--------------020302000507060706010207 Content-Type: text/x-vcard; charset=utf-8; name="venturan.vcf" Content-Disposition: attachment; filename="venturan.vcf" Content-Transfer-Encoding: 7bit begin:vcard fn:Noelia Ventura Campos n:Ventura Campos;Noelia org;quoted-printable:Universitat Jaume I;Departament de Psicolog=C3=ADa B=C3=A0sica, Cl=C3=ADnica y Psicobiolog=C3= =ADa adr;quoted-printable;quoted-printable;quoted-printable:Avda. Sos Baynat, s/n;;Edifici d'investigaci=C3=B3 II;Castell=C3=B3 de la Plana;;E-12071;Espa=C3=B1a (Spain) title:PDI tel;work:+34 964 387658 tel;fax:+34 964 729267 url:www.fmri.uji.es version:2.1 end:vcard --------------020302000507060706010207-- ========================================================================= Date: Tue, 10 May 2011 15:44:25 +0100 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: inaccurate segmentation of gray matter Comments: To: "Moeller, C." <[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 don't have any good suggestions at the moment, other than maybe try Christian Gaser's toolbox to see if this handles the data better. I suspect that it might, as its final segmentation relies less heavily on the tissue probability maps. Best regards, -John On 9 May 2011 10:37, Moeller, C. <[log in to unmask]> wrote: > Dear SPM experts, > > I am doing VBM analysis on scans of AD patients and healthy controls. I > just finished the segmentation and found a lot of inaccurate > segmentations, especially in scans with a lot of atrophy. I attached a > screenshot of the segmentation for illustration of the problem. It seems > as if the space between the cortex and the dura is also segmented as > gray matter. I used 'New Segment' of SPM8. > > Does anyone have an idea why this happens and how I can solve this > problem? > > Another issue is, that often the dura around the brain is also segmented > as gray matter. In the older version of the 'Segment' step there was a > 'Clean up' option. Does anyone know how to solve this in the 'New > Segment' step of SPM? > > Thanks in advance. > > Best regards, > Christiane > ========================================================================= Date: Tue, 10 May 2011 10:49:35 -0400 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: Ancova in SPM8 Comments: To: Ryan Herringa <[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]> Did you mean-center the covariate-condition interaction terms in design2? There's often more than one way to do mean centering. I don't recall how S= PM sets things up here. Obviously if you take the first such term, ie the = first such column, the entries are nontrivial for the first (13?) rows, and= all zeros after that. I think that means the mean centering was done "ove= r condition", _if_ it was done. (Which would be the right way to do it in = this case.) The model without mean centering is equivalent (but not identical) to the o= ne with mean centering; the one with mean centering is easily to understand= , I think. Stephen J. Fromm, PhD Contractor, NIMH/MAP (301) 451--9265 ________________________________________ From: Ryan Herringa [[log in to unmask]] Sent: Monday, May 09, 2011 4:11 PM To: [log in to unmask]; Fromm, Stephen (NIH/NIMH) [C] Subject: Re: Ancova in SPM8 Thanks Stephen, Your first assumption is correct, that the covariates vary across subjects = but not condition. (not that I explained it terribly well) The second level scans represent intra-subject contrasts (emotion vs. shape= ) so I think you are right that the subject effects should have been subtra= cted out. Thanks for helping to clarify the models - sounds like there is some utilit= y in both which I'll have to give some more thought. For model 2, am I correct in assuming that if I select either a single cond= ition, or a single covariate interaction term in the contrast manager, that= I will be looking at the condition x covariate interaction?= ========================================================================= Date: Tue, 10 May 2011 15:57:39 +0100 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_affreg 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 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You only need the one shear, so that you have 6 parameters in total. You can test this by looking for affine transforms that can not be represented by those six parameters: for i=3D1:10000 M0=3D[randn(2,3)*10; 0 0 1]; P1=3Dspm_imatrix2D(M0); M1=3Dspm_matrix2D(P1); t=3Dsum(sum((M1-M0).^2)); if t>1e-9, disp('Problem'); break; end end Best regards, -John On 6 May 2011 21:48, Siddharth Srivastava <[log in to unmask]> wrote: > Hi John, please ignore the first part of my previous mail: made some erro= rs > in > cut and paste. It runs fine now. The question about the shears, however, > still lingers: do I have > to somehow use both Sx and Sy, or as your implementation suggests, only o= ne > would suffice? > > thanks again, and sorry for being such a bother... > sid. > > On Fri, May 6, 2011 at 12:23 PM, Siddharth Srivastava <[log in to unmask]> > wrote: >> >> Thanks John, for the answers. The code, however exits with "Problem". M0 >> and M1 differ. >> Also, why is there just one shear component in the matrix? I have seen >> general shears modeled as [1,h;g,1]: is this superfluous, and just one "= h" >> will be sufficient for 2d case? >> >> Thanks, >> Sid. >> >> >> >> On Fri, May 6, 2011 at 11:01 AM, John Ashburner <[log in to unmask]> >> wrote: >>> >>> Your model uses 7 parameters, but there are only 6 elements in >>> A(1:2,1:3). =A0Also, you need to be able to deal with a broader range o= f >>> angles, so atan2(s,c) is used to compute the angle. =A0I've tested it >>> with the following code, so hopefully it works for all cases. >>> >>> for i=3D1:10000 >>> =A0P=3Drandn(1,6)*10; >>> =A0M0=3Dspm_matrix2D(P); >>> =A0P1=3Dspm_imatrix2D(M0); >>> =A0M1=3Dspm_matrix2D(P1); >>> =A0t=3Dsum(sum((M1-M0).^2)); >>> =A0if t>1e-9, disp('Problem'); break; end >>> end >>> >>> Note that spm_imatrix2D(spm_matrix2D(P)) does not necessarily have to >>> equal P, but the matrices generated from the two parameter sets should >>> be the same. >>> >>> Best regards, >>> -John >>> >>> On 6 May 2011 17:11, Siddharth Srivastava <[log in to unmask]> wrote: >>> > Hi everyone, >>> > =A0=A0=A0=A0=A0 Can someone help me check the 2D versions of the spm_= matrix and >>> > spm_imatrix? My >>> > implementation currently is as follows: >>> > >>> > ---2D spm_matrix --- >>> > function [A] =3D spm_matrix2D(P) >>> > % P =3D [Tx, Ty, R, Zx, Zy, Sx,Sy] >>> > p0 =3D [0,0,0,1,1,0,0]; >>> > P =3D [P, p0((length(P)+1):7)] >>> > >>> > A =3D eye(3); >>> > % T -> R -> Zoom -> Shear >>> > =A0A =3D=A0=A0=A0=A0 A * [1,0,P(1); ... >>> > =A0=A0=A0=A0=A0=A0=A0=A0=A0 0,1,P(2); ... >>> > =A0 =A0=A0=A0 =A0 0,0,=A0=A0 1]; >>> > =A0A =3D=A0=A0=A0=A0 A * [cos(P(3)), sin(P(3)), 0; ... >>> > =A0=A0=A0=A0=A0=A0=A0=A0=A0 -sin(P(3)),=A0 cos(P(3)), 0; ... >>> > =A0=A0=A0 =A0 0,=A0=A0=A0=A0=A0=A0=A0=A0=A0 0,=A0=A0=A0=A0=A0=A0=A0= =A0 1]; >>> > =A0A =3D=A0=A0=A0=A0 A * [P(4), 0, 0; ... >>> > =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 0,=A0=A0 P(5), 0; ... >>> > =A0=A0=A0 =A0=A0=A0=A0=A0 0, 0, 1]; >>> > =A0A =3D=A0=A0=A0=A0 A * [1, P(6), 0; ... >>> > =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 P(7)=A0 , 1, 0; ... >>> > =A0=A0=A0 =A0=A0=A0=A0=A0 0, 0, 1]; >>> > -------------------------------------------------- >>> > -----2D spm_imatrix ------------------------ >>> > function P =3D=A0 spm_imatrix2D(M); >>> > >>> > >>> > >>> > R =3D M(1:2,1:2); >>> > C =3D chol(R' * R); >>> > P =3D [M(1:2,3)',0,diag(C)',0,0]; >>> > if(det(R) < 0) P(4) =3D -P(4); end >>> > C =3D diag(diag(C))\C; >>> > P(6:7) =3D C([2,3]); >>> > >>> > R0 =3D spm_matrix2D([0,0,0,P(4:end)]); >>> > R0 =3D R0(1:2,1:2); >>> > R1 =3D R/R0; >>> > >>> > th =3D asin(rang(R1(1,2))); >>> > P(3) =3D th; >>> > >>> > % There may be slight rounding errors making b>1 or b<-1. >>> > function=A0 a =3D rang(b) >>> > a =3D min(max(b, -1), 1); >>> > return; >>> > >>> > ---------------------------------------------------------------------= ------ >>> > >>> > I know there is something not correct, since when, for a parameter se= t >>> > 'p', >>> > i do spm_imatrix2D(spm_matrix2D(p)), I do not recover P for all >>> > entries, >>> > specially >>> > rotation and shears. C(2) above always evaluates to zero. The result = is >>> > that >>> > these errors >>> > accumulate (or are not accounted for) on repeated application of this >>> > sequence of >>> > operations in the registration routine. >>> > >>> > Thanks in anticipation, >>> > Sid. >>> > >>> > >>> > >>> > On Fri, Apr 22, 2011 at 9:13 AM, Siddharth Srivastava >>> > <[log in to unmask]> >>> > wrote: >>> >> >>> >> Hi John, >>> >> =A0=A0=A0=A0=A0 Thanks a lot for your answer, and it has been very h= elpful for >>> >> my >>> >> understanding >>> >> of the details of the implementation, and my own development efforts= . >>> >> I >>> >> have >>> >> 2 very quick questions, though: >>> >> >>> >> 1) the parameter set x(:) that is returned from spm_mireg, and the >>> >> "params" that >>> >> spm_affsub3 returns (in spm99, which is also the version that I am >>> >> looking >>> >> at) , >>> >> are they equivalent? In other words, If I am collating the parameter= s >>> >> from >>> >> the mireg >>> >> routine, would the distribution x(7:12) be used to estimate the >>> >> icovar0 >>> >> entries in affsub3? >>> >> >>> >> 2) also would VG.mat\spm_matrix(x)*VF.mat transform G to F similar t= o >>> >> =A0=A0=A0=A0 VG.mat\spm_matrix(params)*VF.mat , or do I have to do >>> >> =A0=A0=A0=A0=A0 M =3D VG.mat\spm_matrix(x(:)')*VF.mat >>> >> =A0=A0=A0=A0=A0 pp =3D spm_imatrix(M) >>> >> =A0=A0=A0=A0=A0 and use pp(7:12) ? >>> >> >>> >> >>> >> >>> >> Thanks again. >>> >> Sid. >>> >> >>> >> On Thu, Apr 14, 2011 at 2:13 PM, John Ashburner <[log in to unmask] m> >>> >> wrote: >>> >>> >>> >>> > I had a look at the code in spm_maff, and >>> >>> > it looks like it also uses the values of isig and mu in the call = to >>> >>> > affreg(). My guess is >>> >>> > that during the initial registration on a large data set for >>> >>> > estimating >>> >>> > the >>> >>> > prior >>> >>> > distribution of parameters, this will be called with typ =3D 'non= e' . >>> >>> > Is >>> >>> > this >>> >>> > correct? >>> >>> >>> >>> Once the registration has locked in on a solution, the priors have >>> >>> less influence. =A0Their main influence is in the early stages of t= he >>> >>> registration, or when there are relatively few slices in the data. = =A0I >>> >>> don't actually remember if the registration was regularised when I >>> >>> computed their values. >>> >>> >>> >>> > >>> >>> > Further, do we have to do a non-rigid registration for the >>> >>> > estimation >>> >>> > (since, in the comments the selected >>> >>> > files are filtered as *seg_inv_sn.mat"? Since only p.Affine is >>> >>> > being >>> >>> > used, >>> >>> > can this work with only >>> >>> > an affine / rigid transformation? >>> >>> >>> >>> Only the affine transform in the files is used, so basically yes. >>> >>> =A0You >>> >>> could just do affine registration. >>> >>> >>> >>> > >>> >>> > I would also be happy if you could also answer my other question >>> >>> > about >>> >>> > examining the >>> >>> > effects of the values in isig and mu (what are the units of mu?) = on >>> >>> > the >>> >>> > transformed image. >>> >>> >>> >>> They are unit-less, and are just a measure of the zooms and shears. >>> >>> >>> >>> > For example, >>> >>> > if i set a mu of [mu1, mu2, ...] and a isig of [sig1, 0, 0...; 0, >>> >>> > sig2, >>> >>> > 0, >>> >>> > 0..; ...] (with only diagonal components), >>> >>> > based on the Hencky tensor penalty, it seems that large deviation= s >>> >>> > of >>> >>> > parameter 1=A0 estimate (T - mu) from >>> >>> > mu1 will be penalized. How is the value of isig1 modifying the >>> >>> > penalty? >>> >>> >>> >>> Its assuming that the elements of the matrix log of the part that >>> >>> does >>> >>> shears and zooms has a multivariate normal distribution, so the >>> >>> penalty is a kind of negative log likelhood. =A0The isig is essenti= ally >>> >>> a precision matrix, which is the inverse of a covariance matrix. >>> >>> >>> >>> p(x|mu,S) =3D 1/sqrt(det(2*pi*S)) * exp(-0.5*(x-mu)'*inv(S)*(x-mu)) >>> >>> >>> >>> Taking logs and negating, you get 0.5*(x-mu)'*inv(S)*(x-mu) + const >>> >>> >>> >>> > How >>> >>> > do the off diagonal terms affect >>> >>> > the penalty? >>> >>> >>> >>> The inverse of isig would just encode the covariance between the >>> >>> various shears and zooms. >>> >>> >>> >>> > >>> >>> > Could you also point me to a reference where I can learn more abo= ut >>> >>> > Hencky >>> >>> > strain tensor (specifically >>> >>> > what information it carries in the context of image processing >>> >>> > etc.) >>> >>> >>> >>> I don't know if this is of any use: >>> >>> >>> >>> http://en.wikipedia.org/wiki/Deformation_(mechanics) >>> >>> >>> >>> Best regards, >>> >>> -John >>> >>> >>> >>> >>> >>> > On Thu, Apr 14, 2011 at 10:17 AM, John Ashburner >>> >>> > <[log in to unmask]> >>> >>> > wrote: >>> >>> >> >>> >>> >> They were computed by affine registering loads of (227) images t= o >>> >>> >> MNI >>> >>> >> space. =A0This gave the affine transforms, which were then used = to >>> >>> >> build >>> >>> >> up a mean and inverse covariance matrix. =A0In the comments of >>> >>> >> spm_maff.m, you should see the following... >>> >>> >> >>> >>> >> %% Values can be derived by... >>> >>> >> %sn =3D spm_select(Inf,'.*seg_inv_sn.mat$'); >>> >>> >> %X =A0=3D zeros(size(sn,1),12); >>> >>> >> %for i=3D1:size(sn,1), >>> >>> >> % =A0 =A0p =A0=3D load(deblank(sn(i,:))); >>> >>> >> % =A0 =A0M =A0=3D p.VF(1).mat*p.Affine/p.VG(1).mat; >>> >>> >> % =A0 =A0J =A0=3D M(1:3,1:3); >>> >>> >> % =A0 =A0V =A0=3D sqrtm(J*J'); >>> >>> >> % =A0 =A0R =A0=3D V\J; >>> >>> >> % =A0 =A0lV =A0 =A0 =A0=3D =A0logm(V); >>> >>> >> % =A0 =A0lR =A0 =A0 =A0=3D -logm(R); >>> >>> >> % =A0 =A0P =A0 =A0 =A0 =3D zeros(12,1); >>> >>> >> % =A0 =A0P(1:3) =A0=3D M(1:3,4); >>> >>> >> % =A0 =A0P(4:6) =A0=3D lR([2 3 6]); >>> >>> >> % =A0 =A0P(7:12) =3D lV([1 2 3 5 6 9]); >>> >>> >> % =A0 =A0X(i,:) =A0=3D P'; >>> >>> >> %end; >>> >>> >> %mu =A0 =3D mean(X(:,7:12)); >>> >>> >> %XR =A0 =3D X(:,7:12) - repmat(mu,[size(X,1),1]); >>> >>> >> %isig =3D inv(XR'*XR/(size(X,1)-1)) >>> >>> >> >>> >>> >> You may be able to re-code this to work with your 2D transforms. >>> >>> >> =A0Note >>> >>> >> that I only use the components that describe shears and zooms. >>> >>> >> =A0Also, >>> >>> >> if I was re-coding it all again, I would probably do things >>> >>> >> slightly >>> >>> >> differently, using the following decomposition: >>> >>> >> >>> >>> >> J=3DM(1:3,1:3); >>> >>> >> L=3Dreal(logm(J)); >>> >>> >> S=3D(L+L')/2; % Symmetric part (for zooms and shears, which shou= ld >>> >>> >> be >>> >>> >> penalised) >>> >>> >> A=3D(L-L')/2; % Anti-symmetric part (for rotations) >>> >>> >> >>> >>> >> I might even base the penalty on lambda(2)*trace(S)^2 + >>> >>> >> lambda(1)*trace(S*S'), which is often used as a measure of >>> >>> >> "elastic >>> >>> >> energy" for penalising non-linear registration. =A0Of course, th= ere >>> >>> >> would also be a bit of annoying extra stuff to deal with due to >>> >>> >> MNI >>> >>> >> space being a bit bigger than average brains are. =A0Figuring ou= t >>> >>> >> the >>> >>> >> mean would require (I think) an iterative process similar to the >>> >>> >> one >>> >>> >> described in "Characterizing volume and surface deformations in = an >>> >>> >> atlas framework: theory, applications, and implementation", by >>> >>> >> Woods >>> >>> >> in NeuroImage (2003). >>> >>> >> >>> >>> >> Best regards, >>> >>> >> -John >>> >>> >> >>> >>> >> On 14 April 2011 17:15, Siddharth Srivastava <[log in to unmask]> >>> >>> >> wrote: >>> >>> >> > Hi all, >>> >>> >> > =A0=A0=A0=A0 My question pertains specifically to the values o= f isig and >>> >>> >> > mu >>> >>> >> > that >>> >>> >> > are >>> >>> >> > incorporated >>> >>> >> > in the code, and I wish to disentangle the effect that these >>> >>> >> > numbers >>> >>> >> > have on >>> >>> >> > individual parameters >>> >>> >> > during the optimization stage. I am trying to map it to a 2D >>> >>> >> > case, >>> >>> >> > and >>> >>> >> > these >>> >>> >> > numbers >>> >>> >> > will have to be calculated ab-initio for my case. Before I beg= in >>> >>> >> > with >>> >>> >> > estimating the prior distribution >>> >>> >> > for these parameters, I need to check the effect that they hav= e >>> >>> >> > on >>> >>> >> > the >>> >>> >> > registered images. Regarding this >>> >>> >> > 1) Can someone suggest a way I can examine the effect on each >>> >>> >> > parameter >>> >>> >> > separately, if it is >>> >>> >> > =A0=A0=A0=A0 at all possible. >>> >>> >> > 2) Some advise on how to estimate these parameters, if it has >>> >>> >> > been >>> >>> >> > done >>> >>> >> > for >>> >>> >> > images other >>> >>> >> > =A0=A0=A0=A0 than brain images, will be really helpful for me. >>> >>> >> > >>> >>> >> > Thanks, >>> >>> >> > Sid. >>> >>> >> > >>> >>> >> > On Mon, Mar 28, 2011 at 7:04 PM, Siddharth Srivastava >>> >>> >> > <[log in to unmask]> >>> >>> >> > wrote: >>> >>> >> >> >>> >>> >> >> Dear John, >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0=A0 Thanks for your answer. I was abl= e to correctly run >>> >>> >> >> my 2D >>> >>> >> >> implementation >>> >>> >> >> based on your advise. However, I am am not very confident of = my >>> >>> >> >> results >>> >>> >> >> and wanted to >>> >>> >> >> consult you on certain aspects of my mapping from 3D to 2D. >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 1) In function reg(..), the co= mment says that the >>> >>> >> >> derivatives >>> >>> >> >> w.r.t rotations and >>> >>> >> >> translations is zero. Consequently, I ran the indices from 4:= 7. >>> >>> >> >> According >>> >>> >> >> to your previous >>> >>> >> >> reply, rotations will be lumped together with scaling and >>> >>> >> >> affine in >>> >>> >> >> 4 >>> >>> >> >> parameters, and >>> >>> >> >> translations, independently, in the last two. I understand th= at >>> >>> >> >> in >>> >>> >> >> this >>> >>> >> >> case, the indices >>> >>> >> >> run over parameters that have been separated into individual >>> >>> >> >> components >>> >>> >> >> (similar to >>> >>> >> >> the form accepted by spm_matrix). Is this correct for the >>> >>> >> >> function >>> >>> >> >> reg(..)?=A0 In fact, I went back to the >>> >>> >> >> old spm code (spm99), and found parameters pdesc and free, an= d >>> >>> >> >> got >>> >>> >> >> more >>> >>> >> >> confused as to when I should >>> >>> >> >> use the 6 parameter, and when to use separate parameters (as = if >>> >>> >> >> I >>> >>> >> >> was >>> >>> >> >> passing them to spm_matrix). >>> >>> >> >> So, when the loop runs over p1 and perturbs p1(i) by ds, does >>> >>> >> >> it >>> >>> >> >> run >>> >>> >> >> over >>> >>> >> >> translations/rotations etc >>> >>> >> >> or does it run over the martix elements that define these >>> >>> >> >> transformations >>> >>> >> >> (2x2 + 1x2)? >>> >>> >> >> >>> >>> >> >> =A0 =A0 =A0 =A0 =A0=A0 2) In function penalty(..), I have use= d unique >>> >>> >> >> elements >>> >>> >> >> as >>> >>> >> >> [1,3,4], and my code looks like >>> >>> >> >> T =3D my_matrix(p)*M; >>> >>> >> >> T =3D T(1:2,1:2); >>> >>> >> >> T =3D 0.5*logm(T'*T); >>> >>> >> >> T >>> >>> >> >> T =3D T(els)' - mu; >>> >>> >> >> h =3D T'*isig*T >>> >>> >> >> >>> >>> >> >> Assuming an application specific isig, is this correct? >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0 3) I have not yet incorporated the p= rior information >>> >>> >> >> in my >>> >>> >> >> code, >>> >>> >> >> But for testing purposes, >>> >>> >> >> can you suggest some neutral values=A0 for mu and isig that I= can >>> >>> >> >> try >>> >>> >> >> to >>> >>> >> >> concentrate on the >>> >>> >> >> accuracy of the implementation minus the priors for now? >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0=A0 4) My solution curently seems to = oscillate around an >>> >>> >> >> optimum. >>> >>> >> >> I >>> >>> >> >> am hoping that after >>> >>> >> >> I have removed any errors after your replies, it will converg= e >>> >>> >> >> gracefully. >>> >>> >> >> >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0=A0 Thanks again for all the help. >>> >>> >> >> =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 Siddharth. >>> >>> >> >> >>> >>> >> >> >>> >>> >> >> On Fri, Mar 18, 2011 at 2:05 AM, John Ashburner >>> >>> >> >> <[log in to unmask]> >>> >>> >> >> wrote: >>> >>> >> >>> >>> >>> >> >>> A 2D affine transform would use 6 parameters, rather than 7:= a >>> >>> >> >>> 2x2 >>> >>> >> >>> matrix to encode rotations, zooms and shears, and a 2x1 vect= or >>> >>> >> >>> for >>> >>> >> >>> translations. >>> >>> >> >>> >>> >>> >> >>> Best regards, >>> >>> >> >>> -John >>> >>> >> >>> >>> >>> >> >>> On 18 March 2011 05:05, Siddharth Srivastava >>> >>> >> >>> <[log in to unmask]> >>> >>> >> >>> wrote: >>> >>> >> >>> > Dear list users, >>> >>> >> >>> > =A0=A0=A0=A0=A0=A0=A0=A0=A0 I am currently trying to under= stand the >>> >>> >> >>> > implementation >>> >>> >> >>> > in >>> >>> >> >>> > spm_affreg, and >>> >>> >> >>> > to make matters simple, I tried to work out a 2D version o= f >>> >>> >> >>> > the >>> >>> >> >>> > registration >>> >>> >> >>> > procedure. I struck a roadblock, however... >>> >>> >> >>> > >>> >>> >> >>> > The function make_A creates part of the design matrix. For >>> >>> >> >>> > the >>> >>> >> >>> > 3D >>> >>> >> >>> > case, >>> >>> >> >>> > the >>> >>> >> >>> > 13 parameters >>> >>> >> >>> > are encoded in columns of A1, which then is used to genera= te >>> >>> >> >>> > AA >>> >>> >> >>> > + >>> >>> >> >>> > A1' >>> >>> >> >>> > A1. AA >>> >>> >> >>> > is 13x13, and everything >>> >>> >> >>> > works. For the 2D case, however, there will be 7+1 >>> >>> >> >>> > parameters( 2 >>> >>> >> >>> > translations, 1 rotation, 2 zoom, 2 shears and 1 intensity >>> >>> >> >>> > scaling). >>> >>> >> >>> > The A1 matrix for 2D, then,=A0 will be (in make_A) >>> >>> >> >>> > A=A0 =3D [x1.*dG1 x1.*dG2 ... >>> >>> >> >>> > =A0=A0=A0=A0=A0 x2.*dG1 x2.*dG2 ... >>> >>> >> >>> > =A0=A0=A0 =A0 dG1=A0=A0=A0=A0 dG2=A0 t]; >>> >>> >> >>> > >>> >>> >> >>> > which is (number of sampled points) x 7. The AA matrix (in >>> >>> >> >>> > function >>> >>> >> >>> > costfun) >>> >>> >> >>> > is 8x8 >>> >>> >> >>> > so, which is the parameter I am missing in modeling a 2D >>> >>> >> >>> > example? I >>> >>> >> >>> > hope I >>> >>> >> >>> > have got the number of parameters >>> >>> >> >>> > right for the 2D case? Is there an extra column that I hav= e >>> >>> >> >>> > to >>> >>> >> >>> > add >>> >>> >> >>> > to >>> >>> >> >>> > A1 to >>> >>> >> >>> > make the rest of the matrix computation >>> >>> >> >>> > consistent? >>> >>> >> >>> > >>> >>> >> >>> > Thanks for the help >>> >>> >> >>> > >>> >>> >> >> >>> >>> >> > >>> >>> >> > >>> >>> > >>> >>> > >>> >> >>> > >>> > >> > > ========================================================================= Date: Tue, 10 May 2011 16:37:48 +0100 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: contrast specification error In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed;boundary="----=_20110510163748_41792" Message-ID: <[log in to unmask]> ------=_20110510163748_41792 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 8bit Hi everyone, I am getting an error that I can't diagnose... any help would be appreciated. Attached are the .mat files for a first level specification. Everything seems to be running fine until it gets to contrast 11 when it crashes. The only hint at a problem I have is from looking at the design matrix (image also attached). Contrast 11 is '1' contrast above the regressor in session 2 where there looks like there is a problem. I've looked at my code, the mat files attached, and the matlabbatch stucture generated by my script to specify all the .mat files and i can't find a problem. Anyone have any idea what's going on? 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 ------=_20110510163748_41792 Content-Type: image/x-png; name="DM for model 79.png" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="DM for model 79.png" iVBORw0KGgoAAAANSUhEUgAAAusAAAPeCAIAAAA6W8PAAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA5BNJREFU eF7tvS/0Pbt15elZq0GDBgEBDRo0CAgIMAgwCDEwMDAwCAgIaBAQMGDAgAFDGjQIaBAQEBAQEBBg YGBgYGJgEGAQYBBgYGAwIMDAwCBTz/Vc1tPR2Tra+lOluruW1/P3d6/+HG3p6nzqSKr6P/7jP/7j a7qkgBSQAlJACkgBKbCXAgfB6JICUkAKSAEpIAWkwF4KfG0vc2WtFJACUkAKSAEpIAW+WEGSClJA CkgBKSAFpIAU2E4BEcx2XSaDpYAUkAJSQApIAcVgNAakgBSQAlJACkiBDRVQDGbDTpPJUkAKSAEp IAU+XgERzMcPAQkgBaSAFJACUmBDBUQwG3aaTJYCUkAKSAEp8PEKiGA+fghIACkgBaSAFJACGyog gtmw02SyFJACUkAKSIGPV0AE8/FDQAJIASkgBaSAFNhQARHMhp0mk6WAFJACUkAKfLwCIpiPHwIS QApIASkgBaTAhgqIYDbsNJksBaSAFJACUuDjFRDBfPwQkABSQApIASkgBTZUQASzYafJZCkgBaSA FJACH6+ACObjh4AEkAJSQApIASmwoQIimA07TSZLASkgBaSAFPh4BUQwHz8EJIAUkAJSQApIgQ0V EMFs2GkyWQpIASkgBaTAxysggvn4ISABpIAUkAJSQApsqIAIZsNOk8lSQApIASkgBT5eARHMxw8B CSAFpIAUkAJSYEMFRDAbdppMlgJSQApIASnw8QqIYD5+CEgAKSAFpIAUkAIbKiCC2bDTZLIUkAJS QApIgY9XQATz8UNAAkgBKSAFpIAU2FABEcyGnSaTpYAUkAJSQAp8vAIimI8fAhJACkgBKSAFpMCG CohgNuw0mSwFpIAUkAJS4OMVEMF8/BCQAFJACkgBKSAFNlRABLNhp8lkKSAFpIAUkAIfr4AI5uOH gASQAlJACkgBKbChAiKYDTtNJksBKSAFpIAU+HgFRDAfPwRqAnzNXLUc+l4KTFdAw3K6xKpACjxe ARHM47voPgNPJ2Hr9z6/z1LV/EEKaFh+UGerqVIAKiCC0QApK1BklzSpl0A3xxpS8xTQsJynrUqW AtspIILZrsuea7Bujp/bNx9smYblB3e+mv5yBUQwL+/gZc2jb46XWaiKPlABDcsP7HQ1+XMUEMF8 Tl+rpVJACkgBKSAF3qOACObOvnzylhFr2/XJnZKp7vkKaFjO11g1SAEpMEABEcwAEYkitlibr0bg iYYry5MV0LB8cu/INikgBTIFRDA3DIkqGVQTcEYT99ZxSxSz4TrlObmqfV1NQLSFGJNHLXFLNCyJ TlEWKbCLAiKYXXqqy07i3rrqJGyCapauNijzuxQgxmSEXTQs3zVM1BopgBQQwbx/fFTBAj/ZxQrk +Z6Ig3m/3GphQAF6TJ5jrJhdwzIgvJJIgVcpIIJ5VXfOaEwwzt/jk2aYXS0za1c1PZ3gqogu4XLb VZHjVQyxKl5dMWWPDcVhaQusKhZJUFyK6mz7cEFm2KMypcDDFRDB3NBBb12bJ26Ob1D/dzfxEbdU dW+eH0o/5/x0VjVXCJC36Oy9kTmpmxY0iovZFLsvMmA6hRouSKc9yi4FHq6ACOaeDuJc4z22NtYa jNk0ljosOcDHtFM4X1LM1VrUkEKqenm1VDMOTNCqTLVqUCA9LPGAGftDHi5IVTElkAJbKyCCua37 xs59kbvtpkn8NG+Zkcu6IRXhqtR+yPkSLlfW9iGFVPX0alnT4wALsOXrhyUGyjVyVXtTCaTAZyog grmh36uzXjVBq9FEgbSPabVtcXrguc+vTns4jOByPYRgqoOkmqCpK4soWS1h/bAMDpiq5UogBaTA cAVEMMMlDRWYOss0g/d5qFCYiHM/8Vzrb445TSKQYd2k7SNbjperehOfitxUSEpaxZ7KSiu2Agy/ YHbMIq2FVHkuWGBGojSIgAFT7dniEG2y33ZxVmn6Tw4Kud+RckmBJygggrmzF7CnHGVZlUJwAjDh Ar/+2Mk00hzQL/arCzrj8FEsBHvcCDBl/QhMxUEmL+PV0ix7xCUXxSl630uHTBBPH88qnP7SCgBK 0QBMgaBngT1XmcFeJjpi1GSicqTAoxQQwTyqO2YZk0554J7bVm/hpoo7s9owrtwqOFZZpOj/qrki PjVYSHZrbnkRfFJ1zPa+3xIPoBzrj6tyWWWa8MVCZETqiA5B1Cs2uRNHgk2IdMS4n45KkgLPUkAE 86z+mGpN1XP3E4z1rFNbRBcev1fOqshYMPNSxRv6appqAgwQnicmTAV06xmJgxlBG7LusEBQRZzT DKCkV0V1CBX7FANW1f5ggiYwxR1RbaYSSIEdFRDB3NBr12x7Q93tVaZBl/TvYkk2QTVLu0UjcwR9 p9fYTvgoitODQZ40wBem3vSEgCLIVlsKPH2VPzCEWd9swQgjRZbeaybuZSBLp/1BoOGwaeSvRWVJ gYcpIIK5oUMyr2m9yw02mSo5I7cjmKBzTeUpKuP5sNNZeqGIYtdzBFNlrDiCgK4HN/oewTTJlRaC dbDFNkkdB2tAZp6kRdvsSAMDIz6cqoU8YTKRDVJgkgIimEnC1ouNz6H1smCKs6LF1V1TcKfxw7NX HS1wHqmPvFRN6SQCH1VPFikEuFV7Qx83NR0nGV3FAcijvap6GYJUe8ryXxx64uNqLMFkImBVuZEQ GRvx5iulFHiyAiKYO3snu1ebZErxJjj1u169TehD1zKp1aBRxbZXPXTmX4ugE3E5mWGtbtgDrKxR 1VvziGMGcFYtH8sFRstVKSAYywFYlmJ1wYFXBSmLjK0lnz80q1hkOEU6ImiPkkmB7RQQwdzWZde0 lc6A86yx1eG63nonZ4khc4eeLxxFMFWX0+S34s4vgmhnG7OW2k8iUkTS4JZa0KzSXrWNxKiuGmkJ ZlSngPZ6VRRlnzerqGQpcK8CIpjb9F9MMN5tK2j/5bqCGrWmDxY7PBkOAGQEk/pRz0FadxLMlVkS r9r2JnbeVd+fYXRaGjbS+m/vE+yPL8WK1RWVqTYKdDQwOxtveLRkv+Ji4iLitKpqS/ZQm6C04T8x FSgF1iggglmjc7mWdPqzQDPWstnAtNe86bklz9lUnVA/fOCqrbzYWbYiTjrYLhIFVQB7sqK4Qrzh FEQE3F9FC72fGyaYCO5UBYlQoAhm7Hyo0t6hgAjmhn6M3BoON2s2wZwu8/rvcPuHFwju9S2OeE4o BQUvV9EZ49qr0QUATEFTg0iUJvP+zowBTj2Tq2iDlRRTQhVHglK3EkwwvU0G7KkOFTDeAAYN/+2o QCnwEAVEMA/piBVm4JBP9Z5+hYmq48MUKI7JIipZqnu9VGeTPYB7ffPVQClQVUAEU5VoSoKV4Yqm kI+dMZvavyDS02SPEjcpsGxY4qUZLzbT1JYsNmM5gC5tWUYcJ1tmhiqSAo9VQARzQ9cUY8U32BGo srrUcpXRxEmBmpVktQK3D0t6sAGlth6WmPNWjw/VJwWep4AI5p4+WXazezaPq44IqHSGcO7pDNX6 OwW4ccLpZ+sKEsxHDUsPYjjNlUsKvEwBEczLOrTQHO7e2mORKqPYOff9EquFjQoUx2Q6crzyPnBY 6gfVOLiU/IMUEMF8RGcT99acq6jyzUfIrUYGFCDG5BVNtMXjgadhGegQJZEC+ykgglnRZ8VQ8IqK O+oQwXSIt0dWDcs9+klWSgEp4Cggglk3NPa6EaS3QO7VzHXd/9Sa9uovDcunjiPZJQVuUEAEc4Po s6u88d76xqpnq6ryexS4d2DcW3uPbsorBaQAUEAE89rhsde99Wu7QQ1LFNCY1HCQAlJgoAIimIFi Prco+jjStd0yXkKaUh7ruWPibsviIyqz9Mx4fJhGVqqt0bCsSqQEUmA7BUQw23VZs8EpRsSRwssV PPRxJYvX2NwwZdhWAW5MntRyNTpeiB2NGpbbjh0ZLgV+r4AI5rbRkK3Nz7MjPtGnNshVzOuRJ5e8 ZlhyY1IE8+SRI9ukwHoFRDDrNf+iRnsLOO+mkPMWnj2KwdwzYpbUumxYcmOy+MM5hdGwXDJAVIkU eJYCIpgb+oODA9rQ7K46vnuA82drbuJpNZTRU2DlsKTHJE3/GpYa+VLgfQqIYG7o05WuorN5rfP+ mb6zUmW/RQENy1tkV6VSQArQCsjZ0NLxGTdyFVwj42EernzlmqGAhuUMVVWmFJAC8xQQwczT1i2Z DqHbjEHrr9DIerZQSCbYR7cnWzwsbxyTxaWo2/WXAVJACrQqIIJpVeye9N7qTGTVxts1Gdz8mE33 ESJZz0n39MrH10oPS25MgqGoYfnxg1ECfKICzyUYOt7wvm6szs4zWITzMRGi2rqDNCyv7usZltzo ognm9cNy69+UjJcCtAJPJBj6xo5WYX3GyxEuqJrb30D7mAUtuqUKDcuBsnNjkiaYgZarKCkgBZ6j wOMIpufG7jmyYks8OJhnv1W1Secmg1M4q9Yyr8ljS642pJpgrD0zSmvq5X4DiDHZQzCvHJb9vaAS pMDWCjyOYLZWM2j8YldxWpUtf1RNtcslkbjR1TT7R7XGtgTHmW39b6gCX3TZ7/6X/j1P53xMBpqD hqWf/WqO/WNe67Yvue0HqdRS4AYFRDB3iO6828UzhYOJGxqWPBpVBLOdA2slGA4mbpFFBMPIfssM okqlQIsCIpgWtQalJYhkl0UKxWAYVxGIPSwoliCSNaGa/raLYBgNB013KkYKzFPgcQRDePd56lRL ttZWs9AJNoKYVBa6vZWMa70+4d0ZnzGoUYVhOahk26iNIOYrw3KaIDf2++CqZ/10Va4UGKbA4wjm 3LExrH3TCjpnQ1u893mPIVVBqgl6alfeU4EtRNaw1HCVAlLgcxR4KCs83FtUzasmaB1hKz1Tq22f k354t46VrmpeNUGrPRqWrYopvRSQAgMVeBzBVCfZaoKB6jytqJ5FK6tbREku19N067enqlU1Qb8N jy2BHpb06KIzPlZDGSYFpAChwOMI5gzX9yzQcPMpl4tQ/MoyY72paI9t2vUJsJ/L1SPIw/P2DEt6 dNEZaTHXDEt6dNEZaUGUUQpIgccq8ESCOcUi5m7Ox3C5RvVo2sxRZXocQ5T/yaGFIA5iVenRRWck etlmWTMs6dFFZxwijgqRAlLgIQo8l2BaBapOal5cp+qB8PyekVar2Wl6rwnn59UGVqu+Smi6z27N RaBn1fJNE1S7DPc4aLU3mL0QRY+AU4dl6+i6GtKaUcOyZwworxR4pgLvIZjF+ladU9CeyM3ukMh5 OuOftkWa0JorUmZQGSUjFBil/5ph2Tq6ivgSGcyjZCF6RFmkgBSYp8DjCGZUvGGeZHYa5epqCoQE gSNy127dxsBcb3UVGpbeIOnpcREMN3UolxSQAl/eujxNiCHxhtmNqs7a1QSchZk4rYWkzBS3sDXX K8P1GpYYcyMxm2IJraMrvX9oZfHOn0/rz03ppYAUmK3A42IwkZgwmAqLniY++cYnYi+C0hRZaXL2 ljniFDJ7GI2KSy2zk6uoVXCaezoz2tZpWHI9rlxSQAo8WYGHEswhGXHD1OpgaFrKbgTj3JMOhewO smp8P8Fcdp7yBsdlU654scHan5asdVjSgtAZ7W+nqajFw7JpdGU/nwvLqg2sJnjaMJM9UkAKRBSI urFIWQPT0N6am6q4XJ3tzVxFhCpSO1ttJqpLTbLZ45GwTqGek50blq09dW9Aixgn9LAk6sruOjQs n/PrkCVSYL0C7yGYqp8oJuByDemn+PSd3fen/4xbEq8OBIoimBU3abuUrQRDjy46Y7+k8XHSPyzj dWXtojP266MSpIAUeI4CDyWYzFNWJ/TrzszDFFDCORHbLvE+v2wLWgU6O52I+0vDo4qb9IlcmWN7 zlgfYklrvIEYXfRgPm3rH0jLhiUxurgYzCkLh/5Dho0KkQJSYIYCjyOYITd2xGxl68Xk4dk5o5NG MRMhS+vUb91nv0OdJGlTsZ3Dsml0pYY1Zew0skmQlCR6upgbkxqWRGcpixR4nwKPI5iNJO6ZuNP7 7Oqt4ZXg+eK8lWCer/xloYal7SwNy40GsEyVAnEFnkgwo4LhcRXolPQd5BVWCVbdrwnn2FpzvdhV 9HdBsK/7k+0yLFtHF01pLx6W/aNFJUiBfRV4HMHQ8QbOwXC50hB6FvOPDwV6+o5XkaXkamzN1eM+ 6abNzsgNS3p00RmLZNzUg02J+2Wnq2vN+Mph2a+/SpACWyvwOIK5puDOGaq6NHNRSDa1xX1V/40d 0cbU7KaRV2xmtQQuV7XYHRMQVOENrWq/0xn7CaY1OpimP82Ody49uuiMcduUUgpIgecr0DDdPL8x xOTrRVOCjU3n66a5+zTVXqDeq3z7R8RajrfiuXqcbsT+fdO0DoyrpUMythaycljGR1fW+/GMGpb7 /nBkuRSoKvBcgrmmnlYuyeasqgTFYAzONWRajM/CGWZxBHMxU/pHRJzrxjp4h91DdRF77k3DDUtu TFrMfd+wbB1dKd6dI03D8t5fhGqXAjcq8FCCoeMNrVjgxWBa71y5LmydvtPJmraQyxjMxenPqbc+ FzcsaU3ojJ3KrB+WwdFl2xXMeJeSnR2h7FJAClRu254pEOcqitGayBxHTHDX/V+/gBEL+2tZE4Mh lBzStDWFcMOS1oTIuN2wbKUlLgZDKLlmRKkWKSAFehR4Wwwmg5g4HKQpq7nOabearNoxrdP3VS9h ADeJE7kyI6sibJSAIxh6TLZmJEZFUfxlw5IYXXTQ9MXDcqNfkEyVAmMVeCjBXAGD+KRsvXskL5fr ci09ENM6fffUNXbQfHJpTY6wZ3R5eSsx1e4XC2hYfvLwVtulwF4KPJdg9tJxgbX9BJOGEOKltebq CRQtkFFVjFUgPpC8eM8VVmkqSsNybD+qNCmwowIPJZjWG8E0ttw0D3ZGU1qn0WyItGZvbVpaHbcC 0pqrx8Ln/36IYXlmIWShM6bVnTTZKuyyYdk6uq6GtGYkRGgVTemlgBRYr0Dz7DbbxP7Ae5OFV3VN uVJgyhxGvBxiFu4Jb7RWx7Xxra6CG5b06KIzFkdjU6e0jpOiMsFfQWtdIpigsEomBT5EgccRTOY4 m7qBu23lcq13FU1SFBOnd+Rxr9aaK15yf4sWl0A0rXN00TUuA+vOLmgdXSnEWAACxhBKdjZN2aWA FJitwHMJpifeMFs1ehrNDGuavreYgnvuyJf1Gl3RLq1rGldWjabsGpb0cFJGKSAFOhV4KMFYT19t ZzqTen+DyMQuE3FVB3wbmvngSGlboGSkIcPTVMcMPSbPCEpKEsONH1hgVYfhYzLVZyOhBmquoqSA FPhiHthFheosyXmLJtbx7lZTH9+kZxMcdAYAqgJ6eNfUok9zLVhVbkyeGl6yEx3XNK46R3XPsCSa dlpLZOzUpPVXoPRSQAosUEAEw7sKYhpNe7Qze+vg4KprzXWlP/9ozd7aqNvTP5BgOjXvzN7UI3Rd rRk/bVg29YISS4F9FXgowRA3TNz9bs/Nbus0mo2S1uw9N7v0nWsrhWSuojX7w39IrcOSG5OdMZjW cXXvsKStbcr47mH58F+NzJMC8xR4KMEQDS46+PNDUBqX6yqwaRq1ZvRkb83LAVBrLrmKLMzmDTA8 wj9kWLaOrvR310STGpbEjKosUuD5CjyXYLIZ6oFS0vPvFRFpmoXHAtA8PVNv0YpZ86waVbKGZVXJ Z3b6u4dltVOUQAq8UoGHEoydBJ85Ld47Jpo0aUo8Ksj0vlWkzjWXewfMmtrjIy2ecrjsdNVrNFQt UkAKRBR4FcFca0bpjXJIha/eWUey9Pvm1jm0M2BDGxy387JwIAAF+2JNMgKs6TF59lfrSKZ7me6y zmEZH100xLx+WK4Z/KpFCjxQgfcQTDoVen8XO4BwS/R0T8/C/UPHepqI84jnsppHyu9v18oSWocK PSaLIBLXM54y+IuYJHJ8dNkfTpCcPmFYTuodFSsFnq/AQwmGuAHlvIU310d8AD3/nsOiNXur+1w8 +DL9IwIutnBIdU1xEW5MgjhKRNXWcUXDwTWMb+Tyap9+yLCs6qAEUuCVCjyXYFrl5liEy9VqW2f6 ok86P4yXzDm2eC7PW8ctfF9KenTRGVdq2D8s46OLxiwNy5VDQnVJgcUKNLjAlZY1+ebLMCJK0eMq 6Pl3VAymp0dGKVwUnyu8pzlr8hLtIsbk7jEYui8Ieb040EcNS1pwZZQCuyvwUIIBkzhWvCnIf9bi Xa1dS8+/1Vm41ZJges5gwHyjlAzaf0syQrTWMfnJw5KQF/98Bv7AbxlvqlQKSAGgwEMJpjO8cVeX 0/PvLRDDWcvluqtHxtarYTlWT1saPbrojLNbpPKlgBSYp8BDCWZeg6eW3DmNdmZvik4F67I+e6qA KnyGAsG+9qruzI7un8zwCjZfwzIolJJJgXcrIILh+7dzGm3NPs+R8BIo5/MUaB1XWQtas2tYPm8I yCIp8CkKiGB26ukeb2HzRkrjcu2kqWztViAykIqV0KOLztjdVhUgBaTAgxQQwfCd0TmNtmanN2Fw mxm5XLyayjlIgdZxZWMw1U/SBNywpEcXnXGQuipGCkiBBynwXIJpjWZfonIZm3J1TqOd2bnhw90o E7malOTacmMurnVcrqOZTRk7x1VndqJTiNF11kJkbFKSaIuySAEpsF6BhxIMMUPRUxs3IdK5UtJq 6m/uZrepiv7EdMf1V72gBK51XC56gNHVcb8gDcsFA09VSAEpUFRABEPe0t0+njod1ST7n2nVqMZy reNy0QQzqrFcOXRjueqCuZ5pVdB4JZMCUsBT4KEE0zN9c7MVl+vegfVMm59p1aie4lrH5er5FYxq L1EO3ViirniWZ1oVt18ppYAUsAo8lGDo0DSXkct1+3hqmpS5XQ6tuTZVMtiVXOu4XCe+2Cto6o3J 4sOydXRdjWrNuKmSN3aiqpYCWyjwUILZQzt/Ho3Y3z8LR2pJ06Suxfu7ALnJ+yPjuVptU/pRCrSO q6ze1uydjMWNySw6pWE5avCoHCmwlwLPJZhsZozLymWkc6X3hcW/geX09B1XA5h01Q5umu1X1Vyc kkSLbsnCtY7LZcMwwSZ3jqvO7EEjMwo5c1VHV5bMDm9vMNNdEG+OUkoBKbBYgYcSDHCcWCAu4/Bc gAl6kKJnFiZYhPAxnJKLBz1dHdc6LhcWP4jFTViwHg64MalhSQ9gZZQCL1NABPOVOz+AF7bj6fmX cxW0I0wblTKQZ0bW0iI2gTtdm/01vxmuC7hcuxAM3bpr+BFj8hQnnrHTyH0H8Mc2fN8uk+VNCohg eIJpnUaLDPSyWfjdMybXOi4XTTAalk03G03T5UaJM8Kz88xGbZGpUsBT4KEE483CkY7kllq4XBF7 RqWhHaEXg4ksddmOqOZ6vpI9PcK1jsvV8yvoaWNT3s5haR1tsPbWjHQXBO15ZrLqr/WZZssqKRBU 4LkEE2zAvclap9HM2tbsPbMwN5dxue7tFNXeOq7uGpb06KIzfuDY6BwMH6iYmryRAo8jmHNuKoZA saxcRi7XaUnnNNqZvXWQcdXFc/Uo2dqW9em51nG5rqFF/Ar2Gpbx0WUZKzgG6C4Ilv/wZLTCD2+X zJMCX3phCUEr0Dk7tGbvDNfTvq3VTlpPZRyiQGd/tWbvHJat1V0S0RmHiLxLIVJpl56SnZwCj4vB eOGN4E+Rm0+5XDQTtM7CxXvx88N4r9M39DYjqJRWMt6QG1NyreNyFUdXsMeDyTwlg9n7hyU3Jk9l NCyDP4RgbwZLUzIp8CgFGlzgGrvpaZHLyOW6MKtpGs0E7J+F1/RIsJYeJYNV3JiMax2Xy/PQQWZt HVcaljeOq9lVdw6G2eapfCnQqcDjCMaLwQTbyd1wcLmCJg1Mds1HxRv0akXZdFZNXwQ1nGsXJYNt t96dyEhrQmckjOzJ0jMsuTFpIe+Th2VP3ymvFNhagYcSTPE2NCg0FxcZkito4ZWsafq+/Jn9I1Kv dYcRB0nk4pSMNOEJabjWcbnoX0HTuLKqNmXvGZbE6PLucKqDme6CJ4y6Hhs+tuE9oinvLgo8lGCy abE6PaVMkM5xwYxcdfT8y83CPa7i9IVERKE1F6fkNr+W32l4NnPq6ErL56qzP4qIzp09HpeFHpNE xncPS9CtH9vwyFBXmhcosAfBxKdFzs135nq+qyAm/Vsw6+G/qM5x0ho866zu+cOylZZAizBNcko+ fDRGzPvYhkfEUZoXKCCC+aITud85Pf9ycHDamV6t44/L3pSLU7K1IXel51rH5fqQYdk0utJ+b8pI d8FdI21UvR/b8FECqpyHK/BQgsmm72C4PiODc44LdkD6U2/KtR4pgi26Kxmn5F3WttbLtY7LRf8K mry7VaAze6uka9LTXbDGvHm1fGzD50mqkp+jQNTBP8fiD7Qk8yg0M6Vk1kRpl+bxXB/YTZ/W5CHD khuTKdtlf39aL6i9UuCTFXgowdCTI5eRy9U/jbZO363p05Ft4SOCI625aCW3+BFyreNynaPLu7Bc PeOEGNV0da2jC5A0Hsy0klsMS2AkrfDuDZf9H6LAQwkGTKNVv+vNp9U5rjg/glyds0Nr9tb02Qjm shO5OP13+b1xreNycb8Cost6SLenOjovkZHugl1Gpv29c/i7aXtl9mcq8FCCATNUHETOHr3ScyzC 5YoMptZZuDW9tSGb0SJG2khA/O4/rn/QktuTccOSy5XhS1zMznHSmr01PXa08S5uGsx0F8TteWZK PFs+02ZZJQXiCohgvkI5l3AR7mn17v1I0TRrxwfBwJTvdhVc67hcNMFoWBZ/aJacMigc+Ct4VFHp VCageVTXyJh+BR5KMN4sHPkFFt18NSOXq78D3lfCu5XkWsfl6vkVvG9cdbaI7oLOeu/Nbu/EqjPh vQardinQpMBzCaapGUosBaSAFJAC1VCTCEaD5E0KPI5gzh9YdsN0/hPrzmXkcr1pBIxqy7uV5FrH 5TrHP/crGNWbrylHSqaTZ3UWfU2/qyEfosDjCObUPYIsxR7iMq7MdZndWmlreu9uLDWgOsrtlIcn wU4jq/bcm4BrHZeL/hXQ1XE/vZ7qWkcXGLqfPCzv/VGodilwowIPJZh0MiWmyDSEExd3Za6MY+Jt JIxMs2R/A3G4XJ0dF++sG1MSXZAFVJqMX1wd14OtRtKji87Y0wVN/fW0xFaxp1koe6QArcCjCSZt FR3/5DKuzNUUFKE1uaVFp9ugR+fzM3Kt43LRYtLVXTTT1BHx6uIpMwPojPRvrUmB5yTuF+o5bZEl UsAq8Gjv0npjRwc2uPvOnly0qbQmqf+Lh3y4XD1GPv9XyrWOy0VHDujquFFNV3e52KYxqWEZ/JmI YIJCKdmmCjyUYFqnswwIWjuDq47LRZvaX112Sx2Z3VIHE7kj7zSS6LjWu+qs1U0GNyWmO7ozI2ck XWlPda2jy/a1LaE4hHqMbB2TKV1lf3tF9YzJqnmRn3m1ECWQAs9U4KEE80yxtraK8xZcrjVCnbbZ /+La0wk96P/WNOcDa6FHF51xgcjEsJw3JtPY2PX3AhFUhRRYo8BzCcb+9oKKcBlX5srud9Oqq943 szOoyZksvRON35m15mpVsjjJVm+ai5N+5JbXo5agIK2tS5WPdzQ9Qjqr47JzmvSMSWIwtxq5clh2 jsmmSUCJpcCbFHgowWQ/6aB3SR1YeidU7TCuOi5X6pyuSZxwvXFNqs0fmIDThGiLl6Va1MVGWcpq xsWja3116Wi8aq/KwvX4wCEXKYozstp2WzU3LHvGZLX5GYpV0yuBFNhIgT0IJuLgi/NvMKO9B4pM Xlwuj2CqpnZWd922NrFday7ayIjg2e/KIki8kGtaz+7m8U+Xax2XyxJMdYT0/ATuGpZZR8SnzqaM /V3QZFia+KKTagncmAyOWNu/VXuUQAo8XwERzBd9xE1wXK4bXYXn4cAwbW1ja/pI1U2/ojjEYB4q Vsq1jsv1CcNymTJ0RXYYcANsZa6Mn4hx3vSLU2IpcKMCDyWYbPpu+v2ns1U848pcRYiJmMoZ2XNr Tkz9hJHp3WdmLUac7AY3ouE5tLiM9LAkNLG9FmwdbeT6YUmMLnowE13AjZOVuaqew46Z+CiqFq4E UuB2BZ5LMLdL8zIDOG/B5WqSLp1Sz78jkyyXKys8Xl1Ti5Q4qAA9uuiMQcPoccINSy5XsC0XUWXU HsyuZFLgyQo8l2CyH17Eq133Z8SPlquOy5Xe7DaZOra64LhssjCNcNib0WKN2fQd7Ggul/VMwero LusZk4SYndVx2XuGZevo8jqiOphbjeQG2Mpc1SYrgRR4vQIPJZhWv5LOa0SfcdVxuWhTO6sjZCGy EEbaG9BIvVyu4r11pLrLtccT0x3dmZHogrRRrdlb0xMC9mchjOQG2MpcQVk4k4KFK5kUuFcBEcwX +hMTHJ2L9k+ckXR1nM8mjORmWC6XCKY63bT2YGv6HlqiBzNhJDfAVuaqdmVxjiKkiFSkNFLgFgUe SjA9fMD9RFfmWjkR9zuMVmWI9DbCf34CfhJelmrtdEYO6Xpy0b+Cqgh4rmnN3pq+f0wSyrQayY2T lbkiDsO2ulWHSC1KIwXuUuChBFOcCCIacRlX5krxxdbb6rMjmlxOtKk6LhenZLwV96bkWsflOp30 gi7LJG2ttDV9hi9EAwlleoy8d8j11561vb9AlSAFnqPAQwnmOQLJEikgBaSAFJACUuCBCohgHtgp MkkKSAEpIAWkgBSoKCCC0RCRAlJACkgBKSAF9lNABLNfn8liKSAFpIAUkAJS4LkEw23xO3qUy7gy 1zXsWittTZ+Nby57a67W9LQaZ8YtqtvCSLoj6Nat7D7aSC7jylzYjXGWyDVKgS0UeCLBnD85K5/3 eTbztmbkquNy0aYuri6Fg7ietJFcRi7X5TXj7eLU6Mm12MhbhuWy7ltWUU+P00YCNzOjzC28moz8 HAUeRzBFdkn7w0vAZVyZK/UT1dumSJODBa5pI1fL6aqb1Lj8BJFrcXVc0xYbGRxF1iq6dXQDiYy0 kVzGlbm48R/v7s/xgmrpvgo8jmD2lVKWSwEpIAWkgBSQAssUEMEsk1oVba+ADctX77nPsEGWLJKL zkhXdwW3mqztqY7O25qxNX0aqGhSg9OwJ1c1EkPYv/0PVQ34GAVEMB/T1WponwKXJ0hdQpVFuFzp csma6i4nmv2RWmL1o1tHN5DISBvJZVyZq4ovTb3Z9/tQbilwgwKPI5jzbql4VX+uREauOi5XdmMX b+Pi6tI7wgVGcq3jcl2BjXi7rA/wnChw8IAPiqPau2/GwMTlSodlakzRExcTtLbOUlG1rvVGcmKu zFWdD5t68wbnoyqlQLcCjyMYfM/X9KMNilO9jY74mGBd3kRczc4ZSVdXdNvzjORax+XiBpit6/yk CSmu9FXLF1fndTduI2ckVxcYyZOM5Fq3MlfrZBgZsdXfuBJIgUcp8ESCqToGoGDVNwzEEa4umioW VyeIyYbKGbbJ7murncLlOn8CK6u7urupUs5Irq70h7PGSK51K3NVIaZJqEd5JhkjBSIKPI5gIi6B o5BiyVx1XK44u9h5pzpV9VDdEGVoTbiMXK4IHA9RI8h/Xiu41nG5bhmWtKmtGVvT02r09Dht5PBf fcRnKI0UeI4CjyOY4t2nd9NWvUuOZLT3TPNygftIXClnJF3dWGM84xebd0t1dMdxGblctDI91dF5 WzO2pqfV4H41PbmqNzYejleZ6TkuSpZIAUTqj1XnnHfSK2gql3FlrmyKjLeRM5KuLp1bFxjJtY7L dYFyvF2cGj25FhtJjxO6C+gGEhlpI7mMK3NFOKZ1nAcnWyWTAvcq8MQYzL2KqHYpIAWkgBSQAlLg +QqIYJ7fR7JQCkgBKSAFpIAUyBV4LsFwYVgivNwT56eN5CpdXN1iI7nWcbl2GSdc67hcWkUq+gdO zJW5tIokx/6xCjyUYOiNZlzGlblSP9E07Dgj6eoufFljJNc6LteJL03t4tToybXYSHqccEouVoY2 ksu4MlcVX4hxrixSYBcFmHl8Qdu4KWDxpE8byU3fi6tbbCTXOi7XLuOEax2XSwTjBWCI6Y7rAi6X CIboIGV5jQIPJRjax9AZuemDy3WLt1imDK0Jl5HLtUwNDgTpEdJZHZed7gK6F4iMtJFcxpW5BDGv 8cdqSKsCDyUYu4ocnBG4jCtzpc7J1gv6jzOSru5yZmuM5FrH5Tr9X1O7ODV6ci02kh4nnJKLlaGN 5DKuzFXFF2Kct3oRpZcCdynwUIK5Sw7VKwWkgBSQAlJACmyhwBMJ5rxpyO7SImpyGVfmyu50423k jKSrSw1bYCTXOi7XFduIt4tToyfXYiPpcUJ3Ad1AIiNtJJdxZa5IAKZ1nEdmWqWRAg9R4HEEk64W eX8XteMyrsyV+onq32kbOSOrVaTFWklbK21Nv9i8W6rbRZPLyVVVGjUslymzrCJOw55cVXxp6s2H OCSZIQWaFBDB/F6BODDR0yI3YS2ubrGRXOu4XOcdPDGzr8y12MiqGh7pcppwo2u9kVzrVuYSwTS5 OiV+pQIimHX+bP0s3OMtWufi1vS0Gj2NWgwHu2jCSUq3ju4FIiNtJJdxZS4RzCtdshrVpMDjCOac pM7rmljxekfqC4mMXHVcLtrUxdWlysc7gjaSy8jlogcYVx2Xa7GRtwzLZcosq4j71fTkqkIMMR82 +Q8llgL3KvBEgrlXEdUuBaSAFJACUkAKPF8BEczz+0gWSgEpIAWkgBSQArkCzyWY4MqR7VIu48pc 5xoBMRi5XIur28JIaVIcflzfrcy1uOMWV8cpWV1LIqYaZZECWyjA+NE1DaN/zFzGlbkWT4uLq+OU XGzk4uqkyag7jcUdt7g6epyAObm1zNb0adU277UR59qNlKUpZsnKrKY5uylNlv0zMvyqtcwoMxtg qUpeR1TtLA5anGtUvfTvhYYKEcyX0nG/Wy4X3c1bVLeFkYu7QJpEXEhwFuPE5HLtMk6eTDCnbRle pB/ar65P7B9XUZECMQSAem0tRUssvTWVWWzLVWbR+PPD/lpsOUS99G8q+EuPJBPBiGDcccINUC7X YlexuDppIoIJTcfU4jIumRh7qbcGMJF6wezv65/nzf0kpwvcMPDHo9y/xZcglmEIs8BXbMtYVSP4 YltXZSwPQCMdF/m9fKFkMN36ZMQPD/R91X6uOi7XYve5uDppIm9d/bnRY5LOuMWwpI0EghNlZg6m +M/UDQPi8XxYkGksJXj8AbxpJg5HMB6vWKfTwxbYhXGWR3K11lvFrJRcLemevU+MzMLUGplr5qU5 m6FLCkgBKSAF1itQnNutD87CBkWfhKEkbVrc69vYQNEfA4Px7UScNqwlHtPEy7RtaSWJCJ1E0rTW GyGYLI0nSyddPCsGczTy/9X1SQp827/AbP4X/vVP/vXv8PrP/vVJHaK2bqMAmPpBGyI3vlea9EbZ Ugv2Sdm3ET8a8eiRcorcc7UlUkLQks4y76qlv14RTPnXJ4LZZvocZOh3/QsQzF/61//2r5/DSwQz qEtVzCIFFhBM6qjuIpgMuXr4Ix77Kfp4AH+cVXfV0l+vCMYlmL9xrkWzgqpZqwBHMP+nf/29f/1/ 8PoD/1oriWqTAiEF5hFMtk0Bs0vmDtP4TbquBLwmcP9FaPBiP1nV2aqWXfRpwg6AL5bz+j/xOrfa 9my9r9WSeL2evF7szX5elTSywPS4VaRfORf4Tf8veIUmAyW6SQEQTQExmP/bv/7Rv34Brz/0r5u0 UbVSACkwnGCKHr264oBzpVRx/aK9HSTFGEk6D4DNKDZUgx2k/RZ/kk1HRfFbyyxGMqp+fUgtlj/6 67WkW0TYU0nAlxFw+f1IaEo9O/HRsN84F/gpe9Bzfq5Z8MkK/E//AgTz//gXIJh/gdfX/evJAsq2 j1VgOMHMnt5VvhTA8Ero87gYjLfbEsxTP4bXx05wKxv+E/+izQAE83/51z/4FyaYP/YvugnKKAXm KSCCIRyestyrgBfSo616A8GAXRHHV2AGAasK8+adV5b8Pf/C7QULgBzB/K1//Qhe3/SvV3aZGrW7 AiIY2u0p440KFBcTaXseRzDebksw3YA78uMrkPGf/Wv32W2x/T/0L2wJt4oE9sH8nX/9AF7f8K/F Yqo6KRBRQARDuz1lfI0CbyAYsCvi+ArMBSB4E5lB1qT5vn+tMSBSC3gKC84ODj//J/8CBAMKBJh1 fKUYTKSjleY5CohgXuOG1RBagY8mGIA+z5mnwMaO5xhJK+kdnj8+BwQDqgNwA5a6jq++5V/P0VmW SIFLgUkEUz2TAuotHpO5Dp4QZ5HOEyvB0zfZHouxZ5E8S/DW1KrlWetWnniycuH1nWpbTil6xg/B MW8gGHpSo/0uXSORcYtA0c/8Czd5OMGAJUWwaHh89Wf+RfSaskiB2QrsQjDWsV1Ozp6qzT7JUqZF FfeEetkzrar1Xp7YGuB56OFlFisiamltS7zexbBSHPAfTTBb7OQFQYXZU2S8fNpIbhUJ5Por/wJL XcdX4P0GcR2UUgosU2ASwaS30WmEwPPlqU9Nwy1X0GKI07VuuPiJ1QRHDnps86Iv8TJB/AZgBMY4 C3ZF1LuqzipqqrfKOh6AWomI6MuXOtA5Z2Q8Gkbs5KWnjOfs5MVbebxv6YYPz0jHYMBOXvCYf3Dg iCYY7YMZPipU4FQFZhOMFymxn6efVHMFWcQjD89DV11v0WcPoY1s6YQoM4IRdC2AYOh6wYJRkWKL caAhIZybYzAn46fXSoIBKw5g6vlX/wKxgeMrkBH43S2WumZs1gEEA14dAB7yi2MwOos01d2q8OEK 0ASTzrflyHzpkampY7Z/e0tFGdycVVcTZ946CwtlLHKVSftjj4qKfrqKSq0E47EajifFa/EIpqfe CMFkaTyDO0MhNxNMZv3RyJUEw619gOfngV0dx1cg41/71/CJb0aBHAseloCGA4IBwARetIT3wfyp f81QTGVKgU4FaIKp+owMO+yyBUcwljyqn3isg1dSbLjlgjY6klG0pAhPcbYohoWshZ21eAQDMPH8 CtQrgin/iBYTDBfeAN4aLwbRbr5zpluQHSwG4dpB8Om/+BdgERBKAS8cOL7STt4F40RVDFRgAcFE 2AXf4jdFXIpFgcgKwQo9BFOMvsTjScFIz4JaIjqnoysexbnktf2uGIz728fPWgVTBkcwIHKDCYYL +Qyc8uYVBRbIcKVg3Q0QDFhF+nP/ApGb4yuw/DRPN5UsBWgF5hHMdQtOEEzmugDBWG8H/J91qAAI ipU2lRC0ZFSZIExilyloCPOoAgRUQJDMhnCKgTEwHgCrgbGdCxJPuiAlF4PxXgZ5fk5PEF5Gbtct /di94fbPKJCGMyALF4MBj3UB3HN8BZafZiimMqVApwLDCabIBNX7b5wrdWDpwoS3gOKtoVwrGp67 taEa7CDtt/iTy/jMEi9WQdtZ9eutlhctsa3orzeNsgBauqqu1hhBjjfsgwHv1jm+6pwmbHYRjNWE XiAD+2AAwYCn64LtLDgGo7NIw38pKnCqAsMJJuIwlEYKjFJABPPl/EC/2XHq/PJRhXPrcYdE3D4Y sG74J/6FCUbPg/moEfuCxopgRrlSlbNMgfh6WdCkN8Rg3r1As8VU+yv/wvaDvvsD//oX//pv/oV3 8mofzBYjTUZeCohggk5OyR6lQHExkbZQBKMp8U4FwAYaQDA/9a8/8i98mvov/OtOgVS3FHAUEMHQ bk8ZX6PAGwhGU9ztCtD7YEAM5g/9C7yvGxAMfqKdYjC3jyIZ0KSACOY1blgNoRV4A8H8G7yaJgUl 5hSg98H8wL8AwQAWARty8VkksB+c00S5pMBUBSYRTM/+yuIxmeuICnEW6TyaFDx9k+2xGHsWybOk 8yxSVuykE09nLdmASYUdVW+xIppOIhnfQDDeY3zPz6dOIiq8UwFAFYBgAPcAgsE7ecGe4s42KrsU mKHALgRz2lncwpmev02TgbO4V1GRAj2IqdZra7EmWSAoNtPaCSzHPATaHqlleL09sBuhk0iaNxAM vZMX3MrPmHFeXCb9lm9AFdwqEv08GHoh7MXdqqY9WYFJBJO64ew2PWORlAOyv7O4C+3aWx2z1QTH bwiU8eIZV9VcmUUeAh9ytdhcPfUWCcaOCtv7VTMi7PLlaIwnXZDyaBjxXiTge46vwBz0c/968sz1 QNvotwqAZ/ICggGnqcHLAfBZJMVgHjiuZBJQYDbBZFEHLy5iFyNwNKUpkGBjEh7TAG86hANwdKSV tIpuPmMgTGP9BIPZzmtvMZBT7KaUYosxrSEhnJtjMOfoTy+CYOgYDP0k2ZUT69/510ozcF3gcBDO CGIw/9W/AMH8sX/hs0hbDIbn9LgsuV0BmmDS+bZYSPEW2Tpdz4l6BHPVm8VyANNYp15kBWDwKA7A eEETzALLZyhme9ALQXkcXAxlEVGSmwnG0jFBMOA+/vgKTDRbLBz82r9un0MvA2gjwf5Z8GQXwCJg FUmnqZ8zYGRJvwI0wVT9ROp1ih7ICyEAR875eI91ipBknWLWkJ64RdGSk8myens+WVOL7YvWekUw 5R/RoSxBMMB9Hl+BmYI+QdM/+8RLoBdo4lX0p6QP8vytfwGC+Rv/AnL9GF56Jm//MFAJKxVYQDAW FLxITDWsQhNMmpFmBbwmErENxwwiJUTSrKmlSDCZzlgxEcxIgqFnDeAI6TKHZ9xidQMcB8OCgLNI gGDAC6jB43rxO8y/41/D+1QFSoF+BeYRzOlHg9GXzB16qwbAawYjN946xeX1s6oBDRBhEuDgcUAo Ai5eUMR2MWG51Qd84g0qW68nb5YSjIeqpNVg4Rc2RBItS8PFYOj3ItFrH/2zT7yELQJFYDsLbimA SLAPBiz/AYL5CbwUg4mPSaV8ggLDCaboa6srDjhX6sBOKsr8lnVjFolsRpwrI7CiUNUSbLznMsNz va1lFiMZVb8+pBbLH/31puySgUta3TUGqjVGwOMNBEPv5N1igWaLA1PgAS0zYjCgxwHB4FUkvZv6 CV5ZNsQVGE4wEYehNFJglAIimC9/7DTBbLGT94f+FZ/sZqeklQS7sMEqEmgODrSAb8Ex7NnqqXwp QCggghnlSlXOMgW8kB5twBtiMMSP/8yyxQINDQe0LETGGZt1AMGAjcP0Pphv+BchiLJIgdkKiGBo t6eMNypQXEyk7RHBlDFm9uwTL38GHMRrD6akn8kL+Oy/+xcgmH/1L7yK9Kf+FRRByaTASgVEMLTb U8bXKPDRBLNyuqHr2iJQRO/k/Wv/AgQD9v/SBAOqo/tOGaXAPAVEMK9xw2oIrYAIZt4MM6bkLQgG bGfBKvyFfwGkANWJYMYMO5XyeAVEMLTbU8bXKPAGgqGfyfv4OWobA8GjVnAbvutff+RfIHLzffbi Ng5v00My9HUKTCKYnhMixYO+1yFb4jT1eQo3eH7YHsMGEgXLTEsoWpIlyGpsrSU9sD31zPYp7GXt qHqzYhdw0hsIhn4m7+vmtNsaRG83BhnBG47AE+0AS+E3O+p5MLeNHlVMKbALwZx2Fg+hpE8QSZOB p4lcRUUKxBBgSwCfWJOKsNJUZrEtKVXYLq4qBvTBthH19sDuKLh5A8FQP/8vMm1xUJlu3cqM9ENr OIIBMZif+Rd+L5IIZuWAUV39CkwimJQ20qiDRxWpT03DLVf4ZJ7TtSV7Lt+LlPTYNrXMIhz045HX 3gi+WMQERmYVFcNjQwDoDQRDP5MXnLwF8wt4Dh5+Ms0WO1q4iZV+qwDYBwNiMOB8Fk0woDpOE+WS AlMVmE0wGbIAgkm/quZqChJYSvC8eNXlF510nGDiq0XxMothJLAQM5tgcMgKxNKK3WRx1oPjnnjM zQRzMn56EW92XPxEu9/415/A6y/9i94JO3WKjBdO2w+eIweQgjtNDd7BdHz1n/wrroNSSoFlCtAE k863xUKKsY30Q/u3594yuDmrria2XhznAgbjqEycNrgohY1bZG3P3LxVZlKkp7NeYGcmqUe0ANSa gOZmgslsPVr7fIL5O//C0aAtnuzCzb+g4bjAb/kXF4P5N//CBPNf/IvTRLmkwFQFaIKpeogMO7zl A8/9F902Fz/A9/3YAHvHH+cVa23RkgvI+lsHauysBbeFq1cEU/4RLSYYblkHeGsQGzi+AtMZZ8nU +bGpcBrOOILhngeDCeY/+1eTFEosBdYosIBgIuwSdJC0j08zZjfucSLpj2TgmAHdOhvaydrbb7nX QcVoEIjJYZrMGmJjZlXcrIJ12TYu26RcHMHQ8wXHDbS3fjHBcEoegoA9uWBF7q/8iz6LpDc70r8j ZbxFgXkEc3rrYvw/9UNFn+StGhT9aBVBiu48nsujgWoJF69YT+zJ3lMmhgwLMRwweVQBAioel/TH YNIB1oMTj1tF8h7nccscUayU9tYvJhi6d/6HfwGCAWEwQDD4uUEiGLoTlfEWBYYTTNEv4hCLFxQp OsvTaWVUYSEjq/HKhd28LdbDF8/7cpZ4MZJgLRFBso7Gdt5Yb8pwgJauJuMOCmLN4wjGc063zBEi mNmyg7NIX/cvQDDg5Ud4FQlUN1sElS8FCAWGE0zQZyiZFBiiwDsJxntACPELV5bnKwBYBCAFWHsC BIOfaAdOij1fRln4gQqIYIb4URWyUoH4elnQqsfFYLw1Gvq59R84tW3UZLCpiAuKgKcUgtdPHl+B 9ayN9JSpn6OACCbo5JTsUQoUFxNpC7chGPAUFjxn0a9N/pyp8MaWcgQDznz9k3/hVSSwO/hGfVS1 FPAUEMHQbk8ZX6PA4wjG+7n+u3/hOe6X/qXJcYECmBvAA47/1L8ApgAkwmfdRTALBoOqGKiACOY1 blgNoRV4P8Fwh5/Bg9EGzkGfUBTYmHJ8BXaffMO/wI4W8IAZQEvHVzqL9Amj8U1tnEQwPfsri8dk riMqxFmk86xT8PSNPcQEJAqWmR01Gnu+ybYuPXjl1UVYflYE2jKqXlsRjSbBjNsQDP3mHe61yd6h 7uPzN02CC9oC4iXHV2BPLiAY8I5GQDC4sXqz44LBoCoGKrALwZx2Frdwpudv02TgLO5VVKRADAG2 BPCJNSnTv9qWIq9Yca5ii8YHa7GCA7mKDYm0rgd2g4BSTfZ+guEe38Jxz8Dp6YFFAW4A1n4PXmDt BhAMoCI6BvNd/3pgX8gkKTCJYFLnl8Y/PKpIfWoabrkcNu10i9yDscNqgiMWPbZlIY2MPDw7iyRR jJHgtrRaPqPeKmMVse8aVEMASARTngm5tad3z6pcGOwH8AKrSOClj2B39p/7F3gXwfGVCObdo/d9 rZtNMJn7AQSTflXNBYIonpcthmeycoA3BTGGOAd4vOJFmILEUIy4eK4d05vXFhDm8WJjVcWqoaMU Uyzj2nBUNdbiJbiZYM52ppc30XDu8yiNO4bNRW7eN0umLeI0watIQDFAMGB3MCAYvA9Gp6nfPXrf 1zqaYNL5tliI9YVFXIi4zAxuLscWjK9YB1/04sDgUZGMImoA/mgimAxZlhEMXW+EYLI0eLTsSjCW 9YYTDHcMm/PW75sl0xZxK2sg1/EVOB/ErSKBUAro0+MrEcy7R+/7WkcTTNVbVGMqxbtqDCWj4geR corck0YFsgTxMrOUTWV6Xt/ySk8tcXLqqVcEU/4RHZoOJxiwdvC+SW1qiziqA+txx1dgJy+IwYCz SCAGgy2htwBP1VyFSwFPgQUEkzoqSy2tMRiOG0CcwDPA8+LxFZ9iCV5opCmeVA17dNYSJBiML0NW kRSD+covl15FAs+t1+TYpABHMCAGdnzF7YMBm4PBi5ZwNAg8fqZJJSWWAmsUmEcw2TYFzC42YGC5 p+jjs5Wpaq44AKUxpKJKcfSpFhW3yuMngAvWeMLySfVmferpkAlYDO9Vg4Igwc37YGznDY/BaE/u mvnUqwXvn/2Of4EYDDjrDggGsOzx1R/7170CqnYpUFRgOMEUF1Oq9/Q4V+axrmUXu9BT/ORMf13B XBmBAYhJv8JhCc+SeAlFkrCtAzEYWwL3SbEt/fVa0k0px7JLtcYI2byfYLjIgSbNUQrgtxEBggGP mAMvPwIEg1eRuNcwjVJJ5UiBVgWGE0zEYSiNFBilgAjmy5Wl1l9+JD04AhzJrjSXAvg0NUcw4Dm/ 9PNg/sS/1JtS4IEKiGBGuVKVs0wBL6RHG/D+GAw39fyrf3EFfmwuHPkAgRYuBkO/HEAxmI8dops2 XARDuz1lvFEBuybYY4wIpjx9bbF7BhxFxtwAVtbAQ1O4iR6w4PEViMGAaAp4xg8gGHyaWgTD9a9y 3aWACKbH8ynvOxR4P8EANw+mHu7ZJ4vnMjAEwTbY4yvANz/3L6514B2Zx1eAYMCLikCfgtcU4Cfa AcW4hiuXFJiqgAjmHT5YrehR4P0E8yv/AvPLFvt/wTEfOgYz/PE5fwcvABwAbkDvgAIB9xxfgeqm +iEVLgU4BSYRTM/+yuJxnuuISnH5AJ8AOg+zVNPYZMVcqWLBMrMsWBy6zMvaGSeeiueVsjNQo+pN Dx/1cEk87/sJ5sVvFQDHg3G8YWXwCZ9h/lv/AjEYwGeAYMBTd4+vtlg35Fydcr1SgV0IxnrQ9GBt 5vPSE7npV3YHaHFPqJc90ypLdlUEaslstuIHy8woByBR8atgLVbVGfX2wG6cUXDK9xPMi59oNyNQ NLxMHA3iYjCgT8FDfvFXerPjK938ixs1iWAybrCeu8gfqWe1EQWMKRGACKbxwMKLvtBAUESilNU8 GCqCV8TsTg1n1FtlrCL2pcOjn2PeTzBb7Gjh5tnhtHGYMbzMX8MLUAVY1gEEA4AJx2DA6wi43lEu KTBVgdkE40VK7OfpJ9VcQRax3hHTBvCmNG0U11+KCyXBEFExNOK1FGNNnL2KMbALIzy2w8znKZN2 7mVhRrdX3iEhnJsJ5tQxvbzfPP1WgeEueeqs1FT4jKYNLxOvZw0nGFAgJhjFYJrGnhLfrgBNMOl8 WyzEescMO4o+CQcJbKVxr5+FhS6biz4y+3YsBxTxJRgdKZJEEQU81x5RzKaZUW+EYLI0HnJ1hmFu JhhLx8MJhtvJe/v09BoD8CoS+BYgBYir0atIIONr+kINeZMCNMFUfUY1psIRjCWP6ifA+2ZQhVHg 4qcgbQQ54IpkRNgCeP2iVWmsIh5xiVgejK+A1olgyj+iQ7LhBMPt5H3TTHdvW8Dzc4+vQMgHEAzY PQMKxHADIjT3CqjapUBRgQUEkzoqSy34rjr7NuLji943C0jQ5RRjM3Ey8KIvHmNFSCLSXkwbETUi FNjaOhHMOoLhdvL+0r80nzYp8M/wAtEUQDCARUBQRwTT1HFK/HAF5hHM6c/SSEwW8AAxmGIuL8ZQ BYjieko8VzE7CN4AIPCKqoaRqgQGAjC2i6ttL7YO64+DQ5Ziiyzo6QDGQ1VSMMJ/X10k0bI0M2Iw 3E7ef/Kvh89r88z7R/8Clf47vABVDCeYv2SveZKqZClAKzCcYIr+uxpLwLlSB3ZSEfDoxWjBlcuz pOiAbUWZXNZ94k9SMzAYeVGToJ1Vv95q+bJ6U7rKwCVFqKtrqi2NgMf798FwW1O5yA09GW2REaAI sB8/yx+s3XAEQ+/k1VmkLQahjLwUGE4wEYehNFJglAIimNC7qTmC0fPNrKvglMRPwgXAAZAC7IMB 6InPIoEAjbymFHigAiKYUa5U5SxTAAfSCDPeH4Phph7OW3N17ZKLW4/DO3mHryLRBPMX/rVLB8nO j1JABEM4PGW5XYHiYiJt1fsJhnvZsghmVAyGfiYviMGAaArALByDEcF8lPt/QWNFMLTbU8bXKPB+ guF2b7xgghveBI7qMMFwq0jcyWd2I+9fDldSBUqBfgVEMK9xw2oIrcD7Cean/tU/iaiEqgLgVNfx FUcw3FIRJhjt5K12pRI8SgERDO32lPE1CryfYLhTRb/xr0fNYs83Bu/kBWs3ADjATl4u11Gg3irw /LEkC1MFJhFMzwmR4kHf65AtcZr6PIUbPD+c7RLFDQmWmYo8/IT2Wbg1G+8UISzPaplXb7GiqbT0 foLhThVxu2cWz7B/61+YGwABgIxc634OrxksAsrkquMarlxSYKoCuxBM0V+mH9rzKeBpIpePLJ5q SR9JArxplgyUiS3J+MbiSBFQcK7r2yKBEZYXKdCWQ9TbA7ujsOb9BPO//QvML9y5m6kTli0cBIq+ B6+L8e0fw1/CgN9NDSJkIJoC9sGAAjHZ6L1Ii0evqutUYBLBpE43jTp4vjz1hWm45XKctNONAEH1 ph9HLHpssyASwbIieNlWAHyJ1GLTzKi3ylh2zKQ4NQSA3k8wwIOCGYTbtdo5JbVmpwNFXFyq1bwz Pf1EO3o9yCMVfBYJ8A3XcOWSAlMVmE0wmfsBBJN+Vc0VCXiAVQ8vYFN1+cUYQ5xgPF7xIkxNxJAZ 77l2r+2YaayFxS6ohqy8lmbjsEixtsYUjnviMTcTjI0BeL/5L59eV/o/PE183792JxgQKMIngP7G v4bDDQiKHF9xyzogXsJtrDloSTGYqe5WhQ9XgCaYdNYtFlJcYijGWophkqJDtZVG/LHFjmIuYLBt YKRejz88Nx8ps0gSgNXGWo5jUZ5tmFqqMZi0dR4s9uDLF+V35h+b/WjkcILhdvIOn25mFIjDG1wY icvFseBRF7dUxHGPVpFmDEKVeZcCNMFUJ+1qTMXSTDUUEfHxkbhFpJwi91w3/ZESgpY0lcnFQi7y K8JipC0z6gXEk5FKOpYA71bHpJdgG4L5gX/hGWR4UOGuCcvW+wKCASeAwKIPYJG/9y8s14tJ9zkj VpYMVGABwdhoiheJmUcw2Y1+xGeDCEoPBxRDDtWGV8NUWQmdtQTZCwe3sqEVKTMrMAOXSwTcNQTH bEMwtIMZHlQYOAepKMAigGDAig8YJ3hlTeNEo3EvBeYRzOlvinfPVYLx7rmLrstbWbD+L21sPJdH A9USrMcFYGE5z/skoxnCrxOWB2MwuIG2XtuWYqvBeKhKGgGabQiGPhwkz/TkefnP/AsQDOhT8GA6 MISOr/RWgSePE9lmFRhOMMXwRvX+G+dKHdhJRZnfsm4sK/DK5VnieeiIS7Zg5H2SmoHBKF4mYAvQ uVixYplF2qh2BIjEVKnIMmg2EjwAilDLVxRuzTA1/dHIHzqXQOSVkzggGBCeAYMBnM+iT0W9Unk1 ancFhhPM1LldhUuBKhIREj0uBuM940QEs/uEW7T/T/2LI5h/9C+8ivRt/3ql8mrU7gqIYAiHpyz3 KoADaYRtjyMY70WMYLr5Fbx2n6febf83/QusImUR3fSf4KA4jsH8sX+9uwvUuk0VEMEQDk9Zbleg uJhIW/UGggGPkT2+2nR62stswA24IWBvCojBgOroVaS9BJe1UkAEQ7s9ZXyNAo8jGO/JdWDCws9M AxnBe4U0PzYpQL8A/B/8CxAMd0Ie7+TVYGjqcSW+XQERzGvcsBpCK/AGgsGeCUw03orV8fnt09Ne BtC7lMDjWwDBgB7/O/8Swew1qGQtVmASwfSccS0ek7kO4hJnkc4TK8HTN/YQE5AoWOZXjr2ULMkS 4M2q1bZMOvG0rN6zv2gcITIuraxq39F4IgaD9zfsPg8CH/+cpv3Mv7CRIAbDPeYfvFsbEwxAn+fo LEukwKXALgRz2lncwnnBzdWW7BN7KPcqKlIgPvZsSwCfFC2x+NJaQpEtrGKePkANa3CQ8IqiFXtq MawUB/xHEwz9jJmV0yjYprzSDFzXP/sXzsjFYMD7xsEWGbzaqFWk5wwnWRJRYBLBpLSR+lfPI6a+ 7fo7/aOKKRE3HExjNcGxlh7bvOhLa5mAGEBEp7WWlIps3oyQgvVWWccDUGBANcyR29aaYWp6LgZD 7+Sl1z4i88tHpfmef2EduAfvgveN/4F/4RgM/dDnj+poNfY5CswmmMz9AIJJv6rmCrKIRx5ekGM2 B3i84kWYrJ2YeHD58VrW1BuJElmc9eC4BypujsGcjJ9exCqS9wiZ83Mw42xBMFsEimgjwWYXgBTg JVngVZGYYEDG5zgtWSIF+leR0vm2HJn/7VaGjAnS+2b7d9XF2ko9Fil6R+9u/rSzeE9fZJqsXTaj 9wkXpcBt8ThgjeUeI+I4FiYki6ce0doBxnHMzQRjo1UEwYCFg+Or3ae8LTCLOxx0dA04/Az2wQCC Aa+KxO+m3kLn3Qez7B+owOwYTAoHmWfiCCaDgDjBeF4zThsXP3USTGZJEZ6wVRFu6KkFa9UUSQKt i8RgsjTVzvpcgqFPUw+cTeYVtYVn9bjz+BwrA+gTEAzYdgNebwSiLMdXgKXmda5KlgK0AgsIJnVC /TEYjmCKoSBrGP7E89xNMRgvNBKkIi/qABpo7/CHaBiJDGHFRDDlX9/Rl0QMhj6LBM6t0NPKZ2ac gVkATMGrA77hX5hg6DDSZ/a4Wn27AvMIpin6YgMGRZgoxgaqAFGEhnguvBwTCQJliyBA87hVgEuq ayvVWrJoGSgwTk62zH6CuQI8XOjl9yTXmX9s9sUEAzbQ3D497WUAvQ8GnAACZYIz2BhTwLfgfNNe fSFrP0SB4QRT9OjVVQmcKyWAdCkniyWAO/4rl2eJF1EAIZOiX8frO6kZGIw624LNHmh5E8FE6rVR ujRXNhKqrBZEizfsg6EnLC4GA1wdHQ3a3X3SzwYEq0iAYP7Jv8BmF7DAdHzFDQZ67CmjFOhUYDjB BH2GkkmBIQpUWS1Sy0cTDBc5AJEbeqcFNxl1zoBPyM4RDFhFAsezRTBP6HHZMEoBbtIY4jYirkVp pIBVAAfSCMU+mmC43RvgZp2OweweAKDf7Ahmc+7VAaALMF+O8isqRwqsUUAEQzg8ZbldgeJiIm3V GwgGuP/jKzCbcARDP/oMVEeXuWaurNbyY//CecGz/MHWWm7RDcdg9FaBai8rwaMUEMHQbk8ZX6PA GwgGPHf/+Gr4pMNxz2EGjtB43w63f0aB3HrcYQk4Ms3pDFgK6083YYaeKlMKVBUQwbzGDashtAJv IBj6rQLVOaKYgPOs7yYY8Ig5LDIItHAxmB/6F35u0O5hMG4wK9e+CkwimJ6NMsXjPNcRFeIs0nli BZ8SOnXIklUP6wbLTEXuL9MroXhiizvxdKkRsXx4vWdH0DhCZFxaWdW+o/HE82DoJ9r93L+Grz29 m2Do9yKBGAxHMN/3L1DX8ZUIZl9f/pmW70Iw1qemjjPzeemJ3PQruwO0uCfUy55plSW7KgK1ZDZb 8atleiVUsSZjEayYbcu8ehfDSnHAv4Fg8PtuwOxG+93PnDFBq3/qX3QMBsS6wMZhEIMRwWjcvkmB SQSTcYP1f0X+SP335ZLToEsEQfrTeGBhISDlKq7e/jKL0aNiEMWjwB7Li+RUhBKvFpAYoGQ6PKpB jWqCNxAMfQJoi60P3Dy1eKamV9a4fTB/T12YYMCDZBaLqeqkQEQBbmaI3DcX3Y+HMqkjzP5uDaIU wypXM3GQo+p6s3JaCcbjlThbFEuwjcpCLKM4KbOzs94qZmUUW4wMRYbi0wnmgvTrD2IVCZxMOb6a sR4UmV9GpZlxUHmUbVc59HuROIIBz4MBBYJHxRxf6a0Cw0eFCpyqAE0w6axbjszr3dTJZo7M0VZR ySOtIELNIxgcsGmqN0IwVWZ6A8Fkv5+jSQTB/BpeuxPMFvszZsRgQMeBk8/082C2CMhN9YgqfC8F aIKp39f+zn97MZXUSXuBE7yOkPlL7PWDVVgfnMaNiosXOK7jxVeuz5vKBKUBSLpwM6gY1qpIFR5a gdaJYMo/Io5gwObN46u9ZiVrLQ0HKxtOu3/wlDlgP466ed+CdaLjK8VgVg4Y1dWvwAKCsUstll2C eIEDAIBgsHcP+vUhazEgZhBpXRGwbDyss5Zid1gNO+sVwYwkGPosUv8ksqCELQjmF/6FJfquf4GM 4L1I/9O/MMHQELZgDKgKKWAVmEcwp69NAxjZPgYQgynmKnJGVqalJW9doxo4yWywQlVLsLYBsCha 3lSCR4GzLe+pN9g7YDxUJa0GC7+wIZJoWRouBoOfKbL73LcFwYCXRWP9wXNyAYuAvUFggQnv5N1C 590Hs+wfqMBwgikGEqqrEjhX6sCu+34vIlJcZ7HRgqLvLIYZgETWfeJPUjM810uXECeJSNstLxY1 9LDSE62qWJFrLU1ecCyC+XI2oM8igf0zAyeaTyiKDmBwq0hgJy84pYTfiySC+YSB+qY2DieYZXeq qkgKeCGcVmXeEIOhCYZ7Fc7f+hfenwHc/O7uk2ZBcD4IOBtAMKB3MMFssWP6TQ5YbelUQATT6u2U /nYFmqJNEWvfQDD0RMA5rd/41y/h9W3/2p1gwIoP7h1AdaBMgJ5ggQkTzBan1umhrozvU0AEE/Fw SvM0BYqLibSRbyAY4OqOr8DMxR0/wdWBb1/8Zkc6BgO6ALxyHMS6ANxggnmfh1OL3q2ACIZ2e8r4 GgXeQDD/Di8wi3GRD/wSAy6osPtUS8dgQOQDEAz46p/9C5x7Or7ilhR37zjZv68CIpjXuGE1hFbg DQSDXR2YocCtPMc99I6cfafR0/Jf+RduGocpIBe9isQNht07Tvbvq8Akguk5IVI8rnIdUSHOIp37 PaunYGyyYq5UsWCZWRYsTrXMzCp7vqn/xNNpMD6ZNa/eUftz40DzBoKhnwfD+V1uMejIte9cWbUc bK3FeUHfAUwBh7dBKAU/D4YOI1XFUQIpMEOBXQjG+tTshK3d3Vk8gnu1Nz21mxWefeUBh1dCqyUW iegSqvSTthTUcjEE1jATE7OdLdMjyzh5jEr5BoJZHPn4N//ClgDnuvsSxg/8i97Jyy0wgVAKfi+S CGaGl1WZ8xSYRDDpbXQaM/A8YkoDabjlcnIELnguM7vFtyVbTTAZ9Njmef14mRxG0ChTjM1kihWZ zwMmkBig5DWoPL5sgpuPJhjuKSY/8i9MMNyz8OfNgANL5pQ8DAAZAcGAI9MgGoRjMCDkM1AoFSUF Rikwm2C8SIn9PP2kmquJTuJBjqrrLeICQRvFhZJIUAQTTzUKUqU3EJoCbefqzUJfxW6yOOvBcROy 5MjVk7k/74lj6UW82ZGOwQzfyYvnJm75adR8N7Ucbj3uMAnERQDwARYBIPLn8FIMZuoIUeHDFaAJ Jp1vi4UUYxvFWEvRrRaDBLbSJq+fgVFWBTAYR2UIgqmiUpUkshJsgV5wIqKYTeNFenrqjRBMlqYq C4cTb4jB0FMDdwaYO4N9GPligqHdP9gHw8VgwGDAp6kVg6F/R8p4iwI0wVRdRTWmYmnGurTMY/V4 X88rV51i1pA4r+Aar8aeWBaMjnheH5PERX7BWiKWRyzB9Ypgyj+iQzUiBkNPH+AJ9KBMLnJDE8wP /Ytu+PCM9JsdAfqAGAygDdCneBVp961Iw/tUBT5cgQUEY6Mpll2CeEETDIhYtNJDFbMuSig2yguN BNkiSxbBiHzF5Le0VAxxtVoeD8BUw2xFI1N2xAZXeRoneEMMBuyKOL4C0xD3TN7FBAM2Dj9nhuWi WYf9HMGALgDHlMBbJI+vFIN5znCSJREF5hHM6bQyJ+SxS+bsi7k8L1hFkCI0xHN5zFEtwQIBwJcg UoC2pF2JKxpleRPBFPGoCGRAt8zydID1QMwbCAa4z+MrMBfQ60GR+WVUmncbyREMiJcAgsGrSHoe zKgRq3LWKDCcYIphkuqqBM6VAs3ptDLfiV3plSXLWHXAVQdZLcFSV2pMUXyiLREzQCQmEsgpajij 3pRR0n7PjLy6pgqFEbJ5A8GAx7AeX4HZhN69sWaGOmuhj/msNNJb+zs+x2aAMBhACtBx/+RfmGC2 GAwr+1R1PVyB4QQTcRhKIwVGKSCC+XKGoZ9o92P/4iYvbq8unYszckYueqkL7E0BBDPjNLUIZsbA UJnzFBDBjHKlKmeZAl5IjzbgDTEY+jQ1F94Ab2H6BrzAyeFv+he97Wbe1GlLprfBAvoEmAKOKX3P v/AT7bQPZuWAUV39CohgaLenjDcqUFxMpO35aILhtpiAdQrsIwEwgVBE/0y3oARuT/RhGEAfQDAg PEOvIoENNAsEVBVSoFUBEQzt9pTxNQq8gWBaf/lXei68Abw1WIk4vtp9Rw6wn2PBo0AQTQFwA0gR 5MLvphbB0L8jZbxFARHMa9ywGkIrIIIpY8wMb33LNLemUo4FD9tANGU4weBn8nIPB1ojr2qRAlYB EQzt9pTxNQo8jmA8v/WcKYz21s9pwnMs+b5/gVUkEAYD70XCMRjwuqvnyCVLpMClwCSC6TkhUjyj ex2yJU5TH20snosGFV1nd3FDIseJ7cHvzjKrbfFOj6d9PcrybFNt51nxy8Ke8UNw1eMI5q+d6zkz lwhmYF+AtRtAMGDXLQjqYILZ4tnHA5VXUbsrsAvBXEhhnVz6BJE0GXiayMk013+zwrOvPG9ardfW Yk3K9K+WWSzB5sIoUK0laPmQehfDSnHAP45gvB0Vu083sr+owM/8C6wicRD5HXgBS9R3UuCBCkwi mBQO0piBRxWpL0zDLVf4hHC6AFOyyEGGMlYTHLHosc2LiwDAyujEtiWCL0XUw2A3qd4iwdjxYHET 8FNrGOZxBOM5JzmYB86h/Sb9i39xbzgCkRscg9EA6+9NlbBSgdkEkyELIJj0q2quJjqxlOARDPCm xWBJPwfQBAOCMTZe1VpLhIq8NDhkZW3DBGNx1oPjVmr5iiY9mfvznoyfXh7BcD/XY0LRk8pWzqqt dQGC4WIwoLu/BS8RTGvfKf29CnBTYjbfliPzZpkmw47ifba91bYe66zd84Wc973KzBxq3B97VFTk iSoq2QBDhBhA24uBGa+WnrriigHSygzLujvSzCaueFwMxpsUwHPk8DzyG/+6dwJS7YcCYCcviMGA 09RggQm/2VEEowG5lwI0wVQ9ROp1ih6II5jqWgbnfYPcUwSdTg5oKtOCSLC9TbUE1QBUdNEJqFcE U/4RHZINJxi9se/eeRnsuj2+AieAwCZfcBYJPJkGvxdJBHPvOFHtrQosIBi71GLZBbvhpohLsSgQ WWnlj3jsJ2JJ//rOXbX01yuCaSaYGe8ObJ0ylJ5Q4KfwAqE1EIMBLELvg1GsjuhcZblRgXkEc67+ BKMvmTv0Vg2A17RfFbknHsLB2bMVMUtpxU8ykLLiV3GqCea8zq3WYls3qd5s0dDrHTAeqpJWg4Vf 2BBJtCwNiMHQBMM9LpZetLpxRntm1UD/46t/9S8uBgNWkfBO3l/61zOFlVUfrsBwgimSBA6xZG7s 8q/Wa55IlIKRdx9vkchmtM4v+8RWlMlVLcE2zWtCcUWm2LqiVdZy7HBbLc941LO2yhPVelO6Kg6A SxPLYTRjvJ9guJO34MFoHz5vtjYfx2DA68G5x/yDl09hggEs1dpkpZcCCxQYTjC0F1FGKUAoUGWm SJkimPJUQ7+qcMHMdVcV3FNrAQvSX4HeAQSDd/IC0rpLcNUrBYACIpiIh1OaRynghfRoI99PMP/s X2B24Nae3j3h/pt/gYb/HF7gSbiAb7wHNx+fA0v0RLt3j89Pa50IhnZ7ynijAsXFRNqe9xMM9zvn 1p7ePYdyVIf3wfyTfwGCAWXS+2AUg3n36H1f67iZbUjonvY3yigFxirwfoIBD0YDk5oIxooDFmiA kr+AFxeDAc+DAU+0w/tgwLP13uf81KIXKCCCGesLVdqOCryfYLgdLfTRpxfMjF4TOKoDBHl89T3/ 4mIwf+VfeBVJMZgXD91XNm0SwfQEaYrHVdIDSq1nkYLnaGyy4WeRPEvSXoic1sFnptLjTl5HVGs5 Tc0MW1OvrXo2Fb2fYLi1D7C68crZ8GoUwBSOYMAp5eOrH/gX6AIQDQIEoxjMu4fup7VuF4I57Sxu 4bSnarNP7KHcq6hIgRgCbAngk6IlFl+aSsBlFo2vKmb1wQeb7SiK19sDu6PI5v0Ew/ldbsVk8Rz6 K//C4wNsruU2yXJ7og+4BKtIYAv2n/oXeLQu3pHzE/9a3K2qTgpEFJhEMCltpJEMzxGmPjUNt1xB C8LpAkzB2BHxx03ggoHpqi5SZmu0Brclruq8equs4wGoNZ4GmvcTTGQusGk+9n2QHPABkQFSHF8B bgAE88f+BQ5MATg7vtJbBbhfinLdpcBsgsncDyCY9KtqriY6iQc5gDfNhIrQRjHAU+QVL8IESrCm Zp94sY1OywfWa1udiXOxb/qHB8c0vnxRYE/m/rxn89LLmw4Wb0zZ4hnzIFAEdv8cX62MMOHIB9h9 ArbI/Hf/AhtysSYimLs8serlFKAJJp1vi4UUb5HTD+3fVUduK434YwsNxVzA4OGRjCoqWWN6eMiL oHi1eHVV8SXFiyrzRQgmS4NloVniZoKxSj2EYMD7CLnpZkYubs8KzgXs5OJS4P0Mx1cAOEAMBhAM HYPRM3lnDFGVOU8BmmCq3uLydtcNdBY44QjG3qZXP/HAKP0c8EHWkLjXj3DAxWSZ76+66ipMZLVf vRBhvogaXjApXq8IpvwjOhR8CMGsjFLQc9xiguHiUuDlAMdXAFPAV9/wL5ALbPI9vtJpanocKuMt CiwgGAsKXiQmEoOp8krR+2b+vtOLB2kjYkl/dOSuWvrrFcGsIxjwOBIw7wzfDnLLHDe2Uu7JOuC0 0fEVt5P36/4FCAY8Reb4Sqepx44WlTZbgXkEc7r5NIARj8EUcxW5ISuzGFax4YqmXMXskRJsGq+o OJZlyqSagMCJXaZohbBJ9WZmeDqA8VCVFIzw31cXSbQszYwYjAhm1EzKPVkHRLOOr77vX2AfzJ/4 FwAmxWBGjQSV8wQFhhNMMbxRXU/BuVIHdlIRiKkUAzlXLuzmbbHYQdpv8SepGRiMvNhMxoVXY6uC FCEG1GLZoij78HrPAtP/FintMkYE8+XuXjybcM+DecIM9TQbOCXBxpTjK/C2SI5gwMuu8VkknaZ+ 2niTPViB4QSz7E5VFUkBL4TTqsz7d/JyO1r+1r8+dmLlVtb+Hl4AOADBcPtg/hJeIpiPHdibNlwE 0+rtlP52BeLrZUFT308wnN8FD4vbdL7rN5tTEp+mBttWwALTn/kX2KyD98GAHTn90qkEKTBcARFM 0Mkp2aMUKC4m0haKYMoTCxe5GT5J4QLBM3LAM9+Or8AzebmlImAnfi8S4AZAMN/0LxDxUQxm8fhU dVMVEMHQbk8ZX6PA+wmGm0SGO3LODJwLBDD+Al7glUNcoAXYiXfygqUisCcXxGDA4W1MMGA9a0bf qUwp0KmACOY1blgNoRUQwZSnkeGOvHO2KmYHh4PAE/mOr1ZGmPAT7cALqDmC0SrSjJGmMh+owCSC 6TkhUjzOcx1OIc4iFc/vFDeBZsdt7OmbGSd6WsssWpV+OOTEU+QsUibsqHpH7c+NA40IZmOCAYEi 8Pzc46uVESa8igQwBSwwgTc7grUnvA9GMZgHOmmZBBTYhWBOO4tbOLOTt1eyK7H9w6ZJC/eO8hZR w5oEPilakhbb0xaPyWwXV2tp0rCz3h7YjTMKTimCKU8Rv/Sv58yqdKCIzki0Ha8igTNfHMGAoA5+ HgxgKaLVyiIFZiswiWAyIAAwYZ//kYZbrrt8wukCTMHY4bl8ixopVzWhTNXre+3FuFMN5xQpMGL5 vHqLBGNHhbXcSkQDjQimPM+ALSazJ6Yh5S9+4QCwGZzqOr7iCAY80Q50HN4HA55MM6RHVIgUGKvA bILJ2MVDGbsIUoSDiK8txmmuZmISAt4UwEGcrjwGShuLqShrSJHSbGn97DWjXmBnkWKLkaEhIZyb CeYc/enl/cgXv5uae/7s2BmqWhqAA+ytwUkl2yPXJ1V7igkAoxxfAdICMRjwVgE6BrM7s3K9o1z7 KkATTPobLxZSvEUGERfrIz2PleFOlU4875uxAjAYR2UIgqmiEo7BZJS2zPKx9UYIJkuDZVEMZvBE tHKnCG06OHeD127AjpA/9y/OTmwJIEVAMCAGA05Tg6YdX4GMXMOVSwpMVYAmmKq3SMMtxUiMpZlq KKInBuPFZqpOMWtInFdwjVdjLyCLtC5CY0UZm2rxLL+YMv0D/w3qFcGUf0SHZA+JwazcKUJPc3Sg CJxU+hv/4uz8NbwA33D7YMDG4e/CSwTD9a9y3aXAAoKxyyJeJGYewWQxjwgrAC9e9NmRMrOMmfiR EiJp1tRS1KcYW/K6VQQjghkw780IFIFDTJzF+Fw3eGwN2JgCziIBEMEE87/9i2u4ckmBqQrMI5jT jwajL8XlieIqkheEAK696FaroZTMcm8hKYIU1aKyRkXaEqnX69xq24HyY+v1YCuzsDiKsgFWDQqC BDfvg7Ek+5AYzNSpZ1ThWwSK8FsFvu1fgGDAPhjwsD78lD9AWqP6S+VIgYEKDCeYoofz4hlF1355 L/vtiUQpGHn38RaJbEZLNtkntiIQNemxJC2Ws8pajj16tRbLFkU1htebsktxAFw6Ww6jIUYEM3BK UVEFBX4DL7CKBB7Q8h3/AqtImGC0iqThu5cCwwmG9iLKKAUIBSyKMYUQeeZlOZo0PAbzC//aa8La 1Fq8igROKoEYzH/yL3A+Cz8PRu+m3nSAfazZIph5nkglT1LAC+nR1b0/BgPeYvixc99zGg4WfUAM BrzZERyKxvtg9ES754wKWRJRQARDuz1lvFGB4mIibc/7CYbb7goOwkQmF6W5FPgpvEAMBhAM2D0D FoPwKhJ4HYF6Uwo8UAERDO32lPE1CryfYLi3GAK3+8C5bI1J3DEl8H6G4yuOYMC7qcET7fDzYEQw a0aRahmlgAjmNW5YDaEVeD/BcAd2OO4ZNTc9sxywJRcYjJ/JCzJyO3nBoWj8nOLv+dczu0NWfbgC Ihja7SnjaxQQwZSnQRGM1QWwCPAl+Jm84AF6YGst2NECCsQEo7cKfDgQbNf8SQTTc0KkeNA3PWLd epr6aGPkJLBNNvw0tWdJ2gvVc87VtqRnzr2OqNZympoZhnONqtdWPRuV3k8w201MjzWYewTwz+AF Tir9i3+B9SCw1KVVpMcOLRlGKLALwZx2Fg+h2OeCZJ/Yx4pcRUUKxBBgSwCfFC2x+NJaAngyStH4 qmJWn6CGV1vi9fbA7iiyEcGUpw5uxYSYhjbKwu2Jxk+0G04wIOSjGMxGg02mVhWYRDApbaQxA+sI U2+a/Z3FXYoE0wQQAFywH63GHmjbvOiLBxkR3IlgREqEEQ3n1VtlHQ+eALe1ko0IRqtI1anyywTc Tl4cgwHAAWIwgEX+2r/A+yyPr8C57qhASicFFiowm2Ay9wMIJv2qmivIIh55eD4beNNMqIjXt2ni q0VVggFBERuvauUkYPnAeoGdKbyefW1JN/28FVny3uzM35n9bF56eTPA/+dfMyYN7YOxqnIzJo7B AOAABANYBD+2DnwLXvQ9Y4CpTCnQqQD3e8zm22IhxVtk64ewq86+Teu1/q/H62c+Eocx+iMuVVSK yJIZeTVhHsFkZmec0VpvhGCyNNUu5lhCMZjyNMKdYOqckh6endvJCx6Se3wFkAIcaAdPdqFXkRSD efjwk3mZAjTBVF1FerOe3bh7HFOFEhsbiHziFYtXUizEXPzUSTCZzUV4qrpqDyaK7DXK8qzh1z9t L1zagtaJYMo/okOy4TGYf/UvMCeKYKw43E5e/FYBAByAYEDkBrwyCe+DEcEIEfZSYAHBWFBYTzBF f48JxvPKVczCfr0YfeksswhbXs9GmC+SJi0/I1QQk8M0aaNKisF8ZT6hV5FADGCvCet2a7mdvPg0 NWARQDCgTPCwO7zABB6Fd7vyMkAKjFrVBW44c2zB6IsNSxTxokgVXrgCu9V4Lq+x1RIsylR1q5Zp G1VsJq6oWguwHBhgIzQZyth6vSxZyuIoOvNWJQWM/ntgiiRalmZGDEZz3ygFuLgUyHV8BeI63GOR /7t/gbqOr0Qwo8aJylmjwPAYTPH23YtnAO+bOqc02fl55resG8tqvHJ5lhRXNKoOEtdbjDcULSnG M2xk5SrQNr8qSBEmHlhvyi6Alq6uEcF8GZfB8wW39rFmDtqrlhkEA8oEBAP69E/8C+8pFsHsNRpl 7XCCWXanqoqkQDXqE5To/Tt5Ob+r+fF2BbhVpD/yLxwN0lsFbu9xGdCkgAgm6OSU7DkK4EAaYec2 BMM9jOSYEUQwTdPi4sSgd8AWbBCD+VP/wjEYvVVgcderuk4FRDCEw1OW2xUoLibSVm1DMFoM6pzv tssOYjCAe+hVJD0PZrsR8uEGi2Bot6eMr1HgcQTjxVq4gzAfPsc9v/mgW7lj8GAVCcdgRDDPHy2y MFVABPMaN6yG0Ao8jmC8FxJpMeiV0zdHMCAXeOjL38FLO3lfOcBe3KhJBNNzQqR4wOc6okKcRTr3 ewbPDdlDTECiYJnZkR8sTqTMbAdr1rr0uJNXV6SWYl6gz6h6R+3PjQPN4wjm184FZiKwg+H46sVT 2Aua9vf+BV6ohN9w5H2L5dI+mBcMp49qwi4Ec9pZ3MKZnr9Nk4GzuFdRkQIxBNgSwCfWpEz/aluu BuLWXcUCCmmyfF69PbAbZxSc8nEE8+/OBeYm3MKPmtS2ayxYuwEEA56DB74Cb0U4vtIzebcbPB9u 8CSCSWkjjRB4fjf13Nff6R8R1z4qjdUERyx66s3CMxl5FAErCyl5abxIRiu4eEGXUfVWGauIfdeg GgJAjyMY79m7YLbCqwMfPs2tab7Hncfn2IAf+RcgGPB0XbDAhE9T/8S/1mioWqRAkwKzCSZzP4Bg 0q+quUAQBXjWai7gTYvBkmLIBwd4QHQkwhbFwotleq49UktRQwtqnfXauJoFOIuzHhz3xGNuJpgT x9KLIBj8rFUwKfzCv5qmEiUGi0FYHC4GA94qABaY/gZeWkXSMN5LAZpg0vm2WEjR4aUf2r+tS8sK sZVG/LF1tMVcwGAclbEZvU8iizuYigDBZMiyjGDoeiMEk6WpCstxzM0EY+mYIBh8xoTbQPOcuQy8 GfE5RtIvnwIrOyAGA6Ip9E5eGsKe0wuy5KMUoAmm6iqqMRWOYKrrLF4MBn/ufWvv+OO8ErTkxLJs 0af6SZUbstq5WiwwDaxXBFP+ER0SEwSDVwfApLbFM2a4eWrxVP5v/oUtAXwGCAYEU77tX/8bXmAt crGYqk4KRBTgZgbv/j4trejq+mMwHMEAv1tlhYwtqoGiYpgk5TlP80g86QSR6/IgAHRQsJbigs7Y ekUwIwkGuLrjKzAXbPGMGeCtI9PcmjT0w1TAGWZARQBFwBPt8CoS3YQ1CqsWKZApMI9gTkeeee4g wRRzFaMUABcwNFTBpcoc1RKqAQwrfqTM4sKKXYgAPRupxaaZUW+RDoFumVXpAKsGBZEgPZmH5z1a RcRgwDZSvJN0i2fMbBEooo0EEAkIBtDG1/0Lx2Do11bIs0qBWxQYTjDFW3z7IXCHl5eyDGHDD5Eg RDFuUY2sZARWFMqGOvAnniUgdoV9/GVkNTpSRZxIW1Jjim0BsZ9IT2XsUhwAVzkeYxFE8YZ9MPQq 0hYHaLfArJ/7F57cATcAggErPmAVSU+0u8XRqtJJCgwnGMJ/KIsUoBWoMlOk5DEEkwJXj1lcDIbe yctt3gQLHwCJjq/ACRowx21BMF7k7PgcT99/7l9cDOY7/gU67vhqix3Tk3yhit1RARFMxMMpzaMU 8EJ6tJEDCAbEi1rN4giGjsFwax9g0Qq3FxyT2X0Jg8YsQDAgrgNOMH3Lv3AMZov9Rjs6Wtk8SQER TKt/UfonKJCtmnWa9NEEQ/tdbkpaXB1nJJfrl/6FCwSP0AUEA2jju/6FCQbskuE0US4pMFUBEUyn 81P2FygwgGCuHUnefp+4TFwMhp4mvHcwHZ+DMsFiEP1svd3hhrYfPIAOEAygDRDowjt5RTD070gZ b1FABBP3LEr5VgXGEMwodRYTDPcIELxoxflyLtct82ax0h/7FzbyL/wLEAzoOBCDwQSjVaTnDCdZ ElFgEsF0bmTMrLoOzXJnkbLb46vw4umbbI8Fbkjk/E6W5mqLp3ykzLNFaUPSVZVRJ56KbV9Qb9a6 UWAAyhlDMFkf0HYvJhhuH8xiguG2G0dmwIFp6FNdXAwG7IMBZ5GAksdX2sk7cDyoqAUK7EIwNjaf bZ20uzvx3kp7Ftcr0IMYr4RWS9IuiJRpgQxgFqAQYOfFEBi/htTbA7s0JOSI3F8QHm1N5S8mmC2e aEc/sH/BHHpVwR2zOrKDkAl4axWgjW/6F47B7B4GW9ndqusJCkwimPQ2Oo06ePN86rmvv9M/Iq59 VBqrCY6L9NRr8SVlNQsZ17dWyYjZNngT/GRevVXGKrb0GlRDAGhADGZfgpHTGjUR06epwbYVQDAg mgKQCERujq9GSaFypMAaBWYTTDWwkfFKFmDI4KA1bOCRh1cO8KZ2YStbjYrY5vGKF2EqEozVJGXE zCqMNXH2slGZs1LMdpjPbKutpBZnPThuCnM8KwZzSXn9QTyTF2yYOL4Cs8kWBEOHN9ZMo2ct3I6i IyNYyAMEA/as/JV/gSPrx1faB7NywKiufgVogkln3WIh1jtmXjBNAIIN1mOljrOJG7y7+RSbMpfs 3eJH6vUalYU9iszhsUWRJIooMNxyS06d9UYIJksDZLmZYK4x5JFd3L6jBIJgwFHe46v+meLeEt6N WQA4QLcCTcDKIH4vks4i3TvOVXurAjTBVCfkFBcydLDsEicYOvIRrMLiRdYQz4lGmKYYa7mALFJC EcIAA101crUAYArGV0C9byOY6k8imIAjGPoMM3g2XetsMi/9FgRDbzcGT7QDBAP2wYCQFch1fLXF pqh5w0wlb6fAAoJJHZXHLkG8aPLxWfAGe9xIyUNK8EIjGTkBTrpu8qtxGhAbs51S/WR4va8imFSd 7O8guKQcSsRg6LcKgMfMP2c62yI2QG83/jP/AgQDdrSAwYD3wWxBis8ZlrLkdgXmEcwVVicIphiz Kfp4GyRoChsAcMHRjki9QUssGGGcyhx/0U7ASZzlNswzpN6sT7NabBUZtF0BnlZIyNIP2MnrjU6v hcBiLgaD763BRMOdpl48c/3GvxZbAqqjd/KC58FwMRiwNRiPE52mfs5wkiURBYYTTNH74hCLF34o OrDr/taLiNg7e++W2Lr57JOqg6yWYJuWRTKs/pEyi8ViQXKfnTxOxouFFDEo60qrGIaJautSRvEI JoVjzGpBshlAMKBhrSZyBAMCAMdXYC7YYuFgC88Kwht4LgZvFQBUBOJSoEC8D4bejBxxNkojBYYr MJxggj5DyaTAEAVa8aBY6RsI5kfwAhPHFgsHWwSKaCNByATEYMDyHwjq6K0Cw52oCrxRARHMED+q QlYq4IX0aBsGEMwVF8qChARhcTEYeifvFgSzhZH0UhcADi4GAx7yi/fB6DT1jc5YVRMKiGBot6eM NypQXEyk7RlDMHT1doWP2MkLjp8cX4Gp4R/8i5hQJmXZgmBo9w/6DhAMYBFwuAnvg9Eq0qQBrGIn KSCCGeV3VM6+CowhmGy/FRF9uXYkEQSDX1QEpg+wTjFp0iGK3YJgaCPBI3QBwYDXMNHP5AXPuyN6 TVmkwGwFRDD7+l1ZPkqBAQST7Tqm8eVcjVpJMJzfpSM3oDrwYLfZ8+CQ8v/Vv3D5YDM1eFoPiJeA JUW8k/ef/WuIRCpECoxVQAQzyguqnH0VGE8wJ4hwinAEgz0TmDW4B/aDo0/0M9O+7l9jZ71JpdHP BgS7a0GZoMfBKhLeLyWCmTQ2VOwkBSYRDD17F2f+61SzdxQ5cka3mubai3lpMvw0ta3C6h+x06pk DznjnSKRWoqdCI5PZ6soxaEVqbfH+5PMwGVLc4GT362FcwRDn6bmYjD0fIRXu7xv6epWZqTPpYNo CiAYwD3gcBPW/4f+tVJJ1SUFggrsQjDXDoEUL9IP7fkU7FPOb4unWrKvPBrzSmi1xPpBUMLV5Goa q1gmHS7Btq5IeB5tAPqxsNXq4oenJ4MlmR3pgOtEeGIVCb+xD0wHP/AvkAt4a3pHTnDOemwy8BZG bDN4fAtHMGAfDH52M/1ihMd2igx7twKTCCZ1bGkkw6OK1F9ef6d/ELhw2UC4as83WX6ia8HRCABY GUkUIayKL0H4s5BnexAHhKqKVVnHq7HIWBzcjCEYrm6bi4vB0Kepud0bYAkDW/LiGMyv/Qt7Ee4s EvduanxmTWeR3u3v39e62QSTuR9AMMU7WBAOiTNNPMgBvClgmh5LrG2YLYLA5IVGimGnCOHNqLeK WRf7pn94cNzDDzcTzNm89CJiMHTkg1v7AM8++QN4gUeVcDtynjMp06epuZ28oDrwgBkQ7zm+EsE8 ZzjJkogCNMGk822xkOItcjHWUnSrRUduKw1638xZFnMBg4v3yVl0gbNkCMEUeaVIY2MJprPeCMEU Ow6EnTiOGUMwFkRIa9aeReK4gXsx8lHXi2Mw9DN5wQog2N4EcoGdvIrBRPyi0uyiAE0w1cm5GlOx NGNd2sAYjOf2vAhKBiint86CAThegmu88nJlYmCypMLVYkMvA+t9FcF4wFj9nRTpeGUMhtvJC7w1 /Y7JXSZNz04umnWUBsQEBAPiJTTB0GGk3ftO9m+qwAKCsW7ei8TMI5jMv0TiJfhGny4BeLpgmVkg qmhnfy02wjS8XhFM+dd3CE0QDD0BcQTD5aKN3CIjrQngBkAw3Jsd8SrS9/xriy6QkZ+mwDyCuYIW BMGk8Zs0nAC8NXD/RXfuhV6yqosLJa2hF1umJ3vVKhsZsmESYDNt+aR6PVMzHcB4GBL7eNwqkvfS vudMT7S3fk4TnmMJeEcjIBjwVgGwDwavIoHXgz5HLlkiBS4FhhNMkSSq6yk4V+rArkhAarl1Y1mB WfzAu/u3oRrsIHG9xZBS0ZJ4W4okYTmg6tdbLV9Wb8ougCZPGausFlzDGUAwVcWDppxN0gwlBaSA FLhRAbwWnLqx7G9gMygTkPo34QWqA1PuwOk6PrErpRSwCgwZiiKYG6dKVS0FpMDjFMDP+AYEAzAF BG7Bc4/+DF4iGGHBdgp4IT26IWMIxv6qOYMUg3ncdC6DpMCHKYDPDHIEA1AELF9+B14iGM7LKNe9 ChQXE2mTBhAMXXcxrPRhs6Wau7cCP/GvvRv2wdbj/VKAYLiXXYDnaopgBjoXFfVKBboI5tqPoxjM B0/4H910bjflR0v2+MbjGMx/9i+QEZyD+xf/EsG80umqUQMV6CKYgXZce78fP7/JQCnwewXANkwg E3iL5PGV9L1XAXA+7vgKEAwwGzyD8Z/8C7/Ja9IqUs/+yuIxGXuj23R+5zq6Us2V7bEYexbpPGgy u8wsFlD0sEWRs5TVNFlbRtV7qjQcDNDO9JWVVevSPph7527V3qoA2IYJigIvpji+arVB6ccqQMdg AKaAJzeChUjweMbjq10I5ro7veb/y8ldcJN9lSXI/KKX60pmsxcdvN1VCj5ZX2YRBaqKWRGALKCK iGKLYaXMc1WqWJlABDN2LlZpsxXwHsB4fA6qxqdduFv52S19WfkAPXHkA8RgwFkk8JgisIoEXrR+ fDWJYFJcSO/UPapIfWoabrmCFkOcbsQxExGLuG3VCJAHYUHLMxqoskXc8gioWbj02mu7G7BOEfuu QTUEgJYGfKowJIJ5mZ94fXM4gqHfpg5+Qa+XemwDAW38PbwAwXCv6wJnkWaMk4jbyLxj8Z+pY04d lRdfifhRm8b6Ua+cqssvOuk4B3A+Po0/RVpn41URksC1zKgX2Fmk2GIcKDIU68xQTTE1wYlj6TV2 nlJpUmCqAhzB0Psb9PKmUb35C/8CIHJ8BQgGvK7r6/4FCGbSKlI634K4RTEqYG/BPQdpueeq1/o/ jyQsNxQJxmZPI0nFIAHn9T2n28lnRalt13C1AIKh640QTJam2sUcaSgGM2pKVDmfqABHMHinBdCR fgf4i/vmZ/7FLeRhrQDBgNPU3PNg/ge8bllF6iEYYp3FY51gbOPy0Ck8cRxQZKmz2IyW4p94HJBZ 2FkLRkwQ4wH1imDKvHVI9uKpVk17nwKLCUbv5LJD6Mf+Bcbbz/0Lx2D+i3+BlSnwegBwMG1SDKZ6 s9sUfYnHYDiCAXGCVlaIx34ilNO/vlMMFHlhHpqTih1ka2mqVwQjgnmfN//EFnEEQyvFeWu6ui0y ggfQcTEYvM8aEAyIwXAE81fwmheDOZ1lGsDI9jHYSMzl0oq5it43K7MYVim61Sq4ZDZ4yzERTLFF eZ6+apVtL2idR5lELZPqLeIXqCuzPB1gVaQGCbSKtMUsLSMfqsBignnxPhh6ZY2LS/3Sv/CbHf/A v8Ar07/tX9/3L/AcvOOr4QRT9OjVNQicKyWA02llbhsHA64sWUacKyOwov+rlmA9dNXptpZZbJ1H SHYZi/5kUr0powDyu2SstjRCNiKYh7pGmbWFAosJZot9ML/2L/D0NnDe+PgKzGUcwQBMwQMPEAyI wYBz0d/zr7+E13CCiTgMpZECoxQQwWzh42TkmxXgCOan8AJ6cd56cQeAw8iAG/DbiECZnCb0C63+ 0L+458H8wL9uXEUa5aVUjhRIFfBCerRKisEsnt5V3asU4AgGvIvg+Gp3ggGBInCMHA+L4cEnYAl+ Cst/9S8QgwERJkAwOC6lGAzt9pTxRgWKi4m0PSKYVzlUNWaxAhzB0EZy8Qa6Oi4jgAPafhp9vCaA fTB47QbEYMAuJcAiYB/MXc/kpd2JMkqBxQqIYLhZWrmkwBcKcAQDtoMcX+2uLN6T632LW02jj1cs 2KyDYzDcKtK3/Avs/wW5jq8Ug1nsLFXdAxUQwezuL2T/nQpwBPPuNztuQTDASJpgAIt8w79Arlt2 8vbsrywexrmOqBBnkbxTRaCi08sOPzc0tcz0eE56UGjUKSrvXNXwem1Fs6FHBHOn/1PduyvAEQzY MHF8tbsmW9gP3iqA7f9v/gV28oLT1IBgbnmi3XCCuZDicmbZUVu7uxOcxb18ZHFPaHqgF3jTLBko E1ti96iCtmDLracvdgRh+bx6e4bKKLIRwWwx38rIhyrAEQz9xj4QvHmoQE81iz5NDXbygn0w3/Ev gLMPfKtAigX2+R9puOUKWox1uhgRIhzQDxnFCFDGZ621ZJYDfEmJsLUW2xc99VYZy/JfGsoaAkAi mKdOsbJrBwU4gqHf7Ai83XPUAo4ccAPWhHvwLtAEvFUAn+vmziKBNzsCuW6Jwdhbdi+wkfFKts4C wiFxpokHOaou30aAIqGX4voL5qR4685FHMxDVoEhBNNZr42rWYCzOJvh7xsI5urC64/nTMSyRApU FeAIhn7+7PBDxdUGEglAoAis3WBv/cf+RVhI7xo+Og6sIoHeAZjyD/5Fx+pAiD6bcospi3fq6Yf2 b2+pKL0Lzxx2JH7gLTxlXhwYPIo2gM/OKKeJYIrFeq49olhTmqyieL0RgsnSeLJ0LicpBjN8AlSB H6QARzA/gheQb/iRnBldhWMY3rfYEvC2Ra4J4CksOBr0R/4FMoLNLuBg2uK3ChRxIUWQLGiBfVL2 bZNnLTJBFZJAvCSFpx5LbBSB4BVrJ27vFS/hLLe5cFzn0hnUK4Ipg9chGTcfKZcUuEUBjmDos0hb EAwmAO/bxQTDPbTm0J8jGLD8Bx43PCkGU73rLd6a98dg7EJD9RMAJRlOZVEZS2PFT+bxB0cbRbgZ bnmRabwATJAmva4sEjAOaFXH51cIrCn17MQimFvcsCqlFeAIBr/9mDbmIRlnnKYe3jSwdoMJ7L/7 F2g4RzB3xWBOpxWMvmTu0PNYwGsCZ190q1XsyGywbqtagsWjZWUCkmiCtoiqODxjQzVFtM3kzbQF 4wG3NAgbWkUaPjeqwA9SgCMYeifvFspuQTDgfYp4FQwQDOgdUCaAm8XPgyn6PM/JFT365b3stycS pWDk3YhbJLIZrfOzzjWCAt56StE2a3zReXeWWfXr1bYXAzlVffrrTdmlOAAuVTPKCcJKMZkIZgun ICMfqgBHMP8Or4c29V1mcZt1jlzcKhK3Reau09Q9HkV5pUBQgSozRcoRwbxrYlZr1irAEcyP4bW2 BR9aG0AKOgYDygSBFrCkeNc+mIjnUBopQCgQX7cKFi6C+dAZXM0eogBHMLRnGmKzCjkUAEtdfwUv EIMBB6ZAkQ98ol3QeSiZFCAUKC4mEuV8uSZF55yR8WibplcpsJECHMHgu/yNmr+vqTTB/Il/gRgM 2NECYjCL98HMmNJVphSYqoBiMPtOwrL8fgU4gqGfaHd/gz/AAnwCiHu2HmCRv/MvHA0CXQF8xpDN B1N9kgqXAnEFRDAfMCWridMUEMFMk/a2gvH+WUAwIJryXf8Cq0gimLgnU8rPVEAEc9tEqYpfoMAW j/l/gc4rm4AJBrzhCCwOftO/wON6b1lF6gnSFA/6pkesW09TH165eIYZVPTl9ojfZmuKRVVPKc84 TX028LLz2iMC6qramZV5CZKpMaPeYtVT0UoEs3JuVF1vU2D4GwffJtCG7cFvaAIEw51FAu83+Da8 Jq0iDScY60Gzh4XY8yngaSKXjyyeaskeNOK1xT6PpPqJNakIBKAtwPKineDD/lpse1OKssxR1Kdn qIzCGhHMhlOsTF6rAPAxWzzmf61a29eG98EAggEtB68O+L5/3ftu6iz+4VFF6tuuv9M/qnAQdO2R cjzXm37OuX+vBI/POmupMk1EjXiaCL6kLa0CEEDJ4yuQvZVsRDDbT7hqwGwFwGuMRDCzxV9fPjgU fXwFCAZkBEtF4P0Gdz3Rruh+AMGkX2XJiqELwrNWKafq8otOmrCkWlFTmUVf7sU2OCqyuYpiggWg Ygle+iLF2hovjmlFljz01Zm/M/vZjPRaP2GpRimAFfi1f4GMv/QvCf5kBWiCATgLYjDg3dSTdvKm 821xAgduNf3Kc9X2Zv0K56R+K+KPLXYUcy3gABtrGRXXsbyygGCK/BGvF6iR9UWGsB4M0SChGMyT 51LZ9ggFwDsAgH3/5l+PaJWMcBTAkY8/9S+APuDI9D/713fgNWkfTOrbLHD0EEyGI3GCwTEA4BRT 93m1JVLvsjKr7HXhZsYcHj5GWheJweB6RTBl5DpU07wqBZ6mAEcw2uR7ez+C5T9gG458AIIBO3lB oAWsIt2yk9fenXvU0hqD4QgmCwxEPDS+0edKWFamFwgZSDDFtjTVK4IRwdw+vcuAqALcQ19ogvmF f0UtVrrfKgCCIkAhOgYDygT7YMBX34LXvBjMtehj14OqMRhv1QB4TYAURbdaDT9kNlhnUy2huG8j spA0A4/sctWoWjCW4XqL61BV3bKoGL149Hsa7i9iYAmKwcj1PFABjmDoTb5gWeE54vzMv55jJLeQ h88ifcO/QMP/0b8AwfwZvIYTTNEvek6uSAkXGdhvr1UJEFOxiJDmikdBMgIreijLRtVPTmOAv6uW ENl9gquIlMCl6a/X0m22Inn1ryVImiK0D+Y5860seagCiwlmi6fk/ci/ntOL4HG3wMi/gBcgGMCs 4HG9YJMvqOv4ajjB0F5EGaUAoUCVmSJlimCeM9/KkocqwBEMfRaJDt6slG8LzPpf/gW0wu/dBGER 0HHAEhCDuWsnb8RzKI0UIBSIr1sFCxfBrJz2VdeWCnAEwy1hHAJtQTBbGMntg8FrN1wMBjwUESwa /ld4KQYTdHJK9igFrpXBIVaJYLb0qTJ6pQIcwYCTKdj4H/vXylbjurYgmJ/4F2gdjnwAvgGBFoAp 4Jm84C2Sx1cimCEuUIVsrYAI5jlOQZY8VAGOYOizSJzfBe4ThCLwignojy0IhjsGj88wgxgM2OzC EcwfwUsEs7XrlfFDFBDBPNRryqznKMARDO3jOfQB0wHYdZud9cj++Zwu4CzhTnXhM8wgBgM2Dv/Q v77nX7fEYHr2VxYP46QHlOxxnur5neIJIFBRduDF+11U67UnehacRfLOXqWtICyvnk4aVW+xoiGk 4vbj1NJbCz905KYq5ZIC8xRYTDBcDAYEAEB4BmzIPb6aJ+maksECDTDgT+D1Tf/i3k0Nnmh3yz6Y 4QRzIcXlDq4q7Kna7JMsZVpUcU+olz3zRNV6L09sDfD0CZaZZQdUVKwoWEtGEkVwxA2JKNYzVFrZ QASzZs5ULS9UgCMYWghwvw7KBCwCPCvgnuMrugkPyfgv/gUsxDt5AcGAGAx4Hgx4XO9dq0ip2wYu PPWm2d/XPy/faYFjxifWz+GIRRwI4lEQXCZutYU8r9645ZGUXL1VxvIA1JpEA41WkR4y2cqM5yqw mGC4VSSwaEWvZ4EuoU9arexmEN4AZgBGOb4CfAPORYOloscSTOZ+PJRJ7++zv9NIAMcr1n975QBv Wgwn4HCOrYXz8SD+BAqMREciXAJaUV1XwuRk25U1JyVXS7pn7V4zm2jmZoI5m5FeKyc41SUFIgpw BANuu3Gl3CGmxQSzxRPtuPczgDcfHV8BguHeMg2eaPeH8AKjCPiAbL4tpizeIls/5DnRIhyk9Xp+ PcINRYIBBuOoTD8HtLbFuu3IJ/FaPA0jtcTJKUIwWRo8Wpqo5St0ReeckfFoZMSjKI0UWKkARzDc awWPdnEhk8UEwwWKVvbaURfHgl+HF4jQAGb9S/962j6YLHByebV+grG36dVPcAyj6hRT46+b/iID BS2x4vQzEGaLi/yyqMm99Ypgyvwjglk8xau6iAIcwXDu87CH270RacjANBxmDTQgUhT3ZB0cgwEn lcCmoj/3L0AwmKXoGEz15jPzqZhd8E1/BkDF8Mzlm4tFFY0hyskYpbUEEB8KsoXn9UEDs57qYa9q DMYLwBRbJ4IRwURmYKV5hAIcwdBRii0WaLYgGIAUYGD9AbwAwYAYDJALrD3dchbpck7B6EvmVou5 PC9YDSQU3Wo8F14WaQKCIop9ZTnjty99xGVmCYocAEgCoN76eottsRaC8YBbWuXsL9ULpluTTDGY R3hsGfFVBTiCoX08jT4r+w3sWl1pBq6LY8Erbl/8Azyx9+/8iyMYbMnwGEzRC+IQi+ezrb9P22Id v/dJpkAxjuJxQAQF4pacLR1epuWAql+vRlMinDSj3pQsiwMgg+NqSyPUcfNOXhsle870J0ukwKkA RzC0evTyE10jkZHe5UPURWfhOg6fYQYxGPDsY/B0YLCK9J/gNZxgIg5DaaTAKAVEMPTMpoxSoEEB zhFyuQ6z6OBNQ5O6k3IvTeyutq0A8IAWUNB34QViMH/lX+DpwMBIfK5bBDPKlaqcZQp4IT3aAMVg 2uZEpf5ABTgW+al/YQ23IBjwAL3njBDOSPxEO0Aw/8O/fu5fgGDu2slLuxNllAJVBa6VwWrKSAIR zHPmW1nyUAU4gtliOwut+BaYxS11gZPPx1fgvY+gx//Vv8A7H/BbshWDiXg4pXm3AiIYeg5Xxk9R gCMYcMbkBcJtQTDcWST8rihAFSAjOCEPYjCYpUQw7/bNal1EARHMC7yJmjBXAY5guADA3JZ8WOlc GAxHPsAmGbCKBJ5MAwgGvyV7EsH07K8sHpO5jqgUlw+qJ2uKJ4BARdmBF88LVustntaZcRYprcg7 e9V0Zip4FmlGvcWqIyBCpxHBfNikrua2K8ARzBbnjdvF2CkHFygCT587vgJ8A6QBB45eTzAXUlxe KjtqWzzFneUCaTJPbP9pvWN67jetqNUSCxZVO6vsZVUiaslIwqPAIpCBD7PW9cAujSxZRhHMTjOy bL1FAY5guG2ktzTwrZVyBIPXbgDf/LV/AUwBT5EBe26OrybFYOytuQcT9vkfabjlLOfynVXXbp0u 8YkHK14MI44y8SgILjNIMFW2iFtuU4IwVVO9VdbJUDWrdwgAiWDeOnurXcMU4AiGc5/DjFZBrAIA RI6vwCoSyAg2RYFYHbZkNsF4kRL7efpJNdfFJRGmiYcfqq63GNuIc0AkNILDOTYWVZQi47ZR7JXp U8QaDylsTxXbkjXf4qwHxz3xmJsJ5oL06w922lE+KTBLAXCXDKoUwczqj8nlgkfdHF8BggHbbsCB IxCeAc82PL6iCSaddYvOA7i39CvP/Rcdua20iWC8u/kr0mMdf9wfxy3pKfPTCKYYuvNgiIaYmwkm X9PSu6knz84qnlAA3AoTpSnLwxWgV5EA+oBNvn/vX3hHDk0wVW9RjalYjrHuOeObCCVE7vUj5dh4 ycVPxMpUMXpxkVNrFKfYRq8KopYmDel6IzEYEczDJzqZ9ykKbPGY/0/pjPntBLRxfAWoAhzeBjEY 8LhebMkCgrHRlJ4YTAYWQRbBXjZIJP1rMTh4EGyLtRaHpuwdPqfh8HpFMOXbgKM7509QqkEKtCkA 1oP+zb/a6lDqxyjwF/DiCAaEZ8AbrcFrCo6v5hFMujRzOT+PXTLnnS33gNWfrGRv7cm6imrYo1ip hZgIdtii8EISLtMudXmxKy9OVm17UdUZ9eIyPd2K4b1qUBAk0CrSYyZOGfJUBQDBcA99wTstnirD p9iFn2gH8AaME7CVasapezTjf82d84vet7oqgXNlHusMOXgRkaI7t1GKou+0xXqo4cUPbPoFZbYS TKTtwTTF8FXTyCl2ZcZYRZSxHEZDjAjmU+ZltXOGAr/yL1AdeFPx8dUMO1VmXAEAIsdXgGBAyARs dsHVcfvBm/wQ7TyUUQr0KIARM1iyCCY+symlFMgVACwCxALPmD++ksoLFABhMPxGaC4GAwgGxGDu 2skbdB5KJgVaFfBCeq3lXOlFMAtmS1XxWgW4R8WAY7fHV0AscPL2tRLPaRh4czh+lj8gGNCtYDM4 IBi9F4l2bMr4WAWuZcEhFopg5kyQKvUzFFhMML/2r3frzS2mAE2+51/4vUiAKkB13DGlu84iDXEt KkQKLFBABPPumV+tm6sARzB46wOwmHtV4VwJTOm/9C8Qb8B7g/6zfw1vHXhm3fEVIBiwBRhskQHr WXhPMWi49sEs8J2q4gkKiGCGT4Aq8IMUWEwwWzyZht6aOjzQAgYieMw/TTBcDAa0+pa3CvTsrywe 57kOnhSXD7gTQKCi63TP888ipcesrEqe/VXFqmeR5tVbrHoq6IhgPsjdqqnDFeAIhjYDLEbQZQ7P CAJFwFvjvUHgiXCc/fRz5EAMBrQO2M/tnul5qwD2KMMJ5kKK3+++/N2BanuqtnoW9/KRxT2hXvas ydV6bS32VDBXZg+XgLZbNbJPZtTbM1RGYY0IhpsAlUsKfKHAYoLZYh/MFjEYsCca758F3wIWAU8+ BJbcEoNJb6PPm/UMQbJPUhpIAwlnOVcJVReb3b4TkFF0ijhiEa8lLbynzKLXj4RVOvWZUS8oE6Bk cUjQQCOCkSeWArwCiwmGN3RhzsUEwz0W+fv+hQkG7K4FBPNj/wI9cy/BFEkl45sLU+zaDfZhQX9s ucEjoaCHDtZra7EBJI9pPCqysaj4J14cK8KF8VoicRqgTFpRiimWcbNhQ+PLF+X0ZO7Pe0H69cfC mVZVSYFeBRYTzBY7eXs1bcz/I/8CJYFlHfwsf45gfuZf3AITPnUPZuZsygVxi8yfFWMtRSDIPFkK N5djC5KE5Yaiz7bQkJXfShuen477+HgJwQBMULE19XpUdBmZ4a/9HHRQE1fcTDCZrUezG6cvJZcC dyqwmGBW7nW9U9aWujmq+6F/4fcigbgIODr0E/8Cbb3rvUip+ylGYop31djFRqIFEe8bKafIPWlU IEuwpkwuFnIR52KCwfWKYMqYJYJpmbqV9n4FOIL5AbxAq0QwVhzufBbIhfcU45Ud71sQg3naTl57 c4yjLxg77L04uB0vFlUMBRXDPMEb/QivRNK0xnWKXj8egBlLMJ31imBEMPd7X1nQrwBHMOB9kMdX /VZ9VAkc1QGCwYwC4iLcPhhgPzBy3lmkdNHHgoJHMzhX0ftGUKa4amPXjLJPithkgSOCKd5qiHVg Vav6CSaiWDGNR6UehEVa5y0D4b4ohvealo3ydZuezMPzKgbzUb7nBY3lCAYcPzm+ArKA1+u8QEyu CRzBgI7DMRhAMGAVCSxagVz0O8zBzFxkAutcLz9UXdkpckDR8acLE5eFOBhwZTn/COY6nStoadH7 VsMSk8rMXbL/8nAv8lG1vJ9gIopZuk1zWXbBHRSkC+2D4aZN5ZICXyjAEQz9XqR/9K+P7Q/wbDqg yc/9Cz8JF0RoQMgEvMQAVEf3KUcwQZ+hZFJgiAIiGPoHroxSYIwCHMHQbxWY4e3GCHFfKdzrwUEX 4P2zHMH8nX+B6m5cRRriolSIFAARJhHMfbOmapYCv1VgMcFwu1bf3VfcKhIIg+EYDPgWWAICRYBg wEHr4yvQrYrBCB0eq0BxMZG2VqtI757e1bq5CnAEQ9vEeWu6ui0ycqepgZJ/Di8uDAY2MAGCoWN1 IhjaIyrjXgqIYLaYpWXkQxUQwdzeMRzVgY7Db3bkNt5yBKN3U+/lTWXtegVEMLfPwDJgYwUWE8zG Sk0znSMYcBwMRz64VaS/9S8Q8Vl8Fmm9+1GNUqBTARHMtJlVBX+AAosJZos3O27R7WDtCb8XCZ8j 874FO3nBawpuWUXq2V9ZPNabHsy2B5KrJ4GLZ5hBRdeR4weepq62xTs9nrr5qmLFs9PcqfXWeouH rjsZBWcXwWwx38rIhyqwmGB2fx4MFy+Z0feg4/A+GG7bDQj5cC9a6nkvUsUl1J5H0rTJJn0KyJUx +9A+6MXmAmlSr5k+kgR40yzZlbLVEuvgW0uwlliVmmqxbQFdUIQ88GHWuh7YHYU1IpgZ06PK/BQF FhMMt410cWeAQBF45RB4xfTxFXiVNNc6cMxnBsGAB/kAgrnrNLV9+JilhMxTpp74ijSkp04iCNKf xvrFauwhAx0PbuLRCAKPilET3JaeWqzOEWy6gltF4MtKACiZDo9+jhHBcBOgckmBLxRYTDBbnKYG gSIQwAA7RY6v/sa/uIH4C//6Nry4LgCtA4tWd+3kLbofLy5yOaTTsWXJggGAODd4lNMaTrD+GH/C +fi0zEgoxUtvP48TzIx6gZ2XYfaPjIOHhHBuJpgL0q8/uPlIuaTALQpwBANyHV+BhjxnFQYYyQWK 8P4SjhuAkWCTLH4vEvdKSKDJ+tPU6axbvAm23hFHXIre3WJQGpJpjXxgEgIGj4pk9LOFVcmLhXiu 3aO3VvbqrzdCMFmaCKQSIZmbCSaz+GjkLX5IlUoBTgGOYOj3InFGLs7FYRbetcqVCRoOQAQ/Rw6v 7HjfAoIBq0g4LgVaBzxB5MY3xYViTCX1RtgdDonBBKsAazEpPA3kgLPYJhqL0EOWpqkWonwPQEG9 IpjyT0wEs9jZqLpOBTiCoVcHOq1dk52jjcUEgzEFfEu/StqDG7CKdDvB2Jt7yy5BvKC5IUMuupxI oCgLNQVjG3GCqbbFQphdA8JWFbtjeL0iGBHMGm+iWuYqwBEMvUNzbmM+qfSf+Bd+CgvoOwA3YAsz +IqO1XXGYE4/Goy+2IBB0cUCzwqgJHO9QeddDPxYFGiCof4ym9ri9WA85IMN9sqJ1+vBVlZycRRl A4xYPPo9j/ZkHp5XMZhPciJvaCtHMPRzPt4g2TPaAECE5gZQJthbAx4BDJ4ic3w1fBWp6NGrqxI4 V+rATioC8QB7Z39lyTJaGrDFFomhGInxIgpjywy2BZtd5AasxrJ6U3ax/JSNBBxtiqOF9sE8Y0KV FXsqsJhg9ES7UcNkBsGAzcjca5jAqa7jq+EEE3cbSikF+hWoslqkChHMqClR5XyiAhzB0EqBm3K6 zOEZwcln8BVeWeP21oCmgY6jd5+AQAvY+QQeP7M4BhNxGEojBXoU8EJ6dJkimOETuAr8IAUWEwx3 UHlxf/zGv37lX9+CF3DzXOvAZheaYEDvAD4Dq0j/AC/FYGi3p4w3KlBcTKTtEcFwE6BySYEvFFhM MMMfizKjF0GgBZzxWRyD+bl/0Ws33EsfwfPzwJN8j69EMLTbU8bXKCCCmTGHq8xPUYAjGPwEfaDd 8MWUGf0EHDnYKUK/h5lrAtiuiw9ag+rAs+n+2b/AaWraEuCfhmw+eI3/U0N2V0AEw02AyiUF+BjM u5/Jyz3ZZfF4AixFRz7As+nAchBYILtlFakHcYqHYq4jKsRZJO/YLajoOlj0tLNIxbbYM1bp0aEi XlTPYdljPsUjYNmWlCH1jjphFOcqEczimVPVvUoBLgbzsU+0e04M6V/8C3MDiBVxz6YDud5BMPas cnbU1u7uBGdxLx9Z3BOaHugF3jRLBsrElqSOtlomPmBsfXaRwKq12LbMq7cHduOMglOKYF7lUNWY xQpwBEM/0Q5skl3c8N2rA10AVnyOr8DKDojBgFNF4Il2P4DXvH0w9tEdKQ0U+SP1rNcdfxp0AZgS AYhgmggHcJZYWPE+iUMGKBPgS0qEw9vSVG+VsSw8pYGoIQAkgtl9Kpb9dyrAEQx9bw12b9ypwoZ1 /8y//glegGAAi4DdwSDX9+E1m2C8SIn9HBCP58OyAInn9eNBjqrrvYrq9PrViuJtKZpUDR21Ekyc kzyksIrZuFrWloxii5GhNxDMBenXHxtOhjL5cxXgCAaEUo6vgJpbnKbeYjSA9SyaYMB6ECgTEAyO BtEEk866YKdF5mCyKAtezfE81uXYWgkGk5CFhkkcEPfxnte3htkye2pZU2+EYLI0EbAjlpYUg9li vpWRD1WAIxg6BrPFaeqHdtVXzfqFf9EEA3YHc0+0m0QwVT9RjalYmrEuLfNYPZEPHAOI+OwL2oLk NKPMqtf3okRpPMOGOqqfzKi3WqYlThHMFhOjjPwsBTiCoXfygsWIz9K9u7XAi2O+BKtIoFtBLnAW 6XaCsQsWXiRmHsEUQ0GjVlI8txrhLW91BrjqaltA6KiHvYbXK4IpTyCH0N1TkwqQAusU4AiG3sn7 S/9a1+ZaTeCJdtxTa2m5gKXgkTz4mbzcQ/nAJl/wTF762XqAz7wViiyLXe4JriJlN99g9QesQxVz VYMN3n2/VYMAF2wS1xYsTrETeyy3MR4QarLjIbL+ZXUA4yE4FHHIUKtItflY30sBXwGOYOh3Uz/n NPKQTRhXbP/440/h9XX/4oYnwCw6BgPeiwSMBDEYvJ5Fd4HnEoqBB7yC4wUGijCRLuUUV0yKgZx0 kARznVZhB2m/rX7SWWbakFQfECABEONFgIIa2rZUeSKiT5Vg0q6p1ojZ5cuWRhItS3M0iZuPlEsK 3KLAYoIBOy1uaX6x0i1iMCAaBA584a/ATl4ANyAGs5hgls3zqkgK2IgOp4liMM+Z+WXJfgpwBEO3 c4sYDIADADf4rQLgW05MsDKF3wgNIAbwJcgFFphEMJxXU67HKhBftwo2QQTDTYDKJQW+UGBxUGQL gtndSPw2IrBLBsRgwK/lO/5Fv98AzP5DQvdB76JkUsAqUFxMpIUSwcgTSwFegcUEwxu6MOcWBAP0 oFeRwFIRiCGBfTA37uSlPYoySoGVCohgFk7tqmpPBcCywu7ees8OmWs1XrsBMRhAMGCccG+0Pg5a D9/Ju9LxqC4pMEQBEczc2VClv0CBX/uXCOYF/Zs1AcdgAMFwL+UGa0+3xGB6lpmKx1WuA8DEWaT0 6Er19E22x+JpZ5FAW7KjSXidpXomyG6SLZ6iSj8snpPKCCNS76j9uXG4EcG8bwZWiwYrAAgG1ASe +jrYPhU3VAG8kxdQBYimgL01YB/MOwjm9EbFLZwX3FweK/skde1emrRwL3vRGVuTwCdFSyxUtZYA ii0SWFWxSw1bMugCTIe2TI/G4uQxKqUIZujkp8LeqMC/+5cI5skdzp1gwqtIgG+4t1Y9+Zm82W16 xiKpN83+zuIuRYJpAgjPiVZv+nHkIA4E1QhQFbBwe6sBj0ka9tRbZSwPQK3sNNCIYJ48A8u2RyjA EQz9DiPgQR8hxz5GgAfvgkbgN0KDVSTQ42DH9/o3O1a9RTGS4d3T28WIFHSagijFIEGQDIA3BU6a IJhqRV6ZXsAGM5ZHToTlGQX21GvjarabUootRoaKSlYHZ96brRnGpj9Hf3rtMz3K0k9RgHvoC31M 6ef+9SmKD2oniJeAGuidvGAfDIAbsPY06TR1Ot8W5/PiLXIWZQFBEY9grnqt/6t6fUxCwGDbQBwL wZZ4TjdSZubjTwGXWZ6GcPrrjRBMlqbaxRxaKAYzaLJUMe9VgCMYepMvHbxZ2QM/8q+VZuC6fuBf ICPmBm4nL8DZ9QRTdRUpLmTo4HFMFUoiPt6m8YotQlK2zmJ9tudEI7Z5/BEvM9KWYpoicNxbrwim /CM6euU5058skQKnAhzB0OrRwRu6RiLjz/yLKG1SlhkvwgRxHa7jwDN58Z5iIBpgFC+K4K1WFJce LMfMI5jM4AhteCRUNRIElvrLBICVwZbXfXTbqxpa27zxgHWwAhYJGABQFa+zBIrBTJo8Vex7FOAI hst1qEYHb1YqvkWgCJyLBlrhE0DcWSTwqBiwDwa/Y3IewaQLHK0E43msouerBhKKvBXP5eFatYSq J7aOtlpmU1swxDShzKR6PejJdADjIQLTVaARwayc9lXXlgpwLMK5TxHMwCHCBUUwwXAxGEClgGAW vxep6BerKzs4V+rAztUQEA8AayhZRuv8bLHYQVZLKIZMesq8mo/bUvXrrZYvqzdllwxc7IoejvpU weX3i3rxpAtSHs0eOH+pKCkwRAGOYLiztUMMXlDIFoEiruPwe5EAwYC4FHhsHXg3NX623tQYzILZ XlV8uAJVVovooxjMgtleVeytAOcIj+e+e9fecvzW+i0IhjMSvyUbRGi4lSlAurc80S7iNpRGCnAK eCE9rrQvAjl0zhkZFYN5gW97XxM4gvmNf71Pome2iFtFAntWjq/Ayg6IpuAyvW8xSykGM8MHqczZ ChQXE+lKRTDPnHtl1YMU4AgGHLt9UNtebcoP/Qu0+2/gBXbXgnPRIBfALDCEjq9EMLTbU8bXKCCC efUUrsaNUIAjGHofDPB2I1rzQWV8z7+ACvgEEHhazLf8Cxw+B6epbzyL9BoPp4a8WwERzAdN6Goq pwBHMPR54xlPMeEavnuuf/Ev0DT8FBZAFeBU0U/9CxCM9sG82/uqdf0KiGB2n6Vl/3QFOILhtpHS m2TBC7TB83NBruOr6cpOroCLZuFn8g4nGLB7ZvFp6tOd9JwQKR70vQ7ZEqepT3uC54ezXaI9J5+L UhQtSX1w1c5qW7yTz0212E5cVm/n+CGARgQzeRJV8fsrwBEM92LkQy1u/ym4Xwf7Oui7/C169Rf+ RcdgAN+AGAzYkQPeTU33DvAEVUCpJmgq3D4XJHVy6RNEUmgATxO5shdPtWQFem2p1mtrKTbEgoW1 qrUtV5lF40dZbssh6u0ZKgSsFLOIYLaYimXknQpwBMO90ZqOwYBtN2A9C+Q6vrpT9BF1cwt5mBsA wQAWAatIIAZz107e1OkCB5x6wezv65/X3T9w7QBKWnNZJ4fjInEgiEdB4mUWIQxEwlrViFsSwReL mFXG8vgV8FMr2YhgRsyUKuPVCnAEAyIfWC1u+Qnkor/avVc5gsH7YLjT1D/xL/CwO7AT+fgK9E5T mMTz+pn78VDmYpRsxQeEQwjPWqUc4E2zBnIcwPl4D0c8BQC+ZAs0YzVsrbeKWSm5WtLNhkortXyF Jnsy9+c925leu0+asv99CnAEQ59F4gSkMYUDJs7Ixbl+5V/AEjoGA05Tg1UkQDCTziKl8205Mv+1 L+5sMyawfijihlPuueq1/g8XlRpT5I/iPX2RaTo5oKfMIgNdltt24XgSQTBAuhkEk5VZ7WIOJxSD WTypqrr9FOAIhgsA7KfOgy0G0RRgNX6OHHjUMiCYH/vX0/bBZLiQIkgWCGklmMyFR2IhHuvYFY2i D7b81EkwVpxWkrAMVJUxjWcM0TCiXoqbHl0VeS5rTnH8YGBqQhkRzIMnYJn2DAU4gqFjGz/3r2fo sY0V3J5o/EQ7QDAgmgIIBuS6fR+MdXVeJKYaVonwStFTFkNB2AfjeAZnSX+ZNqz1ldWQ38a9gmky lKwSiRc68oJbGUOIYKJQdSi1zewoQz9GgcUEwz2H7WN6o6Gh3Glq/D5FsA+Gi8EAgsE7cubtgzkd WzD6krk3754b0AlAishdvnXnmQ3eckwTyvSX6UVfimEhz2XGQz7A4OJXgHKKwOTBVjAGkw6wKB+U 0ikG0zAhKulnKsARDK0VFzmgq+Mycgs0XF10Lu6tAvgpLOAsEtj59AP/4t5FcMDZcIIpevTq/TfO lTrL02llnhL49QukbEacKyOwooOslmA9dNXpEm2JCAIiIl7YJuuUS8CsO4pBIAxPIEvKLhaSbNWY mYJYI4Khp0dl/BQFFhPMFhtowKPwnjMsOBYEDwA8vgIEA17fCMoEue46ixR0HkomBXoUEME8Z6qU JW9WgCOYxaepF3cA/by+lXZyZ5HAThf8FVgPAtEgEIPB1Q2PwfR4I+WVAhEFvJBeJG85lkbnnJHx aN7KCU51SYGIAhzB0FEKegtwpC2j0nDhjVG1B8sBO1pACWDF5/gKUAV4wxEgGBCDEcHM8DIq814F iouJtElaRQpOhkr2uQpwBAMc4QukfDFmgXNDx1dglwy3HgR2z2BLFIOh3Z4yvkYBEcwLvImaMFcB jmAWP9FurgSm9C0IhtOEjsEAgvm+f4FtT9gSEcxr3LAaQisgguFmOeX6IAU4gnmxj9+l77k3OwLa wF+BNxyB5/wCgsHVTSKYnv2VxcM41xEV4iySd6oIVHQdz8ENWX8WqdiW7CxSemjIs3+I5ZPqtQe4 aDQJZhTB7DIby87bFBDB3CZ9X8UcROK3CoDzQWAfDHgyDdhRBM49HV/tQjAXUlw+KTtqa3d3grO4 l48s7glND/QCb5olA2ViS1IvWy0THzC2DrtIMNVabFvm1dsDu0FAqSYTwfTNkcr9AQpwBKMH090+ NLjtxvh5MCAuAt4PAAYDwKy7TlPbR3ekNFDkj9SzXvf3adAFYEoEIIJpIhzAWWJhxfvEg4wI7ljI a62lCHZZsSANwKaURG0bi1VYeEoDUUMASARz+zQrA56uAEcw4O7h6Q1+i31cDIYmGHB+HpxFArmw JZNiMN5NvHcrn65HZH8XuSfIIt5aiccfVddbhIN4SKOfLVpLwDQ2z/JIvTauZgnmsjCj2yvvGwjm gvTrj7dMnmrHexTgCAa81AZLs8WjVrboXQ4iMTeAuAh4TjHI9b/8S++mtl6/SDDFkIDnIHtiMD1l Vr0+RpwUBwkKjBBevHXVtgDerTYT/GwLdNWUenbio9lbzIwy8qMU4AiGPov0G//6KNn7G8s9VBDv PgGrSMBgsLcGAOtdz4NJ3U/miuz9tLckkVHFKG6IlFPknis+FCnBa1QWVWoq0/PcXrQpjVXEIy4R yz3+yPKC1olgyiwkgumftVXCcAU4gqFfDgBuyrmmcYspXF2PyvXv/gXsxNwAoimgx0FADvTOXaep M5/qUYvnVlP3Zu/FW+MHRWOKVYCSU3/DEQwgg57oCMaXzE0OtLyzXhGMCOZRU72MQQpwBENzA7f/ FDwC+LyXKl7qeKsAfiM0iMGAjuMIZtIqUiSUft1/Z1jg0Yzd3FCNwURQxjraplzF7JESbJoiilkw ApBhSwCt8/qICMZMqjfjtizC5FWafu71TmR8/r66ptSzEysGI4/yQAUWEwyHPmCdAryv54FqDzSJ Y0EQAzu+4laROILBlgzfyVv0vl7sAQdXig7sYmgvImLv7DPs9hZirDdNCazotiJBiCxNf5lZCcXW Vf16v+WT6rV0m/aLZZdqSyO8obNIAydMFfVOBTiCobXgCAZ4a+AIQV3HV3QTHpKRUxLsnjm+AqeK /sK/wEFrYORigok4DKWRAqMUEME8ZJ6UGS9XYDHBcGpy3loEY9X+F3hxr7sCLxwAr5+85Zm8o/yT ypECVgEvpEdrpRgM5y+U64MU+Ff/eo4KIphRffFTeAGq+Cv/AuEZEGi5aycv7U6UUQpUFSguJlZz uRuD6JwzMmofzKhZWOUMVIDbTjHQABW1UgH8JFwuBvMd/8IvMQDfDt8HM2NKV5lSYKoCisGsnBtV 15YKiGC27DbWaHwCCBAMCIOB8AxYRcKnokQwU12jCt9CAREMO88p38coADwTd4v8Mcpt2dAZMRiA KeARwCDX8dUkgunZX1k8JnMdUSHOIp2HWYKnb7I9FrghwTKzY1OdZdq2pMeCrsbidZYhlk+q1x4K m41BIpgtJ1kZvVIBQDDgKSzAQnC29vhqZdNUl1UAx2B+7F/g3dRAZy6og0+KAbdRBZRqgqbC0zO0 V8bsQ7u70+YCaVKvmR7oBd40S3albLUk45usRs+Y4G7WYkeMshz0crzenqEyimxEMJrDpQCvAEcw INfxFW+Nco5QAD+TlyOYP/MvEMajSbcJMsCBkTRm4FFF6lOvv9M/CKcbQYpiGtAWy09NtVhY8T7x 2otxJ7McYMSZMq7qvHqrrOPxqzWeBhoRzIg5T2V8qgLcc+vxOsWnavmUduPeAQQD9kuBs0gg5HPX Pphi8AAQTPqVF1+JBFFwcAL77KrLH0Uw1YqqBJPJZQv0YhudGg6sN6WoIntd7Jv+kUXLhoRwbiaY s3np9ZRpTHZIgYACHMGAlz4eXwWqVZJeBQCm4Dc7gifaAYIBT7QDMZhJBJPOt8V73+Itcvqh/du6 NItBV73VxHYByPJTGocABuOoDKYiz06uTFBa5stnEEyRDul6IwSTpamCHReGuZlgLL71zkzKLwUW KsA97A68AvD4aqH5n1sVTTA/8i+wXwoQDFgquuuZvNnNul224Agmi4JEIgqYIapOsRh1iNRb9PfW JWcBBry+UwwCgcjQVVpTLRHL7bpSUWdQrwimzFuHZJ87p6rlGyrAEQz9JNzf+NeG4t1pMoiX4BNA 3CoSt8n3doKJsIvnMm0MhiMYECdojaDEYz8RysEcECkhEheZUUt/vSIYEcyd07fqHqUARzB07cC5 0mV+ZsZf+hd+xByIwYDQ2nf9C+Cs3k1dXE+pgktxycmiQBNk9JdpSyiW6S0h4QAPaMukeu1KX9HC rPZieI9bP/qyup7Mw/MqBvOZ7mTfVi8mGLCBZl8NOy3/mX+BkgELYoIBMRhQHUcw2BJQHZiZgYMs ekEcYsnc2MUWRa95rkqAmApYy8gy2lbYYiMo4EU4ijEGa3ymM2FVRJDWWixbFGupdkRrvVfvpwZ4 I8EDoFao0D6YzvlT2T9aAY5g6B2a3MuP3t1DICgCGv5v/gUeMXd89RP/AitT4M2O4HG92JLhBNPq PJReCvQogBEzWLII5t3Tu1o3VwGOYMB2luMrYLEIxorDvfMBKIn3wYAXV3/bv8D5JrDJF5+KEsEE nZySPUcBL6RHWyiCmevhVPq7FeAIBj8zTQTTNGbA7hNQDsc9R4EgBvOX/gV6HDwq5pa3CtC+RBml QESB4mJiJGMxjQimabZUYinwFQU4gtHzYAYOIy4uxXUcJhhw4AhEUwD30KuN3D4Y2osooxS4SwER zMC5VEV9nAKcI6SfBwNu5T9O+t81mCOY/+lffwMvEIMBBANYhHtUDH5/lgjmLoeqehcrIIL52Jlf DR+gAEcw9PNgwP7TAY2ZXwRHGzPsAk+0wxEyQDBgTy4Apm/5l1aRFrtDVbedAiKYGdOjyvwUBRYT DLfnY3Fn/MK/QJQCn7sBj2/hWgeqwxEyQDDgwBF4Nt03/GvxaerTe/WcECkeJ06PWEcOD0fORYOK rlZscZo6E/zaIwJOblfPbNtOLJaWbapNqy7vOPlaTgtFhXvGD8FPIhhuAlQuKfCFAhzB0NptQTAg 3kDHYECZnJh0DOan/sXFYL7pX7e8m7rHAwHnWjyEkj5BJOWn4kNELvfm5bo8t82eucZqvaAoT59q mUWrbK6smZMsH1Jvz1AhYEU7ebm5TrmkgKvAYoIZ7shndC13zAdHPrgyQetADAZzA4jB/Kl/AUvA KtL34QXK7NwHk/paABOpF8z+zuIuNtIw6RPbcByxqGJHMSjFlZnaBiAMRMIsBUY+mVcviMFkDbTx ns5o3+85bxQKDSnnaOeMKVVlSoFJCiwmmC3ei8QFWvDeIK5M0OkAifDbiP7Vv8A+6z/wL2DJXW8V KLofD2WOzwHxBAMPVY9ugyLZJ8CbFsMYxZAPqCUSGsFlFksoxkJwmGeI5Z31VjHrGhXpHym1pMOm Bx5uXkU6m5FekzyNipUCMxRYTDDglMyM1g0vczGmAPvpjTXgJQYgYvJf/Yt+th4dg0nn23Jk/rc7 HjI/mvo8+7d1aRaDrnqrib3avchNpz/OIgTFWoDPDsaTvBIsr8wgmEgt8XojBJOliUAqgTI3E4yl 4+GTpgqUAvMUWEwwW7wX6Vf+BeIN4FDx8dXP/YvrXLBUBJbqjq8AwYAYzJ/4F9g9c28M5nTMWRDF 45gqlEQowabxim2NdqTw1GOJjSJ4jtlrSxV3bIKrF0ZZXgw1NdUrginD1tFD3HykXFLgFgU4ggG7 QY+vQEOGL6bMEA34XWA/gJvjK3Aqh2vCD/wLFwgIBjT86/4FqPSuJ9o1RV8wdmQARKyAFI0hygku 4hBEUoWSpjK9QMjAWopd1lSvCEYEw028yvUsBTiCAVGK46vdCYYLFOHIx/CdvDP2wYDzTSAGA85g 3/hepHj0JXOHxZiNty5Tde1FtxrPhZdFmkIaRRRL3RhnlZfLW0+p1mLjK00QGa8369MMEG2lmeXp ACMWj35fXU/m4XkVg3mWf5Y1NQU4gqmVuvf3M458Dy8TFIifaAd28oK4zh/5FyAY/HRgeh8M9lJZ eAOHWLzAQNGBnU4LxFSKa0ZXLs+SYjyg6iAt3FQ/6S+zWEJVkKy/qnZatlhTb8ooHsGctlkOo1lC +2D29hay/l4FOIJ593uRZix1DS8TgAjebgxWkX7kX6kbzv4G+2AWEwztRZRRChAKWBRjCiHyzMui GMy9/li1tyrAEQz9VoEf+ler5fPSD6eNw9ThZdIFAvQBvfOH/qV3U89zKCr5aQrgQBphrWIw82Zy lfx+BRYTDHhRzvu1dloIdp8ATUAoBT9bD2QEq0j/zb/AISzFYAiXpiwPV6C4mEjbLIL52JlfDR+g AEcw9PtuuE2yA9r54CK4J7v8u3/htoKXa3I7eUEM5q6zSLQ7UUYpsFgBEcyD52aZ9ngFOIIBj9Y9 vtr9LNLiTqPXgzw78bluQDDgiXbgmbwgBqN3Uy92h6puOwVEMIvnW1X3KgU4gsHvYRbBNA0R7qA1 eCQPeIH28RUXgwH7YADBNOmQJgZ+aMj2ye38nAx+qwIiGHqWUEYpQL6bmj6LRK99vLiruBgMWKAB h4OOr8DTgbm3Cvylf+Fn5IA+FcG81WGrXZkCIpgXz+1q2nQFuBgM3isKjAaPOJve1HdV8Av/wr0D MoJ9MGAnLyAYrSLJYUsBrIAI5l0Ts1qzVgGOYOjT1NrJa7sXPOAYjAVwzAe/m5ojGPBEO0AwNz6T V45TCmyhgAhmrcdTbe9SYDHBDH807Qt6g4tLgRUf/D5FsIoEtjeBGMx3/Qs8Iu/4SqtIW7hYGTlV ARHMC+ZwNeE2BTiC+TG8tJO3qTu5uBTgHjoGA95NDQjmO/6l09RTnZ8Kf4ECIpim2VKJpcBXFOAI RqepBw4jLi4FTjDR+2CGx2D0RLsXuFg1YaoCIpiBc6mK+jgFOILBd/lAxL/1r4+T/ncN5s4ige0s eJcSyMjFYMA+GMVgpjo/Ff4CBUQwHzvzq+EDFOAIBj8zDZgF7vIHNGZQEeAMMAhvYG7gAi2gQTTB gEcAg975E/8Cz+Sln92s09Qv8M1qQkQBEcygaVvFfKQCHMG8+ywSWCP7Y//6NrzAE+G4cQfCYLh3 AMGAGAz3ZkcQcju+0k7eiIdTmncrIILhJkDlkgJfKLCYYIaHImb0IniKCdh1i3efcNt1QevofTCA YMDuYBCDAWeRtIr0bu+r1vUrIIKZMYerzE9RgCMYWh1uzwddHZcR0AaIfODnz4KMnJHgUDS2hFtF +rp/AYLB57oVg+n3fyphdwVEMNwEqFxSgI/B0NptQTBbBIpAF+ATQIBZuRgMWD3TPpjd/avsn62A CIb2JsooBchVpHcLtwVmgVUkHIMBr6YCwAEw5Vv+dcszeXte/WjzHp+cH55/XP+8HFsxS+r2bJaz tMw1ZsmKubJibQn4kyFlesanghSFiitWrGJNvV7V8zhGBPNub6LWzVVg8SrS3MYMKn0LggFG/jW8 QI+DRZ8/8y9AMLfEYIYTzOm90mKvvy+4ydxzlqCYPVKg15ZqvVeN2BKLRNaqzIYiwxXbUvT6hOWX /tgS3O/FenuGyiimEcEMmrZVzEcqIILZtNvxgSPwLYjBgKND4KEv3/QvcLjp+GrePpjUbQMXnnq1 7O/rn5fDxq6dc8w2l/WLxWiNB0wWXKqhC4/PPCjBBBPBiLTGiKoRguHqLeayo8ICaKTjgogjgtl0 BpbZj1BABPOIbhhqBKCN4yvwIkmw3RjsbgbV3fVM3szBFP+ZOntAPBaAACXgUASmHOBNwdpQnJzA Ig4dIipGeiziFKtusrya+KzU4xhAYx4ypuTqMQ3Gpj0I5hLu+mPoXKTCpMBcBbjXCs61SaX3KYAJ 5tf+BYADRFMA3IBz6cdXdAwmnXWDSxWZry36JOzIbaXx+EFacjFX8Z4+7o/jlvSUGUG3IvCtIRgb TAL1VhPbhngUFSQVL9njYjCWafSJFJACUkAKjFKg6jNS95O5Issu+Aa96JI9Zxa514/TRsY9aVSg ZxUpEyfeFs+pFxeqshAXUUsxwJORRxHIrrqAYiKY6o+osNu8nue3KTxMxtlX5lps5OLqOCUXG7m4 OmniRZuDP+o0GScml2uXcQJkJBqeZfGoxXOrqXvrJ5iiMcUqQJDDGz89ZECUmTLoaa39JKMrohbL Z8PrFcHUJy7ihwdkrdbHVcflWjwtLq5OmshbV39u9JikM24xLGkjxxLM5VwtKHg0c6XMkAUQDACO Yi7rmL1PcPZIvTbNkDI9D2UL90YCh1wz6vV+iZmFYDwMGe3PWkXyeDMyIQJ0jWTn1ORyaRYu9ggn JpdrcRdsYaQ0uX1Y0uNkCMGktV9+yH6YuUOcK3VgVyQAz/NZgVn8oOqMLUsFu9WKn31yWtIkdTE9 rmhILbfUm7ILYLKrgUNGuwjmywHJqcnlWuwqFlcnTewcJ02kybz7KFwyPfYiBiuNFKAVGDIyn0sw tC7KKAWkgBSQAlJACjxNAS+kR9spgqGlU0YpIAWkgBSQAlKgQYHiYmJD/q8mFcHQ0imjFJACUkAK SAEpcJsCIpjbpFfFUkAKSAEpIAWkAK2ACIaWThmlgBSQAlJACkiB2xQQwdwmvSqWAlJACkgBKSAF aAVEMLR0yigFpIAUkAJSQArcpoAI5jbpVbEUkAJSQApIASlAKyCCoaVTRikgBaSAFJACUuA2BUQw t0mviqWAFJACUkAKSAFaAREMLZ0ySgEpIAWkgBSQArcpIIK5TXpV/GIFhrzy48X6qGlSQApIgX4F RDD9GqoEKZArIILRmJACUkAKzFZABDNbYZX/FAWul79nbxdLX2efviD+sDt7hYf99mpbmtL7+yzw +u9V/lMEkh1SQApIga0UEMFs1V0ytkMBQCopW1x8Y1+jepWQ/pESScYooISMYzqapaxSQApIgQ9V QATzoR3/gc3GL3bPoiOZPl70xeZKPykSTBq2+cBeUJOlgBSQAqMUEMGMUlLlPF2BIsFcKz5FgvG+ zeI0abKMYCLlP1042ScFpIAUeKQCIphHdouMmqBAJCLixWniMZjTcC+ik5avYMyETlaRUkAKfJAC IpgP6uwPb2pwH0wRLOIEQ6wiFbHmwztLzZcCUkAKVBUQwVQlUoKXKHCxRRZouRZ6rthJCjHFb+1u 3zTZWU5anU1/hWqELy8ZXmqGFJACyxUQwSyXXBXepIBY4SbhVa0UkAJSYIoCIpgpsqrQByoggnlg p8gkKSAFpACtgAiGlk4ZN1NABLNZh8lcKSAFpABUQASjASIFpIAUkAJSQArsp4AIZr8+k8VSQApI ASkgBaSACEZjQApIASkgBaSAFNhPARHMfn0mi6WAFJACUkAKSAERjMaAFJACUkAKSAEpsJ8CIpj9 +kwWSwEpIAWkgBSQAiIYjQEpIAWkgBSQAlJgPwVEMPv1mSyWAlJACkgBKSAFRDAaA1JACkgBKSAF pMB+Cohg9uszWSwFpIAUkAJSQAqIYDQGpIAUkAJSQApIgf0UEMHs12eyWApIASkgBaSAFBDBaAxI ASkgBaSAFJAC+ykggtmvz2SxFJACUkAKSAEpIILRGJACUkAKSAEpIAX2U0AEs1+fyWIpIAWkgBSQ AlJABKMxIAWkgBSQAlJACuyngAhmvz6TxVJACkgBKSAFpIAIRmNACkgBKSAFpIAU2E8BEcx+fSaL pYAUkAJSQApIARGMxoAUkAJSQApIASmwnwIimP36TBZLASkgBaSAFJACIhiNASkgBaSAFJACUmA/ BUQw+/WZLJYCUkAKSAEpIAVEMBoDUkAKSAEpIAWkwH4KiGD26zNZLAWkgBSQAlJACohgNAakgBSQ AlJACkiB/RQQwezXZ7JYCkgBKSAFpIAUEMFoDEgBKSAFpIAUkAL7KSCC2a/PZLEUkAJSQApIASkg gtEYkAJSQApIASkgBfZTQASzX5/JYikgBaSAFJACUkAEozEgBaSAFJACUkAK7KeACGa/PpPFUkAK SAEpIAWkgAhGY0AKSAEpIAWkgBTYTwERzH59JoulgBSQAlJACkgBEYzGgBSQAlJACkgBKbCfAiKY /fpMFksBKSAFpIAUkALPIpiv6ZICUkAKSIGZCsjtSYHXKPA4gvGUPX7Ry746KlpZHahLlhQ7Xb1j ZZmhyf/rXBqxTfrv8it+jVdTQz5HARFMua9n+AOCwHaZ+1bKJU2WUZ0IJpOaG+e7jNjPcXtq6WsU EMGIYH6vgO6tX3lvzfndI5cIRgTzGlenhrxSARGMCEYEQy4a7nJvLYJpAlNaLuAhhpfJFYhH7Cs9 nBr1bgVEMCIYEYwIpvArUAxmFPfsQrrvdnVq3SsVEMGIYEQwIhgRTOhXMCPywZXJ5VIM5pVe/JMb JYIRwYTm7l3uI7mZncv1ek20D0b7YD7ZO6rtz1dABCOCEcEoBqMYTOhX8G7Sfb67koVSIL+peJQi 3AQxPNfr7629TtdZpKatD68fJ4rBKAbzKAchY6SACOZLBeSt5a0vBTgIFsEU51NOzOG5Xt87w+9D nu8drycVP99UWbhGAa0iaRUpFD+XP/hAb60YjGIwpwL4fm+Rr0oey87ZQ+QiskTui0YpdpoXB7sr /SgDbi9HBCOCEcFoH0zhV6DT1E1ByhfEdLmgzjIflirMgQWXi27gsuriFV0p41no5q/JKIIRwYhg RDAimNCvgFvq2iV+WSWY817/ak52Q59FAgjgiFOgTZlGI9IoSGrz9bcNLGXtsp6+2PasnKICWIfU pGoTrupS865K7YeZDuk4FMFMASxughiea5cZZ3jD4zNI1v2yZNT9OqfkpBGrVaQh43xS71SBo5ig 1XVhX26/zfjGsgL2HBlkpImLDt46aWBA1nbLFkWeyBx/yjpp64qlZV3vGWCbVq0lQ65IqwHATXHn SwpVDKYsM+dIhufaZe4b3nCxVBMSTRonIhgRTJBRLGoUfWrQqRV//hYRipVGfLnlKkwMHp1ECAbo ECGqFNHsz9zjGGxYK1YGe+2WZCIYEczvFRA3PIEbvIlgfe+IYEQwcYI5b/G9MEZGG2li728s/ij3 H6GQIMFkCmTSeViGFfbKtGZfNAOyYAq8BUE6KxXBiGBEMNoHU/gVHJOdCEYEEySYomu0gY2gu6rG YLxoRARHsqhGJEuQYLzRUtXBJvCyFLktTWyl86ilKHKwg56TTAQjghHBiGBEMKFfAZj0sT/gMg7P ZR1/1RVZgrmiLBHX2Oomr8KL0VAQQiAgI20aZgiQEqhRDXhk2qZtrzKNpZZIlqpJ1fHwtAQiGBFM aO7Gc9/wqfYF/uAFmigGoxhM5vPA7f7pgDMEaSKYyK/eVpEGUbK/s8QWjzL+ABhkScX7JOMSa146 qIrUYjUv9sI1J2fsUuwFj2+eRiSt9ohgRDAiGMVgFIMJ/Qo4Kt2F/j3nYVsd55J4yqrrGlhUtS6Q gDMjkiuSpsfyjJxGFXVvOSIYEUxo7t5lFuZ8DJfr9ZooBqMYTHajjyMKdjId6JUHFtXjdDkzIrki aXosF8GMUs8th3Mkw3O93jPFb7aCg354F+Af88rqPtkSEYwIJkUWuzyBYxVjXfLY0mhn1mpGuk6E K20teVkT6IoWZFQMRjEYxWC0ilT4FRzzqQhGBLPACakKKUArIIIRwYhgRDAimNCvgAv+7RLTpb2I MkqBuxQQwYhgQnP3LrMw52O4XK/XRDEYxWDu8kyqVwpEFBDBiGBEMIrBKAYT+hW8m3QjDkNppMCj FBDBiGBCc/fr4w3ez1I7eW0k5pM1efE4eZRnkjFSIKLA4wjm2u6uP6SAFJACUmC4AhHHoDRSYAsF nkUwW0gmI6WAFJACUkAKSIHbFRDB3N4FMkAKSAEpIAWkgBRoVkAE0yyZMkgBKSAFpIAUkAK3KyCC ub0LZIAUkAJSQApIASnQrIAIplkyZZACUkAKSAEpIAVuV0AEc3sXyAApIAWkgBSQAlKgWQERTLNk yiAFpIAUkAJSQArcroAI5vYukAFSQApIASkgBaRAswIimGbJlEEKSAEpIAWkgBS4XQERzO1dIAOk gBSQAlJACkiBZgVEMM2SKcO7FcAv/Wlt+1laa5k2fVoOV2ar5UovBaSAFHi4AiKYh3eQzFutQCtt YPu40rxc6edcyavVVH1SQApIgWkKiGCmSauCxylwRR0yF36+9O6sJ4tMXO/D8769rEtTen/b8ovZ r7pSgy8jrYVeIba9WQwmfdtfWsg4yVWSFJACUuDpCohgnt5Dsu+kB49UUrbI0mTskmGBXYuxKzXF EtIesUGRop226nhdNiXOqwEjBaSAFPgQBUQwH9LRezcTr55YFrGQYeGGIBgrIiAYG/vxbADWZrGf Irvg5u/d8bJeCkgBKeArIILR6NhAgSLBZCspaZorbJOt4FxAkEVurqKqn2dipTakgAIiMdYGYK0I ZoPRKROlgBS4SQERzE3Cq9oWBXCow4um2ChIkWAslKTJrm8zQkoLLzbFW/3BNkQiQ1kaxWBahpLS SgEp8B4FRDDv6csXt+QKpWQxiSKjZPxR9PfVcqrLQ5ZgAFgQNoAoDia2Fw8DNU0KSAEpkCoggtF4 2ECBy2dnYGFXf9KQSfHb4maUjJDS6mx6G7NJs59slK0oYYK5slwZbXuLEZ2qbRt0rUyUAlJACrAK iGBY5ZRvoQLFFZyF9T+9Kunz9B6SfVJACkxQQAQzQVQVOVoBeWisqPQZPeJUnhSQAhsoIILZoJNk ojy0NwayBSwNFSkgBaTA5ygggvmcvlZLpYAUkAJSQAq8RwERzHv6Ui2RAlJACkgBKfA5CohgPqev 1VIpIAWkgBSQAu9RQATznr5US6SAFJACUkAKfI4CIpjP6Wu1VApIASkgBaTAexQQwbynL9USKSAF pIAUkAKfo4AI5nP6Wi2VAlJACkgBKfAeBUQw7+lLtUQKSAEpIAWkwOcoIIL5nL5WS6WAFJACUkAK vEcBEcx7+lItkQJSQApIASnwOQqIYD6nr9VSKSAFpIAUkALvUUAE856+VEukgBSQAlJACnyOAiKY z+lrtVQKSAEpIAWkwHsUEMG8py8/qyVf+9p/6H9SQArMU+CzJhS1dksFRDBbdpuMFr5IASkwVwHN MlLg8QqIYB7fRTKwqMC8W0+VLAWkwKGALinweAU0TB/fRTJQBCOHKgXWK6CZRwo8XgERzOO7SAZK ASkgBaSAFJACRgERjAaFFJACUkAKSAEpsJ8CIpj9+kwWSwEpIAWkgBSQAiIYjQEpIAWkgBSQAlJg PwVEMPv1mSyWAlJACkgBKSAFRDAaA1JACkgBKSAFpMB+Cohg9uszWSwFpIAUkAJSQAqIYDQGpIAU kAJSQApIgf0UEMHs12eyWApIASkgBaSAFBDBaAxIASkgBaSAFJAC+ykggtmvz2SxFJACUkAKSAEp IILRGJACUkAKSAEpIAX2U0AEs1+fyWIpIAWkgBSQAlJABKMxIAWkgBSQAlJACuyngAhmvz6TxVJA CkgBKSAFpIAIRmNACkgBKSAFpIAU2E8BEcx+fSaLpYAUkAJSQApIARGMxoAUkAJSQApIASmwnwIi mP36TBZLASkgBaSAFJACIpj3j4Gvla7HNvs09rHmyTApIAWkgBR4iAJyFQ/piIlmFAmmhxKmQsbU wi+V47WkQsVzTexOFS0FpIAUkAK/VUAE8/6BYP1upyfuzI4Vn1p4K8Fkxqyx7f0jUi2UAlJACoxQ QAQzQsVnl1H0u491xo8y7FHGPHuUyTopIAWkwGoFRDCrFV9fX4Rg0pWm1MLi5yCok351/X0VAtqe Jc6CJV6NoGnF1Z9iLZGqz9KAGVa0K33Pgt360aIapYAUkAK7KCCC2aWneDtpgknxpYgmp004WfZt sRleCVnhRawpLvRkzGRtsM2xrIZz2YYD8wQx/PBVTikgBaSAo4AI5v1Do0owHp3EP09DFJe3LmKE lbuIICkbjf3bKy0z2ybLYjBp6zxgKhby/gGnFkoBKSAFliggglki862VBAkGBCEy86vxmKr7Twus EgxnGKiiyCJZBKXahKrZxQJvHQiqXApIASnwKgVEMK/qzmJjaIK5PD3nrUGuToKJGCaCef/IVgul gBT4bAVEMO/v/yDBACHAehAHN00Ewxkmgnn/yFYLpYAU+GwFRDDv739LMEHsGLIPprqYUtxUaxdx 4sbgvN63w/fBVBv+/pGnFkoBKSAFZioggpmp7jPKzs7UZAGV1KMXycbbhpJ56GCyoiTWwmLh1RqP BFlRllcAwVhlskZhfTxkaWWvZ4waWSEFpIAUeLoCIpin91C/fYAPMChkQACCCpeTDm7yzRqVogOg hDSXBSbblojBVxvTP4oVFRMAHCzWHqGZ/h5XCVJACkiBT1BABPMJvbyujcU9N+uqp2ra0Waqocok BaSAFHiVAiKYV3Xn+sYEDxytNyxeowgmrpVSSgEpIAWeo4AI5jl9saUlkSWqhzdMBPPwDpJ5UkAK SIGiAiIYDYxeBcCWlN6il+QXwSyRWZVIASkgBQYrIIIZLKiKkwJSQApIASkgBRYoIIJZILKqkAJS QApIASkgBQYrIIIZLKiKkwJSQApIASkgBRYoIIJZILKqkAJSQApIASkgBQYrIIIZLKiKkwJSQApI ASkgBRYoIIJZILKqkAJSQApIASkgBQYrIIIZLKiKkwJSQApIASkgBRYoIIJZILKqkAJSQApIASkg BQYrIIIZLKiKkwJSQApIASkgBRYoIIJZILKqkAJSQApIASkgBQYrIIIZLKiKkwJSQApIASkgBRYo IIJZILKqkAJSQApIASkgBQYrIIIZLKiKkwJSQApIASkgBRYoIIJZILKqkAJSQApIASkgBQYrIIIZ LKiKkwJSQApIASkgBRYoIIJZILKqkAJSQApIASkgBQYrIIIZLChR3NfMRRcSz3jWGU9fTXmVNrzk atVpgmqj7jWvqS1KLAWkgBSQAkCBkT5MQnMKWIKpumFbUZNjvmrkDMa1N1kyyoCrnKp01QTDTVKB UkAKSAEpMEMBEcwMVdvKzFz+AgIYXsXwAtsUTFJXAaWagK5aGaWAFJACUmClAiKYlWqX6wIEk4Zn 0sw2iFIspOitszLTjMW/LZ1ktYMCD5uLTbjKrBaeSVZFpbPJVg3Pkvu7XxZIASkgBaQApYAIhpJt aCa7inQWH/w8TVzMWISAIkNYggH8kZXgQUm1BFCplTlCMBe3pX/YD4f2oQqTAlJACkiB1QqIYFYr 7nll6+mr0ZGTcjyCuT7HHBCsxVZEZ8xiJKAcom8uOzNxrqLSBET5yiIFpIAUkAIPUUAEc39H2CWP zA17qzap6cVIhte2UfCRYkGEpYJpeiBDBHP/gJYFUkAKSIElCohglsgMK/EIxltFSoMuRSYoJsC4 Uw3kgIriMZUqwVQtr/aWCKYqkRJIASkgBd6hgAjm/n7EBFNkiypw2ATDCWZUICcrB1te7S0RTFUi JZACUkAKvEMBEcz9/WhjLacb9mIw2ecezWRrT5hg0jIxHmWG2ahJEcjASllx/StLfxnvfZ4msH8X seb+jpcFUkAKSAEp0KGACKZDvEFZwWrR5bA9LPBcO8CXC0Fs3khYJSWYrIQUvOxXGUZ4y1JNltse UAxm0KhUMVJACkiBpysggnl6D8k+KSAFpIAUkAJSoHDLKlGkgBSQAlJACkgBKbCdAorBbNdlMlgK SAEpIAWkgBT4DxGMBoEUkAJSQApIASmwnwIimP36TBZLASkgBaSAFJACIpi2MQBOBbcV9NXU1UPC PYWfeRdUsbiioib4KFO/jCpBCkgBKSAFHqKACKatIzIHOQoLRpXjNWalX5/dFtBheu5L22hWaikg BaTAzgqIYNp6b2uCaWvqhqlTgjnDThs2QiZLASkgBaRASAFN8SGZrkSAYKqPg/MSXEs8xUiJ/TB9 zJ21Pn0+3vmt/STNFaw0LQdn9x6+V3yinQ3YdD7RLrOtrXeVWgpIASkgBfZRQATT1lcZDVSpxdJJ WkL2XFoMH8XEnQQTMcZikGd2mtIynyWYYHstMAX7TDGYoFBKJgWkgBTYUQERTFuvZfGPOMFY796U F8RvPAcfyZKlyYwEFhab09nGi/auctr65quphS896imvFJACUuD5Cohg2vooJZhsvaOJSKy3zgIb 1chH0e6IDZZ4MmePAzPB7D000w8f/SW0DQullgJSQApIgeUKiGDaJL+FYDIT7d6RK0ErwWScZLGj WLVHPD3UUgWjeD8JX+JaKaUUkAJSYF8FRDBtfWcRAcRO+j16BkxBwsD1ZqwAEuM1piPjjLZX24g7 TPjSNqCVWgpIASmwrQIimLauqxKMtzs1QjPZstSJCLhAa71Nf5WDE2c4Ulwji3941VW1pyipF2cC 8aeUqNo6VamlgBSQAlJgQwVEMG2dlnlQ75+R1Zyg5/awxrO7SgxZRlt+Sk5ZwMYCRNW8qj1FDb1Q SpVgiszX1sdKLQWkgBSQAjsoIILZoZdkoxSQAlJACkgBKfBVBUQwGhFSQApIASkgBaTAfgqIYPbr M1ksBaSAFJACUkAKiGA0BqSAFJACUkAKSIH9FBDB7NdnslgKSAEpIAWkgBQQwYwfA9lxmOJhn7NW fLImkmC89XNKrLZ0bLXZUSbQBWm9XK6xlqs0KSAFpIAUCCogggkK1ZBMBGPFupFgUi7xDmlfQHlZ Hs/VMDKUVApIASkgBcYpIIIZp6Upqd9t95cwsXlPLdo+YAYEWlJkAdSC0eepSsguKSAFpMCbFRDB TOxd7/lvR5WnR/QeagfcKmCa66um584VffO9j7mLtNHrtktYnCD7lss1ceioaCkgBaSAFKgpIIKp KdTxPSCJjGDswtNZrYc4GDsy/sgKT0suskI1/VV7a8qMzGywpFhgFkGJhENAmuFfdQwQZZUCUkAK SAFeAREMr101J4jBZIBiScUjmOtzW3u1EC+BDUikjBI3tWhztSEZD9n0VZ2LUhRzYfrxvo0wE2Gk skgBKSAFpECPAiKYHvUqeeMEk7ptDCI4hABooBiY8dw8jh7ZGExaTlbRcwimCiJeZGviEFHRUkAK SAEpwCoggmGVC+SLE0y2elIEkYxyiBiM5QzQiFHE8xCCqeLLYadNE8kVGAhKIgWkgBSQAuMVEMGM 1/QqsZVgqs7eJvCgpBjIia8iAUuyQoopqw2prhxZ6Zr6KSOPIIhwuZoMU2IpIAWkgBQYpYAIZpSS hXJaCSaNxHgQUFyjycIzWb1ZgAdj0BmK8CwpBmbiH2ZsV7QzTmmg51IWCeLL2fDUwokjQ0VLASkg BaRAtwIimG4J/QLiBHNxQ/pH9nfKFsU6r+q8ejMPDVx7kZN6PgRQlWHNDIIpMpnV0OpjSW7icFHR UkAKSAEp0KKACKZFrWen7Vx5eXbjZJ0UkAJSQApIga8oIIJ5z4AQwbynL9USKSAFpIAUqCkggqkp tM/3Iph9+kqWSgEpIAWkQK8CIpheBZVfCkgBKSAFpIAUWK+ACGa95qpRCkgBKSAFpIAU6FVABNOr YDV/cHEnfui3WuOQBPeaHazdthScNr8SFwvHNfZ8O6Q7VIgUkAJSQApkCohgpg+JiDOOpJlu6Fcr iJgUScOZzZUceaDLlaaY2OPIKl9WE3A6KJcUkAJSQAp4Cohgpo+NiDOOpJluaHsF88zmSs4wwlJF MUE11yFMFVCqCdrVVQ4pIAWkgBRACohgpo+P1Blff9sPi5/YhY80hGDdvF1DuT7JQg7nPwEoDDc7 qyuTwvs2jxn+Nl2wz+YRTFHnoFVKJgWkgBSQAkMUiDqDIZV9ZiEerHguPGUOyyspeVR5JSuqWJoH McPNLjIKttAOGIBcxcQWgNJPLjHthyBjxoJnyjhXfeavQK2WAlJACgxXQAQzXNK8wGIwI4t/BAMe NiST+s4MiSJVZGkyR54Rz+Wq8edee9O6rKnAWq6HPKSwxmcpixmL1FLlHs5y5ZICUkAKSIGIAiKY iEpdaTiCyeIrXgDDYw77ecSMfoKJmG2xKY3EWE4i1McRkZSfUgS8qrY1WsqJcA9hubJIASkgBaRA UAERTFAoPlkEHUDcAnh0jAsXKGA+8NZlZpjtRYnGEkx8QedMWWWRajgHoA8/bpRTCkgBKSAFoAIi mOkDhEYBvBxjycbjA5DShkMuOWaYnUIVZ1W1t6rRl7SEIMFklFNcUYpjU7UJSiAFpIAUkAIRBUQw EZW60sRR4HKo1ZhEmiBzqGlgJkt2payGfDKywU2Im+0RTLW9Ra6yvVLFiCJ8pIASCbeIYLp+D8os BaSAFBikgAhmkJB+Ma0EY918MVKSkkrm4C3TBIkki09UiSezwZqUtj0NujRZ6FlVJJgi2xUhL8tu Tc3qtWZ4PDR9SKkCKSAFpIAUOPYASAQpIAWqCjRFd6qlKYEUkAJSQAr0KyCC6ddQJbxfARHM+/tY LZQCUmA3BUQwu/WY7L1DgeoaUxVx7rBadUoBKSAF3qyACObNvau2SQEpIAWkgBR4qwIimLf2rNol BaSAFJACUuDNCohg7uzdbG0icsg5NRcvbSxrGHFme/aaS/GgVlGQqiWXyEG1swI9S3qWpYKWLBsA qkgKSAEpcIsCIphbZP+y0s8kmNkOuOmQc4Rgzt6qprTJPEvSY+o0V905cFW3FJACUuABCohg7uwE QDARs2ajQMSG07VXvXtreClYtU1mLcG2RSyPE4zt0CxmZkmoaEDcKlooZZQCUkAK7K6ACObOHozH YIprGZEFDi/NtboRWfXwlkIu1+654TTYcP5t67WFpzZn3VOlJUAwNiICqk6bltns4cXVQG9IFRNE CKZo550DV3VLASkgBR6ggAjmzk5IXWPmpWzQAvh+QBi2igwjQEVFL14MexR5yJZsCSYzz9bYTzBX CZZgsohIyls2lwcu1kJMMGmlGIYuNWhj7hzcqlsKSAEpMFkBEcxkgWHxTQSTenf8d1onwIhqgcUE nQRzQUxWeNFOom8AZ2CCyTJamukkGFt+q6lZtxLiKIsUkAJS4E0KiGDu7M3UbXuunfi8SjApPRRj MLYE7G69pZCUA8DfIP7U2j2tWFCMu5yaXyplf2CTPCmK/BGJwWQDoMmYVvWUXgpIASmwkQIimDs7 6y6CKS4tXUJkPJHhjtUra0VWTpod/13M2No9lgmCa0PzYjBeyR6LxLGmVRyllwJSQAq8SQERzJ29 eS/BFHki+GEWUeiMwWRhBg+JIl31NIIB9oBoSnHB61RJMZjIMFAaKSAFPkEBEcydvdxEMGngJBjM uMiguEzjFYgTj4rBnM44iwbZdmXVReAm4v6DgRkCGrzaM+bDIBJpwp0DV3VLASkgBR6ggAjmzk4I EkwaokizeH8XAyReXcXP7cIHQAduFSllCKADQTApGHnZs9qvZLYtlnWKAae0hKw0S4pphxbHX1V/ bMOdY1p1SwEpIAVWKSCCWaX0oHoiQYgiwQyqX8WsUKAKKNUEK6xUHVJACkiBWxUQwdwqf6zySKzF K6mVeGIWKdVcBaqAUk0w1z6VLgWkgBR4gAIimAd0QsCE4kpEIF/okf+RcpRmpQKYO0WlK/tCdUkB KfBYBUQwj+0aGSYFpIAUkAJSQAq4CohgNDikgBSQAlJACkiB/RQQwezXZ7JYCkgBKSAFpIAUEMFM GQPetpXIDoZImsvopsTFptIlRDJ6h5aniP6uQjv36nZmf5eWao0UkALvVEAEM75fU3yJP+mEg5II RuAW0iVUM/YcoRrfK7uV2Ikgndl3U0v2SgEp8IkKiGDG97r13E2hiCoZpBY3JV4cg+m3bXzf7FNi J4J0Zt9HJ1kqBaTA5yogghnc99Ztg1BEcbEpC+FksZl4UOcqp6kEL9dZiNcW25Dsk6LZXlsA+lSp 6PTcVzLbHNsQEP3KSsvGiqWE9JPiSEjrwiSKC79a4TUnheZ4pYN/DCpOCkgBKTBTARHMYHWxi407 8iIBeKAAgivWl2dOvWoSppYLF4DBGfpkWYqA5ckYIZjUeWNHngFHhgWW2KzOxRLOjLa06/OMTiK0 UbTNcoyFlVYLB/8eVJwUkAJSYJoCIpjB0lp/XyUA6ymtD44UkrYkA5dIFWmaSHW2itZagumbeqjV YROIk+mMoSGlmSLBRGjGVuFBkm1OldIyC5vUVmIpIAWkwI0KiGAGiz+JYK67bRxBSV1d5hojJRQj HBGauVgkkrg1TVMPRQgmDRdZlTLzivQQRwrLBxZZivZ43QfsKVruEQwWoUlzJZYCUkAK3KKACGaw 7BYCWh12MX3mb2yQJmuGZ0ZaTso09u8iDNG29YjQ1EMRgvEKvIzEgRkrdYYpXljlEjktoUgqlnsy cCliUJoLN8GrtElqJZYCUkAK3KuACGa8/hk99Djv1KcWqcWSSsYiTSUsSHx62WpbvHZVe6uVYOKe HqOGV28RbiKJuepsc4p1YcaqiqwEUkAKSIEnKCCCGd8LaZzj+rsY5EhTZjGPYkabHnj6LLG1KmhS NSNOcDrLItUVPyxalYkD+qxKMMVARfYhxpqs9quBxchKkRU8gABkA2IwHg5eSnodnSUY/0tQiVJA CkiBmQqIYKaoW0STJkeeEUyaN3X8EYKx7h+UYOuNVH0VaEt+IMHYjsh8fBPBpPSTQQwIddivQOIi MxXr8iz3MCurdMqPQYVKASkgBeYoIIKZo6tKlQJSQApIASkgBWYqIIKZqa7KlgJSQApIASkgBeYo IIKZo6tKlQJSQApIASkgBWYqIIKZqa7KlgJSQApIASkgBeYoIIKZo6tKlQJSQApIASkgBWYqIIJp Vtc7Z9RckJMBHC8aVcWacoJnptYY01nLk8/s2NPUT7a2syOUXQpIASlwKSCCaRsMKb5MQo1Jxba1 c0TqNxHMCD2mlyFwmS6xKpACUuBJCohg2nrDe95JWykw9SsJZqA+KspTQASjsSEFpMBHKSCCaehu yxbFMEP6DLcsS/HJb6cFWXTHmgWeGpcmto+kKy57AUtwaampvw/lJXVkH6aty/4GenoYF8E7q4C1 wVpe1dAmyD65lAGEYccDyBUUIRtvZ4HY2oZBr6RSQApIgacqIIJp6BnsPu0Ck0UHbxEqsjhFlG/B 6PJtRI0er+BGeQQTF6fJGafOu/g3lyBSVKptEUBTAYuyWCQq4siVt/pHmj3DmoZxr6RSQApIgUcq IIJp6JaUYDwH7Dls7nPPeVtLsvKrARuvBFDjqCqqxl/g1dA3v0ta9NM2JpH5/iI6cDxhmSNel8dA xc/th+ATzypCYWWRAlJACjxEARFMQ0fECSZ19q0O24v0xMu56OpqW5y3LMFY9+9RQlZv3GCMdw09 9NukVkAQcCqqXSyhCCKYLTLLQbFZ4Z7BmM8wwbTKqPRSQApIgYcrIIJp6CDPNXoOOM4NREQkdVfF vy1FRZDCygG4hKsiYoaHcfHeSp190fFnrFZseIqA/QRjS7B0WKzFmlE0XgQTHx5KKQWkwAsUEMG0 dWLmWVudcWv6zFFVqeUq3/6RBSeCzOTBWVpaa6Na0zf1UBFcIqCAoynFb08RqlwCEkQMq9YCwKUJ 45p0VmIpIAWkwO0KiGDauiANq4DghE0GUOCiARuz4QgmNazVEltj1szMWmB8K6l4pnoiFHvO89lV 1AgSTJUn0gSZ5dc/PRItpq9SDg69eOzVNu6VWgpIASnwPAVEMM194nFG6rAvv57+Af62HGDNigBB Wk7qL083FiyhCDH4w6vkSBVeGk+0JoIpKnCWkHVQ+qGlBNub2IxItKPIgp5h8c8xwYBymoe+MkgB KSAFnqSACOZJvSFbfqdAkTbeJ09GTu9roFokBaSAFJingAhmnrYquU0BEJtpK2if1CKYffpKlkoB KfA4BUQwj+uSTzYI7wR6nzIimPf1qVokBaTAMgVEMMukVkVSQApIASkgBaTAMAVEMMOkVEFSQApI ASkgBaTAMgVEMBWpgyePiqU0bUdtSrxgfAzZlVI8NjzVeLsuo5WaqYKrcCkgBaTAXQqIYJDyKb54 h6VB/iYoaUq8YLj0E0x/CUQzrYwiGEJGZZECUkAKPF8BEUydYM4U2cM8IsARSXNV35R4wcDqt6e/ BKKZIhhCNGWRAlJACuyogAjG7bWiL/SWRYqLTVkIJ4OVYFDnSubZ45VzmlrMXswC7PHSgxZlgjTV CNCnSkVXq1Pb0j72EKdYcrW6HX/zslkKSAEp8A4FRDANBAMcYYRgUqSw6T1nmWHQhVBpWAh8aLMX 2QLbk9pmsaxoIU0wRSXjkSpMMJdQVrEr0mbRJ038jp+9WiEFpIAUeIECIpgQwbQ6+Msdpi4zJRiQ IDPI0gPOW4x2dNoDbEjDPJP+bvqZWUYpUstpapxaBDFNvaDEUkAKSIEFCohgVhNMChMeGXjBnlaa CSJFMZyD67IefSDkZWY3/QwGEoyNNjVZosRSQApIASkwVQERTIhg4mGPKmQU11yAz64WCDAlQjAR e7K4Tko8Gf30WBvhueqPIVse8mItrTGYar1KIAWkgBSQAosVEMEgwVtXZOLEgJFoZQyGYI7MeKIE u7iGOSz+q8iCQ5ltmbBXQ67Pq0tOcUuUUgpIASkgBaYqIIKpE0xxNaEIN+DDzGfjNZcIwWTBj9QH Z3EIjAutMRirBi7hrD0ul2U7T41izwGCOc0A1JIm8BJP/TWqcCkgBaSAFIgrIIKpaGVRo+hii8m8 jRSXO4+sm4A0oNIi0FSDJdUEaduLnGRLuJdgMiixLOVRCwap+A9MKaWAFJACUmCSAiKYScKqWCkg BaSAFJACUmCiAiKYieKqaCkgBaSAFJACUmCSAiKYScKqWCkgBaSAFJACUmCiAiKYieKqaCkgBaSA FJACUmCSAiKYScKqWCkgBaSAFJACUmCiAiKYieKqaCkgBaSAFJACUmCSAiKYScKqWCkgBaSAFJAC UmCiAiKYieKqaCkgBaSAFJACUmCSAiKYScKqWCkgBaSAFJACUmCiAiKYieKqaCkgBaSAFJACUmCS AiKYScKqWCkgBaSAFJACUmCiAiKYieKqaCkgBaSAFJACUmCSAiKYScKqWCkgBaSAFJACUmCiAiKY ieKqaCkgBaSAFJACUmCSAiKYScKqWCkgBaSAFJACUmCiAiKYieKqaCkgBaSAFJACUmCSAiKYScKq WCkgBaSAFJACUmCiAv8/AR6kWQu2gU8AAAAASUVORK5CYII= ------=_20110510163748_41792 Content-Type: application/octet-stream; name="GLMspec_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="GLMspec_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA2OjAzIDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAAUS4AAHiczNXLjtMwFAbgpCodLtKIB2AxmhWLAcXnuDdWIxixgwVl WWnkprdIuVRNyk1CgrfgBdjzCDwCz8GKR8DO7w4ZKFN1dyo5rh3b/ZMe6TsOguDX51bQsf1t28IA n1uN8bbdsy0zVWomE1PFy3rdsW3fGvtbN+zHfLu+F/o15Sq7Omd54DkdP3Z9WZmq9OecHHjOXT92 /TxbJ5flahb7++68H58OO+/Ijx/YNk3WbrpKsiRfBOWsdCEnxn75c/4X/wz73v/Ve2qsb+9Y32+s v+/HF0/GF6Yy41GxLF6aN8nCVEmRj6rN9H09/6KYztL+cPzMVLNFsU5ik45Hkap/7zwMDnr+7fyJ bZs8qf+XV69xv36/1baP6vOjPc/TvvY8bfsW49LtGzT2dfbkuuPnT9N3H76eHZ3//bv79rfst1P/ /g/dF/p93z/eXEetYHddPrStjE2OkomLfIo12Satksv1bBEsV3O/r64PCv9bTz/Df+upWX+73r8J rteTG++rp+ebPHYzJj1xY3LFNLZVby9v15P50yiiqMePItu7S9Svr65Tj5NscaaE5Rsg30BqviHy DYXmU1Gdz3Uy8ynkU1Lz4V92ncx8jHwsNZ9GPi01Xxf5ulLz9ZCvJzUf/FBS/VDwQ0n1Q8EPJdUP gh8k1Q+CHyTVD4IfJNUPgh8k1Q+CHyTVD4IfJNUPgh8k1Q+CHyTVD4IfJNUPgh8k1Q+GHyzVD4Yf LNUPhh8s1Q+GHyzVD4YfLNUPhh8s1Q+GHyzVD4YfLNUPhh8s1Q+GHyzVDw0/tFQ/NPzQUv3Q8ENL 9UPDDy3VDw0/tFQ/NPzQUv3Q8ENL9UPDDy3VDw0/tFQ/NPzQUv3owo+uOD9+AwAA///M1TtOxDAU hWGQAFEhlsACANnJ5D4o0YiSJm0aM8AQCSYSkwGxE5aL7ZOGHZxINzd29UspvhCamxBCF8u7rng7 fmyv4xGeizy/ec7ynOc5We5Pl/NxnrTs8lwu5/XdsE5zGvrpbXpMX+M2zeO06+fD80+9fzjsNuUm vV+VczP0IQ77l31+fX8+vd7nriBtiUJfg76Gta9FX8vat0LfirWvQ1/H2ifoE9Y+RZ+y9hn6jLXP 0eekfRJqX1mcffBDWP0Q+CGsfuCzLs4++CGsfgj8EFY/BH4Iqx8CP4TVD4EfwuqHwA9h9UPhh7L6 ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4UfyuqHwg9l9UPhh7L6ofBDWf0w+GGsfhj8MFY/DH4Y qx8GP4zVD4MfxuqHwQ9j9cPgh7H6YfDDWP0w+GGsfhj8MFY/HH44qx8OP5zVD4cfzuqHww9n9cPh h7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zUjxiqH3Vx9kX0kfoR8Zfr+t/3BwAA///M1T1Ow0AQ xXEjAaJCHIEDANp5M+sklCiipEnrxnwFSxBLxAFxE47L2s8XoHsr7Y53G/+733lVVb9ln5Z9VvZx xXUy34/Kbuc5rov5vr5t1u3QNpv+rX9ov7ptO3T9bjMcnn+m9/vD7ml8ad8vxzuaTbJm/7Ivx/fn 4+tdSki1X6cyy2Fp+pyG3XQf2yub/6fSF+wL1b7MvqzaV7OvVu1bsG+h2rdk31K1b8W+lWifpalv HJp9xj5T7QP7oNpHP0zVD6MfpuqH0Q9T9cPoh6n6YfTDVP0w+mGqfhj9MFU/QD+g6gfoB1T9AP2A qh+gH1D1A/QDqn6AfkDVD9APqPoB+gFVP0A/oOoH6AdU/XD64ap+OP1wVT+cfriqH04/XNUPpx+u 6ofTD1f1w+mHq/rh9MNV/XD64ap+OP1wVT+CfoSqH0E/QtWPoB+h6kfQj1D1I+hHqPoR9CNU/Qj6 Eap+BP0IVT+CfoSqH0E/QtWPTD+yqh+ZfmRVPzL9yKp+ZPqR/+3HHwAAAP//zNU7TsNAFIVhIwGi QiyBBQCaGcf3QYkiShq3bkyAYAliiTggdsJymfHxEhA6ke5czzT5u++oqqo+T9nld7Hc17fdup/6 rh1fx4f+c9j20zDu2unw9D2/3x92m/LSv12We+raELv98z4fXx+PL3chpCD1dcg7H7FZlc95xZvh fXsVl/87z/OT5zTPWZ7j5f1kuf9XX4O+hrVP0CesfYo+Ze0z9Blrn6PPSfskzH1lcfZF9EXWvoS+ xNpXo69m7YMfwuqHwA9h9UPgh7D6IfBDWP0Q+CGsfgj8EFY/FH4oqx8KP5TVD4UfyuqHwg9l9UPh h7L6ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4MfxuqHwQ9j9cPgh7H6YfDDWP0w+GGsfhj8MFY/ DH4Yqx8GP4zVD4MfxuqHwQ9j9cPhh7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zVD4cfzuqHww9n 9cPhh7P64fDDSf1IYfZjXpx9EX2kfqT5xOLsw2cg9SOFFfr+3I9fAAAA///M1TtOw0AUhWGQAFEh lsACCJr7chJKFFHSpHUzBAiWIJaIA2InLJcZHy8BoWNpfO9M9Xffun/tH/Jnt81D1+/Ww+Hpe5WH 3N4fdpv6kt+u6l3bdZJ2/7wvv6+Px5e7lDQ1Nktlzuoe41qH3HTv22s5wndRzk85Z+Wcl3MyvZ9O 9+Ny8jTrdzndV7ft2PFXfQ36Gta+OfrmrH0L9C1Y+5boW5L2SRrXOjj7BH3C2qfoU9Y+rHVw9jn6 nLUPfgirHwI/hNUPgR/C6ofAD2H1Q+CHsPqh8ENZ/VD4oax+KPxQVj8Uq7L6ofBDWf1Q+KGsfij8 UFY/FH4oqx8KP5TVD4UfyuqHwQ9j9cPgh7H6YfDDWP0wrMbqh8EPY/XD4Iex+mHww1j9MPhhrH4Y /DBWPwx+GKsfDj+c1Q+HH87qh8MPZ/XDsTqrHw4/nNUPhx/O6ofDD2f1w+GHs/rh8MNZ/XD44ax+ BPwIVj8CfgSrHwE/gtWPwBqsfgT8CFY/An7Ev/vxCwAA///M1TFOw0AQRmGQAFEhjsABAK3teGeG EkWUNGndmAAhEiQSSUDchOOy9u8DUD5LuzN249d9/fvVvN/3dbdIVbd72ZXr+/Pp9T6lOuXmJpVZ rrrN4zqM6nb9sbqujvRclPNbzlk55+WcTN9Pp/fjcvppDs/l9D6/64b/dovt2/ax/1qv+v16u1ns D88/4/eHw2Y5fPlvn6nPqH2uPqf2hfoC2pfTuA6D2Vepr6L21eqrqX1ah8Hsm6lvRu1r1ddS++RH pvqR5Uem+pHlR6b6keVHpvph8sOofpj8MKofJj+M6odpNaofJj+M6ofJD6P6YfLDqH6Y/DCqHyY/ jOqHyQ+j+uHyw6l+uPxwqh8uP5zqh2t1qh8uP5zqh8sPp/rh8sOpfrj8cKofLj+c6ofLD6f6EfIj qH6E/AiqHyE/gupHaA2qHyE/gupHyI+g+hHyI6h+hPwIqh8hP4LqR8iPgPrRpNGPcTD7KvVB/WjG W4PZ16gP6keTZuqD+tGkVn1QP5qU1Yfz4w8AAP//zNU9SkNBFIZhBRUrcQkuQGXm/MTEUoKlTdo0 178Y0ARMorgTl+vcvLdzA9/AmcNM9XZPKXZVipebdrPq9fJjcVkPOGdtftuctDltczT8Hw/vwzbd sPtzPrynt/Npt+3ms/Xb+qH7Wi667XK9mm13zz/7//vd6qn/6d4v+rfNZ6XONy+bdn1/Pr7eta4y 8j6KvjF9Y9W+CX0T0b5a9n390uyr9FXVPqPPVPucPlftC/pCtS/pS9W+EX0j1T78qKp+VPyoqn5U /Kiqfhh+mKofhh+m6ofhh6n6Yfhhqn4YfpiqH4YfpuqH4Yep+mH4Yap+GH6Yqh+GH6bqh+OHq/rh +OGqfjh+uKofjh+u6ofjh6v64fjhqn44friqH44fruqH44er+uH44ap+BH6Eqh+BH6HqR+BHqPoR +BGqfgR+hKofgR+h6kfgR6j6EfgRqn4EfoSqH4EfoepH4keq+pH4kap+JH6kqh+JH6nqR+JHqvqR +JGqfiR+pKofiR+p6kfiR/7z4w8AAP//zNU9TsQwEIbhIAGiQhyBAwAaJ45nhhKtKGnSpgl/SyTY SGwWxE04Lo6/XIDus2RP7CZv95xXVfWb92neZ3kfV1gn6/0o72Gdy7pY75vbfjPMQ99Nb9PD8DVu h3mcdt18eP4p7/eH3dPyMrxfLve67yT0+5d9Pr4/H1/vRGpJzbXkmY+m9eWzjHAzfmyvwvo/kr4k pW8ZnH0BfYG1r0ZfzdrXoK9h7Yvoi6x9Lfpa1r6EvsTap+hT1j5Dn7H2wY/E6ofCD2X1Q+GHsvqh 8ENZ/VD4oax+KPxQVj8UfiirHwo/lNUPhR/K6ofCD2X1Q+GHsvph8MNY/TD4Yax+GPwwVj8Mfhir HwY/jNUPgx/G6ofBD2P1w+CHsfph8MNY/TD4Yax+OPxwVj8cfjirHw4/nNUPhx/O6ofDD2f1w+GH s/rh8MNZ/XD44ax+OPxwVj8cfjipH1GKH2Vw9gX0kfoRy4nB2degj9SPKBF9pH5EadFH6keUhD5S P6Io+kj9iGLoI/UjiqPv3378AQAA///M1U9Kw0AYhvEKKq7EI3gAleT7k7Yupbh00202sWotaAu2 VbyJx3XSJ0cQeQPfTGY28+x+R6PRqCvT7/13MZxnt+2s23XtfPO6eeg+V8tut9qs57v90/fh/n6/ XvQ33dtlf7Z2XtXt9nlblq+Px5e7qrKq8euq7GWJuup/D1t9s3pfXtXDe+dlfsqcljkrczzcnwzn /+qr6atV+4w+U+1z+ly1L+gL1b6kL1X7Gvoa1b4xfWPVvgl9E9W+KX1T0T7DD1P1w/DDVP0w/DBV Pww/TNUPww9T9cPww1T9MPwwVT8MP0zVD8MPU/XD8MNU/XD8cFU/HD9c1Q/HD1f1w/HDVf1w/HBV Pxw/XNUPxw9X9cPxw1X9cPxwVT8cP1zVj8CPUPUj8CNU/Qj8CFU/Aj9C1Y/Aj1D1I/AjVP0I/AhV PwI/QtWPwI9Q9SPwI1T9SPxIVT8SP1LVj8SPVPUj8SNV/Uj8SFU/Ej9S1Y/Ej1T1I/EjVf1I/EhV PxI/UtWPBj+aP/fjFwAA///clX9sTFkUx8fQpihpxM9Yq1l/MrxpO3Pv4w9HtaxUqK0I0ojXdlRl Zl7ND3RRuvqLYqs/RNIt8QeVRhARQUQI8WM3EbJ/bKR/iTRiN1LiDyES7n3nTk2eMZNt/HHqJmfO PXcy533vOe/Mp8TcbK4wtlVVGpEqM1gSiVbUFBgRo3RJNFguTwx/toxzSks0d2nYFxYf20Nlm/I1 LUfz5ro04cVHntctt5Zzz60KVM5xO3CNF3ZMWLqwDGGj1HmaikcIM5SXK0vFBfNLLR3fSl8O6suh qi8X9eVS1ZeH+vKo6vOgPg9VfV7U56Wqj6E+RlUfR32cqj4d9elE9THN0icdTX3ID0aVHwz5wajy gyE/GFV+MOQHo8oPhvxgVPnBkB+MKj8Y8oNR5QdDfjCq/GDID0aVHxz5wanygyM/OFV+cOQHp8oP jvzgVPnBkR+cKj848oNT5QdHfnCq/ODID06VHxz5wanygyM/OFV+6MgPnSo/dOSHTpUfOvJDp8oP HfmhU+WHjvzQqfJDR37oVPmhIz/0hPy4Mv2zPmcCfZlx+tLEDUar2CUsaAR81hdmMOyLyE1FNGRp dUQCZoX1VXVso54HKeqRZatHlvWcWA2yQ2Y04pN5fo7Lk27LM1LliOWRmqcKWzPedbF7nhv2bLke aZ9ZB7P63tVuqWmDoebbOfHc0qf11XCxe6DnricEj34SCRv9Vj4tSb4RtnxOtZO/K3Z8vR+xFd+P dBVPGOxHtREyAo5q01/zf+o+zlZ3GZebwUjI9GPRE+Sx38uZoE4y10LPhmvLekPQLwt0+wAMNU+Z TJAWgF+u/NjwX1H4u6lz3Pu9tFDluZDkXrEZia/PXmH7F23sf1TGYP6l+9tunF8L+27+ebLwuAFn f1hfsaDHB3+dLLz8pjAA00aJSaipg42ynmd+A5d8gaP1OBcnGtBPaUJ/rxle9dwtYr1twOQDXO2w XG68HfBYvu/nO0CO1UBxZ8q+2nU71dlo2xqOfR1j66uMY/NjNVXlaU5yr7EJ+rpWWGjzkZe7ZoSh afKTh2+NKIhooefDDnj4tuTZqXe/on+xGzKds5sm/7MH+zapBQZk3zYchPe14kA7DKe77mRrV1th 9bNTXQX3jqTsl12PU53FtWrwXsOtXynmsKBY5bmZ5F4TEvSrVdgF2bA/8uHo4hb/86JVOHe+cvQN lej7g9inj3U4X637MH6u5rCyEX2TmsOVzejn7cc5fNEGXHpfO/pbym/tgHw52Bmd2Pf6zpR8s9/D qc7Svlif6zvc+p1sPq1mO4Y+nxbf/g3jHD7YDiLKdD7ZqeJaxb0W9H0H0Xcdwv/dvsMg2zr19e8g x/vv163o16Wez08AAAD//9RXC0xTVxgutSJGhoqJOh+xm0Q0iGuv8nJm58cVFR/IRDd0OKlQQAat loKACOMNk1cpRDKjAsah08wnmCEqc9NF3JjxsYFMEB0OZEznYoibut3T/1ZLeXQpcd6d5PTP+e9/ Tv/vnP98370jBAKBFddpE7Kd+oY+b3q/HdslbLdmuw1naRvKja16rWP1bJ6v0TyhyTxDs3rmF+lj 6die7Up5pEIg2ChXyyMFG1URcYZ4ui4YrSvqIx87o3VHcWOlPEYcpFKGijXRamUU+mIHwDXcBBcd L2L7hZjTuyeQlVCitE+tfaCCpW7ZnoHtqWiPZ6D9NhPGi961c96aDac0uvGibB3crzi/xM21GNin bZfKisEUh7n/F3I+a0Mz2g9Lz8d9SP/nQ6OGWHg+7mbOx1rQ83ysufNZ6Gv5uay1K18RW3id2D5o Wv1FRBVZmfPV04+PfU9Ck31rN51oI0HbRsb9+vY1EpLcHJ599SQpPfie/YrudhJ55XiJrv1HUjOZ TLvllUYs2U9DzVmK21dmOe54ySPRdIcu4pjqODn+XAUJn5XYFX3mChnj6VlfuqCRbBvrdfxsXAsp F83tcLU7TB5+crosdFEr+Wnx0j0j1v9Aro0sLQ5flfxCcQ8zwT2Mw+21UGYxblmLdt53lfdI91Ub obVbK1kqXnUoteYu8Tig3Z0+8SYJmlF4SFTYQUSNb01bvP860VUlTDi2/xZp355Rtaethey76xbz RHxzULjBDO5RJrhHcbiDFRpVtBqZyFL858SH1WFSb3h6NbZjWkcwKN5sWZ6bHwavUt5ZnQJJ4TUa 3cJ0OLrzXsX5vCzkoZE65J8nOhxvL+IFD70snRhpcj50rFZEaYyFgq7jPwCuoX2cz3S2E5cPqr3L Y+HTHV+LD8cm4Xm45KI+MPmgDtN2PX6tAEzvj7n1hZzPsPeD2fcG4cvh/774QL/vrABYut/Fu45s CW1uJqqHviFxde0En94nzUcuPrZ3biTSk1N2nT3V+p/znB6Xr8xiXGN8dgx3W/YzWR61all9SQup Krwqqztzk3Sm+Sdoa++QBrXjiE0pt14oLhtBT1w2BlwsgVuKa3LlzMa1KZ0ks23MNeHFO6TuLjP8 8iutZF7D7gntFZ3k74oVEzvr2weFa5EZXKNNcI024DImaO45XS9wgDwMd8MYJ81bzwPVGuQB/wTk 45k50Hbp9aZHf+UCfX7BXot8oNWa5QPT/xFyPgMZ85GHzZ3DOJNzGKdfL2ZDqFyzQaUUr5Arg1WR YkWMQqnRr1dr1T8+po9zKGAHvyWwO73GE6q9bYVOjgsB9XM5SG44WCcr/MBn/Sh/D5k/lE5444bD 2HXgKLU5OMk+CHV1czCUe1X98SRNAax3TfDUUHz/XxOG1v5DuFjGBlRGoN2rBDp9TY0Keb8lGXkf UmBq06PEcG0K7GM/B7IhFXax5bDkUiqwqn10p08a2stpOM8pHb8jstNx7JgBd2jdlGVgHQkz0R+V if5T3HeHcxba1CyMq8rCuCnZmEd4NjjTB02F8Gci+8Bah9ZBB+70BeF9bpysw7jPdRBIC7ZDh/k6 FcF86ggpwri8IviGheN5sAjxdRXhe8iMYrRexUDN0YRinLcTv3/CBqhT03MUcj5L2//xXvTLTz1u xPP13Ae4F68Jet8LGyuOn+qi9DzkHa+Bw5SIZkUDvS7VqTGQObaxvlu7Ge3aWKjv9ru99/dYjJsf D9Ttd2QLCJ3YCEiADOpw2YpxZYl437yToIE6TiQh7+VvA1ougbY5QIeJ7+QALc/xX+ZgvU7KxXrd kovxLblAw8Kd8kBHC/uzPKDhoon5QN01e/Nh5e29O2TjCoCmE7S6QM+3kgMFwKKyTfmlAGh6PrZa 0HYlkEovLbBsrG5P0upxRFYi7w6kY6b7J+R8/7bxsf5qjdZ9Pr/vfGh8gWDgel0n6FmvdCybGyCT a+QBfqowlc8zTvfTRAfH6f0LopVB1COPENMxE+AnkQZEKaKkAeqN69aHzJdIGInrbGcJa+mPxE3/ S410liZWY9G+fsThybK36hf/Cave+EvM4Jeb4JcPAj8TELVZ3QP/7F74N0SGzpTyLD93zM+dr/l5 YH4ePM1PKtHnRw0/85NiflK+5ocsQQ0/88MqpIaf+c3B/ObwNT8XzM+Fr/m5Yn6ufM0P9UPKV/2Q on5I+aofUtQPKV/1g0H9YPiqHwzqB8NX/WBQPxi+6geD+sHwVT8Y1A+Gr/rBoH4wfNUPBvWDeeH6 8Q8AAAD//8zVO07DQBRGYZAAUSGWwAIA2Z77SChRREmT1o15hUiQSCQBsROWi+2TFdDwj3Tnelyd 7ts9fc+6bdfe7VaP2+V61b1dDO+mnVd1u3neNO3m6+Ph5baqmirKVVVV49XkcI+rvl6+Ly7rA85Z Pz/9nPRz2s/R/v/x/n3YT7ffwznfv2c37dgxX7+u77vP5aIbaubbv/ZN6Juo9k3pm4r2lWrsG5Zm X01frdrX0Neo9hX6imqf0WeqfU6fq/YFfaHahx9F1Y+CH0XVj4IfRdUPww9T9cPww1T9MPwwVT8M P0zVD8MPU/XD8MNU/TD8MFU/DD9M1Q/DD1P1w/DDVP1w/HBVPxw/XNUPxw9X9cPxw1X9cPxwVT8c P1zVD8cPV/XD8cNV/XD8cFU/HD9c1Y/Aj1D1I/AjVP0I/AhVP/gcl2YffoSqH4EfoepH4Eeo+hH4 Eap+BH6Eqh+BH6HqR+JHqvqR+JGqfiR+pKofiR+p6kfiR6r6kfiRqn4kfqSqH4kf+e9+/AIAAP// zNUxTsNQEIRhkABRIY7AAQDtTBLbS4kiSpq0bkyAEAkSiSQgbsJxsd+4pZ8nrVfvVX9h6WO7+/58 er2PYFSTm4gon7oZvmXhdv2xusaRzkU/v/2c9XPez8n4fjrej/vpxj2cy/E+v2vn3b5rF9u37WP3 tV51+/V2s9gfnn/K+8Nhsxxeuver4c52EWh3L7v/+lJ9adrXROkblmcf1AfXPqqPrn0T9U1c+6bq m7r2zdQ3c+2r1Fe59tXqq1375Efj6kcjPxpXP1J+pKsfKT/S1Y+UH+nqR8qPdPUj5Ue6+pHyI139 SPmRrn6k/EhXP1J+pKsfKT/S1A9E8aMszz6oz9QPBNVn6gf0F5bl2TdVn6kfiJn6TP1AVOoz9QNR q8/UD0SjPlM/EKk+Vz8gP+DqB+QHXP2A/ICrH5AfcPUD8gOufkB+wNUPyA+4+gH5AVc/ID/g6gfk B1z9oPygqx+UH3T1g/KDrn5QftDVD8oPuvpB+UFXPyg/6OoH5Qdd/aD8oKsflB/MPwAAAP//zNU5 TsNQFEZhkABRIZbAAgh6tu/gUKKIkiatGzOFSJBIJAGxE5aLnZOe9n/SfVN1um9Sqpvlx+K6OmJd DPM7zNkw58OcHP5PD+/jYfrDOa7Lw3t22836bd/N12/rh/5ruei3y/Vqvt09/+z/73erp/Gnf78a 33U3L1W3ednU3eb78/H1rpS6RDMppYxb1ZTxuj80+yr6KtW+mr5ata+hr1HtM/pMtc/pc9W+oC9U +5K+VO1r6WtV+6b0TUX7DD9M1Q/DD1P1w/DDVP0w/DBVPww/TNUPww9T9cPww1T9MPwwVT8MP0zV D8MPU/XD8cNV/XD8cFU/HD9c1Q/HD1f1w/HDVf1w/HBVPxw/XNUPxw9X9cPxw1X9cPxwVT8CP0LV j8CPUPUj8CNU/eC6PzT78CNU/Qj8CFU/Aj9C1Y/Aj1D1I/AjVP0I/AhVPxI/UtWPxI9U9SPxI1X9 SPxIVT8SP1LVj8SPVPUj8SNV/Uj8SFU/Ej9S1Y/Ej1T1o8WP9l8//gAAAP//zNU9TsNQEMRxkABR IY7AAQDtzHMSLyWKKGnSujFfIRIkEklA3ITjYnt8AwrG0nr9XuN/9zs60HPczWk3h9204+6f8/E8 v2nm7a5tFpvXzX37uVq2u9Vmvdjtn76H+7v9+rG/ad8u+jObRaDZPm/ZbL8+Hl5uIxjTchUR/Qs1 +s9h4Xr1vrzE+L+zbn66ORl7/quP6qNrX1Ffce2r1Fe59k3UN3Htm6pv6to3U9/Mta9WX+3al+pL 076Moa9fnn3yI139SPmRrn6k/EhXP1J+pKsfKT/S1Y+UH+nqR8qPdPUj5Ue6+pHyI039YAx+DMuz D+oz9YNB9Zn6weGt5dlXqc/UD8ZEfaZ+dN/qM/WDMVOfqR+MWn2mfjBSfa5+QH7A1Q/ID7j6AfkB Vz8gP+DqB+QHXP2A/ICrH5AfcPUD8gOufkB+wNUPyA+4+kH5QVc/KD/o6gflB139oPygqx+UH3T1 g/KDrn5QftDVD8oPuvpB+UFXPyg/6OpHkR/F1Y8iP8qf/fgFAAD//8zVO07DQBSFYUcCRIWyBBYA aGzfx4QSRZQ0bt0Mr8QSxBJ2QOyE5TL28RYiHUvXM3Orv/vWRVGkPNv7dpvG1Db9vn9K390ujV1/ aMbj6++8fzweXqZN+rie3lXbhLId3oaqHX6+nt8fQqiC1bchhOlX1dV0nY/yrvvc3ZQFvqs8f3ku 8lzmOVv258t7tfSslv2p+mr01ax9gj5h7VP0KWufoc9Y+xx9ztoX0RdZ+zbo25D2SZj7poOzr0Rf ydoHP4TVD4EfwuqHwA9h9UPgh7D6IfBDWP0Q+CGsfgj8EFY/BH4Iqx8KP5TVD4UfyuqHwg9l9UPh h7L6ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4UfyuqHwQ9j9cPgh7H6YfDDWP3AdT44++CHsfph 8MNY/TD4Yax+GPwwVj8MfhirHwY/jNUPhx/O6ofDD2f1w+GHs/rh8MNZ/XD44ax+OPxwVj8cfjir Hw4/nNUPhx/O6ofDD2f1I8KPyOpHhB+R1Y8IP+LJ/fgHAAD//8zVO07DUBCFYZAAUSGWwAIAzZyx HQ8liihp0roxr2AJEok4IHbCcrF9vAIazpXGd66rv/v2T9/Ltm+bu/3mse+2m/btYnyjWZk3u+cd mt3Xx8PLrRmsiiszGz+op3W6/Lp7X1/6Ac/ZMD/DnAxzOszR/P94fh8O0873eM7n9/KmmTpW29ft ffvZrduxZtX/ta9gX6HaV7KvVO2r2Fep9i3Yt1Dtq9lXq/Yl+1K0L23qGy/NPmefq/aBfVDtox+p 6kfSj1T1I+lHqvqR9CNV/Uj6kap+JP1IVT+SfqSoH2GTH9Ol2efsE/UjDOwT9SOMq4n6EVawT9SP sJJ9on6EVewT9SNswT5RP8Jq9on6EZbsU/XD6Yer+uH0w1X9cPrhqn44V1f1w+mHq/rh9MNV/XD6 4ap+OP1wVT+cfriqH04/XNUP0A+o+gH6AVU/QD+g6ge4QtUP0A+o+gH6AVU/QD+g6gfoB1T9AP2A qh+gH1D1I+hHqPoR9CNU/Qj6Eap+BNf4dz9+AQAA///M1TtOw0AUhWGQAFEhlsACAI3t+woliihp 0roZAgRLEEvYAbETlovHxy39sXQ9d6b6u69uh+/Pp9f7lOpkzU1KqfyaRua1HNVt97G7ro7wXUzz O83ZNOfTnCzvp8v9eJq8nOW7XO7ru3adx9xu+rf+MX91uzx2/X4zHp5/5veHw35bXvL7VbnX7SZV 7fAy/Nen6FPWPkOfsfY5+py1L9AXrH0r9K1I+yTNazk4+yr0Vax9Nfpq1j6s5eDsgx/C6ofAD2H1 Q+CHsPoh8ENY/RD4Iax+CPwQVj8UfiirHwo/lNUPhR/K6odiVVY/FH4oqx8KP5TVD4UfyuqHwg9l 9UPhh7L6ofBDWf0w+GGsfhj8MFY/DH4Yqx/Laqx+GPwwVj8MfhirHwY/jNUPgx/G6ofBD2P1w+CH sfrh8MNZ/XD44ax+OPxwVj8cq7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zVD4cfzupHwI9g9SPg R7D6EfAjWP0IrMHqR8CPYPUj4EfoHwAAAP//zNU9TsNAEMVxkABRIY7AAQiaN7NJvJQooqRx68Z8 BUuQSNgBcROOi9fPPe2zNJ7drf7db2G46T621zjidzHO7zhn45yPczK/n87343HaeZfvcr5vbptN O7RNvX/bP7Rf3bYduv2uHg7PP9P7/WH3VF7a96ty96Y2NP1L703//fn4emfmtoqFmZVfVKvpWJZm 35p9a9W+in2Val9mXxbtyzYdy9LsA/ug2ufsc9U+HsvS7EvsS6p9S/YtVfvoR1b1I9OPrOpHph9Z 1Y9MP7KoH8kmP6al2Qf2ifqRzNkn6kea/lyafYl9on4kW7JP1I9kK/aJ+pFszT5RP5JV7BP1I1lm n6ofoB9Q9QP0A6p+gH5A1Q/QD6j6AfoBVT9AP6DqB+gHVP0A/YCqH6AfUPUD9AOqfjj9cFU/nH64 qh9OP1zVD6cfruqH0w9X9cPph6v64fTDVf1w+uGqfjj9cFU/nH64qh9BP0LVj6AfoepH0I9Q9SPo R6j6EfQjVP0I+hGqfgT9iH/9+AMAAP//3BgJUJRVeFlhAVEEmkliaNpSK8ajBSSwrPdVoKloq6AR Q8bKrmLB7rIHiELArrjcsquGMx1qY545aR6Vo2SJVjaZVlaAZYV4VIqNlmnU9N7/vc3lb90d1zGh N/P2e8f+3/uO913PX4ItgPYg2v1oV3HIWhifJz+QlawyqbLSdLm6KarCuXNUprk6bZrJrC4W1seZ tTlsRZUnZ/O4rDRFbJZRY4zLMhYZZs1+VKGIU9wfP1KhULCf0fGJbCiA2FFz8+eMiOXnhdK+nHYZ p+dm0ZeE9CX1VvrGIH1jeil9oxUCfQz0Tvpikb7Y3kpfHNIX11vpi0f64nsrfaORvtG9lb4EpC/B LX1RV+iTuqFvgAt9AZSDYD4fSbtWla8RNnRao8bEBmqzQaBVYsrXqYUtvXPAzwMv8ggTySNMOMcp A7lBZzZpxHhkIjxSjsOJJ5jj2fZhYfOKB56D5doI657nFoGveDLVDx57YtVs6P5i3um7h+kEPAoP ePxEeKR8xL5TSq4uf2dzlb+MzyP+kb9eZVDlS/S6vOJrkfNAkZzZPEenNRl0eShkjudxD3z1cyOf SNrNSyL9Z4yKhb+YfPzmw5svd63d39wAvuI7QtHEnpsN59bun5S4twC2MXyk4H8jd5f7PT6F46ny wFeIGzll0B513zdDZao8oNIKer1OD9Y9B1alrDECnUVntpiBAXVIEcSxwaZFUMz+uNsGY489Ud9U VAUNTdQwVlWDsp4O0mvhPfr5DlOdVzsR0yPla8FX2j/y6Wv66i/SF5s77URQFsezzgNfg9zoax77 hl7natVDsOGXzybPypgBU2aFZYzpzgQddU8Hhs2CW5gC16tRf6vnc3sqg8vPP7vL1F4B7Zfo4CUr UGsLHWlZCKmJ1Y9kn2uAmTsnDLCMbYQ1L7bIFel2mDhAOjzwgh0S6EbdYodXfYrplfK14J6tT+rT i/0lKzkevQe+XHlz6pOd+/lkqsB3tGh/eQUISwwcmuA7ambaITaMPyHVaF8LaxAeRztLuoZzpXwt gLe+qA9P9iUog+PZ7IEvZ87gqo9y2qkZSIcPHgfUK1Ysq50K3eupoW2YiXa0ZQHCqaUID5dBGTWr JfdWIPzOgjBpIcKwSrS7g4vhzs2G3PCmRoSBdsi1nyl5dweHoxwI93u3MzHdUr4WIGp9Ua+hIr2G CvgK5VS3c+Qms0Fr9D2/uPAnVdjZAoxjE6oRptR7zQfE+KR0RSaTXZcfWyf1nD/3k/gmX1c/4E6+ MklP+cq4fMcrfZfrF4lBv1ibfyAWs4EMHNJBFt2+Ky513Hbii3ycd8BXPpTJvvORM6l0b/P6VnJo 2ddRp63fkNLEsNWnAjfeUD4CRXwEcj5Sxif7zEfZ4H32jHfOkMxJIe2GfT+QVdO6J276vf26+AAv fFyl/pKrNSad2YCWy/BkeDg/wA0/99Jeseyx2rwiHbC0dMdwIwh11HQzfEyn5wt5XDxf4zUOivFL +ZqMNUnfzDMHSXrKnc0NGqPJ1WEyPPM88OWszV3lzu5dUfOKqPsq0zGvfC0b88mhJRjXynm8U/A8 clwl5o27eP6YZAcjDWjdP3uPZ+LzpXxN5mwu8vBVP0n9bo6/dWffgn6ow/VVL5H6kJVZ958gqQP3 +UVt/Zk8Pehk9kI4TuovNy9t+KSV3PNVwpdHnjlLio92OiKmbidpSbfHrJ7fRrpaFMqTLcfIxgnP DG2YefA/92sC38pkn/le3jp7y7Yd7WTEkTvDu+d/Tzoeqrp1zJZO8mtbsCX4wrfkxxMHa5e++z2x 7gkvOqx+hWz4oDzHf0sbGXEm0RbT+jnZqY/c+j7svaF8B0l68h3k5Js6dF/5Ti8PrjWm/kQCN+2U v53dSWzHtekHIrqItdZWU36pi+zU/lZ8x5cnyKnTuS2nluwnY2sjp/fv30XkkpYVrSUniKZlr7Vs 6eHr4ts1DrnjO1zEd7iTb9cAwPcZvmwPdDhtz1UOjG7bra0HL3akor9vK8W6NaYc/U10I9Cs3fZC mx3nmQ6v8UB8jpSvOZ1Ob4wH3vQwWKSHwZIedeo0lVaty5drCjVak4Dvdw/83eZGDx/STqPxyT9u 0+L7Q64OhHe2LD3Wq8kFwNRz4EgBvErD85/pBtzvNuA7ks0I0SyAP2nC/TAzsGck7Rozrr9RiO9K ITb8TmXD/22yYdx/uAp+ZOd0VoGGTusfrsb62FoN97AAlVgD7JgHD9Xgu9SEWghmC0F1WC8fqsP3 qxjM+5XXwL+Ur3lrffHeXNV+e9yYK/hW+l2dv1GSf98bvR+vq5uSQU7rX/vuiSDkFylKvEfr0rDe Hj6D191PAUv7TnZkwjA6fez80/DWeVrATc7Ge3FUhfq8mIN1eMEC9AsRJQgvl2B+uLsU85aS53n9 Xgadh+5qvxRdzut1DnMrYAh7GHu/Augs0t/C6/hoK3wwiToaixVGsgfpUP5eVsDr+6Mc+lfiOqkE mg29+bJlMdBynmwPacT3s/hGIHTw0YJG6HjtxZaVWzk8w2GMHf3XFDswP1fUZAcmr42f2RFPF8+n 7nLg/hQH7lsc8OnFtI5hux1wtoQkfNvhAAP735Al4M3PivUk5WvX2m70ff8bAAD//2NkgABWBhYG NiAN4gsBcV5ibioDQ0FiUWIuQ0F+TiVUGQMfEB8AYpBaDqh6iH4EnxFJHKR+ApJ6Fizq45HUC0D5 LlYxLokliTHB+Rn5follmemJJZn5ecElpSmVYHG30rxkkEhijgKIbxQTbGAYU5xabBRTVBCflOZk YGBkYGasa2BgACYMzMEkiDLUK6koAbvLAMldbAT8wQRkNWDxPxMBfaBwZYXyQXRGURo8XCpINIcd ygfRKalFmWXFDET5gwnFHxCbANccyO4= ------=_20110510163748_41792 Content-Type: application/octet-stream; name="GLMest_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="GLMest_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA2OjE2IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAA6AAAAHiclZC/CsIwEMavRes/KD6J6CS6OOiqCF0cAnLaagutEXOK bj6Go4/jY5m0p2QQtYHj8l2++yUXHwBuLoCnc12HA8WqWvoVLR0ZUoqrFdI6zn2+joXV737pL+qV /Mxhj9pnb067JMdjbbIiJMWch1OO02Bt8iY7JMtIER/nvHNJXo21X8ynvwyyiGIZcu0O//336/69 5a988I8sf5v1ZCgmSCgCGcsZnpItUiJ3AR3DS16fyjBK+wMxRoq28pCsMRVBtyeC+bRjHmzuvUK5 uZusTR6nqJShWnN0LZ73g+fqndk/AfYVMeY= ------=_20110510163748_41792 Content-Type: application/octet-stream; name="CONspec_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="CONspec_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA4OjExIDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAALAIAAHic7ZbdTsIwFMcLIpCQoCZKuOTSK4NXftxoIgZvMERMvNkF ZTSwuK1krSS+gY/gpY/gI/km2O4DujKYbCUmQJNmO+X0cPo7J/+uDAD4rgCQZ88imxngjX3BDmaJ TQtSE/Z6kOpD16/M5kjYn12y31vPub9lfB8ysqZxTleMk/dt/iQUUuLH+TlJno+O7Wk+nyvGCdar 3rkYKjceQYSAPjIRRV7cL/A33lO+gn8uwv9G8D/07ca11oAUah08xI9wbAwgNbDdoW/9d3e9hVlG F1faHaRogB1Dh6bWqZ9rnXbrjCfO/7d7vDzPspxnJjl36oN3+2DFOCJ3G1oIeHUcI53VgcF30MiN +xDD8UjiyO0Zu1rPxPorEc4rxstHxKsK8bKSLQ8erx6TXy6UXw6wc5l831AR99qauN/GnKsEwty5 /YQIFYkn5b0IeBreH4p43/5Tn1ck3txmfd7CDnoeQjuE3tWBFbjv+VwzYDKJJh89trn/D0C4Htx+ 4TesYQ9qFh4biCTXmzX0/4brvAPtPrYETol0RxriPbmtfb5Q5wXiqngHDjudn/GO0vkAfVKdjxvy PZCmHl1F9aiuqR6XMefKg3A9uC3yV6IzCvp+EznbcFxrtlNznrtNd5znObcb6ftZBr3NnAsS54LP +b7ZSK8bEugd5zBnh1+STDhU6LOIesc5gnNbQT9LoLeZcxGEORcDzkw41HxvzECn4Szui8pD5JNl b/z9F9NrLTM= ------=_20110510163748_41792-- ========================================================================= Date: Tue, 10 May 2011 11:48:33 -0400 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: contrast specification error Comments: To: Lorelei Howard <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=002354186d846eacec04a2ede3cd Message-ID: <[log in to unmask]> --002354186d846eacec04a2ede3cd Content-Type: text/plain; charset=ISO-8859-1 That column looks to be all 0s. This is the problem. Basically, you can't form a contrast for a regressor that does not exist. 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, May 10, 2011 at 11:37 AM, Lorelei Howard <[log in to unmask]> wrote: > Hi everyone, > > I am getting an error that I can't diagnose... any help would be > appreciated. > > Attached are the .mat files for a first level specification. Everything > seems to be running fine until it gets to contrast 11 when it crashes. The > only hint at a problem I have is from looking at the design matrix (image > also attached). Contrast 11 is '1' contrast above the regressor in session > 2 where there looks like there is a problem. I've looked at my code, the > mat files attached, and the matlabbatch stucture generated by my script to > specify all the .mat files and i can't find a problem. > > Anyone have any idea what's going on? > > 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 --002354186d846eacec04a2ede3cd Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable That column looks to be all 0s. This is the problem. <br><br>Basically, you= can't form a contrast for a regressor that does not exist.<br clear=3D= "all"><br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br> Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Departm= ent of Neurology, Massachusetts General Hospital and Harvard Medical School= <br>Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDENTIAL INFORMATION which m= ay contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILE= GED and which is intended only for the use of the individual or entity name= d above. If the reader of the e-mail is not the intended recipient or the e= mployee or agent responsible for delivering it to the intended recipient, y= ou are hereby notified that you are in possession of confidential and privi= leged information. Any unauthorized use, disclosure, copying or the taking = of any action in reliance on the contents of this information is strictly p= rohibited and may be unlawful. If you have received this e-mail unintention= ally, please immediately notify the sender via telephone at (773) 406-2464 = or email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 11:37 AM, Lorele= i Howard <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">l.howa= [log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmail_quote" sty= le=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);= padding-left: 1ex;"> Hi everyone,<br> <br> I am getting an error that I can't diagnose... any help would be apprec= iated.<br> <br> Attached are the .mat files for a first level specification. Everything<br> seems to be running fine until it gets to contrast 11 when it crashes. The<= br> only hint at a problem I have is from looking at the design matrix (image<b= r> also attached). Contrast 11 is '1' contrast above the regressor in = session<br> 2 where there looks like there is a problem. I've looked at my code, th= e<br> mat files attached, and the matlabbatch stucture generated by my script to<= br> specify all the .mat files and i can't find a problem.<br> <br> Anyone have any idea what's going on?<br> <br> Thanks,<br> Lorelei<br> <br> <br> <br> <br> --<br> Lorelei Howard<br> <br> Ph.D. Student<br> <br> Institute of Behavioural Neuroscience<br> Cognitive, Perceptual and Brain Sciences Department<br> University College London<br> 26 Bedford Way<br> WC1H 0AP<br> <a href=3D"tel:%2B44%20%280%2920%207679%208553" value=3D"+442076798553">+44= (0)20 7679 8553</a></blockquote></div><br> --002354186d846eacec04a2ede3cd-- ========================================================================= Date: Tue, 10 May 2011 16:56:24 +0100 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: contrast specification error Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed;boundary="----=_20110510165623_38470" Message-ID: <[log in to unmask]> ------=_20110510165623_38470 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 8bit Dear Donald I appreciate the reply, thank you. I was aware that it is a column of zeros. The problem is that I am unsure why this is. As I said in the previous email, all my specification seems to be working correctly. The .mat files I attached to the previous email may not come through as I got an error message through after sending the email. I will try to attach these again here and also a copy of my script and the .mat files they use for regressor specification for the first subject (where the error is occuring). Thanks, Lorelei > That column looks to be all 0s. This is the problem. > > Basically, you can't form a contrast for a regressor that does not exist. > > 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, May 10, 2011 at 11:37 AM, Lorelei Howard <[log in to unmask]> > wrote: > >> Hi everyone, >> >> I am getting an error that I can't diagnose... any help would be >> appreciated. >> >> Attached are the .mat files for a first level specification. Everything >> seems to be running fine until it gets to contrast 11 when it crashes. >> The >> only hint at a problem I have is from looking at the design matrix >> (image >> also attached). Contrast 11 is '1' contrast above the regressor in >> session >> 2 where there looks like there is a problem. I've looked at my code, the >> mat files attached, and the matlabbatch stucture generated by my script >> to >> specify all the .mat files and i can't find a problem. >> >> Anyone have any idea what's going on? >> >> 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 ------=_20110510165623_38470 Content-Type: application/octet-stream; name="GLMspec_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="GLMspec_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA2OjAzIDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAAUS4AAHiczNXLjtMwFAbgpCodLtKIB2AxmhWLAcXnuDdWIxixgwVl WWnkprdIuVRNyk1CgrfgBdjzCDwCz8GKR8DO7w4ZKFN1dyo5rh3b/ZMe6TsOguDX51bQsf1t28IA n1uN8bbdsy0zVWomE1PFy3rdsW3fGvtbN+zHfLu+F/o15Sq7Omd54DkdP3Z9WZmq9OecHHjOXT92 /TxbJ5flahb7++68H58OO+/Ijx/YNk3WbrpKsiRfBOWsdCEnxn75c/4X/wz73v/Ve2qsb+9Y32+s v+/HF0/GF6Yy41GxLF6aN8nCVEmRj6rN9H09/6KYztL+cPzMVLNFsU5ik45Hkap/7zwMDnr+7fyJ bZs8qf+XV69xv36/1baP6vOjPc/TvvY8bfsW49LtGzT2dfbkuuPnT9N3H76eHZ3//bv79rfst1P/ /g/dF/p93z/eXEetYHddPrStjE2OkomLfIo12Satksv1bBEsV3O/r64PCv9bTz/Df+upWX+73r8J rteTG++rp+ebPHYzJj1xY3LFNLZVby9v15P50yiiqMePItu7S9Svr65Tj5NscaaE5Rsg30BqviHy DYXmU1Gdz3Uy8ynkU1Lz4V92ncx8jHwsNZ9GPi01Xxf5ulLz9ZCvJzUf/FBS/VDwQ0n1Q8EPJdUP gh8k1Q+CHyTVD4IfJNUPgh8k1Q+CHyTVD4IfJNUPgh8k1Q+CHyTVD4IfJNUPgh8k1Q+GHyzVD4Yf LNUPhh8s1Q+GHyzVD4YfLNUPhh8s1Q+GHyzVD4YfLNUPhh8s1Q+GHyzVDw0/tFQ/NPzQUv3Q8ENL 9UPDDy3VDw0/tFQ/NPzQUv3Q8ENL9UPDDy3VDw0/tFQ/NPzQUv3owo+uOD9+AwAA///M1TtOxDAU hWGQAFEhlsACANnJ5D4o0YiSJm0aM8AQCSYSkwGxE5aL7ZOGHZxINzd29UspvhCamxBCF8u7rng7 fmyv4xGeizy/ec7ynOc5We5Pl/NxnrTs8lwu5/XdsE5zGvrpbXpMX+M2zeO06+fD80+9fzjsNuUm vV+VczP0IQ77l31+fX8+vd7nriBtiUJfg76Gta9FX8vat0LfirWvQ1/H2ifoE9Y+RZ+y9hn6jLXP 0eekfRJqX1mcffBDWP0Q+CGsfuCzLs4++CGsfgj8EFY/BH4Iqx8CP4TVD4EfwuqHwA9h9UPhh7L6 ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4UfyuqHwg9l9UPhh7L6ofBDWf0w+GGsfhj8MFY/DH4Y qx8GP4zVD4MfxuqHwQ9j9cPgh7H6YfDDWP0w+GGsfhj8MFY/HH44qx8OP5zVD4cfzuqHww9n9cPh h7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zUjxiqH3Vx9kX0kfoR8Zfr+t/3BwAA///M1T1Ow0AQ xXEjAaJCHIEDANp5M+sklCiipEnrxnwFSxBLxAFxE47L2s8XoHsr7Y53G/+733lVVb9ln5Z9VvZx xXUy34/Kbuc5rov5vr5t1u3QNpv+rX9ov7ptO3T9bjMcnn+m9/vD7ml8ad8vxzuaTbJm/7Ivx/fn 4+tdSki1X6cyy2Fp+pyG3XQf2yub/6fSF+wL1b7MvqzaV7OvVu1bsG+h2rdk31K1b8W+lWifpalv HJp9xj5T7QP7oNpHP0zVD6MfpuqH0Q9T9cPoh6n6YfTDVP0w+mGqfhj9MFU/QD+g6gfoB1T9AP2A qh+gH1D1A/QDqn6AfkDVD9APqPoB+gFVP0A/oOoH6AdU/XD64ap+OP1wVT+cfriqH04/XNUPpx+u 6ofTD1f1w+mHq/rh9MNV/XD64ap+OP1wVT+CfoSqH0E/QtWPoB+h6kfQj1D1I+hHqPoR9CNU/Qj6 Eap+BP0IVT+CfoSqH0E/QtWPTD+yqh+ZfmRVPzL9yKp+ZPqR/+3HHwAAAP//zNU7TsNAFIVhIwGi QiyBBQCaGcf3QYkiShq3bkyAYAliiTggdsJymfHxEhA6ke5czzT5u++oqqo+T9nld7Hc17fdup/6 rh1fx4f+c9j20zDu2unw9D2/3x92m/LSv12We+raELv98z4fXx+PL3chpCD1dcg7H7FZlc95xZvh fXsVl/87z/OT5zTPWZ7j5f1kuf9XX4O+hrVP0CesfYo+Ze0z9Blrn6PPSfskzH1lcfZF9EXWvoS+ xNpXo69m7YMfwuqHwA9h9UPgh7D6IfBDWP0Q+CGsfgj8EFY/FH4oqx8KP5TVD4UfyuqHwg9l9UPh h7L6ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4MfxuqHwQ9j9cPgh7H6YfDDWP0w+GGsfhj8MFY/ DH4Yqx8GP4zVD4MfxuqHwQ9j9cPhh7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zVD4cfzuqHww9n 9cPhh7P64fDDSf1IYfZjXpx9EX2kfqT5xOLsw2cg9SOFFfr+3I9fAAAA///M1TtOw0AUhWGQAFEh lsACCJr7chJKFFHSpHUzBAiWIJaIA2InLJcZHy8BoWNpfO9M9Xffun/tH/Jnt81D1+/Ww+Hpe5WH 3N4fdpv6kt+u6l3bdZJ2/7wvv6+Px5e7lDQ1Nktlzuoe41qH3HTv22s5wndRzk85Z+Wcl3MyvZ9O 9+Ny8jTrdzndV7ft2PFXfQ36Gta+OfrmrH0L9C1Y+5boW5L2SRrXOjj7BH3C2qfoU9Y+rHVw9jn6 nLUPfgirHwI/hNUPgR/C6ofAD2H1Q+CHsPqh8ENZ/VD4oax+KPxQVj8Uq7L6ofBDWf1Q+KGsfij8 UFY/FH4oqx8KP5TVD4UfyuqHwQ9j9cPgh7H6YfDDWP0wrMbqh8EPY/XD4Iex+mHww1j9MPhhrH4Y /DBWPwx+GKsfDj+c1Q+HH87qh8MPZ/XDsTqrHw4/nNUPhx/O6ofDD2f1w+GHs/rh8MNZ/XD44ax+ BPwIVj8CfgSrHwE/gtWPwBqsfgT8CFY/An7Ev/vxCwAA///M1TFOw0AQRmGQAFEhjsABAK3teGeG EkWUNGndmAAhEiQSSUDchOOy9u8DUD5LuzN249d9/fvVvN/3dbdIVbd72ZXr+/Pp9T6lOuXmJpVZ rrrN4zqM6nb9sbqujvRclPNbzlk55+WcTN9Pp/fjcvppDs/l9D6/64b/dovt2/ax/1qv+v16u1ns D88/4/eHw2Y5fPlvn6nPqH2uPqf2hfoC2pfTuA6D2Vepr6L21eqrqX1ah8Hsm6lvRu1r1ddS++RH pvqR5Uem+pHlR6b6keVHpvph8sOofpj8MKofJj+M6odpNaofJj+M6ofJD6P6YfLDqH6Y/DCqHyY/ jOqHyQ+j+uHyw6l+uPxwqh8uP5zqh2t1qh8uP5zqh8sPp/rh8sOpfrj8cKofLj+c6ofLD6f6EfIj qH6E/AiqHyE/gupHaA2qHyE/gupHyI+g+hHyI6h+hPwIqh8hP4LqR8iPgPrRpNGPcTD7KvVB/WjG W4PZ16gP6keTZuqD+tGkVn1QP5qU1Yfz4w8AAP//zNU9SkNBFIZhBRUrcQkuQGXm/MTEUoKlTdo0 178Y0ARMorgTl+vcvLdzA9/AmcNM9XZPKXZVipebdrPq9fJjcVkPOGdtftuctDltczT8Hw/vwzbd sPtzPrynt/Npt+3ms/Xb+qH7Wi667XK9mm13zz/7//vd6qn/6d4v+rfNZ6XONy+bdn1/Pr7eta4y 8j6KvjF9Y9W+CX0T0b5a9n390uyr9FXVPqPPVPucPlftC/pCtS/pS9W+EX0j1T78qKp+VPyoqn5U /Kiqfhh+mKofhh+m6ofhh6n6Yfhhqn4YfpiqH4YfpuqH4Yep+mH4Yap+GH6Yqh+GH6bqh+OHq/rh +OGqfjh+uKofjh+u6ofjh6v64fjhqn44friqH44fruqH44er+uH44ap+BH6Eqh+BH6HqR+BHqPoR +BGqfgR+hKofgR+h6kfgR6j6EfgRqn4EfoSqH4EfoepH4keq+pH4kap+JH6kqh+JH6nqR+JHqvqR +JGqfiR+pKofiR+p6kfiR/7z4w8AAP//zNU9TsQwEIbhIAGiQhyBAwAaJ45nhhKtKGnSpgl/SyTY SGwWxE04Lo6/XIDus2RP7CZv95xXVfWb92neZ3kfV1gn6/0o72Gdy7pY75vbfjPMQ99Nb9PD8DVu h3mcdt18eP4p7/eH3dPyMrxfLve67yT0+5d9Pr4/H1/vRGpJzbXkmY+m9eWzjHAzfmyvwvo/kr4k pW8ZnH0BfYG1r0ZfzdrXoK9h7Yvoi6x9Lfpa1r6EvsTap+hT1j5Dn7H2wY/E6ofCD2X1Q+GHsvqh 8ENZ/VD4oax+KPxQVj8UfiirHwo/lNUPhR/K6ofCD2X1Q+GHsvph8MNY/TD4Yax+GPwwVj8Mfhir HwY/jNUPgx/G6ofBD2P1w+CHsfph8MNY/TD4Yax+OPxwVj8cfjirHw4/nNUPhx/O6ofDD2f1w+GH s/rh8MNZ/XD44ax+OPxwVj8cfjipH1GKH2Vw9gX0kfoRy4nB2degj9SPKBF9pH5EadFH6keUhD5S P6Io+kj9iGLoI/UjiqPv3378AQAA///M1U9Kw0AYhvEKKq7EI3gAleT7k7Yupbh00202sWotaAu2 VbyJx3XSJ0cQeQPfTGY28+x+R6PRqCvT7/13MZxnt+2s23XtfPO6eeg+V8tut9qs57v90/fh/n6/ XvQ33dtlf7Z2XtXt9nlblq+Px5e7qrKq8euq7GWJuup/D1t9s3pfXtXDe+dlfsqcljkrczzcnwzn /+qr6atV+4w+U+1z+ly1L+gL1b6kL1X7Gvoa1b4xfWPVvgl9E9W+KX1T0T7DD1P1w/DDVP0w/DBV Pww/TNUPww9T9cPww1T9MPwwVT8MP0zVD8MPU/XD8MNU/XD8cFU/HD9c1Q/HD1f1w/HDVf1w/HBV Pxw/XNUPxw9X9cPxw1X9cPxwVT8cP1zVj8CPUPUj8CNU/Qj8CFU/Aj9C1Y/Aj1D1I/AjVP0I/AhV PwI/QtWPwI9Q9SPwI1T9SPxIVT8SP1LVj8SPVPUj8SNV/Uj8SFU/Ej9S1Y/Ej1T1I/EjVf1I/EhV PxI/UtWPBj+aP/fjFwAA///clX9sTFkUx8fQpihpxM9Yq1l/MrxpO3Pv4w9HtaxUqK0I0ojXdlRl Zl7ND3RRuvqLYqs/RNIt8QeVRhARQUQI8WM3EbJ/bKR/iTRiN1LiDyES7n3nTk2eMZNt/HHqJmfO PXcy533vOe/Mp8TcbK4wtlVVGpEqM1gSiVbUFBgRo3RJNFguTwx/toxzSks0d2nYFxYf20Nlm/I1 LUfz5ro04cVHntctt5Zzz60KVM5xO3CNF3ZMWLqwDGGj1HmaikcIM5SXK0vFBfNLLR3fSl8O6suh qi8X9eVS1ZeH+vKo6vOgPg9VfV7U56Wqj6E+RlUfR32cqj4d9elE9THN0icdTX3ID0aVHwz5wajy gyE/GFV+MOQHo8oPhvxgVPnBkB+MKj8Y8oNR5QdDfjCq/GDID0aVHxz5wanygyM/OFV+cOQHp8oP jvzgVPnBkR+cKj848oNT5QdHfnCq/ODID06VHxz5wanygyM/OFV+6MgPnSo/dOSHTpUfOvJDp8oP HfmhU+WHjvzQqfJDR37oVPmhIz/0hPy4Mv2zPmcCfZlx+tLEDUar2CUsaAR81hdmMOyLyE1FNGRp dUQCZoX1VXVso54HKeqRZatHlvWcWA2yQ2Y04pN5fo7Lk27LM1LliOWRmqcKWzPedbF7nhv2bLke aZ9ZB7P63tVuqWmDoebbOfHc0qf11XCxe6DnricEj34SCRv9Vj4tSb4RtnxOtZO/K3Z8vR+xFd+P dBVPGOxHtREyAo5q01/zf+o+zlZ3GZebwUjI9GPRE+Sx38uZoE4y10LPhmvLekPQLwt0+wAMNU+Z TJAWgF+u/NjwX1H4u6lz3Pu9tFDluZDkXrEZia/PXmH7F23sf1TGYP6l+9tunF8L+27+ebLwuAFn f1hfsaDHB3+dLLz8pjAA00aJSaipg42ynmd+A5d8gaP1OBcnGtBPaUJ/rxle9dwtYr1twOQDXO2w XG68HfBYvu/nO0CO1UBxZ8q+2nU71dlo2xqOfR1j66uMY/NjNVXlaU5yr7EJ+rpWWGjzkZe7ZoSh afKTh2+NKIhooefDDnj4tuTZqXe/on+xGzKds5sm/7MH+zapBQZk3zYchPe14kA7DKe77mRrV1th 9bNTXQX3jqTsl12PU53FtWrwXsOtXynmsKBY5bmZ5F4TEvSrVdgF2bA/8uHo4hb/86JVOHe+cvQN lej7g9inj3U4X637MH6u5rCyEX2TmsOVzejn7cc5fNEGXHpfO/pbym/tgHw52Bmd2Pf6zpR8s9/D qc7Svlif6zvc+p1sPq1mO4Y+nxbf/g3jHD7YDiLKdD7ZqeJaxb0W9H0H0Xcdwv/dvsMg2zr19e8g x/vv163o16Wez08AAAD//9RXC0xTVxgutSJGhoqJOh+xm0Q0iGuv8nJm58cVFR/IRDd0OKlQQAat loKACOMNk1cpRDKjAsah08wnmCEqc9NF3JjxsYFMEB0OZEznYoibut3T/1ZLeXQpcd6d5PTP+e9/ Tv/vnP98370jBAKBFddpE7Kd+oY+b3q/HdslbLdmuw1naRvKja16rWP1bJ6v0TyhyTxDs3rmF+lj 6die7Up5pEIg2ChXyyMFG1URcYZ4ui4YrSvqIx87o3VHcWOlPEYcpFKGijXRamUU+mIHwDXcBBcd L2L7hZjTuyeQlVCitE+tfaCCpW7ZnoHtqWiPZ6D9NhPGi961c96aDac0uvGibB3crzi/xM21GNin bZfKisEUh7n/F3I+a0Mz2g9Lz8d9SP/nQ6OGWHg+7mbOx1rQ83ysufNZ6Gv5uay1K18RW3id2D5o Wv1FRBVZmfPV04+PfU9Ck31rN51oI0HbRsb9+vY1EpLcHJ599SQpPfie/YrudhJ55XiJrv1HUjOZ TLvllUYs2U9DzVmK21dmOe54ySPRdIcu4pjqODn+XAUJn5XYFX3mChnj6VlfuqCRbBvrdfxsXAsp F83tcLU7TB5+crosdFEr+Wnx0j0j1v9Aro0sLQ5flfxCcQ8zwT2Mw+21UGYxblmLdt53lfdI91Ub obVbK1kqXnUoteYu8Tig3Z0+8SYJmlF4SFTYQUSNb01bvP860VUlTDi2/xZp355Rtaethey76xbz RHxzULjBDO5RJrhHcbiDFRpVtBqZyFL858SH1WFSb3h6NbZjWkcwKN5sWZ6bHwavUt5ZnQJJ4TUa 3cJ0OLrzXsX5vCzkoZE65J8nOhxvL+IFD70snRhpcj50rFZEaYyFgq7jPwCuoX2cz3S2E5cPqr3L Y+HTHV+LD8cm4Xm45KI+MPmgDtN2PX6tAEzvj7n1hZzPsPeD2fcG4cvh/774QL/vrABYut/Fu45s CW1uJqqHviFxde0En94nzUcuPrZ3biTSk1N2nT3V+p/znB6Xr8xiXGN8dgx3W/YzWR61all9SQup Krwqqztzk3Sm+Sdoa++QBrXjiE0pt14oLhtBT1w2BlwsgVuKa3LlzMa1KZ0ks23MNeHFO6TuLjP8 8iutZF7D7gntFZ3k74oVEzvr2weFa5EZXKNNcI024DImaO45XS9wgDwMd8MYJ81bzwPVGuQB/wTk 45k50Hbp9aZHf+UCfX7BXot8oNWa5QPT/xFyPgMZ85GHzZ3DOJNzGKdfL2ZDqFyzQaUUr5Arg1WR YkWMQqnRr1dr1T8+po9zKGAHvyWwO73GE6q9bYVOjgsB9XM5SG44WCcr/MBn/Sh/D5k/lE5444bD 2HXgKLU5OMk+CHV1czCUe1X98SRNAax3TfDUUHz/XxOG1v5DuFjGBlRGoN2rBDp9TY0Keb8lGXkf UmBq06PEcG0K7GM/B7IhFXax5bDkUiqwqn10p08a2stpOM8pHb8jstNx7JgBd2jdlGVgHQkz0R+V if5T3HeHcxba1CyMq8rCuCnZmEd4NjjTB02F8Gci+8Bah9ZBB+70BeF9bpysw7jPdRBIC7ZDh/k6 FcF86ggpwri8IviGheN5sAjxdRXhe8iMYrRexUDN0YRinLcTv3/CBqhT03MUcj5L2//xXvTLTz1u xPP13Ae4F68Jet8LGyuOn+qi9DzkHa+Bw5SIZkUDvS7VqTGQObaxvlu7Ge3aWKjv9ru99/dYjJsf D9Ttd2QLCJ3YCEiADOpw2YpxZYl437yToIE6TiQh7+VvA1ougbY5QIeJ7+QALc/xX+ZgvU7KxXrd kovxLblAw8Kd8kBHC/uzPKDhoon5QN01e/Nh5e29O2TjCoCmE7S6QM+3kgMFwKKyTfmlAGh6PrZa 0HYlkEovLbBsrG5P0upxRFYi7w6kY6b7J+R8/7bxsf5qjdZ9Pr/vfGh8gWDgel0n6FmvdCybGyCT a+QBfqowlc8zTvfTRAfH6f0LopVB1COPENMxE+AnkQZEKaKkAeqN69aHzJdIGInrbGcJa+mPxE3/ S410liZWY9G+fsThybK36hf/Cave+EvM4Jeb4JcPAj8TELVZ3QP/7F74N0SGzpTyLD93zM+dr/l5 YH4ePM1PKtHnRw0/85NiflK+5ocsQQ0/88MqpIaf+c3B/ObwNT8XzM+Fr/m5Yn6ufM0P9UPKV/2Q on5I+aofUtQPKV/1g0H9YPiqHwzqB8NX/WBQPxi+6geD+sHwVT8Y1A+Gr/rBoH4wfNUPBvWDeeH6 8Q8AAAD//8zVO07DQBRGYZAAUSGWwAIA2Z77SChRREmT1o15hUiQSCQBsROWi+2TFdDwj3Tnelyd 7ts9fc+6bdfe7VaP2+V61b1dDO+mnVd1u3neNO3m6+Ph5baqmirKVVVV49XkcI+rvl6+Ly7rA85Z Pz/9nPRz2s/R/v/x/n3YT7ffwznfv2c37dgxX7+u77vP5aIbaubbv/ZN6Juo9k3pm4r2lWrsG5Zm X01frdrX0Neo9hX6imqf0WeqfU6fq/YFfaHahx9F1Y+CH0XVj4IfRdUPww9T9cPww1T9MPwwVT8M P0zVD8MPU/XD8MNU/TD8MFU/DD9M1Q/DD1P1w/DDVP1w/HBVPxw/XNUPxw9X9cPxw1X9cPxwVT8c P1zVD8cPV/XD8cNV/XD8cFU/HD9c1Y/Aj1D1I/AjVP0I/AhVP/gcl2YffoSqH4EfoepH4Eeo+hH4 Eap+BH6Eqh+BH6HqR+JHqvqR+JGqfiR+pKofiR+p6kfiR6r6kfiRqn4kfqSqH4kf+e9+/AIAAP// zNUxTsNQEIRhkABRIY7AAQDtTBLbS4kiSpq0bkyAEAkSiSQgbsJxsd+4pZ8nrVfvVX9h6WO7+/58 er2PYFSTm4gon7oZvmXhdv2xusaRzkU/v/2c9XPez8n4fjrej/vpxj2cy/E+v2vn3b5rF9u37WP3 tV51+/V2s9gfnn/K+8Nhsxxeuver4c52EWh3L7v/+lJ9adrXROkblmcf1AfXPqqPrn0T9U1c+6bq m7r2zdQ3c+2r1Fe59tXqq1375Efj6kcjPxpXP1J+pKsfKT/S1Y+UH+nqR8qPdPUj5Ue6+pHyI139 SPmRrn6k/EhXP1J+pKsfKT/S1A9E8aMszz6oz9QPBNVn6gf0F5bl2TdVn6kfiJn6TP1AVOoz9QNR q8/UD0SjPlM/EKk+Vz8gP+DqB+QHXP2A/ICrH5AfcPUD8gOufkB+wNUPyA+4+gH5AVc/ID/g6gfk B1z9oPygqx+UH3T1g/KDrn5QftDVD8oPuvpB+UFXPyg/6OoH5Qdd/aD8oKsflB/MPwAAAP//zNU5 TsNQFEZhkABRIZbAAgh6tu/gUKKIkiatGzOFSJBIJAGxE5aLnZOe9n/SfVN1um9Sqpvlx+K6OmJd DPM7zNkw58OcHP5PD+/jYfrDOa7Lw3t22836bd/N12/rh/5ruei3y/Vqvt09/+z/73erp/Gnf78a 33U3L1W3ednU3eb78/H1rpS6RDMppYxb1ZTxuj80+yr6KtW+mr5ata+hr1HtM/pMtc/pc9W+oC9U +5K+VO1r6WtV+6b0TUX7DD9M1Q/DD1P1w/DDVP0w/DBVPww/TNUPww9T9cPww1T9MPwwVT8MP0zV D8MPU/XD8cNV/XD8cFU/HD9c1Q/HD1f1w/HDVf1w/HBVPxw/XNUPxw9X9cPxw1X9cPxwVT8CP0LV j8CPUPUj8CNU/eC6PzT78CNU/Qj8CFU/Aj9C1Y/Aj1D1I/AjVP0I/AhVPxI/UtWPxI9U9SPxI1X9 SPxIVT8SP1LVj8SPVPUj8SNV/Uj8SFU/Ej9S1Y/Ej1T1o8WP9l8//gAAAP//zNU9TsNQEMRxkABR IY7AAQDtzHMSLyWKKGnSujFfIRIkEklA3ITjYnt8AwrG0nr9XuN/9zs60HPczWk3h9204+6f8/E8 v2nm7a5tFpvXzX37uVq2u9Vmvdjtn76H+7v9+rG/ad8u+jObRaDZPm/ZbL8+Hl5uIxjTchUR/Qs1 +s9h4Xr1vrzE+L+zbn66ORl7/quP6qNrX1Ffce2r1Fe59k3UN3Htm6pv6to3U9/Mta9WX+3al+pL 076Moa9fnn3yI139SPmRrn6k/EhXP1J+pKsfKT/S1Y+UH+nqR8qPdPUj5Ue6+pHyI039YAx+DMuz D+oz9YNB9Zn6weGt5dlXqc/UD8ZEfaZ+dN/qM/WDMVOfqR+MWn2mfjBSfa5+QH7A1Q/ID7j6AfkB Vz8gP+DqB+QHXP2A/ICrH5AfcPUD8gOufkB+wNUPyA+4+kH5QVc/KD/o6gflB139oPygqx+UH3T1 g/KDrn5QftDVD8oPuvpB+UFXPyg/6OpHkR/F1Y8iP8qf/fgFAAD//8zVO07DQBSFYUcCRIWyBBYA aGzfx4QSRZQ0bt0Mr8QSxBJ2QOyE5TL28RYiHUvXM3Orv/vWRVGkPNv7dpvG1Db9vn9K390ujV1/ aMbj6++8fzweXqZN+rie3lXbhLId3oaqHX6+nt8fQqiC1bchhOlX1dV0nY/yrvvc3ZQFvqs8f3ku 8lzmOVv258t7tfSslv2p+mr01ax9gj5h7VP0KWufoc9Y+xx9ztoX0RdZ+zbo25D2SZj7poOzr0Rf ydoHP4TVD4EfwuqHwA9h9UPgh7D6IfBDWP0Q+CGsfgj8EFY/BH4Iqx8KP5TVD4UfyuqHwg9l9UPh h7L6ofBDWf1Q+KGsfij8UFY/FH4oqx8KP5TVD4UfyuqHwQ9j9cPgh7H6YfDDWP3AdT44++CHsfph 8MNY/TD4Yax+GPwwVj8MfhirHwY/jNUPhx/O6ofDD2f1w+GHs/rh8MNZ/XD44ax+OPxwVj8cfjir Hw4/nNUPhx/O6ofDD2f1I8KPyOpHhB+R1Y8IP+LJ/fgHAAD//8zVO07DUBCFYZAAUSGWwAIAzZyx HQ8liihp0roxr2AJEok4IHbCcrF9vAIazpXGd66rv/v2T9/Ltm+bu/3mse+2m/btYnyjWZk3u+cd mt3Xx8PLrRmsiiszGz+op3W6/Lp7X1/6Ac/ZMD/DnAxzOszR/P94fh8O0873eM7n9/KmmTpW29ft ffvZrduxZtX/ta9gX6HaV7KvVO2r2Fep9i3Yt1Dtq9lXq/Yl+1K0L23qGy/NPmefq/aBfVDtox+p 6kfSj1T1I+lHqvqR9CNV/Uj6kap+JP1IVT+SfqSoH2GTH9Ol2efsE/UjDOwT9SOMq4n6EVawT9SP sJJ9on6EVewT9SNswT5RP8Jq9on6EZbsU/XD6Yer+uH0w1X9cPrhqn44V1f1w+mHq/rh9MNV/XD6 4ap+OP1wVT+cfriqH04/XNUP0A+o+gH6AVU/QD+g6ge4QtUP0A+o+gH6AVU/QD+g6gfoB1T9AP2A qh+gH1D1I+hHqPoR9CNU/Qj6Eap+BNf4dz9+AQAA///M1TtOw0AUhWGQAFEhlsACAI3t+woliihp 0roZAgRLEEvYAbETlovHxy39sXQ9d6b6u69uh+/Pp9f7lOpkzU1KqfyaRua1HNVt97G7ro7wXUzz O83ZNOfTnCzvp8v9eJq8nOW7XO7ru3adx9xu+rf+MX91uzx2/X4zHp5/5veHw35bXvL7VbnX7SZV 7fAy/Nen6FPWPkOfsfY5+py1L9AXrH0r9K1I+yTNazk4+yr0Vax9Nfpq1j6s5eDsgx/C6ofAD2H1 Q+CHsPoh8ENY/RD4Iax+CPwQVj8UfiirHwo/lNUPhR/K6odiVVY/FH4oqx8KP5TVD4UfyuqHwg9l 9UPhh7L6ofBDWf0w+GGsfhj8MFY/DH4Yqx/Laqx+GPwwVj8MfhirHwY/jNUPgx/G6ofBD2P1w+CH sfrh8MNZ/XD44ax+OPxwVj8cq7P64fDDWf1w+OGsfjj8cFY/HH44qx8OP5zVD4cfzupHwI9g9SPg R7D6EfAjWP0IrMHqR8CPYPUj4EfoHwAAAP//zNU9TsNAEMVxkABRIY7AAQiaN7NJvJQooqRx68Z8 BUuQSNgBcROOi9fPPe2zNJ7drf7db2G46T621zjidzHO7zhn45yPczK/n87343HaeZfvcr5vbptN O7RNvX/bP7Rf3bYduv2uHg7PP9P7/WH3VF7a96ty96Y2NP1L703//fn4emfmtoqFmZVfVKvpWJZm 35p9a9W+in2Val9mXxbtyzYdy9LsA/ug2ufsc9U+HsvS7EvsS6p9S/YtVfvoR1b1I9OPrOpHph9Z 1Y9MP7KoH8kmP6al2Qf2ifqRzNkn6kea/lyafYl9on4kW7JP1I9kK/aJ+pFszT5RP5JV7BP1I1lm n6ofoB9Q9QP0A6p+gH5A1Q/QD6j6AfoBVT9AP6DqB+gHVP0A/YCqH6AfUPUD9AOqfjj9cFU/nH64 qh9OP1zVD6cfruqH0w9X9cPph6v64fTDVf1w+uGqfjj9cFU/nH64qh9BP0LVj6AfoepH0I9Q9SPo R6j6EfQjVP0I+hGqfgT9iH/9+AMAAP//3BgJUJRVeFlhAVEEmkliaNpSK8ajBSSwrPdVoKloq6AR Q8bKrmLB7rIHiELArrjcsquGMx1qY545aR6Vo2SJVjaZVlaAZYV4VIqNlmnU9N7/vc3lb90d1zGh N/P2e8f+3/uO913PX4ItgPYg2v1oV3HIWhifJz+QlawyqbLSdLm6KarCuXNUprk6bZrJrC4W1seZ tTlsRZUnZ/O4rDRFbJZRY4zLMhYZZs1+VKGIU9wfP1KhULCf0fGJbCiA2FFz8+eMiOXnhdK+nHYZ p+dm0ZeE9CX1VvrGIH1jeil9oxUCfQz0Tvpikb7Y3kpfHNIX11vpi0f64nsrfaORvtG9lb4EpC/B LX1RV+iTuqFvgAt9AZSDYD4fSbtWla8RNnRao8bEBmqzQaBVYsrXqYUtvXPAzwMv8ggTySNMOMcp A7lBZzZpxHhkIjxSjsOJJ5jj2fZhYfOKB56D5doI657nFoGveDLVDx57YtVs6P5i3um7h+kEPAoP ePxEeKR8xL5TSq4uf2dzlb+MzyP+kb9eZVDlS/S6vOJrkfNAkZzZPEenNRl0eShkjudxD3z1cyOf SNrNSyL9Z4yKhb+YfPzmw5svd63d39wAvuI7QtHEnpsN59bun5S4twC2MXyk4H8jd5f7PT6F46ny wFeIGzll0B513zdDZao8oNIKer1OD9Y9B1alrDECnUVntpiBAXVIEcSxwaZFUMz+uNsGY489Ud9U VAUNTdQwVlWDsp4O0mvhPfr5DlOdVzsR0yPla8FX2j/y6Wv66i/SF5s77URQFsezzgNfg9zoax77 hl7natVDsOGXzybPypgBU2aFZYzpzgQddU8Hhs2CW5gC16tRf6vnc3sqg8vPP7vL1F4B7Zfo4CUr UGsLHWlZCKmJ1Y9kn2uAmTsnDLCMbYQ1L7bIFel2mDhAOjzwgh0S6EbdYodXfYrplfK14J6tT+rT i/0lKzkevQe+XHlz6pOd+/lkqsB3tGh/eQUISwwcmuA7ambaITaMPyHVaF8LaxAeRztLuoZzpXwt gLe+qA9P9iUog+PZ7IEvZ87gqo9y2qkZSIcPHgfUK1Ysq50K3eupoW2YiXa0ZQHCqaUID5dBGTWr JfdWIPzOgjBpIcKwSrS7g4vhzs2G3PCmRoSBdsi1nyl5dweHoxwI93u3MzHdUr4WIGp9Ua+hIr2G CvgK5VS3c+Qms0Fr9D2/uPAnVdjZAoxjE6oRptR7zQfE+KR0RSaTXZcfWyf1nD/3k/gmX1c/4E6+ MklP+cq4fMcrfZfrF4lBv1ibfyAWs4EMHNJBFt2+Ky513Hbii3ycd8BXPpTJvvORM6l0b/P6VnJo 2ddRp63fkNLEsNWnAjfeUD4CRXwEcj5Sxif7zEfZ4H32jHfOkMxJIe2GfT+QVdO6J276vf26+AAv fFyl/pKrNSad2YCWy/BkeDg/wA0/99Jeseyx2rwiHbC0dMdwIwh11HQzfEyn5wt5XDxf4zUOivFL +ZqMNUnfzDMHSXrKnc0NGqPJ1WEyPPM88OWszV3lzu5dUfOKqPsq0zGvfC0b88mhJRjXynm8U/A8 clwl5o27eP6YZAcjDWjdP3uPZ+LzpXxN5mwu8vBVP0n9bo6/dWffgn6ow/VVL5H6kJVZ958gqQP3 +UVt/Zk8Pehk9kI4TuovNy9t+KSV3PNVwpdHnjlLio92OiKmbidpSbfHrJ7fRrpaFMqTLcfIxgnP DG2YefA/92sC38pkn/le3jp7y7Yd7WTEkTvDu+d/Tzoeqrp1zJZO8mtbsCX4wrfkxxMHa5e++z2x 7gkvOqx+hWz4oDzHf0sbGXEm0RbT+jnZqY/c+j7svaF8B0l68h3k5Js6dF/5Ti8PrjWm/kQCN+2U v53dSWzHtekHIrqItdZWU36pi+zU/lZ8x5cnyKnTuS2nluwnY2sjp/fv30XkkpYVrSUniKZlr7Vs 6eHr4ts1DrnjO1zEd7iTb9cAwPcZvmwPdDhtz1UOjG7bra0HL3akor9vK8W6NaYc/U10I9Cs3fZC mx3nmQ6v8UB8jpSvOZ1Ob4wH3vQwWKSHwZIedeo0lVaty5drCjVak4Dvdw/83eZGDx/STqPxyT9u 0+L7Q64OhHe2LD3Wq8kFwNRz4EgBvErD85/pBtzvNuA7ks0I0SyAP2nC/TAzsGck7Rozrr9RiO9K ITb8TmXD/22yYdx/uAp+ZOd0VoGGTusfrsb62FoN97AAlVgD7JgHD9Xgu9SEWghmC0F1WC8fqsP3 qxjM+5XXwL+Ur3lrffHeXNV+e9yYK/hW+l2dv1GSf98bvR+vq5uSQU7rX/vuiSDkFylKvEfr0rDe Hj6D191PAUv7TnZkwjA6fez80/DWeVrATc7Ge3FUhfq8mIN1eMEC9AsRJQgvl2B+uLsU85aS53n9 Xgadh+5qvxRdzut1DnMrYAh7GHu/Augs0t/C6/hoK3wwiToaixVGsgfpUP5eVsDr+6Mc+lfiOqkE mg29+bJlMdBynmwPacT3s/hGIHTw0YJG6HjtxZaVWzk8w2GMHf3XFDswP1fUZAcmr42f2RFPF8+n 7nLg/hQH7lsc8OnFtI5hux1wtoQkfNvhAAP735Al4M3PivUk5WvX2m70ff8bAAD//2NkgABWBhYG NiAN4gsBcV5ibioDQ0FiUWIuQ0F+TiVUGQMfEB8AYpBaDqh6iH4EnxFJHKR+ApJ6Fizq45HUC0D5 LlYxLokliTHB+Rn5follmemJJZn5ecElpSmVYHG30rxkkEhijgKIbxQTbGAYU5xabBRTVBCflOZk YGBkYGasa2BgACYMzMEkiDLUK6koAbvLAMldbAT8wQRkNWDxPxMBfaBwZYXyQXRGURo8XCpINIcd ygfRKalFmWXFDET5gwnFHxCbANccyO4= ------=_20110510165623_38470 Content-Type: application/octet-stream; name="GLMest_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="GLMest_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA2OjE2IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAA6AAAAHiclZC/CsIwEMavRes/KD6J6CS6OOiqCF0cAnLaagutEXOK bj6Go4/jY5m0p2QQtYHj8l2++yUXHwBuLoCnc12HA8WqWvoVLR0ZUoqrFdI6zn2+joXV737pL+qV /Mxhj9pnb067JMdjbbIiJMWch1OO02Bt8iY7JMtIER/nvHNJXo21X8ynvwyyiGIZcu0O//336/69 5a988I8sf5v1ZCgmSCgCGcsZnpItUiJ3AR3DS16fyjBK+wMxRoq28pCsMRVBtyeC+bRjHmzuvUK5 uZusTR6nqJShWnN0LZ73g+fqndk/AfYVMeY= ------=_20110510165623_38470 Content-Type: application/octet-stream; name="CONspec_S01.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="CONspec_S01.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDEyOjA4OjExIDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAALAIAAHic7ZbdTsIwFMcLIpCQoCZKuOTSK4NXftxoIgZvMERMvNkF ZTSwuK1krSS+gY/gpY/gI/km2O4DujKYbCUmQJNmO+X0cPo7J/+uDAD4rgCQZ88imxngjX3BDmaJ TQtSE/Z6kOpD16/M5kjYn12y31vPub9lfB8ysqZxTleMk/dt/iQUUuLH+TlJno+O7Wk+nyvGCdar 3rkYKjceQYSAPjIRRV7cL/A33lO+gn8uwv9G8D/07ca11oAUah08xI9wbAwgNbDdoW/9d3e9hVlG F1faHaRogB1Dh6bWqZ9rnXbrjCfO/7d7vDzPspxnJjl36oN3+2DFOCJ3G1oIeHUcI53VgcF30MiN +xDD8UjiyO0Zu1rPxPorEc4rxstHxKsK8bKSLQ8erx6TXy6UXw6wc5l831AR99qauN/GnKsEwty5 /YQIFYkn5b0IeBreH4p43/5Tn1ck3txmfd7CDnoeQjuE3tWBFbjv+VwzYDKJJh89trn/D0C4Htx+ 4TesYQ9qFh4biCTXmzX0/4brvAPtPrYETol0RxriPbmtfb5Q5wXiqngHDjudn/GO0vkAfVKdjxvy PZCmHl1F9aiuqR6XMefKg3A9uC3yV6IzCvp+EznbcFxrtlNznrtNd5znObcb6ftZBr3NnAsS54LP +b7ZSK8bEugd5zBnh1+STDhU6LOIesc5gnNbQT9LoLeZcxGEORcDzkw41HxvzECn4Szui8pD5JNl b/z9F9NrLTM= ------=_20110510165623_38470 Content-Type: application/octet-stream; name="SohoNavSpec_model_79.m" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="SohoNavSpec_model_79.m" JSUgc3BlYyBhbmQgcnVuICgxc3QgbGV2ZWwgYW5kIHR3byB2ZXJzaW9ucyBvZiBzZWNvbmQgbGV2 ZWwpCgpjbGVhciBhbGw7CgpNb2RObyA9ICc3OSc7CgolIFNlbGVjdCBzdWJqZWN0cywgZGVwZW5k aW5nIG9uIHdoZXRoZXIgdGVzdGluZyBzY3JpcHQgb3Igbm90CiUtLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KCnN1Ympl Y3RfZnVuYyA9IHsKICAgICdTMDEnLC4uLgogICAgfTsKCiUgc3ViamVjdF9mdW5jID0gewolICAg ICAnUzAxJywuLi4KJSAgICAgJ1MwMicsLi4uCiUgICAgICdTMDMnLC4uLgolICAgICAnUzA0Jywu Li4KJSAgICAgJ1MwNScsLi4uCiUgICAgICdTMDYnLC4uLgolICAgICAnUzA3JywuLi4KJSAgICAg J1MwOCcsLi4uCiUgICAgICdTMDknLC4uLgolICAgICAnUzEwJywuLi4KJSAgICAgJ1MxMScsLi4u CiUgICAgICdTMTInLC4uLgolICAgICAnUzEzJywuLi4KJSAgICAgJ1MxNCcsLi4uCiUgICAgICdT MTUnLC4uLgolICAgICAnUzE2JywuLi4KJSAgICAgJ1MxOCcsLi4uCiUgICAgICdTMTknLC4uLgol ICAgICAnUzIwJywuLi4KJSAgICAgJ1MyMScsLi4uCiUgICAgICdTMjInLC4uLgolICAgICAnUzIz JywuLi4KJSAgICAgJ1MyNCcsLi4uCiUgICAgICdTMjUnfTsKCmZ1bmN0aW9uYWxwYXRoID0gJ0Q6 XERhdGFcU29ob05hdmlnYXRpb25TdHVkeURhdGFcRnVuY3Rpb25hbCBEYXRhMic7CnN0cnVjdHVy YWxwYXRoID0gJ0Q6XERhdGFcU29ob05hdmlnYXRpb25TdHVkeURhdGFcU3RydWN0dXJhbCBEYXRh JzsKbXlkYXRhcGF0aCA9IChbJ0Q6XERhdGFcU29ob05hdmlnYXRpb25TdHVkeURhdGFcTW9kZWwn IE1vZE5vLCdcQ2F0ZWdvcmljYWwnXSk7CmpvYnBhdGggPSAoWydEOlxEYXRhXFNvaG9OYXZpZ2F0 aW9uU3R1ZHlEYXRhXE1vZGVsJyBNb2RObyAnXEpvYnMnXSk7Ck1SSXNwZWNwYXRoID0gKFsnRDpc RGF0YVxTb2hvTmF2aWdhdGlvblN0dWR5RGF0YVxNb2RlbCcgTW9kTm8gJ1xKb2JzXE1SSSBzcGVj IG1hdCBmaWxlcyddKTsKcmVzdWx0c3BhdGgxID0gKFsnRDpcRGF0YVxTb2hvTmF2aWdhdGlvblN0 dWR5RGF0YVxNb2RlbCcgTW9kTm8gJ1xSZXN1bHRzJ10pOwpyZXN1bHRzcGF0aDIgPSAoWydEOlxE YXRhXFNvaG9OYXZpZ2F0aW9uU3R1ZHlEYXRhXE1vZGVsJyBNb2RObyAnXFJlc3VsdHMgd2l0aCBi ZXR0ZXIgcGFyaWV0YWwnXSk7CgolIExvYWQgY29udHJhc3QgaW5mbzogJ25hbWVfY29udHJhc3Rz JywgJ2NvbnRyYXN0X3ZlY3RvcnMnLCAnY29udHJhc3RzJwolLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCmNkIChqb2Jw YXRoKTsKbG9hZCBjb250cmFzdGluZm8ubWF0OyAKCm51bWJfc3ViaiA9IG51bWVsKHN1YmplY3Rf ZnVuYyk7Cm51bWJfc2VzcyA9IDI7Cm51bWJfY29udHJhc3RzID0gbnVtZWwoY29udHJhc3RfdmVj dG9ycyk7CgpzcG1fam9ibWFuKCdpbml0Y2ZnJyk7CgolJSBmaXJzdCBsZXZlbApmb3Igcz0xOm51 bWJfc3ViajsKICAgIAogICAgICAgICUgTU9ERUwgU1BFQ0lGSUNBVElPTgogICAgICAgICU9PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT0KICAgICAgICAlIGNoZWNrIGZvciBzdWJqZWN0IGZvbGRlciBpbiBDYXRlZ29yaWNh bCBmb2xkZXIuIGlmIGl0IGV4aXN0cyBkZWxldGUgc28gY2FuIG92ZXJ3cml0ZSBiZWxvdwogICAg ICAgICUtLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0gICAKICAgICAgICBuZXdkaXIgPSBzcHJpbnRmKHN1YmplY3RfZnVu Y3tzfSk7CiAgICAgICAgaWYgZXhpc3QgKGZ1bGxmaWxlKG15ZGF0YXBhdGgsIG5ld2RpcikpOwog ICAgICAgICAgICBybWRpciAoW215ZGF0YXBhdGgsIGZpbGVzZXAsIG5ld2Rpcl0sJ3MnKTsKICAg ICAgICBlbmQKICAgICAgICAKICAgICAgICAlIERpcmVjdG9yeQogICAgICAgICUtLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0gICAgICAgIAogICAgICAgIG1rZGlyKG15ZGF0YXBhdGgsIG5ld2Rpcik7ICUgbWFrZSBmaXJz dAogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuZGlyID0gY2VsbHN0 cihbbXlkYXRhcGF0aCBmaWxlc2VwIG5ld2Rpcl0pOyAgICAgICAKICAgICAgICAKICAgICAgICAl IFRpbWluZwogICAgICAgICUtLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0u c3RhdHMuZm1yaV9zcGVjLnRpbWluZy51bml0cyA9ICdzZWNzJzsKICAgICAgICBtYXRsYWJiYXRj aHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnRpbWluZy5SVCA9IDIuODk2ODsKICAgICAgICBtYXRs YWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnRpbWluZy5mbXJpX3QgPSAzNDsKICAgICAg ICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnRpbWluZy5mbXJpX3QwID0gMTsK ICAgICAgICAKICAgICAgICBmb3IgZT0xOm51bWJfc2VzczsKICAgICAgICAgICAgCiAgICAgICAg ICAgICUgU2NhbnMgKHN1YmogYW5kIHNlc3Mgc3BlY2lmaWMpCiAgICAgICAgICAgICUtLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0KICAgICAgICAgICAgZmlsZXM9c3BtX3NlbGVjdCgnRXh0RlBMaXN0JyxbZnVuY3Rpb25h bHBhdGggZmlsZXNlcCBzdWJqZWN0X2Z1bmN7c30gZmlsZXNlcCBzcHJpbnRmKCdzZXMlcycsbnVt MnN0cihlKSldLCdec3dyYmYuKlwuaW1nJCcpOyAlIGZpbHRlciB0byBzZWxlY3QgZnVuY3Rpb25h bHMKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEs ZSkuc2NhbnMgPSBjZWxsc3RyKGZpbGVzKTsKICAgICAgICAgICAgCiAgICAgICAgICAgICUgUmVn cmVzc29ycyAoc3ViaiBhbmQgc2VzcyBzcGVjaWZpYykKICAgICAgICAgICAgJS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LQogICAgICAgICAgICAlIGxvYWQgc3ViamVjdCBhbmQgc2Vzc2lvbiBzcGVjaWZpYyByZWdyZXNz b3IgZmlsZQogICAgICAgICAgICBjbGVhciByZWc7CiAgICAgICAgICAgIHJlZz1sb2FkKGZ1bGxm aWxlKGpvYnBhdGgsIHN1YmplY3RfZnVuY3tzfSwgc3ByaW50Zignc2VzJXMnLG51bTJzdHIoZSkp LCBbc3ViamVjdF9mdW5je3N9IHNwcmludGYoJ19zZXNzJXMubWF0JywgbnVtMnN0cihlKSldKSk7 CiAgICAgICAgICAgICUgbG9hZCBjb25kaXRpb25zIDEtNgogICAgICAgICAgICBmb3IgaSA9IDE6 NiAgJSBmb3IgdGhlIG5vbi1wYXJhbWV0cmljYWxseSBtb2R1bGF0ZWQgcmVncmVzc29ycwogICAg ICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSku Y29uZChpKS5uYW1lID0gcmVnLm5hbWVze2l9OwogICAgICAgICAgICAgICAgbWF0bGFiYmF0Y2h7 MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZChpKS5vbnNldCA9IHJlZy5vbnNl dHN7aX07CiAgICAgICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVj LnNlc3MoMSxlKS5jb25kKGkpLmR1cmF0aW9uID0gcmVnLmR1cmF0aW9uc3tpfTsKICAgICAgICAg ICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQo aSkudG1vZCA9IDA7CiAgICAgICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1y aV9zcGVjLnNlc3MoMSxlKS5jb25kKGkpLnBtb2QgPSBzdHJ1Y3QoJ25hbWUnLCB7fSwgJ3BhcmFt Jywge30sICdwb2x5Jywge30pOwogICAgICAgICAgICBlbmQKICAgICAgICAgICAgJSBsb2FkIGNv bmRpdGlvbiA3ICh3aXRoIHBtb2QpCiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0 cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQoNykubmFtZSA9IHJlZy5uYW1lc3s3fTsgICAlIE5h diBEUAogICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3Mo MSxlKS5jb25kKDcpLm9uc2V0ID0gcmVnLm9uc2V0c3s3fTsKICAgICAgICAgICAgbWF0bGFiYmF0 Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg3KS5kdXJhdGlvbiA9IHJl Zy5kdXJhdGlvbnN7N307CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJp X3NwZWMuc2VzcygxLGUpLmNvbmQoNykudG1vZCA9IDA7CiAgICAgICAgICAgIG1hdGxhYmJhdGNo ezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQoNykucG1vZCgxKS5uYW1lID0g cmVnLnBtb2QoMSkubmFtZTsgICUgR29hbFByb3hpbWl0eQogICAgICAgICAgICBtYXRsYWJiYXRj aHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDcpLnBtb2QoMSkucGFyYW0g PSByZWcucG1vZCgxKS5wYXJhbTsKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRz LmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg3KS5wbW9kKDEpLnBvbHkgPSAxOwogICAgICAgICAg ICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDcpLnBt b2QoMikubmFtZSA9IHJlZy5wbW9kKDMpLm5hbWU7ICAlIAogICAgICAgICAgICBtYXRsYWJiYXRj aHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDcpLnBtb2QoMikucGFyYW0g PSByZWcucG1vZCgzKS5wYXJhbTsKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRz LmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg3KS5wbW9kKDIpLnBvbHkgPSAxOwogICAgICAgICAg ICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDcpLnBt b2QoMykubmFtZSA9IHJlZy5wbW9kKDUpLm5hbWU7ICAlIEVHRAogICAgICAgICAgICBtYXRsYWJi YXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDcpLnBtb2QoMykucGFy YW0gPSByZWcucG1vZCg1KS5wYXJhbTsKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0 YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg3KS5wbW9kKDMpLnBvbHkgPSAxOwogICAgICAg ICAgICAlIGxvYWQgY29uZGl0aW9uIDgKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0 YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg4KS5uYW1lID0gcmVnLm5hbWVzezh9OwogICAg ICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25k KDgpLm9uc2V0ID0gcmVnLm9uc2V0c3s4fTsKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3Bt LnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg4KS5kdXJhdGlvbiA9IHJlZy5kdXJhdGlv bnN7OH07CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuc2Vz cygxLGUpLmNvbmQoOCkudG1vZCA9IDA7CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5z dGF0cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQoOCkucG1vZCA9IHN0cnVjdCgnbmFtZScsIHt9 LCAncGFyYW0nLCB7fSwgJ3BvbHknLCB7fSk7CiAgICAgICAgICAgICUgbG9hZCBjb25kaXRpb24g OSAod2l0aCBwbW9kKQogICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9z cGVjLnNlc3MoMSxlKS5jb25kKDkpLm5hbWUgPSByZWcubmFtZXN7OX07ICAgJVJlc3QgRFAKICAg ICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29u ZCg5KS5vbnNldCA9IHJlZy5vbnNldHN7OX07CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNw bS5zdGF0cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQoOSkuZHVyYXRpb24gPSByZWcuZHVyYXRp b25zezl9OwogICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNl c3MoMSxlKS5jb25kKDkpLnRtb2QgPSAwOwogICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0u c3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5jb25kKDkpLnBtb2QoMSkubmFtZSA9IHJlZy5wbW9k KDIpLm5hbWU7ICAlIEdvYWxQcm94aW1pdHkKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3Bt LnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg5KS5wbW9kKDEpLnBhcmFtID0gcmVnLnBt b2QoMikucGFyYW07CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3Nw ZWMuc2VzcygxLGUpLmNvbmQoOSkucG1vZCgxKS5wb2x5ID0gMTsKICAgICAgICAgICAgbWF0bGFi YmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg5KS5wbW9kKDIpLm5h bWUgPSByZWcucG1vZCg0KS5uYW1lOyAgJSAKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3Bt LnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg5KS5wbW9kKDIpLnBhcmFtID0gcmVnLnBt b2QoNCkucGFyYW07CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3Nw ZWMuc2VzcygxLGUpLmNvbmQoOSkucG1vZCgyKS5wb2x5ID0gMTsKICAgICAgICAgICAgbWF0bGFi YmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg5KS5wbW9kKDMpLm5h bWUgPSByZWcucG1vZCg2KS5uYW1lOyAgJSBFR0QKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0u c3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZCg5KS5wbW9kKDMpLnBhcmFtID0gcmVn LnBtb2QoNikucGFyYW07CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJp X3NwZWMuc2VzcygxLGUpLmNvbmQoOSkucG1vZCgzKS5wb2x5ID0gMTsKICAgICAgICAgICAgJSBs b2FkIGNvbmRpdGlvbnMgMTAtMTIKICAgICAgICAgICAgZm9yIGkgPSAxMDoxMiAgJSBmb3IgdGhl IG5vbi1wYXJhbWV0cmljYWxseSBtb2R1bGF0ZWQgcmVncmVzc29ycwogICAgICAgICAgICAgICAg bWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZChpKS5uYW1l ID0gcmVnLm5hbWVze2l9OwogICAgICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRz LmZtcmlfc3BlYy5zZXNzKDEsZSkuY29uZChpKS5vbnNldCA9IHJlZy5vbnNldHN7aX07CiAgICAg ICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5j b25kKGkpLmR1cmF0aW9uID0gcmVnLmR1cmF0aW9uc3tpfTsKICAgICAgICAgICAgICAgIG1hdGxh YmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuc2VzcygxLGUpLmNvbmQoaSkudG1vZCA9IDA7 CiAgICAgICAgICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3Mo MSxlKS5jb25kKGkpLnBtb2QgPSBzdHJ1Y3QoJ25hbWUnLCB7fSwgJ3BhcmFtJywge30sICdwb2x5 Jywge30pOyAgICAgICAgICAgICAgICAKICAgICAgICAgICAgZW5kCiAgICAgICAgICAgICVzZWxl Y3QgcmVhbGlnbm1lbnQgaW5mbyBwYXJhbWV0ZXJzCiAgICAgICAgICAgIHJlYWxpZ25wYXJhbXM9 c3BtX3NlbGVjdCgnRlBMaXN0JywgW2Z1bmN0aW9uYWxwYXRoIGZpbGVzZXAgc3ViamVjdF9mdW5j e3N9IGZpbGVzZXAgc3ByaW50Zignc2VzJXMnLG51bTJzdHIoZSkpXSwgJ15ycF9iZi4qXC50eHQk Jyk7CiAgICAgICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJpX3NwZWMuc2Vzcygx LGUpLm11bHRpX3JlZyA9IGNlbGxzdHIocmVhbGlnbnBhcmFtcyk7CiAgICAgICAgICAgIAogICAg ICAgICAgICAlIEhpZ2gtcGFzcyBmaWx0ZXIKICAgICAgICAgICAgJS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQogICAg ICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1yaV9zcGVjLnNlc3MoMSxlKS5ocGYg PSAxMjg7CiAgICAgICAgZW5kCgogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJp X3NwZWMuYmFzZXMuaHJmLmRlcml2cyA9IFswIDBdOyAlY291bGQgd2FudCB0byBjaGFuZ2UgdGhp cyBpbiBHVUkKICAgICAgICAKICAgICAgICBzYXZlKFtNUklzcGVjcGF0aCwgZmlsZXNlcCwgc3By aW50ZihbJ0dMTXNwZWNfJyBzdWJqZWN0X2Z1bmN7c30gJy5tYXQnXSldLCAnbWF0bGFiYmF0Y2gn KTsgJXNhdmUgYXMgc3ViamVjdCBzcGVjaWZpYwogICAgICAgIHNwbV9qb2JtYW4oJ3J1bicsbWF0 bGFiYmF0Y2gpOwogICAgICAgIGNsZWFyIG1hdGxhYmJhdGNoOwogICAgICAgICAgICAKICAgICAg ICAlIE1PREVMIEVTVElNQVRJT04KICAgICAgICAlPT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09CiAgICAgICAgJXNlbGVj dCAubWF0IGZpbGUgZnJvbSBkZXNpZ24gc3BlYwogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5z dGF0cy5mbXJpX2VzdC5zcG1tYXQgPSBjZWxsc3RyKFtteWRhdGFwYXRoIGZpbGVzZXAgc3ViamVj dF9mdW5je3N9IGZpbGVzZXAgJ1NQTS5tYXQnXSk7CiAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3Bt LnN0YXRzLmZtcmlfZXN0Lm1ldGhvZC5DbGFzc2ljYWwgPSAxOwoKICAgICAgICBzYXZlKFtNUklz cGVjcGF0aCwgZmlsZXNlcCwgc3ByaW50ZihbJ0dMTWVzdF8nIHN1YmplY3RfZnVuY3tzfSAnLm1h dCddKV0sICdtYXRsYWJiYXRjaCcpOyAlc2F2ZSBhcyBzdWJqZWN0IHNwZWNpZmljCiAgICAgICAg c3BtX2pvYm1hbigncnVuJyxtYXRsYWJiYXRjaCk7CiAgICAgICAgY2xlYXIgbWF0bGFiYmF0Y2g7 CiAgICAgICAgCiAgICAgICAgJSBJTkZFUkVOQ0UgJiBSRVNVTFRTCiAgICAgICAgJT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PQogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5jb24uc3BtbWF0ID0gY2VsbHN0 cihbbXlkYXRhcGF0aCBmaWxlc2VwIHN1YmplY3RfZnVuY3tzfSBmaWxlc2VwICdTUE0ubWF0J10p OwoKICAgICAgICBmb3IgYz0xOm51bWJfY29udHJhc3RzOwogICAgICAgICAgICBtYXRsYWJiYXRj aHsxfS5zcG0uc3RhdHMuY29uLmNvbnNlc3N7MSxjfS50Y29uLm5hbWUgPSBuYW1lX2NvbnRyYXN0 c3tjfTsKICAgICAgICAgICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmNvbi5jb25zZXNzezEs Y30udGNvbi5jb252ZWMgPSBjb250cmFzdF92ZWN0b3Jze2N9OwogICAgICAgICAgICBtYXRsYWJi YXRjaHsxfS5zcG0uc3RhdHMuY29uLmNvbnNlc3N7MSxjfS50Y29uLnNlc3NyZXAgPSAncmVwbCc7 ICVzaG91bGQgcmVwbGljYXRlIGFjcm9zcyBzZXNzaW9ucwoKICAgICAgICBlbmQKICAgICAgICBt YXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuY29uLmRlbGV0ZSA9IDE7ICUxPWRlbGV0ZSBvbGQgY29u dHJhc3RzLCAwPUFQUEVORAoKICAgICAgICBzYXZlKFtNUklzcGVjcGF0aCwgZmlsZXNlcCwgc3By aW50ZihbJ0NPTnNwZWNfJyBzdWJqZWN0X2Z1bmN7c30gJy5tYXQnXSldLCAnbWF0bGFiYmF0Y2gn KTsgJXNhdmUgYXMgc3ViamVjdCBzcGVjaWZpYwogICAgICAgIHNwbV9qb2JtYW4oJ3J1bicsbWF0 bGFiYmF0Y2gpOwogICAgICAgIGNsZWFyIG1hdGxhYmJhdGNoOwplbmQKCgoKCgoKCgolJSBzZWNv bmQgbGV2ZWwKCmZvciBjPTE6bnVtYl9jb250cmFzdHM7IAogICAgCiAgICAlIE1PREVMIFNQRUNJ RklDQVRJT04KICAgICU9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT0KCiAgICAlIFNwZWNpZnkgT3V0cHV0IERpcmVjdG9y eSAobWFrZSBpZiBkb2Vzbid0IGV4aXN0KQogICAgJS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQogICAgbmV3ZGlyID0g c3ByaW50ZihuYW1lX2NvbnRyYXN0c3tjfSk7CiAgICBpZiBleGlzdCAoZnVsbGZpbGUocmVzdWx0 c3BhdGgxLCBuZXdkaXIpKTsKICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZmFjdG9y aWFsX2Rlc2lnbi5kaXIgPSBjZWxsc3RyKFtyZXN1bHRzcGF0aDEgZmlsZXNlcCBuZXdkaXJdKTsK ICAgIGVsc2UKICAgICAgICBta2RpcihyZXN1bHRzcGF0aDEsIG5ld2Rpcik7ICVtYWtlIGRpcmVj dG9yeQogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mYWN0b3JpYWxfZGVzaWduLmRp ciA9IGNlbGxzdHIoW3Jlc3VsdHNwYXRoMSBmaWxlc2VwIG5ld2Rpcl0pOwogICAgZW5kCgogICAg JSBTZWxlY3QgY29udHJhc3QgaW1hZ2UgZm9yIGVhY2ggc3ViamVjdAogICAgJS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LQogICAgY29udHJhc3RpbWFnZXM9W107CiAgICBmb3Igcz0xOm51bWJfc3ViajsgJXRvIHNlbGVj dCBhbGwgc3ViamVjdHMKICAgICAgICBzdWJqaW1hZ2U9c3BtX3NlbGVjdCgnRlBMaXN0JyxbbXlk YXRhcGF0aCwgZmlsZXNlcCwgc3ViamVjdF9mdW5je3N9XSwgY29udHJhc3Rze2N9KTsKICAgICAg ICBjb250cmFzdGltYWdlcz1bY29udHJhc3RpbWFnZXM7IHN1YmppbWFnZV07CiAgICBlbmQKCiAg ICAlIEVudGVyIGltYWdlcyBpbnRvIE1hdGxhYmJhdGNoCiAgICAlLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCiAgICBt YXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZmFjdG9yaWFsX2Rlc2lnbi5kZXMudDEuc2NhbnMgPSBj ZWxsc3RyKGNvbnRyYXN0aW1hZ2VzKTsKCiAgICBzYXZlKFtNUklzcGVjcGF0aCwgZmlsZXNlcCwg c3ByaW50ZihbJzJuZExFVkVMc3BlY18nIHN1YmplY3RfZnVuY3tzfSAnLm1hdCddKV0sICdtYXRs YWJiYXRjaCcpOyAlc2F2ZSBhcyBzdWJqZWN0IHNwZWNpZmljCiAgICBzcG1fam9ibWFuKCdydW4n LG1hdGxhYmJhdGNoKTsKICAgIGNsZWFyIG1hdGxhYmJhdGNoOwoKICAgICUgTU9ERUwgRVNUSU1B VElPTgogICAgJT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PQogICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZtcmlf ZXN0LnNwbW1hdCA9IGNlbGxzdHIoW3Jlc3VsdHNwYXRoMSBmaWxlc2VwIG5hbWVfY29udHJhc3Rz e2N9IGZpbGVzZXAgJ1NQTS5tYXQnXSk7CiAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZm1y aV9lc3QubWV0aG9kLkNsYXNzaWNhbCA9IDE7CgogICAgc2F2ZShbTVJJc3BlY3BhdGgsIGZpbGVz ZXAsIHNwcmludGYoWycybmRMRVZFTGVzdF8nIHN1YmplY3RfZnVuY3tzfSAnLm1hdCddKV0sICdt YXRsYWJiYXRjaCcpOyAlc2F2ZSBhcyBzdWJqZWN0IHNwZWNpZmljCiAgICBzcG1fam9ibWFuKCdy dW4nLG1hdGxhYmJhdGNoKTsKICAgIGNsZWFyIG1hdGxhYmJhdGNoOwogICAgCiAgICAlIENPTlRS QVNUIFNQRUNJRklDQVRJT04KICAgICU9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0KICAgIG1hdGxhYmJhdGNoezF9LnNw bS5zdGF0cy5jb24uc3BtbWF0ID0gY2VsbHN0cihbcmVzdWx0c3BhdGgxIGZpbGVzZXAgbmFtZV9j b250cmFzdHN7Y30gZmlsZXNlcCAnU1BNLm1hdCddKTsKICAgIG1hdGxhYmJhdGNoezF9LnNwbS5z dGF0cy5jb24uY29uc2Vzc3sxfS50Y29uLm5hbWUgPSAnUG9zaXRpdmUnOwogICAgbWF0bGFiYmF0 Y2h7MX0uc3BtLnN0YXRzLmNvbi5jb25zZXNzezF9LnRjb24uY29udmVjID0gMTsKICAgIG1hdGxh YmJhdGNoezF9LnNwbS5zdGF0cy5jb24uY29uc2Vzc3sxfS50Y29uLnNlc3NyZXAgPSAnbm9uZSc7 CiAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuY29uLmNvbnNlc3N7Mn0udGNvbi5uYW1lID0g J05lZ2F0aXZlJzsKICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5jb24uY29uc2Vzc3syfS50 Y29uLmNvbnZlYyA9IC0xOwogICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmNvbi5jb25zZXNz ezJ9LnRjb24uc2Vzc3JlcCA9ICdub25lJzsKICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5j b24uZGVsZXRlID0gMTsKCiAgICBzYXZlKFtNUklzcGVjcGF0aCwgZmlsZXNlcCwgc3ByaW50Zihb JzJuZExFVkVMZXN0X0NPTmVzdF8nIHN1YmplY3RfZnVuY3tzfSAnLm1hdCddKV0sICdtYXRsYWJi YXRjaCcpOyAlc2F2ZSBhcyBzdWJqZWN0IHNwZWNpZmljCiAgICBzcG1fam9ibWFuKCdydW4nLG1h dGxhYmJhdGNoKTsKICAgIGNsZWFyIG1hdGxhYmJhdGNoOwplbmQKCgoKCgoKCgolJSBzZWNvbmQg bGV2ZWwKCiUgU2VsZWN0IHN1YnNldCBvZiBzdWJqZWN0cwolLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCmNsZWFyIHN1 YmplY3RfZnVuYyBudW1iX3N1YmoKCnN1YmplY3RfZnVuYyA9IHsKICAgICdTMDEnLC4uLgogICAg J1MwMicsLi4uCiAgICAnUzAzJywuLi4KICAgICdTMDUnLC4uLgogICAgJ1MwOCcsLi4uCiAgICAn UzA5JywuLi4KICAgICdTMTAnLC4uLgogICAgJ1MxMScsLi4uCiAgICAnUzEyJywuLi4KICAgICdT MTMnLC4uLgogICAgJ1MxNCcsLi4uCiAgICAnUzE1JywuLi4KICAgICdTMTYnLC4uLgogICAgJ1Mx OCcsLi4uCiAgICAnUzIwJywuLi4KICAgICdTMjEnLC4uLgogICAgJ1MyMicsLi4uCiAgICAnUzIz JywuLi4KICAgICdTMjQnLC4uLgogICAgJ1MyNSd9OwoKbnVtYl9zdWJqID0gbnVtZWwoc3ViamVj dF9mdW5jKTsKCmZvciBjPTE6bnVtYl9jb250cmFzdHM7IAogICAgCiAgICAlIE1PREVMIFNQRUNJ RklDQVRJT04KICAgICU9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT0KCiAgICAlIFNwZWNpZnkgT3V0cHV0IERpcmVjdG9y eSAobWFrZSBpZiBkb2Vzbid0IGV4aXN0KQogICAgJS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQogICAgbmV3ZGlyID0g c3ByaW50ZihuYW1lX2NvbnRyYXN0c3tjfSk7CiAgICBpZiBleGlzdCAoZnVsbGZpbGUocmVzdWx0 c3BhdGgyLCBuZXdkaXIpKTsKICAgICAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZmFjdG9y aWFsX2Rlc2lnbi5kaXIgPSBjZWxsc3RyKFtyZXN1bHRzcGF0aDIgZmlsZXNlcCBuZXdkaXJdKTsK ICAgIGVsc2UKICAgICAgICBta2RpcihyZXN1bHRzcGF0aDIsIG5ld2Rpcik7ICVtYWtlIGRpcmVj dG9yeQogICAgICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mYWN0b3JpYWxfZGVzaWduLmRp ciA9IGNlbGxzdHIoW3Jlc3VsdHNwYXRoMiBmaWxlc2VwIG5ld2Rpcl0pOwogICAgZW5kCgogICAg JSBTZWxlY3QgY29udHJhc3QgaW1hZ2UgZm9yIGVhY2ggc3ViamVjdAogICAgJS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LQogICAgY29udHJhc3RpbWFnZXM9W107CiAgICBmb3Igcz0xOm51bWJfc3ViajsgJXRvIHNlbGVj dCBhbGwgc3ViamVjdHMKICAgICAgICBzdWJqaW1hZ2U9c3BtX3NlbGVjdCgnRlBMaXN0JyxbbXlk YXRhcGF0aCwgZmlsZXNlcCwgc3ViamVjdF9mdW5je3N9XSwgY29udHJhc3Rze2N9KTsKICAgICAg ICBjb250cmFzdGltYWdlcz1bY29udHJhc3RpbWFnZXM7IHN1YmppbWFnZV07CiAgICBlbmQKCiAg ICAlIEVudGVyIGltYWdlcyBpbnRvIE1hdGxhYmJhdGNoCiAgICAlLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCiAgICBt YXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuZmFjdG9yaWFsX2Rlc2lnbi5kZXMudDEuc2NhbnMgPSBj ZWxsc3RyKGNvbnRyYXN0aW1hZ2VzKTsKCiAgICBzYXZlKFtNUklzcGVjcGF0aCwgZmlsZXNlcCwg c3ByaW50ZihbJzJuZExFVkVMc3BlYzJfJyBzdWJqZWN0X2Z1bmN7c30gJy5tYXQnXSldLCAnbWF0 bGFiYmF0Y2gnKTsgJXNhdmUgYXMgc3ViamVjdCBzcGVjaWZpYwogICAgc3BtX2pvYm1hbigncnVu JyxtYXRsYWJiYXRjaCk7CiAgICBjbGVhciBtYXRsYWJiYXRjaDsKCiAgICAlIE1PREVMIEVTVElN QVRJT04KICAgICU9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT0KICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5mbXJp X2VzdC5zcG1tYXQgPSBjZWxsc3RyKFtyZXN1bHRzcGF0aDIgZmlsZXNlcCBuYW1lX2NvbnRyYXN0 c3tjfSBmaWxlc2VwICdTUE0ubWF0J10pOwogICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmZt cmlfZXN0Lm1ldGhvZC5DbGFzc2ljYWwgPSAxOwoKICAgIHNhdmUoW01SSXNwZWNwYXRoLCBmaWxl c2VwLCBzcHJpbnRmKFsnMm5kTEVWRUxlc3QyXycgc3ViamVjdF9mdW5je3N9ICcubWF0J10pXSwg J21hdGxhYmJhdGNoJyk7ICVzYXZlIGFzIHN1YmplY3Qgc3BlY2lmaWMKICAgIHNwbV9qb2JtYW4o J3J1bicsbWF0bGFiYmF0Y2gpOwogICAgY2xlYXIgbWF0bGFiYmF0Y2g7CiAgICAKICAgICUgQ09O VFJBU1QgU1BFQ0lGSUNBVElPTgogICAgJT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PQogICAgbWF0bGFiYmF0Y2h7MX0u c3BtLnN0YXRzLmNvbi5zcG1tYXQgPSBjZWxsc3RyKFtyZXN1bHRzcGF0aDIgZmlsZXNlcCBuYW1l X2NvbnRyYXN0c3tjfSBmaWxlc2VwICdTUE0ubWF0J10pOwogICAgbWF0bGFiYmF0Y2h7MX0uc3Bt LnN0YXRzLmNvbi5jb25zZXNzezF9LnRjb24ubmFtZSA9ICdQb3NpdGl2ZSc7CiAgICBtYXRsYWJi YXRjaHsxfS5zcG0uc3RhdHMuY29uLmNvbnNlc3N7MX0udGNvbi5jb252ZWMgPSAxOwogICAgbWF0 bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmNvbi5jb25zZXNzezF9LnRjb24uc2Vzc3JlcCA9ICdub25l JzsKICAgIG1hdGxhYmJhdGNoezF9LnNwbS5zdGF0cy5jb24uY29uc2Vzc3syfS50Y29uLm5hbWUg PSAnTmVnYXRpdmUnOwogICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRzLmNvbi5jb25zZXNzezJ9 LnRjb24uY29udmVjID0gLTE7CiAgICBtYXRsYWJiYXRjaHsxfS5zcG0uc3RhdHMuY29uLmNvbnNl c3N7Mn0udGNvbi5zZXNzcmVwID0gJ25vbmUnOwogICAgbWF0bGFiYmF0Y2h7MX0uc3BtLnN0YXRz LmNvbi5kZWxldGUgPSAxOwoKICAgIHNhdmUoW01SSXNwZWNwYXRoLCBmaWxlc2VwLCBzcHJpbnRm KFsnMm5kTEVWRUxlc3RfQ09OZXN0Ml8nIHN1YmplY3RfZnVuY3tzfSAnLm1hdCddKV0sICdtYXRs YWJiYXRjaCcpOyAlc2F2ZSBhcyBzdWJqZWN0IHNwZWNpZmljCiAgICBzcG1fam9ibWFuKCdydW4n LG1hdGxhYmJhdGNoKTsKICAgIGNsZWFyIG1hdGxhYmJhdGNoOwplbmQ= ------=_20110510165623_38470 Content-Type: application/octet-stream; name="S01_sess1.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="S01_sess1.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDE1OjQ1OjU1IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAAyAAAAHicrdFLCoMwEAbgKX1QpE115TZHcdHSLktvEGoQoU4gRs/U YzYxFmMQDOjAMEwgHz8JAYDvFuCg51H3Bmztnf3UT3OGrOK1nkR3BsO93cS92PHivpG1ZcFUKZBK 0Sge4pw9x+xvgUqKj0UC80w5Tp77LdCJPCdy8nTIOnmuzxXydEigQzyH2DxUWwVVjcR60b/TnCvR SCuFOBfPMbvktXIDGecx4ySek/wdNxAM7zTnpZ6XwujfXgxzUVHeclTL8o0kWz+h9k86DwAAAFUC AAB4nONjYGC4wMrAwAakOYCYiQECWKF8RjjNwlCQm5/CCqTZoOI8QJyXmJvKwFCQWJSYC9bHB8QW DAjzWLCYB9MPAgJQfl5imYJ7AER/BZJ+NjT9nFC9MP0gvgcQx/ItCqqYdMue59PtyJ052+xDeg7/ 69p83j69MeBA4fan9snd/JVvnK/apzXey+q8stt+wZpwoaDvL+xzL2+ZMfnFdfs9svZqj1xb7Ilx Pzua+0H8otTiEpAHQPoj8LifFYv7NYB46ryN1en37tnnfw1Iqzz1wh4i+8H+3sbTf4R0b9ob7paf d2jvQ6Lchyt8A1zID98qg58sGipv7dWb1WWrji63z9Kre1u6/7K9sKPjuQVuN+27xVy3HKq8b7+I xeqlGd8G+6+z9i1M93hof8fLZzF30jX7q/wLpmaFNlIWvkAPkBu+wn6zOc19n9j7F4f6nptx337b pCsup/Y/sH/dElEz8cAz+xtF6tyFTY/Idh8ofF3dXcgOX5f7E23Obn1v//0KBxOb+UN7H4XQ9c17 Xtlbrp44v1X6gX2y5qT1LJNe2rPctFPzWnnLfvK2GqnNKx/Zv5jetm3x0/v2K16Zl/1VeECU+zkY UN3PAQtfoAfIDV/ZrTo3Y5te27c/Fb7KdPqZ/alXRpyXeB/a29yYL/Vi+Wv7/8uDpF+fe0G2+0Dh G5yaXkxu+ELAE2i+OgClb0BpWH57gCb/AEMcZL8DAffD3ABzPycsfEEegIqTHc5zvksfL7xjL7b1 Z0ho6FX7j4YsRTLHH9r/lGCy7Hv8Ak4DAPbs6scPAAAAJwQAAHicnVVrTJtVGP5A1nSxQdJEhhOz zRHRVASWMND94AXXTDIDLiymYEQ2qBDcIFIuHZfWttCL9PbxtWqC2+wSZEHi5jJmJGxhUYdMs+Al UUIWfizIajIR/bGgm3rO9xyiMbU/1uTk6Tk957087/O+TZckqUQrSRqGDKQUCZ9N/9rrBPI7ba0W c4dFktLZ9+ekf95p/vPuPvFmw95mtrLYejE979zx/EKyt0x3KNsctHNx3dZybJi4PUpiLzWBvQy2 SovqpirG22l5/tHF9U/fVO2cTWInI4GdN9jyldUvzx8upmfOf9F16YyJXDNXY8YTh2giu7Zxz5iZ vowZL/xmPEoPpbEMjjmonjs87SSWzepY5wDyOTkI3OIBznrpl7Er+4vHh6mYO8hT6Hn+ZXeEvmbh 2s5EiNOx+kJUjdubJO77E8RtYqu9Wb7V94iFPJkL124f6iS2Ky26Y6Vrt6tvjK73AOP9pEs1eDK/ tyOuB4dolcdV56ffbeygIEjvj3y2veCTMB28MTqyd1ZW45lJEo8+QTxhzj0P6N1yeuvZoSMr+w+A N3MDcLAJuNyKOP5ygJ+wC/sVwWOTG+gRPFZ5gfk+8BgfphKOZgV4WeDrESrnhdFGkdfAvfOq6uon C/ib6ya206Uu9Iq9TehtCLjoB44EoIfFIPFws9ZCxMvy7VoYWANerUni2ZwgHt5nc12XTm4tPUjv tOpdM7+2QUc3XcDzbuBXHsri+uz30UXmPs2ngN/dUVL1HYves//Pt7PKFlbQn99Z44/FG8m8Z6kq EGpGP9Q4UZ99g8TTvxL0wv8DCvzeVbB/O6L6NyXxvymB/8c36nHKqur0rFXouCiAvHeFiMvuzo6w ar8+iX1NAvsFG/anOmDf1Ic8nhR1/SNA/Pc5vQw/suiPlP/3sytRf7DNz6w962rLiMvJkLuPwGsV FVzP0TjM1VR5OMP09F4Tvbc1/3pO5iuUW6idyNY3gO/uRjrF2ufugJm0fDDtbIIeapuB+tfQX5NH gKOtxJ/XTreBryUH+CIn5q7spNNMHj5y0Qk+EOZdpM7nygHgN6IfDYPQlU/Mt1w3/ch5ibnBU6ro U4sH5xeFDvO8QJcX9y6IPt7mQxwtPtEvw+hXjQLMEf38ktg7FNz7UMHcjSuI1yD6/dUI7gUjNMvS KZuIIL9bEejziSjQGCUO5/qieHcc/VCSpI47EtRRmyL0ctWi6qKip4PUufdUJ/HyTrm6MI/lbuDL Yh6vWXGvvEedB9Uf9RIfywvUR25+UNSPezEb9FFhpx/4wcd26DA0RDy9ep2f+NZ2wI85c9kPfrMD 4Lc3gPtLAeLXWgxBUnghxoPEr6c9HCJ+PD0awrzfEiYeTkNNGP8DH4TVeedcCRMPr1Inq/8rk0aZ WHe037TLah5HJ2X6G+Xxo/wPAAAA1AAAAHic42NgYLjBzMDABqQ5gJiRAQJYkfg8UJoTiFNKixJL MvPziqHq+IDYgwGhnw1NPzNUL8xckBkSQFwtss79YUuBw6a575cfMy1yuKB0+2ddW44DyDwHPOYx YTFPAIiTngJNYM11CNoh1/rau5igOQJo5jBBxTjRACFzuLGYw41qDlHhJITFHJAYKwZAhDs57kI3 iJA5nFjMAYmxwQADce4hxRwLPOawYjGHFWYOEfrZsOhng7sDoj8Dj34jLPqNGMgHIPsi8NiniMU+ RRLMBwCI5SJU ------=_20110510165623_38470 Content-Type: application/octet-stream; name="S01_sess2.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="S01_sess2.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBUdWUgTWF5 IDEwIDE1OjQ1OjU1IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0PAAAAyAAAAHicrdFLCoMwEAbgKX1QpE115TZHcdHSLktvEGoQoU4gRs/U YzYxFmMQDOjAMEwgHz8JAYDvFuCg51H3Bmztnf3UT3OGrOK1nkR3BsO93cS92PHivpG1ZcFUKZBK 0Sge4pw9x+xvgUqKj0UC80w5Tp77LdCJPCdy8nTIOnmuzxXydEigQzyH2DxUWwVVjcR60b/TnCvR SCuFOBfPMbvktXIDGecx4ySek/wdNxAM7zTnpZ6XwujfXgxzUVHeclTL8o0kWz+h9k86DwAAACEC AAB4nONjYGCYwMrAwAakOYCYiQECWKF8RjjNwlCQm5/CCqTZoOI8QJyXmJvKwFCQWJSYC9bHB8QW DAjzWLCYB9MPAgJQfl5imYJ7AES/B5J+NjT9zFC9MP2cQCwBxFfMOT4273tk31RaZM+r/Ni+TXaP kY/bVnti3MOO5h4Qvyi1uATkIJD+Cjzu4cTiHpD7JQq4F8SYPbP34T3KKLX5jX0s//OEFocn9r2/ 9k3pO3vTXv266bWr8e/sK+88nSQUuNU+2EJWc0nVLfv3RwwCnh+5b7/aM16lL+4cUe7HFZ4BLuSH Z7J37eF9K2/aX5h6Q+pl8137WnOBJS/YV1MWnkAHkRueM26mbdyy7ba9zlVFwT9VD+0f23aIWW58 av/1FmcT55d79q+eneuesv+hffMBwfKLKfPsVx1vSGbZeMte5615u+bNy/a7CiQ2H3I4TLb7QeHp 6u5CdnjWix+dGLHzrX2UN/ftoqOP7BcG/fFa++M2Ue7hYEB1DwcsPIEOIjc8Qxo4u4t9Xtuzr92l sCPhqX37k7yQU0Lv7Zu727safr6335X3rVL+2jP7Fy8zjryYfMzeplsilIvrvb0Cw5H5N2ue2ace OdxcP+Ui2e4HhWdwanoxueEJATfsIfQDKH0A7B4HAu6BhQnMPZyw8AQ5CCpObriWqjaHhoY+tZed 8136eOEd+58STJZ9j1/g4O+Bi4tt/RkSGnoVLg4ArfreXw8AAADuAwAAeJydVW1Mk1cUfmHIR0pY 55INTX84ESchIrgY/Jo8RjHDCWpkjDWbAcSETFLoF2VShLbUUijQt0S7xBk/okzdzJzbWLYIcwth jiWO+fFj4tSkGuMydQsmTIfLzu25y5bl3ftjTU6ee9977znnPvc5pxmKojSnKEoyYSpZgsK/Gf+Y p0sUexos9u0Ou6Jk0BjK3+eS/3UuUZ75y18amZHs43NNwweW78Dblpm+szt2Q/h5ScfPExp+Msmc /ZlJFXkF+ONS8535CTtxev/9d0eHe+P+OnX8GTT8VZLNXvTjvOTqetyZX5D6fqgRvrNjh4oH7KCZ yTzihIBagwuLxeDkbrwlNg4FsPL6xp6oqxO9UbrQoSA29dCgvBtf0vFBRyiezzGdfJ7UyKdZnBl9 uTBY/SJO/HqhtKayAmU1xspl02Y0EG1j2TV4WiR4vJbzO7JT8tCGR7vePOOY8GDiIQ3e8YFYyljo 7cCGwuDqql96sfXzknTvyjAG9o3MyS9XsT49MTflgYoltBDqi8TzbdTJN1UjX6GDi6WU4GcW5q/e yui2SXTgBtFkyQrwuxuCzE9HF+NN5umUTlyjRtx2MrpOYu6z60Cv59nTvRnTx4mwE1uZjw9bGDe3 Mn7fhjaip3+Bh/GGl3FpB6PRz/yd78Nzp2x1T0XDjCkq6tS77i8GJeZFGEcj/1u/Dx5TQvesrKeS IGNxT9xfpY6/GRr+FpB59qzprnc1QMh2MNcOc+2K6xtfceJbmk42Sd4nu+L+m3X8p2n4F/dzDR+Y vchfzvo7WsW6m+dm3toln/lSb+v8rK8zUmdLVdiJsOmfma8qnfjJGvHzyQLP/HB+KraB73GllfWc 087+TWGQCgJ7r6g8N3Oc33TizNKIc46MWLz9+ywL111dAy6TbApeb2Qdr7VChB+7bMVhovVxuY3X p23cHwJ2mATxrzp43eiEaA+WASd//6CJ+4UhwOeqA7zvZIDfa1UnfhJxbnViO017VgW5bnxBPC8I L+yCCLNivIv7TUk30sSH1BDX0XiI+1IO6+hgwn/fP0/j/o0Jsp6iazGHdK8OrUf83Ys3MR/HtnCd 5VbIensNQna3Y2Zk03TN5Bv4dJKEXVrF97tazXlNbeP6s7bw+810Mz5ysz6HWllP7l2ybttwa3zu xENTu6xTiXUeZInG9pUHNMtM8sr6NfnwNbXL1V4fFoo/ggzZ76yyrq9KTPLz9yI/SKWn93v7QGVc 9IkhzP3vhTCKaPBNSxixo/tGDn4k8a7EHJV1VqZC6NEVVSH4eu+Cyn7uS53PjfB6WYTXvRF8N7Ul lj0UwT130ZJrsQhsYl9WP/4EBkQy8Q8AAADnAAAAeJzjY2BgWMHMwMAGpDmAmJEBAliR+DxQmhOI U0qLEksy8/OKoer4gNiBAaGfDU0/E1QvzFyQGQJAHJVifd9/YZrDnysVL9VU8x1A5njgMYcZizkS QHwVqN3wQ5rDh+XHvM0PFzpsmft++TH7QgdC7uJGM48JKsaJAET5jx+LOfyo5oDNApljgcccDizm gMRYoYCQOwSw6BdA0o9sjgEJ4cwEFGFjYyPoflYs9oPE2ECAiHDkxKKfE6YfagYsPvC5gw2LOWxw cyD6A/Dol8SiX5KBMACZm4DHXD0s5uoRYS46AACsdCIt ------=_20110510165623_38470-- ========================================================================= Date: Tue, 10 May 2011 17:03:36 +0100 Reply-To: Marta Garrido <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marta Garrido <[log in to unmask]> Subject: Re: DCM question Comments: To: Keith Kawabata Duncan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf30549f05459f9604a2ee19ab Message-ID: <[log in to unmask]> --20cf30549f05459f9604a2ee19ab Content-Type: text/plain; charset=ISO-8859-1 Hi Keith, If you are interested in model comparison, then I'd say you can use whichever number of subjects, irrespectively of the number of connections. In fact, you can make statistical inferences about models in only one individual subject. However, if you then want to go on and do classical statistical tests on the connectivity parameters, then it might be a good idea to increase the number subjects/parameters per cell. I'm CCing this to the SPM list in case other people want to comment on it. Hope this helps! Marta On 9 May 2011 10:44, Keith Kawabata Duncan <[log in to unmask]> wrote: > Hi Marta, > > Maria thought you might be able to help me with a DCM question. I was > wondering if increasing the number of connections in a model requires > increasing the number of subjects - a bit like increasing the number of > cells in a factorial design? > > Keith > -- Dr. Marta I. Garrido Research Associate Wellcome Trust Centre for Neuroimaging University College London 12 Queen Square London WC1N 3BG United Kingdom Tel: +44(0)20 7833 7472 (ext: 4366) Fax: +44(0)20 7813 1420 Email: [log in to unmask] www: http://www.fil.ion.ucl.ac.uk --20cf30549f05459f9604a2ee19ab Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi Keith,<br><br>If you are interested in model comparison, then I'd sa= y you can use whichever number of subjects, irrespectively of the number of= connections. In fact, you can make statistical inferences about models in = only one individual subject. <br> However, if you then want to go on and do classical statistical tests on th= e connectivity parameters, then it might be a good idea to increase the num= ber subjects/parameters per cell.<br><br>I'm CCing this to the SPM list= in case other people want to comment on it.<br> Hope this helps!<br> <br>Marta<br><br><br><div class=3D"gmail_quote">On 9 May 2011 10:44, Keith = Kawabata Duncan <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]"= target=3D"_blank">[log in to unmask]</a>></span> wrote:<br><blockquote = class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid= ;padding-left:1ex"> =A0Hi Marta,<br> <br> Maria thought you might be able to help me with a DCM question. I was wonde= ring if increasing the number of connections in a model requires increasing= the number of subjects - a bit like increasing the number of cells in a fa= ctorial design?<br> <font color=3D"#888888"> <br> Keith<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Dr. Marta I. Gar= rido<br>Research Associate<br>Wellcome Trust Centre for Neuroimaging<br>Uni= versity College London<br>12 Queen Square<br>London<br>WC1N 3BG<br>United K= ingdom<br> <br>Tel: +44(0)20 7833 7472 (ext: 4366)<br>Fax: +44(0)20 7813 1420<br>Email= : <a href=3D"mailto:[log in to unmask]" target=3D"_blank">m.garrid= [log in to unmask]</a><br>www: <a href=3D"http://www.fil.ion.ucl.ac.uk" ta= rget=3D"_blank">http://www.fil.ion.ucl.ac.uk</a><br> <br> --20cf30549f05459f9604a2ee19ab-- ========================================================================= Date: Tue, 10 May 2011 18:22:48 +0200 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: reverse from MNI space to native space Comments: cc: Marie-Christine Ottet <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=90e6ba6e8d42e9620004a2ee5db4 Message-ID: <[log in to unmask]> --90e6ba6e8d42e9620004a2ee5db4 Content-Type: multipart/alternative; boundary=90e6ba6e8d42e961f004a2ee5db2 --90e6ba6e8d42e961f004a2ee5db2 Content-Type: text/plain; charset=ISO-8859-1 Dear SPMers, We would like to extract the activation clusters for each subject in its own native space. I need to transform back the activation clusters from MNI to native space. I tried using the deformation batch but I'm not sure which images to use in "Apply to", the originally normalized images? For the parameter file I should use the seg_inv_sn file right? Should I keep the default parameters for bounding box and voxel size? I added the screen shot of the batch to make myself more clear, Best Regards, -- *Marine* --90e6ba6e8d42e961f004a2ee5db2 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPMers,<div><br></div><div>We would like to extract the activation clu= sters for each subject in its own native space.<br><div>I need to transform= back the activation clusters from MNI to native space.</div><div>I tried u= sing the deformation batch but I'm not sure which images to use in &quo= t;Apply to", the originally normalized images?</div> <div>For the parameter file I should use the seg_inv_sn file right?</div><d= iv>Should I keep the default parameters for bounding box and voxel size?</d= iv><div><br></div><div>I added the screen shot of the batch to make myself = more clear,</div> <div><font class=3D"Apple-style-span" face=3D"monospace"><span class=3D"App= le-style-span" style=3D"white-space: pre-wrap;"><font class=3D"Apple-style-= span" face=3D"arial"><span class=3D"Apple-style-span" style=3D"white-space:= normal;"><br> </span></font></span></font></div><div><font class=3D"Apple-style-span" fac= e=3D"monospace"><span class=3D"Apple-style-span" style=3D"white-space: pre-= wrap;"><font class=3D"Apple-style-span" face=3D"arial"><span class=3D"Apple= -style-span" style=3D"white-space: normal;">Best Regards,</span></font></sp= an></font></div> <div><br>-- <br><b><i><font face=3D"georgia, serif">Marine</font></i></b><b= r> </div></div> --90e6ba6e8d42e961f004a2ee5db2-- --90e6ba6e8d42e9620004a2ee5db4 Content-Type: image/png; name="Screen shot 2011-05-10 at 6.16.29 PM.png" Content-Disposition: attachment; filename="Screen shot 2011-05-10 at 6.16.29 PM.png" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gnj1mh5r0 iVBORw0KGgoAAAANSUhEUgAAAgoAAAEtCAIAAADm4t8RAAAWVmlDQ1BJQ0MgUHJvZmlsZQAAeAHV WGVUVV23Xvs0cahDdzcISCPd3Sl16G5QQURClFAkRBBECUFBQEVAsAgRUSSUUAkJKeEVkJCUu9Gh 33vHuPff/XPXGHvv58y1ztzrnGetNed8AGDMI4aEBCCoAAgMigiz0NXgtLN34MSOACRgACRAAMgR 3cND1M3MjMD/2jaHAHTY+V7s0Nf/Oux/7qD28Ax3BwAyg7vdPMLdA2H8EMYr7iFhEQAgymDcdyIi BMZI+AK0YfAEYXzhEHv/xoWH2O03vvdrjJWFJjzmBQA4ciIxzBsAsj7Yzhnl7g37IFsBAEMT5OEb BAANCsYq7j5EDwAYteAxooGBwYc4BMaCbv/y4/0vTCS6/fVJJHr/xb9/C/xN+MVavuEhAcRTvz78 X94CAyLh/+tXo4Hv5EEBJofc4OBr3oOoZfgHhwT84uyX3TPI2vKPPcjNxPQP9grTsfiDQyI0/oXN rP7Yo300Tf5gz3Dtv378iAZmf+xhkRbWf3B4lKX2HxztY2X7B3t4av21e/nq6P+x+0bo/32Xf7Dh 3zkAX2AMiMA9wvPkIe9AMzjkVJivt08Epzq8yjxFOfWD3MVFOaUkJCUPu//ftMP99Xuyaxa/9g1E 3/sfW2QrAMfS4D0w+x+bYx4A9ZkAUCr8x8YbCwD+CwBtFO6RYVG//aEOH2hACigBLWAC7IAHCAIx IAVkgRJQA9rAAJgCK2APnIE78AGBIAycAKdBPEgGaeAyuAKugRugFNwG1eABaARPQCt4Cd6APjAI RsAEmAGLYAVsgl0IgrAQHiJATBAHxAeJQFKQPKQCaUNGkAVkD7lC3lAQFAmdhhKhNCgLugYVQ3eg +1AT1Ap1Qf3QR2gSWoC+QzsIJIIcQYtgQ/AjjiDkEeoIQ4QVwgnhjQhFRCOSEJcQeYgSxF1EA6IV 8QYxiJhALCI2kABJhqRHciHFkPJITaQp0gHphQxDnkGmInORJcgaZDOyE/keOYFcQm6jMCgCihMl hlJC6aGsUe6oUNQZVDrqGuo2qgH1AvUeNYlaQf1E49GsaBG0IlofbYf2Rp9AJ6Nz0eXoenQHehA9 g97EYDD0GAGMHEYPY4/xw8Rg0jHXMbWYFkw/ZhqzgcVimbAiWGWsKZaIjcAmY/Oxd7HPse+wM9gt HBmOAyeF08E54IJwCbhcXCXuGe4dbg63S0JFwkeiSGJK4kFyiiSDpIykmaSXZIZkl5SaVIBUmdSK 1I80njSPtIa0g3SUdI2MjIybTIHMnMyX7CxZHtk9sldkk2Tb5DTkwuSa5I7kkeSXyCvIW8g/kq/h 8Xh+vBreAR+Bv4S/g2/Hj+O3KAgU4hT6FB4UcRQFFA0U7yi+UZJQ8lGqUzpTRlPmUtZR9lIuUZFQ 8VNpUhGpzlAVUDVRDVNtUBOoJalNqQOp06krqbuo52mwNPw02jQeNEk0pTTtNNMEJIGHoElwJyQS yggdhBlaDK0ArT6tH20abTVtD+0KHQ2dNJ0N3Um6ArqndBP0SHp+en36APoM+gf0Q/Q7DGwM6gye DCkMNQzvGH4wsjCqMXoypjLWMg4y7jBxMmkz+TNlMjUyjTGjmIWZzZlPMBcxdzAvsdCyKLG4s6Sy PGD5xIpgFWa1YI1hLWXtZt1gY2fTZQthy2drZ1tip2dXY/djz2F/xr7AQeBQ4fDlyOF4zvGVk45T nTOAM4/zBecKFyuXHlckVzFXD9cutwC3NXcCdy33GA8pjzyPF08OTxvPCi8HrzHvad4q3k98JHzy fD58V/k6+X7wC/Db8p/nb+SfF2AU0BeIFqgSGBXEC6oKhgqWCA4IYYTkhfyFrgv1CSOEZYR9hAuE e0UQIrIiviLXRfpF0aIKokGiJaLDYuRi6mJRYlVik+L04kbiCeKN4t+O8B5xOJJ5pPPITwkZiQCJ MokRSRpJA8kEyWbJ71LCUu5SBVIDR/FHdY7GHX10dFVaRNpTukj6gwxBxljmvEybzL6snGyYbI3s ghyvnKtcodywPK28mXy6/CsFtIKGQpzCE4VtRVnFCMUHistKYkr+SpVK88cEjnkeKzs2rcytTFQu Vp5Q4VRxVbmpMqHKpUpULVGdUuNR81ArV5tTF1L3U7+r/k1DQiNMo17jh6aiZqxmixZSS1crVatH m0bbWvua9rgOt463TpXOiq6Mboxuix5az1AvU29Yn03fXf+O/oqBnEGswQtDckNLw2uGU0bCRmFG zcYIYwPjbONREz6TIJNGU2Cqb5ptOmYmYBZq9tgcY25mXmA+ayFpcdqi05Jg6WJZablppWGVYTVi LWgdad1mQ2njaHPH5oetlm2W7YTdEbtYuzf2zPa+9o8csA42DuUOG8e1j185PuMo45jsOOQk4HTS qcuZ2TnA+akLpQvRpc4V7WrrWum6RzQllhA33PTdCt1W3DXdr7oveqh55HgseCp7ZnnOeSl7ZXnN eyt7Z3sv+Kj65Pos+Wr6XvNd9dPzu+H3w9/Uv8L/IMA2oDYQF+ga2BREE+Qf9CKYPfhkcH+ISEhy yESoYuiV0JUww7DycCjcKfxRBC2cyHRHCkaei5yMUokqiNo6YXOi7iT1yaCT3aeET6WcmovWib4V g4pxj2k7zXU6/vRkrHps8RnojNuZtjieuKS4mbO6Z2/Hk8b7x79NkEjISlhPtE1sTmJLOps0fU73 XFUyRXJY8vB5pfM3LqAu+F7oSTmakp/yM9Uj9XWaRFpu2l66e/rri5IX8y4eXPK61JMhm1F0GXM5 6PJQpmrm7SzqrOis6Wzj7IYczpzUnPUrLle6cqVzb1wlvRp5dSLPKO9RPm/+5fy9az7XBgs0CmoL WQtTCn9c97j+rkitqOYG2420Gzs3fW9+KNYtbijhL8ktxZRGlc6W2ZR13pK/daecuTytfL8iqGLi tsXtF3fk7typZK3MqEJURVYt3HW821etVf2oRqymuJa+Nu0euBd57+t91/tDDwwftNXJ19U85HtY WE+oT22AGk41rDT6NE48sn/U32TQ1Nas1Fz/WPxxxROuJwVP6Z5mPCN9lvTs4Hn0842WkJalVu/W 6TaXtpF2u/aBF+YvejoMO1691HnZ3qne+fyV8qsnXYpdTa/lXze+kX3T0C3TXf9W5m19j2xPQ69c 76M+hb7m/mP9z96pvmt9r/X+5YD+wJtBk8H+IeuhD8OOwxMfPD7Mfwz4uPop6tPuyNlR9GjqGNVY 7jjreMlnoc+1E7ITTye1JrunLKdGpt2nF7+Ef9mbSZrFz+bOcczdmZeaf7Kgs9D39fjXmcWQxd2l 5H+o/yn8Jvjt4bLacveK3crMatjqwff0Naa1inXp9bYNs43xzcDN3R+pW0xbt7fltzt3bHfmdk/s Yffy9oX2m38a/hw9CDw4CCGGEX/lAkj4jvDyAuB7BZwn2ANAgPNfUorf+e+vEXB6DCftCBjbQOLQ IuI6MhClj+bB4LEIHIKElJSRTJRcG+9DkU3ZSrVFw0twpS2l+8zAyujIVMw8xcrDdpz9Ckc/F45b jofIm8J3m79F4L3gmNC08IzIlOiY2Afx90feSwxKfpKaOjonvSTzXXZTbkt+Bz6FtpR+HFtX/q6y prqutq2B1KTUYtLm1RHVldZT0Fc10DLUNTIwNjYxN7UxczUPtIi1vGxVYl1n027bbzduv+jwwxHh hHdmdOFzlSZqudm5B3uc8yzwuuf9wueD71e/3QDyQLYgiWCdEJfQuLCy8DcRa1EsJzROep1Kia6K eXN68Qw2juusTLxmgmGiSZLpOdNks/OmF8xSzFLN08zTzS9aXLLMsL3snOmfFZudlXPnyvPcoauL +eAaoYC/UOG6UZHzjdCbycWFJXWl3WVfbu1X0N8Wv6NT6VQVfjeluqjmfm3bvb77ow9m6749XK/f bYQeYZsomgmPmZ6wP+V5Jvj8SItCq26bXXvAi4SOvJfVne2vhrq+vt7vpn7L0yPdq95n0G/0Tu+9 2oDMoOAQ8zD58M8Pyx8/f+obaRm9N1Y0nv75xITzpPoUx9TOdP+XipnTs2ZzfHM7830Lt77GLJot 8S3t/NPzrXg5dEVlFbP68nv8muLa6nr5huMmzWbXj7gtma3F7eId2138btveiX3R/cmfuQcGBwf/ 4t8ZxYf6ih7EPMVW4ypIKkkfknWQj+A3KbmpNKkDaAoIL2l/0oszuDBmM3Ux77BKwSvgAkcd5weu bR56XiE+eX41AVXBY0JSwrwi9KI40S2xefGPR95ItEg+lKo6Wix9VSZdNk4uXN5DwUpRW0n2GJ8y QQWpsq66oDalPqExoTmpNa09ozOrO6c3q//FYNJw3OiT8ZDJgOk7s3fm7ywGLIetPlmP2ozbfrab sJ9w+Hx8zHHUacR5xGXMdZq45PbDA+VJ48XuLeIj56vhZ+xvF+AWGBB0MvhcSG5oZVhr+MeI71Fk J3hPHjtlGe0bc+Z0ZmzJmbq4lrPd8QMJo4lTSXPnFpNXzq9f+JGym/ozHbqIvkSaQXmZIZM7SyJb Lcf8intuxNVzeVfyy681FHQUDl7/UrR+E1PMUiJZql/mdiumPKfiLnyqjVau3SWt5qiRqdW/53g/ 6EFcXcbDovrqhseNXY+Gm740f3/88ynZM8bngi2KrWZtfu3JL8o62l5OdO51Mb2WfmPW7fc2qaeg 935fW3/vu+H34wNTg3NDi8PfPqx+3Pi0NbIzuje2P77/eXtiZXJqqm+6+UvpTNps+JzDvMaC0FfK r5uLo0ut/1R8S132X9Ff5V7d/N6xdnndYYNnY3Gz7kf0lvo2brt3J3fXcY97b2G/5mfEgcK/+E9C KqBwqB70I0weNgEXRxJLGk+WTl6Er6N4R/kPNT2NGsGLNpuujX6VkYPJlDmOpZL1IzuKQ5zTmCuI O5XnJu99vqf8LwQ6BDuE2oVbRJpFH4jdFS8/ckMiTzJHKuPoBelEmdOyUXJB8p4KjormStrH5JWF VdhVadSwaj/VtzQ2NFe1lrTndaZ1R/UG9d/CuUKz0T3jcpNC0xyzS+ZpFmmWqVZp1mk2qbYX7M7b JznEH491POUU6RzqEuwaTAx3O+We4JHqme1V4F3sc9u31q/O/1HA08DWoI7g7pCh0Mmw5fCDSIoo thMiJ+VPaUebxtifdov1OxMaF3U2Kj48ITDRM+n4ObNknfNKF46k8KYypVGko9K3Ly5fms2YuDye OZk1n72as5OLukqRx5DPeU2w4Eih3HWVIp0bpjftiz1LwksTyjJvFZffq3h+uwdeBV+rtqsxNYRa jnui9+UeaNQZP7StJzb4NYY9imlKbE59nPkk/+nNZ7ef32951vq2baJ9vQP3krVT4pV2l93rgDdx 3ZlvS3se9D7v64LXQd/7voGewe6hruGOD20fn31qHmkYfTB2d7zic8lEwWTOVPp04pfomdBZ7zmn ecsF/a8qi0eXBP9h+0azjF3eW1lZnf4+tPZqvXnj7ub1HxlbZ7dDdoi75nvq+5I/OQ+oDvn/rYMc xgSMLAC3nsABAS6ojT4CUKoAAJ8UHD/g2tMMD4CVAoC+0QKoswZANbR/4wcEULBSRAnrRZxAGMgA dWACHIE/iIGryuugFrSBYbAE14yMkASkD7lDsVA+1AANQOsIOoQcwgERhyhH9CK2kfxIa+R55GPk Glyx+aEqUUtoCXQU+gkGhTHC5GKmsZLYeOwATgh3FveJRJYkh2SD1Ja0mYyDLIlsgdyMvAnPi8+i QFBEUnylJFKOUNlSDVJbUw/R2NJ8IDgQRmldaefoQuh26VMZmBmqGTUYPzGFM1MyV7HoscyyJrFx s7Wze3KQcjzgPM6F5Wrg9uZh5unlTeJT5FvmLxGwEsQINgkFCfMJj4sUiDqIsYhNwGs7REJZklJy Turl0bvSeTIXZGPkQuR9FIiKTkrHjzkqu6h4qPqphahHaERqRmiFaYfqhOlG6sXoJxlkGBYZ1Rq3 mQybLpvjLLgslaxsrENtMmxr7Prs148zO2o6BTtfd+klIt2U3E94NHkBbyOfQt9Vf72AksCfwU4h z8J4wlMj1qOIJ3pOKUaXnSaPDT0zdFYpvjQRnxR9bvG884WBVKO0zoval15fts1czs65opV7kNdy 7VyhaRHrjaXi1tL8WxEVVnfkq3iqGWqZ7ovUGdWfaqxtWnwi/Cyw5UHbbode57WulW7Dntp+7vdV QzYf2Ue2xkcn275Uz13/mv1PxkrWWsFm5XbL3tiv8+M3/1Sw4sANqw0KQAfWGNxBOEgCubCW8AT0 g1mwD9FBYpAu5AadgQpgJeAjtI1gRaghPBHpiHrEBBKPVEYGI8uQYyhGlC0qHzWG5kEHoBth7i0w JZh1rC72OnYDZ4arJsGThJAMkh4jLScjkMWTrZITyfvx6vApxU9RQElDmU6FozpPTUJ9kYZAU0gQ INTRqtP20RHp1ujPwzVnA6M54zJTBrMU8weWs3D9OMSWyC7BPsaRyinPOceVx23Eg+Bp5o3kk+Jb 5a8TOAFHM4RQp3C6iLkog+i4WIV42BFVCUqJSckGqUtHfaV1ZQRl8XBe80V+UOG1YpvS02PNys0q T1Xb1F6rD8AR7ZvWvg5el1VPRF/BQNfQ1sjH+LRJtulds07zGUuMlaC1sU2EbYFdm/3CcSpHeSei 80WXZtevbmzuFh4pnu3ewEfNN8HvVQB1oHNQbQgi1D7sQQRFZGBUz0mpU3kxiNOBsSNx+mcbErgS U5KWk63PP07hSb2Utncx8NLEZZvM3mzDnNe5Jlc/5PsVoAorisxu/CyuLXW/xVw+eDur0uIuXfVo bfn9qDrTeulG/ia+x+JPVZ5btga0X+i409nTtdnN02PVl/Kuc5B6OODj2KjH+M5kwRfN2dWFsiWn ZZbV8fWKH5E7+vtcf/knBdSAGdabjsBKkx6wBV6wsnQBFIAa0A4+gG8QBmKHNSILWBdKg7WgV9AC ggwhjrBEnIIVnm7EFrz3bWEFpwW5hZKGVZr7qDW0PDoO3YmhwbhgqjH7WDNsGXYXZ4WrhfkPJOkj lSUtIiMhiyT7Qm5F3oGXw1dScFLkwfxnUFFSZVLTUxfRCNLUE7QIA7RetFt0qfQc9PUMxgzzjMlM /HD2EsRCz9LKGsTGxvaW/QyHOMdnzstcWlzb3Pd5AniFeOf5qvhDBOQEIcG3QteEvUWkRRGifWI3 YfZ1JdgltiQHpRqO5kvHyfjIWslpyx9TkFdUVFI/ZqR8XCVA9azaVfV7Gt2aX7VJdcR0LfSi9PMM Gg0HjFZM8KaiZsbmoRZ5lm1W32zYbU3tEu2bHJYd+Z2cnK+4vCXi3LTcEz1eeOG8TXyu+k75SwTE Bw4Gi4QkhU6Gq0WURGFPBJ/8FK0X0xQreqb0LGt8XiJ90pVk2vOZKfjU83DGEntp53J05l52/BVc blYeR/69As3C0aKYm1zF/aUpt3QryG6/ryy6G1CjfI/6/kJde31xY2KT92Ozp8rPxVt529k6mDtZ ujje8L+V7FXtN3/vORg7nPexYWR4bG+Cf8rqS/Lsk/nNRel/IpabvkPrxpvXt1Z2dfeLD/kP9zoK xwi4QeQasPw4fnCwxg8ANguA/cyDg92Sg4P9UrjYGAWgJeC3tn44GEMFQOHYIeqs3zl8/Lf2X1lt XDUqYxroAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgAElEQVR4Aey9D1wU17n/fwi7AsYlgIIKGvSi qcbr2mhyNTGaAGkq18a1ibaJwm1IUtZfmgrk3mKhibcX8o0F723A3uYutCm2gm0Cti5pimkCJJBU bAKJa5VNhAgaUPnvLrKLu4bfc2Z2Zmd3Z/YPsCj6nBcvdubMOc95znt3z7MzZ85nAgb72onbZB3q O99jJnJZ+JzZM2T2oubBS2ZZcNiMIHuWN1vmS+cHzUQ2Y+6sWz0WN5tHoExwsI9NQB1opXfISlx9 7hswk/A5M4Md2/aqL7547mjecc+jHQnnHa14sWe9DBBCwmaHOfZWkqqP7Q4NXhwYssqCZ0TOuk3w ufDCMSyCBJDAVCAQ4DE8TIVeoI9IAAkgASQwwQRumWB7aA4JIAEkgARuCAIYHm6ItxE7gQSQABKY aAIYHiaaKNpDAkgACdwQBDA83BBvI3YCCSABJDDRBDA8TDRRtIcEkAASuCEIYHi4Id5G7AQSQAJI YKIJONyw/v+q2on1Chm5PNGtoD0kgASQABKYCgSCbiWyaT/ZtAB8dQgPEYrpP37sganQA/QRCSAB JIAE/ELgZ4c+Yu06XFwK/IquUqbJ3Lw5ICBgxb4Bdtf237hvhWumQwky1BgfEKAsanbM9fce4xg4 LEwJmbUdZo8NH9tHOwqpweix7PgLWPUNVbUneqihoUZoNLfREfD4W0ALSAAJIAFHAmfPnu3q6rpw 4UJ3d3dPT09/f//AwIDBYDAajcPDwyaTaWRk5MqVK1ar1WKxQFU+EDiEB6FNBewsDHI4uSAkdCEh M50zhbXgrCQU6jlkTcYOdYwkl1dXa7XVNfXVRWnxpK4wccHqqg6r2+YHPj2sJSSjprX7XtphfyfT X9erEqu/oM3IZuUnp319ltzfTaJ9JIAEkMDYCDiN/2Mz4lzL4JhhNYMyj7uGrGajySJXKBy1gRyN eN6LT3x0wwbWRMK6B6Onr95aqFMVvjv6ygbpuoogiGaqFevjInn/JsYZ8SZDQuNJ/G3T6cHgRVkH ioWlfG3XbAbpqmC3XIXmcRsJIAEk4BsBybMH1ozjj1vHPdJTVaBmLswEJKTk1bY5XCexdtTmqDP3 V1XmJATIQ+QBAZv3N3SIuGbU71MnyENCQ0NDAhLUVScYI2Z9QYp6f21tcWYCaz/zYDN7FtDdWJmT U1B7VvSq0Qg9L7Kl4C17ypJhu/BPJ2xlXb01Hyn4wStw8qD9XWrmvjYoJuFMXor6YEPDvhS4srb5 WI/Nt31q2IVLbeBzW8P+HNbPzTmVLAVrT3OB2nbZCvp18FgHsbbtU295pY7UvZqekldlNLcWqNVV rUPUX4l2pSB0HTsIpkMgyQNSCqqYa1W2buMLEkACSGDCCIAkH/9X8EbDKJtMTXRsVWbUt7S06NgE W4258YTEawy0TL9GRX1IztZUlBdBNqTyFsOoqWkTIQ8WNhmaNEweic/WVFeXUmuEFDX22+yzL5bW DCY/u1Rboy1l7JFSMGJoYreJKkNTWpSspIU0OtpsU/5y2P6vo452Rg20ss0xewv1+eCXimlT1Nvu +tJsxnNlRn55u9GTM0SZnFHUOsD5Bj3XZDOuUfeyi4py4YoWtFeqG7X1S5lbqq2u0DDZRNvaUVGU TfulSssvrTEZmx4kJL+pnytMvIRgaa0AGyQ5t6K6WpPLcM2usfcZt5AAEkACjgQ6Ojo6OzvPnz9/ 8eJFmH7o6+uD6YdLly7B9MPly5dh+gEuRcD0A0w8wAwEVIVAwAYFwscG2HAOD3QcckkqGh6663Ph QHZ1p82N/no68GVX28ODjoYHZYbWxJboZgowdXnPWyvSoEx+fbctp7uGGT1LDSZmCE4uZeLQqKUd fuGTtPIWWszU39l50WjhbbAb4uFBp6GjZ1FTv6S3oxYmrpSCPQ/OqIra2UZZ39LK2X7piqjLpUzo GrXoYCe+qGnUpMtQKvlYaGoqYt0Yhebg4pJGx3Sk6UE4t2nq99CuCwRTSylYS9O2sz1v1GRn5GtZ UE5QcBcJIAEkAATGHB74S+4w5rgkZa7u3R0zmblsOCaXG0q/u3QXM7Fw7pMmyGks35vzNjEREhIy TIfwtz4f2B0ltPLsU/G2+YTIezariNZArILDw30X4bf0Q/dE2vIi72PKjLBlslMfZmeLZbMXwjB/ uneYFgsOj44WmHC7aSFwN5Jq9eLwc6US3r68ljEwAuXcO6NKio8VoMrYuNbWL/ooCpVyMeOplbu4 Fbx873uv/0V7IOcPJ08db9TW6Tg3TfTOMOE1ME/tukKQhcyBU5YS1YISpSpjS8I3Nz69d2WcwDWu KXxFAkgACYyPgNuBZWHUgshIdoxmWlFEhRLChAd5EM2OmR8RFhYUMjJCguZqNEUjQXeFSHpjGzod 2qNGhPeTcsMr08icML5li7CQZAvEIfYQ0vPuqxCzkqfDjIknb6lX0s7AQYNgTAcACxdGOLgBjtvC Bc22dh1ZFZMEMSE5Izfpmbzk7761dUeJQ3nhjnS70JArBFnshuOmzto/v/nuO9V7dqcX7k4n8fmd tVleB01h27iNBJDATUcAzidgrtSbbjsM195UYMvImQe43f9Etno5a6Fnf+bz3WsfDibMb3zOUPOJ TrJ8Cd0ztx+FsZq50s8dhN/RMOyf+bKfrGTHNuMpepdpsoKLMcIzDXsl6a0griIt0rz/hV0wQmds XRpMWiW9FRjz4IygJGxa3PnW8qYGWi5vtWxjftb31B6HGkFyG+o+R0u+QtBX5jz7TuyhYnXCFvXL xdbavIcTdx9pG8iKDneyi7tIAAkggXERkLhzibkuAycKUqPgkseeg+vsO5Rb9jecaNM3FKREpRaW Bc2GkwuHVLJ9aV7lsQ59Q97jSvj9nPvCI/wZAZRbuuEpQnSqmJSq5rautoYc1RqIDpqsRwQ/xB2s wY5+P71XStPM3PDjdLDud7kFTMrLSUkIWJUKDabp9myCUdmNt+w6QOjmGJxxap8wJz+AYObiO+FQ 9aHD+jb9kf05UYm7qecnTrO3UOleK6481saD9bXdkGmmupIdqXmVbV1dbc3vvltXR8ia2zE2OL8Z uI8EkMB4CUicPcjls8FyqNgKODYEKFaXtmhTl6pS18OQTlNGeVPWumhivgDb827jb4FV7t66ho6O MFQX1WcnOFwCkcVu6qzXJK3foVpVxhQh+doW9XIFMROnOANBRRHK2JzGFhT9X7dnF4yVbFKm5WZm ZTwZx4YaKW+JkZ5XxNBuunGGFmFbZ2w7+MZctmKbhMkZgHYxiETf93S+as+uXVvLdsERZX6p5kTq jsLtqY8+0rR4g4rsKtm6Jq7f9BBrx027Dg0BAQZC7L/+qDTteOrurVoWK0x11z8Xa/MAX5AAEkAC XhHw5hKTw7OmS2rO/Wjr/V7ZthUydnV0W0hI6OyocMcFWsYTxaHKHXCX6pMx5o7+4emhMZHhEqHI PNDR2Q+/pkOj4oQTHb644WVZSW/t9SfMGWtPV+ewRR4RE62AfpuNAyaiCGc3jRZ5iMIRF/GxXehJ p8Ekl4dExcZSo5iQABJAAhIEQFRDJpPdwiS4+sJusP8DAwPZHPY/xAy5XL634oO0xPlgbJxDiyI6 Vni5yNk7w7CVKCLdFqE3I8XGTc7FEQ/eUu8nzBlZZLTgN32wIpy7ZBYsujrcx3ahJ0sczsScyeM+ EkACSMAjAffnEBJzDx6tYgEkgASQABK4oQmM8+xBko1i+dMGw7YQhbtzC8nKeAAJIAEkgASuNQF3 Zw/6qgK4ICVMK9T7u/h7bjy4LlMo/HxV3K6JjULZHt4MPIwEkAAS8JWAZHjoqs1bqqJ33uRWNHb3 93e2NOYnK3UlqUm7j3gbIHz1xdfydk1sFMr2lR2WRwJIAAl4ICAVHrpey6Q3TubXdL64ZXVkeHj0 ktVZB/4ICnq6PRqdYBEzyFAbjewN/R5aEj0MWt+u+VI2QTfKoTijib1p0QwQ9XASymYybYalrLm2 izlIAAkgASTAExAPD2b9X3dTUYjynQ4rFeJSGJG7QSMzpruVoXajd+1O61vUJiHiEtasJrZeN1FC 2TwU3EACSAAJIAFxxVYDo7dq11KFu5+ckkctbmm9a0mtbwmbkhLWrCb2309NiFC2U/9wFwkgASRw YxBwUmzt7e0FQe/BwUEQ9B4aGnIj6C1+51LnCT2NnNPEj8KRtsMFhYwWd9Y6qrea0H375qjE1B8f euwPSloR9K6Lt8GN/veNnFKma0Hv+klYC22NbypRGgwWwix/Bq3vv7y8Ccps6I4zRq1P3/PGA8nN ojY3/owxufXftmyIJRs2fD1q/h8uDcH1LXbiOyhwzpaduUOH9xiSnst6cjk8JRtKw1poDx6CUPaB J+G2qqfjF5QtUNm1oWhTmJAAEkACSICIX1yavWQhsLltukt4sJoHegZgAkBU/hp0TdmZBA961wx2 J61v0HcaEtP3BpuEk7AOWLE5M2/fwD1P783a5HjDrLdC2byHrkLZ+FlAAkgACSABIQHx8MCGheq6 U8KisK1/7fGIqIjfthhZiWzBUQctbhG9a0FRl01b3UChfhEtZMtnJaxrKjTZawjoVyetWiRPKOhy seKcIWENiokKZTtXx30kgASQwI1FAK6V+dQh8fCgUD4MNylp09Mq9YK7lLpqf7wDBPgyHoIrRZz8 ta0xVos7htPidqt3zVah13PYxGp9w7aEzbPwvOr0N++i+tWH4YFrNfBE0zqQsLbV5l/6+C12Q8Ia J/otcseUkwHcRQJIAAncMAR8jQ3QcfHwQGRLcurz4S7WrUtDM/cdbDh27MjBghUxiTQ4aJ+LG5v8 Nad3zeJ21fq+W0LfO8w7CetxCmXfMB8C7AgSQAJIwJUAu8AZ8mHD9T+b6VTLZXaBOx65Lquzad6u 1O2F6dthxphJqvLGV7etplJw3spQC6/wcHrXNmPEWetbRiT0vZdKSlhzktch4xfK5rzCVySABJDA TUcAzi1cI4RHQW8qTG0wWYksNCY2klMd5dj5KEPNVvOg9S1h06OENazPG79QNtcxfEUCSAAJ3CAE nAS9WRFv4X8Q94bYAP8hSPgk6E2Fqem9q6LJRxlqoQ1JrW8Jmx4lrCdEKFvoIW4jASSABG5mAhJz DzczEuw7EkACSAAJjPtxQGNBiFrfY6GGdZAAEkACk0tgXGcPzUXxAQHKxiFfXR6b1rdAtdvLBo3N CQEBKwroOurrOPner+u4M+gaEkACNwyBcYUHeRDcOrRw2iTBEKh2e9minFD/nOfTvaw8acV879ek uYYNIQEkcBMTGFd4GDM3n3S8uVYEqt1clodXGVV3gjXSPiVn2XCfKo+lsO/9GksrWAcJIAEk4BuB CQoPZn1Binp/be1E6nib9Xkp6oMNDftSVgQEfOPfn9rySh2pezU9Ja+KWcndU1WghpuxICWk5NXy q6gHThSo4ZISpJR9ZdVnCD2B8DJJyIbbulacyZoNyDzY7Lzk2ovugw/WnuYC9WbGN3BaffBYB7G2 OaiRe+koFkMCSAAJTAKBwb52/q/gjQa479X7pPu/TYRsajKOjhqaVKyvE6fjbbdJlMk7c//3lWza hCotv7TGNNqvYdqD1irKi+KZpstbDKOcJHhyrkaTn8Z6pCpq8qZHkrLhfNdUGZrSomQltarRGRxs 8mWku8/5pswt1VZXaNIYp7WtHQ5q5A5GcQcJIAEkMAEEnAS9+/r6QND70qVLIOh9+fJlN4Le4s97 8NIje3gwMeEBdLyZmroiOniDjjfds+hgJ76oiX2GBOh4s2VGu+uZ4V7zaQUdx+3PluiuYfJLDaxN VVG7hXXHUhpP4jU62Omuz4Uq2dWd7IHRfsZUdnVrBShFkdzqdja/s4YW8zI8mFpKoXCa1la3UZOd ka+lHWDdAAFwxqilHYRFQLC8xdY0++JF90dNugylsqixn6tRBHaKmmDX3i8Hm7iDBJAAEpgIAk7h gX3egzfhQVJUAwYvX9MYdLy1EjreWk4bXJUUH2vz0a7afe6TJvCtsXxvztvEBI8SDRmmY/Zbn38W dhzCwcYHY1nPo+/brCK7vZx7kHGy4SVKVcaWhG9ufHrvyjiejjcC4B66H7x873uv/0V7IOcPJ08d b9TWwdP42GTvF5eDr0gACSCBa09gguYemBngidXxZtkYRuxS4TwtOSPlFDM/Imzu3IiIiJCwOI2m qChzWU9bHS0jqOH4WAjegMiGG9lwbwTAoYz77lu7jqyKWKpKTT9H5iU9kweXl0ScwCwkgASQwHVD gP99PBEeeanjvXwJbYzV8YZL8Jzy9kqq9UcIqw2ezGmDM3n8P1a1Ww5PgyPk/iey1ctZ/3v2Zz7f vfbh+JVwXUr7Rb91JfMkOfPppjI4m+Aru93QV+Y8+07soWJ1AlUOt9bmPZy4G2TDs6JtCuDOs9Ei xtx2v+VNDZwvlLdatjHnJD21cKJDguQ2/s5q5CLWMQsJIAEkMGEE4KoV3Cbj3pw3Zw/G/XC7TUKx 4MkP7m06Hh2rjrfocgVWtXvRY8/BoL9DuWV/w4k2fUNBSlRqYVnQ7NClVBKcbF2QWnlMrz9W+bgy 1dEVd3sh3smGC0x4h4Xr/szFd0Ld6kOH9W36I/tzohJ3w67+xGkzY9FJjVzQCm4iASSABK4NgXGe PcBdo3BZhSaH+0cnRMebGThjQpknU9MWBKrdo1mlLdpUuFSznk46QMoob8paB2cfm9prihYkpm9d A6cNRKmKJ9o6hd0CU1TiX+y/epQNt9WEC1auNj12P/q+p/NVe3bt2lq2i7qWX6o5kbqjcHvqo480 2dXIR7PCJdzDbCSABJDAhBNwfw7hUdB7wvwZm463U/OOqt0g8t1tISGhs6PCgwVxztjT0W0goRGx ka6DrbFD32mV8yGHM2+xhMxeEB0e7FE2nKswtleqjj5skUfERNOrX2bjgIkowtlNMTXysTWCtZAA EkACAgJOgt4g3O2a4EITZEK08EnQW9DIRGz6quPt1KajajeIfIvNPSsiYxUSGuTm0y8sXUXPLFwS 3KtavG2JR9lwl3o+ZVB1dHuFYEU4dwXNsV/2IriFBJAAErhWBAQ/uq+VC5PZbvDKA6OjByazRWwL CSABJDA1CUxeeEAd76n5CUGvkQASuEkJiISHp3/TcpPCwG4jASSABG44Av/50K1j65NIeABDp/Qe 7od125jsFz9ddHfQlaONhsuyW2AxQNicW++Ng6UKo9W/+jz39Kjbun49KPt1waJlHRfu/eXgmJph +jXdclx3edheP0BuGv7ZX60vZ8w+V3X2hS9ufefFuaTt/DeKL9mLXEdbgWnfuz11WdDJP3/xTP0V xq/pbxTcPh/kAo0j58m0+YoAMjL0Hy9++SGRyieJG29/6YHpsPjw5GeWuK9Nh9mT439q3XHU+sMf 3LEt9hYycuXcsGx++C3k6vDen57947QZf3xx3lyYhh8Y7iHBNJ9YDxa2/qKLiJcfYWG5+ukMUaq6 j/nTND/9pxXQm2HTyYHAZTFUnF4Ax96oj2Yleydlh2lp0no92e552Ts7br9uKW79LfttPe7FYia/ ejJe414NaHcuGfuQ6826hzF1wmr6ubb3xUPdudru54vP7DsJg1HA7fNdbhkak+2xVroK+hVm81dj rc7UGxl+qezC8/a/8z88dKnTePl7eV+8wH3axEPuuFqdgMqPPnb70YLFEBuEtu5KmEljQ2//E3ln vpP3ubbDSoJmpH0zSCofVvI9sQZG06+qf9X2zGtnnz9CHwX1tZUzyKzbNkFsIFf2vfTFd/Z8ru38 igROT/7W9CX/fBvEBrD/7T1nIb+yDb6QshVLpkmVB2uifgp9ptsSzfmaH3ZnGI0NV00v/bTjmaIv Xnj/Mthe9kDEPKf2Jqg5Sfcmt9eSbkh1c5zueVndiTkh8xSB8xw+rS4lxpbh+G0dmw3RWv5yWLQx mjkRA5qkcXrAf0PZLXDz0Jdc29WfmnYum0aYaB0We1vRtsg7wpmmR64crbvwfO0wUcw4mBF14e+D of8SuUxx9WBB64FbxYv9NiPK0HwpRDlrWXgAuXrl3aqLg3fM2rKMLm4eujCQ9fOLn9BGg/J+EPNQ LP0xaDYOv1114Wd6+a+fn/u1ICJbNKc6PTizqFvvWub4FVc3ftFLzQnSLTMEO7ZN+D3y3Oxzf4Gz B+ExFx/A/rVLs8IoDad0z0L6/Xvvzz3MOzX6s78aVN+PuH1B8D1W8fx5d0+H+NKr68w9TS19Utvz 8d1BoResa1dHAJbzzRd/z/z8/9nhvm/+IDJ8ljyOWeD+3p+62fO11xovb4m7DT4Ga1ffJloebIr6 SRsTJKnqvubPmUWZnHnvwluM8Zq3+n/wwK1zpwUKP7pwxFezvpaHJiaz15Psnpe9Y94B27/EhDk/ 3hDGftEGO/pf+mX3h7YjEt+pWTN+/dScZbNk5Krl+MdGWWyI6eTFH77NLjkVGma27d/WYHYwkS+b uWIW/aF8Xnf+qQrLz9LnxowM/ajoop4pvuTu2Xu/NeP02+eePyr5/RV3WDFDxH7ZJYdrF1wZ9wOa yJj5YYDLgObS03Fn+O3sgZAeu3OBO+7nLn4F3fqbH8y9IzzwzMn+6mZjL5l274bb8+4MIEGBkYpp 9yZGLVOQwV5z54hksWjFtLsfiFwmMx//zGQNnPbQt+dDbDjXNnRm4KsZc8ILvgcL1OCKwUKIDYMX jPW6IaKYrtr+Tz//Ounrt9DPy4i1e/DqkGiZu2XOboisFL/lrthpa2ODmL/gxMVB9JfmNNnt4dPm hwqvyIn5APavXSp5rS0pS/8s83uf9+KKFU48refOcaefZ83w2ZUFy6TyI0MDoW6/KTjv6dv/mLXw oDr8b2Vt3zs0tGwejLOjfz9Kf33TxKAOjgo5/e6Fg0e6yplYEqYI+vcH6cfAavlKqjzAFPWTMWr/ J1V9lYQbUvnBvcOfX7jcdI752QLmZ8mZG6WF7yNtdKKak3Jjknst5YZU/jjd87I6/+6GrZj9EsQG 43D9h73HOy1hsRH//fxM5qjEdypoxh+z5kFsON926fgFsmJ1xLI5If+8QPq8g/+2BgWyg8kKuGJ8 cmjwKpmrnPuzldaeQPmsmPDti9mPQcB3EsJnTZd1dzH6B7yXgg1Jh0Xt3+s4CHBl3A1oomPm175y HNAEDk3cpqOvE2eXBCkO5N3B2gsOYoLQyFApXO9WBJOroyffOfNMLQ3F89bPq/jWjLmz5OQcU3Z4 6KU9X74Fv0AVocluig0Mbt1zAX7wPqlepI6Tffyn1h8ehasioe/kRcuCA9Z+cw5cMTj3Yft3tMzP h+iIdzKi7l0bcu/Pz/4i72v/fK73e781rP3m7SJl1oeRz5gPAe+GK5CgW3f+4J+E2ZUF+v9hfjIL Pz7iPoD9j3uFdSd3exSGfpOJiwRM24arsCt/aOOMkj/AZaKAtMdnhkF++LRb+sTz45mzjTtWR95B vho0krlxQTszwh748xfvm6H81X4uOhDjcOswuXta4K29w7+oBYuyX+9ZtIxGFkimA0ett98hXp75 2S7iJ1uT/2+UaO6qj/kBp/q/d6qfNXvX3bNyH5sFv1jPfdzPnIDyrZGJak7KvUnutZQbUvnjdM/L 6jzuGXI6XPSe6t2lhTm+3h8+HRsfHAgybTNFv9frwx5dEgoXMM98eHYbLU82Pnb7C6uncwGftyrY YM4B6LeV+doS46XUvPNwohB2Z1T1kxExcdMK3zM+9G3F3esV5LSBzApdN4uQgUFNB3xixZOUw3px +8HkKL0ka0tsGbcDGpkWKDJmhl19ptg+oHHmPLy6XyPtWtlv4YF81dNrtr1JgWS4e6i0op+eIRoN T/3iq3//RtQbWdPCFfIZQQ6/1IbOGWhs8FTs/OcGiA000QasJ/7BtDPNZmrZAnrFIFI5942v0Wtn VmsAPUuNDL6LBNKpDxktJl2GfmzsbsCOU7pqqqzq6yO2s65p8q8+gjMM2qBDkrZPnIYeh2qTvvPH sguqvNvvWDnv6IqvrIG3sJ8Gc8+V35RdekAsv5/ApXpi7h14voBexIPz7l99J3zFNyJPtrp3/erv q85/PSJ49T1h86eH/PTp28ptnwz3texHn3w6Tv0128QVTB1/YD/isOXyPtiOSuWzh5fcedt/qGYv o9PmXx2v+XLH28N+as69Gw49YXYm2Q0/uefaL/c5Q8NU23LW6tuP3m09f8H86Se9T9Vfhl82aRLf 69hL8MZZ34ML1Ex666+XMlbTT6mX6VzzAHsRafAcPXWecWug/mjfmU2KhYvC1oJi0Fp6jevkhwNw SCpJOcyWd7Xvasf9gCY9ZtoHNFebrjm+xgawwA4IrqbGnTNifL7o/JcuZsIWz3rz+3CNEK4gDbd9 NnQpaMb6r9k/ljJm7IZKbopB3f4ux4uAcB4puArEXBgh5sswbzMqB4PW0eOfXZFZ4c4Ze3JfhnfD XoHfslpfPzrk3C97D2zl3NvnjV0HG8Pfe/H0kxtn3T9PbjVfOXku4NEN4RBR4VYe0fxphAbXzxp6 2SCn/7j302+F3x1I5MGQf4vgfH7abPiGDn+1LGHW44orv9Aaao5eqiGXyFvGN/bcPn/BjKgvxcvD 0ztE07kzl8/MkMOwARK3rZeuTpNoLsDHfGhuo2r+C2vhktfoGV33L+EXDPPrxE/NSbk3yb2WckMq f5zuSVUXfaMhc/BU77q8oR9vnLlyYcj8mBlzY2YkfePyCy+ek/pOmWRUeyDIPgjQn/kwSniToJjB yP9UCeBqmd/UX9m5bPqmO4OJEiY1h39vu81P3KSUwzWMG2L2HexAo+4HNDeDoYMhTzvsY4yhFCvU 6vSf3XWyYfsV7JQ7Ebt0ato1bV4fBjiOln+WVHB2R1l31QX63sDFaKeSHooxp59OVfjdEebt7jh6 7nu/PLet6Oy2X3ZbZgZNH7nyJYk+amIAACAASURBVFOC/QS4L8ObEt2g5yKe0njse7I9kceZ24QW zvnHxWeKv9zx2+4PQoLgq3a23SyV/9EZer0uKpoPBME0DMCd0KdhWL3lXzfa2My79za4IWqw9fLK 9bPWr537XTg9Z1OQLIS5xKQTL29kf8dxpe2vNbUXthWxb+i5nx2/+pFE9Tof8y/cGQWxwTpgfOHF z7aV2WIDtOqn5qTcm+ReS7khlT9O96Sq299dx60nHo+te27m23/o/M6e1nuzPqN3wQWFfD0aJg1p OdfvtZlmB4Zz+mobE+jvfX7Ipwc9JJEx8PfvDILZ9U/cDleYBk/2w0DvJkk5zFURsc8d4l7dDmju B0N2QOMMjf0Vzi1cK3tjnLnfP9CY+mKn4zstlW9rRfRXw7kLFvI12dL7o54glxd/fWYSc8dRVNyt 8/7h4JuXxRzqMEMWfCx+/1bfU8vmrPh23C9Cu+v6ZKqkqDsU5HP+rtMFM/MTrHsky9gukDtY9n3H vQ++23NfQ/hGSG2LW/h7q4Wsnr5xW8wF7QBZHPH02unk6lDJ2yMdK8TzPyH95zbMmL96/sGQnjf1 XyUmzYYwcO7jgSMfBjy14db5y+b9WnX+A3PI9sTbYGHEn44YTq4Kuzdx+pb02Fvf6Tsrm/6v6yMg Upz/dPDIh0SsvOCarLi/ttxPPgQ3XJsb+sQ46lN+0sYosCiTBT7x5Px/Y78KcLo5OPST3/Z/KXBg opqTck/QlLtNf7vhJ/fcdUnsmMFyS3C4oiB99v/95RKZO2MlvafIpO8ib0l8Zw/rjOqvhSd9Py7s /b5uRahqpQ9XlsTaZ/K6Lv19IGo9c73x3Xc8fCylHCZignCMdeGXVNIFwg1okoMhcyeIjB3Qaun1 tzEnqetOXkS2sbV5VbxaTX3v58MkLDZ85/Z5ScuCPz9phF7NVc75LhP8zXRqkSY3xRx+F9Abb7ig NzJKf0fA4d7BrDf6h4js7sToH32Hxga4X+179C63q+1wshI4bf2GiIWSZWjrvBt0xylJ9EvoFV1a 4da+k8nJ3B2yOHTgy+MXKuHKW7hC/eTt6rUzZFevVGo6YYpIKh9OtNN+eeH8SMBCZdTO78xZpgg4 f/JCGkwJjlxO+1UPvJXL1s5VJ8LPt9H6NzpKesmHb3dWtl2BX39J35qn3hAxfzo5/1n3zkOXpcrz KJz85PNtGxLNSZqVKB/EXsxUTF8Wd+sdscxfzPQ7FoU4nyBKVPe1OcnyXPcmp9eSbkh1c5zueVmd K/bWn89/DHchxoT/6PsLfvStWfODYELo4ltwVOI7NXjq4rN/Ghi8Kr/3gTkQG85foNcHB+BnqHTi v638BlsWdrnv/tU/fEgvWFt7B1/rkjbEHJF02OUkRmDfbtPBB7EBTXIwnCUY0Oz2JnJLRNAbRDXG t2rao38BaxeHhMm/amsz08l9hewuBTnTZXWJfl4Wk2pOlngnXCq52nVuBH4W8YXmzZLNGLmqt+WI l+ELj3vD3/bH7SBjYF508L9Ew/0D1tpTI8J3QSofrl7COwiXlS52mj7stbMlQdMeXQI/wm+51DlU 02v3jdqJhNOyUS/L22u635JoTsoNyXz3rfBHJ6o5KTt8Q+43pKr7O9+9V/xRKTf4ArYN2ca7g4Ms gg8Pmy8PuNRlqum6umTx9Ltuu2XE8tUp/bDtFiCuotP3Oix2xs6vT/uwtr/mSkDYyOjg4qij3484 +ef2Pw3L3Nh3dsfz/pgd9mzaixKSg6HjgCZiCVZNg6iGTCZjRbxhjiEwMNDpPxyCHFdB72sSHkT6 gFlIAAncRAQUt1W/ODdMrMO9ui8fKfNwPUdYb969MRXfVlh7L73yes+ZsNDcx6NmBY5WH7h4b8qc CbFva2viHBY6Pwnb4wkP3sw9TEIXsAkkgARuJgLGS0lZE6NL9uXRnqPrbr131m0/+gHMeEEa/fjP Z3JPXCFZwjPhcbOdOIfH7crkGRAPD+NRcZo837ElJIAEkAAZ+XXVZ7925HAnrKPDNH4Cg33t/F/B Gw0whT3OVJOtBK/yG/uFdiztFdTVeI1BmDuebePRBwlZXtjklQ3jUWj8v446uORVRSyEBJCA/wgY GuPpuAApt9/UxG5nV7cLGjRoYDhRFrn76vpU0afCAj9GfaroU2EfWunPZ2m5DLBCG67bHR0dnZ2d 58+fv3jxYnd3d19fX39//6VLlwwGw+XLl4eHh81m88jIiMViuXLlClSHQMAGhYm/c+m+f8uELpT/ 4ZhwRr6lTguZGRkbJO/14rrt7atsGogrLfSytGxWfnLa10G6AxMSQALXDwG5HL7F2Vpdd/dzcOti DOPYnqQXmgWrXIPgSz4zSPwqB9cRnyr6VJhrgb76VNGnwl63Ev7/9ffrtNlQPnhSBrOJDw/BSx7O VRJdYXkLvc+UTT3VvysjRPV4fCyXQ6xmo9FoL8Hn+7RhkCgNwZAT9GBKBC/KOlC8aZHz/YoStUWy nQ2KFMEsJIAEfCYAX+E5CxdERkJ04FNZ6h4q1OV78qmiT4WFvvhU0afCnltRhIcvXniHsJxftyc+ PBASvfnZZELKqj+yyVhYO47uqoNzh6dWsecORv0+dYI8JDQ0NCQgQV11YgDuNq7KU6ekZFa1sQHD eKQgE3aPdLC7PVUFarjvClJCSl5tG5SXTF3HDm4OCAiBJA9IKaiyeWBuLVCrq1qHzG1V6hQ1mzJp UqvzqrhfKuKtiBuUbB8PIAEk4CMBqrFEE3wTVbma7Hii25O4/wT3vWSPMf+tPc0Favh+MylBffBY B3vQY0WBjSnfisUmJSjsk2/bcPnIywr+CA9k+SNb4YLhrj+CjCpNLXV04iH/8XX0DNHalhm6NL2k LrtUW6MtVdWVqJQR+/WW+M33lpUVqhblwhveVbU7aVehbv43H4oFiYeB4s1Rql0lydmaivIiUrY7 cVHEQb3IR4exXRmzZrs2ObeiulqTm1y2SxWVw/wMsRqqS0r0BguUgTR9+vSZM2ceL4RUUlJ5nskT b8XaJmGQqYP/kAASmGAC8x/IfrUUbKYmFzovR7O2/Shq1a6SM7ml2uoKTRop2b5mQRU8wIpNbiq6 uuim8JRrxbV3E5jDz0vDxoRMTTMTI/0aFfiYrDPBXnd+PGxntFrokdaKNNjJr+9misHBGlpQVQpT 1i3l9JBSpYLQQpS5nUyJ7vpc2MuuZvdGR/vrafnsapgp2kTIg45T06YW+sFK09qmtho12Rn5WjoZ bmp6kJDCJsH8lqGRNkbiq9sZFyVakTRo8x5fkAASGAcBZgq3iP1imprgqx1fRG82qYEzCBg+Slvg q0tHEvaWFpMuQ6ks4u55MTUVQRla12NFoYMeC1/3rRh0GlvHhf1yu+00Nd3b23vNpqbBdXhiwIan Muj1peNG0kOvLMXnPxrHzC4N912EaPDQPZxeX+R9m+HtN1C5rSXb/rsiTanTanUkvubdF6MZQ+c+ aYLXxvK9OczFoJy9ZXSO+63PB5ijTv9kIXMgtJSoFgSs2JyZt2/gnqf3Zm0SmQw3NqtD15QQUqrT bqAnKESqFaOXBp38wF0kgATGQSBhdxH8eitLffbYgEnBf4GDl+997/UFLQdyMtWbE1aErEp3bUG8 oms5Jke88NRsRaKL7rIhoLg7zBzzy8UlsBwb/zgM++V//bD5/TrYfeaxe2yuBPHvNpthu+DD7vA6 3Ve4y5FypnzM/IiwuXMjIiJCwuI0mqKizLvos0Ndkix2w3FTZ02FJnsNKdydnrRqkTyhoMupmLUj 7/5VEBvyazqfXG5zRqoVhTcGnezjLhJAAuMkELz8Z41wclCXlvurNs6UtevIqoilqtT0c2Re0jN5 9PKSaxKr6FrKliNWeKq2ItnJcR1wf8PYOEwrVj2VplTtTloFNpT534ijP9JpGoFpgzNf9pOV7NmB 8dRhOB1IVsBwf2L/s9tLdPHJyX1lZUlJezqP0xMI5lnF5P4nstXLWVd79mc+37324WBie/oHa5X9 r6/Mefad2EPF6oQt6peLrbV5DyfuPtI2kBVtDyY9xVsW7NaR3Or2rATWA1pVqpV2KYPhwmZxGwkg gQkmEL76WW3Ga6rCXTowDL80YQrzTQ1sl7datjEXInpqj0NmEDwAxDG5VnQ87rDnWnjqtuLQMa93 4BwCJvqlintz9mDcDzcLJBS7TAdL5bNtydY9tZ3dSs7cxF1LIks3PEWIThWTUtXc1tXWkKNaA9FB k/UI0R9UppZBIDl04ECZJpnodicXNED1JY89B5+NHcot+xtOtOkbClKiUgvLgmaHivYnZJqprmRH al5lW1dXW/O779bBicua2+1DubU256Ed0B58qvo/LGbSvn2VMLkl1Yong6JeYCYSQALjJyDblFua zJphbmCfufhO2Ks+dFjfpj+yPycqcTfs6k+cdrk73rmiW1ecC0/lVtx2dGwHvZiaNpTCCC2y4Fkq n5slsbTA/APUrOHmodkDnfV0ISSf8rUwAdUJSyWgpJaZKIYJ6yLm90JpC51X7m/RMnu2GhnlzEpp Zmo6uVTHNca8WjpL0+ikFpfiNfXMnDZTuLDpLPdx447TV1UjbUSiFSmDDq3iDhJAAmMi4DA1rYNg oNI46CB01+cz31F668qoqTXfPhAo80vhVyQkZX23p4pC10yeCl/3rUzU1PTg4CCsmh4aGnKzalpE sZUy93cyD3R09sN0dGhUXKTTZIR408aujm4LCQmdHRUe7Hw66VQDinYaTHJ5SFRsrMJDWdeqIq2M w6CTfdxFAkhAQMDcnBCy6rs6k3o5d/FZcFBs09rT1TlskUfERNOvttk4YCKKcB+/5WJ2HfOu61as +v3ypalwy9bOlfYLI47+O++dPXtWKOjNKns7/YdLTK6C3r4Nn87Njnk/ODw2ztu+MY0oomO9CiNQ GIousU8r+OSieCvjMOhT61gYCdx0BOAycfXvSxRn7n9000ovQoQsMjrWzihYEe5FHXt5b7eu21as J44crnv7d972Y9zlvJl7GHcjaAAJIAEkIEJAPpsQ7Z707YUfiT57WKTGTZ1latJsTS+EKVVlqMuc vD/AXKOzB390BW0iASQwtQgELy8eHS2eWj5fS28VTx4efXISHfDD2QNcT2RFUYT/E3KOdbncYjCB /TTSRlcUNJOhRmg2t3FgPLaNzfvASEHzuIyMxwGsiwSQgH8I9FTmpbAj04qElIMNHR5aMZ4oSGHH sxWb1QUNHS73bzrV5wcip3xPu5JjzlBjfECAsqjZkwG/HPfL2QNcT1RmaP730TtBQJyQoX+8+Zv0 wj1rkmMNtWpvJxB87aycQKOhcCGS0e5eMk7tbmYdhF+uavraLyyPBJDAhBGg0mr01nZVdtGqvvTd JdvrynrdzPGa9ZmhykK4pTItdw2p21OyS1vyt8b+w6vdTJvyA5GvPkuNOcyTC2BwuybJL+EBerJw yX3r1i1nu5SQcK+5VrurrrndTPg7FEDQ22SRKxQTNAjLqPw5vT2a0e5m2x3n/yA5XU0HUt6yYOeb pSbY+XE6itWRABLwgoC1o4HGBmV2++GXYYJ7y9q4mMRd6a9Upx3YJjoMGU+/D7FBmaH96yubZOTF pGWb16drj30+sNpNfOAHIs4fn8YKqTGHWfjBWZzEVz9cXLJ5L1TLUEQthFxunbOIoDcMw/q8lJRK XoqV3zXrC1LU+2trizNtl6wyDzbDHbE0DZwoULOZKfvKqs/A2QNkctrdYFCyIjiQycgCr0gprqra l5Ozv8FZegMsfVBdAm0y0uAJ+2q5tf0izrtXI6eeYkICSOCaEzD1tYMPuf+byd78FJ3wGF2Y1dn/ J4mnCfR/1gzH834EsYGmezdupi+uz+ERHYigpMhYIT3QUdMSYw5ziPkn/tAB+3FPW7ACxFMRx+Ne LIsTrirxYpvVRMyt7uyGB9h1dra31pfnQpvKjAoqjmpppW8JiK6ygt7MNl3+ZqCPEsznRVX5XQPV caRJlaEpLUpW0k2NzsDbSc7VaPJt6isqkHs0Nj3I2pGsyC7WIxlFpSD6Tc3BySOjE8n3jV14AvnK tPzycnb1DaMyK+G8QVfKmMkGqdhOLe2fMruaEajlTeIGEkAC15iAqb0+Pzu/3rb2dnTU0EjHlvii sxLfX4uhvbERxhqaLKZubS4tTgcfYeLGBOeBiMv3cqCTHHOYJb2MNDUrg03YRxuwq3/LmYXDQndE t1nF1q6urgsXLsDDRFnFVm+WxRF/hAfboMsMmfy/DG0ruC4p6C1cPwnlmBjDq/WCtq/tTWqHk0OS Vt7SWkFHYZBOYnF01tAIRMMDr93NWBCpyMiG8xXbtVxFAVf2rVLl19vy+htpj+I1n/qoRi4wiZtI AAlcRwT6W6rTmN+a7KOtRZ8mwLlrKGJKwhgAP2sdJSBgQBMfiHwd6KTGHAMXHiQfbcB56ebVSdDb 9VnT8KBpeMr0ZDxrGhDC7H58bnV3P3v60NnSVJ2tUsKzfvY1D7gR9Gboc/9MFuHltuzUh9k5bdns hTBSn+4d7vriOISDjQ+yp4kkmtUF52rzr64Vh40XhRVjH95oOzvh63AbSUlft22G35nIFBryUY2c s4SvSAAJXC8EjB3N8LTKiKVJoP+pqW9/eQMdQ0SfJsB5HLKlrKm+pgIGMUL2fL+4mcunr1ID0dgG Otcxx8o1JvXQgQGuwDhfIbq4WvDX3ENoVFRkeGQ0k5as3LB799PQ9uF3PyduBb0Jt9bD/OWndZyz ECfmhPF3PFmYO8tGzrUxxwUTHHwJrh6dqXapaDtI76iyJW5Om9t39+rW+StcTV6NnMvAVySABK4L Ah1HCkIXrEov6cstr++21KrX0djAJtfvb8exI5VHTpiJLHr5ynUJW14+XJWtJNrXPxKMyGbJgcjt WCE60HGOiL9KPXTArkYtXs+rXNHYADX9FR6cZnA6v7wAja1YMocX9LZ5zQp6x1BB71BCTn7WzeZ3 fuZ0PzIfRNnjQctXwu957RdUt4km8+mmMnZqmt23/3eqyB44M8DdvtzxHhUUh6ZdU/OJTlumtfM4 FIpRBHFq5LZ8gfO8GrmS1IEauchMt2sDmIMEkMAkErB2VC1I2qVM03Rajr+4bV2k4LZNse+v+cM9 SVuTkj/ixgpiNfTpqLuCesGSA5H0WOFmoHMdc/jRn3/oQFZW1ouQslKD9B9dmhEpes/VhEH1Yu5B SplVIp+96K9Ky+dSdjI7j5IMzwG0MJMH8IQHbVNrZ2s989BAZraHmXuAyz7l9brGiny2exrmSYFQ 2fa4QYhx7Lx3URNvp6KxpaWxgrn24zz3IFGRPvgaHCivaaypoBMPkOikhSDx00S5FY2tLfXMjBT1 gW/UyXlTSzm1osyHp5XqmJnseH7eQmAWN5EAEriGBFpKmXtYknOLirixKTc3v7R+QOL7y17uJ/HZ 1U0tukZtNjPKsHOofC/4McFpIOLzncYKGMGY0dB5oJMac6D8JvapyexEOjx4uV4Hg1J+Mh1y8llF at4biQ2Pcw9w+z5MP7jOPXgzNS0RBkYl8hnJXOq7IMFtR/wcu5igN+1WCzNLzFbKyKcK3/zUtDA8 ABZWx7u9pohvQamizGk+Q5M+Vpqf3GaRmZr4iv26CvYOKKiSkU1nltjpKZ4t+4hpogQXbCm7Qsce FXPenRo5bxM3kAASuLYE2J9u3Heae43P/wn9oos+TcBUT59qak8Zmnp6+6VjEh+I4CZGkScX0Jqi A53kmMMMaOyIJ/5oA0dnRPfGHB6uL0Fv80BPv8kSoojyVqPX2NPRbSChEbGR4fb30O2WtUf/blOP 8sF10TKrFVRuu6oCYuDkwbBzpevkBQF/LhqGp0fERAo1g31WI3frEB5EAkjgOiZgHujq7AfBQFlo lOM4IPRZaiCSGCvcDHTiY469LR8ebcBXchL0DgwMBFkR4X+Q8oac613QOxhms70d55m+KyJjFfyT 6Hga7jasF95PStqhzCj9Y85jEYZPf/wonDEq75jDX+JzqAv+xLr647MauYNN3EECSGAKEQgOj/b8 8AGpgUhirHAz0ImPOXZe4g8dsB+f0C3BLMuE2r1ujQUv/055xuvbC1MXFaYyTipLm97bEH3Tcbhu 3yB0DAkggeuEwE04LIZve6X20d1dnXDZaNgyc/HyaJGrStfJu4NuIAEkgASuGQHpG1sHmjNXsMK3 3P8V6oYu0ftEr5n3Y26YnjAuWbJ8JcaGMSPEikgACbgnYNU3VNWe6HFfSOKoeF2zvhKG4+Jm/mZb idoTlC0VHqxVP15VqCMZmupWuvpZp8mIJ7qS9cmvjq2vE+QtmkECSAAJTBUCpr+uVyVWfzEmd0Xr tuUu3QrWRsi4fqbD3U1euiQRHswtFSVUZWiPekMcXf28XP3K60Vw72jd4S8mKW45+w935prHxcTZ IO4jASSABPxJICQ0nsTfNt2bJlzGN5G6tXnf3+ONrYkrIxEe2AZWzBUsyYt8+BlYIrCC7au1p7lA zWhiw6lOgvrgsQ5i7SjOTFHnVfLhw6ivVG9OOXhigDHmWYpWxCZTs+vYQWgJhLVD5AEpBVV4+jJx 7z5aQgJIwD8ErG371FteqSN1r6an5FUxo6L4GCgyvonUJQPH9iXurotPhuVbk5gkVk23ZzM+xGfk a+ub2rv7HeSpbVq1ytxSbXWFJo1ZBahtN2qZFSTaTraspZouOFPWdMOuF1K04jYtllZmkXNybkV1 tU1/O7tGdOkHZiIBJIAErhcCls6KImadNehHlNaYJMZA8fHNuS5oj9NVvaDL0G7QwYZ9mbB3vXVa FicU9L58+fLw8LDPq6ZNrTUw3SBM0M36VkZX26TLUCqLQCKDSaYmunoZPGbXhSdrmAXG7OMW0iog OHglRSthk11MmKa1CXc3arIhYLHi3t6RwVJIAAkggWtCwFIKF5eY8VBqDJQe3+x1QUqIeZKAko6C Fh2MypMWHiRvbA2OS3ildjQXVgyeafuH7pN3Dr9WQh+1Wl7d2bQhevne917/i/ZAzh9OnjreqK1j dKoIUSx/JFe5Y/erh3+uXk4+rNRCN9SJ0IBdivZtAqsPQ0KG4RB56/OBlzfY15wFi9uUhcyBFe8l qgUlSlXGloRvbnx678o4SaeF0Qy3kQASQALXkoBpBFofoerQUmOgUX2HxPhmr9tRlbu1hGRrqzaB vKzZEjqhPYKwCfMDUibF5x7MbUcyUzKPdJgV4dFLVq7b8uTO4sPHWyvggpPu8wtGa9eRVRFLVanp 58i8pGfy4PISZz16a2Ya0e1+v6Pr/XKYRMndspKO/95I0UrZlMVuOG7qrKnQZK8hhbvTk1YtkicU dHHt4SsSQAJI4PonIDUGKjyPbwPaF+mE9B7VArrCIGQV/LZOXxUREJDSbPZ7v8V/iFsufV5YVnjm nsc37FzNuxAxfw7dtpCWNzVwvlDeatnG/I7vqT0O2UHMoxqWqECdtWRrajKpA4G8rdFMZV6KVr2c ba5nf+bz3WsfFsx7S9rUV+Y8+07soWJ1whb1y8XW2ryHE3cfaRvI8k17g3ED/yEBJIAEJplAH9Oe 1BjY7nZ8Y+oqNv6vNnqATCMEnkgxbUj/4vZdyvzSrUviFgoHUP/0SvzsQaFU5cLjFNLXJGQWN5zQ n2g+VlmcE7EmHe51vWO+YubiO8GZ6kOH9W36I/tzohJ3w67+xGkazMLveRbOJergWT3xaaolrM9L HnsOhI12KLfsbzjRpm8oSIlKLSwLmu1wkiRl85ZpprqSHal5lW1dXW3N775LLa+53X5Nim0B/yMB JIAErkcCuteKK4+1LZIYA0Pcjm9M3Y7YdZu2bKKJ/v9O0kJC7kl6fNOmdZMxCkrcuQR3G+nyedlr Fnt8RnULMx1tas2H8d6WIJIxjzggynpmyri/SQNHlBla4c1OnqVopWz2d5ayt0bZmoPn/3Vek2km bBQJIAEk4AsBS41toKRPghEfAy1S45tDXXujTs8psB9wt+XmzqWhoSE3dy55EPQGndrOPphOlofO jIqOFIoTWXu6Ooct8oiYaKp1bTYOmIjCgwy3RylaSZtQs9NgkstDomJjhdLaXIjCVySABJDA9UjA bDRa5CGKYPa6uvgYKDW+OdYde++cBL1BuNs1wbwGZEKQkcvleys+SEucD+2Jzz3wjoBO7RJxwWxZ ZDTMo3MpWBHu+UKYRylaSZtQcwk7j8E1iK9IAAkggeufQLBCIRgaxcdAqfHNse416Kv43MM1cASb RAJIAAkggeuJAIaH6+ndQF+QABJAAtcNAU/hYaCBVfUuPsFrKfnu+1BjfECAsqjZu5riSrbe1cVS SAAJIIEJJ9CRt2LFfscxUH+keDM7OK7YXFzb5tSkG+XtY/tsanUN5xvhin9u4wDxbYR0asqPux7C Q1vNH9kl0a/+/qOxeyGbBjexwv1Y3iVRJVvvqmIpJIAEkMAEEzA37y/crdMZLHbJ6K7avKVJO7S6 5CJNbrxOuyNxkeMPaDfK2wOfHoaVbRk1rd33hs/KT077+iw58W2EHHv3YObZp8ruw0PPW68Wwk2q Slgtvaf0hNgiPauEyrZrvsFbv0SUbK0whW8Ua95bm1gOCSABJOAzgbaqPLpSORWGQWHqqXwJFnvF V3ce2Kl+8VBjERx7vfoUX8Kt8rYiCH4sq1asj4uUBS/KOlC8adEMtqI3I6SL7jffpucNX2MDWHQX Hqxt76fTxc+lr1NFqLLDf7NpaVs7anPUmfurKnMSAuSgsh2weX9DB9iSyuccN1TmqFNyDrIC35Bp PFGZsjmlUnjK5qpka9TvUyfIQ0JDQ0NAObzKJg/OmcRXJIAEkIDfCFw63+Rq26x/CwbGtPJfbWBu pwxf/URpRvKGJbexJd0qb5uPFPzgFTh50P4uNXNf22BrgVpd1Trk2ITXut+O1TzuwYUsSFBM9D+b 6WTEXXhoOvQqlE6KX7nkQ7MxIQAAIABJREFUvo2wsbv0ffYHvKnv9J6SwlTV1sY1murq0mSiTV2/ YN+xAal8rsnQCNJYtmd7TRtrxlr3m61lWl3EHOFyipDoZXfSy1ALFy+fP0NubcsMXZpeUpddqq3R lqrqSlTKiP36ccyCcK7gKxJAAkjAI4GV6kr6QALm/IAvbLpEf+jHzeioKs7LzMzM2/fOmt0HsjYx IhHG5p2gLgFrhV/NEn0yw61RM2dSQ32z5s6SWQ3VJSV6A9Xs49JA8eYo1a6S5GxNRXkRKduduCji oN5obauMWbNdyz3XoGyXKiqnlqsyMa/i5xaSq6YtLXDKAHK0zFJog4Yuk46vZxZNs8LdsC7aBCYh ddfTgypNl04Dr675BlPTJkIeLGwytZbTgkVNtJZA8Zvu2pNdybaV0bHNr++2HeyuYRoqRUFvOy3c QgJIwM8E2BGPl9FuYmUiYCwj8HieePpKiKYJBkdvlLctpTCKxZdSUQlT04OEFEJFboT0Xffbq547 rZru6+vr7++/dOmSwWBw/7wHyWVxA0erSqDTdR8cPBgUREaq4YSI1JXVtK3bEgdbkJ59Kt623CPy ns0qojXYnn8qlQ9VguO+kR9PdqVXdu1cKf/oLTBZ9BRV/HZMdiXb4b6LEE0euodbmBd5H9PQiH2G yLEm7iEBJIAE/E1AzjQQn1Fx6JUt4YS8+pPK+5du3fEfb6zL6PBCeRtEKCCNwDUQqOuUfNf9djIw wbtSF5fMNWW7oCmlUvfq9tTU7a+eofPTpOTVtwZEHLCdHLkM9K75kY9Rxb497+m7jr4OczsZqtWu iAQNBAmvO0G+8CxMUAw3kQASQAKTRcBC6PXt7z71LXbwUiz5Via9rDH8Z1+Ut11GS+r9OHS/afUJ T6JOEjLw0atw7qAq/eDwk9wIbT6YErK9LL2h41n2bKr5RCdZzlxuM7cf1cLpks03qXz2cFwiVfze /t0kooPLUSkCXQ6HrvWxezTEnvmyn6xkFTWMp+gtYcmKEIeyuIMEkAASmDwCC7+eAJPL+jMXyXJ2 AOv8nA6Awd/8X+0/jU95e2y63/7rufjZg766rI6Q7J0Pc7EBHAh+cHsuvPxGyy6EgDF+aV7lsQ59 Q97jSggluS88wt6f5ZovMAInVPdmZCiJDowodzxCz0hEE6uCu3jDU3BLrSompaq5rautIUe1Bt4F TdYjAg0T0dqYiQSQABLwF4Hw1Sp4MlqhalPewQa9/ti+lEfheT3ZWf+6wjvlbfoIObjPU8w7qWcf uNf9FrM0QXliU9MGOnlC0nS2qWdu9oN9HHa85iwzBQ3jO+9CWlE9zLSwEziu+ezES3KpjjVkYBS/ 4dREYobZQcm2s15jb4aQfG0L5w2+IgEkgAQmg4Cphd5Tw09NQ5OW7sYM7noJHMoobRQ+v4D6JKm8 zYyuyczox8xI81PT7Ajpo+63V90f89S0B0FvPgAIN4wnikOVO0pbDE/GmDv6h6eHxkSG04tUUvnC urRY877QVem5NZ0vJrDXjJyO010HJVvzQEdnPwTb0Kg4B01xkXqYhQSQABKYHAL0AQQGE4xLMZET /JgB33S/PfbWSdA7MDAQVjkI/4OUN+T4LOjtpmHDsJWA3rfDlSNaXCofDlkH2o5+crL6pXQ4Ffs3 6dgAJR2UbIPDY+PCqWlMSAAJIIHrhQB9AAF3V+XE+uSb7vfEti20JjE1LSwycdumM4fWJ9IbovJr npOalJ641tASEkACSAAJjJ3AWMKDYvnTBsO2EIXziYNUPu+dQrmzuzOZhEREevHwIL4WbiABJIAE kMBEEYD5ClEJDVf74ncuseU6aveBFceUUkU1LWQKhejFNjZfWpFbFhwZHe02NkjXFfpubE4ICFhR 0CzM825bYH+IU9P1rqag1IQYEdjDTSSABJDA9UdAMjyAYu2CRJgkIGlF1e3d3Z3tOg2dqi9TLb2/ qsO9eOp4FLm9qysnoSDLNJb7WwX2ZZyars/vyoQY8blVrIAEkAASmEwCUheXul7LhFXNJKOi5ZUt zNo3Eql+5S8zyeqthboXC9/b9MoGaS9FFLmhMEh8y2zP45auSsTrOleQ0WXtXurfElmwoFmBfUZN 19myV/vujID2uMkid3i+rFc2sRASQAJIYPIIeHOJSfzswdr2t92wcE1VmmuLDazTwVtyS2BFhK7w T/TZD2Z9XkpKJa+fyu6ePL5PveWVOlL3anpKXtWAhPS3N3XpunVhGjhRoIZLSpBS9pVVn4H7XG1H vda/dVILN3NqumZ9QYp6f21tcSZrPyDzYDO7aMXa01ygtj3aCeTEDx7rIFJGwBlR7XFp48LO4TYS QAJI4HojIB4eOj75EBzNz1a5zD6v/vdcuMR00QLqR5bhurKyL+D2VjbZdqcJFbmvSkh/e1OX1b2y GQdl7wjlrpK65FyNJn96euou29Jt4pP+rZNaOKemaxn+W1lJamLijjMrNKVFyUpSuH3Va/AUCmvb j6JW7So5k1uqra7QpME68TULqjrkwg7KeUleKe1xKeO2juELEkACSOCaEYBzCHdti62aHm0ppWLe RU0i65p1jJhteasJlgVCoLCvJLSvErQrcktJf4PEt8e6wuWArRUZ4E9udTub2VmTC7sgDO67/q3d N/D/QU5NF06J4LFHbG8t7aDcAc/6aBk16TKUyqJGRsScroIsgnymvyJGJLXHGSwixoXdw20kgASQ gN8IOK2a7u3tBUHvwcFBEPQeGhoaHh6Gh9CNjIxYLJYrV66AFwVvNLBxQXzuwTIyDENhkMMPeMig yUJl8lRLolzmhU0WbjLArsjNVnEj8c0WINJ12QJdXxyHRjc+aFssEU2FvXdDc77r3zr7ZnMA1uml 2gSmZLMXJhNyuneYBK/c+97rf9EeyPnDyVPHG7V13BkLETHiXntcxDjfMG4gASSABK5LAuIXlxbc cw94W1132sXntgPp8ONaIecjh9wWYMxfflrnUtolw1Hi29u65nNtjG2Bnjd71Wui9G8h0swJ4y+k WdhpD2vXkVURS1Wp6efIvKRn8ujlJTdJWntc1LgbS3gICSABJHA9EBAPD4o7H4axUJueetDh2c7W hn0/hWdyxxc9t5w5eYDJ4ZOfdbPd6PysQ9ifPsEOlfhmEyv9zWx7WZcpG7x8JVz+0X5BhZdoMp9u KmOmpnn926ysrBchZaUG6T+6NCOyHR6Dnf7mXVvULxcfBvmsGpgvqTvSNsDWJkLfbFn0hZtE4bJa 3tTA+UJ5q+XAKy+qt216YPE8OBLEhTRnI5z2uK02qz0ew2uPOxvnGsFXJIAEkMB1SkA8PJDgJf9Z kw/3KG1XRqgL9jcca25uOFKQsmp9ehmJzy99djXbG/hdXLL13w82nDhWWbBoK4jakiCum6wi91Vm V1Ti22Nd4YC6lCp7k60LUiuPgYBu5ePKVLadsenfsr4J7XNeO7zOXHwn7FcfOqxv0x/ZnxOVSO/0 1Z84zS76cDLCeIja4w4AcQcJIIGpTUB0apqdI+nWVcBtPMKkyq3gnvtMi7Roc/mjGfm5UJadua3J p3O9cOuTlPS3N3VtM8KsK6Oj7TV0ZphNShVMbMN0L1UI91H/VqAWLlDTBXeFc+zJrHFTq60ftFV4 uDj7hFllvUHMyOiouPa4fcae6YmpyWac6xe+IgEkgAT8SmDMU9MeBb2tPR0dfSaY0yYz5y2IdtFK Mg/09JssIYqocEeVDVaRm5x+TVT6mx3l3ddVCBazseWJsaej20BCI2Ijw2053IGujm4LLKmbHRUu qAWquKC3K5eHRMXGCr1zUAt3NOSyRzV7hy3yiJhoasFsHDARBdNVcSOoPe5CEDOQABK4tgScBL1B uNs1wYIyyIQoJZfL91Z8kJY4H3wWv3NJ0BlZZGycG9Ha4PDIaKexmqnMKnKzc7xSEt/u6wp84Dap frioL77p3zqohXO2JV6pZq/9ULCCj4/iRlB73A4Lt5AAEpjaBCTmHqZ2p9B7JIAEkAASGC8Bj2cP 42rAo8T3uKxjZSSABJAAEvCaAFw7gotIXhcn4z170FcVUBkkQVqh3t9lvytISvrbew89lhTIa7sp y2uAj13H28m6d+06VcJdJIAEkMC1IACxwddmxxUeQPR7qYo+/S23orG7v7+zpTE/WakrSU3afcQe IHz1yOfyAnltN3V5DfCx63g7WfeuXadKuIsEkAASuBYE2N/w0DJsuP5nM538Gk94sIl+59d0vrhl dWR4ePSS1VkH/gjqSLo9Gp2z4KpTuxO4K5DXdmOV1wBndLw3LZrhpqzoIVDqNpqFD7oQaZeWMQrL iFrCTCSABJDA9UVA9Nxi7OHBrP8rFf1OLt+ZEC3oaFwKszxg0GgVV+0GAXAq/a0+2NCwL2VFQMDm Yz2OuzSuiGl0iypjO8lrC/ygm6Ia4AIdbwc3pNpl7aSskIeEhoaErNicU9tGxVyFuuW0qoSat6CJ jXv+Q52Sc5Bbu02MJypTNqdUgjQsJiSABJDAtSMgGhuoO26WxUEdN4lVY82vF66TcyxuoLKs+U3c +jZ+19DErpqDtWbJGUWtA467ln4Nczg5W1NRXkTXv4GyRYthlK+lymBltyFf88lnFUXZtLgqLb+0 xiRs39JKVV4hflENcJtcEoi8jhqbHmS94g2ybki1a2ph7YA7FZpsxmR2u6XToV2urexSbY22lO1d qdBnpokDu+giwwoQu6XJoqV2lTXS/IS9wW0kgASQwNgIOC2L6+vrA8XWS5cugWLr5cuX3Si2jj08 tJTT4S2f07sW8VtKtZvVuFYVtVuYSo67UhrdIKhNh11X2e1Rgby2wAkpDXBnHW/ODal2WaVuXku8 nRnUqZ65oF0Pat5cE6bWcugBDVGQ2OCUVsEyEDiOm0gACSCBiSQw5vAw9htbZy9ZCIPdbdNdLFjN AwOmkHB+ARmUYpJdtZvuqpLiYwVV+V0pje6B3VFQS0wZW0ReG0pKaYDTtgXJY7sXYi8KtcRjN71i Mu1lHk9qHAE7I1RF1r2aN99EcNw38uPJrvTKrp0r5R+9Bcq3RU8lChgI3MJNJIAEkMC1JjD2uQd2 XKuuO+XUBf1rj0dERfy2hbukzkmcOil+G5iBla/L70ppdIcwD5d2ld3mLThuSGqAOxYjHtsNYpW6 BVriThborrSaNxzkmyAk8rFn4TLXnvf0XUdfB4G/DNVqsRXnIg1gFhJAAkhgsgmM/cerQvkwXF0q TE+rfPiDLUu4hyV01f54B/wsznhouYKY6eOgqeI3bBPipPgt1VFeo1u9nPWtZ3/m891rHw4m9AlF rrLbrB1neW1i1wBfycgtsRrg7KwAW8Xpv1S7QX0Q57RfdFtZOx1VmQtUhTB/sCWOGrC1y6l5r2Qn 6Vk172RezdveVFxicjw8lPS7SURHVJoUgV6HvQxuIQEkgASuBwLenD0Y928OCEgo5k4HOLdlS3Lq qej31qWhmfsONhw7duRgwYqYRBoctM8xgydxo9rNWXF+ldLodi7nuO8krw0HpTTAHevZ96TaXfrI DphQ3rooteqYvvnIvk2qQljmcV+c7WF5bLuLqd64d2re4fdmZCiJDm75Uu54hM5UY0ICSAAJXKcE vLhzyUDvxYnXiDx4GlSsm8odRb9V5Y2d/KyKuOI3M8nMynHTkk67UhrdTDER2W142g+nH87dI2Vr X1wD3FHH2+6GVLujo92NpfaBXJlWDbck0eTQrhs1b2ETUM3QpKEfBZXt0daMKfyHBJAAEvAXgTFP TXsU9PYmqlHVa4PJSmShMbGRTg+hllLt9mQX1LhFNLqlaonLa0NpSQ1wKUsS7VqNPQOw3k0WHhku vB7n0K53at7G5n2hq9JzYS2hw3oRKX8wHwkgASQwLgJOgt6BgYGwRlr4H6S8IWcMgt7euEVVr0WF tqGylGq3J7viGt1StcTltaG0pAa4lCWJdmWKyEhufkVQ1aFdT2re1oG2o5+crH4pHe7A+jeMDQKM uIkEkMCkEYCTFFEJDVcHhD+FXY9izkQSMJ05tD6RSlTl1zyHk9ITSRZtIQEk4AcCGB78AFXCpEK5 s7szmYRERLqsCZGogdlIAAkggWtGQPLOpeai+IAAZeOQG8/8r2g91EidKGqWdkLgw4QpdUu3Ns4j suDI6GiMDeOkiNWRwCQSGHB+YgFcl+FSg9PdnPwQxA9cfM4keuxlU3CJyWNJybMHeVAoIaHT3Bmg itbp+Y2jy6XmHdxV9uqYbBp1wl1RgQ+MUveSWXJ3xfEYEkACSMAHAoqkau0/DZFp06b1Hy9P3V1G 4nO1WSvIFUgRcbBYV5j4IYgfuPgcYbGpsy0ZHrzogoiitRe1bEWsZqss2Ll1EMQ2WeQKhcPdT7B4 QjoJfGCUuoUlRa0JC+A2EkACSMAtAdnyDZuWMyWsd1yA8KD67uZNG9gMl3r8EMSI+tOBi89xKXud ZLifppa8uGT33lslbXEVboGi9ea/narNUWfur6rMSQiQh8hBzXt/Q4etIVFBbLsTxNrTXKCG5XlM SlAfPNbhrKrNK3VDLVFroh0RNIGbSAAJIAEpAiYLVVljZdZsZZyfTdBaoFZXtQquyPODkrvBR2zk BH0I1xEPWnVq0enqls2tiXuRWhan+79NhGxqMnLaotCiOyVtTyrcjKL18b8zK8JgjV22prq6NJnp RRFovkoJYjNL2B4sbOIKKHNLtdUVmjRG5lvb2uGgqs0rdUtZ4xW8hR3Ria7289f6FLSLBJDAFCXA PsLAprjM9oEfUrhnEzzIPiyAH7j4QYkv6Tz4SIyctkHMccQDjWveju0xBF6xdFoW19vbC4Leg4OD IOg9NDQ0FkFve3jwQklbSg3bpsLNKVqzfJUZWtuDGbrrqQiSSvNpBX0eg/3REd01TH6pgads0mUo lTSQMMnUVATlmRXUAjVvUxO8N4VN/R7ktUUkwb1CjIWQABK4mQmIhAd2bOTGN+HDAuDHNf1dyw1K tpHQZfCRHjklRjynFr17P8YcHpyv/sOwK5rcK2m7V+HmFa1Zy88+FW+bW4i8Z7OKaA1kqI+KZj90 DzfFHXkfkz9if2B18PK9773+F+2BnD+cPHW8UVsHmkVsElHzdi+vLdYRzhi+IgEkgAR8JOA0vrmp 7Tr4nOttgvKN5Xtz3iYmQkJChkGzjrz1+cDLGyRGPGre+xZp6XEkL+YevFDSdqPCDb4JFK2dPLXJ ZAe6FcSGOtauI6silqpS08+ReUnP5MHlJSdDDrvS1mCyyGtJcAeTuIMEkAASECUgPb45FBcdfKRG TrnbEc/LFh2aH9OOt2cP7pW0pdSwORVuB9eaT3SS5Utolrn9KMRKmEjwJIjd8qYGzhfKWy3b4qjD PbXH4X8Q9ySJPmpLkDxYs5+TCOrgJhJAAkjA3wScBx+pkfPMm//jZsTzt5e8fa/OHvjSrhusovWi x56D2YIdyi37G0606RsKUqJSC8uCZouvWCjZvjSv8liHviHvcWUJqGO/8MjdngSxZy6+E5quPnRY 36Y/sj8nKhGepUP0J04z948RJzVvRsrbO3lt1/5gDhJAAkhgsghIPUfA/Yg3Wd4RN2cPMLjD+RBN TsO8ApTuQmH1WcjiDSqyq2Trmrj+0azSFm0qXP5ZTy+dQcoob8paF03MF2A7hhYWJuXurWvoAE9I WlF9dkK0jGwCQeyk9TtUq8rYcvnaFjXzQCHYnXebPPq+p/NVe3bt2lpGJYuU+aWaE6k7CrenPvpI k90H00Osn7JYSWsSHWHbxP9IAAkgAUkCcuanPjP0OZQRjm/CEQYGLkIsfA6/wVa2jaKKlRIjp8SI 110K1YUtOrgy0TvjFfR2ULQmEmrYnNPGE8Whyh2lLYYnY8wd/cPTQ2Mc5LE9CGJT2fBhizwiJpo+ /81sHDARRTi7abTIQxROi+w8WON8wlckgASQwDUmIDpySo54vjrrJOgNwt2uCRaUQSbcCSWXy/dW fJCWOB9acXP24JUPDorWREIN29GSYdjK6Gw75sKeB0FsKhturxOs4HXtHH3giniwxhXDVySABJDA NSYgOnJKjnhjdhZGfwgD3lcf79yD9y1hSSSABJAAErhWBCA2+Nr0eM8efGpPsfxpg2FbiAIuu2FC AkgACSCBySPAChJBe+wJhNN/0bOKSQ0PcC1LgbFh8j4P2BISQAJIwCsCoted8OKSV+ywEBJAAkjg RiUgdd0Jw8ON+o5jv5AAEkAC4yKA4WFc+LAyEkACSOBGJYDh4UZ9Z7FfSAAJIIFxEcDwMC58WBkJ IAEkcKMSwPBwo76z2C8kgASQwLgIYHgYFz6sjASQABK4UQlgeLhR31nsFxJAAkhgXAQwPIwLH1ZG AkgACdyoBDA83KjvLPYLCSABJCBCQGoRnGtRDA+uTDAHCSABJIAECIYH/BAgASSABJCACAEMDyJQ MAsJIAEkcGMT8OYSE4aHG/szgL1DAkgACYyRAIaHMYLDakgACSCBqU7A/TkEhoep/v6i/0gACSAB vxDA8OAXrGgUCSABJDDVCWB4mOrvIPqPBJAAEvALAQwPfsGKRpEAEkACU50Ahoep/g6i/0gACSAB vxDA8OAXrGgUCSABJDDVCWB4mOrvIPqPBJAAEvALAQwPfsGKRpEAEkACU50Ahoep/g6i/0gACSAB vxDA8OAXrGgUCSABJHC9EXC/RtrVWwwPrkwwBwkgASRwoxHwNTZA/zE83GgfAuwPEkACSMCVQACT IB9eXf+zmU61MDw4AcFdJIAEkMBNR0D03ALDw033OcAOIwEkgASEBERjAxTA8CCkhNtIAAkgASRg I4DhAT8KSAAJIAEkIEIAw4MIFMxCAkgACSABDA/4GUACSAAJIAERAhgeRKBgFhJAAkgACWB4wM8A EkACSAAJiBDA8CACBbOQABJAAkgAwwN+BpAAEkACSECEAIYHESiYhQSQABK4UQlILYJz7S+GB1cm mIMEkAASQAK4aho/A0gACSABJCBGAM8exKhgHhJAAkjghibgzSUmDA839EcAO4cEkAASGCsBDA9j JYf1kAASQAJTnID7cwgMD1P87UX3kQASQAL+IYDhwT9c0SoSQAJIYIoTwPAwxd9AdB8JIAEk4B8C GB78wxWtIgEkgASmOAEMD1P8DUT3kQASQAL+IYDhwT9c0SoSQAJIYIoTwPAwxd9AdB8JIAEk4B8C GB78wxWtIgEkgASmOAEMD1P8DUT3kQASQAL+IYDhwT9c0SoSQAJI4Doj4H6NtKuzGB5cmWAOEkAC SOBGI+BrbID+Y3i40T4E2B8kgASQgCuBACZBPry6/mcznWpheHACgrtIAAkggZuOgOi5BYaHm+5z gB1GAkgACQgJiMYGKIDhQUgJt5EAEkACSMBGAMMDfhSQABJAAkhAhACGBxEomIUEkAASQAIYHvAz gASQABJAAiIEMDyIQMEsJIAEkAASwPCAnwEkgASQABIQIYDhQQQKZiEBJIAEkACGB/wMIAEkgASQ gAgBDA8iUDALCSABJHCjEpBaBOfaXwwPrkwwBwkgASSABHDVNH4GkAASQAJIQIwAnj2IUcE8JIAE kMANTcCbS0wYHm7ojwB2DgkgASQwVgIYHsZKDushASSABKY4AffnEBgepvjbi+4jASSABPxDAMOD f7iiVSSABJDAFCeA4WGKv4HoPhJAAkjAPwQwPPiHK1pFAkgACUxxAhgepvgbiO4jASSABPxDAMOD f7iiVSSABJDAFCeA4WGKv4HoPhJAAkjAPwQwPPiHK1pFAkgACUxxAhgepvgbiO4jASSABPxDAMOD f7iiVSSABJDAdUbA/RppV2cxPLgywRwkgASQwI1GwNfYAP3H8HCjfQiwP0gACSABVwIBTIJ8eHX9 z2Y61cLw4AQEd5EAEkACNx0B0XMLDA833ecAO4wEkAASEBIQjQ1QAMODkBJuIwEkgASQgI0Ahgf8 KCABJIAEkIAIAQwPIlAwCwkgASSABDA84GcACSABJIAERAhgeBCBgllIAAkgASSA4QE/A0gACSAB JCBCAMODCBTMQgJIAAkgAQwP+BlAAkgACSABEQIYHkSgYBYSQAJI4EYlILUIzrW/GB5cmWAOEkAC SAAJ4Kpp/AwgASSABJCAGAE8exCjgnlIAAkggRuagDeXmDA83NAfAewcEkACSGCsBDA8jJUc1kMC SAAJTHEC7s8hMDxM8bcX3UcCSAAJ+IcAhgf/cEWrSAAJIIEpTgDDwxR/A9F9JIAEkIB/CGB48A9X tIoEkAASmOIEMDxM8TcQ3UcCSAAJ+IcAhgf/cEWrSAAJIIEpTgDDwxR/A9F9JIAEkIB/CGB48A9X tIoEkAASmOIEMDxM8TcQ3UcCSAAJ+IcAhgf/cEWrSAAJIIHrjID7NdKuzjqEh6u3BLmWwBwkgASQ ABKY6gS8jw18IJAJ+2wlgT879FHgVyPCTNxGAkgACSCBm4QAxAYIBGxnHcLDvLlzhkau3iQUsJtI AAkgASTgSmBGEIQHC+QHDPa1ux7GHCSABJAAErjJCTjMPdzkLLD7SAAJIAEkwBPA8MCjwA0kgASQ ABKwE8DwYGeBW0gACSABJMATwPDAo8ANJIAEkAASsBNwuHOJzx48/beS/9uv/fgs5CxY992fZKbc OUu8JF9lojaGhi5ZSXDYDIcVGOYv/1bw8z+bFybk/PChGYSIlpkYB8wdmrzisyFhwdTciNkUdPtd 92/ZfN9k9d65E2PvKe3I77pDhBhHyLzEnQ9ZfppdPqz8zs9/9FDwhb+9XABUv7n7hw8w/XVuHfeR ABK4mQmI3Ll0vrF46cY9jlCW/P5jbdJC4VjjeHyi9sz/eCTmWw3k3nc7fn83xAEuDR0vnZfwX+T+ /9eu3R4mUYYrO77XoX9sj/3WW0427v/Pdm1qmFPmJOyOp6eiHSHZJ2vJsgR4c7Pb+9QyG9Vr1LtJ AIhNIAEkMA4CrueptjVhAAAFi0lEQVQEnb+yxYaHf1v7k6WXav/l2/9FiP6J/NoLmqTx/Ma0mq2y YNfmHH2XhcylGbOnO7Ukn8aWo/XFynhlnDXh/r+M3EoLLMkp/o+V4eTkW//9n7/Vkw/+650z27ZO QnQkxKEj4+mprSPk2f8puosNtFeuTJu3MuZrM97903LLbbGQZ7ZRnSZ8VxwccM8KjyIBJHBDE3Ce ezCf/tvPmQ6/UvuKakXsHetTPyl9mmZUfNze+m7GjoyMX7xvpvuXq/fmbN+x73MzMX/5fkZKRkHl +xV708Jmpv3tM4fdj4fI+SZt2voFs2IWhc18oqjqH1YYmL58P+f5nJyXioteygibuQBqldZ3EGtn 6X/mv0GNH96VVXqGaYbuCZNLGRHjZ97P2ZFRsL+69CXwZ8HalH0fn/6H5vkn6PaOfccHoX2aek/9 /zu2eu22Q7c+QriopIZPgIuLs0t+38IibZDM208g19zaO98X5FoFAbvIpgUHP4A9gux3oGexqCHO PegeIcanqCEPtB0DBCQk+IeGgFFUqL+d8o83d7ft3LL75B1oKCBpQHcAktQoczQERkNgBIYAcsMR 5P3Xd++DKJv6QH1wM5qBQdGv7M7NLGCjneXVlgWrNzA8128AD1W/uLRs6za3wh6GP2+fLNi2gQGI QMCN8T0K9/eNpZru1UCJsPiAqws31Cf63J+5t0X13rSFyyDqw2wYVh3ZVRjIYHS3+vWVXWBBhsNn Xn+HsNDJP8hq3t5Y6uKGafiTaUB3ApG2JbBsv7qtz2VbH5S9us+eQf7FDH9g5+Tp3qbUhhsMles/ lBiiW8Lw7RuwqBVg+PHm2ZOrUEngAJdZGLAjpZFQaP1619y+4uM3GXbMNET17BXsagi658+1pZpo Hpm+WAopNHD5FDnkWTG8Aez2bVizRxfUe/j96xe/mZcV79v7fTOWMdho56UxIMf9F0wHzNw7IUQZ i5GjQqMhMBoCIyME0HsP7x7dAXscOpgDZrOLiAiLiPDDShPoWAREBahIghZLbgsPn3zxcpouHyTk INz+V3NAxbd3w7Kuts75S4HVDMOCxSe+QoY1tLNOPZ01a+PZRhug8K7jD0XK1u4IA+l2O7avTAtt fAkkDqyk5JHU5D+dgdVwsEqbyjuHlu/cVQ/iaBddP7R87z4w+/kXSLdE3L5y2pTutV5yYNVoxC4X ZVAvQUI9ENybiXJS5//DqtTfUNQ4ubkmJ85FHqz+1x80v2vy4VaDzz0/907C8MjSR1mI0MDrUwZo yOsjzdbA/HOjLT0lMhqIMuMTox4AfQ6rQ2CxCVGIzQGLT2DpjcDMHaVHQ2A0BIZ9CKCWEgwMQnIq wJIa6G2ExJ+3164+/83KKw0JDGjpjxEyXq7uWuLAIh1apkC5XyHcrQ1RWxtgWo4cvPfNHsSRl5YC 1QEcIlAz/zD8gI55fAMWZFgKO7AJCDV/8BnOxwM0m4UTXIvJC/MCtSJPYDAwSOjZR+mBDcRG2IYG gGdBGGS0HUMivdU4GP6I8T0+dbxvW199LoYGmN9Z8KjB5x7sHrn7Vh9i07cf2BVAgxFmO4azgAKW s/e1mPAx/AaFKws4tLGpYsBu/t0v0diqHKwmjAqOhsBoCAy3EEDUAhCfSeroghhHqpeccMmwEAcy z8yrdKkEVhjARS/gAvsTWOGP+we3gRlw4hOwLY0E4NyvIMGEmTt6QlS+PLlz6d47BjYhJa6zIFGI USAWDECdw4U+NQ2TB9HIaog0HNMi4CTw148fvv/h4OHnwTJh7tY5YQJa9+XYjMq+bTcSeld3JJje W1BgVQwZTAO7DOZZvGqQ/IDpHmweUeaHhCg4NLApgAUjasgj2cPAwKumrqyI1A/D2SHAZr4yrhoa xYpRzmgIjIbA8AwBAAkywdDStm1OAAAAAElFTkSuQmCC --90e6ba6e8d42e9620004a2ee5db4-- ========================================================================= Date: Tue, 10 May 2011 18:32:38 +0100 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: reverse from MNI space to native space 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 Message-ID: <[log in to unmask]> You may want to add an additional component to the job, (by selecting "Identity (reference image)". This should make sure that the inverse warped images are written out with the same dimensions etc as the reference image that you select. You would use the seg_inv_sn file, and if the idea is to warp t statistic images back to native space, you'd specify the spmT images that you hope to inverse warp. I would suggest leaving the bounding box and voxel sizes as they are. Overall, the batch (if saved as a .m file) should have the following structure: matlabbatch{1}.spm.util.defs.comp{1}.sn2def.matname = {'image_seg_inv_sn.mat'}; matlabbatch{1}.spm.util.defs.comp{1}.sn2def.vox = [NaN NaN NaN]; matlabbatch{1}.spm.util.defs.comp{1}.sn2def.bb = [NaN NaN NaN NaN NaN NaN]; matlabbatch{1}.spm.util.defs.comp{2}.id.space = {'image.nii,1'}; matlabbatch{1}.spm.util.defs.ofname = ''; matlabbatch{1}.spm.util.defs.fnames = {'wimage.nii,1'}; matlabbatch{1}.spm.util.defs.savedir.savepwd = 1; matlabbatch{1}.spm.util.defs.interp = 1; Best regards, -John On 10 May 2011 17:22, Marine Lazouret <[log in to unmask]> wrote: > Dear SPMers, > We would like to extract the activation clusters for each subject in its own > native space. > I need to transform back the activation clusters from MNI to native space. > I tried using the deformation batch but I'm not sure which images to use in > "Apply to", the originally normalized images? > For the parameter file I should use the seg_inv_sn file right? > Should I keep the default parameters for bounding box and voxel size? > I added the screen shot of the batch to make myself more clear, > Best Regards, > -- > Marine > ========================================================================= Date: Tue, 10 May 2011 19:51:18 +0100 Reply-To: Andrew Westphal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andrew Westphal <[log in to unmask]> Subject: Con Image Issue - Validity Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello fellow SPM users, With some investigation into con images, my lab found that con images app= ear to be sums of the beta images that one specifies depending on the wei= ght that is assigned (i.e. 1 0 0 0 0 1 0 0 0 0 - you would add the two be= ta images together). This appears to be the way it is calculated instead = of a mean value. So does SPM account for the fact that subjects will have= varied block counts, such as one subject having 5 sessions and another h= aving 2 sessions? Or is the subject with 5 sessions going to contribute 2= .5 times as much at the group level as the subject with two sessions? For= it to be valid, does one have to put in a contrast vector like 1/5 0 0 0= 0 1/5 0 0 for the 5 session subject and 1/2 0 0 0 0 1/2 0 0 for the 2 se= ssion subject? Andrew ========================================================================= Date: Tue, 10 May 2011 15:09:42 -0400 Reply-To: Bedda Rosario <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bedda Rosario <[log in to unmask]> Subject: Cluster information without corresponding SPM.mat MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba6e8eccc8748304a2f0b2a2 Message-ID: <[log in to unmask]> --90e6ba6e8eccc8748304a2f0b2a2 Content-Type: text/plain; charset=ISO-8859-1 Dear SPM users, I have created an image (and hdr file) using the results of a t test analysis in SPM8 (that is, using spmT_0001.img). I would like to know if it possible to get a cluster report from the image without a corresponding SPM.mat file. Thanks for the help, Bedda --90e6ba6e8eccc8748304a2f0b2a2 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM users, <br><br>I have created an image (and hdr file) using the re= sults of a t test analysis in SPM8 (that is, using spmT_0001.img).=A0 I wou= ld like to know if it possible to get a cluster report from the image witho= ut a corresponding SPM.mat file.=A0 <br> <br>Thanks for the help, <br>Bedda<br> --90e6ba6e8eccc8748304a2f0b2a2-- ========================================================================= Date: Tue, 10 May 2011 15:11:50 -0400 Reply-To: zhang sheng <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: zhang sheng <[log in to unmask]> Subject: Flexible factorial questions MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=0016e657b16a74e69304a2f0bae2 Message-ID: <[log in to unmask]> --0016e657b16a74e69304a2f0bae2 Content-Type: multipart/alternative; boundary=0016e657b16a74e68504a2f0bae0 --0016e657b16a74e68504a2f0bae0 Content-Type: text/plain; charset=ISO-8859-1 Dear all, I have a problem for the Flexible factorial analysis. In second level analysis, I tried to do a Flexible factorial analysis with two factors: "group" (e.g. patient vs. control) and "gender". So in the design, I have three factors: subject, group, and gender. As I have only one scan for each subject, the factor matrices should be [1 1] for group1 and male, [1 2] for group1 and female, [2 1] for group2 and male, and [2 2] for group2 and female for each subject (see attached file for design matrix)? However, when I estimated the model, I got an error message: *Please check*your *data*: *There* are *no significant voxels. *So, first, did I do something wrong with the model design? If I'm right, could someone tell me what happened for the estimating? Thanks, and any suggestion is highly appreciated! Sheng --0016e657b16a74e68504a2f0bae0 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear all,<br><br>I have a problem for the Flexible factorial analysis. <br>= <br>In second level analysis, I tried to do a Flexible factorial analysis w= ith two factors: "group" (e.g. patient vs. control) and "gen= der". So in the design, I have three factors: subject, group, and gend= er. As I have only one scan for each subject, the factor matrices should be= [1 1] for group1 and male, [1 2] for group1 and female, [2 1] for group2 a= nd male, and [2 2] for group2 and female for each subject (see attached fil= e for design matrix)? However, when I estimated the model, I got an error m= essage: <em>Please check</em> your <em>data</em>: <em>There</em> are <em>no= significant voxels.=A0</em>So, first, did I do something wrong with the mo= del design? If I'm right, could someone tell me what happened for the e= stimating? <br> <br>Thanks, and any suggestion is highly appreciated!<br><br>Sheng<br>=20 --0016e657b16a74e68504a2f0bae0-- --0016e657b16a74e69304a2f0bae2 Content-Type: image/jpeg; name="FF_design.jpg" Content-Disposition: attachment; filename="FF_design.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gnj7veb60 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAOoAooDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKz9dtby+8PanZ6dcfZ76e0lit5t 5Ty5GQhW3DkYJByORQBHD4l0G51Q6XBremy6gHZDaJdI0oZc7hsBzkYORjjBrQE8LXD26yxmdEV3 jDDcqsSFJHUAlWAPfafSvB7PS/CGneH9K8PeLfDOpeEtUtnhA1+KNQrXWdwKXi7hlgSxDfKmCMjY COg8MSeJofjP47lWHTZ7RHt2u4oiwndBA/2ZYdxCbyNofeQM5wQKAPXKpyatpsKQvLqFoiTXH2WJ mmUB5txXy155fcrDaOcgjtXmdl448cQ+KrHw3q0fhRtT1K0nMVvaTSl7KdIfNQXAy3ynphevJB45 5Dwhcanb/DjwL/aNlpV1pkniW2j07mYTRMZp/Md8Mq7g33eq4J3A0AfQ9V5r+zt/tHn3cEX2aITz 75AvlRndh2z91fkbk8fKfQ15HL8U/E134q1iw0mPwwU07U/sKaZeXbQ3t2A+wmJ2KxksQ2ByV6EN xuz5ZPEMPjX4ryyw6NPp6WQa/iczBniFpL9nVNpHJXaJOR32kcUAe4QTw3VvFcW8sc0EqB45I2DK 6kZBBHBBHOakrydPGXiCPSfC3h3wboljLqcnh+HU5VupGEEUAjCrGmX3Fi4Cgs3GRknJZegvvE3i m+8JaXqmi6LBpd1Pvkv/AO3yYobGONW3b8Mr8sBtYKRt5YLmgDsLq/s7HyPtl3Bb+fKsEPnSBPMk b7qLnqxwcAcmrFeH3viy88XaH4YuL9LH7VY+OraxeawctBPs3ESR552kMMcnOM8ZwPYNcuNRtNDv Z9IsvtupLE32aAlQHkPC7izKNoOCeQcA45wKANCivJ7T4jeJLTUfEuk6t/wjl3faZok+pxy6TLI8 cckRKmGUMc7s4JAKkAd88Fn488cRReFdX1fS9Di0bXru2tFhhklNypmU4kzygUkFwvJAIUnOWAB6 xVeG/s7i8ubOG7gkurXb9ohSQF4twyu5RyuRyM9a8z8cePPF/hC4u9SktfD8OixXCpbWd5Oy397G DGHaII7LjLkjIyq8svY4+seJdS8H+M/iPr2n29pcJaXGjm7huCwLwtEUIQjo+5l5OQBng8CgD2yi vO9c+I81r4slsNHTTb/T7DQptavZBcFnkUKTHHGVyFJzG2TnKvkdBuz/AIfeP/E3inVNOF03hi8s Lu3klnTTbhkurErjBlikbcQW+T5QRkg7sY3AHpljf2ep2cd5YXcF3ayZ2TQSCRGwSDhhwcEEfhVi vB9K+IV14c+HHge20i00PS21eW7XzL55zaWwjmYYPzM43M4+YsQvOcA5X0TQ9Z8X654IubuGHw+N aFw0VrcR3LTWFzGrgGQGMlgMb1wTncuSBnAAOwnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1l2Piz w3qd5HZ2HiDSru6kzshgvY5HbAJOFBycAE/hVfx3/wAk88S/9gq6/wDRTV5/c+CfD8/wPs9Xh02C y1az0SLUodQso1hnE8cAcMXUZOSOc+ueCAQAewUV5Pd/EnVIdO8Naba3mh22r3miQapdX+vTeRbM GAXagTGZC25scABTjOeI9a+L9y3h/wAI3+jwabYP4geZWm1l3MFsYiEZSY+cFzw5wABkgZJUA9Uu r+zsfI+2XcFv58qwQ+dIE8yRvuouerHBwByasV5HqXiHUtU8P+ErvXtN0a4nfxbb28NxZXTS28qg uFuIij5BByu1yeVJK84FjVfiD4vmfxPqPh3RdNl0Pw881rOb2VlnmmjU+Y6BTgImVYqcFlHB3HCg HqlFeVjx34y1TWdE0jQtO0aS41Hw5Bq0st2ZEjt3dvmJwxLJ0UKBnLAk4BrqPAHiXUvEml6kNYt7 SHUNM1OfTpzaFjFI0ePmUNyB82Oc9M8ZwADrKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKp6tpsOs6NfaXcNIsF7byW8jRkBgrqVJGQRnB9DVyigDyN/h 9461Pw/aeDdX1rRv+EXgdI5Lm1icXs1vGcxoQw2KflQZHI2jJfkNuXPg7xJH428R6hpmp2NvpniG 0SK4lKyC7tJEgeONotpCnDFWJJB6gYxk+gUUAeP+DvhZreg694Yv7uLw5BHpH2lJjp6Sia4EkW1X eRx+8bcW4+UKOmQdqyaX8NvFVlomhaDcXmjS6foeuwahazR+akssKvK8m8EEBzvXaBx1y3Qn1yig Dxvxh8LPE3ivVL+OeTwxJZ3V6s8epvaNHqEEXA8vMYCyBVyoDk7uCSDjbuax4I8SS+JfFlzpl1pT aZ4l0/7PcLciRZoZEt3ij2FQVK7ipJPOCQBxk+kUUAeX3ngDxTYRaBqfhjV7G212x0SPRrpbpS0D xqud6HYSGD8jIwcDOACrHib4ba3f6H4YtLXV4NZm0eV5biPX3laG9duQ77CSdhyFU5+VsEkZDeoU UAeT2Hwv1iz062tfP0pfJ8YJruLdXijFuABsRMHa3HCZIA43Gu48caDeeJ/BeqaNYXv2K6u4tiTE kDgglWxztYAqevDHg9D0FFAHj+l/CzW7WXU5ZIvDll9s8NT6QkOmJLGiys3yu5YMz/KBucnd2wcb j0l/4K1K68K+BtLSe0E+g3thcXTM7bXWBCrhPlyST0yB74rvKKAPE/Efwg8Q6ve+KWjn8P3A1a4F xb318kzXcQDqRCrcrEiruXKglgACACNnYXHhiGy1bx7qmvajaWmi+ILe2t/O88RtCqwmFixcbVJL jbyf6V3lV76ws9Ts5LO/tILu1kxvhnjEiNggjKng4IB/CgDyP4IaRqdx4F1XWnvJLbUNVRLS0uSk brHFbxeTC4QdSrbgQ33tg9cmx4U+GWvWPjfR9f1eHwxZjTLeSM/2LA8bXbMhQGRcKgPzM25QPTGM bfWIIIbW3it7eKOGCJAkccahVRQMAADgADjFSUAeT2/w78U6f8P/AA3odtL4cvJtNlne7s9TtjPa T72cowYpvVkDdgM7jk4GDqeDfBniHwR4D1Cw0yTRjrVzetdxRP5xtIQxRdm7PmMAiHBPOSAc4yfR KKAMvxLps2s+FdX0u3aNZ72ymt42kJChnQqCcAnGT6GvP7bwh8Qb7w1a+EtY1Dw/a6CtvFaT3Gmt cfazCgA2qWwmWC7WJGMMeD0PqlFAHm/ij4bPNqOm6l4es9DuZLPT10sWGvQtPbLAp3IyEZYSD7uT nIY9COZNa8D64vhrQtL0abw/fJp7u11aavpcS2tyzA/OEiT92VZmwFAyG+Zjg7vRKKAPI9G+FOq6 X4a0ew+0aas9v4oi1ueKFpPKiiUbfLjZgWY4C43Y6kEnGTzfiC6/s2Lx3Y6D420OPSr6W7lvbO+G 2+jutrebFCrBQ6uQqbucA/LllJb6ArLm8NaDc6oNUn0TTZdQDq4u3tUaUMuNp3kZyMDBzxgUAcf4 L8Kala6z4e8QXAjhgi8JW2lyW8m5Z0mDK5ypGAAOOuc9q3PBfhu88Of8JD9skgf+0tbudQh8lids cm3aGyBhuDkDI966iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiuH8ZfDv/hLdXiv/AO1PsnlwCHZ9n8zOGY5zuH979KipKUVeKuzpwtKjVqctepyR72b/ AAR3FFeTf8KT/wCph/8AJL/7ZR/wpP8A6mH/AMkv/tlYe1r/APPv8Uen9Qyv/oL/APKcv8z1mivJ v+FJ/wDUw/8Akl/9so/4Un/1MP8A5Jf/AGyj2tf/AJ9/ig+oZX/0F/8AlOX+Z6zRXk3/AApP/qYf /JL/AO2Uf8KT/wCph/8AJL/7ZR7Wv/z7/FB9Qyv/AKC//Kcv8z1mivJv+FJ/9TD/AOSX/wBso/4U n/1MP/kl/wDbKPa1/wDn3+KD6hlf/QX/AOU5f5nrNFeTf8KT/wCph/8AJL/7ZR/wpP8A6mH/AMkv /tlHta//AD7/ABQfUMr/AOgv/wApy/zPWaK8m/4Un/1MP/kl/wDbKP8AhSf/AFMP/kl/9so9rX/5 9/ig+oZX/wBBf/lOX+Z6zRXk3/Ck/wDqYf8AyS/+2Uf8KT/6mH/yS/8AtlHta/8Az7/FB9Qyv/oL /wDKcv8AM9Zoryb/AIUn/wBTD/5Jf/bKP+FJ/wDUw/8Akl/9so9rX/59/ig+oZX/ANBf/lOX+Z6z RXk3/Ck/+ph/8kv/ALZR/wAKT/6mH/yS/wDtlHta/wDz7/FB9Qyv/oL/APKcv8z1mivJv+FJ/wDU w/8Akl/9so/4Un/1MP8A5Jf/AGyj2tf/AJ9/ig+oZX/0F/8AlOX+Z6zRXk3/AApP/qYf/JL/AO2U f8KT/wCph/8AJL/7ZR7Wv/z7/FB9Qyv/AKC//Kcv8z1mivJv+FJ/9TD/AOSX/wBso/4Un/1MP/kl /wDbKPa1/wDn3+KD6hlf/QX/AOU5f5nrNFeTf8KT/wCph/8AJL/7ZR/wpP8A6mH/AMkv/tlHta// AD7/ABQfUMr/AOgv/wApy/zPWaK8m/4Un/1MP/kl/wDbKP8AhSf/AFMP/kl/9so9rX/59/ig+oZX /wBBf/lOX+Z6zRXk3/Ck/wDqYf8AyS/+2Uf8KT/6mH/yS/8AtlHta/8Az7/FB9Qyv/oL/wDKcv8A M9ZooorqPDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKz/AO3NLXR/7Wlv4ILAcNPcP5SxndtKvvxsYN8pVsENwQDxWhXkemGG 1lupp/hpaGSB5VmtrHQhHItu0C7tkr4jnKu0sJVDmUHeo2jDAHpmma7o+t+b/ZOq2N/5OPM+yXCS 7M5xnaTjOD19DVia+t7e8trWWTZNc7hCCpw5UZKhum7GSFzkhWIBCtjLsJ9V1DVPtUltqWl28aBH s71bZ0mzuO5GidnVwdudzbccBcncuP4/Vc6S83hm01i3W4VZpJtNa+aFGkjDqiKCykoXffgj9yFI JdaANy/8U6Npk9zDeXnltaxPLM3lOUUKhkZdwGDIEBfywd+35sY5rYrzu2KXGiT6M/w8u1SS3VGk tLKzigkj3yNE6xzyDad26TynUmNn+YHOW7yxhuLezjiurr7VMmQZjGELjJ2lgON2MZIABOSAoO0A EaarYvb3dwbmOOKzd0uWl/d+SUGW37sbRtwwJ4KkMMggmvpviXQdZuGt9L1vTb6dULtHa3SSsFyB khSTjJAz7iuDdPK8ZXznwDYy3UV3HPbvFo+JJv3kvmP9rbEQk2iKcEleWMX3/mHQabfapdajpFhD p8+jwwWjTukUG+zaIGIRx5aJGVtjSYX92yNGdyOu3IB1F1fW9j5BuZPLWaVYUYqdu9vugnouT8oz jLFVHLAGvda3p1lqMFhcXGy4m24GxiqbjtTewG1NzAqu4jcwIXJGKw/iIJv+EVkeDQLTWpI3LrBd WZu1RgjlSIh8zFm2x5X7vmFj8qsDl6bJb23nwQeBYE0hvPzNFpxt2mtn8sPi3Kbiwwquj7DIsQaN ZPuKAdxYX1vqenW1/ZyeZa3USTQvtI3IwBU4PIyCOtWKw/CEV3D4V04XfmIWt43jt5YEiktlKKfK cIqKShyowicADbkEncoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCnqWmQarbrBcSXaIrhwbW7 lt2zgjlo2Ukc9M46egrz/wAMwx23jG3s5PFFpcXNq9wkluPEs91JcNzwbVziMoAcAtJtAIbe2JF9 MrzvwZf2iy2UFnrfiCTTzcPbW0N5FaCCQmAXEYXy4xIqGJ96D5Qoj2ELhUIB6JXB+MdIttOe31T+ 2JLSJ7h/OivvE13YxSsykhVcMwUD5jsVRnAwwClW7yuP8Zy2sOraEzajqtnfGUxwtpqQE7JJIoWa Qyocxh5YQVGcllbadgKgGp4PVU8J2CpqkepgIw+1pdtdCQ7jn96xJcg8FvlBIOFQYUal9ZRahZyW szzpG+MmCd4XGCDw6EMOnY89OlU/D1yLvRIZPtV3dOryRySXaxrKJEdldGEYCZVlZcqMHbkE9TqU AeXizh07xpDYSeKYI3TUElSKfxTctMUIG2A2jMd2cjkyHLHdgofJr1CvO7C/tLbxHqC2mt+IIrRd TjaaPyrT7KXmnkh2qPL8wIZ4XjbGCWbfkhmkr0SgDj/GejQizk1carPYsksTztNrtzZQMgIXZuVi kW47QSEOckDDMHWx4DMR0OdotXg1JWu5G3Q6o+oCHOCI/Oc5bAxgbVwCAQxy7x/EG5srLQYLu7ut Stntrj7RA2mrEZy0cckjBTKCoHlrIW5G5QV53bW1PD8paK+tpdRvr24tLtoZmvUhV0O1WUDykVSp RkcdT8+DggqADYooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKAMO/8ZeGdK1T+zNQ1/TbW8CF2inuVQoBt+9k4UkOpAOCRkjIB xXh+IPg2dC6eKtGADsnz3saHKsVPDEHGRwehGCMgg0eJfOtNU0jU4JtNEsbyWkUF9dm0E0ku3aFk CMWPyH91ghiQ3WNaueG7XytHtrmW4gu7iaIf6VE/m7odzPEnmn5pVRXwHbluWOCxoA0L6/s9Ms5L y/u4LS1jxvmnkEaLkgDLHgZJA/GufT4jeDpYrV4fEVjO11gQwwyeZMxKlgvlrlwxAwFIyWIXG4gH qK4PQLSaZ7PQ5L3Rp7LT0uLW5S3vjOb1QoWSN7YoEhAZ422gt5QxGuEegDrNO1vTtVleKyuPMkSJ JnQoylFZnQBgQMMGikUqfmUqQQDVfVvFfh/QbyC01bWrGyuJ+Y4551Q4wx3HJ+VfkYbjgZ4zkgVo Q2Fnb3lzeQ2kEd1dbftEyRgPLtGF3MOWwOBnpWP4uXytOtNRNzYwLp12lyxvrj7NG3DIFMwBMfLg 5AO4AofldqAI4/H3he4eZLPVo754n2MtjG9yS20MAvlq24lSSAM5CSH/AJZvt3Pt9n/Z39o/a4Ps Plef9p8weX5eN2/d0245z0xWPoun/bbO7l1YWN408txEIkk+1JbxsQssAldQXUvGxKkALnZjCCty CCG1t4re3ijhgiQJHHGoVUUDAAA4AA4xQBzf/CxvBzQebF4isZ2MvkrDbyebNI+/YAkSZd8t02g5 HIyOasaH4y0nxFqMlnpy3zbbSO8WaaxlhjkjkLBSrOoznbkdiPuk7W249ul5BrkmkW2p6VGV1X7X JJHqBE48zdL5JtAmz5og6/e5w0+N+a6yz0nTdPuLq4stPtLae7ffcyQwqjTNknLkDLHLE5PqfWgC vrXiTRfDqQvrGqWliJ3CRefKFLncq8DqQCy5PRQcnA5rPj8f+FJ72aztdctLqeK3+0stqTMNhcIA CgILlmVQgJYlhgHIq54ptftnhy7Q3EECx7J2knfy0CxushzJ1iyFI8wfNHneOVFZ+i2/9uz3Goan /ZV3CJYJra2t7n7dHbzKm4TJI6LsZkeMhVUAAbwSZDQB0FjfW+o2cd1ayeZC+QCVKkEEhlZTgqwI IKkAggggEVz7/EfwVHFdSHxRpRW1z5gW5UlsKG+QA5k4I+7nnI6giugsbCz0yzjs7C0gtLWPOyGC MRouSScKOBkkn8a4/WEvLfXNUsrPU9Kt59T8idZZNQME9pjZCjiBUIm/eAcsy+ZuWJvlVaAOgsPF OjanPbQ2d55jXUSSwt5ThGDIJFXcRgSFCH8snft+bGOasazrml+HtOe/1e/gsrVcjfM+NxAJ2qOr NgHCjJOOBRa6Fo9jqM+o2elWNvfT7vOuYbdEkk3Hc25gMnJAJz1NSatZNqWjX1gkscT3NvJCskkK zKpZSMlG4cDP3TwehoAw5viN4OivLa1HiKxnmuNxQWsnnhQoyzO0eRGoGSWYgAAnOAcbmm6tpus2 7XGl6haX0CuUaS1mWVQ2AcEqSM4IOPcVzfhtn1fUba9e50qW1tLQLDFZ6o2o7zlkSbe6KUYBZ03D Jk3MGPyAV2FAHPzeOvCdtqNxYXHiTSobq3wJUlu0TacsNuScbgVOV6jjIGRksPG3h3U/sxstQ85L nZ5cqwyeX82Am5yu1dzZQbiMurIMurKM/wASs+na412tzpQ+26e1v5N5qjae6pHueR1dEZnwr5zx 5W1ipHmNW5ZaHbQiyub2G0vNWgQF9QNqiSPKY1jeQYHyllQAgHoAOgFAFy+v7PTLOS8v7uC0tY8b 5p5BGi5IAyx4GSQPxrn0+I3g6WK1eHxFYztdYEMMMnmTMSpYL5a5cMQMBSMliFxuIB6iuD0C0mme z0OS90aey09Li1uUt74zm9UKFkje2KBIQGeNtoLeUMRrhHoA6jRfEOmeIEmbTp5HMDlJI5oJIXUh mXlJFVsbkdc4xlGHVSBXv/GXhnStU/szUNf021vAhdop7lUKAbfvZOFJDqQDgkZIyAcaltYWdnt+ y2kEG2JIB5UYXEaZ2Jx/Cu5sDoMnHWsPxL51pqmkanBNpoljeS0igvrs2gmkl27QsgRix+Q/usEM SG6xrQBy+peM4r7WVuNI8eeH4NPDhNkuoQxbSrFW3xPEzyjIJBWWIMpAXb/rG9InnhtbeW4uJY4Y IkLySSMFVFAySSeAAOc1l+G7XytHtrmW4gu7iaIf6VE/m7odzPEnmn5pVRXwHbluWOCxrYoA5Of4 jeEGsJZLfxZo0chcwxtJMr7ZN+wMUDBim7nOQCvzbgvzUeFNfm1a4EVx4p8ManOLffJa6QpLRtlc ncZnLICcZ2LnI6dKp26XkGuSaRbanpUZXVftckkeoETjzN0vkm0CbPmiDr97nDT435rtIIIbW3it 7eKOGCJAkccahVRQMAADgADjFAElFU9V1KHSNLuNQuFkaKBNzBAM/mSAo9WYhVGSxABIw/8AhMZj P9mj8La5LdLL5UsMX2ZvJJTeu9xNsXK543ZHy5A3x7wDqKKr2NxLd2cc81lPZSNnME5QumCRyUZl 568E9fXisfUvFkGmajNatp19Mlv5X2ieLygsXmHCfIziSTJ4Hlo25squ5wVAB0FV7+8TT9Oub2UZ jt4nlYb1TIUEn5nIUdOrEAdyBWHZ+JNSmv7G1ufD13bi7uJEEgLOsMSJIS0h2YV9yKuMlG8xSjyA HG5f/bP7Ouf7O8j7d5T/AGf7Rny/Mwdu/HO3OM45xQBn6N4r8P8AiHYNI1qxvZGiE3kwzqZFQ45Z M7l6gHIGCcHmtiuD0vR9V8RxLZeMdKkubCB3k26otszPIs5a3dPIJGfKJWQNgHChQQXz2Gm6Tpuj W7W+l6faWMDOXaO1hWJS2AMkKAM4AGfYUAZ+peL9B0bWV0vVdTtLCd7cXEbXU6Rq67ipAy2QQcdQ M5O3dtbbcg13R7nf9n1Wxl2eTv8ALuEbb52PKzg8b8jb/eyMZrD8TSeKYtbsW0qG7m0soyzrYG2E oJSUFj55AyGMBTbxxLvB+UG5B4WsrmzsZNVs4P7QilS8l+ySyJEtzlHfYMg+WZEVyh4ZgGYFuaAO gryexuNf07UbrU7Xw/pX9oiK5mvktvDU9vPLtO8R/aDJsdpCuCUMmHKlRKoLj0TXtBtPEdhHZ3kt 3HHHcRXANrcPC26NwwBKkHGR+HBGGAIy/D3iC1f7BpEGn30UHlPFa3EqQKsohwjfu4m3RYxgho4w rYQhWKqQDqK4/wAeRzzxabAbCxu7BpXa4N5osupiNgvyFY4m3BjlhkjbgtllO1X7CuPm0+w8LeI5 NUtbbVdQ1HWJZcW6zRsM7Iy2xpmUJxCDsV8sAflZYl8sAseDLzWJLNbLVdOgtVhtIJIGtbR7aFVY yJ5SozMRtEatztbbKgZEIIrqKp6VqUOr6Xb6hbrIsU6blDgZ/MEhh6MpKsMFSQQTHrmj2/iDQ73S Lt547e8iaKRoJTG4B9CP5HIPQggkEA4OdNZbxvLcW+laNHqJvTFHdyeGp3mW32bRIboSCInHGN4J TsH/AHNegaTeTajo1je3FpJZz3FvHLJbSZ3QsyglDkA5BOOg6dBXP6XrVlpNwuiW9hqTWyXr2ovJ DEczMTIVK7/OY/MTvKEsv71mZSZD1lAHN+Nxcv4fEUFnaXcUlxGtzHd6c99GIs5LGFGDsQQpG0Nz jIUZdM/wVc6rCYtPn0u0t9MZLh7ZrPS5LCOMJImMxuxIMnmscMEIMb43qQ9WNZ0vTdI8QL4qZNSu b+V4bZLaOVWjZsSIoAlISInzT825Mn5VOZWWTc0fVk1izedLae3aOV4ZIptpKOpwRuRmRsHg7WOC CpwysAAaFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQBwfxAtWltdPvbm503TJILhlS6n1dbUACVJY1Bkt5FYsYI3I2ggx4BZ S2djwf8AborAwXFlaJaFBdQX1rqH2pbt5nd5HJ8uPBLEPwu0+YNuAMDP8QaLqa6pbahbX/ie7lR5 TENPGnILdWxlCZkUsh44JblFJ5VTWp4J8j/hELH7P9u2fvM/2h5X2nd5jbvN8v5fM3Z3Z+bOd/z7 qANyQzB4RFHGyF8SlnKlV2nlRg7ju2jBxwSc8YPk+h2d3purXMWkjRtUudPdriPRo/EiO1vJHCLY AAWivlYwIv3j45y3zfNXqGpWU99brFb6nd6e4cMZbVYmYjB+U+YjjHOemeBz1zweg2Mmm+I9DtLy PxXFFH9oFlHqLacbVCUJKjyfmDBSQiryqhgoEYcUAekVx/xAsP7S8M3KXhsbaCOUhbqfUvsojjki MTsXaF1DHzXj2lSMNuDBsAdhXJ+KtFvrp4ryC/8AEE3l3CSQ2umixUwMqn51adAcHkMN5yHK4Kki gCPwXJqJ3yyRWN7aX++5k1e01Vbrzpl2RBSFhiUfIu3KDA8o5+Y5PWTGZUBgjjd96gh3KjbuG45A PIXJA7kAZGcjm/A+3+ztRz/av2n+0JftP9rfZ/tHmYXr5HG3G3bnnZt2/JsroL63lu7OSCG9nspG xieAIXTBB4Dqy89OQevrzQB5nJprQ+Oru2tLnRnmluFm/seTxAqM5jle5jYxi1MqnzJHmwHI+bGS gAr0ywmuLjTraa8tfsl1JEjzW/mCTynIBZNw4bByMjrivO302fTNZtIrxvFz2T6wkgeU6Z9jeYtw 7qoDKjMNwGFJkYMB5rDPplAGH4nhmuNEvlKWixQpFcRTz3hgEckb+ZvZvLcKE2I4JDBiCGUDry/g CK7tBbizGm6tYbI9Pk1C11tLpraGGN2ijKrbxKQC2O7/AL0E5AGOg8W6Lc6rYSmO/wBZ8vYo+xaa LQM7B8h1edMq6nDAh1xsBX5utPwbv/tjW/tf9uf2h+487+2Psm/btbZ5f2f/AJZ/e/2d2/HzeZQB 1k5mW3la3jjknCExpI5RWbHALAEgZ74OPQ15n4s0xG8aSoL3SrdtRiMLWM+urbvfJMI43Bja2dxv EEcf7tx/q8rtYtn0yeNpreWJJpIHdCqyxhSyEj7w3AjI68gj1BrzPxHpNzpv9pyXdz4yurGfyjeX Fv8A2WsEwGBudWVTtAwrsygFEw5KLwAeiaXPfXNgsmpWUdld73V4Y5/OUAOQrB8LkMoDcgEbsEAi o9YtH1DTruwa0gubW5tJopEluGi3lgAEyqkhWBbLDlcDAOeNCsfxFpc2qadJHHfX0UYikElraR2z /agR9widGXnkdVHzHJx0AOH8HfbY7/7TYT6V4g8qVkleDX453tEuZxJM4WO1jU5Yb8Mf+WeExyD6 hXB+FIZbbxUIL8+Jzdx6ZshOtyWTqYg6g7GhJdnzt3nJz8m/ny67ygDzfxtDLb6xpOrS6jpWi3wi U+bNraW++RFkXaoltZA6oLiQBgFJ83leFx2HhqK8tNJXT7rSoNNjsdttbRwXhuUeFY12sGZVbjlc MM/LnkEGuT8Q6LqdjcT3a3/jW/LWTRzXOmDTkYx5J2Y2I7OvJUqCV3naQWYV2mheR/wj2mfZf+Pf 7JF5X+q+7sGP9V+76f3Pl/u8YoAuSGYPCIo42QviUs5Uqu08qMHcd20YOOCTnjB8n0Ozu9N1a5i0 kaNqlzp7tcR6NH4kR2t5I4RbAAC0V8rGBF+8fHOW+b5q9Q1KynvrdYrfU7vT3DhjLarEzEYPynzE cY5z0zwOeueD0Gxk03xHodpeR+K4oo/tAso9RbTjaoShJUeT8wYKSEVeVUMFAjDigD0iuD+IFq0t rp97c3Om6ZJBcMqXU+rragASpLGoMlvIrFjBG5G0EGPALKWz3lcX4g0XU11S21C2v/E93KjymIae NOQW6tjKEzIpZDxwS3KKTyqmgDQ8H/borAwXFlaJaFBdQX1rqH2pbt5nd5HJ8uPBLEPwu0+YNuAM DoJjMqAwRxu+9QQ7lRt3DccgHkLkgdyAMjORh+CfI/4RCx+z/btn7zP9oeV9p3eY27zfL+XzN2d2 fmznf8+6ti+t5buzkghvZ7KRsYngCF0wQeA6svPTkHr680AeZyaa0Pjq7trS50Z5pbhZv7Hk8QKj OY5XuY2MYtTKp8yR5sByPmxkoAK9MsJri4062mvLX7JdSRI81v5gk8pyAWTcOGwcjI64rzt9Nn0z WbSK8bxc9k+sJIHlOmfY3mLcO6qAyozDcBhSZGDAeawz6ZQBj+KYop/Dl3HNDBNGdmY59Ne/Q/Ov WBCGf8OnXoK5vwPotoL+8vX0DRoDbuq2V5a+H302Q5Qh8rKS+eccDbgjDMSypueNr/8AszwhfXpu /sqReWZJfM8v5PMUOu8fMu5cruTLjOUVnCqc/wAC3UVz9vNvrFjdwr5f+i2msvqnkt82XM0mHG8Y GzG0eWSOWbAB2FeZ+L9PjuPEF01vomjX947xKFu/CM9y0nCg5utwiJxnGSqjAVmUAsPTK8v8R6xE PF+p2MviOx0+5g8owTXeuvafZFaMbSLZcx3GGDPiUqzbgrAJsZgD0ixsLPTLOOzsLSC0tY87IYIx Gi5JJwo4GSSfxrP8RaLomradJJrVrYvHbxSFbq7gik+ygj5nBlVlXGAeRj5RkEVsVT1aRYdGvpXh u50S3kZorMsJ3AU/LHtIO89Bgg5xgigDh/AOh6VpusyTWlrHBOtktuPJ8OXOnq0asDl5Jyxd846M C3JbftUp6JXnfw61DR7mZYNKjkAht5YSlvrk9/b24im8sKVc7UDqqPGdoLKHwAF+b0SgDy/xZ4O8 Kw6isdrpmlQySWjRy2sXhyW92Ix4kxbFGjY7SA7E/cOzaQ+70TSYoYNGsYbfzPIjt41j8yAQttCg DMYVQhx/DtXHTA6Vw/jnUNHsvFmmLqMckE8tu8Md9Nrk+nRKCsj7VMZwwLQoJD1XfDw2QB3GkyLN o1jKkN3Aj28bLFeFjOgKj5ZNxJ3jocknOck0AXK8r0XQNPvfEFrbv4Z8P3FgySNcsfBs1g0eB8mH nbaQTwcZbphcFmT1SvL/AATrEWp6xYuniOxkuW8wXCf269zNesqsGH2Q/u4PmHmZiZlATahZG3UA eoVx/jq1tbn7B9ps7G52+Zt+1+G59V2/dzjyiPL/AB+9xj7prsK4fx/qkVheaPBc6jBbQXPnDy59 WfTUd1CkMZosyDA3jbjYd/zMGEYYA1PBOlQ2Hh+C4Ojabpl/dIGulsLEWqvgtt+TJbGDkb8NzyqE lR0lY/haTzvDlpINSg1FW37biCfz0272wgl6ybBhN5+Ztm44JNbFAHl95o9reeLRGvh/Q71ZtQYX XneD50fyyxLP9pdhE7ercBuSoY7Ub0yCCG1t4re3ijhgiQJHHGoVUUDAAA4AA4xXmdrrEV14yntz 4jsYL2HVWhZptdcSMok4hWxGYWyhEQYNuyd5AlDIPUKAOb8a28Fzo0KXFtaXCC4UhLrRJdUUHa3I ijIKn/b6DkfxCqfgPSLe2s5799D0qxu2lkijnstINgZIMjGUcmQZI5DbeRwGUK7SfEDUV0zRrOSW +jtYJb1IZfMvWs1kDK21TMn7yMbtrZQMflwwCF2Wx4KnW40aZ49VtL+L7QwRbXUGvlthtXMZnb55 DnL5YAjzAo4UGgDpKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiisfxPqF5pmiiewMC3Ul3a2yNPGZEXzZ44ixUMpOA5OMjpQBn+ LNCl1u80wDStKv4Y/NDNf26SiFmChWIYZMYAYlUKszrENyrvYXPB8c8XhOwWeaOYFGaBkEQAgLEw r+6AjyIigOz5cg4JGCY/sfjD/oO6H/4Jpv8A5KqnpOg+KtG0ax0u31/RmgsreO3jaTR5SxVFCgnF yBnA9BQB1led+HfDt5pHiDSHWw0bS52Sc3sdlbW8ZuIgMNgqofZ5rw+Wo6RpmVi7hBH4k8S+MPD2 ox2n27Q7jfEJd39mTJjJIxj7QfSuak8Y+KpdZttUafRvPt7ea3RRYS7SsjRsxP7/ADnMS457nr2A Pba5/wAXaVLq+nWkCaXY6kiXaSSwXaIwKgMBtLqyr8xUM2CwjMm0F9orz7/hYfjD/nrof/gvm/8A j9H/AAsPxh/z10P/AMF83/x+gDvfBdlPp+l3lu8to1ql7KtpHawxRJEi4V1CRfKo85ZiASzAMA53 BgOkrxLTPGPirSrV7eCfRmR7ie4JewlJ3SytKw4nHG5yB7Y69auf8LD8Yf8APXQ//BfN/wDH6AN2 Tw7eW3iWG/isNGsL2XWNyXcVtbiS4jJLuoYrvAMCvuxukaV2OUiTLeiV4le+MfFV9dadcSz6MHsL g3EQWwlwWMUkWG/f9NsjHjHIH0Nz/hYfjD/nrof/AIL5v/j9AHpPimwl1Pw5d2cNpBdtJs3QzRpI GUOpbasnyGQKCU3/AC7wu7jNZfg/TJ9Lv9YhWPTbWyDxAWWnxRJHBOU3SY2KGI2NAN0mGYqzBUQo K4r/AIWH4w/566H/AOC+b/4/VOy8Y+KrG61G4in0Yvf3AuJQ1hLgMIo4sL+/6bY1POeSfoAD22vO /FPh28n1TUtSgsNGt7tngFhqkltbtKsvypF80ik7/NPzMc4jWNY1MjErhf8ACw/GH/PXQ/8AwXzf /H6p6n4x8Varapbzz6MqJcQXAKWEoO6KVZVHM543IAfbPTrQB7bVPVoJrrRr63t4rSaeW3kSOO8U tA7FSAJAOShPBHpmvKv+Fh+MP+euh/8Agvm/+P0f8LD8Yf8APXQ//BfN/wDH6AOp8J6LNpHiFlgt bHT7U6epubOCC2jkZi+IXkMSgmTCTlsbYwXVUDbWc9xXiUfjHxVFrNzqiz6N59xbw27qbCXaFjaR lI/f5zmVs89h073P+Fh+MP8Anrof/gvm/wDj9AHU+MtCW/1GW/vNK0qexh087ru4t7eSSEKWZ+Zh jdjaI8kRqWlaTOEVus0mO+h0axi1SaOfUEt41upYxhXlCjew4HBbJ6D6CvHtW8Y+KtZ0a+0u4n0Z YL23kt5GjsJQwV1KkjM5GcH0NXP+Fh+MP+euh/8Agvm/+P0Aew1534d8O3mkeINIdbDRtLnZJzex 2Vtbxm4iAw2Cqh9nmvD5ajpGmZWLuEGF/wALD8Yf89dD/wDBfN/8fqnJ4x8VS6zbao0+jefb281u iiwl2lZGjZif3+c5iXHPc9ewB7bXL+LNCl1u80wDStKv4Y/NDNf26SiFmChWIYZMYAYlUKszrENy rvYcN/wsPxh/z10P/wAF83/x+j/hYfjD/nrof/gvm/8Aj9AHovhCOeHwxawXE0crwvLEuwRDy0WR lSJhEBGHRQqMEGAysBnrW5XiWmeMfFWlWr28E+jMj3E9wS9hKTullaVhxOONzkD2x161c/4WH4w/ 566H/wCC+b/4/QBuyeHby28Sw38Vho1hey6xuS7itrcSXEZJd1DFd4BgV92N0jSuxykSZb0SvEr3 xj4qvrrTriWfRg9hcG4iC2EuCxikiw37/ptkY8Y5A+huf8LD8Yf89dD/APBfN/8AH6APTfEcepy+ H7xNGmkh1AoDDJGIyykEE4EgKMcZ+VtoPTcmd6834efxQ2tF57y+ubFbv7PPb6gbLzbZPIL7iLcf e3+Vg7uVlOYxtEjbH2Pxh/0HdD/8E03/AMlVTstB8VWN1qNxFr+jF7+4FxKG0eXAYRRxYX/Sem2N Tznkn6AA6yuD8TDxVDrNw9lqWpW1nM9uloYjYC3RmYKwkaZfNBYkABVkwSGG8nylj8T654w8N/Zf +Jjodx9o3/8AMLmTbt2/9PJz96uS1Pxj4q1W1S3nn0ZUS4guAUsJQd0UqyqOZzxuQA+2enWgD13R f7RGmBNU5uo5ZYw525kjWRljdtvG5kCMcADLHhegsX6XEmnXKWb7LponELbgu18HaclWA5xyVb6H pXk3/Cw/GH/PXQ//AAXzf/H67mwXxhfada3f9taGnnxJLt/seY7dwBxn7Vz1oAw9IbxmNSQS3+pT vbpam5tNRXT494eXZJIFg3ME2CVlBdSGiA/e5IX0SuTj0HxVFrNzqi6/o3n3FvDbup0eXaFjaRlI /wBJznMrZ57Dp3p+JNS8YeHtOju/7U0O43yiLb/ZMyYyCc5+0n0oAk8aN4itrhrvTr/UrXT47Jy7 Wa2LLFKCT5kv2nbhAvUKx3f9M9uX3PDqazFa3EGtP5skUqiGcsjNKhiRmJKKgOJGkQHYmVRSRk5P lWreMfFWs6NfaXcT6MsF7byW8jR2EoYK6lSRmcjOD6Grn/Cw/GH/AD10P/wXzf8Ax+gD2GvM1Hju G4MI1LUpL+K3uZ0t7o6cILpkI8tYyi+ayZZEYssWQ+7MRwh3NDuPGGs6PBqH9r6HD5u75P7ImbGG K9ftI9Kkk0HxVLrNtqja/o3n29vNboo0eXaVkaNmJ/0nOcxLjnuevYA6yub8Vx66z6fJo82pJAju LqPThamVwV+Xi5G0AHksGz22tu3JT1y48YaNo8+of2voc3lbfk/siZc5YL1+0n1rjv8AhYfjD/nr of8A4L5v/j9AHc+DH157NX1e8+3281pBcQXZMJLu5k3KPJAULsELADdgyMBJIADXUV4lpPjHxVo2 jWOl28+jNBZW8dvG0lhKWKooUE4nAzgegq5/wsPxh/z10P8A8F83/wAfoA6G8i8W/wDCRi2Osara 29zqDCB1/s/yPI2FtiMyGYyAKzcxn7pQkgGau00mS+m0axl1SGODUHt42uoozlUlKjeo5PAbI6n6 mvHr3xj4qvrrTriWfRg9hcG4iC2EuCxikiw37/ptkY8Y5A+huf8ACw/GH/PXQ/8AwXzf/H6APRfF UesSaXEdFmu4p0uEaX7IIDK0XO5VE42EnjqVx97LY2Pl+En8RvdeZqF5Pe2Ev2kFro2vmW7xyosa EW42hjmYN8z8xKcxklBx3/Cw/GH/AD10P/wXzf8Ax+qemeMfFWlWr28E+jMj3E9wS9hKTullaVhx OONzkD2x160Ae20Vw/gLxXrHiHUNWtNW+wn7JFbyRtaQPFnzDKCCGds/6sdMdTXcUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/wCM v+QHbf8AYV03/wBLYa6CuP8AGU2seXbRfYbH+zP7V03/AEj7Y/nf8fcP/LLytv3uPv8ATn2oA7Ci iigDy74k/wDIxW//AF6L/wChvXHV2PxJ/wCRit/+vRf/AEN646gAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigD6CooooA8++J//MK/7bf+yV59XoPxP/5hX/bb /wBkrz6gAr3LQf8AkXdM/wCvSL/0AV4bXuWg/wDIu6Z/16Rf+gCgDQrjviT/AMi7b/8AX2v/AKA9 djXHfEn/AJF23/6+1/8AQHoA8uooooA9i8D/APIn2H/bT/0Y1dBXP+B/+RPsP+2n/oxq6CgDn/HH /In3/wD2z/8ARi147XsXjj/kT7//ALZ/+jFrx2gAooooAKKKKACiiigDsfhX/wAjD4h/69bL/wBD ua9Rrx74fTaxF4h1z+ybGxus2tn5n2u8e32/PcYxtifPfrjGB1zx6D4v/caZZ6ivyyafqFtP5p6Q xmQRzu3baIJJsk8KCW4IBAB0FFcPZeKdYv7DR/Ll0qO61zyZbb5Hc2cckE0wEsW8GTiAoHDIGJY7 V2YaPw7441LWr6yklsbSHT724it4lWRmlRpNPW8yxxghfmXgfNvB+XZ84B2l1f2dj5H2y7gt/PlW CHzpAnmSN91Fz1Y4OAOTUbatpqveo2oWgewQPeKZlzbqVLAyc/ICoJyccDNcX8R/+Xz/ALFTWf8A 22rn7z/j70b/ALCt/wD+n+0oA9YS/s5IrWWO7geO7x9mdZARNlS42H+L5QW47AnpVivN9Osbf+0f Dl+Y83Q8S6rCrsxO1M6gSFB4XJxnGN21c52rjU1rXNStPG8WlaXa6aJ7tLWJrm4jbcFZL5+SpywQ wAqvGdzjK7tygHaUV5va/EDWJ2tXmt9Kto9QtLS9txLM4S3jkiupmEspxnK2uNwUBPMJxIE+btPD WpTaz4V0jVLhY1nvbKG4kWMEKGdAxAyScZPqaANSiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAK5/wAZf8gO2/7Cum/+lsNdBXM+PblLLwut1IGKQ6jp8jBepAvIScfl QB01Fcd/wsnR/wDn2vv+/af/ABVH/CydH/59r7/v2n/xVAHP/En/AJGK3/69F/8AQ3rjq73UtNm8 e3K6ppbRwwRILdluiVYsCWyNoYYw47+tU/8AhW2sf8/Nj/38f/4mgDjqK6u78AarZWU91JcWRSGN pGCu2SAMnHy+1cpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH 0FRRRQB598T/APmFf9tv/ZK8+r0H4n/8wr/tt/7JXn1ABXuWg/8AIu6Z/wBekX/oArw2vR9M8f6V ZaVZ2slvel4YEjYqi4JCgHHze1AHd1x3xJ/5F23/AOvtf/QHo/4WTo//AD7X3/ftP/iqp6lqUPj2 3XS9LWSGeJxcM10AqlQCuBtLHOXHb1oA86orsf8AhW2sf8/Nj/38f/4mj/hW2sf8/Nj/AN/H/wDi aAOx8D/8ifYf9tP/AEY1dBXDWPiSz8IWcehahFPLdWud726hkO4lxgkg9GHarH/CydH/AOfa+/79 p/8AFUAaPjj/AJE+/wD+2f8A6MWvHa9JvvEln4vs5NC0+KeK6usbHuFCoNpDnJBJ6Ke1Y/8AwrbW P+fmx/7+P/8AE0AcdRXY/wDCttY/5+bH/v4//wATWBreiXOg3qWt08Tu8YkBiJIwSR3A9DQBm0UU UAFFFFAHY/Cv/kYfEP8A162X/odzXpM1hZ3H2jz7SCX7TEIJ98YbzYxuwjZ+8vztwePmPqa82+Ff /Iw+If8Ar1sv/Q7mvUaAM99C0eSzurOTSrF7W7lM9zC1uhSaQkEu64wzZAOTzwKkn0y2mErJHHBc O5lW5jiQyJKY/L80FlI3hPlyQeODkcVcooAx7bQFG3+1b+fWvLlSe3/tCC3P2eRc4dPLiTDc9Tkj HGOc3G0nTWe9dtPtC9+gS8Ywrm4UKVAk4+cBSRg54OKuUUAV0sLOOK1ijtIEjtMfZkWMAQ4UoNg/ h+UleOxI6VHHpOmxXAuI9PtEnDlxIsKhgxLknOM5zLKc/wDTR/7xzcooAx7/AMOWt1ZxW9o/9m+X 5IV7S3gJ2REmJMSRuoVGO5cD5SOCOc6FhY2+madbWFnH5draxJDCm4naigBRk8nAA61YooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArj/ih/yIN1/wBfVl/6VRV2 Fef/ABQ1m1/4Rm60vyr77R9qsvn+wT+T/wAfETf67Z5fT/a68deKAPPaKKKAPUfht/yLtx/19t/6 AldjXHfDb/kXbj/r7b/0BK7GgDP17/kXdT/69Jf/AEA14bXuWvf8i7qf/XpL/wCgGvDaACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+gqKKKAPPvif/wAwr/tt/wCy V59XoPxP/wCYV/22/wDZK8+oAKKKKACux+G3/IxXH/Xo3/oaVx1dj8Nv+RiuP+vRv/Q0oA9Roooo A8e8cf8AI4X/AP2z/wDRa1z1dD44/wCRwv8A/tn/AOi1rnqAOh8D/wDI4WH/AG0/9FtXsNePeB/+ RwsP+2n/AKLavYaACvLviT/yMVv/ANei/wDob16jXl3xJ/5GK3/69F/9DegDjqKKKACiiigDsfhX /wAjD4h/69bL/wBDua9Rrx74faza6R4h1z7TFfSeba2e37JYT3OMPcZz5SNt698Z5x0Nek+Ib640 2CwvIJMRrqFvDPFtH75JnEAGT93a0qycdfL28BiQAbFFc3F4ytp7KC5g03UpTeOgsIxGim9R0aRX jZnCAFI3ba7KwC8qCyhjTPG+kavqMdpaLdskrokNy0BWKRnt1uUCk85MRZsY42ENt3JvAOkorh/H 9/eWu/7NdzwfZtE1HUo/JkKf6RB5PlM2PvqPMfKNlGz8wOBjDu9W1JbyLbqF2P7Tvbq2uQJmA8uP Vre1QRjP7oiGV13R7SSdxJYBgAeqUV5/p9zqjXnhpn1Wc2i63f2H2fqZUiF6qea5JaTCxRAZ7hmY uxBXc1TxVbaR4g/s94dSuriZIEit7eJGXe4uWXB4IJ+zsGLHauEPyjewAOkork7f4haRdOBBbalI kiQyWzC1ObqORZW3xofnwFt5mwVBYJ8gfcu7oNJ1KHWdGsdUt1kWC9t47iNZAAwV1DAHBIzg+poA uUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXH/FD/kQbr/r6sv/ AEqirsK4/wCKH/Ig3X/X1Zf+lUVAHltFFFAHqPw2/wCRduP+vtv/AEBK7GuE8AanYWWgzx3V9bQO blmCyyqpI2rzgn2NdX/b2j/9Bax/8CU/xoANe/5F3U/+vSX/ANANeG17Nq+r6bdaNfW9vqFpNPLb yJHFHMrM7FSAAAckk8Yryn+wdY/6BN9/4DP/AIUAZ9FaH9g6x/0Cb7/wGf8AwqvdWF5Y7PtdpPb7 87fNjKbsdcZ69RQBXooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+gqKKK APPvif8A8wr/ALbf+yV59XoPxP8A+YV/22/9krz6gAooq9HouqyxrJHpl66OAyssDEMD0IOKAKNd j8Nv+RiuP+vRv/Q0rnv7B1j/AKBN9/4DP/hXS+CbebRtZmuNUiksYGt2RZbpTEpYspwC2BnAJx7G gD02is/+3tH/AOgtY/8AgSn+NH9vaP8A9Bax/wDAlP8AGgDy7xx/yOF//wBs/wD0Wtc9XU+KrC81 PxJd3mn2k93aybNk1vGZEbCKDhhkHBBH4Vjf2DrH/QJvv/AZ/wDCgDR8D/8AI4WH/bT/ANFtXsNe TeFbC80zxJaXmoWk9pax7981xGY0XKMBljgDJIH416T/AG9o/wD0FrH/AMCU/wAaANCvLviT/wAj Fb/9ei/+hvXoP9vaP/0FrH/wJT/GvOPH93bXuvQSWtxFOgtlUtE4YA7m4yPqKAOUooooAKKKKAOx +Ff/ACMPiH/r1sv/AEO5r0HVNJTVrO7tbi5nWG4iVFEe0GBwSRLG23IkBKkEk4KKQAc58++Ff/Iw +If+vWy/9Dua9RoA5uLwbbQWUFtBqWpRGzdDYSCRGNkiI0apGrIUICSOu51ZiG5YlVKkHg+x0qOJ 9LSQPaXAu7W3kmxHvWz+yIhbazBNgHPJzzz0rpKKAOXudBvPEm7+37WCx2xPbf8AEvvjP9ot5ced C++Fdqtsj5X5+OGXnMkvgrTZZp5Wnuxud5LZQ64tJHmW4d4/l5JmjSTEm8ArgAKSp6SigDHg8N2d vFpiLJOzWF3Jeq7MMyzSLKsjPxj5jPI2FCgEjAAGKjbwvbSeIIdamu7uW7hdWXcUC4UXKquAo4C3 Tj1+VMkncW3KKAOLvfBC2tlYjSEkuLm0t7WzjafUGtSkUCTJuDxxMd7LPIhwBw2VKMoNdJoWmf2J 4e0zSfO877DaRW3m7du/YgXdjJxnGcZNaFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFcf8AFD/kQbr/AK+rL/0qirsK4/4of8iDdf8AX1Zf+lUVAHltFFFABRRR QBoaD/yMWmf9fcX/AKGK9yrw3Qf+Ri0z/r7i/wDQxXuVABXn3xP/AOYV/wBtv/ZK9Brz74n/APMK /wC23/slAHn1FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH0FRRRQB598T /wDmFf8Abb/2SvPq9B+J/wDzCv8Att/7JXn1ABXuWg/8i7pn/XpF/wCgCvDa9y0H/kXdM/69Iv8A 0AUAaFcd8Sf+Rdt/+vtf/QHrsa474k/8i7b/APX2v/oD0AeXUUUUAexeB/8AkT7D/tp/6Maugrn/ AAP/AMifYf8AbT/0Y1dBQBz/AI4/5E+//wC2f/oxa8dr2Lxx/wAiff8A/bP/ANGLXjtABRRRQAUU UUAFFFFAHY/Cv/kYfEP/AF62X/odzXqNeXfCv/kYfEP/AF62X/odzXoOq6n/AGX9ikeHdbz3cdtL Lux5PmZVGxglsyGNMDpv3HhTQBoUVnvrujx2d1eSarYpa2kpguZmuECQyAgFHbOFbJAweeRVhL+z kvGs0u4GukzuhEgLrgITlevAkjJ/319RQBYorl/F3iS80L/jzjgbydPu9Tm85S3mR2/l7olwRtZv NGHO4Lt+62eMu48a6lFclFgtNl5cTWtnlGzbtFfRWReQ7v3oLTCTaNmAu3JzvAB3lFcfaeKdUnvN DgexgENzqF1YXV1uwGeEXI/dJkkZNtuO4/KHVQXOWXoLnWLOxvJYr28sbaNIlkBluQr8iRm3KcYU LEzBsnO1+AEyQDQoqmdW00XFxbnULTz7d4knj85d0TSECMMM5BYkBQeueM1YgnhureK4t5Y5oJUD xyRsGV1IyCCOCCOc0ASUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABXn/wAUNC0f/hGbrV/7Ksf7T+1WX+m/Z087/j4iX7+N33eOvTivQK4/4of8iDdf9fVl/wClUVAH ltFFFABRRRQBoaD/AMjFpn/X3F/6GK9yrw3Qf+Ri0z/r7i/9DFe5UAFeffE//mFf9tv/AGSvQa8+ +J//ADCv+23/ALJQB59RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9BUU UUAeffE//mFf9tv/AGSvPq9B+J//ADCv+23/ALJXn1ABXuWg/wDIu6Z/16Rf+gCvDa9y0H/kXdM/ 69Iv/QBQBoVx3xJ/5F23/wCvtf8A0B67GuO+JP8AyLtv/wBfa/8AoD0AeXUUUUAexeB/+RPsP+2n /oxq6Cuf8D/8ifYf9tP/AEY1dBQBz/jj/kT7/wD7Z/8Aoxa8dr2Lxx/yJ9//ANs//Ri147QAUUUU AFFFFABRRRQB0Pw+0LR9b8Q65/a2lWN/5NrZ+X9rt0l2Ze4zjcDjOB09BXpPiDT7zVdMubW3ECSR +Tc2ckkhw1xFJ5iLIoXiPckeSCSQzAbSATw3wr/5GHxD/wBetl/6Hc16jQBw9l4W1iwsNH8uLSpL rQ/JitvndDeRxwTQgyy7CY+Jy4QK4Uhhubfla+j+B38NG0umkgmWwu4Z5J4oWM00EOmG1A2KCS28 swQE8McEk4PoFFAHD63aN4083+yhPDnT7nTLj+0LK4tdkdzszKnmRjzGTyfuDAO7ll4zHceCtSlu S6z2myzuJrqzy7ZuGlvor0pINv7oBoRHuG/IbdgY2HvKKAOXtPDd5BFoZkkg8y01W61K5VWJA89b klEOPm2tcAZIXIUnAPy1XvvC15qfjSz1m7isTa28sD+SXMh/ci9CMMoBuzcQsPQq2CdoLdhRQB5X L4Ml8O6bpLyR2jpaWVnZvbw6fNdxyyrFeRzs8cSElD9qL8gbyCrFN+6u88J2Nxpng3Q7C8j8u6td Pt4Zk3A7XWNQwyODgg9K2KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAK4/4of8iDdf8AX1Zf+lUVdhXH/FD/AJEG6/6+rL/0qioA8tooooAKKKKAHRyPFIskbsjo QyspwVI6EGr39vax/wBBa+/8CX/xrPooA0P7e1j/AKC19/4Ev/jXY+BP+J3/AGh/a3+n+T5flfa/ 3uzO7ON2cZwM49BXn1eg/DD/AJiv/bH/ANnoA7H+wdH/AOgTY/8AgMn+FH9g6P8A9Amx/wDAZP8A CtCigD59ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD6CooooA8++J//MK/7bf+ yV59XoPxP/5hX/bb/wBkrz6gAr3LQf8AkXdM/wCvSL/0AV4bXuWg/wDIu6Z/16Rf+gCgDQrjviT/ AMi7b/8AX2v/AKA9djXHfEn/AJF23/6+1/8AQHoA8uooooA9i8D/APIn2H/bT/0Y1dBXP+B/+RPs P+2n/oxq6CgDn/HH/In3/wD2z/8ARi147XsXjj/kT7//ALZ/+jFrx2gAooooAKKKKACiiigDsfhX /wAjD4h/69bL/wBDua9Rry74V/8AIw+If+vWy/8AQ7mvSbi+t7Se0hnk2SXcphgG0ne4RpCOOnyo x59PXFAFiiiigAorH1zxJZ6B5f2mOeTMUlzJ5Kg+TbxbfNmbJGVXemQuXO75VODinL4102KaeJoL s7XeO2YIuLuRJlt3SP5uCJpEjzJsBLZBKgsADpKK5+DxjpdxeaZZJ5/2u/lkh8nZk27xiXeJSCVX 5oJVGCdxRtu5QWG4Jla4eACTeiK5JjYLhiQMNjBPynIByOM4yMgElFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFef/ABQ066/4Rm6vf7Zvvs/2qy/0DZB5P/Hx EOvl+Z1+b7/X24r0CuP+KH/Ig3X/AF9WX/pVFQB5bRRRQAUUUUAFFFFABXoPww/5iv8A2x/9nrz6 vQfhh/zFf+2P/s9AHoNFFFAHz7RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH0F RRRQB598T/8AmFf9tv8A2SvPq9B+J/8AzCv+23/slefUAFe5aD/yLumf9ekX/oArw2vctB/5F3TP +vSL/wBAFAGhXHfEn/kXbf8A6+1/9AeuxrjviT/yLtv/ANfa/wDoD0AeXUUUUAexeB/+RPsP+2n/ AKMaugrn/A//ACJ9h/20/wDRjV0FAHP+OP8AkT7/AP7Z/wDoxa8dr2Lxx/yJ9/8A9s//AEYteO0A FFFFABRRRQAUUUUAdD8PtOur/wAQ659m1m+03Za2e77IkDeZl7jGfNjfpjtjqc54x6D4tD3Gizx2 8E801lLa6g0ccLEypFOspSM4w0hETAKD1K5wGBrjvhX/AMjD4h/69bL/ANDua9RoA83tv7T/ALFs ft3/AAke3zYv7e2+du83yZfM+z7P323zvIz5P7rbjZ8vm1J4dj8Vx6rZXOrzak08l7Fb3cLAGBE/ sxXkZQo2gG5UDcDgMCFxvcN6JUc8EN1by29xFHNBKhSSORQyupGCCDwQRxigDi/H9heXW/7NaTz/ AGnRNR02PyYy/wDpE/k+UrY+4p8t8u2EXHzEZGcO70nUmvItun3Z/sy9urm5IhYjy5NWt7pDGcfv SYYnbbHuII2kBiFPommaFo+ieb/ZOlWNh52PM+yW6Rb8ZxnaBnGT19TWhQBw+n2F4sXhqRrSdVPi C/vWDRkGOGVb1o2cdUyJY+GwQWAIB4qPWI9V1Xxvaw282s2+ku8Ec7wCSFcKmoCUZI4DMIQWGDzG ysDsau8ooA8rs4/FYFlcXU3iCX7VZafcX5jAWRZ2ju8rGhAjQiUWgZQAuADLlS5PceC55rrwL4eu LiWSaeXTLZ5JJGLM7GJSSSeSSec1oalpOm6zbrb6pp9pfQK4dY7qFZVDYIyAwIzgkZ9zVygAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuP+KH/Ig3X/X1Zf8ApVFX YVx/xQ/5EG6/6+rL/wBKoqAPLaKKKACiiigAooooAK2NC8SXnh77R9kigfz9u7zVJxtzjGCPU1j0 UAdj/wALJ1j/AJ9rH/v2/wD8VR/wsnWP+fax/wC/b/8AxVcdRQB6j/wrbR/+fm+/7+J/8TR/wrbR /wDn5vv+/if/ABNdjRQB5h4t8JWGg6VFdWs1y7vOIyJWUjBVj2UegrjK9R+JP/Iu2/8A19r/AOgP Xl1ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB9BUUUUAeffE/8A5hX/AG2/9krz6vQfif8A 8wr/ALbf+yV59QAV7loP/Iu6Z/16Rf8AoArw2vctB/5F3TP+vSL/ANAFAGhXHfEn/kXbf/r7X/0B 67GuO+JP/Iu2/wD19r/6A9AHl1FFFAHsXgf/AJE+w/7af+jGroK5/wAD/wDIn2H/AG0/9GNXQUAc /wCOP+RPv/8Atn/6MWvHa9i8cf8AIn3/AP2z/wDRi147QAUUUUAFFFFABRRRQB2Pwr/5GHxD/wBe tl/6Hc16jXl3wr/5GHxD/wBetl/6Hc16jQAUUUUAFFV7q/s7HyPtl3Bb+fKsEPnSBPMkb7qLnqxw cAcmo21bTVe9RtQtA9gge8UzLm3UqWBk5+QFQTk44GaALlFV0v7OSK1lju4Hju8fZnWQETZUuNh/ i+UFuOwJ6VYoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AK8/+KE2sf8ACM3UX2Gx/sz7VZf6R9sfzv8Aj4i/5ZeVt+9x9/pz7V6BXH/FD/kQbr/r6sv/AEqi oA8tooooAKKKKACiiigAooooAKKKKAPoKiiigDjviT/yLtv/ANfa/wDoD15dXqPxJ/5F23/6+1/9 AevLqACiiigAooooAKKKKACiiigAooooAKKKKACiiigD6CooooA8++J//MK/7bf+yV59XoPxP/5h X/bb/wBkrz6gAr3LQf8AkXdM/wCvSL/0AV4bXuWg/wDIu6Z/16Rf+gCgDQrjviT/AMi7b/8AX2v/ AKA9djXHfEn/AJF23/6+1/8AQHoA8uooooA9i8D/APIn2H/bT/0Y1dBXP+B/+RPsP+2n/oxq6CgD n/HH/In3/wD2z/8ARi147XsXjj/kT7//ALZ/+jFrx2gAooooAKKKKACiiigDofh9NrEXiHXP7Jsb G6za2fmfa7x7fb89xjG2J89+uMYHXPHoPi/9xplnqK/LJp+oW0/mnpDGZBHO7dtogkmyTwoJbggE cd8K/wDkYfEP/XrZf+h3NekzWFncfaPPtIJftMQgn3xhvNjG7CNn7y/O3B4+Y+poA4+y8U6xf2Gj +XLpUd1rnky23yO5s45IJpgJYt4MnEBQOGQMSx2rsw0fh3xxqWtX1lJLY2kOn3txFbxKsjNKjSae t5ljjBC/MvA+beD8uz5+sfQtHks7qzk0qxe1u5TPcwtboUmkJBLuuMM2QDk88CpJ9MtphKyRxwXD uZVuY4kMiSmPy/NBZSN4T5ckHjg5HFAHF/Ef/l8/7FTWf/baufvP+PvRv+wrf/8Ap/tK9IttAUbf 7Vv59a8uVJ7f+0ILc/Z5Fzh08uJMNz1OSMcY5zcbSdNZ7120+0L36BLxjCubhQpUCTj5wFJGDng4 oA4PTrG3/tHw5fmPN0PEuqwq7MTtTOoEhQeFycZxjdtXOdq41Na1zUrTxvFpWl2umie7S1ia5uI2 3BWS+fkqcsEMAKrxnc4yu7cvWJYWccVrFHaQJHaY+zIsYAhwpQbB/D8pK8diR0qOPSdNiuBcR6fa JOHLiRYVDBiXJOcZzmWU5/6aP/eOQDg7X4gaxO1q81vpVtHqFpaXtuJZnCW8ckV1MwllOM5W1xuC gJ5hOJAnzdp4a1KbWfCukapcLGs97ZQ3EixghQzoGIGSTjJ9TUd/4ctbqzit7R/7N8vyQr2lvATs iJMSYkjdQqMdy4HykcEc50LCxt9M062sLOPy7W1iSGFNxO1FACjJ5OAB1oAsUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARzzw2tvLcXEscMESF5JJGCqigZJJPAAHOajhv7O 4vLmzhu4JLq12/aIUkBeLcMruUcrkcjPWsvxcYR4dkEscju9xbJb7HCFbhp0ELbiGACylGOVYYB+ Vvunn/B8kJ1WzW4tJI7z7PqCIwuRMokjvdt2x/dpzJIYXHGOCAsYHzgHeVx/xQ/5EG6/6+rL/wBK oq7CuP8Aih/yIN1/19WX/pVFQB5bRRRQAUUUUAFFFFABRRRQAUUUUAeo/wDCydH/AOfa+/79p/8A FUf8LJ0f/n2vv+/af/FV5dRQB6LqWpQ+PbddL0tZIZ4nFwzXQCqVAK4G0sc5cdvWsv8A4VtrH/Pz Y/8Afx//AImj4bf8jFcf9ejf+hpXqNAHl3/CttY/5+bH/v4//wATXKXds9lez2shUvDI0bFehIOD j8q98rw3Xv8AkYtT/wCvuX/0M0AZ9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAeo/8LJ0f/n2vv+/a f/FUf8LJ0f8A59r7/v2n/wAVXl1FAHoOqf8AFwvK/sn9z9hz5v2v5c78Yxt3f3DnOO1Z/wDwrbWP +fmx/wC/j/8AxNaHww/5iv8A2x/9nr0GgDy7/hW2sf8APzY/9/H/APia3LfxtpujW8Wl3EF209kg t5GjRSpZBtJGWBxkegrta8N17/kYtT/6+5f/AEM0Aeg/8LJ0f/n2vv8Av2n/AMVVPUtSh8e266Xp ayQzxOLhmugFUqAVwNpY5y47etedV2Pw2/5GK4/69G/9DSgA/wCFbax/z82P/fx//iaP+Fbax/z8 2P8A38f/AOJr1GigDhrHxJZ+ELOPQtQinlurXO97dQyHcS4wSQejDtVj/hZOj/8APtff9+0/+Krj /HH/ACOF/wD9s/8A0Wtc9QB3viPxtpur6Dc2NvBdrLLt2mRFC8MDzhj6VwVFFABRRRQAVNaWz3t7 BaxlQ80ixqW6Ak4GfzqGtDQf+Ri0z/r7i/8AQxQB0P8AwrbWP+fmx/7+P/8AE0f8K21j/n5sf+/j /wDxNeo0UAcD4G0S50Hxbr1rdPE7vY2MgMRJGDJdDuB6Gu+rn7P/AJKHrP8A2CrD/wBG3ddBQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGH4w iu7jwnfwWXmebKixsyQJMVjZgJG8plYSgIWJjxlwCq4JBHB6fpl1o0t/ceEpNVEYtA4hk0GCxE10 GxDFJ/osbSRvvOWUr5YRizAOCvoHim1+2eHLuM3EEMabJpTcvsheON1d45G5xG6qyMSCArHIYcHj /AmleG7LxXey6Df+HJf3U/GmzRvNLHJP5q7lUfuli3eWNpIcFCduxFAB6RXn/wAUNZtf+EZutL8q ++0farL5/sE/k/8AHxE3+u2eX0/2uvHXivQK4/4of8iDdf8AX1Zf+lUVAHltFFFABRRRQAUUUUAF FFFABRRRQAUUUUAdj8Nv+RiuP+vRv/Q0r1GvLvht/wAjFcf9ejf+hpXqNABXhuvf8jFqf/X3L/6G a9yrw3Xv+Ri1P/r7l/8AQzQBn0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeg/DD/AJiv/bH/ ANnr0GvPvhh/zFf+2P8A7PXoNABXhuvf8jFqf/X3L/6Ga9yrw3Xv+Ri1P/r7l/8AQzQBn12Pw2/5 GK4/69G/9DSuOrsfht/yMVx/16N/6GlAHqNFFFAHj3jj/kcL/wD7Z/8Aota56uh8cf8AI4X/AP2z /wDRa1z1ABRRRQAUUUUAFaGg/wDIxaZ/19xf+his+tDQf+Ri0z/r7i/9DFAHuVFFFAHH3Gs2ukfE PVPtMV9J5ulWO37JYT3OMS3ec+UjbevfGecdDWx4hvrjTYLC8gkxGuoW8M8W0fvkmcQAZP3drSrJ x18vbwGJFez/AOSh6z/2CrD/ANG3daGqaSmrWd3a3FzOsNxEqKI9oMDgkiWNtuRICVIJJwUUgA5y AZcXjK2nsoLmDTdSlN46CwjEaKb1HRpFeNmcIAUjdtrsrALyoLKGNM8b6Rq+ox2lot2ySuiQ3LQF YpGe3W5QKTzkxFmxjjYQ23cm8i8G20FlBbQalqURs3Q2EgkRjZIiNGqRqyFCAkjrudWYhuWJVSpB 4PsdKjifS0kD2lwLu1t5JsR71s/siIW2swTYBzyc889KAMvx/f3lrv8As13PB9m0TUdSj8mQp/pE Hk+UzY++o8x8o2UbPzA4GMO71bUlvItuoXY/tO9ura5AmYDy49Wt7VBGM/uiIZXXdHtJJ3ElgGHW XOg3niTd/b9rBY7Yntv+JffGf7Rby486F98K7VbZHyvz8cMvOZJfBWmyzTytPdjc7yWyh1xaSPMt w7x/LyTNGkmJN4BXAAUlSAYen3OqNeeGmfVZzaLrd/YfZ+plSIXqp5rklpMLFEBnuGZi7EFdzVPF VtpHiD+z3h1K6uJkgSK3t4kZd7i5ZcHggn7OwYsdq4Q/KN7C5B4bs7eLTEWSdmsLuS9V2YZlmkWV ZGfjHzGeRsKFAJGAAMVG3he2k8QQ61Nd3ct3C6su4oFwouVVcBRwFunHr8qZJO4sAZ9v8QtIunAg ttSkSRIZLZhanN1HIsrb40Pz4C28zYKgsE+QPuXd0Gk6lDrOjWOqW6yLBe28dxGsgAYK6hgDgkZw fU1y974IW1srEaQklxc2lva2cbT6g1qUigSZNweOJjvZZ5EOAOGypRlBrpNC0z+xPD2maT53nfYb SK283bt37EC7sZOM4zjJoA0KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA OX8e3sFr4akDzQLMstvcKslzFC6rHcRFpEMrKm5CVI3Hbu2BgQ205fgrxLFrOszW6a3qV8Vt2fy7 q60uVR8yjIFoS+ecZPy8nPOK7yigArj/AIof8iDdf9fVl/6VRV2Fcf8AFD/kQbr/AK+rL/0qioA8 tooooAKKKKACiiigAooooAKKKKACiiigDsfht/yMVx/16N/6Gleo18+0UAfQVeG69/yMWp/9fcv/ AKGaz69y0H/kXdM/69Iv/QBQB4bRX0FXP+OP+RPv/wDtn/6MWgDx2iiigAooooAKKKKACiiigAoo ooAKKKKAPQfhh/zFf+2P/s9eg1598MP+Yr/2x/8AZ69BoAK8N17/AJGLU/8Ar7l/9DNe5UUAfPtd j8Nv+RiuP+vRv/Q0r1GuO+JP/Iu2/wD19r/6A9AHY0V8+0UAdD44/wCRwv8A/tn/AOi1rnqKKACi iigAoor1H4bf8i7cf9fbf+gJQB5dWhoP/IxaZ/19xf8AoYr3Ks/Xv+Rd1P8A69Jf/QDQBoUV8+0U AexWf/JQ9Z/7BVh/6Nu66CvLvhX/AMjD4h/69bL/ANDua9RoAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4/4of8iDdf9fVl/wClUVdh Xn/xQ0LR/wDhGbrV/wCyrH+0/tVl/pv2dPO/4+Il+/jd93jr04oA89ooooAKKKKACiiigAooooAK KKKACiiigAooooAK9y0H/kXdM/69Iv8A0AV4bXuWg/8AIu6Z/wBekX/oAoA0K5/xx/yJ9/8A9s// AEYtdBXP+OP+RPv/APtn/wCjFoA8dooooAKKKKACiiigAooooAKKKKACiiigD0H4Yf8AMV/7Y/8A s9eg1598MP8AmK/9sf8A2evQaACiiigArjviT/yLtv8A9fa/+gPXY1x3xJ/5F23/AOvtf/QHoA8u ooooAKKKKACiiigAr1H4bf8AIu3H/X23/oCV5dXqPw2/5F24/wCvtv8A0BKAOxrP17/kXdT/AOvS X/0A1oVn69/yLup/9ekv/oBoA8NooooA7H4V/wDIw+If+vWy/wDQ7mvQdV1P+y/sUjw7ree7jtpZ d2PJ8zKo2MEtmQxpgdN+48Ka8u+H2haPrfiHXP7W0qxv/JtbPy/tdukuzL3GcbgcZwOnoK9J8Qaf earplza24gSSPybmzkkkOGuIpPMRZFC8R7kjyQSSGYDaQCQCw+u6PHZ3V5JqtilraSmC5ma4QJDI CAUds4VskDB55FWEv7OS8azS7ga6TO6ESAuuAhOV68CSMn/fX1FcfZeFtYsLDR/Li0qS60PyYrb5 3Q3kccE0IMsuwmPicuECuFIYbm35Wvo/gd/DRtLppIJlsLuGeSeKFjNNBDphtQNigktvLMEBPDHB JOCAbHi7xJeaF/x5xwN5On3epzecpbzI7fy90S4I2s3mjDncF2/dbPGXceNdSiuSiwWmy8uJrWzy jZt2ivorIvId370FphJtGzAXbk53iTW7RvGnm/2UJ4c6fc6Zcf2hZXFrsjudmZU8yMeYyeT9wYB3 csvGY7jwVqUtyXWe02WdxNdWeXbNw0t9FelJBt/dANCI9w35DbsDGwgFy08U6pPeaHA9jAIbnULq wurrdgM8IuR+6TJIybbcdx+UOqgucsvQXOsWdjeSxXt5Y20aRLIDLchX5EjNuU4woWJmDZOdr8AJ k49p4bvIItDMkkHmWmq3WpXKqxIHnrckohx821rgDJC5Ck4B+Wq994WvNT8aWes3cVibW3lgfyS5 kP7kXoRhlAN2biFh6FWwTtBYA6Q6tpouLi3OoWnn27xJPH5y7omkIEYYZyCxICg9c8ZqxBPDdW8V xbyxzQSoHjkjYMrqRkEEcEEc5ry+XwZL4d03SXkjtHS0srOze3h0+a7jllWK8jnZ44kJKH7UX5A3 kFWKb91d54TsbjTPBuh2F5H5d1a6fbwzJuB2usahhkcHBB6UAbFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVx/xQ/5EG6/6+rL/wBKoq7CuP8Aih/yIN1/19WX/pVF QB5bRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV7loP8AyLumf9ekX/oArw2ty38Y69a28VvD f7YokCIvkocKBgDlaAPZq5/xx/yJ9/8A9s//AEYteff8Jx4i/wCgj/5Aj/8Aia0ND1zUfEmsQaTq 1x9osbjd5sWxU3bVLDlQCOVB4NAHHUV7F/wg/h3/AKB3/keT/wCKo/4Qfw7/ANA7/wAjyf8AxVAH jtFbHiqxttN8SXdpaR+XBHs2ruJxlFJ5PPUmsegAooooAKKKKACiiigAooooA9B+GH/MV/7Y/wDs 9eg1598MP+Yr/wBsf/Z69BoAKKKKACuO+JP/ACLtv/19r/6A9djXHfEn/kXbf/r7X/0B6APLqKKK ACiiigAooooAK9R+G3/Iu3H/AF9t/wCgJXl1eo/Db/kXbj/r7b/0BKAOxrP17/kXdT/69Jf/AEA1 oVn69/yLup/9ekv/AKAaAPDaKKKAOx+Ff/Iw+If+vWy/9Dua9Rry74V/8jD4h/69bL/0O5r1GgAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igArj/ih/wAiDdf9fVl/6VRV2Fef/FDTrr/hGbq9/tm++z/arL/QNkHk/wDHxEOvl+Z1+b7/AF9u KAPPaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArofA//I4WH/bT/wBFtXPV0Pgf/kcL D/tp/wCi2oA9hooooA8e8cf8jhf/APbP/wBFrXPV0Pjj/kcL/wD7Z/8Aota56gAooooAKKKKACii igAooooA9B+GH/MV/wC2P/s9eg1598MP+Yr/ANsf/Z69BoAKKKKACuO+JP8AyLtv/wBfa/8AoD12 Ncd8Sf8AkXbf/r7X/wBAegDy6iiigAooooAKKKKACvUfht/yLtx/19t/6AleXV6j8Nv+RduP+vtv /QEoA7Gs/Xv+Rd1P/r0l/wDQDWhWfr3/ACLup/8AXpL/AOgGgDw2iiigDsfhX/yMPiH/AK9bL/0O 5r0m4vre0ntIZ5Nkl3KYYBtJ3uEaQjjp8qMefT1xXk3w+066v/EOufZtZvtN2Wtnu+yJA3mZe4xn zY36Y7Y6nOeMeg+LQ9xos8dvBPNNZS2uoNHHCxMqRTrKUjOMNIREwCg9SucBgaAOgorze2/tP+xb H7d/wke3zYv7e2+du83yZfM+z7P323zvIz5P7rbjZ8vm1J4dj8Vx6rZXOrzak08l7Fb3cLAGBE/s xXkZQo2gG5UDcDgMCFxvcMAdZrniSz0Dy/tMc8mYpLmTyVB8m3i2+bM2SMqu9Mhcud3yqcHFOXxr psU08TQXZ2u8dswRcXciTLbukfzcETSJHmTYCWyCVBYZfj+wvLrf9mtJ5/tOiajpsfkxl/8ASJ/J 8pWx9xT5b5dsIuPmIyM4d3pOpNeRbdPuz/Zl7dXNyRCxHlyatb3SGM4/ekwxO22PcQRtIDEKQDtI PGOl3F5plknn/a7+WSHydmTbvGJd4lIJVfmglUYJ3FG27lBYbgmVrh4AJN6IrkmNguGJAw2ME/Kc gHI4zjIzxen2F4sXhqRrSdVPiC/vWDRkGOGVb1o2cdUyJY+GwQWAIB4qPWI9V1Xxvaw282s2+ku8 Ec7wCSFcKmoCUZI4DMIQWGDzGysDsagDvKK8rs4/FYFlcXU3iCX7VZafcX5jAWRZ2ju8rGhAjQiU WgZQAuADLlS5PceC55rrwL4euLiWSaeXTLZ5JJGLM7GJSSSeSSec0AblFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVx/wAUP+RBuv8Ar6sv/SqKuwrj/ih/yIN1/wBf Vl/6VRUAeW0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXQ+B/8AkcLD/tp/6LauerQ0 PVP7G1iDUPJ87yt3ybtucqV64PrQB7lRXn3/AAs//qD/APkz/wDYUf8ACz/+oP8A+TP/ANhQBz/j j/kcL/8A7Z/+i1rnq9B/4Rj/AITL/if/AGz7H9r/AOWHleZt2/J97Iznbnp3o/4Vh/1GP/Jb/wCz oA8+orub/wCHX2HTrq7/ALV3+RE8u37PjdtBOM7uOlcNQAUUUUAFFFFABRRRQB6D8MP+Yr/2x/8A Z69Brz74Yf8AMV/7Y/8As9eg0AFFFFABXHfEn/kXbf8A6+1/9AeuxrjviT/yLtv/ANfa/wDoD0Ae XUUUUAFFFFABRRRQAV6j8Nv+RduP+vtv/QEry6vUfht/yLtx/wBfbf8AoCUAdjWfr3/Iu6n/ANek v/oBrQrP17/kXdT/AOvSX/0A0AeG0UUUAdj8K/8AkYfEP/XrZf8AodzXqNeXfCv/AJGHxD/162X/ AKHc16jQAVHPBDdW8tvcRRzQSoUkjkUMrqRggg8EEcYqSigDP0zQtH0Tzf7J0qxsPOx5n2S3SLfj OM7QM4yevqa0KKKACiiigCnqWk6brNutvqmn2l9Arh1juoVlUNgjIDAjOCRn3NXKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4/wCKH/Ig3X/X1Zf+lUVdhXn/ AMUJtY/4Rm6i+w2P9mfarL/SPtj+d/x8Rf8ALLytv3uPv9OfagDz2iiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigD2LwP8A8ifYf9tP/RjV0Fc/4H/5E+w/7af+jGroKAM/Xv8A kXdT/wCvSX/0A14bXuWvf8i7qf8A16S/+gGvDaACiiigAooooAKKKKAPQfhh/wAxX/tj/wCz16DX n3ww/wCYr/2x/wDZ69BoAKKKKACuO+JP/Iu2/wD19r/6A9djXHfEn/kXbf8A6+1/9AegDy6iiigA ooooAKKKKACvUfht/wAi7cf9fbf+gJXl1eo/Db/kXbj/AK+2/wDQEoA7Gs/Xv+Rd1P8A69Jf/QDW hWfr3/Iu6n/16S/+gGgDw2iiigDsfhX/AMjD4h/69bL/ANDua9Rrx74fTaxF4h1z+ybGxus2tn5n 2u8e32/PcYxtifPfrjGB1zx6D4v/AHGmWeor8smn6hbT+aekMZkEc7t22iCSbJPCgluCAQAdBRXD 2XinWL+w0fy5dKjutc8mW2+R3NnHJBNMBLFvBk4gKBwyBiWO1dmGj8O+ONS1q+spJbG0h0+9uIre JVkZpUaTT1vMscYIX5l4Hzbwfl2fOAdpdX9nY+R9su4Lfz5Vgh86QJ5kjfdRc9WODgDk1G2raar3 qNqFoHsED3imZc26lSwMnPyAqCcnHAzXF/Ef/l8/7FTWf/baufvP+PvRv+wrf/8Ap/tKAPWEv7OS K1lju4Hju8fZnWQETZUuNh/i+UFuOwJ6VYrzfTrG3/tHw5fmPN0PEuqwq7MTtTOoEhQeFycZxjdt XOdq41Na1zUrTxvFpWl2umie7S1ia5uI23BWS+fkqcsEMAKrxnc4yu7coB2lFeb2vxA1idrV5rfS raPULS0vbcSzOEt45IrqZhLKcZytrjcFATzCcSBPm7Tw1qU2s+FdI1S4WNZ72yhuJFjBChnQMQMk nGT6mgDUooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuP+KH/Ig3 X/X1Zf8ApVFXYVx/xQ/5EG6/6+rL/wBKoqAPLaKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKAPYvA//ACJ9h/20/wDRjV0FcN4V8VaLpvhu0tLu98uePfuXynOMuxHIGOhFbP8A wnHh3/oI/wDkCT/4mgDQ17/kXdT/AOvSX/0A14bXrN/4q0XU9OutPs73zbq6ieGFPKddzsCqjJAA ySOtcP8A8IP4i/6B3/keP/4qgDnqK6H/AIQfxF/0Dv8AyPH/APFVlalpV7pFytvfQ+VKyBwu4N8u SM8E+hoAp0UUUAFFFFAHoPww/wCYr/2x/wDZ69Brz74Yf8xX/tj/AOz16DQAUUVh3HjHQbW5lt5r /bLE5R18lzhgcEcLQBuVx3xJ/wCRdt/+vtf/AEB60f8AhOPDv/QR/wDIEn/xNY3iS+tvF2nR6foU n2u6jlEzJtMeEAKk5fA6sPzoA82orof+EH8Rf9A7/wAjx/8AxVH/AAg/iL/oHf8AkeP/AOKoA56i rF9Y3Om3klpdx+XPHjcu4HGQCORx0IqvQAUUUUAFeo/Db/kXbj/r7b/0BK8urvfBPiPStI0aa3vr rypWuGcL5bN8u1RngH0NAHotZ+vf8i7qf/XpL/6Aaz/+E48O/wDQR/8AIEn/AMTVa/8AFWi6np11 p9ne+bdXUTwwp5TrudgVUZIAGSR1oA8morof+EH8Rf8AQO/8jx//ABVH/CD+Iv8AoHf+R4//AIqg DQ+Ff/Iw+If+vWy/9Dua9JmsLO4+0efaQS/aYhBPvjDebGN2EbP3l+duDx8x9TXB/D3Sr3SPFOv2 99D5UrWVk4XcG+XfcjPBPoa9EoAz30LR5LO6s5NKsXtbuUz3MLW6FJpCQS7rjDNkA5PPAqSfTLaY SskccFw7mVbmOJDIkpj8vzQWUjeE+XJB44ORxVyigDHttAUbf7Vv59a8uVJ7f+0ILc/Z5Fzh08uJ MNz1OSMcY5zcbSdNZ7120+0L36BLxjCubhQpUCTj5wFJGDng4q5RQBXSws44rWKO0gSO0x9mRYwB DhSg2D+H5SV47EjpUcek6bFcC4j0+0ScOXEiwqGDEuSc4znMspz/ANNH/vHNyigDHv8Aw5a3VnFb 2j/2b5fkhXtLeAnZESYkxJG6hUY7lwPlI4I5zoWFjb6Zp1tYWcfl2trEkMKbidqKAFGTycADrVii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuP+KH/Ig3X/AF9W X/pVFXYV5/8AFDWbX/hGbrS/KvvtH2qy+f7BP5P/AB8RN/rtnl9P9rrx14oA89ooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKANDQf+Ri0z/r7i/8AQxXuVeG6D/yMWmf9 fcX/AKGK9yoAK8u+JP8AyMVv/wBei/8Aob16jXl3xJ/5GK3/AOvRf/Q3oA46iiigAooooA9B+GH/ ADFf+2P/ALPXoNeffDD/AJiv/bH/ANnr0GgArw3Xv+Ri1P8A6+5f/QzXuVeG69/yMWp/9fcv/oZo Az67H4bf8jFcf9ejf+hpXHV2Pw2/5GK4/wCvRv8A0NKAPUaKKKAPHvHH/I4X/wD2z/8ARa1z1dD4 4/5HC/8A+2f/AKLWueoAKKKKACiiigArQ0H/AJGLTP8Ar7i/9DFZ9aGg/wDIxaZ/19xf+higD3Ki iigDn7P/AJKHrP8A2CrD/wBG3ddBXH3Gs2ukfEPVPtMV9J5ulWO37JYT3OMS3ec+UjbevfGecdDW x4hvrjTYLC8gkxGuoW8M8W0fvkmcQAZP3drSrJx18vbwGJABsUVzcXjK2nsoLmDTdSlN46CwjEaK b1HRpFeNmcIAUjdtrsrALyoLKGNM8b6Rq+ox2lot2ySuiQ3LQFYpGe3W5QKTzkxFmxjjYQ23cm8A 6SiuH8f395a7/s13PB9m0TUdSj8mQp/pEHk+UzY++o8x8o2UbPzA4GMO71bUlvItuoXY/tO9ura5 AmYDy49Wt7VBGM/uiIZXXdHtJJ3ElgGAB6pRXn+n3OqNeeGmfVZzaLrd/YfZ+plSIXqp5rklpMLF EBnuGZi7EFdzVPFVtpHiD+z3h1K6uJkgSK3t4kZd7i5ZcHggn7OwYsdq4Q/KN7AA6SiuTt/iFpF0 4EFtqUiSJDJbMLU5uo5FlbfGh+fAW3mbBUFgnyB9y7ug0nUodZ0ax1S3WRYL23juI1kADBXUMAcE jOD6mgC5RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcf8UP+RBu v+vqy/8ASqKuwrj/AIof8iDdf9fVl/6VRUAeW0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFAGhoP/ACMWmf8AX3F/6GK9yrw3Qf8AkYtM/wCvuL/0MV7lQAV5d8Sf+Rit /wDr0X/0N69Rry74k/8AIxW//Xov/ob0AcdRRRQAUUUUAeg/DD/mK/8AbH/2evQa8++GH/MV/wC2 P/s9eg0AFeG69/yMWp/9fcv/AKGa9yrw3Xv+Ri1P/r7l/wDQzQBn12Pw2/5GK4/69G/9DSuOrsfh t/yMVx/16N/6GlAHqNFFFAHj3jj/AJHC/wD+2f8A6LWuerofHH/I4X//AGz/APRa1z1ABRRRQAUU UUAFaGg/8jFpn/X3F/6GKz60NB/5GLTP+vuL/wBDFAHuVFFFAHP2f/JQ9Z/7BVh/6Nu60NU0lNWs 7u1uLmdYbiJUUR7QYHBJEsbbciQEqQSTgopABznPs/8Akoes/wDYKsP/AEbd10FAHNxeDbaCygto NS1KI2bobCQSIxskRGjVI1ZChASR13OrMQ3LEqpUg8H2OlRxPpaSB7S4F3a28k2I962f2RELbWYJ sA55OeeeldJRQBy9zoN54k3f2/awWO2J7b/iX3xn+0W8uPOhffCu1W2R8r8/HDLzmSXwVpss08rT 3Y3O8lsodcWkjzLcO8fy8kzRpJiTeAVwAFJU9JRQBjweG7O3i0xFknZrC7kvVdmGZZpFlWRn4x8x nkbChQCRgADFRt4XtpPEEOtTXd3LdwurLuKBcKLlVXAUcBbpx6/KmSTuLblFAHF3vghbWysRpCSX FzaW9rZxtPqDWpSKBJk3B44mO9lnkQ4A4bKlGUGuk0LTP7E8PaZpPned9htIrbzdu3fsQLuxk4zj OMmtCigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuP+KH/Ig3 X/X1Zf8ApVFXYVx/xQ/5EG6/6+rL/wBKoqAPLaKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAK9R+G3/ACLtx/19t/6AleXV6j8Nv+RduP8Ar7b/ANASgDsaKKKA Pn2iiigD0H4Yf8xX/tj/AOz16DXn3ww/5iv/AGx/9nr0GgArw3Xv+Ri1P/r7l/8AQzXuVeG69/yM Wp/9fcv/AKGaAM+ux+G3/IxXH/Xo3/oaVx1dj8Nv+RiuP+vRv/Q0oA9RooooA8e8cf8AI4X/AP2z /wDRa1z1dD44/wCRwv8A/tn/AOi1rnqACiiigAooooAK0NB/5GLTP+vuL/0MVn1oaD/yMWmf9fcX /oYoA9yooooA5+z/AOSh6z/2CrD/ANG3ddBXP2f/ACUPWf8AsFWH/o27rQ1XU/7L+xSPDut57uO2 ll3Y8nzMqjYwS2ZDGmB037jwpoA0KKz313R47O6vJNVsUtbSUwXMzXCBIZAQCjtnCtkgYPPIqwl/ ZyXjWaXcDXSZ3QiQF1wEJyvXgSRk/wC+vqKALFFcv4u8SXmhf8eccDeTp93qc3nKW8yO38vdEuCN rN5ow53Bdv3Wzxl3HjXUorkosFpsvLia1s8o2bdor6KyLyHd+9BaYSbRswF25Od4AO8orj7TxTqk 95ocD2MAhudQurC6ut2Azwi5H7pMkjJttx3H5Q6qC5yy9Bc6xZ2N5LFe3ljbRpEsgMtyFfkSM25T jChYmYNk52vwAmSAaFFUzq2mi4uLc6haefbvEk8fnLuiaQgRhhnILEgKD1zxmrEE8N1bxXFvLHNB KgeOSNgyupGQQRwQRzmgCSiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAK8/+KGhaP/wjN1q/9lWP9p/arL/Tfs6ed/x8RL9/G77vHXpxXoFcf8UP+RBuv+vqy/8ASqKg Dy2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvUfht/wAi 7cf9fbf+gJXl1eo/Db/kXbj/AK+2/wDQEoA7GiiigD59ooooA9B+GH/MV/7Y/wDs9eg1598MP+Yr /wBsf/Z69BoAK8N17/kYtT/6+5f/AEM17lXhuvf8jFqf/X3L/wChmgDPrsfht/yMVx/16N/6Glcd XY/Db/kYrj/r0b/0NKAPUaKKKAPHvHH/ACOF/wD9s/8A0Wtc9XQ+OP8AkcL/AP7Z/wDota56gAoo ooAKKKKACtDQf+Ri0z/r7i/9DFZ9aGg/8jFpn/X3F/6GKAPcqKKKAOPuNC0fW/iHqn9raVY3/k6V Y+X9rt0l2Zlu843A4zgdPQVseINPvNV0y5tbcQJJH5NzZySSHDXEUnmIsiheI9yR5IJJDMBtIBNe z/5KHrP/AGCrD/0bd10FAHD2XhbWLCw0fy4tKkutD8mK2+d0N5HHBNCDLLsJj4nLhArhSGG5t+Vr 6P4Hfw0bS6aSCZbC7hnknihYzTQQ6YbUDYoJLbyzBATwxwSTg+gUUAcPrdo3jTzf7KE8OdPudMuP 7Qsri12R3OzMqeZGPMZPJ+4MA7uWXjMdx4K1KW5LrPabLO4murPLtm4aW+ivSkg2/ugGhEe4b8ht 2BjYe8ooA5e08N3kEWhmSSDzLTVbrUrlVYkDz1uSUQ4+ba1wBkhchScA/LVe+8LXmp+NLPWbuKxN rbywP5JcyH9yL0IwygG7NxCw9CrYJ2gt2FFAHlcvgyXw7pukvJHaOlpZWdm9vDp813HLKsV5HOzx xISUP2ovyBvIKsU37q7zwnY3GmeDdDsLyPy7q10+3hmTcDtdY1DDI4OCD0rYooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArL8RaFb+JNEm0q6mngileN/MgKh1KO rqRuBHVR1BrUooA8/wD+FTWH/Qxa5+dt/wDGap6T8KvO0axl1TXNZg1B7eNrqKM2pVJSo3qP3R4D ZHU/U16ZRQB5/wD8KmsP+hi1z87b/wCM1Tk+FWNZtoo9c1k6e1vM08pNruWUNH5aj910KmUng/dH I7+mUUAef/8ACprD/oYtc/O2/wDjNH/CprD/AKGLXPztv/jNegUUAeZ6Z8KvNtXbUdc1mGcXE6qq G1IMQlYRN/qjyYwhPuTwOguf8KmsP+hi1z87b/4zXoFFAHmd78Ktl1py2muazJA9wVvGY2uY4vKk IZf3Q58wRjvwx47i5/wqaw/6GLXPztv/AIzXoFFAHn//AAqaw/6GLXPztv8A4zVOy+FW+61FbvXN ZjgS4C2bKbXMkXlRks37o8+YZB24Ucdz6ZRQB5//AMKmsP8AoYtc/O2/+M1T1P4VeVao2na5rM05 uIFZXNqAIjKolb/VDkRlyPcDg9D6ZRQB5/8A8KmsP+hi1z87b/4zR/wqaw/6GLXPztv/AIzXoFFA HmcfwqzrNzFJrmsjT1t4WglBtdzSlpPMU/uugUREcD7x5Pa5/wAKmsP+hi1z87b/AOM16BRQB5nq 3wq8nRr6XS9c1mfUEt5GtYpDahXlCnYp/dDgtgdR9RVz/hU1h/0MWufnbf8AxmvQKKAPP/8AhU1h /wBDFrn523/xmqcnwqxrNtFHrmsnT2t5mnlJtdyyho/LUfuuhUyk8H7o5Hf0yigDz/8A4VNYf9DF rn523/xmj/hU1h/0MWufnbf/ABmvQKKAPM9M+FXm2rtqOuazDOLidVVDakGISsIm/wBUeTGEJ9ye B0HQab4Jn0i3a3sfFmuRRM5crss2+bAGebc+grrKKAOTvdC8RJdactp4s1mSB7greM0VjmOLypCG X9wOfMEY78MeO4uf8I9qn/Q565/35sv/AJHroKKAPP8A/hU1h/0MWufnbf8Axmqdl8Kt91qK3eua zHAlwFs2U2uZIvKjJZv3R58wyDtwo47n0yigDj9L8BPo3m/2f4q1yHzcb/ktGzjOOsB9TUmp6F4i itUbTvFmszTm4gVleKxAERlUSt/qByIy5HuBweh6yigDn/8AhHtU/wChz1z/AL82X/yPWHcfC21u rmW4m8Sa40srl3b/AEUZYnJPEFd5RQB5nH8Ks6zcxSa5rI09beFoJQbXc0paTzFP7roFERHA+8eT 22NN+Ha6RctcWPijXIpWQoW22jfLkHHMB9BXaUUAcnq2heIodGvpdL8WazPqCW8jWsUkViFeUKdi n9wOC2B1H1FXP+Ee1T/oc9c/782X/wAj10FFAHD33w0h1K8ku7vxNrkk8mNzYtRnAAHAgx0ArLk+ FWNZtoo9c1k6e1vM08pNruWUNH5aj910KmUng/dHI7+mUUAef/8ACprD/oYtc/O2/wDjNH/CprD/ AKGLXPztv/jNegUUAeZ6T8KvO0axl1TXNZg1B7eNrqKM2pVJSo3qP3R4DZHU/U1c/wCFTWH/AEMW ufnbf/Ga9AooA8zvfhVsutOW01zWZIHuCt4zG1zHF5UhDL+6HPmCMd+GPHcaFv8AC21tbmK4h8Sa 4ssTh0b/AEU4YHIPMFd5RQBz/wDwj2qf9Dnrn/fmy/8AkeqemaF4iltXbUfFmswzi4nVVSKxIMQl YRN/qDyYwhPuTwOg6yigDH0nQTpmo3d/Pqt9qN1dRRQs92IRtSMyFQBFGg6yt1z2rYoooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDD8RalqVjLo9tpa2hn1C 9Nqz3QYrGvkTSbwFIJIMYO3jdyMrncOX1Lx5qFjpMt9m0L2CXJuYIbG4uGuDBNLETlDi1R/JJV5C 4+Zhz5RLdR4i0Bdfl0dZlja3s703MyszK2PImRShXkOHkRgQQRtyCCBUd54K0G+sxay2s6wmJoZB BeTQmdGJJErI4MuS7k7y3LuerNkAyx4k1ltJur2STTbYHU7ixtdttPcyFYppkLeTGd0jkRgbFI2h XkLEfIuXbeP9bui0lvYWMkMH2eJxN5tu8sst7PZj5SGMSkxrIQwZkwUwxbcnaS+HtMmsPsRgkSIX El0rRTyRyJLI7O7pIrB1JLvnaRwxXocVXtfCGh2cTRw2kmGeJ2aS4lkZmjne4QlmYkkSyO2Sec4O RxQBydx4/wBWDzw29n5t1YRSPLFb6ZcXP250uJ4NiGMkW242xIL78eZ32Etn2HjLxFp8eoWkj/2l dWkt1chINJurhrofa7mNYAyOwg5gIVn3AK6jB8slu8l8IaHMctaSAM8jSqlxKiz75GkZZVDASoWk kOx9yjewAAYgxnwVoJlmk+yzjz5XlnQXkwSYuxdldN+14yzOfLYFBvfA+dsgGWPEPiGXSbq+WC0W IancWcckNpNdNDFFNMnmvEhDyFikabE+7kyE4yi9ZYXP2zTra63QN50SSZt5fNjOQD8j4G5eeGwM jnAqnL4e0yaw+xGCRIhcSXStFPJHIksjs7ukisHUku+dpHDFehxWhBBDa28VvbxRwwRIEjjjUKqK BgAAcAAcYoAkooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK5n4ganfa R4NurzTblra6E9tGsyorFQ88aNgMCPuseoNAHTUV4d/wk/iz/oab7/wGtP8A4zR/wk/iz/oab7/w GtP/AIzQB7jRXglr4w8Xz3F9G3ii8AgnEa4tbXkGNG5/c+rGrX/CT+LP+hpvv/Aa0/8AjNAHuNFe Cal4w8X2el3d1H4ovC8MDyKGtbXBIUkZ/c+1Wv8AhJ/Fn/Q033/gNaf/ABmgD3GivDv+En8Wf9DT ff8AgNaf/Gaqx+MPF7apcWp8UXmyOCKQH7La5yzSA/8ALH/YH60Ae90V4d/wk/iz/oab7/wGtP8A 4zR/wk/iz/oab7/wGtP/AIzQB7jRXgmm+MPF95pdpdSeKLwPNAkjBbW1wCVBOP3PvVr/AISfxZ/0 NN9/4DWn/wAZoA9xorwSTxh4vXVLe1Hii82SQSyE/ZbXOVZAP+WP+2f0q1/wk/iz/oab7/wGtP8A 4zQB7jRXh3/CT+LP+hpvv/Aa0/8AjNVbDxh4vurd5H8UXgInlj+W1teiSMo/5Y+gFAHvdFeHf8JP 4s/6Gm+/8BrT/wCM1VuvGHi+C4sY18UXhE85jbNra8ARu3H7n1UUAe90V4d/wk/iz/oab7/wGtP/ AIzR/wAJP4s/6Gm+/wDAa0/+M0Ae40V4Ja+MPF89xfRt4ovAIJxGuLW15BjRuf3Pqxq1/wAJP4s/ 6Gm+/wDAa0/+M0Ae40V4Jf8AjDxfa26SJ4ovCTPFH81ra9HkVT/yx9Catf8ACT+LP+hpvv8AwGtP /jNAHuNFeHf8JP4s/wChpvv/AAGtP/jNVY/GHi9tUuLU+KLzZHBFID9ltc5ZpAf+WP8AsD9aAPe6 K8O/4SfxZ/0NN9/4DWn/AMZqrqXjDxfZ6Xd3Ufii8LwwPIoa1tcEhSRn9z7UAe90V4d/wk/iz/oa b7/wGtP/AIzR/wAJP4s/6Gm+/wDAa0/+M0Ae40V4JJ4w8XrqlvajxRebJIJZCfstrnKsgH/LH/bP 6Va/4SfxZ/0NN9/4DWn/AMZoA9xorw7/AISfxZ/0NN9/4DWn/wAZqrpvjDxfeaXaXUnii8DzQJIw W1tcAlQTj9z70Ae90V4d/wAJP4s/6Gm+/wDAa0/+M1VuvGHi+C4sY18UXhE85jbNra8ARu3H7n1U UAe90V4d/wAJP4s/6Gm+/wDAa0/+M0f8JP4s/wChpvv/AAGtP/jNAHuNFeCWHjDxfdW7yP4ovARP LH8tra9EkZR/yx9AKtf8JP4s/wChpvv/AAGtP/jNAHuNFeCX/jDxfa26SJ4ovCTPFH81ra9HkVT/ AMsfQmrX/CT+LP8Aoab7/wABrT/4zQB7jRXh3/CT+LP+hpvv/Aa0/wDjNVbXxh4vnuL6NvFF4BBO I1xa2vIMaNz+59WNAHvdFeHf8JP4s/6Gm+/8BrT/AOM1V1Lxh4vs9Lu7qPxReF4YHkUNa2uCQpIz +59qAPe6K8O/4SfxZ/0NN9/4DWn/AMZo/wCEn8Wf9DTff+A1p/8AGaAPcaK89+G+uazqmqa1a6rq kt8lvBbSRGWKJChdpg3+rRc52L1z0r0KgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigArj/ih/yIN1/wBfVl/6VRV2Fcf8UP8AkQbr/r6s v/SqKgDy2iiigDP0/wD4/tW/6+l/9ExVoVn6f/x/at/19L/6JirQoAz9d/5F7Uv+vWX/ANANaFZ+ u/8AIval/wBesv8A6Aa0KACs+H/kYbz/AK9YP/Q5q0Kz4f8AkYbz/r1g/wDQ5qANCiiigDP0L/kX tN/69Yv/AEAVoVn6F/yL2m/9esX/AKAK0KAM+b/kYbP/AK9Z/wD0OGtCs+b/AJGGz/69Z/8A0OGt CgArP0b/AI8ZP+vq5/8ARz1oVn6N/wAeMn/X1c/+jnoA0Kz9Q/4/tJ/6+m/9Ey1oVn6h/wAf2k/9 fTf+iZaANCiiigDP0/8A4/tW/wCvpf8A0TFWhWfp/wDx/at/19L/AOiYq0KAM/Wf+PGP/r6tv/Ry VoVn6z/x4x/9fVt/6OStCgArPh/5GG8/69YP/Q5q0Kz4f+RhvP8Ar1g/9DmoA0Kz9d/5F7Uv+vWX /wBANaFZ+u/8i9qX/XrL/wCgGgDQooooAz5v+Rhs/wDr1n/9DhrQrPm/5GGz/wCvWf8A9DhrQoAK z9C/5F7Tf+vWL/0AVoVn6F/yL2m/9esX/oAoA0Kz9Q/4/tJ/6+m/9Ey1oVn6h/x/aT/19N/6JloA 0KKKKAM/Rv8Ajxk/6+rn/wBHPWhWfo3/AB4yf9fVz/6OetCgDP1n/jxj/wCvq2/9HJWhWfrP/HjH /wBfVt/6OStCgArP0/8A4/tW/wCvpf8A0TFWhWfp/wDx/at/19L/AOiYqANCs/Xf+Re1L/r1l/8A QDWhWfrv/Ival/16y/8AoBoA0KKKKAOx+Ff/ACMPiH/r1sv/AEO5r1GvLvhX/wAjD4h/69bL/wBD ua9RoA8/svGVxbXt6Gsp7xpdVWKZvPCx2qSXjWUOxTk7cW7OwH8bjAw7GOSy+IdzMmi3N3osdva6 qlsyrHcvNPCJ2VI2dVi8tELt8peRSyg4G4FBYm8ArPrwu3ubT7Al6t9FD9kbz43EgmwJfM2kGbzG yyEqs0qKVDVsN4Q0Nk05BaSImnJClusdxKg2wsGiD4YeYEYZUPuwST3OQCPQvE/9t38tmtn5MlrE xuiZchZBPLBtTj513W8x3HacbPlyxC2L7xLYafeSWs1vqryJjJg0m6mQ5APDpGVPXseOnWrGmaZ/ Z8+pXDTebNf3ZuZMLtVcIkaqBk9EjTJzy248AhRoUAc3oHiuPWNZv9KaC7We3QXCSSafPbK0LswU ESqCHBUjuGxuX+JUz9F8Q30viNEvp5Psl/cXtrboYP3ay288iIkbKuQTFDI7+a3JK+XwHVestbG3 svPNvHtaeVppWLFmdz3JPJwAFHoqqowAAMfRvCOnaRdSXm3zbt7u5uvMyyoGmld93l7ivmBH8vzM bioxkL8tAGfqXjxNI1DVI7zS5/sNhK8JuYpFYyutn9rIVDjHyBwckc7MZ3NsjsPEutXvjy30eWzt LeCC3uVvkjujIPNUWzqyExKWAW4Qc7cl34+RS/QT+HNIupZZLixjmMtwbmRZCWV5DB9nJKk4IMXy 7cY74zzUdp4X0myvLW8ghnF1bebtme7ld38wKH8xmYmXiOMDfuxsXGNowAYd74+a0S3KaPJcPNcX MCxx3ChiYr+Kz43ADLebv5IAxgnncI4PHHm6sbeLSZ5b+Ty7cwpd5RpEkvBIqbsKMfZJdrEKX3Rh tgGV3JPB+gSakdRbTYxdlw/mK7LyJUm6A4x5savjpuLnq77hvB+gSGcyabG/nvvk3uzbj5ksh6no WuJgR0ZZGU5U7aAOfg+JkLaBFrFxpckcBcCRI5g7Kv8AZ324kZABOPkxxnrkdKrv40vftsWqXOm3 drFZaZqE11aP5sSTLE9oxkjEsaM5CO4Xci/NuXIB31uab4C0K0sNLiu7KC9urG0S2890IEuIPIZi mSPnQ7WBzkKgOdi40IPDllp/7/Tk8u/SKaOO6uZJLhiZNmTIWfdJ/qohy2QqBQQKAJNP1dtQsL68 hs5HSC4nghiR18yYwu0bcNhVJdHAy2MbSSMkLzc/j+4tLOC4m0TeptLu8neC7DLHFbmLzMb1Vy2J GTYyowkTaQFzIOosNGs7Hw9baHs+0WMFolnsuAH8yNUCYcYwcgc8YPpWG3w90NtUgunikniRJ/Mj uppbh5ZJfIXcZJHLY2QeWV6MrlTwSCAVx4/L3eoQxaNdmOG4+yW07JIkUk32hbba7mPYoMjggo0h 2KxIVhsPN+O/Emp299bRvpc8P2aJbq/ii1ya381VhvmCIYl+6fIZw3ys37tWUY+T0B/C2jSS3UjW eWuclv3r4jJYOWiGf3TF1VyybSXVWJLAGq8/gnw7cxGObT96tEYWJmkyylZlJY7sliLmfLH5iZCS ScEAGWPH5e71CGLRrsxw3H2S2nZJEikm+0LbbXcx7FBkcEFGkOxWJCsNhr+ENTvp9ah+0yXeL9NU Z7e6l3m3NvflUVcMVB2zlWwSCI4wDheekfwto0kt1I1nlrnJb96+IyWDlohn90xdVcsm0l1ViSwB qPS/C9jo+pLPYxxwWkFu8NraRptW3MsplmI55DsIvlxhfL+XAJFAG5RRRQAUUUUAFFFFABRRRQAU UUUAFFFFABVHWNHsde0yXTdShaW1lKMyLI0ZyrBlIZSCMMoPB7VeooA4/wD4Vf4T/wCfO+/8Gt3/ APHaP+FX+E/+fO+/8Gt3/wDHa7CigDjV+FfhBGdlsbxS53MRql0NxwBk/vOeAB+FO/4Vf4T/AOfO +/8ABrd//Ha7CigDjZPhX4QljaOSxvHRwVZW1S6IIPUEeZTv+FX+E/8Anzvv/Brd/wDx2uwooA4/ /hV/hP8A5877/wAGt3/8dpo+FfhASNILG8DsApb+1LrJAzgZ8z3P5muyooA4/wD4Vf4T/wCfO+/8 Gt3/APHaP+FX+E/+fO+/8Gt3/wDHa7CigDjY/hX4QijWOOxvERAFVV1S6AAHQAeZTv8AhV/hP/nz vv8Awa3f/wAdrsKKAONPwr8IGRZDY3hdQVDf2pdZAOMjPmew/IU7/hV/hP8A5877/wAGt3/8drsK KAOP/wCFX+E/+fO+/wDBrd//AB2mp8K/CEalUsbxQSWwuqXQ5JyT/rO5JNdlRQBx/wDwq/wn/wA+ d9/4Nbv/AOO01vhX4QdkZrG8Yodyk6pdHacEZH7zjgkfjXZVT1TVbHRbBr/UrmO1tEdEeaThVLuE XJ7Dcw5PA6nA5oA5v/hV/hP/AJ877/wa3f8A8do/4Vf4T/5877/wa3f/AMdrUPi7RxbpKJLt3d2T 7NHYzvcKVAJ3QBDIoAZDllAw6H+Nci+MNAke6SLUo5jaojy+SjSAB1Ro8FQQxcSJsAyXOQuSrAAG Svwr8IIzstjeKXO5iNUuhuOAMn95zwAPwp3/AAq/wn/z533/AINbv/47Wg/jbw7F5In1D7O83mYi uIZIpF2bC+9GUMm1ZEc7gMId/wBwFhIfF+h/Z0mhu5LoO7KqWdvLcSHaAS2yNWbZh0IfG0iSMgkO pIBkv8K/CEihXsbxgCGw2qXR5ByD/rOxANO/4Vf4T/5877/wa3f/AMdqSy+IOj3d1qMKi7cWtwIo WtrOec3KGKN/NQJGSUBkA3DIwY2ziRM7Gn+I9I1W/mstPvo7qeBFeTyQWVVZEdDuA24ZZFKnPzYb GdrYAMP/AIVf4T/5877/AMGt3/8AHaaPhX4QEjSCxvA7AKW/tS6yQM4GfM9z+ZraPinRlvJraS88 ryt4aeWJ0gJQEuqzMBGzKFfcoYkbHyBtbGPdfEPTY9a0/TYIp2a74Z57a4h8l/OhjCODESrETbhu 29Y8kCVWoAP+FX+E/wDnzvv/AAa3f/x2myfCvwhLG0cljeOjgqytql0QQeoI8ytKw8ceGtT+zfY9 XgkN1sMIwwLBsBWwRwpYiPcePM/d53/LVe/8eaNaWLXUc+6NJYFMk6PbxvFJNHE00cjqFkjXzAxZ CVwV5AYGgCv/AMKv8J/8+d9/4Nbv/wCO0f8ACr/Cf/Pnff8Ag1u//jtWLzxtZRPZi1XzmuJUiaGY SQTRFri3hO6N0yuBcq+G2kgpgENuWPwn480/xTdTWkUckVwiCSP91MEmj8qF2dGeNOA06gAgEja2 AGFAEJ+FfhAyLIbG8LqCob+1LrIBxkZ8z2H5Cnf8Kv8ACf8Az533/g1u/wD47XUWF9b6np1tf2cn mWt1Ek0L7SNyMAVODyMgjrVigDj/APhV/hP/AJ877/wa3f8A8dpsfwr8IRRrHHY3iIgCqq6pdAAD oAPMrsqKAOP/AOFX+E/+fO+/8Gt3/wDHaa3wr8IOyM1jeMUO5SdUujtOCMj95xwSPxrsqKAOP/4V f4T/AOfO+/8ABrd//HaP+FX+E/8Anzvv/Brd/wDx2uwooA41PhX4QjUqljeKCS2F1S6HJOSf9Z3J Jp3/AAq/wn/z533/AINbv/47XYUUAca/wr8ISKFexvGAIbDapdHkHIP+s7EA07/hV/hP/nzvv/Br d/8Ax2uwooA4/wD4Vf4T/wCfO+/8Gt3/APHaavwr8IIzstjeKXO5iNUuhuOAMn95zwAPwrsqKAOP /wCFX+E/+fO+/wDBrd//AB2myfCvwhLG0cljeOjgqytql0QQeoI8yuyooA4//hV/hP8A5877/wAG t3/8do/4Vf4T/wCfO+/8Gt3/APHa7CigDF0Hwno3hqS5k0q2lie5CLK0tzLMWCbtozIzYxvbp61t UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc345EzeG41t5I45zqen iN5ELqrfbIcEqCCRntkZ9RXSUUAcPceG9fma4vC0HmXt21xcafBqs9qit5UMSMLmJBI2BCx2lFU+ bzzGpJoPhHWNG8H3elreQJeS/ZdrwyuoZYra2hkTftDJv8mRQ6gsocMPmGK7iqN7rWladMIb7U7O 1lZdwSedUYjpnBPTg/lSbS3KhCU3aKuzgY/AGsvDrhka0hfULK/t40fUJ7so08NpGhaWRA7DNu5O RwCoGe2hqvgzUJ/EV3rEBjmMlxI0duup3FidjwWqFmlhUtkNan5MEEODkFcV03/CU+Hv+g9pf/gZ H/jR/wAJT4e/6D2l/wDgZH/jS549zX6rX/kf3M4lPh9qllqPnWyWktvAgit4oNVurAlTb2sTZaMM 4Cm0GFLPuEmSQUG7rPCnh5/DlvfWxaAwySwmAQhgFRLWCHGGJI5ibALNwRkk5q1/wlPh7/oPaX/4 GR/40f8ACU+Hv+g9pf8A4GR/40c8e4fVa/8AI/uZzMPgW4XVLgypaTW5uLy6iluru5nQtceb8hs9 yxKB5zKWDHcAeFL5WvP4O8Q3F3pEzTWhit7jdJDNfzTtbRC4tJtqSum+Yk2sh+fbjzQoOEFdd/wl Ph7/AKD2l/8AgZH/AI0f8JT4e/6D2l/+Bkf+NHPHuH1Wv/I/uZzNl4K1K206yt3ntC8FloluxDtg tZ3DSykfL0KnC+p6461nz+BvEtzqENy93afaIEQfbLi/uLkTyLd20xk+zsAkIIt3PlxtjLKuQACO 2/4Snw9/0HtL/wDAyP8Axo/4Snw9/wBB7S//AAMj/wAaOePcPqtf+R/czlbjwXrF9rSavO9jDNLd rczwJM7rHtmsCFVyg35SyY5KrhnA5ALVhzeEptL8PW/h/VLi0a4u7izjtY7W4JlmU2cVhdkRsgOI 4mkkDDOOCwAUg+jf8JT4e/6D2l/+Bkf+NH/CU+Hv+g9pf/gZH/jRzx7h9Vr/AMj+5mtRWT/wlPh7 /oPaX/4GR/40f8JT4e/6D2l/+Bkf+NHPHuH1Wv8AyP7ma1FZP/CU+Hv+g9pf/gZH/jR/wlPh7/oP aX/4GR/40c8e4fVa/wDI/uZrUVk/8JT4e/6D2l/+Bkf+NH/CU+Hv+g9pf/gZH/jRzx7h9Vr/AMj+ 5mtRWT/wlPh7/oPaX/4GR/40f8JT4e/6D2l/+Bkf+NHPHuH1Wv8AyP7ma1FZP/CU+Hv+g9pf/gZH /jR/wlPh7/oPaX/4GR/40c8e4fVa/wDI/uZrUVk/8JT4e/6D2l/+Bkf+NH/CU+Hv+g9pf/gZH/jR zx7h9Vr/AMj+5mtRWT/wlPh7/oPaX/4GR/40f8JT4e/6D2l/+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/4GR/40f8JT4e/6D2l/+Bkf+NHPHuH1Wv8A yP7ma1FFFUYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXK+JfAOleKtS jvr64vI5Y4RCBA6hcAk91PPzGuqoqZwjNWkjfD4mrh5+0oys+555/wAKb8Pf8/mqf9/Y/wD4ij/h Tfh7/n81T/v7H/8AEV6HRWX1aj/Kd39uZj/z9Z55/wAKb8Pf8/mqf9/Y/wD4ij/hTfh7/n81T/v7 H/8AEV6HRR9Wo/yh/bmY/wDP1nnn/Cm/D3/P5qn/AH9j/wDiKP8AhTfh7/n81T/v7H/8RXodFH1a j/KH9uZj/wA/Weef8Kb8Pf8AP5qn/f2P/wCIo/4U34e/5/NU/wC/sf8A8RXodFH1aj/KH9uZj/z9 Z55/wpvw9/z+ap/39j/+Io/4U34e/wCfzVP+/sf/AMRXodFH1aj/ACh/bmY/8/Weef8ACm/D3/P5 qn/f2P8A+Io/4U34e/5/NU/7+x//ABFeh0UfVqP8of25mP8Az9Z55/wpvw9/z+ap/wB/Y/8A4ij/ AIU34e/5/NU/7+x//EV6HRR9Wo/yh/bmY/8AP1nnn/Cm/D3/AD+ap/39j/8AiKP+FN+Hv+fzVP8A v7H/APEV6HRR9Wo/yh/bmY/8/Weef8Kb8Pf8/mqf9/Y//iKP+FN+Hv8An81T/v7H/wDEV6HRR9Wo /wAof25mP/P1nnn/AApvw9/z+ap/39j/APiKP+FN+Hv+fzVP+/sf/wARXodFH1aj/KH9uZj/AM/W eef8Kb8Pf8/mqf8Af2P/AOIo/wCFN+Hv+fzVP+/sf/xFeh0UfVqP8of25mP/AD9Z55/wpvw9/wA/ mqf9/Y//AIij/hTfh7/n81T/AL+x/wDxFeh0UfVqP8of25mP/P1nnn/Cm/D3/P5qn/f2P/4ij/hT fh7/AJ/NU/7+x/8AxFeh0UfVqP8AKH9uZj/z9Z55/wAKb8Pf8/mqf9/Y/wD4ij/hTfh7/n81T/v7 H/8AEV6HRR9Wo/yh/bmY/wDP1nnn/Cm/D3/P5qn/AH9j/wDiKP8AhTfh7/n81T/v7H/8RXodFH1a j/KH9uZj/wA/WFFFFbnlBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeV6/478ZWXifxTFpGnaNdaT4 bS2nuo5jIlxLE8YkfY27ZkAOeR0AwGPB9Urx/wAQ6D42HirxvFo3h+C4sfE0VrarqE97GiW6LD5c jGPO9uHboONucN0IB2lx8SfCljb6LLqOqx2L6xbx3NtFODuCOBtMm3IQc4yxA4bng4uXXjjw1Y+K IPDVzq8EerzbQluQ3VhlVLY2qx7KSCcrgfMM+Z+Lfhrrz6lo0OnaZaa3Zx6Fb6NK1xePbRxtFKre bKiOrOmBkKrNgjOCVXO5rPhnxK3xQjv9I0yCGwu7u2m1C/N2rxzwRBD5clvIGxMrRnZJHjAcdDlg Ablr4+0+x0vXNS8Q6tpsVpYaxNp8b20c2RtxtjZWXLyjJJ2ArgZHAOLn/CxfCP8AZ39o/wBtwfYf 7Q/s37Ttfy/tGN23djG3HO/7mO9ed6r4Z1zTNLuLhra0F3/wnv8Aa9ja3F9FCb6M/cRGJwHbk7Tg 4U8E4Br6Hour67b3LW9nG09l8SGvbxI5gVijQDeQzbSwBPoCfSgD2DQPEekeKdLXUtFvo7u0LlN6 gqVYdQysAVPQ4IHBB6EVx/ib4qaZp/iXSvD2jXtpd6pLrFvY30LRyMIonOHKuMLvBKjGTg5BHBxq eANE1HRP+Eo/tG38n7d4gu723+dW3wvt2twTjODwcH2rg5vCHjCLxKLBNBjm0s+Ml8Qf2ml7GAIi RlPLbDZAPJ9QQAeCQD0y18ceGr7xRP4attXgk1eHcHtwG6qMsobG1mHdQSRhsj5Tg0Hxx4a8T6je WGjavBeXVpzKiBhxnG5SQA65/iXI5HPIzwfhrwRr2m/FS61C70a0bTxqd9qEWpvfOxCzoAscUIYB X7MzocgcH5VJj8K+BvEq/wBt6NfWMGjaFJpU9hbotyt4qzzbRJLblv3qQts3mJ3+8wPJ+6Adpa/E vwfe+H77XbfWo5NPsHRLqQQyboi5AXKbd+CTgHGOD6HFeLx9p8/iOcQ6tpsuhwaPNqDtHHM0/wC6 naN5AwXY0Q2MOMsSMjKkGuHk8I+MNS+E2t6PceGNNsdQe30+0tUgljNxdC38sM80u7YRhflGQQMj ngnc8d6JqP8AwkPiXX/s/wDxLP8AhCrqy8/ev+u3s+3bnd93nOMe9AGxd/Fjwimh6tqOn6rBfSad aLctAm9S+/AjUHb3ZkUkA7C2GweKj8HeO5fFOpaVGt1ppiutCW+lgjhmWdZxL5UhBYbPKDBlxktk Z5HNcf4f8Ma/reg3OqSafHAG8DxaNpyJcLIL0vGXD5O0xkHapVhjJOGIGTc0TwT4ivrqCPVtP/su F/BTaDJL50c/lzCUqDhW5ygD8cDO3ORQB3Gj/ETwrr2napf6Zqvn2ulxedeP9nlXykwxzhlBPCN0 z0qxoHjjw14ovJrTRdXgvLiGJJnjQMCEYAgjIGcZAOPuk4bB4rzfR/B3iq907VJtT0X+zrq38H/8 I9Z2/wBqim+1vhj5m5WxHyFG0/3vvcVsaF4c8RaJ4h0zVv7J877D4Ki0/wAr7TGu+8Rw3k5ycZxj fgr70AeoUVz/APa/iL/oV/8AmFfav+P+P/j8/wCfTp/5F+7WxYTXFxp1tNeWv2S6kiR5rfzBJ5Tk Asm4cNg5GR1xQBYooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqOeeG1t5bi4ljhgiQvJJIwVUUDJJJ4AA5zQ BHfWFnqdnJZ39pBd2smN8M8YkRsEEZU8HBAP4UWNhZ6ZZx2dhaQWlrHnZDBGI0XJJOFHAyST+NV9 M13R9b83+ydVsb/yceZ9kuEl2ZzjO0nGcHr6GtCgAorPtNb06+1jUdJtrjffab5X2uLYw8vzF3Jy Rg5AzwTjvWhQAUUUUAFRzwQ3VvLb3EUc0EqFJI5FDK6kYIIPBBHGKyz4s8NreTWZ8QaULqHf5sJv Y98ewEvuXORtCsTnpg56VsUARwQQ2tvFb28UcMESBI441CqigYAAHAAHGKkqveX9np8QlvbuC2jO 7DzSBAdql25Poqsx9ApPQVJJPDC8KSyxo8z7IlZgC7bS2F9TtVjgdgT2oAkooqvY39nqdnHeWF3B d2smdk0EgkRsEg4YcHBBH4UAWKKjknhheFJZY0eZ9kSswBdtpbC+p2qxwOwJ7VJQAUVHDPDcoXgl jlQOyFkYMAysVYcdwwII7EEUQzw3KF4JY5UDshZGDAMrFWHHcMCCOxBFAElFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVz/i3xP/wjFnp7 R2f2u61HUIdPto2l8tPMkJwXbDFVAB5CsegxzkYcXxGmh1vS9L1jQpNLe6uL+Gee4nKxRraoJPOR mRfMiZT94hcYPXFAHeUVweufFbQ9P8MX+racJL6e1t4bpbSaOW0aaGWRY1kQyJlky33lBHQcZFbB 8e+G1t7iV7+SJ4LiK1ktpbWZLjzZADGggZBIxYHIwpyASOhwAdJRXJ6V48sdc8X2+j6YsdzZ3Gj/ ANqxX6S8MPO8rZsxkEHOcnIIIIBFXF8ceGjrFxpZ1eBLq380SGQMkYMSq0qiQgIWRWBZQxKjOQMH AB0Fc/47/wCSeeJf+wVdf+imrHu/ifoxi0qTSD/aH23VbXTpEffbvAJ1LpKUdNxUqARwA3Y8Grjf Evwetre3R1qPyLJBJK4hkIZDKYt8fy/vU8wbdybgD3oA8YvNVvhp39p+GbmSWew8BWdrPcWP7w2z m4TzEYrnY4jEh7FQpPGMjT1LWdatvDMN3P4p8zSE1DUzGLbUbuIzKsWYoY71oP3jI3m7SxKyFQo3 bcL6hr3xC07SNYttJtk+2Xx1Wz027iy0f2b7SrMj5KkPwucA/Uiqet/Fzwvpeiahf2dxJqUtom5I IInAmy5jDq5XaYt6lTIMqDgckqCAed3XiXxA+tagLvUdVs7E6h4fXUUluGjNnBLAWmDOoTycvtDM oj544zijw5qV1rHjbS9L/t/VbjSJvEGswoYtVn/eQRQRNCPMV9zKOo+bufU57zUvFFnqF/rFpqOv Sabb2Vxp7WP9nR3EV+ssybljkjZCJS3aMK3y53KCOJPDuv8Ag3w1Zi3stSvru61bULuaXdZzS3M1 0pBmDxRxgxsoK/LsXjBx1JAJPhrq02s/B/TtQ17UJGLW863N3JMYmEaSOm4yAgghVHz5B4znPNdp YG3bTrY2c/n2piQwzecZfMTA2tvJJfIwdxJz1yar2Ot6dqehx61YXH2uwkiMySQI0hZRnICgbiww RtxuyMYzxVyCZbm3inQSBJEDqJI2RgCM8qwBU+xAI70Aebyx6kPhr40klu7RtPKa2IoFtmWVW8+4 5aTzCGH3uAg6jnjmOz1G6m8UXMeo6pBaqt3fLfodYnEy2SiYRsbYKEtlCiFhOGUkBTuzJg9g3jbw 7HBJcTah5FumwieeGSKORWdUEkbsoWSPc6ZdSVAdSSAQajuPHGhx2rzW97HM8bxLJEEl3rulSNk2 KjOZVLqDHt3KXQNs3g0Aed+IdU1SfwddDUr2+F/LFLHPBOnkEwf2RcOhMQOPmkDs3HEitHlhCrVY 1vUbxbsN4f1C+1HyMzW8kuZpBqBsdR8xNrD5JBtgzAFUISBsXdg+meG9aXxF4a03WEgkgF7bpN5T hgUJHI+YAkZ6NjDDBHBFalAHD+C5G1L+14Vv4LjTHiiRBY61cahskPmCQi6dVZWK+X8isSmA3y7w Txeh6qdL0vwpDbXskVxs0qJY73WZFd45vJ8zyLTBSaLEkg3scqQ4XAiQD2yigDxPWLwv4Z0HU4dY 1KXxBE5aaI3MjLDqTWNy3lhfuiUzKimDouQoRRKwfc8+K98S6fpuh+IL6fQJ7uFXuLbU3uN8ht75 povPZmYfLHASobK5Vl2sQ1eoUUAeN+IPEdzY6PqFy2oyRXlq+pS2kl5q72iF0u7kIIUVSLp1CIDF IdqjygAA7V6J4N/5Adz/ANhXUv8A0tmroKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAoorh/Guu6jY+KvC2i2l7PZWupfbJbqe0t1mn2wQ7wq KyODknkBCxwAPcA0PG/hu88RWekNYSQLdaXqtvqSRzsVSbyycoWAJXIY87W6YxzkcHF8HdSMsJNz ptnBLcavLPDbhmW1W8gEMaRDaocIFBOdnoBxWp4T+Iusao+laSlpY63dvpTahcX1tcPbrMqXDwkR xyRDMh2g4YxqWJ5VcGo4PjG1yFtodFtJdQOp2Wn7YdUWW3BuY2ZT5yIclWRlYBSARwWoAy9T+FHi TWNFkgmuNKt7qLw/aaJbqk8kiSeVOkrSsxjUpwmAoDdeoxzuXHgjxVcX+vazDqFpYXmrXtjLLZWt 3KFkt7dNrRm5CK6F8k5VMjaBkhjixa/E6a/v7PR7XRIzrU+p3unPFLeFbdGtUDuwlEZYghkx+7HJ OcYBNPRPiFqNj8CYPGmrJ/al8m7zFysHmZuTEOVXAwCOi84980AHw7+HGseEdY028v7mxkjtdEl0 9xA7kmRrt5wRlR8u1gM9c9sc1qaN4U8TaJf+Ibey1LTYNP1S9u9QS7MTSXEcsyKEAjOEARgSSS28 ADC5JGWvxg8nUxa3uheXHFLqNvdSQXfmFJLKMySbFKLvVlKYJKnJYEAAFpNG+LY1rRtQu4dKtEns ns/NZ9VjS1jjuFDBpJ2UFSh3KyhGO4ADOTgAw9N+FPiePU4b6/vrGWYarpV9M73s1w8gtY5ElJd0 zuYsGC9BnGQAK0Ivhr4kPwxvvAsuoaUtikRFlcKkjSTSfaTMGk6CNcbV2gOQSTuONpkt/jFNqVql xpnh6N0GhS61OLm+MRVYpXikjXbG247kJBO3IPO3pXSa98QLPR/CWi6/Hb7o9YltorZbqYQJH5y7 w0z/ADbFVQSSA3T05ABy918O/EmqeL59fum0q287W9M1AwRXUku2O1jkRxuMS5Y7lIGAOuSMc5d9 8IvFWryay2qa5aXE93pj2iXkk0rtLILxbhCYyu2FNqhNiEheoBya6y2+JN1qV/pthpfh77beT6f/ AGhcxRalAwjjE4hYRSKTHKwO84LIMKOQxKin4p+Js+lajq+hLZR2t4LK9eynS8imlR4bfzVklhGR GjDJXcSTt5UA8AFfVPhzr2o+J9Q8QCXTY55NT0rUoLXz3Ks1tGySRtJ5eVBLnawU5xyozxJonw41 iw8X6Xr9zc2PyarqmpXcEbu3l/ao1RERio342ZJITrwDjnD0L4j31neM7293qeoag+iWjLdah5du stzal98aLEfKG4fMBnJORjGK6i1+J01/f2ej2uiRnWp9TvdOeKW8K26NaoHdhKIyxBDJj92OSc4w CQDY8BeHdS8H/Dyy0Sc2lxqFokxGyVhE7NI7qNxXIHzAE7Tjng11EBma3ia4jjjnKAyJG5dVbHID EAkZ74GfQVz/AIQ8UTeL/BFr4gt7CO3nuUlMdrJcEruR2QAyBMgEr12nGehxXQQGZreJriOOOcoD Ikbl1VscgMQCRnvgZ9BQBwcfgvWHttEtZnsUj0OK2tbeRJnY3UcdzayM7KUHlttteFBcEvjcAuTJ P4K1KWfT3We0xb3tzcPl25WTU4LtQPl6+XEwP+0QORyLknjWWSxudVsbD7RY293FYfZ/NQXMs8k0 cfTdiLaHB2SbXJOGEW3LSR+K9SuL9tHt9JtDrUTyieKS+ZbdVRIHJWURFmOLmHgxrzv5+UFgDU8K afeaR4S0nTL8QC6srSO2cwSF0bYoUMCVU8gA4xxnHOMnYrn5PE/n6Lol9pln502t7PscVzL5KjdC 037xlD7cIjdA3zYHQ5Gfo3jW91q/uLaLw7On2aKSWYG6j3qVnuYRGBnaZGa3GPm2Dc2XG1d4B2FF cHa/EVry6isLKy03Ub+W4jhH9m6qs9uokindS0pRSCDbtuXaSFYFd5+So9a8dai/hzWpdK07bead p88t1J56k28ivPCGiDLiVRJbyMd2w7ApCsx2AA9Aork/Hev33hyHRLuzaMQNqYW/3ruH2RYZZJm9 cqsZcY5JUDBztOHN8Q7ix8Ua5FcPYjTVltrXTHupxaw78XInaWUhivz20sY+U5KJgYYuQD0iiuDu /iXbWuiW+sNHpq2bJM0jy6oiGcwuySLaDaTOcoSM+WCHj5BYhdTTvFlxeajBFPpX2ezuNQutNgn+ 0B3eWEzZbYBxGVgbkncG42lcOQDqKK5fUvGSaXqeqQTWW63020e6m2zqLhkSPzDIkLY3Q8+XvDf6 zK7cAsDU/E2o6Pp0U2o2OlWMzSmNpb3V1htDwCNkpQuzEE4Uxr/q5OcBS4B1FFeZwfEad7+S6htb u8tLl4mtLQNFGwSVNNCqcjlw145GXA6gnG0p0Efi6+kv20pNGjk1aB5TcwR3n7vZGkDt5UjIN7lb mLCsqDO8FgACwB1lFcnpvjFphoct7bRpFr1vFPZrDIrSRFo1ZkdCQ8gBYHzEUgAkuqBN7dZQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcXq8nhLxI9prsPiu0tp9C dmXULK+gYW4mXyyr7w6APwBuGcjg9c7HjSCa68C+Ibe3ikmnl0y5SOONSzOxiYAADkknjFfPniK+ iv8AwdP9mjsbyO18H6bDJfW28vbt9pg/0aQiQx7sq7/dVu3RTkA9r0v4XeHNJKrCt28A0d9GeCSb KyQvIZHYkANvZmbkEAZ4A4rL0P4Y+GJYrXUrDXL7UYVu7K6huEuIXRjZq0USgpGAVAJU9zt65znl /wC3dY/4S/y/7Vvv7b/4TX7H/Z/2h/8AkF+X1+zZ2+Xs+bzNmf4t2eaxPh5q01jpfhCHRtQ1KZ3s tWfV7KwmNy8SpuaErA5ZI3Lbdp2jcWAOdxyAen3HgLw9pV/DqJ1q703UJtYuLy1umnhDefdIEeFF kQowYKMKVLccHrVz/hXGj/8ACuP+EH+0339mf89d6ed/rvN67dv3uPu9PzrxjTfEF5rN9pdvJfT3 NjB4l0Ka3Se/N68bSQyGQGVgCWJUblwFVtwAHfu/H11et461q3TU9St4LTwbPqEMdrfSwKtwkrBZ CEYAnHHOQeM9KAOk/wCFVeH31N72aW+m8y7v7qSF5VCObyMRyqdqhgu0cYIIz1NSR/DXTV0a306T VtZnNncW1xZXEtwrNaNAoWIRpt8oADOcod24lsnBHlmo+K/EE+napez61fR66NP0SbQ7eOdovtLy hTMUgUhZ8uWByrY6cAYFue71C5uJZ31nWQ8nxCOksI9TuEUWhOfKCq4Cj3ABHY0AegaZ8JtB0q1e 3gu9SZH0efRiXkQnyZZWlZuEHz7nIB6YxwetaGrWui6H4V0/SLjxBd6UmnW6yWs0NyEuHS2QFjsw RKAg+ZdjAg/d6Y8Y0HxbrTaZJfXureILrf4SluJVs7omTel6YBIu4MsZEaANIFyBvflsk15tf1K9 uLW4bUpJX0+48SRWM8V205ijWyV02TnDyAFiVc84x04AAPW9H+HPhqTTNEvtCv8AVbW3TT0hjntb lonu7Z5BPh2I3LufLHZsPzkcDABqnwt8NtfX+r3OoX1pbv8AbbiaPzo1hia5h8ueTcyFhlRu5baC OABxXD6R4g1uPxL4Y+06tfX0l5FpKQWcF7LHIkT25M0skTxGO4jJ8xmkDFlKqNynpyniHWdS8Q2P iWxjudZnt00w30lneXrTTpNFqGxmmiVVWEiNmJhUbFUIxyQCAD1fTPAPhCbxFc2VnqepSaho1xpV xcQkqAjW8BS3BJjAYMhJbB6/3elSar4DXSdTtdT0W01W6um1W71CS5tLu3We1a4j2uEjmTynjO1Q QxDLwQTzXH6zr9/D4q1m2s9cvv8AhHk1XQopZ0vpGSGykhYuwm3ZRWITMgYFs8k55NF1HU9X8X6B pc+sarJok+t61DavFqEyfabWKNGiPnKwaVVfdhix7jOBigD0j4d+G9R8O/DPTdDv5Ps1/HFLveBl cwtI7uMEgqWXcOxXI7jr1kEbQ28UTzSTuiBWlkChnIH3jtAGT14AHoBXD/DXVptZ+D+nahr2oSMW t51ubuSYxMI0kdNxkBBBCqPnyDxnOea7SwNu2nWxs5/PtTEhhm84y+YmBtbeSS+Rg7iTnrk0AU9T 0Cx1WZJ5lkSdXgYyRNtLiKZZkVh0YBk4yMgM+0jcc07jwnBJqdxqdpqN9Y388rSNcQeUxVWjhjZA siMu0/Z4jyN2V4IBIrm9Xg1e1gmOo3N2mrTXtnFbapasCkdu97CrpGNgEROQTG+8sCoLzBDsryXc Fp4iutJ1nWbuz8O2lxPHHPPqcsBEvkWckaNc7w7E+dcsFZzkZ4wg2gHUazoottB0y10mzu/+JW6C 1FlNGJ4FWNogY/PzG52sVIkP3WYg7goOf4a8J2Nvp2pWN+ZHn1C3kjurKW68x47aS4upIw7Alt5E 8is24glDhjgkx3d/eHwf4Tm167nsY7nyf7anaQ2hjzbSMd7rtMWZxGOCvJC9GweP0nUnk1R5dR1a +XTZZViv765ma1kWBbnVfL8xxsMPzpCuBs5wmADtoA9AtPC1qniNb+612+1HU7byZds7QKUQJcxp lY41+U+fNyepXrwRVfVPA+jtpmpifVL6ys7qK4/tB0uEjEkbySzfMxX5VRppSMEAg4fevFcO+pOl xrNy2rX0d8mlf8SXbMwF26XV8LT5/wDluxQR7EZm84MWKyYyDxrrjiTWLWG82yXEWpW9xb3Gqs9w YltLhlzZbRHFHmOMpIp3MmwkkyNkA9U1vQLHxDDBBqCyPBE8jGNW2iQPDJCysRzjbK3Qg5xzWWvg WwilFzb3t9BfDYy3aNGXWUNcM8oDIU3ObqfIK7Ru+VVwMGuWz6j4y0fT3vr63s30+9lmitLlofOK yWwXLKQwxvJypB6jO0sDx+i67eaha6deXuqznxDNLpZtbUXBj8+1kitmuJBbKQki5e6JfYdu1sEe WNoB2Go+CYNRs57c6zqsLXdobO+mR4ne7jJc4bzI2C4MsuPLCAb8AAKoXQh8N2cH2LbJOfseoXGo R5YcyTeduB4+6PPfA68Lyec+bvcapa+DdEv017VWuv8AhFLzV5He43ebPHHamPcCMbVJHAxu53bt 8m/Q1PUXsrm+02/1Sd7e11Bo47i/1htNhJNtbSEPcRLuMheaRkiGE2+ZgARoFAOs1DRNF1zVpre9 1CS6ykgfTDdDajmERO4A/eKfKnClQwUeaG27mDGR/CxbyZ113VV1KLzB/aG6FpGR9m5NjRmJVPlR /dReUznLOW8z0XWNSvTLdTarJbvd2Qu7uTc0aSN9m0gsXMY/dAq8imUD90HZxjbVzxH4kez8OJdw 309vcW+nyzWUl/rbQRyOryBTb7U/077iEeb95DCTzK5IB1GmeB/DaywxWWqTzyWPk5RbiNivlNbI u4BePm05VPTkSjg/dsa34fNvqb6pY2mq3E13K7XB0+6himCtHBG0Y8zaBGwt0JZXEiso2nDHbweh 3cFjq+o6c2s3cOix6m6ak76nKBbAz6nt3Sl90JZo7XJDKXJXOd53aH2zUpdO8SagNY1IJpOjvd6a FuW2PtuL77PMx6ygxRRcsSsgILh/lIAPQPCugL4f0GwtpVja/jsra2uZkZmDGKMIApbkIDuIAAGW Y4yzE7lc3p1rfL4luJ7BJLTRS8nnQz8CafPzSRR7AyAtzv37WIciM7xKekoAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvO9e+J02h6p4kjbRI5 tP8ADz2YvJxeFZXW424Mcfl4JXJ4LrnHXnj0SuDT4dQX3jfxLrGtpHdafqT2b29qtzKFJhTB86MY RxuCkBtw69MmgDQb4l+D1tb26OtR+RZIJJXEMhDIZTFvj+X96nmDbuTcAe9WIvHvhuTVINMN/JFe TOkaxXFrNCUd92xH3oBG7BGKq2CwwQCCM+Z6p8I/GGrHWHvdYsby6u9PNmt3PczEyt9tWcMU2EQr sXbsQlQRx1JrqPFPgfxJrvjnT9UTVIJdJtdQsryK3muJI/s6wlvNVYlUpIzlgwdiGGNvSgDUHxY8 FNYPfDV5Ps6Ishc2VwPkZygcDZkpvUoWHAbCkgkA2NT+JHhfS31CGS+knuLFJTJDbwO5dolVpERs bGdQ6lgG+UZLbQCR5n/wpTxJ/wAI5/Z323SvO/sT+z93mybfM/tD7Tn/AFf3dnGeu7tjmugHwlup Ne1yWeexNrfy6jPBeM87zQm7iEZVYQyxLty2XJYuAowuAQAdRa/Erw6dH0e91K7/ALPm1K0juvIk jkPkKzKm6Rto2R72CiRtqt1BxVh/iH4YjluozfTlrW7NjIVsZyGuAwXyUITEkmSPlXJxk4wCa4e+ +FGraiuiG6/sqb7PokOj3cElzcCICKVWEo8vY0uVBPlsUAbadzYzWp/wrrUP7L1aKVNNup5vFEmt 2qPc3EICNgAebFtaOUDcQQHAOOD1AB3ljrenanocetWFx9rsJIjMkkCNIWUZyAoG4sMEbcbsjGM8 VcgmW5t4p0EgSRA6iSNkYAjPKsAVPsQCO9YfhbStY0HwXaaff3/9ravDE5eeeZ8SyEswUuQzbRkL uwTgZ29q3IDM1vE1xHHHOUBkSNy6q2OQGIBIz3wM+goA5+18Xwz2Wq3c2n3dullex2cUTqBLcNIk LRfKcbC7TqoDkYyC+zkLoWGtwz2V5Ne+XZS6e5jv1eQFIGCK5O84BTYyuG44YbgpBUU/7GvI08SF Usbj+07tZo4LkFo5I/s8MTRyccbvLcZwwAYHDcrVO18GLH4Q1zSPMjtp9aSYztFulSF5IRFkFiGk ICqWdiC7bmO3dgAEmp6/pN9p0TRalqtrceadi2VnK13Gygbt9uY2bbtkXPmRlR5kbDBKGjRte8NW OzR7LVPtMglBkm2s6tLPiYO8ir5a+aZcr91WZiqDI2gmsvEj6jZa19m0p76CKe2+xfapEjSOUwtu 87yyXYGHp5ajEmP4Mvn6Z4IvNL8PPpK3UE23UNNuI5SCu6O2S0VsjBwx+zOQMkcrz1wAWL34iaTB fw29u3nxvEZmk2SglfPgiRo0WNmlV/POx0BVimAcZKmhfEPTdX0WHUJYp4pJdmLS3tri4mUmGKRs osQYqPNUbwChDId2XAGWngrXmGg28s+mi00W3gtIiruXmWK5tJfMb5cKWS2YbBnacfOwbKx+F/B3 iTw0lteeXpVzfQRG1+z/AGyRI2j+z2cW/wAzyid2bPO3ZjEn3vl5AOsi8YaBcX/2KDUo5pd8aFo0 Zo1MiK8W6QDYocOuwkgOTtXJBA3K4fTPBF5pfh59JW6gm26hptxHKQV3R2yWitkYOGP2ZyBkjlee uOkhSHxFohXVtJj+zzu3+iXcYcPGrny2ZGHBZQj7WGVJweRQBXuPEsJMMOl28mo3Ur3AEKER/Lby COc5fAyGIQD+JmXkLudbD+IdMTRrTVhPJJaXiI9t5UEkkkwddy7I1Uux25YgLkAEnABxh2vgSz8P XUd/4Xt7G0vh9ojcy24CPHPKJDnywpPllV2LnG1SmRu3rcuPDs1lpHh620cxzPoToYI7uUoJlWB4 MM6qdp2ybshDkrjAzkAFweKdGa8hto7zzfN2BZ4oneAFwCitMoMaswZNqlgTvTAO5cjeKdGWzs7o 3n7m9tPtlsRE5MsWYwNq4yWJmjATG4lwACax5fDesSXlxDJJYyWt7qFpqVzdKzxukkAgyiQ4YFWN sOTICvmHhtvzR2/hDUrEa9Ja6hGst648nazRlo/tM9w6s45jLfaXi3LkqFEg5O1QDYj8X6HLcWls LuRbq7dkhtpLeVJiylNwMbKGUgSI+GA+Q7/ugsK9t430eSytZ5JpCZ7eKZmtraeaJGkQOqeYIwN7 Bl2owV23phcuoOX4W8JanpXiOXVbzyI45PtP7oX8146+Ylmq5llUM3/Hs5Oem5QMjpnw+B9fjs9C spZ4LiOw/s7Mg1KeJIVtzEXRbdU8ubJjdg74b94BwEWgDoNC8d6TrWnaZcHz7ea9iiZka3lMcEkg BEbylAgYll2gkFw6FQQ656ivM/D3w+1TThYR36WkoR7OWZ49VuvLja3jijXFuoRJSfIVtzEbS+CH EY39xCkPiLRCuraTH9nndv8ARLuMOHjVz5bMjDgsoR9rDKk4PIoAr3HiWEmGHS7eTUbqV7gCFCI/ lt5BHOcvgZDEIB/EzLyF3OseoeLtOsbDQ9RDeZYatKqRTAMDtaCSVCqbdzM2xVCAbiXAGTwc+18C Wfh66jv/AAvb2NpfD7RG5ltwEeOeUSHPlhSfLKrsXONqlMjdvWxeeG7y00vwzaaHJA39hSoVF8x/ expbSwhSUHDHePmxgcttbG0gEi+NdMN7PETI0CW8EsbRRSSSyvI86tEIVUvvT7OxZcZGGyF2Grie KdGkltY1vMtc4C/unxGSxQLKcfumLqyBX2kurKAWBFcvbeC9YsdWTWInsZrqOUXS2zzOiNI8l80i GTYSFUXww20lvL5Vc8aGi+G9Y0XVGu4pLFvt/N7uZz9n/wBJuLgrGMDzM/aWj3EpjYH2tnYADY8O eIbfxFpNrdxLsmltILiaEEuITLGHCF8AFgCCR1wVJADLnQvb630+BZrqTy42ljhB2k5eR1jQcerM o9s88VyfhnR9S8FaNZaYkEd5G6WUQhgZsRTbQly4O3asW1PNGcFpGkBO51FbGveENB8SmN9U0y0n njeIrO8CNJtSQSbNzKTsJBBHcMw70AFv4lhuNUS3+zyLaT3EtnbXRIxLcRb/ADU29VA8uQBjwTG/ QeWZJG8U6MmsSaUbz/TIpUhlTynKxO6qyB3xtTfvULkjc2VXLAgZ+m+E/wCzbyzt4Xgj0jTrua+s oYk2uJJhKGjbHyiNfOk24AJ3IOPLJkLrw3eT/wBrbZIB9s1uy1CPLHiOH7LuB4+8fIfA6cryOcAF yfxVpiWumTw3MbjU0imtd6yKJInlhj3cISDmePAIGSwyVGWAPF+hvbvNHdySBXVVSO3leSbcCVaJ Au6VGCuQ6BlIRyCQrEcungrXmGg28s+mi00W3gtIiruXmWK5tJfMb5cKWS2YbBnacfOwbK3E8Ma/ Bp01tDcwRW4lj26fb308McqqHBKTAGS1UloiIY9yoIdoJEjGgDYfxt4dSVYm1DEhiE7J5MmYoyzo XkG392qtG6uXwIyMPtJGbD+KdGjluo2vMNbZDfunxIQwQrEcfvWDsqFU3EOyqQGIFcnp/gXVbfw7 4jsZZLRZ9V0yW1iH2mSYRu895IN8jqGYAXKAsQSSGOOmdC78K6rc6Pf6P51oLM3rX8DrLJHLM7XY utjMuDCAd0e5d5OQ42ldrAHUabqlpq1u01o8hCOUkSWJ4pI2wDh0cBlOCCAQMhgehBq5WH4X0aXR 7K5E8UcU91cGeRFvJrsg7EQbppjuc4QdlAGFwcbm3KACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooA4+01jxAth4ledILi+stQjiiigiZ0t4mgt3bCjDzeWJHbAw 0m3ChNwVac/iq+l8AeKr3TLmO6utHSWO3v3XyxOqwJKJtuwqSFk4wNkhUMNquNvSQ6VZ3Nrf3Nlf zhdWljvRc20w+VhFGiNGQMFcRIcHcrZIIKnFEnhuzm8Paho8kk7LqMUkd3clh50zSJsZycY3YwBx tUBVACqAADk/EPjnWrXwrf31vptpbuyXltbTLeF2jubdJTIShixsH2eYqckthAVTc2w8R/EC70y0 1OykTTbPVFsrkwRQailxcQzR27zB3hKACIiMlWJJIaPci7iF1LjwTplxE+k3ms300c8VzJHau8Kl XlVknuF2xhix+0OSCSimXhQNgBd/D21vrU2lxrWqvZn7Q32f9wF82eKWOWXIi3bm8+V8Z2hm4UKA oANjxZfXGmeDdcv7OTy7q10+4mhfaDtdY2KnB4OCB1rLufF93ZpepeabaWdxbpBOTeaikUEUMzSK hml2nY+6JlKoJBuZMMQSy6F/YNeaDr9vr19HFYXiTJuRlUWtsY9h+dgBnAaQlhhS5XkKCTUvC9tq GqNqgu7u1vwkKxTwFCYTH5wDKHVlJK3EqncCMEYAIBoAx4fiDDPpxuY7OOR5bdjYiK5DpeXCXBtm jRwuAnmtAFkbAImBwNrYrp47u4RqxGlSXFvpST3d3cSXSKVhS5uoiqKEG59tvlFOARkM4IDPqWnh HR2W1ia8nvZ9Nu5ZZJHlTe0kkq3RWQIoA/eCGQABT8qj7rMGkHgrTRa63b+fd7NYt5be4O9cqskt xKSny8HdcyAZzwF9CSARw+LLibWLK3Glf6Be6hcWEV39oG5XgWbfvjxxloGC4LZXJYocKbD32qJ4 +tbGSSBdMm0+5ljiRcu7xvbjezHp/rXUKPTJJ3AJjw+H73/hL7KVbS+gs7HULi9Be6ja0IljmDeW q4kMjvMrnzFITEiowXG/rJNNhl1m21Rmk8+3t5rdFBG0rI0bMTxnOYlxz3PXsAZfhi+1S7udei1a SBprXUFijSBfkiQ20EmwE8vgyN8xAz1wowow9Y8cXdjLa3/2GSPRY729jlljkR5JVtoLkyIyMBsJ khyhVjkL8xTO09hZabDY3Wo3ETSF7+4FxKGIwGEUcWF46bY1POeSfoOb1jwl4fiikvNZ1OeHSY5Z 5Tbz3SxW0ZuFkjmycAneZ2OWYlScIVBKkAr2PxB/tGf+z7OHSr3U3ljji+xap59p86TP88wj3KwW 3lyoQ9Y+fmJUtfiFLOupTzaDPBa6ZaTXd4xuEZ41jluYigUcNITbZAB2/M3z/Ku+TVdP8l47ZNX1 LVdchdbuNVurVLuCIK8XmRQsiwsP3jqS6jIc/MWRFEnhvwZb2ui38OoxTn+04pYLi2luC5WB57iV VZwSxk23JVm3NyOCfvEAz/8AhaFmsUkbyaG10JYo1mh1gPYr5iysPMuPLBRsW8ny7Dy0fPzHboaZ 47TVrrTYbW0gdbveC/21QJSkrxObbIAuFQxs7HKERsjAEttrQfwsZola413VZ76KUS2967Qh4CFd PlQRiLlZJASUJO7k/Km0TQLO+W1f+1768ht5QZla5EiXE0UpcF+PlZJQTiPYPlCEFVVQAR694om0 i4vlgsI7iDTLJdQv3e4MbLCTJjylCMJHxDJwxQfd+bk7ebfWfFz+B7q8E8CalN4gNlGy3CYt4ftg t9qE2+DggrllY4JfOcRjrNY8L22sXE0sl3dwJdW4tL2KEptu4AWxG+5SVH7yQZQo3znnhcRzaVo9 vbW2hzX/AJcl1qDahbxPMgllkW5+1MFBHzKG6gDIXvnmgDD/AOFisujaNe3Nlpumy6vbm6t11TVV gi8pVjzmQI3zlpRtUDlAWJU5QFv8UNNu7+zjhFokFw9pH5U96qXpa5SNoyluAQ6ATJubeMbZMA7R u3B4Tgg07Sbax1G+sptLtBZQXcPlNIYcICrB0ZDkxRknaDleCASDJF4Yhtr/AO0W2o6lDAzxyT2q zhluJERUV3kYGUnbHGCA4DbfmB3NuAObPjrUWv8ASbo6d5dhqOntcWcfnqwnaWe0jt/MbbujZfPO 8AMoD5BkIwLF54/uNOnuLa60TdPYxXM1/wDZ7sNHCkCW8rFCyqXzHcLj5R842nCkyCw3gfR7eWzS XVL4NHF9i02OS4QCBVaOaNYl24ZozbqwLBmYKd5cAYkfwbpV09/bXGpXdxf3VlcQXkjSRiV0uVjj 8wqqBVO22VVIUD5GyGOTQBsaLq7aqL1JrOS0uLO48iaF3V9pMaSryvGdkiZxkBtwBYAMdSs/TrWz t77Vpba482a5u1luk3hvKkEMSBcD7vyIjYPPzZ6EVoUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB4v4V1DU9mgWwvYIJki0tLOOXU5kke 1NvbmXZZqhSZSTOPNb7p3ElRECLEGuX9zdXselXk8l0ulXGpG0GqyXd21zBLA8cc8G0C2ZiXR4Ys A7nXjauPYKKAPJ9V1LVLm7sbiHVr62XVLuYxtbzfIsQ1Kwt4niDZUq0Pz8hlbznONr4q5JeGz8RX WlXusala6PZ3E6WsouZHl+0eRZyRRhzuedyZrkrE28P02sEAHplZ+p6ZLqHlNb6pfadNHkeZaFDu U4yCsiuh5A527hggEAsCAcnaTzTeB/Aup38sktpElrdalNMxcbfsr7ZJM9Qsxicsc7Soc4ClhJ/p mm/Dv/lvZw/2h7xNb6e97+BhVbZv9kxqP4SvHYWFjb6Zp1tYWcfl2trEkMKbidqKAFGTycADrVig Dxf+0RDquoLBqnmaFJqEx+16hrE2nI7ra2IhH2lF3SfIZdoJPmKN5LlQx0G1S/T7KNV1u+TXRqGk xJDCZIluLZ/svms0BUbY2laYGQqh3fuiQP3Z9YrHvfD6X2orcTahfG1Esc72G9TC8sZVkbJUuuGR DtVlUlckHc24A87F3e2vhXwfLfarJLa3+mG6vLrU9cl09WuCkHlr58ak52mXEfAfDO25gWMllqWv vr1nBqGpQLqYlsFj82+ngkmhMUBnKWAiCyKzG4HmOAUO7Ozyvl9YooA4/wAC3j3H2+GS9nvpIvLM twZ2kjeU7g2VdQ1vN8oL2/3YgUxgs2eHuHGr6foUTapfXFxL9il1yJNRmJtr03looVlD/wCjt+8u cRgIMrkDMalfaKKAPM9c08y/Fu3WK/1K1+2JaR3H2e8kQPGYdRJTbnAB8lOQAVO5lKsS1Z91q3iE X+rtY3kY1iF9SxbJfzT3DRIk/wBnH2HyzHEMi3Ik43jbyTLhvXKKAPJ7TUmLKJdagTw093Cl3PZ6 9cXaQ/urliWvXCGPLrajYr8fLnHm4fPtNSeDQ9TL6tfRXUcV5NoYlma1ku703t5keSNgmkytvmJk IBYDYN5B9oooA8jvr7V7ez1O7i13UgyW+vXqoZQVVrS6C26DIyEUuxIz83CsWQBBqfEK2f8A4S7Q bm2vr6zumiESzW1yy7Q1/YofkJKHKyvkMpDfLuB2rj0iigDzu7ujZ+IJrVdTu11S31Oxt9Ns3vpC 01iRbiZ/JLYmGGucysrMNrHcNg24+h32rw6dpFw+u6lO32LQrlhPKGDyXdwYZS3GSPKXaFzjJLkG T569cooA5fxDY2//AAlnhO/MeboahJCrsxO1Psl0SFB4XJxnGN21c52rg0mxt7L4h+IDbx7Wn0+y mlYsWZ3Mt3ySeTgAKPRVVRgAAdRRQB5P4k1LVIvEHiZ7fVr6BdOtNQu4Yo5vkLxWmnvGCpz8od2b AxnLA5VnVrGp6i9lc32m3+qTvb2uoNHHcX+sNpsJJtraQh7iJdxkLzSMkQwm3zMACNAvqFFAHj9l 4gvL7TtMvNQ1q+j1me70fyoIZCiz2sotDKzRKNojMrzAy4Hzfu92DsJoup6tdaZJLcazBbzf2esm qquqXFw6T+ZDuEw8vGn8eejbCNm5mAIhyvpF74fS+1FbibUL42oljnew3qYXljKsjZKl1wyIdqsq krkg7m3bFAGP4Wuftfhy0mDTsp3qrTS+aWUOwBWTAMkZABSQjc6bWOSSa2KKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q== --0016e657b16a74e69304a2f0bae2-- ========================================================================= Date: Tue, 10 May 2011 16:46:23 -0400 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: Con Image Issue - Validity Comments: To: Andrew Westphal <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0023545309c897c1ed04a2f20c06 Message-ID: <[log in to unmask]> --0023545309c897c1ed04a2f20c06 Content-Type: text/plain; charset=ISO-8859-1 Andrew, If you are wanting to say what the activity is in a subject, the best thing to do is to average across runs based on the number of trials. If its a block design, you probably have equal number of blocks in each run, and it can be simplified to averaging across runs. The idea of using 1s and 0s was simple that it was easier than entering fractions and as long as all subjects had the same number of runs, then it wasn't a problem. Additionally, the spmT or spmF maps are the same for the 1s and 0s or if you use 1/N and 0s where N is the numebr of runs. Since you want to compare across subjects with varying numbers of runs/sessions, you need to make the sum of the contrast (assuming its A versus baseline) equal to 1. 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, May 10, 2011 at 2:51 PM, Andrew Westphal <[log in to unmask]>wrote: > Hello fellow SPM users, > > With some investigation into con images, my lab found that con images > appear to be sums of the beta images that one specifies depending on the > weight that is assigned (i.e. 1 0 0 0 0 1 0 0 0 0 - you would add the two > beta images together). This appears to be the way it is calculated instead > of a mean value. So does SPM account for the fact that subjects will have > varied block counts, such as one subject having 5 sessions and another > having 2 sessions? Or is the subject with 5 sessions going to contribute 2.5 > times as much at the group level as the subject with two sessions? For it to > be valid, does one have to put in a contrast vector like 1/5 0 0 0 0 1/5 0 0 > for the 5 session subject and 1/2 0 0 0 0 1/2 0 0 for the 2 session subject? > > Andrew > --0023545309c897c1ed04a2f20c06 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Andrew,<br><br>If you are wanting to say what the activity is in a subject,= the best thing to do is to average across runs based on the number of tria= ls. If its a block design, you probably have equal number of blocks in each= run, and it can be simplified to averaging across runs.<br> <br>The idea of using 1s and 0s was simple that it was easier than entering= fractions and as long as all subjects had the same number of runs, then it= wasn't a problem. Additionally, the spmT or spmF maps are the same for= the 1s and 0s or if you use 1/N and 0s where N is the numebr of runs.<br> <br>Since you want to compare across subjects with varying numbers of runs/= sessions, you need to make the sum of the contrast (assuming its A versus b= aseline) equal to 1.<br clear=3D"all"><br>Best Regards, Donald McLaren<br> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<= br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Depa= rtment of Neurology, Massachusetts General Hospital and Harvard Medical Sch= ool<br>Office: (773) 406-2464<br> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>This e-m= ail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCAR= E 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 f= or delivering it to the intended recipient, you are hereby notified that yo= u are in possession of confidential and privileged information. Any unautho= rized use, disclosure, copying or the taking of any action in reliance on t= he contents of this information is strictly prohibited and may be unlawful.= If you have received this e-mail unintentionally, please immediately notif= y the sender via telephone at (773) 406-2464 or email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 2:51 PM, Andrew = Westphal <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">aj= [log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmail_qu= ote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 20= 4, 204); padding-left: 1ex;"> Hello fellow SPM users,<br> <br> With some investigation into con images, my lab found that con images appea= r to be sums of the beta images that one specifies depending on the weight = that is assigned (i.e. 1 0 0 0 0 1 0 0 0 0 - you would add the two beta ima= ges together). This appears to be the way it is calculated instead of a mea= n value. So does SPM account for the fact that subjects will have varied bl= ock counts, such as one subject having 5 sessions and another having 2 sess= ions? Or is the subject with 5 sessions going to contribute 2.5 times as mu= ch at the group level as the subject with two sessions? For it to be valid,= does one have to put in a contrast vector like 1/5 0 0 0 0 1/5 0 0 for the= 5 session subject and 1/2 0 0 0 0 1/2 0 0 for the 2 session subject?<br> <font color=3D"#888888"><br> Andrew<br> </font></blockquote></div><br> --0023545309c897c1ed04a2f20c06-- ========================================================================= Date: Tue, 10 May 2011 21:59:55 +0100 Reply-To: Andrew Westphal <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andrew Westphal <[log in to unmask]> Subject: Re: Con Image Issue - Validity 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 Dr. McLaren, Thanks for responding to me. The design is event-related with varying num= bers of sessions due to blocks being removed due to movement and such. Is= the weighting with whole integers still a problem in the second level an= alysis if the contrast sums to zero? For example, 2 session subject (1 -1= 0 0 1 -1 0 0) and 5 session subject (1 -1 0 0 1 -1 0 0 1 -1 0 0 1 -1 0 0= 1 -1 0 0). Andrew ========================================================================= Date: Tue, 10 May 2011 17:05:09 -0400 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: Con Image Issue - Validity Comments: To: Andrew Westphal <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0015174beb46b4a85904a2f24f2c Message-ID: <[log in to unmask]> --0015174beb46b4a85904a2f24f2c Content-Type: text/plain; charset=ISO-8859-1 You want the positives to sum to 1 and the negatives sum to 1. 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, May 10, 2011 at 4:59 PM, Andrew Westphal <[log in to unmask]>wrote: > Dear Dr. McLaren, > > Thanks for responding to me. The design is event-related with varying > numbers of sessions due to blocks being removed due to movement and such. Is > the weighting with whole integers still a problem in the second level > analysis if the contrast sums to zero? For example, 2 session subject (1 -1 > 0 0 1 -1 0 0) and 5 session subject (1 -1 0 0 1 -1 0 0 1 -1 0 0 1 -1 0 0 1 > -1 0 0). > > Andrew > > > --0015174beb46b4a85904a2f24f2c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable You want the positives to sum to 1 and the negatives sum to 1.<br clear=3D"= all"><br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, = GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusett= s General Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 4:59 PM, Andrew = Westphal <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">aj= [log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmail_qu= ote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex= ;"> Dear Dr. McLaren,<br> <br> Thanks for responding to me. The design is event-related with varying numbe= rs of sessions due to blocks being removed due to movement and such. Is the= weighting with whole integers still a problem in the second level analysis= if the contrast sums to zero? For example, 2 session subject (1 -1 0 0 1 -= 1 0 0) and 5 session subject (1 -1 0 0 1 -1 0 0 1 -1 0 0 1 -1 0 0 1 -1 0 0)= .<br> <font color=3D"#888888"><br> Andrew<br> <br> <br> </font></blockquote></div><br> --0015174beb46b4a85904a2f24f2c-- ========================================================================= Date: Tue, 10 May 2011 17:21:42 -0400 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: Cluster information without corresponding SPM.mat Comments: To: Bedda Rosario <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0023545309c8d97cc104a2f28a9c Message-ID: <[log in to unmask]> --0023545309c8d97cc104a2f28a9c Content-Type: text/plain; charset=ISO-8859-1 See the following posts: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R25661&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R16468&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 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, May 10, 2011 at 3:09 PM, Bedda Rosario <[log in to unmask]> wrote: > Dear SPM users, > > I have created an image (and hdr file) using the results of a t test > analysis in SPM8 (that is, using spmT_0001.img). I would like to know if it > possible to get a cluster report from the image without a corresponding > SPM.mat file. > > Thanks for the help, > Bedda > --0023545309c8d97cc104a2f28a9c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable See the following posts:<div><a href=3D"http://www.jiscmail.ac.uk/cgi-bin/w= a.exe?A2=3Dind1103&L=3DSPM&P=3DR25661&I=3D-3&d=3DNo+Match%3= BMatch%3BMatches&m=3D41734">http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2= =3Dind1103&L=3DSPM&P=3DR25661&I=3D-3&d=3DNo+Match%3BMatch%3= BMatches&m=3D41734</a></div> <div><br></div><div><a href=3D"http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2= =3Dind1103&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match%3BMatch%3= BMatches&m=3D41734">http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind11= 03&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match%3BMatch%3BMatches= &m=3D41734</a><br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: <a href=3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target= =3D"_blank">(773) 406-2464</a><br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDENTIAL INFORMATION w= hich may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY P= RIVILEGED and which is intended only for the use of the individual or entit= y 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 recipi= ent, you are hereby notified that you are in possession of confidential and= privileged information. Any unauthorized use, disclosure, copying or the t= aking of any action in reliance on the contents of this information is stri= ctly prohibited and may be unlawful. If you have received this e-mail unint= entionally, please immediately notify the sender via telephone at <a href= =3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target=3D"_blank">(773= ) 406-2464</a> or email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 3:09 PM, Bedda R= osario <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]" target= =3D"_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class= =3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padd= ing-left:1ex"> Dear SPM users, <br><br>I have created an image (and hdr file) using the re= sults of a t test analysis in SPM8 (that is, using spmT_0001.img).=A0 I wou= ld like to know if it possible to get a cluster report from the image witho= ut a corresponding SPM.mat file.=A0 <br> <br>Thanks for the help, <br><font color=3D"#888888">Bedda<br> </font></blockquote></div><br> </div> --0023545309c8d97cc104a2f28a9c-- ========================================================================= Date: Tue, 10 May 2011 17:38:41 -0400 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: Flexible factorial questions Comments: To: zhang sheng <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3054a4bf9f614704a2f2c721 Message-ID: <[log in to unmask]> --20cf3054a4bf9f614704a2f2c721 Content-Type: text/plain; charset=ISO-8859-1 Zhang, Remove subject from the model and the flexible factorial setup. Subject CAN only be AND MUST be included in within-subject designs. Your design is purely between subjects. When you specify subjects the error term goes to 0. When this happens, SPM produces this message. There are several other potential causes as well (e.g. no overlapping voxels in ALL subjects, using the same scan twice, etc.), but this is why it appeared in this case. 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, May 10, 2011 at 3:11 PM, zhang sheng <[log in to unmask]> wrote: > Dear all, > > I have a problem for the Flexible factorial analysis. > > In second level analysis, I tried to do a Flexible factorial analysis with > two factors: "group" (e.g. patient vs. control) and "gender". So in the > design, I have three factors: subject, group, and gender. As I have only one > scan for each subject, the factor matrices should be [1 1] for group1 and > male, [1 2] for group1 and female, [2 1] for group2 and male, and [2 2] for > group2 and female for each subject (see attached file for design matrix)? > However, when I estimated the model, I got an error message: *Please check > * your *data*: *There* are *no significant voxels. *So, first, did I do > something wrong with the model design? If I'm right, could someone tell me > what happened for the estimating? > > Thanks, and any suggestion is highly appreciated! > > Sheng > --20cf3054a4bf9f614704a2f2c721 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Zhang,<div><br></div><div>Remove subject from the model and the flexible fa= ctorial setup. Subject CAN only be AND MUST be included in within-subject d= esigns. Your design is purely between subjects.</div><div><br></div><div> When you specify subjects the error term goes to 0. When this happens, SPM = produces this message. There are several other potential causes as well (e.= g. no overlapping voxels in ALL subjects, using the same scan twice, etc.),= but this is why it appeared in this case.<br> <div><br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, = GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusett= s General Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 3:11 PM, zhang s= heng <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">szhang570= @gmail.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style= =3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> Dear all,<br><br>I have a problem for the Flexible factorial analysis. <br>= <br>In second level analysis, I tried to do a Flexible factorial analysis w= ith two factors: "group" (e.g. patient vs. control) and "gen= der". So in the design, I have three factors: subject, group, and gend= er. As I have only one scan for each subject, the factor matrices should be= [1 1] for group1 and male, [1 2] for group1 and female, [2 1] for group2 a= nd male, and [2 2] for group2 and female for each subject (see attached fil= e for design matrix)? However, when I estimated the model, I got an error m= essage: <em>Please check</em> your <em>data</em>: <em>There</em> are <em>no= significant voxels.=A0</em>So, first, did I do something wrong with the mo= del design? If I'm right, could someone tell me what happened for the e= stimating? <br> <br>Thanks, and any suggestion is highly appreciated!<br><font color=3D"#88= 8888"><br>Sheng<br>=20 </font></blockquote></div><br></div></div> --20cf3054a4bf9f614704a2f2c721-- ========================================================================= Date: Wed, 11 May 2011 08:01:20 +0000 Reply-To: "Fellhauer, Iven" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Fellhauer, Iven" <[log in to unmask]> Subject: AW: [SPM] inaccurate segmentation of gray matter 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]> Dear Christiane, Maybe you can use the TOM8 toolbox (https://irc.cchmc.org/software/tom.php)= to create your own Tissue Probability Map (TPM). Take a look in Christian = Gaser's vbm8 manual on page 24. Best regards, Iven -----Urspr=FCngliche Nachricht----- Von: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] Im Au= ftrag von John Ashburner Gesendet: Dienstag, 10. Mai 2011 16:44 An: [log in to unmask] Betreff: Re: [SPM] inaccurate segmentation of gray matter I don't have any good suggestions at the moment, other than maybe try Chris= tian Gaser's toolbox to see if this handles the data better. I suspect tha= t it might, as its final segmentation relies less heavily on the tissue pro= bability maps. Best regards, -John On 9 May 2011 10:37, Moeller, C. <[log in to unmask]> wrote: > Dear SPM experts, > > I am doing VBM analysis on scans of AD patients and healthy controls.=20 > I just finished the segmentation and found a lot of inaccurate=20 > segmentations, especially in scans with a lot of atrophy. I attached a=20 > screenshot of the segmentation for illustration of the problem. It=20 > seems as if the space between the cortex and the dura is also=20 > segmented as gray matter. I used 'New Segment' of SPM8. > > Does anyone have an idea why this happens and how I can solve this=20 > problem? > > Another issue is, that often the dura around the brain is also=20 > segmented as gray matter. In the older version of the 'Segment' step=20 > there was a 'Clean up' option. Does anyone know how to solve this in=20 > the 'New Segment' step of SPM? > > Thanks in advance. > > Best regards, > Christiane > ========================================================================= Date: Wed, 11 May 2011 10:55:10 +0200 Reply-To: Nicole David <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nicole David <[log in to unmask]> Subject: Job Announcement (PhD Position) Content-Type: multipart/alternative; boundary=Apple-Mail-7-379396246 Mime-Version: 1.0 (Apple Message framework v936) Message-ID: <[log in to unmask]> --Apple-Mail-7-379396246 Content-Type: text/plain; charset="utf-8"; format=flowed; delsp=yes Content-Transfer-Encoding: base64 X-NAIMIME-Disclaimer: 1 X-NAIMIME-Modified: 1 RGVhciBTUE0gbGlzdCwKCkkgd291bGQgbGlrZSB0byBhbm5vdW5jZSBhbiBvcGVuIFBoRCBwb3Np dGlvbiB3aXRoIFByb2YuIEFuZHJlYXMgRW5nZWwgIAphdCB0aGUgRGVwdC4gb2YgTmV1cm9waHlz aW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3ksIFVuaXZlcnNpdHkgIApNZWRpY2FsIENlbnRlciBI YW1idXJnLUVwcGVuZG9yZi4KCkZvciBkZXRhaWxzLCBzZWUgYmVsb3cuCgpCZXN0LApOaWNvbGUg RGF2aWQKLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQoKVGhlIERlcGFydG1lbnQgb2Yg TmV1cm9waHlzaW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3kgYXQgdGhlICAKVW5pdmVyc2l0eSBN ZWRpY2FsIENlbnRlciBIYW1idXJnLUVwcGVuZG9yZiAoRGlyZWN0b3I6IFByb2YuIERyLiAgCkFu ZHJlYXMgSy4gRW5nZWwpIGludml0ZXMgYXBwbGljYXRpb25zIGZvciBhIFBoRCBzdHVkZW50IHBv c2l0aW9uLiBUaGUgIApwb3NpdGlvbiBpcyBhdmFpbGFibGUgd2l0aCBpbW1lZGlhdGUgc3RhcnQu IFRoZSBhcHBvaW50bWVudCB3aWxsIGJlICAKZm9yIDMgeWVhcnMuCgpUaGUgcG9zaXRpb24gaXMg YXQgdGhlIGludGVyc2VjdGlvbiBvZiBwc3ljaG9sb2d5LCBjb2duaXRpdmUgIApuZXVyb3NjaWVu Y2UgYW5kIHBoaWxvc29waHkuIE1vcmUgc3BlY2lmaWNhbGx5LCBleHBlcmltZW50cyAgCmludmVz dGlnYXRpbmcgdGhlIHNlbnNlIG9mIGFnZW5jeSBzaGFsbCBiZSBjYXJyaWVkIG91dCwgdXNpbmcg IApuZXVyb3NjaWVuY2UgdGVjaG5pcXVlcyBzdWNoIGFzIG1hZ25ldG9lbmNlcGhhbG9ncmFwaHkg KE1FRykuIFRoZSAgCnNlbnNlIG9mIGFnZW5jeSByZWZlcnMgdG8gdGhlIG1vbml0b3Jpbmcgb2Yg YWN0aW9ucyBhbmQgdGhlICAKZXhwZXJpZW5jZSwgb3IgYXdhcmVuZXNzLCBvZiBzdWNoIGFzIHNl bGYtIG9yIG90aGVyLWdlbmVyYXRlZC4KCkFwcGxpY2FudHMgc2hvdWxkIGhhdmUgZXhwZXJpZW5j ZSBpbiBhbmFseXppbmcgaHVtYW4gRUVHIG9yIE1FRyAgCnN0dWRpZXMuIFByb2dyYW1taW5nIGV4 cGVyaWVuY2UgKE1hdGxhYikgYXMgd2VsbCBhcyBmbHVlbnQgd3JpdHRlbiBhbmQgIApzcG9rZW4g RW5nbGlzaCBpcyBjb25zaWRlcmVkIG1hbmRhdG9yeS4gQXBwbGljYW50cyB3aWxsIGJlIHJlc3Bv bnNpYmxlICAKZm9yIHRoZSBtYW5hZ2VtZW50IGFuZCBpbXBsZW1lbnRhdGlvbiBvZiByZXNlYXJj aCBwYXJhZGlnbXMsIGRhdGEgIAphY3F1aXNpdGlvbiBhbmQgYW5hbHlzaXMuIEtub3dsZWRnZSBp biB0aGVvcmllcyBvZiBhY3Rpb24gYW5kICAKcGVyY2VwdGlvbiwgbXVsdGlzZW5zb3J5IHByb2Nl c3NpbmcsIHNlbGYtYXdhcmVuZXNzIGFuZCBhIGJhc2ljICAKdW5kZXJzdGFuZGluZyBvZiBpbmZv cm1hdGlvbiBwcm9jZXNzaW5nIGluIHRoZSBodW1hbiBicmFpbiB3b3VsZCBiZSAgCnByZWZlcnJl ZC4KClRoZSBzdWNjZXNzZnVsIGNhbmRpZGF0ZSB3aWxsIGJlIG1lbWJlciBvZiB0aGUgRXVyb3Bl YW4gY29sbGFib3JhdGl2ZSAgCnByb2plY3Qg4oCcRXh0ZW5kaW5nIFNlbnNvcmltb3RvciBDb250 aW5nZW5jaWVzIHRvIENvZ25pdGlvbiDigJMgZVNNQ3PigJ0gIAp0aGF0IGhhcyBzdGFydGVkIGlu IEphbnVhcnkgMjAxMS4gVGhlIG1haW4gb2JqZWN0aXZlIG9mIHRoZSBwcm9qZWN0IGlzICAKdG8g ZXh0ZW5kIHRoZSBTZW5zb3JpbW90b3IgQ29udGluZ2VuY3kgVGhlb3J5IGFzIGFuIGFjdGlvbi1i YXNlZCAgCmFwcHJvYWNoIHRvIHBlcmNlcHRpb24gYW5kIGNvZ25pdGlvbiwgYW5kIHRvIGludmVz dGlnYXRlIGl0cyAgCmltcGxpY2F0aW9ucyBpbiBzdHVkaWVzIG9mIG5hdHVyYWwgYW5kIGFydGlm aWNpYWwgYWdlbnRzLiBNb3JlICAKaW5mb3JtYXRpb24gb24gdGhlIHByb2plY3QgaXMgdG8gYXBw ZWFyIGF0IGh0dHA6Ly9lU01Dcy5ldS4gIApJbmZvcm1hdGlvbiBvbiB0aGUgRGVwdC4gb2YgTmV1 cm9waHlzaW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3kgaXMgIAphdmFpbGFibGUgYXQgaHR0cDov L3d3dy51a2UuZGUvbmV1cm9waHlzaW9sb2dpZSBhbmQgYXQgaHR0cDovL3d3dy40MGh6LmRlIAou CgpQbGVhc2Ugc2VuZCB5b3VyIGFwcGxpY2F0aW9uIChpbmNsdWRpbmcgYSBDVikgdG8KCkRyLiBO aWNvbGUgRGF2aWQgb3IgUHJvZi4gRHIuIEFuZHJlYXMgSy4gRW5nZWwKRGVwdC4gb2YgTmV1cm9w aHlzaW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3kKVW5pdmVyc2l0eSBNZWRpY2FsIENlbnRlciBI YW1idXJnLUVwcGVuZG9yZgpNYXJ0aW5pc3RyYXNzZSA1MgoyMDI0NiBIYW1idXJnCkdlcm1hbnkK ClN1Ym1pc3Npb24gb2YgYXBwbGljYXRpb25zIHRocm91Z2ggZW1haWwgKG5kYXZpZEB1a2UuZGUs ICAKYWsuZW5nZWxAdWtlLmRlKSBpcyBlbmNvdXJhZ2VkLgoKLS0gClBmbGljaHRhbmdhYmVuIGdl bcOkw58gR2VzZXR6IMO8YmVyIGVsZWt0cm9uaXNjaGUgSGFuZGVsc3JlZ2lzdGVyIHVuZCBHZW5v c3NlbnNjaGFmdHNyZWdpc3RlciBzb3dpZSBkYXMgVW50ZXJuZWhtZW5zcmVnaXN0ZXIgKEVIVUcp OgoKVW5pdmVyc2l0w6R0c2tsaW5pa3VtIEhhbWJ1cmctRXBwZW5kb3JmCkvDtnJwZXJzY2hhZnQg ZGVzIMO2ZmZlbnRsaWNoZW4gUmVjaHRzCkdlcmljaHRzc3RhbmQ6IEhhbWJ1cmcKClZvcnN0YW5k c21pdGdsaWVkZXI6ClByb2YuIERyLiBKw7ZyZyBGLiBEZWJhdGluIChWb3JzaXR6ZW5kZXIpCkRy LiBBbGV4YW5kZXIgS2lyc3RlaW4KSm9hY2hpbSBQcsO2bMOfClByb2YuIERyLiBEci4gVXdlIEtv Y2gtR3JvbXVzCg== --Apple-Mail-7-379396246 Content-Type: text/HTML; charset="utf-8" Content-Transfer-Encoding: base64 X-NAIMIME-Disclaimer: 1 X-NAIMIME-Modified: 1 PGh0bWw+PGJvZHkgc3R5bGU9IndvcmQtd3JhcDogYnJlYWstd29yZDsgLXdlYmtpdC1uYnNwLW1v ZGU6IHNwYWNlOyAtd2Via2l0LWxpbmUtYnJlYWs6IGFmdGVyLXdoaXRlLXNwYWNlOyAiPjxmb250 IGNsYXNzPSJBcHBsZS1zdHlsZS1zcGFuIiBmYWNlPSJBcmlhbCI+RGVhciBTUE0gbGlzdCw8L2Zv bnQ+PGRpdj48Zm9udCBjbGFzcz0iQXBwbGUtc3R5bGUtc3BhbiIgZmFjZT0iQXJpYWwiPjxicj48 L2ZvbnQ+PC9kaXY+PGRpdj48Zm9udCBjbGFzcz0iQXBwbGUtc3R5bGUtc3BhbiIgZmFjZT0iQXJp YWwiPkkgd291bGQgbGlrZSB0byBhbm5vdW5jZSBhbiBvcGVuIFBoRCBwb3NpdGlvbiB3aXRoIFBy b2YuIEFuZHJlYXMgRW5nZWwgYXQgdGhlIERlcHQuIG9mIE5ldXJvcGh5c2lvbG9neSBhbmQgUGF0 aG9waHlzaW9sb2d5LCBVbml2ZXJzaXR5IE1lZGljYWwgQ2VudGVyIEhhbWJ1cmctRXBwZW5kb3Jm LjwvZm9udD48L2Rpdj48ZGl2Pjxmb250IGNsYXNzPSJBcHBsZS1zdHlsZS1zcGFuIiBmYWNlPSJB cmlhbCI+PGJyPjwvZm9udD48L2Rpdj48ZGl2Pjxmb250IGNsYXNzPSJBcHBsZS1zdHlsZS1zcGFu IiBmYWNlPSJBcmlhbCI+Rm9yIGRldGFpbHMsIHNlZSBiZWxvdy48L2ZvbnQ+PC9kaXY+PGRpdj48 Zm9udCBjbGFzcz0iQXBwbGUtc3R5bGUtc3BhbiIgZmFjZT0iQXJpYWwiPjxicj48L2ZvbnQ+PC9k aXY+PGRpdj48Zm9udCBjbGFzcz0iQXBwbGUtc3R5bGUtc3BhbiIgZmFjZT0iQXJpYWwiPkJlc3Qs PC9mb250PjwvZGl2PjxkaXY+PGZvbnQgY2xhc3M9IkFwcGxlLXN0eWxlLXNwYW4iIGZhY2U9IkFy aWFsIj5OaWNvbGUgRGF2aWQ8L2ZvbnQ+PC9kaXY+PGRpdj48Zm9udCBjbGFzcz0iQXBwbGUtc3R5 bGUtc3BhbiIgZmFjZT0iQXJpYWwiPi0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS08L2Zv bnQ+PC9kaXY+PGRpdj48Zm9udCBjbGFzcz0iQXBwbGUtc3R5bGUtc3BhbiIgZmFjZT0iQXJpYWwi Pjxicj48L2ZvbnQ+PC9kaXY+PGRpdj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdp bi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IGZvbnQ6 IG5vcm1hbCBub3JtYWwgbm9ybWFsIDExcHgvbm9ybWFsIEFyaWFsOyAiPjxmb250IGNsYXNzPSJB cHBsZS1zdHlsZS1zcGFuIiBmYWNlPSJIZWx2ZXRpY2EiPjxzcGFuIGNsYXNzPSJBcHBsZS1zdHls ZS1zcGFuIiBzdHlsZT0iZm9udC1zaXplOiBtZWRpdW07ICI+PGZvbnQgY2xhc3M9IkFwcGxlLXN0 eWxlLXNwYW4iIGZhY2U9IkFyaWFsIj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdp bi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQt YWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEycHgvbm9ybWFsIEFy aWFsOyAiPlRoZSBEZXBhcnRtZW50IG9mIE5ldXJvcGh5c2lvbG9neSBhbmQgUGF0aG9waHlzaW9s b2d5IGF0IHRoZSBVbml2ZXJzaXR5IE1lZGljYWwgQ2VudGVyIEhhbWJ1cmctRXBwZW5kb3JmIChE aXJlY3RvcjogUHJvZi4gRHIuIEFuZHJlYXMgSy4gRW5nZWwpIGludml0ZXMgYXBwbGljYXRpb25z IGZvciBhJm5ic3A7UGhEIHN0dWRlbnQmbmJzcDtwb3NpdGlvbi4mbmJzcDtUaGUgcG9zaXRpb24g aXMgYXZhaWxhYmxlIHdpdGggaW1tZWRpYXRlIHN0YXJ0LiBUaGUgYXBwb2ludG1lbnQgd2lsbCBi ZSBmb3IgMyB5ZWFycy48L2Rpdj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdpbi1y aWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQtYWxp Z246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEycHgvbm9ybWFsIEFyaWFs OyBtaW4taGVpZ2h0OiAxNHB4OyAiPjxicj48L2Rpdj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAw cHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAw cHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEycHgv bm9ybWFsIEFyaWFsOyAiPlRoZSBwb3NpdGlvbiBpcyBhdCB0aGUgaW50ZXJzZWN0aW9uIG9mIHBz eWNob2xvZ3ksIGNvZ25pdGl2ZSBuZXVyb3NjaWVuY2UgYW5kIHBoaWxvc29waHkuIE1vcmUgc3Bl Y2lmaWNhbGx5LCBleHBlcmltZW50cyBpbnZlc3RpZ2F0aW5nIHRoZSBzZW5zZSBvZiBhZ2VuY3kg c2hhbGwgYmUgY2FycmllZCBvdXQsIHVzaW5nIG5ldXJvc2NpZW5jZSB0ZWNobmlxdWVzIHN1Y2gg YXMgbWFnbmV0b2VuY2VwaGFsb2dyYXBoeSAoTUVHKS4gVGhlIHNlbnNlIG9mIGFnZW5jeSByZWZl cnMgdG8gdGhlIG1vbml0b3Jpbmcgb2YgYWN0aW9ucyBhbmQgdGhlIGV4cGVyaWVuY2UsIG9yIGF3 YXJlbmVzcywgb2Ygc3VjaCBhcyBzZWxmLSBvciBvdGhlci1nZW5lcmF0ZWQuPC9kaXY+PGRpdiBz dHlsZT0ibWFyZ2luLXRvcDogMHB4OyBtYXJnaW4tcmlnaHQ6IDBweDsgbWFyZ2luLWJvdHRvbTog MHB4OyBtYXJnaW4tbGVmdDogMHB4OyB0ZXh0LWFsaWduOiBqdXN0aWZ5OyBmb250OiBub3JtYWwg bm9ybWFsIG5vcm1hbCAxMnB4L25vcm1hbCBBcmlhbDsgbWluLWhlaWdodDogMTRweDsgIj48YnI+ PC9kaXY+PGRpdiBzdHlsZT0ibWFyZ2luLXRvcDogMHB4OyBtYXJnaW4tcmlnaHQ6IDBweDsgbWFy Z2luLWJvdHRvbTogMHB4OyBtYXJnaW4tbGVmdDogMHB4OyB0ZXh0LWFsaWduOiBqdXN0aWZ5OyBm b250OiBub3JtYWwgbm9ybWFsIG5vcm1hbCAxMnB4L25vcm1hbCBBcmlhbDsgIj5BcHBsaWNhbnRz IHNob3VsZCBoYXZlIGV4cGVyaWVuY2UgaW4gYW5hbHl6aW5nIGh1bWFuIEVFRyBvciBNRUcgc3R1 ZGllcy4gUHJvZ3JhbW1pbmcgZXhwZXJpZW5jZSAoTWF0bGFiKSZuYnNwOzxzcGFuIGxhbmc9IkVO LVVTIj5hcyB3ZWxsIGFzIGZsdWVudCB3cml0dGVuIGFuZCBzcG9rZW4gRW5nbGlzaCBpcyBjb25z aWRlcmVkIG1hbmRhdG9yeTwvc3Bhbj4uIEFwcGxpY2FudHMgd2lsbCBiZSByZXNwb25zaWJsZSBm b3IgdGhlIG1hbmFnZW1lbnQgYW5kIGltcGxlbWVudGF0aW9uIG9mIHJlc2VhcmNoIHBhcmFkaWdt cywgZGF0YSBhY3F1aXNpdGlvbiBhbmQgYW5hbHlzaXMuIEtub3dsZWRnZSBpbiB0aGVvcmllcyBv ZiBhY3Rpb24gYW5kIHBlcmNlcHRpb24sIG11bHRpc2Vuc29yeSBwcm9jZXNzaW5nLCBzZWxmLWF3 YXJlbmVzcyBhbmQgYSBiYXNpYyB1bmRlcnN0YW5kaW5nIG9mIGluZm9ybWF0aW9uIHByb2Nlc3Np bmcgaW4gdGhlIGh1bWFuIGJyYWluIHdvdWxkIGJlIHByZWZlcnJlZC48L2Rpdj48ZGl2IHN0eWxl PSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7 IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3Jt YWwgbm9ybWFsIDEycHgvbm9ybWFsIEFyaWFsOyBtaW4taGVpZ2h0OiAxNHB4OyAiPjxicj48L2Rp dj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4t Ym90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6 IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEycHgvbm9ybWFsIEFyaWFsOyAiPlRoZSBzdWNjZXNzZnVs IGNhbmRpZGF0ZSB3aWxsIGJlIG1lbWJlciBvZiB0aGUgRXVyb3BlYW4gY29sbGFib3JhdGl2ZSBw cm9qZWN0IOKAnEV4dGVuZGluZyBTZW5zb3JpbW90b3IgQ29udGluZ2VuY2llcyB0byBDb2duaXRp b24g4oCTIGVTTUNz4oCdIHRoYXQgaGFzIHN0YXJ0ZWQgaW4gSmFudWFyeSAyMDExLiBUaGUgbWFp biBvYmplY3RpdmUgb2YgdGhlIHByb2plY3QgaXMgdG8gZXh0ZW5kIHRoZSBTZW5zb3JpbW90b3Ig Q29udGluZ2VuY3kgVGhlb3J5IGFzIGFuIGFjdGlvbi1iYXNlZCBhcHByb2FjaCB0byBwZXJjZXB0 aW9uIGFuZCBjb2duaXRpb24sIGFuZCB0byBpbnZlc3RpZ2F0ZSBpdHMgaW1wbGljYXRpb25zIGlu IHN0dWRpZXMgb2YgbmF0dXJhbCBhbmQgYXJ0aWZpY2lhbCBhZ2VudHMuIE1vcmUgaW5mb3JtYXRp b24gb24gdGhlIHByb2plY3QgaXMgdG8gYXBwZWFyIGF0Jm5ic3A7PGEgaHJlZj0iaHR0cDovL2VT TUNzLmRlLyI+PHNwYW4gc3R5bGU9InRleHQtZGVjb3JhdGlvbjogdW5kZXJsaW5lOyBjb2xvcjog cmdiKDAsIDgwLCAxNzQpOyAiPmh0dHA6Ly9lU01Dcy5ldTwvc3Bhbj48L2E+LiBJbmZvcm1hdGlv biBvbiB0aGUgRGVwdC4gb2YgTmV1cm9waHlzaW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3kgaXMg YXZhaWxhYmxlIGF0Jm5ic3A7PGEgaHJlZj0iaHR0cDovL3d3dy51a2UuZGUvbmV1cm9waHlzaW9s b2dpZSI+PHNwYW4gc3R5bGU9InRleHQtZGVjb3JhdGlvbjogdW5kZXJsaW5lOyBjb2xvcjogcmdi KDAsIDgwLCAxNzQpOyAiPmh0dHA6Ly93d3cudWtlLmRlL25ldXJvcGh5c2lvbG9naWU8L3NwYW4+ PC9hPiZuYnNwO2FuZCBhdCZuYnNwOzxhIGhyZWY9Imh0dHA6Ly93d3cuNDBIei5kZS8iPjxzcGFu IHN0eWxlPSJ0ZXh0LWRlY29yYXRpb246IHVuZGVybGluZTsgY29sb3I6IHJnYigwLCA4MCwgMTc0 KTsgIj5odHRwOi8vd3d3LjQwaHouZGU8L3NwYW4+PC9hPi48L2Rpdj48ZGl2IHN0eWxlPSJtYXJn aW4tdG9wOiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdp bi1sZWZ0OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3JtYWwgbm9y bWFsIDEycHgvbm9ybWFsIEFyaWFsOyBtaW4taGVpZ2h0OiAxNHB4OyAiPjxicj48L2Rpdj48ZGl2 IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9t OiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1h bCBub3JtYWwgbm9ybWFsIDEycHgvbm9ybWFsIEFyaWFsOyAiPlBsZWFzZSBzZW5kIHlvdXIgYXBw bGljYXRpb24gKGluY2x1ZGluZyBhIENWKSB0bzwvZGl2PjxkaXYgc3R5bGU9Im1hcmdpbi10b3A6 IDBweDsgbWFyZ2luLXJpZ2h0OiAwcHg7IG1hcmdpbi1ib3R0b206IDBweDsgbWFyZ2luLWxlZnQ6 IDBweDsgdGV4dC1hbGlnbjoganVzdGlmeTsgZm9udDogbm9ybWFsIG5vcm1hbCBub3JtYWwgMTJw eC9ub3JtYWwgQXJpYWw7IG1pbi1oZWlnaHQ6IDE0cHg7ICI+PGJyPjwvZGl2PjxkaXYgc3R5bGU9 Im1hcmdpbi10b3A6IDBweDsgbWFyZ2luLXJpZ2h0OiAwcHg7IG1hcmdpbi1ib3R0b206IDBweDsg bWFyZ2luLWxlZnQ6IDBweDsgdGV4dC1hbGlnbjoganVzdGlmeTsgZm9udDogbm9ybWFsIG5vcm1h bCBub3JtYWwgMTJweC9ub3JtYWwgQXJpYWw7ICI+RHIuIE5pY29sZSBEYXZpZCBvciBQcm9mLiBE ci4gQW5kcmVhcyBLLiBFbmdlbDwvZGl2PjxkaXYgc3R5bGU9Im1hcmdpbi10b3A6IDBweDsgbWFy Z2luLXJpZ2h0OiAwcHg7IG1hcmdpbi1ib3R0b206IDBweDsgbWFyZ2luLWxlZnQ6IDBweDsgdGV4 dC1hbGlnbjoganVzdGlmeTsgZm9udDogbm9ybWFsIG5vcm1hbCBub3JtYWwgMTJweC9ub3JtYWwg QXJpYWw7ICI+RGVwdC4gb2YgTmV1cm9waHlzaW9sb2d5IGFuZCBQYXRob3BoeXNpb2xvZ3k8L2Rp dj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9wOiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4t Ym90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6 IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEycHgvbm9ybWFsIEFyaWFsOyAiPlVuaXZlcnNpdHkgTWVk aWNhbCBDZW50ZXIgSGFtYnVyZy1FcHBlbmRvcmY8L2Rpdj48ZGl2IHN0eWxlPSJtYXJnaW4tdG9w OiAwcHg7IG1hcmdpbi1yaWdodDogMHB4OyBtYXJnaW4tYm90dG9tOiAwcHg7IG1hcmdpbi1sZWZ0 OiAwcHg7IHRleHQtYWxpZ246IGp1c3RpZnk7IGZvbnQ6IG5vcm1hbCBub3JtYWwgbm9ybWFsIDEy cHgvbm9ybWFsIEFyaWFsOyAiPk1hcnRpbmlzdHJhc3NlIDUyPC9kaXY+PGRpdiBzdHlsZT0ibWFy Z2luLXRvcDogMHB4OyBtYXJnaW4tcmlnaHQ6IDBweDsgbWFyZ2luLWJvdHRvbTogMHB4OyBtYXJn aW4tbGVmdDogMHB4OyB0ZXh0LWFsaWduOiBqdXN0aWZ5OyBmb250OiBub3JtYWwgbm9ybWFsIG5v cm1hbCAxMnB4L25vcm1hbCBBcmlhbDsgIj4yMDI0NiBIYW1idXJnPC9kaXY+PGRpdiBzdHlsZT0i bWFyZ2luLXRvcDogMHB4OyBtYXJnaW4tcmlnaHQ6IDBweDsgbWFyZ2luLWJvdHRvbTogMHB4OyBt YXJnaW4tbGVmdDogMHB4OyB0ZXh0LWFsaWduOiBqdXN0aWZ5OyBmb250OiBub3JtYWwgbm9ybWFs IG5vcm1hbCAxMnB4L25vcm1hbCBBcmlhbDsgIj5HZXJtYW55PC9kaXY+PHAgc3R5bGU9Im1hcmdp bi10b3A6IDBweDsgbWFyZ2luLXJpZ2h0OiAwcHg7IG1hcmdpbi1ib3R0b206IDBweDsgbWFyZ2lu LWxlZnQ6IDBweDsgdGV4dC1hbGlnbjoganVzdGlmeTsgZm9udDogbm9ybWFsIG5vcm1hbCBub3Jt YWwgMTJweC9ub3JtYWwgQXJpYWw7ICI+Jm5ic3A7PC9wPjxkaXYgc3R5bGU9Im1hcmdpbi10b3A6 IDBweDsgbWFyZ2luLXJpZ2h0OiAwcHg7IG1hcmdpbi1ib3R0b206IDBweDsgbWFyZ2luLWxlZnQ6 IDBweDsgdGV4dC1hbGlnbjoganVzdGlmeTsgZm9udDogbm9ybWFsIG5vcm1hbCBub3JtYWwgMTJw eC9ub3JtYWwgQXJpYWw7ICI+U3VibWlzc2lvbiBvZiBhcHBsaWNhdGlvbnMgdGhyb3VnaCBlbWFp bCAoPGEgaHJlZj0ibWFpbHRvOm5kYXZpZEB1a2UuZGUiPm5kYXZpZEB1a2UuZGU8L2E+LCZuYnNw OzxhIGhyZWY9Im1haWx0bzphay5lbmdlbEB1a2UuZGUiPmFrLmVuZ2VsQHVrZS5kZTwvYT4pIGlz IGVuY291cmFnZWQuPC9kaXY+PC9mb250Pjwvc3Bhbj48L2ZvbnQ+PC9kaXY+PC9kaXY+CjxESVY+ PFA+PEhSPgotLSA8QlI+ClBmbGljaHRhbmdhYmVuIGdlbSZhdW1sOyZzemxpZzsgR2VzZXR6ICZ1 dW1sO2JlciBlbGVrdHJvbmlzY2hlIEhhbmRlbHNyZWdpc3RlciB1bmQgR2Vub3NzZW5zY2hhZnRz cmVnaXN0ZXIgc293aWUgZGFzIFVudGVybmVobWVuc3JlZ2lzdGVyIChFSFVHKTo8QlI+CjxCUj4K VW5pdmVyc2l0JmF1bWw7dHNrbGluaWt1bSBIYW1idXJnLUVwcGVuZG9yZjxCUj4KSyZvdW1sO3Jw ZXJzY2hhZnQgZGVzICZvdW1sO2ZmZW50bGljaGVuIFJlY2h0czxCUj4KR2VyaWNodHNzdGFuZDog SGFtYnVyZzxCUj4KPEJSPgpWb3JzdGFuZHNtaXRnbGllZGVyOjxCUj4KUHJvZi4gRHIuIEomb3Vt bDtyZyBGLiBEZWJhdGluIChWb3JzaXR6ZW5kZXIpPEJSPgpEci4gQWxleGFuZGVyIEtpcnN0ZWlu PEJSPgpKb2FjaGltIFByJm91bWw7bCZzemxpZzs8QlI+ClByb2YuIERyLiBEci4gVXdlIEtvY2gt R3JvbXVzCjwvUD48L0RJVj4KPC9ib2R5PjwvaHRtbD4= --Apple-Mail-7-379396246-- ========================================================================= Date: Wed, 11 May 2011 11:37:11 +0100 Reply-To: Subscribe Spm J <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Subscribe Spm J <[log in to unmask]> Subject: MAUDSLEY ANNUAL REVIEWS 2011 (Monday, 27 June 2011 to Wednesday, 29 June 2011) Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> MAUDSLEY ANNUAL REVIEWS 2011=20 Institute of Psychiatry, London, UK=20 Monday, 27 June 2011 to Wednesday, 29 June 2011 (event website: http://www.iop.kcl.ac.uk/events/?id=3D1202)=20 =20 We are delighted to announce the 2011 Maudsley Annual Reviews of Psychiat= ry that build on the success of our inaugural meeting and aim to bridge t= he gap between scientific developments in mental health and their applica= tion in clinical care.=20 Each year we select three themes that represent topics in psychiatry wher= e substantial developments were achieved in understanding or treating men= tal illness that are directly relevant to clinical practice. Each year we= bring together internationally renowned researchers and clinicians to di= scuss their findings and share their perspective on each of their expert = themes in a clinically relevant manner.=20 The three themes covered in the 2011 Maudsley Annual Reviews of Psychiatr= y focus on advances in Major Depressive Disorder, in promoting the physic= al wellbeing of patients with mental disorders and in improving clinical = and functional outcomes through better adherence.=20 Key Speakers for the Core Scientific programme: Norman Sartorius, Rudolf = Uher, Marieke Wichers, George Papakostas, Mark De Hert, Peter Haddad, And= re Tylee, John Hague, Munk-J=C3=B9rgensen and Francesc Colom.=20 =20 Meeting Chairs: Sophia Frangou, Philip McGuire, Rudolf Uher=20 =20 The event has been awarded 9 ECMEC credits from the European Accreditatio= n Council for Continuing Medical Education=20 =20 Programme, Registration form, Venue and Accommodation details are availab= le from the website =20 ========================================================================= Date: Wed, 11 May 2011 12:57:36 +0100 Reply-To: Michael Thomas Smith <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Thomas Smith <[log in to unmask]> Subject: After estimation design matrix loses most regressors. 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]> I'm interested in the correlation between a time series and the voxels =20 in the brain. To find out more I made a GLM (see http://www.sal.mvm.ed.ac.uk/images/problem1.png ). Before estimation it looks fine (I think?). During estimation however, 6 of the 8 columns are set to zero (see =20 inset in bottom-left corner of the linked image). Just sessions 1 and =20 3 output beta images. There are no errors or warnings expressed during =20 estimation, and the matlab output appears ok. - I've confirmed the number of images in each session is equal to the =20 number of values in the .txt regressor files. - I've tried adding conditions to each session (just a single event =20 near the start of each session) to see if it's the lack of conditions =20 that's upsetting SPM. (this didn't make any difference). - I've rebuilt the design matrix from scratch by hand, using the =20 interface, but that didn't make any difference. Looking forward to any suggestions! Thanks, Mike Smith --=20 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ========================================================================= Date: Wed, 11 May 2011 14:10:12 +0100 Reply-To: Ryan Herringa <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ryan Herringa <[log in to unmask]> Subject: Re: Ancova in SPM8 Comments: To: "Stephen J. Fromm" <[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]> Thanks Stephen, For each covariate in spm8, I selected: Interactions: "with Factor 1" Centering: "Overall mean" spm8 gives other options for centering: Factor 1 mean Factor 2 mean Factor 3 mean No centering User specified value As implied by Ancova GM My understanding was that it is centering on the condition mean so I thin= k this was the correct selection? Best, Ryan Herringa, MD, PhD University of Pittsburgh ========================================================================= Date: Wed, 11 May 2011 21:16:42 +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 make a mask in order to exclude activation located within ventricles Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=====003_Dragon115107410027_=====" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --=====003_Dragon115107410027_===== Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 RGVhciBhbGwsDQoNCkknbSBkb2luZyBzb21lIGZNUkkgZGF0YSBhbmFseXNpcyBhbmQgdGhlIEdM TSBhbmFseXNpcyByZXN1bHRzIEkgZ290IGhhdmUgYWN0aXZhdGlvbiB3aXRoaW4gdmVudHJpY2xl cy4gSSB3YW50IHRvIGV4Y2x1ZGUgdGhlIGFjdGl2YXRpb24gbGlrZSB0aGlzIHRocm91Z2ggYSBz cGVjaWZpYyBtYXNrIHdoaWNoIGNhbiBiZSBhcHBsaWVkIGFzIHRoZSBleHBsaWNpdCBtYXNrIGR1 cmluZyBkZXNpZ24gc2V0dGluZy4gDQoNCkkgaGF2ZSB0cmllZCBXRlUgUGlja0F0bGFzIFRvb2xi b3ggYW5kIEkgbWFkZSBhIG1hc2sgd2hpY2ggaW5jbHVkaW5nIGFsbCB0aGUgVEQgbGFibGVzIG9m IGh1bWFuIGF0bGFzIGV4Y2VwdCBmb3IgdGhlIHZlbnRyaWNsZXMuIEhvd2V2ZXIsIHRoZSByZXN1 bHQgd2FzIG5vdCB2ZXJ5IHdlbGwgYW5kIHRoZXJlIHdhcyBzdGlsbCBhY3RpdmF0aW9uIGluIGxh dGVyYWwgdmVudHJpY2xlLiBJIGNvbXBhcmVkIHRoZSBtYXNrIEkgbWFkZSB3aXRoIHN0YW5kYXJk IFQxIGFuYXRvbWljYWwgdGVtcGxhdGUsIGl0IHNlZW1zIHRoYXQgdGhlIG1hc2sgaXMgbm90ICBw cmVjaXNlIGVub3VnaC4gSSBhbHNvIHRyaWVkIHRvIG1ha2UgYSBtYXNrIHRocm91Z2ggdGhlIGdy YXktbWF0dGVyIHNlZ21lbnRhdGlvbiByZXN1bHQgb2YgdGhlIHN1YmplY3RzJyBUMSBhbmF0b21p Y2FsIHRlbXBsYXRlIChwcm9kdWNlZCBkdXJpbmcgRGFydGVsLT5DcmVhdGUgdGVtcGxhdGUpLCBp dCB3YXMgbm90IHByZWNpc2UgZW5vdWdoIGVpdGhlci4NCg0KU28sIGFueSBzdWdnZXN0aW9uIG9u IHRoaXMgaXNzdWUgd2lsbCBiZSBhcHByZWNpYXRlZCBhIGxvdC4gVGhhbmsgeW91IGFsbCBpbiBh ZHZhbmNlIQ0KIA0KMjAxMS0wNS0xMSANCg0KDQoNCsDuwdXB4Q0KDQpMaW5saW5nIExpIA0KUmVz ZWFyY2ggQ2VudHJlIGZvciBOZXVyYWwgRW5naW5lZXJpbmcNCi0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tDQpQaG9uZTogKzg2LTc1NS04NjM5MjIzMg0KRW1haWw6ICBsbC5s aTFAc2lhdC5hYy5jbg0KU2hlbnpoZW4gSW5zdGl0dXRlcyBvZiBBZHZhbmNlZCBUZWNobm9sb2d5 LA0KQ2hpbmVzZSBBY2FkZW15IG9mIFNjaWVuY2VzLg0K --=====003_Dragon115107410027_===== Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: base64 PCFET0NUWVBFIEhUTUwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4NCjxIVE1MPjxIRUFEPg0KPFNUWUxFIHR5cGU9dGV4dC9jc3M+QGltcG9ydCB1cmwoIEM6 XERvY3VtZW50cyBhbmQgU2V0dGluZ3NcQWRtaW5pc3RyYXRvclxMb2NhbCBTZXR0aW5nc1xUZW1w b3JhcnkgSW50ZXJuZXQgRmlsZXNcc2Nyb2xsYmFyLmNzcyApOw0KPC9TVFlMRT4NCg0KPE1FVEEg Y29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PUdCMjMxMiIgaHR0cC1lcXVpdj1Db250ZW50LVR5 cGU+DQo8TUVUQSBuYW1lPUdFTkVSQVRPUiBjb250ZW50PSJNU0hUTUwgOC4wMC42MDAxLjE5MDQ2 Ij48TElOSyByZWw9c3R5bGVzaGVldCANCmhyZWY9IkJMT0NLUVVPVEV7bWFyZ2luLVRvcDogMHB4 OyBtYXJnaW4tQm90dG9tOiAwcHg7IG1hcmdpbi1MZWZ0OiAyZW19Ij48L0hFQUQ+DQo8Qk9EWSBz dHlsZT0iTUFSR0lOOiAxMHB4OyBGT05ULUZBTUlMWTogdmVyZGFuYTsgRk9OVC1TSVpFOiAxMHB0 Ij4NCjxESVY+PEZPTlQgc2l6ZT00IGZhY2U9IlRpbWVzIE5ldyBSb21hbiI+RGVhciBhbGwsPC9G T05UPjwvRElWPg0KPERJVj48Rk9OVCBzaXplPTQgZmFjZT0iVGltZXMgTmV3IFJvbWFuIj48L0ZP TlQ+Jm5ic3A7PC9ESVY+DQo8RElWPjxGT05UIHNpemU9NCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4i PkknbSBkb2luZyBzb21lIGZNUkkgZGF0YSBhbmFseXNpcyBhbmQgDQp0aGUgR0xNIGFuYWx5c2lz IHJlc3VsdHMgSSBnb3QgaGF2ZSBhY3RpdmF0aW9uIHdpdGhpbiB2ZW50cmljbGVzLiBJIHdhbnQg dG8gDQpleGNsdWRlIHRoZSBhY3RpdmF0aW9uIGxpa2UgdGhpcyB0aHJvdWdoIGEgc3BlY2lmaWMg bWFzayB3aGljaCBjYW4gYmUgYXBwbGllZCBhcyANCnRoZSBleHBsaWNpdCBtYXNrIGR1cmluZyBk ZXNpZ24gc2V0dGluZy4gPC9GT05UPjwvRElWPg0KPERJVj48Rk9OVCBzaXplPTQgZmFjZT0iVGlt ZXMgTmV3IFJvbWFuIj48L0ZPTlQ+Jm5ic3A7PC9ESVY+DQo8RElWPjxGT05UIHNpemU9NCBmYWNl PSJUaW1lcyBOZXcgUm9tYW4iPkkgaGF2ZSB0cmllZCBXRlUgUGlja0F0bGFzIFRvb2xib3ggYW5k IA0KSSBtYWRlIGEgbWFzayB3aGljaCBpbmNsdWRpbmcgYWxsIHRoZSBURCBsYWJsZXMgb2YgaHVt YW4gYXRsYXMgZXhjZXB0IGZvciB0aGUgDQp2ZW50cmljbGVzLiZuYnNwO0hvd2V2ZXIsPC9GT05U PjxGT05UIHNpemU9NCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPiB0aGUgcmVzdWx0IA0Kd2FzIG5v dCB2ZXJ5IHdlbGwgYW5kIHRoZXJlIHdhcyBzdGlsbCBhY3RpdmF0aW9uIGluIGxhdGVyYWwgdmVu dHJpY2xlLiBJIA0KY29tcGFyZWQgdGhlIG1hc2sgSSBtYWRlIHdpdGggc3RhbmRhcmQgVDEgYW5h dG9taWNhbCB0ZW1wbGF0ZSwgaXQgc2VlbXMgdGhhdCB0aGUgDQptYXNrIGlzIG5vdCZuYnNwOyBw cmVjaXNlIGVub3VnaC4gSSBhbHNvIHRyaWVkIHRvIG1ha2UgYSBtYXNrIHRocm91Z2ggdGhlIA0K Z3JheS1tYXR0ZXIgc2VnbWVudGF0aW9uIHJlc3VsdCBvZiB0aGUgc3ViamVjdHMnIFQxIGFuYXRv bWljYWwgdGVtcGxhdGUgDQoocHJvZHVjZWQgZHVyaW5nIERhcnRlbC0mZ3Q7Q3JlYXRlIHRlbXBs YXRlKSwgaXQgd2FzIG5vdCBwcmVjaXNlIGVub3VnaCANCmVpdGhlci48L0ZPTlQ+PC9ESVY+DQo8 RElWPjxGT05UIHNpemU9NCBmYWNlPSJUaW1lcyBOZXcgUm9tYW4iPjwvRk9OVD4mbmJzcDs8L0RJ Vj4NCjxESVY+PEZPTlQgc2l6ZT00IGZhY2U9IlRpbWVzIE5ldyBSb21hbiI+U28sIGFueSBzdWdn ZXN0aW9uIG9uIHRoaXMgaXNzdWUgd2lsbCANCmJlIGFwcHJlY2lhdGVkIGEgbG90LiBUaGFuayB5 b3UgYWxsIGluIGFkdmFuY2UhPC9GT05UPjwvRElWPg0KPERJVj48Rk9OVCBzaXplPTQgZmFjZT0i VGltZXMgTmV3IFJvbWFuIj4mbmJzcDs8L0ZPTlQ+PC9ESVY+DQo8RElWIGFsaWduPWxlZnQ+PEZP TlQgY29sb3I9I2MwYzBjMCBzaXplPTI+MjAxMS0wNS0xMSA8L0ZPTlQ+PC9ESVY+PEZPTlQgc2l6 ZT0yPg0KPEhSIHN0eWxlPSJXSURUSDogMTIycHg7IEhFSUdIVDogMnB4IiBhbGlnbj1sZWZ0IFNJ WkU9Mj4NCg0KPERJVj48Rk9OVCBjb2xvcj0jYzBjMGMwIHNpemU9Mj48U1BBTj4NCjxESVY+DQo8 RElWPsDuwdXB4TxCUj48QlI+TGlubGluZyBMaSA8L0RJVj4NCjxESVY+UmVzZWFyY2ggQ2VudHJl IGZvciBOZXVyYWwgDQpFbmdpbmVlcmluZzxCUj4tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLTxCUj5QaG9uZTogDQorODYtNzU1LTg2MzkyMjMyPEJSPkVtYWlsOiZuYnNwOyZu YnNwOzxBIA0KaHJlZj0ibWFpbHRvOmxsLmxpMUBzaWF0LmFjLmNuIj5sbC5saTFAc2lhdC5hYy5j bjwvQT48QlI+U2hlbnpoZW4gSW5zdGl0dXRlcyBvZiANCkFkdmFuY2VkIFRlY2hub2xvZ3ksPEJS PkNoaW5lc2UgQWNhZGVteSBvZiANClNjaWVuY2VzLjwvRElWPjwvRElWPjwvU1BBTj48L0ZPTlQ+ PC9ESVY+PC9GT05UPjwvQk9EWT48L0hUTUw+DQo= --=====003_Dragon115107410027_=====-- ========================================================================= Date: Tue, 10 May 2011 19:39:41 +0000 Reply-To: James Rowe <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: James Rowe <[log in to unmask]> Subject: Re: DCM question Comments: To: Marta Garrido <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_000_E639F95C329FB7489DCB4B0C33A840363935A208WSR21mrccbsuloc_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_E639F95C329FB7489DCB4B0C33A840363935A208WSR21mrccbsuloc_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi Keith, Increasing the number of connections does not require more subjects, but th= ere are two other considerations to bear in mind. More complex models face a higher penalty in the Free energy estimate of lo= g-model-evidence (F), as this depends on the difference between model accur= acy and model complexity. Unlike AIC and BIC (SPM2.SPM5), the F penalty fo= r complexity (SPM8, DCM10) is not fixed by the number of parameters/observa= tions. Rather, it also accomodates the dependencies among parameters (via t= he KL divergence). Bayesian model selection will then be able help you fi= nd the right balance of model complexity and accuracy, for a given data set= , for one or more subjects. The dependencies among parameters need careful consideration, especially if= you wish to go on to perform classical statistics on the connectivity para= meters, as Marta suggests (and as have been successfully undertaken in many= published DCM papers). Relevant parameter dependecies are more likely for = complex models. If the parameters do covary, then the estimates of indidual= parameters become unreliable, increasing type II error in the classical st= ats. Note that this does not affect F-based model comparisons (e.g. RFX & F= FX Bayesian model selection). Klaas offered a clear account of these issues= with useful refs in his recent mail #046065<http://www.jiscmail.ac.uk/cgi-= bin/wa.exe?A2=3Dind1104&L=3DSPM&P=3DR18510&1=3DSPM&9=3DA&J=3Don&d=3DNo+Matc= h%3BMatch%3BMatches&z=3D4> "DCM-conditional dependencies". If you frame you= r hypothesis in terms of model selection (with one or many subjects) then t= his problem does not arise. Best wishes, James . ________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf o= f Marta Garrido [[log in to unmask]] Sent: 10 May 2011 17:03 To: [log in to unmask] Subject: Re: [SPM] DCM question Hi Keith, If you are interested in model comparison, then I'd say you can use whichev= er number of subjects, irrespectively of the number of connections. In fact= , you can make statistical inferences about models in only one individual s= ubject. However, if you then want to go on and do classical statistical tests on th= e connectivity parameters, then it might be a good idea to increase the num= ber subjects/parameters per cell. I'm CCing this to the SPM list in case other people want to comment on it. Hope this helps! Marta On 9 May 2011 10:44, Keith Kawabata Duncan <[log in to unmask]<mailto:k.dun= [log in to unmask]>> wrote: Hi Marta, Maria thought you might be able to help me with a DCM question. I was wonde= ring if increasing the number of connections in a model requires increasing= the number of subjects - a bit like increasing the number of cells in a fa= ctorial design? Keith -- Dr. Marta I. Garrido Research Associate Wellcome Trust Centre for Neuroimaging University College London 12 Queen Square London WC1N 3BG United Kingdom Tel: +44(0)20 7833 7472 (ext: 4366) Fax: +44(0)20 7813 1420 Email: [log in to unmask]<mailto:[log in to unmask]> www: http://www.fil.ion.ucl.ac.uk --_000_E639F95C329FB7489DCB4B0C33A840363935A208WSR21mrccbsuloc_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html dir=3D"ltr"> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Diso-8859-= 1"> <style id=3D"owaParaStyle">P { MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px } </style> </head> <body fPStyle=3D"1" ocsi=3D"0"> <div style=3D"direction: ltr;font-family: Tahoma;color: #000000;font-size: = 10pt;"> <p>Hi Keith, </p> <p> </p> <p>Increasing the number of connections does not require more subjects, but= there are two other considerations to bear in mind. </p> <p> </p> <p>More complex models face a higher penalty in the Free energy estimate of= log-model-evidence (F), as this depends on the difference between model ac= curacy and model complexity. Unlike AIC and BIC (SPM2.SPM5), the = ;F penalty for complexity (SPM8, DCM10) is not fixed by the number of parameters/observations. Rather, it also accomo= dates the dependencies among parameters (via the KL divergence).  = ; Bayesian model selection will then be able help you find the right balanc= e of model complexity and accuracy, for a given data set, for one or more subjects.</p> <p> </p> <p>The dependencies among parameters need careful consideration, especially= if you wish to go on to perform classical statistics on the connectivity p= arameters, as Marta suggests (and as have been successfully undertaken in m= any published DCM papers). Relevant parameter dependecies are more likely for complex models. If the parameter= s do covary, then the estimates of indidual parameters become unreliable, i= ncreasing type II error in the classical stats. Note that this does not aff= ect F-based model comparisons (e.g. RFX & FFX Bayesian model selection). Klaas offered a clear account of = these issues with useful refs in his recent mail #<a href=3D"http://www.jis= cmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1104&L=3DSPM&P=3DR18510&1=3D= SPM&9=3DA&J=3Don&d=3DNo+Match%3BMatch%3BMatches&z=3D4">= <strong><font color=3D"#3366cc">046065</font></strong></a> "DCM-c= onditional dependencies". If you frame your hypothesis in terms of model selecti= on (with one or many subjects) then this problem does not arise. </p> <p> </p> <p>Best wishes, </p> <p> </p> <p>James</p> <p> </p> <p>. </p> <p> </p> <p> </p> <p> </p> <div style=3D"FONT-FAMILY: Times New Roman; COLOR: #000000; FONT-SIZE: 16px= "> <hr tabindex=3D"-1"> <div style=3D"DIRECTION: ltr" id=3D"divRpF279207"><font color=3D"#000000" s= ize=3D"2" face=3D"Tahoma"><b>From:</b> SPM (Statistical Parametric Mapping)= [[log in to unmask]] on behalf of Marta Garrido [[log in to unmask] UK]<br> <b>Sent:</b> 10 May 2011 17:03<br> <b>To:</b> [log in to unmask]<br> <b>Subject:</b> Re: [SPM] DCM question<br> </font><br> </div> <div></div> <div>Hi Keith,<br> <br> If you are interested in model comparison, then I'd say you can use whichev= er number of subjects, irrespectively of the number of connections. In fact= , you can make statistical inferences about models in only one individual s= ubject. <br> However, if you then want to go on and do classical statistical tests on th= e connectivity parameters, then it might be a good idea to increase the num= ber subjects/parameters per cell.<br> <br> I'm CCing this to the SPM list in case other people want to comment on it.<= br> Hope this helps!<br> <br> Marta<br> <br> <br> <div class=3D"gmail_quote">On 9 May 2011 10:44, Keith Kawabata Duncan <span= dir=3D"ltr"> <<a href=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] .uk</a>></span> wrote:<br> <blockquote style=3D"BORDER-LEFT: #ccc 1px solid; MARGIN: 0px 0px 0px 0.8ex= ; PADDING-LEFT: 1ex" class=3D"gmail_quote"> Hi Marta,<br> <br> Maria thought you might be able to help me with a DCM question. I was wonde= ring if increasing the number of connections in a model requires increasing= the number of subjects - a bit like increasing the number of cells in a fa= ctorial design?<br> <font color=3D"#888888"><br> Keith<br> </font></blockquote> </div> <br> <br clear=3D"all"> <br> -- <br> Dr. Marta I. Garrido<br> Research Associate<br> Wellcome Trust Centre for Neuroimaging<br> University College London<br> 12 Queen Square<br> London<br> WC1N 3BG<br> United Kingdom<br> <br> Tel: +44(0)20 7833 7472 (ext: 4366)<br> Fax: +44(0)20 7813 1420<br> Email: <a href=3D"mailto:[log in to unmask]" target=3D"_blank">m.g= [log in to unmask]</a><br> www: <a href=3D"http://www.fil.ion.ucl.ac.uk" target=3D"_blank">http://www.= fil.ion.ucl.ac.uk</a><br> <br> </div> </div> </div> </body> </html> --_000_E639F95C329FB7489DCB4B0C33A840363935A208WSR21mrccbsuloc_-- ========================================================================= Date: Wed, 11 May 2011 16:34:35 +0100 Reply-To: Carolina Valencia <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carolina Valencia <[log in to unmask]> Subject: Activation orbit Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Deas SPM users, I have some weird results for a task of thinking in verbs related, I got = activation in both orbits, how I interpret this? is an error for some pre= processing step? Thanks a lot, Best regards, Carolina ========================================================================= Date: Wed, 11 May 2011 16:43:07 +0100 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: SPM5 Missing ResMS.hdr file after design matrix estimation.. Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear listserv users: I am using SPM5 to process some data, and I am getting no ResMS.hdr file = after estimating the design matrix of a subject's data while all beta ima= ges and mask image are produced as both ".img" and ".hdr" files. I do not= have this problem with any of my other subjects. I appreciate if anybody= has an idea about what is causing this problem and how to fix it. Thanks in advance, Regards, Soha ========================================================================= Date: Wed, 11 May 2011 18:48:02 +0200 Reply-To: "S.F.W. Neggers" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "S.F.W. Neggers" <[log in to unmask]> Subject: question regarding matlabbatch struct/class 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 list, I created a matlab batch job in the SPM8 batching system, and saved it to disk as a mat file. Now I want to use it in my own pipeline (matlab software) to do some repetative number crunching. I work with matlab version 7.9.0529 (R2009b), and SPM8 rev 4290. When loading this matlabbatch structure from disk, I obtained the following warning: --- snip --- > load testbatch_spm8.mat Warning: Class ':all:' is an unknown object class. Element(s) of this class in array 'matlabbatch' have been converted to structures. --- When I now fill in some fields according to my needs, and save this structure again to disk, will all be OK? Apparently matlabbatch originally is a class with methods in an OOP sense, and I dont want to break things by irreversibly convert classes to fields of a structure. I never had this warning in SPM5, and I am in the process of moving my pipeline to spm8. Thanks for your pointers, Bas -- -------------------------------------------------- Dr. S.F.W. Neggers Division of Brain Research Rudolf Magnus Institute for Neuroscience Utrecht University Medical Center Visiting : Heidelberglaan 100, 3584 CX Utrecht Room B.01.1.03 Mail : Huispost B01.206, P.O. Box 3508 GA Utrecht, the Netherlands Tel : +31 (0)88 7559609 Fax : +31 (0)88 7555443 E-mail : [log in to unmask] Web : http://www.neuromri.nl/people/bas-neggers -------------------------------------------------- ------------------------------------------------------------------------------ De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht ontvangt, wordt u verzocht de inhoud niet te gebruiken en de afzender direct te informeren door het bericht te retourneren. Het Universitair Medisch Centrum Utrecht is een publiekrechtelijke rechtspersoon in de zin van de W.H.W. (Wet Hoger Onderwijs en Wetenschappelijk Onderzoek) en staat geregistreerd bij de Kamer van Koophandel voor Midden-Nederland onder nr. 30244197. Denk s.v.p aan het milieu voor u deze e-mail afdrukt. ------------------------------------------------------------------------------ This message may contain confidential information and is intended exclusively for the addressee. If you receive this message unintentionally, please do not use the contents but notify the sender immediately by return e-mail. University Medical Center Utrecht is a legal person by public law and is registered at the Chamber of Commerce for Midden-Nederland under no. 30244197. Please consider the environment before printing this e-mail. ========================================================================= Date: Wed, 11 May 2011 18:36:06 +0100 Reply-To: Markus Bauer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Markus Bauer <[log in to unmask]> Subject: Channel labels for converting and reading EEG data 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 have a question concerning the image conversion of individual virtual EEG channels (time-frequency data) The data were imported from fieldtrip with the spm_eeg_ft2spm routine Presumably since the labels are arbitrary (and only 4 channels) SPM did not assign a channel type, meaning that the image conversion routine does not recognize these channels. How can I force it to assign the type 'MEEG' / manipulate the data-structure in such a way that they can be treated that way? I also had problems converting an EEG raw data set (from BrainVision *.eeg file) - this gets stuck in ft_senstype (even when converting from raw format) Our cap was custom-made and therefore has arbitrary channel names (from 1 to 128), apparently SPM cant deal with that situation ??? thanks Markus ========================================================================= Date: Wed, 11 May 2011 21:18:50 +0100 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: Channel labels for converting and reading EEG data Comments: To: Markus Bauer <[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]> Hi Markus, On Wed, May 11, 2011 at 6:36 PM, Markus Bauer <[log in to unmask]> wrote: > Dear all > > I have a question concerning the image conversion of individual virtual EEG > channels (time-frequency data) > The data were imported from fieldtrip with the spm_eeg_ft2spm routine > > Presumably since the labels are arbitrary (and only 4 channels) SPM did not > assign a channel type, meaning that the image conversion routine does not > recognize these channels. > How can I force it to assign the type 'MEEG' / manipulate the data-structure > in such a way that they can be treated that way? Use Other/Prepare > > I also had problems converting an EEG raw data set (from BrainVision *.eeg > file) - this gets stuck in ft_senstype (even when converting from raw > format) > Our cap was custom-made and therefore has arbitrary channel names (from 1 to > 128), apparently SPM cant deal with that situation ??? > ft_senstype is a Fieldtrip function so if it can't deal with this situation, neither can SPM ;-) I suspect this might be related to the fact that your channel labels are actually numbers. This is not what SPM or Fieldtrip would expect. I suggest that you look at ft_read_header and make sure that the labels are read as strings, i.e. '1', '2'... rather than 1, 2... Then you will have to use Prepare again to set the channel types and load sensor locations because SPM will not be able to do it automatically. Best, Vladimir > thanks > Markus > ========================================================================= Date: Thu, 12 May 2011 10:50:35 +0530 Reply-To: megha sharda <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: megha sharda <[log in to unmask]> Subject: Interpreting parametric modulation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Message-ID: <[log in to unmask]> Hello all, I have a task with three conditions A, B, C. The trials in each of the conditions are varied on a parameter which is a feature of the trial stimulus. I used two models to assess activation differences across three categories. The first model looked at each condition, A, B, C and i compared across conditions (A-B, A-C, B-C...etc). In the next model, I additionally used a parametric modulator for each of the conditions A, Apm, B, Bpm, C, Cpm and used the second column as the regressor for each condition. However, now when i compare across conditions I do not see any differences. My question is, would it be correct to infer that the difference I see across the conditions A, B, and C is due to the parameter in question and not any other feature of the stimulus? Thanks! Megha -- Megha Sharda Graduate Student National Brain Research Centre Manesar, Gurgaon -122050 India Ph: +919810943202 ========================================================================= Date: Thu, 12 May 2011 11:23:31 +0200 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "<Frank> <lJessen>" <[log in to unmask]> Subject: job offer: 2 post-doc positions (structural imaging, functional imaging) in Bonn MIME-Version: 1.0 Content-type: multipart/alternative; Boundary="0__=4EBBF21DDFA00AA28f9e8a93df938690918c4EBBF21DDFA00AA2" Content-Disposition: inline Message-ID: <[log in to unmask]> --0__=4EBBF21DDFA00AA28f9e8a93df938690918c4EBBF21DDFA00AA2 Content-type: text/plain; charset=ISO-8859-1 Content-transfer-encoding: quoted-printable Two post-doc positions for neuroimaging are offered at the Department o= f Psychiatry, University of Bonn, within the clinical dementia research g= roup (Prof. Dr. F. Jessen). The positions focus on MR-imaging in early neurodegenerative disease and risk states. The research group operates within the Clinical Research Center for Neurodegenerative Disorders at = the University of Bonn and the MR facilities of Life&Brain. The Clinical Research Center for Neurodegenerative Disorders is the partner of the German Center of Neurodegenerative Diseases (DZNE) clinical research branch. In addition, the research group closely interacts with partner groups from basis science, genomics and epidemiology.One post-doc posit= ion will focus on advanced structural MRI analyses, including DTI. The seco= nd position will focus on functional MR imaging. The position will be for = two years and can be extended. Please send CV and publication record to: Prof. Dr. Frank Jessen Klinik f=FCr Psychiatrie und Psychotherapie Sigmund-Freud-Str. 25, 53125 Bonn Germany [log in to unmask] = --0__=4EBBF21DDFA00AA28f9e8a93df938690918c4EBBF21DDFA00AA2 Content-type: text/html; charset=ISO-8859-1 Content-Disposition: inline Content-transfer-encoding: quoted-printable <html><body> <p><font size=3D"3" face=3D"sans-serif">Two post-doc positions for neur= oimaging are offered at the Department of Psychiatry, University of Bon= n, within the clinical dementia research group (Prof. Dr. F. Jessen). T= he positions focus on MR-imaging in early neurodegenerative disease and= risk states. The research group operates within the Clinical Research = Center for Neurodegenerative Disorders at the University of Bonn and th= e MR facilities of Life&Brain. The Clinical Research Center for Neu= rodegenerative Disorders is the partner of the German Center of Neurode= generative Diseases (DZNE) clinical research branch. In addition, the r= esearch group closely interacts with partner groups from basis science,= genomics and epidemiology.One post-doc position will focus on advanced= structural MRI analyses, including DTI. The second position will focus= on functional MR imaging. The position will be for two years and can b= e extended. Please send CV and publication record to:<br> <br> Prof. Dr. Frank Jessen<br> Klinik f=FCr Psychiatrie und Psychotherapie<br> Sigmund-Freud-Str. 25, 53125 Bonn<br> Germany<br> [log in to unmask]</font><font size=3D"2" face=3D"sans-serif"= ><br> </font><font size=3D"2" face=3D"sans-serif"><br> <br> <br> <br> </font></body></html>= --0__=4EBBF21DDFA00AA28f9e8a93df938690918c4EBBF21DDFA00AA2-- ========================================================================= Date: Thu, 12 May 2011 11:06:23 +0100 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jing Kan <[log in to unmask]> Subject: "the MNE toolbox is not installed" MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Dear SPM experts, I am quite new on using SPM now. There is a initial problem which stuck my operation here. I am trying to import the MEG data (.fif) into the SPM with "spm_eeg_convert". Each time the matlab shows the problem as follows: spm_eeg_convert('mathieu_pilotebis_session1_mc.fif') ??? Error using ==> hastoolbox at 250 the MNE toolbox is not installed, see http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php Error in ==> ft_read_header at 893 hastoolbox('mne', 1); Error in ==> spm_eeg_convert at 72 hdr = ft_read_header(S.dataset, 'fallback', 'biosig', 'headerformat', S.inputformat); I actually have MNE and Fieldtrip installed and path added in my matlab. May I ask how to fix this problem? Many thanks, Jing. ========================================================================= Date: Thu, 12 May 2011 10:24:26 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: "the MNE toolbox is not installed" Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="Windows-1252" MIME-Version: 1.0 Message-ID: <[log in to unmask]> RGVhciBKaW5nLA0KDQpNYWtlIHN1cmUgdGhhdCB5b3UgaGF2ZSBNTkUgTWF0bGFiIHRvb2xib3gg cmF0aGVyIHRoYW4ganVzdCBNTkUgc29mdHdhcmUuIFRoZXNlIGFyZSBkaWZmZXJlbnQgdGhpbmdz Lg0KDQpCZXN0LA0KDQpWbGFkaW1pcg0KU2VudCB1c2luZyBCbGFja0JlcnJ5riBmcm9tIE9yYW5n ZQ0KDQotLS0tLU9yaWdpbmFsIE1lc3NhZ2UtLS0tLQ0KRnJvbTogICAgICAgICBKaW5nIEthbiA8 a2FuamluZ0BDUy5ZT1JLLkFDLlVLPg0KU2VuZGVyOiAgICAgICAiU1BNIChTdGF0aXN0aWNhbCBQ YXJhbWV0cmljIE1hcHBpbmcpIiA8U1BNQEpJU0NNQUlMLkFDLlVLPg0KRGF0ZTogICAgICAgICBU aHUsIDEyIE1heSAyMDExIDExOjA2OjIzIA0KVG86IDxTUE1ASklTQ01BSUwuQUMuVUs+DQpSZXBs eS1UbzogICAgIGthbmppbmdAQ1MuWU9SSy5BQy5VSw0KU3ViamVjdDogW1NQTV0gInRoZSBNTkUg dG9vbGJveCBpcyBub3QgaW5zdGFsbGVkIg0KDQpEZWFyIFNQTSBleHBlcnRzLA0KDQpJIGFtIHF1 aXRlIG5ldyBvbiB1c2luZyBTUE0gbm93LiBUaGVyZSBpcyBhIGluaXRpYWwgcHJvYmxlbSB3aGlj aCBzdHVjayBteQ0Kb3BlcmF0aW9uIGhlcmUuIEkgYW0gdHJ5aW5nIHRvIGltcG9ydCB0aGUgTUVH IGRhdGEgKC5maWYpIGludG8gdGhlIFNQTQ0Kd2l0aCAic3BtX2VlZ19jb252ZXJ0Ii4gRWFjaCB0 aW1lIHRoZSBtYXRsYWIgIHNob3dzIHRoZSBwcm9ibGVtIGFzDQpmb2xsb3dzOg0KDQpzcG1fZWVn X2NvbnZlcnQoJ21hdGhpZXVfcGlsb3RlYmlzX3Nlc3Npb24xX21jLmZpZicpDQo/Pz8gRXJyb3Ig dXNpbmcgPT0+IGhhc3Rvb2xib3ggYXQgMjUwDQp0aGUgTU5FIHRvb2xib3ggaXMgbm90IGluc3Rh bGxlZCwgc2VlDQpodHRwOi8vd3d3Lm5tci5tZ2guaGFydmFyZC5lZHUvbWFydGlub3MvdXNlcklu Zm8vZGF0YS9zb2ZNTkUucGhwDQoNCkVycm9yIGluID09PiBmdF9yZWFkX2hlYWRlciBhdCA4OTMN CiAgICBoYXN0b29sYm94KCdtbmUnLCAxKTsNCg0KRXJyb3IgaW4gPT0+IHNwbV9lZWdfY29udmVy dCBhdCA3Mg0KaGRyID0gZnRfcmVhZF9oZWFkZXIoUy5kYXRhc2V0LCAnZmFsbGJhY2snLCAnYmlv c2lnJywgJ2hlYWRlcmZvcm1hdCcsDQpTLmlucHV0Zm9ybWF0KTsNCg0KSSBhY3R1YWxseSBoYXZl IE1ORSBhbmQgRmllbGR0cmlwIGluc3RhbGxlZCBhbmQgcGF0aCBhZGRlZCBpbiBteSBtYXRsYWIu DQpNYXkgSSBhc2sgaG93IHRvIGZpeCB0aGlzIHByb2JsZW0/DQoNCk1hbnkgdGhhbmtzLA0KDQpK aW5nLg0K ========================================================================= Date: Thu, 12 May 2011 18:23:27 +0800 Reply-To: Syu-Jyun Peng <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Syu-JyunPeng <[log in to unmask]> Subject: Consult MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_Part_280034_767240815.1305195807457" Message-ID: <1627981024.1305195808016.JavaMail.nobody@sg2000-ap-1> ------=_Part_280034_767240815.1305195807457 Content-Type: multipart/alternative; boundary="----=_Part_280032_1783101196.1305195807457" ------=_Part_280032_1783101196.1305195807457 Content-Type: text/plain;charset=big5 Content-Transfer-Encoding: quoted-printable <div><br />Dear Sir</div> <div> <div> <div> <div> <p>I'm a&nbsp;EE PhD student in Taiwan.</p> <p>I have a question.</p> <p>I have 20 sets of control subject&nbsp;SPGR MRI.</p> <p>How could I use SPM&nbsp; software to&nbsp;obtain template= &nbsp;like as&nbsp;the attached file?</p> <p>Could you&nbsp;<span lang=3D"EN-US" style=3D"= ;font-size: 10pt; color: black; font-family: &quot;Times New Roman&= quot;; mso-fareast-font-family: =B7s=B2=D3=A9=FA=C5=E9; mso-bidi-font-famil= y: Arial; mso-ansi-language: EN-US; mso-fareast-language: ZH-TW; mso-bidi-l= anguage: AR-SA;">give the&nbsp;instructions step by step for me= ?<br /><br /></span></p> <div v:shape=3D"_x0000_s1026" class=3D"O"> <div style=3D"mso-char-wrap: 1; mso-kinsoku-overflow: 1;">&= lt;span lang=3D"EN-US" style=3D"mso-fareast-font-family: =B7= s=B2=D3=A9=FA=C5=E9; mso-fareast-language: ZH-TW; mso-hansi-font-family: Ar= ial; text-shadow: auto;"><span style=3D"font-size: small;&q= uot;>It would be greatly appreciated, if you could look<span style=3D= "mso-spacerun: yes;">&nbsp; </span>into this for us.= <br /></span></span></div> <div style=3D"mso-char-wrap: 1; mso-kinsoku-overflow: 1;">&= lt;span lang=3D"EN-US" style=3D"mso-fareast-font-family: =B7= s=B2=D3=A9=FA=C5=E9; mso-fareast-language: ZH-TW; mso-hansi-font-family: Ar= ial; text-shadow: auto;"><span style=3D"font-size: small;&q= uot;><br />Thank you. <br /></span></span><br= />Best regards,<br /><br />Syu-Jyun Peng</div> </div> </div> </div> </div> </div> ------=_Part_280032_1783101196.1305195807457 Content-Type: text/html;charset=big5 Content-Transfer-Encoding: quoted-printable <div><br />Dear Sir</div> <div> <div> <div> <div> <p>I'm a EE PhD student in Taiwan.</p> <p>I have a question.</p> <p>I have 20 sets of control subject SPGR MRI.</p> <p>How could I use SPM software to obtain template like as&= nbsp;the attached file?</p> <p>Could you <span lang=3D"EN-US" style=3D"font-size: 10pt; color: bla= ck; font-family: "Times New Roman"; mso-fareast-font-family: =B7s= =B2=D3=A9=FA=C5=E9; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; = mso-fareast-language: ZH-TW; mso-bidi-language: AR-SA;">give the instr= uctions step by step for me?<br /><br /></span></p> <div v:shape=3D"_x0000_s1026" class=3D"O"> <div style=3D"mso-char-wrap: 1; mso-kinsoku-overflow: 1;"><span lang=3D"EN-= US" style=3D"mso-fareast-font-family: =B7s=B2=D3=A9=FA=C5=E9; mso-fareast-l= anguage: ZH-TW; mso-hansi-font-family: Arial; text-shadow: auto;"><span sty= le=3D"font-size: small;">It would be greatly appreciated, if you could look= <span style=3D"mso-spacerun: yes;"> </span>into this for us. <br /></= span></span></div> <div style=3D"mso-char-wrap: 1; mso-kinsoku-overflow: 1;"><span lang=3D"EN-= US" style=3D"mso-fareast-font-family: =B7s=B2=D3=A9=FA=C5=E9; mso-fareast-l= anguage: ZH-TW; mso-hansi-font-family: Arial; text-shadow: auto;"><span sty= le=3D"font-size: small;"><br />Thank you. <br /></span></span><br />Best re= gards,<br /><br />Syu-Jyun Peng</div> </div> </div> </div> </div> </div> ------=_Part_280032_1783101196.1305195807457-- ------=_Part_280034_767240815.1305195807457 Content-Type: image/pjpeg; name=Question.JPG Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename=Question.JPG /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAOEBvADASIA 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 KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK8t+JF5 qf8AwnHhPRNF1W+s59SnY3fkzkL5K7cnacgHG7BGOnOaOqXcOjfY9SorkfEGiaxZaTPe+HNbvo76 2jMqW90/2iK4wM7GD5YZxjKkYzVHSNeu/iV4AsbvSbqXSp7mZY7yaBgXt9hy4TP97AAPYPkjiha7 Bta53lFeQ+MRqOneM/CXhjR/EGrm4v5jJePJeFm8hcZ46AkB+QB92u4g8OXln4os72DWtUlsFhkE 1rcXBkjZ+Ap557t6jgdO4tVf+tAejsdNRXluiXGpax8Z9esItVv/AOwtJhj3W/2lirTsBxnrj7/G e3pxXqVC1SfcHo2uwUVieKdM1XVdMhg0fWTpVwtzHI8wTdvQHlPx4+uMdDW1nAyT+NAC0Um5d23c NxGcZ5paACiim7137Nw34ztzzigB1FJkAgEjJ6ChXVwSrBgDjg5oAWisO50zVpfGFjqUOsmLS4bZ 45tO2ZErk8Pn24/L3Ncz42WS8+IHg/S7W7vYXmnkubpYbqRUaGIbtrIDtOWwMkZoW6Xf+v8Agg9L +X9f8D1PQqKKRXV1DIwZT3ByKAFoppkRV3F1AzjJPenUAFFNWRG+66nBxwe/pS7gWKgjI6igBaKQ MGzgg4ODjtS0AFFIzKilmYKB3JxQGUkgEEjqM0ALRSbhzyOOvtQCGUFSCD0IoAWio5jH5bJI+0MC OG2nGOcEcj8K4H4R+deaHqmsvdXVxBf6jMbM3Nw8xW3RiqAFyT2ahatg9Fc9CoopMjnkcdfagBaK bvXZv3DbjO7PGKdQAUU0ugbaWUNjOM84rG8XXcFl4O1a+mklWKC1kl3QTvExIUkYdCCOcdDSk+VN jSu7G3RXD+GbuTwn8IbXVNXuJ7maCwN5cPPIWkdmBfaSeSeQop+ieH9Tv7DQdZvtXvINSMgvb2NX JSQOh/cBc4VFyvAH8OTyc1TVpOPb+v0ZCleKfc7Wim7037Ny7wM7c80pIAJJwB3NIoWik3DdtyM4 zijI55HHX2oAWim712b9w24zuzxinUAFFNLoG2llDYzjPOKq6lqtho+my6jqF3Fb2cS7nmkbCgf1 J7DvQ3bUEr6Fyiqum3yappdrfxxyRx3MSyosgwwVhkZHY4PSs3wtpmraVpckGsaydWuGuJJFnKbd qE8Lj2/Tp2p2s7MOl0blFNLoHCFlDHoM8mndKQBRSF1BUFhlunPWhmCqWYgAdSaAFopAysAVYHIz waWgAooooAKKKKACiiigAoqjrGrWehaPd6rfyeXa2sZkkbqcDsPUnoB71yPhaPVvGtrF4i1y4uLP Trj95YaVbSmJRH/C8zKQzlhztztx2PYWrB6HeUVia3ZX11aW+k6TcPpscufOuoEG6GIdQmRgOxIA J6DJ6ivMvEmka18MNS0jXdL8T6zqVhPex2l7Z6ncGfeHP3gcAA8HnGQcc9RQtXbuD0TfbU9ooqOd JJIWSKXymPG8KCR9M8Z+ua4H4R6lqWp6Rrz6pqE99PDrM8AlmIztVUAAAwFHfAAHJoWra8r/AIpf qHS56FRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeUaDjxL8fte1QlXt9BtEsocc4kbOT9c+YK9QvGu ktJGsYoZbkD92k0hjQn3YKxH5GvOfAnhbxn4Nt9UE1poOoXOo3jXcs/9ozRHLDpj7O2ecnr3oj8V +yf37flcH8Nu9v8AP/I7XxVrMHh/wtqeq3LBUt7dmGf4mxhV+pJA/GuL+BGi3GkfDWGS5Vka/ne7 VW7IQqqfxCg/jWvqHgu98V3kEviy/il0+BxJHpNkpWFnHeV2O6T6YUe1dVffbYLArpVvayTqNscc 8pijHHHKqx/DFCbjzPuD1sux5p4dz4k+PPiLVyQ1tolqlhCevzt978QRIPxFen3t3FYWNxeTsFhg jaV2J4CqMn+VcP8ADLwjr3hC11OLWjp1xPf3jXct1bTuWYsBwVaMd8nr/FWx490nW9f8J3mj6IbJ Jb2MwyTXczoEQ9cBUbJIyO3WlK6gkt7fj/w442c23tf8P+GOZ+CVpLL4X1DxFdD/AEnXNQlumOOi 7iAPz3H8a9NrC8HaTdaD4S03SbyK2SazgWEm2kZ0cgctkqp5OTjHfrW7VysnZbImN2rvdnmWsafa az8eNDSO2jEmlWEl7cyhBlix2Rqx7kdRmoPiBBDqfxR8G6bBaxS3kTS3rsQSVCD93u/2dwJP+7gc mt7SPC+s2XxD8Q63LNaiy1JoCkiuTKEjTHl7SuBk9Tk8DAAzkXNM8MXcPxD1nxNfyW8iz28VpYqh JaKJeX3ZAwS3PGamK0j5Xf5tfjb7uw5byt1svy/4JzF3olq3xu8PLbFmvLGwnvb+6ckvMH/doGPQ DJJA6AcACvUa4rT/AAzrdv8AEfXdclntRYXy26Qurs0yRxrzGFK4UMxyTk8Dpk5Ha018KX9b3B/E 3/Wx5/8AEbxZc6de6V4Z0ud4NQ1Zz5lzGhd7a3X77qoBJcjIXjsfaq+meGxqHivRr/TdHbS9I0gy S/aruIpd38roUOQ37wLySWkwWOOO9XvFPhbW5PHGk+LvD7Wc1zaQNaz2l5I0ayRkk5VlU4b5j1Hp 9D0NvNqlvbXGp65La20cUTMbW2YyJGByWaRlUscDsqgZPXg1KaiuZ+f/AAP677g7uVlt/V/8vQ4L R9As9d+MfijUFtY4dPsoIrGVYlCC6lYB33Edewb14B4yC7wnYW2nfG/xJZ6Hbx2ukw6fCLq3gXbE LgkFcKOAdu7p71L8M49f/wCEIn1K3srRLzW7ybUBNczttUSNgMyhckgDIUEAjHzLnjsPD/hiLw5p VzDbztPf3bvPdXso+aeZurEDoPQDoKesbeS/FrX7rv8AAHaV/N/guvzscj8M7C0uPF3jXxDaW0cF vNf/AGKARoFXEQw7ADszHP1FXdCH9s/GLxHqpX9zpNpDpkLZ4LN+8k/I4FXfht4Z1bwr4YttM1OS 2DQ7/ltnLiRmcsXZio55AAA4HUnPDPA3hjWNBbVBqT2oF1qU96Xt5Wdpy543ZUbQB2Gcn0xy9ml2 X46L8dRN7+b/AA3/AESOZt/FEXjPxLqlxc2d9qGh6bMbWx021tmlS8nH3pJTjYFHG3eQoznqK0Yd IPhf4deKtS16xsd1609++moivDCSgCR9MMflXJAxk/jUng/wz4u8FRXmi2UekXmlSXTzW11PcSJL ErHnegQhyPQFc+ozxH8U1vJ/Dmj+F0mF5fazqMcL7z5QdFPmP90HaowB3IHqescqcVHvZffa/wCP /AKvaTfRXfyV7fh/wSlo3gjR9L+DCf8ACSWkVy8OnSXDecufs5YM+EH8Lc8sOSe+AALfhSx13Uv2 f4LNJ5F1S406Vbd2YhtpLeWM9vl2gGuj1vQL/wAVpFpuoiKz0JWV54IZC8t1tORGxwAiZAJwSW6f L3uX9vrcGuabNpMVm+lwwSRXNvJKY2OSmwphSMjaRg4HNXP3uZP7X6X/AMyY6cvl/wAA5D4cxeHf FGl6TqsWlW1lrOiK1ldwrbhHR9hRlPfackjPuPWj+z7TwZ8ZtOOnW0drYeI7SWGWGFAqCeL5w+Bw Mg447knvXReFvC82k+IfEWu3Qhin1iaNvs8DFljVFwCSQMsxJJ4wPfqa/jrRLnX73w7Bp9yba9td QW6aZQCYoQjBzyMc5UD3I9DTbvJS/rXf7r3BRtFx/rTb8jR8OaNZWGq67qFhG0ceoXe9xuJV5FGH cA9Mtkf8BrbvLuGwsp7y5cJBBG0sjn+FVGSfyFPt4I7W2jt4V2xxqEUegFZ3ibSG1/wvqmkJKIXv LWSBZCMhSykAn2qJXUdCo76nLeDoW8WWTeMPEUKOlyzPp1pOA0dnbjIVtp48xhkl+uCAMDis74ax adF4S8R+I3RP7P1C9uJ4wwwq2sWUReeigKcDoBx2q9pXh3xY3gM+HNSGlW4t7A2sTW8zv9oYIVTf lRsXpkDcW56Dgwp4K8R/8Kkbwqt3YQXr2qWmEZvKRM/vG3bdzMw3dgOQPVi5faUe1l/n+H5hHWzl 3u/6+f4HP/D/AEuzj+Ed3q2rWoXTZRc6hLa8hJjzjcOpQKqgA8E5PPymtDw/qsvw8+BmkTtEbjUL hALS2Jxvlncsic9gGyfoa6fxj4Uu9T+G0/hnQXggkMMUEfmsUTy1ZcgkAnlQR0rO8XeDNc1vQtFN nPYrqWl38V7HbOWFuAgwIgQM4HHJHPPAyAG7XaW2i+S/4H5Eq9k3vq/n/wAPcp+LdLg8N/C7Vb/V JBe6/PbeW9+6gytcPwojP8Kqx+VVwBjPXJrtPCejjw/4S0nSeN1rapG5AxlsfMfxOa5Lxf4V8T+L LTRZpk01J7PUorqSxFy5h8tc5y+zLMTj+EADOM9T6BaxzxW6rczCabku4XaMnsB2A6DqfUmhbP1/ L/h2FtV/X9bEOrT21ro97c3qq1rDA8koboVUEn+VePeDbC3tPgne6vqloBYvHc3xs+VS4c527u5U AKFB478/Lj0vx1oupeIvB9/o+lXEFvcXirE0k5IURlhv6A8lcj8apeLfCEuteDLTwzppggsllt45 xIzL/o8bAlVwD8x2gdh71DjdPzsv8/0+4tOzXlr/AF+J59daYun/ALO8TarFLKfsKx2do5JxLK3y uV7sC+VB+6AMYOa6/W9T1Dwn4Q8N+GdLkD69frFp9vI/zeXtQeZKQeoUc/UitLx14b1LX7XRINL+ yiKy1KG6nhnkaNZEjzhchW744x2qh4o8KeILrxB4Z8QaXPZ3l7pLTedDds0SSiQYO0qG245Aznty cc6Nptt7N/h/wbtP/gGaVkrbpfj/AEjI+I+gaVpPgOLSLWMHVdVvILOK8k+a4mldgruz/eJKbgee h29OK0PibbofDGieEbVDt1W/t7HapxthQhmP4BB+dSa/4U8R6xr3hrWN+mNcWE8ks8bs/lwgrhRH xl8HJJO3cQPujpb13wzrV7408N6vay2s8GmQTIzXTlWWSQBfMCquGOP4cr9RUpvS/f8AL+rfMp6a rt+L/pP/AIJj/GHSdO1DTND042FvJe6hqVvYxTmIGSKLdufa2MgYHOMdak8SNDpGsaT4G8I21vpF zrLNNeXFlCsbQ26D5mGBw7YKhj0x64q5qlo+qfFfw5Z+ZJLDotlLfTMx6vJ+7j3e/Dn8DWhqvhm9 HxB0/wAWacIJmjs3srm3mcodhbcHQ4PzA9QcZHeiOy7Nt/p+a+5g+tt0l/X3M5n4pafY+G/B+nHQ 7OKDV11KBdPkjX960xbLZbqxYA7iTznmpfi75Fy/hTTfsMd1qF5q0RQbAW2JywB6hckAnsCTXU/8 I3Pq3iW11vXDFjT932CxhcukbHgyuxA3PjAAxheeSeaiufDF5ffEyz8RXUtu2nWFi8NrCCTIs7t8 7njAG3jrQul+9/ut/l+IPr6W+/8AyuQy+ENF0bUL/wAX3zzz3wsHjvHllJjkQfMx2HO37uABgAds 81wfg2wt7T4J3ur6paAWLx3N8bPlUuHOdu7uVAChQeO/Py49L8daLqXiLwff6PpVxBb3F4qxNJOS FEZYb+gPJXI/GqXi3whLrXgy08M6aYILJZbeOcSMy/6PGwJVcA/MdoHYe9Ty3TXfT/P9PuKTs19/ 4afqefXWmLp/7O8TarFLKfsKx2do5JxLK3yuV7sC+VB+6AMYOa6/W9T1Dwn4Q8N+GdLkD69frFp9 vI/zeXtQeZKQeoUc/UitLx14b1LX7XRINL+yiKy1KG6nhnkaNZEjzhchW744x2qh4o8KeILrxB4Z 8QaXPZ3l7pLTedDds0SSiQYO0qG245Azntycc6Nptt7N/h/wbtP/AIBmlZK26X4/0jI+I+gaVpPg OLSLWMHVdVvILOK8k+a4mldgruz/AHiSm4HnodvTirHxV0ewuvD3h7w6llBJPd38FjbyMgLwRDBd lJ5Hypg47Grmv+FPEesa94a1jfpjXFhPJLPG7P5cIK4UR8ZfBySTt3ED7o6W9d8Ma3f+NvDmq289 rLa6ZBMrSXDEOJZAF8wIq4Y4HTIFSm9L9/y/pr5lPRadvz/pMpfEmw0Xw98KNXtbPSbOKKRRHBbw wKB50hCBgAPvDOc9eKh1zULvwJ4G8N+GNG2DWr7ytPtnK7hG2B5kpHfGc/UjNa/jzwzqfiG20GPT zbyiw1KK7mjupSglVAf4grdz6UniPwpqOo6j4c1q2uILjUtGneRo58xxzLIMOAQCVI/hznpye9Hr 1a+5f8Ow226J/f8A0kZHjzQdH8N/CnVpHjEt5HCrC/m+a4kucgJIZPvbt2MHt0GBWN8Qw+q+EfA2 l6raedq+pXNsJkx+8ACBpQPQkkA54Gea7vU/Dtz4o1GzbWhFFpVlMLiOxikLm4lH3WlOAAqnOEGc nBJ7U268MXd98SrHxDcyW7afp9i8VrFkmQTu3zPjGANvHX8KFvd90/u1/Hb7vQHtp2f4/wCW5yfj bQobnxb4JtGw+sz6l9qkuAT+7hhXcyIP4U+6AO/U5OTWn8Xfttroel6xDai9sNL1CO6v7MjIlhGQ SR0O0nPPA69qval4Z1u6+Jlvr1vPaLp8emmzVnZjLA7PuZ0TbtJKgDJPfJBxg3jb+JEutcjktdPv bC7bNkslyysg8tUKyDYRtJBOQSeelJ35dN73+aa/yv8AeOyb122/r77EnhXTtBxP4h0CKCO31mOK VvJjCBsBuSB3+bkeoPfNdHXP+CPDX/CIeD9P0Mz+e9urb5AMAszFjj2ySBXQVc7cztsTG9tQoooq RhRRRQAUUUUAeYfHozH4ciFG2wzX0CTt/dTJOf8AvoLVrxP8MtEk8My3GnLc22sWNqWstQW7kMyM i5Ubi33e2OwPGK63xT4dtPFfhu90W9LLFcpgOvVGByrD6EA1jC08YzeF30S4TTheNEbY6ms7FShG 3zfL2Z34525xn+LFS0+SSjvuvut+H6lXXNFvZf53/r0J/hvr1z4m+H2karenddSxFZXxjeyMULce u3P41HqOnr4x8QWG75tG0e5+0M3a5ulyFUeqpkknu2B2NV9W8Na/pvgix8N+C5rGAQxrDLcX0rox QddvlqTuY5ycjGTjnBGVBp/xdgt47WG58GW8CKEQQxz/ALtRx8o2449K0k05trvoQk1FJnpdeafB j/kEeJf+xguv5JXoKRT2unRxQkXE0aBd08hXeQOpYA9fpXI/DjwxrnhS01S31Uae4vdQkvQ9rcO+ 3eB8uGjXpjrnvSjpJ+jX4x/yH9lev6M7iiiikAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFZfiHRh4g0O60p7y4tIrpDHLJb7d5QjBALAgZHHStSik1cadtUZ2haQmg 6JaaVFPJNDaRLDG0gUNtUADO0AdvStGiiqbbd2JKwUUUUgCub1TwhHqvivTPEEup3iTaaH+zQIsf lLvGGJBUkkj34xxXSUUdbh5BRRRQBT1bTxq2k3Vgbq5tRcRmPz7Z9kiZ7q3Y1Douiw6JZLAlxdXc u1VkuryUyzS4GBuY/wAhgcn1NaVFAbhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWZ4g0O38R6PLp t1LPFG7o4kgba6sjBlIPPdRWnRQBnaZo8Omy3Vx5stxd3Thp7ibG5sDCrwAAqjgAD1JySSdGiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvL/iv4l1TQprC PTrloBLwxGPUV6hXi/xvP+naV9f6ioqNqLsdGEipVop7XX5nAn4n+KR/zFJPyH+FN/4Wh4p/6Ckn 5D/CuMY80zdV8q/psPby7L/wGP8Akdt/wtHxT/0E5PyH+FKPij4q/wCgpJ+S/wCFcPuo3D1pcq/p sPby7L/wGP8Akdx/wtHxV/0FJPyX/Cl/4Wj4q/6Ckn5L/hXD7qC1HKv6bD28uy/8Bj/kd0nxS8T8 7tUlHphV/wAKafin4qzxqkmPov8AhXDbqN1HIv6bD28uy/8AAY/5Hcf8LT8Vf9BOT8l/wo/4Wn4q /wCgnJ+S/wCFcPuo3Uci/psPby7L/wABj/kdz/wtPxV/0FJPyX/Cj/hafir/AKCkn5L/AIVw2fej dRyr+mw9vLsv/AY/5Hc/8LU8V/8AQUk/Jf8ACj/hafir/oKSfkv+FcNupN3NHIv6bH9Yl2X/AIDH /I7v/hanir/oKSfkv+FH/C0/Ff8A0FJPyX/CuFzShsDFHIv6bD6xLsv/AAGP+R3H/C1PFf8A0FJP yX/Cl/4Wn4r/AOgpJ+S/4Vw26k3Uci/psPrEuy/8Bj/kdz/wtTxX/wBBST8l/wAKX/haniv/AKCk n5L/AIVwm6nBqORf02Ht5dl/4DH/ACO5/wCFqeK/+gpL+S/4Uf8AC1PFf/QUl/Jf8K4XdTiwAo5F /TYvby7L/wABj/kdx/wtXxV/0FJfyX/Cj/haviv/AKCkv5L/AIVwwbNBajkX9Nh7eXZf+Ax/yO6/ 4Wp4rxn+1JPyX/Cj/haniv8A6Ckv5L/hXChuaC+TRyL+mw9vLsv/AAGP+R3X/C1fFX/QUl/Jf8KB 8VPFZ/5ikv5L/hXCg08H8qORf02Ht5dl/wCAx/yO5PxT8Vj/AJikn5L/AIUn/C0/Ff8A0FJfyX/C uGYkmjcaOVf02Ht5dl/4DH/I7j/hanivOP7Ul/Jf8KX/AIWn4q/6Ckn5L/hXC7scmjfmjkX9Nh7e XZf+Ax/yO5PxT8Vj/mKS/kv+FH/C1PFf/QTl/Jf8K4Xd60oanyL+mw9vLsv/AAGP+R3I+Knis/8A MTl/Jf8ACj/havir/oJy/kv+FcNupu6lyr+mw9vLsv8AwGP+R3f/AAtXxV/0E5fyX/Cj/havir/o Jy/kv+FcJvo3e9PlX9Nh7eXZf+Ax/wAjvP8Ahavir/oJy/kv+FJ/wtXxV/0E5fyX/CuELUbqXIv6 bD28uy/8Bj/kd3/wtbxV/wBBOX8l/wAKcvxV8UZG7VJgO/C/4VwW6k3GjkX9Nh9Yl2X/AIDH/I77 /havintqkv5L/wDE0f8AC1fFOP8AkJy/kv8A8TXBbqN9HIv6bD28uy/8Bj/kd6Pit4p/6Ccv5L/8 TR/wtbxT/wBBOX8l/wDia4LdRuo5F/TYe3l2X/gMf8j0L/ha3iiNEJv3feN2SBxyR2HtSf8AC2/E 4/5fW/T/AArhJT+6t/8Armf/AEJqh3UQ2DEJc+3Rfkj0D/hbficn/j8P6f4Uv/C2/E//AD+H9P8A CvPt1G6rMLI9A/4W34n/AOfw/p/hS/8AC3PE/wDz+H9P8K8+3UbqAseg/wDC3PE//P4f0/wo/wCF ueJ8/wDH4f0/wrz7dRmgLI9B/wCFueKP+fw/p/hS/wDC3fE//P2f0/wrz9csaUqRx1oCx3//AAt3 xP8A8/Z/T/Cj/hb3if8A5+z+n+Fee5pN1A7I9D/4W74n/wCfs/p/hR/wt7xR/wA/Z/T/AArz3dRm gVj0L/hb3if/AJ+z+n+FH/C3vFH/AD9n9P8ACvPN1G6gdj0P/hb3ij/n7P6f4UD4veKP+fo/p/hX nu/Jo3UCseh/8Le8Uf8AP0f0/wAKP+Fv+J/+fo/5/CvPN/FJuoCyPQ/+Fv8Aif8A5+j/AJ/Cl/4W /wCKP+fr+X+FeebqN1AWR6H/AMLf8T/8/P6//Wo/4XB4nH/Lz+v/ANavPN1G7IoHZHon/C4fE/8A z8fr/wDWoHxh8T/8/H6//WrzvdRuoFZHoh+MPif/AJ+P1/8ArUo+MXib/nv+v/1q853U5TkgZA9z SHZHon/C4fE3/Pf9f/rUv/C4vE2P9f8Ar/8AWrzkkBiAwI9RRuphZHow+MXib/nv+v8A9aj/AIXF 4m/57/r/APWrzjdRupBY9I/4XH4m/wCew/P/AOtR/wALj8S/89h+f/1q833UoagND0j/AIXH4l/5 6j8//rUf8Lk8S/8APUfn/wDWrzfd70bqAsek/wDC4/Ev/PUfn/8AWpP+FyeJP+eo/P8A+tXm+6jd QFj0j/hcviT/AJ6D8/8A61L/AMLl8SZ/1g/P/wCtXm27PejdQFj0r/hcviT++Pz/APrUf8Lm8R/3 1/P/AOtXmu6jdQFj0r/hcviM/wAa/n/9al/4XN4j/vL+f/1q803570bu1AWPS/8Ahc3iT+8v5/8A 1qD8ZfEf95f++v8A61eaFqNxxQFj0sfGbxH/AHl/76/+tSf8Lm8Sf3l/76/+tXmu6jdQFkel/wDC 5vEfqv8A31/9aj/hc/iMd1/76/8ArV5qGo3UBY9K/wCFz+JPVf8Avr/61J/wufxH/sf99f8A1q81 3UbqAself8Ln8Sf7H/fX/wBal/4XR4k/2P8Avr/61eabqN3NArHpf/C6PEnon/fX/wBaj/hdHiP0 T/vr/wCtXmm7mjcaBnpf/C6PEfon/fX/ANaj/hdPiP0T/vr/AOtXmmaN1AWPS/8AhdPiT+6n/fX/ ANalHxp8Sf3U/wC+v/rV5nv9KTdQB6b/AMLp8R/3U/76/wDrUf8AC6vEf91P++v/AK1eZ7qTdQB6 b/wunxH/AHU/77/+tR/wurxH/cT/AL7/APrV5nuo3UWA9N/4XV4j/uJ/33/9aj/hdXiL+5H/AN9/ /WrzLdRuosB6b/wuvxF/zzj/AO+//rUf8Lr8Rf8APOP/AL7/APrV5lu96N1AWPTv+F2eIv8AnnH/ AN9//Wo/4XX4i/55R/8Aff8A9avMN1LuosB6f/wuvxEP+WUf/ff/ANak/wCF2eIv+eUf/ff/ANav MS1G6iwHp/8AwuzxD/zyj/77/wDrUf8AC7PEP/PGP/vv/wCtXmG73o30WA9Q/wCF2eIf+eUf/ff/ ANagfG3xB/zxj/77P+FeX7qQNRYD1IfGzxB/zxj/AO+z/hR/wu3xB/zwj/77P+FeXb6N3vRYLHqP /C7tf/54R/8AfZ/wo/4Xbr//ADwj/wC+z/hXl26jd70Aepf8Lu1//nhF/wB9n/Cg/G3X/wDn3i/7 7P8AhXlu7FAb3osB6l/wu7X/APn3i/77P+FH/C7tf/594v8Avs/4V5bmjcaLAep/8Lu1/wD594v+ +z/hSf8AC79e/wCfaL/vs/4V5Zuo3UWA9UHxv17/AJ9ov++z/hS/8Lw17/n1i/7+H/CvK93FG6iw Hqn/AAvDXv8An0h/7+H/AAo/4Xhr3e0h/wC/h/wryvdRuosB6p/wvHXv+fSH/v4f8KP+F469/wA+ cP8A38P+FeV7qN1FgPVf+F4a9/z5w/8Afw/4Un/C8de/584f+/h/wrywSYOaTdmiwHqn/C8de/58 of8Av4f8KX/heOvZ/wCPOH/v4f8ACvKt1G6iwHqv/C8te/58of8Av4f8KP8AheWvf8+UP/fw/wCF eVb6N1FgPVf+F5a9/wA+UH/fw/4Uf8Ly17/nyh/7+H/CvKt1G7NFgPVf+F5a6f8Alyh/7+H/AAo/ 4Xnrv/PjB/38P+FeVbqC2MUWA9W/4Xnrv/PjB/38P+FH/C89d/58oP8Av4f8K8p3Um6iwHq//C89 dP8Ay4wf9/D/AIUn/C8td/58oP8Av4f8K8q3UbqLAeq/8Lz13/nxg/7+H/Cl/wCF567/AM+MH/fw /wCFeU7qA+KLAerD4567/wA+MH/fw/4UH4567n/jxg/7+H/CvKd1BagD1f8A4Xnrg62MH/fw/wCF KPjlrp6WMH/fw/4V5PuoD46GiwHq5+Oeuf8APjB/38P+FH/C89c/58YP+/h/wryjfmjfQB6uPjlr n/PjB/38P+FH/C8tc/58YP8Av4f8K8oDUb6LAer/APC89c/58Yf+/h/wpf8Aheet/wDPjB/38P8A hXk++jfRZAesf8Lz1vH/AB4w/wDfw/4Uf8Ly1v8A58YP+/h/wryfdRuosgPWP+F563/z4wf9/D/h R/wvPWv+fCD/AL+H/CvJ91JvosgPWh8dNa/58IP+/h/wo/4XprX/AD4Qf9/D/hXk26jd70WQHrX/ AAvTWf8AoHwf9/D/AIUf8L01n/oHwf8Afw/4V5Luo3UWA9b/AOF6az/0D4P+/h/wpP8Ahems/wDQ Pg/7+n/CvJQ5o3e9FgPW/wDhemtf9A6D/v6f8KP+F66z/wBA6D/v6f8ACvJN3vRuosB62PjrrP8A 0Dbf/v6f8KX/AIXrrP8A0Dbf/v6f8K8j3Uu+iwHrf/C9tZ/6Btv/AN/T/hS/8L21n/oG2/8A39P+ FeRbqXdRYLHrn/C99Z/6Blv/AN/T/hQPjxrH/QMt/wDv6f8ACvIt9KWpWA9c/wCF8ax/0C7f/v6f 8KP+F76v/wBAu3/7+n/CvIt9G+iyA9e/4Xvq/wD0C7f/AL+n/Ck/4XxrH/QLt/8Av6f8K8i30u6n ZAeu/wDC+NY/6BVv/wB/T/hR/wAL31f/AKBdv/39P+FeRbqN1FgPXh8dtX/6Bdv/AN/T/hXoPwz8 dXnjUap9rtY4Psnlbdjlt2/fn/0EV8xK3Ne4/s+nI8Rf9u3/ALVosJntdFFFIQUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXi3xv8A+P7Svr/U V7TXi3xw/wCP7Svr/UVnV+FnTg/48PVfmeFt1qMmnvUZrUwD3zSUdOKUUABOBxQMZ5bFNwfWigBx ODjOaQHmkFFAD2xn5TmkzSZNKDk0AFJmnEU0juDQAZ9KMmjpSUAP3cU3NIKKAHZozSc0dKAA8UA0 HpRigBQaCaKQ5oAUHFKTnpSdRzSY54oAOnWpAynHGD61EevJFA60ATkDPWkXqeaj7Uo60ASk4FNJ BHFNJpT04AoAMZ60nTHNKCMZNNLUABNICc0opCMDigAJNJmgZpecUAFJ+NAyKXGR1pgBNGaSlFIA 6Gk604HmkPWgAzRR0o70AJmlFGKQ/pQBYlH7m3Of+WZ/9CaoM+9TTcw2/wD1zP8A6E1QY5qYbff+ ZviPjXpH/wBJQ7BpDwKM4HUUAjHNWYCdqOaXI7UmfakAtJzmndBmmknvQA4cdKNxBzmmgigmgB8j AnIpueKTqKKADdSZNL0NGeaAEz0oJpMd6AeKYC5NGeKB60HpSAMnFGaTtSZwKYDs9sUHigEkc0Ug DdSg0h4pKAFyaM4pOtHAoAUGlzSEUY4pgBPHFKDTeadghQxHBOM0ABOKRaCc0gJBoAdQM0maB0oA cXO0L2BzSZoPvSUgFzRnrQKCBQAmcUE0UZFAC7qDx1ooLZoAUcCk5o7UUALQDSd6KAF60nSnDoSa acUAAY+tLmmjrS4HrQAvakoNLxQAmaO9GeaQGgBxNGcU00vWgAzRmkwOpowKAF3YpMjFHSjimAtJ Sc54NLj5eetIAJozxSdDRmmA7NAPFNzmjtSAdnFGc02lyaAFzRk02l60AKSaOnekIpAPWgBwpCaS lzx0pgGTSjNNBGaKAHUpJpnfNKe1IA3HNLuyDSe1IaYCnkUpYk5NNHSjpQA7NK4KHBx7c0wjjg0m O1ADs0vam9qOaQDgcUZ4zTcUo4FMAzSg03PNL1oAXvRupKSkA7NKDTKUdOaAFzRu5pO9FAC5o3Un SlwOaADdRuFIMdKQ80APyKTNJ1FHSmAtJmkNABJpAOJpM0Yo4oAMmlBpKM0ALu5pCSabS0ALupSa SjvQAmcGnBvam4oxxmgBwNB5pvSlzQAoNGaaTQKAHA0dKTNL9aADNJ1FNOaUUAOzQDSY96KAFzS5 pAMmkI5oAXNGc0h9KPrQAvQ9aCaQnikzmgBw9aM+lIMUHg4FAC5pc5pAaOc0ALnmkJpwCk/MTimG gBc5FHGKTFFAC5pc0zNLQA7dSAmkpR1oAkU17n+z508Rf9u3/tWvDF617n+z508Rf9u3/tWgTPbK KKKQgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACvFvjh/x/aT9f6ivaa8W+OH/H9pP1/qKzq/Czpwf8eHqvzPCmphzUj44xUZNamAmCeaQ9aduw KTPNADc/nS4FH4UErQAEZpDwelLk+1Ic0AFOZ2YIDjCDAwP50mKAODQA7dxSZ70xs0AUAP4NIRzQ Vz1GKcRnHrQAzvR7AU/ym7A1LHbsT3/KgCIIacIieBVtbNz0z+VWI7ByB978qLgZwt29KX7NJxxW wunvj+L8qcLFvU/lSuMwXiZTzTCDWpc2JQFsk1nMmHwcihMVhu3inCNj0FPhiLyBRnk4rWttKZ2+ 8R+FMDI+yynnbTDGyHBHNdOdKKKBuY/hUEum5BPP5UrjOezSZPpWtJpmex/KoW05h0zxRcRRU89K X1qV7d4zwCai2sG5BpgKRik4zSmm5NADsj0ph5NOpNpxQAmKXHy5zSE54PagD8qACkpaSmAHpQD2 oI9KMAdRQAA4pePSjIxQCPSkAcGjjNHQ0pPNAARxmm1IHyMcCm4x1oAllP7q3/65n/0JqgJ5qeYf urf/AK5n/wBCaocVMNvv/M3xHxr0j/6ShvSlz2o60Y46VZgKaB9abk96MZNAE6qCOWA/ClaEYODm oVLDoM1Oolf+E0gK+3B4oNPZGUnPFNI96AEFIPrRnilxxTAO9N70veg8UAA6UlFLkAYoAKKQdaXF AAKQ0dKcOaAG0dqVhikoAO+KPxpvWl28ZNACignmgdKmWznZQ4XINAEOKXpU32OYH7lMaJ05YUgG c04PiNlPORx7U00nUUAL2oyDQM4oHNMBcU7ApnSjJoAU0gpfrSGgAApwHNIKQkigAJFGKTtRnmgB enFLnijjvSEdqQCg0ZoBwKAM0AGeeKKMYNLQAmcUcGjHbNLjAoATFFA5ofrQAA54oNApOPWmAo96 COfakwMUCgBeBRnnFJgml2nGaQCYJXPalC0oxsxk5HSjPFACYppzTjjvTeooAXrzScgUuAKCOKYC N7UZz2pRSYoABijrRjmg9MUAFLjjOabS5+WgAo6UgxjOaXrQAZyaOPWkOBS44oAKMmkHFL3oATvz TsUHqPelXhgTzQAztxTu1J0pQOKAE9aUFdmMfNnr7UmaOtAAfakGe9LR2oAKXikozigAFBpAKdji gBOwpRg0h5oIOKAAjnNFOXB4pvSgBc9RigZxSdKXNACYpRSfWlxmgBc80ZzSHgUdDSAUj1pD6UY9 6XFACe4ooHFGaYBjmjFIelKuaQCUAkClPpRjnmgBM0uBRgCgc5oAQ9aO9Lik6UAApRzSdKADQApp B7UUZoAXnn0pDk8UZPapI/lUyMAcfd+tMCLml+lJkk0DigBSpo70ZJNJjmkAvend6ZinDtQAh4ox xkUGgexoAKADkEdqXHekHNAC5waUjvTT05oycUAKe9JQAemaXFAB15o7UnejHNAC0D3oxRQAZFLR ij6CgA70hoxQaAAUvOKQZFOxigBvajApfxpNp9aAA8UopDnNKKAJFNe5/s+dPEX/AG7f+1a8MXrX uf7PnTxF/wBu3/tWgTPbKKKKQgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACvFvjh/x/aT9f6ivaa8W+OH/AB/aT9f6is6vws6cH/Hh6r8zwpqY BUjim4NamA0imY5p5FAXJoAZR3p5XFJigBtBPPHFP29+KaynOeKAE59TSgnGMCjBI60oX1NAACvc Gk2DqHH0p4XJ61MsQ2+v4UARKjH3qaOF2bpVi2hBzkGtG1gTJ+U0AV47STI+X9KsJaOG5FaixjcP lqYRr/dNTcZUghAXkCrsUXTgU9I1HarMaLxgUWFchMXsKrSxEHpWwI1I5Wqs6Lk8d6GgTMW6hJjO BWFPaSGTha6qRRzxVCZVDdKSGZdjauLhMr3rqbaD5ugrPtETzFOO9bUGAaokd9nYj7tVprV8n5a3 4ERkHGeKe9sjD7tDQXOVe2PdRVd4OcbRXSXVuqgYWsuRAHPy0rDuYc1qSchRVOWxkbkJ+lbzIuel MZB6UhnKy2cgZgVxVXYV4NdPPCpJJXtWJcRKG4U1SYigT2pQCe/FSMnfGBUeT24pgMKkGgAnvSk8 UikZoAXbz96jb70pGBmkyMYoAXtQQMUnajPFACDHTFKMZ6cU3PpTh0oAf8mOhzTeOtNNHagCVVjJ BJxT5tm3I61XzxS545oAlnz5Vv8A9cz/AOhNUNTzn91b/wDXM/8AoTVBn0qYbff+ZviPjXpH/wBJ QdKQGl60hBqjAM0oPFJRimA9Gx61YSdcYy351UB5pcc8UgLZ+dDzmqZyOKlDkDFRk8k0AAGc0h6U obFBPNACH0pD0peM80hPagApPwpcigHmmADNKQTzmjOKTJoAO1GcUZoyM4pAKTmnRQPM+xF3E9hS AHoBXS+HrZQVkMZ3EdSKBNmA+n3MQy8TAVAQQcEYr0ieJSh+QH8K4nUYyLyX5CBn0pXGjPSNm+6M 10tnbyvZRqqkkdqxrMZJrp9HxuUE9/WmIqvYXW0/umrPu9NuygxCxr0V0TYeBVfygf4P0pWC55VL BJExDqVqLa1dr4htzsG2Ennstc1LC+f9U/8A3yaaYzP9qUGpZImXkoy/UYqMDmgAwaUc9RSgHoBm ja/90/lQAhpCaXvikoAOppSMmkoNMAPFKCAO1NzRQA/ilx3pnenE8cUgG4xT0IzimdaUcUwHHrSE +lJmikAcmkOadRmgBoNLjNAFLQABeKaB60u45pM0AGD1oxS54xSbuaAFwetKAc0meKNxFABmjsaT OTQOnFMBD1zSU48AUcUAJg0UZ9KDwaQBSZyaUUUwDFGKM0tADaUCjPNOz8vSgBoAxRj1oB5pwIzk jI9KAGkZpDTmYFjgY9BTOe6mgBR0ozSAn0pe/SgBeKUUINzgY611Nr4ftZYUZs5IoFc5Yindq9At /BthKuWzVTV/CtnZWryR5LAZFJhc4fFHFPZCGIwevpTCKBgBnp1opMHORS5pgGKO1FFAAORxS4NI KWgBDxQORS9cZoPFAApKnjHTFA4ooJ5xQAuQR0pO1LuAUjbyehpKQC4pM80vIpOlAAQSaTnNLmlz 6UwEFOwQoOODTenFLnjGaQDcUo60+OJpX2qOfU9BViKx3OEeQKzHC7ecmk2kK5UIOaBU32eTP8P4 mmOux9uc9zTuMZil5xS55pM0AIKTvS96QdfagBepxSHg80tIaAE60oNAPHSlxxQAgoopc4NMBO1L 260DGaMZ7UAIBzScZFWreylunCxoa0/+EauQOWUfjSEYhFHStiXQLiNC24H2zWe9nMrFdhOKBleg GrK2FywyIW/Kk+xzqfuH8qAK7dRxScg1bFlcNyImP4Un2Kc5yhFAFajGKurp0zLnAGKU6dIR0yaA KOKOtWXsp1/gNQMjIcEEUANNBBIzS0UAIBxSnOaKMUAJjNGOaXFFAB3pc45pp9qM8YoACcdKM5op cCgBORS0UZ7UAJRRjNFAC0oGOaBThxQAqjmvc/2fOniL/t2/9q14YK9z/Z86eIv+3b/2rQJntlFF FIQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABXi3xw/4/tJ+v8AUV7TXi3xw/4/tJ+v9RWdX4WdOD/jw9V+Z4WzHPWoy7epqRhzUeK1MBuT60hY +tPwKTAoAaSSKTOO9OyKQ/SgBvNLyeKXJAoyeaAGgNTwCadGjMRir0FrlvmFAEVtbPIMgcVoRWUm 0cVcsoAoIxWtbwqYulJgZMNu8ecgVbhU5NXXtwO1IsODwKQCxf6wVb7URQjeMCrq2xPOOKLCKBQ5 q5a27sRitK204OuStbFrpioFJWmG5hm1kA7Vm3akHHoa7C7ijjOMVymoOvnNj1oAznQ7TWZco3mV r5DVVnRfM6VIyrajDD61qwjLCqC7VccVcgky3FMTOgslIUZ9K0o4i0ZIrK05yep7V0thGJIelUSt zDvom2DisOZdshBruL6xLoMLXN3enP5x+XtS3KOeuOoqMrgZ7VpTWm0/MKrvGBxilYdzOdc5rNuI fmHSth1+YgUw2qt1FIZzlzEQKzpI2A6V2EtmhX7tZs2m5QnFO4HNMMNil4q7NZvGTlaqyRlT0qhD D0poHrS4NB6UwFIx0pKPakpAKOT7U7HpTM0ooAXBxQBRnikFACY9KB1pe+aOpzQBNP8A6q3/AOuZ /wDQmqAcfWrEo/dW/wD1zP8A6E1Q96mG33/mb4j416R/9JQU0ZzR3xR0NUYCGlFLgYoApgFKTjij pTScc0AL2ppNOCnvTStIBBS4pQua0tL0qXUJ1CLldwBoEZmBS7c16bH4DtsfPGen96uX1Pw69n5r KvyqeKGNHMlcVNbwG4mWMHBY4pGjK9RWt4fsWn1GF9uUB5poTdiceFJjj5xVHUdHfT9u8g5r1MC0 TGVPFZmsWNpqSqI0OV96TBM8sIoUZrr28G3BYlV4PTmmjwq9ud0w4+tFxlXRdDae/hDEbSM4/Cur eBLFjEFHy8VTa/tLSJWgUrIgAzWbPqks8rPu6mi4jeSUMcYrJ1C3R2lO0ZIqvBeyb8FqtMryqWzw algjAgtCCeauQyG1lTvzUztHbEhxyayJ7otPx0zTA7eLU90qLt6mtyMBhnHavOLe+kE8fPeuvstR II3HjFMRryGEffjU/Wqky2zvkQJ+VSG6ikAyKTfEecUwMvWPDw1OJPIVUI9K56bwXdRn74ruYb+J W2noKvQy2065YZpWC55/p3hOXzT5hUithvDKpav8q5x1rso47JOQpz9akYWroVx196BnhuoWbWkz Bv7xFUiK9Y1XwlBfupiQ53EnmsDV/BZsdPedUOVPrS2GcHjmjNWHtnUZIqJkxTAYCKWjGDQwwOKY BR0FAooAAaWjg0fSgAx70ZxSGnDHSgAoooHJxSAdtPl7+2cU3NHQ47UUANyc0Hnk0p60nfmmAoPF IOc0vU0h60ALg0lOzQaAEpPpQaTHJoAd/DzSd+KO1ANAAeO9J2pcc80Y5zQAAcUdKB9ad7UgGnFJ nmncZ5o4zQA0HtThyK0YdE1C4jWSODKHoc1Y/wCEcvR99Np9KYrmOo5wK1rPw9f3UkeLZxG5+9jj FXLLTDayZnAOPUV00WuQwxKo/hHaldC1OcuPDyWMpWTllqB7WEocIK1r+/S6kZwetVoUEg2g8mkM 56a32scCmpFxyK6C40mbkgDFZ0sJiIU9RRcY2yt1aYHYDiuwtZIkiVTjIrkIJjE2SOKvx3uWB5x9 adxHbx3O1PlNR3EqzIVkAIPas6yu0YYb0rRWWBhgkA/SgRjy6XZSMcQqM+grMuvChkbdbA7SORXV GKMnIYflVm3nijG0kflRYLnm9z4dvYBkRM2PQVlzW8sDbZUKn0Nez+XFMO3PtUZ0ezkO540Y+60B c8WIOaMGvRta8GG9uxLbSJGmMbQtY0/gS/jXMcgkPoBQO5yWCD1ozityTwnrEe4m0O1epzWO8Zjc owwQcGgZHzQDQevFHbFMBT04pKXqetIeKQBzmlFIOTS0ABzmjj8aSjtTAOxPelB4oNAxigApDSkU Y4pAWrGQecIGzslO047VFJm3uXVHI8tiFYVGpKOGBwQcihmLMWbkk5NK2tybahktks2SaBScUUyg B5pR3pD0xSc0ALijt0oyMe9B4FACd6Mc0ZozQAdOtL9DTc0uMimAvFGM44oxk8VsabYxuqySj5s8 CkIZa6JcXChiNoPrWzZ+Ho0A8zDGtq0i+VRt4rRWJV6CgVzMgsIrYfIgBPpSzKemKvyMoOKjIRut JgYc6yZ+SqLRzKcnFdM8MR571UmiRhjFLYZlQ3lwPkAGKvRWck7Av0qe3sUZlO3vXQwWSBeVqkhN mOlqsS4FUZrVSxNdHPDGprDuWAcgGkxoy3tsZwaltrQN1qVQrHk1agRQ3FJAQvZKRis640mOUn5e T3rqY7XeuQuaqTpscjFOwXORn0BusR/OqcujXMY4G76V2vB44pBb7m+7QM4f+y7of8s2/Ko2sZ1O PLY/QV6BLZL5f3az3s1V9xBouI4p4ZIv9YhXPrUeK7K4tklTaUBrP/sBHyVB9uaExnO0nWrl3p81 o5Dr8vrVMj0NMA96BSe1KOKAFx604KDnJxTc0uaAE280YpMmjNAC5Ipc0mDmlFAD1617l+z308Rf 9u3/ALVrw1a9z/Z86eIv+3b/ANq0CZ7ZRRRSEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4r8cP8AkIaT9f6ivaq8V+OH/IQ0n6/1FZ1fhZ04 P+PD1X5nhjVHmntTMVqYDSaSlNJnigAPFJn2ozR1oAASxxipETI6UxAc1ftoHdcgUAOtYSQCEJ59 K1reDk7o6dYW7rGCR3rSApNgRwxYHCYq5FuC4ANOgGVqwsZIpBcjHI5FKqZPSpfKNSJGRmmIWBQJ RmtiBFOOARWQgIbJrYsAXCgU0Jm5YwJ5X3a1BEqwA7cVWsYHEXNaEwK23NSykc3qrYfr2rir5iZX 5711WsTBZsGuSuTvlbHrQHUYmeM0OuWyRmpFHApT1oEQNGuM7Rmmxna3pUzKevaoZOMUDNexl2t9 7AxXY6GwdFy2ea4K3bIH0rsPDcgXYD6mq6E9Tr2tkccpmsa8s185sRdvSuigcOeKbPAWJIFSWec3 dt+8+5WPcxFZCNtdnqFnIJORXOahEyPyKZNjEdF5OOarAtnjNXZFOTUUUTAmkAxF3A7h+dRSqojP Aq6UIU1VkGVoY0YtzGCD8lZdxEN33K6GWNmUgCqFxC4PIoA51wQW+WoQCDWncREbuKz34qkwG0Zp QeKDQAY9KUfKOlJnApWwAMMD/SgA69sUntRk0d6AFHXmkPWjnNJ35oAsTf6m3/65n/0JqgI/Gppj +6t/+uZ/9Cao+1TDb7/zN8R8a9I/+kobRwKMHGTQCCaowFHNHf2pwwBjvSE8UAMPNNPIwRT+1N5o AkTJHNKiFnC4zz0p9tG0jbVG5vQVsLpptJI55Tj5h8tAFNbE8fuW/Ku48JWttFayNIiq4YEZrNOp WiR/dHAqOPW7fOFyB7Gi5L1O+e76BZR+dUr+1gntn3KGJrnbfVYWbqfzrVTUIigHP50C2ObvtIt0 jBW359hW/wCF9Pso9NMkkapKGON3Bp093BgZUVRlv40bAJA9qFoN6nTSx2+wn5c1Rj8lXbLKB9az BqUboBk/nUMgab7hNFwNe5vAsR8uZc+xrEvbyR1H73NQPZzjJJOKrywvGuWpMaRVJDEh8Yz3rNmc rMyoeM8ValdSpA61AsDu27HBoQBBK/mjk1vQOfJXJrJhgYSDitSMbYxmhgU78AuMisaSMebwO9at 84MgqOCAyuCBmhDZFDH++T5e9dBFlQMccVXW2KsCVHFXFIPApCK8t3KhwHIp6XkpXmU0Nptxcn92 ue9MfSrqP7ykU0A5LmQufnNaFtdSqv8ArDXO/bY7aQh88VZh1a3x1oA6H7dcf89DTlv7n/noayE1 KBhwasR30O2gRsw6jcKcmUjinyXTXkZhnlDRt1BNYE97GY8A1VF2P7xouOxravoemR6bI8MSbwOM Vwd9aeWV2RsPwrsVu1ZQGYke9NcwN1VT+FIaODML/wDPNvypBbyMwHltyfSvQIobdsfuk/Kr6WEL ciFM/SqQjhZ/Dk0NsJVJY4zishoJE5ZGA9SK9bjsmfjaMemKdfeGhqFg0QhRCf4gtAXPHTxSjpV7 U7FtPvpLdudh64qjjBoTGJmgetIetLjmmAp6Uv0oxxmkFAATR2pcUrDbxQA3FB+lLnigmgBuaM80 EZPWlIFAAKXP0ptLigBTzTcgdqQmkxQAvU0gHOcUqj5qv2iRbSZULemKAJtJ0V9VZ8Ps211ul+DL VFb7YRLnp7Vzlvfta8QLtFdDa6tOFAckGi5LH6j4QskiY26BW7YFY8HgyeZj++x+NdUuoF1G4g1X nvWj5T9KVw1KmneC7SFW+2MJCTx7VPc+GNIiClYl/Kq76lPnqakS5lmADZouBpw3MFpbCKONQq9K y7rUw0vAAoNu7KTuNZk9p+8yWNJsaRDc3rO59KreZnk024Ty245qoXbOKBkzyncQvFWrKRhIpzVF FJOTVu2+WQUCZ0bT5TBrn7xVacmr7uwQ1kyuTKaQyGSP3p8UZ45qZIxIRVkQqi0xF6FjGop5u3Vx g1ShlMrbelaFvZCXLFuRV0o800mZ1ZOMG0PTUmUlWqeG5aQk+9VZLdCTkcisW51Ca0uGROgNdOIw rp+8tjjwmNVb3Op31lKx2itH5z0Neb23iK6Vl4rZi8R3BA4rkud1jsAH7mpEkZDmuPbxHcDjbUMv iSfHAouOx35v1eIxsoIPBrnj4O0S6ld2jwzZPTvXLN4gufMHWrttrtxv6mkM5jxDoR0m9dUOY8/L 9KxNpHWvSbgR6iA06hj70tr4a029kCyxkD/ZOKEFzzXoKSu28TeDV05XurWTEA/gbk1xmKdxjOcj FL3xS4FIaAA5pBmlwaTIpgL2oB7UA0Y70AKfYUnNKKXGaQDT9aU9M0h4GDRyQBTAKKXGe1GBj3oA TFIc4pc0HmkAmCvBBBoPSgknqc0nNMBaMgds0UUgDNA60EY5qSGJpnCgdaALen2vnMWK/KK1IW8u UcdDV3T7JYINnUnrVxbBS2fLNIRes7nci8VpdRWWkYjGAMYqzbyMXAzTEOliO7NRHK8VtrbxvECR zVC4t8SHCnFDQJmVIWDcGlhVpHAPep5IxuxipbWHMg4pIZetbEhQ2avO4jWkXKQHsRWPd3UnduKZ Nrkl7c7n49Kw53y7VLJMzHrWVcytvOGqdy1oPEpDda0LSQlutc+zSZ4zmrli0/md6BHcafIGTBFN urAzMWFQ6PvI+YVvxQ715WrJOX+wsrY9KsxwkY6VtzWyBSdtYV3K0bHacc0hkkvyx9qzJ4/Mbjip FmZ/vGpk8sj5jzSGUo7Q7uatLCEXoKl3Rg8EU7KMuM80CMPUbQTZDAYNcZe2ptp2XGB2r0OdAetc pr0GUEgP3T0pFbnPUAelFIDg1QC0UnXmlPTigAoHWkBpcigBaBkUd6AaAJFr3P8AZ86eIv8At2/9 q14Wte6fs+dPEX/bt/7VoEz2yiiikIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigArxX44f8hDSfr/UV7VXivxw/5CGk/X+orOr8LOnB/wAeHqvz PC2phNPamGtTAac0mOMU4mm0AJ0NP7c0zqakRSzAUAPiXcw4zXQ2EIEXKVV0+zUgMc1uxxhVwBSb ASFQMACrqRg/w02GEkg4NaEUH1pICuiY6CrcK/J0qxDah+DmrSWS471RJWSIEfdzQ8RHRTWpDbED ABNTfZuu4GgDEWIluVP5Vu6VD+8X5DVfYM1uaUg+WgEbNrGBH0qDUXCwEZxVxHCrgVh6tKWVx2qS zkdYkLT8GsJ87zxWzfIWlzWc6YNFiSJelKetIxxUDTMDjAoCxORxUbqD2pEl3EA4qY4xQAlv16V0 2iPtKc45rnIyAa09Pn2OuCKpCZ6Rpsu6Q5bNawAK1x+j3p85s46V1ltJvhDZpNFJmdqEKk5K1xet RgTHC16BeLuArj9agzL3pIbONkX94Rg1LHEMdKuyWo8w9akjtsetUiDLmTHGKqNGPSt2a0DDPNZ8 sXlqTg0mhmVKABwKqSKCelaE4yvFVSp7ipGY9xCCH+U/lWLNEQ3A4rq5FyDxWXcwZ7EU7gYDAA9K TjNTXEZRj1quG55qgHcZo4BpOCaMZ70AOGKQ8Uo4HrTSfUUwDNJnuKO9FICeX/VW/wD1zP8A6E1R GpJv9Vbf9cz/AOhNUZFTDb7/AMzfEfGvSP8A6ShDnFAGOaODTgMduKowFHA96aTQf0pD0oATOe1T Wtu1zOsSD5mOKix+tb/hyBYbvz5wylfuj1oEzX07RDZMkm0Fx1JrR1NLR7Yny+QKma9iKnmsrUL6 IQMAc5pMEYsrJsam2MAmcEDjNQvKHUgd60NIjZevqKEDNiKwVTkKB+NQyQXIztIHpzWtHhjwanGm zSDIHWgVznBBeseWH51attPmkx5mDz61tjR7heoqWOzkgGXIGKAuVItMXj5B+da1npiZO5B09arr dIrda0LO/hLHntVCK+oWkUduSEwRXJ6r8qLtrr7+USQsFHeuW1K3kkQYB4qWNHOQRNJNg85rVjgR I8FahtbWRJskirsikLSGRBYwc4qTAK8VExwM09ZBgcGgZn3UJMgOKvafEi9RzUcylnBFT2x2daBF uTbsOOuKp+YVPWp2lUqRVZgD3oGbGm3e1hknpWp50Ei/vASfpXOWsyREbj2rRiuEcZFUmTYhvdAt 7g7oohk+prPbw/5R4jA/Gu0s7KadQUXPFF1pF1uzt7elDQJnGLphU/cH51OllgYKituSwmj6ioGi ZTg0rDuZ66cZWACjH1pZ9K8qIvsAx71r2kDtISOgFS6kuyycnpRbQEzjZ5PKjJBwRWdJfyD+M1bu 3V0YLWVJEzdBSQzTtL+ZmXEh/KunsJJ5Tw2a5Sws5W2EV2mlW7RAliOlWSzptIszM2GANdlbaXb/ AGcboxmuS0rUbe3c75AK7Ozv7ea3DLIuDSY0eZ/E/wAO2MemtdwQATgj5q8WYV9H+ONNn1bRpY7U b2PTFfPV/ZT2Fy9vOhWRDgg1MdymUwKMZpc4ozzVCFz0pO9BNJmgBT6UAe9JmlpgJ2pBnpj8adnn rRt546UgE5zRS4IpOnWmAc0EkDFJnmkyaQBzmlHTigenetKy09/OR5k/d9aBGrpem2Mum+ZPH+9y eTVHERlcDjBwBirdxdIpKR5CDgCqNum6XPvSA1rXTxJHnYDUM8U8RIXit+wAWHmnvaiUnC0Ajl45 LouAGNbFnHPJw+TWpBoxOGEf6VpwWPknLKBTsK5UtNO3x5MYzWlBp0YwCgp32mOE7TSpqCF+DTEX hp8Xlf6sVzmpWsaXGAuOK6tLxDCBXPaorPc7h0xSY0cRqf8AriFFUFhYuOOK3L20ZpicVGLcKAcc ikiioqbFIIGKktwPNHHFSyJlTUCnYw5oEaxRSOgxWZPCPOOBxVpJt2DnikcKxyAc96QEdrGu/pU0 qgI3FCBUNJJKpBGaARmROwm4NbukSMZsMeM1kIo3kitCwcRP1q4uzuTOPMrGrfQmObOBhhxVE6fF MGYx5PrW5EYbpB5nOOlaUGlCSLMSZBrurYlVKXL1PJoYKdOvzp6HBy6eI8lUApqROOorsrvR5FB/ d/pWRJZlG5XFeeewZBjYjgVE0DscAVqtFtPAqS3tyzdKAMtLJyuSlI5ES+ldJJb7IG+XoK5K+m+8 Ae9IEON8ytgua1dKvZGmXa561ybFmbOa3vD8ZNxGfeqQmd0IXvYNkg3hh0NefeIvC95ZXEs8Vsfs 45JFesaZ5aBd1at6La4snjZVYEdDSkOJ81kYzTSCa3tW8N6lZi4u5LR1txIfmxxyeKwu9NMYmKBz S5pOpzQAY59qOvFODgHoKTjmgBKMHrRS5xxQAh5pAcdad2pvWmApPHFJk9aUA5o5BpAJmjNHeg8U wDpRjgnvRz6UoHfNACZ7UuKdxV620u6uCpSJipPXFIRDY2b3dwqKCR3reGkfZsMqc1uaXpEdnEpC /OR8xNXZ7fctDEZmnxn+MVtJtwBisxR5ZINWYp+AKSBlsWu/kCnC1KEHbU1tIAASafJcAsKoReto mMYyKhuo8Z4rT05hJH0p1zYlySCKGFjjpkYynitPTbVjIDtq9/Yru+dwrVtLL7PgkjilsMzry3ZI X4xxXKzIxY8V2+oShsr7VgG0BY5YUtw2Ofkhz2xWe1oTISV4rprm2Ct1FZzx4J5pWHczVtwONtXb K2+bJUUoHNWYmweKEDN/TIwq4xW/EmIxxXPadNxjFdFCd0a/SqYkVblcowArmL+3Y7iQetdqYNw7 VmXmnlkbkUhnGIhUkUyXI6Vfu7ZoZeTVWQDZSAomRgepqzDIxXJJqvsy9XIY8JQDI5ZPl5Nc9qTC RShOea3LtD2rCa2eRz9aBnPSRlHIxUfeuqXTFK5YA1nXul7RmEEn0p3Ax6SrTWFyiljC2B3qqQaY ABR2pMcUtACd6cOtJigUASLXuf7PfTxF/wBu3/tWvDEPNe5/s99PEX/bt/7VoEz22iiikIKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArxT44f8 hDSfr/UV7XXivxw/5CGk/X+orOr8LOnB/wAeHqvzPDGqOnv3pnatTAawwKZg09jSDk0AOVc1fggL PHxUNsgZuVzW9bwKFjOyhgT2kBjStKJNy1BEjcYU4q9EmF6YqQLFsnArRiiOapW4xjNacLAng1SJ JYkIJq/BCWUGq9uuSeM1q2yfuxxQ2CJIYdq02dQFNWVGBUFxjbyaksyCPmrZ004VaxiRnrWpYH5R g1T2IW5s7+Kx7/ndWogJX1qjeRnaflNSWczdKA1ZMv3j9a2bxH3/AHTVGSAkH5P0qiTJcZJqBoST mrckbBj8poRRg5FTYZSWMq4qyBmnMnzcLU9vCWblc00hEKxMatW4KSDNaEdqNo/d9vSmfZyJvuYF OwjU0mQ+efpXa6fJ/ow+tcXp0eJjgdq62xz9nHXrQxo0p+QK5rVYy8tdCxJAzWPqC5k6VJRzMkeJ CKfHHU80RMp+WnKhxwtVcixVljwKzLxN0RA9a3miJ6rVKaAbT8lAHNGBqrywHNbUyKoPy4qjNtz2 pNAZLREZqrNDv6VoyclsVVbjtSKOd1C1KgnjrWO64JFdPfpuQ8Z5rn5Yzk/L3poCtS98UjZFL1NM Axz1oyRSjNDUAJn15owMZ6UmKXGaAJph+6tsH/lmf/QmqHB6VLMP3Vt7Rn/0JqYBUw2+/wDM3xHx r0j/AOkoFHBFKemKX0NBqjAjYE0mDUmM0hUjvQBb0rT5tR1GK3hALZ3c+grsNSsWgdFwAVHOK57w o7Ra9Gw6hG/lXV6rcRPvYyLnHrQ9hdTJkkAQ1kTuHGKle4G1vnqmHDMOc1KGKsZ3itqz+UCssbc9 asQzMHAU8UxHUWbBpMD0rrrWM+Whrh9PkImOTjiu4s54/JQFxn601sSy1cLwMCsm9QspA9K15mRl 4cVm3eACc0IDBa2kGTToUKE5qV5P9riow+TwaBmirrsAxVK/AZOBWhFCWUcZpZbEyKRsP5U2JHIh SjFj0zQ7hlxWtcaa6o37tuvpWPcqYcgjGPWoKGMuRgVIkDFQcVWWbJ+9WhBIpiHzCgCFoWB5qB1K nmrsjpnlhUMgViMHNAyqxwpNQCVT61oNCrLjNQfZAOi0AQg56Vq6ehZPxrNERDdDWtp3yp+NNEs9 N0GHFumQOlXruMeg6Vk6JdYjRd3QVsM3mjPWhjRzt/bkgEAVhz2rmTpXYXMG4cisS7hCSU0DRTs4 WTeT0xVbXBnTJAOtTvdLErfOM/Ws3ULsS2bLuBz70MSOMS1kaQAVI1jKp5ArQhRRIp96tOivSKM+ 3IgwG7VrRXqFCATWTcrtY4qsl0VJ+bFFxWNvz954Y/nXU6XdbLJQXb864KG55+8K3rS/CW4HmD86 aEz1bSJVlsFzznPWvHfibpqLqMl4rAHcV2gV2+n68ltp6s06qPc1574x1OLUpZh9oU4JIwal7lLY 4M9aTAob73WjOBVAFHWkzzijIHGaAHDikJ9KM0YFAB1FHTrS5AFNJzTAdnmjd1ppPak7UAO3D0pf lNRjk08UgJreETTogOMmt+RGhiC54ArT8O6RZPogup4l88McFuuKpamUVTtIoYkYcm8yE1btBt5N VSQT1qxGSAMGgDfguSqjtXRWEXnxhq5O0+Zfm5rt9FVfs69KaJZqR2oRBj0qtcqccVt7Y/JGMZxW VdKAeKAsYNxG5kzmo44mVsk1ZuCRJxUIY+tIZpRShUApZDHIckZqkGbHWpwCVHBqhGVeQqXJFZjI dxGa3Z4yc5FZ7wjk7eahlJmZIgGaq+VuPC1PdPtZhnBqtFIxkHNJAyyluV749qc4KiplwaZIM8UA UHmOeDURcnnNXJYEAGBzT47WIpyooAoKxzwamjmKuOat/ZYh/AKQ28a8hRQBsaa5eNT716ZoFksl gjEda8y075YwBxzXp3hydvsKAtVdBdSXUdMTymOO1cVf2IVzivSbkCSIg88VyOoWy7icVKKaONe2 O+rVpZtvHpVyeFFfgYpYmCEYIq7EEd/DttpPpXnV0hMr8967rULpv3i7+CK42VVaRvXNS9ylsVIr QsM9q2tLb7K6E9jTIIV8scUOfL6cUXCx19vrA2jAqaXXHIKg1xcd3Ip4c4qyLmQn75ouFjQ8T6lL L4bliY5DMP515n3r0yC3i1GIQXHzI3UGuI8RWCWGrzQwptiUnbQCZkk0mecUY9aAOKYxaKB0pQvG fSmAowR701hindRxTSOaQCD6UvU+lApcUAISaQc07HFCqe9ACYoHJp2AKC4FABjNKEA703fk0oOT igC/pdsk90A3Qc12dntUqigAVzelxrbpub7zV0dkN0goEbUK5IGKtG3yp/wpLcDirwAAz2pkXOau 4yjnj9KpLIQ+MV0V6Fc4AGax3tm3lgKmxRZgfKDipG4OaZAhVRmllBJGKYHRaI+Vrd25ANYWgLxz XR8BOlDCJXJAqrdTBV64/GrUpBBOKwNUl+Q4OKSGyrfXIG7nt61iNefN979ajuWYluaxpyR3ouFj aacPzu/WomcEHkVm2wZwetSSQSMOKQDpJADwf1q1aHecVlLbSJJubpWlZ3KQthutCBnR6eCp5rpL ZgYxXJW99GpzmtW21CPaDuqiTod+BUE75UioVu43j4NIHD8A5qSrmBqq/MTisOX71dVqkDeSSFrl JlYS80xCIuW6VPjaKIU5zSzc8UAQSHd2piW4JzgflViKFnPFWzbMAOKLDuUGiG3pWfcR/KeK3ZU2 pyKzHAL4NJgjPiiL8EcfSs3U9GAUywL8xPIrp40VRnFVrhhyMUDRwLIVJB4Ipo4rW1a0EcnmIOD1 rKIpoBM80oApcDFJTAkXFe5fs99PEX/bt/7Vrw1eTXuf7PnTxF/27f8AtWgTPbKKKKQgooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvFPjh/wAh DSfr/UV7XXivxw/5CGk/X+orOr8LOnB/x4eq/M8LbmmZp7ZzUZrUwEPNOReRTaswRl5FoA0LC3Vn 5zXQQQr8g7VU0+0KNk46VtRRDK8UmBJFEoXFWEiGO9SQwbqvxW+FwQKaRNyrFCpHerkUIU8VYitw egFXobTB5AoDcSyhBB61qxRgLxTrOFFXlathUHapZSRXKcCqF8MCtOYgYxVC7G5aBsw2JFaenHO2 qDpVqwfbKAaog6i2jDR0XFspQ0+yYNFWgIA6dKktHI3dnGz96pNZIfWuxnsAx+6KqtpwAPAoCxwN 3Zou485rLZADXa39iAH+Wuflthv6CmtSXoUYIFbr61oW0Co/FEMGOgq/awHzBkZqhF+3s0Makg8i myWUe89a37G2BjXK9qnbT1Zs7agpIwbCzRZD1roraFVhGKdb6cEY8CrywhExigdihIcVmXnJrQvG APFZcuXY80hlUxA0qQin96nhUNTEV2iXFUpYxsNbvkgqeKyruMrGaBM5u+T5DWLMMNXQXCkgisq6 hO7pVEmWVGc1Wl+9V11IJFRGP1FTYZmyRKw5rKurZFRzXRvBuHAqncWmY2GBSKOLkA3EUzGDWhd2 5SYjAqlINpxVoQ3dgU3dk0UvQ9KAEHBp3HajqKQdaAJpv9XbH/pmf/QmpmcdamlUmG3/AOuZ/wDQ mqBhnAqYbff+ZviPjXpH/wBJQhYkcUA5xS4yKBjv0qjAXOOtNJyaVsE8UgHPNAG94TtZbjVdyDKq jKT9RW1rOjXNvkswxj0rD8P6rLpUx8sKfMYZya7LUrr7bECxAyvah7CW5wTwsAc1WX5T1roJrFfL bk1jSW+3saSYxu/vV20bc4PvVLycjvV2yQqR9aYmb0cgjOTWza3q5Tr+dc68mAK0rViSnFJCZ1Ud yrnAqO7fKHFV7cPuOFNTSRu8ZJGDVCMeSQDNJbSBmNPmhwD1qtF+6Y1IzsrCMuUHqK3Y7Fnz0/Ku d0q6HmRjI6V2ensJXIyKbCJkXeky+ScBfyrgPEGmyiRxx1r2iaz3xYANcfqvh/7TcODuAz6VJVjy SSzlhTcSMVJFKFjCnrXcan4UVLYkM/8A3zXMyaKY5CuH49qBGfIS3IqNrtIRhhk1otZBTg5FYd5G xuCgBxnFAyzHqsTSqoU8n1rXSVXHArFtdIzcxj5uWHauzi0DagOW6elOwrmBIQTgCrFtwv41LPp7 JIww3X0qSCzYL0b8qEDOo0eTaF+ldTZuGj6VyOnfuwAfSum0yUsAAO9NiiaEsLMtc5qcDLIT7V2A i3JWHqdoWduD0pFHmeozeUWz6ms03auNozk1q65aOjNhScse1Y9vau9wilWGfajcWxKFZcMelSpI DWrLpG2DdlunpVBrXyuueaLBcp3MZYEiseW2kLcV0LohGC2KpTRxp/GKQzIkV4MbjTk1BETaSfzp b50bGGBqiYs8g0wNm5vRJpACsRz61yczkytkknNdQ9oo0RXLHrXLTACVue9HUFsRd6QnNLimkUwH KMmm9Dk08dKQjmgBufSjkmnYooASjpS460mAKAFIHGaTHoaXpSUAAHetnSNCn1Mq4wsWcE1kKpPA 5rrdAnNvbrH/AHuTzTEy3cl7S3aBH+VRjiuYuZXbOWNdDeuH3+9YM0RweDUIZSDGrMTnFQ7SD0qx FGTxiqEa9k2EzXRWF/5agA9K5mEFU4q9byHrigVjurfVPMwue1STS7652ycjaQO1aySs3VaYirdD MnBqoSVNXpvv81VcZNJjJ4fmK10VrYebFkCubgOHX613Gj4NtT6C6mbJozkH5c/hWZPpBAb5a9ES 2DR1nXGnsQ/yn8qm5VjxzU9OkFwwANZhtJYjnnAr0TUdNY3DfK35VhX2nsgPB/KiwHPrLwOuaPMO eTV37PhTwfyqlOpQ4xSAGnUdakjukPGKzDuzjFaNrblgpwevpTsBpFUKA4qFwo7Vr/ZCsAOD+VUr iAjnB/KhoEwtD8vFdzoM5W1TJri7WNtvQ/lXVaV8sKg00SdlHNvUZ71SvbTfk4qS0fcqir0ibk6U mWcBq0BiY4rn57h488mu21u2Jfoa4++tyu7g/lR0Ec7dXzmUjJJNVkjZn3YqaeJvtC/KfyrQgtjw cH8qSBlONiODTLgErxV2aErIeD+VRMMj0oGZgR93Wrccb+VwacyqncUCZEXhl/OgCezuXt5VJbpV LxZIbuKOVUHHUgVLFiW4Bz+VW9XsBLpYXfimJbnAUoOelSTxmJymc+9RDimMeKBmk6mlzzQAu3HS jGKC3FITQAuaQkCm80vWgBQ1GSelJijoeKAEzRnmjrS9R0oATFXLG3aaZeOF5NQQJvlVSOCa6eO2 Ece2NcD2oYiEJulAXgVtWzNDhiKpwWuCCc9atOGC4U1IGxa34MgFbkUnmx5FcjZI/nKSTXU2ORHt 61aJZWujh6gUbuO1WL2Nt2Rms7zWjOKQyxIVjU81VEu9uKjluC2eaW1UvIOM0XA7LQY/lrdePC9a yNHHlxjjmtVpM0mNFeYbVNcnqlyo3D3ro7+48tTz2riL+5MkrdOtAMqTSBg1ZckTO1X3yeaIIPMf lTSALKLYhz61M3cAVbS2VRgCoXQBjTYkUZjheaz2mVZq0rlMqeaqJZrI+SDSKJY7oKoq5BfKF4qo 1qEHC06OHA4GKBG7b6jgAc4rcsZRI6kd642IsGHNdJpUzeYoqkJm7qEW62NcfeW5WUk9K7xovPt8 Gue1PTuSRmkM59R6GlMZYirAtGU9D+VWYbTd1Bp2ER2kBA5FX5YxtHHanxQBBgipJFAXpTEYF0cB s5rLzukrRvifMcVStoDJLzmoK6FhY8rVGZcswPrXQpZL5Z7VjXcISRsHPNDQIzJrdZI2UgciuPuI jFO8Z/hNdsTjrXP67bfMsy/TAoRRiGlHWgjFHQ1Qh617n+z508Rf9u3/ALVrwxete5/s+dPEX/bt /wC1aBM9sooopCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAK8V+OH/ACENJ+v9RXtVeK/HD/kIaT9f6is6vws6cH/Hh6r8zwxqjNSkE7iOwzUX vWpgAFaFiv7xOO9Z6Dca3NOgUlCQaAOitEwenatFAMrxVSBcdKvRgfKaQi7COKvx9Kownir0XSqJ ZZi4IrQifJrPh5IrQiQdallI0LY5XinsDnimWwwpqcikUVJGOeagkBerEq/NTUjBbvSAzJ7chCQK qRsY5c10VxbL5Z4Nc9cLsmIFUmS0dJplyPJ5rpbVg8a4rgbS6ZEAFdhpNyZI48kdKGhpmyYc84qC SDg8VoRKHXmlkgGKLFXOR1C3Gxvl5rlLuHbL0r0C9tlZWzXM3dhGZeQaS0E1cyLSHJHy9617W1y4 wop1rYoGAwetbVpZIH4BpiSLdlb4QcdqviDjOKsWlsoQZFW/LULjFA7maYwvaqk0oViK0L0iNQRX M312VnYZHSkxopX0+6Tg0yAeYPWqMk29zk1paaoZTmhCGPC248VJBGR2q68IqNF2k0APRPlIxVK8 tsxHArTiXINMnQGM0Achc2xAOFrOmg55WujnjBJFUZbZSaaZLRzUtr8zELVOSLaeRXRS24DGsu7i APSmIx5GCVWdgykVNdDr9arAZNSykYd9bsbjIWsW6TbNgiuuniUyVzuqxBJyR6UIZl46UucikyO5 pSeBiqEITinDmgDPWndBQBLMSIrf08s/+hNUHU1PN/qrf/rmf/QmqEcVMNvv/M3xHxr0j/6SgxgZ NITiguTx2pp9KowF6GlH3qZnmpI13ZPpQBesArSDIBORXRTu4C/MQMetZmitHHDPvTJ7Gjyprx8o xAHFJiLjMxH3jVK4wBV1NEucg+Z+tTXGiyiI884pAYQJLcVaiJFKdInA4an2ulzs4yeM0wJ4SC3z HP1ro7FVzHxUGnaHK0pB6fSuutNLWNUJUcU0IfaIhfkCp7lEELEAZq0TFDyFFU7qZXyAKNwObuOr fWsq4JGMetdHexqLdjiuO1G42EdetJjR0OmSN56cmvQPDjE3LZOeK8x0OYvfRKehr1bw1GPtT/Sq J6nawKpC5APFVbyKPzGworTh2kDA7U6SFXXoKll3OauYImiIKA/hXN3dpb+Y/wC6X8q76ayUxnis yWwjYtlBSHueW6jbwLJwi/lXOyQQ/avuL19K9cutFikbOwVk3PhhGfciAU7ktHIW0cPnphRnPpXV Io2DjtUieHSpBGMirg0uUADNNsEjm540aVsqOtWLaCIxn5B+Vba6OdxJAqT7AIlIxSuFjmWCrIcD Fbmjfc/Gs272xMfl5zT7K8CMAOOadxbM7SPoKr3Kg54qK1n3KPpT5pBzSZZy+oW0LsdyA8ntWZLb QIhZY1BHfFbF/IqtyO9Zs0ivGVA5NCJZzl1cTfMokYD0zWXPJIcZcn8a2JtNnkc471GNFmP3hRYR hMc9aoXxGBXXHSCq8gZrJ1CwUDoKLFJnGN14NKC2Opq3cQBegqoYZCcigDQnkk/shV3HGfWuckPz mukntZBoyvnjNc5IuHP1o6h0EHPFJipUjJ5qNxhqYBtPak6g04dOtMxg0AJTu1Jg5p3TrQA3tSd6 UnjFA6UAFJjJoPNOHJ4oA2dIskmjMjdfStmG1EI3AYrK0e5jiXY2etb9zMnkjb3FDEZc0o3kZFVH fcKe9tLKSw6H3p6adM3p+dIZQYAN1qaJgByakk06YMajjtJi2OPzoEXoeRWhawbuear2dhIyj/Gu jsNKkKA8U7CJrC3y6jHatYwhR0qW108xEMT0FSzAUxGRcIQ/SqUvy5zW40O7msXVQYDxSYIrrKA4 +bvXb6FIWtevevLlvwJsYPWvRfCsnn2RYetC2H1PRdOQPEOKvtaKVJ21Ho9vm3VjWt5YK4pDucje 6bG0hOwVz+paQhRjsr0SawEjZzWXfaVlSM0D0Z5RLpa8jZWFe6diTG2vVn0Pqcism88NtI2QRQKx 5a1kqsPlrUtYAu0Yrp5/C0o7ili8OTqRyKaEyMxgQqMVUmtVkI4roG0efaBx+dNGizk9qZNmY8Fo qp92rsUezpWtHpDxpyRUEtqY3IzSuOxf0587c1vhVKCuZt5fKZa6C3l3oKRSM/U4VbtXL3lqpLDb xXaXkRdawbq1PNJA0cjJpkZy2BVGVfs+SBXUS2xAJ9KxL60d1O3rVCMCadnc1WIaTPFbcGg3U5yA PzrTi8LXIjy2AKVh3OIuIiAciseQFXPWu/vNAkQnkVzF7pzI7jIzQBnWdwUlXtWhqV+32MLurLW3 k87A61Lf2k62gcjj60MDDmYO5PeoT9KdICGpAe1MYA9KOc0oxRQAHpSGjJoNABSUvajtTAQ5oGaM +lKKAE+lORGdsAUg6Vs6LCCzOQM9s0CLGkaU4fzZV+gNdJFDj+GmWy4UdK0IUzzUgUpVxnAqFAWY cVpPBubpxQtqQfu0WC4kC/MOOa39PXjnrWXFD8w4rasYyoxiqRLHXEe7+HNc/eW0gkYheK7NYAy9 KoXNrnI20h7HE7Dv6c1vaPb7pRuWpP7OJbPl85rZ0yzMb5K4o2Dc1YY1SMYGOKVsg1IPSkYcVJRg aqGYkD0rkpoJDIflrvbiAO3TNUW05c52U0JnJQ27FgGXvWxFZqq/crRNiFcfu6ui3CRnK9qok5mZ fLbB4rLnb5jg1raqreeNvTFYkuQTmpZSAYY81PCmW4FU4vv1pWgBcZoQMdLGNo4qEha6CO0WSMfL msm9tWjlICHGabQrlHaM1r6UrmYcHFZCq3mAYNdTpEQ3Jx2oQM6W2OIADUUsSuTlc1L0UAdKQ1I0 UZLRTjCCo/ICD7orQLCoJGAouOxmyr8/AptwPlHHarEiFjkCo7hSQOKoixzs8O6Zjt6miFUhOWGK 0pYxycc1hX5kzxnrRsNFq5vQUwjVlO5djk5piJKx6GrCRkH5lqdyii6H0qtcwhozuXNbLKvpVS6R GGKQ0zhbuMRzsB0zVfGTWnqEB89gB3rOCndiqQhV617n+z508Rf9u3/tWvD12qfU17l+z6cjxEf+ vb/2rTEz2uiiikIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigArxX43/APIQ0n6/1Fe1V4p8cP8AkI6T9f6is6vws6cH/Hh6r8zwx+aYRmnt1pFX ccCtTAltUDSYPpXV2MSiJMDmud0+3Lz4weldZaw7Y1yDSYFuNDmtCFCSoqnHgHrV6EjctCEXFjIP FW41OKijCtg1cjVAKokfAhJFacK1Vtog2CK1be3yeQallosW0WVqz5HFLAgQdKlOKRRUNuM9qkgt xu6CnkjNSwYyc0AJcW48k1x2ow7JmNdzOMxVyGrRYZjR1E9jGEhU10eiXhEiLzXJzTCN8ZrX0m72 Sodwx9KsjY9Osp9yZxWixDpXM6ffqY/vCtZL8bRgj8qkqw25jBBrFniXf0FbEkocHmsy4H7ykURQ xASLwK2LWIbxxWVEcSCtSCXYwJNAjYUbQAKjnk2Rk1ELsEdap3t9iM8imJIoateFI1PNcTf6iTdM OelbOtaj+6XDD8q426uXe5Y8flSWrGy/av5rHmuq0iL9zXLabGWycV2GlJtgHFNiRbZRyKrbcGrb hec1V3DNIomiFOljyhpISDU7AFaBGBdw7RmqDDHWti9jylY8+UNAFSdQFY4rInAbORV6a4bLLkVn SyKDyapENmRcwgseB1qi8Q5xWlO4yeapMASaTGUGgZmzkViazbFctkcCupWNSOTWPrdupgcjPC0i kcWxweaUcmnSLhsUKuOaoQ4YA5oOD0ppakFAE85/dW4/6Zn/ANCaoR6VLP8A6u3/AOuZ/wDQmqDO Dmpht9/5m+I+Nekf/SUOPHamk9qXNJmqMBF5PrWxpKQG5j85VIJ5zWQmC3FWYpNsoPpQI6a9e1iw IdqgjnbUOnMDIMetY0s28jrxV3Tbny25GeaVgZ20QHy8elWblU8vlRWOmqKoX5P1qabVVlUDZ+tA g8tCfuitDT7WMuD5QPPpWQt2CwGK3dOuVVhx3FCBnTWVpGHOIh+VWJwEjbAAxSWN2ruRt7UXhzE9 DGjHlm45amI28561WuW2oKlsWyBn1qiBL8f6K1cNqiZK4HevQL2Evbtg8Vx97aM5HPQ1L3KQaGCL +L6V6z4acLcPuPavLtLhMV4jE9K9A0W8EczcdapbC6npdtKhwAw6Vc7Vy1tdgMpz+tdBa3KzIMdc UimTsMiqMygE1odqp3I5JpAjNkUZ5qNkXb0ps0wVsU5GDigohVPm6VYWJSPu08Q45p4wKQEDIo7C qdwFB6VZnlC1SlkDGgDltVT5iQO9UYOJF+tbmoQ7+lY8i+VKKaJkdBZS/wC12qeV8g81kWd0CcYq 604KGhjRkai2SOe9VIBumUHnJp19MGkA96S0YfaU+tNbEvc6W30+BgpMK/lRd2MKKNsQH4VeglVY 1JFV7+9QADFJlJHH6oUidwMLiuP1Gbdna+a6LXphJPJjiuRl5Y/WkFjIkSRz0JNXre3TyRvQZqRQ EPIzSG6VTjFAFnUoh/YSrGvzZ6D61xMsbiUjac56V20d2kkezFZ76aZbreuME+lNbh0I9L0tprZW a1LdecVg6lbvb30iNGUGeARXt/h/SgdLjUquee1ea/EO1+y64i4wCp6D3oe4LY4803NPPTimk4pg JnilY8UnIPPSlOOx5oAaBninbjtAxSGm84oAXNSRrk+1Q81OhITFMDcsLNFUSGQc1fmuU8vaGBwM Vz6XDrGADxSCZs9etJiN+Bi21R3rYgs2Nc9ZSEMhrqba4YAcCkIqS2TbzzUEWmncee9aEs7Fz0qW 3JJGfWhAyxYaQ7pxk11Nno8iRDrVjQoEaEZFdQkKrGMCqBK5y00RhQ57VnuxfoK3dWiAV/rWTaxh 2pIGJHC7J0Nc54ijZCK76G3XyxXNeJ7RDg0mxpWPMAT9o6HrXq3gdC+msT/erz1rdfNPHevRPBre TYMo6bqpbCe56zpS7bRav1Q0ly1oufSr9SwCq1zEXUkVZpCOKARhvA3pVSS2Ymt6ZADVN0GelBRh zWTMM1XFuwIGK6B0BHSoRApagDPSyaT1qUaew9a37WBAM4zVh4UI6UBc5V7VlyKxry2IlJrr54hu NY17ENxpDOcVSH5rds2GwVjzHaxx61bspWxTQjVmPFZF3yTWixLJWbdDrSAzZELAj1qtHpck77R3 q+oG4CtWzRQw4poTRDp/hxo0DF+1S6jAtjbEs44HetOa7aCLgVwXi/V53jZM4GKGxpWKOpapHlgC DXGX13ukYhc0yW5kZjk01fm5NIRFZW73E6nGM1oa5pzw6YMcmprQiMqQo4q9dTGeLa4GKb2F1PMZ kKyYYEVB0rp9WskMwI4plpo8MsEjv/CpIoRRzQ607NOkQLKwHQGmimAZ9aTBzTjTfemAHrR0GKAK O9ACgEngc0g4o79aKAHICWwO9dLYWzQ26kjkjmsG0hM1wip1zXZwwMVC46UmIS1LbgDnFbMOQRVa 2tTnOK0YrdsgAUIRZhj3Y4q6LYnotFtbsAOOa14YdoBamIy0tiG5WtK0iC9RTnAPSpoFwOaVxpE4 B7UNHkcipVAAp5Ubc0iiosKjooFThQBxS0daAI1yG5qXrTDgmnKBikAFR6UBBjpzUiIGqRUANAFc xjPSopkGw8VdePJqG4iKxkmgDk9RVRJ07Vy18wUsa6fVXCT4PpXK6gchqbEihBdZmxurcscsfXmu ThJFzn3rpdLlJP40ITO40kKUwwFWrqzjk5CCsWyuPLA5rctblJVAJ5psEYx0sCbiIdfStizs1iwd o6Ve8kNyKQIwNK47DXbBApMknFKYyTS7cUhkTIT0pPLNS5paAKrxMai2ZPSr/WojGR2oAzp7cbCQ OayZbdWPKitybILZrJnUkHHWmiWUmhSMZAAqjcSA5Aq3MkpTgHNZzxyZORQxlaR6ruCzZxV3yGJx ig2z+lKw7mPcWAky+zJrlL2F4JyjDHtXo3kEJyK4vxDAyXQfHFMDGXrXun7PnTxF/wBu3/tWvDV6 17j+z308Rf8Abt/7VpiZ7bRRRSEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAV4p8cP+QlpP1/qK9rrxP44f8hLSfr/AFFZ1fhZ04P+PD1X5nhr VNaRl5MVC1XtMXM/4VqYGppluRc9e1dFHHhcVmacmJz9K3ootwHSkIgWNuwqwhKkcdKuw2vPUVI1 icE7hTsIbDdLwMVfjlBFZPlGOTGavwj5aYmjasJASBjvXQwVzOn8FT710to27tUsqJaA4prOBTz0 qrM2GNIsTzlDnNTQzrmsqWT5zT7eb5ulCEbrSb021z2sQN5TN2rchO5gKi1O23255FDA8yvUIm5q zYXAR1XHSptUtCLjqKo267bjFUiGdtpt8ojPFbCX8aoCa5C0m2IeKlk1DC4APFJoaZ1y6pGTgUkl yrtmuQh1A+YDtNasd35i5waTQ0zWWdFlHNWWv4lAyawWm5zVS8vtijg0DOrTU4scGqd/qMZiaubt tS+Y/KaLq58yMnFFguQ6ndJIq4rDkOZc1LeTYA4qujb8H3qkSdPo0JZTXVWS+XEM1iaFDmM/St8L 5cYpMaGyyDJqn5gLGnTTYJ4qkku5jxSGaluwq2RxWfanNaGMigCjdL8hrDvVw9b90MJWFfDL0Ac9 cEB3rJuJFLda1bpPncZrHngKnrVEFSVgelQ1MYTnrR5WOaQEQBFZ+pwtLbyY9K1GYDjFVrj5kI9q TGjgLqJo5iDVdiRxW3qcGbk9KxphtfFNDYylB4pOKARTAnmx5Vv/ANcz/wChNUI5qab/AFVv/wBc z/6E1QZ4qYbff+ZviPjXpH/0lBnmkPNOFIfYVRgPiXMgAqwsTCXkVFanbOtaTDndjigCAw5GaIQ6 HgVMG96mt0DNmkIcslwcYzVqJLreoIJqdIwCDWjADvBoAiigkMi/Ka6G0t3XBKmqKvtINW/7T2pt DrTRLNuC5W3bcWxT7jUEeIjePzrlLm+kIHIqBr5tnUUXBI1Lu5LKMGnWFw3mqM8ZrCa9yOWFaOmS h2U570Jg0dU5Eke3NUJNLEhyFP5VPDIrSBdwz6V0thaeavQ9KbBHHLp4hfdggj2q5Bc/Z23E4zW7 qGm+XEzBTnNcnqpaFFJGKVwsdLBrfzAbx+ddXourBghLjp614pDqhEuNy10una95EKt5qjigdz2Z dSjJ+8Pzpk1wrqzA15la+J2mlCpKjH0xWwPFFskGyS4RZMYIoY0y9qV8sUoG4CorPWIvMCtIOvrX Ea3rUstwDEysvrisRNalR2bcuR7UkDZ7ct/C2AJB+dLJcKBndXjNn4su3uol3pywHSuz/tiZoxuI xj0osFzoLi5DMQDmow+4cmuWk1d0YnctWLbVmdeWWiwXNe4Qt0FY95AxfODWxFcCRByKSZFYE0D3 MKEmJvmGKma6AUjcKj1A7AMVhXN6Y2IyKNydizcyjeCT3p1nKGukAI61z0+oEsNzDGan02/H2+Ib h1p3Cx6hEMwj6VUvIC2OKvWeJIUI5yKt/Zt/UUmUjzzVtOdndghP4VyN3ZtE3KEfhXtNxpcbqc5r jfEelRRLuGe9ILHnUoArInkYSkVsXJVCcmsKeRPNPNCAsRXLJjmtmylEjR5IyTXMPKE4yKuWV40b x8jANMTPX7HUYbCzQySBQB3NcR8RL7T7+1jkgZHmDD5gecVn61rPmWARXUnHSuVjSW/3qGA2IXPH pRuxrRFE0m72pTSHpTAQnPakxTt2KZznmgANJTgDSd6AFVC7BR1qWVDGdvcUWwzOn1qxeRkSmgCG MMwxU6wnvmiBcDNWwQBQBHHLJEwK9q0Y9RvBgKtZ2cGr1rcLuGR0oEXI5b6QglOprrtO02WWJWYY NYVrMuxTjvXZaZqESxqMdKaJZv6XG1rCFNbq30YjAOM1zo1GPb0qF9Tj54P50ncpMsazfR7H2+tY tnfHcSoBqvfXqSBgO5qLT2BJoQmzt9OuPNgBZRUOp6dHeryKXShi3+taAQsDikxo4WXwsoZmyfyq axkOmoY1PfvXWz27mM1x2oqY7og00xNHc6P4oCRhGx+ddNDrMUgXpzXjdvfxxuAc11Vlq8OYhz+d DA9MjcSKGBp9ZenXkbQKavidD3pAEyjaSe1ZFxdrG+DWlczqqHNcTq+rwx3BU/zoKRvR30cjYzWh CiPg1wljq0Ly4Hr611VrqUW1RQFzoEUIOKGcKOaqrdq0dRPcKF5NArBMQzHArHvVy5q+1yhqhdMH ORSKOavFKlttQWty6E5q7eISSazGYRdaEJmyl8CnzVXnuEfODWU14ijFQyXqYzTsK5pIy7h83eti 2ToQa5SK8jaRfrXVWUquq49KdgTItREzDCMRXC+INMu5yxDE16TJE0nSs+60qaYHaB+VIZ48dFuB y2aX+znQcsa9BvNGuI92QMfSuXvIzC7K3UUCMCRpID97gU06i6rzzUl8Qd1ZEsoVcUgLztHdMCwr a0/TInt3UcbxjNcrDcruArrrG/jitckdqaEzmtf8KRabatcxXDysTkrgVyfINd7qutW7Wsi4JPTr XAu2XJ9TQNAaKQk+lApjDNHWlpM0wDHFKKSnopJAA5oA2NAthLceYWPy12sduQetZOiWSxWysE+Y 9TXSRISeRQQPtoMj6VeT5McZqW0iG37tXFgQtytAtyBJ2XsKmW5dzg1dS2hIHyini3iB+6KVx2ZF FEODmraJgU5VjxximyEBeDSKRJuA4zSiUAYrFnnkEmAxqe3d2xliaLBc1MZFKOBREOBmrCIpBoGV PLyamSLC9aQjDGnA4WkBIiDpmpRF71nNOFfBfFWoryEgAygmmBKw2sKhu2BhIHWrG+KTGCDVe7jb YcCgDhNa3/aRx2rnrzIjY45rvLi0EkhLpk1iajpwMbbYSfwosK557vYTZ2jrW/ozlmIIHWnrpZMn /HuxOfSt3StKK5P2dh+FNCZJFEzAYre0yxYqDmls7IZw0ZArobOFI0AAxTuJII7YrGMmh0CjrUs0 gUEA1T8zccE1JZGz4filB39acyjdTkUZoArkENT1XIzRIOaWMjHWkAbMU0ntUwweM1E4AoAiktg4 PPWqEtiuc7jWluHrUFwRsJzQFjIuIEjX71YtzIkbMfetW9djGea5i7Z2duvWncROLwA5Cika9yeg rNO8etQuz7xzSuOxtrLvXpXO69aLPEzdxyKvxzME5eqd1JvBG7PFFwRxgGDjFe5fs+dPEX/bt/7V rxW5iMVw2ehOa9r/AGff+Zi/7dv/AGrVdBM9rooopCCiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8T+OH/ACEtJ+v9RXtleJ/HD/kJ6T9f6is6 vws6cH/Hh6r8zw1uta2jRhrjmsojNb+iQneDWpzm3bRKsmQO1bFufuis+3TbJV6JfnHNJAzat0BN aH2QGPODVCzcIwzzXTWoSRYwcc02JI5yWxG/dsb8qcluQPut+VdqmnJJ6U86Qo9KVyuU5qxgIAyD W/aLtqVbERdhQ+I6Vx2HuwA61m3Eh8w8064uMNis+WTc5NAyKeRg/FSWsjFqaIjJU8EJV6CTatGJ kFXrqIPAao2anzRWu8ZMOKGNHnusQBbngGsUIFnz712WrWv+kciuWnjxMw96cSZIckpUHFUry8aM cMM5qVjtBrnNUnIY9etNsSRqQajIZgCwxXS2VwWiJzXmtrcM1yBzXeaPG0loTmjoHU1nmxGxzWHq l86KPmFalxEy2znJ6Vx+uuyovWpKNGx1B3dgWFaJnZouvFcZpMxMz5zXT27b7fGaokJhvwCafFAC B9aYIye9alnbFo1PvTA6/Q7YLCDg9K07hdqU7SYMQDjtUl8uBUstHN3UjCVhniqkcpBNWbz/AFzV nAkNUgbVlMT3rWRs965q3n2dq0YL0FxTA0LhAy1lXVsGOa0fPD8U0oHosFzlZ7JS78GsybTwTyp/ Ku3NmGbpTH09T2FO4rHAtp6g/dNV5LRQDweK7a7s1QdBWLcWowx4ouKxyEy7XxVWboa2rqEeZ2rP nh5PpQ0By2oR7ps+1c/eJtmxXYXdvmQ9K5XVIylyRSRRR20YpR70YyetUImlH7q3/wCuZ/8AQmqE jmp5hiO3/wCuZ/8AQmqDmpht9/5m+I+Nekf/AElCgDFNp3G3rRjJ45qjAs2S5ckitNot8DY7dKz4 0a3xnvVhrtioUcY9KQIb90nNW7OROcms95GbJJqS1yaBM6BXXjmtC3ZS3FYsYORWtaKd/NIC4x+U 1UYgyDmrT/cNZzNiUUMCzIMjiqzlQpyamMnHFVJRlTSGiPK4rX0eRFZQT3NYlXtPbay/WqRLOwtX Q3Iwa7vRBuBx/drzayb/AEhTXpPhg72Yf7NUC3J9ThdrZsCvPvElvIIVO2vXriz8yEjHWuP8S6Tu tqgs8WAKuSwwMmr322CG0Xe+KfqFl5ccnP8AEf51yuoSnBj7A0IVjstE1K0e+RVk5NXrqaJr1iG7 151pM5t76OQDpWm+tv8AbOnG4VRNjsbjBIxWDIQskua0oLsTxZ9qwtQnKPJSY0S6fKn22Dn+IV6H JcRxwKXbHFeRWV0Uu4m9GFdTqOtsYEGOlO9kK2p0k1xFJ91s81YtpE29e9cbZ6l5h5Hatm1uiygj 1p3FY7+xkQoOe1Xm+aM4rD0cmXA9q6OC3ylS0UjnNSjYgYFcxewSGUkLxXo9zpnnDmse70YKTz2o Bo8zu1KckY5pNOkRb+Ik8Zq34oi+yREg55Ncva3zC5T60LcHsfQekTRtDFhs8Ct1CK878OannyFP Tiu1jvAe9NgmXJ3AU5rifE8yMmAeea1NX1nyEcDsK881PWmuZQMcZqSmzmNQR+gFc7Mjeaciu/ew 8/BzisS60f8AfN81PYVzk2Rs9DU8OAVz2q3d2/kSFc9Kz3bDGi4Fq8YPGNvNGlApJcFhgeQwzUUM mcA1dQfupv8AcNMRh8etBU+1JntR2oGNINH1pefWhvbFACEjHFJj0o496XHHUUAS2y/vQSa3RZrN GHY9qw4Mg5FbMF5iMKSKGIqPiOQqO1MaXtRK26UmoSGY4Apxi5bBKSjuWUww5q3ahTJirVtpiG0D vkNjNVLcFJSPetqmHnBJs5qeLp1JOKexvwxfuwR0q5BK4OAeBVe1BMC8VcSLHI71znQatrK7pkt0 qKeVgTzU1lFiPmq90ACabAz5JzzzV3SZ9zmsiZiGOemav6UcPmhA7HoukyA2wrYgIY1zelyYt+tb ljJlgCaOo0azW4aI8dq4bW7QJemvSEiBhH0rmtY0sTXG7aelLqU9jye+kaKY49aji1mSOReTwa1N d00xTthWxXKzxmMM3PFFybHpum+M1trVVkyTXR6b4rjvlG3IJr51udXuPNISQgAY4Nbnh/xTcWjo HlGB/eNPcNj3y61CQqRmvOPEF1IL1uantPFovB/rYySccGsfWbjzZi+4HNK1gvcZpt/Is/3u9dnY 6g5KZNea2twyzHpjNdLZ6mUKfMOKaJZ6pJfrBApPcVUfVFkHBNcLr/i0W8MQEqDj1qhpfio3cmPN Rh7GkXex6J9u96ZNe5XrWHFfb1ySKRrxmPUYoFc0ml8wVmXy4GRU8EpYip5rTzU6GkPc5K7kdelZ 8s0mDlq6a80rjO1qwb+08lCcGquTYpQ3bJKvJ4Ndxod6ZZUXPWvNGmKXAGcDNdX4dvz9sQbhihA1 Y9VgjDAVaMarGTiqNpOGjBBFOvb3ybdjkdKTLRmavKiRv9K8m1q6P2qTFdlrGsM5YBlrhb1TNIzY yTSEczeXcjFhnFZjSvkjOa2r2wIDMAc1jSRlSeKaQN2GrIetXxqEgg2A9qzCSO1KJDVOLW5KaewS O0mQT1NVZ4xHMVBzjFW8BiKq3f8Ax9N9BUlEfGKTNAxilxmmAY4pMUuMHiimAVYtf9cKr+1WrJd8 6igTPRtJ/wCPKP1rcgTc1YumjbaRitm1LM9BBqwKqL1p7TqvaoQh25qu7kGgZpx3I28ipPPVuKxz dFV9qgGogN0NKw0zoQcd6U/MOKxE1Qs2AK0LW6LLyKVh3EktC75zVu1syMc0qMG5xVuI4AouFix5 exKZ5oXpTy3yVWk60DHPIM1FJMoXrSSLxVGcNg80gMbVdT8mY1hp4lCy4wfzo19ysjH2rhJL1xM3 1pisetab4miJQMD1rtbTUbe8TAPOK8EsdQkUKfSut0jxJJBKAckUCPTpbISNlSKryWm0EFQfwqlp niRJ4/mXvW/GUuowQOtAzItraIT5Kr+Vaq+VGudq/lTGsfKJYGq8s3ljBoGPeRGbhQPwoDqoyKy7 i/WKsi58SLESoWkBt3V0qs2TWemooJOtcRqni1xLIAtY8HieVpx9aaEexLOsiAqakjfLc1zfh+9a 7hDGulij75oBDjFvzg1G0BSrCyKvFKzqeopDKyqV6USdKkYjPFQSPzxQBCetV7h1VDU7d6zbxyFI oQMzL65VV5rKe6iYkY5+lSX7MwArKwRI1MReYJKMDiqk9sc/Kc0ed5dSRXCycEUgMyaJgazX3Bjm ullt1f5hxWPcW4Dn0oGc3fkmcfSvZv2fOniL/t2/9q143qIAuce1eyfs+dPEX/bt/wC1afQGe2UU UUEhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFeJfHA/wDE10ke/wDUV7bXiPxw/wCQtpP1/qKip8LOjCfx4eq/M8SAy1dZpMRESHFczaxh5sEZ FdnYxhYFwMVozAtRcNk1ciYFhVRRlqtwx8ikgNO3kXdit2znUSRZY8VhQxBTnFXodwkTBpknf2Uy MgINXty1ydpdtEANxrQGp+rmpNLmhcuBmsi6lBxg0y61EEH5jWZLeq38RosFx8zZbrTAM9KgWcO5 5qzFg0CuaFnESucVbWE5HyipdMh3xcitD7OB2oGQWy4kHFa4/wBVWfHFtcHFXt2I6YGDqijzelcV e8XL8d67TU2zJwa4u8z9pf60kKRUblTXKaujM5wP4q64rkVkX1qjsflByapolHO2MbfaFGO9el+H 4W+xHK9xXK2WnJ5ynYM16FodsEtCCuORR0H1IbyMrZyZUdK4XXIy8Y2rmvS9QgBsZMDtXBatCVUZ FSNnK2MEiSuSuBXRWEbbATmq9rbhmPFbtnajyfu1RLGIvPSt2wiYwr8ves1oQvQVtaewWFQfWkxo 7fTFxb9O1Q3wycVPp7gQ9e1R3Q3GhlI5u5iJkbiqBt3/ALtb8sBMhOKZ9l9qQzCMTL1FRltpznFb NzAFU8dqwrjO049aBF21nXfyxrUhlVhwa5iFm3da1LSbapy1MR0CAFQaSUcVXiuF2r81SSTKR1oG jGv2Ck5PesS4mTa3NaeqPndg965md3O4ZoSE2UbmRfN61TlYHNWHiJbJGTVedNuaZJmXOC54rkdb GLrpXYyqC1cvr0OJQ1R1LRgEdxQRgc09gAcUhHFWIfMf3Vt/1zP/AKE1QdasTgeTb/8AXM/+hNUI qYbff+ZviPjXpH/0lCHpVvTYklvoo5D8pPPNVD3pYzhgelUYHY6lBGyIIQhAHbFc/PbyISSuBVjT GZ2bkmtmWEm2+7S2EcyqN6Grlmp3DgdatSx7R9zFVlOGFFwN6MLlelakZRT2rm45PmHzVcklwv3q ANiZ0MZwwrEnJEobPAqLzhg5eoWlUofmpAX4rhGP3qWaeIRn5hWPEcEnNV5n4b5qLAW57lcDDVNp s4M6DcTk1gk5rT0f/j7iPvVJAz0CxifzEODivTPCIxI2fQVxFmB5MZx2rsvDDATNz2pko73aCvQV ja/AHtSAoPHpW0h+QfSqepLm3PFSykeF67bMttKdn8R7e9ec6lERK/HevZvEkQFlN8v8X9a8h1vi Vx71KGZMJCPk8VXkf9+TnvRIfl61B361YG/pt4FjO6Rvzq5exyPa+Yq5UjrWJYAENXXXgA8Prgdj SaEtzioctMgU8k8c1rXVjdiJSRkezZrBBIIIrc0KR5LsqzFhjpTAhsXeKY7yRx611Oky74fvZ5rn r2MrM3GOa09JJEX40IGev+Gbd32kLniu4s7N9mSlc54LAMKZ/u13kYAQUMSehlzxLGvzACue1O5t 4ywLKDitXxDcbEHz4ryjxBdsbxv3p6HvUl30Oa8b3Mci4R8/OelcTbvtnUk8ZrW1h9+cnPJrGA5p oTO/0fUY0ki/e459a6qLVkGf35/OvJbByLhOT1roElJ/iNNsmxt6xqQklkxMSPrXPQy75fvZ57mq 1458xuaqxSYb72KSKO+tbmED5mHSnu8DsSNprkUlyPv1fhl/dD5qLisZ+uxgu5RR17VzDxvuPymu ulKu5BINVpYU2nCCkmUcsVcHoa09JR2NzkEgQMeatW8SNcYZQa24o4o7S7KoAfIb+VO5JwNKR8po pM0xjcccU08U4EDGKRxlqAEyQ2KUYLAHj3oAxSkUAW9Pi82Yj/ZNTNbzIfu1WsZjDMG/CuohjM8I IHahiOaOQcEc10ulwxtZoxRScdcVk3VqUlPFaWl3YCrBtORXbgXHn1PNzNT9leJrbRjHaoZLeNl4 UZ9qnFFe3KEZKzPmIVJQldMbC+yML6VYhuATgmoMUm0ZziuCeXxeqZ6tPN5rSSOgtblRHgsKr3Us RY5asjJHejrSjl8b6sqebyt7qElAZmA6Gkt5fs0nLEL606mMgYYIroqYWEocqRyUsfUjU5pM7LRr sSW2Q+RXXaV85U15voTCGOQMeMivQdFmXYvNeHUp8krM+noVVUgpI763hUwpn0plzZq5yFzUdpL8 qc1p9RmsmbXseY+JtNVSx2V5ZqUASKY45ANe7eI7cuj8dq8Z1iArFccetIZ5lIT5rfWkDEGnzLtk bp1qLmqQFiC7mgkDxyEEHPWujtNSa7g+d8uK5Tmtvw/F5kkme2KLCZqhJEy3NVZ9WePKI/zCuiuo RHbE4HSvP7s4unI9anqNE13dXM7q8zE46c0lneSwXQkRsZPOKrbpJTtJJPagoyH5gRVID1vRbszW ilnycCt62haVcgZrzrw7dlLNQWPWvWvDcfnWKtjOaZCRZsbBiVyhroY7NQgytT2sG0DgVJdSLGnp Us0SMLVVgiQ5IBrz/X7iLy3CsK1fFerKs2zea89vr0TFlUkk0Ri5OyInJRV2Z8pka5DKMrWzpNy0 NypY4qnbRFI/m61Nt5z3r1o5enG/U8KebNVLW0PU9L1Vfs4zIOlV9c1YG3YLJ2rhrHUfIj2OTSX2 oCZTtJ6VxTwtRO1j0qeOpSjdsklnMpJLUsaoV+YisqOV6kaVmIwcVrSwM5PU562Z0ofDqWL6KIxt twaxUtI2mLMowPWr5JPU1RvWYEBWIzXUsLCgueWpxSx88S/Zw0uFxFapG2VUHHHFcm4O84HetyW2 ll+8d2PWqr2rKeVrhxFeNR+6j1MJh5UV7zuZ6Doc856U2/TbdtkY4Fa0NkTKvyjrV3xrawW8dgY4 1V2Q7iO9cx2nJAYo6UlKaBhSd+lLigUwAVraNatPPuA4FZQrovDspjLYx1oEzsLa3lSJRg4Fatpk Pk1DBOzwL06VPEdp5oILzThU61lXN6oJ2tSajNiP5Wrn3kZieaTZSRflvpCCN3FVWu2/vVTkkIGM 0yJWeQA8ipGatvdyl1xXTaXI8ifNWDawIpBxWklw0Awpq0Szp1ZVUcig3QTjIrm/7Qlb+L9aQ3Tn q1Kw7nUC+HcipEuI5DyRXK/aDszv5psV3KG4NFgudXO3y/KagVGlU5FVbO4ZyA7VrRY25FKw9zgv E1syu3HavMpgRO2R3r2/XLVZlYsM8V5RrFmsUrkKevpSGU4ZMRACrNvcvG4O6sxGZWx2qV2IHBpi OrstZeJeH712mieLSmxHcYryaJjt61PBO6SjDUAe/nXIZoch15Fc5rWtrCMq4riba+mWIEP29ayd av5mABb9aTGjW1LxK5JCvXN3etzyOcHvWVJIznmkRN1OwBNcSSuSxPNXdLh8y5UEVW2KO/Na2kpm 4TFMTPUfDkKxWq4roRIwFY2gQn7KCc1pzy+UpHFJhEkEuGyTTZLtF7isa4vGz94Csm6vH3cP+tFg udWt2jH7wpJJe61ysFy55zmtGO9YDkj86LBc1TI2ORWfeMu00n24kYyKryHzAcGiwNmXdICOtZrA BiO9aF7uRay0JaU5oYDZU+WolBVuKuuvFQng0hiiUgc1VndMHPWpnIx2rNuslW+lAI57UmBuiRXs n7Pn/Mxf9u3/ALVrxW6B83Ne0/s+f8zF/wBu3/tWq6Az22iiikSFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4j8cP+QrpP1/qK9urxH44f8hX Sfr/AFFRU+FnThP48PVfmePaeo+0e9dhaj90orkdOwbn8K7G0A2DNaM5yzEvzVaVtpFRRBd1TEDF JAXYJixwa04eSlYEcjKeDWrBKfk+aqEbO7atU5rt1fApzS5X71UpDlqlIGx8l47cGoDMTUMzEPxn 8qj8w+/5VQF6KU5rUtXJQVhRuc1q2bHaOaTBHb6OMwZrT2cVn6EM23NbIjyDxSsWipjBod8LUjrg VTuX2oecUhmPqb/vK5G8P79vrXQ38xM33qwJ1DSscZyaETIq5qnOuW/GtIxD+6ap3C47d6sglsUH mrXdaYmLeuIsR++Tg12+ntiCkxolvh/okg9q4jWYtyDmu2u2zbuM9q5PVIwUGRU9S3sYVmu1zW7a Y8qsuKNVY4FaVoeAKpkLcslQ1XbYYVfrVYKPSp4mxipLOysJP3Y+lTv81Z1hITGOa0F+YUAiEpk0 jpirIT2psyAY4osMxdR+VeK5uZ/lP1rpNV4SuTuHIQ/WkJiK205qZbllrMaZh/FTfPf+9V2IudFF dthasm6YiuXS7lBGH4q9DdO33nFKw0y3dfvAaxZYvmPNarShl5YVmzMMtyKEDM+VcSVn3h5P0q5O +JOtUbg7gabEUH5Nc/ribtv1roH4NY2sKrIpPrUlI5SYYkIqPGRmpbsATnFQk8cVSAmmGYrf/rmf /QmqCp5h+6t/+uZ/9CaoSCKmG33/AJm+I+Nekf8A0lCZ/OnKuaaBnrXQ+FNLOo3zbcExgHB71Rzt lPS3kjkOF4J7iuuhkzbDft/KrOoaY9pjdGq8dq567ukjypJyKTYWLd+U2HAHQ9K53dl6tm8jdSAT VYEFhigCdCciprokJkE0qRsSvFSXaEx8CkCMhnbHU0Rux6k1L5Rz0p4QheRTAVKpsuZD1q4hCnnp U7ywiI8c/SgCgsKDrVq0cRTptI4NVZXVwNpqEK28EZ/CmgZ6np167RRLuHSuhtr2a2IMTYJrzHSb xFliUu2RXomnyrKo284FMg9X0u8aWCPewyVrTdVkQg8iuF068VXQbj0rqbO9jbILE0izA8RadA1h KCnevn3xbAsN9OFBAD19KaxJG1o9eA+Noi95cFR/HUdSjz5+lRd6uTQui5IqoRzViNbSY1ZGLdK6 nU8LoShfQ1xNtLsXBOK6JryOXSNgYlgKHsLqcmOBXQ+GUaPVcSLjKg4PpWAAcdKvaZcLBch3YgUI GdnqNpC4B29+1FlbIoAAOM063mWeFCpzx3rb02IMmdo60JaibPS/CCBIY8f3a6m6uWjt22noKwNC KxW6Z44q7eyh42CnqKGNI4rX7+4lOGfoT0rzPWrib7W/zGvStVtpByRwSa4HWYv9LbipH1OKkdnd gxzzTQoz0qzcxN5pwO9JBA5nUEUwYyNCjggGtG2duc1f8jan3BSKoXqAKLiKc8YdSTnNZE+Yzxmu ikeMKc1kXrI5AUUhlKK6lB+9WjFcSlAcmqMFu7McLW9bWrCAZSmIrQ5L7iTmtFACnQUBAowRzTTI q8GkMakMfm5xzVqWNRY3RBOfKbv7VltdxLKcsamW+iktblQ5yYm/lQBxpODSdTSHpSd6oAPWg8ik 4FGTQAUbjmjOKQcmgCaJtsgzXX6ZcxtAFA5xXOaZZfbZynTAJrYt0NoSuaGIlu0DMSKr2AAvcCp5 XyvvVKORo7kMp71rh5KNRNmGKi5UmkdKKKZG26MHPan19KndHxclaTQUUUUEiGlxRSUAGKKWimBP b3IgBGM5NdnouojykPTiuErV0vUDEwjb8DXm4zDpx5orU9nLcY4y5JPQ9bstXj/dru5rrIH8yINX k1jd4eM+9ekaXeb7Zc147R9GnfYXVbXzYmPqK8n1rRm8m4445r2aZRLCee1cRrNkDbz/AENQyj5p 1O3+zXskY9c1SNbXiRNusSgdqxSKa2AK6LwzG7PIQODjmucHFb3h+9MJZMgDjrVCZ1us7obEnuFr zeVy8rMepNd5q9751kwyPu1wLffb61K3H0Lemx+ZeqvXir2q2bIwYKcCqWlyCO+Vj0ANdFMUvYmG R0oYIh0ISeSMkYzxXuXg2InS4yfavHdKsxHGFHTNe2eDk26TGPYUyep1SIFTmue1vUBGpAIrflfE R+lcNr0m52pWu7FN2Vzz7xJcSXN6cEflWKtqu4Oc5rS1Fw942O3FVq9/D4aEYp21Pk8ZjaspuN9B MUtFFdh5twxSYo3D1paNGPVBiiiigQVTu1LSpirlQyR75Vrmxf8ACZ2YD+OhsUO4HPWo5LTmrY/d mmPIMjNfOH2KK8VnJ5i49ah8cqwFiG7Ka1reQGVRkVR+IKAGxPqppgtzhSKPalIG3gfNnrSYpjF4 xSYpQBij3oAMVsaQ20n61jDOa29PHlxgnvQJnb2U37pBuq7NcBVGDXL22oRphSavS6jEwwGpXFYk vLrcCN1ZZnwTzVbULxSpC8ms6GclhuzSKNveCMnFCXGGGMVTeVPLwG5xUCv8w60IR1Ftc8rlqss/ mEfNXPQ3aAgc1owXSHvTuFjWX5F5NRGU7+vFLFKjr1pkpQdKYiRpeODUkEjFuRVRHTcOauwbQ3PS hAX4pmRhzxW7YXQKAE1zpkjAzmpra6UEKDzTEjpLuMSxE9eK8y8RWyxlzjvXpts3mW1cR4uhARiB 3qC0ebuB5mM0rDimy8TH61ICCKYD04FJG37wU3pTYv8AWigDobaQlAM9qzdWPzDmr0H+rH0rM1M/ MKXUEZ61OoG2oo0LCpGQ4GOKYDMgvXRaDCWuk461zaD94B713Phq2Z7iPjtTRLPTdMRYtPUYxVPU mHZquI3l2qoetYepTBSxLUuo+hlX0hB4NZ4LO3NTPMsr461LEijkimInt4wIwar3ExVsZxU7TKFw DWbMxZu9DAvwyZAyauRSDPUVixuRjmplm56mi4GjeIskXaufIKTsB2Na5fMfWsiWQJKxPrSY0PZz t5qCSTmka5QjiomcScikMillIaqk024EZFLcxyEkgmsuUkE5JosBFcqAQx5APIFewfs+9fEf/bt/ 7Vrx8kNCwPJxXsH7Pv8AzMf/AG7f+1aoGe2UUUUiQooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvEvjeP8Aia6V/nuK9trxL44f8hXSfr/UVFT4 WdOE/jw9V+Z5Dpo/0j8K6+3X92K5PSBm6/Cu3t48xLWjOcYrFTmphNlaSSPA4qHBzikBbjIJ61cT qtZ0KHPWtFBgLzTQi2HPSnZqINgCl30xDjgmmlQaaTzSUAHCnrWpYqGRee9ZLLurQs2KKo96APS/ D8Ia0BrcCBV4rF8MtusRW7SY7mfMvB4rB1VjHCxrqJYwQTXN6zGWhYVLLRxl5Kxl6UQx7sE1NPB8 9SRx7UFPoTbUhlT0FUWtSzHitRhzTkjBPSi47Fe0ssOpxW3G3lLtpYIcKOKrXr7JsUgSJ3mDHbnr WdqFv5qcVVa5xeoOa0TKHHTtQBzphKsRV20XAGane33MTSpDtp3FYsKAajY7Xp8akGkaPJzUlG3p dwWUit23+Za5bSAQz5rq7MfuxVIRcjjBAptygGKmQgAVWvJQMChgtzC1jiOuHvJMIR7112uXGFAr iLmTepHvSQSKjTjpSCTPSqzRncafGhUHNWQWAeOtSLKF71RaTqKhLEHqaB2Nf7SMdaryXAIPNZ+8 +tRuxAzSAklfL5JqJ3GKrSSZambstSuMkbk1ha38u361vBsDpWLrZyq8d6Q0cldHMx47VBzVi7/4 +D9KgqkBLNxFb/8AXM/+hNUOcmppv9Vb/wDXM/8AoTVD07VMNvv/ADN8R8a9I/8ApKAda7vwki2l p9pjOHY4JrhAa7DTrh7fw8GVCfmPQVZzM2Nc1GWQjMmeK4q8kZ3YnmrF5fSzsNyMOO9VVbc2D+tS UVgW7CpYpGEgye9WNq+ophi3dj+VAGpHMpK/NV0rHIME1z4t2TBy1TxXDRuDz+dAjTe3jCEjtWfI cHA6VM97uUjH61Rkcs3SkMkwDTpYh5WaiTcD0NWCSyYxTEZpXb0qSPPBGc1bW23nGcfhVuCzVU3E j8RQMq2h2TKRxiu+8O3bO7At2rgZWVCcEfhWz4ZuyLiQc9B396aJZ7FYpllOO1btuWTPGKoaLGJI 4j6rXQva/u2IHb0pMpHP6tdMLRvmryfXwJrmXdzlq9D8QbobGRsk8/1rz14ftl0ys23Pc0gZx+rw pHbgisA9a7bxNpi29jvWUNz0Fcbs5poBqirCzusZUHg0sdvkZzUpgwuAD+VMCkRxSxqNwqQxHHf8 qSJSHGQevpQB6H4fskktoiyn7tdjp1jEqYxjmsHwvfIbWKIoo2p1NdELoE/KB+Bpsg6+wwI1APAF XvK3jJFYeiSmR8GuphQeSTUstHIeJP3USYrzHWJCbtua9K8ZgrFH16mvJdVci6brSGzMlXdJn3p0 SYmU5qJWzIamTO8UyTULEx1RupGQDFXETgc1n6qD8tIaMq5vJA7DNQROZW+aop1PmE5p9odrU0DO jsLdM960gwT5c1UtGGBjHSo5ixlPWhiRVvLx1mYKRVB7yUk80y4gc3LNlqVbY43En8qBkaAyv81d B4f0mK8u3idTtaM9Kg01YzIqkLXceGliiv8AcQn3T6UxHkWqWos9QngAIVHIGfSqfvXZfEny/wDh JSY1VQY1+7+NcWSRSQxWpucmlwaPwpgKFyvWkAwetFKuMj60AdP4NVBfymUZXy2/lTr+dFnbZ0zW rBqFjHo0SRlRKF5wOa5WebzJmIPeh7iRNJdEnrVYz85zzTihZahaLFC0Bq6Nqz1dVjWMqSfWtaGc SDkYrj1O2r1te7Wwc/nXdSxs47nmV8sp1LuO51G4etAIPSs9J1aLINVYdREErK2SCetdlPHxlKzP Nq5TOEbrU26KojUrc/x1OlzE4+Vs12qrB7M86WGqx3RPRSA5FLWhgFAJB4OKKSk1dWGnZ3Ov02cC GLnJr0DSrhhbCvF7SR1uYvmONw717ZosXm2KMB1FeDjKHs3fufVZdifbRs+hrC7Ih5Nc7qtwrW03 Pati6iZYj9K4XU7krFMDn864WemeP+J7KRtUmlUZFc4ylTggg13OoW8lzcsyjOa5DUreSC6ZXUjF NAUs05ZGQ5Bx9KZ3opgW21Cdk2lzj3qryT1FJ14o6UAOG4HIz+FdB4ehluWdQSawIwGbBbA9a9A8 ARW4eTfKOemRTQmbmnaNIEBK16d4fj+zaeinrisNBEoG0git2wfdGMUrgka8hZlOK4XxlIbWDcnU 13qLmOvOfH06ALFn5vSt8LHmqJHLjZ8lFtM4BmLuWJ5NFJS19GlZHxsm27sKax4p1Mb7wGKyrz5I Nm2Gh7SoolK4JjfIY9amtrpZiUAIIFMvY3C521S0xitzLu4FeVhK8nOzZ7+OwkPZXitTapMiqk9/ FGpG7JqO0k3AOx4JzXfXxcKZ5OGy+pV30L54Qt2qublC/uKbeS5jO2sdTJ5hODXlVsXOpp0Pdw+X U6LUupqT3qKetZtzqLbvlPFRT72I+U1XELOwrksegTxXtwZF2uetel3PgtvEWj2lzKz7kQ9K4rTN NzcRZxzjtXulhLFbaHEhIztpvYS3PmjWLH+zdUuLTn905XmqFdF4zgkj8TXruuFeQsp9RXO4pLYp iYxS9qQ0YJqgJYI/MlA/GtiJCRhRVLT0yGOK6jTbVWjDFaQjE+zTbs4NK4kUd66mS3QIflrNuLcM BxSAx44JJW6EirD2EoTIWt7TbRNvKjNXZ7cKp+WgVzh2V4m+bPFTLMpA962p7KOQklazZbXYOB0o GNhYFxV0tsHFV7VAHGRU12dijFDAuQXOB3qZ7guOAayrQuc8Vt26DauRQA23jdmBINa0S8VEIxgY FTwLhuaaQmQzEk7atWULEg082wc7sVat4/LAFMVzobAFbbmuV8W7TC31rq7I5t65LxacRt9ahlI8 vuEP2hh70gBQc0+Y7rk/WnMuRTGRbs9jToB+9FSKmAeKdbR5nGR3oEzaQBYM47Vi6i25hXRmPbbd O1c7qQG8GkMrwntUj9BzVeNsGpJJMgUxCW67rlR716j4YtiJIyR2rzLT1DXcf1r2Tw3Gqqh/2aro J7mtdqVirktYkxkV1upP+7NcXqpJkNSMo20o83mtRAJI8isXOOlWYbl0XGaEwLM8bKeP0qszAH5j VgT71+Y1nXZweKGCJi47GlRxnlhWO9yVzzUS3rE4pXHY6Rpown3gay7ohs4Oearq7PgE1IseTnNI ZCqEnpVyGE7eBQkfOcVaiX5TTQmytLD8pyOa5y/QrK3FdVKetYF6A0nTNAGKgLAgA817N+z8MN4j H/Xt/wC1a8vtbQE5K1678EIliuPEIUY/49v/AGrTEevUUUUCCiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8S+N/wDyFNJ+v9RXtteJfG//AJCu lfX+oqKnws6cH/Hh6r8zyXSOLnj0rurJl2LuNcPo4zcfhXa2qjYK0OctyeWw4xVdkXOcCnsAKbmg B0Y54rQRRsHrVCNsGraMfl5oET4NRtkHvSM7ButIWJpgOyTSr1pgOTSucCgCSrEMmCOaqLytSLwR QI9M8K3H+ikbuK6iN94zmuF8Ksfs55rsrI5U0mO2hZf7prB1BA4YYzW833TWRcgbiDSZUTnJbNC2 dlVZ41jTgYrbnABOK5vUpWDPg0hsiZgDU9t8z81jea5PJNbGnfMR9Kdibm/AgKjiqV/ArTZ21rwq BEOKrXIUyUikchdx7L5cDAq0hJ6VYv41Nz07UyBQCaBWIsnPJpQeafKADxVSdyqkg0hl6IqTzVtY VZc4rmxPJn71aVrcyeWPm71VhXN6xhCZ+Wt+04jFY9sfkBrXtDlKBl7dhayb+YgjmtFvumsLVCci hgjn9enY4AauUkLEdTW/rB+ZaxGAxTRMirTWYDvSz/KvFViSaYhCw3HmmO1NY9ajJpXAUtjvTsgj BqM8img80hiSRjdwKYF56VYoxQAioNvI5rC14YVcetbuawtc5VfrSYI5G6yZyfQVBzVi74nP0qD3 qkMlm/1Vv/1zP/oTVETUs/8Aqrf/AK5n/wBCaoetTDb7/wAzfEfGvSP/AKSgAr0DQLuBtBS2IzJu Nef9a6vQHEVujvwuTzVdDnZY1mIKVG3tXMS8SEV1Gr3cLkbWB4rmJgXkJAqUMdb8n8a2LePnkVkW yMDyD1rWimRCNxxTEX3iJTGO1Y11byIuSK2PtcLYG8ZqtfgyR/Ic0hmKFNXba3eQjAzzUKwybvum tawRkIyO9MTJfsUgH3BVM20gk6d66HcDWbKcMxNIEVwBGcmoLu6TymQE5xUskisMCsy7XLnFAyng ljitzQEYTNn0H86yUifj5a3dFRkkJYY4FUiWe8eHUJjgH+zXXsQkLA9cVy3hpSEgOONtdNdkKpye tNjRxHiCFpLKQKOd39a8y1KF4ZHLcc163qcDyQOFGTmvNvEdpMm7cuOagbOO1FWmtyo5Nc5JAySH I6V1kkbBeRxXOXkiiaQEjNCAijYEcVu2llJLa7lUEEVg2kbTBtilseldzpVtNHp4LqQMd6oRzn9n TAZKVGbKTstdJKNkTMxAAHrWZ9stz/y0WpGTafJ9kILnAxiun0y6SWLIbvXISyxsow4/OtrRbq3j hw0ig59apEM9Q0QhArH0rrLWQNDXGaPcxSRKEcE47V1dg48mhlIwvFkTSRJgZ615BrkRW+YEV7T4 i5hXArxzxGf+Ji4+tQUzmvMVXI96ljlUyAZqrIhDkkcZojYCUGqEbkRwQT0qvqU0Y25p6uoQHNZ2 pMrbcGkCM242uxI71EkbbhgU/YWfgVajhk67TVAaelqwJzW2sORnbWTYuIyd3FbkUyFBg0ElNrX9 4TsGKpXaKkbjHattjkZ7Vj3wLCTAqWNGHbTBLjJNdHp2pRx3Gd56VyhUrKc8VatZo45MswAphYZ4 snFxqvmKcjaBmsA8CtHV5lluMoc8VmnNCGJn9adjNM70oOKYBjHanA9qbRQBp6RAbiZ1zjCk1HID FIRnoa0PDARruTf08tv5VUvkH2htvTNDBEa3B6YqQfvRVdUOelXbePGN3SgAWzyud1Mlt/KGc1oR tFuCnNWZ7KKWP5Dzj1pXAyI7tlUKKVh5gyaa9sUkK4PFX4LPcozTAoLb55zU8bGJ1wa2YbG32/MD n61YOk2zLkBvzq4VGncznBSi0OgfzIVb1FS1XTbDiPPTgVOK+hoVVUjdHx+LoOlUaYtFIOtFbnKP hO2dD/tCvePCX77R42PoK8Hi5mT/AHhXvXgxCuix59BXk5j0PfybaRsXdurQH6V5vren7YLhs+te oyKGQg9MVyGq6aJYJh6g15DPfR4Fe+IG06+aMRhttc9qmqtqMzSMgXPpWt4u0xrTVpjngVzB601q Aw80oxnmgigAVQB70VKRGq9dx9qDGjLuDY9sUgIwa1tH1STT7hSoyCayO/FSjKEH+KmI9o0rVjc2 6tjHFd9on722Vj3rxPw5qMptV+ZevpXsvhqYPp0TEjOBQJHVoAsf4V494/z/AGynpg16+r7l/CvM viFpTfaEuww2gYxXZgZJVNTz80i3QdjgaWkFLXvHybCnRDMoph6VUhuZBP1GM8Vx42oo07Pqd+XU pTqprobN5aZgDetclflrV5Qp+8MV0NzqEnkhSQa5bUZXmlOa8FTcXdH1jimrMoiVs5bk1fgviEC4 qgIGPNTxxkYzQ3fcaSWxrxSecQPWrsdmHPUVUsxHvQYOfrW1GqKM/wBakZny2QXristwIp8Y71u3 cyjpWFON0uc96SDobunzhZYziuovvE5traGMKTxXD21yYmU5HFVNY1mUvGqkYHtTBDvGF+b+6ikx j5K5jPard7eNd+Xu6qMVToQwNHNJSiqA2tFAYsMZrtNPiHkDgVx2gIWkbHWu1s1KxDrSJY9oeehq pcwgdq6GKNGhGRVS6swcbVJoaC5nWQwwFbEkKvD05xUEFptYHYa0OFXFMRzc8JVz8pqvJbBlPy10 RhDsTtzUctoNp+WlYdzmBahDkLTJIQ5AYZraeEKpyKrbB5uSKVh3HWenqIt22p0i2uBt4q9Bjy8C lUIOW65qrEhFGCM4qdEXcBipEZCuBimsUTvzQIsoABipEXLDioLRDITjJrUhgZQPloGkXrZdkHSu I8XyAo4z3rs5ZligILY4rzPxVdhy4D55qGWjjpWxMcetKshJ5NRsQxyDyT0p6JuamgLUfIq7YRBp gcZqO0tWkUkKTXUaPo5d0Jjb86diWxksR+z/AHe1cvq0TBx8pFepzaUEtv8AVnpXJa3pp258s0mh pnCKMHmnuAQDUt1bmGQgqRVYnHFAF7TF/wBLj+tex6ARsT/drxzTziZCeOa9T8PXALIA3amJ7mzq TEnFctqPMldVqCFgDiua1GEZJ5pD6mUyjHSo+acxINMLDvSAlQ8daHRWHNRhh2NOVuaAMq6gIDEK etQW1uS/zKa3nVCKZGq7uAKBkcVuAOlSrCFPNWF4U4FRSZIoEA2jpilD7QcGqTsV70qSMw45oHYf JIWJFVjahzkqatxwsxzg1fWLao+WmkK5nCBIYCQOa9F+Cpzc+If+3b/2rXB3YHlHFd38FP8Aj58Q /wDbt/7VoDoet0UUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAK8T+N4/4melfX+or2yvE/jf/wAhPSvr/UVFT4WdOD/jw9V+Z5Po+BPz6V2N u42DFcdpA3T8+ldjAgEa1oc5OrgnmpAoPQVEFIPFSq2MUAKU2Gnqr5B7U5V81qsCDgc0CK7hyeDT CsnrVh4ytNVS3WmMiAcHrUuc9akEG7nNPW1B7mgQyPmrMcZbBqW30xnG5TWlBpTbRk0rhY3vDaFL c5rrbBjyK57SLUww4rct38rJpFW0NCd9kZNYF1eKrnOav3V+BCc9K4XXPEMVpu4yRSBaF+91aNXx lvyrnLi6M87YJwTWDd+KPNlyqjFT6feG6kU4xmqSE2b1rbBxkgGuk02zQBSVHSs/TbPzI85rp9Ps GwPTFIaJ0gygwKils9zZIFa8dsFHNI1sCaB3Obm0oSy52j86VdGC5yg/OuiFoM5zTzbKR3oC6OJv 9OZMbFA59awL2J485r0e60vzRwa5bWdGZQ5BPSlsG5xU0jKBg4qNNTaIhd7flU+o2sluoJ71z00x SUg9qq5Fj0XS9YR0wzMce1dVp92ksWVz+VeOWHiAQZBArv8Aw3rSXVuOMGgaZ2m7cuazL2PzCOM1 ainDIDTZU31LLRy+p2HmEHaKwp7EohOB+ddvdWu7vWPd6cfKPNFxNHFXMeVwBzVXym9BW7c6c3OK qGwZRzmqTIsURa5XlRUbWYzworXW0faAKVrFxQBiG1x2FN+xt1wPzrZ/s9ic80fZSBigDFNrID0H 50xoWXritprXJzmqtzbhFJJ7UAZDqQeOlY+sIWVcjvW1I4DYrG1d8quPWpY0cffKVuT9KrZq1fMf tDZ9KqYpoZPMf3Vv/wBcz/6E1Q96lm/1Vv8A9cz/AOhNUVKG33/mb4j416R/9JQ5AXbAHJruWsJ7 HwsvngDPzDB7GuJgB85PrXsN3ph1DQIYjkZjX+Qqnsc3U8ukO48U+KNmYAVuah4eFkQNx6d6yFby JcDtSRRdtbSUg8CmXmnz/ewMY9atWF0WJz61uJbJdRkFuTQK5xK20m8f41q28Ds2BW3/AMI6c5G6 p7bRWjkycigDJ+ySY+6KRUMbANXRyaeFjJ3ViXUYjfOaTGieNlYnFVLlSEamJdBD1FPkfzYz70AZ UuABVVomkfK1ptab+KsW2mZTcc0IRmrEyqOBWjp6kP8AlQ9ttyOamsIm8zoetNPUTPetCQrY27f7 ArUuWyBmqujxY0y3P+wKdqkwtrZpP7ozTkykVL24jgt2d+gryXxR4jsJLyaBWferc/LVnxH4+uFs 5VSNeGI5HvXld9qkl5eSXDABnOeKlagzdv8AV7UwEAtz7VykriSdmXOCadJM0owRS20HmzRp03MB TSA6TwoAiSlgOa6q/wBUt1sVjyQwHYUuieD3hti4LHcM1matp8iTmLY3FNkow9Quo3tJFVmyRXMf NnqfzrrpdEleMgK3SsW90mW0UMytzSRRntJ8gGTmpIpOR8zfnUDKRwaVOCKaEz2vwC4cryT8vf6V 6haDEVeH+BtdW3m2sQMD+ley6HeC+tPMUg89qchRE1dC8QxXjfihCurPn3r2+9i3pXi/jFfL1mQZ 9azNDlbnBiYCqGw1ZEnnSFPepDaZPXimSNDqIQMnNUrhhxV1okA27+aqXEXI5pgFuMsK10GFxWNC 2yQCtLz8MoFAi3FY3E5wi5rWg0q6WMAoM/Wr/hq3+1SkZxXeQ+GxJEG30xHn4s5o4/nFUriE7G4r vNW0Q20BbdmuHvpTCZFx0pMaRxeoRMJG4rPIK9a1L2fzJG4rOlBI6UIZUmILZqM4wMU+VSOoqE5p gOxxxSZH40L1oOM0AHXnvR7UAjOKDnNAF/Tb02kxYKDuBXr61PICzlsdTVKziL3MakcE10c1skWB ihiRlpG3Xafyq3FGdn3T+Va9rCjoMgVprYxMg4FILnIOGQn5TVmwnLSFSMV0UumREH5RVUaSiHKj FAzJlUmU/KevpT43KkAit0WSbOlVZ7VFPSkG4W75FaKj5OtY/wDqiAKsi6O371ArCXKAPkn+KpFY EcHNUrmUuuM55qSzz5fPrXq5bUs3E8POKN0plvvQaQ02RiBXrt2R8+ld2Jrb5rmMf7Qr6B8Krs0a L6CvB9Ht997CT3Yfzr6F0mNYtPiUdNteFi6/tH6H1OX4X2Mb33LjuADmsm7UGJzkdKXUrswI7A9B Xl+t+NbuK3uAvGM4rgZ6ljifiF5Y1O4w6k46Zrzw1oapqE2o3sk8rEsxqhg04obGnpRipViZ/uqT 9KcbeUdUb8qoRBVlLsLb+V5YJ9aj8pgclTSeU3900ARgYOaUsWOTTijDqppuKQGlpuotZkgLuBI7 17v4HuvtOjwyYxkCvnlDhhXsngXUmi0qCPPoKZL0Z6/CcoK4/wAeqzafwpJ9hXUWUvmRKc9RTdRt I7iFhIgbjvV0J8k0zPEU/aU3E8DwQcEYNLWjr8KwarKirtGeBWaK+lhLmjc+LqQ5JuIN901iI7LO 2cjnvW5UEtrHL1H5VzYrD+2Wh14HFLDybZnXEwdQAR+dZsgJl5BrdawhAzg8VkXFwZLjYq4VTivK q4V0leTPfoY5V5WggWH1pjA7sBT+VTh8YJFa1hAkrJlQcmuQ77mZZlgynafyrYVmK5wa6mDQbZo1 OwZPtTrjRY41wAKGhJnFzNk4IrIucq5612F7YpE3QVzN8o8wjFSUZ5mOKz7xyzg81eZMZ5qlOMNT Aqk9KTrStQAfTgUwAjigUh65py0wOo8JQiSWTviu8hs1EQPrXFeDW2mU13UUu6MA0EEgXy09hUD3 JzxUsu7yjisdy6460gRp/aSqH1qo96+eartK+zFVSXJ70XHY3La43AZqw7ZU1jWpYAda0XmxFjFM RXuh+6JrKc4YGr0sxZCDWbcE5BqWNGnaykjAqd8A5zWdZyLjrV90GzduzVALFMQ2BUzLvYZ6VQSQ hsYq6jkkUIRtaYio3FbHAArE04Nv6GtgkiPNTIcTG1iUqGAPavKtand5nBPevRdadtzc9q811ZGM rHB60ikZ0a7mFalpaGSQDNVLOAs659a7LSNJMkq4XIqiWWdI0lWiJ56+ldxplhFDEpI5+lO0jTor e3IdR171ZkmWNtoxj2obBIkmQMhAFYOp2COnNbaTKx5ao7lVkTAANSM8n1vSwJSVrlLiIxSkV6/q ekCXJKgV55rWmGG4baO9MZmWZ+ZPrXoHhu4xcoK87g3JKo967bQZCtxHzimSz0e5Aa2B9q5y+jBB zXTKhksFbGeK5zUm2ORjFIZz9wm01VdN1XbghulVGyO1IYxUYVMsZIqMMT2IqWGdEzuoAhlJUH2q OCXL4xU8zRuCQRzUduiB+DmkBfiwTipjbqVz3pqBUXNXrVUlwKpCMWez3Cn2liADk10MunqUyFp0 NmiDlRTsK5RitFRciobhwpIrQnYRqQMCueuJZGkb0oYIivJf3Zrvvgg26fxCf+vb/wBq15leSP5R r0n4FZ8zxDn/AKdv/atSU9j2KiiimSFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAV4n8b/+QnpP1/qK9srxL43/APIU0r6/1FRU+FnThP48PVfm eV6OMSg+1ddbsSoFchpLAyY9q7OyTci1oc5Ko5qZId3NPSIg84qUdaBBCm1qtDpTUUE1OE4piIGT d1oSHAq5FDuI4FXo7QY6Ck3YaVzKSEk4xV2Czyec1pwWG4ggCtODTyOwpXKUSrY2K+V3rTislCir drbeWmCBVoRgdqRVhlsgjSpXcqppcYFRS/dNAGTqd60duxryXxLqckjuOOteka4T9jb615LqMLz3 ci5796BMpQu0oyRXc+GrDzWhJB5ArJ0fw9PPACAn416r4b0AwQQllXIA6VSJ3NDS9OCx9DXQW8Ai QAU6GFYlwBUtILhSZpjzKlUpb5Q2KAsaFLWYmoKWA5q5FcLJxQFieqGoWa3EbE9avdaCARg0AnY8 813R1aNcZrz3UtOMV1IMHpXvN5YrcKAFFcVq3h9muJCFTBFSVueLyExSYxXY+E9ReNduKxtY0eaC 4x8vU1b0KCSItkiqiTI9Z0+4MtujetagGQK5/RCfsMefWujjHyihlIhdM1UuIQYzWk4wDVOVgQRS Gc7NZgk1VkswD1rfljB7VRnQA9KBGZ9nApjJU7uFJqq86g85oELtGKhkjGDzQ12nvVR7pTnrTsJt CPgHrVC9b9230p0tyoeqF5chkYDPSmIzZPvVk6tgIvPetUkHmsbWMlFqSkctff68/SqoJzzVi8z5 5+lVwRmqQE03+qt8f88z/wChNUVSzn91b/8AXM/+hNUI5qYbff8Amb4j416R/wDSUT2wzcJ/vV9A 2kezSbZiBjyl/kK+elYq2QcEd69k8KahcXfh2ISzNI4JUZPYVe6OZ7lPxUytImB/Ca4C4Uidia7/ AMQRvuQsh6Vxd9GFJO3FR1L6EdpIqdT3roNNvIxMgJPJrk2dgRjitrRfmlUsDw3eqRLPTIyrBOBy B2pL1PLj3YAqC3cZj+bsK0dQiZrZsKScUMS1OcmuFKNXNalIOfoa27mGZYnPlsPwrkdSkk3kEkda ljSKm8NwCc1qxOPLWsCNjk81KtzIOBIaBnT2g8xyAO1ascDLblyBgVyVheyJIcykcdzV2fVZBbsq 3P4ZpiLU0ykkd6taT80xwPSuWhupZJwDITk123hm382Vsx54Hb3oSEz27TTjToR/sCs3xK+NNl/3 auWjbbOMZx8tZHiVydMlwf4aJMuJ8/a4+6KXk/fP865k9a39WEpWTION5/nVfSNLmvrpUW1ebI6K M0R2BmSore0XRby5urSWNAUMinOfetxfCdwG/wCQTP1/uV7D4Q8L2MWh2jTWKpMvJ3LyOaaJNfS7 BksYwyLnbXM6voVxLelliUgn2r0IKsahV4AqpLGjPkgUnuO2hwy+H58j90n6Vm+IvCV7c2n7mBMg EnpXp8MMZkXIGKuXFpG8RGwcihgrI+OtSsZbW7kjkUBlOCKolSK+l9U8CaNczvJJYIWY5J215J4w 8NR6fq7RWlkwj2A/KvFCY7HLaRJ5c/3iPpXvvwzlEmiPyT8/f6V4Ha2lykxxA/5V7f8ADDzo9JYS Ky5fofpVkPc769+WPJrwzxxIDrco+te46gxMI+leN+LrQSatI3lE9ecVk1qadDz21OLsn3rSlmRY yaqSQmGRiEK8+lQzSMUOc0ySNZQZs5NSu4NUSSDnGKZJKwGM0wJi6+dVpZA7risxGJbPWtSyjLyD 5SeaYmeh+DgVmYn2r1izmQWyg15p4agCMTsIrrxdOi7Q2BQwiT+IbqIWbA4/KvKtVIZ5SOnNbXiX VpRvTz8YI4zXGT3pfcDLnPvUjZiTqfMbjvSQwNI2ABmp3RnJKoW+lPgjmR8+W4/CmgKWqWMsVksz IApYjNYhPFdLq8znSljdiDvJwa5vFNANzS9aKKADAHNOUZbFNHWjPemBdsvkvk9M1vX8uXAFc/au Hu4iPUVu3oJYcUmCL1jMFUZrcguEKryK5WGQKvNaFtcIpGTSEdPDsmcLnrWkdKGzORWJYSr5qHNd QJlMfXtTEc9NEI3ZazrraOprXu1JlYiufvZAGOe1JjRlXd2I5MCqRuSzHt+NF24aUkVCtrM2Cqkg 1UIOWwpzjHc0LYmYgA54rUgQoCDWfplpLDJvcYBFate3gqHJHma1PmcyxTqS5E9BTUMrdB61MTxV aRwZlXuDmumvPkptnFhKfPVSOp0KDF1b59RXutoUWzjGf4a8U0VW86A9sivX7dwbZPpXzs3c+wpx srGT4juBFbP9DXhWuXoMM59TXs/ith9kb6GvB9XIaKUAH71Ymxy5VnOQDVyw0q5v50jjjPzGtbRd MnuoVKQlst6V33hrQb2O+h3W5Az6VokQ2bHgjwMbW3j+0RI5LZO5RXaX/hWzZABbQjjsgrodPtmh iUMADinXbDOKTY0eY3/g2OQ4WKMc9lFOg8DIUAMaf98iu4kIzVq3AO2kmFjyrxJ8NpLmyxAFQ5HI UV5lrngy/wBH5KNIuMkgV9Y3EXmQbQOa5fXNGluIsCEMMelFxo+UzG6nDIwPuK67wtqht4fLOeG9 a0/E+iTwXrj7Nt+grmtOQwzMjKwO6mmJo+hvDN6Li3i9SK6OdMofpXEeC8iCHIPSu5n/ANV+FD0B bHi3jZAmtHA7Vzgrq/HCMdUyB2rlBX0GDqc9NHyWY0nCs33FooorqPPGsMisW6sDHJ5i8jOTW5UF 0CYWAFYYikqkHc68HiJUqisYuMmtjSyVkjGO9ZYVvMxitzTIn82M7eM189KNnY+vjLmjc9F0sK0U efSp9SEMcf3hVK3cRW67uOKyNZv4uRvqGUirqWx+hFcdqEZ804rbe7jYY3ZrGvDubI5pDRliPLc1 MNFluhuTHFI6Njoa1tPvYbeNlkcKcd6YHF3cJguJIm6ocGougqzqbrLqVwynKlyQarY4poYU4U2l GRQB1XhRwvm/WuztZVIwa4zwogbza7KCOJFBLc0yDUxuj4qnLZM/Q1BLfeWCFamW+pMzctQFieLT pBmmXFmyITgVs2UiygEmprq1ypIHFIDmoQVHSrEh+TmpjaMCcA0SQfu+aYGQRlqr3UbFa0DCAc1W uSBwTUtDRUtlINaQOY6pRACrkfKUICNDhs1egYNIBVdYRWhawKHBNUhM3tOXI6VosP3fSqFk6pwD V5iWXipZSOX1hAWauA1Gzd5GI9a9PvrTzWOQeaw5dDEjng0WC5xen6bIZ48H+KvStDsXiwWx0qKw 8OxoVYjGDmugWNbdMCncVrkxbaMVg392sTMT2rQmutuea43WL5y7AHipGzVi1ePdzmtjT75JzgV5 /azM8gyK6nSH2NnpVbi2Omnh8xelcJ4g09zM2AK7+GTzE61nalpwnyTUlHjr2Lrc9O9dNotq5uI/ atKXQx9oPBrV03TPJlU4NUiWdXbLt0wA+lchrv8ArD9a7JGC22w+lc1rNp5rEgGp6ldDmFTfUMkZ BrS+yvHng1VmQhulOwiqF46VUuImY8cVpKlQumTSBGU6tGCSTTLef98Ku3MZ2NWZGrLLzSGdFG+5 K1dIgZpwe2KxrLDHBrp9JQJIp7YqkJl65jKRjJqKOMupIqxftmLiqUdx5SkGmhMr3MBZzzWPcwck itC7vQrnBFZclyXJ5pMaMm7TrXo3wO/1/iL/ALdv/ateb6hKEBr0T4ETCabxEcdPs3/tWkhvY9ko oopkhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFeJ/G/wD5Cek/X+or2yvE/jf/AMhPSvr/AFFZ1PhZ04P+PD1X5nkmlMBPye1draTbIwQa4Gxl WOfLdMV2VrIrwqQe1anOav2s46inpckkDIrOzUsZAIpXA2opxnrVlZQSBmsmIqT1q4hGV5qhNGzb bTjJrTiMe371YcMiqBzV6KVCvBqWNM6GyWMgYNasca9qwdPlQAc9624Zk9aRaLiDAp4AqFZRinCU ZoAm21DKvymrEbBhRIuVNAHJavbNJasAD1rho/DktxqRyjYJr1Ge3dlIApthpc/2gPxihCZn6F4Z WG1AIb867OztltoVUdhT7eLyo8HrU3amSFQzzCJc5pXnROprJv7yIZJPegEgnvl5yQKw7zWI45tu 8VSvNVt13AmuR1HVbdrngmkNux20WtQmVRvFa9rqsZbhhXlEGowNdJ1610lnqVv5g5qhXPT7e5WV Ac81YBzXMafqELIuG7VuQXkTIBmkDRcqncWSTbic5IqwJkbowp+QehoDY828QeFxLMrAN371i2+i PazMArYr1W9gaQgjpWPNYyGTgClsVoytpNqUtEGD1rfSLCjNQ2dq6xjOKuSDaOTTAqXB2A1ltNk4 FXr1wF61jxkF+KQyaRziqNy4zzVuQjbWbdsNw5oEUJ3I3EVlyTsSavzsArVlP1NNEsaZDVZpTkip 2PFVSjZJpklW4Y7+lVZssDxV6RctUMi/KQKCjNIxWPrUgjRee9bj5BrlfEUi+Yq96kaMG6YNOSDw RUA4pzkFqaKoCaf/AFVt/wBcz/6E1Qg1LP8A6u3/AOuZ/wDQmqIZxzUw2+/8zfEfGvSP/pKHA816 N8K7qKTV3tLmQ7WA2AnjNecdWC9z0q7Z3l1p0/mW0jRyD+Ne1WjnaPaPiEsdhNAsJBDKT69680mm M7FSBVddZ1HVCftV1JMVHG7tUaSYlyxqLajJxZoxznpWjZjyGGPWq0EsZU5pZJ1U8EigDrLS/k8+ McdRXd27/aBtbHSvHYNRIlXDnrXfaBqhkuwjSE5qtxbG5fabG1rIPavK/ENmkMzEHoDXtUyq8JGM 5Fec+K9MyXZYx0NSyjzMSc0ZxzUlzbNAFLDGaibHl+9MQjTsvSozMznBpqgsavWVi9xIgVc5NMRc 0y1Dzx5Jr1XwxapCzH1UVzml6CUMTtCOBXY2wS0A425oF1OuhmO1VqtqUIubdkboRUVlcLI6jPat YIkgOQDUstHmk3gO2vi0ZlK7mJzzXY+DvAtnozpMpDsq45FaP2ZVbKrg1s6adgAPpTQmjR+zQ/8A PGP/AL5FRSKqnCgAe1WN49aoXNwoZuaBIjlbBrBv9Se3mwBU91qCoeXxXFa7q2JW2yHNIpnWW+tS NMgwOTXTWl2ZiFNeI2mszfaY/wB83Wu+0LVnlnwZCeBVWJO5kt0k6iub1jRreWfcyKTjuK37e6V0 GTzVO+KySZHpSY0cH/wjdqJmwqdf7tdBpNilnGFQADPalYIJDxVy227cii47Et2N0YrjdY09Jrhm OOldlcH5a5rUpFExBpAzzTXdMSONiMda5uSyXb1rvtcjSaPCjvXNy2f7s4WiwrnMy2ihetU5bQcY Nb1xbMEPy1S+zMTyKaAzUhCnFbukIPMHHcVDb6ZLLIMJkGur0nQpFO5ohjimJnaaFAvXjpV25AWR hWcl2lig528ViXniFfPf98aT1BHK+KpCL2cZPUVyTykEnJrb1e4a7vJTuJBrGkhbnikijS0zUCrq CgP1Fbp1EOMeUg/AVyMJMZGOKsvdyKvDkU2TYr+I5zLcgYwMdqw6uX8rSzZZiTiqgxTQxtBOOKXG TR0oAQUuOaXPAFJmgC3pi7r+If7QrptQ2q+3HIrlbSXyblJB1Brfurky7XPBYUMEQtu7U6PcMHNP iQSfxVOtqMZ3UgLdrftHt9RXR6dqLTNtJ4rk1iCN1ra0pgsvJoTE0dJLDvBIPUVyupQlXbNdtFEJ IAR3FYGraeQxOGOfam0JM4VlD3ATPU4roYIljiVcdqpx2CiZmYEEHjNaA4GK9rA0eWPMz53NMRzS 5YsXFFJmlr0DxhCcCoLNBc35IOQOKmYZGKvaHppErSNuAJz0rgx8moJHq5VFOo2dfptqUWJvTFeg 2UpNutcTayIiIu4cV2GlHzbYHNeIz6aJBq9g19CVHcV55dfDy4nLrngmvWzGNtQ+Xg1KKZgeEPA8 OmWcYmQFg2a7aOwt4iCsSgj2os2zEBVgnAqmybCOdqEisK/uSrGtC5vQoZRiuV1XUsOQMVJSRDca t5RzmrNrrikrzXF6pqe3nj86zIPEeyZR8vX1piue5Wd0LhBVkqGGCK47wzri3PHy9PWuujmWQcEU MRzms+H7a8nLvHmuZ/4QmxW4ZxEMk5r0G7APOazGIDmkWRaXpMdqqKi4xWldpsTGafaEZFLfL8v1 psDzXxTZrNc7yOa8+u4HgmIYYHavUfECr5uCa8/1xQrAivQy+o1LlPHzaknDmMejNAor3D5gWmnn rS54qKVmCnYMt6CplJRV2XTg5SsilI6rd4xXa6FaJMIjt5JrkILKa5nDtG4OfSvQtCtRAkRY42nv XztaSlNtH2GGg400mbWo6YU08lODgV5b4g86GZst3r1fWNUiSwZRImQB3ryLxDeefO+MHntXMzrM I3ci96lhvgSA/rVNsntUagh84700JnX232ZwuUzkVzPiTal4nl/KK0Le8Me3pwKxdYuDcTqxxxQC M4ncxJ6mimjrSjp1pjFHSgUlFMDqfCkgUyiupdsoDmuD0K6+z3uC2FIOa6+O5WVBtOaTJGTNnIya S14fqac6ZBOKjiyrcVIzqtJYcc10y4ZOlcjpEqpjNdVFMrqAtUxIilgBUnj8qx7siNDk1vuuUOTW LewbkNCBowXlBBqhPJl8YrQlgZR0qjLAxfNIaJIRleKtIpAAqOFNi1OiliMU0IkB5FXEuAMADmok tmI6VMtk2c80xGhZPulFb0K5QVi2NsyyjI4roLcAKM0mOJE8YJ6CohAAc7R+VXJMA8Cog2TjFSUI AFXpVadsKSatnpVS4AKmgDGuWBbg9q5PUFJlbnvXV3IAY1yd/IDKwB702ShtlETKuK6izj2jBrnd NwJVJNdNb4c8GmthM3bM/IKtSLvWqlphV5NaC7WWkUjNNuPNzgflVhEUEYUU8x/McGpFj4pDI25q ncFRnIq6RiqV0hOaAMm5QvnaKzZYiPvLith14xVWWEsOaokw5jtbAFRBSxq9cW+1iaqpIASMUhkE 8PyGqCWpaSteVC4JAotbYtJRYLhaWrLyf5V0enJgj6VTitXOMVpRJ9nj3Me1MQ68KhOTWHeygHg1 PqF4Chwe9Y004c9aQynczHcetUPOKk9fzrQkiEhzVSS1YZxSGYupXO58c16p+z/97xH/ANu3/tWv JdShKNljivWv2ff+Zi/7dv8A2rTWwM9rooooJCiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAK8T+N/8AyEtJ+v8AUV7ZXifxw/5CWk/X+orOp8LO nB/x4eq/M8SjIDV1+myAwKPauNzg10uhyF2Ck8YrU5zdBB6U9TSKnPFSbPlzUgSxSAGriTqMcVmD PapQSAKaYGzHOpHSrkU6helc/FIQwyauLPjoaYjpLS/RCBtNblpeo2eDXE282SOa2bCb5jzQ0CZ2 EU4KdKlEw9KzLJy0fWrgpFmlbzDHSp/MDVlpJt4zVmCTcTQMslM1dtgEHNVF68055gg60hM0TIvr UEl4igisea/2nG6s6bURk/PTFY1ri7XNc/qt6iqeD1pkt8W53Vg6pcM4OD3pDMq+ulZ34Nc9cHfK SOlW7guXbOetUn+9TJFhG2ZT71sW0wDdKyYhkg1bVsEU0I7jSbtFAGD0robe8XYDzXA2VxsAO7tW iup7ExvpMaZ3MV8m7ofzrShvUKCvN4NX+Y5krZttXTyx+8oHc7gyq44qvIMtms23vlZRh6uJKHHW gaLUcgVMVBcTANQWAHWs27mwRzQBHfTAkCqca4bNQ3UpaRcGp0OPypAJKPlrNuR81W7qTCcGsm5n w3JoArz9GrNfrVuWVTnmqjEE1SIbInBqMnANWtgK5qvIuAcUCKUxy9Qt0NPmOHqpM5UHFIorXE6o 5BFcbr0wkusAVtahcFZjzXMai5efOaXUopGlHSkPWlFUIln/ANVb/wDXM/8AoTVFmppv9Vb/APXM /wDoTVARzUw2+/8AM3xHxr0j/wCkoUHkHHIpwdh/EaaBR3qjA6HwzB55m+Xdj/CodSRopW4xWn4H kj824iZgHf7oPfineILCRC7cY+tJgmc/DMw6uR+NTvISPvZqkylCM1Krbl4oAkWUgg7jXWeD7ovq 8amQnPvXHbCeK6XwXEya5Ex6ZpoTPco1LBRjtWdrGnebG37vPHpWpbyrlBV25j8yMgY5FJjR4zr2 jHykxB39K4mazmEjDYcCvdtV0yR4QABXBXehXAdzhevrSA4a3s5GY4jNdX4dsH3xlo/4jU1po8yu SQOldRo9g8cYJA4JqiWaqIscK8Y4qldy5IwavT8Rmsib5jQgZqabcsLlMscV1tpcqxPzVxFqpR1Y 9K3bO6VGOaGOJ0m9T3q1C2FGDWPDOGYCtKNwsYJpFFia4KITuxXOX2qKsjjzavapdpHasTXnuo6j H50pyaQMNX1grNxPj8a5C+1MSTHdNn8ar6rfo84PNc/NJvlOKBHQwXiCZCJO9d14Zvka7/1nb1ry eLIkXnvXV6BqMdpdl3JxiqQme3Wl2D/H2qdpw2ea4nTtft5TgbunpXQ2l2lxFuXOKQ0x88qg9ant Z0CferMvARzUMVwIxg0hm9czjZ96uN1e4JuzhuK15r1CuK5zUG3zkimhNlRdszMDzTZrZPLOFFMs v9c9XmGRiqIOdmsy2QI6ZFphY/6o100dqzsAAK0LTTpCfuikPUo6PpCeXGWh/SuqFlFHCcIBxU1l Zska5Aq5NHtXmhlJHBazbuR8qmuGurWb7S3yHrXrF/bFuwrl7m1KztkCpB6HDi1wcsnNV7mNFR+O cVv3hEUr5Fczd3SPO6DqTigB9nYNMQRGWzU97pMkcQPkkfhW/oZWzt45pQNtS+IfE1jHaj5TnP8A dpsSPMtSiMVxtYYOKpEdq0NXvY76+M0Y+XaB0xWcc5poYnSlFJ0peQM0wENL8uCCDnsabyKcM0AS RLmRR710l5b+XbQHbyVBrnrQf6RHn1Fei6npjvp9o6DIMYNJ7C6nFeaUbg4q3Hc/KATVG6ieGdlI 71EJgDSGapuVB+9mrNnfASD5sc1gNNk5FOt5GMmB1600hM9u0MiWwiPXNad9pu9M+Xnj0rJ8JSBt Jt89a9CFoJIVPtQwSueM6rpssdwxWPArM6HB616vquiGVmI/lXnmo6LcQXDlVJFejg8WoLlkePmG AdT3obmXSilEEwHzIcjjpSMGU4II+tetCpGezPn6lGdN2khK6DRrjMDKzDI6CuezVzT3KzHB7Vji 4KVNnTgJuFZWNkXmJsbu9d/4buw1jy3evK8ky5x3rsNEv/ItNrcc18+fWo9ISUFOtG4ZrAtNUjZB 8361eW7U4wamxZu28gUUy9vBHC3OKpx3AC5rD8QaosNrJz+tA7EdzqqHd+85+tchqmpr5zfvP1rM l1pTu5/WuZ1LVCZj3z70C3LupXu9T89YAmzcAgnGaimvTKCP61Cm4sDihCPVfBtztlPz/rXolrf4 P368Z8M6ilrIfMOPxrs7bxBbluHH502CO/a6DD71UXnHmHmsy21BJ4tynNVp7ohyakq51drOvBzU 91cAp17Vx9vqwUgHH51am1ZGTr+tMV0c94ouwt7gN2rgrud5pjuOQK3PEt+DfZXk4rnCxZifWvZw FFxXMz53NcQpPkiwFHalVGYfKpP0FW4dNnmxlSBXdOrGC1Z5NKhOo7RRROSOBmtTw/prXF2pdCcn vWha6HIUHB/Kus8P6O0LqxGOfSvHxGLdTRbH0eDwEaXvPcng0WGNB+5UH6VR1GHyUfaMYrsmiwtY eo2jSK+B1rgbPVseWatcSHzBvbr61zzRmSQk5Oa7jVtLZd7EH8q51rcI5z/KkIxJIAv8OKgSLc5A Her99MiNtJo0pBJL7ZFNAxVsGK/cPSuXvgVunQ9q9ZmS0tLDzWlUEL0OK8lv5ftF5JKAOWNAIrgc Up6UL1pSOaYxmDjrThkU/gCgqVbDDtmmARthwRXYaR80AbOa45j2Are0S7wvlk4x70mI6GSRuRUK sRRvDNyasRW4Y1Iy7p0h3rn1rrrB/mFczZWwVl4rorYFCCDir6EGu65XNVJoQVOBTjc9sj86VXEn HFSUZjWYbqKo3dmqdBXSC3HUmmy2iN1FO4rHJCBielX7aywykityOyiB5AqyIUUYCii4WKsNogA4 5qylsg7VIkfzdasmLA4pXHYrogDYAq3GpA5FRw4RulSvKTwOBQMkwpFQSgJzUkce4ZJqC5baOtAE Dy4OM1BPKNhrPurlldsHgVlT6jIeM/rRYm5PeyEscHHFcvdKPMY+9bXnGVSWNc/dykSHjvQwRYtS Q4xW9YO3PNYemL5soBNdJBEEFNCZp20r7utdDbAPCMjnFYlhFkZIrobePEYpMpFYjD4qY8L0psqg OacCCBzSGVmXJqGWPIxV7Zz1pkkeecUDMv7MC2abLaqe1XwAjVIVUigVjmbq0XnisWey2N8vrXbP ArnkVA2nxMeVp3FY5KKNuAelalhboz8itU6dHu4FXbSyjjPAouFiuLVUTIFZd8Xwyg9K3r0+Uny1 iMPNlIakgZztzA7g5qk1uytiuquIkjXPBrHupkR+AKYEMVsuzmq04AJAqSW+bHGAKyp7s/MSwpNj SOd12bddCMdBzXrX7PvTxF/27f8AtWvG9Rl8y83ZBr2T9n7/AJmL/t2/9q01sDPa6KKKCQooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvE/jh/y EtJ+v9RXtleJ/G//AJCek/X+orOp8LOnB/x4eq/M8NatnRLoRTYIrGan277JM5IrUwZ39jdCeUqB jirjrgGuO0e6VLolpCBj1rsYpEkhBBzkUCIlODUgo2+1PwMUgGgZqZelRinhWPQUwLMD4cCtqwOX NYluhEi5Fb1gVVjmhi6nU6WuYa0NhFVNJdDBwa0xgipLRUxzVi2O00xxzwKWMHNAzQ8zis6+uwiH ippGwnWud1KX73zGgBlxfKWrKlv1Eh4NUbmQ+YfmP51QMn7z7xp2IubX28H+E1Vnl8ztVMSD1qUM CopiuULjvWbIh3VsygDJrOlK76QyGNSCKsVGpG4Yp56GmBZinCDkUkl0CTgGqHzc9aUA5oAuJcBT nFX4LxdoODWQKtQgmMUCOysNSUJ0NdHYXqyR5ANefRMQgwSK3tLuMQ4LmkNM7AzgrWZdSbmpsUwK fepJPmxikWVZAS4NTiTApjDmmSHC0CI7lsrWLeLlhV2aTHUmqczBsYoEzNbgkU0HFTuBk1FgVZAo kwKjd/lNI54qpI/B+akMrTnMlUrlsRn6VNLktWffyCOB8nHFSxnPalIDcH6VgXJ3SGrl/MGuCQxN Z7nLU0hjB1p+eKaBTqYEs3+qt/8Armf/AEJqiqac/urf/rmf/QmqDPNTDb7/AMzfEfGvSP8A6Shc 0hzRSfjVGBZsLprG8S4UfMtdfFqUWsgROjBm71xFbug3SR3SBuuaBF7UdFWMZRugNYIBQ4rtLsG7 iYpxxXIz27wzAOaSGPji3OOetdPoQSzvVlGTjmubj++tb9rKI3yfSi4j0GDxOFkUbGrqLDWRer90 g9K8nj1BFcEqa7DQdXhOAFPUUxHby24mXBrJm0RJN3PWta3uVmOAKcwwaRRzo8PKnOaf9jFnGRnO Oa3GGaz76M7D9KAsYE9wG3Lg1UCgmppozuNRxoc00Qy4qfIMVNCH38YpI0JAAq1bWshfrSKSNOzi fzFyR0qbUr42NvnGcVPa27qykntWV4nby7U59aRRzmveIpDZYVTzXDXGoyyl2OOa2dblH2MVy0ky 4agRTuGMz7umKp+X89WTMO1V2k+emBIcLz3FSR3rRMCBVcvkUzdlh9aAPQ/DfmXBDZHKk16Jo8JW 2wT3rg/CUD+Wjdihr0HTW8uHB9abJRbuLLeg5rJurVoicV0WfMQAVnX1rI+SD2qSzl7iZo+tZk8+ WORWnqNvIhG6sS4kCSYNUiGOtTtkY+tXQ+WFZcV0gfpVlLtNwpiNu2ZQy9a17adFznNc7BeICMir qajEvUGk0UmdRFeoqAYNMudRjx0NYa6xAqDiql3rtsMcGkO5bvtTAHANcfearJ9pfA4zV+51WGUf KK525lVpmI70C3MXUtRnM79MVgu7NPv75zWrfRsZXbtWVsJlx70IZZk8RXMcIhXGBWbc3816u2Tt zUF0pWQ1XyR0NOwATTc5pT0pKYBnmlzTetOoATgr75ozzSgUmKAJEOGBHbmvStE146lp0VsyAeUg XNeZgVfsL+azb902M0CZ1uraEzsZt33q5C5t/JkZc9DXc2uoi8sk3nJArC1axyzuo4PNTsNHOqua 0dNtTJN2qqseJMGt3SIgsuTTEz0zw4nkWUK9cV6RaXW6FRjtXkVrqPkxKoPSun0nX2YbWb2oeoLQ 7mWPzMmsC+0wSMxrasbgTRAmpZIFbmpKPP5dC2uTnvUTeF4b190jlSPSu4ls0btUBtBH0rWFWcPh ZjUw9OorSRxb+DLRD/rnrOudFhsn/dOxPvXaXYKyYrn75S0tU8TUkrNmMcFRg+ZIwlszuzWig8tQ tSrDyKn+xyNyBxWZuFrc7WArp7VtxSsew0pnkBYcV1sFgiqnHIpMtIhuLgQRn6V594r1rEUiAV1+ uzeTvXPQV5lrsgnLg96ko5d9T3NwKrTEyHd61O1moPTmh4gqimIzwpWrKtgCmMnzU7Hy4oAsx3gR sEVraddCSUAetc7Gu+cKfSum8PWQkn5HemkSz0vQIs2YPrV66s8jdntUujWyxWiitY2yyLz6UmNb HDzxGMk5qBrkgY7Cuov9KUo20Vyt5bPCxGKaYmjn9WQTzeYvXpissAltvetW5JEp3VWHlh92Oa9G hjeSPKzyMTlqqT5omvp1qsMIzyTya17eIM4ArAjvgpAzWnaaiu8YNcdSo5u7PQo0Y0opJHY2VqBG M1r2yhAK5q11ZQgGatjWVVevSsToTR0THIqlN3rDl8RYz81Vn8Qbl60FcyKviGZUgkGBmvOL+92u Riuv1u9+0RPg9a4O/B8wkmkIyb+YSyg1PaagLXJwMmqsygtUNxGyID6imIuavrZvrURYxisEUMTz k00Gmhj+MUmeaMUnWmA4HihmZzySTjFIBSHOKAF5qWCVoJAwNQ07kCgDuNKSO6iR859a6SKCKM5G K4Hw9qHkT+UxwrV2qyg9DS2JNPKxJuBFQPqLoODVKafEfWsqS4JJ+Y0mwSN5NVcuMsK1YLsEghhm uGE3zdTWraTMHB3GmmDR2qXbnGTxWjbssietcxBcBsfNWpaXIVsbuKGhJmq688UA4qVMSICKaygC pLBPvCrTMNuKqq6ilMo7mgB4Gaeq5pI2XGTT/NQc5pgTbf3fWsy8B2GrrTgjg1TnYMKQHLXrN5jD nFZFwa6S9hOWYL2rnLw84waokgWbCkVj3c2ZCPetIjCng1zl47eecZ61I0bumSlZl5rqrWff1riN LZjIM56V09m7A9apCZ2Vk+EFaiXe1QK5u0mYR9afJfbeN+DSY0zeaTe2c07Zhcg1hRX4GMvV+K/V yFDUh3NCI/NUztuTgVDGVIzmpgMjIoAqSR5NSRRgLzzT5D6kUI4xzQMrygK2KYKnkVWOetREBTxQ AYFPXC96jzSY+tIBlyAy4NZFyoQEjrWy0eRzWZfIu04poTOdv52CYDViTl3fO6t+7tlZc4NZ0tsA eBTsJGVJkISTXNXt0wlYZNdRdR4Bx6Vyd+iozE9TSW5RnZy+T3r3D9n3/mYv+3b/ANq14cDXuP7P v/Mxf9u3/tWqJZ7XRRRSEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAV4n8b/8AkJ6V9f6ivbK8T+OH/IT0n6/1FZ1fhZ04P+PD1X5nhre9Mzg0 9hUZrUwLFtKUfOa6/TtQUxxpv5riFGO9aOnzss8eCOtAj0FG3EVYVAxAqhYyeZ1I6VoZ2jNICcW6 7c0g2ocVH9obbUZck80wLaSKDV2CU84NZKckVoW/FAmdbo1x+6IJ5reilBXrXI6ZKVU1vW0xMYpM pGso3U8LjoKjtmylWQAaRRUmztNYGox5zXTyRgrWJqEQwaQHIXMOJDxWXLFtYkCuiuo8tWXLEMmq RD0Mwu4NWUdtookgUtQmFOKYhk28rWfIrb+la5wapTqA/FA0VACCKmQFj0pvRsVZt1y3NAETxBQD 61GV9KvyxhgBUYgBNAFVEJNXItypgCpI7cA1MIwOKBAhJxWnYbz06VUhhDZrc023AShgkX7ZHKLk VfKgAZp0EIEYp0i8ipLK0gGeKqzjKVddRmopEGw0AYk6kg1WKHuK0pFGTUDrQKxnPGOc1X2CtCSP INVHXbVJktFOUgA1mSMCxq9dHauRWXI2ATQwRBNMEbGa5rXb7h0BHK1a1G9ZLjAIrmNSnaack+lT uWUnkLNz1poOaTvSjmqEJmnZpBSjpQBNN/qrf/rmf/QmqA1PN/qrf/rmf/QmqHvUw2+/8zfEfGvS P/pKCkpaTvVGAdDxUkMzwyrIh+YVERgUCgDqdK1kuvlzDJYgVf1PSRIPNQ9BXH2rsJ0wcfMK9DtB 5tkxY54oaEckIzGdx6LUx1RAPlU5rSv4UW1kIXB+lcxbfNKAelJajNIao2elbGl6/NbuCB3FZUcE ZdflrcgtohHkIPyoEzrdN8bOkx3ocYrrrTX47rZ8pBavJHATlRg1uabcyLLF85/OhA9D1VWDdKr3 iAxMazNOuXdzls8etX7hibZuaGNMwpoxzTIYQT1odvnPNT2vLUC6l23s+QSa0oIFQ5pYVAVTillb ZjHFIo0oiARWN4ktjPanHrViOY5HzfrT58Sx4Y5oA8x16wdLLPFee3UjpcMh7V7xqlnBJbEMgNeY avYQLcz4jHHtQI5a1hMwJzU0mnuBuBFWrRFVWwMVbcDZQBgm3cLnimeUVI6da12Rdp6VX8tSw470 0wOv0LVGtLaMY6LitxPFbRcBa5m1QC2jwO1Eg+emyUei6b4qSfhlIxW7DexXkW4ZrySzmdGO1sV1 +h3EjIMuevrRYaZu3+mi4A2muevfD772bPauzi+ZRn0ps0SlTkUhnlFxay28uOOuKdAkzyqOOa6j UrWLfnZ3PaoLO3jN0g2fpTRLC20qV9vTmrTaNIB1FdPb28YC4UVYaBMdKVxpHnd5HJbsy8cVgXlx MT0HFd/q9tH5knyiuYltomzlRRuGxyU2oyQjkCsyXXTvPy10Op2sS9FFcfdRqLhsL3pDJJNS84nI 61cstJe6eOQEBWNY5UA12cKiLwt5ycOI8g1VtBM4rWbX7NdSLnOKyc1YuZZJZWZ3LHPc1Bx1oGJ2 o7UD1o6imAUAflRg0uMUgEPBoJzS5z2ox70AANPVtpyKjpwIAxTA27DUmiUKScV0kIhv4gAQSRXD RyBRg10mhX8MUkYY96VhPQmm8PTpIXCHbRDiybMnFdk9zHPZHZ1NcbrymMDIpMFqWP7Xjzw9X7HX BCch+M1xiKWwa1Le1kKDg0Aeq6T45tooVSVhXUW3jDT50GJBk+9eGLaSL1FbGmMIyAx6GmGx7bFe RXGCjA55p0mDXN6BdxuY1B/hrpGIIpNDTMW+H72seeHfJWvfyAT4NZwkDSYHrSQMSKxJIrbttNDR glarwIcrXQWy4iHFMEhlrZJHj5RWoI1CiqquFPNT/aEx1pDOP8UW8jtKUz92vLtZjmgDs4PFezan GZg+0ZyK848VaZO1rIQtAHnD3vNWIyZ4wetUZbOVCcr0rQsl2wjPFMRDNC6jIFQbiBgrWu+CvFU3 ibB4pAVrdSbkEDjFdd4dmWCUlwBzXM2ikS81uWPUmquJnpFt4it4UC7hW7Y65azoDvFeTs43VsaV coiAFu9G4rnqRMVwnBBzWDqWjmTJUVZ0y7jdEw1bDAOoxUl7nlGr6TPDLuCkiudmSdCeCK9a1mze RflWuHv9Om3t8n6UEvQ5mMzFgPmrWtYblmGA1S2+nTB1+TvXS2NhKGUlKpCKENtdLGCd1Q3dxNbo Qcg12QtHMQG3mub16zkAPy0XCxy8+oT8kMap/wBrSow3Mcd6tS2smD8tZN1byKGyKTGiefWUdSrP msi7uo5DkEGqVxGY+T1NQqhc8UDLkVjJdNmME/SpdZto7CGJJlIdkJHFdB4dgb7OW2/d5Nc5401O G+v4o4hzCpVvrQxI5djkn0pAaDSgc0xigUdelIfSlHpTAGBBIyOPSik6Z4o96ACnDJFNJzShsCgB 8UjRSBx1BzXZ6dqwuYwTgNjkCuJzWjpdx5UpXsaTEdqr+b34p4s1cdqr6f8AOgrURNppWEVBZKP4 RUpjEQBFXkTcKguICFp2AjiuSWAFa9i7E5PIrCiiYOK27F9i/NQhM6yznzFUrn5c1nWMyFcd60WG 5OKTKRCZVHeoHnBPFRzowJIqABiwzQkJsti6Kg1SudUMbYzilnDItYt8xLU7Bc001tQMZqcamGGd wrkW3buKljuXTGegpAdaLlJl2kDmqV5pyScgCs+21BdyjvmtYXO8UxGLLZpECNua5+8s0dydo611 8+CT9K524IErZ9aTGivZW4RxwMV0NnGD2rKthlxituyGD83FNCZcb91HwcVzt5dObhgD3rZv7gLH gVzMswe4b60mM17RnYKSTWvA5jYGs2wQtGlbawDaKYjRtpyVBJq/BNk+1ZEbLGME1YiuFBxmpLNO RdwyKh5FPikDKcGmt1oAeoAGaYzAmkZwOtQu2elAwadY85xioDfJnris68mOWHNY9xe+WKLCudJN foF++KznuTITzxXOHVNxxz1rVtpVkjU56imhMLyYhOlZNxJnnOK1blNydawtRPlmhgjKvLjDN81c jeXDTSnPQVf1O7IkbHfiscnJJNJFDlB69q9y/Z+6eIv+3b/2rXhqnjHavcf2feniL/t2/wDatUSz 2yiiikIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigArxP44f8hPSfr/UV7ZXifxw/wCQlpP1/qKzqfCzpwf8eHqvzPDW60w09qjNamAlSRNtcHNR kUDigDr9DvUEhDOeldOJFeMbecivM7S4eJ8qcV2un3LPHACRyBQxGqCQcGpV6VA55zQkjYpAW16i rkWSaz4myRmtKAimJmlYttBya3raVRGOa5uJiOladszGMUmNM6a1mXb1rQt5AT1rmYZ2QcGtKwnY nrSKN5hkdKy7yIlTxWjG+SATSSxhgc0DOMu4WEn3ayJ42BPFdtcWkbNkg1jXlog3cGhOwmrnMFD6 VVuEYD5RW89sg9arvboetO5FjFXcBzVWdvnrZlgQZrIuowJaYEGfmqzbklqrAfOBV+zRd9CBkrBs DikjDbxkVe8oMBxTkgG7oaAGRJk4xVuOE7c7afbwAOeKvxxgDFJsaRXhhI/hrf02E+X92oYLdSBm tizQJHgUiiVEwnSqlwdrVfJwDWTeP83WgY1mBPWkkPyVSllZWGDSm4ZhgkUCIZTjNV3PPSpJWqrL IQeDQIH5BrPuDhqleZueapTSEnJNNCbKt24KcVh3coWJyTjFaV7IFTIIzXIaleyGGZcihgjC1K43 3ZKscVnO25s5p0jFnyaZimhiduaMUuD17UvbrQAnalpMc9aWgCWb/VW//XM/+hNUNTzY8q3/AOuZ /wDQmqDPNTDb7/zN8R8a9I/+koM8UhFKelJniqMBMUDPpS5OKCaAFUlSD6V2fhfUjtMT/Nk4rigT V/Tb2W0nUpgAnmgTPVL+0hksXAjGStcEdOFuyt710cGtPc7ImdSGAFO1zTUtIVcZ696TBGQm1WXi tm3kRlxtrnxMM9RWlZTMxGPWhAzSkhVhwKiUsjgg9KvwxmRiCKimgCAkZoA19CvZDcNlj0ro5bpj ZO1cdo7bJmPtW/NdKunPlhnmhiTMKXVnEjfWr2k6m0shBrkbi7O98Edau6Hdyec3T/JoTG1Y9rtI Q8CHHUVT1qExQgrxVjSpt0EQJH3afrKK9ufYUmUjgZNRmiJbeeDTU8QSDgsTVPVP3cLkf3jXNvdy LIcEU7knaf220vynPNZ93FHOHcqMkVz8F9N5g6VvwMZbYFupFPQTOQvoPIlwvArCkuZPPK7jjNdZ q8AE44Nc09ov2gkg9akoELs4BNXY4eRmmrboGByatLtGOaLgbUPy26DHaqN1cbJcVZikzEoBrG1K V1ucKOMU2JF60mJc811ejXDKgOehrg7W5ZSc4rYtNWkhXClaEwseqWt+SvPpWhHN5q5rzax1+diQ StdTpuqPJGMletAJmzPZRyrkqKrixjhYOFHFaqAPEGJHSqt2QluzZoKIhfBOMVIl9vrlrjUWjJwR xUMetup+8tArnU3UYlQsRnNchrMq2jgBetbMWqmS3GWWuS8TXf7xcEUtg3KE90kv3lzWLO0HmtmM VEt9IxPSnCPzPmI5NAIyp4xcTmOMba6K5dbPwp5cj4JTA+tMtNKt93nysV4NYPiLVo54/sURDJG2 QwpiOdY5JPvTBnNO9sim/jTGGOaMYpQDig9aAEzTmOQKTrR/KgBCSKKDzQMZpgB4pM0tGBwKAFFW 7aYJIuOtU+nSnxnaQaBHoek36tHGhzzUniSx8+BSmOlcxpeqCKSMNjg12XmpqsWyM5IHakwW5xsV uVYKe1dRY24MY4rFmhMNyyk9DWxY3ibABSBmibDcCcVU8jy2NbEEqyJ1qtPBhiR0p2Ea2gXoinjB zwK7dL4MvevNtNk8m5XPSurh1BNvUUMa0G6pd5uTisxL3EuOetQ6pqCi54xWQNSHndR1oSZLaueh WM4dk96663h/cg157pd+hkiyR2r0mzKvagg9qGikzIvpTCSfSsWTXNmc54rX1pcBiK4G8uwgcntU obOgPiBG4IP5Vm6nfJdwMuOvtXMnViG7YqxFqAnITuaqyFdnPX9lhmwBmsGaQwsVNdrfWjNuIrjN TgZJzSGtSsl97nNW1uNwFY6o+7pWnBG21cqaBF23QmUE+la9ooVWxVC2jO8fStW1iJVsUICrPKUf FPtbo5PJ4NVr3KzYxUME4ViCR1pIGj0HRtR2lBk13FrfLJGOteS6fqkcLLkjiuwsPEFuVA3CqEtD sZQJVzWbcaUJQTgc1Ysb2O6j+U1pLFvXipL3OZXSdjjIHFXVRYgOK0ZrYjJrKu5xEpz2pisWlnU8 VQ1S1FxETiqkerQhsMwq79thuICAw6UAcjdwCAHIrmr0q0hwM54xXT63MiB8GuOlvU3kHFFyTKvr VQSSc1mBCsmErbuf3oIUdaq22nTyTk4+Wkhs6HQpzDps5fqFrza9m8+7ll/vNmuo1TWH0uIQW7I7 NkOD2rjmYsxJ7092CDqeKU0i5xTuKoYGkGTS0A0gBqQCl60EcUAGKbzmnZ4waT8KYBUkbmNwR2qO lHNIDutBuFmiGOtdBtJ6V5/oN+ba5EZbCtXcw3Occigk0oSqrz1qGaVWOAKqSXDKTg1VF0xbrRcL GmiDcOKuRR7jgcVnwy7scitK3ZM8vzTEzUs4WXoa2Y/uDPpWRDKijhxWhDOjADcKTGh0iBsiolt8 HNWigajbtFSUY+oyqgxWDcSCRhgcVu6rHhC2Oa5K4mkSUgU0SWvLB7VBOgC8cUQ3JI+Y4p0rI6/e FMCqjNHICK1ba+YnDCsk4DdaejkNwaVwOgeVWGfasOeHfKxHrVuKUBfmaomZNxIIpsENt02N1rSi lHUVSjCmrIMaR9QDQIqaheZO2suCMySls45q5MomlPerlpZRAZPWlYdzRs49kae1aLXG1cCqCsqL gN0qN7g802JE0l8Q2MVNb3hZhWI8paXrV6yDMc4oGdVZThhVtyDyKyrJWAyeKsST7BjNSUidmyaY ehqhJfbehqudTJyMiiwXHXyjDGuZvR1rXubzcGy3FYtw4bvTEUQnNadvceWqjuBVBetPDgN1pAaz T+YuOlc5rlyI2INajTqiA7gK43xNdkzBVbqKGNGFdzGaZjnjNQUnWlBqgHr1r3H9n3/mYv8At2/9 q14cvWvcf2ff+Zi/7dv/AGrQJntlFFFIQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXifxvH/Ez0r6/wBRXtleJ/G//kJaV9f6is6nws6cH/Hh 6r8zw1utRnOKkbrUZIxWpgIRzRQOlGKYCg4OcVp2WpGKeHg4U+tZdKhw1Aj0WxvFuYw2APxq8VFc dot4ke1WbnNdjDMkiZBpAKnDir1s3J5qnwTxTlO2gRsxOB3FaltKPKHIrlo5wh5NaUE4aMYNG4bG 21wB3/WrlhdgP1/WucMnvU1vNhxhu9Kw7nfRTjcOf1q6rhh1rl7SfMijdWzBJyMmkUXygIqhdW25 TwPyq+jqRQ6gjgUhnJ3cBV8Y/Ss6UY4rrbm3LH7tYN3bOrn5e9AmjBmUnNZVxCWkro2gJJ+Wqc9u Q/3aq5NrGB5DCQVfs49r1Yki2gkrS24+fpTEatugdRx2q9Fahl6Cq9svyjjtWlbj5RUlIbHZc9vy qytnhRU0PU1PuUCgZGibasxyhBVVpFJ4NQyzqo60AXJboDPP61z1/fjzOP50txPkt81c/cy5frQk JsuyXpZup/OkW6IPX9ayWmVTyaT7Qp/iqrE3Zsm7yMf1qvLNk9f1ql5gI61GzE9DRYLltpRtNZt1 cqmef1p8twqxnLYxXLatqEauRv5xSY0ivrOrAAgevY1yck7O7Ek8n1p1zIZJGO4kZqt3oQxGOaTP FFAORimAZoFLj0oxTAM80maUe1H4UgJpv9Vb/wDXM/8AoTVDU05xFb/9cz/6E1RD3qYbff8Amb4j 416R/wDSUNpcZpSOaXOBiqMBAvrRgDrR1pDzQAvAoDYNN5o7UAaemzt9ug+bA3jv716j4qKT6USC rFVz8prxxGKkEHBBrr/C19Je3kltMxcPGcZoauLZ3MYyODnn8q1dMupAR8w6jtW3e+HhHbyP8vFc sWNrMBnoe1K49ztYLyQMfmXpRLO7KckVzdtqZLnIPSrf2wsvANFxWNEXEkXKMAapahql2IWUS8Y6 VAJi3rVW6RpSfmxSuOxSS4kMnJzmug0aUrISCOlYa2pyORWlZK0TfepoTPYtIu5CIhuHSti8dpIW z6Vxmg3DGeIZPSu2jTzkZfahjR5zrSYtpDz96uLlYiducV6vreilrR+R1ryrWrF7e4kAbo1SOxJa P++HIrsLAI1tGCRz715unmRHdvNaNrrrxMifNwapEtHYaxZwiVTjPFcbft5MjbcD6111rerfxbiv I9a5XX7NlkZw2BQ0NMxmvJcfeFMW8mLYLVU2t61Yt4S7YoA3LS6faDuHSqt9dP5vUdKpSpJAMh8A +lVn3s2SxoAljmdnOT+VaNsN6gkmiw08t8xwcitiCzWNOQKBEdplCcZro9NuJVQYbvWRGigngVYj uBEcU0I786nNHZDDjO0VkX+tXJtHy4rmr/WGFuANwrDudXkMDZLUhl251SdyQXFVft0x/jrnXvHY n5jTobphnJJpDOtj1O4S3AEg6Vk3t5NcON75rLlv2CYBYVJpZN1MATnkdaYbGpp9jHIxyCa6KKwt 1gGR0q79lh0mEO8atuGeK4rVvFgWeWOFCpBIFAitr+sOhktYXAUHHArk2OSSafPM1xM0j8s1Q00h gfWlAGetIelOGKAFBABpvWl2ij2pgIKUmgUbeKAG9aXABpSOKOM0AJSZ9qd9KTrQADpSrRjilC9q QFjeoIxXX+C7tPtUiu2OO5rh+Qav6ZcGC5VgSOe1MTOo1VW+3TMFyM9arWM6KeT3rpBafaNO87AO Vzk1xL/u7hx6Gp2Hud3ZXMflj5qmmnVuhrl7GfMYwTWoH3R5zTuKxaWdVfOanOqRoMb6wpHOTzSR qJGyRxW2HourKyObFYiNCHMy/dXHnyZBNUnU7gwPepQoA4pcV7qw8eTlZ8s8XP2nOmdDpl3GrxZb nIr1rSbtHsVIbPFeCxsVkU5xzXq/haUvpa5JNePisP7Jn0eBxft0a+sNuiYj0rzbUciOQ4716Zcx 74D9K4LVLbEUvAriR6DOEeXEhBNaGmSqbhBmsXUH2TsvQ0unXO2ZeTmmhM9GFqZUyBnNcZq9myXb hkrrtJvtyIpY1na/Hum3+tDBHDvAF64609XRR98VDqhKjKnFYpmbPLGktRnV291Fv++K17G5iL4L 8Vwls7bs81fjuWj4y34VoqcjN1IrqdFqLRtMSGFYE04WR8Z61oW8Xmx7pPmz60/7JBn/AFa13Qy9 yV7nl1M2jCTjYzUuTtHJzWtYXuzBLGmfZYR/AKZJEqD5QBTll7ir3FTzWM5KNj0bw1qqbcGSu/sr uN4x84rxLRpPLHJrs7TUTHEPmYV5zR68Wd5c3USo2XH51xes6hEI3xIKzNQ1k/MPMbpXGahqDOrZ djzUlF+41NfOOJP1q9p+tRrC26WvP5rjdJkE05bnZE3zGmKx1Wq6okpbEmRXOBmknAGTk1RE3msB k8+tdZodgJDESqnnvQkDFsbJm2lkwPepNS1Ox0yBsspkHBArW8S3CaTpTPjGMfdryK+uPtd1JNz8 xzzQCEv7gXV28q5wxqrSmkAx2pjHCjA3A0najnNMApwPNJQfXNADt2KaTRQOKADPajnFJ3zRQA44 IpOlJml9qAJraQRzK56A13unzJdRK6MOlee/hWro2omzuAGYhGpMTR3EqMQaqeWQaniu1ljyGBzT 1cZpAPhLAjiraNtOSai3YXOKYSzdMimI0ob4AYORWhbXq7lINc3uYHkkVZgnwRzRcVjt4rtWQHNT CdT3FcsL5VT7+KamoFmAD0WHc3r+ISx8EVzF7YM0pIFbEUjyD7xNTiIFckZoC5xskDRkgg1FtboM 10t5bZc4Ws/7KQx+SiwGasbNgY5qxHaseoq+luyqTt6Uhk2HkUWApyQMp71SdyjGt9DHKDnGazJ7 YFzgCk0BFb3BZguKnnJ2ioo0EZzjpRLLuXigAgYK/NaCuSBismNXduAa6Gxs5JIV+UmmgZVV2B6G pWG9cYNa403C5K00W2GwFoAx4NPeSXIBrdsbNogNy1oWtsI1ztFWwvHApXHYrN+7TpWHqN8Eety6 HyGuT1VT5mcUkNiG+V1yOtUGvGBPB61CCQ1OkTjORTESNch1xmowqsfvVTOdxp65Hei4FkxqB96q 0zrECS1BkCjlq57Wb8LGVVsnPak2NIl1TWI1i2xtlvauXnne4k3yN/8AWqJmLMT60hNNIAJx0OaA abnNKKYEqnmvcv2ff+Zi/wC3b/2rXhqda9z/AGff+Zi/7dv/AGrQJntdFFFIQUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXinxwGNR0g+p/qK9 rrxX44/8hDSPqf5is6nws6cJ/Hh6r8zwtqjNStTDzWpgM7UZxSmkxQAmM80tJilFAE0EjI4KnFdV pd65t/mcZrjxxUiyFRjJFAj02ykEkYJYE59a0PKRhXn+j6nFbhVkZs5NdVZ6zaPnDN+VAFmUBZCB V22JEYqss8U53LnH0q1FjZxQhFkEkc1LHwaiQZHFSLwOaANWzncTDkV0lrKWUZIri4ZQsgOa3rG6 Tjk0MaZ0aysOhqzDITjJrJjnQjrV6GRTjmpKLrDNZ9xArdRWgrLjmoZsYoGc9PCiE4BrPkQF627s rtNY0v3+tIRQukXaarQL8/FWLlhvxSWw+erRD3Ne3GI1+lXYcYFZ6yqFAqaOZRzmpLRpByvQ1G0z ZPIqq9wp6E1C0me5oAseaw71VnlYnk0q1DMppAV5GJzWXcIN1ahFQSKM1SJZjTKueRVfgmtS6ChT xzWJI2wEmmBoxgY61XupRGeGArMn1e3gT5mYfhXPapq8MsoKO+PpRcVjSv7+QJLhx7Vx95dSTPl2 B+lLNchy2GPNU2PPWkkUB5pMGhQobJGfalYk9BgelMBhFJg9qdmkoAADS45popeaYC4x0pcUlHNA E84/d2//AFzP/oTVB3qafiO3/wCuZ/8AQmqIGoht9/5m+I+Nekf/AElC4ooPSl4HUZqjATFJilJG eOKKAG4NIeacRTT9aADBHvWroF4bLV4XzgE7T9Kys05HKMGU4IOQaEJo9pmuLK5tdivlmHSuK1bR ZllLRxnH1rK0zxLNauomHmAHqa7SDUF1W3DKu0HihoFocMYbm3OSuM0gvpF4LD8q7C80J5kAD4xX LXOjtDvJfoan1GLBelm5apzcA9TWZCuxiae82OMUwNRGjOOauwlM1hJdbcfLVuG/55WkB3+i36x3 UeWGAK7/AEjUI7h2G8GvHbC/CzKdvau18O6sqTtlOtVuTsd9d20dxCVPOa898Q+GxJI7LGxyfWu/ iuw6jjqKqX8fmqfrUlnkN14eeKLPkt+dYcmnPHMSEPB9a9c1CzLQEA1xl5YMJpOaBGbpl48EZDED 8KTVCLmMkc5Haq8yeS22pox5qhaL3DY5xrJwM7Tx7063Xyny4xXQyWB2H5qyry0MSg5zQMgujGyD mmw2iyANgnn1pyxedhemK1LKzPlde9CEy/a28Uca9elJPMseQDUp/doBWRqF0I3PHahiRKdQC5+Y flQLwvyCD+FYDXYY9KtQXIEfSgZfubvcoVmHPtVKV0eMjNQTTB3XjoaVcO4UDrRYZWMI7Uqwvn5V rWi0t3I+arkWkupPzUxGVbaRcXW39yxB9DXWaL4VS2V5p4nXaM8tVvSY/I2KeSK19Y1pLGzZCmdy 0PQW5yfibxEgRY4ZV4GOlec3MpmneQ8kmrep3oupcgYwTWduoQxvNHegnNJTAXApRSD0p3FMAzmk ozjiigA69KWgGg8UAFGOaM0negBQMU2lzQM0AFA60uPekxQAVJG21g3pUdOFAHq2iXUVzoSDPOMY rlNS06WK4dtvGc0uh6vDb28cTSYOeldfqGnfardZkXcGXqKT7iWhwMVz5JwcjFaaanEUA3YqhqFk YZyNpH4Vn5CHntS3GzooZVmkwrZx2q8qgdKwdHlDTtk9q3gQe9e5gIpQufL5rKbq26C0UmR601n4 wOtdzkkrs8yMJSdkh4HIr0zwjME0pQx5yK87iiQqpY811uiXohtggbgGvHxteFRWifQ5bhalF3ke lJD51v8AL3Fc5qWjyNDLgDn2rd0u7ElsuDnirM6h4yPUV5jPbPAda0qZLuRSnPrisb7NLbtuKniv V9b0vzLl22Hn2rk9R0wpG3yHH0oEZNjqvkFdxYY963pLiO+gVg351yM8GzOBzWnY3ASAKTg00xNF XWbFvL4A/KuVWIpP8w4Brt7yTzkIJrnpbXLk4P5U4uzBq6sXLVbaRQFVc/SrP2aL+4Pyqhp8Yjl7 1q17+G5Zwu0fKY5zp1WkxAoUYHSilorrPObuFQyrkgepqaopTgqfeoq/AzWh/ERftFMS81ek1FYo OpGKpRuGiBBzVG/lxCRXzEnqfaxWhDd6urSH5mP41jz3nmAgZ/OoJAS2aYAc4xUlijml2PIu1QTm r1taeYBwfyrd0jRhNMo2MefSqFcw9P0a8nlTERxmvRIbUaNo32m6wgQZJIrY07SY7SHzHUqFGckV 5r448VXF3dTabFIDajI4NDfRAlco+JfE0epxywoG2kjBJ44rkT1oY4pPrQhiDmnUAYpQM0wEx6UU p4pARQAo5oIxQTikyMUgCjOKMD1oGAaYCUtKQOtLgUAIBS4wad0OQaDjuaQDTQDg0ZGeKDQBq6bq TQnY5JXtzXR2t8juBzXD5KnNaNlfsjqCenrRYD0SJ1cDAqzHEWPSufsb9nI5H51uW0565FCJC4tm B6VQcMprYeTeDuIrLuMZIBoYIqtKxPU1ds8lxVAKc1saXAJJRuz1pIGdBYW7ugIrTSEqAGFSW0KQ xDHpTy43UNjSKslspycVQe3+Y8VruwK1AIlc9aLg0UWgAiJx2rFvVG7iukuUCwsAe1c7eKaNxWsZ Tysj4BxUscuetVZ8iTpVb7U6Z4ouOxpuAynbUYhY8VWt7l5HANbNpCJOSTQtRbFjTbEvjiuvsrRU hUbR0rHsI1iAINa8VywXihsaHzALmqQK7uOtTzykqxPWsxZG8ykNmokm0c1ZjlVkrDnuymADRBqL b9uR+dFguas67hWDqVvk5ArXFxvXk1n3jBj1oQM5a4iZHPFVWYnvWvdxFmNZjxhTimxFYKSc02YM BxVoxrtyWrM1C8S2iLbhUsaKOoXfkx8tzXLTztNIxJ4zUt7fPdyZPCjoKpmmkMccU2k7UvbNUIQc /SnAUlODYGKAHL1Fe5fs+f8AMxf9u3/tWvDV6ivcv2fP+Zj/AO3b/wBq0CZ7bRRRSEFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4r8cP+P/SP r/UV7VXifxxP/Ey0ge/9RUVPhZ0YT+PD1X5nhjmmjJPSkdskZ6Cvcfg74TsZ9Em1XUoEZJW2pv7Y NaGJ4gR6jFIRXr3xo8LW2mtaalYQhLdl2ttHGSa8hzQA38KKXqaaaAF4zQRzSe9HNADlYg5zViK8 lj+6+KrUg68UwOu0fVwsJE0nP1rorfVIXiBD/rXmSuy9DirEd5MigK5AoFY9RjuywyjcVZhnJzuN cDpepTeWQ0o/GtyyvmcndKPzpCOn3+laFpNsIyawYZwXGXH51eSbnhhTA6iG4yuQav290QRk1ysU 7AcNWvbSZRSWGamw0zpI7oEfeqKe8AH3hWUJWHQ1Su5mC8NzmkVct3F3nPzVi3N5iTAaoJrh8Elq 569u388/PT2Jvc2JbwGblqsWt2u4/NXGXF46kkSDP1ptvqMu7mUfnRcLHfidmPBpftDLxmsCyvXK j94OlaCylkyWBNMRpx3RzyauRuGTNYAkPrV2GZtgG6k0NM2FximvzVUXBA+8Kilusfxj86Vh3Hyu FJ5rOmuVVuWqhe37K7/vB0rl7zVJg/8ArhT2FudHqF+sY4cdK5a+1gCFsPzWVqGozP8A8tc1hyTu w5bNG4yzc30kxOWyM1SaQseTTTyaMUwFzzSgUAetO7UAMNBPFKaSgBOtIeuKU4x1o4oAQYFGaDSY 4oAdmjmkAp2e1AEswzFb/wDXM/8AoTVFU03+qt/+uZ/9CaoRkVMNvv8AzN8R8a9I/wDpKHcU3GTx mnE8V23wosrfUPGttBcxLJGc5Uj2NUYHEBG7qfyowR2NfSviXVvA/ha+S01GzCyOCRtizUejt4D8 cCays7YGRVyQUKGgVz5upuK6Txr4fXw34lutPQ5jQ5Q+1c4aBiUUYozxQAo4rV0zWZ7OVF8zEQ7V kbqWgD03TNWW/wAlZQRWtc6RZy2bOQSxFeR29zLbtuicqa6PTfE1350Uck/y9DkUE7GncaTBEoIQ 1Sm06LYSEbNdfDfwTtiWWEj6ir8C2UrBS0RB9xSsPmPK3i2k4U1GhYGvXp/D9hJGSIQSfSua1XQo LcAx27dfQ0hnM2c0gkXngV02j3cqTcMK5CfzYSxVGXB9KS2vrlXyGIpoGj26zv5SV+cdKtXF6+zl hXldhql356AynGK2Lq/uGtgfMOaGJM6ye7Jj5cVi3Cq5Zietcre392kOVds1Rj1S/Z1UuxBP92hA zUvYk82nWEStLg+tTR2xnQNIjZ+lVrofY0ZkypHrRsPc35bOERtj0rlddj8uJdnrVNdcuyRmcYrW 0q5F7c7J2Rl98Ubi2OTt55RMRniti0u5Vjxkda6rUNBszAjpDyf7tY0mjxI+BE+PxoGZ9zfTADDC sm6uJJCSxGa6CTSYz/yzf9aibRoiOYn/ACNIDlsnNXLfdsreh0KKQkGCQ/ga39N8OW5jXdbv17g1 QHKWNmlxOA4Nblto1qZ1BDV1WuaVa6RojXFvGEkCjk1wF9r13Falo5VD56gClcR2y6TaRx5Gc/Ws 3UfLtsbGAzXnz+KNWZcG54+lUbjVLy6wZZmOKYHZf2vcQ3eEkXANZPiLX7qeYRiQFcc4rmDM5OS7 Z+tIWLck5PvSsMMkkk004pQc0mc9qYBzSBcU4DijpQAYpaBSYpgLRijFFACHril60h65paQABzRj BoHSlNMBMUq8mk6CvVPhT8PoPEBbVtUX/Q4jhVPG4ikB5gIJXUssbEeuKjOQcEEV9Fah8Q/BehXn 9mx2cUixnazeVnH445qPxL4S8O+NvDD6zoqolwE3rtGM+2O1FwPng0CnzRtDPJCw+ZGKn6g0w0wH A4OR1rsfD3jWazCWt4jTREhVx2rjOopwyBkGgVj3X+x9L1SET+WwLDPUVzt94Ps/MYpkVyWieL7z S4/KOZE6AE9K77RfFVlqVuGlZUk7g0gOKutFaxLNC+CDiqfnXsI++p+or1yS10++j+VkOec1Sfw1 aSdNtXCtOGzM50YT+JHlT3t3n5mH4Uov50wS2a7678Jp5nyKCPpXPan4ZlhJIQ4+lU685bsSw9Nb IxV1ifI5rotM1OUwZz3rl306VGPynirdnKIYyrNg5rK5rY9a0HXZViVTXYxX5kRc968V0zVQhA31 29nrQPljfSYLQ6+eOOUksKwdVs4TC4xRNrIUHmsa/wBdUowLUh3ObvrONWauclLRzsqniuiuLxZi SKxZY3eZiE4oEixYRvcSBWIwa6BfDsbQ7yeax9Lf7NIXkXFbTeKIUQx8cUw1Oc1O2/s9wU5qpDeg kKw5NbU+zW22qwyKqv4aki+cZOK3p4ipT+FnLXwdKt8SBE3rkMBQUwcbhSCKaIbdpqpcNKDwpzWv 1+t3OZ5TQsWjwcZqrdnEZNZxmuVYkA5qNri4lG1lOK7HjoyhZ7nHTyucKnMtjWtbkrbDNVbmV5si pLO2llhwFNaNpoU00oyh5ryGrs95aIxI7JpMDI5rVsvD/myAEiupi0SC2jDS4BXk5qZNS0yzOWkX igCtDoVvbQAtkn2rNvvECaFIGijZgvpUeveMrb/V27Zwewrz/UNQlvp2d24PQUrjsdHqnxE1a9Dx QsI4WGMd64+RzI5cnLMck0wk5oHSnYYhzQOlL2oHIoAToacOtIRxS5OMdhQApxTeDSnGKQDtTAXI HFKoTacg57U3PtTsUANIzR26Up4FHagBeoGKKCcCtvwXDHdeMtKgmUPG9wAynuKQGIwb0P5UhDY+ 6a+ovEsvg3wlBbNqdkqiYfLtj3dKydN1P4d+LZv7OhgAkcYAKbM/jRcR8545pTXcfEfwSfCOsDyc tZzf6s+h64riG6UDGn1oBxSGgelMDRsdSeCQBjx611en6kkycSKTXB8VIkzxH5GI/GlYD0sTkr96 q0k5zgEGuPttZmU7ZHJFa9vqCSYO7mpEbkAZiCRWzYu0bjArCt7ocYrWtZiXHNNCZ2cEzPAORVW5 mZXwDTLa4RIgCaguZVZyQaLahfQnSZipy3NMNzJH3qGO4jA5PNRXMwxkUxCS3srSbS3BpyoJRyM1 n78yZNaFpKqnk0AUruzAbhK5u9TY5GMV2F9fRJx0OK8+1TXIo7p0IzzUspF+wH74Emuz0hI26gYr z3S9ViuJtoGCOa63S74K2AaaBnWMgXpT42xVGK8DAE1aS4ixk0guLK5yeeKzXnKEkEVcnuYtpArC ubgZwKaBjruZ35z+VUorl0mxn86sL+9TiqdxCVfIoA27e7ZhjdTpHyeWrnBcPDznFRz6tsHLUXCx vyygAgYNY95KFbORWJceIlUn5qwL/W5bg4QkCle40jY1HWBBGwDAt0AFcvc3ktycuxI9KheRnOWJ JpmaaQ7iUfhQTzRmmIbnmnA9qNtLQAlAPNKQTSY5oAkXqK9y/Z8/5mP/ALdv/ateGrwRXuX7Pn/M x/8Abt/7VoEz22iiikIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigArw/wCOR/4m2kD3/qK9wrxD45D/AIm2kH3/AKioqfCzown8eHqvzPErO0kv r2K2iBLyttUD1r6E8YXaeC/hla2VuQk8gRlxxzwTXm3wj0H+1/GMU7LmO1xL7cGvYPFnjLwha3/9 m6wUkkhAO0rkDP4VZgZ10ieNvg/FIx3zxw+aw6ncM184yI0UrxsMMpwc19VeEfFHhnWxLpmjlQNp JjC44/Kvnrx/oraJ4vvLcrhXbzV+jE0AcvmkxzSk4pN1MYtJRmjpQAZpTSZzS9qAGnIpeaO1BPfN AD0kK8c1Ygu5I24NUuck0qsQcUAdDDrMysOBW5ZazJJhSorhhIwPWrMN7JEwIagVj0i21JwvAFac OrumPlrzWHWnRcF6sJrr8fvKAseoR6w7j7opZbouucV51D4gKjmSpH8SnbjzDSA6S/v2CMAOlcrd 37ed0qhca1JJuG/rWZLduzZ3UDLl3eszn6VVF04YHNVnkLNyabuwaYHU2GqSIo47Vt22rOYwMVwM dy6HhquRam6JjfQKx30OpMSflFWV1RwPuivPk1llP36k/t5tv36QWOzn1uVTwKxrzxJMshGK5mbV ZXPD1TkuJHbJbOaBmvc65NI7HHWsuW6eRsk1XZjnmkz70wHNIWptJmlOMUAJinZpBilzmgBQeKXN NzRQAuaO1J3oPrmgBGFIBmlzSZOaADpQM0macOaYCj3pM0uab1pATzf6q3/65n/0Jqh61LN/qbf/ AK5n/wBCaoetTDb7/wAzfEfGvSP/AKSgNehfBzjx5afj/I15+BXoPwd/5Hy0/H+RqjnPWPHPgnw1 4h1WO51bWHtJlUgIrAZGfepPBXgzw54dubi50bUDe3BTHzEHHPtXK/Ffwp4h1rxDBPpcEjxKhBKv jnNQfDHwV4n0vxGl7qIlhtkwWDSZDe3WkB5/8RLjULjxheSajCYpS2AvtXJE5Nem/Gq8tbnxWqW5 UvGCJCvrxXmXSmMO1NNO4ApO1ACA4pc0nagGgBwp30pgNGaAJRIwP3jWlp2qNBcRmR2KqeTWTmlB oFY9hsPHejwxxrI54HPStn/hMtBv02xkE474rwXPepobqSBsxtigD1zVtHa8tSbaEHccjArmp/DG oqVxbn8qp6d4rvyVT7SoCjHIretfEc8isJLlD6UrDuVbXQr9Z1JiIA9q2hpF35AHlms8a9KP+XhK tweIZCADcJQIZc6NdeXzF+lPs9KkWSPdEOCO1Lca8WT/AI+ErnLrxhJDI4SUFl6cUAemyRwwqoZA OK4Hxrdw4KocH0Fc7d+M9TuowPMCkd8Vh3Wo3F22Zn3UWGiHec9a0NJvVtroNI5ArLLCgP3HWmB7 xoOpWd7ZxICGYLnmtkWsEo3LGpr5+tdcv7PHkShcDHSryeMdbQYW6AH0osJHtrWcIPMS/lSpaW5c fulrxI+MdbJ5uh/3zTl8Ya0DkXI/75pWGfRFhp9ryTAnT0q/5NrEP9WoxXztB481+MHF2n/fNJL8 QNfOQbtP++KBnovxP1i0XRzbK4Ej8ACvFJpWMeCTzVjUtZvNVYNdyByDkYGKoGRvLMfG0nPTmhIL kZJpD9aU0gGaYhuKdtoA7UDNMAHHelowDR3oAeCMcU08UUDGTmgBAe1HWjI7Cj3pAKvSg9KT1NLn imAmO9LmgH0opAAOB9aKOwP4UopgNavpXQmOk/Bee5t+JBbeZkevFfNhxX0P8LdStfEfgW40KeQC UAoVJ/h4xSA+erhzLcSSMcszkn861NN8Ua1pFs1vYahNBCxyURsA10OtfDDxHZavLBDZNLEzko4I wQTXead8M9F0DwXNqHiSNvtYTftEm3HHSgDw2SRppXkdtzuSzE9yaaakuTGbuYwjERdtn0zxUX1o ABzSjgUUUALmnrI6cqxH0qPvRmmB1OkeLZLCKKJwzBRgmulh8e2yn5ga8yzS5pCse2ab4vtLqEtj vXQLHFqtsGjQHIr5/tdSmtUKoeM10+l/EPUNOjCKoZRx0FKwHaXvhafbIwQVwOoaRcQ3bKeDXXRf E6KVVWfAz1+Wo7rW/DN/KJZLzaxHIwaLDuchbxvFIAc5FdNazlWjOTV8aNpMoE0d0CrDI61Mtjpy kD7SOKBBLd70wCelZF1G8uQGOTXRLZ2JHE4/Oj7FYK25phj60wMCy0a6l245zW5D4fl28qM1Yj8U eHdNk8qW6AdOvyk4qCb4h6MkhEc4K+u2kBj+ILV9OgJbjIrz6W9JlY5Ndd4q8V2erRBYZM8elcE7 AsT60kUdb4V1JUvsOTgivS7CeG7idQBXh1ldm0nEi9a6rSPGLWjESYwT6VRNj0SXSt7EhBVKTQZH YkIPas1PiFabRmQZ+lP/AOFiWY6OPypBYkm8M3BBAQZqKLwheP8AwCr1r8RdLZl86UAd/lrorf4g eF1UFr1Qf900AU9I8IXEcWZEFdNDoy20WWReBXKan8YdNsLnybK2NzHjO8HH86wNW+Mk11aNHZ2Z hkPRmIIFJtsaSGeL/ENvb3VxahsOARxXmVxeSysfnYjPenX+oXGpXTXNywaVjkkDFUicimkMcSSc 00Z70fjS5piG0UUZpgKetJ3oPrSqOaAAfSgd6MnPFHNABmg0vQZopAJ3pc8e9GKQimAcg9aXtSYp T04oARuldD4DH/FdaN/18rXO4ro/An/I86MP+nlaAPU/j5/x5aT9G/pXj/hqaS38SadJHkMsykfn Xu/xg8M6p4htNNGm2xmMYO7BxjpXHeB/hTrI1+3vdUg+z28Dh+TnOO1IR1PxrRJ/B+lzyf6wEsPq VFfP3avYfjj4ht7maz0a1kDfZjufB9RjH6V44DQMDTeKl2g96Ty/cUAM7dKPrTzGfWgxtTAZ0qWG Zo2BU81GUYHpSDPcUgOhstWXAV8AiujsdSiLD5hXngYg1at7+aAjaRj3osI9cgv4pFxuHSmzXiZw DmuAtfE3lKA6ZNWW8VRn/lmfzpXYWOqN7g9KU6kCORXI/wDCTRk8xn86P+Ekh/55n86LhY60XSt8 1NfUhCc4rl18TwBceUfzqjeeImnBESbfrzRdhY6DUtaRgWPYVws8plmdyc5NLNcyzNl2qE5oSGW7 K6+yzh8ZHQ12WmX8Lr5kbD6VwdXLK9azfcDkelDA9Pt9UQAZFaSXkciA7hXnlprkTj5vlNa9trUR UYYEUXFY6S4vdgIC5rKNwZWwRio31WKQY4596gW7iDZP86Vx2Ohs2RYuWqO9ZAMhqyP7UREwuKrX GqBkySKLiSFu7gAdRWDf3XOMU681EE8EVh3d4ZThenekiiK4mDvkVBmjqKTpViHUgFANGaACijrR igBwpaQUmeKACijNAOTQA9ete5fs9/8AMx/9u3/tWvDVPNe4/s9f8zH/ANu3/tWgTPbqKKKQgooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvE/j gP8AiZaQff8AqK9srxT44f8AIR0j6/1FRU+FnTg/48PVfmeaeFfHF94QWUWUEbNIeWYkH6Vgavqd xrGpS31ycyyHJ5qu3Wo81oYGv4Z8SXnhXVBf2XMmwqQTgc1P4q8WXXiy9W7vIUSUALlSTkCsA0me KAENGKXrQKAExk0d6MilzQAgoHHWjOaDQAE5pMUY70d6YAOtGOaPejigBc4oFFA96AHA04S47Uzm ikBL5zDpSGVjTKSgB28k0ZzSdqO1ABRSY5pc4oAMEUmTmlzSZoAM80A80ZzRjmgApM0UcCgBc8UU GgcigAzRRgYoIoAKXpzSYpRQAtAGTSUZoAM80E0UhoADSZJo20vA4oAQCnD9KT6UZoAU+1FApe1A E0wHlW//AFzP/oTVD9Kmm/1Vv/1zP/oTVD3qYbff+ZviPjXpH/0lBWv4c8QXPhrVo9QtVVpU6AnF ZOKMDFUYHp3/AAvDXz/y7Q/99mql/wDGTxJdwNHGy2+4Y3I3P8q88I9KTFAElzdTXlw888hklc5L HvUROTSnim96ADtQMeppevSkHWgAx6UmKcOaDkcUAIOlAFGKOaAFozgUmMUuOKAG0dTS859qCeaA DJx1xRuIHU/nTT1FOHJoANxx1P509GbOQx6etRlTSjpQA7LZyScfWgnJo6rTcc9aAHZGKSlXFNP3 uKAEakFLijpQAuQOKM0nencYoAM0oOKYeTSk0APzwabnNIMmjoKAFzxTM96XIHSgYoAbmjOadSAU wEzSjrS0mM0gDpSUveigBM0vaijtTAMd6cKb1pfUUgA0lKKOlMBOnel7UGgUAAIHalAXGcn6U09a KAFzWjo2uX+g3yXenztFIp7HrWeOlJSA9btfjzq8Nv5cumW0zgY3u7Zrj/FXxA1nxYdl1J5cAORC pyK5THPFLg5oAbS9KUc0nWgApOaXGOaMd6YCjmkIo5zSn1pANxTjwKQClPvQAgpc0nSkJ4oAcTRm mDpTqALqareogVZ2CjgCl/ta9H/LdqoZwKTJoA0xrV+P+Xh6P7a1Ajm4fFZo5OM0uaLATNcSuzsz nLjDH1qL0pM0n40AKTTaUHNJ3oAWlzTaBzTAcGzS5pvQUA5pAPDYpQ1Rk0ZoAfupN1NBo6mgBSaQ /Sl60hoADRk0lOHSmA3oaO9KRQKQABRg0oODRu6+9ACUY/KkGc0uOcUAGKKXocUNzQAopCM0lFMA pc4FJRmkAVc0rUZdJ1W2v4QDJA4dQfWqY604UAenD446+AB9mhwBj75qrqPxl8SXtu0UbC2yMFkb n9a88HBzR9aAH3NzNeTvPPIzyOSWY9zUJJOM9ulOJFJkZoAOTR0NGaM80AGT60bm9aBQaAF80jrz TvMVuCMVHjNHegB/lg8q1HlmmHPanB2XvmmAmCOtGc04SZ+8PyowpHy0gE70UjA0g75oAdkjrSA4 pM5NKaAFoJ5oxRkUAHUUDnvSGlAFADwcAnNKsrqOHIqM9MdqTNAE4uplPEhp/wBvuf8Anoaqg0HN FgLZv5z/ABmmteTkf6w1X/GkPSlYLjmldzksabwaSl70wFNA560ck4AyaKADpR3oxQKAAUcUnejj tQA4GjtQOKOaAEPSijmjr1oAkWvcf2e+viP/ALdv/ateGrXuf7Pf/Mx/9u3/ALVoEz22iiikIKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArxT4 4f8AIS0j6/1Fe114p8cP+QlpH1/qKzq/Czpwf8eHqvzPDGqM1IQS2AMmoyOcVqYCHpRkdKOaCKAE 70YpcU7OMYoAaRgDA+tJTieKTqMUAIaKO1KPpQAhzSdvenUmKAEFFKBS4pgFJ+FKBmjvSABQBS4o 6UAJiiloxQAAHGSDSZp2TtwScelIKACk70uKNtABSUuOaKAEApaKSgAowDQBmlNABxRRik70ALRk GgjijjFACUUYooAKXtSYpfwoADRS0hFACdRRilA5pQMUAN5p2BRQOKADpSdqU0YoAkmJ8q2/65n/ ANCao+lTTf6q3/65n/0JqhqYbff+ZviPjXpH/wBJQpfIFJyaB9KXkCqMBSuB1puTS9BR1FADOcUU uOaQ+lADsgDA6Gk96AppSMUANxSk5NFGKAEpaTFKBQAlO7daQikIoAO9JjmloPpigBCB0pNpFLil AyKAHDsKQj2o6GngDbk0AMxijG0AmndTQRx7UAN60Y5zTgoppOTxQA3OCaCaXHNLgCgAIwKbml78 UmKADPpQTijHA96MCmABjzSdaMc0uKAEoBpcYowaAE55o6dqUcnmlx3pANo5BHpTsUnamAh60UpG OlHakAn1oxxxSjmgAigBOaKXkDmigApM0uKMUAIO1PHANN6cUooAaeaBS4oxTAUdDQMUmCKXb3pA LkUNjaMHnvTOlLQAA4pMmlNLQA3NGaUikAoAcDmnDmmUuTQA7FIelHNIc4oAQ9Kbxml780EZNMAx SZwKXHNGM0gEowfWlxg0fWmAmOaKdSY5pAIKXGaXGefSlC8UAMAxRmnYpMUwDHHNApfrR3pAJjml IAbg8ZowAKKAGnr7UU7GRSYoATNLn3oxRxnpTATNLS7KB1pAJjnFA96UClI4oAQkUmfSjg0YxQAU DpQRS9sUAGcdKMkEEUfSkxz1oAO9KaMUGgBMe9GM0uBSjg0AJjmkNPpuM0AJjilxg9eooxxSEZoA XPNBNGKQCgA5paQCjmgBRxSUuKMUAIOaU8ml4xSUAAxg0mO9LilxQAwg0Yp+OaSgBq049OKKCKAE yaXg9aQjNPZgzZ2hfYdKAE246UuKQnHSgHnmgBSeMUzvS5+alI5oATBzS87cUoFB60AJmm+9O7Ck xzQAAUnenYoIoATGOho7daKMUAJS4oxxQBmgAHqKWl4xSdKAFpDx1o5zSkZoAbRS4pOaAFFGeKQd KUdaAEB5p1H4Ud6AHLXuX7PfXxH/ANu3/tWvDlHNe4/s99fEf/bt/wC1aBM9uooopCCiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8S+Ob7L7TG xnaCcfiK9trzX4n+C9Q8Uy2j2IBMQIOfwqZK6saUpOEuZbo+c28gH/VS/wDfwf8AxNM/0f8A55S/ 9/B/8TXpJ+DfiI9UT9f8Kafgz4h/uJ+v+FHLH+mzb28+y/8AAY/5Hm/+j/8APKX/AL+D/wCJpf8A R/8AnlL/AN/B/wDE16Mfg14iH8Cfr/hTT8HPEWf9Uv5H/Cjlj5/ew9vPsv8AwGP+R51/o/8Azyl/ 7+D/AOJpQLcD/VS/9/B/8TXof/CnPEX/ADyX8j/hR/wp3xF/zxH5H/Ci0fP72Ht59l/4DH/I87P2 f/nlL/38H/xNH+j/APPKX/v4P/ia9DPwe8R/88B+R/woPwe8Rn/liPyP+FFo/wBNh7efZf8AgMf8 jzz/AEf/AJ5S/wDfwf8AxNH+j/8APKX/AL+D/wCJr0P/AIU74j/54j8j/hSf8Kd8R/8APEfkf8KO WP8ATYe3n2X/AIDH/I89/wBH/wCeUv8A38H/AMTR/o//ADyl/wC/g/8Aia9D/wCFPeJP+eI/I/4U n/Cn/En/ADwH5N/hRyx/psPbz7L/AMBj/kefAW7Z/dS8c/6wf/E0n+j/APPKX/v4P/ia9CPwf8Sf 88B+Tf4U0/CHxJ/z7/8Ajrf4UWj/AE2Ht59l/wCAx/yPP/8AR/8AnlL/AN/B/wDE0f6P/wA8pf8A v4P/AImvQP8AhUPiUf8ALv8A+Ot/hSf8Kh8SD/l2/wDHW/wotH+mw9vPsv8AwGP+RwGLf/nlL/38 H/xNLm3/AOeUv/fwf/E133/CofEn/Pt/463+FH/CovEn/Pt/463+FFo+f3sPbz7L/wABj/kcD/o/ /PKX/v4P/iaTNv8A88pf+/g/+Jrv/wDhUXiX/n2/8db/AAo/4VH4k/59T/3y3+FK0f6bD28+y/8A AY/5HAf6P/zyl/7+D/4ml/0cf8spf+/g/wDia74/CLxL/wA+x/75b/Cj/hUXiX/n2P8A3y3+FO0f 6bD28+y/8Bj/AJHA/wCj/wDPKX/v4P8A4mjNv/zyl/7+D/4mu9/4VJ4l/wCfU/8AfLf4UH4SeJf+ fU/98t/hRyx/psPbz7L/AMBj/kcEPs5P+ql/7+D/AOJpD9nz/qpf+/g/+Jrvh8JPEv8Az6n/AL5b /Ck/4VJ4l/59D/3y3+FHLHz+9h7efZf+Ax/yOC/0f/nlL/38H/xNH+j/APPKX/v4P/ia77/hUniX /n1P/fLf4Un/AAqTxLn/AI9D/wB8t/hRyx/psPbz7L/wGP8AkcF/o/8Azyl/7+D/AOJpf9H/AOeU v/fwf/E13n/CpPEo/wCXU/8AfLf4Uv8AwqPxKR/x6n/vlv8ACjlj/TYe3n2X/gMf8jgf9H/55S/9 /B/8TR/o/wDzyl/7+D/4mu9/4VH4l/59T/3y3+FL/wAKj8S/8+p/75b/AAo5Y/02Ht59l/4DH/I4 L/R/+eUv/fwf/E0n+j/88pf+/g/+Jrvh8I/Ep/5df/HW/wAKP+FReJf+fb/x1v8ACjlj/TYe3n2X /gMf8jgf9H/55S/9/B/8TR/o/wDzyl/7+D/4mu//AOFQ+Jf+fb/x1v8ACj/hUXiXp9m/8db/AAo5 V/TYfWJ9l/4DH/I4H/R/+eUv/fwf/E0Zt/8AnlL/AN/B/wDE13h+EfiYf8up/wC+W/wo/wCFR+Js f8ep/wC+W/wo5Y/02H1ifZf+Ax/yOC/0f/nlL/38H/xNKTb/APPKX/v4P/ia7z/hUfib/n1P/fLf 4UH4R+Jv+fX/AMdb/Cjlj/TYfWJ9l/4DH/I4PNv/AM8pf+/g/wDiaM2//PKX/v4P/ia7wfCPxN/z 6n/vlv8ACkPwk8Tj/l0P/fLf4Ucsf6bD6xPsv/AY/wCRwn+j/wDPKX/v4P8A4mlzb/8APKX/AL+D /wCJrux8JPE3/Pof++W/wpP+FTeJs/8AHo3/AHw3+FHLH+mxfWJ9l/4DH/I4X/R/+eMv/fwf/E0Z t/8AnlL/AN/B/wDE13R+E/ib/nzb/vhv8Kb/AMKo8Tf8+T/98N/hRyr+mx/WJ9l/4DH/ACOFlZX2 BVKhF2jJz3J9Peo9td7/AMKp8TZ/48ZP+/bf4Uf8Kp8Tf8+Mn/ftv8KpWSsjKc5TlzS3OD2mjZk1 3n/CqfE3/PjJ/wB+2/woPwq8TD/lxk/79t/hRdE2OCxSYNd6fhT4m/58ZP8Av23+FB+FXib/AJ8J f+/bf4U7oLHBgHBGKQrXeH4WeJsY/s+X/v03+FJ/wqzxN/0D5v8Av03+FFwscMq55ob5iOK7n/hV vib/AKB83/fp/wDCkPwu8TD/AJh05/7ZP/hRdBY4XZQVruv+FX+Jv+gbP/36f/Cmn4X+Jh/zDbg/ 9sX/AMKLoLHD7aNtdx/wq/xN/wBA24/78v8A4Un/AArDxN/0DLj/AL8v/hRcLHEYo2812x+GPiYf 8wu5/wC/L/4Uh+GXif8A6Bdz/wB+H/wouKxxW2kK812v/Cs/E/8A0Crr/vw/+FB+Gnif/oFXX/fh /wDCgdjittKFrtB8NfE+P+QTdf8Afh/8KT/hW3ij/oE3X/fh/wDCgDjdtKBzwK7H/hWvij/oE3X/ AH4f/Cj/AIVt4o/6BF3/AN+H/wAKAsccUowcV2J+G3ij/oE3X/fh/wDCj/hW3ijH/IIuv+/D/wCF AHG4zxSbDXZf8K28Uf8AQIu/+/D/AOFB+G/ijP8AyCLv/vw/+FAHGbaNtdl/wrbxR/0CLv8A8B3/ AMKT/hW/ikf8we7/APAd/wDCgDjttLtrsD8N/FP/AEB7v/wHf/Cj/hW/inP/ACB7v/wHf/CgLHH7 fak212B+G/in/oD3n/gO/wDhR/wrfxT/ANAe8/8AAd/8KBHH7cUba6//AIVz4p/6A15/4Dv/AIUH 4c+Kf+gNe/8AgO/+FAHH7aXbXXf8K58U/wDQGvf/AAGf/Cj/AIVz4p/6At7/AOA0n+FAHIbaXYTX XH4deKR/zBb3/wABpP8ACk/4V54p6f2Jff8AgNJ/hQByYQ0bK6s/D3xUP+YJf/8AgLJ/hR/wr7xV j/kB3/8A4Cyf4UAcnto2811f/CvvFP8A0Ar/AP8AAWT/AAo/4V94pH/MDv8A/wABZP8ACgDlljJ7 UuyurHgHxTtx/Yeof+Asn+FJ/wAID4pyc6FqH/gLJ/hQByrRkUzbXWt4C8UY/wCQFqJ/7dZP8Kj/ AOEA8U/9AHUf/AST/CgDlttG2uoPgLxSP+YBqX/gJJ/8TSf8IH4p/wChf1P/AMA5P/iaYHMbaNtd N/wgnin/AKF/U/8AwDk/+Jo/4QXxSP8AmXtT/wDAOT/4mgDmdtG2um/4QXxT/wBC9qn/AIByf/E0 h8DeKc/8i7qn/gHJ/wDE0Ac1toK5FdIfA3in/oXdV/8AAKT/AOJo/wCEH8U/9C7qv/gFJ/8AE0Ac 1tpdvHSuk/4QbxT/ANC7qv8A4BSf/E0v/CD+Kcf8i9qv/gHJ/wDE0gOa2mk210v/AAg/in/oXdV/ 8ApP/iaP+EH8U/8AQu6r/wCAUn/xNAHN7aNtdJ/wg/in/oXdV/8AAKT/AOJpP+EH8U/9C7qv/gFJ /wDE0Ac3to210v8Awg/in/oXdV/8ApP/AImkHgfxTn/kXNV/8ApP/iaYHN7aXb7V0f8Awg/in/oX NV/8ApP/AIml/wCEI8VH/mXNV/8AAKT/AOJpAc1syaNuK6UeCPFX/Quar/4BSf8AxNJ/wg3inP8A yLuqf+AUn/xNAHN7aNtdJ/wg/ir/AKF3Vf8AwCk/+Jo/4QfxV/0Lmq/+AUn/AMTTA5vaaNhNdJ/w g/ir/oXNV/8AAKT/AOJoHgfxV/0Lmq/+AUn/AMTQBzeyl210f/CEeKv+hc1X/wAApP8A4mj/AIQj xTj/AJFzVf8AwCl/+JoDQ5vbzS4rof8AhCPFWf8AkXNV/wDAKX/4mj/hCfFWf+Rb1b/wCl/+JoA5 4ikK10Z8FeKic/8ACN6t/wCAUv8A8TSf8IV4p/6FvVv/AABl/wDiaLCujnNlKErof+EK8Vf9C1q/ /gDL/wDE0f8ACF+Kv+ha1f8A8AZf/iaLBdHPbaNtdB/whnin/oWdX/8AAGX/AOJo/wCEM8U/9Czr H/gDL/8AE0WHdHP7aNtb58GeKf8AoWdY/wDACX/4mj/hDfFP/Qs6z/4AS/8AxNFgMDbxijbW/wD8 Ib4p/wChY1n/AMAJf/iaT/hDvFP/AELGs/8AgBL/APE0WAwtpNG2t3/hD/FP/Qsa1/4AS/8AxNH/ AAh/in/oV9a/8AJf/iaLAYO2jaa3f+EQ8U/9CvrX/gBL/wDE0f8ACIeKf+hX1v8A8AJf/iaAMLbR trd/4RDxT/0K+t/+C+X/AOJo/wCEQ8U/9Ctrf/gvl/8AiaLAYW2jbW7/AMIh4p/6FfW//BfL/wDE 0f8ACIeKf+hX1v8A8F8v/wATQBhbaNvtW7/wiPin/oVtb/8ABfL/APE0n/CI+Kf+hW1v/wAF8v8A 8TQBibaTbW7/AMIj4p/6FbW//BfL/wDE0f8ACIeKP+hX1v8A8F8v/wATQBhbaXbW5/wiPij/AKFb W/8AwXy//E0f8Ij4p/6FbW//AAXy/wDxNAGGVzQFxW7/AMIh4p/6FfW//BfL/wDE0f8ACIeKf+hX 1r/wXy//ABNAGEUFAWt0+EPFP/Qr61/4AS//ABNH/CH+Kf8AoWNa/wDACX/4mgDC20m2t7/hD/FP /Qsa1/4AS/8AxNH/AAh/in/oWNa/8AJf/iaQGFt46Um2t8eD/FP/AELGtf8AgBL/APE0f8If4o/6 FjWv/ACX/wCJpgYOKTbW/wD8Id4p/wChY1n/AMAJf/iaP+EO8U/9CxrX/gBL/wDE0AYO32pNtb// AAh/in/oWNa/8AJf/iaP+EO8Uf8AQsaz/wCAEv8A8TQBg7aNtb3/AAh3in/oWNZ/8AJf/iaP+EO8 U/8AQsaz/wCAEv8A8TQBgbaNtb//AAh3in/oWNZ/8AJf/iaP+EO8U4/5FjWv/ACX/wCJoAwdgzxR trd/4Q/xT/0LGtf+AEv/AMTS/wDCH+KOv/CL61/4AS//ABNFgMAJSlcVvf8ACIeKc/8AIr61/wCA Ev8A8TR/wiHij/oV9a/8AJf/AImiwGBt5pdtbv8Awh/ij/oV9a/8AJf/AImj/hEPFH/Qr63/AOAE v/xNFgMLFG2t3/hEPFH/AEK+t/8Agvl/+Jo/4RHxT/0K+t/+C+X/AOJosBhbaCgyOa3P+EQ8U/8A Qr63/wCC+X/4mj/hEPFH/Qr63/4L5f8A4miwzD20Ba3T4Q8Uf9Cvrf8A4L5f/iaT/hEfFH/Qra3/ AOC+X/4mgRhkUba3P+ER8Uf9Ctrf/gvl/wDiaP8AhEvFH/Qra3/4L5f/AImkBhbaXbW5/wAIj4o/ 6FbW/wDwXy//ABNJ/wAIl4o/6FbXP/BfL/8AE0wMTbRt9q2z4S8Uf9Ctrn/gvl/+JpT4S8Ucf8Ut rn/gvl/+JoAwitG2tz/hEfFH/Qra5/4L5f8A4mlHhLxR/wBCtrn/AIL5f/iaQGHtoxW5/wAIl4o/ 6FbXP/BfL/8AE0f8Ij4o/wChW1z/AMF8v/xNAzD2nNG2tz/hEfFH/Qra5/4L5f8A4mj/AIRHxR/0 K2t/+C+X/wCJpiMLbmjbW9/wiHij/oV9b/8ABfL/APE0f8Ih4o/6FfW//BfL/wDE0AYW3mjFb3/C H+KD/wAyxrf/AIAS/wDxNH/CH+KP+hY1r/wAl/8AiaAMRRzXuP7PowfEf/bt/wC1a8rXwf4o/wCh Y1r/AMAJf/ia9h+BukappR1/+0tMvbHzfs/l/aYGi348zONwGcZH5ikJnr9FFFIQUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXN6VpUOo21x c3NzqLSte3S/JqNwigLPIqgKrgAAADgdqlt3sjanTi4uc3ZKy0V97+a7HSUVk/8ACOWP/PfVP/Br c/8Axyj/AIRyx/576p/4Nbn/AOOUXl2/H/gD5aH8z+5f/JGtRWT/AMI5Y/8APfVP/Brc/wDxyj/h HLH/AJ76p/4Nbn/45ReXb8f+AHLQ/mf3L/5I1qKyf+Ecsf8Anvqn/g1uf/jlH/COWP8Az31T/wAG tz/8covLt+P/AAA5aH8z+5f/ACRrUVk/8I5Y/wDPfVP/AAa3P/xyj/hHLH/nvqn/AINbn/45ReXb 8f8AgBy0P5n9y/8AkjWorJ/4Ryx/576p/wCDW5/+OUf8I5Y/899U/wDBrc//AByi8u34/wDADlof zP7l/wDJGtRWT/wjlj/z31T/AMGtz/8AHKP+Ecsf+e+qf+DW5/8AjlF5dvx/4ActD+Z/cv8A5I1q Kyf+Ecsf+e+qf+DW5/8AjlH/AAjlj/z31T/wa3P/AMcovLt+P/ADlofzP7l/8ka1FZP/AAjlj/z3 1T/wa3P/AMco/wCEcsf+e+qf+DW5/wDjlF5dvx/4ActD+Z/cv/kjWorJ/wCEcsf+e+qf+DW5/wDj lH/COWP/AD31T/wa3P8A8covLt+P/ADlofzP7l/8ka1FZP8Awjlj/wA99U/8Gtz/APHKP+Ecsf8A nvqn/g1uf/jlF5dvx/4ActD+Z/cv/kjWorJ/4Ryx/wCe+qf+DW5/+OUf8I5Y/wDPfVP/AAa3P/xy i8u34/8AADlofzP7l/8AJGtRWT/wjlj/AM99U/8ABrc//HKP+Ecsf+e+qf8Ag1uf/jlF5dvx/wCA HLQ/mf3L/wCSNaisn/hHLH/nvqn/AINbn/45R/wjlj/z31T/AMGtz/8AHKLy7fj/AMAOWh/M/uX/ AMka1FZP/COWP/PfVP8Awa3P/wAco/4Ryx/576p/4Nbn/wCOUXl2/H/gBy0P5n9y/wDkjWorJ/4R yx/576p/4Nbn/wCOUf8ACOWP/PfVP/Brc/8Axyi8u34/8AOWh/M/uX/yRrUVk/8ACOWP/PfVP/Br c/8Axyj/AIRyx/576p/4Nbn/AOOUXl2/H/gBy0P5n9y/+SNaisn/AIRyx/576p/4Nbn/AOOUf8I5 Y/8APfVP/Brc/wDxyi8u34/8AOWh/M/uX/yRrUVk/wDCOWP/AD31T/wa3P8A8co/4Ryx/wCe+qf+ DW5/+OUXl2/H/gBy0P5n9y/+SNaisn/hHLH/AJ76p/4Nbn/45R/wjlj/AM99U/8ABrc//HKLy7fj /wAAOWh/M/uX/wAka1FZP/COWP8Az31T/wAGtz/8co/4Ryx/576p/wCDW5/+OUXl2/H/AIActD+Z /cv/AJI1qKyf+Ecsf+e+qf8Ag1uf/jlH/COWP/PfVP8Awa3P/wAcovLt+P8AwA5aH8z+5f8AyRrU Vk/8I5Y/899U/wDBrc//AByj/hHLH/nvqn/g1uf/AI5ReXb8f+AHLQ/mf3L/AOSNaisn/hHLH/nv qn/g1uf/AI5R/wAI5Y/899U/8Gtz/wDHKLy7fj/wA5aH8z+5f/JGtRWT/wAI5Y/899U/8Gtz/wDH KP8AhHLH/nvqn/g1uf8A45ReXb8f+AHLQ/mf3L/5I1qKyf8AhHLH/nvqn/g1uf8A45R/wjlj/wA9 9U/8Gtz/APHKLy7fj/wA5aH8z+5f/JGtRWT/AMI5Y/8APfVP/Brc/wDxyj/hHLH/AJ76p/4Nbn/4 5ReXb8f+AHLQ/mf3L/5I1qKyf+Ecsf8Anvqn/g1uf/jlH/COWP8Az31T/wAGtz/8covLt+P/AAA5 aH8z+5f/ACRrUVk/8I5Y/wDPfVP/AAa3P/xyj/hHLH/nvqn/AINbn/45ReXb8f8AgBy0P5n9y/8A kjWorJ/4Ryx/576p/wCDW5/+OUf8I5Y/899U/wDBrc//AByi8u34/wDADlofzP7l/wDJGtRWT/wj lj/z31T/AMGtz/8AHKP+Ecsf+e+qf+DW5/8AjlF5dvx/4ActD+Z/cv8A5I1qKyf+Ecsf+e+qf+DW 5/8AjlH/AAjlj/z31T/wa3P/AMcovLt+P/ADlofzP7l/8ka1FZP/AAjlj/z31T/wa3P/AMco/wCE csf+e+qf+DW5/wDjlF5dvx/4ActD+Z/cv/kjWorJ/wCEcsf+e+qf+DW5/wDjlH/COWP/AD31T/wa 3P8A8covLt+P/ADlofzP7l/8ka1FZP8Awjlj/wA99U/8Gtz/APHKP+Ecsf8Anvqn/g1uf/jlF5dv x/4ActD+Z/cv/kjWorJ/4Ryx/wCe+qf+DW5/+OUf8I5Y/wDPfVP/AAa3P/xyi8u34/8AADlofzP7 l/8AJGtRWNoMZgudZthNPJFBehY/PneVlBghYjc5JxlievetmnF3RFWHJKyd9n96uFFFFMzCiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigDG8U6reaH4dvdVs7aG5azhed4pZTHuVRkgEA84Fcn4D8f674+0a71Gy0bTrZYJvJCT3r5Zto PaM4HI9a6Xx1/wAiB4h/7B0//oBrwT4e6h4lsfg34kbQ9Ot54jPIJrg3JWWFTGgdlj24bC853Z9j jlwXNJr0/OxVtI+bPXbzxr4msNG8Qy3HhdX1PS/JMNtaztMtwshxvB2A4GDxjPB6V1XhvU7vWfDl hqN9YPYXVxEHktnzmM+nPP51zXxSvbzRfh7qGsaXdSWd/AsW2aLGSN4GDkHIwx/OuZ1Xxzr+k/AL TfEMEpm1W5REe6dA3l7mILkYx2AHGMkVLdk32siEm3Hzu/ut+R6/RXhXjPVNa8J6F4R1PTPEeqvc arsW9DT+d5+5VYtGsm5UPJACgDkV7faxPBaQxSTyTuiBWlkADOQPvHAAyfYAVTVr+TsCkna3VXJq yvEGuxeHtJuNQls7y6SCJpmS2i3HaoyTk4A/E/TNatYnjH/kSNe/7B1x/wCi2qJu0WzSnHmmovqZ Xw68cf8ACfaJd6otj9jjiu2t44zJvJUKpyTgc/N0rsK8g/Zy/wCSf33/AGE3/wDRcdev1rUiouy7 L8kZxdzhbzxxrcPj8+Ebbw/Yy3DWv2yOeTU2RDHkjkCEkNkdOfrV2z8bbPE8HhvXdMk0vU7lDJas somguAOoSTAOR6FR/LPI6zezWH7RFtNBp13qD/2DjybUxh8eY3P7x1GPxrVudA1vxb4+0jX7/S20 rTtESRraC4mRp7iVgOSI2ZVUYH8RPHTmoj0v53/EHfW2/T8D0ao5J4oWjWSRUMjbEBP3mwTgfgD+ VeV+IvEmu+GfAyavf6o3/CS288Ul3YwsskKo8uPLYKpCLsPDEg5H3j3teOrO4ufih4HEeq31ss7X ICwsm2MrFncoZSNxyQSc8dMU+V6etvwuNdfS53Wq3uq2t7pken6Wt5BPPsu5TME+zx4+/g/e57Ct SuG8aanrGian4Sis9TcW93qcVncq0SFplIJJLY4zj+EDrSaj4gn1TxPqmjwy6vb2umrEkkmmQK7y Suu45Yq20AFcYwSc546rp8/8v8xX1+S/U7qivO9D1Hxa3hTxLFqj3dtc2JlbTtRuLZFkmiCsULJj aWGOeB1FY15qfiofCSz8Zp4klS+gs4rk26wR+TMMjd5mVLFiCehAHGAOtJtK9+lvxKSb09fwPWZp 4rePzJpFjTIXcxwMk4A/EkCpK8w+KizX2neELmO+u7ZZ9YtFaOFlC/NyGIIOSpAxnj2NekWdu9ra pDJdTXTr1mn272577QB+QFVbR372/L/MXbzVySVzHEzhHkIHCp1PsM1z3g/xYPFsOqSjT5bL7Bfv ZGOZ1ZyUC5J25A5JGAT0610leefCjp4w/wCxju//AGWiKvf0/Vf5g9r+f6M9DorFvtP1qbxRpl7a 6ssGkwRyC7sjECZ2I+U7u2Dz+HvW1S6AcxH4quT8Rm8Ky6ckcf2A3qXQn3FxvCY2bRjv3NdPXmGs W19efHSKCwv/ALC7eHjvnWIO6r538Ab5Q3TkgjrxVrQdU1/QviNL4R1nU31W0u7M3lheTRKsiFTh o22gBu5z9PXgWqXz/Bv9EOa5W/l+KX6s6/T77WLi81WO70lLaG3lC2UpuAftK4+8cAlefWs/wX4q m8V2epzT2C2UljqEti0azeaCY8ZO7aO5PaqHg/V9WvfGXi/TtRvvtUOn3ECWw8pUCKyFiOBz16kn pVD4UlhY+LSgBf8A4SK92gngnK04rRt/y3/L/Mm/5/5nolRyTxQtGskioZG2ICfvNgnA/AH8q8r8 ReJNd8M+Bk1e/wBUb/hJbeeKS7sYWWSFUeXHlsFUhF2HhiQcj7x72vHVncXPxQ8DiPVb62WdrkBY WTbGVizuUMpG45IJOeOmKOV6etvwuNdfS56dRXFa3r1xH4kt/DME2pYisRdXVzZwrJO2W2Kv3SFz hiSAO2Mdq/hO78UHXNa0u9/tCTSlRZNN1HULZVlBwNyMAAGwTwSM8HrS/r7g/r7zvaK8w8CTeLfF /hnSdZufE5t2ivpDPHHaRkXUayEFW4G3gYG3oOTknibX9U1zS/DfiO/1LVWs9WgM8+m2toySBYE+ 4zIFOVbHzFs4z1XsbK/9dP6+TG007edjovHniq58G+HJNYh06O9jjdEkVrjyyu5goI+U55I9K6ZT lQfUV5b8StRfWPgSmpyIEku4rKdlXoC0kbED869ST7i/SrceVNPdNr8ETe9vQzfEOuWvhvQL3WLw MYLWMuVXGWPQKM9ySB+NVbG/8Qtrotb7SbZdOktvNW8guN3lyZH7plIBPUkMOOOlcl8crWWX4cXt yl/cwpE0Qa3j2eXLmVMbsqW47YI981Z8cTa34R+Gmpajp/iG8mvIQjrPdwwuygsqlQFRVxyTyCaj aPM+/wDX5lqN2orqeg0V5/rOoa14Q0fUfF+oa2by1SwUJpXkKsaTsVCkSD5iOcHPJyTxwBY1mz8R 2PgyTV7PX7iTWba3+1ujRIYJ8Dc0Xl7eFxkAg7umSaJWje/Tf8SY+9a3X+v1O4orzPxF4w1S88B+ GfEei3f2F9QvLVJoSiurCRsMhJGQAe4wcVc1K517w5488NpJrs1/Y6xLLb3NtNDGqRsE3KYtqggD GMEscdSetGzs+9vn/TC91ddr/L+kd4J4jcG3EimZVDlM8hSSAf0P5VJXl3hfT7lvjF4yzrOolYPs j7S0ZEilWbYcpwoyQAuOvUnmt3RtUuvGGu68I9QntNN0u6NhFFbbVaSRQC8jMQTwTgAYHBznIwdF 6XG1Zv5flc7SivMI/E/iCyHjPw9dahv1HRbQ3tlqLQLumiKFhvUALuU4GQMH04pkUfjPVfhtY+Jb TxVNHqa6el0tsttF5MxC7iHyuSzeoIA7AUnJJX6f1/kHK9v66f5o9SoriItd1rxJ4M8O6tpRhtEv Xik1KYuqmCEAmQpuBHUY6E4P4iDRPEc8nxNvvD1tfy3mmPpSX0Ekw3bH37TsfALoQQc5IyDg9qvl d3H1/BXJUk0mjvqy4b3VX8SXVnLpappSQI8N95wJkkJ5TZ1GPWuB0KTxf4r/AOEntE8VGwk07WHt 7e4js42JRcfIVIxtx75JPXAwduy1jVk+LeoaNdah5umx6Sl3HCIlUIxk2k5HJ4Hc96la2f8AW1xy 9267O342O3qOaeK3j8yaRY0yF3McDJOAPxJAry9tf1nxP4Zm1rS7nX7W8lWSTT4bS0RoMAsIwxZD v3YG7JwCeMY5p+OJdY1bwr4IvNTe80vUJdXtYrq1iKqokLH58EHkFcqDkDPQ00m2l5pfeP8A4P4H sFBzg4GTXN65Drtrp2m22m3zOn2pf7Qvrl41kS3GSzD5QueAPu9D+IxtC8SSyfE+98PwX0t7pb6W l9C8ozsbftOx8DehBBzlhnOD2oSu7L+tLibt/XnY1fDniq41rxN4h0a506O0fR3hTek/mCXzFLA/ dGOAOOetdRXnvgz/AJKr8Qv+utl/6JNehU2rJei/IXV+oUV5/p+o6p4tuPFiQ6vc6ZPpl41nZxwq mI9qgiRwynduYng8YHGDk0vgq58R+KfhmmoXOsyxapewt5Mot408l1LKDjGGBIB6dOlT9ly9H95V tbedvuO/orzvwr4pvdQ+Gd1cXlzK+v2kkllcKxQMLsNsRRhcDJKY4796PEV/4k0fxT4L0e11nzE1 AzRXTTQITIyRg7yQPUk4GBwBR1t6fj/X4iSbX3/geiUHODgZNcVNpvi3StNgt18Qf2gsupeZdX08 cUMltZ4ywHG0nIxnH8XAGMivoXiSWT4n3vh+C+lvdLfS0voXlGdjb9p2Pgb0IIOcsM5we1NK7sv6 0v8A16A9P687Gr4c8VXGteJvEOjXOnR2j6O8Kb0n8wS+YpYH7oxwBxz1rqK898Gf8lV+IX/XWy/9 EmvQqbVkvRfkLq/UK5rx14nuPB/he41qDT0vlgKiSNp/KwGYKCPlOeT04qnfTaourawdW1J7Cx2q mkR2jqZZSE3O+wKWZgf4eRgfd7nivEHiC88Ufs1SavfbTdzRosrKMBmW4CbsDpnbnj1pJX19PxLi k5qL6nskT+ZEjkY3KDTq8v8AF7eL/CehJ4rtfEL3q2hje70xraNYHiJAIQgblxnOSSfftV271fW7 34kaZpthrLQaXqOkPdhDbozRHcPmUkctg8bsgdcHpSb1t/W1yFtd/wBbL9Ueh0VwvhPUNWtPG3iL w5qWqS6lbWcUFzbXFwiCVVcHcrFFUHkccUvha+vvHWj3Wt/2td2VtPPLFYxWgQeSiMVDtuU7mJBJ ByuMDHUlJp7f10G1bf8ArqdzRXmuk+LddvfBfiuG6uI4de8PNPC10sAKzbELJJsPALY6dPzxV3wj b+J9c0nw7r914neOOWxDXNmlrGVlLJw+7GQ2TuPGOAAByS1r/XfYdrfivu3O9ory/wALy+J/FHg7 UZ7jxTc293a3lzDFNb28SlzGSF3gqQV6cKF9yahg1TxLr3wjXxaPEE9hfw2Mk6RWsMXlSNHnmTch JLbScAqBnGDjJSd483TR/fqJauy819zserUHODgZNcdJqmu6z4S8OajprQ2q3vkT6lMXVTBAU3OU 3AjOcDp0PbqKeheJJZPife+H4L6W90t9LS+heUZ2Nv2nY+BvQgg5ywznB7VfI+Zx6q/4aivpc1fD niu51rxN4h0a506O1fR3hTek/mCXzFLA/dGOAOOetamg3uq31lLJq+mLp06zuiRCYSbowflfI6ZH auO8IB2+KPxEEbhHMllhiM4/cntVrwJqWreIfDmvJqepzPcw6ldWiXMSJG6ImANoAwCPcGlLTVfy p/l/mF9bedvzOze632k8tmFuJIw6qgcAM65+XPbkY9qr6Jdahe6La3Gq2AsL6RMzWwkEgjPpuHBr zv4T2t+Phet7FrN35r/adqTKkiIwlf5x8oYkkZOWI5PFXdE8bX9t8FrXxRqDfbdRePALKFEkrSlE yFAwMkdB0FDVr/L8b/19w3pKz8/wPR6K8w1W+8VWFxpt9or+IdRl+0qt9aXlkiQyQnO4rhQVI4xg /XPOb2v+L7bTPH40bXdTn0jT5rNHsZ1wkcsu5t++Qg4KjbgHC8nOcil28/8Ahw/r9D0GiqWkx3UW lwR3t19quFBDXGFHm8nDYUADIweBisfx8usr4N1G50C9ktdStojPEY0V9+3kphgeozj3xSk+VXY4 rmdkdLRXlXi3xZeQeBvDPijS9Zu4beaWA3cSRpI0sJGZP4DhlweRgcH2rtriea88RaVbWV/MsMcL XVyI9hWWM/LGGyCRubJBGPuNVNWbXnYm90n5XLcN7qr+JLqzl0tU0pIEeG+84EySE8ps6jHrV9Z4 5HmjidXkiOHUH7pIyAfTgj8642z1XV2+LmoaHPqBfT10lbqGJYlXy3aTbnOMnp3OPasb4VWFyupe KppNY1CYQa5cRNHK0bCbCqAznZuz06EDgcUoq6+Tf42H3fml+FzqvCHimbxM+tJcWCWcmmag9iVW bzQ5UD5s7R1z0xXTV5P4J0zUNUvvG8VrrVxpkQ8QXB3WkaGRmwvUurDb04AB966v4b61qOueEI5t VmWe+t7ia1lmVAokMbld2BwMgClF3S72T/Ic1yya82vzOtrJ1rWjp3hm81qxhjvlt7drhU87Ysiq CThsHsPSqfj2OWTwFrnk3dxaullLIJIGCt8qk4yQcA4wcc47iuX0azltvgTJJJf3Nys2gFljm2bY h5B4XaoOPqSeOtKV+WT7f8H/ACLhG8op9X/l/mdt4b1dtf8ADOm6u0IhN7bJOYg27ZuGcZ70are6 ra3umR6fpa3kE8+y7lMwT7PHj7+D97nsK8wQeINK+C2meItP8Qz28thpsE0dmkMZgdAFyHypckju GA9BXTeLNe1e0uvBs9je+RbanqEMNzAIlO9WUtjcckdO2Kt6z5V3X4vQxvaF32f4He1HO0yQO0Ea ySgfIjvsDH0Jwcfka5rU9Wur3xnbeGLG5a0C2Zvry4jVTJs3hUjXcCBuO7JwcAcYJyJ9HsfENh4j 1JLu/F5oTxo1n5xBmik/iUkAZXvk5PSp6FbCeCfE03i3w9/ac9itlJ9olhaAS+ZtKMV+9gZ6eldH Xj/w61lbvQZ/C+mX62+sPeXc7yAjNvF5xG8KR87c8Dp3PYHrfEOsXOiXOjaBBdahPdah5jSXaRJJ OscagsyqF27iSo+7gAk46URfNFPul+WoPRtdm/z0Ozorz7Q7zxLb+OvsW3V7vw7c2xb7RqNuqPbT gk4DAAlSMdQeSOag8PP4k8R3vinT7jxNc28dhqbQQTW9vEJdu0FVJKldoz6bj6jpQ3+V/wAbB/X4 XPSKK820Xx1qNt8I5de1PZd6pbSyWm5V2rNKJfKQkD1JXOPfFQ6rfeKrC402+0V/EOoy/aVW+tLy yRIZITncVwoKkcYwfrnnLWrVtmD0v8/wPT65nSfFF1qHjXWPD1xpyW40+GKZJln8zzg+cHG0bcY6 c1XGr3OveOb/AEK1u5LWz0mCJ7toQPMmllBKpkg7VCjJxySRyACDjeFbe5tfjH4riubt7rFjamOS RQH2fNgNgAEg5GcdMZ5pfaXnf8n/AF6A9E/l+a/zPR6KK5DSr278XahrEkeoXFnp1jdvYQx220NI 6Ab5GYgnhiQFGBwSc5GGB19Fee3+veJvCfgq2i1m5tLnXrvUV0+0uQnyMHfCSOoAGQuTgegqTxtN rng3w6PEWn6td3xsGRr21ulQpdRkhWPyqNjDOcrgeoNF1v0Gk3p1O+qOSeKFo1kkVDI2xAT95sE4 H4A/lXnl1rWvaj8S7TR7DWPs+l32iG9TFum+Il8BhkHc2MYB45JwcYrH13RdYtfHHgCy1DxTqF3d M12r3McccYyqEhghVhu2ttJOeBximk20v66/5Ba97dr/AIXPX6K4rW9euI/Elv4Zgm1LEViLq6ub OFZJ2y2xV+6QucMSQB2xjtD4QuvEja/rGl6kupy6OEWTT9QvYVjmGQAyEgAHBPBIzwetJa/12E9P 67nd0V5Va6p4nsvFOoeC9R1S7lvryRZ9L1QrEoW16vxtwXXBGMHJIOAOa9Rt4jBbpE00kxUYMkmN ze5wAPyFC1VwejsSVkeHSF0qdmIAF9ekk9v9JlrXrzLxrez2nws1GK3kMb3uqT2ZcdQsl46t/wCO kj8ah/Gl3/Vo3jZUJN7XX5SO80rVBq8TXVvF/oDf8e85bmcd2A/uehzz1xjBOJ498c2ngXTLK8uk 8z7TdxwbfRCcu/4KD+OK6iCGO2t4oIlCxxoEVQOAAMAV5L8UNFtvGnh7XrpJ4muNK+SxUuMlo+Zs Drls7MesYqpyUXddPyMYJvf+meuI6yIrowZWGQR0IqlDqanVH025QQ3O0ywjdkTRg4LKeORkZHbI 6gg1w3wS8UnxH4AggncteaYfssuTyVAyjf8AfPH1U1f+JtxJpen6JrUD7JrDVoCSBy0chMbr+Ib9 KqS5ZJea/HYhSvFt9P0O5rhPGPxHbwRr+mWuq6Uh0q/YqL+K6JaLGAS0ezoNw6MeM/Su7rlPEWl2 et+J9P03UIFntLjTb1JEbuC8H5HuD2pK91/XQ0ik9GdFNPK9gZ9OSC5kZA0IeYoj56fOFbA98Gsr wnrWpeINHXUNQ0qLTfMZhHCl357YVipLfIoHI7Z4rhPB2qXnw88TL4C8QXLyafOS2h38vRlz/qWP qO3vx0Kiugk8Rp4T+FU+ttGJGtkkMcZOAzmVlUH2yRSlJLVbMlXcuXr/AF+B3NFcPrNn4jsfBkmr 2ev3Ems21v8Aa3RokME+BuaLy9vC4yAQd3TJNZHiPxpq9z4L8Ja/otwlmdVvrWGaBowwIcncu4jI GRjIGcU3o7ei+8Fqr+Tf3bnp9Fee+KIPFHhvwn4m1dPFk08ixLPaKbOIfZ9udyjggqc8ZGRjqTkm j4r1HxJonhDTfFkGvSvOrWpmsDDGLaVJCqkfdLg/MPm3HvgChauy8vxE2l+P4f8ADnqFFed+J7vX PC+s+HNRGvXN3DqGpxWN1ZSRRLCFkBGY8LuXBGeWJ9TW7qkmpL4nH229Wx8OragRvHMqSTXTNjb0 3cAcAYyT36Atpf8AruP+v0N++mnt7Gaa2gSeZFLLG8mwNjtuwcflXM6H4u1HxH8OE8S6bo6yX80c jQ2HnjDFXKgbyB6Z6Cqvw/8AEN74g8OayL6aSeTT9RubJJpYwkkiJgqXUAYbDAHgdOlcv4SvdQ0z 9m6LUNLuxa3VraXMySeUr8rI5xhuP0NOStCTfl+KbBbpebPWrWSaWzhkuIfJnaNWki3btjEcrkdc HjNTVz1imo634N0h01Oa0uZ7eCWe6hVPMOUBbAZWXJPt3Ncno+oa3rfxJnsdK1++m0DRvk1CaZIW Fxcf88kKxjAH8R68EccGm177j6kxfuKR6bRXnba9deJLrVvIudds7ezu5LO3OmWyuGaPAZ3ZkbJ3 bhtzjGM8niC28Y+JdO+H9i+uWf2fxHd3yaZCZ4tqu7thZSo7bckgYBIxxmpWv4fjt/X/AASv6+46 Xxl4ouvCtrYXEOnJdxXN5FaOzT7DFvbAbG07vpkV01eXfE7TL200PRH/ALXurlBq9otwtwEPmHfw w2qNpzjgYGCeOleo0o9fX9ED3+X6sKK5zxDJqqatphS6istBQSvqV0ZVjYYAEagt0BJOSMHjqO+R 4M1671TxJ4t0OW7uJ7bTZofss8qbJQkiElT8oyAQcEjJBHJ61SV7/wBeQPQ7qqGtXN/Z6NdXGl2I vr6OMmG2MgQSN6bjwK85tdU8T2XinUPBeo6pdy315Is+l6oViULa9X424LrgjGDkkHAHNdd40uNS 0XwBql5puovHeWVo8q3EsayMxUZ5BG3J+n4VEn7vN/X9Ia+Kx0NrJNLZwyXEPkztGrSRbt2xiOVy OuDxmpjnBwMmuOku/EF54R8N3FjcRobgW8mp3bsitHBs3SMu4YyTgdO/GOop6F4klk+J974fgvpb 3S30tL6F5RnY2/adj4G9CCDnLDOcHtWri+Zx9fw1Ii/dT9DV8OeKrjWvE3iHRrnTo7R9HeFN6T+Y JfMUsD90Y4A45611Fee+DP8AkqvxC/662X/ok16FSasl6L8h9X6hRXHX02qLq2sHVtSewsdqppEd o6mWUhNzvsClmYH+HkYH3e55t/GGu6j8Ah4qgvvsmrJblmmSJGDlJChO1gQMgZ4HGahu0XLtb8f+ GKSvJR7nqtZdxe6rH4js7OHS1l0uSF2nvfOAMTj7q7Opz61xPiW88R6Bb6D4gXXpZVuL22t7qwMM YgMcnB2/Lv3DIOSx/AcVqaxq2rWfxU8OaWl//wASy+guXkthEoyyKMEt17+oqkryt6r7lclv3b+j /E7aivPtf8X22mfEAaNrupz6Rp81mj2M64SOWXc2/dIQcFRtwDheTnORVnWdb1PTP+Ea8OJqAbVd XneJtQ2ISIkBZpFXGzeRtxxtBPQ4xUp3s++n42/ryH1a/rudxUbTxJOkDSKJZAWRCeWAxkj6ZH51 wfizU9V8D32j6pFqFxe6Rc3aWd9a3IVjHv6SowUMCCOQcg5GAKzr3T7m4+PaRprOowK2iGZRG0ZE Y80AooZCApwCeM5701q18/wVx20f3/e7HqNFeaRXXinW/Hfi3w7D4iNnBaQW7W08dqheEupbABHO T1JOcDAxnI0dO1LUtV8XT+GG1aQR6JZQG/uYY1SS8nkX6HYuBuwuDkjnA5Fql5/8H/IGrXv0/W3+ Z3VFcJp2tatpHxIl8J6hePfWd5Zm80+6mRRJGQcNExUAMByQTz6k5rN8NSeJ/FU3ie0uPFM9qmn6 tLawy2trEH2qBgHKkbec8DJP8Xalft/Wtgt/l96v+R6bRXlOn6t4x1fwd4l0xdTMfifQLlokuoYk xdKBuXKkEAsMjjHb3rdsfEMviD4eaPd6bfzR3+pGKBZcoXSXP73OV2kqFkOMfw01rt5fjsL/AIP4 bnc0U1F2IqlmYgY3N1Pua4v4narq2ieHba/0i/NrKL6CJ18pXWRHcKQcjI69iDSbtb5L79AvZNvo dtRXnPim58R+GvEHh64ttfkuo9UvhYz2lxBH5CF1JDIFAYbSCcFiT0zU093rHhz4laBp0mt3epWG tR3KyRXSRDyXjUOGQoi4HbB/Wqim/wAfwVwPQKK46+m1RdW1g6tqT2FjtVNIjtHUyykJud9gUszA /wAPIwPu9zy1z411+7+AEfiy3vVtdVSIeZKsKtvIm8snBGBnr0pfZcu1vxGleSiup61RXmfjA+Lv D/h0eLLLxFJcPaKk1zpr28Yt5IuNwXjeCM5yWJ69OlW/FPjWDStV8ONqN1caf4f1KB3e7iU/60hT GjsASq4LHjHIHOAaOtv6/r9SbnoNFZmg+adPLvqQ1KGSQyW11lDviOCvKAA4zjPfGam1h9Qj0a9f SYo5dRWBzbRynCtJj5QT6ZxQ9Fca1ZdqNp4knSBpFEsgLIhPLAYyR9Mj86821XxFf+H7rwko1eW9 vbvUIbDVYcrJFudfm5VQEZWwQBtyOxFR3un3Nx8e0jXWdRgVtEMyiNoyIx5oBRQyEBTgE8Zz3quV 3Xz/AAVwWqb/AK3seo1HJPFC0aySKhkbYgJ+82CcD8AfyqSvMfHdlcXHxS8ELHqt9bLM1yAsLJtj Kx53KGUjcckEnPHTFStWkNK6Z0//AAlVwvxGXwrJp6LG9g16l0J8lgHC7dm3jnPc109eYalaXknx vsbW21B4Jf8AhHGVrooryY87lgCNu73II9q1fC+oarY+ONf8Oalqs2pWtrbw3dtPcIglRXyGVigA PI44pJqyv5/hf9EOas9PL8Uv1Z3VFeVtr+s+J/DM2taXc6/a3kqySafDaWiNBgFhGGLId+7A3ZOA TxjHPfeGL3UtR8NWF1rFmbPUXiH2mAjG1xwcD0OMj61Vu5JrUVzHj/Wrzw/4RuNSs1l/dSxefJCg d4oS4EjqDkEhc9eB1PSl8Lajaay51LRtfbU9IkiC+W7hmhlBzzwHBIPKt0wMAZpLX+v6/wCHB6HT UV5tr+qa5pfhvxHf6lqrWerQGefTbW0ZJAsCfcZkCnKtj5i2cZ6r2Xxb4m1lPhxoXiLTb37FcXT2 bTRiJXVxLt3LyMgc9iDR0v6L79hNpXv0v+H9aHpFFec+KbnxH4a8QeHri21+S6j1S+FjPaXEEfkI XUkMgUBhtIJwWJPTNTT3eseHPiVoGnSa3d6lYa1HcrJFdJEPJeNQ4ZCiLgdsH9acU3+P4K4z0Civ O21668SXWreRc67Z29ndyWdudMtlcM0eAzuzI2Tu3DbnGMZ5PG74Bv8AxBqHhWFvE9m9vqkTtFIX jCeaB919o4GQecdwaS1Vwen5GjpH/IT1/wD6/wBf/SaCtasnSP8AkJ6//wBf6/8ApNBWtUw2+/8A M3xHxr0j/wCkoKKKKowCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiisCKK/1HVNWA1m8tYra5WGOKCOAqB5MTnl42Ocue9TKVjWlS9pf VKyvrful0T7jfHEd/deENTsNN02e/ury2kt0SKSNApZSAzF2Xj6ZPtXEfBvw3r/hTQ73RNf8PzxL dXDS+cJ4JItpRV2sFkLduwPWvQv7Ivv+hj1T/v3bf/GaP7Ivv+hj1T/v3bf/ABmhSab039C3Si0l 7Raf4v8A5EwfinpWqa74AvtI0fTpLy7uiiqqyRoEAcMSS7Dj5ccZPNcnqh17wp+z4tnLpMUeoW8S 200N35Uy7S2CwAZlbqMA9+1elf2Rff8AQx6p/wB+7b/4zWZrPgpNehto9Q1zU5vstwl1BkQLslX7 rfLGM4z0PFS22mrb27f5gqULp+0Wl/5uvy8keL6X420zRJtPvNS+GOsJDp6BY7m5vp7gWw45jSVd icgdCtel+KNS8canqXhe/wDBOx9EulSa6Z1jGUYqRvD/ADAbSfu89e+K3bjwrqmpWVzY6p4pvLiy uY2ikjitoYiVYYI3BSenpirtr4ensrSG1ttf1KK3hRY441itgFUDAA/c9hV873a2d+nzuT7CFre0 Wv8Ai/yNysDxkL+fwtqVlp2mT39zd2ssCLFJEgUspUFi7rxz2z0qz/ZF9/0Meqf9+7b/AOM0f2Rf f9DHqn/fu2/+M1Lbatb8i404xaaqL/yb/I4T4LeH/EHhHQLrSNc0Se1eS7a4ScTwyJgoowdrls5X 0716jWT/AGRff9DHqn/fu2/+M0f2Rff9DHqn/fu2/wDjNVKpKWrX5f5kKjBf8vF/5N/8icXPpmvn 41xeI08PXTaUmnGxMv2i3BLbi24L5mdvIHr7V6BqUd1NpV3HZSCK7eF1hkbojlTtJ+hxVP8Asi+/ 6GPVP+/dt/8AGaP7Ivv+hj1T/v3bf/GaltuPLZ/gUqUFLm9ov/Jv/kTyq68P+MNS+Ds3hc+Fvs9/ EUeaWS9jJunEodnXBOWbGSWK9eCa6jxNY6/ea14N8SWuhTSvp0k32uwW4i82NZE2g5LBCRjnDf41 1v8AZF9/0Meqf9+7b/4zR/ZF9/0Meqf9+7b/AOM1XtJdut+glRgtqi2a+11+Ry/jmw13VrzwrLZa HNP9h1OO9utlxCAiAEEDcylm+bsMcdao6np/izwv49vvEfh7RRrOn6xDH9ssjdJDJDKg2hgWyMY9 M9+nBrtv7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/GaSk1sn+H9dA9jDrNf+Tf5GTI/iTUfC 2qT32krBeXFq8VvpUFykhQlSMvKdqkkkdOAB3rnLnQvEEnwKTw3Hoc51f7GlobczwgAgjLbt+3GB nrn2ruf7Ivv+hj1T/v3bf/GaP7Ivv+hj1T/v3bf/ABmpeqa5d/67+Y1Timn7Raf4v8vI5Pxpo2ta t4P8PSWGlyPf6ZfWt3LYvLGHYR8MobcVzz6/4V3NjPcXNok1zaNaSPz5DurMg9GKkjP0JHvVL+yL 7/oY9U/7923/AMZo/si+/wChj1T/AL923/xmrc276b6/1qL2MLJe0Wit9r/5HzNSR2SNmWNpCBwi 4yfzIFcL8NtL1jSD4gXVdInsvt2rTXsJeWJx5b4wDsdsHjmum/si+/6GPVP+/dt/8Zo/si+/6GPV P+/dt/8AGaSm1fT8v8/IPZQtb2i/8m/+RIr6+16HxVpdpZ6XFNo00chvLwyANCwHyADPOenQ9e2K 26yf7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/GaOZ22/IPZQ/wCfi/8AJv8AI5zVdM1Wx+KV t4lg0ya/sG0s2MgtpIxJG/mbtxV2XK49CT7VestHutR8Z/8ACUX9rJarbWZtLK0kZGkG5svIxUkA nCgAE8ZzgnA1f7Ivv+hj1T/v3bf/ABmj+yL7/oY9U/7923/xmjmemm1+3W/+bG6UHvUXT+bpby8j mPClhrVh438Xaje6HcwWmpSxSWrmaFiwRNpBAckE8EfXnFQ+B9G8Q6bonim3uNPk029v9Quryzkk licDzANmdjtgg9a63+yL7/oY9U/7923/AMZo/si+/wChj1T/AL923/xmjmdrW6W6eX+QlRgnfnW9 /tf5HlV14f8AGGpfB2bwufC32e/iKPNLJexk3TiUOzrgnLNjJLFevBNdR4msdfvNa8G+JLXQppX0 6Sb7XYLcRebGsibQclghIxzhv8a63+yL7/oY9U/7923/AMZo/si+/wChj1T/AL923/xmn7SXbrfo CowW1RbNfa6/I47xRpXimx8X6b4z8PaZHfXH2M2V/pjXKoWj3FlKueMgnn8MA810Wj6h4lv4ZL/V tD/sxI0Ij06K6SeaZv7zN8qKPQZ75JGMVf8A7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/Gam 7taz/D/MHRhe/tF/5N/kc78K9K1bQvBsek6xpktlcwzSvlpYnVw7lhgozevfHSuasNG8ZDwl4q8P 3fh9ZNQ1BronVZLyMJchwQvAJbOMAAgAADJFej/2Rff9DHqn/fu2/wDjNH9kX3/Qx6p/37tv/jND bfTpbp/mN04t3dRb3+15+Xmee67oviXVvgpZeHYfD1wuppHbQGE3MA2iIoSxJcDB2nAGTyM4r1O3 d5LeNpIXhYjmNypK+x2kj8jWd/ZF9/0Meqf9+7b/AOM0f2Rff9DHqn/fu2/+M1cqkpXut3fp/mT7 CGn7xaf4v/kTnvitpOra/wCBbrSNH02W8url4yNssaKgV1Ykl2XsO2aZ8QbHWPEfw0u9M0/RbltQ vEjUW7zQqY8MrHc3mbex6E10n9kX3/Qx6p/37tv/AIzR/ZF9/wBDHqn/AH7tv/jNQ5Nx5bfkWqcV JSVRaf4v8jO1vQG8X+ALjRb2CSwlubdU2ylWMUgwVJ2MQQGA6Gsy3m8VTeCjodzoc0es/ZjZm8M0 TWxO0r5+d27GOdu3dk4x3rpP7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/GaJPmveO/8AXclU oK1qi0/xf/InG+KfCeoWfgfw54f0HTZtRGnXdtJIyyxR/JEdzH52HJPYetX/ABdY6zqHirwjeWOi 3E9tYXLz3TiaFTGGTaBhnGSMknHp3ro/7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/GaLtu7X W/TfTz8gVGCVlNbW+1/l5nL2Gm65pHxU17UE0h7nTtYhttl0k6KsLRqVYOCd3/fIPb3waVo2q+Dv GWtz2uny6hoesyi7/wBHdBJaz/xBldhlWznK5xjGO9dR/ZF9/wBDHqn/AH7tv/jNH9kX3/Qx6p/3 7tv/AIzTUmrafl/mN0oPeoun83TT+U5C/wBDu10/xv4n1GI29xqGmPBBasys0EKRNwxUkbmYknBI HHJqLwrea1dfCnSdMsdEnFzNpscEV28sX2dVaPAkPzb8AH7u3OeOnNdLrXhnU9T0e6sIvE96n2iN onM9tBIhVhgghUQ9D2YVD4e8J6pomi22myeKLp47aMRQi2tIYlVBwBh1cnjHJap3TTjpp2879fMb pwsrVFe77+Vvs+RgeJPC+r6TovhCx0GwOrado86G8sfMWM3AVflc7iAcNlseuD2p0Fl4lHxYh8ST eHmWyudI+xsEu42aAiTf8/I5xxhdwyRz1I7L+yL7/oY9U/7923/xmj+yL7/oY9U/7923/wAZq1OS d7d+3VWI9hTtbnW1vtd79jmvh9pusaXqfic6no9xZxahqsl7byPNC4KMAACEckHj0702DTdYPxfv NYk0W4XSZtLWyFyZofvh92dofdtwT2zx0rp/7Ivv+hj1T/v3bf8Axmj+yL7/AKGPVP8Av3bf/Gal Satptp07W7lSpQk23UWrv9rvfsefeGYfHngKKTw1beF01vS4p3NjerqCQbYmOcOGBPGT2+meK2PH WheINS8NaRNBBHqGp2GqwX8tvE4QMqk5RC2BwCBk4zgnrxXU/wBkX3/Qx6p/37tv/jNH9kX3/Qx6 p/37tv8A4zT5paOzv8unzF7KCbtUXX+br8jlPGtt4l1SLw5f2ehvc29rfCe90hriMO6gfIWOdhKn nGSM49OIbex8S/8AC2oPEc/h8pZXOkiybbdxsbciXfmTpzjsu4dOeuOx/si+/wChj1T/AL923/xm j+yL7/oY9U/7923/AMZojNxd0vy6q3cHRg/+Xi/8m73/AJTmvCWmaxZ/EHxdqd7pE9tZapJbm2le WFsiNCpyFckZ4I4/Ku7rJ/si+/6GPVP+/dt/8ZpDo16ykHxHqmDx/q7b/wCM0ObaWm3oHsYXb9ov /Jv8jzHUJtWufFWr6jbeA7jVIJLg24v9L1trVJ44+MOiY8wg7gd2ecr0Fej+E9WTUtKMI0S50V7M iE2M8QTywANuzHBTHAI9CO1YmneCNf0NIrHRfGMlto8R+S2msEnkUE5IErN65x8uB6VoX3hG9uNO vbe18U6tBPdfMZj5eQ3GDlFVwOOistHM0rJP8P8AMHRg5X9ov/Jv8jHi8MSRfF66u7W5C6bcW0d9 e2o5zcqWSNvYEZbjulS+L9O1i88eeEtRsNGuLqz0uWZ7mVJoVGJECjAZwTjqePpmtTSPCN3pcLs3 ibU5byfa1xPthPmMFCjAdHKjA4G49/U1pf2Rff8AQx6p/wB+7b/4zRdqytt6f15eiQKlC7fOtf8A F2/w+v3nOfEKw168l8PXWlaadStLO9E97p3mohlAHyn5iFO0849cHtVG3sfEv/C2oPEc/h8pZXOk iybbdxsbciXfmTpzjsu4dOeuOx/si+/6GPVP+/dt/wDGaP7Ivv8AoY9U/wC/dt/8ZojNxd0u/bqr A6MH9tf+Td79jmvCWmaxZ/EHxdqd7pE9tZapJbm2leWFsiNCpyFckZ4I4/Ku7rJ/si+/6GPVP+/d t/8AGaP7Ivv+hj1T/v3bf/GaHNu2n5f5h7GF2/aL/wAm/wAjjtMtPFem+NPEzz6EL5NRlBs9Ta7R EihC/LGw5cAc8KpySTjnNYmn+DvEtx8CrrwhNpQttSgJEXm3CFZ/33mZUqTgY4Gcc+g5r0z+yL7/ AKGPVP8Av3bf/GaP7Ivv+hj1T/v3bf8AxmhSko2SfTt02GqcFLmU1vf7X+Rz+t2+q+L/AA0dAOj3 ulpdqsd3cXTwkRxhhvChHYszAEDjHOSRjBin0nVIvirpWoW2jTHR7TTHsvtCywgKxYEfKXDbQFA6 d+ldL/ZF9/0Meqf9+7b/AOM0f2Rff9DHqn/fu2/+M0ubW/L+Xa3fzF7GFre0X/k3/wAj5HO6Rp2r x/FTXtTudHmi0y8tIYIbkzREMUznKhywB3ccducVW8IaXrHgNdR0T+ybnUtJ+0Pc6dcWkkeVV+TE 6u6kEHoeQc9RXV/2Rff9DHqn/fu2/wDjNH9kX3/Qx6p/37tv/jNCbSsl+Xe/cbpQf/Lxf+Tdrdjl o/DGpWPhHxVI1q11rOvtPI1vC6ARb0KRpuYqDtGMnPXOM1s+B7fUNL8CaXY6hpk9veWVqkLwmSJi 5UY+Uq5GDjuRWh/ZF9/0Meqf9+7b/wCM0f2Rff8AQx6p/wB+7b/4zRdrZdu3QPZQtb2i6v7XXf7J y3gXTta0Xwhq9tf6JcxXct5c3EUAmgYyCQ5UAiTAPPOSKpaHoev2XwQm8Oz6JMurCzntlgE8JDF9 207t+McjOTn2Ndt/ZF9/0Meqf9+7b/4zR/ZF9/0Meqf9+7b/AOM0X93ltpZLp007ijRhFqSqK6bf 2uuvY8/1DRfFMfhPwRFDocl0ukPENS0triIGbYgCsDuKsFYEgE9cHHFX7ex8S/8AC2oPEc/h8pZX OkiybbdxsbciXfmTpzjsu4dOeuOx/si+/wChj1T/AL923/xmqOtWmpadoWoX0PiLUWltraSZA8Vs VJVSRnEXTiqdWSbm13fTqrPqVDDRm1BTV3Zfa7/4TG8L6drNh498Yard6Lcw2mpPA1q3nQMX8tCp yBIcZ4xn8cU/4baXrGlWGtW+r6VNYtdancXcReaKQMkhBA+Rjgjn/Gu6oqr/AJW/L/I5Wtb+dzzf wFY+IvDvgu40G98PTGW0a48uWO5hK3AdmZdmWGOvO7b/AEFXSvBOraj8EP8AhEdStH03Uo4yIy8q OvmLIZEOUZuMgZ79a9SopNtp+dvwKbvLm9fxPP8AQtZ+Il5DDpmpeFYLCZUEc2qvfo6cDl1iUEkn sM4z1I6Vp65Zf2vPe6RrfhqXVdHMaPbzqYmO/bhlwWDq3cMPU8iutool724lpscx8PtDvPDngyz0 y+Z/MiaQojyB2ijLkohYcEqpAOOM9OK6YgEEEZB6ilooeoHnnhfwBLYpr2maptk0dpJ7fTIR/Bbz 4eTofU7R3G0+ta3w88P6j4f8OrBrE4nvgfJD8cQx5SJf++Ru+rGutooWn9dgerv/AFqcJbafrK/G G61p9GuF0qXTEs1uTND98PuztD7tuCe2eOlR+DNO1zw/4h8S2lzo7vZ32pyX8F8s8ewo4Hy7c7t3 HpjryOM9/RQnb8vxuHfzs/uVjgvAWnazo914ql1HRbmBb7VJb62/fQtvRgAF+VzhuO+B71Y+GWm6 tpHh66tNX0yWxna+nnUPLHIGWRtw5Rm5GcHNdrRSSS18rfl/kEnzO773/P8AzMzxHp8uq+GNV063 Kia6tJYY9xwNzIQM+2TXHaVbeIT8JJdCuPDtxBfxaWbFIjcQkyvsKBgd+AvQ8kH2PWvRKKGrprv/ AMH/ADKUmmmun/A/yPNbnRdek+ByeHE0S4Orf2elmbfz4RhgAC27ft28Z659qXxJpniDULXwSLXw /dO+mXsFzeKbi3Hlqi7WAzJ8x5zxxx1r0miqv73N1un9xHKrW8mvvPO/F2jeJLHxlp/jTwxYpf3C 2hsr3TpZljaSLduG1jwCCffoODzXQ6HqPiS+tJNQ1jQxppVCItNhuknlkOfvM/yqPYZ7nJ6AdHRS 6WH1ueM2PgfxAPBi3Eelz6d4q0u/nvdOfzYGEokcsYiQ5G1gcHJHTv32fEmneLdZi8P+KNP0Q2ni HRpG8zTp7mIpPHIoEiq6sR24zg9e+K9NooWiSXT+vx6g9Xd+f4/1ocroOqeK9amWXVfD6aDaxHc0 ZvFuJbg44UbQAq55JJycYwBzWf4KsdZ0zVfFlxqGiXMCX2oNd2376FvMTaFA4c4b5c84HPWu6opW V7/1/WgdLHlOleCdZ1T4S6r4Z1OxfS9QmuZbiBnmjddxl81OY2bjOAeK1tC1n4iXkMOmal4VgsJl QRzaq9+jpwOXWJQSSewzjPUjpXoFFNaK3T/IHr+P4nnVzpHiDwv8Rr3xDpOlvq+mavBFHewxTIk0 UkY2q43lQwI9+56YFTeH9P8AEg+Jusa5e6NFaadfWkEaM92rSIUB4KqDluTnnA7Fq7+ihdPIHrfz /T/hgryuGx8Z+A/Eutf2H4ej1/RdWumvUUXqW728r/fBLdR+HYcjpXqlFHW4dLHDeLPC2seLvBqx zy29prcVwl9aIrFo7eRPupuwC3GctjqcgYAFGuRa74y8KnQpdFudKmvQsd7PNLC8cKAgvsKuS5IB C8D3xXc0UWW3QLvfqefHR9VtvizYapbaHcto9tpB07zxNCMHfuBCl923AA6Z9qn8b6Xq7+KvCmva Xpr6jHpk8wuIIpUSTbIgUMN5AOPr6fUd1RR2Hff0t+FjzjxRpXimx8X6b4z8PaZHfXH2M2V/pjXK oWj3FlKueMgnn8MA810ejah4lv4nv9W0QaYkaERadHdJPNM3qW+VF9AM98kjFdJRR0sJ66nmPiPw jq+u6AfECWUtv4yguBc2Sq8W6AKcLDu37ShXJPzckk47V1Nzq3ib/hCZb+28PbPECxDbp888bKZM gEhlfBXqRyCcdq6WijpZDvqmyCzknlsYJLqEQXDxq0sQbcEYjlc98HjNcN4i0O58QfDbVLSxG69i 1C5ubdf70kd3I4X8cY/GvQKyfDn/ACDJv+v+8/8ASmWolrNNf1sbQ0oST11X5SIrjWrq48Jrquia fJqFzPAHt7dXSM7mHG4uVAAPXvweKoaDoNkvhu3OoeG0F+kX79LmK3eWaTGWbcrFSWbJySOvOK6G 2sIbOad4NyLO29owfkD92A7E98cE89SSbNXpr5mHbyPCvh/4U8Y+DPiHqN7D4ZuE8O38joYvtdtv iTdmNtokwSvTAPQnrXoPj60fXZtB8PQjc1xfx3Vxx9y3hO5ifTLbFHu1dpVW20+3trm4uVUtcTke ZK5yxAztX2UZOAOOSepJL6RT6fpqgevM+/6lquV1CTV/+E70y4g0G7m0+C2nhlulmgADSGMghTIG IHlnPHcda6qiktHcLnO+NfCFl408PS6bdHy5gfMtrhR80Eo6MP6juKx4fBdzqnwiTwnq0uy9a2MT zFt+JQ24PnqRkA+9d1RSaTTXcE7NS7HC283iqbwUdDudDmj1n7MbM3hmia2J2lfPzu3Yxzt27snG O9ZvirwlqNp4R8K6FoOmT6iuk31tcSOssUeVizu++6/MSc4HHvXplFN6u/p+ALTRdmvvOW8e2mo6 x4A1Sw07TZp728tzEkHmRKVJ7sWcLgexNYXjPSNd1b4W2ej2GiXEuo4tg8DTwr5fllWbLF8fw4GC a9GooWjuvL8P+HE0nv5/icF4+07Wdah8NHTtFuZmtNVgvbhfOhUxomcjlwC3PbI96iubfxPZfFGb V10F9U06exSC0f7TGn2Js5cEMeMnklQTgDGcYr0Kii/5t/erD6Wfp+NzzjwPpviHQIPFVtqGhSE3 Oo3F9A8FxGyzeZjCpuZT26tt7fhR0nw94hsvgTN4Xl0Of+1mtp7cQieDBLsxDbvMxjDDPf2r1Wim 23FxfZL7tA63+Zwl6fFll8LLSy0XRZRr4tI7Xy2nhHkEIA0m7ftOMHHOc44pnhe2v/DHhyx0LS/C eqxMHQT3dzNaYJZh5krbZmYnGTgA9AK76im5Xk5PqJJJJdjyy2tPGfgXxNrSaP4dXX9E1S7a9i23 qQPbyv8AfB39Rn8OnPUVpeLvDPiLxR4ThnzbW+vWl7FqFpbCTdFEydIy+BuJBOWwBnjgc16DRU9E u1vwH1v/AFqeX+Kx4w8XeHtPt4fCL2d3b39vcTpc3sO07GydhUnIyOSQOOgY16bCZGhRpkVJCAWV W3AH0BwM/lT6KO/nr/X3AcF4stPEMfj7QtXstIfWdKtYZFa1jnjjaGZuBKA7AE44B7fN0zVfw3Ye I9J+IPirU73QWa21VLeWOS2uo2UGOMrs+YqSxJ9AOvPTPotFCulZef46g9fw/A8x8R+EdX13QD4g Sylt/GUFwLmyVXi3QBThYd2/aUK5J+bkknHatvxD/b2v/DLULVvD9xDrF5aNbtZ/aICFdlwTv37d uT659q7Oik1o49x31TPLtZ0rxQ/hvwX5GhyXK6XJEdS0priIGXYgCtu3bWCsCQCeuDjirVvY+Jf+ FtQeI5/D5SyudJFk227jY25Eu/MnTnHZdw6c9cej0VfM+Zy9X96sSkkreVvudzhPCWmaxZ/EHxdq d7pE9tZapJbm2leWFsiNCpyFckZ4I4/Ku7oopN3sPq2ecaZaeK9N8aeJnn0IXyajKDZ6m12iJFCF +WNhy4A54VTkknHOaw7Hw34ph+BV34Sbw9L/AGmoeCMfaYdsm6UuXBLYCgHvyT2717HRU29zk6af gO/vc3nc898a6drmr+ENDtbDQrmW6hvLaeeEzwKYljOWGTJgnjAwTUut2OtXfxN8M6vb6Hcvp9jB OlxL50AKmRQBhTJk4I5+vGa72iqTtLm87/erE2VreVvxucnr1n/bNzf6TrfhiTVNGaNXt5kMRw+C GUAuHVvRh6nkYrkY/h9rtj4S8M3FmfM1rQLuS4t7WecZeB3J8gv90Ns2jP3cgjOOa9aoqUra/wBf 1+hV76HFa7p+oeNhpdjNpd1pthBdx3l4100RL7MkRKEdsktjJ4GOhPSq+sabrVn8V7DxFY6S+oWM mmNYy+VNGjQv5m4MQ5Hy/TJ68dAe9oprTb+rq35C7/d9zv8AmcF4e0/W7b4n+I9Wu9EuILDUobdI pzPCwBjXacgPuwc8cduQKdd6HqmgfEebxRpdk+oWOp262+oW0Lqssbp9yVQ7AMMDBGc85Ga7uiha Wt0G3e9+tvwt/kclZ6Neap47Hie/tns4bSzNnZ20pUyEs2XkbaSACAABnPXOOlcz4C1K7sdY8brD o93fRtr1wQ9s8WQ2F+Uh3XGeMHkdc47+pNuKEIQGxwSMgH6VyvhHwnf+Gr7WJ59Wt7yPVLx710Sz MRR26gHzG44Hb8aSWvyf53Hf3X3bX4Jr/In8I6DcaUup39+EGoateNdzohyIhgKkee+FAyfUmsLw j4Yk0rx34hMVyH0eG4861thyIbiZFaX6YGMD0kNdH4u0HUPEOjpaaZr11otykyyi5t13EgfwkZGQ fTPbv0q7omkR6JpiWaTzXD5Ly3E7bpJpGOWdj6k/gBgDgU1p8lb5aEtf5mjXFfE7TNV1rw3BY6Tp kt7N9tgmbZLEgVUcMc72XnjjFdrRStt5Wf3aiaumn1OE8d2Gs6te+FpdN0W4uFsdTjvbn99CnloA QR8zjLfNnjI460eJtN1i9+InhLVLTR7iax037QbmUTQrt8xAowC4Jwev6Zru6KpO35/erD7+ljzj S7TxXpvjTxM8+gi+TUZQbPU2u0RIoQvyxsOXAHPCqckk45zXJ3umavoX7N2qaLrGmtZz2QCBjKji XdcBty7ScDkDnB9q9zrnPG/hm48X+F7nQ4tQjsY7naJZWtzKcBg3A3rjke9Q78nKvL8C4NKopPvc ytYh1fxV4WHh9dIubBbyNIrq7mkiaOOLjcU2sWYsMhflHXnHStPVrd/Ph0ibw+dT8PyWvlyAeUwi dT8oKOwJBHcZwQPrW3p0F1bWEMF5cRTzRoFaSKIxq2BjO0s2Pzq1VSScr/1/X6mcbpI434deGZvC +nanbeTNa2M9+81lZzSiR7eIgDBIJAyQTjJ4IzzmtPxtZatqPgvVrPQpTHqcsBWBg+0k9wG7EjIB 7ZrfopSXMrMqLs7nkWr6Z4o1Lw74TW28HfYxomo21xJZreRb3EYIOwA7dvPVmB9u9bup6fr1t8UN N8SW2iveWsulGynSK4jVreQvvy24jK+4z346A+gUVfM/xb+9WYlorLtb8biLuKjcAGxyAcgGuG8b 6Xq7+KvCmvaXpr6jHpk8wuIIpUSTbIgUMN5AOPr6fUd1RU9bjTscB/Z+uS/F2z16TRJk09dJNlJM LiFgshk39N4YgDjp1/OptN03Vh8Vda1O40eeLS7uxito7kzREFkJJyocsAc8cdu1dzRSstPK/wCN /wDMTbe/l+Fv8jyjwzD488BRSeGrbwumt6XFO5sb1dQSDbExzhwwJ4ye30zxXpmnR3kdkn9oSxyX TZaTyh8ik/wrnnA6ZPJxnvVuinfuFtdDL1241G1s4ptN097+QToJbZHRGeI8NguQuQDnBIzjFcpo Hg+LTfiLPrmkaVJo+nS2JiuYCyqk8xYFSsasQNoByeBkjGck139FJKzv/W1v68we1jyKw0bxkPCX irw/d+H1k1DUGuidVkvIwlyHBC8Als4wACAAAMkVNquj+JdR+FGhaLH4cuRf2r2iyRfaYPlWHbli S4HO3gAnqM4r1eimtEl6f+S7CklK/nf8dzhPHdhrOrXvhaXTdFuLhbHU4725/fQp5aAEEfM4y3zZ 4yOOtHibTdYvfiJ4S1S00e4msdN+0G5lE0K7fMQKMAuCcHr+ma7uimnb8/vVh9/Sx5ZbWnjPwL4l 1pNH8Orr2iapdtexbb1IHt5X++Du6jP4dOeor0LR/wC1HtDPq6xRXMx3fZoX3pAuOF3YG49ycDk8 cCtGiktrA9XcydI/5Cev/wDX+v8A6TQVrVk6R/yE9f8A+v8AX/0mgrWqYbff+ZviPjXpH/0lBRRR VGAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAVk6R/yE9f/wCv9f8A0mgrWrJ0j/kJ6/8A9f6/+k0FTLdf10N6PwVPT/26JrEgDJOAKhWa K7gY21ypByBJEwbB/UVz3xA07VdU8I3EGjRxzXiSxTC2lbCXKo4Zoj7MBjB4PQ9axvA3irRPEmvX RGly6L4khtxFe6fPFscqCCGBwNwGSOQD83TGKpau39bGD01Jfh7qmsa5o/iOO/1N57q11a5soLlo kBRUChTtUAHHWur0K1vLHRbW11DUf7Ru4l2y3flhPMOeuB09PwrgPh5bRXfh7xtBMpaN9evwwDEZ 6elZWka5ceHf2a7TULWUwziHykmAyYt85TePcBs/hVNdu0fxQutvN/mewC4hM5gE0ZmAyY9w3Aeu KWWaKCMyTSJGg6s7AAfia5bUfAGialpOn2tupsnsriO6hurYAS7wcklzyS2Tknkk5rDElz4l+IPi Oze00q9i0kW8MMGoAsIw8e5nUbSMsSQT6KB9Zt0QX6no6srqGVgykZBByDSSKzxsquY2IwGUAke/ PFcj4D8Lah4Tj1S1ubq2ayubprm0tbfdttQxO5Fz/DnGMe/rXYUMZxPw01jVNXsNd/tW+a8ls9Zu LSORkVD5abQBhQB69u9dkLiAzmATR+cBkx7hux9K8Ustbu/Dvww+IepWDMl0mv3aRuvVC7ou4e43 Zrtda8H6PdfDaSCztoY5oLM3NpeJxKkwXcJfMHzbieS2cnJ9aJySXN0Sj+Q0ru3dv8zuqjhuIbgM YZo5ApwSjA4PpxXlNp4iuPEln8O9L1d90WswSz3ysOLoxR8Iw/usTuI6HGOhruV8H6fD4vt/EdoW tJ47VrWSCABI5lJyu4Ackdvw9Kpxadn5/h/wdCFK6uvL8f8AgG49zBHKkTzRrI/3UZgC30Fcb8Vt V1XQvBFxqukajJZ3EEsSnbGjhw0iqQdynHXtXN63a2SeB/HKWWdakdrq4udQnRVSGQDIjVuS7RgD GBgEYyp4o8eXM17+zzaXVxIZJ5rWwkkdurMXjJJp043cX5x/H+vmVfV/P8P6+R6LqllqN3daXPZ6 ubGG3nD3MXlB/tKEY2ZP3ee9ajyRx43uq59TivO/iRbRf254Gudp87+24U3bj93axxjp2FbnjY+H NJ0u48Sa/YW159ihKQpPCsh3E8Km4cFjgfh7VG0b+b/Jf1YUdXbyX5s6dZY3OEkVj6A5pv2mDz/I 86PzsZ8vcN2Pp1rgfhp4QfRdKvNe1SCG11nWMzTLEgRbWPqkajoMDk+/XOK5HxSINN+Dfm6NuvhZ XcU6a5MgjaaYzjdJH1ZjkkbjgEHIZqpRvJR66fj/AJBe6uvM9c1LxFp2l6rpumXE6i81B2SCLPOF UsWPoBjGfUijVbLULu80yey1f7Fb28/mXMQiDi5QjGzJPy89xXDeONL0+9+KPgT7VYWs/wBoN0s3 mwq3mBYgVDZHIBORnpU3xGsbaDWfAskUSxmPW4YkC8KqbW4C9AOBSSvy+b/UJ6Rv5X/M9FkljhjM krqiDqzHAH40qSJKgeN1dDyGU5BrgLC5j1/4wa7ZajGssWi2sAsoJQCqtICXlCn+LkLnsOO5qvbW x8N/GdNO01RFpOtadJcT2kfEazocGQL0UkYBx1zzUp3t53/C/wDkOWl/K342/wAz0Z5oo1ZpJERV +8WYAD604EMAQQQehFeWeCvC+i6lq/jW2vtOhurWPWn8u3mXfGhKKSwU8BuevUdsVs/CAuvw/ht2 kd0tbu4t4i7ZIRZWCjPsOKIyUkvNX/L/ADHJW++35/5HcySJFGzuwVQMkk1wHhu81XxtoOneIbTX 5bCWS9kkNssaPG1usrL5RU85KqDuznJPbAHa6rYWep6ZPa39pBd27Llop4xIhI5GQRjg815Z8M4L DQ/gt/wk1rpdn/asNpdv9pWBRK+13IUvjJHyjjPYU9oyk+lv1/yHa9kurPW2uIEmWF5o1lb7qFgC foKkJAGScAVwHhDw7pHiP4Y2R1G1hvJtVthPeXMqhpJJmHLluu4Hgf3cADGK4fULu91b9nDVDq80 lxeafM1qtwznMgSdVBJ78cZPXFKT5brqv+GFBc7jbq7HugljZyiyKXA3FQecetK7pGjO7BUUZZmO AB6muf0Twlo2nX6a5b2pGqzWiwTXTOxaVeCdwzgnIHOOgAHHFbl5bw3VnNBcQxzQyIVeORQysPQg 8GnP3VoKLvY5i/8AE8es+ANW1rw/qBiMEFw8Fwiq+TFuGQGBBBK+nSrngzVZdR8FaDeahcrJeXdn FI7MQpkcqCSAP6V554F02xh+AupXsVlbJdyWN+jzrEokZQZMAtjJHA49qgvvDOjv+z/b6u1lGdUg 0mGeK9PM0bKFKhX6qo9BxSm1Dmb2Vv1Lcdl1vJfke1kgDJ4FRw3ENwheCWOVQcbkYMM/hXnviTU7 u+1XwZoZFrLFqcEtxPDdZ8u4KRKQjYByMsWx3Kj8bWk+DNR0vx6PEELaXp9nNbfZ7uysVZVnbJKu RgDcCQM4zjPrVctnZ+f4f8EzUk1deX9fqd5UU1xBb7fOmjj3HC72AyfbNS1wbWli3ijxOIgdbvbq KNJ4ZkUQ2MYjOI2c54Y5baoJ5BI5yZbsUjunkSMAu6qD6nFIZYwgcyKFPRs8V5b4M0VPHHwCsdM1 FhLJNbyxRSyDJjZJHWNh9ML+AxU3hq/g8VeAtH8O3ltEbtJ/sWoW5RSI/sxBkJXphsIOOhlFXKNp OPb+r/IlO6T/AK/rc9Lknhhi82WVEjH8bMAPzp6srqGVgykZBByDXm6GfxF4/wDEVh9j0m7g0dbe CC2vlJWJXj3MyqFIy2cE+igfWGLwfeeG/AXjGyvbm3exniubuztbZnVbXKMSi5x8oOCAOKhuybfa 5cY80lFd7HpnnRF1QSJuYEqu4ZIHXFN+0wef5HnR+djPl7hux9OtcB4B8JaLL4T8L+IJoCdVt9NT y7xpCGjVo8Y64KgE4BHHJ6kmuS8UiDTfg35ujbr4WV3FOmuTII2mmM43SR9WY5JG44BByGarUbyU fO33uxCd1c9wZlRSzMFUDJJOAKbFNFPGJIZEkQ9GRgQfxFcH8Q7mSz1TwpeX6bvDsV6TqRZcxoxX ETyDptDnOTwDg9cVnaFoWkah8XL7XNFitbjRzYxu1xatmE3m8/cKnax2j5sdCRnk0oq7+/8ABXG9 Ff8Arex6ZNcQ26hp5o4gTgF2C5PpzUleX6LFceM7rxHdXOn6LqAj1KewC36lmhjjwoRRtOAcbuOp JPpilfWuseBvh/o3hi71Xz2vtXh08XMDMrQ20jZKgnkcBlB7A8dKSTdvO34/1/XU/S/4f8MdL8Rd Z1DTNO0650jVvIb+07e2uYo0jfejvggkglT9MV3FeY/FLQdLtNB0GW0sYLZ7XVrSOIwxhMIXxt4/ h6HHqBXp1KPX1/RA916fqzI1u11i/wDLtdM1D+zIz8012sSySY7Igb5QT3Yg4HQZORzfhG88RWXj XWvDWs6n/a9tbW8V1b3hhWN0Dkjy32gDPyk/hnvgdtdRST20kUVxJbO64WaIKWQ+oDAjP1Brzbw7 Za34O+J8mj3WrSarpmuwy3qTXCATJNHtB3EdRtKj06AAY5cfit6/1/X6il8N/T8/6/pG7YaxeeKP F2s2VpeyWmlaOy2zNAql7i4Iy2WYHCpwMDBznPHFTeFdfurnW9c8N6nMJ77SZEK3G0KZ4ZF3IzAY AYcg4AHGcDNYXwpR4L7xvBNxMviCdyp67WAKn6EU/Rw0vx48RyxH93DpVvFL/vk7h+maUXrFd1+l /wDgfMctpPs/1t/Xmei1G1xCkywtNGsr/dQsAx+grO8Tao+ieFtW1WJQ0lnaSzqp6EqpI/lXLab4 P0jxR8NLWC5/eXGp2sV1NqKqPPeYgN5m485B6DsBjpT11fb9f+GDsd6zKilmIVQMkk4ApsU0U8fm QypIh/iRgR+YrzzUZVvPirofhW/ZrjT7TSmvQk5BFzOGCqzjoxUAsPc57CmeI7X/AIRr4neF9S0h Ft01eZ7LUYIhtWcBco7KOCy8/N14x0o0uvN2/T8WD0Tv0V/wv+R6O0iJnc6jAycnoPWm+fF5Bn81 PKC7i+4bceufSvNNO8P6XffGDxZb3VnHNavZ2kslvIN0UjkNyyHhunfvz1qx8PNPtYP+E10IQo+l 2+rSJFayANGiMisUCnjbk9KhSur+Tf3Ow2rP5r8Vc7PQfEGn+JNO+36bMJbYySRo4P39jFSw9iRx 7Vo+dEXVBIm5gSq7hkgdcV414K0bTW+AeoXYsYEuns74NPGgSRgrSYBYYJAwOM9hWp4e0PStN+Ge j+MvsXn63puiGa3uGdtw/cn5cA4KgEgAjjk9STVytG9+n/B/yK5He3dtfdb/ADPUGuIEmWF5o1lb 7qFgCfoKwfHV1faf4J1fUNNvXtLq0tZJ45FRH5VScEMCMcVheEPDukeI/hjZHUbWG8m1W2E95cyq GkkmYcuW67geB/dwAMYrB0nUtR1H9n7X11Sd7i5s7a9tDO5yZBGGAJPfjjPfFKeikuqREWnZ9Gej eFbue/8ACGjXl1IZLiexhllcgDczICTx7mtckAZJwBWF4J/5EPw9/wBg23/9FrVX4gadquqeEbiD Ro45rxJYphbSthLlUcM0R9mAxg8HoetaVrKo/X9SYaxR0UVxBcR+ZDNHInPzIwI468inCWMqWDqV HU54rgfA/inQ/Euu3f8AxK5NF8RxW4ivtPuIgjsgIIYHA3AZI5APzdMYqH4Y2tuT41tjBGYP+Egu V8oqNuMLxjpUdPlf8bF20v52/C5pfFLVtT0bwDfato2otaXNuUw6Ro4YM6qQdwOOCeRXXQXEbLHG ZUMxQMU3Dd064rwiNQv7Ld4qjAFw4AHb/S66X4ieCtL0zwRL4j0eH7HrumBLuO/VyZXII3b2Jy+R nrn8qG1GN3t/wEVKGqit9V9x6HrlhqV6dPbT9YOmrBdpLcfuQ/nxDrHyflzxzWnLNFBGZJpEjQdW dgAPxNeZfEpVvNJ8FalNEUun1mxJGT8u7JIx9f5VYElz4l+IXiOye00q9i0kW8MNvqClhGHj3M6j aRliSCfRQPq7PVdm/wBP8yLqyl3S/Nno6srqGVgykZBByDTBcQmcwCaMzAZMe4bgPXFeT31lq/wx +HvieZdQt44bi5D2MVsG22IlkCttB7LuBAHeuw1HwBompaTp9rbqbJ7K4juobq2AEu8HJJc8ktk5 J5JOaLdemn/B+78RP/P+vn+BrSeI9Nj8SRaB9oQ6g9u1y0YI+RAQMt6ZJ4+hrSmuIbcKZpo4gx2q XYLk+gzXml5o2l3fx9jW502zmV9CMziSBWDSCbAc5HLY4z1pfGeoaj4T8YSeIbzQX1rw7PZLbTNC oeSx2lix2njawIz0BwMkYGV0i31v+b/yKaabS8vxt/menZz0rJ8U/wDIoa1/14T/APotqj8JXGkX Xhexl0GZZdLKH7OVGAq5Py47bemPapPFP/Ioa1/14T/+i2qaitFm2Ed60PVfmXdR1C20rTbnUL2U RW1tE0srn+FVGTWXZ/2jquiyXlzcT2El1GJIYYgu62XGVySDl+eew6AcZPP/ABgYnwE0J/1c99ax Sj1UyrkfpXcygC3cDoFP8qG7xk/l+Cf6mCfvJfP+vuZ4n8HvEXibxvJr0Wr+JL3NosawtDHCu0sX y33Dn7o68Vs/CXx7rniTV9b0HW3jupdNY7L2OIIXAcrhgPlzxkYA71wvwI0qfV/+Eqt4dXv9NJEI MlmYwxyZO7K2Pwx1roPhBry6J4x1XwDPp9ostvJKFv4Iyr3BjOCZMk5JHI9OlaW95L+7t+vy/EKi te3R/wBfeer2eqPba62hX8m+doTcWsxABnjBwwIHG5SRnAwQwPrjargvHZNv4z8CXUeRN/aUkGc/ wPGQw/QV3tRF3Xpp+v5NA371vn+f+R5/qdn4ssfDGs61ceLb2GeGO4ube1is7YJGi7jGh3RFjwBk 5rP0JPGevfDiz1+08X3n9qz2xnWB7O1MTsCfkwIgwzjGc8ZrsvG3/IieIP8AsHT/APotqzPhT/yS 3w9/16/+zGqj18rfqD0cfPm/9t/zIP7X1WL4xw6M+oO+mS6O139mMaAJIJAudwG48eprtY5Y5k3x SI65xlWyK821jTrXVPjtaWt7F51u3h5y8TH5ZB53Rh/EvseDWejw+CPFnj7+xbeK2toNGj1BLWNd sSzBXGQo4GcDOKjmSSv5/hd/kipRtKy/u/il+rPWDcQCcQGaMTEZEZYbiPpUleSv4IvfFfgWy+zx 6LFeXEUN1HrChzc+Z8reYXCg7jjnn+VepWKzpp9ul1KktwsarLInRnAwxH45q3G1090QndJrqTlg oyxAHqaas0TStEsiGReWQMMj6ivOvjBaRT2Hhp23rJ/b1pGHRypAJOcY7+9UfiD4X03wyNH8T+H7 RLHU7bUoY3aAEG5SR9rK/wDeJz1OT19alPXXvb77f5lTXKr+V/z/AMj1KaeG3TfNKkSdNzsFH61H eX9pp9hLf3dxHDaQoZJJnbCqo75rlr+GyHxEW4Msuo6gdOEUWmLGpW2Qv80zMxwgbgepCnAbGBxX h5Bd/Cjx9ZXlvA0Nlf6ikFvgPHAFXcAmQOFYkjgfhRryuS6K/wCNv6+4cVeaj5pfev6/M9JN1J4q 8H/aNE1BrCW+thJb3OwO0W4ZBKnvitezSSOygSaf7RKsah5toHmHHLYHAz1ryx9F0yP9neSWKxgh kk0NZpHiQIZHEWQzEY3HPrmrmtapcWXw/wDBVjbyRxjVJrKzlMjEKUMe4qSOQG2hT7E1co2lJLul 99zNS2b7N/dY9JiuIJmdYpo5GQ4cIwJU+/pTmkRM7nUYGTk9B61583gXUY/Fuja7Yx6Lo4sCyXKW KMv2mFsAo3AHGOM/0qnp3h/S774weLLe6s45rV7O0lkt5BuikchuWQ8N079+etRfVLvf8Cu79Pzs ejXWo2dlp0uoXNzFHZxIZHmZhtCjvmotH1W21vSLXUrQnybmJZUB6hWGRkdjivKtO060t/BvxN0d YEbT7K6uWtYHUMkOYg4Cg9AG5GOldt8NdOsrLwBok1rZ28EtxYQPM8USqZG2DliByeTyfWnG0o83 p+NxtW+9r7rHW1V1C3nubGWK2vZbKZlOyeJUZkPrh1IP5VapH+430pS2YLc8r+GfxB1W9vV8O+MG C6tPELqwuSioLuFhkYC/LuHPTsD3Bz1euQayfFuipZeIbq0sbkyi4tkggbOxNw2syEjJ68n2xXOX ngmPxh8L/D7W0gttasbOGbT7scNHIFBAJ/ukgfTAPao/B/jZ/E+s6Pp+qxfZPEWmvcQ39q3BJEeP MUf3T+h9sE1PRtLdfir7/wCf3iqe63bZ/wBf8Memyzw26Bp5kjXpudgozTmkRIjKzqIwNxYngD1z Xn3gqWDxR4m8XX+pwR3FxZ6i2nwRzKHEECgcKD03HJPrgegqt4Xs10vx54t8IiJJNANvFeW9o4DR weZnegUjAUnJ29OPc1N9PVX/AAv+Q3pe/R2/G35ncaD4g0/xJp32/TZhLbGSSNHB+/sYqWHsSOPa tBbiB5mhWaNpVGWQMCw+orw7w5cQ+G/gBJrdhBbWuoyyPbNepGFkVXudmdwGeAePTA9K6rVvh/qN /wD2VcaXBoOkXen3K3Ed5aI4kZRncjHaMhs85zn8atxs/IT0bXqel1lal4i07S9V03TLidReag7J BFnnCqWLH0AxjPqRWoDkA5Bz3FeZ+OtL0+8+KngYXVhazidrtJvNhVvMVYsqGyOQDyM9KlK8khpX T9DV8T6vqlh8RPB9la6g66fqUlwtxbCNCH2R7lO7G7qfWu1jmilLCORHKnDBWBwfevOPHVjby/ED 4fWWzy7fzbtNkRKAKIR8o24wMccdqItH07w58aNMi0azhsIL7SZvtEFsgjjco4KttHGeetKLWi7t /lcc9En5L82j0V7mCOVInmjWR/uozAFvoKz9c8Q6d4ehtnv51Rrq4S2gTPzSO7AAAe2cn0ArznW7 WyTwP45SyzrUjtdXFzqE6KqQyAZEatyXaMAYwMAjGVPFV/F8UOreBfhze6hBDc3M99p6yyyxhmYO mXBJ7E9R3qox5kn5x/EXVr1/A9iVgwypBHqDWdr2u6f4b0a41XU51htoFySTyx7KB3JPAFXLW0tr G2S2s7eK3gThIoUCKvfgDgVwnxrtoJ/hbqkksEcjwmJo2dAShMigkHscEjj1pPyHBc0kib4g65qW mWnh270rUWt0utWtrWeNURhJG5ORkgkdO2K6STQfDtzdSmTSdLluGJeUtbRs5J5JPGcn1NcJ8SNL sbHwx4XtbG1hs4ZNfsyVtUEXJyCflxz71L4k0HS/D3j3wXfaRZQ2VxcX0tvcPAu0zo0ZJ8wj75yM 5OTSajez72++36lRqTpxUoO3u3006s7OXw54ZgjMk2i6RGg6s9rGAPxIpy+GPDjqGXQtKZSMgi0j IP6Vx4kufEvxC8R2T2mlXsWki3hht9QUsIw8e5nUbSMsSQT6KB9djwH4W1DwnHqlrc3Vs1lc3TXN pa2+7bahidyLn+HOMY9/WkqceqLeKr9Jv72bX/CLeHv+gDpf/gHH/hWR4k8PaHFoWoCysdKs75La SWJ1soHYFVJB2spBHHpW14i1NtF8NapqiJve0tZJ1X1KqSB+lcbpui6fqfwfa6vYUubvUNNN5c3T qDK8zR7t+7qCDwPQAAcCoqQjySdtl/n/AJFwxNfmXvvXzZX0TwxD4m+GWh3ca6bY6jNFDcT3n9mw vvGcsNuABmtLx5F4f8J+DNR1aLQdIa4ijAhV7OMguzBVJGOQCQa4vXLaKX9nTQJ3UmSJLModx4zI oPHToa6T436fZXHwzvbyazt5Lq3aEQzvEC8YaVAwViMjI6461r7KDla32rfkZU8XXurze3d+ZNd+ FtK8Ore+IZXsrvTbfTneWxmsYDumUbg6sFG3IBG0cc9qj8OeELXWbTw74g32AE1qs9/ZHT4Gjl8x MgL8uU2kjB5yBzk5NXvGfhjQLX4Za5Hb6HpkKR2U1wix2kahZRGQJAAOGx/F1rBdrTwj8C7bV9It LTT7660+zjmu4YVjcmTYpdmAySN7HJzzzQqcddOqt87lLFV+Ve++vV+R3y+HvC7zNCuj6O0qjLIL aIsPqMU8+GPDi/e0PSh9bSP/AArjNW+H+o3/APZVxpcGg6Rd6fcrcR3lojiRlGdyMdoyGzznOfxq HVNHstR+Otvb3KGS3uPD7tPEXJSUebjBH93pwOuB2qOSOmn9WuNYmu03zvS3V97Hb/8ACOeGDD53 9jaR5WM7/sse3HrnFLF4b8MzxiSLRdJkRujJaxkH8cVw7abBZ+OLTwXpNlYHStP0s3sNjfM7xtI8 xy/O4krjjPTcfbGv4X8F3mheItaupX0+LSdURT/ZlorCOOUAAsAQByM5wPT0p+zjvbTW3y/r+ui+ tV9ud/ezoH8O+GIxl9G0hRu25a1iHPp061J/wi3h7/oA6X/4Bx/4V5d4Q8J6Jqnwi1L7dYR3DRNf JA0o3m3Ad8eXn7h4ByOT3zV5teuV+FngW3mu5UOs3FnY3NwHIfyyMsN3UFgu3P8AtGlGMJaJa6fi OWJrr7b69X0O+j8O+F5ZHjj0fR3dPvqttESv1GOKJvDvhe3UNPo+jxAnAL20S5PpyKq3fgjSJtW0 TUbOMadLpLnyhZosYeMjBjbA+704/wAa5TRYrjxndeI7q50/RdQEepT2AW/Us0MceFCKNpwDjdx1 JJ9MHJHov60/zF9ar9Zv735/5Hc/8It4d/6AOl/+Acf+FR3HhLQpbaWOHRtMhlZCqSfYozsJHBxj nFU/APh/UfC3heLRtS1CO9e3dvJdM/JEeVXnnjkD2xW1q1tFd6RdwTKWjeJgQGIzwfSlUhBJ2Q44 qvf4397MXQ/Bmk2GiW1tqNnpeo3cSbZrs2ESeYQTzgDA9Pwq7/wjnhnzBH/Yukb2G4L9ljyR64xX lK2sNz+ywpmjDmK0Z0J/hYTHBFbHjbQdN0vRPDWu2dssWrR6nZZvR/rpAxCsHfqwIOME47DjiqlC Cm4tdUtu7sRHFV+RNTez6voehf8ACLeHv+gDpf8A4Bx/4Vla5b+DfD0Ns9/pGko11cJbQJ9kj3SO 7AAAY7ZyfQCurrzb4vWNndQ+FnuLWCVjr1tEWkjDEoxO5eexwMjoaFTi2lbdr8y1ia7v772fV9ER eJtOsbH4h+D7O0stNXTtSkuEuLcWEBV9keQd2zd1PrXax+G/DMpYR6LpDlThgtrGcH34rivGul2S +Pfh7p0NulvaedeKIrceUAPKBIG3GAe+PU1JFo+neHPjRpkWjWcNhBfaTN9ogtkEcblHBVto4zz1 pRjDRW3b/K454muknzvZdX3aOvm8O+F7dQ0+j6PECcAvbRLk+nIqT/hFvDv/AEAdL/8AAOP/AArh tFiuPGd14jurnT9F1AR6lPYBb9SzQxx4UIo2nAON3HUkn0x1PgHw/qPhbwvFo2pahHevbu3kumfk iPKrzzxyB7YpqnG2q7P7yXiq/Sb3tuzR/wCEW8Pf9AHS/wDwDj/wqKLw94XnZ1i0fR5Ch2sEtojt PoeOKk8U2F7qnhTVLDTZxBe3Fs8cMhYrhiMDkdPTNcL4O8T6XqHiKw0bWdBPh7xTYwNFHCYgqTxY wRE46r8obHTjgnk0lCLdrA8VXSvzv72dNpfgjTrTUtUlvYNMvILiUSW1udOiT7KmMbcgfNk9zWhJ 4d8Lwsiy6Po6M5woa1iBY+3HNct4Ct4rX4hePoYVKoLu2IBJPWLJ6+5rKtrjRTrXjXSvHDWcV3d3 JNqb0qpmtCuIhCW64IbheQxPc0OEUlp0v+WgRxNd399723f3/wBeh6EfC/h0Ak6FpYA6k2kf+FM/ 4Rzwz5gj/sXSN7DcF+yx5I9cYryu58M/2Z+zhdLqdj5WoG1E0gk3BwRJmMkH7rBSMj6itbxtoOm6 XonhrXbO2WLVo9Tss3o/10gYhWDv1YEHGCcdhxxTcIKbjbZpffoH1qvy35316vod/J4a8NRRmSTR NJRB1ZrSMAfjim/8Ix4cubctBo+lYcfLJHaRNj3HykVmeJ4rE+KvD9xc3M89xD55ttIijVzcuVA8 z5iAuwZ+YkAbsZGcHH8ASzx/ELx7YtDHbQR3FrMttE2URniJYjgctgE8dfXrTjSjJPT+rpfqDxVd fbf3sg+Gmm2Or2Gu/wBq6fpt5LZ6zcWkcjWEKHy02gDCoB69u9dq/hnw3GjO+h6UqKMszWkYAHqe K4n4d350vQvGN6tld3pi8R3h+z2iB5X+ZR8oJAPXPWuo0vxAniiO7srvwxrFjD5JLjVbRFjlB42j DNn6GicIpKyWy/JAsVX6ze76vuR6Da+DvEmnfb9N0fSpbYySRo4tI/n2MVLDjoSOPar7eHvC6TLC +j6OsrfdQ20QJ+gxXn/w0FroXwYn1+00+2/tCGC8cyrEod9juQpYDJHA49q3PCHh3SPEfwxsjqNr DeTarbCe8uZVDSSTMOXLddwPA/u4AGMUpU4pvTYbxNdOzm931fQ6n/hFvD3/AEAdL/8AAOP/AApD 4X8OgEnQtLAHUm0j/wAK4XwP4y1Ox8BWf9o6XrOuXUN1PZmawhWZiImwGcsy9jjPOdvNT/EO9i8R /B3W7y40i+sWjTKQ6hEI5VZWXDYBPH41M4wUXJK63COJruSi5ve279DsP+Ec8M+YI/7F0jew3Bfs seSPXGKe3hjw4ilm0LSlUDJJtIwB+lefeNtB03S9E8Na7Z2yxatHqdlm9H+ukDEKwd+rAg4wTjsO OK2Nau11X4v6T4cvlD6dDp0l+IH5SafftUsD97aASB2PPYVXJC9rdbfhcSxVe1+d7J7vq7HTReHP DM8fmQ6LpEiH+JLWMj8wKztL8Eadaalqkt7Bpl5BcSiS2tzp0SfZUxjbkD5snuawPEloPDfxN8L6 jpCLbprEz2WowQjas425RyBxuXn5uvGOlTeAreK1+IXj6GFSqC7tiAST1iyevuaI04O7S7/mv807 ili66sud9Or8/wDJnUnw94XWcQHR9HExG4Rm2i3Y9cYp7+GfDcaF30PSlVRks1pGAP0rzB9Xi8L6 heeH/HWjlLO91B7mz8QJGJI3dnLoXOMqy4AHoABgKM10uuXdxrHxNh0FYtPuba20sXyW17kxyO0m 3fgA5KhRj03E/RKnFpWW/wDldjeKrq95vTzfex1MXhvwzPGJItF0mRG6MlrGQfxxT/8AhFvD3/QB 0v8A8A4/8K57wj4Q1Dw34o1e/Mthb6ZqKoy6dZhhHFKoALqCABkA5wPT0rtmUMpU9CMHBxT9nC2w fWq9/jf3sx38N+GYwS+i6QoBAO61jHJ6dqbN4e8L2+3ztH0ePccLvtohk+2RXnfgzwdofiKXxpZa taNdWsXiCYRRPKwEZAX5lIOQe2c9OO5z0Zs7A+J/Eywg63eXMUcc8MyKIbGMRnEbOc8Ny21QTyCR zkrlhy3t0uU8RXUnFze9t33NTXrPwd4b0a41XU9I0mK2gXJJtI8seygY5JPAFQa14O0zV9HjGkw6 XpkvmRytcLp8UvyA5ZcEY5HevNLlv7T/AGWLe5vlW4ngQLFJKoZk23GwYJ6fLx9K6j4paNptj8Lx 9jsYLYR3Vq6pAgjXcZEBO1cAkjvVujFS5WuqRDxVfkUud7Pq+h3f/CLeHcf8gHS//AOP/Co4fD3h e4DGHR9HkCnB2W0RwfwFc148vjL4m8H+HJ/+Qdql1IbtScLMsaZWNvVSxGR3xjvVT4k2KeHpND8T 6LDHaX9tqENtL5ChPtFu5wY2AHzDOMZ6dRUKEHuutvy/zG8VXSvzva+78/8AI7X/AIRbw9/0AdL/ APAOP/CuHXTLKH4yNo0llp0mlPo5vFt20+ACN/NC53BMkY9T3r06vMdX0my1r46w2uoRefbf8I+W eEsQkn7/AKMB94ex44FOMI8y0XX8mUsTW5X776dX3R2sXhvwzMgeLRdJkQ9GW1jI/lTZPD3heKRI 5NH0dHc4RWtogW+gxzXEadpUfgr4w/YNFj8jR9W0yS5ls0J2RzRt98D+HIwPx+lUtC0W6+IPw/e/ ubLQ7m41ZJS15cKzTRMWYLg7eNnRQDwB9aShFrmS/q7X6Mn61WvZ1H977X/U7TXLfwb4ehtnv9I0 lGurhLaBPske6R3YAADHbOT6AVo/8I54YyB/Y2kc9P8ARYv8K858b6E9p4V8D2Wtvbalf2+s2trL ctHuMiEnIJbnBAXIPXFanxI0Wy0GLQPFdhp1tGmg3iGaOKEAC2c7XwoGOCcj05NP2cF062/LX8R/ WcRZe+9r7vz0/A7P/hG/DO/Z/Yukbs4x9ljz/KlHhnw2WKjQ9JLDqBaR5H6V59q81jpfxj0vxHb2 kBsboJpN3dAD/j4kXzIyOOuAgLZ6MB2ro7i6XTdD8V+Mre2hW6MEpt32DLRwKQhJ7gsGYeoK0nGK je3f+vyD6zXbsqj6dWbZ8PeFxOIDo+jiYjIjNtFuI+mKe/hnw3Ghd9D0pVUZLNaRgD9K89fwRe+K /Atl9nj0WK8uIobqPWFDm58z5W8wuFB3HHPP8q0b6fUNU8f2Xhy5Gm3xs9HS7eG7B8qaYybGkwAc ldvGem4/hTpJPlsr/wDA/wCB/XSVi6zXNzu3q/L/ADOwi8N+GZ4xJFoukyI3RktYyD+OKR/DvhiM ZfRtIUbtuWtYhz6dOtc/4X8F3mheItaupX0+LSdURT/ZlorCOOUAAsAQByM5wPT0rkvCHhPRNU+E WpfbrCO4aJr5IGlG824Dvjy8/cPAORye+azkoRV7dLlrEV27c73tu+x6j/wi3h7/AKAOl/8AgHH/ AIVm69Z+DvDejXGq6npGkw20C5JNpHlj2UDHJJ4Ap3w8uZrz4d+H57iRpJXsYtzsclvlxkmsT412 0E/wt1SSWCOR4TE0bOgJQmRQSD2OCRx61cqcU7JCp4mvNr33r5sqfEzT9O0r4c3+s6LY6baXUCxu rpYQSZDOoIO5COjV1tt4e8NNDAj6NpJmaNW2m1j3HjrjFcr8TtPstN+CusQ2Fnb2sRjhYpBEqKWM iZOAOtUvHvh7StI0HRNcsbOOHVodSs2+2r/rpNzBWDv95gQehNLkgm1bql9+gPFV/ZqXO+vV9LM9 MtbKw0q2dLS2trODJdlijWNc45JxgdAOfap4pY54xJFIsiN0ZDkH8a4HXbm51f4nReHxDYXFva6W L1ba+yY3kaXbvwAclQoxnpuJ+lrwj4Q1Dw34o1e/Mthb6ZqKoy6dZhhHFKoALqCABkA5wPT0q4rT sv6/r+tMJycm23dnb0UUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACsnSP+Qnr/AP1/r/6TQVrVjHTNVg1C+nsdRs44ruZZik9k0jKR GifeEq8fID071Er6NHRQcbTjJ2uvPun0TJtc0++1C0hXTr9LG6hnSZJXg81SB1UruXIIJHUdaz4v DdxJ4pHiK7ubY3sVm1nAIbcqoVmDEuSxLcgYHGMnrnIu/Z/EP/QU0v8A8Fsn/wAfo+z+If8AoKaX /wCC2T/4/Qnbp+Xa3fsL2UP+fi/8m9exh+G/Bup+HdL1q1XWrWeXU7ua7802DKInkxn5fNORxwMj 60mjeAI7P4dt4N1a9XULIxtEJUg8lgCxYH7zDIJ4PsK3fs/iH/oKaX/4LZP/AI/R9n8Q/wDQU0v/ AMFsn/x+m5NqzXbt02D2UE7+0X/k3/yJy2geBPEmmGGx1HxvcahocC7EsvsUaO6Dory8sV7EA8jj gcVL4h8A3934pHiXw14hfQ9TkhEFz/oyzxzqOmVJAyBxnnoOldJ9n8Q/9BTS/wDwWyf/AB+j7P4h /wCgppf/AILZP/j9Dk272/L/ADBUYLTnX/k3/wAiN0HRrrTIGk1LVZdV1GUAS3UkaxggZwqovCqM n1JzyTWtIHMbCNlV8fKWXIB9xkZ/Osv7P4h/6Cml/wDgtk/+P0fZ/EP/AEFNL/8ABbJ/8fo5n2/I FRgv+Xi/8m/yMDQPAH9naLruk6tfw6laazczXMypamAq0uNwB3twMcdx60628IazF4Z/4RmbX4pt K8s23m/ZCLn7Pgjy9+/bu28b9vTtnmt37P4h/wCgppf/AILZP/j9H2fxD/0FNL/8Fsn/AMfo5m+n bt02D2UN/aL/AMm/yMXxT8P7LX9E02ysbmTSbrSWVtOuoFyYMADGMjIwBxkdBVjw54d1yyl+0eJP E0mtzpnyFFpHbxxZGCcJ95sZGT0BOBya0vs/iH/oKaX/AOC2T/4/R9n8Q/8AQU0v/wAFsn/x+jme um/p/mHsYaL2i/8AJv8A5E46w+Gmp2Wg6v4dHil/7EvfPMUS2a+bH5mcqZCxyvJzwCfUVPqHw91T VPhxbeEZ/EFuoiESG5FgT8kZUooXzBg/KMkk554FdV9n8Q/9BTS//BbJ/wDH6Ps/iH/oKaX/AOC2 T/4/QpyWyfT8Nuo3Sg3f2i6/zdd/smP4m8Jan4in0KUaza2zaVdpeYFgziV14x/rRhcE8cnnrUXi zwZqPibWdHvRrFrFb6Y/nLZT2DSxSzdnbEqnjsO3PXNbv2fxD/0FNL/8Fsn/AMfpix687Mq6vpLF DhgNPkOD7/v6OZ9vPoL2MP512+1/8iVG0HWdRaaPWtZtJ7SS1lt/JsrFrc5kAG8s0r8gZAAx96uV f4XazdeAX8JXvizzbONUW1MdgqFQrhlEmWJYDAAAK+5Ndx9n8Q/9BTS//BbJ/wDH6Ps/iH/oKaX/ AOC2T/4/RzNO6X5D9lH/AJ+L/wAm/wDkTD1vwdqmrTeHdRj1yGLWdHkd/Paz3RTB12sPLDggY6fN /iJPE3hHUvEM+hyrrcMB0q7S8y9l5hmkXscOoC8ngDPvWx9n8Q/9BTS//BbJ/wDH6aYfEAYKdW0o E9B/Z0nP/kejnfbrfoL2MGre0W1vtf5GbqfhGeXxRbeJtJ1COy1ZIPs1z5kBkhuouu1lDAgg8hge OhzVzTvDrQ69ca9qFxHc6pLALaN44jHHDCCW2qpYnJJySTzgdOlWPs/iH/oKaX/4LZP/AI/R9n8Q /wDQU0v/AMFsn/x+hSa6fl/mHsof8/F/5N/kZXhvwrqPh+9165fV7a5Oq3TXQH2Ip5TkYx/rDuUA Djg8dal8D+F7rwjokmm3Opx6gDPJOsi23kkFzuYEb2B5J9K0Ps/iH/oKaX/4LZP/AI/R9n8Q/wDQ U0v/AMFsn/x+knbaPS3TYHSg/wDl4u/2v/kS9exXE1lLFazRQzOpCySxGRVPqVDLn8xXO+CfCM/h Pwsnh+61CHUbSPeEYWpiYq5JIb52B5Y+lan2fxD/ANBTS/8AwWyf/H6Ps/iH/oKaX/4LZP8A4/T5 nZq2/oHsof8APxf+Tf5GHovhDVvDWnTaRo2twJpRdmtVubQyS2qsclVcSAMMk4yOM87ulN1n4fQ3 nw9/4Q7SrxLCzZQryyQGZ2wwcnhl+YsMk+/St77P4h/6Cml/+C2T/wCP0fZ/EP8A0FNL/wDBbJ/8 fpOTas0/6+Y1SindVF/5N/8AIlzToLm20+CC7ninmjQK0kURjVsDGdpZsfnVkgEEHoayvs/iH/oK aX/4LZP/AI/R9n8Q/wDQU0v/AMFsn/x+m5N7r8hKjBbVF/5N/kctpPw/1XSPCepeGofEMLafcLOl vvsMvEspOdx3jdjccY28+o4FqbwRfzfDNfB39s264t1tTd/YjzGMD7nmfewOufwrf+z+If8AoKaX /wCC2T/4/R9n8Q/9BTS//BbJ/wDH6Td001+X+Y/Zxvf2i7/a6/8AbpgeIfAMniDw/pFs2rm01jSW R7TUreDbtYAA/IWPBAGRu6gfSrnh/wAOa7a3CXPiTxNJrMsRzBGlolvHGcYLELyzYJ6nAz0zzWn9 n8Q/9BTS/wDwWyf/AB+j7P4h/wCgppf/AILZP/j9Pnd27b+hPsIWS51p/i/yNauHsvA2q6Z4j1m8 sPEjQaZq0/2ie1+yK8iORg7JGJAz7qeOMd66L7P4h/6Cml/+C2T/AOP0fZ/EP/QU0v8A8Fsn/wAf pX8vy/zK9lG1vaL/AMm/+RM3wH4UuvBvh5dGm1Nb63hdvs5Fv5ZRSxYhuTuOWPPH0qfSPCNlo/in WtdgZjLqhQtH/DGQMMV92IBP0FW/s/iH/oKaX/4LZP8A4/TBHrxkMY1fSS4AJX+z5MgeuPPpucm7 ta/L/MXsYWt7Rf8Ak3/yJz3iLwDqF54q/wCEm8NeIX0TU5IRBc/6Ms8c6joSpOMjpnnoOlaDeFNR l8NahY3WvG71TUIGt59QuLUbQhBGEiRlCgbj36nJzWp9n8Q/9BTS/wDwWyf/AB+j7P4h/wCgppf/ AILZP/j9K+lrfl/mP2ceZS9or/8Ab3/yJS0XwxLp3glPDN9fpdRJaGzE8MBhby9pXpubnHeuUf4X azdeAX8JXvizzbONUW1MdgqFQrhlEmWJYDAAAK+5Ndx9n8Q/9BTS/wDwWyf/AB+j7P4h/wCgppf/ AILZP/j9Pmd721+X+YlSgkl7Rf8Ak3/yJx/iKLWdS1nRfDmn+KVstetYHvp5/scbRSL9wYiYnnOc dcAEk5xm5pEPjjRdXsYdb8Q6ZrFpcuY2T7L5E44J3Jt4OOM5HT3qbxJ4DHi5Il13+xrsxfcc6fMj qPQMtwGx7ZxT/D3ghvCsLRaINDswww7rpkrO49Gdpyx/E0KT6r8v6QnRhsqi/wDJv8jOvfh5rFr4 nv8AWfCnip9FGpMHvLZ7JLiNn7uoYjBPX6557Vqav4Btdd8Jy6NqGoXU13JItwdSO0SidejgAAKB 02jAx781rNDr6IXfVtKVQMknTpAB/wCR6bEuu3EYkh1nSJIz0ZNPcg/iJ6V3a1v6+8r2UL39ov8A yb/5E5jVfAniPxDo9pp+seLkY2lzFOk1tpwRnKHOXy7AnpjAAB6huld9CjRwojytKygAyMACx9Tg AflWZ9n8Q/8AQU0v/wAFsn/x+kaHxAoy2raUB6nTpP8A4/T5n2/IXsYf8/F/5N/kV9csPE0+pWl1 oWt2lpBGpWa0urPzklJ6NuDKwx6A1Np+iSpqf9rapcxXepCEwI8MJiiijJyQilmIJIXJLHO0dOlP +z+If+gppf8A4LZP/j9H2fxD/wBBTS//AAWyf/H6Sk10/L/MHRg/+Xi/8m/yKh8OzWXiW71vSbmK F76NUvLeaMskrIMJICCCrAcHqCMdMZqXw94dTRHv7uaf7VqWozefd3OzYGIGFVVycKoGAMn6mpvs /iH/AKCml/8Agtk/+P0fZ/EP/QU0v/wWyf8Ax+nzPt+X+YOjB/8ALxf+Tf5GjdW0N7aTWtxGJIJk aORG6MpGCPyrz3Rfhz4h0B10+w8dXkfh1HJWy+xxtKqH+ATHJX6gD2APNdcI9eMhjGr6TvAyV/s9 8/l59P8As/iH/oKaX/4LZP8A4/Su73t+X+Yeyha3tF/5N/kZuv8AgyPUrvSdS0y6/s3VNJOLWfy/ MQxkYaN0yNyke4I6g1PF4cnutftNa1q5gubmxR0s4oITHHCXADucsxZiBgHgAds81ZWLX3zs1bSm wcHGnSH/ANr077P4h/6Cml/+C2T/AOP01Jrp+X+Yexh/z8X/AJN/kZmneFr+x8c6r4jfVoJY9Qhj hNr9jKmMIPlw/mHPU545z2o8L+Fb3w/qeuXdzqsN6mq3RumjS0MRjYjGAd7ZGAB0zxWn9n8Q/wDQ U0v/AMFsn/x+j7P4h/6Cml/+C2T/AOP0k7fZ/IHSg/8Al4v/ACb0/lOW0n4f6tpHhTUfDUHiKFtP nEyW++wy8SS53BiJBuI3HGNvPqOB0fhnQJtD8LWuhXt3Dfx20At1dbcxbkAxhgWbnFTfZ/EP/QU0 v/wWyf8Ax+j7P4h/6Cml/wDgtk/+P0Xfb8hunF/8vF3+11+Rh6L4Q1bw1p02kaNrcCaUXZrVbm0M ktqrHJVXEgDDJOMjjPO7pVm78FxJ4Al8JaRcpZW8lu1uZpYTM2HzvYgMuWOSc+p6Vp/Z/EP/AEFN L/8ABbJ/8fo+z+If+gppf/gtk/8Aj9Dk2rNP+vmJUoJ39ov/ACb/AORH+HtLm0Xw/YaXPcpctZwJ AJUiMe4KoUErubnj1o1zT77ULSFdOv0sbqGdJkleDzVIHVSu5cggkdR1pn2fxD/0FNL/APBbJ/8A H6Ps/iH/AKCml/8Agtk/+P05Tcndrz6AqMErKov/ACb/ACKlv4clfxaniO/nt3uorM2kKW8JQBWY MxYliW5AwOAMnrnjMsfBWqaR4h1W60vxALfS9UuTdXFo1oHkSQj5ikhbjPupx29a3vs/iH/oKaX/ AOC2T/4/SeT4gLFRq2lbhyR/Z0mR/wCR6V32/L17j9lH/n4v/Jv8jl7b4YpF8OLzwdLrE0kE7s8c whUGEmTzAAMksM9cnJ9q1rjw1qWtWMOneINRsrnT1ZGmhtbNojcFGDKGLSNhcgZUDn1AyDp/Z/EP /QU0v/wWyf8Ax+j7P4h/6Cml/wDgtk/+P01Jrp+X+YOlF/8ALxf+Tdfkcn8TQL6z8KpaFZt3iK1A 8s7h8pbPT0wc+mKseIvAOoXnir/hJvDXiF9E1OSEQXP+jLPHOo6EqTjI6Z56DpU+l+Bn0a8e6sJd LikeVpgps52RHbhmRGuCqEgkEqBxxW79n8Q/9BTS/wDwWyf/AB+hSdttd/wS7g6cHpzq1rfa7t/y maPBi3/hy+0zxFqU+r3F/F5VxdNGsWAOVEaKNqAHkdTnkk1kaB4E8SaYYbHUfG9xqGhwLsSy+xRo 7oOivLyxXsQDyOOBxXTiHxASQNW0oleCBp0nH/kenfZ/EP8A0FNL/wDBbJ/8fpczve35C9jC1vaL /wAm/wAjJ1fwje3Xjay8TaXqyWU8Vo1lPFLbeaskRbd8vzLtbPfkdOOoOg2mazDq91d2mqWzWtxG i/Zri1LFZFGN4dXHUYyuOwwRUkcevSoHj1fSXQ9GXT5CD/5Hp32fxD/0FNL/APBbJ/8AH6Lva39f eHsoXv7Rf+Tf5GPF4MuNO8ATeHNG1d7C7k3sL6KLGx3fe+1QflHJAAOQMckjNaOuxSweA9ThnnNx NHpkqyTFdpkYREFsdsnmp/s/iH/oKaX/AOC2T/4/VbUNK13UdNurGbVtOWK5heFymnOGAYEHGZuv NKTbi0l+RrRhThVjOVRaO/2u6v0JfFvh+PxT4XvtHkk8s3Cfu5P+ecikMjfgwBptpdahq3h7y4ZY LHVkURXKzQmZYZcfMNoZCQeoOQCCDW5TRGgkMgRfMIClsckDoM/ifzq7aNHJc8w8E/CvWfAUl++l eKLKY3oUSfatJZsbc4I2zr/ePrW94R+G+m+FdWvdae5n1DWb5mae7mAX7x3MFUcKCfr9a7Oiqu9x PXc5KWx/4SPx1YaiMnTtDWURvnia5cbTj1CKCCf7zY6qa62moiRRqiKqIowFAwAPSnE4GT0pdLB1 MjxNpN3rvh+90u0vYrM3cLQvLJbmbCsMHADLzgn1+lVvBnh678K+G7XRbjUIb2O1TZFIlsYW25J+ b52yee2K1bfVdOu7h7e2v7WadPvRxzKzL9QDkVZV0cZV1bkrwc8jqKFp8wetvI5eXwneyfESHxWN VgVI7M2X2Q2ZJMZbcTv8z72e+3HtUcfgy4bxjq2tXuo29xaanaCzlsvsZXEYzj5/MOTyc/L37V11 FKy/P8f+HG23r6fht+R5xonw78S6Aq6bYePLqPQEY+Xa/YY2mRCc7BK2cdeoH0Ar0K2t4rS1itoF 2xRKEQZzgD3PWpaKdybK9zzv4wI76P4dSOQxu3iC0CuACVOWwcHg4roJfDt/qt9YTa9f2tzb2Mou Ire2tWiV5hkK77nbIGchRjnnJq3r/hXSPE6266tBNMtvIJYlS6liCuOjYRhyOx6itaKJYYljQuVU YBdy5/Ekkn8aFp99/wAipO9l5W/F/wCZx934M1RPHVz4k0jxALFb6COC7t5LQTbtnRkJYbTjpkEZ ycHOKi0P4fz6TZeJNNn1prrTtZlnl2m3VZY3lGGYuDg+2FA/p3FNEiGQxh1LgAlc8geuKEtLBzO9 /T8DhoPA2tD4fS+E5/EVu8bWv2OOcafjbFjHI8zlscZyB7HrVrUvASa34AtvDGp6hvltY4xBfQQ+ WyPGMK+0sefXkdTjFdjRQ9b36iWlrHGaH4U8SQTR/wDCR+MJdYtYSGjtkso7cMynKmRhlmxwcZ6j nNWtO8LX9j451XxG+rQSx6hDHCbX7GVMYQfLh/MOepzxzntXU0UdbhY4vS/Atzax+KIb7V47qDX3 keQRWnlNCXXbwS7AgDHUdqteHfDWs6L4ROiTa+k0kNqbazuIrQR+SAuFYgsdzDjuBx+NdVRStpb5 fcO7vf5mZ4e0690nQLKw1DUpNSu4I9kl3IMNKfU8ntxknJxVq/iu5rKWOxuIbe4YYWWaEyqvvtDL n86s0U5e9uJabGL4W0e90Dw/a6VeahFffZY1ijmS3MJKgYG4b2yfcY+lQTeENPfxta+Koh5V/FA9 vNtHEyEcZ9x6+nHpjcFzA05gE0ZmUZMYYbgPp1qWm3d3DyOU/wCERudO8WXmv6DqEVq2oKov7S4g MscrL92RcMpRsZB6g56Z5rQ0Tw6ml3WoahcTC61TUnVrq4WPYpCrtVVXJ2qB0BJPJyTW3TRIhkMY dS4AJXPIHriktFYDg9H+Gz2fhXUPCuo6sLzQ5zL5ES2wjljDsW5ckhip5GAOfbgN0PwL4nsI4dP1 LxxcX2iwqIxapZJFJIg6I0uS2OxxyR3FegUUA9d/6uIAFUKAABwAO1cv4r8J3Wu6roeradqaWF/p MzvG0tv50bq67WUruU9B6+v1HU013SNGd2VUUZLMcACjzBabHJ6z4Q1HVvEfh/WBrUKNo7OwSSyL +czrtbJEi4GOmBx71Le+Fr+78e2HiVdVgSKzt3txaGzJLK/LHf5nXgY+Xt3rqQQRkHINRNcwJMsL TRrK3RC4DH8KLf16g9Vr/XU89sPhpqdloOr+HR4pf+xL3zzFEtmvmx+ZnKmQscryc8An1FWbv4fX 974G0jQ5ddQX2kzwT2l2lphF8oYQFN3PHU56+3Fd9RQtFb0/DYd9b+v47laxiuobREvblbm56vIk XlqT/srk4H1JPvWZ4w8OR+LfCl/oclw1uLpABMq7tjBgwOO4yBxxW5RQ9Qi3F3Rwus+CNc17R9Jt L3xHa+fp95Fd+aunHa7R/dG3zRgHPPJz2x0rQ8ReFdR13WNBv49Wt7YaTP8AaPLNkX81yMHnzBgY z69etdVRR1v53+YulvK3yOF8Q+AdQu/FI8S+G/ET6JqckIguj9mWeOdR0JVjjI6Z56DpXRaDo11p kDSalqsuq6jKAJbqSNYwQM4VUXhVGT6k55JrX3KWKgjcOSM8iloWisD1d2RXVtDe2k1rcRiSCZGj kQ9GUjBH5Vwel+ANc0fQ7nw7aeK86HIkkcKyWQa5gRgflWTdjGT1Kn0GOMeg0Umk7+Y02jz28+G1 9c/Du08IJ4kxHAYybmWxDNhGDKqqHXAyB1LH3rX8Z+E7/wAYeE20JtWt7TzShnmWzZ921gw2r5g2 8gdSa6okAEk4A6k0AhgCCCDyCKq7/G/zElbYwNa0PUta8IXWiSanaxXF1btby3K2bFdrDBKp5nB6 /wARqtb+DVm8ADwlrF3He2wtVtRNDAYW2qAFbBZvmGAc9MjpXU0Unqmn1BaWt0PP9D8C+J7COHT9 S8cXF9osKiMWqWSRSSIOiNLktjscckdxWnN4Sv3+IMHimLVraNIbM2QtDZE5jLbvv+YOc98Y9q62 ijrcFomkcb4w8DT+INV0/XNH1mTRtbsVaOO6SESq8Z6oykjI6+3J4Pa/oegavYxvc6x4gbVtUKFI pntViihU9ljQjOcDJJycdq6OiklZWB66s4vQfBWpaH4Kv/Dw1y3me5MxS6+wlTGZcliV8whuSccj 8ajf4cxXvw4tvCOp6h532RVFtewQ+U8bJ9xsFm5HQ88gnpXcUUWX5fhsO7vf1/Hc43w94U8R2dwh 8ReMJtatoNphthZxwDcOjOwyz464J6jJzWfe/DzWLXxPf6z4U8VPoo1Jg95bPZJcRs/d1DEYJ6/X PPavQqKfW4uljP0fS20uzMc13Ne3Uh3z3U2A0r4xnAACjAAAAwBU+oQXFzp88FrPHBNIhVZJIjIq 5GM7Qy5/OrBZQwUsAT0GetLQ1fRgtNjz6P4c38fwvbwT/b8BjKmMXf2A7ghfeRt83Ge2c9O1aPiT wfqfiHw9pemf21bW8llPDO8wsSwlaP7o2+b8ozgnk12FFD1d36/cC0VvX8RsYcRqJGVnx8xVcAn2 GTj865zxr4Vk8WaTa29vf/YbuzvIry3nMXmKHQnAK5GRye9dLTWkRGVXdVLnCgnGT6CgFocdqXg7 V9U1/wAP6xPr1t5+kNI+z+zztlZ1CsB+9G0Y6dSO5NWrzwtf3Xj2w8SrqsCRWdu9uLQ2ZJZX5Y7/ ADOvAx8vbvXU0ULQHqrP+up57e/DzWLXxPf6z4U8VPoo1Jg95bPZJcRs/d1DEYJ6/XPPauy0fS20 uzMc13Ne3Uh3z3U2A0r4xnAACjAAAAwBWhRQtFZA9XdlDWrG41LSLi0tbv7HcSKPKuPL3+WwIIbb kZwR0zWRN4avNS1vR9T1W8tJJNKLvD9ntTGXkZdpJJdsLgk7R3A54xXTUUra3A5Tw/4VvtD8T67r Vxq8FymrOkkkC2Zj8sopUYbzG7Yzkdu1cppdj4u8SPeeIfC/jiO20y+u3eK3uLCKZgqnaAW6gfLw vUDGec16tXGXfwn8C3uqNqM/h23Nyz722u6oT6lAwU+/HNPqvuDo/vKlzo3iHxd4O1fw3q2saatw ZBC1/aWzOrJ8rEFCy7X7HBIwR3q94k8H6n4h8PaXpn9tW1vJZTwzvMLEsJWj+6Nvm/KM4J5NdVa2 ltY2sdraQRW9vEu2OKJAqoPQAcCpqLK/3fgC2t6/icbr3g7VNQ8U6Z4j0vXI7DULS3a1lD2nmxTI xz93eCOff054o0XwXqOieLtX1uPXvtCaokZnintF3GRFKqdykALyeAAenPr2VNd0jRndlVFGSzHA AoWmwPU5jwX4Uu/CkeppcanDfC/vpL5ilqYdjvjIHztkcV1BAIIPQ0oIIyDkGkZlXG5gMnAyepoe ujA5Hwb4Mu/C2mzaRNq0d9pO+UwQG1COFcklXbcd3U9AO/0DNF8Iat4a06bSNG1uBNKLs1qtzaGS W1VjkqriQBhknGRxnnd0rsqKB31v8zO0HRLLw5oltpOnoVtrdcLuOWYk5LE9ySST9ao+M/D0/ivw veaJDfpZC7UI8zQeaQuQSANy8nHWt+iiXvbiTtqjj/Eng/U/EPh7S9M/tq2t5LKeGd5hYlhK0f3R t835RnBPJqz4h8JNrN9per2t8LLXNMYmG6EO+N1YYdHj3DKkf7QI7GuleRIl3SOqDIGWOOT0p1HW /nf5h5fI56Pw5Pd69a61rN1Bc3VjG6WUcEBjjhLgB3OWYsxxjqAB2zzVXw94Uv8ARfFGuazPq0F0 uryJI8CWZj8sou1cN5jZ4xnI7dq6aO5gmdkimjd0+8quCR9aloTsD13ONvfCGqap4YuPDuo6vaXF jOxUyfYiJUi3bgg+cruA4D44wDtJpvizwFLreoaZrGi6xJous6chiiuUiEqvGf4GUkZH+J4PbtKR mVFLMwUDuTikklsO5geHtB1OwY3Wu67JrOobSiSGBII4lPJCovc4GWJJ47Dit9txU7SA2OCRkA/S lopiSscp4S8J3/hq+1mefVre8j1S8e9dEszEY3bqAfMbjgdvxqlZeBtV0zxHrN5YeJGg0zVp/tE9 r9kV5EcjB2SMSBn3U8cY713FFKy/Cw227/f8zzuy+GM8XwzvfBV1rYltpMi1mS1CGEeZ5g3Dcd53 deRxx71c1/wVrniTwcmh3viO3WXfG0lwmncHYQV2r5nByBkknPYCu2SRJVLRurgEjKnPI6inVV23 f+tBeXr+JzXiXwkPE+k2cVzeG31OymW5tb63jx5Uy9G2EnK+qk8+tJL4bvdYu9Ol8QXtrdRafKtz FBa2zRK86ghXbc7cDOQo78knpXShlbO0g4ODg96WktNfmHSwVy2qeFbybxlb+KNL1GG3u47M2UkN zbmWN4y+7ja6lWz35HtXU03em/ZvXfjO3POKOtx30sY2m+HzBrM+t6jPHd6rNCLcSxxeWkUIYsER SzEZJySScn0AAHJWnw317QdRu18MeM5NL0e6nM7WLWEc/lFuoRmPA9OOO+a9IBBGQciigRyPiXwP /bXhyx06z1KS1urG8jvobuZPOLSqSSXGRnJJ6Y/LituXSX1Hw/caXrE6XZuoWindIvLUhhg7VySB 6ZJPvWnRSaumu4JtWfY41/h7ZSfDZPB8lw7BYlH2vnf5oIbzBzx8w6Z4HFdMNLsxo40nyFNj5H2b yu3l7duPyq5RTet79QWlrdDzjRPh34l0BV02w8eXUegIx8u1+wxtMiE52CVs469QPoBWh4n+Hzap eaVqeg6vJomraZGYYLlYhMGiP8Dqx+b6nPU5Brt6KAOc0PQNXsY3udY8QNq2qFCkUz2qxRQqeyxo RnOBkk5OO1Z2g+CtS0PwVf8Ah4a5bzPcmYpdfYSpjMuSxK+YQ3JOOR+NdpRSaTvf0BO23qYvhPQ5 /DXhix0ae8jvPscYiSZIDFlR0yNzc++ab4w8OR+LfCl/oclw1uLpABMq7tjBgwOO4yBxxW5RTl72 4R921uhxeueDtY8ReBZvDt/r8HnThFluUsONqkEbU8zgnaMkk+wFTeKPCOpeJPDthpZ1m2tnt5op pJhYlxI0ZyuF80bRkDPJrrqKN3f5/cHTl6f5nF+LPAt1r+o6brema22ka/YIY1vIoA6SIeqshPTO cZJ6nrWp4e0HU7Bjda7rsms6htKJIYEgjiU8kKi9zgZYknjsOK6CihaA9dwooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOZ8 e+LB4M8KXWrC0muZFwkaIhKhjwC5H3Vz3P0HJouPHOkafY2FxqAv7cXsy20Pm2Eyl5SBhcFcjOeD 0PPpWT8Zv+SU619Iv/RqVo+NfD7eJ/AF1p8BK3fkrNaupwVlTDIQe3Ix+NK9otvv+BXLe3nf9C/f eLNK07XrLRLlrlb++UtbRi2kYSADJwwG3gdeeKvPq1smsx6Uwm+1SQtMuIWKbAQCd+No5I4znmvI 9Z1288X+FtL8a6Wp87w2kd3NGoGZJiR58WSMgLGCT67h6V2f2m417wv4h8Q6S7PJeWTxaYQCDsRG wQD3Mhc57jbRL3U32v8A8D+vJij7zS2vb7+v4fia1x410a3tLi93Xc1hbOUmvILWSSJCvDfMoO4D uVyBg5PBrbtbq3vrSK6tJo57eZA8csbbldT0IIrkvhjcWl/8K9D8sI0K2YhlUgY3LlXB/EGqXwZg kt/h5CmWNr9ruDaFhgmHzDtP48n8aq1m49v6/wCG+ZN7pPudvqF/baXYTXt27JbwoXkZUZyFHJOF BJ/Kubm+JPhiHS7fVPtVxJp8wBN1FZyvFECcDzGC4Q8jhsHnpXQav/yBb/8A695P/QTXmenqD+zI 4IGP7FmP6NUc3xeVvxv/AJD+1GPe/wCFv8ztb7x3oGnXUEVxPceVNIsS3aWsrWwdjgKZguzOeOvH fFamo61ZaZc2trO7NdXbMtvbxIXeTaMsQB0AHUnAHHPIrzfx0ir+zvEoUBVsbLAA4HzR10ms39o/ jfQdNtrO3k182ks0V3cZK2sJADHaCN7MRgDI4BOR31lGzt5tfgJO8FPuv6/M3rDxHpuoRXzRSuj2 Enl3cUsbI8LYzypHOQQQRkHsTWG/xR8KDSxqcV5c3Fnlt0tvZzSCMKcEvhfkGQcbsZ6jNZHgVJI/ ih8QkknedhJZZdgAT+6bsABx0/DnPWpfg1HG3wutlkVSjz3O8EcEea4OfwqJK33J/fYq2jfnb8/8 jrT4m0k6ZY6hDdC4gv2CWnkKXadiCcKBz0BJzjGDnGDVnTtWt9SkuYoknjmtmCTRzRMhUkZHXhhj uCR715J4a8KXWrfDvT4tJ1VtL1G21K6vNEmI3L5YdlwQRyrBj68HOCMiuy8C+ItY1HUtU0bxNpcV prunrEZZrc5iuYm3bHX06H/63QCaf9eV/vXUnX+vW346WO3JABJOAK5fwxN4Yl1zxAdBYtfG4RtS OJMeYQcY3cdAfu8V1Fef+Cv+SkfED/r6tf8A0TRFXb9P1QSdkvX/ADOlfxXpiCSQfaZLaKf7M9zH bu8aybtpXIHOG4LAbQcgkYNbdePagviL4eR3mr6a0WveCLmVri4spOJ7RZGJcoe65Oe/0HLV69FI s0KSrna6hhn0NJO6uN6Mqalq9npKw/apGDzyeVDFGheSVuuFVQSeASewAJOAK8+WewuPj1YSW1u8 F1/Y8wuVkiaNid42k54PGeRkds8Vcvr4xfHrS7e8YLbvosq2W7oZjIC+Pfao/CpNQ2f8L10bG3zP 7Fnz648wY/r+tHWL73/Jr9By0TXp+aOpsfEmm6jqupaZbSSte6cFNzC0LKV3AlcZGGyB2zSaT4n0 nWoL+a0uGCafM0F0Zo2i8p1GWB3AdB36VyvhcgfGPxyM8+TYnH/bM1w95PLH4D+Js1tvkQeImMvl Hkxb49/4bc59qL7ea/VIpxtf1X4q56r/AMJzoq6haWk7Xlr9tfy7We5s5Yopn7KrsoGTjjOM9s5r pK4XUPDfh/xH4dtdR1DXdQutKQpeQyNcqFBA+VhhevOMV3KHcitgjIzg9aq1tHuZp31Rx/ifxynh /wAVaJon2G8lN+7tJNFbPIAiqThAoJZs4yBnA61v3uvWGnwWkly8qvdsFt4BCxmkYjOBGBuyByeO O+K4/wAZcfFT4f5/56Xn/ooUzVLmS2+POhi7OLW40iaGzJ6edu3Pj32qP0pLZebZclbXyX52OtsP E+mahq02kq8sGpQoJGtbmJo3KH+JcjDr7qTjvWxXnnje18z4k+AprXP25LmfdtH/ACw2fPn26D/g Veh0LVX/AK/r9bkvR2Me58S6fb3l5aILi6nskEl0ltC0hhBGRnHcgZ2jLY7ciob3xjolj4Yj8RyX TvpMiq4uYYXkAU8AkAZHPHI4PWsLTbu21XxD4qXQ4obFYJhDqN7jfNcTLHjCqflQKMDcQcnPHeqP wnsLfV/gjp2n3ieZb3UNxDIp7q0sgNFm4OS8vxv/AJBpzJPa7R2tx4gsbbQV1qT7R9iZFcFIHZ9r dDsA3dx271Jf61aadDC84nMk/wDqoIoWklf1wignjPJPA7kV554Du7m8tbfwVfgtc+Hrtku2ZeHh iINuevG4lCPaNqs6xOp+NtrY3l/dWUdzo2yykikCB5PNJdASDyQFP/AR7U7JtW2e3pa4tk77rf1v b/gnXaT4u0fWY70288kctgwW7t7iFopYCRkbkYA8joRwazofiV4VuF01rfUHlj1K4+zWzpbyFS+4 qATt+XJBAzjOCRwM1JpnhvR9F8W3OoR3d3PrF/b4m86XdujTADEAADHABrF+CsaL8NLVgigtdXDM QOp81hk/gB+VJNX17fqVb3W/O33ps6S68ZaPawX1wXnltNPkMd3cwwM8cLDG4Egc4zztzjnOMGtG +1jT9O0v+0rq5VbQhSsigvv3YChQMliSQAACTnivM7u7ttY+GHjG+0KKHTtGZb1sIN8t1IAd7sTk IrHI24Jxggr0qxqem3Wo/B/wjNZTRi9sl0+7t4ZGwLmRVXEWfVs8e/XA5DS92/8Ah/H+tBdbev4f 1qdZJ4+0C31ddJvZrmz1BlDpBPaSAup/iUhSCPx7Gm2E/heX4g6h9jbd4iNmouiPMwIQ3y9fk6nt zVL+ytR8QeN9D8QXWl3Gkx6TDOrJcSxM87SKFAHluw2Dk5JBzjiqlr/yXu//AOwBF/6ONCWq+f5P +vmTJ6P5fmjdl8c6BFqN9p32meS+sgvnW0VrK8hLcgKoXL8DPy5wOTirug+JdK8SabJfabcF4YpG ilEiGN4nX7yurAFSPeuY0NR/wunxU2BkafZjP/fVUPDFil8/xKsDMLaO41GaMyjgR7oQC365rJT9 35N/c7GllzW81+KudHdfEPw7Y21teXVxcxWF0/lwXptJTDK3YBgvQ4OD0I5BIrorK8g1CzjurVy8 MgyrFSue3QgGvLbfRNc8SfDHTvCNxpMkGwQINVE0fkNFG6kSIN3mbii8AoOTyQK9YAwAPStmkr+p F9iC9vrXTbKa9vZ44LaFS8kshwqj3rHbxlpENxZRXZu7IXzBLWS7tZIkkY9F3MPlY9g2CfSub+Mc 8tp4Z0u6wTYwavay33GR5QfPPtu2/pVr4uw2158LNXEnzZSNoCoyTJvXZj6kgfjUXsm/P/L/AD0K Su0u5m+NfFUUHxB8L6PNa6hJZrNPNcxpYTSCZlizHsAU+aAWyducEAnGK7Pw7o+kaXaTz6PZPZxa hKbuSJ0eM72ABOxuU6D5cD6VxWtidPiX8NFuzm5FvdiUnu/kLn9a9OrRqyXz/P8A4BN72fkjkvHP jQeEYtMVbK5uJb+8jt1aOFnWMFhuJwOWxnCjkmud+Kctnd6T4U1QwvEV161w9zE0Tou5s5DAFRxn mrvxY4tfCpPT/hIrT+bUnxbAbTfDIYAg+IbMEHvy1Qtk/wC9/l/mapW/8Bf6/wCR0Np440O812HR lmuYrudWa3+0WksSXAXqY2ZQHHfI69qs6p4nsNKllieO9uZIVDzLZ2kk/lD/AGioIBxzjrjnGK5v x4APGHgNwBuGqOAe+DE2RWX4EUatq3i2zn1bULXUoNZnaWCKVV3RkgRvgqTjaAPoB7Uovmul0v8A p/n+Bm3Z/d+v+R6JpGr2GvaXBqemXKXNnOu6OVM4PboeQQeCDyKnu4ZLi0khinaB3XaJVGWXPUj3 9PesTwhomj+HNOudK0WSV7eG4YyeZJv2yMAWXP5ZHbNdD0qnboCPJvGfgnQNNu9C/wCEeiSy8UPf xtayLOfNlUNmVpCxJcbdxJOSTgd8He+IOoyy6t4Z8KwytGus3h+1MjbS1vENzpkcjdwD7ZHeqfxU 8FaFfeFtT1xbOO21q1j+0QXtuNkrSrjaCR97JAAzk8jHNQ+Lbe5h8W/DnW70YEM72tyeyyzRADP/ AAIEflSjrZPuvx/4bX1FLRtrs/w/4fT0LXjlYPBs2g+INKtorRY76KyvEgQIsls+RhgOu04K+nPq a9Drz34uwtqOh6PosKl59R1e3iVR1Cglmb6ALzXoQGABRF3Tv3/RDfxL0/VlHUtXstJEP2qRvMnf y4YYo2kklb0VVBJwOSegHJwKraV4m0zWNQvNOt5JI7+y2/aLWeMxyRhuQcHqD6jIrlL3VI7L462N vqDCOG40Vo7F5DhTKZcuo/2iFX8h61ueJLW2S01ubS4Yh4in0uQJJGv70qAdmT2+Y8Z649uE3aPN 6/hf/IaV5cvp+NiW48a6Nb2lxe7ruawtnKTXkFrJJEhXhvmUHcB3K5Awcng1LqXjDQdJsbC9u79R a38iR200aM6SM/3fmUEAH1JArI+GNxaX/wAK9D8sI0K2YhlUgY3LlXB/EGvNbO2aH4N6HE25rQ+J Y/sm8dYTcHB+h5P405aS5fT8Xb/golO8eb1/Jv8ASzPXLTxvod5r0WjJPOl3OrNb+dbSRpcBepjd lAcAc5BORyM1mXvj6O0+ISeGTp9+USza4kmjtJJNx3KFChQSV+9lumcDNVvHCj/hOfAT4G4ahMAe 4BiORUUvH7QEHv4db/0fRBqT+/8AK5TWj9E/xsbV78Q/DNjJqUUl+zy6aqtdRxQSOybgSMADngEn GQAMnFXv+Eq0p4tPaCWW4k1GAXFrDDCzSPGQDvK4+VeRy2BkgdeK5bw3Gh+NXjdyilxbWShiOQDG cj9B+VN0O5eH45eJ7W94km0+2eyz3hXIYD/gbGlF3S8/0uOa5b+Vvxt/mdfpHiPS9dmu7W0mcXVo wS5tZ4miliJ6ZVgDg9iOD2Ncb4DtoLP4nfEGC2iSKITWZCIMAExEn9STUt1a4+PdhcWmQ50ST7bg cbPMwmfcn/0Gl8Gf8lV+IX/XWy/9Emrp6pvyf/pSX6ET7ea/K/6noVYj+K9MQSSD7TJbRT/ZnuY7 d3jWTdtK5A5w3BYDaDkEjBrbrx7UF8RfDyO81fTWi17wRcytcXFlJxPaLIxLlD3XJz3+g5aovrZj s2tD0y88RabYa7YaNcyyR3t/u+zL5TFZNoy3zAYGB6mm/wDCQ6XN4ibw47yDUTbmfyXhcK0WdpYM RtIzx1rkvF0qyfEz4eSg4V5LsjPHWEYp1wyn9oGzUMCV8OvkZ6fvqfa/n+BVla67J/jYwfAviTRP CKeLoLjzVVNeuWWC1t3mZIwF+YhAdq8Hk4HFek2HiHRtb8PDV7O8SfTZEOZVB6dCCOoPtjNcp8LV X7R4zbAyfEVyCcdeFpvwzsRZXnjP7KCNMfWpfswxhdwAEm32DfL/AMBod7Wf8qf4L/MJ6Tdv5mvz /wAja8IXvhSw8DwXHh6Xy9AiL+U7eZ13nON/zElsgDqe1WrPxlpF3rUWkP8AbLS+nQyW8V7aSQee o5JQuBkjuOo9K8d0+8+wfB7wjcTSyw6eniEm7liOPLTzZcMeD0baenUCvUb/AMLaNJe6TrOo6rqF zLZzq9iz3AILuQAAABu3YH4VW7u+/wCi/wA/66TJcrce1/za/Q3LzxBZWd69kq3Fzdxx+bJDawtK Y17FiBhc9gTk4OAcVzviH4k6Zp/gCXxRpKyalCcxw+XE2Fkzt/eZGUAPXOPbrVT4a6pHPq/jGwum C6tHrU0kkbn52iIVY2x/d2qB+A9aqfE+30q0+EfiKDSYYYYlnUyiFcL5plQsfQnJ59+O1R0XnZ/f b/MqCvO3nY7SPxJYrof9qXP2m3hUqhE9rJG7OcABUKhmJJAGAcnpViy1m3vb6Sx8q5guo4xK0c8L L8hOAQ33TyOxJHfFZXirRIfEfhSLTGv3sZ5Wie1uY+THMvzocd+V9qwvB+u+JrfxSfDHjKyt5NSS 0aa01S1+5cxBlDAjjDZx2H0HBNbya/rb8/0IV+VP+v6/U9DoqGG7triSaOC4ileB9kqo4YxtgHDA dDgg4PrU1IZyekX48V+IdUmJDaXpF19kgj7S3CgGSRvXaSFUdAQT1xjzfxzr914x+L+neAI7iSHR o5VF9HGxX7QQvmMrEc7doxj1JPpjtvhPlNC1mB/9dDrd4soPUNvzz+BFeba/bN4Q/aTsNY1D93p+ oTB452Hy/PH5ZBPThjz6Ag0R1lBPqk/nZMTfuza8/uTse3X3hTQ9Q0ddKm0y2W0RQIlijCGEjoUI 5UjsRWZ4W8JPpPg2PQ764eaaCed4rrdmTLSuySZ7NhgT+I6V1mRjOePWq1jf2upW32mzmWaAsyiR QdrEHBwe4yOo4o7ruPoZHhHX217TJxcbBf2FzJZXgQYHmxnBYDsGGGA7Zx2rV1OytdQ024tby3iu IHQho5UDKePQ1xvgAF/FXjuZDmBtWCKQeN6xqH/Hpmu5uP8Aj3l/3D/Kpk7wu+qT+9Dj8VvN/meO fBzwh4b1X4axXWpaPYz3DXEwa5kiHmqA2Bh/vLjHYjFbvwe1S/v7LX7aa9nvtMstSkg066mcuzxA njeeWA4wff8ALhfCPgiXxP8ABWOfTJ5V1SG7lmjheZmguNr/AOreMnYQcenXr3r1P4feKdP8S+Fc WFnHp93ZA29zpyIE+zSj+ELxhc9Py6g1o3u/Jfpr+hNtl5v9dP1+Roar410jRxO9x9te3tji5uLe zllihx13Oqkcd8Zx3xWhc67plpoy6vLeR/YHVWjmTL+ZuxtChclicgAAEnPFec/DTT7bxR8Oxa3O rakswM1tqNos6ja7M28Ebcjduz17mrniTw9bJ8PNKtPDV0HXSNRimso55CRcvHIf3QbHOSSAenHp zU7aPy+7+thqz/H+v8+x0snj7QLfV10m9mubPUGUOkE9pIC6n+JSFII/HsabYT+F5fiDqH2Nt3iI 2ai6I8zAhDfL1+Tqe3NUv7K1HxB430PxBdaXcaTHpMM6slxLEzztIoUAeW7DYOTkkHOOKqWv/Je7 /wD7AEX/AKONNLVfP8n/AF8yZPR/L80dXd+IbG21B9PQT3N7HGJZILaFpDGp6FiOFzzgE5ODgHFQ W3jDQrvQ7rWIbwmzsyy3P7lxJAy/eDx7d6kehFct8N7mT/hKfHdnen/iYJq5mYHqYWUCL8Nq1nra +V4/+I8trn7I+lRfaAB8vn+UxH47eT/vVnKVo38r/hf/AIHqaJLmt52/G3/B9DqJviV4Ygs7W9e8 nNlchCLpbSUwx7/uiRwu1CcjgkEZ5Arf1HVrTTI4GuHYvcSCKCONS7yuQThQPYE56AAk4FeYXkaH 9mFVKLj+yI2xjvkHP58103ibw/ceItA0RdM1Y6Zrloq3VhORuUsEwysMHIIbnr9CMiqbs2uzX3P/ AIYhapPvf9P8zqdO1a31KS5iiSeOa2YJNHNEyFSRkdeGGO4JHvV6uI8C+ItY1HUtU0bxNpcVprun rEZZrc5iuYm3bHX06H/63QdF4nF43hTVxp+ftps5fI29d+w4x75ok+WNyoq8uVlO48a6Nb2lxe7r uawtnKTXkFrJJEhXhvmUHcB3K5Awcng1D4s8UwaV4Iu9asWluQ9q72s1rC0yZ2EqxKAhV4+8cD3q n8Mbi0v/AIV6H5YRoVsxDKpAxuXKuD+INcf4Zge2/Z415MsbXyL82hYYzDl9p/Hk/jRUVlKPZf1/ wAg7uL7v+v8AgnQeA7LQtd0bw5qT6ZdDVdMs0kF3NazQ73kTDkOwAlySxJy3XPevQiQqlmIAAySe 1c/4D/5J94c/7Btv/wCi1ravBCbG4FwcQGNvMP8As45/StK/uylbuyYapGXbeKtNujaNELo296/l 2tz9nfypjyeGxwCAcE4B7E5FUrGfwvL8QdQ+xtu8RGzUXRHmYEIb5evydT25rirJ/E3w3u9M0zUD Fr3g+e5it7O6AxcWZZh5Qb1UH6/UcLW7a/8AJe7/AP7AEX/o41KScl8/Xb+vkKTaWvl+f9fM6q/8 RWNhetZYuLm7SHz3gtYGlZI84DNgcZwcDqcHAODVbRPGugeI7eSfSb5rlIlLPi3kUgA4PBUHPt1r nhDqWh/FHWdRsrGbWLXUbSDz4bWWNZbV0yq5EjIpVhk8NnjpjrseB/Dtx4e0zUftW1JtQ1Ge+MCt uEAkPCZ7kADOOMk4yOaS2v5fjf8Ar+mVLR6f1p/noQH4m+FzpA1WG7ubiyBbfJBZTSeUqnBaQBco Mg/exnBxmrviS/8ADd34LuLnWrhH0K6twzyJvIeMgEEbPm9Olcl4CjRvhBq6lFKvJqG4EcH53HNM iOf2ajnn/iQN/wCgGs3K8H6J/en/AJFWtJLzf4M6rWtettB+Hcuq6XHK0EWnmSzCxO+AI8oW7gYA yT+Jrjl1PwhN8NNMbxPYaiYTbwXU95/Z1wP35Ckyecq8Esfvbuc4rc1X/kg9z/2Lx/8ARFYXiv8A 5Nni/wCwTZfzireouXnfZr9SaHv8kdr/APAPTL3VrHTNOW9u5vLgbaE+UszlvuqqgEsx7AAk1StP FWl3WtnRmea21LyftC29zC0bPHnG5SRgj2zkdxXI+LNUTS/Gvw/lv2EemM06tI5wiTmILGSfX5mA z6muw1g6FZ3MeqalFAbqC3m8uUpukWILukx3xgc9uQO9RL3demv9f10FG7slvp/X9dSsfG2iC1hv TNN/Z004t477yG8kuW2j5sfdLcBvun1qzq3ivRdD1Kz0/Urz7PcXm7yA0bbW2jLfPjaMD1NeUeMH F18BGvNPii0zR2MDWVhD87BDOMGRznk/ewuMHgs1dj42UN428ABgD/p8x5/64mnJWSfnb8v8wTvr 5N/mdBpXjPRtY1S50y2e6jvreLzmt7m0lgkaPON6q6gsM+lZfhDx5H4r1HVbb+z763W2vHtovMtJ AMKoyXbG1GJz8pIOMVV1AAfHLRmAAZtFuAT3IEi4FRfC4/6T40HceIrn+S0oNS18n+ErFtWTt3X4 q5V8FJbaX8R/iL5MBSCKS0fy4YyTzEWOFHJOSTgV2nh/xNpnijTG1HSZJZrUMU3tCyZYdQAwBNcr 4M/5Kr8Qv+utl/6JNZAuJ/AnjbWvD9sClv4jH2vSSEJVLpiEkXjsCQ57ACnK94r+6retl+hHVvz/ AAPTNL1S21ixF5aeb5LMygyRNGSVODwwBxkHnvVx3WNGd2CqoyWJwAPWoLCyh07T7eyt12w28axI PQAYFch8XpruD4W649nu3mJVfb18suof/wAdJqZO2xUFzNIxvinrejav8L9TlMU8sDxg2l09s4jZ wwwVbHQ9m4DZ4JzXbJq9ppWjab5/mvLLCgihgiaWR8KM4VQTgdz0Hciua+IdxZXfwU1O4tmie0ks I2hIxtIyu3H6VkvOrfFXR7G8v7qyjufD8aWUkMgQPJvy6AkHkgKf+Aj2oS9+UfT8n+OhLd4qXr+c f8zvdC8T6X4ha7isZZVubNwlzbXELRSwkjI3IwBwR0PQ1UvfHOh2FrNezS3B0+CTypb1LZ2gRs4+ +ByM8ErkA8E5rA1vw9Z6BD4k1TTLu+n8R3ukzffl3syomFbAAxg4ANX/AAXcaFr3wr0xGFtLpgsE huYpCCibUAdWz0wQev1p9G+1v1/y/q2r6pd/+B/n/VzY1DxfoOmX9lY3eoxpcXys9uoUsHULuJ3A YAxzyfSoNO8b6FqmlT6ja3E5hguTaMjW0glMoAOwR7dzNg9AM/ka5jxYlrJ4/wDhsIIlFt5tyYkK YAAhBXg9MYH0p3j66S08e+Cxd3U9np80lzGbiJwoSdkUJkkEZILD8T70LXTzt/X5eo2rJN9r/mv0 v6HV6V4s0vV9VuNKia4t9RgQSvaXdu8MhQnAcBgNy57jNMuvGWj2sF9cF55bTT5DHd3MMDPHCwxu BIHOM87c45zjBqkPC+kWPizT9bub++uNW8tra3M0wbcmCWG0AZAyTntXI3d3bax8MPGN9oUUOnaM y3rYQb5bqQA73YnIRWORtwTjBBXpSb0+X9f13Dr81+X9fI9KvdasbG3tZpJTILt1S2WFS7TMQSAo HXgE56AAkkCnafq1vqT3MUSzxzWzBJo5omQoSMjrwwx3BI964E+H7jxD8OPBy6Zqx0zXLSygurCc jcpYQgMrDnIIbnr9CMioLfxJ4jvvDXjHSNY0tbTxPp2nMfOtDlLlWR/LdOcg8H8fQ8CqnuSku1/w FBOXL5nW3vj3QdPKSXUl3HZNIIvt5tJPswYnA/e7duMnG77vvWxqOsWWlpAbmU77hxHBFGhd5Wxn CqoJPGST0ABJwK4Pw/oOieMfhnYLNq9/JpUlpGk0H2hVSMoBlTheNpX9Kit7yC3+MXh+0MrnTj4f ZNNeU/ffeMkE9WKIPwp8tpcr/rRv9P6sJO8eb+t1/n/VwWewuPj1YSW1u8F1/Y8wuVkiaNid42k5 4PGeRkds8V6fXAahs/4Xro2Nvmf2LPn1x5gx/X9a7+s4fCvn+bG/ify/JGPc+JdPt7y8tEFxdT2S CS6S2haQwgjIzjuQM7RlsduRUV54x0Gx8MR+I5r7/iUyIrrcRxO4IPA4UEjk45HBrB027ttV8Q+K l0OKGxWCYQ6je43zXEyx4wqn5UCjA3EHJzx3ridN5/ZSlzz/AKNN/wClDU5O1Nz7W/G/+Q0rzS87 Hpj+PfD8ep2di9zMrXkgigna2kEEkh6IJduwt2wD1460zxPP4XGtaAmuNnUFvAdNUeZ/rjx/Dx/3 1xXP/EVEHgfw7hVGzU9PKcfd+YDj04qx8S/+Qp4I/wCw/D/6C1UrOVu0kvy/zIb9y/8Adv8An/kd bd65aWt+bBUnuLtIhNJFbxFzHGSQGbsMkHA6nBwDg09dc006H/bTXSxaf5fmmaZTHtX3DAEfQjNc X4r0LxGniWbxL4J1GA6lFCkF9pl0P3VyqgsmDxhvm9R9Rznn/FviZta+GGha6tg9jaR61C2p2rc+ WElIcH1G8D9KlO687/rb+vMu33f8C9vzPRG8ZaRDcWUV2buyF8wS1ku7WSJJGPRdzD5WPYNgn0rO 8T+OU8P+KtE0T7DeSm/d2kmitnkARVJwgUEs2cZAzgdarfF2G2vPhZq4k+bKRtAVGSZN67MfUkD8 azdfE6eP/hmt2c3AW5EpPd/IGf1zVR1dvP8Ar/g/IFt8n+B1Vx458P2moHT5rx1vVtvtbW/2eQuI 8gfd253ZONv3s9qdaeNdCvdFsNVt7mSSDUHMdrGsLmWVwSCAgG7jacnGABknHNc5dIj/ALQNgWVS U8PSFSR0PnYyPwJ/Om6pcPafHjQluuLS40ieKzz087fufHvtUfpSTvbzv+F/8hyXLfyt+Nv8zrbD xPpmoatNpKvLBqUKCRrW5iaNyh/iXIw6+6k471Rn8feHIb+9sBeSzXllt863gtpJJMt0CqqktwMn GQB1xWF43tfM+JPgKa1z9uS5n3bR/wAsNnz59ug/4FS+GgP+F0eNzgZFvYjP/bM0k72+f4f194rb +ST+92N2x8e+HtS0G91m0u5JLWxJF2ogfzYCOu6PG4Y57dj6Vo2HiHT9T0BdbtGlewaMyrIYWUsg 7hSM/pXJaFp8Z+L/AIult41NnLZWyXQx8rTkHg9s7MZ/3vesHw/5mjXmqfC+UMUe6EliSpIawkJe QZ7YAdcnuwoT5tt3/nZ/5ryFtv0f4Wv/AMD1PV7G8i1GxgvIBIIZ0EieZGUbaeRlSAR+NZfi+TQk 8L3y+JH26U8ZW4+/kr/wD5vyrbACqABgDgAVzPxH/wCSbeI/+wfN/wCgmlUtyscL3VzQj1XSNL8M 2d8svk6Z5MQt/lZmZWACKq8szHIAABJNcJ4su9Pu/iZ4GdbWaDUBdy58+Bo2aPyz0JGCM44zkZ5x mqt9fG01D4Ti6YJprJ87N93zjbqsWffLHFbvjrZ/wnfgEnb5n9oTYz1x5Rz/AE/SrqK0/wDt634/ 18iIfB/27f8ABnWanr1npciwyJc3FwylxBaW7zPt9SFBwO2TjJ4HNVLPxjoV9oFzrcF4xsrUutyT C++Fk+8rx43Aj0xXIaY4vPi74s0261K9srtktpLVYZVXzYRHzjIOQGJ6f3j71rL4f0fw9pviyPTr i4mu7u2kub3zpd+HZHwTxgE88e1RKXLBy8r/ANf1uaRSclHz/r/geRam+JXhiCztb17yc2VyEIul tJTDHv8AuiRwu1CcjgkEZ5Ao8ceNF8JQ6YEs7i4k1C8it1eOFnRFZhuJIHLYzhRyTXH3kaH9mFVK Lj+yI2xjvkHP581o/EQ/8U/4JYn/AJjdhkn6Gr05+Xs1+LJjqk/Jv7lc3vF2o+FLrw1bzeJhPHYP PG8ccsM0cnmBvlyoAYc+uBWd8TPER0yDRNNRLny9R1S3guDHA7B4SxLICByW242jJIJ45o+Mv/JO bn/r7tf/AEclQ/FL/XeCP+xktP5NTpxTa/xL9BXs36f5ls6h4Pk8baK7WF7Z63tkgsWksJ7YSLty y5KqrADnBziun1PXrPS5FhkS5uLhlLiC0t3mfb6kKDgdsnGTwOa5Hxr/AMlN+Hv/AF8Xn/okVT0x xefF3xZpt1qV7ZXbJbSWqwyqvmwiPnGQcgMT0/vH3qVeS+/+vxuXJctn5L82v0Oz07xZomqaJcav b36LZWzOtw8wMZgZfvK6sAVI9CK4P4o6lpmo6Z4fka1uY7k6tatayXFq8ZZd/OCQMHH8Jwe+OKzv HOl6T4QghNlc3EkV3r1nda0ZpN4CbmYFuMDJAOPYV0/xaMTeG9IYlDnWbMoeP7/b8M0nZ2fml+X+ ZL0TXk/1/wAj0CsfXvFGl+GzZjU5Jo/tkwggKQPIGkPRcqDgn3rYrmvH/h9/E3gvUNPgJW7CCa1c HBWZDuTB7cjH40SdlcaV3Ys33izStO16y0S5a5W/vlLW0YtpGEgAycMBt4HXnirk+s2dvqq6bKZV naFpwfKYpsUgE78YHJHGc815LrOu3ni/wtpfjXS1PneG0ju5o1AzJMSPPiyRkBYwSfXcPSvSPDF1 Hrj3XiOJy9tebYrMkEfuUzzg+rlznuNtVbddr/8AA/rrZk328/z6/h+JV8KXnhi18O6jc+GUmlsI bqZ5lijld2m6uFVvmPbgcelQ+C/F0Hjzw/JNPp9xEsxlBjlt3EflhyoG8jazYxkA9c+lZ/wr/wCQ N4k/7D17/wChCl+DBH/CrbD/AK63H/o16TS5fkvxQ18N/wC8/wBTA+GXi7R/DPwvsxetclIZbhpT bWkkwgTzW5kKKQgx64zXrVnd2+oWUF5aSrNbzoJIpFPDKRkEV5p8MURvgnMCqkML3cCOvzv1rpPh eSfhj4dyc/6GtCd7rsl+N/8AIqppNpd5fg/+Cb+tPeRaFqEmnJvvltpDbr6ybTtH54ryv4aw+BvF /hiKzuLO2PiWNG+3PONt95ufmlWQ/P1Ocg8Zxx0r1bVdSg0fTJ9QuiVt4Bvlb+6ueW+gHP4Vwnj/ AOGekeIrWbX9Lk/szXYUNxDf2rbBIQMjfj/0IcjjkgYqW0rt7BvaK3J/E/h+Ky+C2o6ZqEUd2+n6 ZMIpJVDnKKdj5PRsAH61leFPBfhq7+E+l30tjb2V4dOEralbAQzxttzv8xcHjrycetSWuu3/AIk/ Z3v9U1MH7XJpVyrvjHmbQ6h8e4Gal8CeCdG1f4c6E+oHUbmOW0jd7eTUrgwE9ceVv2Y9sY9qqUWn Jen6kRa91+v6HPWkNz8QPgTaSeIEWS/kuoraG+kjBk2m4RA4PXOCQfXHvXVfCLW7i78Nz+H9TONV 0CY2U6nqUGQjfTAIz321v+KYLey8MW1vbxRwQRXtkkcaKFVFFxFgADoK4jxi8vgH4o6f4ttreWWw 1mM2F9DEMlpgP3ZA9ThR+B9aptb93+Nlb79vmPl/drur/d2+X6GZ8eGk1bRJ9krCz0m4hR1VhiS4 k6hv91CD/wBtPavZbCytdPsYbWzt4re3jUBIokCqo9gK8o+LWny6b8HBFcsHvJb2Ka5cdGldyzY9 gTgewFevJ9xfoKUdINeb/KISd2n5fqVtT1Sy0bTpr/UJ1gtYRl3IJ74AAHJJJAAHJJqnb+JLCbV4 tKkFxbXs0JnhiuIWTzUGMlT0JGRkZyM9Ky/iFqGlaf4X/wCJtYJfx3FzDDBau+xZJi4KZb+EAjJP PAPB6VzeuxXsfxh8BNfXSSyvHfZSGPZGn7rtnLHtkk844A6UQXM7ev4K4S0X9dzp734heGbGXUop L9nl01Va6jigd2TcCRgAc8Ak4yABk4q5J4s0sWVpcwfarv7XAtzDFa20kshjIyGKgZUH/axk8da5 jw3Gh+NXjdyilxbWShiOQDGcj9B+VVNLZbv4u+LNMutSvbK6aO2ktFhkVPNgEfOMg5CsT0/vH3qY 6pd/6/r7xzXK38vxS/4b7ju9C8Q6Z4ksGvNLuDLGkjRSKyMjxSL1R1YAqR6EVp1zXhrw/o/h7VNW j064uJru7dbm982XfhznBPGATzx7V0tU7dBBRRRSAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooApatpNjrmlXGmalb rcWdwmyWNiRkfUcg55yOlM0fRbHQrFbOwSRYlAGZZnlYgdAWck4HQDOB2rQooAoWWi6fp+nS2Fra pHayvK8kfUMZGLP+ZY/yqexsrfTrC3sbSMRW1vGsUSDoqqMAflViigDnv+EJ0NJrt4YLi3S8fzLm C3u5YopWPUlFYLk45wBnvmt2GCK2gjggjSKGNQiIi4VQOAAB0FSUUAV7+xg1Kxms7kSGCZSjiOVo yQeo3KQR+BrIXwXoSeGT4cW2nGklSpt/tk33Tzt3b9232zit+igDn7vwToF/4ch8P3VrPJpUIUJb m8mwAuNoJ35IGBgE8YqPVfAnh/WpbCa+tZ3nsFK2863cySqp6guGDH8TWjoev6d4js5bvTZWkhin e3cshXDocEYNadF76/P/AIIf8Mc3b+EvDHh7U7jxBBaxWExiCzSCZkiwBjcVztzj+IjPJ9TXCfB/ SdE1rwCiNcyvMZ5hdW8F/JGGHmMRvRGA5UjqORgHI4r1+ihbjvpbzMq+8O6ZqCWCywPGLBt1r9mm eDyjt28bCOMcYPGO1Tafo1lpk9zcQI7XN0VM88sjSO+BhRlicAc4UYAyeOTV+igQhGQQc8+hrF0v wlo2jateapYwTx3l6Q1zI93NJ5pGcZDMRxk444rboo8w8jn18FaItpJZiK6NlJKZpLV7yZonYtuO VLEbSeSv3T3FdABgYHSiijZWAxvEPhXRfFMMEer2QnNu/mQSq7RyRN6q6kMOg79hVS28BeGrbVLb VP7N87ULZSsdzczSTSdc5LOxLH0JzgcDAq7rnibSvDgtf7TneH7VMsEO2F3DSN0XKggZ9yK16F3Q PszntU8EaBrGtJrF3Zyfb1j8oyw3EkRdM52uEYBh7HNT2PhLQdOOoG10yBBqJP2pSCyygjBBB4Ax xgcVtVg6t4x0PRNKl1S+u3Syjk8ozJBJIpbOMAqpzycZ6ZyM5BoS6L+rh1MzR/hX4M0LVU1Kw0ZU uY23xmSaSRY29VVmIB98ZHauxpEYOisOjDIrJuPE+lW3iK00CWeRdSu1ZoYjA+HCgliHxt4A9aeu weY3XvC2keJfsjanbu8lnJ5tvLFM8UkbdDhkIIz9ew9KfqfhnR9X0+CxvLMPDbuskBR2R4XHRkdS GVvcHNa1FIDNsNBsNOu3u41mlu3QRme5neZwv90Fydo74GATyea0qKga8gS9W0Z9szrvQMCAwHXB 6EjuOtAGC/gDwzJr1zrL6bm7uiDcDzn8qYjoXj3bG/EdeevNXfDvhbSPCtpJa6PbNBBI5coZXcDv hdxO0cngYHNbFFC00QPXcqW+l2VrqN5qEFuiXV5s+0SgcybBhc/QGs7xN4O0HxhaxW+uael0sTFo m3MjoT1wykHn06HArcoosO7MHRPBug+HNMn0/SbI2sM/MrxzOJXPvJnf9OeO1T+H/DGk+F7NrTR4 JYLdmLeU1zLKoJ6kB2OM+1a9FAuljk4vhp4ShlvmXSQUvi7TQvNI0RZurCMttVvQgZHbFcv450rw 74d0HRdB1DS7k+EjcNJeXQ82drcov7sbgSygk4yM4AIA5yPVKydO8S6VqusahpNnO73un7PtMbQu mzdnbywGc47ZpeS/qw79X/VzzDwtoXgS7u4rnwDda4LmKZWMkH2hIAAQWEhlUIVx1XO4jpXpUfhH RovEj+IUguBqrx+U05vJjlOu3aX27cgHGMVt0VVybGLa+FNHstfutct4Z01G7ULPKbuYiQAYAKlt vHbjjtWBr2gWXhLwv4h1DQNHuLu8vlLXMBnmuBMWOHcxs53EAk4GCcYzXc0VLWlik9bngujaP8Hr 66SDQZtbbUwgGyyS8Ewb1J24U57khR9K9s0W3uLXRbO3u5ZZZ44lV3mYNI2P7xHBb1I4JzV6iquT bUhu7S2v7SW0u4I57eZSkkUihldT1BBrHsvB2jWC2qRx3UkNmQ1tDcXks0cJHQqrsRx2z93tit6s zXdf07w3YLe6nK0UDTJCGVC3zMcDgUtmN7alXUvB+i6trVrrF5Dcvf2mfs8qXs8flZ67VVwBnvxz 3rcUBVAGcAY5OaWijpYPMy9f8O6X4n0z+z9WtvPt96yKA7IyuvRlZSCD9DVK/wDBOg6pZWdne21x LDZyCaH/AE2dWEg6OWDgs3+0STXQ0UAYmp+EtH1m8sLu/huZZ7Bt9s4vJk8tvXCuATx1OTWdr/w0 8JeJtTGpappQkvMBWmjmkiLgcDdtYZ44z1x3rqLi4itbaW4ncJFEhd2PZQMk1neHfEmleKtJXU9G uvtFqzFN2wqQw6gggEUtHp8w2Lmn6dZ6TYQ2Gn20dtawrtjijXCqKZqulWet6Vc6ZqEPnWlyhSWP cV3D6ggirlFN+9uC02Oe0rwXpGkiFYmv7hIMeTHeX008ceMbdqOxUEY4OMjtWvqWm2er2Etjf26T 20owyN7HIII5BBwQRyDVqih67gtNjKtvD1hb6hFqDCa4u4YzFDLczNKYlPULuJwT3PU9ya1aKKAM TxL4R0LxfZx2uuael1HG26M7irIe+GUgj6dDTvDnhXRPCVg1lolglrC7b3wxZnPqzMST+fFbNFC0 2B67nPf8IToaTXbwwXFul4/mXMFvdyxRSsepKKwXJxzgDPfNTax4R0TXrK0s7+zZrazdXt4oZ5IF jZRhSBGy9O3pW3RQBial4R0bV7/T76+huJbnTzm1kF5MpjPGT8rjJOBknOaTVfCOi61q1pql7bSG 9tEMcc0U8kTbDyVbaw3L7HI5Pqau6zrNjoGmS6jqUrxWsI3SSLE8m0epCgnFQXPiTTLTwufEcszj TPs63PmCMk+WQCDt69xReyv2DfQgsfB+i6brk+s2kFxHf3ACyyG8mYOB0BUuVwOwxx2qfV/Del63 cWtzeQP9rtGLW9zBK8MseeCA6EHB7jofStC1uYr20huoGLQzRrIjEEZUjI4PtU1DVtOwJ31XUoad o1jpbTvbRv51wczTSytJJJjpl2JOB2GcDtiqmmeFNH0fVr3VLKCdL29INzI93NJ5pGcZDMRxk444 raooAK59fBWiLaSWYiujZSSmaS1e8maJ2LbjlSxG0nkr909xVm/8TaVpmtWGj3U7pfX7FbaPyXIc gZPzY2jA9TWvR5h5GP4g8L6P4otYLfVrUyrbyCWFkkaN4nHQqykEfnUNr4L8P2eo2uoRaePtlrGY 4p3ld3AJySSxO5j/AHjk+9b1FC01QPXQ58+C9FW+u7u3S8tJLxi9yLS+mgSVj1YqjgbuPvAA+9Mv vEXh7wpc2Wh3Dpp6zxMbbKbYjj+EN03HPTqfqa6OszXdf07w3YLe6nK0UDTJCGVC3zMcDgUmugX6 s5bwF4TRfhXaaH4gsVkS4WSSe2mB4DyM6g9wwBHuD9K0vDnw48KeFb03ukaUsVzgqsskrylAeoXc Tt/Dmuqoqm9boG3Lc5PxF8NfCXivUV1DV9IWa7ACtKkrxlwOgbaRn6nmte48N6Pc+HG8PyWEP9kt F5P2ZRtUL2xjkHPORznmtWilbSwX1v1MBfBmiDRI9IMFwbWORJFJvJvNDIcqRJu3jHbnirtpoVjZ 6g2oKsst40Xk+dPM0jKmc7V3E7QTgnGM4Gc4FaVFHmFjJ0jw3pWh3upXmn2xin1Kbz7pt7Nvfnnk 8dTwPWtaiigPMwIdLbRfEV3f2kTPZ6oyNdRoMmKYDaJQO4YYDem0HHLEXNc8PaT4l05rDWbCG8ti c7ZByp9VI5U+4INadFKytYd9bnJ2/wAO9EgjWBp9Xns1AUWU+qTvBtH8JjL4K+xyK372SWysBHp9 mJZsCOCJfkRTjjcf4VHf9ATgVdopiWhk+HdDj8P6QtmshmmeR57icjBmlclnbHbJPA7DAq9e2cOo WklrOZRFINreVM8TY9mQgj8DViih67gtDE8O+EtF8J272+iW0ttA5LGI3UsiAnqQrsQDx2pIvCGh weJJvEEFm8OpzACaaG4kQS4/vIGCt+Ircoo63FZWscbqXwp8F6trMmrXeio13K2+UpNIiyN6sqsA c9+Oe+ay/iedHt9P0bTNc0tz4WMxa7uIIWYWoRf3ajZygJOMgdAR3yPRqKXSyKvrdnjXhbQvAl3d xXPgG61wXMUysZIPtCQAAgsJDKoQrjqudxHSvSo/COjReJH8QpBcDVXj8ppzeTHKddu0vt25AOMY rboqrk2Me/8AC+lahq0eqyRSw6jGnlC5tp3hdk67WKEbh7NnHanDw1pI0W50kW7i0ug32gCeQSTb hhi8m7exI4JJzUtxrVnbx3jgzTCz/wBeLeFpCpxnHyg5IHJAyRkccisDRvib4a8Qi4OkS316LZQ0 3kafM2wHOOi9Tg8Dnil5Fa7l+TwP4fl8MDw3JaTtpAwBbG8mxgHIG7fuwD2zirE/hbSri206B0ug NObdaut5MrxnG374bc3HGGJGKfofibRfEccjaTqEVy0R2yx8rJEemHRgGU8HqBV9LyCS7ktA+J4w GKMCCVPcZ6jtkd+KH5iK+n6NZaZPc3ECO1zdFTPPLI0jvgYUZYnAHOFGAMnjk1forB1Pxlomk6v/ AGVdTXTXwiExht7GechCSAT5aMByDQAz/hCdDSa7eGC4t0vH8y5gt7uWKKVj1JRWC5OOcAZ75q/q OgabqujNpF1bsNPaPyjBBK8KlMY2/IQduOMdKxLf4l+F7trhba41CZrZtk4j0m7bym9GxF8p9jVi z8feG9QuNJgtL95X1bf9i/0eVRLszu5KgDGDwcGi2lgvrc1tH0ay0HTotP05JY7WIBY45J5JdgHQ AuxIHtVyaJLiCSGQbo5FKMM4yCMGn0UP3twWmxhW3g/R7WGygSO5ktrFle2gnu5ZY42X7pwzHOO2 c7cDGMU6Pwjo0XiR/EKQXA1V4/Kac3kxynXbtL7duQDjGK26Kd3e4eR4V4jf4e6h4u1eX4g2lzpu qC48m1CxXAE0CABJA0YIctzz24HbJ7/wToVjZRXf9kTa1/YtxGoSO/klTDc5aIOBIgweScZ4x0zX bUUlorA9Xc56x8EaBpmg3OiWdrcRabc7hLB9tnIO772CXyue+CM04eC9CHhn/hHBbXH9k7dv2f7b N93+7u37tvtnFb9FD1C5jN4V0h/Df/CPNBOdL8vyfI+1y52Yxt3bt23HGM4qG58F6FeeG4/D1xbT yaVGqqtubyb7q4wpbfuIGBgE44rR1jVrPQdIutUv3ZLW2QySMqliB9BVi1uYr20huoGLQzRrIjEE ZUjI4PtRe9w2sZmpeFdF1jQV0TUrP7XYKAFSeV3ZcdCHJ3Z985qp4d8A+GfCtvcw6RpUcIul2Ts7 NI0i/wB0liTj26V0lFHfzDt5HGxfCvwdFpU+mDSS1pN/yzkuZXEfOf3e5jsOe64J75rQm8D6BPLp ksttctJpefsb/bpwYyepyH5J7k5J71t3l3DYWkl1cFxFGpZikbOQPooJP4Cq2h63p/iPSINV0uYz WU+7y5ChXdhip4IBHIPWjz7AVZvCmjz+JIfEEkE51SGPyknF3KAE/u7Q23HJ4xTLTwfodjrt3rNt aPHeXb+ZNtnk8tn/AL/l7tu73xnk+prdooB6nOx6BonhebVvENta3f2maMzXbLczTNNsBI+VnIJA yAKyPDmuWPxD1Ow16ysbhNO05JDDNdwhGadxtIXk5CqGBI4JYYPBruaKAYVHPBDdW8lvcRJLDKpS SN1BVlIwQQeoqSigDj0+F3hFLGaw/s6ZrGXObV7yZokJOSVQvhTnuMEdjVvV/h/4W1zR7XSr7R4G s7T/AI90jJjMXrtZSDz3HfAq9ceJ9KtvEVpoEs8i6ldqzQxGB8OFBLEPjbwB61JceINOtvENpoUs rC/u4nmhTYSCq9eego/X9Avb+u5D4d8KaJ4Us5LXRbBLZJDukbczvIf9pmJJ/PisVPhP4Ij1wawm gwi6Enmgb38sP6+Xnb+GMe1dnRR1uFtLGJqnhLR9a1Wz1O+huHvLI7raRLyaMRHuQquBzjnjnvU2 u+HNI8TaUdM1iyS7tCQwV2IKkdCGByD7g9zWrRRbSw7u9znfDPgXw54QMraLpywSygK8rO0jlfTc xJA9hxVWL4aeEoZb5l0kFL4u00LzSNEWbqwjLbVb0IGR2xXWUUPUS02Oct/A2g2un2FlBBcxx6fI ZbZkvZg8bEbfvBskY4wTjHGMVp2Gi2Wmz3NxBG7XF0QZ5ppGkd8DAGWJwoycKMAZPHJqvq3iXT9H M6zefNJbwfaJo7aFpGjiyRuYDoODjucHA4NXtO1C11bTbbULKXzbW5jWWJ8EblYZBweR+NG9/wCv 62/ADlI/hJ4Hi1c6mmgxCYyeZs81/K3Zzny9238MY9q3PEHhTRfFEMEer2QmNu++CRXaOSJvVXUg joO/YVs1k6d4l0rVdY1DSbOd3vdP2faY2hdNm7O3lgM5x2zR5B/eKNt4C8NW2qW2qf2b52oWylY7 m5mkmk65yWdiWPoTnA4GBXSUUUAc0/gDwzJr1zrL6bm7uiDcDzn8qYjoXj3bG/EdeevNQxfDjwtD oNxoa2Eo0y4ffJb/AGyYKecgDD5C552jjNdXRRbSw7u9zA1LwXoesaZZ6df29zPa2TK8Cm9nBVl4 U7g+SR2JJp+r+ENF12Wxl1KC4mexcSWxF5MnluOjfK4y3HU81uUUXEY8vhnTpNSl1FTeRXc0SwyS Q3kqb0XOAQGwSMn5sZ5PNWIdC0uDRToyWUR04oUaBxvVw3Lbs5LEkkknJJJJ5rQqjrGrWeg6Rdap fuyWtshkkZVLED6Ck7Ja7DW+hn2Xg7RrBbVI47qSGzIa2huLyWaOEjoVV2I47Z+72xUuveFtI8S/ ZG1O3d5LOTzbeWKZ4pI26HDIQRn69h6Vp2tzFe2kN1AxaGaNZEYgjKkZHB9qmqne+pKaaujn18Fa CmuQ6yltcLfwx+VHKt7OAqf3du/bjPOMdeava1oGmeILaODU7bzVikEsTq7RyROOjI6kMp9wRWlR SGZthoNhp1293Gs0t26CMz3M7zOEH8ILk7R3wMAnk815to50XV/jX4tV9RAm8m0Fu1tetE7EJh1B RhuwcZHPOOMgV63WL4f8WaL4o+2f2PeC4NnMYZxsZSrfiBkcHkcUl8S+Y72i13/4f9CDULnSfAPh a81EWlw1pb/vphDmaaQkgFmZjlj6sx6DrxVPwxdW/irVG8WQ2csNsbZbWxkuItkkiE73fH90naB/ uk9CK62imt7i6WCqGs6LY6/psunalHJLaSjbJGkzxbx6EoQSPasu58daDa6pd6a017Ld2jBZ0ttO uZxGSAwyyRkdCD1qlD8UfCdxYPfw3l9JZx533CaVdGNcdcsIsDFLRoeqZo3Pgvw/e+G4/D11p/2j S4goihmmdzHgYG1yxZcDgYPSqg+HPhZpLSS4057yW0ffBJeXMs7IcYxl2PyjHC9M84zXR2V5BqFj b3trIJLe4jWWJwCNysMg4PPQ1PVO6eu4t0c14l8AeGfF0sE2s6Ys08C7Y5UkaNwvXGVIyPY9OcVa tfCOhWXhyTQLSxFvpkqsskUUroXDcNucHcSR1JOa26KXSwX1uc9J4H8Py+GB4bktJ20gYAtjeTYw DkDdv3YB7ZxU2peEdF1bw/Fol9bSTWMJRoleeQuhX7pEm7dkeua26KAOf1LwVoOsaNHpGoWs89jG wbyzeTAuw7uwcFz7sTUmqeENF1lLFdQhuZ/sEgltib2dSjjo2Q4JYepyas67r+neG7Bb3U5WigaZ IQyoW+ZjgcCtOhPt/TDyMXUfCej6tqdjqN7DcSXdgSbaRbyZPLJ4JAVwOQOc9e9VPE3gHw14vmgn 1rTVnngG2OZZHjcDOcZUjI+vTJrpaydO8S6VqusahpNnO73un7PtMbQumzdnbywGc47Zo8h67kNt 4O8PWnhyTw/DpVuNKkB8y3YFg5PdieS3A+YnPA54rMHww8JGxjsZ9OlubWJlaKK5vJpVi29AgZzt HqBjPfNdfRR5i6WGQwx28KQwoscaAKqKMAAdhT6KKAKFloun6fp0tha2qR2sryvJH1DGRiz/AJlj /KpbXTray0yHTraMxWsMSwxpGxUqoGAARyOKpWHibStT1u+0e0nd76xVWuY2hdNgbpywAOfbNa9H T1DqY2g+FdH8MLcLpNvNAtzIZZVe6llDuerYdjycDJqDSvBOg6J9qGnWs1utyzMyJdS7ULfe2Ddi PP8As4q7pniDTtX1DUrGzlZ59NlENypQqFYjIwT1/CtOjdfL8A/r5mBp/gvQtK0KfRLK3uIdOn3b 4RezH733sMXyucnOCKv6Lomn+HtLi03TInhs4s+XG8zybR6AuSQPatCigCtqFhbarptzp95H5ltc xNFKmSNysMEZHtXPv8PtBe0NiBqKaew2tZJqVwsBX+7sD4C/7IwPaupooAytQ8N6VqeiDRbi2ZdN CCL7PbzPAuwDG392V+XHbpTtD0DTvDmnpYaXFLDapwkT3EkoQei72OBz0FadFAWMnXfDemeJLeOD VI7iWKNw6pHdywjcDkE7GXJB5GehFWpdKsri2t7e4h8+O3kSWLzmMhV1OVbLEkkHuTVyigDD8R+D 9E8Wwxw63ay3UMbbliF1LGm7nnajAE8nk1q2dpFYWkdtCZTHGNq+bM8rY92ckn8TWHc+OtBtdUu9 Naa9lu7Rgs6W2nXM4jJAYZZIyOhB61Sh+KPhO4sHv4by+ks4877hNKujGuOuWEWBihbaBubuv+H9 L8T6TJpesWoubSQhihYqQQcggggg1jn4ceFmexkOnyGeyYtFObmUynPBDPu3MMcYJIxx04q9/wAJ jof2/SbIXbGfV4/NsgIJMSrt3Z3bcDjnBOa3aLWC9zDsvCOjafr1zrdtDcpqNyAJpTezMJAOgKly pA7DHHaq3ibwD4a8XzQz61pqzzwjbHMkjRuBnOMqRkfXpk10tFAXM7RNB0vw5pqafpFnHa2qknYm SST1JJyWPuSTWjRRQFrBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBjeKPEEXhnRvt8qK5aaOCMO+xN7sFBZs HaozknB4FLDeawt5NbXFpaMot/NguI5GCu2cFGUj5cZHOTnPQYqt40l0JfDzW/iRIm0m7lS3naVt qpuPysT2w2OeMdc8Vx/gnT7zwx4zm8N6Zrr6x4bawNwiyuJHsX3BVTeOAGGSBxnBwOCTKd7r1/K9 v1/Ab/r7zd0jxvdax8NpfFkWmwxyxRzyNatcHaREWBAfb32+lV9R+Is9j4S8N66uitOusy28RjS4 AMLSjIAyPmPX0+orj/COvafafCLV/DE0xXXbeG/iksNp81SS5zj+7gj5ug9aZqt3CPg98PZSXCQa lp4kzGwIKg5wMZPTt17VT1em3u/iW4xV1/i/DY7TUPHGs+H9c06HX9AhttJ1G4FtDeW955rQyN90 SLtGM+xIHPJrX1LxLIur3Gk6V9ga7to1kuHvbkxRx787FGFJZjjJHGAQe4FYXjW5sPGdppWhaNeW 99NPfQXEr20qyCCBG3NIxGcZxtGepP1rB1HWNJ8F/FPXH8W2sY0rWUhnsr2S1Mqo6IEZDgE9s8dO PWktdHpv89F/wfuM3e+nZfm/+B99zpdJ+Ip1Lw1r98dLB1HQnlju7SK5DI2wE7kkIGVIBxxmp9C8 WeINdTRb638MqNJv7Yyy3BvFDwvtyBsIBKk4UHvycAYzS1HXNDvfAHiG/wBKt4bXSpNPlSO7eH7O LlyjABQwBIHQHHJPGcVo/DbULKX4Z6FJHdwMkFjEkrCQYjZVwQ3oQQetPq/l+v8Aw43aya6t/pYb oPjK61rwzrOqRaOkVxp11PB9kNz98xdcvt4JOexrMb4g65L4Eg8X2nhy3awWD7Rcxy3u2XYD83lg IQQBnlipODxWR8Pta0seDPGUh1C1CLqd9KWMoA2NyrfQ9jUNhqNkv7Mr7ryAf8SqWH/WD/WHcAn+ 9nt1qW2k32UX96dyU25KPdy/Bqx02uePNT0jRbbxINFhbw7IYjJI90VuVjkIAfywhGPmBxuz6gdr 3iTxjc6F4k0PSIdHe8GrNIscqzqpBRd2Np+o5JGOetcX441Oxl/Z3tyl5A3m2lmiAOMswaPIA9Rz kdq0fGmqWMPjX4dai93D9iEl0xuA2UC+SBuyOMep6Dqa1nFJ2X81vloOn70U3u039yNm18a6rZ+M bPw94k0SCxOoozWN1a3RnjdlGSjZVSDjvj0rUj8Q3eq61qOnaLbQOmnERXN1cSEL5xG7y1VQScAj JyMZAwecYutm28WeNfDMWlTxXcOlXD3t3cQOHSIbMIhYZG5iQcdcAn0rO8JXUfg7xt4o0bW50tE1 K9bUtPuLhgiXCv8AfVWPBZeOOvfpWa10fn89v+D91wem3l+v/A+82Lb4iwJpniJtU097TU/D6lry 0STzA6kEo0b4GVYDuAR3FbVhqeqTT2DT21lJZ3sRcT2s7MI227gvK4YEZ+bI6dOa4uL7A/iPxj4x u4I5vDstnDZ+YRuS6QAebIP7ygHGRnODiqXh/SV8IeN9HtfCGvnUPDuq+a8+mmYTi2QKWEqMCcLn C5PUkAliRgi72T7L9fz38gfW3T/gflqjsbzxVdXF1fwaGmly/YZDDK99eGLfKACUQBTwM4LE9cjB waraV8SdN1PwIniY20sbNKLY2SsGc3BYKI1PAOSRg8cHJxXF6V4h8PeCvEviPRfGttDA8uoy31ld zWZlWaKU7sAhSePy6jtWl46Ml74O07XtD0iVLDTNWg1EQRwGN7iFPvSeXgEDnjIBwM9KatZNvR21 7d/68vUb3a66/Pe336f1Ym+J1zrI0PRFvrG1WObV7Te1vOz+S2/ODlRuHUbuOccc16jXkXxD8b+H td8K6VLpOoLej+1LSV1t0Z2iUPn51Ayp4wAeSegNetQyrPCkqbtrgMNylTj3B5H40orfTr+iE916 fqzkviDqdxbWOlaRaO8c+tahFYtKjbWjiOTIQfXaCM9t2e1Z3xghitfhDqsEEaxwxJAiIgwFUSoA AKm+I8LxXfhPVtpMFhrUPnn+4kmU3H2DFfzp3xhhln+FmspDE8jhY22opY4EiknA9ACaael33/y/ 4JdO/tY/L83/AMAq6x471zwvb2mp6t4aWLw87JHLcJdh57cNgB3jC46noGP17Unid1k+MPgF0IKt DfEEdx5S07xlqmm+KvAU2h6HeWWp3+pRJFBDBMsmPmXMjYztVOpJ6Yx1IrN164sNN+LHw+sGvoN1 na3UT7pACuYgq7vTO049auHx6+f5P+vmYv4NO36r+vkdePFE6/ET/hFpLKPy2083yXKynOA+zaVx +uah03xg8/ifxHo+oW0FrHosMU73KzFldHUtkgqNuAPU1zviDVLLw58atO1TWJ1s9PudGe1juZeI /NEu7aW6Dj1rKRT4g8afEaysvNWTVNJgS0Z4mUP+5YZ6cDJGM4z1FZN6Jrs/wub2jdp+X42v+bN/ WPiLe6VoSeJBp+ny6P8AI7Qi9IuxE5AD7Nu0Nzkpn8fTofEw/tXwVc3umyH7Qlv9tsZV4IkVd6Ef XoR3BIPWuA8IePPBT6JYaVq2mpB4igQW01h/ZbPK8qjbxhCMnGeTx3xXous3g0/wPf3l1brbLDYS O0CkYjwh+UY446ccU6vuwk4622f9ff8A0iKesop/NE/hnWV8Q+GdN1hFCC8t0lKA52kjkfgcik8R 6/beG9Ie/uEeUl0hhgjxvmlc7URc9yT+HWqHw902fSPh9oVjcqVnjtEMinqpPzEfhnFZHxZ0rUL/ AML2l7pkD3NxpOoQ6h9njGWlVCcgDucHOPaqqWUmul/1IpXcE3vb8bFvXvFuq+E7K21TW9MtjpbO qXclnOzvabjgNgoPMXOASNp9jU3iLxfcaHruhWEGlfbYdWd40mjnAZWVdwAUjBz67hjmsXxrrul+ L/h9Pp2hXVtqN7q6JFa20cgLgllyzL1UJ1JIGMc4qr4uuLHR/Fnw50+4vYUe2ndWMjhcAQ7ATnpk kAetS73t5pf5/cV007P/AIH9eR0Ol+K9Ubxo3hrXNJt7SeS0N5azWtyZo3QNtZSWRSGGR2rrq841 LVLBfjvosDXkAlGkTIVLjO5nBVfqQCcda9Hoi7xTfn+bB/E16fkjzvQdc8T3vxS8SafPHYPZ2K2y KguHXyo2DNuUbDucg852jgDPeqWnXOpQfGDxuulWMV1cvBY48+byokAjPLMFY/QBTn261Jo2p2ek /GrxbbX84gmv4bNrRHBzOAhB2D+LB9PQ+hpfC2pWMnxp8bIl5bszwWgQCQfNsQhseuO/pTX2fRmj suf5fobOj+Pkm0LW73XLP+z7nRLhre7hjk80MwxtKHAzuyABjqap6t8QLzw9cadPq1npn9mXtyts zWl8ZZrZmztZlKAMOOcHj378Rdj/AISLTPibaaLPDd3o1KG6ihjYOZVj2E4A+99wj3PFdPofj7wJ r9vbwafpcM2tSoP+JaunHesncFtm0AHqxOAPypQd0pPfTT5f8P6GT0v8/wAH/Xqen1yfiXxhdaD4 l0TR4dGe8/tVpFSVZ1Ugou7G0/UckjHPWurXdsXdjdjnHTNecfEG8t9P+IfgG6u5Vht457svI33U HlAZJ7D1PQdTQ90vMuKvf0f5Gja+NdVs/GNn4e8SaJBYnUUZrG6tbozxuyjJRsqpBx3x6Vo/8JFq F9rGtafpljEP7JVA8t2zKs0jLv2rgcADHzc9fu+uRrZtvFnjXwzFpU8V3DpVw97d3EDh0iGzCIWG RuYkHHXAJ9KzIfF1lqnjDxHo/iG4kjks5fIsNJCMRcJsP7zYoJlLZztOQAAcdTSu7eev6a/p8rkr fy0/X+vmL4q8d6hf/BY+K/D6R2rXEYEhlcl4QX8ttmBgsG4BOPX2ra8QeK9V8K+D4tUv9GtbhxLF GUguyUUMVVWJZAScnoB+NeaWNxFefsuXdlbMZbqyB+0xKpLRf6SW+b0+Xn2HNdP8TvE2i6j8JxNZ 6lbzRyT2uwq33yJFZgPUgA5A6Y5raSiptLbmX3Cd+Rd/e/A9A13xBHo/2O3RYpL++dktopZfLQ7V 3MzNg7VA74PJA71iaV44nk8aN4W1S3slu5bY3VpcWVyZY5VBIKnKgqwxnuDWF8R72LTtW8KeLnt0 1HQbRpob0xoJlVJQoEncYBXr9B3rd8OeJ/Cev3yHwtaQXJjyZ7qOyaFLdcd2ZR8x6bRzySeKzX+f 9fqDen3f1+n4kWneMtf1uXW7TTfD1sLvS7xrZvtF8REwAByGCEljn7uMDu3ahfGt/rHwqvPE2lWc VrqEUExaC6clYnjJD8gfNjacDjPGcVn/AA61fTpNd8dMl/bEDV3lJ80fcCKN3+7kEZ6cVleCriHV Pgp4ihsZVuJj/aGI4zub5i5XjryCMfWs7tw/7dv+X+ZrFLnS/vW/M7T4f3eraj4J0y41VLctNZxO kqTNI0uVyWcFRgn0BPU81FcPY/DrwvJOsNsZLi7QP5aLbQmaVlQHABCIBj1OF53Hks+F+s6fqfgH RYbK6SeS2soop1TnynCgFW9Dx09OelaPjSXQl8PNb+JEibSbuVLedpW2qm4/KxPbDY54x1zxW1V2 k7d/12+ZlDWOv9aF2yu9T/tN7S+trfyTD5sVzA7Yc5wVKkcYyOcnOe2K0znBwMn0rzDwPYXnhfxt L4c03XX1jw2bE3CLK4kexfcoVN44AYZIHGcHA4JPp5IAJJwB1Jqeif8AW4+tjh9O8Za/rcut2mm+ HrYXel3jWzfaL4iJgADkMEJLHP3cYHdu1XtF8d2WoeAW8V30L2MMCSG5hY7mjaNirKPXkcfUVg/D rV9Ok13x0yX9sQNXeUnzR9wIo3f7uQRnpxXM6HYf8Jh8CNd0vSJ0mu2u7iRI0YbiRN5iqR23ADGf Ws+aXL/26n+X+f4F2XNr3t+f+R32peKNf0vw2PEU+hQNZogmntEuT9oih6lvu7WYDkrwOuGNR6j8 QVt9S8Mwafpkl9a6+rPb3CyhTgIGA24zzkDJxjnPSqo8a6Tqvw4lPmxPqUtk1q+lk4nNwUKmHyz8 2d2eMdOelcnLDb+FNZ+FWkajfW0VzYrOLlWlUeWzxDGeeBuOAe9av47ea/X/AIH3kR1WvZ/gtDrd V8b+IvD/AIf/ALS1jwtFC/8AaK2nlxX4cGNiAsqnbzySMHaeO1WNW8Z6xoGuaZDqWgx/2fqcxtrd 7e68ydZSMqroVCjd04Ygckms/wCMl/aQeDLVZbqGNpNRtWQM4G4CQEkewAJqL4narp8WpeCC99bg HW4Zc+YPubWG7/dyRz05pQ1ev8yX4L/NkybTdu1/z/yRtx+LNVs/Gmn6BrelWlumpxyvZz2t203z RgEq4KLg45yOO3NWv+Ei1C+1jWtP0yxiH9kqgeW7ZlWaRl37VwOABj5uev3fXmvGOp2Efxb8Bh7y 3XZ9rLZkA2hogFz6ZPT1qKHxdZap4w8R6R4huJI5LOXyLDSVRiLhNh/ebFBMpbOdpyAADjqaH8Kt vZ/gzS1n939fgaWr+IoPFnwQ1XXLeJokutKuGMbHJRgrKwz3wQeantNak8O/B3TdWjsPtotdIhle HzBGCoiUnkg/yNcL4Z1fT0/Zp1C0e8iS4gsruCWN22lZHaTanP8AEcjjryK6LUdSsR+zwWN5AFfQ lhU+YOZPKA2/72eMdauraKqcvdf+3EQTbin5/odgmr6tfaPpF5penWssl7Cs0q3Fw0aQqVB+8qMS cnAGBnmsnQPGmp61401DQf7LtDbaag+131vds6JKekYBRct6+mD3rA1bx9beHfhPoTabeWzalqFp Da2bNKoSN9ihncnhQmec9DjNafgu58KeH9GsPDek69p2p6leO3nPbXSSyTTFS0kjbSTjCnr6AZpS Vpytsr/18v66ijdwjfdk+o+O7z+x7zWtHttLutOtRIwW4vjHNcLGSGKKEIA+U7ck7hzxxnQHjmzu fDujanp0DXFxrLLHY2rNsLOQSwdudoUBixwenAPArzrwT4q8K+GNI/4RfxjaQ2mt6dM8B8zT2lNw u4lWUqhJyCPrwRnNbHjO4l0jU/Bvi9tMltdH064nS5gSL5reKVdqyMgHy9yR2yB1pK3fTTX+u/4F dXprrp/X9Mt+LZ9T/wCFheBIL61gEZvZXSeCQkZ8o5UggY68HJzzwMV6ZXlHizxbomp+L/A8+nXo voI7+QyS2iNMqkx4CkqD83IO3qBycCvVwcgH1qYrR+v6IHuvT9WYWsa/NY69pWi2loZbnUBK/nPk RwpGASTgHJyQAOPqO9fQvFE2q6lr2kS2kSalo8qI4jlJjlV03IwJGVyMgjnGOprD8aeLU0nxtoui 6pfPpeiXdvLLNdqxTzZBwIvMHKDuSMHlRkc5x/A2p6TZfFDxxErrZx3ItZreOWNoSyLEd74YAgZO ST6+9ONnFvyf4Nf8H8wldfgatl4/8Q6v4Pn1/TvDFvi1M32iGe+IJEZIKxkId5wD1CjsM1e1zx+L DwHYeKbTSjd292IG8uSYJ5XmEAZ4OSCewrmvAWsaavwb1mY31uEie+Zz5gyoZnK5HUZBGPXNYmta tpw/Zw0WP7dbmTFomwSAtuSRSwx1yADn6UoNtpP+7+O4T0vb+9+Gx6z4g8RLo02lWqrEbnU7n7NC Zn2RodpYknvwMAdyQOOtJN4gl0ew1a81+GK3t9OQS+dA5YTJjOQCBhsgjbk9ueayvGi+E9e0nT9K 8QzQGw1JyLW6EwUJKFypR+gJGcdj05ziuEfQPEGpeE/GfgyDWH1yztI4X067Y7nLbt5t2fOCwCgY zxuHQEAJt2l3X/AHFXa7He6l4o1/S/DY8RT6FA1miCae0S5P2iKHqW+7tZgOSvA64Y1k+PPFWp/2 J4YvvDkts1jq2oWyGSR2R3VyGVfunapxhj1HTFWB410nVfhxKfNifUpbJrV9LJxObgoVMPln5s7s 8Y6c9K5rxRpUvhT4YeB4L84Gl6pZyXkg5WIZYsSR2BOM/StLLnt0uvz1/T7xR1Sv2l+C0/X7jstY 8Z6loet6BpN1oiS3GrNKm+2udyoyLnAyoJzkcnaBye1H/CXaxY2UUWsaDHbazeX7WdhZxXgkScAb vMLhflQDcSSueOmSBXO+L/EWjXPxE+H9zFqdq0Cz3JMnmgLhowqtn0J4B6HtV/4kQ3FlrXhTxdFG 89jpF0/2wRqW2QyqFMuB1Cjrj1qE+/e35f8ADFNLp2v89f8AgGvqXi298Oa1plrrtjAthqUotob6 1lZhHOfuo6FeAezA9uQKZqXjLULXxyvhe00L7RNJYtdxTNdBFbDbcNwdq5zk8npgGsvx1NY+NbPR tG0S6ttQmm1CC6eS3lWQW8CElpSRnA42jPUnAqHV9Rs9L+PNhPfXMdtC2gMnmSttUEz8ZJ4GenPf A6kUK+l+7/K/5iS0fon+NvyNXR/GmqXHiW98Maxo8FhrMdt9qtdlyZILmPOMhtoIweOnrVvwT4un 8V6TfXV1ZRWF1Z3MlrNbecXMTp13HaP0rPhji8R/FO11nT5En0/SbCSB7qJgySTSN9xWHDbQCTjo SBWTr+k6jpPxHaPTImbT/FsH2e82sV8iWMfNLnsTFux70K7t53+++n3r80J6X8mvutr9z/I77w/q Nzq2i2+oXVvHbtcDzI0Ry37s/dJJAOSMHGOM1Pq13PYaVdXdtbLcywxNIsLSeWHwM43YOPyq0iLG iogCqowAOwrP8QXVvZ+HtRnuZo4YltpNzyMFA+U9zU1ZcsG49BwV2kzjW+IOuS+BIPF9p4ct2sFg +0XMct7tl2A/N5YCEEAZ5YqTg8VZ1zx5qekaLbeJBosLeHZDEZJHuityschAD+WEIx8wON2fUDtz NhqNkv7Mr7ryAf8AEqlh/wBYP9YdwCf72e3WjxxqdjL+zvblLyBvNtLNEAcZZg0eQB6jnI7VtypT a6Jr8WyKTc1G/Vf5Hoer+IJbPXdJ0aztPNuNREriZ8iOFEAJJwDknIAHHXqO9fRPFM+p6jr+ky2S DUtGlRHWKXKTK6bkYEgbcjOQc4x1Nc54m8a21n4o8P6ZeamLHw9fWjzPfQybRO44WPzV5QdyQQfu jI5zS8BX2mw/FXxtawD7P9s+yS2sLwtG0irEdzhSAcZOcnrn3qUtH6P701/wfzHra/p+JpaV488T eIvCg1zRfCkEiq0oeGe/2s2xiNseEO48dTtGeBmup8JeJrXxd4btdZtI3iScENE/3o3U4ZT9CK4T 4S+JtE0v4bxRX2p20E0M9wzQySBXIMrY2qeWz0GM5PHWtTwxNH8P/hlc6xrkM0CtPLfS26Ll4xLJ 8iY4+bBUHPQk0k++1l9//B/QucWp8se7Xy/q33noVcx8QdeufDngu+vbLH219lvbE/wySMEVufTO fwrobO6ivrKC7gJMM8ayISMEqwyOO3WuN+LVpNcfD+6ngjMjWU0N4yL1KRyBm/Jcn8KJKzs+6v6X 1/AlNtXjq+n6HTWenQ6T4eSwgzshgKlmOWc45Zj3JOST3JNeJfszdPE3/bt/7Vr27UtUsbPQ5tQu 7uGC0MO7zpXCrgjjk+teAfs9a/o+if8ACQjVdVsrEy/ZzH9pnWPfjzM7dxGcZHT1q4P3p33t+omv cVi18Q7l/Bvx90jV9NJiN8kLXSLwJAzmNwR3yAPxGa9Z+IlxNpPhlvENmpN3pMiXCgHG+PcBKh9i hP4gHqBXAXHhu++JHxgtvEBtJ7fw5pQjWO4njMf2ooS42KwBKlj16YHqa7z4pSFfhvrEKIZJrqNb WFF6vJI4RQPxNZPmVJW31t9+n/DF6Oq+1l+Wp1lvOl1bRXERzHKgdT6gjIpkdnDFeT3Sp++nCq7d yFzgfTk/maZplqbHSrO0Y7mggSMn1KqB/Sp5ZY4ImlmkSONRlndgAB7k1crJu2xnC7ir7nmfwo/5 GPx//wBht/5tSeMrePSPH/gJbG0aT/S7+VYUIG53TceTgAbmJqn8ItX0648T+N44b63d7jVnlhUS DMqZb5lHce4rR8d6jZW/xS8ArNdwRtHNdFw8gG0NGAufTJ4HrS/kX+H8jSL1qf8Ab/6m5pnivVf+ Ez/4RvXdJtrSaa0N3az2l0Zo3VWwyncikMMg9KfeeKrq4ur+DQ00uX7DIYZXvrwxb5QASiAKeBnB YnrkYODWJq+p2C/HPw/A15AJV024QoZBkMxBUfU4OB7VzuleIfD3grxL4j0XxrbQwPLqMt9ZXc1m ZVmilO7AIUnj8uo7VMXzJX03/B2/Inq7d1+V/wAz0jwV4stvGnhqDV7eFrdmZo5oGYMYpFOCMjqO 4PoR0rT1jVrTQtIutTvnK29um9sDJPYADuScAD1NVvD11a3tg1zYaf8AY7GRt0GYfJMq4Hz7CAVB 7ZGcDPcVjfFDTL7Vvh5qdvpsZlu0Ec8cQXcZPLkVyoHckKeO9VNpeQR121/r+tShq3xAvPD1xp0+ rWemf2Ze3K2zNaXxlmtmbO1mUoAw45wePfv0Nz4gkk8RnQdLgjnu4oVnupZXKx26MSEHAJZmwcLx wCSRwDx2h+PvAmv29vBp+lwza1Kg/wCJaunHesncFtm0AHqxOAPyqtFrtt4H+L2vnxHKLSz12C3l tLtwfK3RJtZN3bkn9PUU0lez8/n5fr+Ar3V15fmdhpHii8vvF2q+HbvT4oJ9Pt45vPjmLpKHJxgF QV6c9efzKeGPFN54jg1xfsEFvc6ZfyWQXzyySFMfNnaCM59DWF4Xv4NT+MHiW5thIYW061CO8ZTe Mt8wBwceh79Rxg1R8E6/pugeJPGGjancfZ9Sn1qW5gtihLzxuBtKAD5s47VEXeOu9n+ErfkW7avz X5X/ADNiz+IM998MLjxf/Yqs0KzM9oLkYxGxU/OV9s/drT1bxPqFl8P08TWOlx3cgtEu5LUzFMIV DNtbackDJ6c4rzfQr+2tv2ctZguZRBKsd5Ftl+XLs77VUnhicjpmvUfBk9rqHgXRjDLFPC1hEjbS GH3ACD7+oqrNxdt9PxTFKyqW6Xf4NEZ8S3E3gqy12xt7e4nvEhaKAysqM0hAC7tpPVh27HpTptfv X8UDw9aWaC4SyF3Pcy7vKXLbQq4HzEkHqRgDv25PwFpWo6drt94YuYn/ALK0C7e4s5TnEizDMSg9 9gaXPuV9Kn1XxhbR/E248P69eyWGmw2kctpEu5ft0jHnLL8zY+6EH3jnIPAptptW6/5fmTZpPy/z /r+kdF4a8TDxPoeoyvbi3ubK5nsbmJX3qJI+CVbAyCCD0HWuZ+F+rWmhfA7TdTvnK29vFM7YGSf3 zgADuScAD1NUPhhq2lWGm+M7SWeKzMOsXc5hlXyvJhIUKSCBtHGADjp7VkaHZ3Gufszx2eklLi+t wZvIX5mJS4Mm0r6kLwO9U7KL/wC3fyd/uHZ3S82dlq3xAvPD1xp0+rWemf2Ze3K2zNaXxlmtmbO1 mUoAw45wePfvo6l4v1Cz8bw+G7fRVuGuLJ7qCf7UFDENtww2/KB1J+Y+gJrntD8feBNft7eDT9Lh m1qVB/xLV0471k7gts2gA9WJwB+VT3+p2SfHbR4Jbu3WcaNLGyeYBh2cEL9SATjrgVDvdLzf5f5i T0b8l+f+Rr6L4r1fUdQ17RLzS7W11rTI0kjC3LPbzK6kq27YGAyMH5ap/CjWde17wpFqGri1kWeW dvPSVi5bzWG3ZtwqjoPmPAFVNE1Kxn+Nfim3iu4GlOn20YQOMsy53AeuNwz6ZpnwW1SyPgqHRvPH 9pWk9wtxbYO+L96x+Yfw9e/07UQd1d9v1a/yKkkl81+Kuel1yVj4rvtX0+71PS7O0ntLa7ktmhac iYhH2s5wpAPBYJ3GORnFdbXiOvWemRJdeOfA2uLp2urPsu9MjlDJeTBirRNF13kk4GOeoAJ3Ur+9 Z/1t/XzC11odZ4q/5LJ4D/65X/8A6KFbd/4plsvHmkeHW0wFNQimkW8Mw42DJAXH07iuX8T6naL8 YvAiz3VvHNHBeechkH7svGNoPpkggZ64qTxRqunw/GrwdHJe26PHb3auDIBsLKNoPpnBxnrWi15F 6/8AtxEm9WvL9Dql1691PU7+z0S2tpE09/JuLm5lKr52A3lqqgk4BGWyMEgAHnFGy8c7/Ckeq6np U2nXj3X2JbKZsb5t20BWIGV77scAE9q47Q/Gdh8OfEXiTQ/Fss1mlzqMuoWN0YHdJo5CDgbQTkf4 jtWl8Rbu61PwlpniSy0yWez0zU4r5reWI757YAqzFGGR97OCOnJxWaasnfR2v5bX+7X+rlNO7XXX 59vv0NX/AITu607xXpWjazbad5WrFktriwvDMI5FA+RwVXrnAYfl6dzXn+heMfBPiG6gh8M2UF5f 5Viqae0f2cZ5d3KALj65J4Ga9Aqugr3Zz914hmk8THw/pVvHNdwwC4u5pnKx26MSEHAJZmweOOAT noDW0nxbNceKL3wxqlilpq0EP2mAxyl4bmEnG9WKggg8FSOOxNc6sw8IfGPVL3VZFg0rxBbQiC8l O2NJ4hjyix4UkZIzjParkcEev/Fu31ywaOXT9K057eW8jYFJJnY/uww4O0ZJweCcVMdbed7/AI/8 BDlpe3l+Nv8Ag/cY/hO98TTfFPxiHsdLZwbJLlTfSbYU2Njyz5Xz8EnBC8/nXqwAUAAAAcACvMPB Wr6bN8X/AB2kd/bO07WnkhZVPmbIyG2884JwcdDXqFW9l6L8gfxM87vtc8Tf8Lii0a2jsHsU0tri OGSd03ZdVLsQh+YYIC4xgnnmqWm3OpQfF/xuul2MV1dPBY48+byokxGeWYKx+gCnPt1qXWNSs9F+ O1hdalOttb3OhtBDLJwryednYD3bHb3HqKTwpqlhN8Z/GgjvICZILPYN4y22M7sD2zz6d6lfZ+f5 s0dlzekf/bTo/C/jBtXsNXfVrRNNutHuHgvUEu+MBRu3hsD5SOeRWNrHxFvdK0JPEg0/T5dH+R2h F6RdiJyAH2bdobnJTP4+mLohg8Rt8UtK028gkuryaRIQsgOcw7AeO24YzUfhDx54KfRLDStW01IP EUCC2msP7LZ5XlUbeMIRk4zyeO+KUXzJPrZO3rv/AF5me3pd/hseuWtzFeWkN1A++GZFkRvVSMg/ lXOeNfF03hKDTZo9La9S9vYrMlZghQuTggEfMeDxx9a6Cx3/AGGDzLdbZtgzCpBEfH3eOOOnFcH8 YJUg0fw7NISEj8QWjMQCTgFieByact9O6/McFda9n+RZ1DxxrPh/XNOh1/QIbbSdRuBbQ3lveea0 MjfdEi7RjPsSBzya1f8AhItQvtY1rT9MsYh/ZKoHlu2ZVmkZd+1cDgAY+bnr931xPGtzYeM7TStC 0a8t76ae+guJXtpVkEECNuaRiM4zjaM9SfrVCHxdZap4w8R6P4huJI5LOXyLDSQjETpsP7zYoJlL ZztOQAAcdTU3dn31/T9Wwtr5afr/AJC+KvHeoX/wWPivw+kdq1xGBIZXJeEF/LbZgYLBuATj19q6 bU9e1TQfA93rF9pVtcTWsBlaC3uiVZAuclmQHPXjaa8osbiK8/Zcu7K2Yy3VkD9piVSWi/0kt83p 8vPsOa9A8X+I9H1H4Q6zdWmowSwS6e8UcgbCu5TAUHu2eMDkHirq2jz8vf8A4YIq6jfz/Q35vFUF p4V07WLiMLJfrCsMAfG6WUDau49hnJPYAntWP/wnd1p3ivStG1m207ytWLJbXFheGYRyKB8jgqvX OAw/L05rxLI9z8KPCWuabCmpQaRJaXdzBGBJviWPa4x6jdznpz6VvaF4x8E+IbqCHwzZQXl/lWKp p7R/Zxnl3coAuPrkngZqpJKbS6Pby/rr5epmm+RN9vx/q2n/AAC6PGGr3PizWPDtloULXVjFFLHN LebYnV8nLkISuMYwAxJ9BzU/hrxVf+IdJ1dW06G01nTLmS0lt5Ji0RkUAhg4XO05HbNYmgapYSfG zxVEl5AZDZWqBfMGSVBLAeuMjPpmk+HOo2V74i8dR213DK7aszqqOCSuwLuHqMgjPTisU24/J/g7 fkay0fzX5X/Mt/CjWde17wpFqGri1kWeWdvPSVi5bzWG3ZtwqjoPmPAFas8Fh4J0zX/EksELSOrX EwtYFi3KuSq4HU/McsTklieBgDnPgtqlkfBUOjeeP7StJ7hbi2wd8X71j8w/h69/p2rt/El3Y2Ph 2+uNUiSXT1iIuUcZUxnhsjvwTxV1HZNx7BpzNef6lew1PVJp7Bp7ayks72IuJ7WdmEbbdwXlcMCM /NkdOnNbleReH9JXwh430e18Ia+dQ8O6r5rz6aZhOLZApYSowJwucLk9SQCWJGPXabtuiFdaMrRW cNoly0SYad2lkPdmIAz+QA/CvOvgSA3w3KsAQb6cEHvyK9F1C9tdPsZrm8uIreBFJaSVwqjjuTXm XwD1Czm8Bvax3ULXKXkztCHG9VJGCV64pQ3kvL9UE/hj/i/Rnp1jZw6dYW9lbLtgt41ijX0VRgD8 hViuQ8fmWO20SaK5uIiNZs0KxSsiurSgEMB94H0ORWr4g8OJ4hSBX1bV9P8AJJOdNvGgL5x97HXp xRe65vO34J/qV1KHjjxVd+EbCzv47CG5s5LqO3uZZJzGLcOcBz8pyoPX8KZrvirUNF8WaFpJ0+2e z1Z2iW8a4ZfLkVc7Su05z/Dzzz0xU1z4MtrjwVqHhua+v76K6jceffzmeVWP3TuPoQCK89TSdb8Z /Dk6newNDreiRCLTywBY3Fu+ZJB7uYwn/AT60rpavZa/Lr936hZvbrp8+n9eR6odSuj4nGmRwQNb La+fNN5p3xkthV24xzhuc/wmuZ1Hx3ef2Pea1o9tpd1p1qJGC3F8Y5rhYyQxRQhAHynbkncOeOM6 3hE3Gp6C2tXkBt7vV1E5iY5MSbQI1/75G7HqzV5p4J8VeFfDGkf8Iv4xtIbTW9OmeA+Zp7Sm4XcS rKVQk5BH14Izmm00+V7/ANflsJNNc1tP6/M7fWfiHFa+ArHxXYaYb20ujF8kkoj8ouwXng5IY449 OtbWveIxpN9pOmwQpNqGqzNHAkj7EUKu53YgE4A7AckgcdRxHxU1G0h+FREkMemia6gaC0cBH2CZ WzsHQ4BYjt35q18S9T0K2i8NavPqxsJ4royWWpxwtcQoNvzq6pncGHGAR65GDTdt/P8ADQSve3l+ Opr2PjHV38dT+FLzRIBPFCtz9qhvPleAnbvCMucg8EZPNYWnXOpQfGDxuulWMV1cvBY48+byokAj PLMFY/QBTn261d8Ga94Y8QeJ5tVi8S2Wqa7Nai2RIbd7ULCrFiEjkJYkk5Jyeg4FV/C2pWMnxp8b Il5bszwWgQCQfNsQhseuO/pStrFdbMtPSfy/NFweN9YvvBfia5h062stc0RpYZopZi8QKLu3qQuW BHIBA561Z8LeIryx+GlprniLyFgjsYphNFM0kk2VH3gVXDEkAAE8nrXK6VeW2pw/Fa2sbiK5nleU xxxMGZx5G3IA68gjjvSGZfFX7P0FjoEyXl/Z2Ns0kEalyHiZGaMj+8dp+XqfxpRd4XfVR/FO4rbW 7yX3WsdHq3xAvPD1xp0+rWemf2Ze3K2zNaXxlmtmbO1mUoAw45wePfv0V1r8sniL+wtLginu44Rc XUsrlY7dGJCdASzMQcDjgEk9AeN0Px94E1+3t4NP0uGbWpUH/EtXTjvWTuC2zaAD1YnAH5VFHq8P gr4va5L4hlFpYa7b272t7JkQh4k2tGW6A9Tz7eoqutn5/wDDf16Ep3Tfp+Zd8Kz3c3xj8V/bbZIJ ksbVMRuXRx8xDKSAcHPTHByOetejngE4z7V5l4Z1uxv/AIz+IXtXlliuNPtRDKsLmOQDJ3BsY288 HODzjNemEgAkkADqTUx+BfP82N/E/l+SOZ8OeKZdb1/xDpcumC0fSZY4yfODmXcu4E4Axxj1qhpv jDXNav8AXdOsNAtkvdLuRAWuL4iFgRkHcqFskdtuPVqyvAmq6fcfEvx6sN7byM9xbsgWQHcFiwxH rggg46U/4f6pp83jPx60d9bODqCOCsqn5RHgnr0BBGfajqv8N/n7v+bJba/8Ct8tf+AXdH8a674k 8PzXmk+H7db20lkgvIby8KIsqEgpGyo289OTtHPU84sQ/EGCf4bQeL4dPmkMyqq2aEkiUv5e0nHQ N3x05x2rD+Fmr6a2g+KZBfW2xNZvJWYygAIxBDZ/un16Vzfh3xbL4e+AVheabOokW8aC5nRRI1nG 875kKeuCMAjqRwarS1/8P47lLa/m191/8j0t/E91p/i/S9A1O3t86pBLJbzQOx2vGAWRlI6YOQ2e 3QU+bxHdXniTUNC0a3tpLjT4I5bmW5lZVDSZKIAoJOQCSe2Rwecee6nrHh1PiZ4F1SxvTNZAXkcm oyb3ErtHhV81h85ySNoPBOMDpVrxN4k0Pwr8Tr67TxAmiX81nEl2l5ps11Ddf3GURlSCo43bsc4x waGlaPnf9bfoC6/L9LnRaB8Rm1HQdV1jVdI/s220uSSC4xdCZvOQgGMDA5ORj1JFR6t8QLzw9cad Pq1npn9mXtytszWl8ZZrZmztZlKAMOOcHj374Wo2Wl638Jtdg8HalFq969yNRuWQZeWbzFkYFOoy FwqkdABz1q7ofj7wJr9vbwafpcM2tSoP+JaunHesncFtm0AHqxOAPyo0v92n9fP8BPa/TX+v63PT 6Kw5vFOn23iuy8MzGX+0rq2a5QKmYwq8HLepw2Pp24zuUh+RWis4bRLlokw07tLIe7MQBn8gB+Fe dfAkBvhuVYAg304IPfkV6LqF7a6fYzXN5cRW8CKS0krhVHHcmvMvgHqFnN4De1juoWuUvJnaEON6 qSMEr1xRDeS8v1Qp/DH/ABfoyfxjAuj+N/hzbWNq8qWzXUUMCEAkCEADJwB9a6DTPFeq/wDCZ/8A CN67pNtaTTWhu7We0ujNG6q2GU7kUhhkHpWH471Gyt/il4BWa7gjaOa6Lh5ANoaMBc+mTwPWpNX1 OwX45+H4GvIBKum3CFDIMhmIKj6nBwPalFttebf5X/Muo9E/Jf8ApTNu88VXVxdX8Ghppcv2GQwy vfXhi3ygAlEAU8DOCxPXIwcGrfgrxZbeNPDUGr28LW7MzRzQMwYxSKcEZHUdwfQjpXm+leIfD3gr xL4j0XxrbQwPLqMt9ZXc1mZVmilO7AIUnj8uo7V6l4eurW9sGubDT/sdjI26DMPkmVcD59hAKg9s jOBnuKcfhvvovv8A66eXqTLR28/w/q2pr0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTURIxhFVRnOA MU6igBuxdxbaMnqcda4f4maZq+r2miQ6TpM181rqsF7KUliQBIycj53XJOeO3vXdUUDTt/XcigO6 IOYDCz8sjbcg++0kfrT3RJF2uqsPRhmnVl6De6rfWUsmr6YunTrO6JEJhJujB+V8jpkdqOotjUoo ooAKKKKACuD8X6drN5488J6hY6NcXVnpcsz3MqTQrxIgUYDOCcdTx9M13lFHVPsNO1xsYCxjbH5Y IztwOPyodEkXa6qw9CM06sbWteXTNQ0rTYo1kvtTmaOBXbaqhELu574AHQdSQOOoBGzTVRELFUUF jkkDrWXoN5rN3FdrrWmRWU0Nw0cTQzeYk8eARIO65zjB54rWoAayI5BZFbacjIzg06uZ13xRdaN4 o0HSRpyS2+rTND9q8/BiKruxs285HfNdNQtQeg1URfuqo5zwO9OoooAr31lbalYz2V5Cs1tOhjkj boynqKZp0Fza2ot7mcXBjO1Jjwzr23dt3YkdevGcC3RQA1URCdqquTk4GM06iigBGVXADKDg5GRS 0UUAN2Jv37F34xuxzis3VNJ/tiWGG7dTp0bLK8AHM7qcqG/2AQDjucZ4BB1KKACiuZ0nxRdah411 jw9cacluNPhimSZZ/M84PnBxtG3GOnNdNQndXQbOw1URWZlRQzdSByadRRQAUUVl3F7qsfiOzs4d LWXS5IXae984AxOPurs6nPrR5Aae1SwbaNwGAcc0tFFABTVRFZmVFDN1IHJp1RieI3BtxIpmVQ5T PIUkgH9D+VAElcH4v07Wbzx54T1Cx0a4urPS5ZnuZUmhXiRAowGcE46nj6ZrvKKOqfYadrjYwFjG 2PywRnbgcflS7V379o3YxnHOKWigQ0IqggKADyQB1pVUKoVQAB0ApaKAAjIwelNRFjUKihVHQAYF OooAKKKKAEVVQYVQB1wBS0VHBPFcwJNBIskTjKupyGHqKAHIiRjCKqjOcAYp1FFABRRRQA3Ym/fs XfjG7HNOrl9S8V3OnePNF8OPp0bQaokzpdC45Xy13EFNv0711FFtEw62CiiigApNq79+0bsYzjnF LRQAiqqjCqACc8ClorLuL3VY/EdnZw6WsulyQu0975wBicfdXZ1OfWjyA1KKKKAGlELhyilh0JHI p3WisvVb3VbW90yPT9LW8gnn2XcpmCfZ48ffwfvc9hQHmaSoqABVCgdABjFOormdJ8UXWoeNdY8P XGnJbjT4YpkmWfzPOD5wcbRtxjpzRfWwdLnSsqtjcoODkZHQ0bV3Fto3EYzjmlooAKKKKAEIBGCA QfWhVVFCooVR0AGAKWsvS73VbnUNTiv9LW0toJgtnMJg/wBoTHLYH3eexoA0tib9+xd+Mbsc0rKG UqwBB6gioJLnKXC2vlzXEPBiL7cNjIBODjgjt3rF8D+J38YeFoNaezFm0skieSJPM27HK/ewM9PS jfQdtLnRdKKKKBDURIwQiKuTk4GK4O707WG+MNvra6HcyaVHpbWLTiaDlzJvztMm7bjjpn2rX8Ze LW8MeDp/EVlZxajDCFYr5/lgqzBQQdpzya6OCQy28chGC6hsfUULe/YeqVu/6NMivjdRabcHToon u1iYwRyHahfHyg46DOK5rwnD4n1GVNX8XWdpYXkURggs7aTeFyQXkY7iMnaoABOADzzx11FC3uLp YKKDnBwMmuX8OeKrjWvE3iHRrnTo7R9HeFN6T+YJfMUsD90Y4A4560JXB6K51FFFFACMqtjcoODk ZHQ1FcMYYZJo7Z55QvCR7Qz+wLED8yKmopNXVgOG+Fmkapofg+HS9Z0mSzuoJpnDPJFIrB3LDBRm 7HvjpXcMqupVgCp4II4NZHinWbjw94bvtXt7JLw2cLTPE03lZVRk4O0849qptr2rXXg/S9Y0nR1v Lu8SCVrXzwgRHALHceuAarf5WX+QSerb63Z0lI6LIjI6hlYYKkZBFLRSAztI019It/sMbq1jEALV f4ok/wCeZ9QOgPXGAemTo0VHNPFbx+ZNIsaZC7mOBknAH4kgUASVl3Ok/wBoatb3V66vb2beZbW4 HHmYx5jepGSFHQZJ5ONpr95qtjp6S6Ppi6jcmZFaEzCPCE/M2T6DtWpQu4eQUUUUAFFFFABTWRHI LIrbTkZGcGnVDeXUNjZT3dw4SCCNpJGPZVGSfyFJtJXY0ruyJqK5bTPEGu6nJot7Do0DaPqcXmvK LnEtqpXchZSMNkYBC9Ce+MnqapprRiuNVEVmZUUM3UgcmhlVgNyhscjI71l63rR03w7qOq2cMd6b KOR2i87YDsBLDdg4IwR0p3hvV21/wzpurtCITe2yTmINu2bhnGe9LcbVjmNEstaX4qa3q91odxba bd2kMEM7zwtkx5ySquSAcnHHbnFd1tXfu2jdjGcc0tFCVkkhdWw6UUUUAJgAk4GT1oKqWDFQWHQ4 5FLRQAgVVJIUAnqQOtLRRQA1URWZlRQzdSByadRXL2viu5l+Ilx4Vn06OJYrD7clytxv3qXCAbdo x37npQld2QeZ1FIFVSSFAJOTgdaWigApvloXD7F3AYDY5Fc5468T3Hg/wvca1Bp6XywFRJG0/lYD MFBHynPJ6cV0cT+ZEjkY3KDR0uD0HUUUUANZEfG5VbByMjODTqKKAGoiRghEVcnJwMZNOoooARlV 1KsAynqCM0ABQAoAA6AUtFABRQc4OBk1y/hzxVca14m8Q6Nc6dHaPo7wpvSfzBL5ilgfujHAHHPW hK4PRXOnKqxBKgkcgkdKWsvS73VbnUNTiv8AS1tLaCYLZzCYP9oTHLYH3eexrQininVmhkVwrFCV OcMDgj8DxQBJTdib9+xd+Mbsc4rN0G91W+spZNX0xdOnWd0SITCTdGD8r5HTI7VqUAFcL8TNM1fV 7TRIdJ0ma+a11WC9lKSxIAkZOR87rknPHb3ruqKBp2/ruRQHdEHMBhZ+WRtuQffaSP1qTau/ftG7 GM45xS0UCGhFUEBQAeSAOtKqhVCqAAOgFLWXDe6q/iS6s5dLVNKSBHhvvOBMkhPKbOox60eQGpTU RIwQiKuTk4GMmmGePzngV1adUD+XnnByAT6AkH8q57wh4pm8TPrSXFglnJpmoPYlVm80OVA+bO0d c9MUDtpc6aiiigQgVVJIUAk5OB1paydb1o6b4d1HVbOGO9NlHI7RedsB2AlhuwcEYI6U7w3q7a/4 Z03V2hEJvbZJzEG3bNwzjPegGrGkqIhYqigsckgdadWXr95qtjp6S6Ppi6jcmZFaEzCPCE/M2T6D tWpQAUVHO0yQO0EaySgfIjvsDH0JwcfkawPBPiabxb4e/tOexWyk+0SwtAJfM2lGK/ewM9PSjrYP Mh8b2WoahaaVFp+ny3bQ6pa3MuySNdkccgZj87DPA6CuoUkqCVKkjoeopaKFored/wAEv0B6hSAA DAAApaKAAAAAAYAppRC4copYdCRyKdRQAVxHiqDxVa+J7XWNI0m312xjtWgOnSXQt2jkLZMoLAqc rheeQM46nOjpPii61DxrrHh6405LcafDFMkyz+Z5wfODjaNuMdOa6aktbNf10Dun/XU5DR7DUtXu LS+1nwvpmiPbSeaixzrcz7sEfeVFCDnnBbI44rr6KKYgpFVUGFUAdcAUtFAxqoiszKihm6kDk0rI rrh1DD0IzTJJ4oWjWSRUMjbEBP3mwTgfgD+VSUAGAOgooooAKKKKACkIDAggEHqDXM+LvFVz4Ym0 YR6clzDqN/FZNIZ9hiLnrt2ndwD3FdPR0uGw3YmFG1fl+7x0+lecSjxvoms6qB4TtPEtve3Rmhu/ 7RSAxR8BYmWQH7o/u8ck9Sa9Joo63DpYwdB0qaC5l1O803T9Pu541jaCyO/Cgk/PJtXeef7oA5xn Oa3FRFZmVFDN1IHJpsM8U6s0UiuFYoSpzhgcEfgakoATapYNtG4DAOOaWiigAooooAKKKKAGsiOQ WRW2nIyM4NOqvfTT29jNNbQJPMilljeTYGx23YOPyrH8E+JW8X+EbHXWtRam63nyQ+/btdl64Gen pQle/kDOgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOO+KGrapoPw/1LVdIvBa3VsEIcxK+QXVT97gc E9jWVrN94h8OzWWpTa5LdLq5hsYtPjtFZbeVvmaRD95sIr4BByx5wOBq/FDS9T1zwDqOkaTYSXl3 dhFRVkjQLh1Ykl2XjAPTNVfGfh/WPEvgzTpNNgay1zTJ4b21huHT/WIMbSVYryCe+M+gpRdm2+6+ 7r/X6lP4VbfX9LfiU473xNY+MNI/s/8At7UdIuWaLUU1G2RRD02yIyquOc5HT2FaKapd698QNc8P m/ubC3020haIW+1XleQEmQkgnC/KABxyc54w/RNY8a61LHBqfhePQYkIM9018k5fB5WNFHGfUngH ua5rxZLqlz45uriw8HS60llClut5pusNZSxlhuaN2TDMeh25wAQf4qfZev8AX3/1sSnv8vz/AMv6 3Nv4cX3iPVbLVzrOrm5lsr6ewRjaxoGKEYkG3GeuMVl6R461HS/h54o17WLj7fc6bqNxbQ5RYw21 lRFwo4GSPeuk8H6z5tlNpx8LX2iT2q+YLWUKRKGJyySZw5zncSc5OT1rmND8FanqvgfxT4e1vTZd NbU7+e6t5XlikADsGQnY55BHI/WiWzt2/VfjuCtzK+1/w1/4BuazZ+I7HwZJq9nr9xJrNtb/AGt0 aJDBPgbmi8vbwuMgEHd0yTWX4i8YapeeA/DPiPRbv7C+oXlqk0JRXVhI2GQkjIAPcYOK0rebxVN4 KOh3OhzR6z9mNmbwzRNbE7Svn53bsY527d2TjHeqPinwnqFn4H8OeH9B02bURp13bSSMssUfyRHc x+dhyT2HrRLd22uvz1/Cwley72f320/Usalc694c8eeG0k12a/sdYllt7m2mhjVI2CblMW1QQBjG CWOOpPWq8viyOfxtrXhzVNcuNE1BXRdJGFSN0KDDgspV2Lkja2RwABkGr/i6x1nUPFXhG8sdFuJ7 awuXnunE0KmMMm0DDOMkZJOPTvUXijRj4p03VdN1zwncXjRO4026ieHcwIypVi4KEHg7sAgDr0qL uzv5/dp+O9vmVpdfL9f+AdzaiYWkIuCDMEXzCP72Of1rnfHXiSbw3otu1p5f26/vIrG1aQZVHkbG 4juAMnHfFaXhmwvNL8L6XYahP595bWscU0oYtucKATk8nnvWT8QfCs/izw0LaxmSDUbWdLuzkfO0 SoeAcdiCRntnNXPf5/hcUNvl+PQzPG02ueDfDo8Rafq13fGwZGvbW6VCl1GSFY/Ko2MM5yuB6g1g +KrSbUvin4GubfXNRhjv47qWExiH/Rh5Kn5N0Z+9337vbFdHrkWu+MvCp0KXRbnSpr0LHezzSwvH CgIL7CrkuSAQvA98VV8QaPq3/CwfCF7puhXM+maNHNHLIk0C8OgRdoaQE4xzwPbNVDSWv9aP8Nrf MHtp2/4b9fwO9tYXt7WKF7iW4dFCmaXbvc+p2gDP0AFc74r124s9T0TQNPmEF9rE7oJ9oYwxIu6R lByC2MAZyMnJBxiunUkqCQQSOh7Vw/xA8PaxeX+g+I/D8Mdzqeizu4tZJAgnicAOoY8A4HGfU+1T 1V/6/r8hrZ+hmeLbC5s/iF4EY6hPcWzXsoMc+0lX8o/MGAB5Gcg8dMY5rS1HxBPqnifVNHhl1e3t dNWJJJNMgV3klddxyxVtoAK4xgk5zx1oa5F4q8Q+IfCupW3hhrWHTrt5Z0vLyJXGU25+Qt8vJ9ye wHNN1XTvFnhXx/f+IfD2jJrWn6vFGLy0F0sLxSoNoYFu2PY9844pRW6fd/kv+D+An3XZfmxNP8W+ IvD/AIQ8QXfiGzuZpLC42abPdRCFrtHbbFvC4AOSASAOD61u6lo/iuKysbnSteafU0uEa6juVRYJ YifnUKF+UDPBB3YHJJ5qprega5478EanY6xBDpNxdxr9mtEm87yXRgwMjgAEkgAgDAHqah8Pav8A ES6WDStW8L29iyIEm1c3yOpx/EkQBJYjpk4z19Krr56f1/mJ7eWv9f5CapqPiGX4rx+HbXWBbWFx pD3PywIWhbzNu5SQdzemeOScHGKY2p6tp2o2XhK71XUb66hs2u7zUbKzXzpA0hWNAuGCjAOW68DB GTie507Wf+FxWutpotzJpceltYtcCaEfOZN+7aX3be3TPtUPjDR/Emm+NrDxj4YsU1ORbU2N9YNO IjJHuLKVZuAQT/Lg81Mdlfz/AFt+hct3y+X5K/6ljwfe+Jk1/WNOv4tQudJRFl0691CFY5CcDdGx UDPJ4OM8HrWF4i8Sa74Z8DJq9/qjf8JLbzxSXdjCyyQqjy48tgqkIuw8MSDkfePfrrG48Wa1pt3P eadFoMxt5I7W2Nytw5lI+WR2UAADsoyecnHSvPrrw/4w1L4OzeFz4W+z38RR5pZL2Mm6cSh2dcE5 ZsZJYr14Jq4tcy5ulvuv/lv/AMOTa/zv+X9f1Y7Dxp4sj8P+ItDg1W6uLDQ7xJRLdwrx53y7EZwC VXBY8Y6DnANXNR1+bwz4GvdYN6NXAcGxmypEokZViBKAAgFgMjkgZ5JqS8lv9SFva6r4VkutKu7X /SYGeGX7PIG4DKW+bI7rnBA+tctafDW4k+HniTw7CJbCC9vGuNLt55t5tgpVkDEFsAupPBJwRnnN Qtnfz/P8+3kC1t/XT8u5Lqt94qsLjTb7RX8Q6jL9pVb60vLJEhkhOdxXCgqRxjB+uec9CNXude8c 3+hWt3Ja2ekwRPdtCB5k0soJVMkHaoUZOOSSOQAQc3QtZ+Il5DDpmpeFYLCZUEc2qvfo6cDl1iUE knsM4z1I6VFc6R4g8L/Ea98Q6Tpb6vpmrwRR3sMUyJNFJGNquN5UMCPfuemBVaXS6a/16fqJbPvp +f5jfCtvc2vxj8VxXN291ixtTHJIoD7PmwGwACQcjOOmM816PXAeH9P8SD4m6xrl7o0Vpp19aQRo z3atIhQHgqoOW5OecDsWrv6mN1BX8/zY38T+X5I821/VNc0vw34jv9S1VrPVoDPPptraMkgWBPuM yBTlWx8xbOM9V7O8U+Jta/4Qbwvrum3gs57+4shPEI1ZHWXGVyQSBz1BBrJsNG8ZDwl4q8P3fh9Z NQ1BronVZLyMJchwQvAJbOMAAgAADJFTalpHia9+G3hfS4/Ddx9t0+5tDPF9pgBCQAZbJcDJxwM/ XFNfCu/u/wDB/wCCN7v/ALe/4H/ANfUrnXvDnjzw2kmuzX9jrEstvc200MapGwTcpi2qCAMYwSxx 1J61a1jVtWs/ip4c0tL/AP4ll9BcvJbCJRlkUYJbr39RTPF1jrOoeKvCN5Y6LcT21hcvPdOJoVMY ZNoGGcZIyScenem69p+s3PxQ8N6rbaLcTadp8NxHPOJoRgyKAMKXDHGOeO/eiF7q/d/l/mTPZ27L 8/8AIpS+LI5/G2teHNU1y40TUFdF0kYVI3QoMOCylXYuSNrZHAAGQa1/Euv39hc+G/D9vcCDU9Zl MclyFDGFETdIygggt2GQRznBxiqXijRj4p07VdN1zwncXjRO4027ieHcwIypVi4MZB4O7AIA69Kq 6v4L1w+HfCV5azR3XiLw4I2KvKQt0NoEkYc9CccE/jjNKOyv5f169ynu7ef39P8AgFjxZqeq+B77 R9Ui1C4vdIubtLO+tbkKxj39JUYKGBBHIOQcjAFZ3hfT7l/jF4xzrOolYPsj7S0ZEilWbYcpwoyQ AuOvUnmtzXdP1DxsNLsZtLutNsILuO8vGumiJfZkiJQjtklsZPAx0J6VWsdN1zR/ipr2oJpDXWm6 vDbeXdJOirC0alSHBO78ge3vio6PXz/T9bg7Wfovvv8A5WKcV14p1vx34t8Ow+IfsdvaQW7W08dq haEupbABHOT1JOcDAxnI0NO1LUtV8XT+GG1aQR6JZQG/uYY1SS8nkX6HYuBuwuDkjnA5boGn6za/ FHxLq1zotzDp2pRW6QXDTQnmNdpyocsAc8cdu1Ld6HqmgfEebxRpdk+oWOp262+oW0Lqssbp9yVQ 7AMMDBGc85GamO0b/wBb2/r0HO13by/S/wCoy01jWdK+IU/hG8v3ura9sjeadeTRr5kJBw0bbQA4 GCQTz0znNZPhD/hL/GvgGPUn8XT2d8ZJ1iaC1i2uyyMB5gKnI4xhdvHXJrprTRrzVPHX/CT39s9n DaWbWdnbSlTISzZeRtpIAIAAGc9c46Vyvwm1XUbT4cQQ22g3V4fPuPJliliEbEytw25gVwevB46Z PFJX672/XT8B20b81+Wv4l3T9b8T+LPhLPf2N01j4lszLE5hRCsksTYK4YEfMB26E+laVj4hl8Qf DzR7vTb+aO/1IxQLLlC6S5/e5yu0lQshxj+Gtvwd4ebwz4chsJZFlund57mRBhWlkYs2PbJwPYCu c8I+GJNK8d+ITFch9HhuPOtbYciG4mRWl+mBjA9JDVK97Py+9b/JmfTTz+57fcd+i7EVSzMQMbm6 n3NcR4z1LW7Dxd4UtdM1IQW+oXEsM8Lwq6nEZYNn73HoCM8V3NcR4x0/V7zxh4Tu7DSJ7u0065km uZUmiXaGTaMB3BPXJ+nek73Xqils/R/kQ6fdazovxQj0G61q41TT77Tnuk+1RxiSKVHAOCiqNpB6 Yqzo2qXXjDXdeEeoT2mm6XdGwiittqtJIoBeRmIJ4JwAMDg5zkYjvrDWJPi5pmrR6PO+lwafJbPd CaIAO7Bvul92Bgdu/SodK0bVfB3jLW57XT5dQ0PWZRd/6O6CS1n/AIgyuwyrZzlc4xjHeiN9L+f5 6fh/WwS628vy1/ExL7WtZk03x14T1TUrg3WlWTXVrqMIWKWeBkLANhduRwpKgZ56EZrq/hpZy23g HRZJL+5uVmsYGWObZtiGwcLtUHH1JPHWqB8J6hqn/CW6vdRC2vtasfsVpayMpMEYjIAdlyNzMSTg kDjk1pfD5dWtfCOnabq2kSafNY2yW7eZMj+YVGMrsJ447kH271UdnffT9f8AgX8wlbT1f6frc6DV NQh0nSbzUbjPk2sLzPjrhQSf5VyWjRa74n8FQa0NeubLU7+H7TbrAkZhtwwyibWU7xjGS2STnBHA HXanYQ6rpV3p9wCYbqF4ZMf3WBB/nXGeEU8R+FfDS+HrzRLi/msMxWd5byxCK4jz8hbc4ZCAcEEH gcZ6VHf+v66f1cO39en6nPXPjzXtV+Hmh65aXC6fqDatFYX0AiDI58zaw+YZUHg8HIyRnvW9qVzr 3hzx54bSTXZr+x1iWW3ubaaGNUjYJuUxbVBAGMYJY46k9aydV8E6zpngPRtH0+xfVb5NVj1G+kik jjXd5hkfG9lz2A/XFbvi6x1nUPFXhG8sdFuJ7awuXnunE0KmMMm0DDOMkZJOPTvT96/zX5K/y3E9 n6P83b57FbxV/wAlk8B/9cr/AP8ARQp/iTVNV8PfETQJZ9TnHhzU3Nq8OECxXGMpltu7a3TGeufp T/EWmaxd/E/wtqtro9xLp+mx3K3E4lhAzKm0YUuGOCOeO/etb4gaNba74G1W1uZRBshM8c5OPKkT 5lbPsR+WaqUrRi+1/wA3+mwWbk0uti1byXV34vu/Lu5RYWcCRvCNpVp2+Y54yNqbD1wd/tW2zBVL E4AGTWJ4Rsbyx8NWn9pyebqc6+feSYAzK3JHHpwo9lFbbKHRlPQjBpSTjougotPU4jR59W8aeGrj XLXV7iwN35v9mxQhNkSqzKjvlSXLYBIJwAcAZBJi1DX9fsLXwp4fvbiCDxDrDmK5uoFDLEsa7pHQ EY3EYAyMAk8EDFYnhq28eeAEfw1ZeGYdc0hJ2axvRqCW/lxsxOHBBJwTnp64zW74v8L61qMGha1Z PDPr+i3JuBFu8tJ1b/WRKT04wAT6c9c0aXVttP6/zH3vvr/wP+AReLNT1XwPfaPqkWoXF7pFzdpZ 31rchWMe/pKjBQwII5ByDkYAq1rGr6tZ/FTw5paX/wDxLL6C5eS2ESjLIowS3Xv6il13T9Q8bDS7 GbS7rTbCC7jvLxrpoiX2ZIiUI7ZJbGTwMdCelQ6/p+s3HxR8N6ra6NcTadp8NxHPcLNCvMigDClw xxjnjv3px3V+7+63+Yp3s7dl+f8AkQW9z4g1L4jeI9B/t+aCwgtreaJooIvNi35yEJUjkjqwY46Y 607wn4mvrHSPFg1+9a+Hh67mQXJRVeSFUDjdgAbscdKsaPYaxB8UvEGq3Gjzx6deWsEMFwZoSGMY OcqH3DOeOO3OKo6B4b1O8/4Tmx1nS57C11y4kaGYzRPmNk2dEckHjOCO/Wslzcvyf56fgW7c2vdf lr+Jc+yeKtZ8Fx6vY609trt1Cl1bwYT7NFuAbyiCp3DHBYknPIwOAnjDV9d0m+8HiO+SBb7UobW9 hiiDB8qS2GYZAyO2DWV4VufiNoNjaeGLnwtaXUdov2eLVzqCpEIhwrGPBZsDHHBPt1rS8c6Xrd/e eFBYaZcaiNN1KK7upxLDHlVBBwGdeec4AxxW3u86ttdfdf8Ay3v/AJmT5uVp72f9f5GiNXudd8c3 +hWt3Ja2ekwRPdtDjfNLLkqmSDtUKMnHJJHIAIOJ4XhuLP4weLUurx7oLY2rJJIoDBPm4baACRzz jpjPNPudL8QeG/iNe+JNK0h9U0zWYIkvbaKaNJoJUGFYbiFYY469z6DMug6b4jb4lazrN/o0drpu oWcMSs12rSJtB4KrnLcnODgdi1Z6+615/k/6XkaStr8v0/4Jltr+s+J/DM2taXc6/a3kqySafDaW iNBgFhGGLId+7A3ZOATxjHPfeGL3UtR8NWF1rFmbPUXiH2mAjG1xwcD0OMj61594Zh8eeAopPDVt 4XTW9Linc2N6uoJBtiY5w4YE8ZPb6Z4r0zTo7yOyT+0JY5LpstJ5Q+RSf4VzzgdMnk4z3q9Labf1 /TJd76+f9f5GL4+XWV8G6jc6BeyWupW0RniMaK+/byUwwPUZx74rj/Fviy8g8DeGfFGl6zdw280s Bu4kjSRpYSMyfwHDLg8jA4PtXqhAIIIyD1Fee+F/AEtimvaZqm2TR2knt9MhH8FvPh5Oh9TtHcbT 61Oqbt6/d0+ZWmn3ff1+R0lzPNeeIdLtrHUJkijga6uQmxlkjPyxhsjI3NkgjGQjVkeEdW1a+8Ye MNM1DUDcRWE8MdtiJU8tXjLHGBz1756VY+H3h/UvD3hwQavN9ov1PkhhjiGPKRKP+Ajdz3c1S8Ia drNn468WX99o89rZ6nNDJbTPNC2QibTkK5IzwRx37VSSu15P81+hDvZPzRk/CywuV1DxXNJrGoTC DW7iJo5WjYTYVQGc7N2enQgcDisnwFp2s3Pwh+1af4huNNa3N3JbpbxRlWYSOf3hdWJGR0Xbx610 vhOw1/w5rfii2m0KWe1vdSlv7e7iuIgro4Hy4LA7uO4A68jjMfgfSNd0P4WXOkXuiXCaiguAkAnh PmeYzFcMHwPvDOSKzm3yNx35V9+hveN2v734anU+DNYn1/wXo+q3QUXF1apJLtGAWxyQPrUXj2OW TwFrnk3dxaullLIJIGCt8qk4yQcA4wcc47iovh5YahpPgTSdN1OyezvLSERSRu6Pkg9QUYjBrU8R 6fLqvhjVdOtyomurSWGPccDcyEDPtk1dZX5rGdB6x5vI8p1qzltv2aWke/ublZtOtGWObZtiG5OF 2qDj6knjrW/4nn8Q+GbLRtei8QTSo15bQXNgYYxbmKQhSE+XeCMg5LH8OlZ1/pHinU/gl/wi48M3 EWpR20NqFa6gwxRlJYHfjbhfXOe2Oa3PHen63rXg7S7TT9DuZrpbq2nlhM0CmIRsGIJMmCeMDBNO b99tfzL7r6/gL/l0l1tL8lYu+IPEMx8VxeHbZtQiVbP7ZczWEIklwX2ogyCFBwxJxngY71B4MvfE i+I9W03Uo9RuNGVUl0++v4Fjl6DdG20DOCeCRng9ap+LNK8TWHjLTvGfhrTRfy/ZDZX2myzrEzR5 LKQxJXIJ7Z6DrzXSeH7zxHqrfa9Z0qPRolUqlmLlZ5HJ/iZlACgDooz1ycYxSj/nf9P02/zCX+Vv 6+/f/I6GvMtA1CHSfH/xO1G4z5NqLWZ8dcLASf5V6aeB0z7V51ovhzUrnxZ45fVtHuLbTNdWKKKV pomyqxGNshXJGc5HH5UJu0rb2f5oeml9rou6NFrvifwVBrQ165stTv4ftNusCRmG3DDKJtZTvGMZ LZJOcEcAZum/EW91PwFpV6IIoNcv9QXSthBMcc24hnweSAoLbc9eM1d8Ip4j8K+Gl8PXmiXF/NYZ is7y3liEVxHn5C25wyEA4IIPA4z0rM1H4a6jH8ObHT9Ou4m8QWF8NWSU8RyXO4sw+nzEDPoM4odk /wC7p67/AOV79diVeyXX/gf52OnuNJ8SWetaPPpusyXdmHKanFfbP3iY4dNqDawPYYHI4qvfTaou rawdW1J7Cx2qmkR2jqZZSE3O+wKWZgf4eRgfd7lPD2s+NdYnSHWPDEWhQxbTNcm+ScykdVRFHy5P ck4HTJ5GVplp4r03xp4mefQhfJqMoNnqbXaIkUIX5Y2HLgDnhVOSScc5pO+3r/X+X/DDXf0/r/P/ AIcgj8QXfij9nm+1e/Km7m0m5WVlAAZlDpuwOBnbn8aj1vVtW0L4KaBqOk332WWO1sUbESuWV9ik fNnHX0qDQdB8S2XwRvvCs+gTjUVt7i1iVbiHEpkZyHBLgBRuHU568VY8SaH4hv8A4P6XoFpoVxJq cUdokkRuIFCGIqWyxfBB24GM9a2k48zttzR+67Is7W8n+h32q2N3fC38jVrjTooyXme3Ee5+OB86 MAOpPfgVxPgPUtb8SeJ9T1OLWbyfwrasba0E6RZvJBw0gZUHyDtjrx6EVofEJPE2q+F4NL0LSrrN +wjvnWaBZLeD+MLukALEccHHXkcVd0SS90+DStF07wpqGm6dBiN5rma2KxxqpPRJXZmLYHTuTmso 7t/16lP4Uv69P6/Uwtf1TXNL8N+I7/UtVaz1aAzz6ba2jJIFgT7jMgU5VsfMWzjPVe2Z49ubnXfC vgPVfttzaNfajYtJFAyhMyDduwQclSOM5HsaSw0bxkPCXirw/d+H1k1DUGuidVkvIwlyHBC8Als4 wACAAAMkVPqWg+JL34aeFYY9EddS0S7s5XsnuI98qwrtYhs7RnqAT09+KqFrJvvH/g/pf7yurv8A 3v8Agf8AANr4i32s+GPAwu9N1iYXENxDG800UbySq8gU5+UKOD2Wrmr65dXXjnTfCdjdNaGS0e/v J0UGTy1YKqJuBA3EnJxkAcYJyKHxFste8S+A/sWn6BO19PPDJ5BuIQYlRw3zsXAzxjCluvWn+INC 1YeLNE8aaRYPLc20LWl9pzSIsksDc/K27ZuUnOC2D61K8+/6K3yuT107frr+Al9q+qeFPH+i6dNe zX2i62XhQXABe1mUZGHABZWyBhskcnPaore58Qal8RvEeg/2/NBYQW1vNE0UEXmxb85CEqRyR1YM cdMda0rrSr3xP4r0XUbqymsNP0dnnVLgoXnmZQFwFZsKoz1IJPbHNV9HsNYg+KfiDVbjR549OvLW CGC486EhjGDnKh9wznjjtzikr3V/P7rafj+Fin1t5fff/L9ST4e6pql1/b+l6tem+m0nUnto7lkC vJHgMu7AA3DPUCup1TUIdJ0m81G4z5NrC8z464UEn+Vcn4G0/WLDXfFU+paRNZw6hqBubaR5onDJ tCjIRyQeM8jv1rrdTsIdV0q70+4BMN1C8MmP7rAg/wA6T5uRd7L77DVufXa/4XOR0aLXfE/gqDWh r1zZanfw/abdYEjMNuGGUTayneMYyWySc4I4AwrrXbzxv8D9T1SeefTr23tbmO7itgu2R41YMp3q SFPB4wR0zWx4RTxH4V8NL4evNEuL+awzFZ3lvLEIriPPyFtzhkIBwQQeBxnpUbeEtQ0X4S6joFpA +parewT+aYWRFaebJJy7KNoJx64HSnPaVtraf1+fyFC9433vr/XraxpfDfT57XwVo08uq3t2k2n2 5SGcRbIBsHCbUU45x8xboK6e+ha4sZ4Unlt2eMqJYSA6cdRkEZ/CsfwTBe2fg3SbDUbCWyurS0jt 5I5HjfJVQpIKMwxxW+w3KR6jFXXs5St5kw0SPG/A1rOPgVql5LqV5OJbK/HkSlCindJlgdu4k98s eppyDxBpXwW0zxFp/iGe3lsNNgmjs0hjMDoAuQ+VLkkdwwHoKu+G9H8TaV8MdV8K3Hh2ZrlIrqGG ZLmHZP5hbaVywI+9zuxx6ngXLnRdek+ByeHE0S4Orf2elmbfz4RhgAC27ft28Z659qio3aTjvZW/ E1fLdLpeX6Gx4g8UzRPoGnWouY59XjeZ5bWISyRRIgZtikEFiWUcggDJ9Ko6Fe+JLbx2LPZq954c ubYt5+owKj204JOAwAJUjHUHkjmq3iXw/wCI57Dwr4g0OyxrmiLtfT7iZB5sboFkTcGK5+Xg5/Xi ug0DU/FetTpJq2gJoFrEdzI14txJOccAbQAqg8knk4xjvV6KTt5/1/XUxTfKr9l/X9dDqa4lNUu9 e+IGueHzf3Nhb6baQtELfaryvICTISQThflAA45Oc8Y7avJ/FkuqXPjm6uLDwdLrSWUKW63mm6w1 lLGWG5o3ZMMx6HbnABB/iqOpfR/11L/g2bxZr/h/X4r3xDL9utLu4sLe4W1iQb4yNsnC4IOcEY9e 9S+FfFN7qHwzuri8uZX1+0kksrhWKBhdhtiKMLgZJTHHfvWz4L1cXNvJpj+GrzQJbZRJ9nnAKSBi csrj75zncTzk5PWsmLwxJF8Xrq7tbkLptxbR317ajnNypZI29gRluO6U2ru3Rr8uvz1+dkK+l10f 59Py/EPHeo+IPDWh+HBYasXuJtStrO5eaJCZ9+d2SB8uSP4R0NXr7S/GenadrEun66NTu7t4vskc 8EcQsxuxIVPRuDkBgcbRncc5g+Jmmavq9nosOk6TNfNa6rBeylJYkASMnI+d15OeKn+I+n65rXgz ydFtGkuDcRSXFi8qoZ4Q2XiLZxzxnnkZHelfd+f+X4FNLRLt+Ov/AACg3iSSx+JPh7SLHVJr/T9U guVn8wh0WSJdwZHC9eGDKDgccCiL/k4O4/7Ftf8A0fVPVLTxPf8AjLwh4gj8LNDa6d9oiktReReZ GJECgtg7Qo/2SxwOgPFaUOmax/wuWbXW0eddLbSRYi4M0PLiTfnaH3bce2eOlaxaTj/29+tv0M2t H8v0O8rjr6bVF1bWDq2pPYWO1U0iO0dTLKQm532BSzMD/DyMD7vc9jXnGmWnivTfGniZ59CF8moy g2eptdoiRQhfljYcuAOeFU5JJxzmsXfZdmWu/mYHiDxBeeKP2apNXvtpu5o0WVlGAzLcBN2B0ztz x61reL28X+E9CTxXa+IXvVtDG93pjW0awPESAQhA3LjOckk+/as3T/B3iW4+BV14Qm0oW2pQEiLz bhCs/wC+8zKlScDHAzjn0HNdZrdvqvi/w0dAOj3ulpdqsd3cXTwkRxhhvChHYszAEDjHOSRjB0qW u+Tvp2/ruCtyxUvP9Cv4o1vWE8ReDRo+piCz1eR1khkgVlYeVvVj0bj0BGeKLG81nQ/iemh3etXG p6de6a92puo4w8MiOAcFFUbSD0xT/FWk6pJ4p8Hy6Xo01xYaVO7zyRyxKEUpsUAM4JxnP0HenX+n avN8WtN1VNHnfSodOktZLkTRYDuwP3S+7Ax6d+lZ6307v7rafiLXl13svvvr+AeFr6+8daPda3/a 13ZW088sVjFaBB5KIxUO25TuYkEkHK4wMdSc7SfFuu3vgvxXDdXEcOveHmnha6WAFZtiFkk2HgFs dOn54qz4Q0vWPAa6jon9k3OpaT9oe5064tJI8qr8mJ1d1IIPQ8g56inR+GNSsfCPiqRrVrrWdfae RreF0Ai3oUjTcxUHaMZOeucZod+V27fjp/wf6sXG3Or7c34a/wDAHeEbfxPrmk+HdfuvE7xxy2Ia 5s0tYyspZOH3YyGydx4xwAAOSczwvL4n8UeDtRnuPFNzb3dreXMMU1vbxKXMZIXeCpBXpwoX3Jrq vA9vqGl+BNLsdQ0ye3vLK1SF4TJExcqMfKVcjBx3IrG8C6drWi+ENXtr/RLmK7lvLm4igE0DGQSH KgESYB55yRTqX5nbaz++6t+pnG/u+qv9z/4BkQap4l174Rr4tHiCewv4bGSdIrWGLypGjzzJuQkl tpOAVAzjBxk9LJqmu6z4S8OajprQ2q3vkT6lMXVTBAU3OU3AjOcDp0PbqMTQ9D1+y+CE3h2fRJl1 YWc9ssAnhIYvu2ndvxjkZyc+xqrqGi+KY/CfgiKHQ5LpdIeIalpbXEQM2xAFYHcVYKwJAJ64OOK0 ly3lbuv1v8trhG/Ir72f5K3/AADZ0LxJLJ8T73w/BfS3ulvpaX0LyjOxt+07HwN6EEHOWGc4Paof Buf+FqfEPHXzbL/0Sajt7HxL/wALag8Rz+Hyllc6SLJtt3GxtyJd+ZOnOOy7h0564t+E9M1iy+IH i/VL3R7i3s9Te3a2kaWFtwjQqchXJGeMfXnFNNW+T/8ASv8AIGvzX5CeHNR1jVfEfjbSL7VZWjsp IorWSGNI2hV4yx28Hnnqc9Ko/Bizuj8Pra7Oq3chle4Aim2MiN5rZf7oYk9Tlj1NX/Cem6za+OPF 17faPPa2eqSxPbTPNC2QiFDkK5IzwRx37Uz4Z2Ou+HPDR0LU9EkjNlLMVuFnjZbgM5Zdg3Z7/wAW 3+ghWt8l/wAH5j6O/wDN/mZ2jeN9UsfAXiPWNTn/ALQvLHVZrK3zGEDHescYwoHGWGe/XqaXVb7x VYXGm32iv4h1GX7Sq31peWSJDJCc7iuFBUjjGD9c85qad4J1rVvAPirQNS0+XTLm/wBRmvrSSSeN 1yXV0yY2YjlcHj862tC1n4iXkMOmal4VgsJlQRzaq9+jpwOXWJQSSewzjPUjpQrad9PyX63uLWzv 5/np/wAATX/F9tpnj8aNrupz6Rp81mj2M64SOWXc2/fIQcFRtwDheTnORXaaTHdRaXBHe3X2q4UE NcYUebycNhQAMjB4GK5/XLL+1573SNb8NS6ro5jR7edTEx37cMuCwdW7hh6nkVN8PtDvPDngyz0y +Z/MiaQojyB2ijLkohYcEqpAOOM9OKmO39d/6+Q3vc19btr270S8h028azvmibyJ1UNsfHGQQQRn rXmc/ibXL/4IJrtlq91ba9any58xIzSTLJseMpsOM9RgDGRnjNeuVwmj+DbrT/iBrF2xX+wZ5Fv4 IMDH2t1KSHr2AJ5HVx6UrPW39W/r8h3tZ9v6/r5l4ao2s6R4bOlalcxvqJSfzV2FzCq7pNwK49E4 AwXFVrHVtW/4W9f6JcX/AJunJpSXUUAiVQjmTaeep4Hc96PAPhC78Ly6lDcy77OGd4tLTg+VbM3m EZ65LNg/7gqK20/WV+MN1rT6NcLpUumJZrcmaH74fdnaH3bcE9s8dK0VnJPvd/etP0+ZDT5Wu1l6 2e/9dDJ8L6fct8YvGWdZ1ErB9kfaWjIkUqzbDlOFGSAFx16k81B4J0zUNUvvG8VrrVxpkQ8QXB3W kaGRmwvUurDb04AB962bPTtc0b4p6/qMejvd6dq8Nv5dzHPGoiaNSpDhiG/IHt74d4C07WdHuvFU uo6LcwLfapLfW376Ft6MAAvyucNx3wPeo6L0/VG0mtbd1+TND4b61qOueEI5tVmWe+t7ia1lmVAo kMbld2BwMgCulvoWuLGeFJ5bdnjKiWEgOnHUZBGfwrkvhlpuraR4eurTV9MlsZ2vp51DyxyBlkbc OUZuRnBzXaMNykeoxSd3BX3svvsZx0fzf5njfga1nHwK1S8l1K8nEtlfjyJShRTukywO3cSe+WPU 05B4g0r4LaZ4i0/xDPby2GmwTR2aQxmB0AXIfKlySO4YD0FXfDej+JtK+GOq+Fbjw7M1ykV1DDMl zDsn8wttK5YEfe53Y49TwLlzouvSfA5PDiaJcHVv7PSzNv58IwwABbdv27eM9c+1Oo3aTjvZW/E0 fLdLpeX6F74h69q2m+AYda0m9+xys9vuAiVyVkZQRls4+96Vp6lq11feMbXwxY3TWgWzN9eXEaqZ Nm8Kka7gQNx3ZODgDjBORheN9K17WfhjaaTYaHcSagfsxeFp4V8vy2Vjli+D93jGetN8UaR4mtPF mm+NvDemC7uVszZX2lzzpG7xbtww2SoIPoT0HXmrfKn5Xf5afK5hHmaXey/PX52Om0ex8Q2HiPUk u78XmhPGjWfnEGaKT+JSQBle+Tk9K4H4dayt3oM/hfTL9bfWHvLud5ARm3i84jeFI+dueB07nsD3 uh6j4kvrSTUNY0MaaVQiLTYbpJ5ZDn7zP8qj2Ge5yegHnlj4H8QDwYtxHpc+neKtLv573Tn82BhK JHLGIkORtYHByR07987e/rtb9V/Xe3mXutO/6P8Ar18ju/E2sT+FtF0+3inuby9vruOzjmkRXky2 Sz7VABIVWIAGM47Vhx3viax8YaR/Z/8Ab2o6Rcs0WopqNsiiHptkRlVcc5yOnsKl8VaT4j8Y+DLK 4i01tJ8R6bdRXsEE08bo8qZyAyMRtOTjOO2cVf0TWPGutSxwan4Xj0GJCDPdNfJOXweVjRRxn1J4 B7mrW/nf8Lf8P5/gJ7eVvx/q3l+JQt7nxBqXxG8R6D/b80FhBbW80TRQRebFvzkISpHJHVgxx0x1 qjpvjPVdD8L+Kzqlx/ad3ouomztZnQK028qIt4UAZBcZwK2NHsNYg+KfiDVbjR549OvLWCGC486E hjGDnKh9wznjjtziue/4QvWte0rx1pt7p0umnVr77XYTyTxMpK7dm7YzEcoM8dDWcbpK+1n+f52L e+ndfl+V9y1qt94qsLjTb7RX8Q6jL9pVb60vLJEhkhOdxXCgqRxjB+uec9CNXude8c3+hWt3Ja2e kwRPdtCB5k0soJVMkHaoUZOOSSOQAQc3QtZ+Il5DDpmpeFYLCZUEc2qvfo6cDl1iUEknsM4z1I6V Fc6R4g8L/Ea98Q6Tpb6vpmrwRR3sMUyJNFJGNquN5UMCPfuemBWml0umv9en6kLZ99Pz/Mb4Vt7m 1+MfiuK5u3usWNqY5JFAfZ82A2AASDkZx0xnmvR64Dw/p/iQfE3WNcvdGitNOvrSCNGe7VpEKA8F VBy3Jzzgdi1d/UxTUVfz/NjfxP5fkjkNKvbvxdqGsSR6hcWenWN29hDHbbQ0joBvkZiCeGJAUYHB JzkYzL/XvE3hPwVbRazc2lzr13qK6faXIT5GDvhJHUADIXJwPQVmw2PjPwH4l1r+w/D0ev6Lq101 6ii9S3e3lf74JbqPw7DkdK2/FnhbWPF3g1Y55be01uK4S+tEVi0dvIn3U3YBbjOWx1OQMACjon6X /C/67dPkPq162/T9P6uReNptc8G+HR4i0/Vru+NgyNe2t0qFLqMkKx+VRsYZzlcD1Bqtda1r2o/E u00ew1j7Ppd9ohvUxbpviJfAYZB3NjGAeOScHGKva5FrvjLwqdCl0W50qa9Cx3s80sLxwoCC+wq5 LkgELwPfFVzo+q23xZsNUttDuW0e20g6d54mhGDv3AhS+7bgAdM+1PW9n3/R/htb5hpy/L9V+l7m Drui6xa+OPAFlqHinULu6ZrtXuY444xlUJDBCrDdtbaSc8DjFdVrevXEfiS38MwTaliKxF1dXNnC sk7ZbYq/dIXOGJIA7Yx2i8b6Xq7+KvCmvaXpr6jHpk8wuIIpUSTbIgUMN5AOPr6fUUvFGleKbHxf pvjPw9pkd9cfYzZX+mNcqhaPcWUq54yCefwwDzTumlfu/wDgBLy3t+uv4FzwhdeJG1/WNL1JdTl0 cIsmn6hewrHMMgBkJAAOCeCRng9axbXVPE9l4p1DwXqOqXct9eSLPpeqFYlC2vV+NuC64Ixg5JBw BzXY6NqHiW/ie/1bRBpiRoRFp0d0k80zepb5UX0Az3ySMVyviPwjq+u6AfECWUtv4yguBc2Sq8W6 AKcLDu37ShXJPzckk47Uno1f5/13/TzFunb+v6/PyPSreIwW6RNNJMVGDJJjc3ucAD8hUlcrqGr+ Kx4He/sPDqp4hVVH2C5njKbsjcQyvgqBkj5geK2tC1CXVtA0/UZrc20tzbpK8Oc7CwBIz3+tNrcS 2RxXxdExsfDAt2RZj4gtfLaRSVDfNgkAjI/EVW8SXnibwJq2k6vNr8+raRd3iWl9bT28a+Vv6OhR QQB6c9sk5rc+IuialrOn6PJpdsLqXTtVgvXgEio0iITkKWIXPPcijWdPvfGjabZ3GmXWnadbXcd5 cm6MRabZkrEoRm/iwWJwMDjOeFHp6/hp/wAEueq+X43dv0I/7W1KL4yLov22RtMl0c3f2dlXCyCX bkHG7p2zVfSb/VL3x54z0CbVro2drb2xtWURh4DIhLFW2889M5o8Q6drmnfErT/FGmaQ+q2p097C 4hhmjjkiO/eGG8gEdutO8O6Nr1v8RvEWsX+nxQ2ep29uqOtwGKMiEFcdT15PA9M9andJev62G3FN /L9L/qUvgpazjwFaXkupXk4lknHkSlCinzmywO3cSe+WPU16TXB/C7Ttc8P+HhoGq6Q1uLSaYi78 9GSZWcspQKS3f+ID8+B3laSd9UQ7czt3Z5tr+qa5pfhvxHf6lqrWerQGefTbW0ZJAsCfcZkCnKtj 5i2cZ6r2b4q8Va6ng/whrOmXUdrLql3ZRzxGMMGEq5K7jkqO3AzWbYaN4yHhLxV4fu/D6yahqDXR OqyXkYS5DgheAS2cYABAAAGSKdqej+J77wB4P06Pw1dfbNKu7SWeP7TAPkgXBOTIBk9h+eKStyr/ ALd/4P8AwR21d/73/A/4Bu6vea54QEklzrk2qPrF9Da2UX2NQbQkMZCoX7/yg7Qe4Gc8mq0d74ms fGGkf2f/AG9qOkXLNFqKajbIoh6bZEZVXHOcjp7CtT4heHtT8S+GrWbSP9H1jTrqO/tI5mABkTPy sQSOQT3xnHNGiax411qWODU/C8egxIQZ7pr5Jy+DysaKOM+pPAPc0Revn+lv+H8/wFLb5fj/AFby /E7OuY1STUl8Tj7berY+HVtQI3jmVJJrpmxt6buAOAMZJ79B09efXlt4msPipcaqmhtq2mzWSQWc q3Mcf2Ns/OCGOQGPJIBOAMZxip6r+ugdH/XVf16Fn4f+Ib3xB4c1kX00k8mn6jc2STSxhJJETBUu oAw2GAPA6dKw/AF7f6b+z3Be6XB519BaXMkEYXdlhI+OO/071d8D6b4h0CDxVbahoUhNzqNxfQPB cRss3mYwqbmU9urbe34TeB9O8R+GvhRb6c+kOus2SuUtmniImJlLYDBiBkHGTjFXJrklbtH8nf8A ENmvVlvwdrtl4mFnqGieI576FYyt/aXBTzFcjh2XaCpyCMLhTk46U7RtUuvGGu68I9QntNN0u6Nh FFbbVaSRQC8jMQTwTgAYHBznIxn/APCJJc/EHRvEmmaHPo08fmtqcjNGizKyEBCqMQz7iDuHGAck nFTaVo2q+DvGWtz2uny6hoesyi7/ANHdBJaz/wAQZXYZVs5yucYxjvU7v7/v/q9v+GC1lp5fd/Vi fwhr2qf8JXr3hPWZ/tc+m7J7a9KBGngfkbwoC7l4GQBn04rt65Xw7od2vifWPE+oxG3uNQWKCC1Z lZoIUHRipI3MxJOCQOOTXVUdFff+v6fmHV9v6/UKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKz9c1WLQ9Cv9TlwV tLeSbaTjdtUnH6VznhyLWNZtPDviD/hIZQJrVZ7+yMaNHL5iZAXum0kYPOQOcnJoSv8A13v/AJAz s64KfwHr1nqd7P4a8bXOk2l7cNdTWstjFdASMcsUZ+VB9Oa7mO4glkeOOaN3T7yqwJX6jtTvMQPs 3ru9M80dbh0sUNK0p9PQvc39zqF24AkubjaCQOgCoFVRz2H1zWjTRIhYqHUsOwPNI80Uas0kiIq/ eLMAB9aAH0UgIYAggg9CK5H4karfaX4I1W90fVPsd/ZxCUFESQ4yOCrA4BB60pSUVdjirux19FVt OkeXTLSSRizvCjMT3JUVT8QWOo3+mrFpmrHS51mSRrgRCTKA5ZcH1Heqas7Ep3VzVopCwVcswA9T TRLG0rRLIhkUZZQwyPqKQx9FFZMniPTY/EkWgfaEOoPbtctGCPkQEDLemSePoaPIDWoriG1fVIvj LForag76XLo7XYtjGgCSCQLncBuPHqa7SOWOZN8UiOucZVsihaq42rOz/rS4+io5LiCKRI5Jo0dz hFZgC30HepKBBRSEgAknAHUmm+dF5gj8xN7DcF3DJHrigB9FMkljhjMkrqiDqzHAH40qSJKgeN1d DyGU5BoAdRTHmijBLyIoBAO5gOT0pS6qSCwBAzye3rQA6ioxPCYfOEsZixneGG386ejq6B0YMpGQ Qcg0ALRUSXMEkrRJNG0ifeRWBK/UVxeuarq1h8VfC2mxajJ/Zuox3TTWpjTGY48qQ23d1Pr2ppXd v62uD0TZ3NFFRzTw26b5pUiTpudgo/WkBJRSKwZQykEEZBHemGeFWRTLGGf7gLD5vp60ASUUhYKM sQB6mmrNE0rRLIhkXlkDDI+ooAfRUc1xDbqGnmjiBOAXYLk+nNSUAFFRRXEFxu8maOTacNsYHB98 U95Y4wS7qoUZJY4wPWgB1FZNxZahL4isb+HV/K05IXSWx8oETueVbfnIxWqWUEAkAnoM9aAI7hZn t5Ft5I45iMI8iF1U+pUEZ/MVzngXwrd+DdBXSJtThvoUkeSN1tTCw3MWIPzsDyT6V0scscy7opFd c4ypyKRriFZlhaaMSsMqhYbj9BQtHcL6WMXxdoOoeIdHS00zXrrRblJllFzbruJA/hIyMg+me3fp V3RNIj0TTEs0nmuHyXluJ23STSMcs7H1J/ADAHArRooWgPUKKjFxAZzAJo/OAyY9w3Y+lPZlRSzE BQMkk8CgBaKZFNFPGJIZEkQ9GRgQfxFI1xCkywtNGsr/AHULAMfoKAJKKytWsdRurzTJrPVzYwW9 xvuYvKD/AGlMY2ZP3ee9ahYLjJAycDNAC0UyOaKUsI5EcocMFYHB9DWbpdjqNpqWqS3urm8guJRJ bW5iCfZUxjbkfeye5oA1aKydI8R6brtxfxafcJMLK4NtI6kEFwoJA9cZx9Qa0ftMHn+R50fnYz5e 4bsfTrQBLXIXvgq81Hxg+q3PiXUG0hxGW0YcRF0wRzn7uRkrjnuSOKq+P9V1bR9T8Ktp+oyQQXus QWdzAI0IkRsk8ldw6Y4Peu5ppac3n/l/mD7BRXEfFbVdV0LwRcarpGoyWdxBLEp2xo4cNIqkHcpx 17V2yn5AT6UW0v8AL8v8we9haKjhuIbgMYZo5ApwdjA4P4VxXhnWNTf4heL9M1HU2uLLTxbNbiRE QRB1LMMqBn6n0pJXY7aXO5opqOsiB0YMpGQynINNNxAJxAZoxMRkRlhuI+lAiSimTTRW8ZkmlSKM dWdgoH4mnKyuoZWBB6EGgBaKKwPGbahB4S1O70zUpbC7tbaSeOSOONwSqk4IdSMHHbBpN2HFOTsj forz74beOL3W1uPD/iRBb+J9O/18ZAXz07SKBx3Gcccgjg1rGDWf+FgrCPEN1/ZZszcGz8iD7+8L jf5e7bjtnOe9U9Gl3/yv+hN9G+3/AAx1dFRy3EEBUTTRxlzhQ7AZPtmo729tdNsZr29nSC2gQvJK 5wqqOpNJu2oyxRVHR9Vttb0i11K0J8m5iWVAeoVhkZHY4q0txA8zQrNG0qjLIGBYfUUNWdmBJRRW VqXiLTtL1XTdMuJ1F5qDskEWecKpYsfQDGM+pFAGrRXDnWNUj+MkWjHUGk0qXR2uxbmNAFkEgXO4 DcePU12scscyb4pEdc4yrZFC1VxtWdn/AFpcfRUT3MEcqRPNGsj/AHUZgC30FZ+ueIdO8PQ2z386 o11cJbQJn5pHdgAAPbOT6AUCNWikVgwypBHqDQWCjLEAepoAWis7WILnUdIvbPTNSFjeshRLlVEh hb121bs0kjsoEmn+0SrGoebaB5hxy2BwM9aAJqKjjuIZXdI5o3dDh1VgSv19K4xtX1SH4yxaK2oO +lyaO12LYxoAkgkC53Abjx6mjrYdtG+3/DHb0UyOWOZN8UiOucZVgRWZpHiPTdduL+LT7hJhZXBt pHUgguFBIHrjOPqDQI1qKKjluIICommjjLnCh2AyfbNAElFFRmeEFAZUBfhAWHzfT1oAkoorK1zx Dp3h6G2e/nVGurhLaBM/NI7sAAB7ZyfQCgDVorh/iLrGp6RD4fuNL1FoEudYt7SdFRGEkbk5GSCR 07EV2qzRNK0ayIZF+8oYZH1FC1G1a39f1sPoqOa4ht1DTzRxAnALsFyfTmpKBBRUVwkkttLHDL5M rIVSTbnYSODjviqmhWt5Y6La2uoaj/aN3Eu2W78sJ5hz1wOnp+FAGhRUbTwrs3SoN5wuWHzH0HrS tNEkixtIgdvuqWGT9BQA+imySJFGZJHVEHVmOAPxojkjmjEkTq6N0ZTkH8aAHUVG1xCsywtNGJWG VQsNx+gqQkAZPAoAKKjhuIbhC8Escqg43IwYZ/CkNzAs4gM0YmI3CMsN2PXFAEtFFZXiCx1G/wBN WLTNWOlzrMkjXAiEmUByy4PqO9AGrXBT+A9es9TvZ/DXja50m0vbhrqa1lsYroCRjlijPyoPpzXd u6Rxl5HVUUZLMcACkiljmjEkUiSIwyGQ5BH1o63DpY52+8LXl14dvbFPEeox6pcxhP7V+USpg5G1 UCqo68LgnPJzzVrwz4ffQNPaO51G41O/mIa5vbj70rAYGB/CoA4H16kk1su6RozuwVFGWZjgAepr I0nXbPxRo0l5pN3iJ2kjinQAn5WK7gDkYyOM9qV7XYW2NmiuJ+GOv3us+ArPUdavhNdyzzI0zhU3 bZGUDAAHQDoK7YkAZPSmDVm0woqOG4huELwSxyqDjcjBhn8KkoAKKa0iICXdVwMnJxgetCSJKgeN 1dDyGU5BoAdRUX2mDz/I86PzsZ8vcN2Pp1rP1LxFp2l6rpumXE6i81B2SCLPOFUsWPoBjGfUigDV orKuLLUJfEdlfRav5WnRwuktj5QInc/dbdnIx6VpSzRQRmSaRI0HVnYAD8TQA+ikVldQysGUjIIO QaYZ4QUBlQF+EBYfN9PWgCSisrXPEOneHobZ7+dUa6uEtoEz80juwAAHtnJ9AKzvHb6t/wAInPLo WsQaZeK6FbiRN4YZxsAw2WY4AABJOAOtJvS40rux01FUbG5ni0S0n1doYLryENz8wCLIVG4Z+uau qyuoZWBB6EGqas7Ep3VxaKKZ50RdUEibmBKjcMkD0pDH0VlaBY6jp9nNDqernU5zcO6ymIR7EJyq YHoO9aIuIWnMAmjMoGTGGG4D6UASUVxHxH1bVdFi8Pz6bqD2y3GsW9pPGI0YSRuTkZIJHTtiu0Wa JpWjWRDIv3lDDI+ooWo2rf1/XYfRWTJ4j02PxJFoH2hDqD27XLRgj5EBAy3pknj6GtajzEFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAQ3 dpBfWsltcxiSCQYdD0Yeh9Qe46EcGpQAAAAAB0ApaKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKAOU+JOn2V98Pdca7tILg29lNNCZow/luI2wy5HDD1H NcHeiz8O/CfwqdNs7ewk1x9PtL+7tY1ikaN1Bcs6gHJGRk/3jXqHijR7jxB4cvtIt7yO0+2QtA8r wGXarDBwAy84PrWP/wAIJHffD+LwlrN4t1FDCkUVzbwmB02Y2MAWb5hgex545o6P1X4XuPt8/wDg GR8TtAs9N8Cy6votvBp2paIFnsri3QRtGAQGTgcqVyNp4PFQeIJBd/EP4a37wrHPcJcs/HIzADjP sSa6C48K6rrOkwaR4h1e2vbBGQ3Hk2ZikughBUOTIwAJA3YHPbbUni3wlPr1zpGoabqQ03U9JmaS 3laASoVYbWRlyOCMd+KadnffX/h/68vMF+jX+X3fqYU1vDH+0DbFIkUy+HnMhVQNx87GT61R8FeF 9F1LV/GttfadDdWsetP5dvMu+NCUUlgp4Dc9eo7YrpLfwXfDxlaeJ7rXmmvIrJrSVFtVVJAW3cDJ 2qD25P8AtVL4b8K6j4fvdeuX1e2uTqt010B9iKeU5GMf6w7lAA44PHWs1H8n+dxylfby/BW/M4PS tfvPDP7P+oXdpNIZrKee0tpGO4xqZzGp/wCAg8fStr4i+GtItvg7qMcNpGGtrZZY5wB5hfIJct1J bncT1yc1raL8PBaeCNQ8K6xqKajaXjyOZY7byHUu24n7zDIbkf1qjP8AD7xFfeDp/C194vSWwMQh ilXTwJ9oI2h23kMABjgAn165JJuDXWy1+X+YJpST6Xf56HdaV/yB7L/r3j/9BFcN8aLaKXwA87qT JFd2xQ7jxmVQeOnQ13GlWUmnaXbWct3JdvDGEM0iqrPgY6KABWJ468LXXjHQDpMGpx2EbSpI8jWp mY7GDAD51A5A9a1bXOn5p/iZxT5OXy/Qwfi5Zwz6FokrriaPWbQJIpwyZfBwag8Q6HpugfEXwXe6 TaR2dxc3U9vcvEMG4UxknzD1c5GcnJzXQ+LfCmo+KNK0+zGr29pJa3UV08n2IuJGjOVAXzBtGevJ PvS+IPCuo63rmganHq9vb/2TK03lGyLiZmG08+YNoxnHXr3rNJp/P8NP+CV0+X46nVV5heaNpd38 fUW502zmV9CMziSBWDSCbAc5HLY4z1r0+uT1fwje3Xjey8T6ZqyWc8Vo1nPFLbeaskRbdx8w2tnv yOnHUGlpJP1/Jjv7rX9bo5/V9OtdU+O1pa3sQlt28PPviY/LIPO+6w/iX2PBqTwxZWnh/wCKninT 9Mt0tbBrC2uvssQ2xrJ8wJVRwMgdq3X8JXzfEKDxSNWh2xWRsvsrWZJZC24neJB82f8AZx7U6x8L X1p481DxJJqsEsV5bpbm0FmVKqnK4fzDk8nPy9+1TZ6W8/1t+gTd23/h/C1/1OJ0LRbr4g/D97+5 stDubjVklLXlwrNNExZguDt42dFAPAH1r0jwxYX+l+G7HT9TvVvby2iEUlwufnx0JzznGM5rjrT4 b69oOo3a+GPGcml6PdTmdrFrCOfyi3UIzHgenHHfNd9p1jHptlHbRvJJtyWklbc8jE5LMfUkk1d9 NNPIl3vrrucj8X7WG5+F2uGaMOYoQ6E/wsGGCK5/xtoOm6XonhrXbO2WLVo9Tss3o/10gYhWDv1Y EHGCcdhxxXc+M/D0/ivwveaJDfpZC7UI8zQeaQuQSANy8nHWs7xJ4P1PxD4e0vTP7atreSynhneY WJYStH90bfN+UZwTyahJqV13j+D1Keqt5P8AFaGdYXMev/GDXbLUY1li0W1gFlBKAVVpAS8oU/xc hc9hx3NV7e1Phz4zrpumqIdJ1rTpLie0i+VEnQ4MgUfdJGAcYyTz2rf1HwjcSeJrbxNpeoR2erpB 9mud8BkhuouuGTcCpB5BDcdDmrdh4deLXbnXr+5iudVlgFtE6QlI4IgSwVVLEnJOSSeccYpx0t5X /X/gMUtb262/S/6/eef+DPB2h+IpfGllq1o11axeIJhFE8rARkBfmUg5B7Zz047nOhaaFpl18afE FtPZxy20ul28stu/McrbiMunRuPUH1610nhLwnf+Gr7WZ59Wt7yPVLx710SzMRjduoB8xuOB2/Gn Wfha+tvHt94mfVLeRLu2S2+yizKlFU5B3+YcnOc/LQk/d8v/AJG352CprzcvV/hzX/I5Twd4b0iX xh410aawgm0m0vIXt7CVA9vE0kYZiIz8uc4xxx2xk0ngiOOPwT420o6kdLsLPVL63guAcCziwDle mACSetdVoPhTUNG8Ta9rEmr29wNXdHaEWRTyii7VwfMOeMZ9cdqyYfhpOfDXibRbzW0mi1y5kuzJ FaGNoZGIP/PQ7lBUccd+ab+Fr+7b56f5Mcd9f5r/AC1/zOf1947CD4dy6TYtb20WrW1rDeSIsUs8 ToQ2EAyFccndtOQPl71v+Kv+SyeA/wDrlf8A/ooU3UPh94g1XSdHhvvFiSXuk3cNzbSDT1EZMYwC 67tzN7hgPbvWtqXhHUdR8YaFr51mBTpMciLC1kSZTIu1yWEgx0GMDj3rVSV/m/xVvzIt7tvJfmPu vG8lrqsliPCPiaYJL5f2mKzQwtzjcDvzt75xUN/DZD4iLcGWXUdQOnCKLTFjUrboX+aZmY4QNwPU hTgNjA7GuMu/BmqJ46ufEmkeIBYrfQRwXdvJaCbds6MhLDacdMgjOTg5xWS3X9dCnqn/AF1OQ8Mq 118LfHtlcoqxWV/qUcMEbnZCFXcFUgD5QxOOB9Kn07wFoWu/BzT7i4s0/tI6SksV8STNE6puXa2c hQf4RxWoPB934T8J+Ns6x9ss7+K7vFje3CyLI8Z3FnBweg6Ad/wPB+jazqvww0bT31iCPTbrT4lf ZakXAiZBlA+/aDzjdt4HYnmlP3k+XR2j99mXolFvvL9Dmdfmm134YfD7UdQaU3s2p2SPNvKsQSwL fU4BzWv8QfC+m+GRo/ifw/aJY6nbalDG7QAg3KSPtZX/ALxOepyevrVv4qWMVn4a8LWFiBbRRa5Z RQ7FB8sDcFwDxxxXTy+Hb/Vb6wm16/tbm3sZRcRW9tatErzDIV33O2QM5CjHPOTVXvJyX81/loQ4 2gov+X9WcnosVx4zuvEd1c6fouoCPUp7ALfqWaGOPChFG04Bxu46kk+mJB4Xl0D4Tz6B4i8SCG3h kUC6hVnIhLgiHaeWzygHOQQMHpVq9+HmsWvie/1nwp4qfRRqTB7y2eyS4jZ+7qGIwT1+uee1Xtf8 Aya34VXTf7buV1NbqO9/tKSMMzTJ0JQYULjgKMAcde8rSKt5X/z/ADf9MHv97/y/y/4ZGHqE8sHx e8FtBpw02K7tbyB1O0SSxqgZQ6rwACAQMk8nhTVfTvDOla18VvHWnajbtcWL29mXt3kbaxKE5POc g5I54Jz1wRuXngbXb/XNB1y48UI2paWZQSLBREySKFYKu7IbHcswzzgdK0NJ8J6hpvjfV/ET6vbT JqaRJJbCyKFRGMLh/MPOM54/Kqdny+V/xv8A5jWifnb8Gv0Rz2o6RZaP8XPA9vYxGOKOwuoVUuzY RI1CjknoKdrOj2N58b9OSeANHcaLN56ZIWYCQYDj+Ic8g8HoeK6DVvCeoal430jxFHq9vCmmpJGl s1kX3iQYbL+YPbGB270668LX0/xAtPE66rAkVvataC0NmSSrHcTv8wc5x/DjjpSd3yt9L3+d/wDM nbmt1t+Fv8jC8I6fbaH8U/Fml6ZClrYNa2tyttENsaSEMCVUcDOBnFckp0nUfhv4h0PxCtuPGPn3 LNBKB9rmuMlomjH3nGNgG3jaMdK9BGiT+HvFOu+M7/WIWsprQCa3WyIMUcQJBD7zk9c/LzntXN6F oHj640u31LSvHduLK7zcxwXGnRSEByWAeRfvNgjJB68DgVMU3Gz7fdrp+CLvZ3XdfgtfzO78JaSd F8LadaSQrFciCNrlVYkebtG85PuKzfibrd34d+HesalYMyXSRBI3Xqhdgu4e43ZrV8M3eo3mio+q PZyXaO8bzWZPky7WIDLnn6+4NXNV0y01rSrrTL6PzLW6jMUqZxkH37Gqq3nfzJpWja/Q5HWvB+j3 Xw2kgs7aGOaCzNzaXicSpMF3CXzB824nktnJyfWue8Ka/L401vw5p+uYmSPQV1F4JFytxOZNgkYd DgKWAxjLZ7CuotvCGsxeGf8AhGZtfim0ryzbeb9kIufs+CPL379u7bxv29O2eai8SfDmPUZNIvfD +pPoWqaTF5FrcRRCRfKxjYyk8j6+pyDTb95vpf8AR6/ivu9BJPlS6/8ADf5P7yTU9HtfBo8UeL9O Z0ll08yPZrgQmSNSQ+0dzgA/j61V03wfpHij4aWsFz+8uNTtYrqbUVUee8xAbzNx5yD0HYDHStrR vDV7FYTp4l1l9euriJoHke3SGNYm6oqJwM9yeTgemK5zRfhz4h0B10+w8dXkfh1HJWy+xxtKqH+A THJX6gD2APNK28f66/8AA+70H/eX9bf18xnxBsIrfVPATl5JZotZgh82RyWYBDye2TjOab4+063u /iZ4DEgkHnzXSSFJGUsoiBxweO/T1Nb/AIn8HXWuz6EbLU4LCDSLpLqKJrMy72QYCk+YuBjI6Z96 wvH8V1L8QPAEdtcpBdefd7ZTFvUMIh1XPIPpkHB6g80dv8X+Q4R1f+F/gmVtb8OWHhD4i+E9Q8O2 qWC6jO9je21sNkcyFCwYqOMrgnP09KveBLWG3+IHxAt40/dfarb5WO7OYsnr9TXSw+Hrq51231jW LyC5uLNHSziggMccJcAM5BZizEDGcjAJGO9VPD3hS/0XxRrmsz6tBdLq8iSPAlmY/LKLtXDeY2eM ZyO3aiOl0+z/ADWn4N/MiSu7ry/XX8V9xzPwv0jS4rvxfcJY2kM1vrlzFDOsKBoU2jhTj5QMnjpX P+KRBpvwb83Rt18LK7inTXJkEbTTGcbpI+rMckjccAg5DNXd6f4DurC/8RRrrWdH1uaWeW2FtiVH kXa2JN2Mf8Bz05HOcd/hdrN14Bfwle+LPNs41RbUx2CoVCuGUSZYlgMAAAr7k04Ss4t9OX8NzSdn J9m3+Oxa+KBzceByf+hjtf5NXolcZr3g7Vtfi8P+frtskulXkd67/YCfOkTOBgSDauCRjk+9dku4 KN5BbHJAwCfpTuuW3m/yX+RC/T/M8/8AjX/yS3Uf+u1v/wCjkpfHl8ZfEvg7w5P/AMg7VLqQ3ak4 WZY0ysbeqliMjvjHetnx34VufGXhyTRotSjsYpXR5JGtjMx2sGAHzrjkD1p/iXwkvifSbSG4vDb6 lZTLc2l9bx48qZejbCTlfVSefWlf3UvO/wCX+Q3v8mjmPiTYp4ek0PxPosMdpf22oQ20vkKE+0W7 nBjYAfMM4xnp1FV9L0DTNe+LnjePVbVbuCNLIiCUkxkmM8lehI7Z6ZOK62Xw3e6xd6dL4gvbW6i0 +VbmKC1tmiV51BCu2524GchR35JPSoR4U1Cw8Yan4g0jU7aP+00iW5t7u1aUAxqVUoyuuOvIIP1F KOm/n+n6p/eO+j9F+f8AkcTpck/w+1jx/pemZOlWOnDVLGBiWW3coxK89iQTjPQfWrD+CL3xX4Fs vs8eixXlxFDdR6woc3PmfK3mFwoO4455/lXeaf4XtbeLVHvit7easMX8xj2CQbdgRVydqBeAMnqc kk5rltE+HfiXQFXTbDx5dR6AjHy7X7DG0yITnYJWzjr1A+gFC7PfTX0v/wAD1sJ73X3fd/wfvDxi fEOjXmgeIE0xfEFvYW7w39tDy6u20GeNcHJG1h0yASOASRv+AtV8P6z4fe+8NkJZyzu72+wIYJGw WQqOhyc8cc8cVbk0bULbUrS40m/gt7SG2+zyWk8DSCQA5Vg4cFSOecHOTWX/AMIXeQeHtetdP1j7 DqusXLXMl7bQbFic7RhF3ZHyrjO7OST7AT3b8/z/AF38tgSWi9Py/r1OxrE8Y/8AIk67/wBg+f8A 9FtWpZxSwWMEM85uJo41WSYrtMjAYLY7ZPNZ3ifSb3XNAu9Lsr+KyN1E0Mk0luZiFYYO0B1wcHqc /Sh6bFU5WkpM5Tx94Mu9Ut7HxN4dxD4n0pA9u44+0IBzE3rkE4z6kd6PBXjS08Z6umoQoYbuHTWj vLRvvwSiQZUj0OOP8c13GmQXltp8MN9cxXNwihWlihMStgddpZsfnWTp/hDT9K8Yah4ish5Uuowr HcxAfKzg53j0JHX16+uU173lr99mv1+/5mbV4een5o5n4aRWXi7wnd63q9pBeXmp3M63PnoJNqBy Ei56Kq4wvuT1NYWmRSL4E+Ifhu+xeWWiyXEdiZ/3nlx+WWRQT/d4x6Z9hXa6b4Qv/Dmo6lJ4e1O3 gsNQlNw1nd2rSiGY/eZGV1O08ZU+nBFXbTwfZWvhrU9I82R31TznvLrADySSghnx0HXgdgBSa92y 7W+en/B+81i0ppv+a/y1/wCB9xxH2mLwp8BLTU9Kht7G7uLG0SW5ijCNmQohckDJIDE5Oeavat8P 9Rv/AOyrjS4NB0i70+5W4jvLRHEjKM7kY7RkNnnOc/jWxpvgWb/hBZPCmvaomoWf2YWsbQ2whKIP uscs2WGBg8DgcHrVLQ/Aviewjh0/UvHFxfaLCojFqlkkUkiDojS5LY7HHJHcVrKV5uS73X9f1uZJ NRSO/ByAcg57ivM/HWl6fefFTwMLqwtZxO12k3mwq3mKsWVDZHIB5GelemABVCgAAcADtXL+K/Cd 1ruq6Hq2namlhf6TM7xtLb+dG6uu1lK7lPQevr9RC+JMuLsnfszmdT0mxvvjjY2E9ujWS+HWBtwM RsomwFKjgr/s9KteFNPtNC+LHifTNMgS1sJLK2ufs0Q2xrIdykqo4GQO1a6+ENR/4T228USa1DIY rI2TQGyILoW3E7hJgHP+z09etS2Pha+tPHmoeJZNVglivLdLc2q2ZUoqcqQ/mHnk5+Xv2pK9187/ AI2/QKjve3938LX/AFON1u1sk8D+OUss61I7XVxc6hOiqkMgGRGrcl2jAGMDAIxlTxVfxfFDq3gX 4c3uoQQ3NzPfaessssYZmDplwSexPUd63LD4aanZaDq/h0eKX/sS988xRLZr5sfmZypkLHK8nPAJ 9RVm7+H1/e+BtI0OXXUF9pM8E9pdpaYRfKGEBTdzx1OevtxVwaSV/wC7+G4dX/29+Ox3FraW1jbJ bWdvFbwJwkUKBFXvwBwK89+MFqk1h4ak3SJL/b1pGHRiCoJPQdM9Oo7V39jFdQ2iJe3K3Nz1eRIv LUn/AGVycD6kn3rn/GvhS88WQadDb6pDYrZXkd6C9oZizpnaP9YuBzz/ADqXuvVfmEba37P8jD8a eDdA0jwH4uu7CwEE93ZtLOyyv87orFWIJxnJJPqTk5qbV01aT4HKmhiU6gdJhEYhzvI2ruC453bd 2Mc+ldH4l0S88Q+FrzRlv4LaS7hMMs/2YuNp4O1d4wcepNc/4ijm8P8Aw6s/D8+siO+ufJ0u0vIo REC5wAWDMQBhTu55GQBkgUO7TXV2t+P+aEtJRl2v+n+Ry2rWXhjxfN4LfwxFYz3MF1ELmG1wGgtN hMiyheUxgAbu5wOta2paTZXvxwsNPuIRJZr4cZTASdjqJsBWH8S+x4Pepm0n4kaDbvdyeNdJvreB c+TeacsCEDtuj5GenetseFr+48e2vi46lDEFsfshsWtCSELbz8/mfez3249qbd382/vQJ2TXkl9z TMfwtptnovxW8UaTp1vHbadLY21wbWNQsSudykqo4GQOcVX+FWkaZFqPiy4j060Se3165ihkWBQ0 SYX5VOMgcngV01h4WvrTx5qHiWTVYJY7y3S3NqtmVKKnKkP5h5yTn5e/aovD/hC+8PeIdYurfWFb TNSu2vWtDbDzFkYDI8zdjbx0256cjnKhdLXs/wA9PwG9mvNflr+J1jttRmxnAzgV5z8NIrLxd4Tu 9b1e0gvLzU7mdbnz0Em1A5CRc9FVcYX3J6mvR65DTfCF/wCHNR1KTw9qdvBYahKbhrO7tWlEMx+8 yMrqdp4yp9OCKS3d+3+QdPn/AJnK+GoZP+EU8eeF75murDSZp4bMysWKQmMsqZ6/Lx9M+wrJvvDm lyfs922sNaqdVt9NhmgvTzNEVKkBH6qo9Bgde/NelQeERY+GNT0uxvBHeakZZLm9lh3l5Zc7n2gj 8BngAdazJ/Ad/N8MV8Gf23AoEK25vBYnJjBHGzzOuBjOfwqZKVnZ62X3q92OLV1fa7+5tWOwsJGm 061lc5d4UZj6kgV598XrGzuofCz3FrBKx162iLSRhiUYncvPY4GR0Nd/pttPZ6ZbW1zPHPNFGqNJ HGY1bAxnaWbH5msTxr4Vk8WaTa29vf8A2G7s7yK8t5zF5ih0JwCuRkcnvWra501tdfmTT0jZ9n+R zXxUsLW10HwxZWkKWtuPEFoqx26iMICW+7jp17UzxJoOl+HvHvgu+0iyhsri4vpbe4eBdpnRoyT5 hH3zkZycmtzxN4P1XxLp2kwTa7bxT2N7HfPL9gLLJImcAKJBtXnoST71P4i8K6jrusaDfx6tb2w0 mf7R5Zsi/muRg8+YMDGfXr1qNb/Nfdpf9Rzd42Xa3z1/zRzOixXHjO68R3Vzp+i6gI9SnsAt+pZo Y48KEUbTgHG7jqST6Y6nwD4f1Hwt4Xi0bUtQjvXt3byXTPyRHlV5545A9sViXvw81m18T3+s+FPF baKNSYPeWzWSXEbSDq6hjwT1+uee1dlo+ltpdmY5rua9upDvnupsBpXxjOAAFGAAABgCnHRfJf1/ Xf1FLf53/r+v0H6tbRXekXcEylo3iYEBiM8H0rgfh/awXvwHs7e5iWSJ7GcMrdD8z16FqEFxc6fP BazxwTSIVWSSIyKuRjO0MufzrmvDvhC/8PeA/wDhGU1e3nZInihumsiNoYk/MnmfNjd6jpUTTdOa W7S/X/Ma0lF9v+AcDb+HdJvP2d01K5so5r+DSnmhupPmlhZMldjHlACBwMD86s+N9IsY/g9beJRb xtr0UNncrqbKDceYWQk7+uOTx0HYCuug8DXsHwybwausQHNu1r9r+xH/AFbZz8nmfeweuce1JrXg a+1n4dQeEn1qCPZHFE92LIkusZUjCeZwTtGeT9BW11ztrun+LuRSTSipdtfw/wCCTeI0sn8TeHLq 6uZp7mJZmttIijVzcuVH7z5iAuwZ+Y4A3YyM4OR4Blnj+IPj6xMMdtDHcWsyW0TZRHeIliOBy2AT x19etaWr+C9XvPEek+INP1+Gz1KztmtZi1kZIpkJz9wyZXn3Pbnio7Lw1P4Q8Qa/4ru/EDT2F1Ak 17HJar5mYkIyGXAAxngLnpz6ymknfs/zT/Qdm7L0OFU6TqPw38Q6H4hW3HjHz7lmglA+1zXGS0TR j7zjGwDbxtGOlbt1b3OmP4I8Im3s1N9BLPfW9wzGK4mjiXKt1yNxLY6EqPxNC0Dx9caXb6lpXju3 Fld5uY4LjTopCA5LAPIv3mwRkg9eBwK1dQ8L33jvw3p8t9qUFlr2m3TPbanpyl49ynGQGIJBAGRn qOpHU20a7X+Wn9eiG3zO687fPX+vUl0nwZqOl+PR4ghbS9Ps5rb7Pd2VirKs7ZJVyMAbgSBnGcZ9 a5R9Xi8L6heeH/HWjlLO91B7mz8QJGJI3dnLoXOMqy4AHoABgKM133h/w5rtrcJc+JPE0msyxHME aWiW8cZxgsQvLNgnqcDPTPNVr3whqmqeGLjw7qOr2lxYzsVMn2IiVIt24IPnK7gOA+OMA7SaWt1/ XUNHe/8AWljsQQwBBBB5BFeefGi2il8APO6kyRXdsUO48ZlUHjp0NehRxrFEkaDCooUD2Fc1468L XXjHQDpMGpx2EbSpI8jWpmY7GDAD51A5A9aeikn5r8xbxa8mUfiJba0dO0vUtG09NUOm3YuZ9NZs faUCkcdcspIYDB5AIBIApfAHiHw/4j/tO90e3exvGkX+0LCWIRyRSgFcsB1yBjP+zzg5FbV/pmrz yabcWuqW0Fxas3n7rQtHcKwwV2+YCvIBBycEd6j0fw4bDxBquu3M0Ul9qCxRsIIvLRUjB29SSzcn LHHAAwMcpbv+uwW0Xc2Ly3hurOaC4hjmhkQq8cihlYehB4Ned/BTTbGH4d2d7FZWyXcjzo86xKJG UStgFsZI4HHtXpJAIIPQ1x/hDwfqnhLS59Li1yKexV5Hs0ezw0e9i3znf84BPbb/AEC6P0LuuW3n /mcR4E8KaJrfwalm1LT4rqbbeCOSYbmhw7/6sn7hyM8YyetSafq99qeh/DnRJWt7hNRs5ZZ47wkp cmJAFR+Dkc7sHqVH49j4b8Fah4c8D3HhuPWbeZn80RXJsSNnmEk5XzPm5Y45FZs/wukuPBui6R/b ph1PRHD2Gp29tsZPZkLnIPGeR0H0ItHrtp+t/uuhTfNJtf3vxtb+uhZ0nwZqOl+PR4ghbS9Ps5rb 7Pd2VirKs7ZJVyMAbgSBnGcZ9a7yuW8P+HNdtbhLnxJ4mk1mWI5gjS0S3jjOMFiF5ZsE9TgZ6Z5r qarokT1ueVaP4d0rU/i942tb60W5tVhsn+zzEvGzFCcspOG55GelWfB2nL4Y+Ininw7YSPDoxtYb 6GLcStq75DBc9AcE/gPSmaVDqUnxn8Ztp13bwMsFl5iXEBkVwYz6MpBHrkjk8dCOts/CcaWusG+u Tc3+sIUvLqNPL+XZsVUXJ2qoJwMk5JOTms1flTXZ/P8Arf5GlRJzt/h/JX/rzPMPFIg034N+bo26 +FldxTprkyCNppjON0kfVmOSRuOAQchmro/HGl6fe/FHwJ9qsLWf7QbpZvNhVvMCxAqGyOQCcjPS h/hdrN14Bfwle+LPNs41RbUx2CoVCuGUSZYlgMAAAr7k1ua34O1TVpvDuox65DFrOjyO/ntZ7opg 67WHlhwQMdPm/wARtdaev5pIhab9mjJ1uxtrT4z+DTbxLHus7tNq8KFVBtAHQAZ7UCS58S/ELxHZ PaaVexaSLeGG31BSwjDx7mdRtIyxJBPooH12b/whql74w0XxAdcgB0uKSMQtY583zBhiWEgx2xgc Y71U8ReAdQvPFX/CS+GvEL6JqckIguv9GWeOdR0JUnGR0zz0HSo0sk/P82/69RW1bXl+mhR07wZc +H/B3ivTtRmtJtOulnubWzh3bLUMrEouei5xjHeuYvvDmlyfs922sNaqdVt9NhmgvTzNEVKkBH6q o9Bgde/NekR+GNR/4R6/tbnXWu9WvoDDJqE9su1QQQAkSlQoGScZ5PUms6fwHfzfDFfBn9twKBCt ubwWJyYwRxs8zrgYzn8KiSl71nrZW+V/+AXFq6v3d/nYxfifa22oaR4MuLu2gmlk1izRmkjDEqwJ Zeex7joa9MtbO2sbZLa0tobeCP7kUSBFXvwBwK5bxH4NuvEHhfTNOOqxwahp1xDcw3aW3yGSPpmM seD6bv8ACumsYrqG0RL25W5ueryJF5ak/wCyuTgfUk+9atrVLu/0J6R9P1ZxHj99a0nW9I8R2Wjn W9Oso5Y7qyTmSPft/fIuDkgAjp0J6AkjY8Bar4f1nw+994bISzlnd3t9gQwSNgshUdDk544544rQ vdO1Vtch1Cw1GCKEQGGa1mty4l5yrBgw2kc9j1NR+G/Dq6B/aUzSpLdaldtd3DRReWgYgDCrk4GA Mkkkkk98CI7a+f5/rv5bA/6+7+kM8bwR3HgXXVkBIFhOwwxHIjYjpXNeAPB+iXXhTwrrlxaGXVId OQR3JkYMFaPBXggFQCQB2yT1JNdl4g02fWNAvtNt7mO2e7heAyyQmUKrAg/KGXnB9aq+GNEvfD3h e00eS/guntIVhinFsYxtUYG5d5ycY6EULRt+n6lNqyXr+hxXw7huofBHi6DSRtu01W/S1BPRxwnJ 98Vy97BoviP4V6TpdhHbt4wtpYQbUgC7W4EgWZpF++AfmZmPHc120GlyfDfwb4hudS1wTw3U8tyJ be0EMkUsxCgKWkYfeIxnGO5xVSw8M/EfSrCBofHmmzW0UIIjn01FjAAzy64JH+1nnrVJ2s+3L+BO yt5y/H+vzHfFHT7ez8O+F7C2QwwLr9oihGIKgluh696TxJoOl+HvHvgu+0iyhsri4vpbe4eBdpnR oyT5hH3zkZycmtbW/D2p+OPD+gySajaafPbXEN/JstmnSSVM42kup2HOeecY6Vd8ReFdR13WNBv4 9Wt7YaTP9o8s2RfzXIwefMGBjPr161Nnf/t78NL/AKhJpxSX8v46/wCaOavNG0u7+PqLc6bZzK+h GZxJArBpBNgOcjlscZ616fXJ6v4RvrrxtZeJ9M1eOzuIrRrKeOW281ZIy275fmG1s9+e3HUHQm0f VJPF1lqqa5KmmwWrQy6d5Y2zOT98nOM9O3GOOpql8KXr+bY5O8m15fkkblFFFIQUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBT1TS7TWdOm0++ WR7aZSkiRzPGWU8EEoQcH0zUejaJYeH9Oj0/TY5Y7WIbY45J5Jdg9AXYkD2rQooAxtf8K6R4nW3X VoJplt5BLEqXUsQVx0bCMOR2PUVrRRLDEsaFyqjALuXP4kkk/jT6KACiiigAooooAKKKKAGyRpNG 8ciK8bgqysMhgeoIrjIvhJ4Dh1H7cvhu1M24ttZ3aPP/AFzLbMe2MV2tFHW4eQ1ESNFRFCoowqqM AD0FOoooAKKKKACiiigArE1Twlo2tarZ6pfQ3D3lmc28iXk0flE9SArgDPfjnvW3RR5gIqhVCjOA McnJ/OloooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAqjq+jabr2nSafqtnFd2sn3o5VyM+o9D7jmr1FJq+4HK6F8NfB3hq8F5pWhW8VyDlZZ GeZkPqpcnb+GK6qiincAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACmyRpNG8ciK8bg qysMhgeoIp1FAHFRfCTwHDqP25fDdqZtxbazu0ef+uZbZj2xiuzREjRURQqKMKqjAA9BTqKOlg8w ooooAKKKKACiiigAooooAKKKKACiiigDDs/CGjafrtzrdtDcpqNzgTym9mbzAOgKlypA7DHFblFF HSweYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBBeWVrqNnLZ3tvFcW0y7ZIpVDKw9CDXK6Z8KfA+k X4vbPw9bCdTlTK7zBTnOQrsQD7gcV2NFC0d0G6sFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA 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 FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAf/9k= ------=_Part_280034_767240815.1305195807457-- ========================================================================= Date: Thu, 12 May 2011 11:51:43 +0100 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: Consult Comments: To: Syu-Jyun Peng <[log in to unmask]> In-Reply-To: <1627981024.1305195808016.JavaMail.nobody@sg2000-ap-1> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Chapter 41 (Using DARTEL) of the spm8 manual would be a good place to begin. You can find it in spm8/man/manual.pdf Best regards, -John 2011/5/12 Syu-JyunPeng <[log in to unmask]>: > > Dear Sir > > I'm a=A0EE PhD student in Taiwan. > > I have a question. > > I have 20 sets of control subject=A0SPGR MRI. > > How could I use SPM=A0 software to=A0obtain template=A0like as=A0the atta= ched file? > > Could you=A0give the=A0instructions step by step for me? > > It would be greatly appreciated, if you could look=A0 into this for us. > > Thank you. > > Best regards, > > Syu-Jyun Peng ========================================================================= Date: Thu, 12 May 2011 12:46:51 +0100 Reply-To: Charalampos Styliadis <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Charalampos Styliadis <[log in to unmask]> Subject: SPM ANOVA Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi to all I used the 2nd level analysis in SPM to do a factorial design for MEG dat= a with gender as the between participants factor and two factors A and B = with two levels (high-low) as the within participants factors.. I got F-= images and T-images with the later denoted as positive effect of gender o= r positive interaction of gender by factor A etc...Are the t-images the a= ctivations and the f-Images activations and deactivations? Thanks in advance for any reply Haris ========================================================================= Date: Thu, 12 May 2011 08:14:10 -0400 Reply-To: hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: hekmatyar <[log in to unmask]> Subject: Re: SPM resting state 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]> Hello SPM experts Is there anyway, I can do detrending and low pass filtering in the SPM for resting state functional connectivity measurements for my rat brain? Is there any procedure or method available in SPM? Please suggest me if you have come across. Thanks Regards Khan Hekmatyar PhD Bioimaging Research Center. University of Georgia, Athens, GA ========================================================================= Date: Thu, 12 May 2011 21:05:56 +0800 Reply-To: =?utf-8?B?5p6X5LqR?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?utf-8?B?5p6X5LqR?= <[log in to unmask]> Subject: Re: SPM resting state Comments: To: hekmatyar <[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: quoted-printable Message-ID: <[log in to unmask]> Dear Khan, There's a software package dedicated to the processing of resting state f= MRI=20 data (Rest, http://www.restfmri.net/forum/). The software is designed for human subjects, but I think it is feasible t= o=20 replace the MNI template by a rat brain template, which may do the trick. Best regards Yun Lin -------------------------------------------------------------------------= ------- Physician, Master Candidate Department of Radiology, Southwest Hospital 3rd Military Medical University, Chongqing, China Phone: +86 150 2312 2673 MSN: [log in to unmask] Skype: lin.yun83 -----=E5=8E=9F=E5=A7=8B=E9=82=AE=E4=BB=B6-----=20 From: hekmatyar Sent: Thursday, May 12, 2011 8:14 PM To: [log in to unmask] Subject: Re: [SPM] SPM resting state Hello SPM experts Is there anyway, I can do detrending and low pass filtering in the SPM fo= r resting state functional connectivity measurements for my rat brain? Is there any procedure or method available in SPM? Please suggest me if you have come across. Thanks Regards Khan Hekmatyar PhD Bioimaging Research Center. University of Georgia, Athens, GA ========================================================================= Date: Thu, 12 May 2011 14:42:07 +0100 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: question regarding matlabbatch struct/class Comments: To: Bas Neggers <[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]> What happens if you put SPM in your matlab path? Usually for me that's e= nough to make the warning disappear. ========================================================================= Date: Thu, 12 May 2011 16:02:22 +0200 Reply-To: "S.F.W. Neggers" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "S.F.W. Neggers" <[log in to unmask]> Subject: Re: question regarding matlabbatch struct/class 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]> Thanks, that kind of worked, it had to do with SPM indeed. I just updated my spm8 version to latest revision, and the warning disappeared. I have to admit I didnt do that in a while. Cheers, Bas Op 12-05-11 15:42, Stephen J. Fromm schreef: > What happens if you put SPM in your matlab path? Usually for me that's enough to make the warning disappear. > > -- -------------------------------------------------- Dr. S.F.W. Neggers Division of Brain Research Rudolf Magnus Institute for Neuroscience Utrecht University Medical Center Visiting : Heidelberglaan 100, 3584 CX Utrecht Room B.01.1.03 Mail : Huispost B01.206, P.O. Box 3508 GA Utrecht, the Netherlands Tel : +31 (0)88 7559609 Fax : +31 (0)88 7555443 E-mail : [log in to unmask] Web : http://www.neuromri.nl/people/bas-neggers -------------------------------------------------- ------------------------------------------------------------------------------ De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht ontvangt, wordt u verzocht de inhoud niet te gebruiken en de afzender direct te informeren door het bericht te retourneren. Het Universitair Medisch Centrum Utrecht is een publiekrechtelijke rechtspersoon in de zin van de W.H.W. (Wet Hoger Onderwijs en Wetenschappelijk Onderzoek) en staat geregistreerd bij de Kamer van Koophandel voor Midden-Nederland onder nr. 30244197. Denk s.v.p aan het milieu voor u deze e-mail afdrukt. ------------------------------------------------------------------------------ This message may contain confidential information and is intended exclusively for the addressee. If you receive this message unintentionally, please do not use the contents but notify the sender immediately by return e-mail. University Medical Center Utrecht is a legal person by public law and is registered at the Chamber of Commerce for Midden-Nederland under no. 30244197. Please consider the environment before printing this e-mail. ========================================================================= Date: Thu, 12 May 2011 11:31:14 -0400 Reply-To: Cornelia McCormick <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cornelia McCormick <[log in to unmask]> Subject: 2nd level resting state problem MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear SPM users, I am analyzing resting state data of a group of healthy individuals with a seed-based approach. In my regression model, I added the extracted timeseries of the seed voxel as well as a few other regressors (motion parameters, CSF, WM). The first level analysis (t-test) shows strong activation in the common default mode areas. However, when I set up the second level random effects model (all con files combined in a one-sample t-test), there is no activation left. No even the seed voxel lights up anymore. There is clearly some mistake in setting up the second level analysis but I don't know what. Does anybody have an idea? Any help would be aprreciated! Thanks a lot, Cornelia ========================================================================= Date: Thu, 12 May 2011 12:24:53 -0400 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: 2nd level resting state problem Comments: To: Cornelia McCormick <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016368e30e911927704a316a1ad Message-ID: <[log in to unmask]> --0016368e30e911927704a316a1ad Content-Type: text/plain; charset=ISO-8859-1 A couple of quick questions, then some thoughts. (1) Are the seeds in the same voxel in all subjects? (2) How big are the seeds? The analysis that you have done is not the typical analysis that others have reported. If you notice, almost all papers use Z-scores as the second level input; rather than the con_ images. To get the Z, you need to convert the t-statistic image to an r-value and then use the fisher Z-transform to get a Z-score. However, it is probably useful also to do the analysis with the con images, although there could be scaling issues. In the past, although not published, I converted to percent signal change to make all the voxels on the same magnitude scale. 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, May 12, 2011 at 11:31 AM, Cornelia McCormick < [log in to unmask]> wrote: > Dear SPM users, > > I am analyzing resting state data of a group of healthy individuals > with a seed-based approach. In my regression model, I added the > extracted timeseries of the seed voxel as well as a few other > regressors (motion parameters, CSF, WM). The first level analysis > (t-test) shows strong activation in the common default mode areas. > However, when I set up the second level random effects model (all con > files combined in a one-sample t-test), there is no activation left. > No even the seed voxel lights up anymore. There is clearly some > mistake in setting up the second level analysis but I don't know what. > Does anybody have an idea? Any help would be aprreciated! > > Thanks a lot, > Cornelia > --0016368e30e911927704a316a1ad Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable A couple of quick questions, then some thoughts.=A0<div>(1) Are the seeds i= n the same voxel in all subjects?</div><div>(2) How big are the seeds?</div= ><div><br></div><div>The analysis that you have done is not the typical ana= lysis that others have reported. If you notice, almost all papers use Z-sco= res as the second level input; rather than the con_ images. To get the Z, y= ou need to convert the t-statistic image to an r-value and then use the fis= her Z-transform to get a Z-score.</div> <div><br></div><div>However, it is probably useful also to do the analysis = with the con images, although there could be scaling issues. In the past, a= lthough not published, I converted to percent signal change to make all the= voxels on the same magnitude scale.<br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Thu, May 12, 2011 at 11:31 AM, Cornel= ia McCormick <span dir=3D"ltr"><<a href=3D"mailto:cornelia.mccormick@goo= glemail.com">[log in to unmask]</a>></span> wrote:<br><bl= ockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #= ccc solid;padding-left:1ex;"> Dear SPM users,<br> <br> I am analyzing resting state data of a group of healthy individuals<br> with a seed-based approach. In my regression model, I added the<br> extracted timeseries of the seed voxel as well as a few other<br> regressors (motion parameters, CSF, WM). The first level analysis<br> (t-test) shows strong activation in the common default mode areas.<br> However, when I set up the second level random effects model (all con<br> files combined in a one-sample t-test), there is no activation left.<br> No even the seed voxel lights up anymore. There is clearly some<br> mistake in setting up the second level analysis but I don't know what.<= br> Does anybody have an idea? Any help would be aprreciated!<br> <br> Thanks a lot,<br> <font color=3D"#888888">Cornelia<br> </font></blockquote></div><br></div> --0016368e30e911927704a316a1ad-- ========================================================================= Date: Thu, 12 May 2011 16:47:36 +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]> Content-Type: multipart/alternative; boundary="_000_3822A22C3FB6974099045A8DB62D39F9391CD531WSR21mrccbsuloc_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_3822A22C3FB6974099045A8DB62D39F9391CD531WSR21mrccbsuloc_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable MRC Cognition and Brain Sciences Unit - Cambridge Neuroimaging Post to study Ageing The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally reno= wned research institute with state-of-the-art cognitive neuroscience facili= ties including MRI, MEG and EEG. A post-doctoral position is available as part of the Cambridge Centre for A= geing and Neuroscience (Cam-CAN), a large group of researchers who seek to = relate changes in the brain to changes in cognition across the adult lifesp= an. You will help develop multivariate, multimodal neuroimaging approaches = to understanding the extent to which the brain can functionally reorganise = despite extensive structural changes with age. This will be an 11.5 month = appointment in the first instance. You will have or be in the final stages of obtaining a PhD in cognitive, co= mputational or systems neuroscience, and experience in human neuroimaging. = You should have skills in MRI, fMRI and/or EEG/MEG, plus good computer prog= ramming skills. It would be desirable, but is not essential, for you to hav= e experience of studying changes in the brain with ageing. Experience in mu= ltivariate statistics is also desirable. You will be thorough, efficient, e= ffective, a good communicator, and enjoy working as part of a diverse and e= nergetic, interdisciplinary team. The starting salary will be in the range of =A326,022 - =A331,758 per annum= , supported by a flexible pay and reward policy, 30 days annual leave enti= tlement, and an optional MRC final salary Pension Scheme. On site car and = cycle parking is available. The appointment is for 11.5 months in the first= instance. For informal discussion please contact Dr Rik Henson by email: rik.henson@m= rc-cbu.cam.ac.uk<mailto:[log in to unmask]>. Applications are handled by the RCUK Shared Services Centre; to apply pleas= e visit our job board at https://ext.ssc.rcuk.ac.uk<https://ext.ssc.rcuk.ac= .uk/> and complete an online application form. Applicants who would like t= o receive this advert in an alternative format (e.g. large print, Braille, = audio or hard copy), or who are unable to apply online should contact us by= telephone on 01793 867003, please quote reference number IRC22048. Closing date: 15th June 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_3822A22C3FB6974099045A8DB62D39F9391CD531WSR21mrccbsuloc_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Diso-8859-= 1"> <meta name=3D"Generator" content=3D"Microsoft Exchange Server"> <!-- converted from rtf --> <style><!-- .EmailQuote { margin-left: 1pt; padding-left: 4pt; border-left:= #800000 2px solid; } --></style> </head> <body> <font face=3D"Calibri" size=3D"2"><span style=3D"font-size:11pt;"> <div> </div> <div><b>MRC Cognition and Brain Sciences Unit – Cambridge</b></div> <div> </div> <div><b>Neuroimaging Post to study Ageing</b></div> <div> </div> <div>The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally= renowned research institute with state-of-the-art cognitive neuroscience f= acilities including MRI, MEG and EEG.</div> <div> </div> <div>A post-doctoral position is available as part of the Cambridge Centre = for Ageing and Neuroscience (Cam-CAN), a large group of researchers who see= k to relate changes in the brain to changes in cognition across the adult l= ifespan. You will help develop multivariate, multimodal neuroimaging approaches to understanding the extent to which the= brain can functionally reorganise despite extensive structural changes wit= h age. This will be an 11.5 month appointment in the first instance.<= /div> <div> </div> <div>You will have or be in the final stages of obtaining a PhD in cog= nitive, computational or systems neuroscience, and experience in human neur= oimaging. You should have skills in MRI, fMRI and/or EEG/MEG, plus good com= puter programming skills. It would be desirable, but is not essential, for you to have experience of studying cha= nges in the brain with ageing. Experience in multivariate statistics is als= o desirable. You will be thorough, efficient, effective, a good communicato= r, and enjoy working as part of a diverse and energetic, interdisciplinary team.</div> <div> </div> <div>The starting salary will be in the range of =A326,022 - =A331,758 per = annum , supported by a flexible pay and reward policy, 30 days annual leave= entitlement, and an optional MRC final salary Pension Scheme. On sit= e car and cycle parking is available. The appointment is for 11.5 months in the first instance.</div> <div> </div> <div>For informal discussion please contact Dr Rik Henson by email: <a href= =3D"mailto:[log in to unmask]"><font color=3D"blue"><u>rik.henson= @mrc-cbu.cam.ac.uk</u></font></a>.</div> <div> </div> <div>Applications are handled by the RCUK Shared Services Centre; to apply = please visit our job board at <a href=3D"https://ext.ssc.rcuk.ac.uk/"><font= color=3D"blue"><u>https://ext.ssc.rcuk.ac.uk</u></font></a> and complete a= n online application form. Applicants who would like to receive this advert in an alternative format (e.g. large = print, Braille, audio or hard copy), or who are unable to apply online shou= ld contact us by telephone on 01793 867003, please quote reference number I= RC22048<b>. </b></div> <div> </div> <div><b>Closing date: 15</b><font size=3D"1"><span style=3D"font-size:7.3pt= ;"><b><sup>th</sup></b></span></font><b> June 2011 </b></div> <div> </div> <div><i>This position is subject to pre-employment screening</i></div> <div><i>The Medical Research Council is an Equal Opportunities Employer</i>= </div> <div> </div> <div> </div> <div> </div> <div>-------------------------------------------------------</div> <div><font face=3D"Courier New"> &= nbsp; Dr Richard Henson</fo= nt></div> <div><font face=3D"Courier New"> &= nbsp; Assistant Director, Neuroimaging</font></div> <div><font face=3D"Courier New"> &= nbsp; MRC Cognition & Brain Sciences Unit</font></div> <div><font face=3D"Courier New"> &= nbsp; 15 Chaucer Road</font= ></div> <div><font face=3D"Courier New"> &= nbsp; Cambridge, CB2 7EF, UK</font></div> <div><font face=3D"Courier New"> </font></div> <div><font face=3D"Courier New"> &= nbsp; Office: +44 (0)1223 355 294 x522</font></div> <div><font face=3D"Courier New"> &= nbsp; Mob: +44 (0)794 1377 345</font></div> <div><font face=3D"Courier New"> &= nbsp; Fax: +44 (0)1223 359 062</font></div> <div><font face=3D"Courier New"> </font></div> <div><font face=3D"Courier New"> <a href=3D"http://www.mr= c-cbu.cam.ac.uk/people/rik.henson"> http://www.mrc-cbu.cam.ac.uk/people/rik.henson</a></font></div> <div><font face=3D"Courier New">-------------------------------------------= ------------</font></div> <div> </div> <div> </div> <div> </div> </span></font> </body> </html> --_000_3822A22C3FB6974099045A8DB62D39F9391CD531WSR21mrccbsuloc_-- ========================================================================= Date: Thu, 12 May 2011 12:02:08 -0700 Reply-To: "Zakrzewski, Jessica" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Zakrzewski, Jessica" <[log in to unmask]> Subject: Post-Doc Position at UCSF Memory & Aging Center Content-Type: multipart/alternative; boundary="_000_25B6F6D6457E2240B2AFFD35C9C8455515B8B2E057EX03netucsfed_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_25B6F6D6457E2240B2AFFD35C9C8455515B8B2E057EX03netucsfed_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable The UCSF Memory and Aging Center (MAC), in the Department of Neurology, is = looking for a candidate interested in post-doctoral fellowship training in = cognitive neuroscience, specifically studying self-awareness in neurodegene= rative disease with an emphasis on psychophysiological recording. Appropria= te candidates would include individuals completing a PhD in clinical psycho= logy, neuropsychology or cognitive neuroscience that focused on emotions re= search, psychophysiology and/or dementia, or MDs with similar skills and in= terests. The work would focus primarily on analysis of psychophysiological = data (including, but not limited to skin conductance, heart rate, facial ex= pression) and structural brain imaging. The MAC is a large, multidisciplinary group that provides clinical services= and has an extensive research program on aging and neurodegenerative disea= se, and is a world leader in research on frontotemporal dementia. MAC inves= tigators direct many projects looking at the clinical, imaging, genetic and= pathological features of aging and typical and atypical neurodegenerative = syndromes. The context for this fellowship would be a study of self-awarene= ss in frontotemporal dementia and Alzheimer's disease, but the fellowship w= ould also give broad exposure to imaging in a variety of other clinical con= texts including Alzheimer's disease and mild cognitive impairment, progress= ive supranuclear palsy, corticobasal degeneration and other disorders. Inte= rested candidates should contact Howard Rosen ([log in to unmask]<mailt= o:[log in to unmask]>) and Jessica Zakrzewski ([log in to unmask] edu<mailto:[log in to unmask]>) for more information. UCSF seeks candidates whose experience, teaching, research, or community se= rvice has prepared them to contribute to our commitment to diversity and ex= cellence. UCSF is an Affirmative Action/Equal Opportunity Employer. The Uni= versity undertakes affirmative action to assure equal employment opportunit= y for underutilized minorities and women, for person with disabilities, and= for covered veterans Jessica Zakrzewski Research Associate UCSF Department of Neurology Memory & Aging Center 432A Irving Street San Francisco, CA 94122 [log in to unmask]<mailto:[log in to unmask]> p: 415-476-9246 --_000_25B6F6D6457E2240B2AFFD35C9C8455515B8B2E057EX03netucsfed_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable <html xmlns:v=3D"urn:schemas-microsoft-com:vml" xmlns:o=3D"urn:schemas-micr= osoft-com:office:office" xmlns:w=3D"urn:schemas-microsoft-com:office:word" = xmlns:x=3D"urn:schemas-microsoft-com:office:excel" xmlns:p=3D"urn:schemas-m= icrosoft-com:office:powerpoint" xmlns:a=3D"urn:schemas-microsoft-com:office= :access" xmlns:dt=3D"uuid:C2F41010-65B3-11d1-A29F-00AA00C14882" xmlns:s=3D"= uuid:BDC6E3F0-6DA3-11d1-A2A3-00AA00C14882" xmlns:rs=3D"urn:schemas-microsof= t-com:rowset" xmlns:z=3D"#RowsetSchema" xmlns:b=3D"urn:schemas-microsoft-co= m:office:publisher" xmlns:ss=3D"urn:schemas-microsoft-com:office:spreadshee= t" xmlns:c=3D"urn:schemas-microsoft-com:office:component:spreadsheet" xmlns= :odc=3D"urn:schemas-microsoft-com:office:odc" xmlns:oa=3D"urn:schemas-micro= soft-com:office:activation" xmlns:html=3D"http://www.w3.org/TR/REC-html40" = xmlns:q=3D"http://schemas.xmlsoap.org/soap/envelope/" xmlns:rtc=3D"http://m= icrosoft.com/officenet/conferencing" xmlns:D=3D"DAV:" xmlns:Repl=3D"http://= schemas.microsoft.com/repl/" xmlns:mt=3D"http://schemas.microsoft.com/share= point/soap/meetings/" xmlns:x2=3D"http://schemas.microsoft.com/office/excel= /2003/xml" xmlns:ppda=3D"http://www.passport.com/NameSpace.xsd" xmlns:ois= =3D"http://schemas.microsoft.com/sharepoint/soap/ois/" xmlns:dir=3D"http://= schemas.microsoft.com/sharepoint/soap/directory/" xmlns:ds=3D"http://www.w3= .org/2000/09/xmldsig#" xmlns:dsp=3D"http://schemas.microsoft.com/sharepoint= /dsp" xmlns:udc=3D"http://schemas.microsoft.com/data/udc" xmlns:xsd=3D"http= ://www.w3.org/2001/XMLSchema" xmlns:sub=3D"http://schemas.microsoft.com/sha= repoint/soap/2002/1/alerts/" xmlns:ec=3D"http://www.w3.org/2001/04/xmlenc#"= xmlns:sp=3D"http://schemas.microsoft.com/sharepoint/" xmlns:sps=3D"http://= schemas.microsoft.com/sharepoint/soap/" xmlns:xsi=3D"http://www.w3.org/2001= /XMLSchema-instance" xmlns:udcs=3D"http://schemas.microsoft.com/data/udc/so= ap" xmlns:udcxf=3D"http://schemas.microsoft.com/data/udc/xmlfile" xmlns:udc= p2p=3D"http://schemas.microsoft.com/data/udc/parttopart" xmlns:wf=3D"http:/= /schemas.microsoft.com/sharepoint/soap/workflow/" xmlns:dsss=3D"http://sche= mas.microsoft.com/office/2006/digsig-setup" xmlns:dssi=3D"http://schemas.mi= crosoft.com/office/2006/digsig" xmlns:mdssi=3D"http://schemas.openxmlformat= s.org/package/2006/digital-signature" xmlns:mver=3D"http://schemas.openxmlf= ormats.org/markup-compatibility/2006" xmlns:m=3D"http://schemas.microsoft.c= om/office/2004/12/omml" xmlns:mrels=3D"http://schemas.openxmlformats.org/pa= ckage/2006/relationships" xmlns:spwp=3D"http://microsoft.com/sharepoint/web= partpages" xmlns:ex12t=3D"http://schemas.microsoft.com/exchange/services/20= 06/types" xmlns:ex12m=3D"http://schemas.microsoft.com/exchange/services/200= 6/messages" xmlns:pptsl=3D"http://schemas.microsoft.com/sharepoint/soap/Sli= deLibrary/" xmlns:spsl=3D"http://microsoft.com/webservices/SharePointPortal= Server/PublishedLinksService" xmlns:Z=3D"urn:schemas-microsoft-com:" xmlns:= st=3D"" xmlns=3D"http://www.w3.org/TR/REC-html40"> <head> <META HTTP-EQUIV=3D"Content-Type" CONTENT=3D"text/html; charset=3Dus-ascii"= > <meta name=3DGenerator content=3D"Microsoft Word 12 (filtered medium)"> <style> <!-- /* Font Definitions */ @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4;} @font-face {font-family:Cambria; panose-1:2 4 5 3 5 4 6 3 2 4;} @font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4;} @font-face {font-family:"Palatino Linotype"; panose-1:2 4 5 2 5 5 5 3 3 4;} @font-face {font-family:Times; panose-1:2 2 6 3 5 4 5 2 3 4;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {margin:0in; margin-bottom:.0001pt; font-size:11.0pt; font-family:"Calibri","sans-serif";} a:link, span.MsoHyperlink {mso-style-priority:99; color:blue; text-decoration:underline;} a:visited, span.MsoHyperlinkFollowed {mso-style-priority:99; color:purple; text-decoration:underline;} span.EmailStyle17 {mso-style-type:personal-compose; font-family:"Calibri","sans-serif"; color:windowtext;} .MsoChpDefault {mso-style-type:export-only;} @page Section1 {size:8.5in 11.0in; margin:1.0in 1.0in 1.0in 1.0in;} div.Section1 {page:Section1;} --> </style> <!--[if gte mso 9]><xml> <o:shapedefaults v:ext=3D"edit" spidmax=3D"1026" /> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext=3D"edit"> <o:idmap v:ext=3D"edit" data=3D"1" /> </o:shapelayout></xml><![endif]--> </head> <body lang=3DEN-US link=3Dblue vlink=3Dpurple> <div class=3DSection1> <p class=3DMsoNormal><span style=3D'font-family:"Cambria","serif"'>The UCSF= Memory and Aging Center (MAC), in the Department of Neurology, is looking for a candidate interested in post-doctoral fellowship training in cognitive neuroscience, specifically studying self-awareness in neurodegenerative dis= ease with an emphasis on psychophysiological recording. Appropriate candidates w= ould include individuals completing a PhD in clinical psychology, neuropsycholog= y or cognitive neuroscience that focused on emotions research, psychophysiology and/or dementia, or MDs with similar skills and interests. The work would f= ocus primarily on analysis of psychophysiological data (including, but not limit= ed to skin conductance, heart rate, facial expression) and structural brain imaging. <o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Cambria","serif"'><o:p>&nb= sp;</o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Cambria","serif"'>The MAC = is a large, multidisciplinary group that provides clinical services and has an extensive research program on aging and neurodegenerative disease, and is a world leader in research on frontotemporal dementia. MAC investigators dire= ct many projects looking at the clinical, imaging, genetic and pathological features of aging and typical and atypical neurodegenerative syndromes. The context for this fellowship would be a study of self-awareness in frontotemporal dementia and Alzheimer’s disease, but the fellowship w= ould also give broad exposure to imaging in a variety of other clinical contexts including Alzheimer’s disease and mild cognitive impairment, progress= ive supranuclear palsy, corticobasal degeneration and other disorders. Interest= ed candidates should contact Howard Rosen (<a href=3D"mailto:[log in to unmask] f.edu">[log in to unmask]</a>) and Jessica Zakrzewski (<a href=3D"mailto:[log in to unmask]">jzak= [log in to unmask]</a>) for more information.</span><span style=3D'font-size:10.0pt;font-family:"Ca= mbria","serif"'> <o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-size:10.0pt;font-family:"Cambria",= "serif"'><o:p> </o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Cambria","serif"'>UCSF see= ks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence.&= nbsp;UCSF is an Affirmative Action/Equal Opportunity Employer. The University undertakes affirmative action to assure equal employment opportunity for underutilized minorities and women, for person with disabilities, and for covered veterans</span><span style=3D'font-family:"Cambria","serif"'><o:p><= /o:p></span></p> <p class=3DMsoNormal><o:p> </o:p></p> <p class=3DMsoNormal><o:p> </o:p></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>Jessica Zakrzewski<o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>Research Associate <o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '><o:p> </o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>UCSF Department of Neurology</span><span style=3D'font-family:"Palatino Linotype= ","serif"'><o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>Memory & Aging Center</span><span style=3D'font-family:"Palatino Linotype","se= rif"'><o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>432A Irving Street</span><span style=3D'font-family:"Palatino Linotype","serif"'= ><o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>San Francisco, CA 94122</span><span style=3D'font-family:"Palatino Linoty= pe","serif"'><o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '><a href=3D"mailto:[log in to unmask]"><span style=3D'color:blue'>jzak= [log in to unmask]</span></a></span><span style=3D'font-family:"Palatino Linotype","serif"'><o:p></o:p></span></p> <p class=3DMsoNormal><span style=3D'font-family:"Palatino Linotype","serif"= '>p: 415-476-9246</span><span style=3D'font-family:"Palatino Linotype","serif"'>= <o:p></o:p></span></p> <p class=3DMsoNormal><o:p> </o:p></p> </div> </body> </html> --_000_25B6F6D6457E2240B2AFFD35C9C8455515B8B2E057EX03netucsfed_-- ========================================================================= Date: Thu, 12 May 2011 15:15:49 -0400 Reply-To: Bedda Rosario <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bedda Rosario <[log in to unmask]> Subject: Re: Cluster information without corresponding SPM.mat Comments: To: "MCLAREN, Donald" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba6e8ecc5c2b5204a31904d1 Message-ID: <[log in to unmask]> --90e6ba6e8ecc5c2b5204a31904d1 Content-Type: text/plain; charset=ISO-8859-1 Hello Donald, Thank you for your response and the information. It is very useful, peak.nii and cluster.nii provides information about cluster size, peak statistic, coordinates, cluster number and region name. Do you know if it is possible to also get the volume for each cluster (or the value (e.g. t-value) from each voxel for a specific cluster)? Thanks for the help, Bedda On Tue, May 10, 2011 at 5:21 PM, MCLAREN, Donald <[log in to unmask]>wrote: > See the following posts: > > http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R25661&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 > > > http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R16468&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 > > 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, May 10, 2011 at 3:09 PM, Bedda Rosario <[log in to unmask]> wrote: > >> Dear SPM users, >> >> I have created an image (and hdr file) using the results of a t test >> analysis in SPM8 (that is, using spmT_0001.img). I would like to know if it >> possible to get a cluster report from the image without a corresponding >> SPM.mat file. >> >> Thanks for the help, >> Bedda >> > > --90e6ba6e8ecc5c2b5204a31904d1 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello Donald, <br><br>Thank you for your response and the information.=A0 I= t is very useful, peak.nii and cluster.nii provides information about clust= er size, peak statistic, coordinates, cluster number and region name.=A0 Do= you know if it is possible to also get the volume for each cluster (or the= value (e.g. t-value) from each voxel for a specific cluster)? <br> <br>Thanks for the help, <br>Bedda<br><br><div class=3D"gmail_quote">On Tue= , May 10, 2011 at 5:21 PM, MCLAREN, Donald <span dir=3D"ltr"><<a href=3D= "mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] m</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex">See the following posts:<div><a href=3D"http= ://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L=3DSPM&P=3DR2566= 1&I=3D-3&d=3DNo+Match%3BMatch%3BMatches&m=3D41734" target=3D"_b= lank">http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L=3DSPM&= ;P=3DR25661&I=3D-3&d=3DNo+Match%3BMatch%3BMatches&m=3D41734</a>= </div> <div><br></div><div><a href=3D"http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2= =3Dind1103&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match%3BMatch%3= BMatches&m=3D41734" target=3D"_blank">http://www.jiscmail.ac.uk/cgi-bin= /wa.exe?A2=3Dind1103&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match= %3BMatch%3BMatches&m=3D41734</a><br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: <a href=3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target= =3D"_blank">(773) 406-2464</a><br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDENTIAL INFORMATION w= hich may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY P= RIVILEGED and which is intended only for the use of the individual or entit= y 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 recipi= ent, you are hereby notified that you are in possession of confidential and= privileged information. Any unauthorized use, disclosure, copying or the t= aking of any action in reliance on the contents of this information is stri= ctly prohibited and may be unlawful. If you have received this e-mail unint= entionally, please immediately notify the sender via telephone at <a href= =3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target=3D"_blank">(773= ) 406-2464</a> or email.<div> <div></div><div><br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 3:09 PM, Bedda R= osario <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]" target= =3D"_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class= =3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padd= ing-left:1ex"> Dear SPM users, <br><br>I have created an image (and hdr file) using the re= sults of a t test analysis in SPM8 (that is, using spmT_0001.img).=A0 I wou= ld like to know if it possible to get a cluster report from the image witho= ut a corresponding SPM.mat file.=A0 <br> <br>Thanks for the help, <br><font color=3D"#888888">Bedda<br> </font></blockquote></div><br> </div></div></div> </blockquote></div><br> --90e6ba6e8ecc5c2b5204a31904d1-- ========================================================================= Date: Thu, 12 May 2011 15:54:26 -0400 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: Cluster information without corresponding SPM.mat Comments: To: Bedda Rosario <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001517491d5471e90404a3198e05 Message-ID: <[log in to unmask]> --001517491d5471e90404a3198e05 Content-Type: text/plain; charset=ISO-8859-1 Bedda, The volume of each cluster is simply the number of voxels*voxelsize, where voxel size is xdim*ydim*zdim. This is easiest enough to do post-hoc on your own. The reason this isn't implemented automatically is that cluster extent is defined using number of contiguous voxels. The output image will be a map of T-statistics in all clusters. If you want to extract those values, I'd recommend AFNI. Specifically, they have a tool called 3dmaskdump that could dump the T-statistics from all voxels using a mask image. Something like: 3dmaskdump -mask *clusters.nii -mrange a b spmT_*.nii/.img where a and b are the numbers above and below the actual cluster number. AFNI is free and easily runs on Mac and Linux boxes. 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 Thu, May 12, 2011 at 3:15 PM, Bedda Rosario <[log in to unmask]> wrote: > Hello Donald, > > Thank you for your response and the information. It is very useful, > peak.nii and cluster.nii provides information about cluster size, peak > statistic, coordinates, cluster number and region name. Do you know if it > is possible to also get the volume for each cluster (or the value (e.g. > t-value) from each voxel for a specific cluster)? > > Thanks for the help, > Bedda > > On Tue, May 10, 2011 at 5:21 PM, MCLAREN, Donald <[log in to unmask] > > wrote: > >> See the following posts: >> >> http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R25661&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 >> >> >> http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R16468&I=-3&d=No+Match%3BMatch%3BMatches&m=41734 >> >> 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, May 10, 2011 at 3:09 PM, Bedda Rosario <[log in to unmask]>wrote: >> >>> Dear SPM users, >>> >>> I have created an image (and hdr file) using the results of a t test >>> analysis in SPM8 (that is, using spmT_0001.img). I would like to know if it >>> possible to get a cluster report from the image without a corresponding >>> SPM.mat file. >>> >>> Thanks for the help, >>> Bedda >>> >> >> > --001517491d5471e90404a3198e05 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Bedda,<br><br>The volume of each cluster is simply the number of voxels*vox= elsize, where voxel size is xdim*ydim*zdim. This is easiest enough to do po= st-hoc on your own. The reason this isn't implemented automatically is = that cluster extent is defined using number of contiguous voxels.<br> <br>The output image will be a map of T-statistics in all clusters. If you = want to extract those values, I'd recommend AFNI. Specifically, they ha= ve a tool called 3dmaskdump that could dump the T-statistics from all voxel= s using a mask image. Something like:<br> 3dmaskdump -mask *clusters.nii -mrange a b spmT_*.nii/.img<br>where a and b= are the numbers above and below the actual cluster number.<br><br>AFNI is = free and easily runs on Mac and Linux boxes.<br><br>Hope this helps.<br> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Thu, May 12, 2011 at 3:15 PM, Bedda R= osario <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">brosario= @gmail.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style= =3D"border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; p= adding-left: 1ex;"> Hello Donald, <br><br>Thank you for your response and the information.=A0 I= t is very useful, peak.nii and cluster.nii provides information about clust= er size, peak statistic, coordinates, cluster number and region name.=A0 Do= you know if it is possible to also get the volume for each cluster (or the= value (e.g. t-value) from each voxel for a specific cluster)? <br> <div class=3D"im"> <br>Thanks for the help, <br>Bedda<br><br></div><div class=3D"gmail_quote">= <div class=3D"im">On Tue, May 10, 2011 at 5:21 PM, MCLAREN, Donald <span di= r=3D"ltr"><<a href=3D"mailto:[log in to unmask]" target=3D"_blank"= >[log in to unmask]</a>></span> wrote:<br> </div><div><div></div><div class=3D"h5"><blockquote class=3D"gmail_quote" s= tyle=3D"border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8e= x; padding-left: 1ex;">See the following posts:<div><a href=3D"http://www.j= iscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L=3DSPM&P=3DR25661&I= =3D-3&d=3DNo+Match%3BMatch%3BMatches&m=3D41734" target=3D"_blank">h= ttp://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L=3DSPM&P=3DR2= 5661&I=3D-3&d=3DNo+Match%3BMatch%3BMatches&m=3D41734</a></div> <div><br></div><div><a href=3D"http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2= =3Dind1103&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match%3BMatch%3= BMatches&m=3D41734" target=3D"_blank">http://www.jiscmail.ac.uk/cgi-bin= /wa.exe?A2=3Dind1103&L=3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match= %3BMatch%3BMatches&m=3D41734</a><br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: <a href=3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target= =3D"_blank">(773) 406-2464</a><br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDENTIAL INFORMATION w= hich may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY P= RIVILEGED and which is intended only for the use of the individual or entit= y 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 recipi= ent, you are hereby notified that you are in possession of confidential and= privileged information. Any unauthorized use, disclosure, copying or the t= aking of any action in reliance on the contents of this information is stri= ctly prohibited and may be unlawful. If you have received this e-mail unint= entionally, please immediately notify the sender via telephone at <a href= =3D"tel:%28773%29%20406-2464" value=3D"+17734062464" target=3D"_blank">(773= ) 406-2464</a> or email.<div> <div></div><div><br> <br><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 3:09 PM, Bedda R= osario <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]" target= =3D"_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class= =3D"gmail_quote" style=3D"border-left: 1px solid rgb(204, 204, 204); margin= : 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"> Dear SPM users, <br><br>I have created an image (and hdr file) using the re= sults of a t test analysis in SPM8 (that is, using spmT_0001.img).=A0 I wou= ld like to know if it possible to get a cluster report from the image witho= ut a corresponding SPM.mat file.=A0 <br> <br>Thanks for the help, <br><font color=3D"#888888">Bedda<br> </font></blockquote></div><br> </div></div></div> </blockquote></div></div></div><br> </blockquote></div><br> --001517491d5471e90404a3198e05-- ========================================================================= Date: Thu, 12 May 2011 20:57:23 +0100 Reply-To: Shamil Hadi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Shamil Hadi <[log in to unmask]> Subject: Modulatory Input in DCM Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello DCM members, =20 Is there any reason to modulate forward connections (B matrix)=20 rather than backward connections in effective connectivity? =20 =20 Sincerely, Shamil, ========================================================================= Date: Thu, 12 May 2011 16:42:46 -0400 Reply-To: Jiansong Xu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jiansong Xu <[log in to unmask]> Subject: Henson's batch code Content-Type: multipart/mixed; boundary=Apple-Mail-91-508252663 Mime-Version: 1.0 (Apple Message framework v936) Message-ID: <[log in to unmask]> --Apple-Mail-91-508252663 Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes Content-Transfer-Encoding: 7bit Dear all: I'm trying to use Henson's batch code for spm5 but got following error message. Any suggestions are appreciated. --Apple-Mail-91-508252663 Content-Disposition: inline; filename="Picture 3.png" Content-Type: image/png; x-mac-hide-extension=yes; x-unix-mode=0644; name="Picture 3.png" Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAApQAAAEjCAIAAAADigY7AAAOZ2lDQ1BJQ0MgUHJvZmlsZQAAeAGt l2dQVMvWhntmyDLkHATJOWeUnHOWKGHIcWDICoggSEZEguQsAgMCIiCIgEgQhCNJRVBBBCTJAZQo 30YPnvp+3Lp/blft6md6r169dr89vWoBQDrgiEZ7wwEAPr6BASaaKqyWVtas+DMABggBEkgAAUcU Bq1sZKQHmfyH9n0KsobauOCpr5bE0v0NqclK1eQ7DxfGo7n+w6SzYZIAaEEAYALQAJXbb1Y6Zaff bHbKIYHoQMjG/ZRR7o7OEF+DWCDAzEQV4vsQk7j95tZTdvrNA6ccjHI7nfsGADwKX2cPXwDw1yBW cHbBoKDXp+vaO2NQPhAnQ/zNx8cP8k966p8HhQ6A5pKWQ8xxui9QD7WgPAAURgCAM/07Zh0DQG0U FJ7Av2NcUCxUrwDomP13bNvk117BaEYxruJiv9zBiFUAwJ07OdmG9go/A4Dj2ycnh6UnJ8dlACDe A/DMGxUUEPzLFvoQGLTyf/v9+5v/mYGAxIEEhunBpRA0OLy4nnjPCeQJ+87ZIQlIsGQOFOSUT6md aJF0FQySjE3MXOddWO6xjrABdjmOMM4mrk0eUV4/vhr+JUF2IWvhFJF20WVxUglxSQspX+k7Ml2y G/J0F1UvuSnEK1YodSlPqayqHqgTaJBpUmsxaDPrMOky69Hp0xrQGvIYKRqbmDibhpjFm2dalFyu tXxs1W09bDNtu2C3eeXYAenI7CSIUnI2cTF3NXLTc1f3UPJU9LroLeMj7SvtJ4OW91cI0MAYBVoH uQaHhiSE5obdD8defXCtNKIgMicq/3pedOGN0piq2Jqb2Li6+Lpb9QnViRVJ+cnpKTGpAWmu6V63 fTMwd4IzQ+4GZvlme+TY55re08iTyecuoCz4WbhSNF7cVlJUeqsMU46u8Kv0rfK971Xt9sCpxqbW tE4Pq16v0CD9UKSRt4mjmeURUwvjY4ZW8ta9ttH24ieBHZqd9J1rT7u6Mp95d6v1MPfs9049f9SX +SK432pAcZB7CDm0+rJrOH0E9Up2lHz069iLv4peR4zbTMhMUk9+mxqaLnsT/tboHce73Zne99mz QXMWH6Q/0nzc+TQ2f38h6rP5It/i4Zehpexl1IrQyt7X3tWkNbN1pvXFjZ7Nkm/Xt+z+lt2m2f62 079b+D3wh/Ye897Gfs9B1qH70cVjiuOlnx0nbicnkP5MsA9wLKIe5x3eBfwUQgKifGIZ5BJpJrky xTpVFo0k7RC9AUM3Eyez7fl4lnrWWTYSdhUONGcp1zQPMa8inx9/ocCw4I4wrYiCqLUYRjxRolDy sdS09IEsrZy0vNFFl0th0BnIVCpSrlSpVq1Wq1Iv0yjSzNXK0L6lc103XC9OP8ugzLDe6Inxc5OX pmNmk+ZvLGYuz1l+tPpsvWSzZrtjd2SPgE4BpdM5pwPUsvOUS59ri1uVe7ZHgmeEF8bbw8fB19bP Cm3hbxFgg3EIREHnwCnEOFQ5TCyc+yrLNboIikiyKNLryGiiG7gxsJij2J2bq3Ef4sdudSXUJt5L Kk4uSilNLUkrTs+7nZGRdCcyE33XMcsoWyGHL5cy9/jeUt5IflNBQWFcUWzxjZLrpVFlV8tDKvwr Paoc7ltWGz7QrFGolawTxHLWn2+ge0jVSNqE27TW3Pcop8XvsXorY+tW20B76ZPIDptOuaeMT0+6 Fp4NdDf35Pfeeh7a5/pCp59rAAy8HWwfuvfy2rDDiNor/lHK0YOxhb8GXmPHMyaCJi9PSU1TTW++ GX777F3jTOX7rNm4ucAP9h+1PonO080fLsx97lws+HJ1yWpZeoVqZfPryCp2rWQ9fyNrM/Vb3FbE 3wHbzjsWu5rfpX5w7JHt/dxfO3hz2Hf06LjiZ+aJ9an+IAYWCg9HlOIs46nitxIqEI0SuyBPSHPJ +SnaqRSpsbQX6ELoexhOmOSY3c9nsQyyHrGJsF/hSOR8zDXPQ8QrwqfP7y5wTTBJKEu4UKREtFis UDxfokCySKpYukAmVzZD7pb81Yvel6wU1BUFlSiVdpWnVdpVC9Qi1W01FDVZtZBax9prOjO6Q3qP 9csN0g3DjVyMDU3kTDnMyMyOzdcsZi+PWD61arAutImxtbOTuUJxZcN+xKHOMc0pAGXprOTC70rn hu924L7hseg55/XG+7XPsO9zv3Y01r8ioBCTF5gblBdcGFIaWhVWHf4AuhdqIu5HlkP3Qlp0zA3/ GKdYm5vGcdrxCrdEEy4kkiYeJC0kD6VgU9PT0Ol6t3ky4Bnv7zRlJt5FZSlnc+aQ5Ozlfro3mFef n1UQVmhXpFzMWUJYslE6XtZeXlyRUImpunJfu1r4AcmDxZqW2pQ6R6xUPXH954anD/Mbw5tsm5Uf cbeQtuw/XmqdaOttb33yoCO10+Opchdj1/dn490Pe273op8b94m+oHix1T82UDN4c8jupcQwwfDM yINXMaNuY0Z/yb3mGEeO70y8mWyfKpiOfGP7VvYd9btvM4PvS2bD50w+8H6EfXz7aWi+c6Huc9Zi 1Bf3JbNl1RXprxKrCmuX16M3Or6Rbt3dVtnl+eF1wHLsf6r/79x3mhPwpAAocwPAUhQAvdsAFJAA wKUHACUTAEZIAMxkAVyICsCJhwFseBj8kz+gvEUAqAE7EAXKwBg4AQyIA7mgDnSDabAOw4HuF0mY IcwTFg8rh/XA5uE4cG64HjwQXgB/hYAjZBH+iBrEIg4Ljh1OHs4MLiOuHW4J7hKeEF4IXg8+Kb4D fiMBHsEVglZCSsIgwgkicaLbRFvnzM+1EV8gTibeR3ogZ0lMSF6SapH2k+mSvSa3IV+iCKEkoCyi kqB6SW1J/YUmlJaYtoROlm6SPoCBguERoxXjCVMVsyHz/vkKFmOWY9aaCzZsRGwd7Fc5lDkJOMe5 CrndeaR48Xgn+Sr4gwW0BBkFV4U6hNNEHEVFxRBi0+J1EomSgVLO0qYyarJCcizySPmji2uX5hTG FUeUBpV7VQZUh9Wm1Zc1jrRotAV0hHRl9ZT1DQ3sDYONMoyxJuOmh+bcFhaXkyz7rXFsVG2T7Cbt mRycHFOdnqG2XDhc7dyy3ac86b3svEt9vvqJoSP8BzHUgU5B9cGHoZph2eEn11IjBaP6o91jiGLr 4gzi1xNSkviS+1Lt0w5vp9/hzezI0s/eym3ICy6QLdwtbir1LuevmK8qrLatYa1dwbY2JDW6Nmu3 yLYytM0/qemM7DLuFuyl7oO92BpYH9oa/jlK9hffuOqk63Ti24cz5bNZH1I+JS7cWkxdKlhpWO1f X/x27m/BHdPvkXulB0NHO7/uDzggBoyAD8gBXWALfEE0yAT3QSd4DZbBCYwWJgLTgbnAomGFsE7Y LOwEzg7XhgdA6o8icBEKiFBEE2ITRwDHG6cOZxNXDDcE9ykePp4JXiHeOr4ifib+CoEqQTHBT0JH wudE3EQJROuQ9p3EvMS5SCLkdeQeCYZkixRDuk92g5yEvIBCiKKH0oZyhyqdmo26iUaTZo42mI6c roHemH6XIY9RjXGTKY9ZB1K+hsWWlYS150IomyDbCnszx01OKy4hbgT3O55m3lQ+d35VARaBI8Fp oUbhdBFvUQ0xNnGY+LzEsORzqU7pRzK1siVyOfKpF2MuhStgFNFKaGU/lUDVSLVE9QKNZs0xrU3t bV2Y3jl9JgMhQw3oPosxqTGdMEdYSFz2sKy0WrRht/Wye3Tl0EHN0d+pDDXlQuiq6hbp3ukJ89L0 TvGZ9ruA9vXvxBAH2gbVBh+F6oeVXD13LSdSNKov2v7GYWxWnHB8X4JN4kZyVCpJWvFtroyWTIW7 PdlGOT/u1eSjCpFFbSXOZSTlLZV29/Gq62vs6yiwgw2xjRrNyEcfH2PbTNqPO8qeWj4j7O7rTejT 76cdWB3qHS5/dXPM77XlhNqU7Bu+dxQzY7NJH3Q+kc1Pfi76gl7W+Mqyerw+tzmwhd2+uxv7w2/f 4lD1WOSX/niADDBD+ssCbWAFvEAESAfloA2Mgi/gJ6S/GMwA5gVLhNXCRmHbcEa4BhwDL4dPIYgR aogIxGPENo4IDhqnEec7rjxuDO4IHj2eB94TfHJ8T/x+Am6CRIK/CW0JB4mkiSrP0Z5LJsYhjiL+ iYwmwSfJIGUlbSLTIftCHkvBQTFA6U9FT9VH7U5DQtNC60hHQtcF/d95GeYZC5lsmBmZZ84XsNiz srEuX8CyhbLrcQhzUnEecS1wD/E08ubx3eD3FDAWlBZiFoYLL4kMizaIZYlHSnhIWknpSSvJiMuy y1HL48rvXly8NK0wrNin9Ez5iUqnarfaS/X3GptaRNocOpK6+nrO+tEGpYZDRt9NeEztzfLMP1zm sgy2GrJhtY2y+2iv6JDt+AUl5hztMuHG737TY9FLw/uBL6lfEHo2QA2DDaIJjghZDDMKH7hmFrEV FR/NcaMr1vrmdnxcAn1iVbJ4SmeaZvpQhvGdybumWVM5Qfd48+YLsoq0i49K68odK6mqhqpjapTr CLATDZWN15sdW/RbL7TNPrnbadxF9myip/h5wAu1AZYh2MuVkYnR/r86x5snG6axb+tmQmaVPlB9 /D7/7vOTLznL6K8yqxvraZtM37K2TrYtduJ3q7+3/2jeq9gPO9A4+HlYdWR0NHPsdLzy89aJ2qn+ v+ulXzUFroe+I1Q7/W+bj3fQmU8KyDOxr5OBIdQTQM8yOtDotA78xZhgU/UzdvXQ0D5jZ0c13TMO d1c1OGPXAA2TM/Z01DE6Yxdfc9MzRnv/qm//WUvlj70LRv2PTbi72eUz+4AgE/Mz9vLT/WPv7KL2 JzZfbwMop/6O2SNQ+0/8wAPoA0eA+l1nQhYAjwyAfO9T6m7uO+3+Xwt0CYVqUABU/dBhAR5u7oGs ylCV7SLAqu2LEhJgFRMRFQX/BxjQO51xgFzIAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgAElEQVR4 Aeydd7wkRdX3a3pyuHEzu+ScM0qUJJJkJYggSRAEBAV51JdHUUAwo4jkLEhOwi45C0gUWBbYZZfN 4e7NcyfP9PR0vd+a41PPvHdXH/97uc92f+6nt7r61KlTv3PqnEo9G9Jaq+AKEAgQCBAIEAgQCBAY Owg4Y0fUQNIAgQCBAIEAgQCBAAGDQBC8AzsIEAgQCBAIEAgQGGMIBMF7jCksEDdAIEAgQCBAIEAg CN6BDQQIBAgECAQIBAiMMQSC4D3GFBaIGyAQIBAgECAQIBAE78AGAgQCBAIEAgQCBMYYAkHwHmMK C8QNEAgQCBAIEAgQCIJ3YAMBAgECAQIBAgECYwyBIHiPMYUF4gYIBAgECAQIBAgEwTuwgQCBAIEA gQCBAIExhkAQvMeYwgJxAwQCBAIEAgQCBILgHdhAgECAQIBAgECAwBhDYC0K3pVKBeXIvVQq1ev1 Vl3ZR9d1yW80GvZttVqVNAl56/u+5MCHhDzyypbiv3uxDC2f1oS8FXrP82q1mvAZlc8r8rnbGqlF iMmHmIq4WjmTFiFHZbY+Uq+lEUBgIlXYiloTIka5XIbMFiRh25vL5eAvj4KJfSQBK3kFgPatyGO5 SXWFQkHyucsr2kjt0kYrJG9FJBKtDAUZ7lYwinBZnpLmbSuBbamAL3eKSMK+tUxGJSwB9UoRkU3a 0lqXGBL0iMFd2mWLF4vFVs42n0zbRgsFmaSlFWhQaiRTEEAMkYSKJNHKuTUtHCCzHESMUcXRhcgj /Fs5CKUURxJbXWvDW+lt2rbXtlSYizCSbrUxW3AsJgRnmgM+kh4cHKQhFnbSki85AiPIUERyLKW8 4s4rgUJybN+RHi3c5JWtyNIAsuAsHOROdZQSdUhBqrC1QCM8SYh2oBRicoaGhoTJ6hYi+f/6Lnys nVuGCEm6VQYRWyTBI7WyFTLpZZDl8/nWtyLYyMiIZFo5rQOEp82EBg5INUoAkZO7JKRGxLA5FLRM YCjCoDsRWGqXtC0irRAxRDbAF/yFUjLXcEe4teECC5oJrNxB3DYZcG0aNEFQHklLQorQ0ywZCYys 9dGmm5jXpYjN/GcJYUKNIgOmJpQig320xRHbSkXm8PBwKz1pYYh92CKrJ3grUAgIpFt54k+xMykl fOSRO52zlRuU8ih8YCI58kgslwQ0IidWKPVKKZoMgeTAXOoSHGg+3EbRU4rWkS/ykJaygpUALmnh Hw6HrYT0DUrZuoSDkImQ9q1AYVsq8iCJACtFVr/jEHt7e6dPnz5hwgR5iySUIm1BEJ5kWu04jgOB 1IiuhZ4cC7uUtfkIKWLYHIhplJXNckYeqVo4AI7gQ+YaL94KJq0ACqXwBECptNUmAUeEh5LigpVU hAC24WusUTIpBVso4YNjkkzLM5PJHHvssdlslvx/h9u/qOgz8kogEmHEGuVOjuBP2wVAwZlMCAQQ 0TKKEEAoYjGxWuOtMJQi3IVGdAcHyZe64CB1QSOCUVxkEzLLVnjSo3lreZK29FKKO3oX8YSDzf83 E9awKS4IwE2EFA5IYmksT0GGDmhzILNdeGBgYJNNNsF7W2mtbEgLN2m7NWze4jfgIChROzSkpThY wQqGlKUIxFY8qw5eWUkkITTWHUlB6EVymy9NE7SlCJVShZCN4imP/+gza3z3vykTaP785z/vuuuu uPW2trYTTjjhtddeE98EXviOVCrFHcU8/PDDogDwJeeoo44SRfK40047hUIh2wl5a69YLCZpCxpM bAewmTYhSpKKSMuj6IlSUiN30rxCi1xS1oYBq1TROuIJE8isSdnqRiUgJmfmzJk777wzYoMJd6ki Eonstdde1i4PPPBAwoxwhoY0xPF4/Etf+tK8efOkgbzlFZncuXC73OFjgRJ5rMDQSwORwWZKDgXJ lLQUJ00DpY2Sj2xckMFHfIqIRw5FoKEhNjRStRDzVpjQdnIEASilFl5JB5YqIIbA9ise/9mFBhmd bLjhhr/85S+Fp1C2lhXOVoxPP/10++23x97WWWed2267TUr19/ePHz8e60Jy7HPffff96KOPhJVl CweaI4C0yoOPs2JLvoXLmk0rfWvaSkUtoiZUYI1ZOkVHRwdFxPD+8pe/bLTRRtDssMMOpAU9i6Fw e/DBB8eNGzdqvNtaKWmLz7Jly+h0NJn7vffeyyu0iV3RqGuvvTadTr///vujyo7RR8BBL4QTHNHR Rx+9xRZboB0yxTxEZSj90EMPbW9vB2EuAVaQFF8h+KApsXlsr6urC0pURo/7/Oc/P2PGDNGjlL3l llsmTZqEBrfddtsnn3yS4piKrdQiKbXzSC3wEYbRaHSrrbZCKWJdqEy6tpFMKSsk4kFg7ceaq2X+ bybs+AB6Wkc8fuSRR0466aSNN96YHGmOJEQA7oi6++67i9nwSuQUIyQNDr/5zW8uuugiwQoO77zz zuc+9zkKggbuCxohFo9BvvQ+AZNHeHIJjXiqq6+++rzzzpN86/ESicQXvvCFG2+80RKTeOqpp0AP lNZff/333nuPHAQQA7jjjju+9a1vTZs2TfiQyUV64cKF9Cli03rrrcejZArNGu9rS/C+5pprzjzz THFA+IVbb72VTmIBwkmRRknge+qpp9K7RGHoEgvGHCWI4l6tRlG8TVPWeklM/N8xXxu0MCxrl/CR jifWxqNcVovi8qzjo6AdYwplX18fCUv/j/It/1CvFKdFmMjTTz/NS7qNtJc08UOaTHr+/Pn0fGkm BSVBccSbO3fuN7/5TYqLqCI2RaARGyUt14oVKyQhUkE5Ch/bHGRgZGCRpBRCCjG1SEVkShclYZtJ Ed6KE5RS3Lls5yQtroEExFQEvW2ylQd60QsWYso3OVinKTmr33fZZRdxH6MoaaltCyBIpdS73377 /exnPwMoMr/97W/DUGQjBwFgwh3Pcv755z/wwANSnQ3/AoJtONDJAFSqBnlpFAyBTsrCzWpndeGF oZVTdCGZpGHy/PPPf+Mb35CCDz30EAIzzuBx1apVF1988VVXXUVa6KkIAZYsWcLIg7aQbzUrxUfd pdWgwbiHjjZr1qyzzz4bGvhYyr/+9a9421FGbt+OoYSAg8Drrrvu8ccf/8QTT9DRWuUnDBMFcfc4 fYgFUgjQtVgOaYtMKyBiqwI1AQ/Xz/RDFHr77bdvt912cIbD3//+d0L7okWLLBMyYU5FJMRCUB+1 SBeQO938tNNOu/nmm6GUtxCL2NT43HPPXXDBBfLI3arb0thX/2PCtpeyCAz95MmTMTxMDluyTgPh pWm2C8yZM4cmX3fddYiHuYrYFJcEo2pxieQA2tZbb/3qq6+Sfvvtt+EP4JSSzkUt0mXELIU/3co6 B0ohJENMLJ+3AqMU4ZER+SmnnEKUgYwLGsYHb731FpK/++67lFq6dCn5UDJwP/300xmXYPNSXNpO w7fccsuvfe1rd999dzKZhPhfdFtTB85W/vlff2fMSBtFwdJYoAE7MQWxD0AUnU2cOFFoGIIR4x97 7DFUdc899xDXxSuJx7SRxnpt6wRhLjT/Alh4IoC1Wuljlh5RxYxE6zyKOaJjEdLGKili+fD4P3Ye kZMg3VqKgtSISdEZ6K4wefTRR+m60mTeMg2yfYNHxGBEb8UgAVtLDGWrGNYQbWcQw4WPsEISiotH I2HDJw2HwNYrfKga5sK/tQkQCzgrV66UfGmpFG+tEZ74GmhEHgpawYxAzSkIb4Wb5KzxjnM54ogj bOuYV1FKBBMmpEUSW1wgoqUwt+3CGCx0Qk8OI3rSwpxHONjmi20IT3lFenVp/0cjpBQSUlD0KBCR tiaN/UMg6ujs7CTNK+ipFABtBOKRV3DbZ5993njjje7u7lF4iqj2DgdpJgNiGiVtl9Yhg3BDDMhw ZywR2YJjNEFDkNxiYnsKIAhuvP3pT396+eWXgyqXNBOzFJRsjjzyFi0IXKSlj5AQ/vg6ACSQ77nn nkQsMnnEYD7++GO6s9AAstRry0oCSvhwp0YrLVNMMm0TSFA79McddxwGDKVYi6W3Vk2pf/+Cre2h rcLYfiF9mVrkrSxIwB9pmYJLRSKkIMMEl4CKbLQUkS677LIrrriCKkS8W2655Qc/+AGleAtn1n5I WJxbxaY6YUgRZswQSy2W2LaXoCtQ/OpXv/rP//xPmAgmBPWf//znwrOnp0cStEvai/yQWc3CXJqM /dvMVnlsem0J3ox9WCeXZqMJayWSg6+xiODvWOsQ9QAia4C4D/A95phjWJLCnwolZLyFTPoAd1Gb PBKJZVnJsl09IfTkM2rjjgWcccYZjBsYMVALlTLxFd1TEYbCUhsJjOz666+HXuyJnDfffJMwzOTv mWeeYaLMcE9kWL1GcrAGCtoFH4pzUZw7b4GFNMMUxrzY6IknnsgoXsY9uG+hgQypuBjLb7PNNlKL NV9iv+RAAGiSBiWxQqoW2XgUhJm7swDLKutmm23GkJO2Sz4FGSMTCah06tSp999/v41DLC7hgwCK oQMgs9n8t7/9TcCEWC5Qkqq5S41UTV0MvelgjHBpI+uNQkMR3rK8hhgMyHgrS7UUtE2w3EYlgIhB 9KjMKVOmiH+RfFCFM+MJauFia7wVTJlnQCmBECdLWUB48cUX2aaxnBlBsseBYbCKyGI7+bDCEWyw wQaAj5wSfSm+6aab4td4K0oRQCyf1RM0E2WRbyURGkzuD3/4AzNv0QhSYWMnn3yysGX+zUT5hhtu oLgUhOwnP/kJi4fkUGnr8GL1Sm0OlKS5U5yCUpd9S+x59tlnv/rVr9qcsZtAIwgPVmIb9BTbKaRR WDtd3jZQcLYWiIWDElGK4hLGoBTFSRHRAhyYYUsO9Nie1Cs5dCVJcIcJ1g6N9B1YWfBFMGioa9Gi RXvssYeIwR0dCYc//vGP+EPLDWmlXa3V2bf/Y0JKSXNEHsQTL42EVCpNtoK1Bs7ly5cjIVUIjUWM caG4VinFEo7ASzORFvfFnqCAJhYrxeEj9UqLZNgqHFjGsJtZtkXCgUeWjg4++GAStIJpN31QXBZl P/zwQ7yKhRcaStllcx7F8m0zabJtBa2DYI3X2hK8gY+9Ohb6mBYIEKIV0sAEWCQAneUUVkFlcwjQ yedOhMC2WGRGnUJJAnoW2wls+FPWCRnTQWNNirfEDyEmvcYLGxVjlbesmLEIKZxRGEum7NDzCvHo 5wyiyUH9yHP44YczBuQVaSZDLF/jKHfbbTeGF6wRffGLX8QI1lijZFJKCAjhIgA5vBLhkRnbpVfz SsKMjccyxJFpGW6dGj/55BMKQg8HMX2cC2lktjLIW6laquCVvH355ZeJ2Rg9RVhcYrwCmLIqS9ci qPOKgjTq0ksvZfoFwiBAN2D4Agf6BivPhGQ2nIShNIQiDALQr8DFIwWJxzg1+h40CxYsOPLII0U7 4hPZUrnrrrukH/7+978HbRt7wMF2JGlF6504LWuYeC4hQyqWLl544QU4IBVwEYbRkZSidsb7TAJk p0ZmD8hJLdZaWHNjaw1XjrS8gjNGywz4gw8+kLacddZZGIP4SkCDvzBHAHbXDjroICshSmFMYDnb /NaE+ErJEQXRBCRnFYFBksjGW6rjQjBUxpAFzO+8807eSkGKgDCLEGJRDAHJl7QQjLrDihzAYbr5 i1/8Ar3zKGYjhiQdgUwgYql5VPEx9yjNkaaJlVqlCBRoAY8xe/ZsRmz0Pjz7r3/9a5pptQM4MsgW M+OVjSuCM06DMe5hhx3G6BaeZBKqWVWGEvvnThcjnqEyxJD4hAyiKek+0IiElEX7PHKxyC+Lf6St uonoLEM2348e88HZkgnBv3+nXsBpLU4OMgt60goZpAqNIMlsmEUFaYKAyVtWDZmBUBAamCAVrkk4 iDzQy3BZmgwOGCG+DhvGuUnHFEr6IwmcFVs8JGAozkHqIgfmMGExXEI7lVrlCgfukydPpkYEo6dI JjSwoseJLiw3pGWNExpRtLTd8mlNrC3BmzaDGu6Gk1YcyGIeQ47VJToDSnSJetjIERxBjVka6QMO OIANSGZ4oNmqFcwIfXARaS655BLGZdiQgAtny7wVbpsWLyk0aBQtEimJhWKUok6IRRIqZSuRR1En 40d6OJRcvCK2ibmQkBxby+oJKpJMEpQVhqRpheRLA8EBryoDSXKkFAkuwYr1KEIddmYLUhwLhsDy l4S0VBoi7aUUr8gHUiZ2pMVALbw0HwFks1yqBmrOfQhDZrFsCUuau8ROElBKPyRtxRAyXqF36kJa ICITStlYQjCcJjFPpBJApFe35gif1e/i+6R19i3jia9//es8CjgMFGSOYhTWrJ2hHhJ+//vfF4Gl UsFW4GWCa5fXaDscaLKUFdtgeg1/cnBSEFsXT+RmHQKGQN2qGivbqITAS0ulsbyVHBIMOIgHUqm8 hS0rpSwAwpnTZMy9LPgIwKBKRj+8Ffyl7Kga7aOARuvwldCzm267jBSU4ESmqMMWHLsJCzJNoMmi d9LSRxhxMiQi9JJDFGdz9He/+500VqIFUFgOVrnoi7AEN0yRhTcZ7wqAKIilJpl9wvCQQw4RvQhP oRHLQRc8ijzWlhhGc1yXBTDJF31RL5SM6rBPayrCUHrxqEx59e/crfaF2E6uRGYqtU0WAvoOsGD8 4szJBByhoY8wocItt9YLvNIQ7tIW6bzQyCMCUPyVV17hSMrmm28uTkaQh4apFO4dMaSBchfFISrD 2ccffxwy6dFohFfQCBn5rciLGAzRyIchd3vJI8RS0Ba3BK2JtSV4C15iH4xAmZ6yrWvNBY8JKKjQ OikBUfBlKoZ/Z62St8AqrKAXzRn9NOeaTI9knVPyLfNWuFvT0knE4jEavDBr+6zkMFBgACHRWqyK SkUwyHB2MBGBSVibsA7O5rTWNSotTcDCyBdrI0EVtBrONAfXzLycySg5zBElYAhnWsewlPUJJBGp hLk4bkpRRNpuEYCMGqVfCbFAxFBXaKy/kCrIpFLSNIo7lzxKWQIhgZzozhSWVX2ZFkiLqEgQs/hI EdrI8gntkkckkSpEDPhbEIRgdXuQ/NXvYChsZUBN26WZrGZLYGO1XI5QCIZwEI1TKads2CixUpEj xUGDFXKRzcoDFPgaaOTi0RZk+wDm0FNj6yQVbhRv5bO6/BY3XsFQhEezbL786Ec/EjB5BSuw/eEP f8iaAY9ISA5rIYxRRIMEBlatLH+EJN1qHvaVJKhXALGmRaBiRYG3vOIiIX6TinC7o4qPuUdpEWJL e0FYeh850qNJkCORW9BmJQMrEgzRi+WAmgTzUSBgwywZsoDHHQKhpyOzYAaAOBbCEv0I5hTkLgQk ROliTsIZvTN0ZhJP5G61H4ogD6fYzjnnHJhYkUZJIo+tU0zsQU7Fk8CM4c8FmbVh0oIMidYakcf2 ZZFcCkLGhBgnQKNIy2VhwRRZF5RMWwV84GBroSEsW0IjbIXM+gEaTrSWBoIPA2IApOdCJrVIL6Y4 A1wmBtCThpKLhPXGpIXS+jpyYAJnoBBicqwMksMr0bttLDSrX2tL8LYtFyXxgYoddtF50CtoyivB EXqUhA6wJIauBG9Mlky6AWRcrRYmzOFjv/SlrO2TturWBBx4lKAFpVgJamZgwS47C+AMohk0QIM8 6JIENGJb5GAKaFck5BUchAaGuADyjRGt6aIKkZyXIGBNGZ5iLqzYwIQ1AAYu4krgDEOqliqoDuE5 qMV6GmkueEJAgnxLIzDaztAkNJTUaJERYoECqXiFSIhBDrVDLKUQVbhRi/UIzJUZ7jDgZYFRzjwL DWUBCoVKLSIYPCXCwVCaSYLaKQKxFUM4UHCN9iDCjLoTLO0KmwhMkxH4wgsv5BA1xMQkvlchIZJI jTxKpThTYYhUNJm+SoLi991335e//GVeQQ9bRh7W3iwavEVTyM/6qiyxggPrJXauIMAKE6lljXdB zIonJoE/wgZgThFqFFbAYjmQSUvpF5LDK1okF42SR0v8zxLWN1EQGrmLJFb7rCfxTd0/4zCG8sFQ DEzAlImgICB+gJVVmiPNJ2EtU97yaI1HyFCBJZYEd86psT8osIAhNFzSDeHDVpRwFgJ7FzGkazAK x4SIVWw1CgEmIZYgj8xKqYW07UqWz6iEEIhsYtsQWJlpjhgbOTZTwEFgm4NVCG50TDJpDkwImUjI GiSPUkS226gRUZl2y/aoNFxax563ODQqhQ9LEexMU5wircBa94gZS13AyDiGCZVtnewpUO/ixYtZ l7Wr5RBIoxhVMHGHgItMYj+1kxCnJK2Ws0RUhzACr8BFEekI0mrbEWztNrG2BG9Rj+iSxhMDWs9u CFgCLgoDTRDE1vH4gpS1XQsrOaIYa6CEE5n3iK1QUHqdcBh1l1IoxhKLRi0ZXQjPKI6Yfo7kUh0E TDcxJqGUMEPaNsEmLKs1JmAIpVincKZFCLx6cQYKcAATSYjk5BAnZHjRyp/iYqBwE+bSz6GRXiFI il2ypseeHGmZtTOiosMIDtg6y1D20DjFpaDlQ4KKKAtbSiEe1UnVvGpthRRkERJnJCIBO92eUY60 hRGDyGN1YYvDkCIwh+caL04Rskhu47fQwO3111/Hg6A+5tDiYuSVCMCd2mEuDkLMQOAVAogJ/6yu k0B+dus5t8XqnPRkcpDcpvEdzBIQUtpIEfsKSSAeZVoiib1L6wQKiMGT7T3Wby2B1fh6662HDHap nHaxVoTAlLLE2BLtkplHa74lkISYnPRHyFABnJkjjioCsBzDBORRxcfco8VQegHyWxsT7XBnGUYW vbFDHNSCBQswHikoNEAtJir6tSC02icF2SSGTEzauiBo2BH/j//4D1tKXtkuI8hDhn5ZR6F2NGKJ SaAseKIO1tKRyo4RW2ls2pqxiGofRX6m9VRnMykl9gA4tmnScOShj1j0hD80RG4kFJltE+Qta5ZE ZdKWlZgZJ/k5XWEzb7rpJjyYFOEOGoIYPLn4RnHvvfdGWpAnVMNQILV1ITwycxJFlkhFBdI67gzc 2QyCLWRwuPLKK1mykrdWlRiAFcaKITjg8MlBbCSxr1ZPrC3BmyNIbHgLfAQMdnPxUGJ/4G47EmAJ fIKU5JMpdgaldfQQoAZhSBE+3GTP2+6y8IqyEK+OuOTAk4s0dzEaltyRkABGJsbK4jBjOrEJWLHr KZugjCjxmHIcGu3ySkyT9WqagySsBVkHsXrtcKZG8b+UFROETFoNKwkhWJUw4Q6Z8JGEQMGdtuNc ABOe0IsYllj6m1iqFUOaTD+BGGlZlucgsRTkDBqzPSlOKSI38y3m1vKW9jJeoUYudsr5CEQGv4hH Wo5o0QThT3XwEegoLgLjdNgeA16KEG6JdrLnTW+USgUByv4Le7ANsQnUxA+BoUGawx0BbJNZuKNS Vj5Fv0gupVixYJZApbfffjud37ISMZBWkKeUnI9FQtYGmXwzaIOYKl544QUqJS2caSOn2wBTRve2 IRCQxghbF/FsdZKw0vJI1cIQhGUfUaCTTMTAyLFJ2adg/2/33XeXvXzbajuIoS1WF6NqHPXIVjdB Avq7774bj8xbmo/xUCk1IgBwUe+oUmPusRVnhMdamBjQTLFSaQ5hgEktxxIxFcIbR3NoOAhADAGU 6BFtiiFJv5BXghUE/AgJSmGcJwzFEqiaIi+++CIOilEymaJWFAQ3Rv+tBkNBxhD2Z0+kLjFIaQIL QvbLPXkrda1+FwnJt3GRisSWWoktk1aIZGDBnUzmMNwFKHFcHAhlIAITW8QeEEFUzqnhN2ijCCCt ozjGycKDbO7wWygESPjLAEVksKt6MOc7Gj64FznZI8A7tcoM7PA/6aSTAEocnX0rOkUMVgQlHNCV OKEip96kU0jMlv5O1RBL6+QOK16JMDSQXmCZj0qsLcGb5Wj8HYM4OgD7tUyMBEewRg0oEpjEsLjL RBCkIBYLEO3yClihFGvGqihIDluw/OwOy4xCZnUj6hmF+KhHa9kMunHl8qMoTKHY0GXBUIjhwzoq vZ0EDtp2Tt7K907YtAwUEEAqpVFrvGzttMKuedIcQYMiVmYaK5k2h8ZCAAcaKC1lzoezIIclX4GC BPRcDCaoQqqj/1gTF3DIF+tkVXnKlCmUZQGc3kUpS4DRc66exSX8C8GJiCvciGScCCWTYQq9gp+J gBUqs+qjXhHVVkoCgcGQ0QaIceIBtO33gUgLZ6n3X9uDCNB6p2oQkB0vyRfQSLOUjcrYXyBNpnWR xGC2V6gUYejS9FiRnLm4+GIRm1aw8MA3MLzFxWCxoIFBYsMkQIN8aMQU2drkHB8/LkRdwk3Ap14q grPI9s/uok3eol9GRUAkLsPmCzfe0hy28BnhMdYkcouTFbbUBYH0F4HUFl9jvQIIR0+weayXyY1U KjYmRf7X/EgLzUGtMkyhsVxABIyoRrDCDICL6Msvr2E2oHHrrbeKcxCgRJUYgCADsdgJxVE9HYfh HUsUcEBZEIs985aNMErhW5iqWs8GE2gQY1Twhi1F5KLrkUBCLqmUO+ZHK2D+L4KKJRYJRX4MQzQr Ri7d31KK0cpdhoBUzfCaqknQVbnjW4Se3o3YSEKryeeiQ1ELwmO9xF3rQwQKWwuuA/8JPUtZdC7J l0qFCXfY4lIYm/IWmRnuM/6WXia9QCyct6DKJShRkMtKyFs8DF6LHFbg6LwCAggIGXe0z52GsFLC W8QQF4oA5EtaJhgi5+r3tSV4C+KtfgEsZNglmaJvLBIL4JUdBEk+vQgyUQBvhRvB1RK3ei4oxWpX h9vmCFtbC/liH2LZ0pOFGFZEWbEwckRa+ygTUPJbWfGI1td4YW28lQEs5ivyIzyZXNJAmFtnLflC AFy2XvKFhiKtItmGk4lIuBUMEUmwSO44C9vl4GC7gdTC3SKMYLYu+Ahb7pKAkkz6v0AtlBQRhyLY ilTC2WpfUIIJO1hTm9+8So5tOPRScJQ9CJ813tk8w29y+gx8wEQktGsl024AACAASURBVMJjOVQh tfCKiiRNxEJgMRvYki9FyIFAZMAkLCCtEgpPK4yIyqMIQALOXCTgAyZWGFvEJuwrJJfLvhI5RRIx AIgtPQmpgjs03K1qLI1ltXpCGJIvxHhhitMucmgpktAWIAVY4F29+JjLEawQG1SlybTRpqU5ArVs M4lOWzuItQTMXtKCPGXBSsAnIW5ECMi09UIm/ElINxG0JU2maIQiVjW2dkSFQOyhVblWJN6OuoQ5 mdZyLLFNII+IJPyhlFZYVpaSV1ySTwJJRDbbOsuK076cLAYEkVP4QyycLX9BiUcQE7aWlQUEqWDC RgOjKCsSCSiFOXebJh+ViVQCFHKKm+VRcqCRFsG5tTni83lrcRD+YIgwlhKCUdfaEryl2QITiHNJ jsAqYIna0AEQC45CI/BZZK1d8lYKQm8htlZiVSJM1ninlLC1FiNk2JZlCAHxTx6RTQSmA1O17V1i i1gqklg7XmONZMLQMudxFBStOWJq4kcsmZ3j2gZK7RSEXsSzCctNgOJRuJGwOXBGZsFN3koaJpKA mAvOIjbV2bLkSxpiKStISiZFSNgxvshGEaYgDIf5kMa6ANGCFVuKt9qDkWBNF0XIpgrWdRiDk6Yt UtzCIuVaMSeHlXMpK3dypLG2lPR88i1iUoo7l5RCbKFHVMtH2jKqOim1xjv0XLY41YmurSeiRRS0 BkDargkJQwjkLQ2nFeAsUgkOa6yUTEpJV4LeKpqESA6YfMI+ql/8M1ZjJd+GCgRuxRO4aLVVtN19 QCkCvpju6joVfHgrBBYHW5DiaEHUgUJbK7XEVhirBTtBxzCETPgLAWLItHVUpa0MSYtRtcqMGGJm kmnM7r/4S1lLLEDxVnK4I3lrdbaglRlbIpMtReuXeBT0hLm1cJFNMuVui7RmkqYIq9+y4s1jq+fn EZHEwgHWtkiUKIBDgwwiNvmIKm9JWGGs0uFmGysJ6YAw+WfXWhS8BQuBm7uFRtQv+YKapMXuRf0g KwlrDVCKAqz12CBhdWw1tEb0rf2J/tAxPKVqoeeRi7SsjZPgUaRqZUgtIoP1dND8s8tWaptvhZS6 hAAxJB8+1EXzuZNji0sOmUJAwtqrTVvOUsoC1aoIMi0HKxIcxOKFgwgmyAgTGAoUIgaP8nZ1SljJ K5TC+qGsBOy4445EbqmXe2tZ6KWK1kxJ82qNl4UFMWxzhLmIZ+1BVAwTqYKENFAay+MonMkXVOFG ESGjFmFuHTcFrbTCWe4wt5yhWePVSkBdYoq2IG+FQGqEAwmbphYBXGh4tG+FRt6usV4yRWYpxWOr 9hFAoCBTmP8zJmMo3zaEJgvOVpUWKHKkRQBo0xYcMinLI+BIEfgAo6ANf8m0fkAQFiQtUCIGr4TY FhdT5NHagBSxsklB6hJKkcSyHZWQUlYw3kopS2bVLc0UUeWtlLVwyStb3AZa2iUycBcmcqe4EAsf oZE0/G290CAelyR4RUXSVaWIpC1PK4+8FVGRAQ5S1uIsCejllby1Ati1UpGktS6h5G6rsDzJXP0y i6hM7IIrQCBAIEAgQCBAIEBgrCDw319njhWJAzkDBAIEAgQCBAIE1nIEguC9lhtA0PwAgQCBAIEA gbGHQBC8x57OAokDBAIEAgQCBNZyBILgvZYbQND8AIEAgQCBAIGxh0AQvMeezgKJAwQCBAIEAgTW cgSC4L2WG0DQ/ACBAIEAgQCBsYdAELzHns4CiQMEAgQCBAIE1nIEguC9lhtA0PwAgQCBAIEAgbGH QBC8x57OAokDBAIEAgQCBNZyBMz/2bI2XPyHHKOaaX9aLngFMgEareYRoBGgIQgEziFwDq194f8X GqNkkMfg51HXCEuQGSAQIBAgECAQIPDZRSBYNv/s6iaQLEAgQCBAIEAgQGCNCATBe42wBJkBAgEC AQIBAgECn10EguD92dVNIFmAQIBAgECAQIDAGhEIgvcaYQkyAwQCBAIEAgQCBD67CATB+7Orm0Cy AIEAgQCBAIEAgTUiEATvNcISZAYIBAgECAQIBAh8dhEIgvdnVzeBZAECAQIBAgECAQJrRCAI3muE JcgMEAgQCBAIEAgQ+OwiEL7kkkv+H+mqvhopKB1Xyl8ZVgMqVFaKn2GL1pUqN3TcKSqVU6qmtF8s xKLhcqjRr0Zqqt5Q5biqKr+i6q4KeeWQLqpGyNMRP0y2Ktd0IlJQylXKa/5FlKe9XMip8M4vD4XM T71pSqiaryNOsaGUo1ytIiFq8mqlSiQa8xpKk+l7CKXVgK+GHYh02K875ufTRkZU2FONqnKLyvHd aqHkubFoLNQIaVe7kRAsI1K3T+N8VdNKN6phlVXVuIoyiiFP+SqEiCHlUm9IOdDXyDLsGyG/VqtE K6QdxPB5C71Ly2mkaQbllK/DtEK7yq+6TqhOBcrI5mjKK1VBcmeonI3F444XUkBaqKpoRPuGDmoF FygRjEIkeCRTcqB3VNnxa8qLqrBbKkaijtZeKBT2KnWnETawQtwUXlPEMU+S0cScxiGB71Xr4bDB uu7Vw7QzhIoa9aYaht1yPBwFXaAIwaGpgqqqlXQxoROGv6Oq9Vok3NCqmlUlWhdTUSM2xH5dFfPN MhQOU7Y6PBKJJwDEtDEcVQ3P1zqE+pQeMlI6sWLdoAzbsIHX9XQYnEVoCoUNURzOOYwqohp+LaLz ygPBRr0aBQPPKAhUwjWjr6pjONHwkKvRuCrVvEQEu6MAyiobfDFjr+GWvbBTM+LSRB2mQMOtOdhq sWlu3EuuLtZD0ZKKDCpjudy0KvkqAh71WtFXTt0Jl+uqjqgR+kId+cCxmi/G/LgqNBS6RZJ60zxI 0SGqXjgCY8PLiyh6AX0KzjQOkUKNpvodf7ia9yPxqgpVPLfu+vFQuFHyHApyQdq8URNJ8HKr5UiE buiVGoWEE6HlVfpIJAbyXtF1omENUtSOiZaKKhYrhFTBV3GU61M2H0YjWd4ZfahGU9NgWG00og5i 0sfp//QkI5/p7IaNaT91G3F8VTVFitmil4iNhOhBfhTLxpy9IraITtCDV2kqFps3RUK+LodC/ZCU DTYxCHzs3ms4oZpq9CmVV6G4TwlaiKmae9NNcOePmpuSgpuRF6MoFVOxmOlQ0lG1qtALYmF0UqI5 jgNNvVaN0gCKh5p+RRuulYobiYbJo3HZJqs4ZWo0vDHgDoXCIKiNLVCTgdk02XPUyH9h4rvawUrB oamGpukp7AmZdLMqB77UTWfzfccJYZ8lfGKjkgAVl3Zp7firvHItHIMKV0U5WkRdYZ5J5QZVKqoq I6pSUOWy8lO1WGg4V0vHm5oAdoQqlVXIKBrflfcaSfCtw9lrhL1B1cgr+nUDg6jU3VAYEyiH6VQ1 GkkfCQ02KlEnGtKe8UoARUEaGDa6C64xh8BqWssVjPPNjaiR7FRdm6Jq3aqeoh9X69hjqFBtq1Um esW2/GA6FsYvhBrVdVRqQi3SXoyobMN4qKZHJggk6I6RqEGkWlFJeiZ2QpAxHjZmeoZ2HOOCVX/B 0WlVxnlA6apYhHeEF/6S9Dq37jgqkUl6DT8SVn7BT7hOZEl/uKwiPThz07MrBBX6TyqjskW8tuno RTcWSbXH08Yz1hqhqOmHdDdF9fwZ7o4RIqLK9WJaxcO60Yy2TQ+EyARK+nmhZMqEDUS1UqHhukmi UToCh6pLj8a7NUhHo5EQHr3Z1z3HK1eLxvvUdazhJTw6L1GOgIUnahCnG7VSZ1uH6dw4EL+q2hL1 epWuRjgxUhmX2VAx0/4q8SatVIKq84RIZMBjQWLiRLUQTxEXvZDDD+Q1IomoQmZCecMzHKA1qBiR +KO5UeXT1mZznVgUzAEdscPFWkXjLJ1w3a0mlZoQS0FPSMjRcJiEVQG/r5xwKFEDZxghDsG4UgtV qt0Nvx0BhofJ9HxwiKpUykfRYae3r0f5bmJcO8MqFUHZxFUXwJ1IqFKp1NxSmsGHV1JJxwSEuK7V qlXtV7yqb8ZJyi9Xkds0SFSWTlA1uBVqRUYb5UYxHo0raqy5uBzGdoa0qdIS0YtSYEXJTLzgGgcc NeqCjHBi/FQ4kuCRdMWrMHgk5RYZdPqdfiRW9020x3LyjYTnZ3zV1lSI49biKhRVupzPJuKxWCRc KDYYayYTxO86vj6sXZSRwTbqNdUWxh+idQqYij2NBqNtESNSlNECYzujIl4iKuZsWtnULaJm4kly am4hFnYyiWh2IBtOQvVflwl3xmpoCVc8kSjXKlhtOpxwGT+gmhTmQvmyEw+bSEEwoLJama7nFQqR usow0G1WHEsklVdX7WGX7obi44jbBMWnnHLrdfKoJJIwFZk3REOeERa2utYgBDHWdVSmKxN3THMy KhSpuQyi001bLIED9saLOtpntFJWxHRGvX0lNVxOqYiv677vRbUTrjGmaKhFA02/YWAzQCE2dypu uVeI0yiw7jlmqOt3pTMGhkTEdJbmsDSWiWpGlXS6MCAZPxPFj3m+WyiVK+U61TW9SlsiFsaG6ZWF Ev0QFTdKFRAgqyPW0RwyGBAFE8wWIOolN2U6ourP50NUx4WhggYaMQ//JaekGXSWK0bBQBRS5Uqh od1UOGZ8Qh5/pRnsjo+mMDnMJqMYdnl0zL7BUoPGRnyVTrq1Gtidsd9+Khymdf2rCt0dcTNORWU4 RGJ9Bnv06tr4vlQkrJk3IUcoVCuXYAvqdBOGDsloPKLxQiGVYzKDMKGaW2kPpwxazamSkbcJstFW cI1BBEb/POq9629bZYqZjHZuODXa3Xnkmac5u21nIveAf/WBhyTGddScRsWvbbXLjoedfZpafz3V 1q16ijfs/pWNoulVXqE+ITVp1+0P/dYp4e22UlEz5iMa+IS6eMTVDYdAgJ9v9snCQG9be0YtXTn7 zodmv/pm/+BIfL0pB/3g3E3324swx1jQdAyX+QvBLIylOk68UVEx37ni4KMnF4qhgeXOlK7jX3pC t2WGVCRD71ox+JPjjq3Wq5MnTx7uGfr5vXerdSYoAj4O0wkxCEcW+onh2nRETJ7xlXRyMkOEfAw5 Gqk3/Gg8YcbsiYQZQTMJDul6PR9FVOZf1VKDIQJ9hnE23Z1hig8bnz7mRulbjHMbZshiZiu+Gsiq cR3lVIhY1O3j3vG7eDUvFonlKkUnEYuFYsxXCCBuM5InVYT5CGSVYkF3tJVUY8gdnhTrbCd8DpdU xVeZZD3pj4TqE4heUIYc163FYkmSFSK6rjPPSIaileF8G4VMf25SNXmaFBf3YlURtFJxnQjj8j1V j6u4gSVXMaOqjpTx38yDivlUKhV2YjQF3NAYQOlcIYnynnl2xo3XLx8ZLEWjg378N8+/yIBmeSGf TEUz4Wgxlx3X0YVbGSrlYun2SrUwJd5uYpjyi9Vyug1vqfMjI5nOThNaYdswEw8vzMzCSTEiwiMx a4g4hXIpZaKRUVAIfx3SZlioGjEVTuGHWOtBopRT0X4ylqgznUo13WrD90qlekSHUykGcVWv0h1p Vw1iJo7cD7kuWBLJaSrKrlRLXVHCmK8qjNJC6oVXH7vp5hVLFutEypsy5fwHHzErTV2sbDCCqSkn US0UE5k0k7haLI1dpAk0PgOIUG04F090GA5xpjzOiFtKxgjXgMYggRWgWiTGYgeh22AYwmC4HGNc 0jpCUVM7DABDhXIxk8qgAbdcRbN+ueykiYzYGLSmgKgViMqNshlVNfMq1VosEg+H4mY1IEGHCecq fjrJJBLzwuIwzOaAIaqWlHIT29pCK5cnJ0yuhaNumHk401G3nTUUJmdxEyTKNS/CKJOO2xxCmbDG HxcCgAXDJ0W9bZiOrvmJpLNqoHfKxHGq5qlazW9P5kMNxnH1eoUVl872DlUsffe4b4wbcCfX65na wAnPPaYmdDWSDNzok+rivQ/fTDntxf6F5aHzP5pdiYWceALUMAaDVbNaJKF+0uQICPVKKZqO13wv xniQ8UHEKZm1MT/N5FejEta8COAhM4mnKGEv7JS8alskrmpVXauEM23KIcyZgZoZ3NHWiF9qeNEI oxiEdxhMxhPppq0o00ma1l/ADYCL0u30eubQDEkZGxiZTMylHtyMQagpLX3MrL9V8h3JNJm1ciHF XJnliaJz7gH7XfP+y6V6KR3DljBsj3F0KRQdqriTk5Gwz3SasWr9R9MP/8Wf7lId0xgHmKWZCCti vosdNhgKEICZqIcq2ov54QxjHreqEvQMj2nLcLWQSbSjNdwLulSFPJ0dB0N3SyVxC2bxsVavpZlW IRmWFnYGlJpqksE1xhBoGdo3JU91JI+/9y6zdMN8MRp/+6brdmzUo9ttrxLh9oldp9x4nerqNBba t3LZM89P3Xbb8B77qHTn+Hj6oLvuU10R5ebUpK5nrvvjVr1D6x6wr3FMMTMBxynUXDfF3AJLK9ej Kaetq0v1Dz978x0H7bLndt86x3TYWv7qm6/edO/dTB8h1FWI3CS0KhNK6TUNhpPw+v7dd6vhEdYi H/jqlxWj5jZmFMZ1JLo7Lpv5qIlMqwauPPlslWg3A39iLB27ObWADC+AxfJHQOLCuzFzqg4PJzOd ZnWsFooyRK370UjE82oRFgYayBCOxpKmxnRbOppiVRN/F9FsFNClCMkhVfRUJhpzqKzihxN1rxbP OWqoMvP7P/zy5T9LbTw1RXOIEI1aNVKPJuOhhtcZTehKNZQI1SplPxGvh3FVyFLX1RrDiGSGgYLP AmI4ho+rmFdMlwdHbjv+hNOefGRCPGmQYY4bcWJQViuhaDildaKhHYOb1xZnlghQJnYYn2Yv8cJp KjIEIdeLxcJxlj9yzOzTZgkctbq1CrEpFO5KJ9VIUaXaIw0cdIwJkl/1klSdL7519c1HnHa62m4z RSTLdLNUoMt6Yls7VTn12vhkJ5O+Gm6ro519k1SCSQsLfFWmihnjPT1WIDrqLI97jSi7Ik6bmXvo bHZV+7iJZsnEr+tIFITb481QxMqhWW+hJY10hoYRy0OV7Egq3m206IXMYAJ9RZ2R7FBnZwfRK5KI RTDdSoU5chvhtFZnETrURphrzpZAxGM2aHaB2nHQAyOxNG2PqOHcm9ffMf2kb6kdtjervbCtVFPd 6FepgSGVSahIPYG3Zf5X9+LRNGzCeFKMrerEo21maJUb9KZk3EikjVDdKIQJ0mxPMBRhqm9CdaPY qDLXTDPVNa/+MYQ1yjFrln61XE4k0m2RlBrMq0SCKMbKj5NOsHrBvNcsMHBJHDMBw8cqzUKqsSvF HKu5rIw/d1R2JJxKdiQzjIlYQY+wCWTm4JRH6WpiWwdixzu7iOixWDTvlroxAt4ze6sw3QyjljRq whAcQHfK9VoqGicMmp5DmDGTbFN9deWqRPcUM5+rqynjxxv9MuirN5xSrTMe1fVcKJVS3W21YiHe lv7j3XeoShzonjhkHwYdbJDU4x5dOeVELp05w4xxs6te/NEFzFJjbd0MyhCHC+DBxV4NhtV+I8oU VtM94/CJA4zBhWGAR9CrMLQLtRHoYsjJ0IEBNk1mGczRFcdPZtodwh9BLsWCc133DYfax6FK42dw EZVaOkS3Yn2Isb4bZxFFO5W6TsvuFVMXpvNejR2jbGm43ek2I+COFDWDhZFWPEtT1mqjxsiBGMvr FGOm/ly4M51ijIipMIqqhdcPs5jWSDP3BQqWysxUoqS6JnQmmQGwxM16FzPv8C9uuIlZuFnxCDFI hydt9SJuNcKwI5/X7e2UZGMliqHK4I8gnUwwNRqHU2KgiF9CKQwoSo1rv/61cx67P0W0zmvW8UKJ cKqdBqJQs2A4CmcLeJD47CMwOnhPf/FR1tmavQKjcne78Ee/POio/3zgftUVPeWVx42to3XWpfTE 9Y4+886vHn/yE/uodueYNx4lOKlOOlKHqnpf+s65vzjypB99fn/VbVYiWSSNMjWIJzF0VimTbUzv vHAhP/f+mXvusJs6dG+zvYYVliPf+fkl+JniyECmnQknK1Z1NZhTOGVWotLJGGuzrEeNV4oIlK8O dTmqzSx4dbCtFlGVSDXJeh+9f8p4D6ePnGaxzzOrT0z5mw7MOJ7m6Bg2+ETCAh4wwhI0U3y20dna wiXi39roFvgFFrrpYPgq9g/TKltS8WiCMOCyqoxjbKihnGqbrBrsdJq99ni0EkvyvxDhslnpTQy9 9ncEVcvZ4PNVZwRflWB9G29XYAIZDbGwl6/FGSRpWsA43a2s6ou1TTIDghwyO/G4F0+Xitl+ldlQ 5V26+qQPFqhVdeNr2E6k+5nw1nQeOJds1UkyI3VVYUStM94sjLP83dz5Y9gEN9N8ummIlfp6AheP VspuGL+BL2AxHidi5v+uGh/LMAcGtkJFVQG5rNpwgoo8E+GGRoi+hb/PVRdvYtbJmZqxNB1jTTql l/fHiYK0HaxqpdT4lFfIJdvMQq4JMKA6MMxQwEQIIrSTVLlGJ9Vlkuxb4L66VIfqGVGdcYYQbKVr 1mBZwmBUxDMmhwrMmYS6iTMAGU8ZY2Pyy2yCZqZCLCd2djLiaXr+ErqIYbpmzYbo0sYMlNbj6Qmm ZjOeXSFWnkvFaiKdCHd0Mqg0L3216v0P1M+2IOqr7rSx/wTjKBeNhRl/MI4xw0fCP9Na9hWay6ds yA/nVT6kUgBeU+M6I0lArMXqjAJZOqcidF0xXj4RrtfLcToAk2Gz8dk0QfQCFNwYGDFpZ4ZNlB2o GHPqzZnp4DiHphUUu6iRBJUwRYS86WtpBH+EoggbVQSDCFGHDoA2oSCweY3CYGJ8d6WWj1AG/OlH Drv0flLXzSScoMXaVSQ8AXiHiO7s8ubVuDaseqAwNDUz0ayLx9mL9qsOJkL/0WWvnDRjWNNJ1aps Itqu+ptLIe1xgwwyjGQVq/GsiLi5UEfC7GN1d6lMW66c7+jKKDbNUmpeuHRYG+slYTa6GX2AUCRR UVOiKtP+1qqF+3cki24lYQas5jCJXGjO9Fm6BzNjlEEIZDaJKfYMKoaJXtVsY1WG4pMNuiO54U7s WXMIgVEFc4+w6skyhmgbn3LNSkDFTE/z9OJ4iMX8UsFsDKBoOpGfVP1FM4IxoR17a6hJ0Q7sCo0T 0dEUm8+paM9Iz9T2bnP+geiOJpqr/E03YXbZ/iGwWVs0+olht8MMI5Kmj7CYgv4YOjBrp172EWiL 6SnIb5bKGXmzNxFhXwi8XTNEVh0d+Eov3VXXIZyKKqLdBnsfRp4w+3SFWEcCZjXNUDvWHODG1Iin cvS1sEqzfcPCIeMDXE04Om+pGqobn0CHamsLJ5rLPliKsRaW2f0uM/oIrrGHwOjgrcZn8FBMsxy8 TKJt6ZMvbL3jrgrPEqnV4qG4jlcHBhLjulXHBPXkm5ttvb1qSxerw5nxxNp4XdeihAnPKbz9waZb bY2frTu6oLwJTBzNhi+Oy01iVZoDZhhZ/J3nXt7yil+zm+RFMW3mKqyN+6yzZVjXqte9OXNv++M1 g/1DuLxTjju5+6Avqo4oExE3WWdwr4YKXjKqykWVbDO7gPCPJZg+hFl1I9QSEHEAjZrH7mQsypmy SJj+anocjsAckGqedTI5dAa826qBmVff0ve3D8LxRGGDrsPOPXXjPXczO7V1Z+nMx2c88fjSxYum jZ904mmnjj9gf3yOKmZ/ftSRP57x/AvXXrXsvYWffDRrm93XO+nCs0LbbM6M7dJjT57QV5johJ46 6Iu5ZNdQZwa39cfXnjJxiA3dxStv+9UVyxcubG9vP/eC86Nf2C1f7ps8acpvz73wB7/4/dwnZj72 6OOOG15ng4nHnPPlzC7bMne8bPoRuy5ZOT6dufagg9S0qdrNLi1nf/v2mypfUEODM3/z+/6FyzgJ k63Xdtj3C7sfdVhms01UhFBqorDsV+JOWOej748or5vDYq7zH1887HdPP/HEdb9f8srfy/1D4WT8 giceNG6zVsl+uujBq28uzlowqX3ckd88LXXQ/sZLZtp+ePyx62TzO40f/9bp35wbK85jDrfBFpc8 8DCLIomODu/dd//ypz/Pn/3xtA3WO+TEYyceui+DghpRrR7/3gEHX/ngI7NuuuXlZ58vjeTZFLz0 pecV05H+vJq36OEbblrR39exwTr7Hn/0BvvtHsOVaP/H+x/68z/dv+D+e2bNmrV4/rxYR+q8e+4w i4UsvGNjvQMPXXfL4rc/iMRj7J4cefap6+26nVlnaDj1j+Y8cvPtQwuXdjiRE845R+2/r4olvEbd JWowY8NhAYqr2s2hARJENX3R8ccklq3ca51xL5x4ZF9n6qPCcHz9dS+eOTOVSoaKxauOOOq8G26Y fdOfH3/h+WjdrcRjP33+mSir/RVv4NU3/vrk39589Y1ttt1qv2MPX//QPVJJxg217++97xX33n3l eT8478TTnR12OPfbp/z27juSG61bKAy3pZtrBjLlwfyaSkEijj5kPP3jE45ND+anpZiYeenNp+16 8rHd+x2E7pp6xOyM3XJh3Aw30pzYCjV+vf8h/+fFGU/fdP2Hf3t3aOGSbadMPuGq38Y2mqqrxVQ4 MnfGEy/cdV9+VTaa7Drx3HOmHLynGXB4zq+OPvbCRx585uG/vH7vI5t1TRwXzRz87W+qg3aYwHoY Y4hyqRFL+KEwiwhgymQ5ojnC0lxAev+TZ2+6a/DTJasG+yfttNWXTj9pwhc+Z8aymZSaNXvmVVd/ On9OYto6R5x9+rQD9osnk/FUGuFDmYjqHfbp+4SJCmtFMYYSZlW+Mx5hglhksAYRLWIvQhpolPPf ISWEDqMD/b0s9qYy7SpXvuzbZ4X6RtgpSY3vSk/tOvXH56r11+3MmNX7S3fe++IZj7/6zCvPPfnM tN4V/cXBiz58L8bgxtMX7rHfr154+pmrrln85rvFfGneqt6b33nLjAtHhv94zNfAf6Vbmrjuxhut s8m+Xzs6uu929UrhipPP/M877mC4k6+WN+kcx4rE9486+oqbsSIq0gAAIABJREFUb1XJbtFFUx0m XNOC5jwWF1VLNJyPH3161j0zej9d3Dl1wlFnndz1pX1MiA2ryYxxh4afuuOWWX97J5NMnHzaCR37 7OVnGlGOwoSdnx053SlzClD1V1b+5o2XOY0SjiZUhaOgvprz0UM33jwwb0Wys/uIC87uPmCvAXdg XHqcyuZUz/CzDz/49tPPbxnrdn09/aLvpvbaHVO/8PBDthgYmZBM/fpLB7ZNnMqpxZWF8sXvvVTP VaIdZiSGyCyrJGShrol8cBtLCPBfF7de+Xqv9nO6UNCLewf+eNefpn9DL16l2SvS1Xo9p+tlXS7q geG519x64yHH6KGir91hXS/oou/ntVfQfT3zrr71jlPO1iv7fN1YpesD2tcepbUu1bTf0H69Uswa ylLxtn2P0ssLupqt6GK+2Kf9sq7kdA1m2dLLzz5y9un6/df1yBLdM3/RL6/UDz+r81WOuizXfVoP 6p7Ft+21uy4P6YbLsq2uaO1WtTfk88od+M0Oe+ilw7pS8DTH3N2Kz96o1nWmdE1K6JHH5bGuRwr6 xVcu3+egnj/dpwcKeukqvXTZz88/RzdKur9X3/Psn/c9xp81S1eyet78q75ybP9jz+qlvXr27Ju2 3eLxyy764InHdV9V91dzV1/18MnTdXWxkS1f1r2Fp3fbV384Hxj1SFlnh3Uppysj7sfv33zKKXre HF3r0/mety+8qPHwo7rWr0dW3bXB5xYe893BJ5/Uuazurw/ceO/jpx6t576mS1mdHdRz3rt3m011 T5+uuHrZfF0dAklmqDdPP1o/9LAe7NGffqALA4uemfnH876jK2Xt0YvrnHqr6AaN5o8W54xGyuXG kO4p/GGrLzx62vmf3H63nj9PF3I/2WtfvWSpLg0Mv/fqbWeept99Qxd69dCydy/6cfmBh3Sp4Gf7 da2sc7lfb7WTXrhYDyzU1R490q/zOT04rGd/9Ofjj9Wz3tS5VWS+cfFl1aef8fPL0Ijuzf56890e O/6Mj668Rveu0D1LLvnil/QAbczqt9998Jiv67fe1PlevXLBy5df1v/UTNSnS0M3brVnz1k/zd31 gF6+VA/05h595M7DjtQrB/VQtvzKa5fue9DSOx9gD1739CD2leeexYBG5wb06+89evypesknemSp zq547pKLlt73iC5iNpURXQINQOFYk1fUxmZKTVBYhMSKBpbetuN2+sP3dGm5bvTrcp+u5o3N9I9c veGW700/dsW1N+ihAd238jv77lGnFaWifvCR+w6Z7r/6ltHvOx/ceNyJfY88oHuW6mXLrtltl5d+ /F392ksffOnYZUefqRf33Xb6N42EulBtGiQH/DlnZCRpSoE4PBuZ6Exuvy4s1z1zs1f98r4TjtaN Im9RnxEGaqjQq64PIb1f0Muzv9tk1/sv+MHKV/+qh7O6L6evv/+e3Q5EX7pWHHno4RsPO0TPflUX evSb795z2nfm3/ln3devFyy/deudH/veBYuefoLep5cu9K6/9+kTz9aFfl/ntVvUdSrNFXXZ1QWt i0Zyung1l33lpd9jJ0+8oOcu09n60Et/v/Pin5u3+RX65ecf/PrX9LxZutRP/33s0p8MPT1D5wca /SswVD7T0KXybXvtrd96x8jG/jENa1RNxyyO6E/mPHjAgTqX59AomPz33395Djp33ujM1V5ZF4Z1 FT+zzNQ71KN7+0buvPN3X9hVD63Q+YoeLD64xQ5vHPqVp39zjS7Vdc/yn263qXZzpvfNW/bgjns9 d+yJC666Vi9bqVcNXLLfIcaSK/26TgdcqWtZXRnUi+br39707BEna46eNNy3fnFl9oGHtYfucrp/ YOixZ5687ApddE0LOOXa1Ana4cFcxr/hN1bqB5667cCvNGb/XReH9Idzbz3syGV332mQn9N/y7o7 vXz6ednHZuilC3R+8KlTT9MznjKdmqtW1IWsBoRs4Q+77K4XLteFOqfGm8K/e90JX9Zz39QjK/TC uW9ffNnKGY/VdaHsDuo3Z1+z+6HGfQ0N6xXL9aJF1537XbqJkZD7nEV3bbOd7luAyVR6l5tMZKU2 3AFy0wDgxhSDawwiwBrl/3PVda7ev0QvG3jwiFPz1z2o+8qaL0jcgk9kpeeUK3rBknvOPH85rpOw VyQoVQbY42rkjEdcvPT+s767/E/3G7/sub2FoVwzVuphojIWw8dCddcjxeFiwlvfTZ87TM9eTsER OgaZ2C6Dg8IQseqJc87Sb/xN9y41fmHlp3pZ7z1HfB2PX24Y3+ARv3sWX7LeVF0ZanhV44XhWsxr na0RO6u913z+AD1vFcG77OeqDDswWPqVmKx4be48luu6N/fMWedVnnhGjxBOVmi3oHN4h6JxasXC X485R894Q+eHjLekT776+u3Hn66XDOqPP7ltk/X1otmm1XlXD1f1ksWXrt/dDN74gprOlX+/0UZ6 yQLTnMqwGawwLslmH/nWt/U7H5puXO3Vg4sZB9x/6Fd0qQfXM2Pa5/Rzn0BjvOdIQy/sv3HPHXXf J7pvhWGy7MPLNupuDkfofMO0dKCMn238bp/9TPDrX66zPTrfr5cv1EVGCUUNLNrFRdY4g950MdwJ 5Ks0/Ef0ksItG+6tZ7yMx9TeiEZ9I0N6eEiPDN777TP02+/onhW6f5HOLtXLF99xyJeag6SiWxgA mR/svKse7CfGeAUcaMHEjKHcg6ecqt97Q/ctZESihwkPq/502FEazzK8TC8dun3rffRfnjb8iz2a oUO+Tw/1697BJ0//tv5grl68xAw++pbqVatu/upXjQPtW3H7Fnvqp99BHl0Fq36d679qp8/DVq8c eOQ736vNfE4PM2hYwdeGJmYztEJB+cHXjztDv/yuHunRFUBbrodX3XDCN3TfENbFqLCkGyMuXyKa gZnxXPwV8MIYHtF6xaXbb6wHFmhvWUkzBCwUCRJchfpV626hH31a9/XqMi1dYQyjUWV0+9djv6Ef fUbnC9rFaEfcF1984hvf1J8u1vMWX7/V5vqT1+F23zqb6Sfe1vN7f7zN9saP+yNERNRmBl6e8ZiI gNUijvH/DFsJEtmFurxUD87Xn865fustm6HOhPl/RG60aEoxjqZPDetF2Vu22FvP/tiUBXYw+aTn ti120cODemTghRPP0E8/p7PztbuMrlp5/uWHTjnDjCZnfXrzRlvqjz6mLW5tpVHTsqGfbLiNiV61 ATNwz+bN6IRh+siwiWFooZDTy5c9/J3v6pkzGZ2YrkqriQR0Fiyq1Pv6Safpt9/Xg0uNstBm78r7 jvua7u3BYCq0mKtU/sOmW+tFy/XgkFevVDnJZ8bdjK4Leu6867bY2gx53eZwRqILd0JhM8o0B6Bs x5eMCxph/DpitOAygBnE5+gVPdfutI1etRTb0P2FRzbdRt/7EL2y2ttrDHhoHl7CNGdh7+1Tt9SP PW8Gjnzf4NJtMR6ajIGxcDWoS9gMI8UV+oPl9224ky7n6qU+PfvDP514gi6C0pBelbvl4OP0kh5d xoPV+cZSgrfEweboqmEwzOXemX6mfvRVMyyALeOMl/8647Rv6vlLCd53b7WXfut9XRg0HYHu8Mab 9x72FawInXrGUwE6bjZ/xRY76EWM+5mZ1PGH9513uv7wNT24oPnXoxf3X7v/oabbNgozTjhH399s FPbpDvjgjwNxq1W3ZJgtWnrFtHUwb08PMn5l+lVgVCaekLsxLLwjWAfX2ENgtWVz34+0jXv7F1ce c/zx6sBdFfvKrDSaDzRYig6xCfrkXfftfdhB0/bf2xyy5OiOilQrlfYIu5iV966689j9D1d7bqW6 OXfLthFnl5pLfhx04jPXKJucHkt57D/FWENPpytmyyfFBhufF5vFOfaczC41i9KFhe99eP1r59Sr FSfKZzvsUkZLHe2sezYPZblhFrqbX1ey0OSH+ZzSLFrVEomiyqbNYlDUHSqY7WzW5pGZFXLN57Us xzaPccliHPs+LGJRm+t98tFHB/32IsURTqfN7HJxLsqtqYz54PPtxZ/ss81m1MJBMHNqaestcz39 ZptW+xyMUVMm5ivF9sQkszfc8CZNmmQW+8zhXjajSuF1xpmtvki9kjBfhpjdw96y+8rHDzz3rZxX Hml3Em3JVLmxslEyh9WUXhQpqfXHmy1VdkxZ4x7XviKf46S0OSHIMWkV7uR0mM/CMZug4Zxf7khO Zh38zB9+776Lf7rJ+Gkbb7BR1w47qK02U20ccmkeTaDy5gaBWQhCMLZHlZrM6QCYFfnMu6723JlN +WqDo1gx8+EWn/hX1apX3//L339Qyo6M1EupDnYU9ZIan6oaPUaTbcjmsZ3M4b66Djt8HAW87JpX Rpb23PTNMz1zTHdctVAbF0sNcr4VWNBvgZ3Qsvr8DuZrm+a2aYMzXWgw78x//8PsiafxPWssGkdh w4lQLxJwEtAJ9bO/t92GfAag42lO6IRCGcds0YVVpbH4gw9jl1xkvllOsjXAZiot4mM88x3YvI/m LDn5jGx7iC+ohnPDqY6uqjan+A2+ZheaL5mMWlmvZTnffAqVjDKkMTugdbe3VjDbuxweSHOQgv2T GAcDElEHX6h22IajumZxuI3zupwyN+ufH86Zu8/WW6HioWpuXLotut0Wiz9dYDD0nQzf/Uzl/F0j Oy6u1h/HeYJ2Gl5hV5OTH+ZUBqe7qEoOZ9EqIx+okjtnwfuPPfrBS8/Fc4VpoWiCTIQwhy7/sSbL waU6w1BjwxgC+6a1Omc2152GPH7CMb1Hl1UXRw34NCgMvPtf/lOV5Dwzh+v9xO7bD150uSrU2cFy yZncTSZfgHCuAzgyk5DTfER0yR4Hb+QlG/lyW0f7qkK20h774TMPmy/Kom2z3nrvqMsvI12N1jix jwzp9gQfR6a89LL3l+VO+M4cd0V8UrtXb5SK9Qld05TmfFvEDfNFg1GUUTr7FKk4J784+Gk+p+IM GmxCjkf3Z5eFwxkIYzo3mbTRlOIIuOnCRlI+y3dDrM/zbUjP0PvX/+nTD2aPFPkULNvdHlUFc1QW y1kZddUuWwNIpINvEsGYM44cxTB+JQ8ye+9hzhPEOJBaiXTQ35s77F5cvTfr/Scef+WVVzqzesLc YpyTCo1SJFFHfamutoH3Ppyw3S7q3Y82mjBZdXXQPdEVkiIm4mNYsjFvJOYLErfx6dxPdtlkM97X OWI+kbP5my/6wVyj76g/Em+oLaYqljScthD9c9q0lStX4nOM6hApzg8MmJPi/KKG+QUBMEL7Jb9n 1pxrTz+HMY9ZRc97U3VbG59QDvG9ZWLZ0sXGPjmBkeTQB8cfGml2Vep8zp40HwqGnA4W5OsqHPM5 LVysFrv5MgKE8bghLInTN6FcIdvZNcEIH1xjCoHRwdvHuSv99gt/3e273+fcFh8uOI26+TiYiy3T ROa5J5899PzvNvsiv+9hPtLl1C7nJzHhOW+9u9MF56uJoVq4Eee4BEYtF6ZtIikmQ56XiiaqtXIi HGN3yRwXCiXNkSRiAEctOKnBQbBEwtP+d/58F/usctzM+DWMtcoXQXhcHFNMDQ9usOHGHP0opyLt MY7xmpAU44QPZ9/qeLP1zUEgHIIOufwAA4dUuYzLa158Dto8s5VGsGikzOk2znCFiO1+rVRKcmKI Ayl8baljFeQBECK6XG61P8fBHM6GNKKxFMKnOsZzBI9QSygo82skHDmPJ3zPczJtnDozXj7BwWYn xkEbvrZMpTn1c8SNt3GIxowVOETDGR9+rYHavdqEzTZSpRHlrMNBc6OEQnnKtA3NEIT4TcjxcP60 iM/JHM6etjnNg2TDxfTuexy34/ZqwQr/g48XzJr11M03HPCNE7aafrhxKiBgrmazaQF/xlFyKMaE uvHrTVPVkurOuOZHW/7xWyDs7UW90JHX3mjOqXHOjp1dRka4S3wrc79KKVIPbbXJxmy0m3Pmgif3 eHxZT+/P7v2z6mScwf5kyhxM64qr9uaRgsFiZ2e3OQWJEbEAYjyaw5kbxCAoff2OO82nwGY3Gm15 qjPNcIpHTsGbswntGQ6lpTm9WCAiNONvPFotkoe+DLcK+uLjZfTF+Sfss+Ef//iTCp8LqpxTI7xx 0hB46xyQiJXLxWg4EWObHH9uvvkSdAhEuXiluvF6m6jholpvPRoAdJFwpFhpJCqVqRtvqopFNamz wcHsUKharvDbF3xTxRn3pqeud7d1Gnxi8RF+Joivm6tegY8UkCYTy2eiil1wP5rCGAjXrkv0ZgiJ ZJzlxvvzZzoHBuZHh5997o0nnzzsqOk7futbJsgNF1ced5w5J8EaNr81lE5ydth0KRPg+PS8OcT0 9dSp6xrx2tr51AnbUJ2ZFYVh05V8xaEK8zkGR53ikbrnReuhhomRfEhSDRPYODJijkeySc+pw1Sd w01I6OtLnn3KnHvC8LggiTJICqsyP9wUS6NZqiCiGmviyyf+GI+l+amasKsPvu3Og6di8HVzJj/G +UECPhe2HOEICvY8edq6JoNf4zGfT9B2TnM1jxDq0DrrrQ9HTqUljek3QRHjNVo2GRF0Rgnz7Ul5 +Wtvvzfz2el7HLjjueeZX2RKxW445ACVZs+YgW+lSvhkyBgOVWPhFOdHqcKcpcfoOIJpRvB8XMop tUiCj1cZj9GE8MdPv/Tp4385Yt+9dzz9dE4cqmXOiyd/zZwr4OB6zDniq0e89JenDt5ol9mPztjv lJNQHR2DAEvTREaUaC7k5Jyix4eKIfMDCeyyNwM8LcVxZVJtZuTK+iCOhTNxGY7DNTtjrWIOYPKD ELGw+San4cdxVpV8mJ14vlaFY5GDg5FqX/Z7d9+hxnebjXOO1nop86MC5oAuRwj4FY2kifT88gpf oJg9fMY5zWOn6I+jkpiQ6S9MQZiAZMxUJldwxqVNlukyXifTg+AagwiIA/5vwYfNV7Xuufc+aPwE P+Bh5t0OP3DW4DNQjpYodeWTT6p8xZz1bfAFSziZSA0XOLfJDMw/8bbrzblx/Jb5oNb0GnNxVpWw 65ixOzaSNkYf9uPx2sjw9p/bNfu314jWfIfYPOSJ2+R3C+hg4XHrr68++th82TjBUe11leagKb8H 0R6raH7kwYyj28f5/MZXKNGeyPALWPwMF79cwDJRnOlgubG4t9cIbpwkkyy+MTEfGptYxhkuRvHN U0LGDzYd57Y775T9ywtqyOOoSBIXwDgdOnqODh264+7q7Q/NDBIk6AKretbnq3eOayYy2b6C4tsh qiG2MVmvNMalOabL4VKGFgxr3RofZ3NoGi9MPUzW+WqZX5HZoFMt/0SV+fqIuXUz5JgztPxiTHrF 8h4T/NhvSCQKHA1t6+hfNmS+fqH3IlIypQkSnG/njLZ2YjWPr5VVPGN+vwwdbr+hc9oRm1x45neu ++3N995tGi7hTdRrnG2zsTgSujDj8Y743KFePL5bMR/ZjeAWyxzSNtRb7fE5tWKR4suATletn1Zd bRwJ9OJJlyPP+IhktG/5EhNimUewmMA0gl92ScV2PPzLatUw35mrSUk1gTUJ0MtrpncE4K4JjTwK Ixg7pQgf9zIDZwKNJpwN+C5r2adqQlxNddS0uJrAWI0zSOZjqk6Xr1QZuLDM4JWNnlhmRjrMTG+7 887lGS+YAFPzjb74tTXz+2oxjvFvvtHm6vU3FF9Zd3MEl0EANsxJbKIMVsWR/2Q6EquV+aRJDVaY e3Pe3+XHf+JxZmDJxAijxglYElP4RolfJfI7zYdD8TrHmxl8KH7EJaxDUZ1Ia+CNxNbdbHP1KtbL 98pa5RvqvY932o2jW/jlyFA7J8w5TRYrIDSj2GooXOJEpeMzncK+zVloiYDG4ZtewpS0WPv95b88 7BeXqB03Vd3NENie6eyeyPlHPskLJeOFmvkhooppD8tIzdFosYSVLsHUAaFsfpXIhHzFr8XBlHFw ftKGG6pFS8zZfiZslUT57/Mmbrm5MbxMYpjPN0IOv3pXzfLTZhnWRrogYz8qFvPaqmrjmNo4pCaU VEdJTWbRqqbGt6l0fJutt1QvvKiW9CW82Dg+Vyp2hfoYMzGWGJ68fqfKLVdpT01uV5M40MrPGdFk vllw+cyiTldta1840KMYfBcqjF9YT0swT6T1ppOqpat6iK+JaIQvp126LPpsHillRF5lGQtrY+kL MPlmMZy+5dbbp19yoZq+t5oQMdXxI4rEYX5YjNY7fDE6wUzN+dHDUHxAxThcbszG2EDERa0YGA6q xiM/bxCPctB/JD/z9tu/cvkvncMPV8zgnYJK1aqlQfObKuFktTgc33G7xqc96r05SwZWqU2nNT+v MFyM4syRc4naPJgrwmd7KrzznnurOUy1q6aBTIAW9qbMuBbMw90Ym8sggz9+B0qpj+fvvOMuDPio 2MR7vqCEaZzftCBmc76VwRafg+rpB09Xy/vMZz6sq01LmC8RxjHq4HMMb6ONN2089bz5wsWNxfhh Pg6x9/JbVY75csEs9iR9M4YzkxBO4bOj5kX8yLhUne/hwXOEH0Zsfhkh0gf3MYWAePf/Fhl7CcXD D337jKv2+wJrxWFd4Xx3PBk1n9fw53jfP+KQS0850fy+JB7fqxPqOzEODDiT/OXJJ1329eMZCqYZ Y2LU8MZ7Nj9/oAITEvGaPp/k5vh2OT5x4kaHHPjMKy/Nf+avxrUxHv1k8VPX3WiCruedfMH3rrnh Jv/jOea3Ixjaz5/38CWX/V/2zgNOzqL847O93+XSQwoQmtQIgQChI0oNLQIBpFoRUBRF/6g0EQ2I goCA0jtIpEoRRJqIgNIJSguSXq7vbd/3/519z/fee9/dvX7Jcs98NpfZqc/8ZnaemWeeeUb/iDUz ZjkBu/cuRVCGQJ6hznQU0hYX65DrchM4Fm9m6YDgi9FZZDoKB3zajkLnAvl/bYU6HRLzHvjNr13/ m98mH31OX45qzrctfO/3P7tIU9/WMvPYeX+68koUZbWwccmq+fMv2fGAL2g5rZHPoJfLL195U0yn CFRjsSaI8Ye5Vax5ZS6/9fY7qRde9RUidawcmBlZCwcKB37nazfedp3CBhm0wZJXrrjlpz8tzQM+ 7Lfp7WkYfshOErTgFMAY0jfl2LmEo+tttFn+o0/UqjZPxhvCTgbpmpv+eMkl7973gBZgUL7yLHz/ wwmTpmji2XJ49PZOL1mYZviU+kLvEZlHQt4Ut2I8nmAYqylcJ4pAv16BxcMzDvz8/Evnt732qmKv UPSkFi255oKf+0PxDtZAMDP2DmTUm0bEoF405hCVsxvYdMcdLzz/IvXqGwRTb/s779xx083apBW7 Cp8fnl1qL6cjNAwhDGJqRCnxvY84/BeX/Wr5M89o4tOZxtffupFC9J4vkIH9UF0SMJmiuA8WTwEX 823YP+cbX7n2yt+2P/kMd8ewz2W89eGCC36hr2DFYjt/4+QFf7i96eWXtRQGIcLC9+/8zeWllaUn h54/mMCguRTMLeCotlaBxTQtlaFGf7x1NQJLP2bDkNr7GHKgggsFVrQ26TFZxHpnoJguRrQJNz3e P3/U3Acf+5NaskJf8v542XXX/n77fffRV/giwVZWfmj7q1ArXU/T4N0cUgS8HvbljBmYt/5t6H9s daled2WxuPvuuy/569Na2oTsYdXqV3/2s4+4vMsBUz6fbGpMRLRtOLZ8XNErsFFj71c3ivtsbWzu 9YoHSQ8rzggQJSZhF5Gra+NmHbL/bddeo95brG8DvvHRdb+6es5Jx+n7hD7M43ExDJFLNBznljac NfFJewucNcXd7ljdUm+qJRhoG9vQMjacxbwSy1MqD3v2OfLwSy65VC38QC1q5NQj9ezrf7zkSs27 Qv5dv3r87Zdf/NZLL2sMDR8/opt/eoGWiCA0QnLEChUGjBCC/xl+GgxvmO03swgLjkS0hbklhjBK y88Io2uAhE9pN6v/Yo9F//YBk6uVXs8HL/5DdwrWT159ZcGPfzJx2gZ6XUvHZNNJ+D9iIb1CQ5YG NFweg4eS11dMxBGWUKw2nab/A3Z61jc+1rDs6Rf0MPCGs+/8e9mvftHOqVmCcwRfeKyWJx944KF/ vuji7fbZXY3Rewd4JoXrQ7fSD6s0UErdCQGs8tvaZp4479Zrr8wtbfazqvto1e8v/OXhIM9FHl9x dTr91M9+qd7ngIgt/vK7b7px1hcPYYrD7oLewPj9WfY/XN8IswjgPif8nsOd4LTtt5t/0c8y/L5Y hcLXF314+xWXY/WWHj/8h9/79dVXLbrrj3pLk/LnPlh652VX6Z8hK0LGWyQ4aePN2j5arlYnsUIw rqivPK5pb8bgIdP7OZ8/4KKDDtd3BcXVIAJMlN3cevx8Mvl333hp8uTJGKP0sfrjN8OylxEKe1y+ cnXbEn94rMJaCfcp/fUYG/GxXWb53NrqS7HDxU4TVxdLI5thp03wlixUm5VQCPLUugZtUJOZdMP1 5p3xrTceefz3v7qxo62jYcNpu510hDZNwLZ+yvqnXfTzB2+7/f0rr1m5bOW2n515xElf1StThhlS N37JIZWdPBZWQgXZJi151VsKJEjxes6bA5tO0+SFQ0WfD0bT2t7G8acmAUEZ0ymsrSQR1Gt+zno/ s8H3rrzsoV9evfo316cwzrTRxP2/eYKeOOqj/hkb7vbVo2/46cXLVi6LJuLHnfb19fZlTYNcfmzL xlOhZ8XqlWPHTlYI/kfHXvMV2pNt8Qnj2SX44vH9vvLl31/0y9Sl1zalsCYa+fmj9zJVj91t1jHe 8EPX3fbfHy2MjxmVndRw6Kkna5hy6ZXc+UYG6GE/Xkw1twULPg/GNDDsXkz7YerN+S999bSbf/Xb ZatWrci1tPmN69gDjY8ffPS85x955P65J6GJNX7KlO332vOHF87XoANISWqnJygaXpKDsPtkxlaI nQs+Y3QdLD+Zz8VHNTBdJn3YszXq/f4x++/9nTFjnrxoWVc3AAAgAElEQVTznpe/d+648RPGb7r5 sScczzFboi6IndeA37ea+ZAZGVkrJkBCMT881fBuecBBW07bcMFvrlpx4SUdmfTWe+x82NyDuO+i V1cq/zFCbL2L9etNJx9EoPAkX6Bup51+eM75f73n3nsu+T0XnSdsutEh+30BQxaMwEXQWhc06pif NDPXs5gWS8B7A2rjDb5z5WULLr/qo99dz9n++InrffGrJ5QE70W185YHB09/4t77/nXm+VPGjB+/ 6Sa7zDtMs2FOdpEKIDvVyyDOuVmMZblqlUpjWjRTx5Bjaz6WS/YYGOMObn07V2wRe9cn/Kx1Jo3S 4lNEHbQBNsDdtzSbdU/s87tsvvq/N//fef/652sbz95uznHzxn1hTz0+uf/G7iqDDCYbhIuw1mEX OBYJQd6DhKYk3mAkaN5SGoQeLdU0VCLwha+c+MT1N/3uZxePqa/baKPp+x10wMS99zj3K18+/6ab YonRxab2UCwaQh6OZXlmW4TemJgNeJomI8/KrUqnQrExHANxrtzM5K5Xt77Egfvslk9d851zC8nc qPU2PPmUb4a22gJTSKoh3hjCGAjWAhD4IxlG/u9bMTlRCAboVG+xMNZbl+poj0Yx3hXhDBVTRyyt E9y023WHb/36l3f/7sbMlTctXbxki+1mfuH4L+ojM24/7/e5eQ11jy1Y8Myvb0inclvN2vGwOfOU Ry/dGHvtLWnEtYUxaAxw4ThjRAMtLR118SgcRO+J21Px6VOxU5OP1BFgOoDEz0cPWv0/wqyUrzQD XHDRJQuuuvaxK29pbWmauvlGXzrhhMX/ePXKH3z/tD/eg5TiQw4REELkDDb/WGzQDWRTjlGjxtUt HBYENPNmbYbah7YWq4VeiZNPP+uFm2644bv/N2H99TbdbPPdD5iz6w47/OTQY3664FZt6wWL0ft8 btGvf/OFAz/PCXyGHUugtIAr9RuEcWoGtZzC6KUFukFYY9x2o8+dfOwdP7ykeemyRCx2/KlfDu25 C9Twq2waG9/5c5/71Rk/bGtrW2/6tIO/cqzaaQb5iy0p7yiWxTltDFCbrGefnFajx+rSg774Efuf vul6D99w+8pzL4vFEh0N8eP/73t6407rpk3+3u+ufPy222/Y+8AxdQ3YhDnijNP0pUqGHDfLi7mj vnHaLfN/27Jsdba5oynkufDlR0fHkYowMbCWKEb0+qlk0sbEXf7WDgJO86jKWKVnnxwzgoH8E10y Dztdhu/oRLalNciRCWMfTYc1y31TJ+jNV4FttzeZbsPGsjZKQPR4X97v1fIvjx+LQQxrbSFQLwLI WLIPoBeFzIKlmRTlrBaOYBn1SOe8xfQqr9b0wRIQA8qrj8D1irt0MMYejnGGtRMYL2c1XCCHf8TQ EeMXWMKb7S9PQmgr1NiZaue4FAbP1lvbuuKAlVz8zMzfW4kqfXLn5Sezph6qUKnBuDof2IPmxJhA odCSAQco5AMlTPERNrKozMTQGjLQoho9Ggkqh1iondax6WE9H/SjhVfMFWNY9ljdpjjr5AdGc4pJ NT6qS4ZW5AeskRFY0a44bEybyCodKnNAy88bE8QlG08AgNSLH3IC446ZGHhCBgI3pmY2W+xgEDiw jKBITiv1/s2j1qxRkybo41RktnA8OocdL2jrluiSSFTIdPjZhWR0Z+hVjN+bQd06EuJggFUQe/wg ynqAhdl2NpT6OC2g7UUEAqhGeUfBtHnPoVkfq0EAB5Acg9BBsJNVK8MI2OGO3NgF/1w7h696b0St wMtfNt/8pS/YXpAFgvhwOoAwGMk/Vm44VmCPDciEYJ8LCzPtTWoC1ti8LW1rJkQbtH0ozec4V2bv jt5XSa6q7YDl2e8W0y3eMTBIeA0TJCZ3kNlyFs7sHcoY6WAsrG2sMiqwScdSFPmi7gk9M+thAHti EsS0GVvMaEBL+7VRMc7mlb4C24g+EXygoXSeQm4ttoaJ6Dcg2OA207QQ8huFURNkwoxJH5YSgJe+ 0wfPWHTRf+vjVFZk01jElk4JJDqQCFpE7dRChasbVUjrFumdEJOvtjTPUTp3o0GYEAy8oHFd9CNl wqFFqBc2JRgD1O1vLeZHYXct3aFNyowapw2AIJ7WBGCdA8lQQu+e6ymHK9aYuEGLLaH33PQvjtU5 Ntux4YVVP3b2mbwvGCxi0pCe9DKwkFXoJmFBD/ai2V4TJlD4jdAFaTWxHtV5PXaRbQA7hhMQxmAK AiaE/fegL5MrhKgaSvJrNHzBho6cNxIsHRgTCPKsvlPtauyGSMh1LZog7fTY4T/z140HERfG/LWF HNRNitpOy5gG3Uas20I1Y5PRidSaUjRLLo06xMVIa9jU6kN2vQ/PBDHsV0ywc2DUweOZzfSgQuDI ugXooYeO46gO+QcHLqVfLtEPPL14TeO0U49jRLUXMBse07MKeXGlnxY/tFJmsOJNJ3LxG+cwr/TI E/1FV6JGp40b0h1Yo2P8l176QZ8gwTxAURzcUFAqv+y//vUms76Zf8C+P7j3ARUbn87nAnXBTLol yiqZMa97M8XvQisisFjA8fNmTgAtflnUy1IVHdhxowoc84AJdtrBqo277My3CPaUmsA5JerlRT/r x0ZIRd4WYe2oixJXUwj4zjvvPDvBWU/E8Ee9MINRdYvDrLhjsSwKXHVo2mSDCZSS2YTBaL1jJmEK Ct7Ob7453RGMRf0M9PY2xeI/6uV3oLmHVz9GwE9P80EmWf1D9DDdwbPQV0plMgHmVlbiGF3CvArz aIK9ZhylFe4uMK6wc6lVvpEiM3XAGpnCwjwgog0QdaBE5015wxHuQmGEBZE2pqJKP0829HAmNk0R tJOwH06N2C1NpZKekgFvaIQMfmn8X1LNNXinoLQzg4MiuW3GgKRmjfwAOGkO+NuybHfglIgl4QQs sPMeuDJTA8ay9YKDFT3HwRGeq2Bbpa2T5rFVGgoyNTA1MaHAXDk8xmQb2y+EDSiKt7cWYmFt8KKe Y+ySBADdvEIhlcNkLDQnA+EQVrGSHW3FjpSfVrN2xm4pQSyikm3eegRlTOiaDzc3Lwuzk2DqYTNK KzC7NqFk6gsg+FVDQGkqhGnqOY0ftd4UwHgwhcP92awvGCo265dd/IkIEZlCPk4DeOWJ1qFKjC49 ds1ZKnHdQM85fmzWsuXmBBadL/of43Br2IrCPzSvTMfGsItqo7H6JQQODjHHARfRen/J1hjME7uz WthbQGSNbKM0FEpLf1gXNshCipEV9alR4TwcMxHm4IZHUbx1oyDGmyvEY4lURyqAXTD24swyIWz8 AEKpv8bF9TklIVob3IP0lVcoIvB++sufUg0RzATlsa6v0aAsRp63LdXGq25FwwPr4dSeJ7+KmHnH UtUo2CrjB7MVXl4uwxqml8dKskkt//bkCuFY0u9l1uWwUotXGczNKz2MGcAP5I2wkfTkg/Az+AG1 8MGx4ulo94+r5wC4OdsWDocRTKHmlgXgEmfyw7aRq8O5sa3tz+axPGh0YK1bn/rr4wVmdvTN24M8 NIKuNUsuP8bmaTVW9koTd7GQzWd84UALi5J8cRTpYAA0gex6LxXWj81p4T8WVFhecDLi/STb7A2E 0i2rwvH6HO/LxeuwZOuNoOHM82XBSMEf4Lfkga9qmVQuh6YnNtl9Xh5rQWMOjU6fB7N8AcYtjisU gExH0xZ+UOiBM2b4CfCb1T8i0/Ka5i48f9eYLiSzrSzPuJjn8cYwP5xpZSyU+CtQdaxAtSKT8+PQ ui4d9pSuhvArNR1o88GMPKfINASBNTyJY/hUs+ZGWrueZR86fMEM9nGjoTT25GF1HLlos95gxfDP taIWHzCoNMem1otdd/qH3z6sLemLGB6WX1qHLKctzTWgrckEFNFXAIC0ufXBh+/f8di5GNHj+TE/ SweazAxlksdfFnqllT8a+GnkZJ5gOplEL1IXggU6ODcLJiiBaJ4RikUzhZQffsm0BmIhDz8Lw8vk 4sGapJeHAJpVy733owgyffZMbDz7g2EACaBumU0HYNiIP5HNJbz5AMtuvVLWkhvq4mAQ+RB3NLBk lYjmPD4MVPFjT/t8Lbm2GKCBAco6URRnl2P60MsaBYfpPh47QSBkjlgTbflbIwg4d97L8wX0dNAl gWV/ooXcaguWlB2qjScGsGNMqzTfK7HA9oIv7uPCFWla821jUd9k44XGGjqV3CqCtyl/Cz8Hdh16 fV1S7WCNyWSFjcVkPhb3p7JtPq0gyxUyVv5MGCEURerCdeye+MExsWUKGaYPeIefPSibY+R73PfQ Z06sBdrYYoe1QrPeIenNZS5dx68RhQ8oZDYpbdcx1Kb1V9lJliYczK8Ra/7uSs8JFLOFduxIsWdP F9I8uVOaSn1ctAhqw5z5dJjp3UgY3KpAnZ3tDRZJ2dJh8wg1Lz1tsXekPE84lMkbIXg8lHWkvbBq vWzXVmbbfRxt+YIYRORmEvvCmH+Vl9s8RbaZQWpkH6bBYcINJlPpcEQ/MhbSQjnkwdSIZik3f0Ls 5cNevQRi2047sPOBBhN7NaiFgbLUYQUD6+pobIuywWJ9ze+ZmbHUTZpIcmpHYJEnLJmWMKJexxyE F8ctGyxCa9t4XDQBSHqKyZF9m+4CdOLpIK5esdllt6aViNkZIovR6fT8U5p19ByIhUyi2L22FTLT fOzqSq8kBtRyP5zWP4Zz8kzaE2L+UIF0UTMVUjPBGsnS/lxfcSntdJiptHIRXFfvZdjGwdeZdxhI iD8zvATJmpF+YgylUeIr6tc8yZsdo4KpXKYDjWtsdTa3BhMJXrnCRm5Yz+7ctUnHvf4QzBKnDVhC hTeXyoeZ4lnslcYwe0OGTJg/YK4XJQDIYo/D+UY6o1WNLtm51bTgAKqOh+0MjiRDhQIbUkaOl5e1 9N1JaAO2kriYQdjCnWJmSAUiRKB/x4JY79kgC62hkioAIBaXe8CLI8pcA6sj1Il9Ph47g85SdcgZ eMYRts0OUss5tGP3HPbxcloEm/EUTImMhlwOQbWetNH0hgq92GBfy8/Lmy8G8tEgYoKsap0Kr9Fs Xq99uajBZeuMhweosnUcjXOkSvkoZGgRPJeXeMVVv68KawTkbBBzra1x9uys2vO+TCHL42YI8TUT ogZ+WFi4Qx/crzXq9YMqqbwPEsI+VjwQGFKN2UxHJLQeZlf1aEMTjZHB0ZZ+nBPZAqdgJUEIBJht pJmaZ+sPB23cVWWVAHip1rZIXYxlB+tkvpfOioOYNIhxCUD5GgtGzOcLI9zCYYMxnYuWmHQjl2C4 fad/bPr3luMNNNbbpV8av3H9rJ/WTOEvEPHwWDHB3JPzXLLfPvzUTrn4p75tNtdUhSLtBrdEI5iI 02lL85tuGy+s+vnN0K20LI88ikf+9BITitlLaAO9TBOIDiiDBIgwUnHEcii7F9DlCGG4NtiRv+SI oyOLV4zv8G2zx7afOf9EtR4n92P0GqJQzKZavKNQawBNJiBGZDro5YmkIOs//ZAuMwFCLGY+nirT 1+MYd3oJz4+Z3ykDezT7LcRpzEIsccIIdvL6QRdO5Liz6Qm2tiTH1o/XcImrKQSczLumiBdiBQFB QBAQBASBkYgAy1pxgoAgIAgIAoKAIFBLCAjzrqXeEloFAUFAEBAEBAEQEOYtw0AQEAQEAUFAEKgx BIR511iHCbmCgCAgCAgCgoAwbxkDgoAgIAgIAoJAjSEgzLvGOkzIFQQEAUFAEBAEhHnLGBAEBAFB QBAQBGoMAWHeNdZhQq4gIAgIAoKAICDMW8aAICAICAKCgCBQYwgI866xDhNyBQFBQBAQBAQBYd4y BgQBQUAQEAQEgRpDQJh3jXWYkCsICAKCgCAgCAjzljEgCAgCgoAgIAjUGALCvGusw4RcQUAQEAQE AUFAmLeMAUFAEBAEBAFBoMYQEOZdYx0m5AoCgoAgIAgIAsK8ZQwIAoKAICAICAI1hoAw7xrrMCFX EBAEBAFBQBAQ5i1jQBAQBAQBQUAQqDEEhHnXWIcJuYKAICAICAKCgL+GIfB4lGHUFv1FVfAoTz5X CPgDqqhU1lBej/aEVHsmH4vo7shm89lcOhGLKlVMGR15T2C5yoxS0XEkLniTxbSKJqKGzpT0Ktof U8qXVx6+ew3Dn/W0J1VrRHn9arQnG/QX84Wwz6cyFKZUXilqCBuGt+DJKVXIk6VgZFpCwagKhJMF 1Z5XHXnVllKGV9UHVIOh6nypXCYQrPOoiI/KCjmV7VA+jwpFlre1JxINlJorKj+UFFWdV3kKBaVo kVfX5VNGUFFP0FDZvDICOgzHgjFSLOo0Ok4VfCqVzsRDIU1hQTdDe1rbVH2CklQ+q4L+nA+cvCqT 9YGb1wOIGVU0lBFTno7WZDSWUOmCopkUHVAZj+pQ+ZDyRqEpXVS5oCoW8zFvMaCCxVz7quXxCRMN 5Svq1MpIFfxBCIVqAoqGx5sDFMLBCXJbkqqlWeUKKpdRHUmVSKhQWMXDakxI5X3qvzmVzKlwSql2 4FE0wR9V8XoVDpQaKn8EAUFAEBgqBDyGg//BEd3OkcadYK2E1CDzNrKpomH4YABwRxwsIltiqD7V kc6FIoGm5paGUfXwSB9sKdkWrUssb2sKJRpCSkXhqb5gNugt+gOFpBGNeHJelS2ogKFCPtge7DmT DxT8MMNP2m4578Ljfn62Z9L4vEclkx314ahqL8Bvzj35hNOv/NmYqRM9yZQKhlWqXdVF2zSrN8Jr 2jz+6A922mODSJ2vPZWtC5z2xL2qLghTzKtge0cuZngC6XYVhUXBJv1ZX6gxna4Lh9MZIx7yFPNG 2O9R+Rzps8l0kBqLqr2YCocjuqGsNjwqZWSDnqAy8tECzffms1l/JNiRy4WCAVYGmY6OUDicz2T9 EAa/b1oTbGjQHNYoZLyG3+v3Gd5cRyoQjbS0tYfq4hTrKeRDHhqvF0DFZC6TzUbGxpJGAQbvU8Vs S3NddFQJYb8RUqmOdDST1pw14Ff+QGs6GwyEw5TPyoYy8PiKoEhm7aXQNEy5+eK994oUjXgwFA6G Fq9e2RGLrYr7r/zbEypZuGbGQXUtqcyU6GpvsrGjKbzexKZQwyXXXBeYNll3rjhBQBAQBIYMgXI7 73WTVbshqBU6bZR7DL8vm1beHBw2mcmGIwkjrLKZTCjvjYYD7PTqRiXYKgbYjDY1RxOjVdY7MTKu 0JbzRdjMBVUQxqmakskxkRiLrCBMtaCynrzylViQN+/xhPW2eOyoVk+hY1ljbPToQtjvjUULqujL JItPvDpn823HjmffaWgeVsyrukRWb8CzmWIhMKY+39w6//k/K1+IDeUP99yNtYJiu5r1dBj5cDQa IGFa/WDn2fMfflRNnKS32uGwodKjQx5zx5xNZoqBkJ/NcTycVZlcIRNmS4/8oNAR8gUKysgkO0Lx eMDjLxYMb0D5ouz/s8EgxOcLRSMUi7KSSfrh8ul4IMiGOegr5nNpfyjkV95Va1ZMHDMBzg13rY/H YazJQiHi8bMK8hiqrT2VqItEPIG29mQkEWnOtBTy2Qn1oxAIpIr5YMjPMinqC59/6GHn3naLqo+o sApr4lVbazoR1WsFPohBYN5Qo1NDSjqv8slV7SsuefVNZfhUc1qNaVCBnApm12RXjwklxo8fe/i9 N6j1AipBF7BjR2bAkkK23bYRL15BQBAYGgSYqcQNIwIZdnnsQFO+TL7O4w+2rMk1rUj4EecWVUfG n80E85ni8qUw00g4rFYu1/vpjPKxS25sU3l4Zya4Ys2kYChYzKhCRqWRYMPDkZV7VSFb2jFqeTSC 8S9948t/e+iRUpltAcTSyTUq7nvz1Rc2nTpR5VKefBp2qaXWq5PBtGrIFevScKpiflS02BBXIb0U qB8T16LlVAeC4rAnpBndx0tVe3brQL0KxFRrOpRMRQt5hPYqk1IdWdWRCxYD4azhT6aC6XZk4j4f +QtGe3Milw0uWxZd1T4BaXsy629rZxeNPNyTaw9kmvztzb5MxtdCIblgNt8QCEb9vkyuLYZ0urXZ T3NaW3yNjRMTdXB/5cll2pqpExJDPl+gJGJvL+ZDoyIpEDCKCRYPrc1jPb4JLHSWLgaWYMiXzXf4 OwwOBUYvb1SZogYzmw+y41cqQS2AjOM8QzPeol7PIrenvQBUKEydMEF/B+dJ41UkxKJnVbYjGEyw 4HjvtVfY3uvTCEQgKc4aYPkdqqNVlyZOEBAEBIGhRKDczrtKfUiqW1rUOeeo229XTU1MbV2nzlWi rr9eXXSR+uQTtcEG6v/+T510UlcNVXJ1JeruI4vlHJtvotra1AUXqJtv1nRusYW66Sa1zTZW8rXv +fj9v91y2xsvvpxsbufQdreD9t/xxKNVih1e6KLd9zj7r48+fuWvP/zww2wmdeCBB258zDylGsH5 iaOO+/zXT73v15cddvq31YSpF//iwjMv+ZFvi82VEYHhwMrgPgl/JF6SFSNKzqjUqM9u9O9zzvvC CUf5omFfcrXSYvPmP7/2xPd/cKcWB69J5p57/bbLr8205CLx8AnfPj64364c/6Y9YZYBKtnCFtSv UirQoWLxglEMwiSXNf/qK6dPXrrGt3r1LTvv1Rb2pePxjwq5K198VnniKmOoZ//2yoP3vfTCc1M2 3nDPk75UN2evQjhCYX6v95aZux1/893qtlsfeOxxr9/zH3/hzJeeVf6CWrLkirlzT7/z/td+d9t7 /3h7VVPrCpU9/88PBOsMajx9z32mReOB9jRLlhlbbL3XV09SMzdV8WioIZRsXROta2CHi7i8Ndnq iyUaC40NgYhqWXPu4ceGGluL6fYxYxqmbTh9r2OOiH5ht0jef9EXjhqzZM0Mv/GH7T4b2HSjt1LJ RSHvdc88o6JhA4mFn3MDVArY9Jcc/8HYEaI3GrG2AmsgxTG6N82Zd06xkBmXZ0nV1LT75hurbLv6 ZJkqjA6OGqU8fhWG7YsTBAQBQWDIEegj84aeffdVBxyg3n1XjRmj7Hy0UtQf/qDmz1cPPKC22kq9 9ZY6/HAVjaqjjupqWZUCuxLZfBbDdtRuJvn61zV5LBSYjq+7Tp14ovrXv2yZB89Lc849Vx1xRJ9K fPqnF8+aNWuXW87Q6x40rv71j5suvujEn1+iUrmNQ/GHv/+9L8w7fN9vnaY6Ol679ob0U38N7zVb 1Ydza1aql1867OfzX7nqmsBGm5x1680PzT97zgXnqjiKaZpvIin3axE3W2nOyr1B2Fg+t9s226q/ /0sduL0+om5uf/+Jv8zYfaYWtTcncws/WHDrbSf96By10VZq9cqHf/j1fbJt4YP3C9RHW5va6yP1 qnmxD900ZXSwE/V4tapaJP7dW29Vzaknv3j4Pndfp+pjWkstQkUp5NfqxYX/uP7WHb9zyvbf+Q4H 9isfuLc4riE+Y/NcALFAYXQ6s/qbp42de/Qht9zMVviaM04tJrPeKJzdOyWXe+688zefuctnLz5B jZ34q7mHKeTpngIH4Fc89KBqz6mmNhWMqXsffvTCS/e/+wqE35yeB2IRkmjeXTDQuGPPHPMhAsip aOR8lpUp2G2bFv4/8sTjV92878wZqn7M2ffew3nD/XvMOuK5J0l2aCyi0Qv5Otpbo1q6TmklXTkO AzRdOsATQEsvmozHf3bIwXVjR0+ZuvFWO8/e5LDDA/FAs5FX0VEfFLIrvvTF9in1o2dsuf72O295 0IEqHFTtLWpcXZ9GhSQWBAQBQaDPCKCw1s2V5IXMXF0fezThTz1lD+jyV4raaSfj6ae7kj37rDFr VtfXSrm6UlT2kdfhCHnvva6wQsHw+bq+Dq5v0SLj5JONGTOMRx/tfcEPb7yL8bd3jHc/ND76wGhc aSz9wEivNJpWGm8uvmmjHYyPPjTal+vPsv8a/3z7hgOPMPIFo7Xpsi02NZ55znj7k4s33z77/n+M VPMF221pLP/YyDalDaM1Y6SzhoEiXCpptBtGY87ga0vG+MvbL877lvHBR0bjcmPp4kfPOM147zVj +UfGJ4vvOuFryUefNBo7jOac8fFqY8GDjx99rNHWbuQMnXdVzvho5cWf2dzIrM4YudVJQ4dT8icr jX+9dvUWGxnNHxmZT4zkx0ZuuZFeYrQu/89Xv2U886bxtzeNhZ8Y/3zPeGXhvYfONdY0GdDXnLxr 1g7G7TcbSxYbK5brVq9ek4Naoha9//DsrRvvuslYudRoTxstWWNl2liVNNJNRqHFWLPCePd94/3F xj8/MP6x+O5pM42WpgJUFtCQK9HJf3xyRh6SW5oy+aSRaTeWLjM+WWp8+LHx+kfGM4ue32xvY/lK I9NmZApGc8dvttzK+Og/xqKFRmp1MdfalEulSiWxAtBt5G/pf7PgotbE7zBaVhmpFmPZUuPF1944 +xcPH/ZV3UzyFbNGa4fRnjHaWpJvvfrPyy+77NDDjA8XG20gKE4QEAQEgaFFoNzO29rall0J7LVX 2WAdWDbqn/9Uu+3WlWWXXdSbb3Z9rZSrW4q+fNl4467UKBDpm0s9ubI7eBOEKlHrr684DvjPf9T5 5+sP5wJlm9+98gN//IMHfnRWeJsNE9Mnb7T55hN22UmtXKkSY1UwmEM0PSGeUckQ6tnokScSHRzN sgeMjcqFomrcOGV4fHV1gY2mcrLqp10cgWeTKlCXoJkcvKIwheaaFiXrY2IteNhso8VNq3bs4C5T RC1vafx4CYpeKsFxtW/Jx4ujs0q7cA5xUe2euc3SX81Hwwy9bq25jjJ2vhCLlNS4uDGFxD2j4pSc 0P9auPgV9RsBvycYKOazXtI3N//1mb88c/+DgY2nLmluquNgOxZtzLeV7pWhDR9elmxXu++oRodV KGCk2jzR8S15NYblYaG4uHFNw4H7qFgiky+E/FodTxk5xZa3pVGtaPzHbXe9/tzzda3ZcauyQbTc EWxzyy4Q75RNc/2MTbdP+bJqbGyU1r9rWq2aGveX6x0AACAASURBVP92283vvfNGx5sfbNcabyaP LjaQMYqhoJGqRxqeVmPROyu0+5BChFpyhQb0AyEGh7K8PsHW+HHGjQYa+gTg488VvaPr2MpvfcrX t376H/+48todr77YKOZTiQiJKT262SbbTZ603TZbXz9//pcv/EUJrO4dL98EAUFAEBhUBMox70Gt oAYKq7JYqRJlNmzTTfXx/91369OEH/xA/fSnPbT3oO0OOei36qP3/7vo40UvvrTg8ivnzPvi1M8f AN9o8iZVRHNj2AfMWgU9GQSwcGVvnstYKssVrGw61apZVDHr0cpmMLmIkeV8ukHzPJKG9O3nZNCD 3DmcSwYC6d2OOmDVQw+PO/JI48m/7DprB1UX0lfCvaq1vUml21RdQ96b8XvbVSi/vHmJChUVCu9e BO9crc4nc23Kk0L5LY3YHZZHdVrFAaYWVymfB8m8EfT6wjo8Vt9YX/fDxx5XY8Now6lAXC8suIPl 8aXCvkiWo2No1sBwi7oQCegr3hBMCFJrmhswuM/GVbBioeDl3lsODfTie3978R8P/emLhx2043eP 12r2Tb4bv3iEyrQH6kblC+kA9SIy52Q6ojpS2UQoSBaVKSx75u8vP//kbvvvscv3j9Vadct8tx4y V/lzimt4oTqgy6DiF+GuWgENdZ/KsRioC0QhoZN5a/atP1wzo538hZvn0+kcF80CAVVncENAzfrM wisv3jGHjqCPXmrOpfysugwUAFvVdpu9ec7r3FLTTRUnCAgCgsBQIsD53hC7mTPVc8911fHyy2rG jK6v64KP7bX7YxLmDnfsxd9/Xx19tLrsMvX44z1zbspE25yN8vRNp+3x+R2POfGb5/3srhtvVuk0 7CTMXjmnGuBU6RQe9frrO+4yG+VqWDUCBMUVaq8nhA0TeFuRHWxYH5kXOGONwm1gRib7acu3w3vR f8MKjBobGj9js3+9+JLKef7y3DPTjj1Kbyf5dLTusPNOak2rWt3qT2eJVc88fcBBB5Vsk5ROfDlC L2RiiShsnsNnzV41GpTKciEYS3BCHFRaebugWrOqNcPedMpWW+TffkMf97I/1vpznOiz+9eX3+CG ExsmqjVtMESAZsMOJ9fDjstehr9+1BjWBB2FTMjDUsDXzoabliYzD954+5cu+Hl425lKb4u5iMW9 rZy2gtKR8msldrW6DbsoqPoZEZgxxbFfTyXvu/2ug79+WsOs2Soa04H+wkpT9zsQKrahBO5NeLmZ 51UtOZXBhIsaq4goctOs0xnwbLTN88guwAkbCIXlq7lfzpJHX6hvatW3899dGJ42ScsGgDndNgob M+iZ01+hupXPvTTjs9tpSsQJAoKAIDDECAw98/7ud9VXv6reeUc3hL+nnKLOOGOIG9XH4tleuz9m Ge5way/+8ce6XUceqU44Qf39772RmVPk89+dX7jlcfX2SpXEtBh3uDKTttpci7J9gYa0/y9fO9/z 2ipPc7168o1Hbrhjl2OOUF4ugKVz+lI3+9FsA1wEXlvwsr9V3jg2xNhuIzo24DZGuKMjP9aLfa9i PtmcLWYQvavNN/Fg2Oz5F9PjRqtUmxYgYx0lGtlzn31v+va56pOcWu1T76z4x/1PTJu8oV4WkIWq sGQSCuf0desiO1z2lx704bGjltdXodefOjn7x/vUwvfV6mbVlkX5WjUmv3TU0Vf87KfqzbfU4uXq w0/Um/+59//OV/mAHzaLEZbmoopMUEkWAAGVNqLs9JEosCJo82RWwd8Tdb64Ryt05+KjR+uTAsMX zQUKD/9VrcmrVET988N3v3veqCw3xwzlQUWOe3JKNcQ7sC7n55CgVDD34Otj4bo69dJCtaqoVgXV n9/4+EeXjEKRDb6LFl89SmSeWdO3UTc9otIJtayg3lnpSXq8eQ4dStwbC2slSb6v4PUVvR74dN7j i47G0Ixak1WNGGurVw+/eM99fz7ke99XMe6UdSAJUE3N6r/L1fKU8dgbT9z9zCGHHanCWkwhThAQ BASBIUWgnNjcsbmkfotj9YMW9LFbW9WcOerDD9Umm+irYnZV834UuI5koUXnnad+//s+kbPrcSf9 +69PPXHL9xtTreM2mDJ95pZf+v5ZbGdVIR0dNe5zexxw07d+1J7Njp488Zhvna4+8xm9A8x71qBu zU2kXGgV8uE8/NBXgEcjJW/N6/vGyIAhwlOMshVt7MD4yLhAfTHTUYSf+fKzDp5zxe+v2ffsU9TG G6KbrTXPE57YzjvPbcxdf+b3Fy1dNHP7bQ7cZ7/AnH1VOJErWR4N10XY7i9uQ14e8xWDbaubEhgn gbNFEyrfPufU0278+fzIgj+t+ngZemXn/P2vqq5ezZp52tlnP7Lgjx++9banLbvFDjvOPelEpOQR KAsHVyJsiCRUOKbtjAbjbY3tiYY6vT9P1K1BcuANtTUlE1ikwdQMjUsmGW+nXH7589de99LNNy5t XrHnDjsfdMKJo19748wvHnHpM39GDTGdzxgeTygU9LCIwV6qn/1/anQgdPJ55z7x88v+e93vGnNt O2677e6f32fejjt854ijf/3QAiMR8Xj8s0/+xtO/vPzDR/682udJj6o75647kKhjC0YjiHFZzb+R erNGKZl31dJ840eHHdbRnhofGzOmGNvlc/scyQBeL45OYCAevuDgLy7715szPvPZaHTs1jvseewp p6utpnLe0achIYkFAUFAEOgHAi7zqP0oQ7L0HoGVCHvhqbCKomYYcAuEtlxtSvnO2GGny175h+IA FZOl8DZYCLe/TH6GKhk7b1gggmcOXzl7xihKY/Lmr39r8X+X+BJxHyZTc20erJR54u05oyPg+fkz jxpBDKZ4vNkgVaTCubTXaCjC+0ucCR7JgS8SaUyKIamOx7ichlHulgDGVTngNmKYhWGVoIkMKH8k 3ZHK1QeMbLLOi7FyTJljUxzToQUV8yl/Mb1mud7yssLTVtO4sQbnQzSeQXGNHa/e1yJhxpBLJFII B9pTyXgkVuD+FztvBAhBtuKGNxzNYX6VIAijZJaUnAQgAOAoIctBQ0DLpTlxaIjnimznS2ZVTL0y vVcuaLviQMoOO4fMIFQ6a0iXjhX8inOB+gR27XJeP3rvIQT+KA2EMfPaHB4/lqwgWpLwY0euiGiA FQWLAQy6BkvGV5SBsD3LrTBE4xHkG1QH2QTqs34UEdAuCGnafDTcr2nwF3OFXCAgm+/e/yQkpSAg CPQHAWHe/UGt/3lWryk9IYLlMQ6bs1qTW9tcg0Wp3+5/4Dcfe0RhK5T70xgiZZMNo8jyvgYycZgc LJNcsCzOX+GF2iyqWtKk4nGtZabNjCJFhofFNTuBi6AvFudwt1E1jNda34mg3tFymAtrpl7YNpyS 8+AYSm5ppfXOsECq74hr82qcwcPjO9LowOsQnipBsE8SeDDmStiVwnKRwCPf5nwa2jB8hhwcRksg GWGuMPLSNfFSYrgdwv8Sa9frFfbWJMaMKGbLWJFoI2w6EK4P52xNqUxSrTdaWyhtT2ldBJhiB4fK OSzHamTg0PqtEeQO0dJrJxl9tZpk8GEK1AsjKG9WCW2dDVmCXkm0daiGMTojEg5t0Z308G9MwKb0 0bgOgV+jVVCkWbq9ZMlin5Z3VHKqYwUrBt2KWJ3K+TX+XH2nOQjMIzHVppX1dBYeL2lI6FWXvvie U+Mn93+ESE5BQBAQBHqBgDDvXoA0WEk8nhemjPOgFYVRbp8v7Pc1rVo9fuyY1samfEd+0uTxHfnC mpYmXyAUr6tfs7opHk8YaKgFfCmfkUGruYgg3O/16CdAPAVfzOOfGIw2r14BuwtHPHWhQD6fb2wp cggcj8eWNibrJ43OFtGpDrQlO9py2Q022GDNilUBjzfkD7R0tBfRA09Ec60tIR9aa0ZdYnSyNYn+ djEWDAX8oVyhtbnRH0Uy7S9wUczra8oWRjXUcVid6UgGEnXchNb6aPl8IZkMQVI2HYkGiqhx6ce/ AphKz2XRT4Op5oLh0PLWlsmTJ6db28kNltFotLm5eeLEic1rmrPZ7NixY/OeQjMkZXPjYw2YmPkk 1RyLxyMFD3e0ku2t4Xgs7/ekc3k/b58EgnF/uKm1BSyQKxSMrDbB2pYenWhIZfLBcOD9pcs2nz51 xcol2rpqoZjN5hINo7WmnS+0anXjhA0mv7nwvekbTM20p8kYwZCbkU+2NoVigYLX4DWYAiQbwWDe F8oFvYYnGGQHnmvPtUcT4WWLV0wcm0hEEosXLx09dkJrW2rUqFHt7e3cGYuNihV8GKgDCooozljZ MlhDRsoRBAQBQaAsAsK8y8IigYKAICAICAKCwLqLACJQcYKAICAICAKCgCBQSwgI866l3hJaBYGa QYD37WrXDZD4AWYfIG69qb03aQZIxqcj+zoMlDDvT8cQk1YIApUR4CB/ON2yZdpyEaqUtej6Qbwb XtoOAhQ1zK4S8esOhf0AhKf/MKrNc1buVrhLIw0pSU+uQXFrqyt7Qbww716AJEkEAUHAQoBp8bOf LSntl0wTWuGmB+NF22+vsKvYrJ9d7+aYVe0Ow4tTp+qPfZ7FZOFee+n7Dr1xGPx3uB7n9z4R328K aTsIzJqljVtUcg40KiUzw93tcodUQd5deG8odOdaKyHz5qnvf1+1t+s7LKZzt90ijDSkxFI1uQbF rctADe27J1L6WkeAi1riBIG+IlBl2GywgfHQQxXLmzmzYuzEid1ybb21fm+QD0/zmY632vbc0+C9 vh7dIYcYv/iF8aUvGU88Ycyd25W8Ctlmoj4RPxAKqe5Pf+pqWheJ//M50PhfcPn/3e1yh1RBvnyh PVFYKdcwh7tb6g5xk9SbNO5clUKqd2WlXEMcLtrmg7I8W4cLYZVqrVjXYTKFtHULgSrDpkrUgw+q G25Q999fvi0bbaQ++KArigv3WUzhYDXgf57jjtPS5gMO6EpTyceOllrmzlULFqhDD1XTp3cmrEKb maJKAjfxFmGWp/cUmtUdfrg68UR18MFl2uFAo0wKW5CbbEeIm3hb7mreKhRWyzaMcY6WUrM7xE1O b9K4c1UJWfeAErF5le6qhaglS9RJJ6kJE/QMiJkzJhcMrZuO4csHZ3qsr1YsZmuxM4/sEfsqZkoz Sv6uFQSqd+WKFdqQfjisJk9W11zTjUD6rmxXlu13MydRSBfPOkuPHMrcbjv1xhudZZq5+OLOXiXK zHznnZpXVXL7798tBuu/CLH5bLqpDqdRG2zQxbmroEFi1Ijuu08tXqweekj77a4SUP0gvjqFbnzs ZJj+449XYFLWOdAom6b3gWWRHyCF7topsNKwIbFVHXMRByvWaQjhvEfFSOMN5cceU5Mmqbfe6iqb swmOqMnCaqYsVj5sTlV2f/2rgq1i0nEgDgorDRur2EpdabbaSjacniHe2UvxQ4zAllsaV19tZDCa YhgrVxq33Wbsvnu3KiuJjwjfaSfjgguM1at1+krJupUlX4YSgSpdSe8gT77rLgPD7i0tBkLjW27p IqXHrnR3LiHHHKNHCyOHMq+91th2264C8bmzWNFVoqZM6RxOVmK754or7N+MZ5811lvPQHqM5Pyl l4x99+0WWwUN0p1yivHAAzo9f/FbDtqqAEWyPhFfnUJ7pZbf4Wls1G0s6xxolE1jBbrJdoRUR96R 2CoWTxUK7clMP+VUHzZmMkbUddcZm23WWQC5Tj/dWLzYQLB/4ol6mpo1qzPqlVeMXXc13n5bf2Vg n3qqHuR299RTxuab2wO032rOCy/o3wKnLW5HLsZVLx0FVh82lFMJKPJa9PSyukFKJgeigwTk2iqm vt744INqlVcaWITzwxC37iBQpSvpLOY+y/373wbHsZbrsSvdY4CQ996zCtD82+fr+orPncWKrhIV CFip+uBhNmcdaS4irWxV0LDSuD3QVgUo0veP+LIUWrVXKZM0/cPEKtz0uKtwhFSvxZHYUXj1vPbE lFN92NgTWyOKXKYeg+WxovbZp5uKQy7XjVWTnpScNzsc4bgXX6zIuYl99FHd12ZKR3b3V5JVHzZm lt4D5a5iCELkzHs4xRxDUNcjj6hTT1Ubb6xfSZ89W2vqNvACmM0h1dFTlstVCncllIA+IwC2ble2 F+zJqnSlu7OsU1hKcMfaiy2bwJ3FEeL4ai+wSpSdKnuW6n4Ouc89V59/H3OMTnjbbWqPPVQVNKqU 5qbNQZI7gVWaI6UVjqcshVaCKmWSpkqxVgk9etxVOEKq1+JI7Kiuel57Ync59hCE4Zdeqp58Uoug eYwAZ455K43bw5GNftzB5hCS22XgnOVfcolCtG53lPPjH6s77lCvv17xRiKieHTOy2ob2Isy/RZh VlRZTMoGWlmG3zMECwIpctgRePNNLQI9/nhj/HgtRbe7SmvPSuH2vOIffgTKdqWjs9goRyJdpDli uyL+53Mn6DHEneB/hVXbzVQX3lol2D3nnNM5Yt2q3SQri4Y9u8PvINsBFIkdCezZKxFfncLqZVaS tdrr7Y3fvecLhbrlq0S8mahKq/tEobscK+SOOwy20ZxitLV1EmZFVfHQLvqounO3nQKRsd93n5bh V3LW5r5SAnu4RaEZ6B42hPcJKHvhQ+YXsfmQQbtWCkb2aJ/WocE99E3CHON1rVD7aa0UbN2fvjbW 3pWOznr8cWPOnK7yHLFdEf/zuRP0GFJp2FCkO+//6jHmzdNTau8dV8647mU6q0bLYy/HjoY93OF3 0OYAisSOBPbsZYnvDYVVygQNih2445CYsxLLLVxoTJ9ufdOessRbKQaLQnc5VgjHHKbmjbtSK43b M3u2cc89Vo7yHiuXFW2FVIHXSmPlquJxJHYPG/JWqatKyUMZJcx7KNEdhrJR97jpps7zQhaM/BLQ vLA7lERI4F7eOsarPYv41woCVbqSzjrssE69ntdf1xM3mj6W67Er3Ql6DKk0bKjUndeiBFaHAlEv HeegNNnSNkJDzbz2jQdXBY0q5UNbFaDI2Cfiq1NokVGlTIgxFeusxD16KM1dIOe+KPQtXapzo+PC HtchYKuOvLtAi4xKFJYlw12OFYLg5IYb9DzDB0U/hgF7X3MXbqVxeziZRlhoamKSEQU06HE4K5cV bg+hhCOOMDgsdzh7GnsU4e4oQqoPG0roE1D2GofML8x7yKAdnoKff16PXXbb/FSmTTPOPFOLd+yO 3wNGMNjNOEatewTbc4l/+BGo0pV0FmJJeDa9jFq4Q9Owx650J+gxpNKwARZ3XjtWcH23hpE9geln l8a1CLuuJZwbzXNT+Zw0VdBwl2aFQFsVoEjWe+J7pNBeqeW3e/pn2QMKy8oeMEcDtkTBJhcssNfT 6a+CfKVWV6GQLO5cVUJQ+MJaDhMR8nz2D1B43nmdUkArl9sD6XB61iXkYmzvvbfBrtfhrFxWuCOk LP+mtLKOvG54Caw+bKoD5S6wbNWDHSjMe7ARlfIEgUFHwDFbDXr5g1ggu1XOXy+5pMxmaBBrGaKi Bot4GD8IgAMF9tXBJxAm98P1ifiBUNgP2gaShUtfXIx0yw4rlUlKZI3WRTVHsrLwVvl99QjUq6/2 s78chPX9qxhpGX4dQalREPj0IrD++uqVV7QaMCaDas4NFvGjRmkEwIEC++pQ2L766r5m0un7RPxA KOwPcQPIc++96pZbtFY5quk9OtKQkvR//GP5tH2Ft0egzjyzn/1Vnr4+hMpVsT6AJUkFgbWDAFNS L9/qWDv0lasVw2femt0bDJD4AWYvB2cfwnpTe2/S9KHKGk9a5fe1DgMlzLvGh52QLwgIAoKAIDDy EKjZpfHI6yppsSAgCAgCgoAgYCIgzFtGgiAgCAgCgoAgUGMICPOusQ4TcgUBQUAQEAQEAWHeMgYE AUFAEBAEBIEaQ0CYd411mJArCAgCgoAgIAgI85YxIAgIAoKAICAI1BgCwrxrrMOEXEFAEBAEBAFB QJi3jAFBQBAQBAQBQaDGEBDmXWMdJuQKAoKAICAICALCvGUMCAKCgCAgCAgCNYaAMO8a6zAhVxAQ BAQBQUAQEOYtY0AQEAQEAUFAEKgxBIR511iHCbmCgCAgCAgCgoAwbxkDgoAgIAgIAoJAjSEgzLvG OkzIFQQEAUFAEBAEhHnLGBAEBAFBQBAQBGoMAWHeNdZhQq4gIAgIAoKAICDMW8aAICAICAKCgCBQ YwgI866xDhNyBQFBQBAQBAQBYd4yBgQBQUAQEAQEgRpDoJaZt8dTY2ALuYOLgAyAwcVTShMEBIHa QcDFvJkQ3Z/aaY9Q6kTgmWfUZz+rgsHObjWj6eL99utKuddeOtaKMgdANKoIf+edrmTugWHl6krU C1//cvWi4BpIYmFIj2y/vfrDH7potqLsHivaHmj5rVjxCAKCwAhDwF+mvYZRJnAdDKoVOtcudCee qK64Qh10kJOK559XHR0KDt3aqt59t1usCWyxqMNvuEHNnq0OP1wnsACHeVj+bjmH98u6QENfobDT /Mkn6sc/VitWqNNO6wavieKf/6z42J09rz1c/IKAIDDyEPAYjhmhr5PRyIOsxlpctkMJnDdPHXqo OuoodffdmknApM2R4EgPCw+HVTbbrdWONN3ievFlgNl7UcPwJRlgW4A3HterKIcj/OCD1b33avBN N8CKHOXLV0FAEKhxBFxi8+rtYQZho3bGGWrcOOX3d8layVUl6vrr1UYbacntppuqG2/sVkOVXN3S 2b6QxfrYgrWX8PZ2ddZZasIEPettt5164w1HkrX8dckSddJJmjzQqKtTxx2n/v73TpIgnk3YkUdq yidPVtdc00UqUS+/rHOB5GOPqUmT1FtvdcVW8pkoEWt6rK9menjD/fdrL3/nzDHDyvz95z/VNtuU Ce9T0JNPatE97WIY3Hprt6zPPad22UWjQdSdd3ZFVQfKalFXhv/5wAeCKZAaH3lEt910ePo3Nqy6 zDI5hrAKNAu3EphfO6N79x+b7x13LJP0V7/SvzKLc5dJIUGCgCAwshFg593NaeFoZUfsTjsZF1xg rF6tE9kTV4q65x5jk02MN9/U6fmL/667tN90lXL9L77a//bardKOOca47TYjkzEKBePaa41tt61W wkDittzSoGl9deS6+mpNHm7lSk3q7rt3lkFz9txTgwPlLS3GIYcYt9zSFXX66cbixcbMmcaJJ+qM s2Z1RvX4nxslshBIFYmEkcsZ9fX6r5XM8ixfblx1lbH11sbbbzsrsdI4I8p9f+klY7PNOgcARdEu K/srrxi77tpZPvScemrX2KgClFWJVY4V8uKLBhkXLtQB1LXvvl11kXiAY4N+ue463Ra7c9Ngj+3R f/zxxr//7UwF8ief7AykokBAd9b++xuXXmq0tTkTyHdBQBAYSQi4WDVzhONjh4Oop56yB3T5K0XB 7J9+uivZs892YzyVcnVlqOwjr8MR8t57XWHMtj5f19fB9S1apGfYGTOMRx/tQ8FMvh98UD49xMOe LcecDuM0HVFUh7M8vW8XWdzODGStcPbZxpw5nSWbyYiyPnA7eKrblS3TncwMOfBAJ0RW9n326WyX mZI1xOabdxZTBSirIqscKwTG5hifVho8gzI2HMhb5Vs09NIDsEccYfBzcDvGldnd7iiWfSxQzjtP A2WuUdxpJEQQEARGAAKumb36ZFQltlIU2wWYqOXwRyLWt66NUVdQr33uGnsT4i6eXO6Pmcwd7qgC FguHY4Hi4BnuWsyQP/3J2GADA6Z15pnGggVGY2NXQkfJRACd6awot6crfwWflcUebwaygYNHspvE Wcksz9KlWm4xcOYdCnUbAPa6iKI6+8dijVWAshpikWqFVKnLndgdYpVj9yAuQtoxZYruDpNUe2wv C7FnwY/shDHwwguOYP311VeNb3+7TLgjiPGG0EKcICAIjFQEhHkPRs8j62Zm//GPe1sW/ACBOSLT 8eO1FN10DjZgX+VYUW5Pj1VaWewpzUBkAHhgJDgrmeUh8IEHjHnz7Pk6/fY0ZaK7B1n82Aq2sjsW dlYCy1MWKCvWKscKqVKXO7E7xCrH8txxh+ay4GCJqR25HF+tjFU8AI7Mo9K+mYXgf/9bJXdXFCsV cYKAIDBSERh65u0QmyP0I8Ry/Zj7quR1l+YOsbJbHtK4P2asO9xRIJJY2Fvvd95WpaYH1QFLDuEo +fHHOwXapLSi3B5Hge6vVhZ7lDvQCrE8Znp2gebWvHp2e6zDj/DffqzL0YBVxezZvdUbsANllW+V Y4UgKrDXhfDZSmN5rMTuECvK8iCZMBUUrBBHLks6YiWo7kkm9Um8XYBvT//EE1onoDeOXfvQ6XP0 hgBJIwgIAmsVgaFn3qbCmqn0xF9mHIfCWr/b75hGKac3If2uzpERxvCVr+jm9OnMG1HnTTd1qvux twYcNmGmg/jDDuvU3nr9dWP6dAN9LiuqkqczReX/3JiQ1h1ohVges0jOoVmaOLaJjjSVK9cxqN3B kDggoL3IG9jIWtmBDtmDqaNHLAwJBExXBajOFOVaQYEc4Zt13XefVga06rI8VbJbUZaHlccNN2jK +XA+TUPY3Fu7cJKhQkiHEttLh7ilip4jp+CQXd1RF2k4837++eoJJVYQEAQ+xQiUY95Mc46PBYB7 BuxNFFs3WBF5UTVnKrS7KgXak5X1u/P2JqRsUf0IZFrn0LqvjgmXCZrdNjxg2jR98m0de0M8QlqA Ioo1gf0Q3WqX29MjAVYWe0p3oBVieaz0bBPh36mUc1SQ0p3YymX3cDSw3noGYl6kFAiN7bngiOxE iaLVe+9tIG8wXRWgrJLt5ViBlMAwY0OM8hproEqCDdKXzW6VY3qQE8ydqwuBQpZZ9DjKYlaZpGHB gdIi1VFabwo0kzn+WpWy0S+rZEACK8vEicYpp1TUaLOKEo8gIAh8qhFwGWkZ2Rfn1mbruSWsp+i+ uCr3gNPpvhQ0sLTrCBnuRjz8sDr77HXujRZmGgAAIABJREFUrr+bTgkRBAQBQaCPCJQzj9rHIiT5 WkNgODl0lUauI2Q4KPz4YzV/vvra1xzB8lUQEAQEgU8BArLzHsZO7IcFrmGkTqqqSQT6Kq2pyUYK 0YKAIOBEQHbeTkSG8LvMs0MIrhQtCAgCgsAIQqCPts1HEDLSVEFAEBAEBAFBYB1FQJj3OtoxQpYg UNsI8DBarbgaIrVWIBU6hx4BYd5Dj/Ew1GB/2OrTcbL+6WvRMAyDdaSKZcvU0Ufrp05rxUEqBEO2 OEGgdhAQ5l07fVWdUg7UrQ/q37yz+e67zhzvvKPDKymH9y+Xsw7XdwcbNr+6UjkDrLZ8ahQFeEt3 /fWdzeQ7276pU9WaNWWirCA7hlbguulByX/77dXMmaq5uZNAiHc4d4gjAV97TMPbrLz6yjutJjju EqyQHpGHVAieNUt9+KGVSTyCwDqOgDDvdbyD+kUeF6/vukudcIJ+wdpy+HlK/I47Kr4S3b9cVvlV PHZObPqrJP60RvF8+7bbKp42d7inntLhY8Y4grt9tQDsFmr70iOrs6UdWu/cueraa9X3vqfZ6pC6 E09UF16ostnONWuVunpEHlIhGLIPP7xKMRIlCKxTCAjzXqe6Y/CIYTP3k58oJjjLfelLOqTs5s9K 079cVnbxVEeA7rj9dmeSW2/t1k3O6Jr6/uCDasoUddBBw0H0okV9qKg3yB9wgJo+XdEEcYJALSAg zLsWeql/NDKHbrmluuwynZu/W2/dq8muSi62d2zfzzpLTZigt+/bbTcIxstee00dfLBib0SBiPTX ytS5ZImWSdAodmBQctxx6u9/74ScJq9YoY48UpM3ebK65pquriDq5Zd1ruuvV489piZNUm+91RVb 1kdLH3lEbxYtx1EFeQ89tDOAojbaSJOx6abqxhutVNU8kMEHZ3qsr2ae557TqFIgxd55Z7dySIk8 +Ywz1Lhxyu/vJqauHmVWQZlIrZFd2x1V2NeL9qh++KsgDw04kxLzb/Xye0TezH788U6UzPDeVFGd AIkVBAYdgU+18dcR07gqVrV5COTXv9Zmw/vkyuaiFh4v53ERHtrieYxrr+3Vw1ZVaMMwOA+TYO7e LPDpp40DDyxDZpUSyqTue9CWW+qHWc3XwzC9TgN50cR0VI1Jc/PpFKyO8zAJ76xYUaefbmD8nLdJ ePCbjLNmdUZV+e/UU/VDJpbDTyGmM5/w4RVUHH+x0G5/wsdMUwmKsuE8bMP7LuabQBBP1fYCyYLJ +gsu6Hwmx15ClSiTDP4yAHizYLPNrADt4dVz3n9zOHvJZpQ7xJGFr6SphHzvC7EXWwV5KxkPDWCE 3+0gpjc0uzNKiCAwZAhwYCSu9hGoMrMwdxNrzuC9b2jZXJRjf8uS6ZvXRHp05HJ8rCwsKao8sWUl I/uQOp4D4Wnzso6qYc+W471RHqQxHVG8LIezPL1BgwdX7Asp/KxgTAcrZfliOZ5sca8GKkFRNpwV mEmhWSYPxPEWmeXIYn/8xgrHUyXKngy/o8llH0h10+YOcRRr0lAJeTNxbwqxF1sFeXuysk2wJxC/ ILBuICDmUQddlrE2CkSsp6fccu7zn1eHHKLuu0/95S/loiuElc3lrsUd4i6vShpk0R0dytvT2U2V EhzVkdLtKiFjpUSUfeqpauON1YwZavZstddeqqGhM9JdNeJiU+5tRbk9VsllPYjE//pXLYT/5BMF ztalAEpGim6hgRY6V5jAx+6suuyB+MuGA28m0y2hz6fy+c6QslnMuCpRHA1ceqlWu0Omncvp5HZ4 LXA66yj95y7NHWJPb/rdaRyFuxO4C3GEVELensxRiz1K/ILAuoRAT/PmukSr0NJnBNBQmzNHnXaa Ouww9cMf9jZ7/3L1tvShTAcjcX96rBBNpY8+Ur/+tdb6fuAB9ZnPdDvbtmeHoXI8PEDHmfptt+ky 0Pw/9tgBFlYtO9QWCt0AsTh3tWyV4zjS/s539FhauLBTzduRFg0A9523QMCRSoVCzpAevw8P8k1N WgNAnCBQEwisGwIAoWJgCJQVIT70kH473HIcVz/wgPWtoqdKLnct7hB3uVXScLTMAXOPrkoJjryk dH8caXr8yqmt9WK3o2oeC58zp7MAK8rtqV4FRw+m7J2zdrtY2yE2f/FFfSbtcFZdjvCywt7Zs6ud SlQqipIrRXG+YGoGWLU7UvJe+333WZGdHoT/HDdYbuFC/Wh9j85Rsh15M68jQY8FkqAS8lZeiKcJ 4gSBWkCAdbm42kfAPZHBFZj629q62oafEPvhLrkcGavnciSmaEeIu0B3mi6CDINjyIYGrSyWSung l14yDjvMHt/pd9RSJsXAgtDqQnHM1LTiIJ9jeFSlTEfVkGRqDLz+uuY6aIFZUZU8nSkq/wdbRU3P qsVMaCqsmXXxd9ttu+mXOepylI3SHE2AeLt79FGtD2hq2xHF4bod3iqoVopizQHZFMWHI3nU9zjz to8xVn4EOtyf/mTsu6+xdKkOZvhxEk+P2x3VuWskpBLyZl53FqvMsgWasWWRtzJSY9kFbpUCrbzi EQSGFwFh3sOL9xDV5pjI2B4xSdm3O2a9hMCoTE5JCLnsO7YeczlqMUuwt8hRoBnlzmXPAi9kLxuL aTYAu3Lv29y12LMPip81BCIKdtvQMG2aceaZBlrHpoP4O+7QPNskz67hZbXL7emRKhT1WbWgre1w hFAXBaJqDpt0O6suRxSMecYM3ZsksKeBxcI4QyFNP8px7F8tZ09mBZqeSlFokM2dq4GiQFYeCxYY 553XJaUw87KMgFs73BNPaJ18yIP9k8vhqM4+Ds1YAishbyVwlGN9LVugGVsJeWIhGwzLuioFlk0v gYLA0CMgCms1cbjRE5H9UN6hSBS1fvYz9be/9VR6r+MHvUCz5v61rtdUV0vYj6pRE6vkKhmmrZS+ FsMxj7rrrurb39Y3yHupHzDow6avBaKB+JvfqMsvV88/X96KEdYIUGkcxF9KLfas0LyOISAKa+tY h/SPHDSA4BnWp5eFoDZ89dW9TNurZINYoNUWPP3Qb+oVuUOTCA5d6TM0Fa5bpWLC75VX1Ouva3M3 vXSDOGzMGvta4KhRmmDIrmR/8MwzB/mX0ktkJJkgUBkB2XlXxkZiBAGWDiNhuzwUHY1+uHXtbSjK H8Qya4jUQWy1FFXjCAjzrvEOFPIFAUFAEBAERh4CIjYfeX0uLRYEBAFBQBCocQSEedd4Bwr5goAg IAgIAiMPAWHeI6/PpcWCgCAgCAgCNY6AMO8a70AhXxAQBAQBQWDkISDMe+T1ubRYEBAEBAFBoMYR EOZd4x0o5AsCgoAgIAiMPASEeY+8PpcWCwKCgCAgCNQ4AsK8a7wDhXxBQBAQBASBkYeAMO+R1+fS YkFAEBAEBIEaR0CYd413oJAvCAgCgoAgMPIQEOY98vpcWiwICAKCgCBQ4wgI867xDhTyBQFBQBAQ BEYeAsK8R16fS4sFAUFAEBAEahwBYd413oFCviAgCAgCgsDIQ0CY98jrc2mxICAICAKCQI0jIMy7 xjtQyBcEBAFBQBAYeQgI8x55fS4tFgQEAUFAEKhxBIR513gHCvmCgCAgCAgCIw8BYd4jr8+lxYKA ICAICAI1joAw7xrvQCFfEBAEBAFBYOQhUMvM2+MZef21brS4ppGvaeLXjf4XKgQBQWCtI+Bi3kxt 7s9aJ1MI6DcCzzyjPvtZFQx2dmu/y5GMg4KA9eOiR7bfXv3hD12lLlmi7r5bnXCC2mijrkDTZ8+1 yy7qkUecCeS7ICAIjDAEXMyb9huG87NuggKd4npE4MQT1YUXqmy2s097TN+bBDWN/OASD1vtk7N+ XPTIffephx9WV17ZWcBee6nHHlMHHKA+/NBZpD3XPfdoHn/nnc408l0QEARGEgIewzGXMRk5QkYS HJ/CtkqHDmmnDhDeYlHF46qjoxuNPZaZz6uxY1Vzc7dc8kUQEARGEgLldt5V2s+00tqqzjhDjRun /H4tibVclajrr9eSQOSEm26qbrzRyqE9VXJ1S2f7QhbrYwvWXsLb29VZZ6kJE1Q4rLbbTr3xhiPJ Wv6KaPSkkzR5oFFXp447Tv39750kQfyKFerIIzXlkyera67pIpWol1/WuUCSzdmkSeqtt7piK/lM lIg1PdZXM6RKgaQs28vucipVbQ8f5ibbqzZbaob0j3izBDMvXcYBBMcQ9gLtCUjWV/fJJ2rHHfua SQHpZpv1OZdkEAQEgU8RAn1k3rR8333VmDHq3XcVy3+HKxvFqd78+eqBB7Tk9o9/VD//uRb62V3Z XPYEDr8lQnSEm1+//nU1Y4ZiTmQ3841vKITGQ+S22qrbgWUva6GxTNaQBxoffKD220/98IddWefN U3PnasoXLtRM+tZbu6Lw/+tf6uqrNXqsSL785a6oSj4TKGItxOwyleoFlu0Uq5xKNZYNH84mlyXA DOwf8eS1MqbT6vTTFQPMXqA9gR3ezkQ9/XfOOeraa3tKZItneYfAHDJEbG5DRbyCwEhEALF5N8dk 5fjYo4l66il7QJe/UtROOxlPP92V7NlnjVmzur5WytWVorKPvA5HyHvvdYUVCobP1/V1cH2LFhkn n2zMmGE8+mgfCq6vNz74oHx6iF+8uCvq3/82tt668ytRVIezPL1vF1nczirH8tgLJLBSL5tFlS3T XYsZMpxNdhPWm5BKlJcNtwNFAnf5ZXO5A1tajCOOMPg5uF3ZMgk0P1OmGG++6c4kIYKAIDCiEOjj mTeCwUrbi0pRCBvZsnj/t8V3HPJVytWbdZQ7b29C3CWTy+3MZlaJMrP85z/q/PO1htFFFykUjnp0 6AmfeqraeGMtHpg9W2dpaOjM5CYe6Nig46wot6fHGq0s9pRWoNtjr86exe63ctkDK/mHs8luwnoT UolyM5wTiksvVU8+qQ81cjkdZv8JuMuvXpoZu2qVOuYYdcEFauedyySvUiY/pT//WZ13nlZCRLVN nCAgCIxUBIR5D0bPI8rm9PoHP1A//WmvioMfvP66noWRjcP7Ee/jHFO2fZVjRbk9PdZnZbGntALd Hjcl9oym38rljqoUMjxNdhPWm5BKNBOOdPqGG7SYeu+9tWYZzlGg42uVoqwoODeaDZyAfOYzVlg3 T49lUgLXzD7+uFsu+SIICAIjCQH/kDd25kz13HNqjz06K0JPik3nOuWYK92ulzvv999XP/mJWrRI Pf54r3beZkWcl/M59li1Zo2aOrWTeTtoYKu3zz6OsBr+OjxN9vkUix5LzPPPfw4UsVNOUStXau3C Si4QqBRTPhyFBtZ5v/+9lr7029FAmilOEBAERjAC/5NmDx0E3/2u+upX1Tvv6Br4y2yIsvo65eDT 7o9JoTvcZOrEsu+hXWyhsKqBxnhvZObk2m03dfPNmmfjmH+feqqbsvHhh3cChUoaQJ17rk5W6244 m7zNNlpLH2BBGNXI004bKHjTpqnbb9cF8mENeuihivUBNxosR410KLG9dHQryoYD4dwsfyHjqqt6 WaEkEwQEgU8nAs4Tfksvxu6xEunzvgquStR11xnTp2t1m002MW64oVv+Krm6pSv3xZ23NyHlSupP GNpkCxb0OePzz2s1pUhEa9JNm2aceabR2NhZCMTfcYcGiqhtt+2mMma1y+3pkQIriz2lFej2kMwK tGex+3tMYE88nE1+/XVj5kwNYEODcfrpGls3qe4QO7UOPyqEc+fq/gqFjD331D1+3nn6q+VeeEEr LQYCuqLelGwmc/w1S3MEml8dUbGYccghxosvWvWLRxAQBEYmAq4z70/nEqUWWtXjSae7EdwIr+TQ bBo2128yarfJw4atVCQICAKCQDkEhHmXQ2WthPWDk60VOgex0hHY5EFET4oSBASBEYyAMO9h7Hx4 lThBYHARsJQwBrdYKU0QEATWbQSGXtt83W7/sFIn8+ywwi2VCQKCgCDwqUVg6LXNP7XQScMEAUFA EBAEBIG1g4Aw77WDu9QqCAwVAr2/t9ZXCoau5L5SIukFgRGPgDDvET8EBIC1gsBQKEAsW6aOPrrT EtxQNAobc5RPLeIEAUFgbSMwkpg372hhUgNrWaNGdXuLkz5gJuXDI6c8XWp/y4tAHv6yHJZYhmLO tcoXjwMB3t/kFU66zOwgM7ZKp5jJ+BuNarM5pmkgK5cVa3kc1fXmK3nXTYfVIGymYtDQ/s734I55 Sqb8WbO0MX9xgoAgsFYRGDHM+8or9ZPYzz9f/i1O+gBtMh45ffVVhU1Nns22HFkwaYnjiWseQhU3 nAjwoisvcPA6C71jV/er0ilmSoygYYMMs+S8Qms6M9wsxO4fzubY6zIpsYcM0M9jsrwu+r3vdRlz HfQxzyqK8qkFU4DiBAFBYK0iMGKuitXV6R2JZfXaATrbKWsy5WBv9OjO7QvhvLGNNcqjjtIPafOU CPzASukoRL4OOgL2frEKr9IpjvR0JQZkzJfZ7NkH0oOOKqxi167nwQf1yLz//m5UDN2Yh3mzrjr4 4G7VyRdBQBAYRgRGzM57yhRtm7o3bskSNWZMV0JmKHNO5O+cOV3hI9AHMiedpCZM0Hs7GAMPbGDU 3XSwNF7MxNI7zHLyZG1g3HJEIfMgF/IMXlGbNEnxwliPjlx8cKbH+mpm7GWnIEThoGSAjhdiEN3T LseRCsUyonbZRaNBFO+PWa46UFaLrPSWB3zMkx1q5ClVEwFi8SBLOOssDSOUbLedwvq93VE73NTh hm7MH398t/Za9Tq6yQoXjyAgCAw6AiPFKixWrzfbTNsSf++9Mk3WUnPDKBSMl14yZs827rmnMw3h LS1GImHkckZ9vf5rpixTxAgI2nJL4+qrjUxGN3XlSuO224zdd+9sNrBg+vuuuzSGIIb97Vtu6YrC zDhGwrE6fuKJOuOsWZ1RPf5XFu0qnWKlX77cuOoqA/vzb7/trMRK44wo953xwLB5800dR1G0y8r+ yivGrrt2lk+TTz1VN990VYDqTKH3/pa304PFcjIuXKi/Ute++3alIfExx2jAAR+Er71WW7+3uylT jNWr7QHaP3RjHqPx663nrI7v0OluV5l0EiQICAIDRcA1gwy0wHU4P6yXZ1HgzbCZp57qRqg56fC8 BNPxn/7UFWXORKQ/+2xjzhwdPpLnJpYvH3zQBY7dByywZ8v9+9+acZqOqEWLtNfy8HBIL11ZtM3A sp1ClPWB28FT3a5sme5kZsiBBxqPPtot0sq+zz6d7TKjGV2bb96ZsgpQVllWOVbI/vuXGZZmLInt i074twNDhm5ZN3RjvlKNZcmQQEFAEBhsBEYS87awY1vD1H/FFVZARZZszrCXXqq33byNhnPPuV2l 1I6PVrg/PZLPsmaDDQyYFgIM3tey3kMrC4s1uVuIuT091mhlsac0A8t2ipV+6VK9Nx048+YxMTil 3VlVEIXf/rEYahWgrKKscqyQKnW5EztCLLSt0hyeQR/zPdboIEC+CgKCwKAiMGIU1hznDU1NasMN uy7VcFan+bLLmeFcjOFQc+VKNW6cPn0sm9KV9VMbwIn1669r3T0OaM8/X33jG7qlDljQFONOsKml b0W5PT1iZGWxpzQDy3aKPT06XJwE24+izULsaezFlvVze5A7CHZnZeeom6fbKqlAkqUsUFZRVjlW SJW63IkdIVOnqtde66arYRVreQZxzFPUVlspjvbFCQKCwFpCYMQorDnwhbvYtdIcsY6v06drhg3n /tQ4pn73p5etY9Y+9lh18836FvV3v1s+E0pe++xTPmqwQnvsFJTaTC25gdS4xRbqP//pKsDOrnbY QS1Y0BXl9vUGKHsuVNXsdXFvu/du11171sccxDHP/fvdd+89dZJSEBAEBh2BEcO8d9tN85tVqzSC TIuwn6uvHnQ0a6ZA1iLuT4/UmxiuWaMTwgmeekrtuGNXJq4PmUZRUIQ+5RR17rldUWvL98tfquuu G9Dt/O9/X+t4s9Gkvbff3k2j+yc/Uaedpi8QEsUHxXvr9nN1oCqhcdFF+ha1WRdXG9Do7r1jPN90 kzP50I35W27RptbczlwRusMlRBAQBAYdgUEVwq/Dhb3wgnH88VpvnINJtNKeeKIbrY4TRCvOHe4O sRJ/6j3PP28ccYQRiWgMp03TJ9/WsTew3HGHMX26juKw2a4PaCHm9vSImJXFntIdaIVYHis9el47 7WSkUt0Op0lmfqxkVTwo2KNZzYH0vHlaVd5exbPPap1womj13nsbjz/eWUwVoKyK7OVYgZSwySYG x8kor6ErDtSmcyd2h6DMb9e1JOMQjXlqmTHDIrmbB6rkLLwbIvJFEBgqBEbqmfegL4JGeIGOI9je oMF95UqOs+Rhc+sIGe72PvywOvts531udzIrBHkSwvNvf1udcYY29DsUDnM3v/mNuvxybalw/fXL 1MC5+6mnqr/9rUyUBAkCgsCgIjA0P/JBJVEK+3QiMJwcugqC6wgZDgrhxPPnq699zRFc7Svc9JVX tOAd+zmmqmC11P2K41EAjLBSC8oEZd2ZZ47o06iymEigIDA0CMjOe2hwHWmlsn9dN7lgbXWEJQZA He+b39Rn6v1wHMBX0YHvR4FWlqEr2apCPIKAINA7BIR59w4nSSUICAKCgCAgCKwzCIwYbfN1BnEh RBAQBAQBQUAQGCACwrwHCKBkFwQEAUFAEBAEhhsBYd7DjbjUJwgIAoKAICAIDBABYd4DBFCyCwKC gCAgCAgCw42AMO/hRlzqEwQEAUFAEBAEBoiAMO8BAijZBQFBQBAQBASB4UZAmPdwIy71CQKCgCAg CAgCA0RAmPcAAZTsgoAgIAgIAoLAcCMgzHu4EZf6BAFBQBAQBASBASIgzHuAAEp2QUAQEAQEAUFg uBEQ5j3ciEt9goAgIAgIAoLAABEQ5j1AACW7ICAICAKCgCAw3AgI8x5uxKU+QUAQEAQEAUFggAgI 8x4ggJJdEBAEBAFBQBAYbgSEeQ834lKfICAICAKCgCAwQASEeQ8QQMkuCAgCgoAgIAgMNwLCvIcb calPEBAEBAFBQBAYIALCvAcIoGQXBAQBQUAQEASGGwFh3sONuNQnCAgCgoAgIAgMEAFh3gMEULIL AoKAICAICALDjYB/uCscxPo8HmUYg1jecBTV1KTCnkwk0KhyIRUOK79KpqLRRKtHFZWqV8qTNFTR o7wqHcrl/b6Y8noMVVBZo5D1Gz5lFFUqo+KxvC/Q3JFqiMZ8OUWkCnqUT3UUUsGAN5tujfqjqimp fA2Up0JF5fv/9q4ETKriWp+79Trd0z3DDoIiiyigCCIqxgWjviQYl7jE3cTEJGri0xijXxIVjTG+ aIhr8CkqokFFRSUuETGCJEaJG4jIJjDDMkvvy91vvb8Y0jNMzwwos8Cz6utv5t5aT/19v/6rTp1z rst8SlFW00QBkqpNl/IGxUIFWTZRThR2CGNQ0aCgSpLFbxSZWZYVChVILhIGVqpJUYhQsaDnq4Ia MZdZiuTzu5DcIY3QipElk6oVfbgiSuXCMZ8jGart0GaH8g6pLnkWVQQIshYkqqxgEceqqPDbKhV1 imDChoaJMJmKGmky7xLCGRaFVEeRcVd0yQdRCMPZkrlF8/uIQjapFmnONiE1kjSHyRIHKp9urOgV 181cENWAm+mQu23irkP9++UlfwUQt11yHLI8ggw6LmzSDOqLp8qVlH5UcKmQp1yGlCB5HoV9FFDI j49K/nB3PC1iDIGAQEAg0A4CEmvFf2DE8tSqTnmFHsnZC8n7+kMO/91br1MlZ0SZfCopqicbhu2E wLGgIBZkEictR7eiqicpfiZJIBjZBQuT4XL6Cagks5Rph8KVIFk/8sFK+YJUFcJ/cFQAzM8JSb3+ uFN+N+cZ6hsGYxu26foDSfLCpFZxvvcc8GgkZHJ6p6BF27928LBfLuSz4WDYNXTLrzLV55CsYMiC XhEKFohUzvc6kWcakqqG8oYdDWkSs4k5oFTyBTK4lCgGYgVhSrZaKPxxwjcjOZ31q0iSlSwUq3r1 0zzf1Q89SCMHFWQWZj5yGOamaLJnFyWQrxdybU8Jylw4rGpkz7RsyR8sMlIksk27Cp1baVCya5MX iYJyLXI1TqrkZ+RkdTUURFvbzmtB8L/jZbJuJp/7bNX6V1/fkGz45l1/0OOxqC1Jpg20MPqqufPn Pfp4sFDsP6z/MT88q/fXjiaKgs5v/sbJgVw2rAbVQDBQHT/nRz8MHH0kYT1VEemRR14MKhAQCAgE mhBoa+e9Z1J1+Te2t8jZQvIJboRSDuUKoT4xvoXEVk8JBAL+HFHOs6pVXy6TjQRDYCxFwgacSQ54 c9t3ZLrfHzHu4Q/e55tRVYmqvnQuUxmp5Jtch5RIuECOQVaEVFC4o8qBglWNDaWqgflSRiESimJr jQFTbjrOYpKmSo4E2vLzbT5JKqfJpOuYKsOY4YpYpmhVhqJBRnouHY6EE4naPvFqkLEHjlX9KT1T EQiogQrMLBTQOMNylUDBkRWoByo1DRn5fKoiEsjpxbgS7tN74HnPz6D+EgWxq5f5Jpl8pGgF3Shq qucjWZNcS4KaQdECTdoUxS/XbEn07hfXZIhV9KshPZMLVEawgKnyaZTO3fWNM69+8nGlby+FtLpU Kh7vndf1eDAIQMDcXs40HStYHXFyRTXokwNVt1zxyyP69T9x6OilCx/wBQLATNK2qW1cZ9WSRUvX f/qLJ/6XawU+/ejxB2dc0H8IGzBYikZvXPAi4OQgGzYlcnPv+/OETG7fk09u8ZWKS4GAQEAg0AMI iDPvbgU9sL6G8iapfC9MjVmCBttxilu3qK7ZW5YDjh0BM5kGMVtp2KrZRZLAHDYVC6j/jZFjqCHD G1qWYtsxkDh4G0ppqOKLdjhvVruSz7TVTF5FSdBH6STpBoriUlDdmg4nUgPIqmbQLMvYbioBxXMs 04IKAEN4+IRVtVLR+khSqFisRJ1pWp4OAAAbbUlEQVRUBhwZlDUll+0TiVEySzk9Yskhw4xLAc10 FdtUXFtiJhVzZJhgfRX7d8eiugSkqvBrVMjEg2FooNd8sAx/ydu2k9azJGPvbxTdghoOB3xBA9pr aB2gEOerAxu7dp50d5+q6gBGMXU/tuXZXFBTA7YbQ2kRCEj9G6DQ9ihRpCIbJAeDlt0HzG1zPrYM W476wdymbqjhEFkA2b3xvgdPvOpamnKiDyoOWfWAKhLUH470ytx5J506lWJBwqppyIALfvSzD+e9 KlVUJKwCBXwU9mcKGSj5qf+A79w07aEHHuLrD5EEAgIBgUCPItDWzrsDgfDrnMnQb35DTzzBOQNa 3NL2t4Oihx+m226jmhrad1+6/nq65JLmETpo1Vxpxys0KaXS6E05KMrlaNo0euwxLueBB9Kjj9LY saXqPX4xMuynXOpfv532Xs3GSCh+2nnnR0/5ZigW4QytG7869r/iples3zKgf68RY0Yeff5ZdNQR 5AtedvIJ++S8A/PSnOOmGMP7rcw1skjk9/Nf4KSHrfmqdSvmzvvbe0vAxwcOHjzlkvPV4yaSpQ/s 7afkplV3zFy7rlZqzA86aP/RP5waO2I82hQLZiikKqqkqBWc7kwLZOqPYDFg0Zr1S597btGit0B4 AaYcOG7csWeeRgeOJdM//ZhvXjV33nu33PzhB0uq9x80asqUUeeeqfauJM3+w/hjf37b72fcctO5 P73SP2TIPffdfc2Vl9HoUdBxY8N63MjRXNG+rp4GhKkqRD6wuBokLU92ximGcPwM0fl349qyqWBv D7V/wb7/1POChXwit1kNKhXx+KX33Uv7DCDZd9Pxp+yTMcZr/tePPWF1MJTpFXP7xH/19JPYH/vI TaYyVX362sxj0CxU+Ip2IQQChno8CyMAnIU7GayNXDnoYkCJCh4Zcv3SldUHHMSNA6wcxWNkhZe+ vviQX/xcC8QSXjEsB4J9B0Nxz5ceidSgfYeRuW3L3uMPkxBAICAQ+CojgDPvHRJ2Ix0klE6axKZN Y42NvFbLyu0VPf00Gz6cLVvG6+MvrufM4ddNqb1W/ynv6H/L0Uu9nXsumz2bmSZzXTZjBhs3rqMe dqfsoIMYpvYF02f9D/hkwmT2why2cQWr3TjnnPMSz7/IwSxk2Pp1rLaera1ln61jK1ey23776tST mFHPzDoGtl79+cuDx7P3a9ina1n9ZpbcynIJ1rDZeOPN2Secbv/xEbaxkX2+iT0z75Vrf2FnEyzb +OABI4uXXsZmP8+Wb2FL1rM7Zj5/4hS2cQ3bmmU2Y55te7qJ/5iC4bJ8kY9SV/vMyVPZ9PvZJ5+w jWvZJx8XH3to7tWXsdp1bEXNg4PGvX3KpeyJv7K1G9jSpauvuzH37PPMSrFU7eyDDmH//Wu2+L0X vjH1zd/8iq346NnTp7KtG1iyliUaZo2dMnfoEU8ddeLrl/9k+ewHWWYTs9L57OZGLwtRIIBd0Jlt ecwyWN5keebpLFtknzWwZZvZ8tXs05XssdlPHXc8y9azQoptSbDlG56ND2Hvr2WfNbKNObahgSUy zLCYXmDMzRYLBmOb8pk0cwuw9HN0L51iyFtfy9ZtuP/QCSyfZ44NizzeakvmnmO+xT5YyerqWTHB kpvZa/+Yc+jxLJNHJwZ6c/IQkNk2K9qzb7iVbcmy+uIX/NpFdYGAQEAg0MkIlFE1GLHVp+WIKFq4 sGVG83V7RSD7v/+9udqiRWzixObb9lo112j/Cm1bJeSsXt2cB/5WlObbzr1av55973vs4IPZK6/s esev9R3K3vkn0z9n1lpmNLKtm28/83yWMlhDmiXzbGuKbapnmzawJYvZhyueHjWWJTZ7rMgKSVaX eHXsSWxZPduwgdkJj4GrUqwmvfiKG/V5C3nbhjpWSDAnlUvVgnVY3nrk0MPY3xexzbUMLJbNszVr Zx85mX2O/hkzWdKwdOYa+QKz+C2zXdcqsLwxZ/JZ7I01bE2K1SZYzUaW28oK61hiDVYVs8YexVav 5xxfqAFhsxfenH/GpWzDRpZJPXrAoeyvi9jKmnvHH8k+WcbSjfdPOIw3L+aYY7FUjqWyLNHI3v/3 srvu+dMpZ7PPNzPTdjgrMswPJMkclkxkYbiWZcYmVvRgzQ4BNjSyZZ8BCrZ01SMjDmb5FKzImZ5n idQfRo1mGzZzuECy6SzWagVYtfFu+CdvM9D4FsfFGjOzbQS+SEln2MbP7xw9gqWAVYpZOaanWb6x 5uFHXjnubPYm1itb2KIlqSnfXTr263wxlAM4RWYWWUOjt2rNM7+/Yx2e5HSSZ4okEBAICAR6FIG2 1OatdNGt9BLHHdcqo/m2zaJ//5uOhu3uf9JRR9GyZf+52fa/zVY71PgiN8OGNdeWZa7Y32lqqYcv VW4CoYOiIUMIxwGrVtHNN/MPzgV2YSKFkEYDe1NALsDZCKe7jOnZIjcjl2E/7dGqlYsXzH9/+bvy 1rrhDUYB3k2BsMU8fwAH4c6mTJI7UMXCTJXTJMfhsyTL7765ePK060jR4fpFEuzJPF+sF9fqSlp9 Ok37DqBYgAXVIuzMA1UNmRRZDsHODGbbfg2Vwzgah8U2/MOKeYpGfZZx9pVXvHT77dQnNHTcqAHD BsSPncjt3HCAXcxmnRwNiuYCEMIIOybOI+rW306xKtjGu4pEAERiOk7QBw/A4YUEHTW035qSkpxo DLJuswcfOXT0PsNGjznsmZv/58zbb1V6qVnLisKVC5ps14tXRmA/X0GapshFWLzr2XeenL1i6VvO htreCTcQi8EpzZVggucoIVmPhklhVOknn8vCAQPo+TSdMc2RuCcXR5PCigxzekwPM2jMpHphIDig ASQo0gNNVTy4nQ06c2rvvDb32mu3UnrSAQdMOO/cRnyhwQCE51MowkbBP+ve+0696NxKHARosuPC sQymdyIJBAQCAoEeQ6At8u4xYXpo4A4WKx0UNQk7YgQ//n/qKTrpJLruOrrllo7nYMIADVZmOI9l FvctVtRIQedma4rs/mvxvJfnfv20rx/903P46eyarTN/cDnZhj+EE3EYkuckEJXikK5LQU3TVHLh sw1Pa3h4uwQrcbh+wYzbtBWPkebnXmOwpobtueQYnu5hccCMgmZz0tn2neP4VyITtuj8wNvTfCF/ Ea5VIS/8rYlTTzqUttau/uD9te/9e/mMGcecceJ+U78FFzUdYviYTgXu41zho4xpYwEA721yi/AO R+eaAiUzyBUrhoKXB3k6qgOjuwhBfZCVfFigwDXLpvHD63/2AenwpralIBYiTiAkB7DiME0+Iwtn 7drGBe889+pLU888cdLVp3NI6+T7vvVtzAjLMQmmbdCQJ5LkwjbeJtipw0adMzRs6BU4qEORoGIs bjgPu3YYBUiuWewVDRHWSQU3Xt0fjnFJkkJYkmiqW8z4wgH/pVO/c9mpcH+jrE6bcs6Y/Snq02U3 iHVVJvvU9HsvuvgiGjmUf02SbKncJ00kgYBAQCDQgwh0vd3s+PG0eHHzDN97jw4+uPl2T7jC9rr8 0yRYeX6rvfiaNfTd79L06fTaaztlbnQZkfzwwKYthaAVpJxLH64YMWx/4s5Wzp1333XGrbdFJx1F Wohsj6p6wyoahl2UQaAUhaIVNvaPRVBhkExfhYm9OEzE9f0mjV3/t7/BR5oadcqC/zQwMZe9WKiq 6gV6g8pXk2HCDgctKYx1gA7zch7lRCM3p6cSRo7bpfslN5+BX7QKD2ZsVs0cVUeHn3TChAsvufjO 6U8/PIv378nRijgljT4GhcHXW9K0Zs2AUcMIW3BNQeJub6ZZHa/i8V6yBROrB+aqxOC8pdquhIkX He6Y5vcl337rwAmjYRUOKzls/BEhBQ5oDCsJGMylGgiGZln9lYcev+Cm38bGjuN+ZQZvGNNguE56 Hl512FbLQ6sGULJIiTy3GDcteLUVknXoTYEpPQzioLYoZPzkhMjzMzsE8cDcsEVnirOxjmylCub2 DgzmLV8ooOt5Hn0FNvOABm0/XzH8pMkpI6mpGNpc/IfpZ3/v+1QZ42IallIohGCcL5JAQCAgEOhR BLqevK++mn7wA1qxgk8Tf3/8Y7rqqh6dctng2F6Xf5pqleeX9uIbNvB5nXUWXXQR/fOfu6IzR5fV adX81T30cYbqNPq4dub/3Dv1qp8QAn7ElejI/TbOfY50jRIyLVy2+brbI4afajKkVVLaALUNHzOW Fr1DOZXWG1QvU15D6LHDzzv92ZlPZGe+SJkwbbLpreVvT38wl0pTZTjTUCDLL/UZiCgnGtylGsxK RHXzRcnMm5YHo+5YsFIKxMDeeddVYpXgfL+pP/fTa2n+AqppoIzBteWfLNtn3KF8tx6IGQ3usuvu p08d+jBPK9PP3T1j6pWXc/Wy7CumdL6VpRCD+5YdIH8V4pDxeG6GUpV2KKtQWqatOtXbNP+dhU+/ NO6S06ifRqrqt6nKk9HSdBAqzVP7VfF2mqqBs198gzbbVO+nN9fSdX+sysi0IREKVnE6b8yNGTyS Fr9PVgUliTLwK6eqcPU2micrm4NW3B9GnBmTCiblbULgGMSXwWopax6EOG1r6qhep7TJw7eRHgwq lDAoj26LtGDBc88+NuL4Q+NRv5pp3HjHXUcPhyE69BWM0nlKpLmj2rqasmdIZAgEBAICgW5F4AtG WMO+s8RereTsoAhnwzgSXreOhg9vw1WsvQ5b9V9+Wz7iruSU9/PlcuCBdtNNdPo2pe4u9/DsgAln 3Drtif+9J0ve/iNGnXjRuTRxDFeGa6q1YePL9z+ydcWnej57zCHjDz30sExN3f1Lllz/3F88Lydr Pnr3k6fvmL65pjYajW6tT9zwxnwaiHNuovfX/mvW3KWvLZRledAhoyadc0bv/zoeR76/OHjcHW++ QVXBrGVEFSjSpV9POuqW1xZQMMb3l5TjOudghO+YEU8UDSQ4lJv0rxUrXn79nQ+X4oC89377DB5z wAlX/JCCUfDu7O9ceP5///KFP99f0BvjQ/qfcOkl2uTDKQ7nt/y9J3/nipmzSJVvOP2026AJCGhX HHnYvYsX8TAwvuBtZ5y1dtnHB40eEQsFJx88YcSJx9NhI/nKAH5hXCVgo37BKAQCIWzavXwxYGhg 2ffue/CTRQuzDTXHHzZx9BFToNS/5tW5dy6Yz+0YoJl46c2/PzLrk001SU0uhLXbn3uKYhVQUeSS qUgvKC0om2yMVsf4lh6V0/l7Lv1JZtnyUQhbR3Kqz8A6kuuzienvLCDEZ3PZTZNOG+z5s0Z6wnGH T77yYtp3oKGFA572x7FHxaFI7xPNuHaqUPBHY45f+/VLL1Gv6l3+zkVFgYBAQCDQ+QiUkXfnDyF6 bIHAljoowDnjIlb2tuAhFI/yW548bmSVz3JmxZbRH0X8EEJ0cx8VAzjQdfw6Ypxje4r4ny5pFXCh LlYisKkbShd9iIWCAGsIbcaLcAAs2zldC0NpDdtrTwrxI2E3l1GQU2Q3H35Kv0As5WXCMBVz3FR9 Y1W8j+nT6phz6/z5PJQKmB36c3B5AHp7uPLblMxRXpp51rnfm/8SPxgGYcNYDAsCReNkiN6TDdwE DHpmHK5Db2/oPOAJrL2gVcAfhFjFeTeOvzFVTAp/AyrzPEnGrl2ybUdDHFas4XiQVY4B5PdBf49Y 6zKCxWaxJuB6+5xO/fsjjjv0+miMIwPQOZ+yz+d6luPTXFl1EIBclnH6D/FVH/TcUDAwmZvxIT5B nmAtqOlQQxCLFbdmQ32jUCiY2O4jnl0KaxdEaTURaRZakIKpS5H+ruFFEB8G3SFaSwQH99vsH6Gl x7lDmEMqkkBAICAQ6CkEBHl3L/LpOkqnqCrOCQDbTPxteX4K8kB0cdhRIzMDFo9ShiECOVV65Bqc 9hCqE9ptnOvi4BzK4SqEQwXTI9YnWBYxyBk/2Q3FuWrXF+BkiVefcLpDG9SBTbsF9TttQQgU2Jzj QDfPyQ9jMZVvT/kyAm8xUQjxwGE9DqMsmJ7hPBgioU6WPTnl6+d+8B504QSrNFhi22BtlXw4VkY0 8yJ/gUdlnECFhsLNubGzxyFxwU+IceZmON/DED3RSOEY5SEkDMsZBYIwmOdB4hQQKOjTJROqfux0 +/KTbBxvQx7QOALMcQ0BTBMQmj2IAHPblg45bicPUg+EeIUIjgMsLgyCwkKRAIFNnb9KRM+SHyFX XdpaT33jJGcpFKV0gHwxrkNCDDu82gXsjsVQHryO4/M0F5UzP8TGEbhHhRQPr4bKEDsQ5uYR+Naq qrr3uRGjCQQEAgKBHRAQ5L0DHF17I0nLB/dNJpNBfwAO1tEKOGr5HQec7Lmua9qWhIDh3J1JTTY2 Duo9wCoYAbXCkWmTkQhEAqqtFzK5Pr2qEomUbMsDBg2sSyQrKip8ktLQuMUX9FVWVhqGJUlSKpuD bVrv6r6FdD6dTAWjFbqKESyf50Uk1s/nq9+UjMdhJo7IKK5eoD6xcLAisqJ2a3xQdaNjythAF+yw L5DKZvYZMDCdaIyqWoR8RcNqYJ4WCTKQN7cKVy2mqKFIsZgLSnrED3pW61M5LdYnihETWyKyT3ED 0DYrfSO5YlKzzaqKCo1JRlGXFb8hSUUY0uE4AN5rlhWGnRokDEiyz9+YKYQiUUlikmtbuQRwUmSf rpPPX2WYsH1jsmobTqMsS7INqzI/XmWSyxfxphZVVgOqhvjwqqrChrzoIbIa3Ng9M5/pF62UGLQT 8CBX/XK/QtoKBKF3MKLxcN4ysIz4fO3a8UOGKJ7+WU19db+YqvZy8WoxySjkUhWVUcuGgsDq37// 6jVrgPlhjXgtCjhcJIGAQEAg0DMICPLuGdzFqAIBgYBAQCAgEPjSCPDzSpEEAgIBgYBAQCAgENiL EBDkvRd9WULUdhDAeXkXpa7ruYsEFt0KBAQCXw0EBHl/Bb5n2Fjtvalj4bds4UFyKvibxbskoWf0 j1FEEggIBAQCexICPU3ejz/OX9mJYJaIXH3BBTzaSSnhVxsfVaX99ydUKyVknnxy6Y5HR+n49725 6t5w9dZbdMghHJCm6e8NIveYjIiTM2ECIYQforiXUuc+UegZ/U+cyKMUiCQQEAgIBPYYBHqUvO+9 lxAt9e23ufPP2rWckn/5yx2QgUEvfIU/+IDwdhNEeiklNEGgUKRsllauLGX/f7i4+GK69VYOCObe WfbMndVPj+DbgfBnnEEzZtDPf87XOk2p058o9Iz+McoXjMbTI1CJQQUCAoGvDgI9am0ehcct3o/V zgICW8/SDzeOHuFZ27TBQv4559Cpp9LZZ/M3giCe18yZzTX39q+u5az39rl0qfwvvsi/93nzdhik 654okDfWVaecssNw4kYgIBAQCPQQAu0QZ/dIM2jQDu8s6WDQTZuoukVASvyGNv1q4+/UqR20290i jHvJJdS3L9/bgRhaKvbBsnV1PLY5AqEMHEh//nPzWCiCRgGtoC149VXEBaPly5tL27tCK3yQmi5K t005HXSImtBAIGJ87978lKGpkzb74b3vLHXzlFuJs+vC/+UvnE1bpa57oi68kDBieWr5NZWXihyB gEBAINBFCPTk28Q/+oiNHMmuuYatXt2GGAj8heS67N132ZFHsqef3l4H+ZkMi0SYbbPKSv63qWYb Xex21kEHsQceYKbJO6qvZ7Nns699bXunGPTYY9mcOVxCyPPtb7NZs5qLrryS1day8ePZxRfzhhMn bi/a6b8254LMDjpE6aRJbNo01tjIuy/voTynAzG6c8rlgu1KTpPwgwZtn2/LuXTdE5VMsgEDWg61 /RoCl8vcRj2RJRAQCAgEOhOBbQTZmR1+wb5AvTNncm4GES5cuEPjpp9FTWOTJ7O//rW5qOm3EvVv uIFNncrzu+7XE4uDtWubh255hUFBz6X02WdszJjtdyhav55fly4UpVRxJxdtzqXUT+miZYfIbAVd qzHa7LNVndJtd065XLBdyWkSFQ9Gm6nrnqj2RmxTDJEpEBAICAS6EoEePfNuqUzA20Ivv5xggnTF FduzoZAsnXm3rNmUf9ddNG0a3Xknff/7XFHcZs1WrVreNl3vtNXLL3Ophg3j7yA/8khu2R6Pb++m fFCo1mFohlQqKr/Y3rj9f6UmLauUMssvWg7XsknL61KrlpntXXfnlMsF25WcJslLaLc3kU5/onY6 YnuSiHyBgEBAINDZCOwx5I2JpVK0337Nbj/lv+NNk2/Kh+sOXMjq6/lBb3s1OwssnFh/9BG3jMMB 9s03049+xDtuNShM6uAT3GQDXyoqv9ipSKUmLWuWMssvyiVp2bDputSqvKi9nO6Zcrlgu5LTJPM+ +9CHH+5gCVE+l058otDV6NEEgwCRBAICAYHAHoAAXh21xyTwX0urtI7lGjp057vtlj2AFcrTTnfe TU3wq43PeedRIkHgjCbybtXbggV0wgmt8vbi2+6ZMt5ghi+95G4Ah8BdT5Mnc2tHOB10kDrxiYL/ /de+1sFQokggIBAQCHQnAj1qbX700fTYY9TQwCeMgBtgxwce6KrJg6fLPzsdrElCcDYSmGDhQjr8 8OZGcB+Cbhbp44/pxz+mG29sLtp7r7pzyojPAyt9AAuEf/e75hOTXUEPT8ujj7au2HVP1KxZPNRa ecKisM11YXlNkSMQEAgIBDoRga48UN9Z3//4B7vwQm43DvMrWKW9/voODcptl5qKy/PLc3boaDdu 3n6bnXkmCwa5hIMHc8N4WB2XxHjySTZ0KC8aN24Hk7GSPOUXO5Wl1KRlzVJm+QWqlTJbNml5vdMK LSt355RhHA6DfAAYj3NzemBbLmp5TklatG1pyYj8LnqiMMrBB5eG3eEC4glDth0QETcCAYFAdyCw J515d+KSpBu6Kj+d3emg8AhvLxlGeyWdn/+lxdjTpgxtDZTnP/sZ93GHg3tXJFgg3n03/elPPA7g kCFtjIBzd5g0LlnSRpHIEggIBAQCXYZA1/zkdZm4e3fH3cnQHSDVnWJ06Vhg06VLefhSxM9pMhXs YNZfrggh9+EBgVEQcqfNdM01XXjW0+aIIlMgIBAQCMBmGtt7gcOXQQD71y5lpi8jUxe32WOn3NLq rXMx6LqeO1dO0ZtAQCDwFUNAkPdX7AsX0xUICAQEAgKBvR+BHrU23/vhEzMQCAgEBAICAYFA9yMg yLv7MRcjCgQEAgIBgYBAYLcQEOS9W/CJxgIBgYBAQCAgEOh+BAR5dz/mYkSBgEBAICAQEAjsFgKC vHcLPtFYICAQEAgIBAQC3Y+AIO/ux1yMKBAQCAgEBAICgd1CQJD3bsEnGgsEBAICAYGAQKD7ERDk 3f2YixEFAgIBgYBAQCCwWwgI8t4t+ERjgYBAQCAgEBAIdD8C/wd9gTSqByueNQAAAABJRU5ErkJg gg== --Apple-Mail-91-508252663 Content-Type: text/plain; charset=US-ASCII; format=flowed; delsp=yes Content-Transfer-Encoding: 7bit Best Regards Jiansong The information contained in this message may be privileged and confidential. If you are NOT the intended recipient, please notify the sender immediately with a copy to [log in to unmask] and destroy this message. Please be aware that email communication can be intercepted in transmission or misdirected. Your use of email to communicate protected health information to us indicates that you acknowledge and accept the possible risks associated with such communication. Please consider communicating any sensitive information by telephone, fax or mail. If you do not wish to have your information sent by email, please contact the sender immediately. =============== Jiansong Xu, M.D., Ph.D. Assistant Professor Dept. of Psychiatry Yale Medical School 2 Church St., Suite 215 New Haven, CT 06519 Tel: 203-785-5306 Fax: 203-737-3591 --Apple-Mail-91-508252663-- ========================================================================= Date: Thu, 12 May 2011 21:57:15 -0400 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: Interpreting parametric modulation Comments: To: megha sharda <[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 Megha, > I have a task with three conditions A, B, C. The trials in each of the > conditions are varied on a parameter which is a feature of the trial > stimulus. I used two models to assess activation differences across > three categories. The first model looked at each condition, A, B, C > and i compared across conditions (A-B, A-C, B-C...etc). In the next > model, I additionally used a parametric modulator for each of the > conditions A, Apm, B, Bpm, C, Cpm and used the second column as the > regressor for each condition. However, now when i compare across > conditions I do not see any differences. My question is, would it be > correct to infer that the difference I see across the conditions A, B, > and C is due to the parameter in question and not any other feature of > the stimulus? You say that in the case in which you added the parametric modulators, you used the second column as the regressor for each condition. Does this mean that if your conditions were A Apm B Bpm C Cpm you did a contrast looking at, for example, for A-B: [0 1 0 -1 0 0] I.e., Apm - Bpm? Or did I misunderstand what you meant about using the second column? If this is the case, it's not surprising that you got different results, as you are testing something different. It could well be that conditions A and B differ (contrast [1 0 -1 0 0 0]), but that the effect of the parametric modulator does not differ by condition (contrast [0 1 0 -1 0 0]). If you want to say that the difference across condition cannot be attributed to the parametric modulator, you would need a slightly different model. As it stands now the parametric modulator tells you how that factor affects activity within each condition, but not about how it may contribute across the whole experiment. To look at this, I think you would want a single column that codes for every event (assuming you have the same number of events in each A,B,C condition), a single parametric modulator, and then additional columns that code for condition A, B, C. So: [any_event pm A B C] In this case, because the parametric modulator goes across all trials/conditions, I think you would be safer in concluding that an A>B difference exists apart from anything that could be explained by the parametric modulator. Hope this helps! 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: Thu, 12 May 2011 19:52:24 -0700 Reply-To: Elsa Baena <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Elsa Baena <[log in to unmask]> Subject: Negative contrast values MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear SPMers, There seems to be much confusion about what a negative contrast value means. My understanding is that a negative contrast value indicates that significant activation is elicited by the a condition for which a -1 was assigned to in a contrast, not the condition that a 1 was assigned to. For example, if a contrast comparing semantic memory retrieval and a control condition yielded a -3 contrast value for a particular brain region, this brain region would show enhanced activation during the control task compared to the semantic memory task. Is this correct? Additionally, what does a negative contrast value mean when performing a parametric modulation examining the linear increase in activation given two conditions (e.g. easier vs. hard)? Does it hold the same principle? Thanks in advance for the help. Best, Elsa Baena -- _________________________________ _________________________________ Elsa Baena Graduate Student Clinical Neuropsychology Program Cognitive Neuroscience Laboratory University of Arizona [log in to unmask] ========================================================================= Date: Thu, 12 May 2011 23:29:33 -0400 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: Negative contrast values Comments: To: Elsa Baena <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016368e30e90f89bd04a31fea99 Message-ID: <[log in to unmask]> --0016368e30e90f89bd04a31fea99 Content-Type: text/plain; charset=ISO-8859-1 See responses below. 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, May 12, 2011 at 10:52 PM, Elsa Baena <[log in to unmask]>wrote: > Dear SPMers, > There seems to be much confusion about what a negative contrast value > means. > > My understanding is that a negative contrast value indicates that > significant activation is elicited by the a condition for which a -1 > was assigned to in a contrast, not the condition that a 1 was assigned > to. For example, if a contrast comparing semantic memory retrieval and > a control condition yielded a -3 contrast value for a particular brain > region, this brain region would show enhanced activation during the > control task compared to the semantic memory task. Is this correct? > Almost. Whenever you are comparing two conditions, you don't know if one the -1 has enhanced activation or decreased deactivation. > > Additionally, what does a negative contrast value mean when performing > a parametric modulation examining the linear increase in activation > given two conditions (e.g. easier vs. hard)? Does it hold the same > principle? > If the PM is -1 for easier and 1 for harder, then a -1 would mean that the activity is less for the harder conditions. > > Thanks in advance for the help. > > Best, > > Elsa Baena > > -- > _________________________________ > _________________________________ > Elsa Baena > Graduate Student > Clinical Neuropsychology Program > Cognitive Neuroscience Laboratory > University of Arizona > [log in to unmask] > --0016368e30e90f89bd04a31fea99 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable See responses below.<br clear=3D"all"><br>Best Regards, Donald McLaren<br>= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<= br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Depa= rtment of Neurology, Massachusetts General Hospital and Harvard Medical Sch= ool<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Thu, May 12, 2011 at 10:52 PM, Elsa B= aena <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">ebae= [log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmail_qu= ote" style=3D"border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0p= t 0.8ex; padding-left: 1ex;"> Dear SPMers,<br> There seems to be much confusion about what a negative contrast value means= .<br> <br> My understanding is that a negative contrast value indicates that<br> significant activation is elicited by the a condition for which a -1<br> was assigned to in a contrast, not the condition that a 1 was assigned<br> to. For example, if a contrast comparing semantic memory retrieval and<br> a control condition yielded a -3 contrast value for a particular brain<br> region, this brain region would show enhanced activation during the<br> control task compared to the semantic memory task. Is this correct?<br></bl= ockquote><div><br>Almost. Whenever you are comparing two conditions, you do= n't know if one the -1 has enhanced activation or decreased deactivatio= n.<br> =A0</div><blockquote class=3D"gmail_quote" style=3D"border-left: 1px solid = rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"> <br> Additionally, what does a negative contrast value mean when performing<br> a parametric modulation examining the linear increase in activation<br> given two conditions (e.g. easier vs. hard)? Does it hold the same<br> principle?<br></blockquote><div><br>If the PM is -1 for easier and 1 for ha= rder, then a -1 would mean that the activity is less for the harder conditi= ons.<br>=A0</div><blockquote class=3D"gmail_quote" style=3D"border-left: 1p= x solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"> <br> Thanks in advance for the help.<br> <br> Best,<br> <br> Elsa Baena<br> <br> --<br> _________________________________<br> _________________________________<br> <font color=3D"#888888">Elsa Baena<br> Graduate Student<br> Clinical Neuropsychology Program<br> Cognitive Neuroscience Laboratory<br> University of Arizona<br> <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a><br= > </font></blockquote></div><br> --0016368e30e90f89bd04a31fea99-- ========================================================================= Date: Thu, 12 May 2011 22:40:57 -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: rest toolbox aal time series MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf30364013e0082104a3201284 Message-ID: <[log in to unmask]> --20cf30364013e0082104a3201284 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello all, Someone knows in which order the rest toolbox gives the aal times series? I see the .mat and the .txt data but I don=B4t know to which region correspon= d each time serie Thanks in advance John Ochoa --20cf30364013e0082104a3201284 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello all,<br><br>Someone knows in which order the rest toolbox gives the a= al times series? I see the .mat and the .txt data but I don=B4t know to whi= ch region correspond each time serie<br><br>Thanks in advance<br><br>John O= choa<br> <br> --20cf30364013e0082104a3201284-- ========================================================================= Date: Fri, 13 May 2011 05:34:15 +0100 Reply-To: Krishna Miyapuram <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Krishna Miyapuram <[log in to unmask]> Subject: Re: Interpreting parametric modulation Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> In addition to what Jonathan mentioned, The design matrix specified in th= e the two cases is as follows Y =3D beta*X+epsilon and in once case, X is simply A B C and in other case X is A Apm B Bpm C = Cpm Firstly, the parametric odulator should not be highly correlated with the= condition itself. You can look at both the design matrices and there sho= uld be row below those design matrices (when you explore design) that men= tions contrast / parameter estimability. This should be ideally white. Gr= ay is still okay, but if you see that, it could be one of the explanation= s of the results you observe.=20 Second, The brain activation in the second design matrix is distributed t= o condition and parametric modulation. As i mentioned earlier the same co= ntrast i.e. differences between conditions A and B should not be severly = affected if your parametric modulator is orthogonal to the conditions. Th= is is rarely the case. When the modulator is highly correlated then the a= ctivation is shown in the regressor contrast instead of the condition con= trast.=20 By extracting the timecourse of activation, you can independently verify = your claim that the activation is related to the parametric modulator. HTH -krishna ========================================================================= Date: Fri, 13 May 2011 08:21:22 +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: Re: Henson's batch code Comments: To: Jiansong Xu <[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]> From the error message, it looks like you haven't quite specified the names= of each condition correctly (in U(i).name{k}) - so trying changing how you= specify the names as cell arrays of strings (which may require a bit of Ma= tlab training) - but without seeing your actual modifications to the script= , I cannot help - and apologies that those scripts are provided for use, wi= thout promise of support. BW,R > -----Original Message----- > From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On > Behalf Of Jiansong Xu > Sent: 12 May 2011 21:43 > To: [log in to unmask] > Subject: [SPM] Henson's batch code >=20 > Dear all: >=20 > I'm trying to use Henson's batch code for spm5 but got following error > message. Any suggestions are appreciated. ========================================================================= Date: Fri, 13 May 2011 10:54:33 +0100 Reply-To: Patrick Fisher <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Patrick Fisher <[log in to unmask]> Subject: viewing individual slices for a single volume Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPMers, Is there a way to view each individual slice of a functional volume at = the same time? This seems like something that should have a straight-for= ward solution, but I cannot figure it out. Perhaps this is not possible = within SPM? Any help would be greatly appreciated. Regards, Patrick ========================================================================= Date: Fri, 13 May 2011 19:06:57 +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: viewing individual slices for a single volume In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_14f04319-6aba-4123-9959-b99c124e6f29_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_14f04319-6aba-4123-9959-b99c124e6f29_ Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: 8bit This should help youhttp://imaging.mrc-cbu.cam.ac.uk/imaging/DisplaySlices Brahim > Date: Fri, 13 May 2011 10:54:33 +0100 > From: [log in to unmask] > Subject: [SPM] viewing individual slices for a single volume > To: [log in to unmask] > > Dear SPMers, > Is there a way to view each individual slice of a functional volume at the same time? This seems like something that should have a straight-forward solution, but I cannot figure it out. Perhaps this is not possible within SPM? > > Any help would be greatly appreciated. > > Regards, > Patrick --_14f04319-6aba-4123-9959-b99c124e6f29_ Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: 8bit <html> <head> <style><!-- .hmmessage P { margin:0px; padding:0px } body.hmmessage { font-size: 10pt; font-family:Tahoma } --></style> </head> <body class='hmmessage'> This should help you<div><a href="http://imaging.mrc-cbu.cam.ac.uk/imaging/DisplaySlices">http://imaging.mrc-cbu.cam.ac.uk/imaging/DisplaySlices</a> <br><br>Brahim <br><br>> Date: Fri, 13 May 2011 10:54:33 +0100<br>> From: [log in to unmask]<br>> Subject: [SPM] viewing individual slices for a single volume<br>> To: [log in to unmask]<br>> <br>> Dear SPMers,<br>> Is there a way to view each individual slice of a functional volume at the same time? This seems like something that should have a straight-forward solution, but I cannot figure it out. Perhaps this is not possible within SPM?<br>> <br>> Any help would be greatly appreciated.<br>> <br>> Regards,<br>> Patrick<br></div> </body> </html> --_14f04319-6aba-4123-9959-b99c124e6f29_-- ========================================================================= Date: Fri, 13 May 2011 12:17:24 +0100 Reply-To: "<Branislava> <Curcic-Blake>" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "<Branislava> <Curcic-Blake>" <[log in to unmask]> Subject: Re: Modulatory Input in DCM Comments: To: Shamil Hadi <[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 Shamill, It all depends on your research question i.e. what do you want to answer = with the DCM. If you are interested if the connections are forward or bac= kward, than you might model 4 possibilities (no modulatory, modulatory on= forward, modulatory on backward and modulatory on both), and compare the= outcome using model selection. But I assume that you are not sure if you may question modulatory on back= ward if it is not on forward? I believe you may. Kind Regards Branislava ========================================================================= Date: Fri, 13 May 2011 12:42:16 +0100 Reply-To: Christian Gaser <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Christian Gaser <[log in to unmask]> Subject: Postdoctoral Position or PhD Student Position Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Postdoctoral Position or PhD Student Position Departments of Psychiatry and Neurology, University of Jena ---------------------------------------------------------------------- Applications are invited for a postdoctoral position or a PhD-student pos= ition at the Departments=20 of Psychiatry and Neurology, University of Jena. The focus of the positio= ns is on developing and applying new=20 methods for computational morphometry. The candidates should have a Ph.D.= or degree in=20 Psychology, Neuroinformatics, Neuroscience, Physics or a related area. Ex= perience in anatomical=20 MRI (data acquisition, processing, statistics) and programming skills (Ma= tlab, C/C++) are=20 desirable. This post is based in the Structural Brain Mapping Group in Jena (http://= dbm.neuro.uni-jena.de), a=20 multi-disciplinary research team in the Departments of Neurology and Psyc= hiatry. The departments have=20 excellent research facilities including an on-site, research-dedicated Si= emens Tim Trio 3T MRI scanner. Salary will be according to "EG 13 TV-L" (PostDoc 100%, PhD Student 50%).= The appointment is=20 initially for 2 years with the option of extension. Expected start date i= s July 2011. Please email CV, and statement of research interests to=20 Christian Gaser, Ph.D. Departments of Psychiatry and Neurology Friedrich-Schiller-University of Jena Jahnstrasse 3, D-07743 Jena, Germany Tel: ++49-3641-934752=09Fax: ++49-3641-934755 e-mail: [log in to unmask] http://dbm.neuro.uni-jena.de ========================================================================= Date: Fri, 13 May 2011 08:12:27 -0400 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: Simple scripting question - batch loading scans MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3054a8ef25942d04a32738b9 Message-ID: <[log in to unmask]> --20cf3054a8ef25942d04a32738b9 Content-Type: text/plain; charset=ISO-8859-1 Hi All, For a second level analysis, I've create a .mat file with the names of all of the scans that I'd like to load into my second level factorial model. Using the SPM batch interface, is there a way to load this .mat into the Scan <-X prompt? Right now, I have to manually select each file. I suspect there's an easy way to input the files as a matlab variable, but I can figure it out - any suggestions?? For example, with a first level analysis, one can easily use a .mat file to load vectors for the onset and duration times. Anyway to do that with scan names? Thanks! --20cf3054a8ef25942d04a32738b9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi All,<div><br></div><div>For a second level analysis, I've create a .= mat file with the names of all of the scans that I'd like to load into = my second level factorial model. =A0Using the SPM batch interface, is there= a way to load this .mat into the Scan <-X prompt? Right now, I have to = manually select each file. =A0I suspect there's an easy way to input th= e files as a matlab variable, but I can figure it out - any suggestions?? = =A0For example, with a first level analysis, one can easily use a .mat file= to load vectors for the onset and duration times. Anyway to do that with s= can names?<br> <br></div><div><br></div><div>Thanks! =A0</div> --20cf3054a8ef25942d04a32738b9-- ========================================================================= Date: Fri, 13 May 2011 14:28:22 +0200 Reply-To: =?UTF-8?B?546L5aqb?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?UTF-8?B?546L5aqb?= <[log in to unmask]> Subject: Simple question: In the 2nd level analysis, which file discribe the P-value map. MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf305643090b799504a3277103 Message-ID: <[log in to unmask]> --20cf305643090b799504a3277103 Content-Type: text/plain; charset=ISO-8859-1 Hello, Just a simple question as the subject. The second level analysis produces a lot of files, I wonder which one contains or is the p-value map. RPV? mask? ResMS? And what exactly does the p-value map show? Is it simply a mask which sets '1s' for the pixels larger than the value,'0s' for others? I am totally a freshman for the analysis procedure, maybe you can explain in a easier way:) Thank you so much. Best Regards, Yuan --20cf305643090b799504a3277103 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Hello,</div> <div>=A0</div> <div>Just a simple question as the subject.</div> <div>The second level analysis produces a lot of files, I wonder which one = contains or is the p-value map.</div> <div>RPV? mask? ResMS?</div> <div>And what exactly does the p-value map show? Is it simply a mask which = sets '1s' for the pixels larger than the value,'0s' for oth= ers?</div> <div>I am totally a freshman for the analysis procedure, maybe you can expl= ain in a easier way:)</div> <div>Thank you so much.</div> <div>=A0</div> <div>Best Regards,</div> <div>Yuan</div> --20cf305643090b799504a3277103-- ========================================================================= Date: Fri, 13 May 2011 13:45:47 +0100 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: resel size in batch script Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi, I've just recently started using batch scripts to run my SPM analyses= instead of the GUI, and I've come across a problem. I found that when ru= nning the second level analysis on exactly the same first level files and= with identical parameters, the scripted analysis results in much smoothe= r activations and larger resels, altering the cluster sizes reported. I'm= not sure how this has come about, or where I can amend this setting in e= ither the script or in the GUI batch interface. Is there some default tha= t I have failed to specify in my script? Thanks for your help, Ciara ========================================================================= Date: Fri, 13 May 2011 09:53:38 -0400 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: Simple question: In the 2nd level analysis, which file discribe the P-value map. Comments: To: =?UTF-8?B?546L5aqb?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001517478f04fce12404a328a1db Message-ID: <[log in to unmask]> --001517478f04fce12404a328a1db Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable There is no p-value map. Rather, you create contrasts (after you estimate the model) that create either: (1) spmT_ and con_ images or (2) spmF_ and ess_ images. The con_ images provide you with a measure of the magnitude of the contrast. The spmT_ and spmF_ images provide you with either the T-statsistics and F-stastistics at each voxel. From those values, you can determine the p-value. The ess_ is the extra sums of squares associated with the numerator for the F-test. For a general introduction, I'd read up on statistics and the general linea= r model. All of the classical analyses in SPM use the general linear model. Best Regards, Donald McLaren =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School Office: (773) 406-2464 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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, May 13, 2011 at 8:28 AM, =E7=8E=8B=E5=AA=9B <[log in to unmask] > wrote: > Hello, > > Just a simple question as the subject. > The second level analysis produces a lot of files, I wonder which one > contains or is the p-value map. > RPV? mask? ResMS? > And what exactly does the p-value map show? Is it simply a mask which set= s > '1s' for the pixels larger than the value,'0s' for others? > I am totally a freshman for the analysis procedure, maybe you can explain > in a easier way:) > Thank you so much. > > Best Regards, > Yuan > --001517478f04fce12404a328a1db Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable There is no p-value map. Rather, you create contrasts (after you estimate t= he model) that create either: (1) spmT_ and con_ images or (2) spmF_ and es= s_ images. The con_ images provide you with a measure of the magnitude of t= he contrast. The spmT_ and spmF_ images provide you with either the T-stats= istics and F-stastistics at each voxel. From those values, you can determin= e the p-value.<div> <br></div><div>The ess_ is the extra sums of squares associated with the nu= merator for the F-test.</div><div><br></div><div>For a general introduction= , I'd read up on statistics and the general linear model. All of the cl= assical analyses in SPM use the general linear model.<br> <div><br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, = GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusett= s General Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Fri, May 13, 2011 at 8:28 AM, =E7=8E= =8B=E5=AA=9B <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask] ">[log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmai= l_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left= :1ex;"> <div>Hello,</div> <div>=C2=A0</div> <div>Just a simple question as the subject.</div> <div>The second level analysis produces a lot of files, I wonder which one = contains or is the p-value map.</div> <div>RPV? mask? ResMS?</div> <div>And what exactly does the p-value map show? Is it simply a mask which = sets '1s' for the pixels larger than the value,'0s' for oth= ers?</div> <div>I am totally a freshman for the analysis procedure, maybe you can expl= ain in a easier way:)</div> <div>Thank you so much.</div> <div>=C2=A0</div> <div>Best Regards,</div> <div>Yuan</div> </blockquote></div><br></div></div> --001517478f04fce12404a328a1db-- ========================================================================= Date: Fri, 13 May 2011 15:09:41 +0100 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: source priors for MEG source inversion Comments: To: Sonja Schall <[log in to unmask]> In-Reply-To: <1613249220.234129.1303994236572.JavaMail.root@zimbra> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=20cf300fb38f5f88d504a328dbed Message-ID: <[log in to unmask]> --20cf300fb38f5f88d504a328dbed Content-Type: text/plain; charset=ISO-8859-1 Dear Sonja, Sorry for the delay. I was a little preoccupied with other things. There are indeed some bugs in the batch and you were apparently the first user to try out that particular option and discover them. Please put the attached file in spm/config and restart your SPM. It should work then. Best, Vladimir On Thu, Apr 28, 2011 at 1:37 PM, Sonja Schall <[log in to unmask]> wrote: > Dear SPM experts, > > I encountered some technical difficulties when I tried to run the M/EEG source inversion batch with source priors. The source prior option seems to work fine for the GUI, but when I use the batch it results in an error. > It seems I don't have the option to specify "MNI" as the image space using the batch, but I don't know whether this is related to the problem. > > Is there any easy way to fix/work around this problem? > > best, > sonja > --20cf300fb38f5f88d504a328dbed Content-Type: application/octet-stream; name="spm_cfg_eeg_inv_invert.m" Content-Disposition: attachment; filename="spm_cfg_eeg_inv_invert.m" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gnn7bdze0 ZnVuY3Rpb24gaW52ZXJ0ID0gc3BtX2NmZ19lZWdfaW52X2ludmVydA0KJSBjb25maWd1cmF0aW9u IGZpbGUgZm9yIGNvbmZpZ3VyaW5nIGltYWdpbmcgc291cmNlIGludmVyc2lvbg0KJSByZWNvbnN0 cnVjdGlvbg0KJV9fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fDQolIENvcHlyaWdodCAoQykgMjAxMCBXZWxsY29tZSBU cnVzdCBDZW50cmUgZm9yIE5ldXJvaW1hZ2luZw0KDQolIFZsYWRpbWlyIExpdHZhaw0KJSAkSWQ6 IHNwbV9jZmdfZWVnX2ludl9pbnZlcnQubSAzOTAzIDIwMTAtMDUtMjggMDk6Mzk6MTNaIHZsYWRp bWlyICQNCg0KRCA9IGNmZ19maWxlczsNCkQudGFnID0gJ0QnOw0KRC5uYW1lID0gJ00vRUVHIGRh dGFzZXRzJzsNCkQuZmlsdGVyID0gJ21hdCc7DQpELm51bSA9IFsxIEluZl07DQpELmhlbHAgPSB7 J1NlbGVjdCB0aGUgTS9FRUcgbWF0IGZpbGVzLid9Ow0KDQp2YWwgPSBjZmdfZW50cnk7DQp2YWwu dGFnID0gJ3ZhbCc7DQp2YWwubmFtZSA9ICdJbnZlcnNpb24gaW5kZXgnOw0KdmFsLnN0cnR5cGUg PSAnbic7DQp2YWwuaGVscCA9IHsnSW5kZXggb2YgdGhlIGNlbGwgaW4gRC5pbnYgd2hlcmUgdGhl IGZvcndhcmQgbW9kZWwgY2FuIGJlIGZvdW5kIGFuZCB0aGUgcmVzdWx0cyB3aWxsIGJlIHN0b3Jl ZC4nfTsNCnZhbC52YWwgPSB7MX07DQoNCmFsbCA9IGNmZ19jb25zdDsNCmFsbC50YWcgPSAnYWxs JzsNCmFsbC5uYW1lID0gJ0FsbCc7DQphbGwudmFsICA9IHsxfTsNCg0KY29uZGxhYmVsID0gY2Zn X2VudHJ5Ow0KY29uZGxhYmVsLnRhZyA9ICdjb25kbGFiZWwnOw0KY29uZGxhYmVsLm5hbWUgPSAn Q29uZGl0aW9uIGxhYmVsJzsNCmNvbmRsYWJlbC5zdHJ0eXBlID0gJ3MnOw0KY29uZGxhYmVsLnZh bCA9IHsnJ307DQoNCmNvbmRpdGlvbnMgPSBjZmdfcmVwZWF0Ow0KY29uZGl0aW9ucy50YWcgPSAn Y29uZGl0aW9ucyc7DQpjb25kaXRpb25zLm5hbWUgPSAnQ29uZGl0aW9ucyc7DQpjb25kaXRpb25z LmhlbHAgPSB7J1NwZWNpZnkgdGhlIGxhYmVscyBvZiB0aGUgY29uZGl0aW9ucyB0byBiZSBpbmNs dWRlZCBpbiB0aGUgaW52ZXJzaW9uJ307DQpjb25kaXRpb25zLm51bSAgPSBbMSBJbmZdOw0KY29u ZGl0aW9ucy52YWx1ZXMgID0ge2NvbmRsYWJlbH07DQpjb25kaXRpb25zLnZhbCA9IHtjb25kbGFi ZWx9Ow0KDQp3aGF0Y29uZGl0aW9ucyA9IGNmZ19jaG9pY2U7DQp3aGF0Y29uZGl0aW9ucy50YWcg PSAnd2hhdGNvbmRpdGlvbnMnOw0Kd2hhdGNvbmRpdGlvbnMubmFtZSA9ICdXaGF0IGNvbmRpdGlv bnMgdG8gaW5jbHVkZT8nOw0Kd2hhdGNvbmRpdGlvbnMudmFsdWVzID0ge2FsbCwgY29uZGl0aW9u c307DQp3aGF0Y29uZGl0aW9ucy52YWwgPSB7YWxsfTsNCg0Kc3RhbmRhcmQgPSBjZmdfY29uc3Q7 DQpzdGFuZGFyZC50YWcgPSAnc3RhbmRhcmQnOw0Kc3RhbmRhcmQubmFtZSA9ICdTdGFuZGFyZCc7 DQpzdGFuZGFyZC5oZWxwID0geydVc2UgZGVmYXVsdCBzZXR0aW5ncyBmb3IgdGhlIGludmVyc2lv bid9Ow0Kc3RhbmRhcmQudmFsICA9IHsxfTsNCg0KaW52dHlwZSA9IGNmZ19tZW51Ow0KaW52dHlw ZS50YWcgPSAnaW52dHlwZSc7DQppbnZ0eXBlLm5hbWUgPSAnSW52ZXJzaW9uIHR5cGUnOw0KaW52 dHlwZS5oZWxwID0geydTZWxlY3QgdGhlIGRlc2lyZWQgaW52ZXJzaW9uIHR5cGUnfTsNCmludnR5 cGUubGFiZWxzID0geydHUycsICdBUkQnLCAnTVNQIChHUytBUkQpJyAnQ09IJywgJ0lJRCd9Ow0K aW52dHlwZS52YWx1ZXMgPSB7J0dTJywgJ0FSRCcsICdNU1AnLCAnTE9SJywgJ0lJRCd9Ow0KaW52 dHlwZS52YWwgPSB7J0dTJ307DQoNCndvaSA9IGNmZ19lbnRyeTsNCndvaS50YWcgPSAnd29pJzsN CndvaS5uYW1lID0gJ1RpbWUgd2luZG93IG9mIGludGVyZXN0JzsNCndvaS5zdHJ0eXBlID0gJ3In Ow0Kd29pLm51bSA9IFsxIDJdOw0Kd29pLnZhbCA9IHtbLUluZiBJbmZdfTsNCndvaS5oZWxwID0g eydUaW1lIHdpbmRvdyB0byBpbmNsdWRlIGluIHRoZSBpbnZlcnNpb24gKG1zKSd9Ow0KDQpmb2kg PSBjZmdfZW50cnk7DQpmb2kudGFnID0gJ2ZvaSc7DQpmb2kubmFtZSA9ICdGcmVxdWVuY3kgd2lu ZG93IG9mIGludGVyZXN0JzsNCmZvaS5zdHJ0eXBlID0gJ3InOw0KZm9pLm51bSA9IFsxIDJdOw0K Zm9pLnZhbCA9IHtbMCAyNTZdfTsNCmZvaS5oZWxwID0geydGcmVxdWVuY3kgd2luZG93ICh0aGUg c2FtZSBhcyBoaWdoLXBhc3MgYW5kIGxvdy1wYXNzIGluIHRoZSBHVUkpJ307DQoNCmhhbm5pbmcg PSBjZmdfbWVudTsNCmhhbm5pbmcudGFnID0gJ2hhbm5pbmcnOw0KaGFubmluZy5uYW1lID0gJ1BT VCBIYW5uaW5nIHdpbmRvdyc7DQpoYW5uaW5nLmhlbHAgPSB7J011bHRpcGx5IHRoZSB0aW1lIHNl cmllcyBieSBhIEhhbm5pbmcgdGFwZXIgdG8gZW1waGFzaXplIHRoZSBjZW50cmFsIHBhcnQgb2Yg dGhlIHJlc3BvbnNlLid9Ow0KaGFubmluZy5sYWJlbHMgPSB7J3llcycsICdubyd9Ow0KaGFubmlu Zy52YWx1ZXMgPSB7MSwgMH07DQpoYW5uaW5nLnZhbCA9IHsxfTsNCg0KcHJpb3JzbWFzayAgPSBj ZmdfZmlsZXM7DQpwcmlvcnNtYXNrLnRhZyA9ICdwcmlvcnNtYXNrJzsNCnByaW9yc21hc2submFt ZSA9ICdQcmlvcnMgZmlsZSc7DQpwcmlvcnNtYXNrLmZpbHRlciA9ICcoLipcLmdpaSQpfCguKlwu bWF0JCl8KC4qXC5uaWkoLFxkKyk/JCl8KC4qXC5pbWcoLFxkKyk/JCknOw0KcHJpb3JzbWFzay5u dW0gPSBbMCAxXTsNCnByaW9yc21hc2suaGVscCA9IHsnU2VsZWN0IGEgbWFzayBvciBhIG1hdCBm aWxlIHdpdGggcHJpb3JzLid9Ow0KcHJpb3JzbWFzay52YWwgPSB7eycnfX07DQoNCnNwYWNlID0g Y2ZnX21lbnU7DQpzcGFjZS50YWcgPSAnc3BhY2UnOw0Kc3BhY2UubmFtZSA9ICdQcmlvciBpbWFn ZSBzcGFjZSc7DQpzcGFjZS5oZWxwID0geydTcGFjZSBvZiB0aGUgbWFzayBpbWFnZS4nfTsNCnNw YWNlLmxhYmVscyA9IHsnTU5JJywgJ05hdGl2ZSd9Ow0Kc3BhY2UudmFsdWVzID0gezEsIDB9Ow0K c3BhY2UudmFsID0gezF9Ow0KDQpwcmlvcnMgPSBjZmdfYnJhbmNoOw0KcHJpb3JzLnRhZyA9ICdw cmlvcnMnOw0KcHJpb3JzLm5hbWUgPSAnU291cmNlIHByaW9ycyc7DQpwcmlvcnMuaGVscCA9IHsn UmVzdHJpY3Qgc29sdXRpb25zIHRvIHByZS1zcGVjaWZpZWQgVk9Jcyd9Ow0KcHJpb3JzLnZhbCAg PSB7cHJpb3JzbWFzaywgc3BhY2V9Ow0KDQpsb2NzICA9IGNmZ19lbnRyeTsNCmxvY3MudGFnID0g J2xvY3MnOw0KbG9jcy5uYW1lID0gJ1NvdXJjZSBsb2NhdGlvbnMnOw0KbG9jcy5zdHJ0eXBlID0g J3InOw0KbG9jcy5udW0gPSBbSW5mIDNdOw0KbG9jcy5oZWxwID0geydJbnB1dCBzb3VyY2UgbG9j YXRpb25zIGFzIG4geCAzIG1hdHJpeCd9Ow0KbG9jcy52YWwgPSB7emVyb3MoMCwgMyl9Ow0KDQpy YWRpdXMgPSBjZmdfZW50cnk7DQpyYWRpdXMudGFnID0gJ3JhZGl1cyc7DQpyYWRpdXMubmFtZSA9 ICdSYWRpdXMgb2YgVk9JIChtbSknOw0KcmFkaXVzLnN0cnR5cGUgPSAncic7DQpyYWRpdXMubnVt ID0gWzEgMV07DQpyYWRpdXMudmFsID0gezMyfTsNCg0KcmVzdHJpY3QgPSBjZmdfYnJhbmNoOw0K cmVzdHJpY3QudGFnID0gJ3Jlc3RyaWN0JzsNCnJlc3RyaWN0Lm5hbWUgPSAnUmVzdHJpY3Qgc29s dXRpb25zJzsNCnJlc3RyaWN0LmhlbHAgPSB7J1Jlc3RyaWN0IHNvbHV0aW9ucyB0byBwcmUtc3Bl Y2lmaWVkIFZPSXMnfTsNCnJlc3RyaWN0LnZhbCAgPSB7bG9jcywgcmFkaXVzfTsNCg0KY3VzdG9t ID0gY2ZnX2JyYW5jaDsNCmN1c3RvbS50YWcgPSAnY3VzdG9tJzsNCmN1c3RvbS5uYW1lID0gJ0N1 c3RvbSc7DQpjdXN0b20uaGVscCA9IHsnRGVmaW5lIGN1c3RvbSBzZXR0aW5ncyBmb3IgdGhlIGlu dmVyc2lvbid9Ow0KY3VzdG9tLnZhbCAgPSB7aW52dHlwZSwgd29pLCBmb2ksIGhhbm5pbmcsIHBy aW9ycywgcmVzdHJpY3R9Ow0KDQppc3N0YW5kYXJkID0gY2ZnX2Nob2ljZTsNCmlzc3RhbmRhcmQu dGFnID0gJ2lzc3RhbmRhcmQnOw0KaXNzdGFuZGFyZC5uYW1lID0gJ0ludmVyc2lvbiBwYXJhbWV0 ZXJzJzsNCmlzc3RhbmRhcmQuaGVscCA9IHsnQ2hvb3NlIHdoZXRoZXIgdG8gdXNlIHN0YW5kYXJk IG9yIGN1c3RvbSBpbnZlcnNpb24gcGFyYW1ldGVycy4nfTsNCmlzc3RhbmRhcmQudmFsdWVzID0g e3N0YW5kYXJkLCBjdXN0b219Ow0KaXNzdGFuZGFyZC52YWwgPSB7c3RhbmRhcmR9Ow0KDQptb2Rh bGl0eSA9IGNmZ19tZW51Ow0KbW9kYWxpdHkudGFnID0gJ21vZGFsaXR5JzsNCm1vZGFsaXR5Lm5h bWUgPSAnU2VsZWN0IG1vZGFsaXRpZXMnOw0KbW9kYWxpdHkuaGVscCA9IHsnU2VsZWN0IG1vZGFs aXRpZXMgZm9yIHRoZSBpbnZlcnNpb24gKG9ubHkgcmVsZXZhbnQgZm9yIG11bHRpbW9kYWwgZGF0 YXNldHMpLid9Ow0KbW9kYWxpdHkubGFiZWxzID0geydBbGwnLCAnRUVHJywgJ01FRycsICdNRUdQ TEFOQVInLCAnRUVHK01FRycsICdNRUcrTUVHUExBTkFSJywgJ0VFRytNRUdQTEFOQVInfTsNCm1v ZGFsaXR5LnZhbHVlcyA9IHsNCiAgICB7J0FsbCd9DQogICAgeydFRUcnfQ0KICAgIHsnTUVHJ30N CiAgICB7J01FR1BMQU5BUid9DQogICAgeydFRUcnLCAnTUVHJ30NCiAgICB7J01FRycsICdNRUdQ TEFOQVInfQ0KICAgIHsnRUVHJywgJ01FR1BMQU5BUid9DQogICAgfSc7DQptb2RhbGl0eS52YWwg PSB7eydBbGwnfX07DQoNCmludmVydCA9IGNmZ19leGJyYW5jaDsNCmludmVydC50YWcgPSAnaW52 ZXJ0JzsNCmludmVydC5uYW1lID0gJ00vRUVHIHNvdXJjZSBpbnZlcnNpb24nOw0KaW52ZXJ0LnZh bCA9IHtELCB2YWwsIHdoYXRjb25kaXRpb25zLCBpc3N0YW5kYXJkLCBtb2RhbGl0eX07DQppbnZl cnQuaGVscCA9IHsnUnVuIGltYWdpbmcgc291cmNlIHJlY29uc3RydWN0aW9uJ307DQppbnZlcnQu cHJvZyA9IEBydW5faW52ZXJzaW9uOw0KaW52ZXJ0LnZvdXQgPSBAdm91dF9pbnZlcnNpb247DQpp bnZlcnQubW9kYWxpdHkgPSB7J0VFRyd9Ow0KDQpmdW5jdGlvbiAgb3V0ID0gcnVuX2ludmVyc2lv bihqb2IpDQoNCkQgPSBzcG1fZWVnX2xvYWQoam9iLkR7MX0pOw0KDQppbnZlcnNlID0gW107DQpp ZiBpc2ZpZWxkKGpvYi53aGF0Y29uZGl0aW9ucywgJ2NvbmRsYWJlbCcpDQogICAgaW52ZXJzZS50 cmlhbHMgPSBqb2Iud2hhdGNvbmRpdGlvbnMuY29uZGxhYmVsOw0KZW5kDQoNCmlmIGlzZmllbGQo am9iLmlzc3RhbmRhcmQsICdjdXN0b20nKQ0KICAgIGludmVyc2UudHlwZSA9IGpvYi5pc3N0YW5k YXJkLmN1c3RvbS5pbnZ0eXBlOw0KICAgIGludmVyc2Uud29pICA9IGZpeChbbWF4KG1pbihqb2Iu aXNzdGFuZGFyZC5jdXN0b20ud29pKSwgMTAwMCpELnRpbWUoMSkpIG1pbihtYXgoam9iLmlzc3Rh bmRhcmQuY3VzdG9tLndvaSksIDEwMDAqRC50aW1lKGVuZCkpXSk7DQogICAgaW52ZXJzZS5IYW4g ID0gam9iLmlzc3RhbmRhcmQuY3VzdG9tLmhhbm5pbmc7DQogICAgaW52ZXJzZS5scGYgID0gIGZp eChtaW4oam9iLmlzc3RhbmRhcmQuY3VzdG9tLmZvaSkpOw0KICAgIGludmVyc2UuaHBmICA9ICBm aXgobWF4KGpvYi5pc3N0YW5kYXJkLmN1c3RvbS5mb2kpKTsNCiAgICANCiAgICBQID0gY2hhcihq b2IuaXNzdGFuZGFyZC5jdXN0b20ucHJpb3JzLnByaW9yc21hc2spOw0KICAgIGlmIH5pc2VtcHR5 KFApICAgICAgICANCiAgICAgICAgW3AsZixlXSA9IGZpbGVwYXJ0cyhQKTsNCiAgICAgICAgc3dp dGNoIGxvd2VyKGUpDQogICAgICAgICAgICBjYXNlICcuZ2lpJw0KICAgICAgICAgICAgICAgIGcg PSBnaWZ0aShQKTsNCiAgICAgICAgICAgICAgICBpbnZlcnNlLnBRID0gY2VsbCgxLHNpemUoZy5j ZGF0YSwyKSk7DQogICAgICAgICAgICAgICAgZm9yIGk9MTpzaXplKGcuY2RhdGEsMikNCiAgICAg ICAgICAgICAgICAgICAgaW52ZXJzZS5wUXtpfSA9IGRvdWJsZShnLmNkYXRhKDosaSkpOw0KICAg ICAgICAgICAgICAgIGVuZA0KICAgICAgICAgICAgY2FzZSAnLm1hdCcNCiAgICAgICAgICAgICAg ICBsb2FkKFApOw0KICAgICAgICAgICAgICAgIGludmVyc2UucFEgPSBwUTsNCiAgICAgICAgICAg IGNhc2UgeycuaW1nJywgJy5uaWknfQ0KICAgICAgICAgICAgICAgIFMuRCA9IEQ7DQogICAgICAg ICAgICAgICAgUy5mbXJpID0gUDsNCiAgICAgICAgICAgICAgICBTLnNwYWNlID0gam9iLmlzc3Rh bmRhcmQuY3VzdG9tLnByaW9ycy5zcGFjZTsNCiAgICAgICAgICAgICAgICBEID0gc3BtX2VlZ19p bnZfZm1yaXByaW9ycyhTKTsNCiAgICAgICAgICAgICAgICBpbnZlcnNlLmZtcmkgPSBELmludntE LnZhbH0uaW52ZXJzZS5mbXJpOw0KICAgICAgICAgICAgICAgIGxvYWQoaW52ZXJzZS5mbXJpLnBy aW9ycyk7DQogICAgICAgICAgICAgICAgaW52ZXJzZS5wUSA9IHBROw0KICAgICAgICAgICAgb3Ro ZXJ3aXNlDQogICAgICAgICAgICAgICAgZXJyb3IoJ1Vua25vd24gZmlsZSB0eXBlLicpOw0KICAg ICAgICBlbmQNCiAgICBlbmQNCiAgICANCiAgICBpZiB+aXNlbXB0eShqb2IuaXNzdGFuZGFyZC5j dXN0b20ucmVzdHJpY3QubG9jcykNCiAgICAgICAgaW52ZXJzZS54eXogPSBqb2IuaXNzdGFuZGFy ZC5jdXN0b20ucmVzdHJpY3QubG9jczsNCiAgICAgICAgaW52ZXJzZS5yYWQgPSBqb2IuaXNzdGFu ZGFyZC5jdXN0b20ucmVzdHJpY3QucmFkaXVzOw0KICAgIGVuZA0KZW5kDQoNClttb2QsIGxpc3Rd ID0gbW9kYWxpdHkoRCwgMSwgMSk7DQppZiBzdHJjbXAoam9iLm1vZGFsaXR5ezF9LCAnQWxsJykN CiAgICBpbnZlcnNlLm1vZGFsaXR5ICA9IGxpc3Q7DQplbHNlDQogICAgaW52ZXJzZS5tb2RhbGl0 eSAgPSBpbnRlcnNlY3QobGlzdCwgam9iLm1vZGFsaXR5KTsNCmVuZA0KDQppZiBudW1lbChpbnZl cnNlLm1vZGFsaXR5KSA9PSAxDQogICAgaW52ZXJzZS5tb2RhbGl0eSA9IGludmVyc2UubW9kYWxp dHl7MX07DQplbmQNCg0KRCA9IHt9Ow0KDQpmb3IgaSA9IDE6bnVtZWwoam9iLkQpDQogICAgRHtp fSA9IHNwbV9lZWdfbG9hZChqb2IuRHtpfSk7DQogICAgDQogICAgRHtpfS52YWwgPSBqb2IudmFs Ow0KICAgIA0KICAgIER7aX0uY29uID0gMTsNCiAgICANCiAgICBpZiB+aXNmaWVsZChEe2l9LCAn aW52JykNCiAgICAgICAgZXJyb3Ioc3ByaW50ZignRm9yd2FyZCBtb2RlbCBpcyBtaXNzaW5nIGZv ciBzdWJqZWN0ICVkJywgaSkpOw0KICAgIGVsc2VpZiAgbnVtZWwoRHtpfS5pbnYpPER7aX0udmFs IHx8IH5pc2ZpZWxkKER7aX0uaW52e0R7aX0udmFsfSwgJ2ZvcndhcmQnKQ0KICAgICAgICBpZiBE e2l9LnZhbD4xICYmIGlzZmllbGQoRHtpfS5pbnZ7RHtpfS52YWwtMX0sICdmb3J3YXJkJykNCiAg ICAgICAgICAgIER7aX0uaW52e0R7aX0udmFsfSA9IER7aX0uaW52e0R7aX0udmFsLTF9Ow0KICAg ICAgICAgICAgd2FybmluZyhzcHJpbnRmKCdEdXBsaWNhdGluZyB0aGUgbGFzdCBmb3J3YXJkIG1v ZGVsIGZvciBzdWJqZWN0ICVkJywgaSkpOw0KICAgICAgICBlbHNlDQogICAgICAgICAgICBlcnJv cihzcHJpbnRmKCdGb3J3YXJkIG1vZGVsIGlzIG1pc3NpbmcgZm9yIHN1YmplY3QgJWQnLCBpKSk7 DQogICAgICAgIGVuZA0KICAgIGVuZA0KICAgIA0KICAgIER7aX0uaW52e0R7aX0udmFsfS5pbnZl cnNlID0gaW52ZXJzZTsNCmVuZA0KDQpEID0gc3BtX2VlZ19pbnZlcnQoRCk7DQoNCmlmIH5pc2Nl bGwoRCkNCiAgICBEID0ge0R9Ow0KZW5kDQoNCmZvciBpID0gMTpudW1lbChEKQ0KICAgIHNhdmUo RHtpfSk7DQplbmQNCg0Kb3V0LkQgPSBqb2IuRDsNCg0KZnVuY3Rpb24gZGVwID0gdm91dF9pbnZl cnNpb24oam9iKQ0KJSBPdXRwdXQgaXMgYWx3YXlzIGluIGZpZWxkICJEIiwgbm8gbWF0dGVyIGhv dyBqb2IgaXMgc3RydWN0dXJlZA0KZGVwID0gY2ZnX2RlcDsNCmRlcC5zbmFtZSA9ICdNL0VFRyBk YXRhc2V0KHMpIGFmdGVyIGltYWdpbmcgc291cmNlIHJlY29uc3RydWN0aW9uJzsNCiUgcmVmZXJl bmNlIGZpZWxkICJEIiBmcm9tIG91dHB1dA0KZGVwLnNyY19vdXRwdXQgPSBzdWJzdHJ1Y3QoJy4n LCdEJyk7DQolIHRoaXMgY2FuIGJlIGVudGVyZWQgaW50byBhbnkgZXZhbHVhdGVkIGlucHV0DQpk ZXAudGd0X3NwZWMgICA9IGNmZ19maW5kc3BlYyh7eydmaWx0ZXInLCdtYXQnfX0pOw0KDQo= --20cf300fb38f5f88d504a328dbed-- ========================================================================= Date: Fri, 13 May 2011 15:06:06 +0100 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: Batch problem--3D Source Reconstruction 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, I tried and everything works fine for me. Make sure your SPM version is up to date and if you can't make it work, send me your saved batch and I'll take another look. Best, Vladimir 2011/5/10 =E9=A3=9E=E9=B8=9F <[log in to unmask]>: > Dear SPM's users, > =E3=80=80=E3=80=80Have you ever used batch interface to do 3D source reco= nstruction for > EEG/MEG data?=C2=A0I choosed three separate=C2=A0modules=C2=A0 "M/EEG hea= d model > specification" "M/EEG source inversion" and "M/EEG inversion results"=C2= =A0so=C2=A0as > to reconstruct the sources.=C2=A0In the "M/EEG head model specification" = module, > I specified ''Meshes<Meshes source<individual structual image'' and then > selected a mri file named "sDP74-0003-00000-000001-01.img,1".=C2=A0 When = all was > ready and then I ran the batch, however,=C2=A0I found that it din't excut= ed the > specified structual image ( as you can see below 'template=3D1') and the > matlab command window appeared those: > > SPM8: spm_eeg_inv_mesh_ui (v3731)=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 21:39:06 - = 09/05/2011 > =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 template: 1 > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Affine: [4x4 double] > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 sMRI: 'C:\Pr= ogram > Files\MATLAB\toolbox\spm8\canonical\single_subj_T1.nii' > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Msize: 2 > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 tess_mni: [1x1 struct] > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 tess_ctx: 'C:\Program > Files\MATLAB\toolbox\spm8\canonical\cortex_8196.surf.gii' > =C2=A0=C2=A0=C2=A0=C2=A0 tess_scalp: 'C:\Program > Files\MATLAB\toolbox\spm8\canonical\scalp_2562.surf.gii' > =C2=A0=C2=A0=C2=A0 tess_oskull: 'C:\Program > Files\MATLAB\toolbox\spm8\canonical\oskull_2562.surf.gii' > =C2=A0=C2=A0=C2=A0 tess_iskull: 'C:\Program > Files\MATLAB\toolbox\spm8\canonical\iskull_2562.surf.gii' > =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 fid: [= 1x1 struct] > > =C2=A0 =E3=80=80I didn't understand why. Anyone who knows the reason ? Th= anks very much > for any help ! > > -- > Haoran LI (MS) > Brain Imaging Lab, > Research Center for Learning Science, > Southeast University > 2 Si Pai Lou , Nanjing, 210096, P.R.China > > ========================================================================= Date: Fri, 13 May 2011 17:31:03 +0200 Reply-To: Laura Zapparoli <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Laura Zapparoli <[log in to unmask]> Subject: SPM8 and visualization of cortical activations MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf303f6c946699a104a329fef9 Message-ID: <[log in to unmask]> --20cf303f6c946699a104a329fef9 Content-Type: text/plain; charset=ISO-8859-1 Dear SPM users, I have a problem with SPM8 and the visualization of cortical activations. With SPM2, I usually select the contrast of interest in "Results"; then I select "Render" and I obtain an image like this (activations on 6 little 3-d brains): In SPM8 I can't do this anymore. Any suggestion to obtain the same result? Thank you, Laura --20cf303f6c946699a104a329fef9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM users,<div><br></div><div>I have a problem with SPM8 and the visua= lization of cortical activations.</div><div>With SPM2, I usually select the= contrast of interest in "Results"; then I select "Render&qu= ot; and I obtain an image like this (activations on 6 little 3-d brains):</= div> <div><br></div><div><img src=3D"webkit-fake-url://0426A476-5A38-4BDD-BF26-1= BF9DF363834/image.tiff"></div><div><br></div><div>In SPM8 I can't do th= is anymore.</div><div><br></div><div>Any suggestion to obtain the same resu= lt?</div> <div><br></div><div>Thank you,</div><div>Laura</div> --20cf303f6c946699a104a329fef9-- ========================================================================= Date: Fri, 13 May 2011 16:52:07 +0100 Reply-To: Iwo Bohr <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Iwo Bohr <[log in to unmask]> Subject: Re: SPM8 and visualization of cortical activations Comments: To: Laura Zapparoli <[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 Message-ID: <[log in to unmask]> Dear Laura, Unfortunately I cannot see your image (it would have been better to attach it), however to render activations you need to (after specifying/selecting your contrast)press button "Overlay" (in "Results" window), select "Render" and then choose a Matlab file to use for rendering From "Previous" drop down menu choose the rend subdirectory in spm installation directory, from somehing like this: /your_installation_dir/spm8/rend/... eg rend_single_subj.mat Once you've done it, you can use "previous render" option from Overlay. Hope it helps Iwo On May 13 2011, Laura Zapparoli wrote: >Dear SPM users, > > I have a problem with SPM8 and the visualization of cortical activations. > With SPM2, I usually select the contrast of interest in "Results"; then I > select "Render" and I obtain an image like this (activations on 6 little > 3-d brains): > > >In SPM8 I can't do this anymore. > >Any suggestion to obtain the same result? > >Thank you, >Laura > -- Iwo Bohr, PhD (Torun) University of Cambridge Research Centre for English and Applied Linguistics 9 West Road Cambridge CB3 9DP UK Tel: +44 1223 767 354 Fax: +44 1223 767 398 http://www.rceal.cam.ac.uk/People/Staffpages/ijb25.html ========================================================================= Date: Fri, 13 May 2011 17:08:50 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: Simple scripting question - batch loading scans Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Fred, unfortunately, file selector does not allow to load a list of filenames f= rom MATLAB variables or .mat files. However, I could think of a number of= ways to help you select the files:=20 * If all files have the same name, but live in different folders in your = project (e.g. all con_0001.img files from all your subjects), then you co= uld navigate to the top level of your project, enter ^con_0001\.img$ into= the filter field and press the 'Rec' button. This will go through all fo= lders and eventually collect all con_0001.img files that are found. * If your files are spread in a more complicated way, you should consider= the 'Save Batch and Script' option of the batch GUI. If you leave the 'S= cans' items blank in the GUI, the script skeleton will allow to fill the= m using standard MATLAB commands. Note, that for the batch system file na= mes are always cell arrays of strings. Hope this helps, Volkmar ========================================================================= Date: Fri, 13 May 2011 17:46:00 +0100 Reply-To: Iwo Bohr <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Iwo Bohr <[log in to unmask]> Subject: Re: Simple scripting question - batch loading scans Comments: To: Volkmar Glauche <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_NextPart_000_0041_01CC1195.9EFA8800" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------=_NextPart_000_0041_01CC1195.9EFA8800 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit This a good quick method with the record button, however in my case it returns many unwanted images from some subdirectories. I've written a small and easy script to go around this problem. It creates a text file with directories to cons of interest (works only for one and two-digit affixes though; didn't need more :), then after using Edit button in 2nd level window you can paste the text, either from a text file or straight from the Matlab command window Of course no guarantee of perfection but easy to test and doesn't mess up your actual data, even if there are bugs in it. I've just played around with comments only, so there shouldn't be any errors. Iwo -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Volkmar Glauche Sent: 13 May 2011 17:09 To: [log in to unmask] Subject: Re: [SPM] Simple scripting question - batch loading scans Dear Fred, unfortunately, file selector does not allow to load a list of filenames from MATLAB variables or .mat files. However, I could think of a number of ways to help you select the files: * If all files have the same name, but live in different folders in your project (e.g. all con_0001.img files from all your subjects), then you could navigate to the top level of your project, enter ^con_0001\.img$ into the filter field and press the 'Rec' button. This will go through all folders and eventually collect all con_0001.img files that are found. * If your files are spread in a more complicated way, you should consider the 'Save Batch and Script' option of the batch GUI. If you leave the 'Scans' items blank in the GUI, the script skeleton will allow to fill them using standard MATLAB commands. Note, that for the batch system file names are always cell arrays of strings. Hope this helps, Volkmar ------=_NextPart_000_0041_01CC1195.9EFA8800 Content-Type: application/octet-stream; name="Level2.m" Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="Level2.m" % print to file con images directories to enter into 2nd level % # of subjects: first=3; last=17; % contrasts to print to file con1=73; con2=73; % components of directory strings dir='/home/ijb25/CGproject/Subject'; dir2='model/'; dir3='con_00'; dir4='.img'; % clear previous version fID=fopen('conIms.txt', 'w'); fclose(fID); % open for appending fID=fopen('conIms.txt', 'a+'); for i=con1:con2 for j=first:last % accounts for two-digit affixes of contrast images names, but not higher! if i<10; dir3=[dir3 '0']; end path=[dir num2str(j) '/' dir2 dir3 num2str(i) dir4]; fprintf(fID, '%s \n', path); end end type ('conIms.txt'); fclose(fID); ------=_NextPart_000_0041_01CC1195.9EFA8800-- ========================================================================= Date: Fri, 13 May 2011 10:47:19 -0700 Reply-To: Shabnam Hakimi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Shabnam Hakimi <[log in to unmask]> Subject: Magnitude of Z-scores in PPI MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=000e0cd48464e05edd04a32be689 Message-ID: <[log in to unmask]> --000e0cd48464e05edd04a32be689 Content-Type: multipart/alternative; boundary=000e0cd48464e05ed604a32be687 --000e0cd48464e05ed604a32be687 Content-Type: text/plain; charset=ISO-8859-1 Hi revered PPI experts, After implementing the PPI code in SPM8 (where there is no mean-centering of the psychological variable; i.e., spm_detrend has been commented out in spm_peb_ppi.m) as per Donald's helpful instruction, we are running into an interesting issue. When we use a dummy variable that only takes values 1 and 0 for the psychological variable with the new algorithm, we end up with z-scores that are several orders of magnitude more significant than we have seen previously. The entire brain is significant until p<10E-7, at which point we begin to see results that are somewhat consistent with previous work. We wanted to look at PPI during intertemporal choice. We chose a seed region in ventromedial PFC, a region that was modulated by subjective value at the time of choice. The psychological variable corresponded to the time of choice (i.e., the dummy variable took the value 1 whenever a choice was made and 0 otherwise). The top figure on the slide shows the results of this model when demeaning of the psychological variable is done (the old method). When we updated our analysis to be consistent with the current implementation of SPM, we got the results on the bottom of the slide. The only difference between the two analyses is the treatment of the psychological variable; everything else is exactly the same. Any ideas on what's going on here? We'd greatly appreciate any help! Thanks, Shabnam -- *Shabnam Hakimi* Graduate Student Rangel Neuroeconomics Laboratory California Institute of Technology Baxter Hall, Room 332E Pasadena, CA 91125 phone: 626-395-5988 | fax: 626-405-9841 e-mail: [log in to unmask] --000e0cd48464e05ed604a32be687 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <meta charset=3D"utf-8"><span class=3D"Apple-style-span" style=3D"border-co= llapse: collapse; font-family: arial, sans-serif; font-size: 12px; ">Hi rev= ered PPI experts,<div><br></div><div>After implementing the PPI code in SPM= 8 (where there is no mean-centering of the psychological variable; i.e., sp= m_detrend has been commented out in spm_peb_ppi.m) as per Donald's help= ful instruction, we are running into an interesting issue.=A0When we use a = dummy variable that only takes values 1 and 0 for the psychological variabl= e with the new algorithm, we end up with z-scores that are several orders o= f magnitude more significant than we have seen previously. The entire brain= is significant until p<10E-7, at which point we begin to see results th= at are somewhat consistent with previous work.</div> <div><br></div><div>We wanted to look at PPI during intertemporal choice. W= e chose a seed region in ventromedial PFC, a region that was modulated by s= ubjective value at the time of choice. The psychological variable correspon= ded to the time of choice (i.e., the dummy variable took the value 1 whenev= er a choice was made and 0 otherwise). The top figure on the slide shows th= e results of this model when demeaning of the psychological variable is don= e (the old method). When we updated our analysis to be consistent with the = current implementation of SPM, we got the results on the bottom of the slid= e. The only difference between the two analyses is the treatment of the psy= chological variable; everything else is exactly the same.=A0</div> <div><br></div><div>Any ideas on what's going on here? We'd greatly= appreciate any help!</div><div><br></div><div>Thanks,</div><div>Shabnam</d= iv></span><br clear=3D"all"><br>-- <br><b>Shabnam Hakimi</b><div>Graduate S= tudent</div> <div>Rangel Neuroeconomics Laboratory</div><div>California Institute of Tec= hnology</div><div>Baxter Hall, Room 332E</div><div>Pasadena, CA 91125</div>= <div>phone: 626-395-5988 | fax: 626-405-9841</div><div><div>e-mail:=A0<a hr= ef=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask]</a>= </div> </div><br> --000e0cd48464e05ed604a32be687-- --000e0cd48464e05edd04a32be689 Content-Type: image/jpeg; name="meancentering.jpg" Content-Disposition: attachment; filename="meancentering.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gnnf671a0 /9j/4AAQSkZJRgABAQEASABIAAD/4gVASUNDX1BST0ZJTEUAAQEAAAUwYXBwbAIgAABtbnRyUkdC IFhZWiAH2QACABkACwAaAAthY3NwQVBQTAAAAABhcHBsAAAAAAAAAAAAAAAAAAAAAAAA9tYAAQAA AADTLWFwcGwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAtk c2NtAAABCAAAAvJkZXNjAAAD/AAAAG9nWFlaAAAEbAAAABR3dHB0AAAEgAAAABRyWFlaAAAElAAA ABRiWFlaAAAEqAAAABRyVFJDAAAEvAAAAA5jcHJ0AAAEzAAAADhjaGFkAAAFBAAAACxnVFJDAAAE vAAAAA5iVFJDAAAEvAAAAA5tbHVjAAAAAAAAABEAAAAMZW5VUwAAACYAAAJ+ZXNFUwAAACYAAAGC ZGFESwAAAC4AAAHqZGVERQAAACwAAAGoZmlGSQAAACgAAADcZnJGVQAAACgAAAEqaXRJVAAAACgA AAJWbmxOTAAAACgAAAIYbmJOTwAAACYAAAEEcHRCUgAAACYAAAGCc3ZTRQAAACYAAAEEamFKUAAA ABoAAAFSa29LUgAAABYAAAJAemhUVwAAABYAAAFsemhDTgAAABYAAAHUcnVSVQAAACIAAAKkcGxQ TAAAACwAAALGAFkAbABlAGkAbgBlAG4AIABSAEcAQgAtAHAAcgBvAGYAaQBpAGwAaQBHAGUAbgBl AHIAaQBzAGsAIABSAEcAQgAtAHAAcgBvAGYAaQBsAFAAcgBvAGYAaQBsACAARwDpAG4A6QByAGkA cQB1AGUAIABSAFYAQk4AgiwAIABSAEcAQgAgMNcw7TDVMKEwpDDrkBp1KAAgAFIARwBCACCCcl9p Y8+P8ABQAGUAcgBmAGkAbAAgAFIARwBCACAARwBlAG4A6QByAGkAYwBvAEEAbABsAGcAZQBtAGUA aQBuAGUAcwAgAFIARwBCAC0AUAByAG8AZgBpAGxmbpAaACAAUgBHAEIAIGPPj/Blh072AEcAZQBu AGUAcgBlAGwAIABSAEcAQgAtAGIAZQBzAGsAcgBpAHYAZQBsAHMAZQBBAGwAZwBlAG0AZQBlAG4A IABSAEcAQgAtAHAAcgBvAGYAaQBlAGzHfLwYACAAUgBHAEIAINUEuFzTDMd8AFAAcgBvAGYAaQBs AG8AIABSAEcAQgAgAEcAZQBuAGUAcgBpAGMAbwBHAGUAbgBlAHIAaQBjACAAUgBHAEIAIABQAHIA bwBmAGkAbABlBB4EMQRJBDgEOQAgBD8EQAQ+BEQEOAQ7BEwAIABSAEcAQgBVAG4AaQB3AGUAcgBz AGEAbABuAHkAIABwAHIAbwBmAGkAbAAgAFIARwBCAABkZXNjAAAAAAAAABRHZW5lcmljIFJHQiBQ cm9maWxlAAAAAAAAAAAAAAAUR2VuZXJpYyBSR0IgUHJvZmlsZQAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWFlaIAAAAAAAAFp1AACscwAAFzRYWVogAAAA AAAA81IAAQAAAAEWz1hZWiAAAAAAAAB0TQAAPe4AAAPQWFlaIAAAAAAAACgaAAAVnwAAuDZjdXJ2 AAAAAAAAAAEBzQAAdGV4dAAAAABDb3B5cmlnaHQgMjAwNyBBcHBsZSBJbmMuLCBhbGwgcmlnaHRz IHJlc2VydmVkLgBzZjMyAAAAAAABDEIAAAXe///zJgAAB5IAAP2R///7ov///aMAAAPcAADAbP/h AHRFeGlmAABNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kA BAAAAAEAAABOAAAAAAAAAEgAAAABAAAASAAAAAEAAqACAAQAAAABAAADI6ADAAQAAAABAAACGwAA AAD/2wBDAAICAgICAQICAgICAgIDAwYEAwMDAwcFBQQGCAcICAgHCAgJCg0LCQkMCggICw8LDA0O Dg4OCQsQEQ8OEQ0ODg7/2wBDAQICAgMDAwYEBAYOCQgJDg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4O Dg4ODg4ODg4ODg4ODg4ODg4ODg4ODg4ODg7/wAARCAIbAyMDASIAAhEBAxEB/8QAHwAAAQUBAQEB AQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh ByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ WmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAEC AwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHB CSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0 dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX 2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9/KKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAr+WL9vL9oL4z/Df/AIKBar4c8DfEPXvDeh/2ZFP9jtGTZ5jSTBm5UnJ2j8q/qdr+QD/gpT/y k41X/sC2/wD6NnoA8U/4bA/aY/6LF4s/77j/APiKP+GwP2mP+ixeLP8AvuP/AOIr5tooA+kv+GwP 2mP+ixeLP++4/wD4ij/hr/8AaYJ4+MPiwn/ej/8AiK8H0LQdS8Q67FYadCZJXPLHhVHqTX3R8Lv2 fdI0XS4vE3iq5trTT4/muL27wPLGOTGp6/WgDzzSP2h/2v8AW1V7H4m+NDE3SSQxqv5lK7ez+J37 Zt2xA+MOso2M7ftcRP5BK9Rufiv8D9FnNp4I8OeJviDqiZU3GpRCC3B6HbjnHfpV3SPjf8SIbCeP QvhX8P7OGQnZNdhmeMH3oA8fu/in+2LZ48z4x+IXycfI6n/2nWLN8a/2xokZh8WPEzgdvOjBP4FK +iv+Fx/HBr23nTwt8KHji/5dWtSVY46nnrWRrfxx8XWcscfi34MfD3V4JG/eNpRdHUf8BzQB8xan +1B+1TpFjEb/AOKnja2nMrK2/wAvGMLg/c+tYP8Aw1/+0xn/AJLD4s/76j/+Ir6Xi+IXwQ8Q+Kbt fEtjr3w3mniiigiS28+23K0hcsT82MNHjjsa1PEf7KeleOrEav4IvfD3iizceb9o0a7QXW0DH7yM ZI6dDjtXpZrThCtFRVvcg/m4Rb/EzpttO/d/mfKn/DYH7TH/AEWLxZ/33H/8RR/w2B+0x/0WLxZ/ 33H/APEVV8Zfs9+IfDWrXdrD9oE0Of3F5H5bnvgHofwrwK+sLzTdSks763ltbmM4aORcEV5pofQ/ /DYH7TH/AEWLxZ/33H/8RR/w2B+0x/0WLxZ/33H/APEV820UAfSsf7X/AO0ubhAfjD4sILDPzx// ABFfpl+2D8WviR4A/YI+EXijwb4w1bw/4g1SawXUL62ZfMuBJp7yOGyCOXAbgDmvw8i/4+Y/94fz r9f/ANvH/lGX8DP+vjTf/TW9AHwd/wANgftMf9Fi8Wf99x//ABFH/DYH7TH/AEWLxZ/33H/8RXzb RQB9Jf8ADYH7TH/RYvFn/fcf/wARR/w2B+0x/wBFi8Wf99x//EV828k4HJr2LwX8Hdf8SyQXF9HJ p1hI2EULmeTPQqmM49+lAHYf8Nf/ALTB/wCaw+LD/wACj/8AiKvwftWftU3IBt/ip43mB6FFQ/8A slfUfhT9mXSPBHgz/hJvFMuh+HNHKkreaxcIbqTj+CM9fwzXOz/EX4E6Zrz2b3vjbxJEkfM1hZIk ZbPQBtpxjvQB4LqP7VP7UlldyA/FbxukC4wzhPQE87Kyf+Gv/wBpjv8AGHxZ/wB9R/8AxFfTuneP v2bb3wraafcal8QtD13cVunudOjazUFmIIKksflK9vWus1f9lnw58RvCR134f6n4X8W2Gzcxs7tY 71cdSY/vfmBXfmtONPHVoRWilJL0TZnSbcIt9j42/wCGwP2mP+ixeLP++4//AIij/hsD9pj/AKLF 4s/77j/+IqLxx+z1r3hzWru208zNLATvtb4eVJ9FPRj7da+fL2yu9P1GW0vYJba4jOHR1wRXAaH0 R/w2B+0x/wBFi8Wf99x//EUf8NgftMf9Fi8Wf99x/wDxFfNtFAH6V/sg/tG/HDx/+3z4O8L+MfiT 4i1/w/dQ3jXNjcsnlylLSV1zhQeGUHr2r+pj4aEn4EeGCSSfsY6/U1/H5+wj/wApNvAf/Xvf/wDp FPX9gfwz/wCSD+GP+vMfzNAHc0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX8gH/BSn/lJxqv8A 2Bbf/wBGz1/X/X8gH/BSn/lJxqv/AGBbf/0bPQB8BVe03T7nVdbt9PtE33Ez7VFUa+ivgb4RhutU uPEOqjydPgQs8pH3IwMuR7ngD60Ae3+A9E8IfCT4dJrPi8yxM6CaFBDvkuHIwAPbNYd/q/i743eI opb2OTT/AAxCdkNnG5UOo5BYetcrcnU/jD8bXv5JGh8LaYxggUNhBGnT5T7V91fs3eL/AII+H/iV rGm/ER9At/D/APZe20l1K1LhpvMT7oAODt3c14XE2dSynLK2NjQnWdNX5IK85apWiur6nThMOq9a NNyUb9XsjwG3stC8F6aIokhku06Kqgj8T2qldeOZbqzK21vbQ8fMwzkV+p3jrRP2bdL/AGZNV+J3 hzwj4B1vT3tJG0mSfTMRXlxlkSP7u4bpF254r8UvElxcT+MJpIbWHSzPM0jW1qSIod7EhF/2Vzge 1fIeG/iRHi+lXq08HUoRpS5H7RJPmXxRtvePW+zdj0M3yf6hyJ1FJyV9O3T7z9Jv2Yv2ern47eE7 XxfceJVsNJtdZe0vbKK3/eyrGsbnbIXG0nzB/CeldR+3R8GNV+Hmv6H498L6X9s0e/IstU2uMpKF UQ7IlGQCFfLdMgetfLn7I9/8cYvj7pXhz4ba7fr5GoC8udIk1GSKwlXKK7zqnJQ/IDgE8Div6OLD T5tS0ZYdXMbTBcuASy5xz1HSv578SeL+KeGfEGhWjiFiU1N06Kago05bwqO2km0pRm735dnY9XCw oVcA4KPKna7tdtrqtflZW3P5YpYbfVtQuLfVtIS8zCgEc0XzKct+P/6q4CXwRf6B4iGpeBtd1TRd UjfcsXnmHaRzgMOD+Nftv+0n8JPClt4l+K+oeFPA+mTeI7bQNKvYHs7ONZA73d6Z5M8clEGT321+ Wmn6vpI8EPJr8Uc0147PbIE3MwOduD1Ff1Lwp4g4Xiyg8RSpunKEaSlFtNpujTlfTp71k+rTPm8T gJYZLW6lez9JNf16mh4Z/aR8YaZ4ag8L/GDw7pnj7w7xFcajKudStVJ5aNxw+Af0FaHjr4OfD/4t /C+bxZ8JdUufE/h+3Ba7S5RYtS0sgdGj6leDzyPevP8AW/C0EPgg6yqPb2+cvbTnOEPp3B9q4HQ9 S1Twl4xtvFHw+1mexvrdw9xEHMcd0oOfLkXgOpxjGK+rOU+ZPFvg/U/CWt+ReRlraQn7POOkg/of auSr9MdXt/B/7Q3gHWL3TLG30jx9Dbtc654ehh2QxgcGe1Y9T0yvXrX57eLfC934V8UyWFwGaFiT bysMGRfXHY+1AHNxf8fMf+8P51+v/wC3j/yjL+Bn/Xxpv/prevyAi/4+Y/8AeH86/X/9vH/lGX8D P+vjTf8A01vQB+PlOVWeRUUFmY4AHc03vX158DPgbfa9fWev31vCscf715Llh5NumM7z6tjkD1oA 7D9n/wDZu1XXtTGpXlvbfaYojNczXbqLayiwDuJPG8dea9yv/jd4M+HV5NpfwX0u28Y6xtaG88Sa 5AVSF+hECDG71DcCuQ8eeMr3xlo9t8LfhXPPF4Jim36lqKOYLnUp1yH34P8AqsHp3qBPhBb6JpNp Na6m7alCyv5TooiOOduB27UAcdF4H8bfES+l1rxBeXOrX1w53XN9McKOu1FPQc+leh6f8GrzR/Ds s1tfRx3CjPleSuz8zz2r3f4aeKruHxodM1rwtpEsqW52o+XiIK8N9R2r6n+DHwtTxr8R4ptWsJJP DdlC88jPGrw3LqUHksD2IfPTtXyXFvGGD4by7EZhj1y0qUU07r327+7Fb30XrfTZ268NhJV5RjB3 b38l3Z+T1n4SvPGes2GnTaZa22nBmjmu1j5LAkH61Prfwev/AAIG1jQtX1Cya3IcXFrP5My+h44P OOK/QHxR4H0PwhqPhefRYkhttYtrmeS1EKrHAYrhoxsx69T71514m8Lza5rNkp/5B8cgeUZGGwOh HpX01DPcPnLqY2hflnOpvunGcotfJprz3Ry+xlSSg+y/FJng2m/tG6vPotl4V+Lnh2x8YaIDtk10 xFNWjU9X9JCBnAGOnWrHiX4K+Dfir8L7vxL8NNU/4SrQ0ZhIlwqw6jZADndH97g/gfWuj8aeC9N8 Z+IYNHskW1FpJve5jiAbpjAP+elfP+raB43+Dnjo+JPDWp3dskRw01vJt+1jgmOZRwV98VuI+TPH fgi/8E+KDaXAkezkJ+zyuMM2OCGHYg9q4av1A8QaJoP7QnwKl8T6LaR/29bQZ8R6eECtaSqMefF/ eDBcn1NfnL4q8L6h4V8SSWV7G3lkkwS44kX1/wDrUAfTX7CP/KTbwH/173//AKRT1/YH8M/+SD+G P+vMfzNfx+fsI/8AKTbwH/173/8A6RT1/YH8M/8Akg/hj/rzH8zQB3NFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAV/IB/wUp/5Scar/ANgW3/8ARs9f1/1/IB/wUp/5Scar/wBgW3/9Gz0AfCmj6dJq 3iWz06I4eeQLnHQdzX1V42lbwp8HNI8E6cfL1TVI0e4MXBEK42rx/ePWvP8A4HaJYy+I7nX9WjB0 +zIMjkZwoBJx+WPxrpNNTUvH3x+vtQWHzUy/2KNT8qop+Xg9OOfwoA9S8D6XbReHdN8NWjLCskYk vpF4aRscrXp8/hHQftq3n9lweei4UjpX6m/smfB3wNqf7G1nBrPhnQLjVZ2mR9Qm0yCS7TJYZErI SCO1fEvxs+EEfws+L82gzalJewzRvPZGOdg/k+YyKH6AvheSABmvyHhPxiyrO+J8dw/yOnXw8pRV 9VU5b8zVlpbTR99Nj1sVlNSjQjWvdNJvyvseXaJoWn+IPDVnpF/431rQJ/7ftrSDSHLtYLbPIvmX BLMI0KbmbBxyM+9fRfx5+B37PPhn9lEXfg/WPD2peNWntwZLXXhcTS/PGJG8sStjPzZwOPwr5A1e ztItOnghlurcshcy+czY/A1z9ssMWn26T+Tc/KAk6jDc89u9e7nHBWOxma4bF0MyqUqdKfPKlFJR qbe7Jrlbjp15nq9RUcyhCjKDpRbatd6tb7XvbfpY96+HfxJT4VftCr8Q/Bfg3SdPmXRWsf7IfUpp Y3bermbzHBbPygY6cV9s/D79v251zx7a6f400Gw8KaVPCdt9ZzS3OZNygKQI/lHJyTwMV+c0XhzX 47GC5l0e/nYISroATg9MjNZkHh/xO6W/l6FfgAOJEfC9TnPWvM4r8GeFeIXKpjKD9ty8qqKc+dJb auTTavvJPzChnGJpNbNJ3tZW/BL8D9vrT4nfCq//AGjfG1zqXi/whPp114W0uLdcahEY3YTX+9eT gkBlyPcetfmB+0j4d8Cad+1pbah4Qu9EvtHvrEXFvb2HlC3tXUBBGuw4Gdu7t1rwLXvBfi/wPqMN 54j0BrKy1K1tpYlFxHMWSYStGSFY7ciNzg9Mc4yKzZ7iCO0EU9u1xbopZCrbZY/p3/KvJ4J8G8Fw 3ncM1wGZOtB0KVOUUo8suWlTV203qnFP71sXiszlUoSozp295vVvTV9PwOmluri+hSx1TRfNLPwq NvjK56k/TtXG3nw707WPHNxJZSNplgkYEggXgv8ATNa+h3FyI4rm2uZruylBDQyvl4Rkjg/481LJ PNP4T1gWq3C/ZpjJCUOHdhkkH2zmv2o8c8QvrHX/AAN8YrbUtCuprLW9OnE1pdK203Kgg4bsQRwR 0Irf+IcGlfG34bXXi/TLKHTvEVuf+JvZRJtitJzwpj6fI+Mn0Jr0vxAf7e+EMGoRaeLnVfLDW7BQ Hjbvye3tXiHhnxJH4P8AjPZavfhW0TUnFtr9gB8kkTHbJwONwBLA9jQB8jPBLbaubeZSk0Uux1PY g4Nfr1+3j/yjL+Bn/Xxpv/prevgD48eCY/DfxMTULFWlsLuUsk4GFlUkFH+rAgmvv/8Abx/5Rl/A z/r403/01vQB+X/wu8LyeLvjLpOlJCZ1aTcUAzuI6L+Jr9C/jpq8PgD4YaN8NfBk08mp38Udrcpb IVke4fG8ADk46D3rwz9jLTobf4i6z4vuIo2TSITJE0i7lMgUlAR7tgV3f2fWvH//AAUs0N9PjjvZ tFjOqtE7hULRB5znJGcbegOccCubGYhUKE6raXKm9XZaLq+i7sunBzmo9z1Xw98IPFnwb0XTItd0 RorrULCO6jDqA6I44yByOnIPORXQ+HtKvdU8RGW43iHJZw/T6V9pfH7xfB4q8MfD3UZ5LTT9YXTo Zb0QwFRIjRk4BOTt3E4XPFeG2sttNb+ZatG0Z7oMCvkfDviTHZ7kNHHY2j7KrJyTir292TjdX6O1 0+p3Zrg44XEunF3Wn4q+vmcNotneRfFDUrma2aOAKFRvXgiv0o/Zy0bw5YeA7nxXDqlxNPJBLb3Y mmZI4j+7ZgIy204OPnxk5618KyOEiLkgDHWtzwre6/qFrrPhrw5rE0OoavYSWtnarPIFuJWKnbGV YJG52/fcgYBGeRXzHjbwjiuIeG5Yahi/YKMoym2rxcE/eUutkve3S0101W2TYqnQrvnje6t5/L8j 6UmtPh38Sf2d/B+jW/i7w9Z6/bX87rJDPC87KbibEXLA4Yspx3wK+bvFmg33hvxbqehXRQTwEqhV 1YMpztJ2kjJGDjtXzF4O8G3mj6rHb6vealpurQymZUgkB8shyQQ65A6djXv5u7i+fzby6ur24I+a aeQu7Y9SeTXr8KcBY3hvNsYqOYe3wlSdWag4r3ZTqOV4ST1i03dO/Rq2t8cRjaVbDU4ulyzikr33 VuqOa8LaBe299OsluZ725k2RJGu93JPAAHJJ9K/TP4E/s++H9H8CXlx480PStau9ZhQyWWq6fDcL a7TIMKW3feDAnpX5pXuteItD8a+Gta0COMzadqUd0RJko+xgwDAEHGRzzX2H4o/a+1v/AIVrF/YX huO11dI1Z3uiWjYjGcBHB9e9fI+MuD4wzCWFwOT4ZVKEneo3JK7TVoyb1UftXjdtpLa6ltln1aKl OpKz6f8AA8z5f/bk8AeB/wBl39pn4SeMfhbof/COxeLNQvR4kS3u5Ps84DQFFEJYoigyP8qqBg9O BXyZ+0r8LtJ8T/CA+PPDwgKzbpZbWJQPsU+MlF/2G617f+138WvD3x8/ZT0GfxHZa1pvxA8OrcfY 4LWULbCRhFmSTeGLIdh2qrBgRzXlXwsvYPGP7K72005lmOhsjqSTmZFIXIPViO9fZeF0cyp8P06O Y0Z06sJTTU5c7spy5bSbblHlsot9FppY58xhCNa8JJppPT0/PufMX7CYK/8ABTjwIpGCIL8Ef9uU 9f2B/DP/AJIP4Y/68x/M1/Iz+xzaJY/8FePDdpGuxIm1FQvp/oc9f1zfDP8A5IP4Y/68x/M1+hnA dzRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAfP8A+0h8e9H/AGfP2epvFl5aJq+uXc4tND0oyFPtU5BJLEAkRooLMfYLwWFf DnhS6/4KLfGHwJZ/EXQvF3hrwXod4v2vSNOuLa1t/tMTcoyI0ErFCD8vnPyADyCCaP8AwVIjvftX wUlYudKxqikKekmbMnPbO3p9DX6vaK2mt4N0ltGMJ0g2URsTFjZ5OwbNuOMbcYxXyE4VcwzSvQlV lCFJRsou13JXu35H9G4bEYDg7gXK80oYGjiMTjpVnKdaCqKEaUlBQjF6JyvdvfddrfAf7Nf7Vvjz WP2jr74A/tBaPa6H8SrYyJZ6gqJB9rlVRJ5Ekafu97RkukkZCOoAAyQWb8Wfj58U/C//AAWS+Gvw k0TxFDaeAtWbTRf6cdNt3aXzpJFk/eshkXIUdGGO1eIftGsh/wCDg34LDw75f9qLcaENS2kff+1u X3Y5/wCPcp15xj2q58ef+ViP4N/7+j/+jpa8epmGLhh5UXUbdOtGPNfVx7O2/mfo2C4O4fxOb0sx hgoQji8uq13S5U4QqpW5oKV+VPeNttbH66V+av7cPxy+Mvwv+PHwx8M/CvxePDaa7ZOJ42061uFl mM6xoS00TlQN2OMD2r9Kq/Iz/goV/wAntfs+/wC6P/S2Ovo+LK1SllspU5OLvHVOz3XVH4x9HvLc FjuNaFLF0Y1YclV8s4qUW1Tk1eMk07NXNfxXff8ABSP4YeCr7xprHibwz4w0PS4mudRtbSx0+UpC gLO7IsEUhQAHOxtwHPQEj7Q/Zj+P2n/tC/s6x+J/sttpXiewn+x6/pkDlkgmAyrpn5vLkX5lznB3 LlipJ+hboWx0y5F55RtPKbz/ADcbNmDuzntjOa/JL/gl5Hc/8JH8b5bcuNICaYuGJ5fdeFPxC7s/ UVyRhVy/M6FGNWU4VVK6k72cVe6Z9BWxWX8Y8DZrmNbAUcNiMDKi4zo01TU41ZODhOK0drXT32Wm t/1Q8ZX93pXwh8VapYS+RfWej3M9tLtDbJEhZlbBBBwQDgjFfjf+zZ+2V8c/EP7aPw80H4k+N31z wXrupPpcsL6PZW4aeSPbDiSKFGBErw5Geje9fsL8Qv8Akgfjj/sX7z/0Q9fzyaJ4evIP+Cbll8U9 HzHrHhL4q7UnUcxJPZ2zq59hLbxAe71xcW4zE0MVQlSm0opyaTdmk1e/fS59P9HjhzJM2yHNKGYY eE5VZU6MJyjFypyqQqKLi2m4vm5dmtbPofpv+3f+0P8AEP4Q3fw78M/CzXzofiLVFur7UpEsYLl/ s8YVYxtmjcAFjKSQAf3fXGax/hL8fPi14l/4Iy/Fz4q634ta+8faNfXkem6r/ZtqhgWOK1ZB5SxC NsGRz8ynrz0FfOXjLxNZ/tI/t/fFDx1p5+1eFfCPwX1C8s8nKrnSX+U+jLc30hx1/d+xrrPgR/yr w/Hn/sJ6h/6Isq85ZniK+Y16kaj9m41OVXdvdSV1876n2M+BsnyvgzK8HXwVP61GthXVk4Rc/wB/ UlJ05Nq9lDlTi3a2hN8I/E//AAUF+NfwhTxt4L+Kvhr+xWvJbUfb9P06GXfHjd8oszx8w5zX1p8C fDn7Z2l/Hdbr46eOfDPiDwL/AGfMrWljFaLJ9oO3y2zFbRtgfN/Fj2NfCX7L3gf9sPX/ANlyLUPg l8TPDHhXwQdVuEWxv2QSCcbfMbm1kOD8v8XboK/Qv9nnwf8AtT+G/iDr1z8fPiD4d8YaDLpwTTbf TmQtFceYpLnFtFxs3DqevSurh/2lX2M5+2bdndy9x/jt8jwvGB4PAf2nhsM8sjGLlFU4UUsTFXtZ NQSU1vdPSx8ifFLUv+Chnwn+DXiH4heJvip4WHhvSmjM4s7HTpZgss6QptU2Yz80i556Zqv8LtW/ 4KG/F34I6R8QPCXxV8Lf2BqTTLbfbbHTopcxTPC2VFmcfNG2OemK+xP25/8AlFh8VP8Ac0//ANOV pVL9g3/lFp8OP+u2pf8Apxua2eWS/tf6r9Yqcvs+b43e/Nb8jzIccUP+IdPPv7IwXt1i/Y/7tT5e T2PPtbfm6326HkvxO+OvxW8Pf8FqPh78JtJ8VtaeANSOni+0oadbOJfND+Z+9aMyjOB0cY7Yr60/ aH8V6/4H/Ym+JPi3wtfnS/EOl6O9xYXYhSXypAygNtkVlPU8EEV+XX7WNl401H/gt/4Tsfh3qVro /jiay01NFvbnb5cE5Em1m3I4x9VP0rpfjP4E/bw079lnxvffEj4p+EdZ8DQ6Yza1ZW3keZPDkZVd tmhznHRh9azjnGIpfXYck52lKzWqjp5vS2+h3VvDbJ8cuF8SsRhsPzUqLnTn7s6z59XaMGpuS928 nq9HofWP7Lfx01vX/wDgm5q3xc+Mfib+0X0q+vnvtRNnDCRBCFKqI4URS3OAAMsSBzXzH4d+NP7Z n7VHizxBqHwTfRPhp8PrO4+zrc3KQlVbAYI9xJFJI821gzeUgCgjOMjdxPhaO/k/4Nn/AB/9hLDb 4lDXAUkExC/s939CfYGvt39gZtMP/BMHwMNPMX2kXmoDUduN3nfbZiN2O/l+V15xilg62Ix08NhZ VZRj7JTbTtKTva19x8TZZk/CuFzvPqGApVaqxssPThOClSpRUefmVPSN38K000t1T+btM/aZ/aO/ Zy/aJ0LwZ+1Lb6d4j8Jayyrb+IbSGFDDGGCvPG8KKJVTcN8boJMYIIyN3sv7dnxq+Ifwj+Cnw+1z 4XeKl0KfVdWkiuLmKztrtZ4fJ3rjzo3XGecr19a4P/gp42mD9l34dpL5f9snxSxtc43eSLWXzsd8 bjBn8PavI/21P7Q/4dbfsr/2r/yE/wCzbP7V6+Z/ZkW7PvnrWWPxeJwlHG4ZVZNQUXGTfvK7V1ff 08jv4TyDI+Icx4azypgKVOWJqVqdWlGC9lP2cZcs1Td4r+8lpzWe52kcf/BSuH4bWfjHT/GfhzxP Yz2Md9Fp1tZaYbiaJ0DgBDbJltp+6rZPQZOK+lP2Q/2n5P2gfh/rOmeKLKw0b4i6A6jUbW1DJFdw NwtwiMSVIYFHXJ2nacgOFH0l8N/+Td/AX/YuWP8A6Tx1+W/7J0e7/gux+0C2ilV0dG17zfLOIyn9 qwhQMcY3Yx7CvS5K2X4rCuFaU41HZqT5ul7q+1j4tYnLeMMhzyOIy6hh6uCiqlOpRpqk9J8rhNR0 lzLbs9ex+vlFFFfan8whRRRQAUUUUAFFFFABRRRQAUUUUAFfyAf8FKf+UnGq/wDYFt//AEbPX9f9 fyJ/8FD7QX3/AAVimtGBKyaXbA/9/Z6APE7a1Phz9nnQ9IgjaDVvEJ2g5wdn8R498V7T8H/CN1B4 eufFAt5FmhkEcS8bXjHy9OpPJ5rhfiBYNbeIPA9sjmJrbS0dAuPlLrj/AAr6r0V9T0rwPo9rpFgL qzjtlMrbgvOATn9aVwO68L/E74s/DHxFFrfhi/tvsV2VWbTblM254YKzgENxuP3SOTzXc/GuX4h/ FzXPBPiqz+G/ji/1WPQ4obuWx0Gc27yMS7+WRu3R/NkHOcV574Ux4l8c6PZ67dxaDolxOFuLl4jK YhzgYB7nAz2zmv0Q+J37RXwn/Z++CkOmaZ/ZvibxRZWEI0rRJLhlkuolIj3mYRMqkAMTnk496/mn xYzZZFxVl+IyTKvrGZVlJcyjJJwa5femrJ27t+6rOTSsfTZZTdXBVFXnamrdrp7+q/Xp1PxO+IFz qvh3xpf6bq9pdaPdWaCO6sL2Fo5VLAMNykZHBB57GsX4V3MXiL4h29jduEtEkyq54HU/0rF+I/iP xh8YPi34k8cXlnbefqMpnnFuojjwiqgAGeyKo98Z715jp2pahokryWkslndoysMHBxX9F5dPEywt KWKio1XFcyi7pSt7yTerSd7eR87VUFN8m19L9uh9yfFv4kXvgLStKXSbK2u7q8dgDNkqirjsCOeR 3rI8M/EGXxbZQRWeqLp3iBlLLaTwfu5D6A//AF6+Yta8WeINeurS98Qs86QDasuMiPPU46c+tesW J8MHwtBLFqMhuAAyyiUh1P1rsuZnX3vxDvNG8b3dr490S2uUe3ijOF3Kiq0hDYOf756Vof8ACPeD PGtudR8D61K2oWxE32Fm4fH8JDcjP1ridRjHj7wZrTPN9u1TT7SLZKgwzqGl6+/Ir548NXHiKy+I dj/wjv25dVFwojSIkZOehx29q9XOLKtD/BT/APTcTKls/V/mfRtxo+taJ4xaTZLYCRQ89m4+Unvj 610ME5j1GG8h+a0lBFwo9exr3Z9Hk1v4TWlz4ntYrXWIrQySOMKYnA559Pavmm31WLUPHSWGlXkU 1lGN87xoc7weRXk3RqbDu4juMagr2M7kQ21qmHZj/Dntn8K8D8b6Iti9+Wtkt8Ms32ffvKDvz6kV 6tLqMlr441xbWGKyVZVE2oTjcsQIH3QO9ef+PEsZdKebTdXk1W/+zu91NnuB8oxTuBpfE3VbPxV+ wh4Ov7hc69o6HTrxigB/duojzxknYvWvpv8Abx/5Rl/Az/r403/01vXyRLHf6x+yN8RZ72NWubXV kmmOwAjch/rX1v8At4/8oy/gZ/18ab/6a3oA8B/Zntksv2b/ABRLgi4u7m38tvUBwzV90f8ABOvw HovjH4jfGHxzqGkRXfiLTNfjtdNv2Zt9vC8UwlQDOMMOORXx38BrZIf2SNNmHyzTagyEjviMkD9K 9Y/Yt+PV/wDB743+OfDEj2p0jUddMuoJMpD/ACq6hg4Bxt3kkYOcdq/LPGjIM2zrg7GYLKn++klZ J8raUk5RT7yinGz0d7PRnqZNiYYfGQqS6d+/R/Jn2T+0n4S8KweOrbS9CF5Z6lIhnvFU5g2l5ATk 8h9wOR0ArwXTb7RLOVdIs51MyDBAB5P19a+ofjjrHwe8SQLfan8QIPCviC/s4msrqKCSdGjaQtko gHLZYckEcelfP8fw+0PTf2fNB8dWGpia+uGMdxA5DGRvMkAcc5UFVX5cfjXx/g1xvQo8L5dg8w9s qzapXqwn702pNWk1Zx91xvf3WrSsmm/WzzKqksVVnT5WkubRrbRarvrfz6GJq5kl0W6toCGdlx15 A9q+J/EHiLxz8I/GaQ6RrN3d6RcsXjgvmMgVs8jPVfwxX1/aXLrrBMoLxsp3epNcN8TfAlp4u8Kr MqmK7gkEyNgENjOQc+2a/oK6Z8qfOeqfGrxyYbHw7pENjYSSoAs6Rs0zF2J6sxAGT6V9MfDBNb0r wksGsajeavNJmSae6kLsjHsCT0HpXmHhLw34e8XeK7HUUhV00mERlVGN0m5sZ46AV9ByJ9i0gQW8 KowHyqgxivTzqyzHEf45/wDpTMqH8OPovyO+0zw94g1XSmvtM0XUtRslk8tpbe2aRd2ASOB1wQcV 6tY/AXxVdXumTXs2j/2ZcSoJkhu8zKhI3fKV4OM9a7j9mDx/pk/hfV/BniGw0e1t7SA3UF6yKjyE lV2txy3J+bPTAxxXG/CnxDc+K/8AgpL4qsx471UeGTj+x9Fd5WgmAi/fMgJCx7CueRyWJFfx7xx4 p8bUMyzfCYajChTwVN1OdxlU54ysoWcXaMndvXSKTctItn3OV5VlsqVKpVbbk7Wuktm3vq7Wt+C1 Z2ni/wDY00S+/Z18XSaZp6atrt1Z5tDfTDbCR1KlUGCQTnOegr8iv2e7ltM8TeNNBdQY9P1cmKI/ MArAqBX9IXjjx74Y8A/Bi/bU9UtbeJLFgGlYszcYHQEnk1/NJ8EPtbfFf4k3Uy/O+oRqwBz8285F e79HLijNM5wWMnjsRKu04PnknbmlF80YvZqKULqOzbvqzzM/pwjODhBRWtku19+/fX/If+zbpq6T /wAFvNJsQCDG2oBsnqfsM2a/q8+Gf/JB/DH/AF5j+Zr+V/4HgD/gvVbKBgifUQRjp/oU1f1QfDP/ AJIP4Y/68x/M1/SZ88dzRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeB/tG/AbRf2g/2e5/CF/dJpGs204u9E1byfMNnOAQc rkFo3UlWGe4PVRXwt4V0v/gor8HPBNv8OfDnhrw5408P2YNro+pT3VpcC2iXhRG7zRusYA+UTJwC AAAAB+stFeLjsjpYiv7eM5QnazcXa689Gfp3C3inmGT5Y8rrYajisNzc8YV4OahLq4NSi1fqr2eu mrv+d37NH7J3jnQf2ir749/H/WbXXfiTctJJZ2CyLcfZZXGwzySL8m8JlERAURSCDkALw37T/wAD /wBonXf+ClHh74wfB/wRba/Do2n2T2V3capZRR/aYXkYq0c06OQNw7YOetfqVRWE+GsI8IsMnJLm 5rp+85d22nqephfG/P6fEM85qU6VSTpOiqcov2UaT+xGEZRsl01e7vc/Mn/hLf8Agpr/ANEv8Df+ Bemf/J1UP2tvgf8AtCfFjxX8D/FnhPwTb6z4j0Xw9G+vKuqWVvHbahvjldAJZ13KHDfcLDA61+ot FKrw5CrQnRq16klK27TtZ30901wPjTicBmmHzHA5XhaNSlzr3Kc0pKceVqf727ST0s1rvc/K3xFY f8FI/iX4QvvBeteH/CPgvRdWia11C+t77T0PkuNrqWjnmkVSCQTGu7HSvsb9mX9n3TP2ePgC/hqO /j1vxFqN19s1vU0i2LNLtCrHGDyI0UYGeSSzcbsD6MorowWR0qFf28pynNKycney8tEjxeJvFTMM 2yt5XRw1HC4eUlKUKEORTktnNuUpO3RXt5aK3N+MbC71X4ReKtLsIvPvrzR7m3totwXfI8LKq5JA GSQMkgV+bPwe/Zb+J9h/wSf+OXwp8b+FU0fxbrl+1/oNk2pWs32iWGG3eD95HKyJulh2ZZhjOTgc 1+pVFbY3KKGKqqpUb0Uo+VpKz6Hm8MeIeaZDgKmDwkY2nUpVbtPmUqMuaFrSStfe6bfRo/LX9mT9 lv4n/Dv9kD9o208YeFU0zx34s8N3GkaFp/8AaVrM0q/ZJwv7yOVo0Eksyr87DHlgnA5rofhN8APi 54Z/4I0fFv4Ua54S+xeP9avryXTNL/tS0k89ZIrVUPmpKYlyY3HzOMY56iv0porhocM4SjCEYt+7 GUem0t29N+35H1WbeOXEGY4ivXrU6fNVrUazspWUqKShFe/pGy95O7fSSPx7+EnhL/goR8FfhGng nwX8LvDA0RbyS6H2/U9Mmk3yY3fN9sHHyjjFfUnwV8Q/tv3/AO0NpFr8avA3hbRPh40M5v7uyuLF pUcRMYgBFdSPzJtBwp684619wUU8Hw8sM4cmIqWjay5lbTpbl2J4k8Y6mdxxDxOUYRVKylzVFTn7 ROX2lJ1X7y3TaevQ+eP2rfA3ir4lfsCeP/BPgrS/7a8T6ktmLKy+0xQeb5d9byv88rKgwiOeWGcY HJAqt+yT4B8W/DH9gfwV4K8caT/YniawkvTd2f2qKfyxJezyp88TMhyjqeGOM4PORX0hRXpf2dT+ u/W7vm5eXyte/rf5nxK4yxq4YfD/ACx9i63t72fPz8ns7Xvy8ttbct79baH50fE/4EfFbxF/wWo+ H/xb0fwr9s+H2mtp5vdV/tO1TyvKD+Z+6aUSnGR0Q57Zr6x/aG8Ka/45/Yn+JHhHwtYf2p4h1XR3 t7C08+OLzZCykLvkZUXoeWIFezUVjSyijTjXim/3rbe3VW00/O56OYeIuZYytldWcIJ4CMI07KWq hLmXP72rb35eXTax8XfstfAzXPDn/BOHVfhL8Y/DCafJqt9fJf6a15BcbreYKAweF3UNwSCDlSAe DivmDQfgb+2R+yx4z160+Bj6P8SvAF9N9o+y3TwKrNwod7eSWN0m2qFJichgFz0AX9cKK5anDmHl SpRjKUZU1ZSTtK3npZ/ce/g/GfOaWOx9etQpVqWMlz1KNSDlScr3Tiubmi10al2veyt+UOlfswft GftEftGaJ42/amvNO0DwtpDI0Hh+1nhkM0e4M0EaQOyxK+0b5GcyYwADgbfav26vgn8RPi98Ffh/ ofwt8LR67PpOqyS3FtHfW1osEJh2LjzpEXGeMLnHpX3pRSXDeF+rVaLlJ+0+KTd5O22trfgXPxsz 153gczhSpQWDuqVGMHGjDmTUrRUlK7vdvmu3bW2h+Vtq/wDwUs/4V9ZeEbHwd4U8M2UFjHYw6jFe 6WZoI0QIG3faZPm2jqEJ7jmvo79kr9lw/s+eE9d1bxJq9r4i+IWv7BqN3bBjDaxKSwhjZwGfLks7 kDcQvA25P2JRV4TIKNGtGtKpKpKO3M729Ekjl4h8XcyzPLa2XUMJQwtKs06ioU3B1LO655SlJtJ6 pJrUKKKK90/KQooooAKKKKACiiigAooooAKKKKACv5N/25hLcf8ABZCKwEjrBPp1sHUdG/eT1/WR X8mP7dExg/4LPWUoYLixteT2zLOK2oYirQmp0pOMl1Ts/vQpRUlZo8y+J9pb2XxZtrPSxDGsdnGC oHyllAz+Zr1nwjqnhvWvDtnCdItFa3izcXBGA0gH3T+p/CvPvjhoN1o3xKh8RLDLLZCBJnZB8ojd QVb6V5pofiBdJ8WWzi436VfoBIAxAVj0PpnPH416S4hzVf8AMVU/8Dl/mZfV6X8q+5H0Vqeq+H4d G8rTtHsJrtjhiyECPnua85uLI2+qaZdtpkN9pdxMBNKeDk54A9Pam2lhrep+KoLr7HFb2CMXiaST 5Zh2H1Neqtoq33h2QyQvZTSN80DgZRiVOR7DFH+sOa/9BVT/AMDl/mH1el/KvuQ258HeF7fwzaSa fb3TTyL/AKc9xCoRXLtxEF527AoO4A5z2ry3X/hreXnw/wBX8Z6fBdW1ha3UcdyNyiO3V5PLjJ7/ ADHiva7G3mtvBt0t95dphtzTGUlBjjIz0rnLj43XPhHQL7QPB2o6Xd/2qYYbqGbTre8jfy3yrETR uAQSTkYNeZjs+4gp4Z/VcTKc7rSdSaVuZOSurtPlvy6PW19Lm1KhhnP342Xkl2/z3KXh/wCG+j3f h4WM/mXL7AZbstnk9lB6YrN8SfCPQtLNmtnrt5aTXDbI4p5ch2x0Fe5aJphg0O2toFAKxjc545xz Xz38YdF8SaT4gh8QvqJutP8AOVItgK/Z2xkfnjtXo/2/mn/QTP8A8Dl/mZewp/yr7iv4b0+18H6r rFpr1pFfI8A+efrGcnBGPpV+O50gWBubS00aK7lGYV8gZJPQfWuD8PazrnijXG0qXQNV8X3uoHy0 g02B5bo7QW+RVBJIBY/Qe1bPiPSoPh/4707SPEbX+gpJCs9omsaXJZ3AGejq6gkA9xxXoZxxRjo4 uFL63JSdOm7c7v8ABHW1zOlhqbi3yLd9PM1rWwvdRinufEJuLNVYqsNu5jTb64B5rWttP022sWa1 0mzgtAMtd3f8X5c102gXFjqfh/zbK8sNRZgWMiPuGST+les+Jofg74Q/Z+0CLVGsfE/j3VYGu7u6 lu5oV0lGVQkaRo3lyMGV+GG75hkdK+azTjjNMHVoU/a1qkqsuVcsm7aNuUm5JRikt27t2STbSOqj l9KpGUrRSiuv/DHz/o8ej3Wo30Fla6dqNzJhpJJ48RJ2wo6mvHvjTF/wjcdt9lnt7W6uAS0dqmxR g9K9YvvF/g9tRtryOeKFLN/MQom3zCOxwOlfKPxH8UXXjj4pvJEuIDJst1U5yOBXrf6wZp/0Ez/8 Dl/mYfV6X8q+49l+Gjf2/wDsjfFm+vo47i8tUtvJmcfMmWwcH3r7C/bqnlt/+CaHwKlhdo5BPpuG X/sFvXyB8NpB4d/ZB+MqXSH54LZYx/eYScj8K+u/28B/xrK+BY/6eNM/9Nb1zVszxlacZ1K0pOOz cm2vRt6fIuNOCVkjyj4UaNpmofsd6TfzWUFzfHWFWa4ZcuqmM4BPoTXn+nTad4Z/aV8RWWqaVDf6 U1qtzhhxFuPUV9Xfs/8Ag8Xv/BPi3KuBPdainlrtyzf6O/I+mc14u+j6QP2pLm31qDfPLpyhEKg5 CEg8Hv3rp/1gzX/oJqf+By/zI+r0v5V9x6/8RbTwD8RPg54f0rwD4T0rw1qlnDFJeXcVusVzcuqY flSdyHrzznrVXwF8I7nW/hzqOt2Wgm60/T2KXd05DCNlUMwOTnoc8VrR+BnTUrHU/DZdo8hXULtw O+RX1Z4W0FNB/ZM8awW2v2GlzXrzXYtxPC0s48kKYthORuK4yBn0r854k4xzfhTKqEcDiZtyqwj7 9SrN2nP3npJybSba6LtZHtYLBUMfiJOtFK0W9FFbLRdj4/sdD8PfZbmW70WytykpjQSJy2ByaLjT fCi6TJjTNOcDqVTke1ZeqXAl8xZTuJzuBPSvm7xdrup6DrdxZ6HqbPZ3ZEska8hCOwNfpH+sWbf9 BVT/AMDl/meL9Wo/yL7keq/DSw0M2d815Y2txK1/IiLIvGNxxXpb2nhBdVksmsNM+2jG6LYcpmvi yXxN4ga3S1tpJrJfO83fBlWZj7ivpL4OaSfFniHSNGXVbc6hd3IF3cXt0qNGCeSWkIGcA4BPJwB1 rp4i4rzDC4nFVamLnGEJTbfPKySbb69EThsJCcYJQTbt0PRbHwho2o+LZLGew0zTtODKEv5lPltu /iOMkAZ9K7G7+Cfjv4Ra54W+IsvgPSvEWjXRliubK9lZo4Ff5EZwpByc7lwTx1xX3RpX7J/hfwjr Wm+I5fHd3qOmWU63E6XdrCsMiIckMS33eOeK+efj5qN/4n8f3mjx+NFTQ7WcfYUSULEymNCQNnDY I75xzX84ZV4543jzO6WX5Djp1MIoyliHP20bx+Hkg/dam+ZNu+iSa6p/S/2LRwWFlWrwSne0Urb7 3flpbb/NfKHxm1rR5tTn1i48O6VoaLZbI7OyLGMSbScgEnk5x+Arw/4M2Nve+D73UNSso7mS61Ub GlXO1Djj8819v+DPgHoXiP4b+Mdf8X+LEsNI0y3Eov7kIIOQ2RvlYKMYHQ9xXh3wS0e3f4cRag9r stmUyeVsB2+Y2Vb8BX6/kPGvNXr5XgsXUvheWMlzVLR5lzJczfvO29m2uup5GKwL5Y1qkF792tF0 8lseZfANzF/wXRs7CHEVlFc6j5UK/dTFlN0r+qb4Z/8AJB/DH/XmP5mv5WvgYI1/4L4qsR3Ri71I KcYyPsU1f1S/DP8A5IP4Y/68x/M16uIxVbET56s3KXdtt/eznjFRVkrHc0UUVgUFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHgn7 TXxP8SfBv9jDxX8RfClpo97rWly2gih1SGSS3KzXUULblR0bpIcYYc461R+D3xsk8Vf8E6tG+N/x COk6PnS7y/1f+z4nSCNLeedD5au7NkrEOCxJY8dQK5v9t+3+0/8ABLf4rR4ztgspP++NQtm/pX5q 6n8QNZ8Sf8EvP2bv2Z/ArG58U+Mru5k1OKNsbbcavdJBG5HRWlUyMf4VgyeDXx2bZxUwWYzV7x9l dR7yc7L+ux/SXh94b4LibgzDyVNRqPHOFSr1jQjQ9pO72SVm1f7Vl1PqX9lz9tL4i/HT9smXwN4h 0DwZpPhqbT7u7tTYW04u08sqY1aR5mVvlJyQgyRkY6V6d8fP2mPHfhT9tT4efAn4OaV4V1vxdrJQ 6xJrNtNPHZCVh5fEUsZXZGskz5z8hQjvXyL4b0/wf+zN/wAFmr2zaUW3hXwx8PjJczYCvctHpCPI +O8ksiMcd2fAr1T9iLw3qHjr4tfFT9rj4iKqXWoXVzBpDy5KQJjdcSJn+COMJAhH8KyDtXlYDMcd Vpxwcqj9q5yUn2jG1/v2R97xbwdwpgcXV4loYKKwMcLSlSptO1SvX51TTV7vlS55q91ZM9Z8NftO /EHxX/wV61r4E6VpnhGTwBo7zrf6h9jnN+PItQZCH87yx/pRCD5DhTjk814r8Rf2pf22/ht4X1Dx T4q+DHgDw34SjvBFFdX1rK7KHYiNW2XxJYjGcLjqeBWR/wAE7rS48b/ta/Hf4xX8TfabnMYd+cyX 11JcyAH1HkLn/eHrXa/8FO/FZtPgJ8N/BEUh83V9dl1CRF6lLWHywD7FroH6r7U54vFVMmqY51pR d5ONrbNpJPTZP8xYbh7I8H4k4ThWGWUaq9nRhVclJtSjTlUqTjaSXNKLV2017q03vl+EP2jf29fH fgfSPE/hf4JfDzU/D2prvs78WUyRyJuKFsNfhgMg9u1ev/Gf9pz4h+C/+Cjfwy+BngjTfB2pRa2u nrrV3qFnPJLA9xcMjmPZMoULCok+YN1719afCrwoPA37M/gDwd5Yjk0bw/aWcw9ZEhVZGPuXDE+5 r8ZPEeq/Fv4gf8FvviJ4u+Cfh2w8YeL/AAvqE4tLW9liW2jhtI105pD5ssakBmBADZ3MDg810ZjP F5fhaK9tOc6ko36tJay5UkeLwXQ4f4vz3MJLLsPh8NhKNdwesIylJqNJ1ZSk0rau6sr302t+yPxg 8dN8M/2XfHvj2NLaW60TRZ7qziuATFJcBCIUfBB2tIUBwQcHg15B+yL8Y/Hvx0/Zn1Dx34807w3p k51yaz06LRrWWGN4Y44iXbzJZCT5jOvBA+Svzq/ad+Jv7ZcH7Mlz4c+OHg/wp4U8G+IL2G0M1hJb PPNJGwuFQeVcyMBmHJJXHGM8jP6Yfsk+E/8AhDf+Cc/wo0povKnuNFXUpgRht12zXXPuBKB7Yx2r 0MFmtXG5tyQUowhC7UlbVvTT02PkOJ/D/A8M+Hv1jESo18TiMQowqUpqolTjC8kpLS/NpJeaPI/2 0f2mPH/7O8vw1XwNpXhTUz4h/tD7YNatJ5iv2f7Ls8vypo8Z8985znAxjnPpf7Jnx11T4/8A7LT+ LfENro9j4lstXn0/UrfTI3jgBUJJGyq7uwBjkTOWOSGxjoPkL/gpZ/yUP9m//r/1P/0ZpteY/D/x 1/wzT8Sv22fhw032CO2024vvDSsdoSQy+Ra7R6sl9bsQO0ftXm1s5r4bPKqqTfslZW6JuHMvvsz7 fLfDPKs78LcBLB4Zf2hUcpqa+KUY4n2Mk/JKpF/JHRx/8FDPinqv7XkHhLQ9A+Hkvgq88YLplhdS 2F01y9m10IkcuLkIZDGQchcZPTHFe7/tV/tTfF34P/tf+DPhj8M/DngzX5Nf0W1nt4tVs55J5bue 7uLdY1ZLiNQp8uMDI6k5OOn5s6Z4K/4RPTP2RdUmi8q98TeIpNWl3D5tn9pW8EX4FIFYf7/vX05+ 3jc+IrP/AIKz/Ba78I2cGo+LINF0qTRbSbGye7XVbswI2WUYaQKDlhweo614tLN8w+oVp1Kr5uaG 26Uley+TP0zHeHXB3+tmXYfCYKn7J0cVdS+GU6MuRSk794t37M9h/wCF1f8ABRP/AKN88D/+AUn/ AMsK+7fhBrPxB8Q/s6eG9Y+KmgWXhfx9cJMdV0uzQrFblZ5Fj2gySEZjEbffPLHp0Hwn/wALL/4K Sf8ARFvAv5Qf/J9feWieKNY0X9lfTvGXxSt7bQNcsfDS6j4phgx5dpLHB5lwq4ZhhSGAwx6dTX1+ SVP3spSnVaS/5eKy9Votf0P5z8UcGlgqFKlhcDGU5qzwlR1JvRrlkueVotvtq0tT4Y/a1/bY8YfB T9pK3+H/AMOtK8H6tJZ6ZHca3LrNrPMYppSWSJfKmjxiPYxznPmDpjn7s+Ffj7T/AIo/s6eDviBp mxbfW9MjuXiRsiCXG2WLPqkiuh91r8c/gBb/AA9+NvxE/aR+Inxm8ceCPCuo+KrC407Rodd1u3t3 glumMgmjWVwxEAjgRWAIwSOxr33/AIJrfEx38M+Ovgrq13DLeaPctqukBJhIrQuwjuVRgcFFk8tw RkHzmNeVkue16uPUqs7063Nyr+Xlenpdfeff+JnhVlOB4RnSwOHccZlvsvbzs7VVVjeTT2lyTaWn wq9zc+M/7cHi/wCDv/BSS/8AhvqWheF7z4Z6dc2Iv7lLWc6ksM9rDLK6uJthZGlYgeXyFC9Tur7A +O3xTvvh7+wz4r+K3go6LrNzZafbXmlvdq01pcJNNCoc7HUspSTIIYdq/Lz43/C23+M3/BeTx18O prs6fNqejKbO6ydsVxFoUcsLNjqnmIoYf3Scc4rB8PfGLV7P/gmJ8d/2aPiN52n+MfCsCDRortv3 jQx38Antcnq0TZZcZzGxx8sdc0M/xVKri4VZe63UUH2lFXt91reZ7mL8JMhzDBcO4nL6K9tGGDni af8Az8pVnFOpbraSkp2+y7uySv8ArB+zZ8S9e+MP7FXgr4jeJ7XSbPXNXF39ph0yJ47dfKvJ4F2q 7uw+WJScseSenQe518k/sLf8orfhZ9NS/wDTnd19bV9xlFWdXA0Zzd24xb9Wkfyx4h4HD4LirM8N h4KNOnXqxilsoxqSSS8klYKKKK9A+PCiiigAooooAKKKKACiiigAooooAKKKKACv5E/+Ch919i/4 KxTXWcGPSrYg5xj97PX9dlfyA/8ABSf/AJSean/2Brf/ANGz0Ae2/EGa3179lL4aeLEginMNqdNv IyNyyrgMGf16ECvlrxF4M06HwjdXmmsyWlzjzFc5Fs+cgr6DPGK+mNLAn/Yl03w7NgTS2qTRknJV tmOBXmuh2EF7pFxokkqTRykxgFckHHGfxFAHH+CvHcMXw9NvevGLjT/3Zz1yMYb6GvYtDni1yytt VmuEKFfljjckH3NeC3Hw61Aaze28RWwvINxhmdcRXCjqjDucZrmLLxNr/hi++x2xuLTIJNncZIB7 lfUe9AH1vqoS48PTRSL5luwKyR5xxXzx4Q8J2d1+0Jb2lu5n02JmmBZc8Lng1Rk8b61faAz3N2gY HBRWK1618F7J7641XXZoghOI0+X65x+dAH0NHOEs/lxhjxjtXkPxuuorf4Q2tiW33t1eqUjJ+bAB yf1FR+NviFceEfGNrptpZQagkkQdkeTaUOTznB9OleJaz4on8X+OYrzUWWARjaEZ8qg9cdqAOg+E fx9sf2b/ANovwj41fw7F4si095GksknEMm5opE4cg/8APTuOgrzr9rP9pnUv2o/2lIfHN14etfC+ n2WnrYabYxSGSVYgS5Mr9HcuzcgAbcDHGTxHjnwRrMuuSvY2d3dQK5dTtyQpAx/6Ca8ofRtRjvPs 728izZxtI5z6VzZ7wzl1XPqObTp3rwowhGV3pFwi3ZXtfV62vbS9h0a81RdNPRtv8TY0KDxDNcwD R73UowzAN5MxG38M17Vp1j4ksFEt7qFtICQBJfp5rE/QnrXnfhPw/wCLNP1q3uYdMvVjLg/cO04N fR9lYQa1LGdQjk0u+QAOjR5Vj6iukRwF/BrrNct5Gl3QngZD5EAjwfXFeFNpGoWXiLdcQSQyrJ8u Tj8RX3BN4QtIdOed9ThMCx5bC42/jXj2teG5EkubuC3ur7TyhMcywkqpHqfSgDR+Lt/Y+Ev2X/Cn hjTY83Otok19IDtIK7WwfUHNfXH7dyl/+CaPwKUdTdaYB/4LHr4f+IVrd+LfgVZ65K6xS6BIkTwg ZLq2FB/DFfoV+1rpq6x+xd+zVpjrvjuNV0pHGM8f2XJQB7t+zn4Hvrv/AIJ+eEVtUjSdb2MSrIvU SKIxn/vrg180ftU+C7fwL8d28ReH5hd3NoouzbouBk5WaEkegJIr9M/hB4D8SN+xr9m8OxLDew2k a20L8K8iKGU/X5a/ID4veLfHWmfHLxjD4v0v7Wg1GRCdxOzBIJwRyGoA7nw34m13xF4atbjTbuCy tJ4w2RKcoK62z8babZarH4f+1PqdyqlpLktkBumDXyX4I8M61c+ArCLRPGb6Ys4c3FpNAxkgYdwQ funPFYPhzS/iB4X8Yz31+l5eWstyEJmyd6k8tnPBHWgD6U8S6zPb2s8V9p8UMpkO2SKPG4ev0r51 8QXVlYXUdzNDbbDMSJJOVXIzkD8BXoPiDxlLd66dNi0+5mh2jddzHEWewBGfpXkHjvR9W8Q3i6fa ReRLHCHRAflYZxjjv3oAybHxrYX3iK60iRbaS5YgW8qRAbn6ge3Wvb/Dt8dJ1y2dSHu5NpKqMOrD vn2I/CvmHSPCWs6P41TVbuwuRGpDJIyfI+MA4PqK+i55P7OstM1a9jlNyPuLEPmkB9B3NejnOuPr 3/nl/wClMzo/w4+iPrrQ/GfiaXR2bXNf1o2s+Y4reXUZWRx3BUtiqvjC20u+0y3lu9RjsCo+TJOT x7V856/8RLbStP0OQR3923m7zbohEiDGcEHvXJ6p8RfE/jm5l0210HVtLtpNqJeXA2pGDxuOenrx Xj4fC0aCapQUU+yS/I2lOUtW7nf/ABD+JnjHxzb6B8I9LvNMt/DOlhftx01JInu0baMXHzlZGOwH JA619taJ8N4dA+Enw4g0WHzvtDPdaqwUbIsKoWJsd8Y4Nfl/Y6pa+B/iRr2m6Zbz65qIjixcs2fM cLljnnjcePpX6q/AnV/FV/8AAvwhrGqaE7RPaOLlWYhWaViqPyDnGPbtWGX5bhcDTdPD01CLbk0u rk7t+rbLrV6lWXNN3e33HwB8H7WS0/4L+KkkRiY3upkr6f6FNX9Sfwz/AOSD+GP+vMfzNfzh6X4Y fQv+C+3h25aMobkX7OMY2sbCU4r+jz4Z/wDJB/DH/XmP5mu4yO5ooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPn79qjQNW8U f8E+fijoWhaXqOt6vdaSPsljYWzzzzus0bhUjQFmPy9AK+Fv+Cfv7OXirQvifrnxU+JfhXxB4avN IhOm+GrDXNPltZg8oJnuFjlVWChG2KwGCZZe6196/Ef4HyfET4gR6+nxj+OHgMLZpb/2b4S8Tixs 22sx8wx+W37w7sFs8hV9K4L/AIZTn/6OX/as/wDC8H/xivk8fllWtmUMU6LlyaL3lZ72ffS5/QnC XG+By3gnFZBHMFT+tPmm/ZVHKN1FSgmvdako2b7No+Bv2zPgj8VviH/wU1lfwd4E8X6ppGsWenWn 9s2+kXD6fExVY2MtwqGNVTALEn5QMmv0P+IvhC4+FX/BKnxR4B+G2iaxrl7p/hB9H0y00qxe4u7m WceS8wjjBYuWleZiBx8x6Csn/hlOf/o5f9qz/wALwf8Axij/AIZTn/6OX/as/wDC8H/xiubDZViK NXEVY0XzVb680dL9v66HtZzx9lOZYHJ8BWzGPscByNR9jV/eOFknL5JpW25mcb+wD8NNe+HP7Fd+ /ivw/q/hvxFrXiGe7mstUspLW5jiRI4Yw0cgDAZjdhkch89CK8X/AGo/h74++LP/AAVr+D2lW3gf xhe/DvR/7Pj1HWU0ad9PQNdNPckzhPLH7oRqct94Y9q+mv8AhlOf/o5f9qz/AMLwf/GKP+GU5/8A o5f9qz/wvB/8Yp1MsxE8BTwfsXyxt9qOttfxZng+OsnocWYziP8AtGLrV1USXsatoc65U11fLHRX PqHV759M8J6pqUVpdahJaWkk6WttGZJZyiFgiKoJZmxgAAkk1+av/BPX4X+O/D3jL4vePviP4T8T eF9c1WS3t7Ua3pc1nLOHeWa4dVlVSVLGHkcZBr6J/wCGU5/+jl/2rP8AwvB/8Yo/4ZTn/wCjl/2r P/C8H/xiuzE0cXXxVGvKi/3d7Lmj1VvwPmsjzPh/K8gzHKaWZr/bPZqU/Y1LpU5c1l095vXyPnL/ AIKCeDfiR8T/AIn/AAe8E+DfBfjDXNHhaafUNS03Rri5tLeS4lihQyyohRNixuxyRhXyeCK/TLTd PtdJ8O2GlWMYhsrO2S3t4x/CiKFUfgAK+Wv+GU5/+jl/2rP/AAvB/wDGK+i/BnhlvB3wy0nw03iH xN4rNjGyHVvEN79qv7nLs2ZZcDcRu2jgcADtXTlmErQxlavUhZ1LdU7WVklY8fjjiHLsTw3lmVYP FKpHCOpooTi5e0fNKcnLTRpJJdGfnx/wUG8B+OPGnjr4AzeDvBnivxZFp17qLag+jaRPeLaB3sCp lMStsB2PjdjO1vQ14j+3v8BviF4g/bY0/wAWfD/wJ4u8V2ev6HbrqM2jaRPdJFcwsYsStGpCDy1g I3EdD6V+zlFc+Y8MUcY6znNr2ji/TlVtPVHr8GeO2Z8OQyyFDDxksHCtDVv31Wmqj5rbcskrW3sf lN+0t8G/F9j8eP2RtE8GeDPFPiTQfCNpZWN9faTpE9zBbLDcWwLyuiEJkIzksR3NZ/7bPhX4oN/w Uu+FHxF8C/DLxr48s/Dmi6bdltJ0S5urd57fUrqfyHkijYKxGzI6gMDjkV+tNFPE8M0qsaqjNx53 F+nKrKxOSeOWPwNbA1KmGjV+r061N3clzqtJyk5W2er2PzJ/4bE/al/6M58df+CjVP8A5Gq1+0B8 R/jd8TP+CWOm2Vp8FviDo3jjxfq0lnq+iafoN7PNp9jbyli0g8oOnmlYgAwG5WfGQDX6WUVrLJsV OlOnUxUpKSa2jpfrou2nzOCl4lZDhsbhcZg8ipUp0KkamlSq+blTsnzSatzWltf3bbNn5x/DH/gn v8HZv2fPB9z8Q9K8SSeN7jS4p9ZEeqyQLFPIu9otg4Gzds9yua8U8QfAvxX+y7/wVJ+H/jH4O+Bv iD4p+GssUR1AaXplzqZtoZN1vdwyNGjEnb++UN3Ix93j9h6Kzq8LYL2cFSioSi01JJX07979TqwH j3xQsXiZ5hWliaFeNSEqU5S5LVP5V9nl+zbZaH5oxeA/HA/4OPbnxyfBniseCTZBR4gOkT/2eT/Y axY+0bfLz5nyfe+9x14rmP2/f2Ytb8UeIdM+MHwz8Narr+vTlLHxJpWkWT3FxcALiG6WOMFmIA8t 8A8CM4wGNfqrRVVuGsPVw1ahN3VSTnfs32Mss8bs3y/O8tzTD01GWEoQw/Ld2qQhf4vW99Nmk1sf MH7Gfh/XvC//AATX+G2heJtE1fw7rlqL/wC06dqdnJbXEO7Ubp13xyAMuVZWGRyCD0NfT9FFe1g8 MsPh4UU7qKS+5WPzHiTOp5xm+KzGcVGVepOo0tk5ycml5K9gooorpPFCiiigAooooAKKKKACiiig AooooAKKKKACv5Hv+CgMMU//AAV6WGZVaJtOtQwYZBHnT1/XDX82n7YHw/8A2YvEX7bep6v8V/jV rvgHxgtmiDTrPT3lUQiSXZJuWJuSS3Ge1AHm2ixLf+C7C3iBJG2FAOijHH0ryuxH9nePtQj+dSt1 kbeCOT0rtbLT/wBlTTYglj+2T8QbZBjATS5O3T/l3qlJ4d/ZGlv2upP2vvHbXDElpDpMmST/ANu9 AHoGpeFLrxf4IB8Po9xq9ofNWP8AjmGOQcfjXy14v8N6tqM8c9vbSG4t32vGYyXjOeR6+2K+g9Pm /Zm0m6E+m/tqfEeylC7d0WmODj/wHrPfT/2VJNTnvH/bH8fNdTPvlk/sl8s3qf8AR+tAHyLf2M1l q5tnDDJGBjr+Ffa+hz2Hhr4T2d1IkcESWaNKQAuW29/euEu/BH7Gl9f/AGq7/ay8aTXGc720d8/+ k9a91o/7J17pB0+6/bD8fT2RABhbSZNuB0/5d6APnvWLvUfF/jy+1jyboxkYjSMFmx2wKn0jw7cf 8JhBb3FrcTbWV5we2Txu9q9103w5+yHpF39o0z9rvxvZzbdu5NHfOPTm3pV8O/sipfz3SftfeOlu JiDK40mTLH3/ANHoAxNe1rSfDc9/e6i0e1LSMJGMZc5k4Ga+fvD/AI+s9T+IupNeabp7abdD90ks SlounIOOD719Hat4G/Y21yNW1f8Aaw8a3wyQGl0eTn2z9n7Z/WsWD4UfsN203mQftReLYn9Ro8n/ AMYrvzKuqtWMkrWjBa+UEv008iKcbL7yXRvFHhxLVYBbEoAcEYAq9qOp6BJ9nnignEqnrG4JPsRT 4/Av7GcWPL/ax8arj00iT/5Hq2nhT9j6P7n7XHjdf+4Q/wD8j1wFnR+AZNA1nxRNa3zQ3SPH+5gk XgHPf1NfQK+EtIvbRbZUiS2KbGgRFCYPtXzNZaF+yTp2oG7sf2v/AB3a3J/5aR6Q4P8A6T10Ed9+ zbE+6P8AbZ+JKN6jTH/+R6AN34nfA/QrD4aaxqdozW9lcWkrfYtmBkdD15r2v412ov8A4IfsnWrI sgl1jSwVY4B/4lUlfPN9ffs26tYrZaj+2x8Sb61wU8mXTZCpB6j/AI9+9fonqn7NJ/aG+DPgDQPC 3ivXtFg8LxWWo6Tq+mvFHPIi2phic+YMDcj7uACDQB9IeKvEE/w8/Yo02ewka2uJ0QHyjtZWMfr3 r8jtfMPirXr691xFvLprlt5lwS4ycE+vWvvDVv2EPjxrnhiHRtX/AGkPjFqGlxY8u2lv7EouBgfw 1wMn/BL/AMbyyl5PjH8S2c9T9rsv8KAPiOysNOg1GbTrWWO1ubeTOI4wDIp9fatDVI0XRtjxq0jS ALgZOa+wD/wSu8VNqzX5+LnxI+1lQpl+12eSB07Val/4JeeNZnVpfjF8S3K9Cbuy4/SgD4SggW20 K4SKBJVVwsytECvXJzVC4srN9WTUfIiC+XhCq8V99L/wS48YqJAvxf8AiQBJ9/F1ZfN9eKhl/wCC WPiuaFI5Pi58R2RBhV+1WQA/SgD4FsrSw1LwbbW1yI5owz4H91txroFs7ZG0tILaM3EO75/LDFRj rjt9a+04P+CVHiS2iZLf4rfESBC24ql1ZAZ9avr/AMEvPGiy71+MPxJD7cbhdWWcflXZmOIjXxdW rHaUpNfNtkU48sEn0R8DLZW83iCO6mgt7jfLhpJ8Et7KMda2dW0OG+0aWJfLtwT1C9AP5V9sH/gl j4rKoD8W/iMQr71/0my4b16VsaX/AMEwvEkXivSZNb+KHxI1nRVvYf7Qs21G0i8633jzF3qNw+Xd 05rjLPzZXwZpQme+sp5pExjzmjBZyOD82emc195fsqeKb1PG+laBq91I+iCNYvLnOY4UU5BweBya +37r/gnd8AY/CV1ZaXYePrSXyHW32eK5f3bkHBGcjqc818Wap/wTY+OGgeBpNR8BfFzx9/wk0Qjd bGbVLOOOVcgyRLJjhiu4KzcZxnigDL+JXhm2sv8Agsj8OvEFk6TW9zPfqZEHc2E/f6LX7V/DP/kg /hj/AK8x/M1+Rfw6/Yf/AGjtE/aB8H+P/GvivxN4uj0+81ArpeqazZSmzhaBobYuykB5W3uW2Ehc gc81+s/wlg120/Z98P2XiW3gtNctRPb3cMLBkQxzyIACCQeFHIPWgZ6NRRRQIKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMjXtf0 Xwv4O1HxD4j1Sx0TRLCEzXl9eTCOKFB3Zjx6AepIA5NfBGtf8FKvgZpvit7HTdA+IXiCwjlKPqNt YQRRyKDgPGssyuwPX5ghri/+CnXjTU9L+C/w48DWV3LBYa9qF1eajEnHnLaiHy1Y913z7seqKewr 7i+G3wX+H/gH9nPR/ANl4V0GfTl02OHVPtFjHKdRkKASyTkg+YXOSc5HOBgACvmMRjcbisdUw2Fk oKmldtXu3qkldaWP3PKOGuGMj4VweeZ7QqYiWMnUVOnCfs1GFNqMpSlytuXM9FtbctfCP40/D343 /DtvEnw/1n+0IIWWO+s54/KurGQjISWM9CcHDAlWwcMcGvlDWv8AgpB8FNC8Y6tol34S+KUt1p97 LazPDp9kUZ43KMVJugcZBxkD6V7T8EP2VPh/8BPiZ4m8UeDNW8WTT61G8Elje3cbWkEJlEiIiLGG JTG0OzE4J9TX5N/BL45+BfgV+2f8ZNa8d+FtS8VWWpXt1a20NnbQTNE4vGcsRKygDAxxzXnZpm+Y YWnh41pRpyk2pO3MtNmfY8BeHXCOf43OamW0K2Mo0I05Uoc3sqjc21KLdmny20dtUj790L/go98F fEHjfRtBs/CfxRiu9SvobOB5tPsgivK4RSxF0TjLDOATjtXtfx4/ap+H37PXizw3pHjTSPF+oza1 byT28uj2sEqRKjhW3+ZMhzk54BrwX4c/tufAr4hfHXwp4J0X4V+INO1bWtRjtLS6udMsVjhkY8Mx WQsAD6AmvFf+Cj2iS+Jf2q/gT4chnjtZtVt5LKOZ1JWNpbqKMMQOoBbNTXzrEwy2rXpV41ZJxStG yV2tN/M6Mr8MslxXG2ByrMMqq4GlOnVnJTrc7koxk1JNRVknF3Wtz9YLTxFpGpfDm38V6Xdx6poV xpwv7W5tiGWeFo/MVlzjOVwR9a+X/Df7aHwy8UfspeP/AIwWHh/x3D4b8I3Vvb6ja3FpbC7lad40 QxKLgoQDIudzrwD1r5h/Y1+LGt+FbH4h/ss/Ewyaf4m0CC+Ph9Lh8khFdri1U/xAczRkZyjOQcBa 0/8AgmRa2t7+zp8U7W9toLu2fxBb74Zow6NiDIyDweQDXVRz6ri6mHhRfK5qfNdX5ZRS/J/ejw8x 8Jsu4ewOcYjMYOssLUwzpuMuVVaFWcrtNJq8oK11flle17HX/wDDzX4Gf9Cf8WP/AAXWP/yXXovw m/bp+FHxi/aA8P8Aw48N+G/iFYa1q5nFtPqdlapbp5UEk7bylw7DKxMBhTyR0HNfK/8AwU+0nStL tPgl/Zmmafp3mvq/mfZrdY9+BY4ztAzjJ/Ov1b0rw/oNlBZXdnoekWl0kQ2TQ2caOuVwcMBkZBI/ Gll9fNKuYVaE60bUnG/u7qSvbfTt1K4vyvgLA8IYDNsPltRVMdGuoXrtqnKlLkUn7nvptqVvd0Vr 9T4a1r/gpB8FNC8Y6tol34S+KUt1p97LazPDp9kUZ43KMVJugcZBxkD6U7Qv+Cj3wV8QeN9G0Gz8 J/FGK71K+hs4Hm0+yCK8rhFLEXROMsM4BOO1fAXwS+OfgX4Fftn/ABk1rx34W1LxVZale3VrbQ2d tBM0Ti8ZyxErKAMDHHNfd3w5/bc+BXxC+OvhTwTovwr8Qadq2tajHaWl1c6ZYrHDIx4ZishYAH0B NeTl/EOIxFufFxhJu3LyXe9lrfqfoPF/g9k+TqbwvD9fEUo01P2qxCjH4OaT5XFv3Xe+utj3b49/ tWfD/wDZ48U+H9J8ZaJ4x1W41i1kubZtFtreREVGCkP5s0ZByewNeEwf8FMvgRJcKkvhb4rW6k4M jaZZkL7nF2T+QNeIf8FIry20/wDap+B1/egtZ21nJNOAu4lFuo2bjvwDxXoXxD/bS/ZP1/4MeJdE tfA2oeJLy+06WC2spfDEESPKyEIS7N8mGIO5csMZAyBXTjc9xMcbiKf1iNNQtZNXvdX7nicNeFWT V+GcoxqyevjJ4pT9pKnU5VT5anKrpxa1WurS0ep9zeC/jZ8PviH+zvqPxO8G6rJrfh2wtp5byOOL ZcwPDH5kkLxvjbJtxgE4OQQSCDXIfBr9pbwP8cPhP4v8Y+FdI8V6dpvh1yt7FqtvBHNIREZf3Yjl cH5Rjkjn86+NP2Kvhv408F/8E9vjr4i8VabqGi2HiPS5X0iyvY2jkeOGznDXGwjIVzIoBP3hHnpg nO/4J6/8mMfHv/r4k/8ASFq7MJnmMq1MKpx5faRk2rdVt9+585n/AIWcO4HCZ9PC1XVWEr0IU5c3 2ajXOnbRuLvG+mqvY+yPgN+1j8Mf2hPE+uaJ4Rt/Emjazplsty1lrkEMUlxCW2tJF5UsgYIxUNkg jevXPGz8Y/2kfBPwS+JPgXwv4p0nxTqN/wCK5jFp8mlW8MkUREkcf70ySoQMyr90NwDX4O/CeXx/ 8LdK0P8AaP8ACC/aNO8P+KBpeoxqSB88Kv5cuP8AllMjSR5/hIHcrX23+2J450D4l/Fr9kPx14Yu ftWiaw7XEBON0ZN3aB43A6OjBkYdmU15eF4sxNTLZznZVYuL20cXJK6/I+8z36PeSYPjbD4ehzTw FWNWL973oVadKU+SUt9bKavurrZXP1e8beOfCfw5+G+oeLvGut2fh/w/ZLme6uGPJPRFUZZ3J4Cq CSegr4Ovf+CmfwXt/ET29p4P+I2oacrEfbFtrVC49VRp84+pB9hXlH7fup6l46/bq+B/wNN/PZ6B e/ZJpRG2AZ769a18wg8Eokfyk9PMf1Nfe3iRf2fP2f8A9n/TdI8U6d4S8IeAp3XTIobjSjPFdOY3 bbIFjcyMyo7Fnzk5ycmvZr5jjMRia0KFSNOFKycpK92/VpJI/NMq4N4bybJMtxOaYOrjcVj+aUKV OTgoQi7J6RlKc5bpbW0drXfR/B/43/Dr45eAJvEHw/1h71LZ1j1CxuYjDd2TsMhZYz6jOGUspwcM cHHrdfnt+y7on7JXhP8AaX1x/gf8T9c8Q+KdetJ1OiTvJ9lithIJtqK1un+r2gBndmxnk5NfoTXs 5PiquIwylVcXLZ8ruv68j8z8RsgwOUZ3UoYGFWFFpSiq8HCok9000rpO6Ura2Pnz41/tOfCb4C/Z Lbxtq15c67dJ5sGiaTCtxetHyPMZSyqiEggF2XJBxnBx4B4U/wCCkHwJ1/xZBpesaZ438IQzz+Wm o6hZQyW0YPRpDFKzr74VgO5xzXzD+y74c0z9or/gqv8AFb4h/Ei1i8SQaRJNfWVhfKJYVc3HlWyM jcMkUSkKpGMqpPTn9Dv2lfg14I+Iv7Hnjaz1Dw9pMeq6ZolxeaLfxWqJNZzwRNJGEcAEISoVlzgq T7EfP0MwzTG0Z4rDyjGCb5YtX5ku7vpfyP2DNOEOA+GMxwuQZvRrVcRUjTdWrCooqlKok0oQ5XzK Kabcnr+C9l1zxroWifAvWviIlwNa8Nadoc2s+dpciTfaraKFpiYW3BH3IvyncAcjkDmvhT/h5r8D P+hP+LH/AILrH/5Lry/9lHxrqniH/giv+0N4X1O7mvE8NaHrEWn+Zz5NtNp0kgjB9BIJiPTdjoBX iX7I37TvwZ+CXwA1/wANfEbwp4g17WLzxBJfW89hpFrdIsLW8EYUtLKhB3RucAY5HPJrkxXEtWo8 PKFVUo1ItttXs07W6H0OQeCGAwkM5pYrBVMdWwlaNOEac/ZuUJLmUnpLo02vlc/S34EftdfDr9oT 4lat4W8HaF410u/07TDqE0ms2tvHE0YkSPCmKeQ7syDqAMA819UV88fs+/Gn4YfHHw1r+v8Aw38N ajoMWmXSWd2b/TLe1lcsu8bfKd8rwOpHPavoevrsrqzqYaM5VFO/VKyfyP5149wWHwedVcPRwU8K oWTpznzyi7Ju8rLe97W0CiiivRPjgooooAKKKKACiiigAr+QD/gpT/yk41X/ALAtv/6Nnr+v+v5A P+ClP/KTjVf+wLb/APo2egD4Cooq7p+nXuq6vDY6fbS3V1KcJHGpJP5UAUq3dG8M67r97HBpWm3d 0XbAdYzs/PpX2f8ADz9ljTLDwVF4x+LWvWPhXR9nmxx3x8t5BjgKhIL5PHAxXWaj8a/hx4N0qPSv hN4Dkm1xG22+tXsgk+bp8kGCOex5oA8s8Efso67dW8ep+Nry08P6c3KC6Yxs4x1VSAW+oBFe6Q/D b4MeHLa1tWNrqShcTPLcCH2yAx9q83u9O+NHjx5tW8Y+IbzT4J/mkXyQG9QMDAX8MV1PhT4c+CEh tY9X0+7n1J/l86eeRgT0z1wM0Aal18LPgFrN7i0tP32MEwXqyHHrgPWPcfszfCiaIzQ6vqUDMfli 2sSB68E12OvfD74a6LHHp0mj/btTxuZbSaTcAfoePxrkZPh/rkepqnw/8UX+iEjL2lwDcDGOhJyR QBxF5+zV4Rl1qTSbLx3p0UMKLMk1wkyKWkLArkp22D865zU/2StcdHbw14u8I65IPuwQX/zv9Ayg n0ruVl+MOnfEWy0SDSrXxVqt88cAS1tcnBLbSQATx85OB0HtX1V4++AnxW+F/hnQ9c8S+ELTWU1C 8+zCTwg13fy25CuxkkXyF2p8p5GeSPWuXizirJ8uzLDYTF4mNOrVhDkjJ2crU43saYXCVqlKVSMW 0m7v5n5Q+LfhT4+8E37wa/4b1O1C/wDLUQMUI9c4rzwggkEEEdQa/U+L4laKmiXWi3HirSJrMyMJ LPU2QncCQyuHwwIORjjFcDrvwp+FvxJmgXRLex8O6q4Il1O0vhNayPjjKqfkGe3NdSdzM/O2ivVv iT8H/F/w0114dXsLibTi5EGoRREwzKP4gw4I/GvKaAJIv+PmP/eH86/sj/Y9/wCSZaN/2J2l/wDo iOv43Iv+PmP/AHh/Ov7I/wBj3/kmWjf9idpf/oiOgD7VooooAKKKKACiiigAooooAKKKKACiiigA rP02OOLTpFiCqpup2IBJ5Mzk9fcmtCqGmsG0+QqQw+1Tjg+kz1Dk+dL+uhaS5Wy/RRRVkBRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQB8Bf8ABQb4NeIPib+zNoXijwrY3Gr6z4Pu5p5dPtoTJNPazqizGMDkshjjYqByoY9sHA+Hf/BR D4PJ+zhpMnjpvEOneOtP01Ib7TLbTGlF7PGgUvDIDsCuRnEjLtzjnGT+jleVax8C/gt4h8UNreuf Cf4d6rqzyGSW7ufD9s8kzE5LSEp85zzls187i8qxUcXLE4SoouaSkpK6dtnp1SP2Xh/j/Ia/D1DI +IsJOrTw85TpTpTUZxU9ZwfMmnGUtb6NdD5V/Yx+Lvx3+NnjP4g+MvGskMPwqNzKNAt5LCNHjmeb cIIZlVWkihjyrM4YksvPDCvib9mr4z/Db4Lftw/G3V/iVPcQafqFzc21mYdPa6JkF6zHIUHHA61+ 59nZWenaVb2Gn2ltY2NvGI4Le3iEccSjgKqgAAD0FeZXPwI+B95qE93d/Br4U3V3PI0k003hKyd5 HY5ZmYxZJJJJJ61yV8hxjjh3CtzTpttuV3e/z2XRH0GVeLXDscRm8MRlrpYbGRpwVOg4xcFTd78z jZylvJ21dz50s/27v2XJdWtobXU9VS5klVIiPDcqkMTgc7eOteD/ALd3/KQT9mD/AK/4v/S+Cv0A j+AfwKinSWL4LfCaORGDI6+ELEFSOhB8rg112veA/A/inxDper+J/BnhTxHqumsG0691TSILmezI YMDE8ilkO4A/KRyAa3xWV4/F4WdGvON24tWTWzu76s8zIuPOE+H8/wAPmOVYWvyxhVjNVJwk25wc YuLUY2Su273vpY/Oz9vz4L6pZyaR+0n8PPPsPE+gvFF4he0GHaFSFhusDqUJEb5zlGXPyoab/wAE v/8AkgXxP/7GCD/0RX6bX1jZapot5pup2drqOnXcLQXVrcxLJFPG4KsjowIZSCQQRgg1g+F/BHgv wRp91aeC/CHhfwha3MgkuYdE0qGySZwMBmWJVDEDjJprh5QzZY2ErKzuvNq1166XIl4xVMR4e1OF 8XScppw5Kl1pTjJSUJdXyvmUdbJO1tNfy8/4Kmf8efwN/wB/WP5WFfrHZ/8AIJtf+uK/yFc14q8A eBPHQsR428FeEvGIst/2Ma5o8F79n37d/l+ajbd2xc4xnaM9BXWABUCqAqgYAA4Fd2Dy2VHHYjEN 3VTlsu3KrHynEfGlDMuFcoyeNNxlg/b3k2rS9rNTVlurWs7n4Ufs1fGf4bfBb9uH426v8Sp7iDT9 Qubm2szDp7XRMgvWY5Cg44HWvvez/bu/Zcl1a2htdT1VLmSVUiI8NyqQxOBzt4619F3PwI+B95qE 93d/Br4U3V3PI0k003hKyd5HY5ZmYxZJJJJJ60yP4B/AqKdJYvgt8Jo5EYMjr4QsQVI6EHyuDXjZ flGa4Kl7KlUhy3b1i76u/c/SeL/EXgLiXGvHY7B4lVXGMXyVaaj7sVFaODfTXU/OH/gonHHN+2J8 AopUSWJ4iro65VgbyIEEdxXuH7X37K2j+IPhAPiP8H/D1l4U+JPhY/bkXw/bLaPqEKHewAiC/wCk JjejD5jtK8krj7Z8RfD/AMBeL9a0/UvFngjwj4o1Gw/48brV9Hgu5bb5g37t5EJTkA8EcjNddXXP h2nVqYl1rNVbW7xsrX9TwcP4z4zL8FkdPL1KE8Cqind+5VU583K0t4tXi797rU/PT9nT9o+X44/8 E+/iLonim9jm+JHhvwzeR6k5wrahbm3kEV0AP4uNj44DAHjeBXk3/BPX/kxj49/9fEn/AKQtX6S6 V8KPhboWuX+p6J8NfAGj6lfQSQXt3Y+HrWGa5il/1kcjqgLq/wDEpJDd81peHvAPgXwjoV/pfhTw X4S8MaZfHN7aaTpEFrDcnbt/eJGoD/LxyDxxUUMlxXtaFStUUnTjKLeut1ZP/M6M08TchWAzXB5b g5UqeLq0akYtxtTdOXNKKt0bvy2tZabI/M7/AIJ9+DNB+In7Afxm8E+J7QXuhaxrn2a6j/iANrFh 1PZ1YBlPZlB7V8E+IvBXjL4UftreGPhH4quri4tfDni+GXSywxFLFcTwETxZ6LIscbFc8MGHXdX9 F3hfwV4N8EaXc2Pgvwl4Z8IWVxL5txb6LpcNnHK+AN7LEqhmwAMnnAqnr3w5+HvirxVZa74o8CeD fEmt2aqtnqGqaLb3VxbqrF1CSSIWUBiWABGCSetefieD3VwVCkppTp9ejV72+/Y+wyX6RywPE+aY +WGlLDYv3lTbXNCagoKae2sbqVrXVtdLH56/8FBvhR4ul8QeA/2gPA1lcahqHhNUh1VYIjK9tFDM bm3udg6xo7S7z2DKem4iTW/2y/2S/jF8A9Kg+Mvh3V7i/tJFu5PDkunTThLoIyFoZY2CMuGYAuy8 NyAen6bkAgggEHqDXkOp/s/fAzWfET6tqnwh+HN7qMjl5Z5PD9vulY9S/wAmHPu2a9HF5LiViKlX DTjapbmjJXV11R8bw94n5LUyjBYDPMPVc8G5exq0Kip1FGTu4SunpfZpppaK2rf5U/sX2ln4r/4K y6546+HvgjVPDvwzgtr3yIzGXh05JIwsUTyZKh2PO3cT1xkAmv21rO0rSNJ0LQoNL0PS9O0bTIBi G0sbZIIYx6KiAAfgK0a7sjyn+z8O6blzNtt6WV32XY+T8VPEBcX5xDGwoulCFONOKcnOXLC9nKTt eTvq/wAW9X+LI1TXP2HP+Cp/izxB4j0HU9T+Fni6S48q9s7fhreaUTr5ROFM0D/IyEglcnjcpr2f 4/8A7enwy1/9mvX/AAb8JW1zxN4t8S2D6ZG76ZJBHZJOpjkJ3gM8u1iFVARuIOeMH9KNc8P6D4n8 PSaR4l0TSPEOlSEGSy1OzjuYHI6Eo4Kn8q4rw38GPhF4P8QLq3hb4Y+A9A1VZC8d7Y6FbxTxk/3H CblHsCBXlLIsdQjUoYaso05tvVXcb72d/wAz7+XitwrmlfCZpnmWzq43DxhG8KijTqun8LqRcW09 Ffl0fpovin4I/BzXPhD/AMEUvjGPFVrLp/ibxJ4W1jVbqxmjKS2UZ0544YZAeQ+1C5BwVMhUjKms j/gm34Z8Oa1+xt4yudZ8P6Jq1wnjOZElvbGOZ1X7HaHaCykgZJOPc1+l1/YWOq6He6Zqllaalpt5 A8F3aXUKywzxOpV43RgQyspIKkEEEg1jeGPBvhDwTok+meDPCvhvwjp005nmtNF0yGzikkKhS7JE qgthVGSM4UDtXXR4fhRxFCcH7tOLjZ7u/U8DM/F/EZlk+a0MRBqvjK0KvNF2jFR05e+1kvJamjpm iaNolvLFo2kaZpEUjbpEsrVIVc9MkKBk1p0UV9EkkrI/GalSdSTlN3b6sKKKKZAUUUUAFFFFABRR RQAV/IB/wUp/5Scar/2Bbf8A9Gz1/X/X8gH/AAUp/wCUnGq/9gW3/wDRs9AHwfp+n3eqaxb2FlC0 9zM4VEUdTX6OeDPBng39m/4H2/xB8a2Nnq3jG9iU6TpMx2yOSB+8bP8ACM9O9eNfsseCdMnv9c+I 3iayE/h7w8onn3nAYDsD05OB+NdJFZat+0N+0Zealq128Xhy0lIht2chY4cnbGgHTgCgCn4QvvGP 7Sf7Xnh8+J9dNhoY1BCz3Fq09pZQq25YvKQcg/c6fxZPGa/Vj4/fsw/Aj4WTQ+P/AA1o9ppOqatr DuyCWWWNmk3uY4kyY4UHUAAABcDsK+U7Lw94e8EaSkvhQWtrfQIEjMPJVgR8xz1PFes3+ueOvH/w zS08Y+I7jVEjdZbFJ0QCJgMZ+VR2JH41+Y8UcJZ9juKMtzLBY50sPR5lVp3a507NaKLT1WvM9F8N ndnqYXF0KeGqU5wvJ7Pt+OlulvmUb/RbLVfCVzp0TQiRlyrxjpXHeFPDEsVsbfVbUkW74jkYYL46 V2Ok2h0HRJpdVv4PV5nbaqgdsmq9t4y8LX9+9rZ+ItNmmUElElGQBX6ceWcXJo841TWrs2bvcGQK jhOSvoKlg8aaR8MPClzc3Okx6p4mvJlXDBW2QYPmJtYEb2+XDdVxx1r0S8aSbRZZtPkiMjpmJwcq T614lb+F746lLd+IGW5vmzlpUOPYAdq5MdgaOLoulVV4u11dq9nfp07rZ7M0pVZU5c0dzG+EXxU0 zw1+1FJ48fSb+505LxpY7BZfMmtIGMqqjPg5KK4yepwa/dPwx8Y/AvjH9n4eLvC+tWmoaKUlEkzZ iMBXfnzFcAp0z8wHHPQ1+IMPgq00T4h6vFb3mm373ej21yWs8lI3k35jPHDLtwfenWl5438N/C+9 8F+Gtdv9E0PVXeTVrWJFMd1uUoysSpIypxwRxX4v48eB2F47qYPGYau6daFOlC8rtSp8sW0005cy u2npd3Ut7r2cnzh4PmpVo3jzN2Vt/wArf0jP+N/wz8IeKP2o/G3iaXw7odlb3N2ksFnp5jMYwigv mI7CXI3nHOWOec15t4q+EenaRommav4KvpfDWsTRBorOF3Ky46BlJI59a9S0fQjeQW5s98dzCyx3 MW4klc88eleo674ftYprTWJp4Ybe0gAKydSR0Ar9iyfLo5fgKGEi7qnGMU9fspLq2+nVt+bPIxNb 2tWVS1rtv7/uPlO1+K2rWOjxeA/i94UuLzQ5h5TTOQEB6GSI4ypAPTNePfHX4DW3hPQNP8deCruH V/BuoxGYNbjd9m6Ha/Jwepr274pG/wDF+msdN0qe5ktpTKrrDgKMfqK1fgVr1t4r8D+Ivg14k+y7 NZhKaPFc43RT4YKoHXO4j8K9ExPzSi/4+Y/94fzr+yP9j3/kmWjf9idpf/oiOv5DPG/ha88HfFrU tDvIXgeC7ZVVgQQA2MfUV/Xn+x7/AMky0b/sTtL/APREdAH2rRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABWXpCsmlSh4VgP225O1TkEGeQhvqRz+NalZWjADSJsRSQ/6ddfK/U/6RJ830PUexFAGrRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAeXfFL4z/Db4L+HtL1X4k+IW8O2Go3DW9nKNPuLnzJFXcVxDG5HHOSAK8ag/bp/ZYnn WNfiisbMcAyeHtSUfmbfA/Gvnf8A4Kgf8m/fDH/sYZ//AEnNR+JfDn7Cg/YX+0apefCO38Uf8InG +7QtVhfVlvfswI2xwyFzJ5vVWG3OQ3Ga+Nx2dY6ONrUaUqcVBJ+/fW6vumf0lwp4ZcLV+GMtzLMK eLq1cVUnC2H5Go8suVNxlBvVPvrqfpN4b8TeHvGPgqw8R+FdZ07xBoV6m+1vrGcSxSAEg4I7gggj qCCDgivnjxl+2b+zl4H8ZXfh/V/iFDd6vaTmG7h0zTri8WBhwwMkcZjJB4IDEggjGRXxR+wxpPj7 xB/wTe/aN0Lw9c3UDX8U9t4ZPmFAt/JYyLJsY4Ck5thuzwee1eBfs1/Ej9nb4W2Ov+Dv2hfg22pe Kv7UkVtX1HRY782ke1FMElvN80RRlc7kUsd/QYrnrcU15UcPKKjT9om3KV+VNO1lbvvr0Pcy/wAB cqpZlnNCrKri/qU4RjSoOCqyjOPNzS5r6RvytRV3JPZaH7YfDb4u/Dj4veFZ9Z+HPizTfE1nAwW6 SENHPbE52iWGQLJHnBxuUA4OM4rmPBX7Rnwc+IPxo1H4d+FvFr3XjWxE32rSbvSbuzlQwtslX9/E gLqeqglsAnGASPmL9lj4P/B2y/aV8UfGD4E/F5Nc8LXsc1vc+EYLJovsEUzB445PMcSgKyZQvGMg EAnk15h+278Ldd+F3x18M/tX/C+M2WoWOoQ/8JEkKfIkwwkVw4HWOVf3Eg4ByvUuxrvqZvj6eAji pQi0n71ndOP80Wn+DufI4Hw64SxvFdfIqWJrQlUh+4dSPs5QrtXVKvGUE3d6KUOVXsle5+j3xG+J vgb4S/Dd/FvxB16Hw9oC3CW4uHgkmZ5XztRY41Z2PBOApwFJPAJqx4J+IXhL4h/CW08deFNTkvPC lysjwahdWc1mrpGxV32zojbAVb5sY4PNfkb4q8Yav+3/APtt+APBPh631TQvhdoWnx3+t7+GgZlR rtyehfcRbRHnnL4wzY9v/wCCiHjCT4b/ALIXw++Evg9IdC0PXZHt5ra1yu2xsUh226+iFpIs+oTH QnMPiWTp18VFJ0aeiet5S9e13bb/ACOmPglRji8pyGrUlHM8W3KpG8XCjSSbV4pXdRxi5W50lazW qkfQutftw/sx6F4rfSLj4kxXs0cpjmn0/Sru6t4yDjPmxxFXHuhYV9D+DPHPhD4ieA7XxP4I8Q6Z 4l0K44S6spdwVu6OPvI4yMowDDuBXzf8N/2NPgV4b/Z00jw14i+HugeJ9bn06P8AtnVtRgL3U1wy DzGjkzuhAYkKIyuAByTkmp+zP+ylefs6fEPxlqVp8R7zxD4f1tTHFobad5SRBJd0EryeYd8qoWUk IoO8+1duDr5wq0Pbwi4S35bpx9bvX5HzHEWV+HEsuxP9lYqvDEUWlFVVGUa6vZ8vJG8Gt1zu1rLd u3s/hT48/Crxt8f/ABJ8L/DPidtS8c6Cbgarpx025iEH2eZYJv3rxiNtsjKvysc5yMjmtn4nfFnw D8HPANr4o+I2unw/odzfpYw3Isp7ndOySSKm2FHYZWJzkjHHXJFfkV8KPi14E+DX/Baz9oPxV8Q9 Xl0XQ59R16xinjs5bgmZ9UjdV2xKzD5Y3OcY49xXW/twftN/Bn4z/sm6D4W+Hfii51rW7bxZBfzQ SaTc2wWBLW7jZt0saqfmlQYznn2NeSuLEsBWqSnBVYuSUb72eml7n6DP6PtSfFmXYKhQxEsDWhSl Oqo3UXON5WmociSfdO3W5+ufh/xHo3ij4d6N4s0S9F3oGq6fFf2N20bRCSCVBIjlXAZcqQcMAR3A r5v8S/tsfs0eF/E0mk3vxJtr+8imMU50vTrm8ijIOCfNjjKMM/3GavkD9pH4l6z4M/4Il/s+eE9D upbGbxf4Y021v5ozhms4rCJ5Ygeo3s0QPqu5Tw1fRn7P37H3wY8PfsteFZfFngXQPGPirVtKhvNW vtYtRcFJJo1cxRB8iNU3bQVAJxknJrseb43E11h8KopqKlJyvZXV0kkfNU/DzhfJcplm+f1K0qdS tUpUadLkUpKnJxlOcpJqyelkr38np9O/Dv4rfDv4s+FJda+HfizTPFFhC+yf7OWSWBj0EkThZI89 tyjPasnxj8cfhZ4A+L3h3wH4v8Vw6P4t10QnStPaxuJTcebMYY8PHGyLmRSvzMMYycDmvy18feGb T9j/AP4LFfDrUfh1LLpfgjxQbb7XpskrPHFbT3Bt7q3yxyyrtWVMk7W29dvPW/to/wDKZD9nD/c0 f/08TVz1OJMTTw0+aCVWnNRa1a16rrqe3gvBXJcXneFVDEVJYLFYepXpy92NROmneE9HFuLsm0kn fS25+ulVNQ1Cw0rRLvU9UvbTTdOtYmlubq6mWKKFFGWZ2YgKoHJJOKt1+Vf/AAUg8c+IbrWvhl8E tDuXtrPX5PtupRqcC6YzLDbI2OdiuJGI6E7D1UV9BnGZLAYSVdq9tl3b0R+P+HHBVXiziChlcKns 1O7lJ68sYpyk7dXZaLva9lqfTuoftz/swaf4jXTn+JIuTuKyXNrot7NBGfd1hIYe67hX0Z4L8c+E PiJ4EtvE3gjxDpniXQpyQl1ZS7gGHVGH3kcZ5VgGHcV4L4P/AGNv2ffDHwgs/C198OvD3ie5Fssd 9q+p2/mXl1Jj55BJndFk5IEZUKMY6V8LfCOzuf2Yv+C5N/8ACHQdQuH+H/iZltxb3Llj5ctsbi15 7yRynyg55Kl+7cePLM8ywlSk8ZGLhNqPu3vFva990fo9PgfgniLB4+PDlWvHEYWnKratyONWnD4u XkScZWaaTvfRd2v1X8ffEnwJ8LvBJ8ReP/E+meGNI37Elu3O6Z8Z2RxqC8jY52opOOcV886T+3Z+ zHq/iKPTV+IE2nvJIEinv9Gu4YGJ7lzHhB7vtA9a+Ib3R/8Ahrn/AILh+KPCXjK7vZvAHg17yBdN hmKKbaylWBkVlwV864YMzD5trYBGFx9efGX9m79lPUPhPfeB5k+F/wAJPFT2gl0nVFnt7S8tmydk jqzq00ZKspDk5G7BDAEZf2vmWK9pVwqgqcG0ua95Nb9Ul5XO7/iHnBeR/UsDns8RPFYiEakvYqPJ RjP4bpxlKbS1lbptd2v9jy+INFj8BS+KV1K1ufDyWBv/ALfav58T24TzPMQpneu0ZG3OR0zXDfC3 4z/Df40eHtU1X4beIW8RWGnXK295KdPuLby5GXcFxNGhPHOQCK8J+E3gjRvhp/wTi8f+BdD+LOl/ Fux0/StRljvbJottistszeQFjlk2ruDuAWzl2r48/Ym8bXHw4/4JnftIeObMQHUNGla5shMMoZxa YiDDuPMK5HeuqpnlWniKEZxSUoylLq1yq+jTseFg/C3AYzKM1r4WtOpUoV6NKi7ckZqrPlTnCUed O1tLqz7o/RH4nftNfBH4QeIDo3jnxzZWGvCISHS7S3lu7lQem9YUbyyRyN5XI5rR+F37Q3wd+Mtz cWvw98bWGsanAm+bTpoZLW6Ve7CKZVZ1HdkBUZGTzX5//sKfADwP8Tfhd4n+M3xV0a18fa/qevT2 9smsp9ohUKEeWdkb5ZJHkkYEsDgJx9417rrP7DPh62/bV8M/Fj4XeKl+Fdnpc0N1Lo+naYZUlnRz uEf71RFFIh2MmCMbuMMQMMJmWb4inDEwpRdOT+G75lHvdu3nY9XiDgrw6ynFYrJcRjq8MXQi71XG LoyqpawUIxdRK/u819Hd7Wv9514RoP7TPwQ8TftES/CrRPHMF946S8uLM6eNOukRprcOZUWZohEx AjfBDkNjjPFaf7QPxNi+EP7H/jjx35iJqFlp7RaWrYO+8lIigGO4DsrEf3Vb0r8JoPh141+Ff7Nf wf8A2prK5umv73xfNIiy5wgidWtnfuRK0N3uzwV2f3ua4g4gq4GtCFKKlZc0/KN0tNdzHwh8Icv4 py3EYnMK8qLlL2OHs0lOvySnaV4v3UktFZu9rpn9Gt3dQWOl3N7dSeVbW8TSzPgnaqgknA54ANeb /DP4z/DP4x6dq138NvFEXia30ySOO+dLOeDyWcMVH76NCchW6Z6Vbi8VaV45/ZRbxloknm6TrXhh 761JOSEktywVvRhnBHYgivzx/wCCXP8AyTX4v/8AYTsP/Rc9ehiM0nDH4ehCzjUUnf0V1Y+PyfgT D4jhPOc0xLlGvg50YqOiV5zcZKSaburaWa13ufqnRRRXtn5eFFFFABRRRQAV/ID/AMFJlL/8FPdT RRlm0a3AH/bWev6/q/kc/wCCglul1/wV0+zyDcj6bagjGf8AlrPQB59qt1eeE/2ZvC3w50F2Oo+J olkvVhPzyDjYpX0Of0r6y8A+FdI8OfDLR4bfSGF5AiG6CgrL5uBkt685r5a1m3gT9rz4frcNJDbW tnbkDBOdoBH619q/24rwwmOCRDM4Ubhg4A6/TFACxeEdK+0T3U8krtPKZfK3Y2k9q6h5o41EEeFR RgAVxur699g0P7XEI7iQuoC7sZB71z9z4tRtN1F7fy4yqBUL5BVj1zmgDstVtLbWLDydUINn0aNn 2qfrXnF94K8P6KkuuaDb26lgFlRGLA4z3JOM1xk/xFtNO0+SG41f+1IgP3qrGzbT6bu1c0PiRpNh DdxWt3cQpKnmLbXTEoc+np+NAH1Xpl9F/YVvFDCkSLGCUzkLkdKjuILae/dsS5xn72R+tePeEfHW n+IrRTYXAMsa4niLcg4HPuK9O0/VIxcYLB1I6ZoA5wo8fxD1QoznFhbnd0P35q2LC8W5uFt7mJZc nBlVeV+vbFVJLzSIvHuqXl3fQ2Fqthb7mmPA+efNUtV8SaebQ/2WIVtCMiZOTJ9K9bOf48P8FP8A 9NxMqOz9X+Z3mleGbKw1+XUYrkHzxgIDwa6TUtLg1PRZLa4iEibcrnoD2NeWeBPD+p22sal4t8RX t2sEyqLCxmkOy3jA+/jPVjz+NdvqGv21/pd2mm6lHHJEhLGPOU4615JqZQ8KpBo0/mRo7BOdo6iv jH4g6ZN8PPjRpfjHRY3hmN6tzGyjBjkQqQB7NjFfWnhnxhdKdTt9YnSe3t490d1nJYnsRXyl8dfE H262kEKyM8EhmCFey4PHtQBV/a70Cx1248L/ABU0K2Edhr1pHezhBkJJIEaQE+u9jX9Gn7Hv/JMt G/7E7S//AERHX83V/qk3jD/gm5IrblOgXjB1Yk7UY7kX2AxX9Iv7Hv8AyTLRv+xO0v8A9ER0Afat FFFABRX45f8ABUDVNQP7YP7Evg2X4oeLfhV4M8T+IdTsPE+raJ4ibSvJtWn0lGmeXIjHlpJIQ0gK rknGCa8U8PeJLz9nz/gs9+zv8Ov2eP2tPGn7SXgzxzeiz8Z6BrHiuLxBFp8G8BpDLETEjiMyTDaF kQQHcSj4IB+/FFcP8SPiT4I+EPwW1z4ifEbxBa+F/BukRo+oalPG7rEHkWNAFjVnZmd0UKoJJYAC vz4/4bU+PXx58QWmnfsefs4eItV8IzXCLP8AEr4hRnTNJ8ncA8ltCzKZ/l5BEhYHGYjQB+n9Fed/ F7xyfhj+yj8TfiQLeK8fwr4V1DWUt5WKrM1rbSTLGSOfmKBePWvwCXxZ8ePhH/wT1+A3/BQS/wD2 gvix4s8X+KfH/leLvCOp6wX0G50x57xRBHbY2p8tqRgDannDywhjBYA/pBoqMyxC1M5kQQhN5fPy 7cZzn0r+cA+Lfj18Wf8Agnf8c/8AgoNYftA/Fjwn4x8MfEDyvCfhDTtYKaDb6Ws9mht3tcbXwt0B gja/knzA5kJAB/SHRX4z/F/4seOP2qv23P2Nv2f9C8f+MfhH4H+IPwwi+IHjG68F6obS/mFxaTzR 2izYJCobZlwcqfO3MrFFrsP2O/jN8atA+Hn7ZHwYupdf/aC8efBPxVJZeDTrOqbNQ1uCaS5it7ea 4cHADWpcu5O0SlchVXAB+tFZOjMr6RMVeVx9uuhmTrkXEgI+g6D2xX48/sd+JP2h7r/g4a+P3hz9 oPxPDd+KI/hxFqE/h7RtSml0bSDPLpcsMFvGx2gxRTCMuASW3ne+4s37D6O8j6TMZXDt9uugCGB4 FxIFHHoMD2xQBq0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFAH5g/8FQP+Tfvhj/2MM//AKTmvQPAf7Bv7OWqfCjwjr2qeHNe v7y+0e1u7lH12dI3eSFHbhGUgZJ4Br6u+KHwZ+Gvxm8P6XpXxK8N/wDCSWGnXDXFnF/aFza+XIy7 S2YJEJ44wSRXoemadZ6P4c0/SdOh+z6fZW0dtaxby3lxooVVyxJOAAMkk180+H6dXMauIrwjKMlG yerVlrurH7ZHxexuA4Ny/J8pxFWhWozqupKL5YyU5Xik4y5nbW90rdLnk/i21f4LfsZ+ID8HfBek NL4c0t7nSvD8UTiGUI2+UYQ72cr5jdSzN1yTXxl8Ovjt+zJ+0v8AB2a6/aK0j4UeHvH9rNJFdLqQ FkZIM5ie3uXbzMbcKVEmdyk4AK1+mVfLXjH9i/8AZv8AG3i2513VPh3b2GqXMnmXEmkX9xZJK3cm KJxGCepIUEk5JrbM8Di5Sg8PyuCVnCS93yasnqvuscHA3FfD1GhXhnPt4YiU1OGJoS/ep/ajLmlG 8Zb3vzcx+df7Olv4X0f/AILn/wBnfs/3mp6r8LBFdJcSvJI0f2X7GTJl2+Z4ludgjZ+SQnJzuP64 fGSztNQ/ZH+KFpfW0F3ayeFNQ3xSoGVsW0hHB9CAR6EVF8Mfg18NPg54autK+HPhSx8OwXTh7uVX eae5IGB5ksjM7Ac4UnAycAZNd9q2l2GueFdT0TVYPtWmahaSWt5DvZPMikUo67lIIypIyCCOxqcm yephMHUpTavNt2Xwq/ReRt4k+JGE4h4kwmPw8Jqnh4UoKU2nVqezbfPO2nO79H037fmn/wAEwbO0 T9nb4lagttAt9L4jihkuAg3vGlurKhPUgF3IHqx9a1P+ClXw31fxN+zr4Q8faTaG7i8JXs6aoIwS 8dtdCJfNx3VXiQH0356ZI+2/hh8Hfhx8GvC+o6L8NvDv/CN6Zf3QuruH7fcXXmS7Qm7dPI5HyqBg ED2r0maGG4tJbe4ijnglQpLHIoZXUjBBB4II4xWVHIG8mWAqtXta62ve6fTqduZ+LkKfiVLizL6c nDnUlGdlJx5FCUXZySbjdJpu2jt0PkH4c/tofAnXf2bdI8S+JPH+ieG9ct9Nj/tnSL2RhdxXCoBI sceN0wLAlSgOQR0OQOf/AGU/2mfiB+0L8V/iIL3wfpWm/DrSZnOlatEskdx88v7i2lBZkeQRZZ2Q rjC8fMDXZaz+xB+zHrnip9XufhpDaTSSNJNBp+q3drbyEnP+qjlCoPQIFFfRfhHwb4V8BeBrTwz4 M0DTPDehW2fKs7GERpk9WPdmPdmJJ7k1eEw2byrU3iKkVGG/Le8vW60+RzcQZ34d0stxccowlade u1yutyKNBXu+Tkk3Jvb3ulr63T/If4DeB/B/j/8A4Lk/tD6L428NaL4q0mK71+5js9TtVniWVdWi UOFYEbgHYZ9GNeif8FA/g/8ACz4f/sY+HNZ8EfD/AMJ+FNWm8ZW1tLd6XpscErxNaXjGMsoBKlkQ 49VHpX354V+BHwp8E/HzxH8T/DHhX+zPHOvG4Oran/ad1L9o+0TLPN+6klaNd0iq3yqMYwMDitn4 m/CfwB8YvAVr4Y+I+gf8JHodtfpfQ2326e22zqkkavugdGOFlcYJxz0yBjgjwzL+zq1BqPtJOTT7 Xemtrn11bxxoPjPLs0pTrLCUIUozp3SbcI2laKnyNN7Xav1sfm/+0b8NdY8a/wDBEL4A+KNDszfX HhHwzpd7fRoCZBZyWEaSuoHXawiZvRQx/hr3/wDZ8/bG+DWv/ss+F7fxj430Twb4s0fSobLVbLV5 /IMjwoE82Jm4kVwu7CksCSCOMn7L8P8Ah7R/C3w+0bwroVmLLQNKsIrGwtDI8oigiQRom5yWbCqB liSe5NfOfif9i39mrxZ4qm1rUfhraWd/PMZbj+y9QubKKUnkjyopFRQT/dUH3roeUY3DV1iMLKLb ioyUr2dlo01qePS8Q+GM5ymeUZ/SrKnTrVKtGpS5HOKqNuUJxm1Gzet0738lr+f3jnxPb/th/wDB Yr4e2Pw9gm1HwP4YNsLnUZYmRJbW3uDcXNwQRlVYsIkBALHZnG7jpv24tRsdH/4K3fs/6tql1DY6 bZWmlXN3cythIYk1aZndj2AUEn6V+oPw8+FXw8+FHhWXRfh54T0vwvYTPvnFsrNLOw6GSVyzyEdt zHHauP8Aid+zh8GPjJ4zsfEPxI8G/wDCR6xZ2Qsra4/ta8ttkId3CbYJkU/M7HJBPPXpXJX4bxU8 JUvOLqzkpPdR02S0b0Posr8a8gwvEODccPVhl+FoVKEEuWVV+0XvTleUY3k9Wk7K2nYo/wDDVH7O n/RY/A3/AIMBXw3/AMFBtAu9c8N/CH9ojwJLba54e01Vil1G1JdFR5EntJsj/lkW8xd3GCyD+Kvq r/hhP9lX/oln/ly6p/8AJNfSWi+DfDHh/wCE2n+BdM0e2TwlZaeun2+mXBa5j+zqu0RN5pYuu3g7 ic98134nLsfj8PUoYvkinazjdtNO63S0Pksk4y4R4SznCZrkCxFWUG1UhXVOMZU5RcZJOEpPm10u rLfXY+ePBv7aH7Pvif4OWfinUviDofha9+yq+oaNqUpS7tpcfPGseN0uDnDRhsjH0r4a+D15eftP f8FxNQ+MOiabcQ+AfDTi5E1yhUiKK3Nvag+kkkg83Z2Af+7z9v6j+w5+zBqXiX+05fhnFbOXLyW9 prF7BA5PP+rSYBR7LtHtX0T4M8D+Efh54EtvDPgnw9pnhrQ4CSlpZRbVLHq7Hq7nuzEk9zWUsszL F1KSxko8lNqXu3vJra99kd9Pjrgnh3B4+XDlGu8RiqcqV63Io0qc/iUeRtylpZN2to77p/kZrGrP +yL/AMFv/EPjTxZY3o+H/jGW8uP7QhhL/wCjXsgmkZAPvGG4UBk+9tXIHzLn1b9pvUf2KPidZXXx N8SfE1tb8WRaB9i0ix8P6kZHlZDK8StCqEqd8pyXKjHpX6K+Ofh54I+Jfgt/D3jzwzpPijSC+9YL 2HcYnwRvjYYaN8EjcpBwTzXzpp/7Cf7MGn+IF1BPh3LdbG3R211rl7LCp91MvzD2bIrlr5Bjacal GioTpTbklO94t+m67Hu5X4t8NYutgszzKWKw+Ow9ONKUsO4ctWEdr8zTi2tJWun06W+Qf2CIZl/Y E/aSnaKRYJLeVUkKkKxWxmyAehIyM+mRWb+xT4IuPiR/wTK/aQ8DWXk/2jrEpt7Hzm2p9o+y7odx 7DzAmT2r9arbwh4YsfhpL4N07QtM0rws9m9odM0+EW0KwupV1VY9u3IJ5GDznOa5P4X/AAZ+G3wZ 8PanpXw18N/8I3YajcLcXkX9oXN15kirtDZnkcjjjAIFaYbhmdJ4eMpJxhGcX0b5u339zhzvxxwu OhnNelRlTr4qth6tPaUY+wa+J3Tu+VOyi1fS/U/OP9hT9oHwP8Mfhf4p+DfxW1m28Ba9puvTXFrJ rLGCE7giSwM7fLHIkkbHDEZD8fdNes+I/wBtfUde/b68FfCv4D6VoHxF0G+ljttWv5UmC+Yz5kkh lU48qGIFmcoyn5scAE/TPxN/Zl+CHxe8QtrXjnwLY3+umMRnU7W4mtLlwOm9oXXzCBwN4bA4rX+F /wAAfhF8Gjcy/DvwVp+h31ymy41B5JLm7kTIJTzpWZwhIBKKQuQDiqw2V5tRpww0asVTi/iV+Zpd LbeRjnnHnh7mWMxWeVcDWnjK8XelJx9hGpJWc1JNVGk/eSsne+2jXwB/wUM8W6v45+M/wr/Zz8H/ AOmatfXkV9d26vhXuJ2MFojH+EKDM7Z4AdW7Vy/iX9nz9u3xD+zLB8KtZ1TwHqHgGxs4IbXSIfsK OiW20xBJFt1k3DYBu35bJ3E7jn9Jz8AfhI37UA+M0vhPz/iUJPMXWJtUu5NreT5AIhaUwjEfyjCc YBGCM17FTnwzLE4ivWxNRrn0Si2ly2sk9PvWwsN440cjyfK8uybB05rDrnlKvTjKXt3JylKm1J2S 0UZaStbRWR+XX7BfxQk139iP4kfCnVpXXV/CVvdTWMUvD/Y7hJGK4PPyTCTPp5qCvLP+CePxb+Gn w18BfE628e+NdA8Jz32oWb2aajciMzKkcwYr64LD86/Svw5+zn8GvCPxc8R+OvDfg7+yPE+vRXMW rXUOrXmy5S5ffMvkmYxKrMAQFUbSBtxgV5p/wwn+yr/0Sz/y5dU/+Sa5aeR5pS+rSjKDlRUlq5Wa ei2XRHu43xS4EzB55Rr0sRTo5hKjUtCNJyjODcp/FUtaU9U93d3Ssj13wn8efg3468bW3hvwf8R/ C3iLXrhHeGxsrwPK4RSzED2UEn6V63Xz34A/ZV+Avwu+J9l4z8CeA/7C8SWkckdvef23fXGxZEKO Nks7IcqxHK8Z4r6Er6zAvF+z/wBpUea/2b2t87an8+8UxyBYxf2JKq6PKr+2UFLmu72UG1y2tbW9 7+QUUUV2HzYUUUUAFfyUft8ShP8AgrwyeTE7tpttiRs7k/ez9OcfmDX9a9fyK/8ABQy7Wx/4KzyX bfdi0u2Y/wDf2etqFeVGanG1/NJr7mmhSSaszj/iMbrS/iP4d1RLqeW6XSYJY7iYLlCBkAbVAxn1 Br1rRfFF/eWllDqF7rrX0qL5a27QyZLDkYMXAx715R8RTFqWofDOa7PkaffWsNvJMrdFGAx/WvqT SPBP2CC2hskhj05QssV80gEuSuMH1GK9D+2sT2j/AOC4f/ImfsY+f3szdL8La3fLCdT1W9020Eg3 Oxgk8tM/ex5XUdcVyPxA8C6x/wAJXqkXh/X9Q8TWkMjpHKVSHzIwx2thVxkrzivtPTNJ8G3XwQaw 8wHxo4OySWRkRlDZOWP7v7gPXr0615la6Za2Nrej7RuuJyTuc8gkYFeBlfF+Ix+JrWg6fspODjOl TipbWqR91txdmoyuk7S00O3E4CNGnDVS5le6bdv7r811XofCdnZNY2d7ZvJNYzqztPbzBe23k5Xn jNc9qXhW+1i41PVNL8m50y2Rczu+NxKg4GMCv0p0rxetp8BZ/h/qnwt8Eaq0ltPFJ4juYYmugZC5 EnMZO5dwA+b+EV4GPC+n3mmXOiWdr/Z3h+Ni9zOkYQyv9K0yjijOcRUrxxWEjRUZNQdqUuePSVox 92/8r1Mq2GoxUeSfNffdW8tdz518C6PquqRNa6ObnQ7qOM+bfKBtY5BC9M/rXrdp4M8arGFbxtf+ Z0wLcYz+Jr2TS/DVrZaXFHZwRRRIAVVECg8Dn611aOZrSys7j7OsNoHMe2BQ53EE7mAy3TjJOO1e vPOsXePLGDXX3IaKz29zXWytpprfSzxVGFndv8f8z5lTwzrMFz4ig8R6pqWuxGxjZUhgjLSZZ8A7 lbA4bpjrVPwJaQxagk+rw+JtL0tX/cbpI38sA8EoyEACvfjcLH8SNUMQEcf2C27dfnnpl/a6VrLY tv8AR7wnG3ZhXOK9nOM5xCrQ0j8FP7EP5I/3TGlSjZ+r6vuX/EWh3mt/DW5XS/F2uSq8BaFv9H2v xx9yJT+teAWWjeIvD/lQy67qK3lyQJAdnT/gSmva/A9n4h0Lx5f6Xd2ryeH7tBLBJvz5Eg4Kgeh6 /WtzWfAbX/ju31U3IS1UhpEYDgjFeV/bWJ7Q/wDBdP8A+RNPYx8/vZ8varF4u0uC4lsr+e5W5lCN EVQY7Z+7/hXmvjzSfFNvaRT6kjag88TY3EDav8Q4x2r74m8I6L9rN88hmVX8zylwVJ96+aPj1raC 1tNJgghhup3xCygAoARzS/tnE9o/+C4f/Ij9lHz+9nlOltbR/sn/ABREd7LbW3mQGK0jCiOYgfMW yueP9kj8a/pM/Y8/5Jjov/YnaX/6Ijr+eT4heHLfwb/wTW8NahIgXV/Es0tzJuUBjCWAjOR1GGr+ hv8AY9/5Jlo3/YnaX/6IjrkxWMqYhrntp2jGP/pKV/mVGKjsfatFFFcpR+T/APwUM+DOvfGL9vz9 hOxX4beJ/iB8PbXxbexeNXsNHuLqysrKa50kP9rliUiCNkSX5nZRhHOflOPuj4Xfsv8A7PnwW8VT 678LvhJ4N8H67LCYW1O0s992Iz95FmkLOqnjIUgHAznAr3iigDjPiF8PPBXxW+D+teAPiJ4dsPFf g/Vo1TUNMvN3lzBHWRDlSGVldFZWUggqCCCK/PQ/sL/Fz4GeKYNa/Yz/AGifFHgnw+t0stz8OPGs zapockZbMiQO6uYCVyATGzkn/Wriv0+ooA88+Lvgf/hZ37KfxM+G4uY7NvFXhXUNFW5kXcsLXVtJ CJCB/dLg/hX4FJ4B/aK+LH7AXwI/4J/6x+zv8UvB+ueFPH3n+LPHGo6YV0CPS0nvGE0V1ny5SFum +6xD+QAhcyYX+jiigDwTVvi14nsP2/8Aw18EYfg1401LwXqnhh9TufiLDbynRrCUG4UWMjCExiUi FODKDiZPl6Z/D8+Af2jfhT/wT2+N/wDwT90z9nj4o+Ltf8VePhN4V8c6dphbw/LpjT2bmaW6/wBX GStqp+ZgE88hyhTa39HdFAH43fGD4PfEP9l/9tn9j39oHwh8O/GHxm8G/Dv4Yw/D/wAXad4N043W oolvaTwx3aQA7mVjcs2fujycMy71Nezf8E9fhj8RNN8YftNftB/ErwZrPw51X4weOTqeleGdZgMV /Y2EMl1JEZkOGRibt02sqsfJ3Ywy1+ldFAH5m/C7wF450/8A4Oi/2k/iDf8AgzxZY+AtT+Gthaab 4kuNInj0y8nWPRw0UVyV8qSQGKTKqxI8tuPlOP0nslVbNwqqo8+U4AxyZGJNW6o6eZjYSfaITDJ9 pmwpPVfNfa34rg/jQBeooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKAGl0D7S6hvTPNOr8nv8Ago34O1Dwz8Q/hT8ffDYNtqen 3iaddXKL/q54XN1Zucd8icE/7KD0r781D4yaHbfsJ3HxwhMbaT/wif8AbUELN952h3pAT/eMhEf+ 9XjUM3jLFV6FSPL7NJ3vvFq99unU/Ssz8OatLI8qzTB1fbLGSlDl5bOFWMlHkb5nfmveL0uuh7GH QuVDqWHUA80F0DhS6hj0BPNflv8A8E3PA11PoHxK+OPiItNqmuXzada3c3DOisJ7qTPcPI0Yz6xN Xwx8a/EvjL4zftIfFr9oDww1wfC/hLWrKC0v42INnB5rQ2UiehZot59Gk968fEcWOlgaWJdHWpe0 b68qu3K9u2vz3P0fKPo+wzDirH5LDMUqeFUFKq4e66tRxjGmo8+7k3G973i/d2P6MaYZIwxBkQH0 LV5t8G/iLZ/Fj9mDwV8QbMxj+19NSS6ijPENyuUnj/4DKrr9BX49+IfhBo/xy/4LrfEz4f65qupa Np9ze3Nw1zYBDKDFbowA3gjB78V6eZ508PSozow5/aNJa23V1rZnw3A3hlDOMfmWGzLEvCrBU5zq P2ftGuSSjJcqlHbXq9tj9zwysuVIYeoNDMqrliFHqTX4mftG/shab+zR8Dbb4o+Afir4ot9XtdUh t0gndbeeUyE4aGWEqQ67dxGDkAnIxz6n8f8Axt4g+I3/AAb9/D/xj4qJk8QX+o2YvZioUztFLcQi YgcAyCMOccfNwBXE+JKtN1oV6HLOnHntzJpr1tp9x9RDwUwGNjluJyvNPbYbFV1h3N0pQlCbV/gc nzKyeqkui9P1gBBXIIIPcUhdFYBmVSegJr57/ZO/5Rw/B7/sXov5tX47/tO634m+Pn7YXxj8deF1 kvfCPw7tIrZLiNziK2iuVt98ZHUvPLNMOnyBj/DW+acQrCYOlXVPmlOzUb+V3rbovI8rgTwclxHx Jjsqli1RpYZyjKq43TftPZwXLzKznLZc3fc/oOphkjDEGRAfQtXiP7OHxRT4w/saeCPG0kyy6tLZ C11kA8reQ/u5iR23MvmAf3XWvyf8RfCPQ/jd/wAF1viZ4B8RatqWi6Zc3tzcPdWJQSq0VujADeCM E+1aZjnjo0aFSjDn9q0lrbdXWtmcXB3hZHM8zzPB5ninhlgYTnUap+0f7uSjJKPNF9bp637an7oB lYZUhh6g0tfhv+0d+zdoX7Kfgnw38QPhl8Y/EVv4rfV0t4rCS5jiu3Qo7maNodp2qUAYMpU7xkjh W+4v2g9f8Q+If+CEmqeJvEkTWPibUvCmjXepIg8srPJcWbOcDG3JJO3tnHasqOf1P38a1HlnSjzW 5k01ZvdLT7j0cx8IsH/wlV8uzH22HxtZUVJ0pU5RlzRi3ySk+ZK97qSV9ND7moJABJOBXxz+wTJJ L/wTD8CvLI8jm81HLO2Sf9Nmr2749/8AJjHxo/7ETV//AEhmr1cPjva4KOJ5bXjzW+V7XPz/ADjh T6hxPWyR1ebkrOlz2te0+Tm5bv1tfyueqedF/wA9Y/8AvoUokjZsK6MfQNX4Pfspfsm/D/49fATX PFnizxzrXhjULLX5NOitrN7cK8awQShz5gJzmVh6cCvvX4JfsTfDX4Q/tIaH8QvDfxC8QeINW0qO 4EdlcPbGNxLA8DFtihuBIT9cV4+W51jsXGnUWGShLrzrRd7W/A/R+NfDDhXh2ti8JPOpzxFFP3Pq 0kpSSuo8/tGkm7Lm1Svc+76KKK+oPwkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr+ QD/gpRx/wU51U/8AUFt//Rs9f1/1/IB/wUp/5Scar/2Bbf8A9Gz0AYFpay/EH9g+DVbWJrrVPC5H nMrYKRt1PP8AtAD8a+gPglr8Pjj4YQyX+s3F1faaFguLaUgKgAwpxj2Pr0r45/Zu+I1j4R+Ic2ha 4Fm0DWGEN3BJna6HII46HnI9wK9O8feGPFfwG+Ki+LfDH+leEdYBmtXf5o5I35CtgjkZHvQB972u u6PNcrpkUySTJ8uNvBx71NeaTa3N6tyUkLqcja+P0r5B+EXxd0nxJr0Frqyx2+oG+WU26g/PHuBb B+meM1+wHwz0v9nf4q+IbprHSUsbzT/9Zbz3EkLv0+faZDlfmxn1r848RPEijwfh4YnE4OrVpO/N KnGLjDVJczco2vfT0dz08uyx4xtRmk10d7v0smfEHiq51nTfB9xfaLon9uXY6WyuAT789a8J8Kap 8R/EutXNvrmlNZWTtlLaCHYkQGevPOf6V+j/AMfvCPhTwf4702Pw5Lo2n20toG+wpebp5SXxvCEk lR3NeCXTm2sJJwQ21ScdM17vBPF+C4nyajmmETUKi2lbmVnazs2r+jZzYzCTw1V057m/8Lvh5qnj u/j8PWXk6bepA0mLli/yqVHX33Vznjrwf4t+H/iGGz8TaNFps1wjGH/SY5dwGMn5GOOo6+teifs0 /GHStO+My6bqel29pf36PbWl9EjNKzMUIQ5baF+UnOM5r5v+LfxZ8c+JPjbeap47+yxaXEnlaf8A ZoyIUGACOSTk7QTk18lg864srcd18F7OMcvp04Sbkvfcpc6TpuL1TlH3uZaJaWb17J0MLHAqbfvu 9rfK979l27mUzSS/EDUtx25sLfG0/wC3NW5ZW+nxXkMt5dAynlI0PepfhN8MPiJ8SvHGqW1pol7Y wTaTa3KXd9bvapJbySSbJI2ZcMGG4qRwcH0qL46fD7VfhJ8YbnTVma+0+O3E1kZZQ0syEAfPgABt 2e3TFfoOZ8c5FW4gp5PSxMZYh0oS5U02kqcN7bPW6XVJvocNPL8QsM67jaN2vxZ1uk+KrC+8Uy6N BkTRRhiMcYrprxYZ9KnhmlMSMhUsDggHvmvnXwD4hS018Ldqg1i6kHnxYJ8lO3P0xVfxd4p1fTPi 9c2BO63lkUxhmIBQgZx2r1jnNrU7fV/COs/b9PvZr/Rif38Ly7wE6kgdc18m+J5r34nftS6HoejP PdtdXqW0ajgoHZQ3X2zXRfEPxpb6Tq+oWWkXzNdTKRNCHLDp+gr0D4G6JZfD/wCB/jH4x+KJbePV BbE6KJhlvMKttYAdMHDH2oA8q/bF8d2t/wDEbw/8PtEuM6H4YtU0+GNcjAiCoc9jkrniv6Rv2Pf+ SZaN/wBidpf/AKIjr+PrxHrMviD4j6jq8zF2ubtnBJzwW4r+wX9j3/kmWjf9idpf/oiOgD7Voooo AKKKKACiiigAooooAKKKKACiiigApqZ2HPXcf506qOnTrcafJIiyKBdTph+uVldSfpkce2KAL1FF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQB4f8AtIfDUfFn9ivx94LigE+qT6c1zpIx832uAiWEA9tzIEJ9HNfiQfj5f3//AASb 079nyBrqfX28YbFhRSxk03IuEi92N23A9FAr+ievz30n9gDw1pf7dUPxeXxv52gw+JX1yDwr/YIV Y33tLHELjzz8iSFT/quVXb3zXxvE2T4vEVoVMLvJOEtvhb3+Wvmf0t4H+JPD2TZbiMHnz92jUjia CtJ3rQi1y6J25vds3ZKzuyx8ZL6D9lf/AIItWHgrT5o7bxLdaRHoFu0TYL3l0rPezKRyMA3Lg9js 5r4H+E3xU1DwP+wT40+EU37O3jLxZD4xSeW81+KaaIP5sSpA8afZXBEW1HX5yC2Txmv0+/aW/Zbu /wBo/wAX+Cpb/wCI0nhfwzoAcvpEWifaGupJHUyP5vnoFJRFRfkbb8x53Yr6xs7O10/SLWwsoI7W ytoVht4YxhY0UBVUDsAABU18hxWIxjcZezpwioR0UrprXR7du5rlXixkGUcNU4VqH13F4qvLEV/f qUuScZJ0/eilz9Z2TcU731sflP8A8E1fiXc248efBHXjPa31lKdX0u2uVKSR8rDdxFW5Uq3lNt65 aQ4614lr/wALpfjD/wAF2Pib4Ih8V6r4MkuLy6nXVNPQtLH5dsjbcBl4PQ81+gt5+ySYP+ClMH7R XhX4g/8ACNSvdpPqPh/+wvOS7zD5NwvnCddvmrk5KNtc7uelX/DH7K//AAjn/BSrX/2h/wDhO/tn 9pm4P/CP/wBi7PK82ER/8fHnndjGf9WM9OOtcSyLGzwtDC1oc0adTe61hrrvfra259PLxX4ZwufZ rn2XYn2VXGYPSPJN8mKfLeGsHF/Dzcz9xu92flf46+EunfAj9urQfDP7SFx4s+IHwzusTWGsWOoS Q+fCXAaRlbe3yHIkhR1fBBVyCu79Av28bbQbP/glFpdr4Vj0+LwzFqmmLpKWOPs4tgj+V5eONmzb jHavpD9ob4CeHf2hPgS3hHWbv+xdUtrhbnR9aS1E8lhKOG+Tcu9HXKsm4A8HOVBHi19+yB4g1j/g nda/s/a38Yf7TtbDWI7vStbfwwRJa26biLUxfajvAZm2tvG1SFwQBVvIMRhY4rD0KfNCpF8rurp/ ytt3a7M5IeLmT55WyDN80xjpYnB1YqrT5ZunKCd/bQjCLhGXScVZvWyslfmtO+KI+EP/AAQF8K+L Le4+z63J4Tj0/RSDhvtdwWjjZfdAWl+kZr4K/Z6+LN78LP2Y/HvhC5/Z68X/ABCtvHKPHqOr29xL bpNZtAYkiQC1kB2+ZMwfd1k6cc/od8SP2MNT+IH7KHwj+Ei/Fs6Longq3cTSr4a87+1JiNqSlftS +XsUyADLf6xua+1PDug6b4W8AaJ4Z0eEW2k6TYQ2VlEP4IokCIPyUVr/AGJj8RiKblL2apwUU7Rl dte9prbt+R5//ET+EsnyjF06VH65UxuJqVakearR5IQnej7yUW7v30k3Zv3rNJH5G/8ABN34jXvh v4xeNfgl4iS605tSj/tTTLS8Ro3iuolCzx7GwQzxbH6dIDXnmv8Awh0T44/8F2fiX8P/ABBqeqaR ptze3Nw9zp+zzgYrZGAG9WGCevFfoL48/ZJPiX/goT4f/aD8LfEH/hC9ZsZbWa+07+wvtS38kI8t yZBPHtWSALEw2ngE55wPMfiT+wVq/jj9qvxb8U9E+Oeo+Cb/AFq8M6wWPh5zLbAoqlBMt4hYHb/d HXpXn1cjx6wdPDSpe0jTqXWqXNDXz0PscB4q8JT4kxmeUMf9Tq43Ccsv3dSfssReK6QtJWSaa0bT vZux86/tB/sV+Hf2e/gFN8YPBHxC1W41Xw/qNpLFZa7Z20yzs86Iuz5QpZSwfayMCqtxXvnxA+J+ t/GH/g3h8RePfEdrFba5e2UMF60MeyOeSDVooTKo7B/L3EDgEkDgVk/8O47zW9Vsh8QP2iPGvjDS YJNxtTphjk9Dsea5mVD77DX2B48+Afh/xN+wZqHwE8LXieCfD8lhb2llcrafavsyxXEc5YoXQyM5 Q7iWBLOWOT16sJkuKj9ZdOj7KE6biocyd5dHvZdtzweIPEzIK39ixxeZ/X8Rh8XCrLEexlT9nRTi 5U7OKlPVc+kXtbtfzH9gX/lF94F/6/NR/wDS6evcfj3/AMmMfGj/ALETV/8A0hmr4R0z/gnZ430T RotO0b9qbxVpGnxkmO1stBmhiQkknCrqAAySSeOpr3X4ZfsoeKPA3wl+L3hXxD8cNf8AHyeNvDcm jwT6jpsoGlGSKeMzKj3Um/8A1wJUFM7MZ549LL5ZjHCQwtTDNJR5ebmi9o22Tvqz4vjGjwZW4hxG fYTPI1JTr+1VL2FaLtKqpNc0ope7Ft+drJXaPgL9kT9j/wAA/tCfs9a/4v8AFXiPxfo9/Y+IpNNi h0mWBYmjW3t5Qx8yJzuzKw64wBxX6J/An9jb4ffAH4y3Xjbwt4k8ZavqM+lSac0Gqy27RCOSSNyw EcSHdmJe+OTxXzbo/wDwTj8XeHdOks/D/wC0/wCI9DtJJDK8Gn+HZbdGcgAsVS/AJwAM9cAV6z8K P2OPHfw4/aE8M+NtV/aR8W+NNP0qd5JtFu9Onjiuw0bptZmvZAMFg3KH7v415OS5TVw3sufA++rX nzx++1+h+g+J3iFgc8+vvC8V/wCz1VLlofV62qtpDmcElzNWu7JX1PvSiiiv0M/jgKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAr+QD/gpT/wApONV/7Atv/wCjZ6/r/r+QD/gpT/yk41X/ ALAtv/6NnoA+BY5HimWSN2SRTlWU4INfd/wP+O/hvWvBTfC/4rWovNCucCC5VS0sDAEBkJzjtkd6 +DacrskgdGZHHRlOCKAPtr4ifs6eJvCt63jXwBcT+IPCJk822vbRhvhDHhWA5B5GRW/8KPj9e+H9 VTRvFEf2Sd22NqERKlj6Pg5B/SvBvhl+0R43+Ht3BAl/Le6YnDQT5kUjGMFWOD+Ir6TT4i/BP4zJ NH4t0ez0DWpRtivNGgFrsPXLR4+bn+6KUoqSs0Cdj6PXxZY3cJ1YTT3dw42RF5S+Ae4J7VBouv3G peBdUtCZPMtpG2uTy618v6n8GvFnhuKHU/CXi+S80fcDb3BuGltmJ6AqPuH2at/Qtf8AjBoMMser eD7PWIwPllsJ4/nH+0N1JRUVZIe597/Aj4D+PrvWPA3j82mnt4dTUI7vz2vkEvlA5J8vqTjtXQft K6NpOi/EdmeKK6hv7TzVt/IC+WIwitz3yWz0FeD+L/2uviJefsweE/C/w/8Ah54w8KeMrExC/lt4 orWw2KsgZIikxbGShAIHQ189av8AF39o/wAZaNZ2viDRLC5u7NZVjvtRm/elZCCVd2f5gNox6Cv5 94SyLjnH8Wwz/NXTpUoe1o+ySak6d24Sbbkm+bld1y6dLXT+nxeJwFLBTwtK8m7ST6KWl106XP02 /Zu+MugWvxSfwtq1wI7qTw5YWFg6W22NI7drtlDHoMKw57mvl79tL4o+DtU/awWw8PahPql7pNnH Z6qnlERoSRLuQ9GG115HfI7V8Qf2D8WLia6W78R6Rp0skK+fsPmL5bM20AgnoQ35iptL+GUTwNBr 3iuS/t1BLJpiGN3b0Y4yR7V9q/BXKMr47/1mw1SUZSowj7Oy5eZ04rmT7cuijbfW/Q8n+2KlTBPD yXV699b/AH36/gdxrnxd8E6GbZ4bq0eSc4ulVcSp059/pXJ+KfHl58U9Oi0vwXpU9xBAFWXVrhfK WIdM5PNdzoXww8Nw6G11YeFIZbQcNe6sFZFb/aL/AHe9Z/iX4ofCv4b6WbaKaDW/EMJ/dWOmw7bK FgM/OQB5nPocV+nnmnR/Dv4E+DfCfhGL4i/EzW5DokCmQzyqCbthz5cSkfMTjGT0r5e/aI+PrfEj Wrfw74Ytk0jwVpYMNjbRk8rx1Pck55rzT4l/Grxr8TdWJ1jUGg0uN821hagxQRD0CA4A+gryGgCS L/j5j/3h/Ov7I/2Pf+SZaN/2J2l/+iI6/jci/wCPmP8A3h/Ov7I/2Pf+SZaN/wBidpf/AKIjoA+1 aKKKACiiigAooooAKKKKACiiigAooooAKpWBhNjJ5CKifaZsgEn5vNfcefVsmrtZulRNDpkqOME3 lw3Xs08jD9DQBpUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABX8ln/BRDwF448R/8FH9V1Hw/wCD vFGuaf8A2RBH9psNLmnj3CWbK7lUjIyOK/rTrOOj6SWJOl6cSTkk2ycn8qAP4Vv+FSfFT/om/jv/ AMENx/8AEUf8Kk+Kn/RN/Hf/AIIbj/4iv7qf7H0j/oF6d/4DJ/hR/Y+kf9AvTv8AwGT/AAoA/hW/ 4VJ8VP8Aom/jv/wQ3H/xFKvwm+KquGX4cePFYdCNCuQR/wCOV/dR/Y+kf9AvTv8AwGT/AAo/sfSP +gXp3/gMn+FAH8V/gef9oPwTfqbXwR8RprTBDxNo10dwIxg/Lgj2r3FvH3xBvUjN78HvH9syqF22 ehXUK/XCLjNf1v8A9j6R/wBAvTv/AAGT/Cj+x9I/6Benf+Ayf4UAfyNS+NPHQAMfwg+JTkeul3nT 3yKq3PxC+KgGbT4MeNz6CXw/Ow+vK1/Xf/Y+kf8AQL07/wABk/wo/sfSP+gXp3/gMn+FAH8er/EX 9oK3mkvNM+Eer29zMghkDeFJyQiElT9zqS7fkPSs2X4i/tOtqU11F8O/E0Mkg5KeF7hdp9sLxX9j n9j6R/0C9O/8Bk/wo/sfSP8AoF6d/wCAyf4V04rEyrzUpLZRX/gKUV+RMYpH8THie3/aN8XS7tb8 K/Ee4GCNi6LdBcHqMbelefH4TfFVmLN8OPHjE9SdCuf/AIiv7qP7H0j/AKBenf8AgMn+FH9j6R/0 C9O/8Bk/wrmKP4Vv+FSfFT/om/jv/wAENx/8RR/wqT4qf9E38d/+CG4/+Ir+6n+x9I/6Benf+Ayf 4Uf2PpH/AEC9O/8AAZP8KAP4WYvhJ8U/tMf/ABbfx394f8wK49f9yv65v2Qopbf4e6XbzxSQzxeE tNSWORSrIwhQFSDyCCCCK+wP7H0j/oF6d/4DJ/hU1vYWNm7NaWVpaswwxhhVCfrgUAW6KKKACiii gAooooAKKKKACiiigAooooAKr2zBrdiHD/vZBkPu6O3GfbpjtjFWKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPx3+Jn/BLT4g+P/2k PiF490/9snxt4Vs/Evia/wBYg0a38O3EkWnrdXMk4t1YakgZUEmwEKududo6VxI/4JFfFMfd/bt+ IC/Twxdf/Lav2+ooA/EUf8Ej/iyvT9vL4iD6eGrr/wCW1PH/AASU+Lq9P29viOPp4cu//ltX7b0U AfiU3/BJr4yrExh/b5+JJfHyg6BeKD+I1Y/yqt/w6i+PS/c/bz+IH/gsvh/7k6/b6igD8Qf+HVP7 Qq/c/b1+IH/gDqA/9yVH/Dq/9o5fuft7fEAf9uuoj/3I1+31FAH4g/8ADrX9phfuft8fEAf9s9SH /uQo/wCHXf7UK/c/b5+IH/lTH/t/X7fUUAfiD/w7C/asX7n7ffxA/wC/uqD/ANvqP+HZH7Wq/c/b 8+IH/gVqo/8Ab6v2+ooA/EH/AIdnftfr9z9v74gD/t/1cf8At7R/w7U/bJX7n7f/AMQB/wBxTWB/ 7eV+31FAH4g/8O2/201+5/wUA+IH/g51kf8At3R/w7h/bcX7n/BQL4gf+D7Wh/7dV+31FAH4g/8A Duf9udfuf8FA/iB/4Uetj/25o/4d2/t4L9z/AIKC/EAf9zPrg/8Abmv2+ooA/EA/8E8/2+R93/go N4//APCs14f+3FMP/BPT9v8A7f8ABQPx4fr4v14f+16/cKigD8OT/wAE9v8AgoJ2/wCCgHjg/Xxl r4/9rVKn7AH/AAUNijCx/t9+LGA/v+LdcY/mXJr9waKAPxB/4YJ/4KKr939vbxEf97xXrX+NJ/ww d/wUcX7v7eWtn6+Ktar9v6KAPxB/4YU/4KRr939u7Uz/AL3irWf/AImk/wCGGf8AgpUv3f267w/X xTrH/wAbr9v6KAPxB/4Yf/4KYr939uaQ/wC94p1f/wCNUn/DEn/BThfu/txofr4o1b/4xX7f0UAf iD/wxV/wU/X7v7b1of8Ae8T6r/8AI1J/wxf/AMFRl+7+23pp+vifVf8A5Fr9v6KAPxB/4Y0/4Kmr 939tbRT/AL3ibVP/AJDpP+GOv+Cqa/d/bT8PH6+JtT/+Qq/b+igD8Qf+GP8A/gq2v3f2zvCx/wB7 xLqX/wAgUn/DIn/BWFfu/tl+Dz9fEuo//K6v2/ooA/EH/hkr/grSv3f2xvA5/wB7xJqP/wAraP8A hk//AIK3jp+2H4AP18Sah/8AKuv2+ooA/EQfsqf8Fch0/bB+Hn4+IL4/+4qlb9lv/grrGmV/a8+H cpz0XXrzP66XX7dUUAfiD/wzL/wV7Xp+1j8P2/7jdwf56ZR/wzZ/wV/Xp+1X8P2/7jMp/nplft9R QB+IP/DOn/BYRen7Unw/b/uLMf56ZR/wz3/wWHXp+098P2/7iYP89Nr9vqKAPxB/4UH/AMFjF6ft MfD9v+4hGf56bR/wor/gsgvT9pL4ft/2/Qn+em1+31FAH4g/8KR/4LKL0/aL+H7f9vdsf56dR/wp j/gswvT9oX4ft/28WZ/np1ft9RQB+IP/AAqD/gs4vT4//D9v+2tgf56dR/wqf/gtAvT48/D9v+B6 af56dX7fUUAfiD/wq/8A4LSL0+OXw/b/AMFZ/np9H/Ctv+C1C9PjZ8P2/wCAaQf56fX7fUUAfiD/ AMK+/wCC1i9PjN8P2/7Y6Kf56fR/wgn/AAWwXp8YPh+3/btoR/nYV+31FAH4g/8ACGf8FtF6fFn4 ft/26eH/AOthTD4P/wCC2w6fFTwEfpaeHv8A5Br9waKAPxAHhf8A4LaqAP8AhY3gCT3Nt4f5/wDJ MUf8I7/wW1X/AJnzwA//AG7+H/8A5Fr9v6KAPxB/sT/gtqv/ADOPgB/+2Ph//wCR6T+yf+C2q/8A Mz+AH/7ZeH//AIxX7f0UAfiD/Z//AAW1X/mOeAH/AO2fh/8A+NUn2X/gtqv/ADEvAD/8B8P/APxF ft/RQB+IPl/8FtV/5b+AH/Dw/wD4Um7/AILar/B4Af8A8J//ABr9v6KAPxB+0f8ABbVf+XHwA/8A wLw//wDFUn23/gtqv/MH8AP/AMD8P/8Axyv2/ooA/EH+1P8Agtqv/MueAH/7a+H/AP49Sf2z/wAF tV/5lPwA/wD228P/APyRX7f0UAfiD/b/APwW1X/mSPAD/wDbfw//APJNJ/wkv/BbVf8Amn3gB/8A t58P/wDyXX7f0UAfiD/wlf8AwW1X/mmfgB/+3rw//wDJtH/CY/8ABbUf80p+H7/9vfh//wCT6/b6 igD8Qj43/wCC2Sfe+EXw+b/t70E/yv6T/hP/APgtcvX4OfD9v+2+if01Cv2+ooA/EH/hY3/Balev wV+H7f8AbTRz/LUKP+Fm/wDBaZevwP8Ah+3/AALSf6ahX7fUUAfiD/wtX/gtCvX4E/D9vw0z+moU f8Lc/wCCzq9fgH8P2/7Z6f8A01Gv2+ooA/EH/hcn/BZlev7Pnw/b/thZH+Wo0f8AC6/+Cyq9f2dv h+3/AG62v9NRr9vqKAPxB/4Xn/wWRXr+zh8P2/7c4D/LUqP+F8/8FjV6/s1fD9v+3CL+mpV+31FA H4g/8NA/8FiV6/sx/D9v+4ap/lqVH/DRH/BYVev7Lvw/b/uFH+mpV+31FAH4g/8ADSH/AAWBXr+y v8P2/wC4PIf5anR/w0v/AMFfV6/so/D9v+4LN/TU6/b6igD8Qf8Ahp//AIK8r1/ZK+H7f9wK5P8A LU6P+Gpv+Cuq9f2Rfh+3/cv3n9NUr9vqKAPxB/4at/4K4r1/Y++H7f8AcuX5/lqlJ/w1n/wVrX73 7HXgQ/Tw3qH/AMs6/b+igD8Qf+GuP+Csi/e/Y28GH/d8Naj/APLGk/4a+/4KvD737GXhM/Tw1qX/ AMsK/b+igDh/hlrHivxD+zb8Pdf8eaLF4b8c6n4asLzxFpEUTRpYX0ttG9xAFZmZQkrOgDMxG3kk 813FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAISApJIA9TQGDLlSGHqDXxH/wAF BvFn/CN/8E4db01JPLuPEer2elpg/NgObl/wK27A+ze9fPX/AATW+JV1at44+CWvGa1u7cjW9Hgu AVYK2yO4jAP1hcAf3nPrXgVs/p0s0hgXH4le9+utlbzt3P13LPCPGY7gTEcU06ulKbj7Pl1cFyqU 1K/2XKzXLsm79D9YN679u5d3pnmnV+T/AMOf+Lpf8HHfxA8Sf6+w8G290kWeURraGPTsD38ySR/r k9qr/Hf4j/FH9pP9vi6/Zp+EHiKTw94P0mR4vEOpWsrIJmi2/aZJnQhjFEx8pYgQHk6k5UrzviWK oSqezbfO4RSesmuvkvvsetDwRryzSjg3i4wgsNDE16kotRoRkruLs25ySta3LzN7JJs/VyDUdPur 6a2tr6zuLmH/AFsUUys8f+8Acj8auV+RXxP/AOCfFn8Nf2fNX+IPw2+JHiyTxv4YspNUY3ASFZ1g UyP5DRYeGQKpZTufJGOM5H1t+xP8add+NP7HS33iy4+2+KtB1J9Kv7wkb71VjjkinYAcMVk2n1aM t3xXRgs4rSxSw2Jo+zk1da8ydt9dNUeTxN4b5bQyGWd5JmP1uhTmqdS9N0pQk17r5W5XjLvdWbtZ 62+vmZVGWYKPUnFCsrDKsGHqDmvzr/4Ka/8AJifhH/sfLb/0hv68u/4Jta/e+HfiB8V/hFrDbLny LbXbGLPBXCxyuPZkltSPas6nEChmscC4aP7V+rTdrW8u51YHwgni+AavFNPFe9BtOlyauMZxjKSn zdOZNrl26n6xmSNWwzop9C1KzqoBZlUHpk1/NL+0dr978SP2tPi18Som87Qx4qGl2k+eHjRJIoNv /bK13H03D1r7/wD+Civ/ACZb8DP+vwf+ka151PjBTpYmoqWlK1tfiTbV9tNvM+0xn0cZ4XH5Jgqm PtPH8yl+7/hSjTjNx/ie/wDFb7PfyP1Z86L/AJ6x/wDfQpysrDKsrfQ5r8nfC3/BPL4P678MfDmu XfxV8TW11qOl293NCktptjeSJXZRlc4BJHNfbf7OfwB8K/AD4d6/ovhPxLqfiez1XUBdyz3piJR1 jCbVMYAxgZ5r2cBj8dWqJVaCjF9edP8ACyPzXizhLhXLMJUlgc2nXrRduR4eVNPWz99zktN9tT6H ZlUZZlUe5xSgggEEEHoRX48/t16x4j+NP7bnhD9n/wABxHU7zQtOmvbm1RsCS8e3a4KnHdLeNMH1 lYV9H/8ABPP4qf8ACb/sYv4L1C583XPBV39j2s2Xayl3SW7H2BEsQHYRLXPhuIqdbMpYPlsldKV9 2rXVrdL9z1868GsXl3BNDiN11KU+SU6XLrTp1HJU6jlzaqbjp7q33dmfexdA4UsoY9ATyacTgZPA r8R/+Cks2oW37ffgC60qS6h1ODwbbS2slsSJY5EvrxlZSOQwIyCOmK+3/h38dbX48/8ABJ34g+IL iWFPF2m+D9RsfElqmBtuVspMSheySrhx2BLLztNKhxHTqY2thXG0oba/FZa9NPxLzbwWxuE4ayvP qdbnpYppTXLZ0nJ2jf3nzJ66+7rZdUfbCsrDKsGHqDmlr87f+CZv/Jhvir/sfLr/ANIrCvujxz4t 0/wF8GvFXjbVUll07QtKn1C4iixvkWKNn2Lnjc2MD3Ir08vzCOJwcMTJcqav3sfD8ZcHVck4lxGS Up+2lTnyJ25eZu1tLu127WuzpLi4t7WzkuLqeG2t4xl5ZXCqo9STwKdFNFPbJNBLHNC4yjxsGVh6 gjrX4yfCD4O+Nv26fEPif4sfGPx7r2m+ErXUmsdK0zTCgCMAsjRwq4KRRxq6DdsZnJJJyCT6R4M/ Zv8A2hf2bv259EHwYvNV8bfCC9khm1uO/wBQgt4jCXKTRzRs6hp0X50kjXPI4xuU+NR4hxNVRrRw rdKTsmneVu/Klt8z9JzPweyTAzr5fWzynHH0YOUqcoONLmSTdNVpSSc9dPds3p0Z+qrOin5nVfqc U3zov+esf/fQr8fv+CklhFqv7U/wS0u4laCC8sZLeSRcZRXukUkZ44Brv/8Ah278F/8Aornir/v7 Z/8AxNXPPMXLF1aFDDqXs7Xbmlur7WMMN4V8PUcgy/NM1zeVB4tTcYxw8qluSXK/eVReT2W/kfqJ vTZu3rt9c8UoIZcqQw9Qa/PD43/CbQvgp/wQu+IvgTw5rN9r+lQS21xHe3ZQu5l1S1Yj5AFwDxXz t/wTw+Pcvhrx1J8DvFlzJBo2uu154WluCQsN0Rl4AT/BKFLL28xSBkyVVXiJUMdRwteHK5xTve9m 21bbXbf8DHBeDM8z4VzHP8rxXtoYWrKHK6bi504xjJ1F7zaaUruDTsk/evofsvvXzNu5d3pnmkMs YYgyICOoLCvypv8A/laQ0X/rwb/1H5a3f2gf2FPAlxofxo+M7eNPFq60bTVvEv2ARwfZ/OCS3PlZ 2btm4Y65x3p/23iZ06s6NDm9nOUX71tIpO+34fiQvC7JsLjMvw+ZZo6X1vD0q0WqLn71WUo+zsqi 2tfndr3+FH6cedF/z1j/AO+hSmSMAZdBkZGT1r8Kf2Sv2PfB/wC0N8ANe8X+IfFfiXQryw8QSabH BpyQmNkW3glDHepO7MpHpgCus/bu8D6f4c+IP7Mvw3gvrqbS9M8MQ6Il5LtErRRyQwCRsDbu2jJ4 xmuJcT4lYB4yeHtHS3v73dv5dD6eXgZkk+LI8OUM4cqyc/af7O0ockHPT97aV7W0atvrsftT50X/ AD1j/wC+hTt6bN29dvrnivy7/wCHbvwX/wCiueKv+/tn/wDE12fxy+E2hfBT/ghX8RPAnhzWb7X9 KgmtbiO9uyhdzLqtqxHyALgHivQWa46FOpUrYdRUYuWk072V7bde58dLgHhbE43B4PLc3lVqV61O laWGlTUVOXK53dRp8t17ul+6P0QEsZOBIhPoGFPr8W/2c/2FfA3xo/Y+8LfEfWfGnizR9T1R7pZb WyjgMMfk3UsIxuUnkRgnnqTUnwIl8X/s+f8ABZhfgLonji/8Z+Crq4a0vbdnPlFWsjcq/lbmWOaJ sBmXkhXHGcDipcSYhKjOth+WFVpJqSfxbaWTPpsf4J5NKeZ4XLM49ticDGpOdOVCVNNUnadp88o3 T27+l2v2eLoH2llDemeaN6eZt3Lu9M81+R3xQ/5WUfh9/vaf/wCkr189fte+J/EXgr/gs94u8Y+F J5rXXtFl0u9tZo1LBCmnWpO4DqhGVYHgqSDwanGcWLD06lR0rqFTk38r32/D8Tfhr6PdTOcZhMLT xyjKvg/rSvDRNyjFU37+15fH/wCSM/fckKpLEAepoBDLlSCPUGvgv43fFTQ/jP8A8ENfG3xA0ErH Ff6VbLe2m/c1lcreW4mgb3Vs4JxuUq3RhUX7OHxCsfhT/wAEG/DvxD1CEXUOiaZqk8duW2ieY6pd JDFntvkZFz23Zr1f7cpPFezXwez9pzX6Xttb53v8j4GXhbmEcieNk2sQsWsJ7Hl153Dmvzc29/dt y+fN0PvG7vbOwszcX13bWduDzLPKEUfiTin29zb3dqs9rPDcwN92SJwyn6EcV+Mvwa/Z98cftprq 3xk+NvxD1+10GW/kttKsrALubYRv8lX3RwQqTsACEswYnplu2H7KHx+/Z1/at8N6/wDs3arqvjHw hcESataalqdvaIQrAPBdIzokysrZR0XcpzgKVDHz6XEGMqQjXjhW6TejTvK3flS/U+wx3g/w5hMT Vyurn9OOOpxblGUHGippXdP20pJX6X5d9LX0P1qopqktGrFShIyVPUe3FfAX7eH7Qfib4U/Dbw34 E+H93cad408VtIW1C1P7+ztUKqRF3EkrPtVxyoR8YbaR7uZZhSwWGlXqbL8eyPyfgvhDHcT51Qyr B256jer2ikm5Sb7JJvz2Wp97S3tnDfw2s13bRXU2fJheUB5MddoJyfwqzX5WeGv+Caunax8NLfVP iL8TPFY+Id5AJrp7NY5be1mYZKMZMvMVJwWDpkg4qD9lv4lfEr4Oft86r+yj8UdcuvE2lgyQ+H7u Zy/2Z1hNzEUZvmEMsPSMk7H2gY+bPj089xFOrTjisP7ONR2T5k9XsmrK1z9HxnhTk2LwOMq5Bm6x dXCRc6kHSlTvCL96dOTlJTUeu2mq3Sf6rmSMMQZEB9C1J5sROBJGT/vCvwz8R/CDR/jl/wAF2PiX 8Ptc1XUtG0+5vbm4a6sVQyqYrZHAG8EYJ68V9b+Gv+Cb3w38M/Ebw/4ktfiB43uLnSdSgvoopY7b ZI0UiyBWwmcErg4qMLnmOxM5+xwycYycb86W3lY6s98LOFskw+G/tHO5QrVqMKygsNKStNXS5lUt umr287H6M0xpI1bDOin0Jp9fip+1d4HsfiV/wXD8KeAtTvLvT7DXLPTbSe5tgpliVlkyV3AjPHcV 6WdZpLA0YzjDmcpKNr2387M+L8MuBKPFeZVsLWxPsIUqU6rlyc+kLXXLzR6Pv02P2qBDLkEEeoNL X4j/ALRf7Ii/sx/B6x+LHw4+KXiaK9s9VhtnimZba4DSbtrwyxFTuUryu3kEnI2kH3T42fH/AOKU n/BEb4Z/EDSry70fxP4puIdM17VrRfLlSMJcq8qED92Zmt1O5cYDkLjIrzFxLKk60MTRcJQjzWTU rrbfTqfbVPBKhjqeXYjJMyjiKOKrew5pU5UnCdubWLcrrlTd0+y6n6ctqFgmqrYPe2i3zLuW3Myi Qj1C5zirRIVcsQB6k1+Kfw5/ZA+Cfxb/AGYoNd8G/HG91v4wXOkLd/2Yb22Rbe98vebeW3YeeAJM p5u8DjcARxX3F+z58NfixqP7E3ij4S/tOaf/AGpazl7OxluNUivbiWykQHa0qM53xOCUYnK/Jj7o xvl+c4rESSlQspK6alzR9G0tPxPL4w8NMhyejOdHNuedKooVKc6TpVLN2c6cJTvUS7e73bSPsoMr DKsGHqDSM6Ljcyrnpk4r8fP2bfFesfslf8FAPFP7PHxH1BYPBmu3Yk0nU7hvLtxMw/0a6BPCpMgE T/3ZFUE/I1UrW0u/26/+Cp9zqEwuJPgf4IIRc5Edxbq52r7PdSKWPQiJcdUGeeHFCnSgo0/30pcv JfZrdt22S1vY9jEeBM8Nj8ROvjUsvp0VXWJULqcJL3Ixhzr35SvFR5+l+qT/AGS6jI5qrHfWUuoy 2cV5ayXcQBlgWVS6A9CVzkV+Yn7XfxO8feL/ANrPwZ+yd8JNUm8LDUUgi125tW8ncJlDLFuX5lgi gBkdVxvDbcEDB53x3/wTkbwp8Gp/E3wp8e+L9W+KOlIt3bxSmK3S9kUgsIGTa8MnUqS7cgAkZ3DW vnuIdWpHDUPaKnpJ3tr1SVndo8/KvCjKIYHBVs8zZYSpjFzUoezc/cbtGdSSlFQjJ7b6at6NL9Zi QASSAB3NAZWXKkMPUGvgPxC3xlT/AIIf/Euy+Ouniz8dWeh3EBna8huJby3GwxSytEzL5nJU85Ow MeSal/4J23tnB/wTshjnu7aGT/hJL07ZJQp/5Z9ia66Wc8+Lp0PZtc0ObXRrW1mrfqeBj/DR4bh7 GZqsVGp7DEKhaHvQnePNzxqKW3ZcvzWx97F0D7SyhvTPNOr8h/i/NDP/AMHGvwtkgljmj36WNyMG H3JO4rv/ANtz41fEC6+Ofg79mv4TahdaVrviEQDV720m8qWQ3MhjhtRIOY0wDJIw6qyjONwPLPiW nClXnOH8OXIkndyfT0/E9/D+CWMxWNyrC4fEr/a6H1icpR5Y0YK/NfV81rb+7dtKy3P0w+22f9q/ Yftdt9t2b/s/mjzNvrtznHvVmvy2b/gmT4e/4V55yfFbxP8A8LA8rzPt5to/sRnxn/V/63bu/i8z OOcdq6D9h/43fEC8+K3jj9nj4s317q3ijwsszaffXcgklRbeVYJ7Z5Osm1mVkY5JXfzgKKqhndeO IhRxdD2fP8L5lJX7OyVmc2aeGGU1snxWY8P5osX9Vs6sXSlSkot254XlLmjffZpavofpTRX5I/sM 3E8v/BT39odJZ5pEVL7arOSB/wATMdK/W6u/KMy+vYf23Ly6tWvfZ28j5PxG4JfCmcf2c63tfchP m5eX44qVrXlte176+QVWivbOe+ntYbu2muYf9dCkoZ4/94A5H41+UH7S/wARfiT8eP8AgoNZfss/ DLXrnw14ftpVg1y7glKC6lEQnnklK4YxQp8oiyAzqc5ym3U8Uf8ABNqy0D4bXGt/DL4l+Lv+Fi6f Abiza6EcUV1Mg3BEaMK8DMRgMXfBxn1rzJ59iKlSosLh/aRg7N8yV2t0lZ3sfcYbwnybB4PBzz/N 1hK2KipwgqUqloS+GVSSlFQUu1nZavZpfqpUU08NtavPcTRW8KDLySOFVfqT0r4A/Yx/ac1Tx/8A sx+O4vihf3Fz4g+H9v8AatR1aZAJLqx2SuHcDGZY/JkVjjkbCSWLGvlj4f8Agz4j/wDBQL43eLfG HjzxjqnhX4YaLdiKy0qyPmJCzgskEKHCb1Ta0kzKWJZeMEBXPiWnOlReGg5zq3tG9tt7vpYjDeCW Kw2YZlTzrFRw2HwPKqlXlc7uf8NU4qzk5pprayeuuh+0NnfWOoWvn2F5a30Gf9ZbyrIv5g1ZZlVc sQo9Sa/Irxv+xF8V/gr8Q/C3jL9lfxT4n1rUftXl31reahbWs0IHzqzuTHFNA2CrRsvpwwY7fbv2 8rjW7v8A4JR6Xc+JbCDSvEUuqaY+qWcEokjguCjmVFYEhlD7gDnkYq/7bxFOhWnXw7jKmr73jL0l b79NDB+F+T4zNcsw+VZvGvSxk+S/Jy1aTvb36Tne38r5kpW6aN/oOCCuQQQe4qtcX1lZyQrd3dra tM+yETSqhkb0XJ5PsK/NzUPjhqHwL/4ISfCnXfD5CeLtX0qDS9FmaNXW1lcSu05Vsg7EjbAII3lM gjNeefDP9gqT4v8AwT074m/GL4l+MpPGviizXUoRCySmBJlDxGZpQzSMVIYqCm3O3tmpnn1WpOFL DUeebipNXsop7K9tX8vM1w3hLgMLh8Rjs6zL6th4Vp0KbVN1J1JQbUmoKS5Yrq3J66dr/rhR0GTX 57fshfD/APaX+EXxa8VeAfH8E+sfB62EsWianc6lFJ5csb/u3t4zIZUhkQsShAAbaRg7s9x+3h8U v+FdfsGa1pVlc+Tr3i6X+xbQK3zLC4LXL49PKDR57GVa645wlgJYqrTcOVO6e912736M+er+G7qc W0Mhy/GQxKrSio1KbvHllq3JJtxcVdyi3dW36n2erKw+Vlb6HNOr8UP2Jda8R/Af9vyT4SeOYm0u DxvoVpPFC7Haly1uLq1JJ/i2SSwkD/loQM/LX21/wUBlki/4Jp+JXikeJxqth8yMQf8Aj4WubCcQ Ktl1TFOnaUL3jfqul7dvI9viDwenlnGWCyCOLVSlinT9nWUdHGo7c3LzPZ3VufW17q59q0V8zfsc O8n/AATP+EzyO0jnTJcsxyT/AKTNX0zXt4PEe3oQq2tzJP71c/MOI8o/srNsVgOfm9jUnC9rX5JO N7Xdr2va7t3Gs6Ljcyrnpk4p1fCf/BQj4af8Jv8AsLT+J7O383WPBt8uooVXLG1fEVwo9gCkh9oa 9J/Y6+JS/En/AIJ7+CNUu7lZdV0W3Oi6qzNyslqAqsx9WhMLknu5rhjmn/CjLCSjb3eZO+/R6W0t 6s+or8C/8YdS4ioV+de1dKpDlt7N25ovm5nzKS/uxs9NT6hLoH2l1DemeaGdV+8yr9TivyC/Zlt2 /aD/AOCy3xL+OF2jXXh3w7LLNpbuMrlwbSxUjpkQRySezID71xX7bWr+I/jt+3td/DTwTE2p2Xw+ 8N3d1cxoxK+dHAbq7YY/i2pDABj/AFi4715FTinlwTxKpXvLlir/ABa77ade+x+iYPwGdXieGSVc coOFBVq83DSi2k+S3OuZpuKveOkr20sftp1FNZ0U4Z1U+5xXxt+wr8VP+Fk/sHaHp99c+d4g8JP/ AGLehmyzRRqDbP64MRVM92javjT9vLw3beMv+CsXwV8IXlxPaWmuaLpWmzzwgGSJJ9VuomZc8bgG JGeM12YvP408uhjKcObmtZXtv52ezPneHvCKrjeMsVw7jcR7F0FUcp8nNdU1e6jzR0lHVa7Pqfso rq2drK30OacSACScAdTX5G/FL/gnp4M+Hn7PfjHx34e+Kfiex1Tw/pM+ownUY4VikMKF/L3JtZWf G1SCcMRwelb/AMAviV4y+If/AAQ/+P8AB4x1G+1ybQND1fT7HUryQySywHTvMEbuclyhcgEnO1lH asoZ/XhXdDE0OSXK5K0lJO3yVj0MT4R5VicqjmeTZr9YpKtTpT5qMqUouo0k0nKSlvqk0fqkrKwy rBh6g5pGZVGWZVHucV+eP/BM7/kwvxR/2Pd1/wCkVjXhH7deseI/jT+254Q/Z/8AAcR1O80LTpr2 5tUbAkvHt2uCpx3S3jTB9ZWFVV4hUMshjPZ3c7Wjfdt7Xt+hhl/g5LFccYrhx4tRp4fnlUrOOkYQ V3Jx5tFdpfHpe9z9hgQQCCCD0IpGdFOGdVPucV8E/wDBPP4qf8Jv+xi/gvULnzdc8FXf2PazZdrK XdJbsfYESxAdhEtfMH7eXhy08Yf8FYvgr4Sv7iezsdb0XStOuJ4cb4o59VuomZcgjIDEjIxkUYji GMMthjKcObmsrXtq9N7PZ6bCyfwcq4jjbFcN4zE+y9gqknPk5rxguZNR5o/FGzXvaX6n7Kq6tnay t9DmnV+Onxq/Ya+HPwf/AGc/EnxE8P8Axf8AEOma7otsbmwTUZIFF1KvKwxmMI4kY8KRnntjkfW/ 7KXjfxt49/4JVtrfjm6vNR1SG21GztNSum3TXtvErKkjt1Zgd0ZY8t5eSSSTV4TOq0sU8NXo8kuX mVpKSsvRKxz8Q+GOWUMhhnWU5l9Zo+1VKXNSlSkpSV1ZSlJSVt7NW++32pRX5Y/8Evp55/ht8XTN NLMRqdhje5bH7uf1r9Tq78nzH69g4Yjl5ea+l77NrfTsfJeI3Br4U4jxOTut7X2Liubl5b80Yy2v K1ua272I/Niz/rI/++hR5sROBJGT/vCvwD+CXwD8J/H79s/4yaH4t8T6l4XtdLvbq7gms2iDSu14 yFT5gIxg54r7U8N/8E8PhDonxE0HWrH4p+KLy90/UIbuCAyWhErxyK4UgLnBK44rxMDn+OxcPaUs MuW7V+ddHbblP0/inwj4V4exDwuOzyarKMZcqwsmveipJcyq2679D9LKKKK+tP56CiiigAooooAK KKKAPyo/4KMXtz4t+MXwE+Dumyn7ZquoPPJGOSXuJorW3OPr5/51zv7VOkyfs0f8FJfhP8fvC1hJ H4cvo47bVLW34Dm3jW3ni9AZLVkC5/iRm7V718SvBfwc1r/gp54a+MPjH9o74daPd+EPItx4Ovbm 1jmhe38x1DytdAowmkMmDFnjHvXXftDan+zl+0B+zldeA9Q+Pnwr0G6F7De6dqo16zuTZzRkgt5f npu3RtImNw+/ntivz3HYL20sXVc4qpzRcPej9haddL6rW3mf2FwvxRHLKPD+Ajh6tTBqjVhirUqt v9plea+C8uRKMrx5k0rRbPAv+CbGlXet3vxt+K2qJ5l9rGrxWizf9NCZLm4GfczQn8K43/gnNdWV l+1n8cdK8SXEEPjyeJBHC7YeQR3M32sLnk4cwkj8e1fVX7Ompfs+fs+/s5R+Arf9oX4V+Jpm1Ke/ utS/tuztPOkk2qP3f2h8YREX7xztzx0rwD42/Bf9mf4i/Gy4+Jfw/wD2nfh78LfGl1Obm9ktfEVp LBPOc7pk23EbwyNnLMrEE5O0EknKnhZYbDYOpCUZTpczlHmir82+t7XR34zPaGdZ3xHg8VTrUcNj 1SjSrKhVkoqhbkvDlU1GaWul+6XT6t/bE+LmifCv9iPxhFdXtuviTxHps+kaJY7x5szzIY5JQvXb Ejly3TO0dWAPnn/BPX4eal4J/YPXWtYt5LW88WarJqsEUibWW12JFCSPRhG0gPdZFNfOvgj9n39m 8fE+18X/ABs/a08G/GLULcq0dleeJ7aOGTachZnkuZJJUzztBQHvkEg/qR4P8Y+BvF+gSSeA/E/h fxLpdiy27toV/Dcw25CjbGfKJC/LjC8cV7WXc+MzD61Wajyq0YqSk9d27H5rxm8Jw5we8gyxVKyq 1FUr15Up04PlVoU4KaTsnq27Xe107L4X/wCCmv8AyYn4R/7Hy2/9Ib+vkj4keI9R/Z1/a7+F3xY0 eGXyvFHwctQwi4D3X9lfZEB/3ZIrSU/41+oP7TXwB/4aM+BOkeCf+Es/4Q77Dr0Wq/bf7L+2+Zsg nh8vZ5seM+fnduP3cY5yPOfjp+x7ZfGr4HfCfwm3jj/hG9R8EaeLGPVRon2n7ZH5EMTAx+emzJgR h8zY5HOc1w53kuNr4mtWox1XI4O63i9evZ9T6jwu8TeGMryTLsszOt+7lLEwrx5Zu1OrGLi9Iu95 xXw3a3aR+U3i3wWfDf8AwRy+GHiGeIreeKfH19f72HzGGOA20Y+mYZGH+/719m/8FFf+TLfgZ/1+ D/0jWvo34tfse2XxL/ZA+FXwk0/xx/wi1r4LiiVdQ/sT7Ub4rb+UzGPz02Fmy5+ZuSR710P7RX7M P/C/fgt4G8H/APCcf8In/wAI5MJPtf8AY32z7T+5EWNnnR7Omep9PeuN8OYunhcRShC/NCmlqtWv i69+59DHxo4exmfZNmGJxPKqOJxdSp7s3yU6mlLaLveKStG7XVI+a/CX/BN74ReIPhV4Z1668afE eG61LSbe7mjiuLQIrSRK5C5tycAscZJr7E+HPw88Ffsrfsj+ILGz1nWb7wro4u9bvLrVHjacARhn UbFRTxHwMZJNfIcP/BPz4jW9pFb2/wC1p41ggiQJHHHo9wqooGAABqGAAO1ehWP7Gfje3/ZU+IPw w1D9ofW9cXxXe2Uk+p6hoUszW0Fuzu0CI96f9YxjJYMOEIIO7I9DL8HVwrcqWA5Jcr154vW3r1Z8 nxjxHgM9pxoY/i36xQdWDdP6vWjaPNZtPk3hBtpdbW3Z+eXwI+O/ijwr+1v4++O958IvEnxR1rxA 9xHHJYTSxQ2DzSrLIA6wSgkKEQDjahI6Guk/Zs+K0vw8/wCCtD6lfeFtV+HfhT4gX01pLomo7v8A Q1upi1uVZkTciThUD7RhC49a/YX4GfCPTPgf+zPoHw602/8A7X+wGWS71I23kNeTSSM7SFNzbeoU DccKoGTivJf2nf2U7D9ozUPB2qQeMH8CeINAMqLqEWlfbGnicqyoR50RUo6llOTjc3HOa4I8NZjQ w1KrCpzVIPm5bRWr+Jc3/Bsz6qr438F5pnWOwOJwfscJiKboe3UqkrU4JqjL2Nmo2dmrR5ot3fU+ Wv2qEST/AILr/szxyIskbwaOrKwyGB1W5yCPSvGPjHoWt/sZ/te+N08PWlxL8IviV4dv7OG0j4jj WaJ08oZ4320sisvcxPjOWbH6C+Ov2WdQ8fftg/B34wat8SETVfBNjp0N5aJ4e+XVpbW4ed5Q32j9 wJGkI27X2+rV6n8ffgj4f+Pv7PV94G1u5/sq685LnStWS3E0mn3C8CQJuXcCpZGXcMqx5BwR1Yrh /E1vrFWK5anNzQd12Sa367a/keFkfi/kmWPJsDVqe2wnsPY4mPLO0X7RyjNXim3B2knG7tdL3j5Y /wCCZv8AyYb4q/7Hy6/9IrCvqH9pfQdR8TfsCfFrRtJjuJtRm8N3EkEMClpJjGvm+WoHJLBCuB1z isf9mb4B/wDDOnwG1XwR/wAJZ/wmP23XpdU+2/2Z9i2b4IIvL2ebJnHkZ3bh97GOMn6Jr6DKsvqR yqGGrKz5bPyv6H5Hx9xfhMRx/iM7y6XtKarKpB2a5uVprSSTWq6o/Oz/AIJseKtH1H9i3W/CkN1b jXdH8RTS3VrkCTyZkjaOXHdSVkXP+x9K+hviP+1D8NPhh+0v4W+FevjXb7xJriReV/ZVqtyts8sg jhjlUMHDOeQFVjjBxgg18/8AxE/YD0bUfi9feOfg38R9d+Dmr3ZaSe0sYpHgEjNubynjljeFCedm WUHoAMAdN8C/2HvDPwt+Ma/Enxr4w1P4o+O4pDNZ3V7beVBbykf64qzyPJMOcOz4Gc7dwDDycDHO MPRp4SNJLlsue6a5V5b3sff8U1vDbN8zxvEVbHzl7dSksMqco1FVktnUs6fKpa3V9NLO2vzH/wAF ItPi1f8Aas+CGlTvJHBe2T28jx43Kr3SKSM8Zwa9g/4dj/Br/oePib/4EWf/AMj16z+07+yTJ+0Z 4/8ACWvxfEV/A8uhWcluiJof2xpS0gcOGFxFsIx6H614n/wwD8S/+jufHX/gpuf/AJYVwYrJ6rzC vVqYP2sZNWfNFWstd2fW5D4k4CHB+U4DCcR/UKtCNRVI+xqzu5TbjrGDWi7N7npX7Svw/wBK+Ff/ AAQ78Y/D7RLzUL/StFsbGC3uL5lM0gOqW75YqqrnLHoBXxtrfwMvPFX/AARr+DHxr8ERz23j7wVZ 3U9zLaZWaeyTUbmTepHO+Bsyqf7pk6kKK+8bX9lfWm/4J1eMfgPr3xc1TxLe67qS3Y8UX+lvJJbK s1tKIvJe5YsP9HI/1o/1hOOOfbfgt8L4/hF+yt4X+GMusJ4ni0iCaJ757L7OLkSzyynMW98D95tx uOcZ74rtrZFPGYn95T5IOko7p8slK6Ss+nfbpc+Zy3xVw3DeStYLG/WcTHHyrP3JxValKjyTb5o2 Sm204v3l8VtEz8iv2ffind/GX/guJ8MvH2pWotNXvNIlg1FVACPcQaHcQySKB0V2QuB23Y7Zr9df jz/yY18Z/wDsRdX/APSGavmX4bfsM6H8Lv29rT4yeGvHDxaFZ3l5PZeFW0X/AFCXNtND5QufP+6h mJXMecKFPPzV9i+PfC//AAm/wN8Z+C/t39mf2/oV3pf2zyfN+z/aIHi8zZuXdt3527hnGMjrW+QZ djaGBxEMQvflKT3Wt0lf5vvY8vxc404ZzXinKcVk0/8AZaFGjBrlkuTkqTk42au+WLWseZPo2fB/ /BMn/kyXxp/2PE//AKRWdeMf8FItPi1f9qz4IaVO8kcF7ZPbyPHjcqvdIpIzxnBr71/Zk/Z+/wCG cvglrXg7/hLf+Ex/tDXH1P7Z/Zf2Ly90MMXl7PNkzjyc7tw+9jHGTwf7Tv7JMn7Rnj/wlr8XxFfw PLoVnJboiaH9saUtIHDhhcRbCMeh+tcuJyjFy4fhhVC9RW0uukr73tt5nvZL4jcPUPF/EZ9PE8uF k6lqnJN/FTcU+VR59/7v4Hk3/Dsf4Nf9Dx8Tf/Aiz/8Akeu0/aY+H+lfCv8A4IceMPh9ol5qF/pW i2dhBb3F8ymaQHVbZ8sVVVzlj0Arzj/hgH4l/wDR3Pjr/wAFNz/8sK9xt/2Vtak/4J0+MfgNr3xd 1TxLe67qSXY8UX+lvLJbKs1tKIvJe5YsP9HIz5ox5hOOOVRy2ao1oUsF7JyhJX5ou91ot+rLzLjb DTzHLMTjeKPrtOhiaNRwdCrDlUZrmndwV+WN9Fdu+ibPzu+G37Jfjzx//wAE3dM+Knw/+JHiBNdl jvJYvCCmSOGcQXM0bRxSLJxI4j3AFMFmwSM5r23/AIJzH4MXev68F0XUbT46WNs5ubrVr3zvPtmf Ej2q7V8sglVkUhnGR85DMB+hPwG+FH/Ckf2WvDnw0/t//hJ/7Ke5b+0vsP2XzvOuJZ/9XvfbjzNv 3jnGeM4r588R/sYqf287f48fDX4jf8K51QXq313pQ8P/AGyC4nORcHcLiLEcykh0weWcg8gDDD8N 1MHLC4ijS5pJJTi2n01km3ZNPs/Q9PN/GrBcSUs9ybMsc6dCpOcsNWjGUbpSfLSqRhHmlCcbfHFt a82trfN/xQ/5WUfh9/vaf/6SvUur6JpXiX/g5d8QeHtdsodS0bUtFe1vrWYZSaKTw6FdT9QTX1p4 o/ZX/wCEk/4KUeHv2hv+E7+xf2Wbc/2B/Yu/zfKiaP8A4+PPG3Oc/wCrOPerqfsx7P8AgqRL+0r/ AMJvnfb+V/wjn9jdP+JeLLP2nzvbf/qvb3q5ZJi5TleGjrqe6+Hvv+G/kc1DxP4epYWmoYq045VL DaRndV3JNQT5fL4/gX8x+T/xLi8W/st6j8cv2d9S+2an4E8YWUN1odw/QhbiOSC5HbdsjeCUDGWQ Hogz9c6D4b1LxV/wa/8A9maTHJNeQ6fd3/lp1aO112W4l47/ALuJzj1Ar67/AGmf2aPD/wC0f8Od G0281j/hFfEWk3Zl07W0sBdNHG4xLC0e9NyNhT94YZAfUHvPgb8Kl+DP7KPhj4Xya2vihNIW5VtQ ax+zC4E1zNOQYt77cebt+8c4zxnAWD4ar0sdWg/4Mqcoxd1pzNO1t9NSuI/G/KsfwtluKgksypYu lXrQtJKbpQlD2nNbl99KCaTunfSyufMn/BPfxno3iH/gnra+DdPvrePxH4cvryC9ttwEqLPM88U2 OpQ+aVDeqEdq+Mv2h7L9qb9nTw/4cvfEP7S2u6/NrFxJFDaadrVysyKigmQq+PlycZHevq3x3/wT 30Of4pXXjD4MfEjX/hBqFw7O9laRPLBEWOSsDxyxyRISM7SXA6DAAAyvCX/BOTQpfHieIvjF8UPE vxMuAwaS1SN7YT4/hlmeSSV16/dKH3rmxOW5rVwlPCeytKCspqdo201a3ei7HuZJxrwBgeIMXxB/ aCnRxUnUnhp4Vzq875nyxqNOEVzSbupWekW9Ln6AeALu61D4E+Cr+9nlur250GzmuJpW3PI7QIzM T3JJJJr8tv8Ago7Y3egftMfBD4jS2k91o1vGYJcJmMSW9ys+wnoGdZDgHqEPoa/WyysrXTdGtNOs YI7WxtYEht4UGFjRFCqo9gABXD/FD4XeDPjD8Ir7wT46006lo1w6yoY5DHNbTLnZNE4+665PPIIJ BBBIP1ec5ZPGZe6EXaWln5rU/n/wz46wvDXF1PNa1Nyo3mpRVr8k04u3S6vdK6va10dT4e8Q6L4r 8C6V4l8PahbaroepWqXNldwPuSWNhkH69iOoIIPIr8i5NTs/iz/wch6XqHg6b+0tI0S7j+1ahZ/N GVsrMiZyw4Keb+53dDlcdRXo3/DurxVpcdzonhT9pHxTovgy7djcaZ/Z0oBU9mWO6SOQkdSVX6V9 c/s+fsx+AP2ePDN4vh03eteJ9QiWPVNevsCadQciNEHyxR552jJJA3M2Bjyq9HMsxnRhWo+zjCSl J8yd2uiS7+Z+gZXmXBfBeFzLFZZmTxlbE0p0acPZThyRqWvKpKdk3FLRRvd9LO6/LzxL8I7D43/8 F1viX4A1LW9R8P2tze3Nw15ZIrSqYrZGAAbjB6V90fBr9hjwz8HP2j/DvxH0/wCIfirXbzSRceXY 3lvEsUvnW8sB3FeeBKWHuBXX+Gf2V/8AhHf+Clev/tD/APCd/bP7SNwf+Ef/ALF2eV5sIi/4+PPO cYz/AKsZ6cda+uqMn4bpQqVK2Jpe/wC0cou/S909Hb7xeJHjXmFfCYTLcmxz+q/VadKrHksuflca kbzgpbWV4u3ZhX4p/tX+Hdf8W/8ABcDwr4a8LeIrnwl4i1Gy06Cw1m3keOSykKyYkVoyGBH+yQa/ ayvkXxn+yv8A8Jd/wUc8K/tA/wDCd/2f/YxtT/YP9i+Z5/kBh/x8eeNu7d/zzOMd67+Jcvq43Dwp 0439+LettOvb/M+T8EuMcBw1nGLxmLqqF8PVjBuLknUaXImkpaNrquXufOE3/BP74leN/EWmn4w/ tH+JfGOi2ku/7PIbm7mAP3hG9xMyxEj+La30NfTHx18Y/An4D/sqeHfh/wDETwjquq/DfULX+ybL SrCyFymyBUZVZmkQq/Rlfdu3KWyCM19Y15b8Yfg/4O+N/wAFbzwP40t7hrGSVZ7W7tWVbmynXO2W JmBAbBZTkEFWYEc03kdPC0Kv1OK9pJbyvK/k7t6GcPFLG57m2A/1jrSeEozvy0VGk4X+3FQjFcyd nffRpNXPgHxb/wAE6fht4m8GReMvg58R9U8P293ZrqGmLqbLeWLoyh4ysy7ZI0IIO8+YR15rr/8A gnb8Y/Gnj74ceOvA/jPV7zxC3hWS1bTNQuZDNL5M3nAxNKSS4UxZUkk4YjOFAHJ/8O8/iJaaFJ4U 0r9prxHa+A5Swk0n7BcLDsY5KmBbvy26nJ4ye1fb3wI+Angr9n/4St4a8JpcXd5dus2savdf6/UJ lBAYgcIigkKg4UE9SWY+LlWU4iGYQrQw/sIpPmXMmpXWlkn0ep+ncf8AiBlGJ4QxWW4nN/7TqznB 0G6LhKioyvJynKKbco+60rv5PT5I/wCClngjw/f/ALK/hvx/LahPE+k63HYQXSYBe2nSRnjf+8A0 asv907sfeNe+/sX+B/D/AIM/4J5+AJtFtRFd6/Yrq+q3DYMlxcTDOSfRVCoo7BR3JJ639o/4H/8A DQX7PCeAv+En/wCES26rDf8A27+zftn+rWQbPL8yPrv67uMdK7/4W+CP+Fa/s7eDfAP9qf21/YOl RWP2/wCzeR9o2DG/y9zbc+m4/WvXo5XKOdVMVye64pX03vr57dT87zPjuhX8MsJkSxLdWFeUnD3r Knytx1tytczb5b3T1sflx8R7uD4a/wDByT4b8X+KZTZaBq0to9teXYxEqT6adPDbjwFWYHLdFxk9 M1+qHxE8eaB8Mvgl4k8eeJZ/K0bR7J7mVVZQ87AfJFHuIBkdiqKCRlmFeafH79nPwH+0L4BtdM8V LdadrWnhzo+t2RHn2ZfG5Sp+WSNtq5RvTgqea+OF/wCCdHiPVrvTtI8Z/tFeKfEHgewlVrbSxYSb lUDG2MS3MkcJA4DBG+lcMaGZZfVrxw9L2iqSck7pWb3un0XkfT1s14K4vwOU1c4zB4Wpg6UKFSHs 5z9pTpt8rpyhdKUk2nzWs3fZa+0fEr4y+E/jp/wSC+MPjbwbb65baUNJuLSSPVbPyJVlQRswGGZW A3j5lYjORnINfEv7LP7FfgH47/ssR+PPEfirxhpGpNq1xZm301oBFtj2YPzxscncc81+o+ufAnwz N+wvq3wI8HyJ4P8AD9zo7adbXItvtLQljuaZ13J5js2WYlhksTmof2dPgn/woH9nNPAH/CTf8JZt 1Ke8+3/2d9jz5u35PL8yTpt67uc9BRXyOri8fRni4KcVCzfTmv0V7jynxUwXDvCmYYXh3Ezw9aeK UqcWrz9jyW96XK4XuldXv2utT8pLT4OaF8Cv+C4/wn8BeHdT1bV9Nj1CxuhcakUMxaRXJHyKowMc cV6R+0rdwfDD/gvD8NfiL4lElv4Wu/7NunvJUJiijTNtKwP/AEz2hyByMg9xX2h4y/ZX/wCEt/4K O+Fv2gf+E7/s/wDsY2p/sH+xfM8/yAw/4+PPG3du/wCeZxjvXqPxw+A3gP4+/DGLw741tblJ7R2l 0rVLN9lzYSsACyE5DKcDcjAqcDoQCOKPDdeOHr06UVFqopwV9Glt6fM+pq+NuU1c4yvF46tKtGeD eHxLUWpRlUb5mrpKTTs3y3Vr2u9D11tR09dAOrNfWa6WLf7QbwzL5Ii27vM3527NvO7OMc1+Qn7K l0nxK/4LffFv4keHRNJ4UhXU7pLyOMrHKk0yxQbvQyLukAPJ2E9jXef8O6fGB0r/AIRhv2lPEp8C AZGk/wBlTeX1+75X2vy/+BY/Cvuj4J/AzwJ8BfhY3hjwTaXBa5kWbU9SvJN9zfyhdodyAAABkBFA VcnjJJPoyoY/MMVQlXo+zhTfM/eTbfS1unqfGUc04S4QyLM6OWZi8biMZD2StSnTjTpt3k5c+8mt Eo3s/XT87v2Fv+UoP7RP+5ff+nMV+ulfmLqP/BO7X3+LfijxZ4f/AGh9Y8K3OtajcXUqaf4bkjdV llaXyy6XqlwCR2GcZwK7T4ffsU+P/BXxw8KeLtQ/ac8Y+J7LSNUhvJ9JuNMuEjvVjYMYmJvnADYx kq30NYZIsywVJUJYVtczd+aOzfa56XijLgjijMJZrRz2MJeyhFU3QrtuUIJW5uVLVq19kfO2g6lY /CT/AIONvE0vjCUaZpuvX1ytpfXg2xj7dAJIWDHjaZD5W7oDkHocfrl4n8S6J4O+H2seKfEeoQaX oel2r3N7dTMAqIoyfqT0AHJJAHJrwj9oX9l34f8A7Q+hWcniB7zQvFdhCYtN16xAaWKMtuMUiH5Z Y8knacEEnawy2fk4f8E6/FOrfZdH8YftIeK9e8HWkim30z+z5ThR2VZLl44jjgEK307VeHo5llsq 1OjRVSM5OUXzJWv0afbyMM2zLgrjShl2MzPMng62HpQo1YeynPnVPaVOUbpOSe0tn0srvxD9jLwb r3xD+F/7XGq6VaXNvHr/AIVudI05FG2KW6uo7lxGD0JT5AQOgkHqK9o/4Jk+M9Fj+FPxC+G9zcw2 vieHXP7WjtJcJLNC8McLFQeW2ND8w/h3j1r9Dvhp8M/B3wj+EWn+CfA2mf2ZodoWfDyGSWeVuXlk c8s7HqegAAAAAA+S/jJ+wh4Q+IHxcufiF4A8X6t8KPGd1O1xdzWEBmtppmJLzKivG8UjZO4q+09d uSSeahkOLwEMNWopTnBSUle1+bXRvt57nuZp4tcO8W4jOcuzGcsLhsU6UqNTlc3B0Uor2kY3bU0t eW/K21rufOn7Uvhv9pn4OaT4n+JrftG6pH4b1DxJIukaBZavcxzRQzSu0caKcKRGmAQvQD0rV+Ou v614o/4N6fhzr/iLVL7Wtbvb6yku768lMk0zebcDczHknAA/Cur0v/gnNca740g1f4w/HDxV48ji OPIhgdZnXOdpuJ5ZSAeMgJn3HWvqX4v/ALNOg/Eb9irSvgl4X1ZPh94f025t5LGVbFr4RJDu+Ta0 qFixYksXznJOc1zwyfMKkcTL2bipwaUXPmd313svvPWxPiPwjg62R0PrUK08PiI1Klanh/ZRVOKa 5bKPPNre/K7+tj86P2hdB1G+/wCCG37M2v2sdxLY6U0cd6I1JWMTRSBJH9AGQKCe8gHcV+qnwG8V aP4z/Y3+Guv6JdW9zaS+HbSKQREYhmjhWOWIjsyOrKR7VV8KfBbw9pP7E+j/AAQ8VvD408PW2jDT L6SW2NsLxAc7wgdjGwOCCHJUgEHIr4mv/wDgnZrek6lqln8Nf2gvFnhDwlqMrfadJktJW/dnja7R XEazcYHzIuQK7qOCx2Arxr0qXtOaEYyV0mnFW66NHyeY8UcKcWZXUyrH4/6q6OJrVaVR05zhOnVm 5NNRXNGV9VdWtZbvT6x8AftQ/DT4kftS+KPhJ4aGu3XiHRHn8y9W1WSwuEhKrJJHMjH5Q7BcsFBO ME5FfmT+2h8Sn8f/APBTPRPDNl4e1Px14Y+H7xRXeiafuLX0nmJNeqCqOUyBHAzbTgxng1+ln7Pf 7MHgv9nfwXqkXhy7n1zxdqcYXUPEGoQKGcL92OONT+7hDfMU3EsernC7eX/Z3/ZQT4HfGjx18Qdb 8eP8Q/FniSMo97Jo32EweZMZrgn99LvMjiM/w42d88PH4DNcfhaVCtZc0ryataKWqVr+9r200Fwl xXwFwpnmPzXL3OoqVJQoRnzKVWpP3alRSUf3SUb2UmpWk7a6L8tf2lvjZ4o+J3xl8C/Fi0+D/in4 Ua/4ZWOP+07uWWaOZo5xNbHc1vEEZHMnc7twHGOfvf8Aa48dad8Tf+CJtt4+0raLPW20u68tWz5M hnUSRE+qOHQ+6mvsr4u/DbS/i9+zf4s+HWrz/Y7bWbIxR3Yh802sysHhmCZG7ZIqNtyM4xkZzXy1 Y/sYarB/wTt1r9ny9+Ln9oafc67HqWn6sfDO02KB0keARfajvVnVmB3rgu3BrmqZJmNGeJin7RVo PX3Y+/stL9V1+89zB+J/B2Y4fJK9Sn9SqZdiY2herVvQbUpPncW7xkrqLd0rqN72PUv2Nf8AlGV8 JP8AsGS/+lM1fTdfl5p//BPHx3pOjwadpX7VXi3TNPgGIba00OeKKMZzhVXUABySeB3r6x/Z1+BH iP4HaN4qtfEPxX1v4pPq80EkEuo2skJsxGJAVXfcTZ3bwTgr90de3tZPXx8IUqFXDOKiknLmi9l2 TvqfmPiPlfCeIxOOzXL86jWqVakpxpexrRdpzu1zyio+6nfXe2mrPfdd0XT/ABH4J1jw9q8AutK1 Sxls72E9JIpUKOv4qxFfgt4D+KmsfsyeDP2qPgnqtxOmr3FvJZaI4BA+1ib7I8qehe3m84E9oF9R n9/q+BPj/wDsI6P8cf2nZ/iPB4/k8HNe2tvHqlhHoQujcyRDZ5ok89NhMaxpjY2Cmec4HPxPluLr qnWwivUjddFpJWe9tuh7PgXxrw/ldTF5bxFO2DrKnPaUrVKU1OOkVJ2krqTS1VkzM/ZS0zTv2d/+ CRGrfFTxDbrHe6nZz+JLlH+VpY9uyygB/wCmirGV95zXwX+zT8cPE/ww+J3xA+J158G/FHxY13xb uR9UtZZIY4w8zS3IyLeUOZJPLJ5GPLxznj9ePj58BZ/jJ+y5pvwp0Dxgnw70C3ubczeXpH20TW9u pEVuF86PaoYRtnJ5jWvRPhJ8ONM+Ef7OHhL4daTcfbLbRbEQvd+T5RupmJeWYpk7d8jO23Jxuxk4 zXHPIMXKvQp05ckKMdJWTvJ6PR+XVrfY+jw3i3w/TyrNcbjqH1rFZlW9+lzVKfJQj70F7SK/msuW MtYpc21j8ef2KfiRJ8OP+Ckeq+E7/Q9T8FeGPHjSW9ro+pbg9lLveWxBLKpcgF4A20bjLnArt/28 NBPir/grN8E/C41C40k6xo2lWAvrcfvLbztVuo/MXkfMu7cORyK+0f2i/wBkuH46fGbwV8QNF8eS fDvxX4fhEYvYtGF604jlE1uw/fRbGjcyHPzZ3jpjmX4s/sqz/FX9tH4X/GO5+IEej3PhGLT1l0uP QTKt81reSXRIkNwPKDl9uNr7cZy3SuGWQ4+OBqYLl5oqcXF3SvF6vS+lv10PqqHi3wpW4qwvE6re wrTw1SnVhy1JuFWK5ab5uRqfOrK+tlFc9mfmf+0r+zd4p+AXiPwrrPivxV4s+Knwlv71Yr2SO8a0 uIpB8xgYuZkRmUMUkwQdrDAIBP6OrF8K1/4IkeO5/g1ZpZeBrn4farNBGzFpxMbWUTCdiSTOHBVs k8rgfKBX0/8AEPwF4d+J3wY8Q+BfFVr9r0TV7UwzAY3xN1SVCejowV1PYqK+UfhN+yB4g+F/7OPx Y+FrfGE+IfC3jTRri0ihk8MmE6XczQtC1yn+lNvBQjdH8u7YnzLg576eQ1MFi6n1enzU6kWr3V4u 2127tP5/gfJ4vxZwfE/D2C/tnGOli8HWhLlUZezrw5leTjTjyRqQV9WldaK7lp5j/wAE+PEml+Dv +CX/AMSPFmtzeRpGj+KtQvrx+4ji0+ydsepwMAdzivij4EfHfxR4V/a38ffHe8+EXiT4o614ge4j jksJpYobB5pVlkAdYJQSFCIBxtQkdDX6MaF+xfqnh7/gnh4q+AWm/FvyYvEHiQapfa4PDOGMHl26 m2EP2ru1uhL7+QSu3nNfSPwM+EemfA/9mfQPh1pt/wD2v9gMsl3qRtvIa8mkkZ2kKbm29QoG44VQ MnFctDJMxqLC0m/ZqlG9/dl73a13sup7eaeKXBuCqZ9jqcfrlTH1lD2f72l+4Su5c/Kmuaejindp K+lz8ev2bPitL8PP+CtD6lfeFtV+HfhT4gX01pLomo7v9DW6mLW5VmRNyJOFQPtGELj1r0z9vHw3 a+Mv+CsvwU8IX09xa2WuaNpWm3E0GPMjSfVbqJmXII3AMSMgjNfb/wC07+ynYftGah4O1SDxg/gT xBoBlRdQi0r7Y08TlWVCPOiKlHUspycbm45zXD/Hn9jHWfjn8VPBfjO7+ML+G/EGheG7XS5rm28N GRrm4hlllN2hF0hhLPLkIC23H3jXPXyHMIYOrhFD2keeMou8VdPWStfT+rHtZX4tcH4riTL+IZ1/ qlV4erRqx5atTklFctGXMoPnut3q1Zc3Vnh3xC/4JufD/wAPfBbxR4h0P4leJ7W/0rS571G1eKB7 Y+VG0hVyioVBC43ZOM5wcYr0v9iz4x+J/ih+wd4/0LxRDavN4Rs2sbG9trRLdJrZ7ZzHGUQBN0ew jKgZUpkZyzctdf8ABO7xZrFn9h8SftQeMtd0p2Bns59HlZZADnGHvXXPHBKnB7V9pfCz4D+DPg7+ zde/DjwZ9rS3vY5mvtTvNslzdTypsMsm0KDgbQFAAAUDrknuyvKMRTxqq08P7GHK01zKXM+m17WP luOfETKMZwxLA4zN/wC08S6sJU5+xlT9jFP39ZRi5c60sr9GfDH/AAS5/wCSa/F//sJ2H/ouev1T r8rtG/4Ju+KPDkM8fh79pnX9BjnIaZdO8Ny24kIzgsEvxnGT19a9y+Cn7JPjb4UftC6T421r9oXx T8QNPs4Z45NFvdPmiimMkTRhizXkgG0sGHyHp2610cP/ANpYPDUsNUwzstHLmj1d72vfS543jAuC uIs6x+eYPPIuVRJxpewrJtxgoqPM4qK5nHd2Svqfnd8A/wBnrwp+0P8AtrfGjQfFmseIdHtdKu7q 7gk0mSJXdmvWQhvMRxjB7AV9/fD3/gn58K/hz8bfDHjrSPF/xAvdT0PUI722gvJ7UwyOhyA4WAHH 0IrzK5/4J1eIE+JniPxL4f8A2itZ8Lz6vfTXEqad4bkiYLJI0nll0vlLAE9wOmcCtGz/AGCPiTa6 va3L/tZ+OJ0hmWRo20q5w4BBx/yEO/SvGy3Ja2Hiva4Dnmm3zc8V1uuvQ/SuNfEzLc4qS+o8WfV6 EqcYOl9XrSXwKMlfk+07/efpdRRRX6UfxGFFFFABRRRQAUUUUAedap8H/hLrfiC71bWvhd8OtX1W 6kMl1e3vhu0mmnc9Wd2jLMfcmqH/AAov4I/9Ec+Ff/hJ2X/xqvVKK5ngsO3d019yPbp8TZxCKjHF 1Elokpy0/E8r/wCFF/BH/ojnwr/8JOy/+NUf8KL+CP8A0Rz4V/8AhJ2X/wAar1Sil9Rw3/Ptfci/ 9ac6/wCgyp/4HL/M8r/4UX8Ef+iOfCv/AMJOy/8AjVdh4b8HeEPBunXFn4Q8K+G/ClpcSeZPBo+m Q2iSvjG5ljVQTjjJrpKKuGFowd4wSfkkc2Kz3MsVTdOviJzi+jlJr7mwooorc8oKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKAPyc/bY+IHx9b/gq9+zZ8B/g78Z9U+D9h460m6+3X1tpUF8iSxvKwkaOQ At8se3Adeua5zwD8W/2o/wBn7/gtZ8Nv2Yfi78Z9F/aL8K+PNIe6jvBokNhqGjssVy6uUi5Xm3yQ 7OrRncu0g1yn7fnw18O/GD/gu5+x38NfFr6pH4c17Rb23vm026NvcBQ80g2SAHacoOcdM192/AP9 hL9nL9nH4o3Hjn4f+F9Uu/G0lu9vHrmvapJfXNtG4w4i3YSMsOC4XeVJXdgkEA+o/FXi7wr4G8DX 3ifxp4k0Pwn4cs13XWp6vfR2ttCO26SQhRnsM818Fx/8FD/CvxE/aC0T4d/sz/DD4g/Ht5dat7PX vFWn6dLaaFo1u8qrNcPO6Fm2IWYAqiNgbZDmvqj9oP4A+A/2lv2bNR+FvxF/tmPQLq5iukuNKuxB c288edkiMVZSRuPDKynPIr4o8AfBf9uD9lbxh4O8JfDTxz4L/aD/AGeo9UtrS50bxDYR6drehWLy qsssMiMqzeWhZstIxbb8sIzQB+iXxF8Z2Pw4/Z98dfELU4JLrTvC/h691m6hjYBpI7W3edlBPAJC ED61+G+n/tSftjeAv2Wfgx+298QPi1oniX4UeOfHR0rWvhjB4at4IdM01prlFkguB+83hbWUqS24 ExF2lBdR+0vx18E33xJ/Yn+L3w90vyv7V8S+C9T0mxMj7UE9xaSxREnsN7Lmv52r3x2PjV/wST/Z 0/YD8PeG/F1v+0RpfxG+yeJtCu9FnhGk2sU98WuZXZcKqrdRs/dBHKWwApYA/pm1vV7PQPBmr69q DOthptlLeXJQZIjjQu2PfCmvwN/4ar/bQX9i9P29z8UNDPwvPj7+zT8I/wDhHbf7MNI8/wAjP2vH m+b5vyZ+9/Hvx+6r9ZPEvxm+H3jf9pv4hfsc2aa9b/EOT4fXF9NPLaKNPS1niWEYlDly4+0Jxs7H mvwaHxAun/4IpRf8E9f+ES8XL+09/wALK/s7/hFm0iYEQfbvtv2rzNuzy8/LnOdvz42fNQB+nHxw +Nfxu+M3/BTn4ffsv/s6fE6H4M6XceAx4x8R+LjoUOo3TRyqWggSKQ4C4MGdrKSZTyQm1un/AGTP 2uPHfij/AIJ6/GHxB8YNH1Lxz8UfhF4gvNC1yDwtpyNdeIHh2iJoYE2r5zuWj2qqglAQozgfO3xH vtJ/Yn/4Lm/Dr4y/E6HV7b4Oa38IIPCcniex06a7gtb60ijTynCKW3MLWEgAEkS56K5X23/glz4U 8QD4EfG74z65omo+HrP4p/Ee817QrW/iMc0lgSzRzFT/AAs8soUjhgmRkEEgHJ/sjftE/tJfFH/g t78afA3xrsL74f6FZeAk1bSfh1JJDKuiLJNp7W5kkRdzzmC4LPuIw0jDYmAi/rhX5Q/CP/lbo/ao /wCyVad/6K0Sv1eoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/Hw/tD/ALdX xX/4KX/tEfB34C3nwEsdF+HOqLGv/CX2F0kkkEhKoA8O/e+VbOQo6V9AfsU/tW/Ej42/EX4y/B/4 3eDtB8LfF/4Zaklrq8+gSs1hfK0k0eUVmcoytF13kOHBAXBFfAfw9+BvjH43/wDBdn9uDT/CPx6+ JfwKk0zWoZbu68HXLQy6isjuoSQrIhwu0kdeWNfqp+y3+yP4B/ZX8KeLV8N614o8Z+MPFl8l74n8 U+IrkTXuoyJvKA4ACqDLK3O5maRizNxgA+j/ABJ4n8N+DvBt74j8XeINF8L+H7NN93qWrXsdrbQL 6vJIQqj6mvmD4T/ts/BP46/tcaj8JfhFdeIfG8mm6VNf6l4ntdMePRoPLeNBCJpNrPIxfKlU2EKx DGvKv24/2INZ/aq1vwR4u8MfEGy8PeJvCUTCx0DxDp5vNC1JjJv/AH6A5Qn7pcJJleNvetD9lPxv 8fvC/wAUE+AHxj/Ze8J/CnTbHSJrzSvGHw8RY/DF95TRJ5YhRSsEzh9wDOrNtb92oFAH1R8a/jP4 K+Af7P8AqfxH8ePq7aLaTRW0VtpVg93d3lxM4SGCGNeruxCjcVXJ5YV8dfsF/tXfFX9pv4p/tI2v xL8MWXgqHwdrtla6N4f+wvBe6YkzXoeC7LnLzJ5EYYlVwwb5RnA/Rae1tbowG5toLgwyiWHzYw3l uM4Zc9GGTyOea/KT/gn/AB+d/wAFKf8AgpZFvePf8U0Xchwy5u9XGR70AeXa5/wUD/ah1b4c/FD9 pr4a+AfhRqP7KfgTxqvh+4sdRkul17VoPNgjN3G4bYmftEJ+7iPzQCsux2r9OIP2mfgtd6r4N0S1 8c6NJ4y8W+HINe8M+F3mC6lqdtcQNPCY4upZ1RsD1Br+f3wl8TfBvwy/4Nw/2nf2afGOtWOkfG+2 +Iz6XF4RmbF/dMbrTw0kcfVkT7NcZYcAxgHl03fv18DfhNpWgfsO/BTw34z8LaTN4u0P4d6Touoz 3VnG11A8VhHDLEJcb1APmLw3GTQB8Q/sZfEH4xp+1F4c8J/tK+Kv2htE+JviTwre6ppvhfxfZadH oN8I7hfNNr5ANxHLCu3CTbSVZmxyoP6w18s/CX9krwN8JfjXZ+O4PGfxR8e6xpWhSaF4Yj8Y64l9 F4esJJFkkgtcRI3zMqgyStI+1QoYDOfqagAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAK+Y/2zPG3in4cf8ABL34z+OPBOsXHh/xXo+gG403UYEVnt5PNjG4BwVJwT1B619O V8df8FA/+UM/7Qf/AGLB/wDR0VAH5oar4g/bo+H/APwSh8Mftjp+2Vba9ZTaXYavP4M13wlZxRzJ cSon2ZZ+fNb5+gWNmAbaVIFfs38BviLf/Fz9i34WfE/VNKj0TUvFPhiy1W7sYt3lwyTQq7BN3Pl5 JKkknaRX5a/sn/8ABNr9mD4j/sMfBL4o+OtI8Z+JtZ1jw7a6lfafc+I5ksHldcsojj2sqf7IbpX7 Labpun6N4dsNI0mxtNM0qxtktrKztYhHDbxIoVI0RcBVVQAAOAABQB8mfHL9uj9nT4DazN4e1/xg 3i/4gCXyYvBnhCH+09VebOPKdEOyF8/wyuhPYGvSf2d/ip44+MnwEn8b+OvhJ4i+C91Pq00Ol6Br zsb6SyVIzHcyqyIY2dmf5CvAUcnqfjvx7/wT/wDFPhP9qLxb+0B+yP8AGW/+FHxV8QX9xqGsaX4i sotT0bUpp5WllXLRtJArO7k/LLjJChe32L+zvrXx71n4CT/8NH+FPCvhT4j2OrTWbDw5c+bY6hbI kZiu4/3jld5ZwVJBBX7q9AAfE3/BRH9qP47fDD4R+NfDHwK8B+KtK/sHTrHUfFXxRmiiSw0iC4uF ijtrXzMia6d2jVhgmNHJCnO9N39oP9qb4gfBX/ggr8IfiloV1Hqnxd8a6D4e0zTtTvYElVdRvrBb ia6eM4Rm2RTsoI27yuVKgqfQ/wDgpp/yg5+O/wD16ab/AOnayr5Q/a++Hfivxv8A8G1/7NOv+D9G vNf1DwJpfhTxNeWVpE0kz2kWkmCVlRQSQn2hJGOPlRHY8A0AeifCP4r/ALRnwH/4K2+Hv2Y/2h/i ta/HHR/G/gSXxFoeuQ6BDYXGnXkC3Es9uFiAMke20uQAck/uSoj+dK7X47fFL4E/HP8A4J/eNPjN fab8a10/4dmSLT9Njm1Xwpc6hqF15EVvBtRonmSSZ4Iw3zBSzY5yD4D8PPiN4f8A2z/+Dir4SfGr 4QWevX/wv+GPw5nTXNYv9NltY0v7mO9jW0BYYMgN6nGfm8mUruVQx/W3x/8ADnwb8UfAcXhjx3o5 13QY9StdRFp9smgU3FrMk8DMYnUsFkjRthJU7RuBHFAHm/7MPwk1T4JfsT+CvAfiHxFrXirxTDa/ a9e1HU9Qku3e9m/eTIjyMx8qNj5aDP3UBOSST79RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFfn58W/22fAnwh+N2seDvGfj7RfDepQXEhhtbqzy5hEjorZD8j5Tz7V 5p/w8p+Cv/RXvDP/AIAn/wCLoA/U6ivyx/4eU/BX/or3hn/wBP8A8XR/w8p+Cv8A0V7wz/4An/4u gD9TqK/LH/h5T8Ff+iveGf8AwBP/AMXR/wAPKfgr/wBFe8M/+AJ/+LoA/U6ivyx/4eU/BX/or3hn /wAAT/8AF1B/w8h+B/8AaBuv+FteF/tBjEZf7EclQc4+/jqTQB+qtFflj/w8p+Cv/RXvDP8A4An/ AOLpf+HlHwX2bv8AhbnhraTgH7Ccf+h00m9gP1Nor8sf+HlPwV/6K94Z/wDAE/8AxdH/AA8p+Cv/ AEV7wz/4An/4ukB+p1Fflj/w8p+Cv/RXvDP/AIAn/wCLo/4eU/BX/or3hn/wBP8A8XQB+p1RCGFb t7gRRCd1CtIFG5gM4BPXAyfzr8tv+HlPwV/6K94Z/wDAE/8AxdH/AA8p+Cv/AEV7wz/4An/4ugD9 Tqj8mH7Z9o8qP7Rs2ebtG7bnOM9cZ5xXwD4r/bF0LwP8NNI8Y+K/F+l6L4a1Rol0+/nsR5c5ljMs eMSd0Bb6V5n/AMPKfgr/ANFe8M/+AJ/+LoA/UqaGG4t2hnijnib7ySKGU9+Qakr8sf8Ah5T8Ff8A or3hn/wBP/xdH/Dyn4K/9Fe8M/8AgCf/AIugD9TqK/LH/h5T8Ff+iveGf/AE/wDxdH/Dyn4K/wDR XvDP/gCf/i6AP1Oor8sf+HlPwV/6K94Z/wDAE/8AxdB/4KUfBdWw3xc8Mg+hsT/8XTs7XA/U6ivy x/4eU/BX/or3hn/wBP8A8XR/w8p+Cv8A0V7wz/4An/4ukB+p1Fflj/w8p+Cv/RXvDP8A4An/AOLo /wCHlPwV/wCiveGf/AE//F0AfqdRX5Y/8PKfgr/0V7wz/wCAJ/8Ai67H4e/t6fDX4jfGnw54K8L/ ABI0HW9c1W8ENvZQWWHlABZ8fP2RWP4UAfo7RXwX8bf23vhj8HP2lfEHw78R/EPQNB1jTFt2nsbm 03yR+bbxzLk7h1WQHp0NeU/8PLfgr/0Vrwr/AOAB/wDi6AP1Kor8tf8Ah5b8Ff8AorXhX/wAP/xd H/Dy34K/9Fa8K/8AgAf/AIugD9SqK/LX/h5b8Ff+iteFf/AA/wDxdH/Dy34K/wDRWvCv/gAf/i6A P1Kor8tf+HlvwV/6K14V/wDAA/8AxdH/AA8t+Cv/AEVrwp/4AH/4ugD9SqK/LX/h5b8Ff+iteFf/ AAAP/wAXSn/gpZ8FxjPxZ8LDIyM6eef/AB+mk2rgfo3ovw/8B+G/iB4h8WeHvBPhHQfFWvMra7rO naPBb3uplSSpuJkQPMQScbycZrrq/LX/AIeW/BX/AKK14V/8AD/8XR/w8t+Cv/RWvCv/AIAH/wCL pAfqVRX5a/8ADy34K/8ARWvCv/gAf/i6P+HlvwV/6K14V/8AAA//ABdAH6lVyfh7wF4G8JeJvEut +FPBnhPwzrPiK7+1+IL/AEnSILW41afLt5tzJGoaaTMkh3OScu3PJr84f+HlvwV/6K14V/8AAA// ABdX9K/4KM/CXW/EljpGlfFHwxe6leTLDbQJYfNI7HAUfP1JoA+7tT+Cvwd1r41WnxI1f4V/DvVP iDauj2/iS78O2suoxsmAji4ZC+5QoCtnKgcEV6bX55ePv23fCfwu1yy03x9410PwzfXcJltobrTx mRAcEjEnrXBf8PLfgr/0Vrwr/wCAB/8Ai6AP1Kor8tf+HlvwV/6K14V/8AD/APF0f8PLfgr/ANFa 8K/+AB/+LoA/Uqivy1/4eW/BX/orXhX/AMAD/wDF0f8ADy34K/8ARWvCv/gAf/i6AP1Kor8tR/wU s+CxOB8WfCpPoLA//F0f8PLfgtnn4teFc/8AXgf/AIunZ2uB+pVFflr/AMPLfgr/ANFa8K/+AB/+ Lo/4eW/BX/orXhX/AMAD/wDF0gP1Kor8tf8Ah5b8Ff8AorXhX/wAP/xdH/Dy34K/9Fa8K/8AgAf/ AIugD9SqK/LX/h5b8Ff+iteFf/AA/wDxdd74C/bd8KfFDWL+w8A+NND8TXllCJrqK108ZjQnAJzJ 60AfobRXK+CNauPEXwt0jWbsqbi5RmcrHsHDsvTJx09a6KW8tIJdk11bwvjO15Ap/WgCxRVP+0dP /wCf+z/7/r/jR/aOn/8AP/Z/9/1/xoAuUVT/ALR0/wD5/wCz/wC/6/40f2jp/wDz/wBn/wB/1/xo AuUVT/tHT/8An/s/+/6/40f2jp//AD/2f/f9f8aALlFU/wC0dP8A+f8As/8Av+v+NH9o6f8A8/8A Z/8Af9f8aALlFU/7R0//AJ/7P/v+v+NH9o6f/wA/9n/3/X/GgC5RVP8AtHT/APn/ALP/AL/r/jR/ aOn/APP/AGf/AH/X/GgC5RVP+0dP/wCf+z/7/r/jR/aOn/8AP/Z/9/1/xoAuUVS/tHT/APn/ALL/ AL/r/jQNT049NQsj9J1/xoAsRQwwRlIIooULFiqKFBJOSeO5PNS1T/tHT/8An/s/+/6/40f2jp// AD/2f/f9f8aALlFU/wC0dP8A+f8As/8Av+v+NH9o6f8A8/8AZ/8Af9f8aALlFU/7R0//AJ/7P/v+ v+NH9o6f/wA/9n/3/X/GgC5RVP8AtHT/APn/ALP/AL/r/jR/aOn/APP/AGf/AH/X/GgC5RVP+0dP /wCf+z/7/r/jR/aOn/8AP/Z/9/1/xoAuUVT/ALR0/wD5/wCz/wC/6/40f2jp/wDz/wBn/wB/1/xo AuUVSOpacOt/ZD/tuv8AjUkN5Z3EpSC7tp3xnbHKGOPXg0AWaKKKACiiigAooooAKKKKACiiigD+ Sv8A4Kq/8pUdX/7Bx/8ASq4r816/Sj/gqr/ylR1f/sHH/wBKrivzXoAKKK9j+F3wuvvG2sxXEsT/ ANniTbs6GTjP5UAcL4a8Ha74r1NbbSrRnBPzSvwi/jX0v4R/Zc1fVtjTx3uoEff8iMqg/wCBHFe+ 3WqeAvgj4dsovsMeu+KSgezsrcAoh6Yb179a5ub4h/HD4kqE1PxFF4f0Vj+7s9PhWDavUAsozQBr 6D+x5p91bzTXNppltDFnzGutTClSPaprz9j/AEaS6tra11LwjDcTHKI2qDJHr1rl/wDhXVpHA1xr et6veyHlyb98N9ea17b4RaHeaja2r6PqySTOFjmeeUbc+/asMRiqNCLlVmopa6u2250YbC1sRNQp Qcm7LRX1exn6l+xdqMMEr20FhqBHQ2WoK35AGvDPFf7OPibQ7htLAe0mjxcbLqM9HyoGQD/cNfXj /s2XsGsW8lj4n1jwxbqM+ba30krlv93K8fjXPeJbr4y/CnxtNb6D4mt/HFhJawmeLV7ZXd490pVe STw3md/4q83hzjzIcfiK+Ew2IU5undWvb44dbWv5XufTZn4fcQYLBfXcRhnCmnZ3tfZ9L3t52sfn z4h+Hvijw2269095bckBZofmVsjP1rimR0Yh1ZSOoIxX6gwfGv4S+IpoIPif8OPEXgnU2PlyapYJ utnc9DsOMDHPfpVzWv2cfCfxI8IXnin4datonjDSBGzOls4hu4eO6dzxXrwnGSvF3PlKlKdN2mrP zPywor13x78I9c8H3E88cctzYxvh1ZCssX+8v9a8iOQcHg1RAUUUUAfsP+2z/wAoifgf/wBfGj/+ muWvx4r9h/22f+URPwP/AOvjR/8A01y1+PFABRRXReGvC+r+KteWw0qDfJgs0j8IgHXJoA54As2A CT6AV3fhr4b+KvFGXsLBooBnM0/yLX2r8Hf2Xnv4/wC0ZUtlSBS8+rai3l28QAyQqn73sa9I1P4n /CHwPp9xZ+EvDl/478TwZR9SlYR2MTdMKv8AF+QoA+LrL9njxBcbVbUbeSQ9VghdsfpU1/8AADxH cW5v4rpIw4wvnQOAdvy9QPUV9Z29z+0dr+n23iTStY8O+F7GaEeTaQWyq0an5gGDKeea1NJ1X9p+ 2+F3mw+IvC+teGrTzZUsJ7GNnfYzs4JChjk7sc968uPGeRwwFaMsVFNVKcXr1tU09dPwPrn4e8S3 hP6lO0otrTde7r+K+8/O3Xfhf4v0GMyXGnm4h/vwHd+nWvP3jkikKSI8bA4IYYNfqjpXx8+E/inR xZ/FX4b6v4f1ofuzq2kHMSkcfNFkEAc5603xr+zj4c8a+DJvGHg280rxj4dlDGCewfy7mLA5LJ1O Meleonc+Saaep+VtFet+MvhJrvhqS6ubZGvLCI5ZdpEsfsymvJSCGIIII6g0CEr7J/4J+/8AKX74 Lf8AYTn/APSSevjavsn/AIJ+/wDKX74Lf9hOf/0knoA9B/4Kn/8AKcv4yf8AXDRv/TPZV+etfoV/ wVP/AOU5fxk/64aN/wCmeyr89aACjvRyTgcmvpX4P/Bi58TXtrqmqQj7K3zIjkgKAfvNQB5T4T+H HiTxdNusrfyLUcmeYEDHsOpr6Z8Lfsp6hqFmLi5tL++jAyz48qP8zzXsmsfEfwx8JJ00TwVpkOv+ NY02ecYw1vDkZzt6ccVzM2ufGX4jET+K/GM1pbMPltrEC3RR0xhMUAdJpf7HmkvoX2y7i0S1RiAh n1UZJ+lVp/2OtOutRlt7DU/CIljTLxjU1JHtyawE+G2lI8Ud9faxqVzK2FQ3sh3H2GajuPAXgqzn kN5a39tNF98C7dXJ9OtTzx5uW+pXK7XtoVtX/Yy1i3tT9ksFuieklneLL+gNeB+IvgH4ntNSe3hk jR7VjA8dwjK2V9wMd69pbw/eWOsC88K+Ldd0ONRxF9udsenet/Sfix8XvBqSrfQ6R488ORzs9zHd WytKzHG47uuTx617GD/3LEf9u/8ApRjP44nwlrfhLxB4fvTBqenTwnkhgNykeuRXOEEHkEH3r9Qt M+K/7Ofju6e317Rdc+HOrycM0oFxalu5Ppz7Vk/ED9lS1vfA7+J/Ds+l+INDn+aDUtIkyVB5G6Pr Xkmh+aVFdz4w8A614P1Erdx+faMTsnjBx9D6GuGoAK9Q+Cn/ACdx8OP+xgtf/Rgry+vUPgp/ydx8 OP8AsYLX/wBGCgD7n/4Kc/8AJxHw+/7As3/o6vzIr9N/+CnP/JxHw+/7As3/AKOr8yKACiiu+8F/ D7WfGV9m1T7PYowEs7g9/wC6O5oA4SOKSaUJFG8jnoqjJNeneH/hF4v8QWK3aW0VhasARJcsRkfQ c192fDr9nPSvDPgKXxdr8tl4f0S1QPc32ot+9nH/AEzQ9DU/iH42eA9A0U2Xwr8EXWo3x+U6zrJz HKfVI89KAPku0/Z912JftZvln8r5m8m3cj8yKpan+z74ogQzQ3MJZ/mEc0ToTnnrjFe5xfEj41ap BdyjxfpWmwXBJe0itEVcf3RlTj8635/2gvjnomqWKeKZ/D/jCztVAW2k06MfIBwMoBzjHWvTl/yL 4/45f+kxMl/EfofDeu+A/E/h6RhqGmy7B/y0jG5f0rjyCrYYEH0Ir9ZLD4s/ADx9FbW3ijwvrPw5 8Qz4D3+fOsmY9yB90Z7Yrgvij+y1Cvh869YR2WqafcL5ltqmkybl2nkbkFeYan5r0V3niv4e674U cyXMYubIsQs8QJH4jtXB0AFfp3/wTJ/5Ld8S/wDsCQf+jq/MSv07/wCCZP8AyW74l/8AYEg/9HUA f0+/Cf8A5N78N/8AXKT/ANGvX86v/BXH4i+O/C3/AAVXs9M8OeL/ABDoennwNp8ptrK+eKMuZroF sA4ycDn2r+ir4T/8m9+G/wDrlJ/6Nev5o/8Agsf/AMpc7H/sQdO/9H3dAH54f8Lo+LX/AEUfxl/4 NZf8aP8AhdHxa/6KP4y/8Gsv+NeZUUAem/8AC6Pi1/0Ufxl/4NZf8antvi98ZLy6WC08f+OLmZjg JFqUzE/gDWB4N8Ear4w1yOC0jdLbd88xQkHnoMd6+9vCHwu8A/CrwpHr/ji7jsbQx/Pbhx9rmPUB c9M4NAHzhpF7+0RqoVm8eeMbFSM/v9UlB/LOa77T/C/7Ql9GzJ8UvF74GT5d3cHj869kPxz0KQNa /C34WvFH0+2623ns/uBjitOy+K/x8OhfZLa78J6NaNyAumKXHfGetAHgd54b/aJtWG34hfECYEEg rcXGDj8a5m8T9oW0id2+IvjAEdEfUJ1J/M19VR/Ej9oEax9si8beHWlKlWik0pNgH0xWVqPxs+MN jqkUGtaJ4I8Z2ef3jJpqqT+IxQB8a6v45+PGmRW6y+MPH5dUImeLUJnG7ccZIP8AdxXKN8Zvi6kr I/xF8aI6nDK2qSgg+h5r7Xs/ir8LNT8SXkPxA8Ia34S1C8ulYXNgc20KiJIyNn+8hbr0auh1X9nT wJ8S/wDiYeCvFvhLxShXesFvdLBdru5wVJBLeuAa9PN4RhXioq3uU383Ti3+JnTba17v8z4F/wCF 0fFr/oo/jL/way/40f8AC6Pi1/0Ufxl/4NZf8a9R8d/s3eIfC99dRJa39jLGT5cN5ER5gz/C3Q18 4ajpl/pOpyWeo2s1pcISCsikZ9x6ivMNDvv+F0fFr/oo/jL/AMGsv+NH/C6Pi1/0Ufxl/wCDWX/G vMqKAPaPDXxj+K03xF0CGX4ieMJIpNSgV1bVJSGBkUEHmv0m/wCCkPjPxZ4QufhN/wAIt4j1nw+L r+0vtAsLtofN2m227tp5xk4+pr8j/Cv/ACU7w5/2FLf/ANGrX6l/8FRP+Pn4Of8AcU/na0AfnH/w uj4tf9FH8Zf+DWX/ABo/4XR8Wv8Aoo/jL/way/415lRQB6b/AMLo+LX/AEUfxl/4NZf8aP8AhdHx a/6KP4y/8Gsv+Ned2tpdX19HbWdvNczuwVUjQsST9K+k/h3+z1rHiLUrf+07a6u5HIzZWikvGM9X YcAUAecW/wAXfjLdy+Xa+PvHV0/92LUZnP6GuktvGX7Qcskbt4q+IaxZBYvfzLxnnqa+4V+F/wAN fhD4KM/i3xT4f0+cx5TSNOcTX3r8xBOPxIrzk/F34bi6vodM+G/irWrUoVguproqeVxuIwe/PWts Mk60U+6FLZny/f8Ajr4+Wkssp8V/EQW247WW+nYYzx0Nc5J8Y/i/DKUl+IfjaJx1V9TlB/nX29b/ ABd+BN9YaTp2p+A/GfhadESK9vzdiaN2CgFwmBgEgn8a7fUv2f8A4b/FHQl1PwH4w8JeIhIMjTGm EV+m7ouCRlvoDWmOio4moltd/mKDvFH5zf8AC6Pi1/0Ufxl/4NZf8aP+F0fFr/oo/jL/AMGsv+Ne o+Pv2c9b8N6pdx2kV3YyQsQLS9jYM+Dj5W6Gvm+/0y/0u/kttQtJ7SZGwVkQj8vWuUo7/wD4XR8W v+ij+Mv/AAay/wCNH/C6Pi1/0Ufxl/4NZf8AGvMqKAP04/4J8/EDxx4r/bO1/T/E3i3xBrtjH4Su JUt769eWNXFzagMAxxnBIz7mv6APgJ/yWq7/AOwTL/6Mir+dD/gmz/yfN4k/7E24/wDSq0r+jD4C f8lpu/8AsEy/+jIqAPsOiiigAooooAKKKKACiiigAooooA/kr/4Kq/8AKVHV/wDsHH/0quK/Nev0 o/4Kq/8AKVHV/wDsHH/0quK/NegDq/Bvh6TxJ43trHY7W4O6bb12+n1J4r7I8S+Ml+EWhab4Z8JG B/FN5aIJC0QcWxbt9cCvO/hZpdn4H+GF/wCONWVS0cayQKed8h/1a4/Ek/Suh+FfhW58Z/ERvGGu RNc3t/eMbOJjw2ctwO3GfyrKvXhRpSqVHaMU232S1ZvhcNUxFaFGkryk0kl1bdkvvPRPhD8EfE/j bxBcandW1xretFDcyB5QMKcdNx9T0r9G/BH7PXh218PWf/CQ6dPNqUMglcRT4jJB3KrKRyOgI6Hm vbvg5oOhaD4RtwsVvFcC3QdBn7q8V7qdJ83TJbu1ti4KnBVc+1f57eKH0js4zDESwmAvh6KejTan LprJNaeSXq3Y/dsBwRl+S15RxKVaW12rRT0eif5/gj4i8W+HPCcF2IF8L6GmM8Cxj9fpXTeGtN07 V5LU6gyld33T2AxxXpnijwUdQcyvauJM9enevHpvDmsaVrLNH5yW6txzXwNDOv7SwihKu+Zd2391 2fvmUTwFTCcmHapz6tJK/wBx7p4t+GegXHhOC602WHeCQcZHb6V8TeIfh1dN8b9Riu0t7lItLtpF 38jBmuAOv+6a+tdE8TXkOnPbXm9o+uGBrxfX7HVNQ+P2ryWKzMG0O0fGOxnuq93w1xuY5eswhKto qTtJ/wDXyn1PKwFfG4bEUcPiKilDn3lqvhkeZeMPh9pfiTwB/ZN3p9texlWGHUHZkMNwyOCM8Gvj XVfg140+HmpDxF4LvruyuLFzLG9tclWiVfmAccBx7V+iVmdVsb7bNaswzhi6dK7S20bQtT01xeC3 M8qkGMkc9eMV9zwz4o5nwzJqnadNvVb/APDev3npcdcLZTn1FLHQvJfDOD95X79GvJ/Kx+eGi/Gn QvinaxeGfjjbQaP4vY+TpPiSzthHBcOflEdwg4HOPm6V8t/HP4Fan4N8T381taKs0OZLhIWDRSJj PmRkdR7V+m/xG/Zh8N+LiLqy0+azmLMzyWzAFiR75x+FfJ2marrfgbW0+D3xmtJk8JSO0Og6zLES 9rMSNrGU8vF8wyO2a/rHgfxPyXiiPLhZ8tRK7hK1/l3+Xlex/IfFnAGYZElUm1UpN2Uo/wDty3i/ Xd3s2fmgQQSCCD6Gkr6K+Pfwru/BHj25njjikiLZuHtzmLkAq6EcFSDXzrX6KfCn7D/ts/8AKIn4 H/8AXxo//prlr8eK/Yf9tn/lET8D/wDr40f/ANNctfkDZ2k99qkFnbJvnmcIi+pNAGloHh/UvEni O303ToS8srYLnhUHck/Sv038A+Dfht8G/ghF428fxXEulGUR6bpcHNzqVwVyGxjITI6ngVynwx+H Phb4c/BWLxj40jZYYojcR22fmuWAHJ/2cnp3rN0C41D42fFBPFXjCFJLS1RorazU7IYYsZRAoPXJ yT7VjiKrpUpTUXKybst3bovN9DqwOGWIxNOi5qCk0uaWkY3drt9Et35GjL4i+Ifx78VW8niKVNL8 I2lxsj0ywzbwxxgg7GC/ffB+8a98+HXwc8OQatLb2+lW8sTrkfagJApGTxkV1FheeGYLG0toha25 4+RABwDiu1tbtxGh0j74Jy8Iz6V/I3HfiDnePlKEIyoQeiWqWj3b0u7/AHH918D+G2Q5NgLU1CvX 3dRpPlutOXflTT+d7jp/B2pxeKX0mJkEW37u7jrjNX/hd4fbR/hqZb7y5I53nXb1xiWQf0rt/D+m ahdr58/2h9R25yR/Dxz+dcZ4b0zVv+FY2T/vBG01xjceD++kFfl8cwqV+H8VRlUWlWgn5+5X/wAj 2MxzKrXzWjQlUS9yd/P3qf8AkcB4u+CvhfxPLfXdtZpBeyFmEiNt5564HPNfOOmfAj4j+G/HMWq+ FvEUWnalbMrrJDI6RsQQfmToy57Gvrz+2rrR5rq2kJEvQArnPOag0XXL648VqJYj5RdeVjyDyOtf XZH4icUZXh5Qp1+eCStze9otrX2ODNvCfIsylLEYjDRbV3eLcXJuzbfLa79fM8I1HxPpd9qQ8I/H zT7PR/FV1Ky6V4n02MJa3j7VIR4x90/OMk8HFfFnx++C954b8QS6tpkEcybBLO0B+SaNgGSVAO2O o9q/VP4xfB3TPiH8GdV1MadDda1aWUxsWaTYY5HTAI59UXn2r4/8Aas2oW5+CHxZElnrVhCw8OX7 rk/MzDy3b+NM4wemK/o3wt8SKHFGBtUaWIp6TWiv5pXva1r7an8n+JHA0clxTr4RN4eTst3yOyvG Tslvfl1b5bXd7n5dHIODwa+yf+Cfv/KX74Lf9hOf/wBJJ64D47fCG+8A+MLycW/lqk5W7jQ5VGPR l/2TXf8A/BP3/lL98Fv+wnP/AOkk9fqh+YnoP/BU/wD5Tl/GT/rho3/pnsq/PWv0K/4Kn/8AKcv4 yf8AXDRv/TPZV+fUMTz3ccMYy7sFUe5oA9Q+FPg1vFXjyEzRNJaxOowO7E8fgOtfSPjP4h32ka3P 8M/h7cRbnIivLxYvmxjBCntyetc7pLw/Cn4A/bQFOu3+YLMY3csPnb8uB9a2fhh4Xj0a2stY1CBr zxFqUq7I2O7ALdT7VM5KMW30HFXaR7P8GP2edd1vTTqsFhJqMyyql3cySg7C3OTnkjjtX6E+GvgV 4P0jTbPULzSZLzUbWE71lkEkEjFcE7COR1IzXu/wy0Pw74d+H0Nhp8NvAM5ZAckkkn6nrXo+oaJK uhO0FqxSQdVXIxX+cPiX9IbOc4xs8NhJPD0E2lZuMpJ6PnaevXRaK/Xc/fci4Vy/KZKOIgqlS61e ya/luvz3Pwz/AGg/GWmaf+0zeWnh+xk8N3GjxpDm1QQh3xu8xdvThsZ68V9jfs7/AAw8AeLvCPhj xn4kX+2Na1Gx33Rv5vOUsScsVIPPHWut+NX7MPhv4k61FrVzb3em6xDEyma1wpnGPlV88YB79a+W /h78OPiP8IfiVpJvLHWFgWZvnsHNyjQ5IKsBkKTx6Gv2CjxPl/F3B1PAZXmDwuKpQs1J+9UtF3ip XTab97rbRWOKWV4jLs4qYitR9rRqy0cVpTu1aTjayaWnS+up9TfG79kvwbqYutb8GrFpWuyW4NuI pCluCv8A0zC4ya+ALr4feJ/Df9pNrkRsIIbySFnMisGdQCRgGv1lsfFM994Tjiu0mVmj+QSRkOue xHavzM+Ivw++Kup/ELxNq0NlqGs6fNrc1rHHbDzDCvBYsF6duTXf9HPjrOo4fGZbm2Li6dPks6j9 5+9bli20vvv5GHiBwjSo0adenScqjuvcWj680lb8jxHWvAcWraMNUu9LF7DjKT2Y2sPqO9c94b8R eNfhh4qXW/AOsXOn3sed9tPmSCQejRNx+NfUfhNvHvhbxDbaHe+Hzb+DXxFfyajakIQ3Gd/UEfWv ozUPgz8IvG3h37Po0lnb6nFtYXNhceZJGPTBYjB96/YOJfFPBZFXj9apSlRltUp2nFLRPn25Wm9l zXX3Hx2U8DYjMaLdKoo1FvCacW+q5d7p93ax8p2XiHwP+0Np7aTqWn23hj4tyxHzbNwEsdSAGS0f ZGPpXwP8VPhdqHgjxFcyR206WKSlJEf70T56H296+6/iJ+y5490y61rV9I017nT7JzLZXlvOPtLR rgg7E53ewrnfC3imP4u6TL8P/HwsrTx7bRmPR76WIRJeRqMGKX1k44Jr7PIOJ8qzqj7bAV41ErXs 02rq9muj8mfM5pk2Ny6pyYmm4vW11o7dn1R+adeofBT/AJO4+HH/AGMFr/6MFVfiV4JufBvj+6tD DItoZCEZuzZ5FWvgp/ydx8OP+xgtf/Rgr3jzD7n/AOCnP/JxHw+/7As3/o6vzIr9N/8Agpz/AMnE fD7/ALAs3/o6vzq8K+H7rxN40s9LtYzIZHG/HZc80Ab/AMPfA974x8aW0HkyDTUcG4k6ZGfuj1Jr 9KZLvwL+zz8L9NkudPTV/iPd22/QtKADRWQH8c/88HrXMJZeHfgJ8ILfWZLaO58YmJV0y0dQywyE cM47nvzXO+CvDB8Ya9deP/F0kl3qFw/nSl3yrN7DsB6UAcReat4t+LvxKgufGGrPcX90cpFzFaxD 0RBwBX1N4G/Z/wBM1GxG4QTzwxg5d/lP0A/rWHLpOk+IrqK2jtEjWM/K8Y2Mg9QRXd/DGO502fUo Y7y6liSUpDuclh2x715Wd0cdVwVSOCqqnVt7smuZL1XZrTy3O/LKmFp4mEsTBzh1Sdn8n5bmX4s+ GsMng7UtNsNKhfUbW3ZwsZVSuO+a6L4e/B/TfC3wj1S58V6fa6lqup+XJGHVHltwF6Bj0BznArpv GngzxRefDrV9b8J3mpWWrRQ+XdrcIFjMZ5O0sOaqeH/DPinw/oVmfESzSTzQq/mCTenIHXsK+GxW ewz+hRwcMwp04xrK8VdVJygoSkl7ytF6NNXbX4/YYfLHlSqV5YSU24Oz0cIqV0m9NWtmnZXPB/GX wjl1W1a70DTYoo0m23M+5Qi/8B7n6V4tFf8AxE+Cfj3zvDuqtBNJGzGGTM9rIOhDRHIH1r7qvxHp +l3Nz5khjVjJ5WflDEYzWL8N9Pt7jxvJreq28F08waGMSRB8A9sEV9fmeYYzL8BisVOCqOClKMY3 TcUtm3fXfVK3kfMYHB4bG4vD4eEnDnspSlZpNvdLTTbdni+k+Kvhr8a9DXS9TsovCPxMEe1oHX/R L4nqycYXJ7V8F/GD4X3/AII8aXcsNo6acZSrrnPlPnkfT0r7/wD2hP2bf+ER8MRePfBE1yjNemW6 UzKGicnI2Y5Az2rG8N3+k/tAfDG60fV/IT4ladD5MilQsV7Gq9fTfgdfWo4W4py/iDARxuBnzQba 801umv601JzvJcVlWKeHxCtJfc13Pyur9O/+CZP/ACW74l/9gSD/ANHV8HfEvwHc+CPGslt5cn2J 2PlluqkHlTX3j/wTJ/5Ld8S/+wJB/wCjq+iPJP6ffhP/AMm9+G/+uUn/AKNev5o/+Cx//KXOx/7E HTv/AEfd1/S58J/+Te/Df/XKT/0a9fzR/wDBY/8A5S52P/Yg6d/6Pu6APyjrf8NaDceI/FUGnW+F B+aRz0VawO9fV/wa0Kx8NeC77xnr6BLaBRIVPVj/AMs0GffJI9qAPW7TVPDHwO8CR2l5p8t14jaM fYbSMglWYZDNn8K5XTNF8TfFDxWPE/jCYNEfmjgJKpGB0OOnSuR8L6Ze+PviFffELxRMfsCuWjUk jODwAOnQV9e/Cf4c6h8c9Y1fRNF8Qab4R0PSo1a5u7nDPvfOxAm5SwO1jnPGPevKzvO8DlGCqY3G 1OSlBXbd3bW2yu3duySV2zbD4epXqKnTV2zzC41XSvDdiLfS1jlmQYLgArWBeeNr28tiRLDCgwSF UA5FekfFP9nr4h/CXTVvtX/sq90S51F7XTtQinDfagAzK5TnaCoz1OM4r5d1C3uJ/FAgB2BiIww4 UmjJs7wGbYSOKwNZVKctpRd1puvVdVugxGHq0JuFSNmerW+v6jrOoWmm6ZHdXd7dOsMcMCbnldiF CqBySSQMDrmtHxLofxG8G3tjH4i8K65oNvdEpbjUbB4fOK4yFLAZIyOnrXH+FNF1D+zrt7K7MOrW pymxiHXnIYHr15zX0NqHxC8ReLvhR4M8I+O9Tt7geEoWTTJHQLK25VBLueXPyjrUY6eZxxlBYeMH RbftLtqSVnZxto/esmnbRt30s1BU+SXM3foeNJbtqc+o2V7p0d2XcbklUFlHlqPwrzu98A/2fq/2 /wALaje+H71WLBnlZRn/AGWHSu7XW5LLx3r2qRnzrRx5SuOQWK4GK7KLV7W18HWmiXtmdS1SdARG EyOea+tzn+PH/BS/9NwOWj8L9X+bH+GP2ifGfh7QrXwz8TLG1+IPgVQEnjeMfbkQDGY5uvUDrWx4 4+E/w/8Ai/8AC2fxn8MLl7/Q4ELXOmTyZ1PTXPZl/iXntXCeLPDdppHhe11ORDHuIFxbD5uv90dj Xn+haxqfhDxzZ+MvAWpPpuq2TZWCXlJyP4XTowwehryjU+Y/F3hHUvCPiFrS9XdE5JhlUHDDPT6+ 1cnX6VavY+EP2gPhjrF9pNg+neL7GMTa/pGADK+fmntQO2ckj0r8+fFXhy88L+L59Mu1IwS0TEfe TJxQBD4V/wCSneHP+wpb/wDo1a/Uv/gqJ/x8/Bz/ALin87Wvy08K/wDJTvDn/YUt/wD0atfqX/wV E/4+fg5/3FP52tAH5LVd07T7rVddtNOs42lubiVY41AzyTiq0UUk91HDEpeR2CqAOpNfe3wa+C1l oXhk+NvFM0en6TawNJfXTH53UclIs9GIGM/WgDsPgt8AtJ0H4fX/AIz8WalZ6NoFjCX1PU532tcB SSYoM/xduO5rU1z4/wB4+nXfhf4K6T/winhm7iMEt5dwCTULnPBO7+EZ6Y5rnPE2raz8c/F2l6fp B/sr4daQQdL0/oYwcBnkxwzsRnnpXTH4aaXo13YXGg3Mq6pbSBlaV96u3uPxoA4nRPg3rOtWqXep zkTynfLJcgvK5z3Jr0C8+EyaH4Eubqzv7iKeKJnYM3yNgE4xXsfgXVtY1C+v9K1mG0SW248yFcBu n+NY3xR1We10O7061hZsWrlyo9VrfC/xoeq/Mmfws+fNB8A33jfW7Z9bgSLR3tgIjEgDNx649qo+ KvhMvg2aHUdFu7uCaOYC3+zysk6tngqw619HfCSR5PAenI4wFtlyCOa67VPCyX3jO31W6YG0tssq 5rTMP96qf4n+Yqfwo+d9N/aC8UWkNl4V+LWip4x8KQx+WM26xakiAdVlx8xzgkn0rV8Q/B/wP8W/ hO/ib4e36avpigvdaXNJjUbEk8K35H16V0Ov+CdP+Ifid/Njkt7SzDqjRnaST3z6cCvnnXfDvi74 ReNl8TeHb6ZFjfEbRkhXTPIlA68Hg1yFnyZ468G33gzxjJYXKubd8vbyFSMrkjB/2hjBriq/ULxZ 4a8P/Hz4H3PjPQbURXsMYOv6dwZ7e5IBaSID+Ak5x6V+cHijwzqHhXxPJp2oJg/ejcdHXPBFAH3N /wAE2f8Ak+bxJ/2Jtx/6VWlf0YfAT/ktN3/2CZf/AEZFX85//BNn/k+bxJ/2Jtx/6VWlf0YfAT/k tN3/ANgmX/0ZFQB9h0UUUAFFFFABRRRQAUUUUAFFFFAH8lf/AAVV/wCUqOr/APYOP/pVcV8DeB9A n8Q/EOws4oTPGsgaVfUZ6fia++f+Cqv/AClR1f8A7Bx/9Krivmj4OWx0Dwhq3jCVCJIF32xIzlgO Bj68/hQB71afDqb4l+PLDwFp0yWnh/QlR9aKuN7zPwEX6hW55xiv1K+Bf7MWj6Z4I04xv80QHk+Y FZk4bvj3r5m/ZB0DTbjR7DX9Q06G2vtSH2nUrjaVaYiRypbPoCa/W7wXrnhW0tI4LaZPNjAUAEcE A1/nx9IvxVzmePrZdg5SjSg7WS7aNu3nf5WP6dyzJ48NZFQqYekpYmrFTlO12lLVRV1pZWv531PK Lj4fav4b1hTEUkjXIA3DpyK988CzXU2lpaXdvHtBwcCurma31GzS4iMTHghj2zUWjanb2mofZpVW Ni5wSOv61/JEs3/tCvCliWrJq8rdL9j5zNc/xGYYZxqwvNFTxB4Z06409pDmLDdQB615dqHgfTZt OkTzWeQ8hiBivQ/HHiayg0dofNUszfwkZ6187X3xR07StDmt9R1K305AOTIwX8eTXo08Dia2Il/Z 93BNWst/Nf5HXw3hM0qUFKm3uWdZ8N6boumrJcuD838IBri/DR0HUv2itZi83yz/AMItZBcrxn7T e5/QiuR1D4wfD+fT0i1LXFui7HayyDHHvXlR8UeE5fjNqd7oesRKh0K0VCZQcsJ7rI4+or9w4M4H z3E5fj+ahVvKk0pKErfxafl5H1WZRpQnRw+MxHs6in1aVrxlum79vvPorxTollYac0kIEhJ+Y8eh ryCF4LLWkuwGc7x8vb8q7LwzqP8AbESRTX8dyH45ftnFeoWvw00OXU7e4nuoIgzAn99gfyr46OYQ ytSo4pts+0hmVLLIuliZOXmupyejXdzd2EtwYoxH/CNuK+LP2sfDMfiD4WGZbSFntrrzpZThWijC ncQevpwK/UCbSdC0ewjtURJYFXiRTuB/Gvl/4y6J4av/AAFf26olwtwjrJE7feBHQ16PhvxbHA8Q 0MXTg7Rkvu6/geHg6lDOo1sHyOMa0XFPtdaX3667H47eErmbxbol58LPEk8d9f20Df8ACPO5ybhC CzxFz1OMbPfgV8deLPDtz4Z8a3mmzoQiyEwsf4lzxX1B49tdR8J/GWe60u1/s260jVfOsD0Kqsm6 Ij24A+lWf2jPDCar8OvDPxKslhFtrNqL0iLpGz7TIh91ZsYr/VTB4qGJoQrQ2kk181c/kXMsDUwW Lq4ap8UJOL9U7dT65/bZ/wCURPwP/wCvjR//AE1y1+ev7OHgiTxv+0po9gAohEgDuxwEB4LfgOa/ Qr9tn/lET8D/APr40f8A9NctfL37INodP0nxr4p25kt4FgiJGQDLmI/+hZrpOI9F/aV8SS+IPin4 U+GGllILRpYrKMQLj9yj7ckDryN3vXu7/DPT/C2mabY2sotGMKqsqDAY45BA+nWvLvhT4S0nx/8A tieIvFGoLJN/wjLw20Kh8LuaOXJIxzyB3r6Z8eakLWG2l8oOkEh+QDtggV+JcUceYulxjgsqwUnZ NKoraPmtbz0jd+R+98F8AYKtwljcyx6i+anOVN31i4KVvJc00lbW69UcHp3hhtHnjurpkupDKI4h nIBY9T64PavXb/TV8C6Da3EbNcz3TbxGegzgn+dcfYXNzqOgC4ltlhn4eFX6bhyp/Oum8T6tcahp en6mzQTQ2sbRymId9qjFeF4u5RjMwz/LcLOdsPVfLttJtX/NdT6HwZz3C5fkGOrwp81SlzTmm7c8 YwvCN9bK6ktup9N/DPU9C1vTbW9mZ7aRoclCM7eV46e9U/CZ0C5+CekoGYstxdFjt/6eZvavhz4f fG7wk/jkM2oS2c0UhimsbgbHYbhkpnAJ4r1uw+Ivgnwn8FrW6l1W3NzI1zt3SqUQ+dKVyMg5PBr8 X4n8Fc5y2li8NhoTqxnWoODitLKGI3eytfW77FYTinKMyxdHMJV/Ze7O8W/hd4adG1bZ217F/wCJ KaaPEqWlgzpPLNgSMmf4Sa5/RtSi0N4YL1klMr4SRU78VgeHvES/EnSl1DT3eZjI+yYRNHypIOA3 OOCPcVnapZX41ho7tozcQrnylzkHrXo5bgcmw2R18szCEvrkHo77bLXW2jT6ddz9ywuFznG5hgpY PFQlgXC80/ik3e0ou17NONtUlZ3Wp9NaBqdrdyfZ3lb7NIuH9xz/APXr5M/a3+GMI0PTfiZ4fvDb XPhxzeOscWXuFLxjZkc4HJroLHUddsJ4t6zR2ynoy9a6fWNcvdS+HGpWzbniNuVZSvXpXyfDlLGZ BnlDH4Wps1dd09GuurX3G3E3ACx+Hq4WU17OqrPye6eltU7O19bWeh4J4yuNJ+Ln7IUUstnu1WDT DJDdbRmWBlz85/vAivlf9gq1ksv+CynwetJQVki1e4Qg+1rPXt/7OutPqPwvvfDN0im4tVm0qRW4 271Jyc+lec/sfWzWn/BeP4bQsrLjXrkjIxwbSav9DUf51NWZf/4Kn/8AKcv4yf8AXDRv/TPZV8o/ CDwrL4g+IsU72/m2kHLE9AeufwHNfV3/AAVP5/4LmfGMDr5Gjf8Apnsq8e8IPN4H/Z8k1BAItT1E GGLcM5EnB/T+dAh+vPJ48+P0GlaVFJPpemlYbaILnkHk/n3r9nfgn+x1a2vh/SfEniO6e48UBPM8 hHUwQ8DEeOQcc855r47/AOCfHg/QovFvjLXdbtYpZ7cRJBcycldwfdtPvx+VfuB4V8R+G009BYzo X6cH6V/Df0l/GPOsBj3keWOVKMLe0mtXLminbyST17v01/UOGsgVDAQzHk55y20uo2dvvuvl8zw6 28I6z4d10rhJUyOM8V9K+FWe/wBAjivLdMCPnAq7exwzxfaYvKPBye5qzoesWqQtAwWKRFOQRjFf xRRzSnmOLprF2ST1lb9PM+kzrO62YYdSlD3l1Ry/iXwrp0wjfe0RIx0HNebar4E0+eJPLk+dTyWA FdV8RPFNrHGscMgd0Bb5en868E8R/GPQbPSo7XUdftNPlx8iM4U8enNevlOS5rjsTy5bCU027KKb du9ldnq5OsdSwsK06vJHu9EjS1vTNI0O7iWdizAZwAMfyrB8BWeh6tpXjby5ish8RXBCleowleX6 r8ZvhvqGsx6dd381xIU5uRIvl/nmvBL6/vG8S32ueAvGkWnNFrs7S2txNut5oiFwSqjJP41/Q/Cv hPxDicqxdDE0qlGU402pShLl0ndXsm1e1tVddTsxueYGfsZwxHNODadnH8LtX/U+n/iD4c0ufQbr S5499ncRFHYY3YPGQccH3r5P+BOt6FoXj3W9Hs5Lm7u/NeGF5X3jajdCMdR655r7A8LCPxDp9nb3 97a3E8ka+YQ+ATgZI74rq/DXwI+Hmk+On1iCz0uz1B9zNKsuGYnk9u9eHknHOByLJMxybMlUn7ZL lUdEpRbs3e2jdr6bI9jPKcFjMJilJOVK/NdXcou2its9/vMhJbh/CrSTRRkOpyNvQGvyG/aX8IvB +0hqF7Yww6ZC8MTWc9viIJIBkj5cYJr92tVtNCt7OW2lh2oF2hlPWvyz/bJ8N6HPrmkf2W8v2h4W kKQfM0rKeM/Svrvow8Qw/wBa3R5Gvawkl20tLX5Jr5ny3iDiI43IpzjDl5ZRl8trfij4lvIP+Fwf BTUTqPlS+MNITbfKq7S0a8JKPX/aNfO3wjsrjTv2zvh/Z3KGOaLxHbKwP/XQV7F4V1+88KfG7StX RI4ra6Js75G6NHJ8jg/TOa6HxJ4Ebwj/AMFEfhxMhR7e4160aN0PyspcFSPwr/Qs/ns9Z/4Kc/8A JxHw+/7As3/o6vKP2N/A0WreOtS8T3yxfYtNi+0EyYwdnIXn1Ner/wDBTn/k4j4ff9gWb/0dVL4C x/8ACNfsZve42vqU7b2I/hjG7igDiviL4ok+IP7Z0VvexvNpthcb7qKFMqM9OPpX0tpuhaNqVksW hXogbZiS3GQo/wAK+fPghbR3vxU8T+JbmLdFfXZWKRhxtDdK+tdRvrXRr23+w2SSSzEfdHU0AaXh fwgkEwtZHVrq5Plhx/Dmun0vwzB4B8W7XuP7QXzPNlBGOvb8Kj069uIPs18YxHcIQ4VuQDWR418V R6dol7r99BvVQXlS3XlvoK+YzHL8fi8xVOUv9klTlGUdLuTdt917t9nue3g8XhaGD5lH9+pxcXrZ JeWz17o+upLrw7e/s9+Ir5JnBexLbSvt9Kv6jD4cvvClhuLeV9gjDHaP7g9q+FPCHxd8J634O1i1 h1iAQ3dowa3k+SVH7ZB7fhXoXin4r+BfBPw7jshrgmla2QtNO4lJyoyqhcHjpjFfyRnngZnGCrQw mBlOUfbylCdtFFxpq7eycGvV7o/ZMq4zy2vzY3FVLScLSj5pvRd1JfdsN1y10+4ubmyijlFqWK/M 2SRnrmuUl8Tad4Ge2ihga5mP3IAMsw9a0/Dl/H4o8LpqekeZdWrxhw2wqQD6g8isa98MwXviaLUp GJnjXaVY9BX9iQweErYSWXVZ+0tHlmm7yaa+1q2r7/kfijxOJo4iONpx5NbxaVldPppbQ988F65Y ePvhzcJ4isP7Otpi8a28rhzjGA3IwDz6V+anxK8NQ/AL9vGwuNHurx9GugjeYAQFEh4UkcE5r6v1 vTri70kwWM0lvIhBVkOCCK+ZPjtF431b4QRQ6lc3mp29hcLOjSsCVKn735V+ccI+GWI4YzuriMBi f9lq3vSd1y/yuL967Wu9rn1vEHGdLO8BGniqT9tC1pq2ve60tfyuav7UGi6b43+DA8U2Wnra6lEq /axGoClwOGH1HWof+CZQI+OHxMB6jRYM/wDf41taZq6eMf2K9TOxXeTTxNu7gxjbj8azP+CaiNH+ 0F8VI2BDLpMQIP8A13NfsJ+fH9Ovwn/5N78N/wDXKT/0a9fzR/8ABY//AJS52P8A2IOnf+j7uv6X PhP/AMm9+G/+uUn/AKNev5o/+Cx//KXOx/7EHTv/AEfd0AfmV4P0NvEHjuzsPm8stucj0H/16+jv idOxm0X4d6dJlLfbNfPGeHcjpn2UVi/BW2s9A8Kah4w1KESRwhjGMZJbGE/U5/Cr/wAOtA1Txh8S 9T1C52yXczB2ZuNqluR9cY/OgD6A+Gfg3VPG2u2vhrwxpF3qkVha+f8AZrdB8+0qCxzgYBYfnXaL oun6f4gjmvLOWzRplW7aEbJCgYBwvTkDOK/Zr9m/4SaJ8LPhGtlY6paazb3NwblLlI0BwyRjaCDy Plz1re+J/wAA/hr8S77TrrxVokl59jR1gFvO8IXdtznYRn7o61/GWM+l9lOHz6vhKuFcsHF8qqQb 57pO7cJKOjdkldONm9dEfVLhufs171p9nt6XX/BMDwD4O+EPxh/ZY8F2R0q38U+H9Os447IapCHk heOMRkNkY3gfKSOOtfLnx4/YH0nWrVL/AOGUVtod9LeI8lpJN5drBGIyD5aqhIYsAfxNfox8PfDf h7wr4E0zQdEi+zadYWi28EW/JVEAUAknJ4A5PJrjfjh4wbwn8ENev9N1Ox0/W0064bS2upEAe4EM jRqAx+ZtwHy85r+aeF+Ns+yvMlichxc0q1WShTnJyTU5fbitOt5NJO579ehTrTdOtFOy1drbLo/y Pxl8S/safF7wM1lrFlat4jvZcqV02bfs27fvhgvXPHXoa8m+Inw18aafNoR8b+Gb7QbSSUgSylUM oAGRgEnI4r9PPFX7URh+Brjw3omqX3js2Kp5lzFHHAs5UBpPvHhTlgu3Bxivz78dan8Vvih4zt9f 8WfZLzUI7FLbA2RIArMwwqADOWPNf3b4V594k5lL/jIMJSo04OScrSVSXbljflUdvee9tr3t8rml DLKcf3E25P0svV7mBovwQ1pvg7qXjaytbXVvCunXkRmihnzPbo1vHMZXU8GNQygnOcnpXPn7PF40 mmQQR3LWwFsZB8vvj8Kp6NrXi/wP4g8S6bHG1vbapavp1/GcNEYpY4yyjPQ4C8jkVmXN4pha2a1T UVAAihH+sz6D1NfutenmNPGVFi5xlHlpODSaaj7KGktWm09mt10PBTpOC5E09b/ezr45L+6kgg1G xtJ9gzLMv3MewPeuSHw/0XWvEmo6jMzWtrK+yBImCgkdTXq/w++FPxB8S+MdL0TS9B12BNSG0tqd jPFFCwQt8zlPlGBj61714g/ZI1zSvg/Yi68c+FNO8dwzCS3029v1gtljEpGRIwDN8gB+71yPevhs 98S+GcnxNPDYvGRjUqWsleTs76vlTtHR3bsl1O7D5Xiq8XKENF8vzPzRaHxL4B+LsWt6DM1hr+ly NLaOFO2dBkYI/iyOo75rS+Iulad8XfhbJ420azFlqqS+XqMAH+qu/vPgdo2ydvucV7J8RtOv28MP FaJZ3Go2U5D3cUgZF2NhmRu6nBx6g14b4E8S2HhT43QnUTI/hnXkNprEAHy4fIEg9Ckm1gf9mvuI TjOKlHZnC007M+VfDKNH8VPD0bgq66rACD2PmrX6k/8ABUT/AI+fg5/3FP52tfEPxI8FT+Ef2pNB 3W7Qxz6pAW4xlvNHOPQivt7/AIKif8fPwc/7in87WqEfCf7O3gSbxz8e7K0SEypGQc44HIyT9Fyf wr6s/aK8S2V7460L4YeFJZLfR5J4oWRmxhFIBJx6sc/QVh/sfQnw98MvFPjFRsvGDWlm+MkO+M/+ Ob65/wAO6Ta+N/21PE97qBNxZWiLFACepBQAjHpzQB9AeGfCOoeEfB6W2lot5bSR8yAZYH3x2rsf Cnh66e+kvLtXQKCVVx1atey0iz8Mzx3EuoXPkMoCxvIWUAexruLe4huLNZ4GDRHoaAOO8L6bf2Wv avcXkewTTExkdxxzWfrHh7xZ4z8Q6nYeGtIN9Dawj7bcllVLVXU4dyTnHyseM9DXf3dxHbWTzSyJ FGBy7HAX3rhfE9003gDVH0xJJvtNo4M1s+M4U4yR1HWonDEyssPJRndWck5JaronG+m2u/fYacV8 SujM8K6BdeEdWsNKutb0nUpPskbyizkLCJmUnyySB8y9+o969NvonutJuIEfa8kZUGvEfAHhy0TR rG4vYNSW5WJZA0jELkjPFe3xSLJbgjDYHrW9enVhVnGrLmkm7u1ru/boTFppNKxgaTYx6D4dm+3T RLuOXbNeYeJtWi8QyXukadp63sCoQ8meB9a7HxXpGo61rOngE/2cgPmop+8c8Zqzdacuh+F3GmWs fn7R0FZjPjPwfrGvfCD9rTSbgkW+n6k+yaKQ/uz1A46Fa7z9qn4Z6Hf/AA0h8beGWWSCVTM0RPzQ S5zIn+7zxXE/GrSjfeBJtdvXmXXLaQlACdqqG+6PevdNMubHxn+yHq7sVknl0uORV9XQDzR+VAHk X/BNn/k+bxJ/2Jtx/wClVpX9GHwE/wCS03f/AGCZf/RkVfztf8E6Y/K/4KB+MYsY2eFLtcfS8ta/ ol+An/Jabv8A7BMv/oyKgD7DooooAKKKKACiiigAooooAKKKKAP5Lf8AgqkrP/wVW1VFGWawwB7/ AGq4ryzRLSTQ1+Dnh6FsLe6hHNdq38QAyv8AM17l/wAFLrRLr/gsDOrSxqRbDEbZy/8ApVx0ry/V nNt8YPhzELG5jls7e32xPt3Sk915xVY/KcTXy6tOHLrGW84Lo+jkmerw9WhDN8LzdKkOjf2l2P0W 8MPLo2hF4EEaFAOF4PWvd/hlqUt9qqCQEO0oA7dmr5l8L+MNV1O3jsk8H61MqIMhDF/V69v0fxVq OgaRHdp4D8UeaoDZBt9px/2096/zi4o4JzWrGpCUKfPL/p9R/wDkz+4eLOIMDONSk4y55be5P5fZ P0R0awH9kxLuIO0bvyrgviPr2n+GNGnuop4xcpCW5Oea+Qrv9rHWrWwk0qHwT4hS6kOAxaMEY9CJ PauP1Lxf4u8UeGbq4vfB/id45lILebEQAfrJX5Xlnghn1KrGpjI04pv/AJ/UdV5fvD8fyvh10sVG vjpSjBvbknr/AOSnBfEb9oDUrB7nVJZYxYW8gV3A6bpNvp7ivlz4nahqnxr8a+GdH0jVprmOa4Mu p3duAqRpkDJ5ALDP3c5rrvih4L13xD8OL7QbTw/q9nc3Tq6STmMqMOG52t7VQ+G3hXXfhN8JdVv9 a0PVbm2jme4MsPlnaNicY3Z/hPSv7T4KyPBcP5JOtgKVH67JuEP3tLmtJLb37b/N7H0vGuAwWaZ3 hsOpTp5bGmpSahJJzi5OzfLzaq13st9z6jsvhR4Qi8JWGgaQk8tuifOzyszEnr1PqTXzL8Svgrrn gbx3rE3gq8vo5rXTILtbbcAsm+WZcElug8smq+g/tTWWl+I1+2+H9etkkIVDsyc547969r8PfGrT PiD8a9Sjh8MeIL1jp0GnyRKItwkimuC33nxj5+vsa4OHOH/EjhbEY3F1dYOnzS5qtNqT9pT39/s3 r5nJnWP4YzqnhsHTkq1GDaXLGXNBckvh926tZO2z5dU0j5W8EftBarYeKbfTvGFrLpE0wCQ3YgK4 O4YJB7e9foF4c8c6xrvgqONm8ydEzHIq/fGTgjjuBms3x98I7OaytH1H4V65JIARHNdwWTsBzwDv JxXa+C7HULKeyuV+HXiqXTrO3VFWFbYZwu3GPM6cV8d4kZvhOJ8HSxVLB0oVk23L21DXZJX51f5n Lwji8tyTC1ebGSxFHT2cXTmnFq9+jXvX2Tt1Ppj4GW48Q+EL5NdRy0TBYs5GRgV5P8fPCA0PS7i8 tVb7OXbksDxj616h4a8fXmn6KVsfhX46wW+YqbUc4HpLXi/7QnjHxLdfCPUnl+HvjDT4kjdzLdSW 4UYU9cSmv56yLhTPK3EFJUqVOKlK38ei736W9puuljz8k4woQ4l+sOThSb1TjKyS3e3zPxx+Os1t L4+1aRUx+4AEw6b05x+Fcl4jvru5/YO1fw41xFe2nh/U5IreWNsgrK3mH/0Gp/if4jlvJr1NZtYt NhxL5O1gzEv/AHiM881laBDa3/7KvxYtbJJb23QQ3CXC42w5QqN2TnnPav8AWHIeFsZgstoYeXLe EYp+/Dov8R+C8W51SzLO8Xi4bVJyktHs27dD6/8A22f+URPwP/6+NH/9NcteCfs22z237N2qygYi v7uJGJ9UO/8ApXv/AO2nGsv/AASQ+ByNKkKmfSPnfOB/xK5a8Q+CdxcaZ+yVZKmm3d5bDV8Ncw7d hzG2FGSDnv0ruwuXV8RHmhb5yivwbR4EqkY7nqf7KDwN4u+K8s022SfXVyT3wj4r6D8SiOPXXF5C 72+CUAXduO418s/Bey1fw94w8ZanFpV/e2mpav5kEcBQNHsLKQ2SOvHT0r6U1bxVq8VmWl8Iayvo zmI9/wDfr+UOL8pzXAcd1cywqpyaasnVpLVQUXpzp9z+y+BsFgMVwXh8uxvOlVi72jK9nNyi0+SS 1Vn10Zi2Woa9c62n+gpDppO1T/F6V0WgQ3esane21wkcURxiJHB6Z5rHXWdXTwRcXi+FtcWMA5lD RYXg/wC3WD4a8YPo/iVtQuPDmuSxbMMIzHnJBxn5vevazjC8X8V5FipyVJRoNNRjOm3J+vtHblWv TqcOUUuEODc7p0cLGpOtXUoqU0/ctrZLkjdzdo7PW1rHS+PP2SPD3jbw/Fqmny3ek62HVpLiFA+9 ADlcFgOpBz14rzzwH+ypY6pLp/ibXtS1HUoIlZIbNlCqHimID7g2f4DxjvX05pXxyms/C0cr+DfE bwFdwdTF0/77rnfAPx4sNO+GCWcng/xBdbZJ/wB5H5OPmlkbu/8AtV+d4HHeJtHhrF4eNVO1Wkl+ 9otqLjWuubnuvs21v26nJjcnyypnUcS8BzT9939nUV3zQ1cUrPd3uuuvQ2bSxs/Dup+VFCsaKPLY ZwMVd8TeDtKuNG0/XdEu2E+0rPH14BY55P0rx3X/AIjXWsXN01v4U1uIO+5NzxcDPf5/Sn+GPGOu yaffoPDOtXcKowyjx/L09XrxMV4ScX4bDrH1qappWvzVKSTT9Z6n6VhOPskr14Rw+NvWV7wUZNtL dNKLtY6a98QCcLYy2yPICASCQeMe1ew+DNJ8MXvwyvxrEZt3lXKksRjpzXzp4d1m7uvEbu3hLW7h 0bdtUxE9T6t7Vs+LPiRc6Tprxy+FNdtESM7gxiAwPo9eXjvD7N8TOGEwyipNp6VqV7+S5+p6+fZ5 gJU/Y05Tppe9KXJUVktW/h0Xc+JPgzcJZ/tAfFG0tXP2ePUVlQ5ycsxBNbv7PVqln/wcV+AreMYR NcnUD6Wc1cj8LLrzPiZ8RPEFvp8s0F/qKoqWwUCLDZwQSOee1dx8AG8//g4k8CXLf6PIfEFxm2k/ 1iZtJuuOPyNf6EvKcXTo89RRVlr78H+Urv5I/wA8K06SqyjCV1d2dmrrv5XMb/gptZNqP/BfX4qW SZzMNEXj0/siyrx/xhbhviZo3hkvizsdNDIn/TZuP54r6a/b809bz/g4g+KJDLM4j0b9wv3z/wAS ix554rwPxBPG/wC0rNcNbyQ+XcInkyEbhjHHp1FaUsoxNSCnHls/78F+DlcydWKdv0Z9c/DS11Lw H8LbPS9G1VLO7mCvc5RW3dTjke9foj8FNSfWtLsmd3klxiR2XbuI25I/Ovz48MeH7nxH4gjvToPi iWONFZ0tZoQB/d4ZuhxzX123xQf4b+Ff7Yn8EeJLKytI90jN5ARR14Cye1fyN9JDheOaShl2Aw9O WMk03JVKKlqrRi/e5pNq1l007n7L4f4jEUsvq1K9WSotNRTU2tHdyWlkl1fXXsfpBZ2KGxXL7Yx1 zXy3+0V8YvDvwd+G2q61Lf20UyjZbxMSWmkI4UAAnr1x0r5H1r9vHVtf08+HvB3g7Vn1KcFLKWeZ YwzkYG4b8hcmvKvjp4L8b/GX4QeHBrPh3WNP1yyInkmaSMwmZo9rhTvJ25Jxmv5w4Q+jZm+X5thP 9ZowoUZy1vWpczS1259E3ZbXVzfAYmnKjWr4WftZx2ioyabb6u1ttbHlnhL9rjxh4l8f3974m1S2 0u0vHxaWixKyW42gEb8AnvyfWvNdWv8AS/HX7bXhaG+1Vn8Li5RLlvPKLg5LYPbnHNfOWt+GdU8O eNbvw7q1vJFfWVxsmSM7ypIBxlcg8EdK7S78E+KvCmlaB4kawnNnfp59sHIbeucZ2jkH2Ir/AEcw HAuW4DBvD5bClQ9tHli4Tppv3XZpqV5NL3k7vvc/Lq+bY6tXVTFc1T2bu1JOy11TVrJN6Wt5WP01 n+B/hfxe8tvpV+ZdHKBLfyQHaMgcnzC2TXwx47g8ZfBb4hajFYzXLaXFfyRRNMN0blcZyAcZ5rpf h3+0zqvw81FIZ9Jnl0wsGntwGVxgfwZIAr3Xw5daB+1HpeoaHdeFvGey41qW9hbTZrcOC4A2ne3t XwWTYDjrhSrjZZpVWJy+EYfvHOkpRXNu/fTuut2773Prs1r8OZvhqc8JTVGu7txSk7tL0at1VkrH knwy+OsHiDxLawXlwugeJ0BFpeIgKEEcj5uOa+pPFXxA+JN34d0i40OQLeW90q3rQReZ5kZICsQR wSc5x0rxXUf2Po/Bnx50HUrHSPGa6NaOJbiz1J7Z5pXDZAG19u32Nfdng+O80SOe+ufhl4seGXCx rClrjH/fyvz3xK4m4dxcMPj8Pg6OKq2unKrRWl7OMrz1TTbW9nZ+vbwvhsZhaFVVcTKnB7JRm229 bxaWlrJPutD6o+HmiWviX4EWV1qyN/aMkHz845xX5mftg+HG8P8AjrRvtMr2WktA/mTg/NndwoPb NfolpnxD1SDQ4bWx+FfjxU24Uq1qM/8AkWvzv/bHvPGuta7oGr/8IbrOm6baoySw6tcRCN3JyrbV cgkepr+fvo+5Dm1Hj7DzlClTg+dW9vSd7xdrJVG79LJG2d517fBYymnLllZ25ZaWafbZdT8uvFVv a3WsR2sb/YQ8/wC7lmyBtIJyfrivUNR1e88R/Er9nzWLwrJINYtIC6nqI3CivK/HOt29/rSjW/K0 +5MqqEi5UBc9K9M8KwLc+E/glcxwSlLbxjDGLrI2SDzQcDnOfwr/AEtnk+JhFyfLZf34f/JH5Eq0 W/8AgM7v/gpoM/tI/Doeujy/+jqj0OKTT/2MdMtSdirYzXEeR/eXFX/+ClFutx+018OlaeKEjSJc b8/N+/8AYUoubgfs9eHbCXR78QzaIyJL8m1/9oc5xUYfK8RXgpwtbzlFfg2mOVWMXZjvgR4c0zUv 2d/3upyadd7HmgdY929wScHkYr2mzvbHSfBNpeXz+dcsuYVcc5r5l+Bur61p/ga9sBpcmsWcMzKF hwShz0OSK7Hxj4wmh8UaM19oepWUeCkMW0ZJ+maxo8OY+FepUlUTjK1oudO0bb296+ul73s9joqY 2jKjCChZxvd6632v008j3bRNe1G+aWW9hSK3xmPA5xW3Bokvi9XtbZA21gDlgMA/XrXnNnrGow+D 9PuZvDurC2u48wPmP5sH/erq/Cnja78Pm/uE8M6zNLLt8sgx7Vwec/NXkcQ4DO3lVWrlEadSttFO pT5b81pX99ba310a+R6WTU8AswhDMnKFPd2Tva11bR76dOp5J8W/2TfFei+NrTxJ4Mhk1PRXjM+p SvJHD9lcHoBuywI54FQ+Ev2SfEt1c6r4k8bXE9jFbkT2EMbJMt0m3dljuyvPGMV9UeLf2giPg/qt rL4M8QIGsyPMLxBR7n56saB+0jpmvfB+3hg8D+IDObYJn9yUOFxnJfkV+HJ+M8MhoUKmDg5+2acl OlzctoO7/eOKvqm+Xbz1P0CNHgn+0J1fae5y6R97lvqu3Npo7X3+44zwONO8PQ/ZdUVV066RUlCk /IB0wBXTfE2yTwrpkOteHPL1DS5UVslvulu3XJryDUNdu7yR5f8AhH9WiQNuAXywB/49Va98YX0n gabRrjRdY+xlxJuGxmTHYfNgCv1zH8B53HOKOZYKqo81lVpupBwlHRcy97ScUkk1ult3+Jwud4Ge BqYPFRvypunJR95PdJ6fC3fTpf7uq0XxPY6xpUt1Af3sQxNGeobHSvTfH+keCb/9gXVL+8222qNp 8k0IMhBaTHQV8jeBPGGneHvjMyTaHq2oWlzC5ktTCrsXI+U4Jxipviz4oi17RdVuL/Tdb063hgKw 2iyqtvAADg7QxGfUgV5nF3BefZ1m2Dw9B+xpUJxqyqKpC0rXXs7cyb83skzryXMcvwGXV61SXPUq JwULO6Wj5m7Wt5b3RxfwBk+1/s93di5LQ+dcRADuoBOK6v8A4J0rs/am+MSjoNPjx/4EGuK+B7S6 V8ILeJdOvbtZ5J5hJBt2kFT6kV3f/BO5VH7UfxdcTRyF9OQlVzlf9IPBr9FxGV4ijDnna3lKL/BN s+QjVi3ZH9L/AMJ/+Te/Df8A1yk/9GvX803/AAWLjab/AIK+adEgy7+BNNVR7me7Ff0s/Cf/AJN7 8N/9cpP/AEa9fzn/APBVvT2v/wDgtroCtGxhHgbTiXx8oInu+9cVOlOpLlhFt+WpbaW58U6/af2b 4N8LeCYIxHJcWv226Yd0XkA/iDX0H8LvB1/pXw7tvEAtyup3EmXB/jQ9OPbA5ryDxvbW0v7QdqqX CSQw28VvvjkyoU7SwyOPWvqj+17m0src6df6MbCGEBUe7QMSB0xmuj+zsX/z6l9zJ9pHuez/AAn8 V/E7w5+0f4K0+18VX1xomp30dnd2Uz74o4mOSqKeFPHUciv2OtUvJdOWNzuz3r8NfAvjK30f4yeE vEeq6rp6adY6jFcXKRzIzBV5OBnmvr74o/8ABQfwj4Y+GmPhtbR654tW7QSwapastssBV9zBkkyX zswPc1/DH0l/BTiLiHiXAPJssk1ODU5xhZc3M7c8rWWnVn1+S5vSpYSSqVFo9E3rt0Pa/wBpH492 f7NXw7tfEN7p17q02p3L2VpHayKnkymJnV2DAggY6V+S+hfHrx/8eviPp978StdS9GmofslvBCsE JcHIbYoALfMw3dccV4D8WfHHxC+N/wARdc8feK9Vt5pLhvNhshPiOCMFlSONCf4Rxk846k15FY3u p6TcR/Y5ZbW8inyCuevHev2zwZ+j7huD8BTrYnDKpjXrKpyt8t7+7Bvaydm1bmtdo8zN8+njJ6St Hb19f60Pr34w/EvxD4U8YaRpHh9YsTQedcExhmYbsbRnp0PSrPh7xfP4qs0trXUb3R/ESxZEdwP3 Uh9Bn+VfLtzrGq3WvQahrzvdo7KkkobLYHGPpXr51bwoNBtzbSFJo1DI4OGU+v1r97/s7Ff8+pfc zxOePc6J/iPqPhTxbq2neMdPgv4pbhftEiIAeIkXgf7oWuk02y8G+Mrq31/wFc/ZNfsJVmjs5nwp OQcFenbtXm2qwRePfhrqdzBLHe69DcosSvIqSTfuY14BPJypryPwro3jHS/jFYWtjHJourRSBy1z KI1RSM8knGMV6Wd4KvLExg6bbdOnpZ7ezgtjOjNcrd+r/Nn3jrPx6+Otp8cdN8R3nie4sbvTIdsV hbwrHbSqd2S0ajbIfmPJHYelfVXgzwBrv7V/wL1LxnqXinTZ/GthcLZ2xW2aGOyizvaORFwruwO4 N2DAdq8NtPB0Xj/wmbaPVvCkerW9sGFxd6rFCgk4yAxP6V98/s26d4Q+EX7PEelXvinwNaeI724l uNVMWvwyLI+9ljOdw/5ZhBX8g+Ns8q4Qy+lWyXCqnj6UoRh7KlHnjF3k1Jct/ZvXmS3uk9Gz6fLa 1bESkq07wkteZ6N6fifjhrOktpnivWPDrXp1u5tb+a28kfLErRuytux2+U8Gvm/xlophubi1gSOW 5F3sEUIzgv8AKAv4kH8K/RH9tLX9Hn/a5il0jVNHg0WTQoPtNzpEkcpkkM05OShPPTJ96+AfGL6f cXsE/huabzIXhIlkJDNL5qnJ/wAa/oPgnH47PMhwmY1MNKEqsFJxad03utl+R4uMpwo15U1JOzOq +LuvQeMPAPwe8SOjf2sbixtdQdurTRyYckfiK92/4Kif8fPwc/7in87WvmeaKS7/AGWvC0s80bXd n4yVXiLDfhp4zux1x15r6c/4KgQyzXvwcWKOSRv+JpkKpOObT0r6WWFrxkoODTeys7nPzJq9zzv4 MxLpv7GNtCqESvqT3OR3VYmH8zXP/Bu1a2+GWueKZFBumv5X3nqQHPy12/gd9Oi/ZV8KWRvLSG6m sbgSI06qwJwOQeR1rkPgRrmkr4I1jw5rLrbRpdSkGUhVYF/U1p/Z2L/59S+5k+0j3PqCygbxJ4Zt 7zVcRxR4fIPVeuD+Vb+m67pN3dvp9kHAjGPu4WvI/EXiXTLPStG0XS9XtDatI3nsJhyCM4610mha nokOjK0eoabHKVJz9pTJ5x3PvR/Z2L/59S+5h7SPc6/xFbf2t4Wu9OVvMikUpKoPLAgjGa+HNe1D xZ8MviCdH0XVbxNHuT+6t5TvTB4ZcHvz+tfZVhrumw302/UtNdCuBm6Tr69a4D4laD4a1zTbfWf7 Q037Zp7G4+S6Ql1A5XAPJ4FdGFy7F+2h+6luuj7kzqR5XqfOupePviF4i8V2nhBdQfT7QhIQkMYj PlhRnLAZPFfX3w/sp9B8C22meZLNaIgCvKdzE9Scn615f4Gg8L67rzeKZpLCG4REghWeVUK7UALY J7/0r2G81jTUslgg1PS8qR0ukxj860zDLsV9aqfupfE+j7ip1Ici1Onu9UtdMsTc3chWEdcDNLa3 dpqFstzBKk1u4z+HvXAX+p6feaFJbHUtNkOAFU3SD+teZab4pXwnrupJPqFo1lcDMKpKHCkDkcGu P+zsX/z6l9zL9pHuel+NdF0vWfAupxwx29zHtKyKeze9eFfs6XElx8H9a0RzvMV3dWPJ4xIK23v9 WvNL1DVbPU9LsbK6jyIJLld0ncEjPBrkf2b5IrPRNRa8ura3afxHvAnlVDjoTyenvR/Z2L/59S+5 h7SPc2v2C7dLT/gp18RLZAFSPw5eqAP+vy1r+gz4Cf8AJabv/sEy/wDoyKvwA/YYVv8Ah6P8Rpdj iN/Dt9tcjhv9Nteh71+//wABP+S03f8A2CZf/RkVc1SnOnLlmrPzKTTPsOiiioGFFFFABRRRQAUU UUAFFFFAH8rP/BRjYf8AgtnaCTGzyhuz/wBfVzXjfxS1Ke0+POjyRBrWe3hVYJFAO0qBhua9V/4K VzC3/wCCyyzt91IFY/T7XcZqh8a/Bn9q+E/CXiewcQ3Gr6StxYsOhkUASIx7DkYqZRUouMldMunU lCSlF2a1TW6Z9XfAnxXpGv8AwtS+l1GwbxGIMXVpBIC0TbmC5XORkDNdhrvizVUa4sY2dt2VXg+t fl58OfHN78O/iimv6jp1xstbf7PqMKOygqWXMoA+/jBx9a+49H+Kej+LtFh1PShvjdAyl0weR71/ GvH/AIX4nLc3q4qjSdShL3r20hd/D6Lof254U8c4PiClThjKkXivh5ZP3pcqvzK+90m3brc9U8O6 Haw3aanqp/eH5grt1yPT869V/wCEgOoxQ6TZzxRIcIAuBXydF8QLpvGdvpksZ2SMwBwcKB707XvF d94euZb6zUz3SRl4YtxAJxXyeN8Nc4xeOo0Kkf3lSPNBXTVu+jdrdb7H12O4hyXF4TE5hVxK5MO3 GTaaSmre6k0rvVWte7eh9l3vh/SdH0Vb28uILi4IGFaQMefYV8u+N/Ed9q3iKawRW/ssDlEThiCe vtg18YXHx++I2t/EK3tLYQ2k8k+wxSb3GM88FvrX1Ja3d68SzXflSNgA7UwM49a+yyjwvzfg+rHN MZ7OvyvRN6J9LJ217W1v0PgeH+J8g4tVTKYYmtGpUi7SUbWXVac1lbduyt1R5/411DwrpelW7eIU ihDSBYU2AyP6kDrgcc+4ry3Rte/4Rn4gXOv+DJ2uhbxo7KpyWyzZ471v/G3QYtQ8AN4ilVY9Rtrh IwwPylCDxj8BXi/w28Hat4m8a6ZZafqh0lp5wHlVvmC4Y5xnn7n61/VXDlSGLyHF4zMXzQq0eaUb XjGN4tpLd6bvq9VY/nPiai8s4jp5ZlUOSeHqezjO/vzne3NJ6LV6JWSUdHfc/T/wf418bfGPQtH1 mylvp7JmKsGQoQBIVbIx9a+/fAsnh+08Hw6PePGl8sQEpLd8nqc18j+CrnSvhj4ESyN9ZxQFGAmm KpuyST6c9TVfx9+0d8Jvh18HbnWpNWuL/UwQHWIFsMzADpkn71f5jcWZHVz3HvCZRh5ex52qXLHV pvTm6f8ABP6O4gjiMXgKVLERjT5IpycNIqVryk77RTvv0P0U0r/hDtP00tLqFkhU7iXnAx+tfDH7 XPxy8O3HwoHhPQLux1GS/vBaXES4LFGUgtx6Zr4B8V/tiy+LLQweFoxh4iPOuJ2hVSe+DjNfNWre O/FV1qV3Pcz6bcXKxGQET7lAA6ryea/c/Cb6NmMw+Pw2YZxFRVFqSjZOUmndc2rsr+j0PxHiTM8m y+nUWHxLr15aJr4I33fN9p9raa76WOI/aC1Owtr2x0OxihjeMEybPwPWtD4IxGX9k744JJ1GnW7D P/XQ1876xqM+v/EBrzUZZJHlmzKSd2BntX0lpFrP4M/Y1+JeqMGt7bXbWG2s8/eYBwQfyr+4j8cP rr9tj/lEP8D/APrvo/8A6a5a4D4FaZcXn7Cwn2qI7TWIpAcff/cuT+QBrv8A9tn/AJREfA//AK+N H/8ATXLXoH7NGgxf8O9NMFxBGY77UkJcjn5bd+B9c4/GgDG+GGoaYvjC60fLC4hkjmkQrgN5m9hg 9+lfSXjPVdA/4RS3t2t0jlIUFthzx/8Aqr4rvYb/AMEfty6TbSxvBazF7aUOcDeod1P5DA+tfY+s a7pOvaDDDLp5UiJdzrgc+vSv4n8aeH/qPFEK0byjUXO7O1m20/XVX+dj+2/CXPKmb5FhJ1Kb/wBn fsm4vpFRcXb0aT7tN9bL1XwXpHgzVvgdeWLPBLcyxuAjPyQVPv718o6/4Xm8PXl4Y1QW7NktI+cc kACu0stetdEcR2Uk2d37sMTjnsfxry/4i+Kb7WtPjCeVHbxk+YUfknIx/KvL8KeHuJo5xUll9Wca NWXvye0euq2228zTjipkmUUq1bN4xrKXvwpzaU2729zqldrma6K7TseTy/FLT47LULS5n/s+W2kU GGXq2eMivOvDHjew8MaReNqF7i3n1J2AUljgkngeleeeNbS617xHFJHbRQFMxsQQM4IwSfxP5Vyb +F7pmaX7TDNHGRv2yA4HfFf3fSyrB/2diP3UffqU29Fq1Gpq138z+Mv7ezKNWk415J04yUWpNOKb V0mtUnd6eZ9veANWf4h+Lbiz0aNjpgQeVKQUdyDzwe1fYPhDwGNBguIZYPMnuDlyzZ3DgfnxXwR8 AvEb+GPG0UNwkDQqgWORCcvk55Ffp/b+KLC7sLHUIAEkY4CkcZBOK/iX6RmbZ1RzhYWLl9XnGNld 8ra8tt+p/SvhbSpQ4bpVqFGLm5TUp2XOn0Tluly202scCnhrw/oWqXF2qypducImcrnv/OvDfipo 7a7b3cMbyQpLE6NIpwUz6V9ra1ofhy/0FNXkvHjuGQsIVAxuwB/SvmP4qeJPDfhL4f6ze6lIpZLf 90qkbySwUYHU8mvxfgrOsVWzGlOhGUqt0l3vdWt6H6vlmfYerCpLF3cXFxnzaLltaWumlr3Pz6+C nhm/h8G3UK83UmoGaTJ+8EOGP6Vpfs/gj/g488GBgQf+EhuMg/8AXpNXs3wd8L6hpfwe068vY3ju L2YQW8jDmSUgM/P905AryL4FpKv/AAcm+EvOCq//AAkt1kKOP+PSav8AT5bH8BPc9D/bLVG/4OWf ikrgNm10jaD3P9kWNfJPi65Nz8dNceRPLmDmRfYg/wD1q+pP23dQj0z/AIOWviLdygNGq6KCD76R Y15T8ePh/eaR8Wb2bSCGviiXMcWQFlgcbhz3brxTEev/AA5+IkOm/sq6xrNj4kC+OZr1IGsRKjPD EhGGCnnBDHk+lefeIPiV4v8AHV7b6Fq+sXF8kbeYluQqxlgMZOBk9elfMtrq1zpGvLq3kyWttO/2 e6jwVKE98fWvoDQfCelWwju0uZpDcICs7SfNH34r5rA8K4GhiquLqQVSrObkpyjHmjokop2vZJaH tYrPcVVoQw8JOEIx5eVN2erbbV7Xbep698O9O+H/AIT1W28e+K5prrUXdYbWxik3+W2T87IpyOV4 zxzX07d66vxtex0DStSvtL0YszX52iB9oXC7S3+1ivkI/Y7tV01onuGRf3ku3G7PHNP1fWbnwp4E kuNIW8uZ4VyEjnZDj6g5NfJcR+GNLM8bLMliZLExv7OUrShTvppDut73+Kz6WPfyrjWWDwywfsI+ yfxW0lL1l57Wt8Oh9deK/DXw/wDgn8LWltP7I1DxLJbmK2m1CRZZrtwcj6tz2Havnm2kluXfUtXf zriZjIxYDamewHQfhXzH4d8WeJPiJ8atHXUZrh7SzkMpieZnAPqdx619cxWtutmXuB5gI+6eRXfw FwNUyCnVqYvEvE4io7yqSVnbpFauyXa9vlY4OJOJf7TVOnSp+ypwVlFbX7vbX+u589+IvGPhHVPi I+mXFtbCyVCkl1sAIf2rI0fxd4g8BSPr/gi+urZo7s+Vc22CUHrjkdPUVmfGvwrp+kazp2pabBHb fbVYyxIMAkHqKT4UeBfEWvfELR7Wx1q30yxlc/amlIdFAHdCec1+l1p4aGT42WKSdNRTkmrrlvrd a306W1PmcNSq1cTShS+Juy6avbXofVHwwg+J/wAavjj4Z1vxcnia78Pw/wCkjWB8sJKEEKWUBcE9 q/Y3R5/C2oeHYrQSwxmPG7LYHA+tfnzP8Vfh18Av2d4bXVtThi0K1mSKcaegml3ueSqKc4znjtXl fi7/AIKCfBSDSbS08Bf8JBqFy3E8t5ZvAEOOMAjnn3r/ADd4/wCHuIvEXM6f9l4GVPDUnKFLkjyw STveTdoxbVtPu3P3h4TAZLhlQxuKtVlaTd+Z7JdNWlqkfsTc694C8L+EZtSvtZ020htotzvJcDgf nX5RftW/GLQ/iX8XtJ8N6LdC/wBDtbYzSmNAIGfOQd3cj618XeKv2gvE/j7VJLy3vbdI3TYBNdNE hHoULYNeMa9438RW/hjUZw9kHU7JHSTLDPoR2r978Jvo54bhnHUM1xtVTxFNaKMVyptWu27uTWtm uX0Py/NuIIVFUpULtS+03rb0W1+u55N8Y9YttQ+Ks8FikaQwfKAg7ivp34Xxbv2b/g/I2C6ePIgf ++1r4p0mKPU/GRa6MsrSE47lmJ4FfZ+ix3XhXRvgj4Suj5Vxc+M4Lsxj+6XXg1/T58md7/wUpBP7 UvwzA6nS5P8A0eK7620uZv2evAGoTIojbTmiKgdt2Aa4v/go4hl/a7+FUY53ae4/8jivsKz8Jref sx+CdJFsn2gaMXi2gbmJOaAPhf4ERDQ/2kdWm1OC5m8PLqzedGuSg/unA9DX1X8SNS8B+OPija6f FaeUtjgm8giIZ/ULngivAPh/rtz4Q/aT1qwmt4CrS+fDFcICrjOGU5719KeIvEXgnV9estce0TTL 2H5RHDghs9sACvznNuH61fivD472c3CNOUeeNRKMG+rg9W/NXW11ofXYDNqdLJKuG54qTmpcri25 JW05lpbydutnqfQGu+E/BWrfsi29t4ZMb3Ftaq8PktufcP1znrXxPfatLoOmGzktriWe3z5ocYct 1PWvbU1++03QZbnRJ44Ukj3Ksynbj6ZGK+dvFt1qJurzVL0QSzzsZCI2G1j/AErzfDPgzOOGauMw uIr+2oTm5wk2+e8rXTW3zvq+xfE+cYDM6NGtTTjVStJW91JbWfl0VtEcpr/xD0nVfAdzbR3UcNxc I8XkOcMpHrWR4d+Ieh+G/DumafqdyVPkdIlLnPocV4nrWkXWqeL571UhtI3+bqFVfWs+Hwzcx3sN w9xC8Zbh9+7mv2qX/Ivj/jl/6TE+JX8R+n+Z9ueA7nXvHWvJb6ZpqSxN86ZyFVD0L+lfUnhj4T21 p8M7/RdVE01xdSGaeVmBaFu2w+gr5P8AgF42n8MeMbW6u47dLQjybpUG7cg6EehzX6O6f4l0qfV7 aS3likiuVBYHHQjmv44+kJxZxTgMZSwuGjy4fScZxvzOUdWm9lZ2dvTvY/cfDLKcrrYepXkuerZx cXayT7LzXX1R8qXGm/Dr4QajfzXF/qGp6vqMGwxywLIYlGcEbRxmvib4px6rrvg7UZtFRl06YlpX kO1jg9hX69+MvCPgVNQh1See0udQ2fKs6JhQR79a/Nz9pS90SPxBZ+FPDK+XrN9cI7tBcL5ZjJ+Y BAOD75r1PA3xAo5piZU5wqzxFaKlUnNq143WitGySa1V2+2lzk49yLlwMa9KUYUacmowSafvWbu9 bu6fZeZzvw00G7tPhfpojXdFa2EnmD03ISKj/wCCcv8AydB8YP8AsHp/6UGvdPD/AIfu9F+GEFo0 XlXkmlyT+W6jmMIQM+9eG/8ABOcFf2o/jAG6/wBnpn/wINf1Cfjx/TN8J/8Ak3vw3/1yk/8ARr1/ P3/wUmthf/8ABfDwzYys3kP4D09mTPB/f3Vf0CfCf/k3vw3/ANcpP/Rr1/Pz/wAFJLhLX/g4G8HS yv5cX/CE6aHb2+0XWaunUnTd4uz8hNJ7nxD49eL/AIX5qraeyRpG4dFC4BxwePoK9x8PeKdK1rw1 9qexsYobeDZEdgyz45z+leSfGjw3e+FPi7qGrm3eS2D7JSvYNyGA7gg9a4LQdfi0jxcyGUS6Zeph S3RXxkc+/P6V0fX8V/z8l97J9nHsfQeteJrWfT47PTbO1iyuJXMQ3dOQorj47Kaz8Y6U7wW02k3W Uw2N6sR1Pvwaj0XQ9ek8Tx6pc/ZEYLm2QDKy+x9PrXps+jwz6JDGzLa3W7d5e7OxhuII9eTR9fxX /PyX3sPZx7FVvD9laeH0dLeKeMys64UHfheFP45ryXxH4IsYbfRb13a0ub+7X7SN/C7jnAHbivdJ /s+l/D7fq80FpbxMWkdBwc9f5V49rvi7SvFXjrQdJ0yM3TPepmUgjYM44/xpfXsT/wA/Jfew9nHs eoaP4B8P33h9bOWzUWigAPnDye5Ncp4n+Gvgy38T2umaddSWd/coxjh80sMj19K9/s9OP2BYYsQR KuFPcYGK+VPiv4c1Xwx4yh16LUri6hvJnMcjcNEy4OPpzR9exP8Az8l97HyR7HUeCfEDfCjWLm5F rp93cWF9HPb/AGuFZV81ACMg9VOeRXe/ET4y6z8T1037XH4bs9Vt0dbf+zNNjtw+7bndtHONox+N fNWmeIro6ettrenPrGmXUoMrgHzF6AlSO4GKnbxb4S0fxw1udXu/ssa/6JJNB80XH3WA9OlXn+Ep Vc0o42avVp06ajK7uk6cbpPzu/vJoO1NxWzb/M/Yf4Kfsvwa9/wTq1bTNS8SaTqmueM0tdSa7FmC 2mFo4XMHDZ42kZyvXpX158Jf2fNB+H3wE0Pw9rEWhay+mrK0t7JZKu8NK8gJ3E9A2OT2r8o9P1Px p8Lvgf4X1Pw18VLPSbfxNp0OpWmn6bqbLcGKSPKyyLwB90LjOQe1cpqX7SHxDsdDvdL1n4s+NdZg uYXiuLU6i8iyowKshAOMEEjFfxtm3hXxrxPVxUlnyqYOrWdRWUoSU0vZ7pbRS5bJ2bV9z6v+0cNh 1FKjaSVuj03/AODfc98/bMj8FR/tM6VoXhLTtNsNRGkxy3d7E6SRTqXlCxrGBgMpViWzyGHHFfkt 8bpItI8YQ2VldO0+0PKV+UbsnPSva9R+JOjLqH9tyR3MlzaIwgLoTwePmP418h67qF940+Kc9zO6 mWeRivOAFyTX9PcFZTieH8jw2WfWZ1fZRtzNyu/PWUmvJXdloj53FTjWqyqcqVz6V8F7tR/YMfUJ wr3SeOLYGQj5sfKcZ9K+lP8Agp5LJFqnwZaNmUltTB2nGebTivmrwzcHRf2Gjp8qHfeeObIwbuMo SATX0p/wU9G7V/gqvrJqQ/W0r6SWJrSkpObbXmzFRS6GT4W0u1n/AGWfh7ciziI/0lJ5TGMl/kIB Pcf414p4N1VPCnxZ8aaXeafbX2nx3yKnmpuKl8dB6ZNfb/grwbHc/sIeCoI2YXTrdTbccY3REHP1 AGPevmzw1b6TF+0P4rs9RtBNdSmOfPdlwAcfQ4rX6/iv+fkvvZPs49jf1vwtonjHwvaPpkdvb3ll J588MeF3jsBXouhfD9Y/gtpfihrbT1sLy4mhtVV1MheLIYMvUDI4PetS28DqPFFnqulOIrRseerM B8vcGt3UNBTTdFl/s1nK7mfbuyBnriuevjcwcoOnXaSfvXu7qz0Wqs72d9dE1bW6qMKet4nC2y6V b6H5uoWljbzuxynlr8q5wOfwrM1m70OXwVeNbwWM0ZhkVXSNT82096ydVnRoZPNH7sDLll7V8weJ NT1Cy8USWWiX10dNaXz0j7Fjwcfka9DC5hivbQ/ey3XV9/UzlTjyvQ+gfhg2mQ+BdPhlt7VrmUNk yoD0Jx1/Gu7t9R8OXWpy2ccNq7pJsmcQDA9cHvXxBNe69eXFrbSvcJFCxESR5XGev519E+Dbi3/4 Ra3FqZC8ACSq6kMG6nrV4/H4pYmp+8l8T6vuFOnHlWh6vHo0Eseovc2VkIkLLaiNQd69ifQ15hae E/7a+IdnqFnYQwadaMwnE5CqxyQMZ617F4Xjk1C7CEgRKuZF9fStjxBoGgzNbWs+qHTXCnbGo+/k 9TXJ9fxX/PyX3sr2cOx8+/Fa+tNF0HV1tbOGSaSApbCGMELj6dOKrfBDTVi8A+FxdWkczTXzSSO6 bjgsSM10nxLi0zwZ8NLwWoXVvMj/ANY/JLMcYr034SaKll8O9LmuISyny4mA/hxyWHr6Gj6/iv8A n5L72Hs4djxn9hGR3/4KgfEZGYlE8PX21c8L/ptr0r+gX4Cf8lpu/wDsEy/+jIq/n4/YR/5SjfEr AIH/AAj99jP/AF+2tf0D/AT/AJLTd/8AYJl/9GRVzzqSm+aTuykkj7DoooqBhRRRQAUUUUAFFFFA BRRRQB/Jj/wVNmaD/gq7qUy5ylhnj/r6uK7Hz5PEn/BOfw7dSFvtWlTh0bPPlsmSoPpxXH/8FS7a W8/4KxahawgGWWx2ICe5uriur8No9n+zlpOgsN8TWKpOO2cYNAHjt7aL4g+Hc0ohR7y3ThSMGQd1 P4c1w3gDxZJ4N8RwRass50o3LGN0OdgIxsPYYNetaBMkXie4s5Yo0ijnKScdVOR/Kp9b8EaVpkk+ pXMC3Wj6gxTyGX5VJ+63sc1hisNDEUZ0Z7STT9GrM7ctzCrgcZSxVH46coyXrFpr8UdxF8U/Bepa 7JHbh4bm3coHkUKHUfxKfQ1U8Q+N9GvIXmF0CQBs8tsngCvk7xLoj6NqkCpdtc2zZAduHjGcAe49 6qRXvkaVJbMCXzw2SDXy+S8EZZlleFeipOUU0m5N2T3sun/BPv8AinxZz/PsHUwmJcFTnJSajBJt ra73drLfXRant3gfT11r9oQ6q8Me23jLybQMZOdv419TjVMaH/ZreWjeYJOWwemM4rxP4OeHbjTP DE2pXoDS3qq8R6/JjiuE+LfiUyfE+2srK7uIRaw4laCQqScnjIr3sxyjB49QWJhzcjUlvo1s9D4v KM/zDK5VJYOq4OcXGVraxe61TOz+M3iGC60Ky8J2L+ZLJMs1zIp4AAOB+tfIPi/xTq2larYnRbu7 0m5tJCqzW8hjcfKOeP8AeNd3Z39xceII5GWW9cuBHvYksc9PpXXeI/hZb+I9ckMlwYZzCsxKqMZb I/8AZRX0eWUofV8RBRXLyWt0tzR0seTVxFV1VVcnzXve+t+997+Z8ua7458ZeJ7CO18ReJtZ1m2j k8xIru5LqrYIyAe+Cfzqx4XstN1XV7e3vp3hYSDbxkHmu5f4SXcviu806zubSd7b5pU84B9vHIH4 16DoPwO/dw3YvJ1kHLJtHr2rx6GHpUYclOKiuyVl+BpXxNavPnqzcn3bu/xHTeGNO00x+XYJdSvg h3+4R/Kp5vDNjOu5LY2zlCG8nuPpXpdjol7oBht5la9s2OIzMmdvtXc21oNTWS303R7ea/WL5VVQ PzNbGJ8Qaj4Rjt9Wma1aVwDllfhgK9M+POszN8AvhzpNnKEsUgxKsZxvbYpw2PevdNV+Dut3tm95 NLbWmpSQsfs8S5De2a8h8VeD9Stfgb4k0HWNMnbVbdRPYsy7igTl8HsMUAfYn7ZttJef8En/AID2 kS75ZrvR1RfU/wBly8V9kfs3+D9LsP2BdG03WlQGBre7y39wsgf6EAmvmr9ojTzqn7CH7MFiq7zJ rOi4XHX/AIlc3Ffpv8LPBXhnTP2O5YvFTvHptxbrEzL95U8vnH44/KgD8uP2p9T8P+Ovilq1n4dk tre7sIlthPFhX8yNt0UuezZABNeWeAv2h9Pb4W2kGu7ILy1hWGd3c75GRQC/TnOM1Q+Mnw6jj+OX ibUvBGo3k1vc38kluwkIyCThTz2Arl/Cfha2tPDWmWviLRdJvDbSOiXMkQLvnnYfUDnk14ed8NZZ nEYxx1FVFHa99PuaPfyLijNcmlKWBrunzWva2ttt0za0T4z6b4j+IM9pZvG0aPlDIxBY+1a/ib9z bTNp144R28xkb36gV55N8H9M0+5l1TTriaGZrhZWwAAMHPGOg9qsXWga/reqvqR1TZbxqAlkz7Vf kjII7962ynI8BlkJQwlNQT3tf9WzTiLi3N8+qQqZjXdRwVldJWXySPLvH2rHRbO3mbmVn3+UGO3o eDXkmh+JtUufiebRNqWl0QkiDoFIBP419Ea54EOp+KI7S9jea2kt8MC2djZ+9n6cVzI+E0dpYLrW m3EsszkjyQoyhU4DA/hX1FK/9n1f8cPyqHzD/iL0f6HdWMr2es6RHBhYSRiZOTgdjX1NZ/EN9O0H S4SolMk2xvnPy57183SeG9UPhrS4NOZbSSU/NcSMAIiAMkev0qprtn4u1H+z9M0zUIUltZAWu0G3 zDgjODXxufcK5VnMYrHUVPlva99L72s0fU8P8YZvkimsDWcFJptWTva9r3T7v7z6g8X/ABYl0Xw7 I4ugxRdzkyn5RjPHvXyL/asvxT+OT6vqq3B0/T44mt2mP3iWJBwewqKbwFrtrcy6l4l8R3+rQblM loAAsgByVJ9O3FYOp2+r6l8RtQurJpNI0a4gjiSKL5SFRQqjA9h1rPh7hLKsko+ywdJR3d95a+e/ y2FxHxbmeeYh1sZUvsrLSOn91afPc/XHwlYaL4w+Enw5tNPAjXTI55LdVAAumUDdIT2wc18IfDHR 30j/AIOVfAykMUl8QXLozdSDaS4r6+/Zl8OaVefBbwzoWna/dzeJLS1V/JZyu3fIwkXd3yuOPavP b3wl/YH/AAcN/B25Ee1X1maMPjG/FlLzX0h82fKP/BSS9k0//g4N+J1xHksG0TgHH/MJsa774ySP ffDH4f8AikFjcJZC3uCOrkEYJrhv+Cj2mzar/wAHCvxMtYACc6IzZPQDSLEmu58Tub34eW2mlPMg giVo88gMBx/KgD588ZaWmq+Bk1SCCN5duJlAxn/a+oNcPpfi3VtP8MQaPOGNymCjFjiZB6H19q91 8KvbahavaXkaLbyKyMNuRmsbXfAmnaVYy6ZfRmQXSmSymxg27f7J/GgDofDXjbQrrw3HtnitrhU/ fK5wwP41LrninS/7Lxb3Cy3GCE2/db2NfLmrabPp3ih7WS485SPkmHBY+hFWFu3uLa2so1InLhQd xHWgD6H+D2lqNd8Q6yIlG9ljTaOAepx7V9ArdQuwBmjRV/1mWHy1wvw+0RvDXw1SG82rcZMkzV81 +LfE8178TtYuLa9vIbbzCqrHIwU47kDrQB2XxY8QReIfiDb29oGay0uMgkD77d6+TvFHifVv+Era SxvbzT02DCwTMn8iK9z8KvcXviiOExNLJMSGdjnavdjmjWvg5barcX11FdGJ4pGXBAwcc9a9XB/7 liP+3f8A0ozl8UT5QkmlmuGlmkeV2OWZmJJNeoeBdF0jWtaiM07Jc7T+7YDBrT074TXmomSaG4tp rOOTZI0UoLKfpXrWh/BP+z2S7jvrk8DgKMg15RoZo8P2FnqAtY9LjfH32l5/KodS8KWE2n3ASOaC Nh8yxHIzXsenWl1pt2tnqNul3tGVkljGSPrXWjRrzXNNkGg6PavKXCu5wqLQB8X6B4cj074iaVPu L2sd4m5m/wB7vXsHijWbnU/+CkXwwjMv+hRanZCGNT8o/eDnHrXe+Ifgtri2M01hMk92WDSW0SYw evBrmbPQpx8WfhJcXNhLHq2n+J7ZLuZl5KGQYyaAPoL9uzRZ9e/b3+DunwKXZrFy2Ow88c1+nmha Z4d0T4NeCdT1gwxSaXZsjlvUJlfz9K+L/jxoB8Qf8FSfhZbhdwTQZieM4/fjmv0d8f8AhHwPbfsa pYeLLiW2naxZ4ZIydwfBA6dqAPxM+PmpadrvjPVvGvh1orRLW9a5C2xCkDOHTjt7VR0n4l+B7Hwn Dq94/mXYi3CKTJIbHcVyHir4Z6rZ6le22hXlxLYPI2/c+UZd2d2Ca6+28J6TqGhNa6tomkwX89ms fnxxDc2Bjf6BqANPwb8To/GV5qGb3dZSnYIAcbF9QKqeJBPEyQ21009sCVCP1NctbfDW28H39tqG k3E37sMdrYG4nscdafH4U1241FtQutUa9WZ/9Q0mwxD2x1oA8R8b6+dJ8Qi2T9+5XaQxJUfSuc+H WtahqviWawvHUWKyGTnsQePwr1zUvh9DqOp6m9/GzeS2+KQnlfaoLX4XHw/q1nNYzSXdvMwZztA2 g9VNelJP+z4/43/6TEzT/eP0PQNDml/4S82zk20WzOY+j5r3/SNfh0rVbDTbKRlXZ5hlJ5B714Zd eH9ZXXbYWV1FptvBEHMsuCXH93HesLVNM8b6/wCJxLpepppUSReXuj7+pwa8uUVJNNXTNYycXdPU 9x+I/wATbGxjeW7SG/JHl72y0hPYCvD/AIXWtjq3xQ/4TLW4BaRrfFY0YfcQHpg+tZtp4TuvDOpw 6j4h1K98SyLIXjhlUbA3Y+p57VymmaNrd/rk8N/f3FlZS3/mpHExGAWyenT6VNKlClBQhFJLZLRL 5FVKk5ycpO7fVn7G65odh4ptdT1+1VoXj0BYvs64AhQocYI65r4F/YDsZNO/bG+M9pICGSwj6/8A Xw1foj8LPDOleIfBcD6DrdxfrBaeTfBiVyqx8cd+a+R/2ZNBOg/8FF/jRAYvJ8zS4329v+Pg1oQf vx8J/wDk3vw3/wBcpP8A0a9fzjf8FY9Q/sz/AILaeH7vcEC+CNMBb0/0i6r+jn4T/wDJvfhv/rlJ /wCjXr+bD/gr7G8v/BZXQ441LO3gnSwAB/08XVAE/wAaJk1PwZ4A8ZLFG4vtJW0vQR8o8vgZHqQa +VPF/hLTbfwcbnTVa3sZnDnBz5Ug6Y9AcV9S+Jgl9+znpHh6U/NbRrKpHXoCf5V5fomn22t+H5NF LtKkittyOVYUAcN4c+IwX4exNdMFvbfEbLjlSCRz7V7XoctpfafDqzTLczSR/LtHArwa6+HV0iXb SOLLUbdSbd2Hy3aD+Bh+XNcRDrfiDw/qcljbyT2EmzLW7HcAPUH0oA+tPEH2Wfw1Kl4EltWQiZGP bHOPevH/AIT+HLZ/jNfXiBms7KLfDu9SRjPvXC3HijUbnRYZJ71ppt3KtX0X8HdMkj8EXWp3UZSe 7nJII7DAH8qAPZhOTbJGpyH6V4H8d72CS08O6DCyvc+a8s655RSF6/kai8YfErV9B+Jd5pWlfZJr aKME+YCSjbQePfmvJF1dvEfjmS91NwZp32Oo7k9FFAHL3PxZ1DwJrkFtolvp99CIySLqHcAcBCB/ 3zXhHiPXbnxL40v9avI4IZ7qUyOkK7UXJ6AV6j4s+G2vTa1cS2No8sMTMD8wyMksP0YV5oPDGrNf fZxay+ZuIGVwMj3r1c5/jx/wU/8A03AzpfD83+bNfw/oeq61qNp9lurg24UJgXByoHYegr2XTdM1 TRxHG2tSQM7fKqIGbPuTmuS8IeC/GOlavBdxWbImNwO4ba9+0/TrTUrwNqcMtjqAOJAmCp968lJI 0PLtW03XZ9O1CEXkd2tzGNzMgU5FeN2OhXlp4rt47iMIzThSQe27HWvtbUPDmi2ukTXD3c00WAAA uT+AFeTa14Q1Wxs7nUYtMvGsSwkindPu8+lMC/8AG3WLfTvHXwx8D6XCkenw3VtdTEdTL5oU19R/ 8FLrdrvxl8C7dAWaS51BQPq1pXxx4zsZNeh8DeM7mcnUbfW7eymhHQL5oIb8+K/RT9s/RB4g/ao/ Z505o/NRn1ViuO4Nnj9aAPq/4X+ALi8/Zl+FWxli8pjBcRsvByPMKn/v2K/PH9orR7DwL+0LqXiX wdIbq3srjJ3n5Wt3x5inHdWx+VfsDpnw78RX37IUMmi3sWm6jGvmReYcKBGMH8wxr8IPH2o+OtN1 HxLpmvwpfG6luImYD5kL5XA9sHNAHuWi6ld69oVvdS63Hb6c6B38s4OMcgVv6b4/sr2/uND00+Zb wx+XHdMckuc9fxr5m0XwImpeA4INN8Ra3Yx/YCz2JAxHNz8m7HQkfrWN4O8L+MfBOvQS3kjS2c05 M4L7hsHOR70Ae5+KdX1CKzFrfwqZAxDSIBgV88+INWh0XUIXkMdvuDESBctnt9Opr0LUfEOtanrs sX9lmDTfMCxXMmWErfh0ryXxh4W1TxL4luVVhHNbbWAHR1xk/lit8N/Gh6r8yZbMy/D3jQ65rM2i AM1yJv8AR5CMcKec/lXvvhzUGsfESWlqDLI/zMrj7v8Ak1846B4L1vwz4qj1G7tDELh8wybsh1c9 R7jive52n0vW9NlSyn1HUnjAKQ9WXPXj+taY/wD3qp/if5ip/Cj6c0C+Gn2luL9oze3PzIFGPlqH xx/YEm2XUrt7ebZxhePxNfO/iD4gXumeJdL/ALN0m81KaGFt8Z+Uo3A21ytzq3jLxzfm18RWP/CO aVLcASXDzfMq9cAdzxiuQstwLeeO/jVDp9tqD3nh7TL4KUbA8x8ZAz3xX6gS+BLawuPByeHBHJo9 loh82XdkSXDk7h7YbvX5I6X4hvdC1fxLpPhPTVAS+dreeTJ+XouT3OBX7GfC618YP8PPDc17ZW8n 2zSYLKRQclWKiQOMdCc45oA/OD9iO2ez/wCCrPxLgkTYw8PX2R/2/W1fvx8BP+S03f8A2CZf/RkV fi1+z74d/wCEe/4LOfEiJV2pL4Zvmz6n7bbZr9pfgJ/yWm7/AOwTL/6MioA+w6KKKACiiigAoooo AKKKKACiiigD+Uf/AIKbCeD/AIK73GoC0uri3gtgzmKEv0u7g44Fc/4Y+KfgoeHra11HVJrTa6sy taSHjGCOlfUv7eH7VXjH4Kft+694T0Hwr4H1qylWS8M+sacZpgz3MwKhtw+X5RxXxl/w8K+Jn/RP fhP/AOCU/wDxdAGZdeOfDMXj2+ubW/ma2lnLpIttJ2PHava9G+KHwr1vRpdE8Wa49tZTRj9+LSQm M44wAvUEV5P/AMPCviZ/0T34T/8AglP/AMXR/wAPCviZ/wBE9+E//glP/wAXQBha7eeANR1K6sH1 8zW8Mu23vBaSfOmeD0ry7XLvR4/EAFhNNcwEgF0tnx6Z6V7d/wAPCviZ/wBE9+E//glP/wAXU9t/ wUA+Kl3cCK2+G/wrnkPRU0Mk/wDoVAG1H8WfCukfCe3t9P1GWbUoLVESJbWQHdgD07V4bpd/o+r+ JLi51fWZLK4mUsbie2kZVOenSvqDS/2qv2h9XsUntPhJ8LyrjKg6C+T/AOPVWvv2sP2jtPkxP8Ff h2Vxnevh58f+hUAeAWeq+ErXxkzLqt0trb7fLlFq+Jjnnt0rZ8ZfFKztLa5k8N/aLyea3WFZI7d/ kILE9R/tCvQJv23/AI4QSskvwh+HSMvUf8I6/H/j1Upf28/i1DYrNJ8M/hejmUoUOgMCMAHP3vf9 K78FOpGFbkje8dfJc0dfvsvmRNK6ufJeg+ItZtfHL6tdx6i00n+tkeF/m/SvbtO+Kc8BXfc3SDGC v2Z8fyrtn/4KDfFCNsSfDr4UofRtDI/9mpn/AA8K+Jn/AET34T/+CU//ABdcBZly/FO2u7WONtQu YiOpa3c/0rqPA3xe0TSfGpk1G7Edsy7ZZjbP8w/75rL/AOHhXxM/6J78J/8AwSn/AOLo/wCHhXxM /wCie/Cf/wAEp/8Ai6APpq0+P3wq8xVuNbCgfxfY5Dx/3zWZ8QfjJ8G9Y+F11b2Grw3mrzWkyF/7 PkDgkcDO38K+eP8Ah4V8TP8Aonvwn/8ABKf/AIuj/h4V8TP+ie/Cf/wSn/4ugD7a+OWo2GifsW/s uarrDSwaZZazosl5IsbMY0GlzZJA5r6C8f8A7YvwDn/ZB0zw54c8fwS6wgjSWGOzmD42ANn5ea+g /gl4Xt/jF4K8MtrP2Gye88MW2rSILJZokkeOElVRjwB5px6AYr3z/hlrwx/z/aZ/4IIf/iqAP58Z fjF4FS9us61JOskxlDC2k4JPT7tYI+MXg2LxhdQfaXk06QCRJRZvhSeo+7X9Fn/DLXhj/n+0z/wQ Q/8AxVH/AAy14Y/5/tM/8EEP/wAVQB/PBqPxX8CPZCC31iQhnBYi0k4A/wCA1jj4keCXsbuA6w0Y D5hxaSYYD/gPvX9G/wDwy14Y/wCf7TP/AAQQ/wDxVH/DLXhj/n+0z/wQQ/8AxVAH8303xN8HiGKf +0pHuGXDhbaQ4/T2qnpHxR8LQ6MILi6ngdJGBU2zncpYkHge9f0m/wDDLXhj/n+0z/wQQ/8AxVH/ AAy14Y/5/tM/8EEP/wAVXTHEOOHlRt8Ti/8AwFSX/txLj7yf9dP8j+dQfE/wPJPbRvrTJBFlwfsk h5I6Y21k/wDCy/BguYLhtQlkcXH3fIkAQf3unNf0g/8ADLXhj/n+0z/wQQ//ABVH/DLXhj/n+0z/ AMEEP/xVcxR/PIPiR4J1SSGwtbqfU725dY4LaKykZ5XYgKijb1JwBXe/8Ib43l0KTUdQ+HXjO0lV HLq3h6b92i5xzs/ujNfvHb/sw+HrXWNPvbfU7GGe0vYbqN49DiVt0UiyAAhsjJXGa+hJ9LvZ7CW3 bVCFdChPkeox/eoA/mq+B/7Q/wANfhz8StP1fWPEL2sFu+5le0kAZs4Cn5a92h+MXwy+MX/BXj4D az4J1q31LUl8RSGeKKB1KL9iuN2SwHIyvHvX6UeKv2LPB3iz4Pal4SvdZtIRc2giivo9Ci823lXB jnUhwdyuFYc8kVyPgz9hCy8E638Pdb0/4kCfxN4f1q51XVdVk8LxCTWpbjaJNwE37r5VCjG4KBwK B9D8Tf8AgoXeHRv+DhL4qatc2t21ksOjjzUgZlH/ABJ7IZ4FZNj8U/Adx4fSO91iSGRIXXY1pJyS OO1fS3/BQH9sDxx8JP8Agqr8RPAujeEPh9q+nWVtpskVzqmlmW4bzdPt5CGbcM4LED2wK+Mv+HhX xM/6J78J/wDwSn/4ugRlaJ498Padrwf7fMkay7g/2V8YP/Aa9rl+IPwj8V+B7mz13xI2m6lbqXsp xZynL9h93p2ryn/h4V8TP+ie/Cf/AMEp/wDi6P8Ah4V8TP8Aonvwn/8ABKf/AIugDk7+78Darcrc zauYbqJjg/ZJMPjpzjvXn9vqOkQfEC2fzZ/sK3Cs0v2Z8AZye1e2f8PCviZ/0T34T/8AglP/AMXV 6x/b3+LepXIisfhn8LblycYTQmP/ALNQBP42+LmgyeCPsOgajNNcTgpIUt5BsHr0ryHQn8Lz2dxL f661lIrbnElq5ab2HFfUFt+1B+0beWP2iD4RfDB1252jQHz/AOhViXv7Xf7RNg7i5+C3w8QL1P8A wjzYP/j1AHjPhXxH4U07W3vZ9SuYfnYLE1q+NvY9KwPH3xNuXsLvT/Da3pjuJWczxQPkg/hXskv7 cvxshDGX4SfDhAvUnw6+B/49VS4/b2+LFv5W74a/C0l4wxH9gtx7fer0MNOosNWUY3Ttd9tdCJJc yPmHwX4o1DRLi4aSK+jErbjvgfr69K9msPivJEm2a7u8HGR9mfj9K6lv+ChHxOViG+HfwoUjsdEP /wAVSf8ADwr4mf8ARPfhP/4JT/8AF155Zkz/ABOtbuVZDqU8eB91rdzn9K9M+HHxp8LaXFcwaxeL Zws24f6LJyfwWuJ/4eFfEz/onvwn/wDBKf8A4uj/AIeFfEz/AKJ78J//AASn/wCLoA+orL4//Ccu DPrwhb+99jlP/stYHir4lfC7xV8RvCFh4RvorzUZNcsmzHYuhciVc5JUV8+f8PCviZ/0T34T/wDg lP8A8XXZ/Dr9u34ieKPjv4R8O3fgT4YWttqWqw20s1tpBSVFdwCVO7g89aAPsb4z+M/CfgT/AIKd /DnX/Gd++l6Knh6ZTc+U7qG+0A4O0Gu3/aQ/a5+Cfi/wppGmeEvHUN9CtsUnWG0mAB7Zytfa/gf4 baV8RPGF9p+oNZxG0tjKks1glwx+cLj5sY65r1T/AIZa8Mf8/wBpn/ggh/8AiqAP57E+MHgNYVgk 1Z5AoK7jayEMPX7tYulfGHwa009tfXckUcchEUxtJPmHY/dr+i3/AIZa8Mf8/wBpn/ggh/8AiqP+ GWvDH/P9pn/ggh/+KoA/nY1H4qeBbqeOJdXkMKKck2kmCe38NZr/ABL8GtpsEg1hxcxt/q/ssgH5 4r+jn/hlrwx/z/aZ/wCCCH/4qj/hlrwx/wA/2mf+CCH/AOKoA/mz1T4k+EfsdxHa38s7yJ0W1fBP 5Vdsvip4QeztknvpYDtAdWtZDtIH+7X9H/8Awy14Y/5/tM/8EEP/AMVR/wAMteGP+f7TP/BBD/8A FV0PEN0FRts2/vSX6E8vvcx/OjL8UPBUjXEzaw3mLGEjX7LId36VSs/ib4Kg1aKRtSmO6P5pTBJ8 v+zjbX9Hf/DLXhj/AJ/tM/8ABBD/APFUf8MteGP+f7TP/BBD/wDFVzlH891l4x0TxTrMOl+EbLUf FOvkF7eystOlllbHU429BXReIfD2veHfBs+u+IPB/irRrCJVe7u7jQpo0iz1y2zjmv6D/Cf7P2ke EPiNYeJNM1S1jurVJFCw6RHEWDjByytmvR/GfgiTxp8OdR8N32riK1vFCyMbQSDAIP3S2D0oA/BP 9nz9qz4TfDe7u01rxKIxcQGJI5LWTGCOT92ui+CnjXwP42/b0+I3iXwZqtvqGmS+H43nmSNkRP35 I5YDtX6M/E39hjw58QfC1lbWPjCLwlrVlc+baatZ+HIXkjUjbIm3eoIZTjk8dawp/wBgi0soNQtf B/xMj8Kafe+Ho9HuII/CscxZVBBk3eep3HJJ9+9K7sOyPsX4RyJL+zr4YkidZI2gcqynII81+Qa/ ne/4KkiE/wDBdPw19oKiMeBdOPzf9druv6I/hN4Lb4dfs7+GPAzapLrX9iW72i3skAhaZVkfaxQE 44x3Nfir/wAFJB+yav8AwUYs5fjRL4/i8ar4SsjGdFB8kW/m3Hl8hx82d+ePSqdr6CR8zWVudW8N 2qqpbMbByO20ZArx/wAPH7F4ieFkbi4OQDg4Na8Xif8AYTgTbD4k+OkS+izuP/atVhrf7BIm8wa5 8bRJ/e818/8Ao2kB7FrHgq88Z+Dxe+H4w2qacpdoCeXQDke5wK+V/EXg7VdU1aG8tYpX8uTbLHjl NuQ3/wCqvWLfxn+w9aMxtvFnx5gLDBMd1IM/+RaqDxL+wisjOPEXxyDMSWInfkn/ALa0AfNv9nTQ +MIbBg24zqAMdia+1da1y38J/DqS8MYG2LbEuMZfHA4ry1tR/YAa7E7ap8aGmByHLNnP182tOfVf 2F722CXGq/Hi6hByA7SMv1/1tAHh0Gka34n1vUdSWznup5pNzLF/AD/EfQV0fhLw7KnjuMtbrJDb TbGk3Z2t/jXpFvr37B9g8gttd+ONozDDhJXUn2P72oo9c/YJidmi1z43RszbiVlcZPr/AK2gDF8X eNNN8I22sTSqtxfPKvkw5xn91GK8U8E+P5ZtZ1SPUliltbmTzAhTO3knA9K99vbr/gn/AHoikv8A UPjNc7lJRpSzZGT/ANNfUGqkTf8ABPOFsw3XxhjP+yCP/atehmVb2tWMrNWjBa+UIq/o7XXk0RTj Zff+Za0vxtprWfkmxULtAU5qzf65ps15DPBp25wvVCR+dV11D/gn+v3dU+NC/R2/+O1Mus/sDp93 WvjYv0kf/wCO155Z6Z8MLzRtS1K+t5kNxOxDIZUztAzkV73H4e0jUJcyxLMhADxHoQOelfH9v4i/ YPtJGe18QfHG3c9THM4J/wDItXl8bfsQIfk8XfHtfpdSf/HaAPSfjD8J/D/h/wACy6paRskrNbzr AcbYyJVOR+Ir6V+O9l9v/b7/AGebfbGx8jWGAcZGR9jNfF1p4t/Yk1TxFpts3ij4530z3MSQx3Vz I0bNvG0MDL0ziv0p+K3wO8H/ABe13w5qPii68Q2OoaF54sJ9J1BrZ187y9+SvX/VrQB9B/tB+Irr wf8As36FpmnvLELmF0O3KleMnpX5OTw22qyCTUo1e7ZispcZIz3578Cv0Gv/APgmlDrFnDHqXjzx xfwoMxpceMrpwufQFawz/wAEqPCzPubxB4kLZzk+Krn/AOIoA/PfTlsJ7x7OJ2ivLN2QxrwCM9/W rWsRF7OG1CZkdiOB0GOtfe6/8En/AAel29wuteIFnc5Zx4ouMk+52VK3/BKbwo0gdtf8Rsw6E+Kb j/4igD86UMkGgxyQIfs6S/vgRkYGB0/Csm+t4YXvLx0VHmgO1iMYGDX6Wf8ADqXwp5ZT+3vEe09R /wAJTcYP/jlRyf8ABKHwhKQZdb8QScY+bxRcH/2StaM1CpGT6NCkrqx+bdpHY6j4c00Oq3C+Unlt 6EAcfpW+0ZN4hgiKeRakSugwfzr9A0/4JPeDY1Aj1nXkAOQF8UXAAP8A3xU//Dqfwr83/E/8R/N9 7/iqrjn/AMcqsVVVWtOa6tv72KKtFI/N3T4I4tct5CsIWbcwVly0h9c1o65o9nd6VuvH8iBG3yYw OO4r9Dv+HUXhEFMa54h+X7v/ABVNxx/45XUeD/8AgmB4E0L4r6Bq+uPf+JtEtrhmvtO1HxHcTRTp 5bgAoVAOHKHk9jWBR+UC+F9KFsL6yimWCQB4FLA/KemT3r9D/wBkvxO8HiqSy127f+zzANzSvnaw XagH5V9reMv2EvgJrXwi1/RNB+HGi6Rq1zp8kNhdxXksRt5CuEYMMlcHHQV8ZeJf+CWWqWWk6fd/ D/xZqUOp299G91aX3i+7iivLfkSR+YsTNG3O5WAPK4PBoA4HSvDtvpP/AAWE1fUrTJtr/wAI35Vi MZIu7Nj/AOhV+kvwE/5LTd/9gmX/ANGRV8hfCD/gn58TfhX8T7XxhceJdP8AE+tz+Fp7G+e78QXE yQ3UtwsuIhJF/qlSONdxwx2kkc19f/AH4bfGPwD4z0aT4lr4Mvo38P8A2W81LS9UllnnvzsdyImg RViwknzbs5A+XngA+waKKKACiiigAooooAKKKKACiiigD+Sv/gqr/wApUdX/AOwcf/Sq4r816/Sj /gqr/wApUdX/AOwcf/Sq4r816ACtLTdI1LWL5bbTbOe8lY4AjQmvT/hN8G/E/wAU/GsFjplpMljn M1yV+VR16/1r7qsD8PfgraDw14F8Kx/FL4hRoqXkkEG+3spWyuHIByRg1nVqwpQc5ySS6vRGtChV rTUKcXKT6JXZ8x+CP2V/F2tWUOreIUk0TRgw865uE8uJFx1LmvedL0j4GfDS6mtdGFt4612GNpZH t8tGAikuQBkuAASTx0qbxf8AD/8AaX8avDB4hu5rDQ5m5tor5RBEpOceWp5Ir6I+FPwP0zwR8OpP +Jba3l6+43V7PCrySEggjJzgEcYH41+acUeK+R5Xg/a0K0a027KMZJ/NtXsvzP1LhPwjznNMU44u EsPTW8pxavrtFO13+C69E/n2P4w63rQtT4f8D6sLSP8A1TwQeTGB+uRjFbjfET4hiH7RqPgPxAtp /eWJZP0KivqSw0PRZNQ8mO0tLZEXG1UCjv0Arb1+301LFbSOOErsAIH41+fYrx//AHsIUcHvveXT ysj9Mwv0daUZOFfGtye3LCyXrdu/Tax8dxfGDw1Lm01nTrXRLgnJOp2IjYn06VD/AGx4Q1jxjLON C8FXlsbSMCMMUVjvkyR833iCM/hX07Z+C9Eu3d9Q0fTbxMcfabZZD/48DXm+r/CHwp4g+KV1pb6T ZR20dhBNGtqTDhjJOpPyEHoB+lfZZB46ZfVWLVTDyio07t3T+3BbWXfueJmn0esRQlCNPHxcnKyv FpbN6tOVtuzPJb/wn8MdbBj1T4awWcT9LnT7tzs7ZAPWvKPE/wCy74X1WwuNR8C+KRaupOzTtRTZ I59uelfY2ufs73Fl4Xsn8KapPod1FcCSRppZJ1kQBv3eGc4BODn2rw/X9L+M/h3Tb28m+HttrtnF JtMljeBnkTdgNsGSBjk+le1w74p8OZxH93iFCV7cs7Rfy1s7+TPz3iDwsz/K22qftoL7VO8l53Vk 1bzVux8CeLfhX448GTZ1nQr1LVifLukiLRP7hq8771+i8HxTls9QsrH4h+GdU0rTjwtnqGZbYg4B GcfLx6VzfxG/Z88M+MfC9x4x+Fl9ALkqZp9EGCyrg8oR94ZHav0OMlJJrY/O5wlGTjJWaPgyitPV tG1PQ9ZlsNVs5rK7jOGjkXBrMpkn9gv7GP8AyJXgn/sntn/6Lta+96+CP2Mf+RK8E/8AZPbP/wBF 2tfe9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH8i/8AwVX/AOU4PxW/68dH/wDTXa1+ddfop/wV X/5Tg/Fb/rx0f/012tfnX1OByaACuh0Hwtr3iTUo7bR9Oubt3OAUQkfnXufwV/Z61/4k6k+pX6nS vDlsPMnu5xhAo6kk19O6h8UvAHw3kXwD8FvC9r4s8SufIGsCDdl/+mKAEls9/aplKMYuUnZIcYuT SSuzx3wd+yjfpb22sfEHUIvDGisNzz3/AO5jx7E8mvUYPEvwM+GNqbLwxY2viW/Q7ftLLu3n1VMn j3zXISeFPjX8VPFUCeO7nxPp+lk7gNRDouBwfLjOMdcZr1jUfgVpfgLwDbas2nJItwdizyIGfPXP tXI8ywi9n+9j+8do6r3nq7LvonsdKwWIfP7j9zWWj0Xn23M+P4ueLNQvDqGmeCdZjgVMK2wRIR7L g1of8LO8XRRifWPBWuiEjOVtlmGPyFd/4L028bVLG3u5YpNGZP3krL91QOpqjrM2qX3iK6ntb1bf RYJilttjIMqjvg9K1WKg67o681r7O1r23ta+m17kfV5+x9rpa9t1e++29vO1jk4fi54L1ArBe2+g 2VyvBtNQtQjP9Rx/OqthdeD9RuNQln8LeDdVjkunKRrMyEDjAXDdK2rnwD4a1rQL7WvE2l6eqIp2 yvGNx/GvDbX4M3XjDxYg8C3d1aCS4aKCFLjylwOQc17FLEU6GXYqrUkoxiott7JJ6tnPCjOrWhCC vJuyS6t9D0jUfh78JfEKvFq3gW58POR/x9WE7SY/4Cf8a8V8X/srhbH+0fA3iW11ZJOUsJvknQeh Getd/r3gv4s/CjxLZ2c8k+sSzWxmFpcXAmG0HBO4dPpWLb/E3xtJfR2yfDye7ffw0Nx/EPTjg14m Bx+GxtCNfDzU4S2ad0/Ro6cVha2GqypVouMluno0fHXiTwT4p8I6k9r4g0W/02Re8sRAI9c1ytfp Dp/xG8Na5rEuhfEzRLtZp08ryNV5aP0KSEdq8e+LH7Nzabp58T/Du+/4SLRJOZLeNP3luTzggV1n OfH9eofBT/k7j4cf9jBa/wDowV5rcW89rePBcRPDMhwyMMEGvSvgp/ydx8OP+xgtf/RgoA/r8+AX /JUtf/7B5/8ARqV9YV8n/AL/AJKlr/8A2Dz/AOjUr6woAKKKKACiiigAooooAKKKKACiiigAoooo ArWmfsr5xnzpOn/XRq/lo/4LH/8AKXOx/wCxB07/ANH3df1K2RJs3ztz58vQ5/5aNX8tX/BY/wD5 S52P/Yg6d/6Pu6mN+VXKla7sflHRRXS+FvCet+MPE0Wl6JYz3k7EbvLXOBVEnNgFmAUFiegFeh+E /hb4y8Y6vb2ulaRdv5xwjeWTn8Otfamh/Ab4Y/CDwnFrfxd1uJdeMe+30ZAHuHY8gEDO0H1NUdW+ Pmp6l9n8MfCHwZYeFZidqXtmCbtgeu5jwo6HjFAGV4X/AGYNB8MWgvfiRr1pZ3HX7GMNIO/PO0fi c+1emeZ8ENIvI7S3ttEubRUAkWZtzn3+UEV5ZN8MPGmpWMmpeN/EWqXBlyzRecdrP7nvXqPhTwZ4 QRbTT5/DdrBczIFM5iDDJ75oAqS6b8A9cuWCaRokZPBaOYIf/HgKif4H/BW/thLa2twd5zI0BWQR j1BHWul13QPBEd//AGRY+FbLVJbfCTzCAKF/GuJf4V3N/qs8vg/XtX8PJECWghfMQPpg0Ac1L8Af hlfaxe2ieINRsLa1kEVtLLYEgqUWQkgNx8zsPyrmdR/ZV0zUN48JeP8ASb+6/gtp4WhZvT7xx0qb Vb34n+E9ZuYbW+h12AShbiWQfebauAcf7JUfhWvYfEu7tbi3fxX4ftbS2YDdd2JJ2fUdc162c/x4 /wCCl/6bgZUvhfq/zZ8z+MPgp8RPBTyvqmgXUtihOLy3AkibnHBBIryllZJCjqyODgqwwRX6WQ/G fwcujx6c/i2NLXqlrdIzIo7jaQRWTqXgX4U/F25Js10Lw7cGEqmoaafleTPV1PIz3NeSan5z0V67 8TPgz4w+Gerj+1dPnl0qXLW19GA0cq54OR+FeRUAb3hX/kp3hz/sKW//AKNWv6sZP9e/1r+U7wr/ AMlO8Of9hS3/APRq1/VjJ/r3+tAH6VRf8e0f+6P5U+mRf8e0f+6P5U+gAooooAKKKKACiiigAooo oAKKKKACq0+37VZZXJ847Tjodj8/zqzVW4IF3ZAnBMxA9/3b0mxotUUUUxBRRRQAUUUUAFFFFABR RRQB/JX/AMFVf+UqOr/9g4/+lVxXwj8PPBGpePviZp2g6fbzTmaZVfy1ycE193f8FVf+UqOr/wDY OP8A6VXFed/s8xad8PPgB4x+JupyeTqv2cLoe4femIOMfQZNAH0faRaN4el0v4EeAZ3s9cntg/iT VrVf30KqUDQKw4BOT+VfW3ws+B/hzwp4YivrDTE+3yLGbu9mTdLcMoI3Oe55PPvXlP7Jvws1jSW1 Px34xNwNU8RKlxPbXcWHhYM/GfcMDX2NdapZabc3lql1HEG+7GG6fWv4R8cfEvFZnm1TLcvqv2NO ydnpJ/a9bPTqtLo/q7w94f8A7Iyum/YpYmfvOVrtReijfW3u6vZ62exP4psfDq/DvcsVvHcqFyR6 15Pbatp3/CC6hbmSGNtrDG7kHBxVbxf4hgi8OzWyzmWZjjKntnrXioi1L+ytS1Kz0m41V1+eFVyB wOefqK+C4J4Rp47lo4rEqlFyupTvb0+fQ/UvqdfL8lqYp051nGSajG13qtrtaLf02KK3N3Jqd4wv haRxHPmMpO7JIAr2LwPoEeo6ONX193eNULbW6NjGB/Ovlqx+Ivit/FM1rq3hOLT7ZQcOzEbj2HvX 1T8MfKvvh9eR3c32UvcMRbuflj+VeAf89a/avFTgqhk/D1HFYOtGSUuWUlvK70t7zWm2mvV+XyHD /irjc6zHFYPFYeeHlaLjG1+WKSUteSMk5N8ycrrordeD8Za9NaassWjxKsJHJXmuJ0vxDcWnxMvL +SIu40q3Bz0/1s/P619J32g6DY2jvcJHNJ1DBfr7186ajrelzfFfVoxZvDb/ANmQRgKv92afn9f0 r5jw+pTzTC46hhcNKfLR1a3a9pTP0bM+Jspw9LB/WLQh7RLmk7XvGS/rt1PXbXxfPrfh1mCoCmQQ DnsasXOoQwfDq4dlQySRkNk9ua57wzpFpe+F3fTp0nkUkuq9qs/2Ybmwu4riTy4kidnz0GAT/Svg 8Rg8PRrypWceSWqas9Oltz2FHATlzUZe5dNW6rpbunuu583+NdMttcSPS5dJsp7e+Urvmj3bVPG5 fevm/wAV+E9b+Eut2Wq+FPE1+tolwF+yGX5Iu+B7etfbl7Z+R4SlvLa2j1OZM/Y2H/LNCOv4da8P 8R+B5vF/hlIb2WaJ1DtM0Y4kY9Otf3nwfRjhcowkYQl76V23dr3b3d3otLK3dH8N+JGJeYcRZhOd WCVJtRUVZS95R5VprJXcpN9pa7HGeOPCWi/Hz9lq48Y6TptlYeOtAtidUjt8brnj75HHTbz7c1+b c0MlvdywSqUljYqynqCK++fhX4guvhb+0eug61E8mg39wljqzyn5GhkOzdjv8rEV4V+0v8NpPh1+ 0frFnHGRYz3MjQPjAkXdlXHswINfYn5of0+fsY/8iV4J/wCye2f/AKLta+96+CP2Mf8AkSvBP/ZP bP8A9F2tfe9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH8i//BVf/lOD8Vv+vHR//TXa18vfAv4V XnxK+KlrAYJTpcDeZcyBflVV5JJ9AK+ov+CqoLf8FxPiqo6my0cD/wAFdrWX4Xu4fhF+wwsllM8P jbxDceTCqjDiF8BiPrwKALfxj+JEt/JZfB34N21z/ZdswS6nsFIkvpOh3Y6qK+x/2av2TNI+Hmka D438X27al4xkQSmO4X91Yk7SAoxnzBggtnv0pn7HXwNk8H+HNS8YeNdIt4vEF+B9iM4JmijK5PsM 5Br6mufG/h1I7jSE1y0FzHMUaMSfdI6iv488Y/ErNc4xdXIshjKUIfxJwvLmTWqXKnaK2eur06a/ uPAnCuFwVOGPx8lGcvhU9OXz169V2XntR+Mlv4NsvDdlqUjwWk8bFY3VgC5x9z6HH6V5prN/4d8U fBC2sN9tcP5S7QOShwAf61D431LT9d8mzkC3dshy4J+UnsR+teXa5q13onh6QeHtEk1WSFRmOPhI wTgEn619n4YeF9WjkOXVsyxFSM6NT2kYt25buyi01dKXVdbnk8U8XQp47FYbCU41IzjySlu5WTbl dPXl6PyL2maHbaXoUlhHKZA4IyR0HpXT23grwr4c+Ct9rnii5M0bofITcExuPYnPNeI+HPiF4k1H ULm21Tw3HYTpKEjQuQzep5r6tTQdE8b/AAci0bULmIkxKrRvyYm69M19n4v55XyzA4Sp7ScKLqxV Vw+JQ33Wqu0ldWb2W54fh/ldLHYmvBxjKooNwUtub066X30W58FeNLnV/FttcWeg77HRIVIjUyY3 j39frWL4G1DxP4a0uTxDpjQ295ply2y3kjLozAYOQCM19g+KPh74X8N+B47K3vd19GpxuiwZhnp1 7V4npOm2bWOs2UhjSJtQfnGOmK/SOEc+y3ibh/FVaEXKjK0HzRa5lzW67prr8nrc+azrKcbkmYU4 VWo1FaWjTs9+mzT/AM1pY5XWvil4k8SePbCTxVpVoXktPLi+zxlflJyeCTXpHiZ/Cl1PoGi+FtMs rC4QJLPcW6YMjMBuB9wabffDZ73SItYsEfUAsJMrKmRbkdBke3NaHgvRxqUTajOgiudOYKxYcyZ4 rwsLgcni6EsNPkhhm4qEXZKUnypSj6/CnprddGdlfFZly1o1o88q6UnJq7air3T9PitqrW7nnHjb wTpnjbxQ+j3en29vDCg/0kLiTIHXPavBvt3ij4OfFO2Sz1661zRZBl4LqTKumeUI5zxX2r40t54N MeTTLJBdT8S3WPuivm7xd8Lr/XfDkl9PezG+iXMIVQF9a+yPmTg/2jPhhoviH4X2Pxh8DWFva2N3 tW/tLblYJAPmz9a+W/gp/wAnc/Dj/sYbX/0YK+2vgLqa67Hr/wAHPEMJhsdUtZvsBmOSLlASqgH1 Ofzr5a8K+F7jwh/wUN8GaJcRNC0Pia3whHT96OKAP6x/gF/yVLX/APsHn/0alfWFfJ/wC/5Klr// AGDz/wCjUr6woAKKKKACiiigAooooAKKKKACiiigAooooAqWQxZuMEf6RKecf89G9K/lq/4LH/8A KXOx/wCxB07/ANH3df1KWIxZP1/4+Juox/y1av5a/wDgsf8A8pc7H/sQdO/9H3dAH5i+FvDWo+LP Gdno2mxGWeaQLx2Ga/RrU77wv+yr8IrWx0q20/VPibqEQMm9A32JCPvZHVuPwrzX9m7R7DwD8Cte +Meu2NtcfY5DBYxTAZeV1whH05NR/DfwZJ8V/ilqPjPxpcvdweazJFPlvO78Z7CgDzrwx4O8a/Gj xjf63rWqXoLAyS3dwxZieyqD2xX1d8NPBukeBobKFrKCbzpGjkvnUGTfn+VdreRaLp+k+X4b8mGS Qqh8lduAOMV2Vv4W8/wKkFxcCO7MnmrIF6E9qANHW9Gs/Efgy4sI2BZRlWXjnFZHhvw9PaaLjUYE NzECIicE9ODWx9o0zwj4U36rqCxRKSWlcZLk+wrH0z4i+ENYnnSy1YyGHBcNGQBzQBgjQL1dLu9t r+/kum+bgEj1NcX4t8VN4V8KTaLosQu9WliYXEijlT9R+Jr2/WYLm+8LXCadceRPPGfKlX3FeTWn hRrCCdbtzNeuuGkZdxZvU0AeH+H9Yuj4M1lo7Z9QmL7p5XH3G8tR3+hpvhPTvDjeJcavMk73MG9F lTKBjnIx0r1vR/DaRp4us44x5slyg+7gcwox/PdWLqXgYSXVgIrcQiyiG1lUfOcDINetnX8eH+Cl /wCm4GVH4X6v82c//wAK28MRPJdPY6fqc4uN6oYVwqnouKg8efCjTodetJvBk8ui+JWjVxHarsjB PZgOK9b8P6FFqeo2V1bKsbRyhbuId8dzXd+JLCx0i/fxFJKfO2gQwBclmryTU+UrX4ma9pU8Xgn4 ueHLfUNBmAHmO5faBx5i9cHoSP0rx79oP4G2vgn7J4x8H3Lar4M1KNZYp0TAiZjzGR6jIr2D4k6X 4h8VWS6lbaa7Gz3MTIQGYE9Md6634N6pD8SPgl4j+CWsyR3OpzRSXWjRyDLK8a7igJ6HCMn40Afn J4V/5Kd4c/7Clv8A+jVr+rGT/Xv9a/ltTQrjw5+0JpWkXKkPBrEABIxkeavNf1JSf69/rQB+lUX/ AB7R/wC6P5U+mRf8e0f+6P5U+gAooooAKKKKACiiigAooooAKKKKACqdyub3TjkDbcE89/3cg/rV yopCPOgzjJfj/vlqAJaKKKACiiigAooooAKKKKACiiigD+TL/gqZA9z/AMFYNRt0BLyWO0Ae93cV 5+26Lxn8FfBcen+dpsXk3F/CYt0cgcjIdcYxx3r1v/gpZbyzf8FiJZFjLRpbrvbGQP8ASrmuGXxT L4e/aD8MTWMlldWF7plvBdyPn9yAc4HoRmsM5yrNauVVqmEw8ptxlaydnpZ2aT21+eh7/CX1Ged4 WGMqclPnjd2v100utG7J9k7n6ljW1tdFtbbSo0icgBVUYC+3Ss6fTr69ukuJpJGmfliD615JofxD 8P38EcMmsWFttA+ZpcGvZdN8XeDLfwr583inSWnRRsDTAmv85sZwNxDgX7uX1XJv/n3NvXzsf23j s5yzL5WjXhzN/wA0Xv8AM43xH4au4pFlMckpY52np1r0v4feC59R8PtF9nx5pK4CDuCK4i4+KXhF Xb7Vr2nMh9Jc/hXpHhL41eBdJ0xZ4/EOkqyknaZMZrjzjhrjL6goU8uquX/Xuf8AkcWc8Xp5f7On Wg5X35l/mc9438DeHvDPh7ytS0yzkvJZP3bPAu4AEd8H3ry8XM2iwW/9mRkLKw3ADjJ4/kBXS/F3 4z+D/EtvbvDq9kzxnnbJnvXFaL8RfB83gRnudWsI5IsnaXGTgV7eT8IcX/2XB4rAV5XesXCdvkrH q5Fm2Cjgo4nE1YOpJ2l70b+XW9i5qHiDXdP1mOa8hN7ZuuG3LgDp9fevO9W0m31P4uX1xpCORLpN tIY1XoWlnz1+lbOm/H/wFrkr6SbiAlcsJHAwa0/C3i7wm/xynH9r6dHbNpVsgdpABkTXBI/Iiv0v hXIuMeHljMRTy6tRqxou37uXvL2lPpy2eh5HEuacN5rhKMMQ6VSCnrapFppRl1i9GtNmmel+BfAm saX4Hn1JY7gW67vMJTAOMn19BXktv4pvPEPxiuvCumW+oXN9uIktYLU7ME4Hz+/UivuLxF8Ufh3p vwgh0DT/ABRoklxMH810mGBuDgZ/MVL8E/HPwN8Eyz3Nzrnh+XxB5W6S5fZhclj8rYz0bFfm/C2P 4g/tLEZlmmUVJyqN8rlSqNRd788oxjrZ/Ztrc+FzDjbDLJJxw1VU1FxjGMHFt04px5byu43VvfXv K2h89XXwd+JFpoVpKvhSX7PPDu2CVAFU+2a8r1DwvqWg63Npuo2P2Tzo2KhgP3RXHGe/Wv1juPjz 8KdT09HXxloEfB4+0AV8K/tKeLfAd54cu9W0TxZo9xdW7NKixTjLYXp9DivveG/ETj+fFWFwdahV q4ZyXvKhOG+lmuXZX/C5+eZfjeHMww+KoVMJSpVJQlafPKTUl7yfvTet0tVq9U9G0flX8fdMtYTZ 30MoS8imMcjKMZBI/l1rN+PNxb/EH9iHwN43upDLrtjZJY3r4y0ksISPJP0Ga534ra0viGK5igv4 7qNo3bcn8LY4H51n6Exl/YU+IPh3WLlDfWEonsopGyWLqdxX8a/ub+wcz/6B5/8AgMv8j8N9tT/m X3n9Gf7GP/IleCf+ye2f/ou1r73r4H/Yx/5ErwT/ANk9sv8A0Xa198V5RoFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAfydf8ABSnTm1T/AIOB/iBZhN6tFopYYzx/ZlpXHaQbPxN/wUJ8KaHqMYGg6G0L PbMm+NxF83KnjBr2D9vuEr/wcRfEO6mGy1W10fdIw+Uf8Su0rwzRb+HSP2zNZ1Nr22jtZcxpdv8A d5XtXVW4dx2Mw0oKjPlmmrqL2atdO33FUMXGhWjO6vFp2fl3P061bxjqV3q1np/hS7hs4ET5vlAD 8fd+bGMYrHuvC2vS6gskNmJ5ZRvlIdVO89R1/WvDtD1vS9T8WWUJ8RWMUaIX8yZ9qsR2NfWtx428 H2HhcXEHibSJr7bwvnc5xxX80cX5fm/h7XwuF4fy5zlKLU26M3J66Oc1a93d20tbtZH7NkP1Timj WrZtXcfeXIlNKK01UYu9raau9797nh2t6dq2irIdRsp4hjOEG/8AlX0P4K+GSa58LLaNrdn+1W6y s/lhXIOGAJzmvmQ/tN6HpOpx6Z4pspJtSSYLcT2WHhVSeGGTk4HWvrTQP2gfh/pPg2K9g8R6U8L2 wcL5mG2kZ6dc+1fEeLuP8U8Tl+Fof2TUpzUuZTpRnKM2vhasm1a9172u53cP5dw9g3Wq4LExqOS5 XGpZOKe6d7J32emmx83/ABw0TQvDOpeHdFslht9Yt3EtywXa7IeFyR159657RPFd/ZI0dlOfNkbJ XbuJ+lfNH7Snx+t/iD+0NNf+FLeX+zrWFIY7lwVMzg5LDnOOcc+ldf4e+MfhiD4Z+HbvVV/snxZp oRJA6jy7zOSXyvPHTmv3nhLhDiChwfgsLmeW1a9Wqm6iqRcrTlea57p6c1ls+W93sfAY/MqFbOq2 IoYmNFQtZwdrpWi+Wz3td262stz3qPUby5nuE1PddJK2ZElX5kB7rnkevFebWWmSXGta/b6ckt2f 7SkCbU+8OOcdqfqX7QXhfV9GjEsulW92U2yzohyQOmD1rvf2dPHfhbU/iXqqXupaVZ2El2832udw pOccfTivpZ0+IMi4TzDNHlE4VIRj+6jdxlaSXMrR+6yu1a62tySp5fjszpYWWM5qbu/aNPmWjfK7 v9dHt1v2MPhrxF8O/gx/wkOrCQWd0V32TxgrluA27J/LFcv8O7//AIWPrF7ZeC9Lu77ypSlw0cW1 N4P5da+n/jt4u8B+PvAFn4J0rxloljaB4fNuPOGNqn5sY7+le1/Bj4i/s9/Db4ZLpvh/VPDdjOpC XFwVjSSZgBkkqBnJ55r+cOEfEXOsNltbGY/K6kcXiJybfsKjjGMeX2fMoxvLdpa301ex72fYSNSn RVOovY04pKPOm3dvmd23a+l9La6I+Vda+Dfjm0s5YdS8PTBFGXHmKw9exrxzVdKiXQ54Qn2d4ydw dcY7Hiv1k1X41fCnU7OT/itNAUOmCPtAr8p/2j/F/hXSPiJLc6D4osNQTU0BmKTBli28fL9a+w8L fEvi3P8AiWrlWYZbJU7NwqRpVYp21s1K9rrz3Vtbny+MwGBWCdalUtKNrpyTunppts/X8D8/vH10 fCP7Rui+I9FuDbzw3Ec52HbyjAn8xmuw+KenaZd/t2fCDxlp+ANY1iznIVcAbnUn9a8O+JUtx4h1 VmtLsTyLKvlSJxu65r1/S7qHVtA+AtzcXEcusWfie2t5kJy6osoxmv6YnkmYwi5Sw80lu+V/5HzK qwb0kj+j34Bf8lS1/wD7B5/9GpX1hXyf8Av+Spa//wBg8/8Ao1K+sK8w0CiiigAooooAKKKKACii igAooooAKKKKAKdj/wAeT/MG/wBIm5B/6aNX8uv/AAWBtjef8FjNHtVzmXwNpice893X9RlmCLRw QVPnynls/wDLRq/mh/4Kk20d3/wXS8NQyruQ+BtOOP8AtvdUAfJ3jRby7ufBfwe8PsWt1hjnu4kO 3dKfmbd2Pyivs3Q9JtNM8N6ZFY6TDH5CeUybQpjOME+9fI2niO2/4KDtd3MMm+GHEcYGefLwK+v5 NZlLQq8RgDKXkyfuAUAaFp4c0OydHWISSCTf8/8ACSc1sy3SncAw+UcY7Vw2s+IXs7eza02TvJMA 6kZyuM8e9crrHi4Hw1fB5hZBpcHeu1kQDnigD0LVLfTbq187WRFJAB/y1GVFcRfaFo+liLVvD0Fs sdxMpkSJBsY8D+QrZ+B/xi+FfhX4h3+pfEVNY1jQEsWi09o7PzwLhvlbjcP4C4ya8g+I/wAUvh/Z /FnxFD8OEvLHwr5kR0+3uEYjHlpv4LEg79/evm6Gf1amd1cteFqKMIKSqtfu5NvWCf8AMrp/ftbX plh4qgqvOrt2t19f68j6Tt9RV7VFQIsa4XaBVWSC3lmnkWHHuDXj3g7xzY+JrBrmzkKFOJYm6qcn 9K9N0/V41LZO5SOh719IcxzVvG8fiTxKULfLdxkHv/x7w10OmXf2iQRXaZg6GYjBH19ay7HUtKXx rrCubiW5utShitbS3iMjzubaEBFUDJJPGBXu3xp07wl4E+C3gHRrDS7e98a67p32vUPOlZbvT2cQ yRxmIEBch3X5lzgHvXHxjxNhsBm2CwLi51cRGmoqNtFGjGUpSu1aKS33baSTZWEoOdOc72Sb/N6H B6Po+kadqM72k8bTXHzFQ2SK3NU0uHUtHeGVEY4/duRkqfUV9F/shfsv+Fm8E654z+JF/b61quqT iVbKC/EiWyOiOqtjDI65I25r7N8b/BP4YeKfs7XmhRyiK3EURhnaLCjpnaR6V+NcZePWQ8PVJqcX VjTdp8ko8y7Wg2m9dHdxt0uejhsrqVkul9j8f7nw3FD4dmXyxKQORgc18U+I5br4Q/tK6F4u0OLy jBeC9UDjLBiZEOOxXPFfbGs3uqeD/FvjDRdd0+80+G31KWPSkniZXeDzXVD83JXC8N3xXwN8a9au Lq/E8UEnm2hDlH7hmK/rmv2fA4yli8PCvSd4zSa9H6HnTg4ycX0Nz9p3whY2f7UvhHxjokSR6Jq9 /azwbMbQskoYKPpzX7vSf69/rX4QeJbufxV+yF8K9dn3ebpWtW1hJ7lZl5P4Ma/d+T/Xv9a6iT9K ov8Aj2j/AN0fyp9Mi/49o/8AdH8qfQAUUUUAFFFFABRRRQAUUUUAFFFFABUMgBmtyQCRISPb5Wqa oZVDT25PVZCR/wB8sP60ATUUUUAFFFFABRRRQAUUUUAFFFFAH8o//BTAgf8ABY7k4H2cc/8Ab3cV 5f8AEO/k0fxH8P8AXrdBNcRaVA8KMDhmA4BxXdf8FTJ3tv8AgrBqNxGcPHY7gf8At6uK8r8XTvP8 FPhl4xt1a6htGUXPcAJggH6+lRVpxqQcJK6ej9Ga4evUoVY1abtKLTT7Napn6v8AhHSIV8AaXfwx ZnljRmKdQTmvoTSdKuZfA8UkkcksQQZ4rwz9nTxto3jLwNYWlsiuXtI3EhxjnJPX0zj8K9o13x5/ wiGoS2U8Ky26ZA29MD2H1r/KXjDD5jDN62AdP95CT0fa/Q/tzNMdicxqRjQXO5JTi+8ZK6a7aM8R +JbWhdbWBhbTBzuUHB4J9a6X4b+A77WdPtz9ouBas/zkygDHevGfHOsW3jHxT9q03zY8SMdmSOCS f610fh74i3/hPwyto5JUAnIY5A5HrX0uIyvH/wBkQo4fSo901qj9AxWV49ZNClQdqnVPoek/Ebwd 4W0tkg+1LIzD5gZQeQfrXl2g6MPsM1nbRGazkyGIJwcgCvKfE/jmDxHr86nVYpLlPu28U5LDJJy3 PFez+C9Qj034Wboy096ylirc4JA7/hX0GZ8J5rw/ldBYybdSpryvdK+jfb779zyMhzaOIy6pHD1n W9nNQctOXmtd2d7vluruyXZsyrf4L6BoVudS0jSrOymLfvJFwvHUjNdD8MrrRf8AhriRbWaBki0e 237m4yJrnI/UVp6h41jXw9JDcKPNxyoQgYINfM+natPp/wAe7y60wEPLYwlOTzmWY9ule7wtled8 RYHNvbylOSoP3nrZc9N6t9ktDxsyoUMPg6GGxEo0YyqcsEkkm5RktEt7trbvdn65fFKfTdT+HAks tjXCxuUeIcZCPjn618l/Cn4kR2HxBu7LxHMkCbxEJZWI3AOwIJ9hUMXjDWl8KQ2+pgoMlchj3z7+ 9eOa54avtS17+0NNcToJDIY1fyycYPr6g1+P8McLUJ0auCx1RRjNpc9tI+en3s5+HuFaWEy3E4PE +8mm4tWvda2Temvm0j9dvCnibwHqWgw29hrWlyynAWMXA3AkdMZ618Aft52Ov3HhfTNE0p57Wz1C 6aOWSHnzEIUFSfQ5NfOgvL6ynk1aCfUbTU7FjugjuWCb15xx161618QPi+nxB+FGmwalAoubUgwO M5DEHrnPoK/YnwHU4ZznI6tOjTr0VOLjVjGzlHb3lrd3ae/Tbt+Q5Rwu6ssVOhOanTjONWErc1Nu Ls4tP3k+krJPWx+XXxU+GtloXg+C4eRoVRCLhkk5J4wT+NcpYtqFh+xr8TNVlgX7BquLeOb+80IP Arq/j74kgbxJp+jC4Z42kLXKg+mMD8a1v2hDZeA/+CdPww8EOyx61qVmdSvYwPmSSfY+1vwNf3zY /nQ/eL9jH/kSvBP/AGT2y/8ARdrX3xXwR+xj/wAiV4J/7J7Z/wDou1r73oAKKKKACiiigAooooAK KKKACiiigAooooA/lh/b+cJ/wcU/ERmOALPR/wD012lfPnxEt2H7S13BGsry3FxGQEB3YOORXrP/ AAUu1BtM/wCC/wB8Q7sNtCwaKGPt/ZlpmuC8VXyeG/2l/Bvi6dPN0q7tYy8jjK5YYJ59KTQH6JeC PBH/AAiWseE4tMt7y/hvbOSa71C4Z3eFiiFYxztA5PUZr69n0qZfDkM1zA8se3gkEjpXnHwa8X6B 458JXNrppjM1iqlnxlSCvGCR/s1vax8UYtIubnSb22Mp2kYXNf5lcaYLirPeI54CWGlUxVG/Mvik 4uTkpdkrSSVtLW7n9S5fmWXYbL4VMNOMaN07tqK1SVltreL31ufEnx28DeGvEnjK81WAS6fdQRZu J48jcwA2kjvgV6b8EP2YDq3gfRNa8aa3rNxb+QZTarqTC3eMg+XnDAjgg4rk/EGqjXNfu7WbT5Y4 bj5XzyMY9a6yL4jeJtG+G02iW01tHbR2oggYkr5agAZJ3egr+t+JOD+L5cIZdlmTVVSqxUVUlJ3k k0k1F/3W3trZKz0PyGnnmUzzrGYuurqTfJZaOz3a01aS30bbujw/9o74F/B/wzqcq+Ghd2niC6X/ AEO1tJDKjScHc5JO0HPXpxXL/Dj4G63qPgmRNV0q31SaV1eK5u2/1QA+6meCPWte2vrOS4ed9UPi DU5ZQkz+Z5vlY/hBOfWvpaw8XnRvAWi2mm24mliQG43oQFx/D+te1isu4q4eyHD4PAVHjMS5Wc6m ySTd91pol70ndv5GWCxWSZnmFStjI+wpKOiha8m7Ls1fVv3UrJfM8l1n4C2uiWsdxcaXpx03ykMx 2pvyfvYA64r6U/ZDtvCHhvxt4gttLjtLSGPVJBtkG1yoAwcenWuB8V+PY9X8LCzgizI6YkYggIfQ Z614T4X17UdC8WanfaTI325dTk2gbiG4HBAIzXl/6ocUca+H2PwuczdDEacq2jJKSs5q73aavppr Zm+YZhk2U5vGOAtUpTjaT3cb3vyvTpZ217XP0i/abvr+28Az634SWP8AtC22SPztDRg5fB9cVwv7 Pvxn8K674Ujk1q+gs4WkPmmVyoDDsT65ryPWviJrWrafDoHibTnsEvrE/v45TKMYxnb2ryzw58OW 8KSand2U0k9hdSB3j3bgpPcDtmvxTgbwiy3G8LTyjiCUaNSdTmo1YuN5X0cYyTcZK6ty3ur7K572 dYvGYblWBlKrCEWqkfeXLbVNxesdH8Wz+R+2Oja54N8Q+H4v7D1jS9Rd4sp5FwrZ/WvxW/bA8Ha7 4s/bSS0vzdpplpaqbaM8RgHJbB7810uk+Lbn4ceJIfFFib6UwMN1sk77CO+FBwKZ8RvjLo/xV1Qa pZuIr+CHZdIYyrDH15r9D4S8McdwxxxhKlPCwnQdKcXVjHls7K3MtbS0te9nzNLqfE1cTh6+V1pR q2lde63q9endat+Vlc/Nb4peD7Pwv4oguI5TEFIMKB8ruA5yK6/TVv8AStD+BulX0Ag8/wAWW93F zyyvKOa5LxneHxl+0xY+HbR3uRJcx28KL3ZmAJ/AZr1n45atpdt+318GPBOmSo8ei6jYW58scEqy g9O+a/qGx8cf0G/AL/kqWv8A/YPP/o1K+sK+T/gF/wAlS1//ALB5/wDRqV9YUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAQwf6lv+uj/APoRr+aX/gqNM4/4Li6BCAgU+BNPJYKA4/fXXRuor+lqAAQt tYsPMfnHfcciv5if+CsV+NN/4LaaBcnp/wAINpq/nPdit8PiatCfPTk0+6FKKasz5c+IX2jRP2g7 q4s554psRGKZ5SzkkDqx57+texaPqN5qLR2Bi1K5unQhZYr6UIBjnOW6ZNeZeN47O7/ad8HSa0FG malbxSOV4zxhf1Ar6rs/CkVjPDFJcW1tp0Tf6JtOJMHkgnvXf/buY/8AP6X3mfsafY5638FXx0kS 32pX0MgwA0N1Icdh1NedeJvhxqV9Nc3Oj3V1qSoQJftM5JfHUA19qpqGiXHgJtC1TRbOAx2v+i6h ZQ4uJJhwnmMT9w5OfcCvPnsbCDw3d6XbTCK4nVgpzkhjXBg+KM2rSnGo5wcXZXd010aa/LdGtTDU opNWdz4ftrBrbRnilh1C38nHnxIrkY3ndtx17VzWqeC7640fUPEiNFDYG6KQLMhDSAfWv0I+HOue I/hnrOpX9lBotytxYG2ke4h3uoJDZUdmyOtef6zb3fjDVJtZ8UtNJBvXyjKfnmcYALevAArGjxJn 8sdUpzdqKS5Zc7cpN7rk5bJLvzO/Ybw9BU018Xa36nzf4P8ACWp+IdEIZrvQoo0VY5YXIM5BOTg9 q9It/hneeTs/4SXXlfb18+vb7fRY4rVWjVQF6ADjFadtDbxSys4JKj7or1P7czD/AJ/S+8y9jDsc F8C/AkyftR+GdK1G71zUY38WWSNcQ3rwvCpWNi+5SCGAYYI5GK+pv2s/hp4N+HPxy8LeJlu/FepS 63bSLd3NzrE00kHkLEqYLNuIw57189aNrN5o/wARdb1XT7iWxu7XUoZreSI4ZGFtDgj34ruPiF8Q tW+Ltj4ftNdaL7dpMDxW8pzum3BAxck8sdgP4mvhuM8JxTV49yzNsLimsPToKFWPM03zUk1ps1zc r7pq51YR4dYOpTkveb008/8AI9l+Aq6RqHiz/hHX8S+KbOw1VDJZy2mv3UavKFB+YrIBjYp698V+ h9t8E9KbQopB4r+IbZTt4svcH/yJX4seANI8Q6Bq+r6be4fRZpfPsnVjuiZvvLnsK+l/B/xi+J3g /wCLmma3f+Pdb1HwtbkLPpN3cM8HlnhsLkcgZxz1r8v8RvDLMs4zetmtDFTanTs6fPKL51tKLWjc lo07WavfWy78LjYRpRpSitHvb8z55/bU8M6vpH7Wmn6d4d1bXbu1m0qISm/1Ka4cMJZhwzsTjjpX wV458Na5o2tWwu3F0jFJZnlOWK7uQM9x1r9Zvjj4s8JfEj4zW3jbRIJnMWmx2+JVA/eB5GJwCePn 61+avx219rrxbbaOCitGpuHZeDgZ4/HGK/TvDHMM7wnCuAoYupONSMEpKT95Pz8zjzGNGeJnKCVm zmFktF/ZPsf3lws8vjSBoYfMYRCMyoAdmcZz3xmv3tk/17/WvxR+POg2/gH4SfA/wgqrHqkn2G41 FSeTI8wbP6V+10n+vf619ficZXxDTqzcmu5yRio7I/SqL/j2j/3R/Kn0yL/j2j/3R/Kn1zFBRRRQ AUUUUAFFFFABRRRQAUUUUAFRSf62D/f/APZTUtRuCZYSBkB8n2+U0ASUUUUAFFFFABRRRQAUUUUA FFFFAH8lf/BVX/lKjq//AGDj/wClVxXmnwFa1+In7OPiz4b3Lv8A2tFGJdIQHG6UA4X8Rn8cV6X/ AMFVf+UqOr/9g4/+lVxXwh8PfG2oeBPiRYa3YymPypld8Z6A5yPegD7z/Zn+J0/gv4kT+BfENw2n XttcGO1ZztzhsMpz3GOPxr9Q47O28aeFIZhOLhposB9/Xoa/Lr4geANI+Nvwmi+KHw9Vl8UW8Svr NmpG5mIyZFA5I659K9T/AGfP2hLew+GP/CK+IsaNrHh2KK1k+0ygCYAMgYFsEn5ckds1/L/jx4c4 jFzjnOAV6ispJL5J9+yP6L8HuLFVpRymclGpG7pyb36uPZW1ktV1Psuw+GcugaxDcC2WTcPmIO4E /lXCfFb4bvqOi3Jinv7C3kiAkktm+dSSORx09a9R8H/FbTdQ0db6aaO5iZAYmR1IOQCCPUVj634w tr3xXHPDHi3JAkBbNfzVkma57l+cwxEoe/Saeuzs72P3rFYfM8w9rhsUm4zg4yae6kraP0ejPi7w n8FrTw3rEt4mrXOosTlkRcu+T3yK/QP4S+C7Cw+HpvdQlgnChiyvJk4wvbHWtaz03w3f+GzdLEbW TC5wc7ulcda3lxp99NHBITbOSqKR2P8A+qvovEPxQzHjPDxw84Kiqb2XXvqfB8McGYHKcPiKOBlN Tm1zOT6LorJLq3fc4vxj/Y114gZbdW4HOCPSvLdHbR9F+PN1cRx+dImmW5QP8wDGWfP6Yrsb9Im1 xjdSMC3oOKboXhnTbr44agWkLoNFtpAc9zNcj+lexwziVg8sx1N1Jcroa2b1/eU9z9OzanhaNPBq veSjPTS/2JbPodx4m1i+8T2drDC5iXdlgBt9R7+teh+AdFt9C8nVL5VuFhhZmTOc8HqOK85vrQpr EUNsAsWQfmPvXpsSw2Hw4vLm7uo4VFserDnqO9flOaSSwcMPS0jJ2st9Tzc3nGOCjQpvljLp11Pn 34jeM9I/4XNrU0MbwLO/7qArhMZOD1618yeKvGNi9zqUbS3OlmMmROMAsBwB6irPxt1qOLUY9V3k 2NrdEyOrAMw6gj1FfKmq+INf+KPjJdO8N6fNJaSHy45Chzzxye1f6AeHmRUavC2XUsRTa9jZpPur pN37rWx/LXHGeYjIuJMwp4CspKrBU5PtFxi2k+6atc2/Avh+/wDjP+1RaPdHdoun3cc+p3CcBYVI Zj+Sk/SvK/2nviPJ8QP2kdVaM40+xmaG2X0RTtUH6AAV9a6lNZfsx/slanYXws28deJrNkljDZkt wQQdw4IJDY+lfmHeXUt7qtxeTsWmmkLuSe5Oa/UT8fP6/P2Mf+RK8E/9k9s//RdrX3vXwR+xj/yJ Xgn/ALJ7Z/8Aou1r73oAKKKKACiiigAooooAKKKKACiiigAooooA/kY/4Kqkr/wXE+KrDqLLRyP/ AAV2tc/o9mvxZ/Ycgk08zTeKvD0pcRL1MIwWOOpxgfhmt/8A4Kr/APKcH4rf9eOj/wDprta+W/gf 8U7v4cfE61mZt2mTNsnQ9w3BH0IoA+7v2cfjobXwWuk3N1FZa5ZfJKHO0SxjjPuRk1+gtxoNp410 GDV9GuoLmWeIPFOJMow/DtzX5RfGb4O29/osPxU+Fgml0O7Ba7gjIL2z9TkL0HoarfAT9qPxH4Cl tfC2sXf/ABJY5Cy74/m3HGQzHoOK/HPEfgDMcfi6Ob5HNUsZS3b0VSPaTW9rWSelnrY+/wCE+KcN hsPUwGYR56E/nyvul876a3Wh+pVj8Pb+11l4tUgjnieMgFc8HPUcV4f8VfhRPrVhLpkt/qmlR7yI pYH+WQc4DfhXtPgr49aXrHgVb29spDeSAnbCwdSOcYP0xXHW/j97z4t3d5dqY/DPA8l0w4J46+ma +B4WzfxPoZvisTmWDUoRg7xvyxk4beza5lzP0tI+kzPB8IVsFSw9CtyvmVpJXfvb8yfK+VfejwXw V8H7bwpA0L6lLJvICleMH1ye9fd9h4a8MeFP2e5tQ12Wzkt/sqtcTM+ce5rJ8T6H4O1L4aSakHMU SxNJGVm+XI6Hdn1FfPmh+NtZv/AHiLw94o8+SKS2Z4C0GxfKXgj69K8LiPijHeI+W0FgubDRoVY+ 2je0neSS5Gt+VXlK6Vnbc7ctyGhw3ip+0nzucXyO2nuq7UtXbmdkmm7q60OD1jWPD934kvrjQ5pX 01XJUyOMY9QfSovh94ts/Cvi2916WGK5tl1J1BK72XIHK8jmvNfEPgnXNB0q8ufDJhvLG5zJ5MrH K55wMdq9P+CPw2TxB8ONXuvGMsMw+1sotLaUgo2ASSQcg+1f0txPmGUZLwfi6eZylKk4wpvrOV2l vpq9220fmvD+Bx+a51CWDjGM7uXaK67a6dLHUal4+v8Ax/8AFc2WmQ5JXbBuXaVB7tyeK+s/Bdhp vgL4fahNqQOoNNEd5kbeAxHbpxmvgXTPBc8n7RVl4fsrhba7stR82R/NI3RIdwXPfjFfZ/xNvLbR /g1+/uV82a4RBECN2D1NfzN4p4eji8TknDGXVuTC1eR8sVd2bVpN310u+nV3P0zhqMqeFzDM8bTb rJtN3tey1itNNbLr0PnPUvHOk3Et6sEQmxI+YgMZ5PTmvmzxn4u0KzsrrW9LNxpuoopQxTADcD14 rE8Z6nF8PvHNxqF9M81m7O8AhbJ57EV5LpeheM/jL45FvpemyLbSyghtpAZc/wBBX9e0qapwjBbJ WPxCpNzk5PqeifADRpn8a678aNbRk0nRLaaW2ccB7jBCdfevnnw34suvGn/BRXwfrt0255/E9uRj /rqOa+kP2gfGuk/C39nez+Cvh17OW/BWTUZ7dt2WI+7n2r42+CpJ/a5+HJPJ/wCEhtv/AEYK0IP6 /PgF/wAlS1//ALB5/wDRqV9YV8n/AAC/5Klr/wD2Dz/6NSvrCgAooooAKKKKACiiigAooooAKKKK ACiiigCKEKITszje3X13HP61/LN/wWNdo/8Agrzp7ocOvgPTSD6ET3df1NxrsQjcW+Zjk+5Jr+WL /gsf/wApc7H/ALEHTv8A0fd0AeGa3ayeOv2LdB8a6ascuo6FKltdFT+8C9UOB6c/pX0N8G/EWjeL vhbb6tPfTy6nb/urtJ5dzKQOuCelfHX7NfxM07w34mn8J+I0W48O6rujuIZAChDDGfYg4IrsviB4 S8YfAT4jSat4ZuxeeGNTTfbzRHzYpEPIB4xkdKAPv3TvE+m6lei1gD5U/KzLgNj0q3NY2bakLgJC bgHI+b5vyr45+EXxas/EItdMvZVtdZikMh3EKrDPY/jX0X4Yk+16zc6jNLJcyRljkHjGelAF/wAc /wDCX23g95fCFpZX16c7lmPKcdQO5ryjwVovxG1F528VpLMS3mASfKqnj5QMdK93stetr3VXtFim jdBlmYfKB9a0NYujYeHLi8idXKRkpk5B4oAjRDb+HYRcLFDLHH86q3yr6fhXJS6i5lkKvb+Wzclc HNUrDxKuvaRNp2pMtrcToyo68A54/OvJ7241rwdc6jDqyzz2RDCzuYoy3ygd8d/egDubUmXxL4jL up/0uP5geP8Aj3iro7CbSLK/UvKJrsDcOcKK8E0LxpFqPhzxD5TzyTyTLKuVIb5Y1Xp/wGr8l7qT 6LFqtoY5LqVFjEJPGTzz6GvWzr+PD/BS/wDTcDKj8L9X+bPpPw54qi1zWNRtI4XVbZgCxHBrd1Zt NudDuLW+mQRMNrgN0rw/4d6+LW+j08q5lLk307Jhd57A/WuR1bX9U0r4s3lhfzpFZtcuxaU4VkOc c15Jqb+pO/hDUp9T0zUYrzRtrfaYEmDEDtgdjXzh4H0dvix+27pUcgeTQo7ozalJKxVUt4t0jgno BhayPG/jGO71S/0nw09xIJGKukQLBiTyRjrXuejw6d8Bf2Ede1rVpBB448QDbbI8f7xYn27jzzgj cvHXNAHzr8fviU3xG/bn0y6hmZ7C01SCGBCAAiiYYUY7AV/QzJ/r3+tfyraBcyXfxg0O6mYtLLq8 DMT6mVa/qpk/17/WgD9Kov8Aj2j/AN0fyp9Mi/49o/8AdH8qfQAUUUUAFFFFABRRRQAUUUUAFFFF ABTGBLx4bADcj14PFPpjAF4yezZH5GgB9FFFABRRRQAUUUUAFFFFABRRRQB/JX/wVV/5So6v/wBg 4/8ApVcV+a9fpR/wVV/5So6v/wBg4/8ApVcV+a9AHrfwo+L3ib4WeOLfUtIvZEtg372I8gjGDx36 9K+1Jovgv+0Roi6l9rg8J+P5QNzbQlvK5wWZv7ueeOlfmhV2x1C802/S5sriS3mU5DKaAPuDUfBH xn+C+tR3Vpcz6zoFuuNlndm5tjH0A+UkLxjgV7v4P/aG8CTabaRa5e3Wlai3+sgubd/lI/2sY/Wv izwH+0p408IytDPObqyk4nQDIkXGMMpODX0BYeKfg38VLZC9nbaJrT/ft50P2d2xk7ccofzHvXxv E3AuVZ5FKvFxa6xsn6PR3P0bg/xSz3h1VIYeanCa+Gd5JNdVqrO2m9rdNEfffhn4i6HruhiPT9Ti eLg5VwRjAx0r1GXW/Dll4RjW4lU3IAIccjFfmfY/DfRvCM00/h7Wte0SW7QNILKZLhCOcfwkDrWr ND4rnhWG4+JPi97fAwn2GEf+yfWvwPOPo4VqmJvhcSlTvfW6f4Jo/XsP455LXpQli8NUhO95KHK4 /Jtp/Jr5s+r/ABf4o8LvEJbXUbIkHkrKvHHNeK6T8c/CWh/EbUL+61uH7MumxQYDjLMkkpKj3+cf nXgsXwa8MRNL9q1fVrtZDuaOW8WLJPc1oWvwu8A2+qS2/kaEkMdqkqm81FGBcu4PPXOFXiv1Xhfw MyvD4LF0a9eVRSp26LTng/P9D5bNvpAZjWp0aVDCQioSv7zlK+jS25bbvudr4z/apuNbso5PCaXO ntvwMxLM8mAeAFORnjk15pq/xK+O3jfR7e3sra8gtom3brm5aEP06qTgjjpXb6fYeEtD1maGxv8A wTpkiIVM0M275f61h658V/hz4Y09l1TxFdeI73kLBpyhYSB0BJwQfcZr6nJuAeH8rjFYfCxTjs2r y+96n5rm/H+f5ldV8TLlaScY+7HTyVkYen+BfGnjG9jPj7UJLhQR5Wn6e37sc9ZCOAK9Y134r/Dz 9nz4dLa6NFpWqfENVMcUNsiy29iSDhgw4Zh19jXyF41/ad8T6vpU+i+EraHwzoT8OiLmaUYx8755 NfMtzcz3l7Jc3MjTTOcs7Hk19gfHHXePPH3iL4iePLvX/EV9NeXUzlhvYkLmuKoooA/sF/Yx/wCR K8E/9k9s/wD0Xa19718EfsY/8iV4J/7J7Z/+i7WvvegAooooAKKKKACiiigAooooAKKKKACiiigD +Rf/AIKr/wDKcH4rf9eOj/8Aprta/Ouv0U/4Kr/8pwfit/146P8A+mu1r866APo34K/tA+Ivhfqj 2Us5vNBuF8u4tpk8xWU9QQe1fT2p/DX4R/GywXWfh9q9r4d8UXA3zWF3KscDHHRWPqcda/NStjSN e1XQr8XOmXclvIPQ8H8KAPse20/4q/A3xA0OpWV1eaAG2sbdjNEvfKOOK9SsfjjoOvWsOmfbZrKc od6XMZjBJ7EnrXz74G/ah8Q6Npq6N4jhj1bQ34ntnTfG4zzlSf1zXtNvF8GvihALjSY4bS8kGZLK 6bawz2jk6Dns2KAPpHwt4pi1DwJouia7rKNodrMN5gkwDGWzhsdRmvobXPH3wvW1it5b2wllhh8q ZpEDArxxnHIr86pvhToOkQTadpmv+KdOif78dvMJo/zAx+tYzfCDSrmfy7rxR4qlzwFK7RX5ZxV4 S5TnmKpVp1J0lDm92m1FOUndyej1fXvp2Push4/x+V0pQjGNRu2s7vRKyjutF07a9z6L1DxdbW9n fXx1WxSyivGktQrgAw54GP6V5unxwt/C+meIdR0DxBZ2uoXNyzRROu5HBxg7c9feuKh+DfhCOJYr y/vr9VP3Z9QWNR+Faun/AA18CRG4ZovC0ZhuWjjF7fqflABHQ8iv1illeFxGT4jC4iCqU7QTUle9 mt7nxsMwxFHFrEUZOE7t3jpa/Y4i8+L/AIu1DxxB4lW8ludfx8v9lRMN4I5zt6fSr2p+Kvj14t1e 21OO38mONdsQvrskD32seteiWMnhjStKultNe8IaJErZdoXL5I/WuP8AEXxx+GvhtNiXGp+LdSQZ KoQkG72PXHtivNo5dhKXJyUorkVlotEtEl2Q6mNxFTm55t8zu9Xq31Zc8O/C/U/EGtw6n41v11XU Vbc3nyBLGAdeWPFb/wAT/wBpLwr8MPBcvhH4UmC516aDy7/VViA2dikZHQe9fH/j/wDaD8beN7M6 Ys0Gj6CvEdjax7FA/PrXgzMWcsxJYnJJrtOY0tX1fUNd8QXGp6ncSXV5O5Z3dsk5Nd78FP8Ak7j4 cf8AYwWv/owV5fXqHwU/5O4+HH/YwWv/AKMFAH9fnwC/5Klr/wD2Dz/6NSvrCvk/4Bf8lS1//sHn /wBGpX1hQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBWtHMlq7EMMTSDn2dh/Sv5aP+Cx/wDylzsf +xB07/0fd1/UxbII7dlDMwMsjZPu7H+tfyz/APBY/wD5S52P/Yg6d/6Pu6APynilkhuEliYpIhyr A9DX3f8ABb4/6BrXgc/Db4r2K6pob5NvMMB4HIA3Kx6e4718GUoJVgVJBHQigD7k8e/s2694cP8A wmXw9v11/wAOs2+3ktZczQjBPKjkYwetanwy+Omq6DeR+HfGEBiTBVboxbMnPRh2+tfPPw3+Pnjj 4eapC9lqlzJapwEznAznv1r6Xtfi98J/i7YvafEDSLe21u4b5dUiRYZYz0BKjCsBnvQB9DL4u0yb Qnksmgea7RgzpIDge1OttWutV+Dcsc2+OWAMvXkpzg183X3wRu9LlXWPBPivfZH/AFNzbSGVAOv7 wclPfIxW5p//AAubSdAuLW4TQdeDoVSVZ/LJH5c0Aeiw3MLaPbQRxz/aYsFXKHv7/hXdxa/DqPg+ W2u7P7QLaRY5i68MD3FfNf2744C2+z2mmaRbKBjdJOrGmxRfGufQ3sp9V0PS1fIdyAzHPXotAHot 1p+laP4j8S6vpUSraQTKZYsj5l8mNuPxLVwB8faUt/b3djZz29sLjzL5ZcKB6Y7da5+L4ceLb2O8 tLzxpeMiyKl0LSEuJCUVgcAdgwH4V0Gl/CnSDAU1FNe8TyRDEMTQsgB75Hf8a9bOf48f8FL/ANNw MqPwv1f5sXWvjv4ZsdZtorCO4vLRl/0hIofmDZ6hhWPrupeJ/jH5EkFg2g+Hlfabm4X9+4x/Cvfj FesweHdC8PaAlxcweHfCthgMDeSr5ij3Q/NXmPi/9ozwX4ST7N4GtJ9W8RQk7NXmVQkTA4+ROQB7 +9eSantGjeC/hV8Afh1b+NvG4F/qcttv07TZJA0tySPvSDqqc5x1r8/vjT8ZNd+LvxIm1O+k8jTI v3dlaINqRxgnaAPQCuE8X+OPEvjjxJLqniPVLnULh3LDe3C5PQCuSoA3vCv/ACU7w5/2FLf/ANGr X9WMn+vf61/Kd4V/5Kd4c/7Clv8A+jVr+rGT/Xv9aAP0qi/49o/90fyp9Mi/49o/90fyp9ABRRRQ AUUUUAFFFFABRRRQAUUUUAFRucSRDAOXxz9DUlQSqzXFqV6LKS302MP5kUAT0UUUAFFFFABRRRQA UUUUAFFFFAH5UftQf8EvNH/aY/afvPiXe/GXU/CM08HlfYIPDSXSqPNkkzvNwmf9Zjp2r50/4cc+ G/8Ao43XP/CMj/8Akuv3iooA/B3/AIcc+G/+jjdc/wDCMj/+S6P+HHPhv/o43XP/AAjI/wD5Lr94 qKAPwd/4cc+G/wDo43XP/CMj/wDkuprf/giFodpcrNbftJa/DKOjJ4NjB/8ASuv3cooA/FS2/wCC P97axhYf2pvEgA6Z8Gx//JdSSf8ABIPUJBg/tUeJV5z8vg6Mf+3dftPRQB+I9x/wRwnuVxN+1R4p Yf8AYnx//JdY0/8AwRO0q6thHP8AtLeIpMOX3HwemTkAY/4+/av3RorSnWnBSUXa6s/S6f5pCauf hE3/AARA0J2y37SWvk4x/wAidH/8l1F/w458N/8ARxuuf+EZH/8AJdfvFRWYz8Hf+HHPhv8A6ON1 z/wjI/8A5Lo/4cc+G/8Ao43XP/CMj/8Akuv3iooA/B3/AIcc+G/+jjdc/wDCMj/+S6P+HHPhv/o4 3XP/AAjI/wD5Lr94qKAPCPg58EIfhHo+jWcXiSXXl0/w/DpKs9iIDII1iXzOHbBPldPfrxXu9FFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH4NftwfsMS/Gv/gpL44+Iy/EKDQRqVtYJ9ibTmlMfk2U EX3gwznZn8a+S/8Ah2FN/wBFatv/AAUN/wDFV/UzRQB/LN/w7Cm/6K1bf+Chv/iqP+HYU3/RWrb/ AMFDf/FV/UzRQB/LN/w7Cm/6K1bf+Chv/iqvWP8AwTU1XTbrzrH4xx20nqmksP8A2av6i6KAP5oo v2DvHUMWxPjoMY76S3/xdMk/YK8cydfjsy/7ulN/8VX9MFFAH8xdz/wTy8WXYIn+OUjg9R/Zbc/+ PVkXH/BNTUrmSN5PjDGWRQuf7Kfn3+9X9RdFaRrTjCUU9Hv8hWP5am/4Jj3bk7vi9C2eudJbn/x6 o/8Ah2FN/wBFatv/AAUN/wDFV/UzRWYz+Wb/AIdhTf8ARWrb/wAFDf8AxVH/AA7Cm/6K1bf+Chv/ AIqv6maKAP5Zv+HYU3/RWrb/AMFDf/FV1Hgn/gnLN4Q+LnhzxSfihb3o0rUIrv7ONKZfM2MG253c ZxX9ONFAHyb+z+274na43TOmk/8AkVK+sqKKACiiigAooooAKKKKACiiigAooooAKKKKAIoVVYWC ksPMc8nPJYk1/N1/wVc+B3xa+In/AAVEs9f8FeBda8R6Mvgmwtzd2qoUEizXRZeWHIDD86/pKooA /iG/4ZT/AGiP+iT+KP8AviP/AOKo/wCGU/2iP+iT+KP++I//AIqv7eaKAP4hv+GU/wBoj/ok/ij/ AL4j/wDiqUfsqftFKwK/CjxSCOhCx/8AxVf28UUAfxpeDPhP+1l4MvVex+Gvi6SAceU2zHX/AHq9 fl0j9pG9uDcal8DfEVzcEct5cX/xVf1nUUAfyUyaD+0cJFaH4Ea8GHOTHGP/AGaqlzpX7WDEmz+D fiG3J6ZSIgf+PV/XDRQB/ILLof7akchlsfAPia1edhJchbaHl1AUc7v7qrWHL4M/bWkNzjwV4vQT fexFFn891f2LUV0YnEzrzUpdFFfKMVFfgiYxSWh/FHrH7PP7U+vXRm1f4d+NL2QjBMhQ/wDs9YX/ AAyn+0R/0SfxR/3xH/8AFV/bzRXOUfxDf8Mp/tEf9En8Uf8AfEf/AMVR/wAMp/tEf9En8Uf98R// ABVf280UAfxPeHf2Wv2g7X4gaFdXHwq8TxQQ6hDJI5WPCqJFJP3vQV/Rm5BmYg5Ga/S6igBkX/Ht H/uj+VPoooAKKKKACiiigAooooAKKKKACiiigAqGQMZrfBGBId2R22tU1FABRRRQAUUUUAFFFFAB RRRQAUUUUAf/2Q== --000e0cd48464e05edd04a32be689-- ========================================================================= Date: Fri, 13 May 2011 19:06:42 +0100 Reply-To: Carlos Faraco <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carlos Faraco <[log in to unmask]> Subject: SPM8 Reorients, but gives errors 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 am reorienting some files with SPM8 and even though it seems to success= fully reorient, it gives some errors. They output is: ??? Error using =3D=3D> spm_input at 843 Input window cleared whilst waiting for response: Bailing out! Error in =3D=3D> spm_orthviews>zoom_op at 795 thr =3D spm_input('Threshold (Y > ...)', '+1', 'r', '0', 1); Error in =3D=3D> spm_orthviews at 449 zoom_op(varargin{:}); Error in =3D=3D> spm_image at 267 spm_orthviews('Zoom',zl(cz),rl(cz)); ??? Error using =3D=3D> waitfor Error while evaluating uicontrol Callback ??? Error using =3D=3D> spm_input at 843 Input window cleared whilst waiting for response: Bailing out! Error in =3D=3D> spm_image at 172 spm_orthviews('Window',1,spm_input('Range','+1','e','',2)); ??? Error using =3D=3D> waitfor Error while evaluating uicontrol Callback ??? Error using =3D=3D> spm_input at 843 Input window cleared whilst waiting for response: Bailing out! Error in =3D=3D> spm_image at 172 spm_orthviews('Window',1,spm_input('Range','+1','e','',2)); ??? Error while evaluating uicontrol Callback =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D Is this just a Display error? Should I be worrying about this? Thanks, Carlos ========================================================================= Date: Fri, 13 May 2011 15:32:47 -0400 Reply-To: Hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Hekmatyar <[log in to unmask]> Subject: Re: imcalc 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]> Hi SPM experts I created a mask from functional and would like to multiply this mask = with a thousand of images. How do I do that? I know, I can do that with = few images. Is that prod(X) work? Please throw me some suggestions. Thanks Khan=20 ========================================================================= Date: Fri, 13 May 2011 20:38:20 +0100 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: Ancova in SPM8 Comments: To: Ryan Herringa <[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]> Hmm...it's hard for me to tell about centering just by reading the SPM op= tions for it. I tried to load your job into SPM but it doesn't generate = the design matrix, because obviously I don't have the data files. That said, I think the centering is probably correct just based on the de= sign matrix schematic that you'd posted previously. First, an aside: when I loaded the job, it showed you had set ANCOVA to = "yes" under the "factor" part of the design spec. That's a "different" A= NCOVA; it's for PET. So change that to "no". (That said, I don't think = it affected the way your design was set up.) So, getting back to the main question: I think the design is fine. It's just a matter of knowing how to interpr= et the results, ie what the betas are. So you can make appropriate contr= asts. In rough, verbal terms, the betas for the first four columns are the main= effect of condition, and the other columns are the condition-by-covariat= e interactions. If you want to more precisely delineate what's going on, write the model = down symbolically: Condition: 1 <=3D i <=3D 4 Subject: 1 <=3D j <=3D 13 Covariate: 1 <=3D k <=3D 2 y_ij =3D beta_i + C_jk * gamma_ik + error (say). It's Einstein summation notation (sum over repeated indices), so = it means y_ij =3D beta_i + C_j1 * gamma_i1 + C_j2 * gamma_i2 = + error * y is the data; it's indexed by condition and subject, but not by covari= ate * beta_i is the "beta" for condition i * C_jk {k=3D1,2} are the mean-centered covariates. They don't depend on = condition, i, but only on subject, j. * gamma_ik is the "beta" for the condition-by-covariate interaction If we average over subject but not over condition, we get y^hat_i. =3D beta_i Why? The C_j1 and C_j2 are mean-centered so go away. The error goes awa= y since we're looking at predicted values in this type of formulation. So, the parameter estimates for the first four columns are the effect of = condition. (Perhaps technically it's the differences of these...I'm not = sure what conditions you're using, though you said in a previous post tha= t they're already differences.) And the remaining parameters, what I've = called the gammas, pretty clearly give the appropriate interactions. You= can think of them as similar to the textbook ANCOVA examples, where the = horizontal axis is the value of the covariate. Only difference with your= case here is that you have two covariates not one. There's a subtlety to ANCOVA that always comes up. Here, e.g., beta_3 is= the mean for condition three. But the meaning of that number is clouded= because it's for a "typical" subject in your set of subjects, and for th= e average such subject the covariate is zero (precisely because it's been= mean-centered). But that's not necessarily indicative of a truly generi= c subject. Though this issue is typically more at hand in cases where AN= COVA is used with a group factor, not a within-subject factor. ========================================================================= Date: Sat, 14 May 2011 11:01:17 +0530 Reply-To: thamodharan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: thamodharan <[log in to unmask]> Subject: SPM8 second level Analysis-reg MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=00163683303a463add04a335bb66 Message-ID: <[log in to unmask]> --00163683303a463add04a335bb66 Content-Type: text/plain; charset=ISO-8859-1 Hi All, Here i have a request that, We are (our team) working with SPM8. We are able to Process the Pre processing levels (1.SliceTiming 2.ReAlignment 3.CoRegister 4.Normalization and 5.Smoothing) and also upto First level analysis. We are able to get the images with activation in the first level analysis. at the end of the first level analysis we are getting con images as well as SPM.img images. while processing the second level analysis we are getting doubts with the images. which images we can take for second level analysis? Please help us to tackle this problem. -- Best Regards, A.Dhamu BE., (09976413557,07760662060) --00163683303a463add04a335bb66 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Hi All,</div> <div>=A0</div> <div>Here i have a request that,=A0=A0=A0 We are (our team)=A0 working with= SPM8. We are able to Process the =A0Pre processing levels (1.SliceTiming= =A0 2.ReAlignment 3.CoRegister 4.Normalization=A0 and 5.Smoothing) and also= upto First level analysis. We are able to get the images with activation i= n the first level analysis. at the end of the first level analysis we are g= etting con images as well as SPM.img images.=A0 while processing the second= level analysis we are getting doubts with the images. which images=A0we = =A0can take for second level analysis? Please help us to tackle this proble= m.</div> <div><br clear=3D"all"><br>-- <br></div> <div><font color=3D"#000099">Best Regards,</font></div> <div><font color=3D"#000099">A.Dhamu BE.,</font></div> <div><font color=3D"#000099">(09976413557,07760662060)</font></div><br> --00163683303a463add04a335bb66-- ========================================================================= Date: Sat, 14 May 2011 11:02:55 +0530 Reply-To: thamodharan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: thamodharan <[log in to unmask]> Subject: SPM8 Second level Analysis-Reg MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e65aef9225e5fa04a335c13a Message-ID: <[log in to unmask]> --0016e65aef9225e5fa04a335c13a Content-Type: text/plain; charset=ISO-8859-1 Hi All, Here i have a request that, We are (our team) working with SPM8. We are able to Process the Pre processing levels (1.SliceTiming 2.ReAlignment 3.CoRegister 4.Normalization and 5.Smoothing) and also upto First level analysis. We are able to get the images with activation in the first level analysis. at the end of the first level analysis we are getting con images as well as SPM.img images. while processing the second level analysis we are getting doubts with the images. which images we can take for second level analysis? Please help us to tackle this problem. Thanking You All. -- Best Regards, A.Dhamu BE., (09976413557,07760662060) --0016e65aef9225e5fa04a335c13a Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div><br clear=3D"all">Hi All,<br>=A0<br>Here i have a request that,=A0=A0= =A0 We are (our team)=A0 working with SPM8. We are able to Process the=A0 P= re processing levels (1.SliceTiming=A0 2.ReAlignment 3.CoRegister 4.Normali= zation=A0 and 5.Smoothing) and also upto First level analysis. We are able = to get the images with activation in the first level analysis. at the end o= f the first level analysis we are getting con images as well as SPM.img ima= ges.=A0 while processing the second level analysis we are getting doubts wi= th the images. which images we=A0 can take for second level analysis? Pleas= e help us to tackle this problem.</div> <div>=A0</div> <div>=A0</div> <div>=A0</div> <div>=A0</div> <div>=A0</div> <div>=A0</div> <div>Thanking You All.</div> <div>=A0</div> <div><br>-- <br></div> <div><font color=3D"#000099">Best Regards,</font></div> <div><font color=3D"#000099">A.Dhamu BE.,</font></div> <div><font color=3D"#000099">(09976413557,07760662060)</font></div><br> --0016e65aef9225e5fa04a335c13a-- ========================================================================= Date: Sat, 14 May 2011 19:46:05 +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: Problem about how to deal with the activation within ventricles of the brain Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=====003_Dragon164668022748_=====" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --=====003_Dragon164668022748_===== Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 RGVhciBhbGwsDQoNCkNvdWxkIGFueW9uZSBnaXZlIG1lIHNvbWUgc3VnZ2VzdGlvbiBhYm91dCBo b3cgdG8gZGVhbCB3aXRoIHRoZSBhY3RpdmF0aW9uIHdpdGhpbiB2ZW50cmljbGVzIG9mIHRoZSBi cmFpbiBpbiBvcmRlciB0byBzaG93IG1vcmUgbWVhbmluZ2Z1bCBhY3RpdmF0aW9uIHBhdHRlcm4/ DQoNClRoYW5rcyBpbiBhZHZhbmNlIQ0KDQoyMDExLTA1LTE0IA0KDQoNCg0KwO7B1cHhDQoNCkxp bmxpbmcgTGkgDQpSZXNlYXJjaCBDZW50cmUgZm9yIE5ldXJhbCBFbmdpbmVlcmluZw0KLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0NClBob25lOiArODYtNzU1LTg2MzkyMjMy DQpFbWFpbDogIGxsLmxpMUBzaWF0LmFjLmNuDQpTaGVuemhlbiBJbnN0aXR1dGVzIG9mIEFkdmFu Y2VkIFRlY2hub2xvZ3ksDQpDaGluZXNlIEFjYWRlbXkgb2YgU2NpZW5jZXMuDQo= --=====003_Dragon164668022748_===== Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: base64 PCFET0NUWVBFIEhUTUwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4NCjxIVE1MPjxIRUFEPg0KPE1FVEEgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PWdi MjMxMiIgaHR0cC1lcXVpdj1Db250ZW50LVR5cGU+DQo8TUVUQSBuYW1lPUdFTkVSQVRPUiBjb250 ZW50PSJNU0hUTUwgOC4wMC42MDAxLjE5MDQ2Ij48TElOSyByZWw9c3R5bGVzaGVldCANCmhyZWY9 IkJMT0NLUVVPVEV7bWFyZ2luLVRvcDogMHB4OyBtYXJnaW4tQm90dG9tOiAwcHg7IG1hcmdpbi1M ZWZ0OiAyZW19Ij48L0hFQUQ+DQo8Qk9EWSBzdHlsZT0iTUFSR0lOOiAxMHB4OyBGT05ULUZBTUlM WTogdmVyZGFuYTsgRk9OVC1TSVpFOiAxMHB0Ij4NCjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVy ZGFuYT5EZWFyIGFsbCw8L0ZPTlQ+PC9ESVY+DQo8RElWPiZuYnNwOzwvRElWPg0KPERJVj5Db3Vs ZCBhbnlvbmUgZ2l2ZSBtZSBzb21lIHN1Z2dlc3Rpb24gYWJvdXQgaG93IHRvIA0KZGVhbCZuYnNw O3dpdGgmbmJzcDt0aGUmbmJzcDthY3RpdmF0aW9uJm5ic3A7d2l0aGluJm5ic3A7dmVudHJpY2xl cyZuYnNwO29mJm5ic3A7dGhlJm5ic3A7YnJhaW4gDQppbiBvcmRlciB0byBzaG93IG1vcmUgbWVh bmluZ2Z1bCBhY3RpdmF0aW9uIHBhdHRlcm4/PC9ESVY+DQo8RElWPiZuYnNwOzwvRElWPg0KPERJ Vj5UaGFua3MgaW4gYWR2YW5jZSE8L0RJVj4NCjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVyZGFu YT48L0ZPTlQ+Jm5ic3A7PC9ESVY+DQo8RElWIGFsaWduPWxlZnQ+PEZPTlQgY29sb3I9I2MwYzBj MCBzaXplPTIgZmFjZT1WZXJkYW5hPjIwMTEtMDUtMTQgDQo8L0ZPTlQ+PC9ESVY+PEZPTlQgc2l6 ZT0yIGZhY2U9VmVyZGFuYT4NCjxIUiBzdHlsZT0iV0lEVEg6IDEyMnB4OyBIRUlHSFQ6IDJweCIg YWxpZ249bGVmdCBTSVpFPTI+DQoNCjxESVY+PEZPTlQgY29sb3I9I2MwYzBjMCBzaXplPTIgZmFj ZT1WZXJkYW5hPjxTUEFOPg0KPERJVj4NCjxESVY+wO7B1cHhPEJSPjxCUj5MaW5saW5nIExpIDwv RElWPg0KPERJVj5SZXNlYXJjaCBDZW50cmUgZm9yIE5ldXJhbCANCkVuZ2luZWVyaW5nPEJSPi0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tPEJSPlBob25lOiANCis4Ni03NTUt ODYzOTIyMzI8QlI+RW1haWw6Jm5ic3A7Jm5ic3A7PEEgDQpocmVmPSJtYWlsdG86bGwubGkxQHNp YXQuYWMuY24iPmxsLmxpMUBzaWF0LmFjLmNuPC9BPjxCUj5TaGVuemhlbiBJbnN0aXR1dGVzIG9m IA0KQWR2YW5jZWQgVGVjaG5vbG9neSw8QlI+Q2hpbmVzZSBBY2FkZW15IG9mIA0KU2NpZW5jZXMu PC9ESVY+PC9ESVY+PC9TUEFOPjwvRk9OVD48L0RJVj48L0ZPTlQ+PC9CT0RZPjwvSFRNTD4NCg== --=====003_Dragon164668022748_=====-- ========================================================================= Date: Sat, 14 May 2011 09:31:35 -0700 Reply-To: Gui Xue <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Gui Xue <[log in to unmask]> Subject: Postdoctoral Scholar positions at USC Comments: To: FSL - FMRIB's Software Library <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636284b10ad215504a33ef4dd Message-ID: <[log in to unmask]> --001636284b10ad215504a33ef4dd Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable *The Department of Psychology at the University of Southern California*announces one or more Postdoctoral Scholar positions to study the neural mechanisms of risky decision-making (under the direction of Professor Stephen J. Read). The postdoctoral fellow is expected to contribute to NIMH-funded research investigating the neural mechanisms of risky sexual decision making in Meth and non-Meth using men who have sex with men (MSM) at USC, as part of a tea= m including Zhong-Lin Lu, Antoine Bechara, Lynn C. Miller, and Robert Appleby= . Using behavioral testing, fMRI, and computational modeling techniques (e.g, Neural Networks), the goal of the decision making project is to develop a detailed model of the neural mechanisms involved in risky sexual decision-making. The individual in this position will be involved in the design and conduct of behavioral experiments in the scanner, in data analysis, and in the computational modeling of the relevant neural circuits/mechanisms. Opportunities exist for interaction with social psychology faculty and with faculty in the Dornsife Cognitive Neuroscience Imaging Center. *Requirements* =96 Candidates should have a Ph.D. in a relevant discipline, experience with fMRI, and experience with behavioral and/or computational approaches to decision-making and cognitive processing. Familiarity with computational and statistical methods (e.g. MatLab, Psychtoolbox, Neural Networks, statistical programs) is highly advantageous. The appointment could begin as early as September 2011 for a period of one year. Renewal is based on performance and availability of grant support. Salary will be commensurate with experience, minimum salary: $40,000. *Application Procedures* =96 Please send a cover letter (including a description of research skills), a CV, and the names/contact information of three (3) references to: Stephen J. Read ([log in to unmask]) --001636284b10ad215504a33ef4dd Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable <style>@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0in 0in 10pt; font-size= : 12pt; font-family: "Times New Roman"; }div.Section1 { page: Section1; }</= style> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><b style=3D""><sp= an style=3D"font-family: Helvetica;">The Department of Psychology at the University of Southern California</span></b= ><span style=3D"font-family: Helvetica;"> announces one or more Postdoctoral Scholar positions to study the neural mechanisms of risky decision-making (under the direction of Professor Stephen J. Read).</span><= /p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">=A0</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">The postdoctoral fellow is expected to contribute to NIMH-funded research investigating the neural mechanisms of r= isky sexual decision making in Meth and non-Meth using men who have sex with men (MSM) at USC, as part of a team including Zhong-Lin Lu, Antoine Bechara, Ly= nn C. Miller, and Robert Appleby. Using behavioral testing, fMRI, and computational modeling techniques (e.g, Neural Networks), the goal of the decision making project is to develop a detailed model of the neural mechan= isms involved in risky sexual decision-making. <span style=3D"">=A0</span>The in= dividual in this position will be involved in the design and conduct of behavioral experiments in the scanner, in data analys= is, and in the computational modeling of the relevant neural circuits/mechanism= s.<span style=3D"">=A0 </span>Opportunities exist for interaction with social psychology faculty and with faculty in the Dornsife Cognitive Neuroscience Imaging Center. </span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">=A0</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><b style=3D""><sp= an style=3D"font-family: Helvetica;">Requirements</span></b><span style=3D"= font-family: Helvetica;"> =96 Candidates should have a Ph.D. in a relevant discipline, experience with fMRI, and experience with behavioral and/or computational approaches to decision-maki= ng and cognitive processing. Familiarity with computational and statistical methods (e.g. MatLab, Psychtoolbox, Neural Networks, statistical programs) = is highly advantageous.</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">=A0</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">The appointment could begin as early as September 2011 for a period of one year. Renewal is based on performance an= d availability of grant support. Salary will be commensurate with experience, minimum salary: $40,000.</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">=A0</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><b style=3D""><sp= an style=3D"font-family: Helvetica;">Application Procedures</span></b><span style=3D"font-family: Helvetica;"> =96 Please se= nd a cover letter (including a description of research skills), a CV, and the names/contact information of three (3) references to= :</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">=A0</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom: 0.0001pt;"><span style=3D"fo= nt-family: Helvetica;">Stephen J. Read (<a href=3D"mailto:[log in to unmask]">rea= [log in to unmask]</a>)</span></p> --001636284b10ad215504a33ef4dd-- ========================================================================= Date: Sat, 14 May 2011 17:04:27 -0400 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: SPM8 Second level Analysis-Reg Comments: To: thamodharan <[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]> Use the con_???? image of interest. ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf O= f thamodharan [[log in to unmask]] Sent: Saturday, May 14, 2011 1:32 AM To: [log in to unmask] Subject: [SPM] SPM8 Second level Analysis-Reg Hi All, Here i have a request that, We are (our team) working with SPM8. We are= able to Process the Pre processing levels (1.SliceTiming 2.ReAlignment 3= .CoRegister 4.Normalization and 5.Smoothing) and also upto First level ana= lysis. We are able to get the images with activation in the first level ana= lysis. at the end of the first level analysis we are getting con images as = well as SPM.img images. while processing the second level analysis we are = getting doubts with the images. which images we can take for second level = analysis? Please help us to tackle this problem. Thanking You All. -- Best Regards, A.Dhamu BE., (09976413557,07760662060) ========================================================================= Date: Sat, 14 May 2011 22:29:31 +0100 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: Re: Compare conjunction Comments: To: Noelia Ventura <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/related; boundary=90e6ba53ab7832445c04a3431ecd Message-ID: <[log in to unmask]> --90e6ba53ab7832445c04a3431ecd Content-Type: multipart/alternative; boundary=90e6ba53ab7832445104a3431ecc --90e6ba53ab7832445104a3431ecc Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Noelia, I wanted to second Sarika's suggestion. But I am confused by your response= : "in this region there is no response for unisensory conditions"; if there is no response for either A>0 or B>0, then you don't have a conjunction of (A>0)(B>0)(C>A)(C>B). So isn't this negative result appropriate? -Tom On Tue, May 10, 2011 at 2:54 PM, Noelia Ventura <[log in to unmask]> wrote= : > Hi Sarika, > > Thank you for your response. > > This does not function for our particular data set as we include a 3 > conditions: A =3D auditory, B =3D visual and C =3D audiovisual. We are no= w > examining the multimodal region. In this region there is no response for = the > unisensory conditions and therefore this leads to a null result in the > conjunction you suggested. > > Do you have a solution for this? Or anyone else? > > Thanks, > > Noelia > > > El 09/05/2011 11:58, sarika cherodath escribi=F3: > > Hi, > > I am not sure whether this could work, but you can make a subtraction of > L1 and L2 for each condition and then do a conjunction to create the > required contrast. > > > On Mon, May 9, 2011 at 2:47 PM, Noelia Ventura <[log in to unmask]>wrot= e: > >> Hi all, >> >> We would like to compare directly the results of two conjunction analyse= s. >> These conjunctions show overlapping clusters. One conjunction shows a >> larger cluster then the other. We would like to examine whether the voxe= ls >> that show up for one conjunction but not for the other differ significan= tly >> from each other. >> >> In more detail, we have two tasks in the native (L1) and non-native >> language (L2). Each task consists of 3 conditions, (A, B, C). In the >> random effects we use a full factorial within subjects design and create= a >> contrast : ( and we do this conjunction for L1 and L2. We would like t= o >> know in which voxels the conjunction of L1 > L2. >> >> >> >> However, in spm you cannot directly compare to conjunctions with each >> other. Or can you? >> >> We would like to use imcalc to test where i1 =3D conj L1, i2 =3D conj L2= and >> our expression is i1 -i2> 0. However, i think we obtain a binary image f= rom >> that but we need a statistical image. Can anyone provide a solution for >> this? >> >> Thanks for your help, >> Noelia >> > > > > -- > Sarika Cherodath > Graduate Student > National Brain Research Centre > Manesar, Gurgaon -122050 > India > > > --=20 ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 --90e6ba53ab7832445104a3431ecc Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear=A0Noelia,<div><br></div><div>I wanted to second Sarika's suggestio= n. =A0But I am confused by your response: =A0"in this region there is = no response for unisensory conditions"; if there is no response for ei= ther A>0 or B>0, then you don't have a conjunction of (A>0)<me= ta charset=3D"utf-8">(B>0)<meta charset=3D"utf-8">(C>A)<meta charset= =3D"utf-8">(C>B). =A0So isn't this negative result appropriate?</div= > <div><br></div><div>-Tom<br><br><div class=3D"gmail_quote">On Tue, May 10, = 2011 at 2:54 PM, Noelia Ventura <span dir=3D"ltr"><<a href=3D"mailto:ven= [log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote = class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid= ;padding-left:1ex;"> =20 =20 =20 <div bgcolor=3D"#ffffff" text=3D"#000000"> Hi Sarika,<br> <br> Thank you for your response.<br> <br> This does not function for our particular data set as we include a 3 conditions: A =3D auditory, B =3D visual and C =3D audiovisual. We are = now examining the multimodal region. In this region there is no response for the unisensory conditions and therefore this leads to a null result in the conjunction you suggested. <br> <br> Do you have a solution for this? Or anyone else?<br> <br> Thanks,<br> <br> Noelia<br> <br> <br> El 09/05/2011 11:58, sarika cherodath escribi=F3: <blockquote type=3D"cite"> <div dir=3D"ltr"> <div>Hi,</div> <div><br> </div> I am not sure whether this could work, but you can make a subtraction of L1 and L2 for each condition and then do a conjunction to create the required contrast.<div><div></div><div cl= ass=3D"h5"><br> <br> <div class=3D"gmail_quote"> On Mon, May 9, 2011 at 2:47 PM, Noelia Ventura <span dir=3D"ltr">= <<a href=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] ji.es</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0pt 0pt 0pt 0.8= ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex"> <div bgcolor=3D"#ffffff" text=3D"#000000"> <span lang=3D"EN-GB"= >Hi all,</span> <p class=3D"MsoNormal"><span lang=3D"EN-GB">We would like to compare directly the results of two conjunction analyses.<span>=A0 </span>These conjunctions show overlapping clusters. One conjunction shows a larger cluster then the other. We would like to examine whether the voxels that show up for one conjunction but not for the other differ significantly from each other. </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-GB">In more detail, w= e have two tasks in the native (L1) and non-native language (L2). <span>=A0</span>Each task consists of 3 conditions, (A, B, C).<span>=A0 </span>In the random effects we use a full factorial within subjects design and create a contrast : </span><span lang=3D"EN-GB">(</sp= an><span style=3D"font-size:11pt;line-height:115%"><img src=3D"cid:part1.07= [log in to unmask]" width=3D"246" height=3D"20"></span><span lang= =3D"EN-GB"><span>=A0 </span>and we do this conjunction for L1 and L2. <span>= =A0</span>We would like to know in which voxels the conjunction of L1 > L2. </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-GB">=A0</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-GB">However, in spm you cannot directly compare to conjunctions with each other. Or can you?</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-GB">We would like to use imcalc to test where i1 =3D conj L1, i2 =3D conj L2 and our expression is i1 -i2> 0. However, i think we obtain a binary image from that but we need a statistical image. Can anyone provide a solution for this? <br> </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-GB">Thanks for your help,<br> Noelia<br> </span></p> </div> </blockquote> </div> <br> <br clear=3D"all"> <br></div></div> -- <br> Sarika Cherodath<br> Graduate Student<br> National Brain Research Centre<br> Manesar, Gurgaon -122050<br> India<br> <br> </div> </blockquote> <br> </div> </blockquote></div><br><br clear=3D"all"><br>-- <br>_______________________= _____________________<br>Thomas Nichols, PhD<br>Principal Research Fellow, = Head of Neuroimaging Statistics<br>Department of Statistics & Warwick M= anufacturing Group<br> University of Warwick<br>Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email= : <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= ><br>Phone, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 = 7652 4532<br> <br> </div> --90e6ba53ab7832445104a3431ecc-- --90e6ba53ab7832445c04a3431ecd Content-Type: image/gif Content-Transfer-Encoding: base64 Content-ID: <[log in to unmask]> X-Attachment-Id: 6e494caf54a5a3a0_0.0.1.1 R0lGODlh9gAUAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQD2 AA4AhAAAAAAAAAAASAAAdABInAB0v0gAAEgASEgAdEhInEicv0ic33QAAHQASHRInHS//5xIAJxI SJxIdJzf/790AL///9+cSN////+/dP/fnP//v///3wECAwECAwECAwECAwX/ICCOZGmeKEkVaWta xOXO9Eq7sHzvqM2fuZ9w5DtlDI8hyRIIDColzFMJ2EAWxKY2SWU6oQApWGjFkqxaFlV05Ianw/IZ kl5TtE2u+HR3jzYREzRiVmokGDEmaAECgkBmIhoMXBZwIoCOLoQQhog6c02NjyWSLHJ/gT99h4kl i6IvkAClVVeuqTSSlHCeUQq2inSyfGoYsH8QfrMMZsaZIxoNzxkIYHugwyY+zpfJpMwi3CTRz1at Fspo2SgYv7JWypLNx5HSyOfKtQHrJdTWcPDOSKhAgZ8IJq2+mWmjbViQg4ZG9JKo5ohBhJ/GgQPA UETBEg8BWIgYJmEYeikw/7bYMPCjCocJR0ZJKG6GSnYVDchyeTAJzxRHLEWaNDRfSAA//ZUo4lGL SRRBxxSdetBk0moqSFJ42iIqCnRIYWa8KpXp1owzvGpzivahBgcymLYgp5GLLhMTa/lR+nIOl7O5 7EEjusxN3oAj+Hp8B4wHXVJwkZI87C0xViKQTv14jOyvyYl36rhQO3ghErwJEbO5jJlEhgOOrgE1 IHSZacOpK68uK+vuDtIq8EzOvZd12MH5RtOWygZ2OKG9MiTQcRRkAK61itUWSdy1ccmsqG8fcdMV p+djQqrmaJypb5vX0SaeXjWjet3syxp6Dx97SfFS5aBadfoYdFAjhRDjl613zOUlUxj7nKAODgie R0RE6yn2HzIVfVfgSrodxVSGxk2UYH4SCoNDMRFmBQAD8UHYIirPtBAaV+uRyNxjGOARQHKY3HDj JznipyFny4h2Bi4pSBJjjzMWWZxUnDnZBEmXMJkClFuYl9waN+T1gwUGgsmOfziUaeaZ8tFA5ppC iAlnWh7usN6cUNVJw5145smcnfj16aegMwAGpqGEEoGmkIviiagSjybqUUIhAAA7 --90e6ba53ab7832445c04a3431ecd-- ========================================================================= Date: Sat, 14 May 2011 22:35:10 +0100 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: Re: SnPM error Comments: To: Matthew Tryon <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba53aab069a96504a34332c5 Message-ID: <[log in to unmask]> --90e6ba53aab069a96504a34332c5 Content-Type: text/plain; charset=ISO-8859-1 Dear Matthew, Can you let me know what version of SPM and SnPM you're using? I just checked and that function (spm_create_image) hasn't been part of the main SnPM code since SnPM3. It does live on in snpm_combo_pp, a toolbox inside the SnPM toolbox; were you trying to do combining inference? -Tom On Tue, May 10, 2011 at 3:20 AM, Matthew Tryon <[log in to unmask]>wrote: > Hi All, > > I'm a novice with using SnPM and I'd be very grateful for any assistance. > I'm in the process of trying to execute the fMRI example as outlined in the > SnPM documentation. The SnPMcfg.mat file is generated fine, but I get the > following error when I click on the compute button: > > SnPM: snpm_cp > ======================================================================== > Initialising... > ??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLimit',N) > to change the limit. Be aware that exceeding your available stack space > can > crash MATLAB and/or your computer. > > Error in ==> spm_create_image > > > ??? Error while evaluating uicontrol Callback > > > I'm using MATLAB 7.10 on OSX. I sincerely appreciate any help. > > Kind Regards, > > Matthew Tryon > -- > > -- ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 --90e6ba53aab069a96504a34332c5 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Matthew,<div><br></div><div>Can you let me know what version of SPM an= d SnPM you're using? =A0I just checked and that function (spm_create_im= age) hasn't been part of the main SnPM code since SnPM3. =A0It does liv= e on in snpm_combo_pp, a toolbox inside the SnPM toolbox; were you trying t= o do combining inference?</div> <div><br></div><div>-Tom<br><br><div class=3D"gmail_quote">On Tue, May 10, = 2011 at 3:20 AM, Matthew Tryon <span dir=3D"ltr"><<a href=3D"mailto:matt= [log in to unmask]">[log in to unmask]</a>></span> wrote:<br><bloc= kquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #cc= c solid;padding-left:1ex;"> <div style=3D"word-wrap:break-word;color:rgb(0, 0, 0);font-size:14px;font-f= amily:Helvetica, sans-serif"><div><div><div><div>Hi All,</div><div><br></di= v><div>I'm a novice with using SnPM and I'd be very grateful for an= y assistance. =A0I'm in the process of=A0trying to execute the fMRI exa= mple as outlined in the SnPM documentation. =A0The SnPMcfg.mat file is gene= rated fine, but I get the following error when I click on the compute butto= n:</div> <div><br></div><div><div>SnPM: snpm_cp</div><div>=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D</div><div>Initialising...</div><= div>??? Maximum recursion limit of 500 reached. Use set(0,'RecursionLim= it',N)</div> <div>to change the limit. =A0Be aware that exceeding your available stack s= pace can</div><div>crash MATLAB and/or your computer.</div><div><br></div><= div>Error in =3D=3D> spm_create_image</div><div><br></div><div><br></div= ><div> ??? Error while evaluating uicontrol Callback</div></div><div><br></div><di= v><br></div><div>I'm using MATLAB 7.10 on OSX. =A0I sincerely appreciat= e any help.</div><div><br></div><div>Kind Regards,</div><div><br></div><div= > Matthew Tryon</div></div><div><div>-- </div><div><br></div></div></div></di= v></div> </blockquote></div><br><br clear=3D"all"><br>-- <br>_______________________= _____________________<br>Thomas Nichols, PhD<br>Principal Research Fellow, = Head of Neuroimaging Statistics<br>Department of Statistics & Warwick M= anufacturing Group<br> University of Warwick<br>Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email= : <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= ><br>Phone, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 = 7652 4532<br> <br> </div> --90e6ba53aab069a96504a34332c5-- ========================================================================= Date: Sat, 14 May 2011 22:49:22 +0100 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: Re: SPM2 Versus SPM8 Comments: To: Min <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec517ae5a2c378404a3436557 Message-ID: <[log in to unmask]> --bcaec517ae5a2c378404a3436557 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear Min, In SPM8 FDR topological inference was introduced, and voxel-wise FDR inference hidden. Topological inference means inference on peaks and clusters; voxel-wise inference is based on every individual voxel in the image (instead of spatial features of the image). Thus "Topological FDR" means inference on clusters based on cluster size (or local peaks based on peak height), controlling the fraction of false positive clusters among all clusters (or false positive peaks among all peaks) on average, over many experiments. While topological FDR results may be easier to interpret, in my experience it is is generally not very sensitivity and yields similar results to FWE-corrected inferences. This would explain your reduction in sensitivity moving from SPM2 to SPM8. If you would like to use voxel-wise FDR in SPM8, edit spm_defaults, changin= g "topoFDR" line to read defaults.stats.topoFDR =3D 0; (quit and re-start SPM to take effect). For more on this see references below. -Tom Chumbley, J., Worsley, K., Flandin, G., & Friston, K. (2010). Topological FDR for neuroimaging. *NeuroImage*, *49*(4), 3057-64. doi: 10.1016/j.neuroimage.2009.10.090. Chumbley, J. R., & Friston, K. J. (2009). False discovery rate revisited: FDR and topological inference using Gaussian random fields. *Neuroimage*, * 44*(1), 62=9670. doi: 10.1016/j.neuroimage.2008.05.021. On Tue, May 10, 2011 at 11:27 AM, Min <[log in to unmask]> wrote: > Dear SPM's users, > > I ran spm2 and spm8, respectively, for the same set of fMRI data. Howeve= r, > though I followed the same steps for the two versions of spm, there are > considerable differences in the results. With spm8, no suprathreshold > clusters were found with fdr(0.05) corrected, while with spm 2, more than= 8 > clusters survive the threshold (T values of height threshold are nearly t= he > same for the two analysis). > > I am unsure how to explain such a big difference between the results from > two versions of spm. > > I will be grateful for any help! > > Sincerely, > Min > --=20 ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 --bcaec517ae5a2c378404a3436557 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear Min,<div><br></div><div>In SPM8 FDR topological inference was introduc= ed, and voxel-wise FDR=A0inference=A0hidden. =A0Topological inference means= inference on peaks and clusters; voxel-wise inference is based on every in= dividual voxel in the image (instead of spatial features of the image). =A0= Thus "Topological FDR" means inference on clusters based on clust= er size (or local peaks based on peak height), controlling the fraction of = false positive=A0clusters among all clusters=A0(or false positive peaks amo= ng all peaks) on average, over many experiments.</div> <div><br></div><div>While=A0topological=A0FDR results may be easier to inte= rpret, in my experience it is is generally not very sensitivity and=A0yield= s=A0similar results to FWE-corrected inferences. =A0This would explain your= reduction in sensitivity moving from SPM2 to SPM8.</div> <div><br></div><div>If you would like to use voxel-wise FDR in SPM8, edit s= pm_defaults, changing "topoFDR" line to read</div><blockquote cla= ss=3D"webkit-indent-blockquote" style=3D"margin: 0 0 0 40px; border: none; = padding: 0px;"> <div><div><font class=3D"Apple-style-span" face=3D"'courier new', m= onospace">defaults.stats.topoFDR =A0 =A0 =A0=3D 0;</font></div></div></bloc= kquote><div>(quit and re-start SPM to take effect).</div><div><br></div><di= v>For more on this see references below.</div> <div><br></div><div>-Tom</div><div><br></div><div><p style=3D"margin-left:2= 4pt;text-indent:-24.0pt">Chumbley, J., Worsley, K., Flandin, G., & Fris= ton, K. (2010). Topological FDR for neuroimaging. <i>NeuroImage</i>, <i>49<= /i>(4), 3057-64. doi: 10.1016/j.neuroimage.2009.10.090.</p> <p style=3D"margin-left:24pt;text-indent:-24.0pt">Chumbley, J. R., & Fr= iston, K. J. (2009). False discovery rate revisited: FDR and topological in= ference using Gaussian random fields. <i>Neuroimage</i>, <i>44</i>(1), 62= =9670. doi: 10.1016/j.neuroimage.2008.05.021.</p> </div><div><br><div class=3D"gmail_quote">On Tue, May 10, 2011 at 11:27 AM,= Min <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">minxu86@gma= il.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style=3D"= margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> Dear SPM's users,<br> <br> I ran spm2 and spm8, respectively, for the same set of fMRI data. =A0Howeve= r, though I followed the same steps for the two versions of spm, there are = considerable differences in the results. =A0With spm8, no suprathreshold cl= usters were found with fdr(0.05) corrected, while with spm 2, more than 8 c= lusters survive the threshold (T values of height threshold are nearly the = same for the two analysis).<br> <br> I am unsure how to explain such a big difference between the results from t= wo versions of spm.<br> <br> I will be grateful for any help!<br> <br> Sincerely,<br> <font color=3D"#888888">Min<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>________________= ____________________________<br>Thomas Nichols, PhD<br>Principal Research F= ellow, Head of Neuroimaging Statistics<br>Department of Statistics & Wa= rwick Manufacturing Group<br> University of Warwick<br>Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email= : <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= ><br>Phone, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 = 7652 4532<br> <br> </div> --bcaec517ae5a2c378404a3436557-- ========================================================================= Date: Sun, 15 May 2011 19:29:15 +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: Re: Batch problem--3D Source Reconstruction Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_Part_65031_125733977.1305458955197" Message-ID: <[log in to unmask]> ------=_Part_65031_125733977.1305458955197 Content-Type: multipart/alternative; boundary="----=_Part_65033_491824730.1305458955198" ------=_Part_65033_491824730.1305458955198 Content-Type: text/plain; charset=GBK Content-Transfer-Encoding: base64 RGVhciBWbGFkaW1pciwKoaGhoUkgdHJpZWQgYWdhaW4gd2l0aCB0aGUgbGF0ZXN0IHZlcnNpb24g b2Ygc3BtOCg0MDI5KSwgdGhlIHByZXZpb3VzIHByb2JsZW0oIHRlbXBsYXRlPTEpIGRpc2FwcGVh cmVkLiBCdXQgdGhpcyB0aW1lLCBJIGNvbmZyb250ZWQgYSBuZXcgcHJvYmxlbS4gSXQgcmVtaW5k ZWQgbWUgIk5vIGV4ZWN1dGFibGUgbW9kdWxlcywgYnV0IHN0aWxsIHVucmVzb2x2ZWQgZGVwZW5k ZW5jaWVzIG9yIGluY29tcGxldGUgbW9kdWxlIGlucHV0cy4iIEJ1dCBJIGRvbid0IGtub3cgd2hl cmUgdGhlIHByb2JsZW1zIGFyZS4gQ291bGQgeW91IGhlbHAgbWUgc2VlIG15IHJlY29uc3RydWN0 aW9uX2JhdGNoLm1hdCBhbmQgZ2l2ZSBtZSBzb21lIGFkdmljZSA/IFRoYW5rcyBhIGxvdC4KIAog ICAgICAgSGFvcmFuLgogCiAgICAgICBUaGVzZSBjb2RlIGFwcGVhcnMgaW4gdGhlIG1hdGxhYiBj b21tYW5kIHdpbmRvdyB3aGVuIEkgcmFuIHRoZSBiYXRjaDoKU1BNODogc3BtX2VlZ19pbnZfbWVz aF91aSAodjQwMjcpICAgICAgICAgICAgICAgICAgMTE6MDk6MTMgLSAxNS8wNS8yMDExCj09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PQogICAgICAgdGVtcGxhdGU6IDAKICAgICAgICAgICBzTVJJOiAnRDpcUHJvY2Vz c2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxLmltZywxJwogICAgICAgICAgICBk ZWY6ICdEOlxQcm9jZXNzZWRcRFAwMVx5X3NLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMS5uaWkn CiAgICAgICAgIEFmZmluZTogWzR4NCBkb3VibGVdCiAgICAgICAgICBNc2l6ZTogMgogICAgICAg dGVzc19tbmk6IFsxeDEgc3RydWN0XQogICAgICAgdGVzc19jdHg6ICdEOlxQcm9jZXNzZWRcRFAw MVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFjb3J0ZXhfODE5Ni5zdXJmLmdpaScKICAgICB0 ZXNzX3NjYWxwOiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAx c2NhbHBfMjU2Mi5zdXJmLmdpaScKICAgIHRlc3Nfb3NrdWxsOiAnRDpcUHJvY2Vzc2VkXERQMDFc c0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxb3NrdWxsXzI1NjIuc3VyZi5naWknCiAgICB0ZXNz X2lza3VsbDogJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMWlz a3VsbF8yNTYyLnN1cmYuZ2lpJwogICAgICAgICAgICBmaWQ6IFsxeDEgc3RydWN0XQpGYWlsZWQg ICdNL0VFRyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb24nClJlZmVyZW5jZSB0byBub24tZXhpc3Rl bnQgZmllbGQgJ2ZpZCcuCkluIGZpbGUgIkM6XFByb2dyYW0gRmlsZXNcTUFUTEFCXHNwbThuZXdc Y29uZmlnXHNwbV9jZmdfZWVnX2ludl9oZWFkbW9kZWwubSIgKHY0MTE4KSwgZnVuY3Rpb24gInNw ZWNpZnlfaGVhZG1vZGVsIiBhdCBsaW5lIDIwOS4KTm8gZXhlY3V0YWJsZSBtb2R1bGVzLCBidXQg c3RpbGwgdW5yZXNvbHZlZCBkZXBlbmRlbmNpZXMgb3IgaW5jb21wbGV0ZSBtb2R1bGUgaW5wdXRz LgpUaGUgZm9sbG93aW5nIG1vZHVsZXMgZGlkIG5vdCBydW46CkZhaWxlZDogTS9FRUcgaGVhZCBt b2RlbCBzcGVjaWZpY2F0aW9uClNraXBwZWQ6IE0vRUVHIHNvdXJjZSBpbnZlcnNpb24KU2tpcHBl ZDogTS9FRUcgaW52ZXJzaW9uIHJlc3VsdHMKIAoKQXQgMjAxMS0wNS0xMyAyMjowNjowNqOsIlZs YWRpbWlyIExpdHZhayIgPGxpdHZhay52bGFkaW1pckBnbWFpbC5jb20+IHdyb3RlOgoKPkRlYXIg SGFvcmFuLAo+Cj5JIHRyaWVkIGFuZCBldmVyeXRoaW5nIHdvcmtzIGZpbmUgZm9yIG1lLiBNYWtl IHN1cmUgeW91ciBTUE0gdmVyc2lvbgo+aXMgdXAgdG8gZGF0ZSBhbmQgaWYgeW91IGNhbid0IG1h a2UgaXQgd29yaywgc2VuZCBtZSB5b3VyIHNhdmVkIGJhdGNoCj5hbmQgSSdsbCB0YWtlIGFub3Ro ZXIgbG9vay4KPgo+QmVzdCwKPgo+VmxhZGltaXIKPgo+MjAxMS81LzEwILfJxPEgPGxpaGFvcmFu MDYwM0AxNjMuY29tPjoKPj4gRGVhciBTUE0ncyB1c2VycywKPj4goaGhoUhhdmUgeW91IGV2ZXIg dXNlZCBiYXRjaCBpbnRlcmZhY2UgdG8gZG8gM0Qgc291cmNlIHJlY29uc3RydWN0aW9uIGZvcgo+ PiBFRUcvTUVHIGRhdGE/IEkgY2hvb3NlZCB0aHJlZSBzZXBhcmF0ZSBtb2R1bGVzICAiTS9FRUcg aGVhZCBtb2RlbAo+PiBzcGVjaWZpY2F0aW9uIiAiTS9FRUcgc291cmNlIGludmVyc2lvbiIgYW5k ICJNL0VFRyBpbnZlcnNpb24gcmVzdWx0cyIgc28gYXMKPj4gdG8gcmVjb25zdHJ1Y3QgdGhlIHNv dXJjZXMuIEluIHRoZSAiTS9FRUcgaGVhZCBtb2RlbCBzcGVjaWZpY2F0aW9uIiBtb2R1bGUsCj4+ IEkgc3BlY2lmaWVkICcnTWVzaGVzPE1lc2hlcyBzb3VyY2U8aW5kaXZpZHVhbCBzdHJ1Y3R1YWwg aW1hZ2UnJyBhbmQgdGhlbgo+PiBzZWxlY3RlZCBhIG1yaSBmaWxlIG5hbWVkICJzRFA3NC0wMDAz LTAwMDAwLTAwMDAwMS0wMS5pbWcsMSIuICBXaGVuIGFsbCB3YXMKPj4gcmVhZHkgYW5kIHRoZW4g SSByYW4gdGhlIGJhdGNoLCBob3dldmVyLCBJIGZvdW5kIHRoYXQgaXQgZGluJ3QgZXhjdXRlZCB0 aGUKPj4gc3BlY2lmaWVkIHN0cnVjdHVhbCBpbWFnZSAoIGFzIHlvdSBjYW4gc2VlIGJlbG93ICd0 ZW1wbGF0ZT0xJykgYW5kIHRoZQo+PiBtYXRsYWIgY29tbWFuZCB3aW5kb3cgYXBwZWFyZWQgdGhv c2U6Cj4+Cj4+IFNQTTg6IHNwbV9lZWdfaW52X21lc2hfdWkgKHYzNzMxKSAgICAgICAgICAgICAg ICAgIDIxOjM5OjA2IC0gMDkvMDUvMjAxMQo+PiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0KPj4gICAgICAgIHRl bXBsYXRlOiAxCj4+ICAgICAgICAgIEFmZmluZTogWzR4NCBkb3VibGVdCj4+ICAgICAgICAgICAg c01SSTogJ0M6XFByb2dyYW0KPj4gRmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxc c2luZ2xlX3N1YmpfVDEubmlpJwo+PiAgICAgICAgICAgTXNpemU6IDIKPj4gICAgICAgIHRlc3Nf bW5pOiBbMXgxIHN0cnVjdF0KPj4gICAgICAgIHRlc3NfY3R4OiAnQzpcUHJvZ3JhbQo+PiBGaWxl c1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxjb3J0ZXhfODE5Ni5zdXJmLmdpaScKPj4g ICAgICB0ZXNzX3NjYWxwOiAnQzpcUHJvZ3JhbQo+PiBGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04 XGNhbm9uaWNhbFxzY2FscF8yNTYyLnN1cmYuZ2lpJwo+PiAgICAgdGVzc19vc2t1bGw6ICdDOlxQ cm9ncmFtCj4+IEZpbGVzXE1BVExBQlx0b29sYm94XHNwbThcY2Fub25pY2FsXG9za3VsbF8yNTYy LnN1cmYuZ2lpJwo+PiAgICAgdGVzc19pc2t1bGw6ICdDOlxQcm9ncmFtCj4+IEZpbGVzXE1BVExB Qlx0b29sYm94XHNwbThcY2Fub25pY2FsXGlza3VsbF8yNTYyLnN1cmYuZ2lpJwo+PiAgICAgICAg ICAgICBmaWQ6IFsxeDEgc3RydWN0XQo+Pgo+PiAgIKGhSSBkaWRuJ3QgdW5kZXJzdGFuZCB3aHku IEFueW9uZSB3aG8ga25vd3MgdGhlIHJlYXNvbiA/IFRoYW5rcyB2ZXJ5IG11Y2gKPj4gZm9yIGFu eSBoZWxwICEKPj4KPj4gLS0KPj4gSGFvcmFuIExJIChNUykKPj4gQnJhaW4gSW1hZ2luZyBMYWIs Cj4+IFJlc2VhcmNoIENlbnRlciBmb3IgTGVhcm5pbmcgU2NpZW5jZSwKPj4gU291dGhlYXN0IFVu aXZlcnNpdHkKPj4gMiBTaSBQYWkgTG91ICwgTmFuamluZywgMjEwMDk2LCBQLlIuQ2hpbmEKPj4K Pj4KCgoK ------=_Part_65033_491824730.1305458955198 Content-Type: text/html; charset=GBK Content-Transfer-Encoding: base64 PERJVj5EZWFyIFZsYWRpbWlyLDwvRElWPgo8RElWPqGhoaFJIHRyaWVkIGFnYWluIHdpdGggdGhl IGxhdGVzdCB2ZXJzaW9uIG9mIHNwbTgoNDAyOSksIHRoZSZuYnNwO3ByZXZpb3VzIHByb2JsZW0o IHRlbXBsYXRlPTEpIGRpc2FwcGVhcmVkLiBCdXQgdGhpcyB0aW1lLCZuYnNwO0kgY29uZnJvbnRl ZCBhIG5ldyBwcm9ibGVtLiBJdCByZW1pbmRlZCBtZSAiTm8gZXhlY3V0YWJsZSBtb2R1bGVzLCBi dXQgc3RpbGwgdW5yZXNvbHZlZCBkZXBlbmRlbmNpZXMgb3IgaW5jb21wbGV0ZSBtb2R1bGUgaW5w dXRzLiIgQnV0IEkgZG9uJ3Qga25vdyB3aGVyZSB0aGUgcHJvYmxlbXMgYXJlLiBDb3VsZCB5b3Ug aGVscCBtZSZuYnNwO3NlZSBteSByZWNvbnN0cnVjdGlvbl9iYXRjaC5tYXQgYW5kIGdpdmUgbWUg c29tZSBhZHZpY2UgPyBUaGFua3MgYSBsb3QuPC9ESVY+CjxESVY+Jm5ic3A7PC9ESVY+CjxESVY+ Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7IEhhb3Jhbi48L0RJVj4KPERJVj4m bmJzcDs8L0RJVj4KPERJVj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgVGhl c2UgY29kZSBhcHBlYXJzIGluIHRoZSBtYXRsYWIgY29tbWFuZCB3aW5kb3cgd2hlbiBJIHJhbiB0 aGUgYmF0Y2g6PC9ESVY+CjxESVY+U1BNODogc3BtX2VlZ19pbnZfbWVzaF91aSAodjQwMjcpJm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7IDExOjA5OjEzIC0gMTUv MDUvMjAxMTxCUj49PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT08QlI+Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7IHRlbXBsYXRlOiAwPEJSPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBzTVJJOiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tF WUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxLmltZywxJzxCUj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgZGVmOiAnRDpcUHJv Y2Vzc2VkXERQMDFceV9zS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDEubmlpJzxCUj4mbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgQWZmaW5lOiBbNHg0IGRv dWJsZV08QlI+Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7IE1zaXplOiAyPEJSPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyB0 ZXNzX21uaTogWzF4MSBzdHJ1Y3RdPEJSPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyB0ZXNzX2N0eDogJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAw MS0wMWNvcnRleF84MTk2LnN1cmYuZ2lpJzxCUj4mbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsgdGVz c19zY2FscDogJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMXNj YWxwXzI1NjIuc3VyZi5naWknPEJSPiZuYnNwOyZuYnNwOyZuYnNwOyB0ZXNzX29za3VsbDogJ0Q6 XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMW9za3VsbF8yNTYyLnN1 cmYuZ2lpJzxCUj4mbmJzcDsmbmJzcDsmbmJzcDsgdGVzc19pc2t1bGw6ICdEOlxQcm9jZXNzZWRc RFAwMVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFpc2t1bGxfMjU2Mi5zdXJmLmdpaSc8QlI+ Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7IGZpZDogWzF4MSBzdHJ1Y3RdPC9ESVY+CjxESVY+RmFpbGVkJm5ic3A7ICdNL0VF RyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb24nPEJSPlJlZmVyZW5jZSB0byBub24tZXhpc3RlbnQg ZmllbGQgJ2ZpZCcuPEJSPkluIGZpbGUgIkM6XFByb2dyYW0gRmlsZXNcTUFUTEFCXHNwbThuZXdc Y29uZmlnXHNwbV9jZmdfZWVnX2ludl9oZWFkbW9kZWwubSIgKHY0MTE4KSwgZnVuY3Rpb24gInNw ZWNpZnlfaGVhZG1vZGVsIiBhdCBsaW5lIDIwOS48L0RJVj4KPERJVj5ObyBleGVjdXRhYmxlIG1v ZHVsZXMsIGJ1dCBzdGlsbCB1bnJlc29sdmVkIGRlcGVuZGVuY2llcyBvciBpbmNvbXBsZXRlIG1v ZHVsZSBpbnB1dHMuPEJSPlRoZSBmb2xsb3dpbmcgbW9kdWxlcyBkaWQgbm90IHJ1bjo8QlI+RmFp bGVkOiBNL0VFRyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb248QlI+U2tpcHBlZDogTS9FRUcgc291 cmNlIGludmVyc2lvbjxCUj5Ta2lwcGVkOiBNL0VFRyBpbnZlcnNpb24gcmVzdWx0czwvRElWPgo8 RElWPiZuYnNwOzwvRElWPjxQUkU+PEJSPjxGT05UIGNvbG9yPSIjODA4MDgwIj5BdCZuYnNwOzIw MTEtMDUtMTMmbmJzcDsyMjowNjowNqOsIlZsYWRpbWlyJm5ic3A7TGl0dmFrIiZuYnNwOyZsdDs8 L0ZPTlQ+PEEgaHJlZj0ibWFpbHRvOmxpdHZhay52bGFkaW1pckBnbWFpbC5jb20iPjxGT05UIGNv bG9yPSIjODA4MDgwIj5saXR2YWsudmxhZGltaXJAZ21haWwuY29tPC9GT05UPjwvQT48Rk9OVCBj b2xvcj0iIzgwODA4MCI+Jmd0OyZuYnNwO3dyb3RlOgoKJmd0O0RlYXImbmJzcDtIYW9yYW4sCiZn dDsKJmd0O0kmbmJzcDt0cmllZCZuYnNwO2FuZCZuYnNwO2V2ZXJ5dGhpbmcmbmJzcDt3b3JrcyZu YnNwO2ZpbmUmbmJzcDtmb3ImbmJzcDttZS4mbmJzcDtNYWtlJm5ic3A7c3VyZSZuYnNwO3lvdXIm bmJzcDtTUE0mbmJzcDt2ZXJzaW9uCiZndDtpcyZuYnNwO3VwJm5ic3A7dG8mbmJzcDtkYXRlJm5i c3A7YW5kJm5ic3A7aWYmbmJzcDt5b3UmbmJzcDtjYW4ndCZuYnNwO21ha2UmbmJzcDtpdCZuYnNw O3dvcmssJm5ic3A7c2VuZCZuYnNwO21lJm5ic3A7eW91ciZuYnNwO3NhdmVkJm5ic3A7YmF0Y2gK Jmd0O2FuZCZuYnNwO0knbGwmbmJzcDt0YWtlJm5ic3A7YW5vdGhlciZuYnNwO2xvb2suCiZndDsK Jmd0O0Jlc3QsCiZndDsKJmd0O1ZsYWRpbWlyCiZndDsKJmd0OzIwMTEvNS8xMCZuYnNwO7fJxPEm bmJzcDsmbHQ7PC9GT05UPjxBIGhyZWY9Im1haWx0bzpsaWhhb3JhbjA2MDNAMTYzLmNvbSI+PEZP TlQgY29sb3I9IiM4MDgwODAiPmxpaGFvcmFuMDYwM0AxNjMuY29tPC9GT05UPjwvQT48Rk9OVCBj b2xvcj0iIzgwODA4MCI+Jmd0OzoKJmd0OyZndDsmbmJzcDtEZWFyJm5ic3A7U1BNJ3MmbmJzcDt1 c2VycywKJmd0OyZndDsmbmJzcDuhoaGhSGF2ZSZuYnNwO3lvdSZuYnNwO2V2ZXImbmJzcDt1c2Vk Jm5ic3A7YmF0Y2gmbmJzcDtpbnRlcmZhY2UmbmJzcDt0byZuYnNwO2RvJm5ic3A7M0QmbmJzcDtz b3VyY2UmbmJzcDtyZWNvbnN0cnVjdGlvbiZuYnNwO2ZvcgomZ3Q7Jmd0OyZuYnNwO0VFRy9NRUcm bmJzcDtkYXRhPyZuYnNwO0kmbmJzcDtjaG9vc2VkJm5ic3A7dGhyZWUmbmJzcDtzZXBhcmF0ZSZu YnNwO21vZHVsZXMmbmJzcDsmbmJzcDsiTS9FRUcmbmJzcDtoZWFkJm5ic3A7bW9kZWwKJmd0OyZn dDsmbmJzcDtzcGVjaWZpY2F0aW9uIiZuYnNwOyJNL0VFRyZuYnNwO3NvdXJjZSZuYnNwO2ludmVy c2lvbiImbmJzcDthbmQmbmJzcDsiTS9FRUcmbmJzcDtpbnZlcnNpb24mbmJzcDtyZXN1bHRzIiZu YnNwO3NvJm5ic3A7YXMKJmd0OyZndDsmbmJzcDt0byZuYnNwO3JlY29uc3RydWN0Jm5ic3A7dGhl Jm5ic3A7c291cmNlcy4mbmJzcDtJbiZuYnNwO3RoZSZuYnNwOyJNL0VFRyZuYnNwO2hlYWQmbmJz cDttb2RlbCZuYnNwO3NwZWNpZmljYXRpb24iJm5ic3A7bW9kdWxlLAomZ3Q7Jmd0OyZuYnNwO0km bmJzcDtzcGVjaWZpZWQmbmJzcDsnJ01lc2hlcyZsdDtNZXNoZXMmbmJzcDtzb3VyY2UmbHQ7aW5k aXZpZHVhbCZuYnNwO3N0cnVjdHVhbCZuYnNwO2ltYWdlJycmbmJzcDthbmQmbmJzcDt0aGVuCiZn dDsmZ3Q7Jm5ic3A7c2VsZWN0ZWQmbmJzcDthJm5ic3A7bXJpJm5ic3A7ZmlsZSZuYnNwO25hbWVk Jm5ic3A7InNEUDc0LTAwMDMtMDAwMDAtMDAwMDAxLTAxLmltZywxIi4mbmJzcDsmbmJzcDtXaGVu Jm5ic3A7YWxsJm5ic3A7d2FzCiZndDsmZ3Q7Jm5ic3A7cmVhZHkmbmJzcDthbmQmbmJzcDt0aGVu Jm5ic3A7SSZuYnNwO3JhbiZuYnNwO3RoZSZuYnNwO2JhdGNoLCZuYnNwO2hvd2V2ZXIsJm5ic3A7 SSZuYnNwO2ZvdW5kJm5ic3A7dGhhdCZuYnNwO2l0Jm5ic3A7ZGluJ3QmbmJzcDtleGN1dGVkJm5i c3A7dGhlCiZndDsmZ3Q7Jm5ic3A7c3BlY2lmaWVkJm5ic3A7c3RydWN0dWFsJm5ic3A7aW1hZ2Um bmJzcDsoJm5ic3A7YXMmbmJzcDt5b3UmbmJzcDtjYW4mbmJzcDtzZWUmbmJzcDtiZWxvdyZuYnNw Oyd0ZW1wbGF0ZT0xJykmbmJzcDthbmQmbmJzcDt0aGUKJmd0OyZndDsmbmJzcDttYXRsYWImbmJz cDtjb21tYW5kJm5ic3A7d2luZG93Jm5ic3A7YXBwZWFyZWQmbmJzcDt0aG9zZToKJmd0OyZndDsK Jmd0OyZndDsmbmJzcDtTUE04OiZuYnNwO3NwbV9lZWdfaW52X21lc2hfdWkmbmJzcDsodjM3MzEp Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7MjE6Mzk6 MDYmbmJzcDstJm5ic3A7MDkvMDUvMjAxMQomZ3Q7Jmd0OyZuYnNwOz09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PQom Z3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rl bXBsYXRlOiZuYnNwOzEKJmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtBZmZpbmU6Jm5ic3A7WzR4NCZuYnNwO2RvdWJsZV0K Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtzTVJJOiZuYnNwOydDOlxQcm9ncmFtCiZndDsmZ3Q7Jm5i c3A7RmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcc2luZ2xlX3N1YmpfVDEubmlp JwomZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwO01zaXplOiZuYnNwOzIKJmd0OyZndDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNzX21uaTombmJzcDtbMXgxJm5ic3A7 c3RydWN0XQomZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwO3Rlc3NfY3R4OiZuYnNwOydDOlxQcm9ncmFtCiZndDsmZ3Q7Jm5ic3A7RmlsZXNcTUFU TEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcY29ydGV4XzgxOTYuc3VyZi5naWknCiZndDsmZ3Q7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19zY2FscDombmJzcDsnQzpc UHJvZ3JhbQomZ3Q7Jmd0OyZuYnNwO0ZpbGVzXE1BVExBQlx0b29sYm94XHNwbThcY2Fub25pY2Fs XHNjYWxwXzI1NjIuc3VyZi5naWknCiZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7dGVzc19vc2t1bGw6Jm5ic3A7J0M6XFByb2dyYW0KJmd0OyZndDsmbmJzcDtGaWxlc1xNQVRM QUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxvc2t1bGxfMjU2Mi5zdXJmLmdpaScKJmd0OyZndDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNzX2lza3VsbDombmJzcDsnQzpcUHJvZ3Jh bQomZ3Q7Jmd0OyZuYnNwO0ZpbGVzXE1BVExBQlx0b29sYm94XHNwbThcY2Fub25pY2FsXGlza3Vs bF8yNTYyLnN1cmYuZ2lpJwomZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO2ZpZDombmJzcDtb MXgxJm5ic3A7c3RydWN0XQomZ3Q7Jmd0OwomZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwO6GhSSZu YnNwO2RpZG4ndCZuYnNwO3VuZGVyc3RhbmQmbmJzcDt3aHkuJm5ic3A7QW55b25lJm5ic3A7d2hv Jm5ic3A7a25vd3MmbmJzcDt0aGUmbmJzcDtyZWFzb24mbmJzcDs/Jm5ic3A7VGhhbmtzJm5ic3A7 dmVyeSZuYnNwO211Y2gKJmd0OyZndDsmbmJzcDtmb3ImbmJzcDthbnkmbmJzcDtoZWxwJm5ic3A7 IQomZ3Q7Jmd0OwomZ3Q7Jmd0OyZuYnNwOy0tCiZndDsmZ3Q7Jm5ic3A7SGFvcmFuJm5ic3A7TEkm bmJzcDsoTVMpCiZndDsmZ3Q7Jm5ic3A7QnJhaW4mbmJzcDtJbWFnaW5nJm5ic3A7TGFiLAomZ3Q7 Jmd0OyZuYnNwO1Jlc2VhcmNoJm5ic3A7Q2VudGVyJm5ic3A7Zm9yJm5ic3A7TGVhcm5pbmcmbmJz cDtTY2llbmNlLAomZ3Q7Jmd0OyZuYnNwO1NvdXRoZWFzdCZuYnNwO1VuaXZlcnNpdHkKJmd0OyZn dDsmbmJzcDsyJm5ic3A7U2kmbmJzcDtQYWkmbmJzcDtMb3UmbmJzcDssJm5ic3A7TmFuamluZywm bmJzcDsyMTAwOTYsJm5ic3A7UC5SLkNoaW5hCiZndDsmZ3Q7CiZndDsmZ3Q7CjwvRk9OVD48L1BS RT48QlI+PEJSPjxTUEFOIHRpdGxlPSJuZXRlYXNlZm9vdGVyIj48U1BBTiBpZD0ibmV0ZWFzZV9t YWlsX2Zvb3RlciI+PC9TUEFOPjwvU1BBTj48YnI+PGJyPjxzcGFuIHRpdGxlPSJuZXRlYXNlZm9v dGVyIj48c3BhbiBpZD0ibmV0ZWFzZV9tYWlsX2Zvb3RlciI+PC9zcGFuPjwvc3Bhbj4= ------=_Part_65033_491824730.1305458955198-- ------=_Part_65031_125733977.1305458955197 Content-Type: application/octet-stream; name="reconstruction_batch.mat" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="reconstruction_batch.mat" TUFUTEFCIDUuMCBNQVQtZmlsZSwgUGxhdGZvcm06IFBDV0lOLCBDcmVhdGVkIG9uOiBTdW4gTWF5 IDE1IDExOjA3OjI1IDIwMTEgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgIAABSU0OAAAAKCIAAAYAAAAIAAAAAQAAAAAAAAAFAAAACAAAAAEAAAADAAAAAQAA AAsAAABtYXRsYWJiYXRjaAAAAAAADgAAAPgIAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAA AQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAAAHNwbQAAAAAADgAAALAIAAAGAAAACAAAAAIAAAAA AAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAAAG1lZWcAAAAADgAAAGgI AAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAcAAAABAAAABwAA AHNvdXJjZQAADgAAACAIAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAA BQAEAAoAAAABAAAACgAAAGhlYWRtb2RlbAAAAAAAAAAOAAAA0AcAAAYAAAAIAAAAAgAAAAAAAAAF AAAACAAAAAEAAAABAAAAAQAAAAAAAAAFAAQADwAAAAEAAABaAAAARAAAAAAAAAAAAAAAAAAAdmFs AAAAAAAAAAAAAAAAY29tbWVudAAAAAAAAAAAbWVzaGluZwAAAAAAAAAAY29yZWdpc3RyYXRpb24A Zm9yd2FyZAAAAAAAAAAAAAAAAAAADgAAAKAAAAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAABAAAA AQAAAAEAAAAAAAAADgAAAHAAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAIAAAAAEAAAAA AAAABAAAAEAAAABEADoAXABQAHIAbwBjAGUAcwBzAGUAZABcAEQAUAAwADEAXABtAGEAZgBmAGIA ZQBEAFAAMAAxAC4AbQBhAHQADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAA AAEAAAAAAAAAAgABAAEAAAAOAAAAMAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAAAAAAAAAAA AQAAAAAAAAAEAAAAAAAAAA4AAACYAQAABgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAAB AAAAAAAAAAUABAAIAAAAAQAAABAAAABtZXNoZXMAAG1lc2hyZXMADgAAABABAAAGAAAACAAAAAIA AAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAAAG1yaQAAAAAADgAA AMgAAAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAADgAAAJgAAAAGAAAA CAAAAAQAAAAAAAAABQAAAAgAAAABAAAAMwAAAAEAAAAAAAAABAAAAGYAAABEADoAXABQAHIAbwBj AGUAcwBzAGUAZABcAEQAUAAwADEAXABzAEsARQBZAEEATgAtADAAMAAwADIALQAwADAAMAAwADAA LQAwADAAMAAwADAAMQAtADAAMQAuAGkAbQBnACwAMQAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAA AAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQACAAAADgAAAJgDAAAGAAAACAAAAAIAAAAAAAAA BQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAA0AAAABAAAADQAAAGNvcmVnc3BlY2lmeQAAAAAO AAAASAMAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAFAAQADQAAAAEA AAAaAAAAZmlkdWNpYWwAAAAAAHVzZWhlYWRzaGFwZQAAAAAAAAAOAAAAsAIAAAYAAAAIAAAAAgAA AAAAAAAFAAAACAAAAAEAAAADAAAAAQAAAAAAAAAFAAQADgAAAAEAAAAcAAAAZmlkbmFtZQAAAAAA AABzcGVjaWZpY2F0aW9uAAAAAAAOAAAAOAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAAD AAAAAQAAAAAAAAAEAAAABgAAAG4AYQBzAAAADgAAAIAAAAAGAAAACAAAAAIAAAAAAAAABQAAAAgA AAABAAAAAQAAAAEAAAAAAAAABQAEAAcAAAABAAAABwAAAHNlbGVjdAAADgAAADgAAAAGAAAACAAA AAQAAAAAAAAABQAAAAgAAAABAAAAAwAAAAEAAAAAAAAABAAAAAYAAABuAGEAcwAAAA4AAAA4AAAA BgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAMAAAABAAAAAAAAAAQAAAAGAAAAbABwAGEAAAAO AAAAgAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAFAAQABwAAAAEA AAAHAAAAc2VsZWN0AAAOAAAAOAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAADAAAAAQAA AAAAAAAEAAAABgAAAGwAcABhAAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAA AwAAAAEAAAAAAAAABAAAAAYAAAByAHAAYQAAAA4AAACAAAAABgAAAAgAAAACAAAAAAAAAAUAAAAI AAAAAQAAAAEAAAABAAAAAAAAAAUABAAHAAAAAQAAAAcAAABzZWxlY3QAAA4AAAA4AAAABgAAAAgA AAAEAAAAAAAAAAUAAAAIAAAAAQAAAAMAAAABAAAAAAAAAAQAAAAGAAAAcgBwAGEAAAAOAAAAMAAA AAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAAAAAA4AAADYAAAA BgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAEAAAAAQAAAAgAAABl ZWcAbWVnAA4AAABAAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAcAAAABAAAAAAAAAAQA AAAOAAAARQBFAEcAIABCAEUATQAAAA4AAABIAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAA AAwAAAABAAAAAAAAAAQAAAAYAAAAUwBpAG4AZwBsAGUAIABTAGgAZQBsAGwADgAAAJgMAAAGAAAA CAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAAAHNwbQAA AAAADgAAAFAMAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAUA AAABAAAABQAAAG1lZWcAAAAADgAAAAgMAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAA AAEAAAAAAAAABQAEAAcAAAABAAAABwAAAHNvdXJjZQAADgAAAMALAAAGAAAACAAAAAIAAAAAAAAA BQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAcAAAABAAAABwAAAGludmVydAAADgAAAHgLAAAG AAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAA8AAAABAAAASwAAAEQA AAAAAAAAAAAAAAAAAHZhbAAAAAAAAAAAAAAAAHdoYXRjb25kaXRpb25zAGlzc3RhbmRhcmQAAAAA AG1vZGFsaXR5AAAAAAAAAAAAAAAADgAAADgJAAAGAAAACAAAAAMAAAAAAAAABQAAAAgAAAABAAAA AQAAAAEAAAAAAAAAAQAAAAcAAABjZmdfZGVwAAUABAANAAAAAQAAAGgAAAB0bmFtZQAAAAAAAAAA dGd0X2V4YnJhbmNoAHRndF9pbnB1dAAAAAB0Z3Rfc3BlYwAAAAAAanRzdWJzAAAAAAAAAHNuYW1l AAAAAAAAAABzcmNfZXhicmFuY2gAc3JjX291dHB1dAAAAA4AAABQAAAABgAAAAgAAAAEAAAAAAAA AAUAAAAIAAAAAQAAAA4AAAABAAAAAAAAAAQAAAAcAAAATQAvAEUARQBHACAAZABhAHQAYQBzAGUA dABzAAAAAAAOAAAASAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAAAAAAAAAAAAQAAAAAAAAAF AAQABQAAAAEAAAAKAAAAdHlwZQBzdWJzAAAAAAAAAA4AAABIAAAABgAAAAgAAAACAAAAAAAAAAUA AAAIAAAAAAAAAAAAAAABAAAAAAAAAAUABAAFAAAAAQAAAAoAAAB0eXBlAHN1YnMAAAAAAAAADgAA AAABAAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAADgAAANAAAAAGAAAA CAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAYAAAABAAAADAAAAG5hbWUA AHZhbHVlAAAAAAAOAAAAQAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAAGAAAAAQAAAAAA AAAEAAAADAAAAGYAaQBsAHQAZQByAAAAAAAOAAAAOAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAA AAEAAAADAAAAAQAAAAAAAAAEAAAABgAAAG0AYQB0AAAADgAAAEgAAAAGAAAACAAAAAIAAAAAAAAA BQAAAAgAAAAAAAAAAAAAAAEAAAAAAAAABQAEAAUAAAABAAAACgAAAHR5cGUAc3VicwAAAAAAAAAO AAAAwAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAABFAAAAAQAAAAAAAAAEAAAAigAAAE0A LwBFAEUARwAgAGgAZQBhAGQAIABtAG8AZABlAGwAIABzAHAAZQBjAGkAZgBpAGMAYQB0AGkAbwBu ADoAIABNAC8ARQBFAEcAIABkAGEAdABhAHMAZQB0ACgAcwApACAAdwBpAHQAaAAgAGEAIABmAG8A cgB3AGEAcgBkACAAbQBvAGQAZQBsAAAAAAAAAA4AAACoBAAABgAAAAgAAAACAAAAAAAAAAUAAAAI AAAAAQAAAAgAAAABAAAAAAAAAAUABAAFAAAAAQAAAAoAAAB0eXBlAHN1YnMAAAAAAAAADgAAADAA AAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABAACAC4AAAAOAAAAOAAA AAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAADAAAAAQAAAAAAAAAEAAAABgAAAHYAYQBsAAAA DgAAADAAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAgAAAAEAAAAAAAAABAAEAHsAfQAO AAAAYAAAAAYAAAAIAAAAAQAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAOAAAAMAAAAAYA AAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAQAAAA4AAAAwAAAABgAA AAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAQAAgAuAAAADgAAADgAAAAGAAAA CAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAwAAAAEAAAAAAAAABAAAAAYAAAB2AGEAbAAAAA4AAAAw AAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAIAAAABAAAAAAAAAAQABAB7AH0ADgAAAGAA AAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAADgAAADAAAAAGAAAACAAA AAYAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAAAgABAAEAAAAOAAAAMAAAAAYAAAAIAAAA BAAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAEAAIALgAAAA4AAAA4AAAABgAAAAgAAAAE AAAAAAAAAAUAAAAIAAAAAQAAAAMAAAABAAAAAAAAAAQAAAAGAAAAdgBhAGwAAAAOAAAAMAAAAAYA AAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAACAAAAAQAAAAAAAAAEAAQAewB9AA4AAABgAAAABgAA AAgAAAABAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAA AAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQABAAAADgAAADAAAAAGAAAACAAAAAQAAAAA AAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABAACAC4AAAAOAAAAOAAAAAYAAAAIAAAABAAAAAAA AAAFAAAACAAAAAEAAAADAAAAAQAAAAAAAAAEAAAABgAAAHYAYQBsAAAADgAAADAAAAAGAAAACAAA AAQAAAAAAAAABQAAAAgAAAABAAAAAgAAAAEAAAAAAAAABAAEAHsAfQAOAAAAYAAAAAYAAAAIAAAA AQAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAAAAAF AAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAQAAAA4AAAC4AAAABgAAAAgAAAACAAAAAAAAAAUA AAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAFAAAAAQAAAAoAAAB0eXBlAHN1YnMAAAAAAAAADgAA ADAAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABAACAC4AAAAOAAAA MAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAEAAIARAAAAA4AAAAw AAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQABAAAADgAAAHgA AAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABQAEAAUAAAABAAAABQAA AGFsbAAAAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAA AgABAAEAAAAOAAAAgAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAAF AAQACQAAAAEAAAAJAAAAc3RhbmRhcmQAAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAAAAUA AAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQABAAAADgAAAGgAAAAGAAAACAAAAAEAAAAAAAAABQAA AAgAAAABAAAAAQAAAAEAAAAAAAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAA AwAAAAEAAAAAAAAABAAAAAYAAABNAEUARwAAAA4AAABIDAAABgAAAAgAAAACAAAAAAAAAAUAAAAI AAAAAQAAAAEAAAABAAAAAAAAAAUABAAFAAAAAQAAAAUAAABzcG0AAAAAAA4AAAAADAAABgAAAAgA AAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAFAAAAAQAAAAUAAABtZWVnAAAA AA4AAAC4CwAABgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAHAAAA AQAAAAcAAABzb3VyY2UAAA4AAABwCwAABgAAAAgAAAACAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAAB AAAAAAAAAAUABAAIAAAAAQAAAAgAAAByZXN1bHRzAA4AAAAoCwAABgAAAAgAAAACAAAAAAAAAAUA AAAIAAAAAQAAAAEAAAABAAAAAAAAAAUABAAKAAAAAQAAAEYAAABEAAAAAAAAAAAAdmFsAAAAAAAA AHdvaQAAAAAAAABmb2kAAAAAAAAAY3R5cGUAAAAAAHNwYWNlAAAAAABzbW9vdGhpbmcAAAAOAAAA QAkAAAYAAAAIAAAAAwAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAABAAAABwAAAGNmZ19k ZXAABQAEAA0AAAABAAAAaAAAAHRuYW1lAAAAAAAAAAB0Z3RfZXhicmFuY2gAdGd0X2lucHV0AAAA AHRndF9zcGVjAAAAAABqdHN1YnMAAAAAAAAAc25hbWUAAAAAAAAAAHNyY19leGJyYW5jaABzcmNf b3V0cHV0AAAADgAAAFAAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAADgAAAAEAAAAAAAAA BAAAABwAAABNAC8ARQBFAEcAIABkAGEAdABhAHMAZQB0AHMAAAAAAA4AAABIAAAABgAAAAgAAAAC AAAAAAAAAAUAAAAIAAAAAAAAAAAAAAABAAAAAAAAAAUABAAFAAAAAQAAAAoAAAB0eXBlAHN1YnMA AAAAAAAADgAAAEgAAAAGAAAACAAAAAIAAAAAAAAABQAAAAgAAAAAAAAAAAAAAAEAAAAAAAAABQAE AAUAAAABAAAACgAAAHR5cGUAc3VicwAAAAAAAAAOAAAAAAEAAAYAAAAIAAAAAQAAAAAAAAAFAAAA CAAAAAEAAAABAAAAAQAAAAAAAAAOAAAA0AAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAAB AAAAAQAAAAAAAAAFAAQABgAAAAEAAAAMAAAAbmFtZQAAdmFsdWUAAAAAAA4AAABAAAAABgAAAAgA AAAEAAAAAAAAAAUAAAAIAAAAAQAAAAYAAAABAAAAAAAAAAQAAAAMAAAAZgBpAGwAdABlAHIAAAAA AA4AAAA4AAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAMAAAABAAAAAAAAAAQAAAAGAAAA bQBhAHQAAAAOAAAASAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAAAAAAAAAAAAQAAAAAAAAAF AAQABQAAAAEAAAAKAAAAdHlwZQBzdWJzAAAAAAAAAA4AAADIAAAABgAAAAgAAAAEAAAAAAAAAAUA AAAIAAAAAQAAAEwAAAABAAAAAAAAAAQAAACYAAAATQAvAEUARQBHACAAcwBvAHUAcgBjAGUAIABp AG4AdgBlAHIAcwBpAG8AbgA6ACAATQAvAEUARQBHACAAZABhAHQAYQBzAGUAdAAoAHMAKQAgAGEA ZgB0AGUAcgAgAGkAbQBhAGcAaQBuAGcAIABzAG8AdQByAGMAZQAgAHIAZQBjAG8AbgBzAHQAcgB1 AGMAdABpAG8AbgAOAAAAqAQAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAAIAAAAAQAAAAAA AAAFAAQABQAAAAEAAAAKAAAAdHlwZQBzdWJzAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAEAAAAAAAA AAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAQAAgAuAAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAA BQAAAAgAAAABAAAAAwAAAAEAAAAAAAAABAAAAAYAAAB2AGEAbAAAAA4AAAAwAAAABgAAAAgAAAAE AAAAAAAAAAUAAAAIAAAAAQAAAAIAAAABAAAAAAAAAAQABAB7AH0ADgAAAGAAAAAGAAAACAAAAAEA AAAAAAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAA AAgAAAABAAAAAQAAAAEAAAAAAAAAAgABAAIAAAAOAAAAMAAAAAYAAAAIAAAABAAAAAAAAAAFAAAA CAAAAAEAAAABAAAAAQAAAAAAAAAEAAIALgAAAA4AAAA4AAAABgAAAAgAAAAEAAAAAAAAAAUAAAAI AAAAAQAAAAMAAAABAAAAAAAAAAQAAAAGAAAAdgBhAGwAAAAOAAAAMAAAAAYAAAAIAAAABAAAAAAA AAAFAAAACAAAAAEAAAACAAAAAQAAAAAAAAAEAAQAewB9AA4AAABgAAAABgAAAAgAAAABAAAAAAAA AAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAAAAUAAAAIAAAA AQAAAAEAAAABAAAAAAAAAAIAAQABAAAADgAAADAAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAAB AAAAAQAAAAEAAAAAAAAABAACAC4AAAAOAAAAOAAAAAYAAAAIAAAABAAAAAAAAAAFAAAACAAAAAEA AAADAAAAAQAAAAAAAAAEAAAABgAAAHYAYQBsAAAADgAAADAAAAAGAAAACAAAAAQAAAAAAAAABQAA AAgAAAABAAAAAgAAAAEAAAAAAAAABAAEAHsAfQAOAAAAYAAAAAYAAAAIAAAAAQAAAAAAAAAFAAAA CAAAAAEAAAABAAAAAQAAAAAAAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAAB AAAAAQAAAAAAAAACAAEAAQAAAA4AAAAwAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAAAQAAAAEA AAABAAAAAAAAAAQAAgAuAAAADgAAADgAAAAGAAAACAAAAAQAAAAAAAAABQAAAAgAAAABAAAAAwAA AAEAAAAAAAAABAAAAAYAAAB2AGEAbAAAAA4AAAAwAAAABgAAAAgAAAAEAAAAAAAAAAUAAAAIAAAA AQAAAAIAAAABAAAAAAAAAAQABAB7AH0ADgAAAGAAAAAGAAAACAAAAAEAAAAAAAAABQAAAAgAAAAB AAAAAQAAAAEAAAAAAAAADgAAADAAAAAGAAAACAAAAAYAAAAAAAAABQAAAAgAAAABAAAAAQAAAAEA AAAAAAAAAgABAAEAAAAOAAAAuAAAAAYAAAAIAAAAAgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAA AAAAAAAFAAQABQAAAAEAAAAKAAAAdHlwZQBzdWJzAAAAAAAAAA4AAAAwAAAABgAAAAgAAAAEAAAA AAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAQAAgAuAAAADgAAADAAAAAGAAAACAAAAAQAAAAA AAAABQAAAAgAAAABAAAAAQAAAAEAAAAAAAAABAACAEQAAAAOAAAAMAAAAAYAAAAIAAAABgAAAAAA AAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAQAAAA4AAAAwAAAABgAAAAgAAAAGAAAAAAAA AAUAAAAIAAAAAQAAAAIAAAABAAAAAAAAAAMABACc/4QDDgAAADAAAAAGAAAACAAAAAYAAAAAAAAA BQAAAAgAAAABAAAAAgAAAAEAAAAAAAAAAgACAAAAAAAOAAAAQAAAAAYAAAAIAAAABAAAAAAAAAAF AAAACAAAAAEAAAAGAAAAAQAAAAAAAAAEAAAADAAAAGUAdgBvAGsAZQBkAAAAAAAOAAAAMAAAAAYA AAAIAAAABgAAAAAAAAAFAAAACAAAAAEAAAABAAAAAQAAAAAAAAACAAEAAQAAAA4AAAAwAAAABgAA AAgAAAAGAAAAAAAAAAUAAAAIAAAAAQAAAAEAAAABAAAAAAAAAAIAAQAIAAAA ------=_Part_65031_125733977.1305458955197-- ========================================================================= Date: Sun, 15 May 2011 23:38:23 +0100 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: Batch problem--3D Source Reconstruction 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: base64 Message-ID: <[log in to unmask]> RGVhciBIYW9yYW4sCgpUaGUgcHJvYmxlbSBzZWVtcyB0byBiZSBub3QgaW4gdGhlIGJhdGNoIGJ1 dCBpbiB5b3VyIE1FRyBkYXRhIChvciB3YXMKaXQgRUVHPykuIFRoZXJlIGFyZSBubyB2YWxpZCBm aWR1Y2lhbHMgaW4gdGhhdCBkYXRhc2V0LiBDb3VsZCB5b3UKcGVyZm9ybSBzb3VyY2UgcmVjb25z dHJ1Y3Rpb24gdXNpbmcgdGhlIEdVST8gSSdkIGJlIHN1cnByaXNlZCBpZiB5b3UKY291bGQuIFNv IHdlIG5lZWQgdG8gZm9jdXMgb24gaG93IHRvIGdldCBmaWR1Y2lhbHMgaW50byB5b3VyIGRhdGFz ZXQKYW5kIGZvciB0aGF0IHlvdSBuZWVkIHRvIHRlbGwgbWUgbW9yZSBhYm91dCB3aGVyZSB0aGF0 IGRhdGEgY29tZXMKZnJvbS4KCkJlc3QsCgpWbGFkaW1pcgoKMjAxMS81LzE1IOmjnum4nyA8bGlo YW9yYW4wNjAzQDE2My5jb20+Ogo+IERlYXIgVmxhZGltaXIsCj4g44CA44CASSB0cmllZCBhZ2Fp biB3aXRoIHRoZSBsYXRlc3QgdmVyc2lvbiBvZiBzcG04KDQwMjkpLCB0aGXCoHByZXZpb3VzIHBy b2JsZW0oCj4gdGVtcGxhdGU9MSkgZGlzYXBwZWFyZWQuIEJ1dCB0aGlzIHRpbWUswqBJIGNvbmZy b250ZWQgYSBuZXcgcHJvYmxlbS4gSXQKPiByZW1pbmRlZCBtZSAiTm8gZXhlY3V0YWJsZSBtb2R1 bGVzLCBidXQgc3RpbGwgdW5yZXNvbHZlZCBkZXBlbmRlbmNpZXMgb3IKPiBpbmNvbXBsZXRlIG1v ZHVsZSBpbnB1dHMuIiBCdXQgSSBkb24ndCBrbm93IHdoZXJlIHRoZSBwcm9ibGVtcyBhcmUuIENv dWxkCj4geW91IGhlbHAgbWXCoHNlZSBteSByZWNvbnN0cnVjdGlvbl9iYXRjaC5tYXQgYW5kIGdp dmUgbWUgc29tZSBhZHZpY2UgPyBUaGFua3MKPiBhIGxvdC4KPgo+IMKgwqDCoMKgwqDCoCBIYW9y YW4uCj4KPiDCoMKgwqDCoMKgwqAgVGhlc2UgY29kZSBhcHBlYXJzIGluIHRoZSBtYXRsYWIgY29t bWFuZCB3aW5kb3cgd2hlbiBJIHJhbiB0aGUgYmF0Y2g6Cj4gU1BNODogc3BtX2VlZ19pbnZfbWVz aF91aSAodjQwMjcpwqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKgwqDCoCAxMTowOToxMyAt IDE1LzA1LzIwMTEKPiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0KPiDCoMKgwqDCoMKgwqAgdGVtcGxhdGU6IDAK PiDCoMKgwqDCoMKgwqDCoMKgwqDCoCBzTVJJOiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAw MDItMDAwMDAtMDAwMDAxLTAxLmltZywxJwo+IMKgwqDCoMKgwqDCoMKgwqDCoMKgwqAgZGVmOiAn RDpcUHJvY2Vzc2VkXERQMDFceV9zS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDEubmlpJwo+IMKg wqDCoMKgwqDCoMKgwqAgQWZmaW5lOiBbNHg0IGRvdWJsZV0KPiDCoMKgwqDCoMKgwqDCoMKgwqAg TXNpemU6IDIKPiDCoMKgwqDCoMKgwqAgdGVzc19tbmk6IFsxeDEgc3RydWN0XQo+IMKgwqDCoMKg wqDCoCB0ZXNzX2N0eDoKPiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAw MDAxLTAxY29ydGV4XzgxOTYuc3VyZi5naWknCj4gwqDCoMKgwqAgdGVzc19zY2FscDoKPiAnRDpc UHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxc2NhbHBfMjU2Mi5zdXJm LmdpaScKPiDCoMKgwqAgdGVzc19vc2t1bGw6Cj4gJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0w MDAyLTAwMDAwLTAwMDAwMS0wMW9za3VsbF8yNTYyLnN1cmYuZ2lpJwo+IMKgwqDCoCB0ZXNzX2lz a3VsbDoKPiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxaXNr dWxsXzI1NjIuc3VyZi5naWknCj4gwqDCoMKgwqDCoMKgwqDCoMKgwqDCoCBmaWQ6IFsxeDEgc3Ry dWN0XQo+IEZhaWxlZMKgICdNL0VFRyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb24nCj4gUmVmZXJl bmNlIHRvIG5vbi1leGlzdGVudCBmaWVsZCAnZmlkJy4KPiBJbiBmaWxlICJDOlxQcm9ncmFtIEZp bGVzXE1BVExBQlxzcG04bmV3XGNvbmZpZ1xzcG1fY2ZnX2VlZ19pbnZfaGVhZG1vZGVsLm0iCj4g KHY0MTE4KSwgZnVuY3Rpb24gInNwZWNpZnlfaGVhZG1vZGVsIiBhdCBsaW5lIDIwOS4KPiBObyBl eGVjdXRhYmxlIG1vZHVsZXMsIGJ1dCBzdGlsbCB1bnJlc29sdmVkIGRlcGVuZGVuY2llcyBvciBp bmNvbXBsZXRlCj4gbW9kdWxlIGlucHV0cy4KPiBUaGUgZm9sbG93aW5nIG1vZHVsZXMgZGlkIG5v dCBydW46Cj4gRmFpbGVkOiBNL0VFRyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb24KPiBTa2lwcGVk OiBNL0VFRyBzb3VyY2UgaW52ZXJzaW9uCj4gU2tpcHBlZDogTS9FRUcgaW52ZXJzaW9uIHJlc3Vs dHMKPgo+Cj4gQXTCoDIwMTEtMDUtMTPCoDIyOjA2OjA277yMIlZsYWRpbWlywqBMaXR2YWsiwqA8 bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbQo+PsKgd3JvdGU6Cj4KPj5EZWFywqBIYW9yYW4sCj4+ Cj4+ScKgdHJpZWTCoGFuZMKgZXZlcnl0aGluZ8Kgd29ya3PCoGZpbmXCoGZvcsKgbWUuwqBNYWtl wqBzdXJlwqB5b3VywqBTUE3CoHZlcnNpb24KPj5pc8KgdXDCoHRvwqBkYXRlwqBhbmTCoGlmwqB5 b3XCoGNhbid0wqBtYWtlwqBpdMKgd29yayzCoHNlbmTCoG1lwqB5b3VywqBzYXZlZMKgYmF0Y2gK Pj5hbmTCoEknbGzCoHRha2XCoGFub3RoZXLCoGxvb2suCj4+Cj4+QmVzdCwKPj4KPj5WbGFkaW1p cgo+Pgo+PjIwMTEvNS8xMMKg6aOe6bifwqA8bGloYW9yYW4wNjAzQDE2My5jb20+Ogo+Pj7CoERl YXLCoFNQTSdzwqB1c2VycywKPj4+wqDjgIDjgIBIYXZlwqB5b3XCoGV2ZXLCoHVzZWTCoGJhdGNo wqBpbnRlcmZhY2XCoHRvwqBkb8KgM0TCoHNvdXJjZcKgcmVjb25zdHJ1Y3Rpb27CoGZvcgo+Pj7C oEVFRy9NRUfCoGRhdGE/wqBJwqBjaG9vc2VkwqB0aHJlZcKgc2VwYXJhdGXCoG1vZHVsZXPCoMKg Ik0vRUVHwqBoZWFkwqBtb2RlbAo+Pj7CoHNwZWNpZmljYXRpb24iwqAiTS9FRUfCoHNvdXJjZcKg aW52ZXJzaW9uIsKgYW5kwqAiTS9FRUfCoGludmVyc2lvbsKgcmVzdWx0cyLCoHNvwqBhcwo+Pj7C oHRvwqByZWNvbnN0cnVjdMKgdGhlwqBzb3VyY2VzLsKgSW7CoHRoZcKgIk0vRUVHwqBoZWFkwqBt b2RlbMKgc3BlY2lmaWNhdGlvbiLCoG1vZHVsZSwKPj4+wqBJwqBzcGVjaWZpZWTCoCcnTWVzaGVz PE1lc2hlc8Kgc291cmNlPGluZGl2aWR1YWzCoHN0cnVjdHVhbMKgaW1hZ2UnJ8KgYW5kwqB0aGVu Cj4+PsKgc2VsZWN0ZWTCoGHCoG1yacKgZmlsZcKgbmFtZWTCoCJzRFA3NC0wMDAzLTAwMDAwLTAw MDAwMS0wMS5pbWcsMSIuwqDCoFdoZW7CoGFsbMKgd2FzCj4+PsKgcmVhZHnCoGFuZMKgdGhlbsKg ScKgcmFuwqB0aGXCoGJhdGNoLMKgaG93ZXZlcizCoEnCoGZvdW5kwqB0aGF0wqBpdMKgZGluJ3TC oGV4Y3V0ZWTCoHRoZQo+Pj7CoHNwZWNpZmllZMKgc3RydWN0dWFswqBpbWFnZcKgKMKgYXPCoHlv dcKgY2FuwqBzZWXCoGJlbG93wqAndGVtcGxhdGU9MScpwqBhbmTCoHRoZQo+Pj7CoG1hdGxhYsKg Y29tbWFuZMKgd2luZG93wqBhcHBlYXJlZMKgdGhvc2U6Cj4+Pgo+Pj7CoFNQTTg6wqBzcG1fZWVn X2ludl9tZXNoX3VpwqAodjM3MzEpwqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKg MjE6Mzk6MDbCoC3CoDA5LzA1LzIwMTEKPj4+wqA9PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0KPj4+wqDCoMKgwqDC oMKgwqDCoHRlbXBsYXRlOsKgMQo+Pj7CoMKgwqDCoMKgwqDCoMKgwqDCoEFmZmluZTrCoFs0eDTC oGRvdWJsZV0KPj4+wqDCoMKgwqDCoMKgwqDCoMKgwqDCoMKgc01SSTrCoCdDOlxQcm9ncmFtCj4+ PsKgRmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcc2luZ2xlX3N1YmpfVDEubmlp Jwo+Pj7CoMKgwqDCoMKgwqDCoMKgwqDCoMKgTXNpemU6wqAyCj4+PsKgwqDCoMKgwqDCoMKgwqB0 ZXNzX21uaTrCoFsxeDHCoHN0cnVjdF0KPj4+wqDCoMKgwqDCoMKgwqDCoHRlc3NfY3R4OsKgJ0M6 XFByb2dyYW0KPj4+wqBGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxjb3J0ZXhf ODE5Ni5zdXJmLmdpaScKPj4+wqDCoMKgwqDCoMKgdGVzc19zY2FscDrCoCdDOlxQcm9ncmFtCj4+ PsKgRmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcc2NhbHBfMjU2Mi5zdXJmLmdp aScKPj4+wqDCoMKgwqDCoHRlc3Nfb3NrdWxsOsKgJ0M6XFByb2dyYW0KPj4+wqBGaWxlc1xNQVRM QUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxvc2t1bGxfMjU2Mi5zdXJmLmdpaScKPj4+wqDCoMKg wqDCoHRlc3NfaXNrdWxsOsKgJ0M6XFByb2dyYW0KPj4+wqBGaWxlc1xNQVRMQUJcdG9vbGJveFxz cG04XGNhbm9uaWNhbFxpc2t1bGxfMjU2Mi5zdXJmLmdpaScKPj4+wqDCoMKgwqDCoMKgwqDCoMKg wqDCoMKgwqBmaWQ6wqBbMXgxwqBzdHJ1Y3RdCj4+Pgo+Pj7CoMKgwqDjgIBJwqBkaWRuJ3TCoHVu ZGVyc3RhbmTCoHdoeS7CoEFueW9uZcKgd2hvwqBrbm93c8KgdGhlwqByZWFzb27CoD/CoFRoYW5r c8KgdmVyecKgbXVjaAo+Pj7CoGZvcsKgYW55wqBoZWxwwqAhCj4+Pgo+Pj7CoC0tCj4+PsKgSGFv cmFuwqBMScKgKE1TKQo+Pj7CoEJyYWluwqBJbWFnaW5nwqBMYWIsCj4+PsKgUmVzZWFyY2jCoENl bnRlcsKgZm9ywqBMZWFybmluZ8KgU2NpZW5jZSwKPj4+wqBTb3V0aGVhc3TCoFVuaXZlcnNpdHkK Pj4+wqAywqBTacKgUGFpwqBMb3XCoCzCoE5hbmppbmcswqAyMTAwOTYswqBQLlIuQ2hpbmEKPj4+ Cj4+Pgo+Cj4KPgo+Cj4K ========================================================================= Date: Sun, 15 May 2011 23:45:30 +0100 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: variable use in spm_imcalc Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi, I'm having a simple problem with spm_imcalc / spm_imcalc_ui. I need to r= ead in a variable to be used in the imcalc equation.=20=20 The basic idea is to do this: spm_imcalc_ui({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)'= ) in a batch script, where 'thresh1' is a value previously calculated for e= ach subject. The function works if I use a simple number, e.g. '1', in p= lace of the variable. However, adapting the command to read in the extra variable, for example:= spm_imcalc({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)',{[= ],[],[],[]},thresh1);=20 has not worked. I think I must be missing something about the syntax. (= Judging by the lack of questions posted to the board on imcalc and variab= les, it's something obvious, or it's a function that SPM people never use= .) Thank you for any help! Best regards, Elliott Dept. of Psychology Columbia University New York, NY ========================================================================= Date: Sun, 15 May 2011 20:17:51 -0400 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: variable use in spm_imcalc Comments: To: G Elliott Wimmer <[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]> It'll probably work if you use sprintf ________________________________________ From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf O= f G Elliott Wimmer [[log in to unmask]] Sent: Sunday, May 15, 2011 6:45 PM To: [log in to unmask] Subject: [SPM] variable use in spm_imcalc Hi, I'm having a simple problem with spm_imcalc / spm_imcalc_ui. I need to rea= d in a variable to be used in the imcalc equation. The basic idea is to do this: spm_imcalc_ui({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)') in a batch script, where 'thresh1' is a value previously calculated for eac= h subject. The function works if I use a simple number, e.g. '1', in place= of the variable. However, adapting the command to read in the extra variable, for example: spm_imcalc({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)',{[],= [],[],[]},thresh1); has not worked. I think I must be missing something about the syntax. (Ju= dging by the lack of questions posted to the board on imcalc and variables,= it's something obvious, or it's a function that SPM people never use.) Thank you for any help! Best regards, Elliott Dept. of Psychology Columbia University New York, NY ========================================================================= Date: Sun, 15 May 2011 20:44:06 -0400 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: variable use in spm_imcalc Comments: To: G Elliott Wimmer <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0023545309c8e42b7804a359f3e4 Message-ID: <[log in to unmask]> --0023545309c8e42b7804a359f3e4 Content-Type: text/plain; charset=ISO-8859-1 I believe the issue is that you are entering a string that contains thresh1, since its a string, it's not looking for a variable. Try changing 'i2.*(i1>thresh1)' to ['i2.*(i1>' thresh1 ')'] 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, May 15, 2011 at 6:45 PM, G Elliott Wimmer <[log in to unmask]>wrote: > Hi, > > I'm having a simple problem with spm_imcalc / spm_imcalc_ui. I need to > read in a variable to be used in the imcalc equation. > > The basic idea is to do this: > > spm_imcalc_ui({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)') > > in a batch script, where 'thresh1' is a value previously calculated for > each subject. The function works if I use a simple number, e.g. '1', in > place of the variable. > > However, adapting the command to read in the extra variable, for example: > > > spm_imcalc({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)',{[],[],[],[]},thresh1); > > has not worked. I think I must be missing something about the syntax. > (Judging by the lack of questions posted to the board on imcalc and > variables, it's something obvious, or it's a function that SPM people never > use.) > > Thank you for any help! > > Best regards, > > Elliott > > > Dept. of Psychology > Columbia University > New York, NY > --0023545309c8e42b7804a359f3e4 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable I believe the issue is that you are entering a string that contains thresh1= , since its a string, it's not looking for a variable.<div><br></div><d= iv>Try changing=A0<meta charset=3D"utf-8"><span class=3D"Apple-style-span" = style=3D"border-collapse: collapse; font-family: arial, sans-serif; font-si= ze: 13px; ">'i2.*(i1>thresh1)' to [</span><meta charset=3D"utf-8= "><span class=3D"Apple-style-span" style=3D"border-collapse: collapse; font= -family: arial, sans-serif; font-size: 13px; ">'i2.*(i1>' thresh= 1 ')']</span><br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Sun, May 15, 2011 at 6:45 PM, G Ellio= tt Wimmer <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">gew= [log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gmail_quote= " style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> Hi,<br> <br> I'm having a simple problem with spm_imcalc / spm_imcalc_ui. =A0I need = to read in a variable to be used in the imcalc equation.<br> <br> The basic idea is to do this:<br> <br> spm_imcalc_ui({'input1.img','input2.img'},'output.img&#= 39;,'i2.*(i1>thresh1)')<br> <br> in a batch script, where 'thresh1' is a value previously calculated= for each subject. =A0The function works if I use a simple number, e.g. = 9;1', in place of the variable.<br> <br> However, adapting the command to read in the extra variable, for example:<b= r> <br> spm_imcalc({'input1.img','input2.img'},'output.img'= ,'i2.*(i1>thresh1)',{[],[],[],[]},thresh1);<br> <br> has not worked. =A0I think I must be missing something about the syntax. = =A0(Judging by the lack of questions posted to the board on imcalc and vari= ables, it's something obvious, or it's a function that SPM people n= ever use.)<br> <br> Thank you for any help!<br> <br> Best regards,<br> <br> Elliott<br> <br> <br> Dept. of Psychology<br> Columbia University<br> New York, NY<br> </blockquote></div><br></div> --0023545309c8e42b7804a359f3e4-- ========================================================================= Date: Sun, 15 May 2011 20:52:22 -0400 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: variable use in spm_imcalc Comments: To: "Watson, Christopher" <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf30549ff1a58ab804a35a1210 Message-ID: <[log in to unmask]> --20cf30549ff1a58ab804a35a1210 Content-Type: text/plain; charset=ISO-8859-1 Dear Christopher, Of course - thank you! For the record, both of these work to enter variables into spm_imcalc: spm_imcalc_ui({'input1.img','input2.img'},'output.img',['i2.*(i1>' sprintf('%d',val1) ')']); spm_imcalc_ui({'input1.img','input2.img'},'output.img',['i2.*(i1>' num2str(thresh1) ')']); (It was a silly issue of brackets; I'd tried num2str before, but failed.) Best regards, Elliott On Sun, May 15, 2011 at 8:17 PM, Watson, Christopher < [log in to unmask]> wrote: > It'll probably work if you use sprintf > ________________________________________ > From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf > Of G Elliott Wimmer [[log in to unmask]] > Sent: Sunday, May 15, 2011 6:45 PM > To: [log in to unmask] > Subject: [SPM] variable use in spm_imcalc > > Hi, > > I'm having a simple problem with spm_imcalc / spm_imcalc_ui. I need to > read in a variable to be used in the imcalc equation. > > The basic idea is to do this: > > spm_imcalc_ui({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)') > > in a batch script, where 'thresh1' is a value previously calculated for > each subject. The function works if I use a simple number, e.g. '1', in > place of the variable. > > However, adapting the command to read in the extra variable, for example: > > > spm_imcalc({'input1.img','input2.img'},'output.img','i2.*(i1>thresh1)',{[],[],[],[]},thresh1); > > has not worked. I think I must be missing something about the syntax. > (Judging by the lack of questions posted to the board on imcalc and > variables, it's something obvious, or it's a function that SPM people never > use.) > > Thank you for any help! > > Best regards, > > Elliott > > > Dept. of Psychology > Columbia University > New York, NY > > --20cf30549ff1a58ab804a35a1210 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Christopher,<div><br></div><div>Of course - thank you!</div><div><br><= /div><div>For the record, both of these work to enter variables into spm_im= calc:</div><div><br></div><div><br></div><div>spm_imcalc_ui({'input1.im= g','input2.img'},'output.img',['i2.*(i1>' sp= rintf('%d',val1) ')']);</div> <div><br></div><div><div>spm_imcalc_ui({'input1.img','input2.im= g'},'output.img',['i2.*(i1>' num2str(thresh1) ')= ']);</div><div><br></div><div><br></div><div>(It was a silly issue of b= rackets; I'd tried num2str before, but failed.)</div> <div><br></div><div><br></div><div><div>Best regards,</div><div><br></div><= div>Elliott</div></div><div><br></div><br><div class=3D"gmail_quote">On Sun= , May 15, 2011 at 8:17 PM, Watson, Christopher <span dir=3D"ltr"><<a hre= f=3D"mailto:[log in to unmask]">Christopher.Watson@ch= ildrens.harvard.edu</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">It'll probably work if you use sprintf<= br> ________________________________________<br> From: SPM (Statistical Parametric Mapping) [<a href=3D"mailto:[log in to unmask] AC.UK">[log in to unmask]</a>] On Behalf Of G Elliott Wimmer [<a href=3D"ma= ilto:[log in to unmask]">[log in to unmask]</a>]<br> Sent: Sunday, May 15, 2011 6:45 PM<br> To: <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a><br> Subject: [SPM] variable use in spm_imcalc<br> <div><div></div><div class=3D"h5"><br> Hi,<br> <br> I'm having a simple problem with spm_imcalc / spm_imcalc_ui. =A0I need = to read in a variable to be used in the imcalc equation.<br> <br> The basic idea is to do this:<br> <br> spm_imcalc_ui({'input1.img','input2.img'},'output.img&#= 39;,'i2.*(i1>thresh1)')<br> <br> in a batch script, where 'thresh1' is a value previously calculated= for each subject. =A0The function works if I use a simple number, e.g. = 9;1', in place of the variable.<br> <br> However, adapting the command to read in the extra variable, for example:<b= r> <br> spm_imcalc({'input1.img','input2.img'},'output.img'= ,'i2.*(i1>thresh1)',{[],[],[],[]},thresh1);<br> <br> has not worked. =A0I think I must be missing something about the syntax. = =A0(Judging by the lack of questions posted to the board on imcalc and vari= ables, it's something obvious, or it's a function that SPM people n= ever use.)<br> <br> Thank you for any help!<br> <br> Best regards,<br> <br> Elliott<br> <br> <br> Dept. of Psychology<br> Columbia University<br> New York, NY<br> <br> </div></div></blockquote></div><br></div> --20cf30549ff1a58ab804a35a1210-- ========================================================================= Date: Sun, 15 May 2011 22:42:48 -0400 Reply-To: Jadzia Jagiellowicz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jadzia Jagiellowicz <[log in to unmask]> Subject: Re: SPM Digest - 6 May 2011 (#2011-133) In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=005045015ed170643e04a35b9c79 Message-ID: <[log in to unmask]> --005045015ed170643e04a35b9c79 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thank you John and Don for all the help with the fMRI processing errors. I've finally figured out the problem. It was actually a problem with the files themselves. Best jadzia On 6 May 2011 19:00, SPM automatic digest system <[log in to unmask]>w= rote: > There are 3 messages totaling 1723 lines in this issue. > > Topics of the day: > > 1. spm_affreg > 2. PPI question > 3. using mri3dx > > ---------------------------------------------------------------------- > > Date: Fri, 6 May 2011 13:48:29 -0700 > From: Siddharth Srivastava <[log in to unmask]> > Subject: Re: spm_affreg > > Hi John, please ignore the first part of my previous mail: made some erro= rs > in > cut and paste. It runs fine now. The question about the shears, however, > still lingers: do I have > to somehow use both Sx and Sy, or as your implementation suggests, only o= ne > would suffice? > > thanks again, and sorry for being such a bother... > sid. > > On Fri, May 6, 2011 at 12:23 PM, Siddharth Srivastava <[log in to unmask] > >wrote: > > > Thanks John, for the answers. The code, however exits with "Problem". M= 0 > > and M1 differ. > > Also, why is there just one shear component in the matrix? I have seen > > general shears modeled as [1,h;g,1]: is this superfluous, and just one > "h" > > will be sufficient for 2d case? > > > > Thanks, > > Sid. > > > > > > > > > > On Fri, May 6, 2011 at 11:01 AM, John Ashburner <[log in to unmask] > >wrote: > > > >> Your model uses 7 parameters, but there are only 6 elements in > >> A(1:2,1:3). Also, you need to be able to deal with a broader range of > >> angles, so atan2(s,c) is used to compute the angle. I've tested it > >> with the following code, so hopefully it works for all cases. > >> > >> for i=3D1:10000 > >> P=3Drandn(1,6)*10; > >> M0=3Dspm_matrix2D(P); > >> P1=3Dspm_imatrix2D(M0); > >> M1=3Dspm_matrix2D(P1); > >> t=3Dsum(sum((M1-M0).^2)); > >> if t>1e-9, disp('Problem'); break; end > >> end > >> > >> Note that spm_imatrix2D(spm_matrix2D(P)) does not necessarily have to > >> equal P, but the matrices generated from the two parameter sets should > >> be the same. > >> > >> Best regards, > >> -John > >> > >> On 6 May 2011 17:11, Siddharth Srivastava <[log in to unmask]> wrote: > >> > Hi everyone, > >> > Can someone help me check the 2D versions of the spm_matrix an= d > >> > spm_imatrix? My > >> > implementation currently is as follows: > >> > > >> > ---2D spm_matrix --- > >> > function [A] =3D spm_matrix2D(P) > >> > % P =3D [Tx, Ty, R, Zx, Zy, Sx,Sy] > >> > p0 =3D [0,0,0,1,1,0,0]; > >> > P =3D [P, p0((length(P)+1):7)] > >> > > >> > A =3D eye(3); > >> > % T -> R -> Zoom -> Shear > >> > A =3D A * [1,0,P(1); ... > >> > 0,1,P(2); ... > >> > 0,0, 1]; > >> > A =3D A * [cos(P(3)), sin(P(3)), 0; ... > >> > -sin(P(3)), cos(P(3)), 0; ... > >> > 0, 0, 1]; > >> > A =3D A * [P(4), 0, 0; ... > >> > 0, P(5), 0; ... > >> > 0, 0, 1]; > >> > A =3D A * [1, P(6), 0; ... > >> > P(7) , 1, 0; ... > >> > 0, 0, 1]; > >> > -------------------------------------------------- > >> > -----2D spm_imatrix ------------------------ > >> > function P =3D spm_imatrix2D(M); > >> > > >> > > >> > > >> > R =3D M(1:2,1:2); > >> > C =3D chol(R' * R); > >> > P =3D [M(1:2,3)',0,diag(C)',0,0]; > >> > if(det(R) < 0) P(4) =3D -P(4); end > >> > C =3D diag(diag(C))\C; > >> > P(6:7) =3D C([2,3]); > >> > > >> > R0 =3D spm_matrix2D([0,0,0,P(4:end)]); > >> > R0 =3D R0(1:2,1:2); > >> > R1 =3D R/R0; > >> > > >> > th =3D asin(rang(R1(1,2))); > >> > P(3) =3D th; > >> > > >> > % There may be slight rounding errors making b>1 or b<-1. > >> > function a =3D rang(b) > >> > a =3D min(max(b, -1), 1); > >> > return; > >> > > >> > -------------------------------------------------------------------------= -- > >> > > >> > I know there is something not correct, since when, for a parameter s= et > >> 'p', > >> > i do spm_imatrix2D(spm_matrix2D(p)), I do not recover P for all > entries, > >> > specially > >> > rotation and shears. C(2) above always evaluates to zero. The result > is > >> that > >> > these errors > >> > accumulate (or are not accounted for) on repeated application of thi= s > >> > sequence of > >> > operations in the registration routine. > >> > > >> > Thanks in anticipation, > >> > Sid. > >> > > >> > > >> > > >> > On Fri, Apr 22, 2011 at 9:13 AM, Siddharth Srivastava < > [log in to unmask] > >> > > >> > wrote: > >> >> > >> >> Hi John, > >> >> Thanks a lot for your answer, and it has been very helpful fo= r > my > >> >> understanding > >> >> of the details of the implementation, and my own development effort= s. > I > >> >> have > >> >> 2 very quick questions, though: > >> >> > >> >> 1) the parameter set x(:) that is returned from spm_mireg, and the > >> >> "params" that > >> >> spm_affsub3 returns (in spm99, which is also the version that I am > >> looking > >> >> at) , > >> >> are they equivalent? In other words, If I am collating the paramete= rs > >> from > >> >> the mireg > >> >> routine, would the distribution x(7:12) be used to estimate the > icovar0 > >> >> entries in affsub3? > >> >> > >> >> 2) also would VG.mat\spm_matrix(x)*VF.mat transform G to F similar = to > >> >> VG.mat\spm_matrix(params)*VF.mat , or do I have to do > >> >> M =3D VG.mat\spm_matrix(x(:)')*VF.mat > >> >> pp =3D spm_imatrix(M) > >> >> and use pp(7:12) ? > >> >> > >> >> > >> >> > >> >> Thanks again. > >> >> Sid. > >> >> > >> >> On Thu, Apr 14, 2011 at 2:13 PM, John Ashburner < > [log in to unmask]> > >> >> wrote: > >> >>> > >> >>> > I had a look at the code in spm_maff, and > >> >>> > it looks like it also uses the values of isig and mu in the call > to > >> >>> > affreg(). My guess is > >> >>> > that during the initial registration on a large data set for > >> estimating > >> >>> > the > >> >>> > prior > >> >>> > distribution of parameters, this will be called with typ =3D 'no= ne' > . > >> Is > >> >>> > this > >> >>> > correct? > >> >>> > >> >>> Once the registration has locked in on a solution, the priors have > >> >>> less influence. Their main influence is in the early stages of th= e > >> >>> registration, or when there are relatively few slices in the data. > I > >> >>> don't actually remember if the registration was regularised when I > >> >>> computed their values. > >> >>> > >> >>> > > >> >>> > Further, do we have to do a non-rigid registration for the > >> estimation > >> >>> > (since, in the comments the selected > >> >>> > files are filtered as *seg_inv_sn.mat"? Since only p.Affine is > being > >> >>> > used, > >> >>> > can this work with only > >> >>> > an affine / rigid transformation? > >> >>> > >> >>> Only the affine transform in the files is used, so basically yes. > You > >> >>> could just do affine registration. > >> >>> > >> >>> > > >> >>> > I would also be happy if you could also answer my other question > >> about > >> >>> > examining the > >> >>> > effects of the values in isig and mu (what are the units of mu?) > on > >> the > >> >>> > transformed image. > >> >>> > >> >>> They are unit-less, and are just a measure of the zooms and shears= . > >> >>> > >> >>> > For example, > >> >>> > if i set a mu of [mu1, mu2, ...] and a isig of [sig1, 0, 0...; 0= , > >> sig2, > >> >>> > 0, > >> >>> > 0..; ...] (with only diagonal components), > >> >>> > based on the Hencky tensor penalty, it seems that large deviatio= ns > >> of > >> >>> > parameter 1 estimate (T - mu) from > >> >>> > mu1 will be penalized. How is the value of isig1 modifying the > >> penalty? > >> >>> > >> >>> Its assuming that the elements of the matrix log of the part that > does > >> >>> shears and zooms has a multivariate normal distribution, so the > >> >>> penalty is a kind of negative log likelhood. The isig is > essentially > >> >>> a precision matrix, which is the inverse of a covariance matrix. > >> >>> > >> >>> p(x|mu,S) =3D 1/sqrt(det(2*pi*S)) * exp(-0.5*(x-mu)'*inv(S)*(x-mu)= ) > >> >>> > >> >>> Taking logs and negating, you get 0.5*(x-mu)'*inv(S)*(x-mu) + cons= t > >> >>> > >> >>> > How > >> >>> > do the off diagonal terms affect > >> >>> > the penalty? > >> >>> > >> >>> The inverse of isig would just encode the covariance between the > >> >>> various shears and zooms. > >> >>> > >> >>> > > >> >>> > Could you also point me to a reference where I can learn more > about > >> >>> > Hencky > >> >>> > strain tensor (specifically > >> >>> > what information it carries in the context of image processing > etc.) > >> >>> > >> >>> I don't know if this is of any use: > >> >>> > >> >>> http://en.wikipedia.org/wiki/Deformation_(mechanics) > >> >>> > >> >>> Best regards, > >> >>> -John > >> >>> > >> >>> > >> >>> > On Thu, Apr 14, 2011 at 10:17 AM, John Ashburner < > >> [log in to unmask]> > >> >>> > wrote: > >> >>> >> > >> >>> >> They were computed by affine registering loads of (227) images = to > >> MNI > >> >>> >> space. This gave the affine transforms, which were then used t= o > >> build > >> >>> >> up a mean and inverse covariance matrix. In the comments of > >> >>> >> spm_maff.m, you should see the following... > >> >>> >> > >> >>> >> %% Values can be derived by... > >> >>> >> %sn =3D spm_select(Inf,'.*seg_inv_sn.mat$'); > >> >>> >> %X =3D zeros(size(sn,1),12); > >> >>> >> %for i=3D1:size(sn,1), > >> >>> >> % p =3D load(deblank(sn(i,:))); > >> >>> >> % M =3D p.VF(1).mat*p.Affine/p.VG(1).mat; > >> >>> >> % J =3D M(1:3,1:3); > >> >>> >> % V =3D sqrtm(J*J'); > >> >>> >> % R =3D V\J; > >> >>> >> % lV =3D logm(V); > >> >>> >> % lR =3D -logm(R); > >> >>> >> % P =3D zeros(12,1); > >> >>> >> % P(1:3) =3D M(1:3,4); > >> >>> >> % P(4:6) =3D lR([2 3 6]); > >> >>> >> % P(7:12) =3D lV([1 2 3 5 6 9]); > >> >>> >> % X(i,:) =3D P'; > >> >>> >> %end; > >> >>> >> %mu =3D mean(X(:,7:12)); > >> >>> >> %XR =3D X(:,7:12) - repmat(mu,[size(X,1),1]); > >> >>> >> %isig =3D inv(XR'*XR/(size(X,1)-1)) > >> >>> >> > >> >>> >> You may be able to re-code this to work with your 2D transforms= . > >> Note > >> >>> >> that I only use the components that describe shears and zooms. > >> Also, > >> >>> >> if I was re-coding it all again, I would probably do things > >> slightly > >> >>> >> differently, using the following decomposition: > >> >>> >> > >> >>> >> J=3DM(1:3,1:3); > >> >>> >> L=3Dreal(logm(J)); > >> >>> >> S=3D(L+L')/2; % Symmetric part (for zooms and shears, which sho= uld > be > >> >>> >> penalised) > >> >>> >> A=3D(L-L')/2; % Anti-symmetric part (for rotations) > >> >>> >> > >> >>> >> I might even base the penalty on lambda(2)*trace(S)^2 + > >> >>> >> lambda(1)*trace(S*S'), which is often used as a measure of > "elastic > >> >>> >> energy" for penalising non-linear registration. Of course, the= re > >> >>> >> would also be a bit of annoying extra stuff to deal with due to > MNI > >> >>> >> space being a bit bigger than average brains are. Figuring out > the > >> >>> >> mean would require (I think) an iterative process similar to th= e > >> one > >> >>> >> described in "Characterizing volume and surface deformations in > an > >> >>> >> atlas framework: theory, applications, and implementation", by > >> Woods > >> >>> >> in NeuroImage (2003). > >> >>> >> > >> >>> >> Best regards, > >> >>> >> -John > >> >>> >> > >> >>> >> On 14 April 2011 17:15, Siddharth Srivastava <[log in to unmask]> > >> wrote: > >> >>> >> > Hi all, > >> >>> >> > My question pertains specifically to the values of isig > and > >> mu > >> >>> >> > that > >> >>> >> > are > >> >>> >> > incorporated > >> >>> >> > in the code, and I wish to disentangle the effect that these > >> numbers > >> >>> >> > have on > >> >>> >> > individual parameters > >> >>> >> > during the optimization stage. I am trying to map it to a 2D > >> case, > >> >>> >> > and > >> >>> >> > these > >> >>> >> > numbers > >> >>> >> > will have to be calculated ab-initio for my case. Before I > begin > >> >>> >> > with > >> >>> >> > estimating the prior distribution > >> >>> >> > for these parameters, I need to check the effect that they ha= ve > >> on > >> >>> >> > the > >> >>> >> > registered images. Regarding this > >> >>> >> > 1) Can someone suggest a way I can examine the effect on each > >> >>> >> > parameter > >> >>> >> > separately, if it is > >> >>> >> > at all possible. > >> >>> >> > 2) Some advise on how to estimate these parameters, if it has > >> been > >> >>> >> > done > >> >>> >> > for > >> >>> >> > images other > >> >>> >> > than brain images, will be really helpful for me. > >> >>> >> > > >> >>> >> > Thanks, > >> >>> >> > Sid. > >> >>> >> > > >> >>> >> > On Mon, Mar 28, 2011 at 7:04 PM, Siddharth Srivastava > >> >>> >> > <[log in to unmask]> > >> >>> >> > wrote: > >> >>> >> >> > >> >>> >> >> Dear John, > >> >>> >> >> Thanks for your answer. I was able to correctly ru= n > my > >> 2D > >> >>> >> >> implementation > >> >>> >> >> based on your advise. However, I am am not very confident of > my > >> >>> >> >> results > >> >>> >> >> and wanted to > >> >>> >> >> consult you on certain aspects of my mapping from 3D to 2D. > >> >>> >> >> 1) In function reg(..), the comment says that the > >> >>> >> >> derivatives > >> >>> >> >> w.r.t rotations and > >> >>> >> >> translations is zero. Consequently, I ran the indices from > 4:7. > >> >>> >> >> According > >> >>> >> >> to your previous > >> >>> >> >> reply, rotations will be lumped together with scaling and > affine > >> in > >> >>> >> >> 4 > >> >>> >> >> parameters, and > >> >>> >> >> translations, independently, in the last two. I understand > that > >> in > >> >>> >> >> this > >> >>> >> >> case, the indices > >> >>> >> >> run over parameters that have been separated into individual > >> >>> >> >> components > >> >>> >> >> (similar to > >> >>> >> >> the form accepted by spm_matrix). Is this correct for the > >> function > >> >>> >> >> reg(..)? In fact, I went back to the > >> >>> >> >> old spm code (spm99), and found parameters pdesc and free, a= nd > >> got > >> >>> >> >> more > >> >>> >> >> confused as to when I should > >> >>> >> >> use the 6 parameter, and when to use separate parameters (as > if > >> I > >> >>> >> >> was > >> >>> >> >> passing them to spm_matrix). > >> >>> >> >> So, when the loop runs over p1 and perturbs p1(i) by ds, doe= s > it > >> >>> >> >> run > >> >>> >> >> over > >> >>> >> >> translations/rotations etc > >> >>> >> >> or does it run over the martix elements that define these > >> >>> >> >> transformations > >> >>> >> >> (2x2 + 1x2)? > >> >>> >> >> > >> >>> >> >> 2) In function penalty(..), I have used unique > >> elements > >> >>> >> >> as > >> >>> >> >> [1,3,4], and my code looks like > >> >>> >> >> T =3D my_matrix(p)*M; > >> >>> >> >> T =3D T(1:2,1:2); > >> >>> >> >> T =3D 0.5*logm(T'*T); > >> >>> >> >> T > >> >>> >> >> T =3D T(els)' - mu; > >> >>> >> >> h =3D T'*isig*T > >> >>> >> >> > >> >>> >> >> Assuming an application specific isig, is this correct? > >> >>> >> >> 3) I have not yet incorporated the prior informatio= n > in > >> my > >> >>> >> >> code, > >> >>> >> >> But for testing purposes, > >> >>> >> >> can you suggest some neutral values for mu and isig that I > can > >> try > >> >>> >> >> to > >> >>> >> >> concentrate on the > >> >>> >> >> accuracy of the implementation minus the priors for now? > >> >>> >> >> 4) My solution curently seems to oscillate around = an > >> >>> >> >> optimum. > >> >>> >> >> I > >> >>> >> >> am hoping that after > >> >>> >> >> I have removed any errors after your replies, it will conver= ge > >> >>> >> >> gracefully. > >> >>> >> >> > >> >>> >> >> Thanks again for all the help. > >> >>> >> >> Siddharth. > >> >>> >> >> > >> >>> >> >> > >> >>> >> >> On Fri, Mar 18, 2011 at 2:05 AM, John Ashburner > >> >>> >> >> <[log in to unmask]> > >> >>> >> >> wrote: > >> >>> >> >>> > >> >>> >> >>> A 2D affine transform would use 6 parameters, rather than 7= : > a > >> 2x2 > >> >>> >> >>> matrix to encode rotations, zooms and shears, and a 2x1 > vector > >> for > >> >>> >> >>> translations. > >> >>> >> >>> > >> >>> >> >>> Best regards, > >> >>> >> >>> -John > >> >>> >> >>> > >> >>> >> >>> On 18 March 2011 05:05, Siddharth Srivastava < > [log in to unmask] > >> > > >> >>> >> >>> wrote: > >> >>> >> >>> > Dear list users, > >> >>> >> >>> > I am currently trying to understand the > >> implementation > >> >>> >> >>> > in > >> >>> >> >>> > spm_affreg, and > >> >>> >> >>> > to make matters simple, I tried to work out a 2D version = of > >> the > >> >>> >> >>> > registration > >> >>> >> >>> > procedure. I struck a roadblock, however... > >> >>> >> >>> > > >> >>> >> >>> > The function make_A creates part of the design matrix. Fo= r > >> the > >> >>> >> >>> > 3D > >> >>> >> >>> > case, > >> >>> >> >>> > the > >> >>> >> >>> > 13 parameters > >> >>> >> >>> > are encoded in columns of A1, which then is used to > generate > >> AA > >> >>> >> >>> > + > >> >>> >> >>> > A1' > >> >>> >> >>> > A1. AA > >> >>> >> >>> > is 13x13, and everything > >> >>> >> >>> > works. For the 2D case, however, there will be 7+1 > >> parameters( 2 > >> >>> >> >>> > translations, 1 rotation, 2 zoom, 2 shears and 1 intensit= y > >> >>> >> >>> > scaling). > >> >>> >> >>> > The A1 matrix for 2D, then, will be (in make_A) > >> >>> >> >>> > A =3D [x1.*dG1 x1.*dG2 ... > >> >>> >> >>> > x2.*dG1 x2.*dG2 ... > >> >>> >> >>> > dG1 dG2 t]; > >> >>> >> >>> > > >> >>> >> >>> > which is (number of sampled points) x 7. The AA matrix (i= n > >> >>> >> >>> > function > >> >>> >> >>> > costfun) > >> >>> >> >>> > is 8x8 > >> >>> >> >>> > so, which is the parameter I am missing in modeling a 2D > >> >>> >> >>> > example? I > >> >>> >> >>> > hope I > >> >>> >> >>> > have got the number of parameters > >> >>> >> >>> > right for the 2D case? Is there an extra column that I ha= ve > >> to > >> >>> >> >>> > add > >> >>> >> >>> > to > >> >>> >> >>> > A1 to > >> >>> >> >>> > make the rest of the matrix computation > >> >>> >> >>> > consistent? > >> >>> >> >>> > > >> >>> >> >>> > Thanks for the help > >> >>> >> >>> > > >> >>> >> >> > >> >>> >> > > >> >>> >> > > >> >>> > > >> >>> > > >> >> > >> > > >> > > >> > > > > > > ------------------------------ > > Date: Sat, 7 May 2011 07:14:27 +1000 > From: Alex Fornito <[log in to unmask]> > Subject: Re: PPI question > > Actually, sorry one more question: > > If Y in spm_peb_ppi is already frequency filtered by spm_regions, then th= e > only difference (barring any additional confounds specified with xX.iG) > between Y and Yc is the additional removal of block effects. > > Since the ppi interaction term is generated from Y and not Yc, I am > presuming then that is solely because it is not desirable to deconvolve a > time course with block effects removed. Is this correct? > > > On 07/05/2011 04:07, "MCLAREN, Donald" <[log in to unmask]> wrote: > > > The null-space of the contrast are any columns of the contrast (which > > must be an F-contrast) that are 0. Thus, you remove the effects of > > motion when you do the adjustment and don't need to worry about them > > being included in the deconvolved data. The code that I have supplied > > has N rows for N conditions of interest after I talked to Darren about > > the ideal contrast to use for adjustment. > > > > As for removing the frequencies, I think it is probably fine to remove > > them, I was just under the impression that they weren't being removed > > because of me commenting out that line during testing of spm_regions. > > I would go with the SPM defaults until their effect is tested. If you > > don't set a high-pass filter, then you won't remove the frequencies, > > so you could test it that way as well. > > > > Best Regards, Donald McLaren > > =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > > 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 > > =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > > 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, May 5, 2011 at 9:46 PM, Alex Fornito <[log in to unmask]> > wrote: > >> Hi Donald, Torben and Darren, > >> Thanks for your responses. > >> > >> Form each of your responses, it seems like best practice would be to > remove > >> motion (and/or other confounds) from the VOI time series prior to > >> deconvolution. > >> > >> It also seems like you are recommending NOT to frequency filter the VO= I > time > >> series. > >> > >> In my reading though, spm_regions does this by default with the call t= o > >> spm_filter (line 135). So, if spm_regions is used to extract a VOI tim= e > >> series, the time series that is input to spm_peb_ppi will already > frequency > >> filtered. So it seems like, in standard practice, the deconvolution is > >> applied to frequency-filtered data. In this case, if xX.iG is empty, t= he > >> only difference between Y and Yc in spm_peb_ppi is the additional > removal of > >> block effects (xX.iB). > >> > >> Based on the current chain of emails, it seems you are recommending th= at > a > >> confound-corrected, whitened, but non-frequency filtered time course > should > >> be input to spm_peb_ppi. IS that correct, or am I missing something? > >> > >> Finally, can I clarify exactly what the null space of the contrast is? > Most > >> of the time in my analyses I don't get the option to adjust for it, so > xY.Ic > >> is empty. > >> > >> Thanks again for all your help, > >> A > >> > >> > >> > >> On 06/05/2011 02:18, "MCLAREN, Donald" <[log in to unmask]> > wrote: > >> > >>> I haven't thoroughly tested this, but if you set iG to be the movemen= t > >>> parameters columns. Then still adjust for the null space (motion > >>> parameters). You will get the best of both. If you don't adjust for > >>> the null space, then movement will contribute to the deconvolution. I= n > >>> summary, if you have iG be the motion parameter columns AND adjust fo= r > >>> the null-space (motion columns), then: > >>> > >>> Y will have the effect of motion removed. Yc (removal of motion twice= ) > >>> will be minimally different since Y is orthogonal to the motion > >>> parameters. So, you have motion removed from both the autocorrelation > >>> estimate, the Y for deconvolution, and Yc. > >>> > >>> Best Regards, Donald McLaren > >>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>> 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 > >>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>> 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 recipien= t > >>> or the employee or agent responsible for delivering it to the intende= d > >>> 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, May 5, 2011 at 9:48 AM, Torben Ellegaard Lund > >>> <[log in to unmask]> wrote: > >>>> Hi Alex > >>>> > >>>> > >>>> Den Uge:18 05/05/2011 kl. 14.15 skrev Alex Fornito: > >>>> > >>>>> Hi Torben, > >>>>> Sorry, I just wanted to clarify: do you mean leaving SPM.xX.iG empt= y? > It > >>>>> seems like this is done by default by spm_fmri_design (?) > >>>> > >>>> leaving it empty will include all user defined regressors in the > effects of > >>>> interest F-test which determines the mask where the serial correlati= on > is > >>>> estimated. I think it would make sense if the motion regressors did > not go > >>>> into this test, just like the DCS HP-filter does not go into the > >>>> F-contrast. > >>>> > >>>>> Are you suggesting manually entering motion parameters into this > field? > >>>> > >>>> Yes, or their indices that is. It should be done after specification= , > but > >>>> before model estimation. You can check if SPM recognized your change= s > by > >>>> looking at the automatically generated effects of interest F-contras= t. > >>>> > >>>> > >>>> Best > >>>> Torben > >>>> > >>>> > >>>> > >>>> > >>>>> > >>>>> On 05/05/2011 18:16, "Torben Ellegaard Lund" <[log in to unmask]> > wrote: > >>>>> > >>>>>> Just as a kind reminder. Leaving SPM.xX.iG is a bit unfortunate, > because > >>>>>> voxels where motion artefacts are significant then constitutes a > large > >>>>>> part > >>>>>> of > >>>>>> the F-test derived mask used to estimate serial correlations. If y= ou > are > >>>>>> picky > >>>>>> about this you can change it yourselves, but it is not as easy as = it > >>>>>> should > >>>>>> be. > >>>>>> > >>>>>> > >>>>>> Best > >>>>>> Torben > >>>>>> > >>>>>> > >>>>>> > >>>>>> Den Uge:18 05/05/2011 kl. 05.03 skrev Alex Fornito: > >>>>>> > >>>>>>> Ahh, I see. I was assuming that SPM.xX.iG contained the indices t= o > the > >>>>>>> nuisance covariates in the design matrix, but you're right - > >>>>>>> spm_fMRI_design > >>>>>>> leaves it empty. > >>>>>>> > >>>>>>> Thanks for your help! > >>>>>>> A > >>>>>>> > >>>>>>> > >>>>>>> On 05/05/2011 12:54, "MCLAREN, Donald" <[log in to unmask]> > wrote: > >>>>>>> > >>>>>>>> I agree that nuisance covariates, such as motion should be > removed; > >>>>>>>> however, motion covariates are not generally stored in X0 in my > >>>>>>>> reading of the SPM code (spm_fMRI_design, spm_regions). They > should be > >>>>>>>> removed in the spm_regions filtering based on the null-space of > the > >>>>>>>> contrast. > >>>>>>>> > >>>>>>>> As for the motion parameters being in the model, if they were in > the > >>>>>>>> standard analysis, they should be included in the PPI analysis. = My > >>>>>>>> gPPI code will do this automatically as well. > >>>>>>>> > >>>>>>>> X0 is made of SPM.xX.iB (block effects -- so removing the mean) > and > >>>>>>>> SPM.xX.iG (nuisance variable that is empty as far as I can tell > (line > >>>>>>>> 369 of spm_fMRI_design)) and the high-pass filtering (K.X0). > >>>>>>>> > >>>>>>>> Best Regards, Donald McLaren > >>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>>>>>>> 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 > >>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>>>>>>> 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 enti= ty > >>>>>>>> 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 t= he > >>>>>>>> contents of this information is strictly prohibited and may be > >>>>>>>> unlawful. If you have received this e-mail unintentionally, plea= se > >>>>>>>> immediately notify the sender via telephone at (773) 406-2464 or > >>>>>>>> email. > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> On Wed, May 4, 2011 at 10:16 PM, Alex Fornito < > [log in to unmask]> > >>>>>>>> wrote: > >>>>>>>>> Hi Donald, > >>>>>>>>> Thanks for your response. When you say "you don't want > >>>>>>>>> to filter out anything related to neural activity", I'm presumi= ng > that > >>>>>>>>> you > >>>>>>>>> are referring to the deconvolved neural signal? > >>>>>>>>> > >>>>>>>>> If so, that makes sense. However, X0 also contains user-specifi= ed > >>>>>>>>> nuisance > >>>>>>>>> covariates. I would have thought it would make sense to remove > those, > >>>>>>>>> depending on what they were (e.g., movement parameters)? I gues= s > they > >>>>>>>>> can > >>>>>>>>> always be entered into the PPI regression model... > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> On 05/05/2011 11:57, "MCLAREN, Donald" <[log in to unmask] m > > > >>>>>>>>> wrote: > >>>>>>>>> > >>>>>>>>>> I could be wrong, Karl or Darren please correct me if I am. > >>>>>>>>>> > >>>>>>>>>> X0 is composed of the high-pass filter and constant term. Thus= , > you > >>>>>>>>>> want to filter these out of the Yc regressor. However, you don= 't > want > >>>>>>>>>> to filter out anything related to neural activity, so you > wouldn't > >>>>>>>>>> want to filter your signal and then predict the neural activit= y > from > >>>>>>>>>> the filtered signal, especially filtering frequencies. > >>>>>>>>>> > >>>>>>>>>> Best Regards, Donald McLaren > >>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>>>>>>>>> D.G. McLaren, Ph.D. > >>>>>>>>>> Postdoctoral Research Fellow, GRECC, Bedford VA > >>>>>>>>>> Research Fellow, Department of Neurology, Massachusetts Genera= l > >>>>>>>>>> Hospital and Harvard Medical School > >>>>>>>>>> Office: (773) 406-2464 > >>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D > >>>>>>>>>> This e-mail contains CONFIDENTIAL INFORMATION which may contai= n > >>>>>>>>>> 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, Apr 30, 2011 at 8:03 PM, Alex Fornito > >>>>>>>>>> <[log in to unmask]> > >>>>>>>>>> wrote: > >>>>>>>>>>> Hi Donald, > >>>>>>>>>>> As far as I can tell, spm_regions filters and whitens the VOI > time > >>>>>>>>>>> series, > >>>>>>>>>>> and adjusts for the null space of the contrast if an adjustme= nt > is > >>>>>>>>>>> specified > >>>>>>>>>>> in xY.Ic. It defines the confound matrix, X0, but does not > correct > >>>>>>>>>>> for > >>>>>>>>>>> it. > >>>>>>>>>>> The following line in spm_peb_ppi corrects for the confounds. > >>>>>>>>>>> > >>>>>>>>>>> Yc =3D Y - X0*inv(X0'*X0)*X0'*Y; > >>>>>>>>>>> PPI.Y =3D Yc(:,1); > >>>>>>>>>>> > >>>>>>>>>>> So the above correction does seem to be doing something > different to > >>>>>>>>>>> spm_regions. I'm wondering why the uncorrected data, Y, is us= ed > when > >>>>>>>>>>> generating the ppi interaction term, while the corrected data > Yc is > >>>>>>>>>>> to > >>>>>>>>>>> be > >>>>>>>>>>> used as a covariate of no interest in the PPI model. I would > has > >>>>>>>>>>> thought > >>>>>>>>>>> that Yc should be used to generate the ppi interaction term a= s > well. > >>>>>>>>>>> > >>>>>>>>>>> In my data, my X0 is a 195 x 7 matrix (I have 195 volumes per > >>>>>>>>>>> session). > >>>>>>>>>>> > >>>>>>>>>>> Regards, > >>>>>>>>>>> Alex > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> On 30/04/2011 02:22, "MCLAREN, Donald" < > [log in to unmask]> > >>>>>>>>>>> wrote: > >>>>>>>>>>> > >>>>>>>>>>>> I'm actually travelling at the moment. However, I know Y has > >>>>>>>>>>>> already > >>>>>>>>>>>> been adjusted as part of the VOI processing. > >>>>>>>>>>>> > >>>>>>>>>>>> Can you check what X0 looks like? > >>>>>>>>>>>> > >>>>>>>>>>>> Best Regards, Donald McLaren > >>>>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>>>>>>>>>>> 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 > >>>>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D > >>>>>>>>>>>> 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 th= e > >>>>>>>>>>>> intended > >>>>>>>>>>>> recipient, you are hereby notified that you are in possessio= n > of > >>>>>>>>>>>> confidential and privileged information. Any unauthorized us= e, > >>>>>>>>>>>> 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) 406-246= 4 > or > >>>>>>>>>>>> email. > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> On Fri, Apr 29, 2011 at 2:55 AM, Alex Fornito > >>>>>>>>>>>> <[log in to unmask]> > >>>>>>>>>>>> wrote: > >>>>>>>>>>>> Hi all, > >>>>>>>>>>>> I have a question concerning the code used to implement a PP= I > >>>>>>>>>>>> analysis. > >>>>>>>>>>>> > >>>>>>>>>>>> In the spm_peb_ppi.m function packaged with SPM8, lines > 329-332 > >>>>>>>>>>>> remove > >>>>>>>>>>>> confounds from the input seed region=B9s time course: > >>>>>>>>>>>> > >>>>>>>>>>>> % Remove confounds and save Y in ouput structure > >>>>>>>>>>>> > > %------------------------------------------------------------------>>>>>>= >>>>>> > - > >>>>>>>>>>>> --- > >>>>>>>>>>>> -- > >>>>>>>>>>>> -- > >>>>>>>>>>>> Yc =3D Y - X0*inv(X0'*X0)*X0'*Y; > >>>>>>>>>>>> PPI.Y =3D Yc(:,1); > >>>>>>>>>>>> > >>>>>>>>>>>> PPI.Y is then to be used as a covariate of no interest when > >>>>>>>>>>>> building > >>>>>>>>>>>> the > >>>>>>>>>>>> PPI > >>>>>>>>>>>> regression model. However, on line 488, it is the uncorrecte= d > seed > >>>>>>>>>>>> time > >>>>>>>>>>>> course, Y, that is input to the deconvolution routine and us= ed > to > >>>>>>>>>>>> generate > >>>>>>>>>>>> the ppi term: > >>>>>>>>>>>> > >>>>>>>>>>>> C =3D spm_PEB(Y,P); > >>>>>>>>>>>> xn =3D xb*C{2}.E(1:N); > >>>>>>>>>>>> xn =3D spm_detrend(xn); > >>>>>>>>>>>> > >>>>>>>>>>>> % Setup psychological variable from inputs and contast > weights > >>>>>>>>>>>> > >>>>>>>>>>>> > > %------------------------------------------------------------------>>>>>>= >>>>>> > - > >>>>>>>>>>>> --- > >>>>>>>>>>>> PSY =3D zeros(N*NT,1); > >>>>>>>>>>>> for i =3D 1:size(U.u,2) > >>>>>>>>>>>> PSY =3D PSY + full(U.u(:,i)*U.w(:,i)); > >>>>>>>>>>>> end > >>>>>>>>>>>> % PSY =3D spm_detrend(PSY); <- removed centering of psych > variable > >>>>>>>>>>>> % prior to multiplication with xn. Based on discussion with > Karl > >>>>>>>>>>>> % and Donald McLaren. > >>>>>>>>>>>> > >>>>>>>>>>>> % Multiply psychological variable by neural signal > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > > %------------------------------------------------------------------>>>>>>= >>>>>> > - > >>>>>>>>>>>> --- > >>>>>>>>>>>> PSYxn =3D PSY.*xn; > >>>>>>>>>>>> > >>>>>>>>>>>> Is there any specific reason why Y, rather than Yc(:,1), is > used to > >>>>>>>>>>>> compute > >>>>>>>>>>>> PSYxn? I thought Yc might be more appropriate (?). > >>>>>>>>>>>> Thanks for your help, > >>>>>>>>>>>> Alex > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> ------------------------------------------ > >>>>>>>>>>>> > >>>>>>>>>>>> Alex Fornito > >>>>>>>>>>>> Research Fellow > >>>>>>>>>>>> Melbourne Neuropsychiatry Centre > >>>>>>>>>>>> University of Melbourne > >>>>>>>>>>>> National Neuroscience Facility > >>>>>>>>>>>> Levels 1 & 2, Alan Gilbert Building > >>>>>>>>>>>> 161 Barry St > >>>>>>>>>>>> Carlton South 3053 > >>>>>>>>>>>> Victoria, Australia > >>>>>>>>>>>> > >>>>>>>>>>>> Email: [log in to unmask] > >>>>>>>>>>>> Phone: +61 3 8344 1876 > >>>>>>>>>>>> Fax: +61 3 9348 0469 > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> ------------------------------------------ > >>>>>>>>>>> > >>>>>>>>>>> Alex Fornito > >>>>>>>>>>> Research Fellow > >>>>>>>>>>> Melbourne Neuropsychiatry Centre > >>>>>>>>>>> University of Melbourne > >>>>>>>>>>> National Neuroscience Facility > >>>>>>>>>>> Levels 1 & 2, Alan Gilbert Building > >>>>>>>>>>> 161 Barry St > >>>>>>>>>>> Carlton South 3053 > >>>>>>>>>>> Victoria, Australia > >>>>>>>>>>> > >>>>>>>>>>> Email: [log in to unmask] > >>>>>>>>>>> Phone: +61 3 8344 1876 > >>>>>>>>>>> Fax: +61 3 9348 0469 > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>> > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> ------------------------------------------ > >>>>>>>>> > >>>>>>>>> Alex Fornito > >>>>>>>>> Research Fellow > >>>>>>>>> Melbourne Neuropsychiatry Centre > >>>>>>>>> University of Melbourne > >>>>>>>>> National Neuroscience Facility > >>>>>>>>> Levels 1 & 2, Alan Gilbert Building > >>>>>>>>> 161 Barry St > >>>>>>>>> Carlton South 3053 > >>>>>>>>> Victoria, Australia > >>>>>>>>> > >>>>>>>>> Email: [log in to unmask] > >>>>>>>>> Phone: +61 3 8344 1876 > >>>>>>>>> Fax: +61 3 9348 0469 > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> > >>>>>>>> > >>>>>>> > >>>>>>> > >>>>>>> ------------------------------------------ > >>>>>>> > >>>>>>> Alex Fornito > >>>>>>> Research Fellow > >>>>>>> Melbourne Neuropsychiatry Centre > >>>>>>> University of Melbourne > >>>>>>> National Neuroscience Facility > >>>>>>> Levels 1 & 2, Alan Gilbert Building > >>>>>>> 161 Barry St > >>>>>>> Carlton South 3053 > >>>>>>> Victoria, Australia > >>>>>>> > >>>>>>> Email: [log in to unmask] > >>>>>>> Phone: +61 3 8344 1876 > >>>>>>> Fax: +61 3 9348 0469 > >>>>>> > >>>>>> > >>>>> > >>>>> > >>>>> ------------------------------------------ > >>>>> > >>>>> Alex Fornito > >>>>> Research Fellow > >>>>> Melbourne Neuropsychiatry Centre > >>>>> University of Melbourne > >>>>> National Neuroscience Facility > >>>>> Levels 1 & 2, Alan Gilbert Building > >>>>> 161 Barry St > >>>>> Carlton South 3053 > >>>>> Victoria, Australia > >>>>> > >>>>> Email: [log in to unmask] > >>>>> Phone: +61 3 8344 1876 > >>>>> Fax: +61 3 9348 0469 > >>>>> > >>>>> > >>>>> > >>>> > >>> > >> > >> > >> ------------------------------------------ > >> > >> Alex Fornito > >> Research Fellow > >> Melbourne Neuropsychiatry Centre > >> University of Melbourne > >> National Neuroscience Facility > >> Levels 1 & 2, Alan Gilbert Building > >> 161 Barry St > >> Carlton South 3053 > >> Victoria, Australia > >> > >> Email: [log in to unmask] > >> Phone: +61 3 8344 1876 > >> Fax: +61 3 9348 0469 > >> > >> > >> > >> > > > > > ------------------------------------------ > > Alex Fornito > Research Fellow > Melbourne Neuropsychiatry Centre > University of Melbourne > National Neuroscience Facility > Levels 1 & 2, Alan Gilbert Building > 161 Barry St > Carlton South 3053 > Victoria, Australia > > Email: [log in to unmask] > Phone: +61 3 8344 1876 > Fax: +61 3 9348 0469 > > ------------------------------ > > Date: Fri, 6 May 2011 16:17:37 -0500 > From: John Fredy <[log in to unmask]> > Subject: using mri3dx > > hello all, > > I am trying to show some activations in the spm5 template in mri3dx > (template spm5 command) but the activations appear more higher than the > template. Is necessary any conversion to show the activations? > > I think that maybe the mri3dX uses tailarach space, I can convert my > normalized activations to tailarach space? > > Thanks in advance > > ------------------------------ > > End of SPM Digest - 6 May 2011 (#2011-133) > ****************************************** > --=20 Jadzia Jagiellowicz Doctoral Candidate Psychology Department B-207 Stony Brook University, Stony Brook, NY 11794-2500 USA http://www.psychology.stonybrook.edu/aronlab-/ "The brave shall inherit the earth." --005045015ed170643e04a35b9c79 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Thank you John and Don for all the help with the fMRI processing error= s. I've finally figured out the problem. It was actually a problem with= the files themselves. </div> <div>Best</div> <div>jadzia<br><br></div> <div class=3D"gmail_quote">On 6 May 2011 19:00, SPM automatic digest system= <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">LISTSERV@= jiscmail.ac.uk</a>></span> wrote:<br> <blockquote style=3D"BORDER-LEFT: #ccc 1px solid; MARGIN: 0px 0px 0px 0.8ex= ; PADDING-LEFT: 1ex" class=3D"gmail_quote">There are 3 messages totaling 17= 23 lines in this issue.<br><br>Topics of the day:<br><br>=A01. spm_affreg<b= r> =A02. PPI question<br>=A03. using mri3dx<br><br>---------------------------= -------------------------------------------<br><br>Date: =A0 =A0Fri, 6 May = 2011 13:48:29 -0700<br>From: =A0 =A0Siddharth Srivastava <<a href=3D"mai= lto:[log in to unmask]">[log in to unmask]</a>><br> Subject: Re: spm_affreg<br><br>Hi John, please ignore the first part of my = previous mail: made some errors<br>in<br>cut and paste. It runs fine now. T= he question about the shears, however,<br>still lingers: do I have<br>to so= mehow use both Sx and Sy, or as your implementation suggests, only one<br> would suffice?<br><br>thanks again, and sorry for being such a bother...<br= >sid.<br><br>On Fri, May 6, 2011 at 12:23 PM, Siddharth Srivastava <<a h= ref=3D"mailto:[log in to unmask]">[log in to unmask]</a>>wrote:<br><br>> = Thanks John, for the answers. The code, however exits with "Problem&qu= ot;. M0<br> > and M1 differ.<br>> Also, why is there just one shear component in = the matrix? I have seen<br>> general shears modeled as [1,h;g,1]: is thi= s superfluous, and just one "h"<br>> will be sufficient for 2d= case?<br> ><br>> Thanks,<br>> Sid.<br>><br>><br>><br>><br>> O= n Fri, May 6, 2011 at 11:01 AM, John Ashburner <<a href=3D"mailto:jashbu= [log in to unmask]">[log in to unmask]</a>>wrote:<br>><br>>> Your= model uses 7 parameters, but there are only 6 elements in<br> >> A(1:2,1:3). =A0Also, you need to be able to deal with a broader ra= nge of<br>>> angles, so atan2(s,c) is used to compute the angle. =A0I= 've tested it<br>>> with the following code, so hopefully it work= s for all cases.<br> >><br>>> for i=3D1:10000<br>>> =A0P=3Drandn(1,6)*10;<br>&= gt;> =A0M0=3Dspm_matrix2D(P);<br>>> =A0P1=3Dspm_imatrix2D(M0);<br>= >> =A0M1=3Dspm_matrix2D(P1);<br>>> =A0t=3Dsum(sum((M1-M0).^2));= <br>>> =A0if t>1e-9, disp('Problem'); break; end<br> >> end<br>>><br>>> Note that spm_imatrix2D(spm_matrix2D(P= )) does not necessarily have to<br>>> equal P, but the matrices gener= ated from the two parameter sets should<br>>> be the same.<br>>>= ;<br> >> Best regards,<br>>> -John<br>>><br>>> On 6 May 2= 011 17:11, Siddharth Srivastava <<a href=3D"mailto:[log in to unmask]">sid= [log in to unmask]</a>> wrote:<br>>> > Hi everyone,<br>>> >= =A0 =A0 =A0 Can someone help me check the 2D versions of the spm_matrix an= d<br> >> > spm_imatrix? My<br>>> > implementation currently is = as follows:<br>>> ><br>>> > ---2D spm_matrix ---<br>>&= gt; > function [A] =3D spm_matrix2D(P)<br>>> > % P =3D [Tx, Ty,= R, Zx, Zy, Sx,Sy]<br> >> > p0 =3D [0,0,0,1,1,0,0];<br>>> > P =3D [P, p0((length= (P)+1):7)]<br>>> ><br>>> > A =3D eye(3);<br>>> >= % T -> R -> Zoom -> Shear<br>>> > =A0A =3D =A0 =A0 A * [= 1,0,P(1); ...<br> >> > =A0 =A0 =A0 =A0 =A0 0,1,P(2); ...<br>>> > =A0 =A0 = =A0 =A0 0,0, =A0 1];<br>>> > =A0A =3D =A0 =A0 A * [cos(P(3)), sin(= P(3)), 0; ...<br>>> > =A0 =A0 =A0 =A0 =A0 -sin(P(3)), =A0cos(P(3))= , 0; ...<br>>> > =A0 =A0 =A0 0, =A0 =A0 =A0 =A0 =A00, =A0 =A0 =A0 = =A0 1];<br> >> > =A0A =3D =A0 =A0 A * [P(4), 0, 0; ...<br>>> > =A0 = =A0 =A0 =A0 =A0 =A0 =A0 0, =A0 P(5), 0; ...<br>>> > =A0 =A0 =A0 = =A0 =A0 0, 0, 1];<br>>> > =A0A =3D =A0 =A0 A * [1, P(6), 0; ...<br= >>> > =A0 =A0 =A0 =A0 =A0 =A0 =A0 P(7) =A0, 1, 0; ...<br> >> > =A0 =A0 =A0 =A0 =A0 0, 0, 1];<br>>> > --------------= ------------------------------------<br>>> > -----2D spm_imatrix -= -----------------------<br>>> > function P =3D =A0spm_imatrix2D(M)= ;<br>>> ><br> >> ><br>>> ><br>>> > R =3D M(1:2,1:2);<br>>&g= t; > C =3D chol(R' * R);<br>>> > P =3D [M(1:2,3)',0,dia= g(C)',0,0];<br>>> > if(det(R) < 0) P(4) =3D -P(4); end<br>&= gt;> > C =3D diag(diag(C))\C;<br> >> > P(6:7) =3D C([2,3]);<br>>> ><br>>> > R0 =3D= spm_matrix2D([0,0,0,P(4:end)]);<br>>> > R0 =3D R0(1:2,1:2);<br>&g= t;> > R1 =3D R/R0;<br>>> ><br>>> > th =3D asin(rang= (R1(1,2)));<br> >> > P(3) =3D th;<br>>> ><br>>> > % There may be= slight rounding errors making b>1 or b<-1.<br>>> > function= =A0a =3D rang(b)<br>>> > a =3D min(max(b, -1), 1);<br>>> &g= t; return;<br> >> ><br>>> -------------------------------------------------= --------------------------<br>>> ><br>>> > I know there i= s something not correct, since when, for a parameter set<br>>> 'p= ',<br> >> > i do spm_imatrix2D(spm_matrix2D(p)), I do not recover P for a= ll entries,<br>>> > specially<br>>> > rotation and shears= . C(2) above always evaluates to zero. The result is<br>>> that<br> >> > these errors<br>>> > accumulate (or are not accounte= d for) on repeated application of this<br>>> > sequence of<br>>= > > operations in the registration routine.<br>>> ><br>>&= gt; > Thanks in anticipation,<br> >> > Sid.<br>>> ><br>>> ><br>>> ><br>&g= t;> > On Fri, Apr 22, 2011 at 9:13 AM, Siddharth Srivastava <<a hr= ef=3D"mailto:[log in to unmask]">[log in to unmask]</a><br>>> ><br>>= ;> > wrote:<br> >> >><br>>> >> Hi John,<br>>> >> =A0 = =A0 =A0 Thanks a lot for your answer, and it has been very helpful for my<b= r>>> >> understanding<br>>> >> of the details of th= e implementation, and my own development efforts. I<br> >> >> have<br>>> >> 2 very quick questions, though:= <br>>> >><br>>> >> 1) the parameter set x(:) that i= s returned from spm_mireg, and the<br>>> >> "params" = that<br> >> >> spm_affsub3 returns (in spm99, which is also the version = that I am<br>>> looking<br>>> >> at) ,<br>>> >&g= t; are they equivalent? In other words, If I am collating the parameters<br= > >> from<br>>> >> the mireg<br>>> >> routine, = would the distribution x(7:12) be used to estimate the icovar0<br>>> = >> entries in affsub3?<br>>> >><br>>> >> 2) a= lso would VG.mat\spm_matrix(x)*VF.mat transform G to F similar to<br> >> >> =A0 =A0 =A0VG.mat\spm_matrix(params)*VF.mat , or do I hav= e to do<br>>> >> =A0 =A0 =A0 M =3D VG.mat\spm_matrix(x(:)')= *VF.mat<br>>> >> =A0 =A0 =A0 pp =3D spm_imatrix(M)<br>>> = >> =A0 =A0 =A0 and use pp(7:12) ?<br> >> >><br>>> >><br>>> >><br>>> >= ;> Thanks again.<br>>> >> Sid.<br>>> >><br>>&= gt; >> On Thu, Apr 14, 2011 at 2:13 PM, John Ashburner <<a href=3D= "mailto:[log in to unmask]">[log in to unmask]</a>><br> >> >> wrote:<br>>> >>><br>>> >>> = > I had a look at the code in spm_maff, and<br>>> >>> >= ; it looks like it also uses the values of isig and mu in the call to<br> >> >>> > affreg(). My guess is<br>>> >>> &= gt; that during the initial registration on a large data set for<br>>>= ; estimating<br>>> >>> > the<br>>> >>> >= ; prior<br> >> >>> > distribution of parameters, this will be called = with typ =3D 'none' .<br>>> Is<br>>> >>> > = this<br>>> >>> > correct?<br>>> >>><br>>= ;> >>> Once the registration has locked in on a solution, the p= riors have<br> >> >>> less influence. =A0Their main influence is in the ear= ly stages of the<br>>> >>> registration, or when there are r= elatively few slices in the data. =A0I<br>>> >>> don't a= ctually remember if the registration was regularised when I<br> >> >>> computed their values.<br>>> >>><br>&g= t;> >>> ><br>>> >>> > Further, do we have = to do a non-rigid registration for the<br>>> estimation<br>>> &= gt;>> > (since, in the comments the selected<br> >> >>> > files are filtered as *seg_inv_sn.mat"? Sin= ce only p.Affine is being<br>>> >>> > used,<br>>> &= gt;>> > can this work with only<br>>> >>> > an a= ffine / rigid transformation?<br> >> >>><br>>> >>> Only the affine transform in= the files is used, so basically yes. =A0You<br>>> >>> could= just do affine registration.<br>>> >>><br>>> >>= > ><br> >> >>> > I would also be happy if you could also answer m= y other question<br>>> about<br>>> >>> > examining = the<br>>> >>> > effects of the values in isig and mu (wha= t are the units of mu?) on<br> >> the<br>>> >>> > transformed image.<br>>> &= gt;>><br>>> >>> They are unit-less, and are just a mea= sure of the zooms and shears.<br>>> >>><br>>> >>= > > For example,<br> >> >>> > if i set a mu of [mu1, mu2, ...] and a isig of [= sig1, 0, 0...; 0,<br>>> sig2,<br>>> >>> > 0,<br>>= ;> >>> > 0..; ...] (with only diagonal components),<br>>&= gt; >>> > based on the Hencky tensor penalty, it seems that lar= ge deviations<br> >> of<br>>> >>> > parameter 1 =A0estimate (T - mu) = from<br>>> >>> > mu1 will be penalized. How is the value = of isig1 modifying the<br>>> penalty?<br>>> >>><br>>= ;> >>> Its assuming that the elements of the matrix log of the = part that does<br> >> >>> shears and zooms has a multivariate normal distributi= on, so the<br>>> >>> penalty is a kind of negative log likel= hood. =A0The isig is essentially<br>>> >>> a precision matri= x, which is the inverse of a covariance matrix.<br> >> >>><br>>> >>> p(x|mu,S) =3D 1/sqrt(det(2*p= i*S)) * exp(-0.5*(x-mu)'*inv(S)*(x-mu))<br>>> >>><br>>= ;> >>> Taking logs and negating, you get 0.5*(x-mu)'*inv(S)= *(x-mu) + const<br> >> >>><br>>> >>> > How<br>>> >>= ;> > do the off diagonal terms affect<br>>> >>> > t= he penalty?<br>>> >>><br>>> >>> The inverse o= f isig would just encode the covariance between the<br> >> >>> various shears and zooms.<br>>> >>><br= >>> >>> ><br>>> >>> > Could you also po= int me to a reference where I can learn more about<br>>> >>>= > Hencky<br> >> >>> > strain tensor (specifically<br>>> >>= > > what information it carries in the context of image processing et= c.)<br>>> >>><br>>> >>> I don't know if t= his is of any use:<br> >> >>><br>>> >>> <a href=3D"http://en.wikiped= ia.org/wiki/Deformation_(mechanics)" target=3D"_blank">http://en.wikipedia.= org/wiki/Deformation_(mechanics)</a><br>>> >>><br>>> &= gt;>> Best regards,<br> >> >>> -John<br>>> >>><br>>> >>&g= t;<br>>> >>> > On Thu, Apr 14, 2011 at 10:17 AM, John Ash= burner <<br>>> <a href=3D"mailto:[log in to unmask]">jashburner@= gmail.com</a>><br> >> >>> > wrote:<br>>> >>> >><br>>= > >>> >> They were computed by affine registering loads o= f (227) images to<br>>> MNI<br>>> >>> >> space. = =A0This gave the affine transforms, which were then used to<br> >> build<br>>> >>> >> up a mean and inverse cova= riance matrix. =A0In the comments of<br>>> >>> >> spm_= maff.m, you should see the following...<br>>> >>> >><b= r> >> >>> >> %% Values can be derived by...<br>>> &= gt;>> >> %sn =3D spm_select(Inf,'.*seg_inv_sn.mat$');<b= r>>> >>> >> %X =A0=3D zeros(size(sn,1),12);<br>>>= ; >>> >> %for i=3D1:size(sn,1),<br> >> >>> >> % =A0 =A0p =A0=3D load(deblank(sn(i,:)));<br= >>> >>> >> % =A0 =A0M =A0=3D p.VF(1).mat*p.Affine/p.VG= (1).mat;<br>>> >>> >> % =A0 =A0J =A0=3D M(1:3,1:3);<br= >>> >>> >> % =A0 =A0V =A0=3D sqrtm(J*J');<br> >> >>> >> % =A0 =A0R =A0=3D V\J;<br>>> >>&= gt; >> % =A0 =A0lV =A0 =A0 =A0=3D =A0logm(V);<br>>> >>>= ; >> % =A0 =A0lR =A0 =A0 =A0=3D -logm(R);<br>>> >>> &g= t;> % =A0 =A0P =A0 =A0 =A0 =3D zeros(12,1);<br> >> >>> >> % =A0 =A0P(1:3) =A0=3D M(1:3,4);<br>>>= >>> >> % =A0 =A0P(4:6) =A0=3D lR([2 3 6]);<br>>> >= >> >> % =A0 =A0P(7:12) =3D lV([1 2 3 5 6 9]);<br>>> >&= gt;> >> % =A0 =A0X(i,:) =A0=3D P';<br> >> >>> >> %end;<br>>> >>> >> %mu = =A0 =3D mean(X(:,7:12));<br>>> >>> >> %XR =A0 =3D X(:,= 7:12) - repmat(mu,[size(X,1),1]);<br>>> >>> >> %isig = =3D inv(XR'*XR/(size(X,1)-1))<br> >> >>> >><br>>> >>> >> You may be= able to re-code this to work with your 2D transforms.<br>>> =A0Note<= br>>> >>> >> that I only use the components that descr= ibe shears and zooms.<br> >> =A0Also,<br>>> >>> >> if I was re-coding it a= ll again, I would probably do things<br>>> slightly<br>>> >&= gt;> >> differently, using the following decomposition:<br>>>= ; >>> >><br> >> >>> >> J=3DM(1:3,1:3);<br>>> >>> >= ;> L=3Dreal(logm(J));<br>>> >>> >> S=3D(L+L')/2= ; % Symmetric part (for zooms and shears, which should be<br>>> >&= gt;> >> penalised)<br> >> >>> >> A=3D(L-L')/2; % Anti-symmetric part (for= rotations)<br>>> >>> >><br>>> >>> >= > I might even base the penalty on lambda(2)*trace(S)^2 +<br>>> &g= t;>> >> lambda(1)*trace(S*S'), which is often used as a mea= sure of "elastic<br> >> >>> >> energy" for penalising non-linear regis= tration. =A0Of course, there<br>>> >>> >> would also b= e a bit of annoying extra stuff to deal with due to MNI<br>>> >>= ;> >> space being a bit bigger than average brains are. =A0Figurin= g out the<br> >> >>> >> mean would require (I think) an iterative pr= ocess similar to the<br>>> one<br>>> >>> >> desc= ribed in "Characterizing volume and surface deformations in an<br> >> >>> >> atlas framework: theory, applications, and i= mplementation", by<br>>> Woods<br>>> >>> >>= in NeuroImage (2003).<br>>> >>> >><br>>> >&g= t;> >> Best regards,<br> >> >>> >> -John<br>>> >>> >><br>&= gt;> >>> >> On 14 April 2011 17:15, Siddharth Srivastava = <<a href=3D"mailto:[log in to unmask]">[log in to unmask]</a>><br>>>= ; wrote:<br> >> >>> >> > Hi all,<br>>> >>> >&g= t; > =A0 =A0 =A0My question pertains specifically to the values of isig = and<br>>> mu<br>>> >>> >> > that<br>>> = >>> >> > are<br> >> >>> >> > incorporated<br>>> >>> &= gt;> > in the code, and I wish to disentangle the effect that these<b= r>>> numbers<br>>> >>> >> > have on<br>>&g= t; >>> >> > individual parameters<br> >> >>> >> > during the optimization stage. I am try= ing to map it to a 2D<br>>> case,<br>>> >>> >> &= gt; and<br>>> >>> >> > these<br>>> >>&g= t; >> > numbers<br> >> >>> >> > will have to be calculated ab-initio fo= r my case. Before I begin<br>>> >>> >> > with<br>&g= t;> >>> >> > estimating the prior distribution<br> >> >>> >> > for these parameters, I need to check t= he effect that they have<br>>> on<br>>> >>> >> &= gt; the<br>>> >>> >> > registered images. Regarding= this<br> >> >>> >> > 1) Can someone suggest a way I can exam= ine the effect on each<br>>> >>> >> > parameter<br>= >> >>> >> > separately, if it is<br>>> >&g= t;> >> > =A0 =A0 =A0at all possible.<br> >> >>> >> > 2) Some advise on how to estimate these= parameters, if it has<br>>> been<br>>> >>> >> &= gt; done<br>>> >>> >> > for<br>>> >>>= ; >> > images other<br> >> >>> >> > =A0 =A0 =A0than brain images, will be r= eally helpful for me.<br>>> >>> >> ><br>>> &g= t;>> >> > Thanks,<br>>> >>> >> > Sid= .<br> >> >>> >> ><br>>> >>> >> > = On Mon, Mar 28, 2011 at 7:04 PM, Siddharth Srivastava<br>>> >>&= gt; >> > <<a href=3D"mailto:[log in to unmask]">[log in to unmask]<= /a>><br> >> >>> >> > wrote:<br>>> >>> >>= ; >><br>>> >>> >> >> Dear John,<br>>>= ; >>> >> >> =A0 =A0 =A0 =A0 =A0 Thanks for your answer= . I was able to correctly run my<br> >> 2D<br>>> >>> >> >> implementation<br>&g= t;> >>> >> >> based on your advise. However, I am a= m not very confident of my<br>>> >>> >> >> resul= ts<br> >> >>> >> >> and wanted to<br>>> >>&= gt; >> >> consult you on certain aspects of my mapping from 3D = to 2D.<br>>> >>> >> >> =A0 =A0 =A0 =A0 =A0 =A01)= In function reg(..), the comment says that the<br> >> >>> >> >> derivatives<br>>> >>>= ; >> >> w.r.t rotations and<br>>> >>> >> &= gt;> translations is zero. Consequently, I ran the indices from 4:7.<br> >> >>> >> >> According<br>>> >>> = >> >> to your previous<br>>> >>> >> >&g= t; reply, rotations will be lumped together with scaling and affine<br> >> in<br>>> >>> >> >> 4<br>>> >&g= t;> >> >> parameters, and<br>>> >>> >> = >> translations, independently, in the last two. I understand that<br= > >> in<br>>> >>> >> >> this<br>>> >= ;>> >> >> case, the indices<br>>> >>> >= > >> run over parameters that have been separated into individual<= br> >> >>> >> >> components<br>>> >>>= >> >> (similar to<br>>> >>> >> >> t= he form accepted by spm_matrix). Is this correct for the<br>>> functi= on<br> >> >>> >> >> reg(..)? =A0In fact, I went back to= the<br>>> >>> >> >> old spm code (spm99), and f= ound parameters pdesc and free, and<br>>> got<br>>> >>>= ; >> >> more<br> >> >>> >> >> confused as to when I should<br>>= ;> >>> >> >> use the 6 parameter, and when to use s= eparate parameters (as if<br>>> I<br>>> >>> >> &= gt;> was<br> >> >>> >> >> passing them to spm_matrix).<br>>= ;> >>> >> >> So, when the loop runs over p1 and per= turbs p1(i) by ds, does it<br>>> >>> >> >> run<b= r> >> >>> >> >> over<br>>> >>> >&= gt; >> translations/rotations etc<br>>> >>> >> &= gt;> or does it run over the martix elements that define these<br>>&g= t; >>> >> >> transformations<br> >> >>> >> >> (2x2 + 1x2)?<br>>> >>&g= t; >> >><br>>> >>> >> >> =A0 =A0 =A0= =A0 =A0 =A02) In function penalty(..), I have used unique<br>>> elem= ents<br> >> >>> >> >> as<br>>> >>> >>= ; >> [1,3,4], and my code looks like<br>>> >>> >>= ; >> T =3D my_matrix(p)*M;<br>>> >>> >> >>= T =3D T(1:2,1:2);<br> >> >>> >> >> T =3D 0.5*logm(T'*T);<br>>&g= t; >>> >> >> T<br>>> >>> >> >&= gt; T =3D T(els)' - mu;<br>>> >>> >> >> h = =3D T'*isig*T<br> >> >>> >> >><br>>> >>> >> &= gt;> Assuming an application specific isig, is this correct?<br>>>= >>> >> >> =A0 =A0 =A0 =A0 =A03) I have not yet incorp= orated the prior information in<br> >> my<br>>> >>> >> >> code,<br>>> &g= t;>> >> >> But for testing purposes,<br>>> >>= > >> >> can you suggest some neutral values =A0for mu and is= ig that I can<br> >> try<br>>> >>> >> >> to<br>>> >= >> >> >> concentrate on the<br>>> >>> >= > >> accuracy of the implementation minus the priors for now?<br> >> >>> >> >> =A0 =A0 =A0 =A0 =A0 4) My solution = curently seems to oscillate around an<br>>> >>> >> >= ;> optimum.<br>>> >>> >> >> I<br>>> >= ;>> >> >> am hoping that after<br> >> >>> >> >> I have removed any errors after you= r replies, it will converge<br>>> >>> >> >> grac= efully.<br>>> >>> >> >><br>>> >>>= >> >> =A0 =A0 =A0 =A0 =A0 Thanks again for all the help.<br> >> >>> >> >> =A0 =A0 =A0 =A0 =A0 =A0Siddharth.<b= r>>> >>> >> >><br>>> >>> >>= >><br>>> >>> >> >> On Fri, Mar 18, 2011 a= t 2:05 AM, John Ashburner<br> >> >>> >> >> <<a href=3D"mailto:jashburner@gm= ail.com">[log in to unmask]</a>><br>>> >>> >> >= ;> wrote:<br>>> >>> >> >>><br>>> >= ;>> >> >>> A 2D affine transform would use 6 parameter= s, rather than 7: a<br> >> 2x2<br>>> >>> >> >>> matrix to encod= e rotations, zooms and shears, and a 2x1 vector<br>>> for<br>>>= >>> >> >>> translations.<br>>> >>> = >> >>><br> >> >>> >> >>> Best regards,<br>>> >&= gt;> >> >>> -John<br>>> >>> >> >&= gt;><br>>> >>> >> >>> On 18 March 2011 05:= 05, Siddharth Srivastava <<a href=3D"mailto:[log in to unmask]">siddys@gma= il.com</a><br> >> ><br>>> >>> >> >>> wrote:<br>>= > >>> >> >>> > Dear list users,<br>>> &= gt;>> >> >>> > =A0 =A0 =A0 =A0 =A0 I am currently t= rying to understand the<br> >> implementation<br>>> >>> >> >>> >= in<br>>> >>> >> >>> > spm_affreg, and<br>= >> >>> >> >>> > to make matters simple, I = tried to work out a 2D version of<br> >> the<br>>> >>> >> >>> > registrati= on<br>>> >>> >> >>> > procedure. I struck = a roadblock, however...<br>>> >>> >> >>> >= <br> >> >>> >> >>> > The function make_A create= s part of the design matrix. For<br>>> the<br>>> >>> &= gt;> >>> > 3D<br>>> >>> >> >>>= > case,<br> >> >>> >> >>> > the<br>>> >>&g= t; >> >>> > 13 parameters<br>>> >>> >&g= t; >>> > are encoded in columns of A1, which then is used to ge= nerate<br> >> AA<br>>> >>> >> >>> > +<br>>&g= t; >>> >> >>> > A1'<br>>> >>>= >> >>> > A1. AA<br>>> >>> >> >&g= t;> > is 13x13, and everything<br> >> >>> >> >>> > works. For the 2D case, ho= wever, there will be 7+1<br>>> parameters( 2<br>>> >>>= >> >>> > translations, 1 rotation, 2 zoom, 2 shears and = 1 intensity<br> >> >>> >> >>> > scaling).<br>>> >= >> >> >>> > The A1 matrix for 2D, then, =A0will be = (in make_A)<br>>> >>> >> >>> > A =A0=3D [x= 1.*dG1 x1.*dG2 ...<br> >> >>> >> >>> > =A0 =A0 =A0 x2.*dG1 x2.*dG= 2 ...<br>>> >>> >> >>> > =A0 =A0 =A0 dG1 = =A0 =A0 dG2 =A0t];<br>>> >>> >> >>> ><br>&= gt;> >>> >> >>> > which is (number of sampled= points) x 7. The AA matrix (in<br> >> >>> >> >>> > function<br>>> >&= gt;> >> >>> > costfun)<br>>> >>> >&g= t; >>> > is 8x8<br>>> >>> >> >>> = > so, which is the parameter I am missing in modeling a 2D<br> >> >>> >> >>> > example? I<br>>> >= ;>> >> >>> > hope I<br>>> >>> >&g= t; >>> > have got the number of parameters<br>>> >>= > >> >>> > right for the 2D case? Is there an extra co= lumn that I have<br> >> to<br>>> >>> >> >>> > add<br>>= > >>> >> >>> > to<br>>> >>> &g= t;> >>> > A1 to<br>>> >>> >> >>&g= t; > make the rest of the matrix computation<br> >> >>> >> >>> > consistent?<br>>> &g= t;>> >> >>> ><br>>> >>> >> >= ;>> > Thanks for the help<br>>> >>> >> >&g= t;> ><br> >> >>> >> >><br>>> >>> >> &= gt;<br>>> >>> >> ><br>>> >>> ><br= >>> >>> ><br>>> >><br>>> ><br>>&g= t; ><br> >><br>><br>><br><br>------------------------------<br><br>Date:= =A0 =A0Sat, 7 May 2011 07:14:27 +1000<br>From: =A0 =A0Alex Fornito <<a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a>><br>= Subject: Re: PPI question<br> <br>Actually, sorry one more question:<br><br>If Y in spm_peb_ppi is alread= y frequency filtered by spm_regions, then the<br>only difference (barring a= ny additional confounds specified with xX.iG)<br>between Y and Yc is the ad= ditional removal of block effects.<br> <br>Since the ppi interaction term is generated from Y and not Yc, I am<br>= presuming then that is solely because it is not desirable to deconvolve a<b= r>time course with block effects removed. Is this correct?<br><br><br>On 07= /05/2011 04:07, "MCLAREN, Donald" <<a href=3D"mailto:mclaren.d= [log in to unmask]">[log in to unmask]</a>> wrote:<br> <br>> The null-space of the contrast are any columns of the contrast (wh= ich<br>> must be an F-contrast) that are 0. Thus, you remove the effects= of<br>> motion when you do the adjustment and don't need to worry a= bout them<br> > being included in the deconvolved data. The code that I have supplied<= br>> has N rows for N conditions of interest after I talked to Darren ab= out<br>> the ideal contrast to use for adjustment.<br>><br>> As fo= r removing the frequencies, I think it is probably fine to remove<br> > them, I was just under the impression that they weren't being remo= ved<br>> because of me commenting out that line during testing of spm_re= gions.<br>> I would go with the SPM defaults until their effect is teste= d. If you<br> > don't set a high-pass filter, then you won't remove the freque= ncies,<br>> so you could test it that way as well.<br>><br>> Best = Regards, Donald McLaren<br>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D<br>> D.G. McLaren, Ph.D.<br> > Postdoctoral Research Fellow, GRECC, Bedford VA<br>> Research Fello= w, Department of Neurology, Massachusetts General<br>> Hospital and Harv= ard Medical School<br>> Office: (773) 406-2464<br>> =3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br> > This e-mail contains CONFIDENTIAL INFORMATION which may contain<br>>= ; PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED<br>&g= t; and which is intended only for the use of the individual or entity<br> > named above. If the reader of the e-mail is not the intended recipient= <br>> or the employee or agent responsible for delivering it to the inte= nded<br>> recipient, you are hereby notified that you are in possession = of<br> > confidential and privileged information. Any unauthorized use,<br>>= disclosure, copying or the taking of any action in reliance on the<br>>= contents of this information is strictly prohibited and may be<br>> unl= awful. If you have received this e-mail unintentionally, please<br> > immediately notify the sender via telephone at (773) 406-2464 or<br>&g= t; email.<br>><br>><br>><br>> On Thu, May 5, 2011 at 9:46 PM, A= lex Fornito <<a href=3D"mailto:[log in to unmask]">fornitoa@unimelb= .edu.au</a>> wrote:<br> >> Hi Donald, Torben and Darren,<br>>> Thanks for your response= s.<br>>><br>>> Form each of your responses, it seems like best = practice would be to remove<br>>> motion (and/or other confounds) fro= m the VOI time series prior to<br> >> deconvolution.<br>>><br>>> It also seems like you are = recommending NOT to frequency filter the VOI time<br>>> series.<br>&g= t;><br>>> In my reading though, spm_regions does this by default w= ith the call to<br> >> spm_filter (line 135). So, if spm_regions is used to extract a VOI= time<br>>> series, the time series that is input to spm_peb_ppi will= already frequency<br>>> filtered. So it seems like, in standard prac= tice, the deconvolution is<br> >> applied to frequency-filtered data. In this case, if xX.iG is empt= y, the<br>>> only difference between Y and Yc in spm_peb_ppi is the a= dditional removal of<br>>> block effects (xX.iB).<br>>><br> >> Based on the current chain of emails, it seems you are recommendin= g that a<br>>> confound-corrected, whitened, but non-frequency filter= ed time course should<br>>> be input to spm_peb_ppi. IS that correct,= or am I missing something?<br> >><br>>> Finally, can I clarify exactly what the null space of = the contrast is? Most<br>>> of the time in my analyses I don't ge= t the option to adjust for it, so xY.Ic<br>>> is empty.<br>>><b= r> >> Thanks again for all your help,<br>>> A<br>>><br>>&= gt;<br>>><br>>> On 06/05/2011 02:18, "MCLAREN, Donald"= ; <<a href=3D"mailto:[log in to unmask]">[log in to unmask]<= /a>> wrote:<br> >><br>>>> I haven't thoroughly tested this, but if you s= et iG to be the movement<br>>>> parameters columns. Then still adj= ust for the null space (motion<br>>>> parameters). You will get th= e best of both. If you don't adjust for<br> >>> the null space, then movement will contribute to the deconvolu= tion. In<br>>>> summary, if you have iG be the motion parameter co= lumns AND adjust for<br>>>> the null-space (motion columns), then:= <br> >>><br>>>> Y will have the effect of motion removed. Yc (= removal of motion twice)<br>>>> will be minimally different since = Y is orthogonal to the motion<br>>>> parameters. So, you have moti= on removed from both the autocorrelation<br> >>> estimate, the Y for deconvolution, and Yc.<br>>>><br>= >>> Best Regards, Donald McLaren<br>>>> =3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>>>> D.G. McLaren, Ph.D.<br= >>>> Postdoctoral Research Fellow, GRECC, Bedford VA<br> >>> Research Fellow, Department of Neurology, Massachusetts Genera= l<br>>>> Hospital and Harvard Medical School<br>>>> Offic= e: (773) 406-2464<br>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D<br>>>> This e-mail contains CONFIDENTIAL = INFORMATION which may contain<br> >>> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVI= LEGED<br>>>> and which is intended only for the use of the individ= ual or entity<br>>>> named above. If the reader of the e-mail is n= ot the intended recipient<br> >>> or the employee or agent responsible for delivering it to the = intended<br>>>> recipient, you are hereby notified that you are in= possession of<br>>>> confidential and privileged information. Any= unauthorized use,<br> >>> disclosure, copying or the taking of any action in reliance on= the<br>>>> contents of this information is strictly prohibited an= d may be<br>>>> unlawful. If you have received this e-mail uninten= tionally, please<br> >>> immediately notify the sender via telephone at (773) 406-2464 = or<br>>>> email.<br>>>><br>>>><br>>>><b= r>>>> On Thu, May 5, 2011 at 9:48 AM, Torben Ellegaard Lund<br> >>> <<a href=3D"mailto:[log in to unmask]">[log in to unmask]</= a>> wrote:<br>>>>> Hi Alex<br>>>>><br>>>&g= t;><br>>>>> Den Uge:18 05/05/2011 kl. 14.15 skrev Alex Forni= to:<br> >>>><br>>>>>> Hi Torben,<br>>>>>>= Sorry, I just wanted to clarify: do you mean leaving SPM.xX.iG empty? It<b= r>>>>>> seems like this is done by default by spm_fmri_desig= n (?)<br> >>>><br>>>>> leaving it empty will include all user= defined regressors in the effects of<br>>>>> interest F-test w= hich determines the mask where the serial correlation is<br>>>>>= ; estimated. I think it would make sense if the motion regressors did not g= o<br> >>>> into this test, just like the DCS HP-filter does not go in= to the<br>>>>> F-contrast.<br>>>>><br>>>>&= gt;> Are you suggesting manually entering motion parameters into this fi= eld?<br> >>>><br>>>>> Yes, or their indices that is. It shou= ld be done after specification, but<br>>>>> before model estima= tion. You can check if SPM recognized your changes by<br>>>>> l= ooking at the automatically generated effects of interest F-contrast.<br> >>>><br>>>>><br>>>>> Best<br>>>&g= t;> Torben<br>>>>><br>>>>><br>>>>><b= r>>>>><br>>>>>><br>>>>>> On 05/05= /2011 18:16, "Torben Ellegaard Lund" <<a href=3D"mailto:torben= [log in to unmask]">[log in to unmask]</a>> wrote:<br> >>>>><br>>>>>>> Just as a kind reminder. L= eaving SPM.xX.iG is a bit unfortunate, because<br>>>>>>> = voxels where motion artefacts are significant then constitutes a large<br> >>>>>> part<br>>>>>>> of<br>>>>= ;>>> the F-test derived mask used to estimate serial correlations.= If you are<br>>>>>>> picky<br>>>>>>> a= bout this you can change it yourselves, but it is not as easy as it<br> >>>>>> should<br>>>>>>> be.<br>>>= >>>><br>>>>>>><br>>>>>>> Be= st<br>>>>>>> Torben<br>>>>>>><br>>&g= t;>>>><br> >>>>>><br>>>>>>> Den Uge:18 05/05/2011 = kl. 05.03 skrev Alex Fornito:<br>>>>>>><br>>>>&g= t;>>> Ahh, I see. I was assuming that SPM.xX.iG contained the indi= ces to the<br> >>>>>>> nuisance covariates in the design matrix, but = you're right -<br>>>>>>>> spm_fMRI_design<br>>&= gt;>>>>> leaves it empty.<br>>>>>>>><br= > >>>>>>> Thanks for your help!<br>>>>>>&= gt;> A<br>>>>>>>><br>>>>>>>><b= r>>>>>>>> On 05/05/2011 12:54, "MCLAREN, Donald&q= uot; <<a href=3D"mailto:[log in to unmask]">[log in to unmask] om</a>> wrote:<br> >>>>>>><br>>>>>>>>> I agree th= at nuisance covariates, such as motion should be removed;<br>>>>&g= t;>>>> however, motion covariates are not generally stored in X= 0 in my<br> >>>>>>>> reading of the SPM code (spm_fMRI_design, = spm_regions). They should be<br>>>>>>>>> removed in= the spm_regions filtering based on the null-space of the<br>>>>&g= t;>>>> contrast.<br> >>>>>>>><br>>>>>>>>> As for= the motion parameters being in the model, if they were in the<br>>>&= gt;>>>>> standard analysis, they should be included in the P= PI analysis. My<br> >>>>>>>> gPPI code will do this automatically as we= ll.<br>>>>>>>>><br>>>>>>>>>= X0 is made of SPM.xX.iB (block effects -- so removing the mean) and<br> >>>>>>>> SPM.xX.iG (nuisance variable that is empty= as far as I can tell (line<br>>>>>>>>> 369 of spm_= fMRI_design)) and the high-pass filtering (K.X0).<br>>>>>>&g= t;>><br> >>>>>>>> Best Regards, Donald McLaren<br>>>&g= t;>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<= br>>>>>>>>> D.G. McLaren, Ph.D.<br>>>>>= >>>> Postdoctoral Research Fellow, GRECC, Bedford VA<br> >>>>>>>> Research Fellow, Department of Neurology, = Massachusetts General<br>>>>>>>>> Hospital and Harv= ard Medical School<br>>>>>>>>> Office: (773) 406-24= 64<br> >>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D<br>>>>>>>>> This e-mail conta= ins CONFIDENTIAL INFORMATION which may contain<br>>>>>>>&= gt;> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED= <br> >>>>>>>> and which is intended only for the use of = the individual or entity<br>>>>>>>>> named above. I= f the reader of the e-mail is not the intended recipient<br>>>>>= ;>>>> or the employee or agent responsible for delivering it to= the intended<br> >>>>>>>> recipient, you are hereby notified that yo= u are in possession of<br>>>>>>>>> confidential and= privileged information. Any unauthorized use,<br>>>>>>>&= gt;> disclosure, copying or the taking of any action in reliance on the<= br> >>>>>>>> contents of this information is strictly p= rohibited and may be<br>>>>>>>>> unlawful. If you h= ave received this e-mail unintentionally, please<br>>>>>>>= ;>> immediately notify the sender via telephone at (773) 406-2464 or<= br> >>>>>>>> email.<br>>>>>>>>>= <br>>>>>>>>><br>>>>>>>>><br= >>>>>>>>> On Wed, May 4, 2011 at 10:16 PM, Alex For= nito <<a href=3D"mailto:[log in to unmask]">[log in to unmask] </a>><br> >>>>>>>> wrote:<br>>>>>>>>>= > Hi Donald,<br>>>>>>>>>> Thanks for your res= ponse. When you say "you don't want<br>>>>>>>>= ;>> to filter out anything related to neural activity", I'm = presuming that<br> >>>>>>>>> you<br>>>>>>>>>= ;> are referring to the deconvolved neural signal?<br>>>>>&g= t;>>>><br>>>>>>>>>> If so, that make= s sense. However, X0 also contains user-specified<br> >>>>>>>>> nuisance<br>>>>>>>&g= t;>> covariates. I would have thought it would make sense to remove t= hose,<br>>>>>>>>>> depending on what they were (= e.g., movement parameters)? I guess they<br> >>>>>>>>> can<br>>>>>>>>>= ;> always be entered into the PPI regression model...<br>>>>>= ;>>>>><br>>>>>>>>>><br>>>&g= t;>>>>>> On 05/05/2011 11:57, "MCLAREN, Donald"= <<a href=3D"mailto:[log in to unmask]">[log in to unmask]</= a>><br> >>>>>>>>> wrote:<br>>>>>>>>= >><br>>>>>>>>>>> I could be wrong, Karl= or Darren please correct me if I am.<br>>>>>>>>>&g= t;><br> >>>>>>>>>> X0 is composed of the high-pass fi= lter and constant term. Thus, you<br>>>>>>>>>>&g= t; want to filter these out of the Yc regressor. However, you don't wan= t<br> >>>>>>>>>> to filter out anything related to = neural activity, so you wouldn't<br>>>>>>>>>>= ;> want to filter your signal and then predict the neural activity from<= br> >>>>>>>>>> the filtered signal, especially fi= ltering frequencies.<br>>>>>>>>>>><br>>>= ;>>>>>>>> Best Regards, Donald McLaren<br>>>&= gt;>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D<br> >>>>>>>>>> D.G. McLaren, Ph.D.<br>>>>= ;>>>>>>> Postdoctoral Research Fellow, GRECC, Bedford = VA<br>>>>>>>>>>> Research Fellow, Department = of Neurology, Massachusetts General<br> >>>>>>>>>> Hospital and Harvard Medical Schoo= l<br>>>>>>>>>>> Office: (773) 406-2464<br>>= ;>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D<br>>>>>>>>>>> Thi= s e-mail contains CONFIDENTIAL INFORMATION which may contain<br> >>>>>>>>>> PROTECTED HEALTHCARE INFORMATION a= nd may also be LEGALLY PRIVILEGED<br>>>>>>>>>>&g= t; and which is intended only for the use of the individual or entity<br> >>>>>>>>>> named above. If the reader of the = e-mail is not the intended<br>>>>>>>>>>> reci= pient<br>>>>>>>>>>> or the employee or agent = responsible for delivering it to the<br> >>>>>>>>>> intended<br>>>>>>&g= t;>>>> recipient, you are hereby notified that you are in posse= ssion of<br>>>>>>>>>>> confidential and privi= leged information. Any unauthorized use,<br> >>>>>>>>>> disclosure, copying or the taking = of any action in reliance on the<br>>>>>>>>>>>= ; contents of this information is strictly prohibited and may be<br>>>= ;>>>>>>>> unlawful. If you have received this e-mai= l unintentionally, please<br> >>>>>>>>>> immediately notify the sender via = telephone at (773) 406-2464 or<br>>>>>>>>>>> = email.<br>>>>>>>>>>><br>>>>>>&= gt;>>>><br> >>>>>>>>>><br>>>>>>>>>= ;>> On Sat, Apr 30, 2011 at 8:03 PM, Alex Fornito<br>>>>>= >>>>>> <<a href=3D"mailto:[log in to unmask]">for= [log in to unmask]</a>><br> >>>>>>>>>> wrote:<br>>>>>>>= >>>>> Hi Donald,<br>>>>>>>>>>>= > As far as I can tell, spm_regions filters and whitens the VOI time<br> >>>>>>>>>>> series,<br>>>>>>= ;>>>>>> and adjusts for the null space of the contrast if= an adjustment is<br>>>>>>>>>>>> specified= <br> >>>>>>>>>>> in xY.Ic. It defines the confo= und matrix, X0, but does not correct<br>>>>>>>>>>= ;>> for<br>>>>>>>>>>>> it.<br>>&g= t;>>>>>>>>> The following line in spm_peb_ppi co= rrects for the confounds.<br> >>>>>>>>>>><br>>>>>>>>= ;>>>> Yc =A0 =A0=3D Y - X0*inv(X0'*X0)*X0'*Y;<br>>&g= t;>>>>>>>>> PPI.Y =3D Yc(:,1);<br>>>>&g= t;>>>>>>><br> >>>>>>>>>>> So the above correction does s= eem to be doing something different to<br>>>>>>>>>&= gt;>> spm_regions. I'm wondering why the uncorrected data, Y, is = used when<br> >>>>>>>>>>> generating the ppi interaction= term, while the corrected data Yc is<br>>>>>>>>>&g= t;>> to<br>>>>>>>>>>>> be<br>>>= ;>>>>>>>>> used as a covariate of no interest in= the PPI model. I would has<br> >>>>>>>>>>> thought<br>>>>>>= ;>>>>>> that Yc should be used to generate the ppi intera= ction term as well.<br>>>>>>>>>>>><br> >>>>>>>>>>> In my data, my X0 is a 195 x 7= matrix (I have 195 volumes per<br>>>>>>>>>>>= > session).<br>>>>>>>>>>>><br>>>&= gt;>>>>>>>> Regards,<br> >>>>>>>>>>> Alex<br>>>>>>&g= t;>>>>><br>>>>>>>>>>>><br>&= gt;>>>>>>>>>> On 30/04/2011 02:22, "MCLA= REN, Donald" <<a href=3D"mailto:[log in to unmask]">mclaren.d= [log in to unmask]</a>><br> >>>>>>>>>>> wrote:<br>>>>>>= >>>>>><br>>>>>>>>>>>>>= ; I'm actually travelling at the moment. However, I know Y has<br>>&= gt;>>>>>>>>>> already<br> >>>>>>>>>>>> been adjusted as part of t= he VOI processing.<br>>>>>>>>>>>>><br>&= gt;>>>>>>>>>>> Can you check what X0 looks= like?<br> >>>>>>>>>>>><br>>>>>>>= ;>>>>>> Best Regards, Donald McLaren<br>>>>>&= gt;>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D<br>>>>>>>>>>>>> D.G. McLaren,= Ph.D.<br> >>>>>>>>>>>> Postdoctoral Research Fell= ow, GRECC, Bedford VA<br>>>>>>>>>>>>> R= esearch Fellow, Department of Neurology, Massachusetts General<br>>>&= gt;>>>>>>>>> Hospital and Harvard Medical School= <br> >>>>>>>>>>>> Office: (773) 406-2464<br>= >>>>>>>>>>>> =3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>>>>>>>>>= >>>> This e-mail contains CONFIDENTIAL INFORMATION which may co= ntain<br> >>>>>>>>>>>> PROTECTED HEALTHCARE INFOR= MATION and may also be LEGALLY PRIVILEGED<br>>>>>>>>&g= t;>>>> and which is intended only for the use of the individual= or entity<br> >>>>>>>>>>>> named above. If the reader= of the e-mail is not the intended<br>>>>>>>>>>&= gt;>> recipient<br>>>>>>>>>>>>> o= r the employee or agent responsible for delivering it to the<br> >>>>>>>>>>>> intended<br>>>>&g= t;>>>>>>>> recipient, you are hereby notified that = you are in possession of<br>>>>>>>>>>>>>= ; confidential and privileged information. Any unauthorized use,<br> >>>>>>>>>>>> disclosure, copying or the= taking of any action in reliance on the<br>>>>>>>>>= ;>>>> contents of this information is strictly prohibited and m= ay be<br> >>>>>>>>>>>> unlawful. If you have rece= ived this e-mail unintentionally, please<br>>>>>>>>>= ;>>>> immediately notify the sender via telephone at (773) 406-= 2464 or<br> >>>>>>>>>>>> email.<br>>>>>= >>>>>>>><br>>>>>>>>>>>= ;>><br>>>>>>>>>>>>><br>>>&g= t;>>>>>>>>> On Fri, Apr 29, 2011 at 2:55 AM, Ale= x Fornito<br> >>>>>>>>>>>> <<a href=3D"mailto:forn= [log in to unmask]">[log in to unmask]</a>><br>>>>>>= ;>>>>>>> wrote:<br>>>>>>>>>>= ;>>> Hi all,<br> >>>>>>>>>>>> I have a question concerni= ng the code used to implement a PPI<br>>>>>>>>>>= >>> analysis.<br>>>>>>>>>>>>><= br> >>>>>>>>>>>> In the spm_peb_ppi.m funct= ion packaged with SPM8, lines 329-332<br>>>>>>>>>&g= t;>>> remove<br>>>>>>>>>>>>> c= onfounds from the input seed region=B9s time course:<br> >>>>>>>>>>>><br>>>>>>>= ;>>>>>> % Remove confounds and save Y in ouput structure<= br>>>>>>>>>>>>><br>%-------------------= ----------------------------------------------->>>>>>>= >>>>><br> -<br>>>>>>>>>>>>> ---<br>>>>&g= t;>>>>>>>> --<br>>>>>>>>>&g= t;>>> --<br>>>>>>>>>>>>> Yc = =A0 =A0=3D Y - X0*inv(X0'*X0)*X0'*Y;<br> >>>>>>>>>>>> PPI.Y =3D Yc(:,1);<br>>= >>>>>>>>>>><br>>>>>>>>= ;>>>>> PPI.Y is then to be used as a covariate of no interes= t when<br> >>>>>>>>>>>> building<br>>>>&g= t;>>>>>>>> the<br>>>>>>>>>&= gt;>>> PPI<br>>>>>>>>>>>>> reg= ression model. However, on line 488, it is the uncorrected seed<br> >>>>>>>>>>>> time<br>>>>>&g= t;>>>>>>> course, Y, that is input to the deconvolutio= n routine and used to<br>>>>>>>>>>>>> g= enerate<br> >>>>>>>>>>>> the ppi term:<br>>>&= gt;>>>>>>>>><br>>>>>>>>>= >>>> =A0C =A0=3D spm_PEB(Y,P);<br>>>>>>>>&= gt;>>>> =A0 =A0xn =3D xb*C{2}.E(1:N);<br> >>>>>>>>>>>> =A0 =A0xn =3D spm_detrend(= xn);<br>>>>>>>>>>>>><br>>>>>= ;>>>>>>>> =A0% Setup psychological variable from in= puts and contast weights<br> >>>>>>>>>>>><br>>>>>>>= ;>>>>>><br>%---------------------------------------------= --------------------->>>>>>>>>>>><br> -<br>>>>>>>>>>>>> ---<br>>>>&g= t;>>>>>>>> =A0PSY =3D zeros(N*NT,1);<br>>>>= ;>>>>>>>>> =A0for i =3D 1:size(U.u,2)<br>>>= ;>>>>>>>>>> =A0 =A0 =A0PSY =3D PSY + full(U.u= (:,i)*U.w(:,i));<br> >>>>>>>>>>>> =A0end<br>>>>>= >>>>>>>> =A0% PSY =3D spm_detrend(PSY); =A0<- re= moved centering of psych variable<br>>>>>>>>>>&g= t;>> =A0% prior to multiplication with xn. Based on discussion with K= arl<br> >>>>>>>>>>>> =A0% and Donald McLaren.<b= r>>>>>>>>>>>>><br>>>>>>&= gt;>>>>>> % Multiply psychological variable by neural sig= nal<br> >>>>>>>>>>>><br>>>>>>>= ;>>>>>><br>>>>>>>>>>>>&g= t;<br>%------------------------------------------------------------------&g= t;>>>>>>>>>>><br> -<br>>>>>>>>>>>>> ---<br>>>>&g= t;>>>>>>>> =A0 PSYxn =3D PSY.*xn;<br>>>>&g= t;>>>>>>>><br>>>>>>>>>>&= gt;>> Is there any specific reason why Y, rather than Yc(:,1), is use= d to<br> >>>>>>>>>>>> compute<br>>>>>= ;>>>>>>>> PSYxn? I thought Yc might be more appropr= iate (?).<br>>>>>>>>>>>>> Thanks for yo= ur help,<br> >>>>>>>>>>>> Alex<br>>>>>&g= t;>>>>>>><br>>>>>>>>>>>&= gt;><br>>>>>>>>>>>>><br>>>>= >>>>>>>>><br> >>>>>>>>>>>> --------------------------= ----------------<br>>>>>>>>>>>>><br>>= ;>>>>>>>>>>> Alex Fornito<br>>>>&= gt;>>>>>>>> Research Fellow<br> >>>>>>>>>>>> Melbourne Neuropsychiatry = Centre<br>>>>>>>>>>>>> University of Me= lbourne<br>>>>>>>>>>>>> National Neuros= cience Facility<br> >>>>>>>>>>>> Levels 1 & 2, Alan Gil= bert Building<br>>>>>>>>>>>>> 161 Barry= St<br>>>>>>>>>>>>> Carlton South 3053<= br> >>>>>>>>>>>> Victoria, Australia<br>>= ;>>>>>>>>>>><br>>>>>>>&g= t;>>>>> Email: <a href=3D"mailto:[log in to unmask]">fo= [log in to unmask]</a><br> >>>>>>>>>>>> Phone: +61 3 8344 1876<br>= >>>>>>>>>>>> Fax: +61 3 9348 0469<br>&g= t;>>>>>>>>>>><br>>>>>>>&= gt;>>>>><br> >>>>>>>>>>>><br>>>>>>>= ;>>>>><br>>>>>>>>>>>><br>&g= t;>>>>>>>>>> --------------------------------= ----------<br> >>>>>>>>>>><br>>>>>>>>= ;>>>> Alex Fornito<br>>>>>>>>>>>&= gt; Research Fellow<br>>>>>>>>>>>> Melbour= ne Neuropsychiatry Centre<br> >>>>>>>>>>> University of Melbourne<br>>= ;>>>>>>>>>> National Neuroscience Facility<br= >>>>>>>>>>>> Levels 1 & 2, Alan Gilber= t Building<br> >>>>>>>>>>> 161 Barry St<br>>>>&g= t;>>>>>>> Carlton South 3053<br>>>>>>&g= t;>>>>> Victoria, Australia<br>>>>>>>>&= gt;>>><br> >>>>>>>>>>> Email: <a href=3D"mailto:forni= [log in to unmask]">[log in to unmask]</a><br>>>>>>>= >>>>> Phone: +61 3 8344 1876<br>>>>>>>>= >>>> Fax: +61 3 9348 0469<br> >>>>>>>>>>><br>>>>>>>>= ;>>>><br>>>>>>>>>>>><br>>&g= t;>>>>>>>>><br>>>>>>>>>&= gt;><br> >>>>>>>>><br>>>>>>>>>>= ;<br>>>>>>>>>> ---------------------------------= ---------<br>>>>>>>>>><br>>>>>>&g= t;>>> Alex Fornito<br> >>>>>>>>> Research Fellow<br>>>>>>= ;>>>> Melbourne Neuropsychiatry Centre<br>>>>>>&= gt;>>> University of Melbourne<br>>>>>>>>>= > National Neuroscience Facility<br> >>>>>>>>> Levels 1 & 2, Alan Gilbert Buildin= g<br>>>>>>>>>> 161 Barry St<br>>>>>&= gt;>>>> Carlton South 3053<br>>>>>>>>>&= gt; Victoria, Australia<br> >>>>>>>>><br>>>>>>>>>>= ; Email: <a href=3D"mailto:[log in to unmask]">[log in to unmask] </a><br>>>>>>>>>> Phone: +61 3 8344 1876<br>>= >>>>>>>> Fax: +61 3 9348 0469<br> >>>>>>>>><br>>>>>>>>>>= ;<br>>>>>>>>>><br>>>>>>>>&g= t;><br>>>>>>>>><br>>>>>>>><= br> >>>>>>><br>>>>>>>> --------------= ----------------------------<br>>>>>>>><br>>>>= ;>>>> Alex Fornito<br>>>>>>>> Research Fel= low<br> >>>>>>> Melbourne Neuropsychiatry Centre<br>>>&g= t;>>>> University of Melbourne<br>>>>>>>> = National Neuroscience Facility<br>>>>>>>> Levels 1 &am= p; 2, Alan Gilbert Building<br> >>>>>>> 161 Barry St<br>>>>>>>> C= arlton South 3053<br>>>>>>>> Victoria, Australia<br>&g= t;>>>>>><br>>>>>>>> Email: <a href= =3D"mailto:[log in to unmask]">[log in to unmask]</a><br> >>>>>>> Phone: +61 3 8344 1876<br>>>>>>= >> Fax: +61 3 9348 0469<br>>>>>>><br>>>>&g= t;>><br>>>>>><br>>>>>><br>>>>&= gt;> ------------------------------------------<br> >>>>><br>>>>>> Alex Fornito<br>>>>&g= t;> Research Fellow<br>>>>>> Melbourne Neuropsychiatry Ce= ntre<br>>>>>> University of Melbourne<br>>>>>>= ; National Neuroscience Facility<br> >>>>> Levels 1 & 2, Alan Gilbert Building<br>>>>= ;>> 161 Barry St<br>>>>>> Carlton South 3053<br>>&g= t;>>> Victoria, Australia<br>>>>>><br>>>>&= gt;> Email: <a href=3D"mailto:[log in to unmask]">[log in to unmask] edu.au</a><br> >>>>> Phone: +61 3 8344 1876<br>>>>>> Fax: +6= 1 3 9348 0469<br>>>>>><br>>>>>><br>>>&g= t;>><br>>>>><br>>>><br>>><br>>><br> >> ------------------------------------------<br>>><br>>>= Alex Fornito<br>>> Research Fellow<br>>> Melbourne Neuropsychi= atry Centre<br>>> University of Melbourne<br>>> National Neuros= cience Facility<br> >> Levels 1 & 2, Alan Gilbert Building<br>>> 161 Barry St<b= r>>> Carlton South 3053<br>>> Victoria, Australia<br>>><b= r>>> Email: <a href=3D"mailto:[log in to unmask]">fornitoa@unime= lb.edu.au</a><br> >> Phone: +61 3 8344 1876<br>>> Fax: +61 3 9348 0469<br>>>= ;<br>>><br>>><br>>><br>><br><br><br>------------------= ------------------------<br><br>Alex Fornito<br>Research Fellow<br>Melbourn= e Neuropsychiatry Centre<br> University of Melbourne<br>National Neuroscience Facility<br>Levels 1 &= 2, Alan Gilbert Building<br>161 Barry St=A0<br>Carlton South 3053<br>Victo= ria, Australia<br><br>Email: <a href=3D"mailto:[log in to unmask]">for= [log in to unmask]</a><br> Phone: +61 3 8344 1876<br>Fax: +61 3 9348 0469 =A0<br><br>-----------------= -------------<br><br>Date: =A0 =A0Fri, 6 May 2011 16:17:37 -0500<br>From: = =A0 =A0John Fredy <<a href=3D"mailto:[log in to unmask]">jfochoaster@= GMAIL.COM</a>><br> Subject: using mri3dx<br><br>hello all,<br><br>I am trying to show some act= ivations in the spm5 template in mri3dx<br>(template spm5 command) but the = activations appear more higher than the<br>template. Is necessary any conve= rsion to show the activations?<br> <br>I think that maybe the mri3dX uses tailarach space, I can convert my<br= >normalized activations to tailarach space?<br><br>Thanks in advance<br><br= >------------------------------<br><br>End of SPM Digest - 6 May 2011 (#201= 1-133)<br> ******************************************<br></blockquote></div><br><br cl= ear=3D"all"><br>-- <br>Jadzia Jagiellowicz<br>Doctoral Candidate <br>Psycho= logy Department B-207<br>Stony Brook University, <br>Stony Brook, NY 11794-= 2500 USA<br> <a href=3D"http://www.psychology.stonybrook.edu/aronlab-/">http://www.psych= ology.stonybrook.edu/aronlab-/</a><br><br>"The brave shall inherit the= earth."<br> --005045015ed170643e04a35b9c79-- ========================================================================= Date: Mon, 16 May 2011 14:54:28 +0800 Reply-To: min xu <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: min xu <[log in to unmask]> Subject: Re: SPM2 Versus SPM8 Comments: To: Thomas Nichols <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636e0b7ac7bb3ac04a35f2038 Message-ID: <[log in to unmask]> --001636e0b7ac7bb3ac04a35f2038 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear Tom, Thanks a lot for you kind reply. Actually, the topoFDR in spm_defaults has been set to 0 in my analysis. Besides, comparisons between uncorrected results of these two versions of spm also reveal large differences. I also try to analyze auditory fMRI sample data (http://www.fil.ion.ucl.ac.uk/spm/data/auditory/<http://www.fi= l.ion.ucl.ac.uk/spm/data/auditory/>) following steps in spm 8 manual with both spm2 and spm8. There's also much more activation for spm2 than spm8 for both uncorrected and FDR05 corrected (topoFDR has been set to 0) results. I am wondering whether there's any other factor or defaut setting affecting results of these two versions of spm. Thank you very much for your help! Best regards, Xu Min On Sun, May 15, 2011 at 5:49 AM, Thomas Nichols <[log in to unmask]>wrote: > Dear Min, > > In SPM8 FDR topological inference was introduced, and voxel-wise > FDR inference hidden. Topological inference means inference on peaks and > clusters; voxel-wise inference is based on every individual voxel in the > image (instead of spatial features of the image). Thus "Topological FDR" > means inference on clusters based on cluster size (or local peaks based o= n > peak height), controlling the fraction of false positive clusters among a= ll > clusters (or false positive peaks among all peaks) on average, over many > experiments. > > While topological FDR results may be easier to interpret, in my experienc= e > it is is generally not very sensitivity and yields similar results to > FWE-corrected inferences. This would explain your reduction in sensitivi= ty > moving from SPM2 to SPM8. > > If you would like to use voxel-wise FDR in SPM8, edit spm_defaults, > changing "topoFDR" line to read > > defaults.stats.topoFDR =3D 0; > > (quit and re-start SPM to take effect). > > For more on this see references below. > > -Tom > > Chumbley, J., Worsley, K., Flandin, G., & Friston, K. (2010). Topologica= l > FDR for neuroimaging. *NeuroImage*, *49*(4), 3057-64. doi: > 10.1016/j.neuroimage.2009.10.090. > > Chumbley, J. R., & Friston, K. J. (2009). False discovery rate revisited: > FDR and topological inference using Gaussian random fields. *Neuroimage*, > *44*(1), 62=9670. doi: 10.1016/j.neuroimage.2008.05.021. > > On Tue, May 10, 2011 at 11:27 AM, Min <[log in to unmask]> wrote: > >> Dear SPM's users, >> >> I ran spm2 and spm8, respectively, for the same set of fMRI data. >> However, though I followed the same steps for the two versions of spm, >> there are considerable differences in the results. With spm8, no >> suprathreshold clusters were found with fdr(0.05) corrected, while with = spm >> 2, more than 8 clusters survive the threshold (T values of height thresh= old >> are nearly the same for the two analysis). >> >> I am unsure how to explain such a big difference between the results fro= m >> two versions of spm. >> >> I will be grateful for any help! >> >> Sincerely, >> Min >> > > > > -- > ____________________________________________ > Thomas Nichols, PhD > Principal Research Fellow, Head of Neuroimaging Statistics > Department of Statistics & Warwick Manufacturing Group > University of Warwick > Coventry CV4 7AL > United Kingdom > > Email: [log in to unmask] > Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 > Fax: +44 24 7652 4532 > > --001636e0b7ac7bb3ac04a35f2038 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable <div>Dear Tom,</div> <div>=A0</div> <div>Thanks a lot for you kind reply. Actually, the topoFDR in spm_defaults= has been set to 0=A0in=A0my analysis.</div> <div>=A0</div> <div>Besides, comparisons between uncorrected results of these two versions= of spm=A0also reveal large differences. I also try to=A0analyze auditory f= MRI sample data=A0 <a href=3D"http://www.fil.ion.ucl.ac.uk/spm/data/auditor= y/">(http://www.fil.ion.ucl.ac.uk/spm/data/auditory/</a>) following steps i= n spm 8 manual=A0with both spm2 and spm8. There's also much more activa= tion for spm2 than spm8 for both uncorrected and FDR05 corrected (topoFDR= =A0has=A0been set to 0) results. I am wondering whether there's any oth= er factor or defaut setting affecting results of these two versions of spm.= </div> <div>=A0</div> <div>Thank you very much for your help!</div> <div>=A0</div> <div>Best regards,</div> <div>Xu Min</div> <div><font face=3D"Courier New"></font><br>=A0</div> <div class=3D"gmail_quote">On Sun, May 15, 2011 at 5:49 AM, Thomas Nichols = <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">t.e.nich= [log in to unmask]</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"PADDING-LEFT: 1ex; MARGIN: 0px 0= px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">Dear Min,=20 <div><br></div> <div>In SPM8 FDR topological inference was introduced, and voxel-wise FDR= =A0inference=A0hidden. =A0Topological inference means inference on peaks an= d clusters; voxel-wise inference is based on every individual voxel in the = image (instead of spatial features of the image). =A0Thus "Topological= FDR" means inference on clusters based on cluster size (or local peak= s based on peak height), controlling the fraction of false positive=A0clust= ers among all clusters=A0(or false positive peaks among all peaks) on avera= ge, over many experiments.</div> <div><br></div> <div>While=A0topological=A0FDR results may be easier to interpret, in my ex= perience it is is generally not very sensitivity and=A0yields=A0similar res= ults to FWE-corrected inferences. =A0This would explain your reduction in s= ensitivity moving from SPM2 to SPM8.</div> <div><br></div> <div>If you would like to use voxel-wise FDR in SPM8, edit spm_defaults, ch= anging "topoFDR" line to read</div> <blockquote style=3D"BORDER-RIGHT: medium none; PADDING-RIGHT: 0px; BORDER-= TOP: medium none; PADDING-LEFT: 0px; PADDING-BOTTOM: 0px; MARGIN: 0px 0px 0= px 40px; BORDER-LEFT: medium none; PADDING-TOP: 0px; BORDER-BOTTOM: medium = none"> <div> <div><font face=3D"'courier new', monospace">defaults.stats.topoFDR= =A0 =A0 =A0=3D 0;</font></div></div></blockquote> <div>(quit and re-start SPM to take effect).</div> <div><br></div> <div>For more on this see references below.</div> <div><br></div> <div>-Tom</div> <div><br></div> <div> <p style=3D"MARGIN-LEFT: 24pt">Chumbley, J., Worsley, K., Flandin, G., &= ; Friston, K. (2010). Topological FDR for neuroimaging. <i>NeuroImage</i>, = <i>49</i>(4), 3057-64. doi: 10.1016/j.neuroimage.2009.10.090.</p> <p style=3D"MARGIN-LEFT: 24pt">Chumbley, J. R., & Friston, K. J. (2009)= . False discovery rate revisited: FDR and topological inference using Gauss= ian random fields. <i>Neuroimage</i>, <i>44</i>(1), 62=9670. doi: 10.1016/j= .neuroimage.2008.05.021.</p> </div> <div> <div> <div></div> <div class=3D"h5"><br> <div class=3D"gmail_quote">On Tue, May 10, 2011 at 11:27 AM, Min <span dir= =3D"ltr"><<a href=3D"mailto:[log in to unmask]" target=3D"_blank">minxu86= @gmail.com</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"PADDING-LEFT: 1ex; MARGIN: 0px 0= px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">Dear SPM's users,<br><br>I r= an spm2 and spm8, respectively, for the same set of fMRI data. =A0However, = though I followed the same steps for the two versions of spm, there are con= siderable differences in the results. =A0With spm8, no suprathreshold clust= ers were found with fdr(0.05) corrected, while with spm 2, more than 8 clus= ters survive the threshold (T values of height threshold are nearly the sam= e for the two analysis).<br> <br>I am unsure how to explain such a big difference between the results fr= om two versions of spm.<br><br>I will be grateful for any help!<br><br>Sinc= erely,<br><font color=3D"#888888">Min<br></font></blockquote></div><br><br = clear=3D"all"> <br></div></div>-- <br>____________________________________________<br>Thom= as Nichols, PhD<br>Principal Research Fellow, Head of Neuroimaging Statisti= cs<br>Department of Statistics & Warwick Manufacturing Group<br>Univers= ity of Warwick<br> Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email: <a href=3D"mailto:t.e.n= [log in to unmask]" target=3D"_blank">[log in to unmask]</a><br>Ph= one, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 7652 45= 32<br> <br></div></blockquote></div><br> --001636e0b7ac7bb3ac04a35f2038-- ========================================================================= Date: Mon, 16 May 2011 10:06:29 +0200 Reply-To: Urs Bachofner <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Urs Bachofner <[log in to unmask]> Subject: Re-referencing of EEG-Data Comments: To: [log in to unmask] Content-Type: multipart/alternative; boundary="========GMXBoundary71961305533189851811" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --========GMXBoundary71961305533189851811 Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p4G86UFN016256 I have a question regarding the concept of EEG data Re-referencing in Spm= 8. The way I see it, re-referencing is a very important step prior to Source= =20 Analysis. To get useful data, the Signals should be re-referenced to=20 average.=20 According to the SPM8 Manual P. 110 we can use spm_eeg_montage to do this= .=20 The data I'd like to process is recorded with a 128-channel EEG-net with=20 the reference electrode at CZ.=20 According to the SPM8 Manual, for average reference the matrix should hav= e=20 (N-1)/N at the diagonal and -1/N elsewhere. In my case this results in 0.9921875 at the diagonal and -0.0078125=20 elsewhere. Two questions: 1. These numbers are so close to the original matrix (1 at diagonal and 0= =20 elsewhere) which is said to not change anything. Is this matrix correct? 2. Since electrodes that are closer to the reference (in this case CZ) ha= ve=20 a higher amplitude recorded, shouldn't such electrodes be weighted=20 differently than electrodes that are more distant to CZ? Am I missing the= =20 point here? And finally, besides Filtering, epoching, Artefact Detection (removing ba= d=20 channels and bad trials), Re-referencing, and baseline correction are the= re=20 other preprocessing steps that are necessary to get good data from my=20 evoked potentials? And is there a certain order in which these steps shou= ld=20 be executed? Thank you very much for you help. =20 =C2=A0 =20 =20 =20 --=20 NEU: FreePhone - kostenlos mobil telefonieren und surfen! =09 Jetzt informieren: http://www.gmx.net/de/go/freephone --========GMXBoundary71961305533189851811 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: quoted-printable <html> <head> <title></title> <style type=3D"text/css" media=3D"screen"> body {font-family: verdana, arial, helvetica, sans-serif;font-size: 12px;pa= dding: 5px;margin: 0;background-color: #FFF;} p, ul, li {margin-top: 0;margin-bottom: 0;} blockquote {margin-left: 5px;} div.signature {color: #666;font-size: 0.9em;} </style> </head> <body> <br /><br />I have a question regarding the concept of EEG data Re-= referencing in Spm8.<br />The way I see it, re-referencing is a very import= ant step prior to Source Analysis. To get useful data, the Signals should b= e re-referenced to average. <br />According to the SPM8 Manual P. 110 we ca= n use spm_eeg_montage to do this. <br /><br />The data I'd like to process = is recorded with a 128-channel EEG-net with the reference electrode at CZ. = <br />According to the SPM8 Manual, for average reference the matrix should= have (N-1)/N at the diagonal and -1/N elsewhere.<br />In my case this resu= lts in 0.9921875 at the diagonal and -0.0078125 elsewhere.<br /><br />Two q= uestions:<br />1. These numbers are so close to the original matrix (1 at d= iagonal and 0 elsewhere) which is said to not change anything. Is this matr= ix correct?<br /><br />2. Since electrodes that are closer to the reference= (in this case CZ) have a higher amplitude recorded, shouldn't such electro= des be weighted differently than electrodes that are more distant to CZ? Am= I missing the point here?<br /><br /><br />And finally, besides Filtering,= epoching, Artefact Detection (removing bad channels and bad trials), Re-re= ferencing, and baseline correction are there other preprocessing steps that= are necessary to get good data from my evoked potentials? And is there a c= ertain order in which these steps should be executed?<br /><br />Thank you = very much for you help.<br /><br /><br /><br /><br /><br /><br /><br /><br = /><br /> <table width=3D"80" cellspacing=3D"0" cellpadding=3D"0" border=3D"0= " style=3D"border-collapse: collapse; width: 60pt;" x:str=3D""> <col width=3D"80" style=3D"width: 60pt;"></col> <tr height=3D"17" style=3D"height: 12.75pt;"> <td height=3D"17" width=3D"80" align=3D"right" x:num=3D"-7.= 8125E-3" style=3D"height: 12.75pt; width: 60pt;"> </td> </tr> </table> <br /> <div class=3D"signature"><br /><br /><br />-- <br />NEU: FreePhone - ko= stenlos mobil telefonieren und surfen! <br />Jetzt informieren: http://ww= w.gmx.net/de/go/freephone</div></body> </html> --========GMXBoundary71961305533189851811-- ========================================================================= Date: Mon, 16 May 2011 17:13:18 +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: Re: Batch problem--3D Source Reconstruction Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_130052_17267360.1305537198329" Message-ID: <[log in to unmask]> ------=_Part_130052_17267360.1305537198329 Content-Type: text/plain; charset=GBK Content-Transfer-Encoding: base64 RGVhciBWbGFkaW1pciwKoaGhoVlvdXIgZ3Vlc3Mgd2FzIHJpZ2h0LiBJIHJlYWxseSBjb3VsZG4n dCBkbyAzRCBzb3VyY2VzIHJlY29uc3RydWN0aW9uIG9mIHRoYXQgZGF0YSBieSBHVUkuIEl0IHJl bWluZGVkIG1lICcnY2hlY2ttZWVnOiBkYXRhIHNjYWxlIG1pc3NpbmcsIGFzc2lnbmluZyBkZWZh dWx0CmNoZWNrbWVlZzogbm8gZmlkdWNpYWxzIGFyZSBkZWZpbmVkJycuIFRoZW4gSSBkaWQgcHJl cHJvY2VzcyBzdGVwcyBhZ2FpbiwgYW5kIGZvdW5kIHRoYXQgSSBjb3VsZG4ndCBvYnRhaW4gdGhl IGZpZHVjaWFscyBhdXRvbWF0aWNhbGx5KHdoZW4gSSBmaW5pc2hlZCBDb252ZXJ0aW5nIHN0ZXAp LiBJIGNoZWNrZWQgdGhlIG90aGVyIGRhdGFzZXRzLCBsdWNraWx5LCB0aGV5IGRpZG4ndCBoYXZl IHRoaXMgcHJvYmxlbS4gSSBndWVzcyB0aGVyZSBtdXN0IGJlIHNvbWUgd3JvbmdzIG9mIG15IG9y aWdpbmFsIGRhdGFzZXQgKG9ubHkgdGhhdCBvbmUpLCByaWdodCA/CqGhoaFBcyBmb3IgcmVjb25z dHJ1Y3Rpb24gYmF0Y2ggcHJvYmxlbSwgd2l0aCB0aGUgbGF0ZXN0IHZlcnNpb24gb2Ygc3BtOCg0 MDI5KSBhbmQgbm9ybWFsIGRhdGFzZXQsIEkgY2FuIGRvIHJlY29uc3RydWN0aW9uIGJ5IHRoYXQg c2F2ZWQgInJlbmNvbnN0cnVjdGlvbl9iYXRjaCIgc3VjY2Vzc2Z1bGx5LiBUaHVzLCB0aGUgcHJv YmxlbXMgcmVsYXRlZCB0byByZWNvbnN0cnVjdGlvbiBiYXRjaCB0aGF0IEkgY29uZnJvbnRlZCBw cmV2aW91c2x5IG1heSByZXN1bHQgZnJvbSB0aGUgZGlmZmVyZW50IHZlcnNpb25zIG9mIHNwbTgu CiAgICAgICBCeSB0aGUgd2F5LCBpbiAnJ01FRUcgaGVhZCBtb2RlbCBzcGVjaWZpY2F0aW9uPiBF RUcgaGVhZCBtb2RsZScnIG1vZHVsZSwgbXVzdCBJIHNwZWNpZnkgdGhlICcnRUVHIGhlYWQgbW9k bGUnJyBpZiBJIGp1c3QgcHJvY2VzcyBNRUcgZGF0YXNldD8KoaGhoVRoYW5rIHlvdSB2ZXJ5IG11 Y2ggZm9yIGhlbHBpbmcgbWUgc29sdmUgdGhlc2UgdGlueSBwcm9ibGVtcyEKoaGhoQqhoaGhSGFv cmFuLgoKIAogCkF0IDIwMTEtMDUtMTYgMDY6Mzg6MjOjrCJWbGFkaW1pciBMaXR2YWsiIDxsaXR2 YWsudmxhZGltaXJAZ21haWwuY29tPiB3cm90ZToKCj5EZWFyIEhhb3JhbiwKPgo+VGhlIHByb2Js ZW0gc2VlbXMgdG8gYmUgbm90IGluIHRoZSBiYXRjaCBidXQgaW4geW91ciBNRUcgZGF0YSAob3Ig d2FzCj5pdCBFRUc/KS4gVGhlcmUgYXJlIG5vIHZhbGlkIGZpZHVjaWFscyBpbiB0aGF0IGRhdGFz ZXQuIENvdWxkIHlvdQo+cGVyZm9ybSBzb3VyY2UgcmVjb25zdHJ1Y3Rpb24gdXNpbmcgdGhlIEdV ST8gSSdkIGJlIHN1cnByaXNlZCBpZiB5b3UKPmNvdWxkLiBTbyB3ZSBuZWVkIHRvIGZvY3VzIG9u IGhvdyB0byBnZXQgZmlkdWNpYWxzIGludG8geW91ciBkYXRhc2V0Cj5hbmQgZm9yIHRoYXQgeW91 IG5lZWQgdG8gdGVsbCBtZSBtb3JlIGFib3V0IHdoZXJlIHRoYXQgZGF0YSBjb21lcwo+ZnJvbS4K Pgo+QmVzdCwKPgo+VmxhZGltaXIKPgo+MjAxMS81LzE1ILfJxPEgPGxpaGFvcmFuMDYwM0AxNjMu Y29tPjoKPj4gRGVhciBWbGFkaW1pciwKPj4goaGhoUkgdHJpZWQgYWdhaW4gd2l0aCB0aGUgbGF0 ZXN0IHZlcnNpb24gb2Ygc3BtOCg0MDI5KSwgdGhlIHByZXZpb3VzIHByb2JsZW0oCj4+IHRlbXBs YXRlPTEpIGRpc2FwcGVhcmVkLiBCdXQgdGhpcyB0aW1lLCBJIGNvbmZyb250ZWQgYSBuZXcgcHJv YmxlbS4gSXQKPj4gcmVtaW5kZWQgbWUgIk5vIGV4ZWN1dGFibGUgbW9kdWxlcywgYnV0IHN0aWxs IHVucmVzb2x2ZWQgZGVwZW5kZW5jaWVzIG9yCj4+IGluY29tcGxldGUgbW9kdWxlIGlucHV0cy4i IEJ1dCBJIGRvbid0IGtub3cgd2hlcmUgdGhlIHByb2JsZW1zIGFyZS4gQ291bGQKPj4geW91IGhl bHAgbWUgc2VlIG15IHJlY29uc3RydWN0aW9uX2JhdGNoLm1hdCBhbmQgZ2l2ZSBtZSBzb21lIGFk dmljZSA/IFRoYW5rcwo+PiBhIGxvdC4KPj4KPj4gICAgICAgIEhhb3Jhbi4KPj4KPj4gICAgICAg IFRoZXNlIGNvZGUgYXBwZWFycyBpbiB0aGUgbWF0bGFiIGNvbW1hbmQgd2luZG93IHdoZW4gSSBy YW4gdGhlIGJhdGNoOgo+PiBTUE04OiBzcG1fZWVnX2ludl9tZXNoX3VpICh2NDAyNykgICAgICAg ICAgICAgICAgICAxMTowOToxMyAtIDE1LzA1LzIwMTEKPj4gPT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09Cj4+ICAg ICAgICB0ZW1wbGF0ZTogMAo+PiAgICAgICAgICAgIHNNUkk6ICdEOlxQcm9jZXNzZWRcRFAwMVxz S0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDEuaW1nLDEnCj4+ICAgICAgICAgICAgIGRlZjogJ0Q6 XFByb2Nlc3NlZFxEUDAxXHlfc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxLm5paScKPj4gICAg ICAgICAgQWZmaW5lOiBbNHg0IGRvdWJsZV0KPj4gICAgICAgICAgIE1zaXplOiAyCj4+ICAgICAg ICB0ZXNzX21uaTogWzF4MSBzdHJ1Y3RdCj4+ICAgICAgICB0ZXNzX2N0eDoKPj4gJ0Q6XFByb2Nl c3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMWNvcnRleF84MTk2LnN1cmYuZ2lp Jwo+PiAgICAgIHRlc3Nfc2NhbHA6Cj4+ICdEOlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4tMDAwMi0w MDAwMC0wMDAwMDEtMDFzY2FscF8yNTYyLnN1cmYuZ2lpJwo+PiAgICAgdGVzc19vc2t1bGw6Cj4+ ICdEOlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFvc2t1bGxfMjU2 Mi5zdXJmLmdpaScKPj4gICAgIHRlc3NfaXNrdWxsOgo+PiAnRDpcUHJvY2Vzc2VkXERQMDFcc0tF WUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxaXNrdWxsXzI1NjIuc3VyZi5naWknCj4+ICAgICAgICAg ICAgIGZpZDogWzF4MSBzdHJ1Y3RdCj4+IEZhaWxlZCAgJ00vRUVHIGhlYWQgbW9kZWwgc3BlY2lm aWNhdGlvbicKPj4gUmVmZXJlbmNlIHRvIG5vbi1leGlzdGVudCBmaWVsZCAnZmlkJy4KPj4gSW4g ZmlsZSAiQzpcUHJvZ3JhbSBGaWxlc1xNQVRMQUJcc3BtOG5ld1xjb25maWdcc3BtX2NmZ19lZWdf aW52X2hlYWRtb2RlbC5tIgo+PiAodjQxMTgpLCBmdW5jdGlvbiAic3BlY2lmeV9oZWFkbW9kZWwi IGF0IGxpbmUgMjA5Lgo+PiBObyBleGVjdXRhYmxlIG1vZHVsZXMsIGJ1dCBzdGlsbCB1bnJlc29s dmVkIGRlcGVuZGVuY2llcyBvciBpbmNvbXBsZXRlCj4+IG1vZHVsZSBpbnB1dHMuCj4+IFRoZSBm b2xsb3dpbmcgbW9kdWxlcyBkaWQgbm90IHJ1bjoKPj4gRmFpbGVkOiBNL0VFRyBoZWFkIG1vZGVs IHNwZWNpZmljYXRpb24KPj4gU2tpcHBlZDogTS9FRUcgc291cmNlIGludmVyc2lvbgo+PiBTa2lw cGVkOiBNL0VFRyBpbnZlcnNpb24gcmVzdWx0cwo+Pgo+Pgo+PiBBdCAyMDExLTA1LTEzIDIyOjA2 OjA2o6wiVmxhZGltaXIgTGl0dmFrIiA8bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbQo+Pj4gd3Jv dGU6Cj4+Cj4+PkRlYXIgSGFvcmFuLAo+Pj4KPj4+SSB0cmllZCBhbmQgZXZlcnl0aGluZyB3b3Jr cyBmaW5lIGZvciBtZS4gTWFrZSBzdXJlIHlvdXIgU1BNIHZlcnNpb24KPj4+aXMgdXAgdG8gZGF0 ZSBhbmQgaWYgeW91IGNhbid0IG1ha2UgaXQgd29yaywgc2VuZCBtZSB5b3VyIHNhdmVkIGJhdGNo Cj4+PmFuZCBJJ2xsIHRha2UgYW5vdGhlciBsb29rLgo+Pj4KPj4+QmVzdCwKPj4+Cj4+PlZsYWRp bWlyCj4+Pgo+Pj4yMDExLzUvMTAgt8nE8SA8bGloYW9yYW4wNjAzQDE2My5jb20+Ogo+Pj4+IERl YXIgU1BNJ3MgdXNlcnMsCj4+Pj4goaGhoUhhdmUgeW91IGV2ZXIgdXNlZCBiYXRjaCBpbnRlcmZh Y2UgdG8gZG8gM0Qgc291cmNlIHJlY29uc3RydWN0aW9uIGZvcgo+Pj4+IEVFRy9NRUcgZGF0YT8g SSBjaG9vc2VkIHRocmVlIHNlcGFyYXRlIG1vZHVsZXMgICJNL0VFRyBoZWFkIG1vZGVsCj4+Pj4g c3BlY2lmaWNhdGlvbiIgIk0vRUVHIHNvdXJjZSBpbnZlcnNpb24iIGFuZCAiTS9FRUcgaW52ZXJz aW9uIHJlc3VsdHMiIHNvIGFzCj4+Pj4gdG8gcmVjb25zdHJ1Y3QgdGhlIHNvdXJjZXMuIEluIHRo ZSAiTS9FRUcgaGVhZCBtb2RlbCBzcGVjaWZpY2F0aW9uIiBtb2R1bGUsCj4+Pj4gSSBzcGVjaWZp ZWQgJydNZXNoZXM8TWVzaGVzIHNvdXJjZTxpbmRpdmlkdWFsIHN0cnVjdHVhbCBpbWFnZScnIGFu ZCB0aGVuCj4+Pj4gc2VsZWN0ZWQgYSBtcmkgZmlsZSBuYW1lZCAic0RQNzQtMDAwMy0wMDAwMC0w MDAwMDEtMDEuaW1nLDEiLiAgV2hlbiBhbGwgd2FzCj4+Pj4gcmVhZHkgYW5kIHRoZW4gSSByYW4g dGhlIGJhdGNoLCBob3dldmVyLCBJIGZvdW5kIHRoYXQgaXQgZGluJ3QgZXhjdXRlZCB0aGUKPj4+ PiBzcGVjaWZpZWQgc3RydWN0dWFsIGltYWdlICggYXMgeW91IGNhbiBzZWUgYmVsb3cgJ3RlbXBs YXRlPTEnKSBhbmQgdGhlCj4+Pj4gbWF0bGFiIGNvbW1hbmQgd2luZG93IGFwcGVhcmVkIHRob3Nl Ogo+Pj4+Cj4+Pj4gU1BNODogc3BtX2VlZ19pbnZfbWVzaF91aSAodjM3MzEpICAgICAgICAgICAg ICAgICAgMjE6Mzk6MDYgLSAwOS8wNS8yMDExCj4+Pj4gPT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09Cj4+Pj4gICAg ICAgIHRlbXBsYXRlOiAxCj4+Pj4gICAgICAgICAgQWZmaW5lOiBbNHg0IGRvdWJsZV0KPj4+PiAg ICAgICAgICAgIHNNUkk6ICdDOlxQcm9ncmFtCj4+Pj4gRmlsZXNcTUFUTEFCXHRvb2xib3hcc3Bt OFxjYW5vbmljYWxcc2luZ2xlX3N1YmpfVDEubmlpJwo+Pj4+ICAgICAgICAgICBNc2l6ZTogMgo+ Pj4+ICAgICAgICB0ZXNzX21uaTogWzF4MSBzdHJ1Y3RdCj4+Pj4gICAgICAgIHRlc3NfY3R4OiAn QzpcUHJvZ3JhbQo+Pj4+IEZpbGVzXE1BVExBQlx0b29sYm94XHNwbThcY2Fub25pY2FsXGNvcnRl eF84MTk2LnN1cmYuZ2lpJwo+Pj4+ICAgICAgdGVzc19zY2FscDogJ0M6XFByb2dyYW0KPj4+PiBG aWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxzY2FscF8yNTYyLnN1cmYuZ2lpJwo+ Pj4+ICAgICB0ZXNzX29za3VsbDogJ0M6XFByb2dyYW0KPj4+PiBGaWxlc1xNQVRMQUJcdG9vbGJv eFxzcG04XGNhbm9uaWNhbFxvc2t1bGxfMjU2Mi5zdXJmLmdpaScKPj4+PiAgICAgdGVzc19pc2t1 bGw6ICdDOlxQcm9ncmFtCj4+Pj4gRmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxc aXNrdWxsXzI1NjIuc3VyZi5naWknCj4+Pj4gICAgICAgICAgICAgZmlkOiBbMXgxIHN0cnVjdF0K Pj4+Pgo+Pj4+ICAgoaFJIGRpZG4ndCB1bmRlcnN0YW5kIHdoeS4gQW55b25lIHdobyBrbm93cyB0 aGUgcmVhc29uID8gVGhhbmtzIHZlcnkgbXVjaAo+Pj4+IGZvciBhbnkgaGVscCAhCj4+Pj4KPj4+ PiAtLQo+Pj4+IEhhb3JhbiBMSSAoTVMpCj4+Pj4gQnJhaW4gSW1hZ2luZyBMYWIsCj4+Pj4gUmVz ZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTY2llbmNlLAo+Pj4+IFNvdXRoZWFzdCBVbml2ZXJz aXR5Cj4+Pj4gMiBTaSBQYWkgTG91ICwgTmFuamluZywgMjEwMDk2LCBQLlIuQ2hpbmEKPj4+Pgo+ Pj4+Cj4+Cj4+Cj4+Cj4+Cj4+Cg== ------=_Part_130052_17267360.1305537198329 Content-Type: text/html; charset=GBK Content-Transfer-Encoding: base64 PERJVj5EZWFyIFZsYWRpbWlyLDwvRElWPgo8RElWPqGhoaFZb3VyIGd1ZXNzIHdhcyZuYnNwO3Jp Z2h0LiBJIHJlYWxseSBjb3VsZG4ndCBkbyAzRCBzb3VyY2VzIHJlY29uc3RydWN0aW9uJm5ic3A7 b2YgdGhhdCBkYXRhIGJ5IEdVSS4gSXQgcmVtaW5kZWQgbWUgJydjaGVja21lZWc6IGRhdGEgc2Nh bGUgbWlzc2luZywgYXNzaWduaW5nIGRlZmF1bHQ8QlI+Y2hlY2ttZWVnOiBubyBmaWR1Y2lhbHMg YXJlIGRlZmluZWQnJy4gVGhlbiBJIGRpZCBwcmVwcm9jZXNzIHN0ZXBzIGFnYWluLCBhbmQgZm91 bmQgdGhhdCBJIGNvdWxkbid0IG9idGFpbiB0aGUgZmlkdWNpYWxzIGF1dG9tYXRpY2FsbHkod2hl biBJIGZpbmlzaGVkIENvbnZlcnRpbmcgc3RlcCkuIEkgY2hlY2tlZCB0aGUgb3RoZXIgZGF0YXNl dHMsIGx1Y2tpbHksIHRoZXkgZGlkbid0IGhhdmUgdGhpcyBwcm9ibGVtLiBJIGd1ZXNzIHRoZXJl IG11c3QgYmUgc29tZSB3cm9uZ3MmbmJzcDtvZiBteSBvcmlnaW5hbCBkYXRhc2V0IChvbmx5IHRo YXQgb25lKSwgcmlnaHQgPzwvRElWPgo8RElWPqGhoaFBcyBmb3IgcmVjb25zdHJ1Y3Rpb24gYmF0 Y2ggcHJvYmxlbSwgd2l0aCB0aGUgbGF0ZXN0IHZlcnNpb24gb2Ygc3BtOCg0MDI5KSBhbmQgbm9y bWFsIGRhdGFzZXQsIEkgY2FuIGRvIHJlY29uc3RydWN0aW9uIGJ5IHRoYXQgc2F2ZWQgInJlbmNv bnN0cnVjdGlvbl9iYXRjaCIgc3VjY2Vzc2Z1bGx5LiBUaHVzLCB0aGUgcHJvYmxlbXMgcmVsYXRl ZCB0byByZWNvbnN0cnVjdGlvbiBiYXRjaCB0aGF0IEkgY29uZnJvbnRlZCBwcmV2aW91c2x5IG1h eSByZXN1bHQgZnJvbSB0aGUgZGlmZmVyZW50IHZlcnNpb25zIG9mIHNwbTguIDwvRElWPgo8RElW PiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO0J5IHRoZSB3YXksIGlu ICcnTUVFRyBoZWFkIG1vZGVsIHNwZWNpZmljYXRpb24mZ3Q7IEVFRyBoZWFkIG1vZGxlJycgbW9k dWxlLCBtdXN0IEkgc3BlY2lmeSB0aGUgJydFRUcgaGVhZCBtb2RsZScnIGlmIEkganVzdCBwcm9j ZXNzIE1FRyBkYXRhc2V0PzwvRElWPgo8RElWPqGhoaFUaGFuayB5b3UgdmVyeSBtdWNoIGZvciBo ZWxwaW5nIG1lIHNvbHZlIHRoZXNlIHRpbnkgcHJvYmxlbXMhPC9ESVY+CjxESVY+oaGhoTwvRElW Pgo8RElWPqGhoaFIYW9yYW4uPEJSPjwvRElWPgo8RElWPiZuYnNwOzwvRElWPgo8RElWPiZuYnNw OzwvRElWPgo8RElWPkF0Jm5ic3A7MjAxMS0wNS0xNiZuYnNwOzA2OjM4OjIzo6wiVmxhZGltaXIm bmJzcDtMaXR2YWsiJm5ic3A7Jmx0O2xpdHZhay52bGFkaW1pckBnbWFpbC5jb20mZ3Q7Jm5ic3A7 d3JvdGU6PEJSPjxCUj4mZ3Q7RGVhciZuYnNwO0hhb3Jhbiw8QlI+Jmd0OzxCUj4mZ3Q7VGhlJm5i c3A7cHJvYmxlbSZuYnNwO3NlZW1zJm5ic3A7dG8mbmJzcDtiZSZuYnNwO25vdCZuYnNwO2luJm5i c3A7dGhlJm5ic3A7YmF0Y2gmbmJzcDtidXQmbmJzcDtpbiZuYnNwO3lvdXImbmJzcDtNRUcmbmJz cDtkYXRhJm5ic3A7KG9yJm5ic3A7d2FzPEJSPiZndDtpdCZuYnNwO0VFRz8pLiZuYnNwO1RoZXJl Jm5ic3A7YXJlJm5ic3A7bm8mbmJzcDt2YWxpZCZuYnNwO2ZpZHVjaWFscyZuYnNwO2luJm5ic3A7 dGhhdCZuYnNwO2RhdGFzZXQuJm5ic3A7Q291bGQmbmJzcDt5b3U8QlI+Jmd0O3BlcmZvcm0mbmJz cDtzb3VyY2UmbmJzcDtyZWNvbnN0cnVjdGlvbiZuYnNwO3VzaW5nJm5ic3A7dGhlJm5ic3A7R1VJ PyZuYnNwO0knZCZuYnNwO2JlJm5ic3A7c3VycHJpc2VkJm5ic3A7aWYmbmJzcDt5b3U8QlI+Jmd0 O2NvdWxkLiZuYnNwO1NvJm5ic3A7d2UmbmJzcDtuZWVkJm5ic3A7dG8mbmJzcDtmb2N1cyZuYnNw O29uJm5ic3A7aG93Jm5ic3A7dG8mbmJzcDtnZXQmbmJzcDtmaWR1Y2lhbHMmbmJzcDtpbnRvJm5i c3A7eW91ciZuYnNwO2RhdGFzZXQ8QlI+Jmd0O2FuZCZuYnNwO2ZvciZuYnNwO3RoYXQmbmJzcDt5 b3UmbmJzcDtuZWVkJm5ic3A7dG8mbmJzcDt0ZWxsJm5ic3A7bWUmbmJzcDttb3JlJm5ic3A7YWJv dXQmbmJzcDt3aGVyZSZuYnNwO3RoYXQmbmJzcDtkYXRhJm5ic3A7Y29tZXM8QlI+Jmd0O2Zyb20u PEJSPiZndDs8QlI+Jmd0O0Jlc3QsPEJSPiZndDs8QlI+Jmd0O1ZsYWRpbWlyPEJSPiZndDs8QlI+ Jmd0OzIwMTEvNS8xNSZuYnNwO7fJxPEmbmJzcDsmbHQ7bGloYW9yYW4wNjAzQDE2My5jb20mZ3Q7 OjxCUj4mZ3Q7Jmd0OyZuYnNwO0RlYXImbmJzcDtWbGFkaW1pciw8QlI+Jmd0OyZndDsmbmJzcDuh oaGhSSZuYnNwO3RyaWVkJm5ic3A7YWdhaW4mbmJzcDt3aXRoJm5ic3A7dGhlJm5ic3A7bGF0ZXN0 Jm5ic3A7dmVyc2lvbiZuYnNwO29mJm5ic3A7c3BtOCg0MDI5KSwmbmJzcDt0aGUmbmJzcDtwcmV2 aW91cyZuYnNwO3Byb2JsZW0oPEJSPiZndDsmZ3Q7Jm5ic3A7dGVtcGxhdGU9MSkmbmJzcDtkaXNh cHBlYXJlZC4mbmJzcDtCdXQmbmJzcDt0aGlzJm5ic3A7dGltZSwmbmJzcDtJJm5ic3A7Y29uZnJv bnRlZCZuYnNwO2EmbmJzcDtuZXcmbmJzcDtwcm9ibGVtLiZuYnNwO0l0PEJSPiZndDsmZ3Q7Jm5i c3A7cmVtaW5kZWQmbmJzcDttZSZuYnNwOyJObyZuYnNwO2V4ZWN1dGFibGUmbmJzcDttb2R1bGVz LCZuYnNwO2J1dCZuYnNwO3N0aWxsJm5ic3A7dW5yZXNvbHZlZCZuYnNwO2RlcGVuZGVuY2llcyZu YnNwO29yPEJSPiZndDsmZ3Q7Jm5ic3A7aW5jb21wbGV0ZSZuYnNwO21vZHVsZSZuYnNwO2lucHV0 cy4iJm5ic3A7QnV0Jm5ic3A7SSZuYnNwO2Rvbid0Jm5ic3A7a25vdyZuYnNwO3doZXJlJm5ic3A7 dGhlJm5ic3A7cHJvYmxlbXMmbmJzcDthcmUuJm5ic3A7Q291bGQ8QlI+Jmd0OyZndDsmbmJzcDt5 b3UmbmJzcDtoZWxwJm5ic3A7bWUmbmJzcDtzZWUmbmJzcDtteSZuYnNwO3JlY29uc3RydWN0aW9u X2JhdGNoLm1hdCZuYnNwO2FuZCZuYnNwO2dpdmUmbmJzcDttZSZuYnNwO3NvbWUmbmJzcDthZHZp Y2UmbmJzcDs/Jm5ic3A7VGhhbmtzPEJSPiZndDsmZ3Q7Jm5ic3A7YSZuYnNwO2xvdC48QlI+Jmd0 OyZndDs8QlI+Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDtIYW9yYW4uPEJSPiZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7VGhlc2UmbmJzcDtjb2RlJm5ic3A7YXBwZWFy cyZuYnNwO2luJm5ic3A7dGhlJm5ic3A7bWF0bGFiJm5ic3A7Y29tbWFuZCZuYnNwO3dpbmRvdyZu YnNwO3doZW4mbmJzcDtJJm5ic3A7cmFuJm5ic3A7dGhlJm5ic3A7YmF0Y2g6PEJSPiZndDsmZ3Q7 Jm5ic3A7U1BNODombmJzcDtzcG1fZWVnX2ludl9tZXNoX3VpJm5ic3A7KHY0MDI3KSZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOzExOjA5OjEzJm5ic3A7 LSZuYnNwOzE1LzA1LzIwMTE8QlI+Jmd0OyZndDsmbmJzcDs9PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT08QlI+Jmd0 OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZW1w bGF0ZTombmJzcDswPEJSPiZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7c01SSTombmJzcDsnRDpcUHJv Y2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxLmltZywxJzxCUj4mZ3Q7Jmd0 OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwO2RlZjombmJzcDsnRDpcUHJvY2Vzc2VkXERQMDFceV9zS0VZ QU4tMDAwMi0wMDAwMC0wMDAwMDEtMDEubmlpJzxCUj4mZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO0FmZmluZTombmJzcDtb NHg0Jm5ic3A7ZG91YmxlXTxCUj4mZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO01zaXplOiZuYnNwOzI8QlI+Jmd0 OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNz X21uaTombmJzcDtbMXgxJm5ic3A7c3RydWN0XTxCUj4mZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3NfY3R4OjxCUj4mZ3Q7Jmd0OyZuYnNw OydEOlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFjb3J0ZXhfODE5 Ni5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDt0ZXNzX3NjYWxwOjxCUj4mZ3Q7Jmd0OyZuYnNwOydEOlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4t MDAwMi0wMDAwMC0wMDAwMDEtMDFzY2FscF8yNTYyLnN1cmYuZ2lpJzxCUj4mZ3Q7Jmd0OyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3Nfb3NrdWxsOjxCUj4mZ3Q7Jmd0OyZuYnNwOydE OlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFvc2t1bGxfMjU2Mi5z dXJmLmdpaSc8QlI+Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNzX2lz a3VsbDo8QlI+Jmd0OyZndDsmbmJzcDsnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAw MDAtMDAwMDAxLTAxaXNrdWxsXzI1NjIuc3VyZi5naWknPEJSPiZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7ZmlkOiZuYnNwO1sxeDEmbmJzcDtzdHJ1Y3RdPEJSPiZndDsmZ3Q7Jm5ic3A7RmFp bGVkJm5ic3A7Jm5ic3A7J00vRUVHJm5ic3A7aGVhZCZuYnNwO21vZGVsJm5ic3A7c3BlY2lmaWNh dGlvbic8QlI+Jmd0OyZndDsmbmJzcDtSZWZlcmVuY2UmbmJzcDt0byZuYnNwO25vbi1leGlzdGVu dCZuYnNwO2ZpZWxkJm5ic3A7J2ZpZCcuPEJSPiZndDsmZ3Q7Jm5ic3A7SW4mbmJzcDtmaWxlJm5i c3A7IkM6XFByb2dyYW0mbmJzcDtGaWxlc1xNQVRMQUJcc3BtOG5ld1xjb25maWdcc3BtX2NmZ19l ZWdfaW52X2hlYWRtb2RlbC5tIjxCUj4mZ3Q7Jmd0OyZuYnNwOyh2NDExOCksJm5ic3A7ZnVuY3Rp b24mbmJzcDsic3BlY2lmeV9oZWFkbW9kZWwiJm5ic3A7YXQmbmJzcDtsaW5lJm5ic3A7MjA5LjxC Uj4mZ3Q7Jmd0OyZuYnNwO05vJm5ic3A7ZXhlY3V0YWJsZSZuYnNwO21vZHVsZXMsJm5ic3A7YnV0 Jm5ic3A7c3RpbGwmbmJzcDt1bnJlc29sdmVkJm5ic3A7ZGVwZW5kZW5jaWVzJm5ic3A7b3ImbmJz cDtpbmNvbXBsZXRlPEJSPiZndDsmZ3Q7Jm5ic3A7bW9kdWxlJm5ic3A7aW5wdXRzLjxCUj4mZ3Q7 Jmd0OyZuYnNwO1RoZSZuYnNwO2ZvbGxvd2luZyZuYnNwO21vZHVsZXMmbmJzcDtkaWQmbmJzcDtu b3QmbmJzcDtydW46PEJSPiZndDsmZ3Q7Jm5ic3A7RmFpbGVkOiZuYnNwO00vRUVHJm5ic3A7aGVh ZCZuYnNwO21vZGVsJm5ic3A7c3BlY2lmaWNhdGlvbjxCUj4mZ3Q7Jmd0OyZuYnNwO1NraXBwZWQ6 Jm5ic3A7TS9FRUcmbmJzcDtzb3VyY2UmbmJzcDtpbnZlcnNpb248QlI+Jmd0OyZndDsmbmJzcDtT a2lwcGVkOiZuYnNwO00vRUVHJm5ic3A7aW52ZXJzaW9uJm5ic3A7cmVzdWx0czxCUj4mZ3Q7Jmd0 OzxCUj4mZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwO0F0Jm5ic3A7MjAxMS0wNS0xMyZuYnNwOzIy OjA2OjA2o6wiVmxhZGltaXImbmJzcDtMaXR2YWsiJm5ic3A7Jmx0O2xpdHZhay52bGFkaW1pckBn bWFpbC5jb208QlI+Jmd0OyZndDsmZ3Q7Jm5ic3A7d3JvdGU6PEJSPiZndDsmZ3Q7PEJSPiZndDsm Z3Q7Jmd0O0RlYXImbmJzcDtIYW9yYW4sPEJSPiZndDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZndDtJ Jm5ic3A7dHJpZWQmbmJzcDthbmQmbmJzcDtldmVyeXRoaW5nJm5ic3A7d29ya3MmbmJzcDtmaW5l Jm5ic3A7Zm9yJm5ic3A7bWUuJm5ic3A7TWFrZSZuYnNwO3N1cmUmbmJzcDt5b3VyJm5ic3A7U1BN Jm5ic3A7dmVyc2lvbjxCUj4mZ3Q7Jmd0OyZndDtpcyZuYnNwO3VwJm5ic3A7dG8mbmJzcDtkYXRl Jm5ic3A7YW5kJm5ic3A7aWYmbmJzcDt5b3UmbmJzcDtjYW4ndCZuYnNwO21ha2UmbmJzcDtpdCZu YnNwO3dvcmssJm5ic3A7c2VuZCZuYnNwO21lJm5ic3A7eW91ciZuYnNwO3NhdmVkJm5ic3A7YmF0 Y2g8QlI+Jmd0OyZndDsmZ3Q7YW5kJm5ic3A7SSdsbCZuYnNwO3Rha2UmbmJzcDthbm90aGVyJm5i c3A7bG9vay48QlI+Jmd0OyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jmd0O0Jlc3QsPEJSPiZndDsmZ3Q7 Jmd0OzxCUj4mZ3Q7Jmd0OyZndDtWbGFkaW1pcjxCUj4mZ3Q7Jmd0OyZndDs8QlI+Jmd0OyZndDsm Z3Q7MjAxMS81LzEwJm5ic3A7t8nE8SZuYnNwOyZsdDtsaWhhb3JhbjA2MDNAMTYzLmNvbSZndDs6 PEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtEZWFyJm5ic3A7U1BNJ3MmbmJzcDt1c2Vycyw8QlI+ Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO6GhoaFIYXZlJm5ic3A7eW91Jm5ic3A7ZXZlciZuYnNwO3Vz ZWQmbmJzcDtiYXRjaCZuYnNwO2ludGVyZmFjZSZuYnNwO3RvJm5ic3A7ZG8mbmJzcDszRCZuYnNw O3NvdXJjZSZuYnNwO3JlY29uc3RydWN0aW9uJm5ic3A7Zm9yPEJSPiZndDsmZ3Q7Jmd0OyZndDsm bmJzcDtFRUcvTUVHJm5ic3A7ZGF0YT8mbmJzcDtJJm5ic3A7Y2hvb3NlZCZuYnNwO3RocmVlJm5i c3A7c2VwYXJhdGUmbmJzcDttb2R1bGVzJm5ic3A7Jm5ic3A7Ik0vRUVHJm5ic3A7aGVhZCZuYnNw O21vZGVsPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtzcGVjaWZpY2F0aW9uIiZuYnNwOyJNL0VF RyZuYnNwO3NvdXJjZSZuYnNwO2ludmVyc2lvbiImbmJzcDthbmQmbmJzcDsiTS9FRUcmbmJzcDtp bnZlcnNpb24mbmJzcDtyZXN1bHRzIiZuYnNwO3NvJm5ic3A7YXM8QlI+Jmd0OyZndDsmZ3Q7Jmd0 OyZuYnNwO3RvJm5ic3A7cmVjb25zdHJ1Y3QmbmJzcDt0aGUmbmJzcDtzb3VyY2VzLiZuYnNwO0lu Jm5ic3A7dGhlJm5ic3A7Ik0vRUVHJm5ic3A7aGVhZCZuYnNwO21vZGVsJm5ic3A7c3BlY2lmaWNh dGlvbiImbmJzcDttb2R1bGUsPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtJJm5ic3A7c3BlY2lm aWVkJm5ic3A7JydNZXNoZXMmbHQ7TWVzaGVzJm5ic3A7c291cmNlJmx0O2luZGl2aWR1YWwmbmJz cDtzdHJ1Y3R1YWwmbmJzcDtpbWFnZScnJm5ic3A7YW5kJm5ic3A7dGhlbjxCUj4mZ3Q7Jmd0OyZn dDsmZ3Q7Jm5ic3A7c2VsZWN0ZWQmbmJzcDthJm5ic3A7bXJpJm5ic3A7ZmlsZSZuYnNwO25hbWVk Jm5ic3A7InNEUDc0LTAwMDMtMDAwMDAtMDAwMDAxLTAxLmltZywxIi4mbmJzcDsmbmJzcDtXaGVu Jm5ic3A7YWxsJm5ic3A7d2FzPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtyZWFkeSZuYnNwO2Fu ZCZuYnNwO3RoZW4mbmJzcDtJJm5ic3A7cmFuJm5ic3A7dGhlJm5ic3A7YmF0Y2gsJm5ic3A7aG93 ZXZlciwmbmJzcDtJJm5ic3A7Zm91bmQmbmJzcDt0aGF0Jm5ic3A7aXQmbmJzcDtkaW4ndCZuYnNw O2V4Y3V0ZWQmbmJzcDt0aGU8QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO3NwZWNpZmllZCZuYnNw O3N0cnVjdHVhbCZuYnNwO2ltYWdlJm5ic3A7KCZuYnNwO2FzJm5ic3A7eW91Jm5ic3A7Y2FuJm5i c3A7c2VlJm5ic3A7YmVsb3cmbmJzcDsndGVtcGxhdGU9MScpJm5ic3A7YW5kJm5ic3A7dGhlPEJS PiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDttYXRsYWImbmJzcDtjb21tYW5kJm5ic3A7d2luZG93Jm5i c3A7YXBwZWFyZWQmbmJzcDt0aG9zZTo8QlI+Jmd0OyZndDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZn dDsmZ3Q7Jm5ic3A7U1BNODombmJzcDtzcG1fZWVnX2ludl9tZXNoX3VpJm5ic3A7KHYzNzMxKSZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOzIxOjM5OjA2 Jm5ic3A7LSZuYnNwOzA5LzA1LzIwMTE8QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOz09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVtcGxhdGU6Jm5ic3A7MTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 QWZmaW5lOiZuYnNwO1s0eDQmbmJzcDtkb3VibGVdPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDtzTVJJOiZuYnNwOydDOlxQcm9ncmFtPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtG aWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxzaW5nbGVfc3Vial9UMS5uaWknPEJS PiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtNc2l6ZTombmJzcDsyPEJSPiZndDsmZ3Q7Jmd0OyZn dDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNzX21u aTombmJzcDtbMXgxJm5ic3A7c3RydWN0XTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19jdHg6Jm5ic3A7J0M6XFBy b2dyYW08QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO0ZpbGVzXE1BVExBQlx0b29sYm94XHNwbThc Y2Fub25pY2FsXGNvcnRleF84MTk2LnN1cmYuZ2lpJzxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19zY2FscDombmJzcDsnQzpcUHJvZ3Jh bTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7RmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5v bmljYWxcc2NhbHBfMjU2Mi5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3Nfb3NrdWxsOiZuYnNwOydDOlxQcm9ncmFtPEJSPiZndDsm Z3Q7Jmd0OyZndDsmbmJzcDtGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxvc2t1 bGxfMjU2Mi5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwO3Rlc3NfaXNrdWxsOiZuYnNwOydDOlxQcm9ncmFtPEJSPiZndDsmZ3Q7Jmd0OyZn dDsmbmJzcDtGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxpc2t1bGxfMjU2Mi5z dXJmLmdpaSc8QlI+Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO2ZpZDombmJz cDtbMXgxJm5ic3A7c3RydWN0XTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jmd0OyZn dDsmbmJzcDsmbmJzcDsmbmJzcDuhoUkmbmJzcDtkaWRuJ3QmbmJzcDt1bmRlcnN0YW5kJm5ic3A7 d2h5LiZuYnNwO0FueW9uZSZuYnNwO3dobyZuYnNwO2tub3dzJm5ic3A7dGhlJm5ic3A7cmVhc29u Jm5ic3A7PyZuYnNwO1RoYW5rcyZuYnNwO3ZlcnkmbmJzcDttdWNoPEJSPiZndDsmZ3Q7Jmd0OyZn dDsmbmJzcDtmb3ImbmJzcDthbnkmbmJzcDtoZWxwJm5ic3A7ITxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7 PEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDstLTxCUj4mZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7SGFv cmFuJm5ic3A7TEkmbmJzcDsoTVMpPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtCcmFpbiZuYnNw O0ltYWdpbmcmbmJzcDtMYWIsPEJSPiZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtSZXNlYXJjaCZuYnNw O0NlbnRlciZuYnNwO2ZvciZuYnNwO0xlYXJuaW5nJm5ic3A7U2NpZW5jZSw8QlI+Jmd0OyZndDsm Z3Q7Jmd0OyZuYnNwO1NvdXRoZWFzdCZuYnNwO1VuaXZlcnNpdHk8QlI+Jmd0OyZndDsmZ3Q7Jmd0 OyZuYnNwOzImbmJzcDtTaSZuYnNwO1BhaSZuYnNwO0xvdSZuYnNwOywmbmJzcDtOYW5qaW5nLCZu YnNwOzIxMDA5NiwmbmJzcDtQLlIuQ2hpbmE8QlI+Jmd0OyZndDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0 OyZndDsmZ3Q7PEJSPiZndDsmZ3Q7PEJSPiZndDsmZ3Q7PEJSPiZndDsmZ3Q7PEJSPiZndDsmZ3Q7 PEJSPiZndDsmZ3Q7PEJSPjwvRElWPjxicj48YnI+PHNwYW4gdGl0bGU9Im5ldGVhc2Vmb290ZXIi PjxzcGFuIGlkPSJuZXRlYXNlX21haWxfZm9vdGVyIj48L3NwYW4+PC9zcGFuPg== ------=_Part_130052_17267360.1305537198329-- ========================================================================= Date: Mon, 16 May 2011 11:48:56 +0200 Reply-To: Lars Meyer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lars Meyer <[log in to unmask]> Subject: dipole waveforms |=?iso-8859-1?Q?=A0units?= Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Mime-Version: 1.0 (Apple Message framework v1084) Message-ID: <[log in to unmask]> dear all, i am using spm_eeg_dipole_waveforms.m to retrieve dipole timecourses for = two dipole priors in a vb-ecd- localisation. everything works fine, so = no worries there, but i was wondering on what the units of the dipole = amplitudes are=E2=80=A6 i remember some older post on the exact same = question, have talked to the asker, but it seems there is no answer yet. or is there a working solution from the programmers of the above = function? thanks in advance, and all best, l. Lars Meyer Max Planck Institute for Human Cognitive & Brain Sciences Department of Neuropsychology Stephanstra=EF=AC=82e 1a 04103 Leipzig | Germany Office | +49 341 99 40 22 66 Mail | [log in to unmask] Web | www.cbs.mpg.de/~lmeyer ========================================================================= Date: Mon, 16 May 2011 17:57:36 +0800 Reply-To: Syu-Jyun Peng <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Syu-JyunPeng <[log in to unmask]> Subject: Re: produce T1W and T2W template Comments: To: JohnAshburner <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_348646_1661673744.1305539856561" Message-ID: <1767218365.1305539856983.JavaMail.nobody@sg2000-ap-1> ------=_Part_348646_1661673744.1305539856561 Content-Type: text/plain;charset=utf-8 Content-Transfer-Encoding: 7bit <div>Dear Sir<br /><br />Thank you very much for your messages.<br /><br />We can&nbsp;obtain&nbsp;information how to produce template&nbsp;in chapter 41.<br /><br />We would like obtain the T1W and T2W template, but the chaper 41 show the WM and GM template.<br /><br />How to get the T1W and T2W template using SPM?<br /><br />It would be greatly appreciated, if you could look&nbsp; into this for us.<br /><br />Best regards,<br /><br />SJ<br /><br /><br /></div> <div id="HiNet_separatrix"></div> <div id="HiNet_advert"></div> <div></div> <div>&gt;&gt;&gt;&gt;&gt; Original Message &lt;&lt;&lt;&lt;&lt;</div> <div><b>From:</b> <a href="mailto:[log in to unmask]">JohnAshburner</a></div> <div><b>To:</b> <a href="mailto:[log in to unmask]">[log in to unmask]</a></div> <div><b>Sent:</b> Thu,12 May 2011 18:51:43 Asia/Taipei</div> <div><b>Subject:</b> Re: [SPM] Consult</div> <div> <pre>Chapter 41 (Using DARTEL) of the spm8 manual would be a good place to begin. You can find it in spm8/man/manual.pdf Best regards, -John 2011/5/12 Syu-JyunPeng &lt;[log in to unmask]&gt;: &gt; &gt; Dear Sir &gt; &gt; I'm a&nbsp;EE PhD student in Taiwan. &gt; &gt; I have a question. &gt; &gt; I have 20 sets of control subject&nbsp;SPGR MRI. &gt; &gt; How could I use SPM&nbsp; software to&nbsp;obtain template&nbsp;like as&nbsp;the attached file? &gt; &gt; Could you&nbsp;give the&nbsp;instructions step by step for me? &gt; &gt; It would be greatly appreciated, if you could look&nbsp; into this for us. &gt; &gt; Thank you. &gt; &gt; Best regards, &gt; &gt; Syu-Jyun Peng </pre> </div> ------=_Part_348646_1661673744.1305539856561 Content-Type: text/html;charset=utf-8 Content-Transfer-Encoding: 7bit <div>Dear Sir<br /><br />Thank you very much for your messages.<br /><br />We can obtain information how to produce template in chapter 41.<br /><br />We would like obtain the T1W and T2W template, but the chaper 41 show the WM and GM template.<br /><br />How to get the T1W and T2W template using SPM?<br /><br />It would be greatly appreciated, if you could look into this for us.<br /><br />Best regards,<br /><br />SJ<br /><br /><br /></div> <div id="HiNet_separatrix"></div> <div id="HiNet_advert"></div> <div></div> <div>>>>>> Original Message <<<<<</div> <div><b>From:</b> <a href="mailto:[log in to unmask]">JohnAshburner</a></div> <div><b>To:</b> <a href="mailto:[log in to unmask]">[log in to unmask]</a></div> <div><b>Sent:</b> Thu,12 May 2011 18:51:43 Asia/Taipei</div> <div><b>Subject:</b> Re: [SPM] Consult</div> <div> <pre>Chapter 41 (Using DARTEL) of the spm8 manual would be a good place to begin. You can find it in spm8/man/manual.pdf Best regards, -John 2011/5/12 Syu-JyunPeng <[log in to unmask]>: > > Dear Sir > > I'm a EE PhD student in Taiwan. > > I have a question. > > I have 20 sets of control subject SPGR MRI. > > How could I use SPM software to obtain template like as the attached file? > > Could you give the instructions step by step for me? > > It would be greatly appreciated, if you could look into this for us. > > Thank you. > > Best regards, > > Syu-Jyun Peng </pre> </div> ------=_Part_348646_1661673744.1305539856561-- ========================================================================= Date: Mon, 16 May 2011 12:04:19 +0100 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: Batch problem--3D Source Reconstruction Comments: To: =?UTF-8?B?6aOe6bif?= <[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: <4553222900002898154@unknownmsgid> Dear Haoran, On 16 May 2011, at 10:13, "=E9=A3=9E=E9=B8=9F" <[log in to unmask]> wrote= : > Dear Vladimir, > =E3=80=80=E3=80=80Your guess was right. I really couldn't do 3D sources r= econstruction of that data by GUI. It reminded me ''checkmeeg: data scale m= issing, assigning default > checkmeeg: no fiducials are defined''. Then I did preprocess steps again,= and found that I couldn't obtain the fiducials automatically(when I finish= ed Converting step). I checked the other datasets, luckily, they didn't hav= e this problem. I guess there must be some wrongs of my original dataset (o= nly that one), right ? right > =E3=80=80=E3=80=80As for reconstruction batch problem, with the latest ve= rsion of spm8(4029) and normal dataset, I can do reconstruction by that sav= ed "renconstruction_batch" successfully. Thus, the problems related to reco= nstruction batch that I confronted previously may result from the different= versions of spm8. > By the way, in ''MEEG head model specification> EEG head modle'' m= odule, must I specify the ''EEG head modle'' if I just process MEG dataset? One peculiarity of batch is that when you build the configuration you can't use information from the dataset which only becomes available when you run the batch. In particular you can't know if the dataset in question is EEG or MEG. So don't worry about the EEG head model. If your dataset is MEG it will be ignored. Best, Vladimir > =E3=80=80=E3=80=80Thank you very much for helping me solve these tiny pro= blems! > > =E3=80=80=E3=80=80Haoran. > > > At 2011-05-16 06:38:23=EF=BC=8C"Vladimir Litvak" <[log in to unmask] om> wrote: > > >Dear Haoran, > > > >The problem seems to be not in the batch but in your MEG data (or was > >it EEG?). There are no valid fiducials in that dataset. Could you > >perform source reconstruction using the GUI? I'd be surprised if you > >could. So we need to focus on how to get fiducials into your dataset > >and for that you need to tell me more about where that data comes > >from. > > > >Best, > > > >Vladimir > > > >2011/5/15 =E9=A3=9E=E9=B8=9F <[log in to unmask]>: > >> Dear Vladimir, > >> =E3=80=80=E3=80=80I tried again with the latest version of spm8(4029),= the previous problem( > >> template=3D1) disappeared. But this time, I confronted a new problem. = It > >> reminded me "No executable modules, but still unresolved dependencies = or > >> incomplete module inputs." But I don't know where the problems are. Co= uld > >> you help me see my reconstruction_batch.mat and give me some advice ? = Thanks > >> a lot. > >> > >> Haoran. > >> > >> These code appears in the matlab command window when I ran the = batch: > >> SPM8: spm_eeg_inv_mesh_ui (v4027) 11:09:13 - 15/05/20= 11 > >> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >> template: 0 > >> sMRI: 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01.img,1' > >> def: 'D:\Processed\DP01\y_sKEYAN-0002-00000-000001-01.nii' > >> Affine: [4x4 double] > >> Msize: 2 > >> tess_mni: [1x1 struct] > >> tess_ctx: > >> 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01cortex_8196.surf.gii' > >> tess_scalp: > >> 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01scalp_2562.surf.gii' > >> tess_oskull: > >> 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01oskull_2562.surf.gii' > >> tess_iskull: > >> 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01iskull_2562.surf.gii' > >> fid: [1x1 struct] > >> Failed 'M/EEG head model specification' > >> Reference to non-existent field 'fid'. > >> In file "C:\Program Files\MATLAB\spm8new\config\spm_cfg_eeg_inv_headmo= del.m" > >> (v4118), function "specify_headmodel" at line 209. > >> No executable modules, but still unresolved dependencies or incomplete > >> module inputs. > >> The following modules did not run: > >> Failed: M/EEG head model specification > >> Skipped: M/EEG source inversion > >> Skipped: M/EEG inversion results > >> > >> > >> At 2011-05-13 22:06:06=EF=BC=8C"Vladimir Litvak" <litvak.vladimir@gmai= l.com > >>> wrote: > >> > >>>Dear Haoran, > >>> > >>>I tried and everything works fine for me. Make sure your SPM version > >>>is up to date and if you can't make it work, send me your saved batch > >>>and I'll take another look. > >>> > >>>Best, > >>> > >>>Vladimir > >>> > >>>2011/5/10 =E9=A3=9E=E9=B8=9F <[log in to unmask]>: > >>>> Dear SPM's users, > >>>> =E3=80=80=E3=80=80Have you ever used batch interface to do 3D source= reconstruction for > >>>> EEG/MEG data? I choosed three separate modules "M/EEG head model > >>>> specification" "M/EEG source inversion" and "M/EEG inversion results= " so as > >>>> to reconstruct the sources. In the "M/EEG head model specification" = module, > >>>> I specified ''Meshes<Meshes source<individual structual image'' and = then > >>>> selected a mri file named "sDP74-0003-00000-000001-01.img,1". When = all was > >>>> ready and then I ran the batch, however, I found that it din't excut= ed the > >>>> specified structual image ( as you can see below 'template=3D1') and= the > >>>> matlab command window appeared those: > >>>> > >>>> SPM8: spm_eeg_inv_mesh_ui (v3731) 21:39:06 - 09/05/= 2011 > >>>> =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D > >>>> template: 1 > >>>> Affine: [4x4 double] > >>>> sMRI: 'C:\Program > >>>> Files\MATLAB\toolbox\spm8\canonical\single_subj_T1.nii' > >>>> Msize: 2 > >>>> tess_mni: [1x1 struct] > >>>> tess_ctx: 'C:\Program > >>>> Files\MATLAB\toolbox\spm8\canonical\cortex_8196.surf.gii' > >>>> tess_scalp: 'C:\Program > >>>> Files\MATLAB\toolbox\spm8\canonical\scalp_2562.surf.gii' > >>>> tess_oskull: 'C:\Program > >>>> Files\MATLAB\toolbox\spm8\canonical\oskull_2562.surf.gii' > >>>> tess_iskull: 'C:\Program > >>>> Files\MATLAB\toolbox\spm8\canonical\iskull_2562.surf.gii' > >>>> fid: [1x1 struct] > >>>> > >>>> =E3=80=80I didn't understand why. Anyone who knows the reason ? Th= anks very much > >>>> for any help ! > >>>> > >>>> -- > >>>> Haoran LI (MS) > >>>> Brain Imaging Lab, > >>>> Research Center for Learning Science, > >>>> Southeast University > >>>> 2 Si Pai Lou , Nanjing, 210096, P.R.China > >>>> > >>>> > >> > >> > >> > >> > >> > > ========================================================================= Date: Mon, 16 May 2011 14:33:37 +0100 Reply-To: Ralph Schnitker <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ralph Schnitker <[log in to unmask]> Subject: Open position as a Leader of Core Facility for Brain Imaging in Aachen Germany Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Leader of Core Facility for Brain Imaging in Aachen Germany =20 The Core Facility "Brain Imaging" at the Interdisciplinary Centre for Cli= nical=20 Research (IZKF-Aachen) of the RWTH Aachen University, Germany, is seeking= for a new technical and operative leader (1.0 TVL 14 for 3 years). We ar= e looking for a researcher at the postdoctoral level with a strong backgr= ound in imaging neuroscience. The position requires training in cognitive= neuroscience, computer sciences or related areas as well as experience i= n functional imaging data analysis (e.g. SPM5/8) and in designing fMRI ex= periments.=20 The successful candidate will not only coordinate and supervise the inter= disciplinary team in cooperation with the methodical leader of the Core F= acility but also develop own research ideas and projects. The Core Facili= ty supports researchers from the medical faculty and affiliated institute= s in conducting brain imaging research projects. It hosts a large computi= ng facility, offers single and group trainings and carries out the MRI ac= quisitions. Beside these support tasks the member will have access to one= 3T and two 1.5T Philips Achieva and a 3T Siemens Trio MR scanners. Mult= imodal approaches encompassing PET, PET-CT, MEG, TMS or tDCS are encourag= ed. Own research projects are required and a progress toward habilitation= is possible.=20 The appointment will be for 3 years with an opportunity of prolongation f= or another 3 years. The position has to be filled 1st July 2011 or as soo= n as possible thereafter. Qualified women are explicitly invited to apply and handicapped candidate= s with equal qualification will be given preference. Do not hesitate to contact one of the three consultants of the Core Facil= ity, Profs. Klaus Mathiak ([log in to unmask]), Klaus Willmes (willmes@= neuropsych.rwth-aachen.de) or Martin Wiesmann ([log in to unmask]). Fo= r further information visit our website at www.izkf.rwth-aachen.de . Applications (English or German) must include a full CV and name and addr= ess of two references and should be submitted until 31th of May by mail o= r email to: [log in to unmask] ========================================================================= Date: Mon, 16 May 2011 17:32:30 +0100 Reply-To: Mike Sugarman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mike Sugarman <[log in to unmask]> Subject: Model Estimation Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello SPM experts, I'm running a flexible factorial design with a library of PET scans. My d= esign will set up properly, however when I attempt to estimate the model = sometimes I get the error message Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =3D -1.000000e+00.=20 > In spm_inv at 22 In spm_reml at 119 In spm_spm at 860 In spm_getSPM at 235 In spm_results_ui at 263 And this message will repeat dozens of times. When I remove a few subject= s, the problem typically resolves. Can anybody tell me what might be the = issue with the scans that are causing this problem? Thanks, -Mike Sugarman ========================================================================= Date: Mon, 16 May 2011 11:18:40 -0700 Reply-To: LiuKarl <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: LiuKarl <[log in to unmask]> Subject: small volume correction Content-Type: multipart/alternative; boundary="_ccc1b30e-71bf-43eb-b4bc-0e0bf67c0598_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_ccc1b30e-71bf-43eb-b4bc-0e0bf67c0598_ Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: 8bit Hello, SPM-users I have a question of small volume correction. Many published paper reported (p = 0.05, svc). Is this p valued uncorrected or corrected by standard? Should FDR be used in SVC? (When you click the SVC button, there is no option for correction at all. So there correction is from when you click the Results button and the option of p value adjustment to control: FWE FDR none ) How is the svc different from WFU ROI analysis? When you start the pickatlas from toolbox and then click the Results button, it will still ask the the option of p value adjustment to control: FWE FDR none. Karl --_ccc1b30e-71bf-43eb-b4bc-0e0bf67c0598_ Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p4GIIfEM018106 <html> <head> <style><!-- .hmmessage P { margin:0px; padding:0px } body.hmmessage { font-size: 10pt; font-family:=CE=A2=C8=ED=D1=C5=BA=DA } --></style> </head> <body class=3D'hmmessage'> <SPAN style=3D"FONT-SIZE: 12pt; FONT-FAMILY: 'Times New Roman'; mso-farea= st-font-family: SimSun; mso-ansi-language: EN-US; mso-fareast-language: Z= H-CN; mso-bidi-language: AR-SA"> <P class=3DMsoNormal style=3D"MARGIN: 0in 0in 0pt">Hello, SPM-users</P> <P class=3DMsoNormal style=3D"MARGIN: 0in 0in 0pt"><?xml:namespace prefix= =3D o ns =3D "urn:schemas-microsoft-com:office:office" /><o:p> </o:= p></P> <P class=3DMsoNormal style=3D"MARGIN: 0in 0in 0pt">I have a question of s= mall volume correction. Many published paper reported (p =3D 0.05,<SPAN s= tyle=3D"mso-spacerun: yes"> </SPAN>svc). Is this p valued uncorrect= ed or corrected by standard? Should FDR be used in SVC? (When you click t= he SVC button, there is no option for correction at all. So there correct= ion is from when you click the Results button and the option of p value a= djustment to control: FWE FDR none )</P> <P class=3DMsoNormal style=3D"MARGIN: 0in 0in 0pt"><o:p> </o:p></P> <P class=3DMsoNormal style=3D"MARGIN: 0in 0in 0pt">How is the svc differe= nt from WFU ROI analysis? <SPAN style=3D"mso-spacerun: yes"> </SPAN>= When you start the pickatlas from toolbox and then click the Results butt= on, it will still ask the the option of p value adjustment to control: FW= E FDR none. </P> <BR> <BR> Karl</SPAN><BR> </body> </html> --_ccc1b30e-71bf-43eb-b4bc-0e0bf67c0598_-- ========================================================================= Date: Mon, 16 May 2011 22:43:30 +0100 Reply-To: Rebecca Ray <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Rebecca Ray <[log in to unmask]> Subject: slicetiming difficulty Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> I have successfully preprocessed all of my data with the exception of thi= s one subject. Our TR is 2.6 but it changes it to 2.7 and gives the foll= owing error message:=20=20 SPM8: spm_slice_timing (v3756) 16:27:38 - 16/05/2011 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Your TR is 2.7 Failed 'Slice Timing' Improper assignment with rectangular empty matrix. In file "/Applications/spm8/spm_slice_timing.m" (v3756), function "spm_sl= ice_timing" at line 232. In file "/Applications/spm8/config/spm_run_st.m" (v2312), function "spm_r= un_st" at line 25. I have spoken with our physicist who does not use SPM, and he does not th= ink it is a problem with the data. I can display the data prior to slicetiming but not after. After it appe= ars to have spit out files but gives an error when you try to display the= se images: Running 'Display Image' Image "/Users/rebeccaray/Documents/UW_Projects/Vivek/Data_Processing/Raw_= Data/022_S2/Functionals/Emotion/scan2/agvscan0329.img" can not be resampl= ed Image "/Users/rebeccaray/Documents/UW_Projects/Vivek/Data_Processing/Raw_= Data/022_S2/Functionals/Emotion/scan2/agvscan0329.img" can not be resampl= ed Done 'Display Image' Done Any advice would be awesome! ========================================================================= Date: Mon, 16 May 2011 23:06:34 +0100 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: Re: Model Estimation Comments: To: Mike Sugarman <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec5485c4e5d9dcc04a36bdee7 Message-ID: <[log in to unmask]> --bcaec5485c4e5d9dcc04a36bdee7 Content-Type: text/plain; charset=ISO-8859-1 Dear Mike, I have seen this before when the input images have dramatically different scale. If, say, some of the images are 1000-times more intense than the others, SPM's ReML will then scale down those images (depending on the design and how the badly scaled images are arranged relative to the design factors). Try looking at the images all at once with the same color scale with CheckReg. Alternatively look at diag(SPM.xVi.V) (the SPM variable in SPM.mat of a troublesome analysis); these values should all be roughly the same or within an order of magnitude. If some are 100x or 1000x different from the others, this would explain the problem (and suggests maybe scalefactor artifacts would explain the differences). (Are maybe some of the PET images in units of "ECAT" counts, and others in a fractional rate? ). -Tom On Mon, May 16, 2011 at 5:32 PM, Mike Sugarman <[log in to unmask]>wrote: > Hello SPM experts, > > I'm running a flexible factorial design with a library of PET scans. My > design will set up properly, however when I attempt to estimate the model > sometimes I get the error message > > Warning: Matrix is close to singular or badly scaled. > Results may be inaccurate. RCOND = -1.000000e+00. > > In spm_inv at 22 > In spm_reml at 119 > In spm_spm at 860 > In spm_getSPM at 235 > In spm_results_ui at 263 > > And this message will repeat dozens of times. When I remove a few subjects, > the problem typically resolves. Can anybody tell me what might be the issue > with the scans that are causing this problem? > > Thanks, > -Mike Sugarman > -- ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 --bcaec5485c4e5d9dcc04a36bdee7 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Mike,<div><br></div><div>I have seen this before when the input images= have dramatically different scale. =A0If, say, some of the images are 1000= -times more intense than the others, SPM's ReML will then scale down th= ose images (depending on the design and how the badly scaled images are arr= anged relative to the design factors).</div> <div><br></div><div>Try looking at the images all at once with the same col= or scale with CheckReg. =A0Alternatively look at diag(SPM.xVi.V) (the SPM v= ariable in SPM.mat of a troublesome analysis); these values should all be r= oughly the same or within an order of magnitude. =A0If some are 100x or 100= 0x different from the others, this would explain the problem (and suggests = maybe scalefactor artifacts would explain the differences). =A0</div> <div><br></div><div>(Are maybe some of the PET images in units of "ECA= T" counts, and others in a fractional rate? ).</div><div><br></div><di= v>-Tom</div><div><br></div><div><br><br><div class=3D"gmail_quote">On Mon, = May 16, 2011 at 5:32 PM, Mike Sugarman <span dir=3D"ltr"><<a href=3D"mai= lto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<= br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">Hello SPM experts,<br> <br> I'm running a flexible factorial design with a library of PET scans. My= design will set up properly, however when I attempt to estimate the model = sometimes I get the error message<br> <br> Warning: Matrix is close to singular or badly scaled.<br> =A0 =A0 =A0 =A0 Results may be inaccurate. RCOND =3D -1.000000e+00.<br> > In spm_inv at 22<br> =A0In spm_reml at 119<br> =A0In spm_spm at 860<br> =A0In spm_getSPM at 235<br> =A0In spm_results_ui at 263<br> <br> And this message will repeat dozens of times. When I remove a few subjects,= the problem typically resolves. Can anybody tell me what might be the issu= e with the scans that are causing this problem?<br> <br> Thanks,<br> <font color=3D"#888888">-Mike Sugarman<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>________________= ____________________________<br>Thomas Nichols, PhD<br>Principal Research F= ellow, Head of Neuroimaging Statistics<br>Department of Statistics & Wa= rwick Manufacturing Group<br> University of Warwick<br>Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email= : <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= ><br>Phone, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 = 7652 4532<br> <br> </div> --bcaec5485c4e5d9dcc04a36bdee7-- ========================================================================= Date: Tue, 17 May 2011 09:31:28 +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: Re: Batch problem--3D Source Reconstruction Comments: To: Vladimir Litvak <[log in to unmask]> In-Reply-To: <4553222900002898154@unknownmsgid> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_13687_12466455.1305595888996" Message-ID: <[log in to unmask]> ------=_Part_13687_12466455.1305595888996 Content-Type: text/plain; charset=GBK Content-Transfer-Encoding: base64 RGVhciBWbGFkaW1pciwKoaGhoVRoYW5rIHlvdSB2ZXJ5IG11Y2ghIE1heSB5b3UgYSBoYXBweSBk YXkhCqGhoaFIYW9yYW4uCgogCgrU2iAyMDExLTA1LTE2IDE5OjA0OjE5o6wiVmxhZGltaXIgTGl0 dmFrIiA8bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbT4g0LS1wKO6Cgo+RGVhciBIYW9yYW4sCj4K Pgo+Cj5PbiAxNiBNYXkgMjAxMSwgYXQgMTA6MTMsICK3ycTxIiA8bGloYW9yYW4wNjAzQDE2My5j b20+IHdyb3RlOgo+Cj4+IERlYXIgVmxhZGltaXIsCj4+IKGhoaFZb3VyIGd1ZXNzIHdhcyByaWdo dC4gSSByZWFsbHkgY291bGRuJ3QgZG8gM0Qgc291cmNlcyByZWNvbnN0cnVjdGlvbiBvZiB0aGF0 IGRhdGEgYnkgR1VJLiBJdCByZW1pbmRlZCBtZSAnJ2NoZWNrbWVlZzogZGF0YSBzY2FsZSBtaXNz aW5nLCBhc3NpZ25pbmcgZGVmYXVsdAo+PiBjaGVja21lZWc6IG5vIGZpZHVjaWFscyBhcmUgZGVm aW5lZCcnLiBUaGVuIEkgZGlkIHByZXByb2Nlc3Mgc3RlcHMgYWdhaW4sIGFuZCBmb3VuZCB0aGF0 IEkgY291bGRuJ3Qgb2J0YWluIHRoZSBmaWR1Y2lhbHMgYXV0b21hdGljYWxseSh3aGVuIEkgZmlu aXNoZWQgQ29udmVydGluZyBzdGVwKS4gSSBjaGVja2VkIHRoZSBvdGhlciBkYXRhc2V0cywgbHVj a2lseSwgdGhleSBkaWRuJ3QgaGF2ZSB0aGlzIHByb2JsZW0uIEkgZ3Vlc3MgdGhlcmUgbXVzdCBi ZSBzb21lIHdyb25ncyBvZiBteSBvcmlnaW5hbCBkYXRhc2V0IChvbmx5IHRoYXQgb25lKSwgcmln aHQgPwo+Cj5yaWdodAo+Cj4+IKGhoaFBcyBmb3IgcmVjb25zdHJ1Y3Rpb24gYmF0Y2ggcHJvYmxl bSwgd2l0aCB0aGUgbGF0ZXN0IHZlcnNpb24gb2Ygc3BtOCg0MDI5KSBhbmQgbm9ybWFsIGRhdGFz ZXQsIEkgY2FuIGRvIHJlY29uc3RydWN0aW9uIGJ5IHRoYXQgc2F2ZWQgInJlbmNvbnN0cnVjdGlv bl9iYXRjaCIgc3VjY2Vzc2Z1bGx5LiBUaHVzLCB0aGUgcHJvYmxlbXMgcmVsYXRlZCB0byByZWNv bnN0cnVjdGlvbiBiYXRjaCB0aGF0IEkgY29uZnJvbnRlZCBwcmV2aW91c2x5IG1heSByZXN1bHQg ZnJvbSB0aGUgZGlmZmVyZW50IHZlcnNpb25zIG9mIHNwbTguCj4+ICAgICAgICBCeSB0aGUgd2F5 LCBpbiAnJ01FRUcgaGVhZCBtb2RlbCBzcGVjaWZpY2F0aW9uPiBFRUcgaGVhZCBtb2RsZScnIG1v ZHVsZSwgbXVzdCBJIHNwZWNpZnkgdGhlICcnRUVHIGhlYWQgbW9kbGUnJyBpZiBJIGp1c3QgcHJv Y2VzcyBNRUcgZGF0YXNldD8KPgo+T25lIHBlY3VsaWFyaXR5IG9mIGJhdGNoIGlzIHRoYXQgd2hl biB5b3UgYnVpbGQgdGhlIGNvbmZpZ3VyYXRpb24geW91Cj5jYW4ndCB1c2UgaW5mb3JtYXRpb24g ZnJvbSB0aGUgZGF0YXNldCB3aGljaCBvbmx5IGJlY29tZXMgYXZhaWxhYmxlCj53aGVuIHlvdSBy dW4gdGhlIGJhdGNoLiBJbiBwYXJ0aWN1bGFyIHlvdSBjYW4ndCBrbm93IGlmIHRoZSBkYXRhc2V0 IGluCj5xdWVzdGlvbiBpcyBFRUcgb3IgTUVHLiBTbyBkb24ndCB3b3JyeSBhYm91dCB0aGUgRUVH IGhlYWQgbW9kZWwuIElmCj55b3VyIGRhdGFzZXQgaXMgTUVHIGl0IHdpbGwgYmUgaWdub3JlZC4K Pgo+QmVzdCwKPgo+VmxhZGltaXIKPgo+Cj4+IKGhoaFUaGFuayB5b3UgdmVyeSBtdWNoIGZvciBo ZWxwaW5nIG1lIHNvbHZlIHRoZXNlIHRpbnkgcHJvYmxlbXMhCj4+Cj4+IKGhoaFIYW9yYW4uCj4+ Cj4+Cj4+IEF0IDIwMTEtMDUtMTYgMDY6Mzg6MjOjrCJWbGFkaW1pciBMaXR2YWsiIDxsaXR2YWsu dmxhZGltaXJAZ21haWwuY29tPiB3cm90ZToKPj4KPj4gPkRlYXIgSGFvcmFuLAo+PiA+Cj4+ID5U aGUgcHJvYmxlbSBzZWVtcyB0byBiZSBub3QgaW4gdGhlIGJhdGNoIGJ1dCBpbiB5b3VyIE1FRyBk YXRhIChvciB3YXMKPj4gPml0IEVFRz8pLiBUaGVyZSBhcmUgbm8gdmFsaWQgZmlkdWNpYWxzIGlu IHRoYXQgZGF0YXNldC4gQ291bGQgeW91Cj4+ID5wZXJmb3JtIHNvdXJjZSByZWNvbnN0cnVjdGlv biB1c2luZyB0aGUgR1VJPyBJJ2QgYmUgc3VycHJpc2VkIGlmIHlvdQo+PiA+Y291bGQuIFNvIHdl IG5lZWQgdG8gZm9jdXMgb24gaG93IHRvIGdldCBmaWR1Y2lhbHMgaW50byB5b3VyIGRhdGFzZXQK Pj4gPmFuZCBmb3IgdGhhdCB5b3UgbmVlZCB0byB0ZWxsIG1lIG1vcmUgYWJvdXQgd2hlcmUgdGhh dCBkYXRhIGNvbWVzCj4+ID5mcm9tLgo+PiA+Cj4+ID5CZXN0LAo+PiA+Cj4+ID5WbGFkaW1pcgo+ PiA+Cj4+ID4yMDExLzUvMTUgt8nE8SA8bGloYW9yYW4wNjAzQDE2My5jb20+Ogo+PiA+PiBEZWFy IFZsYWRpbWlyLAo+PiA+PiChoaGhSSB0cmllZCBhZ2FpbiB3aXRoIHRoZSBsYXRlc3QgdmVyc2lv biBvZiBzcG04KDQwMjkpLCB0aGUgcHJldmlvdXMgcHJvYmxlbSgKPj4gPj4gdGVtcGxhdGU9MSkg ZGlzYXBwZWFyZWQuIEJ1dCB0aGlzIHRpbWUsIEkgY29uZnJvbnRlZCBhIG5ldyBwcm9ibGVtLiBJ dAo+PiA+PiByZW1pbmRlZCBtZSAiTm8gZXhlY3V0YWJsZSBtb2R1bGVzLCBidXQgc3RpbGwgdW5y ZXNvbHZlZCBkZXBlbmRlbmNpZXMgb3IKPj4gPj4gaW5jb21wbGV0ZSBtb2R1bGUgaW5wdXRzLiIg QnV0IEkgZG9uJ3Qga25vdyB3aGVyZSB0aGUgcHJvYmxlbXMgYXJlLiBDb3VsZAo+PiA+PiB5b3Ug aGVscCBtZSBzZWUgbXkgcmVjb25zdHJ1Y3Rpb25fYmF0Y2gubWF0IGFuZCBnaXZlIG1lIHNvbWUg YWR2aWNlID8gVGhhbmtzCj4+ID4+IGEgbG90Lgo+PiA+Pgo+PiA+PiAgICAgICAgSGFvcmFuLgo+ PiA+Pgo+PiA+PiAgICAgICAgVGhlc2UgY29kZSBhcHBlYXJzIGluIHRoZSBtYXRsYWIgY29tbWFu ZCB3aW5kb3cgd2hlbiBJIHJhbiB0aGUgYmF0Y2g6Cj4+ID4+IFNQTTg6IHNwbV9lZWdfaW52X21l c2hfdWkgKHY0MDI3KSAgICAgICAgICAgICAgICAgIDExOjA5OjEzIC0gMTUvMDUvMjAxMQo+PiA+ PiA9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT0KPj4gPj4gICAgICAgIHRlbXBsYXRlOiAwCj4+ID4+ICAgICAgICAg ICAgc01SSTogJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMS5p bWcsMScKPj4gPj4gICAgICAgICAgICAgZGVmOiAnRDpcUHJvY2Vzc2VkXERQMDFceV9zS0VZQU4t MDAwMi0wMDAwMC0wMDAwMDEtMDEubmlpJwo+PiA+PiAgICAgICAgICBBZmZpbmU6IFs0eDQgZG91 YmxlXQo+PiA+PiAgICAgICAgICAgTXNpemU6IDIKPj4gPj4gICAgICAgIHRlc3NfbW5pOiBbMXgx IHN0cnVjdF0KPj4gPj4gICAgICAgIHRlc3NfY3R4Ogo+PiA+PiAnRDpcUHJvY2Vzc2VkXERQMDFc c0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxY29ydGV4XzgxOTYuc3VyZi5naWknCj4+ID4+ICAg ICAgdGVzc19zY2FscDoKPj4gPj4gJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAw LTAwMDAwMS0wMXNjYWxwXzI1NjIuc3VyZi5naWknCj4+ID4+ICAgICB0ZXNzX29za3VsbDoKPj4g Pj4gJ0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMW9za3VsbF8y NTYyLnN1cmYuZ2lpJwo+PiA+PiAgICAgdGVzc19pc2t1bGw6Cj4+ID4+ICdEOlxQcm9jZXNzZWRc RFAwMVxzS0VZQU4tMDAwMi0wMDAwMC0wMDAwMDEtMDFpc2t1bGxfMjU2Mi5zdXJmLmdpaScKPj4g Pj4gICAgICAgICAgICAgZmlkOiBbMXgxIHN0cnVjdF0KPj4gPj4gRmFpbGVkICAnTS9FRUcgaGVh ZCBtb2RlbCBzcGVjaWZpY2F0aW9uJwo+PiA+PiBSZWZlcmVuY2UgdG8gbm9uLWV4aXN0ZW50IGZp ZWxkICdmaWQnLgo+PiA+PiBJbiBmaWxlICJDOlxQcm9ncmFtIEZpbGVzXE1BVExBQlxzcG04bmV3 XGNvbmZpZ1xzcG1fY2ZnX2VlZ19pbnZfaGVhZG1vZGVsLm0iCj4+ID4+ICh2NDExOCksIGZ1bmN0 aW9uICJzcGVjaWZ5X2hlYWRtb2RlbCIgYXQgbGluZSAyMDkuCj4+ID4+IE5vIGV4ZWN1dGFibGUg bW9kdWxlcywgYnV0IHN0aWxsIHVucmVzb2x2ZWQgZGVwZW5kZW5jaWVzIG9yIGluY29tcGxldGUK Pj4gPj4gbW9kdWxlIGlucHV0cy4KPj4gPj4gVGhlIGZvbGxvd2luZyBtb2R1bGVzIGRpZCBub3Qg cnVuOgo+PiA+PiBGYWlsZWQ6IE0vRUVHIGhlYWQgbW9kZWwgc3BlY2lmaWNhdGlvbgo+PiA+PiBT a2lwcGVkOiBNL0VFRyBzb3VyY2UgaW52ZXJzaW9uCj4+ID4+IFNraXBwZWQ6IE0vRUVHIGludmVy c2lvbiByZXN1bHRzCj4+ID4+Cj4+ID4+Cj4+ID4+IEF0IDIwMTEtMDUtMTMgMjI6MDY6MDajrCJW bGFkaW1pciBMaXR2YWsiIDxsaXR2YWsudmxhZGltaXJAZ21haWwuY29tCj4+ID4+PiB3cm90ZToK Pj4gPj4KPj4gPj4+RGVhciBIYW9yYW4sCj4+ID4+Pgo+PiA+Pj5JIHRyaWVkIGFuZCBldmVyeXRo aW5nIHdvcmtzIGZpbmUgZm9yIG1lLiBNYWtlIHN1cmUgeW91ciBTUE0gdmVyc2lvbgo+PiA+Pj5p cyB1cCB0byBkYXRlIGFuZCBpZiB5b3UgY2FuJ3QgbWFrZSBpdCB3b3JrLCBzZW5kIG1lIHlvdXIg c2F2ZWQgYmF0Y2gKPj4gPj4+YW5kIEknbGwgdGFrZSBhbm90aGVyIGxvb2suCj4+ID4+Pgo+PiA+ Pj5CZXN0LAo+PiA+Pj4KPj4gPj4+VmxhZGltaXIKPj4gPj4+Cj4+ID4+PjIwMTEvNS8xMCC3ycTx IDxsaWhhb3JhbjA2MDNAMTYzLmNvbT46Cj4+ID4+Pj4gRGVhciBTUE0ncyB1c2VycywKPj4gPj4+ PiChoaGhSGF2ZSB5b3UgZXZlciB1c2VkIGJhdGNoIGludGVyZmFjZSB0byBkbyAzRCBzb3VyY2Ug cmVjb25zdHJ1Y3Rpb24gZm9yCj4+ID4+Pj4gRUVHL01FRyBkYXRhPyBJIGNob29zZWQgdGhyZWUg c2VwYXJhdGUgbW9kdWxlcyAgIk0vRUVHIGhlYWQgbW9kZWwKPj4gPj4+PiBzcGVjaWZpY2F0aW9u IiAiTS9FRUcgc291cmNlIGludmVyc2lvbiIgYW5kICJNL0VFRyBpbnZlcnNpb24gcmVzdWx0cyIg c28gYXMKPj4gPj4+PiB0byByZWNvbnN0cnVjdCB0aGUgc291cmNlcy4gSW4gdGhlICJNL0VFRyBo ZWFkIG1vZGVsIHNwZWNpZmljYXRpb24iIG1vZHVsZSwKPj4gPj4+PiBJIHNwZWNpZmllZCAnJ01l c2hlczxNZXNoZXMgc291cmNlPGluZGl2aWR1YWwgc3RydWN0dWFsIGltYWdlJycgYW5kIHRoZW4K Pj4gPj4+PiBzZWxlY3RlZCBhIG1yaSBmaWxlIG5hbWVkICJzRFA3NC0wMDAzLTAwMDAwLTAwMDAw MS0wMS5pbWcsMSIuICBXaGVuIGFsbCB3YXMKPj4gPj4+PiByZWFkeSBhbmQgdGhlbiBJIHJhbiB0 aGUgYmF0Y2gsIGhvd2V2ZXIsIEkgZm91bmQgdGhhdCBpdCBkaW4ndCBleGN1dGVkIHRoZQo+PiA+ Pj4+IHNwZWNpZmllZCBzdHJ1Y3R1YWwgaW1hZ2UgKCBhcyB5b3UgY2FuIHNlZSBiZWxvdyAndGVt cGxhdGU9MScpIGFuZCB0aGUKPj4gPj4+PiBtYXRsYWIgY29tbWFuZCB3aW5kb3cgYXBwZWFyZWQg dGhvc2U6Cj4+ID4+Pj4KPj4gPj4+PiBTUE04OiBzcG1fZWVnX2ludl9tZXNoX3VpICh2MzczMSkg ICAgICAgICAgICAgICAgICAyMTozOTowNiAtIDA5LzA1LzIwMTEKPj4gPj4+PiA9PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT0KPj4gPj4+PiAgICAgICAgdGVtcGxhdGU6IDEKPj4gPj4+PiAgICAgICAgICBBZmZpbmU6 IFs0eDQgZG91YmxlXQo+PiA+Pj4+ICAgICAgICAgICAgc01SSTogJ0M6XFByb2dyYW0KPj4gPj4+ PiBGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxzaW5nbGVfc3Vial9UMS5uaWkn Cj4+ID4+Pj4gICAgICAgICAgIE1zaXplOiAyCj4+ID4+Pj4gICAgICAgIHRlc3NfbW5pOiBbMXgx IHN0cnVjdF0KPj4gPj4+PiAgICAgICAgdGVzc19jdHg6ICdDOlxQcm9ncmFtCj4+ID4+Pj4gRmls ZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcY29ydGV4XzgxOTYuc3VyZi5naWknCj4+ ID4+Pj4gICAgICB0ZXNzX3NjYWxwOiAnQzpcUHJvZ3JhbQo+PiA+Pj4+IEZpbGVzXE1BVExBQlx0 b29sYm94XHNwbThcY2Fub25pY2FsXHNjYWxwXzI1NjIuc3VyZi5naWknCj4+ID4+Pj4gICAgIHRl c3Nfb3NrdWxsOiAnQzpcUHJvZ3JhbQo+PiA+Pj4+IEZpbGVzXE1BVExBQlx0b29sYm94XHNwbThc Y2Fub25pY2FsXG9za3VsbF8yNTYyLnN1cmYuZ2lpJwo+PiA+Pj4+ICAgICB0ZXNzX2lza3VsbDog J0M6XFByb2dyYW0KPj4gPj4+PiBGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxp c2t1bGxfMjU2Mi5zdXJmLmdpaScKPj4gPj4+PiAgICAgICAgICAgICBmaWQ6IFsxeDEgc3RydWN0 XQo+PiA+Pj4+Cj4+ID4+Pj4gICChoUkgZGlkbid0IHVuZGVyc3RhbmQgd2h5LiBBbnlvbmUgd2hv IGtub3dzIHRoZSByZWFzb24gPyBUaGFua3MgdmVyeSBtdWNoCj4+ID4+Pj4gZm9yIGFueSBoZWxw ICEKPj4gPj4+Pgo+PiA+Pj4+IC0tCj4+ID4+Pj4gSGFvcmFuIExJIChNUykKPj4gPj4+PiBCcmFp biBJbWFnaW5nIExhYiwKPj4gPj4+PiBSZXNlYXJjaCBDZW50ZXIgZm9yIExlYXJuaW5nIFNjaWVu Y2UsCj4+ID4+Pj4gU291dGhlYXN0IFVuaXZlcnNpdHkKPj4gPj4+PiAyIFNpIFBhaSBMb3UgLCBO YW5qaW5nLCAyMTAwOTYsIFAuUi5DaGluYQo+PiA+Pj4+Cj4+ID4+Pj4KPj4gPj4KPj4gPj4KPj4g Pj4KPj4gPj4KPj4gPj4KPj4KPj4KCgoKCgotLQoKSGFvcmFuIExJIChNUykKQnJhaW4gSW1hZ2lu ZyBMYWIsClJlc2VhcmNoIENlbnRlciBmb3IgTGVhcm5pbmcgU2NpZW5jZSwKU291dGhlYXN0IFVu aXZlcnNpdHkKMiBTaSBQYWkgTG91ICwgTmFuamluZywgMjEwMDk2LCBQLlIuQ2hpbmE= ------=_Part_13687_12466455.1305595888996 Content-Type: text/html; charset=GBK Content-Transfer-Encoding: base64 PERJVj5EZWFyIFZsYWRpbWlyLDwvRElWPgo8RElWPqGhoaFUaGFuayB5b3UgdmVyeSBtdWNoISBN YXkgeW91IGEgaGFwcHkgZGF5ITwvRElWPgo8RElWPqGhoaFIYW9yYW4uPEJSPjwvRElWPgo8RElW PiZuYnNwOzwvRElWPgo8RElWPjxCUj7U2iZuYnNwOzIwMTEtMDUtMTYmbmJzcDsxOTowNDoxOaOs IlZsYWRpbWlyJm5ic3A7TGl0dmFrIiZuYnNwOyZsdDs8QSBocmVmPSJtYWlsdG86bGl0dmFrLnZs YWRpbWlyQGdtYWlsLmNvbSI+bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbTwvQT4mZ3Q7Jm5ic3A7 0LS1wKO6PEJSPjxCUj4mZ3Q7RGVhciZuYnNwO0hhb3Jhbiw8QlI+Jmd0OzxCUj4mZ3Q7PEJSPiZn dDs8QlI+Jmd0O09uJm5ic3A7MTYmbmJzcDtNYXkmbmJzcDsyMDExLCZuYnNwO2F0Jm5ic3A7MTA6 MTMsJm5ic3A7IrfJxPEiJm5ic3A7Jmx0OzxBIGhyZWY9Im1haWx0bzpsaWhhb3JhbjA2MDNAMTYz LmNvbSI+bGloYW9yYW4wNjAzQDE2My5jb208L0E+Jmd0OyZuYnNwO3dyb3RlOjxCUj4mZ3Q7PEJS PiZndDsmZ3Q7Jm5ic3A7RGVhciZuYnNwO1ZsYWRpbWlyLDxCUj4mZ3Q7Jmd0OyZuYnNwO6GhoaFZ b3VyJm5ic3A7Z3Vlc3MmbmJzcDt3YXMmbmJzcDtyaWdodC4mbmJzcDtJJm5ic3A7cmVhbGx5Jm5i c3A7Y291bGRuJ3QmbmJzcDtkbyZuYnNwOzNEJm5ic3A7c291cmNlcyZuYnNwO3JlY29uc3RydWN0 aW9uJm5ic3A7b2YmbmJzcDt0aGF0Jm5ic3A7ZGF0YSZuYnNwO2J5Jm5ic3A7R1VJLiZuYnNwO0l0 Jm5ic3A7cmVtaW5kZWQmbmJzcDttZSZuYnNwOycnY2hlY2ttZWVnOiZuYnNwO2RhdGEmbmJzcDtz Y2FsZSZuYnNwO21pc3NpbmcsJm5ic3A7YXNzaWduaW5nJm5ic3A7ZGVmYXVsdDxCUj4mZ3Q7Jmd0 OyZuYnNwO2NoZWNrbWVlZzombmJzcDtubyZuYnNwO2ZpZHVjaWFscyZuYnNwO2FyZSZuYnNwO2Rl ZmluZWQnJy4mbmJzcDtUaGVuJm5ic3A7SSZuYnNwO2RpZCZuYnNwO3ByZXByb2Nlc3MmbmJzcDtz dGVwcyZuYnNwO2FnYWluLCZuYnNwO2FuZCZuYnNwO2ZvdW5kJm5ic3A7dGhhdCZuYnNwO0kmbmJz cDtjb3VsZG4ndCZuYnNwO29idGFpbiZuYnNwO3RoZSZuYnNwO2ZpZHVjaWFscyZuYnNwO2F1dG9t YXRpY2FsbHkod2hlbiZuYnNwO0kmbmJzcDtmaW5pc2hlZCZuYnNwO0NvbnZlcnRpbmcmbmJzcDtz dGVwKS4mbmJzcDtJJm5ic3A7Y2hlY2tlZCZuYnNwO3RoZSZuYnNwO290aGVyJm5ic3A7ZGF0YXNl dHMsJm5ic3A7bHVja2lseSwmbmJzcDt0aGV5Jm5ic3A7ZGlkbid0Jm5ic3A7aGF2ZSZuYnNwO3Ro aXMmbmJzcDtwcm9ibGVtLiZuYnNwO0kmbmJzcDtndWVzcyZuYnNwO3RoZXJlJm5ic3A7bXVzdCZu YnNwO2JlJm5ic3A7c29tZSZuYnNwO3dyb25ncyZuYnNwO29mJm5ic3A7bXkmbmJzcDtvcmlnaW5h bCZuYnNwO2RhdGFzZXQmbmJzcDsob25seSZuYnNwO3RoYXQmbmJzcDtvbmUpLCZuYnNwO3JpZ2h0 Jm5ic3A7PzxCUj4mZ3Q7PEJSPiZndDtyaWdodDxCUj4mZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7oaGh oUFzJm5ic3A7Zm9yJm5ic3A7cmVjb25zdHJ1Y3Rpb24mbmJzcDtiYXRjaCZuYnNwO3Byb2JsZW0s Jm5ic3A7d2l0aCZuYnNwO3RoZSZuYnNwO2xhdGVzdCZuYnNwO3ZlcnNpb24mbmJzcDtvZiZuYnNw O3NwbTgoNDAyOSkmbmJzcDthbmQmbmJzcDtub3JtYWwmbmJzcDtkYXRhc2V0LCZuYnNwO0kmbmJz cDtjYW4mbmJzcDtkbyZuYnNwO3JlY29uc3RydWN0aW9uJm5ic3A7YnkmbmJzcDt0aGF0Jm5ic3A7 c2F2ZWQmbmJzcDsicmVuY29uc3RydWN0aW9uX2JhdGNoIiZuYnNwO3N1Y2Nlc3NmdWxseS4mbmJz cDtUaHVzLCZuYnNwO3RoZSZuYnNwO3Byb2JsZW1zJm5ic3A7cmVsYXRlZCZuYnNwO3RvJm5ic3A7 cmVjb25zdHJ1Y3Rpb24mbmJzcDtiYXRjaCZuYnNwO3RoYXQmbmJzcDtJJm5ic3A7Y29uZnJvbnRl ZCZuYnNwO3ByZXZpb3VzbHkmbmJzcDttYXkmbmJzcDtyZXN1bHQmbmJzcDtmcm9tJm5ic3A7dGhl Jm5ic3A7ZGlmZmVyZW50Jm5ic3A7dmVyc2lvbnMmbmJzcDtvZiZuYnNwO3NwbTguPEJSPiZndDsm Z3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7QnkmbmJz cDt0aGUmbmJzcDt3YXksJm5ic3A7aW4mbmJzcDsnJ01FRUcmbmJzcDtoZWFkJm5ic3A7bW9kZWwm bmJzcDtzcGVjaWZpY2F0aW9uJmd0OyZuYnNwO0VFRyZuYnNwO2hlYWQmbmJzcDttb2RsZScnJm5i c3A7bW9kdWxlLCZuYnNwO211c3QmbmJzcDtJJm5ic3A7c3BlY2lmeSZuYnNwO3RoZSZuYnNwOycn RUVHJm5ic3A7aGVhZCZuYnNwO21vZGxlJycmbmJzcDtpZiZuYnNwO0kmbmJzcDtqdXN0Jm5ic3A7 cHJvY2VzcyZuYnNwO01FRyZuYnNwO2RhdGFzZXQ/PEJSPiZndDs8QlI+Jmd0O09uZSZuYnNwO3Bl Y3VsaWFyaXR5Jm5ic3A7b2YmbmJzcDtiYXRjaCZuYnNwO2lzJm5ic3A7dGhhdCZuYnNwO3doZW4m bmJzcDt5b3UmbmJzcDtidWlsZCZuYnNwO3RoZSZuYnNwO2NvbmZpZ3VyYXRpb24mbmJzcDt5b3U8 QlI+Jmd0O2Nhbid0Jm5ic3A7dXNlJm5ic3A7aW5mb3JtYXRpb24mbmJzcDtmcm9tJm5ic3A7dGhl Jm5ic3A7ZGF0YXNldCZuYnNwO3doaWNoJm5ic3A7b25seSZuYnNwO2JlY29tZXMmbmJzcDthdmFp bGFibGU8QlI+Jmd0O3doZW4mbmJzcDt5b3UmbmJzcDtydW4mbmJzcDt0aGUmbmJzcDtiYXRjaC4m bmJzcDtJbiZuYnNwO3BhcnRpY3VsYXImbmJzcDt5b3UmbmJzcDtjYW4ndCZuYnNwO2tub3cmbmJz cDtpZiZuYnNwO3RoZSZuYnNwO2RhdGFzZXQmbmJzcDtpbjxCUj4mZ3Q7cXVlc3Rpb24mbmJzcDtp cyZuYnNwO0VFRyZuYnNwO29yJm5ic3A7TUVHLiZuYnNwO1NvJm5ic3A7ZG9uJ3QmbmJzcDt3b3Jy eSZuYnNwO2Fib3V0Jm5ic3A7dGhlJm5ic3A7RUVHJm5ic3A7aGVhZCZuYnNwO21vZGVsLiZuYnNw O0lmPEJSPiZndDt5b3VyJm5ic3A7ZGF0YXNldCZuYnNwO2lzJm5ic3A7TUVHJm5ic3A7aXQmbmJz cDt3aWxsJm5ic3A7YmUmbmJzcDtpZ25vcmVkLjxCUj4mZ3Q7PEJSPiZndDtCZXN0LDxCUj4mZ3Q7 PEJSPiZndDtWbGFkaW1pcjxCUj4mZ3Q7PEJSPiZndDs8QlI+Jmd0OyZndDsmbmJzcDuhoaGhVGhh bmsmbmJzcDt5b3UmbmJzcDt2ZXJ5Jm5ic3A7bXVjaCZuYnNwO2ZvciZuYnNwO2hlbHBpbmcmbmJz cDttZSZuYnNwO3NvbHZlJm5ic3A7dGhlc2UmbmJzcDt0aW55Jm5ic3A7cHJvYmxlbXMhPEJSPiZn dDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7oaGhoUhhb3Jhbi48QlI+Jmd0OyZndDs8QlI+Jmd0OyZn dDs8QlI+Jmd0OyZndDsmbmJzcDtBdCZuYnNwOzIwMTEtMDUtMTYmbmJzcDswNjozODoyM6OsIlZs YWRpbWlyJm5ic3A7TGl0dmFrIiZuYnNwOyZsdDs8QSBocmVmPSJtYWlsdG86bGl0dmFrLnZsYWRp bWlyQGdtYWlsLmNvbSI+bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbTwvQT4mZ3Q7Jm5ic3A7d3Jv dGU6PEJSPiZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0O0RlYXImbmJzcDtIYW9yYW4sPEJS PiZndDsmZ3Q7Jm5ic3A7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDtUaGUmbmJzcDtwcm9ibGVt Jm5ic3A7c2VlbXMmbmJzcDt0byZuYnNwO2JlJm5ic3A7bm90Jm5ic3A7aW4mbmJzcDt0aGUmbmJz cDtiYXRjaCZuYnNwO2J1dCZuYnNwO2luJm5ic3A7eW91ciZuYnNwO01FRyZuYnNwO2RhdGEmbmJz cDsob3ImbmJzcDt3YXM8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7aXQmbmJzcDtFRUc/KS4mbmJzcDtU aGVyZSZuYnNwO2FyZSZuYnNwO25vJm5ic3A7dmFsaWQmbmJzcDtmaWR1Y2lhbHMmbmJzcDtpbiZu YnNwO3RoYXQmbmJzcDtkYXRhc2V0LiZuYnNwO0NvdWxkJm5ic3A7eW91PEJSPiZndDsmZ3Q7Jm5i c3A7Jmd0O3BlcmZvcm0mbmJzcDtzb3VyY2UmbmJzcDtyZWNvbnN0cnVjdGlvbiZuYnNwO3VzaW5n Jm5ic3A7dGhlJm5ic3A7R1VJPyZuYnNwO0knZCZuYnNwO2JlJm5ic3A7c3VycHJpc2VkJm5ic3A7 aWYmbmJzcDt5b3U8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Y291bGQuJm5ic3A7U28mbmJzcDt3ZSZu YnNwO25lZWQmbmJzcDt0byZuYnNwO2ZvY3VzJm5ic3A7b24mbmJzcDtob3cmbmJzcDt0byZuYnNw O2dldCZuYnNwO2ZpZHVjaWFscyZuYnNwO2ludG8mbmJzcDt5b3VyJm5ic3A7ZGF0YXNldDxCUj4m Z3Q7Jmd0OyZuYnNwOyZndDthbmQmbmJzcDtmb3ImbmJzcDt0aGF0Jm5ic3A7eW91Jm5ic3A7bmVl ZCZuYnNwO3RvJm5ic3A7dGVsbCZuYnNwO21lJm5ic3A7bW9yZSZuYnNwO2Fib3V0Jm5ic3A7d2hl cmUmbmJzcDt0aGF0Jm5ic3A7ZGF0YSZuYnNwO2NvbWVzPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0O2Zy b20uPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDtCZXN0LDxCUj4m Z3Q7Jmd0OyZuYnNwOyZndDs8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7VmxhZGltaXI8QlI+Jmd0OyZn dDsmbmJzcDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OzIwMTEvNS8xNSZuYnNwO7fJxPEmbmJz cDsmbHQ7PEEgaHJlZj0ibWFpbHRvOmxpaGFvcmFuMDYwM0AxNjMuY29tIj5saWhhb3JhbjA2MDNA MTYzLmNvbTwvQT4mZ3Q7OjxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7RGVhciZuYnNw O1ZsYWRpbWlyLDxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7oaGhoUkmbmJzcDt0cmll ZCZuYnNwO2FnYWluJm5ic3A7d2l0aCZuYnNwO3RoZSZuYnNwO2xhdGVzdCZuYnNwO3ZlcnNpb24m bmJzcDtvZiZuYnNwO3NwbTgoNDAyOSksJm5ic3A7dGhlJm5ic3A7cHJldmlvdXMmbmJzcDtwcm9i bGVtKDxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7dGVtcGxhdGU9MSkmbmJzcDtkaXNh cHBlYXJlZC4mbmJzcDtCdXQmbmJzcDt0aGlzJm5ic3A7dGltZSwmbmJzcDtJJm5ic3A7Y29uZnJv bnRlZCZuYnNwO2EmbmJzcDtuZXcmbmJzcDtwcm9ibGVtLiZuYnNwO0l0PEJSPiZndDsmZ3Q7Jm5i c3A7Jmd0OyZndDsmbmJzcDtyZW1pbmRlZCZuYnNwO21lJm5ic3A7Ik5vJm5ic3A7ZXhlY3V0YWJs ZSZuYnNwO21vZHVsZXMsJm5ic3A7YnV0Jm5ic3A7c3RpbGwmbmJzcDt1bnJlc29sdmVkJm5ic3A7 ZGVwZW5kZW5jaWVzJm5ic3A7b3I8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZuYnNwO2luY29t cGxldGUmbmJzcDttb2R1bGUmbmJzcDtpbnB1dHMuIiZuYnNwO0J1dCZuYnNwO0kmbmJzcDtkb24n dCZuYnNwO2tub3cmbmJzcDt3aGVyZSZuYnNwO3RoZSZuYnNwO3Byb2JsZW1zJm5ic3A7YXJlLiZu YnNwO0NvdWxkPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDt5b3UmbmJzcDtoZWxwJm5i c3A7bWUmbmJzcDtzZWUmbmJzcDtteSZuYnNwO3JlY29uc3RydWN0aW9uX2JhdGNoLm1hdCZuYnNw O2FuZCZuYnNwO2dpdmUmbmJzcDttZSZuYnNwO3NvbWUmbmJzcDthZHZpY2UmbmJzcDs/Jm5ic3A7 VGhhbmtzPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDthJm5ic3A7bG90LjxCUj4mZ3Q7 Jmd0OyZuYnNwOyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtIYW9yYW4uPEJSPiZndDsmZ3Q7Jm5i c3A7Jmd0OyZndDs8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO1RoZXNlJm5ic3A7Y29kZSZuYnNwO2FwcGVhcnMm bmJzcDtpbiZuYnNwO3RoZSZuYnNwO21hdGxhYiZuYnNwO2NvbW1hbmQmbmJzcDt3aW5kb3cmbmJz cDt3aGVuJm5ic3A7SSZuYnNwO3JhbiZuYnNwO3RoZSZuYnNwO2JhdGNoOjxCUj4mZ3Q7Jmd0OyZu YnNwOyZndDsmZ3Q7Jm5ic3A7U1BNODombmJzcDtzcG1fZWVnX2ludl9tZXNoX3VpJm5ic3A7KHY0 MDI3KSZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOzEx OjA5OjEzJm5ic3A7LSZuYnNwOzE1LzA1LzIwMTE8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZu YnNwOz09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PTxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVtcGxhdGU6Jm5ic3A7MDxCUj4m Z3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7c01SSTombmJzcDsnRDpcUHJvY2Vz c2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAwMDAxLTAxLmltZywxJzxCUj4mZ3Q7Jmd0OyZu YnNwOyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7ZGVmOiZuYnNwOydEOlxQcm9jZXNzZWRc RFAwMVx5X3NLRVlBTi0wMDAyLTAwMDAwLTAwMDAwMS0wMS5uaWknPEJSPiZndDsmZ3Q7Jm5ic3A7 Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDtBZmZpbmU6Jm5ic3A7WzR4NCZuYnNwO2RvdWJsZV08QlI+Jmd0OyZndDsmbmJz cDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwO01zaXplOiZuYnNwOzI8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0 OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3NfbW5p OiZuYnNwO1sxeDEmbmJzcDtzdHJ1Y3RdPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDt0ZXNzX2N0eDo8QlI+Jmd0 OyZndDsmbmJzcDsmZ3Q7Jmd0OyZuYnNwOydEOlxQcm9jZXNzZWRcRFAwMVxzS0VZQU4tMDAwMi0w MDAwMC0wMDAwMDEtMDFjb3J0ZXhfODE5Ni5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7 Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3Nfc2NhbHA6PEJSPiZn dDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDsnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDIt MDAwMDAtMDAwMDAxLTAxc2NhbHBfMjU2Mi5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7 Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3Nfb3NrdWxsOjxCUj4mZ3Q7Jmd0 OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7J0Q6XFByb2Nlc3NlZFxEUDAxXHNLRVlBTi0wMDAyLTAwMDAw LTAwMDAwMS0wMW9za3VsbF8yNTYyLnN1cmYuZ2lpJzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19pc2t1bGw6PEJSPiZndDsmZ3Q7Jm5i c3A7Jmd0OyZndDsmbmJzcDsnRDpcUHJvY2Vzc2VkXERQMDFcc0tFWUFOLTAwMDItMDAwMDAtMDAw MDAxLTAxaXNrdWxsXzI1NjIuc3VyZi5naWknPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDtmaWQ6Jm5ic3A7WzF4MSZuYnNwO3N0cnVjdF08QlI+Jmd0OyZndDsm bmJzcDsmZ3Q7Jmd0OyZuYnNwO0ZhaWxlZCZuYnNwOyZuYnNwOydNL0VFRyZuYnNwO2hlYWQmbmJz cDttb2RlbCZuYnNwO3NwZWNpZmljYXRpb24nPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJz cDtSZWZlcmVuY2UmbmJzcDt0byZuYnNwO25vbi1leGlzdGVudCZuYnNwO2ZpZWxkJm5ic3A7J2Zp ZCcuPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDtJbiZuYnNwO2ZpbGUmbmJzcDsiQzpc UHJvZ3JhbSZuYnNwO0ZpbGVzXE1BVExBQlxzcG04bmV3XGNvbmZpZ1xzcG1fY2ZnX2VlZ19pbnZf aGVhZG1vZGVsLm0iPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDsodjQxMTgpLCZuYnNw O2Z1bmN0aW9uJm5ic3A7InNwZWNpZnlfaGVhZG1vZGVsIiZuYnNwO2F0Jm5ic3A7bGluZSZuYnNw OzIwOS48QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZuYnNwO05vJm5ic3A7ZXhlY3V0YWJsZSZu YnNwO21vZHVsZXMsJm5ic3A7YnV0Jm5ic3A7c3RpbGwmbmJzcDt1bnJlc29sdmVkJm5ic3A7ZGVw ZW5kZW5jaWVzJm5ic3A7b3ImbmJzcDtpbmNvbXBsZXRlPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZn dDsmbmJzcDttb2R1bGUmbmJzcDtpbnB1dHMuPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJz cDtUaGUmbmJzcDtmb2xsb3dpbmcmbmJzcDttb2R1bGVzJm5ic3A7ZGlkJm5ic3A7bm90Jm5ic3A7 cnVuOjxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7RmFpbGVkOiZuYnNwO00vRUVHJm5i c3A7aGVhZCZuYnNwO21vZGVsJm5ic3A7c3BlY2lmaWNhdGlvbjxCUj4mZ3Q7Jmd0OyZuYnNwOyZn dDsmZ3Q7Jm5ic3A7U2tpcHBlZDombmJzcDtNL0VFRyZuYnNwO3NvdXJjZSZuYnNwO2ludmVyc2lv bjxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jm5ic3A7U2tpcHBlZDombmJzcDtNL0VFRyZuYnNw O2ludmVyc2lvbiZuYnNwO3Jlc3VsdHM8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OzxCUj4mZ3Q7 Jmd0OyZuYnNwOyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmbmJzcDtBdCZuYnNw OzIwMTEtMDUtMTMmbmJzcDsyMjowNjowNqOsIlZsYWRpbWlyJm5ic3A7TGl0dmFrIiZuYnNwOyZs dDs8QSBocmVmPSJtYWlsdG86bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbSI+bGl0dmFrLnZsYWRp bWlyQGdtYWlsLmNvbTwvQT48QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmbmJzcDt3cm90 ZTo8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0 O0RlYXImbmJzcDtIYW9yYW4sPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7PEJSPiZndDsm Z3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7SSZuYnNwO3RyaWVkJm5ic3A7YW5kJm5ic3A7ZXZlcnl0aGlu ZyZuYnNwO3dvcmtzJm5ic3A7ZmluZSZuYnNwO2ZvciZuYnNwO21lLiZuYnNwO01ha2UmbmJzcDtz dXJlJm5ic3A7eW91ciZuYnNwO1NQTSZuYnNwO3ZlcnNpb248QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7 Jmd0OyZndDtpcyZuYnNwO3VwJm5ic3A7dG8mbmJzcDtkYXRlJm5ic3A7YW5kJm5ic3A7aWYmbmJz cDt5b3UmbmJzcDtjYW4ndCZuYnNwO21ha2UmbmJzcDtpdCZuYnNwO3dvcmssJm5ic3A7c2VuZCZu YnNwO21lJm5ic3A7eW91ciZuYnNwO3NhdmVkJm5ic3A7YmF0Y2g8QlI+Jmd0OyZndDsmbmJzcDsm Z3Q7Jmd0OyZndDthbmQmbmJzcDtJJ2xsJm5ic3A7dGFrZSZuYnNwO2Fub3RoZXImbmJzcDtsb29r LjxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7 Jmd0O0Jlc3QsPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7 Jmd0OyZndDsmZ3Q7VmxhZGltaXI8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDs8QlI+Jmd0 OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsyMDExLzUvMTAmbmJzcDu3ycTxJm5ic3A7Jmx0OzxBIGhy ZWY9Im1haWx0bzpsaWhhb3JhbjA2MDNAMTYzLmNvbSI+bGloYW9yYW4wNjAzQDE2My5jb208L0E+ Jmd0Ozo8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7RGVhciZuYnNwO1NQ TSdzJm5ic3A7dXNlcnMsPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO6Gh oaFIYXZlJm5ic3A7eW91Jm5ic3A7ZXZlciZuYnNwO3VzZWQmbmJzcDtiYXRjaCZuYnNwO2ludGVy ZmFjZSZuYnNwO3RvJm5ic3A7ZG8mbmJzcDszRCZuYnNwO3NvdXJjZSZuYnNwO3JlY29uc3RydWN0 aW9uJm5ic3A7Zm9yPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO0VFRy9N RUcmbmJzcDtkYXRhPyZuYnNwO0kmbmJzcDtjaG9vc2VkJm5ic3A7dGhyZWUmbmJzcDtzZXBhcmF0 ZSZuYnNwO21vZHVsZXMmbmJzcDsmbmJzcDsiTS9FRUcmbmJzcDtoZWFkJm5ic3A7bW9kZWw8QlI+ Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7c3BlY2lmaWNhdGlvbiImbmJzcDsi TS9FRUcmbmJzcDtzb3VyY2UmbmJzcDtpbnZlcnNpb24iJm5ic3A7YW5kJm5ic3A7Ik0vRUVHJm5i c3A7aW52ZXJzaW9uJm5ic3A7cmVzdWx0cyImbmJzcDtzbyZuYnNwO2FzPEJSPiZndDsmZ3Q7Jm5i c3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO3RvJm5ic3A7cmVjb25zdHJ1Y3QmbmJzcDt0aGUmbmJz cDtzb3VyY2VzLiZuYnNwO0luJm5ic3A7dGhlJm5ic3A7Ik0vRUVHJm5ic3A7aGVhZCZuYnNwO21v ZGVsJm5ic3A7c3BlY2lmaWNhdGlvbiImbmJzcDttb2R1bGUsPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0 OyZndDsmZ3Q7Jmd0OyZuYnNwO0kmbmJzcDtzcGVjaWZpZWQmbmJzcDsnJ01lc2hlcyZsdDtNZXNo ZXMmbmJzcDtzb3VyY2UmbHQ7aW5kaXZpZHVhbCZuYnNwO3N0cnVjdHVhbCZuYnNwO2ltYWdlJycm bmJzcDthbmQmbmJzcDt0aGVuPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNw O3NlbGVjdGVkJm5ic3A7YSZuYnNwO21yaSZuYnNwO2ZpbGUmbmJzcDtuYW1lZCZuYnNwOyJzRFA3 NC0wMDAzLTAwMDAwLTAwMDAwMS0wMS5pbWcsMSIuJm5ic3A7Jm5ic3A7V2hlbiZuYnNwO2FsbCZu YnNwO3dhczxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtyZWFkeSZuYnNw O2FuZCZuYnNwO3RoZW4mbmJzcDtJJm5ic3A7cmFuJm5ic3A7dGhlJm5ic3A7YmF0Y2gsJm5ic3A7 aG93ZXZlciwmbmJzcDtJJm5ic3A7Zm91bmQmbmJzcDt0aGF0Jm5ic3A7aXQmbmJzcDtkaW4ndCZu YnNwO2V4Y3V0ZWQmbmJzcDt0aGU8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5i c3A7c3BlY2lmaWVkJm5ic3A7c3RydWN0dWFsJm5ic3A7aW1hZ2UmbmJzcDsoJm5ic3A7YXMmbmJz cDt5b3UmbmJzcDtjYW4mbmJzcDtzZWUmbmJzcDtiZWxvdyZuYnNwOyd0ZW1wbGF0ZT0xJykmbmJz cDthbmQmbmJzcDt0aGU8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7bWF0 bGFiJm5ic3A7Y29tbWFuZCZuYnNwO3dpbmRvdyZuYnNwO2FwcGVhcmVkJm5ic3A7dGhvc2U6PEJS PiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7 Jmd0OyZndDsmbmJzcDtTUE04OiZuYnNwO3NwbV9lZWdfaW52X21lc2hfdWkmbmJzcDsodjM3MzEp Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7MjE6Mzk6 MDYmbmJzcDstJm5ic3A7MDkvMDUvMjAxMTxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0OyZn dDsmbmJzcDs9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVtcGxhdGU6 Jm5ic3A7MTxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtBZmZpbmU6Jm5ic3A7 WzR4NCZuYnNwO2RvdWJsZV08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7c01SSTombmJzcDsnQzpcUHJvZ3JhbTxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7 Jmd0OyZndDsmbmJzcDtGaWxlc1xNQVRMQUJcdG9vbGJveFxzcG04XGNhbm9uaWNhbFxzaW5nbGVf c3Vial9UMS5uaWknPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO01z aXplOiZuYnNwOzI8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19tbmk6Jm5ic3A7WzF4MSZu YnNwO3N0cnVjdF08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19jdHg6Jm5ic3A7J0M6XFBy b2dyYW08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7RmlsZXNcTUFUTEFC XHRvb2xib3hcc3BtOFxjYW5vbmljYWxcY29ydGV4XzgxOTYuc3VyZi5naWknPEJSPiZndDsmZ3Q7 Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw O3Rlc3Nfc2NhbHA6Jm5ic3A7J0M6XFByb2dyYW08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZn dDsmZ3Q7Jm5ic3A7RmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxjYW5vbmljYWxcc2NhbHBfMjU2 Mi5zdXJmLmdpaSc8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7dGVzc19vc2t1bGw6Jm5ic3A7J0M6XFByb2dyYW08QlI+Jmd0OyZn dDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7RmlsZXNcTUFUTEFCXHRvb2xib3hcc3BtOFxj YW5vbmljYWxcb3NrdWxsXzI1NjIuc3VyZi5naWknPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsm Z3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwO3Rlc3NfaXNrdWxsOiZuYnNwOydD OlxQcm9ncmFtPEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO0ZpbGVzXE1B VExBQlx0b29sYm94XHNwbThcY2Fub25pY2FsXGlza3VsbF8yNTYyLnN1cmYuZ2lpJzxCUj4mZ3Q7 Jmd0OyZuYnNwOyZndDsmZ3Q7Jmd0OyZndDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDtmaWQ6Jm5ic3A7 WzF4MSZuYnNwO3N0cnVjdF08QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7PEJSPiZn dDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOyZuYnNwOyZuYnNwO6GhSSZuYnNwO2Rp ZG4ndCZuYnNwO3VuZGVyc3RhbmQmbmJzcDt3aHkuJm5ic3A7QW55b25lJm5ic3A7d2hvJm5ic3A7 a25vd3MmbmJzcDt0aGUmbmJzcDtyZWFzb24mbmJzcDs/Jm5ic3A7VGhhbmtzJm5ic3A7dmVyeSZu YnNwO211Y2g8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7Jm5ic3A7Zm9yJm5ic3A7 YW55Jm5ic3A7aGVscCZuYnNwOyE8QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7PEJS PiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOy0tPEJSPiZndDsmZ3Q7Jm5ic3A7 Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO0hhb3JhbiZuYnNwO0xJJm5ic3A7KE1TKTxCUj4mZ3Q7Jmd0 OyZuYnNwOyZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtCcmFpbiZuYnNwO0ltYWdpbmcmbmJzcDtMYWIs PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwO1Jlc2VhcmNoJm5ic3A7Q2Vu dGVyJm5ic3A7Zm9yJm5ic3A7TGVhcm5pbmcmbmJzcDtTY2llbmNlLDxCUj4mZ3Q7Jmd0OyZuYnNw OyZndDsmZ3Q7Jmd0OyZndDsmbmJzcDtTb3V0aGVhc3QmbmJzcDtVbml2ZXJzaXR5PEJSPiZndDsm Z3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7Jmd0OyZuYnNwOzImbmJzcDtTaSZuYnNwO1BhaSZuYnNwO0xv dSZuYnNwOywmbmJzcDtOYW5qaW5nLCZuYnNwOzIxMDA5NiwmbmJzcDtQLlIuQ2hpbmE8QlI+Jmd0 OyZndDsmbmJzcDsmZ3Q7Jmd0OyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDsmZ3Q7 Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7PEJSPiZndDsmZ3Q7Jm5ic3A7Jmd0OyZndDs8 QlI+Jmd0OyZndDsmbmJzcDsmZ3Q7Jmd0OzxCUj4mZ3Q7Jmd0OyZuYnNwOyZndDsmZ3Q7PEJSPiZn dDsmZ3Q7Jm5ic3A7Jmd0OyZndDs8QlI+Jmd0OyZndDs8QlI+Jmd0OyZndDs8QlI+PC9ESVY+CjxE SVY+PEJSPjxCUj48QlI+PC9ESVY+CjxESVY+LS08QlI+CjxESVY+PEZPTlQgY29sb3I9IiMwMDgw MDAiIGZhY2U9IkFyaWFsIj5IYW9yYW4gTEkgKE1TKTxCUj5CcmFpbiBJbWFnaW5nIExhYiw8QlI+ UmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTPEZPTlQgY29sb3I9IiMwMDgwMDAiPmNpPC9G T05UPmVuY2UsPEJSPlNvdXRoZWFzdCBVbml2ZXJzaXR5PEJSPjIgU2kgUGFpIExvdSAsIE5hbmpp bmcsIDIxMDA5NiwgUC5SLkNoaW5hPC9GT05UPjwvRElWPjwvRElWPjxicj48YnI+PHNwYW4gdGl0 bGU9Im5ldGVhc2Vmb290ZXIiPjxzcGFuIGlkPSJuZXRlYXNlX21haWxfZm9vdGVyIj48L3NwYW4+ PC9zcGFuPg== ------=_Part_13687_12466455.1305595888996-- ========================================================================= Date: Tue, 17 May 2011 05:10:34 +0100 Reply-To: David Yang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Yang <[log in to unmask]> Subject: Re: Corregistro PET-T1 with DARTEL 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]> Dear John: Thanks for your kind and useful reply. I wanted to make sure my interpretation was right: Step 1: was done by SPM function: Coreg:estimate and the registration inf= ormation would be stored in the header of source images (no other output = would be found) Step 2 and Step 3:just as routine DARTEL process on T1 images Step4: as in DARTEL toolbox: normalise to MNI space option and use source= images (PET) from Step1. For receptor/transporter binding potential images, I suspected we should = choose Preserve Amount in Step 4 according to previous discussion: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;f3f7b65.0901 Furthermore, considering the image features of PET or even SPECT images, = it might have to smooth such images with large amount (ex 12 mm compared = to default 8 mm in DARTEL). I wonder if such was suitable if we wanted to= perform further analysis by BPM that used both VBM and PET/SPECT images = concurrently as in this subject: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;dd737de1.1012 Thanks for your help~ Sincerely yours, David ========================================================================= Date: Tue, 17 May 2011 11:45:40 +0530 Reply-To: cognitive dept neuro science <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cognitive dept neuro science <[log in to unmask]> Subject: SPM 2nd level analysis - reg MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0003255599869246b304a372b321 Message-ID: <[log in to unmask]> --0003255599869246b304a372b321 Content-Type: text/plain; charset=ISO-8859-1 Dear All, We are(our team) processing our datas using SPM8. We are able to process upto first level. We are able to get the activations at the end of first level result. But we cant further process our second level analysis. Please help us to process further. Thanks in advance. -- Regards, Cognitive Neurosciences Centre, Dept of Clinical Psychology, NIMHANS-Banglore 560 029. --0003255599869246b304a372b321 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div><br clear=3D"all">Dear All,</div> <div>We are(our team) processing our datas=A0using SPM8. We are able to pro= cess upto first level. We are able to get the activations at the end of fir= st level result. But we cant further process our second level analysis. Ple= ase help us to process further. </div> <div>=A0</div> <div>=A0</div> <div>Thanks in advance.</div> <div><br>-- <br><br>Regards,<br>Cognitive Neurosciences Centre,<br>Dept of = Clinical Psychology,<br>NIMHANS-Banglore 560 029.<br><br></div> --0003255599869246b304a372b321-- ========================================================================= Date: Tue, 17 May 2011 11:59:50 +0530 Reply-To: cognitive dept neuro science <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cognitive dept neuro science <[log in to unmask]> Subject: SPM 2nd level analysis-reg MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e6d9a16e362e2b04a372e667 Message-ID: <[log in to unmask]> --0016e6d9a16e362e2b04a372e667 Content-Type: text/plain; charset=ISO-8859-1 Dear All, We are(our team) processing our datas using SPM8. We are able to process upto first level. We are able to get the activations at the end of first level result. But we cant further process our second level analysis. Please help us to process further. Thanks in advance. -- Regards, Cognitive Neurosciences Centre, Dept of Clinical Psychology, NIMHANS-Banglore 560 029. --0016e6d9a16e362e2b04a372e667 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div><br clear=3D"all"><br>Dear All,<br>We are(our team) processing our dat= as using SPM8. We are able to process upto first level. We are able to get = the activations at the end of first level result. But we cant further proce= ss our second level analysis. Please help us to process further. <br> =A0<br>=A0<br>Thanks in advance.</div> <div><br>-- <br><br>Regards,<br>Cognitive Neurosciences Centre,<br>Dept of = Clinical Psychology,<br>NIMHANS-Banglore 560 029.<br><br></div> --0016e6d9a16e362e2b04a372e667-- ========================================================================= Date: Tue, 17 May 2011 08:08:58 +0100 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: Time frequency question Comments: To: Tommy Ng <[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]> http://en.wikipedia.org/wiki/Decibel On Tue, May 17, 2011 at 4:50 AM, Tommy Ng <[log in to unmask]> wrote: > HI Sir > Sorry to bother you again, I have a basic question regarding TF analysis in > SPM. > As instructed in SPM manual, I averaged TF from single data series using the > option LogR. The ensuing source power maps appear reasonable but I do not > know the units of the Z axis. The manual says Db. What is Db? > Thank you for your time and help. > Regards > Tommy Ng ========================================================================= Date: Tue, 17 May 2011 10:20:02 +0100 Reply-To: Andreas Finkelmeyer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andreas Finkelmeyer <[log in to unmask]> Subject: Re: slicetiming difficulty Comments: To: Rebecca Ray <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 MIME-Version: 1.0 Message-ID: <[log in to unmask]> Q2hlY2sgdG8gc2VlIGlmIHRoZSBudW1iZXIgb2Ygc2xpY2VzIG9mIHRoaXMgc3ViamVjdCBpcyBp ZGVudGljYWwgdG8gdGhlIG90aGVyIHN1YmplY3RzLCBhbmQgYXMgeW91IGV4cGVjdCB0aGVtIHRv IGJlIC0gbW9zdCBsaWtlbHkgdGhlcmUgYXJlIG1vcmUgc2xpY2VzLiBUaGUgZWFzaWVzdCB3YXkg dG8gZG8gdGhpcyBpcyB0byBjaGVjayB0aGUgbGFzdCBudW1iZXIgaW4gJ0RpbWVuc2lvbnMnLCB3 aGVuIHlvdSB1c2UgdGhlICdEaXNwbGF5JyBidXR0b24gdG8gbG9vayBhdCB0aGUgaW1hZ2UuDQoN Ckdvb2QgbHVjayENCg0KDQo9PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT0NCkFuZHJlYXMgRmlua2VsbWV5ZXIsIFBoLkQuDQpJbnN0aXR1dGUgb2YgTmV1 cm9zY2llbmNlLCBOZXdjYXN0bGUgVW5pdmVyc2l0eQ0KQWNhZGVtaWMgUHN5Y2hpYXRyeSwgQnVp bGRpbmcgMTUsIE5ld2Nhc3RsZSBHZW5lcmFsIEhvc3BpdGFsDQpXZXN0Z2F0ZSBSb2FkLCBOZXdj YXN0bGUgTkU0IDZCRSwgVUsNCg0KVGVsLjogKzQ0ICgwKTE5MSAyNTYgMzI5NiAgRmF4OiArNDQg KDApMTkxIDI1NiAzMzI0DQpXZWI6IHd3dy5uY2wuYWMudWsvaW9uDQogDQoNCg0KDQo+LS0tLS1P cmlnaW5hbCBNZXNzYWdlLS0tLS0NCj5Gcm9tOiBTUE0gKFN0YXRpc3RpY2FsIFBhcmFtZXRyaWMg TWFwcGluZykgW21haWx0bzpTUE1ASklTQ01BSUwuQUMuVUtdDQo+T24gQmVoYWxmIE9mIFJlYmVj Y2EgUmF5DQo+U2VudDogMTYgTWF5IDIwMTEgMjI6NDQNCj5UbzogU1BNQEpJU0NNQUlMLkFDLlVL DQo+U3ViamVjdDogW1NQTV0gc2xpY2V0aW1pbmcgZGlmZmljdWx0eQ0KPg0KPkkgaGF2ZSBzdWNj ZXNzZnVsbHkgcHJlcHJvY2Vzc2VkIGFsbCBvZiBteSBkYXRhIHdpdGggdGhlIGV4Y2VwdGlvbiBv ZiB0aGlzIG9uZQ0KPnN1YmplY3QuICBPdXIgVFIgaXMgMi42IGJ1dCBpdCBjaGFuZ2VzIGl0IHRv IDIuNyBhbmQgZ2l2ZXMgdGhlIGZvbGxvd2luZyBlcnJvcg0KPm1lc3NhZ2U6DQo+DQo+U1BNODog c3BtX3NsaWNlX3RpbWluZyAodjM3NTYpICAgICAgICAgICAgICAgICAgICAgMTY6Mjc6MzggLSAx Ni8wNS8yMDExDQo+PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT0NCj49PT09PT09PT09PT09DQo+WW91ciBUUiBpcyAyLjcNCj5GYWlsZWQg ICdTbGljZSBUaW1pbmcnDQo+SW1wcm9wZXIgYXNzaWdubWVudCB3aXRoIHJlY3Rhbmd1bGFyIGVt cHR5IG1hdHJpeC4NCj5JbiBmaWxlICIvQXBwbGljYXRpb25zL3NwbTgvc3BtX3NsaWNlX3RpbWlu Zy5tIiAodjM3NTYpLCBmdW5jdGlvbg0KPiJzcG1fc2xpY2VfdGltaW5nIiBhdCBsaW5lIDIzMi4N Cj5JbiBmaWxlICIvQXBwbGljYXRpb25zL3NwbTgvY29uZmlnL3NwbV9ydW5fc3QubSIgKHYyMzEy KSwgZnVuY3Rpb24NCj4ic3BtX3J1bl9zdCIgYXQgbGluZSAyNS4NCj4NCj5JIGhhdmUgc3Bva2Vu IHdpdGggb3VyIHBoeXNpY2lzdCB3aG8gZG9lcyBub3QgdXNlIFNQTSwgYW5kIGhlIGRvZXMgbm90 DQo+dGhpbmsgaXQgaXMgYSBwcm9ibGVtIHdpdGggdGhlIGRhdGEuDQo+DQo+SSBjYW4gZGlzcGxh eSB0aGUgZGF0YSBwcmlvciB0byBzbGljZXRpbWluZyBidXQgbm90IGFmdGVyLiAgQWZ0ZXIgaXQg YXBwZWFycyB0bw0KPmhhdmUgc3BpdCBvdXQgZmlsZXMgYnV0IGdpdmVzIGFuIGVycm9yIHdoZW4g eW91IHRyeSB0byBkaXNwbGF5IHRoZXNlIGltYWdlczoNCj5SdW5uaW5nICdEaXNwbGF5IEltYWdl Jw0KPkltYWdlDQo+Ii9Vc2Vycy9yZWJlY2NhcmF5L0RvY3VtZW50cy9VV19Qcm9qZWN0cy9WaXZl ay9EYXRhX1Byb2Nlc3NpbmcvUmF3Xw0KPkRhdGEvMDIyX1MyL0Z1bmN0aW9uYWxzL0Vtb3Rpb24v c2NhbjIvYWd2c2NhbjAzMjkuaW1nIiBjYW4gbm90IGJlDQo+cmVzYW1wbGVkDQo+SW1hZ2UNCj4i L1VzZXJzL3JlYmVjY2FyYXkvRG9jdW1lbnRzL1VXX1Byb2plY3RzL1ZpdmVrL0RhdGFfUHJvY2Vz c2luZy9SYXdfDQo+RGF0YS8wMjJfUzIvRnVuY3Rpb25hbHMvRW1vdGlvbi9zY2FuMi9hZ3ZzY2Fu MDMyOS5pbWciIGNhbiBub3QgYmUNCj5yZXNhbXBsZWQNCj5Eb25lICAgICdEaXNwbGF5IEltYWdl Jw0KPkRvbmUNCj4NCj5BbnkgYWR2aWNlIHdvdWxkIGJlIGF3ZXNvbWUhDQo= ========================================================================= Date: Tue, 17 May 2011 12:51:23 +0100 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: Re: After estimation design matrix loses most regressors. Comments: To: Michael Thomas Smith <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec54a386226069f04a377647b Message-ID: <[log in to unmask]> --bcaec54a386226069f04a377647b Content-Type: text/plain; charset=ISO-8859-1 Dear Mike, Before estimation, the displayed design matrix is original matrix X (with the exception that DCT drift basis elements aren't shown). After estimation, the 'filtered and whitened' design matrix (xX.xKXs.X = K*W*X) is shown. If certain columns appear to be zeroed out by whitening, that is because the corresponding scans were found to have super-high variance. (See my earlier post on errors with PET data). I can't figure out why this is happening, though, as each session's data is scaled separately. You can check out the scan-by-scan scale factors used to bring each session to a Grand Mean of 100 by looking at SPM.xGX.gSF; if there is crazy variation in this (e.g. super-low values for sessions 1 & 3) it would explain the effect. Then the question is why are the computed globals so different over sessions... maybe crazy-large artifactual values in sessions 2, 4, 5,..,8? Hope this helps with the detective work! -Tom On Wed, May 11, 2011 at 12:57 PM, Michael Thomas Smith < [log in to unmask]> wrote: > I'm interested in the correlation between a time series and the voxels in > the brain. To find out more I made a GLM > (see http://www.sal.mvm.ed.ac.uk/images/problem1.png ). > > Before estimation it looks fine (I think?). > > During estimation however, 6 of the 8 columns are set to zero (see inset in > bottom-left corner of the linked image). Just sessions 1 and 3 output beta > images. There are no errors or warnings expressed during estimation, and the > matlab output appears ok. > > - I've confirmed the number of images in each session is equal to the > number of values in the .txt regressor files. > - I've tried adding conditions to each session (just a single event near > the start of each session) to see if it's the lack of conditions that's > upsetting SPM. (this didn't make any difference). > - I've rebuilt the design matrix from scratch by hand, using the interface, > but that didn't make any difference. > > Looking forward to any suggestions! > > Thanks, > > Mike Smith > > -- > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. > -- ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 --bcaec54a386226069f04a377647b Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Mike,=A0<div><br></div><div>Before estimation, the displayed design ma= trix is original matrix X (with the exception that DCT drift basis elements= aren't shown). =A0After estimation, the 'filtered and whitened'= ; design matrix (xX.xKXs.X =3D K*W*X) is shown. =A0If certain columns appea= r to be zeroed out by whitening, that is because the corresponding scans we= re found to have super-high variance. =A0(See my earlier post on errors wit= h PET data).</div> <div><br></div><div>I can't figure out why this is happening, though, a= s each session's data is scaled separately. =A0You can check out the sc= an-by-scan scale factors used to bring each session to a Grand Mean of 100 = by looking at SPM.xGX.gSF; if there is crazy variation in this (e.g. super-= low values for sessions 1 & 3) it would explain the effect. =A0Then the= question is why are the computed globals so different over sessions... may= be crazy-large artifactual values in=A0sessions=A02, 4, 5,..,8?</div> <div><br></div><div>Hope this helps with the detective work!</div><div><br>= </div><div>-Tom</div><div><br></div><div><br></div><div><div class=3D"gmail= _quote">On Wed, May 11, 2011 at 12:57 PM, Michael Thomas Smith <span dir=3D= "ltr"><<a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= >></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">I'm interested in the correlation betwe= en a time series and the voxels in the brain. To find out more I made a GLM= <br> (see <a href=3D"http://www.sal.mvm.ed.ac.uk/images/problem1.png" target=3D"= _blank">http://www.sal.mvm.ed.ac.uk/images/problem1.png</a> ).<br> <br> Before estimation it looks fine (I think?).<br> <br> During estimation however, 6 of the 8 columns are set to zero (see inset in= bottom-left corner of the linked image). Just sessions 1 and 3 output beta= images. There are no errors or warnings expressed during estimation, and t= he matlab output appears ok.<br> <br> - I've confirmed the number of images in each session is equal to the n= umber of values in the .txt regressor files.<br> - I've tried adding conditions to each session (just a single event nea= r the start of each session) to see if it's the lack of conditions that= 's upsetting SPM. (this didn't make any difference).<br> - I've rebuilt the design matrix from scratch by hand, using the interf= ace, but that didn't make any difference.<br> <br> Looking forward to any suggestions!<br> <br> Thanks,<br> <br> Mike Smith<br><font color=3D"#888888"> <br> -- <br> The University of Edinburgh is a charitable body, registered in<br> Scotland, with registration number SC005336.<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>________________= ____________________________<br>Thomas Nichols, PhD<br>Principal Research F= ellow, Head of Neuroimaging Statistics<br>Department of Statistics & Wa= rwick Manufacturing Group<br> University of Warwick<br>Coventry=A0 CV4 7AL<br>United Kingdom<br><br>Email= : <a href=3D"mailto:[log in to unmask]">[log in to unmask]</a= ><br>Phone, Stats: +44 24761 51086, WMG: +44 24761 50752<br>Fax:=A0 +44 24 = 7652 4532<br> <br> </div> --bcaec54a386226069f04a377647b-- ========================================================================= Date: Tue, 17 May 2011 09:27:05 -0400 Reply-To: "Rajagopalan, Venkateswaran" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Rajagopalan, Venkateswaran" <[log in to unmask]> Subject: SnPM for VBM analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CC1496.1D148B90" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CC1496.1D148B90 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear All, =20 I am interested in using SnPM for my VBM analysis. I am in the learning = process. I am wondering if somebody could help me with the following =20 I opened up SnPM GUI and opened the setup option and I choose >2 groups = Between groups ANOVA 1 scan/subject option since I have 5 groups including = control group in total. After I choose my plug a window pops up to open the= = =66iles so for instance I chose 4 controls (smwrp1 files), 3 from patient = group 1, 5 from from patient group 2. Then when I started to enter subject= = index A A A A B B B C C... the option to enter subject index doesn't seem t= o= stop at the end of 12th index since I chose only 12 subjects data the = option to enter subject index keeps going on for ever what am I doing wrong= = here.=20 =20 Thanks. =20 Venkat =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Please consider the environment before printing this e-mail Cleveland Clinic is ranked one of the top hospitals in America by U.S.News & World Report (2010). =20 Visit us online at http://www.clevelandclinic.org for a complete listing of our services, staff and locations. Confidentiality Note: This message is intended for use only by the individual or entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and destroy the material in its entirety, whether electronic or hard copy. Thank you. ------_=_NextPart_001_01CC1496.1D148B90 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable <HTML dir=3Dltr><HEAD> <META http-equiv=3DContent-Type content=3D"text/html; charset=3Dunicode"> <META content=3D"MSHTML 6.00.2900.3698" name=3DGENERATOR></HEAD> <BODY> <DIV><FONT face=3DArial color=3D#000000 size=3D2>Dear All,</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>I am interested in using SnPM for my VBM = analysis. I am in the learning process. I am wondering if somebody could = help me with the following</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>I opened up SnPM GUI and opened the setup = option and I choose >2 groups Between groups ANOVA 1 scan/subject = option since I have 5 groups including control group in total. After I = choose my plug a window pops up to open the files so for instance I chose 4= = controls (smwrp1 files), 3 from patient group 1, 5 from from = patient group 2. Then when I started to enter subject index A A A A B B = B C C... the option to enter subject index doesn't seem to stop at the= = end of 12th index since I chose only 12 subjects data the option to = enter subject index keeps going on for ever what am I doing wrong= = here. </FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>Thanks.</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>Venkat</FONT></DIV> <P><pre = wrap>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D <p class=3DMsoNormal><font size=3D5 color=3Dgreen><span style=3D'font-size:18.0pt;color:green'></span></font><font size=3D2 color=3Dnavy face=3DCalibri><span = style=3D'font-size:10.0pt;font-family:Calibri; color:navy'> </span></font><font size=3D1 color=3Dgreen face=3DArial><span style=3D'font-size:7.0pt;font-family:Arial;color:green'>Please consider the= = environment before printing this e-mail</span></font><o:p></o:p></p> Cleveland Clinic is ranked one of the top hospitals in America by U.S.News & World Report (2010). =20 Visit us online at http://www.clevelandclinic.org for a complete listing of our services, staff and locations. Confidentiality Note: This message is intended for use only by the individual or entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and destroy the material in its entirety, whether electronic or hard copy. Thank you. </pre></P></BODY></HTML> ------_=_NextPart_001_01CC1496.1D148B90-- ========================================================================= Date: Tue, 17 May 2011 17:45:15 +0200 Reply-To: Joana Braga Pereira <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Joana Braga Pereira <[log in to unmask]> Subject: Re: Correcting for non-stationary smoothnees in vbm Comments: To: Christian Gaser <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=00163646cede81e8f504a37aa859 Message-ID: <[log in to unmask]> --00163646cede81e8f504a37aa859 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear Christian, I've replaced the cg_spmT2x.m by the one you sent on your previous email. However, i still get the following error: Running 'Threshold and transform spmT-maps' Use local RPV values to correct for non-stationary of smoothness. Failed 'Threshold and transform spmT-maps' Subscript indices must either be real positive integers or logicals. In file "C:\Program Files\spm8\spm8\toolbox\vbm8\cg_spmT2x.m" (v412), function "cg_spmT2x" at line 335. Any ideas why this might be happening? Many thanks! Joana 2011/5/3 Christian Gaser <[log in to unmask]> > Dear Aleksander, > > Raphael found the bug. I have attached the corrected version. However, it > is a little bit odd that this failure will occur, because that means that > your RPV image (Resels per volume) was not correct. > > Regards, > > Christian > > On Tue, 3 May 2011 17:16:48 +0200, [log in to unmask] <[log in to unmask]> wrote: > > >Dear Christian, > > > >we tried the newest VBM 8 Version r414. We still get following Error > > > >Running 'Threshold and transform spmT-maps' > >Use local RPV values to correct for non-stationary of smoothness. > >Failed 'Threshold and transform spmT-maps' > >Undefined function or variable 'V2R'. > >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m" (v412), functio= n > "cg_spmT2x" at line 312. > > > >The following modules did not run: > >Failed: Threshold and transform spmT-maps. > > > >We do not know if it is a result of an operation error or an error in th= e > tool itself? > > > >With kind regards > > > >Aleksander > > > > > > > > > > > >Dnia 2 maja 2011 22:37 Christian Gaser <[log in to unmask]> > napisa=C5=82(a): > > > >> Dear Aleksander, > >> > >> as Darren assumed there was some debugging code in the function, which= I > forgot to remove. With the newest update r413 this problem should be now > solved. > >> > >> Regards, > >> > >> Christian > >> > >> > _________________________________________________________________________= ___ > >> > >> Christian Gaser, Ph.D. > >> Departments of Psychiatry and Neurology > >> Friedrich-Schiller-University of Jena > >> Jahnstrasse 3, D-07743 Jena, Germany > >> Tel: ++49-3641-934752 Fax: ++49-3641-934755 > >> e-mail: [log in to unmask] > >> http://dbm.neuro.uni-jena.de > >> > >> > >> On Wed, 27 Apr 2011 11:13:34 +0200, [log in to unmask] <[log in to unmask]> wrote: > >> > >> >Hallo, > >> > > >> >we are trying to correct for non-stationary of smoothness > >> >and we get following error report > >> > > >> >Use local RPV values to correct for non-stationary of smoothness. > >> >Failed 'Threshold and transform spmT-maps' > >> >Undefined function or variable 'K'. > >> >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m" (v404), > function > >> >"cg_spmT2x" at line 321. > >> > > >> >The following modules did not run: > >> >Failed: Threshold and transform spmT-maps > >> > > >> > > >> >We found this : > >> >"Hi, I have removed the interface to the non-stationarity correction = in > >> >VBM8 because > >> >of the interferences with the SPM8 GUI. However, there is still a > hidden > >> >option if > >> >you use the conversion tools for transforming and thresholding T-or > >> >F-maps: > >> >VBM8|Data presentation|Threshodl and transform T-maps" > >> > > >> >And we followed this sequence > >> > > >> >The non-stationary cluster correction in VBM8 can be > >> >implemented > >> >by the following sequence of menu steps: > >> >Toolbox > VBM8 > Data presentation > Threshold and transform spmT-map= s > > > >> >.Cluster > >> >extent threshold > ..k-value > ...Correct for non-isotropic smoothnes= s > >> >yes. > >> > > >> >but without success. > >> > > >> >Do you have some experience with this part of the Vbm8 tool? > >> > > >> >We would be thankful for any help. > >> > > >> >With kind regards > >> > > >> >Aleksander > >> > > --00163646cede81e8f504a37aa859 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear Christian,<div><br></div><div>I've replaced the cg_spmT2x.m by the= one you sent on your previous email.</div><div><br></div><div>However, i s= till get the following error:</div><div><br></div><div><span class=3D"Apple= -style-span" style=3D"border-collapse: collapse; font-family: arial, sans-s= erif; font-size: 13px; ">Running 'Threshold and transform spmT-maps'= ;<br> <div class=3D"im" style=3D"color: rgb(80, 0, 80); ">Use local RPV values to= correct for=C2=A0<span class=3D"il" style=3D"background-image: initial; ba= ckground-attachment: initial; background-origin: initial; background-clip: = initial; background-color: rgb(94, 160, 227); color: rgb(255, 255, 255); ba= ckground-position: initial initial; background-repeat: initial initial; ">n= on</span>-<span class=3D"il" style=3D"background-image: initial; background= -attachment: initial; background-origin: initial; background-clip: initial;= background-color: rgb(94, 160, 227); color: rgb(255, 255, 255); background= -position: initial initial; background-repeat: initial initial; ">stationar= y</span>=C2=A0of smoothness.<br> Failed =C2=A0'Threshold and transform spmT-maps'<br></div>Subscript= indices must either be real positive integers or logicals.<br>In file &quo= t;C:\Program Files\<span class=3D"il" style=3D"background-image: initial; b= ackground-attachment: initial; background-origin: initial; background-clip:= initial; background-color: rgb(94, 160, 227); color: rgb(255, 255, 255); b= ackground-position: initial initial; background-repeat: initial initial; ">= spm8</span>\</span><span class=3D"Apple-style-span" style=3D"border-collaps= e: collapse; font-family: arial, sans-serif; font-size: 13px; "><span class= =3D"il" style=3D"background-image: initial; background-attachment: initial;= background-origin: initial; background-clip: initial; background-color: rg= b(94, 160, 227); color: rgb(255, 255, 255); ">spm8</span>\</span><span clas= s=3D"Apple-style-span" style=3D"border-collapse: collapse; font-family: ari= al, sans-serif; font-size: 13px; ">toolbox\vbm8\cg_spmT2x.m" (v412), f= unction "cg_spmT2x" at line 335.</span></div> <div><span class=3D"Apple-style-span" style=3D"border-collapse: collapse; f= ont-family: arial, sans-serif; font-size: 13px; "><br></span></div><div>Any= ideas why this might be happening?</div><div><br></div><div>Many thanks!</= div> <div><br></div><div>Joana<br><br></div><div><br></div><div><br><div class= =3D"gmail_quote">2011/5/3 Christian Gaser <span dir=3D"ltr"><<a href=3D"= mailto:[log in to unmask]">[log in to unmask]</a>></sp= an><br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">Dear Aleksander,<br> <br> Raphael found the bug. I have attached the corrected version. However, it i= s a little bit odd that this failure will occur, because that means that yo= ur RPV image (Resels per volume) was not correct.<br> <br> Regards,<br> <font color=3D"#888888"><br> Christian<br> </font><div><div></div><div class=3D"h5"><br> On Tue, 3 May 2011 17:16:48 +0200, <a href=3D"mailto:[log in to unmask]">[log in to unmask] pl</a> <<a href=3D"mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br> <br> >Dear Christian,<br> ><br> >we tried the newest VBM 8 Version r414. We still get following Error<br= > ><br> >Running 'Threshold and transform spmT-maps'<br> >Use local RPV values to correct for non-stationary of smoothness.<br> >Failed =C2=A0'Threshold and transform spmT-maps'<br> >Undefined function or variable 'V2R'.<br> >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m" (v41= 2), function "cg_spmT2x" at line 312.<br> ><br> >The following modules did not run:<br> >Failed: Threshold and transform spmT-maps.<br> ><br> >We do not know if it is a result of an operation error or an error in t= he tool itself?<br> ><br> >With kind regards<br> ><br> >Aleksander<br> ><br> ><br> ><br> ><br> ><br> >Dnia 2 maja 2011 22:37 Christian Gaser <<a href=3D"mailto:christian.= [log in to unmask]">[log in to unmask]</a>> napisa=C5=82(a):<br> ><br> >> Dear Aleksander,<br> >><br> >> as Darren assumed there was some debugging code in the function, w= hich I forgot to remove. With the newest update r413 this problem should be= now solved.<br> >><br> >> Regards,<br> >><br> >> Christian<br> >><br> >> __________________________________________________________________= __________<br> >><br> >> Christian Gaser, Ph.D.<br> >> Departments of Psychiatry and Neurology<br> >> Friedrich-Schiller-University of Jena<br> >> Jahnstrasse 3, D-07743 Jena, Germany<br> >> Tel: ++49-3641-934752 =C2=A0 =C2=A0 =C2=A0 =C2=A0Fax: =C2=A0 ++49-= 3641-934755<br> >> e-mail: <a href=3D"mailto:[log in to unmask]">christian.g= [log in to unmask]</a><br> >> <a href=3D"http://dbm.neuro.uni-jena.de" target=3D"_blank">http://= dbm.neuro.uni-jena.de</a><br> >><br> >><br> >> On Wed, 27 Apr 2011 11:13:34 +0200, <a href=3D"mailto:[log in to unmask]"= >[log in to unmask]</a> <<a href=3D"mailto:[log in to unmask]">[log in to unmask]</a>> wro= te:<br> >><br> >> >Hallo,<br> >> ><br> >> >we are trying to correct for non-stationary of smoothness<br> >> >and we get following error report<br> >> ><br> >> >Use local RPV values to correct for non-stationary of smoothne= ss.<br> >> >Failed =C2=A0'Threshold and transform spmT-maps'<br> >> >Undefined function or variable 'K'.<br> >> >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m&q= uot; (v404), function<br> >> >"cg_spmT2x" at line 321.<br> >> ><br> >> >The following modules did not run:<br> >> >Failed: Threshold and transform spmT-maps<br> >> ><br> >> ><br> >> >We found this :<br> >> >"Hi, I have removed the interface to the non-stationarity= correction in<br> >> >VBM8 because<br> >> >of the interferences with the SPM8 GUI. However, there is stil= l a hidden<br> >> >option if<br> >> >you use the conversion tools for transforming and thresholding= T-or<br> >> >F-maps:<br> >> >VBM8|Data presentation|Threshodl and transform T-maps"<br= > >> ><br> >> >And we followed this sequence<br> >> ><br> >> >The non-stationary cluster correction in VBM8 can be<br> >> >implemented<br> >> >by the following sequence of menu steps:<br> >> >Toolbox > VBM8 > Data presentation > Threshold and tr= ansform spmT-maps ><br> >> >.Cluster<br> >> >extent threshold > ..k-value > ...Correct for non-isotro= pic smoothness<br> >> >yes.<br> >> ><br> >> >but without success.<br> >> ><br> >> >Do you have some experience with this part of the Vbm8 tool?<b= r> >> ><br> >> >We would be thankful for any help.<br> >> ><br> >> >With kind regards<br> >> ><br> >> >Aleksander<br> >><br> <br> </div></div></blockquote></div><br></div> --00163646cede81e8f504a37aa859-- ========================================================================= Date: Tue, 17 May 2011 18:16:59 +0100 Reply-To: Michael Thomas Smith <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michael Thomas Smith <[log in to unmask]> Subject: Re: After estimation design matrix loses most regressors. Comments: To: Thomas Nichols <[log in to unmask]> 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]> Thanks for the reply. It seems likely it's something to do with my install of SPM, as Thomas =20 was able to run exactly the same job on his computer and get the =20 correct result. I'll reinstall SPM and try again. (by the way: I found =20 adding extra regressors caused the left-out columns to change...all =20 very weird). Thanks again for the suggestions and help, I'll be back with more questions if the reinstall doesn't work! Mike. Quoting Thomas Nichols <[log in to unmask]> on Tue, 17 May 2011 =20 12:51:23 +0100: > Dear Mike, > > Before estimation, the displayed design matrix is original matrix X (with > the exception that DCT drift basis elements aren't shown). After > estimation, the 'filtered and whitened' design matrix (xX.xKXs.X =3D K*W*X= ) is > shown. If certain columns appear to be zeroed out by whitening, that is > because the corresponding scans were found to have super-high variance. > (See my earlier post on errors with PET data). > > I can't figure out why this is happening, though, as each session's data i= s > scaled separately. You can check out the scan-by-scan scale factors used = to > bring each session to a Grand Mean of 100 by looking at SPM.xGX.gSF; if > there is crazy variation in this (e.g. super-low values for sessions 1 & 3= ) > it would explain the effect. Then the question is why are the computed > globals so different over sessions... maybe crazy-large artifactual values > in sessions 2, 4, 5,..,8? > > Hope this helps with the detective work! > > -Tom > > > On Wed, May 11, 2011 at 12:57 PM, Michael Thomas Smith < > [log in to unmask]> wrote: > >> I'm interested in the correlation between a time series and the voxels in >> the brain. To find out more I made a GLM >> (see http://www.sal.mvm.ed.ac.uk/images/problem1.png ). >> >> Before estimation it looks fine (I think?). >> >> During estimation however, 6 of the 8 columns are set to zero (see inset = in >> bottom-left corner of the linked image). Just sessions 1 and 3 output bet= a >> images. There are no errors or warnings expressed during estimation, and = the >> matlab output appears ok. >> >> - I've confirmed the number of images in each session is equal to the >> number of values in the .txt regressor files. >> - I've tried adding conditions to each session (just a single event near >> the start of each session) to see if it's the lack of conditions that's >> upsetting SPM. (this didn't make any difference). >> - I've rebuilt the design matrix from scratch by hand, using the interfac= e, >> but that didn't make any difference. >> >> Looking forward to any suggestions! >> >> Thanks, >> >> Mike Smith >> >> -- >> The University of Edinburgh is a charitable body, registered in >> Scotland, with registration number SC005336. >> > > > > -- > ____________________________________________ > Thomas Nichols, PhD > Principal Research Fellow, Head of Neuroimaging Statistics > Department of Statistics & Warwick Manufacturing Group > University of Warwick > Coventry CV4 7AL > United Kingdom > > Email: [log in to unmask] > Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 > Fax: +44 24 7652 4532 > --=20 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ========================================================================= Date: Tue, 17 May 2011 21:53:26 +0100 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: Re-Referencing of EEG Data Comments: To: Urs Bachofner <[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]> Dear Urs, On Tue, May 17, 2011 at 10:50 AM, Urs Bachofner <[log in to unmask]> wrot= e: > I have a question regarding the concept of EEG data Re-referencing in Spm= 8. > The way I see it, re-referencing is a very important step prior to Source > Analysis. To get useful data, the Signals should be re-referenced to > average. > According to the SPM8 Manual P. 110 we can use spm_eeg_montage to do this= . > > The data I'd like to process is recorded with a 128-channel EEG-net with = the > reference electrode at CZ. > According to the SPM8 Manual, for average reference the matrix should hav= e > (N-1)/N at the diagonal and -1/N elsewhere. > In my case this results in 0.9921875 at the diagonal and -0.0078125 > elsewhere. > > Two questions: > 1. These numbers are so close to the original matrix (1 at diagonal and 0 > elsewhere) which is said to not change anything. Is this matrix correct? > Yes, it's all OK. The more electrodes you have the closer the average will be to zero but remember that the actual values also depend on the data, not only on the weights so you should still re-reference. > 2. Since electrodes that are closer to the reference (in this case CZ) ha= ve > a higher amplitude recorded, shouldn't such electrodes be weighted > differently than electrodes that are more distant to CZ? Am I missing the > point here? > > No, the average reference is just what it is, every channel minus the average of all channels. It does not depend on where the original reference is. > And finally, besides Filtering, epoching, Artefact Detection (removing ba= d > channels and bad trials), Re-referencing, and baseline correction are the= re > other preprocessing steps that are necessary to get good data from my evo= ked > potentials? No, these are the basic steps. You might try using the robust averaging option. Also there is an option in MEEGTools for correcting eye blinks. You might or might not need it depending on your data. > And is there a certain order in which these steps should be > executed? > I've just presented a slide about that at the SPM course last week. Basically it says the following: ---- Considerations for order of processing steps: It=92s better to filter before epoching unless only small part of the data is relevant. As an alternative one could pad the epochs of interest with extra data and discard it later. Downsampling speeds up the other steps and reduces disk space usage, but it involves low-pass filtering and... Low-pass or band-pass filtering before high-pass can generate ringing at the edges, which is especially problematic for epoched data. So high-pass should come first, but... Only some channel types (EEG, MEG, LFP etc.) are filtered. So channel types should be set correctly first (Prepare, Montage). --- Best, Vladimir > Thank you very much for you help. > > > > ========================================================================= Date: Tue, 17 May 2011 16:47:10 -0400 Reply-To: Mobin Anandwala <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mobin Anandwala <[log in to unmask]> Subject: uj in bilinear state equation MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e6408ac242b46f04a37ee0d5 Message-ID: <[log in to unmask]> --0016e6408ac242b46f04a37ee0d5 Content-Type: text/plain; charset=ISO-8859-1 Good Afternoon This is Mobin Anandwala and I have a question regarding the variable uj in the bilinear state equation. The equation as stated is z' = (A + sigma(uj*Bj))*z + Cu. As I understand the u vector times the C matrix is a vector consisting of input functions. My question is that since the u vector consists of input functions is the uj variable a scalar that is the maximum of the input function or is it a vector? When I tried to use uj as a vector I obtained a matrix multiplication error in Matlab with Bj as the dimensions did not match (u being an n x 1 vector and Bj being 4 x 4). I am currently using uj as a scalar constant with it being the maximum of input function (current data that I have has u2(t) in the u vector and the uj being its maximum). Thank you Mobin Anandwala --0016e6408ac242b46f04a37ee0d5 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Good Afternoon<br><br>This is Mobin Anandwala and I have a question regardi= ng the variable uj in the bilinear state equation.=A0 The equation as state= d is z' =3D (A + sigma(uj*Bj))*z + Cu.=A0 As I understand the u vector = times the C matrix is a vector consisting of input functions.=A0 My questio= n is that since the u vector consists of input functions is the uj variable= a scalar that is the maximum of the input function or is it a vector?=A0 W= hen I tried to use uj as a vector I obtained a matrix multiplication error = in Matlab with Bj as the dimensions did not match (u being an n x 1 vector = and Bj being 4 x 4).=A0 I am currently using uj as a scalar constant with i= t being the maximum of input function (current data that I have has u2(t) i= n the u vector and the uj being its maximum).<br> <br>Thank you<br>Mobin Anandwala<br> --0016e6408ac242b46f04a37ee0d5-- ========================================================================= Date: Tue, 17 May 2011 16:07:10 -0700 Reply-To: Jeannine Morrone-Strupinsky <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jeannine Morrone-Strupinsky <[log in to unmask]> Subject: Re: SPM Digest - 16 May 2011 to 17 May 2011 (#2011-145) 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: <002101cc14e7$26f19830$74d4c890$@net> -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] = On Behalf Of SPM automatic digest system Sent: Tuesday, May 17, 2011 4:00 PM To: [log in to unmask] Subject: SPM Digest - 16 May 2011 to 17 May 2011 (#2011-145) There are 12 messages totaling 1451 lines in this issue. Topics of the day: 1. Batch problem--3D Source Reconstruction 2. Corregistro PET-T1 with DARTEL 3. SPM 2nd level analysis - reg 4. SPM 2nd level analysis-reg 5. Time frequency question 6. slicetiming difficulty 7. After estimation design matrix loses most regressors. (2) 8. SnPM for VBM analysis 9. Correcting for non-stationary smoothnees in vbm 10. Re-Referencing of EEG Data 11. uj in bilinear state equation ---------------------------------------------------------------------- Date: Tue, 17 May 2011 09:31:28 +0800 From: =E9=A3=9E=E9=B8=9F <[log in to unmask]> Subject: Re: Batch problem--3D Source Reconstruction Dear Vladimir, =E3=80=80=E3=80=80Thank you very much! May you a happy day! =E3=80=80=E3=80=80Haoran. =20 =E5=9C=A8 2011-05-16 19:04:19=EF=BC=8C"Vladimir Litvak" = <[log in to unmask]> =E5=86=99=E9=81=93=EF=BC=9A >Dear Haoran, > > > >On 16 May 2011, at 10:13, "=E9=A3=9E=E9=B8=9F" <[log in to unmask]> = wrote: > >> Dear Vladimir, >> =E3=80=80=E3=80=80Your guess was right. I really couldn't do 3D = sources reconstruction of that data by GUI. It reminded me ''checkmeeg: = data scale missing, assigning default >> checkmeeg: no fiducials are defined''. Then I did preprocess steps = again, and found that I couldn't obtain the fiducials automatically(when = I finished Converting step). I checked the other datasets, luckily, they = didn't have this problem. I guess there must be some wrongs of my = original dataset (only that one), right ? > >right > >> =E3=80=80=E3=80=80As for reconstruction batch problem, with the = latest version of spm8(4029) and normal dataset, I can do reconstruction = by that saved "renconstruction_batch" successfully. Thus, the problems = related to reconstruction batch that I confronted previously may result = from the different versions of spm8. >> By the way, in ''MEEG head model specification> EEG head = modle'' module, must I specify the ''EEG head modle'' if I just process = MEG dataset? > >One peculiarity of batch is that when you build the configuration you >can't use information from the dataset which only becomes available >when you run the batch. In particular you can't know if the dataset in >question is EEG or MEG. So don't worry about the EEG head model. If >your dataset is MEG it will be ignored. > >Best, > >Vladimir > > >> =E3=80=80=E3=80=80Thank you very much for helping me solve these tiny = problems! >> >> =E3=80=80=E3=80=80Haoran. >> >> >> At 2011-05-16 06:38:23=EF=BC=8C"Vladimir Litvak" = <[log in to unmask]> wrote: >> >> >Dear Haoran, >> > >> >The problem seems to be not in the batch but in your MEG data (or = was >> >it EEG?). There are no valid fiducials in that dataset. Could you >> >perform source reconstruction using the GUI? I'd be surprised if you >> >could. So we need to focus on how to get fiducials into your dataset >> >and for that you need to tell me more about where that data comes >> >from. >> > >> >Best, >> > >> >Vladimir >> > >> >2011/5/15 =E9=A3=9E=E9=B8=9F <[log in to unmask]>: >> >> Dear Vladimir, >> >> =E3=80=80=E3=80=80I tried again with the latest version of = spm8(4029), the previous problem( >> >> template=3D1) disappeared. But this time, I confronted a new = problem. It >> >> reminded me "No executable modules, but still unresolved = dependencies or >> >> incomplete module inputs." But I don't know where the problems = are. Could >> >> you help me see my reconstruction_batch.mat and give me some = advice ? Thanks >> >> a lot. >> >> >> >> Haoran. >> >> >> >> These code appears in the matlab command window when I ran = the batch: >> >> SPM8: spm_eeg_inv_mesh_ui (v4027) 11:09:13 - = 15/05/2011 >> >> = =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >> >> template: 0 >> >> sMRI: = 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01.img,1' >> >> def: = 'D:\Processed\DP01\y_sKEYAN-0002-00000-000001-01.nii' >> >> Affine: [4x4 double] >> >> Msize: 2 >> >> tess_mni: [1x1 struct] >> >> tess_ctx: >> >> = 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01cortex_8196.surf.gii' >> >> tess_scalp: >> >> 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01scalp_2562.surf.gii' >> >> tess_oskull: >> >> = 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01oskull_2562.surf.gii' >> >> tess_iskull: >> >> = 'D:\Processed\DP01\sKEYAN-0002-00000-000001-01iskull_2562.surf.gii' >> >> fid: [1x1 struct] >> >> Failed 'M/EEG head model specification' >> >> Reference to non-existent field 'fid'. >> >> In file "C:\Program = Files\MATLAB\spm8new\config\spm_cfg_eeg_inv_headmodel.m" >> >> (v4118), function "specify_headmodel" at line 209. >> >> No executable modules, but still unresolved dependencies or = incomplete >> >> module inputs. >> >> The following modules did not run: >> >> Failed: M/EEG head model specification >> >> Skipped: M/EEG source inversion >> >> Skipped: M/EEG inversion results >> >> >> >> >> >> At 2011-05-13 22:06:06=EF=BC=8C"Vladimir Litvak" = <[log in to unmask] >> >>> wrote: >> >> >> >>>Dear Haoran, >> >>> >> >>>I tried and everything works fine for me. Make sure your SPM = version >> >>>is up to date and if you can't make it work, send me your saved = batch >> >>>and I'll take another look. >> >>> >> >>>Best, >> >>> >> >>>Vladimir >> >>> >> >>>2011/5/10 =E9=A3=9E=E9=B8=9F <[log in to unmask]>: >> >>>> Dear SPM's users, >> >>>> =E3=80=80=E3=80=80Have you ever used batch interface to do 3D = source reconstruction for >> >>>> EEG/MEG data? I choosed three separate modules "M/EEG head = model >> >>>> specification" "M/EEG source inversion" and "M/EEG inversion = results" so as >> >>>> to reconstruct the sources. In the "M/EEG head model = specification" module, >> >>>> I specified ''Meshes<Meshes source<individual structual image'' = and then >> >>>> selected a mri file named "sDP74-0003-00000-000001-01.img,1". = When all was >> >>>> ready and then I ran the batch, however, I found that it din't = excuted the >> >>>> specified structual image ( as you can see below 'template=3D1') = and the >> >>>> matlab command window appeared those: >> >>>> >> >>>> SPM8: spm_eeg_inv_mesh_ui (v3731) 21:39:06 - = 09/05/2011 >> >>>> = =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >> >>>> template: 1 >> >>>> Affine: [4x4 double] >> >>>> sMRI: 'C:\Program >> >>>> Files\MATLAB\toolbox\spm8\canonical\single_subj_T1.nii' >> >>>> Msize: 2 >> >>>> tess_mni: [1x1 struct] >> >>>> tess_ctx: 'C:\Program >> >>>> Files\MATLAB\toolbox\spm8\canonical\cortex_8196.surf.gii' >> >>>> tess_scalp: 'C:\Program >> >>>> Files\MATLAB\toolbox\spm8\canonical\scalp_2562.surf.gii' >> >>>> tess_oskull: 'C:\Program >> >>>> Files\MATLAB\toolbox\spm8\canonical\oskull_2562.surf.gii' >> >>>> tess_iskull: 'C:\Program >> >>>> Files\MATLAB\toolbox\spm8\canonical\iskull_2562.surf.gii' >> >>>> fid: [1x1 struct] >> >>>> >> >>>> =E3=80=80I didn't understand why. Anyone who knows the reason = ? Thanks very much >> >>>> for any help ! >> >>>> >> >>>> -- >> >>>> Haoran LI (MS) >> >>>> Brain Imaging Lab, >> >>>> Research Center for Learning Science, >> >>>> Southeast University >> >>>> 2 Si Pai Lou , Nanjing, 210096, P.R.China >> >>>> >> >>>> >> >> >> >> >> >> >> >> >> >> >> >> -- Haoran LI (MS) Brain Imaging Lab, Research Center for Learning Science, Southeast Un ------------------------------ Date: Tue, 17 May 2011 05:10:34 +0100 From: David Yang <[log in to unmask]> Subject: Re: Corregistro PET-T1 with DARTEL Dear John: Thanks for your kind and useful reply. I wanted to make sure my interpretation was right: Step 1: was done by SPM function: Coreg:estimate and the registration = information would be stored in the header of source images (no other = output would be found) Step 2 and Step 3:just as routine DARTEL process on T1 images Step4: as in DARTEL toolbox: normalise to MNI space option and use = source images (PET) from Step1. For receptor/transporter binding potential images, I suspected we should = choose Preserve Amount in Step 4 according to previous discussion: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;f3f7b65.0901 Furthermore, considering the image features of PET or even SPECT images, = it might have to smooth such images with large amount (ex 12 mm compared = to default 8 mm in DARTEL). I wonder if such was suitable if we wanted = to perform further analysis by BPM that used both VBM and PET/SPECT = images concurrently as in this subject: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;dd737de1.1012 Thanks for your help~ Sincerely yours, David ------------------------------ Date: Tue, 17 May 2011 11:45:40 +0530 From: cognitive dept neuro science = <[log in to unmask]> Subject: SPM 2nd level analysis - reg Dear All, We are(our team) processing our datas using SPM8. We are able to process upto first level. We are able to get the activations at the end of first level result. But we cant further process our second level analysis. = Please help us to process further. Thanks in advance. --=20 Regards, Cognitive Neurosciences Centre, Dept of Clinical Psychology, NIMHANS-Banglore 560 029. ------------------------------ Date: Tue, 17 May 2011 11:59:50 +0530 From: cognitive dept neuro science = <[log in to unmask]> Subject: SPM 2nd level analysis-reg Dear All, We are(our team) processing our datas using SPM8. We are able to process upto first level. We are able to get the activations at the end of first level result. But we cant further process our second level analysis. = Please help us to process further. Thanks in advance. --=20 Regards, Cognitive Neurosciences Centre, Dept of Clinical Psychology, NIMHANS-Banglore 560 029. ------------------------------ Date: Tue, 17 May 2011 08:08:58 +0100 From: Vladimir Litvak <[log in to unmask]> Subject: Re: Time frequency question http://en.wikipedia.org/wiki/Decibel On Tue, May 17, 2011 at 4:50 AM, Tommy Ng <[log in to unmask]> wrote: > HI Sir > Sorry to bother you again, I have a basic question regarding TF = analysis in > SPM. > As instructed in SPM manual, I averaged TF from single data series = using the > option LogR. The ensuing source power maps appear reasonable but I do = not > know the units of the Z axis. The manual says Db. What is Db? > Thank you for your time and help. > Regards > Tommy Ng ------------------------------ Date: Tue, 17 May 2011 10:20:02 +0100 From: Andreas Finkelmeyer <[log in to unmask]> Subject: Re: slicetiming difficulty Check to see if the number of slices of this subject is identical to the = other subjects, and as you expect them to be - most likely there are = more slices. The easiest way to do this is to check the last number in = 'Dimensions', when you use the 'Display' button to look at the image. Good luck! =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D Andreas Finkelmeyer, Ph.D. Institute of Neuroscience, Newcastle University Academic Psychiatry, Building 15, Newcastle General Hospital Westgate Road, Newcastle NE4 6BE, UK Tel.: +44 (0)191 256 3296 Fax: +44 (0)191 256 3324 Web: www.ncl.ac.uk/ion =20 >-----Original Message----- >From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] >On Behalf Of Rebecca Ray >Sent: 16 May 2011 22:44 >To: [log in to unmask] >Subject: [SPM] slicetiming difficulty > >I have successfully preprocessed all of my data with the exception of = this one >subject. Our TR is 2.6 but it changes it to 2.7 and gives the = following error >message: > >SPM8: spm_slice_timing (v3756) 16:27:38 - = 16/05/2011 >=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >Your TR is 2.7 >Failed 'Slice Timing' >Improper assignment with rectangular empty matrix. >In file "/Applications/spm8/spm_slice_timing.m" (v3756), function >"spm_slice_timing" at line 232. >In file "/Applications/spm8/config/spm_run_st.m" (v2312), function >"spm_run_st" at line 25. > >I have spoken with our physicist who does not use SPM, and he does not >think it is a problem with the data. > >I can display the data prior to slicetiming but not after. After it = appears to >have spit out files but gives an error when you try to display these = images: >Running 'Display Image' >Image >"/Users/rebeccaray/Documents/UW_Projects/Vivek/Data_Processing/Raw_ >Data/022_S2/Functionals/Emotion/scan2/agvscan0329.img" can not be >resampled >Image >"/Users/rebeccaray/Documents/UW_Projects/Vivek/Data_Processing/Raw_ >Data/022_S2/Functionals/Emotion/scan2/agvscan0329.img" can not be >resampled >Done 'Display Image' >Done > >Any advice would be awesome! ------------------------------ Date: Tue, 17 May 2011 12:51:23 +0100 From: Thomas Nichols <[log in to unmask]> Subject: Re: After estimation design matrix loses most regressors. Dear Mike, Before estimation, the displayed design matrix is original matrix X = (with the exception that DCT drift basis elements aren't shown). After estimation, the 'filtered and whitened' design matrix (xX.xKXs.X =3D = K*W*X) is shown. If certain columns appear to be zeroed out by whitening, that is because the corresponding scans were found to have super-high variance. (See my earlier post on errors with PET data). I can't figure out why this is happening, though, as each session's data = is scaled separately. You can check out the scan-by-scan scale factors = used to bring each session to a Grand Mean of 100 by looking at SPM.xGX.gSF; if there is crazy variation in this (e.g. super-low values for sessions 1 & = 3) it would explain the effect. Then the question is why are the computed globals so different over sessions... maybe crazy-large artifactual = values in sessions 2, 4, 5,..,8? Hope this helps with the detective work! -Tom On Wed, May 11, 2011 at 12:57 PM, Michael Thomas Smith < [log in to unmask]> wrote: > I'm interested in the correlation between a time series and the voxels = in > the brain. To find out more I made a GLM > (see http://www.sal.mvm.ed.ac.uk/images/problem1.png ). > > Before estimation it looks fine (I think?). > > During estimation however, 6 of the 8 columns are set to zero (see = inset in > bottom-left corner of the linked image). Just sessions 1 and 3 output = beta > images. There are no errors or warnings expressed during estimation, = and the > matlab output appears ok. > > - I've confirmed the number of images in each session is equal to the > number of values in the .txt regressor files. > - I've tried adding conditions to each session (just a single event = near > the start of each session) to see if it's the lack of conditions = that's > upsetting SPM. (this didn't make any difference). > - I've rebuilt the design matrix from scratch by hand, using the = interface, > but that didn't make any difference. > > Looking forward to any suggestions! > > Thanks, > > Mike Smith > > -- > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. > --=20 ____________________________________________ Thomas Nichols, PhD Principal Research Fellow, Head of Neuroimaging Statistics Department of Statistics & Warwick Manufacturing Group University of Warwick Coventry CV4 7AL United Kingdom Email: [log in to unmask] Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 Fax: +44 24 7652 4532 ------------------------------ Date: Tue, 17 May 2011 09:27:05 -0400 From: "Rajagopalan, Venkateswaran" <[log in to unmask]> Subject: SnPM for VBM analysis Dear All, =20 I am interested in using SnPM for my VBM analysis. I am in the learning = process. I am wondering if somebody could help me with the following =20 I opened up SnPM GUI and opened the setup option and I choose >2 groups = Between groups ANOVA 1 scan/subject option since I have 5 groups = including control group in total. After I choose my plug a window pops = up to open the files so for instance I chose 4 controls (smwrp1 files), = 3 from patient group 1, 5 from from patient group 2. Then when I = started to enter subject index A A A A B B B C C... the option to enter = subject index doesn't seem to stop at the end of 12th index since I = chose only 12 subjects data the option to enter subject index keeps = going on for ever what am I doing wrong here.=20 =20 Thanks. =20 Venkat =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Please consider the environment before printing this e-mail Cleveland Clinic is ranked one of the top hospitals in America by U.S.News & World Report (2010). =20 Visit us online at http://www.clevelandclinic.org for a complete listing of our services, staff and locations. Confidentiality Note: This message is intended for use only by the individual or entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and destroy the material in its entirety, whether electronic or hard copy. Thank you. ------------------------------ Date: Tue, 17 May 2011 17:45:15 +0200 From: Joana Braga Pereira <[log in to unmask]> Subject: Re: Correcting for non-stationary smoothnees in vbm Dear Christian, I've replaced the cg_spmT2x.m by the one you sent on your previous = email. However, i still get the following error: Running 'Threshold and transform spmT-maps' Use local RPV values to correct for non-stationary of smoothness. Failed 'Threshold and transform spmT-maps' Subscript indices must either be real positive integers or logicals. In file "C:\Program Files\spm8\spm8\toolbox\vbm8\cg_spmT2x.m" (v412), function "cg_spmT2x" at line 335. Any ideas why this might be happening? Many thanks! Joana 2011/5/3 Christian Gaser <[log in to unmask]> > Dear Aleksander, > > Raphael found the bug. I have attached the corrected version. However, = it > is a little bit odd that this failure will occur, because that means = that > your RPV image (Resels per volume) was not correct. > > Regards, > > Christian > > On Tue, 3 May 2011 17:16:48 +0200, [log in to unmask] <[log in to unmask]> wrote: > > >Dear Christian, > > > >we tried the newest VBM 8 Version r414. We still get following Error > > > >Running 'Threshold and transform spmT-maps' > >Use local RPV values to correct for non-stationary of smoothness. > >Failed 'Threshold and transform spmT-maps' > >Undefined function or variable 'V2R'. > >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m" (v412), = function > "cg_spmT2x" at line 312. > > > >The following modules did not run: > >Failed: Threshold and transform spmT-maps. > > > >We do not know if it is a result of an operation error or an error in = the > tool itself? > > > >With kind regards > > > >Aleksander > > > > > > > > > > > >Dnia 2 maja 2011 22:37 Christian Gaser <[log in to unmask]> > napisa=C5=82(a): > > > >> Dear Aleksander, > >> > >> as Darren assumed there was some debugging code in the function, = which I > forgot to remove. With the newest update r413 this problem should be = now > solved. > >> > >> Regards, > >> > >> Christian > >> > >> > = _________________________________________________________________________= ___ > >> > >> Christian Gaser, Ph.D. > >> Departments of Psychiatry and Neurology > >> Friedrich-Schiller-University of Jena > >> Jahnstrasse 3, D-07743 Jena, Germany > >> Tel: ++49-3641-934752 Fax: ++49-3641-934755 > >> e-mail: [log in to unmask] > >> http://dbm.neuro.uni-jena.de > >> > >> > >> On Wed, 27 Apr 2011 11:13:34 +0200, [log in to unmask] <[log in to unmask]> = wrote: > >> > >> >Hallo, > >> > > >> >we are trying to correct for non-stationary of smoothness > >> >and we get following error report > >> > > >> >Use local RPV values to correct for non-stationary of smoothness. > >> >Failed 'Threshold and transform spmT-maps' > >> >Undefined function or variable 'K'. > >> >In file "C:\Program Files\spm8\toolbox\vbm8\cg_spmT2x.m" (v404), > function > >> >"cg_spmT2x" at line 321. > >> > > >> >The following modules did not run: > >> >Failed: Threshold and transform spmT-maps > >> > > >> > > >> >We found this : > >> >"Hi, I have removed the interface to the non-stationarity = correction in > >> >VBM8 because > >> >of the interferences with the SPM8 GUI. However, there is still a > hidden > >> >option if > >> >you use the conversion tools for transforming and thresholding = T-or > >> >F-maps: > >> >VBM8|Data presentation|Threshodl and transform T-maps" > >> > > >> >And we followed this sequence > >> > > >> >The non-stationary cluster correction in VBM8 can be > >> >implemented > >> >by the following sequence of menu steps: > >> >Toolbox > VBM8 > Data presentation > Threshold and transform = spmT-maps > > > >> >.Cluster > >> >extent threshold > ..k-value > ...Correct for non-isotropic = smoothness > >> >yes. > >> > > >> >but without success. > >> > > >> >Do you have some experience with this part of the Vbm8 tool? > >> > > >> >We would be thankful for any help. > >> > > >> >With kind regards > >> > > >> >Aleksander > >> > > ------------------------------ Date: Tue, 17 May 2011 18:16:59 +0100 From: Michael Thomas Smith <[log in to unmask]> Subject: Re: After estimation design matrix loses most regressors. Thanks for the reply. It seems likely it's something to do with my install of SPM, as Thomas =20 was able to run exactly the same job on his computer and get the =20 correct result. I'll reinstall SPM and try again. (by the way: I found =20 adding extra regressors caused the left-out columns to change...all =20 very weird). Thanks again for the suggestions and help, I'll be back with more questions if the reinstall doesn't work! Mike. Quoting Thomas Nichols <[log in to unmask]> on Tue, 17 May 2011 =20 12:51:23 +0100: > Dear Mike, > > Before estimation, the displayed design matrix is original matrix X = (with > the exception that DCT drift basis elements aren't shown). After > estimation, the 'filtered and whitened' design matrix (xX.xKXs.X =3D = K*W*X) is > shown. If certain columns appear to be zeroed out by whitening, that = is > because the corresponding scans were found to have super-high = variance. > (See my earlier post on errors with PET data). > > I can't figure out why this is happening, though, as each session's = data is > scaled separately. You can check out the scan-by-scan scale factors = used to > bring each session to a Grand Mean of 100 by looking at SPM.xGX.gSF; = if > there is crazy variation in this (e.g. super-low values for sessions 1 = & 3) > it would explain the effect. Then the question is why are the = computed > globals so different over sessions... maybe crazy-large artifactual = values > in sessions 2, 4, 5,..,8? > > Hope this helps with the detective work! > > -Tom > > > On Wed, May 11, 2011 at 12:57 PM, Michael Thomas Smith < > [log in to unmask]> wrote: > >> I'm interested in the correlation between a time series and the = voxels in >> the brain. To find out more I made a GLM >> (see http://www.sal.mvm.ed.ac.uk/images/problem1.png ). >> >> Before estimation it looks fine (I think?). >> >> During estimation however, 6 of the 8 columns are set to zero (see = inset in >> bottom-left corner of the linked image). Just sessions 1 and 3 output = beta >> images. There are no errors or warnings expressed during estimation, = and the >> matlab output appears ok. >> >> - I've confirmed the number of images in each session is equal to the >> number of values in the .txt regressor files. >> - I've tried adding conditions to each session (just a single event = near >> the start of each session) to see if it's the lack of conditions = that's >> upsetting SPM. (this didn't make any difference). >> - I've rebuilt the design matrix from scratch by hand, using the = interface, >> but that didn't make any difference. >> >> Looking forward to any suggestions! >> >> Thanks, >> >> Mike Smith >> >> -- >> The University of Edinburgh is a charitable body, registered in >> Scotland, with registration number SC005336. >> > > > > -- > ____________________________________________ > Thomas Nichols, PhD > Principal Research Fellow, Head of Neuroimaging Statistics > Department of Statistics & Warwick Manufacturing Group > University of Warwick > Coventry CV4 7AL > United Kingdom > > Email: [log in to unmask] > Phone, Stats: +44 24761 51086, WMG: +44 24761 50752 > Fax: +44 24 7652 4532 > --=20 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. ------------------------------ Date: Tue, 17 May 2011 21:53:26 +0100 From: Vladimir Litvak <[log in to unmask]> Subject: Re: Re-Referencing of EEG Data Dear Urs, On Tue, May 17, 2011 at 10:50 AM, Urs Bachofner <[log in to unmask]> = wrote: > I have a question regarding the concept of EEG data Re-referencing in = Spm8. > The way I see it, re-referencing is a very important step prior to = Source > Analysis. To get useful data, the Signals should be re-referenced to > average. > According to the SPM8 Manual P. 110 we can use spm_eeg_montage to do = this. > > The data I'd like to process is recorded with a 128-channel EEG-net = with the > reference electrode at CZ. > According to the SPM8 Manual, for average reference the matrix should = have > (N-1)/N at the diagonal and -1/N elsewhere. > In my case this results in 0.9921875 at the diagonal and -0.0078125 > elsewhere. > > Two questions: > 1. These numbers are so close to the original matrix (1 at diagonal = and 0 > elsewhere) which is said to not change anything. Is this matrix = correct? > Yes, it's all OK. The more electrodes you have the closer the average will be to zero but remember that the actual values also depend on the data, not only on the weights so you should still re-reference. > 2. Since electrodes that are closer to the reference (in this case CZ) = have > a higher amplitude recorded, shouldn't such electrodes be weighted > differently than electrodes that are more distant to CZ? Am I missing = the > point here? > > No, the average reference is just what it is, every channel minus the average of all channels. It does not depend on where the original reference is. > And finally, besides Filtering, epoching, Artefact Detection (removing = bad > channels and bad trials), Re-referencing, and baseline correction are = there > other preprocessing steps that are necessary to get good data from my = evoked > potentials? No, these are the basic steps. You might try using the robust averaging option. Also there is an option in MEEGTools for correcting eye blinks. You might or might not need it depending on your data. > And is there a certain order in which these steps should be > executed? > I've just presented a slide about that at the SPM course last week. Basically it says the following: ---- Considerations for order of processing steps: It=E2=80=99s better to filter before epoching unless only small part of = the data is relevant. As an alternative one could pad the epochs of interest with extra data and discard it later. Downsampling speeds up the other steps and reduces disk space usage, but it involves low-pass filtering and... Low-pass or band-pass filtering before high-pass can generate ringing at the edges, which is especially problematic for epoched data. So high-pass should come first, but... Only some channel types (EEG, MEG, LFP etc.) are filtered. So channel types should be set correctly first (Prepare, Montage). --- Best, Vladimir > Thank you very much for you help. > > > > ------------------------------ Date: Tue, 17 May 2011 16:47:10 -0400 From: Mobin Anandwala <[log in to unmask]> Subject: uj in bilinear state equation Good Afternoon This is Mobin Anandwala and I have a question regarding the variable uj = in the bilinear state equation. The equation as stated is z' =3D (A + sigma(uj*Bj))*z + Cu. As I understand the u vector times the C matrix = is a vector consisting of input functions. My question is that since the u vector consists of input functions is the uj variable a scalar that is = the maximum of the input function or is it a vector? When I tried to use uj = as a vector I obtained a matrix multiplication error in Matlab with Bj as = the dimensions did not match (u being an n x 1 vector and Bj being 4 x 4). = I am currently using uj as a scalar constant with it being the maximum of = input function (current data that I have has u2(t) in the u vector and the uj being its maximum). Thank you Mobin Anandwala ------------------------------ End of SPM Digest - 16 May 2011 to 17 May 2011 (#2011-145) ********************************************************** ========================================================================= Date: Wed, 18 May 2011 09:27:47 +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: Re: Problem about how to deal with the activation withinventricles of the brain Comments: To: Burak Erdeniz <[log in to unmask]> Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=====003_Dragon000002560673_=====" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --=====003_Dragon000002560673_===== Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 RGVhciBCdXJhaywNCg0KVGhhbmsgeW91IGZvciB5b3VyIHJlcGx5IGFuZCBJIHdpbGwgdHJ5IHRo YXQgbWV0aG9kIHlvdSBzdWdnZXN0ZWQuDQoNCg0KDQoyMDExLTA1LTE4IA0KDQoNCg0KwO7B1cHh DQoNCkxpbmxpbmcgTGkgDQpSZXNlYXJjaCBDZW50cmUgZm9yIE5ldXJhbCBFbmdpbmVlcmluZw0K LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0NClBob25lOiArODYtNzU1LTg2 MzkyMjMyDQpFbWFpbDogIGxsLmxpMUBzaWF0LmFjLmNuDQpTaGVuemhlbiBJbnN0aXR1dGVzIG9m IEFkdmFuY2VkIFRlY2hub2xvZ3ksDQpDaGluZXNlIEFjYWRlbXkgb2YgU2NpZW5jZXMuDQoNCg0K DQq3orz+yMujuiBCdXJhayBFcmRlbml6IA0Kt6LLzcqxvOSjuiAyMDExLTA1LTE0ICAyMDo0MDo1 NCANCsrVvP7Iy6O6IExpbmxpbmcgTGkgDQqzrcvNo7ogDQrW98zio7ogUmU6IFtTUE1dIFByb2Js ZW0gYWJvdXQgaG93IHRvIGRlYWwgd2l0aCB0aGUgYWN0aXZhdGlvbiB3aXRoaW52ZW50cmljbGVz IG9mIHRoZSBicmFpbiANCiANCkhpIExpbmxpbmcsDQoNClVzdWFsbHkgdGhlIGJlc3R3YXkgaXMg dG8gZGVmaW5lIGFuIFZPSSBhbmQgdGhhbiBnZXQgdGhlIGFjdGl2YXRpb25zIGluIHRoYXQgcmVn aW9uIGFuZCB1c2UgaXQgYXMgYW4gYWRkaXRpb25hbCByZWdyZXNzb3Igc2ltaWxhciB0byB1c2lu ZyBtb3Rpb24gcGFyYW1ldGVycy4gUGVvcGxlIHVzdWFsbHkgY2hvb3NlIGV5ZWJhbGxzIGZvciBW T0kuDQoNCkNoZWVycw0KQnVyYWsNCg0KDQoyMDExLzUvMTQgTGlubGluZyBMaSA8bGwubGkxQHNp YXQuYWMuY24+DQoNCkRlYXIgYWxsLA0KDQpDb3VsZCBhbnlvbmUgZ2l2ZSBtZSBzb21lIHN1Z2dl c3Rpb24gYWJvdXQgaG93IHRvIGRlYWwgd2l0aCB0aGUgYWN0aXZhdGlvbiB3aXRoaW4gdmVudHJp Y2xlcyBvZiB0aGUgYnJhaW4gaW4gb3JkZXIgdG8gc2hvdyBtb3JlIG1lYW5pbmdmdWwgYWN0aXZh dGlvbiBwYXR0ZXJuPw0KDQpUaGFua3MgaW4gYWR2YW5jZSENCg0KMjAxMS0wNS0xNCANCg0KDQoN CsDuwdXB4Q0KDQpMaW5saW5nIExpIA0KUmVzZWFyY2ggQ2VudHJlIGZvciBOZXVyYWwgRW5naW5l ZXJpbmcNCi0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tDQpQaG9uZTogKzg2 LTc1NS04NjM5MjIzMg0KRW1haWw6ICBsbC5saTFAc2lhdC5hYy5jbg0KU2hlbnpoZW4gSW5zdGl0 dXRlcyBvZiBBZHZhbmNlZCBUZWNobm9sb2d5LA0KQ2hpbmVzZSBBY2FkZW15IG9mIFNjaWVuY2Vz Lg0KDQoNCg0KLS0gDQpCdXJhayBFcmRlbml6DQpQaGQgU3R1ZGVudA0KUHN5Y2hvbG9neSAmIENv bXB1dGVyIFNjaWVuY2UgRGVwYXJ0bWVudA0KSGVhbHRoICYgSHVtYW4gU2NpZW5jZXMgUmVzZWFy Y2ggSW5zdGl0dXRlDQoyZjI0MCBIZWFsdGggQnVpbGRpbmcNClVuaXZlcnNpdHkgb2YgSGVydGZv cmRzaGlyZQ0KQ29sbGVnZSBMYW5lDQpIYXRmaWVsZA0KSGVydHMNCkFMIDEwIDlBQg0Kb21uaWEg dmluY2l0IGFtb3INCg== --=====003_Dragon000002560673_===== Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: base64 PCFET0NUWVBFIEhUTUwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4NCjxIVE1MPjxIRUFEPg0KPE1FVEEgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PUdC MjMxMiIgaHR0cC1lcXVpdj1Db250ZW50LVR5cGU+DQo8TUVUQSBuYW1lPUdFTkVSQVRPUiBjb250 ZW50PSJNU0hUTUwgOC4wMC42MDAxLjE5MDQ2Ij4NCjxTVFlMRT5AZm9udC1mYWNlIHsNCglmb250 LWZhbWlseTogy87M5TsNCn0NCkBmb250LWZhY2Ugew0KCWZvbnQtZmFtaWx5OiBWZXJkYW5hOw0K fQ0KQGZvbnQtZmFjZSB7DQoJZm9udC1mYW1pbHk6IEDLzszlOw0KfQ0KQHBhZ2UgU2VjdGlvbjEg e3NpemU6IDU5NS4zcHQgODQxLjlwdDsgbWFyZ2luOiA3Mi4wcHQgOTAuMHB0IDcyLjBwdCA5MC4w cHQ7IGxheW91dC1ncmlkOiAxNS42cHQ7IH0NClAuTXNvTm9ybWFsIHsNCglURVhULUpVU1RJRlk6 IGludGVyLWlkZW9ncmFwaDsgVEVYVC1BTElHTjoganVzdGlmeTsgTUFSR0lOOiAwY20gMGNtIDBw dDsgRk9OVC1GQU1JTFk6ICJUaW1lcyBOZXcgUm9tYW4iOyBGT05ULVNJWkU6IDEwLjVwdA0KfQ0K TEkuTXNvTm9ybWFsIHsNCglURVhULUpVU1RJRlk6IGludGVyLWlkZW9ncmFwaDsgVEVYVC1BTElH TjoganVzdGlmeTsgTUFSR0lOOiAwY20gMGNtIDBwdDsgRk9OVC1GQU1JTFk6ICJUaW1lcyBOZXcg Um9tYW4iOyBGT05ULVNJWkU6IDEwLjVwdA0KfQ0KRElWLk1zb05vcm1hbCB7DQoJVEVYVC1KVVNU SUZZOiBpbnRlci1pZGVvZ3JhcGg7IFRFWFQtQUxJR046IGp1c3RpZnk7IE1BUkdJTjogMGNtIDBj bSAwcHQ7IEZPTlQtRkFNSUxZOiAiVGltZXMgTmV3IFJvbWFuIjsgRk9OVC1TSVpFOiAxMC41cHQN Cn0NCkE6bGluayB7DQoJQ09MT1I6IGJsdWU7IFRFWFQtREVDT1JBVElPTjogdW5kZXJsaW5lDQp9 DQpTUEFOLk1zb0h5cGVybGluayB7DQoJQ09MT1I6IGJsdWU7IFRFWFQtREVDT1JBVElPTjogdW5k ZXJsaW5lDQp9DQpBOnZpc2l0ZWQgew0KCUNPTE9SOiBwdXJwbGU7IFRFWFQtREVDT1JBVElPTjog dW5kZXJsaW5lDQp9DQpTUEFOLk1zb0h5cGVybGlua0ZvbGxvd2VkIHsNCglDT0xPUjogcHVycGxl OyBURVhULURFQ09SQVRJT046IHVuZGVybGluZQ0KfQ0KU1BBTi5FbWFpbFN0eWxlMTcgew0KCUZP TlQtU1RZTEU6IG5vcm1hbDsgRk9OVC1GQU1JTFk6IFZlcmRhbmE7IENPTE9SOiB3aW5kb3d0ZXh0 OyBGT05ULVdFSUdIVDogbm9ybWFsOyBURVhULURFQ09SQVRJT046IG5vbmU7IG1zby1zdHlsZS10 eXBlOiBwZXJzb25hbC1jb21wb3NlDQp9DQpESVYuU2VjdGlvbjEgew0KCXBhZ2U6IFNlY3Rpb24x DQp9DQpVTktOT1dOIHsNCglGT05ULVNJWkU6IDEwcHQNCn0NCkJMT0NLUVVPVEUgew0KCU1BUkdJ Ti1UT1A6IDBweDsgTUFSR0lOLUJPVFRPTTogMHB4OyBNQVJHSU4tTEVGVDogMmVtDQp9DQpPTCB7 DQoJTUFSR0lOLVRPUDogMHB4OyBNQVJHSU4tQk9UVE9NOiAwcHgNCn0NClVMIHsNCglNQVJHSU4t VE9QOiAwcHg7IE1BUkdJTi1CT1RUT006IDBweA0KfQ0KPC9TVFlMRT4NCjwvSEVBRD4NCjxCT0RZ IHN0eWxlPSJNQVJHSU46IDEwcHg7IEZPTlQtRkFNSUxZOiB2ZXJkYW5hOyBGT05ULVNJWkU6IDEw cHQiPg0KPERJVj48Rk9OVCBzaXplPTIgZmFjZT1WZXJkYW5hPkRlYXIgQnVyYWssPC9GT05UPjwv RElWPg0KPERJVj4mbmJzcDs8L0RJVj4NCjxESVY+VGhhbmsgeW91IGZvciB5b3VyIHJlcGx5IGFu ZCBJIHdpbGwgdHJ5IHRoYXQgbWV0aG9kIHlvdSBzdWdnZXN0ZWQuPC9ESVY+DQo8RElWPiZuYnNw OzwvRElWPg0KPERJVj48Rk9OVCBjb2xvcj0jMDAwMDgwIHNpemU9MiBmYWNlPVZlcmRhbmE+PC9G T05UPiZuYnNwOzwvRElWPg0KPERJVj48Rk9OVCBjb2xvcj0jMDAwMDgwIHNpemU9MiBmYWNlPVZl cmRhbmE+PC9GT05UPiZuYnNwOzwvRElWPg0KPERJVj48Rk9OVCBjb2xvcj0jYzBjMGMwIHNpemU9 MiBmYWNlPVZlcmRhbmE+MjAxMS0wNS0xOCA8L0ZPTlQ+PC9ESVY+PEZPTlQgDQpjb2xvcj0jMDAw MDgwIHNpemU9MiBmYWNlPVZlcmRhbmE+DQo8SFIgc3R5bGU9IldJRFRIOiAxMDBweCIgYWxpZ249 bGVmdCBjb2xvcj0jYjVjNGRmIFNJWkU9MT4NCjwvRk9OVD4NCjxESVY+PEZPTlQgY29sb3I9I2Mw YzBjMCBzaXplPTIgZmFjZT1WZXJkYW5hPjxTUEFOPg0KPERJVj4NCjxESVY+wO7B1cHhPEJSPjxC Uj5MaW5saW5nIExpIDwvRElWPg0KPERJVj5SZXNlYXJjaCBDZW50cmUgZm9yIE5ldXJhbCANCkVu Z2luZWVyaW5nPEJSPi0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tPEJSPlBo b25lOiANCis4Ni03NTUtODYzOTIyMzI8QlI+RW1haWw6Jm5ic3A7Jm5ic3A7PEEgDQpocmVmPSJt YWlsdG86bGwubGkxQHNpYXQuYWMuY24iPmxsLmxpMUBzaWF0LmFjLmNuPC9BPjxCUj5TaGVuemhl biBJbnN0aXR1dGVzIG9mIA0KQWR2YW5jZWQgVGVjaG5vbG9neSw8QlI+Q2hpbmVzZSBBY2FkZW15 IG9mIA0KU2NpZW5jZXMuPC9ESVY+PC9ESVY+PC9TUEFOPjwvRk9OVD48L0RJVj4NCjxIUiBjb2xv cj0jYjVjNGRmIFNJWkU9MT4NCg0KPERJVj48Rk9OVCBzaXplPTIgZmFjZT1WZXJkYW5hPjxTVFJP Tkc+t6K8/sjLo7o8L1NUUk9ORz4gQnVyYWsgRXJkZW5peiA8L0ZPTlQ+PC9ESVY+DQo8RElWPjxG T05UIHNpemU9MiBmYWNlPVZlcmRhbmE+PFNUUk9ORz63osvNyrG85KO6PC9TVFJPTkc+IDIwMTEt MDUtMTQmbmJzcDsgMjA6NDA6NTQgDQo8L0ZPTlQ+PC9ESVY+DQo8RElWPjxGT05UIHNpemU9MiBm YWNlPVZlcmRhbmE+PFNUUk9ORz7K1bz+yMujujwvU1RST05HPiBMaW5saW5nIExpIDwvRk9OVD48 L0RJVj4NCjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVyZGFuYT48U1RST05HPrOty82jujwvU1RS T05HPiA8L0ZPTlQ+PC9ESVY+DQo8RElWPjxGT05UIHNpemU9MiBmYWNlPVZlcmRhbmE+PFNUUk9O Rz7W98zio7o8L1NUUk9ORz4gUmU6IFtTUE1dIFByb2JsZW0gYWJvdXQgaG93IA0KdG8gZGVhbCB3 aXRoIHRoZSBhY3RpdmF0aW9uIHdpdGhpbnZlbnRyaWNsZXMgb2YgdGhlIGJyYWluIDwvRk9OVD48 L0RJVj4NCjxESVY+PEZPTlQgc2l6ZT0yIGZhY2U9VmVyZGFuYT48L0ZPTlQ+IDwvRElWPg0KPERJ Vj48Rk9OVCBzaXplPTIgZmFjZT1WZXJkYW5hPkhpIExpbmxpbmcsPEJSPjxCUj5Vc3VhbGx5IHRo ZSBiZXN0d2F5IGlzIHRvIA0KZGVmaW5lIGFuIFZPSSBhbmQgdGhhbiBnZXQgdGhlIGFjdGl2YXRp b25zIGluIHRoYXQgcmVnaW9uIGFuZCB1c2UgaXQgYXMgYW4gDQphZGRpdGlvbmFsIHJlZ3Jlc3Nv ciBzaW1pbGFyIHRvIHVzaW5nIG1vdGlvbiBwYXJhbWV0ZXJzLiBQZW9wbGUgdXN1YWxseSBjaG9v c2UgDQpleWViYWxscyBmb3IgVk9JLjxCUj48QlI+Q2hlZXJzPEJSPkJ1cmFrPEJSPjxCUj4NCjxE SVYgY2xhc3M9Z21haWxfcXVvdGU+MjAxMS81LzE0IExpbmxpbmcgTGkgPFNQQU4gZGlyPWx0cj4m bHQ7PEEgDQpocmVmPSJtYWlsdG86bGwubGkxQHNpYXQuYWMuY24iPmxsLmxpMUBzaWF0LmFjLmNu PC9BPiZndDs8L1NQQU4+PEJSPg0KPEJMT0NLUVVPVEUgDQpzdHlsZT0iQk9SREVSLUxFRlQ6ICNj Y2MgMXB4IHNvbGlkOyBNQVJHSU46IDBweCAwcHggMHB4IDAuOGV4OyBQQURESU5HLUxFRlQ6IDFl eCIgDQpjbGFzcz1nbWFpbF9xdW90ZT4NCiAgPERJViBzdHlsZT0iTUFSR0lOOiAxMHB4OyBGT05U LUZBTUlMWTogdmVyZGFuYTsgRk9OVC1TSVpFOiAxMHB0Ij4NCiAgPERJVj48Rk9OVCBzaXplPTIg ZmFjZT1WZXJkYW5hPkRlYXIgYWxsLDwvRk9OVD48L0RJVj4NCiAgPERJVj4mbmJzcDs8L0RJVj4N CiAgPERJVj5Db3VsZCBhbnlvbmUgZ2l2ZSBtZSBzb21lIHN1Z2dlc3Rpb24gYWJvdXQgaG93IHRv IA0KICBkZWFsJm5ic3A7d2l0aCZuYnNwO3RoZSZuYnNwO2FjdGl2YXRpb24mbmJzcDt3aXRoaW4m bmJzcDt2ZW50cmljbGVzJm5ic3A7b2YmbmJzcDt0aGUmbmJzcDticmFpbiANCiAgaW4gb3JkZXIg dG8gc2hvdyBtb3JlIG1lYW5pbmdmdWwgYWN0aXZhdGlvbiBwYXR0ZXJuPzwvRElWPg0KICA8RElW PiZuYnNwOzwvRElWPg0KICA8RElWPlRoYW5rcyBpbiBhZHZhbmNlITwvRElWPg0KICA8RElWPjxG T05UIHNpemU9MiBmYWNlPVZlcmRhbmE+PC9GT05UPiZuYnNwOzwvRElWPg0KICA8RElWIGFsaWdu PWxlZnQ+PEZPTlQgY29sb3I9I2MwYzBjMCBzaXplPTIgZmFjZT1WZXJkYW5hPjIwMTEtMDUtMTQg DQogIDwvRk9OVD48L0RJVj48Rk9OVCBzaXplPTIgZmFjZT1WZXJkYW5hPg0KICA8SFIgc3R5bGU9 Ik1JTi1IRUlHSFQ6IDJweDsgV0lEVEg6IDEyMnB4IiBhbGlnbj1sZWZ0IFNJWkU9Mj4NCg0KICA8 RElWPjxGT05UIGNvbG9yPSNjMGMwYzAgc2l6ZT0yIGZhY2U9VmVyZGFuYT48U1BBTj4NCiAgPERJ Vj4NCiAgPERJVj7A7sHVweE8QlI+PEJSPkxpbmxpbmcgTGkgPC9ESVY+DQogIDxESVY+UmVzZWFy Y2ggQ2VudHJlIGZvciBOZXVyYWwgDQogIEVuZ2luZWVyaW5nPEJSPi0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tPEJSPlBob25lOiANCiAgKzg2LTc1NS04NjM5MjIzMjxCUj5F bWFpbDombmJzcDsmbmJzcDs8QSBocmVmPSJtYWlsdG86bGwubGkxQHNpYXQuYWMuY24iIA0KICB0 YXJnZXQ9X2JsYW5rPmxsLmxpMUBzaWF0LmFjLmNuPC9BPjxCUj5TaGVuemhlbiBJbnN0aXR1dGVz IG9mIEFkdmFuY2VkIA0KICBUZWNobm9sb2d5LDxCUj5DaGluZXNlIEFjYWRlbXkgb2YgDQogIFNj aWVuY2VzLjwvRElWPjwvRElWPjwvU1BBTj48L0ZPTlQ+PC9ESVY+PC9GT05UPjwvRElWPjwvQkxP Q0tRVU9URT48L0RJVj48QlI+PEJSIA0KY2xlYXI9YWxsPjxCUj4tLSA8QlI+QnVyYWsgRXJkZW5p ejxCUj5QaGQgU3R1ZGVudDxCUj5Qc3ljaG9sb2d5ICZhbXA7IENvbXB1dGVyIA0KU2NpZW5jZSBE ZXBhcnRtZW50PEJSPkhlYWx0aCAmYW1wOyBIdW1hbiBTY2llbmNlcyBSZXNlYXJjaCBJbnN0aXR1 dGU8QlI+MmYyNDAgDQpIZWFsdGggQnVpbGRpbmc8QlI+VW5pdmVyc2l0eSBvZiBIZXJ0Zm9yZHNo aXJlPEJSPkNvbGxlZ2UgDQpMYW5lPEJSPkhhdGZpZWxkPEJSPkhlcnRzPEJSPkFMIDEwIDlBQjxC Uj5vbW5pYSB2aW5jaXQgDQphbW9yPEJSPjxCUj48L0ZPTlQ+PC9ESVY+PC9CT0RZPjwvSFRNTD4N Cg== --=====003_Dragon000002560673_=====-- ========================================================================= Date: Wed, 18 May 2011 08:49:54 +0200 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: FIR MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf305e2565cb289704a3874b5c Message-ID: <[log in to unmask]> --20cf305e2565cb289704a3874b5c Content-Type: text/plain; charset=ISO-8859-1 Dear all, I am planning to do a FIR analysis and as wondering about the timebins: would it be ok to choose seconds instead of TR's as timeunits, or are there reasons to advise against that? Thanks, Maaike --20cf305e2565cb289704a3874b5c Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Dear all,</div> <div>=A0</div> <div>I am planning to do a FIR analysis and as wondering about the timebins= : would it be ok to choose seconds instead of TR's as timeunits, or are= there reasons to advise against that?</div> <div>=A0</div> <div>Thanks, Maaike</div> --20cf305e2565cb289704a3874b5c-- ========================================================================= Date: Wed, 18 May 2011 10:06:16 +0200 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: Problem about how to deal with the activation withinventricles of the brain Comments: To: Linling Li <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-type: multipart/alternative; boundary="Boundary_(ID_t9pmUlO15I4ov7Zxtd0LVw)" Message-ID: <[log in to unmask]> --Boundary_(ID_t9pmUlO15I4ov7Zxtd0LVw) Content-type: text/plain; charset=GB2312 Content-transfer-encoding: quoted-printable Dear Linling To avoid this in the future you may want to record pulse and respiration = and use for instance RETROICOR (Glover et al. 2000). Best Torben Den Uge:20 18/05/2011 kl. 03.27 skrev Linling Li: > Dear Burak, > =20 > Thank you for your reply and I will try that method you suggested. > =20 > =20 > =20 > 2011-05-18 > =C0=EE=C1=D5=C1=E1 >=20 > 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. > =B7=A2=BC=FE=C8=CB=A3=BA Burak Erdeniz > =B7=A2=CB=CD=CA=B1=BC=E4=A3=BA 2011-05-14 20:40:54 > =CA=D5=BC=FE=C8=CB=A3=BA Linling Li > =B3=AD=CB=CD=A3=BA > =D6=F7=CC=E2=A3=BA Re: [SPM] Problem about how to deal with the = activation withinventricles of the brain > Hi Linling, >=20 > Usually the bestway is to define an VOI and than get the activations = in that region and use it as an additional regressor similar to using = motion parameters. People usually choose eyeballs for VOI. >=20 > Cheers > Burak >=20 > 2011/5/14 Linling Li <[log in to unmask]> > Dear all, > =20 > Could anyone give me some suggestion about how to deal with the = activation within ventricles of the brain in order to show more = meaningful activation pattern? > =20 > Thanks in advance! > =20 > 2011-05-14 > =C0=EE=C1=D5=C1=E1 >=20 > 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. >=20 >=20 >=20 > --=20 > Burak Erdeniz > Phd Student > Psychology & Computer Science Department > Health & Human Sciences Research Institute > 2f240 Health Building > University of Hertfordshire > College Lane > Hatfield > Herts > AL 10 9AB > omnia vincit amor >=20 --Boundary_(ID_t9pmUlO15I4ov7Zxtd0LVw) Content-type: text/html; charset=GB2312 Content-transfer-encoding: quoted-printable <html><head><Bass href=3D"x-msg://42/"></head><body style=3D"word-wrap: = break-word; -webkit-nbsp-mode: space; -webkit-line-break: = after-white-space; ">Dear Linling<div><br></div><div>To avoid this = in the future you may want to record pulse and respiration and use for = instance RETROICOR (Glover et al. = 2000).</div><div><br></div><div><br></div><div>Best</div><div>Torben<br><d= iv><font class=3D"Apple-style-span" face=3D"Verdana"><span = class=3D"Apple-style-span" style=3D"font-size: small; = "><br></span></font></div><div><font class=3D"Apple-style-span" = face=3D"Verdana"><span class=3D"Apple-style-span" style=3D"font-size: = small;"><br></span></font></div><div><font class=3D"Apple-style-span" = face=3D"Verdana"><span class=3D"Apple-style-span" style=3D"font-size: = small;"><br></span></font><div><div>Den Uge:20 18/05/2011 kl. 03.27 = skrev Linling Li:</div><br class=3D"Apple-interchange-newline"><blockquote= type=3D"cite"><span class=3D"Apple-style-span" style=3D"border-collapse: = separate; font-family: Helvetica; font-style: normal; font-variant: = normal; font-weight: normal; letter-spacing: normal; line-height: = normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: = normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: = 0px; -webkit-border-vertical-spacing: 0px; = -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: = auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div = style=3D"margin-top: 10px; margin-right: 10px; margin-bottom: 10px; = margin-left: 10px; font-family: verdana; font-size: 10pt; "><div><font = size=3D"2" face=3D"Verdana">Dear = Burak,</font></div><div> </div><div>Thank you for your reply and I = will try that method you suggested.</div><div> </div><div><font = color=3D"#000080" size=3D"2" = face=3D"Verdana"></font> </div><div><font color=3D"#000080" = size=3D"2" face=3D"Verdana"></font> </div><div><font = color=3D"#c0c0c0" size=3D"2" face=3D"Verdana">2011-05-18</font></div><font= color=3D"#000080" size=3D"2" face=3D"Verdana"><hr align=3D"left" = color=3D"#b5c4df" size=3D"1" style=3D"width: 100px; "></font><div><font = color=3D"#c0c0c0" size=3D"2" = face=3D"Verdana"><span><div><div>=C0=EE=C1=D5=C1=E1<br><br>Linling = Li</div><div>Research Centre for Neural = Engineering<br>--------------------------------------<br>Phone: = +86-755-86392232<br>Email: <a href=3D"mailto:[log in to unmask]"= style=3D"color: blue; text-decoration: underline; = ">[log in to unmask]</a><br>Shenzhen Institutes of Advanced = Technology,<br>Chinese Academy of = Sciences.</div></div></span></font></div><hr color=3D"#b5c4df" = size=3D"1"><div><font size=3D"2" = face=3D"Verdana"><strong>=B7=A2=BC=FE=C8=CB=A3=BA</strong><span = class=3D"Apple-converted-space"> </span>Burak = Erdeniz</font></div><div><font size=3D"2" = face=3D"Verdana"><strong>=B7=A2=CB=CD=CA=B1=BC=E4=A3=BA</strong><span = class=3D"Apple-converted-space"> </span>2011-05-14 = 20:40:54</font></div><div><font size=3D"2" = face=3D"Verdana"><strong>=CA=D5=BC=FE=C8=CB=A3=BA</strong><span = class=3D"Apple-converted-space"> </span>Linling = Li</font></div><div><font size=3D"2" = face=3D"Verdana"><strong>=B3=AD=CB=CD=A3=BA</strong></font></div><div><fon= t size=3D"2" face=3D"Verdana"><strong>=D6=F7=CC=E2=A3=BA</strong><span = class=3D"Apple-converted-space"> </span>Re: [SPM] Problem about how = to deal with the activation withinventricles of the = brain</font></div><div><font size=3D"2" = face=3D"Verdana"></font></div><div><font size=3D"2" face=3D"Verdana">Hi = Linling,<br><br>Usually the bestway is to define an VOI and than get the = activations in that region and use it as an additional regressor similar = to using motion parameters. People usually choose eyeballs for = VOI.<br><br>Cheers<br>Burak<br><br><div class=3D"gmail_quote">2011/5/14 = Linling Li<span class=3D"Apple-converted-space"> </span><span = dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]" style=3D"color: = blue; text-decoration: underline; = ">[log in to unmask]</a>></span><br><blockquote class=3D"gmail_quote" = style=3D"margin-top: 0px; margin-bottom: 0px; margin-left: 0.8ex; = border-left-color: rgb(204, 204, 204); border-left-width: 1px; = border-left-style: solid; margin-right: 0px; padding-left: 1ex; "><div = style=3D"margin-top: 10px; margin-right: 10px; margin-bottom: 10px; = margin-left: 10px; font-family: verdana; font-size: 10pt; "><div><font = size=3D"2" face=3D"Verdana">Dear = all,</font></div><div> </div><div>Could anyone give me some = suggestion about how to = deal with the activation within ventricles o= f the brain in order to show more meaningful activation = pattern?</div><div> </div><div>Thanks in advance!</div><div><font = size=3D"2" face=3D"Verdana"></font> </div><div align=3D"left"><font = color=3D"#c0c0c0" size=3D"2" face=3D"Verdana">2011-05-14</font></div><font= size=3D"2" face=3D"Verdana"><hr align=3D"left" size=3D"2" = style=3D"min-height: 2px; width: 122px; "><div><font color=3D"#c0c0c0" = size=3D"2" face=3D"Verdana"><span><div><div>=C0=EE=C1=D5=C1=E1<br><br>Linl= ing Li</div><div>Research Centre for Neural = Engineering<br>--------------------------------------<br>Phone: = +86-755-86392232<br>Email: <a href=3D"mailto:[log in to unmask]"= target=3D"_blank" style=3D"color: blue; text-decoration: underline; = ">[log in to unmask]</a><br>Shenzhen Institutes of Advanced = Technology,<br>Chinese Academy of = Sciences.</div></div></span></font></div></font></div></blockquote></div><= br><br clear=3D"all"><br>--<span = class=3D"Apple-converted-space"> </span><br>Burak Erdeniz<br>Phd = Student<br>Psychology & Computer Science Department<br>Health & = Human Sciences Research Institute<br>2f240 Health Building<br>University = of Hertfordshire<br>College Lane<br>Hatfield<br>Herts<br>AL 10 = 9AB<br>omnia vincit = amor<br><br></font></div></div></span></blockquote></div><br></div></div><= /body></html>= --Boundary_(ID_t9pmUlO15I4ov7Zxtd0LVw)-- ========================================================================= Date: Wed, 18 May 2011 09:33:40 +0100 Reply-To: Chris Leatherday <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chris Leatherday <[log in to unmask]> Subject: Normalising a point 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 series of points that I would like to translate from native spac= e to MNI space. I'm sorry if this is an obvious question, and I've noted = that it seems to have been discussed a couple of times already. In attemp= ting to follow Gem5 of John's Gems 2, I am unable to do the part where h= e asks the user to choose the deformations inversion option...=20 I am running SPM8, and I can't seem to get anything that looks like iy*.i= mg. Running the "inverse" option in deformations just gives me another y_= *.nii file, and I can't seem to find a method of creating an iy*.img file= . Could somebody please point me in the right direction? Thank you Chris ========================================================================= Date: Wed, 18 May 2011 10:10:46 +0100 Reply-To: Marco Sperduti <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Marco Sperduti <[log in to unmask]> Subject: Small Group MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-569334406-1305709846=:4119" Message-ID: <[log in to unmask]> --0-569334406-1305709846=:4119 Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear all, For preliminary results I would like to compare activation between two expe= rimental conditions in two groups. My problem is that the control group is = made of 20 subjects while in the patient group I have only 4 subjects. Which is the best statistical model? Should I just use a full factorial des= ign?=20 Thank you, Marco=A0=20 --0-569334406-1305709846=:4119 Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable <table cellspacing=3D"0" cellpadding=3D"0" border=3D"0" ><tr><td valign=3D"= top" style=3D"font: inherit;">Dear all,<br><br>For preliminary results I wo= uld like to compare activation between two experimental conditions in two g= roups. My problem is that the control group is made of 20 subjects while in= the patient group I have only 4 subjects.<br><br>Which is the best statist= ical model? Should I just use a full factorial design? <br><br>Thank you,<b= r><br>Marco <br></td></tr></table> --0-569334406-1305709846=:4119-- ========================================================================= Date: Wed, 18 May 2011 14:16:12 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: channel selection Comments: To: Fahimeh Mamashli <[log in to unmask]> Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="Windows-1252" MIME-Version: 1.0 Message-ID: <[log in to unmask]> RGVhciBGYWhpbWVoLA0KDQpUaGUgbW9zdCBzdHJhaWdodGZvcndhcmQgd2F5IHRvIGV4Y2x1ZGUg c29tZSBjaGFubmVscyBmcm9tIGFuYWx5c2lzIGlzIHRvIG1hcmsgdGhlbSBhcyBiYWQgaW4gdGhl IGRhdGFzZXQuIFlvdSBjYW4gZG8gaXQgaW4gdGhlIHJldmlld2luZyB0b29sLiBIb3dldmVyLCBJ J20gbm90IHN1cmUgdGhpcyBpcyBhIGdvb2Qgc3RyYXRlZ3kgaW4geW91ciBjYXNlLiBJIHdvdWxk IHN1Z2dlc3QgeW91IHRvIGp1c3Qgc3RhcnQgd2l0aCBhIHN5bW1ldHJpYyBtb2RlbCBmb3IgYm90 aCBsZWZ0IGFuZCByaWdodCBoZW1pc3BoZXJlcy4gVGhlbiBpZiB5b3Ugd2FudCB0byBleHBsb3Jl IHRoZSBhc3ltbWV0cmllcyB5b3UgY2FuIGRvIGl0IGFzIGEgc2Vjb25kIHN0YWdlLg0KDQpCZXN0 LA0KDQpWbGFkaW1pcg0KLS0tLS0tT3JpZ2luYWwgTWVzc2FnZS0tLS0tLQ0KRnJvbTogRmFoaW1l aCBNYW1hc2hsaQ0KVG86IFZsYWRpbWlyIExpdHZhaw0KU3ViamVjdDogY2hhbm5lbCBzZWxlY3Rp b24NClNlbnQ6IDE4IE1heSAyMDExIDE1OjA0DQoNCg0KRGVhciBWbGFkaW1pciwgDQpUaGFua3Mg Zm9yIHlvdXIgYXR0ZW50aW9uLiBJIGFtIHRoZSBvbmUgd2l0aCBzY2FyZiBpbiBTUE0tTUVHIGNv dXJzZSBvbiBNYXktMjAxMSBpZiB5b3UgcmVtZW1iZXIgbWUuIEkgd2FudGVkIHRvIGRvIERDTSBm b3IgdmVyYiBhbmQgbm91biB0b2dldGhlci4gOikNCkkgaGF2ZSBhIHF1ZXN0aW9uOiB0aGUgZGlm ZmVyZW5jZSBiZXR3ZWVuIHR3byBjb25kaXRpb25zIG9mIG15IE1FRyBkYXRhIGlzIHNwcmVhZCBp biBib3RoIGhlbWlzcGhlcmUsIGJ1dCBpdCBpcyBzbyBjb21wbGljYXRlZCBpZiBJIHdhbnQgdG8g bW9kZWwgZnJvbSB0aGUgYmVnaW5uaW5nIGJvdGggaGVtaXNwaGVyZS4gd2Ugd2FudCB0byBvbmx5 IGNob29zZSBsZWZ0IGhlbWlzcGhlcmUgY2hhbm5lbHMuIGlzIGl0IHBvc3NpYmxlPyANCkkgdXNl ZCBEQ00ueFkubmFtZSA9IFsnY2hhbm5lbCBuYW1lcyddOyBidXQgd2hlbiBEQ00gcnVubmluZyBm aW5pc2hlZCBhbmQgSSBsb29rZWQgYXQgRENNLnhZLm5hbWUsIGl0IGp1c3Qgc2hvdyBncmFkaW9t ZXRlcnMuIHdoZXJlIEkgY2FuIGRvIGNoYW5uZWwgc2VsZWN0aW9uIGZvciBEQ00gYW5hbHlzaXM/ IA0KDQpCZXN0IHdpc2hlcywNCkZhaGltZWgNCg0KLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tDQpQ aEQgc3R1ZGVudA0KTWF4IFBsYW5jayBJbnN0aXR1dGUgZm9yIEh1bWFuIENvZ25pdGl2ZSBhbmQg QnJhaW4gU2NpZW5jZXMNClN0ZXBoYW5zdHJh32UgMUEgDQowNDEwMyBMZWlwemlnDQpHZXJtYW55 DQoNClRlbDogKzQ5IDM0MSA5OTQwIC0gMjU3MA0KDQoNCg0KU2VudCB1c2luZyBCbGFja0JlcnJ5 riBmcm9tIE9yYW5nZQ== ========================================================================= Date: Wed, 18 May 2011 15:25:17 +0100 Reply-To: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> Subject: export betas over multiple coordinates Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello, I wonder how to export the betas over the coordinates which showed a sign= ificant effect. I know how to export one beta of one coordinate which sho= wed a significant effect. However, I would like to know how to export bet= as over all of the coordinates in the statistic table at once.=20 Thank you for any advice! Sincerely, Yihui=20 ========================================================================= Date: Wed, 18 May 2011 15:35:23 +0100 Reply-To: Subscribe Spm J <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Subscribe Spm J <[log in to unmask]> Subject: MAUDSLEY ANNUAL REVIEWS 2011 - Institute of Psychiatry, KCL London, UK Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> MAUDSLEY ANNUAL REVIEWS 2011=20 Institute of Psychiatry, KCL London, UK=20 Monday, 27 June 2011 to Wednesday, 29 June 2011 (event website: http://www.iop.kcl.ac.uk/events/?id=3D1202)=20 =20 We are delighted to announce the 2011 Maudsley Annual Reviews of Psychiat= ry that build on the success of our inaugural meeting and aim to bridge t= he gap between scientific developments in mental health and their applica= tion in clinical care.=20 Each year we select three themes that represent topics in psychiatry wher= e substantial developments were achieved in understanding or treating men= tal illness that are directly relevant to clinical practice. Each year we= bring together internationally renowned researchers and clinicians to di= scuss their findings and share their perspective on each of their expert = themes in a clinically relevant manner.=20 The three themes covered in the 2011 Maudsley Annual Reviews of Psychiatr= y focus on advances in Major Depressive Disorder, in promoting the physic= al wellbeing of patients with mental disorders and in improving clinical = and functional outcomes through better adherence.=20 Key Speakers for the Core Scientific programme: Norman Sartorius, Rudolf = Uher, Marieke Wichers, George Papakostas, Mark De Hert, Peter Haddad, And= re Tylee, John Hague, Munk-J=C3=B9rgensen and Francesc Colom.=20 =20 Meeting Chairs: Sophia Frangou, Philip McGuire, Rudolf Uher=20 =20 The event has been awarded 9 ECMEC credits from the European Accreditatio= n Council for Continuing Medical Education=20 =20 Programme, Registration form, Venue and Accommodation details are availab= le from the website =20 ========================================================================= Date: Wed, 18 May 2011 10:41:28 -0400 Reply-To: "Rajagopalan, Venkateswaran" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Rajagopalan, Venkateswaran" <[log in to unmask]> Subject: SnPM->2 group ANOVA results MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CC1569.AB9D1A7F" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CC1569.AB9D1A7F Content-Type: text/plain; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable Dear All, =20 I have one control and 4 patient groups in whom I perfomed T1-w VBM and I a= m= trying to run non-parametric tests=20 =20 I ran >2 group ANOVA 1 scan/subject using SnPM. I chose the following=20 When I run the results part in SnPM=20 my F-statistic comes automatically with positive effects=20 write filtered statistic image - NO results for which image - F Inference method -Voxelwise Voxelwise:Use corrected thresh - FWE =46WE-CORRECTED P VALUE -0.05 =20 I get a significant MIP image, I would like to do get T-test results on the= = individual comparisons like control>patient_group1, = control>patient_group2... how to run these or get these results. In SPM = parametric method I design my own design and contrast matrix I am wondering= = how to give my own contrasts of interests here in SnPM. =20 Thanks =20 Venkateswaran =20 =20 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Please consider the environment before printing this e-mail Cleveland Clinic is ranked one of the top hospitals in America by U.S.News & World Report (2010). =20 Visit us online at http://www.clevelandclinic.org for a complete listing of our services, staff and locations. Confidentiality Note: This message is intended for use only by the individual or entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and destroy the material in its entirety, whether electronic or hard copy. Thank you. ------_=_NextPart_001_01CC1569.AB9D1A7F Content-Type: text/html; charset=iso-8859-1 Content-Transfer-Encoding: quoted-printable <HTML dir=3Dltr><HEAD> <META http-equiv=3DContent-Type content=3D"text/html; charset=3Dunicode"> <META content=3D"MSHTML 6.00.2900.3698" name=3DGENERATOR></HEAD> <BODY> <DIV><FONT face=3DArial color=3D#000000 size=3D2>Dear All,</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>I have one control and 4 patient groups in= = whom I perfomed T1-w VBM and I am trying to run non-parametric = tests </FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>I ran >2 group ANOVA 1 scan/subject = using SnPM. I chose the following </FONT></DIV> <DIV><FONT face=3DArial size=3D2>When I run the results part in SnPM = </FONT></DIV> <DIV><FONT face=3DArial size=3D2>my F-statistic comes automatically with = positive effects </FONT></DIV> <DIV><FONT face=3DArial size=3D2>write filtered statistic image - = NO</FONT></DIV> <DIV><FONT face=3DArial size=3D2>results for which image - F</FONT></DIV> <DIV><FONT face=3DArial size=3D2>Inference method -Voxelwise</FONT></DIV> <DIV><FONT face=3DArial size=3D2>Voxelwise:Use corrected thresh - = =46WE</FONT></DIV> <DIV><FONT face=3DArial size=3D2>FWE-CORRECTED P VALUE -0.05</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>I get a significant MIP image, I would lik= e= to do get T-test results on the individual comparisons like = control>patient_group1, control>patient_group2... how to run these or= = get these results. In SPM parametric method I design my own design and = contrast matrix I am wondering how to give my own contrasts of interests = here in SnPM.</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>Thanks</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2>Venkateswaran</FONT></DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <DIV><FONT face=3DArial size=3D2></FONT> </DIV> <P><pre = wrap>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D <p class=3DMsoNormal><font size=3D5 color=3Dgreen><span style=3D'font-size:18.0pt;color:green'></span></font><font size=3D2 color=3Dnavy face=3DCalibri><span = style=3D'font-size:10.0pt;font-family:Calibri; color:navy'> </span></font><font size=3D1 color=3Dgreen face=3DArial><span style=3D'font-size:7.0pt;font-family:Arial;color:green'>Please consider the= = environment before printing this e-mail</span></font><o:p></o:p></p> Cleveland Clinic is ranked one of the top hospitals in America by U.S.News & World Report (2010). =20 Visit us online at http://www.clevelandclinic.org for a complete listing of our services, staff and locations. Confidentiality Note: This message is intended for use only by the individual or entity to which it is addressed and may contain information that is privileged, confidential, and exempt from disclosure under applicable law. If the reader of this message is not the intended recipient or the employee or agent responsible for delivering the message to the intended recipient, you are hereby notified that any dissemination, distribution or copying of this communication is strictly prohibited. If you have received this communication in error, please contact the sender immediately and destroy the material in its entirety, whether electronic or hard copy. Thank you. </pre></P></BODY></HTML> ------_=_NextPart_001_01CC1569.AB9D1A7F-- ========================================================================= Date: Wed, 18 May 2011 15:36:02 +0100 Reply-To: Chantal Roggeman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chantal Roggeman <[log in to unmask]> Subject: artifacts after Dartel Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPMmers, I have a problem with Dartel - normalise to MNI space. It runs all right,= but my images are distorted after the Dartel normalisation.=20 For the whole preprocessing, I followed basically the manual: realignment= (estimate and reslice), coregistration (estimate), New Segment, run Dart= el (create Template), then Dartel normalize to MNI space. Everything ran without errors, but my functional images are distorted aft= er the last step. The original and realigned images were all right, so it= must happen in a later stage. It looks like some part of the brain is st= retched out. The distortion looks different in different sessions, but is= the same for all images in the same session.=20 Is this a known problem? Does anybody know why it happens and how I can a= void it? Since everything ran smoothly without errors, I have no idea wha= t the problem could be... thanks a lot! Chantal ========================================================================= Date: Wed, 18 May 2011 20:30:19 +0530 Reply-To: Kavita Vemuri <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Kavita Vemuri <[log in to unmask]> Subject: Onsets & Duration - in task independent experiment MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Hi My experiment has no explicit task(well, that is an oxymoron!) - requires free viewing of moving images. The fMRI collected is collected TR of 2 seconds and the total duration of the experiment is around 8 minutes . At the 1st level analysis phase, I notice big variations when 'Onset'(block) is changed from 20 to 22 with a duration of 20, for example. The design is in seconds. Is this expected? For example: At an onset = 20 & duration = 20, I see not even one voxel highlighted but when the onset=22 and the duration= 22 or 25, there is huge response. Question: in an task independent/free viewing paradigm, can blocks be selected in the design? Regards Kavita ========================================================================= Date: Wed, 18 May 2011 16:32:30 +0100 Reply-To: "Dylan D. Wagner" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Dylan D. Wagner" <[log in to unmask]> Subject: Neuroscience software survey: What is popular, what has problems? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear list, The developers of the excellent neurodebian repository have requested t= hat I post this to the spm list. It's quick and informative! --- Dear neuroscience researchers and their IT staff We invite you to participate in a survey on software usage and computing environments in neuroscience research. It will take no more than five minutes to fill it out. Immediately after sending your answers you will get to see a summary of how other participants have responded before. This data will eventually be made available to software vendors and distributors to determine advantages and issue of popular computing environments in neuroscience research. Learn about what fellow neuroscientists are doing to address their comput= ing demands -- take the survey! http://goo.gl/euIMc Thanks, --=20 PyMVPA/NeuroDebian Team: Yaroslav O. Halchenko & Michael Hanke Postdoctoral Fellows, Department of Psychological and Brain Sciences Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 ========================================================================= Date: Wed, 18 May 2011 18:44:18 +0000 Reply-To: Giuseppe Signorello <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Giuseppe Signorello <[log in to unmask]> Subject: time series extraction Content-Type: multipart/alternative; boundary="_9bf33669-5ed3-4d41-9cc4-eb3288627bb9_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_9bf33669-5ed3-4d41-9cc4-eb3288627bb9_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi=2C after the execution of preprocessing of Fmri files=2C I would like to extr= act the time series but if I consider the steps of manual I have several pr= oblem.=20 - I obtain the design matrix (from first level specification)=2C but=20 when I use the contrast manager and I define the Photic=2C Motion=2C= Attention contrasts using the spm examples (the same vectors of the two t-= contrast and the same matrix of f-contrast) and I run the batch file --> th= ere's an error and I do not understand the reason. Could the contrasts be wrong? I have a dataset with 425 scans=2C TR=3D 2.7. In the Spm-manual I do not find information about it. Could you help me? Excuse me for my english.Thanks for your answers. G.S.=20 = --_9bf33669-5ed3-4d41-9cc4-eb3288627bb9_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> Hi=2C<br>after the execution of preprocessing of Fmri files=2C =3B I wo= uld like to extract the time series but if I consider the steps of manual I= have several problem. <br>- I obtain the design matrix (from first level s= pecification)=2C but <br> =3B =3B =3B =3B =3B =3B w= hen I use the contrast manager and I define the Photic=2C Motion=2C Attenti= on contrasts using the spm examples (the same vectors of the two t-contrast= and the same matrix of f-contrast) and I run the batch file -->=3B there= 's an error and I do not understand the reason.<br><br> =3BCould the co= ntrasts be wrong?<br>I have a dataset with 425 scans=2C TR=3D 2.7.<br><br>I= n the Spm-manual I do not find information about it.<br>Could you help me?<= br><br>Excuse me for my english.Thanks for your answers.<br><br>G.S. <br> = </body> </html>= --_9bf33669-5ed3-4d41-9cc4-eb3288627bb9_-- ========================================================================= Date: Wed, 18 May 2011 20:31:42 +0100 Reply-To: Chaleece Sandberg <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Chaleece Sandberg <[log in to unmask]> Subject: trouble with mask in small volume correction Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello SPMers, I am trying to do a small volume correction on a group analysis using an = anatomical mask. I created a mask for left IFG using MRIcro following ins= tructions on http://imaging.mrc-cbu.cam.ac.uk/imaging/SmallVolumeCorrecti= on. I checked the mask against the uncorrected activation peaks in MRIcro= N to make sure that the activation I was interested in fell within my ana= tomical mask. When I selected the mask as the image for the svc, the only= area of activation that came up was a random dot in the right hemisphere= . How is that possible if I'm restricting the region to the left IFG? I d= ouble-checked to make sure the mask was definitely in left hemisphere and= the activation that I am interested in is in left hemisphere. When I do = a small volume correction using a 10mm sphere around the uncorrected peak= , I get that peak as significant at FWE p<.05, but I'd prefer to use the = anatomical mask. Any help would be greatly appreciated! Thanks, Chaleece ========================================================================= Date: Wed, 18 May 2011 14:14:03 -0700 Reply-To: Nicholas DiQuattro <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nicholas DiQuattro <[log in to unmask]> Subject: Sold State Drives MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec520f21968725804a3935fd2 Message-ID: <[log in to unmask]> --bcaec520f21968725804a3935fd2 Content-Type: text/plain; charset=ISO-8859-1 Hello SPM, Has anyone had any experience with running SPM on solid state drives? Do you see much of an improvement in processesing speeds? thanks, Nick -- Nicholas DiQuattro Junior Specialist Integrated Attention Lab Center for Mind and Brain 267 Cousteau Place Davis, CA 95618 tel: (530) 297-4472 --bcaec520f21968725804a3935fd2 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello SPM,<div><br></div><div>Has anyone had any experience with running SP= M on solid state drives? Do you see much of an improvement in=A0processesin= g=A0speeds?<br clear=3D"all"><br></div><div>thanks,</div><div>Nick</div><di= v><br> -- <br>Nicholas DiQuattro<br>Junior Specialist<br>Integrated Attention Lab<= br>Center for Mind and Brain<br>267 Cousteau Place=A0=A0 Davis, CA=A0 95618= <br>tel: (530) 297-4472 <br> </div> --bcaec520f21968725804a3935fd2-- ========================================================================= Date: Wed, 18 May 2011 18:39:36 -0400 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: artifacts after Dartel Comments: To: Chantal Roggeman <[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 Chantal, Most likely what you are describing is a result of how DARTEL moves the data to MNI space, and is not something to be concerned about. See a number of previous posts (try searching for "functional DARTEL" or something similar). For example: https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind0908&L=3DSPM&P=3DR895 or https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=3Dind0910&L=3DSPM&P=3DR3826&= 1=3DSPM Hope this helps! Jonathan On Wed, May 18, 2011 at 10:36 AM, Chantal Roggeman <[log in to unmask]> wrote: > Dear SPMmers, > > I have a problem with Dartel - normalise to MNI space. It runs all right,= but my images are distorted after the Dartel normalisation. > > For the whole preprocessing, I followed basically the manual: realignment= (estimate and reslice), coregistration (estimate), New Segment, run Dartel= (create Template), then Dartel normalize to MNI space. > > Everything ran without errors, but my functional images are distorted aft= er the last step. The original and realigned images were all right, so it m= ust happen in a later stage. It looks like some part of the brain is stretc= hed out. The distortion looks different in different sessions, but is the s= ame for all images in the same session. > > Is this a known problem? Does anybody know why it happens and how I can a= void it? Since everything ran smoothly without errors, I have no idea what = the problem could be... > > thanks a lot! > Chantal > ========================================================================= Date: Thu, 19 May 2011 12:32:18 +1000 Reply-To: Gemma Lamp <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Gemma Lamp <[log in to unmask]> Subject: anatomy toolbox: small volume correction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=000e0ce0d8b4697dcc04a397d048 Message-ID: <[log in to unmask]> --000e0ce0d8b4697dcc04a397d048 Content-Type: text/plain; charset=ISO-8859-1 Hi, I am currently doing an analysis where I'm limiting my ROIs to specified sensory areas. When looking at the stats I use a pre-made mask in SPM to do a small volume correction. However, when I put it into anatomy toolbox, using the same statistical procedure and then use the anatomy toolbox SVC with the same ROI mask, i'm getting different cluster sizes and different areas showing to that in my original stats. Is there a bug in the SVC through anatomy toolbox? Or is there something I am doing wrong? Is there an alternative way to do a small volume correction with anatomy toolbox to keep consistent? Thanks, Gemma -- Gemma Lamp --000e0ce0d8b4697dcc04a397d048 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi,<br><br>I am currently doing an analysis where I'm limiting my ROIs = to specified sensory areas. When looking at the stats I use a pre-made mask= in SPM to do a small volume correction. However, when I put it into anatom= y toolbox, using the same statistical procedure and then use the anatomy to= olbox SVC with the same ROI mask, i'm getting different cluster sizes a= nd different areas showing to that in my original stats. Is there a bug in = the SVC through anatomy toolbox? Or is there something I am doing wrong? Is= there an alternative way to do a small volume correction with anatomy tool= box to keep consistent?<br> <br>Thanks,<br>Gemma<br clear=3D"all"><br>-- <br>Gemma Lamp<br><br><img src= =3D"http://www.discoveriesneeddollars.org/uploads/DND_email_signature.jpg">= <br><br> --000e0ce0d8b4697dcc04a397d048-- ========================================================================= Date: Thu, 19 May 2011 02:12:16 -0700 Reply-To: Mazly Mohamad <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mazly Mohamad <[log in to unmask]> Subject: BMA MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-806656023-1305796336=:86840" Message-ID: <[log in to unmask]> --0-806656023-1305796336=:86840 Content-Type: text/plain; charset=us-ascii Dear SPMers Needs help regarding BMA, I would like to retrieved the mean values from Region A to region B In the manual pg 335 under 34.3.4 Summary Statistics and Group Analyses, I've tried using command for for i=1:6, b(i)=BMS.DCM.rfx.bma.Eps(i).B(4,1,1); end ??? Reference to non-existent field 'Eps'. I've checked the .bma parameters only have .mEp .sEp .mEps and .sEps Thanks in advanced --0-806656023-1305796336=:86840 Content-Type: multipart/related; boundary="0-1120825588-1305796336=:86840" --0-1120825588-1305796336=:86840 Content-Type: text/html; charset=us-ascii <html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:bookman old style,new york,times,serif;font-size:8pt;color:#003366;"><table width="100%" cellspacing="0" cellpadding="0" align="center"><tbody><tr><td width="100%" height="100%" bgcolor="#eff6fc" valign="top"><table width="100%" height="100%" style="table-layout:fixed;" cellspacing="0" cellpadding="0" align="center"><tbody><tr><td width="10"> </td><td valign="top" style="overflow: hidden; text-overflow: ellipsis; white-space: nowrap;"><table width="100%" style="white-space:normal;" cellspacing="0" cellpadding="0"><tbody><tr><td height="10"> </td></tr><tr><td><font face="bookman old style, new york, times, serif" size="1" color="#003366" style="font-family:bookman old style, new york, times, serif;font-size:8;color:#003366;"><div style="text-align:undefined;"><div>Dear SPMers<br><br>Needs help regarding BMA, I would like to retrieved the mean values from Region A to region B<br><br>In the manual pg 335 under 34.3.4 Summary Statistics and Group Analyses, I've tried using command for <br> for i=1:6,<br>b(i)=BMS.DCM.rfx.bma.Eps(i).B(4,1,1);<br>end<br><span style="color: rgb(255, 0, 0);">??? Reference to non-existent field 'Eps'.</span><br><br>I've checked the .bma parameters only have .mEp .sEp .mEps and .sEps<br><br>Thanks in advanced<br></div> </div></font></td></tr><tr><td height="10"> </td></tr></tbody></table></td><td width="10"> </td></tr></tbody></table></td></tr><tr><td height="120" bgcolor="#eff6fc" background="cid:1305795868922@dclient.mail.yahoo.com" align="start"> </td></tr></tbody></table></div></body></html> --0-1120825588-1305796336=:86840 Content-Type: image/jpeg; name="--static--hills_bottom.jpg" Content-Transfer-Encoding: base64 Content-Id: <[log in to unmask]> Content-Disposition: inline /9j/4AAQSkZJRgABAgAAZABkAAD/7AARRHVja3kAAQAEAAAAUAAA/+4ADkFk b2JlAGTAAAAAAf/bAIQAAgICAgICAgICAgMCAgIDBAMCAgMEBQQEBAQEBQYF BQUFBQUGBgcHCAcHBgkJCgoJCQwMDAwMDAwMDAwMDAwMDAEDAwMFBAUJBgYJ DQsJCw0PDg4ODg8PDAwMDAwPDwwMDAwMDA8MDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwM/8AAEQgAeAMFAwERAAIRAQMRAf/EAKcAAAIDAQEBAQEAAAAA AAAAAAABAgMFBAYHCAkBAQEBAQEBAQAAAAAAAAAAAAABAgMEBQYQAAIBAwMC BAQEAwYEBQUAAAECAwARBCESBTFBUWEiE3GBFAaRoTJCsVIj8MFighUH0XKy M+HxUyQWosLSNEURAAICAQQBAgIJAwQDAQAAAAABEQIDITFBEgRRE2EicYGR obHBMhQF8NFCUmIjJOHxchX/2gAMAwEAAhEDEQA/AP7pV6zyBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ FvtmhqCD7I1LyMsaD9TsQAPmaztuZVZ2K2IMTSRFXspZTe4NuuoqppiI0HCf dDEHcFaysPCwI/jVLBaE3DcrAg9CKCCJUigFY0IFjQBQCoAoAoAoA+NAFAO1 6Ae00KkG00DQrWoQVAPrQBY0AwtzY0BL2xQAUAFx1oCFjQCoAoB2oWAAvQgW NAKgCgCgHY0AWNAKgCgCgJbfOgDaaFgNpoA2mgAKT16UEAUAPS9CC2+VAPb8 qAYQmo2VIZj8qogWzwqJhaj9sVSENvlQEilrWoCNjQDCk+VAG00AWNAI6UAq AKACQBckAdyaAYF6ACLUAqAKAYF6ALGgJbAetCpC20IR+FAFASCk0BL2xQAY x8aFI7T4UArGhAoAGptQoypHnQDCEi9ALaaAVjQg9poVIYS/TSgSGY2HWgaI WtQgqAKAfWgCxoB2NAAW5/voUn7ZoWAMflQkC9sW/uoBBL9qJiCXtmhYOsR3 rnJo8h954cr8fBkoSY8SW88fQFZAFDH/AJT/ABrxedV2x6H1P4fLWmf5uVBk 4Oc/D4qSFRLDLE+7GjOrNtJV1BsAbizePXtXzPD8x0zPG9U/uZ6v5Hx/ddrU WxNs/Jy+EAxwcWExIcpr2kZ3Augt+kDoT1PlXXyf5NWy+1j35f5HDwfB+ets i31S/M2vtOGSPimkcbY5pmbHH+AALe3xBr6PhprGpOP8rats767LQ9EVJa/j Xsk+ZAth61ewgQUkWp2EDK2Xp86IEQveqEIrQjIbLkUEFmwihYDZUCQ9tCkV U0BbQBQFQW/QUmCbkthHWkiCSpreo2EgYA0WhWhhTRsnUVtb3oijqggVub0A tlAAU6GqSBt4eNQo1FhVIg3eRoJAjyrMhoW0Vewgj7R8KogmF2/OswUCL1QR 2UAbKARUDtQhYOgoUYAN6ywBFqAiBpQDoRokACKFSEQBQC0oRoBQqRLabUBE gedAO1utAMAGiAiBQjRG1AkO3TShSRAGtAQ71rgnJBgLnSqJGEB7VCgU8LVS M8tk8uxyM/DlwmUYgvG1/W3ZWUWtY/Gvm5fPWG/WyaXqfQp/HO+JZK2Tb49C njMo5fMY+WJGAy8V92OToqpYAW6ddT51z8bNfLnvOiW30HXNjrTxEo/y3+rU 9gU3a2r6iPloPb8qoC1SSQMKR1qFFa/lQDdb6VsjEFIqFI7TVJA9lQpMCwFA BNqEI7db0kQT2269KjKOwqAgVF6qZGiJjvr41ZECC2I0qgsNZZRAWomRsdVF EReowSFu4qAZIIA8qAhtFVAjsqgYTypJIJhfKo2IC3lSSwBQ/qtSRA1W/ajY gt2d6gD27/8ACpJSJj6ikkGItKSUWztSQddhUKc2Xjx5kE2LKLxTo0bjyYWv 8qz1lQy0s6tNcM+W8ticjhB+JvDmO8LNHlLu3opG1br2J7WNfFyeLTHlq29Z R99Zl5OG19a6Q3xJDAx+S5D2+JjxfpDjRqZcgyARtHe28A6k+ItpUweOr5LW 9XJ0vnx+PROZcQvsPqkUUcMMUEKhIolCRoOygWAr7tVofmm5cstCiqwBXSgI hLXoBOt7mgKNBVIKgEBagJfCrAFUI0FCQxihUgsaFCxoB7SKAmvnQCa/QUBE 9B496AYJ6UBE0AqAY1NAMLcmgAqRQC2/jQEgpNrCgJ+3egGFtQFb2vpQDoCu gCgCrAHY1AStpQCt4UArEUBZtv16dqANtvhQFfegLQBbpQEALkigJbQNfCoU mACL9qSBlQbVSEWSoUgVAFUglU60AMLWoBC1rUBPaPGgIsulARsaAYHiKAZG nSgI2Nr0Bn8hgQZkZd42ORCjfTyxsUcEjpcW08q458NcqhqTthz3xP5Xo9zP 4vg4sBYpYS2NkMB9TaxLi99rda8/j+PasWbhxqjt5HlPJNf8Z0+B6Cx+Fe48 YrnxoC1SLdKAZW/agEUt2oCFut+1AG2gHtte+vhapIIka96oFQBQE7aUAE+V AMfhQDuLa9aAhY0AbTa9ALaaAltt1/KkAjbwoBkAeNARoAoAoAoCaqTQF22o UAmtJBYU0sakiCvbrpQEtKoJKOhqAmRpehSK9KEJWHlQpXc0IAOtzrQGTjcW UyXy8mUZEhlLqQu2/wDLuuT+kaADSvH+0Ty+5Zy+Fwj0Wzt0VUoRCfiW+ojy MScYrxuGClNwAP6gLEaEdjUr4ark71cTuuCryPk6W1Rrga+XYV7jyloXrUNC PShCsm3xqgRNx0qMhQWQh2Uhwl923U6dtKz3UNrWCtMgJIzCJxcIU366G1r1 HlXTvDj7ypS4IQy/URrKqkK3TW4+RrPj5vdr2iC3o6uGW2NdzAWNAG00BcBY UBMiwNAQoCJvf+FAC/nQDb4UBXrQAL96AZ1FARsaALGgJi4vrQAQTrfSgGL9 qgL0W41qFGRrpQFbda0QqKgUA9poCLLbpQBYeFAFhQEttAFu1ATHnQCIufKg HQESD40BDad1AWDQUBEAhqAZYAgaXPQd6gANQobjVIG7pU2KK+4Aggg6g3ve iZBr30tVAN2oCG0jXtQFtAFAA86ADQENpoAK96AgQe1QCsaAmq+JqgewUAgP CgLVoCwLca/KslK2UeAqoFXeqQeoGp1qAR61QQINAFjQBY0AWagGAbjWgLCN KAjQEhppUKSqkGQLfGgK9tgbUBXY/GgGR4CgAKT5UBznIRclcVls7qWQk/qs L6DwryPyks3tw/pN9Pl7FpcLIkZBJkBINtAF63Nd7ZErKvqZS0kuRl3Fbgsv 6gCCR8a0rJshbu8qrKO/hQFtwagIEfnQEANaoLBe3lUAiTfrQEaAdAKgCgLV 6UKJgLX70DK760IO58aAWtUELE62qkAqT5edQGbG8UPIvAiMnvAF9PSXGoK6 9xe9h1FeNZKUy9Eob1OnW1lJk4/vNHi4sIfIi91pc7d+1QxCxi3YlST5Dzrn 3tWqSTcvX4f+Aoup2hHoN8ZmVVnQ3BAiBBJI1017V6e67wrfVoZa0mDpsa7y QLGkgNvwpICxoANz2qkGB41ChtBowT2W7VJEFe03Jv8AKqAsaSBEWGutJBz5 fvjFnbFXdkBCYltc3HlWMjfVxuapHZTsZEc/IJGsKTJkyTw+/jZUgAARbb92 0KD1Fv8Awr4+P+QydHK14PVbFjd1GiNTCknlxo5MhAHa5BUWDLf0tbtca19X x8lslFZ6M82WqraEzqtftau0mCQXS1AAFAWX0qAiT86pCFvKqAt5UAWPhQDs aklDb8KSA2/CkgNtJAgNSLfOgK/dj944+8e8qe4Yu4UmwP41FdNxyCzyqyQd jSSi72pJDlbIAzVxAAWMLTSG/QAhRp5k/lWe/wA3UF4kQyiHcPd2e5s77b2v +NWdYBj8y+XhexymMXmhxLjkMFdRJC3V1HZ06jxFxXn8i1qRdbLdf16FYlyY p+axDHIJI5ONeWBh+4PKhJt8AKLKrZFHKkFvKOxfi8dBYZWbGsn/ACRgyEfP aK3ls06pcsFfL5Cnj+ROPJuyOOEcrqL+llKyAE+aj86mXIurh6ohsKVKiS1k YBtfAi/8K66NSU8lByUsHBcRj4VpORz1MGCDqF2kgyMPBBrXi/cNY69f1W2/ uQ9Vi47QQQwNK87xIqtPIbs7d2J8zXtonVQ3IGkqTxLLAwkRwTGw6G2nx6ir 29ClWHkLmYsOSq2Eq+pL3CsNCL97Gs0v2UkOoDt4VuSgRagFa/aqQhDLFPGs sDrLE1wrrqLgkEfIi1ZrZWUoFne1Uo9tJAbfhSQG2kgNtAKx8KpAt5UAWPhQ FoJ8qyUidb2oCIUGrIFtt50kEdPCgM3IlzxlxxY6xiIpvG8H+oB+pQexGn8a +f5Xl2xXSS0Z6MOOlqt2evBzRTZ0+ThSpJ/QzN0gxSFskAGjMbXvqO/U2rng 8vLlzNR8pp0osev6jcv2r6h5CVqFFa3b50kEwt6Aft69qAVh4UBEjwFAHa1j QBY0AbaSAsaSCL6KSWCgAksegsOprNnC/MnJm5QabDIx5Fnyls8Uia7WGqtt BJt4jwryXyNY5r8zXpB1rWXDcHPizBsyXKlZ0cYg+oxTqsTo9mVfmtVXhuzn aY9CPX5Ujr4/Y8TThCJZHb33I6te/pNyNovYWrXiulk715JetquGaFvKvWYA XHaoUsHjUA7/ADoBWvQFvTShSDUIQoAoCO9d4j6sVLW8hpegJUBMH8KBEm6f GhSoi/l50IckOXHNJNGNye1e7PYAhDZiNb2Hwrz4/Kpe9qLeu5p0aSb2Y8TK TMjM0assZYhC4sSB3H41rDl9yYTUfeLVaOqu5gKhTMz5FieCZo2Aw2Er5BHo WNjscX8bG9q82W8WWm2s/kWNDFx02h0yhI0MhGT9NDckPI2/bJt8BYbTXl7O v6pjTb8zVaprTc9LFj4ylZYoI0YCyMgAAHkBp3r6CpWZSMv0OmuhAoAoAoAo BVANQdPGgRZQpWRrQhVLNDAhlnlSGNSA0jkKAT01NS1lVSypN6I53z8JXhib IUvOFMYW7AhtB6hcC/a9Ytmomk2tSrHZptIzY4s33pjmY02TZz7LJIuwrfQB d62t5ivl+Vi8u1/ken0npnEq/EyMjjZZc7FyISsGNJMYhBJI7AkBmdSB+xyC LX868N1Npnbc9FMlVjdXXXg18CPLbJ90BMWJGKZmKsjONwGgCkADqCCO1fS/ j1drt3mvp6HlyukRHzG+Oor6h5iZA7CgIdKAVUBagHQBQEFkjdpER1Z4iBKo NypIuAR20NRWTISoUf8Aa9AKqDkm5DDg+pEmQobDQS5UYuzojdGKjW3nWHeq 3BwnlDBy0WDkIgxuQjV+LzFJs7aBo3vpc3upHW9cXn636vZ7A5+FdsrP5vMb VGnTHhPfZED08ta4+Lbve9/jC+oi5K8fmGg+3F5mZWyGO9zESAxLTlAoPbaN K6+91x93/WoPS+Hn0r0I1Bg8lMvHclx2e5K4+VfCzHv6Ru9UTH4G4+Brz5b9 L1fD0JBnQ58I+7+QhldIvaw1jZnYKLjY3UkfzVzrk/7Fpf8AijMuYIcTz/FT ZnMZWRyOPGXmSLGVpBf2owQtreJJPzqYfIrNnZrc1Bozfc/28oKvnCZWBVlj jkkuDoR6VNdbeXi/1IdZPnnHcwvH8vDMyTZOFib4oCiEN7Um/oHK9yDavlYv IrjyJzotPqL1Zp8r92mfI47IwOOyLYDtIyTe2u8sAoAs57Xrrm/kKWtVrgdW YCc3yMQ5GMwSSR8kntz3ZD6mNzJ+rr1FePH5PXtrPYiqz1X/AMygbijivgZa 5hxPZ3WjMZcpsvcSXAvr0r3/AP6NFSNZgvVmb9t8thYeQ0/JM8IgiMGCvtSN tVnJJuoNr/31z8TPjq/ma0UIdT30f3HwUn6eUhXyfcn/AFAV9NeVi/1IkGZw nMYDchzOGmZA8MeQ2TikSLba+rga+OvzrhgyrvZTotgZ/B8zDjfbeXlWu2Lk PHCg/fLIA6gfNq5+Nm64W36sldj2HG4pw8HGgbc0ixhp3bUtI/qdj8WJr24l FVJSGfnLg/Rb4y65uVHjbgbbN9/Ufwq5Miol8XAKhkOOcbC3ExtgCfZfRWWU pceZB/Kp2fePgDM4/Ki4+f7hhyH9uDCnOSh7BJdSAPEm3zNeTDkVL5E9Eof3 GU9Wjv4/k2yMbEyM2NcR+TlYcdii7OY7XUtp12gsewFerHm7VVnpJTTgyIMp WfHmSdEdo2dDcB10ZbjuK6qyexS+qBUAdaIEVkjcuqOrmJtkgU3Kta9j4Gxp 2QJ1QFAFAFANQCagQEWoUpmSRo5FhcRyspEchFwrdjapZONAo5PJ5kGQ2Lmw ssYlxkMk2cZZHcPa+xDa4JXr4A1+c8itlkdbXl/ge/HeiaslojpwcF8OP6ab GfJmABMsUt1EZJ2qqsU0U36Ux481qxievJM2Wtr9moR24rz4v1U2bvx8GMKI Elbe972uLFjY6AC9fW8a2TFSczOOatLQqbneM/CKwv8AVRqs5tDuYKWPS1ms b30r1LLR7M4e29fgdddDJYvShQPehCuqB0AUAUAUAUAiAwKkAqdCDqDUakGX l4+DFFGpxyDvGxoR/UWx3EgjUDS35V5c6pjrtGvBus2Zio0okyN7e7Jyg+lV rWYFH2kOLaMY27gfp1rlVtS7c7GbRKg9RAymJGWJ4EA2rC42lQunT5V6sVpq tI+AZfXYgUByDKjbLbDO5JVTetx6WGl7H51565pu6RsaddEwx8tMlpFRXUx7 SSwFiGvYgi/hUweTTM2qzpuLUddzsXqK9BksJAoUqJvQgqAh7qe37u7+na9/ 7eelAcOFOs0WXyDttheVxGx6CGH0g/MgmuGLJ2Ts9gdkeRG2MmU4MUTxiUl+ oUi+tdO6ieClGHkS5e6faI8d9MaM/rYDq7eF+wqYru6nghoW0FdClUqye2/t W93afb3fp3W0vWXMOCHnmu2VFBPAIc7OVYcyxBVo1O4urC1wQpX42rxJfPqo s9GatZtRwb/up73097S7N4S3Vb2uPga9iamCHKMy3IPgSRhLxiTHkv8Ar/mB HYixtWfd+fqILcwYzY7w5Y3w5JEDR9Sxk0CgDvVyQ1D5CR5yLImysOTgsuUt nxz/AE2VIASz46jf7g06tGtvia8au7VeO26/D1CXBpSzIzY3F4KmFslRLlML q0UTatfW4dunle9da3qopX+kIa0NVGhWT6WMqHjjDCFf2p0W/h5V6FbhEZGH JhnieZG2xxu6SFtLGMkNf8L0WRNNzsEcOLmTchks0C+1gY5s7sPVK9tFF/0g Xue/SuOLM8tm1+lBmlDNHM06pciCT2mbsWABIHwvau9byEXWHhWpKVSSRQ+3 7rBPekEUV+7tcgfO1ZtaAXWFabAlFqAlQEWF7WoDL5IwMkMMzFHZ/cie4AUx 9SxOlrHpXi8+1Pbi06+h1wqyco8riT5M2TlJLG0MKZAlX2Vj1cnZG39ViAhM e4adTrXxVXVLn/H4nrsqpaOZ3PSY0+bjHEg5C0j5Usixy6blCgFA+2y3OvTy r7mK16pLI/mbPJltSfl2OXAx0VZlzcgNjxStjYRYhSbEXYG99xIsLeFeXD4d Js7ayzd8/aI0g2sfDjxfd9su7ykGSRzuJsLAXsBoK92DBXCorsc8l3kcs6gP xrucx0AitzegKMrJx8LHlysqUQY0IBllYEhQSBc2vprUdktwXDawBBBB6EdD VTlSQlYeFJKY2fmyLLHgYBDZ04vubVYk/nYePgP7Hz5crT613CR2YuHHhQiG IE3JaWVtWkc/qdj3JrrSvVEI5uI86LJA3t5mMS+JJ23W1Rh3VhoRVsUysflV zs7jYUBiOyeTIiJ1EkYC7fMC5Neavkq11VEW8HorX6C5r1yIPFfdM2LiPicj HlQxZ+Mfbki9xd5jfpuS92UG4It0NfO819YvV6r70ZurJaHlJOaxpvtyHDAk /wBRwMq+AnttZI1Y7bO20ABTbr2FeW/lUtj9GnKK02tNGR4r7l5DjYstPo0l OY/ve7JLt9titmACq19fOuOLzvaTSUy5GOlo13LY4+fm4WfAVUgghnQRY3ss ZnkkbcACzWsDr0rk/Lu6dI0O68dxqc+ZyP3FDK0Ody+SshG51hdEC37f0lFj 5XrNvOy25FqddzNyGnmUJk5U+XE/qAnmkkB+TMRXO3kXtu2ZRV9IkdwsaC4D 6KNQax2bcyUewhrXsB3FZAWI1661lgBt3AkAjS9VMEvbYsFUXBvs87VUBLqp QjX9veogRsbXtoDapAA2JuBbyqtgdiD1sCLi9OqAe2HC3UEk2AIFFK2BBsSI KsvtpdmO1gLHTS+lbWRrkHYuZyMSrJHymZGW/Sq5ElrDyLEV1/dZVtYQaKP9 yZ8ONKeQfJw48mNi0ixSGJwdiswAV7XPjW/3mS8JuTSwtpM7peW5zjuXyM/M xIMl41+jLKHhjYA7hY3k1vc12r/IWV+9lxEEtiac8GDk8pk5mdPNk47R4vIT o+ZEjCQLGm0AD9BOmvSsvyVe7b2cfceb27T8Da5b7ghy+UkGHIY8Y46YuK7q YQBIbyBSwFr2AJv+kWr05/KVrRR7wp/Et+ycep9J4uHDhwcbHwpY8iGBAvuQ sHVj+5rqT1NzX1MKrWqVeDcHRlKpxckElP6L+sGxHpOoPlXS9oTKkeexs6Tm IsPDjYxiWBJOUnU2IUgXRD4ue/YedefF5HupRyZUHpgioqqihFQAKoGgA6AV 6imRm480EjclgqPeVbZmOdFmjX/7l7H5Vwyq1X2r9fxIaGJkw5kCZEJukl9D 1Ug2KkeIreLKr1lBHTYeFdJKck+Tj4zY6TzLE+VKIMZT1eRrkKoHkL1LXS3I dNvKrJSYAHSgC1AIDU6aUBmyYeHBJJkzSe3HI+6RJHCxb27m/c/GvHfxMbv7 j3OqyuIM2EPhZHIzZEzSxYMAGOttWjf1D4m67RXHx8FcFrNbGsmZWqlBDL/1 CVRJkM6YUscJjixxEWEhAOvuak7ulq5+bW9luunJcTpGk9jJwpZcmXPXkIlW NXBdk2qZDF6CiHcRpsu1j1Ohrw06KytZPqtj0Pb5HLe8nuY2WVElU3SVQ6ny IuNK/R1co8DUOGWgWFqpANAIL5UmAVTTQ48TzTMI4oxd3N9L6D8zUdo1EFpU d9PKnYQIi1WRBymT3jlQRuYZoTsL2BKllDKwB6jWsdp0EHHhcg0ssmHlosOd CbMoPpewvdb69NbeFccWduzrbRr7yI0VmjfIlxQCZYUR38LPewv46V39xTHo U4so/U4y5nHssk2KWaEi9m26PE3fUAi3Y61i7msrgGdkyY+ZBj8nioxyMc/U AgX/AOzYvG9u+1iB+VeW9q3i6Wu/2COStcqDN5KXMd78VxLLFjvY7TkyaGQ+ SA7Qe161TIsl2/8AFafWE5PTOVRWdiFWMFnY9AB1Net2jUQcWBlNm45yDEIk aR1iF7nYpsCelifCsYsvdSII5UySYQzYAZRin6iK1wWEdw4F/FbgVL2XXsgz k45XAgGHCq4J3NkTsbGR21JjAHQHS9ccCafyr5d38TTv23NzbrXsMjsKgMuL LZMo4WTb3HG7EnXRZVPbyYfnXL3Yt1YOp50SaHGYEPkK7RN2JS11+Njetu6l IhnjeeRkwjcIjpmIL9UZWDD4CUA/Oufd9+v1/UUxgXPCcDxMB/r8nGgkbrti /U7H8a8ibWOlFzH2E4SKfuTkIo5sTiIyyQRhTOiXLMFF1QW8vzIrj5ub5ljW 3P8AYlnDg9LxeJLGhyclduVkKo9rtFGP0xgdBbvX0cKaUvdmjUseldZLA7H4 UkQzzGZnGeR4FxRDynHZMb4scjfrR3CFgw/awbXyrxXydrRGq2JoQyskY0mN M+Q2TNgZAjzp7BUVcjqot/KR07VLZOsOZh6/WGyz7gviTcZyCgXjmWJ2PgTc fluHzp5dujrf0cfaLaHWHGVy0zsQcTiENj295wdx+KgEV1V+2R+lfxCkx+Sm iys1syJ0PH4WEkmTPGRuyTM94YQw12kpdvw8a45r17d09EtSEcDMOPgS8m39 fN5KRlxh13bWtusfFtB/lFcsGR1x+4/1W2Cc6nqcDDOLDZz7mTMfcy5u7yHr r4DoK+hip1rrvyaPG5eXLFLncdjrulnzTaPsSTtUfAnU/CvlZsjl4q7uxg9S 4j4jjQqHd7K7VY6l5DqWPxNzX0bNYcenBqDPlyzxuLjYEDL9fKm6SSQgrEXu zO/S9tT8q5+57aVFu/uDUGpxhvhIV910uxTInN5JgdTIfDcb2Hh4V6MdprIg y5co8j9vjNdAsoZW2reyyRzAC3fqK89svuYXYk6HZkzPl5HHYkd1Se2Vlkf+ mo3BP8zWrra/ayqvSTRqiSNpGiWQNMgDSRg6gNexI87G1duxBu6xI0kjCNEB LOTYADvR2gsGfx+Tk5rZOQyCLD3bMOMj1tt/U7HzOgHa1YxZHfXgQdeTLjY8 YmyyqxIw/qOLqpOgJNjb4mtXSjUuqPNPxGGmbhGaVsqPkUlx2yVbZcAiSJPS dQfUK8NPEx0iVMs7WyvjQfIZWRNFmZuPslh46dFxYejMwup17ksVtVyWd03X /F6fmebc7OH4WXCtNnypl5oB2uoO1Lm7Fd2t7n+2tdfHwOr7Wct/cVI9BY16 ywznycrHwoxNlzpjwllT3XNl3MbKCe1zWXaNWILwdwDAgqRcMDcEHvVTEHHH mLJn5GAiEtiwxyyy3FgZSwVLeNlvWK5Js16AvyMZcqCfHkF0yI2ifvo4IP8A GtXqrJoQzI+3JXfjIoXYu+GfY3E3JUAFT+Bt8q4eNeaR6aENXLyFxMeTIcFt g9KDqzHRVHmTpXXLkVKuz4D0MrhcdmXJ5CY758lyGk1tZTZrX7bhYeQFebxF KeS27/AqTLsrnuFxLCbk8cMbgRo3utp/hTca7W8ilN2IPP5H3xhIduHhZGVr ZnfbCtvEbrsfwryX/k8a21LB4eTkc9uUflsd0w5HkMkMSDeFurKwu+hvuPav l28xrI71UE66iyuR5LOWT6rkMiZW1aLeVT4bU2i1YyeZkv8A5GoM721SxCqp YXJAANcLNtFmC1QhC30IuG+PagLYzAHhbKjeXHW/uxIwVmt0F/41HsapCcs9 k0jZsvETrOePXMDjEwyXbd7AJAkcEaPcj4dOtdK5Kpc6HrrfSI1Z4zLfGkle THx5MUEn3YZH3gNfWxtf8a5uOTy5GnEFZN4wh0ZSQB8f/KoYagk7syxm9mQb D8K0gV2IJudQKQBX/hU+kAR6bjsfVVgHWh2Ih7xSBh5hqjYKpLRzSADuCv8A GiYB5FPu2FhIBbyPejYKwo0uf3WPzokC9wAgGhMaMB+OlWQc4NibekdqME3Y sIoxosY1+J61NAKRtzaD0qAAe1ZkG1xbY5gzVjEmLJHjmbJ5AuSQqEEIiLt/ WdNe1dKWjfY9OO9Uk0pjc7uWyIY8DExMuJ8t5ofqsPPjcrZpLmzKxOoJ166V b2VtFwMllytGeZO2yA2G1LnzNc5PKVsV0APRQGPmaokEjEbxvCfakJ9MkZKM D43WxrVcl6vRl3NI8zzLY2Rh/wCpTPDkI0cgltIQrCx2swLDTzr1LzsqUNyZ aO3g/uCbhUyUlw1zRO4kVkf23UDotiGBGvjXXxfOrh0aMqkaHtMb7y4WbSdp cBrAn30JXz9abx+NfTx+fivyWD0WLnYOaAcTMhyrjcBE6sbdjYG9epZK22Y6 mHCf9K5WXHttxMtlZR/KZNFI/wA10Pltrw1t7ObrxYmzPS2PhX0ZLB5sj6z7 lS7XTioHMUd9DJIFDN8g9q8nbvm/+SHpbGvWWGcXH5qZ8LyKhjaGaWCaMkEq 8TlCDbxtesUv2khPJzMXCVHy8hMdZHEcW82LOxACqOpJJ7UeRLdg6iDW5LDI NGsiskiK8bAh0bUEHqCKy9RDPFZvF5mDkp9PKsmBnMMT23veIsd0ZJ6WB0/C vnZPHtVwn8rf2GWjUmkhaHPgz2ueIczwe2TGfb2nYLg9bHb867N1v2pZbfhw bpd11W5Dj8PD4mDFOTMsf1EKKMJ1B/rOQz7QLliWJ7aVnF41Mdu3qtjbu3WO UenAAA06dhXtTMNSZGPmzLmTYGaFEobdhzqLLLG2oBHZhqPO1cq5Yv1ZDWrv JDPzB9XhTnEmBmju0Ei62liP6TbzFiK5ZNU43KZuZkpmQcP6bJl5UTyoexQg 2P8Amrz3v2rT4tENE5jHlv8ATlVdoxfqHfvu37QPC1ga6vL/AMnT4SXkzeTy 58PM96IurRoN+O7XiyIhqWX+V476+XjWc2R11X/sFjZcP1GHycLEQZKnHyh0 2lTuG4eKm9YtlUq62ehGUfcMLwrDycAtJjsI5mH8pPob/K35E1jzqwlkrvX8 CNcos4TIGbk8plKP1tGG8jYggfC1b8W6yWtb6CrXUszt/F5S8jECcbJYR58I /m6LIPPt56VrLb2n245DRh5GyDlGwo8g48PIhMjCykOiO1/bcDQEbrqR3U2r x2sq5ek6W1X0knWPU1+HaCXjIeLykigyyJ8fIxEtZjCxSR1HgdDc+NezDDp1 e5pbQZ+VnyxcFPjzHdlYs64cyt1Zb6E/FVtXnzZmsNlzMfeZb0+JqZhfjOES FbfUOiY620vJL+q34k16Mr9vFH1Gnoc2QHfHOLjyh8WN4cLkMR/SY1DKrOp6 m4vfxBrFnMpPTZr0+Ik1ONz0z2yBjwFMTGYRwZFxtkt1Cj/Dauvj5e+y0WiI tjUINelssCFSQeG+4oZOPj3pvGKz+7iyJc+zLe5QeTDUedfM85OtZ4nf0f8A YzeYOyXLfkeFxuYxgDl8a/uSAag7be5b4rrW1l97Esi3X9MbqTtfLgGbjcrv /wDbtxM8p17LJE1vztXR5F2WT/a/xRT8+RfcPOwmJo+VyFaBPbhfdcon8oJH TSvAstk0/QyQ/wBc5j6ps3/UZvq3Nzkabr+OorHZ9u/I5k1I/vj7ritbl5JC P/USN/4rXp/dXXJZNGH/AHI+6YmUvJiZAH6leAC/zRlqry7Duzah/wB1c4ED J4WCRf3GKZkPyDK1bXmvlF7nRmf7jcVmoHPEZGPnRi2Nl3jk9oki56qSPKs5 PJrdbQ/UO0lk33f9rNwkvGYxy0lba/uzxaySBgxZypOptWclsftOld/zI38u h2c795/budxHs4+dvyleNhC8Ui3K33WJW1b8nLW2PRy9BbVGl9ufdH2/HgyN lcvjw5MuRI8okYqxGgU6jwFa8S6WObbttmqvTUw8/lODQcnBx3I4gizM6OSN UkBBtCOgvou8n514vJqotWuzsvsMXWkI1OOzeMn5xIv9Rx1wuHiWOAvKgVig 2pa5FyTdjbyrtir28jX9NVCCU2Pern8ef/6GMR3PvJ/xr6ndep1Ucng/t+WD kvuTkMwyrsx900asR+qRmCa36hSTXyvFXfPa74OVNbNmx9zZiRZHGRP6scEz SBdd3qC208Reuvm5Er0T23+wt4lGHxSycvzOSJ3LIPXkHWx/SXAv4kqo8ga8 3h2efJaz+v6OEKuWfSH2rE5FlVENuwAAr7WyNo8Ji5Ht/aM7W1XICIvjudGH 8a+PW3/Tb+n8Tmv0SLH5lYmypof6uXKIMPAitcE7Rc272NhXWmeG/XSq+zX7 y1cybuRj/wClcdFIzl8g5cE2bkE3Mjl13knwtoPKvbPtV1NPQ5eXzmyM1ONh fbGjWklPQMvqZiPCMdPFtK8vk5Xa6xLbdslrawbXFM0sQljX2cAKI8CC2pRd PcY/4raDw+NezC5rK0RUaborqUZQytoynUEHSxrq9tSnhuU47kcaOdOKT3sJ wZYQHH9CaP1IRc6bSOo6jQ+NfPyVvS001rrp6My05LcWEYvFfb0RmG3Nyo8n LlI2iyo0xHwG0D5VrCvbx1UzOslSg3eOy2z8rKmRiMXH2xRRf4yNzM3nYjTt XXBn91trbYLU2SPCvSaUlE+PHkwy488YkhmUrIh7g+dS1VZQynneHkl47Jfg 8t9wS5wZdBdbFrfMajzDDpavJgbx29t/V9BhaaHRwZ92fmsgr65szVh12qu1 L/ICr4rns/iFJp5nI4PHAPm5kWKL6CRgGJ8l6n5CvRfJWq1ZdT5xhfc+Jxku WMTGkzkzXkMDXES2ilcDduFxcOO1fLr5tcVr86yRbslN9wZ3IQTZWUkOPDiE ri48e47piLXZm8LgDTua83keY8ySiEZyPhHmD9Tl4yrl5mRNjQ/0wryE6Aaq q3tfoL20vXL3rtTZ6ehv4Iqx44Y2EKIsaFLKigALfpYV5XL1ZokwXZE9rfsc +Y71JBEEI5Q6gHU/Gq9AAtaRQdWYAD50SBs8Xi4mTnATyofbJRsKRC3vAgiy Ed7+VXmDvjVYmdfQ5s2BXgGRFizQmELA0SrvACdHdut2vYadutVxwbzY0vi4 4ORcHLeCGdIWeGYy7bftEVgzN4de9Zag4+24TRzTzZDwNkPMCMRFWJQbMqLY jTp16mrVGLZWokqEoW0aLvnYB1a+iqNSx8b1VUw7azydk+ZPliMzhC8d7yqt mbcbm+tupJ0GtLuXMQdLWnQrAukhPkDWUZJR+pgSP1Ar+VUCTaPbv+0kN8KA akG6qOoN/kdKSCP6kRQdVuCKkAr1J8SakALX3H+XrVSA9SG06amiBdfc8lh+ pb28xR6gpYBW+Kg0bgAQdquDodKBF8mbkyRR4+2MwxAFoEFt2y50Jue57261 usOsQW1tDiaZXeAQHamTJsVQetrmx+FqiWph2hQdoXLnK4an6kY+8wxp6iBo WAvYmxHQfKsxqdVd3UBJizwywRTRuXmjilVY13HbJ2Fr69RrSDTxrskehxcP FYTY0kHtfTQOychOmiPKVPrRbi6g26/hWmtDvWqVZMGRY1JWKX3kgcATWtuv pp5XqfE8t1VPRyUXCmTzNhQyRNyHfzCqKkIHUAisoIFoY9zebHpWSHJHBDNI khULMN3tzDQg31Uka2NrH8a7Y7tBya2Nl5KZcePmZU02PtMPtyyM4AexUqWJ Othb41u+W7iXMGLPQ9JF94Z2FLJi5uHHmLjkKciNjG7KRdHIIZTcddRrX0Mf 8nEJrQ1RyifB89xP12Zl5WQMJ8+NZIlm0AEsjuQXHpBA2jrXTxvIo72be5K6 yfQo3SVBJE6yxtqsiEMp+Yr6asmXU8/gTR4uf9w+4Vix1kGS8h6D02cn5AV5 cdoyXXG5EUcbA/Kcg/M5a2jxyY+PgOoUjqfiv/VfwFTCvdt7j24Lu5PVWNew 0BGnT40JqeQzs5zgcrgySBs3jyZMeUi272WWVL+e23xrxPPraj3RmTk57Fy2 5fAyMJPf+siZ8nD0s3sBWQ3uLjdtuPKueWtlkV6atrYj3NvicCWJjmchaTk5 heQkhmjU9gRpr5adhXfx8bWt3Ni1XqbttNBcV6jWp5PlfcUtHPd8nFUzYs6i xmhv6l06OhAIt4DxrweVLXxWq+Jlk25X6rio2LD6lsnHhlI/cC6tuHkyg/nW 8Xk98fbZ7ETk6OUZ+IyBy0Cn6aVwnKQDox6LIPA9r/CtZrPF8/HJfieb/wBQ hXLx8eNw8EHIgxv4xuyEGvDbKvcSXF19jRhvU30kv925CWtbDCjzsFb++vSr f9pr/b+ZV+o1+RwRm47RAWmT1Y7eDjpr4Hoa9WWnZfHg2z5jjZyjFzMWVm2N IgS4N1YEbG8vQSpr4Vcri9Hzt9KOU7o+j4hi5Hh8cZLKFy8YJIGI1JG0nX4X r7WKyyYlPKOiUweR+z+Qx4JeSxMjKiRkbWR3VQWjZlYeo97givD/AB76WvR+ pmkqUevzsri8nEyMeXksWNZkKhzNH6T2OrdjrX0cnW9XWdzW58uflcNuOhMu fDHkcXkNGB7iklHuSB4hXS4t418JqzxxzS2n0HN7HqeI5/7Zx8vmMyflsOPI zMgESe4CTH7aEgWvpuJv519TDaqtZvlm09WeY537i4Z+Yb6fPimw5pIJJZI9 zLdQQx9Kn+xryeQ/+XTVOJ+0y1qbPM/e/wBtZUmAIc15kxstJ59sMguq66XA vXqz5aW66yk5Nt6mXyX3v9uS5ceZjRZsrvaPkMf21RJ4hqLkuLMp6Hw0rGTL Tt2U+jJKRJv90MHHhSHj+BkRYgFijklSNFA7AIGra8utVFUOxk5H+6PNSKVx +Pw8Zj0dt8v5EqKy/NfCHcx5P9wvuuTQZsUQ7mOCMfxBrm/LuTsZ2R92/cuU rRzcxM0TizxAIFI8xtrnbPeyh8hts5Mbn+aw45IsXk54I5TukRCLMbW10rGO 7xqK7ETgied5kqqHk5zGsZiWLd6AjEEra3QkA07uILJlVghJQGvr0HWgI2NU BUAUAUAUAdKoDvfvQkB5UkoiAeov5UAtifyirLAbVHQWqJtbEgnuf0/1HGz9 NmOnw1o3O5TQxOX5XALNhclk4zOLMySG5F721v3rVLOn6dAtDXX71+6UBU8v JKrKVZJUjcEEWN7rf866/ub+pezGPvDlxxycZ7eM0CS+9vMZ3M3nZgPyrk2n i9vZEjSOCzhvus8bmxZeTx65vsm8aLIU2kLsB9Qbtr8amBLFfs9QtD2HLf7i 8ZynGNjrh5WLke7HIFYK6+hgSNyn+6vXn8hXpC3NWtKMSD7jwcjNBnzTAkuy J52R0sHfdM5NiL2/Ovm463tebaJuWYW+p9fg+6ftYqiQ85gqgAVEMyrYAaCx tavv1yV4Z2UGnDyvF5J24/J4k7aHbHPGx16aBjW+1XyUzsvjZYllyOHmWF7M ZsRjeGRSNRp+knxFefJiabdWZa9DCkwTncN9n4rTPjmXYHkXUi+OzkeGtrVy yYe9K1bgjrMHssPDx8HHTHxlKxpc3JuzEm5Zj3JPWvVixVxViuxtKDqJCgsx CqOrHQD511kGBl/dHCYhKtmjIcXumODLa3iV9I+ZrzX8rHTdg8Zyf3LDnZWD Ph40sE8RIjklKhSy+uPdtJNty269GNfNz+bWzVqzKMX0UnnIeZ5GSI+3lSYs OXGk00UR2ksVsfUPV1HjXl/dXrKThSZpaTlWCSciVTZ29TuxJaTcNNT27371 yyS1NnLZ0kossMqWvtWZnfy9yPdp80Nc2vwMLSxoZTGPEwcVDZpCGZT3Zhc3 /wAz05OaiZ4Lsxo1lTEha8HHIqOxFt0retjf5itXtwjrXXUx1hkDzSm/rEbJ 2sCot16XvellCSN11ehtQcVkucuGaMxyRY5niW9wTewNxcHpauba4PVXClPb 0K+TwWwVwxLdciaMvKhI0Ata1vDUGlbdp+ByvjVUvU4kRRLHfoBvetHIksku OGz8ecxTIzC1huCsupUnw/GmjCyOjkmk0smZjTez7zIFhgjJZQCDdAXUg6Fr 9aNaHTFeXVGtyeWqz5xXJPJrO64s8Cu0Q3hA5ZXG4MAQQLr163rXaUeh3Sql B5i6vHMGBEioQyHqLg9R4VlaOTxN6McCBI43YgSsi72Gh6dPhRtzoZVdC1Cp crcXYXtfXTyqcGi1rnZY/wDcXUeYqrYpXcqfbNw5uw8BbxPbrTq+CNwMJdGY k3Uj86LQpF967vbUFgLqvifjVSlhhAhjhkDauq+tz0LE+o/jUe+gLVC2j+BN SWBLoiA63a5+VWQVpIm94tSzLfdbTqNCR3qpONQW26kdww/CpoiqC+LE9wxu 88YgeORg5YA3jFyhXqG1/wCFehUqrbyj3Y8ONXmZqcxWSOMLJG6BrEblI1t5 iuDq4mDyXxWWsOCF/wDFtve9jbQ1nU5aM5jGqzwbQAh3kgdjtAuPiDWm9CQp OzGy1xZlyUgOSuNcjbIYlVl6HcoYm3gKlUdKWVWb8suzi8yEZYz3hyR9XCxd CN6jZKlmuwDG2ul/CrZ6wkerJeU3HBix5eTBEmNGTGTviC22+iS7MrBtNaM8 lszTQCNEeaFZPcBA2uBYHb4eVZZlOSlhZUbu1wR8KIp6DH4V8hONkjBkxspS 0rA/vVbkAjxtasO/C3PVTDV2UvRmO+LkqgmZCIpHMaSkgbiLjQdSNOta0mDj bE0u3EnNjxPG0obRZpJCp62I23/Mg10eyOXwO7JcTcfHOGtlYLnFna37f1RH 5XFVuYOb9B8lIpiTKU3aXGYfEqA6/wATWKslLQmcUeKZGl2EpJpGWGthEiqb A6db0XBvHsXwS5WFIDjzyYshud8LFVZl6kW0PwNd/dvj/TZwWWdKc5mh5/eZ ZzlRyy5kp0ZhCye2thYepgAfK9b/AHV329WkY7atHtuN+7+Mgx8fFlx8mBYo wDMQsgZu5O031OvSvpYfPxJRsdEeswuW43kf/wBPOinYgH2g1nF/FDZvyr3U y0vs5KaNdJB5P7o4jHycHNz1d8fKx8d2MidHVASFYd/AGvH5HjVv82zRi9J1 DkIM88pw0eA6RStgTxtM+vtqDDdlXu3YUvWza6vWCurnQ2sbEw+P3SNKDOwt NkzONzW8b2sK7Y6Vov7lgql+4OBhv7vNYEdtDfIj/wDyro71XJZPJ/cn3N9u S4ayY/M40uXjSK8SROWLKSFdfSD2N/iBXj8yL0cPVbGbbHzmH7oxYVx4jLJJ FDkKzbYmF40k3IfVboDavk4nettdm5OKlHquU/3O43JgyMWDh8jJiyEKM0zr GLMLXsA/SvrZPLo00dXY+b//ACDNWX3IoolsxYK25rdPMd1r5SxJNOZiPuOT Wsmjk/e/PT53+oRyQYuTs9vfFEDpa1xv3a2Neq2V+535iDSesnNJ94fdMv6u dyh/yFU/6VFX9zf1L2ZgSz5E5YzZM0hf9V5G179L1whTMGYK2LNozMw6WJJ/ jWuziCkNifyipLAbEH7RSQO2t+9JA6SRoKBIO96CAqFCgHY0AqAkq7ja9qAj qLg9RQBQBQFiEBHPc0Ah+n+6gEguTu6CqwR7jzoCclr2HhUQIUAUAUAUAUAU AUAAEmwoAqgfYnwqAVAHTqLXoAqgKAOv99JBDYg/aunewpILYGMMqvFK8BJA aSJmU2PX9Gta72h6g1sf7l5zDOOkXKT+1jP7mJHIfdWNlBVSokvbQ2tW/euk tSyelX/c37n9lo92E8hWyzmA3B8bK4F/lXZ+VfYvZmBN9z8rnHfyTnObqAzs qA+SaqPkK8uRWvvZjuXR/cK+j3MJkKKV9DhgR4G4WvM/H9GVZCE/Owtjv7Mc scsXqiDqLXB3DUE9xUXjtPUjsmjtxMlMjMeAm8Kg+8b2vGkjs1viCBXO1XVS zCcJ+ptYeUs3uTl0EksiuyIwIUN+0W8BYVLNtnVPQ4M+T21msLqpHr/zlbf/ AF1K6mL6NMhHkn3sWRjrCjHXpYsAL/hR13OSOjHdJxjLIxIzpWnmHf22bQfM ELVR2nrU75fbyZJpJJBBDkuXSTYzgRrZYwVBB1AvUuzrhqno3Ghs5fKZGPx8 ZxwvJRcf7aSZyK8e9WZS0DoysX3WAulz41vHHXRaydb3tErU7M3IzZcOHNxu O3zZSh8rkWaNmLW/QI1ZiAt9ATp4XrObq7prk7UdknClnjvUZFs6Iu0B2e9g p+ANZWu54mdHJYZxINhmjmXbvGwk9V6i4Fx8KJanN2WxyxtLNBBjSsNgH/bH QsbXJ8ToKsmqKCcWI+QwSGD3JIRu0K3Av1AJ7GhrsU5m5I5JJC5nSNyfcvu0 vob9jUW5nQvxzEkCl4PekYrd3/Svl4nXt0qtmYsyLrIzyvs3Mg/qFF0A+XQV g6VrB1ZWLNhyQwTqFnMYlCA9BJ0B86uxu1WjlaW0u9CFlSxCnXy1HgehrSca nNlkkallaC4x8lfciU6lCDZ4yf8ACwIHlajCfA0XbjTz31eRMeO/+Ib3I+QA +dSuiE6wc2YrHZiqP6ZW+S1/2DqPixNvxotmylpQkRgD1HRQOwqSyiEQYO7E iCMlSw0LPa+0fAak/wB5rVV6mZAqAYzoCTcKNNB0qOzZoTEEBldJLSlZIQxD WIuQWF7VukJyzVL1q/mUo1sHJiZMtxF9DFhwoWjxf1TEMDZne5uLGxFq1/x2 htbPQ9mJ956qKo4+QaWTKZnyZMuJ/XDNIbkqfG2l/hVydkpezJ5db0VVPyvV ELqhffCkyBgdjaG/YhuorgfOtVtyjiy9q5MQhVoo5WZTGeqekk9NNbaWpwyJ taHZFiz5SLDixNLFYExD/thh8dLiqblIp9pCLhAvsroAQQLG1wRU7M0myjIl kmnx/eZXUKUDNoTtHp3HQaCtLY5NQ5NafDeLHXLXKgLyXRELNc9tAF1qQarZ MrwZMpJIzip70hvujt1XS+ulvjeo4g9ODsrTVSel5PksrAGDBj8e2BNnSI0m OrowSQMPWhTcEMgJU7lAJN+o17Y0lRRqxktZbI4+VlTMXHWfITjnwf6acWsM jstgLMX9IYkdwLVjJCvoaaT0bgxpGBxsqNhtlYpk4vkwGx1J8DoPnVW0Hjb6 ufU4XygHcK148+FWVbabkO1vnqKy67Mzk1ho4Tks0eHEeh2RgeZJQ/gavXcw bONKWjkkf0M5Fh/zOWP8awmdqrRFDzLJJkwxMjtf3YCGG4SR9Rp2ZSQa3MIl nDTPOHlVshBOwyqHFr+gvLJf8lrosTMv9UnUfuDGW+zHmchdqttVQB46teov Hb3N9kckvOmQIgwVCINC8mpPjov99dFgjaw7mjg/fX3Jx8i+xko+P3xsgNMP kzMGFvI17MWa9OZ+kz7jJ8h/uB9xcjFJjST48MEylZYIYVG4eBLFjr5V0v5F 2oK7No8/k8zy2eWmzeVy55dloy0riwYjcqhSAAe+lcbZbtrUjcmVsS50BJ63 161l2ZCQRR+lQPgKkgen4UJA6FCkgDcW060AW7UAVAOxPQXt1oBUAUAUAUAU AUAUAUBZYe2D3vQCTU7T0tQEe/TvQErAS3B0vQA/6jQEKAKABoCPGgGCRQBc 2I8aAXhY2tVAyb9agFQBQBQBQBQBQEtLde1ACHa1/KgFZSb3oCS2DG/Q0Akt 6wfDSgJ23xnW5WgK7C2h7VSNiABIB1oUbLtZqgIbV1uL3oACqDcKAe5HegGQ NQfwoCBRTqPSezDyrXYhMBrakHzAtUkpNQDe/bpUBWdbg6g6GqQtEhVWVLpu UqSD2bU/nUgQUe1GRqo+Nta1IO2GeREeNpHaJ127SxNrEEWv8K5uibD2OhuQ ZhKFW8hjVVftZd2pHbVqLCyVo3siyLlTHJG8iFt6iNmi02oBra/lp86y8XBp ubSba/cGDINknuxEuGbchI2joAVvXG2C6NyieRn4GWsTY8yNI0wMliVKi+hN 7EWtUVHV7FbZoepU90uY5p5DuyLtcXOrNbU+Jrmn6nWjfZawbMH27lHNEOSb Yi45yhkA7A0YIAF9bEXude3nVdG1KR29uqu5eh5uQK2NI1yyEMoOu0qOh00q LRnmvXdpHdD7DLgPEtyL+5KwIJLAenXsBR6GK/EnEZYchJ4G2ujMvxBvcHyI qSWyk4+SyTPCyrjvGLMNjbbKW67DuvtPh+FVbnOtWi0QyPjzNf2ooxu909z0 ATxNEbdoUIuwsnI4x48sRufciIjLEhH7XYL+u2vpNVODpW/Utm5BsiLEgAKw 4yIzqwUn3dd53W3WPgTSz7M3fJ2RDHgGTJlRhR7hjMkI7kob287gmi0OFtzP u8bxMLskjEquvUgKx+fpohOsna0i/T8cjH982Q1vAsEH5Cm6JV6sk8RM7Ai2 5gZRbpYbiL+W63xq3caGqkxHJLOmPBb3JurdkHifIDWspTuVs58maKRhHjgj FhPtYqjUuL6vYdWdtfwq2fBFsEsbe77DruySQnshrLHewCM382uo6Cqh254O GYfSK6pH7UiuY1iC/vOliPzP41lKdyWhovUGKIxpKH99V98gEG6m/wCdaV4U How+Q6UdI3JNZblT6BbUnoKxLehzd292RgEmZ9O5kEAmay7tB6gSga3lSySO dm0ivN91XiZ4mEiyi8fQn0sNCbaHxrSWn1BudjTTNmMLxwo0chi2yTGwATpt iAJAFv7XqPQyqvkqCBIchPCML+JGlZ5OhyZRxjJgqi+3MF9cYvYMF23B8710 40MJN6QanGcaOTzZML3Pbl9kyY92sS19Tr1t1tUUvZHpxUrD7aFL4GZi4UHI Tt9MuSdkMAuGYA2bUEHte3gKWTUpo1WnWjcwzPzwsUUm30ROY5NgNgxB0+JF zaiPPZuGVzcvxkQaJJ1L2UsIwWIYddQDWlis+A7cnFlfcONIJfYilck7kDAK NRZh16MK6Uw2T1MtpmQc+UNCqKDGrMwXXdZhqPxsa6rDJziVC1AZ4ZkK+lY5 WdNb9XLD+NR4WtGRpmdKWnI952ltqAzFh+B0roklsaIxf0HV4f6bC9yote47 2q21WpGpJEK9idCugP4/8aCAUAmxNgKQJF3oUgysTa9l7gaE/OgBUUAWUCkg kNb+ANqgIiNB0Udbj40BIADpQD2jaxtVJySUA28fCoUANzgdu9XgE2tvt2Uf nUBBepJNrdKAV9Dca0BJSFDHv2oCFAM9qAVAFAFAFAFAFAO5ta+nhQADY3oA v3oBUAUAUAUBIKSGP8vSgI0AUAUAUAUAUAUAUAUAUAUAAEkDsaAZFiQNR2oB UAEG1xQDBK3t360AAkXt3qkgXwoUkzXN/KgBFDOvnpa9AAW7qo7m1QjG4Dyl VHe1vhpVJIrBX2m9hQsgCLMp0IOlBJocWmOMvHm5DHkm49pTDIVuBvZdBfTU XBtWbTELc3WE5tsc0mFlxjJdoWMeI6x5Eyi6Iz/pDHsTVV0/pJ1e50cZxWby 87QYUYcxgNMzMAEUm243628qze6otS0o77GftNzbUDvWzIA2+HjQHp8XmI+N wPZxcdc58d5LyOmy8cwsyse9jYjzr14vK6JVaPVi8x469YMlMbL5mfLmxMVf cXYzYcQ/SrEICo8AbXJ8b1l0eZt1RmHns2kl8CzjsjAxWycbkcVZ4pCV91dT G6mxNwRcadjXky1uph6nBqG0Q5CDDjWQYkomhKqZgH3gEtpqdbHwPSueOzs5 aMKS3gXlXkoIYxFKk11eHJdhCABcsRfqANNNelayr5T0ePLvG59elOFlPHgc fnxR/S+39bHAtsdXmZVhRhuIJYr06W6iuNX8qbcfme1WVW01P5HkvuDnXmgl ibkYZjjSNHkcUYjizIyabdtmD2PSzW71itHa0+pwveKtK31Hm4eexwkayRzR Fep2h9e59JJ/Kt28e3B5uyNSPmuO3hTmRgs247iU6i37gKx7VlwaTQsiaPIw 8swOjSRwn29rBtRqNAayqudQ9Uav1Pv40ePsOyKK5JPW2oAHYd6zJzrQ4ct2 jAjXc8e5CIQbi5/trWk5NWqt+S5PaKMIy7f0f6jPpdupIHYdqjNIvWUYk6ZV iTAI2IHg3pP8aLUzfYz5XL4uURZRjZDkW0soLAfwFXkm9UXwiN83GhOkcKRt MT02C7kfPSsrSfpOddE/idUDGZpJnbcZLMT5uS5/jWrL5md0cr5LRQzzKwR8 0mGN+6ob7iPDQE/hWVabHJubHVAv0UUcqjbn5AK4kZ1+nj7yH/Fbp/51Uyu3 ZmfrEQYv1IwZSdbkG+p8zUk6RoGVk/6jnPkFSgghVHAGvuWIZvjYAVtPQxVa alkasIYpGBZZlBDt1O02a/n41l1g1VyN0JGQg7XPyVSxv+FKbktoiUAv7a3s Qb3H8wGn8KzuyusqAzslsufjAUKhHIlB1AXax0v0FwNK2lKZlVaZU2dh44cS ZEUexWXV16k/GnRvg2cM3OYG2QrP7nuEWWJGY6D4WrSw2Y7JHAeaSbIxxFG0 RU+jIkcRKraWYkbiLdb2rp7LS1Yrf5lB9FxM+TmInx4+YhyPojEz5UUBRY3d gsaCa6kszfygafhXOi6qH8sntpdK0uGV8zk4cvETTYb4eXn4StFmLlXXIR4h tcXDCxBFwOhrVl/yJa7b8MLVWag+OrukkRmZpJC62d2LG99NTc167bHzJPQ4 n+i4sYy81RmTXJOKGud9zoFuAAO5NedvI3C2ImzMhxMrl8qc4WOGcspeCOw2 K7bVNvAdzXsx4bW24O+LDbJsdXHcpLxTzY0OJDlSSSPGuTqHUkFCVI/EV3xZ /amsSdMWd4U6xrO4uXyMbMyDPCPZMapBHiKm0KsY638zXHLkeVyc8uZ5XLMg Ak2Arkcjv43i8zlppsfCQSTwx+57RO0sNwU2J00vesXuqKWXHV3ehw7JDJ7I QmXf7YjGpLXtYfE1qYUkhtwauJix4ycq/KYrj6aJ8dYr7XTJJAXcLi20a1G+ zUGqpKexmNFJFIYp0aGRLb43G1hcXFwela0aky01uIkHc3boKgEilkZv5TqK r3MyTQBonPdCG08DUtoJK0G4Nf8AaNarNCsBYeWlQEr+m3iapIECRrUKAJB3 DrVAG5J7k9qgFbxFjQBQDAJBPhVI2KoUZN6AVAFAFAFAFAFAFAFAFqABqwXx oBkWJHhQCoAoCxP+29ARXU60BE0AUAUAUAwLkDzoAYeo0AifSRVJJI+lV8xQ IQF/hUKB8qoJp3bwqAiDoTVI2RqFLE1VgfCgIW9IagERoPA61QWyKpVJUFlf QjwPhUImOKMNuLC6opJow2UjsV6rqKok6owPqlNtCN6jt/a9ZnQjZVB+qWZu i3PzJ0o/QhUCS2vW9zWiotSCWeVI4InmmlIWOJAWZj4ADU1HZJSypehpYeXy AR+KWKbJhMhlHHovrWVOrWsWBABuKzaunaY+J0TvHU6OTado5pTIy8ZkZEcr wggP7jxk/pPXbtI8qUiPiY726xwckUWZxuRHu3IMiASv7D7m+nk67ivS46il mmjUWx87o42RYZfbvf03a3QX1AHytVT7amEypioBHga0SS+KVUgzPVb3FRVU d9aw1NkhJbiZMuCv1mPM8GWGtiyRtZltqxI7g9LHrXSl3W0o3Wzq5RRlZEmZ I2RMwaaXWRgqqPkFAFW93Zyy5LO7llmLHA5SHImOMk7F/dOinaCALm9tT1tX Gza1SmDDcHZyGPwsWMi4k8s2aQLro0dvFjb8hVo7W1a0FddSGHnSx8dm4Cn2 k3LlmaMkOZIyqxi/gpuR51LUTsmzosjVevxOTNzcjkMmTMy2V8mYL7rqNu4q oW5HjYa1qlVWsLYl79nJy1oyG3cbfMCgI7E/lU+dh/GrI1Jq0iHck0qHuVkc fwNZaT4EnQM7NBVvq5G26gNZhf8AzA1l0q+Cyzrj5rPjLXMTqwIIKW6+YIrD w1iC9zpP3DLJFkRS4gBliMaPG+oPY2Yf31F48bMd5RVLy8MmDmQgTQy5JaxK 3GrbuqFqiwtOQnFYO3CyseSeb/3KFnUhFLgMVWO3Q2PWsWo1wckoNwyBIEWI i8jFVI7btB+Fcex3bhFUzE5uPAo9EEW8r21Nh8xtpVfK2eZNwy2J97ZGSxux AVGPS3QH+JqwdsdYUnNk8vxkGOyxzpPICFCRXc6ddVBHWtrDZ8G+yMyH7ijg xJsVMOR3nN5Z2IGh6gXua7LA/U5yVN9xTnGbGTEjX+r7sUjMzFCRY6WF709g 12OFuY5Jw4+pCGRSrlEA0OhGu6tLFRcEdpORsrLcWfMnb4OV/wCm1bVarZEl +pzkE/qZnsbgMS3/AFE1oagF19K6nwoA6ihIHSSmrg52SEx+NExiwzlrkOqC zNILBSzdTtsCBWLUTc8nT3GqpfE583MPI8hLnZSLGciQPkLF4gAMwvfUkXpW vWqSM3v2crQ0p8PgWi9zF5CSIqhO6QgPutoNhHY+FY7XWkSc41MSWzOZApCz DeL9dev511rpp6Gjug5XKxInxYJ3ixZyPqo47Kzr3XfbcAfAGutMtq16rY2s llXqigFIM2NwQYg4ZWH8raj8Aa4zNZMNFMxHuyWNwzk3+JrS2MySDAlmsLKL UEnThtLCpycYyfUOrRx+2SHDE+rp221i1o0Za2ddih8WWDGgzn0jyi/0bI6l g8RF9wvdR4VrmPtNdWkrHoZRnKmR9VDJnZhnM2S0PqCpAu0SMQCLX1162rFv hoWztd9uTCnl5HlZcrOmEmY8ah8mdUuqJ0BbaLAVv5a6Es7Wbszkt2PxvVZC cFhJsJ0k9P8AwqPUy0SiuDkx2/YR+FHwyEBu9g9zI35LpR7lTHAgkk2P1IO3 40Kyo6aeFVQEWSoqBFIvIfUx8PAVEUr1uKAmgtIAaAiSCTbxoAAuQKoJJ+sr 2N9KEghaxI8DUKAAJAIoAoCRNgv+KqZkiepFDRIC4b/DUBGgCgCgCgJr4UBF TeQX7VWBt+pvjQiI1Cn/2Q== --0-1120825588-1305796336=:86840-- --0-806656023-1305796336=:86840-- ========================================================================= Date: Thu, 19 May 2011 11:32:30 +0200 Reply-To: Maximilian Eck <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Maximilian Eck <[log in to unmask]> MIME-Version: 1.0 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Message-ID: <1436250731.1221221.1305797550468.JavaMail.fmail@mwmweb053> <html><head></head><body bgcolor=3D'#FFFFFF' style=3D'font-size:12px;backgr= ound-color:#FFFFFF;font-family:Verdana, Arial, sans-serif;'>Dear SPMers,<br= /><br/>I just tried to extract the VOI timeseries from my fMRI data using a= mask, which I got from the wfu_pickatlas.<br/><br/>>...<br/>>mask = =3D spm_select(1, 'img', 'Select mask');<br/>>matlabbatch{1}.spm.util.vo= i.roi{1}.spm.mask.image =3D cellstr(mask); <br/>>matlabbatch{1}.sp= m.util.voi.roi{1}.spm.mask.thresh =3D 0.05;<br/>>matlabbatch{1}.spm.util= .voi.roi{1}.spm.mask.mtype =3D 0;<br/>>...<br/><br/>Using these lines I = got the error message:<br/><br/>-------------------------------------------= -----------------------------<br/>Running job #2<br/>----------------------= --------------------------------------------------<br/>No executable module= s, but still unresolved dependencies or incomplete module inputs.<br/>The f= ollowing modules did not run:<br/>Skipped: Volume of Interest<br/><br/>??? = Error using =3D=3D> cfg_util at 835<br/>Job execution failed. The full l= og of this run can be found in MATLAB command window, starting with the lin= es (look for the line showing the exact #job as<br/>displayed in this error= message)<br/>------------------<br/>Running job #2<br/>------------------<= br/><br/>Error in =3D=3D> spm_jobman at 217<br/><br/>Is there anything I= did wrong or anything I forgot to specify?<br/><br/>Thanks a lot.<br/><br/= >Max <br><br><table cellpadding=3D"0" cellspacing=3D"0" border= =3D"0"><tr><td bgcolor=3D"#000000"><img src=3D"https://img.ui-portal.de/p.g= if" width=3D"1" height=3D"1" border=3D"0" alt=3D"" /></td></tr><tr><td styl= e=3D"font-family:verdana; font-size:12px; line-height:17px;">Schon gehö= ;rt? WEB.DE hat einen genialen Phishing-Filter in die <br>= Toolbar eingebaut! <a href=3D"http://produkte.web.de/go/toolbar"><b>http://= produkte.web.de/go/toolbar</b></a></td></tr></table> </body></html> ========================================================================= Date: Thu, 19 May 2011 13:27:57 +0100 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: channel selection Comments: To: Fahimeh Mamashli <[log in to unmask]> In-Reply-To: <1564594267.2181.1305803731716.JavaMail.root@zimbra> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You set DCM.options.location =3D 1; See also line 57 in spm_L_priors. You might want to play with the value the= re. Vladimir On Thu, May 19, 2011 at 12:15 PM, Fahimeh Mamashli <[log in to unmask]> wr= ote: > > Dear Vladimir, > Thanks for your reply. how can I optimize the prior locations in the scri= pt? > > Best wishes, > Fahimeh > > ------------------------ > PhD student > Max Planck Institute for Human Cognitive and Brain Sciences > Stephanstra=DFe 1A > 04103 Leipzig > Germany > > Tel: +49 341 9940 - 2570 > > ----- Original Message ----- > From: "litvak vladimir" <[log in to unmask]> > To: "Fahimeh Mamashli" <[log in to unmask]> > Cc: [log in to unmask] > Sent: Wednesday, May 18, 2011 4:16:12 PM > Subject: Re: channel selection > > Dear Fahimeh, > > The most straightforward way to exclude some channels from analysis is to= mark them as bad in the dataset. You can do it in the reviewing tool. Howe= ver, I'm not sure this is a good strategy in your case. I would suggest you= to just start with a symmetric model for both left and right hemispheres. = Then if you want to explore the asymmetries you can do it as a second stage= . > > Best, > > Vladimir > ------Original Message------ > From: Fahimeh Mamashli > To: Vladimir Litvak > Subject: channel selection > Sent: 18 May 2011 15:04 > > > Dear Vladimir, > Thanks for your attention. I am the one with scarf in SPM-MEG course on M= ay-2011 if you remember me. I wanted to do DCM for verb and noun together. = :) > I have a question: the difference between two conditions of my MEG data i= s spread in both hemisphere, but it is so complicated if I want to model fr= om the beginning both hemisphere. we want to only choose left hemisphere ch= annels. is it possible? > I used DCM.xY.name =3D ['channel names']; but when DCM running finished a= nd I looked at DCM.xY.name, it just show gradiometers. where I can do chann= el selection for DCM analysis? > > Best wishes, > Fahimeh > > ------------------------ > PhD student > Max Planck Institute for Human Cognitive and Brain Sciences > Stephanstra=DFe 1A > 04103 Leipzig > Germany > > Tel: +49 341 9940 - 2570 > > > > Sent using BlackBerry=AE from Orange > ========================================================================= Date: Thu, 19 May 2011 15:40:11 +0200 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: Sold State Drives Comments: To: Nicholas DiQuattro <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-version: 1.0 Content-type: multipart/alternative; boundary="Boundary_(ID_vz2rUCfDb1jElw6VP/AbSA)" Message-ID: <[log in to unmask]> --Boundary_(ID_vz2rUCfDb1jElw6VP/AbSA) Content-type: text/plain; CHARSET=US-ASCII Content-transfer-encoding: 7BIT Dear Nick For fMRI most of the time is used on disk I/O and SSD is really great for this, but actually the biggest change comes after changing defaults.stats.maxmem in spm_defaults to fit your computer e.g. 2^30, and line 569 in spm_spm from: nbz = max(1,min(zdim,floor(mmv/(xdim*ydim)))); nbz = 1; %-# planes to: nbz = max(1,min(zdim,floor(mmv/(xdim*ydim)))); %-# planes In order to load as much data as possible into the RAM at once. 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:20 18/05/2011 kl. 23.14 skrev Nicholas DiQuattro: > Hello SPM, > > Has anyone had any experience with running SPM on solid state drives? Do you see much of an improvement in processesing speeds? > > thanks, > Nick > > -- > Nicholas DiQuattro > Junior Specialist > Integrated Attention Lab > Center for Mind and Brain > 267 Cousteau Place Davis, CA 95618 > tel: (530) 297-4472 --Boundary_(ID_vz2rUCfDb1jElw6VP/AbSA) Content-type: text/html; CHARSET=US-ASCII Content-transfer-encoding: quoted-printable <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear = Nick<div><br></div><div>For fMRI most of the time is used on disk I/O = and SSD is really great for this, but actually the biggest change comes = after changing defaults.stats.maxmem in spm_defaults to fit your = computer e.g. 2^30, and line 569 in spm_spm = from:<div><br></div><div><div style=3D"margin-top: 0px; margin-right: = 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 10px/normal Courier; ">nbz =3D = max(1,min(zdim,floor(mmv/(xdim*ydim)))); nbz =3D 1; <span = style=3D"color: #00a32d">%-# planes</span></div></div><div><span = style=3D"color: = #00a32d"><br></span></div><div>to:</div><div><br></div><div><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 10px/normal Courier; = ">nbz =3D = max(1,min(zdim,floor(mmv/(xdim*ydim)))); <span style=3D"color:= #00a32d">%-# planes</span></div></div><div><font = class=3D"Apple-style-span" color=3D"#00A32D"><br></font></div><div>In = order to load as much data as possible into the RAM at = once.</div><div><br></div><div><br></div><div>Best</div><div>Torben <= /div><div><br></div><div><br></div><div><br></div><div><div>Torben = Ellegaard Lund<br>Associate Professor, PhD<br>The Danish National = Research Foundation's Center of Functionally Integrative = Neuroscience (CFIN)<br>Aarhus University<br>Aarhus University = Hospital<br>Building 10G, 5th floor, room 31<br>Noerrebrogade 44<br>8000 = Aarhus C<br>Denmark<br>Phone: +4589494380<br>Fax: +4589494400<br><a = href=3D"http://www.cfin.au.dk">http://www.cfin.au.dk</a><br>torbenelund@cf= in.dk</div><div><br></div></div><div><br><div><div>Den Uge:20 18/05/2011 = kl. 23.14 skrev Nicholas DiQuattro:</div><br = class=3D"Apple-interchange-newline"><blockquote type=3D"cite">Hello = SPM,<div><br></div><div>Has anyone had any experience with running SPM = on solid state drives? Do you see much of an improvement = in processesing speeds?<br = clear=3D"all"><br></div><div>thanks,</div><div>Nick</div><div><br> -- <br>Nicholas DiQuattro<br>Junior Specialist<br>Integrated Attention = Lab<br>Center for Mind and Brain<br>267 Cousteau Place = Davis, CA 95618<br>tel: (530) 297-4472 <br> </div> </blockquote></div><br></div></div><br><br><div><br = class=3D"Apple-interchange-newline"> </div> <br></body></html>= --Boundary_(ID_vz2rUCfDb1jElw6VP/AbSA)-- ========================================================================= Date: Thu, 19 May 2011 16:20:15 +0200 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Simona Spinelli <[log in to unmask]> Subject: bounding box MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="=_alternative 004F7F82C1257895_=" Message-ID: <[log in to unmask]> Dies ist eine mehrteilige Nachricht im MIME-Format. --=_alternative 004F7F82C1257895_= Content-Type: text/plain; charset="US-ASCII" I would like to expand the bunding box to include the entire cerebellum. Will this change have any consequences for the analysis procedure? For example, can I still use pickatlas or marsbar for ROI analysis? Thank you very much! Simona --=_alternative 004F7F82C1257895_= Content-Type: text/html; charset="US-ASCII" <font size=2 face="sans-serif">I would like to expand the bunding box to include the entire cerebellum.</font> <br> <br><font size=2 face="sans-serif">Will this change have any consequences for the analysis procedure? </font> <br><font size=2 face="sans-serif">For example, can I still use pickatlas or marsbar for ROI analysis?</font> <br> <br><font size=2 face="sans-serif">Thank you very much!</font> <br><font size=2 face="sans-serif"><br> Simona <br> </font> --=_alternative 004F7F82C1257895_=-- ========================================================================= Date: Thu, 19 May 2011 15:40:40 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: VOI extract batch Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi Maximilian, to check your batch you could load it into the GUI and look for any <-X m= arks left. To do this, either save matlabbatch to a .mat file and open it= through the GUI or run spm_jobman('interactive',matlabbatch) from MATLAB= . Hope this helps, Volkmar ========================================================================= Date: Thu, 19 May 2011 14:09:38 -0400 Reply-To: Jadzia Jagiellowicz <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jadzia Jagiellowicz <[log in to unmask]> Subject: glass brain display MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636eef7318a4ac304a3a4e813 Message-ID: <[log in to unmask]> --001636eef7318a4ac304a3a4e813 Content-Type: text/plain; charset=ISO-8859-1 Hello all When I display activation on the "glass brain" after making T contrasts, the "glass brain " never shows up. The activation shows up on a square grid. Have I specified something wrong in the normalization? I am choosing the default bounding box in SPM5. Some of my subjects have glass brains, others only show the activation on a square grid. Thanks in advance, Jadzia -- Jadzia Jagiellowicz Doctoral Candidate Psychology Department B-207 Stony Brook University, Stony Brook, NY 11794-2500 USA http://www.psychology.stonybrook.edu/aronlab-/ "The brave shall inherit the earth." --001636eef7318a4ac304a3a4e813 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Hello all</div> <div>=A0</div> <div>When I display activation on the "glass brain" after making = T contrasts,</div> <div>the "glass brain " never shows up. The activation shows up o= n a square grid. </div> <div>=A0</div> <div>Have I specified something wrong in the normalization? I am choosing t= he </div> <div>default bounding box in SPM5. Some of my subjects have glass brains, o= thers only show the </div> <div>activation on a square grid.</div> <div>Thanks in advance, <br clear=3D"all">Jadzia <br>-- <br>Jadzia Jagiello= wicz<br>Doctoral Candidate <br>Psychology Department B-207<br>Stony Brook U= niversity, <br>Stony Brook, NY 11794-2500 USA<br><a href=3D"http://www.psyc= hology.stonybrook.edu/aronlab-/">http://www.psychology.stonybrook.edu/aronl= ab-/</a><br> <br>"The brave shall inherit the earth."<br></div> --001636eef7318a4ac304a3a4e813-- ========================================================================= Date: Thu, 19 May 2011 19:29:23 +0100 Reply-To: Jennifer Barredo <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jennifer Barredo <[log in to unmask]> Subject: signrank for spm5 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi, I've been trying to generate a SPM for a signrank test rather than a t-te= st. I've been able to get the signrank map written (using Matlab's Statis= tics Toolbox & spm_write_vol), but in the process it seems like it's conv= erting negative values to positive ones. Is there a default in the spm_wr= ite_vol that causes this to happen? Thanks, Jennifer ========================================================================= Date: Thu, 19 May 2011 21:13:30 +0100 Reply-To: Joshua Goh <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Joshua Goh <[log in to unmask]> Subject: Use my own t-maps Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear list, I have a .img/.hdr file that consists of t-values computed externally to = SPM. Is there a way I can display or use this t-map in spm_results_ui? Th= is is so that I can apply the cluster threshold as well as spm_list to id= entify global and local peaks. Thanks, Josh ========================================================================= Date: Thu, 19 May 2011 16:25:24 -0400 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: Use my own t-maps Comments: To: Joshua Goh <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=20cf307f35f41abbb704a3a6ce71 Message-ID: <[log in to unmask]> --20cf307f35f41abbb704a3a6ce71 Content-Type: text/plain; charset=ISO-8859-1 Dear Josh, Try this. Jason On Thu, May 19, 2011 at 4:13 PM, Joshua Goh <[log in to unmask]> wrote: > Dear list, > > I have a .img/.hdr file that consists of t-values computed externally to SPM. Is there a way I can display or use this t-map in spm_results_ui? This is so that I can apply the cluster threshold as well as spm_list to identify global and local peaks. > > Thanks, > Josh > -- Jason Steffener, Ph.D. Department of Neurology Columbia University http://www.cogneurosci.org/steffener.html --20cf307f35f41abbb704a3a6ce71 Content-Type: application/octet-stream; name="SPMDisplayThresholdImage.m" Content-Disposition: attachment; filename="SPMDisplayThresholdImage.m" Content-Transfer-Encoding: base64 X-Attachment-Id: f_gnw5g2ah0 JSBGaWxlbmFtZSA9IFNQTURpc3BsYXlUaHJlc2hvbGRJbWFnZS5tCiUgRGVwZW5kZW50IG9uOiAg RGlzcGxheUNvdi5tCiUgICAgICAgICAgICAgICAgIGpyc19yZXN1bHRzX3VpLm0KJSBXcml0dGVu IGJ5OiBKYXNvbiBTdGVmZmVuZXIKJSBEYXRlOiAwOS8wNwolCgpjbGVhciBtbmkgdGFsIHZhbHVl IGNsdXN0ZXI7Cgp3YXJuaW5nKCdvZmYnLCdhbGwnKQogICAgU0NDU2lkID0gJyRSZXY6IDgxNiAk JzsKICAgJS1Jbml0aWFsaXNlCiAgICAlLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQogICAgU1BNaWQgICAgICA9IHNw bSgnRm5CYW5uZXInLG1maWxlbmFtZSxTQ0NTaWQpOwogICAgW0ZpbnRlcixGZ3JhcGgsQ21kTGlu ZV0gPSBzcG0oJ0ZuVUlzZXR1cCcsJ1N0YXRzOiBSZXN1bHRzJyk7CiAgICBGUyAgICAgICAgID0g c3BtKCdGb250U2l6ZXMnKTsKCiAgICAlIGNsZWFyIHNhdGZpZyBpZiBpdCBleGlzdHMKICAgICUt LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tCiAgICBoU2F0ICAgICAgID0gZmluZG9iaigndGFnJywnU2F0ZWxsaXRlJyk7 CiAgICBzcG1fZmlndXJlKCdjbGVhcicsaFNhdCk7CgogICAgJS1HZXQgdGhyZXNob2xkZWQgeFNQ TSBkYXRhIGFuZCBwYXJhbWV0ZXJzIG9mIGRlc2lnbgogICAgJT09PT09PT09PT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09CiAgICBb U1BNIHhTUE1dID0gRGlzcGxheUNvdjsKJQlbU1BNLHhTUE1dID0gc3BtX2dldFNQTTsKCgogICAg aWYgaXNlbXB0eSh4U1BNKSAKCXZhcmFyZ291dCA9IHtbXSxbXSxbXX07CglyZXR1cm47CiAgICBl bmQKCiAgICBNICAgICAgICAgPSBTUE0ueFZvbC5NOwogICAgRElNICAgICAgID0gU1BNLnhWb2wu RElNOwogICAgdHJ5CiAgICAgICAgdW5pdHMgPSBTUE0ueFZvbC51bml0czsKICAgIGNhdGNoCiAg ICAgICAgdW5pdHMgPSB7J21tJyAnbW0nICdtbSd9OwogICAgZW5kCiAgICAlIGVuc3VyZSBwd2Qg PSBzd2Qgc28gdGhhdCByZWxhdGl2ZSBmaWxlbmFtZXMgYXJlIHZhbGlkCiAgICAlLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLQogICAgY2QoU1BNLnN3ZCkKCiAgICAlLVNldHVwIFJlc3VsdHMgVXNlciBJbnRlcmZhY2U7 IERpc3BsYXkgTUlQLCBkZXNpZ24gbWF0cml4ICYgcGFyYW1ldGVycwogICAgJT09PT09PT09PT09 PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09 PT0KICAgIHNwbSgnRmlnTmFtZScsWydTUE17Jyx4U1BNLlNUQVQsJ306IFJlc3VsdHMnXSxGaW50 ZXIsQ21kTGluZSk7CgoKCgogICAgJS1TZXR1cCByZXN1bHRzIEdVSQogICAgJS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0KICAgIHNwbV9maWd1cmUoJ0NsZWFyJyxGaW50ZXIpCiAgICBoUmVnICAgICAgPSBqcnNfcmVz dWx0c191aSgnU2V0dXBHVUknLE0sRElNLHhTUE0sRmludGVyKTsKCiAgICAlLVNldHVwIGRlc2ln biBpbnRlcnJvZ2F0aW9uIG1lbnUKICAgICUtLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCiUgICAgIGhEZXNSZXBVSSA9 IHNwbV9EZXNSZXAoJ0Rlc1JlcFVJJyxTUE0pOwolICAgICBmaWd1cmUoRmludGVyKQoKICAgICUt U2V0dXAgTWF4aW1pdW0gaW50ZW5zaXR5IHByb2plY3Rpb24gKE1JUCkgJiByZWdpc3RlcgogICAg JS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0KICAgIGhNSVBheCA9IGF4ZXMoJ1BhcmVudCcsRmdyYXBoLCdQb3NpdGlv bicsWzAuMDUgMC42MCAwLjU1IDAuMzZdLCdWaXNpYmxlJywnb2ZmJyk7CiAgICBoTUlQYXggPSBz cG1fbWlwX3VpKHhTUE0uWix4U1BNLlhZWm1tLE0sRElNLGhNSVBheCx1bml0cyk7CgogICAgc3Bt X1hZWnJlZygnWFJlZycsaFJlZyxoTUlQYXgsJ3NwbV9taXBfdWknKTsKICAgIGlmIHhTUE0uU1RB VCA9PSAnUCcKICAgICAgICBzdHIgPSB4U1BNLlNUQVRzdHI7CiAgICBlbHNlCiAgICAgICAgc3Ry ID0gWydTUE1ceycseFNQTS5TVEFUc3RyLCdcfSddOwogICAgZW5kCiAgICB0ZXh0KDI0MCwyNjAs c3RyLC4uLgogICAgICAgICdJbnRlcnByZXRlcicsJ1RlWCcsLi4uCiAgICAgICAgJ0ZvbnRTaXpl JyxGUygxNCksJ0ZvbnR3ZWlnaHQnLCdCb2xkJywuLi4KICAgICAgICAnUGFyZW50JyxoTUlQYXgp CgoKICAgICUtUHJpbnQgY29tcGFyaXNvbiB0aXRsZQogICAgJS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KICAgIGhU aXRBeCA9IGF4ZXMoJ1BhcmVudCcsRmdyYXBoLC4uLgogICAgICAgICdQb3NpdGlvbicsWzAuMDIg MC45NSAwLjk2IDAuMDJdLC4uLgogICAgICAgICdWaXNpYmxlJywnb2ZmJyk7CgogICAgdGV4dCgw LjUsMCx4U1BNLnRpdGxlLCdQYXJlbnQnLGhUaXRBeCwuLi4KICAgICAgICAnSG9yaXpvbnRhbEFs aWdubWVudCcsJ2NlbnRlcicsLi4uCiAgICAgICAgJ1ZlcnRpY2FsQWxpZ25tZW50JywnYmFzZWxp bmUnLC4uLgogICAgICAgICdGb250V2VpZ2h0JywnQm9sZCcsJ0ZvbnRTaXplJyxGUygxNCkpCgoK ICAgICUtUHJpbnQgU1BNcmVzdWx0czogUmVzdWx0cyBkaXJlY3RvcnkgJiB0aHJlc2hvbGRpbmcg aW5mbwogICAgJS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KICAgIGhSZXNBeCA9IGF4ZXMoJ1BhcmVudCcsRmdyYXBo LC4uLgogICAgICAgICdQb3NpdGlvbicsWzAuMDUgMC41NSAwLjQ1IDAuMDVdLC4uLgogICAgICAg ICdEZWZhdWx0VGV4dFZlcnRpY2FsQWxpZ25tZW50JywnYmFzZWxpbmUnLC4uLgogICAgICAgICdE ZWZhdWx0VGV4dEZvbnRTaXplJyxGUyg5KSwuLi4KICAgICAgICAnRGVmYXVsdFRleHRDb2xvcics WzEsMSwxXSouNywuLi4KICAgICAgICAnVW5pdHMnLCdwb2ludHMnLC4uLgogICAgICAgICdWaXNp YmxlJywnb2ZmJyk7CiAgICBBeFBvcyA9IGdldChoUmVzQXgsJ1Bvc2l0aW9uJyk7IHNldChoUmVz QXgsJ1lMaW0nLFswLEF4UG9zKDQpXSkKICAgIGggICAgID0gdGV4dCgwLDI0LCdTUE1yZXN1bHRz OicsJ1BhcmVudCcsaFJlc0F4LC4uLgogICAgICAgICdGb250V2VpZ2h0JywnQm9sZCcsJ0ZvbnRT aXplJyxGUygxNCkpOwogICAgdGV4dChnZXQoaCwnRXh0ZW50JykqWzA7MDsxOzBdLDI0LHNwbV9z dHJfbWFuaXAoU1BNLnN3ZCwnYTMwJyksJ1BhcmVudCcsaFJlc0F4KQogICAgdHJ5CiAgICAgICAg dGhyZXNEZXNjID0geFNQTS50aHJlc0Rlc2M7CiAgICAgICAgdGV4dCgwLDEyLHNwcmludGYoJ0hl aWdodCB0aHJlc2hvbGQgJWMgPSAlMC42ZiAgeyVzfScseFNQTS5TVEFULHhTUE0udSx0aHJlc0Rl c2MpLCdQYXJlbnQnLGhSZXNBeCkKICAgIGNhdGNoCiAgICAgICAgdGV4dCgwLDEyLHNwcmludGYo J0hlaWdodCB0aHJlc2hvbGQgJWMgPSAlMC42ZicseFNQTS5TVEFULHhTUE0udSksJ1BhcmVudCcs aFJlc0F4KQogICAgZW5kCiAgICB0ZXh0KDAsMDAsc3ByaW50ZignRXh0ZW50IHRocmVzaG9sZCBr ID0gJTAuMGYgdm94ZWxzJyx4U1BNLmspLCAnUGFyZW50JyxoUmVzQXgpCgogICAgJS1TdG9yZSBo YW5kbGVzIG9mIHJlc3VsdHMgc2VjdGlvbiBHcmFwaGljcyB3aW5kb3cgb2JqZWN0cwogICAgJS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0KICAgIEggID0gZ2V0KEZncmFwaCwnQ2hpbGRyZW4nKTsKICAgIEggID0gZmlu ZG9iaihILCdmbGF0JywnSGFuZGxlVmlzaWJpbGl0eScsJ29uJyk7CiAgICBIICA9IGZpbmRvYmoo SCk7CiAgICBIdiA9IGdldChILCdWaXNpYmxlJyk7CiAgICBzZXQoaFJlc0F4LCdUYWcnLCdQZXJt UmVzJywnVXNlckRhdGEnLHN0cnVjdCgnSCcsSCwnSHYnLHtIdn0pKQoKICAgICUtRmluaXNoZWQg cmVzdWx0cyBzZXR1cAogICAgJS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KICAgIHZhcmFyZ291dCA9IHtoUmVnLHhT UE0sU1BNfTsKICAgIHNwbSgnUG9pbnRlcicsJ0Fycm93JykKCiAgICBzcG1fbGlzdCgnbGlzdCcs eFNQTSxoUmVnKQoKJSUlIExpdHRsZSBhZGQtb24gYnkgQ2hyaXMKCmNsZWFyICptbmkgdGFsICpj bHVzdGVyICp2YWx1ZSBpbmRleDsKdGVtcD1hbnM7CgoKCgoKCgpmb3IgaT0xOnNpemUodGVtcC5k YXQsMSkKICBtbW5pKGksOik9dGVtcC5kYXR7aSwxMX0nOyAgICVNTkkgY29vcmRpbmF0ZXMKICBt dmFsdWUoaSk9dGVtcC5kYXR7aSw4fTsgICAgICV2b3hlbCB2YWx1ZXMKCiAgaWYgaXNlbXB0eSh0 ZW1wLmRhdHtpLDR9KQogICAgIHRlbXAuZGF0e2ksNH09LTE7CiAgZW5kCiAgCiAgbWNsdXN0ZXIo aSk9dGVtcC5kYXR7aSw0fTsKZW5kCgoKJXRocmVzaG9sZCB3aXRoIGNsdXN0ZXIgdmFsdWUgY2hv c2VuCmluZGV4PWZpbmQobWNsdXN0ZXI+PXhTUE0uayk7CgoKbW5pPW1tbmkoaW5kZXgsOikqZGlh ZyhzaWduKHhTUE0uVk9YKSkKJSBsb29rIHdoZXRoZXIgdGhlcmUgd2FzIGxlZnQtcmlnaHQgZmxp cCBiZWNhdXNlIG9mIFNQTSB0cmFuc2l0aW9uIGlzc3VlcwolJSBUSGlzIGxpbmUgaXMgbm93IGly cmVsZXZhbnQgPT08IGl0IHdhcyBhbHJlYWR5IGltcGxlbWVudGVkIGFib3ZlLCBJREVNUE9URU5U CgoKdGFsPXJvdW5kKG1uaTJ0YWwobW5pKSkKdmFsdWU9bXZhbHVlKGluZGV4KQpjbHVzdGVyPW1j bHVzdGVyKGluZGV4KQoKCgo= --20cf307f35f41abbb704a3a6ce71-- ========================================================================= Date: Thu, 19 May 2011 16:36:34 -0400 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: Use my own t-maps Comments: To: Joshua Goh <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0023545309c80ea4ad04a3a6f6d9 Message-ID: <[log in to unmask]> --0023545309c80ea4ad04a3a6f6d9 Content-Type: text/plain; charset=ISO-8859-1 You need to add the xCon structure to the SPM variable and properly fill the xCon with the correct fields. If you have an old SPM.mat file you can see what the fields should be. If you are simple wanting to cluster and find the peaks, the SPM.mat file is not actually needed with the peak_nii.m program that I recently wrote. There is a previous post on usage of peak_nii.m. It is available for download from http://www.martinos.org/~mclaren/ftp/Utilities_DGM/. 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, May 19, 2011 at 4:13 PM, Joshua Goh <[log in to unmask]> wrote: > Dear list, > > I have a .img/.hdr file that consists of t-values computed externally to > SPM. Is there a way I can display or use this t-map in spm_results_ui? This > is so that I can apply the cluster threshold as well as spm_list to identify > global and local peaks. > > Thanks, > Josh > --0023545309c80ea4ad04a3a6f6d9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable You need to add the xCon structure to the SPM variable and properly fill the xCon with the correct fields. If you have an old SPM.mat file you=20 can see what the fields should be.<br> <br> If you are simple wanting to cluster and find the peaks, the SPM.mat=20 file is not actually needed with the peak_nii.m program that I recently=20 wrote. There is a previous post on usage of peak_nii.m. It is available=20 for download from <a href=3D"http://www.martinos.org/~mclaren/ftp/Utilities= _DGM/">http://www.martinos.org/~mclaren/ftp/Utilities_DGM/</a>.<br clear=3D= "all"><br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br> Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Departm= ent of Neurology, Massachusetts General Hospital and Harvard Medical School= <br>Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDENTIAL INFORMATION which m= ay contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILE= GED and which is intended only for the use of the individual or entity name= d above. If the reader of the e-mail is not the intended recipient or the e= mployee or agent responsible for delivering it to the intended recipient, y= ou are hereby notified that you are in possession of confidential and privi= leged information. Any unauthorized use, disclosure, copying or the taking = of any action in reliance on the contents of this information is strictly p= rohibited and may be unlawful. If you have received this e-mail unintention= ally, please immediately notify the sender via telephone at (773) 406-2464 = or email.<br> <br><br><div class=3D"gmail_quote">On Thu, May 19, 2011 at 4:13 PM, Joshua = Goh <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">joshgoh@gmai= l.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style=3D"m= argin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> Dear list,<br> <br> I have a .img/.hdr file that consists of t-values computed externally to SP= M. Is there a way I can display or use this t-map in spm_results_ui? This i= s so that I can apply the cluster threshold as well as spm_list to identify= global and local peaks.<br> <br> Thanks,<br> Josh<br> </blockquote></div><br> --0023545309c80ea4ad04a3a6f6d9-- ========================================================================= Date: Thu, 19 May 2011 14:01:24 -0700 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: signrank for spm5 Comments: To: Jennifer Barredo <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3040ea429b5f7d04a3a750e8 Message-ID: <[log in to unmask]> --20cf3040ea429b5f7d04a3a750e8 Content-Type: text/plain; charset=UTF-8 I expect that the output is a p-value, correct? so the p-value will obviously only be positive. you could possibly do a couple things to retain the signs. A) you could find the indices of all negative values, and then after the signrank test multiply all of those indices by -1, or B) output separate maps for positive and negative values. Cheers, Michael On Thu, May 19, 2011 at 11:29 AM, Jennifer Barredo < [log in to unmask]> wrote: > Hi, > > I've been trying to generate a SPM for a signrank test rather than a > t-test. I've been able to get the signrank map written (using Matlab's > Statistics Toolbox & spm_write_vol), but in the process it seems like it's > converting negative values to positive ones. Is there a default in the > spm_write_vol that causes this to happen? > > Thanks, > Jennifer > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf3040ea429b5f7d04a3a750e8 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I expect that the output is a p-value, correct? so the p-value will obvious= ly only be positive. you could possibly do a couple things to retain the si= gns. A) you could find the indices of all negative values, and then after t= he signrank test multiply all of those indices by -1, or B) output separate= maps for positive and negative values.<br> <br>Cheers,<br>Michael<br><br><div class=3D"gmail_quote">On Thu, May 19, 20= 11 at 11:29 AM, Jennifer Barredo <span dir=3D"ltr"><<a href=3D"mailto:Je= [log in to unmask]">[log in to unmask]</a>></span> wrote:<= br> <blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; borde= r-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Hi,<br> <br> I've been trying to generate a SPM for a signrank test rather than a t-= test. I've been able to get the signrank map written (using Matlab'= s Statistics Toolbox & spm_write_vol), but in the process it seems like= it's converting negative values to positive ones. Is there a default i= n the spm_write_vol that causes this to happen?<br> <br> Thanks,<br> <font color=3D"#888888">Jennifer<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br>Gazzaley Lab<br>Department of Neurology<br>University of California, = San Francisco<br> --20cf3040ea429b5f7d04a3a750e8-- ========================================================================= Date: Thu, 19 May 2011 23:31:40 +0100 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: Normalising a point Comments: To: Chris Leatherday <[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 should try to be a bit more consistent with my nomenclature for deformation fields. Some functions generate an iy* to indicate an inverse, whereas others just generate another y*. The inverse of the deformation from the Deformations utility will just be named y*. Best regards, -John On 18 May 2011 09:33, Chris Leatherday <[log in to unmask]> wrote: > Dear SPMers > > > I have a series of points that I would like to translate from native spac= e to MNI space. I'm sorry if this is an obvious question, and I've noted th= at it seems to have been discussed a couple of times already. In attempting= to follow Gem5 of John's Gems 2, I am unable to =A0do the part where he as= ks the user to choose the deformations inversion option... > > I am running SPM8, and I can't seem to get anything that looks like iy*.i= mg. Running the "inverse" option in deformations just gives me another y_*.= nii file, and I can't seem to find a method of creating an iy*.img file. > > Could somebody please point me in the right direction? > > Thank you > > Chris > ========================================================================= Date: Thu, 19 May 2011 18:40:55 -0400 Reply-To: Ji Won Bang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Ji Won Bang <[log in to unmask]> Subject: converting fif file to mat file MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e65b646ac0809c04a3a8b214 Message-ID: <[log in to unmask]> --0016e65b646ac0809c04a3a8b214 Content-Type: text/plain; charset=ISO-8859-1 Dear. all. This is Ji won Bang from Dr. Sasaki's group at MGH martino's center. I was trying to convert fif file(MEG) to mat file using SPM8. And this process created 1 dat file and 1 mat file. However, for some reason the mat file is empty, whereas the dat file is 3.8GB. Is there anyone who had same problem with converting? I will appreciate that fi you email me. Thanks a lot. Best regards, Ji Won Bang --0016e65b646ac0809c04a3a8b214 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear. all.<div><br></div><div>This is Ji won Bang from Dr. Sasaki's gro= up at MGH martino's center.=A0</div><div><br></div><div>I was trying to= convert fif file(MEG) to mat file using SPM8.</div><div><br></div><div>And= this process created 1 dat file and 1 mat file.=A0</div> <div><br></div><div>However, for some reason the mat file is empty, whereas= the dat file is 3.8GB.</div><div><br></div><div>Is there anyone who had sa= me problem with converting?</div><div><br></div><div>I will appreciate that= fi you email me.=A0</div> <div><br></div><div>Thanks a lot.</div><div><br></div><div>Best regards,</d= iv><div>Ji Won Bang</div><div><br></div> --0016e65b646ac0809c04a3a8b214-- ========================================================================= Date: Thu, 19 May 2011 15:57:34 -0700 Reply-To: Steven Berman <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Steven Berman <[log in to unmask]> Subject: Are multiple T1s per subject helpful in DARTEL VBM? Mime-Version: 1.0 Content-Type: text/html; charset="us-ascii" Message-ID: <[log in to unmask]> <html> <body> <font color="#008000"><b>Dear SPM Mavens,<br> We are comparing two groups of 13 subjects with DARTEL VBM8. I know this is underpowered, but wondered if there is any way to use the multiple T1 images that were acquired for some of the subjects (in the same session). My initial thought is to process all T1s and use the quality check to see if there is a "better" image when I have two for a given subject. Are there better ways to use multiple T1s?<br> Thanks,<br><br> </b></font><x-sigsep><p></x-sigsep> Steven Berman, Ph.D.<br> <x-tab> </x-tab>UCLA Dept. of Psychiatry & Biobehavioral Sciences, <br> Brain Research Institute, and Center for Addictive Behaviors<br> Tel. (310) 825-0616 Fax: (310) 825-0812 <br><br> <font color="#0000FF"> <a href="http://ibs.med.ucla.edu/" eudora="autourl"> http://ibs.med.ucla.edu<br> </a> <a href="http://uclamindbody.org/" eudora="autourl"> http://uclamindbody.org<br> </a> <a href="http://www.bri.ucla.edu/" eudora="autourl"> http://www.bri.ucla.edu</a></font> <br> <font color="#0000FF"> <a href="http://www.semel.ucla.edu/cab/" eudora="autourl"> http://www.semel.ucla.edu/cab/</a></font> </body> </html> ========================================================================= Date: Fri, 20 May 2011 09:47:01 +1000 Reply-To: Greig de Zubicaray <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Greig de Zubicaray <[log in to unmask]> Subject: Call for papers: Special Issue of Twin Research and Human Genetics on The Genetics of Brain Imaging Phenotypes. Content-Type: multipart/alternative; boundary=Apple-Mail-22--1023376358 Mime-Version: 1.0 (Apple Message framework v1084) Message-ID: <[log in to unmask]> --Apple-Mail-22--1023376358 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 We are putting together a special issue of Twin Research and Human = Genetics on The genetics of brain imaging phenotypes. =20 =20 Scope: We welcome papers on any aspect of brain imaging with a genetic = angle. This could include twin and family studies or genetic association = studies, either GWAS, or candidate genes. Papers can be original = research contributions, reviews, or theoretical treatments. We can also = include =93baseline=94 papers describing a study with full = methodological detail beyond that allowed by most journals. It is also = an opportunity to publish that manuscript lying around your lab that has = been derailed by unreasonable demands from editors of other journals =96 = our editors will consider any reasonable case ! Guidelines for = manuscript preparation are at = http://www.ists.qimr.edu.au/journal.html#Instructions =20 Costs: The upside is that there will be no page limits or other = restrictions on length. The downside is that because TRHG is not backed = by a large publisher, we have to charge for each page published. Normal = charges are $100/page for members of ISTS or $150/page for non-members, = so you only have to publish 4 pages for it to be cheaper to become a = member. Because for this special issue we will be using high-gloss paper = (so you can submit unlimited colour images) there will be a = supplementary charge of $30/page, but no further charges for colour. So = a 10-page paper will cost you $1300 (member), $1800 non-member. This is = still cheap compared with many journals. [If anyone has a bright idea on = how to obtain funds (e.g. a private foundation) to subsidise production = of this issue please let us know]. =20 =20 Timing : The special issue will appear in February 2012. This means that = all final copy must be to the publisher December 1. So editors will need = to receive all manuscripts by September 1 latest to allow time for = reviewing and editing =96 but submissions earlier than this would be = much appreciated. =20 =20 Contacts: Guest editors for this issue will be Dirk Smit (VU, = Amsterdam), Jason Stein (UCLA), Dennis van 't Ent (VU, Amsterdam), and = Greig de Zubicaray (UQ, Brisbane) whose contact details are below; = please email them immediately to indicate your interest. Manuscripts = should be submitted as an email attachment to [log in to unmask] Dirk Smit Department of Psychology, Free University, Amsterdam [log in to unmask] =20 Jason Stein Laboratory of Neuroimaging, UCLA, Los Angeles [log in to unmask] =20 Dennis van 't Ent Department of Biological Psychology, Free University, Amsterdam [log in to unmask] Greig de Zubicaray Department of Psychology, University of Queensland, Brisbane [log in to unmask] -- Assoc Prof Greig de Zubicaray | ARC Future Fellow | Functional = Imaging Laboratory School of Psychology | University of Queensland | Brisbane, QLD 4072 = | AUSTRALIA Phone: +61-7-3365-6802 (direct) | +61-7-3365-6230 (office) | = Fax: +61-7-3365-4466 Email: [log in to unmask] | Web: = http://www.fmrilab.net/ CRICOS Provider Number 00025B --Apple-Mail-22--1023376358 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; = "><div><br></div><div><span class=3D"Apple-style-span" = style=3D"font-family: 'Times New Roman', serif; font-size: 16px; ">We = are putting together a special issue of </span><span = class=3D"Apple-style-span" style=3D"font-family: 'Times New Roman', = serif; font-size: 16px; "><i>Twin Research and Human = Genetics</i></span><span class=3D"Apple-style-span" style=3D"font-family: = 'Times New Roman', serif; font-size: 16px; "> on </span><span = class=3D"Apple-style-span" style=3D"font-family: 'Times New Roman', = serif; font-size: 16px; "><b>The genetics of brain imaging = phenotypes</b></span><span class=3D"Apple-style-span" = style=3D"font-family: 'Times New Roman', serif; font-size: 16px; ">. = </span></div><div><div style=3D"margin-top: 0cm; margin-right: = 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: 12pt; = font-family: 'Times New Roman', serif; "><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><span lang=3D"NL"> </span><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><b><span lang=3D"NL">Scope:</span></b><span lang=3D"NL"> We = welcome papers on any aspect of brain imaging with a genetic angle. This = could include twin and family studies or genetic association studies, = either GWAS, or candidate genes. Papers can be original research = contributions, reviews, or theoretical treatments. We can also include = =93baseline=94 papers describing a study with full methodological detail = beyond that allowed by most journals. It is also an opportunity to = publish that manuscript lying around your lab that has been derailed by = unreasonable demands from editors of other journals =96 our editors will = consider any reasonable case ! Guidelines for manuscript = preparation are at <a = href=3D"http://www.ists.qimr.edu.au/journal.html#Instructions" = target=3D"_blank" style=3D"color: blue; text-decoration: underline; = ">http://www.ists.qimr.edu.au/journal.html#Instructions</a></span><o:p></o= :p></div><div style=3D"margin-top: 0cm; margin-right: 0cm; = margin-bottom: 0.0001pt; margin-left: 0cm; font-size: 12pt; font-family: = 'Times New Roman', serif; "><span = lang=3D"NL"> </span><o:p></o:p></div><div style=3D"margin-top: 0cm; = margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: = 12pt; font-family: 'Times New Roman', serif; "><b><span = lang=3D"NL">Costs:</span></b><span lang=3D"NL"> The upside is that = there will be no page limits or other restrictions on length. The = downside is that because TRHG is not backed by a large publisher, we = have to charge for each page published. Normal charges are $100/page for = members of ISTS or $150/page for non-members, so you only have to = publish 4 pages for it to be cheaper to become a member. Because for = this special issue we will be using high-gloss paper (so you can submit = unlimited colour images) there will be a supplementary charge of = $30/page, but no further charges for colour. So a 10-page paper will = cost you $1300 (member), $1800 non-member. This is still cheap compared = with many journals. [If anyone has a bright idea on how to obtain funds = (e.g. a private foundation) to subsidise production of this issue please = let us know]. <b> </b></span><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><span lang=3D"NL"> </span><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><b><span lang=3D"NL">Timing :</span></b><span = lang=3D"NL"> The special issue will appear in February 2012. This = means that all final copy must be to the publisher December 1. So = editors will need to receive all manuscripts by September 1 latest to = allow time for reviewing and editing =96 but submissions earlier than = this would be much appreciated. </span><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><span lang=3D"NL"> </span><o:p></o:p></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><b><span lang=3D"NL">Contacts:</span></b><span = lang=3D"NL"> Guest editors for this issue will be Dirk Smit (VU, = Amsterdam), Jason Stein (UCLA), </span>Dennis van 't Ent (VU, = Amsterdam), and Greig de Zubicaray (UQ, Brisbane) whose contact = details are below; <u>please email them immediately to indicate = your interest</u>. <b><i> </i></b>Manuscripts should be = submitted as an email attachment to <a = href=3D"mailto:[log in to unmask]" target=3D"_blank" style=3D"color: blue; = text-decoration: underline; = ">[log in to unmask]</a></div></div><div><br></div><div style=3D"margin-top:= 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; = font-size: 12pt; font-family: 'Times New Roman', serif; "><span = lang=3D"NL">Dirk Smit<br>Department of Psychology, Free University, = Amsterdam<br><a href=3D"mailto:[log in to unmask]" target=3D"_blank" = style=3D"color: blue; text-decoration: underline; = ">[log in to unmask]</a></span><o:p></o:p></div><div style=3D"margin-top: = 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; = font-size: 12pt; font-family: 'Times New Roman', serif; "><span = lang=3D"NL"> </span><o:p></o:p></div><div style=3D"margin-top: 0cm; = margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: = 12pt; font-family: 'Times New Roman', serif; "><span lang=3D"NL">Jason = Stein<br>Laboratory of Neuroimaging, UCLA, Los Angeles<br><a = href=3D"mailto:[log in to unmask]" target=3D"_blank" style=3D"color: blue; = text-decoration: underline; = ">[log in to unmask]</a></span><o:p></o:p></div><div style=3D"margin-top: = 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; = font-size: 12pt; font-family: 'Times New Roman', serif; "><span = lang=3D"NL"> </span><o:p></o:p></div><div style=3D"margin-top: 0cm; = margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: = 12pt; font-family: 'Times New Roman', serif; "><span lang=3D"NL">Dennis = van 't Ent</span></div><div style=3D"margin-top: 0cm; margin-right: 0cm; = margin-bottom: 0.0001pt; margin-left: 0cm; "><span lang=3D"NL"><font = class=3D"Apple-style-span" face=3D"'Times New Roman', serif" = size=3D"4"><span class=3D"Apple-style-span" style=3D"font-size: = 16px;">Department of Biological Psychology, Free University, = Amsterdam</span></font></span></div><div style=3D"margin-top: 0cm; = margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: = 12pt; font-family: 'Times New Roman', serif; "><span lang=3D"NL"><a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a></span></div>= <div style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: = 0.0001pt; margin-left: 0cm; font-size: 12pt; font-family: 'Times New = Roman', serif; "><span lang=3D"NL"><br></span></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><span lang=3D"NL">Greig de Zubicaray<br>Department of = Psychology, University of Queensland, Brisbane<br><a = href=3D"mailto:[log in to unmask]" target=3D"_blank" = style=3D"color: blue; text-decoration: underline; = ">[log in to unmask]</a></span></div><div style=3D"margin-top: = 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; = font-size: 12pt; font-family: 'Times New Roman', serif; "><br></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><br></div><div style=3D"margin-top: 0cm; margin-right: 0cm; = margin-bottom: 0.0001pt; margin-left: 0cm; font-size: 12pt; font-family: = 'Times New Roman', serif; "><br></div><div style=3D"margin-top: 0cm; = margin-right: 0cm; margin-bottom: 0.0001pt; margin-left: 0cm; font-size: = 12pt; font-family: 'Times New Roman', serif; "><br></div><div = style=3D"margin-top: 0cm; margin-right: 0cm; margin-bottom: 0.0001pt; = margin-left: 0cm; font-size: 12pt; font-family: 'Times New Roman', = serif; "><br></div><div style=3D"margin-top: 0cm; margin-right: 0cm; = margin-bottom: 0.0001pt; margin-left: 0cm; font-size: 12pt; font-family: = 'Times New Roman', serif; "><br></div><div> <div style=3D"word-wrap: break-word; -webkit-nbsp-mode: space; = -webkit-line-break: after-white-space; "><div style=3D"word-wrap: = break-word; -webkit-nbsp-mode: space; -webkit-line-break: = after-white-space; "><div><span class=3D"Apple-style-span" = style=3D"font-size: 12px; = "><b><div><div><div><div><div><div><div><div><div><div><div><div><div><div= ><div><div><div><div><div><div><div><div><div><div><div><div><div><div><di= v><div><div><div><div><div><div><div><div><div><div><div><div><div><div><d= iv><div><div><div><div><div><div><div><div><div><div><div><div><div><div><= div><div><div><div><div><div><div><div><div><div><div><div><div><div><div>= <div><div><div><div><div><div><div><div><div><div><div><div><div><div><div= ><div><div><div><div><div><div><div><div><div><div><div><div>--</div><div>= Assoc Prof Greig de Zubicaray | ARC Future Fellow = | Functional Imaging Laboratory<br><span = class=3D"Apple-style-span" style=3D"font-weight: normal; ">School of = Psychology | University of Queensland | = Brisbane, QLD 4072 | AUSTRALIA<br>Phone: = +61-7-3365-6802 (direct) | = +61-7-3365-6230 (office) | Fax: = +61-7-3365-4466<br>Email: <a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a= > | Web: <a = href=3D"http://www.fmrilab.net/">http://www.fmrilab.net/</a><br>CRICOS = Provider Number 00025B</span></div><div><span class=3D"Apple-style-span" = style=3D"font-weight: normal; = "><br></span></div></div></div></div></div></div></div></div></div></div><= /div></div></div></div></div></div></div></div></div></div></div></div></d= iv></div></div></div></div></div></div></div></div></div></div></div></div= ></div></div></div></div></div></div></div></div></div></div></div></div><= /div></div></div></div></div></div></div></div></div></div></div></div></d= iv></div></div></div></div></div></div></div></div></div></div></div></div= ></div></div></div></div></div></div></div></div></div></div></div></div><= /div></div></div></div></div></div></div></div></div></div></div></div></d= iv></div></div></div></b></span></div></div></div><br = class=3D"Apple-interchange-newline"><br = class=3D"Apple-interchange-newline"> </div> <br></body></html>= --Apple-Mail-22--1023376358-- ========================================================================= Date: Fri, 20 May 2011 10:29:04 +1000 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: which extra regressors for an n-back task? Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Hi list, I am analyzing a simple n-back task arranged in a block design with two con= ditions of interest (2-back, 0-back), and I want to rigorously examine diff= erences between these conditions (1, -1), while controlling for differences= in accuracy between the conditions. For example, fewer correct responses (as well as more misses and more error= s) occurred in the 2-back condition than the 0-back condition, which may ac= count for much of the difference between the conditions of interest. My question is how do I control for these extraneous differences between co= nditions in the first-level design? To illustrate, I have a column for the = 2-back blocks and a column for the 0-back blocks in the first-level design = matrix. I think I also need a third column for correct responses, which wil= l account for variation due to correct answers. But it also occurs to me th= at this might be better done in two columns, with one column for correct re= sponses during 2-back and a separate column for correct responses during 0-= back. So in general terms my question is, when including regressors-of-no-interes= t which systematically vary between conditions of interest, is it sufficien= t to include a single regressor to control for variation, or is it necessar= y to include separate regressors-of-no-interest for the events in each cond= ition-of-interest? I imagine the issue of how to analyze an n-back task has been done to death= , but as I'm new to this task, I'm unaware of where these discussions have = taken place. Any pointers would be appreciated. Ta, Rich= ========================================================================= Date: Thu, 19 May 2011 23:53:52 -0400 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: Are multiple T1s per subject helpful in DARTEL VBM? Comments: To: Steven Berman <[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]> Dear Steven, > =A0 We are comparing two groups of 13 subjects with DARTEL VBM8. I know t= his > is underpowered, but wondered if there is any way to use the multiple T1 > images that were acquired for some of the subjects (in the same session).= My > initial thought is to process all T1s and use the quality check to see if > there is a "better" image when I have two for a given subject. Are there > better ways to use multiple T1s? In the "new segment" option in SPM8, you can specify multiple modalities of images for segmentation. I assume you could also provide SPM with 2 T1 images. SPM will then use the combined information across images to assign tissue class probabilities, and in general I would think that more data would provide a more accurate result. However, I think you'd want to process all of your subjects the same way. So if you only have multiple T1s for a subset, I would probably stick with the approach you suggest=97see if you can identify a "better" image. (Or, if this is hard to determine, then just use the first.) But if anyone else has alternate approaches, I'd be very interested to hear! Best regards, Jonathan ========================================================================= Date: Fri, 20 May 2011 00:20:38 -0400 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: Are multiple T1s per subject helpful in DARTEL VBM? Comments: To: Jonathan Peelle <[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]> In my honest opinion, all subjects need to be processed the same way. If you have 2 scans, you could use the suggested approaches, or you could coregister them, then average them in. After averaging , you can do the rest of the processing. Averaging should give similar as increasing the NEX. On Thursday, May 19, 2011, Jonathan Peelle <[log in to unmask]> wrote: > Dear Steven, > >> =A0 We are comparing two groups of 13 subjects with DARTEL VBM8. I know = this >> is underpowered, but wondered if there is any way to use the multiple T1 >> images that were acquired for some of the subjects (in the same session)= . My >> initial thought is to process all T1s and use the quality check to see i= f >> there is a "better" image when I have two for a given subject. Are there >> better ways to use multiple T1s? > > In the "new segment" option in SPM8, you can specify multiple > modalities of images for segmentation. =A0I assume you could also > provide SPM with 2 T1 images. =A0SPM will then use the combined > information across images to assign tissue class probabilities, and in > general I would think that more data would provide a more accurate > result. =A0However, I think you'd want to process all of your subjects > the same way. =A0So if you only have multiple T1s for a subset, I would > probably stick with the approach you suggest=97see if you can identify a > "better" image. =A0(Or, if this is hard to determine, then just use the > first.) =A0But if anyone else has alternate approaches, I'd be very > interested to hear! > > Best regards, > Jonathan > --=20 Best Regards, Donald McLaren =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School Office: (773) 406-2464 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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, 20 May 2011 09:10:14 +0100 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: Time derivative in event related design Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hello fellow SPM:ers, I am a bit confused with regards to using the time derivative in SPM.=20 The issue is that I have an event related paradigm where I do post hoc so= rting of stimuli to 2 conditions. At the last SPM course I learned that s= lice time correction should be avoided, so instead of using time slice co= rrection I wanted to include a second basis function to account for timin= g differences in data acquisition between slices. To check for regions significantly more activated in cond 1 vs cond 2 I d= id an F-contrast [1 0 -1 0; 0 1 0 -1] to look for voxels associated with = either the HRF or td. I masked this contrast inclusive with a t-contrast = [1 0 -1 0] to only look at voxels with activation corresponding to the HR= F. The problem here is that I'm telling SPM to look for voxels with diffe= rent timing depending on condition, while I'm interested in allowing for = timing differences between regions regardless of condition. Another variant I've thought of is to merely do a t-contrast [1 0 -1 0], = controlling for any variance associated with the td, but I read that this= may give biased results. The next issue is how to perform the 2nd level analysis. With the latter = method it's straightforward, but from what I understand I can't do a rand= om effects on F-contrasts? Thank you for any suggestions! Bert regards, Jonas Persson ========================================================================= Date: Fri, 20 May 2011 12:59:17 +0400 Reply-To: Alexander Ivanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Ivanov <[log in to unmask]> Subject: Question regarding kernel utilities MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636988b5c2d2e9104a3b156c6 Message-ID: <[log in to unmask]> --001636988b5c2d2e9104a3b156c6 Content-Type: text/plain; charset=ISO-8859-1 *Dear SPMers!* I have a question regarding kernel utilities... I have searched the archives but haven't found any info on this topic... I would just like to be sure that I understood its application correctly... After preprocessing steps (for example, using vbm8 toolbox) I have got m0wrp* images... Afterwards I want to generate dot-product matrices for each set of images (gm, wm, csf). After all I have (N)x(M) dp-matrix (calculated using "Kernel from images" option), where N-th row reflects N-th subject (is that correct?)... Next I'm training a classifier (in my case it's SVM) with training subset of dp-matrix and testing it using corresponding subset. My last question may appear to be stupid, but.... As far as I understand, if I want to make a class prediction of a new subject I can't use my classifier anymore and I have to recalculate dp-matrix again in order to get (N+1)x(M+1) quadratic matrix... - right? Please excuse my for my weird questions... === Best Regards, Alex --001636988b5c2d2e9104a3b156c6 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <b>Dear SPMers!</b><div><br></div><div>I have a question regarding kernel u= tilities... I have searched the archives but haven't found any info on = this topic... I would just like to be sure that I understood its applicatio= n correctly... After preprocessing steps (for example, using vbm8 toolbox) = I have got m0wrp* images... Afterwards I want to generate dot-product matri= ces for each set of images (gm, wm, csf). After all I have (N)x(M) dp-matri= x (calculated using "Kernel from images" option), where N-th row = reflects N-th subject (is that correct?)... Next I'm training a classif= ier (in my case it's SVM) with training subset of dp-matrix and testing= it using corresponding subset.</div> <div>My last question may appear to be stupid, but.... As far as I understa= nd, if I want to make a class prediction of a new subject I can't use m= y classifier anymore and I have to recalculate dp-matrix again in order to = get (N+1)x(M+1) quadratic matrix... - right?</div> <div><br></div><div>Please excuse my for my weird questions...</div><div><b= r></div><div>=3D=3D=3D</div><div><br></div><div>Best Regards,</div><div>Ale= x</div><div><br></div> --001636988b5c2d2e9104a3b156c6-- ========================================================================= Date: Fri, 20 May 2011 10:51:25 +0100 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jing Kan <[log in to unmask]> Subject: the order of the preprocessing MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Dear SPM experts, May I ask a basic question about the data-preprocessing? In terms of the SPM tutorial I read, we can do the preprocessing either with the order "epoch" and then "filter" or "filter" and then "epoch". May I ask is it any effect with these two different ways? Many thanks, Jing ========================================================================= Date: Fri, 20 May 2011 10:55:11 +0100 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: Re: which extra regressors for an n-back task? Comments: To: [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 Rich, only some ideas to that topic.. this regressor of no interest should be highly correlated to the paradigm= , mostly the errors will occur in the 2back-blocks. So for that it seems = to be problematic to model this extra regressor of no interest, like I wo= uld not use extra regressors of no interest for movement parameter, if th= ere is task-related movement, because in that case you can not differ bet= ween nuisance variance and good variance you want to explain. But if you= do so, a single regressor will be enough I would suggest. The only reaso= n to model two accuracy regressors of no interest should be, that you wil= l include one of them into your contrast in a different way than the othe= r. Depending on your intention other suggestion should be a parametric modul= ation of the amplitude of your n back regressors (in that case with trial= onsets) in order to to model out this accuracy related variance.=20 May be you could use only the session data in the analysis, where the acc= uracy was over a specific theshold.. Kind regards,=20 Christoph ========================================================================= Date: Fri, 20 May 2011 11:19:13 +0100 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: the order of the preprocessing 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=ISO-8859-1 Message-ID: <8999032608280567062@unknownmsgid> see http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;5681190f.1105 On 20 May 2011, at 10:54, Jing Kan <[log in to unmask]> wrote: > Dear SPM experts, > > May I ask a basic question about the data-preprocessing? > > In terms of the SPM tutorial I read, we can do the preprocessing either > with the order "epoch" and then "filter" or "filter" and then "epoch". > > May I ask is it any effect with these two different ways? > > Many thanks, > > Jing ========================================================================= Date: Fri, 20 May 2011 11:32:58 +0100 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: the order of the preprocessing 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=ISO-8859-1 Message-ID: <2015229298723303602@unknownmsgid> That was the only slide about order. Other slides are the same as in previous years (see SPM website). We'll make this year's slides available soon. Vladimir On 20 May 2011, at 11:22, "[log in to unmask]" <[log in to unmask]> wrote: > Dear Vladimir, > > Thanks for your speedy help! It is quite clear. I really appreciate it! > > May I ask is it possible that I can have the slides you mentioned about this? > > Many thanks, > > Jing > > >> see >> >> http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;5681190f.1105 >> >> >> On 20 May 2011, at 10:54, Jing Kan <[log in to unmask]> wrote: >> >>> Dear SPM experts, >>> >>> May I ask a basic question about the data-preprocessing? >>> >>> In terms of the SPM tutorial I read, we can do the preprocessing either >>> with the order "epoch" and then "filter" or "filter" and then "epoch". >>> >>> May I ask is it any effect with these two different ways? >>> >>> Many thanks, >>> >>> Jing >> > > ========================================================================= Date: Fri, 20 May 2011 11:20:24 +0100 Reply-To: Michiko Yoshie <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Michiko Yoshie <[log in to unmask]> Subject: DICOM Import error MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear all, I am using the latest version of SPM8 on MATLAB 7.9.0 (R2009b). When I convert DICOM to NIfTI (.nii) using DICOM Import on Batch Editor, the following error message appears after the conversion of all the DICOM files has been completed: --------------------------------------------------------------------- Error using ==> <a href=matlab: opentoline('cfg_util.m',835,0)">cfg_util at 835</a> Job execution failed. The full log of this run can be found in MATLAB command window, starting with the lines (look for the line showing the exact #job as displayed in this error message) --------------------------------------------------------------------- Then I see the following log in MATLAB command window: ------------------------------------------------------------------------ Running job #1 ------------------------------------------------------------------------ Running 'DICOM Import' Changing directory to: C:\[specified output directory]\ Failed 'DICOM Import' Error using ==> horzcat CAT arguments dimensions are not consistent. In file "C:\Program Files\spm\spm8\spm_dicom_convert.m" (v4213), function "spm_dicom_convert" at line 61. In file "C:\Program Files\spm\spm8\config\spm_run_dicom.m" (v2094), function "spm_run_dicom" at line 32. The following modules did not run: Failed: DICOM Import -------------------------------------------------------------------------------------- Despite this error message, it seems that all the NIfTI files have been created in the specified output directory. I wonder whether this error would affect my data or not. Could anyone suggest how I could avoid this error? Many thanks for your help, Michiko Michiko Yoshie, PhD Clinical Imaging Sciences Centre University of Sussex ========================================================================= Date: Fri, 20 May 2011 11:46:43 +0100 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jing Kan <[log in to unmask]> Subject: "normal average" for MEG data MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Dear SPM experts, May I also ask another initial question: When we do the averaging of the data in the preprocessing, there are "robust average" and "normal average" two methods are provided, and the "robust average" method are recommended. In the MEG data analysis after the artefact rejection, can we directly use "normal average"? Many thanls, Jing ========================================================================= Date: Fri, 20 May 2011 12:20:26 +0100 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: Question regarding kernel utilities Comments: To: Alexander Ivanov <[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 a good point. I realised a few weeks ago that I probably need to extend the framework so that new subjects can be added more easily. Essentially, when you compute the initial dot-products, you obtain X1*X1'. You actually want [X1; X2]*[X1; X2]', where X2 contains data from your new subject(s). X = [X1; X2]; % XX = X*X'; XX = [X1*X1' X1*X2'; (X1*X2')' X2*X2']; Best regards, -John On 20 May 2011 09:59, Alexander Ivanov <[log in to unmask]> wrote: > Dear SPMers! > I have a question regarding kernel utilities... I have searched the archives > but haven't found any info on this topic... I would just like to be sure > that I understood its application correctly... After preprocessing steps > (for example, using vbm8 toolbox) I have got m0wrp* images... Afterwards I > want to generate dot-product matrices for each set of images (gm, wm, csf). > After all I have (N)x(M) dp-matrix (calculated using "Kernel from images" > option), where N-th row reflects N-th subject (is that correct?)... Next I'm > training a classifier (in my case it's SVM) with training subset of > dp-matrix and testing it using corresponding subset. > My last question may appear to be stupid, but.... As far as I understand, if > I want to make a class prediction of a new subject I can't use my classifier > anymore and I have to recalculate dp-matrix again in order to get > (N+1)x(M+1) quadratic matrix... - right? > Please excuse my for my weird questions... > === > Best Regards, > Alex > ========================================================================= Date: Fri, 20 May 2011 13:13:05 +0100 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: the order of the preprocessing Comments: To: Urs Bachofner <[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: <4487417340626282225@unknownmsgid> Dear Urs, It's actually the other way around. Electrodes close to the reference have lower amplitude responses and the reference itself will be zero. This would be taken into account if we had forward model computation for different references. Since at the moment we don't have this feature you should switch to average reference and then it wouldn't matter what the original reference was. Best, Vladimir On 20 May 2011, at 11:42, Urs Bachofner <[log in to unmask]> wrote: > Hi Vladimir! > > Thanks a lot for your last answer, apparently I'm not the only one who ha= d this question. > > But I have another one that I already mentioned in my last E-mail: > > Considering the location of the reference electrode in different EEG nets= , it should be clear that electrodes closer to that reference will have a h= igher amplitude recorded than electrodes which are more distant. I'm not ex= actly sure how big and relevant this difference is but shouldn't we have a = preprocessing step that alleviates this bias by checking the coordinates of= the electrodes involved and their distance to the reference electrode? > I think if we don't eliminate this bias we will always have an overestima= ted activation around the reference electrode in 3D source localisation. > > Thanks a lot for your opinion in this matter. > > Urs > > > Am 20.05.2011 12:19, schrieb Vladimir Litvak: >> see >> >> http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;5681190f.1105 >> >> >> On 20 May 2011, at 10:54, Jing Kan<[log in to unmask]> wrote: >> >>> Dear SPM experts, >>> >>> May I ask a basic question about the data-preprocessing? >>> >>> In terms of the SPM tutorial I read, we can do the preprocessing eithe= r >>> with the order "epoch" and then "filter" or "filter" and then "epoch". >>> >>> May I ask is it any effect with these two different ways? >>> >>> Many thanks, >>> >>> Jing > ========================================================================= Date: Fri, 20 May 2011 13:17:40 +0100 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: "normal average" for MEG data 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=ISO-8859-1 Message-ID: <-3967746890312576192@unknownmsgid> Robust averaging can be used either as an alternative or in combination with artefact rejection. If your data is clean you don't have to use it. Vladimir On 20 May 2011, at 11:48, Jing Kan <[log in to unmask]> wrote: > Dear SPM experts, > > May I also ask another initial question: > > When we do the averaging of the data in the preprocessing, there are > "robust average" and "normal average" two methods are provided, and the > "robust average" method are recommended. In the MEG data analysis after > the artefact rejection, can we directly use "normal average"? > > Many thanls, > > Jing ========================================================================= Date: Fri, 20 May 2011 15:14:51 +0100 Reply-To: Carolina Valencia <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carolina Valencia <[log in to unmask]> Subject: Split Dicom folder Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi SPM users I have a set of DICOM files from multiple series in one folder, and I wou= ld like to organize them into separate folders based on the different ser= ies (mprage, bold and so on), I can do this in Dicomviewer with the tool= export Dicom series, but I'm not working on windows, I would like to kno= w is there is a similar tool for ubuntu. Thanks a lot, Best regards, Carolina ========================================================================= Date: Fri, 20 May 2011 22:20:18 +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: Split Dicom folder Comments: To: Carolina Valencia <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba6e8c6843dd1b04a3b5d206 Message-ID: <[log in to unmask]> --90e6ba6e8c6843dd1b04a3b5d206 Content-Type: text/plain; charset=ISO-8859-1 Dear Carolina, You can try REST DICOM sorter. (From www.restfmri.net, REST->Utilities->REST DICOM sorter). Best wishes! Chaogan On Fri, May 20, 2011 at 10:14 PM, Carolina Valencia <[log in to unmask] > wrote: > Hi SPM users > > I have a set of DICOM files from multiple series in one folder, and I would > like to organize them into separate folders based on the different series > (mprage, bold and so on), I can do this in Dicomviewer with the tool export > Dicom series, but I'm not working on windows, I would like to know is there > is a similar tool for ubuntu. > > Thanks a lot, > > Best regards, > > Carolina > -- YAN Chao-Gan, Ph.D. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing, China, 100875 [log in to unmask] http://www.restfmri.net/forum/yanchaogan --90e6ba6e8c6843dd1b04a3b5d206 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear Carolina,<br><br>You can try REST DICOM sorter. (From <a href=3D"http:= //www.restfmri.net">www.restfmri.net</a>, REST->Utilities->REST DICOM= sorter).<br><br>Best wishes!<br><br>Chaogan<br><br><br><div class=3D"gmail= _quote"> On Fri, May 20, 2011 at 10:14 PM, Carolina Valencia <span dir=3D"ltr"><<= a href=3D"mailto:[log in to unmask]">[log in to unmask]</a>></= span> wrote:<br><blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8e= x;border-left:1px #ccc solid;padding-left:1ex;"> Hi SPM users<br> <br> I have a set of DICOM files from multiple series in one folder, and I would= like to organize them into separate folders based on the different series = (mprage, bold and so on), =A0I can do this in Dicomviewer with the tool exp= ort Dicom series, but I'm not working on windows, I would like to know = is there is a similar tool for ubuntu.<br> <br> Thanks a lot,<br> <br> Best regards,<br> <font color=3D"#888888"><br> Carolina<br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>YAN Chao-Gan, Ph= .D.<br>State Key Laboratory of Cognitive Neuroscience and Learning<br>Beiji= ng Normal University<br>Beijing, China, 100875 <br><a href=3D"mailto:ycg.ya= [log in to unmask]" target=3D"_blank">[log in to unmask]</a><br> <a href=3D"http://www.restfmri.net/forum/yanchaogan" target=3D"_blank">http= ://www.restfmri.net/forum/yanchaogan</a><br> --90e6ba6e8c6843dd1b04a3b5d206-- ========================================================================= Date: Fri, 20 May 2011 08:26:48 -0700 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: Re: Split Dicom folder Comments: To: Carolina Valencia <[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]> You can use dicominfo (MATLAB command) figure out the protocol name and sor= t them. It's simply a for-next loop with a call to dicominfo for each dicom= file then move them into different folder. ---------------------------------------------------------------------------= --------------------------------------------------- Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,C= A 90095 Tel: (310)206-1423|Email: [log in to unmask] -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On B= ehalf Of Carolina Valencia Sent: Friday, May 20, 2011 7:15 AM To: [log in to unmask] Subject: [SPM] Split Dicom folder Hi SPM users I have a set of DICOM files from multiple series in one folder, and I would= like to organize them into separate folders based on the different series = (mprage, bold and so on), I can do this in Dicomviewer with the tool expor= t Dicom series, but I'm not working on windows, I would like to know is the= re is a similar tool for ubuntu. Thanks a lot, Best regards, Carolina IMPORTANT WARNING: This email (and any attachments) is only intended for t= he use of the person or entity to which it is addressed, and may contain in= formation that is privileged and confidential. You, the recipient, are obl= igated to maintain it in a safe, secure and confidential manner. Unauthori= zed 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 co= mputer. ========================================================================= Date: Fri, 20 May 2011 19:29:51 +0400 Reply-To: Alexander Ivanov <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alexander Ivanov <[log in to unmask]> Subject: Re: Question regarding kernel utilities Comments: To: John Ashburner <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636988b5cf418a004a3b6ca21 Message-ID: <[log in to unmask]> --001636988b5cf418a004a3b6ca21 Content-Type: text/plain; charset=ISO-8859-1 Thank you very much for such a quick response! Therefore, "Phi" is a quadratic matrix where each column corresponds to each subject, isn't it? (I previously thought otherwise : Phi(n , : ) is n-th subject...) ==== Best Regards, Alex --001636988b5cf418a004a3b6ca21 Content-Type: text/html; charset=ISO-8859-1 Thank you very much for such a quick response!<div><br></div><div>Therefore, "Phi" is a quadratic matrix where each column corresponds to each subject, isn't it? (I previously thought otherwise : Phi(n , : ) is n-th subject...)</div> <div>====</div><div>Best Regards,</div><div><br></div><div>Alex</div> --001636988b5cf418a004a3b6ca21-- ========================================================================= Date: Fri, 20 May 2011 12:19:23 -0400 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: "cohort" and group effects MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Hello all, I have data from 6 different "cohorts" that I would like to analysis in one big group analysis. I have two age groups (yng and old). The yng data are from 4 separate studies and the old are from 2 separate studies. The data were collected in "chunks" over a couple of years. So 20 young here, then another 20 young the following year etc. Although, the training and task were the same I want to account for the fact that the data were not collected continuously. My interest is in age differences and want to make sure that I account for any "cohort" effects so I can correctly interpret any age group effects. Based on box-plots of the behavioral data there is evidence of cohort effects. Thank you for any guidance, Jason -- Jason Steffener, Ph.D. Department of Neurology Columbia University http://www.cogneurosci.org/steffener.html ========================================================================= Date: Fri, 20 May 2011 17:36:28 +0100 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: Question regarding kernel utilities Comments: To: Alexander Ivanov <[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]> Element i,j of the matrix corresponds to a dot-product between data from subjects i and j. Best regards, -John On 20 May 2011 16:29, Alexander Ivanov <[log in to unmask]> wrote: > Thank you very much for such a quick response! > Therefore, "Phi" is a quadratic matrix where each column corresponds to each > subject, isn't it? (I previously thought otherwise : Phi(n , : ) is n-th > subject...) > ==== > Best Regards, > Alex ========================================================================= Date: Fri, 20 May 2011 12:46:16 -0400 Reply-To: Cameron Craddock <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cameron Craddock <[log in to unmask]> Subject: NEURO-BUREAU: ADHD-200 Processed Data New Additions Comments: To: [log in to unmask], FSL - FMRIB's Software Library <[log in to unmask]>, [log in to unmask], [log in to unmask] Mime-version: 1.0 Content-type: multipart/alternative; boundary="B_3388740383_19782828" 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_3388740383_19782828 Content-type: text/plain; charset="US-ASCII" Content-transfer-encoding: 7bit We would like to announce some valuable new additions to the ADHD-200 preprocessed dataset: In order to further reduce barriers to competing in the ADHD-200 global competition, the Neuro Bureau has reduced the functional neuroimaging data to region of interest (ROI) time courses. ROIs were specified using the automated anatomical labeling atlas, the Eickhoff-Zilles atlas, the Harvard-Oxford atlas, the Talairach-Tournoux atlas as well as 200 and 400 region functional parcellations (Craddock et al. HBM, in press). The resulting time courses are available as tab-delimited text files which can easily be imported into any statistical or mathematical software. (http://www.nitrc.org/plugins/mwiki/index.php/neurobureau:AthenaPipeline#Ext racted_Time_Courses) For those of you that prefer voxel-based morphometry to functional connectivity, we would like to announce the release of the Burner pipeline data. The results of this pipeline are modulated and normalized grey matter maps derived using SPM. (http://www.nitrc.org/plugins/mwiki/index.php/neurobureau:BurnerPipeline) Although we are targeting the release dates of the preprocessed data for the ADHD-200 global competition, this data will remain available long after the competition has run its course. We hope that you find the ADHD-200 preprocessed data useful. Cheers, Cameron Craddock and The Neuro Bureau --B_3388740383_19782828 Content-type: text/html; charset="US-ASCII" Content-transfer-encoding: quoted-printable <html><head> <meta name=3D"Title" content=3D""> <meta name=3D"Keywords" content=3D""> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Dutf-8"> <meta name=3D"ProgId" content=3D"Word.Document"> <meta name=3D"Generator" content=3D"Microsoft Word 14"> <meta name=3D"Originator" content=3D"Microsoft Word 14"> <link rel=3D"File-List" href=3D"file://localhost/Users/cameron/Library/Caches/T= emporaryItems/msoclip/0clip_filelist.xml"> <!--[if gte mso 9]><xml> <o:DocumentProperties> <o:Revision>0</o:Revision> <o:TotalTime>0</o:TotalTime> <o:Pages>1</o:Pages> <o:Words>205</o:Words> <o:Characters>1173</o:Characters> <o:Company>Virginia Tech Carilion Research Institute</o:Company> <o:Lines>9</o:Lines> <o:Paragraphs>2</o:Paragraphs> <o:CharactersWithSpaces>1376</o:CharactersWithSpaces> <o:Version>14.0</o:Version> </o:DocumentProperties> <o:OfficeDocumentSettings> <o:AllowPNG/> </o:OfficeDocumentSettings> </xml><![endif]--> <link rel=3D"themeData" href=3D"file://localhost/Users/cameron/Library/Caches/T= emporaryItems/msoclip/0clip_themedata.xml"> <!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>EN-US</w:LidThemeOther> <w:LidThemeAsian>JA</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:EnableOpenTypeKerning/> <w:DontFlipMirrorIndents/> <w:OverrideTableStyleHps/> <w:UseFELayout/> </w:Compatibility> <m:mathPr> <m:mathFont m:val=3D"Cambria Math"/> <m:brkBin m:val=3D"before"/> <m:brkBinSub m:val=3D"--"/> <m:smallFrac m:val=3D"off"/> <m:dispDef/> <m:lMargin m:val=3D"0"/> <m:rMargin m:val=3D"0"/> <m:defJc m:val=3D"centerGroup"/> <m:wrapIndent m:val=3D"1440"/> <m:intLim m:val=3D"subSup"/> <m:naryLim m:val=3D"undOvr"/> </m:mathPr></w:WordDocument> </xml><![endif]--><!--[if gte mso 9]><xml> <w:LatentStyles DefLockedState=3D"false" DefUnhideWhenUsed=3D"true" DefSemiHidden=3D"true" DefQFormat=3D"false" DefPriority=3D"99" LatentStyleCount=3D"276"> <w:LsdException Locked=3D"false" Priority=3D"0" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Normal"/> <w:LsdException Locked=3D"false" Priority=3D"9" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"heading 1"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 2"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 3"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 4"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 5"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 6"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 7"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 8"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"heading = 9"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 1"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 2"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 3"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 4"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 5"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 6"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 7"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 8"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 9"/> <w:LsdException Locked=3D"false" Priority=3D"35" QFormat=3D"true" Name=3D"caption= "/> <w:LsdException Locked=3D"false" Priority=3D"10" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Title"/> <w:LsdException Locked=3D"false" Priority=3D"1" Name=3D"Default Paragraph Font"= /> <w:LsdException Locked=3D"false" Priority=3D"11" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtitle"/> <w:LsdException Locked=3D"false" Priority=3D"22" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Strong"/> <w:LsdException Locked=3D"false" Priority=3D"20" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"59" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Table Grid"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Placeholder T= ext"/> <w:LsdException Locked=3D"false" Priority=3D"1" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"No Spacing"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 1"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Revision"/> <w:LsdException Locked=3D"false" Priority=3D"34" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"List Paragraph"/> <w:LsdException Locked=3D"false" Priority=3D"29" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Quote"/> <w:LsdException Locked=3D"false" Priority=3D"30" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Quote"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"19" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"21" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"31" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Reference"/> <w:LsdException Locked=3D"false" Priority=3D"32" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Reference"/> <w:LsdException Locked=3D"false" Priority=3D"33" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Book Title"/> <w:LsdException Locked=3D"false" Priority=3D"37" Name=3D"Bibliography"/> <w:LsdException Locked=3D"false" Priority=3D"39" QFormat=3D"true" Name=3D"TOC Hea= ding"/> </w:LatentStyles> </xml><![endif]--> <style> <!-- /* Font Definitions */ @font-face {font-family:"?? ??"; panose-1:0 0 0 0 0 0 0 0 0 0; mso-font-charset:128; mso-generic-font-family:roman; mso-font-format:other; mso-font-pitch:fixed; mso-font-signature:1 134676480 16 0 131072 0;} @font-face {font-family:"?? ??"; panose-1:0 0 0 0 0 0 0 0 0 0; mso-font-charset:128; mso-generic-font-family:roman; mso-font-format:other; mso-font-pitch:fixed; mso-font-signature:1 134676480 16 0 131072 0;} @font-face {font-family:Cambria; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"?? ??"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"?? ??"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} @page WordSection1 {size:8.5in 11.0in; margin:1.0in 1.25in 1.0in 1.25in; mso-header-margin:.5in; mso-footer-margin:.5in; mso-paper-source:0;} div.WordSection1 {page:WordSection1;} --> </style> <!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} </style> <![endif]--> </head><body style=3D"word-wrap: break-word; -webkit-nbsp-mode: space; -webki= t-line-break: after-white-space; color: rgb(0, 0, 0); font-size: 14px; font-= family: Calibri, sans-serif; "><div><!--StartFragment--> <p class=3D"MsoNormal">We would like to announce some valuable new additions = to the ADHD-200 preprocessed dataset:<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">In order to further reduce barriers to competing in th= e ADHD-200 global competition, the Neuro Bureau has reduced the functional neuroimagin= g data to region of interest (ROI) time courses.<span style=3D"mso-spacerun:yes= "> </span>ROIs were specified using the automated anatomical labeling atlas, t= he Eickhoff-Zilles atlas, the Harvard-Oxford atlas, the Talairach-Tournoux atl= as as well as 200 and 400 region functional parcellations (Craddock et al. HBM= , <i style=3D"mso-bidi-font-style:normal">in press</i>).<span style=3D"mso-space= run:yes"> </span>The resulting time courses are available as tab-delimited text files which can easily be imported into any statistical = or mathematical software. (http://www.nitrc.org/plugins/mwiki/index.php/neurob= ureau:AthenaPipeline#Extracted_Time_Courses)<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">For those of you that prefer voxel-based morphometry t= o functional connectivity, we would like to announce the release of the Burne= r pipeline data.<span style=3D"mso-spacerun:yes"> </span>The results of t= his pipeline are modulated and normalized grey matter maps derived using SPM.<o= :p></o:p></p> <p class=3D"MsoNormal">(http://www.nitrc.org/plugins/mwiki/index.php/neurobur= eau:BurnerPipeline)<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">Although we are targeting the release dates of the preprocessed data for the ADHD-200 global competition, this data will remai= n available long after the competition has run its course.<span style=3D"mso-sp= acerun:yes"> </span>We hope that you find the ADHD-200 preprocessed data useful. <o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">Cheers,<o:p></o:p></p> <p class=3D"MsoNormal">Cameron Craddock and The Neuro Bureau<o:p></o:p></p> <!--EndFragment--> </div></body></html> --B_3388740383_19782828-- ========================================================================= Date: Fri, 20 May 2011 12:07:15 -0700 Reply-To: Mazly Mohamad <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mazly Mohamad <[log in to unmask]> Subject: Question regarding SPM8 DCM Batch MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="0-1506575205-1305918436=:28942" Message-ID: <[log in to unmask]> --0-1506575205-1305918436=:28942 Content-Type: multipart/alternative; boundary="0-1675381938-1305918436=:28942" --0-1675381938-1305918436=:28942 Content-Type: text/plain; charset=us-ascii Dear SPMers By using limited knowledge in Matlab command, I've tried to change dcm_ spm8_batch command. Unfortunately... ??? Error using ==> inlineeval at 15 Error in inline expression ==> spm_int(P,M,U) ??? Error using ==> mtimes Inner matrix dimensions must agree. Error in ==> inline.feval at 36 INLINE_OUT_ = inlineeval(INLINE_INPUTS_, INLINE_OBJ_.inputExpr, INLINE_OBJ_.expr); Error in ==> spm_diff at 80 f0 = feval(f,x{:}); Error in ==> spm_nlsi_GN at 253 [dfdp f] = spm_diff(IS,Ep,M,U,1,{V}); Error in ==> spm_dcm_estimate at 172 [Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y); Error in ==> dcmtest at 57 DCM_K1M1 = spm_dcm_estimate(fullfile(data_path,'DCM_K1_M1')); I've attach my command . Can Anybody direct me the proper way? Thank you very much --0-1675381938-1305918436=:28942 Content-Type: multipart/related; boundary="0-867640889-1305918436=:28942" --0-867640889-1305918436=:28942 Content-Type: text/html; charset=us-ascii <html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:bookman old style,new york,times,serif;font-size:8pt;color:#003366;"><table width="100%" cellspacing="0" cellpadding="0" align="center"><tbody><tr><td width="100%" height="100%" bgcolor="#eff6fc" valign="top"><table width="100%" height="100%" style="table-layout:fixed;" cellspacing="0" cellpadding="0" align="center"><tbody><tr><td width="10"> </td><td valign="top" style="overflow: hidden; text-overflow: ellipsis; white-space: nowrap;"><table width="100%" style="white-space:normal;" cellspacing="0" cellpadding="0"><tbody><tr><td height="10"> </td></tr><tr><td><font face="bookman old style, new york, times, serif" size="1" color="#003366" style="font-family:bookman old style, new york, times, serif;font-size:8;color:#003366;"><div style="text-align:undefined;"><div>Dear SPMers<br><br><br>By using limited knowledge in Matlab command, I've tried to change dcm_ spm8_batch command. Unfortunately... <br><br>??? Error using ==> inlineeval at 15<br>Error in inline expression ==> spm_int(P,M,U)<br>??? Error using ==> mtimes<br>Inner matrix dimensions must agree.<br><br>Error in ==> inline.feval at 36<br> INLINE_OUT_ = inlineeval(INLINE_INPUTS_,<br> INLINE_OBJ_.inputExpr, INLINE_OBJ_.expr);<br><br>Error in ==> spm_diff at 80<br> f0 = feval(f,x{:});<br><br>Error in ==> spm_nlsi_GN at 253<br> [dfdp f] = spm_diff(IS,Ep,M,U,1,{V});<br><br>Error in ==> spm_dcm_estimate at 172<br>[Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y);<br><br>Error in ==> dcmtest at 57<br>DCM_K1M1 =<br> spm_dcm_estimate(fullfile(data_path,'DCM_K1_M1'));<br><br><br><br><br>I've attach my command . Can Anybody direct me the proper way? Thank you very much<br></div> </div></font></td></tr><tr><td height="10"> </td></tr></tbody></table></td><td width="10"> </td></tr></tbody></table></td></tr><tr><td height="120" bgcolor="#eff6fc" background="cid:1305917907186@dclient.mail.yahoo.com" align="start"> </td></tr></tbody></table></div></body></html> --0-867640889-1305918436=:28942 Content-Type: image/jpeg; name="--static--hills_bottom.jpg" Content-Transfer-Encoding: base64 Content-Id: <[log in to unmask]> Content-Disposition: inline /9j/4AAQSkZJRgABAgAAZABkAAD/7AARRHVja3kAAQAEAAAAUAAA/+4ADkFk b2JlAGTAAAAAAf/bAIQAAgICAgICAgICAgMCAgIDBAMCAgMEBQQEBAQEBQYF BQUFBQUGBgcHCAcHBgkJCgoJCQwMDAwMDAwMDAwMDAwMDAEDAwMFBAUJBgYJ DQsJCw0PDg4ODg8PDAwMDAwPDwwMDAwMDA8MDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwM/8AAEQgAeAMFAwERAAIRAQMRAf/EAKcAAAIDAQEBAQEAAAAA AAAAAAABAgMFBAYHCAkBAQEBAQEBAQAAAAAAAAAAAAABAgMEBQYQAAIBAwMC BAQEAwYEBQUAAAECAwARBCESBTFBUWEiE3GBFAaRoTJCsVIj8MFighUH0XKy M+HxUyQWosLSNEURAAICAQQBAgIJAwQDAQAAAAABEQIDITFBEgRRE2EicYGR obHBMhQF8NFCUmIjJOHxchX/2gAMAwEAAhEDEQA/AP7pV6zyBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ BQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQBQ FvtmhqCD7I1LyMsaD9TsQAPmaztuZVZ2K2IMTSRFXspZTe4NuuoqppiI0HCf dDEHcFaysPCwI/jVLBaE3DcrAg9CKCCJUigFY0IFjQBQCoAoAoAoA+NAFAO1 6Ae00KkG00DQrWoQVAPrQBY0AwtzY0BL2xQAUAFx1oCFjQCoAoB2oWAAvQgW NAKgCgCgHY0AWNAKgCgCgJbfOgDaaFgNpoA2mgAKT16UEAUAPS9CC2+VAPb8 qAYQmo2VIZj8qogWzwqJhaj9sVSENvlQEilrWoCNjQDCk+VAG00AWNAI6UAq AKACQBckAdyaAYF6ACLUAqAKAYF6ALGgJbAetCpC20IR+FAFASCk0BL2xQAY x8aFI7T4UArGhAoAGptQoypHnQDCEi9ALaaAVjQg9poVIYS/TSgSGY2HWgaI WtQgqAKAfWgCxoB2NAAW5/voUn7ZoWAMflQkC9sW/uoBBL9qJiCXtmhYOsR3 rnJo8h954cr8fBkoSY8SW88fQFZAFDH/AJT/ABrxedV2x6H1P4fLWmf5uVBk 4Oc/D4qSFRLDLE+7GjOrNtJV1BsAbizePXtXzPD8x0zPG9U/uZ6v5Hx/ddrU WxNs/Jy+EAxwcWExIcpr2kZ3Augt+kDoT1PlXXyf5NWy+1j35f5HDwfB+ets i31S/M2vtOGSPimkcbY5pmbHH+AALe3xBr6PhprGpOP8rats767LQ9EVJa/j Xsk+ZAth61ewgQUkWp2EDK2Xp86IEQveqEIrQjIbLkUEFmwihYDZUCQ9tCkV U0BbQBQFQW/QUmCbkthHWkiCSpreo2EgYA0WhWhhTRsnUVtb3oijqggVub0A tlAAU6GqSBt4eNQo1FhVIg3eRoJAjyrMhoW0Vewgj7R8KogmF2/OswUCL1QR 2UAbKARUDtQhYOgoUYAN6ywBFqAiBpQDoRokACKFSEQBQC0oRoBQqRLabUBE gedAO1utAMAGiAiBQjRG1AkO3TShSRAGtAQ71rgnJBgLnSqJGEB7VCgU8LVS M8tk8uxyM/DlwmUYgvG1/W3ZWUWtY/Gvm5fPWG/WyaXqfQp/HO+JZK2Tb49C njMo5fMY+WJGAy8V92OToqpYAW6ddT51z8bNfLnvOiW30HXNjrTxEo/y3+rU 9gU3a2r6iPloPb8qoC1SSQMKR1qFFa/lQDdb6VsjEFIqFI7TVJA9lQpMCwFA BNqEI7db0kQT2269KjKOwqAgVF6qZGiJjvr41ZECC2I0qgsNZZRAWomRsdVF EReowSFu4qAZIIA8qAhtFVAjsqgYTypJIJhfKo2IC3lSSwBQ/qtSRA1W/ajY gt2d6gD27/8ACpJSJj6ikkGItKSUWztSQddhUKc2Xjx5kE2LKLxTo0bjyYWv 8qz1lQy0s6tNcM+W8ticjhB+JvDmO8LNHlLu3opG1br2J7WNfFyeLTHlq29Z R99Zl5OG19a6Q3xJDAx+S5D2+JjxfpDjRqZcgyARtHe28A6k+ItpUweOr5LW 9XJ0vnx+PROZcQvsPqkUUcMMUEKhIolCRoOygWAr7tVofmm5cstCiqwBXSgI hLXoBOt7mgKNBVIKgEBagJfCrAFUI0FCQxihUgsaFCxoB7SKAmvnQCa/QUBE 9B496AYJ6UBE0AqAY1NAMLcmgAqRQC2/jQEgpNrCgJ+3egGFtQFb2vpQDoCu gCgCrAHY1AStpQCt4UArEUBZtv16dqANtvhQFfegLQBbpQEALkigJbQNfCoU mACL9qSBlQbVSEWSoUgVAFUglU60AMLWoBC1rUBPaPGgIsulARsaAYHiKAZG nSgI2Nr0Bn8hgQZkZd42ORCjfTyxsUcEjpcW08q458NcqhqTthz3xP5Xo9zP 4vg4sBYpYS2NkMB9TaxLi99rda8/j+PasWbhxqjt5HlPJNf8Z0+B6Cx+Fe48 YrnxoC1SLdKAZW/agEUt2oCFut+1AG2gHtte+vhapIIka96oFQBQE7aUAE+V AMfhQDuLa9aAhY0AbTa9ALaaAltt1/KkAjbwoBkAeNARoAoAoAoCaqTQF22o UAmtJBYU0sakiCvbrpQEtKoJKOhqAmRpehSK9KEJWHlQpXc0IAOtzrQGTjcW UyXy8mUZEhlLqQu2/wDLuuT+kaADSvH+0Ty+5Zy+Fwj0Wzt0VUoRCfiW+ojy MScYrxuGClNwAP6gLEaEdjUr4ark71cTuuCryPk6W1Rrga+XYV7jyloXrUNC PShCsm3xqgRNx0qMhQWQh2Uhwl923U6dtKz3UNrWCtMgJIzCJxcIU366G1r1 HlXTvDj7ypS4IQy/URrKqkK3TW4+RrPj5vdr2iC3o6uGW2NdzAWNAG00BcBY UBMiwNAQoCJvf+FAC/nQDb4UBXrQAL96AZ1FARsaALGgJi4vrQAQTrfSgGL9 qgL0W41qFGRrpQFbda0QqKgUA9poCLLbpQBYeFAFhQEttAFu1ATHnQCIufKg HQESD40BDad1AWDQUBEAhqAZYAgaXPQd6gANQobjVIG7pU2KK+4Aggg6g3ve iZBr30tVAN2oCG0jXtQFtAFAA86ADQENpoAK96AgQe1QCsaAmq+JqgewUAgP CgLVoCwLca/KslK2UeAqoFXeqQeoGp1qAR61QQINAFjQBY0AWagGAbjWgLCN KAjQEhppUKSqkGQLfGgK9tgbUBXY/GgGR4CgAKT5UBznIRclcVls7qWQk/qs L6DwryPyks3tw/pN9Pl7FpcLIkZBJkBINtAF63Nd7ZErKvqZS0kuRl3Fbgsv 6gCCR8a0rJshbu8qrKO/hQFtwagIEfnQEANaoLBe3lUAiTfrQEaAdAKgCgLV 6UKJgLX70DK760IO58aAWtUELE62qkAqT5edQGbG8UPIvAiMnvAF9PSXGoK6 9xe9h1FeNZKUy9Eob1OnW1lJk4/vNHi4sIfIi91pc7d+1QxCxi3YlST5Dzrn 3tWqSTcvX4f+Aoup2hHoN8ZmVVnQ3BAiBBJI1017V6e67wrfVoZa0mDpsa7y QLGkgNvwpICxoANz2qkGB41ChtBowT2W7VJEFe03Jv8AKqAsaSBEWGutJBz5 fvjFnbFXdkBCYltc3HlWMjfVxuapHZTsZEc/IJGsKTJkyTw+/jZUgAARbb92 0KD1Fv8Awr4+P+QydHK14PVbFjd1GiNTCknlxo5MhAHa5BUWDLf0tbtca19X x8lslFZ6M82WqraEzqtftau0mCQXS1AAFAWX0qAiT86pCFvKqAt5UAWPhQDs aklDb8KSA2/CkgNtJAgNSLfOgK/dj944+8e8qe4Yu4UmwP41FdNxyCzyqyQd jSSi72pJDlbIAzVxAAWMLTSG/QAhRp5k/lWe/wA3UF4kQyiHcPd2e5s77b2v +NWdYBj8y+XhexymMXmhxLjkMFdRJC3V1HZ06jxFxXn8i1qRdbLdf16FYlyY p+axDHIJI5ONeWBh+4PKhJt8AKLKrZFHKkFvKOxfi8dBYZWbGsn/ACRgyEfP aK3ls06pcsFfL5Cnj+ROPJuyOOEcrqL+llKyAE+aj86mXIurh6ohsKVKiS1k YBtfAi/8K66NSU8lByUsHBcRj4VpORz1MGCDqF2kgyMPBBrXi/cNY69f1W2/ uQ9Vi47QQQwNK87xIqtPIbs7d2J8zXtonVQ3IGkqTxLLAwkRwTGw6G2nx6ir 29ClWHkLmYsOSq2Eq+pL3CsNCL97Gs0v2UkOoDt4VuSgRagFa/aqQhDLFPGs sDrLE1wrrqLgkEfIi1ZrZWUoFne1Uo9tJAbfhSQG2kgNtAKx8KpAt5UAWPhQ FoJ8qyUidb2oCIUGrIFtt50kEdPCgM3IlzxlxxY6xiIpvG8H+oB+pQexGn8a +f5Xl2xXSS0Z6MOOlqt2evBzRTZ0+ThSpJ/QzN0gxSFskAGjMbXvqO/U2rng 8vLlzNR8pp0osev6jcv2r6h5CVqFFa3b50kEwt6Aft69qAVh4UBEjwFAHa1j QBY0AbaSAsaSCL6KSWCgAksegsOprNnC/MnJm5QabDIx5Fnyls8Uia7WGqtt BJt4jwryXyNY5r8zXpB1rWXDcHPizBsyXKlZ0cYg+oxTqsTo9mVfmtVXhuzn aY9CPX5Ujr4/Y8TThCJZHb33I6te/pNyNovYWrXiulk715JetquGaFvKvWYA XHaoUsHjUA7/ADoBWvQFvTShSDUIQoAoCO9d4j6sVLW8hpegJUBMH8KBEm6f GhSoi/l50IckOXHNJNGNye1e7PYAhDZiNb2Hwrz4/Kpe9qLeu5p0aSb2Y8TK TMjM0assZYhC4sSB3H41rDl9yYTUfeLVaOqu5gKhTMz5FieCZo2Aw2Er5BHo WNjscX8bG9q82W8WWm2s/kWNDFx02h0yhI0MhGT9NDckPI2/bJt8BYbTXl7O v6pjTb8zVaprTc9LFj4ylZYoI0YCyMgAAHkBp3r6CpWZSMv0OmuhAoAoAoAo BVANQdPGgRZQpWRrQhVLNDAhlnlSGNSA0jkKAT01NS1lVSypN6I53z8JXhib IUvOFMYW7AhtB6hcC/a9Ytmomk2tSrHZptIzY4s33pjmY02TZz7LJIuwrfQB d62t5ivl+Vi8u1/ken0npnEq/EyMjjZZc7FyISsGNJMYhBJI7AkBmdSB+xyC LX868N1Npnbc9FMlVjdXXXg18CPLbJ90BMWJGKZmKsjONwGgCkADqCCO1fS/ j1drt3mvp6HlyukRHzG+Oor6h5iZA7CgIdKAVUBagHQBQEFkjdpER1Z4iBKo NypIuAR20NRWTISoUf8Aa9AKqDkm5DDg+pEmQobDQS5UYuzojdGKjW3nWHeq 3BwnlDBy0WDkIgxuQjV+LzFJs7aBo3vpc3upHW9cXn636vZ7A5+FdsrP5vMb VGnTHhPfZED08ta4+Lbve9/jC+oi5K8fmGg+3F5mZWyGO9zESAxLTlAoPbaN K6+91x93/WoPS+Hn0r0I1Bg8lMvHclx2e5K4+VfCzHv6Ru9UTH4G4+Brz5b9 L1fD0JBnQ58I+7+QhldIvaw1jZnYKLjY3UkfzVzrk/7Fpf8AijMuYIcTz/FT ZnMZWRyOPGXmSLGVpBf2owQtreJJPzqYfIrNnZrc1Bozfc/28oKvnCZWBVlj jkkuDoR6VNdbeXi/1IdZPnnHcwvH8vDMyTZOFib4oCiEN7Um/oHK9yDavlYv IrjyJzotPqL1Zp8r92mfI47IwOOyLYDtIyTe2u8sAoAs57Xrrm/kKWtVrgdW YCc3yMQ5GMwSSR8kntz3ZD6mNzJ+rr1FePH5PXtrPYiqz1X/AMygbijivgZa 5hxPZ3WjMZcpsvcSXAvr0r3/AP6NFSNZgvVmb9t8thYeQ0/JM8IgiMGCvtSN tVnJJuoNr/31z8TPjq/ma0UIdT30f3HwUn6eUhXyfcn/AFAV9NeVi/1IkGZw nMYDchzOGmZA8MeQ2TikSLba+rga+OvzrhgyrvZTotgZ/B8zDjfbeXlWu2Lk PHCg/fLIA6gfNq5+Nm64W36sldj2HG4pw8HGgbc0ixhp3bUtI/qdj8WJr24l FVJSGfnLg/Rb4y65uVHjbgbbN9/Ufwq5Miol8XAKhkOOcbC3ExtgCfZfRWWU pceZB/Kp2fePgDM4/Ki4+f7hhyH9uDCnOSh7BJdSAPEm3zNeTDkVL5E9Eof3 GU9Wjv4/k2yMbEyM2NcR+TlYcdii7OY7XUtp12gsewFerHm7VVnpJTTgyIMp WfHmSdEdo2dDcB10ZbjuK6qyexS+qBUAdaIEVkjcuqOrmJtkgU3Kta9j4Gxp 2QJ1QFAFAFANQCagQEWoUpmSRo5FhcRyspEchFwrdjapZONAo5PJ5kGQ2Lmw ssYlxkMk2cZZHcPa+xDa4JXr4A1+c8itlkdbXl/ge/HeiaslojpwcF8OP6ab GfJmABMsUt1EZJ2qqsU0U36Ux481qxievJM2Wtr9moR24rz4v1U2bvx8GMKI Elbe972uLFjY6AC9fW8a2TFSczOOatLQqbneM/CKwv8AVRqs5tDuYKWPS1ms b30r1LLR7M4e29fgdddDJYvShQPehCuqB0AUAUAUAUAiAwKkAqdCDqDUakGX l4+DFFGpxyDvGxoR/UWx3EgjUDS35V5c6pjrtGvBus2Zio0okyN7e7Jyg+lV rWYFH2kOLaMY27gfp1rlVtS7c7GbRKg9RAymJGWJ4EA2rC42lQunT5V6sVpq tI+AZfXYgUByDKjbLbDO5JVTetx6WGl7H51565pu6RsaddEwx8tMlpFRXUx7 SSwFiGvYgi/hUweTTM2qzpuLUddzsXqK9BksJAoUqJvQgqAh7qe37u7+na9/ 7eelAcOFOs0WXyDttheVxGx6CGH0g/MgmuGLJ2Ts9gdkeRG2MmU4MUTxiUl+ oUi+tdO6ieClGHkS5e6faI8d9MaM/rYDq7eF+wqYru6nghoW0FdClUqye2/t W93afb3fp3W0vWXMOCHnmu2VFBPAIc7OVYcyxBVo1O4urC1wQpX42rxJfPqo s9GatZtRwb/up73097S7N4S3Vb2uPga9iamCHKMy3IPgSRhLxiTHkv8Ar/mB HYixtWfd+fqILcwYzY7w5Y3w5JEDR9Sxk0CgDvVyQ1D5CR5yLImysOTgsuUt nxz/AE2VIASz46jf7g06tGtvia8au7VeO26/D1CXBpSzIzY3F4KmFslRLlML q0UTatfW4dunle9da3qopX+kIa0NVGhWT6WMqHjjDCFf2p0W/h5V6FbhEZGH JhnieZG2xxu6SFtLGMkNf8L0WRNNzsEcOLmTchks0C+1gY5s7sPVK9tFF/0g Xue/SuOLM8tm1+lBmlDNHM06pciCT2mbsWABIHwvau9byEXWHhWpKVSSRQ+3 7rBPekEUV+7tcgfO1ZtaAXWFabAlFqAlQEWF7WoDL5IwMkMMzFHZ/cie4AUx 9SxOlrHpXi8+1Pbi06+h1wqyco8riT5M2TlJLG0MKZAlX2Vj1cnZG39ViAhM e4adTrXxVXVLn/H4nrsqpaOZ3PSY0+bjHEg5C0j5Usixy6blCgFA+2y3OvTy r7mK16pLI/mbPJltSfl2OXAx0VZlzcgNjxStjYRYhSbEXYG99xIsLeFeXD4d Js7ayzd8/aI0g2sfDjxfd9su7ykGSRzuJsLAXsBoK92DBXCorsc8l3kcs6gP xrucx0AitzegKMrJx8LHlysqUQY0IBllYEhQSBc2vprUdktwXDawBBBB6EdD VTlSQlYeFJKY2fmyLLHgYBDZ04vubVYk/nYePgP7Hz5crT613CR2YuHHhQiG IE3JaWVtWkc/qdj3JrrSvVEI5uI86LJA3t5mMS+JJ23W1Rh3VhoRVsUysflV zs7jYUBiOyeTIiJ1EkYC7fMC5Neavkq11VEW8HorX6C5r1yIPFfdM2LiPicj HlQxZ+Mfbki9xd5jfpuS92UG4It0NfO819YvV6r70ZurJaHlJOaxpvtyHDAk /wBRwMq+AnttZI1Y7bO20ABTbr2FeW/lUtj9GnKK02tNGR4r7l5DjYstPo0l OY/ve7JLt9titmACq19fOuOLzvaTSUy5GOlo13LY4+fm4WfAVUgghnQRY3ss ZnkkbcACzWsDr0rk/Lu6dI0O68dxqc+ZyP3FDK0Ody+SshG51hdEC37f0lFj 5XrNvOy25FqddzNyGnmUJk5U+XE/qAnmkkB+TMRXO3kXtu2ZRV9IkdwsaC4D 6KNQax2bcyUewhrXsB3FZAWI1661lgBt3AkAjS9VMEvbYsFUXBvs87VUBLqp QjX9veogRsbXtoDapAA2JuBbyqtgdiD1sCLi9OqAe2HC3UEk2AIFFK2BBsSI KsvtpdmO1gLHTS+lbWRrkHYuZyMSrJHymZGW/Sq5ElrDyLEV1/dZVtYQaKP9 yZ8ONKeQfJw48mNi0ixSGJwdiswAV7XPjW/3mS8JuTSwtpM7peW5zjuXyM/M xIMl41+jLKHhjYA7hY3k1vc12r/IWV+9lxEEtiac8GDk8pk5mdPNk47R4vIT o+ZEjCQLGm0AD9BOmvSsvyVe7b2cfceb27T8Da5b7ghy+UkGHIY8Y46YuK7q YQBIbyBSwFr2AJv+kWr05/KVrRR7wp/Et+ycep9J4uHDhwcbHwpY8iGBAvuQ sHVj+5rqT1NzX1MKrWqVeDcHRlKpxckElP6L+sGxHpOoPlXS9oTKkeexs6Tm IsPDjYxiWBJOUnU2IUgXRD4ue/YedefF5HupRyZUHpgioqqihFQAKoGgA6AV 6imRm480EjclgqPeVbZmOdFmjX/7l7H5Vwyq1X2r9fxIaGJkw5kCZEJukl9D 1Ug2KkeIreLKr1lBHTYeFdJKck+Tj4zY6TzLE+VKIMZT1eRrkKoHkL1LXS3I dNvKrJSYAHSgC1AIDU6aUBmyYeHBJJkzSe3HI+6RJHCxb27m/c/GvHfxMbv7 j3OqyuIM2EPhZHIzZEzSxYMAGOttWjf1D4m67RXHx8FcFrNbGsmZWqlBDL/1 CVRJkM6YUscJjixxEWEhAOvuak7ulq5+bW9luunJcTpGk9jJwpZcmXPXkIlW NXBdk2qZDF6CiHcRpsu1j1Ohrw06KytZPqtj0Pb5HLe8nuY2WVElU3SVQ6ny IuNK/R1co8DUOGWgWFqpANAIL5UmAVTTQ48TzTMI4oxd3N9L6D8zUdo1EFpU d9PKnYQIi1WRBymT3jlQRuYZoTsL2BKllDKwB6jWsdp0EHHhcg0ssmHlosOd CbMoPpewvdb69NbeFccWduzrbRr7yI0VmjfIlxQCZYUR38LPewv46V39xTHo U4so/U4y5nHssk2KWaEi9m26PE3fUAi3Y61i7msrgGdkyY+ZBj8nioxyMc/U AgX/AOzYvG9u+1iB+VeW9q3i6Wu/2COStcqDN5KXMd78VxLLFjvY7TkyaGQ+ SA7Qe161TIsl2/8AFafWE5PTOVRWdiFWMFnY9AB1Net2jUQcWBlNm45yDEIk aR1iF7nYpsCelifCsYsvdSII5UySYQzYAZRin6iK1wWEdw4F/FbgVL2XXsgz k45XAgGHCq4J3NkTsbGR21JjAHQHS9ccCafyr5d38TTv23NzbrXsMjsKgMuL LZMo4WTb3HG7EnXRZVPbyYfnXL3Yt1YOp50SaHGYEPkK7RN2JS11+Njetu6l IhnjeeRkwjcIjpmIL9UZWDD4CUA/Oufd9+v1/UUxgXPCcDxMB/r8nGgkbrti /U7H8a8ibWOlFzH2E4SKfuTkIo5sTiIyyQRhTOiXLMFF1QW8vzIrj5ub5ljW 3P8AYlnDg9LxeJLGhyclduVkKo9rtFGP0xgdBbvX0cKaUvdmjUseldZLA7H4 UkQzzGZnGeR4FxRDynHZMb4scjfrR3CFgw/awbXyrxXydrRGq2JoQyskY0mN M+Q2TNgZAjzp7BUVcjqot/KR07VLZOsOZh6/WGyz7gviTcZyCgXjmWJ2PgTc fluHzp5dujrf0cfaLaHWHGVy0zsQcTiENj295wdx+KgEV1V+2R+lfxCkx+Sm iys1syJ0PH4WEkmTPGRuyTM94YQw12kpdvw8a45r17d09EtSEcDMOPgS8m39 fN5KRlxh13bWtusfFtB/lFcsGR1x+4/1W2Cc6nqcDDOLDZz7mTMfcy5u7yHr r4DoK+hip1rrvyaPG5eXLFLncdjrulnzTaPsSTtUfAnU/CvlZsjl4q7uxg9S 4j4jjQqHd7K7VY6l5DqWPxNzX0bNYcenBqDPlyzxuLjYEDL9fKm6SSQgrEXu zO/S9tT8q5+57aVFu/uDUGpxhvhIV910uxTInN5JgdTIfDcb2Hh4V6MdprIg y5co8j9vjNdAsoZW2reyyRzAC3fqK89svuYXYk6HZkzPl5HHYkd1Se2Vlkf+ mo3BP8zWrra/ayqvSTRqiSNpGiWQNMgDSRg6gNexI87G1duxBu6xI0kjCNEB LOTYADvR2gsGfx+Tk5rZOQyCLD3bMOMj1tt/U7HzOgHa1YxZHfXgQdeTLjY8 YmyyqxIw/qOLqpOgJNjb4mtXSjUuqPNPxGGmbhGaVsqPkUlx2yVbZcAiSJPS dQfUK8NPEx0iVMs7WyvjQfIZWRNFmZuPslh46dFxYejMwup17ksVtVyWd03X /F6fmebc7OH4WXCtNnypl5oB2uoO1Lm7Fd2t7n+2tdfHwOr7Wct/cVI9BY16 ywznycrHwoxNlzpjwllT3XNl3MbKCe1zWXaNWILwdwDAgqRcMDcEHvVTEHHH mLJn5GAiEtiwxyyy3FgZSwVLeNlvWK5Js16AvyMZcqCfHkF0yI2ifvo4IP8A GtXqrJoQzI+3JXfjIoXYu+GfY3E3JUAFT+Bt8q4eNeaR6aENXLyFxMeTIcFt g9KDqzHRVHmTpXXLkVKuz4D0MrhcdmXJ5CY758lyGk1tZTZrX7bhYeQFebxF KeS27/AqTLsrnuFxLCbk8cMbgRo3utp/hTca7W8ilN2IPP5H3xhIduHhZGVr ZnfbCtvEbrsfwryX/k8a21LB4eTkc9uUflsd0w5HkMkMSDeFurKwu+hvuPav l28xrI71UE66iyuR5LOWT6rkMiZW1aLeVT4bU2i1YyeZkv8A5GoM721SxCqp YXJAANcLNtFmC1QhC30IuG+PagLYzAHhbKjeXHW/uxIwVmt0F/41HsapCcs9 k0jZsvETrOePXMDjEwyXbd7AJAkcEaPcj4dOtdK5Kpc6HrrfSI1Z4zLfGkle THx5MUEn3YZH3gNfWxtf8a5uOTy5GnEFZN4wh0ZSQB8f/KoYagk7syxm9mQb D8K0gV2IJudQKQBX/hU+kAR6bjsfVVgHWh2Ih7xSBh5hqjYKpLRzSADuCv8A GiYB5FPu2FhIBbyPejYKwo0uf3WPzokC9wAgGhMaMB+OlWQc4NibekdqME3Y sIoxosY1+J61NAKRtzaD0qAAe1ZkG1xbY5gzVjEmLJHjmbJ5AuSQqEEIiLt/ WdNe1dKWjfY9OO9Uk0pjc7uWyIY8DExMuJ8t5ofqsPPjcrZpLmzKxOoJ166V b2VtFwMllytGeZO2yA2G1LnzNc5PKVsV0APRQGPmaokEjEbxvCfakJ9MkZKM D43WxrVcl6vRl3NI8zzLY2Rh/wCpTPDkI0cgltIQrCx2swLDTzr1LzsqUNyZ aO3g/uCbhUyUlw1zRO4kVkf23UDotiGBGvjXXxfOrh0aMqkaHtMb7y4WbSdp cBrAn30JXz9abx+NfTx+fivyWD0WLnYOaAcTMhyrjcBE6sbdjYG9epZK22Y6 mHCf9K5WXHttxMtlZR/KZNFI/wA10Pltrw1t7ObrxYmzPS2PhX0ZLB5sj6z7 lS7XTioHMUd9DJIFDN8g9q8nbvm/+SHpbGvWWGcXH5qZ8LyKhjaGaWCaMkEq 8TlCDbxtesUv2khPJzMXCVHy8hMdZHEcW82LOxACqOpJJ7UeRLdg6iDW5LDI NGsiskiK8bAh0bUEHqCKy9RDPFZvF5mDkp9PKsmBnMMT23veIsd0ZJ6WB0/C vnZPHtVwn8rf2GWjUmkhaHPgz2ueIczwe2TGfb2nYLg9bHb867N1v2pZbfhw bpd11W5Dj8PD4mDFOTMsf1EKKMJ1B/rOQz7QLliWJ7aVnF41Mdu3qtjbu3WO UenAAA06dhXtTMNSZGPmzLmTYGaFEobdhzqLLLG2oBHZhqPO1cq5Yv1ZDWrv JDPzB9XhTnEmBmju0Ei62liP6TbzFiK5ZNU43KZuZkpmQcP6bJl5UTyoexQg 2P8Amrz3v2rT4tENE5jHlv8ATlVdoxfqHfvu37QPC1ga6vL/AMnT4SXkzeTy 58PM96IurRoN+O7XiyIhqWX+V476+XjWc2R11X/sFjZcP1GHycLEQZKnHyh0 2lTuG4eKm9YtlUq62ehGUfcMLwrDycAtJjsI5mH8pPob/K35E1jzqwlkrvX8 CNcos4TIGbk8plKP1tGG8jYggfC1b8W6yWtb6CrXUszt/F5S8jECcbJYR58I /m6LIPPt56VrLb2n245DRh5GyDlGwo8g48PIhMjCykOiO1/bcDQEbrqR3U2r x2sq5ek6W1X0knWPU1+HaCXjIeLykigyyJ8fIxEtZjCxSR1HgdDc+NezDDp1 e5pbQZ+VnyxcFPjzHdlYs64cyt1Zb6E/FVtXnzZmsNlzMfeZb0+JqZhfjOES FbfUOiY620vJL+q34k16Mr9vFH1Gnoc2QHfHOLjyh8WN4cLkMR/SY1DKrOp6 m4vfxBrFnMpPTZr0+Ik1ONz0z2yBjwFMTGYRwZFxtkt1Cj/Dauvj5e+y0WiI tjUINelssCFSQeG+4oZOPj3pvGKz+7iyJc+zLe5QeTDUedfM85OtZ4nf0f8A YzeYOyXLfkeFxuYxgDl8a/uSAag7be5b4rrW1l97Esi3X9MbqTtfLgGbjcrv /wDbtxM8p17LJE1vztXR5F2WT/a/xRT8+RfcPOwmJo+VyFaBPbhfdcon8oJH TSvAstk0/QyQ/wBc5j6ps3/UZvq3Nzkabr+OorHZ9u/I5k1I/vj7ritbl5JC P/USN/4rXp/dXXJZNGH/AHI+6YmUvJiZAH6leAC/zRlqry7Duzah/wB1c4ED J4WCRf3GKZkPyDK1bXmvlF7nRmf7jcVmoHPEZGPnRi2Nl3jk9oki56qSPKs5 PJrdbQ/UO0lk33f9rNwkvGYxy0lba/uzxaySBgxZypOptWclsftOld/zI38u h2c795/budxHs4+dvyleNhC8Ui3K33WJW1b8nLW2PRy9BbVGl9ufdH2/HgyN lcvjw5MuRI8okYqxGgU6jwFa8S6WObbttmqvTUw8/lODQcnBx3I4gizM6OSN UkBBtCOgvou8n514vJqotWuzsvsMXWkI1OOzeMn5xIv9Rx1wuHiWOAvKgVig 2pa5FyTdjbyrtir28jX9NVCCU2Pern8ef/6GMR3PvJ/xr6ndep1Ucng/t+WD kvuTkMwyrsx900asR+qRmCa36hSTXyvFXfPa74OVNbNmx9zZiRZHGRP6scEz SBdd3qC208Reuvm5Er0T23+wt4lGHxSycvzOSJ3LIPXkHWx/SXAv4kqo8ga8 3h2efJaz+v6OEKuWfSH2rE5FlVENuwAAr7WyNo8Ji5Ht/aM7W1XICIvjudGH 8a+PW3/Tb+n8Tmv0SLH5lYmypof6uXKIMPAitcE7Rc272NhXWmeG/XSq+zX7 y1cybuRj/wClcdFIzl8g5cE2bkE3Mjl13knwtoPKvbPtV1NPQ5eXzmyM1ONh fbGjWklPQMvqZiPCMdPFtK8vk5Xa6xLbdslrawbXFM0sQljX2cAKI8CC2pRd PcY/4raDw+NezC5rK0RUaborqUZQytoynUEHSxrq9tSnhuU47kcaOdOKT3sJ wZYQHH9CaP1IRc6bSOo6jQ+NfPyVvS001rrp6My05LcWEYvFfb0RmG3Nyo8n LlI2iyo0xHwG0D5VrCvbx1UzOslSg3eOy2z8rKmRiMXH2xRRf4yNzM3nYjTt XXBn91trbYLU2SPCvSaUlE+PHkwy488YkhmUrIh7g+dS1VZQynneHkl47Jfg 8t9wS5wZdBdbFrfMajzDDpavJgbx29t/V9BhaaHRwZ92fmsgr65szVh12qu1 L/ICr4rns/iFJp5nI4PHAPm5kWKL6CRgGJ8l6n5CvRfJWq1ZdT5xhfc+Jxku WMTGkzkzXkMDXES2ilcDduFxcOO1fLr5tcVr86yRbslN9wZ3IQTZWUkOPDiE ri48e47piLXZm8LgDTua83keY8ySiEZyPhHmD9Tl4yrl5mRNjQ/0wryE6Aaq q3tfoL20vXL3rtTZ6ehv4Iqx44Y2EKIsaFLKigALfpYV5XL1ZokwXZE9rfsc +Y71JBEEI5Q6gHU/Gq9AAtaRQdWYAD50SBs8Xi4mTnATyofbJRsKRC3vAgiy Ed7+VXmDvjVYmdfQ5s2BXgGRFizQmELA0SrvACdHdut2vYadutVxwbzY0vi4 4ORcHLeCGdIWeGYy7bftEVgzN4de9Zag4+24TRzTzZDwNkPMCMRFWJQbMqLY jTp16mrVGLZWokqEoW0aLvnYB1a+iqNSx8b1VUw7azydk+ZPliMzhC8d7yqt mbcbm+tupJ0GtLuXMQdLWnQrAukhPkDWUZJR+pgSP1Ar+VUCTaPbv+0kN8KA akG6qOoN/kdKSCP6kRQdVuCKkAr1J8SakALX3H+XrVSA9SG06amiBdfc8lh+ pb28xR6gpYBW+Kg0bgAQdquDodKBF8mbkyRR4+2MwxAFoEFt2y50Jue57261 usOsQW1tDiaZXeAQHamTJsVQetrmx+FqiWph2hQdoXLnK4an6kY+8wxp6iBo WAvYmxHQfKsxqdVd3UBJizwywRTRuXmjilVY13HbJ2Fr69RrSDTxrskehxcP FYTY0kHtfTQOychOmiPKVPrRbi6g26/hWmtDvWqVZMGRY1JWKX3kgcATWtuv pp5XqfE8t1VPRyUXCmTzNhQyRNyHfzCqKkIHUAisoIFoY9zebHpWSHJHBDNI khULMN3tzDQg31Uka2NrH8a7Y7tBya2Nl5KZcePmZU02PtMPtyyM4AexUqWJ Othb41u+W7iXMGLPQ9JF94Z2FLJi5uHHmLjkKciNjG7KRdHIIZTcddRrX0Mf 8nEJrQ1RyifB89xP12Zl5WQMJ8+NZIlm0AEsjuQXHpBA2jrXTxvIo72be5K6 yfQo3SVBJE6yxtqsiEMp+Yr6asmXU8/gTR4uf9w+4Vix1kGS8h6D02cn5AV5 cdoyXXG5EUcbA/Kcg/M5a2jxyY+PgOoUjqfiv/VfwFTCvdt7j24Lu5PVWNew 0BGnT40JqeQzs5zgcrgySBs3jyZMeUi272WWVL+e23xrxPPraj3RmTk57Fy2 5fAyMJPf+siZ8nD0s3sBWQ3uLjdtuPKueWtlkV6atrYj3NvicCWJjmchaTk5 heQkhmjU9gRpr5adhXfx8bWt3Ni1XqbttNBcV6jWp5PlfcUtHPd8nFUzYs6i xmhv6l06OhAIt4DxrweVLXxWq+Jlk25X6rio2LD6lsnHhlI/cC6tuHkyg/nW 8Xk98fbZ7ETk6OUZ+IyBy0Cn6aVwnKQDox6LIPA9r/CtZrPF8/HJfieb/wBQ hXLx8eNw8EHIgxv4xuyEGvDbKvcSXF19jRhvU30kv925CWtbDCjzsFb++vSr f9pr/b+ZV+o1+RwRm47RAWmT1Y7eDjpr4Hoa9WWnZfHg2z5jjZyjFzMWVm2N IgS4N1YEbG8vQSpr4Vcri9Hzt9KOU7o+j4hi5Hh8cZLKFy8YJIGI1JG0nX4X r7WKyyYlPKOiUweR+z+Qx4JeSxMjKiRkbWR3VQWjZlYeo97givD/AB76WvR+ pmkqUevzsri8nEyMeXksWNZkKhzNH6T2OrdjrX0cnW9XWdzW58uflcNuOhMu fDHkcXkNGB7iklHuSB4hXS4t418JqzxxzS2n0HN7HqeI5/7Zx8vmMyflsOPI zMgESe4CTH7aEgWvpuJv519TDaqtZvlm09WeY537i4Z+Yb6fPimw5pIJJZI9 zLdQQx9Kn+xryeQ/+XTVOJ+0y1qbPM/e/wBtZUmAIc15kxstJ59sMguq66XA vXqz5aW66yk5Nt6mXyX3v9uS5ceZjRZsrvaPkMf21RJ4hqLkuLMp6Hw0rGTL Tt2U+jJKRJv90MHHhSHj+BkRYgFijklSNFA7AIGra8utVFUOxk5H+6PNSKVx +Pw8Zj0dt8v5EqKy/NfCHcx5P9wvuuTQZsUQ7mOCMfxBrm/LuTsZ2R92/cuU rRzcxM0TizxAIFI8xtrnbPeyh8hts5Mbn+aw45IsXk54I5TukRCLMbW10rGO 7xqK7ETgied5kqqHk5zGsZiWLd6AjEEra3QkA07uILJlVghJQGvr0HWgI2NU BUAUAUAUAdKoDvfvQkB5UkoiAeov5UAtifyirLAbVHQWqJtbEgnuf0/1HGz9 NmOnw1o3O5TQxOX5XALNhclk4zOLMySG5F721v3rVLOn6dAtDXX71+6UBU8v JKrKVZJUjcEEWN7rf866/ub+pezGPvDlxxycZ7eM0CS+9vMZ3M3nZgPyrk2n i9vZEjSOCzhvus8bmxZeTx65vsm8aLIU2kLsB9Qbtr8amBLFfs9QtD2HLf7i 8ZynGNjrh5WLke7HIFYK6+hgSNyn+6vXn8hXpC3NWtKMSD7jwcjNBnzTAkuy J52R0sHfdM5NiL2/Ovm463tebaJuWYW+p9fg+6ftYqiQ85gqgAVEMyrYAaCx tavv1yV4Z2UGnDyvF5J24/J4k7aHbHPGx16aBjW+1XyUzsvjZYllyOHmWF7M ZsRjeGRSNRp+knxFefJiabdWZa9DCkwTncN9n4rTPjmXYHkXUi+OzkeGtrVy yYe9K1bgjrMHssPDx8HHTHxlKxpc3JuzEm5Zj3JPWvVixVxViuxtKDqJCgsx CqOrHQD511kGBl/dHCYhKtmjIcXumODLa3iV9I+ZrzX8rHTdg8Zyf3LDnZWD Ph40sE8RIjklKhSy+uPdtJNty269GNfNz+bWzVqzKMX0UnnIeZ5GSI+3lSYs OXGk00UR2ksVsfUPV1HjXl/dXrKThSZpaTlWCSciVTZ29TuxJaTcNNT27371 yyS1NnLZ0kossMqWvtWZnfy9yPdp80Nc2vwMLSxoZTGPEwcVDZpCGZT3Zhc3 /wAz05OaiZ4Lsxo1lTEha8HHIqOxFt0retjf5itXtwjrXXUx1hkDzSm/rEbJ 2sCot16XvellCSN11ehtQcVkucuGaMxyRY5niW9wTewNxcHpauba4PVXClPb 0K+TwWwVwxLdciaMvKhI0Ata1vDUGlbdp+ByvjVUvU4kRRLHfoBvetHIksku OGz8ecxTIzC1huCsupUnw/GmjCyOjkmk0smZjTez7zIFhgjJZQCDdAXUg6Fr 9aNaHTFeXVGtyeWqz5xXJPJrO64s8Cu0Q3hA5ZXG4MAQQLr163rXaUeh3Sql B5i6vHMGBEioQyHqLg9R4VlaOTxN6McCBI43YgSsi72Gh6dPhRtzoZVdC1Cp crcXYXtfXTyqcGi1rnZY/wDcXUeYqrYpXcqfbNw5uw8BbxPbrTq+CNwMJdGY k3Uj86LQpF967vbUFgLqvifjVSlhhAhjhkDauq+tz0LE+o/jUe+gLVC2j+BN SWBLoiA63a5+VWQVpIm94tSzLfdbTqNCR3qpONQW26kdww/CpoiqC+LE9wxu 88YgeORg5YA3jFyhXqG1/wCFehUqrbyj3Y8ONXmZqcxWSOMLJG6BrEblI1t5 iuDq4mDyXxWWsOCF/wDFtve9jbQ1nU5aM5jGqzwbQAh3kgdjtAuPiDWm9CQp OzGy1xZlyUgOSuNcjbIYlVl6HcoYm3gKlUdKWVWb8suzi8yEZYz3hyR9XCxd CN6jZKlmuwDG2ul/CrZ6wkerJeU3HBix5eTBEmNGTGTviC22+iS7MrBtNaM8 lszTQCNEeaFZPcBA2uBYHb4eVZZlOSlhZUbu1wR8KIp6DH4V8hONkjBkxspS 0rA/vVbkAjxtasO/C3PVTDV2UvRmO+LkqgmZCIpHMaSkgbiLjQdSNOta0mDj bE0u3EnNjxPG0obRZpJCp62I23/Mg10eyOXwO7JcTcfHOGtlYLnFna37f1RH 5XFVuYOb9B8lIpiTKU3aXGYfEqA6/wATWKslLQmcUeKZGl2EpJpGWGthEiqb A6db0XBvHsXwS5WFIDjzyYshud8LFVZl6kW0PwNd/dvj/TZwWWdKc5mh5/eZ ZzlRyy5kp0ZhCye2thYepgAfK9b/AHV329WkY7atHtuN+7+Mgx8fFlx8mBYo wDMQsgZu5O031OvSvpYfPxJRsdEeswuW43kf/wBPOinYgH2g1nF/FDZvyr3U y0vs5KaNdJB5P7o4jHycHNz1d8fKx8d2MidHVASFYd/AGvH5HjVv82zRi9J1 DkIM88pw0eA6RStgTxtM+vtqDDdlXu3YUvWza6vWCurnQ2sbEw+P3SNKDOwt NkzONzW8b2sK7Y6Vov7lgql+4OBhv7vNYEdtDfIj/wDyro71XJZPJ/cn3N9u S4ayY/M40uXjSK8SROWLKSFdfSD2N/iBXj8yL0cPVbGbbHzmH7oxYVx4jLJJ FDkKzbYmF40k3IfVboDavk4nettdm5OKlHquU/3O43JgyMWDh8jJiyEKM0zr GLMLXsA/SvrZPLo00dXY+b//ACDNWX3IoolsxYK25rdPMd1r5SxJNOZiPuOT Wsmjk/e/PT53+oRyQYuTs9vfFEDpa1xv3a2Neq2V+535iDSesnNJ94fdMv6u dyh/yFU/6VFX9zf1L2ZgSz5E5YzZM0hf9V5G179L1whTMGYK2LNozMw6WJJ/ jWuziCkNifyipLAbEH7RSQO2t+9JA6SRoKBIO96CAqFCgHY0AqAkq7ja9qAj qLg9RQBQBQFiEBHPc0Ah+n+6gEguTu6CqwR7jzoCclr2HhUQIUAUAUAUAUAU AUAAEmwoAqgfYnwqAVAHTqLXoAqgKAOv99JBDYg/aunewpILYGMMqvFK8BJA aSJmU2PX9Gta72h6g1sf7l5zDOOkXKT+1jP7mJHIfdWNlBVSokvbQ2tW/euk tSyelX/c37n9lo92E8hWyzmA3B8bK4F/lXZ+VfYvZmBN9z8rnHfyTnObqAzs qA+SaqPkK8uRWvvZjuXR/cK+j3MJkKKV9DhgR4G4WvM/H9GVZCE/Owtjv7Mc scsXqiDqLXB3DUE9xUXjtPUjsmjtxMlMjMeAm8Kg+8b2vGkjs1viCBXO1XVS zCcJ+ptYeUs3uTl0EksiuyIwIUN+0W8BYVLNtnVPQ4M+T21msLqpHr/zlbf/ AF1K6mL6NMhHkn3sWRjrCjHXpYsAL/hR13OSOjHdJxjLIxIzpWnmHf22bQfM ELVR2nrU75fbyZJpJJBBDkuXSTYzgRrZYwVBB1AvUuzrhqno3Ghs5fKZGPx8 ZxwvJRcf7aSZyK8e9WZS0DoysX3WAulz41vHHXRaydb3tErU7M3IzZcOHNxu O3zZSh8rkWaNmLW/QI1ZiAt9ATp4XrObq7prk7UdknClnjvUZFs6Iu0B2e9g p+ANZWu54mdHJYZxINhmjmXbvGwk9V6i4Fx8KJanN2WxyxtLNBBjSsNgH/bH QsbXJ8ToKsmqKCcWI+QwSGD3JIRu0K3Av1AJ7GhrsU5m5I5JJC5nSNyfcvu0 vob9jUW5nQvxzEkCl4PekYrd3/Svl4nXt0qtmYsyLrIzyvs3Mg/qFF0A+XQV g6VrB1ZWLNhyQwTqFnMYlCA9BJ0B86uxu1WjlaW0u9CFlSxCnXy1HgehrSca nNlkkallaC4x8lfciU6lCDZ4yf8ACwIHlajCfA0XbjTz31eRMeO/+Ib3I+QA +dSuiE6wc2YrHZiqP6ZW+S1/2DqPixNvxotmylpQkRgD1HRQOwqSyiEQYO7E iCMlSw0LPa+0fAak/wB5rVV6mZAqAYzoCTcKNNB0qOzZoTEEBldJLSlZIQxD WIuQWF7VukJyzVL1q/mUo1sHJiZMtxF9DFhwoWjxf1TEMDZne5uLGxFq1/x2 htbPQ9mJ956qKo4+QaWTKZnyZMuJ/XDNIbkqfG2l/hVydkpezJ5db0VVPyvV ELqhffCkyBgdjaG/YhuorgfOtVtyjiy9q5MQhVoo5WZTGeqekk9NNbaWpwyJ taHZFiz5SLDixNLFYExD/thh8dLiqblIp9pCLhAvsroAQQLG1wRU7M0myjIl kmnx/eZXUKUDNoTtHp3HQaCtLY5NQ5NafDeLHXLXKgLyXRELNc9tAF1qQarZ MrwZMpJIzip70hvujt1XS+ulvjeo4g9ODsrTVSel5PksrAGDBj8e2BNnSI0m OrowSQMPWhTcEMgJU7lAJN+o17Y0lRRqxktZbI4+VlTMXHWfITjnwf6acWsM jstgLMX9IYkdwLVjJCvoaaT0bgxpGBxsqNhtlYpk4vkwGx1J8DoPnVW0Hjb6 ufU4XygHcK148+FWVbabkO1vnqKy67Mzk1ho4Tks0eHEeh2RgeZJQ/gavXcw bONKWjkkf0M5Fh/zOWP8awmdqrRFDzLJJkwxMjtf3YCGG4SR9Rp2ZSQa3MIl nDTPOHlVshBOwyqHFr+gvLJf8lrosTMv9UnUfuDGW+zHmchdqttVQB46teov Hb3N9kckvOmQIgwVCINC8mpPjov99dFgjaw7mjg/fX3Jx8i+xko+P3xsgNMP kzMGFvI17MWa9OZ+kz7jJ8h/uB9xcjFJjST48MEylZYIYVG4eBLFjr5V0v5F 2oK7No8/k8zy2eWmzeVy55dloy0riwYjcqhSAAe+lcbZbtrUjcmVsS50BJ63 161l2ZCQRR+lQPgKkgen4UJA6FCkgDcW060AW7UAVAOxPQXt1oBUAUAUAUAU AUAUAUBZYe2D3vQCTU7T0tQEe/TvQErAS3B0vQA/6jQEKAKABoCPGgGCRQBc 2I8aAXhY2tVAyb9agFQBQBQBQBQBQEtLde1ACHa1/KgFZSb3oCS2DG/Q0Akt 6wfDSgJ23xnW5WgK7C2h7VSNiABIB1oUbLtZqgIbV1uL3oACqDcKAe5HegGQ NQfwoCBRTqPSezDyrXYhMBrakHzAtUkpNQDe/bpUBWdbg6g6GqQtEhVWVLpu UqSD2bU/nUgQUe1GRqo+Nta1IO2GeREeNpHaJ127SxNrEEWv8K5uibD2OhuQ ZhKFW8hjVVftZd2pHbVqLCyVo3siyLlTHJG8iFt6iNmi02oBra/lp86y8XBp ubSba/cGDINknuxEuGbchI2joAVvXG2C6NyieRn4GWsTY8yNI0wMliVKi+hN 7EWtUVHV7FbZoepU90uY5p5DuyLtcXOrNbU+Jrmn6nWjfZawbMH27lHNEOSb Yi45yhkA7A0YIAF9bEXude3nVdG1KR29uqu5eh5uQK2NI1yyEMoOu0qOh00q LRnmvXdpHdD7DLgPEtyL+5KwIJLAenXsBR6GK/EnEZYchJ4G2ujMvxBvcHyI qSWyk4+SyTPCyrjvGLMNjbbKW67DuvtPh+FVbnOtWi0QyPjzNf2ooxu909z0 ATxNEbdoUIuwsnI4x48sRufciIjLEhH7XYL+u2vpNVODpW/Utm5BsiLEgAKw 4yIzqwUn3dd53W3WPgTSz7M3fJ2RDHgGTJlRhR7hjMkI7kob287gmi0OFtzP u8bxMLskjEquvUgKx+fpohOsna0i/T8cjH982Q1vAsEH5Cm6JV6sk8RM7Ai2 5gZRbpYbiL+W63xq3caGqkxHJLOmPBb3JurdkHifIDWspTuVs58maKRhHjgj FhPtYqjUuL6vYdWdtfwq2fBFsEsbe77DruySQnshrLHewCM382uo6Cqh254O GYfSK6pH7UiuY1iC/vOliPzP41lKdyWhovUGKIxpKH99V98gEG6m/wCdaV4U How+Q6UdI3JNZblT6BbUnoKxLehzd292RgEmZ9O5kEAmay7tB6gSga3lSySO dm0ivN91XiZ4mEiyi8fQn0sNCbaHxrSWn1BudjTTNmMLxwo0chi2yTGwATpt iAJAFv7XqPQyqvkqCBIchPCML+JGlZ5OhyZRxjJgqi+3MF9cYvYMF23B8710 40MJN6QanGcaOTzZML3Pbl9kyY92sS19Tr1t1tUUvZHpxUrD7aFL4GZi4UHI Tt9MuSdkMAuGYA2bUEHte3gKWTUpo1WnWjcwzPzwsUUm30ROY5NgNgxB0+JF zaiPPZuGVzcvxkQaJJ1L2UsIwWIYddQDWlis+A7cnFlfcONIJfYilck7kDAK NRZh16MK6Uw2T1MtpmQc+UNCqKDGrMwXXdZhqPxsa6rDJziVC1AZ4ZkK+lY5 WdNb9XLD+NR4WtGRpmdKWnI952ltqAzFh+B0roklsaIxf0HV4f6bC9yote47 2q21WpGpJEK9idCugP4/8aCAUAmxNgKQJF3oUgysTa9l7gaE/OgBUUAWUCkg kNb+ANqgIiNB0Udbj40BIADpQD2jaxtVJySUA28fCoUANzgdu9XgE2tvt2Uf nUBBepJNrdKAV9Dca0BJSFDHv2oCFAM9qAVAFAFAFAFAFAO5ta+nhQADY3oA v3oBUAUAUAUBIKSGP8vSgI0AUAUAUAUAUAUAUAUAUAUAAEkDsaAZFiQNR2oB UAEG1xQDBK3t360AAkXt3qkgXwoUkzXN/KgBFDOvnpa9AAW7qo7m1QjG4Dyl VHe1vhpVJIrBX2m9hQsgCLMp0IOlBJocWmOMvHm5DHkm49pTDIVuBvZdBfTU XBtWbTELc3WE5tsc0mFlxjJdoWMeI6x5Eyi6Iz/pDHsTVV0/pJ1e50cZxWby 87QYUYcxgNMzMAEUm243628qze6otS0o77GftNzbUDvWzIA2+HjQHp8XmI+N wPZxcdc58d5LyOmy8cwsyse9jYjzr14vK6JVaPVi8x469YMlMbL5mfLmxMVf cXYzYcQ/SrEICo8AbXJ8b1l0eZt1RmHns2kl8CzjsjAxWycbkcVZ4pCV91dT G6mxNwRcadjXky1uph6nBqG0Q5CDDjWQYkomhKqZgH3gEtpqdbHwPSueOzs5 aMKS3gXlXkoIYxFKk11eHJdhCABcsRfqANNNelayr5T0ePLvG59elOFlPHgc fnxR/S+39bHAtsdXmZVhRhuIJYr06W6iuNX8qbcfme1WVW01P5HkvuDnXmgl ibkYZjjSNHkcUYjizIyabdtmD2PSzW71itHa0+pwveKtK31Hm4eexwkayRzR Fep2h9e59JJ/Kt28e3B5uyNSPmuO3hTmRgs247iU6i37gKx7VlwaTQsiaPIw 8swOjSRwn29rBtRqNAayqudQ9Uav1Pv40ePsOyKK5JPW2oAHYd6zJzrQ4ct2 jAjXc8e5CIQbi5/trWk5NWqt+S5PaKMIy7f0f6jPpdupIHYdqjNIvWUYk6ZV iTAI2IHg3pP8aLUzfYz5XL4uURZRjZDkW0soLAfwFXkm9UXwiN83GhOkcKRt MT02C7kfPSsrSfpOddE/idUDGZpJnbcZLMT5uS5/jWrL5md0cr5LRQzzKwR8 0mGN+6ob7iPDQE/hWVabHJubHVAv0UUcqjbn5AK4kZ1+nj7yH/Fbp/51Uyu3 ZmfrEQYv1IwZSdbkG+p8zUk6RoGVk/6jnPkFSgghVHAGvuWIZvjYAVtPQxVa alkasIYpGBZZlBDt1O02a/n41l1g1VyN0JGQg7XPyVSxv+FKbktoiUAv7a3s Qb3H8wGn8KzuyusqAzslsufjAUKhHIlB1AXax0v0FwNK2lKZlVaZU2dh44cS ZEUexWXV16k/GnRvg2cM3OYG2QrP7nuEWWJGY6D4WrSw2Y7JHAeaSbIxxFG0 RU+jIkcRKraWYkbiLdb2rp7LS1Yrf5lB9FxM+TmInx4+YhyPojEz5UUBRY3d gsaCa6kszfygafhXOi6qH8sntpdK0uGV8zk4cvETTYb4eXn4StFmLlXXIR4h tcXDCxBFwOhrVl/yJa7b8MLVWag+OrukkRmZpJC62d2LG99NTc167bHzJPQ4 n+i4sYy81RmTXJOKGud9zoFuAAO5NedvI3C2ImzMhxMrl8qc4WOGcspeCOw2 K7bVNvAdzXsx4bW24O+LDbJsdXHcpLxTzY0OJDlSSSPGuTqHUkFCVI/EV3xZ /amsSdMWd4U6xrO4uXyMbMyDPCPZMapBHiKm0KsY638zXHLkeVyc8uZ5XLMg Ak2Arkcjv43i8zlppsfCQSTwx+57RO0sNwU2J00vesXuqKWXHV3ehw7JDJ7I QmXf7YjGpLXtYfE1qYUkhtwauJix4ycq/KYrj6aJ8dYr7XTJJAXcLi20a1G+ zUGqpKexmNFJFIYp0aGRLb43G1hcXFwela0aky01uIkHc3boKgEilkZv5TqK r3MyTQBonPdCG08DUtoJK0G4Nf8AaNarNCsBYeWlQEr+m3iapIECRrUKAJB3 DrVAG5J7k9qgFbxFjQBQDAJBPhVI2KoUZN6AVAFAFAFAFAFAFAFAFqABqwXx oBkWJHhQCoAoCxP+29ARXU60BE0AUAUAUAwLkDzoAYeo0AifSRVJJI+lV8xQ IQF/hUKB8qoJp3bwqAiDoTVI2RqFLE1VgfCgIW9IagERoPA61QWyKpVJUFlf QjwPhUImOKMNuLC6opJow2UjsV6rqKok6owPqlNtCN6jt/a9ZnQjZVB+qWZu i3PzJ0o/QhUCS2vW9zWiotSCWeVI4InmmlIWOJAWZj4ADU1HZJSypehpYeXy AR+KWKbJhMhlHHovrWVOrWsWBABuKzaunaY+J0TvHU6OTado5pTIy8ZkZEcr wggP7jxk/pPXbtI8qUiPiY726xwckUWZxuRHu3IMiASv7D7m+nk67ivS46il mmjUWx87o42RYZfbvf03a3QX1AHytVT7amEypioBHga0SS+KVUgzPVb3FRVU d9aw1NkhJbiZMuCv1mPM8GWGtiyRtZltqxI7g9LHrXSl3W0o3Wzq5RRlZEmZ I2RMwaaXWRgqqPkFAFW93Zyy5LO7llmLHA5SHImOMk7F/dOinaCALm9tT1tX Gza1SmDDcHZyGPwsWMi4k8s2aQLro0dvFjb8hVo7W1a0FddSGHnSx8dm4Cn2 k3LlmaMkOZIyqxi/gpuR51LUTsmzosjVevxOTNzcjkMmTMy2V8mYL7rqNu4q oW5HjYa1qlVWsLYl79nJy1oyG3cbfMCgI7E/lU+dh/GrI1Jq0iHck0qHuVkc fwNZaT4EnQM7NBVvq5G26gNZhf8AzA1l0q+Cyzrj5rPjLXMTqwIIKW6+YIrD w1iC9zpP3DLJFkRS4gBliMaPG+oPY2Yf31F48bMd5RVLy8MmDmQgTQy5JaxK 3GrbuqFqiwtOQnFYO3CyseSeb/3KFnUhFLgMVWO3Q2PWsWo1wckoNwyBIEWI i8jFVI7btB+Fcex3bhFUzE5uPAo9EEW8r21Nh8xtpVfK2eZNwy2J97ZGSxux AVGPS3QH+JqwdsdYUnNk8vxkGOyxzpPICFCRXc6ddVBHWtrDZ8G+yMyH7ijg xJsVMOR3nN5Z2IGh6gXua7LA/U5yVN9xTnGbGTEjX+r7sUjMzFCRY6WF709g 12OFuY5Jw4+pCGRSrlEA0OhGu6tLFRcEdpORsrLcWfMnb4OV/wCm1bVarZEl +pzkE/qZnsbgMS3/AFE1oagF19K6nwoA6ihIHSSmrg52SEx+NExiwzlrkOqC zNILBSzdTtsCBWLUTc8nT3GqpfE583MPI8hLnZSLGciQPkLF4gAMwvfUkXpW vWqSM3v2crQ0p8PgWi9zF5CSIqhO6QgPutoNhHY+FY7XWkSc41MSWzOZApCz DeL9dev511rpp6Gjug5XKxInxYJ3ixZyPqo47Kzr3XfbcAfAGutMtq16rY2s llXqigFIM2NwQYg4ZWH8raj8Aa4zNZMNFMxHuyWNwzk3+JrS2MySDAlmsLKL UEnThtLCpycYyfUOrRx+2SHDE+rp221i1o0Za2ddih8WWDGgzn0jyi/0bI6l g8RF9wvdR4VrmPtNdWkrHoZRnKmR9VDJnZhnM2S0PqCpAu0SMQCLX1162rFv hoWztd9uTCnl5HlZcrOmEmY8ah8mdUuqJ0BbaLAVv5a6Es7Wbszkt2PxvVZC cFhJsJ0k9P8AwqPUy0SiuDkx2/YR+FHwyEBu9g9zI35LpR7lTHAgkk2P1IO3 40Kyo6aeFVQEWSoqBFIvIfUx8PAVEUr1uKAmgtIAaAiSCTbxoAAuQKoJJ+sr 2N9KEghaxI8DUKAAJAIoAoCRNgv+KqZkiepFDRIC4b/DUBGgCgCgCgJr4UBF TeQX7VWBt+pvjQiI1Cn/2Q== --0-867640889-1305918436=:28942-- --0-1675381938-1305918436=:28942-- --0-1506575205-1305918436=:28942 Content-Type: application/octet-stream; name="dcmtest.m" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="dcmtest.m" JSBkYXRhX3BhdGggPSAnQzpcZGF0YVxwYXRoLXRvLWRhdGEnOw0KZGF0YV9w YXRoID0gc3BtX3NlbGVjdCgxLCdkaXInLCdkaXJlY3RvcnknKTsNCg0KbG9h ZChmdWxsZmlsZShkYXRhX3BhdGgsJ1NQTS5tYXQnKSk7DQoNCmxvYWQoZnVs bGZpbGUoZGF0YV9wYXRoLCdWT0lfMUxQQUNfMS5tYXQnKSwneFknKTsNCkRD TS54WSgxKSA9IHhZOw0KbG9hZChmdWxsZmlsZShkYXRhX3BhdGgsJ1ZPSV8y TFNUR18xLm1hdCcpLCd4WScpOw0KRENNLnhZKDIpID0geFk7DQpsb2FkKGZ1 bGxmaWxlKGRhdGFfcGF0aCwnVk9JXzNMSUZHXzEubWF0JyksJ3hZJyk7DQpE Q00ueFkoMykgPSB4WTsNCmxvYWQoZnVsbGZpbGUoZGF0YV9wYXRoLCdWT0lf NExJUExfMS5tYXQnKSwneFknKTsNCkRDTS54WSg0KSA9IHhZOw0KbG9hZChm dWxsZmlsZShkYXRhX3BhdGgsJ1ZPSV81TEFDR18xLm1hdCcpLCd4WScpOw0K RENNLnhZKDUpID0geFk7DQoNCkRDTS5uID0gbGVuZ3RoKERDTS54WSk7ICAg ICAgJSBudW1iZXIgb2YgcmVnaW9ucw0KRENNLnYgPSBsZW5ndGgoRENNLnhZ KDEpLnUpOyAlIG51bWJlciBvZiB0aW1lIHBvaW50cw0KDQpEQ00uWS5kdCAg PSBTUE0ueFkuUlQ7DQpEQ00uWS5YMCAgPSBEQ00ueFkoMSkuWDA7DQpmb3Ig aSA9IDE6RENNLm4NCiAgICBEQ00uWS55KDosaSkgID0gRENNLnhZKGkpLnU7 DQogICAgRENNLlkubmFtZXtpfSA9IERDTS54WShpKS5uYW1lOw0KZW5kDQoN CkRDTS5ZLlEgICAgPSBzcG1fQ2Uob25lcygxLERDTS5uKSpEQ00udik7DQoN CkRDTS5VLmR0ICAgPSAgU1BNLlNlc3MuVSgxKS5kdDsNCkRDTS5VLm5hbWUg PSBbU1BNLlNlc3MuVS5uYW1lXTsNCkRDTS5VLnUgICAgPSBbU1BNLlNlc3Mu VSgxKS51KDMzOmVuZCwxKSAuLi4NCiAgICAgICAgICAgICAgU1BNLlNlc3Mu VSgyKS51KDMzOmVuZCwxKSAuLi4NCiAgICAgICAgICAgICAgU1BNLlNlc3Mu VSgzKS51KDMzOmVuZCwxKV07DQpEQ00uZGVsYXlzID0gcmVwbWF0KFNQTS54 WS5SVCwzLDEpOw0KRENNLlRFICAgICA9IDAuMDU7DQoNCkRDTS5vcHRpb25z Lm5vbmxpbmVhciAgPSAwOw0KRENNLm9wdGlvbnMudHdvX3N0YXRlICA9IDA7 DQpEQ00ub3B0aW9ucy5zdG9jaGFzdGljID0gMDsNCkRDTS5vcHRpb25zLm5v Z3JhcGggICAgPSAxOw0KDQpEQ00uYSA9IFswIDEgMSAxIDE7IDEgMCAxIDEg MCA7IDEgMSAwIDEgMDsgMSAxIDEgMCAwOyAxIDAgMCAwIDBdOw0KRENNLmIg PSB6ZXJvcyg1LDUsMSk7ICBEQ00uYigyLDEsMikgPSAxOyAgDQpEQ00uYyA9 IFsxIDAgMCAwIDA7IDAgMCAwIDAgMDsgMCAwIDAgMCAwXTsNCkRDTS5kID0g emVyb3MoNSw1LDApOw0KDQpzYXZlKGZ1bGxmaWxlKGRhdGFfcGF0aCwnRENN X0sxX00xJyksJ0RDTScpOw0KDQolIFNQRUNJRklDQVRJT04gRENNICJhdHRl bnRpb25hbCBtb2R1bGF0aW9uIG9mIGZvcndhcmQgY29ubmVjdGlvbiINCiUt LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQ0KRENNLmIgPSB6ZXJvcyg1 LDUsMSk7ICBEQ00uYigzLDEsMikgPSAxOw0KDQpzYXZlKGZ1bGxmaWxlKGRh dGFfcGF0aCwnRENNX0sxX00yJyksJ0RDTScpOw0KDQolIEVTVElNQVRJT04N CiUtLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLQ0KJURDTV9LMU0xID0g c3BtX2RjbV9lc3RpbWF0ZShmdWxsZmlsZShkYXRhX3BhdGgsJ0RDTV9LMV9N MScpKTsNCiVEQ01fSzJNMiA9IHNwbV9kY21fZXN0aW1hdGUoZnVsbGZpbGUo ZGF0YV9wYXRoLCdEQ01fSzFfTTInKSk7DQoNCg== --0-1506575205-1305918436=:28942-- ========================================================================= Date: Fri, 20 May 2011 14:34:50 -0600 Reply-To: Andrea Burdine <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Andrea Burdine <[log in to unmask]> Subject: postdoc in data driven methods for fMRI and/or data fusion of multimodal imaging and genetics, ABQ, NM MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e649872eb3997604a3bb0d80 Message-ID: <[log in to unmask]> --0016e649872eb3997604a3bb0d80 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hello all, A postdoctoral fellowship position is currently available in Dr. Calhoun=92= s laboratory (see http://mialab.mrn.org) at The Mind Research Network (MRN) and the Department of Electrical and Computer Engineering at the University of New Mexico. The MRN houses a dedicated Siemens TIM Trio 3T MRI scanner and a 1.5T Mobile MRI scanner in addition to MEG, EEG, and a genetics lab. This position will focus on the development and application of statistical signal/image processing techniques and software for brain imaging data (e.g= . functional MRI, diffusion tensor imaging, structural MRI) and genome-wide genetic and epigenetic data of psychiatric patients. Of particular focus will be developing algorithms based upon the incorporation of prior information (neuroanatomical, functional, biological, etc.) within a source separation framework (independent component analysis, canonical correlation analysis, etc). The ideal candidate will have experience with image/signal processing and/or statistics, will be eager to learn about brain imaging, and is looking for an opportunity in a richly interdisciplinary research center. In addition to working on ongoing projects it is expected that the individual will pursue his/her own research interests. Our ideal candidate will have a Ph.D. in engineering, mathematics, statistics, or related field= . Prior expertise in brain image analysis preferred, but not required. Visit mrn.org and click on Jobs to apply. --=20 Andrea Burdine Mind Research Network Administrative Assistant for Vince D. Calhoun, Ph.D. Chief Technology Officer & Director, Image Analysis and MR Research 1101 Yale Blvd NE Albuquerque, NM 87106 505-301-5567 Office 505-272-8002 Fax --0016e649872eb3997604a3bb0d80 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hello all,<br> <p class=3D"MsoNormal">A postdoctoral fellowship position is currently avai= lable in Dr. Calhoun=92s laboratory (see <a href=3D"http://mialab.mrn.org" target=3D= "_blank">http://mialab.mrn.org</a>) at The Mind Research Network (MRN) and the Department of Electrical and Computer Engineering at = the University of New Mexico. The MRN houses a dedicated Siemens TIM Trio 3T MRI scanner and a 1.5T Mobile MRI scanner in addition t= o MEG, EEG, and a genetics lab. This position will focus on the development a= nd application of statistical signal/image processing techniques and software = for brain imaging data (e.g. functional MRI, diffusion tensor imaging, structural MRI= ) and genome-wide genetic and epigenetic data of psychiatric patients. Of particular focus will be developing algorithms based upon the incorporation= of prior information (neuroanatomical, functional, biological, etc.) within a source separation framework (independent component analysis, canonical correlation analysis, etc). The ideal candidate will have experience with image/signal processing and/or statistics, will be eager to learn about bra= in imaging, and is looking for an opportunity in a richly interdisciplinary research center. In addition to working on ongoing projects it is expected = that the individual will pursue his/her own research interests. Our ideal candid= ate will have a Ph.D. in engineering, mathematics, statistics, or related field= . Prior expertise in brain image analysis preferred, but not required. Visit <a href=3D"http://mrn.org" target=3D"_blank">mrn.org</a> and click on Jobs = to apply.</p> <br clear=3D"all"><br>-- <br>Andrea Burdine<br>Mind Research Network<br>Adm= inistrative Assistant for<br>Vince D. Calhoun, Ph.D.<br> Chief Technology Officer &<br> Director, Image Analysis and MR Research<br>1101 Yale Blvd NE<br>Albuquerqu= e, NM 87106<br><a href=3D"tel:505-301-5567" value=3D"+15053015567" target= =3D"_blank">505-301-5567</a> Office<br><a href=3D"tel:505-272-8002" value= =3D"+15052728002" target=3D"_blank">505-272-8002</a> Fax<br> <br> --0016e649872eb3997604a3bb0d80-- ========================================================================= Date: Fri, 20 May 2011 16:54:57 -0400 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: "cohort" and group effects Comments: To: Jason Steffener <[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]> Jason, If I understand you, you want to identify regions where OLD and YOUNG are different. I assume there is only 1 image per subject for the following scenarios: A) use 6 groups in a one-way ANOVA, then create an old/young contrast. This means that each cohort will have a different mean and variance that is unaffected by the other groups. B) if there are young and old from the same cohort of subjects, then I would have a variable for age group and variables for cohort. Then the contrast would be between the age group variables. The cohort variable is then just an extra term that reduces the variance. Personally, I like model A better as it doesn't infer that the cohort effect has to be same across the age groups as model B assumes. If you have repeated measures, subtract then you can only look at the age*measure interaction since main effects of group aren't valid in SPM. Let me know if you have any questions. On Friday, May 20, 2011, Jason Steffener <[log in to unmask]> wrote: > Hello all, > > I have data from 6 different "cohorts" that I would like to analysis > in one big group analysis. > > I have two age groups (yng and old). The yng data are from 4 separate > studies and the old are from 2 separate studies. > The data were collected in "chunks" over a couple of years. So 20 > young here, then another 20 young the following year etc. Although, > the training and task were the same I want to account for the fact > that the data were not collected continuously. > > My interest is in age differences and want to make sure that I account > for any "cohort" effects so I can correctly interpret any age group > effects. > > Based on box-plots of the behavioral data there is evidence of cohort effects. > > Thank you for any guidance, > Jason > > > > -- > Jason Steffener, Ph.D. > Department of Neurology > Columbia University > http://www.cogneurosci.org/steffener.html > -- 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, 20 May 2011 17:00:39 -0400 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: Time derivative in event related design 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]> See Investigating hemodynamic response variability at the group level using basis functions by Steffener et al. On Friday, May 20, 2011, Jonas Persson <[log in to unmask]> wrote: > Hello fellow SPM:ers, > > I am a bit confused with regards to using the time derivative in SPM. > > The issue is that I have an event related paradigm where I do post hoc so= rting of stimuli to 2 conditions. At the last SPM course I learned that sli= ce time correction should be avoided, so instead of using time slice correc= tion I wanted to include a second basis function to account for timing diff= erences in data acquisition between slices. > > To check for regions significantly more activated in cond 1 vs cond 2 I d= id an F-contrast [1 0 -1 0; 0 1 0 -1] to look for voxels associated with ei= ther the HRF or td. I masked this contrast inclusive with a t-contrast [1 0= -1 0] to only look at voxels with activation corresponding to the HRF. The= problem here is that I'm telling SPM to look for voxels with different tim= ing depending on condition, while I'm interested in allowing for timing dif= ferences between regions regardless of condition. > > Another variant I've thought of is to merely do a t-contrast [1 0 -1 0], = controlling for any variance associated with the td, but I read that this m= ay give biased results. > > The next issue is how to perform the 2nd level analysis. With the latter = method it's straightforward, but from what I understand I can't do a random= effects on F-contrasts? > > Thank you for any suggestions! > > Bert regards, > > Jonas Persson > --=20 Best Regards, Donald McLaren =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School Office: (773) 406-2464 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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, 21 May 2011 04:55:04 +0100 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 parameters of PPI Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all, =20=20=20 I wonder that how to calculate the PPI.P, PPI.Y and PPI.ppi in SPM: 1) Is PPI.P =3D conv(design, spm_hrf(TR)) correct? (Note: design is a one= -dimension array, which means the experimental design for scanning, such = as off-on-off-on-off, the most basic form of block-design) I've plot the two value, finding the shape nearly the same (but actually = the values were a little bit different) 2) Is PPI.Y =3D conv(Y, spm_hrf(TR)) correct? (Note: Y is generated via "= eigenvariate" button, which means the mean response in VOI)=20 It seems that values of PPI.Y ranges from (-2,2), indicating that it has = been standardized, whereas values of Y are much larger (e.g. (130, 160)) 3) Is PPI.ppi =3D PPI.P.*PPI.Y correct? I've also plot the two value, finding the shape nearly the same (but actu= ally the values were a little bit different) I hope someone can make me understood. Thanks a lot! Best wishes, Yang Hu ICN, ECNU, Shanghai, China ========================================================================= Date: Sat, 21 May 2011 10:57:02 +0100 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: question about spm_peb_ppi.m 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 would like to use spm_peb_ppi.m to generate the PPI. After reading = the comment of this file, I was only not clear about the parameter called= 'Uu'. Uu - Matrix of input variables and contrast weights. This is an= [n x 3] matrix. The first column indexes SPM.Sess.U(i). = The second column indexes the name of the input or cause, se= e SPM.Sess.U(i).name{j}. The third column is the contrast weight. Unless there are parametric effects the second colulmn will generally be a 1. =20 What does the n here mean? Does it mean the number of scans within o= ne run? What's more, SPM.Sess.U(i) is a structure with several fields so = I don't know how to put it in one column. Any suggestions are welcom. Thanks a lot! =20 Best wishes, Yang ICN, ECNU, Shanghai, China ========================================================================= Date: Sat, 21 May 2011 11:02:30 +0000 Reply-To: Giuseppe Signorello <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Giuseppe Signorello <[log in to unmask]> Subject: Time series Content-Type: multipart/alternative; boundary="_c89d05a1-43e7-43c8-ab9b-f5304159c54e_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_c89d05a1-43e7-43c8-ab9b-f5304159c54e_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi Spmrs=2C I'm working FMRI files and then the preprocessing (realignment=2C time slic= ing=2C etc.)=2C I would like to extract time series (temporal behaviour of = the signals from cerebellum). I have obtained the design matrix through the First model Specification in= SPm. From manual=2C I saw that I should define the T and F contrasts . Is it correct? How do I define these contrasts? Thanks for your answers. G.S. = --_c89d05a1-43e7-43c8-ab9b-f5304159c54e_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> Hi Spmrs=2C<br>I'm working FMRI files and then the preprocessing (realignme= nt=2C time slicing=2C etc.)=2C I would like to extract time series (tempora= l behaviour of the signals from cerebellum).<br>I =3B have obtained the= design matrix through the First model Specification in SPm.<br>From manual= =2C I saw that I should define the T and F contrasts .<br>Is it correct?<br= >How do I define these contrasts?<br><br><br>Thanks for your answers.<br><b= r>G.S.<br><br><br> </body> </html>= --_c89d05a1-43e7-43c8-ab9b-f5304159c54e_-- ========================================================================= Date: Sat, 21 May 2011 13:24:06 -0700 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: Time series Comments: To: Giuseppe Signorello <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf305e27dfe574b904a3cf0608 Message-ID: <[log in to unmask]> --20cf305e27dfe574b904a3cf0608 Content-Type: text/plain; charset=UTF-8 Check out MarsBaR for the time series extraction. Cheers On Sat, May 21, 2011 at 4:02 AM, Giuseppe Signorello <[log in to unmask]>wrote: > Hi Spmrs, > I'm working FMRI files and then the preprocessing (realignment, time > slicing, etc.), I would like to extract time series (temporal behaviour of > the signals from cerebellum). > I have obtained the design matrix through the First model Specification in > SPm. > From manual, I saw that I should define the T and F contrasts . > Is it correct? > How do I define these contrasts? > > > Thanks for your answers. > > G.S. > > > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf305e27dfe574b904a3cf0608 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Check out MarsBaR for the time series extraction. <br><br>Cheers<br><br><di= v class=3D"gmail_quote">On Sat, May 21, 2011 at 4:02 AM, Giuseppe Signorell= o <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">signo87@hotma= il.it</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; borde= r-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> <div> Hi Spmrs,<br>I'm working FMRI files and then the preprocessing (realign= ment, time slicing, etc.), I would like to extract time series (temporal be= haviour of the signals from cerebellum).<br>I=C2=A0 have obtained the desig= n matrix through the First model Specification in SPm.<br> From manual, I saw that I should define the T and F contrasts .<br>Is it co= rrect?<br>How do I define these contrasts?<br><br><br>Thanks for your answe= rs.<br><br>G.S.<br><br><br> </div> </blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associate<br>G= azzaley Lab<br>Department of Neurology<br>University of California, San Fra= ncisco<br> --20cf305e27dfe574b904a3cf0608-- ========================================================================= Date: Sun, 22 May 2011 17:58:03 +0100 Reply-To: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> Subject: three questions about SPM8 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hello,=20 I used SPM8 for my experiment. I manipulated three factors (e.g., factor = A, B, C) in my experiment. The activation maps under factor A was used as= a mask to constrain the effects of factor B and factor C. The problem is= that the factor A and factor B and factor C cannot be put in the same mo= del at second level analysis. I tried to put the .img file resulting from= factor A across participants in the =E2=80=9Cexplicit mask=E2=80=9D in t= he second level analysis. However, the estimation stopped by showing no i= nmask voxels. I searched through forum and did not find any useful soluti= ons. Can someone help me out? Moreover, is the threshold masking related = to the p value? If not, how should I set criteria for the activation map = of the mask?=20=20=20 I did notice that one person in the forum suggested that one can create a= n activation maps in which the factor B is modeled with the mask of facto= r A in first level analysis. Then, submit the resulting .img file into se= cond level analysis. My question is how to create and save the img file.=20= Finally, I posted a question regarding how to export beta values over mul= tiple coordinates a week ago. However, no one provide any answers. I wond= er whether it is due that no one has the answer or due to that there alre= ady is an answer in the forum. Thank you for your patience and advice. Sincerely, Yihui=20 =20 ========================================================================= Date: Sun, 22 May 2011 22:23:57 +0200 Reply-To: "Eickhoff, Simon" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Eickhoff, Simon" <[log in to unmask]> Subject: AW: [SPM] anatomy toolbox: small volume correction Comments: To: Gemma Lamp <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_000_4DEC2DB0EC56FA45A57A530E212839B5C0D24B6740MBXCLUSTER01a_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_4DEC2DB0EC56FA45A57A530E212839B5C0D24B6740MBXCLUSTER01a_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear Gemma Please note, that there are some differences in the way that SVC is perform= ed in SPM and the Anatomy toolbox. In SPM, the statistical image is first thresholded at the selected level. Y= ou then supply the SVC mask and get a new stats-table indicating which of t= he supro-threshold voxels (based on the previous threshold) are significant= at the SVC level. Voxels not passing the initial threshold are not conside= red, the display is not updated. In the Anatomy toolbox, the statistical image is re-loaded when you perform= the SVC, masked by the selected region of interest and thresholded such th= at only the SVC-corrected voxels are displayed. Hope this helps Simon ________________________________ Von: SPM (Statistical Parametric Mapping) [[log in to unmask]] im Auftrag v= on Gemma Lamp [[log in to unmask]] Gesendet: Donnerstag, 19. Mai 2011 04:32 An: [log in to unmask] Betreff: [SPM] anatomy toolbox: small volume correction Hi, I am currently doing an analysis where I'm limiting my ROIs to specified se= nsory areas. When looking at the stats I use a pre-made mask in SPM to do a= small volume correction. However, when I put it into anatomy toolbox, usin= g the same statistical procedure and then use the anatomy toolbox SVC with = the same ROI mask, i'm getting different cluster sizes and different areas = showing to that in my original stats. Is there a bug in the SVC through ana= tomy toolbox? Or is there something I am doing wrong? Is there an alternati= ve way to do a small volume correction with anatomy toolbox to keep consist= ent? Thanks, Gemma -- Gemma Lamp [http://www.discoveriesneeddollars.org/uploads/DND_email_signature.jpg] ---------------------------------------------------------------------------= --------------------- ---------------------------------------------------------------------------= --------------------- Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDirig Dr. Karl Eugen Huthmacher Geschaeftsfuehrung: Prof. Dr. Achim Bachem (Vorsitzender), Dr. Ulrich Krafft (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt, Prof. Dr. Sebastian M. Schmidt ---------------------------------------------------------------------------= --------------------- ---------------------------------------------------------------------------= --------------------- Besuchen Sie uns auf unserem neuen Webauftritt unter www.fz-juelich.de --_000_4DEC2DB0EC56FA45A57A530E212839B5C0D24B6740MBXCLUSTER01a_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html dir=3D"ltr"> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Diso-8859-= 1"> <meta name=3D"GENERATOR" content=3D"MSHTML 8.00.7600.16766"> <style title=3D"owaParaStyle"><!--P { MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px } --></style> </head> <body ocsi=3D"x"> <div dir=3D"ltr"><font color=3D"#000000" size=3D"2" face=3D"Tahoma">Dear Ge= mma</font></div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma">Please note, that there a= re some differences in the way that SVC is performed in SPM and the Anatomy= toolbox.</font></div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma">In SPM, the statistical i= mage is first thresholded at the selected level. You then supply the SVC ma= sk and get a new stats-table indicating which of the supro-threshold voxels= (based on the previous threshold) are significant at the SVC level. Voxels not passing the initial threshold are= not considered, the display is not updated.</font></div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma">In the Anatomy toolbox, t= he statistical image is re-loaded when you perform the SVC, masked by the s= elected region of interest and thresholded such that only the SVC-corrected= voxels are displayed.</font></div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma">Hope this helps</font></d= iv> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma">Simon</font></div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"><font size=3D"2" face=3D"tahoma"></font> </div> <div dir=3D"ltr"> </div> <div dir=3D"ltr"> </div> <div dir=3D"ltr"> <hr tabindex=3D"-1"> <font size=3D"2" face=3D"Tahoma"><b>Von:</b> SPM (Statistical Parametric Ma= pping) [[log in to unmask]] im Auftrag von Gemma Lamp [[log in to unmask] ]<br> <b>Gesendet:</b> Donnerstag, 19. Mai 2011 04:32<br> <b>An:</b> [log in to unmask]<br> <b>Betreff:</b> [SPM] anatomy toolbox: small volume correction<br> </font><br> </div> <div></div> <div>Hi,<br> <br> I am currently doing an analysis where I'm limiting my ROIs to specified se= nsory areas. When looking at the stats I use a pre-made mask in SPM to do a= small volume correction. However, when I put it into anatomy toolbox, usin= g the same statistical procedure and then use the anatomy toolbox SVC with the same ROI mask, i'm getting d= ifferent cluster sizes and different areas showing to that in my original s= tats. Is there a bug in the SVC through anatomy toolbox? Or is there someth= ing I am doing wrong? Is there an alternative way to do a small volume correction with anatomy toolbox to ke= ep consistent?<br> <br> Thanks,<br> Gemma<br clear=3D"all"> <br> -- <br> Gemma Lamp<br> <br> <img src=3D"http://www.discoveriesneeddollars.org/uploads/DND_email_signatu= re.jpg"><br> <br> </div> <br> <font face=3D"Arial" color=3D"Black" size=3D"1">---------------------------= ---------------------------------------------------------------------<br> ---------------------------------------------------------------------------= ---------------------<br> Forschungszentrum Juelich GmbH<br> 52425 Juelich<br> Sitz der Gesellschaft: Juelich<br> Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498<br> Vorsitzender des Aufsichtsrats: MinDirig Dr. Karl Eugen Huthmacher<br> Geschaeftsfuehrung: Prof. Dr. Achim Bachem (Vorsitzender),<br> Dr. Ulrich Krafft (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt,<br> Prof. Dr. Sebastian M. Schmidt<br> ---------------------------------------------------------------------------= ---------------------<br> ---------------------------------------------------------------------------= ---------------------<br> <br> Besuchen Sie uns auf unserem neuen Webauftritt unter www.fz-juelich.de<br> </font> </body> </html> --_000_4DEC2DB0EC56FA45A57A530E212839B5C0D24B6740MBXCLUSTER01a_-- ========================================================================= Date: Sun, 22 May 2011 19:19:26 -0700 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: three questions about SPM8 Comments: To: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3005df64823a9f04a3e81b08 Message-ID: <[log in to unmask]> --20cf3005df64823a9f04a3e81b08 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable when you say you tried to use the contrast as a mask, did you just use the raw contrast or did you binarize it? If you did the former than it is clear why you had no inmask voxels since any non-zero voxels would all be seen as part of the mask. So, what you want to do is load the mask, apply a threshold and binarize it by setting anything below the threshold to 0, and anything above to 1 (though the latter may not be completely necessary). Then, you could just use that mask to mask out the resulting contrast map from the other analysis. Here is code to do it: %Make mask V =3D spm_vol('contrast_image_2turn_into_mask') Y =3D spm_read_vols(V); threshold =3D X; %any value you choose. Y(find(Y<threshold)) =3D 0; Y(find(Y)) =3D 1; % Use mask VV =3D spm_vol('contrast_image_2mask'); YY =3D spm_vol(VV); YY(find(Y)) =3D 0; %apply mask VV.fname =3D 'masked_image.img'; spm_write_vol(VV,YY); %% There ya go Cheers, Michael On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE SPM Yihui Hung < [log in to unmask]> wrote: > Hello, > > I used SPM8 for my experiment. I manipulated three factors (e.g., factor = A, > B, C) in my experiment. The activation maps under factor A was used as a > mask to constrain the effects of factor B and factor C. The problem is th= at > the factor A and factor B and factor C cannot be put in the same model at > second level analysis. I tried to put the .img file resulting from factor= A > across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the second = level analysis. > However, the estimation stopped by showing no inmask voxels. I searched > through forum and did not find any useful solutions. Can someone help me > out? Moreover, is the threshold masking related to the p value? If not, h= ow > should I set criteria for the activation map of the mask? > > I did notice that one person in the forum suggested that one can create a= n > activation maps in which the factor B is modeled with the mask of factor = A > in first level analysis. Then, submit the resulting .img file into second > level analysis. My question is how to create and save the img file. > > Finally, I posted a question regarding how to export beta values over > multiple coordinates a week ago. However, no one provide any answers. I > wonder whether it is due that no one has the answer or due to that there > already is an answer in the forum. > > Thank you for your patience and advice. > Sincerely, > > Yihui > > --=20 Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf3005df64823a9f04a3e81b08 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable when you say you tried to use the contrast as a mask, did you just use the = raw contrast or did you binarize it? If you did the former than it is clear= why you had no inmask voxels since any non-zero voxels would all be seen a= s part of the mask. So, what you want to do is load the mask, apply a thres= hold and binarize it by setting anything below the threshold to 0, and anyt= hing above to 1 (though the latter may not be completely necessary). Then, = you could just use that mask to mask out the resulting contrast map from th= e other analysis. Here is code to do it:<br> <br>%Make mask<br><br>V =3D spm_vol('contrast_image_2turn_into_mask'= ;)<br>Y =3D spm_read_vols(V);<br><br>threshold =3D X; %any value you choose= .<br><br>Y(find(Y<threshold)) =3D 0;<br>Y(find(Y)) =3D 1;<br><br>% Use m= ask<br> <br>VV =3D spm_vol('contrast_image_2mask');<br>YY =3D spm_vol(VV);<= br><br>YY(find(Y)) =3D 0; %apply mask<br><br>VV.fname =3D 'masked_image= .img';<br>spm_write_vol(VV,YY);<br><br>%% There ya go<br><br>Cheers,<br= >Michael<br> <br><div class=3D"gmail_quote">On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE S= PM Yihui Hung <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask] tw">[log in to unmask]</a>></span> wrote:<br><blockquote class=3D"g= mail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> Hello,<br> <br> I used SPM8 for my experiment. I manipulated three factors (e.g., factor A,= B, C) in my experiment. The activation maps under factor A was used as a m= ask to constrain the effects of factor B and factor C. The problem is that = the factor A and factor B and factor C cannot be put in the same model at s= econd level analysis. I tried to put the .img file resulting from factor A = across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the second le= vel analysis. However, the estimation stopped by showing no inmask voxels. = I searched through forum and did not find any useful solutions. Can someone= help me out? Moreover, is the threshold masking related to the p value? If= not, how should I set criteria for the activation map of the mask?<br> <br> I did notice that one person in the forum suggested that one can create an = activation maps in which the factor B is modeled with the mask of factor A = in first level analysis. Then, submit the resulting .img file into second l= evel analysis. My question is how to create and save the img file.<br> <br> Finally, I posted a question regarding how to export beta values over multi= ple coordinates a week ago. However, no one provide any answers. I wonder w= hether it is due that no one has the answer or due to that there already is= an answer in the forum.<br> <br> Thank you for your patience and advice.<br> Sincerely,<br> <font color=3D"#888888"><br> Yihui<br> <br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br>Gazzaley Lab<br>Department of Neurology<br>University of California, = San Francisco<br> --20cf3005df64823a9f04a3e81b08-- ========================================================================= Date: Mon, 23 May 2011 10:40:54 +0200 Reply-To: Bart Boets <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bart Boets <[log in to unmask]> Subject: Vacancy for one postdoc and one PhD student in Leuven, Belgium Content-Type: multipart/alternative; boundary="_000_D290D66D539A4249887B042F194BE70D01957F6B21A1ICTSSEXC1CA_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_D290D66D539A4249887B042F194BE70D01957F6B21A1ICTSSEXC1CA_ Content-Type: text/plain; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable Can you please distribute this message to potentially interested people? We are looking for two bright and motivated researchers to work on a resear= ch project in which we will use brain imaging and advanced brain decoding m= ethods to investigate the neural basis of deviant mental representations in= neurodevelopmental disorders such as autism, dyslexia, and dyscalculia. Th= is project is an interdisciplinary collaboration between the Laboratory of = Biological Psychology (Hans Op de Beeck), the Parenting and Special Educati= on Research Group (Bert De Smedt), and Child Psychiatry (Jean Steyaert, Bar= t Boets), all part of the University of Leuven (K.U.Leuven) in Belgium. We offer one PhD position and one Post-doc position. Eligible candidates sh= ould have or should soon obtain a master=92s degree (for the PhD position) = and a PhD (for the postdoc position) in a relevant field such as neuroscien= ce, psychology, psychiatry, neurobiology, or medical engineering. Specific = positive points for the evaluation will be experience with brain imaging (i= n particular functional magnetic resonance imaging), a computational backgr= ound in a relevant field (e.g., brain imaging data analysis, neural network= s, etc.), and good computer skills (e.g., Matlab). A background in neurodev= elopmental disorders is a plus, but not mandatory. Good English (oral and w= ritten) communicative skills are necessary. The two positions are full-time, for a period of 4 years (PhD position) and= 2-3 years (postdoc), and start in October 2011. The two positions are stro= ngly research oriented, and teaching and/or administration load will be min= imal. The research will be embedded in a very stimulating environment with = state-of-the-art technical facilities and with ample opportunities for inte= raction with experts on neurodevelopmental disorders and brain imaging. The= K.U.Leuven is consistently ranked within the top of European Universities = and is located in the city of Leuven, which has an strong international app= eal. For more information about our university, please visit http://www.kul= euven.be/about/ Formal applications should be sent by email to Hans Op de Beeck, and should= include a scientific CV (mentioning past research positions and education,= and publications), motivation letter, and the names and contact informatio= n of 2 senior researchers that are willing to write a recommendation letter= if necessary (no letters have to be sent yet). Application deadline is Jun= e 20th 2011. For further information please contact Hans Op de Beeck (hans.opdebeeck@psy= .kuleuven.be<mailto:[log in to unmask]>), Bert De Smedt (Bert.D= [log in to unmask]<mailto:[log in to unmask]>), or Bart Boets= ([log in to unmask]<mailto:[log in to unmask]>). Bart Boets, PhD Postdoctoral fellow of the Research Foundation - Flanders Leuven Autism Research Consortium Child and Adolescent Psychiatry Herestraat 49 - box 7003, B-3000 Leuven, BELGIUM Tel. +32 (0)16/ 34.22.45 Fax. +32 (0)16/ 34.38.30 Email: [log in to unmask]<mailto:[log in to unmask]> --_000_D290D66D539A4249887B042F194BE70D01957F6B21A1ICTSSEXC1CA_ Content-Type: text/html; charset="Windows-1252" Content-Transfer-Encoding: quoted-printable <html dir=3D"ltr"><head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3DWindows-1= 252"> <style id=3D"owaTempEditStyle"></style><style title=3D"owaParaStyle"><!--P = { MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px } --></style> </head> <body ocsi=3D"x"> <div style=3D"FONT-FAMILY: Tahoma; DIRECTION: ltr; COLOR: #000000; FONT-SIZ= E: 13px"> <div></div> <div dir=3D"ltr"><font color=3D"#000000" size=3D"2" face=3D"Tahoma"> <p class=3D"MsoNormal">Can you please distribute this message to potentiall= y interested people?</p> <p class=3D"MsoNormal"> </p> <p class=3D"MsoNormal">We are looking for two bright and motivated research= ers to work on a research project in which we will use <b>brain imaging and advanced brain decoding methods to investigate the neu= ral basis of deviant mental representations in neurodevelopmental disorders= such as autism, dyslexia, and dyscalculia</b>. This project is an interdis= ciplinary collaboration between the Laboratory of Biological Psychology (Hans Op de Beeck), the Parenting = and Special Education Research Group (Bert De Smedt), and Child Psychiatry = (Jean Steyaert, Bart Boets), all part of the University of Leuven (K.U.Leuv= en) in Belgium. </p> <p class=3D"MsoNormal">We offer one PhD position and one Post-doc position.= Eligible candidates should have or should soon obtain a master=92s degree = (for the PhD position) and a PhD (for the postdoc position) in a relevant f= ield such as neuroscience, psychology, psychiatry, neurobiology, or medical engineering. Specific positive points= for the evaluation will be experience with brain imaging (in particular fu= nctional magnetic resonance imaging), a computational background in a relev= ant field (e.g., brain imaging data analysis, neural networks, etc.), and good computer skills (e.g., Matlab).= A background in neurodevelopmental disorders is a plus, but not mandatory.= Good English (oral and written) communicative skills are necessary.</p> <p style=3D"LINE-HEIGHT: 115%" class=3D"MsoPlainText">The two positions are= full-time, for a period of 4 years (PhD position) and 2-3 years (postdoc),= and start in October 2011. The two positions are strongly research oriente= d, and teaching and/or administration load will be minimal. The research will be embedded in a very stimulating = environment with state-of-the-art technical facilities and with ample oppor= tunities for interaction with experts on neurodevelopmental disorders and b= rain imaging. The K.U.Leuven is consistently ranked within the top of European Universities and is located= in the city of Leuven, which has an strong international appeal. For more = information about our university, please visit <a href=3D"http://www.kuleuven.be/about/" target=3D"_blank"><font color=3D"= #0000ff">http://www.kuleuven.be/about/</font></a> </p> <p style=3D"LINE-HEIGHT: 115%" class=3D"MsoPlainText"> </p> <p class=3D"MsoNormal">Formal applications should be sent by email to Hans = Op de Beeck, and should include a scientific CV (mentioning past research p= ositions and education, and publications), motivation letter, and the names= and contact information of 2 senior researchers that are willing to write a recommendation letter if necessary= (no letters have to be sent yet). Application deadline is June 20<sup>th</= sup> 2011.</p> <p class=3D"MsoNormal">For further information please contact Hans Op de Be= eck (<a href=3D"mailto:[log in to unmask]"><font color=3D"#0000= ff">[log in to unmask]</font></a>), Bert De Smedt (<a href=3D"m= ailto:[log in to unmask]"><font color=3D"#0000ff">Bert.DeSmedt@pe= d.kuleuven.be</font></a>), or Bart Boets (<a href=3D"mailto:[log in to unmask]"><font color= =3D"#0000ff">[log in to unmask]</font></a>). </p> <p class=3D"MsoNormal"><font face=3D"tahoma"></font> </p> <p class=3D"MsoNormal"> </p> </font></div> <div><font size=3D"2" face=3D"Tahoma"> <div> <div class=3D"MsoNormal"><font color=3D"navy" size=3D"1" face=3D"Arial"><sp= an style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 9pt" lang=3D"EN-GB"= >Bart Boets</span></font><font color=3D"navy" size=3D"1" face=3D"Arial"><sp= an style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 9pt" lang=3D"EN-GB"= >, PhD<br> Postdoctoral fellow of the Research Foundation - Flanders <br> Leuven Autism Research Consortium</span></font></div> <div class=3D"MsoNormal"><font color=3D"navy" size=3D"1" face=3D"Arial"><sp= an style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 9pt" lang=3D"EN-GB"= ><font size=3D"2" face=3D"arial">Child and Adolescent Psychiatry</font> <br> Herestraat 49 - box 7003, </span></font><font color=3D"navy" size=3D"1" fac= e=3D"Arial"><span style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 9pt"= lang=3D"EN-GB">B-3000 Leuven, BELGIUM</span></font><font color=3D"navy" si= ze=3D"1" face=3D"Arial"><span style=3D"FONT-FAMILY: Arial; COLOR: navy; FON= T-SIZE: 9pt" lang=3D"EN-GB"></span></font></div> <div class=3D"MsoNormal"><font color=3D"navy" size=3D"1" face=3D"Arial"><sp= an style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 7.5pt" lang=3D"EN-G= B"></span></font> </div> <div class=3D"MsoNormal"><font color=3D"navy" size=3D"1" face=3D"Arial"><sp= an style=3D"FONT-FAMILY: Arial; COLOR: navy; FONT-SIZE: 9pt" lang=3D"EN-GB"= >Tel. +32 (0)16/ 34.22.45<br> Fax. +32 (0)16/ 34.38.30<br> Email:</span></font><font color=3D"blue" size=3D"1" face=3D"Arial"><span st= yle=3D"FONT-FAMILY: Arial; COLOR: blue; FONT-SIZE: 9pt" lang=3D"EN-GB"> </span></font><font color=3D"blue" size=3D"1" face=3D"Arial"><span style=3D= "FONT-FAMILY: Arial; COLOR: blue; FONT-SIZE: 9pt"><a href=3D"mailto:bart.bo= [log in to unmask]"><span lang=3D"EN-GB">[log in to unmask]</span>= </a></span></font><font color=3D"blue" size=3D"1" face=3D"Arial"><span styl= e=3D"FONT-FAMILY: Arial; COLOR: blue; FONT-SIZE: 9pt"> <span lang=3D"EN-GB"></span></span></font></div> </div> </font></div> </div> </body> </html> --_000_D290D66D539A4249887B042F194BE70D01957F6B21A1ICTSSEXC1CA_-- ========================================================================= Date: Mon, 23 May 2011 11:11:40 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: Split Dicom folder Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Carolina, if it is sufficient to have the converted NIfTI files sorted, you can try= SPM DICOM import in "Expert mode". In SPMs batch GUI, select SPM->Util->= DICOM Import. There is a menu item called "Directory structure for conver= ted files". Here you can specify whether SPM should put converted files i= nto subject and session specific folders. Note that this option is not av= ailable if you run DICOM Import via the button in the SPM menu window. Hope this helps, Volkmar ========================================================================= Date: Mon, 23 May 2011 11:22:58 +0100 Reply-To: Sven Collette <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Sven Collette <[log in to unmask]> Subject: Re: question about spm_peb_ppi.m Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Yang, the uU in spm_peb_ppi.m defines your [nx3] PPI matrix, where n is the num= ber of your regressors. let's say you have 4 regressors [A B C D] , no parametric modulation, and= you want your psychological variable to be C-D. in this case your [nx3] = matrix would look like this: [ [1:4]' [1 1 1 1]' [0 0 1 -1]']. hope this helps, Sven ========================================================================= Date: Mon, 23 May 2011 14:13:28 +0100 Reply-To: Inci Ayhan <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Inci Ayhan <[log in to unmask]> Subject: converting from .IMA to nifti format MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Hi, I am trying to convert 3D EPI images (Siemens - .IMA format) into nifti format using SPM. Because the images do not have a .dcm extension, I cannot use "dicom2nifti" function. I would so much appreciate any help to suggest a way to do so. Thank you very much. Inci ========================================================================= Date: Mon, 23 May 2011 15:26:57 +0200 Reply-To: Francesco Barban <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Francesco Barban <[log in to unmask]> Subject: DCM.A DCM.B MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear DCM experts, is it allowed to test the modulatory effect (DCM.B) on a non significant (one sample t test) latent connection (DCM.A)? Thank you Francesco Barban ========================================================================= Date: Mon, 23 May 2011 09:11:05 -0500 Reply-To: Carolina Valencia <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carolina Valencia <[log in to unmask]> Subject: Re: Split Dicom folder Comments: To: Volkmar Glauche <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3056446dd6229004a3f20a12 Message-ID: <[log in to unmask]> --20cf3056446dd6229004a3f20a12 Content-Type: text/plain; charset=ISO-8859-1 Thanks very much for the help, It works! I just want to know (for simple curiosity) if there is a tool that split Dicom different from SPM to Ubuntu OS Thanks, Best regards, Carolina 2011/5/23 Volkmar Glauche <[log in to unmask]> > Dear Carolina, > > if it is sufficient to have the converted NIfTI files sorted, you can try > SPM DICOM import in "Expert mode". In SPMs batch GUI, select > SPM->Util->DICOM Import. There is a menu item called "Directory structure > for converted files". Here you can specify whether SPM should put converted > files into subject and session specific folders. Note that this option is > not available if you run DICOM Import via the button in the SPM menu window. > > Hope this helps, > > Volkmar > --20cf3056446dd6229004a3f20a12 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks very much for the help, It works!<div><br></div><div>I just want to = know (for simple curiosity) if there is a tool that split Dicom different f= rom SPM to Ubuntu OS</div><div><br></div><div>Thanks,</div><div><br></div> <div>Best regards,</div><div><br></div><div>Carolina<br><br><div class=3D"g= mail_quote">2011/5/23 Volkmar Glauche <span dir=3D"ltr"><<a href=3D"mail= to:[log in to unmask]">volkmar.glauche@uniklinik-freibur= g.de</a>></span><br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">Dear Carolina,<br> <br> if it is sufficient to have the converted NIfTI files sorted, you can try S= PM DICOM import in "Expert mode". In SPMs batch GUI, select SPM-&= gt;Util->DICOM Import. There is a menu item called "Directory struc= ture for converted files". Here you can specify whether SPM should put= converted files into subject and session specific folders. Note that this = option is not available if you run DICOM Import via the button in the SPM m= enu window.<br> <br> Hope this helps,<br> <font color=3D"#888888"><br> Volkmar<br> </font></blockquote></div><br><br clear=3D"all"><br> </div> --20cf3056446dd6229004a3f20a12-- ========================================================================= Date: Mon, 23 May 2011 17:19:14 +0200 Reply-To: Anne-Laure Fouque <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anne-Laure Fouque <[log in to unmask]> Subject: Problem with Dartel normalize to MNI / Warp to template MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=485b3979db46b397dc04a3f2ff1b Message-ID: <[log in to unmask]> --485b3979db46b397dc04a3f2ff1b Content-Type: text/plain; charset=ISO-8859-1 Hello everybody, I ran into a problem using Dartel, I'm not sure what I did wrong. Here is what the images went through: * new segmentation -> c1*, c2*, rc1*, rc2* ... everything looks OK * Dartel create template -> Template*, u_rc1* everything still looks OK * Dartel normalize to MNI - this doesn't work... smwc1* images are empty (filled with zeros) or there is just a weird white blob that doesn't look like a brain at all I tried changing some options, it didn't do any good. Just warping the images to the template (without the affine transformation to MNI) doesn't work any better. Does anyone have an idea about what could be wrong here ? Thanks in advance. --485b3979db46b397dc04a3f2ff1b Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello everybody,<div><br></div><div>I ran into a problem using Dartel, I= 9;m not sure what I did wrong.</div><div><br></div><div>Here is what the im= ages went through:</div><div>* new segmentation -> c1*, c2*, rc1*, rc2* = ... everything looks OK</div> <div>* Dartel create template -> Template*, u_rc1* =A0everything still l= ooks OK</div><div>* Dartel normalize to MNI - this doesn't work... smwc= 1* images are empty (filled with zeros) or there is just a weird white blob= that doesn't look like a brain at all</div> <div><br></div><div>I tried changing some options, it didn't do any goo= d. Just warping the images to the template (without the affine transformati= on to MNI) doesn't work any better.</div><div><br></div><div>Does anyon= e have an idea about what could be wrong here ?</div> <div><br></div><div>Thanks in advance.</div> --485b3979db46b397dc04a3f2ff1b-- ========================================================================= Date: Mon, 23 May 2011 11:44:39 -0400 Reply-To: adams adams <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: adams adams <[log in to unmask]> Subject: Unit for design Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_1b0d0039-e2e6-4eb1-8b53-ae4514dc3633_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_1b0d0039-e2e6-4eb1-8b53-ae4514dc3633_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable hello Everyone=2C Just a quick a question=2C during the first level analysis=2C for the unit design do we use Scan or Se= cond=2C are they the same.... Than you for your help = --_1b0d0039-e2e6-4eb1-8b53-ae4514dc3633_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> <br><br> <meta http-equiv=3D"Content-Type" content=3D"text/html=3B charset=3Dunicode= "> <meta name=3D"Generator" content=3D"Microsoft SafeHTML"> <style> .ExternalClass .ecxhmmessage P {padding:0px=3B} .ExternalClass body.ecxhmmessage {font-size:10pt=3Bfont-family:Tahoma=3B} </style> hello Everyone=2C<br>Just a quick a question=2C<br>during the first level a= nalysis=2C for the unit design do we use Scan or Second=2C are they the sam= e....<br>Than you =3B for your help<br><br><br> </body> </html>= --_1b0d0039-e2e6-4eb1-8b53-ae4514dc3633_-- ========================================================================= Date: Mon, 23 May 2011 11:44:10 -0400 Reply-To: adams adams <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: adams adams <[log in to unmask]> Subject: Re: Problem with Dartel normalize to MNI / Warp to template Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="_57de950d-93fb-48d9-94cb-c5a619dd2b91_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_57de950d-93fb-48d9-94cb-c5a619dd2b91_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable hello Everyone=2C Just a quick a question=2C during the first level analysis=2C for the unit design do we use Scan or Se= cond=2C are they the same.... Than you for your help Date: Mon=2C 23 May 2011 17:19:14 +0200 From: [log in to unmask] Subject: [SPM] Problem with Dartel normalize to MNI / Warp to template To: [log in to unmask] Hello everybody=2C I ran into a problem using Dartel=2C I'm not sure what I did wrong. Here is what the images went through:* new segmentation -> c1*=2C c2*=2C rc= 1*=2C rc2* ... everything looks OK * Dartel create template -> Template*=2C u_rc1* everything still looks OK*= Dartel normalize to MNI - this doesn't work... smwc1* images are empty (fi= lled with zeros) or there is just a weird white blob that doesn't look like= a brain at all I tried changing some options=2C it didn't do any good. Just warping the im= ages to the template (without the affine transformation to MNI) doesn't wor= k any better. Does anyone have an idea about what could be wrong here ? Thanks in advance. = --_57de950d-93fb-48d9-94cb-c5a619dd2b91_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> hello Everyone=2C<br>Just a quick a question=2C<br>during the first level a= nalysis=2C for the unit design do we use Scan or Second=2C are they the sam= e....<br>Than you =3B for your help<br><br><br><hr id=3D"stopSpelling">= Date: Mon=2C 23 May 2011 17:19:14 +0200<br>From: [log in to unmask] <br>Subject: [SPM] Problem with Dartel normalize to MNI / Warp to template<= br>To: [log in to unmask]<br><br>Hello everybody=2C<div><br></div><div>I ra= n into a problem using Dartel=2C I'm not sure what I did wrong.</div><div><= br></div><div>Here is what the images went through:</div><div>* new segment= ation ->=3B c1*=2C c2*=2C rc1*=2C rc2* ... everything looks OK</div> <div>* Dartel create template ->=3B Template*=2C u_rc1*  =3Beverythin= g still looks OK</div><div>* Dartel normalize to MNI - this doesn't work...= smwc1* images are empty (filled with zeros) or there is just a weird white= blob that doesn't look like a brain at all</div> <div><br></div><div>I tried changing some options=2C it didn't do any good.= Just warping the images to the template (without the affine transformation= to MNI) doesn't work any better.</div><div><br></div><div>Does anyone have= an idea about what could be wrong here ?</div> <div><br></div><div>Thanks in advance.</div> </body> </html>= --_57de950d-93fb-48d9-94cb-c5a619dd2b91_-- ========================================================================= Date: Mon, 23 May 2011 11:50:06 -0400 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: Unit for design Comments: To: adams adams <[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 refers to how the onset times and durations are encoded. If the insets are in seconds, select seconds. If the onset time is in scans/TR select scans. On Monday, May 23, 2011, adams adams <[log in to unmask]> wrote: > > > > > > > > hello Everyone, > Just a quick a question, > during the first level analysis, for the unit design do we use Scan or Se= cond, are they the same.... > Than you=A0 for your help > > > =09 > --=20 Best Regards, Donald McLaren =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School Office: (773) 406-2464 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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, 23 May 2011 10:26:12 -0700 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: =?UTF-8?Q?=E5=9B=9E=E8=A6=86=EF=B9=95_?=[SPM] three questions about SPM8 Comments: To: a489930026 <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba53a2e05ba28104a3f4c667 Message-ID: <[log in to unmask]> --90e6ba53a2e05ba28104a3f4c667 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable threshold needs to be>0. if your map is p values, then just do: Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant. then set your p threshold as 1-threshold, so .95 =3D=3D .05 Determining whether you use the mask inclusively or exlusively can be done when you apply it, so: YY(find(Y)) =3D 0; % exclusive mask YY(find(~Y)) =3D 0; %inclusive mask Try running each line separately (or within a script) to see where exactly the error is occurring. Cheers, Michael On Mon, May 23, 2011 at 12:48 AM, a489930026 <[log in to unmask]>wrote= : > Dear Michael, > > Thank you for the advice. I did what you suggested but a result "Assignme= nt > between unlike types is not allowed." What could be the problem? > > Please see the script below: > > ********************************************************* > >> V=3D spm_vol('L:\2stlevel\wordVSblk\spmF_0001.img') > Y =3D spm_read_vols(V); > > threshold =3D 0; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('L:\2stlevel\radpos_sdXloca_sd\spmF_0001.img'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > V =3D > > fname: 'L:\2stlevel\wordVSblk\spmF_0001.img' > mat: [4x4 double] > dim: [79 95 68] > dt: [16 0] > pinfo: [3x1 double] > n: [1 1] > descrip: 'SPM{F_[1.0,15.0]} - contrast 1: w-b' > private: [1x1 nifti] > > ??? Assignment between unlike types is not allowed. > > ************************************************************************* > > Besides, is the threshold a cutoff for the activation maps of the mask? > Specially, does setting threshold =3D 0.05 mean that the activated voxels= below p =3D0.05 are included. If not, how should I set a cutoff on the p > value, or it does matter? > > Moreover, how can I determine the mask is inclusive (i.e., the voxles in > the to-be-masked activation maps are within the volxels in the activation > maps of the mask) or exclusive (i.e., the voxles in the to-be-masked > activation maps are NOT within the volxels in the activation maps of the > mask). > Thank you very much! > > Sincerely, > Yihui > > > > > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* SUBSCRIBE SPM Yihui Hung <a4899300= [log in to unmask]> > *=E5=89=AF=E6=9C=AC=EF=BC=9A* [log in to unmask] > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/23 (=E4=B8=80) 10:= 19 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: [SPM] three questions about SPM8 > > when you say you tried to use the contrast as a mask, did you just use th= e > raw contrast or did you binarize it? If you did the former than it is > clear why you had no inmask voxels since any non-zero voxels would all be > seen as part of the mask. So, what you want to do is load the mask, apply= a > threshold and binarize it by setting anything below the threshold to 0, > and anything above to 1 (though the latter may not be completely necesary= ). > Then, you could just use that mask to mask out the resulting contrast map > from the other analysis. Here is code to do it: > > %Make mask > > V =3D spm_vol('contrast_image_2turn_into_mask') > Y =3D spm_read_vols(V); > > threshold =3D X; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('contrast_image_2mask'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > Cheers, > Michael > > On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE SPM Yihui Hung < > [log in to unmask]> wrote: > > Hello, > > I used SPM8 for my experiment. I manipulated three factors (e.g., factor > A, B, C) in my experiment. The activation maps under factor A was used as= a > mask to constrain the effects of factor B and factor C. The problem is th= at > the factor A and factor B and factor C cannot be put in the same model at > second level analysis. I tried to put the .img file resulting from factor > A across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the secon= d level analysis. > However, the estimation stopped by showing no inmask voxels. I searched > through forum and did not find any useful solutions. Can someone help me > out? Moreover, is the threshold masking related to the p value? If not, h= ow > should I set criteria for the activation map of the mask? > > I did notice that one person in the forum suggested that one can create a= n > activation maps in which the factor B is modeled with the mask of factor = A > in first level analysis. Then, submit the resulting .img file into second > level analysis. My question is how to create and save the img file. > > Finally, I posted a question regarding how to export beta values over > multiple coordinates a week ago. However, no one provide any answers. I > wonder whether it is due that no one has the answer or due to that there > already is an answer in the forum. > > Thank you for your patience and advice. > Sincerely, > > Yihui > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > --=20 Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --90e6ba53a2e05ba28104a3f4c667 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable threshold needs to be>0. if your map is p values, then just do:<br><br>Y= =3D 1-Y; %now p =3D 1-p, so higher values are more significant.<br><br>the= n set your p threshold as 1-threshold, so .95 =3D=3D .05<br><br>Determining= whether you use the mask inclusively or exlusively can be done when you ap= ply it, so: <br> <br><span><span>YY</span></span>(find(Y)) =3D 0; % exclusive mask<br><br>YY= (find(~Y)) =3D 0; %inclusive mask<br><br><br>Try running each line separate= ly (or within a script) to see where exactly the error is occurring.<br><br= > <br>Cheers,<br>Michael<br><br><div class=3D"gmail_quote">On Mon, May 23, 20= 11 at 12:48 AM, a489930026 <span dir=3D"ltr"><<a href=3D"mailto:a4899300= [log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquo= te class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1= px solid rgb(204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-family: arial,helvetica,sans-serif; font-size: 12pt;"><div style=3D= "font-size: 12pt; font-family: arial,helvetica,sans-serif;"><span>Dear Mich= ael,</span></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><s= pan><br></span></div><div style=3D"font-size: 12pt; font-family: arial,helv= etica,sans-serif;">Thank you for the advice. I did what you suggested but a= result "Assignment between unlike types is not allowed." What co= uld be the problem?</div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-se= rif;">Please see the script below:</div><div style=3D"font-size: 12pt; font= -family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;">*********************************************************</div><div= ><div>>> V=3D <span><span>spm</span></span>_vol('L:\2<span><span>= stlevel</span></span>\<span><span>wordVSblk</span></span>\<span><span>spmF<= /span></span>_0001.<span><span>img</span></span>')</div> <div>Y =3D <span><span>spm</span></span>_read_vols(V);</div><div><br></div>= <div>threshold =3D 0; %any value you choose.</div><div><br></div><div>Y(fin= d(Y<threshold)) =3D 0;</div><div>Y(find(Y)) =3D 1;</div><div><br></div><div>% Use mask</div><d= iv><br></div><div><span><span>VV</span></span> =3D <span><span>spm</span></= span>_vol('L:\2<span><span>stlevel</span></span>\<span><span>radpos</sp= an></span>_<span><span>sdXloca</span></span>_<span><span>sd</span></span>\<= span><span>spmF</span></span>_0001.<span><span>img</span></span>');</di= v> <div><span><span>YY</span></span> =3D <span><span>spm</span></span>_vol(<sp= an><span>VV</span></span>);</div><div><br></div><div><span><span>YY</span><= /span>(find(Y)) =3D 0; %apply mask</div><div><br></div><div><span><span>VV<= /span></span>.<span><span>fname</span></span> =3D 'masked_image.<span><= span>img</span></span>';</div> <div><span><span>spm</span></span>_write_vol(<span><span>VV</span></span>,<= span><span>YY</span></span>);</div><div><br></div><div>%% There ya go</div>= <div><br></div><div>V =3D=C2=A0</div><div><br></div><div>=C2=A0 =C2=A0 =C2= =A0 <span><span>fname</span></span>: 'L:\2<span><span>stlevel</span></s= pan>\<span><span>wordVSblk</span></span>\<span><span>spmF</span></span>_000= 1.<span><span>img</span></span>'</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 mat: [4x4 double]</div><div>=C2=A0 =C2=A0 = =C2=A0 =C2=A0 dim: [79 95 68]</div><div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0<= span><span>dt</span></span>: [16 0]</div><div>=C2=A0 =C2=A0 =C2=A0 <span><s= pan>pinfo</span></span>: [3x1 double]</div><div>=C2=A0 =C2=A0 =C2=A0 =C2=A0= =C2=A0 n: [1 1]</div><div> =C2=A0 =C2=A0 <span><span>descrip</span></span>: 'SPM{F_[1.0,15.0]} - contrast 1: w-b'</div><div>=C2=A0 =C2=A0 private: [1x1 <span><span>nif= ti</span></span>]</div><div><br></div><div>??? Assignment between unlike ty= pes is not allowed.</div><div><br></div><div>******************************= *******************************************</div> <div><br></div><div>Besides, is the threshold a cutoff for the activation m= aps of the mask? Specially, does setting threshold =3D 0.05 mean that the a= ctivated <span><span>voxels</span></span> below p =3D0.05 are included. If = not, how should I set a cutoff on the p value, or it does matter?=C2=A0</di= v> <div><br></div><div>Moreover, how can I determine the mask is inclusive (i.= e., the <span><span>voxles</span></span> in the to-be-masked activation maps are within the <span><span>volxels</span></sp= an> in the activation maps of the mask) or exclusive=C2=A0(i.e., the <span>= <span>voxles</span></span> in the to-be-masked activation maps are NOT with= in the <span><span>volxels</span></span> in <span>the activation</span> map= s of the mask).=C2=A0</div> <div>Thank you very much!</div><div><br></div><div>Sincerely,</div><div><sp= an><span>Yihui</span></span></div></div><div style=3D"font-size: 12pt; font= -family: arial,helvetica,sans-serif;">=C2=A0</div><div style=3D"font-size: = 12pt; font-family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial,helveti= ca,sans-serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial= ,helvetica,sans-serif;"> <br><blockquote style=3D"border-left: 2px solid rgb(16, 16, 255); margin-le= ft: 5px; padding-left: 5px;"><div style=3D"font-size: 12pt; font-family: ar= ial,helvetica,sans-serif;"><div style=3D"font-size: 12pt; font-family: '= ;times new roman','new york',times,serif;"> <font face=3D"Arial" size=3D"2"><hr size=3D"1"><b><span style=3D"font-weigh= t: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> Michael T Rubens = <<a href=3D"mailto:[log in to unmask]" target=3D"_blank">MikeRubens@gm= ail.com</a>><br><b><span style=3D"font-weight: bold;">=E6=94=B6=E4=BB=B6= =E8=80=85=EF=BC=9A</span></b> SUBSCRIBE <span><span>SPM</span></span> <span= ><span>Yihui</span></span> Hung <<a href=3D"mailto:[log in to unmask] tw" target=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=89=AF=E6=9C=AC=EF=BC=9A</span></b= > <a href=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] k</a><br><b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E6=97=A5= =E6=9C=9F=EF=BC=9A</span></b> 2011/5/23 (=E4=B8=80) 10:19 AM<br><b><span st= yle=3D"font-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b> Re: [<spa= n><span>SPM</span></span>] three questions about <span><span>SPM</span></sp= an>8<br> </font><br><div>when you say you tried to use the contrast as a mask, did y= ou just use the raw contrast or did you <span><span>binarize</span></span> it? If you did the former than it is clear why you had no <span><span>inma= sk</span></span> <span><span>voxels</span></span> since any non-zero <span>= <span>voxels</span></span> would all be seen as part of the mask. So, what = you want to do is load the mask, apply a threshold and <span><span>binarize= </span></span> it by setting anything below the threshold to 0, and anythin= g above to 1 (though the latter may not be completely <span><span>necesary<= /span></span>). Then, you could just use that mask to mask out the resulting contrast map from the other analysis. Here is code to do it:<br> <br>%Make mask<br><br>V =3D <span><span>spm</span></span>_vol('contrast= _image_2turn_into_mask')<br>Y =3D <span><span>spm</span></span>_read_vo= ls(V);<br><br>threshold =3D X; %any value you choose.<br><br>Y(find(Y<th= reshold)) =3D 0;<br> Y(find(Y)) =3D 1;<br><br>% Use mask<br> <br><span><span>VV</span></span> =3D <span><span>spm</span></span>_vol('= ;contrast_image_2mask');<br><span><span>YY</span></span> =3D <span><spa= n>spm</span></span>_vol(<span><span>VV</span></span>);<br><br><span><span>Y= Y</span></span>(find(Y)) =3D 0; %apply mask<br> <br><span><span>VV</span></span>.<span><span>fname</span></span> =3D 'm= asked_image.<span><span>img</span></span>';<br><span><span>spm</span></= span>_write_vol(<span><span>VV</span></span>,<span><span>YY</span></span>);= <br> <br>%% There ya go<br><br>Cheers,<br>Michael<br> <br><div>On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE <span><span>SPM</span><= /span> <span><span>Yihui</span></span> Hung <span dir=3D"ltr"><<a rel=3D= "nofollow" href=3D"mailto:[log in to unmask]" target=3D"_blank">a48993= [log in to unmask]</a>></span> wrote:<br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> Hello,<br> <br> I used <span><span>SPM</span></span>8 for my experiment. I manipulated thre= e factors (e.g., factor A, B, C) in my experiment. The activation maps unde= r factor A was used as a mask to constrain the effects of factor B and fact= or C. The problem is that the factor A and factor B and factor C cannot be = put in the same model at second level analysis. I tried to put the .<span><= span>img</span></span> file resulting from factor A across participants in = the =E2=80=9Cexplicit mask=E2=80=9D in the second level analysis. However, = the estimation stopped by showing no <span><span>inmask</span></span> <span= ><span>voxels</span></span>. I searched through forum and did not find any = useful solutions. Can someone help me out? Moreover, is the threshold maski= ng related to the p value? If not, how should I set criteria for the activa= tion map of the mask?<br> <br> I did notice that one person in the forum suggested that one can create an = activation maps in which the factor B is <span><span>modeled</span></span> = with the mask of factor A in first level analysis. Then, submit the resulti= ng .<span><span>img</span></span> file into second level analysis. My quest= ion is how to create and save the <span><span>img</span></span> file.<br> <br> Finally, I posted a question regarding how to export beta values over multi= ple coordinates a week ago. However, no one provide any answers. I wonder w= hether it is due that no one has the answer or due to that there already is= an answer in the forum.<br> <br> Thank you for your patience and advice.<br> Sincerely,<br> <font color=3D"#888888"><br> <span><span>Yihui</span></span><br> <br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br><span><span>Gazzaley</span></span> Lab<br>Department of Neurology<br>= University of California, San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Dep= artment of Neurology<br>University of California, San Francisco<br> --90e6ba53a2e05ba28104a3f4c667-- ========================================================================= Date: Mon, 23 May 2011 19:34:20 +0200 Reply-To: Artur Marchewka <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Artur Marchewka <[log in to unmask]> Subject: MRI Brain Imaging Core Facility leader - The Nencki Institute of Experimental Biology, Polish Academy of Sciences Content-Type: multipart/alternative; boundary=Apple-Mail-14--700137924 Mime-Version: 1.0 (Apple Message framework v1084) Message-ID: <[log in to unmask]> --Apple-Mail-14--700137924 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 The Nencki Institute of Experimental Biology, Polish Academy of = Sciences, is currently seeking a highly motivated experienced individual = to lead its newly forming MRI Brain Imaging Core Facility, within the = newly constructed Neurobiology Centre. This position is created using funds from the FP7 Capacities contract = number 264173 co-financed by the European Commission: Bio-imaging in = research, innovation and education (BIO-IMAGINE). Offer: A fixed-term position for the initial term of 2 years with a maximum = gross salary of 4200 euro / month (depending on qualifications and = experience). This position is a subject to extension to 5 years. The = position can be converted into a permanent position. Possibility to hire additional technical support staff and/or = post-doctoral associates to be discussed during the negotiations = process. Active participation in shaping up and launching of the newly = constructed Brain imaging core facility (approx. 150 m2) with a purchase = of new 3T MRI valued at approx 2 mln euro to be purchased and installed = at the end of 2011 (see additional information). Active engagement in Euro-BioImaging pan-European research = infrastructure project (FP7) and the National imaging research centre in = biological and biomedical sciences (see additional information). Expectations: It is expected that this person should have a strong research and = technology background in the field of neuro-imaging using functional = magnetic resonance and related technologies. A successful candidate will = develop new research approaches and collaborations in neuro-imaging of = the brain and the nervous system and train the institute researchers on = using the equipment and setting up experiments. Successful candidate = should facilitate research experiments for scientists at the Institute = and in partnering institutions of the National imaging research centre = in biological and biomedical sciences. S/he will also be expected to = develop a vision of facility development and secure research, clinical = and private sector external users. It is also expected that the core = facility leader will create networking among national and international = brain imaging facilities. It is also expected that the leader will seek = external financing and actively pursue collaboration with clinical = facilities and diagnostic centres. New MRI scanner is expected to be = installed towards the end of 2011. It would be desirable for the = successful candidate to assist in this process. Application process: The application should contain the following documents/information: =95 A cover letter with a vision of functioning and strategy for = development of the forming core facility and its scientific/technical = team =95 Candidate=92s CV with contact information to at least 3 = references=95 Copy of Candidate=92s PhD diploma (or equivalent) =95 = Candidate=92s contact information, including e-mail address and phone = number =95 Candidate may include additional information or copies = of documents/certificates in support of the application. Application = documents in English (pdf format) may be uploaded at https://recruitment.nencki.gov.pl?call=3Dfbicf Application deadline: July 15, 2011 Selection criteria: The key = evaluation criteria will be the Candidate=92s: =95 leadership, management and communication skills and experience =95= experience (5 years or more) in using and developing advanced = magnetic resonance imaging methods, image processing and data analysis =95 = experience in working at one or more of the recognized fMRI imaging = facilities at leading research institutions =95 technical knowledge in the area = of fMRI functioning, experiment design, data management and image post-processing techniques =95 publications or = other verifiable scientific achievements in the field Short-listed candidates will be invited for personal interviews. = Interviews will take place at the Nencki Institute around mid- = September. Top candidate will be presented an offer by the end of September and = will be expected to finally accept or decline the offer by mid-October. The employment start target date: January 1, 2012= --Apple-Mail-14--700137924 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 12px/normal Arial; "><b>The = Nencki Institute of Experimental Biology, Polish Academy of Sciences, is = currently seeking a highly motivated experienced individual to lead its = newly forming MRI Brain Imaging Core Facility, within the newly = constructed Neurobiology Centre.</b></div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">This position is created using funds = from the FP7 Capacities contract number 264173 co-financed by the = European Commission: Bio-imaging in research, innovation and education = (BIO-IMAGINE).</div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; "><b>Offer:</b></div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">A fixed-term position for the initial = term of 2 years with a maximum gross salary of 4200 euro / month = (depending on qualifications and experience). This position is a subject = to extension to 5 years. The position can be converted into a permanent = position.</div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; ">Possibility to hire additional technical support = staff and/or post-doctoral associates to be discussed during the = negotiations process.</div><div style=3D"margin-top: 0px; margin-right: = 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; ">Active participation in shaping up and launching of = the newly constructed Brain imaging core facility (approx. 150 m<span = style=3D"font: 8.0px Arial">2</span>) with a purchase of new 3T MRI = valued at approx 2 mln euro to be purchased and installed at the end of = 2011 (see additional information).</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">Active engagement in Euro-BioImaging = pan-European research infrastructure project (FP7) and the National = imaging research centre in biological and biomedical sciences (see = additional information).</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; "><b>Expectations:</b></div><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 12px/normal Arial; ">It is = expected that this person should have a strong research and technology = background in the field of neuro-imaging using functional magnetic = resonance and related technologies. A successful candidate will develop = new research approaches and collaborations in neuro-imaging of the brain = and the nervous system and train the institute researchers on using the = equipment and setting up experiments. Successful candidate should = facilitate research experiments for scientists at the Institute and in = partnering institutions of the National imaging research centre in = biological and biomedical sciences. S/he will also be expected to = develop a vision of facility development and secure research, clinical = and private sector external users. It is also expected that the core = facility leader will create networking among national and international = brain imaging facilities. It is also expected that the leader will seek = external financing and actively pursue collaboration with clinical = facilities and diagnostic centres. New MRI scanner is expected to be = installed towards the end of 2011. It would be desirable for the = successful candidate to assist in this process.</div><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 12px/normal Arial; = "><b>Application process:</b></div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">The application should contain the = following documents/information:</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; "><span style=3D"font: 10.0px = Helvetica">=95<span class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>A cover letter with a vision of functioning and strategy = for development of the forming core facility and its = scientific/technical team</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; "><span style=3D"font: 10.0px = Helvetica">=95<span class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>Candidate=92s CV with contact information to at least 3 = references<span style=3D"font: 10.0px Helvetica">=95<span = class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>Copy of Candidate=92s PhD diploma (or equivalent) <span = style=3D"font: 10.0px Helvetica">=95<span class=3D"Apple-tab-span" = style=3D"white-space:pre"> </span></span>Candidate=92s contact = information, including e-mail address and phone number <span = style=3D"font: 10.0px Helvetica">=95<span class=3D"Apple-tab-span" = style=3D"white-space:pre"> </span></span>Candidate may include = additional information or copies of</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">documents/certificates in support of = the application. Application documents in English (pdf format) may be = uploaded at</div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; color: rgb(62, 0, 255); "><a = href=3D"https://recruitment.nencki.gov.pl?call=3Dfbicf">https://recruitmen= t.nencki.gov.pl?call=3Dfbicf</a></div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; "><b>Application deadline: </b><span = style=3D"color: #a60000"><b>July 15, 2011 </b></span><b>Selection = criteria: </b>The key evaluation criteria will be the = Candidate=92s:</div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; "><span style=3D"font: 10.0px Helvetica">=95<span = class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>leadership, management and communication skills and = experience <span style=3D"font: 10.0px Helvetica">=95<span = class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>experience (5 years or more) in using and developing = advanced magnetic</div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; ">resonance imaging methods, image processing and = data analysis <span style=3D"font: 10.0px Helvetica">=95<span = class=3D"Apple-tab-span" style=3D"white-space:pre"> = </span></span>experience in working at one or more of the recognized = fMRI imaging facilities</div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; ">at leading research institutions = <span style=3D"font: 10.0px Helvetica">=95<span class=3D"Apple-tab-span" = style=3D"white-space:pre"> </span></span>technical knowledge in the = area of fMRI functioning, experiment design, data</div><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 12px/normal Arial; = ">management and image post-processing techniques <span style=3D"font: = 10.0px Helvetica">=95<span class=3D"Apple-tab-span" = style=3D"white-space:pre"> </span></span>publications or other = verifiable scientific achievements in the field</div><div = style=3D"margin-top: 0px; margin-right: 0px; margin-bottom: 0px; = margin-left: 0px; font: normal normal normal 12px/normal Arial; = ">Short-listed candidates will be invited for personal interviews. = Interviews will take place at the Nencki Institute <b>around mid- = September.</b></div><div style=3D"margin-top: 0px; margin-right: 0px; = margin-bottom: 0px; margin-left: 0px; font: normal normal normal = 12px/normal Arial; ">Top candidate will be presented an offer <b>by the = end of September </b>and will be expected to finally accept or decline = the offer by <b>mid-October.</b></div><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal = normal normal 12px/normal Arial; "><b>The employment start target date: = </b><span style=3D"color: #a60000"><b>January 1, = 2012</b></span></div></body></html>= --Apple-Mail-14--700137924-- ========================================================================= Date: Mon, 23 May 2011 19:44:43 +0100 Reply-To: Carlos Faraco <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Carlos Faraco <[log in to unmask]> Subject: Re: SPM8 Reorients, but gives errors Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Does anyone have any idea about this? Thanks. ========================================================================= Date: Mon, 23 May 2011 12:16:58 -0700 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: =?UTF-8?Q?=E5=9B=9E=E8=A6=86=EF=B9=95_=E5=9B=9E=E8=A6=86=EF=B9=95_?=[SPM] three questions about SPM8 Comments: To: a489930026 <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3040ea427d213e04a3f652b9 Message-ID: <[log in to unmask]> --20cf3040ea427d213e04a3f652b9 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I see now, I made a mistake in the original code I sent. This line: YY =3D spm_vol(VV); should actually be: YY =3D spm_read_vols(VV); %% Cheers, Michael On Mon, May 23, 2011 at 11:03 AM, a489930026 <[log in to unmask]>wrote= : > Dear Michael, > > Every step went smoothly until the step "YY(find(Y)) =3D 0" and then the = it > showed "Assignment between unlike types is not allowed." > > Yihui > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* a489930026 <[log in to unmask] w>; spm <[log in to unmask]> > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/24 (=E4=BA=8C) 1:2= 6 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: =E5=9B=9E=E8=A6=86=EF=B9=95 [SPM] three= questions about SPM8 > > threshold needs to be>0. if your map is p values, then just do: > > Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant. > > then set your p threshold as 1-threshold, so .95 =3D=3D .05 > > Determining whether you use the mask inclusively or exlusively can be don= e > when you apply it, so: > > YY(find(Y)) =3D 0; % exclusive mask > > YY(find(~Y)) =3D 0; %inclusive mask > > > Try running each line separately (or within a script) to see where exactl= y > the error is occurring. > > > Cheers, > Michael > > On Mon, May 23, 2011 at 12:48 AM, a489930026 <[log in to unmask]>wro= te: > > Dear Michael, > > Thank you for the advice. I did what you suggested but a result "Assignme= nt > between unlike types is not allowed." What could be the problem? > > Please see the script below: > > ********************************************************* > >> V=3D spm_vol('L:\2stlevel\wordVSblk\spmF_0001.img') > Y =3D spm_read_vols(V); > > threshold =3D 0; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('L:\2stlevel\radpos_sdXloca_sd\spmF_0001.img'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > V =3D > > fname: 'L:\2stlevel\wordVSblk\spmF_0001.img' > mat: [4x4 double] > dim: [79 95 68] > dt: [16 0] > pinfo: [3x1 double] > n: [1 1] > descrip: 'SPM{F_[1.0,15.0]} - contrast 1: w-b' > private: [1x1 nifti] > > ??? Assignment between unlike types is not allowed. > > ************************************************************************* > > Besides, is the threshold a cutoff for the activation maps of the mask? > Specially, does setting threshold =3D 0.05 mean that the activated voxels= below p =3D0.05 are included. If not, how should I set a cutoff on the p > value, or it does matter? > > Moreover, how can I determine the mask is inclusive (i.e., the voxles in > the to-be-masked activation maps are within the volxels in the activation > maps of the mask) or exclusive (i.e., the voxles in the to-be-masked > activation maps are NOT within the volxels in the activation maps of the > mask). > Thank you very much! > > Sincerely, > Yihui > > > > > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* SUBSCRIBE SPM Yihui Hung <a4899300= [log in to unmask]> > *=E5=89=AF=E6=9C=AC=EF=BC=9A* [log in to unmask] > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/23 (=E4=B8=80) 10:= 19 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: [SPM] three questions about SPM8 > > when you say you tried to use the contrast as a mask, did you just use th= e > raw contrast or did you binarize it? If you did the former than it is > clear why you had no inmask voxels since any non-zero voxels would all be > seen as part of the mask. So, what you want to do is load the mask, apply= a > threshold and binarize it by setting anything below the threshold to 0, > and anything above to 1 (though the latter may not be completely necesary= ). > Then, you could just use that mask to mask out the resulting contrast map > from the other analysis. Here is code to do it: > > %Make mask > > V =3D spm_vol('contrast_image_2turn_into_mask') > Y =3D spm_read_vols(V); > > threshold =3D X; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('contrast_image_2mask'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > Cheers, > Michael > > On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE SPM Yihui Hung < > [log in to unmask]> wrote: > > Hello, > > I used SPM8 for my experiment. I manipulated three factors (e.g., factor > A, B, C) in my experiment. The activation maps under factor A was used as= a > mask to constrain the effects of factor B and factor C. The problem is th= at > the factor A and factor B and factor C cannot be put in the same model at > second level analysis. I tried to put the .img file resulting from factor > A across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the secon= d level analysis. > However, the estimation stopped by showing no inmask voxels. I searched > through forum and did not find any useful solutions. Can someone help me > out? Moreover, is the threshold masking related to the p value? If not, h= ow > should I set criteria for the activation map of the mask? > > I did notice that one person in the forum suggested that one can create a= n > activation maps in which the factor B is modeled with the mask of factor = A > in first level analysis. Then, submit the resulting .img file into second > level analysis. My question is how to create and save the img file. > > Finally, I posted a question regarding how to export beta values over > multiple coordinates a week ago. However, no one provide any answers. I > wonder whether it is due that no one has the answer or due to that there > already is an answer in the forum. > > Thank you for your patience and advice. > Sincerely, > > Yihui > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > --=20 Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf3040ea427d213e04a3f652b9 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I see now, I made a mistake in the original code I sent. This line: <br><br= ><span><span>YY</span></span> =3D <span><span>spm</span></span>_vol(<span><= span>VV</span></span>);<br><br>should actually be:<br><br><span><span>YY</s= pan></span> =3D <span><span>spm</span></span>_read_vols(<span><span>VV</spa= n></span>);<br> <br><br>%%<br><br>Cheers,<br>Michael<br><div class=3D"gmail_quote">On Mon, = May 23, 2011 at 11:03 AM, a489930026 <span dir=3D"ltr"><<a href=3D"mailt= o:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br= > <blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; borde= r-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-family: arial,helvetica,sans-serif; font-size: 12pt;"><div><span><d= iv>Dear Michael,</div><div><br></div><div>Every step went smoothly until th= e step "YY(find(Y)) =3D 0" and then the it showed "Assignmen= t between unlike types is not allowed."</div> <div><br></div><div>Yihui</div></span></div><div><br><blockquote style=3D"b= order-left: 2px solid rgb(16, 16, 255); margin-left: 5px; padding-left: 5px= ;"><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"= > <div style=3D"font-size: 12pt; font-family: 'times new roman','= new york',times,serif;"><font face=3D"Arial" size=3D"2"><hr size=3D"1">= <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> Michael T Rubens <<a href=3D"mailto:[log in to unmask]" tar= get=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> a489930026 <<a href=3D"mailto:[log in to unmask]" target= =3D"_blank">[log in to unmask]</a>>; spm <<a href=3D"mailto:SPM@= jiscmail.ac.uk" target=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F= =EF=BC=9A</span></b> 2011/5/24 (=E4=BA=8C) 1:26 AM<br><b><span style=3D"fon= t-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b> Re: =E5=9B=9E=E8=A6= =86=EF=B9=95 [SPM] three questions about SPM8<br></font><br><div>threshold = needs to be>0. if your map is p values, then just do:<br> <br>Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant.<br><b= r>then set your p threshold as 1-threshold, so .95 =3D=3D .05<br><br>Determ= ining whether you use the mask inclusively or exlusively can be done when y= ou apply it, so: <br> <br><span><span>YY</span></span>(find(Y)) =3D 0; % exclusive mask<br><br>YY= (find(~Y)) =3D 0; %inclusive mask<br><br><br>Try running each line separate= ly (or within a script) to see where exactly the error is occurring.<br><br= > <br>Cheers,<br>Michael<br><br><div>On Mon, May 23, 2011 at 12:48 AM, a48993= 0026 <span dir=3D"ltr"><<a rel=3D"nofollow" href=3D"mailto:a489930026@ya= hoo.com.tw" target=3D"_blank">[log in to unmask]</a>></span> wrote:= <br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-size: 12pt; font-family: arial,helvetica,sans-serif;"><div style=3D= "font-size: 12pt; font-family: arial,helvetica,sans-serif;"><span>Dear Mich= ael,</span></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><s= pan><br></span></div><div style=3D"font-size: 12pt; font-family: arial,helv= etica,sans-serif;">Thank you for the advice. I did what you suggested but a= result "Assignment between unlike types is not allowed." What co= uld be the problem?</div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-se= rif;">Please see the script below:</div><div style=3D"font-size: 12pt; font= -family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;">*********************************************************</div><div= ><div>>> V=3D <span><span>spm</span></span>_vol('L:\2<span><span>= stlevel</span></span>\<span><span>wordVSblk</span></span>\<span><span>spmF<= /span></span>_0001.<span><span>img</span></span>')</div> <div>Y =3D <span><span>spm</span></span>_read_vols(V);</div><div><br></div>= <div>threshold =3D 0; %any value you choose.</div><div><br></div><div>Y(fin= d(Y<threshold)) =3D 0;</div><div>Y(find(Y)) =3D 1;</div><div><br></div><div>% Use mask</div><d= iv><br></div><div><span><span>VV</span></span> =3D <span><span>spm</span></= span>_vol('L:\2<span><span>stlevel</span></span>\<span><span>radpos</sp= an></span>_<span><span>sdXloca</span></span>_<span><span>sd</span></span>\<= span><span>spmF</span></span>_0001.<span><span>img</span></span>');</di= v> <div><span><span>YY</span></span> =3D <span><span>spm</span></span>_vol(<sp= an><span>VV</span></span>);</div><div><br></div><div><span><span>YY</span><= /span>(find(Y)) =3D 0; %apply mask</div><div><br></div><div><span><span>VV<= /span></span>.<span><span>fname</span></span> =3D 'masked_image.<span><= span>img</span></span>';</div> <div><span><span>spm</span></span>_write_vol(<span><span>VV</span></span>,<= span><span>YY</span></span>);</div><div><br></div><div>%% There ya go</div>= <div><br></div><div>V =3D=C2=A0</div><div><br></div><div>=C2=A0 =C2=A0 =C2= =A0 <span><span>fname</span></span>: 'L:\2<span><span>stlevel</span></s= pan>\<span><span>wordVSblk</span></span>\<span><span>spmF</span></span>_000= 1.<span><span>img</span></span>'</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 mat: [4x4 double]</div><div>=C2=A0 =C2=A0 = =C2=A0 =C2=A0 dim: [79 95 68]</div><div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0<= span><span>dt</span></span>: [16 0]</div><div>=C2=A0 =C2=A0 =C2=A0 <span><s= pan>pinfo</span></span>: [3x1 double]</div><div>=C2=A0 =C2=A0 =C2=A0 =C2=A0= =C2=A0 n: [1 1]</div><div> =C2=A0 =C2=A0 <span><span>descrip</span></span>: 'SPM{F_[1.0,15.0]} - contrast 1: w-b'</div><div>=C2=A0 =C2=A0 private: [1x1 <span><span>nif= ti</span></span>]</div><div><br></div><div>??? Assignment between unlike ty= pes is not allowed.</div><div><br></div><div>******************************= *******************************************</div> <div><br></div><div>Besides, is the threshold a cutoff for the activation m= aps of the mask? Specially, does setting threshold =3D 0.05 mean that the a= ctivated <span><span>voxels</span></span> below p =3D0.05 are included. If = not, how should I set a cutoff on the p value, or it does matter?=C2=A0</di= v> <div><br></div><div>Moreover, how can I determine the mask is inclusive (i.= e., the <span><span>voxles</span></span> in the to-be-masked activation maps are within the <span><span>volxels</span></sp= an> in the activation maps of the mask) or exclusive=C2=A0(i.e., the <span>= <span>voxles</span></span> in the to-be-masked activation maps are NOT with= in the <span><span>volxels</span></span> in <span>the activation</span> map= s of the mask).=C2=A0</div> <div>Thank you very much!</div><div><br></div><div>Sincerely,</div><div><sp= an><span>Yihui</span></span></div></div><div style=3D"font-size: 12pt; font= -family: arial,helvetica,sans-serif;">=C2=A0</div><div style=3D"font-size: = 12pt; font-family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial,helveti= ca,sans-serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial= ,helvetica,sans-serif;"> <br><blockquote style=3D"border-left: 2px solid rgb(16, 16, 255); margin-le= ft: 5px; padding-left: 5px;"><div style=3D"font-size: 12pt; font-family: ar= ial,helvetica,sans-serif;"><div style=3D"font-size: 12pt;"> <font face=3D"Arial" size=3D"2"><hr size=3D"1"><b><span style=3D"font-weigh= t: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> Michael T Rubens = <<a rel=3D"nofollow" href=3D"mailto:[log in to unmask]" target=3D"_bla= nk">[log in to unmask]</a>><br><b><span style=3D"font-weight: bold;">= =E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> SUBSCRIBE <span><span>SPM</= span></span> <span><span>Yihui</span></span> Hung <<a rel=3D"nofollow" h= ref=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] om.tw</a>><br> <b><span style=3D"font-weight: bold;">=E5=89=AF=E6=9C=AC=EF=BC=9A</span></b= > <a rel=3D"nofollow" href=3D"mailto:[log in to unmask]" target=3D"_blank">= [log in to unmask]</a><br><b><span style=3D"font-weight: bold;">=E5=AF=84= =E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A</span></b> 2011/5/23 (=E4=B8=80) 10:19= AM<br> <b><span style=3D"font-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b= > Re: [<span><span>SPM</span></span>] three questions about <span><span>SPM= </span></span>8<br> </font><br><div>when you say you tried to use the contrast as a mask, did y= ou just use the raw contrast or did you <span><span>binarize</span></span> it? If you did the former than it is clear why you had no <span><span>inma= sk</span></span> <span><span>voxels</span></span> since any non-zero <span>= <span>voxels</span></span> would all be seen as part of the mask. So, what = you want to do is load the mask, apply a threshold and <span><span>binarize= </span></span> it by setting anything below the threshold to 0, and anythin= g above to 1 (though the latter may not be completely <span><span>necesary<= /span></span>). Then, you could just use that mask to mask out the resulting contrast map from the other analysis. Here is code to do it:<br> <br>%Make mask<br><br>V =3D <span><span>spm</span></span>_vol('contrast= _image_2turn_into_mask')<br>Y =3D <span><span>spm</span></span>_read_vo= ls(V);<br><br>threshold =3D X; %any value you choose.<br><br>Y(find(Y<th= reshold)) =3D 0;<br> Y(find(Y)) =3D 1;<br><br>% Use mask<br> <br><span><span>VV</span></span> =3D <span><span>spm</span></span>_vol('= ;contrast_image_2mask');<br><span><span>YY</span></span> =3D <span><spa= n>spm</span></span>_vol(<span><span>VV</span></span>);<br><br><span><span>Y= Y</span></span>(find(Y)) =3D 0; %apply mask<br> <br><span><span>VV</span></span>.<span><span>fname</span></span> =3D 'm= asked_image.<span><span>img</span></span>';<br><span><span>spm</span></= span>_write_vol(<span><span>VV</span></span>,<span><span>YY</span></span>);= <br> <br>%% There ya go<br><br>Cheers,<br>Michael<br> <br><div>On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE <span><span>SPM</span><= /span> <span><span>Yihui</span></span> Hung <span dir=3D"ltr"><<a rel=3D= "nofollow" href=3D"mailto:[log in to unmask]" target=3D"_blank">a48993= [log in to unmask]</a>></span> wrote:<br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> Hello,<br> <br> I used <span><span>SPM</span></span>8 for my experiment. I manipulated thre= e factors (e.g., factor A, B, C) in my experiment. The activation maps unde= r factor A was used as a mask to constrain the effects of factor B and fact= or C. The problem is that the factor A and factor B and factor C cannot be = put in the same model at second level analysis. I tried to put the .<span><= span>img</span></span> file resulting from factor A across participants in = the =E2=80=9Cexplicit mask=E2=80=9D in the second level analysis. However, = the estimation stopped by showing no <span><span>inmask</span></span> <span= ><span>voxels</span></span>. I searched through forum and did not find any = useful solutions. Can someone help me out? Moreover, is the threshold maski= ng related to the p value? If not, how should I set criteria for the activa= tion map of the mask?<br> <br> I did notice that one person in the forum suggested that one can create an = activation maps in which the factor B is <span><span>modeled</span></span> = with the mask of factor A in first level analysis. Then, submit the resulti= ng .<span><span>img</span></span> file into second level analysis. My quest= ion is how to create and save the <span><span>img</span></span> file.<br> <br> Finally, I posted a question regarding how to export beta values over multi= ple coordinates a week ago. However, no one provide any answers. I wonder w= hether it is due that no one has the answer or due to that there already is= an answer in the forum.<br> <br> Thank you for your patience and advice.<br> Sincerely,<br> <font color=3D"#888888"><br> <span><span>Yihui</span></span><br> <br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br><span><span>Gazzaley</span></span> Lab<br>Department of Neurology<br>= University of California, San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Dep= artment of Neurology<br>University of California, San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Dep= artment of Neurology<br>University of California, San Francisco<br> --20cf3040ea427d213e04a3f652b9-- ========================================================================= Date: Mon, 23 May 2011 15:20:50 -0600 Reply-To: "Kent A. Kiehl" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Kent A. Kiehl" <[log in to unmask]> Subject: fMRI image acquisition and analyses course with SPM5/8 and ICA - July 14-16, 2011 - Second Call (course 50% full) Content-Type: multipart/alternative; boundary=Apple-Mail-54--686547355 Mime-Version: 1.0 (Apple Message framework v1084) Message-ID: <[log in to unmask]> --Apple-Mail-54--686547355 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 > fMRI image acquisition and analyses course with SPM5/8 and ICA - July = 14-16, 2011 - Second call =20 >=20 >=20 >> The Mind Research Network and the University of New Mexico are = pleased to announce the next upcoming fMRI Image Acquisition and = Analyses course to be held July 14-16, 2011 at the Mind Research Network = on the campus of the University of New Mexico in Albuquerque, NM. The = course will include overview of both SPM5 and SPM8. >>=20 >> The course faculty include: >> =95 Kent Kiehl (The Mind Research Network and the University of New = Mexico) >> =95 Vince Calhoun (The Mind Research Network and the University of = New Mexico) >> =95 Tor Wager (University of Colorado =96 Boulder) >>=20 >> The course is designed for fMRI researchers who range from beginning = to intermediate skill levels. It will provide those who are just getting = started with fMRI a comprehensive set of tools and software to get = started with your own studies, and those who are more advanced will = benefit from custom code and supplements to standard analyses developed = by the instructors. We cover Statistical Parametric Mapping (SPM5/8), = independent component analyses (ICA) of fMRI data, mediation analysis of = fMRI, and statistical nonparametric mapping (SnPM). >>=20 >> The course provides comprehensive coverage of all aspects of = experimental design, image acquisition, image preprocessing, and = analysis using the general linear model and ICA. >>=20 >> The course is unique in that it pairs interactive lectures with = hands-on demonstrations and work-through sessions. Each student works on = their own laptop. Software will be installed on each student's laptop = during the beginning of the course, including Matlab (a trial version), = SPM5/8, the Group ICA of fMRI Toolbox (GIFT), and related SPM toolboxes, = including statistical nonparametric mapping (SnPM) and the multi-level = mediation fMRI toolbox (M3). In addition, alongside the lectures, = participants will be trained to analyze example fMRI data on their = laptops using these tools. >>=20 >> The course will be small and interactive with many opportunities to = work closely with the faculty. Registration will be on a first-come, = first-served basis; we apologize in advance if we cannot accommodate all = who wish to attend, but we will admit as many people as possible given = the interactive nature of the course. >>=20 >> To register, go to = http://www.mrn.org/courses/abq-fmri-course-july-2011 >>=20 >> For inquiries, contact Kent Kiehl [log in to unmask] >>=20 >> _________________________________ >> Kent A. Kiehl, Ph.D. >> Director of Mobile Imaging Core and=20 >> Clinical Cognitive Neuroscience >> Mind Research Network=20 >>=20 >> Associate Professor of Psychology and Neuroscience >> University of New Mexico >>=20 >> Mailing Address:=20 >> Mind Research Network >> 1101 Yale Blvd NE >> Albuquerque, NM, 87106 >> Office: 505-925-4516=20 >> [log in to unmask] >>=20 >> _________________________________ >> Kent A. Kiehl, Ph.D. >> Director of Mobile Imaging Core and=20 >> Clinical Cognitive Neuroscience >> Mind Research Network=20 >>=20 >> Associate Professor of Psychology and Neuroscience >> University of New Mexico >>=20 >> Mailing Address:=20 >> Mind Research Network >> 1101 Yale Blvd NE >> Albuquerque, NM, 87106 >> Office: 505-925-4516=20 >> [log in to unmask] >>=20 >>=20 >>=20 >>=20 _________________________________ Kent A. Kiehl, Ph.D. Director of Mobile Imaging Core and=20 Clinical Cognitive Neuroscience Mind Research Network=20 Associate Professor of Psychology and Neuroscience University of New Mexico Mailing Address:=20 Mind Research Network 1101 Yale Blvd NE Albuquerque, NM, 87106 Office: 505-925-4516=20 [log in to unmask] --Apple-Mail-54--686547355 Content-Type: multipart/mixed; boundary=Apple-Mail-55--686547355 --Apple-Mail-55--686547355 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; = "><div><blockquote type=3D"cite"><div style=3D"margin-top: 0px; = margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><span = style=3D"font-family: Helvetica; font-size: medium; "><b>fMRI image = acquisition and analyses course with SPM5/8 and ICA - July 14-16, 2011 - = Second call </b><br></span></div><br><div style=3D"word-wrap: = break-word; -webkit-nbsp-mode: space; -webkit-line-break: = after-white-space; "><div><br><blockquote type=3D"cite"><div = style=3D"word-wrap: break-word; -webkit-nbsp-mode: space; = -webkit-line-break: after-white-space; ">The Mind Research Network and = the University of New Mexico are pleased to announce the next upcoming = fMRI Image Acquisition and Analyses course to be held July 14-16, 2011 = at the Mind Research Network on the campus of the University of New = Mexico in Albuquerque, NM. The course will include overview of both SPM5 = and SPM8.<br><br>The course faculty include:<br>=95 Kent Kiehl = (The Mind Research Network and the University of New Mexico)<br>=95 = Vince Calhoun (The Mind Research Network and the University of New = Mexico)<br>=95 Tor Wager (University of Colorado =96 Boulder)<br><br>The = course is designed for fMRI researchers who range from beginning to = intermediate skill levels. It will provide those who are just getting = started with fMRI a comprehensive set of tools and software to get = started with your own studies, and those who are more advanced will = benefit from custom code and supplements to standard analyses developed = by the instructors. We cover Statistical Parametric Mapping (SPM5/8), = independent component analyses (ICA) of fMRI data, mediation analysis of = fMRI, and statistical nonparametric mapping (SnPM).<br><br>The course = provides comprehensive coverage of all aspects of experimental design, = image acquisition, image preprocessing, and analysis using the general = linear model and ICA.<br><br>The course is unique in that it pairs = interactive lectures with hands-on demonstrations and work-through = sessions. Each student works on their own laptop. Software will be = installed on each student's laptop during the beginning of the course, = including Matlab (a trial version), SPM5/8, the Group ICA of fMRI = Toolbox (GIFT), and related SPM toolboxes, including statistical = nonparametric mapping (SnPM) and the multi-level mediation fMRI toolbox = (M3). In addition, alongside the lectures, participants will be trained = to analyze example fMRI data on their laptops using these = tools.<br><br>The course will be small and interactive with many = opportunities to work closely with the faculty. Registration will be on = a first-come, first-served basis; we apologize in advance if we cannot = accommodate all who wish to attend, but we will admit as many people as = possible given the interactive nature of the course.<br><br>To register, = go to <font class=3D"Apple-style-span" color=3D"#144FAE"><u><a = href=3D"http://www.mrn.org/courses/abq-fmri-course-july-2011">http://www.m= rn.org/courses/abq-fmri-course-july-2011</a></u></font><div><font = class=3D"Apple-style-span" = color=3D"#144FAE"><u></u></font></div></div></blockquote></div></div></blo= ckquote></div></body></html>= --Apple-Mail-55--686547355 Content-Disposition: inline; filename=agenda_fmri_course.pdf Content-Type: application/pdf; name="agenda_fmri_course.pdf" Content-Transfer-Encoding: base64 JVBERi0xLjUNJeLjz9MNCjU2MyAwIG9iag08PC9MaW5lYXJpemVkIDEvTCAzMDczNy9PIDU2NS9F IDk5MDQvTiAzL1QgMzAzNzcvSCBbIDQ3OSAxODRdPj4NZW5kb2JqDSAgICAgICAgICAgICAgICAg DQo1NzYgMCBvYmoNPDwvRGVjb2RlUGFybXM8PC9Db2x1bW5zIDQvUHJlZGljdG9yIDEyPj4vRmls dGVyL0ZsYXRlRGVjb2RlL0lEWzw3QUQwMzJGRjhCRUM3NzQxOUE3MThDRTdFOUQ2OUJFNT48QzI1 RUM2NzUxREZFMDc0RUEzODM3MzVGQkVDMUMzNjk+XS9JbmRleFs1NjMgMjBdL0luZm8gNTYyIDAg Ui9MZW5ndGggNzIvUHJldiAzMDM3OC9Sb290IDU2NCAwIFIvU2l6ZSA1ODMvVHlwZS9YUmVmL1db MSAyIDFdPj5zdHJlYW0NCmjeYmJkEGBgYmBqBxKMrSBCHEgwxwAJ1mYQ6z6QYFkLIgpA3PMg1gEQ kQ3ignTcfs7AxMh0CMhiYGBEJ/7/X/kHIMAABFQNCg0KZW5kc3RyZWFtDWVuZG9iag1zdGFydHhy ZWYNCjANCiUlRU9GDQogICAgICAgDQo1ODIgMCBvYmoNPDwvQyA5NS9GaWx0ZXIvRmxhdGVEZWNv ZGUvSSAxMTcvTGVuZ3RoIDk4L1MgNTU+PnN0cmVhbQ0KaN5iYGBgAqIUBhYGBoEHDLwMCMALlGEB Qo4JDFMXSB5gWMEgJcXBwQFmIwFuKGZgFGfgYlCJ3XRi6dJZj1ZOVXtgAJZnZWDIWwCk2RgYpIWQ DGeYrQLSBET+AAEGADo+EPkNCmVuZHN0cmVhbQ1lbmRvYmoNNTY0IDAgb2JqDTw8L01hcmtJbmZv PDwvTWFya2VkIHRydWU+Pi9NZXRhZGF0YSAxMSAwIFIvUGFnZUxheW91dC9PbmVDb2x1bW4vUGFn ZXMgNTYxIDAgUi9TdHJ1Y3RUcmVlUm9vdCAxNSAwIFIvVHlwZS9DYXRhbG9nPj4NZW5kb2JqDTU2 NSAwIG9iag08PC9Db250ZW50c1s1NjcgMCBSIDU2OCAwIFIgNTY5IDAgUiA1NzAgMCBSIDU3MSAw IFIgNTcyIDAgUiA1NzMgMCBSIDU3NCAwIFJdL0Nyb3BCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0v TWVkaWFCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vUGFyZW50IDU2MSAwIFIvUmVzb3VyY2VzPDwv Q29sb3JTcGFjZTw8L0NTMCA1NzcgMCBSPj4vRm9udDw8L1RUMCA1NzkgMCBSL1RUMSA1ODEgMCBS Pj4+Pi9Sb3RhdGUgMC9TdHJ1Y3RQYXJlbnRzIDAvVHlwZS9QYWdlPj4NZW5kb2JqDTU2NiAwIG9i ag08PC9GaWx0ZXIvRmxhdGVEZWNvZGUvRmlyc3QgMzkvTGVuZ3RoIDc2NC9OIDUvVHlwZS9PYmpT dG0+PnN0cmVhbQ0KaN7UVd1r2zAQ/1f0uD0UfViybCiFJltYYSujCeug9MFLTGJI7GK7bP3vd3e2 ZMVN0i0vYw8XyboP/e5094uxlglmbMJkCkvKlJHMJIJJrSxsJJOxTtkDv5lOJ1mTr8DIgMfd4+Ul v26WedkyKVTMp9nTp7xYb1oWxxH/kHeqi0hYPttm64ZFms+qsp1Mql8PF8YkpGNKCEEBHkk7y3bF 9uXdotjlDbvNf7K7apeV70l3m+1yThpQ0PnX+cWk2q6+LEg/b+u8XW74bVXvsi0d3XeIrBD8ps22 xfK6XG9zJvi8zXffmIxivnh5yskWIdfFU1vV/HufiTb26gryxMTR5NjtH8tltSrKNb8vyuuyKfz3 rKibdrrJarhy/w4qOZSRf856C2UMnz//aBHPon7OCZhHB6FX7aZ5sOD2L0UZwaIoYsYYZuDpDD0f /CRwqMAAlWSASiu8A66od05viY9jxSBwnkaCWaWgyyytbh9L6UFGSUq2uEddqnVni9/ODr6Niffi UCJh0ChICmfBASOQ4KwhsFvDc+/T75N+7/TeB4BGgY5A9QUgfaqZUoJWg6vpi2MCHO4Fwsp1BxDV f6ALpWQG9zCEC90Ze68eIAJDC91XUo0AOEGwDoPVcJvT2eGMksMz4BXUR66oFoqgh47pzjt/awYx wSONxb8gtUQglEgg3ulAjyNY1yJjwfbxL3lA0AaLNBbsglB8JxyKo9O9TvEy+DwSL3n+BZL9S/6N 9/lX2HP49w3a1UdoNxGnWVcnJ1n3XMKFP7X/mHC1GLgT6QR1MhnNcazP5lvfmBDDCTZuquTeQIVD 4IaCMPRDhbokGDBnh77Et1J6nefl3i+kWx2nnkmIZntK9NQYsE84VMoN8aH/E8AV1mRMtxrqqXCf 4BDKg3SLdqfpFrMntpXyFds6OnjFtmlyBtkGCGyMqce+DdzZKbLVJtonW/mabHXwRGPBRN1fbygu WSfH/Kn88fH4jvMO8SkK3v3HXHtMHNceF+Da3wIMAIXHfPQNCmVuZHN0cmVhbQ1lbmRvYmoNNTY3 IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMTA4Mz4+c3RyZWFtDQpIiexWS3PT SBC+61fMcURFk3lpJKUoqkjCVsFWtsgiloOXg8t2CLtBKuwY8L/f7h6NLAnJmE2OHCLF0+/v6+nW eRmdvmZPn55eXby8ZJI9e3Z+ecGi04s3ki02cMA2iyo6LUvJFCtvokQKKS0rF6z55ytTGtQkvlSe C+VYZq0oHCs/RTN+EydK8as/40Tzl3GScha/L19FrZekdTMDueGfYpXxeWz5B/hbxTnoJ0ry57Hj C/j1efsx1nwDivi+jzN616BbMbSqlgxVq3msLL8D0Q4OUR1d4duHH6lixi8wxzpOLN/GSc7XG0x3 5dMlVVQDB+U/kRPGsUQJlbJyCVW+2qLZHTrYKUsmPEFFI1SGhsvIG3Ll6HXCtFT+gJ4kfHGFyO/Z UIENKXSxDzeurFvqylI1XKlCpGRnfJpQoeH1dg04QHEKirOc/dYSoltCNJZqhDM+9xkwAvUtANkt YHp3T5zsYq35GWMDhIzQpluyFsoGN7+DNQat7hEqoEpzwfDwI/5GyS2BeMJeY8BbcYkaJz5ELgrd dTxEwAQERthNyLjDWIlQ3GKQFbtCoiED6Bs8WOKDQcdmfOV7YL6mLl6QPvuDckUMHP/a9gyq8H9J Yd5xc49umzhvUZlkFOwLWMAxGW7aFLzfXQwP3/rkHa9RiEwxfdKrb+SDDBf1gAidivwQXrbFizQ7 4PwVGKkWdAuhDiKCUQPhPSOWbutt1WeKEm7oGg+aHiBJhhwufxGEWLkWK5N1CfLavUs2/YNwNgFn Qzc0XMeyAcfwNXsXJwVQOzEdjZB5sPpAYK0HxSihs6Axen1n/C38qqivvqwQkHYO0RniivNaS87q GxzVflxRq9Woi423rJvu+ltrtx+dyojUhfDnsBbq7R35W2L3+lyHg2kId9aZoGHbGYPTC5HPBoYv eoszD7afo7QQzjKnMpFqZgstHIPscqnYehW9Y1V0XnYi7Jens8Cx0zDrc786Ebz5jqkzyh7iXT/A e5EL2/HeFPIwn0qqvtOzR3Ga4nJ99FTzvtf+zmbN0n5oFJsK20Ok3fMP9eyKgedH8ZoN8w1YU4tf o2P4nnNGCQNGwub+AV5vnvxQiCl5IaaU6Z441cTHhPFhqY/rq6VzLHZgOi7t3NjiuxsrYUBYyFUU qmiN9uip8QsrHVp9h133M679qkaulIKh2kZzWqSHoh3+3Fap0J0MZvwNfrjSRqONCP92uvrnInfC 4PY5rlLVqzQznXipOTpelg/Kwkm4DRuAPh1pY9RVr7z/GU5D9x9Xne5Wp3XWCadSK8yR8YzulTcd z3TjmVyJfB8vk8fWZ/JsMtp15zJBMROX2AvxYvTusJIFLt0J29BtXord1jfO9CHjhksvRS57xrD6 DxgHZryUmOlZI5Jm0jrg3FgjzqOTaxIxhNOTNJw+AbAJaYPIuDSUPCJl/wkwABl2UhYNCmVuZHN0 cmVhbQ1lbmRvYmoNNTY4IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggOTIwPj5z dHJlYW0NCkiJnFZLU9swEL77V+hoUSL08EMuDAceB9oy03Q8w4HpgQIp6ZCkQNL+/e7q4cjESpUM g2Jrvfr2W6320+QgU1owVZGKKyYLwlmhiVBMk9fHbHKQlaXcYj36Sk5Ojq7Pry6IKMjp6dnFOcle srJhVUFKrVlTkaJhjWg6rxsyz87a7KhtBRGknWRCEg5/8FMV8EnZCPRqZ1lOaPsru2yzy2tYNYQq PdQYwITgjHdolWRlGpoQJZNJcFUPrlYBXKmS4WqdCFeHcFLWAZwoC6bS8KSSiXg6xMNq0Gu8mqfy U7qOoo2xIgRWkWR1ZavIDK7GTLk4I5aLDK2CN6yM+rrdd1bc/b5zLbc52710VtzLnrOU1RZntzPO anam592dq0Fvl2fvjXnueXfnLpYxSKfbpPeH0iUsZrUZiVgd5YjVUYpYXcgRa1hxzUarqAuE3bVV 1DV6Qbnd5l+oEPkjHVX5PS3yJR3JnAgm6Pf2E5bheH8owQuT7A4sepIkH+pLFm/nvhRw0x+RDufk DqjNQkp7Y0C1yiRKYqj3Wbide19AiRtKivfI7Lt6Y+s9gY0caq0W77+tFTINv3/JRosNaJ3TkcoX q1eq8jc6woIscnJFhcrnpiTRsHhYdSU6xWExD7OwY1RBA5Y6NQ1qqOM7wB07fkD+M3WEkPXTMz4d EpMRrFvz/rRYzQ/JDR5TnPtJBbeH9jVMwd4RFY02HSiSgnHQN30/3ZAhZ4zJUMQ3uITAXEyGYs7r KwXMxWQo4hxcELDhxmQo4h3IPXrHZCieMdMZzT75hg9p62csZnY5iZg964jZ04qYfdwRc3gYNu+t QjOtvUKU0I3h4KfIUQV3ZL395A3eXC2e695JeE4jOkCvEWqLRuyBYlUiQmts3AJu3TV5S8SQ1CJc Cxe67C9Tr/fDHwwbulOEtNCt4gQJEk5EhxRnj/Wd5iRsuR7SHIvou3sSpFObgFJLhRMbspjgeI2a 8w0GlRvRIR9Qf+78rehlZdqzUSbzFGqQW2KdnvfixyVp78nIPQ3oYLe3t/kDSMEdrfIlDsfkDQJd TmcrmH3GAd/Jb4p6WEL/r8wE/s5hzvgspwt4xNdjGxJHcI647QEA/DAag7X+x8aPWehkhswspzVJ Yzevc2DPrfIcm2/t8pv8bk1UU/jSBoJhPduvTQj4fg//L2BDUlPzuQ+8HzNsc4NPD1vqJLgU+67s qsQJ4EaVkH8CDADoeEP0DQplbmRzdHJlYW0NZW5kb2JqDTU2OSAwIG9iag08PC9GaWx0ZXIvRmxh dGVEZWNvZGUvTGVuZ3RoIDg3MD4+c3RyZWFtDQpIiaxXS2/UMBC+51f46CDW6/ErCVQ99HEoUImi SBwqDqXaVQvtbh+sEP8ejx9bp9hpWFAl18pk8vmbxzfeed8DAdIvK844F6S/JLOw+0lAEG7/7D/Z NkQbyURL+tvqnL6vFb1e1IZe1YLekPpL/6467quz6r6SLbBWEQ0ta1sCDWcaOqI1ExzIw6L6TFbV QV/Nt8BPKAoMMyoBoqTuv+GHj08Pif267hgYohvFeEs4U2GxX12+QiM6e6PqWCdSK/CO6aIvgCW9 dTaC6aFzI8acG5k4a/nMWQgz4ixEkziDVkwOvDGcsugdgx28MdgDb63FiHcIp8+Ue45pehawgjVE JG+NlPPWSClvjUfOW+cfyd7e/PTw5IhITvb3D45sYdzH3HPFjEu9rTiQrH2h3oyyr2jeWKc/ii0F ggiE9R1LxWFhpUzDAkASW7Bz2r2pZ4JaEhe2l27TDtoVwdaOmEJHDOiE4nVgWLsTwZp2SIcHOgMi O36785X3MhOZMomd5NBcI43BOZVTqHd2A2ood0KKIbvDetbQdT1TdFnPwC+LBTmoJX1we7twevEd 3xio4d8dKjkBxmBSNlUag6gHHi5o7wQ4p+95rLNEJlRJdJ2tqLlZz1jlzlhU3LxrqCtnLOpt1jXm wxnLapv1TSabGtHaUpwwhi4v7nFMShKmvDEEImuMVLPGyCVrjIfNGtPy0s9FVjUGiQeVteHUXTdF Z1UnMdiunT7U4HrG0Esrfj9QOQgwkTbOzmBgKYFJ4MqtYzK6HhCD7E5DdNKe8gOvhrws7rvAGD6A KfNq/iuvBpXI3t1GIdvMTAmQQfinQbqxMghliGRmruzyfT9apgSxy8yWgBh1fBKknyUppaWdGqc4 NT7ZRdKTWkhK7jY3SPTRTxM7RXDnt/cbXFeX2C+4c++8zo2ZfzhfkuVzO9OkQ8LuXK8cnmtSfL7Z tuw1LuvVW9e+Xy/iydxjfIncXdktp79qOxuHpsf/dHhpVbidVqKKZwZlxAyTchqmnZVpOu3dQFJk 7xJ4td6scr+HdkFSkjPTFqv1LJkqIEqj2RuLsznvm1xBQZSnc8H56doHojyf887JhSncafMTOu+d 3H9AjMzoYsRwcvhMuecCr0SDgJWsPiIFa6BcsAZKBWs4cs5KfgswAFXRNkoNCmVuZHN0cmVhbQ1l bmRvYmoNNTcwIDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggODA5Pj5zdHJlYW0N CkiJrFdbT9swGH3Pr/CjjVTjz7EdZ0I8cHnYNCSmReIB9sCg7AYtY2Pbz5+vqdPaJaVVpbbKyefj 79g+J7nbq/bP0cHB/tnx2xMkAB0eHp0co+pnJVuqBBJCU2F+WtpCi3hDZduip2l1gWbVUVftdx0g QN1dBRwx8zE/SlCNhJKUC2aghwoj0n2vTrvq9MyMnNLxSPfBEAIwynpGxakcywhgyAaUlxjgDZlw zBi6JgI/kE/dOzuFLYkUWyIq91bvtDcJtnAtoRgQNnVCKOsNCBu9KmbQcqDi6xnalko1UkaZdsXN +AtOkILWY0l5zVfa+kgmAp/brzMyAex2yuwWdQTwfH5vW/48J8DwP4MyjL7ZK7Nf9tbf9q+93d11 T7RpINFmi3nWUq6Ic8W5CvpsO7qCovS7EbjfqJf4xugzf7ACPT47wabmwhOpMbLI1ymZKHfPj51p B6a7cYdFpduq1kD1grJhL+3liTnNZo7dDQp//qJkFrpZ2mcXbv9cfyEKT5/SXjcmXrAI0LRd50Uf rIODWetGWT0ZFdp/mXHv9qK9e9DaO09RYP6I5mujm3nUutmwuOHrioNveNT6xqDYbPU1xXFzeNRt jkG1FbQuVke5Q7WVe1AtJV9THeT0a+Wum8moJcEKaFAkj8aW82hsKY/GKefRdMc3K9FeC9eQj3ao qR4V7HVjq9YeLp2LPs8Wom8EWwi+ns6kEI+R/pjN840ZTJq3fFRHbS5bPV9IvjF8PlmTjphrqM7m 6sajh1R9uRvJcpnq+aLfFgmN1DD0u2D8SVvvCQB+njmXR1fYOP6jtf35H5egtzYGbp0lXpGc84+c Ser7amzrkPP9QBjsdwyhtfgCW++7oqGtKvmuB4u+m6/tnyIdWvTdQnF8XnNo0XfzxX0oO7Tsu/nq PuZ8ddF3i4pZOf0iuetxhRLBCmhQJI/GlvNobCmPxinn0XTH8RXfZSZedPJKpUb5LudU6+R89Y9P 7tEKmcmmZ+mVVMB8JvRk5ZOUfb/xfIv3mxf5vMknvQWTX/LE1w4v/PqvtrPdqHJpVJNHOxg2xNEI 7eOrHvovwADmOTifDQplbmRzdHJlYW0NZW5kb2JqDTU3MSAwIG9iag08PC9GaWx0ZXIvRmxhdGVE ZWNvZGUvTGVuZ3RoIDkwND4+c3RyZWFtDQpIibRWyW7bMBC96yt4JAtY5qolDXLIckjTAE6jooeg ByN1Gre2s9RAfr8zJLWlpCI7KAIwskbDN29myDfJVfKUiFylXBPNs5QXxKjUiJLIPM3I8yL5RjbJ cZVMq0oQQaq7REjC4Q/+ibxIJdFSpkVBqnVyQ8UBm0jKOftefUrOquRdu5dlarLO9pSw6hfuenZ5 QpLpjBweTi9Pzk+JMeTo6PgUXiKehL1bPGF0qoYAecq5JNUtmfinF9LGIJV8xfCcCUM3TNLtMzP0 AR7I1q7XyHyGyyUT9CM5ZZrOWUa3uJBbWO7ZpKALeMAfv/HHEjf6yYSE77sp+w8UeIlbWQpf2ETY ODBuZLHEH5ZTGxD5gQF6AvuH1sahhMAktoHEy5l1y6kK8GsBcz7cPzYFGpPhH7q5UEXeK+YFlmvJ JhldQHE0XXWZ7gjcomih0kxHG/cqMWUqoLEVfFZAmNovsOvdBzSiszPqMi1l1yq4OxRhXyG4LY6z ZjI1fedcDjn7c+qseE57zlJmA851WzirbYueNyZTRb3rVHtvTHXP2xg54O3T6epk39dF6iQsYvUZ CVtrymFrTSlsrUMOW7vdntfd/uRrrzKNPlh7aDgtUv1Gw8H3BVGlhuRw4Tr7MxN4xid42Wi6xT4n sFG3v/cEE1xjurtw8ZNcdE+y706PiN05ElEYuMH6/JzQKE4e1z2t2RcCGraUI0mVPVLu1HhEr25j EK12hkgF1XP3/Z16jmKU8YB+esT6ho9CDl64Tnz6LK9RWB7xup1vsUHt/Ttf4UpmaHu2+mS/sI8P +HS7+MMmhtrFemzApEGrMnpgXQMiNZZCRyA0xKtHZk20WdtVjlXBGxFq5BjFdrW06osyvG5UeeqG i9USBwd8Syqm6HLdqvW0ob9f/WRdv3C0Wd4MD183GMrL3I4OAusI6O/IPbfIr1Bz3UONl0AGJoUa 2Qv2mKrDWNDv0Qu4NO1UdA88VyQwFoxFCfHTIrNXb7TLmhGBG5CMyIRgbdEBIehZzwfWGB0Pwq71 FI/G6HAQdG1GRjTGR4OgbzOEWd/oYBDLEwqVK5R9D1UC/16iomaXjJjZE46ZPaeY2YcdM3c7XP0z HUDJG72GSbMY3XZuTADoNw6VDom2RfWCuhOqV+8a9obKYeneH8hr+NsETUjALa7X191wnZK3BLkn GJLx/VG8nvfZkb8CDACtGj98DQplbmRzdHJlYW0NZW5kb2JqDTU3MiAwIG9iag08PC9GaWx0ZXIv RmxhdGVEZWNvZGUvTGVuZ3RoIDg4OT4+c3RyZWFtDQpIiaxXS08bMRC+76/w0YsUx6/17iLEAcih DyoiVuoB9RDRAGkJ5RGpUn99Z/xovIkdNlChOMaz48/fzHi+zaQrJuenpBhfkKOj8fnphzNiDDk+ PjmDxWnxVEhZM66J0i1riKg0U6IlQsE/z/PiK3koTrpi3HWCCNLdFCPOODzdXROYCJj8JkISDn/w JZVkkqhKgnO3LK7oaTmq6a9ypOlNORJumM/JSanos53DwOnsZ/mt+1hMujech9ujbByjNayKzkFJ 2f3A7bcCUceBUI1gzT/gmrNqX2DV1DnUaVG1TBiijGIGYqcbN8DWNwdoM9rbAL6VsVHwllU5TyG4 jZY1Gom8Y2std7jWau1aqQ1XKU3eNeTIGm2Oer4YSZXzDWF2vhjmnm8F8cv6+hjaDNnlkJ4oTGmj D0TSGKgmjYFL0hgOmzTGhdaEQnsKueY1phmTDVWmBdODq8xogFCqgcBx4e7Z51LYy2TodanpqhxJ SgSr4mv1bljBtQ1+BJy/WG18sUKNOmws0r2xRYWNpcdZHSJNzsnjMub5H8Cgnls5jGjNe0T9jXLY eKX2xq6bLaLCE+2xfD9S667LIJYiJRgOO3ToLHhPMXhaMXp8L8tRQx9RNGYrLOkFDrN7HMkF2px0 2Cfs1ArM9fylHFXUDtbjAUya3paGHqbkZeDpk21etUzqHaGb2j2i+MkQv/XGWzGQmsFzQTaRlQR6 FVxrAyyEpIuXUtDVMxKcwdpqgU88wGdMLrFCHmHqDDjeE8u6O4D9vkBTCNstrXFhjVf0jw1ztBsu rzlXrG5w9j0QRHaTPje1bm2htfvYegl9W2VYLe3VxSckaVM7v8Nj38dp3RN6jaOhY5vdyVyrTpC5 bel2xqx2p31Dr3LWrHpnnH0LcNasfqedo5csJXcoeNo7elNScoeGZyOG4XSZsushTVHAMlYfkbQ1 UE5bA6W0NRw5bY3rXW9KuWxtBXlNzb4wbgg4vqSC1w75NtvyvTeUF+01WL7HVwnJ9nheRYfgWaGO uemdMr0/gBPnIYRMQpo9nhfMIXhWkGNC3BJSKTnef3cnwkPY1AkJ9niv/0iC9iqxz478bFuCY4ZJ NbGLSxQJ/KzuYFig+NwmxHXouaJuLwVK6pBANInfbAHwtR9trypOHIYhejMUeEtvckynUXvUut87 yV8BBgA4UUTSDQplbmRzdHJlYW0NZW5kb2JqDTU3MyAwIG9iag08PC9GaWx0ZXIvRmxhdGVEZWNv ZGUvTGVuZ3RoIDgwND4+c3RyZWFtDQpIibSXXU/bMBSG7/MrfOkgxfVn4kyICz40MQ2JTpG4QLuo oJ06QWFlE39/59hOcULcpbQTUhP19PjNc3x88rI4ykzNSk0Ur5jWRNesloQzbcl6ni2OMsFrZso2 7AJxVHDGN8mlZKabXMltyZWKko3qJUtZbkmWsoqShdFMdbKVFUwlszFq37Ir3tM2Rm7JhoqJksha YeHc90KxXsFSUV+RRDQgJ6IBKRENj5yITq7J8fHk6uzynFQ1OTk5PT8j2a+w97I0KIt7L2oChTV1 jXk3ZJWdNtmkaQQRpFlkAooEf3ApNSwtrZNsHrNb+jUXgs7zoqR3uaa/80JSIliVf2++ZBdNNt1H THDtCr6RoyRvfuKyF1eAEbFZ3rKhYOjOoIjdOVpRGCY7fPoTIilOnh9jpj0koGVrOQpKdKD8qQmK eGpGK1a2ByUcFOcdoo+vX/vOH0EkY6JwlIOiO8pjJaWSHST6OSh+aN0C9hISmjsSbl5JJKU5joy4 fPNVXmg6X+cF9P7sAZufxKXcBwx2QcZqcMD+G5nlODxjsiWyAJ0AvBnyHQwMR2pHjF4dDEz0wJTo zqgn3K57T+V36/JQVNqwqkNV7EMVLwxD0w6dqQ8tjNWSWK0i3HX6wB+m0uJirmDXOB7WuaHLVS5h sJd0+Qw3D3O4ezlUp0P3xar7Acbrqh7NDF5Mq3uyyBX0HLT0N/hQ9GAtoLg7se9B3k0/FU+/YEVa SbQie5xlZatOy98AI6ezH7BhMKci0p1l3zS0sNveXNPIH8FlwDyhC/DBlN1M5LZvWx9N2c1Ucnix +WjKbiaS28bw0aTdTGS3xQ7ZKbuZrhiW0++U+x4epuwVLBENFRmOtsjD0RZpONo+8nA07nb9zm4a waRtHWBrUv9pNqHmkLX1YJkh9+fVgjUboRa830bulprt3m9ngeD8RgCVQ87P6wVnNkbP+74IiAeg Id+38+rB9Y2gqYZcn9drR+0IwfCainCm8E76M4fp/oJj3f3vscSPp9UEL27uu9Ar/BB/tx6a+Ts/ iBLSneoR5HZo4gfBMHrHCOJw36iRvwIMAGrBOOsNCmVuZHN0cmVhbQ1lbmRvYmoNNTc0IDAgb2Jq DTw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggODMwPj5zdHJlYW0NCkiJrFbbTtwwEH3PV/gx rrpe35MghFQuD22FaNVIPNA+rCiwIFguAvX3O/baWYeMs9G2WokNPnvmxDPjM74ov1JZ3tKZLK+o Lpf37ukjOaczWy5g4YYKDgj890IVAEd0pjzgf7h8fFvRX+2X4qQtvhfPhaoFqzWRRjBZE1FxZkRD hGI1ebkqzsmqOGyLedsKIkh7XQhJOHzgSzc1UxaI2hHbh6IktL1zYU9OjwjENg0TgFvDeE040+EP RL3+4ECrI6gb1sgUFbxhJssVgjPeka1kpk+u5Bi5UgnZqHdkKe0IWcoqIQvYuuqxXTJVlh1THdgu 1T22MXKEHdK5rpNfj0VKEpZBQ0ZwNG4ZR+OWcDS+Mo7Ov5H9/fnp0edjUjfk4ODwGBrjOdZeVb7w odu42NJvVkNcqfmw1RKZhkcZ19uxUUDJdclUJSEMk1HqorR77txwTp4e0oOza3BomkZu34jobSQ0 LWi5jp2sVdVsgpRMpWKLg5Tv76laUsk0aZ/Aon7fucQ9vjkb6nnOzhrQx+4MbNuQSjcUT50TS9xt q5iqK1ypczbDWdbYPJb1NZQZG8qDWVfDqaE/PJj1NJQaa+HBvKOh3G52eG7Wz3J5cjmEquS8DINC EhAobhKB4h4QKL4iAqUNpQfmxetQX2gnyKCdZF7SwA9HO9dg9uW1gscMtXjGvkBM2fVhrPbgBDoD ozNdoiY2LoGYmPWF7zTyO7KYj3m54GRT5NY+tj19FeZkXiz6TDZ/kAcuSXtJZuHpDxmYW5LSH97a wOQuqS1vF/BwT87e3OIrNeXtyj3dwCrBfG/aGyVWxI3nbdL9E9o9JOGfAgvxLjDZOapLm+4SqPsJ VHCwmn4GT6iAG6m7w9bl03JBhS5Xr7T6PxkLBYPtmSB36G/BLzsEH44huO2YNHq+IWtsEq31wiya oOcnEd783SxStjPf4TBag9lphHO7+41Hs/MoQ44XFo9mJxJO7q4HHs3PJJzdzfs1OzuVshlz6fQl 8svwLpUcgGqMiYNhaqiRQuCqCYpEThxcjZQpE3qDYqE3bq1GioiHTlAkdHL01FiJ8dgJisROjpka a4BMjTcoVsdRlPwVYABbSCn9DQplbmRzdHJlYW0NZW5kb2JqDTU3NSAwIG9iag08PC9GaWx0ZXIv RmxhdGVEZWNvZGUvTGVuZ3RoIDIxNi9OIDE+PnN0cmVhbQ0KSIliYGCc4eji5MokwMCQm1dS5B7k GBkRGaXAfp6BjYGZAQwSk4sLHAMCfEDsvPy8VAYM8O0aAyOIvqwLMgtTHi9gTS4oKgHSB4DYKCW1 OBlIfwHizPKSAqA4YwKQLZKUDWaD1IlkhwQ5A9kdQDZfSWoFSIzBOb+gsigzPaNEwdDS0lLBMSU/ KVUhuLK4JDW3WMEzLzm/qCC/KLEkNQWoFmoHCPC7FyVWKrgn5uYmKhjpGZHociIAKCwhrM8h4DBi FDuPEEOA5NKiMiiTkcmYgQEgwABJxjgvDQplbmRzdHJlYW0NZW5kb2JqDTEgMCBvYmoNPDwvQ29u dGVudHMgMiAwIFIvQ3JvcEJveFswLjAgMC4wIDYxMi4wIDc5Mi4wXS9NZWRpYUJveFswLjAgMC4w IDYxMi4wIDc5Mi4wXS9QYXJlbnQgNTYxIDAgUi9SZXNvdXJjZXM8PC9Db2xvclNwYWNlPDwvQ1Mw IDU3NyAwIFI+Pi9Gb250PDwvVFQwIDU4MSAwIFIvVFQxIDU3OSAwIFI+Pj4+L1JvdGF0ZSAwL1N0 cnVjdFBhcmVudHMgMS9UeXBlL1BhZ2U+Pg1lbmRvYmoNMiAwIG9iag08PC9GaWx0ZXIvRmxhdGVE ZWNvZGUvTGVuZ3RoIDQxMzU+PnN0cmVhbQ0KSImsV01zGzkOvftX8Ni9FbWb7O9kag5xUilPxlVR 4q0cMnvQ2HKirCJlZDvZ/PsFQLKbsoAWJftgWWo0+ACQfA94eXly+k799tvpxdn5K5Wr339/+epM nZyefcjV1S08ULdXq5PTy8tcaXV5c6INPIPvRhW1asoqM+ry20mi0suvJ68vwPP11oLaL/jPSdVl daka02SdUWVnslrpImvVZn7yUa1OXl4iin6IUpfwSlPkWd0i0KfkVWqS2S9lnqf/ufwD0aZHr921 WRms7ZJ4zIo619tLPn+CJasMS/20Ybbba35K/rhPJ2WyTCcm+aV09fjiajgb5VYtnimT68fHXncP 1n2CNZuHsfoa05Ge4rIaj7vOGnDKytZ+wKo3/9prxICsEQPy7zhzZWgnBOdxq8W1udJzn2rgyluD G2p2bii8B//KLut0R0653q7fDhXYS6pr9NupXoBVeCzcKq3zLO/hapNV43ATeB3ev7xS7stPFeyg RiYaQviUfEgnbXKXTupktkknGr4GR/pQ7ACoaLI2KtlyK9mmCACr4gDApn2QGTLg/SYtkllaYoYm WeDHerWV4dGAxuRxu1mFCRo4aAOersqsiAYszFaGImAdAhatztoBsMnjMyzaRoSbBtcKKEG4ztaI V2TrNuu8yyrR1x86a8VDt+3cmDFnt5/Wivu55WxMPeLsN8daaXO2vLGWhejtK+28sdIsh4kVw3La bXrIQ75ggtVVhLf6lHmrT4m3+pB5a3DkmofsWHcdegbsGNXA5CbTZvRwtww3OrCAn/aRhaXBnLIb Q+sYcnJoAVfEUdN+NJ0zVOHgQqqII4oIPM0whccLmCKGJwS0aXCw4Z/AE9Yo8gTvG4iTrmWeEJwH 3te1zBO8c0Diuh7hCd47YGRdj/CEWDHM1m6SwBOS1VZEsLqUBatLSbC6kAVreOJ22qi60X7v4bRB YeuYJqpuoV/bc7i5LsqhOaaIQLNMMcB9Strn2EXkucK24hvTLR2OAQfIRKXE9UoOztFRDBzRUZiS dikxjdHhq3f2CEZkwzVGDs+znQg42uVaAgwzfJ9OmmT+A2e3Bfa685+YsbrBjpfa3jWavqFJvYLf BprGicmTX6mGJzoszIGBBjQJ75gyqjL1IyqzU4amRD8qwz/3czi0t7bPHxpiOsqra/uPrD+hd55j NTZPkzpMjJh6H4qcesNJkgN0khQDCJIU7v/b1CWL+X9Z4rdn6iyd2OGAfn9Z36+eqY80CcGzz6nO mRIcHVHZtUSRwu5Pe2JvcW7iddIZJZ0UfPsmiaySTkrOvuchq6STgnPfwZBV1EnBu+9HrLekk3LF sJx2n+g5BNOYBxWTzK4mgtlnLZh9WoLZxy2Yw8vQ7qglyFHZerWsUJti1BKcoUR0E/5Mtbbn+gpO ObGAgqY7PONHQumcbvcAJl/yjpNmi+dkMwbPSnOQW7dXmg/GsNK8PyWTc9Js4Zx4xsBZaQ5SGpPm g1d30hyRjeYEyOJ52hcBt6RZ89IcZPiGaHmF+jtHKdbJbEk6DCe1sWoNRg3WGSm1QmK+SCeV1exr a7Mu5/jCedolUDUUbk68IrMItKSqx871lPyC2vUt7mHdSllnuSvJyxR06RZTpPTpm6LP+V3/4Blm r2b3+I+eUjmu1lSPTd/Y2MrM6I2Few1eoHrbJahIFGHhIywwwlxNgKEqdfkKQpqlGn0KrHcBjdMC Q1jixx3FBeYNPcO9KkA9G3hpje9u1M0F4rynLTkntJyAEMTVEgv5eruMxcB9R2+dK61u0W/0wJdc 2+HwnMjHHfiHu0udSHDcocGA8zv7nNZ43JnOIhI06CwgvU4mqb6zgBVrsbOwRrGz4H37gYesYmch OPsBg6xiZ8E7950oWeXOgvfumzjrLXYWYsWwnHaf6LnfpKBggtVVhLf6lHmrT4m3+pB5a3jSq52e IteZ8Tqvi6yNailyFMbRK1VzIk9YTn8jsJzGezDQw3yvxh8KUechhJxPwyk8gTkJjgGzAj/kY9Mp WH0/dG0n7/szaTl1JzTPrSIcoxM7dDtkd+YEp0hublAi5pwgxwEHKHgAy5g8O47ULZyj1wg4InAe azpwgcSrYBFJlfEa+lSZTjm3viOUiZRxG3R1hEIZv0GqRsiTrwlWi3aAHvvyB0XhjS511ugTZI0+ C9boQ2WNwUEq8oecWUHBdU+aUMZavC1a+1ZYb98W4tGqpdNhRzMYy6DlvUonDXZV0EYpszuXRUIP OF2FmzwAJZlxh/gRi7phL1hVuoWFZnTA4TmWjsEjIQirpT11ikpwOAhJQUxOhtECB+cIOwaOxGAr J3naO3x1Kwcx2RSMHjg8z8siIMqAwRM+cd92BSHM8AMm+A4/LkAP1BvYun+jSJynBmacm5SmuhZG F/hF7y5WqYFxQptkOYee2T68h2d/43tf8dkV/N1xs95T5tDUuBTlcAuR380QFL+dYgj/w2C+Q1TL NXxg9BTsNfzAL/jegpLAbE7xJwaNaywXNBDYLI9PIdAr4BXc9z5ged+5occDOoEcpbbRoSfc9be0 k7jP8y84Cy6ZwScWOBx8CuIu4YT36pznWWMkgbZGUaN5377VJKuo1IKzb+/IKuo179w3TWSVVZv3 7nsg6y1qt1gxpHy7T/Tcb9JQMMlqKyJYXcqC1aUkWF3IgjU87TuDT1W1WI1g8sl1zOxT1VTF0avF DT8OLxhN9uI52esBQSK0l73vrOYdgQGq15morLgRyCEGc8p+RCt8QVYjY9Ax6zvpi8iIG4UcYjiS CJDY1bFqEaT2Z6p1cr+6AqX7ov5K0iL5voGP9Y8Fpnw9h+fXJAF/pRz/R8cSsK+uY9PnJiQPGYxI +yGR8AW8aU8cBm8dz8POKPGw4Ns3emSVeFhy9o0VWSUeFpx7eSaryMOCdy951lviYbliWE67TcIc JVldRXirT5m3+pR4qw+ZtwZnrtwdpgqQm/qA4cOycNlgsMMdm2N/gdfsDi+WgnCY4elQKD/n9GDi XSrZOcfiHTznBLlp44YCgfAPRnB0H5ERO+VYvIOnnDCjkSnn4NUd1Udkw045Fm9ve73V7eqS5fwg wzeY4HyFre58A+Suk9kST6eCg9rYRhiMGqwzNEPMwP8X6YSmH6sHYLMu5/jC8PGcHg6lE+riYioK TI9iOksnoDoruhwoQDO4Kbe4Ij25tWHgsnIFy+HmegZz9Xv8tBBU7yPJ4ewzTEdQPWZSiAQNJ4V2 7MxPA0oFDhMUyhpFheJ9g7asqGWFEpyHDqioZYXinYMGoqhHFIr3DnqBoh5RKLFiWE67T9KkIFhd RXirT5m3+pR4qw+Zt4bnfHdSyEGI20A2yjZuUtAdQo5oVMlo1OFgXqV6OPkOs4OJRRw0JALR6lSQ nx6XqSMg4Mh2Jiopdi6xiIOURCBaqWKS4qTqiPWdWEVkxM4lFjEQKwkSSdYg207cN0augiw/YJLv 8OMiBRV4j1owB/a9hV/3qUmWd7d4eJ8renGxgkefUw3P8SXFTS1PGmle9dLgI/ob/r4i+hX8YXTJ C/UdI13Dx93dEOILdQ1fF/gG2Wfw/q9HRRzoVkWBDfHJ28nOWQ5ykDEJcr96Bpv5lvYIeWb+BZuJ Jaeg0dChhhbETsLhnQakb1pJQ61R1FDeN+h0TStrqOA8NJamlTWUdw66RNOOaCjvHbQrph3RULFi WE67U5KGClZXEd7qU+atPiXe6kPmrcGJr3amvLJt/d7DkcPZMI/S0LLT6Dd2vSpu9HJ4TnGi8Kyo DYCfkvHZ6xgIK2oxSXHTl0N0ohOHSKIWJpVTUgUnasesb0UtJiNuAnOInnhHIIkFC8+CBScVYZYv ocvaQL/VwaSjk//i9PMCJyx1hty4xt83N2SfM3oQH1YQA1SijTuwJaMHHtKRchQkkr+ENw0oxEiE nNuyCXzMegYdnZHZmHcdWicjczHrGui0GWFi1jdQPTPCw1KdoIZub+g5bAxUa6tQotkWQzK7hCWz y0kyu7Alc3jcdgaass7dvlv5r6OYuGnGRplqd5Q5FMbNMQ5Ivj7cEGOxhgFjL5Yle5+TcaQoMP2h izua35cIN7hYrGGu2ItlCd4nMjKyHLyyo/Z9WXDDisUK+ul9YI7BfRrnqS6SFZ2tTVoka0Vf1wpm k5yO3Xq1msO//gQu8OMHfeJvA22+yRM1A/vqmhtSIiOMmFDKuoBPCvsbDiU0cuCkcbfAeWR1BLje KU+bh0jyZnCjhkMb2n0+1b1zRr87H2kbZp8hx/mGGTAiAcPpohUvzLQn4SaTJov/s17tum3EQPBX XNqA4dwdpdOpDFIFqVylcOsgRpBIhg0hvx/ug9Q6mjmTQSrLGi2Hu+TOcA1jRoYj6ytOQGZkJLQ8 lwRkRoZD6wNDQGpkOLa+FDSWGRmtk9RQD4aMEwT0QkCwpArBkgsEy2YhGC70fDlJ5KYb+91rozds xcBmYGC9VMXBKhlt1BkOLcbX7WIht7RuZN0E7mQNCcGBxfi63SwkNK6MK92ru6M1ZAOHFePrd7WQ zt/G9niqlnb2sePhjd99vplyzKN+FtN7/qauVv+35aDJ/euO01bidMefZFY6/hKK5+Mh8H0UcxOD /Zl34ob7mj9aDq//ZTspZcfbhP3wE4NDlRO2O5H6XDgvzd6TnK6fjqfDFTK8bp5NGu7mhV7Fanr5 z46Nbw5S28OxRQoMpcZHgr3zDKXWh4PLLTCUmx+OLqX2aGp/tGJSTjsnZoAE9YpgtKSM0ZISRsuW MRov9+UIN+60GmZNefJbmlxwGiRqtY/gdGVs7hsNbG5Mle7heuuDCTGmbgI3poaE4JRlfG4dLXxm TCGhtUmre3U3poZs4LRlfEVXGwhd5kM691nNT6LuKuHRiz7IHx2kFPqdfyi/e0EC372RNE56jxsy h6ONE7rwthCKwIe8v9x4opLTk6r87dWl6t9efZUHo3z3/UZ9N/8HZ5/uHW32i6oAKUG1gjRK4xMr MJBaAY6tr0JFqRWQ4PIIU5RaAQ6uDwJFuRXg6Oq6Fk2tgFZMymnnpN+XQwoFI6hXBKMlZYyWlDBa tozR0Aa7y4FoSErbaQWDbnat43ZwRjG2biuodA/X87oVdBO4FTQkBGcU4+u2ggY6OEQYXb9WhwLK y/vxh6rzSaaIA3z79xPtttoZDZnBx7YTdmoxYbsPfZobg8iegVT2cGx4c6SZyx4JPlt8mrns4eBg k2lekT0cHRwmzSuyRyumh6uHxGSPoF4RjJaUMVpSwmjZMkbjjbt4AadlfzctUfaGsUX40n6SuNXr jd7Azhd06V0+k74WQvRGdcIgTO8TqjS1EKJnpBOmaVsV411GEyfGeB9u3bCwJjaQNjGODW4xLLyJ SfBZ+oeFNzEODvqaUd7EODqIpUTTJqYVy+WUgxrP7ZLL9hZd7mYeykBtKQXZSTDaCsOla/soyg6K rl1gvHbpFEXZObK1KwzXrk2hqDYFOih6FgXG9V5FpddCr9bBB3poXmWXH42x+6T1cgv+EWAA3R3K uw0KZW5kc3RyZWFtDWVuZG9iag0zIDAgb2JqDTw8L0NvbnRlbnRzIDQgMCBSL0Nyb3BCb3hbMC4w IDAuMCA2MTIuMCA3OTIuMF0vTWVkaWFCb3hbMC4wIDAuMCA2MTIuMCA3OTIuMF0vUGFyZW50IDU2 MSAwIFIvUmVzb3VyY2VzPDwvQ29sb3JTcGFjZTw8L0NTMCA1NzcgMCBSPj4vRm9udDw8L1RUMCA1 ODEgMCBSL1RUMSA1NzkgMCBSPj4+Pi9Sb3RhdGUgMC9TdHJ1Y3RQYXJlbnRzIDIvVHlwZS9QYWdl Pj4NZW5kb2JqDTQgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCA0MDMyPj5zdHJl YW0NCkiJrFdNc9w2Er3rV+BIbnloAgS/klSqVrI35WRdFcWq8kHeg1YaW4rlGa0+nPjfbzfQIDEz 3Rxw5INHY/Y0HroBvvf6+Ozo5e/qp59evj1580qV6uefj1+dqKOXJ+9KdfkAD9TD5ero5dlZqbQ6 +3ikDTyD70ZVjWptXRh19uUoU/nZn0ev30Lm640FdVjwf0d1XzRWtaYteqNsb4pG6aro1P3y6L1a HR2fIYreRmks/KStyqLpEOg8e5Wb7OKbqn7I/3P2K6KdHrx23xU2WpuKeM6KutQbS55nvz7lC5vd 5guTfVO6ef6mdQW7jluSvVCm1M/femO21v0ea27vNbTYXZVTXFbjNdJFC0n4W/cBq378x94gbsgH cUPhNxSuTVHJydNRj+trdc9DqVEqH41uvtm5+fA7+GP7ote9Syr1Zv92XjF/+XWDeTvdi7CqgIVH pXVZlAMcnGo9DbeAn8Pvzy4VfflLRSeo8Q0ft3CevcsXXfaYL5rs4j5faPgaXem52PG1bosuqVi7 UWxbRYB1NQOw7bYqQ2Z5us+r7CK3WKHJbvBjvdqo8GBAY8q006zjAg1ctBFP17aokgErs1GhCNjE gFWni24EbMv0CquuFeFOo9fKWul19kF8RTbeZl32RS3mhkvno3jpNpNbM5VM5+mjeJ4bycY0E8nh cHzUHc5GNvayErNDpykbO81ymNgxbKc/pm0eCg0TotQRPhpK5qOhJD4atsxHoyvXbrNj0/eYGbFj EjeWDm/qcncMNxJYxE/7yMLT4H60niEnQou4Io2a9qPpkqEKgoupIo0oEvA0wxQBL2KKFJ4Q0E6j iw1/BJ7wQZEn+NxInHQj84SQPPK+bmSe4JMjEtfNBE/w2REj62aCJ8SOYbX+kASekKK+I0KUShai VJIQpS0L0fjG7dioprNFP0EU/ATRdC1avcnLzbkoQpvLFAjXN95WdD+giyhLhbbiC+OW5mPABYox 5JI4r0Rwc+koLql0JVUlY4zmr977K5hQDWeMCG8v2026XE+AcYV/5Is2W37F2e0Gve7yL6xYrfHB R7S96hU+uICPMvuW6yZ7yBd1pnTckplbjAhSu7kp6skimsUOX9YY55DGZc33WbZLPUHOaQbAefoR H9ZvOVn1Jbxh17f47YU6yRfeybv/X6+fVi/Ueze2wLNPuS7h1/C/+/jADt6R7TvHZ0ILTgcW7nDI 4UWNgpKoCbmDo3FRSdSk5GBQXFQSNSF5sBsuKoqakD2YB58tiZrcMWynP6cgH9C2zY5JYeqJEA5V C+FQlhAO+xbC8cuw64EtHEcXpM3qwqZ44AYOsen8m/DvXGt/ry/DDKvAFcR3/EAoXVrX8gFMfslZ x+3xSONS8LyORrV1JDqyjs7G8DqaUBJr6z0cKV0KnNfRqCRN1oDT0dmrk47ur8awU4PHC7QvAqJ6 GpTRBX1jdDSq8HVeZX/niy67y5GekW/r7OYLflvBk8cL+HILWmpd8CHX2Q1ws3FBxWlp4jYjsaga dH5RXz7AG87o3vyVrdlaWQ3LTk5aDbyDHTXoOG+9Qq3xjf2MCnalwGCUTqdWVwqffMWP1aPrR4sS XqqzKzIFjLEpi6ryPzmHfoNZWd464QM+AKOydKtfoZMZOvyMLlBJtsE8V5Jbeun8kPNOn9xhwzNv kS7cd8LHrSydoXIfN26Hl95yud9djg7rOfehdA3avK14fuiyhq3Lbww79xI0uYSUC4O+JXo53vtz xv4sWR+SiMAVZ8FC9jK5nUYSakVH4oOiI+Fzo6nGTjgSIXmcIuyEI+GTIwdrpxwJnx2ZPzvlSMSO YTv9ibnn4biihglR6ggfDSXz0VASHw1b5qPxBTc7XgSmLXIH0NUm+d55S1JBK6cciWEcyYGAwZgE SPklrjhfAqhkGOahensy1tnvdSeHInmTsr88y3kUACULMQ/UW5WxvCmncigGGZb9ldWcXwHUwPqz YEmvxtJ+AatxDzZl/XTnDm8VhkYDsgPa80DCZPw31wj1L+Bs9+xvL2Eo2y8igUrdhqZN4Aa84K/d Iudwh9wYSy7A/W8b0HmDj5CJ/+gdg4/HYa9+Q3Jjm/GtD+yHbSW9EdvKWI4tkRu7u1fjUgB3ZY2/ NIOqgbI3kqi5mKhpbOYwYGBQVDQ+Ndh5DIp6xqYOFgeDspqxuYODcLmilkl9wh7CwbiH4VSiJnEh agITCkUyoVADEwpbZELxFW53hKss0ZWTlOiq6PZcKa9YZYVZkzTEzrUejYg9AY2EY4ADai33Ssds kKbcAJFrYgdbD0ecngLn1SKqKZTEycXs1Ukn9ldTsYOtxwtaIQJukJq2LFtHFZ7kizZb44QyTi/L pToGJvYjl+e9z9zMkrijCL5v8AVOaQE7qRAg0WwCoCNxAW0gWW0HVtllWR8UaZbPHaySi4pEKyQH O+KiItXyyYOrcFGZbPnsQct8tki3Ysewnf6Q3PNwQlHDhCh1hI+GkvloKImPhi3z0fjG7YwOdd1h N4iBxal1i4Hrri+snRoaqt2hYTYUTQsjmPwmceMC4RETp+A5uo9r08FKi3Q/H8TRfUpN3IxAcETI KXCO7jdqkqeD+at7uk+phpsLCC+QqwiIbK+R7Rf0bZfu4wrf5Ysq+x0/3qp7OLdlXmcPT7nGQUHj DdXZg2K4PnU7MddX7maP9X+A95ha8MyV+62V1fdYtirN5rLn2acctC/MVG4Sefrvn3hFcGAZXmkX +NG93NLgBEdjhkMy7CG1NaI72DuY5G7X8PH4eLOCP7ANk/2oruDrzQOclYtfoGbDYIcRfPjlAkaT 21u/hXFSK9VCF+A9z17Bwl/XOA9mT19CDQqLWN87sa+2mMpNaevV9OhV7Y5eofOk0mLn905f8Un8 RjuC6esaC79lRrBU4HgK276jvEMA8TBWcgg+KDoEPjdyxMbKDkFIHh2osbJD4JMj82bshEPgsyMn ZuyEQxA7hqLlz8k9D4c0NkyK+o4IUSpZiFJJQpS2LETju74zo9XWIuzMGa22LWZNSgI3oxHa3Blt hAOBM0G071jFno0Ait2bpIq4CY3w5k5ocUXygDZ/cVAsTNhfjOUGNMLbOw7hWMYqQFSVM45PK+Ti a/UhA26+czL01ZHyFVL3lRvMPuScWCfuJKJb3aSWzg1mAXDeYCahnUY8AS8mT7sUlGhXyI2cadWI tCslj06wakTaFZIjW1I1Mu0K2ZHCVY1Mu3LHsJ3+kITBTIpSR/hoKJmPhpL4aNgyH41v3O5gBre1 GmgXGtsk0W5VYRsmLzc7K3k0IsUENKLdAW6g3Uqi3dkIRLsJFbGTkscjZkzB87QbVzQxKc1enSal hGrYScnjBbZLACS+jcr5BRx18Pk4zq7wA72yQYtdeot/M5h9V7w6A9d97X2z9c+v0V+DV79yP125 8YGdphK3PGmRqQrd41KuiosVWuMr/FDOK7v/f8SdLe/hE/64J5fLZ+wpnsPaQsc7kM+t4USDAEk0 DmuC05HoIN87Vbz4xI0H3wPPltt3FTo7Dp8zoeJJpJt6o08jDi87SRJ9UJREPjeyfhAVJVFIHq1W 2cmSyCdHbgWisiTy2ZH1wGxREsWOYTv9ObnnsJnWbHVMClNPhHCoWgiHsoRw2LcQjt+qnXnEdsDT 3WxhLGts0Wg8l8gd6D0fHc9VhY1fpwOhdGldywcwmS244YfwZqtwVJueFOH5AHBhe5NUEDf7EN5s Ed4piBXh+auTCO+vpuaGH8KbL8JROW9yXWUrP9egpN7hx8oNOyt/E0/yBWj0F/x+t15FAUbPUneU oLG217iU2+Q/nbKjil6g1jfgD+AD1b/OlvCnhs1gAW9Qdd/kPXqFJlNOhN9i+A/41kLk8B1H6mdK fKfG/cmHxo1tATBdppzcRkfmDiR4JbA/TytGeOfDWPxNJ17GQQ3hFe9ENfRBUQ353MGRu6iohkJy MMAuKqohnzzYMBeV1ZDPHoyHzxbVUOwYXiN/TkFyms2GSVHfESFKJQtRKkmI0paFaHy3dwZE29qg TGGm3CeCtm2nXyBuNEQcEowEHK9IBHSeTc+EM5f2WrS3CG4aRCTSiRQkp0JDEVQDp0Dzlu0rd1H2 FcANgIgUSFOE2qB5zdN8KApYrc3Ww+zkB6ilOoZR795990PGZ47DU/YSj1ANvqh7y+bmJwdFnJoA hdTN4pwOr39bSGTqYxKX8pmDr8KgxKRCajAxGJR4lE8dtBSDIovyuYNeuVyJQ8U+YQ/hTNzDcCBR k7gQNYEJhSKZUKiBCYUtMqH4Ou0ODrZ2/DG4+brvk1gTzhVGk4nRoWZGh/lgNDyMcPKrwg4PHnH0 9gmInqyj+ipy2wJfHwBBpJ1QFDtAeMTR4icgevKOijITI8QB6/sh4v+cV01v2zAM/Ss+DQ7QBZKt KMFuRYEBxU7ZZYeeCiTYUqTtUHTb359ISjK3PPojl8TOC0WTst57nFFRhEOEZFQG2EpJbO77wuY9 ZHNV5f0dW/Xk3dMr2iRm73pmdSfG3JEz94GseRfb+09s5tVM8U9q0pGOMn/MVyB12FEdZajZtM8/ V117Pq5i+0wfL+nuXTQk3b2fXlc0UlA6xyXRmnHd93R1GOmhH45xoa7cwcFqT28aC4Nq1qWnv2m+ pCc80S3NXD/OyORfkTlER/RsvS5VmLaDAlwqk4CmNOHYarIYNcXJCC4Gh1FTnnBw9QmM2gKFo6v0 S7QpUWbHqJ2yU5bNN9DcEYyWkjFaSsJoeWSM6tf90ub3nr8WOv0+UNQoO0GzL9kW+/2a7qHdjEvI 4gRZQGYUBI2/5Fvs/VVBfkQ+Fq+exWNGNXAKkHzLBgGHBwFV4VeaBY6/aRg4kbc5/mFj8yHxYMMU /lmokVg9/efxTJfNnv/0yD8d6EP+e4smhpnPrQh7081tFJwbcsKFo4Pqyf9qkK5uGiQb36gZ9Nv3 VaqeveFbGqIapB+Ln2rjhFaMNuwV3YVgqYeApnrgWGX5QrDVwwge3FUItnrgYGWUQhhRDxytpDqE EfUwO0btlH2yhhwDzR3BaCkZo6UkjJZHxqg+Cpczj99y6KAezs/Sj85R3Oi5g0OI5FP0PpkvK0hN +NBGRLjXr99L52dUBCcQyagofjqjSMh0wi0cECSh5srJjJnSVQ9vE30dnqiPr7+Ijl4QK1+TKb3l flY3tx7xck6pGHAyJTOzkW+vTmz6MghQQJMAcawyLD7aBGgED/7AR5sAcbASTR9HCBBHK63xcYQA zY5Rq2SbLogog3G925mRFshsxKC5EThrReHKlRgYNbfJWLqgeOnCAIyam4iXrihcup5ARu0txmtX FK5dj5qsbb4Axh4XFO/jKEoHWBFAnWXgqU6ruLh2O3Wo6USnk/1XgAEAdkIJvQ0KZW5kc3RyZWFt DWVuZG9iag01IDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9GaXJzdCA4MDcvTGVuZ3RoIDE5 NTIvTiAxMDAvVHlwZS9PYmpTdG0+PnN0cmVhbQ0KaN6kmN2OFDcQhV/FT8B0lf8lhMRPkCISQCy5 QnsxLC1YscyshlkJ3j7H1cckIVu5SYTinrV92vX16Sq7JYclSAmtBKlBFU0LWmKQHlLuQdGrGT2C vywRI4ImyUFxmRLahH+1BMVlXjBuXEZI4DIXzB9qyxK0D1UJEZelaMCllh5DhF6FSIRezSVE6NXW QoRew80j9FrGeOi1hnHQ61hvhF5PLaQh3ZaQhrRoSBi6pBQSpBesC5dRFozLaDE4YapUjKu49YJx uJXiJqmjLS1k3DJivRl6EYsbt44lhQy92EvI0EsIKkMvFYwbS+4YB72MOPJYOv4IdDEjjjJCQBwF egViI/SCOEA4VsQB6VixiAK9ijgK9BriKNBrWDyWFBviAMrYEUeFXkccFXodcYxQFsRRI1rEURHS gkk1AwniGAgEcdSKFjerQCGIo+LxKuIY6PCAQhO0iKNBLyKOBr2IONpAiTga9BLiaAMp4mjQS+hs A+3wz7AL4ujQyxDt0CuIA5KpIA486lSwmA69Cp906FU8TCwF9smhQ6/BRB16fYzreBR4yCOGvICG LIKHghXLAtrSRlfE48EDk2U8H4FDl4wLIJFlPCl4SnCrnHDT4YGM/3AB5YIVi4ynBy6CmXmsRWQ8 R9xZBMoNVETGE0WMDx/uXozXZAlvdq/3p/Vwfnta1/HK/PMvL9dv5xfr9xB3b4436+/72/E6jSFv v9+uu4vz6e7Kxr05Hs+PHg3Vd4MyRsAulW1j27cW69haYatso7WJ86Rtv7Vktts43Zq+jQIKNJe7 10Fs2O5id7FebWt5effl67tlvPmmN959Exgv/5hkgx4fDsfz/nx9POwubveH3dNP+9N59/z6491p 3T273n887b/8+Hk63j7d386fvxw+YO66ezn+9xwE/vr16+Hm+rBefNqDEke/ujuPv213Abnrz+vx 7syfd++/Xp2ub3/8vF1Pf//DWzyGJ8dvu8dY2h+HD+vph9KjR/8b+EgHW5vZUq9Qr1CvUK9Sr1Kv Uq9Sr1KvUq9Sr1KvUq9Sr1GvUa9RjwZA3mBLvUa9Rr1GvUa9Tr1OvU69Tr1OvU69Tj1aKnbq9U0P +YitsFW2NOyS2Ga2hW1l29hST6gn1BPqCfWEekI9oZ5QT6hHYyf6Oin1+IYkpZ5ST6mn1FPqKfWU epF6kXqRepF6kXqRepF6kXqRepF6iXqJeol6iXqJeol6iXqJeol6iXqZepl6mXp5JpBN73B3c3P5 bqYRoYuELhK6SNpMN40t0wVdJHSR0EVCFwldJHSR0EVCFwldJHSR0kVKFyldpHSR0kVKFyldpHSR 0kVKFyldpHSR0kVKFyldpHSR0kVKFyldpHSRzuxIF6nOdEs9ukjpIqWLlC5SukjpIqWLlC5Sukjp IqWLlC5SukjpIqWLlC5SukjpIqWLlC5SukjpIqWLlC5SukjpIqWLlC5SukjpIqWLNFOPWVaZZZVZ VplllVlWmWVnudpcOGHOtXIqR24NdSnLXMZUxkzGRMY8xjQ2szaTKnMhUxgzDxMG33O+nnyr+NJs DV89vnl88TYVAiMv4iItwiIroiIpgiInYiIlQmIlYiFiHWIZYhViEWINYgliBWIBYv1h+WH1YfFh 7WHpYeVh4WHdYdlhvmC6YLZgsmCuYKpgpmCiYJ5gmmCW4PbEtlvL2KRwy/UxRO5WXm/bpiCjd1ve fd1q3dHrjtadvO5k3dnrztbtLq1Yd/W6q8XlrrxZd/e6u4kvLhajtj3ne/s3bur2G7jighMjV1xy YuiKi06MXfEfq8ErLjwxetmlJ4Yvu/jE+BWXn26uc/mp8SsuPzV+2eWnxi+7/NT4ZZefGr/s8lPj l11+avyS/94Yv+TyU+OXXX7R+GWXXzR+2eUXjV/yX1zjl1x+0fgll180fsnlF41fcvlF4xddftH4 RZdfNH7J5ZeMX3L5JeOXXH7J+EWXXzJ+0c98xi+6/JLxiy6/ZPyiyy8ZP3X5JeOnLr9k/KLLLxu/ 6PLLxi+6/LLxU5dfNn7q8svGT/3SYfzU5ZeNn7r8svETl182fuLyy8ZPXX7F+KnLrxg/dfkV4ycu v2L8xOVXjJ+4/Irx87cFxfiJy68Yv+ryKcav+XyMX3P5VOPXXD518OtueFX/c9PTyTVshyf0vH3G z0iMaa59rnGuhfccH362A9eY+2b7ohO2oxdEePYSnr2EZy/h2Ut49hKevYRnL+HZS3j2Ep69hGcv 4dlLePYSnr22KC7/Chlr2r+/WW1ZT54cv70r6cH4YFkejG+HeUkP7BOnPuhdL3evdr/tv49vQq9v 9lfrl/Vw3j25OV59/rFVaJPWTxEPWoyEm19u9uaubm7fLu+fzL01d2lzOza3XXN75U3mnbl5515p 7onm3seZzLMBNzhzIzM3LHNj4k3mnXn44C5j7ibmrsGZzLMNtwZzCzBL/Szp3mTemYcn1udZh2e9 dSbzbMaiOovnLJKzGHqTeWce/ljZZgWblcqZzLMly9EsO7O8zDLiTeadeXhlTZi5f+Z4ZzI/Nsj8 FsuPDUzMMwF7k3lnftFgNp1Zc2bHeyfzGGc5VH9KMTzb/avvTwEGAIyteiANCmVuZHN0cmVhbQ1l bmRvYmoNNiAwIG9iag08PC9FeHRlbmRzIDUgMCBSL0ZpbHRlci9GbGF0ZURlY29kZS9GaXJzdCA4 NTkvTGVuZ3RoIDc4OC9OIDEwMC9UeXBlL09ialN0bT4+c3RyZWFtDQpo3oSUQZJbIQxEr8INBkkI QdVUVtllM5XkDrn/DdKNx/GCSLPAxn6Nvrq//hfx1pvIbLrwFc0nvlZbA18bS5soBANLpcmESvHf hkytqUKHpU6dowh1s1mnLpoZdavZpG4329BZb4M1TdoY0Jm2EdCZNe/Q2Whu1Dm6oW42X9RFm0Ld anNQt9sMPb1FZ4/Sgi6GtqCNYS3oA9pFH8Nhi7rZFn2MaIs+0MOmj7Hbpg/vbdOHw2+nEf7Z6cQN G1pxrE4v7syIYuZGNx7MkWJGQD++mRXE84SDjylMBXzSJj1No2+IWd3oajqdUcwW6WsGPczHTRh0 Nje7hDjYAb2FsBVwlnC6C+NlIA4eoJVwlqCYmAYjqOMv/mALsblQjLqFgcAHFiKUhRKL5Rcqr735 NwZCuXFOBk+g1j7Ho2nnwMCtdj8nsDknelMRnhBsBk8oNuxlc7LY2B4cMRTcmC2Nx+CpMdWNyme8 NiobLaMVZWLKNRC4dlQesIMWsOHRbpxV9sKhnRSjsvPKHZhBaUfl6RRDN0/jqBzKfrHC2SYqc7gU 6evi4yOovDAAsIINYnt/f/uBQcPj9fPt4zxJ3P16+/3927cHi4KtnOHePJlc53rBpGBaMMsZhurJ +sW8YLNgUbCVs/kvl76vc71gUjAtmOVs/sulr4t5wWbBomArZ/7KJa5zvWBSMC2Y5cxfucyLecFm waJgK2fjlYtf53rBpGBaMMvZeOUyLuYFmwWLgq2c2SsXu871gknBtGCWM3vlcr0HzQs2CxYFWznT Vy7Xe9B6waRgWjDLmb5yud6D6gWbBYuCrYI9+9zXa3A9I9vX07d7jiRHmqLO7j7t/WnPxD4+qYDa 8+m8sRI/H5gb28Erw+PgnWE/1+4ZngdLhuMUTztfB1uG98EjjaUf7ik/uY081hOcpcHJSc7S5ORE Z2l0crIbaXYyv+CP9NJw5cRnabpy8rM0Xj35WZqvnvwszVf1C25f8Ed+6f3Rk5+m90dPfpreHz35 aXp/9JFfmr8+8kvzt5OfpvnbyU//l/9fAQYAHw8OfA0KZW5kc3RyZWFtDWVuZG9iag03IDAgb2Jq DTw8L0V4dGVuZHMgNSAwIFIvRmlsdGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDg2My9MZW5ndGggMTEy MS9OIDEwMC9UeXBlL09ialN0bT4+c3RyZWFtDQpo3nyXQW6cNwyFr6ITeESRlCggCNCguxSokXZn eJEGQRd1m00KtLfvI4djI3GphS3NPIqP/H5pNDNIW2+DZuOBYbUpGKztiWE3GtbG6I0mggY12oga /oewwW3ovM7N4xRJPA7J1GNWY/M4a0Iet5sI4rg3McQxNSXEwVkFccxNF+JY2uwep6jG42aby+NW W93jrC32uN3WRBxirCNOqJl3IaOZtyHczPtA7u19iKItj5ttex+yGnUvHkVR904EHXdvRdEyeS+K nsmbwR+Rd6MMKt6OCibej6pz6gGD2DtSZGZvSZGZvSdFZvGmUACJdzWRWbyticzqfU1kVjQ9UDyp dzCReZKvQublFhOZDY0NNE4Wy5F5A9lYyLy930VtdDgPl6OLxf5Q8Wr54/DClvpz8GAH6RTXcmIe 7Kicz9remz/cKBeBRl4B9GsuvG3sKRDoaNZ2SV1GMvMFTtX8RbwNeZMnxILtXWyk2F7mpsa9Iz2M uXsZmzHRCMQklmJ/EbboQJNMeIpYiom3AmNsPWTeGxP2pR2T6cmQ+ZoeL3x/Ij0meNSMBcxoBz6N pSMZiLBgb7G/EOCHMyZRwvZdjaWEFIrtgFowARJ/5Dz9bYI8sS1RHSag+ubN5T3sL/f4JzhoHy73 vzeKyS+X+7dvrzqHrqUuoc9SV9cBptJn6FbqK/Rd6hb+vdR36FTp0iP/KHUKnUs9+FHJT4Iflfwk +FHJT4JfL/lJ8OslPwl+veQnwY9KfhL8qOSnwa+X/DT49ZKfBr9e8tPg10t+Gvx6yU+dn5/FSp+h l/x0hV7y0+DXS34a/HrJb/bIX/KbFHrJb47QS36TQy/5TQm95DeDn5X8ZvCzkt8Mflbymxb+Jb+5 Qy/5reBnJb8V/Kzkt4KflfxW8LOS3wp+VvJbcX619o/zq7V/nF+t/YPfrPM7v/rjxW/6kOJiv2q/ /ngVH7LsW3m3Mm52j7FKb6s+xKof4vuAv/X+wb8QXOMtx30dV8+Rchw5co6SY/qtmWPmW5lvZT7L fJb5rq08vnSO8j7+9vQ5Knz37ss/D1PucHea3Sm2vna6w726ZNzhu9fj5efLTx///fL318v908dP n//8/NfXy7unL5/+eL7SxjOz77oHM7nWyGI57mTXc6SSna8euSrJS5IXzXGeVnN6c3pzekt6y9Gb 05vTm9Ob05uP3iO9R3qP9Ob05qP3SO+R3iO9R3qPozelN6U3pfdI73H0pvSm9Kb0pvSmo3dP757e Pb0pveno3dO7p3dP757e/eSd19rt+rpdU7fr6HbtlKvz1O08dTtP3c5Tt4/elt6W3pZncedZ3Edv S29Lb0tvS287eMfPnDyARt99aMVPn4OoJ3GexHUQxZ7F/lrcB1H7SaSTOE6ez4Sun5HfinIS9STO k7gOIj8Tun54fyvugyj9JNJJHCfPF0LrtSgnUU/iPInrII4XQvO1uA8i95NIJ3GcPF8I6WtRTqL+ r/ifAAMAswK1Gg0KZW5kc3RyZWFtDWVuZG9iag04IDAgb2JqDTw8L0V4dGVuZHMgNSAwIFIvRmls dGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDg2MS9MZW5ndGggODczL04gMTAwL1R5cGUvT2JqU3RtPj5z dHJlYW0NCmjehJU9shMxEISvohs8za+kKoqIjIQCMoqY+9+AnjHvOVjcG7gkb7d6Z7+ZtU1izGGS Qw+WNWJj2WMvLGcIBNM5xAOrDFmOVYdOw2pDTbH60BSsgRCEafZZU2Q44hSfhT3u4BOazeGGPJPh iTxk+EGe2Qhkm/mIQB48sZFnObIybY2MOrdH7jp3xhKc8zmW45zLWOXHfk/c321seM197KxaYuyD HM9xUKv5Gie8azy7cvC8UxAUeOCJNEMVMhciQ7E5yAwDk3ogHBQJpKIikY3YyCEKnIarol7SxmbV KSSbIDCRXHUZeIktmBPJPmFOJDseyYBEfJUZyTFRYCI5rDxIjpaQnLOOIznrFKqUXEhe1aUKXFrt ghlXZdW90DxpMvgiu8pYSN7VG3zkVIULyae6jbrlVPFAqrM4b8GmnmvXAOCbgZpKPTLaoeJlxgxI wQQRVSnzwqbauTc2u8wHc1MDgmHRImoHyWW0g2THnBnmQb2afZDsu8xIDi0zkqPIHyTHKTOSszqK DmlGjRmSExk+kbwwiD6RvEDdMbnoSWCD5N0zieSd1sOp+5QZyafGcyL5ZJmRfBDvgmmYmFQvWBMN 8ZoYzA02KE4wtC5eb9QZnz69fcUzJ96w72/fcNvo3Y+3n18+f/4nLiLiPXwX/SoeIuKVJaIwUdk9 7UO0q+hMDCYmExcR55OQXsVDRJlMFCYqu+fzpFxFZaK9FH/htatLMMW/tYn9fhW1Xt9HzrMT8yom E9dL8Zec7gRM51nZvFY2Wf6HmOcqChOVicZEJ+L+QJX7KiYTFxM3Ew8Tn9WuC9t4xl7f3DhEzPla nLj8/n7+GZHvv1Lf3mu60fVGtxvdb/S40fNGXzf6hh5BDKcNQgjNdihxSDuMOLQdThzWjiCOQhnO ehntWMSR7djEsdpxiOOBdBJHM3XCVJupE6baTJ0w1WbqhKk2UydMtZkam/9maoSp5q2jmRqhrs3U CHV9MCXUrZkaoW7N1Ah1a6ZGqFszNULdHkwJdWumyn5VmqkSprZuHc1UCXVrpkqo+4Mpoe7NVAl1 b6ZKqLvdOpqpkr74gynpiz+Ykr54M6W/5s1UCHVvpkKoRzMVQj0eTAn1aKZCqEczFcI0mqkQptFM hTCNZiqEaTTTyf4Bm+n8P9O/AgwA7O9Rgg0KZW5kc3RyZWFtDWVuZG9iag05IDAgb2JqDTw8L0V4 dGVuZHMgNSAwIFIvRmlsdGVyL0ZsYXRlRGVjb2RlL0ZpcnN0IDg2Ni9MZW5ndGggMTE0OS9OIDEw MC9UeXBlL09ialN0bT4+c3RyZWFtDQpo3oRXzY6cNRB8FT/BrvvP3ZYiJCJuQWIVuK32EKKIA4Fc ggRvT7WnJyvYTOcwsnerXNWutr9vRsnGHEpriGLwsQJDDJqMcQ+SNZTnIAeLafAEjXmwgMcy2MFj HTKTZ1BJHtQ8eT50Jg8fTd4e6uDJHEbgCQ1T8ISHOTgiYxF4KGVp8gzlJG8Np+T5cE1eDI/k7RGo XOEZBp7SiNDjtSk9ZWwDDx47kme5ryQuTCyZjkkkFVsmTi72TAYyBIk2yEYDCMiQIjaQTTDZIEOC hJNsmVSSoSyBfRqUc6liAa3cuUF55aqVcSJLXVD2EEygHKhOF5T3RGwLynth1TJkLklemOwk+2Cy JMdgpiRvTDzDnWgMUlBHp3SCjPZwaqijV5YNQWOwiSRDeWWFaAk7JRnKno1CM9g9ISjHxL7QBo7c RUA5MgQ0gCMgiA3wzjYget65i4DyzgOA0GVmGeDJRJsVWeOoQBkVyMx48REEjQlhku1FRkKoRbFb 4TxYGweMcRp044QxstENZc40NpQlG7ehLLmdDWVBvYa6Jc+yTShnqw2HWnK7hprEpmECZcN5yz/E EL/BTwwlGNogC20yHGFZloJQXogkD684kjeiPOkQxLEVR6KGHGEOQRxYCZzoV6/u3wzb9w9HaI63 9w+/DVt2pj/fP3z33WGseRi7YdA3GZwMFHebIUeDGoYeBjcMOwxpGOswtGH4YVjDCDCyzbcZ+zD8 NsPnYTSpOx1Gk6nzNxmXTJvUXY9Gk7rbYTSp+zqMJnX3w2hS90umTep+Mo0m9TiZRpN6nEyjST1O ptFkGnIqbTKNk2k0mcbJNJpM42QaTaZxMo0m0ziZRpNpnEy9yXSfTL3JdJ9Mvcl0n0y9yXSfTKPJ dJ+7b10d5+5bV8e5+9bVce7+6uo4mVqTab55D3jeuBfslx8u4GPt4FrntZqr51X56az26+q3Z/X3 54Wd/3rzqLVOr+sW1cg1So1ao9W4avQao8bS89Lz0vPS89Lz0vPSu2wx66Uvm33368cPp+LXrz/9 /bj0Dq/s6Xdx3m50h69MS/gOL7Kn+5/uf3z3z6e/Pt8/fHz3/sMfH/78fP/646f3v1eWVgl+JY3M kipDrlFq1BpvZ4nVeknCtDqi1RGtjthsV5e3lreWt5a3tt5S3lLeUt5S3tp6S3lLeUt5S3lL683l zeXN5c3lLa03lzeXN5c3lze33lTeVN5U3lTe3HpTeVN5U3lTeVPrPct7lvcs71ne1HrP8p7lPct7 lvfsvOu7wvUbwfW9f313X9/QN1fXXdx1F3fdxV13cbfeUd5R3lHeUTd+t95R3lHeUd5R3tF6e3l7 eXt5e3lH431+E9Xld/vfg/T8TmpA78DowN2B8wuoL0HqQO5A6UBtQHlOSF6CqwO9A6MDdwc+J8Qv QepA7kDpQG1AfgbpJWgduG6Cj1oPT708PJ9uSexGn57B+XLl7EDqQO5AaUD6Al6+DPwX1A60m+Cj 1jNfL8/8p1sS0ejPZzBegrsBaXYgfRX8V4ABAPQy2kINCmVuZHN0cmVhbQ1lbmRvYmoNMTAgMCBv YmoNPDwvRXh0ZW5kcyA1IDAgUi9GaWx0ZXIvRmxhdGVEZWNvZGUvRmlyc3QgMzc1L0xlbmd0aCAz OTIvTiA0Ni9UeXBlL09ialN0bT4+c3RyZWFtDQpo3nyTTW4bMQxGr8IbWPwVCRhZdZdN0WRX9P7X 6Ce7jYDIw8WAgh/1TH6YcXYa5BwkhTLJEyUpceIiBnAZxOaoTDwNVUiGoiqJMaqRTEjEIYFFglSg kUnq8OCste4XGXpdB5njvjJZCioehkfxm8Gj6Al4FHcKHg0KuFwnhWMeTYpanqIpzzvT4cAsM+Ex oWR4TCnR42aUAQ92yILHggozuk0qx39YUiV8hn0HQ+hYeMDijo3HhBJ65gEn5mZeUbnhsMKCgXnF hdFYVl7YmWUFBsyyEnOYdUUWMCv2vd9v72QsyP7X7ScZxlunj9vnj7e3J0T7fzhPaB30DkYH5zXU 2jC+wd9ajwXQVKv+edkF/7hUAHIH5Rpq7Zt+Tib/JtM9mZ8Ku1Q8Xt4GRgNzQzvh7GB2sBpYo7u5 oR5RJT+jStlR6anQSwWgddAbODeUE0YHZwezg9XBHRWfkDsoHdQOWgNjJzROGB2cHcwOVge/pvU6 vlj/is/Pz9m5g9JBfQn/CjAAgH1pJw0KZW5kc3RyZWFtDWVuZG9iag0xMSAwIG9iag08PC9MZW5n dGggNDIzNy9TdWJ0eXBlL1hNTC9UeXBlL01ldGFkYXRhPj5zdHJlYW0NCjw/eHBhY2tldCBiZWdp bj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+Cjx4OnhtcG1ldGEgeG1sbnM6 eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDQuMi4xLWMwNDMgNTIu MzcyNzI4LCAyMDA5LzAxLzE4LTE1OjA4OjA0ICAgICAgICAiPgogICA8cmRmOlJERiB4bWxuczpy ZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8 cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczp4bXA9Imh0dHA6 Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPgogICAgICAgICA8eG1wOk1vZGlmeURhdGU+MjAxMS0w My0xMFQwNzo0ODoxNC0wNzowMDwveG1wOk1vZGlmeURhdGU+CiAgICAgICAgIDx4bXA6Q3JlYXRl RGF0ZT4yMDExLTAzLTEwVDA3OjQ4OjEyLTA3OjAwPC94bXA6Q3JlYXRlRGF0ZT4KICAgICAgICAg PHhtcDpNZXRhZGF0YURhdGU+MjAxMS0wMy0xMFQwNzo0ODoxNC0wNzowMDwveG1wOk1ldGFkYXRh RGF0ZT4KICAgICAgICAgPHhtcDpDcmVhdG9yVG9vbD5BY3JvYmF0IFBERk1ha2VyIDkuMSBmb3Ig V29yZDwveG1wOkNyZWF0b3JUb29sPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgICAgPHJk ZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6eG1wTU09Imh0dHA6 Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iPgogICAgICAgICA8eG1wTU06RG9jdW1lbnRJRD51 dWlkOmY1ODExZTYwLWJlMGEtNGIwNy04YzE5LTkxOWE3MWI5NjQ1ZTwveG1wTU06RG9jdW1lbnRJ RD4KICAgICAgICAgPHhtcE1NOkluc3RhbmNlSUQ+dXVpZDo2NWQ2NTJlYi1hMmRjLTRiMGYtOTlj NC1lMWU0MzI4N2RjZmE8L3htcE1NOkluc3RhbmNlSUQ+CiAgICAgICAgIDx4bXBNTTpzdWJqZWN0 PgogICAgICAgICAgICA8cmRmOlNlcT4KICAgICAgICAgICAgICAgPHJkZjpsaT4yPC9yZGY6bGk+ CiAgICAgICAgICAgIDwvcmRmOlNlcT4KICAgICAgICAgPC94bXBNTTpzdWJqZWN0PgogICAgICA8 L3JkZjpEZXNjcmlwdGlvbj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAg ICAgICAgICAgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIj4KICAg ICAgICAgPGRjOmZvcm1hdD5hcHBsaWNhdGlvbi9wZGY8L2RjOmZvcm1hdD4KICAgICAgICAgPGRj OnRpdGxlPgogICAgICAgICAgICA8cmRmOkFsdD4KICAgICAgICAgICAgICAgPHJkZjpsaSB4bWw6 bGFuZz0ieC1kZWZhdWx0Ij5PbGluIE5ldXJvcHN5Y2hpYXRyeSBJbWFnZSBBY3F1aXNpdGlvbiBh bmQgQW5hbHlzZXMgQ291cnNlPC9yZGY6bGk+CiAgICAgICAgICAgIDwvcmRmOkFsdD4KICAgICAg ICAgPC9kYzp0aXRsZT4KICAgICAgICAgPGRjOmRlc2NyaXB0aW9uPgogICAgICAgICAgICA8cmRm OkFsdD4KICAgICAgICAgICAgICAgPHJkZjpsaSB4bWw6bGFuZz0ieC1kZWZhdWx0Ii8+CiAgICAg ICAgICAgIDwvcmRmOkFsdD4KICAgICAgICAgPC9kYzpkZXNjcmlwdGlvbj4KICAgICAgICAgPGRj OmNyZWF0b3I+CiAgICAgICAgICAgIDxyZGY6U2VxPgogICAgICAgICAgICAgICA8cmRmOmxpPktl bnQgQSBLaWVobDwvcmRmOmxpPgogICAgICAgICAgICA8L3JkZjpTZXE+CiAgICAgICAgIDwvZGM6 Y3JlYXRvcj4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24g cmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOnBkZj0iaHR0cDovL25zLmFkb2JlLmNvbS9w ZGYvMS4zLyI+CiAgICAgICAgIDxwZGY6UHJvZHVjZXI+QWRvYmUgUERGIExpYnJhcnkgOS4wPC9w ZGY6UHJvZHVjZXI+CiAgICAgICAgIDxwZGY6S2V5d29yZHMvPgogICAgICA8L3JkZjpEZXNjcmlw dGlvbj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1s bnM6cGRmeD0iaHR0cDovL25zLmFkb2JlLmNvbS9wZGZ4LzEuMy8iPgogICAgICAgICA8cGRmeDpT b3VyY2VNb2RpZmllZD5EOjIwMTEwMzEwMTQzNDAwPC9wZGZ4OlNvdXJjZU1vZGlmaWVkPgogICAg ICAgICA8cGRmeDpDb21wYW55PkhhcnRmb3JkIEhvc3BpdGFsPC9wZGZ4OkNvbXBhbnk+CiAgICAg IDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgogICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAK ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgCjw/eHBhY2tldCBlbmQ9 InciPz4NCmVuZHN0cmVhbQ1lbmRvYmoNMTIgMCBvYmoNPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0Zp cnN0IDYvTGVuZ3RoIDU4L04gMS9UeXBlL09ialN0bT4+c3RyZWFtDQpo3jI1M1QwULCx0XfOL80r UTDW985MKY42NTMFigYpGIJJYxAZqx9SWZCqH5CYnlpsZwcQYACBSg54DQplbmRzdHJlYW0NZW5k b2JqDTEzIDAgb2JqDTw8L0ZpbHRlci9GbGF0ZURlY29kZS9GaXJzdCA2L0xlbmd0aCAyNDEvTiAx L1R5cGUvT2JqU3RtPj5zdHJlYW0NCmjebM9PS8NAEAXwrzK3JgfNbhqtSimEBqnEakHB82R3albT nbh/kHx7NyDiobeB9/g95uq6BAHrdVHH0LPLWrIBamgN9UNebPk0op2yHbpwZKdhx340AefIEQbD tsFAWXNXCinFUgqxqm5keSFWCyEWv63E1spxhwEOzf0eP8nB7aWEJMJbUvOipek7HT7Liz3rc2T1 Rx4c66gomZo7mkV4NJ1DNyVU5MULR6coMeZoSP9zZLWsxFyI3QepkLZeTRgoex6MhSeKjkc/qd5g SNTDCd8JavUVjTfzn4BWQ21xmDx52KYRT/lm8yPAAE9YZDgNCmVuZHN0cmVhbQ1lbmRvYmoNMTQg MCBvYmoNPDwvRGVjb2RlUGFybXM8PC9Db2x1bW5zIDQvUHJlZGljdG9yIDEyPj4vRmlsdGVyL0Zs YXRlRGVjb2RlL0lEWzw3QUQwMzJGRjhCRUM3NzQxOUE3MThDRTdFOUQ2OUJFNT48QzI1RUM2NzUx REZFMDc0RUEzODM3MzVGQkVDMUMzNjk+XS9JbmZvIDU2MiAwIFIvTGVuZ3RoIDEwMC9Sb290IDU2 NCAwIFIvU2l6ZSA1NjMvVHlwZS9YUmVmL1dbMSAyIDFdPj5zdHJlYW0NCmjeYmIAAiZGtQ0MTAyM t4CEQC6cxQYkOJiABEszkGC9CGLdABFvgQTTdyAheBukeDqI8Aea0hUNZDEwMI4SI4hgnDsaBqNx PkqMxvkoMRrno8RonA8FgukyyKsMAAEGAA8MFHYNCmVuZHN0cmVhbQ1lbmRvYmoNc3RhcnR4cmVm DQoxMTYNCiUlRU9GDQo= --Apple-Mail-55--686547355 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=us-ascii <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; = "><div><blockquote type=3D"cite"><div style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; = "><div><blockquote type=3D"cite"><div style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; = "><div><font class=3D"Apple-style-span" = color=3D"#144FAE"><u><br></u></font>For inquiries, contact Kent Kiehl = <a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a> <br><br>___________= ______________________<br>Kent A. Kiehl, Ph.D.<br>Director of Mobile = Imaging Core and <br>Clinical Cognitive Neuroscience<br>Mind = Research Network <br><br>Associate Professor of Psychology and = Neuroscience<br>University of New Mexico<br><br>Mailing = Address: <br>Mind Research Network<br>1101 Yale Blvd = NE<br>Albuquerque, NM, 87106<br>Office: 505-925-4516 <br><a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a><br><br><div><div = style=3D"word-wrap: break-word; -webkit-nbsp-mode: space; = -webkit-line-break: after-white-space; = "><div><div>_________________________________</div><div>Kent A. Kiehl, = Ph.D.</div><div>Director of Mobile Imaging Core = and </div><div>Clinical Cognitive Neuroscience</div><div>Mind = Research Network </div><div><br></div><div>Associate Professor of = Psychology and Neuroscience</div><div>University of New = Mexico</div><div><br></div><div>Mailing Address: </div><div>Mind = Research Network</div><div>1101 Yale Blvd NE</div><div>Albuquerque, NM, = 87106</div><div>Office: 505-925-4516 </div><div><a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a></div></div><br></div><br= class=3D"Apple-interchange-newline"><br = class=3D"Apple-interchange-newline"></div><br></div></div></blockquote></d= iv></div></blockquote></div><div> <span class=3D"Apple-style-span" style=3D"border-collapse: separate; = color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; = font-style: normal; font-variant: normal; font-weight: normal; = letter-spacing: normal; line-height: normal; orphans: 2; text-align: = auto; text-indent: 0px; text-transform: none; white-space: normal; = widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; = -webkit-border-vertical-spacing: 0px; = -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: = auto; -webkit-text-stroke-width: 0px; "><span class=3D"Apple-style-span" = style=3D"border-collapse: separate; color: rgb(0, 0, 0); font-family: = Helvetica; font-size: medium; font-style: normal; font-variant: normal; = font-weight: normal; letter-spacing: normal; line-height: normal; = orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; = widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; = -webkit-border-vertical-spacing: 0px; = -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: = auto; -webkit-text-stroke-width: 0px; "><div style=3D"word-wrap: = break-word; -webkit-nbsp-mode: space; -webkit-line-break: = after-white-space; = "><div><div>_________________________________</div><div>Kent A. Kiehl, = Ph.D.</div><div>Director of Mobile Imaging Core = and </div><div>Clinical Cognitive Neuroscience</div><div>Mind = Research Network </div><div><br></div><div>Associate Professor of = Psychology and Neuroscience</div><div>University of New = Mexico</div><div><br></div><div>Mailing Address: </div><div>Mind = Research Network</div><div>1101 Yale Blvd NE</div><div>Albuquerque, NM, = 87106</div><div>Office: 505-925-4516 </div><div><a = href=3D"mailto:[log in to unmask]">[log in to unmask]</a></div></div><br></div></s= pan><br class=3D"Apple-interchange-newline"></span><br = class=3D"Apple-interchange-newline"> </div> <br></body></html>= --Apple-Mail-55--686547355-- --Apple-Mail-54--686547355-- ========================================================================= Date: Mon, 23 May 2011 22:39:44 +0100 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: DCM source time courses Comments: To: Fahimeh Mamashli <[log in to unmask]> In-Reply-To: <1774541387.2017.1306145983719.JavaMail.root@zimbra> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Dear Fahimeh, You can find some relevant code in spm_dcm_erp_results around line 204. It appears that the time courses are stored in DCM.K field. In general you can do the same thing I've just done and look at the code of that function to find where things are stored or how they can be computed. Best, Vladimir On Mon, May 23, 2011 at 11:19 AM, Fahimeh Mamashli <[log in to unmask]> wr= ote: > > Dear Vladimir, > Thanks for your attention. I would like to have the time courses of each = source in DCM. I mean the time course of pyramidal cells which we plot in E= RP(sources) with GUI. how I can have access to them? > > Best wishes, > Fahimeh > > ------------------------ > PhD student > Max Planck Institute for Human Cognitive and Brain Sciences > Stephanstra=DFe 1A > 04103 Leipzig > Germany > > Tel: +49 341 9940 - 2570 > > ========================================================================= Date: Mon, 23 May 2011 21:06:58 -0700 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: =?UTF-8?Q?=E5=9B=9E=E8=A6=86=EF=B9=95_=E5=9B=9E=E8=A6=86=EF=B9=95_=E5=9B?= =?UTF-8?Q?=9E=E8=A6=86=EF=B9=95_?=[SPM] three questions about SPM8 Comments: To: a489930026 <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba53a2e0eb932804a3fdb959 Message-ID: <[log in to unmask]> --90e6ba53a2e0eb932804a3fdb959 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable You're welcome. Those functions should be included with the spm distribution. If the script worked then you must have at least spm_vol, spm_read_vols and spm_write_vol. otherwise, maybe they are only in older versions, so in that case you can try SPM5. Cheers, Michael On Mon, May 23, 2011 at 8:26 PM, a489930026 <[log in to unmask]> wrote= : > Dear Michael, > > The script works!! Thank you very much for your help and your patience. > My final question is: how can I find these useful matlab functions (e.g., > spm_vol, spm_get_data) for SPM8, so I can try to help myself, instead of > bugging you from the beginning, when I encounter the problem in the > future. Thank you! > > Sincerely, > Yihui > > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* a489930026 <[log in to unmask] w>; spm <[log in to unmask]> > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/24 (=E4=BA=8C) 3:1= 6 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: =E5=9B=9E=E8=A6=86=EF=B9=95 =E5=9B=9E= =E8=A6=86=EF=B9=95 [SPM] three questions about SPM8 > > I see now, I made a mistake in the original code I sent. This line: > > YY =3D spm_vol(VV); > > should actually be: > > YY =3D spm_read_vols(VV); > > > %% > > Cheers, > Michael > On Mon, May 23, 2011 at 11:03 AM, a489930026 <[log in to unmask]>wro= te: > > Dear Michael, > > Every step went smoothly until the step "YY(find(Y)) =3D 0" and then the = it > showed "Assignment between unlike types is not allowed." > > Yihui > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* a489930026 <[log in to unmask] w>; spm <[log in to unmask]> > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/24 (=E4=BA=8C) 1:2= 6 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: =E5=9B=9E=E8=A6=86=EF=B9=95 [SPM] three= questions about SPM8 > > threshold needs to be>0. if your map is p values, then just do: > > Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant. > > then set your p threshold as 1-threshold, so .95 =3D=3D .05 > > Determining whether you use the mask inclusively or exlusively can be don= e > when you apply it, so: > > YY(find(Y)) =3D 0; % exclusive mask > > YY(find(~Y)) =3D 0; %inclusive mask > > > Try running each line separately (or within a script) to see where exactl= y > the error is occurring. > > > Cheers, > Michael > > On Mon, May 23, 2011 at 12:48 AM, a489930026 <[log in to unmask]>wro= te: > > Dear Michael, > > Thank you for the advice. I did what you suggested but a result "Assignme= nt > between unlike types is not allowed." What could be the problem? > > Please see the script below: > > ********************************************************* > >> V=3D spm_vol('L:\2stlevel\wordVSblk\spmF_0001.img') > Y =3D spm_read_vols(V); > > threshold =3D 0; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('L:\2stlevel\radpos_sdXloca_sd\spmF_0001.img'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > V =3D > > fname: 'L:\2stlevel\wordVSblk\spmF_0001.img' > mat: [4x4 double] > dim: [79 95 68] > dt: [16 0] > pinfo: [3x1 double] > n: [1 1] > descrip: 'SPM{F_[1.0,15.0]} - contrast 1: w-b' > private: [1x1 nifti] > > ??? Assignment between unlike types is not allowed. > > ************************************************************************* > > Besides, is the threshold a cutoff for the activation maps of the mask? > Specially, does setting threshold =3D 0.05 mean that the activated voxels= below p =3D0.05 are included. If not, how should I set a cutoff on the p > value, or it does matter? > > Moreover, how can I determine the mask is inclusive (i.e., the voxles in > the to-be-masked activation maps are within the volxels in the activation > maps of the mask) or exclusive (i.e., the voxles in the to-be-masked > activation maps are NOT within the volxels in the activation maps of the > mask). > Thank you very much! > > Sincerely, > Yihui > > > > > > ------------------------------ > *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmail= .com> > *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* SUBSCRIBE SPM Yihui Hung <a4899300= [log in to unmask]> > *=E5=89=AF=E6=9C=AC=EF=BC=9A* [log in to unmask] > *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/23 (=E4=B8=80) 10:= 19 AM > *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: [SPM] three questions about SPM8 > > when you say you tried to use the contrast as a mask, did you just use th= e > raw contrast or did you binarize it? If you did the former than it is > clear why you had no inmask voxels since any non-zero voxels would all be > seen as part of the mask. So, what you want to do is load the mask, apply= a > threshold and binarize it by setting anything below the threshold to 0, > and anything above to 1 (though the latter may not be completely necesary= ). > Then, you could just use that mask to mask out the resulting contrast map > from the other analysis. Here is code to do it: > > %Make mask > > V =3D spm_vol('contrast_image_2turn_into_mask') > Y =3D spm_read_vols(V); > > threshold =3D X; %any value you choose. > > Y(find(Y<threshold)) =3D 0; > Y(find(Y)) =3D 1; > > % Use mask > > VV =3D spm_vol('contrast_image_2mask'); > YY =3D spm_vol(VV); > > YY(find(Y)) =3D 0; %apply mask > > VV.fname =3D 'masked_image.img'; > spm_write_vol(VV,YY); > > %% There ya go > > Cheers, > Michael > > On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE SPM Yihui Hung < > [log in to unmask]> wrote: > > Hello, > > I used SPM8 for my experiment. I manipulated three factors (e.g., factor > A, B, C) in my experiment. The activation maps under factor A was used as= a > mask to constrain the effects of factor B and factor C. The problem is th= at > the factor A and factor B and factor C cannot be put in the same model at > second level analysis. I tried to put the .img file resulting from factor > A across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the secon= d level analysis. > However, the estimation stopped by showing no inmask voxels. I searched > through forum and did not find any useful solutions. Can someone help me > out? Moreover, is the threshold masking related to the p value? If not, h= ow > should I set criteria for the activation map of the mask? > > I did notice that one person in the forum suggested that one can create a= n > activation maps in which the factor B is modeled with the mask of factor = A > in first level analysis. Then, submit the resulting .img file into second > level analysis. My question is how to create and save the img file. > > Finally, I posted a question regarding how to export beta values over > multiple coordinates a week ago. However, no one provide any answers. I > wonder whether it is due that no one has the answer or due to that there > already is an answer in the forum. > > Thank you for your patience and advice. > Sincerely, > > Yihui > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > --=20 Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --90e6ba53a2e0eb932804a3fdb959 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable You're welcome. Those functions should be included with the spm distrib= ution. If the script worked then you must have at least spm_vol, spm_read_v= ols and spm_write_vol. otherwise, maybe they are only in older versions, so= in that case you can try SPM5.<br> <br>Cheers,<br>Michael<br><br><div class=3D"gmail_quote">On Mon, May 23, 20= 11 at 8:26 PM, a489930026 <span dir=3D"ltr"><<a href=3D"mailto:a48993002= [log in to unmask]" target=3D"_blank">[log in to unmask]</a>></span> wr= ote:<br> <blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; borde= r-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-family: arial,helvetica,sans-serif; font-size: 12pt;"><div style=3D= "font-size: 12pt; font-family: arial,helvetica,sans-serif;">Dear Michael,<b= r> </div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-seri= f;"><br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,s= ans-serif;">The script works!!=C2=A0Thank you very much for your help and y= our patience.=C2=A0</div> <div><font size=3D"3">My final question is: how can I find these useful <sp= an><span>matlab</span></span>=C2=A0</font>functions<font size=3D"3">=C2=A0(= e.g.,=C2=A0</font><span style=3D"font-size: 12pt; font-family: 'times n= ew roman','new york',times,serif;"><span><span><span><span>spm<= /span></span></span></span></span><span style=3D"font-size: 12pt; font-fami= ly: 'times new roman','new york',times,serif;">_vol, <span>= <span>spm</span></span>_get_data</span><font size=3D"3">) for <span><span>S= PM</span></span>8, so I can try to help myself, instead of bugging you from= the <span><span>beginning</span></span>, when I encounter the problem in t= he future.=C2=A0</font>Thank you!</div> <div><font size=3D"3"><br></font></div><div><font size=3D"3">Sincerely,</fo= nt></div><div><font size=3D"3"><span><span>Yihui</span></span></font></div>= <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-se= rif;"><blockquote style=3D"border-left: 2px solid rgb(16, 16, 255); margin-= left: 5px; padding-left: 5px;"> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><d= iv style=3D"font-size: 12pt; font-family: 'times new roman','ne= w york',times,serif;"><font face=3D"Arial" size=3D"2"><hr size=3D"1"><b= ><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A</s= pan></b> Michael T Rubens <<a href=3D"mailto:[log in to unmask]" targe= t=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> a489930026 <<a href=3D"mailto:[log in to unmask]" target= =3D"_blank">[log in to unmask]</a>>; <span><span>spm</span></span> = <<a href=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] .uk</a>><br> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F= =EF=BC=9A</span></b> 2011/5/24 (=E4=BA=8C) 3:16 AM<br><b><span style=3D"fon= t-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b> Re: =E5=9B=9E=E8=A6= =86=EF=B9=95 =E5=9B=9E=E8=A6=86=EF=B9=95 [<span><span>SPM</span></span>] th= ree questions about <span><span>SPM</span></span>8<br> </font><br><div>I see now, I made a mistake in the original code I sent. Th= is line: <br><br><span><span><span><span>YY</span></span></span></span> =3D= <span><span><span><span>spm</span></span></span></span>_vol(<span><span><s= pan><span>VV</span></span></span></span>);<br> <br>should actually be:<br><br><span><span><span><span>YY</span></span></sp= an></span> =3D <span><span><span><span>spm</span></span></span></span>_read= _vols(<span><span><span><span>VV</span></span></span></span>);<br> <br><br>%%<br><br>Cheers,<br>Michael<br><div>On Mon, May 23, 2011 at 11:03 = AM, a489930026 <span dir=3D"ltr"><<a rel=3D"nofollow" href=3D"mailto:a48= [log in to unmask]" target=3D"_blank">[log in to unmask]</a>></sp= an> wrote:<br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-size: 12pt; font-family: arial,helvetica,sans-serif;"><div><span><d= iv>Dear Michael,</div><div><br></div><div>Every step went smoothly until th= e step "<span><span>YY</span></span>(find(Y)) =3D 0" and then the= it showed "Assignment between unlike types is not allowed."</div= > <div><br></div><div><span><span>Yihui</span></span></div></span></div><div>= <br><blockquote style=3D"border-left: 2px solid rgb(16, 16, 255); margin-le= ft: 5px; padding-left: 5px;"><div style=3D"font-size: 12pt; font-family: ar= ial,helvetica,sans-serif;"> <div style=3D"font-size: 12pt;"><font face=3D"Arial" size=3D"2"><hr size=3D= "1"><b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC= =9A</span></b> Michael T Rubens <<a rel=3D"nofollow" href=3D"mailto:Mike= [log in to unmask]" target=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> a489930026 <<a rel=3D"nofollow" href=3D"mailto:a489930026@yah= oo.com.tw" target=3D"_blank">[log in to unmask]</a>>; <span><span>s= pm</span></span> <<a rel=3D"nofollow" href=3D"mailto:[log in to unmask]"= target=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F= =EF=BC=9A</span></b> 2011/5/24 (=E4=BA=8C) 1:26 AM<br><b><span style=3D"fon= t-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b> Re: =E5=9B=9E=E8=A6= =86=EF=B9=95 [<span><span>SPM</span></span>] three questions about <span><s= pan>SPM</span></span>8<br> </font><br><div>threshold needs to be>0. if your map is p values, then j= ust do:<br> <br>Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant.<br><b= r>then set your p threshold as 1-threshold, so .95 =3D=3D .05<br><br>Determ= ining whether you use the mask inclusively or <span><span>exlusively</span>= </span> can be done when you apply it, so: <br> <br><span><span><span><span>YY</span></span></span></span>(find(Y)) =3D 0; = % exclusive mask<br><br><span><span>YY</span></span>(find(~Y)) =3D 0; %incl= usive mask<br><br><br>Try running each line separately (or within a script)= to see where exactly the error is occurring.<br> <br> <br>Cheers,<br>Michael<br><br><div>On Mon, May 23, 2011 at 12:48 AM, a48993= 0026 <span dir=3D"ltr"><<a rel=3D"nofollow" href=3D"mailto:a489930026@ya= hoo.com.tw" target=3D"_blank">[log in to unmask]</a>></span> wrote:= <br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> <div><div style=3D"color: rgb(0, 0, 0); background-color: rgb(255, 255, 255= ); font-size: 12pt; font-family: arial,helvetica,sans-serif;"><div style=3D= "font-size: 12pt; font-family: arial,helvetica,sans-serif;"><span>Dear Mich= ael,</span></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><s= pan><br></span></div><div style=3D"font-size: 12pt; font-family: arial,helv= etica,sans-serif;">Thank you for the advice. I did what you suggested but a= result "Assignment between unlike types is not allowed." What co= uld be the problem?</div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-se= rif;">Please see the script below:</div><div style=3D"font-size: 12pt; font= -family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;">*********************************************************</div><div= ><div>>> V=3D <span><span><span><span>spm</span></span></span></span>= _vol('L:\2<span><span><span><span>stlevel</span></span></span></span>\<= span><span><span><span>wordVSblk</span></span></span></span>\<span><span><s= pan><span>spmF</span></span></span></span>_0001.<span><span><span><span>img= </span></span></span></span>')</div> <div>Y =3D <span><span><span><span>spm</span></span></span></span>_read_vol= s(V);</div><div><br></div><div>threshold =3D 0; %any value you choose.</div= ><div><br></div><div>Y(find(Y<threshold)) =3D 0;</div><div>Y(find(Y)) =3D 1;</div><div><br></div><div>% Use mask</div><d= iv><br></div><div><span><span><span><span>VV</span></span></span></span> = =3D <span><span><span><span>spm</span></span></span></span>_vol('L:\2<s= pan><span><span><span>stlevel</span></span></span></span>\<span><span><span= ><span>radpos</span></span></span></span>_<span><span><span><span>sdXloca</= span></span></span></span>_<span><span><span><span>sd</span></span></span><= /span>\<span><span><span><span>spmF</span></span></span></span>_0001.<span>= <span><span><span>img</span></span></span></span>');</div> <div><span><span><span><span>YY</span></span></span></span> =3D <span><span= ><span><span>spm</span></span></span></span>_vol(<span><span><span><span>VV= </span></span></span></span>);</div><div><br></div><div><span><span><span><= span>YY</span></span></span></span>(find(Y)) =3D 0; %apply mask</div> <div><br></div><div><span><span><span><span>VV</span></span></span></span>.= <span><span><span><span>fname</span></span></span></span> =3D 'masked_image.<span><span><span><span>img</span></span></span></span>&= #39;;</div> <div><span><span><span><span>spm</span></span></span></span>_write_vol(<spa= n><span><span><span>VV</span></span></span></span>,<span><span><span><span>= YY</span></span></span></span>);</div><div><br></div><div>%% There ya go</d= iv> <div><br></div><div>V =3D=C2=A0</div><div><br></div><div>=C2=A0 =C2=A0 =C2= =A0 <span><span><span><span>fname</span></span></span></span>: 'L:\2<sp= an><span><span><span>stlevel</span></span></span></span>\<span><span><span>= <span>wordVSblk</span></span></span></span>\<span><span><span><span>spmF</s= pan></span></span></span>_0001.<span><span><span><span>img</span></span></s= pan></span>'</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 mat: [4x4 double]</div><div>=C2=A0 =C2=A0 = =C2=A0 =C2=A0 dim: [79 95 68]</div><div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0<= span><span><span><span>dt</span></span></span></span>: [16 0]</div><div>=C2= =A0 =C2=A0 =C2=A0 <span><span><span><span>pinfo</span></span></span></span>= : [3x1 double]</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 n: [1 1]</div><div> =C2=A0 =C2=A0 <span><span><span><span>descrip</span></span></span></span>: = 'SPM{F_[1.0,15.0]} - contrast 1: w-b'</div><div>=C2=A0 =C2=A0 private: [1x1 <span><span><sp= an><span>nifti</span></span></span></span>]</div><div><br></div><div>??? As= signment between unlike types is not allowed.</div><div><br></div><div>****= *********************************************************************</div> <div><br></div><div>Besides, is the threshold a cutoff for the activation m= aps of the mask? Specially, does setting threshold =3D 0.05 mean that the a= ctivated <span><span><span><span>voxels</span></span></span></span> below p= =3D0.05 are included. If not, how should I set a cutoff on the p value, or= it does matter?=C2=A0</div> <div><br></div><div>Moreover, how can I determine the mask is inclusive (i.= e., the <span><span><span><span>voxles</span></span></span></span> in the to-be-masked activation maps are within the <span><span><span><span>volxel= s</span></span></span></span> in the activation maps of the mask) or exclus= ive=C2=A0(i.e., the <span><span><span><span>voxles</span></span></span></sp= an> in the to-be-masked activation maps are NOT within the <span><span><spa= n><span>volxels</span></span></span></span> in <span>the activation</span> = maps of the mask).=C2=A0</div> <div>Thank you very much!</div><div><br></div><div>Sincerely,</div><div><sp= an><span><span><span>Yihui</span></span></span></span></div></div><div styl= e=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;">=C2=A0</div= ><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"> <br></div><div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-= serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial,helveti= ca,sans-serif;"><br></div><div style=3D"font-size: 12pt; font-family: arial= ,helvetica,sans-serif;"> <br><blockquote style=3D"border-left: 2px solid rgb(16, 16, 255); margin-le= ft: 5px; padding-left: 5px;"><div style=3D"font-size: 12pt; font-family: ar= ial,helvetica,sans-serif;"><div style=3D"font-size: 12pt;"> <font face=3D"Arial" size=3D"2"><hr size=3D"1"><b><span style=3D"font-weigh= t: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> Michael T Rubens = <<a rel=3D"nofollow" href=3D"mailto:[log in to unmask]" target=3D"_bla= nk">[log in to unmask]</a>><br><b><span style=3D"font-weight: bold;">= =E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> SUBSCRIBE <span><span><span= ><span>SPM</span></span></span></span> <span><span><span><span>Yihui</span>= </span></span></span> Hung <<a rel=3D"nofollow" href=3D"mailto:a48993002= [log in to unmask]" target=3D"_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=89=AF=E6=9C=AC=EF=BC=9A</span></b= > <a rel=3D"nofollow" href=3D"mailto:[log in to unmask]" target=3D"_blank">= [log in to unmask]</a><br><b><span style=3D"font-weight: bold;">=E5=AF=84= =E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A</span></b> 2011/5/23 (=E4=B8=80) 10:19= AM<br> <b><span style=3D"font-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b= > Re: [<span><span><span><span>SPM</span></span></span></span>] three quest= ions about <span><span><span><span>SPM</span></span></span></span>8<br> </font><br><div>when you say you tried to use the contrast as a mask, did y= ou just use the raw contrast or did you <span><span><span><span>binarize</s= pan></span></span></span> it? If you did the former than it is clear why you had no <span><span><spa= n><span>inmask</span></span></span></span> <span><span><span><span>voxels</= span></span></span></span> since any non-zero <span><span><span><span>voxel= s</span></span></span></span> would all be seen as part of the mask. So, wh= at you want to do is load the mask, apply a threshold and <span><span><span= ><span>binarize</span></span></span></span> it by setting anything below th= e threshold to 0, and anything above to 1 (though the latter may not be com= pletely <span><span><span><span>necesary</span></span></span></span>). Then= , you could just use that mask to mask out the resulting contrast map from the other analysis. Here is code to do it:<br> <br>%Make mask<br><br>V =3D <span><span><span><span>spm</span></span></span= ></span>_vol('contrast_image_2turn_into_mask')<br>Y =3D <span><span= ><span><span>spm</span></span></span></span>_read_vols(V);<br><br>threshold= =3D X; %any value you choose.<br> <br>Y(find(Y<threshold)) =3D 0;<br> Y(find(Y)) =3D 1;<br><br>% Use mask<br> <br><span><span><span><span>VV</span></span></span></span> =3D <span><span>= <span><span>spm</span></span></span></span>_vol('contrast_image_2mask&#= 39;);<br><span><span><span><span>YY</span></span></span></span> =3D <span><= span><span><span>spm</span></span></span></span>_vol(<span><span><span><spa= n>VV</span></span></span></span>);<br> <br><span><span><span><span>YY</span></span></span></span>(find(Y)) =3D 0; = %apply mask<br> <br><span><span><span><span>VV</span></span></span></span>.<span><span><spa= n><span>fname</span></span></span></span> =3D 'masked_image.<span><span= ><span><span>img</span></span></span></span>';<br><span><span><span><sp= an>spm</span></span></span></span>_write_vol(<span><span><span><span>VV</sp= an></span></span></span>,<span><span><span><span>YY</span></span></span></s= pan>);<br> <br>%% There ya go<br><br>Cheers,<br>Michael<br> <br><div>On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE <span><span><span><span= >SPM</span></span></span></span> <span><span><span><span>Yihui</span></span= ></span></span> Hung <span dir=3D"ltr"><<a rel=3D"nofollow" href=3D"mail= to:[log in to unmask]" target=3D"_blank">[log in to unmask]</a>&g= t;</span> wrote:<br> <blockquote style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(= 204, 204, 204); padding-left: 1ex;"> Hello,<br> <br> I used <span><span><span><span>SPM</span></span></span></span>8 for my expe= riment. I manipulated three factors (e.g., factor A, B, C) in my experiment= . The activation maps under factor A was used as a mask to constrain the ef= fects of factor B and factor C. The problem is that the factor A and factor= B and factor C cannot be put in the same model at second level analysis. I= tried to put the .<span><span><span><span>img</span></span></span></span> = file resulting from factor A across participants in the =E2=80=9Cexplicit m= ask=E2=80=9D in the second level analysis. However, the estimation stopped = by showing no <span><span><span><span>inmask</span></span></span></span> <s= pan><span><span><span>voxels</span></span></span></span>. I searched throug= h forum and did not find any useful solutions. Can someone help me out? Mor= eover, is the threshold masking related to the p value? If not, how should = I set criteria for the activation map of the mask?<br> <br> I did notice that one person in the forum suggested that one can create an = activation maps in which the factor B is <span><span><span><span>modeled</s= pan></span></span></span> with the mask of factor A in first level analysis= . Then, submit the resulting .<span><span><span><span>img</span></span></sp= an></span> file into second level analysis. My question is how to create an= d save the <span><span><span><span>img</span></span></span></span> file.<br= > <br> Finally, I posted a question regarding how to export beta values over multi= ple coordinates a week ago. However, no one provide any answers. I wonder w= hether it is due that no one has the answer or due to that there already is= an answer in the forum.<br> <br> Thank you for your patience and advice.<br> Sincerely,<br> <font color=3D"#888888"><br> <span><span><span><span>Yihui</span></span></span></span><br> <br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br><span><span><span><span>Gazzaley</span></span></span></span> Lab<br>D= epartment of Neurology<br>University of California, San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br><span><span>Gazzale= y</span></span> Lab<br>Department of Neurology<br>University of California,= San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br><span><span>Gazzale= y</span></span> Lab<br>Department of Neurology<br>University of California,= San Francisco<br> </div><br><br></div></div></blockquote></div></div></div></blockquote></div= ><br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Dep= artment of Neurology<br>University of California, San Francisco<br> --90e6ba53a2e0eb932804a3fdb959-- ========================================================================= Date: Tue, 24 May 2011 09:10:20 +0200 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: Error when using unified segmentation in spm8 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, has anyone else encountered this error when trying to segment a dataset=20 via the GUI in spm8 (on Matlab 2010b)? ------------------------------------------------------------------------ Running job #1 ------------------------------------------------------------------------ Running 'Segment' Failed 'Segment' Reference to non-existent field 'fudge'. In file "B:\Util\spm8\spm_preproc.m" (v4178), function "spm_preproc" at=20 line 105. In file "B:\Util\spm8\config\spm_run_preproc.m" (v4185), function=20 "spm_run_preproc" at line 19. The following modules did not run: Failed: Segment ------------------------------------------------------------------------ I re-applied the last updates which did not fix it. I usually segment=20 data from within scripts and it works fine on the same dataset, likely=20 because I specify 'fudge' as '5' in my options. I would appreciate=20 feedback as to why this happens and whether something went wrong during=20 my update process. Thanks, Marko --=20 ____________________________________________________ PD Dr. med. Marko Wilke Facharzt f=FCr Kinder- und Jugendmedizin Leiter, Experimentelle P=E4diatrische Neurobildgebung Universit=E4ts-Kinderklinik Abt. III (Neurop=E4diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 T=FCbingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================= Date: Tue, 24 May 2011 09:20:43 +0100 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: How to do PPI GLM estimation and contrasts using matlab script Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear all,=20 Having set up PPI.mat (PPI variables) of each individual by spm_peb_p= pi.m, I would like to do PPI GLM estimation (3 regressors in the design m= atrix: PPI.ppi, PPI.Y, PPI.P)and contrasts (e.g. [1 0 0 0]) for each indi= vidual. Could someone provide me with the matlab script to do the two abo= ve steps? Any suggestions are welcome. Thanks a lot! Best wishes,=20 Yang Hu ICN, ECNU, Shanghai, China PR ========================================================================= Date: Tue, 24 May 2011 10:11:38 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: Error when using unified segmentation in spm8 Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi Marco, you might want to check whether there is a spm_defaults.m outside the SPM= 8 folder on your MATLAB path. With the last set of updates, some previous= ly hardcoded defaults have moved into spm_defaults.m. If your version of = spm_defaults.m is missing them, this could cause such an error message. Best, Volkmar ========================================================================= Date: Tue, 24 May 2011 11:32:30 +0200 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: t-test FWE correction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba4fc23059d62304a402441a Message-ID: <[log in to unmask]> --90e6ba4fc23059d62304a402441a Content-Type: text/plain; charset=ISO-8859-1 Hello SPMers, I'm having difficulty understanding my results here. I realized an independent component analysis with the GIFT implementation, and now I just wanted to extract the anatomical labels of the regions activated. So I realized a one sample t-test with the images from my group. Why is it that before Family Wise Error Correction I obtain less significantly activated regions than after? My threshold is definitely higher after correction so how could this happen? Cheers, -- *Marine* --90e6ba4fc23059d62304a402441a Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello SPMers,<div><br></div><div>I'm having difficulty understanding my= results here. I realized an independent component analysis with the GIFT i= mplementation, and now I just wanted to extract the anatomical labels of th= e regions activated. So I realized a one sample t-test with the images from= my group.</div> <div>Why is it that before Family Wise Error Correction I obtain less signi= ficantly activated regions than after?</div><div>My threshold is definitely= higher after correction so how could this happen?</div><div><br></div> <div>Cheers,<br clear=3D"all"><br>-- <br><b><i><font face=3D"georgia, serif= ">Marine</font></i></b><br> </div> --90e6ba4fc23059d62304a402441a-- ========================================================================= Date: Tue, 24 May 2011 11:40:10 +0100 Reply-To: Niccolo Morandotti <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Niccolo Morandotti <[log in to unmask]> Subject: help in "modeling categorical responses" Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi i'm new to matlab and spm and i'm trying to learn how to use them i'm reading the spm8 manual and i'm going through the dataset "face fMRI = data", chapter 29 everything ran smoothly in the pre-processing until the smoothing but i'm= now experiencing a problem in the next step, modeling categorical respon= ses, page 243: as a first step i should type "load sots", and this ran sm= oothly, but as a second step i should type "movepars.txt" but when i try = to do it, the following message appears: ??? Error using =3D=3D> load Unable to read file movepars.txt: No such file or directory. please note that i previously saved the file movepars in the directory, a= s it was mentioned at the bottom of page 237 is anybody able to highlighting me? thank you in advance for helping me in this learning step regards Niccol=C3=B2 ========================================================================= Date: Tue, 24 May 2011 12:52:25 +0200 Reply-To: Nico Bunzeck <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Nico Bunzeck <[log in to unmask]> Subject: JOB ANNOUNCEMENT (Hamburg, Germany) MIME-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Postdoc Position (E13) in imaging neuroscience of learning and memory.=20 =20 We are seeking a highly motivated and talented Postdoctoral Research Associate to work at the Institute of Systems Neuroscience at the = University Medical Center Hamburg-Eppendorf (head Prof. B=FCchel).=20 The project will investigate the relationship between dopaminergic neuromodulation, motivation and long-term memory. Methodologically, it involves fMRI and M/EEG in combination with psychopharmacology.=20 The position is funded by the German research foundation (DFG) and is initially available for 2 years with a possible extension. Salary will = be according to German Public service regulations (E13).=20 Candidates should have a Ph.D. (or equivalent) in psychology, biology, neuroscience or a related field. Furthermore, they should be familiar = with fMRI and/or M/EEG and have a strong background in neuroscience of = learning and memory. Programming skills (preferably Matlab) are advantageous. The Institute of Systems Neuroscience provides an excellent multi-disciplinary and interactive neuroimaging environment with its own physics, psychology and clinical neuroscience groups as well as a = research dedicated 3T MR scanner and a whole head MEG system.=20 The position is available immediately and applications will be = considered until the position is filled. Candidates should submit a CV, names of = two references and a brief statement of research interests by e-mail to Dr = Nico Bunzeck at [log in to unmask] For questions or informal enquiries about the position please contact me = via email: [log in to unmask] __________________________ Dr Nico Bunzeck Department of Systems Neuroscience, Bldg W34 University Medical Center Hamburg-Eppendorf Martinistrasse 52 20246 Hamburg, Germany Phone: +49-(0)40-7410-59902 Fax: +49-(0)40-7410-59955 E-mail: [log in to unmask] ========================================================================= Date: Tue, 24 May 2011 12:51:25 +0200 Reply-To: Birgitta Metternich <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Birgitta Metternich <[log in to unmask]> Subject: SPM.ps files stored in wrong folder MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-15; format=flowed Content-Transfer-Encoding: 7bit Message-ID: <[log in to unmask]> Dear all, I am using SPM8 (updated) and have a problem with my results being stored in Matlab's current directory. I searched the archive, but couldn't find a solution to this problem. In my batch I have of course specified a results directory, but only part of the results are written into that directory as an SPM.ps file, e.g. the realignment parameters of subject 6 are written into the SPM.ps file in the folder of subject 5, so that the resulting SPM-file is a mix-up of two subjects. The remaining results are then written to a different SPM.ps in the correct folder (in this case subject 6). This happens every time, unless I change the current directory in Matlab accordingly for each subject I analyse. Oddly, my colleagues, doing similar analyses with similar batches do not have this problem. Their results are always saved in the correct subject folder. Maybe someone has an idea? Birgitta ========================================================================= Date: Tue, 24 May 2011 12:01:30 +0100 Reply-To: Henrike Hemingway <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Henrike Hemingway <[log in to unmask]> Subject: SPM 8 Flexible Factorial Design Question Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-HQJBAPlABbaAkA1xRlRmTAIZOHcm16VdA1OgAA8uoomQc" Message-ID: <[log in to unmask]> --part-HQJBAPlABbaAkA1xRlRmTAIZOHcm16VdA1OgAA8uoomQc Content-Type: multipart/mixed; boundary="part-dyuwpTUhwkAOIhQSfA1llCApznCCaqzCAyviLfedidTMW" This is a multi-part message in MIME format. --part-dyuwpTUhwkAOIhQSfA1llCApznCCaqzCAyviLfedidTMW Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Dear SPMers, I have question regarding the flexible factorial design as implemented in= SPM8. I have realized an experiment with 5 conditions for each subject (4 emoti= on conditions, 1 neutral condition). I was interested in the difference b= etween the emotion and the neutral conditions.Therefore, I set up the 2nd= level analysis using a flexible factorial design in SPM8 in two differen= t ways. The main effect of subject and the main effect of condition were = estimated in both of the design matrices.=20 In the first design matrix I specified the effect of condition with 5 lev= els, including the con images of all 5 conditions separately. The contras= ts for each of the 4 conditions > neutral were computed on the 2nd level.= For the second flexible factorial design I computed the differences betw= een each of the 4 conditions > neutral on the first level and by this did= n=E2=80=99t have to estimate difference contrasts on the 2nd level. Thus,= I specified the effect of condition with only 4 levels each representing= the difference of the condition > neutral and estimated the main effect = for each of the four conditions.=20 The results for the contrasts representing the same difference (Condition= > Neutral), however, were not similar across both designs as you can see= in the attached screen shots. The model including the difference contras= ts computed on the first level resulted in much higher t-values and large= r number of voxels in the clusters of interest. After some research in th= e archives of the SPM mailing list I still have not found an explanation = for the diverging results. Though I found some information about a compar= able difference observed when comparing designs explicitly modeling the s= ubject factor or not. Does anyone know what exactly is causing the pronounced difference in bot= h designs? When looking at the con images computed on the 2nd level they = contain the exact same values in each voxel. Is one of the models practic= ally wrong? I would be glad if anyone could help me out with more detailed informatio= n or helpful links for further readings on that issue. Thanks a lot. Henrike --part-dyuwpTUhwkAOIhQSfA1llCApznCCaqzCAyviLfedidTMW-- --part-HQJBAPlABbaAkA1xRlRmTAIZOHcm16VdA1OgAA8uoomQc Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="FLEXFAC_4.jpg" /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQY GBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYa KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wgAR CAMgAjUDASIAAhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAQFAgMGAQf/xAAYAQEBAQEB AAAAAAAAAAAAAAAAAQIDBP/aAAwDAQACEAMQAAAB+qAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI3LHZKXMt3GWpf AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5iP1+k4Cb2vhyzqhmAAAAAAAAAAAAAAAAAAAAAD Rjq0ktF1E7ZjuMMvMTzHnox1Sv2kvyNiT90KaDwj+RfCXlH1k/XzsQ7LXQ9EaSKS90CWbwAA AAAAAAAAAAAAAAAAAAQdG/SbKroYRvkV1iVNlCmHFcB9G4E6Ds/k3Xnb40lwS5sKaPPfCtzz FbT31YUXMXfNn07p+F7k91bOdLaVznRm8AAAAA8PTUbWGYAAAAAAAAAAAABBgWtWS5HPYELt uMtzyHbSTnaXqop8j6K4jDpudin0+Zo3jz3w0UdhzxOxjTz5znvhHUdpynRCJZVho6WvyLUA Bp3ADnOjHNZ9EOYdOKe4AAAAAAAAAAAABjlqKzPm70o+Q7niy7teapj6bSaI5J9p9U329z8M +t3ECTsvCRu07h574fPOe6KkL/z5t1pbyuemy9HHo+pFZq4ezt+k5DviaACvsIE8AAAAAc90 PPmy8pLsAAAAAAAAAAA1x/Kc27qGET+evMzXZU0490bN8vOd/U9HZWWjAy3w5g898Kvm7inO d6WuFpnG1G7oqKJL0nKz1nQWNNcG0FdYhAnwJ4AAAAA5/oOfNt3SXYAAAAAAAAIhLafDeCDt z9NdF0gobbeKaRMFPx/0bmS8y2biNrm+Gubq2jz0VdR0fhyM/o8ypW2JBjXI4Os76qLOd5uM gActlabCnW4qFx4VC48Khb+lOt/Snqet9KboY+8kAAAAAAAARJcAmeNZIAAAAAAAAAAAAAAA AAB87+U9ZCOcQxMQxMQxMQxMQxMlVMg6/teK7U74AAAAAAACusa8n62skgAAAAAAAAAAAAAA AAINAfLpEfkybC7/AIAAAAAToMg6T6DyfQH0kAAAAAAACLKGPmYAg6d0cx47ouIOn7D5h0x1 FZBjl/v5n06DXQ9GYYZ+G6bCmgAAAAAAAAHwHk+s5MvJfMdscS36AAABMhyjofovE9cfQgAA AAAAAAAAQqi3FBUzeeJcXp5xVSZsc21uMg+V/WuG6Y7P2klkyXp3AFVv4ivPo2XIcOfZo3z/ ALw32nFdqafcefOnABpq7WEfE+T6zkxJjDuOH6evKgAACXElnQ/QeJ7E+hAAAAAAAAAAR5A+ c9lv3FZzfVQTm+hh7iZTQopnAlchNX03lesudXUUMc+o7YsoA5ewuA5nphz0u2HKQOxpipv8 bsAAMYx8M5PrOTAPe+4C0KzzquVAAEmNKOk+h8P1x9DAAAAAAAAAABjzlniVW/l7g6LKiuit 9kTTha/sK2ah9jqsbnTEt8Bt1bQAAAAAAACFT9KPgPJ9ZyYAB3PHR/oZ88AAlxJZ0/b8b2B9 DAAAAAAAAAABH1qs56F3FeYe0fHn1nPnbcl5wOIO6s9NgZYbYp7JhzAAAAAAAAAg0h8q5PrO TAAHQ88Luk7vjCOBJjTTofofB9cfRwAAAAAAAAAAQdEzUVFJ2cE4nzrN5s3WHp88t+g8JOHm J7HkeHs2NJAAAAAAAAPMcx8B5PrOTAAALK55TszjFhXiTGnHR/QOA7Y+hAAAAAAAAAAAAAAA AAAAAAAAAAAArcN8Y+Mcn1nJgAADLEfQ/nlrYHNSI846zr+F7k+hAAAAAAAAAAqsZAiritNM nLI0bNmg24ebiHJ14kyRF2nvmvw2YY4m9qzM2GZt1MTLHVmZZw9pu814krIAAPgPJ9ZyYAAA A7vhNxsx6zmTquz4D6Cd6AAAAAAAAACFnliat2vI9w36TLVsxNuPobtGwYZ+GWHnp55j4bN3 mwiZSRG27BF3bBojzxHxlAACgg9aPgPJ95zBVLUVS1FUtRVLUVS1FnjB7gquq5XujtQAAAAA AAAAAAAAAAAAAAAAAAAAAAANW0U1zS3QxeFVYxZpsYZlRN0SjeAAAAAAAAAAAAAAAAAAAAAA AAAAAAAACnrOrHGz+jFJC6gc7YWQAAAAAAAAAAAoeN7XlMejPyFKnTbWzMC/jQPTu4dHVnTR omgmeUu4tp/LzCZCgQjso1VrJcquwLbKDpOnpMa86DVU4k+95WYStdf6XGiqzOhv+InnUOXH UOXHUOXHUOXHUOXHUOXHUOXHUOXHUOXHUOb6TXANcgAAAAAAFHcVs3XW8GzlpPbDWab2DYXG KPGasUdLjXzdBG15zDVjK2FNsmRSXZw8iUrpJIYbbjF5XNWSOlkI4kKyWSK+RDImi0qCz0Tx USZOkmTaWWT0eKWSOJDDbcYodPZ0iohHSZUXhfOZ6I2AAAAAAAV02HNSJFfgtnCRy1RPTCrt cCl3T/SulWGBTxOjjmjTY7xTWXpv1xdpD1WmBW9HWSiVz1vpKqNf4GqJZ7ShztRWbpW4gWsX 0sEDwsK2QKW6j+ljz9trKnZZ6DbPrZBKRdBYq/YTFZZsjTZuAAAAAAAgTxX6LcR6y7GGYadc oVHtsGjeI1fciqtQixLUUMyyEWHbCnuAV9hUEuvlVhdyoE8r8dXh5Y0tuNGUczyay3BTYY4H QxpMM2Vk+mJ1vz/QGqJLqTdPgSirv+V6czrcMS3AAAAAAAAAAAaI5Pc9kX5EJaJTHSKO8CJT nRuY6A3oEAvnO9EKe4qjOrtqws7CDOKfzZ4QrertSXVbMDTtw3FkChw9xOihTYYpbuoPehob 41V1tHIEqLZnPdLy3Uimy1l2AAAAAAAAAAAAAAAAAAABT3FWe1lxVllYQppT+bRX3FZamnCQ IvvvhaAocJmouoU2IeUl9TjoaK9EeRHIFnX2JznTUl2VCTHLYAAAAAAAAAAGnCRrKz2d6SI8 jA11ltpIlrp3Giqt9ZUXWmWR4VjiV1xGkiss68yrrGGTpkSWVnmzEi2dfYmiPYwDLPKMW4KP zPUX8WVGFTb1h7dVNsKu0gmqRrllB0dNcGuDu0FoAAAAAAAAAAAYGbT6bTEyY6SQ17AxwNrX sDDE2tG8VNtWHtfZQidPgzip82eEK2rbMlVtlWmGefpOjSYZHi2lcXcOZEFNd1BlfUt0KW6g EefGklL0NJeEWPNglmAAAAAAAAAADRq26CD7ryL2NJiirs6glXFFelZqlaRNiSzVBmQDy+o7 wVtlVm+ssawt5cKaVvnnhpsqq0JVXaQCNK14lpEl0xPqbepL+LKiGVXZ1JuuqO8NUOxjEfPV vKbpeZ6YgYe6y1AAAAAAAAAABoj79BW5Y5F/ElxBTXNGTrmnuCurbiuJsjfoI8CfANfRcx04 qrWqNtZZ1hazoM4qvPfCLa1dsSa6xpDf5rkFjSXdIWlPaU50cOZDMqe3qDZfUN8KydUG2fD2 FV0vNdKKTZ4XAAAAAAAAAAANEeRqKrLL0vIkuMY011Tm68q7Qr4NnFMbDfoI8CygGjoqS7FV a1hnWW1WWc6HMKrHZ4Q7attCRSXdObsNcksKS7pybUXNeXMOZFPKi5qhfUl2aq62rzRv1bio 6aivSoSNRaAAAAAAAAAAA1adsciZaNpcx5EUyq7KoJFzS3RE0bIBabHhqhyYhttOd6IV9hWG +DLhFlKhTSvYeCXVWRMqrWqJHsTYWdfYVRKrrOqL6NJiGyssakk21LdGqtt602+x8yJec50Z A1zIxYAAAAAAAAAAA1aN+or2z0tY8jQY1lpBPbWLKIsKw0k3Vt1GjRJike8pboV9hXmUeTCJ s2usSvxyGmZXWRJrrGrMt0XeT66xryRVWcAuY0mMe1lnWG22qLcVdpXnsqJkQL3n+gNEP3Is AAAAAAAAAAAGiOT3O7C+IhLRKY6RUW4V8o3Gg3oEEvXM9MKq1qjbAnwCxnVdoVWOXhrn1doS 4E+oN2vRvLWqtagl19nTHRRJcM9qLeoNl9RXorJ1Sb5UKyKLouW6Yg68ci1AAAAAAAAAABH0 b9BVTonp0ESXEFNc0xIvKW6K7Tvil1o36CPAnwiN0tDfCqtao2wZ0Im2FbZFV576Q7KvtCVU 21Qe45bCyp7inJ1Pb0x0kOZDMqe4pzbe0V6aqi8jESwrN5U9NzHTlT7ngWoAAAAAAAAAANGq ZiUG64GcWV4RqTpMCst8ciDI2+jRvEKJbeFJfYZiss/CFDuPCFYeelZlYYlRYb8j2rtMSu9n jOptvCNU3/hlFleEepvsSnu8chU22JXTNwoOgwzFTbeHoAAAAAAAAAAAAADV4bmjM2NWw9eR iU07DJp2GTzWbXnhk88MkbeZNeBvaNpk88MnmBsAAAAAAAAAAAAAABjhCo5rqXJ5L1Tnx0Dm R0zlbQtlTuLBSV5Kj3EQi7bGER9TwtvMNRG9j7SxgdOOW2dLAIOnXibfdQ3Q7WOSPK8b/Ym8 nVN5rKu7wiF65mUXjlupAAAAPdmvYyFgAAAAAAFbH2xiBYa9p6D3TjsMJkCyPN8HcY6c4pnq kTjdpq7Q3QpEcgyNAkeTthSy5+snQpsQo4N3rI03zaTqKzhE72DsNmMO0NeG2MboeiUSY23A j2Gnw8s9MMk4RJB5r83mOzRkYa7TEiarTSWIAAAAAAKqNKqzTKgWxCsItgQNG6QQ9+mOStvs sha9usxuKScZZxdRdwt2oxq7etOlyrZBKRRK0bxzWv2SbNErWSMdc4prHPYU1ht9NcaZ6V8n buKrVaCv0Xuk2wrCGRPdok19tgc3a75pGlQt5j7F8JdhS3QAAAAABDhXI5x0Yr9VqKzRdCBW 9COfsZ4p43QihnWAqIHTCplyxD0WYpsLwRI9mGjeKWs60UdlKAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/xAAz EAACAgEEAQIDCQACAgMBAAACAwEEAAUREhMUEBUGIUAgIiMkMDEyNVAzNEJgJUGQQ//aAAgB AQABBQL/APJ+y9dZHvojOqagGn15u76erWmsfOq1/cf/AEH4jSbdMbr1WFaiT7WufDBmsdO/ vtR/vv8A0IVLE4EYngPOBGCkRkv8sj2ntztyG525ynIKZzeck5ie3O3O3O3Ibvgnyn17c7cl udmQUzks2Lec7M7c7c7cAuUf5LN+c75885TGRGfPI3wykBttIpDUcUyGD8+Xzwd9lfz9J/aN 9vnvIzx8mIyxZIxTekCrXWHYjfjw+Zb588T/AA/yWb853wRmZeyea5KV/PO4vKMeQX1sVjbR AdGxLIU7lnzwd9lfz9J/aN9piQFZE+LSXCxpcAq2eyzRgewd+Pz3ZHymJxP8P8ln8+MzMDtk oDFN3zbAr7POOQ6g2YzUwGJo2eshszgXYxfzBX8/Sf2SO+Mkd1V+GanZ2m43dGnRBPrOlTw/ hEfe1VhBlCx2in+H6cTExnYMzyjn9UyPvtbKWg+CI/kobWz4+cWOeK7RlqOWXKgtT1wBaftJ WogS0p0NFUbH6T+yo+4f8uzaLSwMdRLbNLEFkfyakxFL3yMmE2hCsVdqf4fo+M2UJptE00zC FV34Nc1rrAQu+q4xLLy43ghGJsDx6RHK9uCn8PuZCdp483RtlvT+5el0WAesKkc0Ds82IiG+ k/s5kJWbYMgjbDX2p1GoW2miXPpkiUuDwa8bzPUQ2VyNBvYP2AZzL/DKeMeWncnLILW848pF iX/g0xBh2RUOMdnbvCLH31WVSUdcZbGGM09ArdG3P0n9tdZMHVMucsCQRzw2DJUYSLNSMFjR OZHu6iO1EzBDz0r+H2KsRDf1Ndjejony036Vo81zQb29XBVsvwYrG0/GMYTYaDSYRgwJZE7q wN5bba5NrRzNla2PWdf7qo+bPSf21qvJMWguFtrgfo3c0TgickerLDZNlAYIdQ5qyXM51FG4 aa+tP2K3/N+prn/S0X+u+m3ncnDBXI3mokIXckQkBQwwHjgGMw+rywaoKh9YGtpL6UN/MMDe BifxfSf2tSPUzgOdSrDKkQIqrxExHGRpqmaaeqLaYsZFCJbUr9MB+3pPlS/K3/N+prn/AEtF /rvpmTPNiD52bPWUXJgmgNiV6UtYunpwRKQUbBMiGcVQLkI8YGOOc9hV/P0n9rMEUSMkUVlq kbPXi2EYracOL7kjd4SNxZyxhQGnycLX8x+xW/5v1Nc/6Wi/130zN+Y/s1UNFmlgUIqEGMje CQOV0xA3U5Ydsxc719y5blg78Vfz9J/aN9nrmDVVNkV9M2makcRqxEwmJg6C2BYqKqlXZzcI yMJ/h9idUqVbXvun577p+e+6fnvun577p+e+6fnvun577p+e+6fnvun577p+e+6fmp6rTsV9 HtVxoKctv0JTuUFMjzmfUg3mA2zbNs2yY3zqjOrGJ5gWixLVVhUvqjOrOqMEOM+vVGdMTnDa Ns2zj89s2y1TCxFXSAUfVgxxj7HW8Hfmcjysnys/M5+a3/M5HlZPlbfmc/Nb/mc/NZ+Zz8zi e36Fn85jceM7/wCJq7FExjbKmeS/PJfnkvzyX55L88l+eS/PJfnkvzyX55L8qutG+bEb/C3y n6Ai5TvsE7yX+JrVjxPijWKeyP00QBOrlYKfhbjz+g2lhf8A18g/xfin+90CwmXW0mhv6VKG TaKapM+F+XL6ApmJL+ERtP8AhXf+udf8L4p/vccv3ih+kjh3K8gw+Dv5fQdc77fLgPqyI5zE YwgWFjVZgqVvngcDj7kYVmBJcraRqHbr2zaOW0YMRsqPv/R/FP8Ae5pT5GzrlcYt/o1OXkzF Rh/DPbz+lZvzKdssbsCxW+dNkwarMDLH88CCOI7VS1pEFezITE9gD96diiFfz+j+Kf7300t3 m1Hh1O/Qrce+QstX8Jksi+lZ/O5E8zOFrMoPDrxwqUjlgj1YDZAnM7sIY427DU2dDPlXglyX 3ZFf/J6t1Gmpj7deuFe5Ws5XtIsim3XcqdSpCCNQqPZjmglamA1f2HM61lcLp+Kf730ruZXb rNZM1v0KvyfEVDL4XJxH9KLAJtjba3ueFuJLGBq1uJw6vvgLxjyWyfvZd04m2EWYqgFsNq7+ Z7fi+q6RLG0xJUqVdrdU05kU6FIYqaLRrKPTfhZKvbMsDzR8NT/8N9hiwaPhI6/in+99dE1E aBanSKhZ+3VnawI2Bn4TlUl9JY36dn9ldZSuwiAA6+8wZd/yEZbxW90jhWJbkvJZzqEZVaD4 cfDNFOCz/wA/Vuh1WP8Abq+2RotTrfQgKWk8x064M6RolO7YbarXbBZo1c6umfa+Kf731j5Y Qjf0Wfl9uvMQ5cVyL4a8jn9LuAtk4yy4cnbgmr+NajaXGY4+CIePCrZFxYmo4s0/T2LzU6x8 dCUwLgHzb+jZV3oXpSxGlp8V7H2SmBjyUbfFP979jTLA1L2tLl3260bvmbIr+E+nl9Ltj2th 3d25vE4DQ2eHKWAqJciONvBPBd1xQPmq4vsVRr9ef/0+huKl1fxX9XxT/e/Z0aQ1BbVksvs1 du9QjDPhTnz+lED7rcDjCkXrncUJImJn7xVZJhCMA9IyQaczkqivYFiGbYMfL/z+j+Kf737W o7a0r7NXfyGE1YfCXTv9Ly2YxgctVSQGszkUv64G6EtI53hsda/utK6ndlsxOuyWRvgz90Z5 N+j+Kf737WlXm151eqFS99ivx7llCS+G4bB/Sn/K2O+TINr+OuFWXRGd5G9DCMZLaW2I4uIz civEpGIGckvuq/n9DdMgrlZb1fFP979uo0LeivUSHetPn5LCkc+EIXBfSsj75RGWFTAy7ivh zxWlkba9PiNqtBA5ZDmnKhmHPEeUbzODHyV/P6GY3jgOfFP979vT7Z0bWtIjq9a/DuqkUM+G hMWfSmJSXWWcDxlIWZOnnGICyBRyz72MTzwEyEcDzrPfgeQs9liUF9H8U/3v6Hw9br14uVHU z9KXZ5TCHf4TFYz/AKs3AgpujA/FP97+gM8SmPedP9EcIcgpz4UEhP8A1W14YRUtx+Kf739H TndVvWUTL8pdnlGSJP4TWKy+jYP4h/8AVHh2qLjZqzMT3F0hZndTTOe2euZmXLcYgDSwWfcz lHPlOc/kLd8kpySneS+cyXpynIPeIZ+JBTObznKZyGfMZ3H7PxT/AHv6WlGWqKsr6rKOHaIt aHwtO5fRmJ8pR+HGEjmML2yF8q4LPOooIkHnD8QVFEKWXYC5gSOBzjAly+W+EMZM75tM5x5F IROScDk/dziGEGfsMntMcokh3j7XxT/e/pKaai1RQ2a9Lsm0U1SZ8L8+X0Z/PDCec/x4RGcf wOG62Bvkhtkh89p3DYcGPnIzyZHIjiZzjPGQnmMEJcZ3CNoP5lxLaBnad94CYyf5wH4fWUxt POf5/Yengw6/4XxT/e/p6PeJEajQXp+ooCznwlO5/R/OD2LnAlvw+4UbjAbCwZ3IZ3kdi2LO P3FxMZEHy2nGQXLad4g+MDOy/kH0PxJStN1n267nt13Pbrue3Xc9uu57ddz267nt13Pbrue3 Xc9uu4NC+JVlMLTYo92fDgWgL/aaUgsrk9XoU7D5TN0M7VehXJEkMlkf7rQqjOco5MGDCKgQ sBha5KI9PDViVwof910M7UV7S5ZDUzXU4gtVjc9ibU4uu2IoLapP/rmtre3T5cdZJ6tYrU12 7DLXuzkIdefIzftMc7V3LvKJkletFXlGpolTtWgHzrA96NRYqlX1pb7mm35ulOtrGzY1SwLp 1CUK93KY996ALUmusaxfbVuHqzEg+4xFN2vAsGasI3E6q15O1Kx32b3RXHVjYPuRExuqFFxG s8laXqQ3i/xNWOuNaHaXtMaTMC7TBMm6YQGWknjn6W1rbGmtarVqiyu3atu2Gp6eAefp03Ab pYB5Om9y3aWvKt+hWkGaUIvbUw7Omm6HaZ1nZ007Naxptd9m9p1kotabwt6jRs526VyizpsW DsaYSis6ZMXdRoXEPtaa5rLemmI2NMFnlaZ11NRo1Q97qZ73Uz3upnvdTPe6me91M97qZ73U z3upnvdTPe6me91M97qZ73Uz3upnvdTPe6me91M97qYGs1TP6FlrrsMsuIGuVGTbIFBqHIK7 SbZ2jNozaM2jNox5cMU78exbleeQwG1rBNl9hczN4oGbslFIu3NozaM2jNozaM2jNozaM2jN ozaM2jNozaMeZw8LvaZW5YqvaE0MsQLp1CVx5Rd1L50tozaM2jNozaM2jNozaM2jNozaM2j6 E4KYNXVKa4GPBs4dTniwgir1oTOHDIxLJdnF2cXYxJsE6+y0oW4SqyWLQSolbCwanGesWSCj DJhsQlkuzi7Ag49D5TBtkGcXZxdnF2cXYTZFnF2cXY2pLmTV5M8Y4ugkwkkEeRU2E0726vkz KZa1fF2ds9vF2cXYEHHpaVLcXTbC6yGKr+G0cTVJVh1QmP8ADacr36/1ryjelK+uPSumU+t0 CbUJbVWu63NjT7ZEibzMkjsTWXYXcW95wNpkVvNZskyKbiYr5WPmi6BNqEU1HIuOKZvN6Y/b LFduNK1CV22yxlooPzW8baXQLmtXgveLiuHuu003QuIf62UGw2NZp8VgkFZ0sUB3m7DcPspt NofTGAnEBVljAqrzapuCa5j+T2hCJjx05KERC1VmDtTzapnjpw01wEU1zH8nxgKsn46caFZQ EFUT3pcONTdaqzBkakF+Tzx0546c4Vexi6yx41OS1VmD46cgKssMKoFtU3BNcxaFZQ8KvYaq y44Vezx0546cEapMYFVeeOnDVXARTXIfye0DUkvHTnjpyQqwxYVWYSEDH5QQ9FsBn64V+Niz Xl8FT3JC+pfiO61jxDHL7UpWS48P5+H6PX2rSuVgFKQxNPqPLSycnxPzrtNFkMrS0krJcHT5 l4c+so/NPCSjwR2rhxjARxsuRzOam5Vw61Wlk5MKPy7lTyRhEDd9H1DcQ0dm5YDmpK+tcVeO LqQtmMXDBOvyPTqo1EuEjU+hDFelVMJP/IlDfPuLYxfhPymswDFIaNyyhrGlVfJ1AJaLyici EO820s2SuuYX/Q6zO+aRzYy2BMTWA1IipYjE1XA7GDJjYrNO5QSxQms5yKViKRxuJpZ36fXO tH+RK3efcFhr8e3vSExXilui7aW43GizzqCYIzoLz76GPyK5Re9DU2Hyh82MuCZorQYIhFnZ KbAuxkFItQfk6ck0g1RMh9VzK3ppyGJ+msc4RVIuEnY3krHpak4TWIuoTscEFYl+W+XjjNwb i5tso2OcIqkXBhWO3nY9ZJvm2iLhzuZUkpHFk3zLBt7SOxzqycpvyUVxOfOvG7Nz830a1oPl r2Ny3JQmvJdMHYxJWO30OW89ON5iz/jay34OWib5OnFZKf8AIlx+4XXda/Ksb0mSxeKcZXbT yFx2WwdQ5Yi8yVIF2924ZjnMvO9DsGD5sPKxlw5WiqyTRFpmybLSf6GTILTTaxTPktth00su OauxSc1jv8ibX5y07pH3L5VXdo4u1zt2bXS4r+x1m9ybTCUmGT5V614wja3uehWSW3zDJ2Wm 9Sqze5EX+WJvdrPQ28SovN4snYPJb42PYYMo2DfP0rmdSUMlkeZO/mej2dS0M7VhdI8Tc7Tx xECw1CDsN1HrW5nUlDJZB3OBeZPrLY8yztAeYvKs/dxbYm1YYK2FYHnWKDTe+SIJcW7pdSIO PM9DMAcxoE3LJQKkFDFQ8SxVgGN9HXQXcotFoG2JJr1eNhKAipthk/SzETALAB6FZ0r9DGDE QEY8dPGFLg8YsWDKFTMVkQExEwCwAelcl0K9ZJXl2euF91XlW4cMAleU6UgwmV+VeQlTVA0Y NEXLBqUIHX8/0Lxe6TpC7LMhCk8CVzrTC2Vyb6NJPk0ySQs2lbCrxTmYGGWBCaTEsn/IkU+f cBcq66XKmICvFCnzbIJ7SXV51YCEZJpm5cUhsASju49sJByFxm6O/LcBKKwhCOursoKsP9HR WO/p41xiwQgkugKkxvBoApqCkT+mAxMe1edgehTAiMwQ96uPMeeEUCMNCWAYn6AYmPYHLuX6 ylXm21gSvDr9lMAFWLSoblhCyaVZEnWAQTnUrzrC4KNllay0SoXYWJqCqj0tAJpQsQT41eBX WQD/AEbC2WKSlKDpXzOvXJOSwBOolKmfSvDtRXCRias7+L6WA7FVwlawqyEIqdTM1FMWKhIl habX8aq8O1FcJGGVOZ+N8/SUfm3gRR4H3a4SMYtHGy9EsMqsyVcOtWdH52/WmwMrkrWW19qL AsNRU9zyyHNaAlaoqyOKqdZ4wIYMpPvpKNa3VxPIpT4WWAlh01GufpbAdqKyyUM0z3mmfpZX LU11kpQUjEU02Ku5dWTq8rd5On1/GRYDtRWWShZTMmDWYJek1y8yyomj4B7VVyscXXIbdiub WFTOTrLlScOvBXLiTesa5RczUqhWoeBNUqlMV8tLlqq4SpMUjjE0zBnoxZEVBJqWaimXUWtq ZZSTWUaxIn6WxByirBiErs7yuz6WoOU1uYqFdnhXFo2cviR1mruFcqQYjYg5RVgxBi7Pb12f WRd5tqDIOu5lSDgcWLvMsC4mkuzzqwcJw1FN23BzIi7zc1Ndhgu+YCt/HLcHKa/MUwuziV2Y b6OFk2tOGwI2OXQ8LnhemnLsBP0tjn0VZLhPkbz5Hpa5dNaS6vx/HiWlfy3BeOuHxbyxz6Ks lwZ5HaXl9npPb5tqS4fnMqcuOL7fMsS2Wl3b1pma+fmfdNQJ3XIs87NT8njaORRJWuWW+XTX kumPIxPkduM58Wlai5p5GS7BOEXlaillrt8mhNiW/S2DlaKrewJts3m2z0tMlaazOxQ3GSCL TDfl58V0eYXfpdg7NWwcrRVb2Ay2wW+Yz1l5+badwDzXZUZLRxbzK5YsEDTuyE1TliM8lvul 1jBSNnlfzU7La42WdavMZzy2yVJrs5pi4zE2mG3GFIDNja7p1krIveSxO44auWrBrs0bBvL6 V59SKzZYM3D38wvSyyVKrs7FeayUeQXnZaOVIB7O3Hn1IrNlgsuELCst4ek2C8uy2Qjzy41m SyMXYIrVixK2ldkZrnLE55Ze5ahbKvktZF3NStlVGwzrUFkpPLLJUquzsVFw5xVwmM9DbI3N PtTaB1jrg7hCjHNkLNKyT5+lccLVXKDGbIZNkPR5wCkFDFxZWYotoZby0zqSxwqelvZjjhaq 5QYnZATB/wA/SXD5VgoAfNVlc4IcBwlZe0FsNsRlY4NOeUudQeXCe3azl10IXaauvXCysoyw cAtJQxfkgWLsgbMYwFCVpHl1mQzGPVudhUIywYgynZVYL6V8iKa3CQk6+TKOOWZCE1+Eqgq/ QtiCt5c4ygTrFaWsVw+QFNbhITKSd2VfWSV5lrhACVYzp8Zr4sleXYlIsbKBZUJZ18kk+43Z SGcgi9moEkBsdcJA60xlqQhKOBK5V5hR15b6WOruodUKstryEuqeLhqEyrdfd9K3jKq8L4SN XJXV55Y4dSIDriKnQMoKzlnj1jFbvxvGVV4Xw4Il4xUkfSYR5dmF9YBW7acKhGBCPKcKOwvH 7K0LFGbJ8y2KNt1eVlyE8XQHWpVNY5Z4dSYDq41NljVhvowEeZUhMC3jwIKcVsNoAdIEL+mc ENUlUrjw/n4k745fatK+sIpR0dMd+OCWLGvEHjghqkqlcHTBhLoyEekpHyrAbjFNUFQSNevg JGLLkcyOoEurjAKzrCb9yoFiRTMW8uIiwNgZJIVhn0sDBqSHWvxgHF1gWz0euGOqLEBdXAof SUacevlZq1xRP+RNdnm2lsYBU2TFRZrHFV2Dbt12ONlZhlVXKUZ0zFqytjM8c/cs1KudlNlc tSquwJy0smKrLJSIpvjE1Grb6PSTLFCvNcSXO50mHTyyqWspVzQ76WwMmiqslAdZ0QSGb5aC WJrAS1BUKEoSa7WXlS6ukZBOWBk0VVkoJru86adjb0lB+baWTQCq6D01LEVcWgxuWEsa3xj8 mouVV8lBTqOo1m2cFBxdzUUFYB481TWcXpbXLE1xJaYqMxNVotxgyQtrvm5pyGoFwkSvCf4R xMiSWeRSQaZ+lsc+irJcG98raUy3LXLprSXUZNEA7/Ny/v40GZXssc+irJcGG3uKbfH0nt82 1JSAE0WUZKUYvt8ywZ95d8updk1sntnUbpnn43uOanNiFWN+pff15b5dNeS6Y8jE+R24znxb NqLmnyZLZ/xtm37blrt8miRlP0tiThFUykJbY3Jlj0tSQprEUq7XeOlj5sZeZKkKaybeWJOE VTKQYxkP3sSv0k2+baMoBbmQ3T5bKMWbZuWTOCNxAVWSJGSbfcbriHOR+bmptepVkihPda9L ckKa5FKYbYxLLEt9DlsHpzHsF5uEWutrpY85G1SNpT9LYPqRWbLBZcZ12bhLfllkqVXZ2KHU CYmLe+pZcdKVpPsTlg+pFZssF1qUt8w9vSbBeZZbIQu3MnRabk4uwRWrLpXJ2iAqrJajJsF5 9uz04NjldzUbc1F2WdSRunM5ZZKlV2diYuHOJuExno+wQW6NgrAPZCksumFf0o25ss+miIiO A5xH0n55tGSAznGPSYiY9YiIjiOcB9do3kYmOA7xERGbRvxHOA5Hy9OMcpAZziPLJiJjbOMe kxvG2cRzaN/TjHKIiMn5xwHb02jf/wBilgQfYPYpoNAjEYFgkO8ZvGJsKdCmgwOUYtoMHeM3 jBMSHeN94zeM3jJeuD3jJMYEHAYrcDI3jN85Rm8YJic/WzMDnYGdgZ2BnYGdgZ2BnYGdgZ2B nYGdgZ2BnYGW0S1/intWpQmy+v3SVMe0qRTYqrhKRDZc1MdXNmdMTW8SQUNMoyaQTldRDdmm MyVXmNaoKbniDLBpHsVdfTCtktqQ421BYBr2QquXMKh5UFaA7AzsDOwM7AzsDOwM7AzsDOwM 7AzsDOwM7A+huzMY2xKQi1MGRsDNS5A1liQDyvwFuJdddnexp8QyvUrrF6WLY6k2GvFs8KPF idPf5EpYtrqcwToP7kH9+z8j3HxZZK2CW4dYZ1hnWGXgAajWLrzWtyxk2yiZuT2MbEVqcy8h eSax3CGCsz5MWGsHsGKh8WBWH8WhMGd8jXaqFE4GxB1hnWGdYZ1hnWGdYZ1hnWGdYZ1h9DeO QGbIqxzxPPMkpt2Ggwr0RA2oka9wiqJtyw1NOdPp2osVifEV+5i1ldjYbysRa6wY2ArKbviy ExW6fGk3+SuwRmTGS4yJ6ZvtEYc4WZdOQqtcdcYYRWG2j51HS2QKGLPUJUq0wgC6ZqRDfzS7 BTblzYcrm8V2C8Py/nWsy1pXeBOscjt2pRjLxDCWdo1zcVio9jCo2mPOpbay3UtMc6q9jCRc +9Op8Fsu7Oi2ZB+rqJDCwZWESOlJdtaRmzWLFyl7O2lhNpTG9TOSVoU+qeKagKyDSqXzT8cm U5HxkSFh6IxIV4xU1lGp4eYS1iwrC4kqyzxldTQKuomDVWHpbnjWZYSyVvrcnPrC0WVuTSXT T5SuRWC3ZbEwlo8T8ZLbhAEV2hD/ADY42HKWSHqkDfU2h1Qw8lLcb4yssuGsxd1Xak0rr1TR ClurAfOqmarEwaa4rkKdcM8ZPZNRe36uofw/DnOuocRFZUdNM8pEE2UqqkLPAmPyoH+CQimn XtjKioGKGY0Kw5101mBCyXVK0CsqxMlYQbQAMIlkjmruGIEfsPZ1JiK61yqkQfgsaKq63m9D VktbchYSCeJYtQSfIXiTRYtfDYmVWpA69kyOmKm+HytqqJWyKQI/DsC1i2KgKe35ZSOlYLel ddkDWdgrTK4tjMS9cGN2uUFbVEFbCI/U1CIkGSoAlQNxNYV46gjiEQjJFA5USkgYiO6r0VY2 E7UIjh0LtZsiZUmrL3BKH2jTOCwYwVq6GilVVqoVUYP4XljMpet0OetKwsLM/SsKvHKmiQq1 lLkELY2aEEoUca4VuKFo4Amt1rCvMLXW4pit+EyoJ4CIFhVBKCqDIvRDpmoMytcrKKkEqNPG FBpyxJ9eHS+v2tiiETXT0j4UcTqgdqNPHrjTg7DpfP8AUsp7sdRFjwotFc1GHYtV5sJOuc16 9aVlUrsrl48eSVNrK9ipMZUTKVoqyqzFNwY2lJwxU2AfShjQ09gYhDFtertQ4Ccg68jI0t20 arK+OV2gSGTZywMsQylxSymzhp9Ka0pV1l/+UH//xAAtEQABAgMIAgECBwAAAAAAAAAAAREC BBUSIlFSYpGx8ANBMCGAIDNAUGFwkP/aAAgBAwEBPwH7TbC2bX40RYlZCKGyrL+stK1n18EU SxK6/wBoWVa16/ZLatZ9fY145TzeWG1BC6FPmcilPmcilPmcilPmcilPmcilPmcilPmcilPm cilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmcilPmci lPmcilPmcilPmcilPmcinl8Hk8P5iN8KJ/I2rkbVyL9PY2obVyNq5G1cjauRtXI2rkbVyL9B Ef2Nq5G1cjauRtXI2rkbVyNq5G1cjauRtXIo4/xJZ9jQY93Ggx7uNBj3caDHu40GPdxoMe7j QY93Ggx7uNBj3caDHu40GPdxoMe7jQY93Ggx7uNBj3caDHu40GPdxoMe7jQY93Ggx7uNBj3c aDHu40GPdxoMe7jQY93Ggx7uK3r/AABSz7LhcLhcLhcLhcLhcLhcLhcLhcLhcLhcLhcLhcLh cFb19gX/xAAiEQACAwABBAIDAAAAAAAAAAAAIQITUQERMVCAA0EwcJD/2gAIAQIBAT8B9Tev 14Tp+Dt+0OvhOn36Nc/JHhc8lsNLYaWw0thpbDS2GlsNLYaWw0thpbDS2GlsNLYaWw0thpbD S2GlsNLYaWw0thpbDS2GlsNLYaRlxLt45jGMYxjGMYxjGMYxjGMYxj/gGxjGMYxjGMYxjGMY xjGMYx+gf//EAEsQAAEDAwEEBgQKCQMDBAIDAAEAAhEDEiExEyJBUQQQMmFxkSMzgaEUIEBC UpKxwdHwMENQYnKCsuHxBTSTJGDCU3Oi0oOQY2Ti/9oACAEBAAY/Av8A9T761YwxgkprukdE r0aDjAquCFVzS+4wGtTOlUKL61wBDG6p1Jv+nV72ReJ7Mqn0Omb6riQY+bH/AGE7ZtvLHB5b zATPg016zoApN1TB0WiysOis3mOdAkrpHQazLH0HSG8gV/q3/wCL+lf6T/8Al/p/7DLmsaHH jCJAAJ170X2i88Yyi4ASdTzQcQJGh5fsyIWi7K7K7K7BXZXZUWFdldlaLsrsqI+J2V2V2V2V 2VFpXZXZK7K7K7Kn9lFBcOoQuHVJVzSoqCSpaR4Hq4IaL2dZ65dCMBbuIUHMK10Qeowsrh+y yh+C1UDgU0rVWAogkIkFZUoTqtQhn3L2dZ6nO4xyRzlYwEXFQgeKH4I/gskLX9lnKblalOcZ lBslalOqF3FQHKJnmrkOSkaIXEoEOK9nWVqnBzirmvwrWnCkqQghngjlCCrXE3L2/pMGepoD gbtIRbO8BPys5TQuMJxlQOa1KLJK1x1RG8FBUcFqrLs8PiFaoysFCprKtCnVDkm7xUsyFOqB gx+jqN2Q2h1df6zKc+LN5tjb5tF2fcq0NAkQ0T+84/eFSuZ2XH52glbOzVloAP72VTBEWUzP tOPsPyskhBQQrJW01UPaEbnDXRZj2LGiwFdTG8t9YQHBY5dZTdFuKXKGLCghNJMALdhG5Y0W QLk/uPxag+g633A/f+xJKOimAjasIXLtBEznqwVD8hQLZV8NmFwhTAWOsoNB4LuVtwuWuEQd FmENnGUbloCuCuBT/H4vSo/9X/xb+lg6XhU/F39R+TFvPqaHByIAhEorCE6rGq3urdKBeC7C mCJTbWHKyCMdZQfBGOrBTnP0ARhS8oSIHehuzCdwKymlRGePxelf+5/4N/Sj+MKn/E/+o/Jy EGkiVED2I6ElEFbrsrmFhoBQmDPUE0ADRARgIYHmvZ1lNvaDyRdosJ1Ng4IlywFwaeSmRoiM XDq4FHx63hppNp4tJaT9/V0r/wBz/wAG/pR/GFT/AIn/ANR+TmFfEqxwLT3rGizxV2Va3Klo W/oo4oFx96A7lhsLRZ6ymYmFaUS0lbmqlyjgpWuFgwVA1UvXt+L0r/3P/Bv6UfxhU/4n/wBR +TlN0VtQNc3wXozagH+as+5RiVE5QhQFSyOyFqtUMjyXs6yuCuRU1CtwoTqrD5x1ZOVHeoEL 2/F6UytUh20HCfmheuP1SvXH6pXrj9Ur1x+qV64/VK9cfqleuP1SvXH6pXrj9Ur1x+qV64/V K9cfqlCnSqy68aiEwOr0gZdq8fSKOyqMfGtpn5DIz7V2c96xaf5uuZK1K1K1K1K1K7RWpRBe 7SFO1NqDQ52FqV2nLtOWp+JqVqVAMLUrUqZK1K1K3iUHOeSVqVHxappmlY83b0yMAfctaPkV k0PIrBoeRWtHyK1oR4Fa0fIrJoeRWDQnwK1o+RWtDyK1o+RRzQ8itaPkVrR8ijtrO635DEcO UqEJjH7F6b0djj8NcRs2j+Fv905j6tUOaYIvXrqn1ivXVPrFeuqfWK9dU+sV66p9Yr11T6xX rqn1ivXVPrFeuqfWK9dU+sV66p9YpjaVV5edAXfirOlsr0nt1fSdqfOPJdKHwjbjdPHGvP5D 6syO9ApodjwP7FqV7b7LTH8qZ/qF3+5ddZHZnP6RoquLWcSBK2dCrS6TTaN1lbgPA4XS7aDq PZkEzOvyEyboOgKysN8v2L0j+X+kJzP9Rqg0QzcFTIBRFRhZORPL9HT2LA+pwaWzPsTmdJpP 6PWHbLM5/h4LpV/SBXwyDdMa4+QzfTHAYWqmGtjl+w3ej2uRu+1NDaJO66zd9W6fcukfy/0j qqdOd6M9HbZa3jGf0bdrdZxtGUG0XUumUgN2i/Vg7wunbtuW48/kLiSCYgYULsjrKGFc7QK2 kwAIXiVLQEZtR3AQmw3guC0Wi0CGF7PknSP5f6R1U6L6tvRnv9ICd32p9TojJ6LiHsG75/om bOntXcGW3T7EW1Q/otYdrEieVvBdJ21dtbDIIfdA3vkxQkhEcFLVau2FLSjzU6FZKy9SIlRM FDTyXs+SdI/l/pHW3/SbLbpO1179FUp62uI/Qtve5jfpNEkJraZp9LpjstA3x3kBdL2VI0ot kF05z8nAB65B1WXLOVIWeSI4qJQJPBYiUM/F2dTpNJr+RcmvrVmMa7Qk6o7CsypGsFOdQqte 1upBTqlKsxzG9pwOia89JpWu0M6oU6PSKb3ngD1GpVcGsGpKa+m4OY7Qj4pdr3KnUaweq2r5 OgXSP5f6R1ipRda8cV0Wp0UB9V4mrZnz/QsimKv7h4o79TotX5wOW+AGq6X8IdcRaAe7PyY5 QKtChTOeS5FF3BEgqFIiVdiPFNYzgrxgpoGi9nxK+zo9E6fTc8lzrvSZX+lHotBz6YrEbF2f Ym9JHQ/gVOnTLSPpz4Kv/wD2+jm2T864tX+r0jdhzmaTwXQxWpU6hbSaMgHgqdTZs2lzt6M6 9VRpgy0jK6OPoyPf8W2oxrxycJTGNba1giGmMd66R/L/AEj4lW6ltNoAPBbFzrjEzH6BnpTS /fHBXinR6Yw6YuPiQM+a6XsGPYN2Q4znPyV0arEpu1mUTKlW8FqgA5a9UnREEFQFCIuyh4fE fVBq07+02m+AV0YMbY3o7rmBvV0Vm/HR3XNz3zldPb0eTU6Rc7J4kLo7KtN1N7GBhDu4Kr8C BJZkXZiSq3RaPSqXSXbIVGVS2ADywv8AUKBu6RUpRYHCx7p1wqFGrF7Rn4/SP5f6R8Wt03pJ v6Y3dB7vBZ+O0uZtB9HmiaHSH9FqfOD5tHcIyuk/Cg26GRAAxvcvkx3TP8KHa+qVbBRH3IOg 2zyUNlAdR+kuK0WhWhUmbVidOX6J9O5zbhq0wVVvqPfUqC2/QgchCfXfWqVqzhbc/l8YlxgB NO1ZD+znVdI/l/pHxaVd7S5rOAQ/1FuKXSDut4j8x8ds1Nn+9yTdtRp9K6P8w8+/GfNdL+Di oBuTfzz8mKPchtNeprQphd/egeaiNPcoOfFYahzURKuhez5EWt1kHWNCi3Bva5mXzaCfeukf y/0j43wTpr2ihRFzM25W81zeUj4zJpmoPogxKceidLd0ar85jzZb3TxXTNo1rSbezx1+THOF 380VkoZwoMpxnihMuARAGVLgvSNBKhuAtShkr2fJOkfy/wBI+O2p0MWt6M037T893xmW1BSP 0yYhA9M6KytR+bUGLu+7UrpfwcvLdybhHP5MUGOVwBhBBAuKJ+aVbCDnAxCtzlQriFoUMFez 5J0j+X+kfHdQp22VyGvlPpUZNMRk/FbtGuc3iGmCi/oXTDTe7VhxaPHiuk7aiykYZ2OPaz8m KaeSh3BOIMwZWEMpoWV2tAeqntcuhQOqAvZ8iJYYMge9A7S1zWuI03yDp/hdI/l/pH6Bv+nU Z+FEznTWU+k/tNMH4jNnUFJ/B5dbHtU9O6He3hWZu3nndxXTdkXFu7qI5/Jigi6nPgiOoFoQ lbuD1ekbMIan2rjMc1ifNDXzXs+RQUN0Y0wukfy/0j9A2vTDS4c10fps+k6VL3DgPiN2t1nG 3VO+C9MbTHKtyXStp0cUDDNJ3u1n5NiENFoPNbzR7FuEe1dgea0HmuyPNdhiNgA9qzHmuC0H muHmpPyTpH8v9I/Q9Ib0px3wAzE80G9Iba4idZ66ewdbU4EkD7V/13RSP/5aWC8+Oh9i6Xsq 21G5wiNf2tVaGvJpxwiZ5KdnU43abkc8rpH8v9I/Qg8l0jpvSoFWg0hoZgaT1t2oLmcQDC/6 TpbDj1VeIYPE4XTL6OyO7jOdf2s91xBcG+yDKjaHMh+O0Cukfy/0j9FR2jyKN4v5QqnSqLP+ kedx3VT2EbThMfeiOmdGfTqDtPp/OP2eS6XZWbVm0yJ7/kjjVvt4WoxUuEpmwc45z4J5JwSV UJziVtHNEcFDg3Sd0oYbHcdFVdA3SQqJ5tTyd7K3g2OYKlzY9/Vb3ShjBV0bqdjIRxgI4wFA CAgT49WBhEq0jwWANJ1WBot0ea9iB+N0j+X+kfo2f6bUAbSYLw5uv5yqtMaMcWpu1us426oN 6L0oVmDSjU+YP5seS6X6AUOxgTnX5I5zHwOIKIu3iZJ6nZ1eSnOndLYCDJW8/wAgg57gbeQT w1+67Kp57LVVbOqk4juRmJPJZnyQdJyhnd8FZONFh2W6p3I9yMRaUfFDAgKM+SxxUa45KOPB BxYCLRkp8ajJRtBPOU0AnX4/SP5f6R+jupPcx3NphdGqdCaKhbTms5o496p7FgfU4NLbp9ic zpFJ/R6w7ZZnP8PBdKv6QK+GQbro1x8kqdr2eCnvUamMKYGpJQBC3RkysDHJO8FgbvJEdyeY tCd4rVYOeSHdyTWnTivbKPHCuzrMdUROdFgwOSiInKBhaZ5ygrTyhNmLj2kcO15r+b4vSXbN 1UPDMEzxKaG0nHddZu+rfPuXSP5f6R+kf0UMBHSSGE8uCZRqPeWW3EgZX/R1mdIZGKbhcWjv BwF030Yp9nA9vyR3a9iM9lN14IeGU3HJNA5ZWJ7k7vWm4iqkCJ0RnzWThR3yt3Q4PUZn+y4r PyKu+l0as9hty1hI7IX+z6T/AMRX+z6T/wARX+z6T/xFf7PpP/EV/s+k/wDEV/s+k/8AEV/s +k/8RX+z6T/xFf7PpP8AxFf7PpP/ABFf7PpP/EUHN6J0kEZHoyqlTpfRatXpwO7taDnYW90T pfR36kikXN9gjC6T8LNQ4aGl4Ixnn+2y5rC8/RCZUbTBGz2r97Qff1kgSeSt2TL7rRFTdOJ1 juTXxEjTrqtdTFzRIF2v4J4e0Nex1pAM/t6nSe+2xobE6t7+otnIzCcx2jhCDLnwDIzog0dk BNz2tOojMQQBPZ8FAkyZJOp/b1e2iXiowNBkRx1VQOuMMcAWntcuP3JtTpEto4EbW36Xf96o VJfOzpau+spl9m5o+OJlUxa4iLXb390zD8QLb+Fv4otruLnzqTOP+3XM6K6HkjExcOS6bTpt rdFrbC8Unuu/mDlSk9HfUFEPdlxJX+pudBoNoB+zLjjcnC6KGUGMpmiH3vuI8JX+rVKllWg3 ZkMvOJ0ghV6fQqFN46PF1zsu7gnUn0WUqYI9bIu7wdE/aUw0A7u9MhUG06W1fVdaBMKekvp9 Hfc5trnjgYTqTKW0cbdla71k/wCCqjLBYGPeHtfd2e5TXbfUYKYJuG853BbFjAWkuAIdLjH7 qf6IMAE9uT7RwTmOYNkA43tfccdyq30zRFKhfYN64nDU+5t5p7Nkl2XOOq6SRRDdmxzhdUg4 5jgqbeksZtQxrqu/ETyHFdDZRYadKvU3ahzc0a44LozWeq7dSNSNIVbb9EtqMY14YH3TJiEK 1To5Dy6LLtPE8Ez0bNoWbQg1REdx4qlRYwOa9wZN+QT3KKPRCbrtnc+LgDqmObQeA2gatSk4 28efmuj1tmSyo5oOYsnivQdFLjBebnWwzgdOKfXYDsW9Ga/ZkgbztBK+DUuj7SpcG9uOElGr X6OaVPYmsDdM8FUAa0FkHdfdg/sWOlhxY44t1BVW4dIqOqNsL3m50cpRB+FEFtp3jkcFUcPh O+zZuzqIhNZPSmsDBThriA4d6q46Q1tUAOa3AxphF8dIYXduw23+KL3/AAkguuNOdwnwTzd0 g3Gd7MeCoPc6s1lIO7OHSU1jaboH7qHSrKm1DNmMaBOa34Ta5hpxdo3ktp/1Haa+OEt0VS34 Ra8EWzgTyTi34Q57tXVN4+Cc0DpFpaWQToNcK2iekEVXtNZ1R0mGp1R3wjeeHkcJCex3wl9z bN8zA5Bbc/CL8TnDo5plVnwmWC1ocZDQnGqK0uZZjlMq0/CHbwcXOMl0aSU249IaW6FhtKY5 o6Qy0RumJ8Uaw+EB118TgGI0VJg+Es2YIBaYOdVUFtaKjBTI7gjRriqWHOivPwgYDS1pgOHI hVmkVhtbbo/d0jknPiuXm7JzqIP2J1OyrYaYpR3BFtM9IM8X7y/WeS/WeS/WeS/WeS/WeS/W eS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeS/WeSa0bSSY0+RFr6YDBOYO YE44J1tJrDukEyOKc352nZTAyleWsaXEg/apFLLR6QfRcXWx9qZeyyLhHPs/itAtAtAtAtAm NY1pc8wJR7Lg5wbgyBhOikIaSC4zHBVm2Bxv3YBMC1v4reptY0MDnfn2KlZOHZxHAovdQ9HA dyxOV6KkHTdbxkDHBVnEauGOW41aBaBaBaBaBaBaBaBaBaBaBaBaBaBU6dJlM3Nc43d0fimG nR9GSASRz9yfLI0cCyci5CrVZZcTDYkjxTiwF1zWBojjLvwXpaIEh0d7hwyg3Y7t1jjB1VAn JNNv2LQLQLQLQLQLQLQLQLQLQLQLT5DuOA9ko131KbYyXW/3T9mWZw70a9Y36ibcaZt03P7q q1j6cuy/0evBYt9jeonaNDf4V6Ou0/yL1rfqL1rfqK172kfwf3RudTDP4P7oim6nDTkWRnvT pczeyd3+6im5jREYYhNRuMjcTiDT3hB3OHmvgxNO1m6Bb3aeUI2vYJ5MUmq36i9HXaf5F61v 1FvuDvZHVumD4Kx1dodr2F61v1F61v1F61v1F61v1EGOrsDiY7HHkvWt+ovWt+omvqPaS0ED d5/4TXk07hpuf3VrafR9JuzpPJOLXsBcZO5qjc5hkQdz+6a0bOGuvG5x56pk2Gp2ps0Rp+hb TYIlufYg7aNHCLV61v1Fs9u2/TsL1rfqL1rfqLfcHeyOqkM2h0uzGIKpMN1oAnf42mffCezP YES6cwnGndccdv8Ac/FXi7t5l87tn/2Rebo2k9vhZ+KDql10/T/c/FNu7UZ/TimwxLhJ5AGU 6Tc5xknrjaFzAIa2NOurTp9p7bfCVSDX1HSzetDRpEfaVZLrbtn2e+Z+r9iaXbSrUgSBbjyW jNCbeM/Q8ShTdDQS8zGlrhCqNc5xBN5ttA1OvsAXRxTrPqXRfbbLTacaIF1J93FxGB3qpAY6 0GInOO14KpdG66AndJpuc2oXbxEaEjmmuDru+QfsVWnT7T22+Eqm0kPdUAbJxo4D/wAim7QU 2zB0OcDHjlXBjSddNN0m3xx71nXq6ZVL4a4YaMzATA11QPqbgLg11p549qpurPdTa/es3RGd MpgsNMHjUGvcO9Ya2fswTb4496ZVpv0O0IA+dEY80Aaz2PslrXW77p0QLm1XsdUe2N3QT+Cf uhsGLXajTePcqbbW2njGvaz/APH3p1Xi5ob5T+PxPR1DTlpYXDUKnSZRfXbGLWnd8gVDu0SX H2merpNZ5neNUU/DTPsV1JjXtE6fOyrfRvHNvzu8JxqACOXgPk++0O8UWBtK8cIQ2jaTZ5hO EUpbrhBzKdMg9yJijAwcKRTpx4L1TPJEmnTAHcrmMpkeCdilu640TcUt7TGq9UzyRc6nTAHc g5tOmQe5F3oYGNFYG0rtYheqZ5K6oymB/CrXNpB0XRHBX+htm2e9OFtKW640VzGUyPBOaRSl uThNxR3tML1TPJeqZ5LZ20r+UKXsptHgrYozEq5jKZHgvVM8kWBtK8cIQa5tIE6YThFKW64Q c2nTIPcrqjKYGnZWztpX8oQL2U2yYyOK2dtK/lC9UzyXqmeSdTa2kXt1EIXspidML1TPJXPp 0wPBAtp0yD3Jxiju64QaG0rjkYXqmeS9UzyQYWU7zwhHZtpOgwYCJdTpgDuQq+iDZw7rNjg6 NY/T7S7GSBHNduDBYccCib+ZbjQlwdn2hWzJkknxMpw+Ei8um+zMctU1uMCMCOp9M6OEIy4F znXOwj6TjLd3Teuymb/8WNcz1WzBkEewyg2ZySccSZTC2oL2QG7uIAIz5lNh+43IEZm237Op zGuDZ4kSmdJ2m9ba4cD+CZvb4e590c/v7+5VTUfhzCwQNAUZcC5zrnYTzfgyRjif8KTUyTLt 3XM46w+/jfb3xCYb2tcx0gkY5fetltNyNOPZs+xOcXBznm4kafnHUXX8zb4/4TCagGgONYzh H0o3Zc3Gkuuz7Qom6SXT4mUWNcGzxIlXvqtcI7FuneMqnLy17HXAjTyW0NTLshvXUufTtfbg 050PiqTyaR2cxNLQEzjPV2rYIdJ7sprQZ5onatlkRI0jn5qnFTDcxGTAjqh0+wkKnvejZwzP nKsY4FvcnNY6xxGHRMKiKlRt7HE3W6z7detw2gcQ1rY5ATH7JFTc2c3fvdmITLBTLmum15wc FFu0Z2e3Ju7FvlxTtpYC502s0HU55s2eT3mY/BU3N2cYmTpmcKpBpRkjJ394GHeUINfF0k7u gyrWNY50yLzohUOzsm7XPZi3w4qi5rKZcx07ztPDCq1pFr9c92nXXfTZSYXgAODodrngujvf FQUi7LnmRvSPd1RTtukHe01VNjrZ4xw8EfUmCIEneyTnzVG40y1oEnjgER1Q17md7Y+9dHqN qNhgIN2uidtbJx2O4J8VniRgQICp0gaNwvbqYE6Ed6IDi3vCoPFr7O095h0ewJ7XOBHjOeJ/ ZIfA2czddwjTzymWU74dlhMSMoiRdZ6279yI88p17BTl0hkzaOpznAWZ3rtdI+9UnMbjGrux n8FVho4/P9ZvAx5SEBUEOziZjOnUKrSQ3V3fiITLBwI10RqgkN49/XXfSYWlwEOkc8ro5qGo 8MLs7v0se7qimLjIxOolUxUG9xzonTTByJbf6zJ/sqN4BgCX3d2R59W46084lNeyZiHExH4p 14iVUBfuOHZAymNjIkRjHXU2uZ4nUn8Pk1TZestNvijfeRdulwgwqnrNc7ugu4c91Uu39XXP H2dR2czI01ic+5N2kzmJHCce5C81bcXmzIMGYxpNqZtbpxcI3Yt/+3U8077gJFupTWu32Npj hAcYOSfHh3reNRnSNpbgd+vgqmy9ZabfFG+8i7dLmwYVW2/j83HdHvTPWRO7u654+zrA3rJ5 Ytt/FMsvDbt4tbJjP3wjh21t7MbvY/8AsnXXFt26XiDH5nqcHXW54Y7lT2d9pjRvfx9ifG04 3buguERz3ZQ2kzJ11ice5Gw1Lp+YJK/W2FnFptBVPYXcdBx4fn3qGl9kb0jH2ddbZmqezANI luuVRbL6bZN0UTnOPCercumRNokxOUzaTce5Ou2sYu3dNdPcqW0v4SIxEfj17jGFve6D9iq/ CmkPD/zCdlw/hEldE2O0u0qSw3afjx6qApl9vzgG4dnnw4qt8KmbsYgDJwOfDPf+yRT3onS3 Fsaz4pjgXBhdDnNbJGv3o7jtrZNlpjsT/VhOMuLboa5wgkdTqZugT83A0hUgy6HREN1z+Cqj f3Zu3OyLhBH8slBzp4xIiROFc1xaZ5TKsvJaWSBCpvpndHzeLkGhzi2MiNOuvDi+0CGkY18F 0dpimCXXDnDo5dVwnUSQNBKpufq7mIT7toBIvNnZydPcqLXhwLgMW4iNfPr3WAtjWUTX7fHk nb1vfyVB9MuvLN6B86OrozWMOzc6HnC6S2swttduzGn7JFHd1tjj2ZlMy0XOtl2gwT9yLobF s2znsXJ+Wm11st0PUaW7xEccR+Kazd4HPHMJw3NSNezDg3PnKD8akY7jCvbGOaNMxbbcFTNt 17w3wCNHd1tjjpP39dUPaCxkRbqZKotp0TD7rroxBjn1XY1Dc95hNqYzyWtMTEGezJIz5KkN 3fjE5yJ+7rttdprGE68ARGiJBA8VcAzaXFoB49VNrLTcdDr3p94AgA475+TPqHIaJTrm2uab TBlH0fGG72u9blM3P4s6ZjquiTIAHiYV0QZIPiDCYBTF74Ld7EEE5+qU2GbjsAzmbbvs6i5j Q4jgf8IUm0zFgcXE8xICpnZ3F5MWu3YAkunkn1DkNBKdc21zTaYMp4swJAzxH+VBp5Bh29pm MdYGzEzZfx0u8k1myY+90BrtOf3I1tju2xd87s3wnM2badhtLW6c/v6nDZgTIv4mFTbsmutj P0cxhP8AQN9JLZ+lDg3PmhDQ2JbA4QYUmmyoAdHqBQYM7O/jNs+ULFFlRs9kmPDgi0U2XRlw Ofb11tp0anGJc3JdJ8FR2fRhUeZMuEWwc+/qywPkhsHTKY60COHLgnN+Ds9JED6XjjuVIiiM w27iMT10+j/OcJ1/PJOtpinnQI0w0l/e02+cJl3RmECd2NI9nUHOY0uGhI0TophkgVMcZ59+ Pk0HIQDWgAJ3o272uNU3cbu9nGnUWuEg8EA0AAaIt2TIJnRXhjbtJjqtqNDh3qTTbNtunDkg wUmWgyBCg5CAa0ABOdY2XYONU30bd3TGnWNz0nZvjjEx5INfTvuOG96L7RFvbj92Y8kRTZZa YLeXU61kP+lGqph7ASNDHZVSaYl2NO3mPtQ2YtbpHJW1WhzeRWzsAqdkH2T9ip7RuC8Bojin tFMCufnRrjrrbWiGxBc9wEHkqFjGOc8ktLY559/Udo24HEc0wsENGncngUQbjpHbVMsYJiA6 NMaddFj2zUyW40T9g20B0HCdLbhy5plTYXUovAt0CkmAmYLmuMXBVNjTs0Jxr+yQ6X7Tx3Zj 7YTRUNTtbth3p/xKOXWWfSNnZ+21HZ3kzvXnenv6nFpffnjjhMe5UtqX3CND38faq0mpn946 yJt9se1NFObc66zx6gDdtBujGJifOFSZ0gTvbptnKeBmowcxjquLXEDJjgFUqVHv3o49+FRs dU2huho73Zn29RFS6JEW6zOFT2RNg/OU+01ZkWw4zqez71SNMvwBGTGmPbHWy5o21MXTjT7V U+DNLQ43EWwnueS1oGoXR2l9WP1bQ4En7llMOQG5gaKsKJJLTa7u7vf8nuY4OaeIT/SM3O1n RN327/Zzr1EuIAHEoFpkHiE521Za0wTdoVZcL4m2c9Rc4gNGpKsD2362zlGxwdBjB6rmODmn iE5t7ZbkidEz0jN/s516w/anadrZ3d0ShtKzqVpkPkCPzKLNo7sequ7rZ8lNOqatxm8mZ6nu FUl+uzu7MqkalZzXaRIF/FVprukZi4ejzdPmJymhjr263TMz1X3+mibZHh4qnfWcy12DjJXr ZqM+ZIx1RXqNY08zEp4e8tbz0t71SdtSZ0Mjfzd9vLqO0fYBm6YiFTbTcS0cdblUPwlwtIze PR509/FUrazpABDLtcRPW1hqb4F1g+1O2D7xPd9yLwxoqH50ZTLq2CTD5G9OvUGF7Q86CclV 3UH3XO3hdNp+TVKcxc0iUSXNJe64wnHaN3TI+tdlM9I3v78zjqiYgh2e4ygJBklx9plMcKrL qcNGMQARn6yZvgtbvd/Zt6nU3VNnPFU2Pqjcbw10iUKV4cBy0VSnMXNIlElzSXuuMJ/pBGSP b/hNmqyXnPfmcdYfeIuvjj2bUx1zQ5jrs6aEfetltBZHLPYsT3OcCXuuMacvu6i+8Rkxxymm 9o0B9hnCPpG7pLm+1wdnyUTMkux3meraXjWbfZCpel2b2ODhyKDy4Q0YHUWFwaDr4J7abmtd 80kTC6K/bQ6m6Y4O5+3qwQCCHZ7imtkHmpFRssi3Hjr5qn6QENg9+kdUEuH8LoVNzXCxrC3e ycx+CO1e17zq5ohPmpVbI4POFTZt2kC7e4QeqmLmhocHRxMJ5qPY9xxLREDl8mqMBguaRKMh oudNrThqf2NZ/j3pz9ip9jH/AMczj7OotETIOeMHRBpjUnHDOibimbY3eD4BEnz9yp1dre1t LZmfZ+HU5jGsLj9IratZSG5Ha4+S2cACcAGY9qqMBguaRKMhoudNrThqqGW5nPE9yoEMpbry 450mcDHf1iruxN08ezEfembrCWum1xwcFFt47MbTj2Lf7p8hrbnTa3QdRqbsZzxMpjt3hP7u ZwnHc1J/j3g6D5Qg0xMk44SdOqnWhu4D4yg0NZPMns+78Eau7EzPHSI6qVruy6YJxpr7E9gd ZI7Q1XRGVNnNIyY7tI93VaI1Bg6HKYzHej6s6Y+nrr5qkSWm2N7jpEdciq8fu4go7UUw867P RE3lw/8ATdFv2LotIloNHhdg4gHqpEBkNIN3FVLiMxp8mqCl27Tb4o3B8F2A4yQFU9Zrnf1F 3DOMeCpdv6+mePPHURTmZGhgxOU0VAZzqZgThC8VSMXjaZJgyRnSYU1GvM02hz7sXdTgxr3O 4BphOewuDNnuCcNMHXvmE+4PAu3Q90kCP8qoKXrLTb4o3B8F2A4yQFVtv45ux3Qmesicb/Zz xznHj1h29ZOt2It0jxTLWvi7ea10EjP9kcu2lvau3exy/iTrg4C7dDzJA6nF11mfnY7sKmWB 8Y0dEZzPsT42nGd/tC4RHLdnkgKkzJ1MmJx1U3gvDADdv4J8FQcynUcWvkw7+6J3rJ1uxEcv HqpDoxdF28GmCcY9kpwtef4TC6PireHbxvxE+PLx+/q9HdMjsmDE5TBUm77E64VYxcL+1rpn HDkqW0vxEm7Gn49dM0w/vN275T9yqjpXavkGZnwT7GlzowAYlU9iHXh0uYX73hPXW+FFxJdg k9507oj5NU2XrLTb4o3Xxdu3awqnrInPhdw/lVLt/k8fZ1HZzMjTWJz7k3aTOYnlOPcjdtp3 NB8753sUi/ZRoRA8ep9l1wEgN4qkC57mtYGukYJjtfZ1VNl6y02+KN18Xbt2sKrbfx8I4R71 0e27YyZxk5xPs6x2rJ9ltv4plt9t29aMxn74R7W1t/l7H/2Trrrbt2/WPzPU6663Ph3Kns74 xEeOZ9irXbWIdMeO7HsVOZmM3a9Wf9vGAPtTT0e+/gAOKaWl+ztJdynh9/VS+DaXb0a6YVS1 tRxA0ZqV0O2TTu39Z9uNOrcu1E26xOUzaTcnXbWMXe/T3KltL+E8oj8er0Vs/vKhj0PzrPD/ AAiam0GezU1CeQ2WAfM7SpmiHE3S6Zujlp1UNnfaO0AMHPPzXSB0kRvbsaR3fJqj2iXNaSAj c5rt6A4aOVTLMGNOxvRn2ZVLs73d28xj7eoubGoyeGdU0uIkzHfnVCXU2gxL4wzBwfIeaY1w AmJZxG7M+eOq8vDMxJEoU2upvfZcWDjjgVtKtt37uiqPaJc1pICNzmu3oDho5VQLd2cRp3pm WGTgx28xjrFLdiYjjFsz9yYWvY0F0F7tBqjui62dlGexM+eE6XNeA6A9ujup1M2xnHEKmGFt roj97PBVS9zGtaHTjsQYHmmvJaZ4t6tha0UgPae/wQd0dzbiQ0SJmU+iHsNureI6qWya3edB LuGJ804ti4R7O9dHAdT3ibubhdAI6rgQMgSeGdUxz4l3vTpdTGkmOxrqqTXWi6N2M6a9UtY5 /c2PvVNjnBt1MnZmLpx/dVdo21zXxbyVT0bgAO2SLftTH7hcZyND4dVBjCyHdoHta8Peql8b safJqlSJtaTCddbIdbI0Kd2NYH7u9blU8Nzr35jHVcImQM95hAmJkjxgwr5pjsO9h4eK2Qt2 cajJnXqe8FotE7wVBtSzfGQPmmOqpUibWkwnXWyHWyNCqghsCVTcNmZqbPTtZ4dYp4tut7+z MplhaLnWy7QYP4IvsER2OPYu/sn3WktdEt0PUaeIz44TQ22Me3MYVQuNNrQHZPzYMZTXOiTy 6vg9kMA7R4mP7KlYybyZdwACbS3C0tLu8dVOxkl51OgxKLhE4VCXU4e5zT3xOfd1SImQM95T XHBK+YJiD9HXXyVIQ0B0Y46T1tp3NtLCY4zhVCWlha+IIIKfLS0NE3u7Kpumne87o+l+HVRb c218iOKfcAIgj5M97tGiSiLLCwxHJP8ARamP4s2qn6P/APzmOoki7QRznCBDYiWxyjCAFCS+ HNbjMg59xQpUwLzTvnyx7+ovsvjgn3U8Np338U6W2uaYI9/3p73aNElEWWFhiOSfNPmJ5qi0 UoBcWfwkA/h1hlmZsu74lNbs77zAb7/uRqWG2O1/LdHknCywsMFvUW2Qci7nCY0smOP0eC6V dRnZied6a5rbRy5dRoBu+Bl8e2FSGzDgXR4LZluoweprnU797y71Uq1ew0ZwqR2EafySYHv6 stukgRzlNcBHdyRAo9qIH0vzCpxT1gXcsT1TUcGhUqYtdUeJB5fnKfDLc5705khz/oIE0ARv OjGg1PVTuph0mA7kq2xiGOgkcfk1Q1OwGm7wW61zbXZDjmVU9E/J8zdGM4z4Ki7ZOt08M8ee eo7QEjAgc5wmmmCAJEd/FOc2jUgAP1g25gjOBqqQbSc2oKcg6QDw93UdoxzmcQHW/eExsOvc 2Bc7URpHgoZ9sqoanYDTd4Lda5trshxzKqDZOc4z7ecLo+6/Xd3uPnn39Y3DfMXcJj8E0Oa5 5Lt0NOZ8fNblIuBEDkd2Yj+FNcxpbdnJk9TrWG/O9w71TD2OcRGeWcTzVfaMqB0Sd/XPjj3J po9jqDbDtrZu4cf7qltg85xDo+9Bpp1L3Aw4mR9uOphrMc+TaADH50VQ1vV270ngqBFJ/A+E nE889XpGlwkCBrKYaYIaOCcBSfJiM6+GcceSpWUzoIPAY/Drp7Wm6Ro7graDHMaPmuKqUqrm OxvMnKYS1xZJHanxzOfDqa50mO/CrWU3MeYunj8meKnYje8F6IkiZM6z3qpvHXOTjPD2qg+4 y3sQT1Ha9lN2R3U8CTTO6dTj8E1wc4vdlszGnD2dW+XgfuT9yowTcB6MZt04cNOp4qdiN7wX oiTmTOs96fDjtO4lUnB0h2QZOc8esZ9Ly74+2ENqXDOCJmVDDmMQcafbCGwcXU+GZ6nWn0nJ U9qTdwz9qqE1HXR9I48E0Uex1auNXtRmBwTNuf3RnWeC19LbHU3blwB3YE58k7a5ZxByqLWY A7Ik8+Pt6jtcNTNn2eCfDjg8CceCp2HMbucadYe8uFUtt4xH2I7Az4p1xgc025xsJI1MknUd TWuMEp46O4nMmST8mfTOjhCO9JJucYR3zrLcab132qlFU2s1bGHHqtmMgg+BlATOST3k5T6e 0O8wU55NH+Uyp9BtrRy6i0Oie6VRMk7Jto6n0zo4QjvSXG5xhP2hJY6d3TXVUmiu4tZJtOZz 1h1+Zvs74hNJqBhY6Q4+X3rZ37sdic9m37FZTfeySQZnqLr5OTbyn/CYXPjQHv4qpWNUA93z cg/cELXXSSZ5yZ6rzUbtAJtxMfgqW0dD2TB8U6oat06MPzfDqAc4BvgngVdnI7Q4KnFUHTT5 0GR1ZdbEOnlCa1pkfanHbDc4/R8fNU4qaZt5wI62X1AI0bxRtqX8J8E9wOzqEesHBURVqCWE w/nPVSca1oH6s8U+10z+yRUuZs5u7+zEJmzsva6YdocEferNyBTtFT502xP5KftbbnOmGaDq dULm7PJHPMfgujva4A03XFvBdI3aYa+CM8R7EGGJycd56tozdacu3jvYjTTllUi1tOWuuy7+ y+EXSyy2OoNpugtcHZ0KezHcujkCmbSS4zznu7+qKcXAg50wUxjoJ4wibqRLSLRneydfNUpe wtbE4zgR1tNtPZjjOZTrokxonG4ukdh3Z+xUaRsDmM2ZzwiOXVS7EBwOdcGV0kudcKjrhz+T VGMMOc0gFEWhoLptGjQq1jKbi6e0cP3uPgF0YCm2xmpLt4eHUWt5jB450TWmOPszgKqHUaZL 3XQYOc50VxY2DTDXPnJI6tmG3AkTmExrokNAMdVRjDDnNIBRFoaC6Q0aNCq1XWVaTmW2n7F0 a1zYpvuLPb1ipiJm7jFsR55TNxrgHSWHR2qG+L49bqexEeeVs6xBIccjx6nVDEZzxKpuAHDj 2cqs8sa6m5pFrj2jPgmsdqOrb2iALZ7vyVRsMBpJLTp3I1MRPa4xGnUxrWgwZzzT22B8jQ8V 0TAa6nbcbtI9nVa0A5Bg8c6JjT/hOlrCMSJ9ZrqqRdBtjenOmnVDXlneF0eq2qHMZgyO5VW1 nB8vuDhxTmsfY4jDomFSpGy5t39kQHFp5hUH4qWnL3QDCdfGQBj5xzvfJqmy9ZabfFG6+Lt2 7WF0kem0dpzndj2LormirrvYOkHX3dR2czI01ic+5N2kzmJ5Tj3J9rekOaXADn+KZl7qcbxI gafj1Osvu4WIYqtZGp0Pd1VNl6y02+KN18Xbt2sLpEMrw1uAPneC6KWSWX7+t32adY7Vk+y2 38Uy2+27et1jP3wnGoKxdsRNul3dwUvvB5O1HU6663Ph3KiGCqAclw0HiukEGpZwwcaaeOUz bAh+dfHHVi/ZgRHDx+xUtmKuTq35visXGlGeQ6mnooneF3OE+24GOAXR7hVFS7P8N3H2dW5d qJt1icpm0m5Ou2sYu9+nuVLaX8J5RH49Xo7Z/eVDHofnWeH+ETUFRuezU1Cd2v5dV0fYtdeL dpM3dVDZ32jUDQ55+aql4qtzgVPk1Q08vDTb4o33Hehpc2CQqnrNc7nZF3DGceK6PBePpCzt Z8MdRNOZkaCTE5TdpN2dRwnCNhrF20O85kGPL7k0Pu7xbiLdfGe/qlu0mY9G2SmMJqRaC70c NOOqoaeXhpt8Uby870NLmwSF0kMNU7jbdzA1mMeCpFr6smpHYGWzqccusN3rJ0txFus+KZYX tF28WtkgZ++EdrtZ2IJbZgO4wvTuLnz2iIn2dTmuuszi3HdlUtkX5IxbiPL8F0v/AHDogtin 9mE0vJLu8R1QC7YxBbbjxmPBUtntZcdW05A8VaC8sjILcD3ff1NPR2OdvC63JjwT9lN47l0b tEudDhbwu1OOXh1SyZkaCTE5TNpNx7k6TUAxcbOzrpjPDmqV92YkW40/Hr3GMLeZfB+xVfhL XNeH8R9nNVLWNtjDgST5QmOY2pUddvOtzHh1UINS06gNkJ21u0GrYh2ZA93yapUAm1pMI3Fp IdbI0K6QQabS1riJGkGMro9Om0FtTJf3d3n1XCJkDPCTCBMTJGOMHVVnYpGnVDYe0jGPeh0e 022XXRqcadQLYkmMpj9Lmg9VSoBNrSYTri0kOtluhTw59OwNLp+j4qn2M/8AyzHs6xS3bZt7 +zMplhaLnW3O0GD+CN7mNbshUt4t8VNWL5g26dRp7sZ8RCplpYQ4gRxXSRcxxZFo5SmvOvUK W7s49s5VMNcy55wD878806iHMNuo4jqa4MJl0ExIATnCJCo4bvGD9aPzr1XCJkDPDKa4xJXz BMZ+jrr5KkIaA6McdJnrZTFtkZ5pxcBjknvJDQ0TLtEx4DDMmef4dfSGlhZs3QARBj5PgBHd GdUMDGnVnqy0eS0GOqCJHxMAI7ozqhujHXMZUECFNolQBA6pjKGBjRHdGdcLHVMCeay0FXQJ 59UESOoYGNOrPUcDOqmBPXMCeawIWVFojrJjJ/7jtLhdEwrJ3olNcxwIOiJJ0EoODhBWq1RN N0gZTXsOHCQtQiWnAJC1C1QOk5yo4rULVahW3CbrfbErUIuLhaNTOiLmuwNZwpa7uzhajq1C 1Tg0g24Py7JAXbb5rtt8122+a7bfNdtvmu23zXbb5rtt8122+a7bfNdtvmu23zXbb5p9RhoZ gZP9iiA+hNjGseXZbaPDmg7bNLBxuEnkNPvVeXUBtBbOuP8ACrFhoW1BAz6vvA5/gnPNai5p kAE8P8p4DqTXnSMxynmq5vptqVBAF0hqAFSluvuuLsvH0TyCYJ6M1o4NOmfD8FRpPqUzDrn9 /H7U1tN9DQXZide4pp29LFHZxOJjX7USX0Sd7Pjp7ka1R9KIIw7+33pznPo3EOz/ABHVW7Sk NQXTJfOs+9GptGWXF2o4+z71eT0ad8ic7x0Ke3a0QDSNIGZwY7uQ5rpDKTqbBUgANxjl9qey +nvG4zUn+XwT31KlKXR7M59wCLdpSiHADlJ/BVGUnUhc+YnEIU3bEtYwiScAudJjGeCftKtH eaGkg65zwTvVAlxO6u23zXbb5rtt8122+a7bfNdtvmu23zXbb5rtt8122+a7bfNdtvmu03z+ Q0LW3ek054KM02BxqOG9BAx4hEFlFuRuv1MujHgnD0QBqG0lugFQD71SbRNNt3At43s/FXbK kbW1C7H0TCDyyj2iJxkeEqoXtpOIFwJGm9GVUBNCMMFSNzjn7l6TZuMu7I/eK6R2ja6Bc4ng E6ns2YVYVGW6QHU4hVJtyGnhjez4areDC4Ejh7ELm0DNMP3B2e4pzNmzCglrrgS5uNwzp9vk ibwSQDU09Fvf58l2xu+r09JvKvAADGN4aSclVrXNcGugVMdnifZnyRdex9FpAbvAF+fDPL2I m651oe8Y3HXdn7fJdhvkuw3yXYb5KqYDcajCeaEVKcN13gCTGp/FAOZ0cb4ZbxP7ypDZUpfB iBnMRk/mVWAp0YaSMxjejn+CovAotL3WlzhgfmFVqHZ5pNgAY45TJbTMNYLiOJ8SqXoKZc5o dGN/MYz+OqpQ/otj26xhnj+QmbNnRxNmS2ZkkfchVNNk+S6M4NAuqcPaq7a2zdD92GxwW8Wv LmS4Y3HcvzyTdmBbu7oA55xqqo3akAFpxn88lIftIdSN3eXZGPzldhvkuw3yXYb5LsN8l2G+ S7DfJdhvkuw3yXYb5Lst8vkNO0ubL4lrZOhVFjxUftXQC9sZVJ1embpJtY4xAcmBtEbzgN48 J/snhjGut2ZHMy6ITvRdnXPEuLftCpehi4+SYalH0xDMTrcnsFHe+geEGDlCqQA/ZXe2Ea1j gPDJ9iqVbXQwEwRCr7YNe+kLtwRKJo0doN4zpIbr9qrubSIdTtYfad37VDKW41rQBy7XH2J1 aJAZfCq30rHM14ynFrD/ADNtlVatam0GkXYb3LZzR2+zuutNtvKJ5ppAYKJIZbGctn70WUtm Dda25sxiV0fZBjXVRtN4TplbZ9OOj2y3d/dnWfuTWVSwkOANojUdVRzdQOUoOaalRrpaGvYG 70Y5JlSp+qsa4N+lngmub2BWg3NjdtE/iukueJYHbsN+bCNrT4ObCbtdkyqahbF2A0OhUzSa xzXPaCSeBICL6TGOLfpcFs4pmm6QIHHx81s3AWZA3eI7/NV2ltNrWsuaSfHVEvtdY6abm7ty dWqsF7S6Wt7jCJqdGgscbYdqdP8AyVr6Vh3oMzMGD9qx0fc3yXT9EwUAaDtm1xdId9FyhtI1 DbdrC3aBc4ai7vhTbGoOeIMKq1xYabMYaQZ803aW21GbRsDQd/mF6RlgLA9ojh5o0qrQBaSN 0iIMQoey1jm3Mx98+CbtLbajNo2BoO/zCFOoCXudu94k/ZCFSpRc1hAcMjQ6fnvT6QZluJ5G JQxaQWz3iQD+mZfZBfEvMAKmHupuLsNLcg5QmmZaTbunO9mPagW03OgThumUx9UQXNaZPDiJ XSKWwNoa0m5sTJcVTtaXENuaGgkwcrZ4O60xHDh9qYBShkOPIgggf+SZuEMduBtvuhWNbiod 6W4nkU19GnbTfo1rIn2J1JrLGH8P7KNjeyizADeEaKo1zN0TO7rn8U30cD8/iU6hfuVw5uM5 wICqU6bW57bVsaRosf8AQbAPknUQ0A5OufJfBvgrXUwDU3X50jT3I9INNl9Mw5wfj2czlPvF zXGY71a9stQeWC4Jgpi1rXXRzxHVUdbfAm3mvSU76Yc2zF0uicexVIbDbfhEx5pzHscXXQYY Tm38EaTWyHY7ODjTyU06GCdGNhVmVGNaxtzuMmPYjTdQbtR6Qtv4CMrpHonFrGB2986Z/BOr toNc5s3Q8492qD6jmMefpOhb1Iv2kUzHImM+afSpNF4ugF/LnyVr6NMUiXG68luuTpzKq3UC bG3ycA5TqhYGuZUNMxnJI/stLm70kNkAXZ96JAuyWkRxOSE4ubObRjOgKmqabLvpGJ4qkWAW lr3TJt1GdCjTtDacvIId9E5+1VOkimWSTcOOCVVr7MMIdDoz+dU+0W4JJjlqtrs3B7pPZM9/ 2p1jIe9xmM/OP90HGC8XQ7uJlbtJo/P9yr7BfpK9GLN4O98/pqVph1+DdEYPcVS+FVfSgyyH Tx8AnNq1nucCYA4b3DGcwgWVawFuQ1uvjjCpOe5+WAbze7E4wqwa6q9wayXVMc+5NHR6z2lz iQW4Ofux7lbc/eAYLQTiAQPvWwNQtgYfwNxnlHBUKVKq5tr8Yg6HTHf4Ju+7ajWcyc6nmuiE mpTwCyySRjw5KtdUe0t+eDmA3J/+SfLqrtKWG6fNxhViXu3iZxxLswY5qpSe6/XFuI5IbRxa xuGy7s6aeS2128MBz5GvKVtS531zHlots1z3b0ta2O0cfeqjyx5NQ2Fs5nSE26pVbVqbrnOc wcYj/CAGg+K+pF1omFsmmpTZSeLHgybiNBzwUAOkOE04kOyW/kIW3y95mcQbfwTqlIm+Q324 bgwntJfg2kWGZ1W1LqlRlpcGcMhVjUdXY+n2nEiS2FVdY+m4NDC3iBw08VUe91enUp77nOtP t9ycajK7HME2h5Bj+UqrtmOp7KHGe7KqVKm0pCnJLXRuTknCNIVn02U22uxbj2hODHOuDbNC PtT+iued+Q42+fCE2ntajcbMhsw4A5n88VbWe5s+k55HFAOqvDNSLeQA5YTc1I7i5qAdVfSB Dog6tGqc51xB3bXNOPdPmqtOlJGSRzzwKc19QkvdcS45P5hVaoZVNwJcWxuzxQZZVuaC5vZ3 gTHgNAm9IG0taSbAJzceXfyVE2PtqBpB8UWl0EQPNTtRGNca6I2kkht2h5TqsZOJ/S05uJv3 QADmO9XdI2lPYQ7QDUwNEaZdUqOa5wDWWwMh34JobVNO4QWbuVtKtTcawAkgcEa1Sq+oXgNG 748B4qg1tWo11rWU3hkjQ8YjQlNFJ7jYQ7I/ctWzbt3MAZcG2xjRNqRUAjdJa0XY7kbzVpaV Cx0QY7/JUNjWINNtoIgyPyFU2dWqLsF1uogA6j91VHbZ8lwOGd8jhnRXMe5xedoIp6b06xzC FVpc59R1toA8fuRq1hVpPpsme6V8GZUq1iB80NwMfiEOjtNmOzMkINfWtEi12NZwn3Pdu+ku jM6ptIt6QL8EQzf4lNDGPe4zLRGI1+1C05+idQr33W6y1pd9iDQ7J7vb10WMdVpAelY98ZH+ CmXVj2CwG7UHVbjw5zHSYAHCOCrWVpcHgugDBmVY+oXb18lo1Wxa4tFtsjEd6dSLpY6ZwnzU c579XJ1Nz72O1kaqoDVe57xbfoQnUnOuY4EHCqsfUc41MFyMuOST5q+TO9704XHeD2/WRFxy Hj6xlZJ7Jb5x+Cebjvfit07huJ8Sf8osqaWvp45OVl3GSbAqhuO/PASJM6oSSI5Qmv2hFvDU eKc4Ehx0Lcf5RF0zlU27R1rQBHh9iFY6hsR9/vPmnMqVXvaWhnLdHBOeXElw4gcoTi103QOW MfpaYJIDXXYMKi4xYwEODsl3tVK54fUbdcbi2Z8F2aYHo94yTgzgqy8s47qpsuuLOMxOO7RM LjNjLWjl+cIaEGGnPKc/2TqpLsxEOITKL3NDWDFvHEJ2y3g7Z4eS7Ryh1mSTu95W0a2kxsRD PnZ1K9E9rToc9ofd7OaZDKLX438yMzhUDUlhabnAO7jxHiqDsRTwbsyPyEw3B7hdO8W8o07m qXbMjWclwxCtwDhdIpEW3AtafYqZoatdPpHk8EKlV5nem1xGscvBNa/ZOA+dG9pCY04EguAT XlwtaZH4dVRjdXNITWUMRHacTju5JnRyLg4NDnRpnMLedMC0feqrvpuu/wD1Q//EACwQAQAC AgEDAwMEAwEBAQAAAAERIQAxQVFhcRCBkaHw8UCxwdEgMOFQYJD/2gAIAQEAAT8h/wDyfuY4 YaLPK7ScYxnFy2pn4MbvEQH9ZI8ggdicT0bVsLfev/gu8ieIsMg0yHgL1qsnysCg39iPrnVs SNuJ589/Tn33Z/8AB7AtEi++XqxQfVgYDCHcPOCJawvyxRLeF+H/AJkvK++MYvffPJ85Ly+c Yxa++D6+TP8ApM+04ZUiTvGA2+cO585vE/nPJ85CGV983oVPqsC4SJl85vEp85CTL5wa0vnA pF845OZ1eVzPxOTidms3SVd8Ylr5zyfOaNFx/wCVqIyzXf8AGeOHglvXIRg8+lv1vrjkisPw o46ZvJ1tOH/Ol54NZ444b5ZPf4ev02UtZvgPnAUB5ykOu+EhiTOHmHKJztABhQoiOcW0BrnA SATJp9Ke/V/f/wArUJ8YJXN/swuQjmsLQjoYiknxkdD4zRsTDWNYTtmugFjJftOHtXNxCG4y P+TBrMT2vw9fpsEFnxjzSEvqw01dcmjfJj7qYsrrkKhTBuGsBvHGJzHZLjvgWIfGdfq/v/5R WwZAhTPbpkcD6V44QhLvAcZDBrJfiwOwDFVE+2JEwKlgQFN5CjAoxEDCfGa0PbDF2bb9fps2 JVm2UAxH9YabJqMY0I7GeMMXBc5dKGsMqOEdOmJt8dMTKoGUZH6mfvv3/wBk6ISkjOvQUAC7 JDbg4aBHZmP2f1c1jL8Jzgwk9XX6YptoDjD7jAi7jGpkBpwwpi75d5YXEE2E2CRm6Tgc0x+U HTrjLLw79fpseNrzllzmgjrBY9ScJcTODFZxGACM0ayXLyVnsSvCtMYk74I3m39/9UhCkkyF 0rUknaYxjGyChNPuvrGWKzrb1PGA4boKIsaNV0wkYtKik21YjHMJg1kOOqY+x4uP1cWDHUx7 lVoxzATnGJDphZcqyohwxhHCDCoRin0HIq7+cmYGGZKcCMTMHyylAlcev02TCESdZIEXgmDe agIHF2anLSVhyeC8h5QDI0pjpiPDbGcwcRjTgOgdf8SHEud//EAVSAxKYS4EM00J2yMgB2ya q8mCE4JrN24iJZaMIoY9IOU1rNZzhzrg9k5peAEQO8oViHXt6/TYJyOBkHcDJWCNY4Q0Yz44 CxRgaeSRxmZ0xaOFTJHpn0KZt9R/iBgCDX+2IAFSj5wAAgK/01GXDGMBb651ytSRkErZwQwk b9cFiSHF3ZkBMY6nLo64icS5MkMIwP8ALDnE0lywpBmS/riQ2jcdvX6bLtCmJEsRl6I4yZLd wQ0XAtomPfDNRqFeBwgJpxsgUneA604YD2yAUJ/4vof+0+x9f1CKiBXVy6l2S/1kSSMAHUld ZthOmI0PIyHNBkC6gxxpM1ePHb0nWUBOajHVgY/hAwyPtrEbhXV6/TZGZDqyZFDpg6oY7YeC 4OQrSzE4NQzWO4dizGPEQaXkVMDU5aTnpk9E3nWczq57+sRNEiMzI01H19Pof+0+x9f1CIET 3nEoO/DeKwjwMBy2yByHEqbaZxFAj04nQXycFZQuuMPB2xUMhC3L0ct3kyAL2catHD6/TZDl SMVjhfE7wkk3qx3NOCGLhiMcXY6jLxqZMG71x9LDqXHI9V+/+P0P/afY+v6hFqE+MnulPTti 9w2O31yWTxaisdg01hOjecIEKDuMRu5nWKSvSuRbXiQZJNdsCoddMV2fj/uH7i+uT3Tbj1+m yjfwx+GfGJc59CJj4wQuCqsdNYslYswoLabydKwnFPqwBSnbPHtrz/jM3EgXD48OfcX8Z+8f cZ8hH3GVdifuM+wv4z7C/jKp4J+4z5CPuM/ePuM+4v4yyPsvGVTwT9xlqALR8uC/9YHDwYRh 8foZhQBxFMnMilUwkRY4P+PUXheIyLX0sl+LBn/LJ/ixREXiMC/4/wBZD8ebJClX9ZwYLq5y IcHa8sn9vIfg/rAo/g/rCkE8+qSRlH9eO0vxgJAOwZL8WT/FjLZGtZK/4sGf8sqWYicBKeDW Q/HlaV8/49FcyRUxH3798F4F1H9mCNh7h/OR9+/fIi4Fv/TI+/fvgvAuo/swzTIUIfzkffv3 xIog5/Jkffv3wJygmvyZH3798j79++U7c8uvf2/Q3XuNWbdGuMERrhBH/i34VxGJKdGHjMkK fn/YcccccccccccvDNkP7MOqoM96mEwYbVmV2+79DO36mI/vl87ZzANf+L05kksTXHXPE8Bh M38f7HEJ2s9pMYdCxcTpPhcA1Lu5zKK/Qig+i4YWYcaxIOfp/wDF+lwXCDO8NDRWW0CYiZU/ 6wCZVBp1bxfM0rciwezAeJRcXe14/Ql+c+45KIwawQYLMt+s5J1yTrknXJOuSdck65J1yTrk nXJOuSdck65J1/RmS/eNN9TmOchmGBqLIYdP6z6X0CDZsWEVn5/1lizPSfYKZzfWjxGgHilw pNwfR+h3UHQAwEUSRrPwvqyQMnVb6dsZGHYxXahLvNRiec/bRwKwXyGD0bqCMZALqdLMK9K5 ygr8Z0rXTPxmaU+MIqRbjx+k+l9AuoikRziDenJ8XSt/6tay2QfzYluPrQUfliUqkXdArXj9 NqE+Mutjp2xL4Y8GRsHCCNZD9HTGKRe2RpjdXgzlTzh8l4vL7cdxmsHQTCcP2ecntHs/SfS+ oU+V2qQ/xRvLU2mImGP9PIiv2AJMshdIhxaHuuRT0Nz5dEfpkTJJxHBOMsTOEyMYRRnkGDNG C0VMjLWUaFsjBxcSMTZXJ75dUeSMntebiZgdP+CMRYSSZ0jR54Y6XridMrrZJRnTXOHK8M+n GrDeaeZT19Nnt1AYCZJVIn+IMDoA5VgPnJcYguiMFW7z6X1Ch50HicmZspOUHwuf9KVKToU+ F51WoqDyAh7uD0Pqvo/po4PbDMRM5qtZWmpjCaK3BkL38pTJ5w+GOHCSO2EkknfDTo2wx+ZB C5CWG8WJL6ZSfV/x/gZwrCDkTZPvg/bCSU3fd4C2ksX2cDDZiQcgRD2Z9sLRRsSTW4/fATTE 0rMOcY1r5DfoAwIjTWSXJl/PXX/G+IzAE++RLYSgPRDeuc+l/wAASpCbjl/eRtxYob/0EhDO hX43izbmyTqEccEk14MBX6Xm9KjOD36ZIaV58YAUyxGdjzijLaIwAWMdSR3gsyx7ljJKoMSw briUNdcfdWNcBRkRMzbft/g0vc+8cxn5IFU9+uJJGCjp0ouaKWThzEse1MEEBSmQJp1mxLC3 Yfacj3ipIAo23lfwhpBGiIOuQfmwGYlWPr/n9L/gEoTZhomWoYJwrlwKgI9H/MvIttT5F4yI WodwkvfAIxazXJ/1f6aEveCckV2+ys2vUlExVDfdiC15JY8JTsmUQ5DGci1paxggRjsThxSS 7gJyBQvJGTRiOJcBIAOSP9Qo1ysZfNfWbmgBd4Gz84o8Af5CUtK8ZyfoUbTFZ9L/AIhzQK7G kykEavARf+YAgZ3n9l5xpD6bRrjtoSFdGOP01FcuSZJKu2OopznWwYNDnIwUjZllZ6cAhkYc JG8Ep8HAmOfCMrQnxhLiIX8+gKWqElZAF6r+P0UwVGDnAxJrWBRESkUwMpYP2z6X/IGI7V3H rztx+GK6E/5clbeXuMeDNiB2WfgZZylpXV/TTqVt4QJHyYOcYGkXOqBkaWT3xGwOpw70DeGm kMYPQYSXYDjoLBw59xz+2ciDbt/H6T6X/MLo0kLJNRPV/lzi6SfeXjGlW/8AYLDrgM3JQ4PE ha/TViMOa+zd5a8arHJkJhyw3F5ASZyO1krNoW4oJzXHjBToaXkbkHSMGrKiYz7hkP7Bk4ww Qv8ASfS/5i7p0LhqvlxxDYNOwd/4svI9oeGHKsmBX0H+rJjqwCA8lt/p8nt2T/2wK36Bk0mI 0ZIJbyQTGEQtYqNkB9M0530xymJmo4wQFB6VPc/RpEl+ImEDuvnHMZoOGGl8e7Ppf9AGpMOk Dm8ZADOief8ABB5xoH8GHWN5DxLfeXBq/wDOv6YnZ84pLO+r0zzIJYkgq4u7zikIwSLVY6ah 1wRtWclmgySB7jKc9hLJ9h7sPd8mGPe3+iBAEdjlIp9pn0v+gCDdA1sjJWb1Taa+f8O63h+E 5W0agI6JRJ8ZIDEgQ8h/TKEQ84q9B6+kNIG2XGI6PRYOaPHI5m5xKLwTyohmHx2y3NMS8mNn o6+kgAhYpLERT+k+l/0gVBESt7O5lN2hCnt6owEtYFdaZKz035hXIO8A0yKZ9jf/AK0Uiqbr QJRNnjDyoEdgoX+ic+l/0hAdqcXbFJg2DPX1bMtok+7ONKHTxgWk1RhQXFA7tv7f+tEAwMbM H5c0SSmFyU7e3XPpf9QK2IEspXJzlPwwwDXT2fTSVPUiY9nziDG5by0sTEFjO0ulk3+kZMfs jGEwRvSdsdkdNxhFCJjxeLdLnwNAQIDtyRUZCz2yeHe9nlg9gM9sNFKMcTGaGS51/wAxeytV /fEkxTED6OsTswsdsm8lV1J5+MA6F1Oyd4Jrntm9YKnPCzescgFmLe04JsE/asJi95IsSGLY wACgnDaSS3XIfjbdWMyE8rw7V3pgJIgUy9t5VETcf5fS/wCsK+XRNHvgmyhF7MZrrnw/Cawt kzFni0vxhpTgD7pt/b9IR+HUGprFb6UDIRIRixyIL04wIBh/Fgu8InAq8cFHvkRxbQuUJHkJ hFuie+CLrEH0ycIVIAHvGCa7BBnH3G2NIMq6r+MpCo8imshEO6hrtkWgf4LMZQjCWKnXTN2k 311GIEkgFk7YlzCoxLoVSyWylNjJljEaIbRlFrsrohmgiFP3zkVjBZqY4xCaGUNxgEhkK/X/ AD+l/wBYox0SVHtkeEa5Iir67wASqoFOreLymhbkaHswDi0XB3teP0gpBLrhpiNrRGqP3yNo L7WFxujtf/MRDZAI75USsL1kf+ZGhGEAjfvjhJSpWPv5yBR6U53/AMwiFPDpesmVTZ7YhJzL 6YmRTy4wAw5GEbWl0nDckp/hiEi9Sa+MFRHrxNuGg6g3qMS3cr8uAVCt+GLA2HT9cAhEjbWv 6yqpgSPvxiFaAjT4yj9RM3xlmlmMmRkfHf8ABkCzTIimjvirYLT2L/xQG/8AQpMeARjIZiga iSGHT+s+l/2Bz+B3Ph84g/gPvOiY4zd9hMdFJxTk839797+ksNFmhDRi0JXIE6euCopSODFw QyvbmM7KNMU4lNu5/wBzoRVOMXSoHtmgTAo63nMnR+7WCiSyD4yBzWZ7sJ+4f8yyne4xw4Pi 7/vgGrMsvU6ZCOYevhnDnf74UAQ/oqlWGjuBn3z/ABn3z/GffP8AGffP8Z98/wAZ98/xn3z/ ABn3z/GffP8AGffP8Z98/wAY6m5Ct+MX8gKDSDWt4Ll9bY9BEPdx6baUjUh3+v8A7YiNqGX5 yXkmSHjq2+mvVnkiQbe2DqdHeWF0ElUdOudRyeh9QSIjm2gmtpOu8rgAQ0JDBwnH/vMBcJ8A 6yue/oKQRC6BmP2clYmKHhwCGJSD49NuV7wicthtHdz6AG70QG3s0fGLFZE5Tu/T2/8AeMSt C33GYvgceMO06iTR9mts577lJ7nUqX8ZR8HZuWx1h5xTElAepwXpGObyhwZGa9uHFyAHSAxN 9mdwBscP6/8AndQhCfkDwuX0EmaYZXTjrkcicyYqYKYhl648Os4GAh9cZhHCljWDEdXOCgww i06bjCZAQwiT2PLgoHhSF6CHv84CwJR5LVeMVrT3GlWfBkL3TvpkNSY0IjUgJ3RQbO2LHAxF G0jXPOXn/QO3AKgRy+AHIK8JBBjBFChBumqFZFiFFwbGpdXgUsmgklkUzxHzhG74YQsICkGc gzolWI1zL3yTt4iXXEqLj65PjJYX9O1Q9GcaOCNdmFEcqee2ThTpasVUz5w3RjYS0kU65I0w GoFNF2KDIZK6Yifb1mMENH0XTpRr3rvgxe7oaEweketYSMh+Qc3vGLkdUKyRdoMHTB4NrA3v ErEVKEATOauIyvGCMQkNVbX8YCzK0skShTWv/FOxQrQWI8ORprKuxJrIKJG1HK+OPGKBKP0r 5J6YZFAKHQBveRmZNSkeTiMUYIJQB0DeQ2uY4HLOOMGVnlHZajtkcVJbUWI1Q/OR8XBJf5xd FnmsnBO7zdNATIii6Ky8ALEusCiehkYEwJobQmseeEFMJqS6xOuWlHSFqsMlikyo0h3yfUzK kaBiemHhC+r64tGbQ1IiBBAbxsW1SxwC1iDQGiKQRe5wsV1tK0SSh0wWf6vBJDY4zZCg8Iro 3a/OBixgKakkoG8gOHbi0LNzif6NQ1BdYvEhBDTPXIQyto3QGEtwPN0iOC1Dtm8LV6STe4w7 4sBG5Od3vJ8ClRXyudr7/Odr7/Odr7/Odr7/ADna+/zna+/zna+/zna+/wA52vv852vv852v v852vv8AOdr7/Odr7/Odr7/Odr7/ADna+/zna+/zna+/zhNwQnr9/wBCg7ByH9mKTtCXNTg6 AhNDFLPF5bKX2b8xmzlKCToI+cBgpwbYREvZ7IwcNTxIUQwmPxmfjM/GZ+Mz8ZngCSFLfxgN PWdCse5kmeUEEckGN/TJlXiTBR0Tw+XJBfIKizQRhIWyZyO5g4QSUURGwaGfZyy40wQq1Hb9 M1lFiuyR9c/GZ+Mz8Zn4zPxmfjM/GZ+Mz8Zn4zPxmfjM/GZ+MyT2AURIVBiclfgJAuY6jnIK IB7QFSE+2UWKkwGIpTg/WUe0rG/+MmLYLxII0RI/RwUYp0DkjEb74S7gV2sM/GZ+Mz8Zn4zP xmfjM/GZ+Mz8Zn4zPxmdp8foSkj1cJ2C4qcThehxmQ1YWybn3xEjG/bnIZIgBpWsJ9LsX5bv QVkh6roZYlmex6QGVdzg+cCUsh3KdJdnfPuH959w/v0IYFH0C6RGsJSLJKFC7Tdby5SRZLCJ 9lYYBBGGjRvHm2fYPzkdR726Oy9Ysw2e+mE69i8plqWngOvQMVBgS/ZwNSwHcp0l2d8+4f3j j2SP7vQQT6zLCUekJ0NS3RWfcP7z7h/efcP7z7h/eGlALxLUpie2fcP7z7h/eFIwPFPPZjK5 5XF1OJaWjIh+5rKTyvZ13mqpdQFn1ZKqkOP7zGmozLwVO93GJpSMpk7g1k9RKuFGE31M+4f3 iCUZETpdSzU59w/vPuH95Mdkir6+iJWIDlC/dMVKcmdwbT1YlXcJi5be+XVGstqRy9Zk84Is cDS4lZU4QatTlYbu3OJ178JHUPd/vMs1rwGnOojvkoWk8SwGvAeo1d2Agd9r6wyzCrHBPtM5 LCYVS4LPi64RIKbHZNOOVHlYzKmY1RdQj3zbibIYDX7LxrILCoUo398IlxBYPYnsMfDlxXC8 BZz074/3UA3RKHjb4zeWuHRI8jX8uFpGmIkg/vDQ1ihPg1BzgXROoe9MhlmFWOCfaZyESbNA gDsuCtDJCyx9z41klGkoV5a7Ej9GShR5R6SNRIEKrirmv7xs+qQSEgIgDIlvgmFSUJaPN4NZ g8iRETtL8aclS+ikPNLsj/DK/DhZlyPLHRpYjoKDmio3kXAAISQjTw3gdLoLGuhWz01vIJkI zeFekEY55Q8Jf4JsxGHbMk5trEqu/wC4wvDBhwp/L0SDEYJlG+3BjJzWYFbAh9u+LgTaF4r4 DTvyaxMUgIJzfu/pzwEXAnFCrGKcNQiggxNxjJhWaGDIc6+MkKcFJIkQ3n4/h9wlU1ncD4cQ v4dhK/j2Px/NgJlOagTIc4yVJhvJKMaBrPx/L93E4ENLCEw2/Uz6MQ9nnBDwMmGO4Hw4AGUE KMBEeDVefj+fj+JFTCapy9exKcsUtMGtzncT4c/H8dKsYpMpFuEXiat6YVmgEyHL+WS6nO1W apwgUBQJTAYMxEJYpz8fz8fy/iQmTOiryN5+P4/B9qcvSEIcjQCiisU5GAFmfj+fj+CU2CMu GsRkBh6YFUpVNYz4qoDf9+qxecSa/wB8hbPYhhN+3TIyhnLzvi90X9M4eiHmBtZB0r5y4ToJ Foq8ucLFowL7V8Rz1zRiLSexx6E0RbTiSMUZUBEaiia0dcXKCR9ceouztWNAVP6XdX5r0oEJ KJsBXSTIkbPyQo6Er1wKBL3VKE2xdJxWAllxmhydIajfPHopfq6LxJkrqNE6eL8ucTG7FeqM dBUcIYJGEmPcLfiP2hRnQERqKJrR1yZIxDcgmWbKZMgNB+pqvz6hRRxLernUPTfOb+yRsNie nVvL8vb77p6eG/jEjlYxoFEvA59CgSEMLKzLOuFZPsMHqutWu9YS4JA5Qe1kHSvnISNIAgVF Ha8UPwnRfJkcWGK5xD3HearKoqkLSPLtkElidrAPP8T39XNXSRcTy5xzHUegYLSIg6ek0QjB SCtu1Yh7BVJuZa6W4McrWkyed/DjCWSCexZmdX09NW8ze/Jj6nVcnuv4T3yA9MEICK+erkpk jYOsYPZ1RAkw9SR6nqUw5BDvF7tuvH/ksA7xl5Keks7yzMBkcELHcdcYy1FTcaPd1b+ci+sK qcBBR0nXPoh9xIvSEjiXORfoCZMStL1HGPdhJklomgXOn2zbLAUyJg7Ex7YhixQEOdN5UFZR PtXL5cZbLrQ9yW8NEyPJAAhHWWZ59Zb5TJEpRSTu6wsYpsgJq0HppgR1CgMPxk5I81bV+zCX Y1IozK0062Y+KDO3hFalnfpPCzMEvocYEIKWQpHWumsq7UASlBNhbk4nYsHqVP1yA1eAukaf bisA9KT5kyFs5hEU0U89tY5YQ1IyZaImqvW//JjbzCJSvswzFb1qG/KPtj0u3VeBE52pALUB E+Rff0N678lojtgMdZMCoK99isQyyVPCgVVB93DMTKNFSS7EHt6PWKCP3CNP3RLtFjyRD9Nl mMXJMovAR1gif7mvQJfJtPQHqdTjJ85oRwmkdFx/fooDJrhEJPcnO3UiXNieYowENnuAOujt 0jHordsQyGOx9I8c22CJY+MF64N8YbzEQT0InGrrQED3wBmioQ6WIJOp7esGshMTKZZ3sRPf X6ZJiRA8KxEhCGUhyQRc7DN0ia+269Rqch87qP2OufHohcsTIgkHW2N3NmgWST0aZVTpuGQ2 5AO28GAew0Ms9dInXHPpaEoEZOl4+TCo3M0CgQ7uMWxVSGIeBLA/TEmJEDwrESEIYSHJBFzs MOGsgaCpKLfdnCXsNz468o9fY+9cuY3U37ZNE6LQw0IX2MYP+hSZ1OsTr5yS3MSwNkHPR6N6 hxrKoxvfOTSIEFCwnSvKM4qDRS6VtZU38YhcMTCkknWmMwIInTtQ1jmCxwC8iNx1wAjF+hWp 77g7M3Z1FjVQ/wBnx6poJArTCMBMEc5X6l3QSzoJe3okYrCAgoeJx/JxduYnpWQZBOUveWtn ZOLOCTutlWN0qfU4Obq/BL98K0TAlRBSgkGbxwjQ3AeC81AYKiZrZUiOiZ9CkgbVkHTECXfG PoDiT2Av6P8AyT7ZCzZfJCN4kopAGOEPANc4xdHo7snrwTkaDVJItIOZPb057BFAFg95fsyn BKYjQIvFp4wTHAy1FC1XJr2zdYQpAISdyHEpCIj2GsnADakfMdMIlWZMi8BXZrvm29L0pUV9 Z9YF+roWDZKqxgDEpaAt5EvHoshUySgF+MkpNVh3FcVi6BAFLQSnbq3ONgpZyRqnhgHrFL05 58ZOQyJdUaCKjHIUQ1JcFteyJUR1mufRfWGRb0bnvPbvkej4DaVY3qff/wAmCZbc3l7VRlZf c+4PoxBRn1K+OMsiZj8A18x7elxQGI05HzDLisIK5Gvzl1aA2snb9AzcDb0ZJHasVA5EnRkM t0G9xeQUpNTCMLWWBts3DLxp6lYyLeiC6y26WpR39Xf0oiFM6kEn5wCuXaRvjLwIYSlA7vZv BFiDzjM7eori2g/k47PUQTZPP784jRBTr74ry0ATNAcT9PQ7YgUsTY6QX9MEhh7f9Nb/AExF FNhzBOCAQ7AmBpg69MYkEr64dBVvesaoq/0urvx6VCdJIsBflywQgpmwvaTAIMndQpRTFEPF 4AWHGbHF0lud+l8IFEr2T9MmdAQCUDwsDyGUjHKTjB0O394RRTQ5gnBEIdgTA0wdemTpOYbs EyRRiJAaD9DXfj1tn5OrOGuB13xjN8GicNqerjeOpdgl0nSO++OcFIicZLBSj6PQeuouhma1 fXIaSaVQYNP6wNrIZW4GtEjrXxigK+GSKO1YhS4XV503gtwRgI6euxv2yIYON4qECVy9se8U iJNkIo6X7etXidI0Cdx16YPtJcRbQ3L0d0EbygvteFy6DqE0+uDgq0jUlJ61uuCGwBOxQK1B 19vVMQqUwilPMy8ZWSy6k3Oj75chHZAOq5qwSPt9DtHD3A6p6av+ivg5tn6Ws4adTr5/TMxE IR5xQ/SAacXv9uTi9/uiPQYe8jeBpGAGs60IOTkBCNFo9LMDMDJ/+y/aLcEy41jET8YzEQhH nFD9IBpxUGKXgwMJz/WQxD7B/YHICDDGXZ57C49I9xp7rrx9NI9t8dk9HgCydpEn1Mjij0CY PrGWdSSjYDPkF5QaTTEhhPkcs90wyYfBZRL4DnywsJs4u04DYEilg99RfqUSw2UOU9T6YMBY 1GMp+1ekBDinZYD5xGfQRHR/eTQBJDssPyO8lMgp7SPhPrGM8m2v0kn4chaXihL18awgPFZ+ jOASkaJn/mORBtWMhEGKiSsdctIcIWmNeGv/ACZ6s6lt8a2+3thIzZMUOo/BjJ8VH2P7GFak bTQcu0e0eg5c6KvwOvL84vU5aCQivh+JxjS5REB8p6WH5Yds2Nu8z6SkEuces7j7Y8donkNt xWSqBCsHgTOkuPR0ZlTo2v3OVObDqJQOs/vhXpkqOS7s1uq9FQGwRQp3mMCG6JVX3XM/XDds 3YB4T9jHFkXOyt7n3HrOO61EHfLnWRHJTCSEQRqI/OK9AuwyIxKEANE9HdjXOAgBHhxWSIGi QyV5woZZJ0sH6YoCrAYT0oWRxOwdj8+mJ3PsPj19ByNKkAYIQ0iSJiLRodHQXrn8fgdY9Cxp KIAxI2Sg06xg6OkM0PTFAVYDCelCyOJogqSR1cFEIdD8evrIw9GhZV70x0xirOtTJyRpGOt3 QwHF30dMRAWZZdbK4j29Lg3SCUSxu4MoxEaSliOpxGPIgXyoQqqNnxjd0mcWpWTqvp++xIi3 Z2mJuJytnEI9Asy3gDXZ5TfPPoEahroZi8ZkQYJRcH++mBTapV5z2judPQLRxGJVIy1xhIWk aWcr7zWRR42pJpW3LL0nMtUI7afHry61iU1Kp564VAtkVD04fzladhJ+vHyuOxoKi+1+m+0K DwGOR4SAb8N3P6acIO0pMl+IRWor4MhC9r1PWdSMXBLOZ9s+joSwnSQt8ZaCkDlKdryHAWvk U3bIKMwEWQ/GL9HknqkeGdnbLKZEq5m6De4yCoaHFQV8T75OEHaUkZL8QitRXwYlNUKLGG+2 F7lkR9W7eqeA3tL4Q4ZoeItyPbG398v7EXkwDx2inoSUQlHaMz8Yl0oGNwYKUQQjTyrU/DHB lpOkpT59O3vKnpa1zE8ay1cCIkEbHxklvoZm+tx6S2GDeZWe5WKTZy91jEhM5FzKqHlO/Pos YpHSQb+MFURVRuZa93N9YJUIVywwylMWR/C/SMYmbH8mHq1YUktz2ZMh3II7cYOkWFMSIo1j ooIEhPPEfGn0n3UKZFPWMUUEKIWIdp/TLClPRJglLjoEaKvXQ3i0vMQr2daru/jIBlc5X4S/ h/HpxBi0gMuzEe+cw1SqaYdiY41ktrjmvQKpb6dL6PPoSp2Vyyt59NYBKg76ckUS2ynkdxNY nWGWKA2BdT7xxiw5T0SYJy46BGir10N4TxJi1kWrRHfeSRt+kQOo+GvVGo/Mvbvl7c5Fclan BbHedce+PLHkdpHSerfHOcuQeaQgo6Trn0ZfLh0FPiOuUPwWLoJUvpxgipAZZlqNAdSn2ziD FpIYdiY9vRAAcKyqZ7E/OSGLdzw5fac2AV1tfYqd+3oTETJArBTcoY045+sa30w64dDxJCrn 6PSEre1QMPmMWEkmjrbX7cZNIhbViCprXy1gMIyto4O1zv29QJk4fIqfrge4TJfwQR9cI6xw FWnbI2fCFVxFwww1x6ATJU8GYCP5PfWJzajqib0de73f0yOQwXhWABkamuWXmeXeahF1ddPe TEDenT937HPpysYTEEB4YnDZS5iRBZtiOuGhLV5ZGklZNNdT9OULG4Jr49OuXZ5+SsZz4oRk kBvyCJycJyrMbZeerEchAvCsADI1NcsvM8u8YkYkNoqAmndwec4ARS3HrhC3P7m5N1Zj3ybK ONZoycy3jF0X7ZFt7TGueMle8q3DbLzPPobuVZMqBKkvgwkyHAOFE3w5xi0LTSSF7Udz75ws YREkF5Yj0SsVZwAvHV10yBCAIg7kB+uCXvW4aC27ce/oUEE7mNpOCYuMlph1T4ZP4wJNg0nG nPw6J9CoCsEhBAZOJ5wgDtMsouJfg5yFISgXtKej9jEOSHRc5Em2lx7+phgd8On2O5l3stCE HsJmsQWb7euk8YiKl1JOk2vLZ6tcqpE7Aa9xuf032YBWbBNDDo5953j+JZrT9bWKPPxBv7+/ TyyO0U72yLW2pd1u9MWZXkyTdgR0x2wzootCptp2Eb+PRCTsVhXBkjahSQmWm0R6fZgFYCya GHRz7zvH2nw6HuYbJ3clrQXBJfb1Uf4vM+te+MSPn2rTnBYexwf0T+3tnLuyIqNze/Q/MeTW q9948QMQFTDweWTEuLWmOvymPfEQmMASXefSZJsDEQ0ynlmCOjPXLilMrfBXg81kIK8N6APq +PRwGiVaqyOwtLxOIExAPYMCgnpoLqAOA5qa9LW72AU9pwuzSWXFxPtihyNJZcvHsxc8jTzZ nvT04xT5IR7ZVeiSLct17PfAedoH1OfasCcygr1xNTkNYwgMtOXdjU+jRORKFo9kAr3OUyxZ lq1Q6fK/pu5ahEMVFsr6Zk+vxhghJy6d9v2MCa3g/UnTu9E6DBwFArsDPtjytiigiAdkh98d b41lVu2IcaYmL1NSfb7HpagE7B9z98EAQv7XLrl7avAls+2gwPMTD3HO5ahEMVA0q+mZPr8Z NHh1RIhXpl+M4BoA+rOl8+op0uraT2Zw24jwMbWcge+MCr7iP7Ls7xTgEFEDJvlT29HCh1mm IXzLkYKKV3BHgXzjaRLVgyE3Bk1iu2VkjfpMmApy6L4CInrHXIVEhQkHGgyqc6ooEi7+PSdJ NogKAcqIDrir9hPFYl2LfbJEMF4BM6SV6F+cSKpGsoFeMdgJg45WexOIICMmk2RbsdN4NkOW 4LUdpr0nhZiQP1GIG3FxYc9Pox7kuqxAw3smMaJjS9RU7fTJXNFCWNFrHtfQfSYrOuCAhz1d AwhHaNLMm+xuHqH6ZwJO8oMkCvCLMnz9MEMbd3dfu4xhUfU++559EOkboQSfEzjxNq9SJHZi ffJCRiwoV69I39MmEmLsFhuqh1z6T12bIQe5gQpV67Lc9unv6OBL3lBOSBXhNGT5+mBhoQck Rb2v6YBlqUuhNqoXnXqCLNT5PaqMB0uZWF/R753RNt9s9PD+sGl4FkZPmPb0RReQNKW+Zw5k xM7n/wBGMidt0/NMyFdO+BGPYb328cekqqzqMmu1r61kL3QWJL5eh5yQi4ApILvlenD6WTSO pEvLAwY81FF6JQl7Xh+QGNpAF6f3em4ZN1ICX5yn8grmF18ZPkM5ExJHK/hvGhET3HI7VHqv tP8Aqe7u4i8UeCBJHm8mj0VQgmV3lh0CSV6H8tekG1kXKFIvAeEwOjNfTdeD9MLStI6Bg0Mj rbdfOSxdi6ubvdyGr/Hi+/pEjKPWQD5ctnXcFI+TCWJQoUC6MZQyrVBDRc6D6Kd1EorEEhCE SDZjEpJ5m4NvAwWleR0DBoZHW0TXzk/tGjciT6mU5brLBXy9dseg1LH4MhkWmQsP8mfQbkuI QaTIgYHjyehzmcHgk+uaUM6UWEe+Sg7MIQhT9sLg0/NZ6Cjp9QJIT5YJjDjh5MX65VjswoyH o8osNVVZdCt4IWzGS9sVMlWQmjeYe3ojao9ZQfvkfdx1CsDTi9DdSX5ZPlUKWYD4PS4QxLkf oYIkQp8x8MKUs1Qi8mSCIOxdTzgLCUYGk6+iOYdSUtYenQhfr8ye36YK5YETMLydG0uKOWWa TlyaZGIh+4nuvviJnKCc6Rb9zr39JgWhsUCHRmLyaFJDZKJdWRu8VpaJCElGjSSLq8BoCkTc J30Rx6GLlYVO60e+Lp7cQk5Nl7ojvOPwQWVUl7rbglywImYXk6PpcUcss0nLhVNCkhkQk06u vODSjZsNZQvu8fR6wZ+rTYid2uNVOUvygalqRFdXbBmkcAzUTvqjVTxkJwZlx3efRoBUJbJF CadcE5oZCOlNnLzlHMPwAhI5ot6ujhhQp5m5Zu5ubln0lV4bSBpU78NOHiwM+HfSXtb0MZKk a1CYO4Gj0A9dUQVmwiOWBlJI0MCkCX/Xtr9Yb7+iCQAWEkiGoZi5ziQTobGevODl1hIN2J6K wXiyGtJIJO7XHv6uWhGPRRrctTw5PRNiRAlRbBk/MYJkRZG9ZURyXEacDsWej6BSlwn9GnLC QHRXDE1rVPU/TGIZslql5fyyiZeS/ntkmchLkL/j5yqkkXKbny9fHoHOirJuZqI5mMGyN3ba tz3mffALGgnWwHPPtvK4My8/h+Xp1e92fywmrmSj+49DEM2S1S8vxriZeS/ntgOKxmFGpjid axdarRzfV19ZUrmSY0eNneMHChNnK1F6n2yDjxQeGonH1YwjmCJejCX3PQyNvMmDUxxOpwga UsuGvl15zQjQuhJJ74K5gxwFvEqszczczPowRjYtVDLoUP1wM2St6eam5jXbBVserRuDidd9 ejJukLtLE2oxcMUgMPjGHYR0Bp9WJ59A5rKMkzM1EXM5bLG4O/OQJRLkWY/frvmg6EC7HaYn v6j5oQHasTGCQZK6rfEZAzR2GE98ZAFFkyzlnp6cMVn7eMIMmHaK2T1u/wBMpKLcdzJA4+E1 HtoxKt3lP1MNK5lZIZl/j05gdiID6mQzuJNhX1XCLhiQTIg82vG87YJEol+gehRtSujLcxND mCfMH19FJR7jokZIHHQmo9tGQiOXwNi+MbwaDBspb49dC+gS7fiHKPiQQSNvbAZtu0dTJf8A bBnZFhZiZfQnRODNMuBnVwOLDxZnBiEAETXreB0BRPJF9X0vDgCAwi3bZfLieKKVwIY76viM QMBU0j0GfS7LaRIum2Hvi7KhFPflES7E5dvrvr6OIy5Okpv4ylklXul/dwAJLIURGWPkw71w qJVjxD6w2AWmXXH0pxBFAgMmDgGysTdYVOg1UI7d3vr6QF6MEOd+cc5cDxE773+ukmJvp/rS XZ6by9q5x86q6Wx9Xtkr87nqsjC1t2TWEE+J9/RGWUQZ8D4wbgKH3JW+DW3LAa0cqzfCetx0 c3iidyP89vB6QvIGaWji+SoxhtPTEeLZHAUbWyzVarrz6DbMDYOtPnzGEiMwpeRn2138Zyi/ 3dMcvhPb0k8S/NAw/GJofegba/bIsmCEAJ4Pu1jyHNyJLA7XPrLqCVI3RxYT1N4NNwPAIOD7 5ciuiDB/Jk2toJEhL4aryekcQYvgBV67R31g5z26oua8B2P03Z8QCmIBwD6Wj3/fEhao1kh+ JHti0kJvGTog/qvQeBZHQQKvIJ74TezgaKkOwMe2XwFCG5k23BJlUkZ3o8vu+jKQIcQz0eQz UNwwSHB6dnxAKYgHBPS0e/75rQ8cRZHSm2Y5yp+SSo5CRokiIv1RHj3KSezhvAtaIH7o+2Pl AH0i5O/hZlJyCn+fQgJ9bSxA+IcrCTazAqeSs1wQBmCT2QxMxMGQS2UcEsH4r0GQQyozB0RT PVoyQgmazFnrDxiDJ02tAe2b9J623kEEJEZL+mNB6AevLDFQEBArjawio3fomsI1ECryZGmp Mw6XB7UYEsKXSDJp3N9MR9xLUBj4zfpNnzpH9xwaAIRJKFrc1xxje5wIsEqcXOSasNg6xgty 9GiW1zr56OIiGgJPnDAFasUJBB74xgy0phsu7J8fpvswCsDsqIuDn3neFpZIvPYflHvkd3OB AQqefR5ZHaKd7ZFhO1Lut3pmywpQblZ01r+8UfoGY+mW1dz0nue8mfbjA4jbDuOHEdW5r0+z AKwOyoi4Ofed4JJM6jB4n74IDUGAJ08gTdcev29yfWvfGiHj3Vpzgmk1Uwl97AgoZv8ADLvz 6e+PPGq9942k0HMUOr3oyByiDxYRa6KSzG0w02CVvaPSAaKkJqsWYdiMor9JV3Db411warVJ IoIDrfT0osEgo5aKfftjjsNFH4xtNRri7wnuv39LW72AU9pwsGaSy4uJ9so5Gne5ePZnVyPd sz3p6edeqI9se4sEkdy2R7L64tyafuHPtWRspH4MEsDAicRMUzLvtPpMVhJG0bDUAr3OBiO5 bbnV1Rr9MD8oETMKySByogbSCOeDN0gRh0k95fsZvSVSuIBLSkrr0hoRFiIJB1Cc38mLCoUn opDxkcaCU7Mc+2l7byCCMSxMLb2Y+HPpDNo9VezB1YyHJMLW8GJGer2ifQH5QImYVkEDKiBt II54MhzKSUk2KNd2JZt3yCFpTtv1g7j9zOjdTftjQxjTpoQ8BrnE3RI2NIQW+7lLNDoc1Ag2 X09O9BUCVIhc3y44z5hulc8H3xbgEDr3NT9cU0Jllb5IL9j0SIsaGULfY0gd5BolcCeKh4t6 me/ZQRww/d49Nn4AAlxynXbNfgREufvr4dZBSISJwKfd4uPRYpgLEQSCHieMeN0XB5helZBt SBe0jifuYNkw6OnKsU0qfb1OHP0fBL98nfWBARBS4DJONAQ7ExXXvvh2DWE58RN67HoXyRH9 LCoVcc5xfu+0AJDy3v8ATLIEnqgyQYWnTMm/34wjMXk4y1zs175eADWEgi1sj29GPKPSBJ7E zj+01JBDsxPvkU5AonymOTiwM2E/MaSH59Cp2ITFLqTp1P4xkiUD0k9FlCT1QTnQCEqRk316 8YRrLRFPJn4MJy+2mjqJtzz6ggSVeT2qjXvk7tzC0uzs3zjMkKqollP8GRmJgClD1Z36M40w Ogt8zi+yjLC7Gf4c0ARDki7uPb23gogpGOyn8d/L6BDqDF1qG6oOOc3Jg6dwdPLXRnIisVTp kl58HpEYaRkrWPp3xZOip7sff8YNPSHdq3Wp+z6VFqbrIEvicpukK5idfE5Pm5BDE0Ra/hvE pET3nB2qNetuNLnO45rW4cIEUfInq/fBiB4rQ849R1lA7Tt7p0fWAECOhtn39o/TiAAaA1il l7G8/iRr0AEAjw4BEARR2yWlyyztlo2UGNejIScJ/gIABoDWXK3obwAgPCtevFqxMXklLoTF WlVLF4SEHAegTAlti8uV2yNZaraLlgAAAOD0hV7VLwkqtSaz9oq/SNLoJiGZBmnvn8KtegIA R4chBRWu2VCrobytQEDHr7LaXhsEN0YBACPDkIomQjT6hABsY3/9HAI2Dx9ucY/Y4aY57jrh Oix4zgoC8S2PnBpgMbvWARBaErqTs74DFJ4spGh1e8AOtXuMOVrQczlEwwxtBCj8ZyB2ZfFn nLkhJsnB2BHzgxzaemh8Zf2J3gWIylB3zraEKfOJKQyhKfDkZLB1nIRMkdcpGp75ckJN3jwx UcMT/J+uOkjuxn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4Rn4RjQ6ATmAevMv01GRQimxw kdUrnn2xhXWaREdAB/xjukwJnKBdEbkSk9OTApDYFIoQ6dO7GeByNhh8i+2bLnkQRsg6vu8h a5xrIW8doMePPbZ0NFVOtYnIYYpyu3NRracop+dI3gfD2y3dCYBElsNpxoyrCrqaOMtjWsUR JdE6HxHzjo8MlLKRw4OrF4jdZGVl2Cu+Or0zi3aWqoXcdMjBxcTLodADES0YosCMCSXeDfnB Ghi9jYg+4vCS3gAiA9i0niTGMROamibNsCJ76x+U6TOinsB846R1wkazwPnOijXBUgp6Tptc g7DbyZIih2Y4EwEXS8ewqVwxlWzISa+mfhGfhGfhGfhGfhGfhGfhGfhGfhGfhGfhGfhGCME3 h+h3QsLxiX+DREMX+57Y7xE3TCLRp39sMnRZVYhv+MBlbkI2IHXUKvOe8QBdPpO/ucGAroK0 aD6xta04nKCwBPyxvjKBhA7mk/u2Y7Uud7HXaq8ZDRAEKE+l6uQu72ItPTEggk6SqRKb1m+C AnTE64iJ6PTFe3CCkl2N1z1nAN1mxOEY+9iPMawatUinXs8v55XqDFqBNVA7/wA8tEwjTex4 uANdciy5IFU+EJwLUFqRJ2dr+/nIBshsGY8nRiNRQn2Ro0hx+MZ+MZ+MYRI3UD34yB0TL3ak 6cdHE5LuaAiSYQpHEXzfGVWwdC1EL+dPLAKpc0AXnMzrhfOCIn1P3E+G+dvIQiQwl4ruYnio yA1tko7YP35+YxgFm6h+2OxhPkyJnlbUHU4yRySBbCytjzhOfUlDcZDD2D3BSTesaUQCCQix z3waCBpsM196cVcE5GSV9jGo6YNPWNYjVa13ZDQUUQgQ9BIinH4xn4xn4xn4xn4xn4xn4xn4 xn4xgDJN4foadadKkoh6dMmBUOPtSDiWY4wCA1aANBbWsh8eUKFrviJOLNk62qMR1VB6EE67 LjWLFCDNCGKU/rJ5lNEcDPGlwOyZhgtfKj5x+yE/cwAAqRwueR4wz2lbYJ5wcjlyERMQrdP0 ySFJpAgCvdwwDaIXfYTW3vkwoXs8hLCYg8ZO4lAlwTHTJ8hQg1RIkYLL1E+oYe8U013ZLuAl eU8jqnWdeGOYTmYjojI0k6BCZpNyfGGAMmoIA0nKfXGTkDEpk27snDvjtC1widV5PRnoop+x h0p5zqsKKcZBVK13LXI+hH5xM2sifhgDqnIsEa3O/fFSGUOn8mRKZD2AZYnZ89s15WcSQ67y 5qFoBjQSOR0OHSapxGMg0vF2DgySjiM5Gh8v0CjFV6kpp3nqmBioE8rRfGQJ1iEoQunZffE8 dhFovqYRdGglTEe9YIYZlJiJjm4xu2FDyCL5vFPQKgOsfOOPNNaP6ByF30+VeVJCZwClYZtW ZY6HOIrGa0u4h46eMZSIbkUXdIyYxpTtkGGinfA3znEalhmKsyx0OcXcFTk7fDJLcoNtt3mD 8skAiUb9j1e8hZT8xZUeGSPp/uq9BKUW5Pj3xnzqqyFDdzD7ZaFFIShBc6VjzmlpioHozOIO uAMcZecQAAaL+YS+XIqvC65AeJvqdcTsKIVrCztIB64QDGghhQjv8p74mgWHXR/kxJ09Rpi3 mgvtkng7o3+h8GQdgIQy3mZ7Z2rcZAht4isqHG1EJsdUWGrAEtE39nfC7wSNIR/O3xDlllWF vFjsgjpilAZWkR0dsT5B6alehmnGAhgdGiUGyl4nsieiApRogEFiTjtJu3CJ+MiHmmJekfti FlwvWInzHOE6iC5Ufx8ekpROuxeIqLomfSOiGfOVp5QleSdSsfDkoHJeVBQSLR/YMFQiyYEo S4SVmxkuVCnelxyt0ngUGNzBHXBtr9QHgwuQcJJHBIuPQ84rCaQe4Fwff4ajYOmAkgSK2Bj1 VG5LyCVUSABYdZuNYdALWSjS9wVy8U5wSFwUysFZoFJCDU0I+mQigEcyUOtS4OXMIWolycQv 0wbENhJPV3vJYhEIEqo6kz5yUGJeSgtDUsi7zrmciRfvPziUunrmZO+9IN5K45Go6HkuvOVO SnAGV4n6s20PFEAi9JZe7g0KUISUPvpkjfduyR44O+WLwl1VH7VgwBbRejE6Sn+5JoHWK6jw TxnBVEDaFkSNhrnBpwZybWOyhL6Y1gXiCLECyZ6TrJcx44iVuyO5ketqSSYgQ+eZxeGfyRVC SKU/mYTqKlFwg8B8u+FgUIhCVk2xL2Vivilk1eBTq1SeM4SxAukGClbEk1WRvuFLT4222RiA NeaiQV0/rm4NRNZLNod2g98h57GsDqHHbE8ZbE2UPI0xN4rEHBuVKdaRkhtbQnwoY43HOQcX v2/L9mBoSxQ5pJu22MAueCItDNcu+uawKSsTnNMI6T3vBDgIDt/jd2VFzGS9wpOxgh9k09si xPsXKzJzJXtxkwpUIurpJ0Mg67ZKkyJZOgxOQs4wHpAiWrrjC03gU2BgiViSJqcItbPeJkIi DzJiGzwiALD3M5W+CCcIQg4g/Obhn4ESMWaT2wfko0PAtT0yNWQ1CCA+Ty5GJoCFSTwRq7yc /iGKVOluvnIPCGLYQpIII8VkjYjE0EcQRy17HFkq2ieljD1JkjOoBULIqW0LpvviOG5HG0jE X2cDM4raiZGOs777yEC21U6dDmUHQydDWRBNlC4XjA74H5CPbhrPe5WDBbmpiXxedJHOU5gi CeAjfXJgMObuMQX3LAmTI0jxgjiefGsmTKBEvT9nHpEoko6l8MU6yUxAIhiikTHGbIBghglB uIYnX+0eEFZEnFNTgx55zEZiK8J74gX2SWdve/rybDOGGy6ObdZDzmNhBAzFe3QxrYjhYkQE 8scTqoSI02HEEN0BOlssS5yUY3FJ3m+OOmBvMGMJp3RLffJqPJubQYT0pPBjz4FsYX8PjE7b lKkggwOlmJfQO94wdWDdT3yJLdzfdotIcgRJBSbdJpROcRCAWUIizZ/3CE+TiJ1VrKdDZMWp 5nJnmy2ooBDccYsQpoChUxrHS4m0SXmrn5rOsLUmgDL2Y5RsuiJhk85OulgAOrBxKyUksNUm Nxcb9EEhsc8IGtI+IC8kiXEUHxZmfYwWW0CnkMTSRgkRIwWkG7jzhtIc8wRmrkcBaYhEo0jn nDRyAQJEh/OMHpAjqDWGoAQgMi1S175xoyoAgiPK++CkgCBM71kpJpYDERxWLMOg8P6weYCo 8yf2x7Wt7xf2xdw/V5ksJxeiWH5tT7h/jKaqDdwE9sKGwL1MX5rApjrBmo6fXAQkle4Gk7xt 7cL9xxR4aoknY898f20iI3LMckJnIAQXAL9shBb9c8U/dGTT+JTceQBhDb0MbEU84FygbQ7x idY01SQg2S1uip/2iZAKRNJs85P8mCJDFnhVyx1FxIufHLgDJ2qsb9+uEUkeBF4mTXPtkOax Encbttx075ALRBYk38EB7OVuMsbQhB1cEKJyeXqQFdQYcj/+SZcrVW+MeYmCgs7t1D9MaFJe MBIwY/kGwRlLyH8t43MImlsrY1v3MRAEGyO5crL0tucMWQ2EYoO5gvNoQuYS14RhSxJx7GD0 AXjgnpN0Cl/fLKXXUITWFgsrMjv8r8Y2UqZJWNs9ckguRsVEGFBzw6ird8r9/bESgsDcXB7x 7YjVJpRCIpp879FpAivEmAFJA6AaTO0a6Ygo2SQCZOJ9vfJ8ApVW2U91DL+l/bB/H/5Q/wD/ 2gAMAwEAAgADAAAAEPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPNOMPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPDPL PPPPPPPPPPPPPPPPPPPPPPKCNJBCCPKHKPCMJPPPPPPPPPPPPPPPPPPPPPPJFJOLLPKANHCH EPPPPPOPPPPPPPPPPPPPPPNIEMJMDHKACBIOPPPNPPHHHPPPPPPPPPPPPOEEADNIONKGNBl9 APPHPPPPPKFPPPPPPPPPPPMBDCF8GHKFAGE0MPPPPPPPPKFPPPPPPPPOOPPMPJFKBPKPCFKK KPPDEGKJFBGPPPPPPPPKBPPPPPPPPPPPPPPPPPPGDDDDDHCPPPPPPPPLFPMMMMMMMNPPPPPP PPPLNPPPPLEPPPPPPPPPLPKPKLJLGPPPPPPPPPPPPPPPPOAPPPPPPPPPPPHJPGAEKHPNHCHN PPDPLPPPPLGPPPPPPPPPPLBIFOOlAPPDHPKDPPOPPLPPPOPPPPPPPPPPPPIBIJPWFPPPPPPP PPDPPPLPPLLPPPPPPPPPPPBPAKEMGHPPPPPPPPNPPPLOPOPPPPPPPPPPPPJPKBAFAPPPPPPP PPLPPPPOPKDPPPPPPPPPPPPPPPPPPPPPPPPPPPOPPPPPNPIPPPPPPPPPPLIGJIIPMIBHGKJE HPPPPPPPLEFPPPPPPPPPPJAMHFPKDJNHLDHHPPHNPPPPPAEPPPPPPPPPPPPPPPPPPPPPPPPP PPPPLNIGPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPHLPPPPPPPPPPPPGTeYadZQTWTWUcSU ZXfffffffffdfPPPPPPPGCtt+cqrrrtst+Msutkqtsssv+effPPPPPPPOdNMLHMAAOGGEIMG IAOOILJOGNMN/PPPPPPPLHPHLDLLPHDDPLCHGIOLPPLHHFOHOPPPPPPPPPPPNPPMPNOOPOEP MHPGPMPKPCKHPPPPPPPPPPPPPPPPPPPPPOIHIOMEPNPNHKCPPPPPPPPPPPPPEPPGNPCIFODF JCGLPNKEFKOGHPPPPPPPPPPPMMPPPMOMPOJPIPLOOIPNPOPFAPPPPPPPPPPPJELDFIMDPLHP CDKJKLPENHLPKPPPPPPPPPPPHPPBPMKKPKAPAPLIPFPCPJPFPPPPPPPPPPPPFNODNCKINLBP HAOAOEOIPHPPOPPPPPPPPPPPFKPEPOIPHOKHIDPLKMPENGLNOPPPPPPPPPPPBLLHPOKCNKLH EPPCOLKBHKCPKPPPPPPPPPPPNNPMHMOPPKCNEGLKLPLCFMKPCPPPPPPPPPPPPCPANMKPPKAP BCLEPEPAFHLPAPPPPPPPPPPPDJPDPFLOFKOFFJLJKCLDHPNFKPPPPPPPPPPPPPPPPOMNNNNN NONPMPPPPPPPPPPPPPPPNfPONOMKEDNMLLDHFFIDMOPPPPPF/PPPPPPPJIJPKDEMALJPGLOP FLGOONEHDADBMPPPPPPPOAKFLMBPHMBPOKOPFLDGFMCKKIKOAHPPPPPPLHPLPHDLLLDHDLDD PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP PPPPPPPPPPPPPPPPPP/EACwRAAEBCAEDAwMFAAAAAAAAAAABEVFhcYGRofHwMUHBICGAMEDR UHCQseH/2gAIAQMBAT8Q+JqpoWjEXpGT/WmE2qop3Cbf6+8RNaFVrt29aKwaRasf3Q7dN+id WX8JOp8Gm87xENca41xrjXGuNca41xrjXGuNca41xrjXGuNca41xrjXGuNca4X0RVS9G/RSV OlL+EGXOJDLnEj3GI1fywRK9mfwMucSGXOJDLnEhlziQy5xIZc4kMucSGlMRW38jI1Upfwij LnEhlziQy5xIZc4kMucSGXOJDLnEhlziQy5xIZc4kKVFYitGnjTxVVev0UKT3K2X+oRFkERZ BEWQRFkERZBEWQRFkERZBEWQRFkERZBEWQRFkERZBEWQRFkERZBEWQRFkERZBEWQRFkERZBE WQRFkERZAlPWq0Z5X+AH2OppVgqwVYKsFWCrBVgqwVYKsFWCrBVgqwVYKsFWCrBVgqwVYKsF WCrBVgqwR/wC/8QAJREAAgEFAAEEAgMAAAAAAAAAABFhAVGRsfBBITBAgCAxUHCQ/9oACAEC AQE/EPqaj/NWtKUdSlXR0+Yjfn2KUpQqf2gjXn+ER3fRqt+gqRCIRCIRCIRCIRCIRCIRCIRC IRCIRCIRCIRCIRCIftr9mtYHGhxop6+BwONDjQ40ONDjQ40ONFPUrVeBxocaHGhxocaHGhxo caHGhxooIXtVfgdnYHZ2B2dgdnYHZ2B2dgdnYHZ2B2dgdnYHZ2B2dgdnYHZ2B2dgdnYHZ2B2 dgdnYHZ2B2dgdnYHZ2B2dgdnYHZ2Cj8/4A1fj5YAAABR+foF/8QALBABAQACAgEDAgUFAQEB AAAAAREAITFBURBhcYGRQKHB8PEgMFCx0eFgkP/aAAgBAQABPxD/APJ/jOY6ywA7VQDy5HFf N125AgvfHeH34IOpbdCKx685wa70ybz8r8Y8xdCgVCdm8sNTra4fuZD69X/4FOkEi1AI1jZ4 H4w+VCcBRPBxw7h7lSi9AxqsejBpWpgolxrQSOoVlKnwz8l/s/8AhIKhebRNaCtcZvQPFHaO frkuUODQ08k0fYyTPpJAhXLDi5ZH8loRrkpzP8ZMIOwHWAi3U4cy/wDcunG86awOqbHTTc5C uhpzimwb2g+MsbkY061hMu3xxNBGgQBB/wB47HWKkdZUEi+zInbl4cYOl+2xxp+xrCRNrZuh P++ts6LkUbeNOMd4cbdOOn8srJYzhkAvfQx0EWbB7ObZbZDZzm/sHEa5qIgWkKTApKxdO8iI eDg7WYxwuvDAiWyH2Z/iuCzrlfBnKFeD7v39stnB3t/5kcYJV93WWV1pT9OuMWtEb7fpni89 q8dfXEKyKC/8+MndnQeGrib565B9cAi1uEHXW8dVK5b1/NwbL91/58Y97g3Vf9ZvSG3LXJ6/ nH+s4IuHK8Yc8sOVJX25/wDMfJWIaKYFDu3L/mI0Bha1uar3BdWf8wFcSQSPWAKsJdeO8cz0 Ck+mQgignNPbjF6HDtc0O3nvb/FBSRrTXR7573t5dvfWPgwXbv8APGEiACO+cDUU5u608411 Hf8Az3lhxGEi995IqEqgv3+MvULApdfxgLedHeEYRB05o9YXV324m7bjjn+fxnFnWzT9cJdm yJE2ev5x/rGmE6h4+Och5seB0t5xWliwXVD9c7ylpGsQRgvy4ir93jKkOJS7YXnOIesNJr2c /c8nzkkhivavL/GOujp3H358ZUdvHO3+KUg2tAeDyYp7rz7ntj6R86+5rFKY2m0X2xPJD7r2 xRsOdT/jHzWlat53imWje0+mDy11GjyYrsmCYIqCGLzK4gOHseA09uMQ5GdH/GMlX4FNnj1/ PP8AWKBmASRn5ZIcGNiV17M1CIIBSnnPLhjk7msPWCoc1Eq69scK+yA6eZca+QDp9mftGT4y kSAoc68Bjb7YaJ0Oznn7YZFvbfd/c4tuEKkSnYiPuehUiFcJwdCgvFQ5TCePa8UL6v2vxaKB 4ITwe2NoVGpd05msSvVWP1GCvRckecp4QT9jIoNmpqnxliIqc/XXt+ePlrbNE8ZaN6D2yf8A c09OgeMEQ1u4R+9v1ypZHjAgyIceRx7YAKpqimz1/OP9YJJ1aT/maEh1veJMqCHj8sZ+Axqi c4F5OnLSrXTxTLARIjPOaphYOKfGatCIRddcYTBIiUEdHtgnBvfCc4wD5P6v7TFHy2VqLHRO tRcI4S4CR0UaNAwzllROFzJhAUv0jrHxJULZBzKTbqJM0DPDU1Q8meKJFwlbinRwPC0A1U0T 8XxkzYPJhJ0gQnn/AJgQOC8rXI9OHcxYUUIO8qj0AAnnreBlewRJ4PgNYSh4SC5r5RDR5MMS vLDJ8Ch884U2zQt7wBDY6OuMalAWCnD5ypq5kVp6/nn+sfq5UbfjEab0JiWNQbwq5aAg8c++ WKi075/czsR6X4wSURQP94HAL9dTFgzEj/pjWc6CUS5Gvrq33/3D43WAHJf6WrgI9mw+gPp/ hIXnVxwYpWLxPHtkHUgCSv39sKybxwDHGiQg+2CnAiveMqHtA1ilW0cgdcYFEB7xawEMfswb FTA6iGkj98AhCgCPgh54ySPYBoyXibMgfzM+X3rLeU79fecuPjBNjTC2Fvvbli9wuO9sAvL8 4yX5LlCQSeMQXC2a61jXrDoe83zTFx3iDhBNYQV3gSe2NFi6v4mbDJR+T/SjtIBKrr8qr/dD mmCj7jBPCwEA4T8Mj4GFcGQFGKeqUhEx98vCzR1hsu1VxgmTVxrkr+eLFQrrBqQ5+cMClgOc SngQW/fJ6yj+/ti/1CUJt5TALqoD28Lk7RoOl74WYpFId+C+u/zP9Y8wGBRsA6w1g5R3MYBQ 8PGJGGOsmzGFaOPrh2qDSqrgMPRgAK3yyh980AMAN/VM1AyG2a8VLUzd3Z4bwEhltzRf8Uly gjONXaPB7YUEF0mk/VhuaBqpx8YF2wbDron64wJaPTBQ64X/AHiGZkNay3jODpx9AJPRxv75 PKhdj9F4xYlM5LgiERXTVvEyLBYorq7XWErGINh7YRAFTV9nsev55/rApcVbQh7fGFhoVHH0 zlTVORv3w8AZONYahEETvrFCO1CUSGkym4Y/ICQ+mI9LDxd7/LEfIENJ1rCEHj4WznGdaIcD 2cPWXqA9ddvWsh3HhJJg2RD4cON8/wCGS5QUzg4EdGBvm6Cl3Jy5KmFQIO83bIbM8y4bz+WM nByPs67zvvFv/mVpk+zjQYEOi+PywkLQ2198cGdklT2dmID1CWfP5YPUBjOhi+wYGAfNV2eu 3yP9ZSIJo8GphLARNvGOnOhp/rBwT305CRKl98QDry+2A3609ZMinSWJ4xXqBIntznmpgbZS /wAau9dbx0QKYfL/ABSXKCHLmtKej3y56zOfNe/zkiM3ODTfuDD0aubm8HPjvGBRAka9d+2G QwwA0C6rfbHcoHzffKiiK9O5vK+ePEkT8tmcHpLvH9VTfQnXPODir3uPv5zk8apf7dZosJuL 8sEO10RDKe/r7blz8YXHiJvJ98JUIOklv/MtiuuSG2d4sEN4BUxVohE5RnP0wra6p0B77ypx SND2G++ccBIiwmI9FgHJl80lvc3gHQIV/wDcvkp6U7f0yq4DvdBLQTqZouqp8fP5MQG6gXjf /Gbg5l7n+GbzqKePCSjqQfd4wMgblD2ecDwlHs3jG5OIe5/lgkm4R4z/ANZourR8/H5sGhap 8OPHX0fG5moCA10BxicA0gFzS9iP1wdHT23ZaZY/b8CgY9NhLWOk134ec0ysOhWVu9H6YNBl 23Bx6LhT1o/MwAFxu+0njN48fU2/LO5Dum35YpLr9v8AjHEZv7JnYNbv2YBnOTf8P3MIEcCe lF/IYZaNKdNaA4n1xhgxXknPGam2a935Ya9n7ft8YACB92F7FEkT8g8etBapNYFB8/8ADCEM Wx/8e+fGVB+mb/evH/GAWAW8fV1igt5w0vPWaRydzT8sAhCQ1pqeMRFqBCmtPWKGxU5VYi/T rFeD11/4wZAq2LtvX9NxgFUDhGI03U0R5M5JZQ5DTy6R9t/OFbrMAvfdenJjDWpUdQDkc7uo abrOSeyNjTpdI+354FxalS9C1D3j8enIIqdpFPEOX3PTk2GG0VSG3o2k3oG7h6cuXPusM7O3 ySp1s3z+BrcBH1zxv7fXJstPY0jJ41xlH5Uc6J2vT/hVYyED4A4Fyd+ce2ZlQYmoz97frn72 /XP3t+ufvb9c/e365+9v1z97frn72/XP3t+ufvb9c/e364p0oQqPIr15xIMUlPQaBt0DRNZI JgneO+DgFmuN/gdmuAAp2EMxkasEXthy+7kgNGwOhdkKa/wris1FWcsZz4w8o1xPhCccPp/c lFIQGOx03OzJsjSRIA5FCMKhSuAPMo7lsw6AU1z+BU6WNCLLC364ZI0Vb+mJV1xiT3eP8NXO w4jQBvpsaOLh157IKgeNf22ap1ErTo0unFSDhk2VD2sWpJhX5kr5TyHMaL+BClHI7qMvhDfH OCZPKrpaUp54+uV8alkRN6NbvfGGynogdoZ7D757D757D757D757D757T757D757T757T757 D757D757T757T74I8I/gkMVVph2IBQ5RoJ3nWpQBw7BURLB+w+lcBJ6ptodi668f22xHaDI8 AWzlMRP4lLaEgG0Ve+cHWtF63W9zj8CK3seineru5HYQVs1n8F9VJTraexnQwfTy+2Sz1sKn xhEKoVQukgT88TgKWNzvGVodulLm1aJADx1jNSaAudPD5c04tZy0/rmj6IAG6cYtS2rSsQF0 ePAuPtMvm4dkxun4Hu7fhq+3QS5wQWSEb1gsxdDUAUvg75/tFhb6SI6RvyntjLdMvegQEelS T3wrJkhpDU1y1sv4YOXNaU9HvjnmgNua9/nOYMJyP3xSArduCGCBcFxMaQ/rieB4f+uS0pLx k8BDSHHf6Ypt7GsEB1EsMnb+ofXJpxMZy2o+d39zDc5PLfxiTGmpEap7/h64o4YaqnUv+xfb FQsz3FU644/shDzppI7Cn3MBJYKgyAaaeU9u8gCjswbCwcE3xz+GrZKaXfBhKkhq1ec5lEij jXaec+na8xmNfbg/VprCAj2GUymAnyYy8JrxjCSjWD+DYBtqaxizHITvAFqmyVuSGEJ2Fp/R IooBLI+G5xgOs13bh1vDL1DA8zme+KRyKDl39Mro+CzKrrW8cgxZRgB7lL84bzVMgFYewL6E Y0ZxUCvyhgBYmkuxP6Uzg8sMB8oPrgG8lBikVoeUNHnXrXHxoKLBHn2UxT9dybILsvHv+ydq uaeukYvOnrCt3NHbbZDxbRFuDaCzVDMdUt3+FWFeMsNTSUmEcA6FUjxnRFWjipXuMNU2LkJz katU43DIqmjxfzw5WKUCvtiuWtXje/0yGQ+Iv2xLxK0D88AHoQI7usfAGhZOt4shzUENXNAB Nie/9ARuPAbKgAcIWvGSjIH07rBtF1PtlL/lKGEA4GvYe0Mnit2Iw153a86IPX0XOR0XnQMe NCBI0CRhs5hgBxE15qa2TXPHoscpGxRG6nzhCkCUYptXA3ya/pBDoMIcMCXDZU1ghQiVFKtb y/019JohuDt7/wDGcww4MTqPx/YJ9HdaHYbvGvOAAejqbCCpz5Md43Cgrm18BwI11z+F5EPl OcAgJ4/+MUkIJNCvJnX1gO8CEQVx4wVNn8GsJ+DxDLdjeHnjj2v1zjiK6mQGzgwcqWwq95Tm mxv9cfuAd8sTbBYY82MIBt+o9YKpfLFN+B/QVLjJm6gd1se3i4Yr6Z0zmLXatXvCRcJMUFat XX9gNaXCPbMiwmhCzn75tm5IIRQpKdzkMHG5E0G4AJR2TW8Mp5NR6wciFHTUZBQ2fsRhU0jY nkw1NCUW1eTR9x/t11LiCPvm23l9ZhN63S/bFpHyET+u7tAPq6qDzrxgkSgWu9VmSTRveB+o Fl4UFVu/k/CoJEE8OUkUMXxehMXIAr6TiQnKf6ETFOHcvB+WPQAUtr7Y8VI5/wBwMkqauHwr vBthEaxddecIErw3CYUDl6cSLuyiN+2KhEap4ecCEbCE76K5cCRO9PIf2gBKXfcif6dPDi2e RzoAgYqG3biKh1ySEhyCst+W/wBIcMV4A7XEnUOrEEK7ah8v9VeHBptmpU7fOKC3EOb+HK4X n+tt/V19XbL7NHeAg4AIOnb219ey4kiA5fSz2E53+GAKD/y4xyVxOpsz5w/YkAC/OawgF4+c npQCyfXTj2Y7QtuIKlqRecWjnQhrKqSptg2hsc3XroVPlm8WacaC/wCsQmy487TCZ/J4yehL Wb+2SCDVz0/R3+CKZFiHmqrSnFxP6+MLFiDzGjff9ddAZgaE36GrNwQhQHkpvr+oo6rdQ3QC nnQ8YWPHfw2bAYbpLrjBRI4yjxim28T4/CvGFXHvUplnbNeCJHzly6OV+CkWyteMgumEWpqY fypFt4ZXJDj+7m9SrbvU/wB5KcDdG8UKQRT64Ba1NHOBE9nvnPUDvf64Rxdu29/i1dEchWqD ua5J1/UUdx4SHaaPGjvE2CQnKkjaPna7xfBAMX7gBNsfb8Ml+w2Ghhiqa9qSOTbvV2MLTkA2 OVCQqHuYTCVTQuXVqZdI4hQtFf37Y9gHXAu2cJZEEr9bi6UAIfrjIz5DeFpmkEtfAydggInZ +LrukXRIs1UBnMOIotItU0A5X+kJd0IMdIB9nCZBXEb1EIGhedY7j5FB8gTd644/Df63+sNr F7dzbMNrEKJW+fzwScqAml/3rFJR4MBIdfXGaIOzXOBNDazKkFR2roflgcDpMRcbSEaafKcX IjtXfv6De62G/S5/t9+5+C63oUQxCGLvQ5dYRXQHNpUUQ6K69f2a4aAjkh/s5w6QM2Acx7/o JUHBTHavovvh8sZfuSIC0c83Cjj+nzxChJtj7H4ZSW9cM6PDiiMvS1298uy1qT9TK6WiKkxl BayhZRtNZzQ9A7ynaS7N++Cp4F9cf55i3TNOvjAtp1LrCzLYhJ1zd42ofzPfv74tuA6D9cNg XabL2dv4IsbQFE9zFnulCI/bx/ZrlOFtWIrEeHBlQJTQkZQ35Xj+hFi+KhEeQS2c44JGO3RK CNcDvrAsnBgXtud8Q1x+Gc8fCy8fGPIHYl70k49/yzYcPe2vywXf3ZVW+N/XKApNJVfkDFVP pNE+jjKps9mvjzjA2NJtrvrNjlmgqu2vAfvgIkxpvxyZAQhqq/5mpM6Jf34woX7j/mNpQba/ 5ioDWKXZ7e342uUl3AdCaU+hhr4GupLU9j6sQZ0VrboavONE66fJcjjQViax6Dg1O75KhNmt /wCWYj/NytYpC+RvmMdoaeJUQa7qD/arxFkN4o3G3iqRBsFq4TXqYeaeUeABucjh7RUD6HQx RKWQuJCtx22u3PwNaP8ALGIl8Mvx58LrRgfCX+NfWXYF3E/tV2ofekDR0J1HNBfj6ea5G+g4 9CyocqKcm3Vw3qN+y5WhroGiazVNeqS8W9zKb5/CawrpKE3Q4cNSgGI7y7wAPdp9wtmNC93R /wCFxa6zKm68Y6c8wozfgx1Yp2EL9Wsc1Wfk4+M1mEdxIlxaSvrsrftgWCkVbUN35fb3w4dY ASvB2O94kuuAHwideiWmCUaATV87xbtoCFA8KTR9cqgpHY0eSP1wxdJvwAPnf2x/FAasQrJ0 PnrHypeFEKydXz1iWAzoNquHxgiepKgE7+XjLGQ7A0ytdSUSnMJvx84zpGbq0v0zgQjS3BT6 CY7qFdiba4fGLQulzuDDW9J45yx4EtYoM4enG6iFJtQJ7axXdeKsHj8DXJpIJpIWib9dGRxJ hUcLNXWBnxFQZH5FnOB65TwyoQps71q4RHcQHe+546a1+ED7MHJBo605rGOQFHgDDCmnCRwS cgD2Uflh5TSjQ0F/LFIEAJ2PM+mGCWyKFPzGXXrcYuBccrJpWvO/GPGVAnYlMErXbZu7XUcO OuV95gW9Jv8AjKLaaix8eff2MfBgoEQV4BDGwEMisDwF4X29sE/kNNFEda2b+MhUt32o1Ulj PtiHsQgXcp76/dygEfiFAhG6Dv8A5jw6am6QidOn4wvQyBHcn5jhkuUMzc4MSMGsOC8GiYjZ XEQjzAOO/rg8xhqiIKzXDhPHEMsjb7a/PArgANAuzbrl9MZQAxscFd6DWMbKECQ2b8TDMDvq jDHuqdcYAAHB+Brp3Bb5OSoZozZNYoyBt0au+ccMnUCtOjS6cReuOTYKS2sTJJhF5Gu5TyHM aL+EqUqCqeyawGgwYkKUqeDxzcEBOmlq8d79r7zAQVog2+/k3pliIJh4Gut/rgBmIEEQV+XC gIuDBXen5m/nGtpFpb4SKfDrWdOJpATsTjtb5mO0AErgpK+BNZBTEgHVLDXCcYiDUnxB+mNJ LGS/KzDBgAQlorbLOuZfORe0KBaJpuucVQBso6FQ+Wy+Y4ETQISAEez/AON1joaEJTKVr5/P 2wrDaRnkQNndl/3kBqknijPzzYAMpsuc66zZQarg8EbJ7YpXgw6lDvf/AF1gFsiUHab5n/rA Y2k0CXmv0b7Zu3Avzp/0OHxVBApNGK2oa3ui79j64N4gQiIbEeHrAY8huUXT/X1/pfdTrTcb 3ESJSbcLapBAw7BURLB+w/3K7fnr0FoTab/TE4WpgRihbHKZCKgjdoEDStFgphnTEq+92m+t Q1wfhE1CUCAh2XpxWC0Rwfket6+urCNQ0kFFvzqPOJsiKq8F35w4WrjsAl/LNUOpdvlvjCgF EB7rzbrrzgoHGt5PAjHqapiubJNz2c/H5YEIqix2hSvMTDb/AEirSefNxp/LvV411OJ7YQbb 0l/Kf942X38Nb98UUugsi3p4j6mcoyldbYPy+2Xgl2EWj1mx58/WZoEEjhZLJeJ1eMjKLrfn 3WfF/BB1jT9BiBiJ8n9xEiRIkSJEiRIkQkmhaDRPIc7U4zZz3HKY23yHrloo0sAm+cqCBNJw DiFCyP8ANnhupD+BQfdwYKOdsC6uBcdo3vXo0baYKGgsK8b1h3XuiGpvBWnu6AdUKL5C9xu/ XkpbQCfmE1oU71jQ2LzaSoi/hFTq/wCeslgELBpRbPY9FuIEWltAT7L9pzp8FjBGPTHDLgdW CbgQgPkU7woZVnQcr29rmiXs+cYPOhfp6CQmnCpCcqg9IkmVeoajvsgA8ADQf54YDoBEoR4N 4FgusFjRwlNghGqw6iWeIpsSMWkSrsaSpCC1EFo7V2iFFfkxEmAFUNA5RfJrOJQn2REgcujM 5EmJt3nMHogVbV4G6uHHCQ6kToNhFraL3/8AOt4OIbm34Gjjxk42hSqPTdTQI9tDMLCEME5o oKpo3p3RLaQiL3I3Zggt01jAiHKvJZ2nW48ZQBpVSBQlHDc0N/cGQCUTj4xiUsekeCvCikFd Li9f+pkX9XziVMiJDRDwjMOc0L6K3IOYYK8XKkQpuiqSuzJlVDoNEQqRu6KFgo0FBGdaUgDR PC4GEQ9WFNAktdErjBreEcRBgMRN6XGVkkKhA03Da2UKwkJKjsXrLs3bpjRi2zudVjA3dBNF s5mLqKLLHRkouVMMg8wCA2IyUK3G1ueKsVBEijeAwwvVJh5C1IQkVQYXIRYc2HA74ScvvEUp dJohAKiCyvOKMrhpUNG0AKcxJjCeU0Q1FgSlG4OqJUZCAl163ya0g4FYlKcurcMAHkDcbnZE D0DzEEPN6wUZnPkPkpRdLclCJxeUQugJq9Y72hVIhKBIE3bdaFkdoRXGApHZ2VcEtSdQIAN3 eef8KWfwPLBEQmkwNkE0JUaNdZSeEI4oczUHBGtYESekEgCFOK8O8OLM5NITqjfS5uuM3hEI NIeBfOENMowAEmpl+ws4U2bYeOsXMsiteh+iawhxQWnBWgudjMNmn1JyqtKqqu1VduWPAQ3T 4Gwp0p3gIBACpr3GIdKPOKWheg4AWgHzN8GGu3Z2LwL27N4LRVRcQ9CqhxVw3DSbGDxqJ+eF Q69+BqqbNoEHrDWKexozFgHzDwZzefxqKptDR4PGH1mCb0BRQB6ZnJiVEQ5B7GXumIgWjQER 9sRKVizULIEbBLzgXqysgRsTrCTUkfmW1au1XnBhMz7EZA9hbJlk7P1hDsIFveColC1lp7FV 7VXa4EDMaUBv3GckzlOwmMI6imM7u1soZNgUI5ceBGrAUrogBOBQ0zEzUWoSCaUl7ObBD6Ot FMPb3z+I4/iOP4jj+I4/iOP4jj+I4/iOP4jj+I4/iOP4jj+I4/iOP4jj+I4/iOP4jj+I41jm aFUL9X4E2Ee5cUHKkJUrcQ4DBesC9iLQQGrvRrbxq6XAAJka6Oe7mhe1SNdgIVqN+DeLqtg5 0Qtx1YUrMnmCBIlMjsRPKac/imfxTP4pn8Uz+KYqYLw2GqC8PjuZMOSdO8ze067y9VURaKa7 akBYtOqoQobl1S7NApu/dmvIB5b3xceGIyZZNAeDWJu3No1CKWxkpNW8HLvMNk2I8aPOsLfo cThniVcaqvefxTP4pn8Uz+KZ/FM/imfxTP4pn8Uz+KZ/FM/imfxTP4plMiHyCAlV78TvHpRg 4AAtEo07m4NTdcAsDVZ4RswfWtiHgNFGnA6F5wn7SUKATptCsdbCQ0U6YQSKg79iKtmIcITZ oII0l71hKgj6hFV5XP4pn8Uz+KZ/FM/imfxTP4pn8Uz+KZ/FM/imBtDfh+BAAbuMniUwhYFO aaXAZqyy4i7NBzaggQDRonOOXBEVszIJ2mBiE0QQQbMUSUIBAqZkQpiAxzj9HdvvCaqFvweP RqhEpBvbz+2H7Su5TaNIwUYx1n8iz/Isp19GKInCJQ/GCiwNQMQlENa+MhFQWQATQUnKi4om lQxk0aPYdaw6eKMX0dSv3xwHUXwJTzi/fNjTi3k+LZpDeOmDlk4gWFUu0HJg4NAQKF14i+Ax rBkVoOc3todym0aRgoxjrP5FmG8mmt+a+m0K9bPin+8dFGXVIQmiKwz+RZ/kWf5Fn+RZJmRN HyC87Wbk3n8iz/IssLGjAE+F9HMltw4jlrQIjSgoMoYNga5Y0NE01KnhMu6kuaQrzgb9sebz ZdQhfBSPS3Jc99Hau2zStd7xpF0iETOrqDLLvQJkho9gQcEbRY65sKCyq19j4ofTP5Fne0k4 II0VRKldFdZ/Is/yLPK6n3R2vXo9XDlBhiKcX/mOHOZWsOxFHM0PWGenQkTote9zV1Vqr2kB WLzoesI7EgSaFBVV7dPGNq2AMZkAkincrgaWWgtD0AkqTneI3Q2tsF38/wB+XKfmwIhGzTXL rNgA0NMsaAFDx5vqWmTysig0IVZrj1hkitQu48FE3J3kZoyEphgCvBfYERroZBm+hi6GiWCi PXKvMDbrbWCYU2vkF3Nyo3tvzg3KB3I4xqV5CE5xsfmxJaF00Jx7qlv0CRiiwqBSCnKN57Ul ojDwAbbDY3w4dJzxvGhq8asJkJbgdWrdrjG8C4CiKHiRGhcbSILQhjdlvjWQiT2oXceCibk7 wlbAvWDtfQfBBVwUCTuxY5o+1uTyaJJyYc5NcNNUk3AUIL3CsPr6OhDugVqqn2E4isHc0cJs FQb3LyZPoTHzQXMMlLjQEVRtJBqnYGl3nQk6oj27x6xdcNO9tGOx5xm0LQhbyNYBvLRUU8iC l7y9zqh0eAwCpHfkyZxFOIbGNkuj+BMd2kD7UI+Wjt1RU2yBoBHnavsf0FSTEjQD0uk8lE4x J7gUAyKpeah/3htBEKJwPgZ9HsIMAbQBfTHVQmVFKTYFEwWHirijTAmBZRSoSTeygqhcY1Lt e0+n4dM5QMD53htQimPie5lUKEdftl08BU6515Q+UwolVYPXjGNY4Npwcdx+z4wr6kEDwjM/ cX6YyEgyA2qzBaUoOFOuM0N3O6sjrzr51mvv+Na86z9xfpg8qowPyw6NVYJ9su6ihiLg49n7 PjCb0QyqKPHhvxvP3F+mPgMVDt4IFy6UEtEAnBsfcwZQSGjLBOPRGeMu55RoOV1wecBpSg4U 64ylGLF8zr3PvigoXkdkNedZ+4v0z9xfpggUjbEvE8C/GadoAgr1xiLNhcOfsTfxvCKkpcKd cZ+4v0wRaxTPie598V2MfXxrWE6jVU8b15Q+WYPKqsH8sHPREGpAgZ/qINLxPG/jCk35gga5 VAMdWmExzxPCZ+4v0z9xfpgVA0w2VJ5yRbUCK9ie598/cX6ZFYcYFYdecEPlNE+2Iqln5awH XkftgOFrKxRNeN5+4v0z9xfphJsjAb417OChQnCxoOR1icIDIDt1iWKEmN6BnY459YqAFK9/ s/b++q44KAhVxNYQm9uBQRpCIIiTBKc1WCQJu4UAvCQDpKsCiIHJsgphWFUJt5wPeNFIxU1i DjPJOAcFAgwDkY8FZ59OSOvARJ98YFZY5EWPuHt5I0IqNAL8GRrxbd5w3ELr9i74Od+jqFHY FgosNKKXZzm1l0RqgvQhqQrK0okcgRAu5AFIER2ekTClSx2NDy09AlhoidoNnvfphYVYeEBK wDYlXm7wsYbVSwAgFurwHGKF6klVEiGwXsZgwCyx0xY6cj28k3pMvklaFIADztxRmrSGiB40 26++/VU3yesdn0Pto6awSpz6saFdpocHjWLjq1jK3bmiz37MbweH7LuA7C3X2M1v4cuqchPQ 4duIVf4SQVsrW93i5KQ3nVY/CQD2KugBWCwYCspwrqbecWUdGYbAN1Dd+mXXYZbFLgpdhCh2 13iY2puA1kCuqznF4fGYDRYQA2HbaIeqqYh0ISZqh0c9dlt1qIDyPchA8ehoOgg2EUtbbNdm GnOZmk5eQW6Jt3gC47I4YJqNUdank6IpalhwTuVtNnHoUQmO0b7Rm+LMDEhqEYlQE2codC4/ 94i5UrbHZtODGXmqExqmmeMJFACBNCJYpYCHXqYCKipTqrfoZoR/xMRoUySNE1b7XXeKCZJn hA3xa3HvjSq22lmpOnfbTVwi7ZgqA0qlpt/Ll2Yr3EqgIBqk0TEo5X/TTyCV6D7ZvWCaBocC CO7oNkZusAEugoAaNcDjN79oMVlHjrXfOIpMi67hoPcp8m8OjtsRwgNRzxx3nPLQV8FoAbDk Qr6qOMkGY61JRBHPIn7CDDIsAjUrCwvo1aGoCywKCIseeHJAO7cmthUFBZrepMTaTEE0l0E4 5l8FlRG8QWFjSO9bvoQD9kI3rSn0uucDJbCJCaKlJATPGC60OpRbsAqTUNuL3xRVEJFpzvLE mkAs1UxYEOUB2HboCvdI+4OGVpQSQhQEMoYu940DxqmtFF+BTSr/AIlTEQ+4/XefJPqFuBw+ ZQ5Gtn+bWSBEhcYTxBvXHfLMn8EceAAG4Jo9KiwoHIhmmvJhdWsVj0RJmgduQ9uFTY+Jwiu9 0GuulyB4eAIUFro1016HRU2EOgegk9FO1xrIgAV53UrkpoaxvFhSEKfYYYCsKPqS3jii7CCu tF26yD1D5rQlkoDxNtYAwwpQXdaBHTY4a7RJcMKSBsD8sfemugZ7BdTl4gVb5RNEIVIq3DKx AcLDdIaN6intv2xmhYRIA8FHknnBzEQDYqBTaW2vKDjw4IgnTovPJN7zbWPM1CSwaNLQt9SU UtVCybLQdHgIH4VwiIAbSI+8zXR1MxtjQbwDyUOkYKmgL5w7fDrD3kyKDk/UnP316M8ANuQk NFQBaEF1gIXeMWBhSqINXQ0Hu+bZSh6QAAJBAo0REA2QSTmgnlp6CDIx0EZqSLP+4FvEdMXq dsQbCGCuuEGECsiSmyuOWRADaRH3ma7OrGNscG8A8lrCOSh9QKNArRowpfWQIJr790399epQ N4TToXqOI2nhvBPaCBSaigUWleN50lpgxrCa94+Ew/nkKbvUQ4ijQckXGim6MHfzlbBSbDNx YUKpbolPk99YEloSsInkNhOAQYmeSGTISCKqINWg6wSmU7s2hhFrr5xsAleF0iC5VbrTrNfS mq9ALTxq700noxCLG0B62NVI5PQbO7OBL1iI9Xnc3f57JSoIitOy+idwdAaINSwAvs4pmijC KkQ2QWzbNLMdB292ZQNkbjm75g8JrmkhMBhoZ1N+sNJJ83uAHXs+mGA0YltyDAbUOcIQRq73 ACX2j8YydTIHOvcliQ2PQD7EoA7pCQUhA2ic7I3zGwFHIkdOV/iYBJII09xNL2nitxCEDyWE HJyTX1YXYhBgBON09pqUuI9KCIRRwMmFBzy4Buo3hSSLt7bOsW/gRuahgTZ7O5rbwgShtPSD 34AYv/VmVBARFNHPBxmo+K+TbK0l2R1zllFDHL1B3tFfpi/ETdBXCtjUFGyY1EAXxNksu9lV OiegvElLX7gKuXS89DcMQUbEwIOTXZK4xpWwaGAtEtjJcF492Q7JQKQKIc8HGJy8t1pRA3SD Zu5tlpLFwaICksm/V3aCUiHWu+uznFOvIpQLM2aTe+3nDRIqL3AIj9Rx/qlCKYJP6U0lJ6SU PsEIogFaBqDeAbJpg4BVOy8abn+JKegtbkMF6vhzd6mbgaEkkOxyLlynxkKDbmUN/i9ed3rG sfUj7+zJuK7XodNBUvuC8aRCa5uhCEkGZBvyJV30d3BloLNlHdySONm93N2NY9dT3DZzpxh3 fUFIMTvePcUFnmA49yZcuRDiUoYg6HlQwLyAhqRsvIHDTNt9VEBs5hrBs2qcmQt2Qx10h2nX CQdhiXHxbwNhQalL5zWAEOgIq7GU9nOY8aIFt2F6tMdVmbxypO3cRHG9tJv0ch9FA6dcJxlw xNgDibQsOALrjFIUUwvu8YpfHKXu1UVroLsPSgCRNGQaDbNrNFLHFghyCb4Ew6a+Pw3NfHoJ B9sb1FJFsCJByN35wiXUUAL8OTrzc6xJdEbj9q7w3zr0VRI5NsEMI1ihdPGOMI7AgLC0xQUl DjKE6uE4A3YAFdlU1OUTCEZnA2PCTfp9bjgZLT7G2J5gJEqapSoAdvGVRRdYANrRxajBMc18 agiD7Y1KKTlBESDkbvy7k2XwSNCgiK86MTZqqJopedd8ft6l7+YUyDyG+zpvHGDIjB6tGk5c OOTlOnt4WkTrvt6YexegCh7BBdN3nlyVP5C0QeGmbccGQ4gYIqWt1KXQ5eMviUIUD7Nzedg7 OKTPSSULTmjU0cZwOAUixBsF0a+TCkTCAxBw7W11pvJixWkUSFBzpzfBmAIowBbe9qUZAvqp MMEbYke5VHtyGPMBpnag1YS1vnJHbKglFGULp104FZgwRiChrQGGng4wK9UydLU2uOu/BkMx Lml5Ssb+r1BdpRZJo+J09LyYwnExQiCF69sR6DeJLyGXaUETV3426wHoG5zsN8jZ3sLicHFw hpQpves1f2iVRgE5Rpxt1+FVaYKgeRMTcI8CCL87c2NXbO1vy3v5zZ35h/5egcqloHuY5tQI C8zF+mb55Pnn74LKyCABAvwT49NWEbQp3nQR0ron2NHu+cEZcwngDgaT4xVpgqB0ieMTcI8C UX525enUhfA+eDBARRna2nje/XZ7YhsQrmxTiatZgm0IZks0B2SnD5yktvR8ATw8JNW6ybts AVCHYaii6T49D028C+a51289WYQlXAjF972wBfPWA9pFkz7nHhUbC47GmYV8RqgKaZd5qZSD Q4Y4fwzyYPK0PBNJbrBcKYzGR1Ft6i40LEkOLsaBmiaS69RVjfE81FtCnPTVTr2oEFCbosqq 1T0ADBsMTa1tG3jLIA76VE6TTXvgDwJCgB0ezSXvIQx4UTzhlcQ4u/VytUxQdV7ADz7Dmiek SYMTlCh7ExwiYIUeAob93AUonS1UOhj2q6LhRuowPlcSRXdYZ2du4MBesEl3HSMppjaKJTW/ 8SuFdDxoWuV9LeAG3ZkVvflp4OMaCTGhApucO+eZ25brJ+ZRteHrg16Ltcxy+UYaaNb1vDeo aBRGIEkndX2DhpyGqgOp37B5MsiAXXlLvv8AvfTYroRVkibS3kgio3qfQGgN5cDcoTeGfcMF BnKu0iKDtHBJ0AQlqTQdFXQ7xYGZKgEJF6Q3dN4eFOOCHwEUaqwMfRsRVPOgZ8Lrzq4CU2+c xrZRW7jeFBfMhCGnVz4g3i4ciFGYNFYpc9w3vb0R2BEKBCmM1Agu5jRzkZmwAig64ZRjsRnA ZyLofnWNE8gZIhVmkYHlpmrOyFH6ZuZia0DwptSXumsKsB6mKCAhWy7UWkPwpMgVVgGU2NN3 ppNO8FWgbhvs5/mmPljtLje/TevRw5DAXKroMHPYwkURNIneWZFA2BuQ2NO94KwpqBFzyS6v HonJRQLlV0GUiBIDTss2b98bpwwR5U4TxhMgVVgGU2NNmZpNO8AjtntEtD3cTmEYw/r/ACer ipIhNwHvaPA1S4E5iYSNSXYHfneMYSQ7TPG3ndfKXeLc6xwLiaB0D6r6CHJxBGkjylOZLng8 DYwTZAfeThmAQ0ZDKG18Aa0bGRd4KGjQBodAb1r061/zDH7SFxpFXFtjXFrAJb3hDrLuYSQ5 g5TZD0aNsOnEH4Fm5iPstocoNIg8jwlo6griB8m0YIRoAZbL3QDIOiORMsxlEAXQ3XZG51rD bYjnCxYFCBRnRl9AwIMEUqURaZr1U0OBYHwEEBAezilTzwSOgrypTyriD4N/CTaN8HNNGFwV bVGlpQTUEYm/Qh+08Pca8PGPpid5qCbRSEt8fhjtFMXYL9LiSGojjjd+sveRwNW9wZ+UJrDn QA/2W+G7r0Jl11AzkaUj7Obd5cBUcuCwt1MazV1oXssRiFDWKjEud3GzR0tfHpFSzpN6ESsv aYtfUK5JLyNBRxYohooBQRgFZVPfLw75TF2K+lxJLURxzu/WXvLD59Pry4R0c4hghL4E/Jzd C+qwgKPYdBvkcWmMrSqaQbDyd8mI86A4J07nwOe5k1HBQIYpXgd8r6PIObRTS8EzXeObHbvm rVSbusESnm7L8TCTZ7wiXXRLOToWHsekfc7+rvc+tcMLAldCJE1OHhV3xkakLmqKGHLsBmrP Qjw0aRPOdn1d4aielG20xmk9zvAyzRgmFE5Re4l9IOqvSBNjSxzkzyLBROWtHN1jO7USBh5G +JwZZwU5HKblFcc+hoM2Um+0ZvjEA2N3gr2ndeXIC41+UCFAGgOua1SbaXShRSg0Zp3zkwGB ZUlhIm17hJ6CTUGAQBCNdjHZsELXiCoZlAEne1bfw3uTxSgP0uWkSAoBWOQfceNmMJNQEe0h Hi3WFmkA/wDg8+jv0XRxXQlUBgJMdcHjCEVDTROkugeg0cGzHMayJdQRy7luAsSQERuRaQtl 8D0WsBtNOiNTrX1wSVautaAYDajGw4xXCUYFIoKqkJo0M9yOKUB+ly0CQFAKxyr3HjYovs9H HhSW+wdg7RPcVzUbbhQQtPQ5rtNYNASb7OxMERMoCIlMk82wexPJwclm/wB19rC6dKge+NLa 02ueXFlsgj1pSBebW8GEEYNZTi8jH2d+2NluZoY7AC3dYcpg6rqQqIUAGGuBwZF5CjDwIckc b8ZgJTJpRm9zXYK1oOxZJKFYNEkhV1t4c+h6VlgUQG1F8CPOJK76KvMFBrV3y8Qxwt9juDBA URJsR9G4nUoLLAwEWPPDlcd2AFYabBQeG9cYC13BrYl0x1x78I9ZtGwE5uyNnLn1FIxr1vc4 eGDXEwML0XQBt+l2eVcabAGtxSyd8v1NYPgl/YTrUsqimN9NZMb8PaVGdbuQVA0zXsN1wL1e LeqfhWCngyJVetzJd8riue2nY064UozEAageJZ5PfOMPVmwKl6f9r9/QnUlgGlAoSNNps5xY FsSrl4gwt2GrymaxNAyl7JBp1LMMJFMUq9sNmiLv39GtBEGDtU3yFR8OcyZzUsk9KgqGmGfG EEKbelG2k44GKlgyJVetzJe8viue2nY0644ExSL8U4X2DbC18UBgu9dpzt9/VwDNBBkVyW2G voxVTiBJiMt+BofcYnaQArWWg8d+DBu0NBnTa6s20nHBhpXauLnTQt2Xly2vsGnRELUQ3ic4 BFUgEgfEDs0GroToC0DSgwZWux28+kZUtpw9aI2Bs25wmEC2em3QzpvjDLKTYcQ8BCWNduPQ qHCgCBsIC1LWaPswJ68Ewk8/LNwNgtk7y6OVnZplGj5tpoRZDHyYl9oZ2FtbQoir524Sh3DG LYKDpeTXlAuF5BDnTTZo+HrzSbmQM4NvOy0kMKNUHa2Qq8Y5BmF7aGRNBwT56wYiBlEpPI7a AmrPVungUQAcNyUEuyH4ZlCo1J2dtczDl0mGzbKadXXkzlMFu8Dp3yOzffN/3h5ifDXbhv39 GX7LFG5HPRm7JuZGDpH1fBrbY23dzemxLYQgrCJv0uIXPZfMW0unhF0bYTkoDo7KaWXZkia4 IUi2dDtF4N4yxUSk7O2uZh30mGzbKadXXkwYcUTg5SkZ2leL1gxIKmgtdzE8pHn1lHmB5Yrx xOdOpm3TBwARAdHA4V4ucJ3tdCdj8aW/cxq61YTfgHhUsnU9Ghyoane8eWN84Q7aEpIZpt03 74iEh92BG17iPRzSv2ECMbcj88+lDSXLL12JDxRxQRUAINBKgNpKFejIKEGyEHdICa+W/Ti0 opAFOJPBHi5D6cwz2pL5eQ3zMdFS2BIKqNqQtFuIshvhRxA3eKb8YFhPTUpKeGC3tnOb5O/e 3VFY85z74sJOb3BRrxrZxr0YHZRyB4LeM3LLnQ1SzBL0q0eK9VIhwowFO+jgXEcwsXFAjY62 4ETg4MoCnxQXRyJg3AIFBaGKQogG3XCYCAoEbBC8+AQ/DagogthCG9phlg1aAQ1HppefvlZQ KNQHuk78DxvHNTREdPsva4PjfoThrO7sA2i2E2QxUDRCcOugMUg2m8e+ra3dxLrVWwME0uBG LBK2U7xTm69CGlUJSAEN+UAVXJkXjKIl2gQIHSMHRo7qoDoatpqEmq6iogthCG9phnhq0A5K PTS8/fOBJNn65YOA8sKlQsIQ/dePA8ephDc3xBbxJwlebrFeOOBmuhvepvy1gI5niLfd/TFu se5Ca7mBrQrt90MdAULQt+vGpo40uOtAwq8QTacTT23gWxbdmlSYUqapvDUWohUDpQYFBQaC y+lHEyusZoU3s2JyEmxnPWqtCrHQ+M1LSrWbB2trIDlsHLNDGWAJIVUDBd5cDe2DoKaORCLZ gN3+HE8rAcKBs6PREssLugDQV5DXOIc6DSWAXhg517byeDb1xeXDmeHXGKAl2FgpoQcErzfQ gn6IDe9KfW74y+UtQ5OOqoDWzWaLNWQJpNDYk0pxhMLtgDV0bD/jeKvtt5oTyI052pgPxKLt HiCpdh6BOMNGiy3bZwR3pX8LM3jUtGfWYAK7bICBWdJXl8BVKsvQOTu3jwJ74c0ATX3TXz4a 9/QxkrB1pSmwU0cnObExL5AJ9gKwG3nIRWaiDL8OnhvhgjmAIImKWZowxvZ6UD4ZKVIbPG8M hOrbrmKVARrfCOTdm1LSPrMAFdskIBWdJXl8HNhz78/drrD3ODTM+Y2NxarovUT7wVt0LZ4s 5besfQeJhSgS/IbHiKeq2aRGn5Htu4sqRJODisnmdr4MMv3IrylicOiTlyq5WqBwh0l23ro5 wBZDyPQ3jwTALYMwTewEEV2AaVqVl9OXp0KK5pCAykOjK2cZHSdQlQYos5SJDRWR4lXaJprZ r0CIxqvilKRCKntEGACPh0jps8UHZzgZGGRFF0QIR9z6abwdnWMJQ2lOOTEEsxWkC100hvXn nFBgEIZhij3cGvJEWfVqzXhwTl569VPD44HN8hJAXXOGDgyLopCQhqmtYZQeIPQDp9OcUhDz qlCtoKrfMbDIn7PuGFpKBJ9cIdQVIprbZ8y9G/woejwqoUPoYgAA6Imw1wdnlzkeenUV7IRv reLGyAqn60666L6cee0iIXURt6w6m8Q3IpqCmuQMAyTNCxwlQ70HeQ9sozELsyOIehWhC5O2 2o8eMsPztH0S693rJC3UlGg0n+xgfiwqoUPoYgAA6Ithrg7PLg2zQioS9zudc4Dqx3DkAN2F Jz9PUDEvBpPc5ZvF1m4jqgDlXRpPmYzrf6ZTmcuZde+A3QuFaLoiN8r6MGgIpbjuEy4JWEgI bB9wa6wOYZIQiTngDcg7BJKEIQiI0QRo79BC8NTQDNiSeK9M2H7YDCgEa88TE0XFoS7OQqnz 8+gQCQXC8jAfsu0N5uHAIXgjurJxvCwaDerIlAghNPSdUiyAk3UoyNnogbCwniJrrLCeNICv BvkvBi+YZgKcvMps1fQwwReC7Z+TiXe/eoXfCJ8pSi2mJTFvAvT4cBa5Kk0HhxveIdCBJlPH Ik59FFQy5QA752pwV6zYAikS3rvhR5pKR/DHTcTQJE71dYAOwqcuMu4hJ4hTEXWpBVy2V5Rt MDYQ6o8A68DmPoIbtHDAVNmKIxpLlQPToGG6bpVNa2qUZq46DoRCgRIq0OIFIDmsDRSPGsvB grAWoiO79GBs/WoNRiFU6EYzNTUWjBUKQDbwBwGATcSAJE71dYQMwqcuMu4hJ4hSynAdAE22 iobTA6JZea1JSoVJFuz62p6hdZK5J8r7DArYLmvqcT0avaLMVlI4CANBYOUYdY8NdreqgAtd AGg9ISeE6zs5LfkMwLlbVGCWEawjHcOcGFFpTcIE6tIqgpXciCXJWEKaFFa2uESm8GrFGyGi RNxcGtFokS1QqQ13SOBhCYUs3PIO5ed7x34SdRVuaUSa2YBDcY1InMLXiPHthh5S4tBltEjn RS1mgTxgI02EQnNMV8FZRCCHa7VRd1048I6ckXBu0WN63to0DbTmSlFIa8PXow0VaQBI6ALU uGzqoyi9JDcEnYOBbElb6FPJ1rfGS9bPoC1E0cDhCGP1MiaGiloIIoognBlVmXNSBhGi2OCF Pwz+GiZRbPRLjDxIBiPkSa6Hsy1BpFYl8tLvxOdZoeg2E6nQ6FhVeyehsxrIgJXyaYm7JhCJ JsoLvbzLsq7y/wBnigUw6NU2oF3g9GHBrwHV243vsvohwJ8jvWjH5YPvBdikexja2X39J+6J lFs9EuMNlaPEfIk10PZkjiMBpV8NK5GrkvyGSxS7NfC+si1SX0pP5A4zg2HsR7s0YeXRi65l 1pZHoSPbxvAJudlQjtpD6ejCRGjs7Q+DRvi4cwJnTF0aSSPL3YkHbDqWHxYt2Nsw9wpIElrs Ku7bv0FDlS8QnOh6vyyD+Y68x2MLtr2YrGrRYqPyGyHArQejjtKDs8KqaTgcSQ3UJdF2qpoK s9sSGAACgT0ihPIN69I4ipBIX7Ak3cNTXZeVu21qqPfuYAyQ7GII3KzufdggNs1RDe3GvC+p hMhbir9zWcsPBjzfiEAA78E6nGAMp3iTkGx+M4EUU4hzI2jg4h6AjWEl2spCU6LK6K416uxC W5i4KM5Z+GVUT8gQZ77xBo6AGiAuk8nD5wokHpNAr4weNa98BaEzAWVNjYaFsoPoaNkILUKd kKdmIziKCiDwaBvUMk81CCGwRgX3GiYnUQ0ibDVGcyBfPp1ksk9iU/3kt5zmwjZeDqG3oqof 5CFPfeINFQA0QF0nk4fOTpsKrUbRCCSTe8TR8GNaUuwVQtlj6refZQL3+FJzu9YKuK8UIKDs OeY+2EDBqFe7AJTU7D1j1XUIC4KgKnPzuvoe+F5BUdx4a884vaIwmuBXbZ3r74JhAEeBt23q lB7biBSKIoSalIdHoyq8YdSHNmSQ3oMF8SAqFK65AVQTvCvpA+INXQoqytlgBlTrg3odoYAc PB1mpb8DyhdWa9ut4GpbTBUGybtV+Dv0OySygS1qUXjWHSyqDyE8cnwTC/o6kZ7NvmmkypQ3 2YN89kDlN+umnDBJEK1Nl19cZT1rHARdgb9/GIKfBWdE5A8mCt6AXbRXCWsD6EwlrKc60UBD kOQu8ZxbiAmhpztrq60fjura3bc8/wBuRxAOiidcd+RJuhXlYVFAgpocHce+A3biFVLltMDg V2yMWVlQSps6sw2tduQpRSJ3upSJbTRN226u5FFqQUmnFZgkJGQCE6KPdBR1jmo8+KHcLvlK 52QxZdMXACvACh8KK4M/ySBqMTY7447wLbK4XUPcN3yevOKp6sEaX0OmuC6w+gDlEEOk4I+V VImmrbGAJmNrqjitZIGLvpG2CRYy2PGWmzvXMC1QUAprfUy6FRJnr3QJohrdgOc+qtyqWnZp EjaeiBKIUwKDgUOxfGJ8gLunu8BomjXZjz+iaM4UQNe/LrLuQCKgC062lNhKudZY21gFQo1b F0uFD0FOhEASbDoNT8NvwqyWAabIubinyQDQBwWAc86WnFGQAuwJacgcvAQlEwPQlqAwCHGO e61OSE0wok21gw03fpguEMgAhMHOBPmdFQSCIDveUjJmQHQnZBRh6HllWceqg0CJsXL2pAzA w4KaOj038VZLgNNkXNxj4IBoA4LAOfvgkWdHhE26FlVnAQLcTwVWgCoDk6IepRTcOpEnJEWU su8BaAf0YNEZDTkdg4vTtCqAotwaML1d5RB+sPVq7211x1fQshU26HpxqVTepneN3qa3aDZK c6xu0xbhLqGBy0AzdCt2iMEAIIQAkAIGI42EovEt9yQx7zk5ibmDiGTZGvCGFUQLdFROBecr xfQHMSxRsUVqadMTkVFZ6oQmnvAsbmE6bQbCqIoJ6I0LrNUA6BNib3g3pDisoNbNA1r4mXzT 7QVVFTvcfbASTskAlKpnOU4voeAaUyb1CT6XWGQLfGSoEmxgmcotioAyKoiQoaLrwI6HcVGq aZ4xCoYHmqeyh5IejaiwSKPcAJ9zLOSREZHQXTVy0hFcnhTt1rrV1+G7z0k7O2uZhMGsMmTb 2afa9s5XXsVezpp0fLOADzmUcVvlxz16fvyyXPRm7JuYFJpNUbPrk3dt3cjWylWXwxj02wOc iG8FcAaB2QaKbIG8JIsE7vu5e7rDF1qd6wCNRU6GvTvvSzs7a5mE4awyZNvZp9r2xW0Ug7gp NaoK01eCyR2tQSEqrapR8UfTjPMnvl6/KPbAFsBBAiD7Dq9XIAeG0Ka0Wp0usbk8GghuAy3h 3DQenvj/APC3yzfnJLxZx4KVBTgAvOlpnaCpSBB0UC+WDlqT46NnfPd3nd9EnzHU3B2WkWi8 G9TTQ+oIUdujmr0MNclGxG4aSltPIyFwuKNmAMS1dV5TbRZdiuteI3UEpstx9BV9eh24PL8j 0/bHEDd4pvxgeh6WFJeLBb3zvH8T+79VvT2+ffP2lwY199ONemp797h8rxnNptbdVmQTjbaP FWYhKcKMBXPRwXPeJu/5G78Y6KuMSB0g086NtMS6RCRBYApgJANumqkgqIKYNFbiDy/hnRij oEid7mss3WXMMXMmvAeagENiA6ieRb5HWHpUgTsyJdQ3vQdY9m4kIIjUsI7OHjEC4zhYGAUY my6OCTfbFdsEpQXlRKgf2iayiIzbuQ7egoC3E65iAcooDCpiqyxmDH7kwAlEpnWzUQSJ3uay zxZcwxcya8B5qngFzUzIglFS63IEzYUkEgDuFOY1U9TpBUFKwuCUI19WINs8Ikra0K0n3N0g cqOAeDVLo8YF3Sa9RaYLoVp4T0eCDUBcaVVnRwYNzaxSKa0Dp7naRbpeE9ysPUi+5oOFMBJT qCaBBYKUAQ9FPQAwvqDYdO2oNtcwFaDf0xAiQBcDLemBMnOXW+VSOTJFVi3iiD5CcK42BsJ0 DCmkUK6Ec8C2gUDzLQHmbVK8HNvLb6DYCJG3HDgP20VAUANkC0CsgoY7RXDXSJUA2nJvxwZt kbTjSBo0/L1hJZKG9wk64ovtzhkMHhbQAKB3m8ftaQ61K6NC1vfGEUKCiHbtPaGjzv0W6jwK SbWjYO07w9aAErNbQiHybdfhdplUloz6zJmwD0RCscpK5fA+awASgJqfAvGCcc6LJ4FexoLG egn0JV1JCXYFKHJzk16p00iK9AVgNvKFR2eTUChU1QA1u4AYddBXhK18J6Q/MYHMaL03zWqC B+kFUQMqD34PTaJVJaR9ZhksAWjArHIlcvgoMKY67Q3KzwRbqigEaAkDmQbGjhDn1MQU2N8C 2b6ezfDA5NomZKClehscyLvkzoUKbAFug584v1pMTQEOAbHmkJ6RDKKfMlicJCTly2kigokM C9U1wbLVzqSKJ5ki7LwtQ3K5sFqhWXaUFhyOMe0xFrYWRtWMF2YSCsCqgKwgZzQ02AxS42ud CIxN4tEV2DgPvTOykqKM8ke+GaPCUIBSnmygznkTWygudHhcB2lAvHigIl1TCU2SnHJjAV4r TQtdIgb1285yn28OYxR7uLWthCzdN2S8XRW3k49boQgy3RAo9gyVbhEIETost33wYz6ykELY 3D2zUcIIjpHaDwvkVD0QBVV7QRKoidvL8MgiJR6xd70APgdcuFkhiEuvF84AEM4p+3x6CUaI UTw4aTDAJHg8Zwe6A15fLgB5Wko8Hg9OVhAkfo4EIceiCIlHrF3vQA+B1y4upBiTT7+cLCGp CW8p49VQooSg8XxiJzakR3ePnCTlSVEkX41nCwgQH0PQIkgEUe7ij3OIafbxgR5bApeR84JR oBAPB6ACBUBR4vPWBIG4N+jxgCBvSDX159GypyJH6OBkQ7Cx4fPOB6nw0/b4+noWdohRPjAR COIPCa8axZTrQmPy+ckTiAUPA+PVQNCRCDxecTIioIF84ORohRPjAHKameQPPrLFEgMcV7/+ jJP61DKC/cffNHeGOqUN+8Z5j4ylJWPckeTXOEjmO7AVZ9MbUMVBaUPn2ylDBWg45wNhdQfq wQ7EwrsYFYwUY7xFOVYrKM8TEFcSonwyg/C6uC+yJ9MGL3UIYMAo8I5EiMOYyVbHcj3gzEhW tz4xK5eEXmf71nTWsFD3zW+LALm/SMPbt41v5YMgZKEaPPxipeHAhVXABuuFwtsIy1AITY8J swKDCIEDEB2ImtiJiSU9iEcFZltrROc+CgxHPhiIp84Ql5u2AH6J9fxxhaYJV98/c365+5v1 z9zfrn7m/XP3N+ufub9c/c365+5v1z9zfrn7m/XP3N+ufub9c/c364MK/nVRg5zrZF5BtfTW YABETBnDRh13giLm2uCCGcKxhOArJtEcIUIeDiUYKIKFSjlUR2ZwbKyEejk5PUmzCZyhKECr KqoMYqirlMqoEkHZsAWB2+foQ1J5huhA8kzVjRZujQkmrRpJjv3IhiweL5dHvCwQ0iQPCarg 1rIwpL7oNIAdjkJOME+6aRpXn/umCzK4k+i2hG7OPZ0KUQVJypjxp98Eto+zRAGHsGtg2EYh rLhOF9oFSHGMqI1CtgJFBHajlFg0UKg5Ib+gdbwkqqgg9CihAUmbDysw1Gouau3HWUK6sG50 m5NBWkLMcFdvByDxoHhZW8OocbAxQql7i5oywII0JBpCMB5csiM2Cqgu0UReXnB3PokNvRsQ TcbHP3N+ufub9c/c365+5v1z9zfrn7m/XP3N+ufub9c/c365+5v1z9zfrn7m/XAiSYBu/P8A AoQCJkCsV1ND9MIZaSARYIldaRi6cTL2RhEIrUNDTbXisjIlAiIJoObrawzgotQLCRyPkRaD OyEoJuiVCsNXC2ZvYCr3JKGdLGMJFSMalUCJOAnvnm86TdrzYIPeamA2WQNQKVSQDkOT0qDr bhpUWjZd4AGl16BwKc5JcGGAh1bytVmjUEwxkqF0GFcq8sQ9gQFkJwCCIwFyUNwo0tdUdcJG VILSqdg2BrnOPm2/hgDQmUvwrtBE81vhS+7xQwnva1O+EePZ6wTBhICMJLOUQj0tUTakxNwq w0h2SuINTNDAxiAD0cq5F3AdALoFNqLOVt/Z36Z+zv0z9nfpjFDaQGlQR07HFz8FTyXdVkEP YqCo9uGlnwrhVGGfHqXQE+LQ3CEwQRe03ZAYQEKjRxPCpXU1AWsDWmBHFhBljSggCFAmF1ctDm5u0wmps15EmK10n2mgQUFHWZ2xIMAa vsOQa1tDh2BNDy8+CLW6e+JfcBopIozizeI3PCoF1CKVOHhuHp0dDPFS8rfPBizQX6wQCSHY /fQ50ACpMSaWKbZVScWdwAKVtaDvC6uEWIhsKTPon7O/TP2d+mfs79M/Z36Z+zv0z9nfpn7O /TP2d+mfs79MCBBolj8vwI3EyD5Ao0dtXCFtNaiaKfAGbUFy0zpRmjiPCxN2Y3gQg0m9HYDy IsdBNRwk4LEDoenfxHdjDojKKPkS6xe/Bd5AK0S7Hd1jaKbVqJwFURhJVmBD6N6AgROLW4db jHRgjLIQJBns1+pkMqbEDAotiLJxZh9irh9gIx88ZvQcmOgjDPe/DGCLAnaXk1Dhi0MRIUob IDsF2UJiiAwTMlU7UgjW04qm8d0CFki0yUU98l46Gk7hXaSWjKIqJFUXlLsJTfMe8lp8mF0B BtArAuXcajaitu7WinVcdv0S6NdCD5G4WKhPHQIqLmBwvLqpSiDI5fN4NG4LvUKYJsG9b0Dl qRPI7+AgS1uycb9E8KICNnCEX2jgF9XDK22RWp8YLB128PNMBD3GaHCg3vB2oKJVOPDO6MSE FBbCeWDBsq0WAQlpxuT7ZpNMgE2Qg0E2mRZCzpoiADcc2Bp3xgs35JcqAKs4KdbMu8CMA3et 2ZHlXDOtUhF2bhADUbghZrQZCuB4qzbc2UcxR3EwqGojrNpL3xBkFYNsvtwKCbCmrOBpDt4E UDbAShoQEIBeY6NWlmv3BQzVURRd2G3YrTXtRKNwXc3O51JALDddiZ1zUwbuoIzsBtURgS1N WV0cyIQa2Ls1oNb1HnZeIMoA3ANr4xeqJ2gbkuYAKacYU6ipSvP0kMvyykSm1Gkd2MBzyJlt 0aC1IHIipGuQuVTythJcwAU04zh83CDTQJUt3JWsx8cxHUQpEGCyGwhSygiYxrRgGxvrEdG0 UwVlBVK1KpB/ug8qFpekEddjAQAF4W98Kt2TQwf4EVk+QTUscYFAR6Nh6Or73WSq+Ld6QIKX tDuY1JpM6QeYQTs4TExLYsDdHuG17BTnqhSXscAdpyMCNIpgbO7d3wDsrFYRWqEQDWgePeTB xoiVIwCEydo5Qxpg6yB5AC8lnZwYolD6qoGxJfQMVOwEFCfBdrU5sw4I2HiJjkTDY1MsgoOP sraJur3uFNKpCZxEERC1SbkzqO1TyCQPQhoy8sApsrBsXjgwcRQms43qDY2PGKosEIesCSAy D0ZvE47W0lajgJBSsEFKANhEdwT58wkGAGAN6x1sk8L5xJ/laAQjjYmlmrgR6+wBUVujT4Ho RiyiECjc3DIKBHIAaNcCfSbG7asQtKFJGc861li6ASDQJ0rxNc5M8E6BTYi6fAxEQ9IGJwAE rzCbwAJuKrcBbHZa45k4KJSROjRNARbN54Dg8tACzznejWOxOEsNDOAGo+Wzh8vcUjAXcULt 3txlLfajcjQt8u8sPmfa4FKWzk6cPJ/dzkWadkCpsRzl2yIEESUiBrmMvXD6vI2EUw/LFoo2 4RIilXungXNJsBVy1stk8S51bGUkDaCJyMc6znZiRpaUV0+WMRMJp2ZGwUAd95v0wtPCcxsI 5CzCXiT3gEqVThqv5yqVg9BCQJttKmIiui0ZnzFdbEnwppw2sekNAOpVmlrnhnQq1E+2ANGq nvh5HG8ya6s33GPLhibk0g8QEa21gR+dtp797ex3NZvfxhuxR4JZ9uJ/d34YNyCIE0StpxiH ex1CIpqwdWNFwSSINJoaQ0EeAMTDHiFvkIzC1sPGAKlwSjuMXLCqDGGAxORQrIwKwSHYrqBM MOCqkU0wImPP181ByWnu6LYlSCZ6XECoIJwG5U4uTzVAi8GgJExMoiaXFC1gaBC1jHkOLADb NFeVfDjVPqNJqoON0uxjU4NkeQUjRQiokEGDJIFOQgpqEAJAcMCSmWqdOqoBCpdzHvLVxAF1 YzDCIEWpFcVAqAIEBTwVRcVn22Y07PN9W+csABXAtfIhEN40SKqBgkkBCUOSh3igkFAbDQYa p4cDNc91gIG/6XNIo8QKz6XCVBGlRVgNIV1s5WuiZ2WJrgJL2KOlAx34qaGD5u+MVDBDLCwd AUDRBXGKDU5INmRQGbMN4ZmopRhIpk1tAKZZf1FAtpMHQbFu1fCspsWks4l4CeQd1ucuCuhg EdckCkymMCBqA5qJsopZeKQCwl3OdPkcLNypxghW32ArAhGpcdQWXyFZBQg4HRB6CgRAtm9C kdsAnxSDLkl0EUCYp7mhzRYJYKBVAyakWU4C42hWQjB3j/OTMVA6BQANiRnMyKcFFGgPuc60 5XXaqclA42SIwozx5OtBRkwh0NAoiq9usCclUmzJEpg5lL40dAABgCHlXAfwXioFR7oQrOBM K/RJaiqAtRyFeTYsCUSeVJoANjJ2qSD5Za1yVsLpMDG6VALKkabCmm8OVxoIioJKiA2TTjj5 GQMUNDtQbMs4JkCsNwSob5l/u0fQTOJUI2bejGpDsNGkVtd1Evkv22Colk0JFeRO8QRaDUqg gt1b+d5JNmiGHYtjtzD3SEqamVbKK6dC6Bxaf5YKwkiawg6mFR7KMaoCLA0oxmsiKI9sKVCs L7Hvjnoy52Gq6PXnhxgvaNJwKkRrB6It54sjBEIjVaTfyMBQ7xQkCm6krJTFKDiBSyoaBNG/ LNz1QJWh2VJRIjwQU2k1BFeQvLV983m77NiSsocAxNBTN3fIVlFhXkiomu8beBl01pF2+546 wpG3oNEQnRVu8VfqpAlil2TjAL7RnSWy+cSRy4NMcAJqKCPAttKRw36TtxAXYgR6pnICwsSq QE3XWDGpQikUBvNum5PRkICIlEwCJEJ+T2kRAMTswePRx3gBNAmaYQx7DN8Dgh1Xe7etYOig EKLhJcgt9ki/w01A6WDZrUkM2IL8xDABTwIJrKm0J0qIVW2t5zTcQerAAEC8c5pJZUE2Ydpr 5wVw1Cd0oCNUtXQBdi9fGJRV52tbvC4SeKpgDQ3xyuV0X6aQXr2/fFp6VJU/ZM+cH5FBNCs1 yIn53D8VaSgb8ciQ9ubjx+8PyB33p93DqVsE1TmvJPq5AgVO8gfDT6mG1pDN5qmgE8W9TBJ1 cpyNEiSg3ftm24lSB2ijtdPxjV0oFa/vSYl2IYmrAIdewccuM34hrJAgICxD3aFmQEouUAAS aNa93DhCqzAKDZA4bMVNKElaEZYFuRasRskh+ZhHm15YdWrUCltC1Rh1Zt6ZjE5LmO4gWJyR f7oICRaJhRGjh6cXAXtEBaNEtjxMO+RjfYgwCR5+mJEhGjXhFTosTbdmcDwgUGA1CnAaHrGF BYgSJEO2yQ6WEtU34qN8gKblOcDdMpzXwocpTRhLIQ834Ci9j4x2mWiCtBEbtbN4cUefIs2d RY62mBkgSvgj0XnVawxjUWGBuaYIcv0mLZAo0ErQdQCeCDhSg5t5aLQK0oxZh6SPINYHXIMQ fOcWVCEbcqlby9zKKagFjYVERVVcM0csgI70EY2xRN3FDWoylDwvUwgCgSKLDZATnl3kb0CI ddGcGGsTTCZNwCQDyd66biUBdAC/C7cLHeD+2wBqYmpum6O8dQl6NaPmd9BnW8WTwqCQFm+8 W1A5DpZgDgnDJAY4V2wgpFXEwzVsVgCbKg4o3bcOMrkXRMb/ANPP/wCUP//Z --part-HQJBAPlABbaAkA1xRlRmTAIZOHcm16VdA1OgAA8uoomQc Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="FELXFAC_5.jpg" /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQY GBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYa KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wgAR CAMgAjUDASIAAhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAQFAgMGAQf/xAAXAQEBAQEA AAAAAAAAAAAAAAAAAQID/9oADAMBAAIQAxAAAAH6oAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAc0dKqIR0jkbEvVZZh zcQ69z20vHMZHSgAAAAAAAAAAAAAAAAAAAAAAAAAAAAcF3uo5fhfseRyPYa9hwG/s9p8mrvs +o5Gl+kZlPF6fEyAAAAAAAAAAAAAAAAAAABox1aSWi+kjZ5tMMvMjVj5Xli1aSX5GxJ+6FNB 4R/IvhLy0yzHXnWlpr1STSRSXugSzeAAAAAAAAAAAAAAAAAAAACDo36TZL98GeGZhnhmVfyL 698wOy6j499QJOEiOSJsKaPPfCtkYyBr2aSt+bddxR9j3VdoNecUxlVlmbwAAAADw9NRtYZg AAAAAAAAAAAAEHb7sMscohrnxdxlljkRoFnmfDu+soRlb10A62Zo3jz3w0bNeskPMj55ouKk 7C3jyBhn4a8tugkgAI8gAc50Y5rPohzDpxT3ACGTAAAAAAAAAAAAaFFeGj5p9J+ZHZ3XHUZ9 UqNNaTfOa8m/pOfxH6/cS8JOJ7u07h574cTQ3Ncdxs+J92XsjjbGXpvOc64zrPfmNn0+x5Ps DeACBPgTwAAAABwfecGd4AAAAAAAAAAADWjZEjXVQS6562jFhNq5xD82zpeU6qPMsywz0GW+ HMHnvhq0+ajkOtqMi236PDTZ+Uct5VbpNkpo3G0FBfhSy08gJ4gJ4gJ4gJ4gJ4gUPWiti2nJ naAAAAAAAAEQltPhvBB25+mFZciPI8ETf4POY6rnS4x35kbVO8Nc3VtHnoq9+3Ir/bL0wx24 mUSWObquypifM83GQAIE+r2k9XiwQPCwQPCwV/pPV4sEDwkcn0ccuQAAAAAAAIkuASzWSAAA AAAAAAAAAAAAAAAfMvmXZ0xQoYmIYmIYmIYmIYmXPNWpd99w/bHfAAAAAAAAV1jXk/W1kkAA AAAAAAAAAAAAAAEaou9B8Nyx5c2xuz4wAAAAdBz94TPofEdmfQwAAAAAAAIsoY+ZgCDp3ajD jO8+enVdFw/ZmeWr0wapJF1ythDwlxjdNhTQAAAAAAAAic8fLeT6zkzotFJ3Bw4AAAF/QXZO +jcJ159HAAAAAAAAAABC255GNPa8SWlf18ojZbBh7jsPiH17jeyLPVuxI8vTuAKrfxFefRsu Q4c+zRvn/eG+04rtRGk0xce1NsAGMM+H8n1nJibCHY8d1FOV4AAF7RXpJ+m/P+0PoYAAAAAA AAAAOY6LXkbKO6xK62hbDdWQ6Y2QJvz+a+hW3yj6Tc7b6khHY7YsoA5ewuA5nphz0u2FVSdh RFFF6CYTJgAQqnox8B5PrOTAJHT8h0Jz/l7RAAC2qegJv0P5/wBsfRAAAAAAAAAAAeYZYjLk emJGUWQeMczl6jtKWam3GrO5z154Dbq2gAAAAAAAEeBYQT4tyfWcmAAdrxsu9OUAAuaboSb3 3BdufRAAAAAAAAAAAR9kfIoInZxj3zneLPrmUaWaPHBH0LzHYMNsU9kw5gAAAAAAAAUMU+U8 n1nJgADsuNEqL23EgC2qehLDv/nXdn0QAAAAAAAAAAEHRM1HkGwjHKSOjnHufmRz0+b4btOG J7HkeHs2NJAAAAAAAAPMcx8B5PrOTAAAOljUnXnHtuoWlX0RZd385+hHfgAAAAAAAAAAAAAA AAAAAAAAAAAAoIPWj4DyfWcmAAALCvHTcz2fLkS5puhLTr+D+inegAAAAAAAAAqsZAiritNM nLI0Z7dBtw82kSTq1Ezbh4b8cdZK16xs2RZB75h6MHplnEkGeOnE3e6sCV54PNmPho15Qz45 yfWcmAAAAbOw4voTnrnLaWfb/O/ox3wAAAAAAAAAIWeWJp368j3DfpMtWzE24+hu0bBhn4Za cvRh54Z7/NhEykiNt2CLu2DRHniPjKAABTRz5FyfecwVS1FUtRVLUVS1FUtR0dDI6Mru05Du jtwAAAAAAAAAAAAAAAAAAAAAAAAAAR49gAEKbrIVjU2w07NJV3lfYDBiU97WWYAAAAAAAAAA AAAAAAAAAAAAAAAAAAAABT1nVjjZ/RikhdQOdsLIAAAAAAAAAAAUPG9pymO+zyDKnTdWzNZ0 Eav9O8h0VWdPGh6Cb5Sbi3n8tMJsKvhnZRqnWTJVdgW2UDSdRSYV50OqoxLC95SYS9dd6XOi pzOiv+HnnUuWHUuWHUuWHUuWHUuWHUuWHUuWHUuWHUuWHUua6XXENcgAAAAAAPKO4rJqvt4F mUvs/War2DYpgjRlskYeV8zQR9eUw14ydhT7JkQmWcLIlq2SSWvemDytWzRhJRhJVcskwN0Q iaLOoLWPOFVIkaSbNpJZYI0Us0YSWG5MESnTo1TCOkyovKvnM9EbAAAAAAAK6ZCllSK3BbWF joLZC9PKuzxKXfNyK+VOwKmH0Uc06bDeZU1j6SNcSWVuuz1lf0dVKJnPWukrI17ga4tjtKPK 0FdukbiFa6o5aK3EtK3dHK+61Ry35+01lXsso5vn1cgmIYmK7MnKu0Q0rNwAAAAAAECeK/Rb iPWXY17A065QqPbYI8gRq+5FVahFhW4orfcIsO2FPcAr7CoJdfKrC7lQJ5X46vDyxpbclwps QjSYXpbw5lQToUmIXEaTDNlZPpidb8/0AjSY5Cs6yxKS/wCa6Ur9WItwAAAAAAAAAAGiOT3P ZF+RCWiUx0ijvArIR0Crmm9AgF853ohT3FUZ1dtWFnYQZxT+bMSLc09qS4M6uPNXmZa01zXG +rs6wvoU2GKW7qD3oaG+EfOuPLOvmFJ0fL9QVeq5qi1AAAAAAAAAAAaMCUpPS6IxJRqsvVPc BFrS8RNhvRa8ulHeCnuK08r7irJ9hFlFPnmIFrCszdV2leate/ws6u0qCXTXtOdBCmxjCkv6 s1dDTXJqr7CuFjXbSuv6W9KrXc1ZaAAAAAAAAAAAa9g1e7A89GOreMMw817RhmGtsGncCrtK 0zrrGET5sKaVfmfhHsIksl1VrVmeqSJtRb1pNprKAXcSXFFTb1RleU1yaq20hGqRq3lL0tLd EXTJiliAAAAAAAAAADDDLUastHpZa9mkyiSIJLlVdoaMUclbIkox05xjbNrbIVNtXmNfaQSb PhTSp834EezrbEk1VrDI+3DAta2yrSVT21OdFDmRjGmvKwwvqm2MaqyrTbOielffc30Jrrtm JbAAAAAAAAAAAeadBM9oci9IpKxjVB0PtLdHiNUHQquabkKAXeXP9AKq1qzZHk1hbTIU0qtm HhskVloSK+wgGjdq2FjVWtMT6q2ojpokuIKi4qTO9o7w1VlxTG7fHkFbd8/0ZXY2dWWgAAAA AAAAAAAAAAAAAAAFPcU5tgT6suZ1dYlP57mQreBZEirtKswz83k6rtKslU9tUnRQpsI9pLuk N3Q890IqbamMpMeWUnTcn1gqN2guAAAAAAAAAAAaI88c9lfhEliJTdIKO8CJVdAKiw3iBAvh zvRAqrWpN1ZZ1ZbToM4qsfRFtam1JlJd1BqlYelpSXdaSau1rS6hzIZ7UXFMbb6hvhG2RSLb 1tiUXQc/0Ap7irLQAAAAAAAAAABrwN6t9LE0m5qgFogTwjayajbTY0RSxVtkK+wqSVE2wSxn wZxX7IvpjZ09iS6+wqjZs0ay4r7CqLGDvrC+jSYZtrJ9MTrahvhFlQzVvh2JV3NFdmVe0lsA AAAAAAAAADDXv8IOUsZato0xbDAjTPPTR5v8NWz3I0aZnhCn45CvsK02RZOBtmx5BXscTTYR phvrbKCY4bMibXWNaSIM+MWUaTFMqyyqiRbU1yKq1gmE2HOKS9ororcsvSwAAAAAAAAAABo0 zRQ+3oRZQiVPQiluggVvQitl7xCgXgoL8FXaVBIrLCAWsyDOKvHzwiXECcTKS7rjTjn4W9Jd 1xuqbimOiiS4ZlUWtMSbzn+gEbZAMJ8Kec51PO9EVev0W4AAAAAAAAAACPoJ7nsi/IhLRKY6 RR3gV0A6BAkG9AgF853ohT3FUZVltWFlYwZxT5sTXMgWhKq7SkN3rIsqu0qCXUXFOdHCmwxS XlQe9DQ3xqrrasE+s2lR1PKdQVePmZbgAAAAAAAAAAAAAAAAAAAVNtVmyBYQSylwppU+Z7SJ aVk4l1tlVnmOeZY1tlVm+qvqM6CHMiCmuqgyv6K9FZZwjRL1bSruuf6ArcdnhaAAAAAAAAAA AAAANXhuaMzY1bD15GJTTsMmnYZatms892eGOzzwwy0SDHPDA368Npiz8MteeBnq3BhmMNMk adwNG8adwadwaM9gAAAAAAAxwg0cvVOSyXq3PeHROY8OocnaluqNpZKKESY1xDI26fCNGp4W /mrWR/Yu0s6/pxy+fSwCDp14m3LSN0Sz0EhW+khD3k+pvNZWXeqIX6rpjrVJgXzwevB68Hrw e7dO5AsAAAAAAArY26MQLDXtPQe6cdhhMgWR5vg7jHTnFM9UicbtNXaG6FI0lfIh+kzyZuKW XP1k6FNiFHBu9ZGm+bSdTzq4l7a2UbMoFkexZkQzixJxKi7cDzL3w8s9MQ9zjDc07TY05GGu 0xImq00liAAAAAACqjSqs0yoFsQrCLYEDRu2kbfpjkrb7LIWvbHMripkmWcXUXerHUaKu8rT p1bIJSNiS9G8c1rykGzRK1kjHXOKaxz2FNYbfTXGmelfJ27iq1Wgr9F7pNsCyhjDESq62wOf l2MkrrKBJMfYvhLsKW6AAAAAAIcK5HOOjFfqtRSTZwgVvQjn7GeKfTfCllWAqIHTCplyxD0W YpsLwVeFuGjeKWs60UdlKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/xAAzEAACAgEEAQIDCQACAgMBAAACAwEE AAUREhMUECEGFUAgIiMkMDEyNVAzQiU0QUOQYP/aAAgBAQABBQL/APJy1qsLs6bfVfVX1ZT9 Tu6wyoa9RiKunXBvVc0zVlXnlrceNUvvc+rqA2NLPVGePpmpnfn/AGqNlOmXI1gWad1XaVP4 jKD0zNH6o+HNLs3TsVqp+FEgek6TNAbdG6itoGngS9F+Gf6P/aNYMyQGQIYKJWEjkLCBwREI lKpEEKApWEngjAD/AI5HtPbnbkNztzlOQUzm85JzE9udududuQ3fAPlPr2525Lc7MgpnJZsW 852Z252524Bco/yWb853z3wImc47Z75G+e+HJQU2wnOM8ffl74PLZX8/Sf2jfb334FtEbZ97 jFqJaD+Zxvx4e5cs98T/AA/yWb853wRmZCJiJ3z3wd898u9nG3eYl+j2mWIIJktijB32V/P0 n9o32AJ5Fvs2GTlt5JrafeJ15S4iB34++5jMxMTGJ/h/ks/nxmZgdsGMmM2wYzbLjJVmvoGC 0rUuhi7fPCkJwY9lfz9J/ZI75t94o9nn1xrTZmpoQCdpbi7hj7sR960UrSpncKf4fpxMTGdg zPKOf1TI++IbZtgxndEu2yIzbGLgzvUVvrlSED0Pjxvj1NqHD0qjY/Sf2VH3Nvco9iDlmre0 aClaXOGfJiPbb3MOQwkRhP8AD9HxmyhNNommmYQqu/BrmtdYCF31XGJZMRnGM7VAXUEEEgeD EZxjJGOyRjbUdOh6tH09lctaV93QucuiIhvpP7RxFazUwiiNuMZq9EmK0lRdhIgiGI47RvIx txjETEx9hTYYX6jLSFt+nmV7zwyxt1XjkbFW1EV6/TMbrGH2hifJ3ytd3MTVM/d5MBZ4lagi NufpP7a87iGnSyWEayVW/hNoIbWKvJX3whWns7EkQDh3Fby5UnW/j9it/wA36mp/2/050mTY 3nLZF466T7Lvl7eui6wiwx5nFpBNjiVfEcpfes2K16i02V+U4Mzxj3Z6T+2vVibFOq4kag+w mz8NteYOA2vqD42XrEufXn8HUGMFTYdDqdZrARGy/sVv+b9TU/7f6ff3nff3xccYtPFQr8aw 0F9ULZzy1VhxV6K0RZors2Fjwj3wd+MT+L6T+224HMLhiK91tMBBVev1lIRz+XoglDwWQ8s8 GOcRxgP29HzbNmKl/fytZytZytZytZytZytZytZytZytZytZytZytZZo2n3DZZANNvTcn6U5 mDiZnLF/qOdRkSsKC5lbS015c3pjZg4om9sngJgT99/fOewq/n6T+wHMDO7GxQVVPzgVlNpt DuLyjPhnzGBlV5LJsGULURQtfuP2K3/N+rY/9f4e/l9KzfmP7MXDBKiBQASAe+FPuBTOGPI7 9njY3kl7ly+9g78Vfz9J/aN9lR96axbJpiEjHtO/P3wkC0DppRibMsd74n+H2K3/ADfq2P8A 1/h7+X0BTuUFMjzmfUg3mA2zbNs2yY3zqjOrOGFpME4EwI9UZ1Z1Rghxn16oyA2zbNs2zj77 Ztjkw0K2lgpnVgxxj7EKet35nI8rJ8rPzOfmt/zOR5W35rPzOfmsnytvzWfmtyGyQ6dT8T6F n85jceM7/wCJrkwxrHWVn5L88l+eS/PJfnkvzyX55L88l+eS/PJfnkvyk62YPbAx8LxtP0BF ynfYJ3kv8TVbng/FGr1fwv06cLmZiw0fhYgIvoNpYX/x7B/h2DkFFasQv4p/vdGcDJcliC/S pE3x2QiB+FZnl9AUzEl/CI2n/CasWhNZUx8U/wB6E8TvT8zpfpUYTIx5EF8JTEl9B1zvt7cB 9WRHOYjD4gL9X4s090WI4jgiOcRyQHsJQ7de2bRy2jBiNlR9/wCj+Kf73NFZHlaqjrt/o05L xvwhD4YEhL6Vm/PaZk08gtabwPTGSDuwZzeeIyRBEN5nvAAcTkjuUrnNiiFfz+htdnTIMmr8 U/3vpTOdVqfo0oXATL+Pwf8Ay+lZ/ONow2CItYLosUhJOn6a1bF8VhDNymY7GcZC7bfWu6ez srcoz7siv/k9W6jTUx9uvXCvcrWcr2kWRTbruVOpUhBGoVHs9LD1V1jMFH2fin+99KtltVnx BUQg/wBCoW1WRTK/hzv5/SxH35/d4di2lK2qjZWbRMCEDhFEOmYmNR0Y7FpJLQMTEwH8dvxf VdIljaYkqVKu1uqacyKdCkMVNFo1lHpvwslXyz01kOzStJnlpfqRQI+Wnj8U/wB766S3mm9W Kpa+3RiBFss4/CXHf6WfL8nac2w6PM+2fI2wYy3YlGFeJxDYYp5aovfT3KtY90qZWKDX/wB/ Vuh1WP8Al1fbI0Wp1voQFLSeY6dXq+FToWL1zT0ag5L1vuW9MprlNP1uK7keG7q+Kf731Q40 O1AO/S/t1JGICFkfw0DgZ9L2RBycZzjIOMgQFjS3FjXDN8WthCwr09QCwZo0+2c6TSmrmrKl g6MLFtA+bf0g00V062krWvT9PTRn7XxT/e/Y0W6ujY1OiypP2qHIVsmdvhLhv9Lt7lPuBcsH J9IiMIYKNU33U6MXYgA/7TG8KGBH/wCz6Gw3pVN2Ov4p/vfsg35pTIZGfs04HgGxF8LNlp/S /vkxjmku4ieYQvadsGM2xy4PF6bxKFjm33tsGPb/AL/QuXDVzSGR+Kf737NJopuayPlfapdv jFI9Xwjw3+l5bMmc1Zc7qc3hB7x5C+UTm+TP4htGM3z/ALb4M/dGeTfo/in+9+1pE+cuyvqf 9ip1bBBHnwzJyf0p/wAhLJjeBUG2o2oHK9kjs1j5rwiiDtPay2mJhX/bJL7qv5/QuY4GHYZA fFP979u6HzGr9igb5VZgVj8JQEF9KyPvzGRO2OswtRJl+V9G4uWEDBBvFiuQ4lA9e0bTI8pn Bj2V/P6GYic4DnxT/e/b0m6aY1imNG361OnBhrI+GZ3P6UxKS6yzgeHV54NcgwZZkcs3LJ58 i7JjgedZ78DyFnssSgvo/in+9/Qq8bOkNAlM9NPOySbEKDPhUVjP+q9PBh1vwvin+9/Qo2jp v1dECv0qeNK0RZifhHfn/r/FP97+job0dViu2seUTfKHiiB+F44l9GwfxD/9UeHaopGzVmYn uLpCzO6mmc9s9czMuW4xBbTmQduXOZyC3yT2wZ3zlM5z99/vGXEeXtynDZxjngzhTtnL3Yzj knm85y2wjkYgtyi0qTK4EB8U/wB7+iBcTub6np+VOjqDvFfwrE8vozE+Uo/DjCRzGF7ZC+Vc FnnUUESDzh+JCyCFgRMhc8fcIn7mTsRzPvPDc9pz/wCZnnnHP+0x7wOxfxmT3gpiZ2+9xyB9 uMzm0zKh4C2tDT8GYR8U/wB7+lorRi5qyeFukTfHaKOHwsvgf0Z++GE85/jwiM4/gcN1sDfJ DbJD32ncNhwY95GeTI5EcTOcS247FESObFsEbCfuXEtoGdp33gJjJ/nAfh9ZTG0wf/f7XxT/ AHv6dEPm9an0wsfI4fCe3L6P3g9i5wJb8PuFG4wGwsGdyGd5HYtizj+GuJjIg99pxkFy2neI PjAzsv2D9dlswPzW9XxJStN1n5ddz5ddz5ddz5ddz5ddz5ddz5ddz5ddz5ddz5ddz5ddysjV K036xrt+CTy+G4s8/wDVNIHnho4+tx01602Jh/o0iEAumXqczAefMh/usCGLb442s5RyekXr 8QN8kojGrFoTSXP++6GdqK9pcshqZrqcQWqxuexNqcXXbEUFtUn/APnNbW9uny46yT1axWpr t2GWvmzkIdefIzftMc7V3LvKJkletFXlGpolTtWgHzrA96NRYqlX1pb7mm35uFOtrGxY1SwL p1CUK+blMfPegC1JrrGsX21bh6sxIPuMRTdrwLBmrCNxOqteTtSsd9m90Vx1Y2D8yImN1Qou I1nkrS9SG8X+Jq51xrQ7TNp+UzAu0wTJ2mEBlpR45+mNa2xprWq1eosrt2rbthqmngHn6dNw G6WAeTpvct+mLyrqFCtIM0oRe2ph2tNN0O0zrO1px2a1jTa77N7TrJRa03hb1GlZzt0vlFnT YsHY00lFZ0yYu6jQuIfa01zWW9NMRsaYLPK0zrqajRqh88q588q588q588q588q588q588q5 88q588q588q588q588q588q588q588q588q588q588q588q4Gs1jP6CY3xlrrsMsOIGuTGTa IFBfggrtJtniOcRziOcRziOPnhimx32LUrzyDBtZ8tl71TM3igZuSUUp7c4jnEc4jnEc4jnE c4jnEc4jnEc4jnEc4jnEceRw8LvaZWuxVeyBoY8RdN/rjyS7qURNLiOcRziOcRziOcRziOcR ziOcRziOcY+hZBTBp6pTWAx63Th0+eLXBFXrdJYcMxJk7ODs4OxiGME62y011uEqkli65KiV NLBp8ZlUMkEsDJFsQkydnB2Lg49Dgpg2SDODs4Ozg7ODsJkgzg7ODsbUJxzUkmeKyLoIMJKu Z5FPYTTM2qvlTKe5q+Ds7J7eDs4OxcHHpaVLcXTbC6yGKr+G0cTVJVh1QmP8Npyvfr/WvKN6 Ur649K6ZT63QJtQltVa7rU2NPtySJvsySOxNYLC7i7DzgbbIreczZBkc3ExXyscGi6BNqEXi ORdaUzeb0x+2WK7MaVqErttljLRQfmt420ugXtavBe8XFcPddppuhcQ/1spNhsazT4rBK1Z0 sWB32bDdPsptNofTMWLIhVWWMVVXnCnuCK5jtT4eMjPFRk168QtNZg8aecKmeKjDRXARRXMd qXGF1JPxUY1VVQdNaGR4UhNevELTWYMhUguNPPFRniozqq9jFVljwqclprMHxUZCqssNVUC4 09wRXMfFRnCp2sVVXnCp2+KjHBUTBIriO1LPFRhorAI165DtT2gKkl4qM8VGeKjATWPCr1xG BqQrDaAEDAMv1gr8bFmvL4KnuSF9S30e9ax4rxy+1KVkuPD9/D9Hr7VpXKwClIYmn1HlpZOS VOZtxp+ynL7UpWS4OnzLw59ZR+aeElHgjtXDjGAjjZcjmc1Nyrh1qwhArlxENGVwVrLYCxD1 9gdIbZYDmpK+tcVeOLqQtnoaRM6Fca0FvxdR508fX7X0aY1P8mUN8+4tjF+E/KazAMUho3LK GsaVV8nUAlowl72LldjjFDYvZfrm6ba5chVaRRlsCYmsBqRFSxGJquB3oayItOQdZDI5LZSY yllyuxtilXYl3+RK3efcFhr8e3vSExXilui7aW43Sl3Ovy6MJO9q1BlPU75nmooY7LAdqulk +lwTNFaDBEIs7JTYF3oYnJack0gyN1lXPxMsrk36eg0l9LY5wiqRcJOxvJWPS1JwmsRdQnY4 IKxL81EnjU7HlY08mmqxzhFUi4MKx287HrJN820RcOdzKklI4sm+ZYNvaR2OdWTlOHDfNvG7 BYc38vw2UWSLqgnbZbkoTXkumDsYkrHbjOcCwm9+nmbF2CkEPZb8X0pE2Z/yJcfzC67rWL7B sokwk4pxldstnmxthbKhyxGcjG9dbIgLG/MMvEYDaZIJG03llw5WiqyTRFpmybLSfjJOBcZx Z09zWw4uKjcfhnvAm4hfp7GmX0r2dSK7JOJuFv5hbZYZK1V2diwuGeIty08tMlSJuGOV2EeP Z1Irsk4ZcID8w9/SbE+VYYQZ588K7JP0XYkrNixK2TaOZSXNWG0xuW7EpzyZi7mo25qw9nSl d8jHLDJWuuztVFw5xVyWHjD6xZaKH6dam2qzahARcPx/Sla8k/phARjrDOsPQogogYjOlfHg PPJjeOoOwAEBwQEY4Bv1L9ZcPm2pEQ81eVCiQxbhm3YaC29oGyp7oyLG9639xYsiL+ag6FYf 3RWzactFAprkLUxYAsVZBjcYyFixkDa05sNU53s9wDWw2SLq5bu/yJlHmWoV1dlTnU64Xi5R 5b+gWSKCKqQEjIYmb9gwSvlEWsuGoctdcJFiOGWeHSjrlW9WYWVaXYRCENYmLFFijBzlgBEi KmGlZnU6OX0plAApgtHyk55KvRhisFmLB8tMiL1k3HtFK+4O1LQeBlAApgtGbKoKLK5L0kUe fcFcq4UuVOFwvFCjzbII7ZlMHXERTk9A3rfVGQ1JXcvmkVOASXBVPS3C5RWEIRwqbKCpD8KI LLC6s26MJgJEcnxyq4TRFtGKvb9K0IYtSuuPDDPDD0cuGgpfWI0hGF0Urs5YV3qmoBHUrBVW 0IYtSuuCpgReL7+krX5dkR6yr1hmrw4YC1xactZN60863GEZ+GVu0pZ4CU+dl2up8WAWSVVa selkRJSQgV9KIhaEg3GLBosp15s1AWAtrVzzprDWMRMZQoXUFV1z9NHvm8ZvH2OUZv75MxGb x6x75vGbx6ygvNtLMwXXJZ1V8AxaCG29PM3huVYeCclUeXcrm7IT+bzUa82U2Fy1UUyictBL FV1ypMVGDiahLbjAhgvqc7VFJKFisZUliDHkJJPsp1+mfpbAdqKyyUM0z3mmfpZXLU11kpYU jEUVCW7LS+1A6fIXNNrnWr2A7UVlkoWUzJnhn6zXLzLKiaPgHtVXKxxdcht2K5tZNQ+VeCFG HXgrlyub86S8zNSqFah4E1QUHCOWly1VcJUmKRxiaZgz0sVWMu0K5VwNRTLKLCr+lJLFT/kS LvmF3sJaws91Ln42KF3m2oeTrCXkyoMhXzrIb15TWjxKb2XlkY2B7FSFqPS5Byir2QiAtbJC 13+jlH5enLasHLYcOTYKv6UosQ7/ACJl3zC6Rdczc3oc4Tipd5tmZkyIuVcpJGbGN69zJUhP l5eg+NqT6RizPpc5QirJ9EFY2SVnv9LM2IuafLZA+/PzZU8eRDaoS6Z+lsHK0VW9gTbZvNtn paZK01mdihuMkEWmG/LZkuvFp4u0uwdmrYOVoqt7AZbYLfMZ6y8/NtO4B5rsqMlo4t5lcsWC Bp22RlQ5ZXzyW/NNRtMTi3fn81Oy2uLz4qC3vOW2SpNdnNMXGYm0w2+jGEJaXZZZSU7DfuOS v0oWisN/yJtfnrb+kPmOVHdwYq1zuWbXU4708qjJajIsHN+/a8YPK/8AI5daaReRCoHtJWWm 9Kaze5Eahvib3Y3GHwGXbWqFmbIn/Bt/jU9KdryG/wCRLh8qwUCIW1kdF4WK2A4ZssKBb3/e VMSvIb+ctNhWSf5rLberGewqscvSwcAtJQxfkgWLsgbPQiTDqVhdgSaEzLtqvpWcLS+lYcLW lnZHmBnlh6NZCgUyGBF0ChdpbDyw3pUx/B6HQ4WHC1pZ2QVsBLzA9Z6PPuCqVQymLKnCAxXR 5tjpFrWoypIzXzuTOoXTUoAYidRzUHJUFnh0ySY9LfDorQvo2qTCvEl/odhYP05iHA6OSp6f CIoAZuJ7KPRv9KYwYLUK48RWeKr0YEMBaxWMU1RAVlgePULl9I8lKFUGMGC1CuCqqIvEV6zC fLswvrhdWZq8OGBCfKeCeUxXk63DpyBTN2wCpmBrxdy6CCCx19QlX45Z4SpMB1cauyxrQ30a uuy5SFAAzbrNVXx8tBWJ1Po3+miYmOUZvHpPt6ch23jfJmIjlGRMF6RMTG8Zyj1mvHmWVSYK R1OQMCGLrwNqzWFxprAt6BEFYSR8qwuSjrnysv1gsqevsWCQBWWV9ikL6lRTiMVTFbfR1cWt po6BLfiVSCqGMGJI+/VriifpbAdqKyyWM0z3mmfpZXLVV1kpYUjEUVCW3LqpegKTQt0K/jIs B2orLJYspmTPDP1lE+bZWTB8Etqy5XGLRI27CCYx9ImDUX1V88b/AMreQT48ch1TNUreRFkJ NMVSkstB2JrhKlRUKMVUJbfQ1yR6dXKuFgZJDaMtq5YSxlmjXNDf8iUt8+4DGLCs8W0gNdbF JbF2ypzHFXmbFNcqr50lFy7XmwK67Bv5cSTIeHauajSPLYGaawmtEVrEYms8HYwZITUfl0FN ULghinV7BV8uJayxQS1U/wCRMu+YXSLr/NzNGD6sVLvNszJHs8GUJZNPOJjevcyV96b2XhPj ak+ne16XOXRWk+iCsbJmz34znxbym1R5cWf8bBs+NlybEWNOh0fTWJOEVTKQltjeWWPS1Jim sRSoW2OCGWJfl8iGs91nyahGQ2JOEVTKQYyxDe2x6ybfNtGUB228qERDizbNywxvabiAqskS M7LHzS6Zwvkfm5qJtELJFCRba3y3JCmuRSmG2MSyxLfRxsizpzHsG0dkFsbdimfLixzosUTc Zf5E2vz1x/SC70sbUb3V8Va53LNrqcywYuSfYnO04u3XsSI2Dm/l1ppG22U14uzvltvSms3u RGob4m92N9HvILFCz5AyZSR2GDVOeIteUHp9g7H+TNiPNtshYzZIsqFuvF2IK3ZkgyHw23UL nXybI/MbriRHZMXM1CyNZZxPEbfpaPrVWZDkxcgsTcFjfQi/N1TIolm5OtEKjLiLGblTsi9n +R1h2GpZh1hzABWOQsIPiOdYc4jaMkBk+A5wDnjAFg7RkqXM4YiYwIxHQnbqXz9CSsmKUtUY SlkOGpZmICJfVywIPsHsU0GgRiMCwSHeM3jE2FOhTQYHKMW0GDvGbxgmJDvG+8ZvGbxkvXB7 xkmMCDgMVuBkbxm+cozeMExOfrZKBzsDOwM7AzsDOwM7AzsDOwM7AzsDOwM7AzsDLaJa/wAU tq1KE2X1+6Spj2lTKbFUISkQ2XNTHINmdUTW8WQUNQoyaQTldUjdmmMyVXmNaoKbnijLBpFs VdfTC9ktqQ421BYBr2QpBcwqFlQVoDsDOwM7AzsDOwM7AzsDOwM7AzsDOwM7AzsD6G9vjX9I RZ2MiMM1KCBrH8A8n8BbZXXW/exp4wyvVrKF6TWx1JgtepkZR4MTp7YsSk1tdT4k6C+5BRzs xEH93xZPrYM7h1LzqXnUvLywGo01V5r2uxk2ZiZtfiMYMVqf48i2VVjtyMFY/Mw5jB5hFQoB gVg/FocTOwHGxDI6DlZVdPASZ1LzqXnUvOpedS86l51LzqXnUv6G8cgM2RVjnieeZJTbsNBh XoiBtRI17hFUTblhqac6fTtRYrE+Ir9zFrK7Gw3lYi11gxsBWU3fFkJit0+NJv8AJXYIzJjJ cZE9M32iMOcLMunIVWuOuMMIrDbR86jpbIFDF+S7otuYMW2kFWG/mhYybQtb5PJrqIWZiv5f vWsk1s3OBFdnnYb4rWXiGEs7Rp2mOdSsk6aNpjzq2mNsVHtJlKyTiRc+9Op8Fsu7Oi2ZB+rq JDCwZWESOlJdtaRmzWLFyl7O2lhNpTG9TOaVoU+qeKagKyDSqXzT8cmU5HxkSFh6IxIV4xU1 lHByN2Zqjg2IJnjrbDK6mgVdRMGqsPS3PGsywlkrfW5OfWFosrcmkummXJI2nB4doCXLR4l4 yGWWwiQcBtCyCxsOUskPVIG+ptDqhhFlLcb4ystPXVxe0W0NBdaqdeAWyqDudVM1WJg01xXI U64Z4yeyai9v1dQ/h+HOddQ4iKyo6aZ5SIJspVVIWeBMflQP8EhFNOvbGVlQMUMxoVhzrprM CFkuqVoFZViZKwg2SKx+51/l4Z9l7OpMRXWuVUiD8FjRVXW83oaslrbkLCQTxLFqCT5C8SaL Fr4bEyq1IHXsmR0xU3w+VtVRK2RSBH4dgWkloAyuCzcmE9KVpelddil17SwhcqnUQEpeuDG7 XKCtqiCthEfqahESDJUASoG4msK8dQRxCIRnFG9VaJliI7qvRVjYTtQiOHQu1m9WZitXCw4J Q+0aZwWDGCtXQ0UqqtVCqjB/C8sZlL1uhjwXk2FQ70rCrxypokKtZS5BC2NmhBKFHGuFbiha OAJrdawrzC11uKYrfhMqCeAiBYVQSgqgyL0Q6ZqDMrXKyipBKjTxhQacsSfXF0vr9rfF3NiY JXgBJnVA7UaePXGnB2HS9/1LKe7HURY8KLRXNRh2LVebCTrnNdFZyyUBKzx48kqbWV7FSYyo mUrRVlVldDaXVCM2KmwD6UNaGnsDEIYtr1dqHATkHXkZilu2jVZXy3X71rQ4XZYGWIZS4pZT Zw0+lNaUq6y//KD/xAAoEQACAQQCAQQABwAAAAAAAAAAIQEEFFKBERIDAhAwgCAzQFBwcZD/ 2gAIAQMBAT8B+pvSevb8cRyoPVHWeJ/Wdp44+CZn1Of5Q6zxz+yd546/Rrx0vm8kdvR6eYLC owksKjCSwqMJLCowksKjCSwqMJLCowksKjCSwqMJLCowksKjCSwqMJLCowksKjCSwqMJLCow ksKjCSwqMJLCowksKjCSwqMJLCowksKjCSwqMJLCowksKjCTy+HyeL8yOPhj+zZv22bNmzZs 2b9tmzZs2bNmzZs37cnPxIQhCEIQhCEIQhCEIQhCEIQif8AUIQhCEIQhCEIQhCEIQhCEIRP0 C//EAB8RAAMBAAICAwEAAAAAAAAAAAACE1EBETFQQXCAkP/aAAgBAgEBPwH8m9/HpOvtjv0n X4a5dePPJVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNKppVNK ppw3DeP6zf/EAEoQAAECBAIHAwcJBwMEAgMBAAECEQADEiEiMQQQEzJBUWFxgZEjM0KhscHR FCAwQFJikuHwUHKCk7LS8QU0wiRTc6JDkBVgY2T/2gAIAQEABj8C/wDqcVI0fR5ukzU79GSY KpVSVJLKSrNJiZoaUKBS7L4KaFV6BOoCqQt7GFTtNlq0QBTeU4wJ8sEJJID6p0kJKJks5E5x Lmo0dazMmmUEA3eEomaBPkpPpqyEHTQghICjT2RJnStCmzUzEV4fRhJGiTZcpQcTDl+29Pla aRKMycZqVH0gY0vSkylIly7IUr040TSF6MgCSvaGbU5UFc4lKTkZqDqHyiaqUis40liMUJCD M0jRP+7Nl0H84Vp2i/7nR56zb008o0EzVmXLOlqdQN0wPk2nzp8xQYIWt4m6HPXRpIStOzIu 5eJKFgpUJVweyNF7D7T+2/KISrtEUlIKeTQygCORgJKU0jg2qkITTybVhAHZFJlobNqYdEpC TzCYCyhJUOLX1MkADkP2QzRlG7G7G7G4Y3Y3YagxuxuxlG7G7DN8zdjdjdjdjdhqTG7G6Y3Y 3Y3Yf9lGBHDUI4auEPZ2MMWeAU3GrhAyju1nXeLNGFoAUziCkZ6i2rh+yzA+EZ+qPygX9UZw b+qM4JSfROpLnVnAv6o7tZ1flGfqjCqCTaGMVBnMD4QfhHDwjP8AZZvCbxmYzgXMZmDc5xmY TxhM1rGEjhAV6MecvAuY7tZjODiMZmHuY6XiwvGzUWEC8G8VAmHdjHf9JYvqSAoGrJoKXxAP 9bN4DmMzGcbO8ZmM4zMJCr2MFJGUGKSY3rRZRdPzDGcZxnF4KecVPcwOUZwbwyrwabfRzE7I bQ5qr85eFLajEmhNb0iq/qicyQHDJD/eUfeIlVI3VH0sg8bOjNFIAP3rxLBDUSy/ebew/WyS IFhGQikkVRWBDgQbCMhCbDIxkI8lZceWgKTCwoWaLctZgVMBGGkxkIyh5Z4mAkiEnlAsINhG QjIQQOB+bMA9BVPq/P6USlzEiYch9YLlLwnKMFLw4MJ2jPDpKTBOGMLCLEWgomgNDApeOEej FqXMW1mJaEtcXgXtFJUitoxlJMKCmKYFDRhpJMOtoUbQzJaErBHZBVzPzdK/8n/BP0qO2V/V 9YKnJS/OE4DC2SRaCeEGGvDNGBLGPK5nVhUYlrUkkkRuGNwxcEW1mJcxIItF8hBuYVMmuUtC 6OJiucT3Qh0FKeRhNCC0GgM+pKmtDfN0r/yf8E/So7ZX9X1giBl4xkPGGAEDaQdmsCCqxIjK ACBBIS56mBlASkWAjIeMCw8Y7tZgOAYOEQVJa0bNAEFVnjdF4JCG6PACRbthQIEBRaGAHjB7 da0orQEg0kAMo8NWlUS5ZG04zCPRT0jzMn+af7Y8zJ/mn+2PMyf5p/tjzMn+af7Y8zJ/mn+2 PMyf5p/tjzMn+af7Y8zJ/mn+2PMyf5p/tjzMn+af7Y8zJ/mn+2PMyf5p/thM9pIam1Z4F+UK VsZNg/nT/bEyqUJdIB3nzf4fVrQDTeKSljAs4gBTiHSlR7TFAS8YAGjyg5wU8YBvGUZRlF9Z i4ggjDCloJvnFMtJMOpMUUwVFMZWixvGHOBXnHf83Sv/ACf8E/TTP3TE/wDcR/y+rGE5RSuk jsgUYW5QkEgxmIs2cZh4S7ZGKUmEFxlGcZwLjwi/LWY4QS4eDUt4csTFmhOXhHCMQGUVuYpf jHCO/wCbpX/k/wCCfppn7pif+4j/AJfUXF++N2/WLUn+LW7mMzGZjMxmY3jG8Y3jG+p4rMww BUbRmY3lRvKjM/MzMWUYzMZmMzDuYzMZmClRMVlRJjMw3zZplKl0LVVidxYCM5PgYuZHgYsZ HgYzk+BjOQ3YYzk+Bi5kP2GBeR4GM5PgYzkeBixkP2GM5PgYzkN2GCCZLHoYmWSKmyJPt+os 3Dk8NAdrfsXSpMhR+W1ppSCXakQpC5s0KTYiqPPTPxGPPTPxGPPTPxGPPTPxGPPTPxGPPTPx GPPTPxGPPTPxGPPTPxGPPTPxGPPTPxGKUK2jndqxH/lFE35To80bzKJ9RjSRt9sGQXvbPn9R 82px1gGE1W7D+xZ0+itgAzt6IhOn1f7hVVH2Xv8ASCqaqWp82sI2UmjTJabhr/nGlbOXs2oc VPz+olzUxyBi8WT4fsMqCkJ6ryhzs8CTMOE4gOXKNI/h/pETJemzXliX5NMw2fpFM5BQc7/R lCZMuYgn7Iq+MAHbSJvpcR8Y0t521ai7nrz+ovXLHAWjOHZKW5fsOlYcQkFL05XjSP4f6RCV ci8TNPVgMlkU87/n9H56ZK0h8KvRHfnBwStO67/5xpbJp3bO/P6iokglmFoaN0azAtFRFhFM pAgVJjIQbDwjIQLDIwYyjKMhAtHd9U0j+H+kakSNImBOircrCiycomqko/6d8Chunv8Aoin5 KJkt7rouO+Bsps2RN4vl6o0muaiZZF0qfn9WMJgpPGHQDnFBtFpiYJSxjgDAdQyMEkjwgCtJ McIsR4QMvCO76k0kOo9WtEjaSl7TYsiz0r5mNI/h/pGuX/pwTs9mK63d+7v+i2g0hUrSAcOH 3wJs+TL0xKslPf1X8Y02zbrDx+ri8OTFodCwCY8rNww1Ufd5wm4yMEOIaq0S1KN2jMQL/N2c zSZSV8iqErnTkISrIk5wdhORMbNjClSJqVpTmQYVMlTkKQneUDlCVnSZVKsi+cCXJ0iWtZ4A 66560oRk5gFJcH52kfw/0jXXo66FMzxL+RgFDYikv9CqrRQtL+du478oBlT1S18Qv4j4RpHy hYWaUMQQbYuX1YmBBEMDxhGovFoQOhg3EJmWp43hEsXYQ8CO75k/ZydE0+WpZKlVeUvH+lHR ZClyxOI2Kr90J0kaH8ily5ZSR9t+yJ//APr0c0ufSqKY/wBXlGqylIyfhGhidKlzCmUkXAPC JczZo2lSsTXz16Um3mznGiEf9pPs+YVKLAQk7QMqwjSP4f6R8yboITj0g2VyhchZcp+g2qNJ 2c9Jwpb3wJ2m6ONISrJbs57RGl0BQGDPv+rWqpeE3jeMO/GBLBNMZmDc5xnGF7RjJAjFVGCK XLQKVZQOz5i5oM2XXvJlrYGNGCE0J0dVSAnVoqMbaOqpN+r3jT06O5maRUq54kRo6JstUtaE BBCugjY6JchynaGNJKVyhpInKQCRhTGkpmThpctIAQsAB5h9G0f6hJV/1E7amSGYMOcSJSt5 CAkt2fMKAzuDfoXgpwY0qQrEcIJ4RpH8P9I+YmbKLLTkWiRpq8WlTVMtXjw7voPKSK0Pchwf hBOj6QdF/wDJ8R8I0v5RvGgj/wBvqxBq8IG9+Ex6X4THpfhMVCp/3Yw1P2GGFUXeFFdlkFot U0CxaApTg8mMBSH8IWVvS3bFny5fR6Ro6JswCcoqKhmHgSpqtvJG7LWhLA88on7H/wCVdXZ0 +fpH8P8ASPmrXNSpQKWtCFzGaa5Hz65ekoRMBtLVx90bXTtF2iVZLQaXPaLRpezqbBmG5/Vx rGvEIApyybhFCxUeZgCjw1MYwiO76kVmErSioUlSr5ARpH8P9I+dPOlkbTR0+SCbfrKGUCD1 +c82QpUp7rTmPdH/AEmlGSP/AOqqfWI0twgFNIwJbn9XGcKaHvG8Y4wbmOMU/dMOtXhGUcYz MC5ju+pUkkcXENtJnGrLE/O0aR/D/SPnSZi91KgTH/5CUG0dRCA9i/zjs9JQA/mSrPuyhPy3 RSE8FyhSSfZGl7N2wZhuf1YwLQJiQYGYDwnnDVB4yjIwmxyMMc4yMZRkYFjHd9U0j+H+kfPH +nzmEhLzHGb/AKMTEB2SogfN8siZS91J+EU6JpICBkmcafbaNK2klMosjdSz731YwAYvGXF4 w8IBJh9QP3THGEVZtrYR3fUtICpopwUkJ3AVEGPPHCFFJt5Rjl/iNI/h/pH0Elehhxo8ppj2 4fN2MmcGUfNE592UBGk6LSsZqlln90aXsiSMOYbn9WMJjj4xZyYvAUqyYYQznxixJeHWMUfn HGLP4wM/GO76lcQMItlaNI/h/pH0CtFSlJRpBoUeIe0bJCioUvf5gTNrF94HLu/ONlok5M6W m4RNs3cq0aU8kSbIsHvn9Ws0DKMh4xiA8YFDWjEgeMZDxjL1wCyfGOEcPGOEZDxjh4w5+qaR /D/SPoVaLLxaYV1AdIUhYZSSx17CSpKkKPmnF+6NlPkKkzU50/CNK2U3aBkcCOf7W0lQlqmh YRYl+J/TQkJkk4VUYfNqfPpGkfw/0j6ETZQBVleNG0l8ekgrV6vjrpnbRMwnzj2HdFOgzE6Q kcCH9RyjTarF028f2xpH8P8ASPotIRpiwSwEoKvzy9UBM9BQoh9WyliWtCj5u1R/5QETZU3R 5oz4+oxpLTttZF72z5/VFGbXTwpgtMqDwjYKUb37IW5sSYmE3s8bRSQ3CGUE5PhMCyW6HKJq mGEkRJPNMLJxXi6UnqkwApLP1iw9cDqIX92DAYCOEEQ8dTDNijdvGG+UEEXhuMMReDZ2i18o uA8XEYhDRNTV5reiWtlFKk124DmY0j+H+kfRJVyLxN/1CbhXJIlhI7fz1YlTUz3sRlAUhUrS gfQXibuMaW8vZ7lvH6opSFsOIMEVYiXJ1KY5rJhSnwlLCAh4xL8BAUtQNPIQsJXhVeJd91MT QD1DCHPsaCFEd0ByHyhPhCVP2wQCLxfIDlF/ZeKukFIHiIfjFzi9UOrOCEnkR0h1ZnlFbKDc 4VV6OcYU8YI4E2EZAdkXPZGKLl4UpSlBwnLoSffCJaZtgjZqqDuI0j+H+kfRolaRMbRS5WlR wm0TFykto6jgIyMFCZMuYgn7OLxzgBW1kzeIzHxjSvKpmvQXBfn9Umb3d2Q/Xwhsy1odhmSY AIjCLl4sLcoV2RYYeUEdIWWpEK7YzixvygdOUAXbpFoYC7eMWBd7m0fGGZ75RYsOUMzPeAWj K/N4EUnk0JdqjvQbKz5x/F8/SP4f6R9IjRj5MaMM+cVbWZL0h8JAw+OcBdMnTQeeI/GNLZJT uWd+f1RW93QX3YTnwgW4XhNuUJA5Xiz9IV1jLBBiYwZ8oL+MXNobq8YcjY6i7/lHGL/UJ4Mn dppvvVFoqpQ6ApS7HgYnrlaNOWg03Sgkboj/AGek/wAox/s9J/lGP9npP8ox/s9J/lGP9npP 8ox/s9J/lGP9npP8ox/s9J/lGP8AZ6T/ACjH+z0n+UY/2ek/yjBMiRpct86ZaoSNF0Ba9Ha4 2Jz7WeC+jaVow6SlLEaV8qE61KUmYCHz/ayqhvAA3hIpLD7x9fP5i5olmZSHYRTQNmFBBVVd 2fLw1koTUepaPMgANUa+Za1r6yUpqPKCpMtJpBK8eQHK1/28pCt1QYw65jLzpe3bqKXuLtFC 3bOEkqWW659upN97KClYsYuVud4vvdvh+359MkrExASC4bjnEwKqLIUAUne5cfdCZmkOmTYN taftdffEiY632crNX4od10YMltxLxLFKiGpVi/OEWXZhTXwp+MFM9RUp8yXt/wDrqkaKplki ztUOUabLlpnaLO2FYlLVV/EFRKc6OuYJIWq6iTH+pqUxkJkBezKjZ0PaNFCJCESzJC611Edj x/q0yZRNkJ2ZCKzZ8mIifL0KRLWNHaqpV1dBCpS5KJUsEedcVdQcoXtJYSAcOJ3ESEy5W1XN VSA7Q+krl6OupSaVLHAtCpSJW0UadlSrzj/4MTEUCgIWsLSurd6Q89NcxAlgmoYlK4RsUIdJ KgCFYi33YX5IIAD77nvHCFIUgbIBRrSuo26RNrlmSJUiugYqibJhdSazL2aHKrqUc40kiSE7 NClCqYxtzHCJadJQjahCVTcbM/IcY0NElBlyp8zDMN6kjO3CNGSjzW/MbMjJonbfRKZiEJWE BdTuWaBOmaOQsqairLtPCEeTRtCjaEGaGboeMSpKEBSVqCHruCekNJ0QmqrZ1LaoA5whSZCw EyDNmSlGnjz8Y0edsyUTFJBu1D8Y8hopUWKzUqlkcDlxhc9AOxToyV7MkDErIPHyaVo+0mVB O+3BzBmz9HMqXsTOBqd4mAJSChjhXVY/sWnSwooUbU5gxNqGkTFTE0FazUpuTwQflRBTScZu OETFD5TjRs1XzDNCUf8AVJQECWyVEBQ6xNw6QlM0AKSksLZWgrbSEFW/Qaa+2CtfykgqqMt8 BPZCy881F8V27IkLUqclEoK3bKcwlCZSmH3YGlUTNqEbMWyEKSn5TSpBltVknlG0/wCo3krZ 7OnKJlI0ilYIpqsH5Qop26lqzVMxHshSQnSKSkoYnIZ2imSdIO1WkzlTFuWTCpivlGJYWQ9n ELQr5SupNGMuw5CNuflFdnvZTc4RNR8pdApSFFwkQozUznUii3J3ik/KFYgoqUXKmycwmo6Q kpyKDSYQpKdIRSGwln7YM4fKAqqtnsCzZRKQPlKNmCAUqY3ziYKZzTECWR0EGTPTNKDfKKyN IFgkpSWChyIickpnDa01N93JuUKW08rNVzfMMfZCpdE2gyxKboIKZfygvxXijKZ4RlM8Iyme EZTPCMpnhGUzwjKZ4RlM8IymeEZTPCMpnhGUzwjKZ4RlM8IymeEZTPCMpnhGUzwjKZ4QlI2j ktl9RvBSuUAgPds2D24QqmUlBwkEgjjCk+lluwgIlVlKElRIPthxJDpHlB9lRVS3thFcuhqg 3Pd+MZCMhGQjIRkIQlCElSyweDuqClBNi4FoU0lLJJBUXbhE5NCVGvCwJYUp+MYpSEJCApX6 7olUcFXwtwMFatHGzYKyaz3jyUkKeqmzuBbhE5RTmoW5YExkIyEZCMhGQjIRkIyEZCMhGQjI RkIyES5cqXLNSVKNXRvjCDLkeTJAJI5+qFugDJQKHuKoEyaiiolks5HbCihNVSUBIbi6vhHl ZKQ4U3VQ4XgJ2IpqoUW4xIJuTLT7IyEZCMhGQjIRkIyEZCMhGQjIRkPqOBVPc8GeuahLXKqY Xs1IvZXk486n8EJqUg05YImpRMQ6rr8nnwh8PcnUTtQE/ux5PSEn+CPPJ/BHnh+CKVzEkfuQ aloCP3IIlrQQk3FDX6wp1pxXOH84aWtCQzWRAeam1xghRCkYgxwcI+TFSKUYQKemXg0GmYgP yRDmcn8EeT0hJ/gjzw/BGNYV3NqwKpPZFCtISFZ7keeH4I88PwR54fgjzw/BAQrSEhRLbnHl Hnk/gjzw/BCVTJiSUggYef8AiErKkVDLBFKUSHZ6r5PyhRTMSCoucGcGpaS4Y4ISkFDJVWMH HnCHKDM3noygy/JJloDOm/dAVtAODUx54fgjZ/KE15bkeeH4I88n8EGtYV3NqlC9IU6rtZjE pBqpAD4+NJf1tC0X3Azqe7Qoy6qjbf8AufGKxVv3db4aP7oKzU20ff4UfGAqZVU/2/ufGE1b zX+nEuWWdQc8gC8Kc1KUXJ1ttCpADJS2WubLl7y009jxKCVzFOjFTSMmb2mKHVTVs93q7/h9 kJKtpNmMHam3hGSMiaeL/Y7TAlqZIJWXbKlQaJiVKUQTWaaQMzn3ARo4lzlzKmrppdJpNsoB VKXVxURYdYmNs1Ugsz3tvdkTKmwqYQrSZalJmFWIhsiRzhKgqrq4PsibLl7y009jxLSSFqmA Jc2yUB/yMJ2glpdjkb2Fu28VBKSc+zCTT229cXz1aZNK2SoWSLuwhASqYFzMAKglVJ5274lq nLVLSvFRhDXyvCBQZYPGYM+g6xZKX9liae23rhE2WvI7QgD0ma3jABnLQuh0pVTjU+UAqTNW hUxafRyD/CF2CWLUqzGWI9IlppTSeLZ71/8A19cKm8VJCfB/j8x5cwy3SUFQzES5SJK56WtS k4fAGGVvElR7y+rSZyy+IzRL7Mr90VSkJWkPl6V4peWsc0+l1EKMwANy7B9XZaQrtgoCZdY4 QNomWl+cKDSnTnAUhCCD0ha2lUo3jyjzSPCPNI8IJMtAA6RUhEsjshVpWHPpCbSsWXWPNI8I KlS0ADpAUmWgg9IKvJMLRQEyqs2jzSPCKpiEAdkBFEushwIChsqTkYJMtAA6RUhEsjshSSJT puYTaViyjzSPCPNI8I2dMuvlDrRLSOyKWlOzxUhEsjsjzSPCCgJl1jhCUqTLBOUKDSnTnAUm Wgg9I80jwjZ0y6+TQK0y0vzjZ0y6+TR5pHhDzUy0jqIKlS0ADpCPNYsuseaR4RUtCAOyAUy0 EHpCi0rDnASBKqNxHmkeEeaR4R5pHhBoRLLGktzglUtAA6Qic0sILUqbnlqSlSgFKyHOFJQo EpsW4fT7Sq1yA3ON9ixQbcDBNfMptkSoKv3iKXcuST2l4nCbMxLa6XDN3wlJLkBn1LlnJQaC 6gVKVUq0HynF04csVV4Rj/etnd9VLsXBHcXgJd7km3El4QUzBWhgnDZgCL+JhLLwJuA13pp9 mpSEqCX4kPA0gTWmUUm1ooSsJcFJATaktl4QuWclBoLqBUpVSrQs12LkW4n/ABDmZcnFhzu9 tYXXxrp6s0INaUqQpwSLcvfGy2mBsuO7R7IUoqClLNRIy/VtRVXzNPb/AIhBKwMgbZte0Hyo wupNsnVVfvEM9TkqftL6kErFaElkdvGEmZMCGcE9DAmFV0JYJ5Px9WpSZiglHpE8oKamyI7o kJXNTvmY32zn79W9SxCnPS8JSC/OCdql0MzjJufjEtplk3Zrlg2uo1v0WRE1CJlYqduKbQaS AesSZS5zKlMEqFvU+qWuoYPu37jCwg2Ucm6n4/skTMGzer726zQigSypKnpWbGxgp2iN3fc1 blPhxhW0oBUp6UZDUpZo2d+0u3wiWpOzazucrvaJjGU1yLnHiBZXg0BMxqnJtkL5akKsyXV1 fL2RJWggKQ/HI84Mw0bN3+9kA2qWpCgChyHLX5wtGHviUg0b9Sm7X9uppdNTg4ss4loVS/Fu HZB8yWIYOcVyb+MSqjLKUgOeNgQ2txNWA27Zo2aiGGTF4UKUq6KyMSJZ2VSEUG9sm5atGmS1 JaWpyFRpJmKSoTFVAjP9khbDZu9VXBsvG8Iol1sq6CWcXghxVR52r7jN43hVaBLdThDvSNSl KAoviqzyb3xKUhNrZq3L/CJ1Ut0kKvVvubeEIrDKa+pEwJAZ3PExJUiWSUqfPKNq7yaKezVL 2fovbg/B+kLQau6NHC5b0l6gRhvb89TSxUXFnzDxLEwYuN8oU8sG4dNfnLn8ok1gFgHXV0uP HXZbJbJoVWGeFCmrpAl7Il1HJW6NUlpZIBcqfK8TK+LDtz+rTNl5yk09sGusirCVBi0TPOZ3 w5CrhzwxK3/w53492o7N3cZZs9/VCdo73Zxwe3qgVmbTas0XBYu1snphG1qe1QbC1P8AdqX8 lDzPWISQZiU7O6dnkWN/ZaF7YKGLDVy8B1iZsvOUmntg11kVYSpLFom018fRt0b1wjzjPhw5 3492sDFQ/K1NPxhFFYTViKUuWv72g2Vtad1sO5/dCqqimrCVhi36fUoKqpvwt0iXs66S2Sev HuhbbTjVhyFQZueF4G0d3OebPb1apdKl7NiVcuyJewq45Djw/XrhafKhA5pwnsOryClpmZCm FbN6rZDhEm82qv7Po1cbcuzVgqdw9Ics94RtHqPSFVbVrVYcs8vVEraV8HDWZvjq8mlKj94t Epq39MAOnxaCpe0F8piWIhagFEgeiHMSdiV3UasGLoPz1q2tWQ3g2K7t6v2SJeJnyptS2b9s IIKggqZSkpcjP3xRdE0pypwjBn+K0EzeeE8xqVLNTB/RsMmiSETCKjyid6SBmQN24y7n8ICl PxZwzh7amJqSrID0YRRMKSrK36tCkOdnybpqStBsM08VQsodwzsHtEnfucODeFTX7r6qg+Yc gZB4lqXmrmGhdW0AcVmjducvVElKwoFQFqbM2fjq8mmo9rRKCSq+8nhE7bpKVJWzGFF2YZs8 JO2UFKJpNPtg0hzyiQFYa7FGfriZtSSzZjjf6tMmM9KSYVU1SVU2g4U3LJ/FTeEYR97xbVUB dwPEtDmxcjwLQgBKal0kcgCCf+MIwgIUw67tWpUwU4Q5qjElLpl1rSDdNnhYW1SFU27AffEy Yz0pJhVTVJVTaF4QwcDu/wAw1KbHF4tbWJbCmqjruvCAhqlqpv2E+6NpRgbJ77lULC2qQqm3 Z+eooYU3A7oCQARZ+8tCwkJyXS/3S14Qo8Q+qXKZJSoE9QBGEA4VLL8h/mBIUmysjql0y6is 58AAHMLmMpVIySHMaOaBjaro5bU6WckC/UwFG0NSl1NT68/CJeEBKmHqfU7KP7oeJaQjyZFz xEFakbPknpExRlzVUAmyYlqKUCaotQ/HkNc8UlNCmDhj9XASkADIAZQrAnFvWzhOFOHdtlqI UHB4QGAtl0gp2aKSXIbOK6RVk+q8bShNf2mvFKEhI5DUAlIAGQAygmlLmxLZwnAnDu2y1hNC XeirjlV4QlFCFVqZlZc/dBnbMNTn6W7XCkhKU0Glk5c/fqUAhIJcVcbf5iWnZpNLHsu1omjZ SzVVnxpLYoTgEvoMtRlUMBhrPEs7QFBEshJdiOPBoKDICSr02z1SwZYW7qchwluMKUAHiQ+j oQjgoJ5lrcvz1XSFOQljleEKCQOXSFJ2UvyhGfHPPwiURLTcBL8Q4fU6qv4Uk+yEqVKATTaZ TfsgqEsSnvQ0TEoQpawMqSx74Q+jy1s+EpZm6apaKRSvi8TvJpTliHHof2SPJja7tbdHbwgI mShMCjZDZnODN2ad1tpT0dvCKZUvZ0lihsjqVTLAmG1bZ/q0Sq5YUU5Ft2NI/wCndfp2GKEm WmlPLUUBA2wF1Q5llSRewyhjLYqG/wA9UsTkJU5tVwhZmoqSbEc4kESMNTJywl21HaprSbU8 4Rs0gJGQbKJgEhJrLtTv/q8SiiUl2ASqnK2Wp1qAHWJQUKphBpLQrYy6BC6iDSLphK9iFSxd ksW1JWtCSpOR5ROEhATSplMMz9WKlFki5h0vyuGhWI4funm1ucJxb2VjqqXlFScocKP4TeEy 0qdSk1js1VrdugeNm+LsiuUXS7QVKLJAcw6X5XDQpJVdOdolgVYyQMJ1g1L2vK9Lt4O0JE1U wYsJQ9T93fBxKpoyc0bvtpg7IrN8RW9T9X1KKVL2l7Xbg7RK2ql1Bsnvfj3xPVtJztdn4coR Q9Od9SXqE1RcBixLfCJe1mLS2K3tMbMKeelOXIagNIWUpWaWHpdIXtDhbwjRQJiseKWm+Li7 ajtSoJcbubvaJeyJoH6vC2XNzGVTi53fXErZqW7CkOWy9rasQBiQZgAnXpt0hWwJ74JsDzhC 9pN2b1VAHxOpEsvUrK1o0g6I28y6RZ/qykKyUGMbyiSXJPGDjXm4yw3f2wnEu2f3rvqpJIyL iAATxPa8CmZMBS1JtYB7eswmdLqSpKaGex/TaiitSAeKYdalK5g8SzP4QZcsqpd78IUhW6oM Y3lEkuSeMKJUu/DlEvyszAqvhc+HXWDWa9+juZ4BmTSikuF2t+ngy1TGFO4VfdpfwhRlzNpU XKuupRCyV508niWFzFV8vtdsTWnKBAVywuXVCAhe0SAwVqBqO0Slm4CE7WYUu6OGIHhEyYlR 2mahwdm1IM5VNJse2zQsTyNkRd8o0amcpRTZJcYrv7dRrUUgXccIQEKdOb84WdsoUHNxg/Tx LCZhcBwl87M+ppqErHJQeJS/NrFgE2qH6MHZrK+DnpC1TZUu4xKI98Sh8sWmWlRINQv2wUrA Uk8DEoIXsinEJaKQ8TPk8yro+7cn3n6vaDfKM/mZiG46rloz12jOMxfWJm0TQ9dLXeloQULQ haFVAqFsiPfCAFopQhk2u7M8KdQUVF7alLMxJTchLXu3wiUp0pWOMLWFoCglk1ej19nhCQ4P Fxx1CYkpT9rmq0JaYlDpMtTjgf8AEGYkpHMDM6gBN2ZSqoHhCku3KJbTUs4KrZsqq2pkqCSC FAnoYQmoKPEwSJ6HQRThyuc/GJXlUlKWLNckBtTEqH7qmiTME0igNSbvaFVzErNhhDZCFqCs RHpElPhCQZiarurm8EF+4tEmhQoll8TkmF4wrJPh9WmIBYqSQ8FwkVKelJsmF7mb/v4nv7Il 7lv/AFu9vZqKQzuDfixygJLZk24XyhNpZpbDwWwIc+PqhBJSQlsXE4aW9+paBTURaoZQJktb JEsITd6bEWHgYMtZCsRIMTEAsVJIeC4SKlPSk2TEwum734npCfN5/gu9tYm4Wep+O6ze+EYU EpU9KjY2MFNY3W2nHcp/OFuEpqU9KchqMzC178S8IVh4P9272iYWlqcKz9Jy9+yEJW1QDWL6 pc5k4Ae14DU7qk34P6UbVkBIHDM6pVKt1TsTbLPuhaAqhxvDONCxp8gbp4d1uWqkNmCxyN4Q i3WD5s5W+3nn4xKJKTS2Ljkza5M5K8KXBSeFuH64QoLa/KCayof9tTU+yEodDue5+74a5hmp lgq+x+yQaTs33qrM2TdsIolqXixICmJF/wAoG1SpVsSwrMU5dtUIE1KkrFjUXOpRUk0XxVZ5 N74lGWhTW9Ldvf1RN2O0Q6WerO4yvyflCQpNJ5atokEhWZLWHIcYQZRUlfs6xVRMCQN6qytS VSwVKTkmzP1eFoxC3DjGh0JZCVY0gtbr3avJpqLh08w94l7UGvjd2hdUpRDioVjHcv7ok7RJ yDqqytceLa0zEAmzXZk++FbarvL++JiSsUEZJTi8XiUlKbJfBa/J+GvSflG6VOg1OG/ZIFK6 Hz9Glva8IKUzCmrEEbzX97QRSva0Z+jue2qPKO75qdzqUFJXTe53Ws3viTTtQ5dwLDtjSk+X ZnTY+qEFT1NdxqdypKuhwj2f5gbLaV8KYSpNTMar26DUlUsl0+gxxeEL2YUVBsuXSJCgVjFu q4Jq492rAFG4enNnvEvahQUefvhdSJzOK/Eu3qiTtETMg54M1+99cjZjyV3bs4+qFbVK05b/ ADa8LoEvLC54xJeuoFVQ4q5apDbRjZTC0L2yViw3vtXdumX1aYtIdSUkgQalJViYKGSomXRY tluYmv3XiVu4um/dre3UVJbMXPC+cJKiHLt1vnAdUtILOtrIsbHwHjCEqAS7OjiMLv421LWk sUh8nhMtSUqXQ5SniaX9sbSbTV93KJi0h1JSSBBqUlWJgoZKiaBThezZdYRdBc2Lb92trErC zs3FqXf3QgpWhIKmK1ZDODhFVL7Jr7jv42hTqSsBTBaclalSzS17cREsIKaVM33r8InXlggK YH0S7JfthKls/TLVsKUiUB3nr2RJ2ISQsl1H2DrE2SZqCyUkJ4jN/dqlbJKcSmJVws/jCzWl BAzPCND8tLaaLjmW+OqoEC4DnhfOEKWzq9cKdUsZOW3M84lJVSKmwtfLPWwkrUOYb4wVzgkK fJPDp2wSAVdBEsok0FSmO0bk/DXpCVpoMtVk8W/ZIkunNqeO67wg1ITUqmpWQsfhBLJ3X2b3 3Kn90KxJXSqmpOR1KlOnjbiGb4whLpuxv6V2tExKaHD24pZQF+13gKUz39uoyiGliw6lniUQ mqpYSSxLCPk1Jaip2PT46kqTuDehRlsVDnEldhUqmls75+F9VTgXAc8HMIXbFy4wcUsXF/sO SL+ESk4RW2HjcO/q1PSpX7sIlmllIKm48PjE0lNNK6WIIgsQDzMSZroFaK/Vu69ISUlOzUwc EW/ZITRd6KurPCU0BZWWCfX7oCqWSbVn92r2QJkoUpchu/UU0cxVzaJKShwosDyt+UTwZNxw NisDuhJSGBGWooKKeAWRvcYQDLdBzJFkwEbPhvakOipOZJFkwrDV05xJeW1bgX5arpqchLds JUA3TlBAlB1sw+12+ES6UZsH5WfWAqna8OcTFSgGCm7esKlhlrA3IrGjgschl3a1MikkBXaD l7Pqylq3UhzBsUkFiDwhWFebD7129sJsq+f3btqqLngwioPyY8IFKFkqalNsQL39RgBNTHJX B2dvDUV0lTcoTL2azVkQ0EgEMWIMKWrdSHMGxSQWIPCFAhVuPOBhX1+7dtYOz8ru19W+EJRN lmZUrCkZv+njbCX6O+1t18v3YUJaChlYkngc9SimW00vj55P7olBYmEpyIVu8InqWmZZCi75 hObXtCNmClPAEvq2WJU5I7k/nEsTUrUFKZgfb0iYhFe2Axl7akCfUqssEjj+UL2gJSbEA5xo 6tlO3qAa90u176iJqa0kiw5vaJeyTSgZDlCwNHOMgt9q5yvbjEpUuTdgEqawtYeGtEk7yr9k TJmjJISpTuePWFApKgRkOMSZhlzaGrDLLp9cFSiAkcTEpCVhZmFhSXhewl0Oyu0Xb3/VlJVd JDGGS+b55wqxvfPK72hNjh66qVZQAl2EMKhyNWX6eApIZsg9sm9mooW9J5FoCr1BNILwQl7l ySXhSVXSQxhkvm+ecKJBxdYFjbrn26x/3fU7e1oG0Ks7Nm8KAuyd0OzM3shWzqzxVZvqVT5z 1dYlbU3JpTfPjE2mslOdD2LvaBsnpvnze+pSgDtRmeH+bxLTNKr4QHN+2CR54+GXtbUk6TkD Y9tvfCjNemJLVsFWN952v36jtN3pCNnu8IWxVnwJ9XriXRm2G9staCtDzkXqbKCNHyhVTgdI 0d1L2aMUs1Hx1STOSNolQKS2XKF7B8hm+XDuz+ruLwbi2cC4vqvqeoNDPfU5sIFxeMJB1OLx mIFxfLWJm1P2tn1ZnhPldmpKnC27vfApmYUoCKOgjCanJL6lTNqTmaOT/wCIkKUsiYg2L5xM WlVy9u294SlJcDjz1JmOkK7LmJflQilT5ZxWZj2slstQC1lFJcKdmhaVKYGJKK7BVQ66mrou DVyaEJCqmzPOFEaSXSRSbYb/AJxLacpkgYbXYM+tKywbpfxhXlDMOTnpBYsecS0fKGCE0VBr pgpVcGJQSsJQn0GzhdKnf6tMlgsVJIeC4SKlOwNkwvczf97FVf2RLum3/rd7aqQzuDfixeAk tmT2OcoT5s0sKeCmBDnx9UIJUCEsauJw0t79VFKVBw4KqfXAnBYUoSwm/YeHLIxs7APYAu3f EyWC1SSHguEipTsDZMTC6bvfiekJ3M/w3e2sTHSz1dd1mhBZBpU9KjY5j3wUbRO7v+luU/nC 6gkVKelOQ1KXha563/xEtWDhxyu9onpCwl0qxA3Ll7+yEItblw1GfWLpFibgcmiWMAYu9WR5 9eyNvtAUqQU0nPhl+uOqTjCaVZEsMoULAWN40dXk8JKgQrJ1Ow58tTBrEHFlYwhFjzhR8maW s+9nn4xKdSTS1+Ngza3E1aegaJqVLrdbg/GJiQ1w14kJStLJUSBVbp2tqlKAlmWnN96NIKpl YWp349/7JC8Ozd6nvus3vhFCEqIU5Qo2VYwK6JiQLqJurC1PY94QiYkJKbWOpS1U0Xu9zlEt SAlrO53b8ImL2UtqSG+1eEoUz3Nu3VtEbp3r9P8AEIbCsWf7PWJk18K/hlqSZfnE5F8oUgi3 bElSqTS3cyn1NLYlwWPG8S0rarixyhWGWWItVv3OcSaqSAA6nvllqZKyjqIQtKQ2zKCt78IV tQkZWSX4ZwpB4iEpTSTiF1br5HtGrRly1JplqcpML2tOQFjvZ4v2SBSuh8/Rpb2vCClMxSas QRvNf3tBSUzNpRvDd3PbVB2lW9Z+WpQUldN7nLg3viTTtQ5dwLDtjSNniJBIKnDHh+ukSts+ 0pxPnqcOpKuhwj9e2BshMr4Ux/8AKEgfwnUlUtyU+ixuYXswoqDZcuLRojJVs6zUDmz2fu1G gKNw4Tmz3iXtQoKPP3wuqXOZxX1uXb1RJ2iJmQc8Ga/e+rydL/eiWPKjiSN3shZO0DmwmQre /hzhNO1fFxPdq0bZDyVWNv1lEz5Q5VzPPl9WmGXvhJp7YNZUcTJKksSImeczvg3RVwte3bEr f/BnfjytqJlu7jIOWe8J2j1XzHB7QKzNAtWdndJYuBbJ2hAmVDKpNOFqc3/e1KKCsK4UBzHk AVJKSWpZsPZzbjC6ioirCVpYkN/mJhl3WEmntg11nEySpLEiJtNfG1Nh2QjzjPbBvX42tbs1 hOKh8qbNTm/bCKCtIqxFKXIF/e0GytpTuU4dz+60KqKiKsJUGJGpSVVUXtTbpeJezrpLZJzv x5WjS/8AcKZilpfstCSskq6htVBf5O1mTn1f3QkySurgAnP1fCKQVlDXBTYer36kbAqCn9FL v0yheyesdIkPXc3w/e7OXZ7tTod3GQcs94RtHqPSFOZgFqjRu55Wvw5xKrquzim2Xx1yxLKy OKabeLe+JvylKkrC+I9nOJpkypamSacVz3NEimsrKjWui/g1ng0AFXUtElBCkk50CpDdS0TN tVwZw369fb+yRJdObU8d1390INaUBSqa1ZCx+EBOBBI3TmMNT9nCELs5F21KlOnjbiGb4xLS Cm7H967WiYkLlsEFWWXrhC7Yg9tVC9xW63Z/mEqlAKHLn0hUrDR2dNSVJ3Bvc4WtACljIRKB Uly2fpYqbdmqpwLgOeDmELti9cHFLFxf7FyL+ESk4RWBh43DvrShJRzKeLQoulTNdPUZQpCU qT98i0VVy6iohNXpQSxV0ESQmlBUdxeed4WZgY5hPIfskS2TnR1yeEBkY1NiyyJ90IVLlIVt EWHHddoYgJWksoM2pSGTxHWzfGJZSEM4DERpEhCEVJAxK4wlTAdnt1DR6Q9D1e6EqTKrSLmA ilDK48dSFKSFOoC/tg0s55xKCky+He6qQ2p7XITfK5aELAEEUy8ZDd5Iv4RKZKcTDrcP7taU 0JugqCuNm+MLdKQAbFPGFIQ9fVJbxhDy0KurFTYBPFoJL9weJRRLCnPpBlZxpASkJoW3b1/Z O0oTXlU14oWhKkciLRXQmtmdrxTLSEp5AaisITWc1NeBYYcukV0prIZ2u0MMtQWUisZFriBh Fri2UV0pryqa+qmYkKTyIeMoQShLo3bZailYCkngYSAkMnLpChskMu6sOcBVCagGBbLWJipa CsekReGlISgfdDaglSEkJyDZakrXLQpSciRcQpSUgKVmWz+uUlQqZ2ih8TPCVIUCDlBJOQeA oKDGM4zgmWpwLwlaDZQcRmIJSbAkRmIzgHJ73huMZiM4zEU1B6qe9njMQVFQpGZfKCpKrDN7 Q6VdL2jMasxGcKCSDTY/XsRAjfT4xvp8Y30+Mb6fGN9PjG+nxjfT4xvp8Y30+Mb6fGN9PjG+ nxjfT4wuYg6Pdhc/kYIC5D0IShZVdNI7OcBW2SUDjUHPIZe+J7qkDaCl3e3+InFBkUzAwv5v qBz+EKWZ0lSS4AJ4f5hYCpKVnJi7cn5xPNctMyYGArcJgATJWFdVRVdY+yeQhAfRkpHBKsr9 nwiTKXNlllVL68fbCUy1yMhVdnz6GEnbyrSdmz2ds/bBJmSScV+3L1QZ0yZKZiLK/L3wpSpk mohV/wB45xTtJIzBVU5W+b+uDM2kuioqzHHu98Vk6M+Mh74jkYWnayQDKMoF3sW6chzjSESl SkCYwATa3L2wtFcvEai8x/4eyFrmTJTqbuvf1AQU7SSzKAHJz8ImIlLlCpbs9mgS1bEpQghy bAqU5a1+EL2k2TiSEkhWd78IVeUCVE4TG+nxjfT4xvp8Y30+Mb6fGN9PjG+nxjfT4xvp8Y30 +Mb6fGN9PjG+nx+oyGTV5TLnYwXlywozFDExAt2iCCiSm4wrzLqa3ZCh5IAzDSSnICYB74lJ k7NNXAp41o+MVbKUaUzCq32S0BZRJ3jdhcdjxMK0ylECoEjLE14mA7BrJExsHG/ujymzUXVu j7xjSLE0qYVKJ4CFS9ki0ThMl05MFS2aFiYQKgLsC179nDOMSUFQJHDugVJkF5YXgG70MKRs kWhiUqqBKk2wF8vb4QTUCSAZlh5LF/nwjfGHzeXlMUT2AAQhPDJzcxOpKVBKmE227xPdfwgq qQuSkgJxAFd+y/LuglwpVIWsWwKq3fb4RuJ8I3E+EbifCJppCbZ5QsyGmS2TniAJLZn4wApG jjGEU8T96JQ2Ml1sWYXuzXP6eJwEqSySRdrYm5/CJKwmSkrVSVKFh+mibMIl3lJYAW43hDol lkoFRHE9piV5CWVKSFNbHdrX+OcSmVotC05tZHb+hCNnL0YPRcpd3JHugTTKQ/hGjKCAKpnD viemds1MvCyG4RiKVlSHULYFcv1yiQ2zEokgpo+6YmLlHaggMaHI5lh74kqsgKIrXSxHj+rw txUKEkPxuq/eAI3E+EbifCNxPhG4nwjcT4RuJ8I3E+EbifCNxPh9Rl0lSXWzpS5yMSULExe1 UwK0teJSp8s1OTShRZgqEBMkYlAYjwf8oWES0qp2ZHMupmhXkt3O/EqKfaIleRao+EIMyT5Y hFnzqhaBJxfYPBixvAmkAL2VXe0GdQoDsue6Jk2lTIBLENE/bBK1yhVgDPBMmTtBiL5OE5+2 J6kyiFS6UHvOH2wyJWFKUgDlvce6FTmcBFbRNrlUKRnxeFFKD/Eml4mzZ0tIMoqsnpGzeTt9 nVVSaaeTPzhJAQJJIRS17pf3wUStmDVSmpLtZ40fZBCVTRtMQfK8bZcttHpdOH7r5v7oSiaU EhQBpDZjVMUnMDk8BSTMmJU6QlaAnE1uUImTP/ioSoJ+1fhCVJ3BOY1JbDSH+MaSpYdAVhZP otBpSexSWgmmWJiasAdTsbf5hCpCK5RSVKNNXLqI2shCFMHxcBGzaWZanAYce3xigbJSOIAu nv8AdE2XQmkJdN884MwoG3QpRSlPMEiJq5qDtJZumlveYJmaMxQo0srM5f8AKChcqjebE7tY +2C2j4MbqfgksYb5P5PE5q+yWhakSlTFKS5xNYcPXGGQVKGYq6tD0tmDfiC0NMRQlSakW/Ps giYAMIULN/ntjyiKAUBaQ3DxgpWilBSSi3I83ijSE0KIqSKW956QRMAGEKFm/wA8L9YEuYCV qVh6hz7GgTJklSUEBQuMjl+usLlBF02fkWeBakgpfqHAP0yK6GK2dZYCJYWqWoqskpuDeA8s ukmnCb4rt3wCmWpTB7JyvCFzQxUlJc8OIeNIlbA0hKSaks7lRiXSkqITUkJBJY3jZ2OFJZuH D2wgCUyGUeRBBA/5QjAQhWAJp9TRQlNphxOmz8jCVyZdMteSUoZ+6FSkooQfh+UNsa0SUWAT wbKJiVIwh3w53+MJ8mw/XxMKkV4J4Um172DCJkuWlN99MbGUZKF/YSwPhAlCUli6iQu46kRN kmSKJYVMpqvYNlEyaNHSZ0reO0szPn3worS6Vsqk8C0UrS6YCygVCECWKUpVU3Ozapiqa2D0 848pLrlhSaLVOpnt3RMZLJp+UO3jCkLQoqqYsgm9PwgykpcKtu2NsvCHlyLE5IS0TUzZaUpR Ucy5bPhBlTNFSZgDlFdqW/Vo0gbFVEuWFXtUC/whU9MhKlJeplm3qzitakS1qviW0JXs1KKi EOOF/wA4mSEoQ+PDV1u/J4KdhLTLUSSqskFmu7fpom1SCaE1ubA3hUwoCVImGWWvckflGVSc TkJcAVX9cEgVXKSG4m5EKKkvekWvkDDzTLRV9os/GELQHwqUwNmsSf1zhYl6PLqIqKhM6uH7 YnaRsUpCXalbhXZE+bswhSF0rpvf9GFrCaVsXUR4xtdmoKV90uw/zGFDKJIte1RHugKLFYqZ XQl4wykj9fnFdAryePJijEFet/ppVJZVdjUzWPQxK+VTfKguhlPx7BCkzZy1KBLAcMXC17tA KJs4Cm4SnPttaJSlqXdAGJPSz2tE4JVNWoJQ6plufSEjR5y0lSiQU2N/db1RTUvEAgUgmzAg e+NgZiksLL4GovybhEiVKmqTSu1mORyt17ITjVtRm93N8zzjRCTMl2BRQ5It2conBUxaSn0w bsE3P/tC3VNVlKsnL0bWicStWIl7cSq7FucTJS1V52ps3KBtFFKE2S6t3LLwjbVYhYKW4z5P G1KlfjLeGULmpdSlsLeqNr5XeLSnFlG3x4xJS65a+ZKBd2yy4cPnLmNVSHaNkkzJaJSxQsFz URkOdjAA0hQeWzhVyn9CBTW61l3sxp+EKmSia3Ce+ybFoWkldjSRQXfONqVTJiKSoI4XETjM VPQuXvKJDlLRNVQuWoJCCniOWXbExa1T5cyXjUpVJ7/VCjMRPQpAekLILfwmJu2QqXsmUX6X iZMmbSUJbkpU2B7k2gyhOXLRLTSq1Nu8QoIUqoJoyI9sL0VSzjcKNPjwaEy9rMTbZkJdlAG7 /rjFM5akv5TnccYAVNWEZkU8gBytCbzG6FSYTXMVKsqkg3KeMKpKxUW3S4tCpaCWQlmFumcb MqYEvyeJs0ImmoEqKWwvxjAFFUu7VIe/TIZQNIpmgBWFFrkq4d5hlIWMgrLC5YQUlTEMPGH2 oa2ds8oNJJITVkeT5xa5s/0st6ia8IABu3WKtI2kvYMrIDMsMoMsqmTFJUoBKKWFwr4QkJmm XUGKMN42k2ZgSgAkgcIM6ZNXMKwEjD28B2xKTLnrCqUoQsBxkW6ZKjZS1TKpbKZSCn0aeMbN O3UgBFQTS1soTMaYA2ElKRVbpBrM2VlMKFMxbr4RI2M4gy00ghi4/QiZs5s0VWKqcwwBzH3Y nrOksxrJpp9J+/KHrLrVWMOTqfPtgTU1KXMVTSAO33QZs4TZS5aHfo8fJkTJs4geiE2FviIG jpNFt13IgJXOpDilVs3tC6lqw+Uqa75wmUU6QK7EMjHxMJCELWou6Q1mz9sCk3+ycxCXc1ZU pKvZGyKsTtkWft1yUIVNlAeVQtbXH+DCKpx3CgGrMHOMKwpSFOWAHBuETqJzqrBUwFi7xQuY VYq3KRnGxSopFNLizdYVKKnQp3tC3mKUteaoVLUutCs3GcTAZqytYpryIhUpSqkKBBtE1C5i lGZYqguo3JPjFbl8XrhQqOILT+KCKjcLH4i8XJ3Snxb4Qs1HF8YwnAaie0n/ADBRMypXLtyV FFXFyaBEw1HG/APcvnAckNyaEr2hFPDMdsKUqYougoFhYRswaU2pbg0JUs1EXLgXvAnHMJZv f6z4wpEyataSkI5YRwhSyokqHEDk0KKVPUw5Wt9LLBJASqqxaJKi1CAQoKuVd8SqlhcxNVRq KXfsjdlgeTxFybF7GKKyjjhiWiqoo4uz26ZQgKKFSk5JfL4xlUVrJWXy/VoVNJVdmZREIkrU kJQLU8bNCtniCtnZZKslQyqLknD1MbRKZSEszI9K+ZiYS24UJ42MIIIsEjM2b2xIMx0FJqUA roeI7YkKs0uxqu4/QhBqC1Cp8RTybLomHVsyM3uVCzRTYG0aRKIpqBSk90SzIzSp/KLJ4QJk 1ZfE9KiM25dkJSvZKA9JsWTQnyclSgGx5CE1qEyWlmc9M9UxCc1JIhKJFmbeUTbpyhGjkVBQ SFKbK92jEp2FI98TVfbVV/8AVD//xAAsEAEAAgIBAgUEAgMBAQEAAAABESEAMUFRYRBxgZGh scHw8UDRIDBQ4ZBg/9oACAEBAAE/If8A5OG3mQfQXrgvQLo6DIwAE2bBhC2rRbUeeRZSRXSa j8rCw+G9MT4Q8kQqzEmFO0IkMk6hKmjJxeU3v/WElXij0OORIiLjj/tmZYZjWuOP7yQtHKuE POMINTySLB5JeMNJp7T4JqxqEaQ+eMsJoBRYzPsxwsJzqCV1x4mkUHUcDlqjPWYjtiOpzPAf XDxnNgf+4DEACaCc7+9yPbG73sJHEKtDEHp4BQAzARPg4DVmBF4ITLYInrgQl0Ac0YiYh64k kOsKHeggP+RLyvrjGL33zzPfJeXvjGLX3wfXuZ/6TPxOGVIk7xgNvfDue+bxP3zzPfIQyvvm 1Cp8VgXCRMvfN4lPnkJMvfBrS98CkXvjk5nV5XM/KcnE7NZukq74xLXvnme+aNFx/wArURlm u/tnlw/EETeS0Ez18LfRvr4RAEDfJkydPXB4JSTyzsGs8uOC92T39nj8bKWs3wHvk04B54mg YpUEy7yKrJhCjBQoiOcW0BrnACEJk0+FPfq/X/lahPbBK5v6MLkI5xEJIlweJvEdD2wcTeI6 XthFzGr0y2tzinBRWTASY6YohHtg1mJym/J4/GwQWe2FSpMH1YZ4G8CERGT/AKJhiM67XKEi 3GDcNYDeOMXtlJfmxpIe2dfq/X/lFbBkCFM9umRwPivGhvt6Y3mu2S/Vif0mS/VhCFSIzGqw AOp1hqCVMBFKjZjpbo1/WL/VYYuzbfj8bNiVZPtB0740+t2xTCi9sIM2FGTfYwoVBBWsbd12 xNvjpj5BE6Y89aUr3z676/7J0QlJGdeAoAF2SG3Bw0COzMfR/lzWMaZJn7ZPLuN5GSlMTksG u+8lhJCJPkxclKvDZkjJ7SvvhiiwG4ljLLw78fjY8bXk53xOXAENTLoyJMLJWxHFYBVZJDlW 2uMFs8YcgiTeAU5dMEbzb9f9UhCkkyF0rUknaYxjGyChNPqvmMsVnW3qeWA4boKIsaNV0wkY tKik21YjHMJgXkKOqY+h5HH8uLBjqZK6zp2z0btjArKI6ZGgv2wFF7Z5sdM/SZBquh3Mm+k8 YqfKdcTO29zzipvoZoR8vPKUCVx4/GyXUlrigXYjJ/qdMXGntmnckyRRjlxKRA0c4pNh0w8u OMLQLDXfP02DbEnHf/EB3derB/2oHaytssH8gHrcmWcM1rplyjrrWdWvjBod5xHePfJgpPbI RGNVi2MwdMuiqGMrl+KzX29sHBfhh+I8jKFYh16ePxsl2FGG8cqHrjncXG84ph2y9/pGUktz OT0pxk9b75No4jpgItDzxqyaYayCSpmD/H4P/inqFsVYewXqdPPDz1ykyMzBnJwZgoZyAEQx gBbXWBpgzqmgOXIniXWSQGXEipHXP2p/eDB8p/eJDaNx28fjYhEI+eFmRtcjKLjA1MoJ5y0l jGDHb+7pgcQAkJfbDDoep/eTCPqjJEzbivXqyCgkLv8Ax+D/AOKeyiBXVxaMp+jGZDA7/BlK hLzkGS+JwqLLpMgUrJnIFAek5cwips4/vBxewY9xiGwgbyfyPtj/ACD4xG4V1ePxsIQaOXEl hjU/+YhKOOVZjd7yLxpfTBd3knfxhcEuFfbjIDIFYiqJ6uNx4SsYYQxzOrnv4yWoraEWfXph oneGr9tj3v8AtkSJEiRIkSJEiRIuAzvecy8EGwDxCqv9A/jGMPnF48OZ7YCOHrgUBXjCB0cO T0XUMSnG81PMMITjgr3MLgp1yVKe7nwsn9uTIAvZxq0cPj8bDRJ64pyw9sLBNSziVUdXHMG5 6YwVIJiMuFRsysaO2ajrhHNREznIS+/HI9V9f8fg/wDcflun8fLUJ7ZPdKenbFQr2O3zlcbi BWRZR3GfkMVQq7RgMPoMQS8h3Mq7NTGbiEOu2BUOumK9n2/9w/kXzk9xtx4/GyjD7MWJAI1x knFPbEXmoxiMRPTvh2pXGIi37MKEFohjqJjh5yapYji+zPL215/4/B/7j8t0/hZTCgDiKZOZ FKphIixwf+PEXheUZxfiyX6sGf8Alk/1YoiDyjAv/H+sh+nFsaAk1/WT0htq5wTwiOMsn6eQ /R/WBR9j+sKQTz8UkjKP68v7Pl/WLT+rJfqyf6sZbI1rJX9rBn/llgBzVZ2baZD9eVpXz/xS B5WSh5VkfnvrgjQZ4T74OYvn+zI/PfXIm4G//TI/PfXDFEhYJ98ScIJv9mR+e+uBNmDj9mCa RwEPvkfjvrkRcC3/ANMQNCH9mB0IccTG3d/g3XqNWbdGuMERrhBH/FdfkBtr2684nJpDp9/9 hxxxxxxxxxx1KPZD5BPwwDO/0FMJxy5B3MB9n+CnbtTEfrl87ZENjX3/APi/J225oc0D8SUn n2/2FPfT8xGfYwAGkMvOK5xeS3kBN/pI/gig+i4YWYcaxIOfp/4bMgTpL+fLEZBltiqE7HWc +Lgm+USI4wecOJpAMV/rc3DMp6E+nIG20+AGnG3EE0QuR0/+P4JfnPyOSiMGsEGCzLfjOSdc k65J1yTrknXJOuSdck65J1yTrknXJOuSdf4aSdox5MmQ6zmaYufsZ8XBTEwYcgREs6L/ANcp s1AecGHOjIceRPrqOy8kn51u9J/BuoOgBgIokjWfpfFkgZOq307YmC8jCq0UVyFuc/VZ5pY/ VY8avuGVoNdMoK+hnStdM/WZpT2wipFuPL+J8XwC3pAqIoma2GAiTiPg4FPP+q3kVrio44zH VXp0G3TeSWKwOPp/jahPbIFJK9O2S5GF1kjMPLDgAKlwZZ/PPCYrem8klrVZuV5DuY4QI/G8 MBjjnNhaNxn4k+uE4fw88ntHo/hTqQAYkJXD1jCuKhS1Yg1xvvnxfETClyKKih8v9WtcQr5x ZOeM0KAkk9Vhxj9MkpRNyYOP8ZEySc4tK9e2JjxgGUQu824uuIUPuxpQQd5SkK1iVUdTuYl0 CdcuNjaQ6u+ftskZPa83EzA6f8EYiwkkzpGjzyY6XridMrrZNRnTXOHK8M+nGrDeaeRT18bV VbgnBKCkTSf5fF8QnasoDWIcosBGeUX/AEwCQpANdGj1OcSzmjIHk37M/JqTD5/jWjPm/bL1 jnJpIYZD2XW8LJiMCoqcZbXxiE2/YwIEY1k/RAaDFphHJkAJGQYspPq/t/gZwrCDkTZPrg/b CSU3fd4C2ksX2cDDZiQcgQD0Z9MLRRsSTW4+uAmmJpWYc4xrXyG/EEhzDWJjvhyCILI0D/As FpXOVi0Oxh8r658X/AS7QjghhOIcp3B+/wDoOiUkd9+Jzg0xwH3g1GN9ICP8bfD1MVjsuZ7d MWn9GF07JSoY0ZL9WJ/SY5E65gyHWUEyeTu1xO8RcRg6BIOYxDClk/siysiJmbb9P8Gl7n1j mM/JAqnv1xJIwUdOlFzRSycOYlj0pggAKUyBNOsNYeACVm0MrEKcCNVfO8HLW1oXp/WDZFTW D4Y+IrmkgP8ABTCgxwMEFPLASrgC1pZLgXcZ8X/ATadMBj0cMkogqQgUoocf6D3Wi9Cz8jKE Fy46c/sziIoqzoj+Mlr0NyRXb8Kzt/g7ZEa2cNop6v6xdH+LjOZjs5SHncoDNIba184FSk4Y HrsvzN/+LDRru5nl14fowEgA5I/1tIXgOQGBj+nGgvfvjIYEZAdgjg/z+L/iIQo4RuR6nTG9 0XwV/f8AmJPHa9VnzcMHpgc+oLXGESOw4elf4xBPLiFddfTBPRNmaPm59bwQrBI7yPAmQOCF mIZdHjNo8AimRmTHZk/DCYJHBsBPTIAvVfb+EACQQ3G2LeC8nylIF6F7866Z8X/INpCYNUeO dM7GKEf5UEm29D+2FxE6ND2G8sPzgL11vz/jRJt98vvt17YHJiay9specU2UXWef3Z6p655/ disrWBfTHUqF4hlc1Xp1z8jn9s5EG3b7fwnV4BBIjJvyw5AmCNKlP6Iz4v8AkCTU6CaHETBo CgdL6df8hPme3OvmevTBhPKboS2jyOMAkPvb5/xqxGHJFy39sX3ZrHuaB88Nz7sFh7TN1rc/ IZ9GDuZZ8LhwofQydpaz8hkP0DJxhghf8T4v+Y1zUPm69LcYFsQPIP8AjU6gcx9HznIigmf+ nRyAgBCHqXv+Prusr2wIgJ3wzIRPEvG3WKk3jChnr18JiFfYyFABY1hUphn2z7fCp6n8NDUg cFJBmZYJnXbGqFTV8JpfTzZ8X/QDtRkUbeun/G7BeM/N9XTJqm/pKfaYW8ln9V/jE7PfDnbf V6ZB2o7seWMm3LGTLk7OTeLJQHq4DwfJYmFwx1/1uAC33YLdV1cn2Hqwd3uYY9bf8IRgZpk3 lIp9Jnxf9AOYNMwcEPfBkaJ73P8Agns9pwPTfswbTQDzdC3hnGlMBCDq2/T+MoRDzxV6D18I Q43pkwJAdrcdod8wzNzjscBlqE+H9ZKAGuuS7ljZ6OvhIAIWKWREU/xPi/6AVrHvuTOsctZK eQuieMgA7Iv5OFCyvy6EqKjBaSRajxv/AKy2L/wpN6AjiCaYGosiHD+s+L/pA9KMJxfkmTRJ nNDavF06QJn3jL746J1DAdb9sJhSLAo6f+x8X/UJOzNNopRjEcQAeng2e2VNe0fRiJj1n0Vp xy4IRGo+R/iGTH6IxhMEb0nbHZHTcYQkiY8rxbpc+BoCBAduSKjIWemTw73s82D2Az0w0Uox xMZoZLnX/mBgF1RxOgk2uX+slQaw7hzkqux+mbQvu3mwIhjDmITbxhKMUYSb9sC2ZCcBnxkk QHATl3/m98VpkIkHjGsgEIZ3LGKoQOjNZKAStZM5AZBW4c5TUoi9y5GbBgByct9RFjjpHrT7 5IiwJnAlYC4YJmu+sDh3IHHyVvPi/wCoIRaWFFDAaSF34bfNnI+fPXHS+gJ9/F54S7JyfX/E I/DqDU1it9KBkIkIxY6IL04wIBh+1gu8InAq8cFHrkRxbQuUJHkJhFuie+MBldg+WPphKeXH IkgEIiNZD9NZ6rhtEYHTc/rH0EoDw/3iF0ab6ZKaPL+jOhMtKo+WESFlMGUSR08GcLzHGMkt qjgdMiDbFF0YEkjAjqnJUsogTWQgJzoI5sugRFRhBywCYjp/7hFKyCGoZjFKLGTFurUDjIpj XBiwSOuHQCw4iIv8Vkhlnldu6567z4v+sSq/YtKFNbDA0Keq7ZOuGZL6B9ORTE3fCNONuWVx V/rzz/EFIJdcNMRtaI1T65G0F9LC43R2vEQ2QCO+VErC9ZH/AMyNCMIBG/XHCSlSsfnvkCj0 pzv/AMwiFPDpesmVTZ6YhJzL4xMinm4wAw5GEbWl0nEIKjdJb1g6CSzM0HTArw5smIJydi3p kAAnywCrTfZiwNh0/OAQiRtrX9ZVUwJH55YhWgI09so/UTN8ZZpZjJkZHt39jJDNMiKaO+LW wWnoX/n8X/YHVEyTm+Wg6gQ8ww50ZAmoxIu6V23jXV0vsT+JYaLNCGjFoTOQJ09cFRSkcGLg lLzcxnbRpinE7nc/9zoRVOMXSoHNAmBR1vDcnR7S1goEsg+WQOazPdhPrH/mWU73GOHB8rv9 cA1Zll4TpkI5h6+TOHO/rhQBD/AbIV1LJl5SYIyAWhUUA6acqVYaO4GfnP2z85+2fnP2z85+ 2fnP2z85+2fnP2z85+2fnP2z85+2fnP2x/VVAk9sgI2mc27TjTnWXg8jFT84GDaXg4zxfz/1 t31kIYFTWtuchlSLJZduXX/BVW0sNE7celtEB9Bq+XPiTQYqB5r0yEVULkSh4FTx4xakqcTm hLvRFvuOP+9d+rynIJ1T5EII69+x08BSCIXQMx9HGNylOoyfTJGhGZpFk7oxYJdZbDaO7nJC n5DkmqTqRVdRT/vmJWhb7jMXwOPGHadRJo+jW2c9dyk9TqVL7ZR8HZuWx1h5xTElAepwXpGO byhwZGa9uHFyAHSAxN9mdwB8cP6//O6hCE/IHhcvoJM0wyunHXI5E5kxUwUxDL1x4dZwQCHz jMI4UsawYjq5wUGGEWnTcYTICGESex5uCgeFIXoIevvgLA1HmWq8sVrT3GlWfIyF6p30yGpM aERqQE7ooNnbFjgYijaRrnnLz/oHbgFQI5HBIcoV4SCDGCqFCDdNUKyLkKLg2NS6vApZNBJL IpniPfCN3wwhYQFIM5BnRKsRrmXrknbxEuuJUXHzk+Mlhf07VD0Zxo4I12YURyp59snCnS1Y qpnzw3RjYSaSKdckaYDUCmi7FBkMldMRPp6zGCWj6Lp0o1613wYvd0NCYPSPWsJGQ/IOb1jF yOqFZIu0GDpg8G1gb1iViKlCAJnNXEZXjBVhCNVbX2wFmVpZIlCmtf8AFHQQ8AsR4cjz2V9i TWAoJG0HK+OPLLQEfpXyT0wiLAKHQBveR2xNEjycRizJhLAOgbyG1zHA5ZxxgCv8UOy1HbIw iS2osRqh98iYOCS4sizyMScE7vCGuAmRFF0VlwAWJ0wKJ6GRSSBJDaE1lQ+CmE1JdYrTLSjp C1WGaYV0RpDvhKpqVGgYnphhAvq+uLRmyNSMgQQG8mRtUscAtYowBtKQRe5wkVVtq0SSh0we d6vBJDY4z4Cg8Iro3a++DixgOakkoG8guHai0LNziv6dQ1BdYvAhBDTPXLQltG6AwluA4ukR wWods3h6eUkm9xh3SYCNyc7veOhKlSXuufi/7z8X/efi/wC8/F/3n4v+8/F/3n4v+8/F/wB5 +L/vPxf95+L/ALz8X/efi/7z8X/efi/7z8X/AHn4v+8/F/3n4v8AvDrghPX6/wAENA+eQfMx Ke0Jc1OCozSQxSzyvOAb7d+cYDO5QSdBHvi5A4NsIiXs9EYCEp4EKIYTH6rP1Wfqs/VZ+qwe wCQpb9sVT1nQrHqZKmFBBHJBjfxkqtxJgo6J4e7kkuEJRZoIwoS2mUjuYCAJKkRGwaGfRy00 0xFVqO34w4ICxXZI+c/VZ+qz9Vn6rP1Wfqs/VZ+qz9Vn6rP1Wfqs/VZ+qyR1AURIVBi4h+Ik C5jqOcWkge0BUhPpgvYqTAYilOK8Mo9pWIn/AMY0roOMgjREj8ODDqZCuSMRvvhLiBXawz9V n6rP1Wfqs/VZ+qz9Vn6rP1Wfqs/VZ+m/gjoV1cJmK4qcTvEaFGZDVhbJufXECMT+OcQkqAGl a5xHte48t3oKw4iy3QyxLM9jwHGddzg98FUkh3KdJdnfPxH95+a/vPwBDvlX8C6RGucQlMko ULtPTeXayNGwifRWHAQRho0bxxsn2D74xVe9ujer1i2Fz10wnXoXlIlS08B16BiYACX8OCqS A7lOkuzvn5r+8XYDiP7vCIh1GWHo5IToaluis/Nf3n5r+8/Nf3n5r+8OLAXiWpTE9s/Mf3n5 r+89M4gU89mMWvK4up3klTsiH0nxlN7Xs67zUWuoCznviUrhx9beVupl4Kne7jJEYxlMncGs cl0q4UYTfUz81/eJpREROl1LNTn5r+8/Ef3nkqIq8ESsQHKF9UxUpyZ3BtPViVdwmLlt75dU ay2pHL1mTzgixwNLiVlThBq1OVhu7c4nXvwkdQ9X+8yjWvAac6iO+SDop5YDXkHjou7AQO+1 8YZZhVjgn0mclhsI0uCz2uuEaCmx2TTjlR5rG5UxTVF1CPXNiFshgNfReWshsKiyN/fDJcQW D2J7DGQ5fNC8BZz0743zUA3RKHjb5ZpKrh0SPMa+7gSRpiJIP7wkNYoT5GoOcsUzqHrTIZZh Vjgn0mcWCTZoEAdlwNoZIWWPqe2slo8lA3lrsSPwyUJDyjwk6iQIVXFXNf3jZ9UgkJARAGRL fBMKkoS0ed4NZg8iRETtL7acmS+ikPPLsj9mV2XCzLkebFRpYroKDmio3kfAAEJIRp4bwWn0 FjXQY2emt5AMUGbyV6QRj7coJf4J8iMO2ZJxaxsqvwXhGGDDhT7vBKMRq5RvtwYya1mBWwIf TviYBtC8V2aNO/M1iYpAQTm+r/HCCCwE4gXYxJwlDKCBiDiGTBWaOPIM440oEefBiSDyZ+gY beJVFZ3I2DEL+RmEb+VmP0DNtLlGa4XIM0SdJg3khCNAa8AXL+JcqiCIpKG35MtNeICHDZxK orO5GwYEEUEFGCkeDUvwB+gYgVOJqTl+xiUZap6a1uc7mTBn6BiJdjEkygH4QvElbkwVmmFy DP0DEgxeopan6ZsfIIF59JdOfoGd0zBl+KlRiFjhqco+vgBmH7UZckoQZxhUUKxboYALPAH6 B4A3ceAMDZhJalXFiK0hNPuPCj+Lb/EmLp+FTLo/75C2exDCb9OmRlDOXnfF7ov4zh6IeYG1 kHSvfLhOgkWirzcXiVAFkVKd6mO2NFFLq7+BNEW04kjFGRARGoomtHXFygkfXHqLs7VjQFT+ l3V+deFAhJRNgK6SZEjZ+SFHQleuBQJe6hQm2LpOKwEsuM0OTpDUb548FL9XReUmNPmXlMUg tQ3GFEKHMiEM01ZltavCaItpxJGKM6AiNRRNaOuTJGIbkEyzZTJkBYj8mq/PxCijiW9XOoem +c39kjYbE9OreX5e333T08m/bEjlYxoFEvA58CgSEMLKzLOuFZPuMHqutWu9YS4JA5Qe1kHS vfISNIAgVFHa/Bx/Rc2PPxHq4RWKRBRPPY3J1HBmLYxsv4D38H4tGBEGUZ66xHgJRGlTfUoy F6YtCRaDwMvQ8JohGCkFbdqxD2CqTcy10twY5WtJk879nGEskE9izM6vp4nHh6B7DGcyUjNA ht4DCwiNIkPTNMmMiqJ2Md/BWxIJsbFsqYBrWEzLEEIsvq6T0D/ksA7xl5KY1LO8szAZHBCx 3HXGMtRU3Gj1dW/fIvrCqnAQUdJ1z4IXcxF6QkcS5yL9ATJiVpeo4x7sJMktE0C50+mbdIDm RMOxMenhqrMHKMH5H6yvBuXCRGtxdVveKBaKLtxGqnfg6oaAEVozHT5yEcGElVM3Xbv5ZeAJ XK5CjkdOa48NMCOoUBh9snJHmravowl2NSKMytNOtmPqgzt4RWpZ346gEgSfE/OShKB6hmDb cZvEkfENOFuOdYIkL2dB7nhsseVmlO8fVwvlZzhc/AeX/JjbzhEpX0YZit61Dfmj6Y9Lt1Xg ROdqQC1ARPmL6+BvXfktEdsBjrJgVBXvsVi4wgC8B8sJMgFzIFm/AoIqHZEB5RPxkonqCQ55 yGQq4alJHN83HPhySSTiULBp296yBEEkmS8xdcc1hIWwAgTEEGoodvBQGTXCISepOdupEubE 8xRgIbOwEjro7dIx6K3bEMhi9Hxnx0nn9cN5iIJ6ETlUJG7E5LiWIBNyExPTp4RYMCdQ0T79 qxcMQC5pt87b/jJMSIHkrESEIZSHJBFzsM3SJr7br1GpyHzuo+h1z48ELliZEEg62xu5s0Cy SejTK6dNwyG3IB23gwD2GhlnrYidcc+DHS0mjqEMvbGHNpFEheLtHKceIhnIwLtN9BiTEiB5 KxEhCGEhyQRc7DDhrIGgqSi31Zwl7Dc+XXmjx9D71y5jdTfpk0TotDDQhfQxg/8AQpM6nWJ1 75J7mJYGyDno8G9Q41lUY3vnJpECChYTpXmjOKg0UulbWVN+2IXDEwpJJ1p4UrRtDtOqbn0w AjF+CtT33B2ZUkfTTg+r4KbQIB2xKPBvA69ZkbXHVicpKNCLBmh4Gm37PBIxWEBBQ8px/Jxd uYnpWQZBOUveWtnZOLOCTutlWN0qfB7kdKY9BwwQItgO2z3PLLTanrNR8YgqKEz5HXJrJmVx IXRUpFIJ8e9/6WjSzqvz/wCSfbIWbL5kI3iSqkAY4Q8A1zlIbMcNr9e56Z1UMm+o0czxx4c9 gigCwe8v4YRkkgVSSZfKo79smpbcoh1I2Nc36M3WEKQCEnch8NLFaaRtIjY3PJkxTIyd9/xO NZC8iIdus3PhdoaFE4Cnvm5prATaHMEuIDQyzdTSteG/BZCpklAL7ZJSarDuK4rF0CAKWglO 3VucbBSzkjVPDAPCdDNpyGTdEGHnH3yZnIAogYpuLJzkvHgenOCCCridNYrnr9UwfGmJ9cT1 aRU5hp1jIpocCNnHlV/xulBPWCcDyJTi0Ne+QUubtR95OMgX9Xt/XwiSUedSH3YhmAuNSlPb DiYrVoE97e+WlEbop9sMeCSibiQZKS7t7NuNkfORsMg6uB0oJ6wTg6TKcWhr3wAn3C5hvtiA KaK2/HBuyydpT8qjBSOAdTAuw+6PpxWBkMg6tGvAmSkybmG/fIPYUm40MCTskahXqnFjUmo8 vAaqTdBv3QyLpJp0ZDvhFEBetAK/bwkqY7kI+g1hoXC4eRmoLqm2h69d+ETkV4UH3xBwmRO4 x9sZAdgrAKMCm4l3K/ZXgci7EKsRhaTYzBG+MnrplNi1PeM2yReGCacl2rPZHueevGmLdSEb R9fSP4ySQ6zh++B5MXv5c9/XFLt2nt6eAlBQokcDIgwA07Yq5KUwurnYHpuOk+AIAR4c+id+ WAhXQwYkkOs4fvgeTOL3yA6OABI+l9vTx+pFWfwHvjGrXnw393rjLwzp23lHfeWB/cuCngq6 Et6zPamMikFCQg/LNEzaGk/JLW6xSo1xxpbPPfr4Q1w47S9LyrahtSp0Ku+MpcAG0QbY9N8d /ARUVCIt+U5dNHlifN6ZISakgCo4Wp14FUV1lICe14IQNDUKrAynYEIkj12w66IpcEO0HhFp ExY+wzY0AuWduMlYQigkky9VwBrG/lFIffKnPOAexRjz0c+E/iQ5hBdR2640x2yyq+DZXv8A 8mRKa45GVm+Tj4U1A7OXYXHXC7kPX9eBZigAcnHmPr4SGYlDMYknEajuqxUD5jKZ0AuPHmVz m5Fh0wo/PguZpQRUx3qNdcIaQ5Kc/l5fWEKaX5x4PdMID1OQeGIcwmI+clELKIcFfW64vwrB QSEyWj3jJPqehWo+plOoUrSw+5tlfZqDYnsnw4wNKjChpAlBE/N+2UZSKQXJPGTmqoJMeWPa axhju9eFjh2L8mKViXgNnrt9Z/jQzip0MUPAZCIe43iXUIeR7DlVYnu9quLqrq/B00GCiW2A gwCsvtHZIwmBlIB2QohEpTZ0cGDCwMNbnXJ4KgZ62Of3jaJiYnUxcZvpkoSxhyWcSdAxQ8Bk Ih7jePyAW2K3DFtlGTp2pEIKjOtNeM92ans/qPTN8MJ0HW2vZjP8FwXPp8kYf2t4IO5qPSPA a49jC+gmiYxOrwlCiK1obyfw1E8kalw1U98KrhDld/fwiDmQisZmInTeRLVRd1uA0Tt1iXZ8 izPPBMHt4Q90JVl0FuBCDk3wucju58gpshV+XgATAGWhSLmYwIfoVZfVc/fDf2YVsB3E8Jxx gLhdoXjZu/AaAOiTgAqSl0FMsahckAkkxR1WzUYSuFer74yRJ3BeQjjuV4GxdlLQnes3XopS ejEP8aX+dF0clAqnJcRcHlrpiy4V7k4681zjAYDrHmK63UeCG4BsERH3MF3gpMWUq+q6ymQm 0QAqIjdLeaVglGAJ9B4TvsEE/I5AJNwo2aDenTtjsjZHNuDtkv8ALi6OSkVT0uIuCOmumRfE 9iExKV2N5EUNXu0jPqajxlkusIXsp1W87tnwmTkjSNZvo6IAeo16xzh5lrSWAcQaDwPOJtIq l1PBk5CAhFBk0q+kYwsuXAhBJd7mY7YUjIBLjyrwSQCodDC+tGCGGSyKM68wavDSLgOkEtbi KnwajMBQ0lbrPnlWoC4GTgAXMkMWuBxHTwkIQ5yGRvE5ByDf9zWRTEDoCwFd25yG0EUkINJ0 vhfIZgCfXJpQ1YACRHQlrrjmbIkKhAUZPjWAZiIfZkXoOX5ZJEMTrjNoEAkcC+1GDisROumA hXsBhQI4n8D+OgShOpnSOq9Zoper34KBKwYMklmUrUUs5DYco8AZAd8uCF6veCOmfBAlCdTL EhJvNXkL34qB7ihvOoemWRSA6hfTD1JgahZu+nliMYbdODbfHgZQpFDdLP4ctiRN5iZgvFo0 NkczL29jLVVrSlmfWfCVE2DZS71r2wmTWRmOYveAJikzJYi78vBzxEAZHWcgHlCo0jOXSsjJ mmU9V3PhCQ2Mkg37Y1wJSImZa9XN4mLQCRbyZSsm2hkXq/CMYmbH7mMXqKCCRXM3flkqCgYA QdW8G2mBGPdGFdqJTHO7+NdsBkB5Re5hlnBboJue+M9kgRwS9t6K/jLClPRJgnLjoEaKvXQ3 i0vMQr2daru+2QDK5yvsl+z7eHEGLSAy7MR65zHVKpph2JjjWS2uOa9Aqlvp0vpLlQ9MaOk9 W+OfBTEgOQXXJtnJSJASuZN13yBOGORZui94sOU9EmCcuOgRoq9dDeE8SYtZFq0R33l7gbQL 7LfTjxRqPvL075enORXJWpwWx3nXHrjyx5HaR0nq3xznLkHmkIKOk658GXbOHQU+UdcofgsX QSpfTjNBak3TGlUg36YKpGqld4PBAAcKyqZ7E++A7smTxUrZHbezJDSiVw8193wJiJkgVg7p Qxpx79Y1vjJpnqeN31NPfwhK3tUDD5xiwkk0dba+nGTSIW1Ygqa17tYDCMraODtc79PEisPA SjhzJN8MQBqPYeR+cuEdY4CrTtgPAyk1/Q7HYniw6qWxEsVB13c/8mKnuqhOdaauvXAkcVKI 0Z6y3g4GjBhBEknIzXnkZqKJO8i+BTfnhDpDdfi8UCjaBOFib2Kn5xtAUHeu5CHCe8gTgnhq WJipjffwrQ1eEWzuBjVuAr0hdS0r4jvtroO9Bhq1E6PLa+uPoOYk+D2I2XePvraM9l+19cas uBvcEFMcDny8EQlSIMSF9pyDSmiFvEvPBncBhNwXqNGNRhEfFPQTo6/hXjOFs7QXp0fLDcBU g6a3bbzzuDA4nhAHqh8YosaZidmNOsT5eMamDxA0HEV6/wDJ8s29T9nA6lt4NMIaK7X+r7Pb BBl0RKS0bLn2nnwcYm+5wfx9MPqxJwSdG3V986MBDdzKPLAWKMImea8K0GsQIuGZWaifJlAF SORD3/8Aa65Li+S+gg11fTv4T3EoILiVEebV4a7BBtlfmicVklUtC9zEvwTFml0KnpOKbgIW binbNM8Ibe46jh9cYBVWqMsG+h4z1rBNzLo60vrnTQhv9hOMMmSuXwntixK4U7KqQg9a+3hY EyDMYYn1jK3OXr/jEdy1CIYqLZX0zJ8+2GCEnLp32+hgTX0PyTp3eCdBg4CgV2Bn0x5WxRQR AOyQ+uOt8ayq3bEONMTFampPp9jwAOxe+OIwQYgEdAlno1HeawJbPpoMDzEw9xzuWoRDFQNK vpmT59smjw6okQr0y+2cA0AfLOl8+Ip0uraT0Zw24jwMbWcgeuMCr7iP0XZ3inAIKIGTfKnp 4OFDrNMQvnLkYKKV3BHkL5x0mp5iEmdSnisXZORd0KSdujyeEyYCnLovkERPWOuUuqZNBMHK 4MlEAnEnyeCdJNogKAcqIDrhnTtw+bCe8xifwDz8EiqRrKBXljsBMHHKz0JxBARk0myLdjpv Bshy3BajtNeIfqtRPeXxne8IBeT0c4zCBwS++SSjRKA0CZWICd+ME8RCiKm/N/5MGyb3W9Oo woC3PlL+HrizbHTv0Xk/rJjA8hoye8enhwCRi9wfPBlmEBXIw8kzgWUw5/ZENffLL1FEaR9v ATjJtvxR/WKzA4IjC18YJmKYMpR0dJ4AiLl1MHAL54doUx0c/E5ZtlrTIZTVIeHMTx6AT84c j0FV3HtORTcCbVA7vRvL+ibrSwdtPBol2IEuKoBxao934MdG2kSIEmebxosGrSeeWelctKBh 3Z+PFqNOUYbZ9fSP+TQL1G2PlBlioZQsP0LIMxnI6L2wWlQCKhHHv6+BLAsHJQn6mD1Qr3k/ RYBIBMA0BbgmtLs1kDxE6DwGD0lE0oMR15mmsgOcriRFsD15gpvLduomSH2OPA6OSSIHLAx9 O+WRMkaYkiGmQQZa7ISPfwQoQB6VQT75H3cdQrADpJURJQejbrj5SRe0wHlB4ufVsYDMS0jp dOo5ukBSX3wYpNcWOeqePFkIHeZ0nvb+Nu3HlGC7WXyvSucQRMmiW478lxjG5zpHmL61U+DE 7AbFWA90wmItWxDCe45cZTYAgXERshrBPUZBZee6tR4VT9hFd7yU3T1jBPWfjG/LRDD5inzl X6PKMF2tvlelcmR/89CERIX3N5opMwYPMX16T43ts+ySsb3a41U4aDgRaC0qRXV2yg8Ih8r7 uqNVPGGb2S5U8ycjTz4WrgHgjRe/JeOBbqESYLd7jnGqoQLuAe8g4nviVDdkPWX6+CeSQrO4 Y2xSdEx2yIq3s0uyTAd4dSJZjVETEx0OfA67F475sKbmsJgtGGzAF8zkgKG6W0ctz1rt4SoQ BuYKdGYvEcFTqlR9cFeoggdBXUlx844ERpCwwTu1x6+LkqTTQhb84fZxQFFyegmj2wUzAvD2 TJk6rGUATl80R7ZuhokBkkTIKEG3t/eQz7KrzWF0dFeX8Y85IOo5ITmqlXfrijyfLb2F3inp lrub63fg+NlDTCIyPvkA6R3tbV63gUIIgUxEAeCF74ldQsQ5I66eCpcyb6MVVNUzMMT60Xkx CwSXW/Qw85IOo5ITmqlXfriHzKSUXtOjRncqmZ8p81+MsV5RLwPLZ6YOGRNt5tRep9MH+ArI kuDTwrOuHW6jc9o8Do3apeinE6nAu4SZ30HrqcH3NXWgiGlYWM74OSbLTzM+D8xkU2iu1Pa5 tz9Cnsh98mCNu5Pw0enhDoR5EvRXWmAKyxNtM1EczGBhJqxRJeaTvnwF1sNmZmojmYy0WNwd +eQWAEWNse/h3yW9RRdjtMT4lMDHgQ7ffJyIwQrRxviNZuES2c+kXg+HAmCPgHLrwjBAVlMS TiXHeps8zSf45IgHUz7qNZ8YXvwQFUBy4IgjI85comFnWcGpMTfhMk6jlossXvDpA6jPhIkH Uy5LC0nWfdRvxRBL1Fy97TjNOOwqnRrSw10kQTwM4u2t61Z/88OBhIl4T3wNdaCNxSJ5gwG9 xgSTl3XrpkLrR9zwSsAoAUJbtIddjJk3Ci3GEwwo+nt4ISBM5PRPL1wIwRfSLxwF4ReV+r9P CTmQGlkJvyyd8WUJnzXdwUDIBWJh67GILFJ7U+B8ZRDmGX9O2QhYiAEAgKyaLipExk66rKwQ l+ReBByjCFbyOU5mcc5cDyid97/jLAFPRJlqkOsRoq9dt4pL6xXtq17vtkAy8p+jX04+3gx4 R6QIPZiPXOY64akMOoTHGso17xdpEUt9Ol9PJ4YY0dPN6c+EIoMlIGdBiwwVs1bEgQaCXqVH ONU2CxwGwLqfWOMWWJPRJm1WNYiIKvXQ3hIEmOoi3YjvvLlWZIL7ZfTjxX1S561XS5mfTnJj Dtx2MebXHrnlkfEx0nq375Pw3SaQgYOk658Hq8wN6U9jFXDRnadBeo4xeuUUQ/lhHVTxnDKU CkEyE8wVPPgImTsUAkOAqM9SOcg8jBIrgFdTbeG0QAQ6Kaqd+BN6rbFQPdGEOcmWS1Uwyj0K j1ydkXK5EIgQHCN+DpwRUKBh7Vi0omjqZa+mCozLKQVNa92sqEifQQ6XO/TxOEjtn3MPGWm2 0C0Lkyxi9sGuuCDHRtxLc1De/CAqTNEhkhhrtU4aosrnF6Hb0/5LRnorlTGpTbNMWhhBdPUf TIw2EdFoTbAjBUkPANNsO7QSOIcrEXIsK6XJJxksdyEWQy125nBQAUO5P38OCLWynSA41hc3 gKUeFbo1D3yJ5blmoADi5fXwqbm1CXL10ZOxIEiCpf2MktFJUkQDXSCovfgjAM5gAFPUwyoO w2o8qMOTsmoii03CddY0b2+EMTWpR8Js+dI/Ucmz6LNVHHEPvk5WwICgOnOTuxFTGR6Uhi7i F/c8Kdyo6aWZ6fXIKgh3yGSrbJ7f8n2i7s17OB6Tn0aYKWCshTgX+PTC85IzqB1vc78GGe+K Zw4KqxJQye51ffExXzhwJuSF0YgOYjqu9HhawVyBFwzDZqJxRCpHIh7/APtdcJMbAo6dHFe/ g1dKihxTCR56vDXYI3Ze5E5ejW5vRqaIc7jwRk3qhFT0nO2ktjcU7ZwSwET3DqOGPCZLVGWD fQ8PPXqiPTOMgh3q/E9DJIepTJ+91WdIq5R6MdXNgEhPv9/XwQ5uWZurr9ozZEhZjZkcQVZ1 7fxgflgRMwrIIOVEDaQRzwZukCMOknrL+DIepenZ8e5x9vCEhEWIgkHUJzfyYsKhSeikPGPl TV4RGwBmHbfQMUegIVo3pE6458CZzqbtpwwC6JixJnb5ERzkQXhWQ2Qcw1xgPygRMwrJIDKi BtII54MOJgIaSoVFu6l8s4S9luK/MeKDuP3M6N1N+mNCGNOmhDwGucY1zPqkz14J1xzkF3hT wbIOZNceHegqBKkQub5cJRAglJCSn0MtwCB17mp+cU0Jllb5IL9DwlQAGFEhlQ4QPNOClz25 5rfXzG89eygjhh9Xl4Fy5rbAPUCWLc1+BES5/Ovk6xms8zJF+g++HTFimAsRBIIeJ4x43RcH mF6VkG1IF7SOJ9TBsmHR05VimlT6eLMB5T1/B2cnfWBARBS4DJOLEGSckV6nfJ8iGzAUjmCJ SsQTgBA94caPsybh1UvGo25rmOgm5Cjt9z/kwbJ5JL06wNgg6iX8PXJhBueUy62f7ZP6+wZ8 OBSMfNHzxHLCAtzMPJM5DJ6WrEIfU9MMlUNleGitBgztLyaHTWXTfPJ8C+b68VjRoY6tHc9+ nhEETLqhwDvnvhyxJGpWL7YabMRAWtfU5mvCQuePQCfnDRegjXcecTkU3QZ1OB3ejeX82ztL B2qPFheSTta+3bc3nJkODZ7JxGJFAfrnBAknEVWK7xvpigMPMchiN1JTRqDztMhIqBpFi+tX /wAkFj3TvOflBnA0PHyMbsWZjgp16T3yThgJi70LwnL4E03cPfT7PxxVKboWWKeMNcXgmfMn IVz7YqUqYjAk0DiS47+AL1b57P8A1hOJSBY1Ad277RyYzoAkZhBNsR6evghFMNDr6DGGY0Vk 9euQMgm2oMh44fjwGUOM6wW98AoHUWVVZF4tg0eoWX9O+QoQbOSY7W/Dxge5JGyEax2k+Hq9 tTzkKoyk/wAUPvhEzb3Ou+J+2ASQdRexePH1EPFsglRu/vhJy5z3vn/kqBQSEFOk4iTGU77M 9mDU6T0wqDahB4AcDACXm4EEi0x5KyxtDFLonpgAACgPBZMUXY6OLhXn3TqYEySJbB0nwXt2 4x9MuWEtLG8Qr2Ez5enhtYgMjggOiHRFYspUghfq9cAGda5HQeniEEoAoPPFa9aCPjwQTQsU 8nhpgFr5DkNGIoMNT1/mQCNg8fjnGPocNMc9x1wlRY8s4KAvEtg9cGmAxu9YBEFoQjqTs74D FJ5WUjQ6veAHWr3GHK1oOZyiYYY2ghR9s5A7Mvizzy5ISbJwdgR88GObT00PbL+xO8CxGUoO +dbQhT3xJSGUJT5ORksHWchEyR1ykanmcuSEm7x4YqOGB+5/OKkDuxn61n61n61n61n61n61 n61n61n61n61n61n61n61jAyATmAevMvxqMhhNNjhI6pXPPpjCvs0iI6AD/xk2lwKWUC6I3I lJ6clBSGwKRQh06d2O8DkbDD5i+2bbrggjZB1fl5A1zhWQt47QZV59tnQ0VU61isnhiHK7c1 Gtpyuj50jeB7PTLt0JgESWw2nGjK8Kuto4y2NaxBAl0Toe0e+NiwSUspHDg6sTgN1kZWXYK7 5OvRODdpaqhdx0yMFFxMuh0AMRLRiywIwJJd5G/PAmhi9jYg+ovCS1gAiA9i0niTIcRObmib NsCJ76xuU6TOinoB88dIa4SNZ5D3weEK4KkFPSdNrmnrbyZIih2Y0EwEHS8ewqVw5ktihJr4 z9az9az9az9az9az9az9az9az9az9az9az9awVgk8n8FDasJsYkeKjUQxf1PTJ/cTdMItGnf 0w6+WVWIb+2Ayg6EbEDrqFXnjeqgLp9J3+ThEHYODBoPrG1rTic4LAEvNjfGCjiRO5pP1bMf snO9jrtVeWRlGCFCfS9XIPN7EWmNZAAEnSVSJTesaB6AzNJQbzDhfTBblCCkl2N1z1nB7rti cIpt7EeY1iKpUinXs8334cbnMFAmqgd/vyUJRGm9jxcAa642X5AqnshOCdSWJEnZ2v6+coPk NgzHmdGDFqE+iNGkOP1bP1bP1bCpC0WPXIHRNPdqTpx0cTjzf0BEkwhSO1+fGV2IdC1EL99P NpIpc0AXnMzrhfOCInwfbifJvnbzIQSGUvFdzE8VGQudslHbB9efeOYxZuofpjsZFJvNEzyt qDqcdIhJAthZWx54SnVJQ3GFg7B7gpJvWb0AIBCLHPfEUADTYZr604d3ITFkmelajANRsXdY SQjRyygzqAsdlSwYiORCxTDwSFVn6tn6tn6tn6tn6tn6tn6tn6tgDID5P4NOtOlSUQ9OmTAq HH2pBxLMcYBAatAGgtrWQ+PNCha74iTCzZOtqjEdVQehBOuy41ixQgzQhilP6yeZTRHAzxpc DsmYYLXuo98fshPzHAACp2XPI8sM9pW2CecHI5chETEK3T8ZJCk0gQBXu4YBtELvsJrb1yYU P2eQlhMQeMncSgS4Jjpk+QgQaokSMFl6ifIYe8U013ZLuAleU8x1TrOvDHMJzMR0RkaSdAhM 0m5PbDAGTUEAaTlPnGTkDEpk27snDvjtC1widV5PBnoop9DDpTznVYUU4yCqVruWuR9CPziZ tZE+zAHVORYI1ud+uKkModP3MrbqZoIgBHrodMCQKOopB0l66yhBDKDqf+Y0EjQ9Dh0mqcRg lQqNQhSyj2TeSIY47tdqrRjJM0FIwcT8Y0BCIPQkUaeuUnWIShC6dl98lRtvKnyYfSsiJtEe tYBJO1GpbH0yKZoRAkAnvlT0CoDrHzxx5prR/QOIKC7ZJhsU74G8m4EG9thPCqUziKxmtLuI eOnllyDlZoaLqcG+cbcdRIMN3b6N5JAINynYLw4KcLuCpydvhkluUG227zB+2SARKN+h6veQ up+YsqPJkj4/3VeglKLcnt64z51VZChu5h9MtCikJQgudKx5zS2qgejM4g64Axxl54gAA0X3 hL5uRVeF1yA8pvqdcTsKIVrEztIB64QDGghhQjv7p74igWHXR+5iTp6jTFvNBfbJPB3Rv9D2 Mg7AQhlvMz2ztW4yBDbyisqHG1EJsdUWGrAEtE3+HfC7wSNIR/O3yhyyyrC3ix2QR0xSgMrS I6O2Q5LSx2HKRvDq6RKsp4SNXZ0wdVUcKIhCp8W8UCXpGh9MiHmmJekfTELrhesRPnHOE6iC 5Ufb28JSiddi8RUXRM+kdEM+eVp5QleSdSsfDkoHJeVBQSLR/gMFQiyYEoS5HWXYumUO9LhK k9Sgm48jhkpSAmGoO2HyDhJI4JFx6HnkLNTkOtLlFj90QW9MRaUQtuhjYybkvFLH93gfUxcY OgFrJRpe4K5eKc4JC4KZWCs0CkhBqaEfGQigEcyUOtS4suYQtRLk4hfjBsQ2Ek9Xe8mAaKI5 IMPMX3ZMVt0TQpUpanTkrwhQQ6mIJ+mK2zc7pIYs39cX0iu8WB6zx2emBADPQbK6fdkM1P0A B5DgaFKEJKH50yRvu3ZI8rV3yxeEuqo+lYMAW0XoxOkp/uSaB1iuo8E8ZwVRA2hZEjYa5wac Gcm1jsoS+mNYF4gixAsmek6yXMeOIlbsjuZHrakomIEPfmcXhn8kVQkilPvMJ1FSi4QeA93f CwCEQhKybYl7KxXxSyavAp1apPGcJYgXSDBStiSarI/3Clp8bbbIxANeaiQV0/rm4NRNZLNo 5tB75Dy2NYHUOO2JistibKHmNMTeKxBwblSnWkZIbW0J7KGONxzkHF79vywtyArFnQJrb8uR qKSgJSoet9C/LKFM8C0pFh4PrhQEz3/xu7Ki5jJe4UnYwQ+iae2RYn2LlZk5kr24yYUqEXV0 k6GQddslSZEsnQYnIWcYD0gRLV1xhabwKbAwRKxJE1OEWtnvEyERB5yYhs8IgBYepnK3wQTh CEHEH3zcM/AiRizSemD8lGh4FqemRqyGoQQHzPLkYmgIVJPIjV3k5/EMUqdLde+QeEMWwhSQ QR5VkjYjE0EcQRy16HFkq2ieljD1JkjOoBULIqW0LpvviKG5HG0jEX2cuUxpnU1GDT104ymy TTA1EkF4zPFlBBKQhjVYNECaokR6cGs9blYMFuamJfK820MCQUfQODXWcn/fK3hFdHgQSJrC sdUu1ZruzjJkygRL0+ji0iUSUdS+GKdZKYgEQxRSJjjNkAwQwSg3EMTr/aPCCsiTimpwY885 iMxFeE9cQL7BLO3vfzyTDOGGy6ObdZBzmNhBAzFenTGtiOFiRATyyMQ4Qib0WUp7YRkxVHHo cGSjG4pO83xx0wN5gxhNO6Jb75NR5NzaDCelJ4MefAtjC/Z7YnbcpUkEGB0syYQgiCCB1zjN 89ckiLVbAZjUiCXERhYKTbpNKJziIQCyhEWbP/cIT5OInVWsp0NkxanmcmebLaigENxxixCm gKFTGsdLibRJeaufes6w9SaAMvZjlGy6ImGTzxotipmAi6PUw0gfkxMUiYuPBBIbHPJA1pHt AXkmS4ig+VmZ9DBZfQKeQxNIcBIiRgtIN3HnhtIc8wRmrkcBaYhEo0jnnDRyAQJEh/eMHpAj qDWGoAQgMi1S175xo2oAgiPNfXBSQBAmd6yUk0sBiI4rFmHQeT+sHmAqPOT9Me1resX6Yu4f l5ksJxeiWH5tT6h+2U1UG7gJ6YUNgXqYvzrApjrBmo6fOAhJK4ljSd4y9uF9RxR4aoknY898 ZklDJm+L0ZK0RU3Uiex7ZbFpJStuK9Mmn8pTceYAwhp6GNiKeeBcoG0O8YnWNNUkINktboqf 9omQCkTSbPPJ/kwRIYs8KuWOouJFz5cuAInaqxv164RSR4EXiZNc+mQ5rESdxu23HTvkJNLh 7Y04V6YpXRykMb8hGTy9SArqDDkf/wAky5Wqt8Y8zMFBZ3bqH4xoUl4wEjBj+QbBGUvMPu3g u0tsgSedQ6OL9NkMsEnMwVtzqMMWQ2EYoO5grNoQuYS14RhaxJx7GD0AXjgnpN0Cl+uWUuuo QmsLBZWZHf3X2xspUySsbZ65NBcjYqIMKCN4dRVu919fTHBJsAshs9v/AExwOLARAUi2Z28+ C0gRXiTACkgdANJnaNdMQUbJIBMnE+nrk+AUqu2U91DL+l+mD7f/ACh//9oADAMBAAIAAwAA ABDzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz zzzzzzzzzzzzzzzzzzzTTzTjzzzzzzzzzzzzzzzzzzzzzzzzzzzzyzhRzyTDzzzzzzzzzzzz zzzzzzzygzSQgAjyhyDADCTzzzzzzzzzzzzzzzzzzzzzyTzyTwDygDgAhxjzzzzzjzzzzzzz zzzzzzzyhBhSSRByggxzTRzzzzzxxxzjzzzzzzzzzzzzyjjhxhjSjxhq/wAg88888888o888 888888888kUUs7cQco4MMDsQ8Mc88888sM888888884488wcUAoU8osIEYAo880w44004888 888888ok888888888888888888gMMMMMcw88888888sU8wwwwwww088888888ooc88884A88 8888888s8o44ckoY888888888w848888sI88888888888cMcA0gEc80cIcsc808sY8884Q88 888888888M8Uw6YQ88Mc84M888888U884088888888888AQ0cMoE888888888M888M88og88 888888888QEAcowY8888888884888sU8os888888888880Icg0EA888888888M8888U88U88 8888888888888888888888888c88888c8E8888888888swY0k80gMMk8IkIsME88888cso88 88888888kgwcU8oMkUcMMcc8880888884s888888888888888888888888888884cwwsc888 88888888888888888888888888888cs888888888888fOyiq+mCKaCaayKSue+++++++++28 88888886J135xp99l1x+6xx5hRJV5xx+77d88888888/+y+qqaa+mSyOuS++Ku+uquie+6+8 8888888scscsM8s8McM8sIcYgoc4oscc440c88888888888088w884484Q8084Y4k8o8gs84 88888888888ww84000088gUsc8I4c4wUEIUo88888888888cMsccsMs84EcgU8g4o4g8YEU0 888888888880kok0cYs84o8gg4so04w0wkUI888888888880goc0IQs888c40oU4oswUc8cs 88888888888888888888880ok8w8U88U8cc088888888888cc8M8Mss8sM0YE8o8E8g0wYU4 88888888888w0800840084U4sokoA8c8o4Q088888888888sk4k0E8M0o48E8sc4csEU8gUo 88888888888Mc8s8ssM8sMcQoo4oMoIUw40888888888888888w844484gckY8Y8E84cYo0k 88888888888888888888oMcQosgs8848o08k88888888888888884w000008Y0swM888sc88 8888888rCGGKOKWuyKOSSyiqqeyOCGCCCC+888888880gk8oMQwAok8Ys48U884wEAYIEMEw 88888884AoYswE0cgUc0ow8EsMYUQIoYAYYAc888888sc8c8cMsssMc8sMM8888888888888 888888888888888888888888888888888888888888888888888888888888888888888888 888888888888888888888888888888888888888888888888888888888888888888888888 8888/8QAKBEAAQIGAgICAQUAAAAAAAAAAAERUYGR4fDxIdExQTCAIEBQcJCx/9oACAEDAQE/ EPqaqbqcf7+aq0QdVFfYfrER5eIfAuOuv8oev4/ZPbcfRp1mIiGmNMaY0xpjTGmNMaY0xpjT GmNMaY0xpjTGmNMaY0xpjTGmNMKqIqpfD/Cjj0qNh+hsP0Kqp4URMP0Nh+hsP0Nh+hsP0Nh+ hsP0Nh+hVVF8ic+WVGw/Q2H6Gw/Q2H6Gw/Q2H6Gw/Q2H6Gw/Q2H6FVUXyOiOiKqr5+FEZyuV GjWlxo1pcaNaXGjWlxo1pcaNaXGjWlxo1pcaNaXGjWlxo1pcaNaXGjWlxo1pcaNaXGjWlxo1 pcaNaXGjWlxo1pcaNaXGjWlxo1pcaNaXGjWlxo1pcZ6/oBThyTk5OTk5OTk5OTk5OTk5OTk5 OTk5OTk5OTk4z19Av//EACMRAQACAwABAgcAAAAAAAAAAAAhYQERgRAwgCAxQFBwcZD/2gAI AQIBAT8Q9pum/jZzjGN5YzvG8fWab36GMYxGPyhpvX2TTe/Y1ndaMqCgoKCgoKCgoKCgoKCg oKCgoKCgoKCgoKCg+dd+jn9OOeOOOOOOOOeOOOOOOOOOOOeNNelKUpSlKUpSlKUpSlKUpSlK UsfwBlKUpSlKUpSlKUpSlKUpSlKWPYF//8QALBABAQACAgEDAwQCAwEBAQAAAREAITFBURBh cYGRoUDB8PGx0SAwUOFgkP/aAAgBAQABPxD/APk4ASQEoUT4RnvzzPm2g+g+8fbppluk0GVq Xiu168syGByUsL/BkJtrOgGG7sD3ZP8AphQ9JxZZ16L+MGJJR8Js6pmywDMOzW7OMNYaUQSW eUD5cXSfKsVl43/nhX6yYPY9zeICDKCkTuifT/27BqeroOTR+rHGwRHOBOIJqhLd+dZByI+s JrEHYnSue7wMlZ/n0EL9zq2A+BIjYmSC+TKJ5RU2fIsxpuhUi0xdBhe05TLDsuTVAMQeZidW YslhHYPd4uPiN3aRCbGd/OEPO6C2E6f/AHAT/LoaPkphY0ABCHBWtQwHgwD5A6chRKynxIhO vQUnCUQ0ZJbu+fRA/GMK5UO/fF5tCIXMSV85XJULUjELxhkKjoN8RTl++AgCiI95wzvPsaDR /wCRMIOwHWAi3U4cy/7y6cbzprA6psdNNzlK6GnOKbBvaD4wh3Ixp1rCZ2+OJoIoCQQf847H WKkdZWJF7jjInfS8OMHS/bY40/Y1gIm1s3Qh+/rbOAuRRt404x3hRtI46fxlZLGcMgF76GOI Vmwezgy12A2c5v7BxGuaiIFpCkwKSsXTvIiGw4O1mPguvDCiWyH2Z/5XBZ1yvgzlCvB938+2 Wzg72/6yVbCl43hovOeXXXGLWiN9v0zu8u1eOvrgwUrvb/r4xnWOVkK/jIcihiG+2sg7KijR TrGRact6/u4Nl+6/6+M5rg3Vf8ZvScpWuT1/Mf4zgi4crxhzyw5UlZNc/wDzNEWEBV3lIGvl xnEIDd7bx+MVSEEsKd/bDSiKVYye2AKsJdeO8c/EFJ9MgFFBOae3GL0PqXzmh2897f8AlBSx 4a6PfPe9v1PfWPgwXZ/vFQEUCLvnnKN36nn3xrqO/wDXvOS5fh0e+EC17/l+MBqLgIpa/wAn 2wtRnuuM7AClSTxcm353i/W5EqnJ/f4zizrZp++E2myJE2ev5j/GNMJ1Dx8c4K/YXgX3c4nY YnLv5xWslQEU8YZyz2DFvD8YjdRY78OCxBrZdh75xL1hpNezn8nk+cInSrvF7/rFqKfz34yg dvHO3/lKQbWgPB5MU9159z2x9A+dfc1gtj95v4y6/mx59saWHOp/ph3a2/A9sIhT3n+n8mRT LxbNdTqZo5Vvab/OH1KkDrFr1ozZ9sOUuFG2C62HE2/GMlX4FNng9fzn+MUDMAkjPxgsAejb nTjHlKjwmz2zVwgSCfTEirBTTW9Y1cFJr2xBX2CKDXXt+c0GMyUGutZ/EZPjDYslOjbx7ZEW ah0+H0+mGRb233f9nFtwhUiU7ER9z0KkQrhODoUF4qHKYTx7XihfV+1+rRQPBCeD2ydBxuWV +c2Dw9a3+MUHfEam99++SSLdnKeJ742vm1r/AFhubvxPHesCAtO9f6xDubJ17OJ+UfrXJ475 cDpWoeXphpvLbzo8ZpGQiBTrr2wAVeKKbPX8h/jBJNVpP9ZTW6HUvLrjjFPTU5mt/GbKYMZ/ rPco9wD/APctgQW22nJ84e8torXrGvzDpDXWfjSl/wAZb2gkeT84Mqnt3b3jAPk/q/6mKPls rUWOidai4RwlwEjoo0aBhnLKicLmTCApfpHWPiSoWyDmUm3USZoGeGpqh5M8USLho3VOjgeF pBqpon6vjJmwesKYBZPk/wBfjN47eDOs3zQIBF0ZtKriDCPGOQxRotefvlXTch1qf4wAAIOC P5252AKRKRzfY9h7N4d44QIG3V4xyhCgRw+rFsBEYASZuMKs2ey5U1cyK09fzn+MJ+AIALzc 050SC3df8Ztm8Wl5ZhJNewUfPzhWTnMQuv8ADgF247LreTPGEDY840ERV7UxxrThOtYXjdLx GsU5TU2PNw2QrCDaf+IeG9eRp+H0/wC1gOGMnE+7o/URaNiWcBN5ZTm6acv2n4wsGyQbG1n0 v5xyOgNtdZGcBAOE2PnDYsrRSm9YOUB8CeDie0+mH7l0Fnf5fvlQEaROR4+TLsrw6L+2VZxt Uqdec+0506n4n0w30PHIbP3/ADiYYFVpj/vPl96y3lO/X3nLj4zj3Am3G53zkmOQvWGpIaIu nb3iLFRGr4wnkIYUKyOTcvr+Cb4wBWWQZqrD6Y2rrCCpHn6DhoRsD2awXJtGjRMOy7jStPzy 4TsqAgRTX6JL/G+X6dKJitRGFnwpg48N27GELhqmfZcttal1xlEEhUgmAgA7bOc3HlYJtwm5 G3y4oDlB7UPtnBUAiah79ZvVUQPH8Ma1ygD5bfS0YlA5jhZikUh34L67/M/xlaWtRonQsy1b EWa6wsivHwY747SlG9sdXGsJAHW3G2HEN1RoGt5cEpZq1Sg585J36ya98+VwIO0ZQ60aXzhQ XV598ESg6G4ecVBAEjm+36JL/G+X6gdx5bR4PbJF2DK8vb5w6Vk2YbcSMbu/GO0Kst8+2a2R HK+Nz+e+GqYDgOPbEdKSp6PPGAj3HZEePGII8u1NNSjPvrDuBQsv7YEpkOp9vOFS9Pb+Os9i +92fuxEAVNV2ex6/nP8AGNOChUTzxhO81KiFT5YluBXyF8p742HKFiO+vfBHVAs5bmsEioLK BAR8Ie+HjahsvkRPoxuxMbNLvXnEMYByzXPHWFYFWva8XJxFoVr244mXqA9ddvX3Pe8Qizvr 5cZftG55we+CgVo6I88qdV/7A4cOHDhw4cOHDh/MG/XtnnjjXviF6AXQL/jhpAdifckny5/T PdZI6dGIBET6jX5yHRFh1S+M2sNo8vxk29oCKcnJi1S7aT4MFvUpXivDrApUF2Fs7yWE1Aaa 7+hjlgWEeGtYWORAY/jLFi6S7w10GdDF9gwMA+ars9dvkf4yY0ACdMCQhkQRWN996zTxIFGx 0B7Y9vOn2/SYtUqC+TZx75bsW15OfxhFXz7iXAxScQjj6YsBjhD77mIJYPFRv+5iLRFbb+Lj ogUw+X6NL/C+efznn9MClzWlPR75c9ZnPmvf5yZN7nDf5TOWnoK33VescKQE/Gu8StQ33+ne NBKUPmfOFRDzvE+/xl4q1Hr6uVU20EvHu5uyazjRrnBxV73H385y/NUv+Os0WE3F+MEOl0RD Ke/r7blz8YfFRN5Pvjxoi35LO/nKBSbV5+cWt3lh+ecsg6AH5Xv+e+X28WrXHO94oDu43/3j UAhGhNd/tkrLoQulnWBht4LYj4xhpa7f+8vkp6U7fo0v8L55/Oef0JAx6bCWsdJrvw85plYd Csrd6L+MGgy7bg2ei4U9aPyYABcbvtJ4zePH1Nvxnch3Tbfxikuv2/0xxCb/AAmdg1u/ZgGc pN/0/ky8AC8oKL+BnN9Qj/RMXBAPKE8ZqbZr3fjDXs/j+PjAAQPuwvYokifgPHrQWqTWBQfP /TLUB5fs+7HROt0afjN/vXj/AEwCwC3j6usUVvOGl56zSOTuafjAIQkNaanjNOl1FKcmsoua 8AN5H/5ivB/Hu+MGQKti7b1/xucMjQcjHa9TRHrOTVvCAUrHhtIp06rzgQuRYYXby3OvyenI jOqmyu6Tpxu9vjeckurEqHsGKe8PjC0y2ooR2dmwmtK3Uc5JMxlCr5pw+7hHMDAl6FqHvHCW 6YmMbqlR1AORzu6hpunenOdEjlZipWvzlp4j89foK2AR9c8b+31ybLTbhpGTxrjKPyo50Tte n/xXgYrIujgHPtzrGKxqYMTWP5t++fzb98/m375/Nv3z+bfvn82/fP5t++fzb98/m375/Nv3 z+bfvgADpfcNEEd79Pvm2QkC0pwRVX4o6gE4BYW0AR0WU43+h2awACnYQzGRqwRe2Bt93JCa NgdC6Qpr/wAU3vdiwXwPjOn2uBaqXw4sfT/sSElNanMD2bevot04wbyzAFUZ41MtzF6y219h N8c/oVMljQiywt+uGSNFW/piVdcYk93j/wAMFDOyKBUEVHVFYUuIt7YptBUK9CmvSvQXht4D QEKEDWb/AB8hpL9x/wCvYK0YYaiBo5Hfvhk6IqJuJlCqncIkRygoYHUg13yN6f0IUo5HdRl8 Ib45wTJ5VdLSlPPH1yvjUsiJvRrd74w2U9EDtDPYffPaffPYffPYffPYffPaffPYffPaffPa ffPYffPYffPaffPaffBHhH9EAdQpNglNiIP0y1krUkLa1KkaaPSu4lCDzGzHvMLkKbQfs6/6 1F3FBoRc6G3ryxEEDgL9p7GdHWIsoOoVc4D238/oRW9j0U71d3I7CCtms/ovqpKdbT2M6GD6 eX2w30aoaGayJBVF1IEyQuiIakbcD473j3zgtOf0OsAAJntyZ5NAmyL+XHd2aQ38YtS2raft iAujx4Fx9pl83DsmJ0/A93b9NXdoW4SditO9hhqZaUvwlwen/q4eRPAaHUa67fOJ0JQq802F 5aBz7YfkyTF1Xg9x3v8ATBS5rSno982jpBvmvf2ck3qAZWdbwq4gqJfbFO2Lq5K8Y+JV0bfb EOIWhHSxuNnA7Hrnn+TDmjUenbx9MFSCqL6YKGFEcHgl5yUW5q8ffziYg4or+OsNzE8t/GJM aakRqnv+iKri5YFpxsD3TJNDI4AoDzFdAA9j61zDa7AN5AXvfHG9f9IdPbQdG3ceD/OmIzoj NbDgujRMJAHbaWlOqnmH6atk5pS8GE7elQzl98igi6avxm6mjasXHbxIQn2fOEtg87Xsf58Y uG5sPO+vnDAYgD2nedMSsSuNFY9TjFYCiHgyal9Ec+zBXX2H86cA2s2StyQwhOwtP+EiigEs j4bnGA6zXduHW8MvUMDzOZ74pHIIOXf0yuj4LMqutbxyDFlGAHuUvzhvdUwAKw9gX11ZryY4 L504HTT9QUR7E/6a7xPIitFNich9sV0TVGBAGvM/6QRS2vTkWGuXl5Iz0FTeaGK4dgcVm2po iQVBXDd1t+mBONQPthOEfjxtg0XGj23MTeUH3zdB2eSaxwpRY8mEFW6JLjy42p0fGEkxiGrX +zlzxaNePBk2GgzF8Lc0cUHS3KRPM4yhBE1DCABNie//AACNx4DZUADhC14yUZA+ndYNouv8 ZS75ChhAOBr2HtDJ4rdmMNed2vOiD19FzkdF50DHjQgSNAkYbOYYAcRNeamtk1zx6qVFUACR uhEEekwfDp3VU09U09m/+Bsg6AM0tGkSgJJwQOkWf8a5Jmhyouzl46TNKV6gxdfA/wCifZrX A5Op5ePfI97Zi1eeBQ617YgshWSN1E6+d/pWxnONGQAPjvNkmdZe3k37ZMMUiTb8ZrHKCgun n5x7Yfahrnjq4q2HOp/phna2/A9sURQKkuvj3wG0yAC3nj4wRJnkn1MQPEkkJh2JtCuZ4xbq oNkmCawgH+mCqXyxTfgf8CpcZM3UDutj28XDFfTOmcxa7Vq94SLhJigrVq6/sBrS4RrZkWE0 IWc5tu5IIRQpKdzkMJ9ED05bqVeDGNyKIdgTFRDWXWsfr7RABK6tXccuFn3k7ngAhrvb5xxk cVKEvVP+FNWmvAAUHSg84mELKaFlYEhQ4Nf8a5IqWCpOBHnswZqyg56dAaDj32kY8/8AOcoJ HBOKB9Xl9ofqaiBwEArXcQDtm4wdlJV2GoW61+mTaaIncHYY9jRW/E+gweibNeb4wgWwb43E rIborgq+y7B/Obc7pFPHnEYs2jg8NseH+s4D9mn4cXnQN2I4ogBTRN41NN3CcdoZxAUNo3wH LgTJ3p5D/rYiHw2UhxCb6XB9CoZjws0RVeWa0BND4Y5mHVf+yui5zENpvRtjtLnKIK3fj/z1 GgaXDbixeLj4yXaEDE0BgRCbNtHGNLFGF1No99fH6YoZTn6YWF2u3damOYKonWK/uXn3xx95 8+H0Z7tCNn3zuURSzEXyNdxUtrjjL62nx4KBfnnOJhDG0PYwoe99+ftg0hREuBgOzEuSCDVz 0/R3+i0fa0miFNA0vRXKjEMiS3rpBsnuB/5V1KwlcUEbd/m4xMdrEfR/5bUpnkhpLvZwefjD UuqPyOQ5QDXxkb7v5G1JXu2/pk6WmkI6zg9b49znAAg6XneSQIEVObgN3iGeecQt833ZHbk8 +0wABxYN1DosbL+coUAFKv1yt2TsM9lnxYET2e+c9QO9/rhHF27b3+iAB3WJbEIxGkR7M0ZN YUwbQFprov8AzrltUQkCgKX7mEC2QIBoRKv+R4CaJWbWS+X4GHi65RWzMLomium1/gtJeOld Tevj9Ml+w2GhhkAQK2e7/eXxNEcdaPxhmdZyLcnnnEpdqgjTwec7tHZBZ85LzV0e2FA8mNh0 U5Y2oah5MmHBrWms4uTHG/OFpmkEtfAydggInZ+rruoAWhmIqn7BvALg52iD9j/joVU2QzpA m+RziiaoEe9JunTMb5kUOhRvCnbya1+m/wAb/GFW43LO2sePfIKYX92mndcZQHAaV7fvgoqH X1wkxO1h0VxcMNYnnLA9R9sxmw7REY1n8b7+PQb3Ww36XP8AL79z9ESBuFpJAKF04bvKaJwz pa1ELx3P+muC0S4iFKvDcT/jJVtIzFUb4OX+GBC0aXRQglE4mm23O2RZXfELEm2PsfplJb1w zo8Ocm1c+Z747MQ0P+7Bs7fSBr1W45JCDeIyAjgV4ZluqwNWvnIVCS0f598d2hEVQE/2YGeB yNIc6ccUdeD98PRh1+Lm42ofzPfv749+A8H74bAu02V5O39EGhmAMeHybcWe6UIj9vH/AE11 SyjzQyFBux2YjHz0pCcHj/hzJ6NI9hf+T23rRmnDyHWpzIb2w5a40L33x01r9M5w+Fl4+MeQ OxL3pJx7/jNhw97a/GBZdqQivxvnLbC2q646yMQfqfjHsCj7CfGEC3/jxhWgqKeX2YDQ1Gn9 OsgJQ1Vf6zUmdEv78YUL9x/rG0oItf6xAJrFLs9vb9ZXShSJsTrFwphBaF+K6tzaEIxoiU1z 6qqkIDCxeI48ZdlV7aLohpUd/XO1gsm3B31s1v8A9ZxoLLNwbuIESk25uRoIHAtioiVB+w/9 NcC0wVAjyPzhx2J54oXYVcq6N+rWSGFEJCvTwOTxljyFBNQ42JTbfDj4SQg26r0fX/8ABV0v AXkgQA29nXjVkILFaS6Xsft6SOoSQKCHwO/T75ZKkk9lKNNVfijpyMUZ5b4Ee4U3z+k1lXSU O6HDhqUAxHeXeAJ7tPuFsxg3u6P+i4tdZlTdeMdOeYUZvwYysU7CF+rWOSrPw4+M1mEdxIlx aSvrsrftgWCkVbUN35fbOICJI9m/DnKziAaAa81kCqEThHhwjEGxeIifn8YKGkEI1TXxWn0x eYWbvQ/vi4cOVzlB4eTeOhTRNb3I4/bDIQkog28eeMAVSNHasD7uFjqdGHdM5piYFaIK6cbn zjfZrTVdjNyOsJSBpQqHX1yXBWQK4bDw/bE2V4dGuVej/eSjEkFSN2M9uMUhACEMRV+gOsCk hV0Kg6+uIRuJbWLtQmhxmMGNqFgDPvlGAEbxDeyEQr7zABgLrzwfYX7Y+t2qlEDNqyF3DnWJ Xk7LFsEEWV0+H/rrk2AA8KN/bLPYWXLWzduE4PRFKppPCIOfsfGVZZyRNdOZPY++bssRq73D gda1r9IX2YOSDR1pM1jHICjwBhhTThI4JqQB7KPxh5TSjQ0F/GKQIATseZ9MMEtkUKfll163 GLgXHKyaVrzvxjxlQJ2JrJYWTcNdPGD/AKVAa3FGrxjnhRBZYPan2xaTRVBsIHu8Y1kQVKoa APllJQ2cjwXxA84OgT6S8KEuvONAsIXJTem/pkWgRoF3pL1p9vbLPVEA3S3X14wXZJUvdjf5 4xqU0A40BPxfnIho0BoAp94PznMx0byODnvnAWNLaAeydLcNySBUCvy7XfvgmKUMgPezh+n0 zQTaYlUC8u/8YQQPAkH6vZhpzRJXNeztPbABCWdWI3R0zGqaq4KN181/GcQAQVjSK32/ziix S7cABz7GbvMBUw8tjXswa9u0OUgXADwODD/srrLehAWup0VHYeDAzLzgYcwBPjEVMhow1ADR yO/fNv8AawjcFiIqp3NJE1R4YrqgJ2TfP6SpSoKp0WawGgwYkCUqeDxzcEBOmlq8d79vrMBB diDb7+TZrCKIJh4Gut/vgBmIEEQV+XCgIuDBXen5N/ONbSLS3wkU+HWs6cTSAnYnHa3zMfoA JXBSV8CayCmJAOSnRrhOMRBqT4g/bGkljJfizDBgAQlorbLOuZfORe4UC0SDdc4oHFa2kiVO nabbjVgJ6gWLzr825xmNLAgJvXxrN0JGlMdiw8fV5wp73SGrtUXnnAAbZTZc511myg1XB4I2 T2xSvBh1KHe/9usAtkSg7TfM/wDrAY2s0CXmv2b7Zu3Avzp/wOHxVBApNGK2oa3ui79j64M4 gQiIbEeHrCY8huUXT/H1/R15+4UqqcISTy4UpvFDCKDe5695ttAaGXVlJsZxOTEjS8DN48B7 b+f0iahKBAQ7L04rJNEcH4PW9fXVhGoaSCi351G3E2DCqvBd+cGFq47AJfxmqHSXb5b4woBR Ae6+brrzgoHGtOXgRj1NUxXNkm57Ofj8YARVFjtFK8xMNt9Iq0nnzcafy71eNXU4nthFtvSX 8T/nGy/4a374opdBZFvTxH1M5RlKdmD8fbLwS7CLR6zY8+frM0CCRYslkvE6vGRlF1vz7rPi /oJgqUSBoHQBlsvHGIgecLFMVE3SyKNwOsafoMQMRPk/7ESJEiRIkSJEiRCR0iGdDPc5zk1H aiD8Q/Ll5O7GOIIN5quzxGloUGfTHgYcf+svkRIURUKFhI750YyZRZrOwoIFLH/g8eu1REqE NbSvs49SDoBANLAWOWtersIAUxQYGAq6WGhxoqZOHa0ci6Pq6S4Rr4rx85MBZLDCORKBOJvf /vGIg4YoI7605HQ30PQ9NCV+JMQItLaAn2X7TkvVM4guS9jNLeRrGMBAlnjARABVes0S9nzj B50L9M0DIp2IiHhEEfJlgtKbILrkAninbf8A3hgOgEShHg3gWC6wWNnCU2CEarDqJZ4imxI6 0iVdjSVIQWogtHau0Qor8mIkwAKhoHKL5NZxKE+yIkDl0ZnIkxNu85g9ECravA3Vw45SHUid BsItbRe//wA63g4hubfgaOPGTjaFKo9N1NAj20MwsIQwTmigqmjendEthCIvcjdmCC3TWMCI cq8lnaZbjxlAGlVIFCUcNzQ39wZAJROPjGJSx6R4K8KKQV0uLV/6mRf1fOJUyIkNEPCMw5zU vorcg5hgrxcqRCm6KpK7MmVUOg0RLSN3RQsFGgoIzrSkAaJ4XAwqHuwpoElrom8ZNbwjiIMB iJvS4zskhUIGm4bWyhWAhJUdi9Zdm7dMaOW2dzqsYG7oJrdnMxdRRZY6MlFyphkHmAQGxGSh W42tzxVioIkUbwGGF6pMPIWpCEiqDC5ALDmw4HfCTl94ilLpKIQCogsrzijK4aVDRtACnMSY wnlNENRYEpRuDqiFOQgJdet8mtIOBWJSnN1uGADyBuNzsiB6B5iCHlesFGZz5D5KUXS3JQic XlELoCavWO9oVSISgSBN23WhZHYEa6wFI7OyrglqTqBABu7zz/4pmfIZyhBCaTB2QTAlRo11 ijoQDihzNQcEa1kAp6YSAIU4rw7wgoz00hOqN9Lm+4zOEQg0h4F84c0ihAASamG2hZwps2w8 dYmbZE16H6JrDnFB6cFaC52MwsSfWnKqtVVVdqq7cufAthPgbCnSneBhIA1Ne4xDpR5xe1Lw HAC0A+Zvgwl27KxeBe3ZvIdaqriHoVUOKuE4aT4weNRPzgkVyz4FVU2bQIPWEtp6TRmLAPwe DN/h+tRVNoaPB4wuMwSegKKAPTMXHlUZDkHsYRwxLAtGgIj7YqXrBmoWQI2CXnOG/K6BGxOs HJSR+ZbVq7VecIGzJsRkD2FsmHm2erCHYQLe8DRKC1lp7FV7VXa4IDMSUBv3GcozluwmMI6i mP6OjWUMmwKEcuOADpQKV0QAnAoaZi5mLQJBNKS9nNsh9rrRTD298/oOP6Dj+g4/oOP6Dj+g 4/oOP6Dj+g4/oOP6Dj+g4/oOP6Dj+g4/oOP6Dj+g4/oONOFkBVC/V+hCAXwLhAtU5KlbiHAY L1gnkRCCE1d6NbeNXocACJGujnu5FeapGuwGVqN+DeQVWxzohbjqwpWZNNEAiEyOxE8ppz+j 5/R8/o+f0fP6PiDFY4w1QXh8dzJBrnE7zN7TrvLVFRFoprtqQFik6qhChuXVLs0Cu3ztvXkA 8t74uNCwSRLJoDwaxNy5lGoRS2MlJq2g9YphsmxHjR51mv5DpwzxKuNVXvP6Pn9Hz+j5/R8/ o+f0fP6Pn9Hz+j5/R8/o+f0fP6Pn9HyuRDpBASq9+J3h7KYKAALRKNO5uDSzXELA1WeEbM03 myDwGijTgdC84b/pOFAPRtCsdbDQ0UTjCCRUHfsRVuxD5CbPBBGkvesNWR9Qiq8rn9Hz+j5/ R8/o+f0fP6Pn9Hz+j5/R8/o+f0fA0Q07D+hCC7XbHiUxRYFOaaXEZqyy4q7NBzaggQDRonOM WBEeRkwT6pDEJqggg2YggoUBAqYkQpiAxzfEOZ94SVQt+Dx6P1InQt7eWFaSu5TaFIwUYx16 wip1dGKInCJY/GQiQPAMQlkNa+MhFAWQATSKTlRcSTSoaZNGj2HWsKmiiF9HUr98YB1HqJTz i/fKmnG/J8ezSG8ROHLJxAsKpdoOTBwSAgULrxF8Bj2TI8A5za1h3KbQpGCjGOvQVRdGmt+V 9NjB6mfFMbFGXVIQiiKw/wCIooopkyJo+QHnazcm/UEUyy0yAnwv8EtuFFcvgCI1oKDKGCYE uUNDYEVSp4TLOpM9IV5wN+2PN5s7oQvipHpbhufPTau27Std7x7B8EETOrqDLLvQMkzRzAg4 I2ix1zYcHlVr7HuIfT0F3dJOCCNBUSpXRX1Fh54M+6O29ej1cOUGGIpxf9Y4c5law7EUczQ9 YZ6dCROi173NXVWqvaQFYvOh6wjsSBJoUFdnt08Y2rYAxmQCSKdyuBpZaC0PQCSpOd4ndDa2 wXfz/wB7MZ+GwIhGzTXLrBQaaDTLGgBQ8eblPOU84YTJ5WRQaEKs1xlPOU84mRmtQu48FE3J 3kBgy0phgCvBfYEXr4ZBm+hi6GiWCuPXqvaG3W2sCwxtfILublRvbfnAucDPI4xqV5CE5x8f mxJaFdNCce6lb9ASMUWFQKQU5QPvaktEYeADbYbGmLDpOeN40NXjVRJSWoHVq9rjH0i4CiKH iRGhcdQILQhjdlvjWJEZ7ULuPBRNyd4etgXrB2voPggn4KBJ3Ysc0fa3JZ9Es5MOcmuGmrI5 AoQXuFYfXKecbCHdArVVPsJxFYK9o4TYqg3uXkybUmfmguYZKXGgMoraSDVOwNLvOhFxRHt2 j1i64ad7WEdjzjNoWhC3kUwBymiop5EFL3ljnXDo8BQCpHfkyVuClENiNkuj+B0q1IH2oR8t HbqgprKhoBHnavsZTzlPOU84YBMSNAPS6TyUTjIjmCgGRaF5qH/ORqIhROB8DOU847lBABtA F9MdVCZSWpNgUTBYeKuINKCYFlEVCSb2UGULjGpdr2n0ynnKecp5ynnKecp+hTDUAB87wuoR SHx9TKcQIq5ZNAUOufqh8oYcWqsHrBYhQwBd1wad+zgZFFE3ehToCDADargtCUHCnWae7ncW R+uvnWaOf2xr669CgxVRgYRCqsEzlUUMBcH4fs+MNuRDCoo/ZvxvP6LjIDER28EC49+w2oAP AgvuZPUjaEHT4ib42YqQgwAKq+MFoSg4U6ytELB8z9z74gJF5DZD66z+i+hQQUjaEvHwL8Zp 3ACCvWJs2FGs/Ym/jfGGUpS4U69CilrFI+PqffE5jHl8aw3UaqHjf1Q+WYHKqsH0K5fpobGh N6L8GeKZKrB2ZS+0XieG+hQ9bsMNFdzDYZDAMQA7O2MA1zwnnWf0XILbjArD84EfKYJiaoZ+ SsB+z9sBytYWKJ9N5/RfQr+i4Oz+opF8J2YkQA2Ad8YYptIpaKCKwOanoxLFTYIMPn7hkzC0 L8A64/71XHBQEKuJrCE3twKCNIRBESYJTmqwSBN3CgF4SAdJVgURC5NkFMKwqhNvOKB+UbNI BawSDDV0dxmWAMKyy8vz6ckdegiT74wOyxyIsfcPbyRoRUaAX4MjXi27zhuIXX8F3wc79HUK OwLBRYaUUuznNrLojVBehDUhWVpRI5BiBd6AKQIjs9ImFKljsaHlp6BLDRE7QbPe/TIwulIm GnyFu27xG2GSTS3mAKVcOS+vARJ77xgFljpix05Ht5JvSZfJK0KQAHnbijNWsNEDxpt199+q pvk9Y7PofbR01glTn1Y0K7TQ4PGsXF0sZW7c0We/ZjeDw/ZdwHYW6+xhvnDl1TkJ6HDtxiv/ AAkgrZWt7vFyUhvOqx+EgHsVdACsFgwFZThXU28+iZCUWQDHDUE1o7xSLlFSHGOgEvsp12kg JpzWDg1yusIuyFQ8AAFXpzkNY2aXbkoEZd7oaBvItpxdEipyd+hoOgg2EUtbbNdmGmOZmk5e QW6Jt3gC47I4YJqNUdank6IpalhwTuVtNnHqGgIHFGlAvqbysmFAETqLRWLbMQAVdZ0oRT2p 84wzQLUHcjT3NA49GKeFomqEzFUISuCo16E0uvea0c1v/wAiI0KZJG0at9rrvFBMkzwgb4tb j3xpVbbSzUnTvtpq4RdswVAaVS02/ly7sX7iVQEA1SaJiUcr/pp5BK9B9sdYpNA0OBBHd0Gy wb2UGqgoAaNcDj0HiJHSy2GwW57NaZdKYgHBb2TaVZBRtMqlthNArqtzXfpbd4YQa6N+lqQU QoAt3EUCg4RQeUEYCLe4jwU2GA6AfQ1eGoCywKCIseeHJAO7cmthUFBZrepMTaTEE0l0E45l 8FtRG8QWFjSO9bvq3vTfSO1X36HGGJbUwhuWymiCu3OaL1T3Dgfh+MfuGSZhwFNg2IblwtOf hQUy0pAQORwp/wAIToC6IEi6F/8AJUxEPuP13nyT6hbgcPmUORrZ/lrJAiQvYTxBvXHfOsnc EceAAG4Jo9KiwqORDNNeTC6tYrHoiTNA7ch7cKkT8xXqxmntfi3CXJLsBFoo2Xnv0N4A4Eh7 ylNOKLrD6uufCNgbrW+MB4F3tITmo+gBvrCWrGRIBoI5EWaLEnaKgVHRtyAgoY6uDExrfbOB RSSCBEmAMMKUF3WgR02OGu0SXDCkgbA/GPvTXQp7BdTl4gVb5RNEIVBVuGViA+j9YSQqjven J11g5iIBsVAptLbXlBznkpo/berlr3XdOwZ8QpVOI48DfcxAg0zYFRyujFlcGjTqts8Cmwh+ mcIiAG0iPvM10dTMbY0G8A8lDpGCpoC+cO3w6w95Mig5P3Jz99ejPADZkJDRUAWhBdYCXvGL AwpVEGroaDXfNspY9IAAEggUaIiAbIJJ1BPLT0M80NmBvwjja+0UHEm62qBMOiDoBtQosIfe 9gq0DnSuWRADaRH3ma7OrGNscG8A8lrCOSh9QKNArRowpfWQYJr790399epQN4TToXqOI2nh vBLaCBSaigUWleN50lpgxrCa94+Ew9nkKbvUQ4ijQdRcaKbowd+sq2Ck2GbiwoVS3RKfJrnW BJaErCJ5DYTgEGJnkhkyEgiqiDVoOvRC0KWkAzAJXN66ua+lIq9ALTxq702VR+23MSnSteTQ dmUWcl6UZsFWzjCQF0iCN6oFwK0NNB4ayGGcKtkQWxQWJ3B0BBQalgBfZxTNFGEVIhsgtm2a WY6Dt7sygbI3HN3zB4TXNJCYDDQzqb9DDqpCNt1d41Prm7kEZztCK+W0bXKcWzwwppNd8g4r 0ZzyyA4bT4YimVCliJ0Degdm30LWwQwO8EiECNsW5/5EAkkEae4ml7TxW4hSB5LCDk5Jr6sr 4zb6G4JC0rRNi4GCkEWETsKFigd2uAbqN4Uki7e2zrF1jOd8MOlQI2uEgqJCOWI7p3cGAiv/ AFZlQQERTRzwcem98chG+cU3JqmrmtJ6dnS0QG6SqAnbW6MQm4e3PkXiaH0e3axhVxsYIwab JlfAUIyGDUAjs4eMZI6nGl/IEvJMTRjGlbBoYC0S2MlwXj3ZDslApAohzwcYmLy3WlEDdINm 7m2WksXBogKSyb9CWjTNDe7H2174i8SKJVi3bNxIa24fGfzUAWKPkT6ALoqluTkO17YnhSDl SiAN6cAFXAlGcDX0M+2HPB0PWkKKByG+O8DJxgUsSCaPE8t/Te8bObEX6Y6+CpXKK3wPs4O0 q17C36uDozTaAhe/9/t16IgcbgGL7Db7GD1q3UsfYrT2TI1Psh3bADNUZHykLbUevqF36A4k CSJXhN61vAUa1ytBTkhKr7MBPEqtyJd8A+Rz3nZzYi/TGfgVK+xW+AfRy56bvhV6VHHjFiqH HNf5G769RhOpLbCONPcrjD5KJNizfCPlMZuai3G31Pubv2ysUCK8bW+AfI+ix4IUraOI8fjL u67CCSexbvrE542ypIeVE6M9ox4qFnpEU+SYFC7p0O16zSvugvBXeJdacp4ZTTcOJXTk09no PF2rTgVq2A2xnhOkJZT0BtVyfKujEZSEAMQdDfoepRyBBWdG30xRpgNKFPbZx+xv7iWC+PJh cmKtNydQR536ED77gW4da5yGMxZujYF9ieZiYEDNKrYyoU6afN2DtArHDuT5xjl01GVYoobu Db7ekxNwSegBKkBeTVn6YEAURHhMatFIRvYNHLx5cGoa7cQhx/LEkp2NwnXprXolDAoDyI8m Tr+yEJPAgaPGIvEttVhIujbvWAsA7zu75Jdz01Z2Qo/TFSkJDUkk0vGstwaCVawNc4CAKIjw 41aKQjewaOXjy4s9q0PcEqezgACUEF9P4epHrzclQGrsHPLiFR2CCBJzq9bI7GberXKa1+ef hm8WoPF2UamoLo3eeXOBbEV8L7Arx7wCZECCgZpovGid63h6o200aAPgLBhgjWCOQICEBCIP bfoookSVfdmiSqtjo0t3rbzEDkiB8nhrCUGvwqkQnMCRN4TOeJI7wAvBJOXZoXwT7Ig48Aqv MLB4zkSshwe3bVQjwvpKYUIqFXmguUN+RKU7nGkGGng4yPcCKWuuwtN8he8TxpdJEBxU52vB N4QVRFVvsM1zJiwYCkYoaMzhO+sBCUGUgsAG6B43KqAsCtfBDm4NtmbZK6pqHJ2LnIuByjD7 0nXiSLfbrIN5Q2iUBTExQ1um/wDkTdnghHmfAjLWZMPNDBIaCUlOHzilK3Jkb4eCJJrnWWm/ KYHeDSgX6tejjPlgk+5q8uerM2r1WiWLss4eL1lzeHjXyVZ7gccZspswKiIokKIxN9+gnvjo LILtVUMBXrFdZQgVecKKQNngYyek28k0aYF5dX6egN16UqV4Ls1y78YpwaWqCHUvbQW5vJu6 SlGzrtR6Dju8YNJOtrZ45wjAoYUFgNE6NYKja529v5lhJe8DIaUCh3k59aOLv0OoTAxfFcDK NSZLfUEea8YzaBPEAdkslOR1iakH2rdr3/OERgzhStqfYarAdzGgtHqc1dYt04DzJE6IL7HP 6YlCY8AVfsYkIkS6aEBiOzhMZLWEJ1HYs5dswRrkUJ/DSN659EEs2LIABVVAAqpjv2pVIRUI ERERBERw4TAeYB0hwERLpy+VqlI4u2q1tnohyAahfIGj3YHbiZdOn6f/AMss3JnGTveKhQUo 74eSmEoaXgFX7GJCJEujBAYjs4Rx1IA9JGgGxF2aykgwlWiC5ub9pU9EBnAVQCk5ei3lNDiq M0ZKpNW40dvFx7LEjJAh9rw9s7x2GsNOoAHhEOjXpRURdZtr2kmeCtmcLQ8Jq6aDG1O0wKN5 mQ08mwhAqdsBvb62u191TrW9ehYFBNyjuAvROrgFWenRGwBNwFDRmcMg6DloKLUt0nvh7b2j CSCCOwPqYTCi6A4HxL41nXYb9wklKqA4dE9LoGnMJpqjADeJgJDbbnGtlKt3G8PB2poAiWrh CU4IMPmkiDt7HJRtGEwSVtiF+uOUF4IgOgSCnc2aCG6a+jUB4IzflymoOYATb/HWBfpwGuyo IWgIHSD6cAyhthWYYOrfaZZy1QQdAE2rF27i/puR5YjBGPw4T3bfxwAAhwHD3pGQmHaLe+g+ i41iREQBwu/p+1ca9BJdpI9qJQGImtjj70my7ICKiQb4kMD4H6zLGwBQnJoTcIRUF4oqgRpx u+haGndzxqfi4xa68XtwGkwJ223hOd5/Cggmhr7uUeURGCMfhwjrbewNAAhwHD3u40YR8uSv Ek1xg44pDXsvsDIb8g+qrwRrlVPJY00spcRphhAHaI5BabzvEz3wIWtNxB4A0u8qo1URQIQQ MA48tcMULN01U2PArKQKYEqiFQ9RxNu3jjWIg0Q0INSibLaOmUFsJSptJRJoON7vpAL/AEUW OpfCtjxu4lqkkbCqPKgTSbxsGKjmA7cKokZ36HabA97CkM6lSI4yYYMS83TtnL0ZtfTG5HbV 7DatnoOG7YggQi0NI3iZevQEGlVCReh11rBuwIqpAljsdErOTXIhusSeyDSDFmn0JF8QjYsC Xbv3wRiuehM7B+Ew4lJSgNEIEjKiNbcMBQBb2Is2HfGUmQpqgHBpo4V4wGsQ8nhHTlqVEAFT aFIqPvvAVLrOgpDoVr1Xh+lWFdGDWPhKOJ7fGvu8YluPtvs8+jIQFVYGASCFEaJiDQbQQfD7 57JOTc8z06ypVDBBVNMaeTzhtIWUbvFhXRg1j4SjnVEopQ8uDjJeF9nn1OyIb3nT0aj2pzvG GDSvYBDyd8mIHlZ1HQkDhqg5cvha5qQEVocrODQBk8LHffAAqaPsxmRIcMBaWmJu6cD19B3m FOVOsK0lsVErVqkrdq+gmYLbuCDSC+LTuZuKJA6vqmqLTbrN2IHoAPFAI1dG/RQJIUTUhsrO Nx3DByM2VA2brYbInI2Y+TyKyhbsC0TfN9H89hUCCmnTnvNUWTTSIrDgq6++CwIo+5hqOicD Os64/fQZoMLIyc79DQZspN9ozfGHghiiukjlVbHi5Do5qMRORFd95IdGHKaViCHE7xS6AQsc AqznvwwAA12Ih8IJ9HIjhLpAXXClvB1hIQhW4wavbxCaCv6b3J4pQH6XLQJAUArHIPuPGzGE moCPaQjxbrCzSAf+h59HfovjiuhKoDASY64PGEYqGmidJdA9Aw4NmOY1kS6gjl3LcNLRWl0c aY3vaegi7WBohPtbrNEF4CZDC0VpkRYuuV9d87lU87ec9yOKUB+ly0CQFAKxyr3HjYovs9HH hSW+wdjcg20BPV4HXV+nqcl2msGgJN9nYmCImUBESmSebcexOzoFtZv919rC6cxQPfGltabX PLiy2Aj1pSBebW8GEEYNZTi8jH2d+2SIkel8w9K7J9ODFY00pRI0B4P39IvIUYeBDkjjfjMQ wZhw9a8uOSq4Ntsq2roKZk42UDQ7MHSssCiAbUXwI84gvvoq8wUGtXfLxDJGYE62LbbgDAHb dcbidSgssDARY88OVh3YAVhpsFB4b1xgLXcGtiXTHXHvwj1m0bATm7I2cufXhhcAQmKVbg6M e6UDpALwVlkXzyY02ANbilk75fqaxSbPEuw6OnCm9lfQSDdmlQuSG1JLUAP/ACUhAEQp2zZt 9J0mJQczJRUBHwmHnWBgTUeoyChgB0swTJcVDta+Vvn07LjAuise6Bta1jPukDvruPrg4rHG K261FXUNLoFwPGcwBNpRrDClM9OJM2JJKpeOc/nFTKaLQjCKjNalyuGorG6iAZs2JbooehzP aECiRBeAFOcFoAJwxqRRsogSOLmm2wclJSgrJZDT0vesGlBUG5bS8XAHoIAGwtsFEtfO3B6u 4ydxkE9yjRCYGiu4orVVagjDeh6tghNqaW2acEYXLEGY4E8jRaKVIuJx82hOkPvSTeRivoWa G7bHYRMT009mVyi7ZAGhUvf/AJIgPB0Tvjvp62JNXK92GCBCKPDh1eri7yx7LA3yHq7YUyzi B2Nq6JodAAPThP0HyDzuoXTcNq7UXIgKhYgBTEKHjOh7cHMLwLq9XLW0KnJSBNj6dLp7T3sT 92bEx8HSAWBJ9URxaKUBQ0wToGx3IUi8PRrrrDjSiNJsHIdZF6k4GymDON2z2yZ2DsFVDLeS 0NWpk7Muf8iG+LfjeCbNpk9uQDAO+dcuVkdhLyU1pfHXHIxZhQ2mkxp63ZNX1PkJka4ooAFI 21ekU4dBcFm3lZ1zNTFklIFh0kK5m5hDKILRB0h1g0utsiUhDkCYQe66PbBMcDmXpK8CZrxu /ptQUQWwhDe0wywatAIaj00vP3ysoFGoD3Sd+B43jmrkRHT7L2uD436E4azu7ANothNkMVA0 QnDroDFINpvHvq2t3cS41VsDBJLARiwStlO8U5uvRnchWAWAjXR+2FShWASXCIlfG59Gjuqg Ohq2moSarqKiC2EIb2mGeGrQDko9NLz984Ek2++WDgPLCpULCEP3XjwPHqYQ3N8QW8ScJXm6 xVjjgZrob3qb8tYCOZ4i33f2xbrHuQmu5ga0K7fdDHQIloW/XjU0caXHWwYVeIJtOJp7byam ep69SIqaFFN4+Ty07UduXsBhZ6UcTK6xmhTezYnI4e7EC4Ca6WBSXeaekuIZO1UFwQTW65Zg YywBJCqgYLvLax5teEKaYhUxBKAdqbG+GgeHt6RPLC7oA0FeQ1ziHOg0lgF4YOde28ng29cX lw5nh1xigJdhYKaEHBK831UOgU72kDT2XtcRDKLgKI5DWhHEgkRgvgoL8oYpCwqdBKs4XRlH 1S0BkdMUKzRIIbl/8nXahbYMQvfXOa3rL6tRNVOxa9hv6EBgdTSDR53fmjcCU5BrPUFkWtu1 8GbRbX6zjiM0QDm4Y4AMeZPCkvRrN9v15CgSoQMQCpcHvR9RAGVigUqWxT0EKMgJXXdAYIJU LsMENQAypLGMDyoecach4zAhqch5E9DSEHsgMBadK6IYSSw4CtkpZQKVJc4VLOoSBAVhTYVG +joNoWbx+xtKcYh9J2ElPcEhzp5ec0B2wq8hbD3dompm0lpbDk7jXDe96noQfoiBzvaa1+cH +zUP47CItBatuCwuEeoSJEM1TWDjCQVvIpr6mVjm8f3b2A5StPXoGBFGzQCcoidky/8Akjtl 0IQTq7TbzCd4fdIqI9VENjjmebhdBUgCiDkrbeSTvElkl2R0LPoHpx43HgV1QNW270apKYDw Zw5LsHtiiX1ANwpFHBtTMHwTwFAQh6JdhMKQtrA4c3ELjStnZqhhiR7AMUBrBoQqpyqw3aOu FwJu3GSVr0VFDRozRa61RxFdTbfa84i2YPITwWCOQenEwYo0lGFFY66cGwdEDYWE8RNdYwqK UDQnZONJ2wg5MIGZT3FbzNO56BAqotN8PPnH1rOqgfAHe/McXhoiwThNOznzhFJ5bLBZstSF Xc9TugWaDAa6G5re/wBM5CLAVAV18GB9k0SgxUrQdLz5uFRCI0wZ1RHurxvACAij6h7fvHG/ QoJNCPNUKgqgXaG8DIooUzopQFFGaU3jBGiAxYkJcQcgUG69TIAMLLZT4Ldem1oEYjtUaPav gcEmK43dmyOJUFQu8QDFDDFKjk4UaMRBwFOBUBXXwYPmDRKDFStJpefNMKAEhTy5KcwDfOTU q3VccMO4fW436hATEBqlm3x6nkmaLuA0t0oejV7Ra6NAJBGqQHTpkbL65yFU2DcD2NPQGvVA 1gjY7GNDbMUiJ16C7b0KLdLgC4UNISoDELB0LllEA5xSI4JxU41IYfk9naNAouRRsWaTTnCu sWwCR7Mc+uJs0qtqYkKZfTb1O6QKOAU105xDCyh4BAFAtDe2YBU+npgNSAoGHb6DIcNDgop0 VEd0mBCDeSqaBjH3C73pwsJKpqTkBIWWs7MKIeJuNkKaAKRZ68bQ8RAd6RPW/axeywQkAhKA SlEhJgWwimTYUF+T5yXGUypWJYQCrYHDp5SweVdGGgIq7FUdVC72DC2YgAP0SqkOiul3+lAq kvQifZwxsWvQStbpOfB4xpQd4PmXbmTTswYwJAF9f+o79GkGogABNiAR8mVACS2xQeSlr2uI 3myJWCoBA1F5wdIwUAEXNBfM16Fy8BR9qhmUVmMG4t5e41ziKtqgYpTXQfAYBVJehE+zhjYt eglS3Sc+DxgBAGiMQWJyHjAbVU063nXqu+/XmdpHMAp+4bPGQGtfZFdzbU8ujFYvsTJNsTvT NZww33r6hXj9p6MNkamC8F8Grey5Hh2GdhPZ7dmIo4NYhbQUHJt7zgw3vzQu1e76D9ngEMrs GLzo4wkOyB1nhEQk6UOXAc2cakC+xs7o9HbGubF37IUjhu8MCligCxsUibsywb/Za17j9z4f QQZAkEw97oTdmHprsvK3ba1VHv3MHcyqKkM7lA4XFAVMNcQ3txruD6owOCBADvJqOucE8Mii BqcjVpqcYy1T4Keex8bxwiDhNxNtPgD1cQSJTEnF2KdQaRAOl+MR7DEFT99pNc/plAVYGGFn hKP1xPcfaff4xLYvbfb59BKCqkAw2wqBonnElBpTF4Xpz7Qy08zx6KAzlYH1wQDDGKeaeccl bIhfpigKsDDCTwlH64m+XoKPL4MHGKc0/b59ZnQeBHYTTVLKD5puYmEbE1Wh8x9sJSpLEolX nue2DwijDbWzoseADr0MQtlHZ6FDCXivOoQJyeCDgafYyZqYSKIoudHKQpurmsuaxV2k5Vqv lfR3Clc/YJ9hU6xvbKuAsbSd5xWuW6BVe3lm9Fh6dWzAU3QaLtxUyxTRButnyaKa1d4kV24D gAvBUN8bd+hMBTCopNEop4zYrNEaXgC00TrEdcKgrKTlF3J3tB6zPRVIXe0NUJNj6KWAjwmg djZ92Ei40FMpCgVXteNGEGiaQ/Kd40YxvLbsDNDdPeYJMERlMbYkge0LabnILZQjOLcQE0NO dtdXWj9NotiltAfpcDlXGUArHIPHJ42UXZ0hR7AD4W6wokUGk/ZT6e/RU8COm5IGbBjDg8JE KgtpHT0B6BhYLCXlxuWdIjSlVuGWeiolTDGm9bZ6GuZzsRii8DimrcEriYHpSrCMKGliFFhM AkoKqkJo0M0wVS2gP0uByLplBpjkH3HjYPWttONmqS32Du7d68BnrcDrrv29dQTsKAgx3Hma wsTZPASFM4cmwexUCL3Keb7bf2phyhy4e+KXxNo3K4yFyhFwM1RGtromK1UHZQm91F7G/bGl SxYggpXo0idMZxlqlUFRCFCxXb6aR8kqHHNo9pwWVVinMjCJFYCgstIMBDBqgUlavJfXJkYh cMIN2rqEec1bYLI+p2FHekxkQFYaw9SShewFgY/1rwFFoxELGXhxaGSSAVDTYKDjW9cYGWsU GylWh1OLe9PQLKpuODbSmIw8vUFuHgT3V385DVdTxgBarLtc7kVmahoFCXcZh5di+w1qIACE ZfTaLgEqgakXamlmsdQxwrVAIQg26Bqf+SoWCkoMX7GFurvGZJ6YpFDTGjsdxyRrF8YxDQXV K2XedCS/RxGH2noaS1ES8eCY29TvJ5U2og0LQTfA64mJN1feM9+6SFscBYQ1OgPoAvAEPTl/ V2JjDVUtmnW8cB6UIrRMLj4EC4qwBU0GQLApZtqr6WpQGLgaEYHRp52ZRtsHlBYzezfPGC3a 4JbNl5m8ciHpYPxr0LHQJwm94+KDKJtCIU0NGvHGU5y1R0ypezblw41bqTQ8OzsU71fQ8A0p k3qEn0usWEjIMQQ2PKOcmEd3nRYFG0Lx0VwBGxl78PXzgxHJ2hAhgYgO0TvDuY1lBCBsSAk2 ejFVc2zW2iHfl7Lr/wAmIJBljvrfT1sTcuBXKCUBGyPBaePa43LkaIHSLKM47wyysi2BDjI8 n0noJp7DpzfndaHTeqUlyiBEAWHDQFaQrnjIn0ToELWkDVuNJ4J1HS9hl4OfTpTuZPe5P3Bs Jh2YIBeBI/ZERou3SIuaAkCDZrsqcHo1mLNFSnGkqiqOEZcgBNhKGuN2p7ZOe0zMkAUJoeRI +jd+SVhG/E3xvGuBtMT2CSDALeffKZRca4Ka5fDXBaYIi2Le0mNPVdkZfTU9+9w+V4xElTRu NACKsV4aOXOaNBBzbXtbGhYYaHI6sfKm/rhkwMhqbdJGcT6PRhJzCYEQMgVdnkAa66ojRRC0 GgAkb2f0s0ejoEid7mss1WXMMXMmvAeagENiA6ieRb5HWDvqzQNz2f8AK+hzNxIQRGpYR2cP GIFxnCwMAoxNl0cDemyypLeAuDuc4MHoAignknd9A0zjbT7Bge0B9zGSy+oohrlx0kUcO/Ps nK6eMNNDnl62aiCRO9zWWfLLmGLmTXgPNRj0KUPnnH2g07KF0wAi6I7eG329TpBUFKwuCUI1 9WIPs8Ikra0K0n3OEqoCNZzvznA1urb210sZaaHPLjwQagLjSqs6ODL/AKaAiatAl5d7eMZp eE9ysPUi+5oOFMBJTqCaBBYKUAQ9O1n+haaCJCOmxcWF1QbrVy9hBr4gZb0wJk5y63yqRyZU 1R+rVqrIAR3qLYGwnQMKaRQroRzwIZEYuNwdfBo99h9Dby2+g2AiRtxw4D9tFQFADZAtArIU MdorhrpEqAbTk344c2yNpxpA0afl6+0d2YZ5K86SHJhkMDhbQAKB3m8qTQ2FomdwTr9sRZja CTqEFrYW84DHG+R9wh9nAIobUkiEEsERRNYUkXaZHxkTUQXVdf8AkPPUbO6i77apzW9ZTy6I zp2Br2m/ZEF7iUPKaRQQ3uOMQl8117BtnxfR6atW8Vu449EA53h6AEUcyeGi9cHKD6BoOlsw G8pLsCVCDSk2D7NPTjgVxkVO81E4dnGFnWiWb4nDQVIVzbQZBQBXktQ6ECNo5XYBHUCIGwaH QmHyRjwg+AteNDs5y+wUwkSKSNjQ8LXIpUPD0fsbdcZoOb1Qp8Ahzp5ecRIiaFOYth7u3Cax FkBFB5Ojxed9eqa1UuJV05ACbNNU0Ut60Qtt3o+2sKLXuJDgLc+DjD3twYIQIBRagLN0AvNJ /AVD845gMy0ycA2DaBDuaSJlEQ1XrITWi/8Ak77kWGwoPenuvtvYddmCCX6EfKOIVlvVUnN1 hzR6jHeCmyYCHzac3TQx4ARFtse11HUu+AXQi1HBISVqNkhzlgiQhFNE6Ls0sXAQ1uf8oKJU VNHZ6BZn5oJA63pVvWtbwe5MCLRBoGUheRB41yC4QlFXi0A2HWasYUCuxjNk1tQ1bmnRlT6I iCuqXiluFGUQFqiq1FFUE5b9HaTo0IR2CGd8YFFGGApteNMfDg1KUTIPYImqp0qH+/VK09kD 3W65ejcDrNSRwGd3p1gJGKneWKFVsNIuuAyGtqmHg5OCsQ2SNKUJ1D2bGKoR7XUdfAK+hkxJ gpAIkG3FgGw5qiitAnBCVI5oDdz/AMlaG2y+KbN8XLl8ETbVEt384EjVTefEvw4y6241zWBo 36Hy4BCcEKmjnAO5QMgR7NKa6Zirm7LhbitdcYGNACAHQehetGIcjkB7mFqpiHlODTt2eXIu TgHkIqe3pC16FE0q1yDnR3IlBYPnl++DB5HFSNzoHHj0EPENN4R05PskUJQjrSmumZR/VuWs Ztd1yOeCD2op7DXr7PDnSCKG3742W6ZPlAb9FE8GA4QkE6norEhjq0oU3vWJs9HLEobg0Xj9 YaflqGUF+4++aPGY6pQ37xnmPjKUlY9yR5Nc4TOY7sBVn0xtQxUFpQ+fbKeiFaDjnA2F1B+r BDsTSdjArGCjHeIpyrFZRniYgqzFE+GUH4XVwX2RPpgxe6hDBgFHhHIkRhzGSrY7ke8GYkK1 ufGJXLwi8z/Os6a1goe+a3xYBc36Rh7dvGt/GDIGShGjz8YqXhwIVVwAbrhcLbCMtQCE2PCb MCgwiBAxAdiJrYiYklPYhHBWZba0TnPgpQjnwxEU+cIS83bAH6J9f1xBKYJB++fzr98/nX75 /Ov3z+dfvn86/fP51++fzr98/nX75/Ov3z+dfvn86/fP51++fzr98CGfzqowc51si8g+nprM AAiJgzhow29wRFzbXBBDOFY6RgIybRHCFCHg4kHCiChUo5VEdmcK2shHo5OT1JswuQoQhAqy qqDGKoo0vlRAkg7NgCwO1/OpDUnmG6EDyTGxBFG6NCSatGkmO/IiGLB4vl0e8Lh2qZA8JquD WsiDkvOg0gB2OQk4wbzppGlef96YPIviT6LaEbs49nwJRBUnKmPGn3xBdTXmiAMPYNbBsEyD WXCcL7QKkOMbXRoFbASKCO1HKbRJoVByQ39A63RRVVBB6FFCApMfb6sQ1Gouau3HWDV1WDc6 TcmgrSFxoZ28HIPGgeFjLg6hxsDFCqXuLkEzAwjQkGkIwHly+OzIKqC7RRF5ecBcegQ29GxB Nxsc/nX75/Ov3z+dfvn86/fP51++fzr98/nX75/Ov3z+dfvn86/fP51++fzr98EIpgG78/od EzEQCsV1ND9Mea6EAiwRK60jt0ySL2RhEIrUNDTapKWRkSgREE0HN0xl5wcWoFhI5HyIhBne qUE3RKhWGrijM34JL3JKGdLGNLlSM6lUCJOAnvnf8bXdrzYI+ZqYHZsgagUqkgHIcnp8DWyD SotGy7wYPLUaHAU5xt0OGAh1bytVmjSNIfJyjRBbNEuFnZUBZCcAgiMCclBqKNFvFHXCRlTi 8qnYNga5zi59v4YA0JlL8K5IAlmt8IX3eKgJb2tTvhHj2esigQkBGElnKIR6WppdQYm4VYaQ 7JTcC0xQwMYgA9HKuFfwHQC6BTaizlbf51+2fzr9s/nX7YmarMDSoImuxwiip9DJa1WQQ9jq hj24bWfBXBtGH/BqXQE+LQ3CEwVI/absgMICFRo4lg0ruVAWsDWmBHG9uihpQQBCgTC6uXzT buzMJqbNeRJipZZdpoEFBR1md2Q2ANX2HINa2hw7Yih5efBFrdPfHv8AgNFJFGcWbwl3whAu oRSpw8OEN3HAzxUvK3zwYm2F+sEAkh2P34qouCkGr4J0D79ZdeY0pZSwQUGyXDVY4CTU4PdN mnI4man7sIeQEitDP51+2fzr9s/nX7Z/Ov2z+dftn86/bP51+2fzr9sFDjRLD9v0I3EyD5Ao 0dtXCFtNaiaKfAGbUFy8zpRmjcHhYncxvAhFpN6OwHkRY6CaDhJwWIHQ9O/iO7GHRGUUfIl1 i9+C7yAVol2O7rG0U2rUTgKojCSrMCH0b0BAicWtw63GOjBGWBAkGezX6mQyi2IGBRbEWTiz D7FXD7ARj54zcg5MdBGGe9+GMEWBO0vJqHDFoYiSpQ2QHYLsoTFEBgnZKp2pBGtpxVN47oEL JFpkop75Lx8NJ3Cu0ktGURUSKovKXYSm+Y95LX5NLoCDaBWBcu41G1Fbd2tFOq47fol0a6EH yNwsVCeOgRUXMDheXVSlEGRy+bwaNwXeoUwTYN63oHLUieR38BAlrdk436J4UQEbmkIvtHAL 6uGVtsiuz4wWDrt4eaYCHuM0OFBveDtQUSqceGd0YkIKC2E8sHjBsq2WAQhM43J9sT5JXBWx GlCnYdE5l0iyzkTqmIYAtQpqFAFWcadbMu8CIBu9bsyPKuP0P8YO5FYaBLUFWJr9IA9R8F5c uNod10qKEd1XjpVFlCUDaUd6/G8QibCmrOBpDt4EWAbQIFc0CEAu2OjVJlCcaEE1qIorbDaa 4vRO0u6pF6bNUlK4QZXhsnW2prBu6gjOwG1RGBLU1ZXRzIhBrYuzWg1vXRgeLskaJFSNPItA fnIm5IfGQwMKdRUpXn6SGX5ZDtC0otATZFSNbI+tUHARNBwI9GN1foZOCRwh8RBAzh43DTTQ JUt3JWsx8cxHUQpEGCyGwhSygiYxrRgGxvrEZC0UwVlBVK1KpB/7QeVC0vSCOuxgIAC8Le+F W7JoYP8AAisnyCeFjjAoCPU2Ho6vvdZKr4t3pAgpe0O5jUukzpB5hBOzhMTE8iwN0e4bXsFO eqFJL24A7TkYEaQTA2d27vgHZWIwitUIgGtA8e8mDiREqRgEJk7RyhjTB1kDyAF5LOzgxRKH 1VQNiS+gYqdgIKE+C7WpzZhwRsPETHImGxqZZBQcfZW0TdvvcKaRSEziIIiFqk3JnUdqnkEg ehDRl5YBTZWDYvHBiiqJVfZJxkXD4c3922m5iIEbit6ZVKrUEZvkBoOmSr0gG7hdNFgT2ebkgwAwBvWOtknhfOJO8rQCEcbE0s1cCPX2 AKit0afA9CMWUQgUbm4YCgEcgBo1wJ9JsbtqxC0oUkZzzrWWLoBINAnSvE1zkzwToFNiLB8D 2xEQ9IGJwAErzCbx4FHqFWBA7QrRTWipU9ATgkbdYSWwuCYOuCgIL7s70axWJwlhoZwA0Hy2 VmI8m8tRd71lNT2oGrcBT5cZmhLUEnUDTmOnDaOYRE0l2ql5SZy7ZECCJKRA1zGX7h9XkbCK YfjFoo24RIilXungXNJsBVy1stk8S50bGUkAqCJyMc6znZiRpaUV0+WXR9QsDgGoUORBUsMd nSheNZM7G6sQQJxexDeOA5cTFKwa+AUiiICVawDaUAjvOlKOVN04nZllAH2ChhqjWtIFV5vT uxjYQ14DDyON5k11ZvuMeXDE3JpB4gY1treBd/bae/e3sdzWb18YbGKPBLPtxP8At34YNyCI E0StpxiHex1CIpqwdWNFwSSINJoaQ0EeAMTDH2hvkIzC1sPGAKlwSjuMXLCqDGGAxOZRVkYF YJDsV1AmGHBVSKaYETHn6+ag5LT3dFsSpBE9LiBUEE4DcqcXJpqgReDQEiYmURNLihawNAha xryHFgBtmivKvhxoFVGk1UHG6XYxqcGyPIKRopIqEggwZLApyEFNQgBIHDAkplqnTqqAQqXc x7y1cQBdWMwwiBFqRXFQKgCBAU8FUXFZ9tm3OzzeOd84/vHdob0AjHVR5y1ZFUOcY+6QpAkD XLLZjtUXDvVKtSqJhFSvvrX/ABc0ijxArPpcJUEaVFWA0hXWzla6JnZYmuAkvYo6UDHfipoY Pm74xUMEMsLB0BQNEFcYoNTkg2ZFAZsw3hmbilGEimTW0Apll7UUK2kwdBsW7V8KymzaSzie gnkHdbnLgroYBHXJApMpjAgaiOaibKKWXikAsJdznT5HCzcqcYIVt9gKwIRqXHUFl8hWQUIO B0QegoEQLZvQpHbAJ8Ugy5JdBFAmKe5oc0WCWCgVQMipFlOAuNoVkIwd4/jkzFQOgUADYkZz cinBRRoD7nOtOOR2hmRKAlLwsNAiOGA4gOYSkhauJqwxUBaxbCQ2Jj5svTIYAOKAIBMB/BeK gVHuhCs4GTukd0FOn3kaGqq/OxCT7BI8oHJns8EDqKpu0JgY3SoBZUjTYU03hy+NBEVBJUQG yaccfIyBihodqDZlmAmQKw3BKhvmX/to+gm8SoRs29GNSHYaNIra7qJfJftsVRLJoSK8id4w i0GpVBBbq387yabNEMOxbHbkT3SEqamVbKK6dC6BzWa2sJTYYU7YbK2hNgLblCrrs8ayIoj2 wpUKwvse+OejLHYaro9eeHGC9o0nAqRGsHoi3niyMEQiNVpN/IwFDvFCQKbqSslMQwR8MYUh EI0OVpTseLqQWGgVqFQgp8JqCK8heWr75vN32bElZQ4BiaCmbu+QrKLCvJFRNd428DLprSLt 9zx1hSNvQaIhOir3ip9VIEsUuycYBfaM6S2XziSOXBphgBNRQR4FtpSOG/SduIC7ECPVMWvR G2EQHY62YAiw7Cb0e4bE1v0ZCAiJRMAixCfk9pEQDE7MGj0cd4ATQJmmEMe0zfA4IdV3u3rW DoqBCi4SXILfZIv8MNQOlg2a1JDNiC/MQwAU8CCayptCdKiFVtrec03EHqwABAvHOaSWVBNm Haa+cFctQndKAjVLV0AXYvXxiUVedrW7wuEHiqYA0K8crldF+mkF69v3xaelSVP2TPnB+RQT QrNciJ+bh+KtJQN+ORIe3Nx4/eH5A770+7h1K2CapzXkn1cgAKneQPhp9TDa0hm88jQCeLep gk6uU5GiRJQbv2zbcSpQ7RR2un4xi6UCtf5pMS6EMTVgEOvYOOXIk4GhTEFeSycbcSBEBaqp zHS0+pxc1CtZXFCppBA91VNKElaEZYFuRasRs0h+ZhHm15YdWrUCltC1Rh1Zt6ZjE5LmO4gW Jybf+0EBItEwojRw9OLgP2iAtGiWx4mHfIxvsQYBI8/TECQmjXhFTovDbdmcDwgUGA1CnAaH rGFBYgSJEO2yQ6WIVPWBAgciZwAoJc14TJbEQbIRjgb04SyEPN+AovY+MdplogrQRG7WzeHH HnyLNnUWOtpgZIEr4I9F51WsMY1FhgbmmCHL9Jh8rPvIlo4BYdnRgSsWSUSex0NVRy4ekjyD WB1yDEHznFFQhG3KpW8vcygmoBVLCoiKqrhmjlkBHegjG2KJu4oa1GUoeF6mEgUCRRYbICc8 u8jegRDrozgw1jaYTJuASB5Heum4lAXQAvwu3Cx3jNENclVNkR0M47AA4tiIghwqkPZ6LJ4V BICzfeLagch0swBwThkgMcK7YQUiriYZq2KyAmyoOKN23DjK5F0TG/8ADz//ACh//9k= --part-HQJBAPlABbaAkA1xRlRmTAIZOHcm16VdA1OgAA8uoomQc-- ========================================================================= Date: Tue, 24 May 2011 14:00:15 +0200 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: Error when using unified segmentation in spm8 Comments: To: Volkmar Glauche <[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 Volkmar, bingo, did not think of that. Thanks, Marko Volkmar Glauche wrote: > Hi Marco, > > you might want to check whether there is a spm_defaults.m outside the S= PM8 folder on your MATLAB path. With the last set of updates, some previo= usly hardcoded defaults have moved into spm_defaults.m. If your version o= f spm_defaults.m is missing them, this could cause such an error message. > > Best, > > Volkmar > --=20 ____________________________________________________ PD Dr. med. Marko Wilke Facharzt f=FCr Kinder- und Jugendmedizin Leiter, Experimentelle P=E4diatrische Neurobildgebung Universit=E4ts-Kinderklinik Abt. III (Neurop=E4diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 T=FCbingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================= Date: Tue, 24 May 2011 20:30:47 +0800 Reply-To: =?gb2312?B?s8K4+yAg?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?gb2312?B?s8K4+yAg?= <[log in to unmask]> Subject: convolution matrix =?gb2312?Q?=A1=AEK=A1=AF?= MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-990975817-1306240247=:78821" Message-ID: <[log in to unmask]> --0-990975817-1306240247=:78821 Content-Type: text/plain; charset=gb2312 Content-Transfer-Encoding: quoted-printable To Whom It May Concern: I am new on fMRI from China. Sorry to bother you with some basis questions. As we know, what any fMRI analysis program does is this: It takes the time = series of stimulus onsets, and convolves it with the HRF. This gives a *pre= diction* of the blood flow response that we should get a given voxel, if th= at voxel is responding to the stimuli. Then, we take that prediction, and g= o and compare it against the measured data. And we see how well they match.= However, in some other paper, for example =A1=B0Analysis of fMRI Time-Seri= es Revisited=A1=B1 from professor J.FRISTON, may use =A1=B0a convolution ma= trix =A1=AEK=A1=AF =A1=AEsee attachment=A1=AF =A1=B1 I wonder, does the =A1= =AEK=A1=AF matrix used here can represent the HRF convolved? Or, it only re= presents temporally smooth? If I make sense, if =A1=AEK=A1=AF only means te= mporally smooth, the X matrix here should be already convolved with HRF. Mo= reover, I can not find more detail information about the =A1=AEK=A1=AF matr= ix. I want to know how to represent =A1=AEK=A1=AF in mathematics. All in al= l, I wonder what =A1=AEK=A1=AF is and how to represent =A1=AEK=A1=AF. Long time confused with these questions. Really look forward your response. --0-990975817-1306240247=:78821 Content-Type: text/html; charset=gb2312 Content-Transfer-Encoding: quoted-printable <table cellspacing=3D"0" cellpadding=3D"0" border=3D"0" ><tr><td valign=3D"= top" style=3D"font: inherit;"><P style=3D"TEXT-ALIGN: left; MARGIN: 0cm 0cm= 0pt; TEXT-AUTOSPACE: ; mso-layout-grid-align: none" class=3DMsoNormal alig= n=3Dleft><FONT face=3D"Times New Roman"><SPAN class=3Dquestion-title><B><SP= AN style=3D"FONT-SIZE: 11.5pt; mso-font-kerning: 18.0pt" lang=3DEN-US>To Wh= om It May Concern:</SPAN></B></SPAN><SPAN style=3D"FONT-FAMILY: 'Courier Ne= w'; FONT-SIZE: 12pt; mso-font-kerning: 0pt; mso-bidi-font-family: 'Times Ne= w Roman'" lang=3DEN-US><?xml:namespace prefix =3D o ns =3D "urn:schemas-mic= rosoft-com:office:office" /><o:p></o:p></SPAN></FONT></DIV> <P style=3D"TEXT-INDENT: 21pt; MARGIN: 0cm 0cm 0pt; mso-char-indent-count: = 2.0" class=3DMsoNormal><SPAN lang=3DEN-US><FONT size=3D3><FONT face=3D"Time= s New Roman">I am new on fMRI from <?xml:namespace prefix =3D st1 ns =3D "u= rn:schemas-microsoft-com:office:smarttags" /><st1:country-region w:st=3D"on= "><st1:place w:st=3D"on">China</st1:place></st1:country-region>. Sorry to b= other you with some basis questions.<o:p></o:p></FONT></FONT></SPAN></DIV> <P style=3D"TEXT-INDENT: 21pt; MARGIN: 0cm 0cm 0pt; mso-char-indent-count: = 2.0" class=3DMsoNormal><SPAN lang=3DEN-US><FONT size=3D3><FONT face=3D"Time= s New Roman">As we know, what any fMRI analysis program does is this: It ta= kes the time series of stimulus onsets, and convolves it with the HRF. This= gives a *prediction* of the blood flow response that we should get a given= voxel, if that voxel is responding to the stimuli. Then, we take that pred= iction, and go and compare it against the measured data. And we see how wel= l they match. However, in some other paper, for example =A1=B0<I style=3D"m= so-bidi-font-style: normal">Analysis of fMRI Time-Series Revisited</I>=A1= =B1 from professor J.FRISTON, may use =A1=B0a convolution matrix =A1=AEK=A1= =AF =A1=AEsee attachment=A1=AF </FONT></FONT><FONT size=3D3><FONT face=3D"T= imes New Roman">=A1=B1 I wonder, does the =A1=AEK=A1=AF matrix used here ca= n represent the HRF convolved? Or, it only represents temporally smooth? If= I make sense, if =A1=AEK=A1=AF only means temporally smooth, the X matrix here should be already convolved with HRF.= Moreover, I can not find more detail information about the =A1=AEK=A1=AF m= atrix. I want to know how to represent =A1=AEK=A1=AF in mathematics. All in= all, I wonder what =A1=AEK=A1=AF is and how to represent =A1=AEK=A1=AF.<o:= p></o:p></FONT></FONT></SPAN></DIV> <P style=3D"TEXT-INDENT: 21pt; MARGIN: 0cm 0cm 0pt; mso-char-indent-count: = 2.0" class=3DMsoNormal><SPAN lang=3DEN-US><FONT size=3D3><FONT face=3D"Time= s New Roman">Long time confused with these questions. Really look forward y= our response.<o:p></o:p></FONT></FONT></SPAN></DIV></td></tr></table> --0-990975817-1306240247=:78821-- ========================================================================= Date: Tue, 24 May 2011 10:54:46 -0400 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 8 Flexible Factorial Design Question Comments: To: Henrike Hemingway <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=002354530970dee77704a406c427 Message-ID: <[log in to unmask]> --002354530970dee77704a406c427 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable As you noted, the con_* at the second level is identical. This is not surprising as the data you are asking about is the same. What is different is the variance terms between the models. From my expierance the model with 4 conditions is wrong. The reason that it is wrong is that the error term represents the unexplained variance between your 4 categories (not the unexplained variance of all conditions). The model with 5 conditions has an error term that represents the unexplained variance of all the conditions. Best Regards, Donald McLaren =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School Office: (773) 406-2464 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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, May 24, 2011 at 7:01 AM, Henrike Hemingway <[log in to unmask] e > wrote: > Dear SPMers, > > I have question regarding the flexible factorial design as implemented in > SPM8. > > I have realized an experiment with 5 conditions for each subject (4 emoti= on > conditions, 1 neutral condition). I was interested in the difference betw= een > the emotion and the neutral conditions.Therefore, I set up the 2nd level > analysis using a flexible factorial design in SPM8 in two different ways. > The main effect of subject and the main effect of condition were estimate= d > in both of the design matrices. > > In the first design matrix I specified the effect of condition with 5 > levels, including the con images of all 5 conditions separately. The > contrasts for each of the 4 conditions > neutral were computed on the 2nd > level. For the second flexible factorial design I computed the difference= s > between each of the 4 conditions > neutral on the first level and by this > didn=92t have to estimate difference contrasts on the 2nd level. Thus, I > specified the effect of condition with only 4 levels each representing th= e > difference of the condition > neutral and estimated the main effect for e= ach > of the four conditions. > > The results for the contrasts representing the same difference (Condition= > > Neutral), however, were not similar across both designs as you can see in > the attached screen shots. The model including the difference contrasts > computed on the first level resulted in much higher t-values and larger > number of voxels in the clusters of interest. After some research in the > archives of the SPM mailing list I still have not found an explanation fo= r > the diverging results. Though I found some information about a comparable > difference observed when comparing designs explicitly modeling the subjec= t > factor or not. > > Does anyone know what exactly is causing the pronounced difference in bot= h > designs? When looking at the con images computed on the 2nd level they > contain the exact same values in each voxel. Is one of the models > practically wrong? > > I would be glad if anyone could help me out with more detailed informatio= n > or helpful links for further readings on that issue. > > Thanks a lot. > > Henrike > > --002354530970dee77704a406c427 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable As you noted, the con_* at the second level is identical. This is not surpr= ising as the data you are asking about is the same. What is different is th= e variance terms between the models. <br><br>From my expierance the model w= ith 4 conditions is wrong. The reason that it is wrong is that the error te= rm represents the unexplained variance between your 4 categories (not the u= nexplained variance of all conditions). The model with 5 conditions has an = error term that represents the unexplained variance of all the conditions.<= br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 24, 2011 at 7:01 AM, Henrike= Hemingway <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask] ">[log in to unmask]</a>></span> wrote:<br><blockquote class=3D"gm= ail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(2= 04, 204, 204); padding-left: 1ex;"> Dear SPMers,<br> <br> I have question regarding the flexible factorial design as implemented in S= PM8.<br> <br> I have realized an experiment with 5 conditions for each subject (4 emotion= conditions, 1 neutral condition). I was interested in the difference betwe= en the emotion and the neutral conditions.Therefore, I set up the 2nd level= analysis using a flexible factorial design in SPM8 in two different ways. = The main effect of subject and the main effect of condition were estimated = in both of the design matrices.<br> <br> In the first design matrix I specified the effect of condition with 5 level= s, including the con images of all 5 conditions separately. The contrasts f= or each of the 4 conditions > neutral were computed on the 2nd level. Fo= r the second flexible factorial design I computed the differences between e= ach of the 4 conditions > neutral on the first level and by this didn=92= t have to estimate difference contrasts on the 2nd level. Thus, I specified= the effect of condition with only 4 levels each representing the differenc= e of the condition > neutral and estimated the main effect for each of t= he four conditions.<br> <br> The results for the contrasts representing the same difference (Condition &= gt; Neutral), however, were not similar across both designs as you can see = in the attached screen shots. The model including the difference contrasts = computed on the first level resulted in much higher t-values and larger num= ber of voxels in the clusters of interest. After some research in the archi= ves of the SPM mailing list I still have not found an explanation for the d= iverging results. Though I found some information about a comparable differ= ence observed when comparing designs explicitly modeling the subject factor= or not.<br> <br> Does anyone know what exactly is causing the pronounced difference in both = designs? When looking at the con images computed on the 2nd level they cont= ain the exact same values in each voxel. Is one of the models practically w= rong?<br> <br> I would be glad if anyone could help me out with more detailed information = or helpful links for further readings on that issue.<br> <br> Thanks a lot.<br> <font color=3D"#888888"><br> Henrike<br> <br> </font></blockquote></div><br> --002354530970dee77704a406c427-- ========================================================================= Date: Tue, 24 May 2011 10:58:41 -0400 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: How to do PPI GLM estimation and contrasts using matlab script Comments: To: Yang Hu <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=00151747af3ee20fde04a406d2e6 Message-ID: <[log in to unmask]> --00151747af3ee20fde04a406d2e6 Content-Type: text/plain; charset=ISO-8859-1 I would suggest that you download the PPI toolbox located at http://www.martinos.org/~mclaren/ftp/Utilities_DGM. Instructions can be found at http://brainmap.wisc.edu/PPI For PPI, using the standard SPM approach, set the method field to 'trad'. 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, May 24, 2011 at 4:20 AM, Yang Hu <[log in to unmask]> wrote: > Dear all, > Having set up PPI.mat (PPI variables) of each individual by > spm_peb_ppi.m, I would like to do PPI GLM estimation (3 regressors in the > design matrix: PPI.ppi, PPI.Y, PPI.P)and contrasts (e.g. [1 0 0 0]) for each > individual. Could someone provide me with the matlab script to do the two > above steps? Any suggestions are welcome. Thanks a lot! > > > Best wishes, > Yang Hu > > ICN, ECNU, Shanghai, China PR > --00151747af3ee20fde04a406d2e6 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable I would suggest that you download the PPI toolbox located at <a href=3D"htt= p://www.martinos.org/~mclaren/ftp/Utilities_DGM">http://www.martinos.org/~m= claren/ftp/Utilities_DGM</a>.<br><br>Instructions can be found at <a href= =3D"http://brainmap.wisc.edu/PPI">http://brainmap.wisc.edu/PPI</a><br> <br>For PPI, using the standard SPM approach, set the method field to '= trad'.<br clear=3D"all"><br>Best Regards, Donald McLaren<br>=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdo= ctoral Research Fellow, GRECC, Bedford VA<br> Research Fellow, Department of Neurology, Massachusetts General Hospital an= d Harvard Medical School<br>Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D<br>This e-mail contains CONFIDEN= TIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may= also be LEGALLY PRIVILEGED and which is intended only for the use of the i= ndividual or entity named above. If the reader of the e-mail is not the int= ended recipient or the employee or agent responsible for delivering it to t= he intended recipient, you are hereby notified that you are in possession o= f confidential and privileged information. Any unauthorized use, disclosure= , copying or the taking of any action in reliance on the contents of this i= nformation is strictly prohibited and may be unlawful. If you have received= this e-mail unintentionally, please immediately notify the sender via tele= phone at (773) 406-2464 or email.<br> <br><br><div class=3D"gmail_quote">On Tue, May 24, 2011 at 4:20 AM, Yang Hu= <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">huyang200606= @126.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style= =3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); p= adding-left: 1ex;"> Dear all,<br> =A0 =A0Having set up PPI.mat (PPI variables) of each individual by spm_peb= _ppi.m, I would like to do PPI GLM estimation (3 regressors in the design m= atrix: PPI.ppi, PPI.Y, PPI.P)and contrasts (e.g. [1 0 0 0]) for each indivi= dual. Could someone provide me with the matlab script to do the two above s= teps? Any suggestions are welcome. Thanks a lot!<br> <br> <br> Best wishes,<br> Yang Hu<br> <br> ICN, ECNU, Shanghai, China PR<br> </blockquote></div><br> --00151747af3ee20fde04a406d2e6-- ========================================================================= Date: Tue, 24 May 2011 11:06:59 -0400 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: export betas over multiple coordinates Comments: To: SUBSCRIBE SPM Yihui Hung <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=00151747af3e96b20b04a406f035 Message-ID: <[log in to unmask]> --00151747af3e96b20b04a406f035 Content-Type: text/plain; charset=ISO-8859-1 See http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind1103&L=SPM&P=R16468&I=-3&d=No+Match%3BMatch%3BMatches 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, May 18, 2011 at 10:25 AM, SUBSCRIBE SPM Yihui Hung < [log in to unmask]> wrote: > Hello, > I wonder how to export the betas over the coordinates which showed a > significant effect. I know how to export one beta of one coordinate which > showed a significant effect. However, I would like to know how to export > betas over all of the coordinates in the statistic table at once. > Thank you for any advice! > > Sincerely, > Yihui > --00151747af3e96b20b04a406f035 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable See <a href=3D"http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L= =3DSPM&P=3DR16468&I=3D-3&d=3DNo+Match%3BMatch%3BMatches">http:/= /www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3Dind1103&L=3DSPM&P=3DR16468&= amp;I=3D-3&d=3DNo+Match%3BMatch%3BMatches</a><br clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Wed, May 18, 2011 at 10:25 AM, SUBSCR= IBE SPM Yihui Hung <span dir=3D"ltr"><<a href=3D"mailto:a489930026@yahoo= .com.tw">[log in to unmask]</a>></span> wrote:<br><blockquote class= =3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid= rgb(204, 204, 204); padding-left: 1ex;"> Hello,<br> I wonder how to export the betas over the coordinates which showed a signif= icant effect. I know how to export one beta of one coordinate which showed = a significant effect. However, I would like to know how to export betas ove= r all of the coordinates in the statistic table at once.<br> Thank you for any advice!<br> <br> Sincerely,<br> <font color=3D"#888888">Yihui<br> </font></blockquote></div><br> --00151747af3e96b20b04a406f035-- ========================================================================= Date: Tue, 24 May 2011 13:50:58 -0400 Reply-To: Anderson Winkler <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Anderson Winkler <[log in to unmask]> Subject: Re: convolution matrix =?GB2312?Q?=A1=AEK=A1=AF?= Comments: To: =?GB2312?B?s8K4+w==?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------050806020102040807020106" Message-ID: <[log in to unmask]> --------------050806020102040807020106 Content-Type: text/plain; charset="GB2312" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p4OHp1jx019650 Dear =B3=C2=B8=FB I think K is a Toeplitz matrix: http://en.wikipedia.org/wiki/Toeplitz_mat= rix It has some nice properties and I believe it was chosen here for ease of implementation, since a computationally expensive convolution can then be done as a simple matrix multiplication in MATLAB, without even the need to Fourier-transform the data. You may also be interested in reading the paper that followed this one that you are reading: http://dx.doi.org/10.1006/nimg.1995.1023 Hope this helps! All the best, Anderson On 05/24/2011 08:30 AM, =B3=C2=B8=FB wrote: > > *To Whom It May Concern:* > > I am new on fMRI from China. Sorry to bother you with some basis > questions. > > As we know, what any fMRI analysis program does is this: It takes the > time series of stimulus onsets, and convolves it with the HRF. This > gives a *prediction* of the blood flow response that we should get a > given voxel, if that voxel is responding to the stimuli. Then, we take > that prediction, and go and compare it against the measured data. And > we see how well they match. However, in some other paper, for example > =A1=B0/Analysis of fMRI Time-Series Revisited/=A1=B1 from professor J.F= RISTON, > may use =A1=B0a convolution matrix =A1=AEK=A1=AF =A1=AEsee attachment=A1= =AF =A1=B1 I wonder, does > the =A1=AEK=A1=AF matrix used here can represent the HRF convolved? Or,= it only > represents temporally smooth? If I make sense, if =A1=AEK=A1=AF only me= ans > temporally smooth, the X matrix here should be already convolved with > HRF. Moreover, I can not find more detail information about the =A1=AEK= =A1=AF > matrix. I want to know how to represent =A1=AEK=A1=AF in mathematics. A= ll in > all, I wonder what =A1=AEK=A1=AF is and how to represent =A1=AEK=A1=AF. > > Long time confused with these questions. Really look forward your > response. > --------------050806020102040807020106 Content-Type: text/html; charset="GB2312" Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p4OHp1jx019650 <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content=3D"text/html; charset=3DGB2312" http-equiv=3D"Content-T= ype"> </head> <body bgcolor=3D"#ffffff" text=3D"#000000"> Dear =B3=C2=B8=FB<br> <br> I think K is a Toeplitz matrix: <a class=3D"moz-txt-link-freetext" href=3D"http://en.wikipedia.org/wi= ki/Toeplitz_matrix">http://en.wikipedia.org/wiki/Toeplitz_matrix</a><br> It has some nice properties and I believe it was chosen here for ease of implementation, since a computationally expensive convolution can then be done as a simple matrix multiplication in MATLAB, without even the need to Fourier-transform the data.<br> <br> You may also be interested in reading the paper that followed this one that you are reading: <a class=3D"moz-txt-link-freetext" href=3D"= http://dx.doi.org/10.1006/nimg.1995.1023">http://dx.doi.org/10.1006/nimg.= 1995.1023</a><br> <br> Hope this helps!<br> <br> All the best,<br> <br> Anderson<br> <br> <br> On 05/24/2011 08:30 AM, =B3=C2=B8=FB wrote: <blockquote cite=3D"mid:[log in to unmask]" type=3D"cite"> <table border=3D"0" cellpadding=3D"0" cellspacing=3D"0"> <tbody> <tr> <td style=3D"font: inherit;" valign=3D"top"> <p style=3D"text-align: left; margin: 0cm 0cm 0pt;" class=3D"MsoNormal" align=3D"left"><font face=3D"Times Ne= w Roman"><span class=3D"question-title"><b><span style=3D"font-size: 11.5pt;" lang=3D"EN-US">To Wh= om It May Concern:</span></b></span><span style=3D"font-family: 'Courier New'; font-size: 12pt;= " lang=3D"EN-US"><o:p></o:p></span></font> </p> <p style=3D"text-indent: 21pt; margin: 0cm 0cm 0pt;" class=3D"MsoNormal"><span lang=3D"EN-US"><font size=3D"3"= ><font face=3D"Times New Roman">I am new on fMRI from <st1= :country-region w:st=3D"on"><st1:place w:st=3D"on">China</st1:pla= ce></st1:country-region>. Sorry to bother you with some basis questions.<o:p>= </o:p></font></font></span> </p> <p style=3D"text-indent: 21pt; margin: 0cm 0cm 0pt;" class=3D"MsoNormal"><span lang=3D"EN-US"><font size=3D"3"= ><font face=3D"Times New Roman">As we know, what any fMRI analysis program does is this: It takes the time series of stimulus onsets, and convolves it with the HRF. This gives a *prediction* of the blood flow response that we should get a given voxel, if that voxel is responding to the stimuli. Then, we take that prediction, and go and compare it against the measured data. And we see how well they match. However, in some other paper, for example =A1=B0<i style=3D"">Analysis of fMRI Time-S= eries Revisited</i>=A1=B1 from professor J.FRISTON, may= use =A1=B0a convolution matrix =A1=AEK=A1=AF =A1=AEsee = attachment=A1=AF </font></font><font size=3D"3"><font face=3D"Times New Roman">=A1=B1 I wo= nder, does the =A1=AEK=A1=AF matrix used here can represe= nt the HRF convolved? Or, it only represents temporally smooth? If I make sense, if =A1=AEK=A1=AF only mean= s temporally smooth, the X matrix here should be already convolved with HRF. Moreover, I can not find more detail information about the =A1=AEK=A1=AF= matrix. I want to know how to represent =A1=AEK=A1=AF in mathematics. All in all, I wonder what =A1=AEK=A1=AF= is and how to represent =A1=AEK=A1=AF.<o:p></o:p></font></= font></span> </p> <p style=3D"text-indent: 21pt; margin: 0cm 0cm 0pt;" class=3D"MsoNormal"><span lang=3D"EN-US"><font size=3D"3"= ><font face=3D"Times New Roman">Long time confused with these questions. Really look forward your response.<o:p></o:p></font></font></span></p> </td> </tr> </tbody> </table> </blockquote> <br> </body> </html> --------------050806020102040807020106-- ========================================================================= Date: Tue, 24 May 2011 11:28:42 -0700 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: three questions about SPM8 Comments: To: =?UTF-8?B?6IOh5p2o?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf305e27dfb7c9c804a409c38b Message-ID: <[log in to unmask]> --20cf305e27dfb7c9c804a409c38b Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable 2011/5/23 =E8=83=A1=E6=9D=A8 <[log in to unmask]> > Dear Dr. Rubens, > It's really helpful of your mail about making masked image via script= . > I have two additional questions about this script: > 1) if my input image is 'spmT_0001.img', should the threshold be T-values= ? > Yes > 2) if I want to limit my extent threshold (namely k voxels), how should I > do with your script? > Try this: http://www.nitrc.org/projects/cluster_correct/ > > Thanks a lot! > > Best wishes, > Yang Hu > -- > > Yang Hu, M.S. candidate > > functional MRI Unit, Institute of Cognitive Neuroscience; > > Department of Psychology, School of Psychology and Cognitive Science, > > East China Normal University, Shanghai, China PR > > At 2011-05-24 03:16:58=EF=BC=8C"Michael T Rubens" <[log in to unmask]> = wrote: > > I see now, I made a mistake in the original code I sent. This line: > > YY =3D spm_vol(VV); > > should actually be: > > YY =3D spm_read_vols(VV); > > > %% > > Cheers, > Michael > On Mon, May 23, 2011 at 11:03 AM, a489930026 <[log in to unmask]>wro= te: > >> Dear Michael, >> >> Every step went smoothly until the step "YY(find(Y)) =3D 0" and then the= it >> showed "Assignment between unlike types is not allowed." >> >> Yihui >> >> ------------------------------ >> *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmai= l.com> >> *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* a489930026 <[log in to unmask] tw>; spm <[log in to unmask]> >> *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/24 (=E4=BA=8C) 1:= 26 AM >> *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: =E5=9B=9E=E8=A6=86=EF=B9=95 [SPM] thre= e questions about SPM8 >> >> threshold needs to be>0. if your map is p values, then just do: >> >> Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant. >> >> then set your p threshold as 1-threshold, so .95 =3D=3D .05 >> >> Determining whether you use the mask inclusively or exlusively can be do= ne >> when you apply it, so: >> >> YY(find(Y)) =3D 0; % exclusive mask >> >> YY(find(~Y)) =3D 0; %inclusive mask >> >> >> Try running each line separately (or within a script) to see where exact= ly >> the error is occurring. >> >> >> Cheers, >> Michael >> >> On Mon, May 23, 2011 at 12:48 AM, a489930026 <[log in to unmask]>wr= ote: >> >> Dear Michael, >> >> Thank you for the advice. I did what you suggested but a result >> "Assignment between unlike types is not allowed." What could be the prob= lem? >> >> Please see the script below: >> >> ********************************************************* >> >> V=3D spm_vol('L:\2stlevel\wordVSblk\spmF_0001.img') >> Y =3D spm_read_vols(V); >> >> threshold =3D 0; %any value you choose. >> >> Y(find(Y<threshold)) =3D 0; >> Y(find(Y)) =3D 1; >> >> % Use mask >> >> VV =3D spm_vol('L:\2stlevel\radpos_sdXloca_sd\spmF_0001.img'); >> YY =3D spm_vol(VV); >> >> YY(find(Y)) =3D 0; %apply mask >> >> VV.fname =3D 'masked_image.img'; >> spm_write_vol(VV,YY); >> >> %% There ya go >> >> V =3D >> >> fname: 'L:\2stlevel\wordVSblk\spmF_0001.img' >> mat: [4x4 double] >> dim: [79 95 68] >> dt: [16 0] >> pinfo: [3x1 double] >> n: [1 1] >> descrip: 'SPM{F_[1.0,15.0]} - contrast 1: w-b' >> private: [1x1 nifti] >> >> ??? Assignment between unlike types is not allowed. >> >> ************************************************************************= * >> >> Besides, is the threshold a cutoff for the activation maps of the mask? >> Specially, does setting threshold =3D 0.05 mean that the activated voxel= sbelow p =3D0.05 are included. If not, how should I set a cutoff on the p >> value, or it does matter? >> >> Moreover, how can I determine the mask is inclusive (i.e., the voxles in >> the to-be-masked activation maps are within the volxels in the activatio= n >> maps of the mask) or exclusive (i.e., the voxles in the to-be-masked >> activation maps are NOT within the volxels in the activation maps of the >> mask). >> Thank you very much! >> >> Sincerely, >> Yihui >> >> >> >> >> >> ------------------------------ >> *=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A* Michael T Rubens <MikeRubens@gmai= l.com> >> *=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A* SUBSCRIBE SPM Yihui Hung <a489930= [log in to unmask]> >> *=E5=89=AF=E6=9C=AC=EF=BC=9A* [log in to unmask] >> *=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A* 2011/5/23 (=E4=B8=80) 10= :19 AM >> *=E4=B8=BB=E6=97=A8=EF=BC=9A* Re: [SPM] three questions about SPM8 >> >> when you say you tried to use the contrast as a mask, did you just use t= he >> raw contrast or did you binarize it? If you did the former than it is >> clear why you had no inmask voxels since any non-zero voxels would all b= e >> seen as part of the mask. So, what you want to do is load the mask, appl= y a >> threshold and binarize it by setting anything below the threshold to 0, >> and anything above to 1 (though the latter may not be completely necesar= y). >> Then, you could just use that mask to mask out the resulting contrast ma= p >> from the other analysis. Here is code to do it: >> >> >> %Make mask >> >> V =3D spm_vol('contrast_image_2turn_into_mask') >> Y =3D spm_read_vols(V); >> >> threshold =3D X; %any value you choose. >> >> Y(find(Y<threshold)) =3D 0; >> Y(find(Y)) =3D 1; >> >> % Use mask >> >> VV =3D spm_vol('contrast_image_2mask'); >> YY =3D spm_vol(VV); >> >> YY(find(Y)) =3D 0; %apply mask >> >> VV.fname =3D 'masked_image.img'; >> spm_write_vol(VV,YY); >> >> %% There ya go >> >> Cheers, >> Michael >> >> On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE SPM Yihui Hung < >> [log in to unmask]> wrote: >> >> Hello, >> >> I used SPM8 for my experiment. I manipulated three factors (e.g., factor >> A, B, C) in my experiment. The activation maps under factor A was used a= s a >> mask to constrain the effects of factor B and factor C. The problem is t= hat >> the factor A and factor B and factor C cannot be put in the same model a= t >> second level analysis. I tried to put the .img file resulting from facto= r >> A across participants in the =E2=80=9Cexplicit mask=E2=80=9D in the seco= nd level analysis. >> However, the estimation stopped by showing no inmask voxels. I searched >> through forum and did not find any useful solutions. Can someone help me >> out? Moreover, is the threshold masking related to the p value? If not, = how >> should I set criteria for the activation map of the mask? >> >> I did notice that one person in the forum suggested that one can create = an >> activation maps in which the factor B is modeled with the mask of factor >> A in first level analysis. Then, submit the resulting .img file into >> second level analysis. My question is how to create and save the imgfile= . >> >> Finally, I posted a question regarding how to export beta values over >> multiple coordinates a week ago. However, no one provide any answers. I >> wonder whether it is due that no one has the answer or due to that there >> already is an answer in the forum. >> >> Thank you for your patience and advice. >> Sincerely, >> >> Yihui >> >> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco >> >> >> >> >> >> -- >> Research Associate >> Gazzaley Lab >> Department of Neurology >> University of California, San Francisco >> >> >> > > > -- > Research Associate > Gazzaley Lab > Department of Neurology > University of California, San Francisco > > > > --=20 Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf305e27dfb7c9c804a409c38b Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable <br><br><div class=3D"gmail_quote">2011/5/23 =E8=83=A1=E6=9D=A8 <span dir= =3D"ltr"><<a href=3D"mailto:[log in to unmask]">[log in to unmask]</= a>></span><br><blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt= 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> <div>Dear Dr. Rubens,</div> <div>=C2=A0=C2=A0=C2=A0 It's really helpful of your mail about making m= asked image via script. I have two additional questions about this script:<= /div> <div>1) if=C2=A0my input image is 'spmT_0001.img', should the thres= hold be T-values?</div></blockquote><div><br>Yes<br>=C2=A0</div><blockquote= class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px= solid rgb(204, 204, 204); padding-left: 1ex;"> <div>2) if I want to limit my extent threshold (namely k voxels), how shoul= d I do with your script?</div></blockquote><div><br>Try this: <a href=3D"ht= tp://www.nitrc.org/projects/cluster_correct/">http://www.nitrc.org/projects= /cluster_correct/</a><br> =C2=A0</div><blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt = 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> <div>=C2=A0</div> <div>=C2=A0=C2=A0=C2=A0 Thanks a lot!</div> <div>=C2=A0</div> <div>Best wishes,</div> <div>Yang Hu=C2=A0=C2=A0<br></div> <div>--<br> <p>Yang Hu, M.S. candidate</p> <p>functional MRI Unit, Institute of Cognitive Neuroscience; </p> <p>Department of Psychology, School of Psychology and Cognitive Science, </= p> <p>East China Normal University, Shanghai, China PR=C2=A0</p></div><br>At 2= 011-05-24 03:16:58=EF=BC=8C"Michael=C2=A0T=C2=A0Rubens"=C2=A0<= <a href=3D"mailto:[log in to unmask]" target=3D"_blank">[log in to unmask] COM</a>> wrote:<br> <blockquote style=3D"padding-left: 1ex; margin: 0px 0px 0px 0.8ex; border-l= eft: 1px solid rgb(204, 204, 204);">I see now, I made a mistake in the orig= inal code I sent. This line: <br><br><span><span>YY</span></span> =3D <span= ><span>spm</span></span>_vol(<span><span>VV</span></span>);<br> <br>should actually be:<br><br><span><span>YY</span></span> =3D <span><span= >spm</span></span>_read_vols(<span><span>VV</span></span>);<br><br><br>%%<b= r><br>Cheers,<br>Michael<br> <div class=3D"gmail_quote">On Mon, May 23, 2011 at 11:03 AM, a489930026 <sp= an dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]" target=3D"_bl= ank">[log in to unmask]</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"padding-left: 1ex; margin: 0pt 0= pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204);"> <div> <div style=3D"font-size: 12pt; color: rgb(0, 0, 0); font-family: arial,helv= etica,sans-serif; background-color: rgb(255, 255, 255);"> <div><span> <div>Dear Michael,</div> <div><br></div> <div>Every step went smoothly until the step "YY(find(Y)) =3D 0" = and then the it showed "Assignment between unlike types is not allowed= ."</div> <div><br></div> <div>Yihui</div></span></div> <div><br> <blockquote style=3D"padding-left: 5px; margin-left: 5px; border-left: 2px = solid rgb(16, 16, 255);"> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"> <div style=3D"font-size: 12pt; font-family: 'times new roman','= new york',times,serif;"><font face=3D"Arial" size=3D"2"> <hr size=3D"1"> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> Michael T Rubens <<a href=3D"mailto:[log in to unmask]" tar= get=3D"_blank">[log in to unmask]</a>><br><b><span style=3D"font-weigh= t: bold;">=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> a489930026 <<a= href=3D"mailto:[log in to unmask]" target=3D"_blank">a489930026@yahoo= .com.tw</a>>; spm <<a href=3D"mailto:[log in to unmask]" target=3D"_b= lank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E6=97=A5=E6=9C=9F= =EF=BC=9A</span></b> 2011/5/24 (=E4=BA=8C) 1:26 AM<br><b><span style=3D"fon= t-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b> Re: =E5=9B=9E=E8=A6= =86=EF=B9=95 [SPM] three questions about SPM8<br></font><br> <div>threshold needs to be>0. if your map is p values, then just do:<br>= <br>Y =3D 1-Y; %now p =3D 1-p, so higher values are more significant.<br><b= r>then set your p threshold as 1-threshold, so .95 =3D=3D .05<br><br>Determ= ining whether you use the mask inclusively or exlusively can be done when y= ou apply it, so: <br> <br><span><span>YY</span></span>(find(Y)) =3D 0; % exclusive mask<br><br>YY= (find(~Y)) =3D 0; %inclusive mask<br><br><br>Try running each line separate= ly (or within a script) to see where exactly the error is occurring.<br><br= > <br>Cheers,<br>Michael<br><br> <div>On Mon, May 23, 2011 at 12:48 AM, a489930026 <span dir=3D"ltr"><<a = href=3D"mailto:[log in to unmask]" rel=3D"nofollow" target=3D"_blank">= [log in to unmask]</a>></span> wrote:<br> <blockquote style=3D"padding-left: 1ex; margin: 0pt 0pt 0pt 0.8ex; border-l= eft: 1px solid rgb(204, 204, 204);"> <div> <div style=3D"font-size: 12pt; color: rgb(0, 0, 0); font-family: arial,helv= etica,sans-serif; background-color: rgb(255, 255, 255);"> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><s= pan>Dear Michael,</span></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><s= pan><br></span></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;">Th= ank you for the advice. I did what you suggested but a result "Assignm= ent between unlike types is not allowed." What could be the problem?</= div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;">Pl= ease see the script below:</div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;">**= *******************************************************</div> <div> <div>>> V=3D <span><span>spm</span></span>_vol('L:\2<span><span>s= tlevel</span></span>\<span><span>wordVSblk</span></span>\<span><span>spmF</= span></span>_0001.<span><span>img</span></span>')</div><div class=3D"im= "> <div>Y =3D <span><span>spm</span></span>_read_vols(V);</div> <div><br></div> <div>threshold =3D 0; %any value you choose.</div> <div><br></div> <div>Y(find(Y<threshold)) =3D 0;</div> <div>Y(find(Y)) =3D 1;</div> <div><br></div> <div>% Use mask</div> <div><br></div> </div><div><span><span>VV</span></span> =3D <span><span>spm</span></span>_v= ol('L:\2<span><span>stlevel</span></span>\<span><span>radpos</span></sp= an>_<span><span>sdXloca</span></span>_<span><span>sd</span></span>\<span><s= pan>spmF</span></span>_0001.<span><span>img</span></span>');</div> <div class=3D"im"> <div><span><span>YY</span></span> =3D <span><span>spm</span></span>_vol(<sp= an><span>VV</span></span>);</div> <div><br></div> <div><span><span>YY</span></span>(find(Y)) =3D 0; %apply mask</div> <div><br></div> <div><span><span>VV</span></span>.<span><span>fname</span></span> =3D '= masked_image.<span><span>img</span></span>';</div> <div><span><span>spm</span></span>_write_vol(<span><span>VV</span></span>,<= span><span>YY</span></span>);</div> <div><br></div> <div>%% There ya go</div> <div><br></div> </div><div>V =3D=C2=A0</div> <div><br></div> <div>=C2=A0 =C2=A0 =C2=A0 <span><span>fname</span></span>: 'L:\2<span><= span>stlevel</span></span>\<span><span>wordVSblk</span></span>\<span><span>= spmF</span></span>_0001.<span><span>img</span></span>'</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 mat: [4x4 double]</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 dim: [79 95 68]</div> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0<span><span>dt</span></span>: [16 0]= </div> <div>=C2=A0 =C2=A0 =C2=A0 <span><span>pinfo</span></span>: [3x1 double]</di= v> <div>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 n: [1 1]</div> <div>=C2=A0 =C2=A0 <span><span>descrip</span></span>: 'SPM{F_[1.0,15.0]= } - contrast 1: w-b'</div> <div>=C2=A0 =C2=A0 private: [1x1 <span><span>nifti</span></span>]</div> <div><br></div> <div>??? Assignment between unlike types is not allowed.</div> <div><br></div> <div>**********************************************************************= ***</div> <div><br></div> <div>Besides, is the threshold a cutoff for the activation maps of the mask= ? Specially, does setting threshold =3D 0.05 mean that the activated <span>= <span>voxels</span></span> below p =3D0.05 are included. If not, how should= I set a cutoff on the p value, or it does matter?=C2=A0</div> <div><br></div> <div>Moreover, how can I determine the mask is inclusive (i.e., the <span><= span>voxles</span></span> in the to-be-masked activation maps are within th= e <span><span>volxels</span></span> in the activation maps of the mask) or = exclusive=C2=A0(i.e., the <span><span>voxles</span></span> in the to-be-mas= ked activation maps are NOT within the <span><span>volxels</span></span> in= <span>the activation</span> maps of the mask).=C2=A0</div> <div>Thank you very much!</div> <div><br></div> <div>Sincerely,</div> <div><span><span>Yihui</span></span></div></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;">= =C2=A0</div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r></div> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"><b= r> <blockquote style=3D"padding-left: 5px; margin-left: 5px; border-left: 2px = solid rgb(16, 16, 255);"> <div style=3D"font-size: 12pt; font-family: arial,helvetica,sans-serif;"> <div style=3D"font-size: 12pt;"><font face=3D"Arial" size=3D"2"> <hr size=3D"1"> <b><span style=3D"font-weight: bold;">=E5=AF=84=E4=BB=B6=E8=80=85=EF=BC=9A<= /span></b> Michael T Rubens <<a href=3D"mailto:[log in to unmask]" rel= =3D"nofollow" target=3D"_blank">[log in to unmask]</a>><br><b><span st= yle=3D"font-weight: bold;">=E6=94=B6=E4=BB=B6=E8=80=85=EF=BC=9A</span></b> = SUBSCRIBE <span><span>SPM</span></span> <span><span>Yihui</span></span> Hun= g <<a href=3D"mailto:[log in to unmask]" rel=3D"nofollow" target=3D= "_blank">[log in to unmask]</a>><br> <b><span style=3D"font-weight: bold;">=E5=89=AF=E6=9C=AC=EF=BC=9A</span></b= > <a href=3D"mailto:[log in to unmask]" rel=3D"nofollow" target=3D"_blank">= [log in to unmask]</a><br><b><span style=3D"font-weight: bold;">=E5=AF=84= =E4=BB=B6=E6=97=A5=E6=9C=9F=EF=BC=9A</span></b> 2011/5/23 (=E4=B8=80) 10:19= AM<br> <b><span style=3D"font-weight: bold;">=E4=B8=BB=E6=97=A8=EF=BC=9A</span></b= > Re: [<span><span>SPM</span></span>] three questions about <span><span>SPM= </span></span>8<br></font><br> <div>when you say you tried to use the contrast as a mask, did you just use= the raw contrast or did you <span><span>binarize</span></span> it? If you = did the former than it is clear why you had no <span><span>inmask</span></s= pan> <span><span>voxels</span></span> since any non-zero <span><span>voxels= </span></span> would all be seen as part of the mask. So, what you want to = do is load the mask, apply a threshold and <span><span>binarize</span></spa= n> it by setting anything below the threshold to 0, and anything above to 1= (though the latter may not be completely <span><span>necesary</span></span= >). Then, you could just use that mask to mask out the resulting contrast m= ap from the other analysis. Here is code to do it:<div> <div></div><div class=3D"h5"><br><br>%Make mask<br><br>V =3D <span><span>sp= m</span></span>_vol('contrast_image_2turn_into_mask')<br>Y =3D <spa= n><span>spm</span></span>_read_vols(V);<br><br>threshold =3D X; %any value = you choose.<br> <br>Y(find(Y<threshold)) =3D 0;<br>Y(find(Y)) =3D 1;<br><br>% Use mask<b= r><br><span><span>VV</span></span> =3D <span><span>spm</span></span>_vol(&#= 39;contrast_image_2mask');<br><span><span>YY</span></span> =3D <span><s= pan>spm</span></span>_vol(<span><span>VV</span></span>);<br> <br><span><span>YY</span></span>(find(Y)) =3D 0; %apply mask<br><br><span><= span>VV</span></span>.<span><span>fname</span></span> =3D 'masked_image= .<span><span>img</span></span>';<br><span><span>spm</span></span>_write= _vol(<span><span>VV</span></span>,<span><span>YY</span></span>);<br> <br>%% There ya go<br><br>Cheers,<br>Michael<br><br> <div>On Sun, May 22, 2011 at 9:58 AM, SUBSCRIBE <span><span>SPM</span></spa= n> <span><span>Yihui</span></span> Hung <span dir=3D"ltr"><<a href=3D"ma= ilto:[log in to unmask]" rel=3D"nofollow" target=3D"_blank">a489930026= @yahoo.com.tw</a>></span> wrote:<br> <blockquote style=3D"padding-left: 1ex; margin: 0pt 0pt 0pt 0.8ex; border-l= eft: 1px solid rgb(204, 204, 204);">Hello,<br><br>I used <span><span>SPM</s= pan></span>8 for my experiment. I manipulated three factors (e.g., factor A= , B, C) in my experiment. The activation maps under factor A was used as a = mask to constrain the effects of factor B and factor C. The problem is that= the factor A and factor B and factor C cannot be put in the same model at = second level analysis. I tried to put the .<span><span>img</span></span> fi= le resulting from factor A across participants in the =E2=80=9Cexplicit mas= k=E2=80=9D in the second level analysis. However, the estimation stopped by= showing no <span><span>inmask</span></span> <span><span>voxels</span></spa= n>. I searched through forum and did not find any useful solutions. Can som= eone help me out? Moreover, is the threshold masking related to the p value= ? If not, how should I set criteria for the activation map of the mask?<br> <br>I did notice that one person in the forum suggested that one can create= an activation maps in which the factor B is <span><span>modeled</span></sp= an> with the mask of factor A in first level analysis. Then, submit the res= ulting .<span><span>img</span></span> file into second level analysis. My q= uestion is how to create and save the <span><span>img</span></span> file.<b= r> <br>Finally, I posted a question regarding how to export beta values over m= ultiple coordinates a week ago. However, no one provide any answers. I wond= er whether it is due that no one has the answer or due to that there alread= y is an answer in the forum.<br> <br>Thank you for your patience and advice.<br>Sincerely,<br><font color=3D= "#888888"><br><span><span>Yihui</span></span><br><br></font></blockquote></= div><br><br clear=3D"all"><br>-- <br>Research Associate<br><span><span>Gazz= aley</span></span> Lab<br> Department of Neurology<br>University of California, San Francisco<br></div= ></div></div><br><br></div></div></blockquote></div></div></div></blockquot= e></div><div><div></div><div class=3D"h5"><br><br clear=3D"all"><br>-- <br> Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University= of California, San Francisco<br></div></div></div><br><br></div></div></bl= ockquote></div></div></div></blockquote></div><div><div></div><div class=3D= "h5"> <br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Depa= rtment of Neurology<br>University of California, San Francisco<br></div></d= iv></blockquote><br><br><span title=3D"neteasefooter"><span></span></span><= /blockquote> </div><br><br clear=3D"all"><br>-- <br>Research Associate<br>Gazzaley Lab<b= r>Department of Neurology<br>University of California, San Francisco<br> --20cf305e27dfb7c9c804a409c38b-- ========================================================================= Date: Tue, 24 May 2011 21:22:11 +0100 Reply-To: Deryk <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Deryk <[log in to unmask]> Subject: Cluster level statistics for VBM Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Is it appropriate to interpret the cluster level statistics provided in t= he results table for a VBM analysis? ========================================================================= Date: Wed, 25 May 2011 05:49:18 +0100 Reply-To: David Yang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Yang <[log in to unmask]> Subject: SPECT transporter image analysis realted to DARTEL and BPM Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear all Our subjects had SPECT transporter images(TRODAT for dopamine transporter= s) and their T1 MRI images (for VBM analysis). We planed to perform corre= lation analysis between such 2 kind of image modalities by WFU Biological= Parametric Mapping Toolbox. Since our MRI images were analysed by DARTEL related process, our SPECT i= mages should also be analysed by DARTEL related ones. According to previous discussion (https://www.jiscmail.ac.uk/cgi-bin/weba= dmin?A2=3DSPM;3779f1d9.0909), we performed following analyses: Step 1: Within subject coregistration: Use SPM function, Coreg:estimate (= without reslicing) and we suspected the registration information would be= stored in the header of source images (no other output would be found)=20= Step 2: Routine DARTEL analysis for MRI data Step 3: as performing the function, "DARTEL toolbox: normalise to MNI spa= ce option" and use source images (SPECT) from Step 1. For receptor/transporter binding potential images, I suspected we should = choose Preserve Amount in Step 3 according to previous discussion: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;f3f7b65.0901 I wondered if there was any mistake in our above analysis methods? Furthermore, considering the image features of PET or even SPECT images, = it might have to smooth such images with large amount (ex 12 mm compared = to default 8 mm in DARTEL). I wonder if such was suitable if we wanted to= perform further analysis by BPM that used both VBM and PET/SPECT images = concurrently as in this subject: http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;dd737de1.1012 Thanks for any help! Sincerely yours, David ========================================================================= Date: Wed, 25 May 2011 10:10:48 +0200 Reply-To: Stefanie Brassen <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Stefanie Brassen <[log in to unmask]> Subject: PhD positions in imaging neuroscience in Hamburg / Germany MIME-Version: 1.0 Content-Type: text/plain; charset="utf-8"; format=flowed Content-Transfer-Encoding: base64 Message-ID: <[log in to unmask]> VHdvIFBoRCBwb3NpdGlvbnMgKDMgeWVhcnMsIDY1JSkgYXJlIGF2YWlsYWJsZSBhdCB0aGUgTmV1 cm9pbWFnaW5nIApDZW50ZXIgKE5ldXJvaW1hZ2VOb3JkKSBvZiB0aGUgVW5pdmVyc2l0eSBNZWRp Y2FsIENlbnRlciAKSGFtYnVyZy1FcHBlbmRvcmYsIERlcGFydG1lbnQgb2YgU3lzdGVtcyBOZXVy b3NjaWVuY2UgKEhlYWQ6IFByb2YuIERyLiAKQ2hyaXN0aWFuIEJ1ZWNoZWwpIGluIHRoZSBhcmVh IG9mIGRlY2lzaW9uLW1ha2luZyBhbmQgcmV3YXJkIHByb2Nlc3NpbmcuIApUaGUgREZHLWZ1bmRl ZCBwcm9qZWN0IHdpbGwgY29tYmluZSBmdW5jdGlvbmFsIG1hZ25ldGljIHJlc29uYW5jZSAKaW1h Z2luZywgY29tcHV0YXRpb25hbCBtb2RlbGxpbmcgYW5kIGJlaGF2aW91cmFsIGludGVydmVudGlv bnMgdG8gCmV4YW1pbmUgZGVjaXNpb24tbWFraW5nIGluIGRpc29yZGVycyBvZiBpbXB1bHNlIGNv bnRyb2wuIFRoZSBkZXBhcnRtZW50IAppcyBzaXR1YXRlZCBpbiB0aGUgYmVhdXRpZnVsIGNpdHkg b2YgSGFtYnVyZyAvIEdlcm1hbnkgYXQgdGhlIAogIFVuaXZlcnNpdHkgTWVkaWNhbCBDZW50ZXIg YW5kIHByb3ZpZGVzIGFuIGV4Y2VsbGVudCBtdWx0aS1kaXNjaXBsaW5hcnkgCmFuZCBpbnRlcmFj dGl2ZSBuZXVyb2ltYWdpbmcgZW52aXJvbm1lbnQgd2l0aCBpdHMgb3duIHBoeXNpY3MsIApwc3lj aG9sb2d5IGFuZCBjbGluaWNhbCBuZXVyb3NjaWVuY2UgZ3JvdXBzIGFzIHdlbGwgYXMgYSAKcmVz ZWFyY2gtZGVkaWNhdGVkIDNUIE1SSSBzY2FubmVyLCBFRUcvTUVHIGZhY2lsaXRpZXMgYW5kIGJl aGF2aW91cmFsIApsYWJzLiBXZSBhcmUgbG9va2luZyBmb3IgZW50aHVzaWFzdGljIGNhbmRpZGF0 ZXMgd2l0aCBhIHN0cm9uZyBpbnRlcmVzdCAKaW4gIGh1bWFuIGNvZ25pdGl2ZSBuZXVyb3NjaWVu Y2UgYW5kIGEgTWFzdGVyIC8gRGlwbG9tYSBpbiBDb2duaXRpdmUgClNjaWVuY2UsIFBzeWNob2xv Z3ksIE5ldXJvc2NpZW5jZSwgQ29tcHV0ZXIgU2NpZW5jZSBvciByZWxhdGVkIGZpZWxkcy4KU3Ry b25nIGNvbXB1dGF0aW9uYWwgc2tpbGxzIGFyZSBhbiBhZHZhbnRhZ2UuCgpGb3IgZnVydGhlciBp bmZvcm1hdGlvbiBwbGVhc2UgY29udGFjdApEci4gSmFuIFBldGVycwpqLnBldGVyc0B1a2UudW5p LWhhbWJ1cmcuZGUKCgoKLS0gCkRyLiBTdGVmYW5pZSBCcmFzc2VuCkRlcGFydG1lbnQgb2YgU3lz dGVtcyBOZXVyb3NjaWVuY2UsIEJsZGcgVzM0ClVuaXZlcnNpdHkgTWVkaWNhbCBDZW50ZXIgSGFt YnVyZy1FcHBlbmRvcmYKTWFydGluaXN0cmFzc2UgNTIKMjAyNDYgSGFtYnVyZywgR2VybWFueQpN YWlsOiBzYnJhc3NlbkB1a2UuZGUKUGhvbmU6ICsrNDktNDAtNzQxMC01NDg2NQpGYXg6ICsrNDkt NDAtNzQxMC01OTk1NQoKCgotLSAKUGZsaWNodGFuZ2FiZW4gZ2Vtw6TDnyBHZXNldHogw7xiZXIg ZWxla3Ryb25pc2NoZSBIYW5kZWxzcmVnaXN0ZXIgdW5kIEdlbm9zc2Vuc2NoYWZ0c3JlZ2lzdGVy IHNvd2llIGRhcyBVbnRlcm5laG1lbnNyZWdpc3RlciAoRUhVRyk6CgpVbml2ZXJzaXTDpHRza2xp bmlrdW0gSGFtYnVyZy1FcHBlbmRvcmYKS8O2cnBlcnNjaGFmdCBkZXMgw7ZmZmVudGxpY2hlbiBS ZWNodHMKR2VyaWNodHNzdGFuZDogSGFtYnVyZwoKVm9yc3RhbmRzbWl0Z2xpZWRlcjoKUHJvZi4g RHIuIErDtnJnIEYuIERlYmF0aW4gKFZvcnNpdHplbmRlcikKRHIuIEFsZXhhbmRlciBLaXJzdGVp bgpKb2FjaGltIFByw7Zsw58KUHJvZi4gRHIuIERyLiBVd2UgS29jaC1Hcm9tdXMK ========================================================================= Date: Wed, 25 May 2011 10:12:13 +0100 Reply-To: David Yang <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Yang <[log in to unmask]> Subject: How to calculate parametric image of ligand-receptor binding in SPECT images by SPM? Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear SPMers: We tried to calculate parametric image of dopamine transporter binding by= SPECT and TRODAT under SPM8. Every case had both SPECT and MRI images. We had coregistered SPECT image to his own MRI images and then normalized= SPECT images to MNI space by DARTEL toolbox: normalise to MNI space and = related process. We also made mask for cerebellum that served as reference region by MasBa= r. We wondered how to further calculate parametric BPND image of each SEPCT?= Some reference, as "The application of statistical parametric mapping to = 123I-FP-CIT SPECT_NeuroImage 23 (2004) 956-966" suggested using "Proporti= onal scaling which scales each image appropriately to a reference region"= .=20 Another reference, as "A novel computer-assisted image analysis of [123I]= =CE=B2-CIT SPECT images improves the diagnostic accuracy of parkinsonian = disorders_Eur J Nucl Med Mol Imaging (2011) 38:702=E2=80=93710" suggested= using "mask" method to calculate such. However, we had several questions regarding these 2 methods: 1. It seems that "Proportional scaling" function was found in the "global= normalisation" item when building up the statistical model and "explicit= ly mask" was option of "Masking" also when building up the statistical mo= del. How can I do both before building up the statistical model? 2. Is it possible to calculate parametric image by "imcal" function of SP= M? Thanks for any help~ Sincerely yours, David ========================================================================= Date: Wed, 25 May 2011 11:50:59 +0100 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: dipole waveforms | units Comments: To: Lars Meyer <[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 Lars, It seems like the answer to your question is not that simple even for your case where it's quite clear what's going on in terms of scaling. I passed it to Fieldtrip developers who will look at how the units are handled by their forward computation. When I hear back from them I'll update you and the list.You can follow their progress via http://bugzilla.fcdonders.nl/show_bug.cgi?id=3D686 Best, Vladimir On Mon, May 16, 2011 at 10:48 AM, Lars Meyer <[log in to unmask]> wrote: > dear all, > > i am using spm_eeg_dipole_waveforms.m to retrieve dipole timecourses for = two dipole priors in a vb-ecd- localisation. everything works fine, so no w= orries there, but i was wondering on what the units of the dipole amplitude= s are=E2=80=A6 i remember some older post on the exact same question, have = talked to the asker, but it seems there is no answer yet. > > or is there a working solution from the programmers of the above function= ? > > > thanks in advance, and all best, > l. > > > > > Lars Meyer > Max Planck Institute for Human Cognitive & Brain Sciences > Department of Neuropsychology > Stephanstra=EF=AC=82e 1a > 04103 Leipzig | Germany > > Office | +49 341 99 40 22 66 > Mail | [log in to unmask] > Web | www.cbs.mpg.de/~lmeyer > ========================================================================= Date: Wed, 25 May 2011 13:00:26 +0100 Reply-To: Gareth Barnes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Gareth Barnes <[log in to unmask]> Subject: Re: dipole waveforms | units 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: <03ad01cc1ad3$55a30370$00e90a50$@[log in to unmask]> Hi Lars For the record, and for MEG only : It looks like (if the units of sensors are Tesla and measurement in mm) = then the moments will be in nAm. (there is an extra scaling factor of = 1000 in the spm code). I am also looking into the EEG case now. Best Gareth -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] = On Behalf Of Vladimir Litvak Sent: 25 May 2011 11:51 To: [log in to unmask] Subject: Re: [SPM] dipole waveforms | units Dear Lars, It seems like the answer to your question is not that simple even for your case where it's quite clear what's going on in terms of scaling. I passed it to Fieldtrip developers who will look at how the units are handled by their forward computation. When I hear back from them I'll update you and the list.You can follow their progress via http://bugzilla.fcdonders.nl/show_bug.cgi?id=3D686 Best, Vladimir On Mon, May 16, 2011 at 10:48 AM, Lars Meyer <[log in to unmask]> wrote: > dear all, > > i am using spm_eeg_dipole_waveforms.m to retrieve dipole timecourses = for two dipole priors in a vb-ecd- localisation. everything works fine, = so no worries there, but i was wondering on what the units of the dipole = amplitudes are=E2=80=A6 i remember some older post on the exact same = question, have talked to the asker, but it seems there is no answer yet. > > or is there a working solution from the programmers of the above = function? > > > thanks in advance, and all best, > l. > > > > > Lars Meyer > Max Planck Institute for Human Cognitive & Brain Sciences > Department of Neuropsychology > Stephanstra=EF=AC=82e 1a > 04103 Leipzig | Germany > > Office | +49 341 99 40 22 66 > Mail | [log in to unmask] > Web | www.cbs.mpg.de/~lmeyer > ========================================================================= Date: Wed, 25 May 2011 13:43:36 +0100 Reply-To: Alec Sproten <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alec Sproten <[log in to unmask]> Subject: using DARTEL appropriately Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="part-B1zxr2PHN81YdAxJOuocT0l3vnl0Yo3UhqgEtDQ01tKlQ" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --part-B1zxr2PHN81YdAxJOuocT0l3vnl0Yo3UhqgEtDQ01tKlQ Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Dear SPM experts, I have started to use the DARTEL tool for the first time and I have some = questions that I could not find any answer for them in the SPM manual. Background:=20 I am investigating effect of 3 experimental manipulations on 2 groups of = subjects (N1=3D27,N2=3D23).=20=20 Each subject undergoes 1 scanning session that is composed of: 1 EPI scan= [event related design] and 1 anatomical scan. We are interested only in functional analysis (not a VBM study). The preprocessing sequence we have used until the DARTEL step: Realignment (Estimate & Reslice; resliced images: all images+mean) =EF=83= =A0 Coregristration (Estimate) =EF=83=A0 New Segment (save bias corrected= was selected; native tissue: Native + DARTEL imported were selected) =EF= =83=A0 DARTEL. Processing the anatomical images with DARTEL:=20 Following the manual, due to the usage of New Segment step, first I proce= ssed the anatomical images. I created a template [using Run DARTEL(create= Template) option]. I selected all the rc1*.nii and the rc2*.nii files i= n the same order =E2=80=93 from all the subjects. Then I used the DARTEL= (normalize to MNI space) process, selected the flow fields and only the g= ray matter c1*.nii images of all the subjects =E2=80=93 again with mainta= ining the same order as before. I chose =E2=80=98Few subjects=E2=80=99 an= d preserve concentrations options. Other factors weren=E2=80=99t changed.= My questions: 1.=09Regarding normalizing the functional images using DARTEL.=20 a.=09Should I need to do the previous step (creating a template) also for= the functional images? And if so, which images are to be selected: the p= ost realignment images from each subject =E2=80=93 in one single selectio= n of all the images from all the subjects [as done with the anatomical on= e]? Or should I separate the process and create a new module for each sub= ject? b.=09In the DARTEL: Normalize to MNI Space,=20 i.=09Which DARTEL Template should I use? I assume if I don=E2=80=99t need= to do the create-template step for the functional images then I should u= se the Template_6.nii that was generated from the DARTEL template of the = anatomical images, is it correct?=20 ii.=09In the manual (page 445. Section 41.2) it is written that the flow = fields should be selected for every subject. It is written that =E2=80=9C= u_c1*.nii=E2=80=9D files should be selected. I wondered is that correct? = Because I thought that after the new segmentation process we should use t= he =E2=80=9Cu_rc1*.nii=E2=80=9D files [Dartel imported and not the native= ones]. Is it typo or did I miss something? c.=09Just to clarify, in this step should I do this step for each subject= independently i.e. selecting each time the flow field and the functional= images for each subject or should I load all the flow fields and the fun= ctional images (ordered) in the same batch? 2.=09If I understood correctly the DARTEL process includes the smoothing = step so the next thing to do is to advance to the other steps (1st analys= is, etc.)? 3.=09In order to match each subject anatomical image with his functional = activity - Would the images after the DARTEL will be coregitered to the a= natomical images or an additional coregisteration process must be employe= d before the 1st analysis? *Notes =E2=80=A2=09I am using SPM8, Matlab 7.10.0 (R2010a). =E2=80=A2=09For now all the processes are done by using the manual batch = system of SPM [without programming scripts]. Thank you very much in advance for your help, Looking forward to learn from you about how to use the DARTEL tool correc= tly, ALEC=20 --part-B1zxr2PHN81YdAxJOuocT0l3vnl0Yo3UhqgEtDQ01tKlQ Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="UTF-8" <html dir=3D"ltr"> <head> <title></title> </head> <body> <p><!--[if gte mso 9]><xml> <o:OfficeDocumentSettings> <o:AllowPNG /> </o:OfficeDocumentSettings> </xml><![endif]--><!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves /> <w:TrackFormatting /> <w:HyphenationZone>21</w:HyphenationZone> <w:PunctuationKerning /> <w:ValidateAgainstSchemas /> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF /> <w:LidThemeOther>DE</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables /> <w:SnapToGridInCell /> <w:WrapTextWithPunct /> <w:UseAsianBreakRules /> <w:DontGrowAutofit /> <w:SplitPgBreakAndParaMark /> <w:EnableOpenTypeKerning /> <w:DontFlipMirrorIndents /> <w:OverrideTableStyleHps /> </w:Compatibility> <m:mathPr> <m:mathFont m:val=3D"Cambria Math" /> <m:brkBin m:val=3D"before" /> <m:brkBinSub m:val=3D"--" /> <m:smallFrac m:val=3D"off" /> <m:dispDef /> <m:lMargin m:val=3D"0" /> <m:rMargin m:val=3D"0" /> <m:defJc m:val=3D"centerGroup" /> <m:wrapIndent m:val=3D"1440" /> <m:intLim m:val=3D"subSup" /> <m:naryLim m:val=3D"undOvr" /> </m:mathPr></w:WordDocument> </xml><![endif]--><!--[if gte mso 9]><xml> <w:LatentStyles DefLockedState=3D"false" DefUnhideWhenUsed=3D"tru= e" DefSemiHidden=3D"true" DefQFormat=3D"false" DefPriority=3D"99" LatentStyleCount=3D"267"> <w:LsdException Locked=3D"false" Priority=3D"0" SemiHidden=3D"fal= se" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Normal" /> <w:LsdException Locked=3D"false" Priority=3D"9" SemiHidden=3D"fal= se" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"heading 1" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 2" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 3" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 4" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 5" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 6" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 7" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 8" /> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" = Name=3D"heading 9" /> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 1" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 2" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 3" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 4" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 5" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 6" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 7" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 8" /= > <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 9" /= > <w:LsdException Locked=3D"false" Priority=3D"35" QFormat=3D"true"= Name=3D"caption" /> <w:LsdException Locked=3D"false" Priority=3D"10" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Title" /> <w:LsdException Locked=3D"false" Priority=3D"1" Name=3D"Default P= aragraph Font" /> <w:LsdException Locked=3D"false" Priority=3D"11" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtitle" /> <w:LsdException Locked=3D"false" Priority=3D"22" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Strong" /> <w:LsdException Locked=3D"false" Priority=3D"20" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Emphasis" /> <w:LsdException Locked=3D"false" Priority=3D"59" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Table Grid" /> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D= "Placeholder Text" /> <w:LsdException Locked=3D"false" Priority=3D"1" SemiHidden=3D"fal= se" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"No Spacing" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 1" /> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D= "Revision" /> <w:LsdException Locked=3D"false" Priority=3D"34" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"List Paragraph"= /> <w:LsdException Locked=3D"false" Priority=3D"29" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Quote" /> <w:LsdException Locked=3D"false" Priority=3D"30" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Quote" = /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 1" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 2" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 3" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 4" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 5" /> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 6" /> <w:LsdException Locked=3D"false" Priority=3D"19" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Emphasis= " /> <w:LsdException Locked=3D"false" Priority=3D"21" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Emphasi= s" /> <w:LsdException Locked=3D"false" Priority=3D"31" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Referenc= e" /> <w:LsdException Locked=3D"false" Priority=3D"32" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Referen= ce" /> <w:LsdException Locked=3D"false" Priority=3D"33" SemiHidden=3D"fa= lse" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Book Title" /> <w:LsdException Locked=3D"false" Priority=3D"37" Name=3D"Bibliogr= aphy" /> <w:LsdException Locked=3D"false" Priority=3D"39" QFormat=3D"true"= Name=3D"TOC Heading" /> </w:LatentStyles> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} </style> <![endif]--></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Dear SPM experts,</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">I have started to use the DARTEL tool for the first time and= I have some questions that I could not find any answer for them in the S= PM manual.</span></p> <p class=3D"MsoNormal"><u><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">Background: </span></u></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">I am investigating effect of 3 experimental manipulations on= 2 groups of subjects (N1=3D27,N2=3D23). <span style=3D"mso-spacerun:yes"= > </span></span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Each subject undergoes 1 scanning session that is composed o= f: 1 EPI scan [event related design] and 1 anatomical scan.</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">We are interested only in functional analysis (not a VBM stu= dy).</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">The preprocessing sequence we have used until the DARTEL ste= p:</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Realignment (Estimate & Reslice; resliced images: all im= ages+mean) </span><span lang=3D"EN-US" style=3D"font-family:Wingdings;mso= -ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin;mso-hansi-font-family:Calibri;ms= o-hansi-theme-font: minor-latin;mso-ansi-language:EN-US;mso-char-type:symbol;mso-symb= ol-font-family: Wingdings"><span style=3D"mso-char-type:symbol;mso-symbol-font-fa= mily:Wingdings">=C3=A0</span></span><span lang=3D"EN-US" style=3D"mso-ans= i-language:EN-US"> Coregristration (Estimate) </span><span lang=3D"EN-US"= style=3D"font-family:Wingdings;mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin;mso-hansi-font-family:Calibri;ms= o-hansi-theme-font: minor-latin;mso-ansi-language:EN-US;mso-char-type:symbol;mso-symb= ol-font-family: Wingdings"><span style=3D"mso-char-type:symbol;mso-symbol-font-fa= mily:Wingdings">=C3=A0</span></span><span lang=3D"EN-US" style=3D"mso-ans= i-language:EN-US"> New Segment (save bias corrected was selected; native = tissue: Native + DARTEL imported were selected) <span style=3D"mso-spacer= un:yes"> </span></span><span lang=3D"EN-US" style=3D"font-family: Wingdings;mso-ascii-font-family:Calibri;mso-ascii-theme-font:mino= r-latin; mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;ms= o-ansi-language: EN-US;mso-char-type:symbol;mso-symbol-font-family:Wingdings"><spa= n style=3D"mso-char-type:symbol;mso-symbol-font-family:Wingdings">=C3=A0<= /span></span><span lang=3D"EN-US" style=3D"mso-ansi-language:EN-US"> DART= EL.</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Processing the anatomical images with DARTEL: </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Following the manual, due to the usage of New Segment step, = first I processed the anatomical images. I created a template [using Run = DARTEL(create Template) option]. <span style=3D"mso-spacerun:yes"> <= /span>I selected all the rc1*.nii and the rc2*.nii files in the same orde= r =E2=80=93 from all the subjects. <span style=3D"mso-spacerun:yes"> = ;</span>Then I used the DARTEL(normalize to MNI space) process, selected = the flow fields and only the gray matter c1*.nii images of all the subjec= ts =E2=80=93 again with maintaining the same order as before. I chose =E2= =80=98Few subjects=E2=80=99 and preserve concentrations options. Other fa= ctors weren=E2=80=99t changed.</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">My questions:</span></p> <p class=3D"MsoListParagraphCxSpFirst" style=3D"text-indent:-18.0= pt;mso-list:l1 level1 lfo1"><span lang=3D"EN-US" style=3D"mso-bidi-font-f= amily:Calibri;mso-bidi-theme-font:minor-latin; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">1.<span = style=3D"font:7.0pt "Times New Roman"">    =    </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">Regarding normalizing the functional images using DARTEL.= </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-left:72.0= pt;mso-add-space: auto;text-indent:-18.0pt;mso-list:l1 level2 lfo1"><span lang=3D"E= N-US" style=3D"mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-lat= in; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">a.<span = style=3D"font:7.0pt "Times New Roman"">    =    </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">Should I need to do the previous step (creating a templat= e) also for the functional images? And if so, which images are to be sele= cted: the post realignment images from each subject =E2=80=93 in one sing= le selection of all the images from all the subjects [as done with the an= atomical one]? Or should I separate the process and create a new module f= or each subject?</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-left:72.0= pt;mso-add-space: auto;text-indent:-18.0pt;mso-list:l1 level2 lfo1"><span lang=3D"E= N-US" style=3D"mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-lat= in; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">b.<span = style=3D"font:7.0pt "Times New Roman"">    =   </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-langua= ge:EN-US">In the DARTEL: Normalize to MNI Space, </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-left:108.= 0pt;mso-add-space: auto;text-indent:-108.0pt;mso-text-indent-alt:-9.0pt;mso-list:l1 = level3 lfo1"><span lang=3D"EN-US" style=3D"mso-bidi-font-family:Calibri;m= so-bidi-theme-font:minor-latin; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore"><span st= yle=3D"font:7.0pt "Times New Roman"">    &#= 160;           = 60;           = 0;            = ;            =           </span>i.<span sty= le=3D"font:7.0pt "Times New Roman"">    = 60; </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-language:= EN-US">Which DARTEL Template should I use? I assume if I don=E2=80=99t ne= ed to do the create-template step for the functional images then I should= use the Template_6.nii that was generated from the DARTEL template of th= e anatomical images, is it correct? </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-left:108.= 0pt;mso-add-space: auto;text-indent:-108.0pt;mso-text-indent-alt:-9.0pt;mso-list:l1 = level3 lfo1"><span lang=3D"EN-US" style=3D"mso-bidi-font-family:Calibri;m= so-bidi-theme-font:minor-latin; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore"><span st= yle=3D"font:7.0pt "Times New Roman"">    &#= 160;           = 60;           = 0;            = ;            =         </span>ii.<span style=3D"font:= 7.0pt "Times New Roman"">      </span>= </span></span><span lang=3D"EN-US" style=3D"mso-ansi-language:EN-US">In t= he manual (page 445. Section 41.2) it is written that the flow fields sho= uld be selected for every subject. It is written that =E2=80=9Cu_c1*.nii=E2= =80=9D files should be selected. I wondered is that correct? Because I th= ought that after the new segmentation process we should use the =E2=80=9C= u_rc1*.nii=E2=80=9D files [Dartel imported and not the native ones]. Is i= t typo or did I miss something?</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-left:72.0= pt;mso-add-space: auto;text-indent:-18.0pt;mso-list:l1 level2 lfo1"><span lang=3D"E= N-US" style=3D"mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-lat= in; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">c.<span = style=3D"font:7.0pt "Times New Roman"">    =    </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">Just to clarify, in this step should I do this step for e= ach subject independently i.e. selecting each time the flow field and the= functional images for each subject or should I load all the flow fields = and the functional images (ordered) in the same batch?</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"text-indent:-18.= 0pt;mso-list:l1 level1 lfo1"><span lang=3D"EN-US" style=3D"mso-bidi-font-= family:Calibri;mso-bidi-theme-font:minor-latin; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">2.<span = style=3D"font:7.0pt "Times New Roman"">    =    </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">If I understood correctly the DARTEL process includes the= smoothing step so the next thing to do is to advance to the other steps = (1<sup>st</sup> analysis, etc.)?</span></p> <p class=3D"MsoListParagraphCxSpLast" style=3D"text-indent:-18.0p= t;mso-list:l1 level1 lfo1"><span lang=3D"EN-US" style=3D"mso-bidi-font-fa= mily:Calibri;mso-bidi-theme-font:minor-latin; mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">3.<span = style=3D"font:7.0pt "Times New Roman"">    =    </span></span></span><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">In order to match each subject anatomical image with his = functional activity - Would the images after the DARTEL will be coregiter= ed to the anatomical images or an additional coregisteration process must= be employed before the 1<sup>st</sup> analysis?</span></p> <p class=3D"MsoNormal"><u><span lang=3D"EN-US" style=3D"mso-ansi-= language:EN-US">Notes</span></u></p> <p class=3D"MsoListParagraphCxSpFirst" style=3D"text-indent:-18.0= pt;mso-list:l0 level1 lfo2"><span lang=3D"EN-US" style=3D"font-family:Sym= bol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">=C2= =B7<span style=3D"font:7.0pt "Times New Roman"">  = 60;      </span></span></span><span lang=3D"EN-U= S" style=3D"mso-ansi-language:EN-US">I am using SPM8, Matlab<span style=3D= "mso-spacerun:yes">  </span>7.10.0 (R2010a).</span></p> <p class=3D"MsoListParagraphCxSpLast" style=3D"text-indent:-18.0p= t;mso-list:l0 level1 lfo2"><span lang=3D"EN-US" style=3D"font-family:Symb= ol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;mso-ansi-language:EN-US"><span style=3D"mso-list:Ignore">=C2= =B7<span style=3D"font:7.0pt "Times New Roman"">  = 60;      </span></span></span><span lang=3D"EN-U= S" style=3D"mso-ansi-language:EN-US">For now all the processes are done b= y using the manual batch system of SPM [without programming scripts].</sp= an></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Thank you very much in advance for your help,</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">Looking forward to learn from you about how to use the DARTE= L tool correctly,</span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US">ALEC </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US"> </span></p> <p class=3D"MsoNormal"><span lang=3D"EN-US" style=3D"mso-ansi-lan= guage:EN-US"> </span></p> </body> </html> --part-B1zxr2PHN81YdAxJOuocT0l3vnl0Yo3UhqgEtDQ01tKlQ-- ========================================================================= Date: Wed, 25 May 2011 14:24:11 +0100 Reply-To: Gareth Barnes <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Gareth Barnes <[log in to unmask]> Subject: Re: dipole waveforms | units 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: <03e001cc1adf$08c85e40$1a591ac0$@[log in to unmask]> Dear Lars, Please ignore my last post. I thought you were asking about the units of = vbecd results (not the output of dipole_waveforms). Apologies Gareth >Hi Lars >For the record, and for MEG only : >It looks like (if the units of sensors are Tesla and measurement in mm) = then the moments will be in nAm. (there is an extra scaling factor of = 1000 in >the spm code). >I am also looking into the EEG case now. >Best >Gareth -----Original Message----- From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] = On Behalf Of Vladimir Litvak Sent: 25 May 2011 11:51 To: [log in to unmask] Subject: Re: [SPM] dipole waveforms | units Dear Lars, It seems like the answer to your question is not that simple even for your case where it's quite clear what's going on in terms of scaling. I passed it to Fieldtrip developers who will look at how the units are handled by their forward computation. When I hear back from them I'll update you and the list.You can follow their progress via http://bugzilla.fcdonders.nl/show_bug.cgi?id=3D686 Best, Vladimir On Mon, May 16, 2011 at 10:48 AM, Lars Meyer <[log in to unmask]> wrote: > dear all, > > i am using spm_eeg_dipole_waveforms.m to retrieve dipole timecourses = for two dipole priors in a vb-ecd- localisation. everything works fine, = so no worries there, but i was wondering on what the units of the dipole = amplitudes are=E2=80=A6 i remember some older post on the exact same = question, have talked to the asker, but it seems there is no answer yet. > > or is there a working solution from the programmers of the above = function? > > > thanks in advance, and all best, > l. > > > > > Lars Meyer > Max Planck Institute for Human Cognitive & Brain Sciences > Department of Neuropsychology > Stephanstra=EF=AC=82e 1a > 04103 Leipzig | Germany > > Office | +49 341 99 40 22 66 > Mail | [log in to unmask] > Web | www.cbs.mpg.de/~lmeyer > ========================================================================= Date: Wed, 25 May 2011 14:58:13 +0100 Reply-To: Debby Klooster <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Debby Klooster <[log in to unmask]> Subject: FIR implementation Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi SPM users, I am trying to implement the finite impulse response (FIR) to estimate my= hemodynamic response function. However, I would like to see the FIR in t= he design matrix simply as white blocks but the blocks in the design matr= ix seemed to be 'blurred'. Can anybody help me with this problem? And doe= s anybody know optimal settings for the window length and the order that = need to be filled in? Thanks heaps! Debby ========================================================================= Date: Wed, 25 May 2011 15:06:04 +0100 Reply-To: Debby Klooster <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Debby Klooster <[log in to unmask]> Subject: Deactivations in SPM Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Hi SPM users, I am trying to model the default mode network (DMN) with SPM. In literatu= re, it is stated that this DMN contains deactivated regions which are usu= ally shown in blue. I can see in my analysis that the HRF has a negative = peak but the responses in the images are shown in red.=20 Does anybody know how to edit the color scale and how I can show deactiva= tions in blue? Thank you in advance! Debby ========================================================================= Date: Wed, 25 May 2011 17:21:34 +0200 Reply-To: "S.F.W. Neggers" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "S.F.W. Neggers" <[log in to unmask]> Subject: Re: question regarding matlabbatch struct/class 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 again, just for the sake of completeness (in case others run into this) a follow up: the (possibly undesirable) conversion of the matlabbatch object to a structure seems to happen as well when spm8 folder is in the matlab path. It first disappears when one has actually first started SPM8 at some point during a running matlab session, regardless of exiting the application again. Apparently some global variables initiated by SPM or so prevent this conversion from happening. So perhaps it is smart to first start SPM8 (and if needed quit it again) and then run scripts using batching code. Not sure whether it makes a difference, but the warning looks scary enough to me. Happy batchin' to all, Bas Op 12-05-11 15:42, Stephen J. Fromm schreef: > What happens if you put SPM in your matlab path? Usually for me that's enough to make the warning disappear. > > Op 11-05-11 18:48, S.F.W. Neggers schreef: > Dear list, > > I created a matlab batch job in the SPM8 batching system, and saved it > to disk as a mat file. Now I want to use it in my own pipeline (matlab > software) to do some repetative number crunching. I work with matlab > version 7.9.0529 (R2009b), and SPM8 rev 4290. > > When loading this matlabbatch structure from disk, I obtained the > following warning: > > --- snip --- > > load testbatch_spm8.mat > Warning: Class ':all:' is an unknown object class. Element(s) of > this class in array 'matlabbatch' have been converted to > structures. > --- > > When I now fill in some fields according to my needs, and save this > structure again to disk, will all be OK? > > Apparently matlabbatch originally is a class with methods in an OOP > sense, and I dont want to break things by irreversibly convert classes > to fields of a structure. I never had this warning in SPM5, and I am > in the process of moving my pipeline to spm8. > > Thanks for your pointers, > > Bas -- -------------------------------------------------- Dr. S.F.W. Neggers Division of Brain Research Rudolf Magnus Institute for Neuroscience Utrecht University Medical Center Visiting : Heidelberglaan 100, 3584 CX Utrecht Room B.01.1.03 Mail : Huispost B01.206, P.O. Box 3508 GA Utrecht, the Netherlands Tel : +31 (0)88 7559609 Fax : +31 (0)88 7555443 E-mail : [log in to unmask] Web : http://www.neuromri.nl/people/bas-neggers -------------------------------------------------- ------------------------------------------------------------------------------ De informatie opgenomen in dit bericht kan vertrouwelijk zijn en is uitsluitend bestemd voor de geadresseerde. Indien u dit bericht onterecht ontvangt, wordt u verzocht de inhoud niet te gebruiken en de afzender direct te informeren door het bericht te retourneren. Het Universitair Medisch Centrum Utrecht is een publiekrechtelijke rechtspersoon in de zin van de W.H.W. (Wet Hoger Onderwijs en Wetenschappelijk Onderzoek) en staat geregistreerd bij de Kamer van Koophandel voor Midden-Nederland onder nr. 30244197. Denk s.v.p aan het milieu voor u deze e-mail afdrukt. ------------------------------------------------------------------------------ This message may contain confidential information and is intended exclusively for the addressee. If you receive this message unintentionally, please do not use the contents but notify the sender immediately by return e-mail. University Medical Center Utrecht is a legal person by public law and is registered at the Chamber of Commerce for Midden-Nederland under no. 30244197. Please consider the environment before printing this e-mail. ========================================================================= Date: Wed, 25 May 2011 16:51:18 +0100 Reply-To: Cedric Clouchoux <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cedric Clouchoux <[log in to unmask]> Subject: Job Announcement: Postdoctoral Position at Children=?UTF-8?Q?=E2=80=99s_?=National Medical Center Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> We are seeking a highly motivated and talented postdoctoral fellow to wor= k in the Advanced Pediatric Brain Imaging Research Laboratory in Diagnost= ic Imaging & Radiology, and Fetal & Transitional Medicine, at Children=E2= =80=99s National Medical Center, George Washington University. The goal of our research program is to study brain development in healthy= and high-risk fetal and neonatal populations, and to develop reliable cl= inical imaging markers that will ultimately improve detection and monitor= ing of the fetus at risk for brain injury. The research project will focus on developing novel mathematical and comp= utational modeling techniques to improve image registration and image rec= onstruction of anatomical and echo planar/diffusion imaging data from in = vivo fetal and neonatal brain imaging studies. The project will include t= he design and development of improved approaches to eliminate motion, opt= imize MR image resolution, to enable automate brain tissue segmentation, = microstructural and functional brain assessment. The research fellow will= gain exposure to a wide range of clinical conditions that affect brain d= evelopment through our fetal and transitional medicine program. Candidates must have a PhD in biomedical engineering, mathematics or comp= uter science. A strong background in neuroimaging is required. The position is funded by the Advanced Pediatric Brain Imaging Laboratory= at Children=E2=80=99s National for one year, renewable for a second year= , and will begin over the summer of 2011. Candidates should submit a CV, = list 2 references, and provide a brief statement of research interests, b= y email to Dr. Limperopoulos. Applications or informal inquires should be= addressed to Catherine Limperopoulos, PhD ([log in to unmask] g). Contact: Catherine Limperopoulos, PhD Director, MRI Research of the Developing Brain Director, Advanced Pediatric Brain Imaging Research Laboratory Diagnostic= Imaging and Radiology/Fetal and Transitional Medicine Children=E2=80=99s National Medical Center=20 Associate Professor of Pediatrics George Washington University School of Medicine and Health Sciences 111 Michigan Ave. N.W. Washington, D.C. 20010 Email: [log in to unmask] ========================================================================= Date: Wed, 25 May 2011 17:34:07 +0100 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: using DARTEL appropriately Comments: To: Alec Sproten <[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]> > I have started to use the DARTEL tool for the first time and I have some > questions that I could not find any answer for them in the SPM manual. > > Background: > > I am investigating effect of 3 experimental manipulations on 2 groups of > subjects (N1=3D27,N2=3D23). > > Each subject undergoes 1 scanning session that is composed of: 1 EPI scan > [event related design] and 1 anatomical scan. > > We are interested only in functional analysis (not a VBM study). > > The preprocessing sequence we have used until the DARTEL step: > > Realignment (Estimate & Reslice; resliced images: all images+mean) =E0 > Coregristration (Estimate) =E0 New Segment (save bias corrected was selec= ted; > native tissue: Native + DARTEL imported were selected) =A0=E0 DARTEL. > > Processing the anatomical images with DARTEL: > > Following the manual, due to the usage of New Segment step, first I > processed the anatomical images. I created a template [using Run > DARTEL(create Template) option]. =A0I selected all the rc1*.nii and the > rc2*.nii files in the same order =96 from all the subjects. =A0Then I use= d the > DARTEL(normalize to MNI space) process, selected the flow fields and only > the gray matter c1*.nii images of all the subjects =96 again with maintai= ning > the same order as before. I chose =91Few subjects=92 and preserve concent= rations > options. Other factors weren=92t changed. > > My questions: > > 1.=A0=A0=A0=A0=A0=A0 Regarding normalizing the functional images using DA= RTEL. > > a.=A0=A0=A0=A0=A0=A0 Should I need to do the previous step (creating a te= mplate) also > for the functional images? And if so, which images are to be selected: th= e > post realignment images from each subject =96 in one single selection of = all > the images from all the subjects [as done with the anatomical one]? Or > should I separate the process and create a new module for each subject? How you do this will depend on what works best for your data. If your fMRI are completely free of distortions, you can simply coregister your fMRI to the anatomical scans and spatially normalise your fMRI (instead of the c1*) using Dartel. Note that if you coregister after estimating the segmentations and warps, then you MUST move the fMRI into alignment with the anatomical data. If you move the anatomical data into alignment with the fMRI, then you won't be able to apply the Dartel transforms any more. In reality, the fMRI will be spatially distorted, which means that the rigid-body alignment between fMRI and anatomical scans may be quite a lot less than perfect. If the dropout artifact is not too strong and the grey and white matter is clearly visible, you could try segmenting the data and aligning the rc1 and rc2 from the fMRI. This occasionally seems to do quite a reasonable job. > > b.=A0=A0=A0=A0=A0 In the DARTEL: Normalize to MNI Space, > > =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0= =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0= =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 i.=A0=A0=A0=A0=A0 Which > DARTEL Template should I use? I assume if I don=92t need to do the > create-template step for the functional images then I should use the > Template_6.nii that was generated from the DARTEL template of the anatomi= cal > images, is it correct? Use Template_6.nii, which is the most crisp of the series. > > =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0= =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0= =A0=A0=A0=A0=A0=A0=A0=A0=A0=A0=A0 ii.=A0=A0=A0=A0=A0 In the > manual (page 445. Section 41.2) it is written that the flow fields should= be > selected for every subject. It is written that =93u_c1*.nii=94 files shou= ld be > selected. I wondered is that correct? Because I thought that after the ne= w > segmentation process we should use the =93u_rc1*.nii=94 files [Dartel imp= orted > and not the native ones]. Is it typo or did I miss something? That must be a typo. You should not actually have any u_c1*.nii files. Only u_rc1*.nii. > > c.=A0=A0=A0=A0=A0=A0 Just to clarify, in this step should I do this step = for each > subject independently i.e. selecting each time the flow field and the > functional images for each subject or should I load all the flow fields a= nd > the functional images (ordered) in the same batch? You'll need to do this for each subject at a time, which is what the =91Few subjects=92 option does. If you can batch it (using some sort of scripts), it may make life easier. > > 2.=A0=A0=A0=A0=A0=A0 If I understood correctly the DARTEL process include= s the smoothing > step so the next thing to do is to advance to the other steps (1st analys= is, > etc.)? Yes. Don't tell Karl I said this, but personally I would do the first level analysis in native space (disabling any threshold masking) in order to compute contrast images, and then spatially normalise the contrast images using Dartel. With one contrast image (or only a small number of them) per subject, you can easily use the 'Many subjects' option for writing the spatially normalised images. Then you'd do the second level analysis on the spatially normalised contrast images. > > 3.=A0=A0=A0=A0=A0=A0 In order to match each subject anatomical image with= his functional > activity - Would the images after the DARTEL will be coregitered to the > anatomical images or an additional coregisteration process must be employ= ed > before the 1st analysis? When processing is treated as a pipeline, the within subject processing should be done before the between subject. Therefore, try to align the native space images. > Looking forward to learn from you about how to use the DARTEL tool > correctly, I'm not entirely sure there is a single correct way. The procedures that work best will depend on the artifacts, contrast etc in your images. Best regards, -John ========================================================================= Date: Wed, 25 May 2011 18:36:59 +0100 Reply-To: Alec Sproten <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Alec Sproten <[log in to unmask]> Subject: Re: using DARTEL appropriately 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, thanks a lot for your help! That really took us a big step further. There're just two small questions remaining: a. You said that we "could try segmenting the data and aligning the rc1 a= nd rc2 from the fMRI". If I unerstood the Dartel correctly, the rc1 and r= c2 are files that we get from the anatomical images? So shouldn't we alig= n the rc1 and rc2 with the fMRI? Or is there a procedure to get these fil= es from the fMRI data? b. If we don't do a VBM study, is it right that we shouldn't normalize th= e anatomical images? Thanks again for your help, Alec ========================================================================= Date: Wed, 25 May 2011 18:47:04 +0100 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: using DARTEL appropriately Comments: To: Alec Sproten <[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. You said that we "could try segmenting the data and aligning the rc1 a= nd rc2 from the fMRI". If I unerstood the Dartel correctly, the rc1 and rc2= are files that we get from the anatomical images? So shouldn't we align th= e rc1 and rc2 with the fMRI? Or is there a procedure to get these files fro= m the fMRI data? If you use New Segment on an fMRI scan (which may or may not work well), then you can get rc1 and rc2 files. > > b. If we don't do a VBM study, is it right that we shouldn't normalize th= e anatomical images? If you don't need them for anything, there's no point in generating them. Usually, when SPM uses a file for any purpose, it asks you to select the file. Best regards, -John ========================================================================= Date: Wed, 25 May 2011 18:54:42 +0100 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: SPECT transporter image analysis realted to DARTEL and BPM Comments: To: David Yang <[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 can't say much about your question on BPM, but your Dartel processing seems to be OK. You are correct in that the headers of the source (and other) images are modified to reflect the new image orientations. A small point about BPM. You should probably smooth your grey matter (c1 images) to match the point-spread function of the SPECT as closely as possible, and then warp the SPECT and sc1 images, probably using "preserve amount" for both types of image. I'm no expert on this, so maybe others could comment further. Best regards, -John On 25 May 2011 05:49, David Yang <[log in to unmask]> wrote: > Dear all > > Our subjects had SPECT transporter images(TRODAT for dopamine transporter= s) and their T1 MRI images (for VBM analysis). We planed to perform correla= tion analysis between such 2 kind of image modalities by WFU Biological Par= ametric Mapping Toolbox. > > Since our MRI images were analysed by DARTEL related process, our SPECT i= mages should also be analysed by DARTEL related ones. > > According to previous discussion (https://www.jiscmail.ac.uk/cgi-bin/weba= dmin?A2=3DSPM;3779f1d9.0909), we performed following analyses: > > Step 1: Within subject coregistration: Use SPM function, Coreg:estimate (= without reslicing) and we suspected the registration information would be s= tored in the header of source images (no other output would be found) > > Step 2: Routine DARTEL analysis for MRI data > > Step 3: as performing the function, "DARTEL toolbox: normalise to MNI spa= ce option" and use source images (SPECT) from Step 1. > > For receptor/transporter binding potential images, I suspected we should = choose Preserve Amount in Step 3 according to previous discussion: > > http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;f3f7b65.0901 > > I wondered if there was any mistake in our above analysis methods? > > Furthermore, considering the image features of PET or even SPECT images, = it might have to smooth such images with large amount (ex 12 mm compared to= default 8 mm in DARTEL). I wonder if such was suitable if we wanted to per= form further analysis by BPM that used both VBM and PET/SPECT images concur= rently as in this subject: > > http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=3DSPM;dd737de1.1012 > > Thanks for any help! > > Sincerely yours, > > David > ========================================================================= Date: Wed, 25 May 2011 18:14:23 +0000 Reply-To: "West, John D." <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "West, John D." <[log in to unmask]> Subject: Large Differences in segmenations between SPM8 and SPM5 Content-Type: multipart/alternative; boundary="_000_793D43FE7976404CB5E172709AB1F4080DE70B26IUMSSGMBX102ads_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_793D43FE7976404CB5E172709AB1F4080DE70B26IUMSSGMBX102ads_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Hello. I'm hoping someone might be able to help us get to the bottom of an issue w= e've run into. Recently we have been trying to reprocess our data using SP= M8. Unfortunately, we have run into a situation where some subjects which = segmented fine in SPM5 are now failing in SPM8. The way we do segmentation is we first register the T1 (MPRAGE) to the T1 t= emplate to give the segmentation a better starting estimate. We then segme= nt from there. However, in SPM8 the registration has consistently come out= differently. It seems that in some cases this new registration is giving = the segmentation a starting estimate that it cannot handle, where in SPM5 t= he starting estimate was fine. Does anyone have any ideas on what would be causing these differences? Is = the coregistration and segmentation algorithms very different between SPM5 = and SPM8? If not, is there some setting we may need to change? If they ar= e different, are there any suggestions you could give so we could successfu= lly segment these cases in SPM8? I appreciate your assistance. Thanks. -John West Systems Administrator IU Center for Neuroimaging --_000_793D43FE7976404CB5E172709AB1F4080DE70B26IUMSSGMBX102ads_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable <html xmlns:v=3D"urn:schemas-microsoft-com:vml" xmlns:o=3D"urn:schemas-micr= osoft-com:office:office" xmlns:w=3D"urn:schemas-microsoft-com:office:word" = xmlns:m=3D"http://schemas.microsoft.com/office/2004/12/omml" xmlns=3D"http:= //www.w3.org/TR/REC-html40"> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Dus-ascii"= > <meta name=3D"Generator" content=3D"Microsoft Word 12 (filtered medium)"> <style><!-- /* Font Definitions */ @font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {margin:0in; margin-bottom:.0001pt; font-size:11.0pt; font-family:"Calibri","sans-serif";} a:link, span.MsoHyperlink {mso-style-priority:99; color:blue; text-decoration:underline;} a:visited, span.MsoHyperlinkFollowed {mso-style-priority:99; color:purple; text-decoration:underline;} span.EmailStyle17 {mso-style-type:personal-compose; font-family:"Calibri","sans-serif"; color:windowtext;} .MsoChpDefault {mso-style-type:export-only;} @page WordSection1 {size:8.5in 11.0in; margin:1.0in 1.0in 1.0in 1.0in;} div.WordSection1 {page:WordSection1;} --></style><!--[if gte mso 9]><xml> <o:shapedefaults v:ext=3D"edit" spidmax=3D"1026" /> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext=3D"edit"> <o:idmap v:ext=3D"edit" data=3D"1" /> </o:shapelayout></xml><![endif]--> </head> <body lang=3D"EN-US" link=3D"blue" vlink=3D"purple"> <div class=3D"WordSection1"> <p class=3D"MsoNormal">Hello.<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">I’m hoping someone might be able to help us ge= t to the bottom of an issue we’ve run into. Recently we have be= en trying to reprocess our data using SPM8. Unfortunately, we have ru= n into a situation where some subjects which segmented fine in SPM5 are now failing in SPM8. <o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">The way we do segmentation is we first register the = T1 (MPRAGE) to the T1 template to give the segmentation a better starting e= stimate. We then segment from there. However, in SPM8 the regis= tration has consistently come out differently. It seems that in some cases this new registration is giving the segmentati= on a starting estimate that it cannot handle, where in SPM5 the starting es= timate was fine.<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">Does anyone have any ideas on what would be causing = these differences? Is the coregistration and segmentation algorithms = very different between SPM5 and SPM8? If not, is there some setting w= e may need to change? If they are different, are there any suggestions you could give so we could successfully segment = these cases in SPM8?<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">I appreciate your assistance.<o:p></o:p></p> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal">Thanks.<o:p></o:p></p> <p class=3D"MsoNormal">-John West<o:p></o:p></p> <p class=3D"MsoNormal">Systems Administrator<o:p></o:p></p> <p class=3D"MsoNormal">IU Center for Neuroimaging<o:p></o:p></p> </div> </body> </html> --_000_793D43FE7976404CB5E172709AB1F4080DE70B26IUMSSGMBX102ads_-- ========================================================================= Date: Wed, 25 May 2011 14:18:04 -0400 Reply-To: cliff <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: cliff <[log in to unmask]> Subject: about the threshold according to different image number MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec53af216bdb7f304a41db981 Message-ID: <[log in to unmask]> --bcaec53af216bdb7f304a41db981 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear experts, I am trying to comapre two resting BOLD datasets' seeded connectivity visua= l pattern difference. One has 320 images, and the other one has 120 images. Is there any way to adjust their thresholds of SPM map to keep this comparison in a fair level. I mean beacuse of the different image number in these two datasets, if I us= e the same threshold to display their connectivity SPM map, it seems unfair t= o the 120 images dataset. So I am seeking for a method to adjust the threshol= d level according to different image number. Thanks=EF=BC=81 --=20 Sincerely, Senhua Zhu --bcaec53af216bdb7f304a41db981 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable <div>Dear experts,</div> <div>=C2=A0</div> <div>I am trying to comapre two resting=C2=A0BOLD datasets' seeded conn= ectivity visual pattern=C2=A0difference. One has 320 images, and the other = one has 120 images.</div> <div>Is there any way to adjust their thresholds of SPM map to keep this co= mparison in a fair level.</div> <div>=C2=A0</div> <div>I mean beacuse of the different image number in these two datasets, if= I use the same threshold to display their connectivity SPM map, it seems u= nfair to the 120 images dataset. So I am seeking for a method to adjust the= threshold level according to different image number.</div> <div>=C2=A0</div> <div>Thanks=EF=BC=81<br clear=3D"all"><br>-- <br></div> <div>Sincerely,</div> <div>Senhua Zhu<br><br></div><br> --bcaec53af216bdb7f304a41db981-- ========================================================================= Date: Wed, 25 May 2011 13:09:54 -0500 Reply-To: Paige Scalf <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Paige Scalf <[log in to unmask]> Subject: job opening MIME-Version: 1.0 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Beginning August 15th, a two-year position is available for a highly motivated and talented postdoctoral fellow in the Department of Psychology at the University of Arizona. The goal of this research program is to study the neural processes that support vision, perception and attention using functional neuoimaging (including ultra high resolution and time resolved fMRI) and behavioral (including eye-tracking) methods. Candidates will be expected to support the research goals of the PI, but will also be given the opportunity to develop more independent lines of research. The candidate will be encouraged to take advantage of the highly collaborative departmental environment. The Department of Psychology is part of the School of Mind, Brain, and Behavior (MBB), and is a member of the College of Science, in keeping with its strong research-oriented approach and scientifically-derived knowledge base. Its faculty and students interact and collaborate with those of other MBB programs and departments=97Neuroscience, Cognitive Science, and Speech, Language and Hearing Science=97as well as with other academic units across campus. The fMRI facilities at the University of Arizona includes a GE 3.0T Signa scanner that is fully available for research use 5 days per week. The University of Arizona is a premier (NSF ranking: # 16) public research university located in Tucson, Arizona. The sun shines nearly every day in Tucson, which is surrounded by four mountain ranges that include the southernmost downhill ski resort in the USA. Tucson also supports great opportunities for hiking and cycling, and is one of the most bicycle friendly cities in the US. Candidates must have a PhD in neuroscience or a related field and the capacity to make a substantive intellectual contribution to the research program. Candidates with previous neuroimaging experience and strong programming skills will will be preferred. Of particular interest are those candidates with experience using python-based programs such as the Vision Egg and SciPy. For more information, contact Dr. Paige Scalf ([log in to unmask]). Applications may be submitted to job #47659 at http://www.uacareertrack.com. ========================================================================= Date: Wed, 25 May 2011 19:26:00 +0100 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: Cluster level statistics for VBM Comments: To: Deryk <[log in to unmask]> In-Reply-To: <[log in to unmask]> Mime-Version: 1.0 (Apple Message framework v1082) Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Hi Deryk, In the initial development of VBM it was observed that cluster-based = inference produces an unreasonable number of false positives, due to = calculation of the expected number of clusters depending on local = variations ("non-stationarity") in the smoothness of the image (see = http://www.fil.ion.ucl.ac.uk/spm/doc/papers/john_vbm_methods.pdf) There is however an SPM toolbox for implementing Keith Worsley's = correction for non-stationarity = (http://fmri.wfubmc.edu/cms/software#NS), which should restore control = over the false-positive rate (see Hayasaka et al. 2004, Neuroimage) Perhaps other stats gurus can add to this if there is more up to date = advice! Hope this helps Best Steve On 24 May 2011, at 21:22, Deryk wrote: > Is it appropriate to interpret the cluster level statistics provided = in the results table for a VBM analysis? ========================================================================= Date: Wed, 25 May 2011 11:30:28 -0700 Reply-To: "Lisa F. Akiyama" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Lisa F. Akiyama" <[log in to unmask]> Subject: Re: converting from .IMA to nifti format Comments: To: Inci Ayhan <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec51ddbf14c6f8b04a41de7de Message-ID: <[log in to unmask]> --bcaec51ddbf14c6f8b04a41de7de Content-Type: text/plain; charset=UTF-8 Hi Inci, I've never done it using SPM, but FreeSurfer worked for me in the past. Lisa On Mon, May 23, 2011 at 6:13 AM, Inci Ayhan <[log in to unmask]> wrote: > Hi, > > I am trying to convert 3D EPI images (Siemens - .IMA format) into nifti > format using SPM. Because the images do not have a .dcm extension, I > cannot use "dicom2nifti" function. > > I would so much appreciate any help to suggest a way to do so. > > Thank you very much. > > Inci > --bcaec51ddbf14c6f8b04a41de7de Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable <div dir=3D"ltr">Hi Inci,<br><br> I've never done it using SPM, but FreeSurfer worked for me in the past.= <div><br></div><div><br></div><div>Lisa<br><br><div class=3D"gmail_quote">O= n Mon, May 23, 2011 at 6:13 AM, Inci Ayhan <span dir=3D"ltr"><<a href=3D= "mailto:[log in to unmask]" target=3D"_blank">[log in to unmask]</a>></spa= n> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex">Hi,<br> <br> I am trying to convert 3D EPI images (Siemens - .IMA format) into nifti<br> format using SPM. Because the images do not have a .dcm extension, I<br> cannot use "dicom2nifti" function.<br> <br> I would so much appreciate any help to suggest a way to do so.<br> <br> Thank you very much.<br> <font color=3D"#888888"><br> Inci<br> </font></blockquote></div><br></div></div> --bcaec51ddbf14c6f8b04a41de7de-- ========================================================================= Date: Wed, 25 May 2011 14:45:34 -0400 Reply-To: shaza zaghlool <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: shaza zaghlool <[log in to unmask]> Subject: Bayesian Analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0050450155a118169204a41e1c83 Message-ID: <[log in to unmask]> --0050450155a118169204a41e1c83 Content-Type: text/plain; charset=ISO-8859-1 I specified and estimated models for each of my 20 subjects with the Bayesian-first level method. I'm at the inference stage and wasn't sure what to input for "Effect size threshold for PPM". In the Auditory fmri dataset tutorial a value of 2 was entered. And in the face fmri tutorial, it says to load the SPM file and then type in Matlab sf =max(SPM.xBF.bf(:,1))/SPM.xBF.dt. I then divided 1/sf to get 4.75. I did that and got 4.75 just like the tutorial says. Which value am I supposed to use 2 or 4.75? Also, when I used 4.75 and a Posterior probability threshold for PPM value of 0.95, I got very little areas of activation when I compared the results to the ones I got from estimating the models using the Classical method. Should the Bayesian-first level method and Classical methods yield the same areas/sizes of activation? Thanks --0050450155a118169204a41e1c83 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>I specified and estimated models for each of my 20 subjects with the B= ayesian-first level method. I'm at the inference stage and wasn't s= ure what to input for "<font size=3D"2" face=3D"CMTI10"><font size=3D"= 2" face=3D"CMTI10">Effect size threshold for PPM". In the Auditory fmr= i dataset tutorial=A0a value of 2 was entered. And in the face fmri tutoria= l, it says to load the SPM file and then <font size=3D"2" face=3D"CMR10"><f= ont size=3D"2" face=3D"CMR10">type in </font></font><font size=3D"2" face= =3D"CMCSC10"><font size=3D"2" face=3D"CMCSC10">Matlab </font></font><font s= ize=3D"2" face=3D"CMTT10"><font size=3D"2" face=3D"CMTT10">sf =3Dmax(<a hre= f=3D"http://SPM.xBF.bf">SPM.xBF.bf</a>(:,1))/SPM.xBF.dt. I then divided 1/s= f to get 4.75. I did that and got 4.75 just like the tutorial says. Which v= alue am I supposed to use 2 or 4.75? Also, when I used 4.75 and a <font siz= e=3D"2" face=3D"CMTI10"><font size=3D"2" face=3D"CMTI10">Posterior probabil= ity threshold for PPM value of 0.95, I got very little areas of activation = when I compared the results to the ones I got from estimating the models us= ing the Classical method. Should the Bayesian-first level method and Classi= cal methods yield the same areas/sizes of activation?</font></font></font><= /font></font></font></div> <div><font size=3D"2" face=3D"CMTI10"><font size=3D"2" face=3D"CMTI10"><fon= t size=3D"2" face=3D"CMTT10"><font size=3D"2" face=3D"CMTT10"><font size=3D= "2" face=3D"CMTI10"><font size=3D"2" face=3D"CMTI10">Thanks</font></font></= font></font></font></font></div> <font size=3D"2" face=3D"CMR10"><font size=3D"2" face=3D"CMR10"></font></fo= nt> --0050450155a118169204a41e1c83-- ========================================================================= Date: Wed, 25 May 2011 14:45:49 -0400 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: Neuroinformatics 2011 - Early bird deadline June 1 MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> 4th INCF Congress of Neuroinformatics: Boston, Sept 4 - 6, 2011 Last day for early bird discount: June 1 Congress registration - http://neuroinformatics2011.org/registration News: - 166 accepted poster and demo abstracts - All speaker abstracts displayed: http://neuroinformatics2011.org/speakers - Spotlight presenters selected: http://neuroinformatics2011.org/program/spotlight-presentations For more information, please visit the Congress website at: www.neuroinformatics2011.org We very much look forward to seeing you in Boston in September! ========================================================================= Date: Wed, 25 May 2011 12:15:25 -0700 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: converting from .IMA to nifti format Comments: To: Inci Ayhan <[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]> Hi, On Mon, May 23, 2011 at 6:13 AM, Inci Ayhan <[log in to unmask]> wrote: > Hi, > > I am trying to convert 3D EPI images (Siemens - .IMA format) into nifti > format using SPM. Because the images do not have a .dcm extension, I > cannot use "dicom2nifti" function. > > I would so much appreciate any help to suggest a way to do so. You can do the conversion more manually from the matlab command line, sort of (untested): P = spm_select(); % select your DICOM files hdrs = spm_dicom_headers(P); cd /some/place/for/the/files spm_dicom_convert(hdrs); I think... Best, Matthew ========================================================================= Date: Wed, 25 May 2011 14:16:19 -0700 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: ROI analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1091898321-1306358179=:26162" Message-ID: <[log in to unmask]> --0-1091898321-1306358179=:26162 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Hi spm experts, I would like to do a covariace (ages) analysis within a defined ROI. I have= the spmTmaps but I first need to extract only the ROI and then do a covari= ance of that region with the ages of the group. What is the best way to do = it? Any specific tool? Thanks for the help, Jose. --0-1091898321-1306358179=:26162 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable <table cellspacing=3D"0" cellpadding=3D"0" border=3D"0" ><tr><td valign=3D"= top" style=3D"font: inherit;">Hi spm experts,<div><br></div><div>I would li= ke to do a covariace (ages) analysis within a defined ROI. I have the spmTm= aps but I first need to extract only the ROI and then do a covariance of th= at region with the ages of the group. What is the best way to do it? Any sp= ecific tool?</div><div><br></div><div>Thanks for the help,</div><div><br></= div><div>Jose.</div></td></tr></table> --0-1091898321-1306358179=:26162-- ========================================================================= Date: Thu, 26 May 2011 00:09:56 -0500 Reply-To: Adam Kristevski <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Adam Kristevski <[log in to unmask]> Subject: segmentation using SPECT scans MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_004A_01CC1B39.3E854930" Message-ID: <77AB2885134A4ACB91BF86FDA38B6186@D5N6MN91> This is a multi-part message in MIME format. ------=_NextPart_000_004A_01CC1B39.3E854930 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hello all I am currently conducting lesion analysis of just one patient using ITK = snap and manually tracing. =20 I'm wondering if using SPM as an adjunct would aid in this process. = Would normalizing, smoothing and segmenting this SPECT scan be = beneficial in terms of having a more accurate representation of the = patient's brain? =20 Am I able to use these functions correctly on a SPECT scan? (i.e., were = these SPM functions designed as SPECT compatible)? Adam ------=_NextPart_000_004A_01CC1B39.3E854930 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"> <HTML><HEAD> <META content=3D"text/html; charset=3Diso-8859-1" = http-equiv=3DContent-Type> <META name=3DGENERATOR content=3D"MSHTML 8.00.6001.19046"> <STYLE></STYLE> </HEAD> <BODY bgColor=3D#ffffff> <DIV><FONT size=3D2 face=3DArial>Hello all</FONT></DIV> <DIV><FONT size=3D2 face=3DArial></FONT> </DIV> <DIV><FONT size=3D2 face=3DArial>I am currently conducting lesion = analysis of just=20 one patient using ITK snap and manually tracing. </FONT></DIV> <DIV><FONT size=3D2 face=3DArial></FONT> </DIV> <DIV><FONT size=3D2 face=3DArial>I'm wondering if using SPM as an = adjunct would aid=20 in this process. Would normalizing, smoothing and segmenting this = SPECT=20 scan be beneficial in terms of having a more accurate representation of = the=20 patient's brain? </FONT></DIV> <DIV><FONT size=3D2 face=3DArial></FONT> </DIV> <DIV><FONT size=3D2 face=3DArial>Am I able to use these functions = correctly on a=20 SPECT scan? (i.e., were these SPM functions designed as SPECT = compatible)?</FONT></DIV> <DIV><FONT size=3D2 face=3DArial></FONT> </DIV> <DIV><FONT size=3D2 face=3DArial>Adam</FONT></DIV></BODY></HTML> ------=_NextPart_000_004A_01CC1B39.3E854930-- ========================================================================= Date: Thu, 26 May 2011 09:02:42 +0100 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: Job announcement: Postdoctoral or PhD position in Tuebingen Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> We are seeking a highly motivated and talented postdoctoral fellow or PhD= student to work in the Section of Neuropsychology of the University of T= uebingen, Germany The goal of the research program is to study the mechanisms and anatomy u= nderlying selective attention in healthy subjects and pathological extinc= tion in neurological patients. We investigate patients with brain lesions= as well as healthy subjects using functional magnetic resonance imaging = (fMRI), transcranial magnetic stimulation (TMS) and behavioural recording= s. Further information on the research environment can be found on http:/= /homepages.uni-tuebingen.de/karnath/Sektion.html The PhD student can acquire a PhD degree in "Neuroscience" in an interdis= ciplinary graduate program. The postdoctoral fellow should have a PhD in = psychology, neuroscience or a related field and should have experience wi= th fMRI and/or TMS. Being able to speak German (or willingness to learn) = is highly desirable. The position is currently funded for one year (with potential extension f= or 3 further years depending on whether or not an already submitted fundi= ng application is successful) and should start as soon as possible but in= November 2011 at the latest. Candidates should submit a CV, list 2 refer= ences and provide a brief statement of research interests. Applications o= r informal inquires should be sent by email to Dr. Bianca de Haan (bianca= [log in to unmask]). ========================================================================= Date: Thu, 26 May 2011 09:04:53 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: question regarding matlabbatch struct/class Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> To run a batch from MATLAB command line, SPM always has to be initialised= . At least,=20 spm_jobman('initcfg') should be run before. If one needs to rely on defaults which can not be s= et in a batch (e.g. in first level fMRI analysis), one should run spm('defaults','fmri') =18r 'pet' or 'eeg', depending on the modality spm_jobman('initcfg') Best, Volkmar ========================================================================= Date: Thu, 26 May 2011 16:08:41 +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: Can DCM deal with short-term signal? MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_181165_30092847.1306397321978" Message-ID: <[log in to unmask]> ------=_Part_181165_30092847.1306397321978 Content-Type: text/plain; charset=GBK Content-Transfer-Encoding: base64 RGVhciBTUE0ncyB1c2VycywKoaGhoVNpbXBseSBwdXQsIGlmIEkgaGF2ZSBhIGxvbmctdGVybSBz aWduYWwsIHN1Y2ggYXMgMH4xMDAwIG1zLCBjb3VsZCBJIGRpdmlkZSBpdCBpbnRvIDIwIHNob3J0 LXRlcm0gc2lnbmFscygwfjUwLDUxfjEwMCwuLi45NTZ+MTAwMCkgdG8gZG8gRENNIGFuYWx5c2lz PyBJbiBvdGhlciB3b3JkcywgZG9lcyBEQ00gaGFzIGFueSBwcmVmZXJlbmNlcyBmb3IgdGhlIHRp bWUgb2Ygc2lnbmFsLCBsb25nIG9yIHNob3J0PyBPciBuZWl0aGVyIG9mIHRoZW0/CiAgICAgICBU aGFuayB5b3UgdmVyeSBtdWNoIGZvciBhbnkgYWR2aWNlIQoKCi0tCgpIYW9yYW4gTEkgKE1TKQpC cmFpbiBJbWFnaW5nIExhYiwKUmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTY2llbmNlLApT b3V0aGVhc3QgVW5pdmVyc2l0eQoyIFNpIFBhaSBMb3UgLCBOYW5qaW5nLCAyMTAwOTYsIFAuUi5D aGluYQoKCg== ------=_Part_181165_30092847.1306397321978 Content-Type: text/html; charset=GBK Content-Transfer-Encoding: base64 PERJVj48L0RJVj4KPERJVj4KPERJVj5EZWFyIFNQTSdzIHVzZXJzLDwvRElWPgo8RElWPqGhoaFT aW1wbHkgcHV0LCBpZiBJIGhhdmUgYSBsb25nLXRlcm0gc2lnbmFsLCBzdWNoIGFzIDB+MTAwMCBt cywgY291bGQgSSBkaXZpZGUgaXQgaW50byAyMCBzaG9ydC10ZXJtIHNpZ25hbHMoMH41MCw1MX4x MDAsLi4uOTU2fjEwMDApIHRvIGRvIERDTSBhbmFseXNpcz8gSW4gb3RoZXIgd29yZHMsIGRvZXMg RENNIGhhcyBhbnkgcHJlZmVyZW5jZXMmbmJzcDtmb3IgdGhlIHRpbWUgb2Ygc2lnbmFsLCBsb25n IG9yIHNob3J0PyBPciBuZWl0aGVyIG9mIHRoZW0/PC9ESVY+CjxESVY+Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7IFRoYW5rIHlvdSB2ZXJ5IG11Y2ggZm9yIGFueSBhZHZpY2Uh PEJSPjxCUj48L0RJVj48L0RJVj4KPERJVj4tLTxCUj4KPERJVj48Rk9OVCBjb2xvcj0iIzAwODAw MCIgZmFjZT0iQXJpYWwiPkhhb3JhbiBMSSAoTVMpPEJSPkJyYWluIEltYWdpbmcgTGFiLDxCUj5S ZXNlYXJjaCBDZW50ZXIgZm9yIExlYXJuaW5nIFM8Rk9OVCBjb2xvcj0iIzAwODAwMCI+Y2k8L0ZP TlQ+ZW5jZSw8QlI+U291dGhlYXN0IFVuaXZlcnNpdHk8QlI+MiBTaSBQYWkgTG91ICwgTmFuamlu ZywgMjEwMDk2LCBQLlIuQ2hpbmE8L0ZPTlQ+PC9ESVY+PC9ESVY+PEJSPjxCUj48U1BBTiB0aXRs ZT0ibmV0ZWFzZWZvb3RlciI+PFNQQU4gaWQ9Im5ldGVhc2VfbWFpbF9mb290ZXIiPjwvU1BBTj48 L1NQQU4+PGJyPjxicj48c3BhbiB0aXRsZT0ibmV0ZWFzZWZvb3RlciI+PHNwYW4gaWQ9Im5ldGVh c2VfbWFpbF9mb290ZXIiPjwvc3Bhbj48L3NwYW4+ ------=_Part_181165_30092847.1306397321978-- ========================================================================= Date: Thu, 26 May 2011 09:42:32 +0100 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: segmentation using SPECT scans Comments: To: Adam Kristevski <[log in to unmask]> In-Reply-To: <77AB2885134A4ACB91BF86FDA38B6186@D5N6MN91> MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> Segmentation etc were not designed for PET or SPECT, as they don't deal with the degree of smoothness of the images. Mohamed's (http://www.fil.ion.ucl.ac.uk/~mseghier/) lesion segmentation tools may be of help with your MRI data though. In general, not everything can be easily automated yet (one of my students is a regular ITK-snap user). In principle though, any labelling that a human expert can do, a computer should be able to do eventually. Best regards, -John On 26 May 2011 06:09, Adam Kristevski <[log in to unmask]> wrote: > Hello all > > I am currently conducting lesion analysis of just one patient using ITK s= nap > and manually tracing. > > I'm wondering if using SPM as an adjunct would aid in this process.=A0 Wo= uld > normalizing, smoothing and segmenting this SPECT scan be beneficial in te= rms > of having a more accurate representation of the patient's brain? > > Am I able to use these functions correctly on a SPECT scan?=A0 (i.e., wer= e > these SPM=A0functions designed as SPECT compatible)? > > Adam ========================================================================= Date: Thu, 26 May 2011 08:46:48 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: source priors for MEG source inversion Comments: To: Sonja Schall <[log in to unmask]> Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="Windows-1252" MIME-Version: 1.0 Message-ID: <[log in to unmask]> RGVhciBTb25qYSwKCklmIHlvdSBsb29rIGF0IFJpayBIZW5zb24ncyBwYXBlciwgaGUgYWxzbyBm b3VuZCB0aGF0IHRoZXJlIGlzIG5vdCBtdWNoIGRpZmZlcmVuY2Ugd2hlbiB1c2luZyBmTVJJIHBy aW9ycyBhbmQgTVNQLiBIb3dldmVyLCB0aGVyZSBzaG91bGQgYmUgYSBkaWZmZXJlbmNlIHdoZW4g eW91IGNob29zZSB0aGUgSUlEIG9wdGlvbiBhbmQgYWRkIHByaW9ycyB0byB0aGF0LiBDb3VsZCB5 b3UgdHJ5IGl0IHdpdGggR1VJIGFuZCBiYXRjaCBhbmQgbGV0IG1lIGtub3cgd2hhdCB5b3UgZ2V0 PwoKVmxhZGltaXIKCi0tLS0tLU9yaWdpbmFsIE1lc3NhZ2UtLS0tLS0KRnJvbTogU29uamEgU2No YWxsClRvOiBWbGFkaW1pciBMaXR2YWsKU3ViamVjdDogUmU6IFtTUE1dIHNvdXJjZSBwcmlvcnMg Zm9yIE1FRyBzb3VyY2UgaW52ZXJzaW9uClNlbnQ6IDI2IE1heSAyMDExIDA5OjA1CgpEZWFyIFZs YWRpbWlyLAoKSSdtIHNvcnJ5IHRvIGJvdGhlciB5b3UgYWdhaW4uLmknbSBzdGlsbCBoYXZpbmcg cHJvYmxlbXMgd2l0aCB0aGUgc291cmNlIHByaW9ycyBvcHRpb24gaW4gdGhlIGludmVyc2lvbiBi YXRjaC4KaSBmb2xsb3dlZCB5b3VyIGxhc3QgaW5zdHJ1Y3Rpb25zIGFuZCB0aGUgc291cmNlIGlu dmVyc2lvbiBpcyBub3cgcnVubmluZy4gaG93ZXZlciwgdGhlIHJlc3VsdHMgbG9vayBleGFjdGx5 IHRoZSBzYW1lIGxpa2Ugd2l0aG91dCBzb3VyY2UgcHJpb3JzLiBpIG5vdGljZWQgYSB3YXJuaW5n IG1lc3NhZ2UgYXQgdGhlIGJlZ2lubmluZyBzYXlpbmc6IENhbiBub3QgaW5pdGlhbGlzZSBwcmlv cnM6IGpvYiBpcyBub3QgYSBzdHJ1Y3QuCgppIGRvbid0IHNlZW0gdG8gZ2V0IHRoaXMgbWVzc2Fn ZSB3aXRoIHRoZSBsYXRlc3Qgb2ZmaWNpYWwgc3BtIHZlcnNpb24sIGJ1dCB0aGUgcmVzdWx0cyBz dGlsbCBsb29rIHRoZSBzYW1lLgoKdGhhbmtzIGEgbG90IGZvciB5b3VyIGhlbHAsCnNvbmphCgot LS0tLSBPcmlnaW5hbCBNZXNzYWdlIC0tLS0tCkZyb206ICJTb25qYSBTY2hhbGwiIDxzY2hhbGxA Y2JzLm1wZy5kZT4KVG86ICJWbGFkaW1pciBMaXR2YWsiIDxsaXR2YWsudmxhZGltaXJAZ21haWwu Y29tPgpTZW50OiBGcmlkYXksIE1heSAxMywgMjAxMSA0OjI0OjMzIFBNClN1YmplY3Q6IFJlOiBb U1BNXSBzb3VyY2UgcHJpb3JzIGZvciBNRUcgc291cmNlIGludmVyc2lvbgoKdGhhbmtzIGEgbG90 IQoKY2hlZXJzLCBzb25qYQoKLS0tLS0gT3JpZ2luYWwgTWVzc2FnZSAtLS0tLQpGcm9tOiAiVmxh ZGltaXIgTGl0dmFrIiA8bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbT4KVG86ICJTb25qYSBTY2hh bGwiIDxzY2hhbGxAY2JzLm1wZy5kZT4KQ2M6IFNQTUBqaXNjbWFpbC5hYy51awpTZW50OiBGcmlk YXksIE1heSAxMywgMjAxMSA0OjA5OjQxIFBNClN1YmplY3Q6IFJlOiBbU1BNXSBzb3VyY2UgcHJp b3JzIGZvciBNRUcgc291cmNlIGludmVyc2lvbgoKRGVhciBTb25qYSwKClNvcnJ5IGZvciB0aGUg ZGVsYXkuIEkgd2FzIGEgbGl0dGxlIHByZW9jY3VwaWVkIHdpdGggb3RoZXIgdGhpbmdzLgpUaGVy ZSBhcmUgaW5kZWVkIHNvbWUgYnVncyBpbiB0aGUgYmF0Y2ggYW5kIHlvdSB3ZXJlIGFwcGFyZW50 bHkgdGhlCmZpcnN0IHVzZXIgdG8gdHJ5IG91dCB0aGF0IHBhcnRpY3VsYXIgb3B0aW9uIGFuZCBk aXNjb3ZlciB0aGVtLiBQbGVhc2UKcHV0IHRoZSBhdHRhY2hlZCBmaWxlIGluIHNwbS9jb25maWcg YW5kIHJlc3RhcnQgeW91ciBTUE0uIEl0IHNob3VsZAp3b3JrIHRoZW4uCgpCZXN0LAoKVmxhZGlt aXIKCk9uIFRodSwgQXByIDI4LCAyMDExIGF0IDE6MzcgUE0sIFNvbmphIFNjaGFsbCA8c2NoYWxs QGNicy5tcGcuZGU+IHdyb3RlOgo+IERlYXIgU1BNIGV4cGVydHMsCj4KPiBJIGVuY291bnRlcmVk IHNvbWUgdGVjaG5pY2FsIGRpZmZpY3VsdGllcyB3aGVuIEkgdHJpZWQgdG8gcnVuIHRoZSBNL0VF RyBzb3VyY2UgaW52ZXJzaW9uIGJhdGNoIHdpdGggc291cmNlIHByaW9ycy4gVGhlIHNvdXJjZSBw cmlvciBvcHRpb24gc2VlbXMgdG8gd29yayBmaW5lIGZvciB0aGUgR1VJLCBidXQgd2hlbiBJIHVz ZSB0aGUgYmF0Y2ggaXQgcmVzdWx0cyBpbiBhbiBlcnJvci4KPiBJdCBzZWVtcyBJIGRvbid0IGhh dmUgdGhlIG9wdGlvbiB0byBzcGVjaWZ5ICJNTkkiIGFzIHRoZSBpbWFnZSBzcGFjZSB1c2luZyB0 aGUgYmF0Y2gsIGJ1dCBJIGRvbid0IGtub3cgd2hldGhlciB0aGlzIGlzIHJlbGF0ZWQgdG8gdGhl IHByb2JsZW0uCj4KPiBJcyB0aGVyZSBhbnkgZWFzeSB3YXkgdG8gZml4L3dvcmsgYXJvdW5kIHRo aXMgcHJvYmxlbT8KPgo+IGJlc3QsCj4gc29uamEKPgoKDQpTZW50IHVzaW5nIEJsYWNrQmVycnmu IGZyb20gT3Jhbmdl ========================================================================= Date: Thu, 26 May 2011 08:56:04 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Can DCM deal with short-term signal? Comments: To: =?gb18030?B?t8nE8Q==?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="part6295-boundary-1772873089-1138862058" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --part6295-boundary-1772873089-1138862058 Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="utf-8" RGVhciBIYW9yYW4sDQoNCklmIHlvdSBhcmUgdGFsa2luZyBhYm91dCBEQ00gZm9yIEVSUCwgeW91 IHNob3VsZCBhbHdheXMgaW5jbHVkZSB0aGUgb25zZXQgb2YgdGhlIEVSUCBpbiB5b3VyIG1vZGVs LiBZb3UgY2Fubm90IHN0YXJ0IG1vZGVsbGluZyBmcm9tIHRoZSBtaWRkbGUgb2YgdGhlIHdhdmVm b3JtIGJlY2F1c2UgdGhlIG1vZGVsIHNob3VsZCAna25vdycgaG93IHRvIGdldCB0byB0aGF0IHBv aW50LiBJZiB5b3UgaW5jbHVkZSB0aGUgb25zZXQgeW91IGNhbiBjaG9vc2Ugd2hlbiB0byBlbmQg dGhlIEVSUC4gVGhpcyB3YXMgZG9uZSBzeXN0ZW1hdGljYWxseSBpbiBNYXJ0YSBHYXJyaWRvJ3Mg cGFwZXIgYWJvdXQgZmVlZGJhY2sgbG9vcHMuDQoNCkJlc3QsDQoNClZsYWRpbWlyDQpTZW50IHVz aW5nIEJsYWNrQmVycnnCriBmcm9tIE9yYW5nZQ0KDQotLS0tLU9yaWdpbmFsIE1lc3NhZ2UtLS0t LQ0KRnJvbTogICAgICAgICDpo57puJ8gPGxpaGFvcmFuMDYwM0AxNjMuQ09NPg0KU2VuZGVyOiAg ICAgICAiU1BNIChTdGF0aXN0aWNhbCBQYXJhbWV0cmljIE1hcHBpbmcpIiA8U1BNQEpJU0NNQUlM LkFDLlVLPg0KRGF0ZTogICAgICAgICBUaHUsIDI2IE1heSAyMDExIDE2OjA4OjQxIA0KVG86IDxT UE1ASklTQ01BSUwuQUMuVUs+DQpSZXBseS1UbzogICAgIOmjnum4nyA8bGloYW9yYW4wNjAzQDE2 My5DT00+DQpTdWJqZWN0OiBbU1BNXSBDYW4gRENNIGRlYWwgd2l0aCBzaG9ydC10ZXJtIHNpZ25h bD8NCg0KRGVhciBTUE0ncyB1c2VycywK44CA44CAU2ltcGx5IHB1dCwgaWYgSSBoYXZlIGEgbG9u Zy10ZXJtIHNpZ25hbCwgc3VjaCBhcyAwfjEwMDAgbXMsIGNvdWxkIEkgZGl2aWRlIGl0IGludG8g MjAgc2hvcnQtdGVybSBzaWduYWxzKDB+NTAsNTF+MTAwLC4uLjk1Nn4xMDAwKSB0byBkbyBEQ00g YW5hbHlzaXM/IEluIG90aGVyIHdvcmRzLCBkb2VzIERDTSBoYXMgYW55IHByZWZlcmVuY2VzIGZv ciB0aGUgdGltZSBvZiBzaWduYWwsIGxvbmcgb3Igc2hvcnQ/IE9yIG5laXRoZXIgb2YgdGhlbT8K ICAgICAgIFRoYW5rIHlvdSB2ZXJ5IG11Y2ggZm9yIGFueSBhZHZpY2UhCgoKLS0KCkhhb3JhbiBM SSAoTVMpCkJyYWluIEltYWdpbmcgTGFiLApSZXNlYXJjaCBDZW50ZXIgZm9yIExlYXJuaW5nIFNj aWVuY2UsClNvdXRoZWFzdCBVbml2ZXJzaXR5CjIgU2kgUGFpIExvdSAsIE5hbmppbmcsIDIxMDA5 NiwgUC5SLkNoaW5hCgoK --part6295-boundary-1772873089-1138862058 Content-Transfer-Encoding: base64 Content-Type: text/html; charset="utf-8" PCFET0NUWVBFIGh0bWwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4gPGh0bWw+PGhlYWQ+IDxtZXRhIGNvbnRlbnQ9InRleHQvaHRtbDsgY2hhcnNldD11dGYt OCIgaHR0cC1lcXVpdj0iQ29udGVudC1UeXBlIj4gPC9oZWFkPkRlYXIgSGFvcmFuLDxici8+PGJy Lz5JZiB5b3UgYXJlIHRhbGtpbmcgYWJvdXQgRENNIGZvciBFUlAsIHlvdSBzaG91bGQgYWx3YXlz IGluY2x1ZGUgdGhlIG9uc2V0IG9mIHRoZSBFUlAgaW4geW91ciBtb2RlbC4gWW91IGNhbm5vdCBz dGFydCBtb2RlbGxpbmcgZnJvbSB0aGUgbWlkZGxlIG9mIHRoZSB3YXZlZm9ybSBiZWNhdXNlIHRo ZSBtb2RlbCBzaG91bGQgJ2tub3cnIGhvdyB0byBnZXQgdG8gdGhhdCBwb2ludC4gSWYgeW91IGlu Y2x1ZGUgdGhlIG9uc2V0IHlvdSBjYW4gY2hvb3NlIHdoZW4gdG8gZW5kIHRoZSBFUlAuIFRoaXMg d2FzIGRvbmUgc3lzdGVtYXRpY2FsbHkgaW4gTWFydGEgR2FycmlkbydzIHBhcGVyIGFib3V0IGZl ZWRiYWNrIGxvb3BzLjxici8+PGJyLz5CZXN0LDxici8+PGJyLz5WbGFkaW1pcjxwPlNlbnQgdXNp bmcgQmxhY2tCZXJyecKuIGZyb20gT3JhbmdlPC9wPjxoci8+PGRpdj48Yj5Gcm9tOiA8L2I+ICAg ICAgICAg6aOe6bifICZsdDtsaWhhb3JhbjA2MDNAMTYzLkNPTSZndDsNCjwvZGl2PjxkaXY+PGI+ U2VuZGVyOiA8L2I+ICAgICAgICJTUE0gKFN0YXRpc3RpY2FsIFBhcmFtZXRyaWMgTWFwcGluZyki ICZsdDtTUE1ASklTQ01BSUwuQUMuVUsmZ3Q7DQo8L2Rpdj48ZGl2PjxiPkRhdGU6IDwvYj5UaHUs IDI2IE1heSAyMDExIDE2OjA4OjQxICswODAwPC9kaXY+PGRpdj48Yj5UbzogPC9iPiZsdDtTUE1A SklTQ01BSUwuQUMuVUsmZ3Q7PC9kaXY+PGRpdj48Yj5SZXBseVRvOiA8L2I+ICAgICDpo57puJ8g Jmx0O2xpaGFvcmFuMDYwM0AxNjMuQ09NJmd0Ow0KPC9kaXY+PGRpdj48Yj5TdWJqZWN0OiA8L2I+ W1NQTV0gQ2FuIERDTSBkZWFsIHdpdGggc2hvcnQtdGVybSBzaWduYWw/PC9kaXY+PGRpdj48YnIv PjwvZGl2PjxESVY+PC9ESVY+CjxESVY+CjxESVY+RGVhciBTUE0ncyB1c2Vycyw8L0RJVj4KPERJ Vj7jgIDjgIBTaW1wbHkgcHV0LCBpZiBJIGhhdmUgYSBsb25nLXRlcm0gc2lnbmFsLCBzdWNoIGFz IDB+MTAwMCBtcywgY291bGQgSSBkaXZpZGUgaXQgaW50byAyMCBzaG9ydC10ZXJtIHNpZ25hbHMo MH41MCw1MX4xMDAsLi4uOTU2fjEwMDApIHRvIGRvIERDTSBhbmFseXNpcz8gSW4gb3RoZXIgd29y ZHMsIGRvZXMgRENNIGhhcyBhbnkgcHJlZmVyZW5jZXMmbmJzcDtmb3IgdGhlIHRpbWUgb2Ygc2ln bmFsLCBsb25nIG9yIHNob3J0PyBPciBuZWl0aGVyIG9mIHRoZW0/PC9ESVY+CjxESVY+Jm5ic3A7 Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7IFRoYW5rIHlvdSB2ZXJ5IG11Y2ggZm9yIGFu eSBhZHZpY2UhPEJSPjxCUj48L0RJVj48L0RJVj4KPERJVj4tLTxCUj4KPERJVj48Rk9OVCBjb2xv cj0iIzAwODAwMCIgZmFjZT0iQXJpYWwiPkhhb3JhbiBMSSAoTVMpPEJSPkJyYWluIEltYWdpbmcg TGFiLDxCUj5SZXNlYXJjaCBDZW50ZXIgZm9yIExlYXJuaW5nIFM8Rk9OVCBjb2xvcj0iIzAwODAw MCI+Y2k8L0ZPTlQ+ZW5jZSw8QlI+U291dGhlYXN0IFVuaXZlcnNpdHk8QlI+MiBTaSBQYWkgTG91 ICwgTmFuamluZywgMjEwMDk2LCBQLlIuQ2hpbmE8L0ZPTlQ+PC9ESVY+PC9ESVY+PEJSPjxCUj48 U1BBTiB0aXRsZT0ibmV0ZWFzZWZvb3RlciI+PFNQQU4gaWQ9Im5ldGVhc2VfbWFpbF9mb290ZXIi PjwvU1BBTj48L1NQQU4+PGJyPjxicj48c3BhbiB0aXRsZT0ibmV0ZWFzZWZvb3RlciI+PHNwYW4g aWQ9Im5ldGVhc2VfbWFpbF9mb290ZXIiPjwvc3Bhbj48L3NwYW4+PC9odG1sPg== --part6295-boundary-1772873089-1138862058-- ========================================================================= Date: Thu, 26 May 2011 17:14:35 +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: Can DCM deal with short-term signal? In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_191472_20857236.1306401275926" Message-ID: <[log in to unmask]> ------=_Part_191472_20857236.1306401275926 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: base64 RGVhciBWbGFkaW1pciwK44CA44CARG8geW91IG1lYW4gaWYgSSBzZXQgdGltZXdpbmRvdz0gWzI1 MCAzMDBdLCBvbnNldCB0aW1lPSA2MCBpcyBpbmFjY2Vzc2libGUgd2hlbiBJIGRvIERDTSBmb3Ig RVJQPwrjgIDjgIBIYW9yYW4uCuOAgOOAgApBdCAyMDExLTA1LTI2IDE2OjU2OjA077yMbGl0dmFr LnZsYWRpbWlyQGdtYWlsLmNvbSB3cm90ZToKCkRlYXIgSGFvcmFuLAoKSWYgeW91IGFyZSB0YWxr aW5nIGFib3V0IERDTSBmb3IgRVJQLCB5b3Ugc2hvdWxkIGFsd2F5cyBpbmNsdWRlIHRoZSBvbnNl dCBvZiB0aGUgRVJQIGluIHlvdXIgbW9kZWwuIFlvdSBjYW5ub3Qgc3RhcnQgbW9kZWxsaW5nIGZy b20gdGhlIG1pZGRsZSBvZiB0aGUgd2F2ZWZvcm0gYmVjYXVzZSB0aGUgbW9kZWwgc2hvdWxkICdr bm93JyBob3cgdG8gZ2V0IHRvIHRoYXQgcG9pbnQuIElmIHlvdSBpbmNsdWRlIHRoZSBvbnNldCB5 b3UgY2FuIGNob29zZSB3aGVuIHRvIGVuZCB0aGUgRVJQLiBUaGlzIHdhcyBkb25lIHN5c3RlbWF0 aWNhbGx5IGluIE1hcnRhIEdhcnJpZG8ncyBwYXBlciBhYm91dCBmZWVkYmFjayBsb29wcy4KCkJl c3QsCgpWbGFkaW1pcgoKU2VudCB1c2luZyBCbGFja0JlcnJ5wq4gZnJvbSBPcmFuZ2UKCkZyb206 6aOe6bifIDxsaWhhb3JhbjA2MDNAMTYzLkNPTT4KU2VuZGVyOiJTUE0gKFN0YXRpc3RpY2FsIFBh cmFtZXRyaWMgTWFwcGluZykiIDxTUE1ASklTQ01BSUwuQUMuVUs+CkRhdGU6VGh1LCAyNiBNYXkg MjAxMSAxNjowODo0MSArMDgwMApUbzo8U1BNQEpJU0NNQUlMLkFDLlVLPgpSZXBseVRvOumjnum4 nyA8bGloYW9yYW4wNjAzQDE2My5DT00+ClN1YmplY3Q6W1NQTV0gQ2FuIERDTSBkZWFsIHdpdGgg c2hvcnQtdGVybSBzaWduYWw/CgoKRGVhciBTUE0ncyB1c2VycywK44CA44CAU2ltcGx5IHB1dCwg aWYgSSBoYXZlIGEgbG9uZy10ZXJtIHNpZ25hbCwgc3VjaCBhcyAwfjEwMDAgbXMsIGNvdWxkIEkg ZGl2aWRlIGl0IGludG8gMjAgc2hvcnQtdGVybSBzaWduYWxzKDB+NTAsNTF+MTAwLC4uLjk1Nn4x MDAwKSB0byBkbyBEQ00gYW5hbHlzaXM/IEluIG90aGVyIHdvcmRzLCBkb2VzIERDTSBoYXMgYW55 IHByZWZlcmVuY2VzIGZvciB0aGUgdGltZSBvZiBzaWduYWwsIGxvbmcgb3Igc2hvcnQ/IE9yIG5l aXRoZXIgb2YgdGhlbT8KICAgICAgIFRoYW5rIHlvdSB2ZXJ5IG11Y2ggZm9yIGFueSBhZHZpY2Uh CgoKLS0KCkhhb3JhbiBMSSAoTVMpCkJyYWluIEltYWdpbmcgTGFiLApSZXNlYXJjaCBDZW50ZXIg Zm9yIExlYXJuaW5nIFNjaWVuY2UsClNvdXRoZWFzdCBVbml2ZXJzaXR5CjIgU2kgUGFpIExvdSAs IE5hbmppbmcsIDIxMDA5NiwgUC5SLkNoaW5hCgoKCgoKCg== ------=_Part_191472_20857236.1306401275926 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: base64 PERJVj5EZWFyIFZsYWRpbWlyLDwvRElWPgo8RElWPuOAgOOAgERvIHlvdSBtZWFuIGlmIEkgc2V0 IHRpbWV3aW5kb3c9IFsyNTAgMzAwXSwgb25zZXQgdGltZT0gNjAgaXMgaW5hY2Nlc3NpYmxlIHdo ZW4gSSBkbyBEQ00gZm9yIEVSUD88QlI+44CA44CASGFvcmFuLjwvRElWPgo8RElWPuOAgOOAgDwv RElWPgo8RElWPkF0IDIwMTEtMDUtMjYgMTY6NTY6MDTvvIw8QSBocmVmPSJtYWlsdG86bGl0dmFr LnZsYWRpbWlyQGdtYWlsLmNvbSI+bGl0dmFrLnZsYWRpbWlyQGdtYWlsLmNvbTwvQT4gd3JvdGU6 PEJSPjwvRElWPgo8QkxPQ0tRVU9URSBzdHlsZT0iQk9SREVSLUxFRlQ6ICNjY2MgMXB4IHNvbGlk OyBNQVJHSU46IDBweCAwcHggMHB4IDAuOGV4OyBQQURESU5HLUxFRlQ6IDFleCIgaWQ9ImlzUmVw bHlDb250ZW50Ij5EZWFyIEhhb3Jhbiw8QlI+PEJSPklmIHlvdSBhcmUgdGFsa2luZyBhYm91dCBE Q00gZm9yIEVSUCwgeW91IHNob3VsZCBhbHdheXMgaW5jbHVkZSB0aGUgb25zZXQgb2YgdGhlIEVS UCBpbiB5b3VyIG1vZGVsLiBZb3UgY2Fubm90IHN0YXJ0IG1vZGVsbGluZyBmcm9tIHRoZSBtaWRk bGUgb2YgdGhlIHdhdmVmb3JtIGJlY2F1c2UgdGhlIG1vZGVsIHNob3VsZCAna25vdycgaG93IHRv IGdldCB0byB0aGF0IHBvaW50LiBJZiB5b3UgaW5jbHVkZSB0aGUgb25zZXQgeW91IGNhbiBjaG9v c2Ugd2hlbiB0byBlbmQgdGhlIEVSUC4gVGhpcyB3YXMgZG9uZSBzeXN0ZW1hdGljYWxseSBpbiBN YXJ0YSBHYXJyaWRvJ3MgcGFwZXIgYWJvdXQgZmVlZGJhY2sgbG9vcHMuPEJSPjxCUj5CZXN0LDxC Uj48QlI+VmxhZGltaXIgCjxQPlNlbnQgdXNpbmcgQmxhY2tCZXJyecKuIGZyb20gT3JhbmdlPC9Q Pgo8SFI+Cgo8RElWPjxCPkZyb206IDwvQj7po57puJ8gJmx0OzxBIGhyZWY9Im1haWx0bzpsaWhh b3JhbjA2MDNAMTYzLkNPTSI+bGloYW9yYW4wNjAzQDE2My5DT008L0E+Jmd0OyA8L0RJVj4KPERJ Vj48Qj5TZW5kZXI6IDwvQj4iU1BNIChTdGF0aXN0aWNhbCBQYXJhbWV0cmljIE1hcHBpbmcpIiAm bHQ7PEEgaHJlZj0ibWFpbHRvOlNQTUBKSVNDTUFJTC5BQy5VSyI+U1BNQEpJU0NNQUlMLkFDLlVL PC9BPiZndDsgPC9ESVY+CjxESVY+PEI+RGF0ZTogPC9CPlRodSwgMjYgTWF5IDIwMTEgMTY6MDg6 NDEgKzA4MDA8L0RJVj4KPERJVj48Qj5UbzogPC9CPiZsdDs8QSBocmVmPSJtYWlsdG86U1BNQEpJ U0NNQUlMLkFDLlVLIj5TUE1ASklTQ01BSUwuQUMuVUs8L0E+Jmd0OzwvRElWPgo8RElWPjxCPlJl cGx5VG86IDwvQj7po57puJ8gJmx0OzxBIGhyZWY9Im1haWx0bzpsaWhhb3JhbjA2MDNAMTYzLkNP TSI+bGloYW9yYW4wNjAzQDE2My5DT008L0E+Jmd0OyA8L0RJVj4KPERJVj48Qj5TdWJqZWN0OiA8 L0I+W1NQTV0gQ2FuIERDTSBkZWFsIHdpdGggc2hvcnQtdGVybSBzaWduYWw/PC9ESVY+CjxESVY+ PEJSPjwvRElWPgo8RElWPjwvRElWPgo8RElWPgo8RElWPkRlYXIgU1BNJ3MgdXNlcnMsPC9ESVY+ CjxESVY+44CA44CAU2ltcGx5IHB1dCwgaWYgSSBoYXZlIGEgbG9uZy10ZXJtIHNpZ25hbCwgc3Vj aCBhcyAwfjEwMDAgbXMsIGNvdWxkIEkgZGl2aWRlIGl0IGludG8gMjAgc2hvcnQtdGVybSBzaWdu YWxzKDB+NTAsNTF+MTAwLC4uLjk1Nn4xMDAwKSB0byBkbyBEQ00gYW5hbHlzaXM/IEluIG90aGVy IHdvcmRzLCBkb2VzIERDTSBoYXMgYW55IHByZWZlcmVuY2VzJm5ic3A7Zm9yIHRoZSB0aW1lIG9m IHNpZ25hbCwgbG9uZyBvciBzaG9ydD8gT3IgbmVpdGhlciBvZiB0aGVtPzwvRElWPgo8RElWPiZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBUaGFuayB5b3UgdmVyeSBtdWNoIGZv ciBhbnkgYWR2aWNlITxCUj48QlI+PC9ESVY+PC9ESVY+CjxESVY+LS08QlI+CjxESVY+PEZPTlQg Y29sb3I9IiMwMDgwMDAiIGZhY2U9IkFyaWFsIj5IYW9yYW4gTEkgKE1TKTxCUj5CcmFpbiBJbWFn aW5nIExhYiw8QlI+UmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTPEZPTlQgY29sb3I9IiMw MDgwMDAiPmNpPC9GT05UPmVuY2UsPEJSPlNvdXRoZWFzdCBVbml2ZXJzaXR5PEJSPjIgU2kgUGFp IExvdSAsIE5hbmppbmcsIDIxMDA5NiwgUC5SLkNoaW5hPC9GT05UPjwvRElWPjwvRElWPjxCUj48 QlI+PFNQQU4gdGl0bGU9Im5ldGVhc2Vmb290ZXIiPjxTUEFOIGlkPSJuZXRlYXNlX21haWxfZm9v dGVyIj48L1NQQU4+PC9TUEFOPjxCUj48QlI+PFNQQU4gdGl0bGU9Im5ldGVhc2Vmb290ZXIiPjxT UEFOIGlkPSJuZXRlYXNlX21haWxfZm9vdGVyIj48L1NQQU4+PC9TUEFOPjwvQkxPQ0tRVU9URT48 QlI+PEJSPjxTUEFOIHRpdGxlPSJuZXRlYXNlZm9vdGVyIj48U1BBTiBpZD0ibmV0ZWFzZV9tYWls X2Zvb3RlciI+PC9TUEFOPjwvU1BBTj48YnI+PGJyPjxzcGFuIHRpdGxlPSJuZXRlYXNlZm9vdGVy Ij48c3BhbiBpZD0ibmV0ZWFzZV9tYWlsX2Zvb3RlciI+PC9zcGFuPjwvc3Bhbj4= ------=_Part_191472_20857236.1306401275926-- ========================================================================= Date: Thu, 26 May 2011 09:19:27 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Can DCM deal with short-term signal? Comments: To: =?utf-8?B?6aOe6bif?= <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/alternative; boundary="part6247-boundary-1877249416-1807820102" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --part6247-boundary-1877249416-1807820102 Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="utf-8" VGhlIGRhdGEgdGhhdCBpcyBub3QgaW5jbHVkZWQgaW4gdGhlIHRpbWUgd2luZG93IGRvZXMgbm90 IGV4aXN0IGZvciBEQ00uDQoNClZsYWRpbWlyDQpTZW50IHVzaW5nIEJsYWNrQmVycnnCriBmcm9t IE9yYW5nZQ0KDQotLS0tLU9yaWdpbmFsIE1lc3NhZ2UtLS0tLQ0KRnJvbTog6aOe6bifIDxsaWhh b3JhbjA2MDNAMTYzLmNvbT4NCkRhdGU6IFRodSwgMjYgTWF5IDIwMTEgMTc6MTM6NTggDQpUbzog PGxpdHZhay52bGFkaW1pckBnbWFpbC5jb20+DQpTdWJqZWN0OiBSZTpSZTogW1NQTV0gQ2FuIERD TSBkZWFsIHdpdGggc2hvcnQtdGVybSBzaWduYWw/DQoNCkRlYXIgVmxhZGltaXIsCuOAgOOAgERv IHlvdSBtZWFuIGlmIEkgc2V0IHRpbWV3aW5kb3c9IFsyNTAgMzAwXSwgb25zZXQgdGltZT0gNjAg aXMgaW5hY2Nlc3NpYmxlIHdoZW4gSSBkbyBEQ00gZm9yIEVSUD8K44CA44CASGFvcmFuLgrjgIDj gIAKQXQgMjAxMS0wNS0yNiAxNjo1NjowNO+8jGxpdHZhay52bGFkaW1pckBnbWFpbC5jb20gd3Jv dGU6CgpEZWFyIEhhb3JhbiwKCklmIHlvdSBhcmUgdGFsa2luZyBhYm91dCBEQ00gZm9yIEVSUCwg eW91IHNob3VsZCBhbHdheXMgaW5jbHVkZSB0aGUgb25zZXQgb2YgdGhlIEVSUCBpbiB5b3VyIG1v ZGVsLiBZb3UgY2Fubm90IHN0YXJ0IG1vZGVsbGluZyBmcm9tIHRoZSBtaWRkbGUgb2YgdGhlIHdh dmVmb3JtIGJlY2F1c2UgdGhlIG1vZGVsIHNob3VsZCAna25vdycgaG93IHRvIGdldCB0byB0aGF0 IHBvaW50LiBJZiB5b3UgaW5jbHVkZSB0aGUgb25zZXQgeW91IGNhbiBjaG9vc2Ugd2hlbiB0byBl bmQgdGhlIEVSUC4gVGhpcyB3YXMgZG9uZSBzeXN0ZW1hdGljYWxseSBpbiBNYXJ0YSBHYXJyaWRv J3MgcGFwZXIgYWJvdXQgZmVlZGJhY2sgbG9vcHMuCgpCZXN0LAoKVmxhZGltaXIKClNlbnQgdXNp bmcgQmxhY2tCZXJyecKuIGZyb20gT3JhbmdlCgpGcm9tOumjnum4nyA8bGloYW9yYW4wNjAzQDE2 My5DT00+ClNlbmRlcjoiU1BNIChTdGF0aXN0aWNhbCBQYXJhbWV0cmljIE1hcHBpbmcpIiA8U1BN QEpJU0NNQUlMLkFDLlVLPgpEYXRlOlRodSwgMjYgTWF5IDIwMTEgMTY6MDg6NDEgKzA4MDAKVG86 PFNQTUBKSVNDTUFJTC5BQy5VSz4KUmVwbHlUbzrpo57puJ8gPGxpaGFvcmFuMDYwM0AxNjMuQ09N PgpTdWJqZWN0OltTUE1dIENhbiBEQ00gZGVhbCB3aXRoIHNob3J0LXRlcm0gc2lnbmFsPwoKCkRl YXIgU1BNJ3MgdXNlcnMsCuOAgOOAgFNpbXBseSBwdXQsIGlmIEkgaGF2ZSBhIGxvbmctdGVybSBz aWduYWwsIHN1Y2ggYXMgMH4xMDAwIG1zLCBjb3VsZCBJIGRpdmlkZSBpdCBpbnRvIDIwIHNob3J0 LXRlcm0gc2lnbmFscygwfjUwLDUxfjEwMCwuLi45NTZ+MTAwMCkgdG8gZG8gRENNIGFuYWx5c2lz PyBJbiBvdGhlciB3b3JkcywgZG9lcyBEQ00gaGFzIGFueSBwcmVmZXJlbmNlcyBmb3IgdGhlIHRp bWUgb2Ygc2lnbmFsLCBsb25nIG9yIHNob3J0PyBPciBuZWl0aGVyIG9mIHRoZW0/CiAgICAgICBU aGFuayB5b3UgdmVyeSBtdWNoIGZvciBhbnkgYWR2aWNlIQoKCi0tCgpIYW9yYW4gTEkgKE1TKQpC cmFpbiBJbWFnaW5nIExhYiwKUmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTY2llbmNlLApT b3V0aGVhc3QgVW5pdmVyc2l0eQoyIFNpIFBhaSBMb3UgLCBOYW5qaW5nLCAyMTAwOTYsIFAuUi5D aGluYQoKCgoK --part6247-boundary-1877249416-1807820102 Content-Transfer-Encoding: base64 Content-Type: text/html; charset="utf-8" PCFET0NUWVBFIGh0bWwgUFVCTElDICItLy9XM0MvL0RURCBIVE1MIDQuMCBUcmFuc2l0aW9uYWwv L0VOIj4gPGh0bWw+PGhlYWQ+IDxtZXRhIGNvbnRlbnQ9InRleHQvaHRtbDsgY2hhcnNldD11dGYt OCIgaHR0cC1lcXVpdj0iQ29udGVudC1UeXBlIj4gPC9oZWFkPlRoZSBkYXRhIHRoYXQgaXMgbm90 IGluY2x1ZGVkIGluIHRoZSB0aW1lIHdpbmRvdyBkb2VzIG5vdCBleGlzdCBmb3IgRENNLjxici8+ PGJyLz5WbGFkaW1pcjxwPlNlbnQgdXNpbmcgQmxhY2tCZXJyecKuIGZyb20gT3JhbmdlPC9wPjxo ci8+PGRpdj48Yj5Gcm9tOiA8L2I+IOmjnum4nyAmbHQ7bGloYW9yYW4wNjAzQDE2My5jb20mZ3Q7 DQo8L2Rpdj48ZGl2PjxiPkRhdGU6IDwvYj5UaHUsIDI2IE1heSAyMDExIDE3OjEzOjU4ICswODAw IChDU1QpPC9kaXY+PGRpdj48Yj5UbzogPC9iPiZsdDtsaXR2YWsudmxhZGltaXJAZ21haWwuY29t Jmd0OzwvZGl2PjxkaXY+PGI+U3ViamVjdDogPC9iPlJlOlJlOiBbU1BNXSBDYW4gRENNIGRlYWwg d2l0aCBzaG9ydC10ZXJtIHNpZ25hbD88L2Rpdj48ZGl2Pjxici8+PC9kaXY+PERJVj5EZWFyIFZs YWRpbWlyLDwvRElWPgo8RElWPuOAgOOAgERvIHlvdSBtZWFuIGlmIEkgc2V0IHRpbWV3aW5kb3c9 IFsyNTAgMzAwXSwgb25zZXQgdGltZT0gNjAgaXMgaW5hY2Nlc3NpYmxlIHdoZW4gSSBkbyBEQ00g Zm9yIEVSUD88QlI+44CA44CASGFvcmFuLjwvRElWPgo8RElWPuOAgOOAgDwvRElWPgo8RElWPkF0 IDIwMTEtMDUtMjYgMTY6NTY6MDTvvIxsaXR2YWsudmxhZGltaXJAZ21haWwuY29tIHdyb3RlOjxC Uj48L0RJVj4KPEJMT0NLUVVPVEUgc3R5bGU9IkJPUkRFUi1MRUZUOiAjY2NjIDFweCBzb2xpZDsg TUFSR0lOOiAwcHggMHB4IDBweCAwLjhleDsgUEFERElORy1MRUZUOiAxZXgiIGlkPSJpc1JlcGx5 Q29udGVudCI+RGVhciBIYW9yYW4sPEJSPjxCUj5JZiB5b3UgYXJlIHRhbGtpbmcgYWJvdXQgRENN IGZvciBFUlAsIHlvdSBzaG91bGQgYWx3YXlzIGluY2x1ZGUgdGhlIG9uc2V0IG9mIHRoZSBFUlAg aW4geW91ciBtb2RlbC4gWW91IGNhbm5vdCBzdGFydCBtb2RlbGxpbmcgZnJvbSB0aGUgbWlkZGxl IG9mIHRoZSB3YXZlZm9ybSBiZWNhdXNlIHRoZSBtb2RlbCBzaG91bGQgJ2tub3cnIGhvdyB0byBn ZXQgdG8gdGhhdCBwb2ludC4gSWYgeW91IGluY2x1ZGUgdGhlIG9uc2V0IHlvdSBjYW4gY2hvb3Nl IHdoZW4gdG8gZW5kIHRoZSBFUlAuIFRoaXMgd2FzIGRvbmUgc3lzdGVtYXRpY2FsbHkgaW4gTWFy dGEgR2FycmlkbydzIHBhcGVyIGFib3V0IGZlZWRiYWNrIGxvb3BzLjxCUj48QlI+QmVzdCw8QlI+ PEJSPlZsYWRpbWlyCjxQPlNlbnQgdXNpbmcgQmxhY2tCZXJyecKuIGZyb20gT3JhbmdlPC9QPgo8 SFI+Cgo8RElWPjxCPkZyb206IDwvQj7po57puJ8gJmx0OzxBIGhyZWY9Im1haWx0bzpsaWhhb3Jh bjA2MDNAMTYzLkNPTSI+bGloYW9yYW4wNjAzQDE2My5DT008L0E+Jmd0OyA8L0RJVj4KPERJVj48 Qj5TZW5kZXI6IDwvQj4iU1BNIChTdGF0aXN0aWNhbCBQYXJhbWV0cmljIE1hcHBpbmcpIiAmbHQ7 PEEgaHJlZj0ibWFpbHRvOlNQTUBKSVNDTUFJTC5BQy5VSyI+U1BNQEpJU0NNQUlMLkFDLlVLPC9B PiZndDsgPC9ESVY+CjxESVY+PEI+RGF0ZTogPC9CPlRodSwgMjYgTWF5IDIwMTEgMTY6MDg6NDEg KzA4MDA8L0RJVj4KPERJVj48Qj5UbzogPC9CPiZsdDs8QSBocmVmPSJtYWlsdG86U1BNQEpJU0NN QUlMLkFDLlVLIj5TUE1ASklTQ01BSUwuQUMuVUs8L0E+Jmd0OzwvRElWPgo8RElWPjxCPlJlcGx5 VG86IDwvQj7po57puJ8gJmx0OzxBIGhyZWY9Im1haWx0bzpsaWhhb3JhbjA2MDNAMTYzLkNPTSI+ bGloYW9yYW4wNjAzQDE2My5DT008L0E+Jmd0OyA8L0RJVj4KPERJVj48Qj5TdWJqZWN0OiA8L0I+ W1NQTV0gQ2FuIERDTSBkZWFsIHdpdGggc2hvcnQtdGVybSBzaWduYWw/PC9ESVY+CjxESVY+PEJS PjwvRElWPgo8RElWPjwvRElWPgo8RElWPgo8RElWPkRlYXIgU1BNJ3MgdXNlcnMsPC9ESVY+CjxE SVY+44CA44CAU2ltcGx5IHB1dCwgaWYgSSBoYXZlIGEgbG9uZy10ZXJtIHNpZ25hbCwgc3VjaCBh cyAwfjEwMDAgbXMsIGNvdWxkIEkgZGl2aWRlIGl0IGludG8gMjAgc2hvcnQtdGVybSBzaWduYWxz KDB+NTAsNTF+MTAwLC4uLjk1Nn4xMDAwKSB0byBkbyBEQ00gYW5hbHlzaXM/IEluIG90aGVyIHdv cmRzLCBkb2VzIERDTSBoYXMgYW55IHByZWZlcmVuY2VzJm5ic3A7Zm9yIHRoZSB0aW1lIG9mIHNp Z25hbCwgbG9uZyBvciBzaG9ydD8gT3IgbmVpdGhlciBvZiB0aGVtPzwvRElWPgo8RElWPiZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBUaGFuayB5b3UgdmVyeSBtdWNoIGZvciBh bnkgYWR2aWNlITxCUj48QlI+PC9ESVY+PC9ESVY+CjxESVY+LS08QlI+CjxESVY+PEZPTlQgY29s b3I9IiMwMDgwMDAiIGZhY2U9IkFyaWFsIj5IYW9yYW4gTEkgKE1TKTxCUj5CcmFpbiBJbWFnaW5n IExhYiw8QlI+UmVzZWFyY2ggQ2VudGVyIGZvciBMZWFybmluZyBTPEZPTlQgY29sb3I9IiMwMDgw MDAiPmNpPC9GT05UPmVuY2UsPEJSPlNvdXRoZWFzdCBVbml2ZXJzaXR5PEJSPjIgU2kgUGFpIExv dSAsIE5hbmppbmcsIDIxMDA5NiwgUC5SLkNoaW5hPC9GT05UPjwvRElWPjwvRElWPjxCUj48QlI+ PFNQQU4gdGl0bGU9Im5ldGVhc2Vmb290ZXIiPjxTUEFOIGlkPSJuZXRlYXNlX21haWxfZm9vdGVy Ij48L1NQQU4+PC9TUEFOPjxCUj48QlI+PFNQQU4gdGl0bGU9Im5ldGVhc2Vmb290ZXIiPjxTUEFO IGlkPSJuZXRlYXNlX21haWxfZm9vdGVyIj48L1NQQU4+PC9TUEFOPjwvQkxPQ0tRVU9URT48YnI+ PGJyPjxzcGFuIHRpdGxlPSJuZXRlYXNlZm9vdGVyIj48c3BhbiBpZD0ibmV0ZWFzZV9tYWlsX2Zv b3RlciI+PC9zcGFuPjwvc3Bhbj48L2h0bWw+ --part6247-boundary-1877249416-1807820102-- ========================================================================= Date: Thu, 26 May 2011 10:43:35 +0100 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jing Kan <[log in to unmask]> Subject: Question about the "MEG" and "MEGPLANAR" modalities of Elekta system. MIME-Version: 1.0 Content-Type: text/plain;charset=iso-8859-1 Content-Transfer-Encoding: 8bit Message-ID: <[log in to unmask]> Dear SPM experts, Thanks for concern! I am doing the "3D source reconstruction" with the MEG data from Elekta system. As I know from the SPM tutorial, SPM treats the MEG data of the Elekta system as two different modalities: "MEG" and "MEGPLANAR". There are two methods used for 3D source reconstruction: "imaging", "VB-ECD" and "DCM". The method "imaging" can let me select both "MEG" and "MEGPLANAR" modalities for reconstruction. However, in the methods "VB-ECD" and "DCM", I can only select either "MEG" or "MEGPLANAR" for reconstruction. May I ask how can I deal with this two modalities in "VB-ECD" and "DCM". How this selection will affect my reconstruction result? Many thanks for your help in advance! Kind regards, Jing ========================================================================= Date: Thu, 26 May 2011 10:10:16 +0000 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Vladimir Litvak <[log in to unmask]> Subject: Re: Question about the "MEG" and "MEGPLANAR" modalities of Elekta system. Comments: To: [log in to unmask] In-Reply-To: <[log in to unmask]> Content-Transfer-Encoding: base64 Content-Type: text/plain; charset="Windows-1252" MIME-Version: 1.0 Message-ID: <[log in to unmask]> RGVhciBKaW5nLA0KDQpBdCB0aGUgbW9tZW50IHRoZXJlIGlzIG5vIHdheSB0byBjb21iaW5lIG1v ZGFsaXRpZXMgZm9yIFZCLUVDRCBhbmQgRENNLiBZb3UgY2FuIG9ubHkgd29yayB3aXRoIGVhY2gg bW9kYWxpdHkgc2VwYXJhdGVseSBhbmQgY29tcGFyZSB0aGUgcmVzdWx0cy4NCg0KQmVzdCwNCg0K VmxhZGltaXINClNlbnQgdXNpbmcgQmxhY2tCZXJyea4gZnJvbSBPcmFuZ2UNCg0KLS0tLS1Pcmln aW5hbCBNZXNzYWdlLS0tLS0NCkZyb206ICAgICAgICAgSmluZyBLYW4gPGthbmppbmdAQ1MuWU9S Sy5BQy5VSz4NClNlbmRlcjogICAgICAgIlNQTSAoU3RhdGlzdGljYWwgUGFyYW1ldHJpYyBNYXBw aW5nKSIgPFNQTUBKSVNDTUFJTC5BQy5VSz4NCkRhdGU6ICAgICAgICAgVGh1LCAyNiBNYXkgMjAx MSAxMDo0MzozNSANClRvOiA8U1BNQEpJU0NNQUlMLkFDLlVLPg0KUmVwbHktVG86ICAgICBrYW5q aW5nQENTLllPUksuQUMuVUsNClN1YmplY3Q6IFtTUE1dIFF1ZXN0aW9uIGFib3V0IHRoZSAiTUVH IiBhbmQgIk1FR1BMQU5BUiIgbW9kYWxpdGllcyBvZiBFbGVrdGEgc3lzdGVtLg0KDQpEZWFyIFNQ TSBleHBlcnRzLA0KDQpUaGFua3MgZm9yIGNvbmNlcm4hIEkgYW0gZG9pbmcgdGhlICIzRCBzb3Vy Y2UgcmVjb25zdHJ1Y3Rpb24iIHdpdGggdGhlIE1FRw0KZGF0YSBmcm9tIEVsZWt0YSBzeXN0ZW0u ICBBcyBJIGtub3cgZnJvbSB0aGUgU1BNIHR1dG9yaWFsLCBTUE0gdHJlYXRzIHRoZQ0KTUVHIGRh dGEgb2YgdGhlIEVsZWt0YSBzeXN0ZW0gYXMgdHdvIGRpZmZlcmVudCBtb2RhbGl0aWVzOiAiTUVH IiBhbmQNCiJNRUdQTEFOQVIiLg0KDQpUaGVyZSBhcmUgdHdvIG1ldGhvZHMgdXNlZCBmb3IgM0Qg c291cmNlIHJlY29uc3RydWN0aW9uOiAiaW1hZ2luZyIsDQoiVkItRUNEIiBhbmQgIkRDTSIuICBU aGUgbWV0aG9kICJpbWFnaW5nIiBjYW4gbGV0IG1lIHNlbGVjdCBib3RoICJNRUciIGFuZA0KIk1F R1BMQU5BUiIgbW9kYWxpdGllcyBmb3IgcmVjb25zdHJ1Y3Rpb24uIEhvd2V2ZXIsICAgaW4gdGhl IG1ldGhvZHMNCiJWQi1FQ0QiIGFuZCAiRENNIiwgSSBjYW4gb25seSBzZWxlY3QgZWl0aGVyICJN RUciIG9yICJNRUdQTEFOQVIiIGZvcg0KcmVjb25zdHJ1Y3Rpb24uDQoNCk1heSBJIGFzayBob3cg Y2FuIEkgZGVhbCB3aXRoIHRoaXMgdHdvIG1vZGFsaXRpZXMgaW4gICJWQi1FQ0QiIGFuZCAiRENN Ii4NCkhvdyB0aGlzIHNlbGVjdGlvbiB3aWxsIGFmZmVjdCBteSByZWNvbnN0cnVjdGlvbiByZXN1 bHQ/DQoNCk1hbnkgdGhhbmtzIGZvciB5b3VyIGhlbHAgaW4gYWR2YW5jZSENCg0KS2luZCByZWdh cmRzLA0KDQpKaW5nDQo= ========================================================================= Date: Thu, 26 May 2011 11:51:10 +0100 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: second level analysis affected by subject order Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA" Message-ID: <[log in to unmask]> --part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA Content-Type: multipart/alternative; boundary="part-yMkeS14zP22j4AARfnvAqArZMxAQUt0SA4XxiEYjSP08f" This is a multi-part message in MIME format. --part-yMkeS14zP22j4AARfnvAqArZMxAQUt0SA4XxiEYjSP08f Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Hi, we've noticed something strange in our second level analyses lately. = The order in which betas from the first level are entered into the cells = of a second level factorial analysis seems to significantly affect the re= sults. I ran a couple of tests to check this out; the design was a 2x2x2 = factorial, leading to 8 cells. In each test, the individual subject betas= were entered into all 8 cells in a consistent order, but I varied that o= rder across tests. For example, in test1 the betas were entered in numeri= cal order (i.e. sub1, sub2, sub3....) in every cell, while in test2 and t= est3 different orders were used (e.g. sub3, sub9 sub1, sub11...), though = in each case the same order was used in every cell. In each case, the same t contrast was then run, and the results were rath= er different. See the attached jpeg files for an illustration - the size,= peak coordinates and p values of the activated clusters changed dependin= g on the order in which the subject data was entered. This seems rather s= trange to me, as the order in which participants are recruited and number= ed is obviously arbitrary, and shouldn't affect the outcome of statistica= l tests. I would be grateful if anyone could shed some light on this stra= nge finding. Regards, Ciara Greene --part-yMkeS14zP22j4AARfnvAqArZMxAQUt0SA4XxiEYjSP08f Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="UTF-8" --part-yMkeS14zP22j4AARfnvAqArZMxAQUt0SA4XxiEYjSP08f-- --part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="test1.jpg" /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRof Hh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwh MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAAR CAMyAjgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiuf8VeJ/wDhGf7E/wBD+0/2nqsGm/63Z5Xmbvn6HONvTjOe oroKACiiuf8A+En/AOLh/wDCKfY/+YV/aX2rzf8Apr5ezZj8c59sUAdBRUc88Nrby3FxLHDB EheSSRgqooGSSTwABzmpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPP/in/AMyV/wBjXY/+ z15Z4mvV/tnxPrE2ralH4/sNdS10O1UNu+zbsRoke3DI6M5PY4XP+sO/6LurCzvvI+2WkFx5 Eqzw+dGH8uRfuuuejDJwRyKjk0nTZtUh1SXT7R9QhTZFdtCplReeFfGQPmbgHufWgDwv4kTa dceNPFr+JdXvrK60nT4J/DCI7RDzCFZnjwvzN5oVSc5xu/55goeIm8a33ia1l0iXyNfk8CxS 3xkjZJ/9bukWJVX5Zi2FAwMZOMHBHul5pOm6hcWtxe6faXM9o++2kmhV2hbIOUJGVOVByPQe lSfYLP8AtH+0fskH27yvI+0+WPM8vO7Zu67c846ZoA5/RLrwsnw1gvLG3gi8MjT2laEoJFSH aTIrqN25h8wcfMS27OTmuP8AAej67beI4bnQ7DVdB8Gjdv03WLkO8nyMB5UJVngxLlmy/wA+ 4EccV6hY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJP41YoA8z+IWl2mtfErwFpt+kj2lymppK iSvGWXyFyNyEHB6EZ5GQeCa4j4dRGHVPhbqS3F213qNvqdrdO9zI4eGHd5Ue0sQqLgEKABkA 9RXvc1hZ3F5bXk1pBJdWu77PM8YLxbhhtrHlcjg461Xt9C0e0+x/ZtKsYfsO/wCyeXbov2ff 9/ZgfLu74xnvQB4R8J7fU28fWGq3N7pUWpXn2/8AtW1jEzX0nzkt9oj2mODEoQj/AFeRx8xO Kp+CfD+n3ifDdJvtezWrfVrXUFS9mQTwxs7LH8rjam7JKrgEkk5zX0Ha6Fo9jqM+o2elWNvf T7vOuYbdEkk3Hc25gMnJAJz1NFvoWj2n2P7NpVjD9h3/AGTy7dF+z7/v7MD5d3fGM96AOT+D E81z8JNBeeWSVwkqBnYsQqzOqjnsFAAHYACu8qvY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJ P41YoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKxqKANmisaom3fa48fc2Nnr1yuPb168+negDeorJj ieVsIPqfSprexxLP5jFh5g24yONq+vvnpx+OaANCiqV3Pa2EYaSEsME8KCePrWbpPiW3vriC xWKYTbcOzfdyBzgkknp35oA36KinnW3QO4JBOOKqX7W9zbLtaOTE8IJUg4/eLQBoUVUksVPM bbfY8iqE8E0N3HuH7vY2SN2M5XHt69efTvQBtUVjVFFu8yfd03jb16bV9eOuenH45oA3qKxq it93lnf13v69Nxx156fh6cYoA3qKxG3botnXzY/XpvGenPT8PXjNbdABRRRQAUVXv939nXWz 7/lPt69cH+7z+XNZ1AGzRWC277XHj7mxs9euVx7evXn071LQBs0VmQ2zzDcMBc9TWbqus2Ol vNavFPJcbCFIA25K+xB7j3oA6WiqGmanDqkLvDGyxrhcOBk8egqOazQ61btnC+RKSo/3o6AN Oiq63Nu0ot4yrZH8OCBSyWkbg7Rsb1H+FAE9FZN5ZzJGpi+f50zjPTcM9Oen4evFMoA2aKwV 3fa5M/c2Ljr1y2fb06c+vapaANmimQ/6iP8A3R/Kn0AFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAY1FFF ABVK/v7fT3jluHYKQV2qMnnHPUDt9fTvUk99FbTCN1YkjdhcdM1yXiHUYrpwJDt5+RN4JAye lAHU2niNLu5hSLFvFkBlYg9/WuggdJGmZJA439s4Hyjjk4/Lj8c15XpcvJHmBmIyCG6c1tRa w1p5kQmlV2bOQ/BPA9fagD0CobaJEhjYKN5QZbaQT9c8/nzXMWfi7zJ3s3jaSYglZQRgce1c /f8AjWTR2jt55isagogDBNoAGOvPT1oA6PxX4sg0aF4EDNMQwOCuPu9OfqK4Hwfr95e67b28 /KPPHg/L2Oewqh4jhvvEMqTwyuoY78s+D0FdT4R0KDTo0ecZuvtELJjBCksA3b+VAHb6hapa 2LGNiSXX7xHv/jXNweL5NOvjHfq8sCoFTZgFenbgHp35+mTXV6xaz3dokcGNwkBPOOMGuN8R aWREihAZ1UBxxyc+v0IoA6mHUdM1OCKa3u4EkmxiN5FDEnsQD1piAiSYEnO/oQRjgev9OPxz XmkUFxBGGV2jIIKMr4IIPb0rR0zx35GozwaqksxnZSksSoCH+7yOOCAOc8Y6cmgDvajh4Q8k /O3UEfxH1/8A1enFSAqwDI6up6MhyD9DUVuipGQowN7n7pHJYk9f/wBR7cUASdXi5I/ep0BP 8Q9P/wBXrxW1WIyK7RBhkebGfuk8hwR0/wD1DvxW3QAUUUUAQXvNjcDJH7puQCSOPQc/lzWb WjfosmnXSMMq0Tg/KW4wew5P4c1nUAM2lrqMAnJVhjBweR36fnz6d61Le2EPzNgv/KoobRYr qORsGQIwB2k4BK9+g6fU9uhq3Lv8p/L+/tO369qAHVQvraEJNcOTu2NgHpnb7c/kc1z+rale 6WWMlyVdw2VDg4HB6dutczfeI79t0txcytauMCIEZ6UAXdQ8Q3NpiAMGj3buq8nH0rW03XH1 GZJJZlG2J1OSoyDtJHTvivNLrXI7qRVS3fOeS7g9vpXRWdlci5t/LbajAHAfGf8APFACWfiS 70zx2lpOAYGdBkMvQrz29/WvVre7t7pQ0MqPkZIVgSPrivCtb0+6HiT+1CxWJNuQH9sVu+Ft cla6Jtrx2UAZ+cEEZoA9am5QckfOvQE/xD0//V68ZpLiATJjOGHQ1ymo65LcxRpGWQpgsMjl gQQfzFaVgbq/091+0yCVR8rbsdSepHPagCRo2iu5FbP3F4wcdTyD0/Ln17UtU47kWs0sV4S8 uAol5JG0twSTyOT/AJxi4QQcEYIoA1of9RH/ALo/lT6ZD/qI/wDdH8qfQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQBjVR1bUo9J097uRd20gKm7BYk9B+GT+FXWZUQszBVUZJJwAK4DxFq8fiS3kt9Pm Bt4nRhJkplsHnkAjrigBlr4j8y5D3UjTSsAOqjHPp2rG8WWl5dyQT6eXXLq2RIBgfMcfyqDT 9Jlhvt8jb0OBy/fiupeQQLDHFycDIz+maAK2l2slrH5kqKzEdmAB5q1q0kIldoo8AgsgyP8A DmhwZboZn4zzlj0zXOeLWvI5V+yllUJj5WxnmgDFs/EN1F4ultjK67S3TGBxmt7VNP8AtU/m TRJMMkqGwRjHv7Yrl4WkEoecEPkkMWzXTzaiqQoBKfMA5H4UAdjY2wbT4T8oJXtgfwit8WiW ek+awDPHcxuG3AkjcvGRXA2/iUPHEjTLG3IwzjngeorrbnXrKXS4rWORQ5aNnwxO7BGe3tQB UtvE1xfXR2zy4Y4GGAAwPQVnXTahcTiSSWR2UY3F8g/5/pWpoNnp8Vwv2cYBcbsucY79fbNb upazZ6dJHBA8ZlCHGBnaCR/F2zjp9OmBkA5CztZ9WhaHGXbG1iQMHNVbLRWeeUXiLuU/KeOu eecV2Ph68je7fkbWXAxzznpV68WwlkuTOyrIh6KQpf5R9c88Zx2x2oAwtP1BNLdbO6ErRNkR kEHaSR9MDqcVrWyqITswVMkhBHQ/OeRye/8AkdK5PxHfJbzpN5WI8lgA3vXReE72HUdKaBpg XWVzEuOVXg4zgZ5JoAvMiu0QYZHmxn7pPIcEdP8A9Q78Vt1jTIYpY1ZN5E0fG3d/EOce3XPb r2rZoAKKKKAK9+iyaddIwyrROD8pbjB7Dk/hzVe0VAWlkICr3bgZqxfnGnXR2b/3T/Ls3buD xjv9KydWLw6bHtYruyWwevIxQAS+ItPFwJRkvHG6jOO5XvnjoO3/ANfnrzxLeTTyNFK6pkgB XxgZ9q891fV7oSmK3Z0bkHD9earaTc6r9sKSvIyspY7pRigCx4v8YXl3rYtdzq4JBYbQPuqe w56UurQaldaZCkDkP1OHCk/LQ0KS6w0jTRmRck5kHpiuq+0eUsY+9x8wJ9hjmgDD8NafPHbi HUwXm38NuDELtHGfwNeg3elGzu7ZkdWjMW4MGznG3Pb3rk5NWiF2sKMIC3TOWBwM+ldZFfW0 kUMNzdgultIAd+csQmB+hoA57xK1vbxqkabg6qxPHXv+orBsYbW0K3UH7hEAykeFyd3oK6K5 EV5E0M7Ex5B+9jFcpqVnOjlIZQsIAB+bqaAN7TdQF1cAebvIx1YetejadNG1pEJLhU8vJG2U YbnuK8l8N6f5MyztcApkfx9Pm9xW7da1FZy+W02xT0G/tmgDuNQsIr9LmVAqyKowU6MASc56 Z68dR36iobacuuyVsy85JPWub03xH9nleVZS6shGcnrzjtzgmohrVw2oNcKS4Yk4yB/SgD0e H/UR/wC6P5U+o4CDbxkEEFRgj6VJQAUUUUAFFFFABRRRQAUUUUAFU9U1KHSbBruZZHAdI0jj ALSSO4REGSBlmZVySAM5JAyauVn61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPI AMu68ZW1ojPJpupAW9v9qv8AMaKbGLc675AzguMxS/6rzMhMjIZS2XrvxESx0PVLyy0q+eW2 +1wW8s0SrDJcweZuXlwzKFieQsOCqlQfM+SrmoeEb7UEulk1mNjqVkLHU2ezyXi3SnEGHAiI E8gBbzOAmdxDFo9V8C/2n4eOk/2j5WbvULnzfI3f8fSXK7cbv4ftOc552ds8AFyx8XjUbCW5 ttB1lpI7h7XyDFGGaZHdZFDF9mF8skuWCHIUMXyoI/GVtcEJZ6bqV3MqE3EMMaFrZ/MeII+X AJMsUiblLKNhZmVMPVd/B003hiPS7i9tLidb2e9k86yL2s7SySSFJIC+WQGXIG/hkRsnGDX0 7wG1jFpURvrQJY3EtwPs+nrEYi87TFbdtxMKNuEbqS4aNQo25JIBHpnjq4u9H0a8uLLy5JtP gu72NVBYvOwit44hvwPNk3FWZjtVMOFLZXcj1yb+1NNSa1kt7TUUkijWeMpLDdR7mMbYJDBk WQhh8v7okM4kSufsfC19pCW2kyXMd1Hc6PDpZuxZbolNszsiyxFmykscrq2WUfuyAytIm3Qt tEmtr/w/pieY8GlPNfy3HllIgzpJEkEanhUAmk2qC3lpCin7ytQB1lFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHJ6veRRwNa+YBNKuCh7oQcnP4Vwt1b5s3ezOED DJ3Vo69d26avMw1OKVmUeWFk3FQV6cDA71P4Yksntrm3uS0m8gBQ23Bx16deP8aAOGg12VtQ a3M7FE24Oe/p096dda3ewW8rtckHICkDlRnp0roLjQdNtdQkk0+CVDJjKtLvwPyrN1i3S3aG OWFmEhUZ3EdSaAMW18QyqnmXN0ynrktjvU03jezimjWbUAof3JB59hWzf/D97vS2miikxtJD ZbGc/T2rk/8AhXEt27pcK0ZTJR8sO445XFAGnc3MF3cJcwuHVifnU9ayNQ1hrfVpIwCwViAC 3t9K9F0TwlZadokaXMnzB3O0yHcBx2wKxtW0ixurnAQsVP8AeI7CgCBNMttURbiG4KtxwWxj I+lPjtNRTxBZQJMxtwUD7nHJ75qlqlteW1vH/Z8rIQ2GCEk4xUZ8R3S3UUKyszgKMg98fSgD 063t2t1KxEbmx0PfHNZ93azzXTFySSBzu7VF4b1CW5jb7Y8iZwFZj3I9x/nNS6vq1rpkwO8y evzDIz07dsUAa+iXDaVBKM/f2gnOccnnp/nNZGoTX7eIpZoSfIfJZdw/vdc/TimQa3ZXflhL pYScFUaQc59MgVrCSNoJIhKFAYnOQCwwB6dcj/OKAMLWRdXEX+jt90YGT71qeF5HsWjmIyMs ZE3fh/hWhptvApmN3I+2PP7snBJGBg8Z71c0a0tpbiQRyTqSrBxuUhlyODx345BB+lAGvdvD NHBPFiX99Gvyrv8A4h27Y657de1aFc/pyCJbm0LtK6XasiSKDhQ6jd25/HtwPXoKACiiigCv fnGnXR2b/wB0/wAuzdu4PGO/0rndXvEMSR/aMOiYJydoOemMV0d6C1jcKEDkxMApAIPHTBIz +YrK1XQLe5SaZTN5jNu2qRjqM9s+poA8N1g3M2qLiQFVY5J9N1TTeKLTS5Pst5chRIGwpBHH 1xXUXug22ms81zOTGSWGX6DP0964zVfClh4k1BZLedwU3ALuPPOf7tAGxa6Ql5ML62mDRuW3 fvAMfnXQPNBGxjlkUIikKd3fFQv4b1DTPCcwto5RKNxDKrHAyPb61zem2uqSeZ9uMrYJADg5 7e31oA2ra8tLiYI1uzOc/N5mMcfSn38V+msRGF8wHacBhzVux0i0hkzJJtk2k8t04PtRLqMU C/Z472KaUE4MT5AyB3x7UAZF7fz/AGoQI7DOOhxVmdxHpylpP3pxxnJqeHTFkcTTOAo2kOzG qt7i3thcSZK8Dr70Ac7ceIHtykQux5vO4KQO/wBKxZNfnuLpRLIzkHHzHoM/Spb/AEyJ5l1A yBVJ3FN+WU7vYVrabZQ30rTvbsmeS244PPNAGn4bvTqk0kC42ruAy3oR7e9dototrAzORnB5 zWFoOmWFreNJCGVm3MfmPQ49a7a502CXTBKblc85VnAJGcen+eKANrw/fi/0tWCbfJIhzuzu wo59utatY3hm3jttNdI84MpJyc84FbNABRRRQAUUVHPBDdW8tvcRRzQSoUkjkUMrqRggg8EE cYoAjs7631CBprWTzI1lkhJ2kYeN2jcc+jKw98ccVYrm/A0ENr4bkt7eKOGCLU9QSOONQqoo vJgAAOAAOMV0lABRRRQAUUUUAFFFFABRRXzDoui6VLoWnySaZZu720bMzQKSSVGSTigD6eor 5v8A7C0f/oFWP/gOn+FUdJ0XSpLORn0yzYi5uFy0CngTOAOnYACgD6eor5v/ALC0f/oFWP8A 4Dp/hVGLRdKOu3cZ0yzKLbQMF8hcAlpcnGO+B+QoA+nqK+b/AOwtH/6BVj/4Dp/hVG/0XSkv NMVdMs1D3LKwECjcPJkODxzyAfwoA+nqK+b/AOwtH/6BVj/4Dp/hVHWtF0qLQtQkj0yzR0tp GVlgUEEKcEHFAH09RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUN3v+xT+Xu3+W23b1zjjHvU1 FAHi6+Er7+0szxuzryeG9M+laWi6FqH9qxhIpFiVxubDbenc4r0h7W6IQxajOQWG7csf3fb5 OtR3i3FrB5i3l1JzggCMY9/uUAZU3hVnuxOkiq21QfnPXv26ZqHVrCe2t4JJoI51iGxQhbnn PPQ+vT/CrA1Zw5V7u4THq0X/AMbpLzVYbbyZl1OSVihztERK5xxuCcdOmO3buAOs9ehitWgu GSF9h8vk5J5rCu1ku72Rg6gk9G7/AONKttaX26eSd9yjK5IBJz9Kgku445mDyuDu4PA/HpzQ BieJ7u606OeYOXKjKIGOD06/nWHa6lcXNh55iKuepXPHAP8AWvQodPh1CUBrtSrZ+YuB0/Ct dfB1k0W9pLgTPHtbLKBnHTABx+BP1oA+etR8ZLbarJaliXUlTlm54+ld74c0azvIo5x8zl4w FVjnJx2/GmeOfBulaT/pCwEztKdzeaxByuaz9JeaXTmt7a7ltHLJtmjY5QjB4wR6etJtpXRd OMZTSk7J9e3mdfftaR6rBpMbyLdptaVFB/dpgnLnHGeP++l9asX3heLU7VrnJKqignfx1A+u ev8A9asDRdUNjZpojwfZ1LFw8AJhlbr1I3bsAj5uy9cYFSzeKTbXS2nnYBUKF78kHrj1AqKc nJXZ0YyjGlNRhtbfTXz029Lu3cor4WmF4jJIdsZBGGOMA/StzTP32rul1LsRTnbuIz81cPf6 xq2lKZjczMCwbBkfBGavaD4ss9U1NdzqJWIyUY929CK0OQ9D8V62sckMmQkbJtYl+Bg//Xro /DskK6ZGqHmSRyCDncQev5Y/KuE1O2t9WjFtczyYViowQO49vatfT9P1uPyrO0uJUtSW2yFm AHflgKAOyvLdWUOi/vDLFk4B4Dqeh47deo7c1brNuLSVI4R9tuJQJY/kdI2Bw4PPyg9BnOc/ U1pUAFFFFAFe/RpNOukUZZonA+UNzg9jwfx4qfcu7bkbsZxnnFQX6NJp10ijLNE4HyhucHse D+PFc9O91aXMjz386OPukgfMM9vl6fpQBzniuC81e7dSkgC5ULz03fSp9F8GR2Nv9s35Kkhg XPfj0+lbV7dpNaCc30xlVGwjLGcHcOMhR1Az07du/lnijxzqVhq6xG9eKJy7EeYyhjnqQCB6 UAewya35Uhgh2sEzl5WJyPXtVy+kgudFlkd0I8okEEEBtp9/0ryy5vr270V54JHWSQEF1Y9m HcfStnw9JeT6JJFcXUpIQ/LnOflA70AQSC3vFmW1kHmspUEseMgivPPEGpReEnRp1zM5Urkt gdcHp7V2qpDaziSOVlx1Xd14rkfEs7X+pxjghVACsTg/5zUzbUbpXNsPThUqqFSXKn13/r8u 7SI4PF0txbpFKWSSQLhSzcc+nap5fEl7AyQiH5FUHeS3PtT7DSdInSORLO5gu4tjMHc4PuM9 QcHn2rok1I2NuAyYQAd8UQd43uPE01TquKVvJu/4rTXfQ5W4iJtPtoVz5w3FWOQCSOlbvhtL /VrKYJCdyKGwFbpuOau6brdnfN9kU4TjoSe/0rt/DcVqsjx291NBJtwSHQhueAAVNUYHNRwz WYCSIQDk9CKYmuTTXj2rqBGM85NdDq1/5eqNEbqRiFKFmCA8E+iitCy8NWVzCt0ZrgPJnJDL jr/u+1AFvw4twts5YxmFmJHXdnA/DGK26it4Et4hGmSB1J6k+tS0AFFFc/ZeMtHubpra4m+w TG7ktLdb1kj+1skrRMYTuO/51I2/eHykqAykgHQUUUUAc/4N/wCQHc/9hXUv/S2augrn/Bv/ ACA7n/sK6l/6WzV0FABRRRQAUUUUAFFFFABXzfoX/IvaZ/16Rf8AoAr6Qr5v0L/kXtM/69Iv /QBQBoVn6N/x4yf9fdz/AOjnrQrP0b/jxk/6+7n/ANHPQBoVnw/8jDef9ekH/oc1aFZ8P/Iw 3n/XpB/6HNQBoVn6j/x/aT/19t/6JlrQrP1H/j+0n/r7b/0TLQBoVn67/wAi9qf/AF6S/wDo BrQrP13/AJF7U/8Ar0l/9ANAH0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBSt900Hli 5kR1BGVC5xkEdR26f/X5qyY2II86QZDDOF4yeD07dv1zWUCQcg4IrQtppZVOQDjPz/8A1qAM fV/C76pO8qajJCzkE/IDj2GCOMYrmovB2pWMpjuLtblXBKBWYkAHrtP1HT/CvQijtndKQMfw jH+NRNEn2uIh3D7Hx8wPGVz159OnHr2oA4eTT54ASsnK5yjAg/TFZGquFILIwc5ySeh4r1GW 3M0bRu+5GHIYf4Yrnp/Cy3U05juwu18bdmQMgHH3s9D35oA4azubq2kLI7Mn93Jrp9N1+6R4 4pC/loMLyAOO3Tp2/lg81d/4RFo4mUzhzjghDj+ef0o0zwquyO6F2u113AKu7gj1z/KgCrf6 Q3iA/Z5Z2dmJIZ+mcHngCufOhWmmqyRzLM2R90MAOPeu1uNFuIlkmiZX2KSqjOWwOn1rhL+5 vbaZlmspITgNh0YEj15+lAFlLV0s5GiUyZIBUKTXLWOgT6tr3nTyLCibThw2TgjpWo+vM9vt A2kYAYEiqWj2l3qfiGExShSWQNyQMbhQBV8QRpcXY05iEVQFDYJ6MRVfw/8AD28g1SOSyjd4 3kVfNWNyi/MOp5xivedK02LTrfCgGZwPNcEkMRn1+pq1Dt82fb18wbvu9dq+nPTHXn8MUAce ng2/YxtNdQBkOflLHPP0FdJptq8NjFH9oceW7AhNhDYds9u+ee/HY5rRqotxFDEdnzHzH+UF eu456cf19ec0AQXYEYgjlmkl/exnLhBzv4PQDuPyGOa0qxpWDyxtJ3mj9Ou8Y+9+Hv6c4rZo AKKKKAK9/t/s663/AHPKfd06YP8Ae4/PisLU7OSXyxLM53DKORnA9P61u3+3+zrrf9zyn3dO mD/e4/Pis2a6nhtGWEZOeMZz1HTFAHGkuk7xh9xwVG71rz3xfoLXmsm2v4jFJE8io5Vtr4bk jpkcV2huryC/xNbtEzfNhlKnBPvU/iq4V9JCFQkjKyrjPqOf1oAboCiLQY7SV26tk4/2jWrF K2kwusKiVZgVfcONvGR+OMfyx1ritBeaFTGAZCSTjk+ldpZ6fqMjFp7eWJFRiTKpUABfVuB+ PFAFB9Ojmczyng87efTpWFPYW810rtBjGO5/xr01fDssiETXCIc9EUsMfjiqMvhNTexwi8xv jZ8+V02lR6/7X6UAc7bQQ+TGCp+VQF56VT12xN7Yt9nDGQFcKAScZ7Yrt08JBVA+259/K/8A sqlh8MhEG+4AfvhSR/OgDyXR9Cn0uE3FwrCRhwpUgjnrzXSWGqNbRyyRg7wBtOe9bd7pM7nD QSAAhQxU4yTir+l+G5Iw7NIY2GNoKEZ6+9AHMWWn6hrWqKJJNkh3EmTd9cmvSLaya2t4oVuZ NqY6Bfm5JOcg9c8/TjFcb4ps9RF5DHYo8s/lbpCgZskscdOegP8AnNdml2Rbq0y7ZSCdmCO9 AFlQVRVLFiBgscZPvxS02MlokY9SoJp1ABXHpq0MUuqaDbeDdVuYY5ZTcRF7UpMJ2Z2cLJOC 0blnxxjhlwCpUdhRQBn6JDBb6PBFbaR/ZEK7ttjsiXyvmOeImZOT83BPXnnNXJ4VubeWBzIE kQoxjkZGAIxwykFT7ggjtUlFAHg+oWsmm6tf2llqeswW8d3NtjTVrkDJkYk/6zkkkknqSSTV fzb7/oN65/4N7n/45Whr3/Iw6n/19y/+hms+gCnZ3epS3Woo+u64VhuAiD+1rngeVG2P9Z6s fzq55t9/0G9c/wDBvc//ABys/Tv+P7Vv+vtf/RMVaFAFOS71JdZtrca7rnlPbzOy/wBrXPJV owD/AKz/AGj+dXPNvv8AoN65/wCDe5/+OVnzf8jDZ/8AXpP/AOhw1oUAU9Tu9St7VHi13XFY 3ECE/wBrXJ4aVVI/1noTVzzb7/oN65/4N7n/AOOVn6z/AMeMf/X3bf8Ao5K0KADzb7/oN65/ 4N7n/wCOV23g/wCHPhm+8E6Dd3FreNNPp1vLIV1K5UFmjUnAEgA5PQDFcTXsHgT/AJJ54a/7 BVr/AOiloAz/APhV/hP/AJ9L7/wa3f8A8dpqfCzwhGpVLG8UElsLql0OSck/6zuSTXZUUAcf /wAKv8J/8+l9/wCDW7/+O00fCzwgJGkFjeB2AUt/al1kgZwM+Z2yfzNdlRQBx/8Awq/wn/z6 X3/g1u//AI7TW+FnhB2Rmsbxih3KTql0dpwRkfvOOCR+NdlRQBx//Cr/AAn/AM+l9/4Nbv8A +O02T4WeEJY2jksbx0cFWVtUuiCD1BHmV2VFABRRRQAUUUUAFFFFABRRRQAUVVluJluvJjgV wED5Mm0tyQQoxgkcdx1FS/aIvsv2nd+52eZuwfu4znH0oAloqr9vh/uXP/gNJ/8AE1JDdRTu ypvDKASHjZDg5x1A9DQBNRRRQBjVq25Bt029MVnPZ3gU7EgLc8NKQOvH8J7ZP14561bt0uYY mVkiJ+Yr+8PXsOnfv6e9AFqom2/a48/f8t8fd6ZXPv6dOPXtTS11k4hhxlsfvT0x8v8AD3PX 096HNz5oMaRlAGGDJjPAwfunvkdenPPQAE9RQ7fNn29fMG77vXavpz0x15/DFKpmO3fHGOec OTgY+nrx9OfamRm53nzEj2kg8SZ2/LyB8o7/AMyeOlAE9ZguE8iAQZG1FAbjpjp8vH5celWL gXsiosaQgHb5mZTx1zj5eccemfbvUisr0RKJBCXAAJ8wnPPJ+6O3PTrxx1oA0becTJnGGHUV DqP/AB7J/wBd4f8A0YtVY7W/DKSlupxyVmY4Oe3y+nP1496muIr6W2CBLdnEivkykZ2uCB93 uAfp79aAL9ZNhoemaTfB7KAxSPEwP7wsCMr2JJ9On49qvl7rBxDDnDY/enrn5f4e/f096qzp qUsnyiBYxuGwTHn+6T8mc9RjoM96AI5GIndlb+I4INRwXMivOFc53/NkKedq+nPTHXn8MVI9 neBjsSArzy0pB6cfwnvkfTnnpRHY3ecv5XLDIDk7Rt7fKM8+v19qAGmSRhhnYj0JqC32+Wdn Te/p13HPTjr+PrzmrKWd4W+dIAvHKyknpz/CO+B9OeOlILK9UKMQtkgsTIeMk5AwvOOMdM98 dwCJtu6Lf082P067xjrx1/H05xW3WXHZ3mUZxCjBlJ2SE/xcjlfT+fbrWpQAUUUUAV7/AG/2 ddb/ALnlPu6dMH+9x+fFZ1atwjyW0qRHEjIQpzjBxx2P8j9KoNZXQzsWE88ZkIyMf7vrx9Of agAsgn20E/6wRsB06ZXPv6dOPXtReeHtKv5DJc2vmMcknzGHXnsaktbe7imV5PLCkYZVkJHQ HP3eSDkduDn2qwGusjMMOMrn96emPm/h7Hp6+1ABZWVvp9olrax+XCmdq5Jxk5PJ56mnXW37 JNv+55bbvu9Mf7XH58etBa4+TEUXO3d+8PHXOPl5xxjpn2publ4iGSONyAMpJnHPJGV7DBHH J496AJ6qSf8AIWt/+uEv/oUdTbrjfjyotnr5hz9702+nP1496hljuDcx3CJEWSORNrOQDllw c49FPbr+dAFuiog1x8+YouN2394eemM/LxnnPXHvTS11k4hhxlsfvT0x8v8AD3PX096AHXG3 yhv6eYn93ruGOvHX8fTnFS1FL55UiMKCG4O/GRjPPynqePpz14pVMx2744xzzhycDH09ePpz 7UAUr1V+1FgPm2KGPy9Mtj39evHp3qvVq4ivZnUhYQnygqZTgf3iPk69vf2qE2d58mEg527v 3p49cfLzjjHTPtQBpQ/6iP8A3R/Kn0yEMsEYcAOFAYKcgHHY0+gAooooAKKKjnjaa3liSaSB 3QqssYUshI+8NwIyOvII9QaAPE9e/wCRh1P/AK+5f/QzWfXQN4L1OS6vH1C38XXNw13ORPaS 6WI5Y/Nby2AcgglNpOQOSeBUcPg950LppvjwAOyfO+lIcqxU8MQcZHB6EYIyCDQByenf8f2r f9fa/wDomKtCtyPwEYnmdNM8chpn3ufP0nk7Qufveij8qB4Pdrh4BpvjzeiK5JfSguGJAw2c E/KcgHI4zjIyAcnN/wAjDZ/9ek//AKHDWhW4fARa4S4OmeOfNRGRW8/SeAxBI+9/sj8qJPB7 xPCjab48JlfYu19KYA7S3zEHCjCnk4GcDqQCAcnrP/HjH/1923/o5K0K3JvARuECS6Z45ZQ6 uB5+kjlWDA/e9QKJ/B721vLO+m+PCkaF2Eb6U7EAZ4VSSx9gCT2oAw69g8Cf8k88Nf8AYKtf /RS15/8A8IRN/wBA/wAc/wDf7SP/AIqu00nUL7RtGsdLt/B/iBoLK3jt42knsSxVFCgnFwBn A9BQB1lFc3B4o1G5t4p08F+IAkiB1EjWaMARnlWuAVPsQCO9Sf8ACQ6p/wBCZrn/AH+sv/ki gDoKK5uPxRqMrzIvgvxADE+xtzWagnaG+Um4wwww5GRnI6ggSf8ACQ6p/wBCZrn/AH+sv/ki gDoKK5s+KNRW4SA+C/EG90ZwQ1mVwpAOW+0YB+YYBOTzjODiT/hIdU/6EzXP+/1l/wDJFAHQ UVzc3ijUYEDv4L8QEF1T5Gs3OWYKOFuCcZPJ6AZJwATUn/CQ6p/0Jmuf9/rL/wCSKAOgoooo AKKKKACiiigAooooAy7vyhfATQ2skpUbWaYJJjc2AgPceuRk+nZ8Fu6WDtD86yW6iO3d96Kc HjOeQcgfh6YxTvb+2N+WW6UxeSA5Vo2Q8nhgTlx14HI7ferZgAFvGAAAFGAEKgcf3T0+lAFD zf8Ap81H/wABP/tdXoA5iSSaNVnZF8zHr6fQEmpait5vPiD+VJGf7si4I7/5/wAaAJaKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooA4hPGWuXMt0bPQdOe3hu57ZXm1R0ZvKlaMsVEDA ZKE4yetP/wCEp8Tf9C/pP/g4k/8AkasvRP8Aj3v/APsK6h/6VzVp0Acp4o+NmoeE9TjsL/wt bSSyQiYGDVGK7SWHeAc/KaxP+GlGzj/hER/4Mv8A7VXHfGr/AJHKz/7B6f8AoySvOKAPd2/a WZevhEf+DL/7VTf+GmD/ANCj/wCVL/7VXg0vao6APff+GmD/ANCj/wCVL/7VR/w0wf8AoUf/ ACpf/aq8CooA99/4aYP/AEKP/lS/+1Uf8NMH/oUf/Kl/9qrwKigD33/hpg/9Cj/5Uv8A7VR/ w0wf+hR/8qX/ANqrwKigD33/AIaYP/Qo/wDlS/8AtVH/AA0wf+hR/wDKl/8Aaq8CooA99/4a YP8A0KP/AJUv/tVH/DTB/wChR/8AKl/9qrwKigD33/hpg/8AQo/+VL/7VR/w0wf+hR/8qX/2 qvAqQ9DQB9+UUUUAFFFFABRRRQAUUUUAc0sEcpxaWCNaYJeW7iWNQnYowG7kZ5OT0NdDOJGt 5BCwWUqQjHoGxway7ezhP2iHULKwQogcvCmBtOR1IyCNp5rYoAzPK/6c9R/8C/8A7ZV+ASLb xiZg0oUB2HQtjk1JUVu8skQaaHyn7ruDD8/89PxoAlooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKAM+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2CcVJpuq2Or27XGn3MdxErlCye uAR+BUqwPRlZWGQwJ4fVrePxSNV0VLG7sZFS9g0+KTTZ44DcSRyo9zJMI9mG8yTaAxBDljud lWPpNGS4vPEOpa1JZz2kM9pbWiRXICyb4nmZzgEjbmYKDn5ijEZUqzAHL6J/x73/AP2FdQ/9 K5q068xHxJ0jw1rmvafe/wBqzyJqt5+7SKExR5uJW+Q5VjncM7ieemBU/wDwurw5/wA+Wq/9 +o//AI5QBx/xq/5HKz/7B6f+jJK84r2HVPDN78YLlfEHh+W3trS3QWTpqDFJC6kuSAgYbcSL 3zkHiqP/AAoPxT/z/wCjf9/pf/jdAHlEvao667xv4B1XwP8AYP7TuLOX7Z5nl/ZnZsbNuc7l H94frXI0AFFBOBTdwoAdRTdwp1ABRRSFgDQAtFN3ClBzQAtFFKqljgUAJSHoak8tvUUjRkKT kdKAPvmiiigAooooAKKKKACiiigDlrm10xZJIYdOaUwpuuJIpziH1wTwxHPHt9cdJcjdazL5 vlZRh5mcbOOv4VCum2iWotkiKRBt+Edgd3qSDk//AFh6VZkjSaJ4pBlHUqw9QaAM77D/ANQn Tv8Avv8A+11egHlxJA0vmSRooYk8ntk/XBqL7BD/AH7n/wACZP8A4qpIbWKB2ZN5ZgAS8jOc DOOpPqaAJqKKKACisF7a3kUq8ETKc8MgI5OT+ZAP1pTBCQQYkIIYEbRyGOW/Pv60AbtFYBs7 Ykk20JJLEnYOSww359/Wo5YbVrxPMhiZ2Rzlh2+VTxjB4wOee3rQB0dFcjLewQpmCGNNnzDc oGDjbkY9uPpTdHubPz5JJYYzkjHlgcEAKMegAyPx96AOworHLadNPFCLWBVAxuaNRswDtx6Y ycfWmiPTLawtpI4YRtjUqigEHjcCcgEgHnPByc0AbVFc9plvFNeFo7a28heoWNPl/iAHfG4Z +tQ6jc2Erxxxww7GdQ/yqTguCwx05xn680AdPRWeNMtZYQyoMOp6qDw3J/PvVCbT7ZL5Yvs0 ckjLI+VUHrtDZHqcj9aAN+isGS1gdyZII2bnJZATyMH9Bio7eOASTLHEg8uQdOx2KPTj5SBx 2/GgDoqKwUtreNtyQRKwxyqAHgYH5AkfSooILaSMEQxEI5C8Z2lWYDGRxjnHYduKAOjornxB bQeSBDEiebEAFG0Z8wY6D1OfTJ9zXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRWPc+J9LtPELaJcT+XdJp76lK7/LHFArhNzOeBkk/QKSccZsaZrFvqvmrG k8E0WC8FzEY5AjZ2PtPO1gCR6EMpAZWVQD458bf8j94k/wCwrdf+jmrCrd8bf8j94k/7Ct1/ 6OasKgD6L+Af/Ii33/YTk/8ARUVep15Z8A/+RFvv+wnJ/wCioq9ToA8M/aL/AOZa/wC3r/2l Xhle5/tF/wDMtf8Ab1/7SrwygBG+7TKe33aZQAVJUdSUAFMb71PpjfeoASnL0ptOXpQA6nx/ eP0plPj+8fpQBLTX+430p1Nf7jfSgD71ooooAKKKKACiiigAooooAKKKKACiiigAooooAwXu ERSxWUjn7sTE8HHQD/8AWOelKZlAJw/AY/6tuxwe35evbNSVatYlH76QgKp4zQBWiaIgvL5q xgsM+U3OOvb8vXtmm6lNBYQ+YqsxOVDspK/wnpjaeO/4DviK71L7TOqRD5Fw2Djr71R1NJr6 23k/LGvOBjHIA6cf19O9AHG6nqTBZAvO4EZKgd6y7G+a2LSySqvzHG7A9Ko65M9jMg9QSRx6 1yGt+J44GS3eCQkg4II45/8ArUAdldeJZ3v38t1KAkEhQe3atW/1KaOyjO8HzBzhRxxWN4Vs LXUdPEq7yWdsAqAePeuun0mKS0j3cbBnkD0oAseFJro6bcurszsuVxHnAK//AF65G8v7i28R Q6euPm28EDqfevQPC12mnObdGLK5yRxzhTj6Vla3pjQ6vJdTqQ67QOn92gC1o/inUoZLeCZ1 ktVKocRjcFHGBjFaet6xDBP9pG5VdRjeCvoOh4PQ8/lXL2t40UgkfEaqwzlhVDX7s65LiIjC qo4I7UAdB/wmtycqkkciIPuMikY6djn9a67TJIdQieRVlRmfAzkjp+g4PfH4nFeGXOoNoLKT H5nygsOPX8av6L4//tbVklQvEWQIynYcjd39PwoA9lYlWxslxx8xiYDkZ6kf5PHWq8Vwmxcp MNznGY3PVjjORx0+g47YrN0a+vJJlFlIPJaTzHjbYC3IBGT/AErpUgee3Mjpsm3vlSTyNxx1 9vw9OMUAUYpllaIosv8ArEOGR0P38eme3T068HNbtYjhw8QUYYTR5+903jPTnp+HrxmtugAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA871DwNrV74g1DfqVo1jq llqUM9wLMiSIziBI1P73DFUiQAhQMQnI3PuHUaPZ3z6zfaxqFvHaS3FvBaC2SXzcCFpWL7sD hmlbAxnaqk4LFE3KKAPijxsQPH/iXn/mK3Xf/ps1YW5fUfnWx45/5KD4l/7Ct1/6NasCgD6A +CfiLRNK8GXkGo6zp9nM2oO4juLpI2K+XGM4Yg4yDz7V6T/wmvhT/oZ9F/8AA+L/AOKr42oo A9z+NH/FYf2J/wAIx/xO/svn/aP7M/0nyd3l7d/l5252tjPXafSvKf8AhCvFf/Qsa1/4AS// ABNerfs6f8zL/wBuv/tWvc6APi6+8L+INOs5Lu+0LU7W2jxvmntJERckAZJGByQPxrGr6w+M n/JKda/7Yf8Ao+Ovk+gAqSo6koAKYwOelPooAjwfSnLwOadTG60AOyPWnxsA3JHSoaKALW9f 7w/OkdlKn5h09arUUAff9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGdawea+5vuqfzrC 8X+Io9PH2XynLKVYsMY5B4rp0jMNowDBX2k7j0BxXn93aJqGoEKy4O0Esw20AafhmWPWXZ/n QhAxzjkZre1WRbeGOLfhfLK989Vx7dj159O9UtHNlpVjiMBpuFbBHqTx3x/9auO8ReIBbyOZ pBncf4gM5PpQBX1/R49Uu03kKOgYAHv+lZOseBrGS1E7LvEQY52r7H0rds5PtqEKTjHHT1rQ k2JCLfdnfkYPbNAHmmn6/Hpl++kWtuwVScsSuPXjiu7fVhLpZiwEYjILMP7vvWX/AMI/9n1V 5yozvYnBHf8ACob7iYhl+TPbtigDLuNQnsb4uV3AE9xxxXQXuqnVLSBo8gmNQc464P8AjWB4 ihuNShgjtEQBDnJI5GK39N02W3tYBKqhiq9D7CgDMgsZLtMK+efQf41etfC90l9DeFtsUe13 Y7cAbh7+4r0Pw7plm1uzlMyhlLEgY4HTp06/pVnWBbw2BtPM/hI2D/eVsnt+Yzzx3oA86u10 rVS8LsjbeWOFOcH/AOvUeieDtBkuJJbWRo2yDlkjB698dO9VbDSVsbqdmLGM/L1BOM/Sul8N tY/apojuJZflIZeuf4vbmgDsNM8Ow6a4ZZmfBz90Dnj/AArRhiLlZJCd6SSY69Cx/vc9Me3p xiqemX0eXtZJkLhjtwwxjPT3Oa0YeEPJPzt1BH8R9f8A9XpxigCpqEbBoJE6maMHr03D056Z 9vXjNX6rXe2SJCHPyzJ93J53Dg4/z68ZqzQAUUUUAFFFfOFumnQ3VnL4rn1zwp4/a7eT/hI7 uFmtp3WXbsA3bGj8t1XgKgAHO3hgD6Por548X/8ACA/8LO8df8Jt5/neVaf2f9n8zzN32Yb9 m35N2dmPM+XPtmsvxpomr3ngX4fadrnmWl/b6ZqkpWSMbljgiEkSFRjBMcaLzyM8gkEUAfTd FfPnizWIfF3xN8LazaT3cdpYXujRLazqMbrovcbxhiAdixqeOSOuFGeng8IaH4++J/jS4160 k1OzsXtLWyk+0SrHEwiJmjUowGQxBZexPYk5APXKK8f+Evgnw7b6x4h1aLT9t9pXiC9srKXz pD5UIUKFxuw3DsMkE89a9goAKKK83+NeiRXvgS41qG3nfV9H2z2M8DuHt8yxmRwFPZVzk/dx njrQB6RRUcE8N1bxXFvLHNBKgeOSNgyupGQQRwQRzmpKACiiigAooooAKKKKAObPi6EXF/N5 Mf8AZFgkpursTgyxeWZA7mHG7yt0UiBs7i6HCFfnrQ0rV2v7i4s7qzksr6BEme3d1f8AdSFv LbcuRn5HUjPDI2Cy7Xbn5fAMNw99aPLHDp9y95K0tsgS6la7VllSRsYKLncODkrCCB5OZNzS dMvIdRu9U1KaB764iitiLZSsYjiMhVsMSdzGV2IzhQVX5tpdwD478c/8lB8S/wDYVuv/AEa1 YFb/AI5/5KD4l/7Ct1/6NasCgAooooA9z/Z0/wCZl/7df/ate514Z+zp/wAzL/26/wDtWvc6 AOF+Mn/JKda/7Yf+j46+T6+sPjJ/ySnWv+2H/o+Ovk+gAqSo6koAKKKKACmN1p9MbrQAlFFF ABRRRQB9/wBFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIzKilmIVQMkk4AFLTJolmgkibIV1K nHXBGKAM7VNUtotLuGjlSVjGwAjcHB2nrXmGl6y11DOyB+CByQKbqcl7omrGOG5u5LGQLtM+ d3IIIIYDuD04xii01YRyqwiAIPZQM/pQB2nhm1muh5spDKNpO4+54rlvG/hKXULxfs08UYEu W3yYyoLZHAPPI4NW9N8W3FzdSWse+MAKflwPbsPejWbmaWBZizhlPJIIPPvQBjzyXGkWyQQS ks2Q5VuDg+uPepdKa8uZN8rMy5zyw4GRV3SnhmfdcAkD2ySc/StK+uoY1lEKsqkkgEEHsP6e 1AFyICRCdi5IOXOK5vV7JobZ5NqspyVPQ9D606LULjzCA7Y54zWna30TosUsRdQMYxntQBy2 lByzSsoKqcDPOOK3ZtSkhljGePlOzjtUeufEDw7okkmli0UXOTyZI1IO3uCM9xxiucTXUaNt QuVd0jK5C4Jx09h3pNpK7LpwlUmoQV29F6nc6Xqk7mSNHeMtjG1setY9/e3IucS3UjybeWZ8 /wCegqTRVvdQjh1WS4EFqpKpZWyeYCcFdzyqMNzu+UdML3BqS/0eS4fzFSTkAg+Wwz/47SjL mVy69H2M+RtN9bdH2v1+V15kGnXqu/zksPXPvXOHUZbDX2EO4IX2kK+ARu610VhY43AKwZRy AM9/YVetvDkTxvczQylgMriBjk59duP1qjE2dMdZ9IuJwv8ApaAvuAAwAQevXNX/AA5q32jd aspJ3uwfPfOT/M81zltqL2BktwjKkm5DwMkHg9RXQ6ALaxgkm23RaR2PFu7DHHovtQBLbxTT XF3JI5MC3YUJ8zfMJFIOB/Pt34rfrHuL+2uZYrZIrhSZ43Ja1kUEhgeeB6dT+tbFABRRRQAV 5PffDvxrqdnJ4Vv/ABPBd+EpLsSvdT7pNSaEEOIixG04cD5s54z0/d16xRQBy+h+G7zTPHfi vXJpIGtdX+x/Z0RiXXyoijbgRgZJ4wT+FR+JPCk2u+M/DGqMLSTT9NS9S8gnyTKs8QjAC4IY dcgkcetdZRQB4/ovws8QWmi6TFqN1pUupWniCyv5biIsN9pbQrEkefLBLKAcAjHJOck1seE7 +zsfi9470g3cFv58tnPbWXmBPMka3LTOid2OAWIGTwTXpFU5NJ02bVIdUl0+0fUIU2RXbQqZ UXnhXxkD5m4B7n1oAw/Bfhu88Of8JD9skgf+0tbudQh8lidscm3aGyBhuDkDI964/wD4W+8m neONUgs/9A0aK1OmmS3ZJJjOCEeRWYZjLbWGNp2HOM16xXk/iPwHrOtz/ExEg8uPWIrB9Pfe h894EyUxuG3LKFy2OueQKALF14+8U2fw/g1aWx0Nr6bVVsFv4boy6cIS2BcsyElY8jYQzAg8 nH3a5/xV47vNY+CGuvqdxY2epvdvY272NwVj1GNJo1kkg3Hc8ZVmU4JBAOcZwJI/AniiHwHM kWhWiSzeKP7Yl8PrdoEe1yB9mZseWRlVOD8uADjI211nw/8AB8tro8sviTQ7GG4Gq3F9ptm2 y4/syN2UiONgNqYZS3yYHIPByAAd5BBDa28VvbxRwwRIEjjjUKqKBgAAcAAcYqSiigAooooA KKKKACiiigAorm5vGdja397FdxyQ2lqkxe4+8wMKB5S0QBdUCshVyMNnsHiMuhpWrtf3FxZ3 VnJZX0CJM9u7q/7qQt5bblyM/I6kZ4ZGwWXa7AHxv45/5KD4l/7Ct1/6NasCt/xz/wAlB8S/ 9hW6/wDRrVgUAMbrSUrdaSgAooooA6b4ff8AI86d/wBtf/RT17nXhnw+/wCR507/ALa/+inr 3OgAr5qr6Vr5qoAKev3aZT1+7QAtRSfeH0qWopPvD6UAMqa2/wBYfpUNTW3+sP0oAtU2T/Vt 9DTqbJ/q2+hoA+8aKKKACiiigAooooAKKKKACiiigAooooAKKKKAOJ8XafaiKOaRl+0Kgwu4 g/xEe3XiuUsLGJLN55yiAPjczVv+IDcS+JbqOcboRt2r5nAXZn8PX6k1hat5n2ERq22JXXHO OxoAv6bp1jHJ9qSQSSnGNrnHX3HtUuoQz+YCeVcZAB965+x1JImWFZiHTGeuD+lad5qb38Uf l3JDqoQEvjj0oAzptUFpdrFgqoPTd71sPPBeRFlkUMScgnB7VkNoNxNJmYq7jgkP796urp0i OdrKMHHJ60AW7W3i5y645/ip5ms7GKSZnHm7G2ruwQ2OPXvWHrhksNNlmSdk2ZY4fnqPSudn uNR1S0t1sZwJZCdzO3YLnuPf9KAOL16zbVvGF7f3Mx2GZsktntjsPavSPCP2afU47OJWAaRF R2fO4kc8Y4xViDwmlkj3EkYfzmySJc5PX+tdBoOkRQ3EF1DbqHjnjxukP3iwHv60AXrrQ4fD d4k1ndS2yOAZrSM/uJBgjdtxw+QvzDsMdyas6PrMl5fG2Eu35dqHPIyR0OMj3x1rX8QWE91b vLIYhEuON5z1wO3v/wDrrgYphYashhmHylTkN7g9RUxio7GlWtOq05u7WhqXEx0xpWikIaQ4 OD2z9PapbHxLcQLKsUmN3PY89M9KoaxungEiEEtg7s0ujaeFl82cNx1G739qozL2razDFpkV 1dANcOz5YnbxlcdBjua6vwteJdaOhDoW3MQqkZAz/jXFa/ZjVEVZSqxL1JfAHI/wosLi500b bK6BhG5fkIJ9+cUAeiX6ApC4j3kTRg/LuwN45x7dc9utXKzVS4isI1uELuZIiQW80g7xnsOB wc9ueuOdKgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5O88ITaj f6qLvUJPsd/bzQSSRsRPJHIm0RMDlAkfLIQucv2PmmfU0nTLyHUbvVNSmge+uIorYi2UrGI4 jIVbDEncxldiM4UFV+baXfYooA+I/HP/ACUHxL/2Fbr/ANGtWBW/45/5KD4l/wCwrdf+jWrA oAY3Wkp5ANG0elADKKftHpRtHpQB0fw+/wCR507/ALa/+inr3OvnPT7+50q+jvbKTyriPOx9 obGQQeDkdCa3P+FgeJ/+gn/5Ai/+JoA9wr5qrpf+FgeJ/wDoJ/8AkCL/AOJr6F/4U94D/wCg F/5Nz/8AxdAHyrT1+7X1P/wp7wH/ANAL/wAm5/8A4uvAPiRpFjoHj7U9M0yDyLODyvLj3s23 dEjHliT1J70AcxUUn3h9KduPrShQ3J5oAhqa2/1h+lGxfSlUBDleDQBZpkgBjYkA4BxTN7et IzsUPPagD72ooooAKKKKACiiigAooooAKKKKACiiigAooooA5a6uYYtSUyRnPl7C2/Gcgj09 D+lcd4o/e6U/2VsOrKcc5x+XvXVa1p01zbiWCRjLHliD/EOTgYHXsP1rlZXkjtHuHLqVYAnO MZoA5DS4biW5YuJPmAHGfXrTmaeIgS3GxVkDckjgZ4rt/D8UGoTLGrAMMAEHuTiuZ8aaW9uW MAIDMD1OepoAo3vjCW1UsszNxzg47/Sk0/xZPqLjDsAPQ98j2FUdN0r7bbPFcK21lx1I71at 9Gt9LlKR7sZPDNnv/wDWoA2LhzqNlNbyvzIOpPcHI7e1O0jTzbv5EuRgZ3g8YwPanmPZCZv4 sHAzXL6j4uura9+zRrlVJB/eH0FAHqbapZWmli1luowx5CmTA+7j/Cl0jxFpyWUsclzEkpmh ZE353BXBP6CvNb5L/VDDc24Zo2QEhdx2naKgu9NvLG/WQPNsBUkBj6fSgD1jWNWub6yaKOcm NipyvGe/YV5VPPe2+tKru7K23jn1FdZpWoTSWCRx72cOinJJ4I/+tWk+mR5RpI23suTyfwoA 57/hJZ4Lq2tDEWj+Xjd1Ocelekw6bbR2EkuWjlJyq7gCR69ye/p/U8vJ4eijWOd4n8wgMoyf Xg1Ytzei5O+SQDdljlsH60AV/EyTtGEhkIXbySeOta/gvTrGey/fO73AZm2ZONvAz0/rWR4h 8y5lSCxMjZH8ZyM5HPAHpXW+HNBbTbRJpJpopgWDLkYK7s85HfFAGzeklIlChiZkyCAeAwOe SOnXPb36VarFlWWSaNTO822WPG5UPAcEnoB07+wI567VABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABWHqur31l4i0Owhs4zZ31w0M1zI/OfImkCoo5zmIbi2AAQAGJJT crP1HTPt99pNz53l/wBn3bXO3bnzMwyxbc54/wBbnPP3cd8gA5+88b/2dFNqd3a50gS3dtEI jmfzLVZmkZgSF2t5EoUA5G1Cc+YRHsaTqd5NqN3pepQwJfW8UVyTbMWjMcpkCrlgDuUxOpOM MArfLuKJTm8GWN280F3JJNpbvPMliflCSzq6zNvBDEESyYGflMr8n92I9DStIawuLi8uryS9 vp0SF7h0VP3UZby12rgZ+d2JxyztgKu1FAPjfxz/AMlB8S/9hW6/9GtWBW/45/5KD4l/7Ct1 /wCjWrAoAKKKKACiiigAooooAK+4q+Ha+4qACvlX4w/8lU1n/th/6Ijr6qr5V+MP/JVNZ/7Y f+iI6AOHpy9KbTl6UAOooooAKRiAp57UtI33T9KAPviiiigAooooAKKKKACiiigAooooAKKK KACiiigDBe5t41LPPEqjPLOAODg/kSB9aiu4bG/sbizupgtvOMyMjgEBGBJ59DjNWqKAPHob 3VPDfiafT2WObyyn72Bm2HOCcHA6E4PoQa9IjsrfXNOtpr2aNGeItgyAHcME7iTnoc8Dpycd 791YW926PKmXQgqcnjH41I+qHT/LM8aMqIUTZxhcqCTnJ/u9OPXtQByB0iKIsI8FOnDE57/y 5rLv7G2Yl1JDkd2r1E2qXsE0sc28zKVRiCMDPT865i98MuzSHIYqxBKZbHAPOOnXv/LFAHFp ukJiBbjPIP6Viy+Go5dTecr95mOSx9PpXosOgeTAxXLFcueD+ftWPeX8UAwe2V+vFAF3RZre ztyo+UAgct7Vd1Oysp4C5Ylsjo3GM4P865+wlW4Z8nC7q7yztY/7HBZjIWuYfmORxvUf1P50 AZGkRaVppBupAibv42PGBjt71uG90lruK6t5o5CsRAAbGcFcck9gD2+vaqvifQkvI/NEywrh V6FiSP8A638qr+D9OXTbmSM3TXDmI4JXbgbh6knuOnp9KAOjlksmeOS4kiSVeQHkwVIG7GM9 sg/jWPrF5G0M6wTpJ5j5AQjB+VfQ89D15/DFaGrQTXBCx3KxrgAruOc/QfhVCxtYoLqVyTMU YY3YGGwDngn8j/hQBB4ctYnka4uCiuG+VGbDA4DA49MAn8K0Wv4hH5XnwhXkJU5Ub9zMVxg8 57HqcZ9allmeY5Y8eg6VWt9vlnZ03v6ddxz046/j685oARJI7oxeRKkn72Nvk2vwHHPX2PPt xyK36xG27ot/TzY/TrvGOvHX8fTnFbdABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRXP6zfapa+JfD0MEkEem3V28M427pJT9nnkA54RVManjJYn+EKd4B0FFcXquu6 v4ce/vJ5Y9S8q3uryawiwiWlvGsjQuJNuQX2KjK24szMyYWJgdjR7y+TWb7R9QuI7uW3t4Ls XKReVkTNKpTbk8K0TYOc7WUHJUu4B8f+Of8AkoPiX/sK3X/o1qwK3/HP/JQfEv8A2Fbr/wBG tWBQA0nBpNxobrSUAPU5paavenUAKBk0u0Ui/ep9ADdor1P/AIX94q/6B+jf9+Zf/jleXVHQ B6r/AML+8Vf9A/Rv+/Mv/wAcrstD+HOkfFTRoPGmuXN9b6lqW7zorF0SFfLYxLtDqzD5YwTl jzn6V88V9bfBj/kk2if9t/8A0fJQBgf8M8eEv+gjrf8A3/i/+N15F8VPB2n+BvE9tpmmTXU0 EtmtwzXLKzbi7rgbVAxhR29a+uK+aP2h/wDkf7D/ALBcf/o2WgDyXcaC5A7UlNbpQAvmN6Cg yEjHFMooA+/6KKKACiiigAooooAKKKKACiiigAooooAKKKKAMd4rpVJFpK554Vkzwcd279fo PXilMFyAT9mc4DHG5ecHgde/b9cVr0UAYxjuwSPsUxwWGdyc4HB+937frikktrkTq4tJXKqy gqY8dAe5zyRj69exraooAxbO0ntSDDbSRBsAgupxxnJ+Y+uPr7c1bt5boSvvsZF8xgxbdGAP lHocnGMc9zxx0v0UAc/qupXot5oxYTJEQAX/ALueuSDj8s9e9c5Do01yu9rC4Ctgg7Tzn6H8 a9DooA8xudEvoG2x6ddH5ieIy2PyJrpLbTdVtUXdA0gWRXMccqjO1x6nHuPp2NdVRQBn3LXM 8LRmzbaQSRvXJweO/ft+uKoi0uEm3rYzcBl4aPkcEHls84wP17VvUUAY7xXSsQLSVxzyrJjg Z7t36fUenNJFa3AZm+zOpkcE5KcfKPQ89Mdzn2xWzRQBjpFdM2DaSoOOWZMcjPZu3T6n05pq W1zEqqtpKQzbjzGNu4knOD274z1781tUUAY6W9xKYzJaSoA6MQxjJGG+pHGM/Q8c1sUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFU73TYb66064laQPYXBuIgpGCx ikiw3HTbIx4xyB9DcooA5+28Krb3l5JJq19c2d7LLLc2FzHbvDL5gIKsfK8xlAwoBc4VVX7o xWhpmj2+lea0bzzzS4Dz3MpkkKLnYm487VBIHqSzElmZm0KKAPiPxz/yUHxL/wBhW6/9GtWB W/45/wCSg+Jf+wrdf+jWrAoAY3WkpxGTSbTQAq96dTR8vWjcKAHr96n1EHAPeneYvoaAH1HS +YvoaNpoASvrb4Mf8km0T/tv/wCj5K+Stpr3H4f/ABl8O+E/BGnaJf2WqSXNt5u94Ioyh3SM 4wS4PRh2oA+gK+aP2h/+R/sP+wXH/wCjZa7z/hofwl/0Dtb/AO/EX/xyuY8R+H7v44ahH4m8 MyQ2llaxCwdNSYxyGRSZCQEDjbiVe+cg8eoB4fTW6V63/wAM9eLP+gjov/f6X/43XPeMvhTr vgrSItS1K706WGWcQKttI7NuKs2fmQDGFPegDg6Kk8hvUUGFgCcjigD77ooooAKKKKACiiig AooooAKKKKACqOq3j2dtHsZUaWVYvMbpHn+LHfGKvVT1PzhZkw20dyAwLxOM7lHXHv0oAppP fxWt27Tx3MIhaSG6TaPmA+7ge/8AnsII7q/t202ae9WeO6ZQYtioRuHUYHIGfbt61HDau51G a2spba3e2ZBE4IZ3POQvbrj+XetDStKtbaC3uBb7LkxDcWJyCRzwehoAq2Go3LaxdJdTqLVW lWPdtHKkZ9+AaTStWnKXk2pSBY4xGwAXhQ+SOnPcVUubSdYbiZbaZ5Ptc6qgU8o6Y3dOnSpL qxljt9WhihlddtusZ2klwoAOPX8KANhtXsEtzObgeUJPKLBSRuxnHA/XpTp9Ts7a3inlnCxy gFDgksMZ6dapa3byPNZzqLkpGzhvs3+sGRwR7cc/Wqsts9vp2nGO2uw8e5leI73iLc4K4G4H v06Yz6gG0l/bSG3CShvtAJiwD82Ov0/Gql7qH/Hv9ll/5fVt5fl/Mcj9RVIC5gj0m6ksn/ci QSRwRjI3Dg7R0z1NRxwXTRI0ltIjnVRKy4J2r657j36UAXrPXrW4+0mSRUWJiV4PMYwN31JP TrVlNXsJLWS5S4BijIDnacjPTjGazDHILfVbSSxnl3zPMu35VZSVxhvXvjHbHtU2mzXEEV5P NbXLoGURloQJnHTBA+9jjn6/QAGxJIkShnOAWC/iTgfqRSqyuMqwIyRkHuODUN4hntZrdeHl iZVJBwOMcn8ayLJbnzYyn2qKMyFgkiOTy7FsknA+Xbyc98fNmgDdLKpUFgCxwAT1PX+hpaxl VnuoC63h8ucElg+OVb8OuAcfL6YBIqzqIuC4aBpRsglYBOhcbdoP68d+e2RQBoUVkYvRNMxe UgSBmVUONokBGDnB+QHhR9ecUwJqJCLI0ysNm5lOcFmiz7HkSfQexFAG1TZJEiUM5wCwX8Sc D9SKySl2rA7rkr+8Vhk8IJVAx77NxB+8fXpSOt05iDLOU8yMoMfwiUn5s8/d2def/HqANd5E RkVjgu21fc4J/kDTqx7SO7Mts07SORKC+UICt5bhuSTkZI6YXpjvVvUWdEjdfPIUk4hBJJ/D v6ZG09D1BoAu0isGGRnqRyCOlZji4YSANdLMZgGZfuhfMG0jPH3PTjru5qui3JuWgDXWAQAN 5wFMrgkk8/cBwfYd9tAG2rK6BlYMrDIIOQRS1mwrInhwLGsqyrbEAEMHDbe2eevT9KhnS9Ej RpLMI1c7H2FiW2oR0I4yX6/KOh7YANiisaZdRxME83gui4P97zMY/OLntz0+anSC8aRgrTqx kw7DOAPNXYVzx9zOcf8AAqANUyIJViJ+dlLAewxn+Yp1YckV4Jjs8/Kq6l2ywC+YnTnJOwdj k44+bNW7KOc3G6aSZkWJdu4FQTufqMk5xjqc9M89ADRooooAKKKKACiiigAooooAKKKKACub 11r6LxT4YeO/kjs5b14XtY12iQ/ZbhyXbqwBRNqjABBJ3Hbt6Sq9xY293PaTTx75LSUzQHcR sco0ZPHX5XYc+vrigDzPVvHrQ+MdYnstXtGi07R9RS301plO+5t/KcyOgIfJPmoFPO2FnU4c mu00Z7iz8Q6losl5PdwwWltdpLckNJvleZXGQANuYQwGPlLsBhQqrsPY28mow37R5uoYpIY3 3H5UcoWGOnJjT8vc1HpulWOkW7W+n20dvEzlyqeuAB+AUKoHRVVVGAoAAPi/xz/yUHxL/wBh W6/9GtWBW/45/wCSg+Jf+wrdf+jWrAoAKKKKAGt2ptObtTaACiiigAqxVerFABTG+9T6Y33q AEr6V/Z6/wCRBvv+wpJ/6Kir5qr6V/Z6/wCRBvv+wpJ/6KioA9Zryn9oD/kQ7H/sJx/+ipa9 Wryn9oD/AJEOx/7Ccf8A6KloA+b6a5+Q8E8U6mv9xvpQB960UUUAFFFFABRRRQAUUUUAFFFF ABTXkRGRWOC7bV9zgn+QNOqpexPLJaBGdMTEl0AJUbG9QR7fjQBZSRHZ1U5KNtb2OAf5EUqs GGRnqRyCOlY0sN5HNKElm2mUkSeXks2xNvC7Rj7w5+Xjmlka6SRS7ThfPUKQeMGcg59sbMfp xuoA1UmjkjikVxtlAKZ43ZGf5UedGZvJDgyYJ2jtjGf/AEIfnWNb28yPp4lFyURYifvfKxRw QQOgGEHt36nJNbzJqF5JCLkOqSOpG4hjtjKgZ4PIPA9MdOKAN2mySJEoZzgFgv4k4H6kVkSx 3vDLNOu6SX+Etgh/kAAI4xk5b5emeMYdcLLJNLEPtWWkRg20kKBIvqCvA5GO2dwyM0Aa9RLc RMkLq2Vm/wBWcHngt/IGs5ftfmoD53yuFi642iRg27sfk24Lde3NR2cdyJLASJMAm3gj5VXy cc+h3bh/P+GgDTa8hS5FufM8w9AImIPTnOMY5HPap6rurHUoGCnaIZATjgElMfyP5VHJBK14 JBHlNwOftUi/+OAY/DvQBLLdxxTiJgxPy7mA4XccLn6njjPvilluVhkVWR9pIBcD5VJOB+Z4 4zjvioLpy97BAyuIQQ7ERMwZs/KNw4GCMnPt2zRdnzXhVI5S4kUqdp2EBxuyOnAGQT7beaAF lvoDNLa+W0si7VaMAfMWBOOcDoCf84pttJbRzRpbWexJ13LJGiqGGM5IBzjnHI6n3qgLa5ju s3EP7r5WkeFmJyTKflwAfvN25Ax1BNXII7kbMoqzQ2iqmRhN7dQcdgUXp6/SgCQ6pb5lCh28 pmVyB0C43Nz2G4e/oDV2sA2Uq21zC8EittMUTRsWDfu0GCcDglV54HBBwOu824oQpAbHBIyA fpQAkciSxh0OVPQ+vuPb3pk84hC/Izs7bVRcZY4J74HQE/hWRpzTuUEDShAEADncir5Kn2yd xX09v4qs7LmJ4ZbgSOY5dzMreZ8uxhwFUc5PPy9xzxwAW5LmJraNghmScYRAB84Iz3wOgJ5q K0lsVmWK0iRTJH5hMce0Y4xn3+YHHv71EqPBa6erxvm3CmUKpbH7tl4x159M/lTrWGSJ7BXQ gx2rI/cBv3fGfwP5UAXjIglWIn52UsB7DGf5il2qHLBRuIAJxyQOn8z+dZ1wZI9WV0Vyu1CV XqwAkGPflk+mQTiqsI1EWwdWnMxAUK/QfuAc8/7YHJ7/AFOQDUmgs0L3c0EW5BvMhjBYY75x njFSLMjzSRAnfGAWBUjg9Oe/Q9KhgkjigLZmWLccGbPAxkkk8gcHlv5YqvpqsjxqylWWzhDh hzn5sfTHOfqOmOQDSooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK5fxDa58WeE7w3E5 xqEkSw78Rrm0uiW2jqxwoyc4C/Ljc24A6iivK57zUon1q/FpHN4gL6nBYSW257yHylkaESR4 x5BQw4TBXe0LkO0+U6zw0beLXNStdKn87RltLSaJkmMyGeTzWkIck5Zk8hzzz5m88yFmAPkv xz/yUHxL/wBhW6/9GtWBW/45/wCSg+Jf+wrdf+jWrAoAUKSOBS7G9KfH90/Wn0AVpFK4yKZU 0/8AD+NQ0AKqljgDJp/kSf3f1og/1y1coAp+RJ/d/WpKsVXoAKjYgMakqF/vmgBdw9a9x+DX j7wz4X8IXdjrOp/ZrmS/eZU8iR8oY4wDlVI6qfyrwup4PuH60AfWX/C4vAX/AEHv/JOf/wCI rlPH+r2PxU0GDQ/Bc/8AampQXK3kkOxoNsSqyFt0oVT80iDGc89ODXz7XrP7PX/I/X//AGC5 P/RsVAGD/wAKZ8f/APQA/wDJyD/4uq+o/CbxvpumXd9d6J5dtbQvNK/2qE7UUEk4D5PAPSvr zPsK5T4jS6ingbW1srS0mgbTboXDzXLRNGvlHlFCMHOM8Er0HPPAB19FFFABRRRQAUUUUAFF FFABRRRQAVHLPFAgaaVI1JwC7ADP41JVHUpPKNo/mxxYm+/IMqPkfryP50AXI5I5oxJE6uh6 MpyD+NNMETTLMYkMqjAcqNwH1/E1Se6fahW7jdWX95JGmVjXJ+ccnHpySO/RWqnaXFxHc+Ql wrIJnyHIyxMjBhgLyQMN2xuyeOAAbtJuUOFLDcQSBnkgdf5j86w4555ZtONxc9fLkyFVQS6S cf8AjoH/AAI+2GyvJBql7JHcZeJJJCjAHgLEQOOx6fQevNAG/SMyoMswAyBknueBWPLdXq4Z ZkAaSULuGB8r4VeASxPPAwTjjBzlbq4d5ZIBcKX82MqhX7uJFAOOCOvOeuQQRyAAbFNEkZCE OpD/AHCD97jPHrxzWWt5cGVEMnIcIq4H7394yMT7hQG4xjPPHFQWU0jNpsbNhV27U2dvIJ3Z +pYe+OOhoA3AysWAYEqcEA9D1/qKWs6GR11OdCdsbzYBHO5vKU4PoMAn8O3Qyyef9sG37X5e 4fd8rZjv1+bH+RQBO9xFHKsbNhmx2OBngZPbJ4GevapazLz/AI/mX+KT7PsHdtspLY9cDk+l TXk08UoSMn98oRCF+424DPvw2ceiH3wAXaKzBdymaVWn2Y37hsB8shwEGOvzDt1PbFRz3l0E jKuiOxYur8BHG3EfQls5JwOW6ggcUAaysrjKsCMkZB7jg0tZEVyYZI0M4RWnkAUqPmJlYd+v 4HgkE5B416AGpHHHu8tFXcxZtoxknufenUUUAFFFFACbVLhio3AEA45APX+Q/KloooARlV0K soZWGCCMgikWONZHkVFDvjcwHLY6ZPenUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BUckEMzwvLFG7wvviZlBKNtK5X0O1mGR2JHepKKAIxBCtw9wsUYndFR5Ao3MqklQT1IBZiB2 3H1ohghtkKQRRxIXZyqKFBZmLMeO5Ykk9ySakooA+I/HP/JQfEv/AGFbr/0a1YFb/jn/AJKD 4l/7Ct1/6NasCgCWP7p+tPpkf3T9adnnFAEU/wDD+NQ1NP8Aw/jUNAEkH+uWrlU4P9ctWydq knsM0ALVerFV6ACoX++amqF/vmgBtTwfcP1qCp4PuH60AS16z+z1/wAj9ff9guT/ANGxV5NX rP7PX/I/X3/YLk/9GxUAfStYHjr/AJJ94l/7BV1/6KatPUtRh0q1S4nV2R7iC3AQAndLKsSn kjjc4z7Z69KzPHX/ACT7xL/2Crr/ANFNQB0lFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYc/gvwrdXEtxceGtGmnlcvJJJYRMzsTkkkrkk nnNR/wDCCeD/APoVND/8F0P/AMTXQUUAc/8A8IJ4P/6FTQ//AAXQ/wDxNV7Xwn4DvvP+x+H/ AA5ceRK0E3k2UD+XIv3kbA4YZGQeRXUVw/g3UPD+q6jFNomo2KwWuni0tLC3uleZrZSu2Sdc lhjoitygkfcd0hVADY/4QTwf/wBCpof/AILof/iaP+EE8H/9Cpof/guh/wDia6CigDn/APhB PB//AEKmh/8Aguh/+JqOfwX4JtbeW4uPDXh+GCJC8kklhCqooGSSSuAAOc10lef/ABdivbvw bd2senT3Om/ZLi4vHheMbDHGTEGDOp2h8SEpk/udu0hzQB0H/CCeD/8AoVND/wDBdD/8TR/w gng//oVND/8ABdD/APE1uQSNNbxSvDJA7oGaKQqWQkfdO0kZHTgkehNSUAc//wAIJ4P/AOhU 0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E10FZfiWG+ufCurwaWZBqEllMlqY5NjCUoQmGyN p3Y5yMUAZdr4T8B33n/Y/D/hy48iVoJvJsoH8uRfvI2BwwyMg8irH/CCeD/+hU0P/wAF0P8A 8TVfwhJv+0rHHBNbQxQwxXsVl9lzjfutvLPIWEkgf3d5Q5eOQnqKAOf/AOEE8H/9Cpof/guh /wDiaP8AhBPB/wD0Kmh/+C6H/wCJroKKAObk8F+CYXhSXw14fR5n2RK1hCC7bS2F+Xk7VY4H YE9qk/4QTwf/ANCpof8A4Lof/iax/GcO/WrP/Rp382JYsKctc4mR/Ktzg+VMNm8vmP5VBz+7 8237igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiivI7V4fCPw98M6xoFlaW+oSaO1zeLFEALmOOweTfMq43ATCEbzyDJ tDDzCGAPXKK87XU/E6PPp7SakhLwMgupdOGpSBlnLLCqN5OP3SEeYMlRPgkquK+meIrvUdYv Et9atLS0dH1I6glmkaSCO0sGBkV+fKP2hi2WEgCqokULQB6ZRXl/h3xH4ivdFh1m61bzVju9 Ktvs32aNVk+0w2fms7AZ4M7MoXbhs53LhV2PEesa7aeJLmLTr+CG3gi0wLDNbCRWkubqWAsx DK20LhtoIJZU+YDcHAO4ory/UvFmtw6LfXVrdX1zdaPFdySx20FsEZYZ540kuWkILK4tzlYA rDEh43IBl2U2sWuheNYDrMlxZWNlqF0ILm0gfzJWub9DuOwAofKDspU5bGCqZRgD2SiuTsdX vpvG9xor38bW9q88wxHiSQbIGETHbjCfaMkjBIaDDMRMK6ygAooooAKKKKACiiigAooooAKK KKACiiigAoorP126vLHw9qd5p1v9ovoLSWW3h2F/MkVCVXaOTkgDA5NAGhRXi/hzxv4lh8F6 p4nk8R2Pia3i0pZzaRWipc2V2SRtkjTb+5HzFnJBIQlQAMnQ0b42aXHoenHVrDXHuIrS2OqX q2GIbZ5MAPIQRhX++u0HKsMDOVAB6xRXjeu/E3U49S8eWhbUtKt9GS1W1uo9NjmMR80I7Msj AMZN4KZIBjXcACDnuLv4haRaeMT4ba21KWeN4Y7i7itS1vbSTf6pJH6gsSoBwRlhzwcAHWUV 43pPxN1PWXZrltS0of8ACWwafEp02MhoHUgWz72yr5QmRhllLjAwQB3Ft8Q9FuvG8nhREu/t YeSKO58sNbyyxoryRK6k/Oqt8wIGCMHkjIB1lFcXonxM0jxCl8+n6drLpb281zbM1kQNQjib a5t+fnIYqNp2nLgY646ywuvt2nW159nnt/PiSXybhNkke4A7XXswzgjsaALFFZcPiXQbnVDp cGt6bLqAdkNol0jShlzuGwHORg5GOMGifxLoNrby3FxremwwRXBtZJJLpFVJgMmMknAcDnb1 oA1KK4/VviJo9pPo8WmXVjqn9oXbxPJDeoI7eGNPMnlaTlf3aFWKkgkNxW4fEugi3uLg63pv kW6RPPJ9qTbEsgBjLHOAGBBUnrnjNAGpRWemu6PJeWtnHqti91dxCe2hW4QvNGQSHRc5ZcAn I44NH9u6P/bH9kf2rY/2n/z5faE877u77md33eenTmgDQorz/WPifBa6+mkaNa2OrSXGnpd2 Ui6rFCl3IbjyfJRmG0sMM3BJO0jGa6yHxLoNyheDW9NlQW7XRZLpGAhVirScH7gYEFugIIoA 1KK5sePvCsl/b2dvr2m3MkqSyMYbyJlijjQs7ud2AAO3J6nG1WKyat4x0fT/AA9Pq1tqFje/ 6JcXNpFHdp/pfkoWdUIznG3BIBx3oA6CiuDv/iVDBZeFEtbW0m1jxEkEkVhPfCEQxyJu3tJs PAbCgYBc52gkYrqIPEug3VvFcW+t6bNBLcC1jkjukZXmIyIwQcFyOdvWgDUorPutd0ex06DU bzVbG3sZ9vk3M1wiRybhuXaxODkAkY6io7nxLoNlcXNvd63psE9qge4jlukVoVJUAuCcqCXQ An+8PUUAalFZcHiXQboxC31vTZjKgeMR3SNvUyeUCMHkGT5M/wB7jrxWH4g+Imj6L/YDW91Y 3kOr3ZiS4+2pHDHCn+tl805Q7DgbcgsTgc0AdhRVea/s7e8trOa7gjurrd9nheQB5doy21Ty 2BycdKp23iXQb24tre01vTZ57pC9vHFdIzTKCwJQA5YAo4JH90+hoA1KK4/w78RNH1vTLrUb q6sdNtVlla2ae9QGe1WTyluCrbTGrPlcMOoxnkVsP4s8Nxy3UUniDSkktM/aUa9jBhwwQ7xn 5fmIXnuQOtAGxRVOTVtNh1SHS5dQtE1CZN8Vo0yiV155VM5I+VuQOx9KuUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZ+maFo+ieb/ZOlWNh52PM+yW6Rb8ZxnaBnGT 19TWhRQBlx+GtBh0ubS4tE01NPmffLaLaoInbjlkxgn5V5I7D0qvrWgNfvDNYnTYZ47gXLfb NPW5RpQqqsuAyMJVCgK4bgZBB+UruUUAZel+HtN0nRrXS4baOSC3SEbpUUtI0SosbvgAFwI0 wccbRjGBVyWws55WlltIJJG8vc7xgk+WxePJ/wBliWHoTkc1YooAy73w1oOpJGl/omm3SRvI 6LPao4Vnbc5GRwWbknueTUkWhaPD9u8rSrGP+0M/bdtug+05znzOPnzubrn7x9a0KKAK62Fm nl7bSBfKleePEYGyR925x6Md75PU7m9TViiigAooooAKKKKACiiigAooooAKKKKACiiigAqv fw3Fxp1zDZ3X2S6kidIbjyxJ5TkEK+08Ng4OD1xVisnxNYajqfh66s9Ku/sl9Js8ubzGj24c E/MvIyAR+NKTsm0aUoKc4xk7Jta9vM4HTPh34m1DVNb1PxRf6Ml5qGhNo3m6bCxMpbrPLuCg uAFGBwRgfKFGeX1P4L+MdY0tLbUNV0a4eCyggs1eW62WbR7YyIhnaA8aKzMUOW4Cj71dH/wg nxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE1ze3n/Iz2P7Kwv/AEFQ/El8X/DbXtav fGJ068037J4it7M4uN6PFNbumBlQQUKK5z1yVGAASbmu/D/WNT+JmneILa40q2s7a7junuY4 XjvtqoEaAlcLLG2wcv8AMA7AfKMNnf8ACCfEP/oa/wDyo3H/AMTR/wAIJ8Q/+hr/APKjcf8A xNHt5/yMP7Kwv/QVD8SV/htry6pKI7zTX0//AIS2HxFGW3rLt+bzUIwRkfIFx1wxJGQBsaD4 S8R6F431Ge11W0j8L3d7PqMlts33E80qICrEqAiKwLLtOccHOcjC/wCEE+If/Q1/+VG4/wDi aP8AhBPiH/0Nf/lRuP8A4mj28/5GH9lYX/oKh+JZ8I/D/wASeGNc1vVkuNDjkm0/7La29nDJ DBczL/q554h8sbcAERjHztjBzu9IsPtn9nW39o+R9u8pPtH2fPl+Zgbtmeduc4zzivLv+EE+ If8A0Nf/AJUbj/4mj/hBPiH/ANDX/wCVG4/+Jo9vP+Rh/ZWF/wCgqH4mR4F8Aa/rl5Y+IvEc f9mRtqGo6lJbwyz21z5lwEj2YG1olHllgQ7EggEYJNSD4ReKra9168sNctIJWuLmbRCZpWeB rh0WWSSUqW3+Smz+PltwII3HT/4QT4h/9DX/AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5 GH9lYX/oKh+JiD4OeKrl9T83U9Ns0d7+5sTFPLcuJbpUjeOZ5EBZDErLvHzgtu56VqL8JdTf SZXkaxi1Jdbtb2GKG8mCfY7ePy4bfzgoZGVGcCQKWOATycif/hBPiH/0Nf8A5Ubj/wCJo/4Q T4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4l3wf8ADrUPC3ixL6xTTdK0spJ9pt7K5uJjdgqv lxuJcgGNvMIlUgsDjYu4is/U/hr4q1/xdPf6rq1o1mz6lHDJ9olkaG3uLfyYkWAqEUp1YhgW zySQCX/8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/E5WP 4e+KrfxrpWlXBjggnS0uP7QsIZbhbSaytHigdneNY8M/JTk9BkZBbb0r4RatbaZdW9/baHdL 9ktLGC1e7uNjQJJ5twDIqoyM82JQ2H2/dxtq/wD8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr /wDKjcf/ABNHt5/yMP7Kwv8A0FQ/EoXXwo8Wa1Y6jFrutWN1eSaJDp1reh33EpMs5EqlPm+Y bBIGztUMUZmOI/EHwZ1C8F9caLFptjPqtkbe+hn1G4nCyi5SbzhK6FpCwjAIIXB5y1af/CCf EP8A6Gv/AMqNx/8AE0f8IJ8Q/wDoa/8Ayo3H/wATR7ef8jD+ysL/ANBUPxOh8XeCLzxd4jgn muoLfTbXSry2t2QEzC4uE8pmZSMNGE5ABB3e1cfpfwf1h9VsG1ubSpNMEtk95bRM8jP9jtfJ iwWQKyuzPvQqMLgBjk1f/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A4mj28/5GH9lY X/oKh+J0PjnwprfiHVtKu9IubG0mscmC+dpVns3aRC7qqkpMrRqyGNwP97BIrk9Q+FGuL4gm 1DTpbRnOuyaolydVubWTyJQPNtwkaMELdDKp3EAcdhb/AOEE+If/AENf/lRuP/iaP+EE+If/ AENf/lRuP/iaPbz/AJGH9lYX/oKh+JiaX8E9eHh2+tdR1HTYtQi0z+z9LmtGdlVWneaXzCyg gtvMeV/gdwVbv0Hir4d69rtwtzZx+H7KRbK8wkSvGHursiOZpCFO8CDgPwzSAEqFO0Rf8IJ8 Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxLPjb4ZaprVr4VsNA1 OC0tdItJrCaa7+eQwSRJC2FC4Ztgf+7yRgjqKer/AAp1VPET3fh6402GzW40+8tkumkJheyg kjjiKgEujEpl9wIGeGPV/wDwgnxD/wChr/8AKjcf/E0f8IJ8Q/8Aoa//ACo3H/xNHt5/yMP7 Kwv/AEFQ/EyLf4KaxbLp9jJe2N3ppitIL0SyujrbrK09zbx7Y/mV5SHVyVcbQvTJMlx8JvFV ylxcPd6ML6dNQuGAkl8oXV4wilA+TPlC3GV/iEnXK8Vp/wDCCfEP/oa//Kjcf/E0f8IJ8Q/+ hr/8qNx/8TR7ef8AIw/srC/9BUPxNXQPhufDvjk6pD5d5p4t7WOB5ruSOW2aG2a3yY1Xy5iy kfMxXbubA9fRK8m/4QT4h/8AQ1/+VG4/+Jo/4QT4h/8AQ1/+VG4/+Jo9vP8AkYf2Vhf+gqH4 nrNFeTf8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/E9Zo ryb/AIQT4h/9DX/5Ubj/AOJo/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ8 Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxPWaK8m/4QT4h/9DX/ AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+J6zRXk3/CCfEP/oa//Kjcf/E0 f8IJ8Q/+hr/8qNx/8TR7ef8AIw/srC/9BUPxPWaK8m/4QT4h/wDQ1/8AlRuP/iaP+EE+If8A 0Nf/AJUbj/4mj28/5GH9lYX/AKCofies0V5N/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6Gv8A 8qNx/wDE0e3n/Iw/srC/9BUPxPWaK8m/4QT4h/8AQ1/+VG4/+Jo/4QT4h/8AQ1/+VG4/+Jo9 vP8AkYf2Vhf+gqH4nrNFeTf8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/y MP7Kwv8A0FQ/E9Zoryb/AIQT4h/9DX/5Ubj/AOJo/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf +gqH4nrNFeTf8IJ8Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxP WaK8m/4QT4h/9DX/AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+J6zRXk3/C CfEP/oa//Kjcf/E0f8IJ8Q/+hr/8qNx/8TR7ef8AIw/srC/9BUPxPWaK8m/4QT4h/wDQ1/8A lRuP/iaP+EE+If8A0Nf/AJUbj/4mj28/5GH9lYX/AKCofies0V5N/wAIJ8Q/+hr/APKjcf8A xNH/AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxPWaK8m/4QT4h/8AQ1/+VG4/+Jo/4QT4 h/8AQ1/+VG4/+Jo9vP8AkYf2Vhf+gqH4nrNFeTf8IJ8Q/wDoa/8Ayo3H/wATXQ+DfDXirRtX luNc1v7datAUWP7VLLh9ykHDgDoCM+9VGtNuzg0ZV8uw9Om5xxEZNdFe7O4oooroPICiiigA ooooAKKKhumuEt3a0iilnGNqSyGNTzzlgrEcZ7GgaV3YxdV1m8s/ENlaRPYraP5YnMpJfdI5 VASD+6ztbYSrLIymPMbbS3F2vi7xRrV54dufssdnBPcJdrapsMtzDLa3Mix4FxjGIiA8mwM7 KdieUwbsryx1DULi1uL3w3oFzPaPvtpJrtnaFsg5Qm3ypyoOR6D0qP8Asq6/6FXw7/x9/bv+ Pk/8fH/Pb/j3/wBZ/tdfep51/SZt9Xn3X/gUf8zkW8Xaxpusa79h0ORZ5b2W8uIbuWBTHFBa WQIZ/OCIG8wHzAz7QOUbJ27mo+N7zTW1GBrWCW60v7fc3SglUkt4YklRUbJIkIubUEkY4lx0 XOtNY6hcuHn8N6BK4uFugz3bMRMqhVk5t/vhQAG6gACrAOtrcPcLo2kCd0VHkF++5lUkqCfI yQCzEDtuPrRzr+kw+rz7r/wKP+Zzuh6lqeo+NdPXWLWO3vLay1CFlUxgsN1i4LIksojOHxtL kkANwGAHeVztnY6hpyQJY+G9AtUgR0hWC7ZBGrsGcLi34DMASB1IBNXPtHiH/oF6X/4MpP8A 4xRzr+kw+rz7r/wKP+ZrUVlxT66ZkE2m6ckRYb2S/dmA7kAwjJ9sj61qU07mc6bhvb5NP8go qvevepCDY28E8u7lZ5zEoHrkI3PTjH41R+0eIf8AoF6X/wCDKT/4xSckioUZTV0196X5s1qK yftHiH/oF6X/AODKT/4xR9o8Q/8AQL0v/wAGUn/xijnX9JlfV591/wCBR/zNasvxBqU2laT5 9usZnkuLe1jMgJVGmmSIOQCCwUvu25GcYyM5DftHiH/oF6X/AODKT/4xUc51u6t5be40bSJo JUKSRyX7srqRgggwYII4xRzr+kw+rz7r/wACj/mcqniGbT/Hm68udNKT29va3tzE58pFiGpu zgk/IQ0I3KS2z5lycbqz9V8Y69qHge4u4brTbJ7jQvtAeIOXWY2vnuEcP8koUjbGwztYSh32 vGOySx1CNERPDegKiJEiqLtgFWJt0QH+j8BGOVH8J5GKryaFJM8Ly+EPDLvDb/ZYmafJSHaV 8tf9H4TazDaOMEjvRzr+kw+rz7r/AMCj/mYI8b+JUM6nTbF/9LFpbSSOsKTOl7FaSEASvIVY uzZKDyvlB83IJPEHiHVtM8SWEzaPOb6HT57WKRhEI7qSW6sot8aCYkKCwba7ISCBuHJHRQaV dW2/7P4V8Oxb/J3+Xcld3k48rOLfnZgbf7uBjFWLmDVrzd9q0DRJ90TwHzb1mzG+N6cwfdba uR0OBnpRzr+kw+rz7r/wKP8AmYJ8Z6vBZol1Z2kV/d27RWaBg6rdJdC1bztkjBULTW52qzMv 71SSVGc/UvE2p6tPJa3GnxwaeNYt0tZS8YZjb6nBC2B5hdwSck7ECcL8+4NXXJBq0cVrFHoG iJHaY+zIt6wEOFKDYPI+X5SV47EjpUZsdQNxcXB8N6B59w8Tzyfa23StGQYyx+z5JUgFSemO MUc6/pMPq8+6/wDAo/5nRUVk/aPEP/QL0v8A8GUn/wAYqu+razHqMNg2n6WLqaKSaNP7Ql+Z EKBjnyMcGRPz9jRzr+kw+rz7r/wKP+ZvUVk/aPEP/QL0v/wZSf8Axij7R4h/6Bel/wDgyk/+ MUc6/pMPq8+6/wDAo/5mtRWT9o8Q/wDQL0v/AMGUn/xij7R4h/6Bel/+DKT/AOMUc6/pMPq8 +6/8Cj/ma1FZP2jxD/0C9L/8GUn/AMYp0U+umZBNpunJEWG9kv3ZgO5AMIyfbI+tHOv6TB4e a6r/AMCj/malFFFUYBRWddTaylw62lhYSwDG15b142PHOVETAc57movtHiH/AKBel/8Agyk/ +MVPMv6RsqE2r3X/AIEv8zWorJ+0eIf+gXpf/gyk/wDjFH2jxD/0C9L/APBlJ/8AGKOdf0mP 6vPuv/Ao/wCZrUVk/aPEP/QL0v8A8GUn/wAYo+0eIf8AoF6X/wCDKT/4xRzr+kw+rz7r/wAC j/ma1FZP2jxD/wBAvS//AAZSf/GKPtHiH/oF6X/4MpP/AIxRzr+kw+rz7r/wKP8Ama1FZP2j xD/0C9L/APBlJ/8AGKPtHiH/AKBel/8Agyk/+MUc6/pMPq8+6/8AAo/5mL4z8WXnhq8tktks WjbT72+kW5cq8n2cRv5ceOrMrOP9n7/IQq2e3jXWZLjUgLSC0t/Nmg0+WSNJTJLFdLbbQgnV pPMdgNxESxkqCWB3VtXenatfata6jc6RpcklrE8Ucb6gxT5pIpNxBt/vK0CEHt9cYkax1Bnv Xbw3oBe/QJeMbts3ChSoEn+j/OApIwc8HFHOv6TD6vPuv/Ao/wCZyuheKL688WyTXFpHZy3F va2d1NMMxxPFd3sRXCscPIyYUbiqlsb3OxZC+8S6xb+Mbz7FoN3BqF7b2FpFDdeRKwA+3ylw qzhGGIyuDIhHJ5wA2lqN3D4TtbdJvC2kW9vI8aRrZpPKgZZd0YPlWpCnzZMqDj5mJXnNaktv e61ZtJdeGtEuIbyKMSJd3D5kRSXRXR7fPyliQrD5STwDmjnX9Jh9Xn3X/gUf8yja+Mb65urO xa2tI7zUHsprcJJ50SQSxPJIGkU4ZwLa6Cso2nMJ6FsY+leJtT8San4Xn1HT47RJb2O8tgHj 3GKayvCoKrI5IGziRgm/J+Rdprsidba4S4bRtIM6IyJIb99yqxBYA+RkAlVJHfaPSq8NjqFs 5eDw3oETm4a6LJdspMzKVaTi3++VJBbqQSKOdf0mH1efdf8AgUf8zoqKyftHiH/oF6X/AODK T/4xUcmoa3C8KS2GkI8z7IlbVHBdtpbC/uOTtVjgdgT2o51/SYfV591/4FH/ADNqiuR0/wAX 3+p6iLCDSoY7pommVLs3ltuRSoYgy2qg4Lr09RWpDqGt3KF4LDSJUDshZNUdgGVirDiDqGBB HYgijnX9Jh9Xn3X/AIFH/M2qKyftHiH/AKBel/8Agyk/+MUfaPEP/QL0v/wZSf8AxijnX9Jh 9Xn3X/gUf8zWorJ+0eIf+gXpf/gyk/8AjFH2jxD/ANAvS/8AwZSf/GKOdf0mH1efdf8AgUf8 zWorJ+0eIf8AoF6X/wCDKT/4xTop9dMyCbTdOSIsN7JfuzAdyAYRk+2R9aOdf0mDw811X/gU f8zUoooqjAKK878dXXjCHVp/7AudSit4rIOiWtlHKry+TeOclo2JO+G2XAI/1gHVgaNLuvGD eNY/tdzqTaW97KjQvZRrEsW6/C/OIw2AILUglufNGc71oA9EoryfUNR8fQXmpz21xqrxr9s+ zwDT42QYGoCLaRFuOPItCMk580ZyHUVqWV14wttJ8W+dc6ld3FvZTPpjzWUYYyrNdomwJGoc lIrdsEHO8EcMBQB6JRXkd9efEG2t9Rit77WZnR5mglOnQliIxqGxRiHBD+RZk8ZPmDaRvFei eGG1FtFK6pLPNdR3d1EJZ41jeSNZ5FjYhVUcoEOQADnPegDYooooAKKKKACiiigAooqG6uUt Ld55FlZFxkRRNI3Jxwqgk/gKBpNuyJq8/wDhzpdxJZ6dr8llBYvd6f5t48LhjqU85SYzvgDG 35gAR8plkVQEVS/Vf8JHY/8APDVP/BVc/wDxuj/hI7H/AJ4ap/4Krn/43U88e5t9Vr/yP7ma 1FZP/CR2P/PDVP8AwVXP/wAbo/4SOx/54ap/4Krn/wCN0c8e4fVa/wDI/uZNrtreX3h7U7PT rj7PfT2ksVvNvKeXIyEK24cjBIORyKp+F9MttNsrk2mgR6HHcXBm+yKyZzsRNzLGSiE7OiEj ADEhmYCb/hI7H/nhqn/gquf/AI3R/wAJHY/88NU/8FVz/wDG6OePcPqtf+R/czWorJ/4SOx/ 54ap/wCCq5/+N0f8JHY/88NU/wDBVc//ABujnj3D6rX/AJH9zNavPzpdxrfjvVrj7FAJrHUL WKHVt4823gjihneBVwD+8MzrkHlZJNxwiI/Vf8JHY/8APDVP/BVc/wDxuj/hI7H/AJ4ap/4K rn/43Rzx7h9Vr/yP7mc7pXxR0nWNJ1HUrWwvmt9PtFvLjElu5SIxyuMhJThv3RUocMCy5AGS NSPxpp8vjObwwsUgvIn2M7XFuoJ8oS/LGZPNYbWAyEIznsCRkppvh9NLTTl/t8W50yLSpx/Z s+bi3j+6rnyeDtaRSU2nErdwhWPUdH8PavfX0mojW7qxvpfPn06bRnaEyeSIQ6sbfzEYKowV cEHp1Io549w+q1/5H9zOqHiXQWsHv11vTTZoiu9wLpPLVWcopLZwAWVlB7lSOoq5Y39nqdnH eWF3Bd2smdk0EgkRsEg4YcHBBH4VwtvoXh+KZriefxNd3b3C3L3FxYTl2kE0UpPEIAB+zwJt AACxKFCksW6DSdR0vRtGsdLt4tXaCyt47eNpNLuSxVFCgnEYGcD0FHPHuH1Wv/I/uZ0VeV2l 8PDGr3Gsa5Z3cd9pemQxX8v2iNl1Ca7nVPPTdIEjQNb4BbYdhVSEWJVrvv8AhI7H/nhqn/gq uf8A43VPU9R0vVbVLeeLV1RLiC4BTS7kHdFKsqjmM8bkAPtnp1o549w+q1/5H9zKL/EXTIre 6uZbG+S1t4i/nqYZElcWoujEhSQ5byiTu+4dpAY8Z1NN8WaVqHh9tbkuI7KxRyjzXU0YjU5A GJVZo2BJAyrEZypwwIHO6lpOharc6pJdTeIjDqG9mt10yYJFK9t9mMqHyN27ysjDMy/MTtzj BeaL4ZuYh5MOt2d1FuWzvbXS51nso2UqYoXMJIjw8mFOQu/5du1Npzx7h9Vr/wAj+5nVXviX QdNSN7/W9NtUkeREae6RAzI21wMnkq3BHY8GtSvPYfD/AIXtjamCLX4/szqyAadcHISS2dFO YuirZwIO5UHJLEtXWf8ACR2P/PDVP/BVc/8Axujnj3D6rX/kf3M1q4vxDoWr6p4vgu/KjuNH gt4YHtThTcLLM32hS24YRVS3kZSp3iPYMBnDb3/CR2P/ADw1T/wVXP8A8bo/4SOx/wCeGqf+ Cq5/+N0c8e4fVa/8j+5lHRPGmn69r19pFrFIk9k8ySF7i3JJjk8tv3ayGUDPQsgGMeoz0lcj pEtno91cvFfeIprWaWaYWc2kyeXG8spkYqVtw/3mbGWPB+mNj/hI7H/nhqn/AIKrn/43Rzx7 h9Vr/wAj+5mD4xt21LxLoemyaPaaxa/Z7u7NndOqp5qGGNJDlSCFE75B7MWAZlRTqWes6PoO h2drqniaxkktIo7aa7urpEMsi7kLNuY4YtFLwSTlHHJU1a/4SOx/54ap/wCCq5/+N1xc/hfw 5dPqDTXHiZxevMxX+zZQIhIt0GVMQZxm9mI3ZOdvOBgnPHuH1Wv/ACP7md8NW01r97BdQtDe I6o9uJl8xWZC6grnIJVWYDuFJ6Co9M13R9b83+ydVsb/AMnHmfZLhJdmc4ztJxnB6+hrkf7G 8OnWv7SY+InCy+bHatY3PkxkzfaG2jys/NOEkJznMarkJlDoeHBovhiwezsk1uSNvKyZtMuC f3cEcC9Ih/DCpPuT24Bzx7h9Vr/yP7mddRWT/wAJHY/88NU/8FVz/wDG6P8AhI7H/nhqn/gq uf8A43Rzx7h9Vr/yP7mN8WX1xpng3XL+zk8u6tdPuJoX2g7XWNipweDggda5O3uLL4b2Qu7j So9Msbt4bCDTrW5iCLLGkrNO8srxplwNu5jvYRxlvmbYnXf8JHY/88NU/wDBVc//ABuqd7qO l311p1xLFq4ewuDcRBdLucFjFJFhv3fTbIx4xyB9Cc8e4fVa/wDI/uZD/wAJ1YLpMepSWV8l vLpUurxZWMl4I44nbADnDfvgoBxyrdsE2NG8Y6TrGnJe/aILSOS7FnEJr23k8yUgEIrRSOpY 54XO7jpjGcV9N8Pvpb6c39vm3GmS6VAP7Nnzb28n3lQ+TydqxqC+44iXuXLU7vw74e1DzpL6 fxFPd3P7u7vF0l4Jrq3OzdbyNFbpujPlrz9/GQGCkgnPHuH1Wv8AyP7mdk/iXQY7i0t31vTV nvUR7WNrpA06ucIUGcsGPAIzntWpXC2em+H7LVLXUU/t9riBxIS+mz4lk/0nc7AQ9Wa8mYhc DO3AAGD0n/CR2P8Azw1T/wAFVz/8bo549w+q1/5H9zIfFFtd3VlbJDayXtotwDfWMTIr3UOx xsG8qpG8xlgWAZVZTkHa3H+HvGmleGvD9lZTxXbvNpkuvkG4jZlimM9x5YMkgkmdQrKWAOcB m25OO2/4SOx/54ap/wCCq5/+N1j6kNF1T+1/PTW1/tXT10+fZplwNsY83BXMRw375uTkcDjr k549w+q1/wCR/cxt38RdMsLy1sryxvoLyaV4ZLdzDvhZRE20gSfvGKzxsEi8xjnAG4EV0k2r abbXosp9QtIrsoriB5lVyrOI1O0nOC5Cg9yQOtcb/ZOhNeR3803iKXU4/NZb86ZMsolcRL5o CwBQwSCNAAoUruDK25s17nwx4RnnbybXW7OybeW0+z0y4iti7oIpH2CL7zRAx8HgMxUK5L0c 8e4fVa/8j+5nbWuu6PfajPp1nqtjcX0G7zraG4R5I9p2tuUHIwSAc9DWhXI6QNF0W/vby2TW 3ku8+YJNMuCBmeefjEQ/iuHH0C98k7H/AAkdj/zw1T/wVXP/AMbo549w+q1/5H9zG67Y3GoT 6PCkfmWa6gk14NwGEjR5Iz68TrAeOuOfl3VsVk/8JHY/88NU/wDBVc//ABuj/hI7H/nhqn/g quf/AI3Rzx7h9Vr/AMj+5mtRWT/wkdj/AM8NU/8ABVc//G6P+Ejsf+eGqf8Agquf/jdHPHuH 1Wv/ACP7ma1cvrs1xZ+LdNu7a1+0zDStQjt4TIIxPPut3SEOeAzCNyPZWPRTWl/wkdj/AM8N U/8ABVc//G6P+Ejsf+eGqf8Agquf/jdHPHuH1Wv/ACP7mc6niAeGtOm1XXNJvoby5ljimuLu 5sYfOOHIWPdc7VjTBwm7PzE/Oxkc6Hg28TU5Ne1SAZtb3UElhcOrqwFpbo2HQlW2uroSpI3K wzxU2pahpWq26wXCa+iK4cG1tb63bOCOWjVSRz0zjp6CrEGu6fbW8UCQ6uUjQIpk067diAMc syEsfckk96OePcPqtf8Akf3M2qKyf+Ejsf8Anhqn/gquf/jdH/CR2P8Azw1T/wAFVz/8bo54 9w+q1/5H9zON8CxJZ6dpviTUVsdIF5p7S3c3nqP7TnlC3DTucLjYqyEA/d3yhQqIGftG8S6C qXrtremhLBwl4xukxbsWKgSc/ISwIwccjFYPimLSPFmmx2N1Jr9qiO7b7XS5gxDxSRMp3wsM FJXHTPQgis+bQvD8hupIp/E1tczuzpcwWE6yQM0ly7GM+TgEi8mXJBwCpGGG6jnj3D6rX/kf 3M6698S6DpqRvf63ptqkjyIjT3SIGZG2uBk8lW4I7Hg1qV57D4f8L2xtTBFr8f2Z1ZANOuDk JJbOinMXRVs4EHcqDkliWrrP+Ejsf+eGqf8Agquf/jdHPHuH1Wv/ACP7ma1FZP8Awkdj/wA8 NU/8FVz/APG6P+Ejsf8Anhqn/gquf/jdHPHuH1Wv/I/uZrUVk/8ACR2P/PDVP/BVc/8Axuj/ AISOx/54ap/4Krn/AON0c8e4fVa/8j+5mtRWT/wkdj/zw1T/AMFVz/8AG6P+Ejsf+eGqf+Cq 5/8AjdHPHuH1Wv8AyP7ma1FZP/CR2P8Azw1T/wAFVz/8bqxZ6tbX8xihjvFYLuzPZTQrj6uo GeenWhTi9mKWHrRV5QaXoy9RRRVGIUUUUAFFFFABRRRQBn6nrenaN5X2+48rzckYRn2quN0j bQdka5G52wq5GSMitCuf8S+GP+Eg27bz7PvtLiwnzFv3W8+zzAvI2yfu12sdwHOVbIxuATfa HZpIzAUUIgQhg2TuJbOCCNuBgYweTngAy4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDn hSxqOfxdo8CWbeZdzG7SR4o7axnnfEbKsm5EQshVmVSGAIPB5BFZdp4F+y6Y9qdR3SRRWVvZ yeRgRx2chkt/MXd+8bcTvIKBhwAh5ovPAiXmmWds13B9oiluJnuXslZ43nkMskluxO6CQOfk bc23jcHIBAB1F/fW+madc395J5draxPNM+0naigljgcnAB6VlyeLdKhsobuVdSRJrj7NFG2l 3IleTYXwsXl7yNqsdwGODzxWpf2aahp1zZSnEdxE8THYr4DAg/K4Knr0YEHuCKw7LwvNY+Fd S0m3v47Se8SURy2NuYIbNmQIDBEHJQAjeRu5dnbI3YABuWF9b6np1tf2cnmWt1Ek0L7SNyMA VODyMgjrVio4IIbW3it7eKOGCJAkccahVRQMAADgADjFSUAFZ91renWWowWFxcbLibbgbGKp uO1N7AbU3MCq7iNzAhckYrQrn9U8Mf2lrSXwvPKhk+y/aYvK3M/2aZpodjZGz52O7IbcuANp 5oA3J54bW3luLiWOGCJC8kkjBVRQMkkngADnNZf/AAlGk/2d9t86fb5vkeR9kl+0eZjds8jb 5m7b8+Nudnzfd5qxqWmf2xpOqaZezf6LfRPbgwrteON49rckkFsliDgDkDBxk4//AAi15s+2 f2nB/bP9of2h5/2Q/Z/M+z/ZseV5m7b5XbzM7+c4+WgDUfxHpCXFpD9ujc3aI8TxgvHtc4jL OoKqHPCFiN54XJ4rUrk18DwwHToba+kWztrext5UkjDSSLZyGSAq4ICncTvyrbhwNh5rrKAC s+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2CcVoVz+l+GP7N1p743nmwx/avs0Xl bWT7TMs029snf86jbgLtXIO480AampapaaTbrNdvIA7hI0iieWSRsE4REBZjgEkAHAUnoCar v4j0hLi0h+3RubtEeJ4wXj2ucRlnUFVDnhCxG88Lk8VHqek3moWumyLewRanYSi4jmNsWhaQ xPE2Y94baVkfA35B28nBBy18DwwHToba+kWztrext5UkjDSSLZyGSAq4ICncTvyrbhwNh5oA 3LXW9OvdRnsLe433EO7I2MFfadr7GI2vtYhW2k7WIDYJxWhXP6X4Y/s3WnvjeebDH9q+zReV tZPtMyzTb2yd/wA6jbgLtXIO4810FAEc88Nrby3FxLHDBEheSSRgqooGSSTwABzmsv8A4SfT Tp321BfSRiXyXji0+4eaN8bsPEEMicYPzKOGU9GGdScTNbyrbyRxzlCI3kQuqtjglQQSM9sj PqK5ePwtqX/CNTaZd3ujX9xcXHnXU97pLSpc8DmSMzcvuVcHIVQqqqAKMAGw/iPSEuLSH7dG 5u0R4njBePa5xGWdQVUOeELEbzwuTxQniHTHuLuITyKlmjvPcvBItugQ4f8AflfLJU5BAbIK sDjacY954LN5e6Oz6lI1vp6W4d38w3V00Dh0MsgkCONwBw8bYLOVKlsgs/BENhe6xcQSWhTU EuAYpLMFZWmcuxucMPPCsSqD5NqMy5O7dQBqf8JRpP8AZ323zp9vm+R5H2SX7R5mN2zyNvmb tvz4252fN93mtSCeG6t4ri3ljmglQPHJGwZXUjIII4II5zXJ2PgmfT7CNodV36qmoHUBcTpL NEZDAbfBV5TIV8o9DLkNznb8ldBpWmf2Pp2nabazbrGytFtgJVzI+wKqNuBAHAbI28kjGMYI BGniPSHuLuH7dGhtEd5XkBSPahxIVdgFYIeHKk7Dw2DxUf8AwlGk/wBnfbfOn2+b5HkfZJft HmY3bPI2+Zu2/PjbnZ833eay28DwznUYbm+kazube+t4kjjCyRreSCScs5JDHcBswq7Rwd55 qT/hFrzZ9s/tOD+2f7Q/tDz/ALIfs/mfZ/s2PK8zdt8rt5md/OcfLQB0kE8N1bxXFvLHNBKg eOSNgyupGQQRwQRzmpKp6TpsOjaNY6XbtI0Flbx28bSEFiqKFBOABnA9BVygArP0zW9O1nzf sFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaFc3oPhebQbedYb+OSdbKHT7R3tztjhgD+T5ih8 u+ZG3EFA3GFTuAamp63p2jeV9vuPK83JGEZ9qrjdI20HZGuRudsKuRkjIo/tvTv7Y/sr7R/p fTbsbZu27/L342+Zs+fZndt+bG3ms/xL4Y/4SDbtvPs++0uLCfMW/dbz7PMC8jbJ+7Xax3Ac 5VsjB/wjH/FS/wBqfbP9H+1/b/I8r5/tH2f7NnfnHl+X/Dtzu53Y+WgDQ0zW9O1nzfsFx5vl YJyjJuVs7ZF3Ab42wdrrlWwcE4NaFc/4a8Mf8I/u3Xn2jZaW9hBiLZtt4N/lhuTuk/eNuYbQ eMKuDnoKAK99fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQ QSGVlOCrAggqQCCCCARVfWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5Eek6VNp NhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FGDkndQBJ/benf2x/ZX2j/S+m3Y2zdt3+Xvxt 8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxqv/AMIx/wAVL/an 2z/R/tf2/wAjyvn+0fZ/s2d+ceX5f8O3O7ndj5az7TwL9l0x7U6jukiisrezk8jAjjs5DJb+ Yu79424neQUDDgBDzQB1FjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYrP0XTP7I0w Wpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToUAY9v4q0K80671C01OC6tLSUQSTW5MqmQh SETbnex3oAFySzbR83FaFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVzdt4f8AE1sN bdPEWmpcam4mWaPSWHkS+XFFuAadgwCRdD/EckkDadjSNNudL0uys/PtCYnYztFbuolB3HI3 SMwcsVZnZnLHcTy2QAWDqtiuspo5uY/7Qe3a6FuOW8pWClz6DcwAz15xnBxHpmt6drPm/YLj zfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwaw08ER23jyLxLaXskSFJvtFs8k8hmkkCgtkzbFAEcYC 7CMKPRClzw14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8YVcHIB0FFFFAGPq3ifTdE vILW9F951xxCINPuJw5wx2ho0YbsIx25zgZxitiuf17RtY1PU9MurDVLG0jsJTcJHPYPOWkM ckZywmT5dsp4xnIznHFZdn4FmsPE91rlreabBPcan9scw6aUd4TGY3gdxJlgx2SZ6eYpYqcg AA6S11qyvtRnsrZp5JIdweQW0nk5U7WUS7fLZgeCoYkEMCMqcaFcv4M8I/8ACJwXkX/Eqb7R K0u+x037K3Lu+1j5jblXftQcbVGOa6igCvfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAA JNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2om8mRZYriKQruCyRSLI m5cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNxNcXTtER5jSu8jhBu+QeY+RndhRg5J3UASf23p3 9sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXGZFc8 IyBg54Usar/8Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTq O6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAg gqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQAUUVHPCtzbyw OZAkiFGMcjIwBGOGUgqfcEEdqAK9hqtjqb3iWNzHObK4NrcbOQkoVWKZ6EgMM46HIPIIFyuX 8IeDx4Sn1UQXfm2d3LG9vb/vj9nREEapmSV93yqoyAvTH3Qqrsano1rq/lfaZb6Pys7fsl/P bZzjOfKdd3TvnHOOpoAE1zS5NcbRI7+CTU0iM0lqj7njQbOWA+7/AKxMZxnORnBo0zW9O1nz fsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NZ93o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTz gN2YRg7cDPIapNA0fUtOuL+51XUbS/ubpwfOhs2gZVBbCcyONihsKBtx8xO5nZiAblFFRzwr c28sDmQJIhRjHIyMARjhlIKn3BBHagCvYarY6m94ljcxzmyuDa3GzkJKFVimehIDDOOhyDyC Bcrl/CHg8eEp9VEF35tndyxvb2/74/Z0RBGqZklfd8qqMgL0x90Kq9RQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABXF+DvEd3e295carfRtBDZW91dSSBEWyuWEhuLYkABREEQ7Xy67/mY5GO0rP0zW9O1nzf sFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NAHN+MfEd3ZW9ncaVfRrBNZXF1ayRhHW9uVEZt7Y EghhKHc7Uw7bPlYYOZP7b1H/AIT3+zvtHyfa/I+w7F/49Psvm/aum/8A1/7nfny/4cb+a6DU 9b07RvK+33HlebkjCM+1VxukbaDsjXI3O2FXIyRkUf23p39sf2V9o/0vpt2Ns3bd/l78bfM2 fPszu2/NjbzQBz/gfW9R1jz/ALbcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv+YnIx2FZ+ma 3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoAx/FF9cadoMtxbSeSwlhSSfaD5ETSo ssvOQNiM75YFRtywIBFV9C1xG0GzuNVv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27KgDArYv r6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEF SAQQQQCKAOX/ALb1H/hPf7O+0fJ9r8j7DsX/AI9Psvm/aum//X/ud+fL/hxv5rH0/wAU6zPo N5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDiu4/tvTv7Y/sr7R/pfTbsbZu27/L34 2+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY0AHhe+uNR0GK4u ZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtiq9jfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKk AggggEVYoA4fR/GOk3jeJ4ZvGNiYbe7JtroXNuDBA8UOGU42lVlkZQzBucAljWh4T8V2Gp+D dDv7zWrGS6uoreCZ/PjG67aNS0WAcCQkn5Bz7VsQa1ZXMWoyQtO/9nSvDcoLaTerqochU27n yrKRtB3ZGM5qxYX1vqenW1/ZyeZa3USTQvtI3IwBU4PIyCOtAHNz6pd/8LIt7BdRkNmbcKdP iVA4k2yMZpFaPcYCPLUSI4AkAQgknEfgXW9R1j7f9vuPP2eXIMIo8hn3boG2geXIm0boW3tH uGZZNw29B/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNR6V4h0zWri4gsJ5JXgRJG LQSIrI5YI6MygSI2xsMpIOODQBqUUUUAcv491Q6RoNvcLrf9kM+oWsJnzCNyPKqyD96rDhC7 9ONmegINe18cb9ensry3sYrEaq2kQ3cN/wCYXuPK81VZSihcjKEBmIk+XB+9XQaxrVloNml1 ftOsLypCphtpJzvc4UbY1Y8nAHHUgdSK0KAOL8AeJLvxMdWu7rUNNnCXDRx21hepOsCrJIgO BGrAOEDBmZt/3gEHy12lU7PVLS/uLqG1eSQ2z7JH8pxGWyQQrkbXIKkMFJ2kYODxVygDH8UX 1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgEVX0LXEbQbO41W/gRri7ltbWeZ1j+2 ASusLL0DNIiq42jDbsqAMCti+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs4 7q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIoA5f+29R/wCE9/s77R8n2vyPsOxf+PT7L5v2rpv/ ANf+5358v+HG/msfT/FOsz6DeXEl5uYRWD3U/lJ/xLZZpSt3FwML9nQB8SBmTOZCw4ruP7b0 7+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiu eEZAwc8KWNAB4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYqvY31vqNnHdW snmQvkAlSpBBIZWU4KsCCCpAIIIIBFWKAM/U5tYi8r+ybGxus58z7XePb7emMbYnz364xgdc 8cnr/inVdK8faJYTxyW2m3d6LWICW223itCxZzvcSArKYwFVR0PLmREHeVn/ANt6d/bH9lfa P9L6bdjbN23f5e/G3zNnz7M7tvzY280Ac/4H1vUdY8/7bcfaMWltPN8ir9lu38zz7X5QMeXt j+R8yLv+YnIx0GpzaxF5X9k2NjdZz5n2u8e329MY2xPnv1xjA654NM1vTtZ837Bceb5WCcoy blbO2RdwG+NsHa65VsHBODWhQBwev+KdV0rx9olhPHJbabd3otYgJbbbeK0LFnO9xICspjAV VHQ8uZEQaHgfXhr9veXCa9aapGXV41jMYkiDA8sicxoxBKRvukUD53LEqm5/benf2x/ZX2j/ AEvpt2Ns3bd/l78bfM2fPszu2/NjbzRputWWry3cdk07/ZZWhkd7aSNC6syMFdlCvhkYHaTj HuKANCuTj14TfEObSU160Hkpsk06Qxq7ExhwEU/vHcA72kyIwpChGbe6dZRQByfgfXhr9veX Ca9aapGXV41jMYkiDA8sicxoxBKRvukUD53LEqnWVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8 bYO11yrYOCcGtCgAooooAKKKKACiiigAooooAKKKKACiiigAoorj9e1vUbLxXbWcFx5an7H9 ntdin7d5s7Jc9RubyYgsnyEbd2XypAoA7Ciqd3ewrZX7xX1pC9qjCWaYhkt2CB8yDcMAKysQ SvBHIzmuHj8X27eCptUk8XRixN75NtfKbY3cylQVjbIEMcrOc/Mo2xY3hG3MoB6JRXB3HiPV 01LSIzfWhkmt7B44bMB4NRaaUpdGMsC7pFGFlBQjaGDPuUgV3lABRRXH6Dreo3viu5s57jzF H2z7Ra7FH2Hyp1S26DcvnRFpPnJ3bcphQRQB2FFcn468QtpfgW61jS9b02zc27yWtxOqyrcH ymZFi+dQXbAKn5hx91qp6z43sLTXvD0kHivQ00i7lcTx+bGXdBFPiTzS+BH5iKvC/eGN3VaA O4ori9B8Q6hfeOtR0t7mO7toUnMzQPDJDbMsqLCi7D5iOUZ94lHLxts+VTXaUAFFU9Vuruy0 u4uLDT5NQu0TMVqkqRmVuw3OQFHcn0BwCcA+f6Z46+2/Dx9W1PXo7CRNTntHuYPsu+Y+Y3lx w5keJCQYxlyw2qzE7SJaAPTKK4O48R6umpaRGb60Mk1vYPHDZgPBqLTSlLoxlgXdIowsoKEb QwZ9ykCu8oAKK5/xZqMun2th/wATD+zLSe78q71D5B9lj8qRg26QFFzIsaZYEfPgfMQRc0PU przRtJfUljttUu7JLiW0IKMrbU8wBGO4BWcA56ZAPWgDUorz8eJtdW61z7Kftt3b2moy/wBn +SG+zSQyhbRdqgP+/jJfDEl8ZTavFV77xLq0egSPYaxBPZDVRaprk80USNb/AGcSGQzCJoR+ +zDu8sj+Dh/moA9IoqnpMzXOjWM7i7DyW8bsLyNUnBKg/vFUAK/qAAAc4q5QAVzeg+F5tBt5 1hv45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYVO/SVxfg7xHd3tveXGq30bQQ2VvdXUkgRFs rlhIbi2JAAURBEO18uu/5mORgA0Ne8Lza9bwLNfxxztZTafduludskM4TzvLUvlHzGu0kuF5 yr9pP+EY/wCKl/tT7Z/o/wBr+3+R5Xz/AGj7P9mzvzjy/L/h253c7sfLWf441vUdH8j7FcfZ 82lzPD8it9qu08vyLX5gc+Zuk+RMSNs+UjByf23qP/Ce/wBnfaPk+1+R9h2L/wAen2XzftXT f/r/ANzvz5f8ON/NAGh4a8Mf8I/u3Xn2jZaW9hBiLZtt4N/lhuTuk/eNuYbQeMKuDnoK4/wP reo6x5/224+0YtLaeb5FX7Ldv5nn2vygY8vbH8j5kXf8xORjsKAM/WtM/tfTDaibyZFliuIp Cu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJKsahxsyznaSX2 jAO480eKL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKj8N6q13olpLfXMbST3E 8FtK21TdojyeXIuMBi8SCTKgAglgAvQAj/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+ Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5o/tvUf+E9/s77 R8n2vyPsOxf+PT7L5v2rpv8A9f8Aud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrd xcDC/Z0AfEgZkzmQsOKAO40XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToVj+F 7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2KAOX07Q/ElhPq1x/belSTahKtx /wAgqQLHIEij6faOV2RdMg7mznA2mxoWjaxonh7TNJ/tSxm+w+VD5v2B1326IF24844kOM78 kf7FWPFN9cad4cu7m1k8qZdiiUqCIgzqrOxOQiqCWLkMEALFXC7TH4a1Vrvw/p81/cxtcXDv FHI21ftBUvhkIwJAyIXDKFDr84VAdoAI/wDhGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs784 8vy/4dud3O7Hy1H4U8Knw6+oTzXUdxcXboF8lJI4oIUXCQxxvI+xFZpGABAG8gAAACO5/taH x3YQprc72t151y1iYIhClvHEkZUNtMhkM0sbg7gNu4dhur+Bdb1HWPt/2+48/Z5cgwijyGfd ugbaB5cibRuhbe0e4Zlk3DaAdhRRRQBj+JdJvNa0yK1sr2C0kS7guTJNbGYHypFkVdodOrIu TnpkdTkY9r4H+w69PrVncWNvfT6q15NJDYbDJbtF5bW7EPk5IEhYnBk+bZVfxprUOma9otu3 jP8AsVbqUpcweZajbF5UzCX97GxGXREznb2xk5qSz+IEMvie60q6TTY4ItT/ALLSeHURI5mM ZkTehRQgYK6feJ8xSgB5IALngzwj/wAInBeRf8SpvtErS77HTfsrcu77WPmNuVd+1BxtUY5r qK4fwF4gvfE91qd/c6tYyxxSvCljYXsc8cQErorn90sgyI8qxdg4YsFUYUdxQBn61pn9r6Yb UTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5pemW9ra3sHmfa5Lm7kltiwk82R5JVj UONmWc7SS+0YB3HmjxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARUfhvVWu9E tJb65jaSe4ngtpW2qbtEeTy5FxgMXiQSZUAEEsAF6AEf/CMf8VL/AGp9s/0f7X9v8jyvn+0f Z/s2d+ceX5f8O3O7ndj5az7TwL9l0x7U6jukiisrezk8jAjjs5DJb+Yu79424neQUDDgBDzR /beo/wDCe/2d9o+T7X5H2HYv/Hp9l837V03/AOv/AHO/Pl/w4381j6f4p1mfQby4kvNzCKwe 6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFAHcaLpn9kaYLUzedI0stxLIF2hpJZGkfauThdztg EkgYBJPJ0Kx/C99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFAGfqehaPrflf 2tpVjf8Ak58v7XbpLszjONwOM4HT0FV5tGuLjxHbalLqG+1ttzw2phGUkZNhw2cbcZI+XeCz ASBGZDY1PUbqw8r7No19qW/O77I8C+XjGM+bInXPbPQ5xxnm49S1NPiVNYz6pJJZu/8Ao9hA Y90aeQCXlRoQ/lbw481ZSN7ImM7gADU8MeGP+Ec+1f6Z5/nbB8sXl79uf3svJ8y4fd+8l437 V+UY56CuP8D63qOsef8Abbj7Ri0tp5vkVfst2/mefa/KBjy9sfyPmRd/zE5GOwoAx5tGuLjx HbalLqG+1ttzw2phGUkZNhw2cbcZI+XeCzASBGZDn+CvCH/CIacbX7VBL+6ihxa2v2eNvLBH msm5szNn53z8wVBgbedj+0br+2PsX9jX32f/AJ/98Hk/dz08zzOvy/c6+3NYfg3Xhrdxqgh1 601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYxAOsrP8A7C0f+2P7X/sqx/tP/n9+zp533dv3 8bvu8denFaFcnJrwHxDh0aHXrR5dm6fTWMa+XF5ZZdv/AC0ecsN3B2CIHcoJRnALnhjwx/wj n2r/AEzz/O2D5YvL37c/vZeT5lw+795Lxv2r8oxz0Fcf4F1vUdY+3/b7jz9nlyDCKPIZ926B toHlyJtG6Ft7R7hmWTcNvYUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVn6Zrenaz5v2C483ysE5Rk3K2dsi7 gN8bYO11yrYOCcGtCub0HwvNoNvOsN/HJOtlDp9o7252xwwB/J8xQ+XfMjbiCgbjCp3ANTU9 b07RvK+33HlebkjCM+1VxukbaDsjXI3O2FXIyRkUf23p39sf2V9o/wBL6bdjbN23f5e/G3zN nz7M7tvzY281l694Xm163gWa/jjnaym0+7dLc7ZIZwnneWpfKPmNdpJcLzlX7Sf8Ix/xUv8A an2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PloA0NM1vTtZ837Bceb5WCcoyblbO2RdwG+ NsHa65VsHBODWhXP+GvDH/CP7t159o2WlvYQYi2bbeDf5Ybk7pP3jbmG0HjCrg56CgCvfX1v p1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAg gggEVX1rTP7X0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yX N3JLbFhJ5sjySrGocbMs52kl9owDuPNAFj+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235 sbearw+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljVf/hGP+Kl/tT7Z/o/2v7f5Hlf P9o+z/Zs7848vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHA CHmgDqLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxWfoumf2RpgtTN50jSy3EsgXa GklkaR9q5OF3O2ASSBgEk8nQoAr399b6Zp1zf3knl2trE80z7SdqKCWOBycAHpRY3sWoWcd1 Ck6RvnAngeFxgkco4DDp3HPXpWf4o0P/AISPQZdM8yBN8sMubiDz428uVJNrpuXcrbMEZHBo 07SbzSdHsbCzvYP3Mu6YyWxZDGWLNHEoceUoyFQEsEVQMNjNAFi41vTrXWLTSZrjF9d58qJU Zuis3zEDCZEchG4jdsbGdpxX0PxRpPiPzP7MmnfZFHP++tJYN0cm7Y6+Yq7lbY2CMjisvVvB Ed94n0/W7O9ktJIL1by6jMk7LcMsYiGFWZUQ7OCdpzgA5Xcrbljpn2TU9Uvnm86S9lRlJXmK NY1URg55UMJHA4AMrcZJJANCq9/fW+madc395J5draxPNM+0naigljgcnAB6VYrH8UaH/wAJ HoMumeZAm+WGXNxB58beXKkm103LuVtmCMjg0AaFjexahZx3UKTpG+cCeB4XGCRyjgMOncc9 elRnVLRdZTSWeQXj27XKKYn2tGrBWIfG0kFlyucjcDjBqnp2k3mk6PY2FnewfuZd0xktiyGM sWaOJQ48pRkKgJYIqgYbGar3ejaxP4ttdYi1SxjtbaJ7dbZ7B2cxyNE0mZPOA3ZhGDtwM8hq ANSw1S01J7xLV5C9ncG2nV4njKyBVbHzAZG1lIYZBBBBNXK5/QdG1jTNT1O6v9UsbuO/lFw8 cFg8BWQRxxjDGZ/l2xDjGcnOccV0FAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASa LG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy 5GV3IuQCCRkAg8ivp+k3ml6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t 6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZ kVzwjIGDnhSxqv8A8Ix/xUv9qfbP9H+1/b/I8r5/tH2f7NnfnHl+X/Dtzu53Y+Ws+08C/ZdM e1Oo7pIorK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ80AdRY31vqNnHdWsnmQvkAlSpBBIZWU4 KsCCCpAIIIIBFWKz9F0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu52wCSQMAknk6FABWf/AG3p 39sf2V9o/wBL6bdjbN23f5e/G3zNnz7M7tvzY280anoWj635X9raVY3/AJOfL+126S7M4zjc DjOB09BVebRri48R22pS6hvtbbc8NqYRlJGTYcNnG3GSPl3gswEgRmQgFjTNb07WfN+wXHm+ VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/AIa8Mf8ACP7t159o2WlvYQYi2bbeDf5Ybk7pP3jb mG0HjCrg56CgArPstb07UdRvbC0uPNuLLb54CMFG4sowxG1vmjdTtJwyMDggitCuT8P+CI/D niW51CyvZP7Peyis4LJ5J5DCsZJX55JmBHzMANg2gjbjLbwDrKz7jW9OtdYtNJmuMX13nyol Rm6KzfMQMJkRyEbiN2xsZ2nGhXJ6t4IjvvE+n63Z3slpJBereXUZknZbhljEQwqzKiHZwTtO cAHK7lYA3NM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhWH4a0fUtHt549R1G0v5 JXEjXEVm0MksmMM8hMjhiQFAAChQoUAKFA3KACiiigAooooAKKKKACiiigAooooAKKKKACii s+61vTrLUYLC4uNlxNtwNjFU3Ham9gNqbmBVdxG5gQuSMUAaFFRzzw2tvLcXEscMESF5JJGC qigZJJPAAHOay/8AhKNJ/s77b50+3zfI8j7JL9o8zG7Z5G3zN2358bc7Pm+7zQBsUVlv4j0h Li0h+3RubtEeJ4wXj2ucRlnUFVDnhCxG88Lk8VqUAFFFZ9rrenXuoz2Fvcb7iHdkbGCvtO19 jEbX2sQrbSdrEBsE4oA0KKp6lqlppNus128gDuEjSKJ5ZJGwThEQFmOASQAcBSegJqu/iPSE uLSH7dG5u0R4njBePa5xGWdQVUOeELEbzwuTxQBqUVn2ut6de6jPYW9xvuId2RsYK+07X2MR tfaxCttJ2sQGwTitCgAoqOeeG1t5bi4ljhgiQvJJIwVUUDJJJ4AA5zWWfFWhDw5N4hOpwf2R Fv3XYJKHY5Q7f73zAgYzu4xnIyAbFFZ91renWWowWFxcbLibbgbGKpuO1N7AbU3MCq7iNzAh ckYotdasr7UZ7K2aeSSHcHkFtJ5OVO1lEu3y2YHgqGJBDAjKnABoUVT1LVLTSbdZrt5AHcJG kUTyySNgnCIgLMcAkgA4Ck9ATViCeG6t4ri3ljmglQPHJGwZXUjIII4II5zQBJRWWniPSHuL uH7dGhtEd5XkBSPahxIVdgFYIeHKk7Dw2DxUf/CUaT/Z323zp9vm+R5H2SX7R5mN2zyNvmbt vz4252fN93mgDYoqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zUlABXF+DvEd3e295carfRtB DZW91dSSBEWyuWEhuLYkABREEQ7Xy67/AJmORjtKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+N sHa65VsHBODQBzfjHxHd2VvZ3GlX0awTWVxdWskYR1vblRGbe2BIIYSh3O1MO2z5WGDm5Nq1 wvju206LUvMD7vOsDAFEUIi3CQHBdmMhAEmRDgMhxIF3bGp63p2jeV9vuPK83JGEZ9qrjdI2 0HZGuRudsKuRkjIo/tvTv7Y/sr7R/pfTbsbZu27/AC9+NvmbPn2Z3bfmxt5oA5f4ceIb3xJZ 3l9e3v2nztlxGkEkcsFskhdlg3rGjeci7Q6sWx8jZG8gdxWfpmt6drPm/YLjzfKwTlGTcrZ2 yLuA3xtg7XXKtg4Jwa0KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQ tcRtBs7jVb+BGuLuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwK2L6+t9Os5Lq6k8uFMAkKWJJI CqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl/7b1H/hPf7O +0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnfny/4cb+ax7Dxfejw5qV9qOq+UsdpZyTTiGPNndzO yzWi52ojIRGAJSTGZA0hZeK7j+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearw+Kd Gns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljQBJ4cu2vfD9ncPqNpqLshDXVo6vG5BI4ZQ AxGMFgFBIJCpnaNSq9jfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYoAz9buNRtdHnm 0my+2Xw2iOHKjqwBb5mUHaCW2ll3bcbhnI5vwL4vt9V8MJc6jqcZkOpz2EU11PbBp28xvKUe S2wuU2jAxuwSAVIY9RqmqWmjWDXt68iwK6J+7ieVizuEUBUBYkswGAD1o0vVLTWbBb2yeRoG d0/eRPEwZHKMCrgMCGUjBA6UAc3/AG3qP/Cx/wCzPtH+if6v7JsXft8nzPP243eXv+Tzt+3d +78rd+8qv4A1zUtevNYlub/7baQeTCJY3t3g+1YZphA0XzNDteHb5hLdQfmDCuo/tvTv7Y/s r7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq+h+KNJ8R+Z/Zk077Io5/31pLBujk3bHXzFXcrb GwRkcUAbFZ+t3Go2ujzzaTZfbL4bRHDlR1YAt8zKDtBLbSy7tuNwzkaFU9U1S00awa9vXkWB XRP3cTysWdwigKgLElmAwAetAHL+BfF9vqvhhLnUdTjMh1Oewimup7YNO3mN5SjyW2Fym0YG N2CQCpDG5c/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3bml6paazYLe2T yNAzun7yJ4mDI5RgVcBgQykYIHSo/wC29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbea AMfwnq1xqV5qcb6l/aFvD5WJTAIikzBjJGFA+RV+QCOQmZDu35BQnqKy9E8Qaf4ht/tGnfa2 g2I6yTWU0CyKwypQyIocEDOVz1HqK1KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+W BUbcsCARVfQtcRtBs7jVb+BGuLuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwK2L6+t9Os5Lq6k 8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl /wC29R/4T3+zvtHyfa/I+w7F/wCPT7L5v2rpv/1/7nfny/4cb+ax9P8AFOsz6DeXEl5uYRWD 3U/lJ/xLZZpSt3FwML9nQB8SBmTOZCw4ruP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bf mxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNAB4XvrjUdBiuLmTzmMsyRz7Q PPiWV1il4wDvRUfKgKd2VABArYqvY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFWKAM /U5tYi8r+ybGxus58z7XePb7emMbYnz364xgdc8cnr/inVdK8faJYTxyW2m3d6LWICW223it CxZzvcSArKYwFVR0PLmREHeVnprmlya42iR38EmppEZpLVH3PGg2csB93/WJjOM5yM4NAHL/ AA48Q3viSzvL69vftPnbLiNIJI5YLZJC7LBvWNG85F2h1Ytj5GyN5A7is/TNb07WfN+wXHm+ VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAFcH4R8U6rqPjXVtH1iOS2uI7KC6+wtLbMLVizhkXy 3Z2BUxHc3fLYjDop6z+2rIax/ZbtPHdHhDLbSJHIdu7akhUI7bcnarE4VuPlODTNb07WfN+w XHm+VgnKMm5WztkXcBvjbB2uuVbBwTg0AaFcvNq1wvju206LUvMD7vOsDAFEUIi3CQHBdmMh AEmRDgMhxIF3dRWXH4g0+bW5tIi+1vdwvslK2UxiRtgkw0uzywdrKcFu4HU4oAx/A+vDX7e8 uE1601SMurxrGYxJEGB5ZE5jRiCUjfdIoHzuWJVOsrP0zWrLWPNNi08kceCJmtpI45Ac4aN2 ULIpxnchIwQc4IzoUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/qnhj+0taS+F55UMn2X7 TF5W5n+zTNNDsbI2fOx3ZDblwBtPNdBRQBn6lpn9saTqmmXs3+i30T24MK7XjjePa3JJBbJY g4A5AwcZOP8A8ItebPtn9pwf2z/aH9oef9kP2fzPs/2bHleZu2+V28zO/nOPlrqKKAOTXwPD AdOhtr6RbO2t7G3lSSMNJItnIZICrggKdxO/KtuHA2HmusoooAK5/S/DH9m6098bzzYY/tX2 aLytrJ9pmWabe2Tv+dRtwF2rkHcea6CigDH1PSbzULXTZFvYItTsJRcRzG2LQtIYnibMe8Nt KyPgb8g7eTgg5a+B4YDp0NtfSLZ21vY28qSRhpJFs5DJAVcEBTuJ35Vtw4Gw811lFAHP6X4Y /s3WnvjeebDH9q+zReVtZPtMyzTb2yd/zqNuAu1cg7jzXQUUUARziZreVbeSOOcoRG8iF1Vs cEqCCRntkZ9RXB33w5utW8OXel6lrEEkzS3M1pcW9vPAIHuHkaYsi3GJOJWVc9ASDuDMD6BR QBy83hF7jUdIvJdQzJYxQxsxjZpAYzuJhd3ZovM+7LuLmRAFJBG4yaJ4Rh0TXr7UYJowl08z lI4BG8jSyeYxmcH96VbIjOF2KzD5s5rpKKAMvWtKm1IWU1pcx295Y3H2i3eWIyx7jG8ZDoGU sNsj4ww5weQCDJpWmf2Pp2nabazbrGytFtgJVzI+wKqNuBAHAbI28kjGMYOhRQBybeB4ZzqM NzfSNZ3NvfW8SRxhZI1vJBJOWckhjuA2YVdo4O881J/wi15s+2f2nB/bP9of2h5/2Q/Z/M+z /ZseV5m7b5XbzM7+c4+WuoooAp6TpsOjaNY6XbtI0Flbx28bSEFiqKFBOABnA9BVyiigArm9 B8LzaDbzrDfxyTrZQ6faO9udscMAfyfMUPl3zI24goG4wqd+kri/B3iO7vbe8uNVvo2ghsre 6upJAiLZXLCQ3FsSAAoiCIdr5dd/zMcjABqeJfDH/CQbdt59n32lxYT5i37refZ5gXkbZP3a 7WO4DnKtkYP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLWX4x8R3dlb2dxp V9GsE1lcXVrJGEdb25URm3tgSCGEodztTDts+Vhg5k/tvUf+E9/s77R8n2vyPsOxf+PT7L5v 2rpv/wBf+5358v8Ahxv5oA0PDXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDaDxhVwc9B XH+B9b1HWPP+23H2jFpbTzfIq/Zbt/M8+1+UDHl7Y/kfMi7/AJicjHYUAZ+taZ/a+mG1E3ky LLFcRSFdwWSKRZE3LkZXci5AIJGQCDyI9J0qbSbCC2S5jkJuJri6doiPMaV3kcIN3yDzHyM7 sKMHJO6o/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFV9C1xG0GzuNVv4Ea4 u5bW1nmdY/tgErrCy9AzSIquNow27KgDAoAP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/ L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaP7b1H/AIT3 +zvtHyfa/I+w7F/49Psvm/aum/8A1/7nfny/4cb+ax9P8U6zPoN5cSXm5hFYPdT+Un/Etlml K3cXAwv2dAHxIGZM5kLDigDuNF0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu52wCSQMAknk6FY /he+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtigArL0fSptH0bTdPS5jlNugF 1M8R3XLbTvf73DtId5J3Zy2ck7hY1S1u7ywaCy1CTT52dD9pjiSRlUOCwAcFclQVyQcZzg4x WP4Z1gf8InpN7rGqxs+pPm1mumjieZZWZ4EIUKpl8sqCFHUHGetAEn/CMf8AFX/279s99vlf vf8AV+X5XmZ/49/+Wnlbf9b8+7tWhY6Z9k1PVL55vOkvZUZSV5ijWNVEYOeVDCRwOADK3GSS cOPXhN8Q5tJTXrQeSmyTTpDGrsTGHART+8dwDvaTIjCkKEZt7oeB9eGv295cJr1pqkZdXjWM xiSIMDyyJzGjEEpG+6RQPncsSqAHWUUVz/jbVrjRPCF9e2c0EF0fLghuLiQJHA8sixLK5II2 oXDHIPC0AXNH0qbR9G03T0uY5TboBdTPEd1y2073+9w7SHeSd2ctnJO4V5NH1KbxZDqk2o2k mnwJiCyazbfExUhnWTzMbznGShwuVXG5y0eg61APDmnXGpaliS6lNvHNeSxA3Mhdgvlsqosi tjMZVQXTa2Mk1Xuf7Wh8d2EKa3O9rdedctYmCIQpbxxJGVDbTIZDNLG4O4DbuHYbgCx4Y8Lp 4b+1CKWDy5tirDa2q20YC5w7InymZt2HdQoYKgCqFroK5fwnq1xqV5qcb6l/aFvD5WJTAIik zBjJGFA+RV+QCOQmZDu35BQnqKAM/WtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB 5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FGDkndUfii+uNO0GW4tpPJYSwpJPt B8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq38CNcXctrazzOsf2wCV1hZegZpEVXG0Ybdl QBgUAH/CMf8AFS/2p9s/0f7X9v8AI8r5/tH2f7NnfnHl+X/Dtzu53Y+Ws+08C/ZdMe1Oo7pI orK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ80f23qP/Ce/2d9o+T7X5H2HYv8Ax6fZfN+1dN/+ v/c78+X/AA4381j6f4p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFAHcaLp n9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0Kx/C99cajoMVxcyecxlmSOfaB58Sy usUvGAd6Kj5UBTuyoAIFbFAGfqejWur+V9plvo/Kzt+yX89tnOM58p13dO+cc46ms+70bWJ/ FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG7MIwduBnkNXQVy82rXC+O7bTotS8wPu86wMAURQi LcJAcF2YyEASZEOAyHEgXcAWPDXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDaDxhVwc9 BXD/AA48Q3viSzvL69vftPnbLiNIJI5YLZJC7LBvWNG85F2h1Ytj5GyN5A7igDDk0GafxZDr MtxaKkCbIhBaFLh1KkeXLNvO+LczPsCr8wQ5+XmPwx4Y/wCEc+1f6Z5/nbB8sXl79uf3svJ8 y4fd+8l437V+UY5pz6pd/wDCyLewXUZDZm3CnT4lQOJNsjGaRWj3GAjy1EiOAJAEIJJweB9e Gv295cJr1pqkZdXjWMxiSIMDyyJzGjEEpG+6RQPncsSqAHWVz/8Awi6Dxf8A28ksEbH5n8q1 VJ5D5fl7HmGC8OMNsYE71U7sKFHQVycevCb4hzaSmvWg8lNkmnSGNXYmMOAin947gHe0mRGF IUIzb3QAseEfCMPhK3lt7eaMwFI4o44YBEu1AQJHAJDzsDh5Pl3bU+Ubeekrj/Aut6jrH2/7 fcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybht7CgAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACs/TNb07WfN+ wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmKHy 75kbcQUDcYVO4BqanrenaN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+yvtH+l 9Nuxtm7bv8vfjb5mz59md235sbeay9e8Lza9bwLNfxxztZTafduludskM4TzvLUvlHzGu0ku F5yr9pP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLQBoaZrenaz5v2C483y sE5Rk3K2dsi7gN8bYO11yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNo PGFXBz0FAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVI IJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3m l6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t6d/bH9lfaP9L6bdjbN23f 5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGq//CMf8VL/ AGp9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5az7TwL9l0x7U6jukiisrezk8jAjjs5DJ b+Yu79424neQUDDgBDzQB1FjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYrP0XTP7I 0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToUAV7++t9M065v7yTy7W1ieaZ9pO1FBLH A5OAD0osb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0rP8UaH/AMJHoMumeZAm+WGXNxB58beX Kkm103LuVtmCMjg0adpN5pOj2NhZ3sH7mXdMZLYshjLFmjiUOPKUZCoCWCKoGGxmgCSTxDpk Wtw6O08hvJX8tQsEjIH2GTY0gXYr7FLbSQcYOMEZk0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42 wdrrlWwcE4NZZ8KmXxuniCa6j8iFGMNpEkiZmKCPzpD5hR3Cb0B2A7XAJO1cXNG0a4068vru 81D7bcXPlp5hhCHy4wQu7k5bkk7dqZJKxoWYsAbFV7++t9M065v7yTy7W1ieaZ9pO1FBLHA5 OAD0qxWP4o0P/hI9Bl0zzIE3ywy5uIPPjby5Uk2um5dytswRkcGgDQsb2LULOO6hSdI3zgTw PC4wSOUcBh07jnr0qvca3p1rrFppM1xi+u8+VEqM3RWb5iBhMiOQjcRu2NjO04r6dpN5pOj2 NhZ3sH7mXdMZLYshjLFmjiUOPKUZCoCWCKoGGxmsvVvBEd94n0/W7O9ktJIL1by6jMk7LcMs YiGFWZUQ7OCdpzgA5XcrAGxpXiHTNauLiCwnkleBEkYtBIisjlgjozKBIjbGwykg44Nalc34 U8Knw6+oTzXUdxcXboF8lJI4oIUXCQxxvI+xFZpGABAG8gAAADpKAK99fW+nWcl1dSeXCmAS FLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVfWtM/tfT DaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJK sahxsyznaSX2jAO480AWP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qvD4p0aezu bqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNV/+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/ L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaAOosb631Gz jurWTzIXyASpUggkMrKcFWBBBUgEEEEAirFZ+i6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2rk4Xc7 YBJIGASTydCgArP/ALb07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5rQrDj0fUk8WTas +o2kto6eXHBJZsZYI9oyiSCTaAzjex2EtwCSFTaAXNN1qy1eW7jsmnf7LK0MjvbSRoXVmRgr soV8MjA7ScY9xWhXL+CvCH/CIacbX7VBL+6ihxa2v2eNvLBHmsm5szNn53z8wVBgbeeooAz3 1qyTXF0bdO180QmKpbSMiId4UtIF2JkxuBuIzjijTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB 2uuVbBwTg1Tk8PtL4sh1trqMCJMKq26rMRtK+U0oOWgyxk8sgnzMNuwAoPDWj6lo9vPHqOo2 l/JK4ka4is2hklkxhnkJkcMSAoAAUKFCgBQoABuVn/23p39sf2V9o/0vpt2Ns3bd/l78bfM2 fPszu2/NjbzWhWPNo1xceI7bUpdQ32ttueG1MIykjJsOGzjbjJHy7wWYCQIzIQCxZa3p2o6j e2FpcebcWW3zwEYKNxZRhiNrfNG6naThkYHBBFaFcn4f8ER+HPEtzqFleyf2e9lFZwWTyTyG FYySvzyTMCPmYAbBtBG3GW39ZQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVx+va3qNl4rtr OC48tT9j+z2uxT9u82dkueo3N5MQWT5CNu7L5UgV2FFAFO7u7Y2V/wD8TGO2FujLPcK6ZtTs Dbm3AqpCsrfMMYIJBBrg/wDhKL+Hwb9rudcg2JqH2dr+G5td08fl7h9nmlWOCVg5wSUQBUlU AugdvSKKAPO7jxXrkd74XSUxxX1/b2TzaXF5SurSOBcGWKQ+cERCShjztKSGT5RXolFFABXH 6Dreo3viu5s57jzFH2z7Ra7FH2Hyp1S26DcvnRFpPnJ3bcphQRXYUUAcn468QtpfgW61jS9b 02zc27yWtxOqyrcHymZFi+dQXbAKn5hx91qp6l4qlm8TaQukanBPps/l7RAUkF3ulaOTy+pm 8sLufY0flD5280HYO4ooA4fwz4t1LVfH2t6Lqdr9i8i0hmgs2mt3eEb3BLlJGYsymJugC8Du rydxRRQBXv3SPTrmSW8+xRrE5a6yo8kAHL5cFRjr8wI45GK4OPX7yXwVNcz+KLS1nivfJt9Q a9t44LobQxVbhoCkgGX5WFTujKY+Vnb0SigDzu98baxa+IvCFrc2MlnZ6k8azPOIIXuXeBiV ETzF4hG5jyPmJJ2g5CiX0SiigDn/ABZqMun2th/xMP7MtJ7vyrvUPkH2WPypGDbpAUXMixpl gR8+B8xBFzQ9SmvNG0l9SWO21S7skuJbQgoyttTzAEY7gFZwDnpkA9a1KKAOH0/xDqMutanF PqMESxRXz3CXEa+XpnkzBLZnAKsFliLSne3zBcoVXNZ9t4vXUvBM0/8AwmmlRanb6hNE1xHe W9tG6+fKsakukwRWjTcvyszBB8x5NekUUAU9JuZrzRrG6uI5I55reOSRJITCysVBIKFmKEE/ d3HHTJ61coooAK4vwd4ju723vLjVb6NoIbK3urqSQIi2VywkNxbEgAKIgiHa+XXf8zHIx2lZ +ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBoA5/xxreo6P5H2K4+z5tLmeH5Fb7Vd p5fkWvzA58zdJ8iYkbZ8pGDk/tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv8A9f8Aud+fL/hx v5roNT1vTtG8r7fceV5uSMIz7VXG6RtoOyNcjc7YVcjJGRR/benf2x/ZX2j/AEvpt2Ns3bd/ l78bfM2fPszu2/NjbzQBz/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/5icjH YVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCgDH8UX1xp2gy3FtJ5LCWFJJ9o PkRNKiyy85A2IzvlgVG3LAgEVH4b1VrvRLSW+uY2knuJ4LaVtqm7RHk8uRcYDF4kEmVABBLA BempfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTg qwIIKkAggggEUAcv/beo/wDCe/2d9o+T7X5H2HYv/Hp9l837V03/AOv/AHO/Pl/w4381j6f4 p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHFdx/benf2x/ZX2j/S+m3Y2zdt 3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxoAPC99caj oMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7G+t9Rs47q1k8yF8gEqVIIJDKynBV gQQVIBBBBAIqxQBT1S1u7ywaCy1CTT52dD9pjiSRlUOCwAcFclQVyQcZzg4xWX4T1GW48L6V dajqHnzahultZJ9iSSxuXkiUqoVfMEONwUYyrEZAzWxf31vpmnXN/eSeXa2sTzTPtJ2ooJY4 HJwAelFjexahZx3UKTpG+cCeB4XGCRyjgMOncc9elAHP3P8Aa0PjuwhTW53tbrzrlrEwRCFL eOJIyobaZDIZpY3B3Abdw7DdX8F+KE1q+1qyn8Q6Vql1b3YMH2AqimDyYSWVA7naHdgWLH5s jI4A6D+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbeaksNUtNSe8S1eQvZ3Btp1eJ4y sgVWx8wGRtZSGGQQQQTQBcqnqlrd3lg0FlqEmnzs6H7THEkjKocFgA4K5Kgrkg4znBxirlV7 ++t9M065v7yTy7W1ieaZ9pO1FBLHA5OAD0oAx/Ceoy3HhfSrrUdQ8+bUN0trJPsSSWNy8kSl VCr5ghxuCjGVYjIGaz7jxQln8SrfRrnxDpS2s1pKFscqkyT7rcRq7FyWZg8hVQq5GeGwCOos b2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0rP/AOEo0n+3v7E86f7d5vkY+yS+X5nleds83bs3 eX82N2cUAZfg3Xhrdxqgh1601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYx6yqdhqlpqT3iW ryF7O4NtOrxPGVkCq2PmAyNrKQwyCCCCauUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGx Gd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL01L6+t9Os5L q6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAi gDl/7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/8AX/ud+fL/AIcb+ax9P8U6zPoN5cSXm5hF YPdT+Un/ABLZZpSt3FwML9nQB8SBmTOZCw4ruP7b07+2P7K+0f6X027G2btu/wAvfjb5mz59 md235sbearw+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljQAeF7641HQYri5k85jLM kc+0Dz4lldYpeMA70VHyoCndlQAQK2Kr2N9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCAR VigDP1PUbqw8r7No19qW/O77I8C+XjGM+bInXPbPQ5xxnk5vGOqwfE2PQ59OkitHsrl7a2E1 sZbtkKFZRmXIDYlVUIH94kjd5XeVnvrVkmuLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFAHN +AvFOoeIrrxDb6pHHDd2N6qfZ0lhcW6tEn7rMbsWKuJAXOMnOApDInaVn6Zrenaz5v2C483y sE5Rk3K2dsi7gN8bYO11yrYOCcGtCgDk5NeA+IcOjQ69aPLs3T6axjXy4vLLLt/5aPOWG7g7 BEDuUEozng3Xhrdxqgh1601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYx3P7b07+2P7K+0f6 X027G2btu/y9+NvmbPn2Z3bfmxt5o0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NA GhXH3HihLP4lW+jXPiHSltZrSULY5VJkn3W4jV2LkszB5CqhVyM8NgEdhVM6paLrKaSzyC8e 3a5RTE+1o1YKxD42kgsuVzkbgcYNAHP+DdeGt3GqCHXrTWLaB0USxGMMsuXEm1E5WDKgR7yX O1zuddjHrKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUVx/ifxQmg+KtBt7nxDpVlYzysLq1nKpNs8mciQuz/ACx70QfdHzcb udtAHYUVl6zqU1toOsXGlLHd6hZW8pS3UGQmYR70RlU5ycoccEhhjqK5P/hIrr+y/wDkYP8A iUf2r9l/4SH9x/x7/ZvM8zzNvkf6/wDcbtuP4fv80AegUV5+fE2utdaH9qP2K7uLTTpf7P8A JC/aZJpSt2u1gX/cRgPhSCmcvuXivQKACiiuP0HW9RvfFdzZz3HmKPtn2i12KPsPlTqlt0G5 fOiLSfOTu25TCgigDsKK5fxdrq2Ph601G11WC3tJ5UzcxXFukkkbIzL5DTnySxIU/NwU34+b FY9z4zuLC58HyazrOlaV9viSTUtPnURTRlraZixZ3ykYkVVwVzuGN3JUgHoFFcP4Z8W6lqvj 7W9F1O1+xeRaQzQWbTW7vCN7glykjMWZTE3QBeB3V5O4oAKKp6rdXdlpdxcWGnyahdomYrVJ UjMrdhucgKO5PoDgE4B83tfF+saz4Dt9Ti1u0sp4dYuLe7vHlgiiMQMuxd6rcJEOYQC+d2AA zb1ZwD1SiqekzNc6NYzuLsPJbxuwvI1ScEqD+8VQAr+oAABzirlABRXP+LNRl0+1sP8AiYf2 ZaT3flXeofIPssflSMG3SAouZFjTLAj58D5iCLmh6lNeaNpL6ksdtql3ZJcS2hBRlbanmAIx 3AKzgHPTIB60AalFcXa63OviXXLZPENpc2cFvNL5sjxTJp7oR8s3lrGYgCz4V2cuI2O5Cjbs ePxx5/gz7U3ijTZLmHU54LyezuLWKXyvNnEXkiZjEpYIhHmE5jDkFmwaAPTKKz9Cury+8PaZ eajb/Z76e0iluIdhTy5GQFl2nkYJIweRWhQAVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmK Hy75kbcQUDcYVO/SVxfg7xHd3tveXGq30bQQ2VvdXUkgRFsrlhIbi2JAAURBEO18uu/5mORg A0Ne8Lza9bwLNfxxztZTafduludskM4TzvLUvlHzGu0kuF5yr9pP+EY/4qX+1Ptn+j/a/t/k eV8/2j7P9mzvzjy/L/h253c7sfLWf441vUdH8j7FcfZ82lzPD8it9qu08vyLX5gc+Zuk+RMS Ns+UjByf23qP/Ce/2d9o+T7X5H2HYv8Ax6fZfN+1dN/+v/c78+X/AA4380AaHhrwx/wj+7de faNlpb2EGItm23g3+WG5O6T9425htB4wq4Oegrj/AAPreo6x5/224+0YtLaeb5FX7Ldv5nn2 vygY8vbH8j5kXf8AMTkY7CgDP1rTP7X0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0 /SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjySrGocbMs52kl9owDuPNHii+uNO0GW4tpPJYSwpJPt B8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJY AL0AI/8AhGP+Kl/tT7Z/o/2v7f5HlfP9o+z/AGbO/OPL8v8Ah253c7sfLWfaeBfsumPanUd0 kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaP7b1H/hPf7O+0fJ9r8j7DsX/AI9Psvm/aum/ /X/ud+fL/hxv5rH0/wAU6zPoN5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDigDuNF 0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu52wCSQMAknk6FY/he+uNR0GK4uZPOYyzJHPtA8+J ZXWKXjAO9FR8qAp3ZUAECtigDH8UaH/wkegy6Z5kCb5YZc3EHnxt5cqSbXTcu5W2YIyODRp2 k3mk6PY2FnewfuZd0xktiyGMsWaOJQ48pRkKgJYIqgYbGauapa3d5YNBZahJp87Oh+0xxJIy qHBYAOCuSoK5IOM5wcYrL8J6jLceF9KutR1Dz5tQ3S2sk+xJJY3LyRKVUKvmCHG4KMZViMgZ oAP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLRoOjaxpmp6ndX+qWN3Hfyi 4eOCweArII44xhjM/wAu2IcYzk5zjis/+29R/wCE9/s77R8n2vyPsOxf+PT7L5v2rpv/ANf+ 5358v+HG/mo/h9qWp31vdxatqkmpXcKRebKhjMCOQ25BiGJ45QRl4nBKAx85JoA7Sqeq2Taj pdxZpLHGZk25lhWVGHdXRuGRh8rDIJBOCpwRcrD8X399pnhi6utOMa3CvEnmSNtWJGkVZJCx VgoRCzbirBduSpAIIBJp2k3mk6PY2FnewfuZd0xktiyGMsWaOJQ48pRkKgJYIqgYbGasXOmf adcsNRebMdnFMqwFcgyPsAkBzwyqsig4ziVhkAkGn4a1IXHh/T5bu8kknuHeOOS4ePNwwLnM ZRUV0KoWRgo3RgNgc1jx6lqafEqaxn1SSSzd/wDR7CAx7o08gEvKjQh/K3hx5qykb2RMZ3AA GpoOjaxpmp6ndX+qWN3Hfyi4eOCweArII44xhjM/y7YhxjOTnOOK6CuL8CeIdQ1y41JLq5jv IIEhP2uF4ZIGnYyeakLxEjylCxlRJ+9AfL9VrtKAM/WtM/tfTDaibyZFliuIpCu4LJFIsibl yMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJKsahxsyznaSX2jAO480eKL64 07QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKj8N6q13olpLfXMbST3E8FtK21Tdojy eXIuMBi8SCTKgAglgAvQAj/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tZ9 p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5o/tvUf8AhPf7O+0fJ9r8j7Ds X/j0+y+b9q6b/wDX/ud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrdxcDC/Z0AfEg ZkzmQsOKAO40XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToVj+F7641HQYri5k 85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2KAM/U9C0fW/K/tbSrG/8AJz5f2u3SXZnGcbgc ZwOnoKpyeH2l8WQ6211GBEmFVbdVmI2lfKaUHLQZYyeWQT5mG3YAUXNT1G6sPK+zaNfalvzu +yPAvl4xjPmyJ1z2z0OccZ5ObxjqsHxNj0OfTpIrR7K5e2thNbGW7ZChWUZlyA2JVVCB/eJI 3eUAbnhrwx/wj+7defaNlpb2EGItm23g3+WG5O6T9425htB4wq4Oegri/AXinUPEV14ht9Uj jhu7G9VPs6SwuLdWiT91mN2LFXEgLnGTnAUhkTtKAMebRri48R22pS6hvtbbc8NqYRlJGTYc NnG3GSPl3gswEgRmQx+GtH1LR7eePUdRtL+SVxI1xFZtDJLJjDPITI4YkBQAAoUKFAChQM+T VZ4viHDp8GsR3UEqYuNPWSKR7U+WWDNGqB40OE/eNIwzIF2fMrLH4F1vUdY+3/b7jz9nlyDC KPIZ926BtoHlyJtG6Ft7R7hmWTcNoB2Fc/d6NrE/i211iLVLGO1tont1tnsHZzHI0TSZk84D dmEYO3AzyGroK5OTXgPiHDo0OvWjy7N0+msY18uLyyy7f+Wjzlhu4OwRA7lBKM4Bc8NeGP8A hH9268+0bLS3sIMRbNtvBv8ALDcndJ+8bcw2g8YVcHPQVx/gXW9R1j7f9vuPP2eXIMIo8hn3 boG2geXIm0boW3tHuGZZNw29hQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVOw1S01J7xLV5C9ncG2nV4njKy BVbHzAZG1lIYZBBBBNXK5vSNE17TrrV7ifWNNnfUH88BNNeMRzCKOJTzO25AsQJXgkk/MOlA Gpqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b07+2P7K+0f6X027G2btu/y9+N vmbPn2Z3bfmxt5rL17wvNr1vAs1/HHO1lNp926W52yQzhPO8tS+UfMa7SS4XnKv2k/4Rj/ip f7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tAGhpmt6drPm/YLjzfKwTlGTcrZ2yLuA 3xtg7XXKtg4Jwa0K5/w14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8YVcHPQUAV76+ t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUg EEEEAiq+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDyK+n6TeaXplva2t7B5n2u S5u5JbYsJPNkeSVY1DjZlnO0kvtGAdx5oAP+Eo0n+3v7E86f7d5vkY+yS+X5nleds83bs3eX 82N2cUQ+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljVi50z7TrlhqLzZjs4plWArkG R9gEgOeGVVkUHGcSsMgEg4+keE7jRbOaO21XFwtpbWNpL9nGI4LcuYhIpJ3sd7B2UpuB+URn mgDoLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxWfommf2Po8FiZvOaPczybdu9mY sx6knknlizHqzMxLHQoAr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdW snmQvkAlSpBBIZWU4KsCCCpAIIIIBFV9bsrzUdHntLC/+w3Eu0CfYXwu4FhhWVhuXK5VlYZy CCAajtLLUrTS7C0iudNjeB1WXybBkiMIyNkcfm/uzt2gElgMH5ecAAkfWrJNcXRt07XzRCYq ltIyIh3hS0gXYmTG4G4jOOKNM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWPb+EPI 8bXfiL7VAPPlE22K12TnECw+U82474fl37No+fac/Lzc8NaPqWj288eo6jaX8kriRriKzaGS WTGGeQmRwxICgABQoUKAFCgAG5Ve+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASasVn6 3ZXmo6PPaWF/9huJdoE+wvhdwLDCsrDcuVyrKwzkEEA0AWLG+t9Rs47q1k8yF8gEqVIIJDKy nBVgQQVIBBBBAIqu+tWSa4ujbp2vmiExVLaRkRDvClpAuxMmNwNxGccVHaWWpWml2FpFc6bG 8Dqsvk2DJEYRkbI4/N/dnbtAJLAYPy84FeTw+0viyHW2uowIkwqrbqsxG0r5TSg5aDLGTyyC fMw27ACgAsaV4h0zWri4gsJ5JXgRJGLQSIrI5YI6MygSI2xsMpIOODWpXN+FPCp8OvqE811H cXF26BfJSSOKCFFwkMcbyPsRWaRgAQBvIAAAA6SgCvfX1vp1nJdXUnlwpgEhSxJJAVVUZLMS QAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2om8mRZYriKQ ruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjySrGocbMs52kl9o wDuPNAFj+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearxeKdGm06+v0vM2tjEZ53M Tj90ASJVBGXjIVtrrlW2naTg1X/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux 8tU9P8FtYaXdWY1COQvZQadAZLVWQW0G/wAtJUYkSFhI6uQUDA/KEPNAHSWN7FqFnHdQpOkb 5wJ4HhcYJHKOAw6dxz16VYrP0TTP7G0eCw87zfK3HIXYi7mLbEXJ2Rrnai5O1QoycZrQoAKz /wC29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbeaNT0LR9b8r+1tKsb/AMnPl/a7dJdm cZxuBxnA6egqvNo1xceI7bUpdQ32ttueG1MIykjJsOGzjbjJHy7wWYCQIzIQCxpmt6drPm/Y LjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0K5/wx4Y/wCEc+1f6Z5/nbB8sXl79uf3svJ8y4fd +8l437V+UY56CgDPuNb0611i00ma4xfXefKiVGborN8xAwmRHIRuI3bGxnacGm61ZavLdx2T Tv8AZZWhkd7aSNC6syMFdlCvhkYHaTjHuKw9W8ER33ifT9bs72S0kgvVvLqMyTstwyxiIYVZ lRDs4J2nOADldytJ4K8If8IhpxtftUEv7qKHFra/Z428sEeaybmzM2fnfPzBUGBt5AOorP8A 7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5rQrDj0fUk8WTas+o2kto6eXHBJZsZYI 9oyiSCTaAzjex2EtwCSFTaAXNM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhXP8A hjwx/wAI59q/0zz/ADtg+WLy9+3P72Xk+ZcPu/eS8b9q/KMc9BQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAVx/ifxQmg+KtBt7nxDpVlYzysLq1nKpNs8mciQuz/LHvRB90fNxu5212FZ91 renWWowWFxcbLibbgbGKpuO1N7AbU3MCq7iNzAhckYoAj1nUprbQdYuNKWO71Cyt5SluoMhM wj3ojKpzk5Q44JDDHUVyf/CRXX9l/wDIwf8AEo/tX7L/AMJD+4/49/s3meZ5m3yP9f8AuN23 H8P3+a7yeeG1t5bi4ljhgiQvJJIwVUUDJJJ4AA5zWPJ4u0eKyhumkuyJbj7KsK2M7TiXYZNj QhPMU7FL8qPlwehBIBzZ8Ta611of2o/Yru4tNOl/s/yQv2mSaUrdrtYF/wBxGA+FIKZy+5eK 9ArHj8U6NN/Z5jvNy38Uc0DeU+3ZJ/qy5xiPeflXft3MCoyQRWxQAVx+g63qN74rubOe48xR 9s+0WuxR9h8qdUtug3L50RaT5yd23KYUEV2FZ9rrenXuoz2Fvcb7iHdkbGCvtO19jEbX2sQr bSdrEBsE4oAx/F2urY+HrTUbXVYLe0nlTNzFcW6SSRsjMvkNOfJLEhT83BTfj5sVj3PjO4sL nwfJrOs6VpX2+JJNS0+dRFNGWtpmLFnfKRiRVXBXO4Y3clT2Gra1ZaJFBJetP/pEvkwpBbST u77WfASNWb7qMenao4vEekTDRyl9GTrCb7BSCGnXy/MJCkZAC8knGMgHkgEA5vwz4t1LVfH2 t6Lqdr9i8i0hmgs2mt3eEb3BLlJGYsymJugC8DurydxVODVbG61S80yC5jkvLJI3uYl5MQk3 bN3YEhScdcYPQjNygCnqt1d2Wl3FxYafJqF2iZitUlSMyt2G5yAo7k+gOATgHzuw8c3d98P1 1W51aC3kTVZ7W5uIp7OKR0DSFFg3yNCGx5fDscorkFiUdvTJ54bW3luLiWOGCJC8kkjBVRQM kkngADnNZa+KNJl0ePVrWae9sZZXhSWxtJbncysykgRqx25RhuxtPGDyMgFjQrq8vvD2mXmo 2/2e+ntIpbiHYU8uRkBZdp5GCSMHkVoVlnxJov2/T7BdUtHu9RTzLSGOUO0ybGcOAM/JtRvm 6cYzkgVqUAc/4s1GXT7Ww/4mH9mWk935V3qHyD7LH5UjBt0gKLmRY0ywI+fA+Ygi5oepTXmj aS+pLHbapd2SXEtoQUZW2p5gCMdwCs4Bz0yAetWNS1S00m3Wa7eQB3CRpFE8skjYJwiICzHA JIAOApPQE1YgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc0AcWniWWTXPENrYeI9KuZLG0neSK7 dI4rSZceWAFPmeWoP752JG4qE2kOi5cfivVbjwhNdR38lvD/AGn9mg1S9ktlBh8kOXknjWSB R5m6MOqOCQsZAcl17hPEekPcXcP26NDaI7yvICke1DiQq7AKwQ8OVJ2HhsHiq8ni7R4rKG6a S7IluPsqwrYztOJdhk2NCE8xTsUvyo+XB6EEgGhpNzNeaNY3VxHJHPNbxySJJCYWVioJBQsx Qgn7u446ZPWrlV7C+t9T062v7OTzLW6iSaF9pG5GAKnB5GQR1qxQAVxfg7xHd3tveXGq30bQ Q2VvdXUkgRFsrlhIbi2JAAURBEO18uu/5mORjtKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+Ns Ha65VsHBODQBz/jjW9R0fyPsVx9nzaXM8PyK32q7Ty/ItfmBz5m6T5ExI2z5SMHJ/beo/wDC e/2d9o+T7X5H2HYv/Hp9l837V03/AOv/AHO/Pl/w43810Gp63p2jeV9vuPK83JGEZ9qrjdI2 0HZGuRudsKuRkjIo/tvTv7Y/sr7R/pfTbsbZu27/AC9+NvmbPn2Z3bfmxt5oA5/wPreo6x5/ 224+0YtLaeb5FX7Ldv5nn2vygY8vbH8j5kXf8xORjsKz9M1vTtZ837Bceb5WCcoyblbO2Rdw G+NsHa65VsHBODWhQBj+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKj8N6q1 3olpLfXMbST3E8FtK21TdojyeXIuMBi8SCTKgAglgAvTUvr6306zkurqTy4UwCQpYkkgKqqM lmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKAOX/tvUf8AhPf7O+0f J9r8j7DsX/j0+y+b9q6b/wDX/ud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrdxcD C/Z0AfEgZkzmQsOK7j+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearw+KdGns7m6j vMw2+0sfKcFw5xG0a4zIrnhGQMHPCljQAeF7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyo CndlQAQK2Kr2N9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVigDD8X399pnhi6utOMa3 CvEnmSNtWJGkVZJCxVgoRCzbirBduSpAIJ4a1IXHh/T5bu8kknuHeOOS4ePNwwLnMZRUV0Ko WRgo3RgNgc1qX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY3sWoWcd1Ck6RvnAnge Fxgkco4DDp3HPXpQBycniHUF+JUOjRXMdxC74lt4HhdbeDyC++Vc+ckplCrkjytjoPvtR4E8 Q6hrlxqSXVzHeQQJCftcLwyQNOxk81IXiJHlKFjKiT96A+X6rXSf23p39sf2V9o/0vpt2Ns3 bd/l78bfM2fPszu2/NjbzRpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4JwaANCsPxff 32meGLq604xrcK8SeZI21YkaRVkkLFWChELNuKsF25KkAg7lV76+t9Os5Lq6k8uFMAkKWJJI CqqjJZiSAFAJJIABJoAy/DWpC48P6fLd3kkk9w7xxyXDx5uGBc5jKKiuhVCyMFG6MBsDmsN9 W1a2+IV1ayX093anLW2l2jRCQKLcNudJIlPllw4Eom272RDj5sdhY3sWoWcd1Ck6RvnAngeF xgkco4DDp3HPXpVOPxBp82tzaRF9re7hfZKVspjEjbBJhpdnlg7WU4LdwOpxQBz/AIC8U6h4 iuvENvqkccN3Y3qp9nSWFxbq0SfusxuxYq4kBc4yc4CkMidpWfpmtWWr+aLVp1kiwXhubaS3 kUHOG2SKrbThgGxglWAOQcaFAGP4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBA IqPw3qrXeiWkt9cxtJPcTwW0rbVN2iPJ5ci4wGLxIJMqACCWAC9NS+vrfTrOS6upPLhTAJCl iSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIoA5f+29R/4T 3+zvtHyfa/I+w7F/49Psvm/aum//AF/7nfny/wCHG/msfT/FOsz6DeXEl5uYRWD3U/lJ/wAS 2WaUrdxcDC/Z0AfEgZkzmQsOK7j+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3mq8 PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY0AHhe+uNR0GK4uZPOYyzJHPtA8+JZXW KXjAO9FR8qAp3ZUAECtiq9jfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYoAK4d9W1a 2+IV1ayX093anLW2l2jRCQKLcNudJIlPllw4Eom272RDj5sdxWf/AG3p39sf2V9o/wBL6bdj bN23f5e/G3zNnz7M7tvzY280Ac34C8U6h4iuvENvqkccN3Y3qp9nSWFxbq0SfusxuxYq4kBc 4yc4CkMidpVOw1Wx1N7xLG5jnNlcG1uNnISUKrFM9CQGGcdDkHkEC5QBxd/4sGm/EFNLm1rT ZYHspjHpkZjjuPtAMHlozPJgu4d9gwgwed2Mg8BeKdQ8RXXiG31SOOG7sb1U+zpLC4t1aJP3 WY3YsVcSAucZOcBSGROk/tqyOsf2WjTyXQ4cxW0jxxnbu2vIFKI23B2swOGXj5hk0zW9O1nz fsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NABqeo3Vh5X2bRr7Ut+d32R4F8vGMZ82ROue2eh zjjOHJrwHxDh0aHXrR5dm6fTWMa+XF5ZZdv/AC0ecsN3B2CIHcoJRn6ys9Nc0uTXG0SO/gk1 NIjNJao+540GzlgPu/6xMZxnORnBoAw/BuvDW7jVBDr1prFtA6KJYjGGWXLiTaicrBlQI95L na53Ouxj1lZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoAKKKKACiiigAoooo AKKKKACiiigAooooAK5/VPDH9pa0l8LzyoZPsv2mLytzP9mmaaHY2Rs+djuyG3LgDaea6CuP 17W9RsvFdtZwXHlqfsf2e12Kft3mzslz1G5vJiCyfIRt3ZfKkCgDpLu1mu7K/tpWtJEnRkiS a3LoFKAYkXd+8G7cSBtyCB2ycPTfCU+neGdX06PV5xf6n5jvfK0rGGRoliVk8yR5PlCIeZCc g4IGANTWbmZ9B1gaVdRrqFvbyqjKplMM3l7k3IoYk/MjbdpJBGAcjPHx+Jvs3gqa+1HxRJEB e+QL9JLWYXBKgqlrKYo4mG4gF3TClZgSAu9QDoL3wjDc6zpt7bzR2sFikSRwpAMwLGxYCBgQ IQ4OyQYO+MKvy4zXSV5/ceIdZPiHQLea9gFrPaWkrHTJEIvpZHYSeSskbmWFAEdirIyRuWJbK49AoAK5/S/DH9m6098bzzYY/tX2aLyt rJ9pmWabe2Tv+dRtwF2rkHcea6CuP0HW9RvfFdzZz3HmKPtn2i12KPsPlTqlt0G5fOiLSfOT u25TCgigDoL+yvL7w9c2H2/7PfT2jw/bLdCnlyMhHmIu7IwTkDdkeveuf1LwDaz+I9I1jTZ/ sDWEsbSQq05SSONGSONEWVUjUK8nAUj5jkEF1e54q16y0/w1FqX9vR2FpM6eXcwGJnn3AlFh aT92Cx2ncwKhQxO0fOuPqPjBtM1Hwn/aPibw/BBeor30MTqA4NvM3mpI78QGRUC/LknHzc7S AaGgeCI/Dviq/wBUs72Q2d1biJbSSSeRlbe0jOXkmYMSzyH7oxu4IJcv1lcX4Z1LU5vF+q2W oapJeBHuGjgiMey2QTYjWVPJSSNyhXZl3EgWRgcAE9pQBHOJmt5Vt5I45yhEbyIXVWxwSoIJ Ge2Rn1FcnB4U1hdButJutR0O9huLuS5KXWjPJGfMleZ1ZDcfN87qVOeAvO4nI6i/m+z6dcz/ AGmC18uJ38+4GY4sAnc4yvyjqeRwOo615/8A8Jc7+Df7Tg8SwS2B1DyUvftFpHePD5e4L+82 wLMX52MFIh6gSUAdBqHhvWJG0NbDXIEj0ja6Pf2b3M00gikhLSOJUzlZCTxncM5xxXUV5/c+ M7iwufB8ms6zpWlfb4kk1LT51EU0Za2mYsWd8pGJFVcFc7hjdyVPoFAGXrWlTakLKa0uY7e8 sbj7RbvLEZY9xjeMh0DKWG2R8YYc4PIBBk0rTP7H07TtNtZt1jZWi2wEq5kfYFVG3AgDgNkb eSRjGMHP8WajLp9rYf8AEw/sy0nu/Ku9Q+QfZY/KkYNukBRcyLGmWBHz4HzEEXND1Ka80bSX 1JY7bVLuyS4ltCCjK21PMARjuAVnAOemQD1oAy4vCHlXV5IbqCaF4ryO2guLXei/apRLN5w3 fvV3qMKNmFyCWJ3A03wlPp3hnV9Oj1ecX+p+Y73ytKxhkaJYlZPMkeT5QiHmQnIOCBgDP0/x DqMutanFPqMESxRXz3CXEa+XpnkzBLZnAKsFliLSne3zBcoVXNZcfjjz/Bn2pvFGmyXMOpzw Xk9ncWsUvlebOIvJEzGJSwRCPMJzGHILNg0AekQQQ2tvFb28UcMESBI441CqigYAAHAAHGKk rP0K6vL7w9pl5qNv9nvp7SKW4h2FPLkZAWXaeRgkjB5FaFABXN6D4Xm0G3nWG/jknWyh0+0d 7c7Y4YA/k+YofLvmRtxBQNxhU79JXF+DvEd3e295carfRtBDZW91dSSBEWyuWEhuLYkABREE Q7Xy67/mY5GADU8S+GP+Eg27bz7PvtLiwnzFv3W8+zzAvI2yfu12sdwHOVbIwf8ACMf8VL/a n2z/AEf7X9v8jyvn+0fZ/s2d+ceX5f8ADtzu53Y+Ws/xxreo6P5H2K4+z5tLmeH5Fb7Vdp5f kWvzA58zdJ8iYkbZ8pGDk/tvUf8AhPf7O+0fJ9r8j7DsX/j0+y+b9q6b/wDX/ud+fL/hxv5o A0PDXhj/AIR/duvPtGy0t7CDEWzbbwb/ACw3J3SfvG3MNoPGFXBz0FcX8Pta1PWbe7l1aeSS 72RPLEjRtBauwbdAMRo6SoRh4nLlAY/mJY12lAGfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5G V3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x8jO7CjByTuqPxRfXGnaDLc W0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARUfhvVWu9EtJb65jaSe4ngtpW2qbtEeTy5Fxg MXiQSZUAEEsAF6AEf/CMf8VL/an2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2X THtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNH9t6j/wAJ7/Z32j5PtfkfYdi/8en2 XzftXTf/AK/9zvz5f8ON/NY+n+KdZn0G8uJLzcwisHup/KT/AIlss0pW7i4GF+zoA+JAzJnM hYcUAdxoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8nQrH8L31xqOgxXFzJ5zGW ZI59oHnxLK6xS8YB3oqPlQFO7KgAgVsUAZfiHRIfEOiTabP5ex3jkAljEiFo3WRQ6HG5CygM uRkEjIzkGkaVNpGl2VjFcxukLsZcxELtbcdkS7v3aKzKFB3bUULz94V/F899beGLqbTr2Oyu EeI/aJOFjTzF8wkmOQKNm75yhC/eOACQeGtSFx4f0+W7vJJJ7h3jjkuHjzcMC5zGUVFdCqFk YKN0YDYHNAFeTwqZ/GcOvS3UaxQP5sVvAkiF5TEYt0v7wxudrMNwjV8BBuwuDJ4Y8Mf8I59q /wBM8/ztg+WLy9+3P72Xk+ZcPu/eS8b9q/KMc05NVni+IcOnwaxHdQSpi409ZIpHtT5ZYM0a oHjQ4T940jDMgXZ8ystf4fa1qes293Lq08kl3sieWJGjaC1dg26AYjR0lQjDxOXKAx/MSxoA 7SsvxDokPiHRJtNn8vY7xyASxiRC0brIodDjchZQGXIyCRkZyNSsPxfPfW3hi6m069jsrhHi P2iThY08xfMJJjkCjZu+coQv3jgAkAFjSNKm0jS7KxiuY3SF2MuYiF2tuOyJd37tFZlCg7tq KF5+8Kf/AAi6Dxf/AG8ksEbH5n8q1VJ5D5fl7HmGC8OMNsYE71U7sKFEnhrUhceH9Plu7ySS e4d445Lh483DAucxlFRXQqhZGCjdGA2BzWPf+LBpvxBTS5ta02WB7KYx6ZGY47j7QDB5aMzy YLuHfYMIMHndjIANjQNBm0m4v7q4uLR571w8iWNobaAsCxMhQu5MrFvmfd8wVBj5cncri/AX inUPEV14ht9Ujjhu7G9VPs6SwuLdWiT91mN2LFXEgLnGTnAUhkTtKAM/WtM/tfTDaibyZFli uIpCu4LJFIsiblyMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd2FG DkndUfii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6JaS31zG0k9xP BbSttU3aI8nlyLjAYvEgkyoAIJYAL0AI/wDhGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs784 8vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmj+29R/4T 3+zvtHyfa/I+w7F/49Psvm/aum//AF/7nfny/wCHG/msfT/FOsz6DeXEl5uYRWD3U/lJ/wAS 2WaUrdxcDC/Z0AfEgZkzmQsOKAO40XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSe ToVj+F7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2KACufvNBv7/xGL251KB9 MSJo4rNbeRJYSyFWkSZZRiQ7sb9uVXIXbuYtoanqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntn oc44zzd/4sGm/EFNLm1rTZYHspjHpkZjjuPtAMHlozPJgu4d9gwgwed2MgAueEPB48JT6qIL vzbO7lje3t/3x+zoiCNUzJK+75VUZAXpj7oVV2NT0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3 zjnHU1zfgLxTqHiK68Q2+qRxw3djeqn2dJYXFurRJ+6zG7FiriQFzjJzgKQyJ2lAHNx+EYYf Gc3iGKaNHmfzZdsAEzt5Qi8tpc8wbVV/LK/6wBt3G2pPDHhj/hHPtX+mef52wfLF5e/bn97L yfMuH3fvJeN+1flGOacmvAfEOHRodetHl2bp9NYxr5cXlll2/wDLR5yw3cHYIgdyglGc8G68 NbuNUEOvWmsW0DooliMYZZcuJNqJysGVAj3kudrnc67GIBuano1rq/lfaZb6Pys7fsl/PbZz jOfKdd3TvnHOOprPu9G1ifxba6xFqljHa20T262z2Ds5jkaJpMyecBuzCMHbgZ5DV0FcnJqs 8XxDh0+DWI7qCVMXGnrJFI9qfLLBmjVA8aHCfvGkYZkC7PmVlALnhrwx/wAI/u3Xn2jZaW9h BiLZtt4N/lhuTuk/eNuYbQeMKuDnoK4/wPreo6x5/wBtuPtGLS2nm+RV+y3b+Z59r8oGPL2x /I+ZF3/MTkY7CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuV bBwTg1oVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYVO4BqanrenaN5X2+4 8rzckYRn2quN0jbQdka5G52wq5GSMipDqlousppLPILx7drlFMT7WjVgrEPjaSCy5XORuBxg 1n61ot9rOlw2UmpxxB0Ed6EtspOp2liqlsqcrwGLoQzK6SA1Hd6NrE/i211iLVLGO1tont1t nsHZzHI0TSZk84DdmEYO3AzyGoA0NM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWh WPo2jXGnXl9d3mofbbi58tPMMIQ+XGCF3cnLcknbtTJJWNCzFtigCvfX1vp1nJdXUnlwpgEh SxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w 2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjySr GocbMs52kl9owDuPNAFj+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbeary+KNJgs7+ 6kmnEdhdrZXAFpKXEzFAqqgXc+7zEwVBB3AgkVX/AOEY/wCKl/tT7Z/o/wBr+3+R5Xz/AGj7 P9mzvzjy/L/h253c7sfLRoOjaxpmp6ndX+qWN3Hfyi4eOCweArII44xhjM/y7YhxjOTnOOKA NTS9UtNZsFvbJ5GgZ3T95E8TBkcowKuAwIZSMEDpVys/Q9M/sbQ7LTjN58kESrLOV2meTq8j DJ+Z2LMSSSSxJJPNaFAEc88Nrby3FxLHDBEheSSRgqooGSSTwABzmq+l6paazYLe2TyNAzun 7yJ4mDI5RgVcBgQykYIHSjVNLtNZsGsr1JGgZ0f93K8TBkcOpDIQwIZQcgjpWH4Z8M6n4a0c WMerwXDPqEl1LJNBM+6N2LMi752KtknDZI7lWYsxANj+29O/tj+yvtH+l9Nuxtm7bv8AL342 +Zs+fZndt+bG3mjTdastXlu47Jp3+yytDI720kaF1ZkYK7KFfDIwO0nGPcVTj0fUk8WTas+o 2kto6eXHBJZsZYI9oyiSCTaAzjex2EtwCSFTaaB4fbRbi/ne6jme7cFhDbrArEFj5rqpw077 vnkAUNtTCrtoA3KjnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1JVPVNLtNZsGsr1JGgZ0f93K 8TBkcOpDIQwIZQcgjpQAaXqlprNgt7ZPI0DO6fvIniYMjlGBVwGBDKRggdKDqlousppLPILx 7drlFMT7WjVgrEPjaSCy5XORuBxg1h+GfDOp+GtHFjHq8Fwz6hJdSyTQTPujdizIu+dirZJw 2SO5VmLMbF3o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTzgN2YRg7cDPIagDQstb07UdRvbC 0uPNuLLb54CMFG4sowxG1vmjdTtJwyMDggitCuT8P+CI/DniW51CyvZP7Peyis4LJ5J5DCsZ JX55JmBHzMANg2gjbjLb+soAr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqN nHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFV9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5Fy AQSMgEHkV9P0m80vTLe1tb2DzPtclzdyS2xYSebI8kqxqHGzLOdpJfaMA7jzQBY/tvTv7Y/s r7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDB zwpY1X/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRW VvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5oA6ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQ QQCKsVn6Lpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0KACqZ1S0XWU0lnkF49u 1yimJ9rRqwViHxtJBZcrnI3A4waj1PQtH1vyv7W0qxv/ACc+X9rt0l2ZxnG4HGcDp6Cs+70b WJ/FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG7MIwduBnkNQBoWWt6dqOo3thaXHm3Flt88BGC jcWUYYja3zRup2k4ZGBwQRWhXJ+H/BEfhzxLc6hZXsn9nvZRWcFk8k8hhWMkr88kzAj5mAGw bQRtxlt/WUAFZ+m61ZavLdx2TTv9llaGR3tpI0LqzIwV2UK+GRgdpOMe4rQrl/BXhD/hENON r9qgl/dRQ4tbX7PG3lgjzWTc2Zmz875+YKgwNvIB1FZ/9t6d/bH9lfaP9L6bdjbN23f5e/G3 zNnz7M7tvzY281oVjzaNcXHiO21KXUN9rbbnhtTCMpIybDhs424yR8u8FmAkCMyEAsaZrena z5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J 3SfvG3MNoPGFXBz0FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRWfca5pdprFppE9/AmpX eTBa78yOArMW2jkLhG+Y8ZGM5wKANCio554bW3luLiWOGCJC8kkjBVRQMkkngADnNZf/AAlG k/2d9t86fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5oA2KKy38R6QlxaQ/bo3N2iPE8YLx7X OIyzqCqhzwhYjeeFyeK1KACiis+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2CcUA aFFU9S1S00m3Wa7eQB3CRpFE8skjYJwiICzHAJIAOApPQE1XPiTRft+n2C6paPd6inmWkMco dpk2M4cAZ+TajfN04xnJAoA1KKpwarY3WqXmmQXMcl5ZJG9zEvJiEm7Zu7AkKTjrjB6EZuUA FFRzzw2tvLcXEscMESF5JJGCqigZJJPAAHOay/8AhKNJ/s77b50+3zfI8j7JL9o8zG7Z5G3z N2358bc7Pm+7zQBsUVHBPDdW8VxbyxzQSoHjkjYMrqRkEEcEEc5qSgAoqnqWqWmk26zXbyAO 4SNIonlkkbBOERAWY4BJABwFJ6AmrEE8N1bxXFvLHNBKgeOSNgyupGQQRwQRzmgCSis/+3NL F5qFqb+BZtNiSa9DPgW6OGKl2PC8Ix5PAwTwRmv/AMJRpP8AZ323zp9vm+R5H2SX7R5mN2zy Nvmbtvz4252fN93mgDYoqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zUlABXF+DvEd3e295ca rfRtBDZW91dSSBEWyuWEhuLYkABREEQ7Xy67/mY5GO0rP0zW9O1nzfsFx5vlYJyjJuVs7ZF3 Ab42wdrrlWwcE4NAHN+MvEd3Y2+l3GkX0YguEedJECOt1gIUijJBEruGJWFdjSYO2WPad1i8 8T2Vn8RrHSZPEFoiTWUqyWDzRArceZB5XbeHdZHwpOCACBwTW5qet6do3lfb7jyvNyRhGfaq 43SNtB2RrkbnbCrkZIyKP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5oAw/A+vDX7 e8uE1601SMurxrGYxJEGB5ZE5jRiCUjfdIoHzuWJVOsrP0zWrLWPNNi08kceCJmtpI45Ac4a N2ULIpxnchIwQc4IzoUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha 4jaDZ3Gq38CNcXctrazzOsf2wCV1hZegZpEVXG0YbdlQBgVsX19b6dZyXV1J5cKYBIUsSSQF VVGSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFAHL/wBt6j/wnv8A Z32j5PtfkfYdi/8AHp9l837V03/6/wDc78+X/DjfzWH4b8V6nrHg7XdUk1eNbiW38+0nVo5r TTzLvMcbukIIeLKeaHDhVCuSAxA7z+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbear w+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljQAeFruW+8OWlxNPPcSNvBmmKEy4dhv VkRFaM4yjBV3IVbGTWxVexvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsUAZ+t3H2XR 55jffYFG0Pd+V5nkqWAZ8dBgEncwKr95gVBFZ+g64j+HNOu9Vv4I3vJTDbyTOsZnJdhEOMK8 jIAT5eUY5KZQqa1NU1S00awa9vXkWBXRP3cTysWdwigKgLElmAwAetSWN7FqFnHdQpOkb5wJ 4HhcYJHKOAw6dxz16UAc/c/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3V /Aut6jrH2/7fcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybht6D+2rIax/ZbtPHdHhDLbSJH Idu7akhUI7bcnarE4VuPlOI9E8Qaf4ht/tGnfa2g2I6yTWU0CyKwypQyIocEDOVz1HqKANSs /W7j7Lo88xvvsCjaHu/K8zyVLAM+OgwCTuYFV+8wKgitCqeqapaaNYNe3ryLAron7uJ5WLO4 RQFQFiSzAYAPWgDL0HXEfw5p13qt/BG95KYbeSZ1jM5LsIhxhXkZACfLyjHJTKFTWPD4hvbr 4pXOlfbdtja7bcWNvJG8jP5HnNPMhj3rDiREDK4+dVBB38dhY3sWoWcd1Ck6RvnAngeFxgkc o4DDp3HPXpVdNc0uTXG0SO/gk1NIjNJao+540GzlgPu/6xMZxnORnBoA5fwBrmpa9eaxLc3/ ANttIPJhEsb27wfasM0wgaL5mh2vDt8wluoPzBhXcVTsNVsdTe8SxuY5zZXBtbjZyElCqxTP QkBhnHQ5B5BAuUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ 3Gq38CNcXctrazzOsf2wCV1hZegZpEVXG0YbdlQBgVsX19b6dZyXV1J5cKYBIUsSSQFVVGSz EkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFAHL/ANt6j/wnv9nfaPk+ 1+R9h2L/AMen2XzftXTf/r/3O/Pl/wAON/NY+n+KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgY X7OgD4kDMmcyFhxXcf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud 5mG32lj5TguHOI2jXGZFc8IyBg54UsaADwvfXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+VA U7sqACBWxVexvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsUAZ+pzaxF5X9k2NjdZz5 n2u8e329MY2xPnv1xjA654w7zxPZWfxGsdJk8QWiJNZSrJYPNECtx5kHldt4d1kfCk4IAIHB NdZWf/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNAHL/DjxDe+JLO8vr29+0+dsuI 0gkjlgtkkLssG9Y0bzkXaHVi2PkbI3kDuKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65V sHBODWhQBycevCb4hzaSmvWg8lNkmnSGNXYmMOAin947gHe0mRGFIUIzb3TP8I+KdV1Hxrq2 j6xHJbXEdlBdfYWltmFqxZwyL5bs7AqYjubvlsRh0U9Z/benf2x/ZX2j/S+m3Y2zdt3+Xvxt 8zZ8+zO7b82NvNGma1Zax5psWnkjjwRM1tJHHIDnDRuyhZFOM7kJGCDnBGQDQrk49eE3xDm0 lNetB5KbJNOkMauxMYcBFP7x3AO9pMiMKQoRm3unWVj/APCUaT/b39iedP8AbvN8jH2SXy/M 8rztnm7dm7y/mxuzigDL8D68Nft7y4TXrTVIy6vGsZjEkQYHlkTmNGIJSN90igfO5YlU6yqd hqtjqb3iWNzHObK4NrcbOQkoVWKZ6EgMM46HIPIIFygAooooAKKKKACiiigAooooAKKKKACi iigArn9e0bWNT1PTLqw1SxtI7CU3CRz2DzlpDHJGcsJk+XbKeMZyM5xxXQUUAZ+paZ/bGk6p pl7N/ot9E9uDCu1443j2tySQWyWIOAOQMHGTj/8ACLXmz7Z/acH9s/2h/aHn/ZD9n8z7P9mx 5XmbtvldvMzv5zj5a6iigDk18DwwHToba+kWztrext5UkjDSSLZyGSAq4ICncTvyrbhwNh5r rKKKACuf0vwx/ZutPfG882GP7V9mi8rayfaZlmm3tk7/AJ1G3AXauQdx5roKKAMPXNFvdZ0a 2tRd2kV5G6u1yYJRhgpDGPy5keMnJGQ5+UspyCaz7jwpqUMPh610fVrS3tNDRBAt3YtO7ssL w5ZllQY2SdAo5Gc44rrKKAOT0DwRH4d8VX+qWd7IbO6txEtpJJPIytvaRnLyTMGJZ5D90Y3c EEuX6yiigCOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPqK5ePw1r0XhqbT49ftF1C4uPNudRWw dXnUgBtwE2Q7Y27lZdq4VAm1SvWUUAV7C2+x6dbWu2BfJiSPFvF5UYwAPkTJ2rxwuTgcZNWK KKAMvWtKm1IWU1pcx295Y3H2i3eWIyx7jG8ZDoGUsNsj4ww5weQCDJpWmf2Pp2nabazbrGyt FtgJVzI+wKqNuBAHAbI28kjGMYOhRQBxen+AI9J1nWLuzu43s9QsjarZ3gnuVDFmdnk8yciQ M0khK4X7xwQWcvJb+D9TsPD13YWGveVfX12Li6u5Y5pty7FQom6fzEysaDcZGYZbaV+XZ2FF AFewtvsenW1rtgXyYkjxbxeVGMAD5Eydq8cLk4HGTViiigArm9B8LzaDbzrDfxyTrZQ6faO9 udscMAfyfMUPl3zI24goG4wqd+kooA5/xL4Y/wCEg27bz7PvtLiwnzFv3W8+zzAvI2yfu12s dwHOVbIwf8Ix/wAVL/an2z/R/tf2/wAjyvn+0fZ/s2d+ceX5f8O3O7ndj5a6CigDm/CPhGHw lby29vNGYCkcUccMAiXagIEjgEh52Bw8ny7tqfKNvPSUUUAZ+taZ/a+mG1E3kyLLFcRSFdwW SKRZE3LkZXci5AIJGQCDyI9J0qbSbCC2S5jkJuJri6doiPMaV3kcIN3yDzHyM7sKMHJO6tSi gDn/APhGP+Kl/tT7Z/o/2v7f5HlfP9o+z/Zs7848vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW 9nJ5GBHHZyGS38xd37xtxO8goGHACHmuwooAz9F0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu5 2wCSQMAknk6FFFAGfrelprWjz2EggKybSBcW6zxkqwYB424ZcgZHBx0KnDCPSNKm0jS7Kxiu Y3SF2MuYiF2tuOyJd37tFZlCg7tqKF5+8NSigDDk0GafxZDrMtxaKkCbIhBaFLh1KkeXLNvO +LczPsCr8wQ5+XmPwx4XTw39qEUsHlzbFWG1tVtowFzh2RPlMzbsO6hQwVAFULXQUUAFZ+t6 WmtaPPYSCArJtIFxbrPGSrBgHjbhlyBkcHHQqcMNCigDL0jSptI0uysYrmN0hdjLmIhdrbjs iXd+7RWZQoO7aihefvDP1LQNVv8AxLDqK6raRWcdvLa/ZxaSCUxSmIyYmWZSr5iG1go256Ej NdJRQBy/hDwePCU+qiC782zu5Y3t7f8AfH7OiII1TMkr7vlVRkBemPuhVXqKKKAM/WtM/tfT DaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75 B5j5Gd2FGDkndWpRQBz/APwjH/FS/wBqfbP9H+1/b/I8r5/tH2f7NnfnHl+X/Dtzu53Y+Ws+ 08C/ZdMe1Oo7pIorK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ812FFAGfoumf2RpgtTN50jSy3 EsgXaGklkaR9q5OF3O2ASSBgEk8nQoooAz9T0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3zjnH U1Tk0fUpvFkOqTajaSafAmILJrNt8TFSGdZPMxvOcZKHC5VcbnLblFAHP+GvDH/CP7t159o2 WlvYQYi2bbeDf5Ybk7pP3jbmG0HjCrg50NT0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3zjnHU 1oUUAYcmj6lN4sh1SbUbSTT4ExBZNZtviYqQzrJ5mN5zjJQ4XKrjc5av4R8Iw+EreW3t5ozA UjijjhgES7UBAkcAkPOwOHk+XdtT5Rt56SigArL1DRV1LVLW6mnk8iC3ni8lCynfJsAlV1IK OqiRQw5xK2COc6lFAHL+EPB48JT6qILvzbO7lje3t/3x+zoiCNUzJK+75VUZAXpj7oVV6iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKAODj8eX1ppMdxqtjpq3cl7eQpbw6hhpkgmMZS FWUPLOTwqAANjJaMsFqvb+MNXlGlWTiMT63etcaRcRsGNxYrcq7h0KARn7K+V5LEA52vgHpI /BWgwwafDHazrHYeWIB9sm6Rv5kav8/7xUblVfcFycAAmpNN8H6Bo4tBp+mxwfZHV4NrsdhE bxjqem2SQ4PBaR2+8xYgFfwj4sPiu3luBpl3aQbI5oJJopFWWKQEry6KC4AywTeg3Lh2zxjt 4y1e+OkSWWkSQvcambc2ssoRyptp5Ak4dQ8LqUjdwFb5SpRpd2D1Fn4c0ixt7q3hsYzBdJ5c scpMimLBAiAYkLEAzARjCLuOAMmq/wDwiGhm3lhe0klE1vPbSvLcSvJLHMEEgd2YsxIjjAYk kBAAQBigDm4/ib5vhS28SJpGdPubSV4j9p+driKCSZ48bOIx5MieYeSy8IVIYyQ/Epb241WP T9Fu7tLF3QNDukLjMAik2xqxEUnnO6sNxMcLMqtyo6STwto039oCSz3LfxSQzr5r7dkn+sCD OI95+Ztm3cwDHJANRnwfoAFx5Omx2zzvE7S2rtBIpjjESbHQhkAQFQFIGGYfxNkAuWN5cano cd1EILe4niJjYOLiHPO11KkeZGeGHKkqRkKcgcm3iW703wNa3t5qcj3f9pvHJNLCm8Qw3LvO jhFCb0toZgxQYJQ7CxKk9hNpVjPpY0xraNbNUVEii/diMLjZs24KFSAVK4KkAjBAqu3hzSJL K1s5LGOW3tnd0jlJcMzo6OXyT5hZZZNxbO4sScnmgDHk8XX0d+ulPo0cerTvEbaCS8/d7JEn dfNkVDscLbS5VVcZ2AMQSVy9b8d3sFrPp0VpBa63LaLDHBFex3E9teyxAxB4wCBD5jpH5rkA uy5Xawc9R/wi+k/2d9i8mfb5vn+f9rl+0eZjbv8AP3eZu2/JndnZ8v3eKrx+CfDsN0k8On+V 5W3yoY5pEhh2yxy/JEG2JmSKNjtUbiOc5OQCPxF4om0S9iht7CO8RLdrm6xcFHjXeiRoihG3 yyszCNCV3lCAeuM+68eNY26/abG0F2txLC0SagpFwYwm5LXKh7iXLhAgRR5iOhZSF3dJdaJp 15dSXNxb75pPs+9t7DPkSmWLgH+F2J984ORxVOTwhocs0MxtJFeK4+0/u7iVA8nnGcFwrASA SszqrZCljgDJoAy9S8Xy2tteS3Fr9ig0/UIbW8uGukATfcxKvJUjaYJFlcnG0OFDbtxjj07x rqVw9ml9oMds8z26TRR3bSSxNOu5I9piXMqLl5UOPLjw4LZwNSfwT4duYjHNp+9WiMLEzSZZ Ssyksd2SxFzPlj8xMhJJOCLk/hzSLqWWS4sY5jLcG5kWQlleQwfZySpOCDF8u3GO+M80AZ+g eL7bWre/un+yQ2logla6hvEngVCGO15F+RZUC5dQWChkIZg1Y6fEO5tbJL3WdFjsYI0ikvFj uXllgEibwmzylLSxqGklTgpHhxvzgdJB4X0m30nUdMEM8lrqW/7WJ7uWZ5d0YjOXdi33FUcH jHFST+HNIupZZLixjmMtwbmRZCWV5DB9nJKk4IMXy7cY74zzQBhy+MtRi1iLRhoG/VptkqWy 3iny7d1l2yytt+Xa8QWQLvC+Yu1pCQpseGPFs/inTrq6tdInt8RJPZm6WWKOdXBKAu0Yw3Hz bBIoDKVZ88alh4e0zTbiO5toJPtEaSJ58s8ksjCQxlt7uxLn91GAWJICADAGKji8LaNDp19Y JZ4tb6IwToZXP7oggRKScpGAzbUXCruO0DJoA4/SvHWuyvp1zeadBJDeafpM0qwzhY7drq4l i3KSpdmIMR2H5QEcb84L6Fv4+uNRa3i0vTbG7murtYIXXUw0ABiml2vLGjgTKITujUMB5iEO Q1dB/wAItozJsls/PUy+cwuJXl8x/s/2Yl95O/MXyndnPU5PNV4/BWgxxXcZtZ5ftcU0M7z3 k0ryJMsaSAu7luVhjHXjbxjmgCneeJbu78FaPqOnW8iahrSW5t7eIozjzFEsgRnwm9YllZS+ FJQZBztJPq99qHhzwyLe/jt7vXHhVr6zjysY8hrh2jSVTwyxMg3jK7wSCVweg1LS7TVrdYbt JCEcPG8UrxSRtgjKOhDKcEgkEZDEdCRVO+8LaNqOkyaXc2e6xbAWFJXQRARiPbHtI8tdmVKp gEMwI+ZsgHJ6N8RNS1LSLO8OmaaUuLdXW4e/aKIssCSzyN+7by4I2Zoi+5sSBUIGSRcTx7eX DNJb6NBFatkQyX96YGXZEkkzzKI2EUcZZomYFtsoCEDOR0EvhbRp4JYZLPdHLFdwuPNcZS5c STjr/EwB9u2BRL4W0aeCWGSz3RyxXcLjzXGUuXEk46/xMAfbtgUAV9V8UJp3h6y1R4oLRrzy wker3S2SwlkL7ZWO4qwAI2qrHd2xlhl2/j5pYU1GbR5LfSJ3lht5JrhY7gyxQvLIskThViCm KaMlpPvIDjadw6DW/D2meIbfyNSgkkTY8ZMU8kLFHGHQsjKSjYGVJwdoyDgYz9P8E6RaRXwn t4557y4nnkmAMbDzJ2mBUg5V1PljeuGPkxnI2LtAOfv/AIj339jX02l6TaXlxaWVzdSXMF75 1nGI1VkcSBVMiNmRRgAmSGROis6+gQGZreJriOOOcoDIkbl1VscgMQCRnvgZ9BWGngrQY7Ga 0W1nEc3lmR/tk3mMUmedW8zfv3CWR33Zzk9cAV0FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWL4q1Cx0vQXudTt7aexNxbxTLclRGqyTIhdiwI wu7dz/d6jrW1VPU9Nh1W1S3naRUS4guAUIB3RSrKo5B43IAfbPTrQNNp3RxtpqngG6a9cado jWtvdm3juLe3SdJFEUUjykopCRoZQrOTtXAywyBVhbjwJG8cV3p+iRTy3b2scaWgbc4uGgRT mMYZmVuMfwSEFlRmq1rXw90bXLqa7uTJ9oluDPvaGCYJuiijZVSaN0wRBGc43Ag4IBINO98D O+sWzWR8u0+1peXNw96wd3W7e6CGFY9rqGkkUZcY83JV2jQ1PJHsbfWq/wDO/vZk6b4l8Eao 3h+G38N2Ul1qvk+akWmmRLbzIpHH7wRbXw8TIcEY2uTjy2ANH8S+CNT1SeyuPDdlZMsqww+b pp3ys1zNbqdhiBVcxx5Y/KrShCQw56a38C2FpPpM1ve30cmlxWsMJDRnekCTRqGyhzuW4kDY x2xtIotvAthb3F/L9tvpPtl3DdFXaPERiunulVcIDtMkj53EnBwCMUckewfWq/8AO/vZRsL3 4ean9mNlY6XMlzs8uVdN/d/NgJucptXc2UG4jLqyDLqyine6z4CsLi3M2jaeLCa3uZxdHTT8 /kmLJjXy8yoVlL70yu1GOSASNYfD3RkuNEuEMnn6RbwW0UkkMErSRwnKAmSNihBLHdHsJ3de FwL8PtK+z3sb3F20t9b3kF1OPLRpjciISSEKgQPiFACFA6khiSaOSPYPrVf+d/eyrfy+DbZ2 ht9D0u4uEu4LYp9iVFbfcRwOyOU2yeW0gDBSdrYVtpNGmS+DdY1y506x0PS544YoHE8VkrAt J5xAYBPlXbCGVydriRCOGUtpSeCtNkuzO093hbgXEEW9dsDG4S5kC/LkiSWJGbcWx0XYOKk0 bwjZ6DeG4sby+CmKG3MMkodBDEJRHGMrlVUS9iCdikkkuXOSPYPrVf8Anf3sydB0+11PU9Tt b/wb4dtI7CUW7yQOJy0hjjkGFMCfLtlHOc5GMY5q5Z2fhbUbe6ls/Cyu9um7yptFNq0hwcKn nogJOMdcDIyRmt6y02GxutRuImkL39wLiUMRgMIo4sLx02xqec8k/QU4vC+j2enX1npNjBpH 22IxSTaZElvIOCAwZR95dxIJzg0ckewfWq/87+9mDBb6M+h6jey+E9ES4sJXhk2+V9mJXG5x OyL+7TJDttypjkAVioBz0vdFnutEii8GaWsN/u8+7mtyttFiURp5cwhKSeYdzR5Kbxs6GRRX VWvh2S10tdPGt6kYo02wlFghMWNmzaI4lGE2cKQVIZgwYYAryeEoYrWFbe4u5vJf7Q1tNcBY ru4EpnV5CEOw+cS5MYUHIBVlVVByR7B9ar/zv72Q6rB4H0S4t7fUtO0i2kuEd4g9knzKhXec hcAKHDEnooZjhVYiS60/wpZ6jBYy+HIWmm27Wh0V5Yxk4G6RIyi8jncRgcnA5qHUvCE2tXGk jVdQkuI7bTLizvZYmMDXTSmANlF+XY6xyAjPy7gVwwDLuXWhaPfajBqN5pVjcX0G3ybma3R5 I9p3LtYjIwSSMdDRyR7B9ar/AM7+9nM7PD3/AAkv9l/8Irpf2f7X9g8/yI9/2j7P9pxs2Y8v y/4t2d3G3HzVHZP4budSngk8KaeluEu3t5I7RZZJBayiGbdGqZB3sNgUuWHXacKd6fwvbTat LqKXd3BK7mdVjKFUuDD5HnjcpO8RfLtJKdyhPNSWHhuz07VpdQiknZj53kxOw2QedIJJtuAC d7qGO4tjGF2jijkj2D61X/nf3s5G11Dw9cwaVcDwhpaw3VpYXNz+6j3Q/bHMcKoNn7zDg7iS mFwRuPyi5qz+G9L03xLf/wDCKafLFoabWVbRS00vlLLtwqHam2SP5+cZckALk60HgrTbdNMj Se78qxt7W3aMuuLlbZt0BkO3IKOS3yFck4bcMCpJPDf2pPE9ldSf6DrnJeJsSR7rdIHXBBHA jVg3OS5BUbcsckewfWq/87+9mT4lTw94f27fCul3Gy0uL+fMEabbeDZ5hX5Duk/eLtU7Qecs uBnPm1Dw9FcSR/8ACIaWVlllt7FvKjBlkjuo7R/NGz92vmypgrvJTcSAQFPVax4Xttcdmu7u 7w6SQMqFAGt5FQSwfdzscxqS33wc7XUcVXl8FabLNPK092NzvJbKHXFpI8y3DvH8vJM0aSYk 3gFcABSVJyR7B9ar/wA7+9mbpCeHtSv7ezl8K6XDJNFcnKwRuBJbTiCZfuD5dzIUbqwJyqEA HoP+EW8Pf9AHS/8AwDj/AMKq2Hhv+z9as7mKTNva2lzHudt0k81xMksrsAAq/NHn5eCZCAEC gHoKOSPYPrVf+d/ezJ/4Rbw9/wBAHS//AADj/wAKP+EW8Pf9AHS//AOP/Ctaijkj2D61X/nf 3syf+EW8Pf8AQB0v/wAA4/8ACj/hFvD3/QB0v/wDj/wrWoo5I9g+tV/5397Mn/hFvD3/AEAd L/8AAOP/AAo/4Rbw9/0AdL/8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/wBAHS//AADj/wAKP+EW 8Pf9AHS//AOP/Ctaijkj2D61X/nf3syf+EW8Pf8AQB0v/wAA4/8ACsfXtM0nTG0yCw8KaJd3 V/dm2RZ0WBFxFJKWLCNz0iIxjvXXVl61oo1kWTLf3djPZXH2iGe1EZYN5bxkESIykFZG7elH JHsH1qv/ADv72YNtJ4HuTbRto+nwzzuY2ik09D5EgkaLZIyqUQmRHRSWw7KQhaq51T4bBygt 9Id96IFj08MWZ1kaMLhPmLrE5QDO8FCud6btKfwDokuradqUUXk3Fjs2sYopjIFkMg3NMjsG Ls7F1KuxcksSARn+G/Az2GpxajfHyGtfJS0tYb1rlI0ijuI0G940woS5KhQo/wBWGLMzMSck ewfWq/8AO/vZMJvAT27zR6Zp8gV1VUj0svJNuBKtEgTdKjBXIdAykI5BIViLlnY+DdQvDbWW k6XcsIlmMsNgrwhWAKjzQuzcVZWC7txVg2MHNZqfCrw7Hp01jGm2BpY5YV+yWp8koHVesJ83 5ZGXM3mHnOd3zVuaZ4WstM1SPUVkkmuIbJLCAtFFGIoV25VRGi8FlDYOQpzsCgkE5I9g+tV/ 5397OZu9T8HC/wBJs9N8O6feyaherbZ/s5kVIijt5wIhYOhCHawwj4YhtqOVr6P4h8C6ibyO 70HT7KeG4kSK3bTHMskSyPGsm0wqQWdCu0bsOUTO9gtdNp/grTdOurK4jnu3exdBbB3XEcMc U0UcPCjKKs8hBOXJI3M2MVHJ4C0ea21S3mM8seo4MqyFGCsLma5UgFcHEk7cMGUhVBBGcnJH sH1qv/O/vZCg8CSS2sa6bpZa5wF/4lwxGSxQLKdn7pi6sgV9pLqygFgRWfDqfgSfUb2CLR9L mht4oHWS3sRNJM8pmwiRIhZvkh8wFQdyNvHy/Mb0Pw40W3utLukOLjTtoST7FafOqytIo2+T tjwztzEEY5ySSARJb/D/AE2zcS2d9qUE8aQpayiZXNqsSyogjV1ZcCOd48MDkfMfny5OSPYP rVf+d/eyi2p/DldUj04WGnyTyvGiPFpLSRMZPK2YlWMpg+fDzuwPMXPUVqf2f4U/tj+y/wDh HIftH9/+xX8n7u7/AF3l+X0/2uvHXioY/h5osFxFLbvdwpC8TRRLICqCM2m1eQSR/oMI5JPL 88jG5/YWj/2x/a/9lWP9p/8AP79nTzvu7fv43fd469OKOSPYPrVf+d/ezkbSXQZ7ywt5fCGl x/b9VvNPhZLdXAS3EuZGPlBVZmhICZ6HIJ2sBY2eHv8AhJf7L/4RXS/s/wBr+wef5Ee/7R9n +042bMeX5f8AFuzu424+atLSPDs0KQjUDGr2WsXmoWht5SwdZmmxvBUYIW4YYGeVB3ckVHfe E5d0l5YajONSOJEln2bFuTELc3ZUJ80giyPL4jbH3VJ3A5I9g+tV/wCd/exupaf4b07VtG08 +GtPkfU7iSEOLJQsYSF5CS2wjPyABSQTkkZ2muXtde8N3ujNqkPhnwytvI8XkSTSqiRI6uwe 5cw4iHyGPK+YPNzHnKkj0a902G+utOuJWkD2FwbiIKRgsYpIsNx02yMeMcgfQ4cPgWwtoo/s 17fQ3Fv5S2dyrRl7SKNZEjiQFCrKqTSrl1djvJLEhSDkj2D61X/nf3sydWuPD2m6daXi+DrL 97p8uqTw3NrHDJBbxCMyDbtOZh5qgIcAkNl1wM6nh/StE1nSftdx4Y0i2nW4uLeSKOFJVDRT PESGKKSCUz0HWprnwVptzZQWQnu4rSC3aySFHXAtGSNHt8lSdjeUpLZ8wHOHA4q54Y0+80zR TBfiBbqS7url1gkMiL5s8koUMVUnAcDOB0o5I9g+tV/5397Hf8It4e/6AOl/+Acf+FH/AAi3 h7/oA6X/AOAcf+Fa1FHJHsH1qv8Azv72ZP8Awi3h7/oA6X/4Bx/4Uf8ACLeHv+gDpf8A4Bx/ 4VrUUckewfWq/wDO/vZk/wDCLeHv+gDpf/gHH/hR/wAIt4e/6AOl/wDgHH/hWtRRyR7B9ar/ AM7+9mT/AMIt4e/6AOl/+Acf+FH/AAi3h7/oA6X/AOAcf+Fa1FHJHsH1qv8Azv72ZP8Awi3h 7/oA6X/4Bx/4Uf8ACLeHv+gDpf8A4Bx/4VrUUckewfWq/wDO/vZk/wDCLeHv+gDpf/gHH/hR /wAIt4e/6AOl/wDgHH/hWtRRyR7B9ar/AM7+9mT/AMIt4e/6AOl/+Acf+FH/AAi3h7/oA6X/ AOAcf+Fa1FHJHsH1qv8Azv72ZP8Awi3h7/oA6X/4Bx/4VYs9F0rTpjNY6ZZ2srLtLwQKjEdc ZA6cD8qvUUKEVshSxFaStKba9WFFFFUYhRRRQAUUUUAFcv8AEK1e88HTQx2/2jN3Zs8ZtGug UW5iZi0S/NIoUElR1ANdRWP4o1z/AIRzQZdT8uB9ksMWLifyI18yVI9zvtbaq78k4PAoA5e0 bVIV0nT9LM9lpMsTC6l0/QvsgtXMpERjhmBKbyXEhKy4Cq2Iw++uX0TUvHGm+G4LK3hvkW30 RktY59Pl8zelqSMKLfaJFnUxrvkG5FH7tiyyP6Rp3i3TrmCxW5vLH7Xef6iOxna6jm+cofKk CL5m3GXwP3Y5bC4Ylt428O3unLfWmofaYW2eWsEMkkkm8EjZGql34V87QcGOQHBjcKAc3Dba /N8Q4BqF/qSxw297Zw3VtYKsTF47WXdkxuFG5pAu5iP9HRSWbf5nJ+G4fGOi+DpJ9ObVUk5j FrLpe2QumkDBIZMlVnijjTAGSCGMhYY9I1nx5o2lQRvBP9vkkltkCWiPNlJnQbwUVs4WRWwO u+IcGVN1weMNANu9wdSjSJHVd0iMm5WBIkXIG6Iqrt5i5TajtuwjEAHNnUfGFh4qmtLq48yy WJ2ScafNLG6+SX3rHFEcMJcoEacMUUDazssjZ9ivi46kmpsb6GR4tMjnD26SvcIb+dWy3lIF UQSb2Xy0ddybipVg3YWPi7Tp9H0S9vG+yTataQ3MUGGkx5jQpt3BezzxLk4+9noDiQeMNANu 9y2pRxW6OqtPMjRxhWBKSb2AHlNtIWTOxiMKxPFAHFyXPjG7Gn3AsZ7q+tZTOqXFt5SRXpsr wSQg4XNurmBVkJIPmH943bc8Ei+/tHWZb9tSBuL15ITc2Xki4iFvaosr8YV8LjGUyTJmNSpW PQ1bxrpmm6bfXCGSW4trKS7WCSKSHcyxGXyizLhJSg3eWfnC/MVxWg/iHTI7fU7lp5Bb6YjP dT+RIY1Chi+19uHK7WDBCSpGDg8UAcn4Og8St8MYLBxPZ6zbRW8MYkC26ogjiICM8Un8Bw+5 G/eeYowAuLmq2N8dB0NPEEMmpQQ3rPqkSp9sEsRjmCbo44U80CRoTgRcEBj90tWwfGGgC3S4 GpRvE7su6NGfaqgEyNgHbEFZG8xsJtdG3YdSZIvFOjTQX00V5vjsJTBcFYnO2UOY/KHHzSbh gIuWO5MAh1yAc+v/AAl1jpnhG1Tz2mNpBDqRbZKfOElsZC7nP/LJbv5s4JwMlimacOq+NDpc kkUF3LqAe2/cy2ShBcNv+1QDPlhoI0CtG5cBmwvmvnbXQQ+NtIeXVRNcRpBYo86zRkyrNbpB BM8oKjGALlBgZJ6jPaPVPHek6ZqdnaN57xy3b2090LeUQQbY5HZvN2GNtpj2sN3yfMWwEbAB yfiW88VXcer6ZHFd3Gm3GjzR2/mWcvmXKmzJDlUtwqTmbK7WkTgYEQJVj1mlaveL4j1GDUpJ /ss92ttpzPAYkd1SV3RUMYbhI8mQu6P1QryguR+L9DluLS2F3It1duyQ20lvKkxZSm4GNlDK QJEfDAfId/3QWFi81Gxg1m1t/s0lzqbJtQQwb2hidhuZ36RodmfmI3+UQoZlxQBz/hPV9f1L XL9b3zJdPjuL6JXa3VFQR3RjhCMD85KiYNnBHlJlRnfLqeGLTxFa/av7fu/tG7Z5P+lRzbeu 77ltDj+Hru/DvT0TxlpFx4a0/Vo7SS1sbtPOnlghLwW0zgSSB3UDjc77pcbAyOHZW4rcutZt bPUYLGWK+aabbtaGwnljGTgbpEQovI53EYHJwOaAOPjsryDwLJpk0N9cTHVbie4D2xLT2q6l umLBVCtvhZjsA/eKxCqRxWXaaTqS31ox0+7BNxA2kMYWH2S3GoTSSqDj/RwbRoVKNsLKBHgl do7DW/F8OkajBZw6fd6g7PIk/wBlUMYmW3knWMD+OVlj4QdAwLEbk3R23i/7R4cl1MWsEsi3 cdnEbW6822nkkeNEaObaN0YaVVZguVKyABivIBydlBqWkeBfE1wIruxnh8OIJ5CrRM2pJFOb iYHgu5JiJmGQ+Bhm28dZc2X9m+MtMvIYZ10y00S7hENvbbo4MSWxVUVF3bmVSAgzkR/KOuSw 8aW+oRaA0MHmf2td3Fr5kTmSFDCspdlkC7WUtFhc7SytuxwQLGga9Z6hqN1pWnWXk6fY2lvJ azqAsc0bmVB5SjpGPJwrcBhyo27WYA4u70nUmvrthp92SLidtXYQsftdudQhkiUnH+kAWizK EXeVUmPALbSWmk6kt9aMdPuwTcQNpDGFh9ktxqE0kqg4/wBHBtGhUo2wsoEeCV2j1SigDn/B ny+H3iHEcOoX0ESDpHGl3KiIo7KqqqgDgAADgV0FRwQQ2tvFb28UcMESBI441CqigYAAHAAH GKkoAKKKKACiiigAooooAK5Pxxo02uHw/aRW1pOg1MvL9tsjdQIotpxmSPcuRuKgEkYYr9D1 lZ+q6n/Zf2KR4d1vPdx20su7Hk+ZlUbGCWzIY0wOm/ceFNAHndzoOtWHiCWSC0kFpZ3EF3my hMcWxRp8ZMUYJOfKgvU8tdzBCV5Ei76+i+HvE1hoKWksccUEOp6L59obNpJXMUdgsjpKsm3Y pjbJCsP3b8+nWN8QtOHh681NU3TRWk95BDlgk6IjSxjzNu1ZHhCy+WfnVWyVwCa1LrxfodnE sk13JhnlRVjt5ZGZo50t3AVVJJEsiLgDnORkc0AcH8NrLxTDq2gLrcM62th4fks18y2MYSQy W8gGdo/5ZNFHzzvglGOCzdx4YtPEVr9q/t+7+0btnk/6VHNt67vuW0OP4eu78O8dr498N3lk t5DfyGBn27ntZkKrsRzIwZAViCyRsZCAgDrlhkVc13xBFonhfU9b+zzzLYxSv5PlOrOyEjH3 SQpI+/grt+b7vNAHLppGop4KtbUtfPPD4lE8gaFd9xH/AGoW3uAnClSJMqFHAP3cg6mq6RY6 54x8P3jaRG72Tz3DX01n8wMOY0hyyggF5jKpzg+TlQc7hoXuu3dlrcdj/ZMk0UySeSYZ0M0r Im8sIzgLF92Pezj94ygqAwY5dz41uLPTmuLjS4PMhu3tZRHfAxzOoB8u2YqGnmOSgTavzxyK WUqNwBoXNncH4h6ZegzvarpV3Ew2Dy43MtsR8wGdzAHgnpHwB82efSyvIPAF1pcsN9cXTahd yuZLYkywjUGZnkVVAdWRtxiXaZULKnXI2NQ8WXGnX9xazaV8x8sWuLgMSXnSCMzYB8lXd8qQ XJRJDgMhSq8PjrzbiOP+zsLFLFb3zefkxSSXUlonlDb+8XzYnyW2EJtIBJKgAp3emX03wa1a wiju4rl7K88iGKL52QmQxokTKTGjLtVYiN0asEzuXNE9tc23jyW/ezuy8d6bhrmO3d86cLHb 5QdQdw+0/N5AJYt84T+Kus/tm1/tj+y/KvvtH9/7BP5P3d3+u2eX0/2uvHXisvUvFhsbjX7e DTLu7n0myhuVjiikLXMkplCxoAhJGY1BddwG45xsagDL8OWF5B4wnmktJ4pP+Jh9vneMqJ99 yjWeXPEu2AOBgt5Y+U7ScVzemXnxBkS3W7vtZzcpbLIzadCpgLtp5kZf3OAQJ7wfNkARHIyh NdpD4pvHg0ueTTIGhvJVgkaC7LssjOybI0aNXZkClpQ6xmNVb7xVgM+w+If9qtDbWWnwPf3f 2d7WJrzCCOaKWaPz2CExSeXA5KBXA3R4YhiVAKejXXjCfwVL9uudSXVJL3T0WZrKNZY4pVtT PtTy9uEMk4yVO3ac/dNY9jefEG5t9OiuL7WYXd4WnlGnQhgJBp+9TmHACefeEcZHlncTsNdh pPjiHWbyx+z2Mi2F68cEc0kgEome1F2AYwCNnlHG7eTv424+atBvF+hqZ1F3JJJC+xooreWS Rm8yWPCIqlnO63myFBwIy33eaAOXvbrxhc6T4S8m51K0uLiyhfU3hsoywlaa0R94eNghCS3D YAGNhJ4UisvT9R8fT3mmT3NxqqRt9j+0QHT41Q5GniXcTFuGfPuycEY8o4wEYV6B/wAJVoXn +T/acG7yvtGcnb5Gzf527p5OOPNzs3fLu3cVn3Pi3RrmVbK4j8yxuLS4a4W5gdXUo0CeS0DL vLOLhcKRlsrgNvFAGP4utIPEPhnRLq3F9rCrF9rsA9lFNBe3BixCLlDHlFcO3zARouWJZCEo 8SW91f8AjDTZ9PSeSYS2wUy2E4NskVy3nmOYqEj8yPcGJYb0iVQHEqlesvNfs7G3tZ5odSZL lN8Yh024lYDAPzqiEoeRwwB6+hxT1XxP/ZniGy0r7H5n2jy/mMu2STe5T9ymD5uzG+XldiEN 82cUAY99Ypb/ABHTW/sd9czxxbJmmsVljgtFhkbfbOib/MMrbCm4uwdvkKhGEfhyxvYvH9/O qyNaBLtbm4ezlgeeR50eHe8gAlCJ5kabNwVUzuAlCLoXfjZYjfxxWkayW+ptpyPezNbREpbC 5eSRihKIFDgEKwOFOdrZEl/4yS38PaVrdtZeZa6hEk6JPOsUjBkDrFGnzGS4YH5YxwxVgXXj IBzenaXd2GjeKrbSLTUlvNRt5DDeXNokF2+oOs7ujyxqilEOwpJym6QhZCMAWLq2WD4d+NPs 1nJZaPMkzWkLW7QeTbfZo1lZIWClSHE7BCEDtzkB99dZqHinRtK1E6feXmy8ESzeQsTu5Riy qQFBzlkKDHVmRfvOgYXxToz+WRefK8TzOxicLbou7cZjjEOCjr+82/MjL1UgAHJpP4osTcWr pd2Wj29wLcSWNijyW8IkumQwRqjbx5f2FDhHADP0ZXK2BqfjTzX3WcgnGmLKkItlMJ1LyCWt jJuyIAdrB+QXJXzhjyzqQ+LdGtJbOyto/IsVtJm2mB4XtzE0CJD5BUOGYTptXAJ+XaDuFdJB MtzbxToJAkiB1EkbIwBGeVYAqfYgEd6APL7a+8SymPUbn7X59vZX0EVz/Z9w8g3yWRUMPs0Z Lk+ZtZYSoC5KybHB6Tw5DLF4tupZ7byb+50Swk1IOULiYNMq5aMBZGOHVmAUDy02ghsJ2FRx wQwvM8UUaPM++VlUAu20LlvU7VUZPYAdqAJKKKKACiiigAooooAKKKKACiiigAooooAKz9a0 lNb0w2UlzPbfvYpkmg270eORZFI3Ky/eQdQa0K5f4hWr3ng6aGO3+0Zu7NnjNo10Ci3MTMWi X5pFCgkqOoBoAuL4aDXtlfXGralcXtkhSKd2jQ7WcNIGVEVGDgIpBU4CKV2vljz9h8MNNPhW x03VZJJ7yC3tkMzFbhYmiRlwizIysmZZyA6nHmcbQqBDTJtSs5tEtNOikg0dkJvPsehtaJA3 nHywkUnzoJCXEhxJgKrYiD+ZWHp/iLxze+DbS/CTy+fFaSTXQtRHIm+OQv5SpHNuU4tTuEb8 zScJtKwgHWSeAbJk2QanqVomyAlLcxKnnQtAUm2eXsDgW0S4A2ABsKNxJj074b6JpcTxWbTw J5qSQ+QsULwhVdNokjRXbMcrpudmcbtwYP8ANXN67eeJtTtY4b7+0rcK+n3h/szSmdCiS2rO 4Lozhw7znyipbFuhKqAwlsabrXjqWym+120iXC3EXnJHZvI8LFJSUTfHFG8XmrAnyvJtV3Yz AbZFAOsTwdpa2eh2zee66NFHDAzPy6IY2UPgDPzwwvxjmMD7pZTXj8B6Wln9ka4vnt/3CKgm 2FIYCzQQq6BWCxu28NnzCQNzsOK5+OTxTpfg3w3Fpcc8TWfhqSe4tmsjI0lxDHb+VCe6sx3q R1K7wAGwyyX+p+MHvk0+GPUrf/SJklvIrSNlCNqECxbCVYErau5JIxyc5ZHCAGpf/DnSdRvr m9uJ5zdXdo9tdT+RbmSYtCYTJ5hiLI2zHCFUyPu8tmxqfhBJ9J8VC1l36nrtpJA084VAB5br Ep8tRlV3kbiGfHBLYAHB2l14883T7+aHUrnUI7KdVebT0HzyQWEqxNgIFQ3G+MtgkKHyV2tI m5Pq3jhtY1O2iXyP9LjSDNnLMiRfa4kVwREqFTAztIPOdu48rawABsaj8N9E1KJPPad7hZXk +0zrFdOQyom0idHU4SKJd23f+7yWJZy2wfDdmdFm0wSTiOS7e9WUMN8czTm4DLxj5ZCCAQR8 oBDDOcfWpPEUOrWlhZXV95Zit447iO2jcTs0hW5eZthSNo4tsifcVnJGJB8gy/D0er+GvCfg mwgtbtLdreMXipYB3SaRov3ciKF2IFknZpDggxKWLElZADUuPhzpd3YXFtPf6q73MTQz3Juf 30iNBDCwZsc7vs8Tn1ZSDlGZDJqHw+0rVLq6uru4u3nu7gS3Dr5aGWMRSxCFtqDKeXNIu7/W YI+f5Vxzfh6+8U6VomhadJZ30MccUJaRtPMqQW66ZwjquHLfaUf5R8/yhSVDoGyzP40s9Sl1 BTrJnvLK3EzSQK/lwrLdYKvHZk78mA7DCXC3D7lG3dGAdpa+Bl0Ozc6Fd+VfiKeGCZ7e3iSL zjDukMcMSq7L5KsAR833SQCCvSXmlWN/cWtzc20b3Fo++3n6SRHIJ2uOQGwAwBww4OQSK4Ob UfGsml3t2bidbgy28NrZ2mntH5rPbQu5WSSJzGvmbwGlTavzq5BKtF1mpz32saJI/hu9jjnW 4eFnb92SY3aORVZo3CkMp5MbggEADcHUAr6N4G0XRrKzs0ikuoLJ1ktY7oh0gdURd6IAFDkx 792MhpJCCN5Fal1oWj32owajeaVY3F9Bt8m5mt0eSPady7WIyMEkjHQ1y5uNdlvLvSlstVhj +yRtYLkAx7RBteS7LOPMEjzB0Pm7khztIOJtS/s/FEnhqzgtb+MasrgzzJMkIZcNxua3lDH7 uSI03EEgIPkoANd8BeGfEl0l1qekWks+8tLKIVDzAxNFtd8biAGBGCCCiEHipP8AhFVOnXtq dWvma/3G8neO3drhiI0yytEU4jjEeAoUqSSC3zDH8Y2F5c+HtOhltJ7vU1iKrLHGZ8XGwAbH TYsMxblLlo9keGJC7tpr6Hb3Vl48vrrZO9n/AKTFc3H2CeOSeWS5jMG9mUeasamSNSu5Y0Qs WVZAqgHUQeG7O3i0xFknZrC7kvVdmGZZpFlWRn4x8xnkbChQCRgADFR6V4Q0HQ9ZuNU0vTLS znnt0t2W3gSNVVWZsjaoOWLDdzzsT0rg4tNt9Ms9SjfQL6/t727jeWbUNKMkhu3M7zfaI7dB 9ohjypTCshdwFkAAaP0zSY1h0axiSa7nRLeNVlvAwncBR80m4A7z1OQDnOQKALlFFFABRRRQ AUUUUAFFFFABRRRQAVT1TTYdWsGtJmkQF0kSSMgNHIjh0cZBGVZVbBBBxggjIq5WH4pgmawt L21ikkudPvYLlfLUuyx7wk5Cj7x8h5htAJOflG7bQBnz/DzRZrSWyR7uCxe3MK20cgKo5t/s vmgsC28Q/JgsV7lS3zVJL4D0uXVFvvtF8rJKZUhE2Y0JuYrpsKQcZli3E9cOwzgIE5/RD4l0 xILK9W+tmMTS3ot7VZUUSW5mmuUcIwa4+2M6iIEjb0iIw1R3+teOodG0+c20kN+zyG+j+xuY 4JgsZjiVYY52kgYF2ZlOSRjzIj+7ABqSfCvQJLC3ti87Nb7RHLNFBOVUQQwkbZYmTlbeMk7d 2QcEAkV1GoaSmqaHf6Td3M7w3sU0Mkg2h1STcMLhcfKGwMg9BnJyTycra1pngqddNiu49Ql1 i98qKOA+ZIr3c7KA5jkSIMCp8yRCmDgldwdaaXviWC/kg0+O+iuI/wC0p4rR9NVbO7fz7nyF MixjYxwjs7SJuGzAYyMygHUXPhY3Gp6lex67qtt/aEXlSxQNCAg8souxzGZE2klwA+A7McfM QYz4Pil0ZNKuNX1KW0CNA0Y8mJXt2UK0BSONUCYUYYKHXJCuoJFV/Bt/qt7b/wDE3nuzKryr EslpIm9AIjukZ7eH5wzMF2qoKk8OUZhh6crT+GfEJ0Sy1XTtS1G0eK1tGs7i3Mc3lSlJHlkU Brhj9+bdjIjXcxCu4B0g8HxZ1ASavqUkV5cfa/Lfyf3M4kWSORWEe9ihRAodmXaqqQQAKIvB WmxTQSrPdna6SXKl1xdyJM1wjyfLwRNI8mI9gJbBBUBRjwRwWXhXX3j0O7k0eW9Q2GmR2ssO YikCMDAq70iMolZ12Hcpc7HDYaOa2tT4e0O0e1vr/Sba7M+oW8ulTqjQulwERbZkLGNJvL2x AMUVY2PADUAdh/YWj/2x/a/9lWP9p/8AP79nTzvu7fv43fd469OKjudDhuL29vFuruC4ureC AyQSBTH5TyOjLx13SHIbKsAAQRkHj9VtpR4V8MabrtnqUuofYlW4vorea8azlVIw8i+UH/0j cT5ch+787Bjyj2Ndtrm+8Ww3MFndu4exSwnNu48gxXbm9w5H7oNDtBJKiVcBd/SgDYtvB8Vl qVtfWur6lE8SFZE/cusxeVpZWO6MlDI7ZbyygO1QANq4ji8EWsW6calfHUvNEseoBIEkiI83 oixCI5+0T5LISTKxJyFK4eqXXjBfGsn2S51JdLS9iRYUso2iaLdYBvnMZbBE90SQ3HlHGNjU eBbrxhNq0H9v3OpS28tkXdLqyjiVJfJs3GCsakHfNcrgk/6sjqpNAHSWHg7S9Mvraez8+OC1 2NDab8xrKsIt1lyRvLCEBMFtuOdu75qjbwVpq3E93az3dpePcfaIrmJ1ZoGJlLBA6spBNxcE 7g3+uOMBUC8Ppl58QZEt1u77Wc3KWyyM2nQqYC7aeZGX9zgECe8HzZAERyMoTWxo114wn8FS /brnUl1SS909FmayjWWOKVbUz7U8vbhDJOMlTt2nP3TQB0A8EaQoeNWuxbPZLpr2wnPltaLG UWEjqQCzMHz5gLEb9p21Ttfh3p1lZvb295PFuinhLR2lmgKTGEuDGsAjbIgUfMp4Zh/d28nY 3nxBubfTori+1mF3eFp5Rp0IYCQafvU5hwAnn3hHGR5Z3E7DW42oXGpfD+1XUbmea/e70m31 SCeER+W8rWvmwlQq5VlkJZTn/WMvT5QAdRL4U8P3GnWNhdaLY3drYRCG1S7gWfykAAwC4J6K v1wM0X/huz1HVotQlknVh5PnRIw2T+TIZId2QSNjsWG0rnOG3DiuL1XUvHVnELuGSR43vb1F Q2T/ALpY52FurrHDLI6Om4lgEyqR4dGJMljWNV8Xxvqz2CXb2iXscSzrA0ZiiCybvKjNtJIS GEIL7Z0cOWXywG8sA6i88MxXIvvJ1C7tXvL2O+leNIZMOkcaKAJY3AA8pGzjIYZBHSo7jwjZ z6THpK3l9DpscX2UWsco2G28tUaA5UkqQmd+fMG5trqDisfR4PEF1o/iCaMfZdWvLu2mSRQ1 sjH7JahynnRSELlXX5oyeCDggkWNf07Wbj4VazY3U08urPp9wCYGSd5ThiqAiFA24YXiNTzw c/NQBqav4U03WzqJvRI4vre3gkU7WVfJkeSNgrAgkNISQ25TgAgjIOenw90ZreztbsyXVraW 88EMBhggVfODrIw8mNCCyuVIBCng43ANVfVtGsT4xvtcfQ5Gex0eRmns7fFxePLldsci7W81 Eg2jDZxOACv8VfT00+LwVqkY0GS9R0mvBoraXNDbBo1QiCJJYhwW2kHb87mRwo5VQC5a/DvT rKze3t7yeLdFPCWjtLNAUmMJcGNYBG2RAo+ZTwzD+7t6iwsbfTNOtrCzj8u1tYkhhTcTtRQA oyeTgAda871jwvr2j6F4f0nR7e0u9N0t9Pwkcz2zS3C3KGSWREjcMhA3HLYXdI5DlUI1NVvf F0Ouamlsk/8AZsfzK8cCOUhb7Iu+Pgl5FAvmCYY5C5UhowwB3FFcPaaj4le/0lJvtwsZN32u b7Cu9UE5FsxGBtaVP9aAreXgfJBncMOxv/Ft9cWM2s28kc9letO2LGeYWzmyu1cALFGJIg3l hQjSEk481tyGgD1SiuH8Py6i+v6DLqSzx3lzpV8bjzmXfKqXEHkl9scfRZGKgorL5jAgMWz3 FABRRRQAUUUUAFFFFABRRRQAUUUUAFY/ijXP+Ec0GXU/LgfZLDFi4n8iNfMlSPc77W2qu/JO DwK2Kp6npsOq2qW87SKiXEFwChAO6KVZVHIPG5AD7Z6daAOPuPiVDZ27SzWtpchbdLjzdNvh cQsuLp5ArlFyVjs3xxy7BTtAL0WPjqFdcg0C3s9GsII38iNLnUhbsyrdT2wWCIRkOcW+duRj eq+9dBq3hLSNbvzd38Mkpe3a3kjEhVXUpIgJxyCFnnUYI/1pJyQpWvZ+D4tO1JL6x1fUoHZF W5RfJZboiWWVmfdGSCzzSk7Cg+bAAwMAFzSfFOja9FFLpd59qSXaVaOJyMMrMGPHyr8jruOB vVkzvBWo4fEsNz4qTRbe3kkjNvPK15kCPzIniR417sQZfmPQEbclgwWn4R8I/wDCN6Tp8b3k 73sVpb29wRL5iOscbDy1LLnyw8kjjowyFyEAQWH8GaIPEMOvWtlBZ6lH5hM9vbxK0hd0Z2Yl CSxCMu7rtkkwQTkAGXZ+O5tRCQ2mlRm8uLhYLdHuiI0YxyyGOdwhMU6LC++IK+0tGMkMStzT fFN5qmsaRFBpkC6ZquntqEFw92RMsYWLIaIRlQ26ZRgORgE56AkPgWwtoo/s17fQ3Fv5S2dy rRl7SKNZEjiQFCrKqTSrl1djvJLEhSLDeG/7Ns7geH5Psl0+n2+m27SNvS2jiLhHAIJZlErH DHDbVBK5LUAV9S8XfYfGUGgf8SqPzIoJd97qXkSSebJIm2GPy28xh5fTcMllHfNZZ+Ik1v4Y tdUv9MtLSe+t7e7tEe/PkNDLJDGWkl8sGMxmdCwKkYI2lvm29hHpsMWs3OqK0nn3FvDbupI2 hY2kZSOM5zK2eew6d8Ox8C2FnplpZS3t9d/Y/sy201w0e+GKCSORIl2oFClok3HG5sDLHau0 AjsPH+kSW8baldWlrJI8gieCY3FvcLGIy7xTBQHRRINxIG0pLn5Y2atTWdc/szR3v4o4Aqym JpNQn+xwxYYqWd3UkKSMKVVtxZcfKdwx9S8B2+o64J2uJ006WK7FxbxzFBmf7OHRVAx5biKU tnnfKXBDYZegvNMlu7MQjVL63mWVpY7mEoHTJPy7SpRlCsVAZW6A/eAagDH1jxj/AGXp2l3n 9mTj7dF53k3TeTIMBT5Crg7rlt2Eh43FH+YbeSDxTeX+rajplhpkD3Vtv2JNdlCuyQIfPAjY w7wS8XD+Yik/LUlz4K025soLIT3cVpBbtZJCjrgWjJGj2+SpOxvKUls+YDnDgcUT+CtNuE1O N57vyr63urdYw64tluW3TmM7cku4DfOWwRhdoyKACHxRM3w5PiyewjR/7MbURaJcFht8syKu 8oOSuM/LwSeuMnHtPHUNta2dra2ejTI6NDbLpGpCe3jZZbaGOMsI12DdcrnCnaq5AbOB0l/p NxrEWtWN/c7dMvrT7JFFDjegZWEkm4rwx3hQvzAeWDn5iBJqmgWOr3tjd3SyGSzfcm1sBhvS QK3t5kUT8YOYwM7SysAce/xThlltxb2+m20Fwhkjm1fUxZqV8i1mAyI3G8i6xt/6Zk5OeO0t 9b067vJLS2uPOmil8mRY0ZgjYY5JAwFyjru+7vRkzuBWsODwDZae9tJpOp6lpssCSRiSAxOW jZYVCESxuMKlvCoIAOEySxJJ1LDw+mmajcXNrqF8sM8plNmzq0KkmR32grkbnlZyc5yqgEKN tAEcnie0g1nVrG4SSGPTre2leZkf940zSKqIu3LnKKBs3bmfaBuGKx7Px811d2MD6PJbvcXE kDQy3CmYlbiSD90qgiUp5e+XDARowIL10Fz4e02+vb24vraO7S8t4Lea3uEWSIrE8jodpHXd ITznouMY5x7D4d6Dpc26wjktonuBcTwQqiJcMszTQhsLkCJ2+XaV4Cq24ACgCx4R8Vr4st5b uGO0S3KRywCO5aSUo4JBkUooUjBU7Wcb1kXdlDWOPiFNLJqEdva6NLPb3v2KO0Grn7QW+2La h5IxETGhLbs5bgqMHORsab4XbRL+0OnXcgtERYpfOKswt4kdYLZAFHyK0rPvJL/KASwbK2J/ C9tNoUulJd3cCPem+WeMoZElNz9pyNylcB+xU8cHPWgDD1P4g/2deW1jLDpVpeHz0uv7T1T7 NDDJGIW2pJ5beZuWdHXhTt6hWBUdBZeJtOvfKjBniu3iilezkhbzovM2fK6gHDL5ibx/AHUt hWBMcPhTTY7hLiYSXUht54Lg3G1/tfnGLzGlGMMSIUUAYUL8oAUKBHYeFE0y8S5ttY1Xd5Ua TLJMri4dREplk3KcyMkCITxgFioVmLUAWLzxDb2PiGLS7hfKjOnz38t1KSkcSRvGv3iNp++S fm+UKMjDA1z9x8RPJa4xo08aw3bQqLmTynnURQyhY12km4kEw8uA4ZtjZKEFR1F5o1nf6jFe XSebstJ7MwuA0ckcxjLhlI5/1Sj0wTkHtz//AArTw3HPey2dt9g+27o7lLOOONZLd0jR7fAT iNvLVsjDhixVl3GgC5onitdc16+sYY7RYLV5ogftLGdnik8twY9m0AHnhyQrxFlXzBXSVj2H huz07VpdQiknZj53kxOw2QedIJJtuACd7qGO4tjGF2jitigAooooAKKKKACsfxVrP/CP+F9R 1NXgSaGIiA3BxGZmO2MOcjCl2UEkgAHJIAJGxVPU9Nh1W1S3naRUS4guAUIB3RSrKo5B43IA fbPTrQBy+neMpotNv73UTHqdnb3DlNS0q2K25tUijeSYlpGBCM7qQjMxKNtUlWxYvPiHotiX aZLtYPs7XMN00YWC4QSRRApIxC4Z5VAJIGBvJCMjtc13whYa/q2najdSzpNYSxyxhBGwJSQS DG9WKZKjJjKFhwxIC4x9C8EXEMF8mqTeTHJFaW9lBb3An+xpbO8kJSQxJu2s4wHV/ufMzhiA AdBY6+ms+HI9X0WD7Z52ViiMqqN4cowZwSu1WByy7shSV38AyeGtSm1nwrpGqXCxrPe2UNxI sYIUM6BiBkk4yfU0XOhw3nhq60O5uruaK7t5YJrh5AZW8wEM2cYB+YkAAKOAAAAK1KACiiig AooooAKKKKACiiigAooooAKrmws2s5rM2kBtZt/mwmMbJN5JfcvQ7izE565OetWKKACiiigA ooooAKKKKACiiigAooooAjEEK3D3CxRid0VHkCjcyqSVBPUgFmIHbcfWpKKKACiiigAooooA qaiT9nQA4zIo/AnFUFSGSfy4llYKSHfzHCj2B/iOeOOmDnB4OleRPNCqoMsHVvyOapfZbkXH npaxpIfvlZcbxjA3cc47dx+JznJajMC/WW81iHTsu8bWd1MsQuXt98kckSoDInzKPnOcZ+hx ijTdO0G/EvlDWbe7t8edbXOoXSyRE5wSGkKuuQwDruRirYJwat6lomqyXSX2nvEl0tvPAA8g Tb5rI24NtcZXYMAoQc81X0/Sdc0y3NtZ6LottHI++V01CRnkcgAu5MOXc4GWY5Pc1rG/KrGb td3Nvwtcy3vhPSLudt009nFLI2MZZlBJwPc0VPoun/2ToVhpvmeZ9kt0g34xu2qBnHOM4ood r6FRvbUv0UUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGfD/aP/AAkN75v/ACDPskH2f7v+ u3zeb/tfd8nrx6d60KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igD/2Q== --part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="test2.jpg" /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRof Hh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwh MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAAR CAMyAjgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiuf8VeJ/wDhGf7E/wBD+0/2nqsGm/63Z5Xmbvn6HONvTjOe oroKACiiuf8A+En/AOLh/wDCKfY/+YV/aX2rzf8Apr5ezZj8c59sUAdBRUc88Nrby3FxLHDB EheSSRgqooGSSTwABzmpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPP/in/AMyV/wBjXY/+ z15Z4mvV/tnxPrE2ralH4/sNdS10O1UNu+zbsRoke3DI6M5PY4XP+sO/6LurCzvvI+2WkFx5 Eqzw+dGH8uRfuuuejDJwRyKjk0nTZtUh1SXT7R9QhTZFdtCplReeFfGQPmbgHufWgDwv4kTa dceNPFr+JdXvrK60nT4J/DCI7RDzCFZnjwvzN5oVSc5xu/55goeIm8a33ia1l0iXyNfk8CxS 3xkjZJ/9bukWJVX5Zi2FAwMZOMHBHul5pOm6hcWtxe6faXM9o++2kmhV2hbIOUJGVOVByPQe lSfYLP8AtH+0fskH27yvI+0+WPM8vO7Zu67c846ZoA5/RLrwsnw1gvLG3gi8MjT2laEoJFSH aTIrqN25h8wcfMS27OTmuP8AAej67beI4bnQ7DVdB8Gjdv03WLkO8nyMB5UJVngxLlmy/wA+ 4EccV6hY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJP41YoA8z+IWl2mtfErwFpt+kj2lymppK iSvGWXyFyNyEHB6EZ5GQeCa4j4dRGHVPhbqS3F213qNvqdrdO9zI4eGHd5Ue0sQqLgEKABkA 9RXvc1hZ3F5bXk1pBJdWu77PM8YLxbhhtrHlcjg461Xt9C0e0+x/ZtKsYfsO/wCyeXbov2ff 9/ZgfLu74xnvQB4R8J7fU28fWGq3N7pUWpXn2/8AtW1jEzX0nzkt9oj2mODEoQj/AFeRx8xO Kp+CfD+n3ifDdJvtezWrfVrXUFS9mQTwxs7LH8rjam7JKrgEkk5zX0Ha6Fo9jqM+o2elWNvf T7vOuYbdEkk3Hc25gMnJAJz1NFvoWj2n2P7NpVjD9h3/AGTy7dF+z7/v7MD5d3fGM96AOT+D E81z8JNBeeWSVwkqBnYsQqzOqjnsFAAHYACu8qvY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJ P41YoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKxqKANmisaom3fa48fc2Nnr1yuPb168+negDeorJj ieVsIPqfSnRwwQS3BubhFVXDDcxXC4Uc59+OOPxzQBqUVWgeym+WB4JCOflYMapvrNtY29sk 77pWQBghyQQBnO47vz59aANWiqy6haNEJDcxKCM/M4GPrzUN+YLmzR1McimeEblIP/LRe9AF +iqklip5jbb7HkVQngmhu49w/d7GyRuxnK49vXrz6d6ANqisaoot3mT7um8bevTavrx1z04/ HNAG9RWNUVvu8s7+u9/XpuOOvPT8PTjFAG9RWI27dFs6+bH69N4z056fh68ZrboAKKKKACiq 9/u/s662ff8AKfb164P93n8uazqANmisFt32uPH3NjZ69crj29evPp3qWgDZorMhtnmG4YC5 6mluVtYLacGaPzhGcB3Iwceg5/Ln0oA0qKrNfWKKA11bqp4AMigVUntYhrEEjMFjEEpYHgD5 o+/YUAalFZtprNhdROVdYo1/56FVBzn3qwYILiMSQsuG5Docg0AWqKybyzmSNTF8/wA6Zxnp uGenPT8PXimUAbNFYK7vtcmfubFx165bPt6dOfXtUtAGzRTIf9RH/uj+VPoAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAxqKKKACmZiFynm3CQqEbO8kA8j3x+fPp3qVADIgbO1mC5A9ay9WiNxdiMYym UXI6DPrQBm+MvEhXQbiCwR0DRMpLBfUfWvEUu7lbqSWVgPmOTxzzXu/iTQ4/7H+zkgSSghiM Y6j2rzHUfCEigrE0e7OeWxnn6UAZw1O808CSKQFQDgrtPH5VQ1XxpcX1rKgiZWBILFgc8Y9K 6ddDaHR3ilKl9rhfmBIrlU02eO6ZZ0R1VjgHB47ZoAj8Ma3cTJcLdHCq4CZK4xg+1ei6Trht ERmJIWRWGMcYwR/Ks2w0Kymi3ImAOSrbcdPpS6pYfZ4CsT7eQTg9sH2oA9O0Dxfa6rI0DkrI CPmZlxyD/h+tdFkNPGwYkFG6A4PI5z0/Pn0714lpWoW2ioJ3Ekkh2/dI9P8A69ej+H/FWmao kDPKIJooWU+eyrwSOAd3+yOo/rkA6Ke0WT5kwr559DWYgIkmBJzv6EEY4Hr/AE4/HNbEc8Mu PLljfI3DawOR61A1pG73DKAsjSBidpGTtUc569ByP5g0AUajh4Q8k/O3UEfxH1//AFenFTSR tE5Vhz/OoLdFSMhRgb3P3SOSxJ6//qPbigCTq8XJH71OgJ/iHp/+r14rarEZFdogwyPNjP3S eQ4I6f8A6h34rboAKKKKAIL3mxuBkj903IBJHHoOfy5rNrRv0WTTrpGGVaJwflLcYPYcn8Oa zqAI2wJ0JbACNnOQOo5Jzgfj+Heqd/4n07RX2n/Spim4GJ12DnGM568enpXH+K/Fji6Fvpgf 92JIpJAygPlhwp54+Uc55zjHrgXBu7+Fzs2OVOGLjg5oAf4z+Jc15LPBaxvEkY+XJVsHC+3P IrzW/wDEl/K32gHMufvYXk49MYruLbwlcylpbhkJbJJ3f/WrSh8MQeYA6RsBkhW2nt6YoA5L QotTuNMaRnQlsOOnHy/St3T9ck0yB4JdzO+BlQMdOf6V6LpOi2b2eIGhUrklQyhuF9Mf55rg vGOkTR3ckkG3aVG3nocHn9KALEV9c3tsiQY8s4ZuRweap3GsTaYEaOTG0DnAx19/el8NPLZ2 +LlVfheCRjv/AI1Ffaf/AGlcPDGAF+XjIxQB1GifFTEUUF8jsq7FDhlUDDDk4xgYH+OelenR XdhrEJexvbe5CfxQSq4GfXB9j+VeB2mm2EM/2a7V8YAJjC9QcdxXUaW7eEZsWtzNskAyiuCp AOemMdz+dAHpLRtFdyK2fuLxg46nkHp+XPr2paltpbbU2eaNoxM0aggHJADN36d/TjPPUVEQ QcEYIoA1of8AUR/7o/lT6ZD/AKiP/dH8qfQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBjU6NDI4RcZPr TQCTgDJNTy2Nw9oywyeVMxHzBsYH1FAFq2ltmXy4JopCBk7GBP1rM1nclzCYP3TkHMiEgkEj IyPoOv8AQ1iWmj63pczzCQ7ABz5oOefepbuW9iKS3iFmZSRkg4BPqOn0/wAKAG6gLqRdk8x3 rkYZ+OtcxeOYJzJLkBG65zkZqC7168u5fNLsijlvmz1P0pmpTN9jYyElXB5z6GgCvqN080zv BIr7ic/vFzjjtUkbW1lI1xKAW2FgpxzxnvXmmseJLjTpZTDuY+Y68NtwAR7V6VZLb3+k2l3f yYLKCAXweVBoAoWOvtLqsxZG8l5GCpkYAAP5dqm1PW4LjUYre2UrkDnjrzWrJ4envpxBp6AM +7JMmM4Gev51gf2CbS/S4nZhLHg7Q4K4x1oAfFEj3qwOo+Y88D0rXi0aOEBopm+7yA4z1+lZ 8cE00yyQOHIYZw3+Nddo3h2/kmgkmUm3lAYtvzhQRn6Hn9KAMtddltPJSOW5RkUDKydVHatz QfHlrc3M6XEMqtIQ+Rt64C+vsK09Q03SooXhhJM+4ZDM3Y/lXmUkEttqe6AKoUYY7wf4uRig D2K/v4EdEWNZg3V1cfL/AI/SqlqoWDhSoZ3bkEZyx56n/Pp0rjo9SuJ/LKT49c/Wu/0+NLjT EHy8M+GUf7R59/60AVWRXaIMMjzYz90nkOCOn/6h34rbrGmQxSxqybyJo+Nu7+Ic49uue3Xt WzQAUUUUAV79Fk066RhlWicH5S3GD2HJ/Dms6W3aeym2TeQw27ZC23ByO/8AnrWjfnGnXR2b /wB0/wAuzdu4PGO/0rh/Ger3Ni1pY27vHvjMshVvvZbA4/4D+tAFu38LNbwFma3mOxiGGWHB 55xj/PsaytYe306DMgRWIJVBjOPYV1ekagq2VvJcKFj8uTbMRkt84BHqPpjnGe1YnjC0t9Zl jlgzIyRFVwdozn3oA5u0u/tluWjJUEEgPgd6y7/WEs3lRfMEikqTkenatSGzuLYMZ/lxkY3c Vw3jrWUs1UMS772xhiOcdOlAHcaFrSJbbvtAikYg73YDgr0z+dWNbvYLpFMZj3bACARknHXi vPNGXUNQs4Z2URRED5S4OflB7fWrOuT3emTI/mHbgcA59TQB0EMSwWzzFQw464PeqV3qUtpb W8sZIBkGQuBnr/hWpoF1bappUSMfn2gnk9efamtBFFcuspDRDBxu5oAuW+hpq9lb6irLExUG RWcDJLcEcc+9QakyyTrbwgtLCpwxI+b5vWrSXMl2kdrZSH5QAqq2Mc967TRfCn2cGTUPml4w qOcDk9ePpQByvh29vlucFnRsFWIcc/lXeXEqtMjKpCy5K/h1rFv9Bv5tcWW1Vo4I1OT5mN2d 2MVs21rcrakXaO8kZLJI8u44OMj+dAGrD/qI/wDdH8qfTIf9RH/uj+VPoAKKKKACiiigAooo oAKKKKACqeqalDpNg13MsjgOkaRxgFpJHcIiDJAyzMq5JAGckgZNXKz9a0z+19MNqJvJkWWK 4ikK7gskUiyJuXIyu5FyAQSMgEHkAGXdeMra0Rnk03UgLe3+1X+Y0U2MW513yBnBcZil/wBV 5mQmRkMpbL134iJY6Hql5ZaVfPLbfa4LeWaJVhkuYPM3Ly4ZlCxPIWHBVSoPmfJVzUPCN9qC XSyazGx1KyFjqbPZ5LxbpTiDDgRECeQAt5nATO4hi0eq+Bf7T8PHSf7R8rN3qFz5vkbv+PpL lduN38P2nOc87O2eAC5Y+LxqNhLc22g6y0kdw9r5BijDNMjusihi+zC+WSXLBDkKGL5UEfjK 2uCEs9N1K7mVCbiGGNC1s/mPEEfLgEmWKRNyllGwszKmHqu/g6abwxHpdxe2lxOt7PeyedZF 7WdpZJJCkkBfLIDLkDfwyI2TjBr6d4Daxi0qI31oEsbiW4H2fT1iMRedpitu24mFG3CN1JcN GoUbckkAj0zx1cXej6NeXFl5ck2nwXd7GqgsXnYRW8cQ34HmybirMx2qmHClsruR65N/ammp NayW9pqKSRRrPGUlhuo9zGNsEhgyLIQw+X90SGcSJXP2Pha+0hLbSZLmO6judHh0s3Yst0Sm 2Z2RZYizZSWOV1bLKP3ZAZWkTboW2iTW1/4f0xPMeDSnmv5bjyykQZ0kiSCNTwqATSbVBby0 hRT95WoA6yiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDLs3ge6MbO pkXGEB5B61b/ALQtDOIROpkJwAOc/jWBpGl6g17JdzXDxlsZYAAnggYBXFaI0NVvvOSZ1UHK 4Iyvp1Hb3oAuXOqWNou6e6iQf72T+QrGs9atNTIhvFQAqRv65JYYGRjb0/HHbHPFfEBby2v5 ktJJsDYU7gZ5x0rl9I1zUVlCzCTMZB6AbsH6UAeu67o9qYw6PFEURjteQgnuMVx8lojpL50u UORgv64rhNY8a66L5Yp5bo7mKgs5GBn6Vfj1m4ktCHkck56tnNAFyXQtIe6kguRvjY5wZCOe D2rpF0WKSGEeXugjT7hkxtAArjP7XtEt3kuG3XCg4+Y8np6Utl49KytFLMCpBGC2MfpQB2eq eM4fD9obe2b987bWcN0AXp90+vWsvSfEOm3qN9uQgu6/OHIGzvwF9Kpy6Zba8Vm+/u+b5WPB Irn/ABVC3h2yDQKVMjhFJP3cqeeR7UAeh202jaZci40wsOjK8khO0/iB6967PStXt777ODPF JOYmLEAZHzLgZ7Z9Mc49ufDtAvpbuGC3uZSxlVSHJxj5c+ldNZ3smjy+c0+xQANwb3HtQB6P 4ggijtfMiAWcyAk7ucc84r588VS39o0rR30kEpJOfMIH3uc16rL4nF8iP9sB4AHI6flXFa/o P/CVyyQK7x8Ebw2ON2c9DQBz2m+K9StrdJA/2tcndIJAB1+nNd14P+LMSXS2WqFYbYK4DMeF bOeyfXv3rDtfBS+H9DeA5nb5vnDli2SPYfyrl7/w3dMfNRHTeTj7wwOPagD6bu2imhtbmICV XkiKkLuBUsCDj9c9uvOK0K4zwRcXN74VjNxvkME8UKLIA21V2cjOOefXjHHv2dABRRRQBXvz jTro7N/7p/l2bt3B4x3+lcTrOtabe3iwAqLu3/dg7jyQ2MdMetdvegtY3ChA5MTAKQCDx0wS M/mK8l+x20/im+k89PK+0uUIfqN5xj1oA34dZt7a2nk1GYzeXExUbz97cOAQDt/LtXMr45W5 1OSCFdqbmCJnOBknrt5rTvNNtRDciWdRC4JBL4wM9zXH3X9kabK00U0buqkfLJnigBmv+JtT glkIVim4/wDLQYxxjtXDX+p3OpXqvc3sQtwxIWTC4OPpWxrWui5tJDA3OcZzkDke1YF5pcd3 PGHaYJIS37tuRx9KAPWNBt4J7eMXGqxxIVXa0kpyRt+hrpNV0W1uJUjkCS5jyGSYHP5H3rzv TtM1CMxxSNIyIoCEZxjH0rore+u11ExXEkkaqAAXJA+7x+tAF3QrSwtr/wCzxnO8gAF+eh/x rXn0q0uJwQrLhduS/bOf61ztqRZ3qTCRTIDkYbv2robfUhNIvnFvmwMhhx29KAOl0XQtN0az W9hUtLLsV3c7gBvHbjGDjntjP13BqViZPLF5AXxnHmCvP9W1aZUS3guJDAo6B+Ac55wPXnmu C1DWNTWdPs006uSRuUk9xQB7ump2Rv5I/OjDbFG4jGcFuN3fHp7988XVeOZG2srr0O05ryKw 1Mx26XOpXTQuwOd+cn5vTFdl4Yvzey7YLqSSPcSShBHHrkd+PSgDq4gRCgIwQop9IoKoqlix AwWOMn34paACiiigAooqOeCG6t5be4ijmglQpJHIoZXUjBBB4II4xQBHZ31vqEDTWsnmRrLJ CTtIw8btG459GVh7444qxXN+BoIbXw3Jb28UcMEWp6gkccahVRReTAAAcAAcYrpKACiiigAo oooAKKKKACiivmHRdF0qXQtPkk0yzd3to2ZmgUkkqMknFAH09RXzf/YWj/8AQKsf/AdP8Ko6 ToulSWcjPplmxFzcLloFPAmcAdOwAFAH09RXzf8A2Fo//QKsf/AdP8KoxaLpR127jOmWZRba BgvkLgEtLk4x3wPyFAH09RXzf/YWj/8AQKsf/AdP8Ko3+i6Ul5pirplmoe5ZWAgUbh5MhweO eQD+FAH09RXzf/YWj/8AQKsf/AdP8Ko61oulRaFqEkemWaOltIyssCgghTgg4oA+nqKKKACi iigAooooAKKKKACiiigAooooAKy9d1qPQrFLqVN6tII8bsdifQ+lalUdW0iz1qyNreozR53K VYqVbBGRj6nrxQByMnxJtjOqRxxKp6lix/oK1LHxxpdy6rLdQqWOOAwwfy5rC1L4ZKzKdKuc RkDP2iTvz0wv0/WsFPBWq2GoCKSGSbYwPmQqzIeM8HbQBf8AEniOwm1MiZ0EoVTwTgj8qxLL xRZyXoiWWI8YGG98VPf+FJNTufNJZWVAvUjAH4Vit4Nj0eYXZaTJIJ3ngHOf7o9KAKQ1a2ud Umgef94uQFD/AO1jip7u0kTDJNJtIJ+8eeazL/QJLPWorq3t3O/5pGwxH3smqviPXb21dTsw qA5B3YwDQBA+l3TXs0zySeT8xAZjgfpVGz0ie51CaOM7+WIK5PGfpU6+MRc2xt0hXEilS245 /DitrQLO8tNSWSEOSd2CwPIx7UAd74I0fUYbUM0MpAxyyt/d+ldJ4ut9LubBbOTytwKscO3X BHBz0wa4aL4pX9nqp0uNQAHZe2RtHup9K3fElo1/opmiu3SZlV8qQSuBnHGPp+NJtpXRdOMZ TSk7J9e3mc/c2VhBfW1pp0nmXIVWdVLN5a4OSSBgDoPXketXNRsxPYx2rZ83Ck8nk5+lVPCG sLZTx6VLH5EUjli8OdrHGTywPOB39PpXcz2OikLd297I8qqG5deGyMZ4Hv27du8U5OSuzoxl GNKajDa2+mvnpt6Xdu55ebC/tLSR23qAy7c7hjrwK7zwfMv2WcXOGi8vksTn72OtcdceLUlv JbO5bGG28FuzdelbFrqTx6bMIWJ3pjj61och2F9dafbwAWs2NyncrPkdfp7Zrjdc8S2X2a4s g8YlbI35b1z2FY1zd6pLKiIkhUnGcNXLSW2o32rTwTRPERK6hirLkDocn1oA9f8Ah/qEl7aP ax/PEkiPuHzAkPF2PGcd+o7c16ZXL6H4Xj8N6Ha2VvPJuMsMk6Da6PLlNxG4AgfL6jGOmeK6 igAooooAr36NJp10ijLNE4HyhucHseD+PFeOvoV5peomS6eVDISyoGYY59COlexX6NJp10ij LNE4HyhucHseD+PFU7vREvgguby5fZnb9wYz9F9qAPIvEN5ILaa2RsFkYDk5rzifTdQaYrvk PmZJyWxj8q9R8T2KrFNdFw7Qq38Xv34ry0+KbmTVHt1RVCFhhS2cAmgC2PD0trblp5RGDk/O SD268Vr+HoLIXeye7iZQhyFkz6VG+pvqUIiuLXfkHJDHNNGkNZn7TYwzM8itlXy23ODjgCgD tjreiwxiSWQqc4GWPp6baw7+6n1DVjNA2YmClNpOCNoNRJ4au7jTRdagkkeSMLhlAG3OeRSX NtHc2kcdrKu2Fdpidm2vwMZIweMZqZtqN0rm2HpwqVVCpLlT67/1+XdpEH2xXvzMt6ojjC7s uQucEcevTNbNtr0VhCN371Wwd5Y/pxXG6dps19qEEEzOsZcb+SOOa7LVtBRNIRLY7imwDBJP 8qIO8b3Hiaap1XFK3k3f8VprvoZep+J7ZCjfagqPnOGI7+netfRLmyeVovtqMxwNrP8ANnNe fWmj3+s3HlT6dMIoWAz5bjAJ55/Cuk8M6K4vJZ5vN3RL5nUgZB+lUYHZ61pJ1R4gsgUKpBG7 jrn0NdH4QbTNCtH86+jEg3bxknHI9vYV57eeJZdOvTHJndJu2qxbp61W1DxCbATymMyIytkb yvHr0oA93sNa03U5GjsryOZ1BLKvUDj/ABFX688+E32W+0K41VIis7XDRbi5PylUbHYdT1xX odABRRXP2XjLR7m6a2uJvsExu5LS3W9ZI/tbJK0TGE7jv+dSNv3h8pKgMpIB0FFFFAHP+Df+ QHc/9hXUv/S2augrn/Bv/IDuf+wrqX/pbNXQUAFFFFABRRRQAUUUUAFfN+hf8i9pn/XpF/6A K+kK+b9C/wCRe0z/AK9Iv/QBQBoVn6N/x4yf9fdz/wCjnrQrP0b/AI8ZP+vu5/8ARz0AaFZ8 P/Iw3n/XpB/6HNWhWfD/AMjDef8AXpB/6HNQBoVn6j/x/aT/ANfbf+iZa0Kz9R/4/tJ/6+2/ 9Ey0AaFZ+u/8i9qf/XpL/wCgGtCs/Xf+Re1P/r0l/wDQDQB9IUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAUrfdNB5YuZEdQRlQucZBHUdun/ANfmrJjYgjzpBkMM4XjJ4PTt2/XNZQJB yDgitC2mllU5AOM/P/8AWoAcYJCSftcwyWOMJxkcD7vbt+uaq32m29/cIs7yElGIAKjA4Hpn qc/Xr2FXSjtndKQMfwjH+NRNEn2uIh3D7Hx8wPGVz159OnHr2oA47WPCdxHZTSQOrhV4AyWP 4AV5vd+GJb+5cSkLjKkMGBHPP4179tkB4kyP9pc/yxWZeaPa6i87sg+0hlXzM4A4U9AfT15/ DFAHm/hv4Y6VczefwhUEEguc9P8Aa966LUNBTTrMXkBMqtHuXuemcHBI/Kuv060OnW5hI3At uLL/AIde1WLNo3soGiOYzGu37vTH+zx+XFAHzzq2mlpZ9SI8pg7F927OcZ4/Wr/h2/v9Yt7q TzWKphApdiCcH/61em+NPDUmqabJ9l5dnLFApJOVOenv/OsPw54SuNO08GZDEVnjGHVgTuKr 3oA88jstaW/Up5iJkZUbwD+FJf61rOkqEnjXe+3gbxgc9QfpX0E2lwyW6ow/eBFUsCewH+FZ eoeGrW8WCInNwoLK5bAOCO2c9+3Hr2oA8a0XSX182zSRFpSysSS3PzdOPXNdfpPgzUrG1nBm a4WLAYMjByxOeF57EGvQNC0G20iEOFVroqUklVjhhuzjBOPT8q04dvmz7evmDd93rtX056Y6 8/higDzd/Cl9etHIkskexiGHltnOa6PRfCUVrfx6lJcFriJmClMdsqc8kcjIx1+hrqqqLcRQ xHZ8x8x/lBXruOenH9fXnNAEF2BGII5ZpJf3sZy4Qc7+D0A7j8hjmtKsaVg8sbSd5o/TrvGP vfh7+nOK2aACiiigCvf7f7Out/3PKfd06YP97j8+KftaRNyzSIG5GAvGRjHI/H6+3FMv9v8A Z11v+55T7unTB/vcfnxVWC6aFdu3cv1oA8aUag2r6g901w1u5kykhbGC2fpXLva2d54gaC3t 0hkO8Ft7HOCTzk19A6siarEbYxHzWhfZ8w45XPHU9unHr2rzqHwRfw6+1z9nlK/P83lPjkmg DlJbPVoLsratIsCkhdhcAiuotNJ8V38M9zbx2lvFErMWuRKhcAA9cYOc13lhYPbBs2cuGBAB VhtPrXRzqFsJFkYsoiIYnHIxz97j8+PWgDyVLzUtTlayn8xtyY53H+E9M1Bovw/1OO6CzXLE MjOA8bAMAOR+o/OvX4rC2gl8yOPa/ruJpsn/ACFrf/rhL/6FHQBx1t8Pi7QyTXawqrIxWNSS yjquSRj9a2H8JQiZjDKUiJ4U8kD/AOt2/WukooA81S5v7DWZbG8tQtvuO1gjAFQ2AQT19Qfb 8KivLyz0nz5rRmLbc4bPPPfiu71nTIr6JWZ/LbKpu69WGOpA7/WsCDwLiT9/coUx/Cpz/MUA ePx6rNqfiiWWaEvIqyBG3MSMkj+RNdZc+HYF0yY3fEjoQVwwxzwa9HtPDul22qkhCZlhH8R5 yzc43Z7f4d6r+IdC/tG4ZzKihgCFOc9h/SgDW8PRrD4a0qJDlUs4VB9ggrSqK1RYrSGNBhVR VA9ABUtABXHpq0MUuqaDbeDdVuYY5ZTcRF7UpMJ2Z2cLJOC0blnxxjhlwCpUdhRQBn6JDBb6 PBFbaR/ZEK7ttjsiXyvmOeImZOT83BPXnnNXJ4VubeWBzIEkQoxjkZGAIxwykFT7ggjtUlFA Hg+oWsmm6tf2llqeswW8d3NtjTVrkDJkYk/6zkkkknqSSTVfzb7/AKDeuf8Ag3uf/jlaGvf8 jDqf/X3L/wChms+gCnZ3epS3Woo+u64VhuAiD+1rngeVG2P9Z6sfzq55t9/0G9c/8G9z/wDH Kz9O/wCP7Vv+vtf/AETFWhQBTku9SXWba3Gu655T28zsv9rXPJVowD/rP9o/nVzzb7/oN65/ 4N7n/wCOVnzf8jDZ/wDXpP8A+hw1oUAU9Tu9St7VHi13XFY3ECE/2tcnhpVUj/WehNXPNvv+ g3rn/g3uf/jlZ+s/8eMf/X3bf+jkrQoAPNvv+g3rn/g3uf8A45XbeD/hz4ZvvBOg3dxa3jTT 6dbyyFdSuVBZo1JwBIAOT0AxXE17B4E/5J54a/7BVr/6KWgDP/4Vf4T/AOfS+/8ABrd//Haa nws8IRqVSxvFBJbC6pdDknJP+s7kk12VFAHH/wDCr/Cf/Ppff+DW7/8AjtNHws8ICRpBY3gd gFLf2pdZIGcDPmdsn8zXZUUAcf8A8Kv8J/8APpff+DW7/wDjtNb4WeEHZGaxvGKHcpOqXR2n BGR+844JH412VFAHH/8ACr/Cf/Ppff8Ag1u//jtNk+FnhCWNo5LG8dHBVlbVLogg9QR5ldlR QAUUUUAFFFFABRRRQAUUUUAFFVZbiZbryY4FcBA+TJtLckEKMYJHHcdRUv2iL7L9p3fudnmb sH7uM5x9KAJaKq/b4f7lz/4DSf8AxNSQ3UU7sqbwygEh42Q4OcdQPQ0ATUUUUAY1atuQbdNv TFZz2d4FOxIC3PDSkDrx/Ce2T9eOetW7dLmGJlZIifmK/vD17Dp37+nvQBaqJtv2uPP3/LfH 3emVz7+nTj17U0tdZOIYcZbH709MfL/D3PX096HNz5oMaRlAGGDJjPAwfunvkdenPPQAE9RQ 7fNn29fMG77vXavpz0x15/DFKpmO3fHGOecOTgY+nrx9OfamRm53nzEj2kg8SZ2/LyB8o7/z J46UAT1mC4TyIBBkbUUBuOmOny8flx6VYuBeyKixpCAdvmZlPHXOPl5xx6Z9u9SKyvREokEJ cAAnzCc88n7o7c9OvHHWgDRt5xMmcYYdRUOo/wDHsn/XeH/0YtVY7W/DKSlupxyVmY4Oe3y+ nP1496muIr6W2CBLdnEivkykZ2uCB93uAfp79aAL9RNt+1x5+/5b4+70yuff06cevaml7rBx DDnDY/enrn5f4e/f096qzpqUsnyiBYxuGwTHn+6T8mc9RjoM96AI5GIndlb+I4INRwXMivOF c53/ADZCnnavpz0x15/DFSPZ3gY7EgK88tKQenH8J75H0556UR2N3nL+VywyA5O0be3yjPPr 9fagBpkkYYZ2I9Cagt9vlnZ03v6ddxz046/j685qylneFvnSALxyspJ6c/wjvgfTnjpSCyvV CjELZILEyHjJOQMLzjjHTPfHcAibbui39PNj9Ou8Y68dfx9OcVt1lx2d5lGcQowZSdkhP8XI 5X0/n261qUAFFFFAFe/2/wBnXW/7nlPu6dMH+9x+fFZ1atwjyW0qRHEjIQpzjBxx2P8AI/Sq DWV0M7FhPPGZCMjH+768fTn2oALIJ9tBP+sEbAdOmVz7+nTj17Vp1Qtbe7imV5PLCkYZVkJH QHP3eSDkduDn2qwGusjMMOMrn96emPm/h7Hp6+1AE9RXW37JNv8AueW277vTH+1x+fHrQWuP kxFFzt3fvDx1zj5eccY6Z9qbm5eIhkjjcgDKSZxzyRlewwRxyePegCeqkn/IWt/+uEv/AKFH U264348qLZ6+Yc/e9Nvpz9ePeoZY7g3MdwiRFkjkTazkA5ZcHOPRT26/nQBboqINcfPmKLjd t/eHnpjPy8Z5z1x700tdZOIYcZbH709MfL/D3PX096AHXG3yhv6eYn93ruGOvHX8fTnFS1FL 55UiMKCG4O/GRjPPynqePpz14pVMx2744xzzhycDH09ePpz7UAUr1V+1FgPm2KGPy9Mtj39e vHp3qvVq4ivZnUhYQnygqZTgf3iPk69vf2qE2d58mEg527v3p49cfLzjjHTPtQBpQ/6iP/dH 8qfTIQywRhwA4UBgpyAcdjT6ACiiigAooqOeNpreWJJpIHdCqyxhSyEj7w3AjI68gj1BoA8T 17/kYdT/AOvuX/0M1n10DeC9Tkurx9Qt/F1zcNdzkT2kuliOWPzW8tgHIIJTaTkDkngVHD4P edC6ab48ADsnzvpSHKsVPDEHGRwehGCMgg0Acnp3/H9q3/X2v/omKtCtyPwEYnmdNM8chpn3 ufP0nk7Qufveij8qB4Pdrh4BpvjzeiK5JfSguGJAw2cE/KcgHI4zjIyAcnN/yMNn/wBek/8A 6HDWhW4fARa4S4OmeOfNRGRW8/SeAxBI+9/sj8qJPB7xPCjab48JlfYu19KYA7S3zEHCjCnk 4GcDqQCAcnrP/HjH/wBfdt/6OStCtybwEbhAkumeOWUOrgefpI5VgwP3vUCifwe9tbyzvpvj wpGhdhG+lOxAGeFUksfYAk9qAMOvYPAn/JPPDX/YKtf/AEUtef8A/CETf9A/xz/3+0j/AOKr tNJ1C+0bRrHS7fwf4gaCyt47eNpJ7EsVRQoJxcAZwPQUAdZRXNweKNRubeKdPBfiAJIgdRI1 mjAEZ5VrgFT7EAjvUn/CQ6p/0Jmuf9/rL/5IoA6Ciubj8UajK8yL4L8QAxPsbc1moJ2hvlJu MMMMORkZyOoIEn/CQ6p/0Jmuf9/rL/5IoA6CiubPijUVuEgPgvxBvdGcENZlcKQDlvtGAfmG ATk84zg4k/4SHVP+hM1z/v8AWX/yRQB0FFc3N4o1GBA7+C/EBBdU+RrNzlmCjhbgnGTyegGS cAE1J/wkOqf9CZrn/f6y/wDkigDoKKKKACiiigAooooAKKKKAMu78oXwE0NrJKVG1mmCSY3N gID3HrkZPp2fBbulg7Q/Osluojt3feinB4znkHIH4emMU72/tjfllulMXkgOVaNkPJ4YE5cd eByO33q2YABbxgAABRgBCoHH909PpQBQ83/p81H/AMBP/tdXoA5iSSaNVnZF8zHr6fQEmpai t5vPiD+VJGf7si4I7/5/xoAlooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDgj481mW5 u1tNAsHggu57ZXl1N0Z/KlaMsVEDAZKk4yetH/Cb+If+hd0v/wAG8n/yPWFp3XUf+wrf/wDp XLV2gDH8SfHK98L6jHZXvha3kleISgw6oxGCSO8I5+U1j/8ADSzZ/wCRRH/gy/8AtVcL8X/+ Rstf+vFP/Rklef0Ae9H9pcj/AJlEf+DL/wC1Un/DTB/6FH/ypf8A2qvBG7U2gD30ftLkn/kU R/4Mv/tVL/w0s2P+RRH/AIMv/tVeBL96n0Ae9/8ADSzf9CiP/Bl/9qpv/DTB/wChR/8AKl/9 qrwao6APff8Ahpg/9Cj/AOVL/wC1Uo/aWJH/ACKI/wDBl/8Aaq8Bp6/doA97/wCGlm/6FEf+ DL/7VS/8NLN/0KI/8GX/ANqrwSigD3v/AIaWb/oUR/4Mv/tVIf2liP8AmUR/4Mv/ALVXgtNb pQB73/w0wf8AoUf/ACpf/aqP+GmD/wBCj/5Uv/tVeBUh6GgD78ooooAKKKKACiiigAooooA5 pYI5Ti0sEa0wS8t3EsahOxRgN3Izycnoa6GcSNbyCFgspUhGPQNjg1l29nCftEOoWVghRA5e FMDacjqRkEbTzWxQBmeV/wBOeo/+Bf8A9sq/AJFt4xMwaUKA7DoWxyakqK3eWSINND5T913B h+f+en40AS0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGfa63p17qM9hb3G+4h3ZGxgr7Ttf YxG19rEK20naxAbBOKk03VbHV7drjT7mO4iVyhZPXAI/AqVYHoysrDIYE8Pq1vH4pGq6Kljd 2MipewafFJps8cBuJI5Ue5kmEezDeZJtAYghyx3OyrH0mjJcXniHUtaks57SGe0trRIrkBZN 8TzM5wCRtzMFBz8xRiMqVZgDhdO66j/2Fb//ANK5au157N8QdL8O+Idfsbz+055E1W8+RI4j HHm4kPynIY5yM7s89MCnf8Lf8P8A/Pnqf/fqP/4ugDlfi/8A8jZa/wDXin/oySvP69hufB2o fGCQeIPD81rbWluPsTpqDMkhdfnJARWG3Ei985B4qH/hnjxb/wBBHRP+/wDL/wDG6APIm7U2 uw8cfDrV/Af2D+1Lmxm+2+Z5f2V3bGzbnO5V/vj171yG00AC/ep9IqEsOlSeW3qKAGVHU/lt 6iodpoASnr92m7TS5xxQA6im7hRuFADqa3SjcKCd3AoAbSHoadtNBU4PNAH31RRRQAUUUUAF FFFABRRRQBy1za6YskkMOnNKYU3XEkU5xD64J4Yjnj2+uOkuRutZl83ysow8zONnHX8KhXTb RLUWyRFIg2/COwO71JByf/rD0qzJGk0TxSDKOpVh6g0AZ32H/qE6d/33/wDa6vQDy4kgaXzJ I0UMSeT2yfrg1F9gh/v3P/gTJ/8AFVJDaxQOzJvLMACXkZzgZx1J9TQBNRRRQAUVgvbW8ilX giZTnhkBHJyfzIB+tKYISCDEhBDAjaOQxy359/WgDdorANnbEkm2hJJYk7ByWGG/Pv61UvpL GCdXnhjdyjnGMsc7VPHQ8YHPOB9aAOqorzGfxXsLrZ2kShTmImLv90dD128VJIgv7eRzEkbE chVxjAAA9uMcf40AelUV4tfPr0Vu9rayxvbgECM20TMBz/ERnPzNjuM8Vd0DVtVjjW3lWMBQ Bg26E/LyO3Y5P1oA9cormrq2/wCJUs0VjAjohwUhAwANwP0zk+ma4OXVvEPnFpLdZYQ3KPaJ tbJyQcAdSM/WgD2GiuM8PX/9q2xS+stlwzlB5UXybWwTnJzyc5rVn023jvUQ2qO7rI4KoMc7 Q2R6nI/WgDeorBktYHcmSCNm5yWQE8jB/QYqO3jgEkyxxIPLkHTsdij04+UgcdvxoA6KisFL a3jbckESsMcqgB4GB+QJH0qKCC2kjBEMRCOQvGdpVmAxkcY5x2HbigDo6K58QW0HkgQxInmx ABRtGfMGOg9Tn0yfc10FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUVj3PifS7TxC2iXE/l3Sae+pSu/yxxQK4TczngZJP0CknHGbGmaxb6r5qxpPBNFgvBcxG OQI2dj7TztYAkehDKQGVlUA+NvHH/JQPEv8A2Fbr/wBHNWDW944/5KB4l/7Ct1/6OasGgD6X /Z4/5EC//wCwpJ/6Kir1uvJf2eCf+EAv+f8AmKSf+ioq9byfU0AeC/tJf8yx/wBvX/tGvBq9 5/aTOf8AhGP+3r/2jXg1ADk++KmqFPvipqACq9WKr0AFMb71PpjfeoASiiigApV60lKvWgB9 I33T9KWkb7p+lAH3xRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBgvcIilispHP3YmJ4OO gH/6xz0pTMoBOH4DH/Vt2OD2/L17ZqSrdnBuPmt0B4HrQBg6leTCBo7QyJNuwXMROAOvX/J7 Vy32kRGSSRtzseSeMnPpXpOpziCyZiu7JAAIBH6/SvNtXsTduBEflznt7/40AYNxqqQzARQx YycfIDjmteLU5DZlnkUbs5BAFZd7pcVq4aWT5h82DgCuL8Z6rcKiLbgD5TyMEdR7UAd4bu1k MhaYLLjjkY/nVKbWpLK6B3I2M8FAM8e1eWWF3qkM6TSLzyCcrxXX2mrWmr3hhkjZHVm+YsOc enFAHa6z4q8QXkNsLWaJI9vzbUQc7Rxz+NW9A+3TRSTX7bmMifwA8ZHp9elcb4hZ4oopEUDq FxjuP/rVZ8JarePP5cx+YyL/AHf6CgD1eG5g0u8jIVQhAJxHtJ45wPx49e1bkWp208ymNnYh XyAr5GMH7oGD9fwGcmsAJBNbW8k7BWBVjwD29f6VNay2f9rwLA8h4wxIGCcrjoenWgDZfUrN p3tpN+9SRgxk5I9Mf5PQc0iW0TlyvmrvcbdwY/wg9CPl9PTPuSKp6nAxU3bweXsK5CkHPJ5/ lRpWrrdw3SvOnnq2VJYDPHHU46jtx+OaAHMSrY2S44+YxMByM9SP8njrVeK4TYuUmG5zjMbn qxxnI46fQcdsVTuPGo0y6aC8tHkUMf3kbgnAOOmMfrW3bbNQsRdQsrbnfaVYkMN5A5P/AOr0 4xQBVimWVoiiy/6xDhkdD9/Hpnt09OvBzW7WI4cPEFGGE0efvdN4z056fh68ZrboAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPO9Q8Da1e+INQ36laNY6pZalDP cCzIkiM4gSNT+9wxVIkAIUDEJyNz7h1Gj2d8+s32sahbx2ktxbwWgtkl83AhaVi+7A4ZpWwM Z2qpOCxRNyigD4l8cYHxA8S8/wDMVuv/AEa1YOR61ueOf+Sg+Jf+wrdf+jWrAoA+iPgT4j0L SPBF7b6nrWnWUzak7rHc3SRsV8uIZAYg4yDz7GvUP+E48Jf9DTon/gwi/wDiq+KV6U6gD3f4 3uvjD+wv+EYYa39l+0faP7MP2nyd3l7d/l5252tjPXafSvI/+EN8U/8AQtax/wCAMv8A8TXq X7Pf/Mx/9u3/ALVr22gD47ufDWvadA11faJqVtbR43zT2joi5OBkkYHJA/GqG5fUfnX078Xf +SXaz/2w/wDR0dfLdAE+5fUfnUFFFABTG+9T6Y33qAEozRTG60AOyPWnIeaip8f3j9KAJaRh 8p+lLSN90/SgD74ooooAKKKKACiiigAooooAKKKKACiiigAooqOeZIIi7sq4HG5goJ9MmgCl awea+5vuqfzq5b3CXMZdAwAOOa4/VfEVrDbqiuSvUjK/e5965+18YtHdI0IIJOOq/T0oA6jx RK/nsuRgBQM4HHWuA/t9FvFt2PJYAfMPXFbviTU5rrSvtYzuITIOOTnFeW6PZ3cmuGeZfkVw w5H97NAHoWt6W0kIuCTlUPHHrXm2pxAXBZ1wQxwOD3rZ8Q+K2e5S2XcScqGG0gHNY8d2by8E UgO4Ps7cc0AaE8UZ0kRs6+aVYFsAd/8ACuYii+w3pl3Ark45Faut3ENlPsmkAbBBIYY6D/Gs y6sTBA8ykMhy/XPbNAHa6frtvPAqzxmPHyg5B6D8Ku3ep2ulW/2hpAwJwpyBzg8detch4XWO 9t5UYrw+TkjuP/rV0mqWem3SR6bJJuk3Bx5bKRnBFAFWDxRcSalDjJVtuPu8gj6V2uiTySSh jIBuXI4Hr0rirMW2nuYHh37AAG4Pb6VoeGL5769lCodqbWUZAxzQB3v9tz3LrpsgxGpypyOu cenv61yOqJd20izpLnZlvujkjnFad3HqFsy3LhVQ4OPlyea4TxH4me2sZiVO84ySRx8w9qAN O68YRSyLDcwssqg8llx16DpXY+EvHtlZoLSVGEJLNlWXrx/h615NazWmsWXmunzFiFZSDg8d 6zLqC60y7k4BwxAII5FAH1bcsLiK0u4D8srxEdeVLA/w+34evGa064nwVkeHIopNyymaOb5Q SMHyxjj/APV68ZrtqACiiigAoor5wt006G6s5fFc+ueFPH7Xbyf8JHdws1tO6y7dgG7Y0flu q8BUAA528MAfR9FfPHi//hAf+FneOv8AhNvP87yrT+z/ALP5nmbvsw37Nvybs7MeZ8ufbNZf jTRNXvPAvw+07XPMtL+30zVJSskY3LHBEJIkKjGCY40XnkZ5BIIoA+m6K+fPFmsQ+Lvib4W1 m0nu47SwvdGiW1nUY3XRe43jDEA7FjU8ckdcKM9PB4Q0Px98T/Glxr1pJqdnYvaWtlJ9olWO JhETNGpRgMhiCy9iexJyAeuUV4/8JfBPh231jxDq0Wn7b7SvEF7ZWUvnSHyoQoULjdhuHYZI J5617BQAUUV5v8a9EivfAlxrUNvO+r6PtnsZ4HcPb5ljMjgKeyrnJ+7jPHWgD0iio4J4bq3i uLeWOaCVA8ckbBldSMggjggjnNSUAFFFFABRRRQAUUUUAc2fF0IuL+byY/7IsElN1dicGWLy zIHcw43eVuikQNncXQ4Qr89aGlau1/cXFndWcllfQIkz27ur/upC3ltuXIz8jqRnhkbBZdrt z8vgGG4e+tHljh0+5e8laW2QJdStdqyypI2MFFzuHByVhBA8nMm5pOmXkOo3eqalNA99cRRW xFspWMRxGQq2GJO5jK7EZwoKr820u4B8d+Of+Sg+Jf8AsK3X/o1qwK3/ABz/AMlB8S/9hW6/ 9GtWBQA5elOpq9KdQB7b+z3/AMzH/wBu3/tWvba8S/Z7/wCZj/7dv/ate20AcT8Xf+SXaz/2 w/8AR0dfLdfUnxd/5JdrP/bD/wBHR18t0AFFFFABTG+9T6Y33qAEpjdafTG60AJT4/vH6Uyn x/eP0oAlpG+6fpS0jAFTx2oA++KKKKACiiigAooooAKKKKACiiigAooooAK5T4gTTwaBE0Ej I5uAMg4yNrcV1dMliWaF4n+66lT9DxQB4GIdQvWGZC4zjluhrSstAmh2yykcEH72f6V6TYjS 0lB+z3RYdjZuV9P7lXNSurWTTynl3CqpGB9mdQO3dcUAeRa/rUto62abvlVTtyK1/BTWl/8A KwjDFRnJBGSaqanokWqXrSRtztAyOv8AKpfDnhm/0u5kaD7QQwHG1mxzngBaAMrVvA1y+pR3 Fp5axq+58uB/Fn0qtfaZBp10WXywckseM9fpXqZgEOjXnnecsrx8ZgkABBzySuB0FeUalb3N 7qUq73wrMc7+Mbu9AHL63oza3epHG20sW5LgZ4Ht7V1es6NIuhwW4XzML5fDDgbcZ5HWoYLq 2S8kgUAmLcTjHat/SPFcazLAyFuSNxYYwB6fhQBz3g3wlc20N8pAYyvlG3jIGDgdOK2rfwTq Mk/28hNsTKpPmDOTwO3qRXpl7qdounpLZxvEZBlwkRVTx0Jxg9ay7vxjZ6doryXcUp8qWJ8R KuWAdfpzxSbSV2XThKpNQgrt6L1Kdn4E+22bNP5aSnaNysp6AdwDzXWaToFjpNrZ2kdvEfJi YZ8rOSSCTkADr6jP61xNh40urrWIL65ZbWwXPl6fayoys2Cu6SQD5urfKMDhT1Bz1MXiWC41 GOUQv5axFQQCT8xUnp9P/wBVKMuZXLr0fYz5G031t0fa/X5XXmVdY02/bUfNkbFnnCZkAAO4 4HPQYryfW9Aiu7mS0miDKUzuBU/xe4r0P4geKh/ZC2liZ45HlXc/3OOeMdewrj/DU1zc5N7J 5pK4Ytg/xVRiVdL0G10bQZvs8asF3AMQu4McdwKoaPZHUNUeObBYs2Dkdvc16SkGny29zbbJ EEqnbtTvkegrmrvTG0eed45D5iFyoY7Tj6EA9PagD1O0WF9IhjSIRpHLEoAUnO1kwcDHt7Dv xWvXE+GdYm1TSYYr0StL9qQqdjOu0bCBlRgc+p46niu2oAKKKKACvJ774d+NdTs5PCt/4ngu /CUl2JXup90mpNCCHERYjacOB82c8Z6fu69YooA5fQ/Dd5pnjvxXrk0kDWur/Y/s6IxLr5UR RtwIwMk8YJ/Co/EnhSbXfGfhjVGFpJp+mpepeQT5JlWeIRgBcEMOuQSOPWusooA8f0X4WeIL TRdJi1G60qXUrTxBZX8txEWG+0toViSPPlgllAOARjknOSa2PCd/Z2Pxe8d6QbuC38+Wzntr LzAnmSNblpnRO7HALEDJ4Jr0iqcmk6bNqkOqS6faPqEKbIrtoVMqLzwr4yB8zcA9z60AYfgv w3eeHP8AhIftkkD/ANpa3c6hD5LE7Y5Nu0NkDDcHIGR71x//AAt95NO8capBZ/6Bo0VqdNMl uySTGcEI8iswzGW2sMbTsOcZr1ivJ/EfgPWdbn+JiJB5cesRWD6e+9D57wJkpjcNuWULlsdc 8gUAWLrx94ps/h/Bq0tjobX02qrYLfw3Rl04QlsC5ZkJKx5GwhmBB5OPu1z/AIq8d3msfBDX X1O4sbPU3u3sbd7G4Kx6jGk0aySQbjueMqzKcEggHOM4EkfgTxRD4DmSLQrRJZvFH9sS+H1u 0CPa5A+zM2PLIyqnB+XABxkba6z4f+D5bXR5ZfEmh2MNwNVuL7TbNtlx/ZkbspEcbAbUwylv kwOQeDkAA7yCCG1t4re3ijhgiQJHHGoVUUDAAA4AA4xUlFFABRRRQAUUUUAFFFFABRXNzeM7 G1v72K7jkhtLVJi9x95gYUDylogC6oFZCrkYbPYPEZdDStXa/uLizurOSyvoESZ7d3V/3Uhb y23LkZ+R1IzwyNgsu12APjrxx/yUDxL/ANhW6/8ARzVg1veOP+SgeJf+wrdf+jmrBoAlj+6f rT6ZH90/Wn0ARy9qjqSXtUdAHbfCL/kqOjf9t/8A0TJX1JXy38Iv+So6N/23/wDRMlfUlABX xDX29XxDQAU9fu0ynr92gBaKKKAClXrSUq9aAH01/uN9KdTX+430oA+9aKKKACiiigAooooA KKKKACiiigAooooAKKKKAKyNDaRASyIjEZOTzUWoRPf2DRW0sYZtpyx7dah1KwnvNrR7FcZB BkIGM8fw9cH8PfrVVI54ZDbC7iWfGADKwz/dHT0/KgDmpdN/sJy88gDjGPnyevHatXQvEumy OFleKKRUILsck8jj2+mOfbHORrrtcOyzMM8HcXz9O1cyNKuIiZYX+U993vQB6xqd1bS6ZOq3 ETZXorg8ZrgL63tja3jREeZtYBt/GTVG01SeKCZZpGOEIO5s4rIfWFHno83yuTwOOCfpQBiW +kMt5eHezSyB/uuO/PBxVjSPDN4bvc7OFbJX96M11Oi2tsZFn3qTyMl8YrTm1eCyMfnsiJjb jJ9PpQBoW9xCLYQXF0hRPlILYwcY/Pj9Ky9bsDDLm3mWRDtCMj5ySMisq/1SynDJaTIHMhLg Ennn1HvUD61LDZndI3XAO7HQcdqAMtdLkt74NGfLAOWRWAQ8enY9PyrfstTl0/Mkh3FVG1cj 1HfBrKjFxeSrtkJWTaT83tWzJYZgjjf720DrUxio7GlWtOq05u7WhiPqQ8QzyecjLtIIG/PJ J9hW9b232FSBlGxg/N94Zq1p2mWNiiyzRh5AQyZmIwQeOAOatNi9mMjrwpzkHH9OelUZlL7c I5Y385Ym/us3XmuI8Zaxer4gm2XRERJ3dMHKj/H8a9Dbw9DqCm4aeKNYVLnzZGGB3PC/SuV1 2DSb1JrR54zKuRuWQ4OCORx7d6AN74cXkt5ZA7GaNJU+TO8fej+bHGMZ/DGa9VrzH4b6dJa6 ZPPEyS2n2pY9u3c2/dF83QcAc5zx1xXp1ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQByd54Qm1G/wBVF3qEn2O/t5oJJI2InkjkTaImBygSPlkIXOX7HzTPqaTp l5DqN3qmpTQPfXEUVsRbKVjEcRkKthiTuYyuxGcKCq/NtLvsUUAfE3jf/koHiX/sK3X/AKOa sGtzxySPiD4l/wCwrdf+jWrA3H1oAsR/dP1p9V0dgOtO3t60AOl7VHSli3U0lAHbfCL/AJKj o3/bf/0TJX1JXxlpOsX+g6nDqemT+ReQ7vLk2K23KlTwwI6E9q6b/hb/AI7/AOg7/wCSkH/x FAH1PXxDXb/8Lf8AHf8A0Hf/ACUg/wDiK9n/AOFS+CP+gJ/5Nzf/ABdAHzDT1+7X03/wqXwR /wBAT/ybm/8Ai68U8eaHp2ieNNQ0/T7fybWHy9ke9mxmNWPJJPUmgDkKKueRH/d/Wq1wojkA XgYoAZSr1qPcfWlVjnrQBNTXAKHIHApNx9aRmO0/SgD73ooooAKKKKACiiigAooooAKKKKAC iiigAooooAgtH324z1Xis7+x5Rqi3Pmh41ZT87Etx+FJGZIydtxKoOclQueue4xwOB7H15p+ rpP9ga4tbqbeOgBwCCfQDqM4/nmgDhPGEM0MzGNZVX5flyc9PpVDR7tjCySkt8oHPUc1Prup 3COFuJCxOCS5J9axtL1qE3DRsybMclW96AKdzrdpOLu2jnQTBTtXdyRmubiaSadw7MPmIGT2 zWhL4Xe18SG5iaRwXPBJIA355wOlWdc0S5U+fa5UHkqS27Oe3FAGtaS/YYjuJK89TWTrC3Wr zbo5JlQHIWNjjoB2FdBczPaeEmvDGTKVc4OccHFcppOs6lqF4U3GJVDMdpYAKB1oA6vwv4Rk upvPnSUODkqcgklTz0roPFHgq3tLUzW5b7OMKVLksTg89K3/AAtqBvtMuZIHk3RfNtGDvJ5y crnPGOO3vzV3X4Jbjw8WLSmX5WEYA5ZmGBjGeM4H65oA8mgu20+1BZSFVgq57YH0rptI13Tr yI/ahh0jG1d+C5yBjOOOMmq1tohvmW1kSQuXBA5yOPpVu+8F/wBlWSzqTzjnfyDnp09P5UAU JtVSa6WMwFeQc787Rn6V14sXtNKkdrWUzyt97BGwAjBx75PXH9TzekFZ50jfdlMfMD+Ga9Ht NrtKUuBIPMB3Ls+YbAOcdeQTng8Y6CgDlLy3ni8N6nK7bX8h0CHhiRg9K8F+2XUmq3IlZx+9 cDPGQDX0Z4iJSFQLp5SQVKnbx0B6Adev+HSsTwz4Osl1seIMus0Tvsbf3IwcgjGCpI/woA6H TNGh0HRLOwt41LmSJpuS+9xtDN8xH90HjGPTtW9WLKssk0amd5tsseNyoeA4JPQDp39gRz12 qACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsPVdXvrLxFodhDZxmzvrh oZrmR+c+RNIFRRznMQ3FsAAgAMSSm5WfqOmfb77SbnzvL/s+7a527c+ZmGWLbnPH+tznn7uO +QAc/eeN/wCzoptTu7XOkCW7tohEcz+ZarM0jMCQu1vIlCgHI2oTnzCI9jSdTvJtRu9L1KGB L63iiuSbZi0ZjlMgVcsAdymJ1JxhgFb5dxRKc3gyxu3mgu5JJtLd55ksT8oSWdXWZt4IYgiW TAz8plfk/uxHoaVpDWFxcXl1eSXt9OiQvcOip+6jLeWu1cDPzuxOOWdsBV2ooB8b+Of+Sg+J f+wrdf8Ao1qwK3/HP/JQfEv/AGFbr/0a1YFADl6U6mr0p1ABRRRQAjfdplPb7tMoAK+0q+La +0qACvnD4o/8lG1X/tj/AOiUr6Pr5w+KP/JRtV/7Y/8AolKAOQqld/60f7tXapXf+tH+7QBB Sr1pKVetAD6RiAp57UtI33T9KAPviiiigAooooAKKKKACiiigAooooAKKKKACiiigDBe5t41 LPPEqjPLOAODg/kSB9as29/FBkNLGI/mZjkcbeGP4Hr6VHRQBxnjvSxqt48trdwneFX5ZOVZ eoOBx/8AXrk9P8OC0nCzOAWIDHceefpXrVxbRXSBJk3KDkDJHP4Vj30BhurZETdtTgj0Dd/z FAEculwxgPHKs+FLDY2ckAnHT0GfpWbCEneRpodx3ErhiMDtWxFcXEmoxurSFHYBlVj83zcg 105t7c213DjEefn6cfKD259OvP4YoA8k1dp5pWgjZvJfICbjtArc8N6DahhzsaQYG58AZxgf mMfWurh8OW7sJY5Q0bEn7pGP1pIWXS2jk2GRGUGMkgdQPTIP4cUAXNJttO06MrbXEbNMA7fv Q24525Htk4+tZ+seIbZWWJJkwksbc5+bDA/0/Srl74msLFYvNfDOOVw3HT2964/xRJZRziWO 3yAqsPnb0zQB0Fpq+nC7jlRIonJG5izHjuefauT+InjtbO6SytIo5lUD95kgknn09h3rKsr6 6v8AUUltYiFj2qI03Ek44x+NGq6BqjhriSGWOWRBgFHBwGAPb6fnQBR8O+KraBybqRFlm24R nPy8n8vxr0Mag2j6Dd3iOjvICqAPxkY9Oc8+x/DFcBpXw4k1aQTy3ItGBBLvEzHr2GR9a9Sh srE2j2ckHnLG55LbeSFPRTn06nP4YoA5nw9cXniK6P2glIg5Yu7HBGeQCe+Nx/4CfSusjure 3tltY5ohEXPl5YfMGZiuOTkHt64/CrEszzHLHj0HSq1vt8s7Om9/TruOenHX8fXnNACJJHdG LyJUk/ext8m1+A456+x59uORW/WI23dFv6ebH6dd4x146/j6c4rboAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKK5/Wb7VLXxL4ehgkgj026u3hnG3dJKfs88gHPCKp jU8ZLE/whTvAOgori9V13V/Dj395PLHqXlW91eTWEWES0t41kaFxJtyC+xUZW3FmZmTCxMDs aPeXyazfaPqFxHdy29vBdi5SLysiZpVKbcnhWibBznayg5Kl3APkHxwoPxA8S/8AYVuv/RrV gbRXQeN/+SgeJf8AsK3X/o5qwaAGk7eBSbjQ3WkoAXcaNxpKKAHZzxRtFIv3qfQA3aK9Z/4X V4j/AOfLSv8Av1J/8cryitCgD0f/AIXV4j/58tK/79Sf/HK6LTPBmnfEPTovFWrTXUF9fZ82 O0ZViGwmMbQyseiDOSec14tX0Z8MP+SdaV/22/8ARr0AY/8Awpfw5/z+6r/39j/+Iry34k+F bHwr4it7GxluJIpLRZiZ2Utku47AcfKK+mK8D+N//I6Wf/YOT/0ZJQB5l5a+ppGUIMipKZJ9 0fWgCPcaCxwaSkPSgD78ooooAKKKKACiiigAooooAKKKKACiiigAooooAx3iulUkWkrnnhWT PBx3bv1+g9eKUwXIBP2ZzgMcbl5weB179v1xWvRQBjGO7BI+xTHBYZ3JzgcH73ft+uKSS2uR Ori0lcqrKCpjx0B7nPJGPr17GtqigDmotEkivEure3kt3LguNwOf4ufm6Z4+vtzUt1Fqcskk kcEsYYAsu5QD8vPCnJ6Y784xx06CigDzfXrjU7S0aVbS4iwMk7WHORxn/PWoLPUvExt/In0a 8aMAhQ8MuFGMDH0r0+igDwePwf4u8ReILm7Nr9ltYpSAt1vjJB4BXIwR347D6Z9DXwm8OlPA 8M11cMx/el1XavA2gF8Huw6enBrtaKAOb0PRX0C2mjtLVt0pLM24ZO3gDliOckj684q3Kl7J OJHtJ2Kh1HzR4PQ5+9nnGB+vatmigDHeK6ViBaSuOeVZMcDPdu/T6j05pIrW4DM32Z1MjgnJ Tj5R6Hnpjuc+2K2aKAMdIrpmwbSVBxyzJjkZ7N26fU+nNNS2uYlVVtJSGbceYxt3Ek5we3fG evfmtqigDHS3uJTGZLSVAHRiGMZIw31I4xn6HjmtiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAqne6bDfXWnXErSB7C4NxEFIwWMUkWG46bZGPGOQPoblFAHP23hV be8vJJNWvrmzvZZZbmwuY7d4ZfMBBVj5XmMoGFALnCqq/dGK0NM0e30rzWjeeeaXAee5lMkh Rc7E3HnaoJA9SWYkszM2hRQB8TeOP+SgeJf+wrdf+jmrBre8cf8AJQPEv/YVuv8A0c1YNADG 60lK3WkoAKKQnFJuFAD1+9T6jVgWFSUAFaFZ9WPtcfo35UAWK+jPhh/yTrSv+23/AKNevmv7 XH6N+VeteDPitoXh/wAJ2Wl3dpqLzweZuaKNCp3SMwxlwehHagD2ivA/jf8A8jpZ/wDYOT/0 ZJXZf8Lv8Nf8+Orf9+o//jlYGuaHc/F29TX9AeG2tLeMWTpfko5dSXJAQMMYkXvnIPFAHkVM k+6PrXpv/CkPEv8Az/aT/wB/ZP8A43WB4v8Ah3q/hDSYtQ1C5sZYpJxABbu7NuKs3dRxhTQB xlIehpNwoLDFAH37RRRQAUUUUAFFFFABRRRQAUUUUAFUdVvHs7aPYyo0sqxeY3SPP8WO+MVe qnqfnCzJhto7kBgXicZ3KOuPfpQBTSe/itbt2njuYRC0kN0m0fMB93A9/wDPYQR3V/btps09 6s8d0ygxbFQjcOowOQM+3b1qOG1dzqM1tZS21u9syCJwQzueche3XH8u9aGlaVa20FvcC32X JiG4sTkEjng9DQBVsNRuW1i6S6nUWqtKse7aOVIz78A0mlatOUvJtSkCxxiNgAvCh8kdOe4q pc2k6w3Ey20zyfa51VAp5R0xu6dOlSXVjLHb6tDFDK67bdYztJLhQAcev4UAbDavYJbmc3A8 oSeUWCkjdjOOB+vSnT6nZ21vFPLOFjlAKHBJYYz061S1u3keaznUXJSNnDfZv9YMjgj245+t VZbZ7fTtOMdtdh49zK8R3vEW5wVwNwPfp0xn1ANpL+2kNuElDfaATFgH5sdfp+NVL3UP+Pf7 LL/y+rby/L+Y5H6iqQFzBHpN1JZP+5EgkjgjGRuHB2jpnqajjgumiRpLaRHOqiVlwTtX1z3H v0oAvWevWtx9pMkiosTErweYxgbvqSenWrKavYSWslylwDFGQHO05GenGM1mGOQW+q2kljPL vmeZdvyqykrjDevfGO2PaptNmuIIryea2uXQMojLQgTOOmCB97HHP1+gANiSRIlDOcAsF/En A/UilVlcZVgRkjIPccGobxDPazW68PLEyqSDgcY5P41kWS3PmxlPtUUZkLBJEcnl2LZJOB8u 3k574+bNAG6WVSoLAFjgAnqev9DS1jKrPdQF1vD5c4JLB8cq34dcA4+X0wCRVnURcFw0DSjZ BKwCdC427Qf14789sigDQorIxeiaZi8pAkDMqocbRICMHOD8gPCj684pgTUSEWRplYbNzKc4 LNFn2PIk+g9iKANqmySJEoZzgFgv4k4H6kVklLtWB3XJX94rDJ4QSqBj32biD94+vSkdbpzE GWcp5kZQY/hEpPzZ5+7s68/+PUAa7yIjIrHBdtq+5wT/ACBp1Y9pHdmW2adpHIlBfKEBW8tw 3JJyMkdML0x3q3qLOiRuvnkKScQgkk/h39Mjaeh6g0AXaRWDDIz1I5BHSsxxcMJAGulmMwDM v3QvmDaRnj7npx13c1XRbk3LQBrrAIAG84CmVwSSefuA4PsO+2gDbVldAysGVhkEHIIpazYV kTw4FjWVZVtiACGDhtvbPPXp+lQzpeiRo0lmEaudj7CxLbUI6EcZL9flHQ9sAGxRWNMuo4mC ebwXRcH+95mMfnFz256fNTpBeNIwVp1YyYdhnAHmrsK54+5nOP8AgVAGqZEEqxE/OylgPYYz /MU6sOSK8Ex2eflVdS7ZYBfMTpzknYOxyccfNmrdlHObjdNJMyLEu3cCoJ3P1GSc4x1Oemee gBo0UUUAFFFFABRRRQAUUUUAFFFFABXN6619F4p8MPHfyR2ct68L2sa7RIfstw5Lt1YAom1R gAgk7jt29JVe4sbe7ntJp498lpKZoDuI2OUaMnjr8rsOfX1xQB5nq3j1ofGOsT2Wr2jRado+ opb6a0ynfc2/lOZHQEPknzUCnnbCzqcOTXaaM9xZ+IdS0WS8nu4YLS2u0luSGk3yvMrjIAG3 MIYDHyl2AwoVV2HsbeTUYb9o83UMUkMb7j8qOULDHTkxp+Xuaj03SrHSLdrfT7aO3iZy5VPX AA/AKFUDoqqqjAUAAHxl43/5KB4l/wCwrdf+jmrBre8cf8lA8S/9hW6/9HNWDQAxutJSt1pK AGt2ptObtTaAHJ98VNUKffFTUAFR1JUdABUyfcFQ1Mn3BQA6vfPgh/yJd5/2EX/9Fx14HXvn wQ/5Eu8/7CL/APouOgD0qvMPjr/yJFl/2Ek/9Fy16fXmHx1/5Eiy/wCwkn/ouWgD58ooooA+ /wCiiigAooooAKKKKACiiigAooooAKa8iIyKxwXbavucE/yBp1VL2J5ZLQIzpiYkugBKjY3q CPb8aALKSI7OqnJRtrexwD/IilVgwyM9SOQR0rGlhvI5pQks20ykiTy8lm2Jt4XaMfeHPy8c 0sjXSSKXacL56hSDxgzkHPtjZj9ON1AGqk0ckcUiuNsoBTPG7Iz/ACo86MzeSHBkwTtHbGM/ +hD86xre3mR9PEouSiLET975WKOCCB0Awg9u/U5JreZNQvJIRch1SR1I3EMdsZUDPB5B4Hpj pxQBu02SRIlDOcAsF/EnA/UisiWO94ZZp13SS/wlsEP8gABHGMnLfL0zxjDrhZZJpYh9qy0i MG2khQJF9QV4HIx2zuGRmgDXqJbiJkhdWys3+rODzwW/kDWcv2vzUB875XCxdcbRIwbd2Pyb cFuvbmo7OO5ElgJEmATbwR8qr5OOfQ7tw/n/AA0AabXkKXItz5nmHoBExB6c5xjHI57VPVd1 Y6lAwU7RDICccAkpj+R/Ko5IJWvBII8puBz9qkX/AMcAx+HegCWW7jinETBifl3MBwu44XP1 PHGffFLLcrDIqsj7SQC4HyqScD8zxxnHfFQXTl72CBlcQgh2IiZgzZ+UbhwMEZOfbtmi7Pmv CqRylxIpU7TsIDjdkdOAMgn2280ALLfQGaW18tpZF2q0YA+YsCcc4HQE/wCcU22kto5o0trP Yk67lkjRVDDGckA5xzjkdT71QFtcx3WbiH918rSPCzE5JlPy4AP3m7cgY6gmrkEdyNmUVZob RVTIwm9uoOOwKL09fpQBIdUt8yhQ7eUzK5A6Bcbm57DcPf0Bq7WAbKVba5heCRW2mKJo2LBv 3aDBOBwSq88Dgg4HXebcUIUgNjgkZAP0oASORJYw6HKnofX3Ht70yecQhfkZ2dtqouMscE98 DoCfwrI05p3KCBpQgCABzuRV8lT7ZO4r6e38VWdlzE8MtwJHMcu5mVvM+XYw4CqOcnn5e454 4ALclzE1tGwQzJOMIgA+cEZ74HQE81FaS2KzLFaRIpkj8wmOPaMcYz7/ADA49/eolR4LXT1e N824UyhVLY/dsvGOvPpn8qdawyRPYK6EGO1ZH7gN+74z+B/KgC8ZEEqxE/OylgPYYz/MUu1Q 5YKNxABOOSB0/mfzrOuDJHqyuiuV2oSq9WAEgx78sn0yCcVVhGoi2Dq05mIChX6D9wDnn/bA 5Pf6nIBqTQWaF7uaCLcg3mQxgsMd84zxipFmR5pIgTvjALAqRwenPfoelQwSRxQFszLFuODN ngYySSeQODy38sVX01WR41ZSrLZwhww5z82PpjnP1HTHIBpUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFcv4htc+LPCd4bic41CSJYd+I1zaXRLbR1Y4UZOcBflxubcAdRRXlc95qU T61fi0jm8QF9TgsJLbc95D5SyNCJI8Y8goYcJgrvaFyHafKdZ4aNvFrmpWulT+doy2lpNEyT GZDPJ5rSEOScsyeQ5558zeeZCzAHyb44IHxA8S/9hW6/9HNWDuHrW545/wCSg+Jf+wrdf+jW rAoAceTxSbT6Uq9KdQBEyMe1Jsb0qaigCNEYOOKl2n0oX71PoAZtPpUe0+lT1HQAzafSpFYK oBPNJTG+9QBLvX1r1z4W+OfDnhvwxc2eraj9nuHvGlVPIkfKlEAOVUjqprx2mN1oA+nf+Fte CP8AoN/+Sk3/AMRXJ/EHXtN+IegwaT4Wuf7QvobpbmSLY0WIwrqWzIFHV1GM55rw2u++Ef8A yNd1/wBeL/8AocdAGV/wrnxZ/wBAr/yYi/8AiqiuvAPiaztJrq40zZDCjSSN58ZwoGScBs9B X0DWH4ue8XwzqQtoIJIzZz+c0kxQoNh5UBTuPXgkdBzzwAe7UUUUAFFFFABRRRQAUUUUAFFF FABUcs8UCBppUjUnALsAM/jUlUdSk8o2j+bHFib78gyo+R+vI/nQBcjkjmjEkTq6HoynIP40 0wRNMsxiQyqMByo3AfX8TVJ7p9qFbuN1Zf3kkaZWNcn5xycenJI79FaqdpcXEdz5CXCsgmfI cjLEyMGGAvJAw3bG7J44ABu0m5Q4UsNxBIGeSB1/mPzrDjnnlm043Fz18uTIVVBLpJx/46B/ wI+2GyvJBql7JHcZeJJJCjAHgLEQOOx6fQevNAG/SMyoMswAyBknueBWPLdXq4ZZkAaSULuG B8r4VeASxPPAwTjjBzlbq4d5ZIBcKX82MqhX7uJFAOOCOvOeuQQRyAAbFNEkZCEOpD/cIP3u M8evHNZa3lwZUQychwirgfvf3jIxPuFAbjGM88cVBZTSM2mxs2FXbtTZ28gndn6lh7446GgD cDKxYBgSpwQD0PX+opazoZHXU50J2xvNgEc7m8pTg+gwCfw7dDLJ5/2wbftfl7h93ytmO/X5 sf5FAE73EUcqxs2GbHY4GeBk9sngZ69qlrMvP+P5l/ik+z7B3bbKS2PXA5PpU15NPFKEjJ/f KEQhfuNuAz78NnHoh98AF2iswXcpmlVp9mN+4bAfLIcBBjr8w7dT2xUc95dBIyrojsWLq/AR xtxH0JbOScDluoIHFAGsrK4yrAjJGQe44NLWRFcmGSNDOEVp5AFKj5iZWHfr+B4JBOQeNegB qRxx7vLRV3MWbaMZJ7n3p1FFABRRRQAm1S4YqNwBAOOQD1/kPypaKKAEZVdCrKGVhggjIIpF jjWR5FRQ743MBy2OmT3p1FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVHJBDM8LyxR u8L74mZQSjbSuV9DtZhkdiR3qSigCMQQrcPcLFGJ3RUeQKNzKpJUE9SAWYgdtx9aIYIbZCkE UcSF2cqihQWZizHjuWJJPckmpKKAPiPxz/yUHxL/ANhW6/8ARrVgVv8Ajn/koPiX/sK3X/o1 qwKAHL0p1NXpTs84oAKKKKAFX71Ppi/epxOAT6UALUdSVHQAUxvvU+mN96gBKY3Wn0xutACV 33wj/wCRruv+vF//AEOOuBrvvhH/AMjXdf8AXi//AKHHQB7PWV4m/wCRU1j/AK8Zv/QDV28v I7GBZZQxVpY4htHOXdUH4ZYZ9qpeJv8AkVNY/wCvGb/0A0Ae30UUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBhz+C/Ct1cS3Fx4a0aaeVy8k klhEzOxOSSSuSSec1H/wgng//oVND/8ABdD/APE10FFAHP8A/CCeD/8AoVND/wDBdD/8TVe1 8J+A77z/ALH4f8OXHkStBN5NlA/lyL95GwOGGRkHkV1FcP4N1Dw/quoxTaJqNisFrp4tLSwt 7pXma2UrtknXJYY6IrcoJH3HdIVQA2P+EE8H/wDQqaH/AOC6H/4mj/hBPB//AEKmh/8Aguh/ +JroKKAOf/4QTwf/ANCpof8A4Lof/iajn8F+CbW3luLjw14fhgiQvJJJYQqqKBkkkrgADnNd JXn/AMXYr278G3drHp09zpv2S4uLx4XjGwxxkxBgzqdofEhKZP7nbtIc0AdB/wAIJ4P/AOhU 0P8A8F0P/wATR/wgng//AKFTQ/8AwXQ//E1uQSNNbxSvDJA7oGaKQqWQkfdO0kZHTgkehNSU Ac//AMIJ4P8A+hU0P/wXQ/8AxNH/AAgng/8A6FTQ/wDwXQ//ABNdBWX4lhvrnwrq8GlmQahJ ZTJamOTYwlKEJhsjad2OcjFAGXa+E/Ad95/2Pw/4cuPIlaCbybKB/LkX7yNgcMMjIPIqx/wg ng//AKFTQ/8AwXQ//E1X8ISb/tKxxwTW0MUMMV7FZfZc437rbyzyFhJIH93eUOXjkJ6igDn/ APhBPB//AEKmh/8Aguh/+Jo/4QTwf/0Kmh/+C6H/AOJroKKAObk8F+CYXhSXw14fR5n2RK1h CC7bS2F+Xk7VY4HYE9qk/wCEE8H/APQqaH/4Lof/AImsfxnDv1qz/wBGnfzYliwpy1ziZH8q 3OD5Uw2by+Y/lUHP7vzbfuKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKK8jtXh8I/D3wzrGgWVpb6hJo7XN4sUQAuY4 7B5N8yrjcBMIRvPIMm0MPMIYA9corztdT8To8+ntJqSEvAyC6l04alIGWcssKo3k4/dIR5gy VE+CSq4r6Z4iu9R1i8S31q0tLR0fUjqCWaRpII7SwYGRX58o/aGLZYSAKqiRQtAHplFeX+Hf EfiK90WHWbrVvNWO70q2+zfZo1WT7TDZ+azsBngzsyhduGzncuFXY8R6xrtp4kuYtOv4IbeC LTAsM1sJFaS5upYCzEMrbQuG2ggllT5gNwcA7iivL9S8Wa3Dot9dWt1fXN1o8V3JLHbQWwRl hnnjSS5aQgsri3OVgCsMSHjcgGXZTaxa6F41gOsyXFlY2WoXQgubSB/Mla5v0O47ACh8oOyl TlsYKplGAPZKK5Ox1e+m8b3Givfxtb2rzzDEeJJBsgYRMduMJ9oySMEhoMMxEwrrKACiiigA ooooAKKKKACiiigAooooAKKKKACiis/Xbq8sfD2p3mnW/wBovoLSWW3h2F/MkVCVXaOTkgDA 5NAGhRXi/hzxv4lh8F6p4nk8R2Pia3i0pZzaRWipc2V2SRtkjTb+5HzFnJBIQlQAMnQ0b42a XHoenHVrDXHuIrS2OqXq2GIbZ5MAPIQRhX++u0HKsMDOVAB6xRXjeu/E3U49S8eWhbUtKt9G S1W1uo9NjmMR80I7MsjAMZN4KZIBjXcACDnuLv4haRaeMT4ba21KWeN4Y7i7itS1vbSTf6pJ H6gsSoBwRlhzwcAHWUV43pPxN1PWXZrltS0of8JbBp8SnTYyGgdSBbPvbKvlCZGGWUuMDBAH cW3xD0W68byeFES7+1h5Io7nyw1vLLGivJErqT86q3zAgYIweSMgHWUVxeifEzSPEKXz6fp2 sulvbzXNszWRA1COJtrm35+chio2nacuBjrjrLC6+3adbXn2ee38+JJfJuE2SR7gDtdezDOC OxoAsUVlw+JdBudUOlwa3psuoB2Q2iXSNKGXO4bAc5GDkY4waJ/Eug2tvLcXGt6bDBFcG1kk kukVUmAyYyScBwOdvWgDUorj9W+Imj2k+jxaZdWOqf2hdvE8kN6gjt4Y08yeVpOV/doVYqSC Q3Fbh8S6CLe4uDrem+RbpE88n2pNsSyAGMsc4AYEFSeueM0AalFZ6a7o8l5a2ceq2L3V3EJ7 aFbhC80ZBIdFzllwCcjjg0f27o/9sf2R/atj/af/AD5faE877u77md33eenTmgDQorz/AFj4 nwWuvppGjWtjq0lxp6XdlIuqxQpdyG48nyUZhtLDDNwSTtIxmush8S6DcoXg1vTZUFu10WS6 RgIVYq0nB+4GBBboCCKANSiubHj7wrJf29nb69ptzJKksjGG8iZYo40LO7ndgADtyepxtVis mreMdH0/w9Pq1tqFje/6JcXNpFHdp/pfkoWdUIznG3BIBx3oA6CiuDv/AIlQwWXhRLW1tJtY 8RJBJFYT3whEMcibt7SbDwGwoGAXOdoJGK6iDxLoN1bxXFvremzQS3AtY5I7pGV5iMiMEHBc jnb1oA1KKz7rXdHsdOg1G81Wxt7Gfb5NzNcIkcm4bl2sTg5AJGOoqO58S6DZXFzb3et6bBPa oHuI5bpFaFSVALgnKgl0AJ/vD1FAGpRWXB4l0G6MQt9b02YyoHjEd0jb1MnlAjB5Bk+TP97j rxWH4g+Imj6L/YDW91Y3kOr3ZiS4+2pHDHCn+tl805Q7DgbcgsTgc0AdhRVea/s7e8trOa7g jurrd9nheQB5doy21Ty2BycdKp23iXQb24tre01vTZ57pC9vHFdIzTKCwJQA5YAo4JH90+ho A1KK4/w78RNH1vTLrUbq6sdNtVlla2ae9QGe1WTyluCrbTGrPlcMOoxnkVsP4s8Nxy3UUniD SkktM/aUa9jBhwwQ7xn5fmIXnuQOtAGxRVOTVtNh1SHS5dQtE1CZN8Vo0yiV155VM5I+VuQO x9KuUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZ+maFo+ieb/ZOlWNh 52PM+yW6Rb8ZxnaBnGT19TWhRQBlx+GtBh0ubS4tE01NPmffLaLaoInbjlkxgn5V5I7D0qvr WgNfvDNYnTYZ47gXLfbNPW5RpQqqsuAyMJVCgK4bgZBB+UruUUAZel+HtN0nRrXS4baOSC3S EbpUUtI0SosbvgAFwI0wccbRjGBVyWws55WlltIJJG8vc7xgk+WxePJ/2WJYehORzViigDLv fDWg6kkaX+iabdJG8jos9qjhWdtzkZHBZuSe55NSRaFo8P27ytKsY/7Qz9t226D7TnOfM4+f O5uufvH1rQooArrYWaeXttIF8qV548RgbJH3bnHox3vk9Tub1NWKKKACiiigAooooAKKKKAC iiigAooooAKKKKACq9/DcXGnXMNndfZLqSJ0huPLEnlOQQr7Tw2Dg4PXFWKyfE1hqOp+Hrqz 0q7+yX0mzy5vMaPbhwT8y8jIBH40pOybRpSgpzjGTsm1r28zgdM+HfibUNU1vU/FF/oyXmoa E2jebpsLEylus8u4KC4AUYHBGB8oUZ5fU/gv4x1jS0ttQ1XRrh4LKCCzV5brZZtHtjIiGdoD xorMxQ5bgKPvV0f/AAgnxD/6Gv8A8qNx/wDE0f8ACCfEP/oa/wDyo3H/AMTXN7ef8jPY/srC /wDQVD8SXxf8Nte1q98YnTrzTfsniK3szi43o8U1u6YGVBBQornPXJUYABJua78P9Y1P4mad 4gtrjSraztruO6e5jheO+2qgRoCVwssbbBy/zAOwHyjDZ3/CCfEP/oa//Kjcf/E0f8IJ8Q/+ hr/8qNx/8TR7ef8AIw/srC/9BUPxJX+G2vLqkojvNNfT/wDhLYfEUZbesu35vNQjBGR8gXHX DEkZAGxoPhLxHoXjfUZ7XVbSPwvd3s+oyW2zfcTzSogKsSoCIrAsu05xwc5yML/hBPiH/wBD X/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6CofiWfCPw/wDEnhjXNb1ZLjQ45JtP +y2tvZwyQwXMy/6ueeIfLG3ABEYx87Ywc7vSLD7Z/Z1t/aPkfbvKT7R9nz5fmYG7ZnnbnOM8 4ry7/hBPiH/0Nf8A5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4mR4F8Aa/rl 5Y+IvEcf9mRtqGo6lJbwyz21z5lwEj2YG1olHllgQ7EggEYJNSD4ReKra9168sNctIJWuLmb RCZpWeBrh0WWSSUqW3+Smz+PltwII3HT/wCEE+If/Q1/+VG4/wDiaP8AhBPiH/0Nf/lRuP8A 4mj28/5GH9lYX/oKh+JiD4OeKrl9T83U9Ns0d7+5sTFPLcuJbpUjeOZ5EBZDErLvHzgtu56V qL8JdTfSZXkaxi1Jdbtb2GKG8mCfY7ePy4bfzgoZGVGcCQKWOATycif/AIQT4h/9DX/5Ubj/ AOJo/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf+gqH4l3wf8OtQ8LeLEvrFNN0rSykn2m3srm4 mN2Cq+XG4lyAY28wiVSCwONi7iKz9T+GvirX/F09/qurWjWbPqUcMn2iWRobe4t/JiRYCoRS nViGBbPJJAJf/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUP xOVj+Hviq38a6VpVwY4IJ0tLj+0LCGW4W0msrR4oHZ3jWPDPyU5PQZGQW29K+EWrW2mXVvf2 2h3S/ZLSxgtXu7jY0CSebcAyKqMjPNiUNh9v3cbav/8ACCfEP/oa/wDyo3H/AMTR/wAIJ8Q/ +hr/APKjcf8AxNHt5/yMP7Kwv/QVD8ShdfCjxZrVjqMWu61Y3V5JokOnWt6HfcSkyzkSqU+b 5hsEgbO1QxRmY4j8QfBnULwX1xosWm2M+q2Rt76GfUbicLKLlJvOEroWkLCMAghcHnLVp/8A CCfEP/oa/wDyo3H/AMTR/wAIJ8Q/+hr/APKjcf8AxNHt5/yMP7Kwv/QVD8TofF3gi88XeI4J 5rqC30210q8trdkBMwuLhPKZmUjDRhOQAQd3tXH6X8H9YfVbBtbm0qTTBLZPeW0TPIz/AGO1 8mLBZArK7M+9CowuAGOTV/8A4QT4h/8AQ1/+VG4/+Jo/4QT4h/8AQ1/+VG4/+Jo9vP8AkYf2 Vhf+gqH4nQ+OfCmt+IdW0q70i5sbSaxyYL52lWezdpELuqqSkytGrIY3A/3sEiuT1D4Ua4vi CbUNOltGc67JqiXJ1W5tZPIlA823CRowQt0MqncQBx2Fv/hBPiH/ANDX/wCVG4/+Jo/4QT4h /wDQ1/8AlRuP/iaPbz/kYf2Vhf8AoKh+JiaX8E9eHh2+tdR1HTYtQi0z+z9LmtGdlVWneaXz CyggtvMeV/gdwVbv0Hir4d69rtwtzZx+H7KRbK8wkSvGHursiOZpCFO8CDgPwzSAEqFO0Rf8 IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/Es+NvhlqmtWv hWw0DU4LS10i0msJprv55DBJEkLYULhm2B/7vJGCOop6v8KdVTxE934euNNhs1uNPvLZLppC YXsoJI44ioBLoxKZfcCBnhj1f/wgnxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE0e3 n/Iw/srC/wDQVD8TIt/gprFsun2Ml7Y3emmK0gvRLK6OtusrT3NvHtj+ZXlIdXJVxtC9MkyX Hwm8VXKXFw93owvp01C4YCSXyhdXjCKUD5M+ULcZX+ISdcrxWn/wgnxD/wChr/8AKjcf/E0f 8IJ8Q/8Aoa//ACo3H/xNHt5/yMP7Kwv/AEFQ/E1dA+G58O+OTqkPl3mni3tY4Hmu5I5bZobZ rfJjVfLmLKR8zFdu5sD19Eryb/hBPiH/ANDX/wCVG4/+Jo/4QT4h/wDQ1/8AlRuP/iaPbz/k Yf2Vhf8AoKh+J6zRXk3/AAgnxD/6Gv8A8qNx/wDE0f8ACCfEP/oa/wDyo3H/AMTR7ef8jD+y sL/0FQ/E9Zoryb/hBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6Cofie s0V5N/wgnxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE0e3n/Iw/srC/wDQVD8T1miv Jv8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6Cofies0V5N/wgnxD /wChr/8AKjcf/E0f8IJ8Q/8Aoa//ACo3H/xNHt5/yMP7Kwv/AEFQ/E9Zoryb/hBPiH/0Nf8A 5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ8Q/+hr/8qNx/8TR/ wgnxD/6Gv/yo3H/xNHt5/wAjD+ysL/0FQ/E9Zoryb/hBPiH/ANDX/wCVG4/+Jo/4QT4h/wDQ 1/8AlRuP/iaPbz/kYf2Vhf8AoKh+J6zRXk3/AAgnxD/6Gv8A8qNx/wDE0f8ACCfEP/oa/wDy o3H/AMTR7ef8jD+ysL/0FQ/E9Zoryb/hBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28 /wCRh/ZWF/6Cofies0V5N/wgnxD/AOhr/wDKjcf/ABNH/CCfEP8A6Gv/AMqNx/8AE0e3n/Iw /srC/wDQVD8T1mivJv8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6 Cofies0V5N/wgnxD/wChr/8AKjcf/E0f8IJ8Q/8Aoa//ACo3H/xNHt5/yMP7Kwv/AEFQ/E9Z oryb/hBPiH/0Nf8A5Ubj/wCJo/4QT4h/9DX/AOVG4/8AiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ 8Q/+hr/8qNx/8TR/wgnxD/6Gv/yo3H/xNHt5/wAjD+ysL/0FQ/E9Zoryb/hBPiH/ANDX/wCV G4/+Jo/4QT4h/wDQ1/8AlRuP/iaPbz/kYf2Vhf8AoKh+J6zRXk3/AAgnxD/6Gv8A8qNx/wDE 10Pg3w14q0bV5bjXNb+3WrQFFj+1Sy4fcpBw4A6AjPvVRrTbs4NGVfLsPTpuccRGTXRXuzuK KKK6DyAooooAKKKKACiiobprhLd2tIopZxjakshjU885YKxHGexoGld2MXVdZvLPxDZWkT2K 2j+WJzKSX3SOVQEg/us7W2EqyyMpjzG20txdr4u8Ua1eeHbn7LHZwT3CXa2qbDLcwy2tzIse BcYxiIgPJsDOynYnlMG7K8sdQ1C4tbi98N6Bcz2j77aSa7Z2hbIOUJt8qcqDkeg9Kj/sq6/6 FXw7/wAff27/AI+T/wAfH/Pb/j3/ANZ/tdfep51/SZt9Xn3X/gUf8zkW8Xaxpusa79h0ORZ5 b2W8uIbuWBTHFBaWQIZ/OCIG8wHzAz7QOUbJ27mo+N7zTW1GBrWCW60v7fc3SglUkt4YklRU bJIkIubUEkY4lx0XOtNY6hcuHn8N6BK4uFugz3bMRMqhVk5t/vhQAG6gACrAOtrcPcLo2kCd 0VHkF++5lUkqCfIyQCzEDtuPrRzr+kw+rz7r/wACj/mc7oepanqPjXT11i1jt7y2stQhZVMY LDdYuCyJLKIzh8bS5JADcBgB3lc7Z2OoackCWPhvQLVIEdIVgu2QRq7BnC4t+AzAEgdSATVz 7R4h/wCgXpf/AIMpP/jFHOv6TD6vPuv/AAKP+ZrUVlxT66ZkE2m6ckRYb2S/dmA7kAwjJ9sj 61qU07mc6bhvb5NP8goqvevepCDY28E8u7lZ5zEoHrkI3PTjH41R+0eIf+gXpf8A4MpP/jFJ ySKhRlNXTX3pfmzWorJ+0eIf+gXpf/gyk/8AjFH2jxD/ANAvS/8AwZSf/GKOdf0mV9Xn3X/g Uf8AM1qy/EGpTaVpPn26xmeS4t7WMyAlUaaZIg5AILBS+7bkZxjIzkN+0eIf+gXpf/gyk/8A jFRznW7q3lt7jRtImglQpJHJfuyupGCCDBggjjFHOv6TD6vPuv8AwKP+ZyqeIZtP8ebry500 pPb29re3MTnykWIam7OCT8hDQjcpLbPmXJxurP1Xxjr2oeB7i7hutNsnuNC+0B4g5dZja+e4 Rw/yShSNsbDO1hKHfa8Y7JLHUI0RE8N6AqIkSKou2AVYm3RAf6PwEY5UfwnkYqvJoUkzwvL4 Q8Mu8Nv9liZp8lIdpXy1/wBH4TazDaOMEjvRzr+kw+rz7r/wKP8AmYI8b+JUM6nTbF/9LFpb SSOsKTOl7FaSEASvIVYuzZKDyvlB83IJPEHiHVtM8SWEzaPOb6HT57WKRhEI7qSW6sot8aCY kKCwba7ISCBuHJHRQaVdW2/7P4V8Oxb/ACd/l3JXd5OPKzi352YG3+7gYxVi5g1a83fatA0S fdE8B829ZsxvjenMH3W2rkdDgZ6Uc6/pMPq8+6/8Cj/mYJ8Z6vBZol1Z2kV/d27RWaBg6rdJ dC1bztkjBULTW52qzMv71SSVGc/UvE2p6tPJa3GnxwaeNYt0tZS8YZjb6nBC2B5hdwSck7EC cL8+4NXXJBq0cVrFHoGiJHaY+zIt6wEOFKDYPI+X5SV47EjpUZsdQNxcXB8N6B59w8Tzyfa2 3StGQYyx+z5JUgFSemOMUc6/pMPq8+6/8Cj/AJnRUVk/aPEP/QL0v/wZSf8Axiq76trMeow2 DafpYupopJo0/tCX5kQoGOfIxwZE/P2NHOv6TD6vPuv/AAKP+ZvUVk/aPEP/AEC9L/8ABlJ/ 8Yo+0eIf+gXpf/gyk/8AjFHOv6TD6vPuv/Ao/wCZrUVk/aPEP/QL0v8A8GUn/wAYo+0eIf8A oF6X/wCDKT/4xRzr+kw+rz7r/wACj/ma1FZP2jxD/wBAvS//AAZSf/GKdFPrpmQTabpyRFhv ZL92YDuQDCMn2yPrRzr+kweHmuq/8Cj/AJmpRRRVGAUVnXU2spcOtpYWEsAxteW9eNjxzlRE wHOe5qL7R4h/6Bel/wDgyk/+MVPMv6RsqE2r3X/gS/zNaisn7R4h/wCgXpf/AIMpP/jFH2jx D/0C9L/8GUn/AMYo51/SY/q8+6/8Cj/ma1FZP2jxD/0C9L/8GUn/AMYo+0eIf+gXpf8A4MpP /jFHOv6TD6vPuv8AwKP+ZrUVk/aPEP8A0C9L/wDBlJ/8Yo+0eIf+gXpf/gyk/wDjFHOv6TD6 vPuv/Ao/5mtRWT9o8Q/9AvS//BlJ/wDGKPtHiH/oF6X/AODKT/4xRzr+kw+rz7r/AMCj/mYv jPxZeeGry2S2SxaNtPvb6RblyryfZxG/lx46sys4/wBn7/IQq2e3jXWZLjUgLSC0t/Nmg0+W SNJTJLFdLbbQgnVpPMdgNxESxkqCWB3VtXenatfata6jc6RpcklrE8Ucb6gxT5pIpNxBt/vK 0CEHt9cYkax1BnvXbw3oBe/QJeMbts3ChSoEn+j/ADgKSMHPBxRzr+kw+rz7r/wKP+ZyuheK L688WyTXFpHZy3Fva2d1NMMxxPFd3sRXCscPIyYUbiqlsb3OxZC+8S6xb+Mbz7FoN3BqF7b2 FpFDdeRKwA+3ylwqzhGGIyuDIhHJ5wA2lqN3D4TtbdJvC2kW9vI8aRrZpPKgZZd0YPlWpCnz ZMqDj5mJXnNaktve61ZtJdeGtEuIbyKMSJd3D5kRSXRXR7fPyliQrD5STwDmjnX9Jh9Xn3X/ AIFH/Mo2vjG+ubqzsWtrSO81B7Ka3CSedEkEsTySBpFOGcC2ugrKNpzCehbGPpXibU/Emp+F 59R0+O0SW9jvLYB49ximsrwqCqyOSBs4kYJvyfkXaa7InW2uEuG0bSDOiMiSG/fcqsQWAPkZ AJVSR32j0qvDY6hbOXg8N6BE5uGuiyXbKTMylWk4t/vlSQW6kEijnX9Jh9Xn3X/gUf8AM6Ki sn7R4h/6Bel/+DKT/wCMVHJqGtwvCkthpCPM+yJW1RwXbaWwv7jk7VY4HYE9qOdf0mH1efdf +BR/zNqiuR0/xff6nqIsINKhjumiaZUuzeW25FKhiDLaqDguvT1FakOoa3coXgsNIlQOyFk1 R2AZWKsOIOoYEEdiCKOdf0mH1efdf+BR/wAzaorJ+0eIf+gXpf8A4MpP/jFH2jxD/wBAvS// AAZSf/GKOdf0mH1efdf+BR/zNaisn7R4h/6Bel/+DKT/AOMUfaPEP/QL0v8A8GUn/wAYo51/ SYfV591/4FH/ADNaisn7R4h/6Bel/wDgyk/+MU6KfXTMgm03TkiLDeyX7swHcgGEZPtkfWjn X9Jg8PNdV/4FH/M1KKKKowCivO/HV14wh1af+wLnUoreKyDolrZRyq8vk3jnJaNiTvhtlwCP 9YB1YGjS7rxg3jWP7Xc6k2lveyo0L2UaxLFuvwvziMNgCC1IJbnzRnO9aAPRKK8n1DUfH0F5 qc9tcaq8a/bPs8A0+NkGBqAi2kRbjjyLQjJOfNGch1FalldeMLbSfFvnXOpXdxb2Uz6Y81lG GMqzXaJsCRqHJSK3bBBzvBHDAUAeiUV5HfXnxBtrfUYre+1mZ0eZoJTp0JYiMahsUYhwQ/kW ZPGT5g2kbxXonhhtRbRSuqSzzXUd3dRCWeNY3kjWeRY2IVVHKBDkAA5z3oA2KKKKACiiigAo oooAKKKhurlLS3eeRZWRcZEUTSNyccKoJP4CgaTbsiavP/hzpdxJZ6dr8llBYvd6f5t48Lhj qU85SYzvgDG35gAR8plkVQEVS/Vf8JHY/wDPDVP/AAVXP/xuj/hI7H/nhqn/AIKrn/43U88e 5t9Vr/yP7ma1FZP/AAkdj/zw1T/wVXP/AMbo/wCEjsf+eGqf+Cq5/wDjdHPHuH1Wv/I/uZNr treX3h7U7PTrj7PfT2ksVvNvKeXIyEK24cjBIORyKp+F9MttNsrk2mgR6HHcXBm+yKyZzsRN zLGSiE7OiEjADEhmYCb/AISOx/54ap/4Krn/AON0f8JHY/8APDVP/BVc/wDxujnj3D6rX/kf 3M1qKyf+Ejsf+eGqf+Cq5/8AjdH/AAkdj/zw1T/wVXP/AMbo549w+q1/5H9zNavPzpdxrfjv Vrj7FAJrHULWKHVt4823gjihneBVwD+8MzrkHlZJNxwiI/Vf8JHY/wDPDVP/AAVXP/xuj/hI 7H/nhqn/AIKrn/43Rzx7h9Vr/wAj+5nO6V8UdJ1jSdR1K1sL5rfT7Rby4xJbuUiMcrjISU4b 90VKHDAsuQBkjUj8aafL4zm8MLFILyJ9jO1xbqCfKEvyxmTzWG1gMhCM57AkZKab4fTS005f 7fFudMi0qcf2bPm4t4/uq58ng7WkUlNpxK3cIVj1HR/D2r319JqI1u6sb6Xz59Om0Z2hMnki EOrG38xGCqMFXBB6dSKOePcPqtf+R/czqh4l0FrB79db002aIrvcC6Ty1VnKKS2cAFlZQe5U jqKuWN/Z6nZx3lhdwXdrJnZNBIJEbBIOGHBwQR+FcLb6F4fima4nn8TXd29wty9xcWE5dpBN FKTxCAAfs8CbQAAsShQpLFug0nUdL0bRrHS7eLV2gsreO3jaTS7ksVRQoJxGBnA9BRzx7h9V r/yP7mdFXldpfDwxq9xrGuWd3HfaXpkMV/L9ojZdQmu51Tz03SBI0DW+AW2HYVUhFiVa77/h I7H/AJ4ap/4Krn/43VPU9R0vVbVLeeLV1RLiC4BTS7kHdFKsqjmM8bkAPtnp1o549w+q1/5H 9zKL/EXTIre6uZbG+S1t4i/nqYZElcWoujEhSQ5byiTu+4dpAY8Z1NN8WaVqHh9tbkuI7KxR yjzXU0YjU5AGJVZo2BJAyrEZypwwIHO6lpOharc6pJdTeIjDqG9mt10yYJFK9t9mMqHyN27y sjDMy/MTtzjBeaL4ZuYh5MOt2d1FuWzvbXS51nso2UqYoXMJIjw8mFOQu/5du1Npzx7h9Vr/ AMj+5nVXviXQdNSN7/W9NtUkeREae6RAzI21wMnkq3BHY8GtSvPYfD/he2NqYItfj+zOrIBp 1wchJLZ0U5i6KtnAg7lQcksS1dZ/wkdj/wA8NU/8FVz/APG6OePcPqtf+R/czWri/EOhavqn i+C78qO40eC3hge1OFNwsszfaFLbhhFVLeRlKneI9gwGcNvf8JHY/wDPDVP/AAVXP/xuj/hI 7H/nhqn/AIKrn/43Rzx7h9Vr/wAj+5lHRPGmn69r19pFrFIk9k8ySF7i3JJjk8tv3ayGUDPQ sgGMeoz0lcjpEtno91cvFfeIprWaWaYWc2kyeXG8spkYqVtw/wB5mxljwfpjY/4SOx/54ap/ 4Krn/wCN0c8e4fVa/wDI/uZg+MbdtS8S6Hpsmj2msWv2e7uzZ3TqqeahhjSQ5UghRO+QezFg GZUU6lnrOj6Dodna6p4msZJLSKO2mu7q6RDLIu5CzbmOGLRS8Ek5RxyVNWv+Ejsf+eGqf+Cq 5/8AjdcXP4X8OXT6g01x4mcXrzMV/s2UCISLdBlTEGcZvZiN2TnbzgYJzx7h9Vr/AMj+5nfD VtNa/ewXULQ3iOqPbiZfMVmQuoK5yCVVmA7hSegqPTNd0fW/N/snVbG/8nHmfZLhJdmc4ztJ xnB6+hrkf7G8OnWv7SY+InCy+bHatY3PkxkzfaG2jys/NOEkJznMarkJlDoeHBovhiwezsk1 uSNvKyZtMuCf3cEcC9Ih/DCpPuT24Bzx7h9Vr/yP7mddRWT/AMJHY/8APDVP/BVc/wDxuj/h I7H/AJ4ap/4Krn/43Rzx7h9Vr/yP7mN8WX1xpng3XL+zk8u6tdPuJoX2g7XWNipweDggda5O 3uLL4b2Qu7jSo9Msbt4bCDTrW5iCLLGkrNO8srxplwNu5jvYRxlvmbYnXf8ACR2P/PDVP/BV c/8Axuqd7qOl311p1xLFq4ewuDcRBdLucFjFJFhv3fTbIx4xyB9Cc8e4fVa/8j+5kP8AwnVg ukx6lJZXyW8ulS6vFlYyXgjjidsAOcN++CgHHKt2wTY0bxjpOsacl79ogtI5LsWcQmvbeTzJ SAQitFI6ljnhc7uOmMZxX03w++lvpzf2+bcaZLpUA/s2fNvbyfeVD5PJ2rGoL7jiJe5ctTu/ Dvh7UPOkvp/EU93c/u7u8XSXgmurc7N1vI0Vum6M+WvP38ZAYKSCc8e4fVa/8j+5nZP4l0GO 4tLd9b01Z71Ee1ja6QNOrnCFBnLBjwCM57VqVwtnpvh+y1S11FP7fa4gcSEvps+JZP8ASdzs BD1ZryZiFwM7cAAYPSf8JHY/88NU/wDBVc//ABujnj3D6rX/AJH9zIfFFtd3VlbJDayXtotw DfWMTIr3UOxxsG8qpG8xlgWAZVZTkHa3H+HvGmleGvD9lZTxXbvNpkuvkG4jZlimM9x5YMkg kmdQrKWAOcBm25OO2/4SOx/54ap/4Krn/wCN1j6kNF1T+1/PTW1/tXT10+fZplwNsY83BXMR w375uTkcDjrk549w+q1/5H9zG3fxF0ywvLWyvLG+gvJpXhkt3MO+FlETbSBJ+8YrPGwSLzGO cAbgRXSTatptteiyn1C0iuyiuIHmVXKs4jU7Sc4LkKD3JA61xv8AZOhNeR3803iKXU4/NZb8 6ZMsolcRL5oCwBQwSCNAAoUruDK25s17nwx4RnnbybXW7OybeW0+z0y4iti7oIpH2CL7zRAx 8HgMxUK5L0c8e4fVa/8AI/uZ21rruj32oz6dZ6rY3F9Bu862huEeSPadrblByMEgHPQ1oVyO kDRdFv728tk1t5LvPmCTTLggZnnn4xEP4rhx9AvfJOx/wkdj/wA8NU/8FVz/APG6OePcPqtf +R/cxuu2NxqE+jwpH5lmuoJNeDcBhI0eSM+vE6wHjrjn5d1bFZP/AAkdj/zw1T/wVXP/AMbo /wCEjsf+eGqf+Cq5/wDjdHPHuH1Wv/I/uZrUVk/8JHY/88NU/wDBVc//ABuj/hI7H/nhqn/g quf/AI3Rzx7h9Vr/AMj+5mtXL67NcWfi3Tbu2tftMw0rUI7eEyCMTz7rd0hDngMwjcj2Vj0U 1pf8JHY/88NU/wDBVc//ABuj/hI7H/nhqn/gquf/AI3Rzx7h9Vr/AMj+5nOp4gHhrTptV1zS b6G8uZY4pri7ubGHzjhyFj3XO1Y0wcJuz8xPzsZHOh4NvE1OTXtUgGbW91BJYXDq6sBaW6Nh 0JVtrq6EqSNysM8VNqWoaVqtusFwmvoiuHBtbW+t2zgjlo1Ukc9M46egqxBrun21vFAkOrlI 0CKZNOu3YgDHLMhLH3JJPejnj3D6rX/kf3M2qKyf+Ejsf+eGqf8Agquf/jdH/CR2P/PDVP8A wVXP/wAbo549w+q1/wCR/czjfAsSWenab4k1FbHSBeae0t3N56j+055Qtw07nC42KshAP3d8 oUKiBn7RvEugql67a3poSwcJeMbpMW7FioEnPyEsCMHHIxWD4pi0jxZpsdjdSa/aoju2+10u YMQ8UkTKd8LDBSVx0z0IIrPm0Lw/IbqSKfxNbXM7s6XMFhOskDNJcuxjPk4BIvJlyQcAqRhh uo549w+q1/5H9zOuvfEug6akb3+t6bapI8iI090iBmRtrgZPJVuCOx4Naleew+H/AAvbG1ME Wvx/ZnVkA064OQkls6KcxdFWzgQdyoOSWJaus/4SOx/54ap/4Krn/wCN0c8e4fVa/wDI/uZr UVk/8JHY/wDPDVP/AAVXP/xuj/hI7H/nhqn/AIKrn/43Rzx7h9Vr/wAj+5mtRWT/AMJHY/8A PDVP/BVc/wDxuj/hI7H/AJ4ap/4Krn/43Rzx7h9Vr/yP7ma1FZP/AAkdj/zw1T/wVXP/AMbo /wCEjsf+eGqf+Cq5/wDjdHPHuH1Wv/I/uZrUVk/8JHY/88NU/wDBVc//ABurFnq1tfzGKGO8 Vgu7M9lNCuPq6gZ56daFOL2YpYetFXlBpejL1FFFUYhRRRQAUUUUAFFFFAGfqet6do3lfb7j yvNyRhGfaq43SNtB2RrkbnbCrkZIyK0K5/xL4Y/4SDbtvPs++0uLCfMW/dbz7PMC8jbJ+7Xa x3Ac5VsjG4BN9odmkjMBRQiBCGDZO4ls4II24GBjB5OeADLh8UaTc2dzdW809xHb7SwgtJZH ZWOEkRFUtJG2Dh0BU7WIJAOJH8R6RHYWl7JfRpBeXCWsG8FWeZn2CPaRuDhsgqRldrbsbTjH 03wdNpWl6laQXtpM94ixH7VZGSKRRkNJPGHHnTyKxEkmV34T5Rtwc/VvhrHqOk6fDDqUkF/Z uu243TiJI/OExijhjmQIm5UC8kqkaAHKqygHcTzLbW8s7iQpGhdhHGzsQBnhVBLH2AJPaseT xdo8VlDdNJdkS3H2VYVsZ2nEuwybGhCeYp2KX5UfLg9CCdicTNbyrbyRxzlCI3kQuqtjglQQ SM9sjPqK5P8A4QZD4e/s1zpUjC7+1JBLpSvYxnZs2pbliUXGW+Vwd7M2cMUIB1kEy3NvFOgk CSIHUSRsjAEZ5VgCp9iAR3qSq9ha/YdOtrP7RPceREkXnXD75JNoA3O3djjJPc1YoAKz7rW9 OstRgsLi42XE23A2MVTcdqb2A2puYFV3EbmBC5IxWhXP6p4Y/tLWkvheeVDJ9l+0xeVuZ/s0 zTQ7GyNnzsd2Q25cAbTzQBuTzw2tvLcXEscMESF5JJGCqigZJJPAAHOay/8AhKNJ/s77b50+ 3zfI8j7JL9o8zG7Z5G3zN2358bc7Pm+7zVjUtM/tjSdU0y9m/wBFvontwYV2vHG8e1uSSC2S xBwByBg4ycf/AIRa82fbP7Tg/tn+0P7Q8/7Ifs/mfZ/s2PK8zdt8rt5md/OcfLQBqP4j0hLi 0h+3RubtEeJ4wXj2ucRlnUFVDnhCxG88Lk8VqVya+B4YDp0NtfSLZ21vY28qSRhpJFs5DJAV cEBTuJ35Vtw4Gw811lABWfa63p17qM9hb3G+4h3ZGxgr7TtfYxG19rEK20naxAbBOK0K5/S/ DH9m6098bzzYY/tX2aLytrJ9pmWabe2Tv+dRtwF2rkHceaANTUtUtNJt1mu3kAdwkaRRPLJI 2CcIiAsxwCSADgKT0BNV38R6QlxaQ/bo3N2iPE8YLx7XOIyzqCqhzwhYjeeFyeKj1PSbzULX TZFvYItTsJRcRzG2LQtIYnibMe8NtKyPgb8g7eTgg5a+B4YDp0NtfSLZ21vY28qSRhpJFs5D JAVcEBTuJ35Vtw4Gw80AblrrenXuoz2Fvcb7iHdkbGCvtO19jEbX2sQrbSdrEBsE4rQrn9L8 Mf2brT3xvPNhj+1fZovK2sn2mZZpt7ZO/wCdRtwF2rkHcea6CgCOeeG1t5bi4ljhgiQvJJIw VUUDJJJ4AA5zWX/wlGk/2d9t86fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5rUnEzW8q28kc c5QiN5ELqrY4JUEEjPbIz6iuT/4RHU38PfYZ9VsZdRN39pfUfsU0cjts2lsx3CushHy7ldVC fuwoXigDcfxHpCXFpD9ujc3aI8TxgvHtc4jLOoKqHPCFiN54XJ4qOw8UaTqerS6ZazTtdR+d kPaSxo3lSCOTa7KFfa5AO0nrWf8A8IteQf2DZ2epwf2NpMUUf2O6tDK8zR4CuzrIg3KFG3Kl Qx3YJCbbmpeGLTWb2/fUnkntLuySz+zq7xmNQ7tJh1YECTMYYDGREoORwACRfFOjSaPHqsF5 9ps5pXhhe2ieZpnVmVhGiAs+Njn5QflUt90ZrUgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc1x +k+CL3SdGgt01mOW/tdTm1G3uJYJZIw0qurK6PMzMMSyYw684Y5O4v0mlaZ/Y+nadptrNusb K0W2AlXMj7Aqo24EAcBsjbySMYxggEaeI9Ie4u4ft0aG0R3leQFI9qHEhV2AVgh4cqTsPDYP FR/8JRpP9nfbfOn2+b5HkfZJftHmY3bPI2+Zu2/PjbnZ833eay28DwznUYbm+kazube+t4kj jCyRreSCScs5JDHcBswq7Rwd55qT/hFrzZ9s/tOD+2f7Q/tDz/sh+z+Z9n+zY8rzN23yu3mZ 385x8tAHSQTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOakqnpOmw6No1jpdu0jQWVvHbxtIQWKo oUE4AGcD0FXKACs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVzeg+F5tBt51h v45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYVO4BqanrenaN5X2+48rzckYRn2quN0jbQdka5 G52wq5GSMij+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3ms/xL4Y/wCEg27bz7Pv tLiwnzFv3W8+zzAvI2yfu12sdwHOVbIwf8Ix/wAVL/an2z/R/tf2/wAjyvn+0fZ/s2d+ceX5 f8O3O7ndj5aANDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/AIa8Mf8ACP7t 159o2WlvYQYi2bbeDf5Ybk7pP3jbmG0HjCrg56CgCvfX1vp1nJdXUnlwpgEhSxJJAVVUZLMS QAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2om8mRZYriKQ ruCyRSLIm5cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNxNcXTtER5jSu8jhBu+QeY+RndhRg5J3 UASf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI 2jXGZFc8IyBg54Usar/8Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPA v2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQS GVlOCrAggqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQBj2/ irQrzTrvULTU4Lq0tJRBJNbkyqZCFIRNud7HegAXJLNtHzcVoWN9b6jZx3VrJ5kL5AJUqQQS GVlOCrAggqQCCCCARXN23h/xNbDW3TxFpqXGpuJlmj0lh5EvlxRbgGnYMAkXQ/xHJJA2nY0j TbnS9LsrPz7QmJ2M7RW7qJQdxyN0jMHLFWZ2Zyx3E8tkAEia5pcmuNokd/BJqaRGaS1R9zxo NnLAfd/1iYzjOcjODUeieINP8Q2/2jTvtbQbEdZJrKaBZFYZUoZEUOCBnK56j1FU7vRtYn8W 2usRapYx2ttE9uts9g7OY5GiaTMnnAbswjB24GeQ1Hhjwunhv7UIpYPLm2KsNrarbRgLnDsi fKZm3Yd1ChgqAKoWgDoKKKKAMfVvE+m6JeQWt6L7zrjiEQafcThzhjtDRow3YRjtznAzjFbF c/r2jaxqep6ZdWGqWNpHYSm4SOewectIY5IzlhMny7ZTxjORnOOKy7PwLNYeJ7rXLW802Ce4 1P7Y5h00o7wmMxvA7iTLBjskz08xSxU5AAB0lrrVlfajPZWzTySQ7g8gtpPJyp2sol2+WzA8 FQxIIYEZU40K5fwZ4R/4ROC8i/4lTfaJWl32Om/ZW5d32sfMbcq79qDjaoxzXUUAV76+t9Os 5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEE Aiq+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDyI9J0qbSbCC2S5jkJuJri6doi PMaV3kcIN3yDzHyM7sKMHJO6gCT+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3mq8 PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/AOEY/wCKl/tT7Z/o/wBr+3+R5Xz/ AGj7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcA IeaAOosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAirFZ+i6Z/ZGmC1M3nSNLLcSyBdo aSWRpH2rk4Xc7YBJIGASTydCgArPTXNLk1xtEjv4JNTSIzSWqPueNBs5YD7v+sTGcZzkZwaN T0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3zjnHU1n3ejaxP4ttdYi1SxjtbaJ7dbZ7B2cxyNE 0mZPOA3ZhGDtwM8hqANDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/hjwx/w jn2r/TPP87YPli8vftz+9l5PmXD7v3kvG/avyjHPQUAUzqtiuspo5uY/7Qe3a6FuOW8pWClz 6DcwAz15xnBwWGq2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg8ggc3J4Ch/4TT+3re+nhhl inS6txcXJeV5Qql1cTAJgJGAAnG0Y5VCljwh4PHhKfVRBd+bZ3csb29v++P2dEQRqmZJX3fK qjIC9MfdCqoB1FZ/9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281oVhyaPqU3iyHVJ tRtJNPgTEFk1m2+JipDOsnmY3nOMlDhcquNzlgC5pmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg 7XXKtg4Jwa0K5/w14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8YVcHPQUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAVxfg7xHd3tveXGq30bQQ2VvdXUkgRFsrlhIbi2JAAURBEO18uu/5mORjtKz9M 1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODQBzfjHxHd2VvZ3GlX0awTWVxdWskYR1v blRGbe2BIIYSh3O1MO2z5WGDmT+29R/4T3+zvtHyfa/I+w7F/wCPT7L5v2rpv/1/7nfny/4c b+a6DU9b07RvK+33HlebkjCM+1VxukbaDsjXI3O2FXIyRkUf23p39sf2V9o/0vpt2Ns3bd/l 78bfM2fPszu2/NjbzQBz/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/5icjHY Vn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCgDH8UX1xp2gy3FtJ5LCWFJJ9oP kRNKiyy85A2IzvlgVG3LAgEVX0LXEbQbO41W/gRri7ltbWeZ1j+2ASusLL0DNIiq42jDbsqA MCti+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynB VgQQVIBBBBAIoA5f+29R/wCE9/s77R8n2vyPsOxf+PT7L5v2rpv/ANf+5358v+HG/msfT/FO sz6DeXEl5uYRWD3U/lJ/xLZZpSt3FwML9nQB8SBmTOZCw4ruP7b07+2P7K+0f6X027G2btu/ y9+NvmbPn2Z3bfmxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNAB4XvrjUdB iuLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYqvY31vqNnHdWsnmQvkAlSpBBIZWU4KsC CCpAIIIIBFWKAOH0fxjpN43ieGbxjYmG3uyba6FzbgwQPFDhlONpVZZGUMwbnAJY1oeE/Fdh qfg3Q7+81qxkurqK3gmfz4xuu2jUtFgHAkJJ+Qc+1bEGtWVzFqMkLTv/AGdK8NygtpN6uqhy FTbufKspG0HdkYzmrFhfW+p6dbX9nJ5lrdRJNC+0jcjAFTg8jII60AcfD4hvbr4pXOlfbdtj a7bcWNvJG8jP5HnNPMhj3rDiREDK4+dVBB38XPA+vDX7e8uE1601SMurxrGYxJEGB5ZE5jRi CUjfdIoHzuWJVNx9ask1xdG3TtfNEJiqW0jIiHeFLSBdiZMbgbiM44qSw1S01J7xLV5C9ncG 2nV4njKyBVbHzAZG1lIYZBBBBNAFyiiigDl/HuqHSNBt7hdb/shn1C1hM+YRuR5VWQfvVYcI Xfpxsz0BBr2vjjfr09leW9jFYjVW0iG7hv8AzC9x5XmqrKUULkZQgMxEny4P3q6DWNastBs0 ur9p1heVIVMNtJOd7nCjbGrHk4A46kDqRWhQBxfgDxJd+Jjq13dahps4S4aOO2sL1J1gVZJE BwI1YBwgYMzNv+8Ag+Wu0qnZ6paX9xdQ2rySG2fZI/lOIy2SCFcja5BUhgpO0jBweKuUAY/i i+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq38CNcXctrazzOsf 2wCV1hZegZpEVXG0YbdlQBgVsX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqN nHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFAHL/23qP8Awnv9nfaPk+1+R9h2L/x6fZfN+1dN /wDr/wBzvz5f8ON/NY+n+KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgYX7OgD4kDMmcyFhxXcf 23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXG ZFc8IyBg54UsaADwvfXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+VAU7sqACBWxVexvrfUbO O6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsUAZ+pzaxF5X9k2NjdZz5n2u8e329MY2xPnv1xj A654w7zxPZWfxGsdJk8QWiJNZSrJYPNECtx5kHldt4d1kfCk4IAIHBNdZVM6paLrKaSzyC8e 3a5RTE+1o1YKxD42kgsuVzkbgcYNAHJ/DjxDe+JLO8vr29+0+dsuI0gkjlgtkkLssG9Y0bzk XaHVi2PkbI3kDqNTm1iLyv7JsbG6znzPtd49vt6YxtifPfrjGB1zwabrVlq8t3HZNO/2WVoZ He2kjQurMjBXZQr4ZGB2k4x7itCgDl5tWuF8d22nRal5gfd51gYAoihEW4SA4LsxkIAkyIcB kOJAu6v4H1vUdY8/7bcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv+YnIx0h1S0XWU0lnkF49 u1yimJ9rRqwViHxtJBZcrnI3A4waj03WrLV5buOyad/ssrQyO9tJGhdWZGCuyhXwyMDtJxj3 FAGhXLzatcL47ttOi1LzA+7zrAwBRFCItwkBwXZjIQBJkQ4DIcSBd3UVnvrVkmuLo26dr5oh MVS2kZEQ7wpaQLsTJjcDcRnHFAHL/DjxDe+JLO8vr29+0+dsuI0gkjlgtkkLssG9Y0bzkXaH Vi2PkbI3kDuKz9N1qy1eW7jsmnf7LK0MjvbSRoXVmRgrsoV8MjA7ScY9xWhQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUVx+va3qNl4rtrOC48tT9j+z2uxT9u82dkueo3N5MQWT5CNu7L 5UgUAdhRWXrOpTW2g6xcaUsd3qFlbylLdQZCZhHvRGVTnJyhxwSGGOork/8AhIrr+y/+Rg/4 lH9q/Zf+Eh/cf8e/2bzPM8zb5H+v/cbtuP4fv80AegUV5/c+M7iwufB8ms6zpWlfb4kk1LT5 1EU0Za2mYsWd8pGJFVcFc7hjdyVPoFABRRXH6Dreo3viu5s57jzFH2z7Ra7FH2Hyp1S26Dcv nRFpPnJ3bcphQRQB2FFc34l1gQ6bpdxbarHZabeXAE+qxtGVghMUjq4dw0YDOsaZYEHzMDkg jDPibXWutD+1H7Fd3Fpp0v8AZ/khftMk0pW7XawL/uIwHwpBTOX3LxQB6BRXH6Dreo3viu5s 57jzFH2z7Ra7FH2Hyp1S26DcvnRFpPnJ3bcphQRXYUAFFV7+b7Pp1zP9pgtfLid/PuBmOLAJ 3OMr8o6nkcDqOtef23jm1m8EzXd54wsUuodQmgmuLHyPMcefKsCxrIzJHvVFIaTcNgYk9ZFA PSKK4e78Syxaj4atpPEelJqN9FBIbOB0FvcKxAlkDuS7KQcQqmGL8neobYeGfFupar4+1vRd TtfsXkWkM0Fm01u7wje4JcpIzFmUxN0AXgd1eQA7iiuf8WajLp9rYf8AEw/sy0nu/Ku9Q+Qf ZY/KkYNukBRcyLGmWBHz4HzEEXND1Ka80bSX1JY7bVLuyS4ltCCjK21PMARjuAVnAOemQD1o A1KK8/HibXVutc+yn7bd29pqMv8AZ/khvs0kMoW0XaoD/v4yXwxJfGU2rxR/wkV1/Zf/ACMH /Eo/tX7L/wAJD+4/49/s3meZ5m3yP9f+43bcfw/f5oA9AorP0K6vL7w9pl5qNv8AZ76e0ilu IdhTy5GQFl2nkYJIweRWhQAVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYV O/SVxfg7xHd3tveXGq30bQQ2VvdXUkgRFsrlhIbi2JAAURBEO18uu/5mORgA0Ne8Lza9bwLN fxxztZTafduludskM4TzvLUvlHzGu0kuF5yr9pP+EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvz jy/L/h253c7sfLWf441vUdH8j7FcfZ82lzPD8it9qu08vyLX5gc+Zuk+RMSNs+UjByf23qP/ AAnv9nfaPk+1+R9h2L/x6fZfN+1dN/8Ar/3O/Pl/w4380AaHhrwx/wAI/u3Xn2jZaW9hBiLZ tt4N/lhuTuk/eNuYbQeMKuDnoK4/wPreo6x5/wBtuPtGLS2nm+RV+y3b+Z59r8oGPL2x/I+Z F3/MTkY7CgDP1rTP7X0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vY PM+1yXN3JLbFhJ5sjySrGocbMs52kl9owDuPNHii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGx Gd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL0AI/wDhGP8A ipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBH HZyGS38xd37xtxO8goGHACHmj+29R/4T3+zvtHyfa/I+w7F/49Psvm/aum//AF/7nfny/wCH G/msfT/FOsz6DeXEl5uYRWD3U/lJ/wAS2WaUrdxcDC/Z0AfEgZkzmQsOKAO40XTP7I0wWpm8 6RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToVj+F7641HQYri5k85jLMkc+0Dz4lldYpeMA70V HyoCndlQAQK2KAOX07Q/ElhPq1x/belSTahKtx/yCpAscgSKPp9o5XZF0yDubOcDabGhaNrG ieHtM0n+1LGb7D5UPm/YHXfbogXbjzjiQ4zvyR/sVy9l42ludO1+JPEulX93bahAiT2ARVhs 3FsJZ1Qu/wAsfmSsXcsoZTn5RtHWeG9Va70S0lvrmNpJ7ieC2lbapu0R5PLkXGAxeJBJlQAQ SwAXoAEnh9pfFkOttdRgRJhVW3VZiNpXymlBy0GWMnlkE+Zht2AFEeg6NrGmanqd1f6pY3cd /KLh44LB4CsgjjjGGMz/AC7YhxjOTnOOKpya8B8Q4dGh160eXZun01jGvlxeWWXb/wAtHnLD dwdgiB3KCUZ4/Aut6jrH2/7fcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybhtAOwooooAx/E uk3mtaZFa2V7BaSJdwXJkmtjMD5UiyKu0OnVkXJz0yOpyMe18D/Yden1qzuLG3vp9Va8mkhs Nhkt2i8trdiHyckCQsTgyfNsqv401qHTNe0W3bxn/Yq3UpS5g8y1G2LypmEv72NiMuiJnO3t jJzUln8QIZfE91pV0mmxwRan/ZaTw6iJHMxjMib0KKEDBXT7xPmKUAPJABc8GeEf+ETgvIv+ JU32iVpd9jpv2VuXd9rHzG3Ku/ag42qMc11FcP4C8QXvie61O/udWsZY4pXhSxsL2OeOICV0 Vz+6WQZEeVYuwcMWCqMKO4oAz9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkV9 P0m80vTLe1tb2DzPtclzdyS2xYSebI8kqxqHGzLOdpJfaMA7jzR4ovrjTtBluLaTyWEsKST7 QfIiaVFll5yBsRnfLAqNuWBAIqPw3qrXeiWkt9cxtJPcTwW0rbVN2iPJ5ci4wGLxIJMqACCW AC9ACP8A4Rj/AIqX+1Ptn+j/AGv7f5HlfP8AaPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR 3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5o/tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv /wBf+5358v8Ahxv5rH0/xTrM+g3lxJebmEVg91P5Sf8AEtlmlK3cXAwv2dAHxIGZM5kLDigD uNF0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu52wCSQMAknk6FY/he+uNR0GK4uZPOYyzJHPtA 8+JZXWKXjAO9FR8qAp3ZUAECtigDP1PQtH1vyv7W0qxv/Jz5f2u3SXZnGcbgcZwOnoKz7vRt Yn8W2usRapYx2ttE9uts9g7OY5GiaTMnnAbswjB24GeQ1dBXFx61qb/EqbTZ55I7NH2W9rA0 e6RPIDmeVGjL+VvLx+asgG9UTbncSAXPBXhD/hENONr9qgl/dRQ4tbX7PG3lgjzWTc2Zmz87 5+YKgwNvOxqehaPrflf2tpVjf+Tny/tdukuzOM43A4zgdPQVzfw+1rU9Zt7uXVp5JLvZE8sS NG0Fq7Bt0AxGjpKhGHicuUBj+YljXSanqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntnoc44yAZ 93o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTzgN2YRg7cDPIaq/grwh/wiGnG1+1QS/uoocW tr9njbywR5rJubMzZ+d8/MFQYG3mvceKEs/iVb6Nc+IdKW1mtJQtjlUmSfdbiNXYuSzMHkKq FXIzw2ARJ4N14a3caoIdetNYtoHRRLEYwyy5cSbUTlYMqBHvJc7XO512MQDrK5e38IeR42u/ EX2qAefKJtsVrsnOIFh8p5tx3w/Lv2bR8+05+XnqKz/7Ruv7Y+xf2NffZ/8An/3weT93PTzP M6/L9zr7c0AY/grwh/wiGnG1+1QS/uoocWtr9njbywR5rJubMzZ+d8/MFQYG3nqK4v4fa1qe s293Lq08kl3sieWJGjaC1dg26AYjR0lQjDxOXKAx/MSxrtKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKz9M1 vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhXN6D4Xm0G3nWG/jknWyh0+0d7c7Y4YA /k+YofLvmRtxBQNxhU7gGpqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b07+2P 7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5rL17wvNr1vAs1/HHO1lNp926W52yQzhPO8tS+U fMa7SS4XnKv2k/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tAGhpmt6drPm /YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0K5/w14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ +8bcw2g8YVcHPQUAV76+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzI XyASpUggkMrKcFWBBBUgEEEEAiq+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AIJGQCDy K+n6TeaXplva2t7B5n2uS5u5JbYsJPNkeSVY1DjZlnO0kvtGAdx5oAsf23p39sf2V9o/0vpt 2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2jXGZFc8IyBg54Usar/8 Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTqO6SKKyt7OTyM COOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVis /RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQBXvr6306zkurqTy4UwCQpYkk gKqqMlmJIAUAkkgAEmo9L1S01mwW9snkaBndP3kTxMGRyjAq4DAhlIwQOlR63pz6ro89lE8E ckm0q88TSKhDAhgEdGDDGVZWBVgCDkVT0fQ7nQfD+m6XZX0bvbuGu7i6ieVrksS0zDMgKu7s WySwGTwaALj61ZJri6Nuna+aITFUtpGREO8KWkC7EyY3A3EZxxUlhqlpqT3iWryF7O4NtOrx PGVkCq2PmAyNrKQwyCCCCaw7fwh5Hja78RfaoB58om2xWuyc4gWHynm3HfD8u/ZtHz7Tn5eb Gg6NrGmanqd1f6pY3cd/KLh44LB4CsgjjjGGMz/LtiHGM5Oc44oA6CiiigDP1jWrLQbNLq/a dYXlSFTDbSTne5wo2xqx5OAOOpA6kVoVj+JdJvNa0yK1sr2C0kS7guTJNbGYHypFkVdodOrI uTnpkdTkY9r4H+w69PrVncWNvfT6q15NJDYbDJbtF5bW7EPk5IEhYnBk+bZQB0lnqlpf3F1D avJIbZ9kj+U4jLZIIVyNrkFSGCk7SMHB4q5XL+DPCP8AwicF5F/xKm+0StLvsdN+yty7vtY+ Y25V37UHG1RjmuooAr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdWsnm QvkAlSpBBIZWU4KsCCCpAIIIIBFV9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEH kV9P0m80vTLe1tb2DzPtclzdyS2xYSebI8kqxqHGzLOdpJfaMA7jzQBY/tvTv7Y/sr7R/pfT bsbZu27/AC9+NvmbPn2Z3bfmxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNV /wDhGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZ W9nJ5GBHHZyGS38xd37xtxO8goGHACHmgDqLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBB BAIqxWfoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8nQoAKz31qyTXF0bdO180Q mKpbSMiId4UtIF2JkxuBuIzjitCsOTw+0viyHW2uowIkwqrbqsxG0r5TSg5aDLGTyyCfMw27 ACgAuaZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCsPw1o+paPbzx6jqNpfySuJG uIrNoZJZMYZ5CZHDEgKAAFChQoAUKBuUAZ/9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tv zY280aZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGqcej6kniybVn1G0ltHTy44JLN jLBHtGUSQSbQGcb2OwluASQqbY/DHhj/AIRz7V/pnn+dsHyxeXv25/ey8nzLh937yXjftX5R jkA6Ciis/wDsLR/7Y/tf+yrH+0/+f37Onnfd2/fxu+7x16cUAGm61ZavLdx2TTv9llaGR3tp I0LqzIwV2UK+GRgdpOMe4rQrl/BXhD/hENONr9qgl/dRQ4tbX7PG3lgjzWTc2Zmz875+YKgw NvPUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZ91renWWowWFxcbLibbgbGKpuO1N7Ab U3MCq7iNzAhckYoA0KKjnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1l/8JRpP9nfbfOn2+b5H kfZJftHmY3bPI2+Zu2/PjbnZ833eaANiist/EekJcWkP26NzdojxPGC8e1ziMs6gqoc8IWI3 nhcnitSgAoorPtdb0691Gewt7jfcQ7sjYwV9p2vsYja+1iFbaTtYgNgnFAGhRWfq2tWWiRQS XrT/AOkS+TCkFtJO7vtZ8BI1Zvuox6dqz/8AhNdB82xjF1O/26K3mhdLOZk2XDbISzhNqbmB A3EUAdBRWfa61ZX2oz2Vs08kkO4PILaTycqdrKJdvlswPBUMSCGBGVONCgAoqOeeG1t5bi4l jhgiQvJJIwVUUDJJJ4AA5zWPP4w0Cz0aXVrzUo7O0icxyfa0aGRXC7thjcB95X5gu3JBBAII oA3KKKKACiqepapaaTbrNdvIA7hI0iieWSRsE4REBZjgEkAHAUnoCasQTw3VvFcW8sc0EqB4 5I2DK6kZBBHBBHOaAJKKxz4q0If2tt1OCX+yIjNf+STJ9nUb8htufmHlvlfvDHTkZr/8Jpon 2Xz999/x9/YvJ/s2587zvK83b5Xl7/8AV/NnbjHegDoKKr2F9b6np1tf2cnmWt1Ek0L7SNyM AVODyMgjrVigAri/B3iO7vbe8uNVvo2ghsre6upJAiLZXLCQ3FsSAAoiCIdr5dd/zMcjHaVn 6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGgDm/GPiO7srezuNKvo1gmsri6tZIwj re3KiM29sCQQwlDudqYdtnysMHMn9t6j/wAJ7/Z32j5PtfkfYdi/8en2XzftXTf/AK/9zvz5 f8ON/NdBqet6do3lfb7jyvNyRhGfaq43SNtB2RrkbnbCrkZIyKP7b07+2P7K+0f6X027G2bt u/y9+NvmbPn2Z3bfmxt5oA5/wPreo6x5/wBtuPtGLS2nm+RV+y3b+Z59r8oGPL2x/I+ZF3/M TkY7Cs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAY/ii+uNO0GW4tpPJYSwp JPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq38CNcXctrazzOsf2wCV1hZegZpEVXG0Y bdlQBgVsX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBI ZWU4KsCCCpAIIIIBFAHL/wBt6j/wnv8AZ32j5PtfkfYdi/8AHp9l837V03/6/wDc78+X/Djf zWPp/inWZ9BvLiS83MIrB7qfyk/4lss0pW7i4GF+zoA+JAzJnMhYcV3H9t6d/bH9lfaP9L6b djbN23f5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGgA8 L31xqOgxXFzJ5zGWZI59oHnxLK6xS8YB3oqPlQFO7KgAgVsVXsb631GzjurWTzIXyASpUggk MrKcFWBBBUgEEEEAirFAGP4pv5dM8OXd5DdwWjR7N000iRhVLqG2tJ8gkKkhN/y7yu7jNZ/h PXDJ4Q0q/wBbv/Lk1GVhaPfPDHJMkkjm3UhMIZDFs+Ve+eM5roL6+t9Os5Lq6k8uFMAkKWJJ ICqqjJZiSAFAJJIABJosb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0oA5ufVLv/hZFvYLqMhs zbhTp8SoHEm2RjNIrR7jAR5aiRHAEgCEEk4PA+vDX7e8uE1601SMurxrGYxJEGB5ZE5jRiCU jfdIoHzuWJVNz+2rI6x/ZaNPJdDhzFbSPHGdu7a8gUojbcHazA4ZePmGZLDVbHU3vEsbmOc2 VwbW42chJQqsUz0JAYZx0OQeQQAC5RRVe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAAS aAOf8e6odI0G3uF1v+yGfULWEz5hG5HlVZB+9Vhwhd+nGzPQEGva+ON+vT2V5b2MViNVbSIb uG/8wvceV5qqylFC5GUIDMRJ8uD96uosb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0qv/AG3p 39sf2V9o/wBL6bdjbN23f5e/G3zNnz7M7tvzY280Ac34A8SXfiY6td3WoabOEuGjjtrC9SdY FWSRAcCNWAcIGDMzb/vAIPlrtKy9E8Qaf4ht/tGnfa2g2I6yTWU0CyKwypQyIocEDOVz1HqK 1KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQtcRtBs7jVb+BGuLuW1 tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwK2L6+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJo sb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl/7b1H/hPf7O+0fJ9r8j7DsX/j0+y+ b9q6b/8AX/ud+fL/AIcb+ax9P8U6zPoN5cSXm5hFYPdT+Un/ABLZZpSt3FwML9nQB8SBmTOZ Cw4ruP7b07+2P7K+0f6X027G2btu/wAvfjb5mz59md235sbearw+KdGns7m6jvMw2+0sfKcF w5xG0a4zIrnhGQMHPCljQAeF7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2Kr 2N9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVigArk59Uu/+FkW9guoyGzNuFOnxKgcS bZGM0itHuMBHlqJEcASAIQSTjrKz01zS5NcbRI7+CTU0iM0lqj7njQbOWA+7/rExnGc5GcGg Dn/A+t6jrHn/AG24+0YtLaeb5FX7Ldv5nn2vygY8vbH8j5kXf8xORjsKz9M1vTtZ837Bceb5 WCcoyblbO2RdwG+NsHa65VsHBODWhQBycevCb4hzaSmvWg8lNkmnSGNXYmMOAin947gHe0mR GFIUIzb3SPwPreo6x5/224+0YtLaeb5FX7Ldv5nn2vygY8vbH8j5kXf8xORjoE1zS5NcbRI7 +CTU0iM0lqj7njQbOWA+7/rExnGc5GcGjTNastY802LTyRx4Ima2kjjkBzho3ZQsinGdyEjB BzgjIAanNrEXlf2TY2N1nPmfa7x7fb0xjbE+e/XGMDrnjDvPE9lZ/Eax0mTxBaIk1lKslg80 QK3HmQeV23h3WR8KTggAgcE11lZ6a5pcmuNokd/BJqaRGaS1R9zxoNnLAfd/1iYzjOcjODQB yfhHxTquo+NdW0fWI5La4jsoLr7C0tswtWLOGRfLdnYFTEdzd8tiMOinvKp2Gq2OpveJY3Mc 5srg2txs5CShVYpnoSAwzjocg8ggXKACiiigAooooAKKKKACiiigAooooAKKKKACuf1Twx/a WtJfC88qGT7L9pi8rcz/AGaZpodjZGz52O7IbcuANp5roK4/Xtb1Gy8V21nBceWp+x/Z7XYp +3ebOyXPUbm8mILJ8hG3dl8qQKAOku7Wa7sr+2la0kSdGSJJrcugUoBiRd37wbtxIG3IIHbJ 5ePwNcQaDNZxapG19Pe/bJbudblyjCMRfum+0CZDsVRkytwXH3W2jpNSv/L0nVJbK7sUurSJ 8vdSYhhkEe9fOI5VcFWPfac1wcfi++bwhNfy6nJb2aan9nivZ57FLm4h8kNmKTd9ldxKWXjj Yjj/AFi0AbkvgOJpdDhivPLsNKitYwoVxPP9nYPHvdZBGygqDtaNsZcqVLZHYV53ceK9cjvf C6SmOK+v7eyebS4vKV1aRwLgyxSHzgiISUMedpSQyfKK9EoAK5/S/DH9m6098bzzYY/tX2aL ytrJ9pmWabe2Tv8AnUbcBdq5B3Hmugrj9B1vUb3xXc2c9x5ij7Z9otdij7D5U6pbdBuXzoi0 nzk7tuUwoIoA6C/sry+8PXNh9v8As99PaPD9st0KeXIyEeYi7sjBOQN2R696ju9EhuBpMMPl 29np9wsv2VIx5cipG6xptGAArFHHBwY1wAQCKfiW7J8MtrFlrU9rY2sTX009hHDM9xAsTNtj MgZOTtYHBzjHGcjLvIvElm/htZ/EMgvLh7S0nght4fImlRXluXZmTfh443ChduGC9ATtANDR PCMOia9fajBNGEunmcpHAI3kaWTzGMzg/vSrZEZwuxWYfNnNdJXF+Gda1O/8X6ra6hPJiJ7h Y7SJo2S3RJtkbSr5YkjeRNrpmRw4MjAKAAO0oAr39r9u065s/tE9v58Txedbvskj3AjcjdmG cg9jXLyeDbweDdQ0Cz1GxtP7Q8xJvK08i3hjePYywQiUeX2flmBdnOPmwOk1a5ms9Gvrq3jk knht5JI0jhMzMwUkAIGUuSR93cM9MjrXDx6/eS+CprmfxRaWs8V75NvqDXtvHBdDaGKrcNAU kAy/Kwqd0ZTHys7AHeWKXkdnGt/PBPdDO+SCExI3JxhSzEcY/iPrx0qxVPSbma80axuriOSO ea3jkkSSEwsrFQSChZihBP3dxx0yetXKAMvWtKm1IWU1pcx295Y3H2i3eWIyx7jG8ZDoGUsN sj4ww5weQCDJpWmf2Pp2nabazbrGytFtgJVzI+wKqNuBAHAbI28kjGMYOf4s1GXT7Ww/4mH9 mWk935V3qHyD7LH5UjBt0gKLmRY0ywI+fA+Ygi5oepTXmjaS+pLHbapd2SXEtoQUZW2p5gCM dwCs4Bz0yAetAFO00bWIPFt1rEuqWMlrcxJbtbJYOriONpWjxJ5xG7MxyduDjgLUg0CZPD+r 2EOoyRXmoPdP9vRSJI2lLeWeGyTGpRAcjiNQMAADDtvEct/4t8Q+Hk8V6ULo2iLpwt0TfbzF rgOChdjLIgSMsOBwPlXJzXk1ybTPC+oXup+KZ4bX+1ZLW1uZo7ZbpxGfKMS5VYQxmjkIZlIE fLbeWQA7yCCG1t4re3ijhgiQJHHGoVUUDAAA4AA4xUlU9JnmutGsbi4ltJp5beN5JLNi0DsV BJjJ5KE8g+mKuUAFc3oPhebQbedYb+OSdbKHT7R3tztjhgD+T5ih8u+ZG3EFA3GFTv0lcX4O 8R3d7b3lxqt9G0ENlb3V1JIERbK5YSG4tiQAFEQRDtfLrv8AmY5GADU8S+GP+Eg27bz7PvtL iwnzFv3W8+zzAvI2yfu12sdwHOVbIwf8Ix/xUv8Aan2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7 c7ud2PlrP8ca3qOj+R9iuPs+bS5nh+RW+1XaeX5Fr8wOfM3SfImJG2fKRg5P7b1H/hPf7O+0 fJ9r8j7DsX/j0+y+b9q6b/8AX/ud+fL/AIcb+aANDw14Y/4R/duvPtGy0t7CDEWzbbwb/LDc ndJ+8bcw2g8YVcHPQVx/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/wCYnIx2 FAGfrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaI jzGld5HCDd8g8x8jO7CjByTuqPxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCAR UfhvVWu9EtJb65jaSe4ngtpW2qbtEeTy5FxgMXiQSZUAEEsAF6AEf/CMf8VL/an2z/R/tf2/ yPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5B QMOAEPNH9t6j/wAJ7/Z32j5PtfkfYdi/8en2XzftXTf/AK/9zvz5f8ON/NY+n+KdZn0G8uJL zcwisHup/KT/AIlss0pW7i4GF+zoA+JAzJnMhYcUAdxoumf2RpgtTN50jSy3EsgXaGklkaR9 q5OF3O2ASSBgEk8nQrH8L31xqOgxXFzJ5zGWZI59oHnxLK6xS8YB3oqPlQFO7KgAgVsUAZfi HRIfEOiTabP5ex3jkAljEiFo3WRQ6HG5CygMuRkEjIzkGkaVNpGl2VjFcxukLsZcxELtbcdk S7v3aKzKFB3bUULz94V/F9/faZ4YurrTjGtwrxJ5kjbViRpFWSQsVYKEQs24qwXbkqQCCeGt SFx4f0+W7vJJJ7h3jjkuHjzcMC5zGUVFdCqFkYKN0YDYHNAFN/Bdu/iq61oT+V9ry05hQxXD kwiHZ56sGEO0K4QDIkUOG4Ao8IeDx4Sn1UQXfm2d3LG9vb/vj9nREEapmSV93yqoyAvTH3Qq rnr4t1IfFW38PXdr9jsZrS4MEbzW5ecqUKzHEhcKQJVCBc8bjn5hFJ8Pta1PWbe7l1aeSS72 RPLEjRtBauwbdAMRo6SoRh4nLlAY/mJY0AdpWX4h0SHxDok2mz+Xsd45AJYxIhaN1kUOhxuQ soDLkZBIyM5GpWH4vv77TPDF1dacY1uFeJPMkbasSNIqySFirBQiFm3FWC7clSAQQCxpGlTa RpdlYxXMbpC7GXMRC7W3HZEu792isyhQd21FC8/eFeTR9Sm8WQ6pNqNpJp8CYgsms23xMVIZ 1k8zG85xkocLlVxucseGtSFx4f0+W7vJJJ7h3jjkuHjzcMC5zGUVFdCqFkYKN0YDYHNZf9t6 j/wnv9nfaPk+1+R9h2L/AMen2XzftXTf/r/3O/Pl/wAON/NAGh4Y8Lp4b+1CKWDy5tirDa2q 20YC5w7InymZt2HdQoYKgCqFroK4vwJ4h1DXLjUkurmO8ggSE/a4XhkgadjJ5qQvESPKULGV En70B8v1Wu0oAz9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkR6TpU2k2EFslz HITcTXF07REeY0rvI4QbvkHmPkZ3YUYOSd1R+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ 3ywKjblgQCKj8N6q13olpLfXMbST3E8FtK21TdojyeXIuMBi8SCTKgAglgAvQAj/AOEY/wCK l/tT7Z/o/wBr+3+R5Xz/AGj7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcd nIZLfzF3fvG3E7yCgYcAIeaP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/8AX/ud+fL/AIcb +ax9P8U6zPoN5cSXm5hFYPdT+Un/ABLZZpSt3FwML9nQB8SBmTOZCw4oA7jRdM/sjTBambzp GlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhWP4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUf KgKd2VABArYoAz9T0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3zjnHU1n3ejaxP4ttdYi1Sxjt baJ7dbZ7B2cxyNE0mZPOA3ZhGDtwM8hq0NT1G6sPK+zaNfalvzu+yPAvl4xjPmyJ1z2z0Occ Z5uPWtTf4lTabPPJHZo+y3tYGj3SJ5Aczyo0Zfyt5ePzVkA3qibc7iQDY0DR9S064v7nVdRt L+5unB86GzaBlUFsJzI42KGwoG3HzE7mdmNzU9GtdX8r7TLfR+Vnb9kv57bOcZz5Tru6d845 x1Nc/wCB9b1HWPP+23H2jFpbTzfIq/Zbt/M8+1+UDHl7Y/kfMi7/AJicjHQanqN1YeV9m0a+ 1Lfnd9keBfLxjGfNkTrntnoc44yAZ93o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTzgN2YRg 7cDPIao/CPhGHwlby29vNGYCkcUccMAiXagIEjgEh52Bw8ny7tqfKNvOfHqWpp8SprGfVJJL N3/0ewgMe6NPIBLyo0Ifyt4ceaspG9kTGdwEngfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx 5e2P5HzIu/5icjAB0Gp6Na6v5X2mW+j8rO37Jfz22c4znynXd075xzjqay9S0DVb/wASw6iu q2kVnHby2v2cWkglMUpiMmJlmUq+YhtYKNuehIzXSVx95rV1a+OxaWmpf2hG0TGbSoZYHmgY RFwfL2o0anamJJJWXdKF2gMrqAWPCHg8eEp9VEF35tndyxvb2/74/Z0RBGqZklfd8qqMgL0x 90Kq9RXF+AvFOoeIrrxDb6pHHDd2N6qfZ0lhcW6tEn7rMbsWKuJAXOMnOApDInaUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCub0HwvNoNvOsN/ HJOtlDp9o7252xwwB/J8xQ+XfMjbiCgbjCp3ANTU9b07RvK+33HlebkjCM+1VxukbaDsjXI3 O2FXIyRkUf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzWXr3hebXreBZr+OOdrKbT 7t0tztkhnCed5al8o+Y12klwvOVftJ/wjH/FS/2p9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O 7ndj5aANDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/hrwx/wj+7defaNlpb 2EGItm23g3+WG5O6T9425htB4wq4OegoAr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQA CTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFV9a0z+19MNqJvJkWWK4ikK7gskUiy JuXIyu5FyAQSMgEHkV9P0m80vTLe1tb2DzPtclzdyS2xYSebI8kqxqHGzLOdpJfaMA7jzQBY /tvTv7Y/sr7R/pfTbsbZu27/AC9+NvmbPn2Z3bfmxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbR rjMiueEZAwc8KWNV/wDhGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy1n2 ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmgDqLG+t9Rs47q1k8yF8gEqVI IJDKynBVgQQVIBBBBAIqxWfoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8nQoAr 399b6Zp1zf3knl2trE80z7SdqKCWOBycAHpRY3sWoWcd1Ck6RvnAngeFxgkco4DDp3HPXpWf 4o0P/hI9Bl0zzIE3ywy5uIPPjby5Uk2um5dytswRkcGjTtJvNJ0exsLO9g/cy7pjJbFkMZYs 0cShx5SjIVASwRVAw2M0AWP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5o03WrLV5 buOyad/ssrQyO9tJGhdWZGCuyhXwyMDtJxj3FU49H1JPFk2rPqNpLaOnlxwSWbGWCPaMokgk 2gM43sdhLcAkhU2mgeH20W4v53uo5nu3BYQ26wKxBY+a6qcNO+755AFDbUwq7aANyq9/fW+m adc395J5draxPNM+0naigljgcnAB6VYqnqtk2o6XcWaSxxmZNuZYVlRh3V0bhkYfKwyCQTgq cEAEljexahZx3UKTpG+cCeB4XGCRyjgMOncc9elU5PEOmRa3Do7TyG8lfy1CwSMgfYZNjSBd ivsUttJBxg4wRmPTtJvNJ0exsLO9g/cy7pjJbFkMZYs0cShx5SjIVASwRVAw2M1TPhUy+N08 QTXUfkQoxhtIkkTMxQR+dIfMKO4TegOwHa4BJ2rgA1NM1vTtZ837Bceb5WCcoyblbO2RdwG+ NsHa65VsHBODWhWH4a0fUtHt549R1G0v5JXEjXEVm0MksmMM8hMjhiQFAAChQoUAKFA3KAK9 9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAgg qQCCCCARVfWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z 7XJc3cktsWEnmyPJKsahxsyznaSX2jAO480AWP7b07+2P7K+0f6X027G2btu/wAvfjb5mz59 md235sbearw+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljVf8A4Rj/AIqX+1Ptn+j/ AGv7f5HlfP8AaPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+ 8bcTvIKBhwAh5oA6ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUze dI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0KACs99ask1xdG3TtfNEJiqW0jIiHeFLSBdiZMb gbiM44o1PQtH1vyv7W0qxv8Ayc+X9rt0l2ZxnG4HGcDp6Cqcnh9pfFkOttdRgRJhVW3VZiNp XymlBy0GWMnlkE+Zht2AFABc0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaFc/4Y 8Mf8I59q/wBM8/ztg+WLy9+3P72Xk+ZcPu/eS8b9q/KMc6Gp6Fo+t+V/a2lWN/5OfL+126S7 M4zjcDjOB09BQAPrVkmuLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFGma3p2s+b9guPN8rBO UZNytnbIu4DfG2Dtdcq2DgnBqnJ4faXxZDrbXUYESYVVt1WYjaV8ppQctBljJ5ZBPmYbdgBR H4Y8Mf8ACOfav9M8/wA7YPli8vftz+9l5PmXD7v3kvG/avyjHIB0FZ/9t6d/bH9lfaP9L6bd jbN23f5e/G3zNnz7M7tvzY281oVjzaNcXHiO21KXUN9rbbnhtTCMpIybDhs424yR8u8FmAkC MyEAsabrVlq8t3HZNO/2WVoZHe2kjQurMjBXZQr4ZGB2k4x7itCuX8FeEP8AhENONr9qgl/d RQ4tbX7PG3lgjzWTc2Zmz875+YKgwNvPUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcfr2 t6jZeK7azguPLU/Y/s9rsU/bvNnZLnqNzeTEFk+Qjbuy+VIFdhRQBn6lf+XpOqS2V3YpdWkT 5e6kxDDII96+cRyq4Kse+05rg4/F983hCa/l1OS3s01P7PFezz2KXNxD5IbMUm77K7iUsvHG xHH+sWvTKKAPO7jxXrkd74XSUxxX1/b2TzaXF5SurSOBcGWKQ+cERCShjztKSGT5RXolFFAB XH6Dreo3viu5s57jzFH2z7Ra7FH2Hyp1S26DcvnRFpPnJ3bcphQRXYUUAc/4luyfDLaxZa1P a2NrE19NPYRwzPcQLEzbYzIGTk7WBwc4xxnIy7yLxJZv4bWfxDILy4e0tJ4IbeHyJpUV5bl2 Zk34eONwoXbhgvQE7e0ooA4vwzrWp3/i/VbXUJ5MRPcLHaRNGyW6JNsjaVfLEkbyJtdMyOHB kYBQAB2lFFAFPVrmaz0a+ureOSSeG3kkjSOEzMzBSQAgZS5JH3dwz0yOtcPHr95L4KmuZ/FF pazxXvk2+oNe28cF0NoYqtw0BSQDL8rCp3RlMfKzt6JRQBT0m5mvNGsbq4jkjnmt45JEkhML KxUEgoWYoQT93ccdMnrVyiigDn/Fmoy6fa2H/Ew/sy0nu/Ku9Q+QfZY/KkYNukBRcyLGmWBH z4HzEEXND1Ka80bSX1JY7bVLuyS4ltCCjK21PMARjuAVnAOemQD1rUooA4e28Ry3/i3xD4eT xXpQujaIunC3RN9vMWuA4KF2MsiBIyw4HA+VcnNeTXJtM8L6he6n4pnhtf7VktbW5mjtlunE Z8oxLlVhDGaOQhmUgR8tt5ZPQKKAKekzzXWjWNxcS2k08tvG8klmxaB2Kgkxk8lCeQfTFXKK KACuL8HeI7u9t7y41W+jaCGyt7q6kkCItlcsJDcWxIACiIIh2vl13/MxyMdpVOw1S01J7xLV 5C9ncG2nV4njKyBVbHzAZG1lIYZBBBBNAHN+ONb1HR/I+xXH2fNpczw/IrfartPL8i1+YHPm bpPkTEjbPlIwcn9t6j/wnv8AZ32j5PtfkfYdi/8AHp9l837V03/6/wDc78+X/DjfzXQanren aN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59m d235sbeaAOf8D63qOsef9tuPtGLS2nm+RV+y3b+Z59r8oGPL2x/I+ZF3/MTkY7Cs/TNb07Wf N+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnI GxGd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL01L6+t9Os 5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEE AigDl/7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnfny/4cb+ax9P8U6zPoN5cSXm5 hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDiu4/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZ ndt+bG3mqZ8X6H9guL0XcjwQPEp2W8rM/mOEiaNQu6RHY4V0BVsHBODgAk8L31xqOgxXFzJ5 zGWZI59oHnxLK6xS8YB3oqPlQFO7KgAgVsVT0vVLTWbBb2yeRoGd0/eRPEwZHKMCrgMCGUjB A6VcoAw/F9/faZ4YurrTjGtwrxJ5kjbViRpFWSQsVYKEQs24qwXbkqQCCeGtSFx4f0+W7vJJ J7h3jjkuHjzcMC5zGUVFdCqFkYKN0YDYHNal9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkk AAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARQBy9x4oSz+JVvo1z4h0pbWa0lC2O VSZJ91uI1di5LMweQqoVcjPDYBB4H1vUdY8/7bcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv +YnIx0H9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280WWt6dqOo3thaXHm3Flt88BG CjcWUYYja3zRup2k4ZGBwQRQBoVh+L57628MXU2nXsdlcI8R+0ScLGnmL5hJMcgUbN3zlCF+ 8cAEjcqvfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNAGX4a1IXHh/T5bu8kknuHeOO S4ePNwwLnMZRUV0KoWRgo3RgNgc1Tuf7Wh8d2EKa3O9rdedctYmCIQpbxxJGVDbTIZDNLG4O 4DbuHYbugsb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAiq/9t6d/bH9lfaP9L6bdjbN2 3f5e/G3zNnz7M7tvzY280Ac34E8Q6hrlxqSXVzHeQQJCftcLwyQNOxk81IXiJHlKFjKiT96A +X6rXaVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCgDH8UX1xp2gy3FtJ5LCW FJJ9oPkRNKiyy85A2IzvlgVG3LAgEVH4b1VrvRLSW+uY2knuJ4LaVtqm7RHk8uRcYDF4kEmV ABBLABempfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEE hlZTgqwIIKkAggggEUAcv/beo/8ACe/2d9o+T7X5H2HYv/Hp9l837V03/wCv/c78+X/DjfzW Pp/inWZ9BvLiS83MIrB7qfyk/wCJbLNKVu4uBhfs6APiQMyZzIWHFdx/benf2x/ZX2j/AEvp t2Ns3bd/l78bfM2fPszu2/NjbzUdn4h0zULe6ntJ5Jktk8xwkEhZ0IJV4125kRtrbWQMHwdp OKAI/C99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7C+t9T062v7OTzLW6i SaF9pG5GAKnB5GQR1qxQAVxcepamnxKmsZ9Ukks3f/R7CAx7o08gEvKjQh/K3hx5qykb2RMZ 3AdpWe+tWSa4ujbp2vmiExVLaRkRDvClpAuxMmNwNxGccUAYfg3Xhrdxqgh1601i2gdFEsRj DLLlxJtROVgyoEe8lztc7nXYx6ys/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1o UAFcf4H1vUdY8/7bcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv8AmJyMdIdUtF1lNJZ5BePb tcopifa0asFYh8bSQWXK5yNwOMGo7LW9O1HUb2wtLjzbiy2+eAjBRuLKMMRtb5o3U7ScMjA4 IIoA0K4dfFupD4q2/h67tfsdjNaXBgjea3LzlShWY4kLhSBKoQLnjcc/MIu4rP8A7b07+2P7 K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5oA5/wAD63qOsef9tuPtGLS2nm+RV+y3b+Z59r8o GPL2x/I+ZF3/ADE5GOwrP0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRXH69reo2Xiu2s4Ljy1P2P7Pa7FP27zZ2S56jc3kxBZP kI27svlSBQB2FFZ+pX/l6Tqktld2KXVpE+XupMQwyCPevnEcquCrHvtOa8//AOEl1u+8Kfbb bWPsVtFqvkrqN/NbJ59v5GSWniimt1/fNtDBQPkCEhycgHqFFcPd+JDBqPhq1Gu/Zb29igca dqSQwyyIxAkafuJCDtRIwv73OQyBtncUAFFFcfoOt6je+K7mznuPMUfbPtFrsUfYfKnVLboN y+dEWk+cndtymFBFAHYUVyfjLU7tNB0+/wBH1u0sreS4VpLx50SJ4Wjcr++aGVEBbyyGK4PC g5YZp3fiQwaj4atRrv2W9vYoHGnakkMMsiMQJGn7iQg7USML+9zkMgbYAdxRXJ6NrwvvGup6 bBr1pfpbI/2i2BjVreQMoVYlHzkKpIkZyRvKhduHResoAKKr3832fTrmf7TBa+XE7+fcDMcW ATucZX5R1PI4HUda8vv/AIialH8ONRvtNvrHUtStpbxLnULaS3EdmgmlWB/KaQE7wqBBlsj5 vnICuAesUVwer+LLxde0FdPuI3j1BLWSKximt5HZJJMSvKu4uyLGdyNASAUkLkoBXeUAFFc/ 4s1GXT7Ww/4mH9mWk935V3qHyD7LH5UjBt0gKLmRY0ywI+fA+Ygi5oepTXmjaS+pLHbapd2S XEtoQUZW2p5gCMdwCs4Bz0yAetAGpRXD6fq2s6rrWp6bFqXkzvFfIy+Qj/2Y8cwjtX24B/ex sZcSE79uU2rkVn3uo69B4eluovE3lQJrcsAvNQMNuwgjR4mV5Ft3ijzcRsVYpypVchmAoA9I oqnpMzXOjWM7i7DyW8bsLyNUnBKg/vFUAK/qAAAc4q5QAVzekaJr2nXWr3E+sabO+oP54Caa 8YjmEUcSnmdtyBYgSvBJJ+YdK6SuL8HeI7u9t7y41W+jaCGyt7q6kkCItlcsJDcWxIACiIIh 2vl13/MxyMAGhr3hebXreBZr+OOdrKbT7t0tztkhnCed5al8o+Y12klwvOVftJ/wjH/FS/2p 9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5az/ABxreo6P5H2K4+z5tLmeH5Fb7Vdp5fkW vzA58zdJ8iYkbZ8pGDk/tvUf+E9/s77R8n2vyPsOxf8Aj0+y+b9q6b/9f+5358v+HG/mgDQ8 NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNoPGFXBz0Fcn4N1WfULjVIW1iPWLSB0Nve xSRTKwYuNpkiSNN4CqTHtJTeDvcOAvWUAZ+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5A IJGQCDyK+n6TeaXplva2t7B5n2uS5u5JbYsJPNkeSVY1DjZlnO0kvtGAdx5o8UX1xp2gy3Ft J5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgEVH4b1VrvRLSW+uY2knuJ4LaVtqm7RHk8uRcYDF 4kEmVABBLABegBJNo1xceI7bUpdQ32ttueG1MIykjJsOGzjbjJHy7wWYCQIzIa/hrwx/wj+7 defaNlpb2EGItm23g3+WG5O6T9425htB4wq4Oc/+29R/4T3+zvtHyfa/I+w7F/49Psvm/aum /wD1/wC5358v+HG/msfT/FOsz6DeXEl5uYRWD3U/lJ/xLZZpSt3FwML9nQB8SBmTOZCw4oA7 jQ9M/sbQ7LTjN58kESrLOV2meTq8jDJ+Z2LMSSSSxJJPNaFY/he+uNR0GK4uZPOYyzJHPtA8 +JZXWKXjAO9FR8qAp3ZUAECtigCvfQ3E9nJHaXX2W44McpjDgEEHDKeqnGCAQcE4KnBGfp2k 3mlaPY2NpewFopd9zJNbFvNVmLOFw4IYluHcu3dt7EsY/F899beGLqbTr2OyuEeI/aJOFjTz F8wkmOQKNm75yhC/eOACQeGtSFx4f0+W7vJJJ7h3jjkuHjzcMC5zGUVFdCqFkYKN0YDYHNAE f/CMf8VL/an2z/R/tf2/yPK+f7R9n+zZ35x5fl/w7c7ud2Plqn4f8ER+HPEtzqFleyf2e9lF ZwWTyTyGFYySvzyTMCPmYAbBtBG3GW3149S1NPiVNYz6pJJZu/8Ao9hAY90aeQCXlRoQ/lbw 481ZSN7ImM7gD4falqd9b3cWrapJqV3CkXmyoYzAjkNuQYhieOUEZeJwSgMfOSaAO0qvfQ3E 9nJHaXX2W44McpjDgEEHDKeqnGCAQcE4KnBFisPxfPfW3hi6m069jsrhHiP2iThY08xfMJJj kCjZu+coQv3jgAkAEmnaTeaVo9jY2l7AWil33Mk1sW81WYs4XDghiW4dy7d23sSxr/8ACMf8 VL/an2z/AEf7X9v8jyvn+0fZ/s2d+ceX5f8ADtzu53Y+WpPDWpC48P6fLd3kkk9w7xxyXDx5 uGBc5jKKiuhVCyMFG6MBsDms+TVZ4viHDp8GsR3UEqYuNPWSKR7U+WWDNGqB40OE/eNIwzIF 2fMrKAXPDXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDaDxhVwc9BXH+C/FCa1fa1ZT+I dK1S6t7sGD7AVRTB5MJLKgdztDuwLFj82RkcAdhQBn61pn9r6YbUTeTIssVxFIV3BZIpFkTc uRldyLkAgkZAIPIr6fpN5pemW9ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmjxRfX GnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARUfhvVWu9EtJb65jaSe4ngtpW2qbtEe Ty5FxgMXiQSZUAEEsAF6AEf/AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7NnfnHl+X/AA7c7ud2 PlqvpHha80KzmWy1OA3S2ltYWsk9oWSO3gL+WHUSAvJiR8sGUH5SFGCDX/tvUf8AhPf7O+0f J9r8j7DsX/j0+y+b9q6b/wDX/ud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrdxcD C/Z0AfEgZkzmQsOKAOw8L6TeaD4es9JvL2C8+xxJbwyw2xh/doiqu4F3y3BJIIHPQVsVj+F7 641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2KACsOTw+0viyHW2uowIkwqrbqsx G0r5TSg5aDLGTyyCfMw27ACjcqOeRobeWVIZJ3RCyxRlQzkD7o3EDJ6ckD1IoAx/DWj6lo9v PHqOo2l/JK4ka4is2hklkxhnkJkcMSAoAAUKFCgBQoG5XF+AvFOoeIrrxDb6pHHDd2N6qfZ0 lhcW6tEn7rMbsWKuJAXOMnOApDInSanqN1YeV9m0a+1Lfnd9keBfLxjGfNkTrntnoc44yAZ9 3o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTzgN2YRg7cDPIaqfh/wRH4c8S3OoWV7J/Z72UV nBZPJPIYVjJK/PJMwI+ZgBsG0EbcZbfJc/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaW NwdwG3cOw3V/Aut6jrH2/wC33Hn7PLkGEUeQz7t0DbQPLkTaN0Lb2j3DMsm4bQDsKx5tGuLj xHbalLqG+1ttzw2phGUkZNhw2cbcZI+XeCzASBGZDsVw6+LdSHxVt/D13a/Y7Ga0uDBG81uX nKlCsxxIXCkCVQgXPG45+YRAGx4Y8Mf8I59q/wBM8/ztg+WLy9+3P72Xk+ZcPu/eS8b9q/KM c9BXH+B9b1HWPP8Attx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/5icjHYUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCub0HwvNoNvOsN/HJOtl Dp9o7252xwwB/J8xQ+XfMjbiCgbjCp3ANTWdb07w/pz3+p3HkW65yQjOxwCxwqgscKrMcDhV YngEguNb0611i00ma4xfXefKiVGborN8xAwmRHIRuI3bGxnacY/irwePFWh/Zri78jU/skls LyHzkQCTb5v7pJV3K2wYV2YDAzuwQadx4B36toup2+oyQ3FncR3N5mW6dbt0hEOQpuMISgIJ O8ngMWG4OAdJputWWry3cdk07/ZZWhkd7aSNC6syMFdlCvhkYHaTjHuK0K5fwV4Q/wCEQ042 v2qCX91FDi1tfs8beWCPNZNzZmbPzvn5gqDA289RQBXvr6306zkurqTy4UwCQpYkkgKqqMlm JIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKr61pn9r6YbUTeTIssVxF IV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5pemW9ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+ 0YB3HmgCx/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPl OC4c4jaNcZkVzwjIGDnhSxqv/wAIx/xUv9qfbP8AR/tf2/yPK+f7R9n+zZ35x5fl/wAO3O7n dj5ap2nhTUholxY6hq1pc3D3EN4l3FYtG73EbrIHmBlbeC0cY2rsAVdq7Rt2gHSWN9b6jZx3 VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsA kkDAJJ5OhQBHPPDa28txcSxwwRIXkkkYKqKBkkk8AAc5qvpeqWms2C3tk8jQM7p+8ieJgyOU YFXAYEMpGCB0o1TS7TWbBrK9SRoGdH/dyvEwZHDqQyEMCGUHII6Vh+GfDOp+GtHFjHq8Fwz6 hJdSyTQTPujdizIu+dirZJw2SO5VmLMQDcOqWi6ymks8gvHt2uUUxPtaNWCsQ+NpILLlc5G4 HGDUema3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrPu9G1ifxba6xFqljHa20T262z 2Ds5jkaJpMyecBuzCMHbgZ5DVJ4a0fUtHt549R1G0v5JXEjXEVm0MksmMM8hMjhiQFAAChQo UAKFAANyo554bW3luLiWOGCJC8kkjBVRQMkkngADnNSVT1TS7TWbBrK9SRoGdH/dyvEwZHDq QyEMCGUHII6UAGl6paazYLe2TyNAzun7yJ4mDI5RgVcBgQykYIHSo/7b07+2P7K+0f6X027G 2btu/wAvfjb5mz59md235sbeax/DPhnU/DWjixj1eC4Z9QkupZJoJn3RuxZkXfOxVsk4bJHc qzFmNj/hGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy0AaGma3p2s+b9gu PN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQrD8NaPqWj288eo6jaX8kriRriKzaGSWTGGeQmRwx ICgABQoUKAFCgblAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8y F8gEqVIIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8 ivp+k3ml6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t6d/bH9lfaP9L6b djbN23f5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGq// AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7NnfnHl+X/AA7c7ud2PlrPtPAv2XTHtTqO6SKKyt7O TyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCAR Vis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQAVTOqWi6ymks8gvHt2uUU xPtaNWCsQ+NpILLlc5G4HGDVyufu9G1ifxba6xFqljHa20T262z2Ds5jkaJpMyecBuzCMHbg Z5DUAaGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQrn/DXhj/hH9268+0bLS3sI MRbNtvBv8sNyd0n7xtzDaDxhVwc6Gp6Fo+t+V/a2lWN/5OfL+126S7M4zjcDjOB09BQAPrVk muLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdc q2DgnBrHt/CHkeNrvxF9qgHnyibbFa7JziBYfKebcd8Py79m0fPtOfl5seGPDH/COfav9M8/ ztg+WLy9+3P72Xk+ZcPu/eS8b9q/KMcgHQVn/wBt6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8zZ8+ zO7b82NvNaFY82jXFx4jttSl1Dfa2254bUwjKSMmw4bONuMkfLvBZgJAjMhALGm61ZavLdx2 TTv9llaGR3tpI0LqzIwV2UK+GRgdpOMe4rQrl/BXhD/hENONr9qgl/dRQ4tbX7PG3lgjzWTc 2Zmz875+YKgwNvPUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZ9xrml2msWmkT38Cald 5MFrvzI4CsxbaOQuEb5jxkYznAoA0KKjnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1l/8JRpP 9nfbfOn2+b5HkfZJftHmY3bPI2+Zu2/PjbnZ833eaANiist/EekJcWkP26NzdojxPGC8e1zi Ms6gqoc8IWI3nhcnitSgAoorPtdb0691Gewt7jfcQ7sjYwV9p2vsYja+1iFbaTtYgNgnFAGh RVPUtUtNJt1mu3kAdwkaRRPLJI2CcIiAsxwCSADgKT0BNVz4k0X7fp9guqWj3eop5lpDHKHa ZNjOHAGfk2o3zdOMZyQKANSis+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2CcVoU AFFRzzw2tvLcXEscMESF5JJGCqigZJJPAAHOay18U6NJo8eqwXn2mzmleGF7aJ5mmdWZWEaI Cz42OflB+VS33RmgDYoqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zUlABRVPUtUtNJt1mu3k AdwkaRRPLJI2CcIiAsxwCSADgKT0BNWIJ4bq3iuLeWOaCVA8ckbBldSMggjggjnNAElFZaeI dMe4u4hPIqWaO89y8Ei26BDh/wB+V8slTkEBsgqwONpxGvinRpNHj1WC8+02c0rwwvbRPM0z qzKwjRAWfGxz8oPyqW+6M0AbFFRwTLc28U6CQJIgdRJGyMARnlWAKn2IBHepKACuL8HeI7u9 t7y41W+jaCGyt7q6kkCItlcsJDcWxIACiIIh2vl13/MxyMdpWfpmt6drPm/YLjzfKwTlGTcr Z2yLuA3xtg7XXKtg4JwaAOT+IHiS8sdL099D1SNZNRRksfsktu0tzO+xYNom+VoMv85XLDKE YXcakh8Q3t18UrnSvtu2xtdtuLG3kjeRn8jzmnmQx71hxIiBlcfOqgg7+Oo1PW9O0byvt9x5 Xm5IwjPtVcbpG2g7I1yNzthVyMkZFCa5pcmuNokd/BJqaRGaS1R9zxoNnLAfd/1iYzjOcjOD QBz/AIF1vUdY+3/b7jz9nlyDCKPIZ926BtoHlyJtG6Ft7R7hmWTcNvYVj6H4o0nxH5n9mTTv sijn/fWksG6OTdsdfMVdytsbBGRxWxQBj+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3yw KjblgQCKr6FriNoNncarfwI1xdy2trPM6x/bAJXWFl6BmkRVcbRht2VAGBWxfX1vp1nJdXUn lwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEUAc3 eeJ7Kz+I1jpMniC0RJrKVZLB5ogVuPMg8rtvDusj4UnBABA4JrDsPF96PDmpX2o6r5Sx2lnJ NOIY82d3M7LNaLnaiMhEYAlJMZkDSFl4ruP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bf mxt5qvD4q0Kazubw6nBDa2+0yTXBMKbWOEkDPgNGxyFcZVsHBODQBJ4cu2vfD9ncPqNpqLsh DXVo6vG5BI4ZQAxGMFgFBIJCpnaNSq9hfW+p6dbX9nJ5lrdRJNC+0jcjAFTg8jII61YoAz9b uPsujzzG++wKNoe78rzPJUsAz46DAJO5gVX7zAqCKz9B1xH8Oadd6rfwRveSmG3kmdYzOS7C IcYV5GQAny8oxyUyhU1qapqlpo1g17evIsCuifu4nlYs7hFAVAWJLMBgA9aksb2LULOO6hSd I3zgTwPC4wSOUcBh07jnr0oA5ufVLv8A4WRb2C6jIbM24U6fEqBxJtkYzSK0e4wEeWokRwBI AhBJOJPCerXGpXmpxvqX9oW8PlYlMAiKTMGMkYUD5FX5AI5CZkO7fkFCdj+29O/tj+yvtH+l 9Nuxtm7bv8vfjb5mz59md235sbeaNM1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODQB oVn63cfZdHnmN99gUbQ935XmeSpYBnx0GASdzAqv3mBUEVoVHPPDa28txcSxwwRIXkkkYKqK Bkkk8AAc5oAw9B1xH8Oadd6rfwRveSmG3kmdYzOS7CIcYV5GQAny8oxyUyhU1h6/4p1XSvH2 iWE8cltpt3ei1iAlttt4rQsWc73EgKymMBVUdDy5kRB2lhfW+p6dbX9nJ5lrdRJNC+0jcjAF Tg8jII61X/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mgDl/hx4hvfElneX17e/af O2XEaQSRywWySF2WDesaN5yLtDqxbHyNkbyB3FZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2D tdcq2DgnBrQoAx/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFV9C1xG0GzuN Vv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27KgDArYvr6306zkurqTy4UwCQpYkkgKqqMlmJI AUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKAOX/tvUf8AhPf7O+0fJ9r8 j7DsX/j0+y+b9q6b/wDX/ud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrdxcDC/Z0 AfEgZkzmQsOK7j+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearw+KdGns7m6jvMw2 +0sfKcFw5xG0a4zIrnhGQMHPCljQAeF7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndl QAQK2Kr2N9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVigArk7zxPZWfxGsdJk8QWiJN ZSrJYPNECtx5kHldt4d1kfCk4IAIHBNdZWf/AG1ZHWP7LRp5LocOYraR44zt3bXkClEbbg7W YHDLx8wyAYfgfXhr9veXCa9aapGXV41jMYkiDA8sicxoxBKRvukUD53LEqm5qc2sReV/ZNjY 3Wc+Z9rvHt9vTGNsT579cYwOueDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUA cPD4hvbr4pXOlfbdtja7bcWNvJG8jP5HnNPMhj3rDiREDK4+dVBB38XPA+vDX7e8uE1601SM urxrGYxJEGB5ZE5jRiCUjfdIoHzuWJVNiPxBp82tzaRF9re7hfZKVspjEjbBJhpdnlg7WU4L dwOpxUmma1Zax5psWnkjjwRM1tJHHIDnDRuyhZFOM7kJGCDnBGQDQrg9f8U6rpXj7RLCeOS2 027vRaxAS2228VoWLOd7iQFZTGAqqOh5cyIg7yigDj/Aut6jrH2/7fcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybht7Cs/TNb07WfN+ wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/r 2jaxqep6ZdWGqWNpHYSm4SOewectIY5IzlhMny7ZTxjORnOOK6CuP17W9RsvFdtZwXHlqfsf 2e12Kft3mzslz1G5vJiCyfIRt3ZfKkCgDoNS0z+2NJ1TTL2b/Rb6J7cGFdrxxvHtbkkgtksQ cAcgYOMnH/4Ra82fbP7Tg/tn+0P7Q8/7Ifs/mfZ/s2PK8zdt8rt5md/OcfLWxqOoywadfS6Z af2pfW3yCzhnRGMhAIVmYgJwysc87TkA5APmcvxF11/hXfazaRxz3kNxeQzaiJLURWwV28oh BMQSQ0aKuWP8X7wbfNAOwXwPDAdOhtr6RbO2t7G3lSSMNJItnIZICrggKdxO/KtuHA2Hmusr j9Tu9Surzw/qGna1PbW+py20cNisdu8TDDzzGSQByd0KMq+W2NwByQ2R2FABXP6X4Y/s3Wnv jeebDH9q+zReVtZPtMyzTb2yd/zqNuAu1cg7jzXQVx+g63qN74rubOe48xR9s+0WuxR9h8qd Utug3L50RaT5yd23KYUEUAamuaLe6zo1tai7tIryN1drkwSjDBSGMflzI8ZOSMhz8pZTkE1n 3HhTUoYfD1ro+rWlvaaGiCBbuxad3ZYXhyzLKgxsk6BRyM5xxR468QtpfgW61jS9b02zc27y WtxOqyrcHymZFi+dQXbAKn5hx91qj1S+ur3UdFvtD8S77G/u4baGG2SCS3k2GSW4Z5NrMcxR OgCEYdRnqSoBqWGj6lD4lvNUv9RtLuKRDHbRLZsj20eVOwOZCCDtyx2gsduThFUblcfoOt6j e+K7mznuPMUfbPtFrsUfYfKnVLboNy+dEWk+cndtymFBFdhQBHOJmt5Vt5I45yhEbyIXVWxw SoIJGe2Rn1FcnB4Z8SW+g3WmReI7GNp7uS48+LTZEYLLK8sqcXGRkvgMpDKucHcQy9Rfuken XMkt59ijWJy11lR5IAOXy4KjHX5gRxyMV53eeLNTtPhpqupDxFpsd3bPMltfTCOeO7Ih3qkM i+Ukr7sqGEYAKMhRypZgD0SwtvsenW1rtgXyYkjxbxeVGMAD5Eydq8cLk4HGTViuH1HxVLNr 9l/ZOpwTWcv2T7JHAUkTUPMuHjucNyW8mNQ/7sjbnL5Xiu4oAy9a0qbUhZTWlzHb3ljcfaLd 5YjLHuMbxkOgZSw2yPjDDnB5AIMmlaZ/Y+nadptrNusbK0W2AlXMj7Aqo24EAcBsjbySMYxg 5/izUZdPtbD/AImH9mWk935V3qHyD7LH5UjBt0gKLmRY0ywI+fA+Ygi5oepTXmjaS+pLHbap d2SXEtoQUZW2p5gCMdwCs4Bz0yAetAGXpfhD+xtW1PULC6giku/PdP8ARcFpJZPMLXBDDztj ZCfcKozLkk7qz28CXl1o4sdTv9KvvJ1CXULdZdKLQ+ZK0xkEsbTHzF/fttAKlSqklsYJp/iH UZda1OKfUYIliivnuEuI18vTPJmCWzOAVYLLEWlO9vmC5Qquax/+EuvdS8IRXFn4t0oXlvqs 8V/dRzRwxiDzLhYssY51hVtsRUuDuAA3FmBIB6RYWv2HTraz+0T3HkRJF51w++STaANzt3Y4 yT3NWKp6TM1zo1jO4uw8lvG7C8jVJwSoP7xVACv6gAAHOKuUAFc3oPhebQbedYb+OSdbKHT7 R3tztjhgD+T5ih8u+ZG3EFA3GFTv0lcX4O8R3d7b3lxqt9G0ENlb3V1JIERbK5YSG4tiQAFE QRDtfLrv+ZjkYANTxL4Y/wCEg27bz7PvtLiwnzFv3W8+zzAvI2yfu12sdwHOVbIwXejaxP4t tdYi1SxjtbaJ7dbZ7B2cxyNE0mZPOA3ZhGDtwM8hqz/HGt6jo/kfYrj7Pm0uZ4fkVvtV2nl+ Ra/MDnzN0nyJiRtnykYOS48UJZ/Eq30a58Q6UtrNaShbHKpMk+63EauxclmYPIVUKuRnhsAg A6Cx0z7JqeqXzzedJeyoykrzFGsaqIwc8qGEjgcAGVuMkk6FcX4E8Q6hrlxqSXVzHeQQJCft cLwyQNOxk81IXiJHlKFjKiT96A+X6rXaUAZ+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5 AIJGQCDyI9J0qbSbCC2S5jkJuJri6doiPMaV3kcIN3yDzHyM7sKMHJO6o/FF9cadoMtxbSeS wlhSSfaD5ETSossvOQNiM75YFRtywIBFR+G9Va70S0lvrmNpJ7ieC2lbapu0R5PLkXGAxeJB JlQAQSwAXoAR/wDCMf8AFS/2p9s/0f7X9v8AI8r5/tH2f7NnfnHl+X/Dtzu53Y+Wq+keFrzQ rOZbLU4DdLaW1hayT2hZI7eAv5YdRIC8mJHywZQflIUYINf+29R/4T3+zvtHyfa/I+w7F/49 Psvm/aum/wD1/wC5358v+HG/ms/QvE+o3Gj31ze61Y2vl2lpPNd38S+TY3cjOJ7VgrR48vbG Ajt5imQbi2QKAOo8L6TeaD4es9JvL2C8+xxJbwyw2xh/doiqu4F3y3BJIIHPQVsVz/gjXE8Q +DdKv/t8F7dNaQi8eF1O2cxqXVgvCtk8rxjPSugoAz9b0tNa0eewkEBWTaQLi3WeMlWDAPG3 DLkDI4OOhU4YR6RpU2kaXZWMVzG6QuxlzEQu1tx2RLu/dorMoUHdtRQvP3hX8Xz31t4YuptO vY7K4R4j9ok4WNPMXzCSY5Ao2bvnKEL944AJB4a1IXHh/T5bu8kknuHeOOS4ePNwwLnMZRUV 0KoWRgo3RgNgc0AR/wDCMf8AFS/2p9s/0f7X9v8AI8r5/tH2f7NnfnHl+X/Dtzu53Y+Wo/C/ hU+H7i9upbqOae6SKIJAkkcEMUZcosaPJIUAMjfKrBANoCjBJp/23qP/AAnv9nfaPk+1+R9h 2L/x6fZfN+1dN/8Ar/3O/Pl/w4381Y8K/wBrJqer2uoa3Pqkdl9ntmkngiiP2jy/MlZFjUYj Kyw4DEkEMOmCQDqKy9f0ODxBpbWc7SKVcTQss0sYWVeUZvLdGYK2GxuHIByCARqVj+J9UvdH 0Oa8sLH7VMn3i0kaJAnVpXMjoCqgE43DPAyoJYAEfhnQ7nw54a0nRxfR3AsU8qSVonzKgDbQ u6RihBK9yMAgKoI215PCpn8Zw69LdRrFA/mxW8CSIXlMRi3S/vDG52sw3CNXwEG7C4J4K8Qj W/BWgajeXMf2u+t0Vizx5mmVTv2hCRn5HbaOQAchSCBT/tvUf+Fj/wBmfaP9E/1f2TYu/b5P meftxu8vf8nnb9u7935W795QBoeGvDH/AAj+7defaNlpb2EGItm23g3+WG5O6T9425htB4wq 4Oegrj/Aut6jrH2/7fcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybht7CgDP1rTP7X0w2om8 mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRHpOlTaTYQWyXMchNxNcXTtER5jSu8jhBu+QeY+R ndhRg5J3VH4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIqPw3qrXeiWkt9cxt JPcTwW0rbVN2iPJ5ci4wGLxIJMqACCWAC9ACP/hGP+Kl/tT7Z/o/2v7f5HlfP9o+z/Zs7848 vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmj+29R/wCE 9/s77R8n2vyPsOxf+PT7L5v2rpv/ANf+5358v+HG/msfT/FOsz6DeXEl5uYRWD3U/lJ/xLZZ pSt3FwML9nQB8SBmTOZCw4oA7jRdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5Oh WP4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYoAK5uPwjDD4zm8QxTRo8z+ bLtgAmdvKEXltLnmDaqv5ZX/AFgDbuNtdJXH3niIw+OxpdtrEF1cJE0kukK0KMI/KLKE3MGe 4Zxn7wRY87gpKs4BqaBo+padcX9zquo2l/c3Tg+dDZtAyqC2E5kcbFDYUDbj5idzOzG5qejW ur+V9plvo/Kzt+yX89tnOM58p13dO+cc46mub8BeKdQ8RXXiG31SOOG7sb1U+zpLC4t1aJP3 WY3YsVcSAucZOcBSGROk1PUbqw8r7No19qW/O77I8C+XjGM+bInXPbPQ5xxkAz/+EXQeL/7e SWCNj8z+Vaqk8h8vy9jzDBeHGG2MCd6qd2FCiPwj4Rh8JW8tvbzRmApHFHHDAIl2oCBI4BIe dgcPJ8u7anyjbzTvNaurXx2LS01L+0I2iYzaVDLA80DCIuD5e1GjU7UxJJKy7pQu0BldY/AX inUPEV14ht9Ujjhu7G9VPs6SwuLdWiT91mN2LFXEgLnGTnAUhkQA7Ss/+xrX+2P7U82++0f3 Pt8/k/d2/wCp3+X0/wBnrz15rQrP/tG6/tj7F/Y199n/AOf/AHweT93PTzPM6/L9zr7c0AU9 A0fUtOuL+51XUbS/ubpwfOhs2gZVBbCcyONihsKBtx8xO5nZjuVxfw+1rU9Zt7uXVp5JLvZE 8sSNG0Fq7Bt0AxGjpKhGHicuUBj+YljXaUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVn6Zrenaz5v2C483ys E5Rk3K2dsi7gN8bYO11yrYOCcGtCub0HwvNoNvOsN/HJOtlDp9o7252xwwB/J8xQ+XfMjbiC gbjCp3ANjUdUtNKS3e8eREnuI7ZGWJ3HmSNtQNtB2gsQNxwMkDPIqP8AtvTv7Y/sr7R/pfTb sbZu27/L342+Zs+fZndt+bG3ms/X9G1jWNMsrWHVLG3kilguLiR7B5BJJFIki7VEy7F3JyCW ODjIIyY4PC8y67FqVxfxyAXAvpIo7cpuuvs32YsCXOIvL52YLBud5Hy0AbFhqlpqT3iWryF7 O4NtOrxPGVkCq2PmAyNrKQwyCCCCauVz+g6NrGmanqd1f6pY3cd/KLh44LB4CsgjjjGGMz/L tiHGM5Oc44roKAK99fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5 AJUqQQSGVlOCrAggqQCCCCARVfWtM/tfTDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5Ff T9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJKsahxsyznaSX2jAO480AWP7b07+2P7K+0f6X027G 2btu/wAvfjb5mz59md235sbeajs/EekX1vdXEN9GILVPMlklBjURYJEoLABoiFYiQZRtpwTg 1T/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tU9O8C2lvYSWV/cyXcAt7Sz g8ovbtHDauzwEsjbjKGYkupUHAwq4OQDoNL1S01mwW9snkaBndP3kTxMGRyjAq4DAhlIwQOl XK5/wf4cn8MaPNY3F/8AbWku5bnzMSjHmNuK/vJJD1J5zznJyxZm6CgCOeeG1t5bi4ljhgiQ vJJIwVUUDJJJ4AA5zVfS9UtNZsFvbJ5GgZ3T95E8TBkcowKuAwIZSMEDpRqml2ms2DWV6kjQ M6P+7leJgyOHUhkIYEMoOQR0rD8M+GdT8NaOLGPV4Lhn1CS6lkmgmfdG7FmRd87FWyThskdy rMWYgGw+tWSa4ujbp2vmiExVLaRkRDvClpAuxMmNwNxGccUaZrenaz5v2C483ysE5Rk3K2ds i7gN8bYO11yrYOCcGse38IeR42u/EX2qAefKJtsVrsnOIFh8p5tx3w/Lv2bR8+05+Xm54a0f UtHt549R1G0v5JXEjXEVm0MksmMM8hMjhiQFAAChQoUAKFAANyo554bW3luLiWOGCJC8kkjB VRQMkkngADnNSVT1TS7TWbBrK9SRoGdH/dyvEwZHDqQyEMCGUHII6UAGl6paazYLe2TyNAzu n7yJ4mDI5RgVcBgQykYIHSo7jW9OtdYtNJmuMX13nyolRm6KzfMQMJkRyEbiN2xsZ2nGP4Z8 M6n4a0cWMerwXDPqEl1LJNBM+6N2LMi752KtknDZI7lWYsxj1bwRHfeJ9P1uzvZLSSC9W8uo zJOy3DLGIhhVmVEOzgnac4AOV3KwBqaH4o0nxH5n9mTTvsijn/fWksG6OTdsdfMVdytsbBGR xWxWfY6Z9k1PVL55vOkvZUZSV5ijWNVEYOeVDCRwOADK3GSSdCgCvfX1vp1nJdXUnlwpgEhS xJJAVVUZLMSQAoBJJAAJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVX1rTP7X0w2 om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjySrG ocbMs52kl9owDuPNAFj+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3mq8PinRp7O5 uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/AOEY/wCKl/tT7Z/o/wBr+3+R5Xz/AGj7P9mz vzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIeaAOosb 631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAirFZ+i6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2r k4Xc7YBJIGASTydCgAqmdUtF1lNJZ5BePbtcopifa0asFYh8bSQWXK5yNwOMGrlc/d6NrE/i 211iLVLGO1tont1tnsHZzHI0TSZk84DdmEYO3AzyGoA0NM1vTtZ837Bceb5WCcoyblbO2Rdw G+NsHa65VsHBODWhXP8Ahjwx/wAI59q/0zz/ADtg+WLy9+3P72Xk+ZcPu/eS8b9q/KMc6Gp6 Fo+t+V/a2lWN/wCTny/tdukuzOM43A4zgdPQUAH9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7 M7tvzY280aZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGqcej6kniybVn1G0ltHTy4 4JLNjLBHtGUSQSbQGcb2OwluASQqbTw1o+paPbzx6jqNpfySuJGuIrNoZJZMYZ5CZHDEgKAA FChQoAUKAAblFFZ/9haP/bH9r/2VY/2n/wA/v2dPO+7t+/jd93jr04oANM1vTtZ837Bceb5W CcoyblbO2RdwG+NsHa65VsHBODWhXP8Ahjwx/wAI59q/0zz/ADtg+WLy9+3P72Xk+ZcPu/eS 8b9q/KMc9BQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVn3GuaXaaxaaRPfwJqV3kwWu/M jgKzFto5C4RvmPGRjOcCgDQoqOeeG1t5bi4ljhgiQvJJIwVUUDJJJ4AA5zWX/wAJRpP9nfbf On2+b5HkfZJftHmY3bPI2+Zu2/PjbnZ833eaANiist/EekJcWkP26NzdojxPGC8e1ziMs6gq oc8IWI3nhcnitSgAoorPtdb0691Gewt7jfcQ7sjYwV9p2vsYja+1iFbaTtYgNgnFAGhRVPUt UtNJt1mu3kAdwkaRRPLJI2CcIiAsxwCSADgKT0BNVz4k0X7fp9guqWj3eop5lpDHKHaZNjOH AGfk2o3zdOMZyQKANSis+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2CcVoUAFFRz zLbW8s7iQpGhdhHGzsQBnhVBLH2AJPasv/hKNJ/s77b50+3zfI8j7JL9o8zG7Z5G3zN2358b c7Pm+7zQBsUVHBPDdW8VxbyxzQSoHjkjYMrqRkEEcEEc5qSgAoqnqWqWmk26zXbyAO4SNIon lkkbBOERAWY4BJABwFJ6AmrEE8N1bxXFvLHNBKgeOSNgyupGQQRwQRzmgCSis/8AtzSxeaha m/gWbTYkmvQz4FujhipdjwvCMeTwME8EZrr4p0aTR49VgvPtNnNK8ML20TzNM6sysI0QFnxs c/KD8qlvujNAGxRUcE8N1bxXFvLHNBKgeOSNgyupGQQRwQRzmpKACuL8HeI7u9t7y41W+jaC Gyt7q6kkCItlcsJDcWxIACiIIh2vl13/ADMcjHaVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8b YO11yrYOCcGgDD8X+J7LStJ0q9h8QWlmLi9tGjYzRbbq3aaNZcbgcoI5CxZcEYByBnMcvi7T B4+02xi8S2L2t3p8h+yC4hIMxeAwlT97c6SvgZwwAIHUnoNT1vTtG8r7fceV5uSMIz7VXG6R toOyNcjc7YVcjJGRR/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNAGH4S8T2Ws6tr 9lD4gtNTMF6WtVjmiZhbmGFjjywNyLI7ruOTxgkkV1lZ+ma3p2s+b9guPN8rBOUZNytnbIu4 DfG2Dtdcq2DgnBrQoAx/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFV9C1xG 0GzuNVv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27KgDArYvr6306zkurqTy4UwCQpYkkgKqq MlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKAOX/tvUf+E9/s77R8 n2vyPsOxf+PT7L5v2rpv/wBf+5358v8Ahxv5rP0LxJ4ivdHvpraP+1L5bS0laHbGv2W7lZxc W/VB+5ARvLdhJzhn+YEdh/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFWhTWd zeHU4IbW32mSa4JhTaxwkgZ8Bo2OQrjKtg4JwaAMv4d+IZPEfhiS5muZLmSC9ubczSPAzOqy NsJ8glM7Cg4xnGRlSrHrKr2F9b6np1tf2cnmWt1Ek0L7SNyMAVODyMgjrVigDP1u41G10eeb SbL7ZfDaI4cqOrAFvmZQdoJbaWXdtxuGcjm/Avi+31XwwlzqOpxmQ6nPYRTXU9sGnbzG8pR5 LbC5TaMDG7BIBUhj1GqapaaNYNe3ryLAron7uJ5WLO4RQFQFiSzAYAPWpLG9i1CzjuoUnSN8 4E8DwuMEjlHAYdO4569KAObn1S7/AOFkW9guoyGzNuFOnxKgcSbZGM0itHuMBHlqJEcASAIQ STin4A1zUtevNYlub/7baQeTCJY3t3g+1YZphA0XzNDteHb5hLdQfmDCuo/tvTv7Y/sr7R/p fTbsbZu27/L342+Zs+fZndt+bG3mjTNastX80WrTrJFgvDc20lvIoOcNskVW2nDANjBKsAcg 4ANCs/W7jUbXR55tJsvtl8Nojhyo6sAW+ZlB2gltpZd23G4ZyNCo554bW3luLiWOGCJC8kkj BVRQMkkngADnNAHH+BfF9vqvhhLnUdTjMh1Oewimup7YNO3mN5SjyW2Fym0YGN2CQCpDGvr/ AIp1XSvH2iWE8cltpt3ei1iAlttt4rQsWc73EgKymMBVUdDy5kRB2lhfW+p6dbX9nJ5lrdRJ NC+0jcjAFTg8jII61X/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mgDD8Hapd6le6 0LnUZL5EuC0JRUEUEbPJthI8tHSdFCiSN9xX5DkbiB1lZeieINP8Q2/2jTvtbQbEdZJrKaBZ FYZUoZEUOCBnK56j1FalAGP4ovrjTtBluLaTyWEsKST7QfIiaVFll5yBsRnfLAqNuWBAIqvo WuI2g2dxqt/AjXF3La2s8zrH9sAldYWXoGaRFVxtGG3ZUAYFbF9fW+nWcl1dSeXCmASFLEkk BVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARQBy/8Abeo/8J7/ AGd9o+T7X5H2HYv/AB6fZfN+1dN/+v8A3O/Pl/w4381j6f4p1mfQby4kvNzCKwe6n8pP+JbL NKVu4uBhfs6APiQMyZzIWHFdx/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFO jT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxoAPC99cajoMVxcyecxlmSOfaB58SyusUvG Ad6Kj5UBTuyoAIFbFV7G+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxQAUUVnprmlya 42iR38EmppEZpLVH3PGg2csB93/WJjOM5yM4NAHJ+EfFOq6j411bR9YjktriOyguvsLS2zC1 Ys4ZF8t2dgVMR3N3y2Iw6Kes1ObWIvK/smxsbrOfM+13j2+3pjG2J89+uMYHXPBpmt6drPm/ YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0KAOTj14TfEObSU160Hkpsk06Qxq7ExhwEU/vHc A72kyIwpChGbe6Z/hHxTquo+NdW0fWI5La4jsoLr7C0tswtWLOGRfLdnYFTEdzd8tiMOinrP 7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5o0zWrLWPNNi08kceCJmtpI45Ac4aN2U LIpxnchIwQc4IyAaFZ/nax/bHlfYbH+zP+fj7Y/nfd/55eVt+9x9/pz7VoVn/wBt6d/bH9lf aP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNAHL/DjxDe+JLO8vr29+0+dsuI0gkjlgtkkLssG9 Y0bzkXaHVi2PkbI3kDuKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAVz+vaNrGp6npl1YapY2kdhKbhI57B5y0hjkjOWEyfLtlP GM5Gc44roKKAM/UtM/tjSdU0y9m/0W+ie3BhXa8cbx7W5JILZLEHAHIGDjJx/wDhFrzZ9s/t OD+2f7Q/tDz/ALIfs/mfZ/s2PK8zdt8rt5md/OcfLXUUUAcmvgeGA6dDbX0i2dtb2NvKkkYa SRbOQyQFXBAU7id+VbcOBsPNdZRRQAVz+l+GP7N1p743nmwx/avs0XlbWT7TMs029snf86jb gLtXIO4810FFAGHrmi3us6NbWou7SK8jdXa5MEowwUhjH5cyPGTkjIc/KWU5BNZ9x4U1KGHw 9a6Pq1pb2mhoggW7sWnd2WF4csyyoMbJOgUcjOccV1lFAGHYaPqUPiW81S/1G0u4pEMdtEtm yPbR5U7A5kIIO3LHaCx25OEVRuUUUARziZreVbeSOOcoRG8iF1VscEqCCRntkZ9RXL23g6ay 022S0vbSC8tb03tuIrIrZwsYmhKJAHyqFXdiA/8ArGLZwStdZRQBT0nTYdG0ax0u3aRoLK3j t42kILFUUKCcADOB6CrlFFAGXrWlTakLKa0uY7e8sbj7RbvLEZY9xjeMh0DKWG2R8YYc4PIB Bk0rTP7H07TtNtZt1jZWi2wEq5kfYFVG3AgDgNkbeSRjGMHQooA4/R/A7eH/ABDqmp6XqGyG 7tPJht7g3E/lyb2k8x2ec78u7kjCn5uCCXLln4T1i2s4lk1qxkurXUJtQtJF051RZJjN5gkX ziXXFw+0BlIwCS3IPYUUAU9J02HRtGsdLt2kaCyt47eNpCCxVFCgnAAzgegq5RRQAVzeg+F5 tBt51hv45J1sodPtHe3O2OGAP5PmKHy75kbcQUDcYVO/SUUAc/4l8Mf8JBt23n2ffaXFhPmL fut59nmBeRtk/drtY7gOcq2RiSTR9Sm8WQ6pNqNpJp8CYgsms23xMVIZ1k8zG85xkocLlVxu ctuUUAYegaPqWnXF/c6rqNpf3N04PnQ2bQMqgthOZHGxQ2FA24+Ynczsx3KKKAM/WtM/tfTD aibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B 5j5Gd2FGDkndWpRQBz//AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7NnfnHl+X/AA7c7ud2Plqv pHha80KzmWy1OA3S2ltYWsk9oWSO3gL+WHUSAvJiR8sGUH5SFGCD1FFAGP4X0m80Hw9Z6TeX sF59jiS3hlhtjD+7RFVdwLvluCSQQOegrYoooAz9b0tNa0eewkEBWTaQLi3WeMlWDAPG3DLk DI4OOhU4YR6RpU2kaXZWMVzG6QuxlzEQu1tx2RLu/dorMoUHdtRQvP3hqUUAc/8A8Ix/xUv9 qfbP9H+1/b/I8r5/tH2f7NnfnHl+X/Dtzu53Y+WpNA0GbSbi/uri4tHnvXDyJY2htoCwLEyF C7kysW+Z93zBUGPlydyigArL1/Q4PEGltZztIpVxNCyzSxhZV5Rm8t0ZgrYbG4cgHIIBGpRQ Bh+GdDufDnhrSdHF9HcCxTypJWifMqANtC7pGKEEr3IwCAqgjbH/AMIx/wAVf/bv2z32+V+9 /wBX5fleZn/j3/5aeVt/1vz7u1dBRQBz/hjwunhv7UIpYPLm2KsNrarbRgLnDsifKZm3Yd1C hgqAKoWugoooAz9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkR6TpU2k2EFslz HITcTXF07REeY0rvI4QbvkHmPkZ3YUYOSd1alFAHP/8ACMf8VL/an2z/AEf7X9v8jyvn+0fZ /s2d+ceX5f8ADtzu53Y+Ws+08C/ZdMe1Oo7pIorK3s5PIwI47OQyW/mLu/eNuJ3kFAw4AQ81 2FFAGfoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8nQoooAz9T0a11fyvtMt9H5 Wdv2S/nts5xnPlOu7p3zjnHU1n3ejaxP4ttdYi1SxjtbaJ7dbZ7B2cxyNE0mZPOA3ZhGDtwM 8hq6CigDD0DR9S064v7nVdRtL+5unB86GzaBlUFsJzI42KGwoG3HzE7mdmNzU9GtdX8r7TLf R+Vnb9kv57bOcZz5Tru6d845x1NaFFAHNyeFTP4zh16W6jWKB/Nit4EkQvKYjFul/eGNztZh uEavgIN2FwTwj4Rh8JW8tvbzRmApHFHHDAIl2oCBI4BIedgcPJ8u7anyjbz0lFABWHJo+pTe LIdUm1G0k0+BMQWTWbb4mKkM6yeZjec4yUOFyq43OW3KKAOf8NeGP+Ef3brz7RstLewgxFs2 28G/yw3J3SfvG3MNoPGFXBz0FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcHH48vrTS Y7jVbHTVu5L28hS3h1DDTJBMYykKsoeWcnhUAAbGS0ZYLVe38YavKNKsnEYn1u9a40i4jYMb ixW5V3DoUAjP2V8ryWIBztfAPSR+CtBhg0+GO1nWOw8sQD7ZN0jfzI1f5/3io3Kq+4Lk4ABN Sab4P0DRxaDT9Njg+yOrwbXY7CI3jHU9NskhweC0jt95ixAK/hHxYfFdvLcDTLu0g2RzQSTR SKssUgJXl0UFwBlgm9BuXDtnjHbxlq98dIkstIkhe41M25tZZQjlTbTyBJw6h4XUpG7gK3yl SjS7sHqLPw5pFjb3VvDYxmC6Ty5Y5SZFMWCBEAxIWIBmAjGEXccAZNV/+EQ0M28sL2kkomt5 7aV5biV5JY5ggkDuzFmJEcYDEkgIACAMUAc3H8TfN8KW3iRNIzp9zaSvEftPztcRQSTPHjZx GPJkTzDyWXhCpDGSH4lLe3Gqx6fot3dpYu6Bod0hcZgEUm2NWIik853VhuJjhZlVuVHSSeFt Gm/tASWe5b+KSGdfNfbsk/1gQZxHvPzNs27mAY5IBqM+D9AAuPJ02O2ed4naW1doJFMcYiTY 6EMgCAqApAwzD+JsgFyxvLjU9DjuohBb3E8RMbBxcQ552upUjzIzww5UlSMhTkDk28S3em+B rW9vNTke7/tN45JpYU3iGG5d50cIoTeltDMGKDBKHYWJUnsJtKsZ9LGmNbRrZqiokUX7sRhc bNm3BQqQCpXBUgEYIFV28OaRJZWtnJYxy29s7ukcpLhmdHRy+SfMLLLJuLZ3FiTk80AY8ni6 +jv10p9Gjj1ad4jbQSXn7vZIk7r5siodjhbaXKqrjOwBiCSuXrfju9gtZ9OitILXW5bRYY4I r2O4ntr2WIGIPGAQIfMdI/NcgF2XK7WDnqP+EX0n+zvsXkz7fN8/z/tcv2jzMbd/n7vM3bfk zuzs+X7vFV4/BPh2G6SeHT/K8rb5UMc0iQw7ZY5fkiDbEzJFGx2qNxHOcnIBH4i8UTaJexQ2 9hHeIlu1zdYuCjxrvRI0RQjb5ZWZhGhK7yhAPXGfdePGsbdftNjaC7W4lhaJNQUi4MYTclrl Q9xLlwgQIo8xHQspC7ukutE068upLm4t980n2fe29hnyJTLFwD/C7E++cHI4qnJ4Q0OWaGY2 kivFcfaf3dxKgeTzjOC4VgJAJWZ1VshSxwBk0AZepeL5bW2vJbi1+xQafqENreXDXSAJvuYl XkqRtMEiyuTjaHCht24xx6d411K4ezS+0GO2eZ7dJoo7tpJYmnXcke0xLmVFy8qHHlx4cFs4 GpP4J8O3MRjm0/erRGFiZpMspWZSWO7JYi5nyx+YmQkknBFyfw5pF1LLJcWMcxluDcyLISyv IYPs5JUnBBi+XbjHfGeaAM/QPF9trVvf3T/ZIbS0QStdQ3iTwKhDHa8i/IsqBcuoLBQyEMwa sdPiHc2tkl7rOix2MEaRSXix3LyywCRN4TZ5SlpY1DSSpwUjw435wOkg8L6Tb6TqOmCGeS11 Lf8AaxPdyzPLujEZy7sW+4qjg8Y4qSfw5pF1LLJcWMcxluDcyLISyvIYPs5JUnBBi+XbjHfG eaAMOXxlqMWsRaMNA36tNslS2W8U+Xbusu2WVtvy7XiCyBd4XzF2tISFNjwx4tn8U6ddXVrp E9viJJ7M3SyxRzq4JQF2jGG4+bYJFAZSrPnjUsPD2mabcR3NtBJ9ojSRPPlnklkYSGMtvd2J c/uowCxJAQAYAxUcXhbRodOvrBLPFrfRGCdDK5/dEECJSTlIwGbai4Vdx2gZNAHH6V4612V9 OubzToJIbzT9JmlWGcLHbtdXEsW5SVLsxBiOw/KAjjfnBfQt/H1xqLW8Wl6bY3c11drBC66m GgAMU0u15Y0cCZRCd0ahgPMQhyGroP8AhFtGZNktn56mXzmFxK8vmP8AZ/sxL7yd+YvlO7Oe pyearx+CtBjiu4zazy/a4poZ3nvJpXkSZY0kBd3LcrDGOvG3jHNAFO88S3d34K0fUdOt5E1D Wktzb28RRnHmKJZAjPhN6xLKyl8KSgyDnaSfV77UPDnhkW9/Hb3euPCrX1nHlYx5DXDtGkqn hliZBvGV3gkErg9BqWl2mrW6w3aSEI4eN4pXikjbBGUdCGU4JBIIyGI6Eiqd94W0bUdJk0u5 s91i2AsKSugiAjEe2PaR5a7MqVTAIZgR8zZAOT0b4ialqWkWd4dM00pcW6utw9+0URZYElnk b923lwRszRF9zYkCoQMki4nj28uGaS30aCK1bIhkv70wMuyJJJnmURsIo4yzRMwLbZQEIGcj oJfC2jTwSwyWe6OWK7hcea4yly4knHX+JgD7dsCiXwto08EsMlnujliu4XHmuMpcuJJx1/iY A+3bAoAr6r4oTTvD1lqjxQWjXnlhI9XulslhLIX2ysdxVgARtVWO7tjLDLt/HzSwpqM2jyW+ kTvLDbyTXCx3BliheWRZInCrEFMU0ZLSfeQHG07h0Gt+HtM8Q2/kalBJImx4yYp5IWKOMOhZ GUlGwMqTg7RkHAxn6f4J0i0ivhPbxzz3lxPPJMAY2HmTtMCpByrqfLG9cMfJjORsXaAc/f8A xHvv7GvptL0m0vLi0srm6kuYL3zrOMRqrI4kCqZEbMijABMkMidFZ19AgMzW8TXEccc5QGRI 3LqrY5AYgEjPfAz6CsNPBWgx2M1otrOI5vLMj/bJvMYpM86t5m/fuEsjvuznJ64AroKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsXxVqFjpegv c6nb209ibi3imW5KiNVkmRC7FgRhd27n+71HWtqqep6bDqtqlvO0iolxBcAoQDuilWVRyDxu QA+2enWgabTujjbTVPAN016407RGtbe7NvHcW9uk6SKIopHlJRSEjQyhWcnauBlhkCrC3HgS N44rvT9Einlu3tY40tA25xcNAinMYwzMrcY/gkILKjNVrWvh7o2uXU13cmT7RLcGfe0MEwTd FFGyqk0bpgiCM5xuBBwQCQad74Gd9Ytmsj5dp9rS8ubh71g7ut290EMKx7XUNJIoy4x5uSrt GhqeSPY2+tV/5397MnTfEvgjVG8Pw2/huykutV8nzUi00yJbeZFI4/eCLa+HiZDgjG1yceWw Bo/iXwRqeqT2Vx4bsrJllWGHzdNO+VmuZrdTsMQKrmOPLH5VaUISGHPTW/gWwtJ9Jmt72+jk 0uK1hhIaM70gSaNQ2UOdy3EgbGO2NpFFt4FsLe4v5ftt9J9su4boq7R4iMV090qrhAdpkkfO 4k4OARijkj2D61X/AJ397KNhe/DzU/sxsrHS5kudnlyrpv7v5sBNzlNq7myg3EZdWQZdWUU7 3WfAVhcW5m0bTxYTW9zOLo6afn8kxZMa+XmVCspfemV2oxyQCRrD4e6Mlxolwhk8/SLeC2ik khglaSOE5QEyRsUIJY7o9hO7rwuBfh9pX2e9je4u2lvre8gupx5aNMbkRCSQhUCB8QoAQoHU kMSTRyR7B9ar/wA7+9lW/l8G2ztDb6HpdxcJdwWxT7EqK2+4jgdkcptk8tpAGCk7WwrbSaNM l8G6xrlzp1joelzxwxQOJ4rJWBaTziAwCfKu2EMrk7XEiEcMpbSk8FabJdmdp7vC3AuIIt67 YGNwlzIF+XJEksSM24tjouwcVJo3hGz0G8NxY3l8FMUNuYZJQ6CGISiOMZXKqol7EE7FJJJc uckewfWq/wDO/vZk6Dp9rqep6na3/g3w7aR2Eot3kgcTlpDHHIMKYE+XbKOc5yMYxzVyzs/C 2o291LZ+Fld7dN3lTaKbVpDg4VPPRAScY64GRkjNb1lpsNjdajcRNIXv7gXEoYjAYRRxYXjp tjU855J+gpxeF9Hs9OvrPSbGDSPtsRikm0yJLeQcEBgyj7y7iQTnBo5I9g+tV/5397MGC30Z 9D1G9l8J6IlxYSvDJt8r7MSuNzidkX92mSHbblTHIArFQDnpe6LPdaJFF4M0tYb/AHefdzW5 W2ixKI08uYQlJPMO5o8lN42dDIorqrXw7Ja6WunjW9SMUabYSiwQmLGzZtEcSjCbOFIKkMwY MMAV5PCUMVrCtvcXc3kv9oa2muAsV3cCUzq8hCHYfOJcmMKDkAqyqqg5I9g+tV/5397IdVg8 D6JcW9vqWnaRbSXCO8QeyT5lQrvOQuAFDhiT0UMxwqsRJdaf4Us9RgsZfDkLTTbdrQ6K8sYy cDdIkZReRzuIwOTgc1DqXhCbWrjSRquoSXEdtplxZ3ssTGBrppTAGyi/LsdY5ARn5dwK4YBl 3LrQtHvtRg1G80qxuL6Db5NzNbo8ke07l2sRkYJJGOho5I9g+tV/5397OZ2eHv8AhJf7L/4R XS/s/wBr+wef5Ee/7R9n+042bMeX5f8AFuzu424+ao7J/DdzqU8EnhTT0twl29vJHaLLJILW UQzbo1TIO9hsClyw67ThTvT+F7abVpdRS7u4JXczqsZQqlwYfI88blJ3iL5dpJTuUJ5qSw8N 2enatLqEUk7MfO8mJ2GyDzpBJNtwATvdQx3FsYwu0cUckewfWq/87+9nI2uoeHrmDSrgeENL WG6tLC5uf3Ue6H7Y5jhVBs/eYcHcSUwuCNx+UXNWfw3pem+Jb/8A4RTT5YtDTayraKWml8pZ duFQ7U2yR/PzjLkgBcnWg8FabbppkaT3flWNva27Rl1xcrbNugMh25BRyW+QrknDbhgVJJ4b +1J4nsrqT/Qdc5LxNiSPdbpA64II4EasG5yXIKjbljkj2D61X/nf3syfEqeHvD+3b4V0u42W lxfz5gjTbbwbPMK/Id0n7xdqnaDzllwM582oeHoriSP/AIRDSyssstvYt5UYMskd1HaP5o2f u182VMFd5KbiQCAp6rWPC9trjs13d3eHSSBlQoA1vIqCWD7udjmNSW++Dna6jiq8vgrTZZp5 Wnuxud5LZQ64tJHmW4d4/l5JmjSTEm8ArgAKSpOSPYPrVf8Anf3szdITw9qV/b2cvhXS4ZJo rk5WCNwJLacQTL9wfLuZCjdWBOVQgA9B/wAIt4e/6AOl/wDgHH/hVWw8N/2frVncxSZt7W0u Y9ztuknmuJklldgAFX5o8/LwTIQAgUA9BRyR7B9ar/zv72ZP/CLeHv8AoA6X/wCAcf8AhR/w i3h7/oA6X/4Bx/4VrUUckewfWq/87+9mT/wi3h7/AKAOl/8AgHH/AIUf8It4e/6AOl/+Acf+ Fa1FHJHsH1qv/O/vZk/8It4e/wCgDpf/AIBx/wCFH/CLeHv+gDpf/gHH/hWtRRyR7B9ar/zv 72ZP/CLeHv8AoA6X/wCAcf8AhR/wi3h7/oA6X/4Bx/4VrUUckewfWq/87+9mT/wi3h7/AKAO l/8AgHH/AIVj69pmk6Y2mQWHhTRLu6v7s2yLOiwIuIpJSxYRuekRGMd666svWtFGsiyZb+7s Z7K4+0Qz2ojLBvLeMgiRGUgrI3b0o5I9g+tV/wCd/ezBtpPA9ybaNtH0+GedzG0UmnofIkEj RbJGVSiEyI6KS2HZSELVXOqfDYOUFvpDvvRAsenhizOsjRhcJ8xdYnKAZ3goVzvTdpT+AdEl 1bTtSii8m4sdm1jFFMZAshkG5pkdgxdnYupV2LkliQCM/wAN+BnsNTi1G+PkNa+Slpaw3rXK RpFHcRoN7xphQlyVChR/qwxZmZiTkj2D61X/AJ397JhN4Ce3eaPTNPkCuqqkell5JtwJVokC bpUYK5DoGUhHIJCsRcs7HwbqF4bay0nS7lhEsxlhsFeEKwBUeaF2birKwXduKsGxg5rNT4Ve HY9OmsY02wNLHLCv2S1PklA6r1hPm/LIy5m8w85zu+atzTPC1lpmqR6iskk1xDZJYQFoooxF Cu3KqI0XgsobByFOdgUEgnJHsH1qv/O/vZzN3qfg4X+k2em+HdPvZNQvVts/2cyKkRR284EQ sHQhDtYYR8MQ21HK19H8Q+BdRN5Hd6Dp9lPDcSJFbtpjmWSJZHjWTaYVILOhXaN2HKJnewWu m0/wVpunXVlcRz3bvYugtg7riOGOKaKOHhRlFWeQgnLkkbmbGKjk8BaPNbapbzGeWPUcGVZC jBWFzNcqQCuDiSduGDKQqggjOTkj2D61X/nf3shQeBJJbWNdN0stc4C/8S4YjJYoFlOz90xd WQK+0l1ZQCwIrPh1PwJPqN7BFo+lzQ28UDrJb2ImkmeUzYRIkQs3yQ+YCoO5G3j5fmN6H4ca Lb3Wl3SHFxp20JJ9itPnVZWkUbfJ2x4Z25iCMc5JJAIkt/h/ptm4ls77UoJ40hS1lEyubVYl lRBGrqy4Ec7x4YHI+Y/PlyckewfWq/8AO/vZRbU/hyuqR6cLDT5J5XjRHi0lpImMnlbMSrGU wfPh53YHmLnqK1P7P8Kf2x/Zf/COQ/aP7/8AYr+T93d/rvL8vp/tdeOvFQx/DzRYLiKW3e7h SF4miiWQFUEZtNq8gkj/AEGEcknl+eRjc/sLR/7Y/tf+yrH+0/8An9+zp533dv38bvu8denF HJHsH1qv/O/vZyNpLoM95YW8vhDS4/t+q3mnwslurgJbiXMjHygqszQkBM9DkE7WAsbPD3/C S/2X/wAIrpf2f7X9g8/yI9/2j7P9pxs2Y8vy/wCLdndxtx81aWkeHZoUhGoGNXstYvNQtDby lg6zNNjeCowQtwwwM8qDu5IqO+8Jy7pLyw1GcakcSJLPs2LcmIW5uyoT5pBFkeXxG2PuqTuB yR7B9ar/AM7+9jdS0/w3p2raNp58NafI+p3EkIcWShYwkLyElthGfkACkgnJIztNcva694bv dGbVIfDPhlbeR4vIkmlVEiR1dg9y5hxEPkMeV8webmPOVJHo17psN9dadcStIHsLg3EQUjBY xSRYbjptkY8Y5A+hw4fAthbRR/Zr2+huLfyls7lWjL2kUayJHEgKFWVUmlXLq7HeSWJCkHJH sH1qv/O/vZk6tceHtN060vF8HWX73T5dUnhubWOGSC3iEZkG3aczDzVAQ4BIbLrgZ1PD+laJ rOk/a7jwxpFtOtxcW8kUcKSqGimeIkMUUkEpnoOtTXPgrTbmygshPdxWkFu1kkKOuBaMkaPb 5Kk7G8pSWz5gOcOBxVzwxp95pmimC/EC3Ul3dXLrBIZEXzZ5JQoYqpOA4GcDpRyR7B9ar/zv 72O/4Rbw9/0AdL/8A4/8KP8AhFvD3/QB0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/0AdL/8 A4/8KP8AhFvD3/QB0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/0AdL/8A4/8KP8AhFvD3/QB 0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/0AdL/8A4/8KP8AhFvD3/QB0v8A8A4/8K1qKOSP YPrVf+d/ezJ/4Rbw9/0AdL/8A4/8KP8AhFvD3/QB0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw 9/0AdL/8A4/8KP8AhFvD3/QB0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/0AdL/8A4/8KP8A hFvD3/QB0v8A8A4/8K1qKOSPYPrVf+d/ezJ/4Rbw9/0AdL/8A4/8KsWei6Vp0xmsdMs7WVl2 l4IFRiOuMgdOB+VXqKFCK2QpYitJWlNterCiiiqMQooooAKKKKACuX+IVq954Omhjt/tGbuz Z4zaNdAotzEzFol+aRQoJKjqAa6isfxRrn/COaDLqflwPslhixcT+RGvmSpHud9rbVXfknB4 FAHL2japCuk6fpZnstJliYXUun6F9kFq5lIiMcMwJTeS4kJWXAVWxGH31y+ial4403w3BZW8 N8i2+iMlrHPp8vmb0tSRhRb7RIs6mNd8g3Io/dsWWR/SNO8W6dcwWK3N5Y/a7z/UR2M7XUc3 zlD5UgRfM24y+B+7HLYXDEtvG3h2905b601D7TC2zy1ghkkkk3gkbI1Uu/CvnaDgxyA4MbhQ Dm4bbX5viHANQv8AUljht72zhurawVYmLx2su7JjcKNzSBdzEf6Oiks2/wAzk/DcPjHRfB0k +nNqqScxi1l0vbIXTSBgkMmSqzxRxpgDJBDGQsMekaz480bSoI3gn+3ySS2yBLRHmykzoN4K K2cLIrYHXfEODKm64PGGgG3e4OpRpEjqu6RGTcrAkSLkDdEVV28xcptR23YRiADmzqPjCw8V TWl1ceZZLE7JONPmljdfJL71jiiOGEuUCNOGKKBtZ2WRs+xXxcdSTU2N9DI8WmRzh7dJXuEN /OrZbykCqIJN7L5aOu5NxUqwbsLHxdp0+j6Je3jfZJtWtIbmKDDSY8xoU27gvZ54lycfez0B xIPGGgG3e5bUo4rdHVWnmRo4wrAlJN7ADym2kLJnYxGFYnigDi5LnxjdjT7gWM91fWspnVLi 28pIr02V4JIQcLm3VzAqyEkHzD+8btueCRff2jrMt+2pA3F68kJubLyRcRC3tUWV+MK+FxjK ZJkzGpUrHoat410zTdNvrhDJLcW1lJdrBJFJDuZYjL5RZlwkpQbvLPzhfmK4rQfxDpkdvqdy 08gt9MRnup/IkMahQxfa+3DldrBghJUjBweKAOT8HQeJW+GMFg4ns9ZtoreGMSBbdUQRxEBG eKT+A4fcjfvPMUYAXFzVbG+Og6GniCGTUoIb1n1SJU+2CWIxzBN0ccKeaBI0JwIuCAx+6WrY PjDQBbpcDUo3id2XdGjPtVQCZGwDtiCsjeY2E2ujbsOpMkXinRpoL6aK83x2EpguCsTnbKHM flDj5pNwwEXLHcmAQ65AOfX/AIS6x0zwjap57TG0gh1ItslPnCS2Mhdzn/lkt382cE4GSxTN OHVfGh0uSSKC7l1APbfuZbJQguG3/aoBnyw0EaBWjcuAzYXzXztroIfG2kPLqomuI0gsUedZ oyZVmt0ggmeUFRjAFygwMk9RntHqnjvSdM1OztG8945bt7ae6FvKIINscjs3m7DG20x7WG75 PmLYCNgA5PxLeeKruPV9Mjiu7jTbjR5o7fzLOXzLlTZkhyqW4VJzNldrSJwMCIEqx6zStXvF 8R6jBqUk/wBlnu1ttOZ4DEjuqSu6KhjDcJHkyF3R+qFeUFyPxfoctxaWwu5Furt2SG2kt5Um LKU3AxsoZSBIj4YD5Dv+6CwsXmo2MGs2tv8AZpLnU2Taghg3tDE7Dczv0jQ7M/MRv8ohQzLi gDn/AAnq+v6lrl+t75kunx3F9ErtbqioI7oxwhGB+clRMGzgjykyozvl1PDFp4itftX9v3f2 jds8n/So5tvXd9y2hx/D13fh3p6J4y0i48Nafq0dpJa2N2nnTywQl4LaZwJJA7qBxud90uNg ZHDsrcVuXWs2tnqMFjLFfNNNt2tDYTyxjJwN0iIUXkc7iMDk4HNAHHx2V5B4Fk0yaG+uJjqt xPcB7Ylp7VdS3TFgqhW3wsx2AfvFYhVI4rLtNJ1Jb60Y6fdgm4gbSGMLD7JbjUJpJVBx/o4N o0KlG2FlAjwSu0dpqnif7JrVnpNjZ/bLq4leB3aXy4YJPIkmRHbDHcwj6AEqrBjjKB49P8R3 1z4YvtWutOtIJba4ng8tb7MQEUjRNI8romxAyOxO0kIMgE/LQBx9lBqWkeBfE1wIruxnh8OI J5CrRM2pJFObiYHgu5JiJmGQ+Bhm28dZc2X9m+MtMvIYZ10y00S7hENvbbo4MSWxVUVF3bmV SAgzkR/KOua6eNp57rRIotK2w3+7z7uZ5VtosSiNPLmERSTzDuaPJTeNnQyKKueEdfh1i3lt 7fTo7KCzSNY44WDLCpBAhcBQIp0C4eLnZuT5jngA4+70nUmvrthp92SLidtXYQsftdudQhki UnH+kAWizKEXeVUmPALbSWmk6kt9aMdPuwTcQNpDGFh9ktxqE0kqg4/0cG0aFSjbCygR4JXa PVKKAOf8GfL4feIcRw6hfQRIOkcaXcqIijsqqqqAOAAAOBXQVHBBDa28VvbxRwwRIEjjjUKq KBgAAcAAcYqSgAooooAKKKKACiiigArk/HGjTa4fD9pFbWk6DUy8v22yN1Aii2nGZI9y5G4q ASRhiv0PWVn6rqf9l/YpHh3W893HbSy7seT5mVRsYJbMhjTA6b9x4U0Aed3Og61YeIJZILSQ WlncQXebKExxbFGnxkxRgk58qC9Ty13MEJXkSLvr6L4e8TWGgpaSxxxQQ6novn2hs2klcxR2 CyOkqybdimNskKw/dvz6dY3xC04eHrzU1TdNFaT3kEOWCToiNLGPM27VkeELL5Z+dVbJXAJr UuvF+h2cSyTXcmGeVFWO3lkZmjnS3cBVUkkSyIuAOc5GRzQBwfw2svFMOraAutwzra2Hh+Sz XzLYxhJDJbyAZ2j/AJZNFHzzvglGOCzdx4YtPEVr9q/t+7+0btnk/wClRzbeu77ltDj+Hru/ DvHa+PfDd5ZLeQ38hgZ9u57WZCq7EcyMGQFYgskbGQgIA65YZFXNd8QRaJ4X1PW/s88y2MUr +T5TqzshIx90kKSPv4K7fm+7zQBy6aRqKeCrW1LXzzw+JRPIGhXfcR/2oW3uAnClSJMqFHAP 3cgmuWl5fePLG+Sw+128f2ZLRLrTjIqSJcyC5cOwxBiMq4b5TKyRbWYKVbpL3Xbuy1uOx/sm SaKZJPJMM6GaVkTeWEZwFi+7HvZx+8ZQVAYMcu58a3FnpzXFxpcHmQ3b2sojvgY5nUA+XbMV DTzHJQJtX545FLKVG4A0LmzuD8Q9MvQZ3tV0q7iYbB5cbmW2I+YDO5gDwT0j4A+bPNyWOpRf D240pVu5LxtTuZpGns2k863GolpGkRAocPExPlrgyKWCgitzUPFlxp1/cWs2lfMfLFri4DEl 50gjM2AfJV3fKkFyUSQ4DIUqvD46824jj/s7CxSxW983n5MUkl1JaJ5Q2/vF82J8lthCbSAS SoAMu608x+A9Kt5bCOO3hvZMWP8AZEksVxCTMsTTW0KcHayTlSqjzFC/uyQVz/ENjrNz4Q0S zZbubXrfTGtSk1nPOEvjDCyzrMoKLKrAospYKplkbf8Au2VvRP7Ztf7Y/svyr77R/f8AsE/k /d3f67Z5fT/a68deKy9S8WGxuNft4NMu7ufSbKG5WOKKQtcySmULGgCEkZjUF13AbjnGxqAO f8dXXjCHVp/7AudSit4rIOiWtlHKry+TeOclo2JO+G2XAI/1gHVgaNLuvGDeNY/tdzqTaW97 KjQvZRrEsW6/C/OIw2AILUglufNGc71rch8U3jwaXPJpkDQ3kqwSNBdl2WRnZNkaNGrsyBS0 odYzGqt94qwGfYfEP+1WhtrLT4Hv7v7O9rE15hBHNFLNH57BCYpPLgclArgbo8MQxKgHP6hq Pj6C81Oe2uNVeNftn2eAafGyDA1ARbSItxx5FoRknPmjOQ6itSyuvGFtpPi3zrnUru4t7KZ9 MeayjDGVZrtE2BI1DkpFbtgg53gjhgK2NJ8cQ6zeWP2exkWwvXjgjmkkAlEz2ouwDGARs8o4 3byd/G3HzVoN4v0NTOou5JJIX2NFFbyySM3mSx4RFUs53W82QoOBGW+7zQB5/fXnxBtrfUYr e+1mZ0eZoJTp0JYiMahsUYhwQ/kWZPGT5g2kbxXYaHeahJp2qWd5f6lcatbpPiPyIY5fK+0X KQSplFjLusYxn5PkU4AYltT/AISrQvP8n+04N3lfaM5O3yNm/wA7d08nHHm52bvl3buKz18W 6NBqIEUflR3MU9xcyNA8UwljNvGqPCVEhkcTRhQRuICAAhloA5vT9KXTvAd7oum6bdizFwjW rPp7RS3Nuhg817iMIpkOTIpjKq80cZAyTvq5caOl58OLTT9Vi1WGzN2ZJEsrRZWESzM8Q+zv G7LDxFiEKzRrtRsqrmusvNfs7G3tZ5odSZLlN8Yh024lYDAPzqiEoeRwwB6+hxT1XxP/AGZ4 hstK+x+Z9o8v5jLtkk3uU/cpg+bsxvl5XYhDfNnFAHJ+IbPUr7QdEhOjyQ3dtZNC9ta2zGK3 uzHCUWDGRE4+YR3WWjhw4OS3Gx4o0sXfi/RL5bS7u7u1eMWsUtpHLaBWmUzuX2lo5URA6ksm SqhNxLqY9Q+I9vbXktvbWXmK32cWVxNKUiuzKLg5XaruVItmCFVYyMyhRtZXOxqXiK4sNDtN UisYLyGSJZpmt7sMhB24SA7czSOWAjXaofuykqCAYcFtc3PjyK/Szuw8l6LhbmS3dMacbHb5 RdgNo+0/N5BIYN85T+KqajWYfGmqT6TBhrrVY4b9o4UYAILFkBcjO37N9pbnaFeRlGW2M/Ya h4p0bStROn3l5svBEs3kLE7uUYsqkBQc5ZCgx1ZkX7zoGjj8Q6B9oFyk8Ylmty80/kMPJijL n9++39yFYSjEhX5hIByGFAHLw6r40OlySRQXcuoB7b9zLZKEFw2/7VAM+WGgjQK0blwGbC+a +dtZ/iW88VXcer6ZHFd3Gm3GjzR2/mWcvmXKmzJDlUtwqTmbK7WkTgYEQJVj2DeNdMF7BEDI sD288sjSxSRyxPG8CrEYWUPvf7QpVcZOVwG3iuggmW5t4p0EgSRA6iSNkYAjPKsAVPsQCO9A Hn+tNfXw1R7+KSaCz1jT20t5oPKQTi5Vdqq0YdRyitMGlVg7Mg6x16JUckEMzwvLFG7wvviZ lBKNtK5X0O1mGR2JHepKACiiigAooooAKKKKACiiigAooooAKKKKACs/WtJTW9MNlJcz2372 KZJoNu9HjkWRSNysv3kHUGtCuX+IVq954Omhjt/tGbuzZ4zaNdAotzEzFol+aRQoJKjqAaAL i+Gg17ZX1xq2pXF7ZIUindo0O1nDSBlRFRg4CKQVOAildr5Y8/YfDDTT4VsdN1WSSe8gt7ZD MxW4WJokZcIsyMrJmWcgOpx5nG0KgQ0ybUrObRLTTopINHZCbz7HobWiQN5x8sJFJ86CQlxI cSYCq2Ig/mVh6f4i8c3vg20vwk8vnxWkk10LURyJvjkL+UqRzblOLU7hG/M0nCbSsIB1kngG yZNkGp6laJsgJS3MSp50LQFJtnl7A4FtEuANgAbCjcSY9O+G+iaXE8Vm08CeakkPkLFC8IVX TaJI0V2zHK6bnZnG7cGD/NXN67eeJtTtY4b7+0rcK+n3h/szSmdCiS2rO4Lozhw7znyipbFu hKqAwlsabrXjqWym+120iXC3EXnJHZvI8LFJSUTfHFG8XmrAnyvJtV3YzAbZFAOsTwdpa2eh 2zee66NFHDAzPy6IY2UPgDPzwwvxjmMD7pZTXj8B6Wln9ka4vnt/3CKgm2FIYCzQQq6BWCxu 28NnzCQNzsOK5+OTxTpfg3w3Fpcc8TWfhqSe4tmsjI0lxDHb+VCe6sx3qR1K7wAGwyyX+p+M Hvk0+GPUrf8A0iZJbyK0jZQjahAsWwlWBK2ruSSMcnOWRwgBqX/w50nUb65vbiec3V3aPbXU /kW5kmLQmEyeYYiyNsxwhVMj7vLZsan4QSfSfFQtZd+p67aSQNPOFQAeW6xKfLUZVd5G4hnx wS2ABwdpdePPN0+/mh1K51COynVXm09B88kFhKsTYCBUNxvjLYJCh8ldrSJuT6t44bWNTtol 8j/S40gzZyzIkX2uJFcERKhUwM7SDznbuPK2sAAbGo/DfRNSiTz2ne4WV5PtM6xXTkMqJtIn R1OEiiXdt3/u8liWctsHw3ZnRZtMEk4jku3vVlDDfHM05uAy8Y+WQggEEfKAQwznH1qTxFDq 1pYWV1feWYreOO4jto3E7NIVuXmbYUjaOLbIn3FZyRiQfIMvw9Hq/hrwn4JsILW7S3a3jF4q WAd0mkaL93IihdiBZJ2aQ4IMSlixJWQA1Lj4c6Xd2FxbT3+qu9zE0M9ybn99IjQQwsGbHO77 PE59WUg5RmQyah8PtK1S6urq7uLt57u4Etw6+WhljEUsQhbagynlzSLu/wBZgj5/lXHN+Hr7 xTpWiaFp0lnfQxxxQlpG08ypBbrpnCOq4ct9pR/lHz/KFJUOgbLM/jSz1KXUFOsme8srcTNJ Ar+XCst1gq8dmTvyYDsMJcLcPuUbd0YB2lr4GXQ7NzoV35V+Ip4YJnt7eJIvOMO6QxwxKrsv kqwBHzfdJAIK9JeaVY39xa3NzbRvcWj77efpJEcgna45AbADAHDDg5BIrg5tR8ayaXe3ZuJ1 uDLbw2tnaae0fms9tC7lZJInMa+ZvAaVNq/OrkEq0XWanPfaxokj+G72OOdbh4Wdv3ZJjdo5 FVmjcKQynkxuCAQANwdQCvo3gbRdGsrOzSKS6gsnWS1juiHSB1RF3ogAUOTHv3YyGkkII3kV qXWhaPfajBqN5pVjcX0G3ybma3R5I9p3LtYjIwSSMdDXLm412W8u9KWy1WGP7JG1guQDHtEG 15Lss48wSPMHQ+buSHO0g4m1L+z8USeGrOC1v4xqyuDPMkyQhlw3G5reUMfu5IjTcQSAg+Sg Cxf+ENB1HW7PWZ9MtP7QtbgXAuBAm+RlRkUOxXJC5DDngoh7VJB4fS10Ox0i21C+gt7K0S2i aN1D5TZskJ28sNnT7jbmDKwOBzfj3TfE194Fks7RY7n/AIlkwv41umWeaXygFCGOL94N28lA se8hRwpZDH4bgv7PxIbq/wBPngum+2LqckUUkiSvLdRmzHmbR5ypFvAYA+UuQ3l8igDoE8I2 aLax/bL5oYpRcTQtKClzMJTOJHG35W80l/3ewHhSCoVRY0Pw3Z6B5n2aSeTMUdtH5zA+Tbxb vKhXAGVXe+C2XO75mOBjzPT9GvbbQVXUtOjZw9qb/wAzQ5Zftl0I5klSZY1Z7hIyYphI25ZJ d2JF3Dy/UPDUN9beFdIg1QyHUI7KFLoySb2MoQB8tk7juzzk5oA1KKKKACiiigAooooAKKKK ACiiigAqnqmmw6tYNaTNIgLpIkkZAaORHDo4yCMqyq2CCDjBBGRVysPxTBM1haXtrFJJc6fe wXK+Wpdlj3hJyFH3j5DzDaASc/KN22gDPn+HmizWktkj3cFi9uYVto5AVRzb/ZfNBYFt4h+T BYr3KlvmqSXwHpcuqLffaL5WSUypCJsxoTcxXTYUg4zLFuJ64dhnAQJz+iHxLpiQWV6t9bMY mlvRb2qyooktzNNco4Rg1x9sZ1EQJG3pERhqjv8AWvHUOjafObaSG/Z5DfR/Y3McEwWMxxKs Mc7SQMC7MynJIx5kR/dgA1JPhXoElhb2xedmt9ojlmignKqIIYSNssTJytvGSdu7IOCASK6j UNJTVNDv9Ju7md4b2KaGSQbQ6pJuGFwuPlDYGQegzk5J5OVta0zwVOumxXceoS6xe+VFHAfM kV7udlAcxyJEGBU+ZIhTBwSu4OtNL3xLBfyQafHfRXEf9pTxWj6aq2d2/n3PkKZFjGxjhHZ2 kTcNmAxkZlAOoufCxuNT1K9j13Vbb+0IvKligaEBB5ZRdjmMyJtJLgB8B2Y4+YgxnwfFLoya VcavqUtoEaBox5MSvbsoVoCkcaoEwowwUOuSFdQSKr+Db/Vb23/4m892ZVeVYlktJE3oBEd0 jPbw/OGZgu1VBUnhyjMMPTlafwz4hOiWWq6dqWo2jxWto1ncW5jm8qUpI8sigNcMfvzbsZEa 7mIV3AOkHg+LOoCTV9SkivLj7X5b+T+5nEiyRyKwj3sUKIFDsy7VVSCABRF4K02KaCVZ7s7X SS5UuuLuRJmuEeT5eCJpHkxHsBLYIKgKMeCOCy8K6+8eh3cmjy3qGw0yO1lhzEUgRgYFXekR lErOuw7lLnY4bDRzW1qfD2h2j2t9f6TbXZn1C3l0qdUaF0uAiLbMhYxpN5e2IBiirGx4AagD sP7C0f8Atj+1/wCyrH+0/wDn9+zp533dv38bvu8denFR3Ohw3F7e3i3V3BcXVvBAZIJApj8p 5HRl467pDkNlWAAIIyDx+q20o8K+GNN12z1KXUPsSrcX0VvNeNZyqkYeRfKD/wCkbifLkP3f nYMeUexrttc33i2G5gs7t3D2KWE5t3HkGK7c3uHI/dBodoJJUSrgLv6UAbFt4PistStr611f UoniQrIn7l1mLytLKx3RkoZHbLeWUB2qABtXEcXgi1i3TjUr46l5olj1AJAkkRHm9EWIRHP2 ifJZCSZWJOQpXD1S68YL41k+yXOpLpaXsSLCllG0TRbrAN85jLYInuiSG48o4xsajwLdeMJt Wg/t+51KW3lsi7pdWUcSpL5Nm4wVjUg75rlcEn/VkdVJoA6Sw8HaXpl9bT2fnxwWuxobTfmN ZVhFusuSN5YQgJgttxzt3fNUbeCtNW4nu7We7tLx7j7RFcxOrNAxMpYIHVlIJuLgncG/1xxg KgXh9MvPiDIlut3fazm5S2WRm06FTAXbTzIy/ucAgT3g+bIAiORlCa2NGuvGE/gqX7dc6kuq SXunoszWUayxxSramfanl7cIZJxkqdu05+6aAOgHgjSFDxq12LZ7JdNe2E58trRYyiwkdSAW Zg+fMBYjftO2qdr8O9OsrN7e3vJ4t0U8JaO0s0BSYwlwY1gEbZECj5lPDMP7u3k7G8+INzb6 dFcX2swu7wtPKNOhDASDT96nMOAE8+8I4yPLO4nYa3G1C41L4f2q6jczzX73ek2+qQTwiPy3 la182EqFXKsshLKc/wCsZenygA6iXwp4fuNOsbC60Wxu7WwiENql3As/lIABgFwT0VfrgZov /DdnqOrRahLJOrDyfOiRhsn8mQyQ7sgkbHYsNpXOcNuHFcXqupeOrOIXcMkjxve3qKhsn/dL HOwt1dY4ZZHR03EsAmVSPDoxJksaxqvi+N9WewS7e0S9jiWdYGjMUQWTd5UZtpJCQwhBfbOj hyy+WA3lgGwfh14eh1Q6ppdpHpN+qIsM+nwQxmAr5gLKChUlllZW3AggLwCoIsTeD4saallq +pWEWmoY7OKDyXWFPLSMACWN8kKhwxy37yQbsNisvR4PEF1o/iCaMfZdWvLu2mSRQ1sjH7Ja hynnRSELlXX5oyeCDggkWNf07Wbj4VazY3U08urPp9wCYGSd5ThiqAiFA24YXiNTzwc/NQBq av4U03WzqJvRI4vre3gkU7WVfJkeSNgrAgkNISQ25TgAgjIOenw90ZreztbsyXVraW88EMBh ggVfODrIw8mNCCyuVIBCng43ANWW+jXFz8SbqW+0yC9t5pSitc2AkEdk1oFfbO2QMzAp5IIO JZWKsHDKafaWWhfDgRN4dnLahuv5dMtrORQplmVvJl8tCSqCRI2G0lo42+RgClAGha/DvTrK ze3t7yeLdFPCWjtLNAUmMJcGNYBG2RAo+ZTwzD+7t6iwsbfTNOtrCzj8u1tYkhhTcTtRQAoy eTgAda831PS9ffQ9GGkRfbNMtru1mSCUz2MizC9DMDAYmYW6LhUUnEaAsRJtQjY1W98XQ65q aWyT/wBmx/MrxwI5SFvsi74+CXkUC+YJhjkLlSGjDAHcUVw9pqPiV7/SUm+3Cxk3fa5vsK71 QTkWzEYG1pU/1oCt5eB8kGdww7G/8W31xYzazbyRz2V607YsZ5hbObK7VwAsUYkiDeWFCNIS TjzW3IaAPVKK4fw/LqL6/oMupLPHeXOlXxuPOZd8qpcQeSX2xx9FkYqCisvmMCAxbPcUAFFF FABRRRQAUUUUAFFFFABRRRQAVj+KNc/4RzQZdT8uB9ksMWLifyI18yVI9zvtbaq78k4PArYq nqemw6rapbztIqJcQXAKEA7opVlUcg8bkAPtnp1oA4+4+JUNnbtLNa2lyFt0uPN02+FxCy4u nkCuUXJWOzfHHLsFO0AvRY+OoV1yDQLez0awgjfyI0udSFuzKt1PbBYIhGQ5xb525GN6r710 GreEtI1u/N3fwySl7dreSMSFVdSkiAnHIIWedRgj/WknJCla9n4Pi07UkvrHV9SgdkVblF8l luiJZZWZ90ZILPNKTsKD5sADAwAXNJ8U6Nr0UUul3n2pJdpVo4nIwyswY8fKvyOu44G9WTO8 Fajh8Sw3PipNFt7eSSM288rXmQI/MieJHjXuxBl+Y9ARtyWDBafhHwj/AMI3pOnxveTvexWl vb3BEvmI6xxsPLUsufLDySOOjDIXIQBBYfwZog8Qw69a2UFnqUfmEz29vErSF3RnZiUJLEIy 7uu2STBBOQAZafEBT4a1zWH0yTGmW/n+VE7SHJDYhl2pmKdSv7xMMIwyksRnGo/iOceNF0IW Gy18oMb24MsQkkIdjHDmPZIwVVYgODgtgHY2I4fBWmw6NqWlie7aC+shp25nXdBaqrrHEmFx hBI+GYMxz8zNgYk1nw/cXk73tjqE8d4uWtUmcNDbTMhiNwqlSSyxs2I8iNj1AZi4AK+peLvs PjKDQP8AiVR+ZFBLvvdS8iSTzZJE2wx+W3mMPL6bhkso75rLPxEmt/DFrql/plpaT31vb3do j358hoZZIYy0kvlgxmMzoWBUjBG0t823sI9Nhi1m51RWk8+4t4bd1JG0LG0jKRxnOZWzz2HT vh2PgWws9MtLKW9vrv7H9mW2muGj3wxQSRyJEu1AoUtEm443NgZY7V2gEdh4/wBIkt421K6t LWSR5BE8ExuLe4WMRl3imCgOiiQbiQNpSXPyxs1ams65/ZmjvfxRwBVlMTSahP8AY4YsMVLO 7qSFJGFKq24suPlO4Y+peA7fUdcE7XE6adLFdi4t45igzP8AZw6KoGPLcRSls875S4IbDL0F 5pkt3ZiEapfW8yytLHcwlA6ZJ+XaVKMoVioDK3QH7wDUAY+seMf7L07S7z+zJx9ui87ybpvJ kGAp8hVwd1y27CQ8bij/ADDbzoJ4gibXNX04284XTLSC5kkWJ2Mnmeb8qKFy+BEOV3ZLFeqk VTufBWm3NlBZCe7itILdrJIUdcC0ZI0e3yVJ2N5SktnzAc4cDitiPTYYtZudUVpPPuLeG3dS RtCxtIykcZzmVs89h07gGX/wk/8Axbz/AISv7H/zCv7S+y+b/wBMvM2b8fhnHvisO08dQ21r Z2trZ6NMjo0NsukakJ7eNlltoY4ywjXYN1yucKdqrkBs4GxB4Xb+wbzw7Pdyf2KdMj0u2RSp lCCNkeVm2gb2DAbcEDywf4io0NU0Cx1e9sbu6WQyWb7k2tgMN6SBW9vMiifjBzGBnaWVgDj3 +KcMstuLe3022guEMkc2r6mLNSvkWswGRG43kXWNv/TMnJzx2lvrenXd5JaW1x500UvkyLGj MEbDHJIGAuUdd33d6MmdwK1hweAbLT3tpNJ1PUtNlgSSMSQGJy0bLCoQiWNxhUt4VBABwmSW JJOpYeH00zUbi5tdQvlhnlMps2dWhUkyO+0Fcjc8rOTnOVUAhRtoAjk1+Z9Z1bSrLTpLi5sL e2lXexiWRpmkH3mXGxRGCWXd1YAFl2mnD4suJoNLuRpX+i3kqwNItwGLuzsgNuoGZo8IZS52 fusOA3zKtzV/C9tq51Fnu7uB7+3t7aVoSn3IpHcDDKwIbzGVgwIZTjHXMaeGJl1a11JvEWqy XEEQhbelsRKnmFyCPJ+Xd8qts2ZCJnlQaAJNH8Sw61rN9Z21vJ9mt7eCeG7YjbcrI0q7kHXZ mI4Y/ezkZXazc+PiFNLJqEdva6NLPb3v2KO0Grn7QW+2Lah5IxETGhLbs5bgqMHORqWngyw0 fxRDquh2VjpsbxeVdrBbxp5iKH2ooCDbuaQMzbv+WEa4IJIuT+F7abQpdKS7u4Ee9N8s8ZQy JKbn7TkblK4D9ip44OetAGHqfxB/s68trGWHSrS8Pnpdf2nqn2aGGSMQttSTy28zcs6OvCnb 1CsCo6Cy8Tade+VGDPFdvFFK9nJC3nReZs+V1AOGXzE3j+AOpbCsCY4fCmmx3CXEwkupDbzw XBuNr/a/OMXmNKMYYkQooAwoX5QAoUCOw8KJpl4lzbaxqu7yo0mWSZXFw6iJTLJuU5kZIEQn jALFQrMWoAsXmvfZvEMWjx2U9xM+nz3wKDbu8t41EalsIWYyH+IbcDOAwNZcvi6+g0ue7fRo 3NncPDdGG83RMV2jZbsUDTSsz+WqbVBkR0LAgbtjU9Dh1O4+0NdXdtOLK4skktpAjIsxjLOp wSHBiUqe3PB7Zf8AwhrrFp6R+I9VjbT94tmSG0AjVlVAAnkbBtVSFIUMA7jOGxQBch8Sw3Pi pNFt7eSSM288rXmQI/MieJHjXuxBl+Y9ARtyWDBdysNPCGgw+JYvEEGmWkGoRpMDJFAil2lK lnYhcl/lIBz0d+ua3KACiiigAooooAKx/FWs/wDCP+F9R1NXgSaGIiA3BxGZmO2MOcjCl2UE kgAHJIAJGxVPU9Nh1W1S3naRUS4guAUIB3RSrKo5B43IAfbPTrQBy+neMpotNv73UTHqdnb3 DlNS0q2K25tUijeSYlpGBCM7qQjMxKNtUlWxYvPiHotiXaZLtYPs7XMN00YWC4QSRRApIxC4 Z5VAJIGBvJCMjtc13whYa/q2najdSzpNYSxyxhBGwJSQSDG9WKZKjJjKFhwxIC4x9C8EXEMF 8mqTeTHJFaW9lBb3An+xpbO8kJSQxJu2s4wHV/ufMzhiAAdBY6+ms+HI9X0WD7Z52ViiMqqN 4cowZwSu1WByy7shSV38AyeGtSm1nwrpGqXCxrPe2UNxIsYIUM6BiBkk4yfU0XOhw3nhq60O 5uruaK7t5YJrh5AZW8wEM2cYB+YkAAKOAAAAK1KACiiigAooooAKKKKACiiigAooooAKrmws 2s5rM2kBtZt/mwmMbJN5JfcvQ7izE565OetWKKACiiigAooooAKKKKACiiigAooooAjEEK3D 3CxRid0VHkCjcyqSVBPUgFmIHbcfWpKKKACiiigAooooAqaiT9nQA4zIo/AnFUFSGSfy4llY KSHfzHCj2B/iOeOOmDnB4OleRPNCqoMsHVvyOapfZbkXHnpaxpIfvlZcbxjA3cc47dx+JznJ ajMC/WW81iHTsu8bWd1MsQuXt98kckSoDInzKPnOcZ+hxijTdO0G/EvlDWbe7t8edbXOoXSy RE5wSGkKuuQwDruRirYJwat6lomqyXSX2nvEl0tvPAA8gTb5rI24NtcZXYMAoQc81X0/Sdc0 y3NtZ6LottHI++V01CRnkcgAu5MOXc4GWY5Pc1rG/KrGbtd3Nvwtcy3vhPSLudt009nFLI2M ZZlBJwPc0VPoun/2ToVhpvmeZ9kt0g34xu2qBnHOM4oodr6FRvbUv0UUUhhRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFAGfD/aP/CQ3vm/8gz7JB9n+7/rt83m/7X3fJ68enetCiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//Z --part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="test3.jpg" /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRof Hh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwh MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAAR CAMyAjgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiuf8VeJ/wDhGf7E/wBD+0/2nqsGm/63Z5Xmbvn6HONvTjOe oroKACiiuf8A+En/AOLh/wDCKfY/+YV/aX2rzf8Apr5ezZj8c59sUAdBRUc88Nrby3FxLHDB EheSSRgqooGSSTwABzmpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPP/in/AMyV/wBjXY/+ z15Z4mvV/tnxPrE2ralH4/sNdS10O1UNu+zbsRoke3DI6M5PY4XP+sO/6LurCzvvI+2WkFx5 Eqzw+dGH8uRfuuuejDJwRyKjk0nTZtUh1SXT7R9QhTZFdtCplReeFfGQPmbgHufWgDwv4kTa dceNPFr+JdXvrK60nT4J/DCI7RDzCFZnjwvzN5oVSc5xu/55goeIm8a33ia1l0iXyNfk8CxS 3xkjZJ/9bukWJVX5Zi2FAwMZOMHBHul5pOm6hcWtxe6faXM9o++2kmhV2hbIOUJGVOVByPQe lSfYLP8AtH+0fskH27yvI+0+WPM8vO7Zu67c846ZoA5/RLrwsnw1gvLG3gi8MjT2laEoJFSH aTIrqN25h8wcfMS27OTmuP8AAej67beI4bnQ7DVdB8Gjdv03WLkO8nyMB5UJVngxLlmy/wA+ 4EccV6hY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJP41YoA8z+IWl2mtfErwFpt+kj2lymppK iSvGWXyFyNyEHB6EZ5GQeCa4j4dRGHVPhbqS3F213qNvqdrdO9zI4eGHd5Ue0sQqLgEKABkA 9RXvc1hZ3F5bXk1pBJdWu77PM8YLxbhhtrHlcjg461Xt9C0e0+x/ZtKsYfsO/wCyeXbov2ff 9/ZgfLu74xnvQB4R8J7fU28fWGq3N7pUWpXn2/8AtW1jEzX0nzkt9oj2mODEoQj/AFeRx8xO Kp+CfD+n3ifDdJvtezWrfVrXUFS9mQTwxs7LH8rjam7JKrgEkk5zX0Ha6Fo9jqM+o2elWNvf T7vOuYbdEkk3Hc25gMnJAJz1NFvoWj2n2P7NpVjD9h3/AGTy7dF+z7/v7MD5d3fGM96AOT+D E81z8JNBeeWSVwkqBnYsQqzOqjnsFAAHYACu8qvY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJ P41YoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKxqKANmisaom3fa48fc2Nnr1yuPb168+negDeorJj ieVsIPqfSprexxLP5jFh5g24yONq+vvnpx+OaANCisPU9d07R3Mc9vKxUZ+RFPYHufem6F4l tdVWG3jjnWXYNxcDGQBnncSfxoA3qKKzNTuLN4FVpoC3nRdXGceYpP8AWgDToql9ngnUvbyq y9PlO4Z+tUp4JobuPcP3exskbsZyuPb168+negDaorGqKLd5k+7pvG3r02r68dc9OPxzQBvU VjVFb7vLO/rvf16bjjrz0/D04xQBvUViNu3RbOvmx+vTeM9Oen4evGa26ACiiigAoqvf7v7O utn3/Kfb164P93n8uazqANmisFt32uPH3NjZ69crj29evPp3qWgDZorMit2kUuSFQdSayNY8 QabpKywvHPNKVKqyY27tvqGB7/WgDqqKx7HxFZX9vviimCYHDKOhH1qWazQ61btnC+RKSo/3 o6ANOio43hkUrE0bKOoUgimSWkbg7Rsb1H+FAE9FZN5ZzJGpi+f50zjPTcM9Oen4evFMoA2a KwV3fa5M/c2Ljr1y2fb06c+vapaANmimQ/6iP/dH8qfQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBjUU UUAFZ+oapa6bcQfaJGy+VEaDLc4wxGQMcY9fTvUl/qUOnpulVm6fKpAJrzq58RQXOpq86Sui /KC7gkfNnjP1NAHrlpqVnOmEcRgdA5Az9OaswsGefD7sPg9ePlHHP9OPxzXlGq6jdEwnTp9i 4OdjD14rb0TxQLK2eK6eSW7lZsvvGNxwASPXgUAd5Ja28xzLBFIfVkBqOztYLe3j8qJEOwDI j2k8d88/nzXJ2XjCV9YNpNl1UsMgrg4HqBViTxjaaYILWSABVQLnzNvQDsefzNAG5qmqx6fG FA3yNkAAj5eOprh5BM9+iuvyuy4bIqxqmoSapdrNAxSNu2fYdxWzYmIWLs6oDFPA29gPlG8Z 57DigDb0yzS0tE2Ekuqls+uO1R3WoLaX6rKrBPLJBB65I98dj159O9SWmrWV622G4jMmcCMu u48Z6A1U14wJEHcKZthC8c4LL36Dp9fTvQBaxb3qh7eRN5G4gMD+YHSqKAiSYEnO/oQRjgev 9OPxzWVaeZBZvOkpQZC53Y6k/wCFVV8TwWuoSQ3qOzTsGEqBeG4XnpxgDntjp1oA6Ko4eEPJ Pzt1BH8R9f8A9XpxUnBGVZXU9GU5B+hqK3RUjIUYG9z90jksSev/AOo9uKAJOrxckfvU6An+ Ien/AOr14rarEZFdogwyPNjP3SeQ4I6f/qHfitugAooooAgvebG4GSP3TcgEkceg5/Lms2tG /RZNOukYZVonB+Utxg9hyfw5rOoAZtLXUYBOSrDGDg8jv0/Pn071orGlnC88pGVBJPp9KjSC G2uo5ZnQSbHAJB4GVzg9PT39OhqHVbi0mtWj+2wq5HCGZVDcjrk0AWodRtLmIt5iKucESMBn 9awPFFnFF5MkShWcnOBgcAelcXrN/fw3rx2sxSOMkZVwQela9vrct7b7bp3kUIeXOe3+e9AG 2mvxeH7GNbmFpN7ZHlkcfKOtbEN3bandW8tvKskb20oJRgSpJj4OM4PNcDqWsWd5Mts0Mxbn cTj07V1HhvThZXUWx2EbRyMF3cZ/d5/mPyFAEVlPdaT4iNpJtMMhUMQwxjbwemeprrlZXUMr BlPQg5Feaa1qNzaXL3pfzVTbwH+926/56U3RvGNze2zWzSurIAQQQO/tzQB6RI6Ou1JBlXXO 3JI+YcHBz/T14zS3EAmTGcMOhrz6fWLrcAJGXLA8PnJByCfxru9MlafTopHYsxzknr1NAFBo 2iu5FbP3F4wcdTyD0/Ln17UtX5bZJbiVwAJDGg3bT2Ld+h6njt36iqBBBwRgigDWh/1Ef+6P 5U+mQ/6iP/dH8qfQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBjVm69rUGg6VJfzgMFZVWPcFLknoM98Z P0BrSJAGScCvN/Hty+rFLaGZGtImV8dMvg85x6NjFAFe68Ute28TyqZDJg8kcDnjAFUJIIr+ DMEaJNxjOAOtQaHpM80m52RoFxj94OOea0nsEik3W0hKjssnvQAkFtcRWw8wEt2+cDPNc34i uL+x1dGSRkQfOoVgA3zHrj8OtaVzc37XBBlcBTg/NnPNYvja7ZTbosmXKsDhs55GOfzoAq6f 47lk8SNayLNGw3ElSpGcfhXVa1PcOUkEYmxwoyowCP16CvPrGwmuJFnXbvBYklsV6Vuge1ig a5QTquMF8HoPWgDr/Bi3DBGkiWUMoO0kY+6a6nxAkMOizIkap5jovyAD+IH+hrlvDl9c2Vqs X2gk54UvnPyjsav6/qYuLtrdJCYgVIwePy/OgDm/B9vev4khl84sgcZG89NpzXW3GhX97NE0 07rIoyNzkqcHpkAj8O/4GqPh23ijv1NtkMXGRu7Dr+ma6S91e0tbhRvjeQRk4AyeSvG7t64x zjt3AKSaHdPps8EjoHcoUG44GCc549D2rAXQ2jmdL+Mh15UnBz+Peuw0q7kvImeQ5HGOnvUu 6B5Z4JdilnHBG0v8q89fm9M+2O1AGZoZSS3ezdGDIWZGJB+Xj+pPFLDCYFZChX9455UjOWPP J7/5x0q1qF1/ZcMRjGIwTlc9eR359adYSxanp/mBFQ734UY2ncT+OeCfXNAFVkV2iDDI82M/ dJ5Dgjp/+od+K26xpkMUsasm8iaPjbu/iHOPbrnt17Vs0AFFFFAFe/RZNOukYZVonB+Utxg9 hyfw5rIn1K20zElwCc/dGQB26k1r35xp10dm/wDdP8uzdu4PGO/0rg/FRad41jdQEUhl39Tu 6/59KALGr+K7e6jMkSbDErLneCTkj/CvK/EHiC/uLtxFcTIuSFIlxj5u34Vf1BmitDGs4UlT uJbk81wl9LdRXhjZmZTnA3+9AGh/wmVz9uj06YzPKWcNIWHOBn+lds7XaWEZScxjHXdjPH86 4XQrXz9ZWZ8O21jwcZ4xXpt1ELvyoxhoVAOdw4OBQA3QZIriZIZ1eSQ9XLDjjtkH0r1YRR21 /aouFRLaXnp0MfJrz/Q4tMsrsEzJ7gS89DXY6rqEZlhktriNswSg7WB4Oz/CgDkPHF7YaOq2 qp50TIrkjacHJGP0rkNNvbGONp7RnWRVGVZlHfrxW9qsCazaPFckgAqcljXFR6ZPazsUK7OM gP1oA6Ow1N5b/BZyc4O5s969Gstet9PsHMzk4GQC4AHJz16V5do1uTcb5nChMHk+9Xtb1KH7 K8SzjJHZvegD0zR/Ellq2pukKgO6Bchwc7cn1/2vT/61+7QrOTjhuRXjfhS8ks9SWdZHwN2M MR2xXpr6291KJAR5XaMN04+nNAHTQ/6iP/dH8qfTITmCMjptH8qfQAUUUUAFFFFABRRRQAUU UUAFU9U1KHSbBruZZHAdI0jjALSSO4REGSBlmZVySAM5JAyauVn61pn9r6YbUTeTIssVxFIV 3BZIpFkTcuRldyLkAgkZAIPIAMu68ZW1ojPJpupAW9v9qv8AMaKbGLc675AzguMxS/6rzMhM jIZS2XrvxESx0PVLyy0q+eW2+1wW8s0SrDJcweZuXlwzKFieQsOCqlQfM+SrmoeEb7UEulk1 mNjqVkLHU2ezyXi3SnEGHAiIE8gBbzOAmdxDFo9V8C/2n4eOk/2j5WbvULnzfI3f8fSXK7cb v4ftOc552ds8AFyx8XjUbCW5ttB1lpI7h7XyDFGGaZHdZFDF9mF8skuWCHIUMXyoI/GVtcEJ Z6bqV3MqE3EMMaFrZ/MeII+XAJMsUiblLKNhZmVMPVd/B003hiPS7i9tLidb2e9k86yL2s7S ySSFJIC+WQGXIG/hkRsnGDX07wG1jFpURvrQJY3EtwPs+nrEYi87TFbdtxMKNuEbqS4aNQo2 5JIBHpnjq4u9H0a8uLLy5JtPgu72NVBYvOwit44hvwPNk3FWZjtVMOFLZXcj1yb+1NNSa1kt 7TUUkijWeMpLDdR7mMbYJDBkWQhh8v7okM4kSufsfC19pCW2kyXMd1Hc6PDpZuxZbolNszsi yxFmykscrq2WUfuyAytIm3QttEmtr/w/pieY8GlPNfy3HllIgzpJEkEanhUAmk2qC3lpCin7 ytQB1lFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHL6tK62MkcLETM COOMAg85/KvFtXGrz2tzFFIyzZTb8445GefpXviC0Qoj6gsVzg4/eJu5ORwR6cdOh7nmuM1X U7GzuCLS2dmbnf52Rz2xt7f/AK80AeLeEZvEcNyyTXkroduEMoIzmt3V77UNPgY+aysSPut0 Ga9a0Xwzpt9Zi6gSWG4Kg4MuVGe3TtiuX8a+G5LBlYgurkEFSSBkt1468UAcXpOo3E0TPdyH 7vUsM9ag1SbcuUmVkYEZ6/nW+dAuLq1Zo45BtUkZB/wqDTfDlw8jRzxsUJzwCMcj2oAwbayu fs6yoR1OSGrD1vUNSg1VglxIuGOMNz0Hevb5dHs9K01PMlUytuOwSZI57jHHWuR1WwtL24bK hxklcMeDigCr4e8T/bpQouZA0fy8lj2+ldlKbn7fD82YX2ljkHP9a4m7s5INPjOnxyLMHxIU y3GP071uWWr3UVvDbytIzEKcMfYe1AHVNJJZxFrR2RsjJVsEfjTIY7i5fzZWYjZwSferOjGO 5QrcyNE7bfLLNgAnv0pt/qWmaVeGK6vkcBeTG44zjHO00AWtMvJ7WaJTMwiEihhu4255+tVb +7vYPEM0gkOwynaQRyN3Bz9MCqX/AAk3hq7mSKz1aJWJGIzJ3PHUqK0p7yyk3Ryz4k3cNtxu GAOcD1zz+HQUAW9R1CadQmTtAOMnJ598VsaJbSRoJY222zs+IyTnqR/TrWRp/wBkkllaa4Zl jB+XONxGOOnv2rb0mRJbdYYpnj8tj8ihfmAJOTweuRnGOnGOlAFnUEH7iTZvbzo1xt3fxDnH t1z2684q7WQl2k7yWpkaaeGZPlkRTwsg+bgKM8/hgcHvr0AFFFFAFe/ONOujs3/un+XZu3cH jHf6V5jqKXT3cqSksS5GQwGOa9PvQWsbhQgcmJgFIBB46YJGfzFYeq6GGneaESM0hLHHOCT9 OlAHimsWty0rBjg87V3dBmqTWzR2wabkNnknPevVNRsJbK2kub2FmCLwMlc8gdce+axI7XSd adY2k8uUZ+Tzc5/SgDltN06SMSXEShYwpx83OafA+pJcySmdxbjdkbuBx6V12rWD2OkTx2vm ZQE5HOeR7Vz/AIdtru7nlF60piy2A2Qe2KAOYh19ri/aOG5dG7jJ9PpXqWmR3MJgWeQuzgH7 +cKQP8KWz8M+Go7+NpnZM8nEpz09K6oW+lLcRQx3yhDFIS/mruXlMDngcZ6igDhvENxDCzW8 dwsch28bqzYrC4FmsxkUoAD8rcnJrQ13w75+stJ9ut3X5cYlznjtxVHUXktbNbW3nBkAX7hy cUAchqeo3lj8qSnf32ketcXfahq9/O6xXUocE4G/AHNdtKLWS4DSX9tuK4ZWmGQcnrV46fZz zLPCuST8x3k96AIfBD6k1oVu3ZnTePMLg56EfzrtbS5nilw0hIx0zWXpcca3JTIQAHJz2/8A 111Y02KGyeaRiGAJGT19qAO00K++36arbNvlkRdc5wBz+taVc94OcPpM2O1wwPt8q10NABRR RQAUUVHPBDdW8tvcRRzQSoUkjkUMrqRggg8EEcYoAjs7631CBprWTzI1lkhJ2kYeN2jcc+jK w98ccVYrm/A0ENr4bkt7eKOGCLU9QSOONQqoovJgAAOAAOMV0lABRRRQAUUUUAFFFFABRRXz Doui6VLoWnySaZZu720bMzQKSSVGSTigD6eor5v/ALC0f/oFWP8A4Dp/hVHSdF0qSzkZ9Ms2 IubhctAp4EzgDp2AAoA+nqK+b/7C0f8A6BVj/wCA6f4VRi0XSjrt3GdMsyi20DBfIXAJaXJx jvgfkKAPp6ivm/8AsLR/+gVY/wDgOn+FUb/RdKS80xV0yzUPcsrAQKNw8mQ4PHPIB/CgD6eo r5v/ALC0f/oFWP8A4Dp/hVHWtF0qLQtQkj0yzR0tpGVlgUEEKcEHFAH09RRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQB5xq9pqNvrjSOzSMoGWUsR09afpGnQarqCRXNvIqDqykjoCf6V3 ItZiMjUbkg/7MX/xFMi094VKx39yATnpH/8AEUATWllBYwiKBSqgAcknOK5fVLu/hkuJGjSQ ZMQwpOPmB69f4a6OSF4gC+p3KgnAJWPr/wB8Vn36oJYPNvp5EIOWCx/KCR3C+x4xzjtjkAx7 a/uv3YEzonpvIHWq01vcT3skoGcseeeR6+9JeDzWIhuGIXuWGRz7AUtve3NkhCXciZbdwQQT x6jmgDA8Um8MU6xQuWxhWVWz2zUGk6bcQWCyyrIsrrnnIbkD9a9EslTWrcGa+uC6k/Kuzpxz 9z6VaTRQ9vGst3cg7FUqBGMcYxgKcfmfrQB4P4m1i+0yZRbwvIXdg+C3GPp9TXWeDWGtwRyX tsySiaJAQSMgkZ610l3ptkt9PE0sjMWI+8MnH4VDJZWtzpE9laarcWFy8kIWVf4PnXnoCMAH +IUm2ldF04xlNKTsn17eZevl05fEMWj20skt6FDtFCGZokwTuc4wo6D1+ZeMHNcnqXge+u9V nuJ2nW32ry5YDsO64zXR+FG/sAw+HbsPZSyFpIrmyCyWtwSN332UsHwGGG7JwcFRWnrlw8F0 I1vpJf3eDuVMjJBxkL7A1FOTkrs6MZRjSmow2tvpr56bel3bueQW3gZLPVUmW4BCMDsEmScN /u112myWmp6oYprlY2B5Ukj+LGOlV9TgltWM4kfduDDax6ZPFSeH7SxudTadd4lxuyrcfeHq K0OQ1NUmOlXKpbsZI5CSXYnA5/Cuu8PXcMemZkOwvM+OCc9PSuU1iCN5FjmeRlA4OQCOfpVz TYNZa7W1tp3itTuKkFhj3JAxQB193ZIZDcJuMrSRZ4BGA6nofp17duav1nXNrMFizeTyfvoz sZIyDhwf7o7DOc/4Vo0AFFFFAFe/RpNOukUZZonA+UNzg9jwfx4qfcu7bkbsZxnnFQX6NJp1 0ijLNE4HyhucHseD+PFcze3NxZ3kjvfzgIxG75em7/doAm8T6XdX/wC7h8542jOcZOCWHA/K uEi8KXOn6yLkSuECkMNx4JJ9uteiW15FdPFKNUmGVYElI8ryOM7OM49O3UY54nxPfTR6ssEV 7MY9zt/rMBjnqQMA9BQB0yTQwQt9ojj8vkmSQsP5GmXupaS2mtIEVQqkK4kJGdvoe1UXijvb AQ3F+Y2Oc7246jjhSalTw9bXFh9hErySNu2hWwDheOoHXnuKAObg1KC9uhJDh8cHk8cVzfir XtS0u+S3tYXkEgUmRS3fIxx9K9Cs/Acdtc/6LeRY5yomyTx/u1y+taFd3TRwfaI9yE7oZWcR SZxjcQQeME1M21G6VzbD04VKqhUlyp9d/wCvy7tIxdK1W5vIfNkkkZwFyWLEBueBnv8A41Zt 49Rk8QJMkMrRkoCdrFcZFbFhHa3blGt57OaEoHgkGBjkDYQMMvykAj0roNMlv7O3cGZohtHE fAPPuKIO8b3Hiaap1XFK3k3f8Vprvoea3fw9MN7LdNJIYi5bBbkZbp92uh0a1RLWRYLZt2OT knHJrsPNub5C0lxK+MdcHjP0qfQks5LoolxPFMuPmVlIJzwMFTiqMDloFkaQxTQZjGexFbE1 zNcM1pIvyAHBGevFdZLpMQu2lmvp1yow7eWMnJz29x279+zhocEjtML24YuSSwKc/wDjtAFH wlZXllDOrvC9rJI0gILFw528dMYwPzrpaiggS3iEaZOOpPUn1qWgAoorn7Lxlo9zdNbXE32C Y3clpbreskf2tklaJjCdx3/OpG37w+UlQGUkA6CiiigDn/Bv/IDuf+wrqX/pbNXQVz/g3/kB 3P8A2FdS/wDS2augoAKKKKACiiigAooooAK+b9C/5F7TP+vSL/0AV9IV836F/wAi9pn/AF6R f+gCgDQrP0b/AI8ZP+vu5/8ARz1oVn6N/wAeMn/X3c/+jnoA0Kz4f+RhvP8Ar0g/9DmrQrPh /wCRhvP+vSD/ANDmoA0Kz9R/4/tJ/wCvtv8A0TLWhWfqP/H9pP8A19t/6JloA0Kz9d/5F7U/ +vSX/wBANaFZ+u/8i9qf/XpL/wCgGgD6QooooAKKKKACiiigAooooAKKKKACiiigAooooApW +6aDyxcyI6gjKhc4yCOo7dP/AK/NWTGxBHnSDIYZwvGTwenbt+uaygSDkHBFaFtNLKpyAcZ+ f/61ABJatKrK1zNtYscYTv0H3e3Ufrmua1LT9WhcyRvG5CsVjiJZgMgEAYz3B/n2rqijtndK QMfwjH+NRNEn2uIh3D7Hx8wPGVz159OnHr2oA84llnsg7SRPvOf3bAgkis2XVVZCWzG4zuQ5 47eles3Vmt5bvbznzIXxuVh6HPUEe1c3N4PS5kuBHeBQHICbMgZAOM7s9COvNAHKaZr1zbnE G/APO1iM+3Sut07xA+EhfO0KAuAowBz0C45HH0HGDzVI+BGjVyLoMSP+eZx/PP6VPo3hIRpH dG8Qh1yqqu4YIHfNAFdrVp7y4lZnLAs27uOpycD0rlJNXSHU2gGW3Fec4xx9K9CvNGuhHK8c isoVjtBOW46Yx1rzG/8ADtzas8kkcsRA3ZeJlwMdaAOiOsSKFCMxO0YGen6dqF1RJLgzXEjM 5XgDt2xnHb+lcVYXl5HdR27LlHPMuWwox0/T9a6K00m51O9tFNzGHyoJG7BG7/69AGPqGqXG oX8kZ/dox+XBPTdxWr4d0/VY9QhNnbzNG0ir5wR9ifMOpHT1NepaLpEGk2myMAyuB5rgnDEZ 7E9smrsO3zZ9vXzBu+712r6c9MdefwxQBgwaS91egXd0shjOSqg84684HWtazthHbokNxKER yMfId2GbOcDufx4HQ5q7VRbiKGI7PmPmP8oK9dxz04/r685oAguwIxBHLNJL+9jOXCDnfweg HcfkMc1pVjSsHljaTvNH6dd4x978Pf05xWzQAUUUUAV7/b/Z11v+55T7unTB/vcfnxWTqujX OrxyH7SkYcDYMdBnOD/P6+1a1/t/s663/c8p93Tpg/3uPz4qrBdNCu3buX60AcC11JYzmDyy rqSuHyO/+Ncnq1xdSaqVnVkwW2MQRuGTyPaugkurkeMJY761ZY5yTvKsoOZO3tWp4m0qzUFL SYKGBxlW456c0AJoEJ1W2j8yKUnksQSeh78fSu0TTYrCzld5Hm2IWO4KAQM5HPHI4Oe3p1rz LR9UfSS9tbyl5SWBZSy+nH6V1dtPrdxvjuILiOMo25pgwXG09S3H58UAXBcfZ78zpI6qOQmQ fw6fh9PfmsTU72S7ujLIfmIPI9PSunPh+Wdf3tyqNn+BSwx+lUZvC269jh+243xs+fK6bSo9 f9r9KAOQnuXDLhjgYAq4LuSW2UB23AAEe1dE/gfec/2j/wCQf/sqgbwjdW8BZZY5COyhsn9K AMc6lHb24j3nPVvr27etZNj4mW2uJJYYdrg8neef0rP1rRtZ83zDp9yIgTlmhfAyeOcV0Pg/ whLJcvNckxgAEHY3UN07UAWdK1C88Rap9n81lkCEliTjaPXj1P5mvQUtTEkaJPKqpjjC/NjO c8d8849OMVxfiCz1A3Ig09X81E5Khskbjg8c9j7frXbzXCQ8HlsZA9aAJFBVFUsWIGCxxk+/ FLTYyWiRj1KgmnUAFcemrQxS6poNt4N1W5hjllNxEXtSkwnZnZwsk4LRuWfHGOGXAKlR2FFA GfokMFvo8EVtpH9kQru22OyJfK+Y54iZk5PzcE9eec1cnhW5t5YHMgSRCjGORkYAjHDKQVPu CCO1SUUAeD6hayabq1/aWWp6zBbx3c22NNWuQMmRiT/rOSSSSepJJNV/Nvv+g3rn/g3uf/jl aGvf8jDqf/X3L/6Gaz6AKdnd6lLdaij67rhWG4CIP7WueB5UbY/1nqx/Ornm33/Qb1z/AMG9 z/8AHKz9O/4/tW/6+1/9ExVoUAU5LvUl1m2txruueU9vM7L/AGtc8lWjAP8ArP8AaP51c82+ /wCg3rn/AIN7n/45WfN/yMNn/wBek/8A6HDWhQBT1O71K3tUeLXdcVjcQIT/AGtcnhpVUj/W ehNXPNvv+g3rn/g3uf8A45WfrP8Ax4x/9fdt/wCjkrQoAPNvv+g3rn/g3uf/AI5XbeD/AIc+ Gb7wToN3cWt400+nW8shXUrlQWaNScASADk9AMVxNeweBP8Aknnhr/sFWv8A6KWgDP8A+FX+ E/8An0vv/Brd/wDx2mp8LPCEalUsbxQSWwuqXQ5JyT/rO5JNdlRQBx//AAq/wn/z6X3/AINb v/47TR8LPCAkaQWN4HYBS39qXWSBnAz5nbJ/M12VFAHH/wDCr/Cf/Ppff+DW7/8AjtNb4WeE HZGaxvGKHcpOqXR2nBGR+844JH412VFAHH/8Kv8ACf8Az6X3/g1u/wD47TZPhZ4QljaOSxvH RwVZW1S6IIPUEeZXZUUAFFFFABRRRQAUUUUAFFFFABRVWW4mW68mOBXAQPkybS3JBCjGCRx3 HUVL9oi+y/ad37nZ5m7B+7jOcfSgCWiqv2+H+5c/+A0n/wATUkN1FO7Km8MoBIeNkODnHUD0 NAE1FFFAGNWrbkG3Tb0xWc9neBTsSAtzw0pA68fwntk/XjnrVu3S5hiZWSIn5iv7w9ew6d+/ p70AWqibb9rjz9/y3x93plc+/p049e1NLXWTiGHGWx+9PTHy/wAPc9fT3oc3PmgxpGUAYYMm M8DB+6e+R16c89AAT1FDt82fb18wbvu9dq+nPTHXn8MUqmY7d8cY55w5OBj6evH059qZGbne fMSPaSDxJnb8vIHyjv8AzJ46UAT1mC4TyIBBkbUUBuOmOny8flx6VYuBeyKixpCAdvmZlPHX OPl5xx6Z9u9SKyvREokEJcAAnzCc88n7o7c9OvHHWgDRt5xMmcYYdRUOo/8AHsn/AF3h/wDR i1Vjtb8MpKW6nHJWZjg57fL6c/Xj3qa4ivpbYIEt2cSK+TKRna4IH3e4B+nv1oAv1mWej6dp t+r2kJjkeJgf3hIIyvYnPp0/HtVwvdYOIYc4bH709c/L/D37+nvVWdNSlk+UQLGNw2CY8/3S fkznqMdBnvQBHIxE7srfxHBBqOC5kV5wrnO/5shTztX056Y68/hipHs7wMdiQFeeWlIPTj+E 98j6c89KI7G7zl/K5YZAcnaNvb5Rnn1+vtQA0ySMMM7EehNQW+3yzs6b39Ou456cdfx9ec1Z SzvC3zpAF45WUk9Of4R3wPpzx0pBZXqhRiFskFiZDxknIGF5xxjpnvjuARNt3Rb+nmx+nXeM deOv4+nOK26y47O8yjOIUYMpOyQn+Lkcr6fz7da1KACiiigCvf7f7Out/wBzyn3dOmD/AHuP z4rOrVuEeS2lSI4kZCFOcYOOOx/kfpVBrK6GdiwnnjMhGRj/AHfXj6c+1ABZBPtoJ/1gjYDp 0yuff06cevakn8O6VdTNNNa7pGJJPmMMk89jUtrb3cUyvJ5YUjDKshI6A5+7yQcjtwc+1WA1 1kZhhxlc/vT0x838PY9PX2oAW1tYbK2S3t02RJnauScZOe/1pbrb9km3/c8tt33emP8Aa4/P j1oLXHyYii527v3h465x8vOOMdM+1NzcvEQyRxuQBlJM455IyvYYI45PHvQBPVST/kLW/wD1 wl/9Cjqbdcb8eVFs9fMOfvem305+vHvUMsdwbmO4RIiyRyJtZyAcsuDnHop7dfzoAt0VEGuP nzFFxu2/vDz0xn5eM856496aWusnEMOMtj96emPl/h7nr6e9ADrjb5Q39PMT+713DHXjr+Pp zipail88qRGFBDcHfjIxnn5T1PH0568UqmY7d8cY55w5OBj6evH059qAKV6q/aiwHzbFDH5e mWx7+vXj071Xq1cRXszqQsIT5QVMpwP7xHyde3v7VCbO8+TCQc7d3708euPl5xxjpn2oA0of 9RH/ALo/lT6ZCGWCMOAHCgMFOQDjsafQAUUUUAFFFRzxtNbyxJNJA7oVWWMKWQkfeG4EZHXk EeoNAHievf8AIw6n/wBfcv8A6Gaz66BvBepyXV4+oW/i65uGu5yJ7SXSxHLH5reWwDkEEptJ yByTwKjh8HvOhdNN8eAB2T530pDlWKnhiDjI4PQjBGQQaAOT07/j+1b/AK+1/wDRMVaFbkfg IxPM6aZ45DTPvc+fpPJ2hc/e9FH5UDwe7XDwDTfHm9EVyS+lBcMSBhs4J+U5AORxnGRkA5Ob /kYbP/r0n/8AQ4a0K3D4CLXCXB0zxz5qIyK3n6TwGIJH3v8AZH5USeD3ieFG03x4TK+xdr6U wB2lvmIOFGFPJwM4HUgEA5PWf+PGP/r7tv8A0claFbk3gI3CBJdM8csodXA8/SRyrBgfveoF E/g97a3lnfTfHhSNC7CN9KdiAM8KpJY+wBJ7UAYdeweBP+SeeGv+wVa/+ilrz/8A4Qib/oH+ Of8Av9pH/wAVXaaTqF9o2jWOl2/g/wAQNBZW8dvG0k9iWKooUE4uAM4HoKAOsorm4PFGo3Nv FOngvxAEkQOokazRgCM8q1wCp9iAR3qT/hIdU/6EzXP+/wBZf/JFAHQUVzcfijUZXmRfBfiA GJ9jbms1BO0N8pNxhhhhyMjOR1BAk/4SHVP+hM1z/v8AWX/yRQB0FFc2fFGorcJAfBfiDe6M 4IazK4UgHLfaMA/MMAnJ5xnBxJ/wkOqf9CZrn/f6y/8AkigDoKK5ubxRqMCB38F+ICC6p8jW bnLMFHC3BOMnk9AMk4AJqT/hIdU/6EzXP+/1l/8AJFAHQUUUUAFFFFABRRRQAUUUUAZd35Qv gJobWSUqNrNMEkxubAQHuPXIyfTs+C3dLB2h+dZLdRHbu+9FODxnPIOQPw9MYp3t/bG/LLdK YvJAcq0bIeTwwJy468DkdvvVswAC3jAAACjACFQOP7p6fSgCh5v/AE+aj/4Cf/a6vQBzEkk0 arOyL5mPX0+gJNS1FbzefEH8qSM/3ZFwR3/z/jQBLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFAHFx+L9du5LprLQdNe3hu57ZHm1R0dvKlaMsVEDAZKE4yetP/AOEl8T/9C/pH/g4l /wDkas7QP+PS+/7Cuof+lc1atAHFeLPjdf8Ag7VYtP1DwtbSzSQCcNb6ozLtLMveAc5U1g/8 NNf9Sj/5Uv8A7VXH/HX/AJHey/7Bqf8AoyWvMKAPoJP2ly+ceERx66l/9qp3/DSbf9CiP/Bl /wDaq8Bg/i/CpqAPdz+0owGf+ERH/gy/+1U3/hpg/wDQo/8AlS/+1V4Q/wBw1DQB77/w0wf+ hR/8qX/2qn/8NJt/0KI/8GX/ANqr5/qxQB7x/wANJt/0KI/8GX/2qkP7SrA/8iiP/Bl/9qrw imN96gD3n/hpZv8AoUR/4Mv/ALVS/wDDSzf9CiP/AAZf/aq8EooA97/4aWb/AKFEf+DL/wC1 Uf8ADSzf9CiP/Bl/9qrwSigD3r/hpZv+hRH/AIMv/tVB/aWIGf8AhER/4Mv/ALVXgtI33T9K APviiiigAooooAKKKKACiiigDmlgjlOLSwRrTBLy3cSxqE7FGA3cjPJyehroZxI1vIIWCylS EY9A2ODWXb2cJ+0Q6hZWCFEDl4UwNpyOpGQRtPNbFAGZ5X/TnqP/AIF//bKvwCRbeMTMGlCg Ow6FscmpKit3lkiDTQ+U/ddwYfn/AJ6fjQBLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ9 rrenXuoz2Fvcb7iHdkbGCvtO19jEbX2sQrbSdrEBsE4qTTdVsdXt2uNPuY7iJXKFk9cAj8Cp VgejKysMhgTw+rW8fikaroqWN3YyKl7Bp8UmmzxwG4kjlR7mSYR7MN5km0BiCHLHc7KsfSaM lxeeIdS1qSzntIZ7S2tEiuQFk3xPMznAJG3MwUHPzFGIypVmAOd0D/j0vv8AsK6h/wClc1at eUr8UdG8La5r2m341e4lTVbz93HFCYos3ErfIcqxzuGdxPPTAqf/AIXr4Y/58NX/AO/MX/xy gDi/jr/yO9l/2DU/9GS15hXqniq3f4qapFrmhlbe1ghFmyX3yOXVmckBNwxiQd/XisP/AIVZ rn/P1p3/AH8f/wCIoA42D+L8KmrV1vwvfeGPI+2y28n2jds8lmONuM5yB/eFZG4UAD/cNQ1M x3DApnlt6igBlWKi8tvUU7zF9DQA+mN96jzF9DTGkXd0NADqKZ5i+hpysGGRQAtFFKBmgBKR vun6U7aaR1O089qAPveiiigAooooAKKKKACiiigDlrm10xZJIYdOaUwpuuJIpziH1wTwxHPH t9cdJcjdazL5vlZRh5mcbOOv4VCum2iWotkiKRBt+Edgd3qSDk//AFh6VZkjSaJ4pBlHUqw9 QaAM77D/ANQnTv8Avv8A+11egHlxJA0vmSRooYk8ntk/XBqL7BD/AH7n/wACZP8A4qpIbWKB 2ZN5ZgAS8jOcDOOpPqaAJqKKKACisF7a3kUq8ETKc8MgI5OT+ZAP1pTBCQQYkIIYEbRyGOW/ Pv60AbtFYBs7Ykk20JJLEnYOSww359/Wo5YLZ71BJDE0jI5yw5I+VTxjB4wOeccetAHR0Vx9 1dfZZVW3togi85KdCBtyMe3H0rIn1JrV2lhWCJiw6IOwCjGemAccf1NAHo9FeZ3Wqz3EaRKI B0AxAg27emDjjqcfU1oHVIdMsokSKHlVJzGp5HzDr6HJ+poA7yiuK0nU4JnWV7S38lDtwtug IH3hj/gWDWlqEunT3MVtALWQPIgfYFb70g3DHTnGenvQB0dFUW0q2ZSAg5DDlQRhjlvz71nz 6bbx3qIbVHd1kcFUGOdobI9TkfrQBvUVgyWsDuTJBGzc5LICeRg/oMVHbxwCSZY4kHlyDp2O xR6cfKQOO340AdFRWCltbxtuSCJWGOVQA8DA/IEj6VFBBbSRgiGIhHIXjO0qzAYyOMc47Dtx QB0dFc+ILaDyQIYkTzYgAo2jPmDHQepz6ZPua6CgAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKx7nxPpdp4hbRLify7pNPfUpXf5Y4oFcJuZzwMkn6BSTjjNjT NYt9V81Y0ngmiwXguYjHIEbOx9p52sASPQhlIDKyqAfGHjv/AJKH4l/7Ct1/6NaufroPHf8A yUPxL/2Fbr/0a1c/QB678LP+RYuf+vxv/QEruK4f4Wf8ixc/9fjf+gJXcUAecfFX/mE/9tv/ AGSvOa9G+Kv/ADCf+23/ALJXnNACr96n0xfvU+gAqvViq9ABTG+9T6Y33qAEqWP7p+tRVLH9 0/WgB9KvWkpV60APpr/cb6U6mv8Acb6UAfetFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA GC9wiKWKykc/diYng46Af/rHPSmzXcFvE0szmONQSWZSAMHH8+nr2zU9chq1y2s3sFsjMltv U7SBktzz+uMUAdRBfWsvzbpGizjekZIP0OOnH49s1Ze8s7NFumjmK/MuSrdfl7Y2/jnsQO9W dLsYrTSoLcKrAIMkqOe/NUNftmk2kKREsfLDoPmGB6d/8O9AHKXWqLdPMNoUFjwBjv8AWsDU blw/zsvlgdW4xzUuoFtPl9T1I49a4zxlrxCxRIm3cucAg45HtQBbudUZLsIsilMkAccVt3F1 DexqBKC5GWAIyOPrXD6VG1/Eru205I7V0dnbokpBOSSTngcUAeleFdPhl0aQP528MAgVCR0A GTjHU/h16Vh3shttYEEecNt3ZXnNdf4U1G3/ALLkQuimMg43AE/KOg/CsnWg91qL3mxQrFQB wSOAOtAGAniTxNpl5FbRNF9jLDe7Imfp+ldlYeMLV4I2vBIsqIQ7DhSc8YGcHgd8e3WsnT7M Sl4pMKpIO5iAOh9a4XWLq9udY+zRQhYlC4IZev8Ak0AesXHiezmhxbjzGzzuXIxj2PWr1tHB co0sYmQSPhdwY/wjsRwOD3xn3JFeLxanc2lwqHhUUEjg5IPWugTxC+qXvmu7LvIBU4Pf9Pwo A9CYlWxslxx8xiYDkZ6kf5PHWq8VwmxcpMNznGY3PVjjORx0+g47YqXT7m4uZD5U+MZkZNoO /kcZ7VeSB57bzHTZNvfKknpuOOvt+HpxigCjFMsrRFFl/wBYhwyOh+/j0z26enXg5rdrEcOH iCjDCaPP3um8Z6c9Pw9eM1t0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFAHneoeBtavfEGob9StGsdUstShnuBZkSRGcQJGp/e4YqkSAEKBiE5G59w6jR7O+fWb7 WNQt47SW4t4LQWyS+bgQtKxfdgcM0rYGM7VUnBYom5RQB8Q+OlY/ELxKQpP/ABNbrt/01auf 2N/dP5V1Hjb/AJH7xJ/2Fbr/ANHNWFQB6P8ADfU9PsPDtxFeX1tbyG7Zgk0qoSNic4J6cGuw /wCEh0X/AKDGn/8AgSn+NeCt1pKAPSPHyP4h/s/+xVbUvI8zzfsQ87y923G7bnGcHGeuDXGf 8Ix4g/6AWp/+Akn+FegfBj/mN/8AbD/2pXqtAHzNPoWsWULXF1pV9BCmN0ktu6quTgZJGOpF Uq99+I//ACIWp/8AbL/0aleBUAFV8H0qxUdAEeD6U1lOehqaigCDa3ofyqSMELyO9PooAKVe tJSr1oAfTXGUPXp606mv9xvpQB960UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZ1rB5r7 m+6p/OqssNhpt3G4iZpARwAuB+nWta3i8qEKevU/WuX17XLS1vyGCsqhcsrgZ/H9KAOrjcSR q4zhgCM0xt32uPH3PLfP3uuVx7evXn071yUXi3zY1+zqyxgBQMq3t1x7VQ1TxZcQou4kgrzy AOT9Pb/PcAp+LL+3iuEWS0BMm4jGOBn6VwHifSkuB9sjHBjOFGMHBJ/rXTFhq8hDZyfu9PWt C+ggttP8kgbiGwMDpQB4npWty2usPp8UR3KWOTjA4+lelyvBHbRu8kcTEAkMQO3rWFenybyX y4VByeQACa17XS2u4ALhU2jnJI44oAvaXO1pM7qAVY7ixYADg1valqIe0MVswYMgIIIPOP8A 9VcrrdjO9lBBZupKNkksORjtWtpGnTR28SzbVJwOWHoBQBTsry7ll8vzQTnpha3oPC1zqVxa upIKgO2AOgP1961PC3hRYtUa+dlZEKnGQc8H2rtlWO1aJfM2oFYDdnByQevT8+fTvQB53daD pTzlEvP3yLlsBME55707R/Ddjd6m0jXBjVBu/hAJyOCKnl8Pqmo3E0Eu+MMW5cE43fT6VY0Y W8M08RDOQP4XHGPXj3oA7Gw0+KwjKp8zEklyBn6fTikWJmCTlS0sUkm1QSAQWI/i56fh6cYp bG/ivkJUqsikhk3AkY//AF1PDwh5J+duoI/iPr/+r04xQBU1CNg0EidTNGD16bh6c9M+3rxm r9Z9xdQzzfZ0Zt8UqMSoJH3lBHBz3+nrxmtCgAooooAKKK+cLdNOhurOXxXPrnhTx+128n/C R3cLNbTusu3YBu2NH5bqvAVAAOdvDAH0fRXzx4v/AOEB/wCFneOv+E28/wA7yrT+z/s/meZu +zDfs2/Juzsx5ny59s1l+NNE1e88C/D7Ttc8y0v7fTNUlKyRjcscEQkiQqMYJjjReeRnkEgi gD6bor588WaxD4u+JvhbWbSe7jtLC90aJbWdRjddF7jeMMQDsWNTxyR1woz08HhDQ/H3xP8A Glxr1pJqdnYvaWtlJ9olWOJhETNGpRgMhiCy9iexJyAeuUV4/wDCXwT4dt9Y8Q6tFp+2+0rx Be2VlL50h8qEKFC43Ybh2GSCeetewUAFFFeb/GvRIr3wJca1Dbzvq+j7Z7GeB3D2+ZYzI4Cn sq5yfu4zx1oA9IoqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zUlABRRRQAUUUUAFFFFAHNnx dCLi/m8mP+yLBJTdXYnBli8syB3MON3lbopEDZ3F0OEK/PWhpWrtf3FxZ3VnJZX0CJM9u7q/ 7qQt5bblyM/I6kZ4ZGwWXa7c/L4BhuHvrR5Y4dPuXvJWltkCXUrXassqSNjBRc7hwclYQQPJ zJuaTpl5DqN3qmpTQPfXEUVsRbKVjEcRkKthiTuYyuxGcKCq/NtLuAfIPjb/AJH7xJ/2Fbr/ ANHNWFW542A/4T/xKcf8xW6/9HNWHQAxutJSt1pKAPVfgx/zG/8Ath/7Ur1WvKvgx/zG/wDt h/7Ur1WgDlviP/yIWp/9sv8A0aleBV778R/+RC1P/tl/6NSvAqACo6kqOgAooooAKKKKAClX rSUq9aAH01/uN9KdTXAKHIHAoA+9aKKKACiiigAooooAKKKKACiiigAooooAKKKKAK91dpaQ eawLAnAx3OK8v1+Bp7aSQDJyvORWveaxNa3/ANmd55rZgrIsoIZeCOjAHrnpxS2upkyOxiJj BGARn+lAEXhDSDJamS5MaxALksRxkn9az9btYbyEW6TokjMMhmAHWuvh1eLU7U2iQyQlACdk ZYH14UHHNczqOjvJdiSHzTscF/3bjAz/ALtADNN06TRbWdnZZW2YQq2cHPXkVhavLeyCa5kL N1bJYZ612UF7axMAxlYg88defpVPVriC8W5WNQvmkkAjGOn+FAHndpem4lMe358kZODW1rE7 6Zor+XuD7WJ5HHy1p6Vp1vY3RuXRWlII4UEY/KtaWKyvVSCeJtq/3V68ehBoA8t8Oarql7eS M8j+WrELlgeMGvV2jjMEcjZ3HZjp+NYWv674e8H3Jt4LKRizkMXCK3AHYgEde9WZPFWnXOiS aiIpRDCY9yx7WOCR+Gee9JtJXZdOEqk1CCu3ovU6ix1Sa3haCEHlgQSRxx9Pp+VUr+4v98TE yPwRvL/dBIz/ACFM8KNd372+s3NzJBZgt5NjawmYMQCm55lX5urfKOBhT1Brf1aOC9/fRpc+ aq7QPssgzyO+30B/T8VGXMrl16PsZ8jab626Ptfr8rrzOet9Vm01ZJFffKVA4IOBnnrXG2l3 e2ertI08hjkb7okPOWrrdO026k1JjcRzpCjfNiFySM9OFPbPWt+fSdLmkll8m5DMcoBZuAOO n3PX0x/WqMS/4eUNpwmKRiRmb5lUAkZ74rSt0VIiFGB5jn7pHJYk9f59D24rkBrT6TceWiyN GD8ytlc888Ee1dBY6lbm0DiKcBmd8JbyMOWJ64Of89OlAEB0por6a5dwY5JUIUbmwfNVhwMe nXt34rbrIudQgu5YbZY7gHzo2y9tIo4YEdh+vH1rXoAKKKKACvJ774d+NdTs5PCt/wCJ4Lvw lJdiV7qfdJqTQghxEWI2nDgfNnPGen7uvWKKAOX0Pw3eaZ478V65NJA1rq/2P7OiMS6+VEUb cCMDJPGCfwqPxJ4Um13xn4Y1RhaSafpqXqXkE+SZVniEYAXBDDrkEjj1rrKKAPH9F+FniC00 XSYtRutKl1K08QWV/LcRFhvtLaFYkjz5YJZQDgEY5Jzkmtjwnf2dj8XvHekG7gt/Pls57ay8 wJ5kjW5aZ0TuxwCxAyeCa9IqnJpOmzapDqkun2j6hCmyK7aFTKi88K+MgfM3APc+tAGH4L8N 3nhz/hIftkkD/wBpa3c6hD5LE7Y5Nu0NkDDcHIGR71x//C33k07xxqkFn/oGjRWp00yW7JJM ZwQjyKzDMZbawxtOw5xmvWK8n8R+A9Z1uf4mIkHlx6xFYPp770PnvAmSmNw25ZQuWx1zyBQB YuvH3imz+H8GrS2OhtfTaqtgt/DdGXThCWwLlmQkrHkbCGYEHk4+7XP+KvHd5rHwQ119TuLG z1N7t7G3exuCseoxpNGskkG47njKsynBIIBzjOBJH4E8UQ+A5ki0K0SWbxR/bEvh9btAj2uQ PszNjyyMqpwflwAcZG2us+H/AIPltdHll8SaHYw3A1W4vtNs22XH9mRuykRxsBtTDKW+TA5B 4OQADvIIIbW3it7eKOGCJAkccahVRQMAADgADjFSUUUAFFFFABRRRQAUUUUAFFc3N4zsbW/v YruOSG0tUmL3H3mBhQPKWiALqgVkKuRhs9g8Rl0NK1dr+4uLO6s5LK+gRJnt3dX/AHUhby23 LkZ+R1IzwyNgsu12APjfxz/yUHxL/wBhW6/9GtWBW/45/wCSg+Jf+wrdf+jWrAoAlj+6frT6 ZH90/Wn0AFFFFAHWfDP/AJKFpf8A21/9FPX0NXzz8M/+ShaX/wBtf/RT19DUAFfJNfW1fJNA BT1+7TKev3aAFooooAKin+4PrUtRT/cH1oAgooooA+/6KKKACiiigAooooAKKKKACiiigAoo ooAKKKKAMO58M2N3OLu6aXzQuDh8BR/nNY12dI0py8k221BABLHJOPp657VqeJbq/tbbzE+S EgglX7/N7Z6f5NeS+KtTmvLWKNGfAlUkhsdj/jQB63ot3oRbz7K6VpJVAOWPH6CqjSq8zFJl L7CW56/MvGf1x7e1eYeHNWkjV7cMyuiDDZ5659Ka1xq/2ksl3MnPJM2O/TrQBtalq0dnqAiL HBOc5PqfarLXEeNwkByT3rDurZLyVTLIJJlHUN1570slnebm+fqcjDdBQB0FtKJHCitCFoIJ FMrgOB93OCOP0rmJrm40nS/tG4hwT0PPUd/xqr/aV1eRwmFgskvVjjso46UAVPGumr4g1eSY nc3mE5DY7D/Cum8F2C6Oba1YYeWaFh82c8j/AAqOXw/doxmKghjn74zXSWMqWttF5kaGQSRs rlyAuGX0H+fegDch8O2ek6uuoafdPp9u2RPYoQLaX5cbgnRXyE+YdlxjkmtVbiGW8jWNkkPl sSygHbyvGc8Z9O+PasDX4r6+tRc2twnkBhkJKeB2yMDnkfSsOyvLy0vEwxZwME7xzyOM/lUx io7GlWtOq05u7Wh12o6m1rcmONgRsHccNmsCXxU8TSIkiq7t94KOuAMnjnp+lLfSyStDK6KG PzEiTryevHH/ANasq10Yy3bGYH5TnJb+VUZk2v6i32W3edlluip5yFwN3AxjHrXT+F59+lBZ JF3+Y21cjIH0+ua5jU9K+23UQKqqqMAmXgZPc4FRwHVLG/IinUWysyrsI69Cc4yRQB3d+gKQ uI95E0YPy7sDeOce3XPbrVyqdw0xt186CPPmxcITJz5g9QOgwc9ueOOblABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQByd54Qm1G/wBVF3qEn2O/t5oJJI2Inkjk TaImBygSPlkIXOX7HzTPqaTpl5DqN3qmpTQPfXEUVsRbKVjEcRkKthiTuYyuxGcKCq/NtLvs UUAfEfjn/koPiX/sK3X/AKNasCuj8bop8f8AiQkf8xW6/wDRzVhbF9KAEj+6frT6iYlDheBS b29aAJqKiV2Penbj60Adf8M/+ShaX/21/wDRT19DV8q6Zqt7o+oRX9hN5N1FnY+0NjIIPBBH Qmuh/wCFneMP+gv/AOS0P/xFAH0TXyTXW/8ACzvGH/QX/wDJaH/4ivon/hTHw/8A+gB/5OT/ APxdAHyTT1+7X1n/AMKY+H//AEAP/Jyf/wCLr58+Jeiad4e+IGqaXpdv9nsoPK8uLez7d0SM eWJJ5JPWgDkKKftHpUb8Nx6UALUU/wBwfWnbj60qqJDhuRQBVoq55Ef939aa8MYRiF5A9aAP vWiiigAooooAKKKKACiiigAooooAKKKKACiiigDPmDXGi3Mahi5hdAAOT8pxivDWkgvY3jH3 wwO0k5PBr2mMyRk7biVQc5Khc9c9xjgcD2PrzXl3jfRP+Ebu5NVgL/YWKbZCT8pIxgnGAcg/ mKAOfiUW8rGPcpJA4J4qOe8lTess7Bc/KSenNdF4PuNP8TSmKR0MiKNzLIeMtgZrO8d6M1rF /owbBYYOSTjLUAZ7XLp+/in3L1JVs45rRs9cN2jIW5QH5vfP096w9BtJntZEuCVTbg9e5NWZ WgsCUjbOQRy3TmgDbW8juV+y3B3wHk5Y89/r2rc0y4s43GNqKBwd/wDjXJ20JnsmuFIwM9Dk 1yt/4outN1F4F+ZEYjl2B6UAfQs2uWFxpwtfPjV0AUAv1+UiuS1DxBbBTDCwO3Bzk9cdelc5 YTz6pbRXVvuJKguBuIBKiqNwkq3boVbnHY+lAHofhXxHb6ikum3M3ySuu1g3Q9fT2FLqdrJo SNc3Myrb8Dzskrz0AOP84rlfDGnyW83nAFTvG3I56fStfxs9xfWcds7sUCptDdF7kDA9vr9a AOOi+Il9c64lorl7NpBhdwxjdj+76Gvf4bdI0miEQWNm7DG4FRkk5ye4zx09snwfwz4NEL/b JYZcKAw5Iyd3HavTtM16+Ny0LoCu/Jdl5fgLye/r69O3FAGtrNssVsEtwU3ghvmPPIqbR9OS HTgJo2Ejltwbjjdx/IGsTUr26nlCCV2VeAMD6dgOuM/yxXRWNrPBZos1w6urszYC4b5mOTx3 yM9+O3NAEt6SUiUKGJmTIIB4DA55I6dc9vfpVqsWVZZJo1M7zbZY8blQ8BwSegHTv7AjnrtU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFYeq6vfWXiLQ7CGzjNnfXDQ zXMj858iaQKijnOYhuLYABAAYklNys/UdM+332k3PneX/Z921zt258zMMsW3OeP9bnPP3cd8 gA5+88b/ANnRTand2udIEt3bRCI5n8y1WZpGYEhdreRKFAORtQnPmER7Gk6neTajd6XqUMCX 1vFFck2zFozHKZAq5YA7lMTqTjDAK3y7iiU5vBljdvNBdySTaW7zzJYn5Qks6uszbwQxBEsm Bn5TK/J/diPQ0rSGsLi4vLq8kvb6dEhe4dFT91GW8tdq4Gfndiccs7YCrtRQD488bEf8J/4l Gf8AmK3X/o5qw63fG3/I/eJP+wrdf+jmrCoAik+8PpTKfJ94fSmUAOXvTqavenUAFFFFABX3 nj6fnXwZX3lQAuPp+dfJ/wAZP+Sr61/2w/8AREdfV9fJ/wAZP+Sra1/2w/8AREdAHC1FJ94f Spaik+8PpQAynx/eP0plPj+8fpQBLTJCBGwJAyDin02T/Vt9DQB940UUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAYL3NvGpZ54lUZ5ZwBwcH8iQPrVTWbDT/EGh3Oj6jOVsrjDStE4DLsc EkE56FRmtCigD530TTdV8BeLb2yaUXKoIx5kbuyN0Y4OB0yQeOCCO1ew2tpH4i063muXiiDx FvmkxyME89enP8+1b19Yw38CxTLkKwdeTwR34+pqIai+jCJZYYmgjRlTYeQpYZJzk9cdOOee 1AHDahoy2sMhjJKY6qxOT1HauM1ewfK7Q4Y5x1/XivoXFhqTZ/1h2/7Q4zXH63pIkuZgkTBF cr3x2PX6Edf8KAPP9DjeLSSkqkOWZSBnBHFYep+E47vVHkyVZmJB3H/CvSY9LEUJUnoScYNZ 8g8u4Zsc89D2oAseFo0s0MWRxhcE88DFdJq+i2ltGLyN42JYDiQnpgN+APFcvZXTxTlwB19T 6V6JFcNc6PCzAArPAvH++lAGHp09tasPtZjSLdg5Yj2/StK4utIu7+3O+OSIRFN28gDkY9+B n8/arOteHl1fJFx5LHGTs3dPxFVPDuhLoV68T373czRHqm0KMj1JJ6jp6c9qANeY6db24t5Z I0jQnCF+QQMnvnoc/jXPN4iSIXEMEcYSQ/LnqPlA69+nf+Qrprsq0ewXBibPVeT+lczY6PBB qr3LSGcREBEI27X4YNwx6dMH/CgB+g+ZdXDXEzW4CybQjMQ394HH0B/I1pNfxCPyvPhCvISp yo37mYrjB5z2PU4z61LLM8xyx49B0qtb7fLOzpvf067jnpx1/H15zQAiSR3Ri8iVJP3sbfJt fgOOevsefbjkVv1iNt3Rb+nmx+nXeMdeOv4+nOK26ACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiuf1m+1S18S+HoYJII9Nurt4Zxt3SSn7PPIBzwiqY1PGSxP8IU7w DoKK4vVdd1fw49/eTyx6l5VvdXk1hFhEtLeNZGhcSbcgvsVGVtxZmZkwsTA7Gj3l8ms32j6h cR3ctvbwXYuUi8rImaVSm3J4Vomwc52soOSpdwD5D8bsR4/8Sf8AYVuv/RzVg7jW743/AOSg eJf+wrdf+jmrBoAjkY7h9KZuNOk+8PpTKAJYznNPqOLvUlACgZNLtFIv3qfQA3aK9e/4aD8W f9A/Rf8AvzL/APHK8jqSgD1j/hoPxZ/0D9F/78y//HK6LS/A2mfFDTovGWtz3dvqOo582Kyd UiXyyYl2hlYj5UBOSec/SvBa+ofhJ/yTDR/+23/o6SgDE/4UR4X/AOf/AFj/AL/Rf/G68p+J ng/T/B/iS30/T5rmWKS0Wcm4ZWbcXdeyjjCivqavnn47/wDI8WX/AGDU/wDRktAHlnlr6mmv +6G5evTmpain+4PrQAzz29BSNMxUjA5FMpD0oA+/KKKKACiiigAooooAKKKKACiiigAooooA KKKKAMd4rpVJFpK554Vkzwcd279foPXilMFyAT9mc4DHG5ecHgde/b9cVr0UAYxjuwSPsUxw WGdyc4HB+937frikktrkTq4tJXKqygqY8dAe5zyRj69exraooA5uPSLmKVJLaOa2HAKhx6Z5 Ibpnj6+3NaULXL70uLBwJWDMQyAD5R6HJxgDnufTppUUAcP4hu7m2WR49JmSJRy5yRzjqeR1 9P1rN0/S7rULcSS2NwofBUlDgggcjpx/kV6VRQB5n/Y19FIcafdf9+yf5ZrrLW1vrSyMT2bz HzEcKkqj7rj1PfGfoOxroKKAKUk94Qdlow4Y/eUng8d+/b9cVSkiu3ufOazmLAMuQY+emD97 POMD9e1bVFAGO8V0rEC0lcc8qyY4Ge7d+n1HpzSRWtwGZvszqZHBOSnHyj0PPTHc59sVs0UA Y6RXTNg2kqDjlmTHIz2bt0+p9OaaltcxKqraSkM248xjbuJJzg9u+M9e/NbVFAGOlvcSmMyW kqAOjEMYyRhvqRxjP0PHNbFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABVO902G+utOuJWkD2FwbiIKRgsYpIsNx02yMeMcgfQ3KKAOftvCq295eSSatfXNneyyy3N hcx27wy+YCCrHyvMZQMKAXOFVV+6MVoaZo9vpXmtG8880uA89zKZJCi52JuPO1QSB6ksxJZm ZtCigD4m8cf8lA8S/wDYVuv/AEc1YNb3jj/koHiX/sK3X/o5qwaAIpPvD6UypXQscjFN8tvU UALF3qSmIpXOafQAq/ep9MX71PoAKkqOl8xfQ0APr6h+En/JMNH/AO23/o6SvlvzF9DXs/gX 4t6D4a8GWGkXlpqUlxb+ZvaGOMod0jMMEuD0YdqAPcq+efjv/wAjxZf9g1P/AEZLXbf8L38L /wDPhrH/AH5i/wDjlc5r+h3Xxjvk8Q+HpIbW0tohYumoEpIXUlyQEDjbiRe+cg8UAeOVFP8A cH1r1T/hRXif/n/0j/v9L/8AG657xh8M9Z8I6RFf39zYSRSTiEC3kdm3FWPdBxhTQBwtIehq XyG9RSNCwUnI4FAH3xRRRQAUUUUAFFFFABRRRQAUUUUAFUdVvHs7aPYyo0sqxeY3SPP8WO+M VeqnqfnCzJhto7kBgXicZ3KOuPfpQBTSe/itbt2njuYRC0kN0m0fMB93A9/89hBHdX9u2mzT 3qzx3TKDFsVCNw6jA5Az7dvWo4bV3OozW1lLbW72zIInBDO55yF7dcfy71oaVpVrbQW9wLfZ cmIbixOQSOeD0NAFWw1G5bWLpLqdRaq0qx7to5UjPvwDSaVq05S8m1KQLHGI2AC8KHyR057i qlzaTrDcTLbTPJ9rnVUCnlHTG7p06VJdWMsdvq0MUMrrtt1jO0kuFABx6/hQBsNq9gluZzcD yhJ5RYKSN2M44H69KdPqdnbW8U8s4WOUAocElhjPTrVLW7eR5rOdRclI2cN9m/1gyOCPbjn6 1Vltnt9O04x212Hj3MrxHe8RbnBXA3A9+nTGfUA2kv7aQ24SUN9oBMWAfmx1+n41UvdQ/wCP f7LL/wAvq28vy/mOR+oqkBcwR6TdSWT/ALkSCSOCMZG4cHaOmepqOOC6aJGktpEc6qJWXBO1 fXPce/SgC9Z69a3H2kySKixMSvB5jGBu+pJ6daspq9hJayXKXAMUZAc7TkZ6cYzWYY5Bb6ra SWM8u+Z5l2/KrKSuMN698Y7Y9qm02a4givJ5ra5dAyiMtCBM46YIH3scc/X6AA2JJEiUM5wC wX8ScD9SKVWVxlWBGSMg9xwahvEM9rNbrw8sTKpIOBxjk/jWRZLc+bGU+1RRmQsEkRyeXYtk k4Hy7eTnvj5s0AbpZVKgsAWOACep6/0NLWMqs91AXW8PlzgksHxyrfh1wDj5fTAJFWdRFwXD QNKNkErAJ0LjbtB/Xjvz2yKANCisjF6JpmLykCQMyqhxtEgIwc4PyA8KPrzimBNRIRZGmVhs 3Mpzgs0WfY8iT6D2IoA2qbJIkShnOAWC/iTgfqRWSUu1YHdclf3isMnhBKoGPfZuIP3j69KR 1unMQZZynmRlBj+ESk/Nnn7uzrz/AOPUAa7yIjIrHBdtq+5wT/IGnVj2kd2ZbZp2kciUF8oQ Fby3DcknIyR0wvTHereos6JG6+eQpJxCCST+Hf0yNp6HqDQBdpFYMMjPUjkEdKzHFwwkAa6W YzAMy/dC+YNpGePuenHXdzVdFuTctAGusAgAbzgKZXBJJ5+4Dg+w77aANtWV0DKwZWGQQcgi lrNhWRPDgWNZVlW2IAIYOG29s89en6VDOl6JGjSWYRq52PsLEttQjoRxkv1+UdD2wAbFFY0y 6jiYJ5vBdFwf73mYx+cXPbnp81OkF40jBWnVjJh2GcAeauwrnj7mc4/4FQBqmRBKsRPzspYD 2GM/zFOrDkivBMdnn5VXUu2WAXzE6c5J2DscnHHzZq3ZRzm43TSTMixLt3AqCdz9RknOMdTn pnnoAaNFFFABRRRQAUUUUAFFFFABRRRQAVzeutfReKfDDx38kdnLevC9rGu0SH7LcOS7dWAK JtUYAIJO47dvSVXuLG3u57SaePfJaSmaA7iNjlGjJ46/K7Dn19cUAeZ6t49aHxjrE9lq9o0W naPqKW+mtMp33Nv5TmR0BD5J81Ap52ws6nDk12mjPcWfiHUtFkvJ7uGC0trtJbkhpN8rzK4y ABtzCGAx8pdgMKFVdh7G3k1GG/aPN1DFJDG+4/KjlCwx05Mafl7mo9N0qx0i3a30+2jt4mcu VT1wAPwChVA6KqqowFAAB8ZeN/8AkoHiX/sK3X/o5qwa3vHH/JQPEv8A2Fbr/wBHNWDQAUUU UAFFFFACr96n0xfvU+gAqOpKjoAKmT7gqGpk+4KAHV9B/Ar/AJEi9/7CT/8AouKvnyvoP4Ff 8iRe/wDYSf8A9FxUAen15h8df+RIsv8AsJJ/6Llr0+vMPjr/AMiRZf8AYST/ANFy0AfPlMkO I24J4NPpsn+rb6GgD7xooooAKKKKACiiigAooooAKKKKACmvIiMiscF22r7nBP8AIGnVUvYn lktAjOmJiS6AEqNjeoI9vxoAspIjs6qclG2t7HAP8iKVWDDIz1I5BHSsaWG8jmlCSzbTKSJP LyWbYm3hdox94c/LxzSyNdJIpdpwvnqFIPGDOQc+2NmP043UAaqTRyRxSK42ygFM8bsjP8qP OjM3khwZME7R2xjP/oQ/Osa3t5kfTxKLkoixE/e+VijgggdAMIPbv1OSa3mTULySEXIdUkdS NxDHbGVAzweQeB6Y6cUAbtNkkSJQznALBfxJwP1IrIljveGWadd0kv8ACWwQ/wAgABHGMnLf L0zxjDrhZZJpYh9qy0iMG2khQJF9QV4HIx2zuGRmgDXqJbiJkhdWys3+rODzwW/kDWcv2vzU B875XCxdcbRIwbd2PybcFuvbmo7OO5ElgJEmATbwR8qr5OOfQ7tw/n/DQBpteQpci3PmeYeg ETEHpznGMcjntU9V3VjqUDBTtEMgJxwCSmP5H8qjkgla8Egjym4HP2qRf/HAMfh3oAllu44p xEwYn5dzAcLuOFz9Txxn3xSy3KwyKrI+0kAuB8qknA/M8cZx3xUF05e9ggZXEIIdiImYM2fl G4cDBGTn27Zouz5rwqkcpcSKVO07CA43ZHTgDIJ9tvNACy30BmltfLaWRdqtGAPmLAnHOB0B P+cU22kto5o0trPYk67lkjRVDDGckA5xzjkdT71QFtcx3WbiH918rSPCzE5JlPy4AP3m7cgY 6gmrkEdyNmUVZobRVTIwm9uoOOwKL09fpQBIdUt8yhQ7eUzK5A6Bcbm57DcPf0Bq7WAbKVba 5heCRW2mKJo2LBv3aDBOBwSq88Dgg4HXebcUIUgNjgkZAP0oASORJYw6HKnofX3Ht70yecQh fkZ2dtqouMscE98DoCfwrI05p3KCBpQgCABzuRV8lT7ZO4r6e38VWdlzE8MtwJHMcu5mVvM+ XYw4CqOcnn5e4544ALclzE1tGwQzJOMIgA+cEZ74HQE81FaS2KzLFaRIpkj8wmOPaMcYz7/M Dj396iVHgtdPV43zbhTKFUtj92y8Y68+mfyp1rDJE9groQY7VkfuA37vjP4H8qALxkQSrET8 7KWA9hjP8xS7VDlgo3EAE45IHT+Z/Os64MkerK6K5XahKr1YASDHvyyfTIJxVWEaiLYOrTmY gKFfoP3AOef9sDk9/qcgGpNBZoXu5oItyDeZDGCwx3zjPGKkWZHmkiBO+MAsCpHB6c9+h6VD BJHFAWzMsW44M2eBjJJJ5A4PLfyxVfTVZHjVlKstnCHDDnPzY+mOc/UdMcgGlRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUVy/iG1z4s8J3huJzjUJIlh34jXNpdEttHVjhRk5wF+XG 5twB1FFeVz3mpRPrV+LSObxAX1OCwkttz3kPlLI0IkjxjyChhwmCu9oXIdp8p1nho28Wuala 6VP52jLaWk0TJMZkM8nmtIQ5JyzJ5DnnnzN55kLMAfJ3jj/koHiX/sK3X/o5qwa3vG//ACUD xL/2Fbr/ANHNWDQAoBNG0+lKvSnUAM2n0o2n0p9FADPu8ngUean979KJf9WarUAWfNT+9+lL sb0qrWhQBDsb0pwdUG1jgipKrS/6w0ATean979K9k+Evjrw34c8K3Vnq2o/Z53vXlVPIkfKl EAOVUjqprxKp4PuH60AfUf8AwtrwR/0G/wDyUm/+IrmPHWq2XxN0SHRfCE39pahBcrdyQ7Wh 2xKrKW3SBR951GM55+teEV6l8Bf+R5vf+wbJ/wCjYqAMP/hUXjr/AKAf/k3B/wDF1X1D4XeM tP026vbrRvLt7eF5ZX+1QnaigknAfJ4HavqyuX+IMmoJ4J1lbO1tZoW065Fw81y0bRr5Z5RQ jBzjPBK9BzzwAd7RRRQAUUUUAFFFFABRRRQAUUUUAFRyzxQIGmlSNScAuwAz+NSVR1KTyjaP 5scWJvvyDKj5H68j+dAFyOSOaMSROroejKcg/jTTBE0yzGJDKowHKjcB9fxNUnun2oVu43Vl /eSRplY1yfnHJx6ckjv0Vqp2lxcR3PkJcKyCZ8hyMsTIwYYC8kDDdsbsnjgAG7SblDhSw3EE gZ5IHX+Y/OsOOeeWbTjcXPXy5MhVUEuknH/joH/Aj7YbK8kGqXskdxl4kkkKMAeAsRA47Hp9 B680Ab9IzKgyzADIGSe54FY8t1erhlmQBpJQu4YHyvhV4BLE88DBOOMHOVurh3lkgFwpfzYy qFfu4kUA44I68565BBHIABsU0SRkIQ6kP9wg/e4zx68c1lreXBlRDJyHCKuB+9/eMjE+4UBu MYzzxxUFlNIzabGzYVdu1NnbyCd2fqWHvjjoaANwMrFgGBKnBAPQ9f6ilrOhkddTnQnbG82A RzubylOD6DAJ/Dt0Msnn/bBt+1+XuH3fK2Y79fmx/kUATvcRRyrGzYZsdjgZ4GT2yeBnr2qW sy8/4/mX+KT7PsHdtspLY9cDk+lTXk08UoSMn98oRCF+424DPvw2ceiH3wAXaKzBdymaVWn2 Y37hsB8shwEGOvzDt1PbFRz3l0EjKuiOxYur8BHG3EfQls5JwOW6ggcUAaysrjKsCMkZB7jg 0tZEVyYZI0M4RWnkAUqPmJlYd+v4HgkE5B416AGpHHHu8tFXcxZtoxknufenUUUAFFFFACbV Lhio3AEA45APX+Q/KloooARlV0KsoZWGCCMgikWONZHkVFDvjcwHLY6ZPenUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABUckEMzwvLFG7wvviZlBKNtK5X0O1mGR2JHepKKAIxBCt w9wsUYndFR5Ao3MqklQT1IBZiB23H1ohghtkKQRRxIXZyqKFBZmLMeO5Ykk9ySakooA+JvHH /JQPEv8A2Fbr/wBHNWDW944/5KB4l/7Ct1/6OasGgBy9KdTV6UuecUALRRRQAyX/AFZqtVmX /Vmq1ABWhWfWhQAVWl/1hqzVaX/WGgBlTwfcP1qCp4PuH60AS16l8Bf+R5vf+wbJ/wCjYq8t r1L4C/8AI83v/YNk/wDRsVAH0RWF42/5ELxF/wBgu5/9FNWlqOoRaZapcTK7I88NuAgBO6WR Y1PJ6ZcZ9s9azfG3/IheIv8AsF3P/opqAOyooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigDDn8F+Fbq4luLjw1o008rl5JJLCJmdickklckk 85qP/hBPB/8A0Kmh/wDguh/+JroKKAOf/wCEE8H/APQqaH/4Lof/AImq9r4T8B33n/Y/D/hy 48iVoJvJsoH8uRfvI2BwwyMg8iuorh/BuoeH9V1GKbRNRsVgtdPFpaWFvdK8zWyldsk65LDH RFblBI+47pCqAGx/wgng/wD6FTQ//BdD/wDE0f8ACCeD/wDoVND/APBdD/8AE10FFAHP/wDC CeD/APoVND/8F0P/AMTUc/gvwTa28txceGvD8MESF5JJLCFVRQMkklcAAc5rpK8/+LsV7d+D bu1j06e5037JcXF48LxjYY4yYgwZ1O0PiQlMn9zt2kOaAOg/4QTwf/0Kmh/+C6H/AOJo/wCE E8H/APQqaH/4Lof/AImtyCRpreKV4ZIHdAzRSFSyEj7p2kjI6cEj0JqSgDn/APhBPB//AEKm h/8Aguh/+Jo/4QTwf/0Kmh/+C6H/AOJroKy/EsN9c+FdXg0syDUJLKZLUxybGEpQhMNkbTux zkYoAy7Xwn4DvvP+x+H/AA5ceRK0E3k2UD+XIv3kbA4YZGQeRVj/AIQTwf8A9Cpof/guh/8A iar+EJN/2lY44JraGKGGK9isvsucb91t5Z5CwkkD+7vKHLxyE9RQBz//AAgng/8A6FTQ/wDw XQ//ABNH/CCeD/8AoVND/wDBdD/8TXQUUAc3J4L8EwvCkvhrw+jzPsiVrCEF22lsL8vJ2qxw OwJ7VJ/wgng//oVND/8ABdD/APE1j+M4d+tWf+jTv5sSxYU5a5xMj+VbnB8qYbN5fMfyqDn9 35tv3FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFeR2rw+Efh74Z1jQLK0t9Qk0drm8WKIAXMcdg8m+ZVxuAmEI3nkG TaGHmEMAeuUV52up+J0efT2k1JCXgZBdS6cNSkDLOWWFUbycfukI8wZKifBJVcV9M8RXeo6x eJb61aWlo6PqR1BLNI0kEdpYMDIr8+UftDFssJAFVRIoWgD0yivL/DviPxFe6LDrN1q3mrHd 6VbfZvs0arJ9phs/NZ2AzwZ2ZQu3DZzuXCrseI9Y1208SXMWnX8ENvBFpgWGa2EitJc3UsBZ iGVtoXDbQQSyp8wG4OAdxRXl+peLNbh0W+urW6vrm60eK7kljtoLYIywzzxpJctIQWVxbnKw BWGJDxuQDLsptYtdC8awHWZLiysbLULoQXNpA/mStc36HcdgBQ+UHZSpy2MFUyjAHslFcnY6 vfTeN7jRXv42t7V55hiPEkg2QMImO3GE+0ZJGCQ0GGYiYV1lABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRWfrt1eWPh7U7zTrf7RfQWkstvDsL+ZIqEqu0cnJAGByaANCivF/DnjfxLD4L 1TxPJ4jsfE1vFpSzm0itFS5srskjbJGm39yPmLOSCQhKgAZOho3xs0uPQ9OOrWGuPcRWlsdU vVsMQ2zyYAeQgjCv99doOVYYGcqAD1iivG9d+Jupx6l48tC2paVb6MlqtrdR6bHMYj5oR2ZZ GAYybwUyQDGu4AEHPcXfxC0i08Ynw21tqUs8bwx3F3Falre2km/1SSP1BYlQDgjLDng4AOso rxvSfibqesuzXLalpQ/4S2DT4lOmxkNA6kC2fe2VfKEyMMspcYGCAO4tviHot143k8KIl39r DyRR3PlhreWWNFeSJXUn51VvmBAwRg8kZAOsori9E+JmkeIUvn0/TtZdLe3mubZmsiBqEcTb XNvz85DFRtO05cDHXHWWF19u062vPs89v58SS+TcJskj3AHa69mGcEdjQBYorLh8S6Dc6odL g1vTZdQDshtEukaUMudw2A5yMHIxxg0T+JdBtbeW4uNb02GCK4NrJJJdIqpMBkxkk4Dgc7et AGpRXH6t8RNHtJ9Hi0y6sdU/tC7eJ5Ib1BHbwxp5k8rScr+7QqxUkEhuK3D4l0EW9xcHW9N8 i3SJ55PtSbYlkAMZY5wAwIKk9c8ZoA1KKz013R5Ly1s49VsXuruIT20K3CF5oyCQ6LnLLgE5 HHBo/t3R/wC2P7I/tWx/tP8A58vtCed93d9zO77vPTpzQBoUV5/rHxPgtdfTSNGtbHVpLjT0 u7KRdVihS7kNx5PkozDaWGGbgknaRjNdZD4l0G5QvBremyoLdrosl0jAQqxVpOD9wMCC3QEE UAalFc2PH3hWS/t7O317TbmSVJZGMN5EyxRxoWd3O7AAHbk9TjarFZNW8Y6Pp/h6fVrbULG9 /wBEuLm0iju0/wBL8lCzqhGc424JAOO9AHQUVwd/8SoYLLwolra2k2seIkgkisJ74QiGORN2 9pNh4DYUDALnO0EjFdRB4l0G6t4ri31vTZoJbgWsckd0jK8xGRGCDguRzt60AalFZ91ruj2O nQajearY29jPt8m5muESOTcNy7WJwcgEjHUVHc+JdBsri5t7vW9NgntUD3Ect0itCpKgFwTl QS6AE/3h6igDUorLg8S6DdGIW+t6bMZUDxiO6Rt6mTygRg8gyfJn+9x14rD8QfETR9F/sBre 6sbyHV7sxJcfbUjhjhT/AFsvmnKHYcDbkFicDmgDsKKrzX9nb3ltZzXcEd1dbvs8LyAPLtGW 2qeWwOTjpVO28S6De3Ftb2mt6bPPdIXt44rpGaZQWBKAHLAFHBI/un0NAGpRXH+HfiJo+t6Z dajdXVjptqssrWzT3qAz2qyeUtwVbaY1Z8rhh1GM8ith/FnhuOW6ik8QaUklpn7SjXsYMOGC HeM/L8xC89yB1oA2KKpyatpsOqQ6XLqFomoTJvitGmUSuvPKpnJHytyB2PpVygAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigArP0zQtH0Tzf7J0qxsPOx5n2S3SLfjOM7Q M4yevqa0KKAMuPw1oMOlzaXFommpp8z75bRbVBE7ccsmME/KvJHYelV9a0Br94ZrE6bDPHcC 5b7Zp63KNKFVVlwGRhKoUBXDcDIIPyldyigDL0vw9puk6Na6XDbRyQW6QjdKilpGiVFjd8AA uBGmDjjaMYwKuS2FnPK0stpBJI3l7neMEny2Lx5P+yxLD0JyOasUUAZd74a0HUkjS/0TTbpI 3kdFntUcKztucjI4LNyT3PJqSLQtHh+3eVpVjH/aGftu23Qfac5z5nHz53N1z94+taFFAFdb CzTy9tpAvlSvPHiMDZI+7c49GO98nqdzepqxRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA VXv4bi4065hs7r7JdSROkNx5Yk8pyCFfaeGwcHB64qxWT4msNR1Pw9dWelXf2S+k2eXN5jR7 cOCfmXkZAI/GlJ2TaNKUFOcYydk2te3mcDpnw78Tahqmt6n4ov8ARkvNQ0JtG83TYWJlLdZ5 dwUFwAowOCMD5Qozy+p/BfxjrGlpbahqujXDwWUEFmry3WyzaPbGREM7QHjRWZihy3AUfero /wDhBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mub28/5Gex/ZWF/6CofiS+L/AIba9rV7 4xOnXmm/ZPEVvZnFxvR4prd0wMqCChRXOeuSowACTc134f6xqfxM07xBbXGlW1nbXcd09zHC 8d9tVAjQErhZY22Dl/mAdgPlGGzv+EE+If8A0Nf/AJUbj/4mj/hBPiH/ANDX/wCVG4/+Jo9v P+Rh/ZWF/wCgqH4kr/DbXl1SUR3mmvp//CWw+Ioy29ZdvzeahGCMj5AuOuGJIyANjQfCXiPQ vG+oz2uq2kfhe7vZ9Rkttm+4nmlRAVYlQERWBZdpzjg5zkYX/CCfEP8A6Gv/AMqNx/8AE0f8 IJ8Q/wDoa/8Ayo3H/wATR7ef8jD+ysL/ANBUPxLPhH4f+JPDGua3qyXGhxyTaf8AZbW3s4ZI YLmZf9XPPEPljbgAiMY+dsYOd3pFh9s/s62/tHyPt3lJ9o+z58vzMDdszztznGecV5d/wgnx D/6Gv/yo3H/xNH/CCfEP/oa//Kjcf/E0e3n/ACMP7Kwv/QVD8TI8C+ANf1y8sfEXiOP+zI21 DUdSkt4ZZ7a58y4CR7MDa0SjyywIdiQQCMEmpB8IvFVte69eWGuWkErXFzNohM0rPA1w6LLJ JKVLb/JTZ/Hy24EEbjp/8IJ8Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL /wBBUPxMQfBzxVcvqfm6nptmjvf3NiYp5blxLdKkbxzPIgLIYlZd4+cFt3PStRfhLqb6TK8j WMWpLrdrewxQ3kwT7Hbx+XDb+cFDIyozgSBSxwCeTkT/APCCfEP/AKGv/wAqNx/8TR/wgnxD /wChr/8AKjcf/E0e3n/Iw/srC/8AQVD8S74P+HWoeFvFiX1imm6VpZST7Tb2VzcTG7BVfLjc S5AMbeYRKpBYHGxdxFZ+p/DXxVr/AIunv9V1a0azZ9Sjhk+0SyNDb3Fv5MSLAVCKU6sQwLZ5 JIBL/wDhBPiH/wBDX/5Ubj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6CoficrH8PfFVv 410rSrgxwQTpaXH9oWEMtwtpNZWjxQOzvGseGfkpyegyMgtt6V8ItWttMure/ttDul+yWljB avd3GxoEk824BkVUZGebEobD7fu421f/AOEE+If/AENf/lRuP/iaP+EE+If/AENf/lRuP/ia Pbz/AJGH9lYX/oKh+JQuvhR4s1qx1GLXdasbq8k0SHTrW9DvuJSZZyJVKfN8w2CQNnaoYozM cR+IPgzqF4L640WLTbGfVbI299DPqNxOFlFyk3nCV0LSFhGAQQuDzlq0/wDhBPiH/wBDX/5U bj/4mj/hBPiH/wBDX/5Ubj/4mj28/wCRh/ZWF/6CofidD4u8EXni7xHBPNdQW+m2ulXltbsg JmFxcJ5TMykYaMJyACDu9q4/S/g/rD6rYNrc2lSaYJbJ7y2iZ5Gf7Ha+TFgsgVldmfehUYXA DHJq/wD8IJ8Q/wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/E6Hx z4U1vxDq2lXekXNjaTWOTBfO0qz2btIhd1VSUmVo1ZDG4H+9gkVyeofCjXF8QTahp0toznXZ NUS5Oq3NrJ5EoHm24SNGCFuhlU7iAOOwt/8ACCfEP/oa/wDyo3H/AMTR/wAIJ8Q/+hr/APKj cf8AxNHt5/yMP7Kwv/QVD8TE0v4J68PDt9a6jqOmxahFpn9n6XNaM7KqtO80vmFlBBbeY8r/ AAO4Kt36DxV8O9e124W5s4/D9lItleYSJXjD3V2RHM0hCneBBwH4ZpACVCnaIv8AhBPiH/0N f/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6CofiWfG3wy1TWrXwrYaBqcFpa6Ra TWE01388hgkiSFsKFwzbA/8Ad5IwR1FPV/hTqqeInu/D1xpsNmtxp95bJdNITC9lBJHHEVAJ dGJTL7gQM8Mer/8AhBPiH/0Nf/lRuP8A4mj/AIQT4h/9DX/5Ubj/AOJo9vP+Rh/ZWF/6Cofi ZFv8FNYtl0+xkvbG700xWkF6JZXR1t1lae5t49sfzK8pDq5KuNoXpkmS4+E3iq5S4uHu9GF9 OmoXDASS+ULq8YRSgfJnyhbjK/xCTrleK0/+EE+If/Q1/wDlRuP/AImj/hBPiH/0Nf8A5Ubj /wCJo9vP+Rh/ZWF/6CofiaugfDc+HfHJ1SHy7zTxb2scDzXckcts0Ns1vkxqvlzFlI+Ziu3c 2B6+iV5N/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxPWa K8m/4QT4h/8AQ1/+VG4/+Jo/4QT4h/8AQ1/+VG4/+Jo9vP8AkYf2Vhf+gqH4nrNFeTf8IJ8Q /wDoa/8Ayo3H/wATR/wgnxD/AOhr/wDKjcf/ABNHt5/yMP7Kwv8A0FQ/E9Zoryb/AIQT4h/9 DX/5Ubj/AOJo/wCEE+If/Q1/+VG4/wDiaPbz/kYf2Vhf+gqH4nrNFeTf8IJ8Q/8Aoa//ACo3 H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxPWaK8m/4QT4h/9DX/AOVG4/8AiaP+ EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+J6zRXk3/CCfEP/oa//Kjcf/E0f8IJ8Q/+hr/8 qNx/8TR7ef8AIw/srC/9BUPxPWaK8m/4QT4h/wDQ1/8AlRuP/iaP+EE+If8A0Nf/AJUbj/4m j28/5GH9lYX/AKCofies0V5N/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+gqH4nrNFeTf 8IJ8Q/8Aoa//ACo3H/xNH/CCfEP/AKGv/wAqNx/8TR7ef8jD+ysL/wBBUPxPWaK8m/4QT4h/ 9DX/AOVG4/8AiaP+EE+If/Q1/wDlRuP/AImj28/5GH9lYX/oKh+J6zRXk3/CCfEP/oa//Kjc f/E0f8IJ8Q/+hr/8qNx/8TR7ef8AIw/srC/9BUPxPWaK8m/4QT4h/wDQ1/8AlRuP/iaP+EE+ If8A0Nf/AJUbj/4mj28/5GH9lYX/AKCofies0V5N/wAIJ8Q/+hr/APKjcf8AxNH/AAgnxD/6 Gv8A8qNx/wDE0e3n/Iw/srC/9BUPxPWaK8m/4QT4h/8AQ1/+VG4/+JrofBvhrxVo2ry3Gua3 9utWgKLH9qllw+5SDhwB0BGfeqjWm3ZwaMq+XYenTc44iMmuivdncUUUV0HkBRRRQAUUUUAF FFQ3TXCW7taRRSzjG1JZDGp55ywViOM9jQNK7sYuq6zeWfiGytInsVtH8sTmUkvukcqgJB/d Z2tsJVlkZTHmNtpbi7Xxd4o1q88O3P2WOzgnuEu1tU2GW5hltbmRY8C4xjERAeTYGdlOxPKY N2V5Y6hqFxa3F74b0C5ntH320k12ztC2QcoTb5U5UHI9B6VH/ZV1/wBCr4d/4+/t3/Hyf+Pj /nt/x7/6z/a6+9Tzr+kzb6vPuv8AwKP+ZyLeLtY03WNd+w6HIs8t7LeXEN3LApjigtLIEM/n BEDeYD5gZ9oHKNk7dzUfG95prajA1rBLdaX9vubpQSqSW8MSSoqNkkSEXNqCSMcS46LnWmsd QuXDz+G9AlcXC3QZ7tmImVQqyc2/3woADdQABVgHW1uHuF0bSBO6KjyC/fcyqSVBPkZIBZiB 23H1o51/SYfV591/4FH/ADOd0PUtT1Hxrp66xax295bWWoQsqmMFhusXBZEllEZw+NpckgBu AwA7yuds7HUNOSBLHw3oFqkCOkKwXbII1dgzhcW/AZgCQOpAJq59o8Q/9AvS/wDwZSf/ABij nX9Jh9Xn3X/gUf8AM1qKy4p9dMyCbTdOSIsN7JfuzAdyAYRk+2R9a1KadzOdNw3t8mn+QUVX vXvUhBsbeCeXdys85iUD1yEbnpxj8ao/aPEP/QL0v/wZSf8Axik5JFQoymrpr70vzZrUVk/a PEP/AEC9L/8ABlJ/8Yo+0eIf+gXpf/gyk/8AjFHOv6TK+rz7r/wKP+ZrVl+INSm0rSfPt1jM 8lxb2sZkBKo00yRByAQWCl923IzjGRnIb9o8Q/8AQL0v/wAGUn/xio5zrd1by29xo2kTQSoU kjkv3ZXUjBBBgwQRxijnX9Jh9Xn3X/gUf8zlU8Qzaf483XlzppSe3t7W9uYnPlIsQ1N2cEn5 CGhG5SW2fMuTjdWfqvjHXtQ8D3F3DdabZPcaF9oDxBy6zG189wjh/klCkbY2GdrCUO+14x2S WOoRoiJ4b0BURIkVRdsAqxNuiA/0fgIxyo/hPIxVeTQpJnheXwh4Zd4bf7LEzT5KQ7Svlr/o /CbWYbRxgkd6Odf0mH1efdf+BR/zMEeN/EqGdTpti/8ApYtLaSR1hSZ0vYrSQgCV5CrF2bJQ eV8oPm5BJ4g8Q6tpniSwmbR5zfQ6fPaxSMIhHdSS3VlFvjQTEhQWDbXZCQQNw5I6KDSrq23/ AGfwr4di3+Tv8u5K7vJx5WcW/OzA2/3cDGKsXMGrXm77VoGiT7ongPm3rNmN8b05g+621cjo cDPSjnX9Jh9Xn3X/AIFH/MwT4z1eCzRLqztIr+7t2is0DB1W6S6Fq3nbJGCoWmtztVmZf3qk kqM5+peJtT1aeS1uNPjg08axbpayl4wzG31OCFsDzC7gk5J2IE4X59wauuSDVo4rWKPQNESO 0x9mRb1gIcKUGweR8vykrx2JHSozY6gbi4uD4b0Dz7h4nnk+1tulaMgxlj9nySpAKk9McYo5 1/SYfV591/4FH/M6Kisn7R4h/wCgXpf/AIMpP/jFV31bWY9RhsG0/SxdTRSTRp/aEvzIhQMc +RjgyJ+fsaOdf0mH1efdf+BR/wAzeorJ+0eIf+gXpf8A4MpP/jFH2jxD/wBAvS//AAZSf/GK Odf0mH1efdf+BR/zNaisn7R4h/6Bel/+DKT/AOMUfaPEP/QL0v8A8GUn/wAYo51/SYfV591/ 4FH/ADNaisn7R4h/6Bel/wDgyk/+MU6KfXTMgm03TkiLDeyX7swHcgGEZPtkfWjnX9Jg8PNd V/4FH/M1KKKKowCis66m1lLh1tLCwlgGNry3rxseOcqImA5z3NRfaPEP/QL0v/wZSf8Axip5 l/SNlQm1e6/8CX+ZrUVk/aPEP/QL0v8A8GUn/wAYo+0eIf8AoF6X/wCDKT/4xRzr+kx/V591 /wCBR/zNaisn7R4h/wCgXpf/AIMpP/jFH2jxD/0C9L/8GUn/AMYo51/SYfV591/4FH/M1qKy ftHiH/oF6X/4MpP/AIxR9o8Q/wDQL0v/AMGUn/xijnX9Jh9Xn3X/AIFH/M1qKyftHiH/AKBe l/8Agyk/+MUfaPEP/QL0v/wZSf8AxijnX9Jh9Xn3X/gUf8zF8Z+LLzw1eWyWyWLRtp97fSLc uVeT7OI38uPHVmVnH+z9/kIVbPbxrrMlxqQFpBaW/mzQafLJGkpkliulttoQTq0nmOwG4iJY yVBLA7q2rvTtWvtWtdRudI0uSS1ieKON9QYp80kUm4g2/wB5WgQg9vrjEjWOoM967eG9AL36 BLxjdtm4UKVAk/0f5wFJGDng4o51/SYfV591/wCBR/zOV0LxRfXni2Sa4tI7OW4t7Wzupphm OJ4ru9iK4Vjh5GTCjcVUtje52LIX3iXWLfxjefYtBu4NQvbewtIobryJWAH2+UuFWcIwxGVw ZEI5POAG0tRu4fCdrbpN4W0i3t5HjSNbNJ5UDLLujB8q1IU+bJlQcfMxK85rUlt73WrNpLrw 1olxDeRRiRLu4fMiKS6K6Pb5+UsSFYfKSeAc0c6/pMPq8+6/8Cj/AJlG18Y31zdWdi1taR3m oPZTW4STzokglieSQNIpwzgW10FZRtOYT0LYx9K8Tan4k1PwvPqOnx2iS3sd5bAPHuMU1leF QVWRyQNnEjBN+T8i7TXZE621wlw2jaQZ0RkSQ377lViCwB8jIBKqSO+0elV4bHULZy8HhvQI nNw10WS7ZSZmUq0nFv8AfKkgt1IJFHOv6TD6vPuv/Ao/5nRUVk/aPEP/AEC9L/8ABlJ/8YqO TUNbheFJbDSEeZ9kStqjgu20thf3HJ2qxwOwJ7Uc6/pMPq8+6/8AAo/5m1RXI6f4vv8AU9RF hBpUMd00TTKl2by23IpUMQZbVQcF16eorUh1DW7lC8FhpEqB2QsmqOwDKxVhxB1DAgjsQRRz r+kw+rz7r/wKP+ZtUVk/aPEP/QL0v/wZSf8Axij7R4h/6Bel/wDgyk/+MUc6/pMPq8+6/wDA o/5mtRWT9o8Q/wDQL0v/AMGUn/xij7R4h/6Bel/+DKT/AOMUc6/pMPq8+6/8Cj/ma1FZP2jx D/0C9L/8GUn/AMYp0U+umZBNpunJEWG9kv3ZgO5AMIyfbI+tHOv6TB4ea6r/AMCj/malFFFU YBRXnfjq68YQ6tP/AGBc6lFbxWQdEtbKOVXl8m8c5LRsSd8NsuAR/rAOrA0aXdeMG8ax/a7n Um0t72VGheyjWJYt1+F+cRhsAQWpBLc+aM53rQB6JRXk+oaj4+gvNTntrjVXjX7Z9ngGnxsg wNQEW0iLcceRaEZJz5ozkOorUsrrxhbaT4t8651K7uLeymfTHmsowxlWa7RNgSNQ5KRW7YIO d4I4YCgD0SivI768+INtb6jFb32szOjzNBKdOhLERjUNijEOCH8izJ4yfMG0jeK9E8MNqLaK V1SWea6ju7qISzxrG8kazyLGxCqo5QIcgAHOe9AGxRRRQAUUUUAFFFFABRRUN1cpaW7zyLKy LjIiiaRuTjhVBJ/AUDSbdkTV5/8ADnS7iSz07X5LKCxe70/zbx4XDHUp5ykxnfAGNvzAAj5T LIqgIql+q/4SOx/54ap/4Krn/wCN0f8ACR2P/PDVP/BVc/8Axup549zb6rX/AJH9zNaisn/h I7H/AJ4ap/4Krn/43R/wkdj/AM8NU/8ABVc//G6OePcPqtf+R/cybXbW8vvD2p2enXH2e+nt JYrebeU8uRkIVtw5GCQcjkVT8L6ZbabZXJtNAj0OO4uDN9kVkznYibmWMlEJ2dEJGAGJDMwE 3/CR2P8Azw1T/wAFVz/8bo/4SOx/54ap/wCCq5/+N0c8e4fVa/8AI/uZrUVk/wDCR2P/ADw1 T/wVXP8A8bo/4SOx/wCeGqf+Cq5/+N0c8e4fVa/8j+5mtXn50u41vx3q1x9igE1jqFrFDq28 ebbwRxQzvAq4B/eGZ1yDyskm44REfqv+Ejsf+eGqf+Cq5/8AjdH/AAkdj/zw1T/wVXP/AMbo 549w+q1/5H9zOd0r4o6TrGk6jqVrYXzW+n2i3lxiS3cpEY5XGQkpw37oqUOGBZcgDJGpH400 +XxnN4YWKQXkT7Gdri3UE+UJfljMnmsNrAZCEZz2BIyU03w+mlppy/2+Lc6ZFpU4/s2fNxbx /dVz5PB2tIpKbTiVu4QrHqOj+HtXvr6TURrd1Y30vnz6dNoztCZPJEIdWNv5iMFUYKuCD06k Uc8e4fVa/wDI/uZ1Q8S6C1g9+ut6abNEV3uBdJ5aqzlFJbOACysoPcqR1FXLG/s9Ts47ywu4 Lu1kzsmgkEiNgkHDDg4II/CuFt9C8PxTNcTz+Jru7e4W5e4uLCcu0gmilJ4hAAP2eBNoAAWJ QoUli3QaTqOl6No1jpdvFq7QWVvHbxtJpdyWKooUE4jAzgego549w+q1/wCR/czoq8rtL4eG NXuNY1yzu477S9Mhiv5ftEbLqE13Oqeem6QJGga3wC2w7CqkIsSrXff8JHY/88NU/wDBVc// ABuqep6jpeq2qW88WrqiXEFwCml3IO6KVZVHMZ43IAfbPTrRzx7h9Vr/AMj+5lF/iLpkVvdX MtjfJa28Rfz1MMiSuLUXRiQpIct5RJ3fcO0gMeM6mm+LNK1Dw+2tyXEdlYo5R5rqaMRqcgDE qs0bAkgZViM5U4YEDndS0nQtVudUkupvERh1DezW66ZMEile2+zGVD5G7d5WRhmZfmJ25xgv NF8M3MQ8mHW7O6i3LZ3trpc6z2UbKVMULmEkR4eTCnIXf8u3am0549w+q1/5H9zOqvfEug6a kb3+t6bapI8iI090iBmRtrgZPJVuCOx4Naleew+H/C9sbUwRa/H9mdWQDTrg5CSWzopzF0Vb OBB3Kg5JYlq6z/hI7H/nhqn/AIKrn/43Rzx7h9Vr/wAj+5mtXF+IdC1fVPF8F35Udxo8FvDA 9qcKbhZZm+0KW3DCKqW8jKVO8R7BgM4be/4SOx/54ap/4Krn/wCN0f8ACR2P/PDVP/BVc/8A xujnj3D6rX/kf3Mo6J400/XtevtItYpEnsnmSQvcW5JMcnlt+7WQygZ6FkAxj1GekrkdIls9 Hurl4r7xFNazSzTCzm0mTy43llMjFStuH+8zYyx4P0xsf8JHY/8APDVP/BVc/wDxujnj3D6r X/kf3MwfGNu2peJdD02TR7TWLX7Pd3Zs7p1VPNQwxpIcqQQonfIPZiwDMqKdSz1nR9B0Oztd U8TWMklpFHbTXd1dIhlkXchZtzHDFopeCSco45KmrX/CR2P/ADw1T/wVXP8A8bri5/C/hy6f UGmuPEzi9eZiv9mygRCRboMqYgzjN7MRuyc7ecDBOePcPqtf+R/czvhq2mtfvYLqFobxHVHt xMvmKzIXUFc5BKqzAdwpPQVHpmu6Prfm/wBk6rY3/k48z7JcJLsznGdpOM4PX0Ncj/Y3h061 /aTHxE4WXzY7VrG58mMmb7Q20eVn5pwkhOc5jVchModDw4NF8MWD2dkmtyRt5WTNplwT+7gj gXpEP4YVJ9ye3AOePcPqtf8Akf3M66isn/hI7H/nhqn/AIKrn/43R/wkdj/zw1T/AMFVz/8A G6OePcPqtf8Akf3Mb4svrjTPBuuX9nJ5d1a6fcTQvtB2usbFTg8HBA61ydvcWXw3shd3GlR6 ZY3bw2EGnWtzEEWWNJWad5ZXjTLgbdzHewjjLfM2xOu/4SOx/wCeGqf+Cq5/+N1TvdR0u+ut OuJYtXD2FwbiILpdzgsYpIsN+76bZGPGOQPoTnj3D6rX/kf3Mh/4TqwXSY9Sksr5LeXSpdXi ysZLwRxxO2AHOG/fBQDjlW7YJsaN4x0nWNOS9+0QWkcl2LOITXtvJ5kpAIRWikdSxzwud3HT GM4r6b4ffS305v7fNuNMl0qAf2bPm3t5PvKh8nk7VjUF9xxEvcuWp3fh3w9qHnSX0/iKe7uf 3d3eLpLwTXVudm63kaK3TdGfLXn7+MgMFJBOePcPqtf+R/czsn8S6DHcWlu+t6as96iPaxtd IGnVzhCgzlgx4BGc9q1K4Wz03w/Zapa6in9vtcQOJCX02fEsn+k7nYCHqzXkzELgZ24AAwek /wCEjsf+eGqf+Cq5/wDjdHPHuH1Wv/I/uZD4otru6srZIbWS9tFuAb6xiZFe6h2ONg3lVI3m MsCwDKrKcg7W4/w9400rw14fsrKeK7d5tMl18g3EbMsUxnuPLBkkEkzqFZSwBzgM23Jx23/C R2P/ADw1T/wVXP8A8brH1IaLqn9r+emtr/aunrp8+zTLgbYx5uCuYjhv3zcnI4HHXJzx7h9V r/yP7mNu/iLplheWtleWN9BeTSvDJbuYd8LKIm2kCT94xWeNgkXmMc4A3Aiukm1bTba9FlPq FpFdlFcQPMquVZxGp2k5wXIUHuSB1rjf7J0JryO/mm8RS6nH5rLfnTJllEriJfNAWAKGCQRo AFCldwZW3Nmvc+GPCM87eTa63Z2Tby2n2emXEVsXdBFI+wRfeaIGPg8BmKhXJejnj3D6rX/k f3M7a113R77UZ9Os9Vsbi+g3edbQ3CPJHtO1tyg5GCQDnoa0K5HSBoui397eWya28l3nzBJp lwQMzzz8YiH8Vw4+gXvknY/4SOx/54ap/wCCq5/+N0c8e4fVa/8AI/uY3XbG41CfR4Uj8yzX UEmvBuAwkaPJGfXidYDx1xz8u6tisn/hI7H/AJ4ap/4Krn/43R/wkdj/AM8NU/8ABVc//G6O ePcPqtf+R/czWorJ/wCEjsf+eGqf+Cq5/wDjdH/CR2P/ADw1T/wVXP8A8bo549w+q1/5H9zN auX12a4s/Fum3dta/aZhpWoR28JkEYnn3W7pCHPAZhG5HsrHoprS/wCEjsf+eGqf+Cq5/wDj dH/CR2P/ADw1T/wVXP8A8bo549w+q1/5H9zOdTxAPDWnTarrmk30N5cyxxTXF3c2MPnHDkLH uudqxpg4Tdn5ifnYyOdDwbeJqcmvapAM2t7qCSwuHV1YC0t0bDoSrbXV0JUkblYZ4qbUtQ0r VbdYLhNfRFcODa2t9btnBHLRqpI56Zx09BViDXdPtreKBIdXKRoEUyadduxAGOWZCWPuSSe9 HPHuH1Wv/I/uZtUVk/8ACR2P/PDVP/BVc/8Axuj/AISOx/54ap/4Krn/AON0c8e4fVa/8j+5 nG+BYks9O03xJqK2OkC809pbubz1H9pzyhbhp3OFxsVZCAfu75QoVEDP2jeJdBVL121vTQlg 4S8Y3SYt2LFQJOfkJYEYOORisHxTFpHizTY7G6k1+1RHdt9rpcwYh4pImU74WGCkrjpnoQRW fNoXh+Q3UkU/ia2uZ3Z0uYLCdZIGaS5djGfJwCReTLkg4BUjDDdRzx7h9Vr/AMj+5nXXviXQ dNSN7/W9NtUkeREae6RAzI21wMnkq3BHY8GtSvPYfD/he2NqYItfj+zOrIBp1wchJLZ0U5i6 KtnAg7lQcksS1dZ/wkdj/wA8NU/8FVz/APG6OePcPqtf+R/czWorJ/4SOx/54ap/4Krn/wCN 0f8ACR2P/PDVP/BVc/8Axujnj3D6rX/kf3M1qKyf+Ejsf+eGqf8Agquf/jdH/CR2P/PDVP8A wVXP/wAbo549w+q1/wCR/czWorJ/4SOx/wCeGqf+Cq5/+N0f8JHY/wDPDVP/AAVXP/xujnj3 D6rX/kf3M1qKyf8AhI7H/nhqn/gquf8A43Viz1a2v5jFDHeKwXdmeymhXH1dQM89OtCnF7MU sPWiryg0vRl6iiiqMQooooAKKKKACiiigDP1PW9O0byvt9x5Xm5IwjPtVcbpG2g7I1yNzthV yMkZFaFc/wCJfDH/AAkG3befZ99pcWE+Yt+63n2eYF5G2T92u1juA5yrZGNwCb7Q7NJGYCih ECEMGydxLZwQRtwMDGDyc8AGXD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNE3inR oLO2upLzENxuKnynJQIcSNIuMxqh4dnChDwxU1lxeFNSmtdQGqataXV7c3EFzFdRWLRlGhl8 2JGBlbdErBQEUpxvOd7l6jvPAv2zTLO1Oo4kjluJpZDBkCSeQyPLCu791MjFvKkJYxhmBDk5 oA7CsdfFOjSaPHqsF59ps5pXhhe2ieZpnVmVhGiAs+Njn5QflUt90Zq5qul2mtaXcabfpI9p cpslRJXjLL3G5CDg9CM8jIPBNcfb/Dkp4aTS7y+tNQlh1OXUYmvbSSeAtIHBWSKSZiwHmORh 1+baxydxcA7iCZbm3inQSBJEDqJI2RgCM8qwBU+xAI71JVewtfsOnW1n9onuPIiSLzrh98km 0AbnbuxxknuasUAFZ91renWWowWFxcbLibbgbGKpuO1N7AbU3MCq7iNzAhckYrQrn9U8Mf2l rSXwvPKhk+y/aYvK3M/2aZpodjZGz52O7IbcuANp5oA3J54bW3luLiWOGCJC8kkjBVRQMkkn gADnNZf/AAlGk/2d9t86fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5qxqWmf2xpOqaZezf6L fRPbgwrteON49rckkFsliDgDkDBxk4//AAi15s+2f2nB/bP9of2h5/2Q/Z/M+z/ZseV5m7b5 XbzM7+c4+WgDUfxHpCXFpD9ujc3aI8TxgvHtc4jLOoKqHPCFiN54XJ4rUrk18DwwHToba+kW ztrext5UkjDSSLZyGSAq4ICncTvyrbhwNh5rrKACs+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr 7WIVtpO1iA2CcVoVz+l+GP7N1p743nmwx/avs0XlbWT7TMs029snf86jbgLtXIO480Aampap aaTbrNdvIA7hI0iieWSRsE4REBZjgEkAHAUnoCarv4j0hLi0h+3RubtEeJ4wXj2ucRlnUFVD nhCxG88Lk8VHqek3moWumyLewRanYSi4jmNsWhaQxPE2Y94baVkfA35B28nBBy18DwwHToba +kWztrext5UkjDSSLZyGSAq4ICncTvyrbhwNh5oA3LXW9OvdRnsLe433EO7I2MFfadr7GI2v tYhW2k7WIDYJxWhXP6X4Y/s3WnvjeebDH9q+zReVtZPtMyzTb2yd/wA6jbgLtXIO4810FAEc 88Nrby3FxLHDBEheSSRgqooGSSTwABzmsv8A4SjSf7O+2+dPt83yPI+yS/aPMxu2eRt8zdt+ fG3Oz5vu81qTiZreVbeSOOcoRG8iF1VscEqCCRntkZ9RXL23hTUodNtvN1a0m1i2vTepfGxY LNI0TRMZk83LHa7AbXQLhAAFTaQDUbxRpP8AaNvYxTT3M1xFFPG1paSzx+XIWCOZEUoqkq3J IGAT05qxa63p17qM9hb3G+4h3ZGxgr7TtfYxG19rEK20naxAbBOKwx4GtobjRGtp40TS7eC2 WZ7ZGutkJyoScYKB+VkBDBlJACZYmNvANre6tqV1qk/m2t5FNbraWjT26COWRXk3fvWG5jGu 4xiMPlywbdwAbC+KdGk0ePVYLz7TZzSvDC9tE8zTOrMrCNEBZ8bHPyg/Kpb7ozWpBPDdW8Vx byxzQSoHjkjYMrqRkEEcEEc5rj9J8EXuk6NBbprMct/a6nNqNvcSwSyRhpVdWV0eZmYYlkxh 15wxydxfpNK0z+x9O07TbWbdY2VotsBKuZH2BVRtwIA4DZG3kkYxjBAI08R6Q9xdw/bo0Noj vK8gKR7UOJCrsArBDw5UnYeGweKj/wCEo0n+zvtvnT7fN8jyPskv2jzMbtnkbfM3bfnxtzs+ b7vNZbeB4ZzqMNzfSNZ3NvfW8SRxhZI1vJBJOWckhjuA2YVdo4O881J/wi15s+2f2nB/bP8A aH9oef8AZD9n8z7P9mx5XmbtvldvMzv5zj5aAOkgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc1 JVPSdNh0bRrHS7dpGgsreO3jaQgsVRQoJwAM4HoKuUAFZ+ma3p2s+b9guPN8rBOUZNytnbIu 4DfG2Dtdcq2DgnBrQrm9B8LzaDbzrDfxyTrZQ6faO9udscMAfyfMUPl3zI24goG4wqdwDU1P W9O0byvt9x5Xm5IwjPtVcbpG2g7I1yNzthVyMkZFH9t6d/bH9lfaP9L6bdjbN23f5e/G3zNn z7M7tvzY281n+JfDH/CQbdt59n32lxYT5i37refZ5gXkbZP3a7WO4DnKtkYP+EY/4qX+1Ptn +j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLQBoaZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO1 1yrYOCcGtCuf8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNoPGFXBz0FAFe+vrfTrOS 6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAI qvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaIjz Gld5HCDd8g8x8jO7CjByTuoAk/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8Pin Rp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/wCEY/4qX+1Ptn+j/a/t/keV8/2j7P8A Zs7848vy/wCHbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5oA 6ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUzedI0stxLIF2hpJZG kfauThdztgEkgYBJPJ0KAMe38VaFeadd6haanBdWlpKIJJrcmVTIQpCJtzvY70AC5JZto+bi tCxvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCK5u28P+JrYa26eItNS41NxMs0eksPIl 8uKLcA07BgEi6H+I5JIG07Gkabc6XpdlZ+faExOxnaK3dRKDuORukZg5YqzOzOWO4nlsgAk/ tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mjTNastX80WrTrJFgvDc20lvIoOcNskV W2nDANjBKsAcg4y5PCpn8Zw69LdRrFA/mxW8CSIXlMRi3S/vDG52sw3CNXwEG7C4NjQNBm0m 4v7q4uLR571w8iWNobaAsCxMhQu5MrFvmfd8wVBj5ckA3KKKKAMfVvE+m6JeQWt6L7zrjiEQ afcThzhjtDRow3YRjtznAzjFbFc/r2jaxqep6ZdWGqWNpHYSm4SOewectIY5IzlhMny7ZTxj ORnOOKy7PwLNYeJ7rXLW802Ce41P7Y5h00o7wmMxvA7iTLBjskz08xSxU5AAB0lrrVlfajPZ WzTySQ7g8gtpPJyp2sol2+WzA8FQxIIYEZU40K5fwZ4R/wCETgvIv+JU32iVpd9jpv2VuXd9 rHzG3Ku/ag42qMc11FAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1 k8yF8gEqVIIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRk Ag8iPSdKm0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x8jO7CjByTuoAk/tvTv7Y/sr7R/pfTbs bZu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/4R j/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx 2chkt/MXd+8bcTvIKBhwAh5oA6ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6L pn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0KACs/wDtqyGsf2W7Tx3R4Qy20iRy Hbu2pIVCO23J2qxOFbj5Tg1PRrXV/K+0y30flZ2/ZL+e2znGc+U67unfOOcdTVOTQZp/FkOs y3FoqQJsiEFoUuHUqR5cs2874tzM+wKvzBDn5eQC5pmt6drPm/YLjzfKwTlGTcrZ2yLuA3xt g7XXKtg4Jwa0K5/w14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8YVcHOhqejWur+V9 plvo/Kzt+yX89tnOM58p13dO+cc46mgA/tqyGsf2W7Tx3R4Qy20iRyHbu2pIVCO23J2qxOFb j5TiSw1Wx1N7xLG5jnNlcG1uNnISUKrFM9CQGGcdDkHkEDLvPDb6h4jGpXFzBHCkTQp9kgaC 6ZGQqY3uBJlo8uzhVVcMEbOV5r+EPB48JT6qILvzbO7lje3t/wB8fs6IgjVMySvu+VVGQF6Y +6FVQDqKpnVbFdZTRzcx/wBoPbtdC3HLeUrBS59BuYAZ684zg4uVx8ngKH/hNP7et76eGGWK dLq3Fxcl5XlCqXVxMAmAkYACcbRjlUKAHSWGq2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg 8ggXK5fwh4PHhKfVRBd+bZ3csb29v++P2dEQRqmZJX3fKqjIC9MfdCqvUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAVxfg7xHd3tveXGq30bQQ2VvdXUkgRFsrlhIbi2JAAURBEO18uu/wCZjkY7Ss/TNb07 WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg0Ac34x8R3dlb2dxpV9GsE1lcXVrJGEdb25UR m3tgSCGEodztTDts+Vhg5k/tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv/wBf+5358v8Ahxv5 roNT1vTtG8r7fceV5uSMIz7VXG6RtoOyNcjc7YVcjJGRR/benf2x/ZX2j/S+m3Y2zdt3+Xvx t8zZ8+zO7b82NvNAHP8AgfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzIu/wCYnIx2 FZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoAx/FF9cadoMtxbSeSwlhSSfaD 5ETSossvOQNiM75YFRtywIBFV9C1xG0GzuNVv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27Kg DArYvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspw VYEEFSAQQQQCKAOX/tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv8A9f8Aud+fL/hxv5rH0/xT rM+g3lxJebmEVg91P5Sf8S2WaUrdxcDC/Z0AfEgZkzmQsOK7j+29O/tj+yvtH+l9Nuxtm7bv 8vfjb5mz59md235sbearw+KdGns7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljQAeF7641HQ Yri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2Kr2N9b6jZx3VrJ5kL5AJUqQQSGVlOCrA ggqQCCCCARVigDzuPx1p/wBi8UkeLbSYWt6v2WWJ7eR0gdIOEwVTAklZFkkyqnG8kKc9Ro+s 2w8P6bcX+uabcvdOII7qK4QxzykkBEYYDvwQdoXcVJCL90XINasrmLUZIWnf+zpXhuUFtJvV 1UOQqbdz5VlI2g7sjGc1YsL631PTra/s5PMtbqJJoX2kbkYAqcHkZBHWgDj4fEN7dfFK50r7 btsbXbbixt5I3kZ/I85p5kMe9YcSIgZXHzqoIO/i54H14a/b3lwmvWmqRl1eNYzGJIgwPLIn MaMQSkb7pFA+dyxKpuPrVkmuLo26dr5ohMVS2kZEQ7wpaQLsTJjcDcRnHFGma3p2s+b9guPN 8rBOUZNytnbIu4DfG2Dtdcq2DgnBoA0KKKKAOX8e6odI0G3uF1v+yGfULWEz5hG5HlVZB+9V hwhd+nGzPQEGva+ON+vT2V5b2MViNVbSIbuG/wDML3HleaqspRQuRlCAzESfLg/eroNY1qy0 GzS6v2nWF5UhUw20k53ucKNsaseTgDjqQOpFaFAHF+APEl34mOrXd1qGmzhLho47awvUnWBV kkQHAjVgHCBgzM2/7wCD5a7Sqdnqlpf3F1DavJIbZ9kj+U4jLZIIVyNrkFSGCk7SMHB4q5QB j+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKr6FriNoNncarfwI1xdy2trPM 6x/bAJXWFl6BmkRVcbRht2VAGBWxfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjfW +o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEUAcv/beo/wDCe/2d9o+T7X5H2HYv/Hp9l837 V03/AOv/AHO/Pl/w4381j6f4p1mfQby4kvNzCKwe6n8pP+JbLNKVu4uBhfs6APiQMyZzIWHF dx/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4ja NcZkVzwjIGDnhSxoAPC99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFV7G+t9 Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxQAVwev+KdV0rx9olhPHJbabd3otYgJbbbeK 0LFnO9xICspjAVVHQ8uZEQd5Wfca3p1rrFppM1xi+u8+VEqM3RWb5iBhMiOQjcRu2NjO04AO T8I+KdV1Hxrq2j6xHJbXEdlBdfYWltmFqxZwyL5bs7AqYjubvlsRh0U9Zqc2sReV/ZNjY3Wc +Z9rvHt9vTGNsT579cYwOueDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAcvc /wBrQ+O7CFNbne1uvOuWsTBEIUt44kjKhtpkMhmljcHcBt3DsN1fwLreo6x9v+33Hn7PLkGE UeQz7t0DbQPLkTaN0Lb2j3DMsm4beg/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3m jTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg0AaFcnHrwm+Ic2kpr1oPJTZJp0hjV2 JjDgIp/eO4B3tJkRhSFCM2906ys/+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbeaAO f8C63qOsfb/t9x5+zy5BhFHkM+7dA20Dy5E2jdC29o9wzLJuG3sKz9M1vTtZ837Bceb5WCco yblbO2RdwG+NsHa65VsHBODWhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVx+va3qNl4r trOC48tT9j+z2uxT9u82dkueo3N5MQWT5CNu7L5UgUAdhRWXrOpTW2g6xcaUsd3qFlbylLdQ ZCZhHvRGVTnJyhxwSGGOori/+Ei1JfBv2hNb3SNqHkPqstxbm1VPL35S5EAjEeQE3tC37wtH jJDqAekUVwdz4k1caz4dTzJLcXVvbSS2klsIpJ2lbbIFiYszGMfM6q6+SDuYzA7R3lABRRXH 6Dreo3viu5s57jzFH2z7Ra7FH2Hyp1S26DcvnRFpPnJ3bcphQRQB2FFc34q1SKPw1FfWfiG0 04SujW9095DDFOGBIXzJIpVwVywwpJ2jkDNZd3r16uo+GnXVPKuryKA3WjyLHHMQ5ALi2KtL xly374CMRlsSbWVgDuKK4/R/FCXHj7V9EufEOlXWyKI2lrblUdH33HmRkb2Z5FVE3dMcHauT nsKACiq9/N9n065n+0wWvlxO/n3AzHFgE7nGV+UdTyOB1HWvM9T8darbfDnUNX0S8j1SFXlE OrPLbb7aMRqVM0e5FMrO2FQLkKyFlLgxsAeqUVw+oeJ719a0w2c/lQ3MVjLa2n7t/tommK3H K7t/kxBZMxNhd25iykV3FABRXP8AizUZdPtbD/iYf2ZaT3flXeofIPssflSMG3SAouZFjTLA j58D5iCLmh6lNeaNpL6ksdtql3ZJcS2hBRlbanmAIx3AKzgHPTIB60AalFcPp/iHUZda1OKf UYIliivnuEuI18vTPJmCWzOAVYLLEWlO9vmC5Qquaz/+Eud/Bv8AacHiWCWwOoeSl79otI7x 4fL3Bf3m2BZi/OxgpEPUCSgD0iis/Qrq8vvD2mXmo2/2e+ntIpbiHYU8uRkBZdp5GCSMHkVo UAFc3oPhebQbedYb+OSdbKHT7R3tztjhgD+T5ih8u+ZG3EFA3GFTv0lcX4O8R3d7b3lxqt9G 0ENlb3V1JIERbK5YSG4tiQAFEQRDtfLrv+ZjkYANDXvC82vW8CzX8cc7WU2n3bpbnbJDOE87 y1L5R8xrtJLhecq/aT/hGP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy1n+ ONb1HR/I+xXH2fNpczw/IrfartPL8i1+YHPmbpPkTEjbPlIwcn9t6j/wnv8AZ32j5PtfkfYd i/8AHp9l837V03/6/wDc78+X/DjfzQBoeGvDH/CP7t159o2WlvYQYi2bbeDf5Ybk7pP3jbmG 0HjCrg56CuP8D63qOsef9tuPtGLS2nm+RV+y3b+Z59r8oGPL2x/I+ZF3/MTkY7CgDP1rTP7X 0w2om8mRZYriKQruCyRSLIm5cjK7kXIBBIyAQeRX0/SbzS9Mt7W1vYPM+1yXN3JLbFhJ5sjy SrGocbMs52kl9owDuPNHii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqt d6JaS31zG0k9xPBbSttU3aI8nlyLjAYvEgkyoAIJYAL0AI/+EY/4qX+1Ptn+j/a/t/keV8/2 j7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG3E7yCgYcAIe aP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnfny/4cb+ax9P8U6zPoN5cSXm5hFYP dT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDigDuNF0z+yNMFqZvOkaWW4lkC7Q0ksjSPtXJwu52w CSQMAknk6FY/he+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtigDl9O0PxJYT6 tcf23pUk2oSrcf8AIKkCxyBIo+n2jldkXTIO5s5wNpsaFo2saJ4e0zSf7UsZvsPlQ+b9gdd9 uiBduPOOJDjO/JH+xVjxTfy6Z4cu7yG7gtGj2bpppEjCqXUNtaT5BIVJCb/l3ld3Gar+FNZl 1Hw5YXWov5c91LNFB5xQPOqvJ5bfIdjM0SByU+U8svy4oAkk8PtL4sh1trqMCJMKq26rMRtK +U0oOWgyxk8sgnzMNuwAoj8NeGP+Ef3brz7RstLewgxFs228G/yw3J3SfvG3MNoPGFXBzTk1 WeL4hw6fBrEd1BKmLjT1kike1PllgzRqgeNDhP3jSMMyBdnzKyng3Xhrdxqgh1601i2gdFEs RjDLLlxJtROVgyoEe8lztc7nXYxAOsooooAx/Euk3mtaZFa2V7BaSJdwXJkmtjMD5UiyKu0O nVkXJz0yOpyMe18D/Yden1qzuLG3vp9Va8mkhsNhkt2i8trdiHyckCQsTgyfNsqv401qHTNe 0W3bxn/Yq3UpS5g8y1G2LypmEv72NiMuiJnO3tjJzUln8QIZfE91pV0mmxwRan/ZaTw6iJHM xjMib0KKEDBXT7xPmKUAPJABc8GeEf8AhE4LyL/iVN9olaXfY6b9lbl3fax8xtyrv2oONqjH NdRXD+AvEF74nutTv7nVrGWOKV4UsbC9jnjiAldFc/ulkGRHlWLsHDFgqjCjuKAM/WtM/tfT DaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJK sahxsyznaSX2jAO480eKL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKj8N6q13 olpLfXMbST3E8FtK21TdojyeXIuMBi8SCTKgAglgAvQAj/4Rj/ipf7U+2f6P9r+3+R5Xz/aP s/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5o /tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv8A9f8Aud+fL/hxv5rH0/xTrM+g3lxJebmEVg91 P5Sf8S2WaUrdxcDC/Z0AfEgZkzmQsOKAO40XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJ JAwCSeToVj+F7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQK2KACuT1bwRHfeJ9 P1uzvZLSSC9W8uozJOy3DLGIhhVmVEOzgnac4AOV3K25qeo3Vh5X2bRr7Ut+d32R4F8vGMZ8 2ROue2ehzjjPLr4t1IfFW38PXdr9jsZrS4MEbzW5ecqUKzHEhcKQJVCBc8bjn5hEAbnhrR9S 0e3nj1HUbS/klcSNcRWbQySyYwzyEyOGJAUAAKFChQAoUC5qehaPrflf2tpVjf8Ak58v7Xbp LszjONwOM4HT0FYfg3Xhrdxqgh1601i2gdFEsRjDLLlxJtROVgyoEe8lztc7nXYx6ygDDj0f Uk8WTas+o2kto6eXHBJZsZYI9oyiSCTaAzjex2EtwCSFTaeGtH1LR7eePUdRtL+SVxI1xFZt DJLJjDPITI4YkBQAAoUKFAChQMu48UJZ/Eq30a58Q6UtrNaShbHKpMk+63EauxclmYPIVUKu RnhsAg8C63qOsfb/ALfcefs8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybhtAOwrDj0fUk8WTas+ o2kto6eXHBJZsZYI9oyiSCTaAzjex2EtwCSFTbuVx9x4oSz+JVvo1z4h0pbWa0lC2OVSZJ91 uI1di5LMweQqoVcjPDYBABoeGPDH/COfav8ATPP87YPli8vftz+9l5PmXD7v3kvG/avyjHPQ Vx/gfW9R1jz/ALbcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv+YnIx2FABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQrm9B8LzaDbzrDfxyTrZQ6f aO9udscMAfyfMUPl3zI24goG4wqdwDU1bWrLRIoJL1p/9Il8mFILaSd3faz4CRqzfdRj07Uf 23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzWH4i8HTeI7jSri4vbRJ7O3lhkkFkWYN IYiZrcl8wSqY8o3z7d3fHNz/AIRj/ir/AO3ftnvt8r97/q/L8rzM/wDHv/y08rb/AK3593ag DUsNUtNSe8S1eQvZ3Btp1eJ4ysgVWx8wGRtZSGGQQQQTVyuf0HRtY0zU9Tur/VLG7jv5RcPH BYPAVkEccYwxmf5dsQ4xnJznHFdBQBXvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmi xvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKr61pn9r6YbUTeTIssVxFIV3BZIpFkTcu RldyLkAgkZAIPIr6fpN5pemW9ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmgCx/be nf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV/wDhKdG83UIxeZbT9guNsTkZdmRVQgfv GLoybU3HepXG7iq//CMf8VL/AGp9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5aw4fhrHZ aldTWOpSQ2Zt7WKztZGnm+ztbSiaLJeYhk3AjaFXCnClSWLAHaWN9b6jZx3VrJ5kL5AJUqQQ SGVlOCrAggqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQBXv r6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEF SAQQQQCKL6G4ns5I7S6+y3HBjlMYcAgg4ZT1U4wQCDgnBU4Iz9O0m80rR7GxtL2AtFLvuZJr Yt5qsxZwuHBDEtw7l27tvYliASSeIdMi1uHR2nkN5K/lqFgkZA+wybGkC7FfYpbaSDjBxgjN iw1S01J7xLV5C9ncG2nV4njKyBVbHzAZG1lIYZBBBBNY58KmXxuniCa6j8iFGMNpEkiZmKCP zpD5hR3Cb0B2A7XAJO1cSaDo2saZqep3V/qljdx38ouHjgsHgKyCOOMYYzP8u2IcYzk5zjig DoKr319b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTViq99DcT2ckdpdfZbjgxymMOAQQc Mp6qcYIBBwTgqcEABY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFV/7b07+2P7K+0f6 X027G2btu/y9+NvmbPn2Z3bfmxt5qvp2k3mlaPY2NpewFopd9zJNbFvNVmLOFw4IYluHcu3d t7Esa/8AwjH/ABV/9u/bPfb5X73/AFfl+V5mf+Pf/lp5W3/W/Pu7UAaGma3p2s+b9guPN8rB OUZNytnbIu4DfG2Dtdcq2DgnBrQrD8NaPqWj288eo6jaX8kriRriKzaGSWTGGeQmRwxICgAB QoUKAFCgblAFe+vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEq VIIJDKynBVgQQVIBBBBAIqvrWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k 3ml6Zb2treweZ9rkubuSW2LCTzZHklWNQ42ZZztJL7RgHceaALH9t6d/bH9lfaP9L6bdjbN2 3f5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U4LhziNo1xmRXPCMgYOeFLGq/8AwjH/ ABUv9qfbP9H+1/b/ACPK+f7R9n+zZ35x5fl/w7c7ud2PlrPtPAv2XTHtTqO6SKKyt7OTyMCO OzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVis/R dM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhQAVnvrVkmuLo26dr5ohMVS2kZEQ 7wpaQLsTJjcDcRnHFGp6Fo+t+V/a2lWN/wCTny/tdukuzOM43A4zgdPQVTk8PtL4sh1trqMC JMKq26rMRtK+U0oOWgyxk8sgnzMNuwAoALmma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2 DgnBrQrn/DXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDaDxhVwc9BQBn/wBt6d/bH9lf aP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2D gnBqvNo1xceI7bUpdQ32ttueG1MIykjJsOGzjbjJHy7wWYCQIzIY/DWj6lo9vPHqOo2l/JK4 ka4is2hklkxhnkJkcMSAoAAUKFCgBQoABuVTOqWi6ymks8gvHt2uUUxPtaNWCsQ+NpILLlc5 G4HGDVyufu9G1ifxba6xFqljHa20T262z2Ds5jkaJpMyecBuzCMHbgZ5DUAaGma3p2s+b9gu PN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQrn/DXhj/AIR/duvPtGy0t7CDEWzbbwb/ACw3J3Sf vG3MNoPGFXBz0FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRWfda3p1lqMFhcXGy4m24Gx iqbjtTewG1NzAqu4jcwIXJGKANCio554bW3luLiWOGCJC8kkjBVRQMkkngADnNZf/CUaT/Z3 23zp9vm+R5H2SX7R5mN2zyNvmbtvz4252fN93mgDYorLfxHpCXFpD9ujc3aI8TxgvHtc4jLO oKqHPCFiN54XJ4rUoAKKKz7XW9OvdRnsLe433EO7I2MFfadr7GI2vtYhW2k7WIDYJxQBoUVT 1LVLTSbdZrt5AHcJGkUTyySNgnCIgLMcAkgA4Ck9ATVd/EekJcWkP26NzdojxPGC8e1ziMs6 gqoc8IWI3nhcnigDUorPtdb0691Gewt7jfcQ7sjYwV9p2vsYja+1iFbaTtYgNgnFaFABRUc8 8Nrby3FxLHDBEheSSRgqooGSSTwABzmseTxdo8VlDdNJdkS3H2VYVsZ2nEuwybGhCeYp2KX5 UfLg9CCQDcorLfxDpkVxaW8s8kct0iOiyQSLsDnCeZlf3RZsqofaWYFRkgipLXWrK+1Geytm nkkh3B5BbSeTlTtZRLt8tmB4KhiQQwIypwAaFFU9S1S00m3Wa7eQB3CRpFE8skjYJwiICzHA JIAOApPQE1YgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc0ASUVlp4j0h7i7h+3RobRHeV5AUj2 ocSFXYBWCHhypOw8Ng8VH/wlGk/2d9t86fb5vkeR9kl+0eZjds8jb5m7b8+Nudnzfd5oA2KK jgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc1JQAVxfg7xHd3tveXGq30bQQ2VvdXUkgRFsrlhI bi2JAAURBEO18uu/5mORjtKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODQByfxA 8SXljpenvoeqRrJqKMlj9klt2luZ32LBtE3ytBl/nK5YZQjC7jUk2ualJ8UrbRLe/wDMhTdN cWtu9u0cdqIOswP75ZvOkjxjClCpGSHrqNT1vTtG8r7fceV5uSMIz7VXG6RtoOyNcjc7YVcj JGRR/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNAHL+ANc1LXrzWJbm/+22kHkwiW N7d4PtWGaYQNF8zQ7Xh2+YS3UH5gwruKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsH BODWhQBj+KL6407QZbi2k8lhLCkk+0HyImlRZZecgbEZ3ywKjblgQCKr6FriNoNncarfwI1x dy2trPM6x/bAJXWFl6BmkRVcbRht2VAGBWxfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJA AJNFjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEUAc3eeJ7Kz+I1jpMniC0RJrKVZLB5 ogVuPMg8rtvDusj4UnBABA4JrH8J6nrvipNaWPX5Ejie1hF9brazRJOGL3C2xUHMRjaIKZdz DdyCykV2n9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U 4LhziNo1xmRXPCMgYOeFLGgCPwfc3d94TsL28upLp7pGuIppVRXMLsXiDhAFDiNkDbRjIOM9 TuVXsb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAirFAGfrdx9l0eeY332BRtD3fleZ5K lgGfHQYBJ3MCq/eYFQRWfoOuI/hzTrvVb+CN7yUw28kzrGZyXYRDjCvIyAE+XlGOSmUKmti+ vrfTrOS6upPLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQV IBBBBAIoA5uPXhN8Q5tJTXrQeSmyTTpDGrsTGHART+8dwDvaTIjCkKEZt7pH4F1vUdY+3/b7 jz9nlyDCKPIZ926BtoHlyJtG6Ft7R7hmWTcNvQf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPs zu2/NjbzRpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4JwaANCs/W7j7Lo88xvvsCjaH u/K8zyVLAM+OgwCTuYFV+8wKgitCqeqapaaNYNe3ryLAron7uJ5WLO4RQFQFiSzAYAPWgDL0 HXEfw5p13qt/BG95KYbeSZ1jM5LsIhxhXkZACfLyjHJTKFTVOPXhN8Q5tJTXrQeSmyTTpDGr sTGHART+8dwDvaTIjCkKEZt7p0Gl6paazYLe2TyNAzun7yJ4mDI5RgVcBgQykYIHSq8fiDT5 tbm0iL7W93C+yUrZTGJG2CTDS7PLB2spwW7gdTigDL8J6tcaleanG+pf2hbw+ViUwCIpMwYy RhQPkVfkAjkJmQ7t+QUJ6isfQ/FGk+I/M/syad9kUc/760lg3Rybtjr5iruVtjYIyOK2KAMf xRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQtcRtBs7jVb+BGuLuW1tZ5nW P7YBK6wsvQM0iKrjaMNuyoAwK2L6+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631 GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl/7b1H/AIT3+zvtHyfa/I+w7F/49Psvm/au m/8A1/7nfny/4cb+ap+HPE093pd/PrOsx20C2VtLcXj+VGNPvJfMWW2yRtUxERYSQM4LjeWy BXWf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI 2jXGZFc8IyBg54UsaAK/gjXE8Q+DdKv/ALfBe3TWkIvHhdTtnMal1YLwrZPK8Yz0roKr2N9b 6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVigDP1ObWIvK/smxsbrOfM+13j2+3pjG2J89 +uMYHXPGPNq1wvju206LUvMD7vOsDAFEUIi3CQHBdmMhAEmRDgMhxIF3dRWf/bVkNY/st2nj ujwhltpEjkO3dtSQqEdtuTtVicK3HynABh+B9eGv295cJr1pqkZdXjWMxiSIMDyyJzGjEEpG +6RQPncsSqdZWfpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwa0KAOTvPE9lZ/Eax0 mTxBaIk1lKslg80QK3HmQeV23h3WR8KTggAgcE1n+EfFOq6j411bR9YjktriOyguvsLS2zC1 Ys4ZF8t2dgVMR3N3y2Iw6Ke8qnYarY6m94ljcxzmyuDa3GzkJKFVimehIDDOOhyDyCAAXKKK z/7b07+2P7K+0f6X027G2btu/wAvfjb5mz59md235sbeaAOX+HHiG98SWd5fXt79p87ZcRpB JHLBbJIXZYN6xo3nIu0OrFsfI2RvIHcVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYO CcGtCgAooooAKKKKACiiigAooooAKKKKACiiigArn9U8Mf2lrSXwvPKhk+y/aYvK3M/2aZpo djZGz52O7IbcuANp5roKKAM/UtM/tjSdU0y9m/0W+ie3BhXa8cbx7W5JILZLEHAHIGDjJx/+ EWvNn2z+04P7Z/tD+0PP+yH7P5n2f7NjyvM3bfK7eZnfznHy11FFAHJr4HhgOnQ219ItnbW9 jbypJGGkkWzkMkBVwQFO4nflW3DgbDzXWUUUAFc/pfhj+zdae+N55sMf2r7NF5W1k+0zLNNv bJ3/ADqNuAu1cg7jzXQUUAY+p6Teaha6bIt7BFqdhKLiOY2xaFpDE8TZj3htpWR8DfkHbycE HLXwPDAdOhtr6RbO2t7G3lSSMNJItnIZICrggKdxO/KtuHA2HmusooA5/S/DH9m6098bzzYY /tX2aLytrJ9pmWabe2Tv+dRtwF2rkHcea6CiigCOcTNbyrbyRxzlCI3kQuqtjglQQSM9sjPq K5P/AIQy4uPD32HU7rStRvftf2pri60kSxzvs2FpY3kJLYJwUZNoCKAEXaewooA5ebwd5mo6 Rc/2nPJ/Z8UMXm3K+bcHyjnckuR5bSfdmO0+YgC/LjNSaJ4Rh0TXr7UYJowl08zlI4BG8jSy eYxmcH96VbIjOF2KzD5s5rpKKAMvWtKm1IWU1pcx295Y3H2i3eWIyx7jG8ZDoGUsNsj4ww5w eQCDJpWmf2Pp2nabazbrGytFtgJVzI+wKqNuBAHAbI28kjGMYOhRQBybeB4ZzqMNzfSNZ3Nv fW8SRxhZI1vJBJOWckhjuA2YVdo4O881J/wi15s+2f2nB/bP9of2h5/2Q/Z/M+z/AGbHleZu 2+V28zO/nOPlrqKKAKek6bDo2jWOl27SNBZW8dvG0hBYqihQTgAZwPQVcoooAK5vQfC82g28 6w38ck62UOn2jvbnbHDAH8nzFD5d8yNuIKBuMKnfpK4vwd4ju723vLjVb6NoIbK3urqSQIi2 VywkNxbEgAKIgiHa+XXf8zHIwAaniXwx/wAJBt23n2ffaXFhPmLfut59nmBeRtk/drtY7gOc q2Rg/wCEY/4qX+1Ptn+j/a/t/keV8/2j7P8AZs7848vy/wCHbndzux8tZfjHxHd2VvZ3GlX0 awTWVxdWskYR1vblRGbe2BIIYSh3O1MO2z5WGDmT+29R/wCE9/s77R8n2vyPsOxf+PT7L5v2 rpv/ANf+5358v+HG/mgDQ8NeGP8AhH9268+0bLS3sIMRbNtvBv8ALDcndJ+8bcw2g8YVcHPQ Vx/gfW9R1jz/ALbcfaMWltPN8ir9lu38zz7X5QMeXtj+R8yLv+YnIx2FAGfrWmf2vphtRN5M iyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x8jO 7CjByTuqPxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQtcRtBs7jVb+BGu LuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwKAD/hGP+Kl/tT7Z/o/2v7f5HlfP9o+z/Zs7848v y/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmj+29R/4T3+ zvtHyfa/I+w7F/49Psvm/aum/wD1/wC5358v+HG/msfT/FOsz6DeXEl5uYRWD3U/lJ/xLZZp St3FwML9nQB8SBmTOZCw4oA7jRdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5OhW P4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYoAz9b0z+2dHnsPO8rzdpyV3 o21g2x1yN8bY2uuRuUsMjOaj0jSptI0uysYrmN0hdjLmIhdrbjsiXd+7RWZQoO7aihefvCxq lrd3lg0FlqEmnzs6H7THEkjKocFgA4K5Kgrkg4znBxisfwzrA/4RPSb3WNVjZ9SfNrNdNHE8 yyszwIQoVTL5ZUEKOoOM9aALEmj6lN4sh1SbUbSTT4ExBZNZtviYqQzrJ5mN5zjJQ4XKrjc5 aPwx4Y/4Rz7V/pnn+dsHyxeXv25/ey8nzLh937yXjftX5Rjmvc/2tD47sIU1ud7W6865axME QhS3jiSMqG2mQyGaWNwdwG3cOw3Z/wAOPEN74ks7y+vb37T52y4jSCSOWC2SQuywb1jRvORd odWLY+RsjeQADuKKKp6pa3d5YNBZahJp87Oh+0xxJIyqHBYAOCuSoK5IOM5wcYoAr6PpU2j6 Npunpcxym3QC6meI7rltp3v97h2kO8k7s5bOSdwp/wDCLoPF/wDbySwRsfmfyrVUnkPl+Xse YYLw4w2xgTvVTuwoUR+GdYH/AAiek3usarGz6k+bWa6aOJ5llZngQhQqmXyyoIUdQcZ61XvP E9lZ/Eax0mTxBaIk1lKslg80QK3HmQeV23h3WR8KTggAgcE0Abljpn2TU9Uvnm86S9lRlJXm KNY1URg55UMJHA4AMrcZJJ0K4f4ceIb3xJZ3l9e3v2nztlxGkEkcsFskhdlg3rGjeci7Q6sW x8jZG8gdxQBn61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIj0nSptJsILZLmOQ m4muLp2iI8xpXeRwg3fIPMfIzuwowck7qj8UX1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2Izvl gVG3LAgEVX0LXEbQbO41W/gRri7ltbWeZ1j+2ASusLL0DNIiq42jDbsqAMCgA/4Rj/ipf7U+ 2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MX d+8bcTvIKBhwAh5o/tvUf+E9/s77R8n2vyPsOxf+PT7L5v2rpv8A9f8Aud+fL/hxv5rH0/xT rM+g3lxJebmEVg91P5Sf8S2WaUrdxcDC/Z0AfEgZkzmQsOKAO40XTP7I0wWpm86RpZbiWQLt DSSyNI+1cnC7nbAJJAwCSeToVj+F7641HQYri5k85jLMkc+0Dz4lldYpeMA70VHyoCndlQAQ K2KACsOTQZp/FkOsy3FoqQJsiEFoUuHUqR5cs2874tzM+wKvzBDn5edyuXm1a4Xx3badFqXm B93nWBgCiKERbhIDguzGQgCTIhwGQ4kC7gC5oGj6lp1xf3Oq6jaX9zdOD50Nm0DKoLYTmRxs UNhQNuPmJ3M7MdyuP8C63qOsfb/t9x5+zy5BhFHkM+7dA20Dy5E2jdC29o9wzLJuG3sKAI54 VubeWBzIEkQoxjkZGAIxwykFT7ggjtXN+EPB48JT6qILvzbO7lje3t/3x+zoiCNUzJK+75VU ZAXpj7oVV2PO1j+2PK+w2P8AZn/Px9sfzvu/88vK2/e4+/059q5Pwj4p1XUfGuraPrEcltcR 2UF19haW2YWrFnDIvluzsCpiO5u+WxGHRSAdZqejWur+V9plvo/Kzt+yX89tnOM58p13dO+c c46mqcmj6lN4sh1SbUbSTT4ExBZNZtviYqQzrJ5mN5zjJQ4XKrjc5a5qc2sReV/ZNjY3Wc+Z 9rvHt9vTGNsT579cYwOueMO88T2Vn8RrHSZPEFoiTWUqyWDzRArceZB5XbeHdZHwpOCACBwT QBoaBo+padcX9zquo2l/c3Tg+dDZtAyqC2E5kcbFDYUDbj5idzOzHcrj/Aut6jrH2/7fcefs 8uQYRR5DPu3QNtA8uRNo3QtvaPcMyybht7CgAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACs/TNb07WfN+wXHm+ VgnKMm5WztkXcBvjbB2uuVbBwTg1oVzeg+F5tBt51hv45J1sodPtHe3O2OGAP5PmKHy75kbc QUDcYVO4BqanrenaN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+yvtH+l9Nuxt m7bv8vfjb5mz59md235sbeay9e8Lza9bwLNfxxztZTafduludskM4TzvLUvlHzGu0kuF5yr9 pP8AhGP+Kl/tT7Z/o/2v7f5HlfP9o+z/AGbO/OPL8v8Ah253c7sfLQBoaZrenaz5v2C483ys E5Rk3K2dsi7gN8bYO11yrYOCcGtCuf8ADXhj/hH9268+0bLS3sIMRbNtvBv8sNyd0n7xtzDa DxhVwc9BQBXvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlS CCQyspwVYEEFSAQQQQCKr61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5 pemW9ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmgCx/benf2x/ZX2j/S+m3Y2zdt3 +Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaWPlOC4c4jaNcZkVzwjIGDnhSxqv/wAIx/xU v9qfbP8AR/tf2/yPK+f7R9n+zZ35x5fl/wAO3O7ndj5az7TwL9l0x7U6jukiisrezk8jAjjs 5DJb+Yu79424neQUDDgBDzQB1FjfW+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEVYrP0XT P7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToUAV7++t9M065v7yTy7W1ieaZ9pO1F BLHA5OAD0osb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0rP8AFGh/8JHoMumeZAm+WGXNxB58 beXKkm103LuVtmCMjg0adpN5pOj2NhZ3sH7mXdMZLYshjLFmjiUOPKUZCoCWCKoGGxmgCSTx DpkWtw6O08hvJX8tQsEjIH2GTY0gXYr7FLbSQcYOMEZk0zW9O1nzfsFx5vlYJyjJuVs7ZF3A b42wdrrlWwcE4NZZ8KmXxuniCa6j8iFGMNpEkiZmKCPzpD5hR3Cb0B2A7XAJO1cWPDWj6lo9 vPHqOo2l/JK4ka4is2hklkxhnkJkcMSAoAAUKFCgBQoABuVXv7630zTrm/vJPLtbWJ5pn2k7 UUEscDk4APSrFY/ijQ/+Ej0GXTPMgTfLDLm4g8+NvLlSTa6bl3K2zBGRwaANCxvYtQs47qFJ 0jfOBPA8LjBI5RwGHTuOevSq9xrenWusWmkzXGL67z5USozdFZvmIGEyI5CNxG7Y2M7Tivp2 k3mk6PY2FnewfuZd0xktiyGMsWaOJQ48pRkKgJYIqgYbGay9W8ER33ifT9bs72S0kgvVvLqM yTstwyxiIYVZlRDs4J2nOADldysAbmma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBr Qrn/AAx4Y/4Rz7V/pnn+dsHyxeXv25/ey8nzLh937yXjftX5RjnoKAK99fW+nWcl1dSeXCmA SFLEkkBVVRksxJACgEkkAAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARVfWtM/tf TDaibyZFliuIpCu4LJFIsiblyMruRcgEEjIBB5FfT9JvNL0y3tbW9g8z7XJc3cktsWEnmyPJ KsahxsyznaSX2jAO480AWP7b07+2P7K+0f6X027G2btu/wAvfjb5mz59md235sbearw+KdGn s7m6jvMw2+0sfKcFw5xG0a4zIrnhGQMHPCljVf8A4Rj/AIqX+1Ptn+j/AGv7f5HlfP8AaPs/ 2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5oA6 ixvrfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUzedI0stxLIF2hpJZGk fauThdztgEkgYBJPJ0KACs+41vTrXWLTSZrjF9d58qJUZuis3zEDCZEchG4jdsbGdpxoVyer eCI77xPp+t2d7JaSQXq3l1GZJ2W4ZYxEMKsyoh2cE7TnAByu5WANyy1vTtR1G9sLS4824stv ngIwUbiyjDEbW+aN1O0nDIwOCCK0K5Pw/wCCI/DniW51CyvZP7Peyis4LJ5J5DCsZJX55JmB HzMANg2gjbjLb9zU9C0fW/K/tbSrG/8AJz5f2u3SXZnGcbgcZwOnoKAC41vTrXWLTSZrjF9d 58qJUZuis3zEDCZEchG4jdsbGdpwaZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGsP VvBEd94n0/W7O9ktJIL1by6jMk7LcMsYiGFWZUQ7OCdpzgA5XcrXPDXhj/hH9268+0bLS3sI MRbNtvBv8sNyd0n7xtzDaDxhVwcgHQVTOqWi6ymks8gvHt2uUUxPtaNWCsQ+NpILLlc5G4HG DUep6Fo+t+V/a2lWN/5OfL+126S7M4zjcDjOB09BWfd6NrE/i211iLVLGO1tont1tnsHZzHI 0TSZk84DdmEYO3AzyGoA0NN1qy1eW7jsmnf7LK0MjvbSRoXVmRgrsoV8MjA7ScY9xWhXL+Cv CH/CIacbX7VBL+6ihxa2v2eNvLBHmsm5szNn53z8wVBgbeeooAKKKKACiiigAooooAKKKKAC iiigAooooAK4/Xtb1Gy8V21nBceWp+x/Z7XYp+3ebOyXPUbm8mILJ8hG3dl8qQK7CigCnd3s K2V+8V9aQvaowlmmIZLdggfMg3DACsrEErwRyM5rz+Pxx5/gz7U3ijTZLmHU54LyezuLWKXy vNnEXkiZjEpYIhHmE5jDkFmwa9MooA8/bxNrp1Hwwtyfsc13aWst1Y+SFaWSU4lVEYF5PLHz MA0ZhXDt5oOwegUUUAFcfoOt6je+K7mznuPMUfbPtFrsUfYfKnVLboNy+dEWk+cndtymFBFd hRQBzfiXWBDpul3Ftqsdlpt5cAT6rG0ZWCExSOrh3DRgM6xplgQfMwOSCMNvE2unUfDC3J+x zXdpay3Vj5IVpZJTiVURgXk8sfMwDRmFcO3mg7B6BRQBw/hnxbqWq+Ptb0XU7X7F5FpDNBZt Nbu8I3uCXKSMxZlMTdAF4HdXk7iiigCnqt1d2Wl3FxYafJqF2iZitUlSMyt2G5yAo7k+gOAT gHz/AEzx19t+Hj6tqevR2EianPaPcwfZd8x8xvLjhzI8SEgxjLlhtVmJ2kS16ZRQBxd5Prkj +G7uPxBGr6i9pEbexiie0lYK89w/mOrSFHiRlTaRghST8xI7SiigDn/Fmoy6fa2H/Ew/sy0n u/Ku9Q+QfZY/KkYNukBRcyLGmWBHz4HzEEXND1Ka80bSX1JY7bVLuyS4ltCCjK21PMARjuAV nAOemQD1rUooA8/HibXVutc+yn7bd29pqMv9n+SG+zSQyhbRdqgP+/jJfDEl8ZTavFV77XNW fQJJ7DxLALK31UQprE8kUSXVubcMSZhBJCuJmKbtgB8vZneefSKKAKekzNc6NYzuLsPJbxuw vI1ScEqD+8VQAr+oAABzirlFFABXF+DvEd3e295carfRtBDZW91dSSBEWyuWEhuLYkABREEQ 7Xy67/mY5GO0rP0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NAHP+ONb1HR/I+xXH 2fNpczw/IrfartPL8i1+YHPmbpPkTEjbPlIwcn9t6j/wnv8AZ32j5PtfkfYdi/8AHp9l837V 03/6/wDc78+X/DjfzXQanrenaN5X2+48rzckYRn2quN0jbQdka5G52wq5GSMij+29O/tj+yv tH+l9Nuxtm7bv8vfjb5mz59md235sbeaAOf8D63qOsef9tuPtGLS2nm+RV+y3b+Z59r8oGPL 2x/I+ZF3/MTkY7Cs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oUAY/ii+uNO0G W4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6JaS31zG0k9xPBbSttU3aI8nlyL jAYvEgkyoAIJYAL01L6+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJosb631GzjurWTzI XyASpUggkMrKcFWBBBUgEEEEAigDl/7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6b/APX/ALnf ny/4cb+ax9P8U6zPoN5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDiu4/tvTv7Y/sr 7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBz wpY0AHhe+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtiq9jfW+o2cd1ayeZC+Q CVKkEEhlZTgqwIIKkAggggEVYoAp6pa3d5YNBZahJp87Oh+0xxJIyqHBYAOCuSoK5IOM5wcY rL8J6jLceF9KutR1Dz5tQ3S2sk+xJJY3LyRKVUKvmCHG4KMZViMgZrYv7630zTrm/vJPLtbW J5pn2k7UUEscDk4APSixvYtQs47qFJ0jfOBPA8LjBI5RwGHTuOevSgDk49a1N/iVNps88kdm j7Le1gaPdInkBzPKjRl/K3l4/NWQDeqJtzuJPAniHUNcuNSS6uY7yCBIT9rheGSBp2MnmpC8 RI8pQsZUSfvQHy/Va6T+29O/tj+yvtH+l9Nuxtm7bv8AL342+Zs+fZndt+bG3mq+h+KNJ8R+ Z/Zk077Io5/31pLBujk3bHXzFXcrbGwRkcUAbFU9Utbu8sGgstQk0+dnQ/aY4kkZVDgsAHBX JUFckHGc4OMVcqvf31vpmnXN/eSeXa2sTzTPtJ2ooJY4HJwAelAGP4T1GW48L6VdajqHnzah ultZJ9iSSxuXkiUqoVfMEONwUYyrEZAzWfceKEs/iVb6Nc+IdKW1mtJQtjlUmSfdbiNXYuSz MHkKqFXIzw2AR1FjexahZx3UKTpG+cCeB4XGCRyjgMOncc9elV/7b07+2P7K+0f6X027G2bt u/y9+NvmbPn2Z3bfmxt5oA5/wX4oTWr7WrKfxDpWqXVvdgwfYCqKYPJhJZUDudod2BYsfmyM jgDsKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQBj+KL6407QZbi2k8lhLCk k+0HyImlRZZecgbEZ3ywKjblgQCKj8N6q13olpLfXMbST3E8FtK21TdojyeXIuMBi8SCTKgA glgAvTUvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO6tZPMhfIBKlSCCQy spwVYEEFSAQQQQCKAOX/ALb1H/hPf7O+0fJ9r8j7DsX/AI9Psvm/aum//X/ud+fL/hxv5rH0 /wAU6zPoN5cSXm5hFYPdT+Un/EtlmlK3cXAwv2dAHxIGZM5kLDiu4/tvTv7Y/sr7R/pfTbsb Zu27/L342+Zs+fZndt+bG3mq8PinRp7O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY0AHhe+ uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAECtiq9jfW+o2cd1ayeZC+QCVKkEEhlZ TgqwIIKkAggggEVYoAz9T1G6sPK+zaNfalvzu+yPAvl4xjPmyJ1z2z0OccZw5NeA+IcOjQ69 aPLs3T6axjXy4vLLLt/5aPOWG7g7BEDuUEoz9ZWe+tWSa4ujbp2vmiExVLaRkRDvClpAuxMm NwNxGccUAc38Pta1PWbe7l1aeSS72RPLEjRtBauwbdAMRo6SoRh4nLlAY/mJY12lZ+ma3p2s +b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoAK4v4fa1qes293Lq08kl3sieWJGjaC1dg2 6AYjR0lQjDxOXKAx/MSxrtKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODQBoVxc epamnxKmsZ9Ukks3f/R7CAx7o08gEvKjQh/K3hx5qykb2RMZ3AdpWf8A23p39sf2V9o/0vpt 2Ns3bd/l78bfM2fPszu2/NjbzQBz/gfW9R1jz/ttx9oxaW083yKv2W7fzPPtflAx5e2P5HzI u/5icjHYVn6brVlq8t3HZNO/2WVoZHe2kjQurMjBXZQr4ZGB2k4x7itCgAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACub0HwvNoNvOsN/HJOtlDp9o7252xwwB/J8xQ+XfMjbiCgbjCp36SuL8HeI7u9t7y41W +jaCGyt7q6kkCItlcsJDcWxIACiIIh2vl13/ADMcjABoa94Xm163gWa/jjnaym0+7dLc7ZIZ wnneWpfKPmNdpJcLzlX7Sf8ACMf8VL/an2z/AEf7X9v8jyvn+0fZ/s2d+ceX5f8ADtzu53Y+ Wsvxj4ju7K3s7jSr6NYJrK4urWSMI63tyojNvbAkEMJQ7namHbZ8rDBzcm1a4Xx3badFqXmB 93nWBgCiKERbhIDguzGQgCTIhwGQ4kC7gCx4a8Mf8I/u3Xn2jZaW9hBiLZtt4N/lhuTuk/eN uYbQeMKuDnoK4Pwj4p1XUfGuraPrEcltcR2UF19haW2YWrFnDIvluzsCpiO5u+WxGHRT3lAG frWmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8ivp+k3ml6Zb2treweZ9rkubuSW2 LCTzZHklWNQ42ZZztJL7RgHceaPFF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIB FV9C1xG0GzuNVv4Ea4u5bW1nmdY/tgErrCy9AzSIquNow27KgDAoAP8AhGP+Kl/tT7Z/o/2v 7f5HlfP9o+z/AGbO/OPL8v8Ah253c7sfLWfaeBfsumPanUd0kUVlb2cnkYEcdnIZLfzF3fvG 3E7yCgYcAIeaP7b1H/hPf7O+0fJ9r8j7DsX/AI9Psvm/aum//X/ud+fL/hxv5rHsPF96PDmp X2o6r5Sx2lnJNOIY82d3M7LNaLnaiMhEYAlJMZkDSFl4oA7jRdM/sjTBambzpGlluJZAu0NJ LI0j7VycLudsAkkDAJJ5OhWX4cu2vfD9ncPqNpqLshDXVo6vG5BI4ZQAxGMFgFBIJCpnaNSg DH8UaH/wkegy6Z5kCb5YZc3EHnxt5cqSbXTcu5W2YIyODRp2k3mk6PY2FnewfuZd0xktiyGM sWaOJQ48pRkKgJYIqgYbGasa3caja6PPNpNl9svhtEcOVHVgC3zMoO0EttLLu243DORzfgXx fb6r4YS51HU4zIdTnsIprqe2DTt5jeUo8lthcptGBjdgkAqQxANybRri48R22pS6hvtbbc8N qYRlJGTYcNnG3GSPl3gswEgRmQ2LHTPsmp6pfPN50l7KjKSvMUaxqojBzyoYSOBwAZW4ySTy 82ualJ8UrbRLe/8AMhTdNcWtu9u0cdqIOswP75ZvOkjxjClCpGSHq54H14a/b3lwmvWmqRl1 eNYzGJIgwPLInMaMQSkb7pFA+dyxKoAdZWP4o0P/AISPQZdM8yBN8sMubiDz428uVJNrpuXc rbMEZHBrYrP1u41G10eebSbL7ZfDaI4cqOrAFvmZQdoJbaWXdtxuGcgAr6dpN5pOj2NhZ3sH 7mXdMZLYshjLFmjiUOPKUZCoCWCKoGGxmq//AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7NnfnH l+X/AA7c7ud2PlrL8C+L7fVfDCXOo6nGZDqc9hFNdT2wadvMbylHktsLlNowMbsEgFSGNy5/ taHx3YQprc72t151y1iYIhClvHEkZUNtMhkM0sbg7gNu4dhuALnhrR9S0e3nj1HUbS/klcSN cRWbQySyYwzyEyOGJAUAAKFChQAoUDcrk/B2qXepXutC51GS+RLgtCUVBFBGzybYSPLR0nRQ okjfcV+Q5G4gdZQBn61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIr6fpN5pemW 9ra3sHmfa5Lm7kltiwk82R5JVjUONmWc7SS+0YB3HmjxRfXGnaDLcW0nksJYUkn2g+RE0qLL LzkDYjO+WBUbcsCARVfQtcRtBs7jVb+BGuLuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwKAD/h GP8Aipf7U+2f6P8Aa/t/keV8/wBo+z/Zs7848vy/4dud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ 5GBHHZyGS38xd37xtxO8goGHACHmj+29R/4T3+zvtHyfa/I+w7F/49Psvm/aum//AF/7nfny /wCHG/msfT/FOsz6DeXEl5uYRWD3U/lJ/wAS2WaUrdxcDC/Z0AfEgZkzmQsOKAO40XTP7I0w Wpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToVj+F7641HQYri5k85jLMkc+0Dz4lldYpeM A70VHyoCndlQAQK2KAM/U9C0fW/K/tbSrG/8nPl/a7dJdmcZxuBxnA6egqnJ4faXxZDrbXUY ESYVVt1WYjaV8ppQctBljJ5ZBPmYbdgBRc1ObWIvK/smxsbrOfM+13j2+3pjG2J89+uMYHXP HJ6/4p1XSvH2iWE8cltpt3ei1iAlttt4rQsWc73EgKymMBVUdDy5kRAAbnhrwx/wj+7defaN lpb2EGItm23g3+WG5O6T9425htB4wq4Oegrg/CPinVdR8a6to+sRyW1xHZQXX2FpbZhasWcM i+W7OwKmI7m75bEYdFPeUAZ/9haP/bH9r/2VY/2n/wA/v2dPO+7t+/jd93jr04qn4a0fUtHt 549R1G0v5JXEjXEVm0MksmMM8hMjhiQFAAChQoUAKFAuedrH9seV9hsf7M/5+Ptj+d93/nl5 W373H3+nPtWH4H14a/b3lwmvWmqRl1eNYzGJIgwPLInMaMQSkb7pFA+dyxKoAdZWHHo+pJ4s m1Z9RtJbR08uOCSzYywR7RlEkEm0BnG9jsJbgEkKm3crl5tWuF8d22nRal5gfd51gYAoihEW 4SA4LsxkIAkyIcBkOJAu4APBXhD/AIRDTja/aoJf3UUOLW1+zxt5YI81k3NmZs/O+fmCoMDb z1FcH4R8U6rqPjXVtH1iOS2uI7KC6+wtLbMLVizhkXy3Z2BUxHc3fLYjDop7ygAooooAKKKK ACiiigAooooAKKKKACiiigArPuNc0u01i00ie/gTUrvJgtd+ZHAVmLbRyFwjfMeMjGc4FaFc /r2jaxqep6ZdWGqWNpHYSm4SOewectIY5IzlhMny7ZTxjORnOOKANyeeG1t5bi4ljhgiQvJJ IwVUUDJJJ4AA5zWX/wAJRpP9nfbfOn2+b5HkfZJftHmY3bPI2+Zu2/PjbnZ833easalpn9sa TqmmXs3+i30T24MK7XjjePa3JJBbJYg4A5AwcZOP/wAItebPtn9pwf2z/aH9oef9kP2fzPs/ 2bHleZu2+V28zO/nOPloA1H8R6QlxaQ/bo3N2iPE8YLx7XOIyzqCqhzwhYjeeFyeK1K5NfA8 MB06G2vpFs7a3sbeVJIw0ki2chkgKuCAp3E78q24cDYea6ygArPtdb0691Gewt7jfcQ7sjYw V9p2vsYja+1iFbaTtYgNgnFaFc/pfhj+zdae+N55sMf2r7NF5W1k+0zLNNvbJ3/Oo24C7VyD uPNAGhq2tWWiRQSXrT/6RL5MKQW0k7u+1nwEjVm+6jHp2rP/AOE10HzbGMXU7/boreaF0s5m TZcNshLOE2puYEDcRWhf2V5feHrmw+3/AGe+ntHh+2W6FPLkZCPMRd2RgnIG7I9e9R3eiQ3A 0mGHy7ez0+4WX7KkY8uRUjdY02jAAVijjg4Ma4AIBABYg1WxutUvNMguY5LyySN7mJeTEJN2 zd2BIUnHXGD0Izcrk9A8ER+HfFV/qlneyGzurcRLaSSTyMrb2kZy8kzBiWeQ/dGN3BBLl+so Ar399b6Zp1zf3knl2trE80z7SdqKCWOBycAHpWe/ijSYNDutaupp7Kwtc+dJe2ktuRjHRZFV mzkAYByTgZPFaF/ZpqGnXNlKcR3ETxMdivgMCD8rgqevRgQe4Iri9Q+GkGo+FZtKe8jguGeR 4GtI5YLW1MiCNvLt0lAwU3cMzDdJIejlSAd5RVewt3tNOtraWXzZIokjaTLHeQACfnZm5/2m Y+pJ5qxQBT1LVLTSbdZrt5AHcJGkUTyySNgnCIgLMcAkgA4Ck9ATViCeG6t4ri3ljmglQPHJ GwZXUjIII4II5zWfrWlTakLKa0uY7e8sbj7RbvLEZY9xjeMh0DKWG2R8YYc4PIBBk0rTP7H0 7TtNtZt1jZWi2wEq5kfYFVG3AgDgNkbeSRjGMEAjTxHpD3F3D9ujQ2iO8ryApHtQ4kKuwCsE PDlSdh4bB4qP/hJ9NGnfbXF9HGZfJSOXT7hJpHxuwkRQSPxk/Kp4Vj0U4z7jwteanea0dX1O C4s9RtJbKJIbQxzWkLgAqjmRl55LHZlm25O1FUV/+ELnj8Pf2dbXljBMbv7SrQ2csMNt8mwr bpFMjw5HJIkOS8ueHIAB1kE8N1bxXFvLHNBKgeOSNgyupGQQRwQRzmpKp6TpsOjaNY6XbtI0 Flbx28bSEFiqKFBOABnA9BVygArP0zW9O1nzfsFx5vlYJyjJuVs7ZF3Ab42wdrrlWwcE4NaF c3oPhebQbedYb+OSdbKHT7R3tztjhgD+T5ih8u+ZG3EFA3GFTuAamp63p2jeV9vuPK83JGEZ 9qrjdI20HZGuRudsKuRkjIo/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3ms/xL4Y/ 4SDbtvPs++0uLCfMW/dbz7PMC8jbJ+7Xax3Ac5VsjB/wjH/FS/2p9s/0f7X9v8jyvn+0fZ/s 2d+ceX5f8O3O7ndj5aANDTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1oVz/hrwx/ wj+7defaNlpb2EGItm23g3+WG5O6T9425htB4wq4OegoAr319b6dZyXV1J5cKYBIUsSSQFVV GSzEkAKASSQACTRY31vqNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFV9a0z+19MNqJvJkWW K4ikK7gskUiyJuXIyu5FyAQSMgEHkR6TpU2k2EFslzHITcTXF07REeY0rvI4QbvkHmPkZ3YU YOSd1AEn9t6d/bH9lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY281Xh8U6NPZ3N1HeZht9pY+U4 LhziNo1xmRXPCMgYOeFLGq//AAjH/FS/2p9s/wBH+1/b/I8r5/tH2f7NnfnHl+X/AA7c7ud2 PlrPtPAv2XTHtTqO6SKKyt7OTyMCOOzkMlv5i7v3jbid5BQMOAEPNAHUWN9b6jZx3VrJ5kL5 AJUqQQSGVlOCrAggqQCCCCARVis/RdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5 OhQBT1TVLTRrBr29eRYFdE/dxPKxZ3CKAqAsSWYDAB60aXqlprNgt7ZPI0DO6fvIniYMjlGB VwGBDKRggdKuVl6PpU2j6Npunpcxym3QC6meI7rltp3v97h2kO8k7s5bOSdwAJP7b07+2P7K +0f6X027G2btu/y9+NvmbPn2Z3bfmxt5o0nXNL16KeXSb+C9hgl8mSWB96b9qtgMOG4dehPp 1BFZ/wDwjH/FS/2p9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5aNB0bWNM1PU7q/1Sxu4 7+UXDxwWDwFZBHHGMMZn+XbEOMZyc5xxQB0FV76+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJ JIABJqxWX4h0SHxDok2mz+Xsd45AJYxIhaN1kUOhxuQsoDLkZBIyM5ABcsb2LULOO6hSdI3z gTwPC4wSOUcBh07jnr0qnH4g0+bW5tIi+1vdwvslK2UxiRtgkw0uzywdrKcFu4HU4o0jSptI 0uysYrmN0hdjLmIhdrbjsiXd+7RWZQoO7aihefvCn/wi6Dxf/bySwRsfmfyrVUnkPl+XseYY Lw4w2xgTvVTuwoUAGhpmtWWseabFp5I48ETNbSRxyA5w0bsoWRTjO5CRgg5wRnQrm/CPhGHw lby29vNGYCkcUccMAiXagIEjgEh52Bw8ny7tqfKNvPSUAV76+t9Os5Lq6k8uFMAkKWJJICqq jJZiSAFAJJIABJosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAiq+taZ/a+mG1E3kyLL FcRSFdwWSKRZE3LkZXci5AIJGQCDyI9J0qbSbCC2S5jkJuJri6doiPMaV3kcIN3yDzHyM7sK MHJO6gCT+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbearw+KdGns7m6jvMw2+0sfKc Fw5xG0a4zIrnhGQMHPCljVf/AIRj/ipf7U+2f6P9r+3+R5Xz/aPs/wBmzvzjy/L/AIdud3O7 Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmgDqLG+t9Rs47q1k8yF8 gEqVIIJDKynBVgQQVIBBBBAIqxWfoumf2RpgtTN50jSy3EsgXaGklkaR9q5OF3O2ASSBgEk8 nQoAKy4/EGnza3NpEX2t7uF9kpWymMSNsEmGl2eWDtZTgt3A6nFalc3P4RhuPE8urmaOISoR KbaAQ3MmY/K2NcIQxixhgpG4OqsHAVVABsWGq2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg 8ggXK5fwh4PHhKfVRBd+bZ3csb29v++P2dEQRqmZJX3fKqjIC9MfdCqvUUAZ6a5pcmuNokd/ BJqaRGaS1R9zxoNnLAfd/wBYmM4znIzg1JYarY6m94ljcxzmyuDa3GzkJKFVimehIDDOOhyD yCBj6loGq3/iWHUV1W0is47eW1+zi0kEpilMRkxMsylXzENrBRtz0JGaj8IeDx4Sn1UQXfm2 d3LG9vb/AL4/Z0RBGqZklfd8qqMgL0x90KqgHUVn/wBt6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8 zZ8+zO7b82NvNGp6Na6v5X2mW+j8rO37Jfz22c4znynXd075xzjqapyaPqU3iyHVJtRtJNPg TEFk1m2+JipDOsnmY3nOMlDhcquNzlgC5pmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7XXKtg4 Jwa0Kw9A0fUtOuL+51XUbS/ubpwfOhs2gZVBbCcyONihsKBtx8xO5nZjuUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFcfr2t6jZeK7azguPLU/Y/s9rsU/bvNnZLnqNzeTEFk+Qjbuy+VIF dhRQBn6lf+XpOqS2V3YpdWkT5e6kxDDII96+cRyq4Kse+05rz/8A4SXW77wp9tttY+xW0Wq+ Suo381snn2/kZJaeKKa3X9820MFA+QISHJz6hRQBweo+LhpWo+Exea/aWFpeIrXlpqaRw3m0 28zB5TuCoN6opCoPnGA2DtrvKKKACuP0HW9RvfFdzZz3HmKPtn2i12KPsPlTqlt0G5fOiLSf OTu25TCgiuwooA5Pxlqd2mg6ff6PrdpZW8lwrSXjzokTwtG5X980MqIC3lkMVweFBywzTu/E hg1Hw1ajXfst7exQONO1JIYZZEYgSNP3EhB2okYX97nIZA2zuKKAOP0fxQlx4+1fRLnxDpV1 siiNpa25VHR99x5kZG9meRVRN3THB2rk57CiigCvfzfZ9OuZ/tMFr5cTv59wMxxYBO5xlflH U8jgdR1rzf8A4S691LwhFcWfi3SheW+qzxX91HNHDGIPMuFiyxjnWFW2xFS4O4ADcWYE+oUU AcHceIdTj1LSIo57uHzbeweG0vII0nvTNKUufMULnfDEFkIj2hCxLZXArvKKKAOf8WajLp9r Yf8AEw/sy0nu/Ku9Q+QfZY/KkYNukBRcyLGmWBHz4HzEEXND1Ka80bSX1JY7bVLuyS4ltCCj K21PMARjuAVnAOemQD1rUooA4fT9W1nVda1PTYtS8md4r5GXyEf+zHjmEdq+3AP72NjLiQnf tym1cis+91HXoPD0t1F4m8qBNblgF5qBht2EEaPEyvItu8UebiNirFOVKrkMwFekUUAU9Jma 50axncXYeS3jdheRqk4JUH94qgBX9QAADnFXKKKACuL8HeI7u9t7y41W+jaCGyt7q6kkCItl csJDcWxIACiIIh2vl13/ADMcjHaVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGg Dn/HGt6jo/kfYrj7Pm0uZ4fkVvtV2nl+Ra/MDnzN0nyJiRtnykYOT+29R/4T3+zvtHyfa/I+ w7F/49Psvm/aum//AF/7nfny/wCHG/mug1PW9O0byvt9x5Xm5IwjPtVcbpG2g7I1yNzthVyM kZFSHVLRdZTSWeQXj27XKKYn2tGrBWIfG0kFlyucjcDjBoA5vwPreo6x5/224+0YtLaeb5FX 7Ldv5nn2vygY8vbH8j5kXf8AMTkY7Cs/TNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwT g1oUAY/ii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAio/Deqtd6JaS31zG0k9x PBbSttU3aI8nlyLjAYvEgkyoAIJYAL01L6+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJ osb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAigDl/wC29R/4T3+zvtHyfa/I+w7F/wCP T7L5v2rpv/1/7nfny/4cb+ax9P8AFOsz6DeXEl5uYRWD3U/lJ/xLZZpSt3FwML9nQB8SBmTO ZCw4ruP7b07+2P7K+0f6X027G2btu/y9+NvmbPn2Z3bfmxt5qOz8Q6ZqFvdT2k8kyWyeY4SC Qs6EEq8a7cyI21trIGD4O0nFAEfhe+uNR0GK4uZPOYyzJHPtA8+JZXWKXjAO9FR8qAp3ZUAE Ctiq9hfW+p6dbX9nJ5lrdRJNC+0jcjAFTg8jII61YoAw/F9/faZ4YurrTjGtwrxJ5kjbViRp FWSQsVYKEQs24qwXbkqQCCeGtSFx4f0+W7vJJJ7h3jjkuHjzcMC5zGUVFdCqFkYKN0YDYHNa l/fW+madc395J5draxPNM+0naigljgcnAB6UWN7FqFnHdQpOkb5wJ4HhcYJHKOAw6dxz16UA c/c/2tD47sIU1ud7W6865axMEQhS3jiSMqG2mQyGaWNwdwG3cOw3V/BfihNavtasp/EOlapd W92DB9gKopg8mEllQO52h3YFix+bIyOAOkOqWi6ymks8gvHt2uUUxPtaNWCsQ+NpILLlc5G4 HGDUema3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBoA0Kw/F9/faZ4YurrTjGtwrxJ5 kjbViRpFWSQsVYKEQs24qwXbkqQCDuVHPPDa28txcSxwwRIXkkkYKqKBkkk8AAc5oAx/DWpC 48P6fLd3kkk9w7xxyXDx5uGBc5jKKiuhVCyMFG6MBsDms+TVZ4viHDp8GsR3UEqYuNPWSKR7 U+WWDNGqB40OE/eNIwzIF2fMrL0Gl6paazYLe2TyNAzun7yJ4mDI5RgVcBgQykYIHSq8niHT Itbh0dp5DeSv5ahYJGQPsMmxpAuxX2KW2kg4wcYIyAYfgfW9R1jz/ttx9oxaW083yKv2W7fz PPtflAx5e2P5HzIu/wCYnIx2FZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBrQoA x/FF9cadoMtxbSeSwlhSSfaD5ETSossvOQNiM75YFRtywIBFR+G9Va70S0lvrmNpJ7ieC2lb apu0R5PLkXGAxeJBJlQAQSwAXpqX19b6dZyXV1J5cKYBIUsSSQFVVGSzEkAKASSQACTRY31v qNnHdWsnmQvkAlSpBBIZWU4KsCCCpAIIIIBFAHL/ANt6j/wnv9nfaPk+1+R9h2L/AMen2Xzf tXTf/r/3O/Pl/wAON/NY+n+KdZn0G8uJLzcwisHup/KT/iWyzSlbuLgYX7OgD4kDMmcyFhxX cf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHOI2j XGZFc8IyBg54UsaADwvfXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+VAU7sqACBWxVexvrfU bOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsUAZ+p6jdWHlfZtGvtS353fZHgXy8YxnzZE65 7Z6HOOM8/ceKEs/iVb6Nc+IdKW1mtJQtjlUmSfdbiNXYuSzMHkKqFXIzw2AR2FZ/9t6d/bH9 lfaP9L6bdjbN23f5e/G3zNnz7M7tvzY280Ac/wCB9b1HWPP+23H2jFpbTzfIq/Zbt/M8+1+U DHl7Y/kfMi7/AJicjHYVn6brVlq8t3HZNO/2WVoZHe2kjQurMjBXZQr4ZGB2k4x7itCgDh18 W6kPirb+Hru1+x2M1pcGCN5rcvOVKFZjiQuFIEqhAueNxz8wiseB9b1HWPP+23H2jFpbTzfI q/Zbt/M8+1+UDHl7Y/kfMi7/AJicjHQf23p39sf2V9o/0vpt2Ns3bd/l78bfM2fPszu2/Njb zRZa3p2o6je2FpcebcWW3zwEYKNxZRhiNrfNG6naThkYHBBFAGhXDvq2rW3xCurWS+nu7U5a 20u0aISBRbhtzpJEp8suHAlE23eyIcfNjuKz31qyTXF0bdO180QmKpbSMiId4UtIF2JkxuBu IzjigDm/AXinUPEV14ht9Ujjhu7G9VPs6SwuLdWiT91mN2LFXEgLnGTnAUhkTtKz9M1vTtZ8 37Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABXN6D4Xm0G3nWG/jknWyh0+0d7c7Y4YA/k+YofLvmRtxBQNx hU79JXF+DvEd3e295carfRtBDZW91dSSBEWyuWEhuLYkABREEQ7Xy67/AJmORgA0Ne8Lza9b wLNfxxztZTafduludskM4TzvLUvlHzGu0kuF5yr9pLvRtYn8W2usRapYx2ttE9uts9g7OY5G iaTMnnAbswjB24GeQ1U/F/iey0rSdKvYfEFpZi4vbRo2M0W26t2mjWXG4HKCOQsWXBGAcgZz HZ+JYNQ8dmxtPEdjJAIlP2PfFmUNEJFMQz5kjYO8yA+WEKqFZtzoAanhrR9S0e3nj1HUbS/k lcSNcRWbQySyYwzyEyOGJAUAAKFChQAoUDcrk/CXiey1nVtfsofEFpqZgvS1qsc0TMLcwwsc eWBuRZHddxyeMEkiusoAz9a0z+19MNqJvJkWWK4ikK7gskUiyJuXIyu5FyAQSMgEHkV9P0m8 0vTLe1tb2DzPtclzdyS2xYSebI8kqxqHGzLOdpJfaMA7jzR4ovrjTtBluLaTyWEsKST7QfIi aVFll5yBsRnfLAqNuWBAIqvoWuI2g2dxqt/AjXF3La2s8zrH9sAldYWXoGaRFVxtGG3ZUAYF AB/wjH/FS/2p9s/0f7X9v8jyvn+0fZ/s2d+ceX5f8O3O7ndj5ar6R4WvNCs5lstTgN0tpbWF rJPaFkjt4C/lh1EgLyYkfLBlB+UhRgg1/wC29R/4T3+zvtHyfa/I+w7F/wCPT7L5v2rpv/1/ 7nfny/4cb+ay9L8S6veeGtZmk1ONLhdMhl+0ywjZp1/KJBJA+1TtSFhEWDhmQMS5I6AHWeF9 JvNB8PWek3l7BefY4kt4ZYbYw/u0RVXcC75bgkkEDnoK2Kx/C13LfeHLS4mnnuJG3gzTFCZc Ow3qyIitGcZRgq7kKtjJrYoAp6rZNqOl3FmkscZmTbmWFZUYd1dG4ZGHysMgkE4KnBFPTtJv NJ0exsLO9g/cy7pjJbFkMZYs0cShx5SjIVASwRVAw2M1Y1u41G10eebSbL7ZfDaI4cqOrAFv mZQdoJbaWXdtxuGcjm/Avi+31XwwlzqOpxmQ6nPYRTXU9sGnbzG8pR5LbC5TaMDG7BIBUhiA al3o2sT+LbXWItUsY7W2ie3W2ewdnMcjRNJmTzgN2YRg7cDPIajwx4Y/4Rz7V/pnn+dsHyxe Xv25/ey8nzLh937yXjftX5RjmnPql3/wsi3sF1GQ2Ztwp0+JUDiTbIxmkVo9xgI8tRIjgCQB CCScU/hx4hvfElneX17e/afO2XEaQSRywWySF2WDesaN5yLtDqxbHyNkbyAAdxVPVNLtNZsG sr1JGgZ0f93K8TBkcOpDIQwIZQcgjpVys/W7jUbXR55tJsvtl8Nojhyo6sAW+ZlB2gltpZd2 3G4ZyADH8M+GdT8NaOLGPV4Lhn1CS6lkmgmfdG7FmRd87FWyThskdyrMWYyHwqZfG6eIJrqP yIUYw2kSSJmYoI/OkPmFHcJvQHYDtcAk7Vxn+BfF9vqvhhLnUdTjMh1Oewimup7YNO3mN5Sj yW2Fym0YGN2CQCpDGvr/AIp1XSvH2iWE8cltpt3ei1iAlttt4rQsWc73EgKymMBVUdDy5kRA Abnhjwx/wjn2r/TPP87YPli8vftz+9l5PmXD7v3kvG/avyjHPQVyfgfXhr9veXCa9aapGXV4 1jMYkiDA8sicxoxBKRvukUD53LEqnWUAZ+taZ/a+mG1E3kyLLFcRSFdwWSKRZE3LkZXci5AI JGQCDyK+n6TeaXplva2t7B5n2uS5u5JbYsJPNkeSVY1DjZlnO0kvtGAdx5o8UX1xp2gy3FtJ 5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgEVX0LXEbQbO41W/gRri7ltbWeZ1j+2ASusLL0DNI iq42jDbsqAMCgA/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OPL8v+Hbndzux8tZ9p4F+y6Y9 qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5o/tvUf8AhPf7O+0fJ9r8j7DsX/j0+y+b 9q6b/wDX/ud+fL/hxv5rH0/xTrM+g3lxJebmEVg91P5Sf8S2WaUrdxcDC/Z0AfEgZkzmQsOK AO40XTP7I0wWpm86RpZbiWQLtDSSyNI+1cnC7nbAJJAwCSeToVj+F7641HQYri5k85jLMkc+ 0Dz4lldYpeMA70VHyoCndlQAQK2KAM/U9C0fW/K/tbSrG/8AJz5f2u3SXZnGcbgcZwOnoKpx 6PqSeLJtWfUbSW0dPLjgks2MsEe0ZRJBJtAZxvY7CW4BJCptuanNrEXlf2TY2N1nPmfa7x7f b0xjbE+e/XGMDrnjDvPE9lZ/Eax0mTxBaIk1lKslg80QK3HmQeV23h3WR8KTggAgcE0ASeCv CH/CIacbX7VBL+6ihxa2v2eNvLBHmsm5szNn53z8wVBgbeeorj/Aut6jrH2/7fcefs8uQYRR 5DPu3QNtA8uRNo3QtvaPcMyybht7CgDHm0a4uPEdtqUuob7W23PDamEZSRk2HDZxtxkj5d4L MBIEZkOX4f8ABEfhzxLc6hZXsn9nvZRWcFk8k8hhWMkr88kzAj5mAGwbQRtxlt+fr/inVdK8 faJYTxyW2m3d6LWICW223itCxZzvcSArKYwFVR0PLmREEnw48Q3viSzvL69vftPnbLiNIJI5 YLZJC7LBvWNG85F2h1Ytj5GyN5AAO4rDk8PtL4sh1trqMCJMKq26rMRtK+U0oOWgyxk8sgnz MNuwAo3K4PX/ABTqulePtEsJ45LbTbu9FrEBLbbbxWhYs53uJAVlMYCqo6HlzIiAA3PDXhj/ AIR/duvPtGy0t7CDEWzbbwb/ACw3J3SfvG3MNoPGFXBz0FcP8OPEN74ks7y+vb37T52y4jSC SOWC2SQuywb1jRvORdodWLY+RsjeQO4oAKKKKACiiigAooooAKKKKACiiigAooooAKz7jXNL tNYtNInv4E1K7yYLXfmRwFZi20chcI3zHjIxnOBWhXP69o2sanqemXVhqljaR2EpuEjnsHnL SGOSM5YTJ8u2U8YzkZzjigDcnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1l/8JRpP9nfbfOn2 +b5HkfZJftHmY3bPI2+Zu2/PjbnZ833easalpn9saTqmmXs3+i30T24MK7XjjePa3JJBbJYg 4A5AwcZOP/wi15s+2f2nB/bP9of2h5/2Q/Z/M+z/AGbHleZu2+V28zO/nOPloA1H8R6QlxaQ /bo3N2iPE8YLx7XOIyzqCqhzwhYjeeFyeK1K5NfA8MB06G2vpFs7a3sbeVJIw0ki2chkgKuC Ap3E78q24cDYea6ygArPtdb0691Gewt7jfcQ7sjYwV9p2vsYja+1iFbaTtYgNgnFaFc/pfhj +zdae+N55sMf2r7NF5W1k+0zLNNvbJ3/ADqNuAu1cg7jzQBoatrVlokUEl60/wDpEvkwpBbS Tu77WfASNWb7qMenas//AITXQfNsYxdTv9uit5oXSzmZNlw2yEs4Tam5gQNxFaF/ZXl94eub D7f9nvp7R4ftluhTy5GQjzEXdkYJyBuyPXvUd3okNwNJhh8u3s9PuFl+ypGPLkVI3WNNowAF Yo44ODGuACAQAWINVsbrVLzTILmOS8skje5iXkxCTds3dgSFJx1xg9CM3K5PQPBEfh3xVf6p Z3shs7q3ES2kkk8jK29pGcvJMwYlnkP3RjdwQS5frKAI554bW3luLiWOGCJC8kkjBVRQMkkn gADnNZf/AAk+mjTvtri+jjMvkpHLp9wk0j43YSIoJH4yflU8Kx6KcXNW02HWdGvtLuGkWC9t 5LeRoyAwV1KkjIIzg+hrn7bwdNZabbJaXtpBeWt6b23EVkVs4WMTQlEgD5VCruxAf/WMWzgl aAOognhureK4t5Y5oJUDxyRsGV1IyCCOCCOc1JVPSdNh0bRrHS7dpGgsreO3jaQgsVRQoJwA M4HoKuUAU9S1S00m3Wa7eQB3CRpFE8skjYJwiICzHAJIAOApPQE1YgnhureK4t5Y5oJUDxyR sGV1IyCCOCCOc1n61pU2pCymtLmO3vLG4+0W7yxGWPcY3jIdAylhtkfGGHODyAQZNK0z+x9O 07TbWbdY2VotsBKuZH2BVRtwIA4DZG3kkYxjBAC11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WI VtpO1iA2CcVTbxfof9l2upRXcl1aXdw9tbvaW8twZpE37gqxqxYDynO4DGFznGKpzeE59U1H VG12+gvtPvYmt47eKGW3kghJU+WJVl+62358KC5wCdqqoz7L4fvb+F20i6vbHUJBqEl8jX9i 1zDlyxKtHLK7cb2IKupzgncS+8A7SCZbm3inQSBJEDqJI2RgCM8qwBU+xAI71JVewtfsOnW1 n9onuPIiSLzrh98km0AbnbuxxknuasUAFZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2 DgnBrQrm9B8LzaDbzrDfxyTrZQ6faO9udscMAfyfMUPl3zI24goG4wqdwDU1PW9O0byvt9x5 Xm5IwjPtVcbpG2g7I1yNzthVyMkZFH9t6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82Nv NZ/iXwx/wkG3befZ99pcWE+Yt+63n2eYF5G2T92u1juA5yrZGD/hGP8Aipf7U+2f6P8Aa/t/ keV8/wBo+z/Zs7848vy/4dud3O7Hy0AaGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2Dg nBrQrn/DXhj/AIR/duvPtGy0t7CDEWzbbwb/ACw3J3SfvG3MNoPGFXBz0FAFe+vrfTrOS6up PLhTAJCliSSAqqoyWYkgBQCSSAASaLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqvr Wmf2vphtRN5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaIjzGld 5HCDd8g8x8jO7CjByTuoAk/tvTv7Y/sr7R/pfTbsbZu27/L342+Zs+fZndt+bG3mq8PinRp7 O5uo7zMNvtLHynBcOcRtGuMyK54RkDBzwpY1X/4Rj/ipf7U+2f6P9r+3+R5Xz/aPs/2bO/OP L8v+Hbndzux8tZ9p4F+y6Y9qdR3SRRWVvZyeRgRx2chkt/MXd+8bcTvIKBhwAh5oA6ixvrfU bOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsVn6Lpn9kaYLUzedI0stxLIF2hpJZGkfauThd ztgEkgYBJPJ0KAK99fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkkAAk0WN7FqFnHdQpOkb5w J4HhcYJHKOAw6dxz16VT8Q6JD4h0SbTZ/L2O8cgEsYkQtG6yKHQ43IWUBlyMgkZGcg0jSptI 0uysYrmN0hdjLmIhdrbjsiXd+7RWZQoO7aihefvAAI/EGnza3NpEX2t7uF9kpWymMSNsEmGl 2eWDtZTgt3A6nFGieINP8Q2/2jTvtbQbEdZJrKaBZFYZUoZEUOCBnK56j1FU/wDhF0Hi/wDt 5JYI2PzP5VqqTyHy/L2PMMF4cYbYwJ3qp3YUKDwx4XTw39qEUsHlzbFWG1tVtowFzh2RPlMz bsO6hQwVAFULQB0FV76+t9Os5Lq6k8uFMAkKWJJICqqjJZiSAFAJJIABJqxWX4h0SHxDok2m z+Xsd45AJYxIhaN1kUOhxuQsoDLkZBIyM5ABcsb2LULOO6hSdI3zgTwPC4wSOUcBh07jnr0q umuaXJrjaJHfwSamkRmktUfc8aDZywH3f9YmM4znIzg1HpGlTaRpdlYxXMbpC7GXMRC7W3HZ Eu792isyhQd21FC8/eFO70bWJ/FtrrEWqWMdrbRPbrbPYOzmORomkzJ5wG7MIwduBnkNQBY0 PxRpPiPzP7MmnfZFHP8AvrSWDdHJu2OvmKu5W2NgjI4rYrPsdM+yanql883nSXsqMpK8xRrG qiMHPKhhI4HABlbjJJOhQBXvr6306zkurqTy4UwCQpYkkgKqqMlmJIAUAkkgAEmixvrfUbOO 6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKr61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAg kZAIPIj0nSptJsILZLmOQm4muLp2iI8xpXeRwg3fIPMfIzuwowck7qAJP7b07+2P7K+0f6X0 27G2btu/y9+NvmbPn2Z3bfmxt5qvD4p0aezubqO8zDb7Sx8pwXDnEbRrjMiueEZAwc8KWNV/ +EY/4qX+1Ptn+j/a/t/keV8/2j7P9mzvzjy/L/h253c7sfLWfaeBfsumPanUd0kUVlb2cnkY EcdnIZLfzF3fvG3E7yCgYcAIeaAOosb631GzjurWTzIXyASpUggkMrKcFWBBBUgEEEEAirFZ +i6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2rk4Xc7YBJIGASTydCgArPTXNLk1xtEjv4JNTSIzSWq PueNBs5YD7v+sTGcZzkZwaNT0a11fyvtMt9H5Wdv2S/nts5xnPlOu7p3zjnHU1n3ejaxP4tt dYi1SxjtbaJ7dbZ7B2cxyNE0mZPOA3ZhGDtwM8hqANDTNastY802LTyRx4Ima2kjjkBzho3Z QsinGdyEjBBzgjOhXN+EfCMPhK3lt7eaMwFI4o44YBEu1AQJHAJDzsDh5Pl3bU+UbedTU9Gt dX8r7TLfR+Vnb9kv57bOcZz5Tru6d845x1NAB/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO 7b82NvNGma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBqnJo+pTeLIdUm1G0k0+BMQW TWbb4mKkM6yeZjec4yUOFyq43OWj8MeGP+Ec+1f6Z5/nbB8sXl79uf3svJ8y4fd+8l437V+U Y5AOgrH/AOEo0n+3v7E86f7d5vkY+yS+X5nleds83bs3eX82N2cVsVl6hoq6lqlrdTTyeRBb zxeShZTvk2ASq6kFHVRIoYc4lbBHOQCxYarY6m94ljcxzmyuDa3GzkJKFVimehIDDOOhyDyC Bcrl/CHg8eEp9VEF35tndyxvb2/74/Z0RBGqZklfd8qqMgL0x90Kq9RQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAVx+va3qNl4rtrOC48tT9j+z2uxT9u82dkueo3N5MQWT5CNu7L5UgV2F Z91renWWowWFxcbLibbgbGKpuO1N7AbU3MCq7iNzAhckYoANSv8Ay9J1SWyu7FLq0ifL3UmI YZBHvXziOVXBVj32nNcHH4vvm8ITX8upyW9mmp/Z4r2eexS5uIfJDZik3fZXcSll442I4/1i 16RPPDa28txcSxwwRIXkkkYKqKBkkk8AAc5rL/4SjSf7O+2+dPt83yPI+yS/aPMxu2eRt8zd t+fG3Oz5vu80AcnceK9cjvfC6SmOK+v7eyebS4vKV1aRwLgyxSHzgiISUMedpSQyfKK9ErDu PF+h217bWjXckj3KQPFJBbyyxFZ3KREyopRQ7AgEkZrcoAK4/Qdb1G98V3NnPceYo+2faLXY o+w+VOqW3Qbl86ItJ85O7blMKCK7Cs+11vTr3UZ7C3uN9xDuyNjBX2na+xiNr7WIVtpO1iA2 CcUAZ/iW7J8MtrFlrU9rY2sTX009hHDM9xAsTNtjMgZOTtYHBzjHGcjm73xT4h0PxF4Q0bVY 4wly8dvd3gltwL+YwMHMas6sqLIYz9wFidox8qydpq2tWWiRQSXrT/6RL5MKQW0k7u+1nwEj Vm+6jHp2qu3ifTV1G3sMXzXU8UUwRdPuG8tJCwQyEJiLJVvv7cbTnGKAMPQfEOoX3jrUdLe5 ju7aFJzM0DwyQ2zLKiwouw+YjlGfeJRy8bbPlU12lU4NVsbrVLzTILmOS8skje5iXkxCTds3 dgSFJx1xg9CM3KAKerXM1no19dW8ckk8NvJJGkcJmZmCkgBAylySPu7hnpkda8/vPFmp2nw0 1XUh4i02O7tnmS2vphHPHdkQ71SGRfKSV92VDCMAFGQo5Us3pE88Nrby3FxLHDBEheSSRgqo oGSSTwABzmseXxj4cg8P/wBvS6xaJpZeREuWfCyMhYME7uco2Aud2OM0Aaljf2ep2cd5YXcF 3ayZ2TQSCRGwSDhhwcEEfhVis+61vTrLUYLC4uNlxNtwNjFU3Ham9gNqbmBVdxG5gQuSMVoU Ac/4s1GXT7Ww/wCJh/ZlpPd+Vd6h8g+yx+VIwbdICi5kWNMsCPnwPmIIuaHqU15o2kvqSx22 qXdklxLaEFGVtqeYAjHcArOAc9MgHrVjUtUtNJt1mu3kAdwkaRRPLJI2CcIiAsxwCSADgKT0 BNWIJ4bq3iuLeWOaCVA8ckbBldSMggjggjnNAHF23iOW/wDFviHw8nivShdG0RdOFuib7eYt cBwULsZZECRlhwOB8q5Oadzreo6b4aury58QyfZf7YltY7m4ezgu/KjBjZELqkBfz4nb5h/q if4sAdhD4k0W4uNSgh1S0d9MQPfFZQVthlx87dFI8t8gnIxzjIqP/hKNJ/s77b50+3zfI8j7 JL9o8zG7Z5G3zN2358bc7Pm+7zQBY0K6vL7w9pl5qNv9nvp7SKW4h2FPLkZAWXaeRgkjB5Fa FRwTw3VvFcW8sc0EqB45I2DK6kZBBHBBHOakoAK4vwd4ju723vLjVb6NoIbK3urqSQIi2Vyw kNxbEgAKIgiHa+XXf8zHIx2lZ+ma3p2s+b9guPN8rBOUZNytnbIu4DfG2Dtdcq2DgnBoA5/x xreo6P5H2K4+z5tLmeH5Fb7Vdp5fkWvzA58zdJ8iYkbZ8pGDk/tvUf8AhPf7O+0fJ9r8j7Ds X/j0+y+b9q6b/wDX/ud+fL/hxv5roNT1vTtG8r7fceV5uSMIz7VXG6RtoOyNcjc7YVcjJGRR /benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNAHP+B9b1HWPP8Attx9oxaW083yKv2W 7fzPPtflAx5e2P5HzIu/5icjHYVn6Zrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtC gDH8UX1xp2gy3FtJ5LCWFJJ9oPkRNKiyy85A2IzvlgVG3LAgEVH4b1VrvRLSW+uY2knuJ4La Vtqm7RHk8uRcYDF4kEmVABBLABempfX1vp1nJdXUnlwpgEhSxJJAVVUZLMSQAoBJJAAJNFjf W+o2cd1ayeZC+QCVKkEEhlZTgqwIIKkAggggEUAcv/beo/8ACe/2d9o+T7X5H2HYv/Hp9l83 7V03/wCv/c78+X/DjfzWPp/inWZ9BvLiS83MIrB7qfyk/wCJbLNKVu4uBhfs6APiQMyZzIWH Fdx/benf2x/ZX2j/AEvpt2Ns3bd/l78bfM2fPszu2/NjbzVeHxTo09nc3Ud5mG32lj5TguHO I2jXGZFc8IyBg54UsaADwvfXGo6DFcXMnnMZZkjn2gefEsrrFLxgHeio+VAU7sqACBWxVexv rfUbOO6tZPMhfIBKlSCCQyspwVYEEFSAQQQQCKsUAYfi+/vtM8MXV1pxjW4V4k8yRtqxI0ir JIWKsFCIWbcVYLtyVIBBPDWpC48P6fLd3kkk9w7xxyXDx5uGBc5jKKiuhVCyMFG6MBsDmtDV NUtNGsGvb15FgV0T93E8rFncIoCoCxJZgMAHrUljexahZx3UKTpG+cCeB4XGCRyjgMOncc9e lAHNyarPF8Q4dPg1iO6glTFxp6yRSPanyywZo1QPGhwn7xpGGZAuz5lZa/gTxDqGuXGpJdXM d5BAkJ+1wvDJA07GTzUheIkeUoWMqJP3oD5fqtdJ/benf2x/ZX2j/S+m3Y2zdt3+Xvxt8zZ8 +zO7b82NvNSWGq2OpveJY3Mc5srg2txs5CShVYpnoSAwzjocg8ggAFysPxff32meGLq604xr cK8SeZI21YkaRVkkLFWChELNuKsF25KkAg7lU9U1S00awa9vXkWBXRP3cTysWdwigKgLElmA wAetAGf4a1IXHh/T5bu8kknuHeOOS4ePNwwLnMZRUV0KoWRgo3RgNgc1l/23qP8Awnv9nfaP k+1+R9h2L/x6fZfN+1dN/wDr/wBzvz5f8ON/NdRY3sWoWcd1Ck6RvnAngeFxgkco4DDp3HPX pVf+2rI6x/ZaNPJdDhzFbSPHGdu7a8gUojbcHazA4ZePmGQDD8G6rPqFxqkLaxHrFpA6G3vY pIplYMXG0yRJGm8BVJj2kpvB3uHAXrKy9E8Qaf4ht/tGnfa2g2I6yTWU0CyKwypQyIocEDOV z1HqK1KAMfxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARUfhvVWu9EtJb65ja Se4ngtpW2qbtEeTy5FxgMXiQSZUAEEsAF6al9fW+nWcl1dSeXCmASFLEkkBVVRksxJACgEkk AAk0WN9b6jZx3VrJ5kL5AJUqQQSGVlOCrAggqQCCCCARQBy/9t6j/wAJ7/Z32j5PtfkfYdi/ 8en2XzftXTf/AK/9zvz5f8ON/NY+n+KdZn0G8uJLzcwisHup/KT/AIlss0pW7i4GF+zoA+JA zJnMhYcV3H9t6d/bH9lfaP8AS+m3Y2zdt3+Xvxt8zZ8+zO7b82NvNV4fFOjT2dzdR3mYbfaW PlOC4c4jaNcZkVzwjIGDnhSxoAPC99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAI FbFV7G+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIqxQBn6nqN1YeV9m0a+1Lfnd9keBf LxjGfNkTrntnoc44zy6+LdSHxVt/D13a/Y7Ga0uDBG81uXnKlCsxxIXCkCVQgXPG45+YRdxR QBxfw+1rU9Zt7uXVp5JLvZE8sSNG0Fq7Bt0AxGjpKhGHicuUBj+YljXSanqN1YeV9m0a+1Lf nd9keBfLxjGfNkTrntnoc44yaZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGtCgDj/ AO29R/4WP/Zn2j/RP9X9k2Lv2+T5nn7cbvL3/J52/bu/d+Vu/eVJ4N14a3caoIdetNYtoHRR LEYwyy5cSbUTlYMqBHvJc7XO512Mdz+29O/tj+yvtH+l9Nuxtm7bv8vfjb5mz59md235sbea ksNVsdTe8SxuY5zZXBtbjZyElCqxTPQkBhnHQ5B5BAALlcXf+LBpvxBTS5ta02WB7KYx6ZGY 47j7QDB5aMzyYLuHfYMIMHndjI7SigDi/AXinUPEV14ht9Ujjhu7G9VPs6SwuLdWiT91mN2L FXEgLnGTnAUhkTtKz9M1vTtZ837Bceb5WCcoyblbO2RdwG+NsHa65VsHBODWhQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAVz+qeGP7S1pL4XnlQyfZftMXlbmf7NM00OxsjZ87HdkNuXAG0 810FFAFO7tZruyv7aVrSRJ0ZIkmty6BSgGJF3fvBu3Egbcggdsnl4/ARTQZrKTUI7q4nvfts /wBtikubaZhGIgkkUkrO6BVVgGkOHVWBwAo7SigDHk0HzLXRLVr2eaHTZUkkNyfMe72RMq+Y eMsHKSbsH5kBwDgjYoooAK5/S/DH9m6098bzzYY/tX2aLytrJ9pmWabe2Tv+dRtwF2rkHcea 6CigDD1zQG17Rra1uzps9xC6yk3enrPbu4UqSYmbIHzEjDgg4+YjIanN4NSTUdIuhe75LCKG JrqeBZLxxEcjbcDDLvyRICGDKSAELMT1FFAHJ6B4Ij8O+Kr/AFSzvZDZ3VuIltJJJ5GVt7SM 5eSZgxLPIfujG7ggly/WUUUARziZreVbeSOOcoRG8iF1VscEqCCRntkZ9RXHt4R15/CeqaC+ u6bs1B7ktMNLcFFuGleUAef13SfKewXkNnI7SigDk73wle6vqWm3mp6jaE2rxSTmztpYGnaK Uyxg/v2UoCF+V1fGXKlS+R1lFFAGXrWlTakLKa0uY7e8sbj7RbvLEZY9xjeMh0DKWG2R8YYc 4PIBBk0rTP7H07TtNtZt1jZWi2wEq5kfYFVG3AgDgNkbeSRjGMHQooA5O78KalqWqazJfata HT9Vsv7PmggsWSVYR52zbIZWG8eeckoQcDCii58HTXum3KXd7aT3l1ei9uBLZFrOZhEsIR4C +WQKiMAX/wBYobOAFrrKKAK9ha/YdOtrP7RPceREkXnXD75JNoA3O3djjJPc1YoooAK5vQfC 82g286w38ck62UOn2jvbnbHDAH8nzFD5d8yNuIKBuMKnfpK4vwd4ju723vLjVb6NoIbK3urq SQIi2VywkNxbEgAKIgiHa+XXf8zHIwAaniXwx/wkG3befZ99pcWE+Yt+63n2eYF5G2T92u1j uA5yrZGD/hGP+Kl/tT7Z/o/2v7f5HlfP9o+z/Zs7848vy/4dud3O7Hy1l+MfEd3ZW9ncaVfR rBNZXF1ayRhHW9uVEZt7YEghhKHc7Uw7bPlYYOZP7b1H/hPf7O+0fJ9r8j7DsX/j0+y+b9q6 b/8AX/ud+fL/AIcb+aANDw14Y/4R/duvPtGy0t7CDEWzbbwb/LDcndJ+8bcw2g8YVcHPQVwf hHxTquo+NdW0fWI5La4jsoLr7C0tswtWLOGRfLdnYFTEdzd8tiMOinvKAM/WtM/tfTDaibyZ FliuIpCu4LJFIsiblyMruRcgEEjIBB5Eek6VNpNhBbJcxyE3E1xdO0RHmNK7yOEG75B5j5Gd 2FGDkndUfii+uNO0GW4tpPJYSwpJPtB8iJpUWWXnIGxGd8sCo25YEAiq+ha4jaDZ3Gq38CNc XctrazzOsf2wCV1hZegZpEVXG0YbdlQBgUAH/CMf8VL/AGp9s/0f7X9v8jyvn+0fZ/s2d+ce X5f8O3O7ndj5az7TwL9l0x7U6jukiisrezk8jAjjs5DJb+Yu79424neQUDDgBDzR/beo/wDC e/2d9o+T7X5H2HYv/Hp9l837V03/AOv/AHO/Pl/w4381j6f4p1mfQby4kvNzCKwe6n8pP+Jb LNKVu4uBhfs6APiQMyZzIWHFAHcaLpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ 0Kx/C99cajoMVxcyecxlmSOfaB58SyusUvGAd6Kj5UBTuyoAIFbFAGfrelprWjz2EggKybSB cW6zxkqwYB424ZcgZHBx0KnDCPSNKm0jS7KxiuY3SF2MuYiF2tuOyJd37tFZlCg7tqKF5+8D xJczWfhrUpra6jtLgW7iG5lUskEhGFd+GwisQWJBAAJPANU9B1qAeHNOuNS1LEl1KbeOa8li BuZC7BfLZVRZFbGYyqgum1sZJoAr3ng8ah4qGr3F35dum5kgtPOgdpGhMJd2Eu1m2OwDqiuM IN+FwTwh4PHhKfVRBd+bZ3csb29v++P2dEQRqmZJX3fKqjIC9MfdCquXr/inVdK8faJYTxyW 2m3d6LWICW223itCxZzvcSArKYwFVR0PLmREGh4H14a/b3lwmvWmqRl1eNYzGJIgwPLInMaM QSkb7pFA+dyxKoAdZWfrelprWjz2EggKybSBcW6zxkqwYB424ZcgZHBx0KnDDQrL8SXM1n4a 1Ka2uo7S4Fu4huZVLJBIRhXfhsIrEFiQQACTwDQAaRpU2kaXZWMVzG6QuxlzEQu1tx2RLu/d orMoUHdtRQvP3hnx+EYYfGc3iGKaNHmfzZdsAEzt5Qi8tpc8wbVV/LK/6wBt3G2pNB1qAeHN OuNS1LEl1KbeOa8liBuZC7BfLZVRZFbGYyqgum1sZJqneeJ7Kz+I1jpMniC0RJrKVZLB5ogV uPMg8rtvDusj4UnBABA4JoAueGPC6eG/tQilg8ubYqw2tqttGAucOyJ8pmbdh3UKGCoAqha6 CuH8Aa5qWvXmsS3N/wDbbSDyYRLG9u8H2rDNMIGi+Zodrw7fMJbqD8wYV3FAGfrWmf2vphtR N5MiyxXEUhXcFkikWRNy5GV3IuQCCRkAg8iPSdKm0mwgtkuY5Cbia4unaIjzGld5HCDd8g8x 8jO7CjByTuqPxRfXGnaDLcW0nksJYUkn2g+RE0qLLLzkDYjO+WBUbcsCARVfQtcRtBs7jVb+ BGuLuW1tZ5nWP7YBK6wsvQM0iKrjaMNuyoAwKAD/AIRj/ipf7U+2f6P9r+3+R5Xz/aPs/wBm zvzjy/L/AIdud3O7Hy1n2ngX7Lpj2p1HdJFFZW9nJ5GBHHZyGS38xd37xtxO8goGHACHmj+2 9R/4T3+zvtHyfa/I+w7F/wCPT7L5v2rpv/1/7nfny/4cb+ax9P8AFOsz6DeXEl5uYRWD3U/l J/xLZZpSt3FwML9nQB8SBmTOZCw4oA7jRdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkD AJJ5OhWP4XvrjUdBiuLmTzmMsyRz7QPPiWV1il4wDvRUfKgKd2VABArYoAKKKz/O1j+2PK+w 2P8AZn/Px9sfzvu/88vK2/e4+/059qAKegaPqWnXF/c6rqNpf3N04PnQ2bQMqgthOZHGxQ2F A24+Ynczsxuano1rq/lfaZb6Pys7fsl/PbZzjOfKdd3TvnHOOprD8D68Nft7y4TXrTVIy6vG sZjEkQYHlkTmNGIJSN90igfO5YlU3NTm1iLyv7JsbG6znzPtd49vt6YxtifPfrjGB1zwAY95 4PGoeKhq9xd+XbpuZILTzoHaRoTCXdhLtZtjsA6orjCDfhcE8IeDx4Sn1UQXfm2d3LG9vb/v j9nREEapmSV93yqoyAvTH3QqrX/tvUf+Fj/2Z9o/0T/V/ZNi79vk+Z5+3G7y9/yedv27v3fl bv3lV/hx4hvfElneX17e/afO2XEaQSRywWySF2WDesaN5yLtDqxbHyNkbyAAdxRRRQBz/hjw x/wjn2r/AEzz/O2D5YvL37c/vZeT5lw+795Lxv2r8oxz0FcP8OPEN74ks7y+vb37T52y4jSC SOWC2SQuywb1jRvORdodWLY+RsjeQO4oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDg4/H l9aaTHcarY6at3Je3kKW8OoYaZIJjGUhVlDyzk8KgADYyWjLBar2/jDV5RpVk4jE+t3rXGkX EbBjcWK3Ku4dCgEZ+yvleSxAOdr4B6SPwVoMMGnwx2s6x2HliAfbJukb+ZGr/P8AvFRuVV9w XJwACak03wfoGji0Gn6bHB9kdXg2ux2ERvGOp6bZJDg8FpHb7zFiAV/CPiw+K7eW4GmXdpBs jmgkmikVZYpASvLooLgDLBN6DcuHbPGO3jLV746RJZaRJC9xqZtzayyhHKm2nkCTh1DwupSN 3AVvlKlGl3YPUWfhzSLG3ureGxjMF0nlyxykyKYsECIBiQsQDMBGMIu44Ayar/8ACIaGbeWF 7SSUTW89tK8txK8kscwQSB3ZizEiOMBiSQEABAGKAObj+Jvm+FLbxImkZ0+5tJXiP2n52uIo JJnjxs4jHkyJ5h5LLwhUhjJD8SlvbjVY9P0W7u0sXdA0O6QuMwCKTbGrERSec7qw3Exwsyq3 KjpJPC2jTf2gJLPct/FJDOvmvt2Sf6wIM4j3n5m2bdzAMckA1GfB+gAXHk6bHbPO8TtLau0E imOMRJsdCGQBAVAUgYZh/E2QC5Y3lxqehx3UQgt7ieImNg4uIc87XUqR5kZ4YcqSpGQpyByb eJbvTfA1re3mpyPd/wBpvHJNLCm8Qw3LvOjhFCb0toZgxQYJQ7CxKk9hNpVjPpY0xraNbNUV Eii/diMLjZs24KFSAVK4KkAjBAqu3hzSJLK1s5LGOW3tnd0jlJcMzo6OXyT5hZZZNxbO4sSc nmgDHk8XX0d+ulPo0cerTvEbaCS8/d7JEndfNkVDscLbS5VVcZ2AMQSVy9b8d3sFrPp0VpBa 63LaLDHBFex3E9teyxAxB4wCBD5jpH5rkAuy5Xawc9R/wi+k/wBnfYvJn2+b5/n/AGuX7R5m Nu/z93mbtvyZ3Z2fL93iq8fgnw7DdJPDp/leVt8qGOaRIYdsscvyRBtiZkijY7VG4jnOTkAj 8ReKJtEvYobewjvES3a5usXBR413okaIoRt8srMwjQld5QgHrjPuvHjWNuv2mxtBdrcSwtEm oKRcGMJuS1yoe4ly4QIEUeYjoWUhd3SXWiadeXUlzcW++aT7Pvbewz5Epli4B/hdiffODkcV Tk8IaHLNDMbSRXiuPtP7u4lQPJ5xnBcKwEgErM6q2QpY4AyaAMvUvF8trbXktxa/YoNP1CG1 vLhrpAE33MSryVI2mCRZXJxtDhQ27cY49O8a6lcPZpfaDHbPM9uk0Ud20ksTTruSPaYlzKi5 eVDjy48OC2cDUn8E+HbmIxzafvVojCxM0mWUrMpLHdksRcz5Y/MTISSTgi5P4c0i6llkuLGO Yy3BuZFkJZXkMH2ckqTggxfLtxjvjPNAGfoHi+21q3v7p/skNpaIJWuobxJ4FQhjteRfkWVA uXUFgoZCGYNWOnxDubWyS91nRY7GCNIpLxY7l5ZYBIm8Js8pS0sahpJU4KR4cb84HSQeF9Jt 9J1HTBDPJa6lv+1ie7lmeXdGIzl3Yt9xVHB4xxUk/hzSLqWWS4sY5jLcG5kWQlleQwfZySpO CDF8u3GO+M80AYcvjLUYtYi0YaBv1abZKlst4p8u3dZdssrbfl2vEFkC7wvmLtaQkKbHhjxb P4p066urXSJ7fEST2ZullijnVwSgLtGMNx82wSKAylWfPGpYeHtM024jubaCT7RGkiefLPJL IwkMZbe7sS5/dRgFiSAgAwBio4vC2jQ6dfWCWeLW+iME6GVz+6IIESknKRgM21Fwq7jtAyaA OP0rx1rsr6dc3mnQSQ3mn6TNKsM4WO3a6uJYtykqXZiDEdh+UBHG/OC+hb+PrjUWt4tL02xu 5rq7WCF11MNAAYppdryxo4EyiE7o1DAeYhDkNXQf8ItozJsls/PUy+cwuJXl8x/s/wBmJfeT vzF8p3Zz1OTzVePwVoMcV3GbWeX7XFNDO895NK8iTLGkgLu5blYYx1428Y5oAp3niW7u/BWj 6jp1vImoa0lube3iKM48xRLIEZ8JvWJZWUvhSUGQc7ST6vfah4c8Mi3v47e71x4Va+s48rGP Ia4do0lU8MsTIN4yu8EglcHoNS0u01a3WG7SQhHDxvFK8UkbYIyjoQynBIJBGQxHQkVTvvC2 jajpMml3NnusWwFhSV0EQEYj2x7SPLXZlSqYBDMCPmbIByejfETUtS0izvDpmmlLi3V1uHv2 iiLLAks8jfu28uCNmaIvubEgVCBkkXE8e3lwzSW+jQRWrZEMl/emBl2RJJM8yiNhFHGWaJmB bbKAhAzkdBL4W0aeCWGSz3RyxXcLjzXGUuXEk46/xMAfbtgUS+FtGnglhks90csV3C481xlL lxJOOv8AEwB9u2BQBX1XxQmneHrLVHigtGvPLCR6vdLZLCWQvtlY7irAAjaqsd3bGWGXb+Pm lhTUZtHkt9IneWG3kmuFjuDLFC8siyROFWIKYpoyWk+8gONp3DoNb8PaZ4ht/I1KCSRNjxkx TyQsUcYdCyMpKNgZUnB2jIOBjP0/wTpFpFfCe3jnnvLieeSYAxsPMnaYFSDlXU+WN64Y+TGc jYu0A5+/+I99/Y19Npek2l5cWllc3UlzBe+dZxiNVZHEgVTIjZkUYAJkhkTorOvoEBma3ia4 jjjnKAyJG5dVbHIDEAkZ74GfQVhp4K0GOxmtFtZxHN5Zkf7ZN5jFJnnVvM379wlkd92c5PXA FdBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVi+ KtQsdL0F7nU7e2nsTcW8Uy3JURqskyIXYsCMLu3c/wB3qOtbVU9T02HVbVLedpFRLiC4BQgH dFKsqjkHjcgB9s9OtA02ndHG2mqeAbpr1xp2iNa292beO4t7dJ0kURRSPKSikJGhlCs5O1cD LDIFWFuPAkbxxXen6JFPLdvaxxpaBtzi4aBFOYxhmZW4x/BIQWVGarWtfD3Rtcupru5Mn2iW 4M+9oYJgm6KKNlVJo3TBEEZzjcCDggEg073wM76xbNZHy7T7Wl5c3D3rB3dbt7oIYVj2uoaS RRlxjzclXaNDU8kext9ar/zv72ZOm+JfBGqN4fht/DdlJdar5PmpFppkS28yKRx+8EW18PEy HBGNrk48tgDR/EvgjU9Unsrjw3ZWTLKsMPm6ad8rNczW6nYYgVXMceWPyq0oQkMOemt/Atha T6TNb3t9HJpcVrDCQ0Z3pAk0ahsoc7luJA2MdsbSKLbwLYW9xfy/bb6T7Zdw3RV2jxEYrp7p VXCA7TJI+dxJwcAjFHJHsH1qv/O/vZRsL34ean9mNlY6XMlzs8uVdN/d/NgJucptXc2UG4jL qyDLqyine6z4CsLi3M2jaeLCa3uZxdHTT8/kmLJjXy8yoVlL70yu1GOSASNYfD3RkuNEuEMn n6RbwW0UkkMErSRwnKAmSNihBLHdHsJ3deFwL8PtK+z3sb3F20t9b3kF1OPLRpjciISSEKgQ PiFACFA6khiSaOSPYPrVf+d/eyrfy+DbZ2ht9D0u4uEu4LYp9iVFbfcRwOyOU2yeW0gDBSdr YVtpNGmS+DdY1y506x0PS544YoHE8VkrAtJ5xAYBPlXbCGVydriRCOGUtpSeCtNkuzO093hb gXEEW9dsDG4S5kC/LkiSWJGbcWx0XYOKk0bwjZ6DeG4sby+CmKG3MMkodBDEJRHGMrlVUS9i CdikkkuXOSPYPrVf+d/ezJ0HT7XU9T1O1v8Awb4dtI7CUW7yQOJy0hjjkGFMCfLtlHOc5GMY 5q5Z2fhbUbe6ls/Cyu9um7yptFNq0hwcKnnogJOMdcDIyRmt6y02GxutRuImkL39wLiUMRgM Io4sLx02xqec8k/QU4vC+j2enX1npNjBpH22IxSTaZElvIOCAwZR95dxIJzg0ckewfWq/wDO /vZgwW+jPoeo3svhPREuLCV4ZNvlfZiVxucTsi/u0yQ7bcqY5AFYqAc9L3RZ7rRIovBmlrDf 7vPu5rcrbRYlEaeXMISknmHc0eSm8bOhkUV1Vr4dktdLXTxrepGKNNsJRYITFjZs2iOJRhNn CkFSGYMGGAK8nhKGK1hW3uLubyX+0NbTXAWK7uBKZ1eQhDsPnEuTGFByAVZVVQckewfWq/8A O/vZDqsHgfRLi3t9S07SLaS4R3iD2SfMqFd5yFwAocMSeihmOFViJLrT/ClnqMFjL4chaabb taHRXljGTgbpEjKLyOdxGBycDmodS8ITa1caSNV1CS4jttMuLO9liYwNdNKYA2UX5djrHICM /LuBXDAMu5daFo99qMGo3mlWNxfQbfJuZrdHkj2ncu1iMjBJIx0NHJHsH1qv/O/vZzOzw9/w kv8AZf8Awiul/Z/tf2Dz/Ij3/aPs/wBpxs2Y8vy/4t2d3G3HzVHZP4budSngk8KaeluEu3t5 I7RZZJBayiGbdGqZB3sNgUuWHXacKd6fwvbTatLqKXd3BK7mdVjKFUuDD5HnjcpO8RfLtJKd yhPNSWHhuz07VpdQiknZj53kxOw2QedIJJtuACd7qGO4tjGF2jijkj2D61X/AJ397ORtdQ8P XMGlXA8IaWsN1aWFzc/uo90P2xzHCqDZ+8w4O4kphcEbj8ouas/hvS9N8S3/APwimnyxaGm1 lW0UtNL5Sy7cKh2ptkj+fnGXJAC5OtB4K023TTI0nu/Ksbe1t2jLri5W2bdAZDtyCjkt8hXJ OG3DAqSTw39qTxPZXUn+g65yXibEke63SB1wQRwI1YNzkuQVG3LHJHsH1qv/ADv72ZPiVPD3 h/bt8K6XcbLS4v58wRptt4NnmFfkO6T94u1TtB5yy4Gc+bUPD0VxJH/wiGllZZZbexbyowZZ I7qO0fzRs/dr5sqYK7yU3EgEBT1WseF7bXHZru7u8OkkDKhQBreRUEsH3c7HMakt98HO11HF V5fBWmyzTytPdjc7yWyh1xaSPMtw7x/LyTNGkmJN4BXAAUlSckewfWq/87+9mbpCeHtSv7ez l8K6XDJNFcnKwRuBJbTiCZfuD5dzIUbqwJyqEAHoP+EW8Pf9AHS//AOP/Cqth4b/ALP1qzuY pM29raXMe523STzXEySyuwACr80efl4JkIAQKAego5I9g+tV/wCd/ezJ/wCEW8Pf9AHS/wDw Dj/wo/4Rbw9/0AdL/wDAOP8AwrWoo5I9g+tV/wCd/ezJ/wCEW8Pf9AHS/wDwDj/wo/4Rbw9/ 0AdL/wDAOP8AwrWoo5I9g+tV/wCd/ezJ/wCEW8Pf9AHS/wDwDj/wo/4Rbw9/0AdL/wDAOP8A wrWoo5I9g+tV/wCd/ezJ/wCEW8Pf9AHS/wDwDj/wo/4Rbw9/0AdL/wDAOP8AwrWoo5I9g+tV /wCd/ezJ/wCEW8Pf9AHS/wDwDj/wrH17TNJ0xtMgsPCmiXd1f3ZtkWdFgRcRSSliwjc9IiMY 7111ZetaKNZFky393Yz2Vx9ohntRGWDeW8ZBEiMpBWRu3pRyR7B9ar/zv72YNtJ4HuTbRto+ nwzzuY2ik09D5EgkaLZIyqUQmRHRSWw7KQhaq51T4bBygt9Id96IFj08MWZ1kaMLhPmLrE5Q DO8FCud6btKfwDokuradqUUXk3Fjs2sYopjIFkMg3NMjsGLs7F1KuxcksSARn+G/Az2Gpxaj fHyGtfJS0tYb1rlI0ijuI0G940woS5KhQo/1YYszMxJyR7B9ar/zv72TCbwE9u80emafIFdV VI9LLyTbgSrRIE3SowVyHQMpCOQSFYi5Z2Pg3ULw21lpOl3LCJZjLDYK8IVgCo80Ls3FWVgu 7cVYNjBzWanwq8Ox6dNYxptgaWOWFfslqfJKB1XrCfN+WRlzN5h5znd81bmmeFrLTNUj1FZJ JriGySwgLRRRiKFduVURovBZQ2DkKc7AoJBOSPYPrVf+d/ezmbvU/Bwv9Js9N8O6feyaherb Z/s5kVIijt5wIhYOhCHawwj4YhtqOVr6P4h8C6ibyO70HT7KeG4kSK3bTHMskSyPGsm0wqQW dCu0bsOUTO9gtdNp/grTdOurK4jnu3exdBbB3XEcMcU0UcPCjKKs8hBOXJI3M2MVHJ4C0ea2 1S3mM8seo4MqyFGCsLma5UgFcHEk7cMGUhVBBGcnJHsH1qv/ADv72QoPAkktrGum6WWucBf+ JcMRksUCynZ+6YurIFfaS6soBYEVnw6n4En1G9gi0fS5obeKB1kt7ETSTPKZsIkSIWb5IfMB UHcjbx8vzG9D8ONFt7rS7pDi407aEk+xWnzqsrSKNvk7Y8M7cxBGOckkgESW/wAP9Ns3Etnf alBPGkKWsomVzarEsqII1dWXAjnePDA5HzH58uTkj2D61X/nf3sotqfw5XVI9OFhp8k8rxoj xaS0kTGTytmJVjKYPnw87sDzFz1Fan9n+FP7Y/sv/hHIftH9/wDsV/J+7u/13l+X0/2uvHXi oY/h5osFxFLbvdwpC8TRRLICqCM2m1eQSR/oMI5JPL88jG5/YWj/ANsf2v8A2VY/2n/z+/Z0 877u37+N33eOvTijkj2D61X/AJ397ORtJdBnvLC3l8IaXH9v1W80+Fkt1cBLcS5kY+UFVmaE gJnocgnawFjZ4e/4SX+y/wDhFdL+z/a/sHn+RHv+0fZ/tONmzHl+X/Fuzu424+atLSPDs0KQ jUDGr2WsXmoWht5SwdZmmxvBUYIW4YYGeVB3ckVHfeE5d0l5YajONSOJEln2bFuTELc3ZUJ8 0giyPL4jbH3VJ3A5I9g+tV/5397G6lp/hvTtW0bTz4a0+R9TuJIQ4slCxhIXkJLbCM/IAFJB OSRnaa5e117w3e6M2qQ+GfDK28jxeRJNKqJEjq7B7lzDiIfIY8r5g83MecqSPRr3TYb66064 laQPYXBuIgpGCxikiw3HTbIx4xyB9Dhw+BbC2ij+zXt9DcW/lLZ3KtGXtIo1kSOJAUKsqpNK uXV2O8ksSFIOSPYPrVf+d/ezJ1a48Pabp1peL4Osv3uny6pPDc2scMkFvEIzINu05mHmqAhw CQ2XXAzqeH9K0TWdJ+13HhjSLadbi4t5Io4UlUNFM8RIYopIJTPQdamufBWm3NlBZCe7itIL drJIUdcC0ZI0e3yVJ2N5SktnzAc4cDirnhjT7zTNFMF+IFupLu6uXWCQyIvmzyShQxVScBwM 4HSjkj2D61X/AJ397Hf8It4e/wCgDpf/AIBx/wCFH/CLeHv+gDpf/gHH/hWtRRyR7B9ar/zv 72ZP/CLeHv8AoA6X/wCAcf8AhR/wi3h7/oA6X/4Bx/4VrUUckewfWq/87+9mT/wi3h7/AKAO l/8AgHH/AIUf8It4e/6AOl/+Acf+Fa1FHJHsH1qv/O/vZk/8It4e/wCgDpf/AIBx/wCFH/CL eHv+gDpf/gHH/hWtRRyR7B9ar/zv72ZP/CLeHv8AoA6X/wCAcf8AhR/wi3h7/oA6X/4Bx/4V rUUckewfWq/87+9mT/wi3h7/AKAOl/8AgHH/AIUf8It4e/6AOl/+Acf+Fa1FHJHsH1qv/O/v Zk/8It4e/wCgDpf/AIBx/wCFH/CLeHv+gDpf/gHH/hWtRRyR7B9ar/zv72ZP/CLeHv8AoA6X /wCAcf8AhViz0XStOmM1jplnaysu0vBAqMR1xkDpwPyq9RQoRWyFLEVpK0ptr1YUUUVRiFFF FABRRRQAVy/xCtXvPB00Mdv9ozd2bPGbRroFFuYmYtEvzSKFBJUdQDXUVj+KNc/4RzQZdT8u B9ksMWLifyI18yVI9zvtbaq78k4PAoA5e0bVIV0nT9LM9lpMsTC6l0/QvsgtXMpERjhmBKby XEhKy4Cq2Iw++uX0TUvHGm+G4LK3hvkW30RktY59Pl8zelqSMKLfaJFnUxrvkG5FH7tiyyP6 Rp3i3TrmCxW5vLH7Xef6iOxna6jm+cofKkCL5m3GXwP3Y5bC4Ylt428O3unLfWmofaYW2eWs EMkkkm8EjZGql34V87QcGOQHBjcKAc3Dba/N8Q4BqF/qSxw297Zw3VtYKsTF47WXdkxuFG5p Au5iP9HRSWbf5nJ+G4fGOi+DpJ9ObVUk5jFrLpe2QumkDBIZMlVnijjTAGSCGMhYY9I1nx5o 2lQRvBP9vkkltkCWiPNlJnQbwUVs4WRWwOu+IcGVN1weMNANu9wdSjSJHVd0iMm5WBIkXIG6 Iqrt5i5TajtuwjEAHNnUfGFh4qmtLq48yyWJ2ScafNLG6+SX3rHFEcMJcoEacMUUDazssjZ9 ivi46kmpsb6GR4tMjnD26SvcIb+dWy3lIFUQSb2Xy0ddybipVg3YWPi7Tp9H0S9vG+yTataQ 3MUGGkx5jQpt3BezzxLk4+9noDiQeMNANu9y2pRxW6OqtPMjRxhWBKSb2AHlNtIWTOxiMKxP FAHFyXPjG7Gn3AsZ7q+tZTOqXFt5SRXpsrwSQg4XNurmBVkJIPmH943bc8Ei+/tHWZb9tSBu L15ITc2Xki4iFvaosr8YV8LjGUyTJmNSpWPQ1bxrpmm6bfXCGSW4trKS7WCSKSHcyxGXyizL hJSg3eWfnC/MVxWg/iHTI7fU7lp5Bb6YjPdT+RIY1Chi+19uHK7WDBCSpGDg8UAcn4Og8St8 MYLBxPZ6zbRW8MYkC26ogjiICM8Un8Bw+5G/eeYowAuLmq2N8dB0NPEEMmpQQ3rPqkSp9sEs RjmCbo44U80CRoTgRcEBj90tWwfGGgC3S4GpRvE7su6NGfaqgEyNgHbEFZG8xsJtdG3YdSZI vFOjTQX00V5vjsJTBcFYnO2UOY/KHHzSbhgIuWO5MAh1yAc+v/CXWOmeEbVPPaY2kEOpFtkp 84SWxkLuc/8ALJbv5s4JwMlimacOq+NDpckkUF3LqAe2/cy2ShBcNv8AtUAz5YaCNArRuXAZ sL5r5210EPjbSHl1UTXEaQWKPOs0ZMqzW6QQTPKCoxgC5QYGSeoz2j1Tx3pOmanZ2jee8ct2 9tPdC3lEEG2OR2bzdhjbaY9rDd8nzFsBGwAcn4lvPFV3Hq+mRxXdxptxo80dv5lnL5lypsyQ 5VLcKk5myu1pE4GBECVY9ZpWr3i+I9Rg1KSf7LPdrbaczwGJHdUld0VDGG4SPJkLuj9UK8oL kfi/Q5bi0thdyLdXbskNtJbypMWUpuBjZQykCRHwwHyHf90FhYvNRsYNZtbf7NJc6mybUEMG 9oYnYbmd+kaHZn5iN/lEKGZcUAc/4T1fX9S1y/W98yXT47i+iV2t1RUEd0Y4QjA/OSomDZwR 5SZUZ3y6nhi08RWv2r+37v7Ru2eT/pUc23ru+5bQ4/h67vw709E8ZaRceGtP1aO0ktbG7Tzp 5YIS8FtM4Ekgd1A43O+6XGwMjh2VuK3LrWbWz1GCxlivmmm27WhsJ5Yxk4G6REKLyOdxGByc DmgDj47K8g8CyaZNDfXEx1W4nuA9sS09qupbpiwVQrb4WY7AP3isQqkcVl2mk6kt9aMdPuwT cQNpDGFh9ktxqE0kqg4/0cG0aFSjbCygR4JXaO01TxP9k1qz0mxs/tl1cSvA7tL5cMEnkSTI jthjuYR9ACVVgxxlA5pGs6xq2g3N4ul2Md9HdzW0dub9zG/lSmJmMnk5XlHIGw8BemTgA4uy g1LSPAvia4EV3Yzw+HEE8hVombUkinNxMDwXckxEzDIfAwzbeOsubL+zfGWmXkMM66ZaaJdw iG3tt0cGJLYqqKi7tzKpAQZyI/lHXNeDx1v0HQ76TTs3ep/ZJHtoJ962sNxKsaSSOVX++MLj LMGAyqs62PB/iez1yJ7O0s4LSO2tLa4hhglDCO3mVjCrqAPLk2pkxjIAK4ZgeADk7vSdSa+u 2Gn3ZIuJ21dhCx+1251CGSJScf6QBaLMoRd5VSY8AttJaaTqS31ox0+7BNxA2kMYWH2S3GoT SSqDj/RwbRoVKNsLKBHgldo9UooA5/wZ8vh94hxHDqF9BEg6RxpdyoiKOyqqqoA4AAA4FdBU cEENrbxW9vFHDBEgSOONQqooGAABwABxipKACiiigAooooAKKKKACuT8caNNrh8P2kVtaToN TLy/bbI3UCKLacZkj3LkbioBJGGK/Q9ZWfqup/2X9ikeHdbz3cdtLLux5PmZVGxglsyGNMDp v3HhTQB53c6DrVh4glkgtJBaWdxBd5soTHFsUafGTFGCTnyoL1PLXcwQleRIu+vovh7xNYaC lpLHHFBDqei+faGzaSVzFHYLI6SrJt2KY2yQrD92/Pp1jfELTh4evNTVN00VpPeQQ5YJOiI0 sY8zbtWR4Qsvln51VslcAmtS68X6HZxLJNdyYZ5UVY7eWRmaOdLdwFVSSRLIi4A5zkZHNAHB /Day8Uw6toC63DOtrYeH5LNfMtjGEkMlvIBnaP8Alk0UfPO+CUY4LN3Hhi08RWv2r+37v7Ru 2eT/AKVHNt67vuW0OP4eu78O8dr498N3lkt5DfyGBn27ntZkKrsRzIwZAViCyRsZCAgDrlhk Vc13xBFonhfU9b+zzzLYxSv5PlOrOyEjH3SQpI+/grt+b7vNAHLppGop4KtbUtfPPD4lE8ga Fd9xH/ahbe4CcKVIkyoUcA/dyCa5aXl948sb5LD7Xbx/ZktEutOMipIlzILlw7DEGIyrhvlM rJFtZgpVukvddu7LW47H+yZJopkk8kwzoZpWRN5YRnAWL7se9nH7xlBUBgxy7nxrcWenNcXG lweZDdvayiO+BjmdQD5dsxUNPMclAm1fnjkUspUbgDQubO4PxD0y9Bne1XSruJhsHlxuZbYj 5gM7mAPBPSPgD5s83JY6lF8PbjSlW7kvG1O5mkaezaTzrcaiWkaREChw8TE+WuDIpYKCK3NQ 8WXGnX9xazaV8x8sWuLgMSXnSCMzYB8lXd8qQXJRJDgMhSq8PjrzbiOP+zsLFLFb3zefkxSS XUlonlDb+8XzYnyW2EJtIBJKgAp3el3cnwa1bTbW0ktJTZXi20FraJE8seZDHmILhXlTaWUK pBkbAQ8Antrm28eS372d2XjvTcNcx27vnThY7fKDqDuH2n5vIBLFvnCfxV1n9s2v9sf2X5V9 9o/v/YJ/J+7u/wBds8vp/tdeOvFZepeLDY3Gv28GmXd3PpNlDcrHFFIWuZJTKFjQBCSMxqC6 7gNxzjY1AGX4csLyDxhPNJaTxSf8TD7fO8ZUT77lGs8ueJdsAcDBbyx8p2k4rm9MvPiDIlut 3fazm5S2WRm06FTAXbTzIy/ucAgT3g+bIAiORlCa7SHxTePBpc8mmQNDeSrBI0F2XZZGdk2R o0auzIFLSh1jMaq33irAZ9h8Q/7VaG2stPge/u/s72sTXmEEc0Us0fnsEJik8uByUCuBujwx DEqAU9GuvGE/gqX7dc6kuqSXunoszWUayxxSramfanl7cIZJxkqdu05+6ax7G8+INzb6dFcX 2swu7wtPKNOhDASDT96nMOAE8+8I4yPLO4nYa7DSfHEOs3lj9nsZFsL144I5pJAJRM9qLsAx gEbPKON28nfxtx81aDeL9DUzqLuSSSF9jRRW8skjN5kseERVLOd1vNkKDgRlvu80Acve3XjC 50nwl5NzqVpcXFlC+pvDZRlhK01oj7w8bBCEluGwAMbCTwpFZen6j4+nvNMnubjVUjb7H9og OnxqhyNPEu4mLcM+fdk4Ix5RxgIwr0D/AISrQvP8n+04N3lfaM5O3yNm/wA7d08nHHm52bvl 3buKz7nxbo1zKtlcR+ZY3FpcNcLcwOrqUaBPJaBl3lnFwuFIy2VwG3igDH8XWkHiHwzol1bi +1hVi+12AeyimgvbgxYhFyhjyiuHb5gI0XLEshCVJr1tc33jPTrq3s7t0R4EikNu4KmO4fz/ AC5CALcbAC5bIuI9qJjGT1F5r9nY29rPNDqTJcpvjEOm3ErAYB+dUQlDyOGAPX0OKeq+J/7M 8Q2WlfY/M+0eX8xl2ySb3KfuUwfN2Y3y8rsQhvmzigDHvrFLf4jprf2O+uZ44tkzTWKyxwWi wyNvtnRN/mGVthTcXYO3yFQjA8N2F5B431KeW0njVvtPnStGVL7plMPmSni5+QHy9uPITMbZ LZqRvHjKJ4jY2iXC3Hkqz6gv2eIiOWV0nmVWEcqJA5dFDhS0fzENuFy/8ZJb+HtK1u2svMtd QiSdEnnWKRgyB1ijT5jJcMD8sY4YqwLrxkA5vTtLu7DRvFVtpFpqS3mo28hhvLm0SC7fUHWd 3R5Y1RSiHYUk5TdIQshGALF1bLB8O/Gn2azkstHmSZrSFrdoPJtvs0ayskLBSpDidghCB25y A++us1DxTo2laidPvLzZeCJZvIWJ3coxZVICg5yyFBjqzIv3nQML4p0Z/LIvPleJ5nYxOFt0 XduMxxiHBR1/ebfmRl6qQADk0n8UWJuLV0u7LR7e4FuJLGxR5LeESXTIYI1Rt48v7ChwjgBn 6MrlbA1Pxp5r7rOQTjTFlSEWymE6l5BLWxk3ZEAO1g/ILkr5wx5Z1IfFujWktnZW0fkWK2kz bTA8L25iaBEh8gqHDMJ02rgE/LtB3CukgmW5t4p0EgSRA6iSNkYAjPKsAVPsQCO9AHl9tfeJ ZTHqNz9r8+3sr6CK5/s+4eQb5LIqGH2aMlyfM2ssJUBclZNjg9J4chli8W3Us9t5N/c6JYSa kHKFxMGmVctGAsjHDqzAKB5abQQ2E7Co44IYXmeKKNHmffKyqAXbaFy3qdqqMnsAO1AElFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFZ+taSmt6YbKS5ntv3sUyTQbd6PHIsikblZfvIOoNaFc v8QrV7zwdNDHb/aM3dmzxm0a6BRbmJmLRL80ihQSVHUA0AXF8NBr2yvrjVtSuL2yQpFO7Rod rOGkDKiKjBwEUgqcBFK7Xyx5+w+GGmnwrY6bqskk95Bb2yGZitwsTRIy4RZkZWTMs5AdTjzO NoVAhpk2pWc2iWmnRSQaOyE3n2PQ2tEgbzj5YSKT50EhLiQ4kwFVsRB/MrD0/wAReOb3wbaX 4SeXz4rSSa6FqI5E3xyF/KVI5tynFqdwjfmaThNpWEA6yTwDZMmyDU9StE2QEpbmJU86FoCk 2zy9gcC2iXAGwANhRuJMenfDfRNLieKzaeBPNSSHyFiheEKrptEkaK7ZjldNzszjduDB/mrm 9dvPE2p2scN9/aVuFfT7w/2ZpTOhRJbVncF0Zw4d5z5RUti3QlVAYS2NN1rx1LZTfa7aRLhb iLzkjs3keFikpKJvjijeLzVgT5Xk2q7sZgNsigHWJ4O0tbPQ7ZvPddGijhgZn5dEMbKHwBn5 4YX4xzGB90sprx+A9LSz+yNcXz2/7hFQTbCkMBZoIVdArBY3beGz5hIG52HFc/HJ4p0vwb4b i0uOeJrPw1JPcWzWRkaS4hjt/KhPdWY71I6ld4ADYZZL/U/GD3yafDHqVv8A6RMkt5FaRsoR tQgWLYSrAlbV3JJGOTnLI4QA1L/4c6TqN9c3txPObq7tHtrqfyLcyTFoTCZPMMRZG2Y4QqmR 93ls2NT8IJPpPioWsu/U9dtJIGnnCoAPLdYlPlqMqu8jcQz44JbAA4O0uvHnm6ffzQ6lc6hH ZTqrzaeg+eSCwlWJsBAqG43xlsEhQ+Su1pE3J9W8cNrGp20S+R/pcaQZs5ZkSL7XEiuCIlQq YGdpB5zt3HlbWAANjUfhvompRJ57TvcLK8n2mdYrpyGVE2kTo6nCRRLu27/3eSxLOW2D4bsz os2mCScRyXb3qyhhvjmac3AZeMfLIQQCCPlAIYZzj61J4ih1a0sLK6vvLMVvHHcR20bidmkK 3LzNsKRtHFtkT7is5IxIPkGX4ej1fw14T8E2EFrdpbtbxi8VLAO6TSNF+7kRQuxAsk7NIcEG JSxYkrIAalx8OdLu7C4tp7/VXe5iaGe5Nz++kRoIYWDNjnd9nic+rKQcozIZNQ+H2lapdXV1 d3F2893cCW4dfLQyxiKWIQttQZTy5pF3f6zBHz/KuOb8PX3inStE0LTpLO+hjjihLSNp5lSC 3XTOEdVw5b7Sj/KPn+UKSodA2WZ/GlnqUuoKdZM95ZW4maSBX8uFZbrBV47MnfkwHYYS4W4f co27owDtLXwMuh2bnQrvyr8RTwwTPb28SRecYd0hjhiVXZfJVgCPm+6SAQV6S80qxv7i1ubm 2je4tH328/SSI5BO1xyA2AGAOGHByCRXBzaj41k0u9uzcTrcGW3htbO009o/NZ7aF3KySROY 18zeA0qbV+dXIJVous1Oe+1jRJH8N3scc63Dws7fuyTG7RyKrNG4UhlPJjcEAgAbg6gFfRvA 2i6NZWdmkUl1BZOslrHdEOkDqiLvRAAocmPfuxkNJIQRvIrUutC0e+1GDUbzSrG4voNvk3M1 ujyR7TuXaxGRgkkY6GuXNxrst5d6UtlqsMf2SNrBcgGPaINryXZZx5gkeYOh83ckOdpBxNqX 9n4ok8NWcFrfxjVlcGeZJkhDLhuNzW8oY/dyRGm4gkBB8lAFi/8ACGg6jrdnrM+mWn9oWtwL gXAgTfIyoyKHYrkhchhzwUQ9qD4Xtj4VTw4Lu7GnrpjaaVBTcyFAgcnb98KDjGB8xyDxjn/H um+Jr7wLJZ2ix3P/ABLJhfxrdMs80vlAKEMcX7wbt5KBY95CjhSyGPw3Bf2fiQ3V/p88F032 xdTkiikkSV5bqM2Y8zaPOVIt4DAHylyG8vkUAdJr3hDQfEpjfVNMtJ543iKzvAjSbUkEmzcy k7CQQR3DMO9SaJ4dg0WW5nF1Pd3VzgSTzJEhKhncDbEiL9+WRidu4lzknjHn82lssV4506eO wN3Dvs59JuJop32zqwnhXzGnkTfHI1xuKTNHGoZSu+vRPDUN9beFdIg1QyHUI7KFLoySb2Mo QB8tk7juzzk5oA1KKKKACiiigAooooAKKKKACiiigAqnqmmw6tYNaTNIgLpIkkZAaORHDo4y CMqyq2CCDjBBGRVysPxTBM1haXtrFJJc6fewXK+Wpdlj3hJyFH3j5DzDaASc/KN22gDPn+Hm izWktkj3cFi9uYVto5AVRzb/AGXzQWBbeIfkwWK9ypb5qkl8B6XLqi332i+VklMqQibMaE3M V02FIOMyxbieuHYZwECc/oh8S6YkFlerfWzGJpb0W9qsqKJLczTXKOEYNcfbGdRECRt6REYa o7/WvHUOjafObaSG/Z5DfR/Y3McEwWMxxKsMc7SQMC7MynJIx5kR/dgA1JPhXoElhb2xedmt 9ojlmignKqIIYSNssTJytvGSdu7IOCASK6jUNJTVNDv9Ju7md4b2KaGSQbQ6pJuGFwuPlDYG Qegzk5J5OVta0zwVOumxXceoS6xe+VFHAfMkV7udlAcxyJEGBU+ZIhTBwSu4OtNL3xLBfyQa fHfRXEf9pTxWj6aq2d2/n3PkKZFjGxjhHZ2kTcNmAxkZlAOoufCxuNT1K9j13Vbb+0IvKlig aEBB5ZRdjmMyJtJLgB8B2Y4+YgxnwfFLoyaVcavqUtoEaBox5MSvbsoVoCkcaoEwowwUOuSF dQSKr+Db/Vb23/4m892ZVeVYlktJE3oBEd0jPbw/OGZgu1VBUnhyjMMPTlafwz4hOiWWq6dq Wo2jxWto1ncW5jm8qUpI8sigNcMfvzbsZEa7mIV3AOkHg+LOoCTV9SkivLj7X5b+T+5nEiyR yKwj3sUKIFDsy7VVSCABRF4K02KaCVZ7s7XSS5UuuLuRJmuEeT5eCJpHkxHsBLYIKgKMeCOC y8K6+8eh3cmjy3qGw0yO1lhzEUgRgYFXekRlErOuw7lLnY4bDRzW1qfD2h2j2t9f6TbXZn1C 3l0qdUaF0uAiLbMhYxpN5e2IBiirGx4AagDsP7C0f+2P7X/sqx/tP/n9+zp533dv38bvu8de nFR3Ohw3F7e3i3V3BcXVvBAZIJApj8p5HRl467pDkNlWAAIIyDx+q20o8K+GNN12z1KXUPsS rcX0VvNeNZyqkYeRfKD/AOkbifLkP3fnYMeUexrttc33i2G5gs7t3D2KWE5t3HkGK7c3uHI/ dBodoJJUSrgLv6UAbFt4PistStr611fUoniQrIn7l1mLytLKx3RkoZHbLeWUB2qABtXEcXgi 1i3TjUr46l5olj1AJAkkRHm9EWIRHP2ifJZCSZWJOQpXD1S68YL41k+yXOpLpaXsSLCllG0T RbrAN85jLYInuiSG48o4xsajwLdeMJtWg/t+51KW3lsi7pdWUcSpL5Nm4wVjUg75rlcEn/Vk dVJoA6Sw8HaXpl9bT2fnxwWuxobTfmNZVhFusuSN5YQgJgttxzt3fNUbeCtNW4nu7We7tLx7 j7RFcxOrNAxMpYIHVlIJuLgncG/1xxgKgXh9MvPiDIlut3fazm5S2WRm06FTAXbTzIy/ucAg T3g+bIAiORlCa2NGuvGE/gqX7dc6kuqSXunoszWUayxxSramfanl7cIZJxkqdu05+6aAOgHg jSFDxq12LZ7JdNe2E58trRYyiwkdSAWZg+fMBYjftO2qdr8O9OsrN7e3vJ4t0U8JaO0s0BSY wlwY1gEbZECj5lPDMP7u3k7G8+INzb6dFcX2swu7wtPKNOhDASDT96nMOAE8+8I4yPLO4nYa 3G1C41L4f2q6jczzX73ek2+qQTwiPy3la182EqFXKsshLKc/6xl6fKADqJfCnh+406xsLrRb G7tbCIQ2qXcCz+UgAGAXBPRV+uBmi/8ADdnqOrRahLJOrDyfOiRhsn8mQyQ7sgkbHYsNpXOc NuHFcXqupeOrOIXcMkjxve3qKhsn/dLHOwt1dY4ZZHR03EsAmVSPDoxJksaxqvi+N9WewS7e 0S9jiWdYGjMUQWTd5UZtpJCQwhBfbOjhyy+WA3lgHQP4PiZJdur6kJ5Hh/0h/JmdY4WZoo/3 kbAhGcsHYGQnBLnFSXHhGzn0mPSVvL6HTY4vsotY5RsNt5ao0BypJUhM78+YNzbXUHFY+jwe ILrR/EE0Y+y6teXdtMkihrZGP2S1DlPOikIXKuvzRk8EHBBIsa/p2s3Hwq1mxupp5dWfT7gE wMk7ynDFUBEKBtwwvEanng5+agDU1fwpputnUTeiRxfW9vBIp2sq+TI8kbBWBBIaQkhtynAB BGQc9Ph7ozW9na3ZkurW0t54IYDDBAq+cHWRh5MaEFlcqQCFPBxuAauf1nSdXl8YaxcQ6fJd XEqSizkSERyw25stuYbxgRE/2gbVQEFfMldlYMGUSyvR4I1GwsrSSITXsc8UaaXLDbtao9v5 4W0OGRCPNBgLbpT5jLkPgAG5a/DvTrKze3t7yeLdFPCWjtLNAUmMJcGNYBG2RAo+ZTwzD+7t 6iwsbfTNOtrCzj8u1tYkhhTcTtRQAoyeTgAda870+0gS70Gym0a7sXt38y3vbfTJRiL7RJ5S qI08u3eVQrTkiPKvtK8/utTVb3xdDrmppbJP/ZsfzK8cCOUhb7Iu+Pgl5FAvmCYY5C5Uhoww B3FFcPaaj4le/wBJSb7cLGTd9rm+wrvVBORbMRgbWlT/AFoCt5eB8kGdww7G/wDFt9cWM2s2 8kc9letO2LGeYWzmyu1cALFGJIg3lhQjSEk481tyGgD1SiuH8Py6i+v6DLqSzx3lzpV8bjzm XfKqXEHkl9scfRZGKgorL5jAgMWz3FABRRRQAUUUUAFFFFABRRRQAUUUUAFY/ijXP+Ec0GXU /LgfZLDFi4n8iNfMlSPc77W2qu/JODwK2Kp6npsOq2qW87SKiXEFwChAO6KVZVHIPG5AD7Z6 daAOPuPiVDZ27SzWtpchbdLjzdNvhcQsuLp5ArlFyVjs3xxy7BTtAL0WPjqFdcg0C3s9GsII 38iNLnUhbsyrdT2wWCIRkOcW+duRjeq+9dBq3hLSNbvzd38Mkpe3a3kjEhVXUpIgJxyCFnnU YI/1pJyQpWvZ+D4tO1JL6x1fUoHZFW5RfJZboiWWVmfdGSCzzSk7Cg+bAAwMAFzSfFOja9FF Lpd59qSXaVaOJyMMrMGPHyr8jruOBvVkzvBWo4fEsNz4qTRbe3kkjNvPK15kCPzIniR417sQ ZfmPQEbclgwWn4R8I/8ACN6Tp8b3k73sVpb29wRL5iOscbDy1LLnyw8kjjowyFyEAQWH8GaI PEMOvWtlBZ6lH5hM9vbxK0hd0Z2YlCSxCMu7rtkkwQTkAGWnxAU+Gtc1h9Mkxplv5/lRO0hy Q2IZdqZinUr+8TDCMMpLEZxsSeIGi8WQ6I1rGRKmVZbhWmA2lvNaIDKwZUx+YSD5mF24IY14 fBWmw6NqWlie7aC+shp25nXdBaqrrHEmFxhBI+GYMxz8zNgYk1nw/cXk73tjqE8d4uWtUmcN DbTMhiNwqlSSyxs2I8iNj1AZi4AK+peLvsPjKDQP+JVH5kUEu+91LyJJPNkkTbDH5beYw8vp uGSyjvmss/ESa38MWuqX+mWlpPfW9vd2iPfnyGhlkhjLSS+WDGYzOhYFSMEbS3zbewj02GLW bnVFaTz7i3ht3UkbQsbSMpHGc5lbPPYdO+HY+BbCz0y0spb2+u/sf2Zbaa4aPfDFBJHIkS7U ChS0Sbjjc2BljtXaAR2Hj/SJLeNtSurS1kkeQRPBMbi3uFjEZd4pgoDookG4kDaUlz8sbNWp rOuf2Zo738UcAVZTE0moT/Y4YsMVLO7qSFJGFKq24suPlO4Y+peA7fUdcE7XE6adLFdi4t45 igzP9nDoqgY8txFKWzzvlLghsMvQXmmS3dmIRql9bzLK0sdzCUDpkn5dpUoyhWKgMrdAfvAN QBj6x4x/svTtLvP7MnH26LzvJum8mQYCnyFXB3XLbsJDxuKP8w28kHjS3Oraja3UHkQ2e8Fl cySqVkEarJEFyjSlgYVBYyryADwZLnwVptzZQWQnu4rSC3aySFHXAtGSNHt8lSdjeUpLZ8wH OHA4qRfBujtdXEl1D9tt5fN2Wd2qSQxebKs0uFK5bfIqv85bBA27RxQBGnitm8Eal4jfTZIX sUvWazklXdm3eRdpZcgE+X23AZ4LYyce08dQ21rZ2trZ6NMjo0NsukakJ7eNlltoY4ywjXYN 1yucKdqrkBs4GhYeCLay0vXtEi8u20XUklRILZEV1Mu8ySZCAA4kWNVIYKsKcnJA2NU0Cx1e 9sbu6WQyWb7k2tgMN6SBW9vMiifjBzGBnaWVgDj3+KcMstuLe3022guEMkc2r6mLNSvkWswG RG43kXWNv/TMnJzx2lvrenXd5JaW1x500UvkyLGjMEbDHJIGAuUdd33d6MmdwK1hweAbLT3t pNJ1PUtNlgSSMSQGJy0bLCoQiWNxhUt4VBABwmSWJJOpYeH00zUbi5tdQvlhnlMps2dWhUky O+0Fcjc8rOTnOVUAhRtoAz9S8V3mmy6pG2hzzyW1o9zaw2zmaWba2xVkRFbyvMb7h+bKq5bY UZRXh8bu11pdvLYQeZebQyw3LMxLStGViV41aRo9u6ZWCGJeoY8VoQeFjby6iya7qu29leco GhBilLBlZXEYdtoVVCuzLsAQgqMVHF4K02KaCVZ7s7XSS5UuuLuRJmuEeT5eCJpHkxHsBLYI KgKAA8I+K18WW8t3DHaJblI5YBHctJKUcEgyKUUKRgqdrON6yLuyhrHHxCmlk1CO3tdGlnt7 37FHaDVz9oLfbFtQ8kYiJjQlt2ctwVGDnI2NN8Ltol/aHTruQWiIsUvnFWYW8SOsFsgCj5Fa Vn3kl/lAJYNlbE/he2m0KXSku7uBHvTfLPGUMiSm5+05G5SuA/YqeODnrQBh6n8Qf7OvLaxl h0q0vD56XX9p6p9mhhkjELbUk8tvM3LOjrwp29QrAqOgsvE2nXvlRgzxXbxRSvZyQt50XmbP ldQDhl8xN4/gDqWwrAmOHwppsdwlxMJLqQ288Fwbja/2vzjF5jSjGGJEKKAMKF+UAKFAjsPC iaZeJc22saru8qNJlkmVxcOoiUyyblOZGSBEJ4wCxUKzFqAJL3X5tP1uO0n06Q2kiSFJYWMs zbE3s4hRSfKHCbs7vMZV2YZWPPx/EO5XRrS9vNFjt7iZ2ElmblxJGVVG8jDxKTdNv+SHADgF g+ME9A3hoNrd7qP9rakqXqBJrVGjVNoQoAsgTzUAyzgLIMMzEfeOac3gWwuYpPtN7fTXFx5q 3lyzRh7uKRY0kicBAqqyQxLlFRhsBDAliQCTRPFa65r19Ywx2iwWrzRA/aWM7PFJ5bgx7NoA PPDkhXiLKvmCukrHsPDdnp2rS6hFJOzHzvJidhsg86QSTbcAE73UMdxbGMLtHFbFABRRRQAU UUUAFY/irWf+Ef8AC+o6mrwJNDERAbg4jMzHbGHORhS7KCSQADkkAEjYqnqemw6rapbztIqJ cQXAKEA7opVlUcg8bkAPtnp1oA5fTvGU0Wm397qJj1Ozt7hympaVbFbc2qRRvJMS0jAhGd1I RmYlG2qSrYsXnxD0WxLtMl2sH2drmG6aMLBcIJIogUkYhcM8qgEkDA3khGR2ua74QsNf1bTt RupZ0msJY5YwgjYEpIJBjerFMlRkxlCw4YkBcY+heCLiGC+TVJvJjkitLeygt7gT/Y0tneSE pIYk3bWcYDq/3PmZwxAAOgsdfTWfDker6LB9s87KxRGVVG8OUYM4JXarA5Zd2QpK7+AZPDWp Taz4V0jVLhY1nvbKG4kWMEKGdAxAyScZPqaLnQ4bzw1daHc3V3NFd28sE1w8gMreYCGbOMA/ MSAAFHAAAAFalABRRRQAUUUUAFFFFABRRRQAUUUUAFVzYWbWc1mbSA2s2/zYTGNkm8kvuXod xZic9cnPWrFFABRRRQAUUUUAFFFFABRRRQAUUUUARiCFbh7hYoxO6KjyBRuZVJKgnqQCzEDt uPrUlFFABRRRQAUUUUAVNRJ+zoAcZkUfgTiqCpDJP5cSysFJDv5jhR7A/wARzxx0wc4PB0ry J5oVVBlg6t+RzVL7Lci489LWNJD98rLjeMYG7jnHbuPxOc5LUZgX6y3msQ6dl3jazupliFy9 vvkjkiVAZE+ZR85zjP0OMUabp2g34l8oazb3dvjzra51C6WSInOCQ0hV1yGAddyMVbBODVvU tE1WS6S+094kult54AHkCbfNZG3BtrjK7BgFCDnmq+n6TrmmW5trPRdFto5H3yumoSM8jkAF 3Jhy7nAyzHJ7mtY35VYzdru5t+FrmW98J6Rdztumns4pZGxjLMoJOB7mip9F0/8AsnQrDTfM 8z7JbpBvxjdtUDOOcZxRQ7X0Kje2pfooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAM+H+0 f+EhvfN/5Bn2SD7P93/Xb5vN/wBr7vk9ePTvWhRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAf/9k= --part-0o3KMXA2xdu1dAjQrVRGm0upYANAAAECvACxGj8A1NHSA-- ========================================================================= Date: Thu, 26 May 2011 12:53:45 +0100 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: Large Differences in segmenations between SPM8 and SPM5 Comments: To: "West, John D." <[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]> > I=92m hoping someone might be able to help us get to the bottom of an iss= ue > we=92ve run into.=A0 Recently we have been trying to reprocess our data u= sing > SPM8.=A0 Unfortunately, we have run into a situation where some subjects = which > segmented fine in SPM5 are now failing in SPM8. > > The way we do segmentation is we first register the T1 (MPRAGE) to the T1 > template to give the segmentation a better starting estimate.=A0 We then > segment from there.=A0 However, in SPM8 the registration has consistently= come > out differently.=A0 It seems that in some cases this new registration is > giving the segmentation a starting estimate that it cannot handle, where = in > SPM5 the starting estimate was fine. Are you actually reslicing the MPRAGE scans? Maybe with B-spline interpola= tion? I've seen a dataset that has been problematic because of this, where the scan had large regions of zeros in it, where bits of the FOV contained no information. Usually, the segmentation ignores zeros, and assumes that they are masked out parts of the FOV. However, if the data has been resliced with high-degree interpolation, these zeros will no longer be exactly zero. The segmentation therefore tried to fit the air class to the approximate zeros, and another tissue class to the normal background noise. If this is the problem, then maybe you could just register the images without actually doing any reslicing. Maybe try windowing the intensities in the image to see if there's anything strange lurking in the background. > > > > Does anyone have any ideas on what would be causing these differences?=A0= Is > the coregistration and segmentation algorithms very different between SPM= 5 > and SPM8?=A0 If not, is there some setting we may need to change?=A0 If t= hey are > different, are there any suggestions you could give so we could successfu= lly > segment these cases in SPM8? The basic algorithms are the same. The implementational details are slightly different though. The older segmentation tried to ignore the background (because it had no model for skull, scalp etc), whereas the newer one tries to model everything in the FOV. Best regards, -John ========================================================================= Date: Thu, 26 May 2011 13:06:26 +0100 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 the "MEG" and "MEGPLANAR" modalities of Elekta system. 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]> Dear Jing, There is no principled way to compare the results between modalities. Bayesian model comparison will not work here because the data is different. Also one wouldn't necessarily expect to get the same results because different modalities might be sensitive to different cortical sources. So you can either decide that you only use one modality throughout and ignore the other (as people who do e.g. beamforming do) or you can look at both results and discuss why they are similar or different. Vladimir On Thu, May 26, 2011 at 11:36 AM, <[log in to unmask]> wrote: > Dear Vladimir, > > Thanks for your reply! May I also ask a silly question here: in this case= , > for instance, I run VB-ECD for both modalities and get two results. > After the comparison, I should choose one of them which is most likely > close to the observation, is it right? > > Many thanks in advance, > > Jing > > > >> Dear Jing, >> >> At the moment there is no way to combine modalities for VB-ECD and DCM. >> You can only work with each modality separately and compare the results. >> >> Best, >> >> Vladimir >> Sent using BlackBerry=AE from Orange >> >> -----Original Message----- >> From: =A0 =A0 =A0 =A0 Jing Kan <[log in to unmask]> >> Sender: =A0 =A0 =A0 "SPM (Statistical Parametric Mapping)" <SPM@JISCMAIL= .AC.UK> >> Date: =A0 =A0 =A0 =A0 Thu, 26 May 2011 10:43:35 >> To: <[log in to unmask]> >> Reply-To: =A0 =A0 [log in to unmask] >> Subject: [SPM] Question about the "MEG" and "MEGPLANAR" modalities of >> Elekta system. >> >> Dear SPM experts, >> >> Thanks for concern! I am doing the "3D source reconstruction" with the >> MEG >> data from Elekta system. =A0As I know from the SPM tutorial, SPM treats = the >> MEG data of the Elekta system as two different modalities: "MEG" and >> "MEGPLANAR". >> >> There are two methods used for 3D source reconstruction: "imaging", >> "VB-ECD" and "DCM". =A0The method "imaging" can let me select both "MEG" >> and >> "MEGPLANAR" modalities for reconstruction. However, =A0 in the methods >> "VB-ECD" and "DCM", I can only select either "MEG" or "MEGPLANAR" for >> reconstruction. >> >> May I ask how can I deal with this two modalities in =A0"VB-ECD" and "DC= M". >> How this selection will affect my reconstruction result? >> >> Many thanks for your help in advance! >> >> Kind regards, >> >> Jing >> > > > ========================================================================= Date: Thu, 26 May 2011 13:19:32 +0100 Reply-To: =?UTF-8?Q?=C4=B0nci_Ayhan?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?UTF-8?Q?=C4=B0nci_Ayhan?= <[log in to unmask]> Subject: Re: converting from .IMA to nifti format Comments: To: Matthew Brett <[log in to unmask]>, [log in to unmask] In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=bcaec544eb726c6c5d04a42cd509 Message-ID: <[log in to unmask]> --bcaec544eb726c6c5d04a42cd509 Content-Type: text/plain; charset=ISO-8859-1 Lisa and Matthew, > > Thanks for the replies. > > Matthew, I tried your code and it seems it perfectly works! Thank you very > much for sharing it! > > I also did this myself which seems to work, too: > > I changes the extension from .IMA to .dcm > and then downloaded the spm extension "dicom2nifti" and the function > converted the images into nifti (.nii) format. > > Best wishes, > > inci > > > > > On Wed, May 25, 2011 at 8:15 PM, Matthew Brett <[log in to unmask]>wrote: > >> Hi, >> >> On Mon, May 23, 2011 at 6:13 AM, Inci Ayhan <[log in to unmask]> wrote: >> > Hi, >> > >> > I am trying to convert 3D EPI images (Siemens - .IMA format) into nifti >> > format using SPM. Because the images do not have a .dcm extension, I >> > cannot use "dicom2nifti" function. >> > >> > I would so much appreciate any help to suggest a way to do so. >> >> You can do the conversion more manually from the matlab command line, >> sort of (untested): >> >> P = spm_select(); % select your DICOM files >> hdrs = spm_dicom_headers(P); >> cd /some/place/for/the/files >> spm_dicom_convert(hdrs); >> >> I think... >> >> Best, >> >> Matthew >> > > --bcaec544eb726c6c5d04a42cd509 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <br><br><div class=3D"gmail_quote"><blockquote class=3D"gmail_quote" style= =3D"margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); p= adding-left: 1ex;">Lisa and Matthew,<br><br>Thanks for the replies.<br><br>= Matthew, I tried your code and it seems it perfectly works! Thank you very = much for sharing it!<br> <br>I also did this myself which seems to work, too:<br><br>I changes the e= xtension from .IMA to .dcm<br> and then downloaded the spm extension "dicom2nifti" and the funct= ion converted the images into nifti (.nii) format.<br><br>Best wishes,<br><= font color=3D"#888888"><br>inci</font><div><div></div><div class=3D"h5"><br= > <br><br><br><div class=3D"gmail_quote">On Wed, May 25, 2011 at 8:15 PM, Mat= thew Brett <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]"= target=3D"_blank">[log in to unmask]</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; borde= r-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Hi,<br> <div><br> On Mon, May 23, 2011 at 6:13 AM, Inci Ayhan <<a href=3D"mailto:i.ayhan@u= cl.ac.uk" target=3D"_blank">[log in to unmask]</a>> wrote:<br> > Hi,<br> ><br> > I am trying to convert 3D EPI images (Siemens - .IMA format) into nift= i<br> > format using SPM. Because the images do not have a .dcm extension, I<b= r> > cannot use "dicom2nifti" function.<br> ><br> > I would so much appreciate any help to suggest a way to do so.<br> <br> </div>You can do the conversion more manually from the matlab command line,= <br> sort of (untested):<br> <br> P =3D spm_select(); =A0% select your DICOM files<br> hdrs =3D spm_dicom_headers(P);<br> cd /some/place/for/the/files<br> spm_dicom_convert(hdrs);<br> <br> I think...<br> <br> Best,<br> <font color=3D"#888888"><br> Matthew<br> </font></blockquote></div><br> </div></div></blockquote></div><br> --bcaec544eb726c6c5d04a42cd509-- ========================================================================= Date: Thu, 26 May 2011 14:29:17 +0000 Reply-To: "West, John D." <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "West, John D." <[log in to unmask]> Subject: Re: Large Differences in segmenations between SPM8 and SPM5 Comments: To: John Ashburner <[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]> Dr. Ashburner, Thanks very much for the reply. In reference to your registration question, no we do not reslice. We actua= lly try to avoid reslicing as much as possible during all our preprocessing= to avoid adding unnecessary data files and to also limit interpolation err= ors. So that is not the issue. However, there is definitely a distinct di= fference between SPM5 and SPM8 in our registration to the template. Is the= re anything that has changed in the registration that would cause this diff= erence in your opinion? The change in the segmentation you mentioned could potentially be the issue= as the failures we are seeing are bringing in more non brain tissue into t= he gray matter compartment. I plan on testing this in the near future by t= rying the following: 1. Register to T1 template in SPM5, but segment in SPM8 2. Register to T1 template in SPM8, but segment in SPM5 Doing that should pinpoint where the issue is stemming from. I would appre= ciate any other suggestions you may have. Thanks. -John West -----Original Message----- From: John Ashburner [mailto:[log in to unmask]]=20 Sent: Thursday, May 26, 2011 7:54 AM To: West, John D. Cc: [log in to unmask] Subject: Re: [SPM] Large Differences in segmenations between SPM8 and SPM5 > I'm hoping someone might be able to help us get to the bottom of an issue > we've run into.=A0 Recently we have been trying to reprocess our data usi= ng > SPM8.=A0 Unfortunately, we have run into a situation where some subjects = which > segmented fine in SPM5 are now failing in SPM8. > > The way we do segmentation is we first register the T1 (MPRAGE) to the T1 > template to give the segmentation a better starting estimate.=A0 We then > segment from there.=A0 However, in SPM8 the registration has consistently= come > out differently.=A0 It seems that in some cases this new registration is > giving the segmentation a starting estimate that it cannot handle, where = in > SPM5 the starting estimate was fine. Are you actually reslicing the MPRAGE scans? Maybe with B-spline interpola= tion? I've seen a dataset that has been problematic because of this, where the scan had large regions of zeros in it, where bits of the FOV contained no information. Usually, the segmentation ignores zeros, and assumes that they are masked out parts of the FOV. However, if the data has been resliced with high-degree interpolation, these zeros will no longer be exactly zero. The segmentation therefore tried to fit the air class to the approximate zeros, and another tissue class to the normal background noise. If this is the problem, then maybe you could just register the images without actually doing any reslicing. Maybe try windowing the intensities in the image to see if there's anything strange lurking in the background. > > > > Does anyone have any ideas on what would be causing these differences?=A0= Is > the coregistration and segmentation algorithms very different between SPM= 5 > and SPM8?=A0 If not, is there some setting we may need to change?=A0 If t= hey are > different, are there any suggestions you could give so we could successfu= lly > segment these cases in SPM8? The basic algorithms are the same. The implementational details are slightly different though. The older segmentation tried to ignore the background (because it had no model for skull, scalp etc), whereas the newer one tries to model everything in the FOV. Best regards, -John ========================================================================= Date: Thu, 26 May 2011 13:18:49 -0400 Reply-To: Phil Greer <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Phil Greer <[log in to unmask]> Subject: VBM Question: Making Custom TPM.nii with TOM8 MIME-version: 1.0 (Apple Message framework v1084) Content-type: text/plain; charset=us-ascii Content-transfer-encoding: quoted-printable Message-ID: <[log in to unmask]> First, thank you for your work in creating a toolbox that will allow = creation of a custom TPM file. It is a very interesting toolbox, but I = feel I may be missing something in the instructions. To be specific, I am having issues in understanding exactly what images = to use as the input when creating the TOM.mat file. I think I am suppose to use the normalized, unmodulated segmented files = for each tissue type (c1* c2*, etc). Is this correct? If one uses the VBM8 toolbox, there is no option to write out all 6 = classes of images, only gm, wm and csf. How does one get the other tissue files? If you use the results of new segment, as per the current VBM = instructions in SPM8, one gets nonlinear transformed segmented images = that may have some segmentation artifacts (at least that is the case for = many of my elderly subjects).=20 So if I want to create a 6 tissue TPM.nii file, what images should I = use? Phil= ========================================================================= Date: Thu, 26 May 2011 22:25:17 +0200 Reply-To: Johannes Gehrig <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Johannes Gehrig <[log in to unmask]> Subject: Memory mapping (MEG, Batch) MIME-Version: 1.0 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Message-ID: <726259888.1242081.1306441517202.JavaMail.fmail@mwmweb063> <html><head></head><body bgcolor=3D'#FFFFFF' style=3D'font-size:12px;backgr= ound-color:#FFFFFF;font-family:Verdana, Arial, sans-serif;'>Dear SPM listus= er,<br/><br/>I'm using the Batch-Mode for MEG preprocessing. I'm working on= a cluster and the problem is that I'm writing to much data to disk. The fi= le on the disk is modified while SPM is "working" on it. Is this = because of the memory mapping? Is there a possibility to load the data in t= he working memory and only save the final result to disk?<br/><br/>Any help= is very much appreciated!<br/><br/>Best Regards,<br/>Johannes <= br><br><table cellpadding=3D"0" cellspacing=3D"0" border=3D"0"><tr><td bgco= lor=3D"#000000"><img src=3D"https://img.ui-portal.de/p.gif" width=3D"1" hei= ght=3D"1" border=3D"0" alt=3D"" /></td></tr><tr><td style=3D"font-family:ve= rdana; font-size:12px; line-height:17px;">Schon gehört? WEB.DE hat ein= en genialen Phishing-Filter in die <br>Toolbar eingebaut! = <a href=3D"http://produkte.web.de/go/toolbar"><b>http://produkte.web.de/go/= toolbar</b></a></td></tr></table> </body></html> ========================================================================= Date: Thu, 26 May 2011 20:21:53 -0400 Reply-To: alekhya mandadi <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: alekhya mandadi <[log in to unmask]> Subject: Onsets and durations in Specify 1st Level MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=000325557c86b46f9104a436ecb9 Message-ID: <[log in to unmask]> --000325557c86b46f9104a436ecb9 Content-Type: text/plain; charset=ISO-8859-1 Hi All, I have a question regarding onsets in specify 1st level. Lets say my onset times are [4 23 56 72 92 120] I want to analyze the data from the start of the onset time to the next 6 seconds. Or to better put it I want to analyze the data from [4 23 56 72 92 120] to [10 29 62 78 98 126] Where do I enter this information? Is it where it says Durations right below Onsets? Your help will be greatly appreciated. Thanks so much! Best, Alekhya Mandadi Research Assistant Center for Neural Decision Making Temple Fox School of Business http://www.fox.temple.edu/minisites/neural/people.html --000325557c86b46f9104a436ecb9 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi All,<br><br>I have a question regarding onsets in specify 1st level.<br>= <br>Lets say my onset times are [4 23 56 72 92 120] <br><br>I want to analy= ze the data from the start of the onset time to the next 6 seconds. Or to b= etter put it I want to analyze the data from [4 23 56 72 92 120]=A0 to [10 = 29 62 78 98 126]<br> <br>Where do I enter this information? Is it where it says Durations right = below Onsets? <br><br>Your help will be greatly appreciated.<br><br>Thanks = so much!<br><br>Best,<br>Alekhya Mandadi<br>Research Assistant <br>Center f= or Neural Decision Making<br> Temple Fox School of Business <br><a href=3D"http://www.fox.temple.edu/mini= sites/neural/people.html">http://www.fox.temple.edu/minisites/neural/people= .html</a><br> --000325557c86b46f9104a436ecb9-- ========================================================================= Date: Thu, 26 May 2011 21:39:23 -0400 Reply-To: hekmatyar <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: hekmatyar <[log in to unmask]> Subject: Re: Explicit masking during statitical analysis 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]> Hello SPMers When I do statistical analysis without masking the outer tissue, I used to see lot of activations from outer tissue, Is there any way to apply mask explicitly during Ist level statistical analysis? Please let me know Thanks Shah ========================================================================= Date: Thu, 26 May 2011 19:02:52 -0700 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: Onsets and durations in Specify 1st Level Comments: To: alekhya mandadi <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf303dd7a4a052b104a43857a1 Message-ID: <[log in to unmask]> --20cf303dd7a4a052b104a43857a1 Content-Type: text/plain; charset=UTF-8 That is basically correct. its a little more complicated then that but if you have a stimulus that lasts for 6 seconds then setting the duration to 6 seconds is the way to go. If however you want to look at the irf progression over those 6 seconds, you should look at using fir. Cheers, Michael On Thu, May 26, 2011 at 5:21 PM, alekhya mandadi <[log in to unmask]>wrote: > Hi All, > > I have a question regarding onsets in specify 1st level. > > Lets say my onset times are [4 23 56 72 92 120] > > I want to analyze the data from the start of the onset time to the next 6 > seconds. Or to better put it I want to analyze the data from [4 23 56 72 92 > 120] to [10 29 62 78 98 126] > > Where do I enter this information? Is it where it says Durations right > below Onsets? > > Your help will be greatly appreciated. > > Thanks so much! > > Best, > Alekhya Mandadi > Research Assistant > Center for Neural Decision Making > Temple Fox School of Business > http://www.fox.temple.edu/minisites/neural/people.html > -- Research Associate Gazzaley Lab Department of Neurology University of California, San Francisco --20cf303dd7a4a052b104a43857a1 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable That is basically correct. its a little more complicated then that but if y= ou have a stimulus that lasts for 6 seconds then setting the duration to 6 = seconds is the way to go. If however you want to look at the irf progressio= n over those 6 seconds, you should look at using fir.<br> <br>Cheers,<br>Michael<br><br><div class=3D"gmail_quote">On Thu, May 26, 20= 11 at 5:21 PM, alekhya mandadi <span dir=3D"ltr"><<a href=3D"mailto:alek= [log in to unmask]">[log in to unmask]</a>></span> wrote:<br><bloc= kquote class=3D"gmail_quote" style=3D"margin: 0pt 0pt 0pt 0.8ex; border-lef= t: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> Hi All,<br><br>I have a question regarding onsets in specify 1st level.<br>= <br>Lets say my onset times are [4 23 56 72 92 120] <br><br>I want to analy= ze the data from the start of the onset time to the next 6 seconds. Or to b= etter put it I want to analyze the data from [4 23 56 72 92 120]=C2=A0 to [= 10 29 62 78 98 126]<br> <br>Where do I enter this information? Is it where it says Durations right = below Onsets? <br><br>Your help will be greatly appreciated.<br><br>Thanks = so much!<br><br>Best,<br><font color=3D"#888888">Alekhya Mandadi<br>Researc= h Assistant <br> Center for Neural Decision Making<br> Temple Fox School of Business <br><a href=3D"http://www.fox.temple.edu/mini= sites/neural/people.html" target=3D"_blank">http://www.fox.temple.edu/minis= ites/neural/people.html</a><br> </font></blockquote></div><br><br clear=3D"all"><br>-- <br>Research Associa= te<br>Gazzaley Lab<br>Department of Neurology<br>University of California, = San Francisco<br> --20cf303dd7a4a052b104a43857a1-- ========================================================================= Date: Fri, 27 May 2011 01:36:32 -0400 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: Explicit masking during statitical analysis Comments: To: hekmatyar <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0015174fed6c020a9504a43b5216 Message-ID: <[log in to unmask]> --0015174fed6c020a9504a43b5216 Content-Type: text/plain; charset=ISO-8859-1 Yes. Under the explicit mask option when you set up the model. 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, May 26, 2011 at 9:39 PM, hekmatyar <[log in to unmask]> wrote: > Hello SPMers > When I do statistical analysis without masking the outer tissue, I used to > see lot of activations from outer tissue, Is there any way to apply mask > explicitly during Ist level statistical analysis? > Please let me know > Thanks > Shah > --0015174fed6c020a9504a43b5216 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Yes. Under the explicit mask option when you set up the model.<div><br>Best= Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedfo= rd VA<br>Research Fellow, Department of Neurology, Massachusetts General Ho= spital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Thu, May 26, 2011 at 9:39 PM, hekmaty= ar <span dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">skhekmat@h= otmail.com</a>></span> wrote:<br><blockquote class=3D"gmail_quote" style= =3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> Hello SPMers<br> When I do statistical analysis without masking the outer tissue, I used to<= br> see lot of activations from outer tissue, Is there any way to apply mask<br= > explicitly during Ist level statistical analysis?<br> Please let me know<br> Thanks<br> Shah<br> </blockquote></div><br></div> --0015174fed6c020a9504a43b5216-- ========================================================================= Date: Fri, 27 May 2011 07:05:19 +0100 Reply-To: Cath <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cath <[log in to unmask]> Subject: small volume correction vs. mask and other related questions 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 a problem regarding the inclusive mask. My conducted a masked pri= ming lexical decision task, and the procedure started with a fixation, a = mask, a prime, another mask and finally a target word (all within one TR)= . The prime could be a word which was visually similar/dissimilar to the = target word. The position of the prime and the target could be a total bl= ank. I defined a prime contrast (dissimilar minus similar) and a word con= trast (target word present minus blank ). I looked for activated voxels o= f the priming effect within the constraint of the activation maps of the = word contrast. So in the second level analysis, I used small volume corre= ction on the prime contrast by entering the image of the word contrast. I= expected that the activated voxel number under the prime contrast should= decrease after I entered the word contrast in the small volume correctio= n. However, the activated voxel number under the prime contrast actually = increased after I entered the word contrast in the small volume correctio= n. Could you please tell me why it was that?=20 What is the difference between using a mask and using the small volume co= rrection? Finally, what is the difference between percent signal changes = and beta value and how to output the percent signal changes over multiple= voxels?=20 Thank you! Sincerely, Cath ========================================================================= Date: Fri, 27 May 2011 16:21:11 +1000 Reply-To: Mayuresh K <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mayuresh K <[log in to unmask]> Subject: Power analysis MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=000e0cd40312af5dfb04a43bf118 Message-ID: <[log in to unmask]> --000e0cd40312af5dfb04a43bf118 Content-Type: text/plain; charset=ISO-8859-1 Hello spm experts, Could anyone guide me how to do a power analysis using fMRI or VBM data? I have performed a random effects analysis comparing two groups of subjects using their fMRI and VBM data. I have found a number of regions with significant differences for both data sets. I can probably calculate the mean and standard deviation of the %signal change (or volume) for each group for the peak voxels say in a particular anatomical region. Then use these to calculate effect sizes and power - Is this a valid approach? Any other suggestions welcome. Thanks, Mayuresh --000e0cd40312af5dfb04a43bf118 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hello spm experts,<br><br>Could anyone guide me how to do a power analysis = using fMRI or VBM data? I have performed a random effects analysis comparin= g two groups of subjects using their fMRI and VBM data. I have found a numb= er of regions with significant differences for both data sets. <br> I can probably calculate the mean and standard deviation of the %signal cha= nge (or volume) for each group for the peak voxels say in a particular anat= omical region. Then use these to calculate effect sizes and power - Is this= a valid approach? <br> Any other suggestions welcome.<br><br>Thanks,<br>Mayuresh=A0 <br><br> --000e0cd40312af5dfb04a43bf118-- ========================================================================= Date: Fri, 27 May 2011 08:34:22 +0200 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: VBM Question: Making Custom TPM.nii with TOM8 Comments: To: Phil Greer <[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 Phil, > First, thank you for your work in creating a toolbox that will allow > creation of a custom TPM file. It is a very interesting toolbox, but > I feel I may be missing something in the instructions. First, thanks for the flowers :) > To be specific, I am having issues in understanding exactly what > images to use as the input when creating the TOM.mat file. I think I > am suppose to use the normalized, unmodulated segmented files for > each tissue type (c1* c2*, etc). Is this correct? Well, yes, you would enter the wc*. I prefer the affine-only registered=20 version, more below. > If one uses the VBM8 toolbox, there is no option to write out all 6 > classes of images, only gm, wm and csf. How does one get the other > tissue files? I have actually never looked at that but it seems that one can trick the toolbox into doing this when modifying lines 75-79 (in the current version) of cg_vbm8_run: % never write class 4-6 for i=3D4:6 tissue(i).warped =3D [0 0 0]; tissue(i).native =3D [0 0 0]; end If you adapt those lines according to what you want, that should do it. > If you use the results of new segment, as per the current VBM > instructions in SPM8, one gets nonlinear transformed segmented images > that may have some segmentation artifacts (at least that is the case > for many of my elderly subjects). I agree that especially for "unusual" subjects, VBM8 is likely to give better results. One way around the non-linear normalization would be to write out the tissue classes in native space and then do an affine-only normalization onto the corresponding tissue class that comes with spm (i.e., normalize c1 to grey.img and take along the other classes). In the TOM paper, we actually normalized each class and averaged the parameters, which seemed like a good idea at that time but I am not sure this really makes that much of a difference. > So if I want to create a 6 tissue TPM.nii file, what images should I > use? Assuming I was right, you have two choices now :) Hope this helps, Marko --=20 ____________________________________________________ PD Dr. med. Marko Wilke Facharzt f=FCr Kinder- und Jugendmedizin Leiter, Experimentelle P=E4diatrische Neurobildgebung Universit=E4ts-Kinderklinik Abt. III (Neurop=E4diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 T=FCbingen, 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, 27 May 2011 08:18:19 +0100 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: small volume correction vs. mask and other related questions Comments: To: Cath <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1306480740-9283-107" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1306480740-9283-107 Received: from [192.168.0.3] (5acc0817.bb.sky.com [90.204.8.23]) (authenticated user=cpernet mech=LOGIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p4R7IxPE024260 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits=256 verify=NOT); Fri, 27 May 2011 08:18:59 +0100 (BST) Message-Id: <[log in to unmask]> To: "Cath" <[log in to unmask]>, [log in to unmask] From: "=?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?=" <[log in to unmask]> Subject: =?utf-8?B?UmU6IFtTUE1dIHNtYWxsIHZvbHVtZSBjb3JyZWN0aW9uIHZzLiBtYXNrIGFuZCBvdGhlciByZWxhdGVkIHF1ZXN0aW9ucw==?= Date: Fri, 27 May 2011 08:18:19 +0100 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_6_1306480699583" 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_6_1306480699583 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SGkgQ2F0aCwKCjFzdCBtYXNraW5nOiB3aGVuIHUgbWFzayB0aGVuIHdob2xl IGFuYWx5c2lzIChpZSB5b3UgZW50ZXJlZCB0aGUgbWFzayBmb3IgdGhlIGRl c2lnbiBtYXRyaXgpIHRoaXMgbWVhbnMgdGhhdCBvbmx5IHRoZXNlIHZveGVs cyBhcmUgYW5hbHl6ZWQgYW5kIHRocmVmb3JlIHRoZSBnbG9iYWxzIHVzZSB0 byBhY2NvdW50IGZvciBnbG9iYWwgZWZmZWN0cyAobWVhbiBvdmVyIGFsbCB2 b3hlbHMgYWJvdmUgc29tZSB0aHJlc2hvbGQpIHdpbGwgYmUgZGlmZmVyZW50 IGZyb20gYSBub24gbWFza2VkIGFuYWx5c2lzLiBXaXRoIHRoZSBTVkMgdGhl IGdsb2JhbCBhcmUgZm9yIHRoZSB3aG9sZSBicmFpbiBidXQgdGhlbiB5b3Ug YXBwbHkgYSBzdGF0aXN0aWNhbCBjb3JyZWN0aW9uIGZvciBhIGdpdmVuIHZv bHVtZSB5b3Ugd2FudCB0byBsb29rIGF0IChzZWUgbXJjIGNiaSB3ZWJzaXRl IGZvciBleHBsYW5hdGlvbnMpIC4uIEJvdGggYXJlIHZhbGlkLgoKMm5kIHRo ZSAlIGJvbGQ6IHRoZSByZWxhdGlvbnNoaXAgaXMgc3RyYWlnaHRmb3J3YXJk LCBiZXRhIHJlZmxlY3QgdGhlIHZhcmlhdGlvbiBhcm91bmQgdGhlIGdyYW5k IG1lYW4gdGhlcmVmb3JlIGJldGEqMTAwL2JldGEgb2YgdGhlIGNvbnN0YW50 ICh0aGUgZnVsbCBjb2x1bW4gb2YgdGhlIGRlc2lnbiBtYXRyaXgpIGlzIHRo ZSAlIG9mIHNpZ25hbCBjaGFuZ2UuIEhvd2V2ZXIsIGlmIHRoZSBkZXNpZ24g bWF0cml4IGhhcyB2YWx1ZSBhYm92ZSAxIHlvdSBuZWVkIHRvIHNjYWxlIHRo aXMgc2ltcGx5IGJ5IG11bHRpcGx5aW5nIGJ5IG1heChTUE0ueFguWCg6KSkg aWUgdGhlIG1heCBvZiB0aGUgZGVzaWduIG1hdHJpeCAuLiBZb3UgY2FuIGZp bmQgYW4gZXhwbGFuYXRpb24gb2YgdGhhdCBvbiBteSB3ZWJzaXRlLiBBcyBm b3IgcmVwb3J0aW5nIG92ZXIgYW4gYXJlYSBleHRyYWN0IHRoZSByZWxldmFu dCBiZXRhIHZhbHVlcyBmb3IgZWFjaCB2b3hlbCwgY29tcHV0ZSB0aGUgJSBz aWduYWwgY2hhbmdlIGFuZCBhdmVyYWdlIChFYXN5Uk9JIGZvciBpbnN0YW5j ZSB3b3VsZCBhbGxvdyB0byBleHRyYWN0IHRoaXMgaQoKSG9wZSB0aGlzIG1h a2VzIHRoaW5ncyBhIGJpdCBjbGVhcmVyIC4uLgpDeXJpbAoKCkRyIEN5cmls IFBlcm5ldApCUklDIC8gU0lOQVBTRQpVbml2ZXJzaXR5IG9mIEVkaW5idXJn aAoKLS0tLS0gUmVwbHkgbWVzc2FnZSAtLS0tLQpGcm9tOiAiQ2F0aCIgPGQ0 OTYwMTAwNEBZTS5FRFUuVFc+CkRhdGU6IEZyaSwgTWF5IDI3LCAyMDExIDA3 OjA1ClN1YmplY3Q6IFtTUE1dIHNtYWxsIHZvbHVtZSBjb3JyZWN0aW9uIHZz LiBtYXNrIGFuZCBvdGhlciByZWxhdGVkIHF1ZXN0aW9ucwpUbzogPFNQTUBK SVNDTUFJTC5BQy5VSz4KCkRlYXIgU1BNIGV4cGVydHMsCgpJIGhhdmUgYSBw cm9ibGVtIHJlZ2FyZGluZyB0aGUgaW5jbHVzaXZlIG1hc2suIE15ICBjb25k dWN0ZWQgYSBtYXNrZWQgcHJpbWluZyBsZXhpY2FsIGRlY2lzaW9uIHRhc2ss IGFuZCB0aGUgcHJvY2VkdXJlIHN0YXJ0ZWQgd2l0aCBhIGZpeGF0aW9uLCBh IG1hc2ssIGEgcHJpbWUsIGFub3RoZXIgbWFzayBhbmQgZmluYWxseSBhIHRh cmdldCB3b3JkIChhbGwgd2l0aGluIG9uZSBUUikuIFRoZSBwcmltZSBjb3Vs ZCBiZSBhIHdvcmQgd2hpY2ggd2FzIHZpc3VhbGx5IHNpbWlsYXIvZGlzc2lt aWxhciB0byB0aGUgdGFyZ2V0IHdvcmQuIFRoZSBwb3NpdGlvbiBvZiB0aGUg cHJpbWUgYW5kIHRoZSB0YXJnZXQgY291bGQgYmUgYSB0b3RhbCBibGFuay4g SSBkZWZpbmVkIGEgcHJpbWUgY29udHJhc3QgKGRpc3NpbWlsYXIgbWludXMg c2ltaWxhcikgYW5kIGEgd29yZCBjb250cmFzdCAodGFyZ2V0IHdvcmQgcHJl c2VudCBtaW51cyBibGFuayApLiBJIGxvb2tlZCBmb3IgYWN0aXZhdGVkIHZv eGVscyBvZiB0aGUgcHJpbWluZyBlZmZlY3Qgd2l0aGluIHRoZSBjb25zdHJh aW50IG9mIHRoZSBhY3RpdmF0aW9uIG1hcHMgb2YgdGhlIHdvcmQgY29udHJh c3QuIFNvIGluIHRoZSBzZWNvbmQgbGV2ZWwgYW5hbHlzaXMsIEkgdXNlZCBz bWFsbCB2b2x1bWUgY29ycmVjdGlvbiBvbiB0aGUgcHJpbWUgY29udHJhc3Qg YnkgZW50ZXJpbmcgdGhlIGltYWdlIG9mIHRoZSB3b3JkIGNvbnRyYXN0LiBJ IGV4cGVjdGVkIHRoYXQgdGhlIGFjdGl2YXRlZCB2b3hlbCBudW1iZXIgdW5k ZXIgdGhlIHByaW1lIGNvbnRyYXN0IHNob3VsZCBkZWNyZWFzZSBhZnRlciBJ IGVudGVyZWQgdGhlIHdvcmQgY29udHJhc3QgaW4gdGhlIHNtYWxsIHZvbHVt ZSBjb3JyZWN0aW9uLiBIb3dldmVyLCB0aGUgYWN0aXZhdGVkIHZveGVsIG51 bWJlciB1bmRlciB0aGUgcHJpbWUgY29udHJhc3QgYWN0dWFsbHkgaW5jcmVh c2VkIGFmdGVyIEkgZW50ZXJlZCB0aGUgd29yZCBjb250cmFzdCBpbiB0aGUg c21hbGwgdm9sdW1lIGNvcnJlY3Rpb24uIENvdWxkIHlvdSBwbGVhc2UgdGVs bCBtZSB3aHkgaXQgd2FzIHRoYXQ/IApXaGF0IGlzIHRoZSBkaWZmZXJlbmNl IGJldHdlZW4gdXNpbmcgYSBtYXNrIGFuZCB1c2luZyB0aGUgc21hbGwgdm9s dW1lIGNvcnJlY3Rpb24/IEZpbmFsbHksIHdoYXQgaXMgdGhlIGRpZmZlcmVu Y2UgYmV0d2VlbiBwZXJjZW50IHNpZ25hbCBjaGFuZ2VzIGFuZCBiZXRhIHZh bHVlIGFuZCBob3cgdG8gb3V0cHV0IHRoZSBwZXJjZW50IHNpZ25hbCBjaGFu Z2VzIG92ZXIgbXVsdGlwbGUgdm94ZWxzPyAKClRoYW5rIHlvdSEKClNpbmNl cmVseSwKQ2F0aAoK ------=_Part_6_1306480699583 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SGkgQ2F0aCw8YnI+PGJyPjFzdCBtYXNraW5nOiB3aGVuIHUgbWFzayB0aGVu IHdob2xlIGFuYWx5c2lzIChpZSB5b3UgZW50ZXJlZCB0aGUgbWFzayBmb3Ig dGhlIGRlc2lnbiBtYXRyaXgpIHRoaXMgbWVhbnMgdGhhdCBvbmx5IHRoZXNl IHZveGVscyBhcmUgYW5hbHl6ZWQgYW5kIHRocmVmb3JlIHRoZSBnbG9iYWxz IHVzZSB0byBhY2NvdW50IGZvciBnbG9iYWwgZWZmZWN0cyAobWVhbiBvdmVy IGFsbCB2b3hlbHMgYWJvdmUgc29tZSB0aHJlc2hvbGQpIHdpbGwgYmUgZGlm ZmVyZW50IGZyb20gYSBub24gbWFza2VkIGFuYWx5c2lzLiBXaXRoIHRoZSBT VkMgdGhlIGdsb2JhbCBhcmUgZm9yIHRoZSB3aG9sZSBicmFpbiBidXQgdGhl biB5b3UgYXBwbHkgYSBzdGF0aXN0aWNhbCBjb3JyZWN0aW9uIGZvciBhIGdp dmVuIHZvbHVtZSB5b3Ugd2FudCB0byBsb29rIGF0IChzZWUgbXJjIGNiaSB3 ZWJzaXRlIGZvciBleHBsYW5hdGlvbnMpIC4uIEJvdGggYXJlIHZhbGlkLjxi cj48YnI+Mm5kIHRoZSAlIGJvbGQ6IHRoZSByZWxhdGlvbnNoaXAgaXMgc3Ry YWlnaHRmb3J3YXJkLCBiZXRhIHJlZmxlY3QgdGhlIHZhcmlhdGlvbiBhcm91 bmQgdGhlIGdyYW5kIG1lYW4gdGhlcmVmb3JlIGJldGEqMTAwL2JldGEgb2Yg dGhlIGNvbnN0YW50ICh0aGUgZnVsbCBjb2x1bW4gb2YgdGhlIGRlc2lnbiBt YXRyaXgpIGlzIHRoZSAlIG9mIHNpZ25hbCBjaGFuZ2UuIEhvd2V2ZXIsIGlm IHRoZSBkZXNpZ24gbWF0cml4IGhhcyB2YWx1ZSBhYm92ZSAxIHlvdSBuZWVk IHRvIHNjYWxlIHRoaXMgc2ltcGx5IGJ5IG11bHRpcGx5aW5nIGJ5IG1heChT UE0ueFguWCg6KSkgaWUgdGhlIG1heCBvZiB0aGUgZGVzaWduIG1hdHJpeCAu LiBZb3UgY2FuIGZpbmQgYW4gZXhwbGFuYXRpb24gb2YgdGhhdCBvbiBteSB3 ZWJzaXRlLiBBcyBmb3IgcmVwb3J0aW5nIG92ZXIgYW4gYXJlYSBleHRyYWN0 IHRoZSByZWxldmFudCBiZXRhIHZhbHVlcyBmb3IgZWFjaCB2b3hlbCwgY29t cHV0ZSB0aGUgJSBzaWduYWwgY2hhbmdlIGFuZCBhdmVyYWdlIChFYXN5Uk9J IGZvciBpbnN0YW5jZSB3b3VsZCBhbGxvdyB0byBleHRyYWN0IHRoaXMgaTxi cj48YnI+SG9wZSB0aGlzIG1ha2VzIHRoaW5ncyBhIGJpdCBjbGVhcmVyIC4u Ljxicj5DeXJpbDxicj48YnI+PGJyPkRyIEN5cmlsIFBlcm5ldDxicj5CUklD IC8gU0lOQVBTRTxicj5Vbml2ZXJzaXR5IG9mIEVkaW5idXJnaDxicj48YnI+ LS0tLS0gUmVwbHkgbWVzc2FnZSAtLS0tLTxicj5Gcm9tOiAmcXVvdDtDYXRo JnF1b3Q7ICZsdDtkNDk2MDEwMDRAWU0uRURVLlRXJmd0Ozxicj5EYXRlOiBG cmksIE1heSAyNywgMjAxMSAwNzowNTxicj5TdWJqZWN0OiBbU1BNXSBzbWFs bCB2b2x1bWUgY29ycmVjdGlvbiB2cy4gbWFzayBhbmQgb3RoZXIgcmVsYXRl ZCBxdWVzdGlvbnM8YnI+VG86ICZsdDtTUE1ASklTQ01BSUwuQUMuVUsmZ3Q7 PGJyPjxicj5EZWFyIFNQTSBleHBlcnRzLDxicj48YnI+SSBoYXZlIGEgcHJv YmxlbSByZWdhcmRpbmcgdGhlIGluY2x1c2l2ZSBtYXNrLiBNeSAmbmJzcDtj b25kdWN0ZWQgYSBtYXNrZWQgcHJpbWluZyBsZXhpY2FsIGRlY2lzaW9uIHRh c2ssIGFuZCB0aGUgcHJvY2VkdXJlIHN0YXJ0ZWQgd2l0aCBhIGZpeGF0aW9u LCBhIG1hc2ssIGEgcHJpbWUsIGFub3RoZXIgbWFzayBhbmQgZmluYWxseSBh IHRhcmdldCB3b3JkIChhbGwgd2l0aGluIG9uZSBUUikuIFRoZSBwcmltZSBj b3VsZCBiZSBhIHdvcmQgd2hpY2ggd2FzIHZpc3VhbGx5IHNpbWlsYXIvZGlz c2ltaWxhciB0byB0aGUgdGFyZ2V0IHdvcmQuIFRoZSBwb3NpdGlvbiBvZiB0 aGUgcHJpbWUgYW5kIHRoZSB0YXJnZXQgY291bGQgYmUgYSB0b3RhbCBibGFu ay4gSSBkZWZpbmVkIGEgcHJpbWUgY29udHJhc3QgKGRpc3NpbWlsYXIgbWlu dXMgc2ltaWxhcikgYW5kIGEgd29yZCBjb250cmFzdCAodGFyZ2V0IHdvcmQg cHJlc2VudCBtaW51cyBibGFuayApLiBJIGxvb2tlZCBmb3IgYWN0aXZhdGVk IHZveGVscyBvZiB0aGUgcHJpbWluZyBlZmZlY3Qgd2l0aGluIHRoZSBjb25z dHJhaW50IG9mIHRoZSBhY3RpdmF0aW9uIG1hcHMgb2YgdGhlIHdvcmQgY29u dHJhc3QuIFNvIGluIHRoZSBzZWNvbmQgbGV2ZWwgYW5hbHlzaXMsIEkgdXNl ZCBzbWFsbCB2b2x1bWUgY29ycmVjdGlvbiBvbiB0aGUgcHJpbWUgY29udHJh c3QgYnkgZW50ZXJpbmcgdGhlIGltYWdlIG9mIHRoZSB3b3JkIGNvbnRyYXN0 LiBJIGV4cGVjdGVkIHRoYXQgdGhlIGFjdGl2YXRlZCB2b3hlbCBudW1iZXIg dW5kZXIgdGhlIHByaW1lIGNvbnRyYXN0IHNob3VsZCBkZWNyZWFzZSBhZnRl ciBJIGVudGVyZWQgdGhlIHdvcmQgY29udHJhc3QgaW4gdGhlIHNtYWxsIHZv bHVtZSBjb3JyZWN0aW9uLiBIb3dldmVyLCB0aGUgYWN0aXZhdGVkIHZveGVs IG51bWJlciB1bmRlciB0aGUgcHJpbWUgY29udHJhc3QgYWN0dWFsbHkgaW5j cmVhc2VkIGFmdGVyIEkgZW50ZXJlZCB0aGUgd29yZCBjb250cmFzdCBpbiB0 aGUgc21hbGwgdm9sdW1lIGNvcnJlY3Rpb24uIENvdWxkIHlvdSBwbGVhc2Ug dGVsbCBtZSB3aHkgaXQgd2FzIHRoYXQ/IDxicj5XaGF0IGlzIHRoZSBkaWZm ZXJlbmNlIGJldHdlZW4gdXNpbmcgYSBtYXNrIGFuZCB1c2luZyB0aGUgc21h bGwgdm9sdW1lIGNvcnJlY3Rpb24/IEZpbmFsbHksIHdoYXQgaXMgdGhlIGRp ZmZlcmVuY2UgYmV0d2VlbiBwZXJjZW50IHNpZ25hbCBjaGFuZ2VzIGFuZCBi ZXRhIHZhbHVlIGFuZCBob3cgdG8gb3V0cHV0IHRoZSBwZXJjZW50IHNpZ25h bCBjaGFuZ2VzIG92ZXIgbXVsdGlwbGUgdm94ZWxzPyA8YnI+PGJyPlRoYW5r IHlvdSE8YnI+PGJyPlNpbmNlcmVseSw8YnI+Q2F0aDxicj48YnI+PGJyPjxi cj4= ------=_Part_6_1306480699583-- ------------=_1306480740-9283-107 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. ------------=_1306480740-9283-107-- ========================================================================= Date: Fri, 27 May 2011 10:46:58 +0100 Reply-To: Molly Henry <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Molly Henry <[log in to unmask]> Subject: comparing trials with different durations 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 know variants of this question appear in the archives. However I still = don't understand what I can practically *do* to help this problem, so I a= sk a new version of an old question.=20 In a design where trials making up two to-be-compared conditions by natur= e have different durations, the assumption is that the longer trials will= have overall a higher HRF amplitude (although why / when this happens de= pends on use of impluse vs. epoch functions). If I compare the short tria= ls to the long trials (the two trial types happen in separate blocks), is= it likely that differences in signal change are an artifact of modeling = assumptions regarding HRF amplitude? If so, which direction should I expe= ct artifactual differences (short < long or long < short)? And finally, w= hat strategy can I use to combat this possible problem.=20 Humbly, Molly ========================================================================= Date: Fri, 27 May 2011 12:43:51 +0200 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: Re: question regarding matlabbatch struct/class Comments: To: Volkmar Glauche <[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 Volkmar & list, When running spm_jobman from the command line in 'serial' mode, what is the= correct input format for dicom files/already imported nifti files, respect= ively ?=20 Best wishes, Martin=20 On May 26, 2011, at 10:04 AM, Volkmar Glauche wrote: > To run a batch from MATLAB command line, SPM always has to be initialised= . At least,=20 >=20 > spm_jobman('initcfg') >=20 > should be run before. If one needs to rely on defaults which can not be s= et in a batch (e.g. in first level fMRI analysis), one should run >=20 > spm('defaults','fmri') =18r 'pet' or 'eeg', depending on the modality > spm_jobman('initcfg') >=20 > Best, >=20 > Volkmar ========================================================================= Date: Fri, 27 May 2011 13:05:35 +0200 Reply-To: Raphael Hilgenstock <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Raphael Hilgenstock <[log in to unmask]> Subject: Re: Power analysis Comments: To: Mayuresh K <[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 Mayuresh, you might want to use the "threshold and transform spm-t/F-maps" option as implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/software/) which allows you to calculate effect size measure 'd' for the respective spm-volume. I hope this helps. Best regards Raphael Am 27.05.2011 08:21, schrieb Mayuresh K: > Hello spm experts, > > Could anyone guide me how to do a power analysis using fMRI or VBM > data? I have performed a random effects analysis comparing two groups > of subjects using their fMRI and VBM data. I have found a number of > regions with significant differences for both data sets. > I can probably calculate the mean and standard deviation of the > %signal change (or volume) for each group for the peak voxels say in a > particular anatomical region. Then use these to calculate effect sizes > and power - Is this a valid approach? > Any other suggestions welcome. > > Thanks, > Mayuresh > ========================================================================= Date: Fri, 27 May 2011 12:58:49 +0100 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: Re: Power analysis In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1306497532-19691-145" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1306497532-19691-145 Received: from mail-pw0-f46.google.com (mail-pw0-f46.google.com [209.85.160.46]) (authenticated user=malexa mech=PLAIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p4RBwol1019477 (version=TLSv1/SSLv3 cipher=RC4-SHA bits=128 verify=NOT) for <[log in to unmask]>; Fri, 27 May 2011 12:58:51 +0100 (BST) Received: by pwi15 with SMTP id 15so1041209pwi.33 for <[log in to unmask]>; Fri, 27 May 2011 04:58:49 -0700 (PDT) MIME-Version: 1.0 Received: by 10.68.12.72 with SMTP id w8mr906003pbb.165.1306497529852; Fri, 27 May 2011 04:58:49 -0700 (PDT) Received: by 10.68.47.70 with HTTP; Fri, 27 May 2011 04:58:49 -0700 (PDT) In-Reply-To: <[log in to unmask]> References: <[log in to unmask]> <[log in to unmask]> Date: Fri, 27 May 2011 12:58:49 +0100 Message-ID: <[log in to unmask]> Subject: Re: [SPM] Power analysis From: Alexa Morcom <[log in to unmask]> To: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> Content-Type: multipart/alternative; boundary=bcaec51f8fed2ab38504a440a916 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 --bcaec51f8fed2ab38504a440a916 Content-Type: text/plain; charset=ISO-8859-1 Content-Disposition: inline Hello Mayuresh Jeanette Mumford has some great resources on this and related issues at: http://mumford.bol.ucla.edu/ and see her 2008 paper with Tom Nichols The software for doing power analyses in FSL is at http://fmripower.org/ - this takes autocorrelation and design into account It would be great to see this or something like it for SPM! cheers Alexa On 27 May 2011 12:05, Raphael Hilgenstock <[log in to unmask]>wrote: > Hi Mayuresh, > > you might want to use the "threshold and transform spm-t/F-maps" option as > implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/software/) > which allows you to calculate effect size measure 'd' for the respective > spm-volume. I hope this helps. > > Best regards > Raphael > > Am 27.05.2011 08:21, schrieb Mayuresh K: > >> Hello spm experts, >> >> Could anyone guide me how to do a power analysis using fMRI or VBM data? I >> have performed a random effects analysis comparing two groups of subjects >> using their fMRI and VBM data. I have found a number of regions with >> significant differences for both data sets. >> I can probably calculate the mean and standard deviation of the %signal >> change (or volume) for each group for the peak voxels say in a particular >> anatomical region. Then use these to calculate effect sizes and power - Is >> this a valid approach? >> Any other suggestions welcome. >> >> Thanks, >> Mayuresh >> >> > -- 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 --bcaec51f8fed2ab38504a440a916 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Hello Mayuresh<br><br>Jeanette Mumford has some great resources on this and= related issues at:<br><br><a href=3D"http://mumford.bol.ucla.edu/">http://= mumford.bol.ucla.edu/</a> and see her 2008 paper with Tom Nichols <br><br> The software for doing power analyses in FSL is at <a href=3D"http://fmripo= wer.org/">http://fmripower.org/</a> - this takes autocorrelation and design= into account<br><br>It would be great to see this or something like it for= SPM!<br> <br>cheers<br><br>Alexa <br><br><div class=3D"gmail_quote">On 27 May 2011 1= 2:05, Raphael Hilgenstock <span dir=3D"ltr"><<a href=3D"mailto:Raphael.H= [log in to unmask]">[log in to unmask]</a>></span> wrot= e:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex;">Hi Mayuresh,<br> <br> you might want to use the "threshold and transform spm-t/F-maps" = option as implemented in the VBM8 toolbox (<a href=3D"http://dbm.neuro.uni-= jena.de/software/" target=3D"_blank">http://dbm.neuro.uni-jena.de/software/= </a>) which allows you to calculate effect size measure 'd' for the= respective spm-volume. I hope this helps.<br> <br> Best regards<br> Raphael<br> <br> Am 27.05.2011 08:21, schrieb Mayuresh K:<br> <blockquote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1p= x #ccc solid;padding-left:1ex"> Hello spm experts,<br> <br> Could anyone guide me how to do a power analysis using fMRI or VBM data? I = have performed a random effects analysis comparing two groups of subjects u= sing their fMRI and VBM data. I have found a number of regions with signifi= cant differences for both data sets.<br> I can probably calculate the mean and standard deviation of the %signal cha= nge (or volume) for each group for the peak voxels say in a particular anat= omical region. Then use these to calculate effect sizes and power - Is this= a valid approach?<br> Any other suggestions welcome.<br> <br> Thanks,<br> Mayuresh<br> <br> </blockquote> <br> </blockquote></div><br><br clear=3D"all"><br>-- <br><font size=3D"2">Dr. Al= exa Morcom<br>RCUK Academic Fellow<br>Centre for Cognitive & Neural Sys= tems/ Centre for Cognitive Ageing & Cognitive Epidemiology<br>Psycholog= y, University of Edinburgh<br> <a href=3D"http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html" t= arget=3D"_blank">http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.h= tml</a> <br><br>The University of Edinburgh is a charitable body, registere= d in Scotland, with registration number SC005336 =A0</font><p> </p> <br> --bcaec51f8fed2ab38504a440a916-- ------------=_1306497532-19691-145 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. ------------=_1306497532-19691-145-- ========================================================================= Date: Fri, 27 May 2011 13:09:41 +0100 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: 1d images Comments: To: Mkael Symmonds <[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 Mkael, According to Guillaume, if the signal of interest is in the first dimension of an image file, everything should work normally. I've never tried it myself. Best, Vladimir On Fri, May 27, 2011 at 1:06 PM, Mkael Symmonds <[log in to unmask]> wrote: > > Hi Vladimir > Do you know if it is possible to use SPM to analyse 1d images (of power at > single band in a single channel over trials) ? > Best > Gareth and Mkael > ========================================================================= Date: Fri, 27 May 2011 16:12:38 +0100 Reply-To: Jasmin Tr=?UTF-8?Q?=C3=B6stl?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Jasmin Tr=?UTF-8?Q?=C3=B6stl?= <[log in to unmask]> Subject: Re: Help with DTI DARTEL 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]> Dear dartel specialists, I am following the outlines from the paper posted in the previous message= : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889147/?tool=3Dpubmed Everything works fine until the following steps: "(c) the custom FA template was normalized to the custom T1-weighted temp= late using 12 parameters in FLIRT [http://www.fmrib.ox.ac.uk/fsl/flirt/in= dex.html, 37]; (d) native space FA and MD images were normalized to the custom FA templa= te (from step (c));=20 (e) the normalization parameters from step (d) were multiplied by the inv= erse transformation parameters for the second T1-weighted segmentation (V= BM step (iv)) and applied to both the FA and MD images;=20 (f) FA and MD images were rigidly aligned using the rigid-body component = of the transformations produced in VBM step (iv) and resampled to 1.5 mm = isotropic voxels;"=20 but step (d) and (e) make problems.=20 In step (d) I just normalize the FA and MD images to the new custom FA te= mplate using Estimate and Normalize from SPM8. In step (e) I use the inverse transformation matrix from the second run o= f the VBM5.1 toolbox estimate and write (segment and normalization to cus= tom prior I calculated from the first run). I just use normalize(write) t= o normalize the images resulting from step (d). In step (f) I apply the non inverse transformation matrix from the second= run of the VBM5.1 on the results of (e) using Normalise(write) with foll= owing options: bounding box filled with NaNs and Voxelsize with 1.5 1.5 1= .5=20 when I look at the results I see the following: after step (d) the images are kind of "compressed" and after step (e) par= ts of the brain are just missing (cut off). I try to follow the guidelines of the paper mentioned above. I am new to = VBM-Dartel methods and I don't know what I am doing wrong.=20 Thank you in advance for every hint and improvement you can give me! Best regards Jasmin Tr=C3=B6stl ========================================================================= Date: Fri, 27 May 2011 16:14:12 +0100 Reply-To: "<Emma> <Smith>" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "<Emma> <Smith>" <[log in to unmask]> Subject: Masking with contrast images from other analysis in spm8 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, Can somebody point out how to use explicit masking with a contrast image = form a different analysis in spm8 (the program allows only for masking wi= th a contrast from the same model)? Thank you. Emma ========================================================================= Date: Fri, 27 May 2011 16:17:21 +0000 Reply-To: Giuseppe Signorello <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Giuseppe Signorello <[log in to unmask]> Subject: T and F Contrasts Content-Type: multipart/alternative; boundary="_98233e86-6917-445c-a5b3-570a3e509750_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_98233e86-6917-445c-a5b3-570a3e509750_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable I'm working with FMRI files. After the preprocessing=2C I should extract time series. How do I define the T and F contrasts? Thank for your answer. G.S. = --_98233e86-6917-445c-a5b3-570a3e509750_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> I'm working with FMRI files.<br>After the preprocessing=2C I should extract= time series.<br>How do I define the T and F contrasts?<br>Thank for your a= nswer.<br>G.S.<br><br> </body> </html>= --_98233e86-6917-445c-a5b3-570a3e509750_-- ========================================================================= Date: Fri, 27 May 2011 09:19:38 -0700 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: Re: T and F Contrasts Comments: To: Giuseppe Signorello <[log in to unmask]> In-Reply-To: <[log in to unmask]> Content-Type: multipart/related; boundary="_004_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_"; type="multipart/alternative" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_004_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_ Content-Type: multipart/alternative; boundary="_000_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_" --_000_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable I think you need to be more specific about what you are asking for so someo= ne can help you. ---------------------------------------------------------------------------= --------------------------------------------------- [cid:image001.jpg@01CC1C4F.33ED5E10] Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,C= A 90095 Tel: (310)206-1423|Email: [log in to unmask] From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On B= ehalf Of Giuseppe Signorello Sent: Friday, May 27, 2011 9:17 AM To: [log in to unmask] Subject: [SPM] T and F Contrasts I'm working with FMRI files. After the preprocessing, I should extract time series. How do I define the T and F contrasts? Thank for your answer. G.S. ________________________________ IMPORTANT WARNING: This email (and any attachments) is only intended for th= e use of the person or entity to which it is addressed, and may contain inf= ormation that is privileged and confidential. You, the recipient, are oblig= ated to maintain it in a safe, secure and confidential manner. Unauthorized= redisclosure or failure to maintain confidentiality may subject you to fed= eral and state penalties. If you are not the intended recipient, please imm= ediately notify us by return email, and delete this message from your compu= ter. --_000_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable <html xmlns:v=3D"urn:schemas-microsoft-com:vml" xmlns:o=3D"urn:schemas-micr= osoft-com:office:office" xmlns:w=3D"urn:schemas-microsoft-com:office:word" = xmlns:m=3D"http://schemas.microsoft.com/office/2004/12/omml" xmlns=3D"http:= //www.w3.org/TR/REC-html40"> <head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Dus-ascii"= > <meta name=3D"Generator" content=3D"Microsoft Word 12 (filtered medium)"> <!--[if !mso]> <style> v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} </style> <![endif]--><style> <!-- /* Font Definitions */ @font-face {font-family:SimSun; panose-1:2 1 6 0 3 1 1 1 1 1;} @font-face {font-family:SimSun; panose-1:2 1 6 0 3 1 1 1 1 1;} @font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4;} @font-face {font-family:Tahoma; panose-1:2 11 6 4 3 5 4 4 2 4;} @font-face {font-family:SimSun; panose-1:2 1 6 0 3 1 1 1 1 1;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {margin:0in; margin-bottom:.0001pt; font-size:12.0pt; font-family:"Times New Roman","serif";} a:link, span.MsoHyperlink {mso-style-priority:99; color:blue; text-decoration:underline;} a:visited, span.MsoHyperlinkFollowed {mso-style-priority:99; color:purple; text-decoration:underline;} p {mso-style-priority:99; mso-margin-top-alt:auto; margin-right:0in; mso-margin-bottom-alt:auto; margin-left:0in; font-size:12.0pt; font-family:"Times New Roman","serif";} p.MsoAcetate, li.MsoAcetate, div.MsoAcetate {mso-style-priority:99; mso-style-link:"Balloon Text Char"; margin:0in; margin-bottom:.0001pt; font-size:8.0pt; font-family:"Tahoma","sans-serif";} span.EmailStyle18 {mso-style-type:personal-reply; font-family:"Calibri","sans-serif"; color:#1F497D;} span.BalloonTextChar {mso-style-name:"Balloon Text Char"; mso-style-priority:99; mso-style-link:"Balloon Text"; font-family:"Tahoma","sans-serif";} .MsoChpDefault {mso-style-type:export-only; font-size:10.0pt;} @page Section1 {size:8.5in 11.0in; margin:1.0in 1.25in 1.0in 1.25in;} div.Section1 {page:Section1;} --> </style><!--[if gte mso 9]><xml> <o:shapedefaults v:ext=3D"edit" spidmax=3D"2050" /> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext=3D"edit"> <o:idmap v:ext=3D"edit" data=3D"1" /> </o:shapelayout></xml><![endif]--> </head> <body lang=3D"EN-US" link=3D"blue" vlink=3D"purple"> <div class=3D"Section1"> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><o:p> </o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D">I think you need to be more specific about what you are aski= ng for so someone can help you. <o:p></o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><o:p> </o:p></span></p> <div> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D">------------------------------------------------------------= ------------------------------------------------------------------<o:p></o:= p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><img width=3D"448" height=3D"48" id=3D"Picture_x0020_1" src= =3D"cid:image001.jpg@01CC1C4F.33ED5E10" alt=3D"logo"><o:p></o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D">Tony Jiang,Ph.D.|Research Scholar|University of California, = Los Angeles<br> Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,C= A 90095<o:p></o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D">Tel: (310)206-1423|Email: [log in to unmask]<o:p></= o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><o:p> </o:p></span></p> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><o:p> </o:p></span></p> </div> <p class=3D"MsoNormal"><span style=3D"font-size:11.0pt;font-family:"Ca= libri","sans-serif"; color:#1F497D"><o:p> </o:p></span></p> <div> <div style=3D"border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0in = 0in 0in"> <p class=3D"MsoNormal"><b><span style=3D"font-size:10.0pt;font-family:"= ;Tahoma","sans-serif"">From:</span></b><span style=3D"font-s= ize:10.0pt;font-family:"Tahoma","sans-serif""> SPM (Sta= tistical Parametric Mapping) [mailto:[log in to unmask]] <b>On Behalf Of </b>Giuseppe Signorello<br> <b>Sent:</b> Friday, May 27, 2011 9:17 AM<br> <b>To:</b> [log in to unmask]<br> <b>Subject:</b> [SPM] T and F Contrasts<o:p></o:p></span></p> </div> </div> <p class=3D"MsoNormal"><o:p> </o:p></p> <p class=3D"MsoNormal" style=3D"margin-bottom:12.0pt"><span style=3D"font-s= ize:10.0pt; font-family:"Tahoma","sans-serif"">I'm working with FMR= I files.<br> After the preprocessing, I should extract time series.<br> How do I define the T and F contrasts?<br> Thank for your answer.<br> G.S.<o:p></o:p></span></p> </div> <br> <hr> <font face=3D"Arial" color=3D"Navy" size=3D"2">IMPORTANT WARNING: This emai= l (and any attachments) is only intended for the use of the person or entit= y 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. Un= authorized 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.<br> </font> </body> </html> --_000_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_-- --_004_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_ Content-Type: image/jpeg; name="image001.jpg" Content-Description: image001.jpg Content-Disposition: inline; filename="image001.jpg"; size=8130; creation-date="Fri, 27 May 2011 09:19:38 GMT"; modification-date="Fri, 27 May 2011 09:19:38 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_D8D86424FFCFA144BDA7076B5BDF7BFC031DCB91F9EMGMB6admedct_-- ========================================================================= Date: Fri, 27 May 2011 13:42:15 -0400 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-classification of gray matter using New segment tools MIME-Version: 1.0 Content-Type: multipart/mixed; boundary=000e0cd1512a8aeab904a44576a2 Message-ID: <[log in to unmask]> --000e0cd1512a8aeab904a44576a2 Content-Type: multipart/alternative; boundary=000e0cd1512a8aeaac04a44576a0 --000e0cd1512a8aeaac04a44576a0 Content-Type: text/plain; charset=ISO-8859-1 Dear experts, I am using the new segment tool to run segmentations on several MRI images. For one of the subject, the tissues outside the cortices are mis-classified as gray matter and warped into the cortices after normalization (please see attached figures). I use the default settings of number of Gaussians for each tissue type. Can someone kindly give me some suggestions on how to avoid this mis-classification? Thank you! Sincerely, -- Xin Di, PhD Postdoctoral Researcher Department of Radiology University of Medicine and Dentistry of New Jersey 30 Bergen Street, ADMC 575 Newark, NJ 07101 --000e0cd1512a8aeaac04a44576a0 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear experts,<br><br>I am using the new segment tool to run segmentations o= n several MRI images. For one of the subject, the tissues outside the corti= ces are mis-classified as gray matter and warped into the cortices after no= rmalization (please see attached figures). I use the default settings of nu= mber of Gaussians for each tissue type. Can someone kindly give me some sug= gestions on how to avoid this mis-classification? Thank you!<br> <br>Sincerely,<br clear=3D"all"><br>-- <br>Xin Di, PhD<br><br>Postdoctoral = Researcher<br>Department of Radiology<br>University of Medicine and Dentist= ry of New Jersey<br> 30 Bergen Street, ADMC 575<br>Newark, NJ 07101<br><br> --000e0cd1512a8aeaac04a44576a0-- --000e0cd1512a8aeab904a44576a2 Content-Type: image/jpeg; name="native_space.jpg" Content-Disposition: attachment; filename="native_space.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_go7eyzmt0 /9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEP ERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4e Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCAIYAhgDASIA AhEBAxEB/8QAHQABAAEFAQEBAAAAAAAAAAAAAAcEBQYICQMCAf/EAFQQAAIBBAECAwQECgUKBAIK AwECAwAEBREGEiEHEzEIIkFRFDdhdRUYIzJWcZSzwdEXQoGR0hYkM1JVkpWh09QJYrHjNPAmQ0ZT V3KEouHxNWOC/8QAHAEBAAIDAQEBAAAAAAAAAAAAAAECBAUGAwcI/8QAOREAAgECBQIFAgUDBAIC AwAAAAECAxEEBRIhMUFRBhMiYXEykRSBodHwQrHxBxUjwVLhFiRTYnL/2gAMAwEAAhEDEQA/ANr+ XZ6145jp8tkbkW9haRiW4c60qg9zs9h+skAfEioou/aA4W6obXm2MhdSdh5rZ1YfaBIDv9RrIPak +pHmP3PL/GuXVUabfJa6SOjv9P8Axr9O+P8A+7D/ANxT+n/jX6d8f/3Yf+4rnFSml9ydS7HR3+n/ AI1+nfH/APdh/wC4p/T/AMa/Tvj/APuw/wDcVzipTS+41LsdHf6f+Nfp3x//AHYf+4p/T/xr9O+P /wC7D/3Fc4qU0vuNS7HR3+n/AI1+nfH/APdh/wC4p/T/AMa/Tvj/APuw/wDcVzipTS+41LsdHf6f +Nfp3x//AHYf+4p/T/xr9O+P/wC7D/3Fc4qU0vuNS7HR3+n/AI1+nfH/APdh/wC4p/T/AMa/Tvj/ APuw/wDcVzipTS+41LsdHf6f+Nfp3x//AHYf+4r0tvH/AIiJg11znEOgB9yF7dNn7SZW7fq1XN6l NL7kal2Olf4wnAv0tx37Xbf46fjCcC/S3Hftdt/jrmpSml9ydS7HSv8AGE4F+luO/a7b/HT8YTgX 6W479rtv8dc1KU0vuNS7HS8e0PwAf/afFn/9XB/1KfjEcA/SbFftcH/UrmhSml9yNS7HS/8AGI4B +k2K/a4P+pT8YjgH6TYr9rg/6lc0KU0vuNS7HS/8YjgH6TYr9rg/6lPxiOAfpNiv2uD/AKlc0KU0 vuNS7HS/8YjgH6TYr9rg/wCpT8YjgH6TYr9rg/6lc0KU0vuNS7HS/wDGI4B+k2K/a4P+pT8YjgH6 TYr9rg/6lc0KU0vuNS7HS8+0PwA//afFj/8AVwf9SvlfaF4CPXlWNb9d3b/weuaVKaX3Gpdjpf8A jEcA/SbFftcH/Up+MRwD9JsV+1wf9SuaFKaX3Gpdjpf+MRwD9JsV+1wf9Sn4xHAP0mxX7XB/1K5o Uppfcal2Ol/4xHAP0mxX7XB/1KfjEcA/SbFftcH/AFK5oUppfcal2Ol/4xHAP0mxX7XB/wBSn4xH AP0mxX7XB/1K5oUppfcal2Ol/wCMRwD9JsV+1wf9Sn4xHAP0mxX7XB/1K5oUppfcal2Olre0LwE+ nKsav6ru3/i9fjePfCZFSeDnmAjKlle3ubuJeoHRDBkDa13GvtP2VzTpTS+41LsdKv6feI/ppxL/ AIr/AO1T+n3iP6acS/4r/wC1XNWlNL7i67HSr+n3iP6acS/4r/7VP6feI/ppxL/iv/tVzVpTS+4u ux0q/p94j+mnEv8Aiv8A7VP6feI/ppxL/iv/ALVc1aU0vuLrsdKv6feI/ppxL/iv/tU/p94j+mnE v+K/+1XNWlNL7i67HSr+n3iP6acS/wCK/wDtU/p94j+mnEv+K/8AtVzVpTS+4uux0q/p94j+mnEv +K/+1X6njzwxpA83PeMwxxgsUhvlkaU9J0vvIoHcg738B6Ddc1KU0vuLrsdK/wAYTgX6W479rtv8 dPxhOBfpbjv2u2/x1zUpTS+5OpdjpX+MJwL9Lcd+123+On4wnAv0tx37Xbf465qUppfcal2Olf4w nAv0tx37Xbf46fjCcC/S3Hftdt/jrmpSml9xqXY6V/jCcC/S3Hftdt/jr0x/j1wOQ3D3PPsJCpl/ JI9zESE6V/1dj87q+Nc0KU0vuRqXY6cf07+HP/4iYH9oT+VP6d/Dn/8AETA/tCfyrmPSml9xddjr rxnPxZ2zx2Rx99HeWN5to5owOmVOliCDr07Dv8aVg3sz/UzwH7tj/dtSphwJclT7Un1I8x+55f41 y6rqL7Un1I8x+55f41y6ouWQ+BSlKsQKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCl KUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQ ClKUApSlAKUpQHTz2Z/qZ4D92x/u2pT2Z/qZ4D92x/u2pVIcFpclT7Un1I8x+55f41y6rqL7Un1I 8x+55f41y6qVyyHwKUpViBSlKAUpSgFKV+6PyNAflK+uhv8AVP8AdTob/VNAfNK/Srf6pr8oBSlK AUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBS lKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgOnnsz/UzwH7tj/dtSnsz/UzwH7tj/dtSqQ4LS5Kn2pP qR5j9zy/xrl1XUX2pPqR5j9zy/xrl1UrlkPgUpSrEClKUAr6RCxHy+dVuNxV1fSRLEh3M/lxDXvS t291F9WPcDSgnuK2M8OPZJ5tnrM3WduoMAp15KzReax0TssgZfdI0Ro779wNaIGuUFmWIIXsfi1e xs+nXU4169vhW+mC9krhlizSX2Qvb1pOlRG2vLiAB3rXSTs63vfw1obq/Qey74ZKCZrKSR9diHZQ D32dBu/Y60d/P11QHO4W6Hetgb1oH1r6azUaBDd/lXRBPZW8IRIrti8noAjpXKXAH6/z91kMvgj4 X2sLtJhbW3jYjbMsa/2bK9qA5nC0USFQJNfZqvKeyA7r3BHqe1dHMn7OPhPmLGK1hspAbZOmOSK8 fqGwNFyjKXPb1ck+vzNRdzD2PkjxTSca5BNPexq5Au1UCU6PSp6FHR36feAPYH3STugNJpYmjJ2N a9a86kLn3AeTcOvJLDP4qW0KSGNZdbjc+93Rvj+ax0dMANlRusIurRo/eBBHz7UBSUpSgFKUoBSl KAUpSgFKUoBSlKAV9KjN6CvS3geVwoUkn0AFX3H4O/lmit4bG4mnl35USRl3k0NnoUAs2h3Oh2He gLRDZFvzj/8AzVTFaIGJG2PwAHbdSJYeDviHeW8Vza8ZvLiKQBl6HjVlBUEdSuylTs60dEEHY9N5 ljPZg8UspY29zDjLay8wEtFfzeU8fft/ow4O/X19NfGgIJ+jq+x06Ir6Nsu9dA7f/PxrabE+yDyS WGE5DKwwzEAyBNFA3bYB31ED5lQfsqrb2Ls9pSOeYs9u4ONk/u/0tAamG3hOuwG/s718PZxdiAQP 11t0/sXZllP/ANPMWDrsBi5Nfvax/lfsic8tHhTA3mNyg6Py8ss/kDY1+aNMSD37H018aA1fksiO 6NsH03Xg1vKN+7vXyqc+S+zv4p4htR8Xub9BEHeW1aMxqfQrt2U7+3p9CPtAwDM8M5JhzdNksRfW 0NoSJbma3kigB6gnaR1CtskAaPf4boDByCDogg/bX5V7+jCaB5BGJEXt1juN/rH2HdW+4s3QFlU+ vpQFJSv0gg6PavygFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgOnnsz/Uz wH7tj/dtSnsz/UzwH7tj/dtSqQ4LS5Kn2pPqR5j9zy/xrl1XUX2pPqR5j9zy/wAa5dVK5ZD4FKUq xArMfCzw/wA7z3PJZYu1fyFYefcsm44v1/M+ulHcnQ7DbDH+N4tsxm7TH+Z5Mc0qpJMRsRr6s2vj pQTr46rb3wtz+I8LuBXeQxMSRY8TCKDJXaCWe+uE92YwWyydKbEZ6CzjoIdpF0pJAkjwk8K/Dnwa ssdnOSS21xy6UdMc9wQ87SFX92CLv0noDjS+ijRJ6d1Z/Gn2iDjIJ7DAXc2OuWGozAIZbhGG297r V44wddJ92Qjq2B8Rr74peJVzy7MZGUXkWOtbnpV55VP0y7h6WjCSBSetNvIQgAUb9NrusHXJYXHq biBDfT9ZHRJDIGlBQ+hKgA9ZHz90E9ydVDaXJeNOct4q5I+T8Z+Z5y6mEt5yHIW56TGDlJrbpToQ Hf0Zo1JLIzbAGutlGh2NXgfFvk2KuBFd4ayhsOhVSOR55nYDp6up2mPUSoYbI3sgnq0Q0Y2vKGmt Imt8R5Xmv0OzXKxhNHQIOiSPmddiCO9esWS+j/SUmuVDPLu3kCO56Af656h1Ej4DWiN9/SpKEqZv xnyk8V/bW2D46uJuwgNpPjCZXQgiWNt6Vg3Yh/h3BVt7GGZrlX4c84Y/hfE7SeVuqW+OJhkkLk7Y /lEYEnuCSD6/A96xq+y2HuMvbySpLJaRjbIWPUx38fl+vtX1k8ysuPtbK1yctmI4+maW2jCtM3SN Psd194EkDWwxG6Arzf8AKLPHo1na22KnJKm9xFnDZXTqD1dPmRRqQNgemt6H27lnw49oPlfHZyct d311EQkcNnkZWmtyvvLoSn8pGQPLZpGaYnT6QbAqF05Na2MjG9WS5eY9UbQL1RAK+1IDn3WGgdbb XYg/L3j5tjL3HNBlYvo0m1GipZTttbHSNEAdz6H5dVRdcFtErarbG+nGudcC57x7H3ueOKxl5dAN HHcXAAZujq1HI6oX93fulVYdLbUaqO/Gb2aeM8kwQyXBktLCZbctDDboqW0+yGGwg7bAIDKO3Vsq 2tVrdZ3cuFvrjH4bIW9vd5CE2txj2EU4mXpY6ZGDADpLMOoa79/XRmLwQ8Zp+MZH/JuS3vBZvJI8 eMmk85FYRx9MVpK7KLdD0vqJuqMH0ePqCmSpqpz3hHIuH5JrTN4i9x79TBVuIwpPTreiCVYDqXup I763vYrF66tcq4fwbxU460eRx8c/UQ4ZgUnt5B89d1b1B+BBI7g9+ePj34W5Xw25hc426tmjgYtN asHDrLB1HpdW0N69GGgVP2FWYCM6UpQClKUApSlAKUpQCpL8JPCHl3O51bFYYyiROqF7ovFARsgM XCnY2D2HwB2V2vVivAMJPm+R20SWD30McitLCNgS/ER7AJHUR070db3o+lbvQ+I1t4ecWis2TAca ztyV8qCdZb8w25YhB5ERRlXS93LKgbts6GwPfiPskcTsEilzuYvb2Xyl8yC1P0eFZdAFkZNS63vQ Lkd/TsNSGcJ4ReGmEnnFnh8dBaq80yQqvUzKp6mKL8dLrZ7DsCa1Z574/wCZ5FZy2NxfXlzEZUYJ BdyWbMfI08Xk2rq6ASbP5SWUHuCR7vTEmQy2Ru3yN5Hii0uTCG+eVhHDcMrbDyQp7hYgdZbtpmY/ MtSdWEPqdjKw+BxOJ3o03L4TZuY3tP8AFvwE+TxHFr+O1WJnRr26t4g7CQxqFS3eeUqXVh1BNaRj 31WE5r2qOT3OZvrDFYjB46xiY+RkSsl/5o7dKiMNCQ2j3Y7G1IAOwa1blyGa8uRLWPFxmJOkCJS7 RhNdlViw+Q1rt/dVO17y+G2ULfzxLFKJR0qqne999DZXv+ae3w1oV5/iaX/kZTyXHJNuk9iZ29pT xxtw0c+UxEjoWdHSwVQepjpXBTuqgkADpbaqSzaYNWp7QniItmz3PKOTC5KkeUkON6Orvr3/AKHv XzPT8/XVQNJmeTM81tG3QbiWSV+lOpkDdI0GbZRF7aII1v1r7yV3yCa2CNlIvLA6xDC/Sy60Apc9 27E9yzb6e5J1v1Uk1dGBOjOnLTPZ+5IeM8XfEe18SL3mmPzk6X1xZJaeVfzmZGRNH8qoiUON+YwC hOkv293atO/A/aTyj3Fxb53N4u/PUnlM2FktTs9RZdrK/wAFHdgo2QAWJ0NP5GykQilhyn0iYdIZ RMVUFfTYYBWHYb36knYPqauOWa4kWe7s3s2BcvcxHqj2BsADR2T2HZj8T8CKm5Vw2unc3gtvap4W zmKWzuRIoYnUNwo0Bv8ArQjv8NdyToDZIFMD7UvhvnMc0ufwefxUaO3Wt1i3uEVVG+smMMNfD5jR +HrpOlvkcpkBObB5lICnpGt9vX/lXrjWwM+UTHR5O6N3KRHELeB5Q0hbpEadGyzE9x0gg79d9qko b2YnlHgD4rcdxuWv7DjtyJS6RQZOyjWe3l6SzxenZ+lNkKTsAEbGjVm5F7N/hNzCKa643fTWNzNK Lh2trz6QuvePl9MhYxqSfRekjQHw1WntrjRev9Pw+Vt8pLbrGPM6T5ltvTBgQpkXQLMOgFupSAOo arLcDz/PcQzkVyYIZbiJCWvY0kiuHVokUDziFkCkxoWMiMxA0QNAAD58ZfZs5xwmNr2GxOcxihf8 8xyNIynQB64tda7Y6GusaGyRUDXMElvL5ci6OgQfgQfQj7DW+3gZ46ZSWG+teWZT/KiG4vyuNlU2 0F+sOtFHgVURwoVpOoMWK9R6QR0V5+0d4CYbxKxL8y8PZIWy0Ue3s4iFW5UbOlB10SDvoHQP5p17 pUDQilVmaxt1iMtdYy9TouLWZ4ZV36MrFWH9hBH9lUdAKUpQClKUApSlAKUpQClKUApSlAKUpQCl KUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+1J9SPMfueX+Ncuq6i+1J 9SPMfueX+Ncuqlcsh8CvW1gmurhLe3jaSWRulVUbJNeVZjjOOckxOLny/wCDVgUmOEXMzhWiMoYj pHWG6woD6CsQhEnZSrNYgyDi34O48FtAIb+fywb21dnaOSVtqsb9DqGVGVi0Tb6/cXWizpV32Q5N yu6W4y+XkvREuluJFUAbCAiFAOhF/JrvQ0db9feNph+ncgyiNdTp5AQRt0qo0qAARKqgKgA1vpAG j0gDuKyGxyNgl9c4yWURdFrJPayRgSgvGjnypACPLDt0KJNkKRoqQ3UmpxuMmpeTQ+rq+x9A8MeH MNKkswzRPyrpRik25N9bLe39y24fjsFtlXvrq5kkMzpH/nCF/JB0OolQ7kKNfmgnQOgx0K/OYXmL jyU2N43cNf2cJdXyElv5PnqG/wBIik/k01obZmJGj7rP0JYr6a3kuriaN1jSaYqZZirSyjXdF9NL 8NAj5Me/TVVaYwpi5bzL2MsVrAPMhtI5Ol20Pz3I0dga16a16D0rEUdH/JXeqTt/i19/5d9DoZVX ir4TK4+XSipNtPZpu+rVpvFNLZ7rpCL+pfOCt0uJJZr0LHBbxgyyFR0kHYAC69Nb/O+Q90Vbs9Zt irl7GKNntzp7V5SD6j3gAOx97XbX69DvV5GPmts2MdJDMlrkEmhnjnj6JonQHqRwQOlkYEa+HfY2 NnNvFvjUrz213Y4mHEY/OY60zuHt4mZorcvEplgDEAbSTq91eyrJH2UEKPZ1mqrq3vHbZ9L7X+U7 39jXQy6FTA08CoaKz1WlH+pxSlpXeM6bi4u99S3I8XI3WBwU0DxtHcXSNEsYcr5IKt7yHuNbOz06 7sN/CqG2x13cGGG8hE2RumBVppn6wnTr3vsUDet70ddiAK98jlLnkUtrYNaR21whaGSRm7BmU+g1 sfmmstwlthly8uKlvTbukYnyNy08cNxLAgBdLcvoPOy6SOMA7ZgSNBmHt+KqRgqbj639kr8v2NX/ ALFgquJqYqnW/wDrQau2rSk9KeiK29T3XCS6diyS4zGW2Eu3tb5bya2ULL58vVGArDsVG+3b9fyO 6xm6W2dei4g+jSsg6Qv+j6u3y7a9fT09SSe1Z9nsUs97nGtpbiKzvb9RYLLM0k8NojgQxFyWJKRB FHcgdPrrvVHluPzCJgmCuMdM3Q0JmVoxeQMT0TAsNspI6g4B30nRPevHD42MHJSk5K/PRcf99ja5 z4ar4mNKrRoxoz0fQr6pNOTaXN2opN6nvdJGQcE8GsRzHD3eTxXirZ2WbxWPF7c2uRtGthbqnuqw mMh3EvSPyig9A6epVYhasmS5BirTJ3FtK0maEM00f0i2SFyxjJLspRh1Rquis3QgcbPQpVqxjO2l /idR5CONrObq15Y616ipG9kDTjZIOvie+9mp58GvE/GTY3H8O5TlsDhuKx2ws7e8lhvWv5Hbaqn0 hZikKr+cXboC+iKoBKbalVjVipRd0fPcwwFbBV3RrRcZLo9n9v57Nl18CvFd8fymzs7iTFXd6Ylt Z8rC6MSgHWsLk6LJssAQfzvRfz+nZrxJ4fx7xd8PZ7KaC3MrozWc88QdrWbpIDEBgfj6BhsH10a0 V5fhbnguYe1vExcOAyl/52Cu7W4PTc2pEahwykrqNDExZwrMeoiRgW65q9lzxQNhfQcXyN6kSBys UsMESxzQIu410D1NpSxBQdkTvvW29TANV/FTg+U4LyWTFZKHoIAIZeoofn0swBZd7AOhvXpWIV0V 9tDgMvLvDuXKWFuksuPAuFMMYeSRf66gAEsSvddEbYKCdbrndcwvBM0T62PiDsEfAgj1H20B50pS gFKUoBXtZ2015dR21uheSQ6UV41e8Gj2kUl2zMmhskeoHy/WfTX6qAzjC5S44IILnD5gRSQAee0V rE3U56ToFwSW/OHYr216aYn0tLbkPIbiK/uFyM8lwYjK8TTXV3K5KxR7YAnbExID3LEquz1AGyYE Jc5NLuZFht7eMKiXDsmtHqZ/kzdI2fQdx8qyfjnOb/id4nJ7G6sDlraZ48aphDLpopI2l7t1JIoc aUqwPodaAfVYnFTlUVKnx1fX8jvslyHDUMFLH4yzlZuMXfTtbednez4su/fYyzgHBsRaot7zr6bx fjLQTrDdJJEl5NdRXCwNGYHR5F0essTHpdDehusNh5IY+IWWGwGCuJbjyLmHMXd3q4W/ErQuqkOh C+UYh09LKQykggk7x7Mcj5DySZ7jOXeXytzbRBLfraS5Kkb/AD3LkgEkkAE62fmwa9cNlvOTXWOw MV3BayXOQs7JbmBCRCs0ojDGPY9P9XY/srxlB01/xxu72bb7931/Lj3NlSxFPFzf4uq4rS5RUY7e hNtRi7qOyd9Sbdk7RaR5815Jzvk2Umus9lLjIX92IgVWVgkYiDBOkAKqKoeUDpI/0rk76jvGrhb2 BxBNM6Txkyyi6Vwvw79Qdg4Ovj2901sHxjwemvOSX9vkORcUzd7irCae4w+EznVe3kkPYWrMyL9G RpOlXk9V3rQJDLDCMkvIPOjtYYkgsBJFCOsxqwIZQOtixUeaQOokjoBJJ71dVKtNKVVLr87e/b9T Flg8vxspUMvqSteKT30pSu9o7eqybf8AT0W7uY5E0ohk/KWaRMVVpoQW2QRreyAfU9zv1+2q26w2 VhsJL4WsaRRRlgGkk8wgDZYqXOh9nr6bA76kO34xfT4O25bBj8DPDcCGNbtc7Yxwi4eIS+Q3myqy zKGIZWUMCG1sdz5ZzjOV47yqKSfMWfIMbe2/ly3uLZ5rC3ueqQi2Euuky+UiyFSFOn9CNFprYnEa XJQ0qO7u+fi39yMtyTKFVjRlifNlVajFxilpvezlqT3bstK+7MMsospmEa3SC3VEhUiZ+tG7/wBU gH12D6emu5NWlRkIUnhuCgaAHQUgBCvbb6HUe3fYJPpvsaz3wxxV9kXscHC9lbXEk1wLy6vpzFFE I4nmaR5OluwRNb1rYHcDvWO5Oxs4I8XM9o9rJcXQEls85fQLb6z9g1sj0796pSxThWkmrRb2+V+f X46nvjshhiMuo1Kc1KtGKdS6tZTskl6bNpvrJO8Xz0tU19ewEx3EN1BBIroRIG8t0I16gA99n0Hx +FU1nZQDIR3+OyM9i0Miyo8ILPbsO6lWDAhuoDW9EfPY7yzdWF7DxyTkVzjpfwKkzQPexgTRROvR tZSm/JJMidKydJbqBXYIrAsticZfPa5rDLrrnEbRjqiDOewYehUg6J1/6+tsPmcpNKtDSn16Hjm3 gelRhOeXYlVZRveG2pWdntfo+lkfWFvPwJ9Ouly2Qyga3RHiVfLcLGvTH7/WWVVX3fd/NB2Oy9rf gb/kFvYDKRxWt7JfX6W7NeGR5bhuhlQFiQOgd+4IOxonXarhdcYy9lbia0ygu2U9civF75/Ue7a7 egO/XXftVtxmWy5YO6XCwr7puUkKRonYb94d9b3on+ys+ji6NZXhI5LMsgzHLKip4qk02r91b5V1 t132Ku3yOIzl230VPod3M3u2dwBJFIdkhQdaPovYgdyANmpi8MPGbNcSyNjBlYLyR7YiISeWZZj7 3T+UXe5U6WA2B1jp375O1hvI3dnnj13sP0to/da4hbpkiG96J7qR8t7H52j6kfPGoJ7jNWnGrq/x cNtp2trvITi1t406XckydJbZYdI2SOra6OwRkmntc2q8fvDDB+LuIuOa8atVsuU28er+1jI1dgAB JANbYkAKGGj6Bt9II0iuoHt5jHJrY7gg7DD4EH4ip88KPE+9wWQto7vJTTYq6KBJpSeq39E6tnsF XWnX83Sl1OwevKPae8K7fJ4FPELjGNZZpZnOVhhIIjk0SX6e2t6G+ke8zFtd2NCDValfpBBIPqK/ KAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgOnnsz/UzwH7tj/dtSnsz/UzwH7tj/dt SqQ4LS5Kn2pPqR5j9zy/xrl1XUX2pPqR5j9zy/xrmZxrGSZfNWtjGqnzZo4z1N0j33VBsgHQ2w76 Ovkalcsh8GS8B4dk73F3PMLjCz3mAxsiLOQ7xCeRmCpBG6o5812IA0p1vZKbVqzXxu5JBkMzHa2S JHjMPa/Q7e3jB6YirhXUjSkL1oi9I2AsQKn3tVt5wPBWPAvZ9yePtMbYm2ixUt3cX1ywt4bi5Eem dyeoqNoCDohQAo7ACtD7S2jF1jYJrbyG9wOpj6dFIgHB+R6n2d/He6irPy4OXYycDhnisTToJ21N L7sybGY3FYqxkvI0PTJD1TzMzN1KBskgk/rrF8xlrjJHyI0kFnbqY7eMHuqdTP2HwJdmbXbuzaAJ Zqrc9ftmctBhbeVY7MSdMjg+8zr0nQ7+g2P7d/ZWUWXCp8nZztieJ5rJ2sUqW9xdWFtJL5chQlVP R73ZRs6BC9SdWutd87hm6U1OqnKUt7c2X7n2LO1DMMPLD4GdOjQpPTqfpUpvlKye3e+zd27pb2rj PHYI8hY7nsba8vG8uC8vS4tbXqXsZWjViqk6HUF6V6u5C7apgt8T7P8AjMbaYvNXGc5dlvpTxZHP 4tJoYFjE2yDE8vS8RQlOqASEqrMpDFTUJWWIyERafj+eSa2Y9kMihE+zQVl/5CmQg5DDPbZK7uhP BbTB5I7Z96Xem7dK7Gt/P+NekMRaTalFyfe9/i3T7mJicpc6MISpVqdKG9oODi27XqOpduW27vHj ZWXGx/ilxvwy5lzu2Hh7zPi0WbvZVmureS7ZbSeSQ9HVHNGrJ9IdtAw9mfq69A7L2zxL4MvEvDLH cau8zZZXm8ecOQxmFxsMl3ctayo6TRRKB1rERG9wzBQnWhBBbTGIMH+EcvkrWy4tFcG+yErfQPoy stxJcAdSvHojoCdPUZCQF1slQN1sb47cq8VeN4qJ585hsZBn0tbSG1sojHkoALdnu59h3ETCXpjJ V5gFePpdHHW+RRdKoqlacLbNP46/mafMoY7BVMHluGxKqWlGUdkmpXSjurvTZ7X5SdrpI1p5tjpL YxPd464sbuzuY/pMNzC8EqIdN0sjAHvtG7j00R61fsLj/wAO8swuBjjgSTIZK2t0mnPSI/MLAyqW AQlOkDoLq7l0VAxJ1Y+cX09zj7gZ/kGUydzcBUikvbuS4ljQNsKGckhASfjrZ+Zq5YhTjFt4ba9u luIAHil84iZSDsOGHcEHWiPTtWmlKnFRk03FN2vdbbdT6TSo42vUr0oyhTrSjBycdMkp+pJaWuGk nvujOvEPG8Z8OMhlOL3rW3J8x1Rhr29aawtLHaq4jWOKUSTuyMSz+airuPp2Q4q1ZnxmTOXGOtr3 gvEMg1gIVsmscZPbiNId9EW1uB5kC9Z/JkNH39DWS3XgDyDk3D8dyjDxw52DIWdvdTW15NNDfSXj qqXcm59CRWkDzCQuOoOegEBC2F3vFX8O7njJ5Fjbcw5RBcJx2K/a1yQjDb866BgcRxMqlenqSUlt AqUcLtHCcHKMY2j+Sjbu3Zts4SOKw+JjSq163mVr2ablKrrezjGGqEIxvffdWt1MDzuWvcrbTxzY T8HpKyu8kNqLeOPp13WOOJI4yen+qANknRJO7AY5cejS9KGwujqSM6EZJ+Kn0+Hw1r7NArmNvj+S ycp4/i7C/juTk7yGwY3JCR+bLIqKWKqWVdt6gEjXofSrxzHH33GM5k8Tlr/E3/4OEiXkmMuHltw3 YCEF4kPmoQ4cgsu3VdKyOCo4mdKk5winFvm754tvvcnMskw+Nx8cLia041YRSUXTikoJatV4NR0L dd7kR5KPyY5La3u5/o08iy+X1ajcqGCsQPVl63XuNjZ9N6rLcHmZYcdi8hZ3v0a/sdRSC2YJIiqQ FbR3sH3dkgqSdEa2KxfGNB+VtxIvTMi/nt+a/c/mnW9ED570PnVRhSGzhjV9ecs0bosffspIHx13 UHsfs3W6Tadj5nUjGcXLhr9Vxf5Ojvgzyu25/wCCy3EmxMkDwTxsS/luNjpBI94KwK7Hr07HbRrn h4i8duMNyG9wkyTJLjbh7VfOK9RjXvGT09tmMx77DvvYrb32IeQR5jj2b4XkjcSpbpBeRHpHlxps r+cACG64urTb9Bo9+lYR9rzHW9j42Z+e0vUmjyMdvf8AQHBUFwyEqNemo02e/c+vpXoYZr6ylWKk aIOjX5VXeNFICy9Ksp7j0JqkoBSlXzhvHZ+RZN7dZfo9tBC9xczmJpBHGvckKvdj9g7mgKTF2KzR vczMBEhChQw6mY+nb4L2Pf8A/nV8xdkMterZQyAiPTvr+p8B318B9vfq18K/Lh45pY7aIt5EPuRq NMVXf/lVeo/b0gn5bq+8Mu7KOzubhISqQQBmm6dO+tlt/wB3/rWDmFeVKi3BbnU+EMsoZhmMY4iS UVu13sm/+ufguXIMhDjLYQOkZhCdJjaQkyqwYEa7nQPSeo73sgbPY4XcIt4hXSBEYrqFQAgA90D1 PxJ/tOyW71keMuoOScjmlmhIs7eEpGknZi2x1Egeg7+n2A/CvfJ4fEKbW2MRx0t8pa2AmRm6dAqz w9ReNXDKyllTzFYMpIO61mDlHDemcXe13be386nc+I6FbPEquHrQVNycaak9Lla12trc7Ru0rb8u 584nK47H8a8mS6a3eBm2semfRckEdW9ggj/n8aqcPnWsr5L8ZTFSKiuGkmhZLpSUcIyAEFZULB0Y dw6qwHbvbr7D4uLEqbtJoJl3HNcqTIIm9QGH+od9tD5em6umFxki4q0x9ljrK+knnCrEGQm6B2dR 7P5SQ+ioNsx0qgkgV4S8nU50r3cvb9H/AG/I2tFZgqEcPjXBU6dJXs5vhWtKKa2a2lt0kk+USh7N s0jXvI8qcHbZpE4lfvFiOpPMuo2aExwCLTM/WqdJIVgCwB/OUGHsT1W2XeWB8euPjthGshuQepR3 2NEnQcv6+u6vOJiu7eaNREpitJVlxt3BL0ywgEFCjA9SsmhpgQewI71LXGuN2PiV4bcmS64pYLyP isMeVscrjcdFJeZicQzdMd2JEkNyzSJ1Sf8A3pce6CD1e2HlTxEfw97Pf3vff8mv3NdnFHGZRWec ODnF6L76XFxWne6eqEm73jZfT0sQnBefR7+78y0tEtLGPzFaPffqG1Pr/wCVvhsHWvWpi5rxXM8a 8LOLCaSJ7e9y99dZlILiKeK2yIjSCGEOo31JFFcK4BZRJ1gk+5US4a7usZftm8Zaxyy468tr6xdy ZIp/JAZAenRI6k94DR0xG/jW33LMdx7x7w2On4lz2O2jxkrNLAIWkXpk2u5rYtHIkm4j5bN/VMmg erYnD0I1aVTR9Vrfom/u+TxzfNauBxuD/EP/AIFNzbt1UpRjbZO0Y209+d9jUHGO8SQf5P5YWF3G twHLwhzMZY3ikJ6vmruNjuN9tEDX5PiLSTK2+SyMUPmyxeXIjb8tpx06P2j3ToHt6fE1MvMuKeG3 FcrdYC95BzS6zdtaRiWa3tLX6Ck7xBh+TcLIy91fpEh7N09ewdRPeuj4qMZOSwZWkQSln6YuzAkD fqex7Vg1nVozUNXdbO7V+eiOqy6OBzDDSxHku1k05x0wlo2i0tUrbXV0ls7tEhpHwXLcAhxFlkL7 Fckx811drc5aGG0tsk0yr1qH86WO3ZYraBFMjojMBvu5K4lnsByDDcgsbDOWF/j7h53gt7S5snje 7mBCahPpN3YAeX1A9a6J2N5jN4cR8Z4vieU86yGRxcuRuCFxNrgS9y0Qk7IZ2mWOOQxak1IFI94d DFCte3ih4nXWQ4liuH8UXJY3AYuxFjFFNcqJcoPIWNRcJGoUKAJAUDMjBuogdKgZNWnC169oysrJ dVxw72+bmmy/G4lT0ZW5VqLlJylNNKMk9TalHTqu99Ol9Ffcjm7t7XD4fAmLleEyMmSs4L0yw3Hm mBpOlpobiKLrkjeMuQQFJIAIGwyL9RTyNcziKL/NIpHSOToZTcaY++iuFZVPqOsAkEbC1cGwfIYO IycjfF38+LhYo16IAsTv1xRdKN2A/KTIo9d6fW+hytsv4xIgt/wnHjprlPKjnmSaRIiAWJ1DG8nc DWwp763obNYdWKqTSUNOp7X/AMLbqdHl9aeDw8pVMV5qoxtJRSTur2Ts5er+myabaV+d8c5jaTpZ JfqI7W8mcQHySd9JPV3Ya/1e516bqzR3lvctJi7h+pQGJdlAQsAQxXY0h13+QO9eg3lE17jGt4rW 9xeRtrRUCB5YmVEBGhsg9j9vw+ded5g7WHi5WOeNpLcm6Fx09mYEsSdfD4dq2WDx6w9NU6ifP5W/ Xj/s4nxF4Tnm2LqYvCTjZQu1f1OStymo/VvZpW9O9jGcY72942HnuGgiDFrLzELMHLjpAYD0Pffw B36HdbXezVybGZfir8V5BGWxhtxi72GQF1MDAiE/mhdqQya949IUsSWrUa2knvcZHcLKzS2Zck+Z pvRSH38NdP8Abon1qTfALPDH8xxl7cwTT21y/wCDb8bjLMWAXfU3cDfRISNHcYHva0d8nvY+TSj6 VJfz/JjHj74a5Lw357eYO5WWW3XT2l04/wDiYiAevfzBPS3p3G9AMoqN66C+0rw2XxD8II89axtc ZzjXX5ihA8ksYH5RBobJK+8FHqwTfYVoLkYhFcMFYEHuCPQj7KseRSUpSgFKUoBSlKAUpSgFKUoB SlKAUpSgFKUoBSlKAUpSgOnnsz/UzwH7tj/dtSnsz/UzwH7tj/dtSqQ4LS5Kn2pPqR5j9zy/xrQn wHtQ/iHhLVoUeOW4PmdSBgxWKSRdb9CGRSD6git9vak+pHmP3PL/ABrRT2cog3iBg5Huo0n+lOYo 3J3IPo8oIXQP+ts70ND12QDK5ZD4N2vH6S0xXsvciW4WOBJMW0SmV+kF3Gl1v4kkAD5kVz65BI73 cyeYzNLF7iq+gHaRh6enfQroB7U/0lfZ3mtbdZvMuJbGKQQpthH5yGXsQR09AcHYI6d7Gt1oDNAt xdWsjPpUWQhenuWDrr3vX1cHXoNfaaTjqVj2w9byZ6/Z/qmj0wWD+mSvefTRaWtkyqrogLMVGyd+ g7n/APitgvBXmWS4xd5zEWVvfXhy+Lu5bSCyHVML+C3eWNowwZFZ0jdCfLkLMIQQVUiod449xjOP m8yFpZEQzi7SK8TrinAYP0Sx77q2ukrvuDqpyz/M8FxLl+PzHEuA4fE52PEwS5G2u47l/wAH3E8E beRHB1RxxskZAZ0TbeYw909YbRec3XeIlO0Yu3HR/B9VWXQp5ZTyelh3KrXpqd27NSi291LhJbXV rp9bmJ4M2/iOmM4z4iZm/bIyJHZ4bkdrEv0qGR16I4LoAbuYDIwYM2pFZm2/TI7LOWY9m7F3vGuK 4/E55uP3WHsRayxFEvoZAzPJL1PqKWRjLIzBiQijq6Yk6zWruPtkvsNFj4XnvPpCC2j/ACbeZKze 4qhdbLEkAADZJGq6I4KS+lj+l5CJYWuRG6WjwgTWwMSExysrujuH69spC6IA3rqb3y6q8VCcKyvZ 245NT4ywEMixWFxOXy0ao6klK+l7Xa3ezv8ADs+hhngx4S4Tw1tpbprpcrnbkGOXJGAQhYd7EMSd TeWnYFveJZtEnQRUhnxF8P8AH8t5VyHnXHPErjObxQf6fnpUuVlmxlssSgFVtlYTajik6QRGSI1B Lt1PWYe2RczXPholxZX0Fxh8Zlolz8McgLxFlXyfN04HSGkhbyyrEl4pB0hNmAuC85uuI2Wasbew weTxnJkS2yMd156zXFuEZTBHLHKFj2HlIcIWBck9QCgWxdTDwaw9WNoWvfe1+2x5eH8Jm2KhUzfA 19WI1aWvS5aXy3rdl0SST7Ijzlt4YeOdGRgha5uWCrGgB8on7d9yPmPjVf5UKuMZPYm7X8GvF9Nn 0PJJZNOra6usdJI6WTvrqLp1xSfGQsLbNWMaXXlxzlXnhjS7Es9qomljRJmVVHmajDHSgFXRgF6t Crs8jjpLKGY3cAV1C7kcAnXw76rSOcqCSivUm79t1tb8j6fTw9HNZzqV5f8AFUjDTfaVoSbmpcWe uyduOjRMvh/7QXLuNcdvMLeWdpmQsMS4a6a3htYbJBG6lGjgVA6KVgKoqp2aTcg0gqTuI864L47c RbhnMjZ4/k6sNWkcojlaYIxW6sS+yfdDkr7xQdSP1I230yu7d8tNOuCy0kKw+5JCQRF1fIfZofIj 5VIXgtnL3B83wkD4PEXH4RzeMt7uPI2qzoricRx3UDdik0Ylk6W2NdbbU6XWzwuNm5xhWldPa1t9 +L37/Y4nPfDWGjhquKy+k4Sg3NTUrxajfUoW2Wlq6vaXO7tYz3xl8D+VcJw2Q5Hxu7GYx9hFJe+Z HKLa7sljEkolOyA3lhE99GDljtYxqoYlisLOKwxRvTf9USKpFk0AMiE9RUFiTHoKys4jYg+9GhGq 3cu7K0zOL5FwX/KXNS5W4w15YT4XK3lvFLciVmH4QVxC7hH8wBXj6oYwegRK0ZjXTHJ47IY6eXE8 hgkx2UsG8u/t2boaKQAEjYPodhgwOmUggkEE+eZYanh4Ly47N/Z9LGX4JzrF5zian4qrepCKSTSt KN/Vq4bt0fS92nw7LzHG3t/awmwjtTLE/X1ydnGvQKda/vrCMNDcRclsYo2Ec7sJXJ+XSesa1vZ0 3p8x6VJmWgvB5VhcwXNgchj1vrGc6BmgZ3RZVHr0kxton85dMPdZSY5jtLrG5Oz93d9FP5ThgSCs jEBiRsD875HZJ9dEV65PKUU6M9mt0uu5rv8AUejQrVKeY4X1RneLkrON1tb52e/XobN+w9m8mnOT YfSg9hdQXCyo8LkrICjKQ4GhvR2GOz0jpH55qm9uzG/RuV2uTZ1WMJJCFI7s8gjca0Na6Y33sj4e tWf2MRLb+OGOIWWRPod+vuoe2zbjfr6dxv5Vl/t/tGZsUqE9f0lev5f6CWt6fKzTq8hWT8omg3bY +Hc6qgqqu5JFkkiP5p7d/wD5+yqWgLjxrD3nIOQWGEx6ddzezrDH2JAJPqdd9AbJ+wGtl/GvimA8 IOCQ8fsYLY566hS0ku/JVJpY9K8pHcnXUe+iR3UduwFk9gThsfIPF98/crC8GBtzOFYsGEr+7GwH oR2f19NDt8Rk/t3zRT8xxyAtI8Rm6Xb1GygZf1bRdfYKA12x6vJKxj8uOZUIjY611t2Xf9pqis8n czWCY5pmWJ3LS99tLsksCfX0+Hzqvs3SHHX89wjtEIwj9BGwHIU638QGJ/srx43jpb26t4i5SW5L SST9fWxQaPw9Nk6O/t39njX0KGqfC3NnlP4meIVDDN6qnp22vfoZPx3G3DYe9li8tLi4UwxSkbBT Xdh8e5La38h8KzXM4vI80HJs7O91JcW4jy2SeCCWVShuI06OvqJhRULsuyemO3cDspK2HFxTI9nE 8jiTHFeqNgfJuVGtBgCGKkDR0yn10QdGpiz2VzVp4YcK5LwyyyPG7Gzu7yPMPjp7hLdr0yQeVJJ5 jHzo36XVesyIhPkliex5ulLzZznKWy3sueyfbbk+0Y+isBhcPh6FFKU7U1ObWlX9Uo2Tck5NOPHC W5D5lQZS7Q27GYwoFRmAEyAtsrvsT72j+sb1utz/AAMtVHCrPl13FheRZq9ZYRlsZaoty1r1pHqa aXoeRowgLhgrjyujpZ022vdtb+G3J7K7u7u9bw8yVp0SPHBZy5Gwu1YkO0EK6khfzJF/JBmRY1HS CBIUmnwHvuG4Dg2Q4jwjleIyfLLh7i8Q31rNZQ3t28b+WfKclmVUhQOIiSFQsQC2zmZZTjTqN6k0 1tv29v5Y5vxzi6uMwcYOlOMoSetOGy1b/Wtmr8WdpKzaTRZJfEb2cM/lMp9J4hEltexSSS51cCF+ ltJouVaL/OhJt2PX0KdqxDehMk4a28L/AAw4k2YxN7Y4nFXwEwufpsl0b38kWQRO7O8u0DMqJsn3 iASSTo7hvJlxllKkUduku54oYyxSENsiNSxZtKG6R1Mx0O5J71drjP8AI7/F4rjV4trb4DByXM1m kLODPJO5dnlBcqWUs4UhV0HYd+o1Ec1ipzc4q6vb36WPSt4CqyoYWOGqz0VHHzE3dRutTltZJLhX vvbc2d5PxTwW8XsdecotOQzY2aKMS5O9sZxazpAnmKGninQ+WNK35RkVisS6YqoFX7wU4n4e4Gwy 2S4HmbjKrcTJaXNzNceaRJApTRAVQAWLS7A03m7X8mYwNMEyME8kjWOTt0DRmG7jZVbzYVljkI79 11JDEwYfFfkTXji84ByjqsfNtLuGM/RMnaz+XKFZdN5brpl/OI90+h+RqYZir6/K3SvJ9V0KYrwb JQ/CrH+iUlGlFtuMttT2TdrO/S3XqjdbxJ8IuMc3kuMj1S43kDqqte2xUiRlVlQzRns6ja7I6XKo q9YAFan868O8/wAQSaLlHHvIxcB81rjyDLaLuQorrMB0gM3ordL++NgdQFY1f4+CaW+zWbjt8lkZ pp7y5uJIVXzZHYuzEAdI2SewGh8BWY+H3iTk8X4fZbicFvFleN5axmtZLCO9+jtYySoVkaCTy3Cg hmJQp09WmGiX6/KrXwuKnezj/wDt+/7mwwGV59kWHVNSjWbV/Kd20tr6Xxs+Yq6fNr2JA8IOX2vI cg/C/FDJwZbjs1ncS2r5Yl5bWeNJJHkW6J649Qm4Jdm90KoRk7hr9jOOezjn2nyWHyeVjsMLYzX2 Qs/PvI/PtgneQ+cvnsI9b/IkHbKG31KK1qm5G2IvJLd7O4uZ/Lu7OLzFERniuLea2WYhS4BAl6yg J7jXV/Wqvvr66SK/x2MujHez2Mkbwi4kiEsUgZCreWyll3o9JJUsq9QYdipYny40414qW73e+21n ftuRjskWKr4qtldWVFqMWoRvG8mnqi43VpWjx3NmOP8AIvC3nuFtvC2wt81x/Gz27W1ljjGi+bKD 9JE3mr1nzo2gL7kbpcu3UJSe0d5TwP8AESPkc9njsK1zjfpjRW9617AsZgLDokk2Q+1U6cKn5wfp Vh07iBbqI8jlsFuJo7hPJu4pLdijxOjbHvr3VgVUg9j37fCq7m3IEvY7U8tys2R8rqFsMhJJdFN6 6unq2V3pdkfIbryqYqGIjGFaDculu38/sZ+FyHEZRVrV8sxUadHiXmO9pJ2v0V+LXf8AVZpl18Ru PX/Bs9Hic1ksTJeojT3a4y6MsVvGE6wszyxxqrsvdUUlz7oA26BsYusdc5HIzRZOfqxqFTFCg6BK T/rH1Ovl6Hsay/BC/s4sH4gRtj57Ka+F7b/TciscuRMV0vniNCTK8nV1EsV0O7E9wGwzKchs7vlN xdQ2UMcjOzSw2ySQ2lnrQZYo3ZiNn4bIHVoaXQHlKk1eVODi+3W3R3fHVc/Bm0MwjLTSxmIjXptq 8k0oudnqhaP1qzjKyjZW9b3drZdYJJuQPiMPYGSa98m2hjiCgtLKxQIGOgN7Q9zob3UiePHhieJX +BymPx2ZNna2EWKzdxdQyvHNcJ1xQzdTO48uYRr0xA6iVYwwQuoqn9n7j0+e8T7DluYsY7LiuMvf whJkLyR4opZYkKxLGRrrKyJ1EA60pDfBW2c5943eE+XtpsBmsgl1i3CrdsLSSZHBOjoIrE9ie2j6 9+wNdLhYSjRipc2R8Uz6vRrZjXlQSUHKVrcWvt+/5ld7P/I0y3A7PN3ojNtkYxa5BfgkyAr16BbS OPQEk9xvuCK0m9pLhf8Akb4jXljbeW2PmYz2RjI6ViYkhAO2gp6lAHbpCep3UzeCfitx3g+c5Vhr ySdeKXrTy2RKbMQXqIJ6Oo7KDagAdx3OyoqH+e56PlWRu8hJ9IubaZh9H6z1SJGX1/VLdwNkhdjY OqyDTkTHsdV+V9zqEnkQHYViAfn3r4oBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoDp57M/ 1M8B+7Y/3bUp7M/1M8B+7Y/3bUqkOC0uSp9qT6keY/c8v8a0o9lVFfxEhRwT/mE4Hf8A/wBkFbr+ 1J9SPMfueX+NaT+y/O0PPVcAkjGza6fUnzIP5VK5ZD4Nv/a2vMjjfZzvr3GQiS6hWDsUL9Kn3XbX x6VJbv2GtnsDWiSCJY7gIiu6TEJ2HUPe7gb/AFD7PSugftA4qbk3g9JYQSRx3V1ZyeUZNiPqe2kX 3iN6/O+01z1MltHnLtFk6kkdyjo+1YE7BB+Xf1qxBk+Kitsvxe1S5hWSGSJVZHjUk9JB2pI2h2o9 5SDrY30swNeqw2v0fH2lsY2mcRxrCqoiljrbO2kQbPdnIA7kmsS4TeRRZqWxa7lWJYfyUUxCgsTs 9I7fAjtoH17VkeZny8VtK9scdaxrvc08rEgfPXTrf9prjsTRlTr+VJ7Xut7Lc/SOR5nQxeUrH0oW qadMmoqUvSrb2astrq7S6k3+zNwHk8nOcJyiKFHwuJkn8y/N7DNHOxt2j6Y3iZlmO5RtlJQFHUt1 qVqT/a0nza+GITG3UkdlNfRwZjy5AnVaujp0EnRIaYwqQvchiCCpYV8+yRGX8LPpcMOcxtpLO8kF nkrhrjasxk+kIz28PuyCRe0YaLSqw1I0u5K5XDiZuI5azzjQ/gqazkW9eSTykS3ZT5pZwR0KFJ94 EEevbVdLSwqpUHTg7Xv939j4jmGf1MbmlPG4iKkoOKtaycYvhpuXPXd89jnzeX0GXyN7l83HDPdm UR3N5fzebKzKoVQWcEkdIUDv6ar7Jtsi9xMLWSezw4jmurxQ30e0d26Yw7j3VJOwOo9z2HeqLiy5 rlEUeGOLs7m/nLQvbzXUdo7v+TEfefojLSGTSojl9ox6QNbvHJMzyK6trPj3JsmmUw2OtUs8FLZB xirpbKEo72zThUmlBDqXAJZm6U2Ci1pFgpuMqk73XFnfnrf+bH1GfijDwq0cLhXTUZ3ctcdFtO+l xsld7JNP6k/Yt9nfxZFZbWO8tY7ll6+q1kEvug62TrW/hqrliLvEfS4jmLXLzWkhj6Vx93Da3UKA kSsEngkEp0V0u4xsaJ97YtFhNLe2UsNpDfY2VR7jT2yoD/y1/wD3S6tchdKtx9Omx8jj3oemOQIw 7bBJ7dvkfjWFFRp1Oi9nv/1/j8zpq0q2Lwdk5ybX1Q9D56NySTXW9tV00npZKvIOBYMYjJ8s4Py2 1zfGrGKB776THKMpZK0hjeSeCOEHylCvL19Kny1bSt0hnz3wK8Gsxa8steWc9slxcOKl83H42Rob lp5+khZnMbOqqm9qAevrAb3Qg69Y7u4vcRjLp4M5b31y88Fybe+tbSeCV40ljjJilVl91LiX7Nt1 a2FI2A9h3nd3Z5C+8NsjkoZcbbQynFrO8UTRypI7yrGAoeQv1O+ix6RH22CendYSlhJzVWK9X52/ K/zsfMfEOPz/AA2GngKtS9K3L062rt2k03d2i3Ld7ct33ny/yvhf4a3+ZzF7f2WOyuWkjv8AJB5p Lq+uNny42Ee3lMS6Kqqr0IA2goBqFuX+KfiTe5PL8g4VySK54ySHRLC3hnkx0QWReq4jlgW4g6hA 8haVfL94BXYarEfaPto7bxwz5hvoLs3cNrdyrHrdrIYhH5L99lumJJO4B1KvbWiXHr6z8MeK4/NY pb/+kTkuCVmuLueOe2xtjJK3TPCqIqu0xiV1R+sx9I6z2Ky2rYmVSdSnJ6Yx6p7+3+DxyzJKWEw+ DxdCHn1a90oSitG31cu6a5UrNbXaSI55BlL28y+X5hnbm8yOTvAhuZmjiDSCNAikLGFUDpVR6D02 awG2vFvJ4rqQRktJJcuWlKsiBukdl11HuAN9tAj41k+cyNpior6/uJVaW7AAt4rOK2iDBen3IoUR EBGt6UbPc7J74m7PDibeGSNVk0Rvq3oaGxs/aPQduw/sjKouU51Xvfq+p6+Pa8aOHwuBppQUU26c WnpbezurXum7bL9TYL2Ibd7rxLW5LrGLOGQIxm1Iwl7ldb/N/JDe1PV8CvS2797eiBLi0AUAfTVO ye53bSk/2V+exDjbayzSZeRZpJr4eRoxgqvQHKkH4Hbts7+Xb5/vt327Jc2i+SyJJfK6s3cn/N5d kdz23/dqtyfNDTrJf/FepPYVS1U5H/4kr/qjXruqagN5v/DyspYuAZG/uI5lWXKskLBCNoI00R27 qWZgT6bU/KsN9uzHBOZ2WSa1iilm64y6KPeVT7uz/aTr5sftrO//AA9srNc+Hc1jIoC2mRmgQqSN oUWXv8z1O3y7aqr9sfiFjm8Ne5iASvkMRpgAhLdMrRhwAB3XYjPUR2AJ2Bs0BpdkBcCzSytLNpxK FllEZL9QU91KgbA2V77+XfudVvHbu2tc1i3lh+iQvE6xgv1BerWiWPz/AOW68bfzUeRll6ekbbt6 jfpqvexexOcs5MsYTH0sCJCOkMda2Pl2P6tj7KxsWk6E0+z4Nz4eqSp5ph5RaTU47y456mYtBkch e3kNpDdQyY+Fr03CQGWP6MoHmu+vRVJGySNe6avvF8/e4S4bIYW5UR3YUXUDIXtchH0svl3EX5s0 ZV3Gm9AxKlTphRYu5ls58hItvj5knx1xZ2ZdWPkSToYpJSAQGPkvMgU9tydRB6QDSW8sjTTRXCFZ 1jBYRuSjKd6I9NHse36u57VyLkoRjKm7SXNun+flpH6JjQlia9ahjKalSl9Llvq5vw2lpWyemMpK zu9N3L2P8N+O8p4bm+bcZz1zh8PiLSSS6sbqA31zb3MavNLCrM0IeNYmg8t2cs3UesqwNRPEl89v Eq30dhkAqlLuMyKttN/VlUqC46G94FQWGtgb1WV8F57y7hM7nAZWGOzeNVkx0tnF9FmZUZVkkCKj tJ3BaTrDv0IHZgoFVfPeMWWN49hOVce4xncVx/I2NtPM9zfxXdpbPNDE0aRPsz9PUzqzzADr6Qug yrWZUhTrU/Nw69UbOW1r/H7HN4TE4zLMY8vziT8uvqjSepSUUukm0m3ZpKTvd2VubZf7QXEcYnHb XxG41NHk5Luboy4wln5tlLKCfPvNrIfoyL5cpckyAv0htMXcwPdWUry3Nzu4vYtCSG2M3TGxOu3r ojt6Ht3Pr21JXFPGHl3A+OSYbCw4W4hUzXcMV1a3NxL2UvKQFmACKoLEIqqB1OwJLMcv+ieGfKuN W3IOfRXXAeT5BmvL1cRcCdL1Q4i+kQxgTRiGZ26wQoZ+mRwzorSNlTowxcfPotRfW90vv+mxocLm GK8P1v8AasxjKrC78vS4ykkrpei72atJKWy7PpE9pwxeSSWMOPyWLy9zBj48hlVnIx64hWGpRMZm /LQR796SHrHu6ZF9wPHHLbWzS3xt7a2kGPu5B5hh2Iiuhv7O++3/APVT62X8F7Lhl/jP8nedyXF8 sKXORIs3vmSKcTqgHmeUq9SICEUdQVd7IDDFMrhbDyrO94ny7B81huSE+i2MTwZVWDP1MLFy0rIv SCSpLaJbp6QWqVBx01KCUrcpS2+Ev1K1MTCp52EzSUqPmW8udSktXN7ykrbr6eUkt2QnLnM0knkT 3LsQx8yKc7G/kR9ny+yvWTK5O66pYcjcxyq3X5ck4CDvsdHcAgAd9jQH/PLpeO3cWZyCPNFNZyTO LyzkQiRZQSGTWxpgdjuex1sbUGvK743bSY60fFYX8HZJFlW5lnuxJHKCdIFj6W6dJsMSxLE9QK+h 9IY3CrZxS/n3/uYWI8NZ9Ja6dWcld9W7pPZqzcXdO6u47322uWq65jlyn+aQQbKKpUr1MCfRxo/E nWtHRA+fenaS4u2i/Ct0y3EzdJlD9Pln/WGiAAoI2fT1/wBY7yvBpeXNw0ubwdhdxJbyW8QnnnV4 ZSADMgVhtlIIUsSPj73aqTFcPsozJJko4p2YkKqjSqu+29a2ft+yvOGLwuGTtFKXtuZ2I8P59nNS m51pTpXvaacErWd2t2rttKybtvsU/Gbq1ssllZbl4IyCscCId9UaAgdJ/rFv7yQfl2ulhk8tyGeG 14xiri7kuCyRkL1OWVSzajG2bSgt2B7A9u1UWa4hYXELNjdW8wHdFbSsN77/ACPyPcfZVB4d8+zP hrzCPOYiwxLZK2jMbpd2wniL9DIT6hkbTHbIVOwRsqzA0o0KGLl5i3e2z6dP5yZWZ5rm3h6ksJUi owepqUX9TbcrXtta9mrR23TVt7/k8fz+xzpwsHHTlcv9CW9NpaW7S3VrC2irSxJtk91lJVgGXqXq A2N5D7O/L+IcY5DfZrlvH7fkNrkoWtJ7vs8mOilV1k/IEOHDAlSNqegP09fUVbG/DPxV5JhOdcn5 bZYTH3d9nZ2lnVMfJKweSVpWghYPuIN7579XaMdm13vHI/wve5rzeVtLjmvbhZZZppYFB0NSMsaM ygIBGOt+ptdIYsFFbmhg6VHeK3PmmaeI8fmSUK024rp0/W7+7fbjY2Y4fwrD8vt4eYczhmx/ErGO O2wGF6mhhkijG0nMan1YEgAAbUAHaqKvHiU/GsZY3WOvMdiGx8cLM5kgt44oox7wQ6XTIEOiG7/H tWd+LFzb4jh0SW2PilIKxW4EPUIlA+A9PQAa/lWlXtRZzKYrJ23HZYntYPo6Xh6dKszyFiCygeq9 IIG9bYHWwpGUaEwDleQx13zDI3CizFvNcyNClmgjh8sjQAVSwIPc733BHYfmjGclc2eOtJbey64p JewjD9Sj/wA3fZB127HX2VYTO4brLN1HvvfevF2Z2LOxZj6k0B80pSgFKUoBSlKAUpSgFKUoBSlK AUpSgFKUoBSlKAUpSgOnnsz/AFM8B+7Y/wB21KezP9TPAfu2P921KpDgtLkqfak+pHmP3PL/ABrQ r2fZQniTgWaF2RzNCJNDUbGMsDsqT3CsPdKn7dbB319qT6keY/c8v8a59eDN0MdzPFy3CM0aXqAh PU+YGhH/AO6VSfsB+PYyuWQ+Dolz8xS+ENndTgyyJFA0ejodbL0d/n2Y/wBuq5t5KxucRko7G8h8 q5t1SOaMkEBgi7Gx29a6OXCHO+BuolkeW2iDdPVskxts9/8A8oNc9/Euzax5llY5HRme8lmJQE9I kcyqDsDZ6JE38O9WIKKytWu+SWqxSrCUHWJJF6hKAVcKBv4E+vb4/Ks4ucdbz3sF+XuIry2YSW88 Nw8bwup2roVI0wIBB9dgVH1sPyKNFLqZGDL27Id6U/3kdvj6Vm2JyNzc4QTxIt3dppXjDhO+x8dd ux3XP5xTrRnGrB2XHa35n1//AE4xeXVsNWy/EU9Un6ndalJLtHfdeyuyavDbxh5vZ8xtLbLZJs7a ZnJ2ttcJdgK0AkKwdUJQAINlGKaKnoOulnZztFkI7G+s5rW6s4by2uIGhuoZlDpJGw6WVgd7Ug6I PqK0hwuZs8HDjr/HWNpkuQx3QuJDlLWT6NYBEXo8pI5QJmMjsRI7KV8lSIlJDVMY8TcTzvwl5LjO VZM8Vnt1hjvLyzillj0yeYkh0p6Y5XhmiMJYlhpOotKm8rLsQ9Hl1J6p8/l8mi8Z5PBYhYzCYZ0s PdQbat6ru7UL3St7JO3vvGfjTe8RyviLFe8OxeKhx+NtzArx2Mb213MesNJ5MgaGRVDAI5jBJ6jt lEZH5FzKxm4t/kxluD4GbHxutzbS4g/gqaO7ELQm5cRo0UkhVv8AUVBrurDQXEpYmicdc9pOkkZm Q29zHMBF1IgkZVPXCrNIgAmWNyW10gggUnGMZHHI1ryO+nbCG5MZbH5CIZTy2R2DJHIDtFdUVnOh +UAXZB6dX5+KdSSbUb9Hxbrz2/U7qWV5FTwdGcYSrOL2nFvzNa3inp3Tk72v9O17cnpIIfwjdC0u rvymUGO2nmjlkh90b26RpvbbIPSO2ho6JOFZVLzL81OJx2LucxdvH5a21nA0sruqFiVVQS2h1E9v QH5VdMnkvoWQxWNt4oLe5VII7mWC2jQSRoTsOUVettMxMrDqIUbJ+GT8dw93yDLx8Yx0v0z8JZHz IrG4lK2y3Cxe9L0n3QVjVyXCltAgbOlMUEqVXW1fUtrJLt249i2aynmGXvDxqKm6MvW5SlNJWbte W8mrrUrcuyLz4PeB/NV55YZ3lPHbvDYCWGRpPLZXnPnxmGNPIXrIHVKpdJkEYRZBKOnqUzZ4KeDn FuD81juMjyRM7yu3gkuLW3Ux2zRwsnktMYOtnckmQFyegl/TYBqPOZ8N5xwXhsWFsLe5uOOPFLke RXdhcIIGnlVIfI8oalaCOKP3iQyt5hdlQIAmAcbeHEZ3C3kMESW2Oy9reSRQKASkU6SsFHYdZCnW +2/l61ta2Njh5whOHbe+y7/Njgcs8L1s3w2IxWGxCdnL0qPqk0np2utOpN25tf5JH9qI5hfGQXmR WwXGyWMX4NUws6zRorhhJ1+47pK/UU0yFfKDqVcq2PZvxG5JyfHvDnmwnI4XVVgkv7Ih7YBgW8mW 1eGROoqvUOog9AGtbBl3xF8bPI4tiLvhNzai5zMczyfSk6rqxVS0XV5RVo9iVXXqZipMZ6VkXqZY 9534pYflXHMhj7nw5w0fKcnEsV1mVgTo8vy1R5lbXmCYaKopLBR0t1t09BxsU4qtLTW09bWuv/f5 m7yGNapl1FV8v81JuCkpuMk09veKu2m4tK/O+xEfOsnGMWYJLSxtZLhRCkNks3Sy795vy0krdWie /VrsO29k4bkIL0QQz3KlYW7R9J2Nfqq58hk+k5AY+3Qi1tgIY1RztuwLa+etAfPYP21SIs960OLE E8sqvs9EbF9je9KOo/3bPb+ys/LaLp0dT5lv2+OPY5TxtmMcXmTo036KS0Ldy3X1bybb9V1d8pIn X2VYr3G5+yys91kkV8lDBFA00gg8npjLuke+kkszAnv+bofHeae3k6/SsSnnNJ+X2ya2E/zafQH/ AK/21+ezzhMIvPOPYfJKlxk8RF9Ljdx5jwvKZGASQqPd6kZfgelQDqrD7cObiuuRwWcfrHK8qn3S CI4jE3fe99UykDXoD39BWwOPNS763dZJZSykdVUdV184MoiR+7H3j+uqGgN1f/D1yVo3Hb/GW8oN 1b35nuUUMColVVQkns2/Jb01oAbHxM0+0CVsOK5/KTtG0aY6TzCFO44TGdk/PRQnt37gVqP7CnJl xHihLhpWiSLKxro9DFzJGSy6I90KFMm9+pKjffVbueLWI/CuBO7eC5tnikguYpEDCVHAAUg9iN/A /OgOZcc4gaZWSM9Y6WLb2vfZ18jTLSCOS2lNu0kKuk2wgKso7EH7fU679hv56uvOsDc4DP3OOuyz yoQxleIR+b6jzAASOltFhon1IOipAsUkT3dsLeUk+WxaPZIXt8CAfQ+m/Uf+sSV1Zl6VR05qa5W5 nKZawu57Ka2zFssXmJ12jHvP7w9w9JVxsbB6SD32CCKqYZ47C6gsLaxub/TK1wTP5fu7Gy0rK3vE emlbXy12rAMXDDDkY57iwkW16VKg7Dxt9h9W0R9h7jXbVSVbyrNbRzW7+YjqCpJ+B+P91cljsP8A hGo2uvfj7rk/QnhXOX4gp1KzkoVNk9Lbkl1tGTajfuk/fezUi8W8RMPxj6OuP8NOP3UMNmiM11J5 l/PcrMswke7MeivWisEEQCsqlSoVVGyfh/ya35fw3F8itoPIW9i3NDssYJgSkkWyB1dDq69WtNrY 7EVpLfXEtvbloLaS6mP5kakDqP2seyj7T/zrIMB40c/4guO49i7DFXdnb2cxFtISWaeT3vMZxo9K SbKqAu0YqSzakGflmPnJtVmrdOF/bocr458JYWhGFTLqc3Uv6t5STvxvJt6m+ivs7tLa+2OS4Vxq XhGf4/x3CYjAPmbJ7Ka5scdHGWJR0VnVOkuF62Otj1IGqiS29m29hi8lOa2ESJH0oow7Iob4H/Te n2a/tr6x/tIYm54/i74YS9mv7ieeLJWdldJI+PWPupJk6A5fqj6daGvM7kx6N5wPj5xS5x13k87d DBxjJTWWPjkWSSW+gQkLdCMRh0RmWRSSvSrRspbqGq2lWlh8RtOzt78HB5fj85yj14bVDzHa+m+p rlJtO9nzYyLhXgrwrC497fLWVlye+l6GmuMlaJIiELoiGMgiNSeo/wBZveALN0rrwxPs+eE+JzVj lsfxh7e9s50urWT8I3LhZEYMraMhB94A6OxWDc29pHj1hFF/kTkLfMX0ztDJbTWNzEYiy7WfqZVD KnSQYhov1rpl6TuP8z4kc3y+Qt7yfk+Qha1naWCK0lNvEm2BCMkehKg6QAJOvsDve23418Zh8ElG 32NjlXhvN/E9SpXU1dPdzb3fbZN/pY2t5Nx7jedkt2z2BxOVktNm3e7tEmMJOt9HUDrfSu9fIeuq gvxhxPhRwPNY6W54Tlsg+RluJpTZZWaNIEQAsViaYJ+dIgCAKoUse3SFbE8X4xeJGNu3mkydnnLZ 3QtbX9ssZVQfeWOSIL0kg626yaIB167yTxymxnO/CfBc/wACWj+jXXlSidWWdIZXEEsfR+aXWZYt n0AjYgkfnR+Ko4mlKdOzaXDRb/YcxyTMKNDGOUITlZShLnpdP2bXKTsY9y3AeH3GLTBZC3XkHL7f O2rT20dzkhYwrCBExl6oYll6yJEAQ9tM+9EDeOvhuCZWVb605Pl+FXLlo5sZc2s+YttDp6HhmHSy 77lvMO+okABQpav45n+OX2CwXEPEiKE2th1WuL5BE3kTWO0CRK4HYroaLHanpj60IDOLHyC1sMfc bx+dtMrbEqsZjtbmKd96BJQxFEAJ33k9Afj2rX16sUlKhGLg+lkt19mdjlGX1ak5UM0q1qeJg09a lKScZbLdKUUuebfOzSoclhcPjeTSTYXLZDKW30KGOW6uoxEs0+iZGjjABSP80ANtgQ3cjRMW8xSG bIS5FJd+dI+k9CAmk2fiCSD2rJeR32asRe2UMJvIPLDiZyNxq29hta36H+z1qg4phhyjnGNxl0rS xk+beCOJgqQoNhDptrsjp2SCC4+wV65fSqTquu7b247fHQwPF+PwmGwMMriptxcm3Plyu7vVsn1W 11uuxJvBPDyzseI2Tyda5Ke4tL+driLTRNGyuIgpPukAshPr7zb7e6PXxTt7xcrb5OCxD28dq0f0 r6QSYXLqdeWSAAR/WUMxIXegO+cSflF6+tVYkbOvnqsb55A9zxa6WW5+jR9KP1f1iFdSU12/OA1/ bW9PlJtzl7nDX/grZXl/k44rKfFwSx3IYOHLRAqR/rb38D9tc6/FyTKX/I1XN5E3MyRstrOxQhoE Huk60QSB32oBbq6e2tZdP4w8mynCbTiN3EsllaLLb2zBjEYupfySsQdkFwF6gVCjXcepirlWaOS8 mLo08carKxl83TDZKrIffZNnemZtH0OgCQLCx2xI9K/KUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlK AUpSgFKUoBSlKA6eezP9TPAfu2P921KezP8AUzwH7tj/AHbUqkOC0uSp9qT6keY/c8v8a5vcayMt tfQXdvHE9xE6vGsh0pkUhk2djt1BfiK6Q+1J9SPMfueX+Ncv7eZoXDCpXLIfB058AM/a5HFzYwzR zRTxi5gJcOsiMBvXqCCNGtdPai8Nr61yM1xChdrQsQT1/lYO7IUGyNjZBGu+/X3QDTezXy/6RgzZ pemGawdejyXkWRIWHp1+g94SAa7KoUaFbX+J2Hx+f8PFvJbcXix2o7S6kMkTqFdWB7N2PffyPzqx BzjkjhsrGO5ghKSlVOi++onq2AvroAKd+h6vsqlweXax5II2kdreVQkwCABQQOnRBHoW9dD113r2 5Li7nC529xd5NNNJaS+U0kiMpkKnROm79yCfXv6gn1qz5VVVVvfK0x0rKoGyCDo7IOtEAb1vvrde VakqtNwlwzOyzHVMvxdPE0n6ou/7r4a2ZnOSz4x1uRDb3OQK726KNL39G+IIGvhV5wmWu2xUl1ay y2cV9C0d3B1sI7qLpdDHKqlS6FXfXcMvVtSraYYnxS2t7+9kyMNx/nEfuuNsTvX53c9gR8D33+rV ZeinfUzlz8PkK5Gu44aSjDaS5f8A12P0VlUa2d0JVcU1KhNWjCya/wD6v9SfKtyn1uWu1ule3htk hnyMkSb854uhOpfQkt8T9mzX3iI8jI5nyq2LyDYia32SgPqCT/Z6fKrlIzhT5aBmA7BjoH+3v/6V RXU8Q6La8SKV5+xtwfM3236FRsdvU6FYynquorn7m5lh1QcZ1ajenhcRfCV73bd+Ltu+6MY5Lnsh bZK4tylhCLZgY5XQsdMD6Hvo67a186r+OXr5US4/IY2GVF6mknjUmMv8R3HZu/w+dWJuH8hv7y5G Nwc0VrbwyX0yrIskcMUalndtnpAAGve1vsPiBV44/LDZYeysr2KSCMflILto1MbhveGyd9B7kaOv T176rdYilRjhY+Wk33T3+dm/tufMsmzDMque1VjZyjT3emUdmm0lFuUY2uuJNRvZIyezkngtspYW sl1Bb39pHZXQcrIk0UfT5Xuv1Dqj6QEfQZNe4Vrx3JaWG7iSS4kjA6miiJZu/r0j/nqvuC8tZ5Ak N5bysRvpSQE/r7GvaVEkQpIgZT6g+laSdapKyqNtLv8A+z6hhcuwlFTqYOMYyateNkn2uo7O3R82 +S2Y22ljy11dwpj/AKJdN5hljdzK56VAB79AUEORobJc7J7BaTKZaa1sLxDLbPdq/lwdOzoM2h1d tA/HX2V58os4YIJruWURRMy76Iu3qB7220dnXfQ186xG9e4vJbY9S2cCp17jl269te8d9ifQD5E/ I1tMJhliZRnJ3Wy47f322ZwniHPKmR0KuHpR0y9Ul6r/AFt7pf0pSd43fN0r72+bC7lWyjliuGVw 7MPd0wG9euh1fMn5mpR8I7B77C5K/ihtnZrmx9940kkUpOzSMOvQG1Ze4PUOnYBIG4slced36fd9 ztoD7Na+FbNexxwy7u7oZvIY9nxt7NHCi6BD+V1t5m/h3kI1ofmb+PbqT4O3d3Jj8LfDXEeG+MzH idNb3N1yC/gLflZ+oRRN0hVBPcA6BI2ddhoaAGpHtC5RMjz+8n+ki5n8iKKZte+jbeQrvQGiJUPb fcfZW8vjvmYrfApjFfqeV9yqFJ0AO2z8P1VzW5bmFv8AJTX8W42upHuPK6+ry2lYyMN9tgFiB2oQ Wm9mCAovR1t+dr4VQUPc7NKAufF81kOPZ21y+MupLW6gfaSpra/3gg/qII+yuovhDzDH+JvhjYZW Oa2a4eNBdRJpxDMvS2tEDYB0d6G/kPSuU9TH7Lni9d+F3MnkmU3GJvo1hvLcIGeVVLFBGSQFcM7a JIX3mDDuCoE0e1pwCfG46bMi1h1DcLH9LIdS0LkhIwB+cRI6gE7ADPrWzvVp2bek9zv27V1LzRwP MeAteLLaZDDZCyMgZgskU8Toex+Gu/8Ayrnh4zeHeU4DyWSzuIpHs5yz2Vy5UC6iAXbr0/Ito9h3 +GtbAwuCYNaypIUVh32y+pHz7j4D4ka7fKqri2ZdoxjmjimSYNKsYOiGGyy9iSB22N+u/h6C0rKY mLghWB3sHfoK8Z1S0lS7tpkimZg6oYzuPRHvAj4HZ+z10Ow1j4qjGtTcX/GbjI8zq5bjI1qb24fv Hqt9vi/Uk6LLWJZ4jMI2iHvdXYen2/8Az2qmxlhYR302TjgvGmkGmkmDE67egPf4fKvHjtvjclbW +WNmoudlizbJ6/Qt8vh2+VXxm0CxGtfEkVxtRqk3CF+zP0pgoTx9OnicRoaXqg0m9mtnvw7bbffo UnUbGxhjtLW6uEUERxmQkopZnIHWfdBZ3OvTbH515mwx9vfTZPyzFLKnTK3oCO3c/L0r1OUsz7sU q3Em9eXbnzG39uvT9Z0PtqizeRvLSxV4hD9JlcCO2kXchXYB0FY9RG99u1IKrOVuG/fkjE1MFh6D qKzhTV0lFNRavxa27va17vhLkt+T5BjrVuqyhSabpAWY6K697WteuiO4GvX1rGr/ACNzkJ0nuJpC 8bEx6PSE79iNdtjf/Ib3XnkLY2dwlhOpimC7WPsQAQTvYOux2PX1/vr6xlus9wqMSIwpaTQ2ekDZ 9f1V1mFwVClFSjv78n59z7xLmmOqypV26aT+helJrv1bXuZHj+VSpbdN3D9IlH9aPSb9PXqOh3Pw 3/ZV/g5PNkOE2tjHZXNxaY2+kubSFG08LuW85CnWFYHrLqxDOp6kHuyfk47l8o3TBEKI2gvW3UQv zqtsL6WxuHtcasNzezIkaROHLdfWpHQFI6iQGUAhv9IdDeiPGpl0YqTobN8ro12NjgvGdevUpQzN 64wacZJLXGS3Ut079mns17mc2E6Xkct7ZS3dnJPbS2crAGNmhkGnjZSNMDv4g6IBGiARd8Bjjl8j MZcgMbirGJ5MjkHg85LYdJKEoGVnJIChE95iw0Do6wnjOXXN3cNlfcit8DGyoBeOhlQsZUUhvd9w 9BkYFtKekAkA9QnG6tuE+HHgTl8PfZDGZbK8jtZfwXdLbkyXSlOuBz776KP1OJAQil1OgSWODQyq rKS836Veyvc6vNfHuCpUpfgF/wAs9Oqajpvbrbe7W/PxuuI78W+S+HC8EOH4lbS3eQt8yJPp1/AY 7qW303vqyaUIfdQRsOrQJZAw6quPDuNy8eyOAucJkDkLPP4u1yuZCMpax3FMFiII6gpkcAfZGO7A 9sX8JsPdx8gseS3li8tparN0SiUL1yPGy9TbGyi6Pf1JZB3AYiQ+Jcrn5BbZG+WFPwfcyrBZMzac 28IUD3Qf9cdtgbA9PWt9SpqEUkrfB8mx+Lliq8qkpOV3zJ3b+S7tcpHMYXcKjoekt/5e/wDGod55 yl89d+XZpJBbRAqvU5BkXt3ZfQd1Gge4HyJIHr4ucmVshFj7VnQWrN1vtSGLKoJGtkaBZfUerbBG jUcS5MRefo7dunpA/VXoYZ7cjuoY44YraVvO6SJSp+B+BrH6/WJYkk7J9a/KAUpSgFKUoBSlKAUp SgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoDp57M/1M8B+7Y/3bUp7M/wBTPAfu2P8AdtSqQ4LS5Kn2 pPqR5j9zy/xrl1XUX2pPqR5j9zy/xrl1UrlkPgybw/5becTy4u7eOKVWHQ6yDsVJBYb9RvQ7j5A6 OtV0W8J+Q2fMPAl762ke4XynDRSMVdenv0n+0Edtg+oJB3XMOpi9mrxsv/CzPS22Ra8v+L30ZS8s ImXqDH0kTq9GA2NbUHfc9hqxBKPtW+HMdxzSPM8O/wA8hv7VZ5IUYEodE72T729eg2e/p61rnOlx au8E4KshZXVl7Aj3WUg/Igg1vLkEXKeGWM5Vgs3bZDD2zsIIiE1CwJVkEgO3H5w130R9lQrzri+L z0V9k5LGS3v5VPS0IPXoAFG6HZUYnQUsdHp7dQGiAICx80tndSXFndPE7MPdAPSF3sjQ9Rs+nbX9 9SFx67lyGNS6mMAncadY0IKH/VO++/SrRzPhd5xiSO4jka5x7oHS88gpFv0IJ2Qh3rQY9+oAFjvW N2Ml2riWyuWRlJHmRtsH7CB2Prvv/Gtdj8vWKj6dpdzs/CXi6pkdZqsnOk/6U+H3S4/Lbkz9QVme J7i9nLD0MXSBv5Mqj/1r7RZEKpFBHbQsdueoBt/YACCf7awxuTZGPatOhlAClm7fbvR7fH5VR5Hk mSnhKPfGNSNfkdA7+e17/EHXbev11qFk9duzat/Ox9El/qNlUIaoxm5dNl9m22/mz+C+8lzbw3qw WyPI0DgsVlcbO/eUKOzkAbIPYf31kEF4kkAkhCmIoDGw/NYEdu47jXz1WIcQyWOtru3jyGIXKWE0 Dxy2aXEsDTAuG6gyOG7NGD73Up6fTYUrasVkpMY4Frk38vf+jLr5evj7p3rv8j8ayquUaqSUdmv1 NDgf9Q1Rx1SpXvKE7cJenbje10uG3u7K22xnqxWt9J71tj51XfTIsoZwfjrS9u+/jS8txZ2013Fk L2BI1LsC3mggD5OCf7iKxWPkmcV1l/ISRkKNeQQDv4g79Tv/AJ1QXV7c3N0BkLx/UsqyydAXY12X sPn31XjTymu5Wk0o/c2WM/1CyqFFyoU5TqvrZQt23Tvb7+585a9yOUmDXskfRoKij0T12ekE7OvX evl6b38w/SJB0gsyqNs4IA7AAs3929n0q84fjOQyV5apNaX9vYSdMkt2LCWRViJHvIFXchIPuhN7 9SQu2E/cR4VxLj8Edhj/APO7xm67nJ3qopjAjAPlAg+Vv3u22I6iCxHaugp0oU1aKsj4/i8diMZU dSvNyk+W/wCfYijgHhk/IsQmWyN5PaxTMq2qiDvOD6N3I6V7+uu/qNggnd3wewKcetMNxqzSeK3x cMs06SyiUmVyR1BvXR6jpfgNa7CsJ8PosbyLMjEz20sNjbeZ+UkyCf1VQidGTYOidBTo+p9O9Xjn virxnwjwuSyOWa8vstfQKLKKVQBkZI4+nzA/oYySpLAHQPYHsK9DEIg9q3xGsbZ8tb2t15V7dExw pGffXWkZwQQVOgdH5nffWq06nlknmeaVizudsT8TVx5Vm7vkXIL7M3xUz3czSuFBCgk+gBJIA9AN nQAA7AVa6AUpSgFKUoDYL2fvaIznELP/ACU5BdNecekgMMXmKXe0be+pCPe0QTte/opXR2G2h5Lm MHnuB2fEOR2Mt2uS3HBdKu440b809YIZX9PT++ublbAey94zx8YzQwXNHbJYa5ZBbteTHos5B2DB tEoutA/1QBvQ2TQFi8RPDW74/dXr2K3E1nZr1TSS6PcMQTpfgNBidAAH10DrBrK5ms71JzGWeLsE Ya7ehXv8x2/u9dCtvPFjlF4ueu77DcUsclbGJZJxBIxbsg6jEqqRIW2exI9B3+NRPzDw2xt+Fnwi pj3YtL7yuYm326Nb2gGtDpBCgnSntqsoqcXF8M9aFeph6satN2lFpp+6MEwKrbdUkEofHzRdUoQs hhkA2elfUA9zodxV9tmt5wZ4kDBwD5jDXV9nfvWMcl4tyPiLwy5SAQQO27e+jbrtZhs6CyD3W307 6CQ2u5UH0sNre3tpeTXdtdOrTMHkK/mvs70FII18vj9taLEZPOpJuMv57/v+h9Xyj/UbC4WlCnVo uye6W9veN+l7+l8X2lbYkj8yIgssK66V6B3H6t+v6tV4WlsiSPctFqU+75jncjKPmfgO2+kdv76x BeWZoAgw2cgGiXAZT6b+0V53/JsleRvCEhjUEeYsalmZDvt39CR8u/f4VhLKcVxZfc6ep/qFkTSm pSbXCcXz88L+c8HjyO4tLvLGWKf6RIqlS6g9I97XQu/l27/Esf1D9wqqblIwrDzNx62fXW/gfmPT 49we1UWYXGWaO1jqR+rcciuwZAd+jA6Pb7P76qsXayZZbWxifpvbpRaxoSRGTIwCt2B0TtPT9Wq6 LB2VJRjey7nxrxIqksfOrUcXKW70ttL83/79ylABmIDEt1djusr4NhpcvzKK3w1zb2czK/RkmkZZ Io1hEZePUgDKXDIWC7G9q2xtMsyHgdzWbCNyOzgtL23P58dmWJSQLtxrQ0Orq167Gu4OwL5iMZku LcoxthnLe0xNpkLboUvK+1uWlMoDMfdUMr70f6x6R8N5RoT45Pxyxmtsdx7O5q2yeXvYpIocoluG ki8tepYtnbOnT1FutiSS2mBI1jVp4bYu2l6LnMPPECA8dvbCNpCN/adD1+dZr4lTvx26wWQa9Co/ nQvGkPW2mQMCAPkEbtrv/wAjbsbewX9nDkLO4jlgmHWjxr7rHfcEf1T6gj++gPvnFza2vhflbK0x BZ7fHi3tiZteVH+a7d+7Hp2dk9+/xqO/8obTGcRsbLAx/Q5riFTdMo6ZFHR/rAD327e8Bvp7jXUh rLuY8ksrTil7Ak1pJNkrVY4ovzjJC7APINHsOjr03pvQ71CGQv10YrdAg/8AJ2UfYAKA+81cF+gs 4dm2T/f61aD3OzX6xLHZJJ+2vygFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAU pSgOnnsz/UzwH7tj/dtSnsz/AFM8B+7Y/wB21KpDgtLkqfak+pHmP3PL/GuXVdRfak+pHmP3PL/G uXVSuWQ+BSlKsQZ74WeI+R4fHcYl4orrDXsge5hce+hA11RnelbsPXsdAHXqJ447c2k8N1k7C8ky kV+oltwz9aqAoBjG+666SSvqGLbrUqsy8MucXnEsmhBL2bt+WiHbqHxI+AbXx+I7H4FQNkLW4a6s BaEMl1E5ZIS2kZDretemiKoMpiLBrtZs7hMfkJygVGu7ZJGK9yCHI3rZJ19pqggyuNyQ+m4i4jki B2zRHRDEA9JHqp79wQCPjVXdXNzdRQebLJOkSiNCU79I9B/6UBMPg1ex8lvUwU0Qxi46z8m2FoqL C0Y/qGPsPT0IqmzfhTwHkZyfHhieN3d6zG4E0cKW99Joks3mxrtj1a3379wfWonXks/F8lZX1hbX M810UgBgTq8kNvbOB6KPifhVyizd0Zsdl0uLmK8tD0htgDe9+6F+H690BkXhL4EfgHi16LzGQ5EP NMt0iTK3nxBmCCQHWyEIBXQG+rt3O8w8NfCfgLXkeXwfDbAwRdbQ3P0pD0yEsHDt09YB2RoDSgaH aq7gWJtZfwhyy8yWcy02UlF0kct0LcWx0o6VYN2Xt6fafnVs5VleQ2t/kp8SZbLE+T09AZXUN8SQ ex38CO9AOcYLgPh/9EnsfD3GNf3sLQwXOIsrfotN+6ZPMfpb0JB1s6PoaiDCcFs+BXN1k8JPaNJe Ro8TwSMzxxFV3EGYknbAsfT1AP5oryyma5reSia7zkWShtgxtYbqIhtEglGdSBrQKj3NjsT1Ed76 15BeYa0iQwpDE5kUBdvHv85SfiN7oC1GS6iiFxCLRMg6xrPcR25AZVJ2F02x6vrZOi2+/fd4wsFo SHna4MTBgUVEZixB9S3b1rG5JzEHjSMLo6LN+b0k1U5DMY/FYsXV/erbQhtCQqSxYj0UDuT2J7eg BJ0AdAZ1hee8O8M+IZHMXfHWnezAt7e3kj+io8oG1iQhSHGiNkA6APb3TrVDxl8TeQeJ3JPwnmJi trAXFjaLrotkY76V/wCXr8qpfEXxAz/Mp0iyF0wsoD+Rt0b3B9voOo/aR/dWH0ApSlAKUpQClKUA pSlASn4a+IV2LP8AyazmVubeycjyryGUxyxD4ozDv0H06h7y7J3rus34K2MNpq2kuLjpj6XWaXrB B9CGJJLdgeo/GtPlJUhlJBHcEfCpT8J+fT46a3x+Qu1FmilQzA7i9Ne9v8z13293Y9FHugTVxkrj Zb+C1t4Htr87v7CaPqt7snfUXQ+6WIJ2wAJGt70Kx7nXhPxvKSi448t1xGaWYytBMGlt5d9yqbIU aOtDt22Nems0wYtr5zcRuYZ10feHu/Lf9tR9jed5vifitlsfy0HLcbJVZbF4dJ0PopLHr3iV0+yD 30QAfzQFzHn8Jp5Li6it8t5JjdvKWe1A6x+cNujnQ79JboHoT0ka344vw6y0F7krfkeLvLi2WINb X+CkiuFSUL+c8LMJJF16gab3NAHe6mf6JiszDLNx6aUNGizCCcEx+W++l4pwOlgR8N9u2/UVY5ly djcpJBEY51cAMknlkLsbIZQQTrZHzIA2PWguRriPC67y91d38sc1vaurRwy3NqzuwCgK3l+i9Skk 9ZDAjsFOiM3w9lDxUxy8cigs79F6EnmjEj9XSoO/T3SVBKr0r9lZ2tzw+LjV3fcosOURtbRvJLPY 5m4th09yzFYyAx7fHfx1WEZK/wCIctsMj/kbm581KqtOmMvSIZwVVdCN+lT+dohtdmPY9gABLPhH zaQJl7eK5gSHIq/TEjhvJuACGUfZsN+o9jUe32Nju4J7DITukMpHmq6ltnfcDsTsH018/tqO+Xcq nwN7aXGJu0aeF28+361bz4gsnmDSgupUxa6tAAnv6HUlWOTtL+OPOY27juAh6/PSUEBgdA73o9/7 iDQEWcl8NfEawvZY8px3JOtoWeyNzdL1m2J6AwUuehyvcrpAen4EAVhtpmctx7dqMtM0UdxIbmMh OhZQ46l10kgH3hoEdz1D5GSPFHxQ8mHM42Hk+Uv8rcXKiSdGHQiKVDL5mg29BgOksQSdkaAEDZPI 3WQuJJbieSTqYn3mJ3QFTyDN32ZvPpF5cyzuFCB3I30jsB2AAA+QAHqfUkm1UpQClKUApSlAKUpQ ClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqV SHBaXJU+1J9SPMfueX+Ncuq6i+1J9SPMfueX+Ncuqlcsh8ClKVYgUpSgMy4Fy+6xN7FFPJNLB0uh jDfnBtfDYHUCBon4EjtvYlPEZe5yPHGucZJb3PXKdxzFojGwP5j9mKsARv5+o0CK16rKOMcoucbM siXUkE3SqFz7ySKCSA6n17k9xo9zojdATJgLOPEWJs7QNFbIxaCJm6hGT3IB9db2e5J7/LQq5xXk vS/UVZiT29P+XrUeXfOr2KBnXHC4USdY3L1GRGLe6OlB0EbUjsQQCN9Wt1WK5TgoLm5jW9gLMwkk W2xk0beZ6SOw77JP2dvQk0BNnAORS2lpJZhy4/qoZPc7HsTvdenJOTzXguGvOm5kmDKrNohHHbYA +Pp3qGYeUYSOcXkd6/4Q6ZUSQ2VywRWK6HQe2tIm9EbIJGtmv3kHLYZ7MNYzX7SOTIvkJHHojtpv OB0CfiFJ7f3gZXeXX0eB7i4HQpUl5HYKqqO+z+r7apcVjVsJL6aycQwXUpuJUAYlpW7sSSx7Ht2A 7aPrvtid5ziG1kSGKzuchF5B6bh/yW5R/VcMqhQRo9QGtnQBqwck5xbXeNvbD8G28S3TkSdxI7jW gx1oK+ghDbcDQ+QoDMMnyvAYvHf5ncWV0kKhYbeGZSPTt+afdUfq/UCdAxhzHmmW5D5ltJOyWHml 44AAFA9B+v8At+JPoO1Y/d3c1wT5kjsCxc9TbLMfVifiT86p6AUpSgFKUoBSlKAUpSgFKUoBX3FI 8UqyxsUdTtSPUGvilASv4beI5xlvDZXcTmOMhVMSF2TbevSfVBv0XuuuwIPbKPEzkljybD297c3m NORguWtrAY6UzedGdFuvsAuthu+wvfR97VQCrFWDKSCPQg1cIMiPKkinhj99CvmJGBJs/Hq7E+p3 37+npQE3cIPNbLh19Y2ucmxVpkZEktV0j9J7+Y3Qf6r6A7ke8GOu+z4qOarnZ7LH80lhtreINJLJ axSflWZisegoGwgUnZ/rA60w1GVryqVZnlnuzNH0usVtLbsY7cMyNqLpkBQKY06dH3ddu/erNLlC kEsFr5/RMFWQzzF+tVGlUr2UhfhsHXwoDZPO8yxvLvD18dlOUwraRXKRXs8MJhSbyyGkQHez1KdA gkab1PxgHlmSxS8lkvONXF7ZRQu4t3jlI6QT/wDVqNeWpJc6DH870XuKxlnZhok6HoPgK+aAzbP+ JGaylnDbqkMDRvHI0/SGkkZCGUt26fzgD2A9Pl2rGJsxk5YvJa+uDF5hkCGVmAc72w2TonqOz8dm qClAfrEsdsST8zX5SlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQC lKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+1J9SPMfueX+Ncuq6i+1J9SPMfueX+Ncu qlcsh8ClKVYgUpSgFKUoD7jlkj/Mkdf1HVejXd0y9LXMpH2ua8KUB9+bJvfmP/vU82T/AO8f++vi lAfrMzHbMSfmTX5SlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCl KUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6 meA/dsf7tqVSHBaXJU+1J9SPMfueX+Ncuq6i+1J9SPMfueX+Ncuqlcsh8ClKVYgUpSgFKUoBSlKA UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSl KAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgOnnsz/UzwH7tj/dtSnsz/UzwH7tj/dt SqQ4LS5Kn2pPqR5j9zy/xrl1XUX2pPqR5j9zy/xrl1UrlkPgUpSrEClKUApSlAKUpQClKUApSlAK UpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSl AKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+ 1J9SPMfueX+Ncuq6i+1J9SPMfueX+Ncuqlcsh8ClKVYgUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFK UoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSg FKUoBSlKAUpSgFKUoBSlKAUpSgOnnsz/AFM8B+7Y/wB21KezP9TPAfu2P921KpDgtLkqfak+pHmP 3PL/ABrl1XUX2pPqR5j9zy/xrl1UrlkPgUpSrEClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUp QClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK UpQClKUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+1J9SPMfueX+Ncuq 6i+1J9SPMfueX+Ncuqlcsh8ClKVYgUpSgFKV9pGzkaHrQHxX6AT6Cq63tE6eqXZG/nqqpYgjAJGq geu6AtAVj6Kf7q/CrD+qf7qvjRjuWXp7D0I2a/AFJ94Dt8dUBY6VcpbZGJC618DVFPA8J94dvgaA 8qUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAp SlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJ U+1J9SPMfueX+Ncuq6i+1J9SPMfueX+Ncuqlcsh8ClKVYgUpVy45hMlyDMW2KxVrJc3VzKIokRSS WP6vsBJPwAJPYGgKCKNpHCqPWs+8PvDflfMLwWmAw13dv1hXdYz0R/m76j6DQcNreyNkA1sH4ZeA fE+G46zzXipdvFkWj8/8EwyhnUBlZesr2XWgCC3SdsvvdjV15N7R9nguPyWHh1x2DD423jCW1w1q EhVQg6d70SBvXR0htqQD6EgWXAex7yi5s1fK8hxmMlJJYeQ1woXfbsGQ71o+vbuO/rWR3/s0+FFm g+l+IN+jpsOlmIpnLfDa9Lnt+oVHl1yzxU5jirvluNyAv8VEVgMdowuSspKHurK8/UFfuFQqoHcL 61RX1tZwyz2lz4mPyTAGz6Y47e8uHklupA2reWVVkQxa6izHuu0BABHUBL9l4c+AsedXCHj+Yu0a 28+XJBJVhjbfSI+lveJIUnSA6+Otjfjd+BXgZyO9MWL5Fk8NLZSdMvmJ5UU+1VtAzp3A9Now7kj1 HaMeP4TwX5RhcXgsjLLh79IWjlvrjO27NHNHJ5flJE5J2/RsHylUqwI6TpRUcij8PLezsLbgfivy vHR2q+VcrfY6diqCMlGVS0XbadPTGrHbrpQAaAyjM+yAbxLy94nzTHX8Oj9Gilj6R1Ad1aRCw7t8 Qo0Pgah3xL8BvEDhFubnK4Xz7JYuuS7tW82GPSszdTD80AKSWYKO699nVSPZYPPX4+mcB8UbTK3U 1sZ7mzysrRXcSIekPFbyxnWiSN6QMQp2291c+Ae0JzmysLK4znn5nAzahW++gvq4kAfzNmQDzOyE lYiAvS3z0ANR8hj57R260KgHuD6iqKt3+ScL8I/F/FS5jjkacVytxOWeZIdWtywZg4ZRroZtk9eh shDtlAB1W8U/DfkPAc02Py9p0+4JEljbrikXttkbQ6gCQD2BHYkAMuwMJpSlAKUpQClKUApSlAKU pQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlA KUpQClKUApSlAdPPZn+pngP3bH+7alPZn+pngP3bH+7alUhwWlyVPtSfUjzH7nl/jXLquovtSfUj zH7nl/jXLqpXLIfApSqvE2Fxk7+KztY5JJZDoKiF2/sUAk/qHerEFy4LxXL8y5PZcewsHnXl5J5c ak6HoWP9ygn9QNbecGw/FfZ+4vPk5BbZXlTQEXd3o9Fq2t+RbhtBm2F2x1sgE6ACrc+H8Uwvs8+F rZi4b6Ty/JWgee5hj62tYSR+Sh2NAMxUFiV2febpVAFhqQy80jzXO8zl7TDY3HtFa2KyWzSS3c7u 3lQxoSiyk/lWO5B0b6m90k0B72nMcPzrkM0niXn7iyjadbuxsra2L+e56gwZ1QsFCrou+uoSvooB oWXJcqePCwYvjdjY8XsYXlYyW86zySzuGV5zczIZA5DFQUCso2OoggLjUlwr+VdXtrYGS2tYrRpY bdI/P6E6d9X5xJ7lnJ6m6u+lCqtlv703Fz51ydKmlAPZVHwH2UBcshlpp2lWfL3t4klylywaaSQe ei9KydUrE9QXS9XY6AHwFU4msJHilkeZ9kiVZXZ2I9AwI0B8/jv7KqMFhjfWRlmkvLS9ViWSSEeW PlrY95f1Hf21+T4u1jw95J9Kinu7cebLFCysqrvfTvp6hsD1Px7jtWA8yoanG7ve3H6/B1sfBWay oxr2jpcXJepdFdxtzq9vnseWVsIYMhGn0wqn0cyqGX8qIuor7regVtnQ3vTdx615xwXUgD2OM85E YqSQNk/aWI38R29CDvVftyiLaW81ssks1yFZwgL9Qj2FYBfkCBv07D5975hbbIY7EhZnYwLGSkCJ 1yrsbCgjWiD8+r9dMfi3h4LTa/Zjwp4dhnGIkq6mqaX1RSsn7t7W54TfsWG7uY5LL6Le28iEbUxl +pUOtH3GB0wB7EfPYPoazvwm5xNwmG6XHxNkcdLD0fg5rl7eISdQPmMoDLIxG9sys+gqqyKNHDcj by5SwN3dwtZ5S0X/ADlNHpkj76Ya3vXr2+0fGrRbTPEwaOQbUbDDRBFe2FxCrxv1XP8AOxrc9yeW V11FO8JK8W1Z27NdJJ7SXck2zyVhNcSci4fJDa3OK/8A8ziIeuFY1DqzzQ+8T9FLhVZQfyfSoYNG epZp4PzbhPiPw6847zTCzCCyczdKRtJNakdWpYSgDuCNho1Bbuy6ZWHVq/BDh73KY67vMa12I3Mk trFKLczqAFG5TG40H8va6J6SR22CLscrHbGPJ4HOTvfQqs0him/KKnXsFvUdtDYIPc6YabRyTSFv 8c/CzM+HPJ5bG6iWa2cebb3UCnybiJiemSP10Do7UklSCNkdLNGtb72uMxXjt4N3OIghh/yhxcEk mHkkkXqib3dwSMvUBG+4z0nv0sh0rICNG8vjZ7adw8EkEisVkgkQq8bAlWUggHYYEHt6igLZSlKA UpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSl KAUpSgFKUoBSlKAUpSgFKUoDp57M/wBTPAfu2P8AdtSnsz/UzwH7tj/dtSqQ4LS5Kn2pPqR5j9zy /wAa5dV1F9qT6keY/c8v8a5dVK5ZD4PqJGkkCL6mto/YY8P48x4gSZu7txLY4mDzGkPUAbhiAiAj 3W0vWzK3puMgd9jXTC2xRi76XQ6mYgnoA7k9hvsNntW+3DuJLwL2ebfjl3OY8nyCRpbua0kaOQIx 6jpxo+6nTGH7HXT6dgLEEN+1Zyz/ACp5sbIOPoVtp99SMI9dQ06/nRsqENvYBEx7dhUIS5A3U1wL fJXMOGedWgtmkdIfMEaxNcdJPTsgFASCSCQda1VTzDkkV3meQxY++lv73KZa6jjdiGVYXdUXTkkM pRVVfTQ36BVLUUEQSKGyjtVuGn6IIyydMfSPVj6+pVmOge4+0VWUlFNvoetGlOrNU4K8pOyXds97 aGXLXQsYZmRFiMis6gqqMW6QAG0WHYb7ggbqjuIpLDIwJdQok0MyyNH1dplH+r8x8e/prv8AOrjH xvJvdMgl+gFYwizxSsw0DsKoGt/H11r4bq0W1pbScnjx+bMty4fy3kMxYM2vd2d9tn4f8+xrXPFR nOdpqUbcLn7naRyGthcNh1Uw8qVd1LKcmtN77Jx3aS6O1nvyjNOPYLOcpzd0lla5rNY7yY5447O1 eSJVk6tLKUXt+YdAnvo+vevPNWuSx+MvbW0tLcOheKSGKEwSRFSVdek794aI1oEEenwraX2R+LTY 7i9znEjhWxzDNCgWdvyC2shRNR+XrqZ3uCx6/wA1ItAlm6YZ8dOWYPP+IN9n8Za2dnYW8bWX0tJR 1ZV0bpE5Ibyyul1Gw2zJoltdKpra2GVOjCrffa0bc/br3Ozy7OpYvMsRl+m8Vr1VVJpxXXeV7xTu orta29yG+NX/ANE5EXnZUs5LZIopenpVQ3vKe+u2+ofYe1StwXh3IubfT7DDyY2W9sbL6VLDJdfR pbkF3GrZfeDaAi6mdowryqCdaYxJh8THlLmzsriR/Ka3+kMwcdXTs9MYHwA6z3Pr8PTQnX2epZbD xy4bZ2k00UEzXVvKisQssQs5W6G+Y6kRtHttQfUDXvi4UamNjCSu3z7dnc1nh7FZnhPDNbE0p6Y0 29L2eq7WqLi09uz2afsYVdce5NDNcZA8czrWOJtJDl5pbGRDaLqJh5isoPUFkRyuuoIesjpBNYTy DH2VljbO9sDK9pKvS7a2ADoqx7b+z+74+uxHtPWecs/Ga45C9nfYi0h8mPD39jLJbLM8luBI/mIw H0huh427hjFBFsdIUmFb6W8sMuZbu5nv7C/d2nNzIZZVlYksxZveYMSSd7OyTXi5RwlZQpXuuU9l Jb/rvsbGnRr+IMuliMfGn5dT6ZRvKVKXpW9/6XpWqzbV99uMXx+Rx4QCVgJfL95ynvBAdsAe3p39 Ce/fvVbhTfpyrFSYXFtkLnLOLPywQn0uSUgRqWbYUluk9wN6OydnXhmMLZ4fJw3Vv3t7jZjJc/kS ASf1rr5+n/OvbCSW1lbRSWl7LCFc6aOUdcR2CHQr3DIxUhjsg9J+Vb6hWjXpqceGfJ81yytleLnh a69UfzTT4afZ8k5+zlm7rB5z6J5N/aZNU+lxwTqRHCsbKJYmUkdLEzdJ0ASBokaAp7X3AbONMf4j cfxRisuQO8mUNqytBb3vbfV2B3KeoE+nWmtdT7Md+HuUv8PfWktwbeG1s70Ql7W6doJYgqJMHLkk LALmNyW7e6ANdJK7a8JhseVcXzfh9lCr2mVtn8tWZh7+tMAR3AIAPYj0bXrXsa051X1l0bZN7Hdl 1VBUmeLPD8jwjm2S49lQ6S20x6GYaEkTe8jjQAO1IBIAHUrgelR/fwBWMqa0aAo6UpQClKUApSlA KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApS lAKUpQClKUB089mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+1J9SPMfueX+NcwbJA8wJG1Xua 6fe1J9SPMfueX+NczsTGRH5g2CT27VK5ZD4JL8A+PHkfiNgbIxyyqLxLmQKRvoiYMNb+cgiUj105 9PUbX+0Dym2xT5O+kjVbXCWRjghaQIrMoJ6N99FmKKPX0HYntWIewpxmxsbDP87vrMia2Q2qzsAR 5aKJGKEjY7syto63EPQisT8fb6LIG1E7SrkLt57uOYEBdBk61YnvrqeJwPTqjU+qirEEI5OSZ8+m OT8Hy2uBtY4jdi3DO7CIKF6tnTBy5C7GiCSCV1VBxlIjylpbuY28YYeUZD0mWRB0ED5j3j+v+8VV 5aJ7Wae+mnkma58vzQvcBYV0SO+mJ1sbA16fE1ceHYrE/wCT9tK8dtPISJWkIBKt8t/DWq12Z11S oNO/q22Ox8D5VUzDNIyhp/4/V6uHZrtv7/luXoLP9JmlN+rQsn5KPywPLOvzg3xq04zGLYQiW1im v7iSORxeMySBJeluj3WdexcgMQdgEnTEdJul477RYb6COWSU+7PGHQr0r0BAOk9Ww5JLEEMoAXpJ anw95eXd9cxkxNbWxWJ5Po5jZ5Qo8wKOth0q2wG3tgASFJKjm4OcIOSattdcftc+24qGGxGKhSqR kp3lpltJp7NtNuajstrpNLZWext/7LnJcPluDvxvH4uXDyYZgrwNetdtKsxZzO0hjjXqeTztqo0u uwAKgQV40eHWK8Ms3isHieTS5aK7iZ0sri1j8+1t0UKHllV131PsIPJAIV/e2m2k3wJscpw7wY5t 4gw2U0eRubB7jHR3Y/ITxW0TvDIEBDhXkkkGyw6lVSvYhm14vhkcrye+5Tnsk+Uzl6qCa6eNYg/S ioPcQBR2UD0+Hz2a2+Mrw/BxVZetrbbj9j514byvEf8AyOtLLZv8PCVpvV9S5t11Xaf7q9yPbjpw eevr63VRcJd9EMAXt0sN60PgQTr0/N/sqQeB8nzljlbLkuPtIrHJY6XzrOSYCSNiVZGVl7HpZWZT rR0xIIOiI/sIYLrks99nVYwqzCSQqfKL9RUBT69OgKkrjuIiymbxeDtbpbJcpdR2i3bh5o4DIQoY oNk9yAB2GyOpkXbDxxkvVTjFeuy3Nl4aoJ0MbXrT/wDquU26fLturtWbXG1mt17G8o5Ff5nw3t+Y 8XwkeWur7Gw31hjbq6Fs0iyIsnltJp1R+lu29r1aBYA9Q0X5tk7ibmXIb4cWs+M291G3H5uOY60t Jo4UikUSoziNVZvPhLiUaZdL0vpV1tL7Wl/lMLwPA/gzK5PGSzZlIWksb2S3kKfRZ26S0bA9O1Hb etgfIVrNJiOYZfG5/nmRWO+xr5fyrq8gSCB4JnEGmeJQoPW06e8isSwkZwN9Rz8wxElenT+pb/l7 HI+EMmoT04zG2dKbdNX6VLJrUuGmrpXur2ujE8tiLjJY2K2ikisVtxtIu8jD3SACQe3r6d/7fSsa yFzKMwy3AaFpo1QopYK82x73Q3fRA1vXy7+tZwQSCJZ5keFuppUUIGXvoEnse3rr/lVp5fFaZbj8 t5a3MbSWZMkc0b76SO5AI/s/t1WpwGMdOpFS3X9r/wB7+59B8WeHaeNwVWdB2qJJ2bXqUE9kr2i4 ptelW+5jC4/Ejk11PfmW4tV6JGhH/wBY0sZcjqBHSA3662K47mMlybg0kuFyl3Y56yMaRXx8uNmv IkimWUBSwCOWXY16FlIrX+S8E+MhKFBDMfN8v0/8p+A2R0gb16Cpe8Bcqj3ORxsJ8uOeFLiGADfV Kn5OV9+o9023b4/Ab6q6s+Ambe0txzkWR8EOI8x5U2LvuTr/AJtk8nbxiEPAwZogV7KzCTSjS+sj aVeoitT36V2j+pOtn4GugGWxX+XPs5cj41Gsct9ZI1xa+azBY3U+bGd7+DKe3cdtaO9Vz/uo18ot GWCP769Q0dEboCz3CeXMygHXwrzqsuY92yvruvqfmN1R0ApSlAKUpQClKUApSlAKUpQClKUApSlA KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAdPPZn+pngP3 bH+7alPZn+pngP3bH+7alUhwWlyVPtSfUjzH7nl/jXNrHJGJYUlLCBdNL3PZB3Y9h30NmukvtSfU jzH7nl/jXPTgFmuR5hiLLymk+kX8EZhEZcSIZV8wEaPu+X1lt9tb3UrlkPg3r4rjP6OfZaFsRam/ urZLeTqkcRSXV1KsR97RIVpZSd9OgG9NDVax+NM87cnUBmnitsdEIIzoKpmkk63Ohs//AA8Y9ddj 271tf47QW0Hh5xzj8kV0xushbflbdd9Btwbnqc67IXhVfT1cDtvdaYe0BeZCXkWUltIZ7eGwNpZN NFMffHlySsSB3UbuI179t6799CxBgeQkurq0tbJJGkmlPlxdBI11d23/AGHRH21lmP43hbGGG2e0 jnuOkt5ki7LMNb1/KrViAn+W8NrCgSO3hkkMba3GW+0diekp6Ej1rJjc2EWYjmz9iJLGJJBbIuXF lI87L0rJ1eXICF2SFI7sF6tqCraDM6s511RjLSrXPrXgjL8NhsrqZnUo+bNy0pWTsla9m9k3fl24 S6lz4hd8VvskZ+RZLKxYc2rAfgowfSEuOpOnfnAqF6fM2D330/bUj8I494I5n6XHBf8AOJ72KCS7 jw8iWkdxlY7eJWdIfo8QWR5FUggOszEO+x3esWvOG4e74bk+W8Qy8+Tlw9yJMta32IMVzDYSuUif zIy0bsnQ8jsCF8vZKxlOlrTxjOcg4rl2ynFclBjLiWzezMv0VZWSN5YpGKB9oGPlBdsrDTN23ojG pShhZRjVitD3vzx7rk3mOoYjPqNavgalRYmn6dN3BLVZP0yfpTV3s+Vs2bs4PK8QzfhtHkrXKW2T 4nLjm67m+nM0TWoQrJ57zEsdAMH8zuCG6u+60f5acHBmo7ficedymJ8zoFxlIYYZFUBQJAVfqkDH rb3o4mUdIKk71sz7Ncl1yvwdzuC5bkL/AJFay3c+Pke+nLzG3khQtG0oIc95H0SdgNoHQAGuniLx TJ8H5jdcLyeQa4uI7SK6tcikSRfSon2DII+pujUiuhB9enfYMKzczl52HjVjFOPd3ur/AAcz4Iof 7dm9bA160oVVsox0uM3G7aepNfHHXdMj1cNYtzeSKQhoRCJ1ttnpDbPw9NbJP9tZBZzY27NxDbOs gj2k0Wjr5aINY5xoynmM4unka8SF1nJ2FPvDp6Qfs/s1r7RXnxvIfg/CZoydKGCaQp73csewH99a /E0Jz21XaUbfmdhkmZ4bD+tUVTjUnWcrrdaFe3Nl19trLubheKOIzXLfZe4dmo7qFzisXa5q/mvr mRpp4Ux7mRgdMXlPWD72t99n41rZi/IjykFxf2TZGFekzwGaa1W7h6w/kyPEwYaZUcfAOqN0t0lT u1e8AmuPBi28Nhnru2a2xNtjxkbeLoLeSiAFkDd436NOnV7yMy9Q3uo94jwTwRwthjn5Jlbe8y0s TTCLkQaxMgKmJj9BnK/kiySFfNRyD3DHSkbbGYOdSrGpB2st2/5/k+feG/EeFwmX1sJioOpqleMI q27W7vfbhWsm01ddSGPFvIYDC57E3/AbW2sMTd4azycqGIXt+lzMGkiRWlaU2sqRtE4MJRtyBix0 hWMLvH5PJ2P0eOK3xdkP9Halfefvv3unsv6tH1Owa2A8RuF5zxAdPEDgJwnKbm/RIMtb4288nVxG gCTsLhh5JaEQh7cnqiYKPynUziPPFbi+AwvK8Px6Lpmz2BsUkztzHdSskl5OquI0VlVQiLoqyj3h IA3vId4eKp1acpVNlGPHX4suOer/ACOjyDGYHGUKWCvKpXrXVRXcXy9TnN+qyXEY8/1ckN4SyabM yQOzRiSQsn0gjuwJDkqNb79yAQdD+0Sd4KX93+H8bEhikuY5pLbINFB7yo1s8nSwZFMepFjQ6Gi0 PYsPWPMsGsc/cyIwiijvlmmbW+iN1Cs2u+wesjt39dVl3BntsVl48lFcw2c4vYGu2uISSmpW85yS WHeOV06gFCqO+j1PW8oz8ynGfdXPleZYX8JiqmH/APCTj9m0bleB12TyS+xrFfIntZHdD/WZWjAH 9zP/AM60j8bsNJhPEnPYpnkcw5CXqkkQr3ciULrZ7BZUX4enoK3B8JrxrXn9iesBblTEwbt2KvoD +3pqD/b4xUth4uQ5NokMGRxsYiZWPUGiZuvY1rR81fj8D2r1ME1lunlV2t+xBI/t+NUlV9yFaSBz 69fSe3qP/k1QUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCl KUApSlAKUpQClKUApSlAKUpQClKUApSlAdPPZn+pngP3bH+7alPZn+pngP3bH+7alUhwWlyVPtSf UjzH7nl/jWlHsq4tsr4t4pupUNqXugWXqVwFERQjY0Py4O//AC6132N1/ak+pHmP3PL/ABrVz2Dc ZJe+JL38UjquPtirAL/pBM4Pr8APox+e+r4a7yuWQ+DZfx6nV8ji8b5LdCQPINE62xCga9O3z+2t M/EmJcjmspe3UZgUZYXCW0kXWJBCDAA29e66xq+9dg+u/qdqvaCv5LPkWRvFXzmsMf5qxSXKxxnS MwBZu0YJHqew3s1qDkjLDhLm0u5BLcL5ZmkRuoSSdwzk76iWdXJYgbJPcnerEGK2FtcXuUmeGOHz pZfKimL9YQqre8CSe3SFPbsewGh6XtbSK3jF/bYC9zkPnNFeZRrJ3hSSOMyOoIXp6ljVnIHoo2dg E1ZeOG4M0sWIjeSW3nZ49srEKAqgk76Tsb+Pfvo9qzzh/O+Y8LaZsVHmsRPc7aVMeFubdmbp3IYn DRK56AOrRbQ1vR76nFyXmqM3tbhNJ39+tj6F4eoVFgJVcLC1TVtKcJOGlJbRaTipN3TbXDsmiVfZ kxWan8TOPZrE4/I/gQR3BnvoUdLR4fLdOjzOySflRH+TBJDKG17hIofaEyvGOQ86nscLxvF2EeEy c4ub+C1jSXIXQ0JGdvKV1CS+epHUyu3vnelr54743eJVplrW8vMjc3tvC3XLaXVtaRx3I1roLRL1 ofiGHYHWww905dz/AMMW8VbSHnHheLK3t+RGSPIWV4ht1tpwJhPcO4Le8ZEWMpGhDOTJ1EMzHGpJ VMM6OGlvfe9r2fNjd42pPB53DMc8opQcbR06tOqO8dV1f9LcW2RIXshY7JW3hpfXmRtJLWDI5Rrj HO+h51sYYlEgHr0syOVJ1tdMNqwJ1X5TFfLyW/vc9e32YzGOnnsZX+nXF0IvLlcNDEZ2LmNX6goY k67nZJJ3q8Ob2XJ8CwlzcZrG5+4axjjvMjjpkltridAFleNkABHmB/QDXpoelaN8uxIwPNOT43GW ENhrN3bJbNH5SQRGVjGFQAe55ZQqBoFSCDoivTM4eThYQi9lZfO3UwfA+K/3LPK+JrwTlJSkna7i 9StpT7Xt8dTAcVkpb7nkd4lo0ME8bwjr0GPQO5I+e6k/wf4t+FvGTjNpYl7dPwiuTvJEt2l6I7Yi YltEBFZ1jj6j2BkX1OgY3s7P8EcrtzkrqFllErxSt7u5HI2O/Ze3oB/HVSVwXmvLuEXFy/HbzE2b 3nSLqR7WSV7pUl8yNXDylVCqZI/ySptZWJ24V1xHUoQxEJ6rQUV33t+x0EcJmuIyjFYfyteIlVly 4rQpJXfP9S4SbvftzvbZXdvkbaG8x9zBd2d1Gs8E8Th1lVgGVlYbUqQQQRWi3iTgIeJc+zXG0y15 kzYzqZry8QLLI8qib3m6m81umVOqQ9JZy/ujsTt74Ocru+b+HGM5JfWcVteTma3njtyfLLxSvExU HuAxTqCknp6tbbWzptcca5Q2XWx5hjHw2Qyl5OL3IZu2e0x8l2RJLKWnEZiIZkfp6OoHY6e3cZ+a wdajFQje7+389zk/ANeOW5lWqYiqqajFpp/1O/C5bs9/Td/JUeGfOeW+H2azt5gosA65VIFK31tJ KT5Ik6T7kia/0rb3v0HprvSeMPiZN4iz2mdzuG/BH0abyLC3sr2d5lQbEiMetYmDMCxZY1bSICxC 1ZeU8VvONZ5ca8eIiyb4+PIWF7i5hNaXttJ2V1bSllLKV95Qe2xsEE2rjeIzHI8lZi7sZMhlbl2g sMXEAA7gkkkHsAAuyzHShSxIA7YMaleC8ib2Vlbr8Lnnvc6qthMsxVT/AHPDQWqWuWttumt1eU76 WnDdKCjvdc7tWHLZQ5G8uXCIoMJjjgRg0hK7ZSdd+oH+qO3b41XcXihewNu7PLbSoglDK0bNGx2V 7HetHp2PgNisx8VOJZfi8l9xjkwxhuzjlvoTZTO8YRmkCgl0U9QMR3oa1qo94vdW9ng4bm5iJWJ+ 0nU/TEHdkLkKD1aBPZh8SAdkA7bAz2dPRpcdrXv78nz3xRh2qkMYq/nRrXlq06d09LWnpwjbLw3y 8s0vHc5dmB7lfKe5jhcFI5l0JF2CfzXDjW+2q+f/ABC8fE2N47kfookmZpbQSbO1Uo0hAG9bJiTu fgD86sPhi0KcUtEtf85eC5mM0YfZWV3MpU69O79hrfT0+vrWbe13lrbl3gHx3leJg3Z3V1BcR/SU 6WEMw8sMAVJ2fMBGip0fUjatnnKGiJBFow6tacdt+verdVwZdW7ghgwf5faKt9AKUpQClKUApSlA KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApS lAKUpQHTz2Z/qZ4D92x/u2pT2Z/qZ4D92x/u2pVIcFpclT7Un1I8x+55f41BP/h8WjNeZrLSSxxQ CCK16eogmSMvISfhrpnX4+obt8529qT6keY/c8v8aij/AMPmOO04ryQyK6dNwk4YoegxtEg7NrRP VE+xvY0CRpgTK5ZD4PDx/OHx0PJ/oj3t5a5GYW8skt9JMwNy0cLmNpC3SqlyVUaXtoaBNaw5eaVb 2d0nuTGnRCLaVT5algknmD0Gz1Ea7n470VAm/wAfOX4vnFjZ53DXc11i7rJhQUiktll8u2mkU6Pq AyoT3PvIQe6kCH+T3dm2ShxsTK7Rwl9yqB3SRYiiHsTpklY738wddhYgx3gRaDF5p3LogiQkxn3x 2buN1KXHeOWvEuEY3PclyX+UGbz+Me4xfHZneNbVHkHlXs9xFN1GMouxHoFi5Ua6GZYk4JdJDcyW 7uojvFMLgsDp+3QSPUA7K779z8KkTCQxZa6xtuOTYbD3qYyHHm35DlL10ha3VgSszW3lQxP0l1iE jAF9A9wi6apTaqVbRTk7NX+3XsfSMFi6csHgdVWdOlB1I1HFu631Jen/AMk1bbuX2e58PL2WLIDD cnwUSyFbu0wmQXKGVFVukxy3ciCJize+vlSbEaBSm36s65h4mRYS84pxrwrzUkeF41Zx3E0oiAW/ nkB/J3CmNCx6SzyaIJknO+iSMEY7g+G8X5flYrLw/wDEzB5jJHy0axyVjNjZW/Js0jRMwYy+9Gx6 FT3VPvMSAWxvkGEznH8nPis1YTYrJrEr/R5wjaDDauGQlXU+m1YjasN7BAxa9bFYeDvBK9vUjocp y3Ic5xVNQxE6ihqflTb325V2rd3ZvjfY3d8OOT2/MeGYzkVsq+XexsZIh1kRTIxSWLqIXqCyK69W h1a2OxrUL2k/DK3tfEPkeVyfK8NaQXc0WQtmusiJ8rNFK3Q0MFsoDuUZCiBikYQoDIOmQpszgYWt PZ4tz4c208E8nGvPwYmjh89ppIOuN5AB5fms7dTH80sWJ7GtHMz+FrW7eezwwnvZHc3c12C1yJ+o +YsvUQ3X1b2WO973W0xdfyoxTV2/eyuu/wCxwXh3K/x9WvOnPTCK3WnXNxk+ityrK8trNotfL1a2 /AUUzGWeKJw5B31aQA9z8zVTxNMdj+MPmY1SWcRs8rgAEED80D0HoP11a1s7rLZ+1XKyXFvcS78x PM6WEYDHpA0Pd2vr8/n2IzjEcIzGcxwxvGOK5mWDKXItRffQLlrNXaTyzI84RlCI2+o7Ouk/KtZV g/Khhldvl24s2dvgcTT/ANwxWeTUacEnCHmO09UYK1lvvsr9d9je7wzwL8W8OsBgJI7NLqwsYlvD bpqKSfQMzrsAnqfqbZAJ6iT3JrSzxM5pDz7xCyHPAL2DGTW0Fpi4byGNJoYFUFgRGTvqlaRh3J0R 8NAbpeIttlbrw65Fa8emuFy82JuY7LypvLl+kGJhGVfY6T1a02xr5itP/CLgd5zzk0uAsZDYWGHC jMTkAT2g2VWFY2GxKxRx7w0nSSwJ0jZ2ZRqTjGhTX1c/COY8E1MFhq9bNMbNJ0leK5blK6uly7fK s2m2uS3pzP6B4cPxnN8WxuZgscn9OsL97pbea1tmnimntkKxF180xyBnDg6l1ohQKnuz4zw/w48G M3zG1xTveXPHpHk/yqVTLL5iFktZo9qi9TMkbRxhesqoPUwBqF8t4u8R4nmZJPDbw+wNlFDdNNaZ /OzS3NxNE0fQxijdhNGrH0AfXTvaAsQMHzfiDzXNQXC5vkOf5Fa5CSGdoWjEVmWjGo26F0I1G+og KAzKGILKCIpTVCPralJbKy/S5fG4aeaVmsNGVCjJ6pKU227u+ry1v8WT+Sl57nMtlZrq/wAte3N7 k8tI46XldxbQMzOYog5YrGvWyqu9Dq/Xvy8KcJHyXOrx+3aaG7Vrw2Mqlo/KvI4Z2t5GdCDGiu6N 1DfvIq9JDbGP3Uec+nJeZIp+Xj0io4KIQ3YdvTQb7d9+9V/HbnIYK+lysNr+XtbpsjAZH2HaHonU kdtp1Rg9jvRI7EbrIwMLU9Td2+X/ADsafxViXUxioRpunTppKMWrNLm7XeTdycfA3I4yWxngsPKR 4ZluTHFCUVopAVjb0HqsevmAoHbtU6+17E9z4C3MsURVYbmzunQAaSOOVJHP/wDyisdfHWgN1r34 Z39wniXnsfkMRPZ39895eskiIkiKlyehCF2CCJS3UGIPcgt1brYfx/yNzH7MeQyd9a3GPvPwWreQ QJnilMfuhujqU6OtkbUDZJ0CazTmDm7OvaYlAupP4irfV0vV6HnXsPy3+qPmKtdAKUpQClKUApSl AKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUAp SlAKUpQHTz2Z/qZ4D92x/u2pT2Z/qZ4D92x/u2pVIcFpclT7Un1I8x+55f41Cf8A4fV15eZ5NbTA BpsdY+XruD0y3X8z/dU2e1J9SPMfueX+Na5ewXmrePmt5ZtKwlvLKIQovfqEUkvmb+QHnx/r6u3o dSuWHwZP7XkEVtm4IIoo1dsj1W+wUiRzZSkFgAdrvq3rR2d/DR188V7fgeJ8SrKHgeKmscSbFJ4Z pcibg3Kywh43aNvfhfbEdDHuOg9I372xntpNcyZKG6/BORs7XGXETPkJlj8mcvbyopi0xYgSSRoe pRoknuoJrUHmslgmRx6W91JcZC3tIoL24W5LoHiRYlRFNvEUKrGPjJva++SCTL4EFd2Kjj+NvbvH xQPEY280WtuiwNLNM8jdoliHcsWOu3r2GjWUcnwXNOIZWTCZiSe0uoYllaO4WIFY2B0/US/WnYjq Vm7hl0CuqxjF3l02QGNihtpLe+6Z40uAZVUt6jsV9D1+v+r8Tre43sw8egzvg3PaZ7GQz4w5ua4w 3W2mtgiLGZLfR67d/OE5BXoZWLMD73UdbKEq1V06ln1Wyat/e/5nbUMTQy7L4YvC6o3emdpThJy3 bs09LitttN9+bu61ImnyVh0XN/dSW/vJLb3gd0ltpyFlhlT3VZQwKupTtoqw18dt/Da44f4/WDX/ AD3ikEHIMPDHBO1lfzxia3kXaSdcRTaM6y9MbM5TRO/fJOq2aky8s99dZUXUFzPei4yM62im4SZ2 JupRESoWVW6l6NoFOx7vqJwyXNOGeFPFuOce8L7WwzuV5DbW+Tu82ZrmPzDC6ASyKG6iGeO4Bg8x QhDqykMytGGkrTk7aVz/AI6e5bPKVRyw9OKk68ktLu+rs1qd3JX+niyfLTSW2VljoLX6AtjObLG2 dsYI7CKKOOAqOgJpQu16AvSoUhdMdg+7rAvEvwj4RzTNy3N9iMrY5O6g/K5jG3ixdJjZQFeMsVd2 UlQzRNpU0WUhKiDwc9oW+/DU+P8AEeTVlf3UjQ5XyRbw2Afp8uCSErsRAhtTFm11AMdAsKz2uOTc qwebw8UWSyGF4y8IZb7HSNCZbzcnVFJLGQwAQKyp2Dbc+90e5lzrU5UnO2pdrX/Q0GGyzG0MfDDu XlTfEnLSrd9S6bdOuxrRNcXt5hcJkrURzXwnUHfu7BB6l/V6brab2Mb3k8iZuKO9SfhtvI0KRTSF pIMgSrOsIPfy+lyzg9uplK9zJWqGUmliu7G6w0sAs7eTojSNS6szDRBA1okdgOw7H3hupA8JeBZL mXLreDGYa4tVhule+zVtuGOy6SjM0U/T/wDEAMpRQOoFlJ0oLDU4W9OpGUVe99l036/B9Cz908Zh K1KrUUXDReUk2pNQ5ptcubTT5ukjfVlUDShd77DQAFQfe+0xwWxyxtpMbnXs4rtrdsnDbRTWzAOU M69EpYxduoMFJI9ASQKxX2mPGfJx38/DuC5H6M9s3lZLK2soaTrKyLJaIvlkh1BjYyI3Uh2vYq2o N43wS95Dg73kFn9ExWLwUHlT5C+vJIoYDsARBoFdnnfr30KG9VB7uobZVsU1NQpq76/y5xOW5DCp hZ4rGS0QstPu2+baW37JWbfVJNq7+PFr4ZQczwTeHOTXI2WXDXt9jo26rfHA9Bj8sdIaAk+azRk7 XQBVV0KyH2csN4bckHIMtzXil3e/guJ82clcSTtDBDF0gwyRIelu6s6Aq3mL5ike5psQseL4JMa1 xe8+tGuvMdTb2+Cvp5XhERK6d47dHcyBV6JOlAD1F+3TV18ROfcduuC2nBeKYmbHYKG6F1dPJd9V 9lbhUHe6MR8tAH94jbD3IukJ0BKxozXm+ZZXtwt7/wA239jc18NN4D8Jqk46r65pwUUrJbvd2Wq0 Ve+q6V9nGuan8zJ3Kiygs9ytcTwWkfRBFLMQ7Rouz0og6FUfLt3r9jtYclNNjp7uG2heTyTNdkxR xuyRBHdtb6fMI94790f6oqniaMTyT3hj85220jMq7b1PckDZ16bGvsArMOCcL5TyzIXycQld77DX Frc9MAWSeV2nJGkdlj93okmIYgEhAAAdjY0YOEEnz1+epxuY4mOJxMqlNWjxFdoraK+bJX9zYHn9 rYYb2g4YobNppb7EIl1d26BwsnS35RmCDQZYlU+8ddMI1726ln2ioY38Actb6Z4mxxj1vpYgxkb9 PX+yoP4ze5rlPj3lY4rOYi2QWMizwJGwdZNDYDdlZduDrupB2OwMpe2tMsXgvJZxRli15any0XYC JPGx2B6ABT/dXqYJzymcSQOWVRI0wPb4bIq0VX5Zgk4RCo17x6fnVBQClKUApSlAKUpQClKUApSl AKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUB0 89mf6meA/dsf7tqU9mf6meA/dsf7tqVSHBaXJU+1J9SPMfueX+NaD+zjy+PiXiXi726lMNoZTFcy AjtE40dk9lUOIpGbsQsZ+0Hfj2pPqR5j9zy/xrl5E7RyK662D8RsH7CPiKlcsh8HUjxpsJeWeF9x PiLy3WVYwrNJAZUVX6RJtQykkKT6Ea9e+tVzz8TLXiAfDTcffJDLXCO2VSaSNrc66BFJCUUHT6ct 1EnejvvW3vsdeK2J5vw5/D7OSTnLxQMRJP74nj30669fnr7muruw0dswYjWnx94rf4PxEyeNvWke WO+kuLfUis0iSv1fmgkroe4vfv5J0O41MuGXpW8yN+LmG3cN3ZQWF5CQoRmRXUFTFK2m2wO9r2bZ HbW/l3u/H83cWedadpEw2UVJF+nWfVBNbykdIIlDe7sM3vAje9fZVXJYNko7a2hiuLgzbjjWG2WV mklCKir0jzHHWiaQdQ2zaA636sZy1tlsflLq2u7V1vcevkXEUqsJEVD7wA7N2OvXuB07+NaWk44h ep79+q7fb9mfTMwp1snqJUY3g3fRdOMtnquuVqV2mu0o7pInPMS4/wASspe568yOF4dyYQQtfR5O 6b8E5PSxxGaKdhu2kCggxEOGAjI3qRqtNl4ScqwuTtZeQ4fE8Is72T6PLlsnmrUwQHTNpVEpMjEK SEUDZHchdsIjschcWbQR2eS2H90xlHCxMoJYFD7p32B1r13V6ucpnMvY2eIsXCR2QfyUUELbCRty eVtiIusjv0jbHv8AMi06N051Ip93dxX5r/J4YXMXGccNgqsotfTBxjVav/8Ajmv+9Nl78S1nuA8Z yFo0/hLlbnlGONu34RxNwHjyMZjjXzLmOKVY3ljfa9QRTqRtICG6UyH2ZvFa6whsODZ+6lv8JdvH bYfIHbyWjMwWO3fX58TEhVbW0JCsAmujDPA3H8TuPEPHW+WzwxNpjLhcjH+TDiWa1DXDJLMzfkQB F1lirBlWRepD0k5NzrD8E8Vr7KS8OvJOI8hyflE2WdEcVheyySdLlDGzlLglkOhsOfRWJd1UJ3aq wtFyfHdfHRlszw6jCeAxSlVhRjdVEm3Tk72Tl/VBtc2Vt1bbaJfGCwntub8tgdLu2uW5RcSTo0ZR 1R5pGiYA62rB1YH00QQD8ZU9k3GXec5NmJ4bJYLvI8ant2yIE4W2cyR+XoxyJrr7vrqWT8l7jJpi bD7XeIurfxS5DJdzReRkEsciTESX8gRi3ZW7ABg8DMNbGtd/gKbg/iRlfDzF5ubFXVhay5YxLHfS S+asVvGkgQRQ9IBl6pS3WzOvZR5Z9ah1I0sR6ujfy7pbfr+ghgq2Oyl+SrqVOmrXSjFxnO8229rK LTfVyfSxmfAvArleDjuc1y/juPvrDj9tcOmKinads08KFoURUB6YpH03vbZujpaLTarDfEXxeyXJ eNWvFszZYSwx+NKvaJisZLAYumNo0KEyOqxhHYD01rsexFZt4K+MWbsshnb/AJbyq8tMQ9hcXXmZ S2e8jhvJZIxBpE/KAFVmYW8bKvRHIwChWYa8QxrfPxuMiQK8PQQJGRgq769nfodKR+o+lTPy/LWm 6i734v39+xXDLFrGTVZQqV6flqD9WlJtRsktO61pttNprh3uZRieGR8q5BbYHA4qOS/uYDLA4nhj SdUXZA8x16zo9Wl94gFu6qSLX+DMbNZLdSpdiF7cyec7vHJE/WwCGPQDHQVgR1Ah1BOwQPTj01p9 KnjxdpkbiAi4Tqe4aGFGkt5YeoOvvOPygLIdhwCrdmNWPkV5GmVvJGZ7iMz9McTMSGYKo91Tsfnb G9dv7hXjh4zqWoxm01u9/j9X7trbg2eb1sNhFPMK2GpyjJqMUkmm2pX3a3jFpO8Yxl6knLYqbIRG 4tkZrYy9LXEpk1pCFA3s7177b6tgAIe5raTwJx/9F/EchyfIyXeKu7+1+mZV5IHLRwxozRRlG7q6 xnbDRPWzAEjp1EXgt4cQ88yElxmZ2x+Ex8SteBWIa6BLuLVXXsNB/fOw3SyaH5Ta33x051Y8tlhw OFNrm43uUZL1SrrDMOkpHEUb88BgS/pradyWC78+RkoeyRZJluX5fkl5HKt1c3U920ZBMfWzdTOr Mql9tMdn0BUABSG1Yvb55WyXOJ4/DLMsbxySsYpiFcp0jpdf6w/KE+o0UX1+Ez+zZxdeIeG8c12t wJ/KJk82QOAAzSMIzoHpDOVGwOyL8q0v9qHPjP8AivlruKRGs4CtrbsjBgY4wezaJ94SPKp/UBr4 kCG7hepZJmDHZGifnVLXvdzea/bXSPTVeFAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKU pQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQHTz2Z/qZ4D92x/u2pT2Z/q Z4D92x/u2pVIcFpclT7Un1I8x+55f41y6rqL7Un1I8x+55f41y6qVyyHwXLjObyXHM9ZZrEXlxZ3 tnMs0U0D9LqwPwJBHpsdwQQSCCCRUx+MnjFY+J9tZ5GTjs1jf2circXInDL0SBlbYAB9YrdthfUF e3q8FVU42+usdeJd2cpimT0I+I+II+IqxBn9jkbQ282Oy0scFtOzeUwHSB395T6ga2Nb7a/uF943 w6/5HnLfDYSN5b+S3kOMna5XUrohk8t3ZgEBRZArb11dC+6rswwVb23yqSzExpNIp85Cvo5BAdQd 71/Yau+CzKWrth8pLDJbun5FwoClCNdJUA+vfYPx367FafFYWVGXnUldctfsfSMiz6lmVBZdjpKE 7aYVLJ8bpSv0TtZqzW265PDklhkcW9zisjYXWFzUUg82C5Qxb6l2D0so6CwYEegPr7pOq9MBJJa4 rICzjlTJ+au4J9hnVAOw+bEBjobrM+RXtzywCXLX9zk5gqRLLds8siIpLBQzHqTXW2hsaLE69d4R dW97i3gmvYJliikHkTdYLxgHtsjYAAJ90+7r06e9Y8cTHEwdJbb8Pr7fHt9uxuK+TVskxEcdL1Jp pzgvpT2bduJWe02rPiS5kX7C8gu8DyzH8lxUpie7eOa3kkZgizx+kb9JB6GHUjAH3lYjsN1deXcd kt3fJcUyD5PEtChlF4gdolZvLWK9Vd+TN1e6G7JNoMhI2FtcAwOUsWsh5XTL77W50h69Akr+r/yn Xc1Z8vNd4q4XGzyw5S2kAMUVyOt9D1Gx3B9T1HY//LqvGi9b8rT6l0f87Wvxwmnc2GYQWFgsw81O lLmUHdXd207O6WpycWtVtUoSi4veu5Dn7vMZM2Wfmz82QvFjjM1/fNOYUQllEcjMxKhiSB6Ak+le 8OCwMGeg6ra8iDe+8ixPOjaBb+19jSqWVSxXqZV2645mMjHlF+kxwR9NlD0rHFs76thgTruqqpOw Pjv4V4WmYkKWcF3dXlvZs58ySGQgFda1rfu9/UDsAO3Y1kvC1lFSbs+qu9/s+yW36mkjnuWSrTox gppNKE9EVoXPDiklqlJ6rWvzGzSJN5xncTfwY/jFl1Q4K3tkT6MQPOvcg6kT3siKW6GIIRQXk6EQ BWUErWEYpVsJSZ2aRsLK8bkR9zA/o3xOx3Pb4VdsdlOM2f8Am+Pmt4wFGzHGT2/8z61/eaoc5yKE xzW1ksLwyKUMjDasdeg/1tj016/A/A4kXXrTcNDs++1lx+W1zoq0MryzDxxDxENUW+HrcpN6t3dO TU1F3aWytbe5QcgyiX2RWLFTPLAtuUMTMY4+sE9ukkdR0fTXwH26kHwR8MRzFczlc1l4sJg8OqDJ 3r2/UgX1MMTNpF6VHUzEt0koWR96WKJ2/BtzIJVYIAxbqO2mLD0I7EAbO99j8t1cMBhc7nsVLYz5 K8scR5wu0tWLMkkpUDzRESB+Z26/iNAb763uFpxpxUIbpdf5yfKM9xdbG1pYnE+mcn9O+35O9l7f n2JI5jy67hy0GI8MMPHxvBYTKkbjub9pMhOXXzTddQVta8sMsq7XoZUdhsVknhNic34g+IyZS4Qy IiMsV60YjgWVnCoAuu47toAllCL1bLAnBcRx+G8vI+PcUxwa5aMxy3LDTdPrtpAD2J9To67aHYCt 0PC/Eca8LvDiXkeYvBZ4y0haTzp9uVGyWYerMzE6Cgb1oAdwKyznys8d+Wjw58K8g9s8ETx48w2f WjMGnPuIGII0CxXZ38TvQ71zP5Hl7jK5Ga4lmMjSOXkfoVPMcnbOVXsCSSdD4mpO9pzxlvvFPlkq 248jBWUrrYwq5IdR7olbYHvMBvuPd6iB6kmHaAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlK AUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoDp57M/1M8B+7Y/3 bUp7M/1M8B+7Y/3bUqkOC0uSp9qT6keY/c8v8a5dV1F9qT6keY/c8v8AGuXVSuWQ+BSlKsQfqMyM GUkEehFXqxySzo1rNHtH11RgnT6O/h6dxVkr9VirBlJBHoRQGWLLlPyQsciX6W2ElZUkX1+Le63w +P8AZXjd5PP3bXNoqz3MQPS8ZHVvoIBHu+vdhvp+dX7wxi45yvKPhuU5q04/I1uBa5CUhIfMUkkS 9te8D+cSNdA7HqJGc8h8E/E/j9u0gxMeVsmhVvPtZFmhbewVXfqBr4gbBH2geDw1Ju+lG1jnmYRp umq0rP3fa1vsRbZ4+5mDzvLZm1lQtGI2bXpoAE91X5jvrWtV9Rxs0n0ZVvUMW1Q7McPqASHX1Gtk Ejvrt3OquLX1zjhJayYhIQsjB1SLoHWp6WGgANgjR/VVZhrnN5GQ2uHxjyyPGWAEeyFBAY716DqH f7R869dKtZmvVaopOUXZvtt/Yt+PmOIvpYsdaxXk0npK/WWbfoenetj5636/M1+22PvEnklgjFl1 gl/L6lVQT8BvsPsHpXlNbZO2kF6YetZD1K8Y6lb5aI7a772K9o8hknEhNpPIGI30qTo/3UcYyVmt hTr1aU9cJNS7p2f3Bx886gPkDdPH1FVmdj0EqR6nvretgfDf9vxFxTJSLFJkLkeTH7wa2SSSUgke 6FCj+/RPz7DtVxZbIRyK34L6mH5olRiW/s7VkGCxHPeUrEcVx/INFLI0aSRQN5fWvV1e8TpddJGz 8e3qQKKKSshKvUnJzk7t9Xu/uyyxYDG4rMxyXljC9uzPI8txL1kEhh5Sxd9n81upvhvvvVZHgHy3 LuSpheMxDzW6mMzAgqg0GfudjXUBv7RUm8K9m/JXVzbT8ry8wm31S2NlE0zMvcaLg6Q70SSCOxHx 2J7sOKcM8JuGXmWzb22JwtsGkkj6+p5XJPulmJLMfQKD27AaA1Vjzbb5LDwHgPGPDPw7uOQclyNh YWVujS3l7c9TF++iPdIYsfzVVSSSdAEkVqT7SHjdl/E/PPY2Ek2P4lZv0Y/Hq2gyqOkSSa9XI2df 1Q3SN6LN9e0j46ZnxVyKYy1i/BfFrF/80sI3J81h282Q6HU3yGtKD8ySYboQKUpQClKUApSlAKUp QClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK UpQClKUApSlAdPPZn+pngP3bH+7alPZn+pngP3bH+7alUhwWlyVPtSfUjzH7nl/jXLquovtSfUjz H7nl/jXLqpXLIfApSlWIFKUoBU6eBftC5Dg0MGG5Lifw9hFbXWkzxXkKH4K4YBwPUBu/wDD4QXSg Ok/D8z4J+JkZvsFyESyxn37CeURzhtA66Jh1HsR7ykj1G9g1l/GeGYy14yY8oHxs1zI7SQ9cJMQ6 yUXq6dE9PTs/r9K5Vxu8biSN2R17hlOiKyPE8+5rirS2s7DlOXgtbZlMUC3T+WoHovTvRX/y+h+V AdMv8j/D61u/Nu7hJmIGlkuQAPt9zX/rVfFgeBoC0UNqisfeZbqRST8D2bv8e9c128YPEFjs5wH5 7tYjv/8AbXufGrxEKBBmYgANdrSL/DQHSKbjfCZtEyR6QdTavWOx9pLH/kd1UJieH4TGyZS4uYba 0ijZ5bu6vmVEQDZLMzABQB8ewrl43iRzrrndOVZePzyS4W7fQJ3vpG/c9f6uvh8qtOe5LyDPTCXM Zm+vnClR50xYAEgkAeg3of3D5UBv14r+074dcBtbix4jJYcgyWtpbWC9NuHYb63mUdBAHqF2xPy9 a0o8WfFfnHidkhc8qzDz28Ujva2USiO3twzEgKg9SAenqbbEAbJrBqUApSlAKUpQClKUApSlAKUp QClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAK UpQClKUApSlAdPPZn+pngP3bH+7alPZn+pngP3bH+7alUhwWlyVPtSfUjzH7nl/jXLquovtSfUjz H7nl/jXLqpXLIfApSlWIFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUo BSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFK UoDp57M/1M8B+7Y/3bUp7M/1M8B+7Y/3bUqkOC0uTNfETj1rynj99gchDPLY31v5M6xEhmQnuNj0 2P7ahP8AFW8K/wBHcr+2zfzpSjW5KY/FW8K/0dyv7bN/On4q3hX+juV/bZv50pUWFx+Kt4V/o7lf 22b+dPxVvCv9Hcr+2zfzpSlhcfireFf6O5X9tm/nT8Vbwr/R3K/ts386UpYXH4q3hX+juV/bZv50 /FW8K/0dyv7bN/OlKWFx+Kt4V/o7lf22b+dPxVvCv9Hcr+2zfzpSlhcfireFf6O5X9tm/nT8Vbwr /R3K/ts386UpYXH4q3hX+juV/bZv50/FW8K/0dyv7bN/OlKWFx+Kt4V/o7lf22b+dPxVvCv9Hcr+ 2zfzpSlhcfireFf6O5X9tm/nT8Vbwr/R3K/ts386UpYXH4q3hX+juV/bZv50/FW8K/0dyv7bN/Ol KWFx+Kt4V/o7lf22b+dPxVvCv9Hcr+2zfzpSlhcfireFf6O5X9tm/nT8Vbwr/R3K/ts386UpYXH4 q3hX+juV/bZv50/FW8K/0dyv7bN/OlKWFx+Kt4V/o7lf22b+dPxVvCv9Hcr+2zfzpSlhcfireFf6 O5X9tm/nT8Vbwr/R3K/ts386UpYXH4q3hX+juV/bZv50/FW8K/0dyv7bN/OlKWFx+Kt4V/o7lf22 b+dPxVvCv9Hcr+2zfzpSlhcfireFf6O5X9tm/nT8Vbwr/R3K/ts386UpYXH4q3hX+juV/bZv50/F W8K/0dyv7bN/OlKWFx+Kt4V/o7lf22b+dPxVvCv9Hcr+2zfzpSlhcfireFf6O5X9tm/nT8Vbwr/R 3K/ts386UpYXH4q3hX+juV/bZv50/FW8K/0dyv7bN/OlKWFx+Kt4V/o7lf22b+deNz7LPhiHSODj eT22yXa7nKjWu3qO53/yNKUsLnz+Kn4df7Eu/wBon/x0/FT8Ov8AYl3+0T/46UqLe5Nx+Kn4df7E u/2if/HT8VPw6/2Jd/tE/wDjpSlvcXH4qfh1/sS7/aJ/8dPxU/Dr/Yl3+0T/AOOlKW9xcfip+HX+ xLv9on/x0/FT8Ov9iXf7RP8A46Upb3Fx+Kn4df7Eu/2if/HT8VPw6/2Jd/tE/wDjpSlvcXH4qfh1 /sS7/aJ/8dPxU/Dr/Yl3+0T/AOOlKW9xcfip+HX+xLv9on/x0/FT8Ov9iXf7RP8A46Upb3Fx+Kn4 df7Eu/2if/HT8VPw6/2Jd/tE/wDjpSlvcXH4qfh1/sS7/aJ/8dPxU/Dr/Yl3+0T/AOOlKW9xcmnw /wCPw8bwWFwdja3ENnjg0ESybJVFDgdz316aJ+z1pSlXirIpI//Z --000e0cd1512a8aeab904a44576a2 Content-Type: image/jpeg; name="MNI_space.jpg" Content-Disposition: attachment; filename="MNI_space.jpg" Content-Transfer-Encoding: base64 X-Attachment-Id: f_go7ezd8m1 /9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEP ERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4e Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCAIYAhUDASIA AhEBAxEB/8QAHQABAAICAwEBAAAAAAAAAAAAAAYIBQcDBAkCAf/EAFsQAAEDAwMCAwQECAoHBQYC CwECAwQABREGEiEHMRNBUQgUImEVMnGBGCNWkZKhscEWJEJSYmRy0dLhMziTorTT8DRTgqPxCUNz g7LCF2N0JSY2VGWEpKWzw//EABwBAQABBQEBAAAAAAAAAAAAAAAFAgMEBgcBCP/EADcRAAIBAwMC BAQFAwQDAQEAAAABAgMEEQUSITFBBhNRYSJxgZEUMqGxwQdC8BUj0eEzUmIWJP/aAAwDAQACEQMR AD8AplSlKAUpSgFKUoBSlKAUpSgFK540SXJWER4r7yiCQG2yokDueKzsHQurJqUqZskkJV2KwE/t OaAjdKnUfpTq51KSpiIznycfwR+quY9I9WdgbcTn/wDif8qA1/Stgjo/rInCW4BOM8SRXUndLNbR W/E+ivGGcfinEqoCE0rMXDS+oreFGZZprQR9YlokD81Yggg4IIPzoD8pSlAKUpQClKUApSlAKUpQ ClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKU pQHoVoz/AFYNBf27R/xbVWOquOjP9WDQX9u0f8W1Vjqoh0KpdRSlKrKTxnpSlAKUpQClKUArs263 zbjJTGgxXpLqzgJbSSalOhdGO3eQ3KnLMeKnC8FByoZPqMY4+/Nb+6b6NnXWVFsWnYPu7aspVN8F WAB9Y5Qn7POgNQ6V6RS5bIkagluWpO7lshG7HPP1vs8qnVm6f6Rt68wIE26OEDJDq/2JH/Wasxpv 2fI0J5ly63r37Csu5ZByPIDdn9dTy53Pp902tZ+kJ9otw2lQS88w064Ep8gSnP1fz0BWewaC1fPl Ny7bo2TF8FlTbLq0rPwLUkkfEoA5KE/Pj7a2DZOimorm02u9TnogV9ZCWWsgDt/KzXBqv2x9A2tl Jtdku11cK9pT4rCAB65Stf7K1jfPbX1RNU9GseibdHDmUtF6Q464M/2dvP2UBvJHs+WjxGw/c5ry TjeoBKcevnXZk+zvpZSiW7jcADxtyDiqVan699UbleHpb14nW9bnZhmXJbS2PRILnFY1rqj1XdbB bv8AqJxAOeJkog/79AXcjezxZVLIkXaaAMhO1KQUjyzyc5/dWOvfQWTDLrtnukp4IBLaS03k8fNX rmqgaf649TbJclPx7pKdeWjYW335JGPsDgNbJsntddTLIAq56ctk5ogJ3O+8Jz58FSzzQGy7t091 9bmSpNsfmNJHOWUjj54VWsdTaSs897w9R2CXDdCcFYcdTgd8+Y9KnumvbdgurZa1BoZ9kqOHHYc1 KgPmErA+fnW8NIdUel3Uq2OrauVsYWtfg+BcH4yZBPyTvV6UBR66dH7Q+2XLFqBxxXJ8N1KD9g7p +QrXF/0fqOyFSp1plIZC9qXQjck/enNekGpOhunrqN8WWqKTkjDDZHn5gD1rV2pOlWqNJeK9GlC6 RAoJCFNOEYPbsMef6qAojSt/a50JbdTpku21kWy7NkuKR4SQh045T8I3eXz71o29W6TabpIt0xBQ 8wsoVlJGceYyAcUB06UpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClK UApSlAKUpQClKUApSlAKUpQClKUApSlAehWjP9WDQX9u0f8AFtVY6q46M/1YNBf27R/xbVWOqiHQ ql1FKUqspPGelKUApSvtltx51LTSCtajhKR3JoD5AJIA7mp7o3Si0PCTLQ06pSPhQRkJz+3ishoP Ra1q3uxTIfO3btBVjt249auN0R6KMRWWrpqW3hIWwPDZLnOTnuMccfPzoCI9E+lEu/Tos64pjotj IS4tpZ3bxk4GAfka37rDUujukWhTdLogwrbECW0oixytbij2AA8zg9yB86177RPXnTHTLS8yy6Yu UB/VTZ93jwWmvFTFVgElwAgJwCOCSc+Rwao/qa9au6i35/VOtLhIktOfGdyyltCR9VCED6qeTgD1 PPNAbO6ze01rLX9yTA0M9dNOWxbPhLZTIbDjqjgqJUEgjsR9b17ZrUMXSs6e29cbzdQlSVc+JudW 53Uefz/nrpsTjbJjjER1ceMSkupbBDqhjyJ/f61kbZatWvNAodabbcTv3LKDnOPQE1aq16dJZnLB IWGlXmoScbWk5teibMVBNhS+Sp5hCPIutrV+rBqezNGa2VfIr0GK2q3rKFPLZcabCRuwoYyFdvSo FFiPe/rFqairU2FBReaQ4gjIwQHAf2ZqXXrUfUwpTIlagubbbUfaBCliOgJGTkpbKQT35IJ7DyFW 3d0lhN4b7GVHQL6alOFNyjHq0m190jaGl9N2KTLbgzbe0icjAU44nxFLOCc7s/L1qaxtJ29k+JEj NDH1SBjn89VXRqnUTksXL+EF0XNinchx+UtwgjkdyQRx2PBqZ9ROrN0kssQdL315Ecoc96cbZ2FW 7AAClDcCAFcpx3HOe16M0yMq28qeOc5Jxqqdp60ajaY1NIi26O42tTbzkZb3iKCsEANpUR3B5xXd 66dN5ekNA3DUMGSyhDKmgpCU5BC3EJ7KJH8r0rUOqtG6ziEM6turSLqhAejWybJXIfdaWrHiIUEr aSCUqyFOJP4s8fVz3bovqDerW/b77q673FhYBVGl3Z91tWDuGUklJwQDz5gVYrXdKi8TZK6f4ev9 Rhvtqba9cPH39SAx5a35iHbgy08yc78NISTwfMAHvXPLZsb8lIjyjCTt5UppagDz6ZPpXFdIsi1S TEeLaSEg4xu4PzxXUSW3EqCihJ2k7injgZxwO57ffV2FTcsrozDuLLyJunPKkuqfqT/RXUnqZoBx uXaNT3H3FZQst+8723EjBHwuA4yBjt2/NV1uhPtLaS6iyGLHJjTrXfBG8RaHUeI24U/W2rQPsPIH f5V52N3Ce14fhTXwlsjakrJSMeWDxj5V2IkObIk+8R5KES3DlCW8oUSQScYAAGM+lXTAawenPV3p rC1HZXpNliRIl5bV4qHgNhXgcpyDgZwKqT1X0Uq5xZdomssM36GQkPH4t2wnjcD55Pesp7OPtGal 0xfIeneplykOWNbKWYz8hjctojsS5kHGCBk54AqzGtNM6c6n6ROptLvMSZrzSVxZTTgSFlOTtVwe cHGPsoeHmFcIj8GWuLJSEuoxkA57jP7669WC6taBXMkOMzLebfqFpn4G1jwg6OCnyweMjyrQUqO/ FfWxJaW06glKkqGCCDg0BxUpSgFKUoBSlKAUpSgFKUoBSlKAUpWa0xpm66gf2wYq1tpI3uHhI5x3 oDEx2Vvr2NgZ+ZqSae0o/OWHHVILeORn5/b8q2jpLpzGacKTE95cA+qhRVzjnPFbz0D0WuN0cb97 gCFFSkFWVFAIz5fCaArfatKwY+1KoUdSk91FO4/rqYWSLZIbKEKszD7ncENpH7auFZei+i4kNDcu CuQ4E4UVO5GfzA1IYGgtF2qKEN2SElCTkrdGT+c0BTJj6NW5/wDu3HQrHbDddl5uMwC45p2MpKsY AS3Vt56ultuOJ0zS0Mn/AL6Uy2f1qrj/AIXdIUlCP4T6IBSPhBnxe3b+dQFRZSLO2kLk6QiKR2Ur a0Tg1iLjB6dSgpL2mmWHFDKlIaxj9EiruMS+mt4bLcWfpeYlzHDEllRP6Jr9k9OtCz2SVWCCtLgy FoGcj7c0BQS6aB0LLt8mfDnC3NstKcWVMvLCQkEk4Cj2APYVG4vSO4Xa0s3bTd7gXOG9uCFqQtgk gkHhQz3BFXv1N0C0TcUue52xtouJIWlbhCDnywEnjvUHufs8TLdGDemdsUJyQhqVtQO54BT60BR7 UOjdQ2FQ+kYaEJJwFJeQoHnHkc1HquRe9K9TNNbVXC2rmxySn42w5j08hUZu1q03cApvUelmWN2c utxtikq8+QcigKu0rd+pelOnrgy45pKaEylDKI70nCc+Y5Tn0861bqbSOodOSVs3S2vISkZ8VA3t keu4cY+2gMFSlKAUpSgFKUoBSlKAUpSgFKUoD0K0Z/qwaC/t2j/i2qsdVcdGf6sGgv7do/4tqrHV RDoVS6ilKVWUnjPSlKAVtDpRor39tu4TIrpeW9tjoKcZxxx95/VUJ0VajeNSw4ZSstFYU6U/yUDu avb7OuhGLo8m/SEvphw3wGAOEuEBRPPfgkUBKOjvRu02iGLhfLcVSjsLbLgG1IASc4BPnxg+la+9 qD2lV6SfGnOndwtr9xZfLUl5I8UMhIQcAY29yU8E9jXL7YXX5WlGWdK6Ol26VPkodTNcz4hjjCm8 YxtznPc+Xaqk6atjFnki5XkqMt5BUhrdt8MnklQTk8g9uMZoDLx7C5cQ9rLXNykzJ0vL6kSVZW86 onGeSSTx5celYRdxk3S4PWyDH93jJyhlCNoSkIP8pKhlR4IOcd+3FfdyuMzWGoPo+eQ14SztUlPC dqdu0p4OeBznyPrWettqjafY95C/FUEbOxTnJB9TUbfX8aHwR/M+huvhbwnV1Vq6rcUIv4nldsN8 dej9AiwWeDHVJmsIfUMbnHApR74HmfUVxl+ZdkBq3hUNpnuW1j4h/J9MDg8V9D36e262294zasbl 7QnPpx91cN/kSLDaGVokfGtxKPqDgBJz6/KoCO+csSlum+mc/sdarfh7eg50qflW0V8W1RTfPHxZ 57dH6rODB6VS/br9JbuDI2ltYSkkHkKT6ZruQ7rLk36VAlkOsfGjYpKcJG/Hp6ZqQKYTMVDk47sK JOfNWw/uqE2RYOq3yRncpXP2uCsyEo3O+bXKX+YNduqFbRnbWtOo3TlN4fqmk2n69cHFqW3x49wU mM5sQ4kqUlKSBnJ4710rFDfclsORUNKW2+k/jG0uJzkY3IX8Khx2IIPY1k9Zuhu6t4VkbPT+ka5+ nzu4yG+5K0D8+az1VqQtfM6s1SenWl3rztGtqbfT5Z4JVdnrgWTPuFwlXGYkBtL8t1TziUA8JCll RAypRx25PrUOsd/ukjUcdt+Y6tDjgSU5wDx6YrIz7i9sVH8ykKxx6/Z8qi0HCLghxw4KVAg1YtqG 6E3V5b7kpreqKhdW0LFuFOLWYp4XD5yTa72RmXdnLjPAMdCQFJ9QE/I57/KsbJtFqTKYmRoRkxiA Pdg6pveSop+se3OD28q+Jt8S5DTblLz4hSDx5Ff2V3orDES0i4lzCWEqKRtPfJx+s1ag61BRcm/R LsyQuaem6pUqxpU0005zllbotvnn0S5wYG+6ekw2VTjGDUdayUt7knw0kEhOc5OAMZ86xkFbsW6s l2YuGAkKS6UeJsSpGU8eeQQPvqcPOXIJZt19Y8BNxiJmwjvSrey58KFfB2zzwcH1FYa9aVTGhOSE LzjaRx6kf0vnWbRvJU5bLjiRqupeHKN3R/FaRmVJLnLWU11Mk25btQ2RES5vJbuexPujgaKVKXgn yAGCAPl288VPugfW/VPSvU8LTV8l+8abDhStElasNoXjC0kbsY2+hxk1H9e9Jn9LdPrP1Atk1bzE mJDdkRVMge7+KwjcveVncC4RwE8b/QVAVXeNNs5g3EDxSolLwzlGASngDB9PvzUpk0VxaPSDWui9 GdV9PnUdgltOz0tLREnwljClpPCVDgHkY5xgH0xVLepWg/fS/wCLb1W69xN/iNJbCS/jdkkHz3gj I9aezh1i1H0l1CnT0tmOi0zZKFym5KNpR8KgCFAZ5yPzfbVt+teko+udMwtW6bUqRMZbKltsHxAp JQXMHkYIOP0u1elJ5sOIW24ptxJStJIUkjBBHlXzU+6sWBMR8XplDg95eIfSeQhWB+bkGoDQClKU ApSlAKUpQClKUAoOTgUqddL9GPXuczPktvJhoUSNoA3kYwM5z3Pp5UB+6C0P9LtIm3APtslY2JA+ uP2+lWA6YdOLhPJg2KzuRmAr8Y6loZOSBySRjGanvRvpW5c4saQ8zJjwGVIShRxykA5xkgmtz6v1 PpXpXpjdIkMRkHettp545WQkqJzgnHGKAx2jel+mtNRDLuaWnXFJTvU+QlKSQAec471GerHtEaD0 LbX2rNcbVc7k0rw0xWnFYBA89iTxz61UXrb7QmsOo0n6PZRFiW5txXhNsMhaljcSDlQz2A/NWqLN ave5v8fU5Ha5KjjBJ9Pl+agN+6p9sDqbcZjzNhZtltZcVtaCI/irx8ioZz91QaZ1T656mmuKXq/U TQc5U0xIMdCQeeEgjjisI89p6z2lLUJtxU9KsBS3FEHPnjGK6rb9/uDweYKY6cJTwEK3DyPNW6lW FNZk8GXaWNxeT2UIOT9j7uR1hNaeevGqro74KCpQfnKcOMehXnt6VGmXpbrh33WSkJHCipR/fUwt uim2wXJS9hSoHbjOQPsVX4Go7L6WTG3oWMk7yKwHqlOTap84Nuh4EvaUIzvH5e7p3f1x0I1D1Nqm 1uA27Ut4jBBGCzNcR27cBVS2y9d+sNq8JEPX16Wlr6qH3Q+MehCwciui8qKmOvxoWZHHHin1+XHa ui5Bt8ptx1weC72Q38SuAPXP21XC+T/NExLjwpKDxSrJvrh5X3ysZ9uptjTvtgdXLYptFxVZrw2g jd7zD8Nah6ZbKfz4rbWhPbRtk5Km9WadjWte8BLjMl1aSCf5oaUePtqnUmy/CVsnjGQP/U1j5cdL LaEFhaXEg71eICFcnHGOOPnWXCtCfRkBc6bc23/kjx69j1Q091I6Wa5a8O3ansFzKQCWXXUhY4z9 VeD+qsXc+jGnbpOkynpjpjyFlaGUITtQCc4B9O1eYzNrkSI6HoYL+VbVJSMFKsA478962ToTrT1V 6brDdvlIbjpSGyxLt6CgpBzjO0K8/WrpgFudcdAm4bDk7Ssib7yjBQhvHzyMFQz5VpbV1tvdmmrs +srAJcUp2+LIZwrYfmM+eKmXSL2yG7ndIdr6gWu3Wtpfwu3Nhx0IB4wS2EK7855A+yrDpmaA6q6e Jt16hXaM8goQuLJwtGeeU5BB47EeVAUC1T0ttdxjmbot51xxIO+JkK7DjBUQeTj171qGbEkwpCo8 uO4w6kkFDiSCMHH7Qaux1U6N3jSM1Fx00Jc5gJ+NfgpIwBuPmSORWr9S2O066Q3Hu61wLgwPgdQo g4/lApVx60BXGlZvV2mLppq4uRp0Z5LQcKWnlIwl0DnIwSOxHnWEoBSlKAUpSgFKUoBSlKA9CtGf 6sGgv7do/wCLaqx1Vx0Z/qwaC/t2j/i2qsdVEOhVLqKUpVZSeM9KVm9C243PVUGOUpU2l0OOBXba nk/soDcPQTTa3YtqiNo3TLzJCeUZKG1qAz2zjCc+nNW2676yt3SDpJLgwEoVLdivCKkOJaUFKWkb iAQe7ncfzaiXsw6XYZcc1nIbaEKCw8yyNuVIKAkEgfZu8qrD7Vmun+ofVAPMl9uJFjtMNtOryEFQ BOAAO5waN4PYxcnhGu7JIEOQ5fLlH97dkOENtupyVLJC/Eyr9vPf8+VtmlZ1zUi5SJUdLL+V7PiV jvjg8VItK2hcZlEuW4h9a0AtZTktIIBCQT2A9BXHqLUrMMPRmmXi8hYSVbgkD7D3qAuNSq1p+VbL 6nW9H8F2OnWqv9al1WVHnr1XKbzx2wd19DNms7SWmUZQEtlTaQkn1P34qNW8m6X5xDYAc+JZW4fL 0/WKwj11XIn+JJC1tqWSQV7j2OODWxIrKn9PxURF+7lbKFJKeMZAJ7ViVKbs45ny5d/Q2Gyu6fiK vtofDTo4aj3lj7Yz68mO1ndEW63mOylxta/qqbO3bgpP76jvTfR0vXlzdh/SLsZtlTYU4WS9grVg cbhjsT38q7crQ019e9d1Q6f6aSf31JOnmpWOj2pv4QSrExqBMmI5DEcSPd9iitte/dsVnGzGMeff is/TaltSxThLMmal40s9avnO8uKLp0oJJLKfGfZ+vsb8neyw/dZkm5xOobtsjTX1yGYTVqPhxUKU SlpOHwNqQQBgAYHYVonW7NuY1o/b4NnhW9OloarFJejoSlVyfiuKSqYtISNq3OCQSsjA+JVXb6x9 SbH0t0rFv1+h3GREfmIhIbgtoWsLU2tYJC1pG3DZ885xxXn1OT7tp2PGQBuW0nJHA5Risi9UYRUI LG4hvDFSrc1pXNxJyVBZS930ItqGYJlxcWkKASpSfiPzNba62MRrL1Fjyk5IlhpJaSkDbs28/PO/ 08vzarhWSTJU4pLrSQFeZP8AdWUtjFwglq0sTnYyXnD7wpl1QS+25tTsUkYyBtPfOd1XJToeW6Tf YxaFvqbvYX0YcuXXjHPBlLnaoarpHlXK4yYVuSgpfciRg88O+3a2paEq5IBysYGTz2Nk+iHssXC0 a9iak13O01dLfECii1NR1TGpe9paPxnipQE7CUKHwryR/JwCdEN+Cyw4z4YIVg8JGP8ArirB+wLc Jzrutba5PlvW+B7gIkVxwlqP4nvCl+GjJCNxGTgDJ5NYumV972NdOjJ7xzpStoq5py4m/ij1w/VP 6E66k6M6d9MNIaw1y7ouxzEuPocjRY1qjslhDrLEP3cK2nDa17lrIHZ5fwqOd1NtdzY2qtTQdRac sUHRzUVlDaY8BY/0qHFKDwKEN4V8SR2z8I59Nqe3gU/hDacJTnOn2P8AiJVaOvNwcF3Rb296ArYO F4HJ9KybqtUU1Cn6ZIPQdOsp2srq6y/i2pLK5aTXQ4r7ZHVpXNfmF+S6ouOuuJJWtZySoqJJJJ5J NSjTFiuGoots0rbZLDM2Yna27IcUhpO1CnTkpBPZBHbviuhMZUuPGjFXxK2jd9oxWF1KkuON20HK kKSNx7HCDUdTqOvKKm84efsbleWlPTKVepaw2ucdi57y+ZPLxctSSb67bl6muSbXb4TFsXBTKcVH U5GZbjuFKNwTtUttSgcAkEEgEmoZq/TSm4r1yafa2lSVFGzaeeP2muO9SRE0xGtyAUuDYCpJwO26 u1ouf4zqLe8FrBZPKjkdwexr2dSum7hPhPp7Fu2s9JnGOkVKf+5KKe/L4qNYxj2fvggJ54q03sT9 YnbFeH9KXlJeiyyylh1Ugp8M7kN9lHGADnj9VaA1bpty0oEgSG3G1eQRtP8AJH76j8V96FLZmR3F NuNrC0KScEEHI/ZUzQrwrQU4Pg5rqul3OmXEre5jiS+XTs+C5ftEdOmo2pblHe2twLzILjKktABC k7HMDIx5kcVTS6wnbdcpMB8fjGHFNq4Izg9+a9HLG7A6zdCba20ouXKMxHc3vIIUkklP1uO6UnP6 6p11v0opTLupmENpLZQxKT2VvSSknvg/yavkYafpSlAKUpQClKUApSueDFemSUx46QpxQJAJx2oD JaMszl91FFgNg7VKCnDjOEjv5VeX2dumiHo0CW/hMCICkpLX11J247jHc/qNam9l/p8qVqOPGbbY K1tgyHD/ADQST58/dVmOs+r7N0m6UyW2wtElLCUoRHSQVKVkbs4IGSk96A/OtvV2zdK7WYCIzTz6 IRcabS8hGMHAATkHP2eorz26rdQrx1CvqLjcSttCEBDbPjLWBgd/iPesd1F1VO1jquZeprz6w6s+ Eh1e4to9PSsRa4Ls55TbK0IKU7sqJ/dVMpKKyy5RozrTVOmsthIXDeSpDg3lOcpP1akVssky8rJZ kJZU3hRWpw8g/IDv99SnTel2LaXFSkxpJWgDlkcEd+9du56giQNyCw8opUU4AAHH31BV9WlUk4W6 z7nVtK/p/QtKUbrWam1f+v8A2n8nwdTS+mfot5b8pbEhxRO07Mkcg5yfPis3MmNxQS4lRARu+Gof edXrdiBLEZ1hRXgKS9g9j6CsM83c5LAmKkbknJ+J5RVjAPp86w/wVavLzLh4ybIvE2naVR/CaRTc lHnPP65WWSa6XU3BX8UDjaG0fFuVtzn5DPpXFpZxty3SbrNSX0NrDYCxvUPsz5fEK7um7jAu7Dgc t6ElCwMKSlQwR/lXbvjkKF4K3YqFoG4hsIGFE4HP6qtyls/2NmH/AJ+5m0qDuEtWddTjh9mll8Jt f/Ppzkw9wixJqhdWlKZisfGtnwhkgY4AzjyP56/HJVmmWKaWIIS4205hxTCArITu8ia7uoLcu5R0 22Ehhh92JLklR+FOyO0XljIGclKFAcYyRkgc1hLA2lOmGreUILkt9JC8cAFwJIP6J/PWTCO6jGo2 +uF8v5Ia6rqlqdWzhGLbpuU3jrJpYST/AC5yunQjSW1PxlPtbUBAIOeDxzXTUXVnZ4ij58k1sibc 0aXitRBFS8lCN5KFbAcrPliun/D6EUjdbXsnuN4NZtO8ryW6nSyuzyjWLvw5pdCSpXd95dRJblsk +fmnj2ItBbgKtiosxlxDhcLnvbW1akpwkBOwgZ5H88fW+XP1arSq53STEsslSywyXULeR4TjoGAU pSkq+L4jxnkD7qnqJEfUVmcEdsx1KxhS0g4xtPl9ta+ddRBvshmQ2H0tOLQfPODjODV61vZVt0ZR w12IvXPDFLTVRq0q26nU6Sxx37Zb6cmPnRX48x2NJQtt9tRC0rSQc/fzXPp+83TT15jXS0zX4kuM 6l1txl1SDuSeOUkH9fnXaus5cmOhlqRITFQoKbjE4bQcHJAzgHJJ7eZpO0/MTZo96aIdivNlSicJ UgpUUkYzyMjg/qFZ8JOSyandUFRk4p5910aLwezV7T0HWSlad1oxFtV0SoeBK96SlmQFrCQk+Krc FAqHYqyPTHOx+q3Raz6xi74DyLbN8XxN6WUBJySSOE58zXmHEZU+6EpWEHI5rfvs9+0hf+ntxdg6 tlXW/WZSAlDKpAUWSlOBt3AnHAHBHarhiGf1tpubCdf0VqxhXgOKLEeb4ZG1YGQoeIMeY7VonqHo mZpO4KAcVKgK2lqQGyAQrOATjGePI16eaitGn+qHT5vLbT8S4Rg/DeeaO5oqGUqxwQfvqqfUrRNy 0HNkWK/hu4ackq2tLSQUpS4Tt+AndkbT50BUOlS7qbpZWnrz48YINtmgvxCnjCCfqkE5GM1EaAUp SgFKUoBSlKA9CtGf6sGgv7do/wCLaqx1Vx0Z/qwaC/t2j/i2qsdVEOhVLqKUpVZSeM9bi9n3Sj1y v0ZLbJMiYlLaST2SpXlx5prUkBluRLQ066lpCs5WogAcfOru+x5pGK7qD6Sej5bhRgpr4eAoKKU/ m/dQE/6vXRjpv7PZskB1tm4+4je2OV4KVKWvy7lJGT9lUJ02qM24m83R1br6FlXJSSrAwO/z+dWX 9tDUrEq9363pvLCXI7UeIxED4S5gLSpZCM5I5cSTjGMjNVKLjipSWgVFsEDaDwfOsa4g6kdmcEzo 1xGzrK5cdzT4z69U/oTOVdLlefEEcLbjjO0N7gpQ/pYJBGDzWQ0lZ1sL99lpSStKhhY5B3cdx6Cv nTLrbVtSn3dCVqwMkYOMCvp+5SIk4jcFMZxgk4HGfXHetcqOWJUaawjtFlCjGVLUb2bnJ/ZN+3sZ y4sxJMUx5CUlskY4HGPTNdO0+LEle4hOYoz4SsHz+IDPbtntXXus1xVsTIZCDnacDn7e1cNluv0i tEYqZbfQD2VlXw8ds57Vixoz8p+n7e5P1tRtnfwS4m0sP/2Wfy5O5qOO/Khu+6rcaebxgpJGcken PaoleHpE2xMom7wppbYBVnk7VZ7/AGVM5cssx1vq2jGM5OB3xUp6I6AsfVvVsuyXNVwjWmBBXJdl 2taBtf8AEbS2haihSRuSXiBgE7DjsazNOU5SjBLv19DW/GU7elRqXDqYbjhx/wDbL4+38Fk/an6b Xvqj09g2HT0u3Rpce6tzFqnOrQgoS08ggFKVHdlweWMA81SdnUVuj2xkFQW6kpwgFJJwn0z616Uq XwSojbmqS+13N0lCvdv0bpPQVjgvNuMz13O3xG21qGXmywUobBKc7VZ3Y4HHnU9eWsK6Tn2OT+Hd cudKqTjbrLnheuP8ya4tsi4XEmSGhGj78gbVIUod+fI8Y/XX5dp7H09FbKPiwjnA81n512rEuT7g PHSljbxsKCnjA9TWBvM9l6/xC2w2nhAykg/yz8q1+nDfVaS4Weh1+9uXa6fTnKb3TlFvcuvPZdjm 1jbPemm3kJ+JIIyB8x8qsl7FtyuuvNd6j1DNuU2xwbCI3uem7K+qPZR47T6F5ikqHdAc4I/GFSjn NaFvC9jCEFIwonJ7elT32LNWXOw9Y29LQm4qrfqY4mrcQouI93YfW34ZCgBlRIOQeO2O9SelVm47 GaP4+0ynCv8AiKfXv9kWp9oPQth1b07v8xzTzMzUUW2Lct0uPDQ5OC2cvNstr2le1SxtKE9w4oDB VmqG/R0tqcWL9a5dru0VQD0aZHLLzfAWnchY3DKVJIz5EHtXpqrCVA5Pft61WP2v+lcid9I9V7VO KHrbBZRLtjFvKlTMObVyFupUMbGljJKThDIyQBxm3lv5scx6o1jw5rK0+uoVlmnJ8r0fHP0KysBS 7o48SkoQggZ9QrNYuNECL9IusnIYyrB8s52+fH66zER6M7BS54rKfEQFKII4yOanHQ/pbN6nalXb bxEvls0miM7IRdYscpRMWhYR4TbyklsHepRPCj+LUMA/EIe2hOpNwj8n8jo2tXNrZ28Lis84e6K9 ZY4+hpC8TUO3eS5uJbCsI59OP2VlNIW5Ds5NxklXgbFEJ9edo7jFWw6vdJunfSHR906gQIEK7XNM SHbYVsvzTD0N5wKYQpwNJQhSny2044ohXJLqsYziukCc1MfcnLiQIYlOOPGLGb8Nlneoq2ITk7UD OAOcACs++fkUdsfl9DVPC0VqeputX6J7vbdlfpydbUjU69wUNxI+xCc53IUM8j0z6GtfKZWlxbS0 kFJIwR6VsNvU5akojojxwk57Kwe2awmv7a1b5TMhrI94W6SNoAHIPH56xrCrOlJUZLCfQmfFthb3 9GWpUKjnKnhT7LnCWDf/ALDvUONaLqu03KaW4/uvhulSDhOCtSOfsGPvqe+0DopEfV12aahEWu6s 7ynPwl1YOcenKTVWugnju6qnwY61IW7EK0lJwrKVp7fco1e7VCl6u6IQNQGMly4sstOOrHKsoBCu Tz3JP31PHKGsM83tX24WnU9xtwRsSxIWlIznCc8Viam/WmKpnW0p9TRbL61KI+YUf8qhFDwUpSgF KUoBWy+lOmZ8l4liKVy5Aw3gZKUbdx8uP8qgVlgquE5LACiBgqwPLIB/bV1fZN0dDkaicfmQPEZj RyfxieASkJH7aA23oey2jpB0zlagvRbiPtxAZLilbjnKiE9hySocfL5VRH2i+pdw6g9QLpJTcXXr Sl7ZGbxtTtQSE8ZPqa3n7dPVKc8/I0RbJ4biMvluSlpRy4djZwrjyJUKp6SSSSSSaA5YjanXg2lO 4nt5+VTaySo2nm1pbT4768BQbAVgcnnsfMVgNPQWXbTc7p9JR48mB4PgxXMb5O9RSrb8QPwjk4B+ eKmNq0q2loPyJTynlgghaO3Pz59KitTqQilGo+H29Tf/AARZXVapKrZQzUj/AHPGIp+merZlm4ky 4NtruK0tJ27koYJHJ/nA+Y/eayTbTDCcNtttJPGEpAFcM2W1CaSt5xtKVHA3KCf21Gbbf3rzdFsl IYbjBTg2LJ8TCgAD8v761uNKrWi5LiKO019QstMqwoye6tPhZ5b+b6Lgy8qJBavjUtTTIWtISPhT yoqzn7fnXy3cQ5Jlw31IQtLhS3uOMpJIHc/Kus3J8cvS5CUjwHFJQM4A28g81menukYWvLhcJMuZ NtqWPDZiyY5BaLpKydwI+PblslIUk4V3GQazbe0lXlsfZdTWtY8QUdLpfiIJJSlnaurT4b+j5MPd XrOmWxFfjNhb3woUG0cZOM81x6bgMQbi+h5Yce8NCmy6QSn64OMjPlUr61dH7topVjvFsusrV8Vf jOzXYtrW2iA2yUK3OKC3AAoKUcnaAEHv5RlEWNNl++R56i4lIRhDgV6+n2mrlxbyt6eyT6r+TB0b V6Ws3buaNNN05LC4Tw44fza6ojzzj8K7xUSpk9C5JInJccIjubcFrv3AO0nd2I4rvs24Sb21c0z4 pQ0obUoe+EYHpj1J867ep7e1MsD0mSSl+O2pQUAAc5888+QqPRIDkZFhU3Lk+DPiLlPHd8KSl95v A8gMNDv6msmEncUnNPDisP0+hDXNKOjX8bWpB1I1ZKpF5+LLwvi+TJPc7pGTMbt0uOiQl8AbkoCg QSQOSfWvp+3afahqdejxmxtJO5CAfPgZHfisbIRHQlVyU4lYhpIaSog+Jj4gc/2lEcelfDkV2+6U N6aiXVySi4hDjTO5UdEQN5U4Rs4+LcCrdjHGPOsehaOp/wCNtJdSX1PxDTs23dwjOU3iCazherfp nPv3GivATe5r8cFuGErSkqwE53px247YrvW9hhudNuclhpTClugKKAQfxuAcnj9dfT7EeBpxhuMg NiUpG5fAIynd3AH80CsrGhtPWiMxuBbKQpXAIVkZP6zmsavWTbn2fH0RNaXps4QhbtJygnP23S6L HokRjWVmty7I7dorfhOLWlQwEpHJx5D7+9R66RFWy4R5Fs3yYyIkdzxVfGnxHGEF1OQMYC1LTjuM YPOakOoveJd2+gWiRHSU/ChJJADee2cd/lWTjC2IaasL7bCdraQpSikKztCjkEetSFC7nb0kn8We flE1HVfD1vrGoVJQapKPwZ7Sqpv7J+pEdSW62yI0W5afClIeyFxwoKdaIKgCpCc7c7c/MEHzrCR4 qpjay0rc+jBKVKA3D5fYakN3tMvTjqvclOPNuqA3lojBAz3Bx5n81ZXVnTi9WOwWG/2tu4zo94iC UQxFUTFSpKFJStaSc534yQnO08eQm6VaFaKnB5Ry/UNNuNOryoXMdsl/nVEr9mrrbfOmWsIcO8XG W5pd3DEuKtBdDKMkhTYJG0gqJ48j2NX9t83RvVrp4JEV1m7WO5NEH+c2rzBH8haT/wBEGvKOdMTK jNILCEONJwpwEZc4A5+dbZ9mzrjqLprqOBbX55e0u6+RKhvrV4bYXtCnE4B2kbQeB6+pq6YBtrrL oBWkr7M0/OgOv6Ym5VAfV8aWUkj4SvA2kKKeMelVc1tpqXpm7e6SEfi1oCml7goKGOeR869QJcPS fWHpyxLadYkxZ8bLEpkhao6zgnBIHIUkZ4HbyqmnVXp69HuUnTGpi4i5Q2g9Ckq2hTwWjOOCQoBR x38qArZSuR9l1h5bLyFNuIUUqSocgg4IrjoBSlKAUpSgPQrRn+rBoL+3aP8Ai2qsdVcdGf6sGgv7 do/4tqrHVRDoVS6ilKVWUnkj0wimTqbgfUZUr9g/fXoR0lt6NGdIpl9ZUHXxbvECSeyhvPP3kVR/ 2coSZWqJjriwhDTHJxnvxV59cuM2L2fGosYqcXPaajpcIwdziio5x6fEKApn7SGqbpLuyLW63HEe 4qVdnSlB3+Kt15IGc424A4x99RrQlmZNsYui3HQ6VLITwE8ZT6fKvzrpJMjXzEVJ3Lhwo8dz+1jf /wDeK7don/RekWHnUAlGcjPkXCPL7aiNWlPy1GHVvH7nRP6e0rb8ZUr3S+GnBy56Jpx5+iMs8sGY 033AA8/nXRnQ48uS40XSlW7JAUM8fKsdJ1O48pBgwA8ogd3cAH7wPlWKuMVUiM5KNlloujiiove/ tljlX/dbN31ePr9+flUXb2cs/HLb9jetY8R27hi3pOus84UkkseuOSQ26IqPIMf4lNDcAduD3z3r avSFN46qxFdF5EuFa7XYEPXm3TkRFOPeOX8KS6C4ErRtlvABOwghByQCFYvoZobpPrGZatOXnV13 GppbRJhw2i2kOJQpxaApbK0/ChJ53YJHHcCrL9JOg+kemerZep7Dcb5Jlyoa4i0TX2lthCloWSAh tJzltPn68VLWtlOM3KTymsfM0DXfEtvXt4UqFNwnCW5N/wBuF7rsyqOi+m2vdb6ih6WvOlNQaXhz t/jXSVaHy3H2IU4nIUED4igJ5UOVDv2N/QkLR8sZr7SkK5IxXGlasqCOw9fOs+hQhQTUFg1LU9Wu tUqRq3MtzSx26fTB03klwlBJANeeNqcVOU/enVIMi4zFSnktjCErcwpQSMk4BPGSa9EnUkL57k1T X2i9BSNEa9VqSDGku2C/Sy9IlyH21FFwfddWplCEgKCAhAIJB9ConisPVKM6tH4exsfgTUbey1Ne evzcJ+j/AOyBONhSlKOfrntUV1w0DfbfsVk4Hz/lVK1KByRyCs4/VUd1FAVJvcR3ccICRx/aNa/Z S21U2+zOweJ6Lr2Dp045blH9zOSEBSEpVkZJ/dX101tgY6laIe8RRKdRW8gFPH/a2q5UOob2qWsB OT5H0FSr2c7BJ191Ws8mDGdesunprU24y2nkNqZWnc7HG1fKkqdZCTtBOM8p4NXdOVWVVKC4zyR/ jKVjRsKk7iS3OLUV3beC9RWpK+cE5yK5HcuJyMZHzrrPLVuUSOPPBr8RISFpIRnntW2nz0aaR7Nm hh1OPUE3XUf0sb39NeD7wz4Af8bxtu3wt2zdxjdnHnnmt2uOJCMrICa41J5JTznnmoL171UvRHS2 6arRC9/MBTJ928fwt/iPIb+ttOMb89j2oe8vgpP7UL0a5+0zquVCeTKZSphsqYWFgLbistrTkZGU qSpJHkQQeRUIellqP4K2FJSnAyVYP7KkTj7d5uc7UhbWwu6SXpZZ37g34ril7d2BnGcZwM1+PRmH mlIUpYJ9D8/srXrm8jOryuEdf0Tw7XoWCcZ4lJZ6LulwQi2sIm3dpolQSc8p5P1T/dUs6kQhItkR 0LVlt0pAAzkFPf8A3a4Jel0trS+y84Vc8KA+ysjfApOmYIVjcPDBx67DVqrcxnWpzpvoZ9hotW10 y9tbunjelLOeuGsdPTqY3oOoRepFoeDrbhktyEqbSfiRhtR59M4q/HQZwSunt3sJUS43KltI4HCS AB8/5Ved/SGQInUW1vkcJ8b9bKxV6ejFwctvVV23FvLNwZcO7P1VYSrt/wDLxn51sxxBlWvad08Y GuL02rxCth1agSCByQTWjqt17ZUdpHUu5oQQQ5FQpScdiW01UWgFKUoBSlKA2H0VtAn3KRJUVYb2 JwP7QJ/dXoFpdMHp102kakkrQsuJYUdzm0EK2ADnAzlRP3VVX2UtLyJqWYpS0lUiQCtSsHCfESn7 +9bk9ve/o0/0ssthYSvEqWn6pxhDSeO3HmPzUBSLqHfZWpdY3K5ylIU5IlLX8KQBknnt9lR7BCiP MV+g5UVEnPf7ayGnYonXhqOVFIUFHI+SSaonNQi5PsZFrbyua0KMOsmkvrwSWz21aNOIkBLpLiQr Gzj63+dZ/UbH0ra4y1K8JQcJIAz6j91d9lSI8OLbwcqDSUjPc4ANdC2ykTULjn4FNYJxz3zWqSrT qT830efo+D6AoaZbWlurHOVOCi1/9R5fPryyP2ZFtvUtEZqPLiux2FeO4qQlxLqspGUp2J2DIPBK u4545yNwtk+PeGZVtgypqGg49KKGlKDDKVIKnFFI+FIyMqPAzWVv+tnpbditkm2pios0BUNDvjb/ AB8eEndgD4fqZxz37193YPNyLbcmGi+u3TG5aWgsI3bDuxuPbOAM1m1qiVzHL+Br+DV9Ms5VNFrO Ef8A+mnLL9eJZX3SOu2w09HedQ5kSEqUcEH6w8q3Z7NejWLj0W1Hc13JuCiDe5anH3iAhpCWI6it RPASACSTxgVo60M2y76vmzb/AHNWkbbNDsh+W8yq4nxlr3FKUNbFAYJPOfqnk5GJL7R+k9P6L0X0 8g6cuku7Qrl71eVTJCA34yn2oY3IRgFtBDaSEq3KG7BJrNsqDpSk1LKNZ8T6tC/oUqc6Lp1F1zn9 MpGZ6fdc5srUdotN1siYsaVOZYdnvSw23FQtwBTi8tgbUglRyRwO4raXtM6Ct2u7GOqmgtQo1NOs kZu2KgWlKZ6JX40cJUyolC0CQtahhWU7eE8k1utMpMq3vOpTgIUQRn5A1sL2c+p0Hpxq1z6SkRI+ mLxhF2lPx3XXWPAbdLPhhvn4nHQDlCuPTk17SvfOm6VSOOx5feGXptvG/tK25r4umOj6rnt34MHp mHqlzUVrMrS10hpakpkLW7EcCUlpQcCTlIxuKdufLPY9qw3VR50dTH1yYq4ztyZ8dSFqPGEqbG0E DjDY9ec/ZV1tUs2jQMWbqrU0hhqxMKT47ymlOFG9aUJ+FKST8SgOAa6Ws7XYuq/QO6Sen7jElVwY 8e3OiN4fjux3wvwsOBsp3LZLe5WAM7uQOb0bKFOlKnDuRlXxLdXmoUru5l+XC6LpnL7d+SkbDz1p VBuzLC3lW99qQlr+eUOheM44zj0NbJmG9dQuiMO72PSV0kTYWsGmnYsRDklZQiMpfiEJRlKfxiU9 vTnmolqSNqPQt8h6c1zZE2m5ymQ+htMlDwLSlKQlWWyod0KGM54rZ/sfLjq6+3V1pZXu0w73H9ZY rCtJVYzdCrHGc8my+IaFjXtqerWNdSdPanHGPfv/AMGqJd48GU/ClRFMyIZIltrXtXGUFbFJcSRl JCiEnOMEgd679uQyttMtlzel1ORhQI557iu51a8XXXUu+zrw4lk2ybItTCYySnLTTyylSioqyr4z nGB24FYex2c2l0pbf8RgJUEhSPi5OeTn91RF5St6eYU5co6L4bvtYvdlzeUk6c1lNNZXu19zERH8 64uW1ILgawBnOcbB2+yurp90tavuK5YDK1PHYFfDuGFds9+MVkpNtgRbg9fZUh1HiOKRtAyM5x5A nyr4vVsTdYCXGVqwEgsq4woHGcg/ZV5VKbW3s0k36NEZUs7uEvNWHUp1JVIxyvijLOG/Th8P147n Bqq/ONNpjuQ9hCwQpS8buPIY+ddXTF+v2hrLOTBjx1t3csF0yY68p8PcpO0hQH8o5yD5dq5IRRqi ztRZDnu7zCis+GnIOSoef3edSC8x2bxH90Q4rc04FHA9AR5/bV6ncqzappY5+L+PuR97oc/Ekal3 OpuTivK6J5f5k0scxxjlc9SN6n0WyvSjWq7Jc27qt3Y9cIkVQeXBDiFOZc2/UCSlSTuA5+yoCa27 0hhWq0anmW3UEt2PG1Ba3rQ2tCdyi48toAJwFYJGcEjA86inVrSMXSepnoVsdkvwE7QlyQpJXuOc g4A9PSthp1I1I7ovg47e2VayrOjWjiS/nk2t7H/XKToC7R9HXNhp6x3KcklwlKFR1KBBO44BG7ae T5H1q3nXvpxG6gWBm6W6SEXW2occjOJJWh0BKiEEDOfjA5HPcV5dgkHIOCKvj7CHVMXzTczR12U5 77CdLzLpK170uKA25JOOT+2rhiFWOr1oZditagZSWnw4WZTRJPxHKsj05JFa0q5/tTaEh2DUqQhQ ch3xDjwSW0jw3UvbynA8sKHNUzfbUy+4yv6yFFJ+0HFAfFKUoBSlKA9CtGf6sGgv7do/4tqrHVXH Rn+rBoL+3aP+Laqx1UQ6FUuopSlVlJ5r+y3Dcck3J3buC1JQlO3nhKiT+z81XE9ocpi6e0zblD8Y h4uYHbDTYz+0VXb2K7M1NuzTK2dyXSpSgTjP4gmt2e07eGhJW0l/47ZEkrWD/wC7Km2yPzjNAUf1 ct669T7pIUokKupbwcZwlYSB+YCpcmC23GbU4j/RJJAzjnJI8/Wtczbk6rUcyTFIW+5PW8g4zklw kVJoU/VMxkKdYT4e7acISM/rzUFqlKpOSeUl7vB1bwLf2dtRnTlTlObx0juS+foSZvx1+GVk7Tgg bfKuJ15tuQsqSs4J/kK/urgZlXEpSksYUlAyNh4xX00X1lapKMAjuB55qD2YfJ1H8SpxShnPuvY7 /SHWFi0t1wsl+1BJXb7VDekePJLLjm3dFdQn4UpKjlSkjgHv6V6AWK6QLzZoF4t0jxoE+M3JivbV JDja0hSVYUARkEHBAIqiHSLSWltXdW7DYNQQhNhzXn/Hj+O43v2xnlp5QoKGClJ4I7VfG0WiBZbF Asttj+BCgsNxYrW9SvDaQkJSnKiVHCQBkkn1ra9OkpUFtWDgPjGjVpapPzZJt88e7fHzML1G13pX QVnEzUt7iwA8FFppStzz2CkENtpyteCpOdoOAcnA5rTnTH2kbZqjXM603dNu0zaGWHHY9xuN1bbT I2uoSlO1SE7VKSoqxuONp7960z7berrndur0jTMiNDEDTAT7k4hKg6v3mOw454hKiDgjjAGB3z3r Q7bbkgASSrYc7TwORjP7RWTKbTz2RC0LanOCi875dPT6/qerjpCiRvCvsrR3tJ3XQWqdCXDTitQ2 CffoUhSoEBN2bS+ielK20J2BYO4KUU7VDGTyKnPSvXNn1Toy23h++2V24m3xnrnHhy0r90fdbBKF IClKR8QWAFc/CR5Gq0e1Bo/SEK4jXegI90GoPpYSpyWgp+OP9K+5KKVBRSQsIzyGwP5I71cfQw45 UljhmsLWuVBWm2XxhcGc04E+G98JUOE7k+SkkpOCMg44Ndh59Ds1CUK3qATnjtkn+6sNKev2obvA vN0eXKdAbT4gbbQNgVuAwhI/nH51+RZqG7xh19KNyWtoPGfiV/lWs3FCm6kpU3k7jo+rXcbSlSvY 7W2ln1WMp8/Q72rZbUezlDi0pWo/CnPJ5TWW6ddSequmtKRLDZtW/RVpib/d2DAjObd61LV8S0lR ypRPJ8/So1qeFJuFzithtS46UK3YIAB+37hWa061p+Tri12LUNzVa7M94vvUhl1CFt4aKkYU4CkZ VtHI88d6vWkpQhGnSfxS5fsRviGjQubqre38G6VJKMV03N/v9GT3RntCdQbPru1L1hrBFz08Hkon siDHbw0sFJc3NNlZ2ZDm1PKtm3zq21g1zpq96PRrK3XZD1jKHHRMcQplCUNqUlalBYBSAUK5IHbP atG6V6T+zzdNJSpTt6euaWX/ABX7lJuqUllLexamlFra2EFIOSU7gHFEKHwlMF61a9b03bWek/SZ dlnaSn2zwy5AeVNeS48+74jKXN6hlWR8JBV+M4xlOJunvhD/AHHlnL7zyLm4f4Sm4x9P8yXN07qC w6lthuFgvVuu8Vt3wlPwZSH0JWEhRQVIJAUAQcHnkVpX26okGX0aiGVP90eZvLDkRrwVL96d8N1J byOEYbU4vcePxe3uoVqL2E9ZSLPrSfoy4XyJAsMmO4+zDlKabLtyU5HaSlKlALU4UAgNg84PGa39 7W+nndR9Fri3AtEu63WFJjyYDMVpxxzxfES2pSUI5V+KcdyCCACT5AiqT3QeCxSpqjcQVTplfbJT G3NbLPFbCiMIHlXYbjJCQ7yVf2TXHEDzMRMOY0qPMj5afYdSUONLSdqkqSeUkEEEHkGudt5QCkZA AHfHz9a06puUmvc+kLNUJUacuq2rH2OVxuS640ErLaRnnaDXV1NMiwreyh55CTvAAJ74BrusSmXU pDclClJzuCVA4rD6vtIuEVGPEcWh3sFAYBBz+wVTQUXViqnCMjVZ1Y2FWdot82l1eV29DXmnpYg3 piUVlARu+IeWUkfvq+mjgE9RNMTi4AhySlBPrubXj9eKoDJaDJ2kKS5wSD6EZq+GkZLbrmk7ol5C mTKguhYOQUqUjnPoQr9dbsnk+XpxcXhkc9tG3rGuPewgbXog+Id+AkVS1aShRSruKtp181HqTUV+ ux1JZxbfdbtLhWz+LOM+8wULb8F/4yd+4EnenCT5Cqo3FITNcSBgDH7BXpSdelKUArlhsqkS2WEj KnFpQPvOK4qkPTyAqfq63tbN6A8CofYCR+ygL3eyDZG47c+UWEENN+GFbScKK93n9lah/wDaK3xm fqiw2yO8s+5sOeIjyCitSf8A7KtB7PdjVZ9ErcdZ8J2VJUo/NKfhH691UE9pvUTuouok+U4+XUiZ IS16JQHllIHP9KgNVhQ2FO3kkc1LtGWCap2PckpUlBSopJA57j1qItgqWAASfQVtvSDqP4NQ2kqK HEt8jGMcn1qL1avOlR+Dvwb5/T/SbbUNRbuG/gW5YeOU1gx+qHU29yHO+FT7SVBTYVyeEp+flz2r uQJK5LIccjLZ9AvI8/mBXS1BaZE18vF153aThAKMDOPQZ8qyiCl5htTROBnKcYxz8+fKoOTh5MV1 fr6ex1ChC4/1GvJpxg8NRePieMOX6LODBaxgKnRWVR0KLiHDnjuCPn9lZSBPlM2OLNuVunxYziG0 sSHIrgbdUUkjarbhR4yACc1hdTTrvFCBHj/CVqAUGyokDtU/19d7HOtOn7NarhGfsMU5ShtzIbWA kMJWT8SVbC78KsE4VkEp4kKduqlrmpyl0x19zUrvWKtnrijZ/DKphT3rEeF8OP8APQwivDlxScK2 OJ80kEgj07+dZDS9h+k+n2orY308TqJFmXJuirv9NCJ9FJfaCd3g5HjbRFKsZOcYwM88GwBkeAkF CW/hwc+XFbU9lt21DT2sot/y79Myfo+ZDKgn+JhCwhWEkLTu8Z4bs87OMFJq3pLk6klF8GX/AFCp 0qdnSnVjmeVhpce/0fY02U+EgNMtKUhZO/GTisVq6G8/avAt7Snkkkq2fFzlP91TXV/8C278Gunt 8n3uy+7NqdkTWihxL5UsLRgtt/CEhs/V/lHnyEUhyJiTsjIbfQTlR4IB+0HirCjOhV7ZiS06ttql gklJQqppccpLjhejOnpbTLTTTUyWgpWk5AVkeZHr9lSbR9/Ggurln1dAtRuT0NpwJjCR4W8uNutZ 3kKxgLz25xj51i7rPnssAsIZ8BCgXdpCiE7gT557Zr4Ym2uR4VxdlNpW0kg/FjGCfI8+dVQr1/M8 9vJYr6VpTtP9LhFRxhtvCb7NpvqWs0f7QOj9ZQp2luosdrT6bmh2ItiTMJZcjLaIUVSAhCW85Wnl QOQMHJFS/pn0z6XaflN6u6bQ4aUS2FxRNi3J2U0634gKgkqcUk4W2AccgpI9apFMYsl7XuVIQ8VA ITtc2k457VZ72Ttf6YsWhpeirve22ZFpcfmx47zZSmPAIbUpZd2hOPFcdPxKKhn+aBiYs73zmoTW JHOPEXhd6bCVzbzUqLeFzl89OiwRX2lun1s0ZqZjV1sdEWLfJbiJcYhSguUvxXlv71KOMhIGwAAY yPStOn8XMfkxdrz60DajcBkEg+fyqyPXDVGh+sOjp2j9CXSNe9Yw30SbVHJXHQtaHAh0tuu7GncM reOApWQCoZxkVvmwZ1occt1yjqiX+CA3KjqxlJGEn5FJGCFJJBBBBIIqP1S3SqebHo+H8/c2/wAC au6lo7Gq/iptyj67cJfDnq11SwdLUkCyXTTcAuasP0+/NUiVYlW1aRFbSHdrnvJwheQls7Rz+M/o mvjRum5H07GtVz1EqzWGQFeNcUQPevACUKUn8Ug71bl4TweM57CvqZpy53x2ELBE8a+yXSEILqUB W1tZV9chI+FJP3cVlvDulgaZtmp2Bb7ilAG10gIXgZ+FYOxWARnaTgnB54q3OrNUYzpRTiuGsZ59 TLoWFtPU6ttfV5RryW6NRSw3B9Ivom+uVgjOpLfYrhqCZdodiOkLZI2CLa1ynJZZ2oCV/jVYUrKg VcjjfgcCstpi1RbU4+41IC/GAzlJT2z6n513otsTqiUmFAhTL5NabU6Y1uZckuIRlIKylkEhOSkZ PGSPWsfGmte+SYElDkWdFWW3462locaWkkKStKhlJBGCDgg1ZuKtevTbaeH1449sEnpOn6TpN5GF OUHNflbl8ecc7lnC68JLodnVkONLtn8ZUlKWyVgk452n5iuLpj7rfNLao0NEtDMmd7vIuMWYXRkq HhtpaAI4553FWOe3nX48xKnuuRbgpSIm4hBTtBV5D9WawaZUrQ+q48rT8pcdUgJae3bV721OZUk5 HAOxPbB+dZWkVVTflN5b+xA/1EsZ3dP8fTpqMY8NviT7dPQg9xhyLfPkQZbfhyI7qmXkZB2rSSFD I4OCPKpP0j1fK0XrOJdI77zSC60HS2vb8IcSrJ48sGuhrkNOX56a24pxybulPEkcOLWoqAx2GfKs AMjBGR6GtgRx9o9K/aKjRdZdJ4GorV4cj3V5p5JSNykIdSAR8j8SM1569QoKoep5awgJaec3IwPk Cf21dr2O9Ssa56dXPSl5nCYWvAWlpzgpSlKMjPyKU/rqpnW63qYuCXtgT4chbKx5ggAcj/wmvTw1 tSlKAUpSgPQrRn+rBoL+3aP+Laqx1Vx0Z/qwaC/t2j/i2qsdVEOhVLqKUpVZSVB9h6DtnNvJbUSy g/cPCA5/PXU9qGetxrqBcFPhlKiiK2Ugc7SlkjkHvtwftOCDg1PfYot6o1hnyVEYUhscp9UoPf7q 0t7U1wQNGTwO9wvTh+t5F5xz7+1AVpshQLvGW6oBIdBUTxW1LVKjKjtpYW0pJVk4V55+2tb2CNCk tKjqEv395YQztbBbA3JJJOcg4CuwPl862FY7Yi221PiqBUgLUoqPPn8hUDrOzjL59DrH9NXcrzNk Fs7yf04+xlAtIS4skckjNYu9yzGt6lNPBKwod8V8O3u2oQptcltCkkk7lpGf11MukHSLUnUvXDMS 92i/2fSbkRU03IxXGBIb2ANhh1SFNrKlLQr0KAsg5ArAs7KdSpyuDb/Efie2srR7JpyeUsPnPJtX 2Pun9omMWzqVLcnP3RKZKWEqcSI7SvEdY3pSlIJPhhQ+JSh8ROM7SLI368W2ywXbperpDt0GOlJd kSnUtNoyQBlSiAMkgD1JFdXR+mbXo7Sdt07aGVNw4EdLDZKEhbmByte0AFajlSjgZUonzqA+182j 8GTVS1nKd0T7f+2MVtUIKnHCRwK4r1Luu51JNtvqyhOqb3eNXXt7UN8nmbdZZT7494baN2xKUI+F ACRhKQOAO3NSK72y3K6JRbqwlJvRvaGo6fF+NUcxip4pRnlIWGMqwcZSMjdzBblELalOtPpVv+ql vkcYB58qn93l6mjaPsDt80uYVojoIgTEQHG0SPGba5K1fColLCSNvf4jyO2O6qVLK5ZLU7B1L5Qq PZBYWfRY6r59fqd3olqq0aUN/TcnXI/vq2CjDDiwrYXc8pSrH1hXe1Lry5X+4ybRaFeDaXmFIfeE chxwKyhaPxg4BSrySD6GsfCtEFDTWWUrJbGSUpPPHyr4ck2qPlLbkYOqXgIaKdxz5Y+2oiWrSlFw px59TotH+n1KjXjc3dVbc529n7HFKWxaIbCGwQhtrOSCrsB3x9la+aNyuT5mtMqUIyQpSko4ABJ8 /vqT65uJQGIrQO55lQIPcZ4Hn9tfa7vfXtz6I8JkD6zbCFpBAHpnvV7Towp099TrIjfGde5u7tWt ksxpdUu3Ca/YylqkRLhHU60+HVIXt+H7BXRf04mXc3356X1IQAWzuAB+HnsPkKx2npTL92/ENOQk 7E/isBsKVheTgd/L81TVTii4PiRtUDxn5VHXO60quNN9jcdE8jxDp9Ordw5T5XZtZXKf0FnuzNl6 Y6g03bcrn3CUpDcdbDhBZcZaQtYc+qCAF4BPcefao/oaH/Bu7WW8zUqZat9zjy31k79qEPIUokJy T8I7Dms+W8tkoQyFdt2OR/0DWJ1FMjQrTLjylhSlo4AIJwePOrsNRrVXCCXTHzZg3HgzT7GFxczk 1uUsdFGOVwlwZDqc9bdI9WLJrDRrTEpiQWb7FD6lqS7I8dTidydwWEkBHHw9z2Pa/HT+8TdQaG09 e5zDTU25WuNMfQ0kpQlxxpK1BIJJwCo4ySceZry6cflPS4z5c8VpnYlCQoqKUJOQMeXH3V6S+zZf JeoPZ/0hdpbLLbzcdyElLaSE+HHdcYbJBJO4oaSTzjJOABgDY4SzLg4zcUnCgnLqnj6GmPag6MN2 63z9daNcU3PmXQO3Fq4XONHhMNu7ytwLfKMKU8W+C4eXCAMYArjqe1a6sLLSJybIXXw2Usw7rDlv FDjfioX4bLq1BCkYUFkbSFJ5+IZ9ItZDSqdOSjrT6J+gTs96+lvC90+unZv8X4Pr7cZ/lYxzivPv V0JlfVPUV4t0iNJtyrjJbt6mVhbQiJcKI6WyOPDS0lCUBJ2hISBwBWHeqhRjvlFZ/k2LwzPVNRrq 1pVpKCxnD6R4XBiNMQnYsRx2era8rHC1pA7n0+WK5tTXFcJhtbQQrxFnPwlXl8vtrJLQgveE6lG0 /L5V+qixZrCErS2sIAPYHyrWnWUqvmTXB26Om1KVi7O1lhpYTfXOctv7mobxKemzfHfaS2sNttYS CAQ2hKAeSecJBPzz2q5OiJCWelemJCVD8VAgLx80hs/uqo2sUsInqSwEAJdcB2gevyqz2g3tnQ60 PZO5MNAAJ7keX6q3GjPzIKR826la/hLqdHOcPqZv2qA5Ij6Vnbf9NaGXEnIwQQ3/AJ1TW8gJuToT 24/YKuJ19LkvQ2h331qQ6bIngnyCxgc/LFU8vgxdHh3+r/8ASKumCdKlKUAraXs7W73zVCFkE5kt Npx6kK/vFatqyPse2pMm/Wd0jG65IUfh74J/uoC9l9U3YdGXFcZaW/dob7jRWf5QSpQ79+a8mNWT TOvkh0qCsOLGR/aNelntSzXYfS9zwVbVuvKR3I48B3Pb7q8wZXEl0f0z+2gOzZGw7c2kKBIO79hr ZyVMWqyR3ikoSEJGSFK7jPlWsbIlxVxbDagFYODn5Vs6JNj/AETHjz45cKUJB3IBQcDg8/KoLV8u ce69Dq/9O3CNCu8qMucSa47cep12byy+Ve7KU4s44DK8fs+2uxbQ8htRdABVj4SRgYz2rmfFshxk vobYYDg3AJCUk5/9ajt0uFykhKLdGdQEnKlLbUAfkCk1GwpqrxBYXqzdLq7lYYncz3zS4jBPv7ck hlMxZOELUlzBzgL5H5q6tjtVrS1fY/g+I23a5NwSjeo7JLCfxThwc/D4ixg/Cd3IOBU66c6a6H6k uLtvu2qOommnkMKe97uk+HFjOYUhJQlagSVkqJA9Ek+VWM6Y9EemWnXLm3DuU+8OXNoMO/SrkaQU t/FuQnCBhK8jcDkHan0qYs7GdKSe/MTnHiPxRb39GVN2+ypxhvquU+H16Ip9pyW67a4zjqgolCdx x/RHpX7b9Waq0xdbi5YbRFlNzEthxchlaseGpwp24UnvvOe/lU96m9KdcaQ6hXSDA0tPu9nmPuzY Dllt7z7Mdhbqg2yohAShxKUjKE5ABTg4Na/1A/fLHckW252G52uQtAcDdwhrZWUFRSFAKwduUqGc eRrCjRrWteU4R/4Nmq6hp2uaTSt7is10ylzLK+Z8S4cK33BEbRs0T7e4hKl+/vIakqeyQUNtK8Na /h2YCUKySQMngfVnVb3I7otryCCobtqs8/fWJXLjRdY2C6vwpTsaDMafkiMyFr8NLiVEAEgE4BwC QPnWe1nedGS2ZE/S1j1JbJ6W0NsMe5Mx4iiF/EpxLaiSrYpQyD3CfIVkVbeNzT8xPbJ9iGsNXr6L efhKkXVpR4UsPKTXOPvz8iNyFToVxUxcllECQCguKCcY24PI7ckVzw42nHSILckPLdOPhcJz944q RaassyZpxrqDcpNgu1vtKXJTtkacL0sOJUoMpkMKGPAU4hKnAVAlkOEHOKwhu0TUWo3tROWux2V6 QsEQrVHEeM1tbSgbG8kpzt3HnkknzryrQlCjvk8P2/Rldhq1G61H8PRiqlPOc1OWk8bor+DpCA3b dTxmIylpj5CiFKB758+9ZZ1u6x73KlWt5bbE+AqDKUgNqC2Vn40fECRnA5GCPI10rrClzpxdZUMb QlK0E/tFdJ+4X2zo9293U+gDO8oWrg/PIqxCVSTjKEluxglrilaUIVaFzRl5DnuTSyljosehnIUh 3TKG7zpmSqLdrecMugJc2Ep8NXwrBSfhWodvOudu5OX6/SLpInuz50iI2ZLq20oJcCWwsbUpSAEq BSMDsBye5wunbgJchcGWhsrfSXiEDtyDg5PfjtXassR216lfWQREcCgg88FWFfIetU1ZThTnRm+e pdsaVtcXtvqVtBKD/wBt4WMPOcv6HfXcrzA1BCFmkGNJjulTbiUIUpOWVhXCwU9lEcjzrK3rULE2 1TXtXQo15v8AEDP0K3IbeQJW5Q94C/d1ISNqcY5T28znPVU22m5mcR8KQDux3ynHB++uOWhuVeLc 4WwoI8U5IyMFPFWba+lRwkvhw8/PBI6z4Wo6jvqzk1Vc4qL9IbksL6Nv6HShas1R081G9qzSgj6V mTmTEUzGQmQyhv4FKSkSC6rlTaVElROc4wOK4rQ3Plaz1I/qMFeoFTHFXLO0fxguLL3CPh+vn6vH pxXQ6kvCTYoLwIG588fcRWT09cLYXXp8i5B2fKAclPyH0qcdcVypSlE5UokkknkmsqrcVKlll9W+ 3syCsNItLTxOo02vLhFPMmsvdDr888mVkHxVt+CpCwFgqwoGsBrCzuPJNySXSthG9JBThO3cocY5 rIJk21xKkwJ7ReKTgeMnv5dvnXUuF2LVqkxZ6FJWULSlYGEq+Egck/bWBbqrTmnD/s23WJ2V5a1K dy8prOU8xylwdLqfptqz6b0hdklanrxCLjxV6hLauP8AaGohCbS9aZKCAC0rxAf/AAn+6uzeb/c7 na7faJspyTGt7zq4qnHFLW2laW0+GCSQEANJwkAYyr14xK1KaUQhRG5ODz3rb0z51nDCfsWD9j+b JgC53GIsExnkpfRgcIWghKv0hj76nvtr9OGbVHlX6Ep8tT3FyAlRBCVICCQDnPOVd/TzrX/sNIM3 Xd8s4PMu3BQBGQShwHt9hNWu9ou2OXjoYw643l1gMOrG3JAUgpUP979VVFk8yyCDg8GlZPVMUwtR 3CMf5D6scY4JyP1GsZQClKUB6FaM/wBWDQX9u0f8W1VjqrpoZBd9mXQDaSAVu2dIz85bNWLqiHQq kKUpVZSV76AKFg6WSrks4K1R0hSe4BQniq5e1OvGkLCkf+8lKWfn8H+dWF6fxnh0RlraUtza9G3J RjG3YkeY9SKrV7TSyvTemyFAp3r4B89iaAgFohtw7tpPIGZFvU8rHmS6+Bn7gB91Sq83OLDcUy+h ZyjPCQRg1xXTTslejdMa9iTYIttrtjEeS0Fq8dThkLSranbtPLo7qHY/LPPEdg3i3iZ7uhe7Kcut jPB++td1eGK0aklmOMfqdl/p3dOppta0ozUau5y554wl+5DdRKtlwUymOy3FKtoW+tvCUDnKjtyo gZycAnjgGr4ezxfZ0rSWnLQiQHYsSyRWkuIJCFhDKEhSQUhWDjPIB9ap7Lh2tpO56JGCA1k/iR/d WwNPaj6kaG6MydY23WVrjWe5Nm36ahiMl2U1IRMG7dvZICAyzKA3LIAKAADtAy9MuVOLgk8LuzXf G+iVLarG4nOLlN42xWH88F5nkueBsK8jafurTvtXXd+1dANSPMNRVusmLt96itSWjmU0OW3UqQru cZBwcEcgGqrsaw17coyJs7WmpDOk5ecU1d32kBSjuO1KFhKBzwlIAHYACtv6L636fX05Y0n1Fsl2 1LJZStuUp+LGkMyEpeJZ3eI4CtSUBrKlJyVJySTyb9PUqE5OLeMepFXngrVLWlTqxhv39optrjPP CK/WO2Wh+ClaIrTqRnBIJPc+td3UbUu8WqFaX7lNehxAAzGfmvKaaCU7UhCckJwDgYHA4rg6m6o0 9/D+RedLuIgWKft8OytxfCegeG0hJ8RIT4Q3r3LHhrXwfi2niuW33O3znlFlaiQnPKSO5qDuadxb VN8ZNrrk6pol5o+tWkberShGpH4XFpJ59u/Y6mq5RjW2zt2mNNjPMslqe6p8KD7mEYUnKjgcL8h3 HHpxz0WqHHanGKnJAUlWzcrOM+f2Vk5LXiNgLZD4znBAOD680mxGH22WnW0FsBPwkf3VZldKbjlY 9ccZJKjoc7aNVU5ufTb5nxbX7P0+XsY+FDiTymZJiJeK8FsuAEhPcfZ3r8ZZbVIbDYSkbhkHz5rJ 5jxg2hOEISAAEg8AVxluKH0KCCkgjsT61b81vPXHYzf9PhFRXw7l+Z92/t+5jL1a0+6qkwWWmZDY AStPwnkj0+RP56+tMvSZdvU+t0PqH1RjBHJHmB6Vmy02V+GpBKVdwTxWPgW8W5TgZSW2iAdoWSBj PkT86KupUnCXXt/wUS0uVC/hcUuINNSSyuezxjH39jo3VrUby3G4W5lKwNpKkDB+7nyroRNN3KS+ h69uokBCgFpLyjlI5xgcVKlPfxJbzYUpaPLzP56xj17U22tKo0jxMdsN8ny/lfZV2lcVtu2mkv3M S/0nTvNVW7qTmnyk3mPyxjH/AEcd0sltbtcgsRG2ShpSkKBIPCTjtzV7egdui2/otoy3W1ksxxaW JKwXFK3OvI8V1WVEn4nFrVjsM4GBgVSLQUaLqTVljg37VFrt8OTeY8Z+1Sm5CX5bCnEhSULYaUhO 8KKAVOIIIJO0YVVpX/aB0JopLuk4emNTe6WFarW34aGFt7Y6iyNqlyN6h8HBVyRyeam9PjKhDdWm uenJy3xdXoatcxpabby+BPdiOPrhdvmZX202t/s5akWpfwoXEVjnke9ND94qnemltq0zBKAVENAf m486sNqT2j9M6lXb4MzSlymWD3xS7tCmW+I8JTCWnQhG1bqgSH/AX5f6M8+R1z1Af6Xaoixm+kkO Ra9USZq3HrG94qD4QDqnEoBKoyBwlwBKwAkEDB+Cvb+kruKjTksrkt+E9QqeHq7q3lGShUW3OOnK eecehEkKS66gqawTn6wFcU9z3SMFMI2kqCfhSO2DX5arlBuTaXYhURzjKSMd/wC418agadegtpYO 1XiA98cYNawotVFGSx8zutStGdnKvQluyspx79OjNQXB1x+U84tSiVOKJz6k1aTQUhwdAbEsE7kq xnjsHFVo3qem0Is+l021iG2+bc0qWWG0pUtwsMklZAyTkqPPmTW6ulynZuiNGaeZGVypDLZQR33u K4+/I/PW9xWFhHyjVm6k3J9WTD2lUl3TegHvEIJ07HJHrwiqcXtaV3N5SPqnbj9EVcz2tmF29/SV qUUoXEsLbakk9sHbx+iapS8oKcKgcg16Wz4pSlAKuL7EbTAYtKltJW4ZStp8xwqqdVcf2Jlj3azq A+ESlJUceZCgP3UBsb22BMTaLIW3SI76nWSjPde3ufuNeer6FNvuNr+slRB+0GvRT2wYjsxrTrag SyJBKfTd5/q21QnqHbvozVEmP4Rb3LWvB/tqH7qAwkJ4x5CXUqKSM8j7K2rptce5WWMVpQ4rwhnc jnIOK1JUz6bXB03RuCp1ZR4SwE+XfNRWrUHOjvj1XJv39P8AVY2uoq2q8xqcY920T5yFFdQhL0dt YQMJCk5A/wCsV00+7uLUiOhACcZwnAruyGnXDhLm1P8AaIP6q4pKokFsKUkICjjIGSftrVoSfTOW d4uKMU3LaoxXVvq/Tn/kxl0hsSmUodaSvarI3VbDpxoi9zOnGmL3754jsq0xZKnFukrUVNJVkknk nPeqXajvMmQ2hNsLzJSslajtAUPLHJqzmkvaj09ZmkRp9mvYtzDKWo8eJEjpSyhIwlKU+KAEgAAA dgK2fTISpQe99exwzxxcUr+4irWDe3OZJcPKWOfbksroozmLaxCnKLrjeUbirOB5VoD2qOl+utYd U4d301ps3OA3aGI7jolR29riX31FOHHEn6q0nIGOftrb3TPqno7X1oRctPXBw7nPCcZeZU0tp3al RbUDwSN4BKSpOexNTpLqVJ7jJHcVJ1acasdr6GkWN7Usa6r08bl6nmZbbtaJiVqa2kAgHLOP3V2J lxtLaRuWhIPl4R5/VV3b70K6R3aYmVI0TCjOpbDey2uuwmyASQShhaElXJ+IjOMDOAKxFz6SdD7D bJcCTo1DjM7YHC5KkPOjYrI8NxTpW1z32KTuHCsjiop6RFyypPBv1L+otWNDbKjFy9ccfuU9tzv0 VOblwQURFuJ9/joxsks5wtCmz8C/gKwN3bdxjvUhv+reiz9iubMjQd2j34W55m3yYgRHZafKCWnF oafSlWFqySUqJAxggAV1WunGv7U1cVy4rFyt9tjLlSJjEtnw/BQgrcUEuFLitozxszwcA8Zhl5Xa p1kky4zbZc2EBfhkHIP2V5SqVraXl1FlepXfWmna3Td7ZzVOollxyk+Fl8Lv7nxo1+Y7Fa3yFqG9 Wdys5qVISy4NshKHCfJSc1GrHZL9bdIM6sVH8Sx5K3H23EZby6WtpSSFE7gOwIwoc98ZC2ams8go SVObyrAy2fX5VhX9tVVRyjHj2Nm8Ja5ZStIUKtZKS4xJ+y9z7utnYaC5kFpuO/tAStBKTyrnt8qx bd9VbSGbsHXkoJG4JSrJPI5Jz2qSXWR/FEiM0pwrAKcBPbv/ACjWFasqripRlsIKjlWFq5HPHb5V YoVIuH+/yv1JXVrOrTuV/pfwza9Pgfzxw30+hzWx6brSWxYdPKVHfkL4dfwhtKU88lOVeQHApcr1 DsaHrRMeLl0t7iorqmkEguNr2LKVEDg4PJxkV11W296edTddPPCHJYV8C0OZwDweFcHv5isNEabu EqbPva0vTHHlOPKVn4nFKUVHCRjk1nqlZ1KKcc4T7dTUpX3iO11KUK7ipThhOSaiksvK9ySRo1v1 BaIzakJc8IqVtXuTjJI8q6du07drXIW7ARazv4IksIkDHlgOIUB91ZS1P2iG3tjktjGCAFY71mJU hLDaVqCiCccVGO7rUJtU84fTJu60DTdUt41Lza6kUlJwfosLtnsRK3WNcW4uy7oljxpDoKRHyhIW Tk4SgBKRnsAAB5AVlNQtIZsy5PhR1uxiXGy60lwZSkkAhQIUM+RBB8812DJjzFbY+XHUnIBH1D68 +dcV+QU2RaJRyVZKgCe2098V669SpWjKb5yUw0qztdOrULaKcNr56/d93n7EK1VKgzLNaZAlWp+7 qW6ZQgQfdUtM7Wi0hSQ22grCvGypIVnjKiAnGFt7CpEkgpSpKU5VnyGaTQ0ZZDCcITwfzmpJ0206 3fE3aU+va1CYBx4m0lStxGPX6hrbovdyfPFen5WYZyTH2OZ0iD1siKjuqbU5DkJJB7jZn91egWoY irv0juMNwjeq3upJP85Gfs8015pdDpv0Z1QtzypComEvoKycFP4pfB9O1eouxplt6At7Maa2oR1K PByCVc+Wd2RVZjHl11vtarbryWnanaoJyR/OGUn/AOmoNVjfaw02lq7XuU02MxZ2dwVnKSpX5/rV XKgFKUoC/MVa2/Y50utpTyXExbaUllexwH3hvBSodj6HyNWfqr8ZWz2ONMLKgnbFtx3Hy/jDfNWg qzTfxNfL+SufQUpSrxQaC9k4XDUfS+7Qr/azbv4422hvx0u7kJbbUFZT2yRjHyqo/Xh9x7Ttsjur yuLJWnB79sc/mFXG9kLerSN2WoHaZaAMjGfxY/vqovtVxkwNbXaCznwG7nJCAeSAFjHP30B2emKG Lh0JvzcmQXkxIc1QjqwpKVJRvScHzBIUD5HBqK6PlpOkypBwpAdOMeYyai2gtZXTR9zEuAGnEEEO NOJGFA4zz3H1RUjOrf4Q64VOcUlHv6xvaSk4SQ0GwM4zztB++o7U6PmUcrtybn4G1JWWpqMnhVFt +TbWGdWB9KXucrxpLjcVCy3tSRyAe3GD2NZK8QbsbOxbmrvcHrdDeLrEBclamGlK3blJQpW1JJWo 5Az8R9TWbLTMVlYSraPELh4J8q+C+1kqUfhUkevyNQH42puzTWF6HWn4ZtHR23st9Trub5T7Y9Ej 9tAUIUULGFJbCVc+YGK5fgDjpwn5/D8xXGiQwltKkDkE+vzrkQsrWdh5UkY4+ysKWdzZs1FwVKNN POMfsYaHp6I5IVLnR2n1OdkqTwnAx6/ZWUt8ZhuQ6G4bTIHAKQORmviJ7y40FPHIHlx+6u+AUuE7 O+fOq61acsqTyY2nafa0lGdKnt5y20stvq28Z6+5x8bCN2eR8q+ZJ2Nhwp+FKck5r8W+GwfEc284 Hw1wwbBfNU2KXdYcfZp6JIW1crnvQfdG0JCnV+ESFr2oUFYSMnsOartrWpXniKLGta5aaVbudaXO OF6tfdmNjKeuEpx0PENNubQMfPn9WKy8plO76+044wmsTCsPhN3R7RN3Tquz2yKmXcZxYEH3fPiZ T4bp3L+FrOU574xnvxqv8ZyOp1J7Z9fIfZWXdWVWnNJLg13QPE+n3ltKVSWJvlpt+rOW63B+1qSp 90uZGQe2OceWayjT6XlhSXcpXwBt+6ovZX9O6mVKGpdbfwYQwlsMD6Lcme8ElW76mNm3Ce/fd8jU kZ0vebVZzqK1n6c0WhbYZvfwRvE3OBtX8XUS6MOqKORzt3djV6ppdRUVPHxLqR9l46tJak7dyflS xtbz1++cPPc7QSlJU2NuD5beO1cJYZIUpTDKiD5tjkcURMZW6kpTnd2OT9lc+9shXw/r+VQ3xROk J0ay4aZ1XYUUvsvssojymylxh9obHGlg5SpKk4IIPIIPeuTwn3I6zMdXJfW4tx155W9bqlLJKlEk kkk5JJJJrhkMrVJZebX8IKRtx8896+blIU3EeIR8Qx5/0hV1OckoKWUR8qdtRnUuJU0pJNZSXK69 vkdhmK02sYbbCSOQED7a+WoEZmUmfEKocwKVtkxlKadTkFJwpBBGQSDzyCajlqvi37t7q8jCU7uc +n3VKEKb92bd/kbif21XVhWt5LnD9izY3Gn6vRklBSim01JfLPD+ZFollk2++BiNOebiK7JSTgfD n+dnuazV3j3a6t+4adaVIlx0qeeSHUt7WkcKOVkA8qTxnNZDY2tbMknlG7H38VE4upE2dV6ukSW2 ZLrTkBLSmzyHsqKwf6JaSMY/leXnnWSd5Xi6n9vX/s1XxPOHh3SqtO0ePNfw8vhfD+XnhLL4RBrx PcuM9yQsBtBV+LaT9VtPYJSPIAAD7qt/7NtqhXLWmk3HtyYzB8ZpkcJC22VKQf0gDVNKuX7Iq/pb WWn8KUG2Lc68lSR2cAUkfqV2raTgx+e2lOU71RQyXApuPbEIGD9QkrJH5zVNatd7Z2nJMHqa26Jy izNjqdfKmh+NBUcp78YBIyPWqqssPPBRaaW4E8nanOKA46UpQCrdew7NT4EGOrCttx24zyCc4/aK qLVkvYpmoj3xhBUAv6Vbxk+qcUBZ32tISndI2q4tl3dCmKWQg4G3wyo5+XwiqMe0Chj+F7L7KAkO sqJIPf8AGKP769J+qFsYuulJMJ9pbiXm3GjtVggKbUCa8zOryZQmQ0y2yhxguMdsZCSP86AgdZi1 OuWu9MOsLIV4SVZ/tNgn9tYesxJTb3LcxObuW6f8DS4fgKGxCUbd+/sfqjj5/KqKkVKLi+5k2daV CvCrB4cWmvozZ1lnGbGaWpZK9g3fbiue4BLiU7mQ8nPAJx99R21yx/BeMY3+mDYCvng48+PWspbJ 7kuKCy146+6vi2+Z9RWmVaDhNyS4TwfS1hqsLi3p0aksylFSzjOfbHOWu/BHG0OvSnG1Qm20gkj6 p865dQR47LLUdtpPiuEo+/t+01L0OfCFPJ2HGDznB9KO+6pKH1DtyDzx5/uq6r1709vT0MF+GIO3 nDzU3Lu0uFn0IvBe1jprTKm7LqGfbIxfMwohu+CveUYJ8RCgrGABjOOAcZrN9KeuPUPT2srWbnru 7vWN64RfpUT3DNxGS4PE2+IFqR8BVnw8E8dyBjluSkTbbIZbP10KSDjuSCP31B2bMptS0KHxhZwf T9dStlqMpRfm9TQfE3g2jQrU1Yr4WuX15/7PSbpnrbTPUGzyL3pW7G5w2XzGccLDjW1xKUqKcOJS ey0nOMc1FurfUDpbpe7MWXXF+NtuDkdMppsQ33stKWpIVltCh3QoYznj7KpPpzUmu9NaUuWkbFfv dbFdQ6J0X3RlfjeK2G3BvUCtOUJA+EjHcYNdm0WuM1HcSW+Crk7j/fWTcapSpRTXJB6P4FvtQqyh Uflpd2s5/UuFpaFY9QWqFqXTLv0zpy6eKwS9GKWn2tymnEKbcAJSSlaSFJwfmKzUvoB0dvRVMm6N jx3ZCEl1qJMkRWRhITw004ltPYZwkZOSckk1Tbphd7VoDrNpvWE9nNviSFNyjuV+KadbLK3eAoq2 Bwr2gZVtwMZzVxLt1Kt9403HvWjpSZ1rnAhmSWFo3ALWhR2rSFDCkEcjyrLt68LmCmkQOr6Zc6Nd St5y+q4yiFdaenmnumvSy43Cx2Vu4WCAWgLQ+8FtqLslCQCt3eThbm7kHkeXlW2437Rl1sNyFw0L B0061Beet8qK8CpcsJIbbIZbTweTlXw/Dz5Vnev93iXDqTbo1wsSZF2diMqYuXvK0FlsPOEp8IfC rsvk8/F8hUbu1vEq3uMH+Wkj7CQR61hX1/8Ah6sYY4fU2Xwt4SWsWNa5UmpxeI49cJnX0nZtcXOB GETT3ve9hDzSly2UFTZSMKAUf+s1+3q5u6avb1j1DATAukdKC80HA5t3oStPxIBScpUk8Hzx3rsW 269QbczGbg6l8FERhMeMn3FhW1tIACeRzgDueay2v+q2qp3R+Xoe9w0zVyi2h65+I22VbXw8n8Uh AAwEhPB8s/Ksd0dPuHti+WTC1Hxho9Pza1PMIruk/wBuTF2+4Rbo3hCQtCk7sEHnB+YrXeqmnouo ZkeMpSEOL3JSlWB2Cv31NNERAzZIjufiU0ePtOawev4qxdWZCON27n/wpFYVjKFK7lTj05/Q2XxV TuL/AECjd1lipmLePSSw1+pjYLsdShFflOtOISVKXuWd3PAwPtrLs3d5hakOyzKQOAlaPq4+ZBqM 3V7wVqQ0vCFqBUnHmBxz95rHvyy5jPcedSrtFWSb6M0KHiGpp0pQjxKL6ptZXbPOH9V+pPdQ3Zi2 wt8JtLT7mUpUkc9u/b1xUYVfJ0hhaZs113IJCTwDx8vvrpQ410vqlRYUdUlxlHiqSnAITkJz5Z5U K7I0zdmfjmwnWkt8qSodwPLvVy3sIU44ksv1MLV/F1zd13Km3GGMbU8L3ylwzm0/pfUGptzlityZ CS74RJkNo+Pvj41D1HParWap0jYWFIY0/YIkBDqSH0wWA3vA7FWO+Nyqrjoe+X5q7IjQmEobaKW0 At5wEgjue/at0/wg1q4x4ybelZSCk7Gx25NSCSRqM6kpvLNJ9VLING6qjvW7e0twLWD5AlShx9xN X20frIa36L23UkF4Imw4zTjgZUVEnJQQeARnaT+eqOdbbwL3GgLcQW5LLikBoA8Jxz3HrxVrPYb0 9Mt3Sue7fI7zMGXHbW3vTtBR4j6lEY54yK9KDW/tAsGXpy/XCUVFx6OXDntu/vyKqRVo/az1HboS bvZbcpClPyfCAUSSGytSsjI/o/rqrlAKUpQF9Up3exjpxO3dmHbxj1/Ht1aOqvMBs+xtpkPEhsxL dvIPIHjt5q0NWqf5n9C5PoKUpV0tmmfZQQW9I3BskY8ZpQA+bY/uqsvtd2sru13lJAK03WWVYHOC 5keXoKt17PkBEPRAcSEgvKQTgf8A5SP76rx7Zlul2SXeJ6m0e7TnC6yQRkKCUA8fas0BTVIO4AAk 5xiu6puVbJrK3EONL4cCeUnAJ/uNcUWSG7m1McTvCXg4pI88KyRW29X6dt2sbSrWcO5LiQ4cRaFs iLuUrw1LUSMqTyc4+7vXjWVgrhNwkpLqjB2m7tXS3hTqHUFtPhqJwskgDny9a7ttt7alpfVIW63y Q2tvgeQ8zUJ08GXnpURDjnhrQvYtSACQSACU54P3mpjZtOyIq2ZDdwJSpsDYElPcfbWt3lKFBySl j2wdr8OX91qsKVWdHzMdZbksc9WuMmZREa8UhLbeCkYG0cdq7SR4bKcADaccfeKx1wurNqhpdkJd d8Ne07QMnuPM1jWdW259a0pjSRkZ5Sn1+2oxUK1Vbkso3epq2m2NTyqlRRm+xnZEpDCnEOb1duwz ++sFc9XQIrTRVFkOE8dkj99ZDTBe6hasgaNsTDLNwuHieG7NcKGk+G2XTkoSo8pQR274q9ejLL/B nSFm0/7172LZAYheN4ezxPDbSjftyduducZOM96lbHSt63Vlg0LxR4+drU8nTpqXq8dOc45XJpTp n0Bh6a1A9d9U3C36jEmIptVtkW4OMR3lLQsqQpxR3bdqkg7EkhWeO1dj2nNQsaL6SPQYlpaXGuCD Z2WWVhlEVDjDoSoAJIISEABAxx5jFb0koQUjGEqPmRVVPa61baLnItGhI7T6pbdwYuDjxbHhFA8Z op5O7dk9sYx51P4jShx0RyNOtf3CUnmUn+5XG+XCLMTbpKbTDhBiCzGKI6APHU2kbnV8D41k5J5+ +u3Eh2hTbjr90uLbaU5SEQkKJVzuBBdGBjbzznJ4GATKI1ncbW6WLg4w0UhLraEYS4nn4SAeRgkY PqayQYZLOMADJ42DngVCVdVhxhZOo2HgK5+JVJ7EunR59W+fXsQTWFglWu4riymWWHWkAlKAdqhu IBGQPMHyrcHseawjJvUzp5fDPuMa8oDcGK7h2IyhDb7j6VIUrCQsHkJSdx71CDZYbLqXENsg/JkC tw+yprewWy+xem69OJjXqXv23SKlBEval58+McJUNqTtT9fP9EVm2V5CtJxiav4n8O3Om0Y162G2 8Nr16p8dM+nsSvqb7PUPUmpI190tcLZpxMSEGkWxi3Bth95KlqC1rbI27tyUk7FEBOee1aO6j2S9 6A1rB05f3IrkiVEEpJgyFuNhBU4kA70IOctny9OavX4XggHcDz6c1xXi22+625623S2xLhAd2+LH lNJcbXghQyhQIOCAR8wDV+4s6VfmS5InR/El9pTUaUvgym1xzj3xlcFEmpaXFNtpC85Bya530FSH ATkHyP213OsFkT086my7TMjREN3BxdxgIgj4Gozjy0toIITtI2H4RkDjBNRWVqS3tbwpiQTu8kJ9 ftrWKtjVp1NsUd10/wAU2V1Z+dVqJZ7enHToZCRBRIQpn4RwFAlGfSsGpn6NmKaffW42SQEpHAz8 Q4JrKMz2p8YqipW2pbYIKsAjBHpWC1qxIZtXjLWlXxo5CjnkVdtVJz8uT6mDrlWhG2d7Qju2rOU3 jHPB103mIjdiIotDGE4GPzVDXlhbq1hOAVE4ruPvlcZDfOeec/OtidDwy8NRzZqQ6+2htwvLG5eD 4hV8XfkgE+uB6VsVtQjTy13OMa1qlW92Rk01H2x1x+xqwggkEYNXJ9huTHN/tUZKSXRBfO4Eds1T uWtDr7j6SQXHFK247AmrfewVGZamwZzrafEdTJZSvz8uPs4NZZAGX9vhlTWpdNSEoVtfhPoJA7lK k9/0hVYGI6bHbUrQUr3qPy/P+arm+3Pby/pGzzdiVe5qeO48FIU7HHH5qqH1Fh7rM2pn4A2tWcef wk0BrKlKUAre3sfSPD15bmdud1zZP6jWia3p7HTYe6mWlBSk5uLff+yaA9GLyWvo54PHCVIUM8cc GvMvrvBbjz5vhyFPpTOccCj/ACcrUMV6IdXJb8PSnisZ3F7BwQOPDWf3VVD2numsTS8ezyEMx1M3 MLLwTnhQ2qGQT8/KgKjVJLbBdXpZqQkjaueUAHOM+GT+6sDOjOQ5j0V3HiNLKFY9Qaydhuz8UMxN yywl5T20HHxFBTVFXOx46mVYqDuaam8LKz8sk40chUZwsLIypKT8Pb6pP76k+wBISn4R8hWCtkhx +XDd24bU0Dyvn6npj1rP1pF226m59z6i8O04U7PyoPKi2l24eH/JxltWSd33eldWXCEhksqCAMHG Rn5Vzy5TcVKS4lRCjgba6wukdxSmg27ntyBj9tWoKp+aJnXNS05pVXy+x148AwmjsWkp3dgMf9dq 6k25sRSrxGlqIBPGOMV2YEa4rvcWFGdS4u5TER2EuOqQlCnFYTng4A+X5qlVx6PTlKlx75OtybjJ HhxkNBbyWwCTu3kJIJKsEAfyRyc8S9nZyuJZl09TnniLxLQ0eiqdGOKnaL9PXOGjWjmtrejBRBkH 5HaM11X9bF9xCWorzQ5yQ9/lUd1RZJmnry7apxbLzYSolByCFAEftrGVMR0u2jzj9TnFXx3rNRY8 xJe0V/wbanpE61O7fhJQSMjPb/0rj6DPQbZ7QWlk3NLrjEiWIf4lCSQuQ2plskKIG0LcST54BwCc CoDZr27BW2D4q0JPIDhGe/8AfUpluMXnTD8xMdCFgH4lgFXwnPf7KxKUJ2M8PmLZsN/dW3iq2cqb 21oRba5eUl9F1ZtP2nJNvR7RbFpgtOhVpgMxZClpSkKcUlbwKcd07HkDnByFcYwTE7743uC3WVbV NAuY3EZwD5isboZDP8HmShtIWlSwTtAzyf3GsjeJSIludcKCrckgY+w/3VGX1bzrrhdOP1N48L6a tO0FqpUyprfnGMZiuPfGDp6YvqbsFxCytt1kEKUVbgrbgE/rrJyGUSGlMLAIz5pyO9Q+06buS5hv EWa20XsrCApSSUq5wSB8/wBVSO8ofGmzuXtkNhAK0qJyQQCc9+atXFKkqyVKXX9GZmj399U0+cr+ k8xTafGJxXTp0f6mSYSmOwlAHwpGAAMVr/Wl0Q/cnGQ2tJZUU5z37f3V9zry8mzLbV4pUDs3eIe4 V3/VXW1pItJtGn4MBtC5bMVT82T4AbW6t4hwJUe69gO0EnsOMdqk9O09xm6kzSPGXi+Ne2p2dssJ 4b+XPHQ+dA6Vc1jfl2tE33Tw2C+pwteJwFJT2yP53r5VZjS2nrbpiI4mBCZQuUQVqbZShSgkcZwO e5/Oa137N1jkQrZLv6xFV9JBLcZQ5dbQha0uAkjgKIQcAnO0Z7Ct1KZ2YWtH1Rip+KwjklSW6TZj GG0XFbj7iilDWVjPPqK0N1YvyJWtFWCLlLaXkB11KueMkjAP2d63T1GuqLLoC7XBlpSCWilG3APx GtOezhp5erur8KRLS0+iU+tS23eeCRz+vFelBv7pRp22saEtgYXFkPSWQ4pRCfETkduATnvUsccg wpbhcZbbBCeAE+Wfs9a4uo+lrBo6+WeHaoKY0t1nxnVM5AUSojzPyUKgvUh6ayUqQ4rlJBO75UBL 2dEaY1re0l1+LCfRuUhTjbakrzkkHz86kureojukNC3W2SoAZ9zjqZaWhZSk4z2zjGRjtVftDXSZ 9IqU48sDB25OfKvj2nL1OOhGors1xYfeSkj1GAcHigNCdSdQPan1pcrw9keK6QgFZVhIOByajlKU ApSlAXzcW417FmnnGW/FcRBt6kI/nEPt4H31aWqsuuKZ9ivT7qASpEGAoAd8h5urTVah1ZXLoKUp V0oIr0tjiJpCIxlO/wAJtagnsMtpH7q0l7c7jE/p1KQEArghXxY/nFv+6twdHJpmaRQ+6tOUBtsn PbDafP76rj7Td5kXDp3qQLWla0lCVHv/AO8SP2CgKWVNOl2qnLNc0W2VIKLZLdAdBGQgkEBXy5Kc /ZUdvVuTBZt7iCoiTEQ8rPkolQx+qscCQQQSCOQRQG4NUxLcox72xND85ie1bS2lxKm0t/Go5AGd wIHn2PavxTN2U2otvR0jJUj4lDgnj9RqC2i4zXW2Yrn4xt64IluOYO8uHg89sc+lSKDqN5F7VCdZ BZBUgKUs+Q4+XlUDqdOVSpugs4R1jwPeULS1dK6k4qckljPdexnilsRdtwkRiSAFbl8bvvqEarYg SHXWbZHU5I8QEqaQkpIxzgp5rYHgR5cZC1sNKC8LwUhVcKYUONvU1BjhY53eEMnJ/wA6ira6VGW7 nPp2Oga5oUtToKlmKhj82G5Yx29yz/TPrb0onw5FotzTWiYdu2eDFuiI1vaX4hUpXhJS4QcEEq4H KwfOp5ZdV6VvZfVZdS2e5hkp8Uwpzb3h7s7d2wnGcHGe+D6VQ5uyszb05KnIQsKxhotDaPhx559A a+bmbjYYedNXi4WcvOAP/R76mfFwDt3bCM4ycZ7ZPrU9S1WnKSg1yzk194Bu6FCpc05LbFvCfVpP Gf5L26p1bpqxw2Tc9Q2W2qfJ8ATZjbHi4xu271DOMjOO2R61SW/BLnUzWSGm2kKRqGccgc4Dx4+y sVdJ141VepL2pZc2anxXHY7Tzi1NR96sqS0lZVsT9UYHkkd8V0RHlRtUyHXp8tZlLU46+8slTjil 5KlK/lKPck8mqLy5p1oOmnz1Mjw3ot3ptxTvKkcxb2td0SrDwdSgNI2qA3EJNdVUttLyWAnKjg8D 1OP3V2nHEsNMqXIBG0clWM4qL2e5ouE5xz8W1sDYHxZzyqoKjSc4yljhHV9S1CFrVpUd2JTb49ks szd0nhhaEiI66SM/A3n99d/p9JubnWLSAhxJTb5vEY7mWlBwNBxHjE452eHv3eW3dnjNcD4HwlO1 ShnnGcVH2pN3GsEPRbrNt8iNktyIbq2nEbm+cKByMjI+wmsmxcIzUmunJBeKoXFW2lQhLLqNRSws cruz0aWkKOd6cq5B3DtX6gbjhJSrJ52nOaoRbeoGvbDqSNMjatv9xLKVHwbhcHn46spUDuRuGcA5 HPBwa7s/W2udQ3ORdXdaagtTj6kj3a2XF6PHbwhKfgb3nGcZPPJJPnU9LUaMY7mcopeDtRq13Rgl ld88Z9C32ob90quqHrRqm96JuKYb5LkO4yozvgPIyk5QsnatOVDtkZIqj2oFaUT1K1Cm3MxPcvpa aIxZS17uGfGXs8PHG3btxjjGMV+2qMFTLgqf4s1995xbsmSd7jylEZUpRGVKJyST3JNcibXBYlqW ITSkYzy0nzx549ajrrUYVU6eH9Dc9C8HV7GVO73Rb6NNZxz/ANdTtRfdHikRXEAKyB4ah2H2fZUb 6gQ324q3y+6tkuISEbyf5Pp28qkiWY8BsTGw22gc7SAAM/P76ieo7g7N94AIWgLylsEqHBxnFYVk pOupR6L1Nn8TzpR0uVCusVJLjHTo+fl14Otq3TiNMSGbe6XnZzQPvT4GYrpOFJ8BRSFHCVJCsjhW fKvq+6iXBtrdlsl+ucthcdDU1Tzm5vckKSUsZAIaIUeCAcbfSsDcbrOmIbMy5SZ61JyfHcUstKzj AKj5gJOR8h5Vja2pLls4DUmnCMV2yKtt7IVtkLtOm5FvU6iY/OeQVhXwpQCskn0+r+uqk1bP2Urt JsugIdxDiGlNTHUxlODjcpQTgfpKqosm5/avZlSNCmK+VOoEJwFY+r4iVIz+tI8qrFa4ka96dkMO o3OeGsg4xg4IH7asl1lnTrl0zW/NV4ivCeWsgYASsNLH3fEarX0okplP3iIVpAQ0laTjOwKBB/WM 0BoSlKUArdnsezExeq9o3LSgGe1yfmlYrSdbG6AzFwtbxJDatq25LS0n0xuNAel3UhiM/paQZWfD aCnOD6IVWifaAsWodcadgSQwZVvbQnwloIACiQR28ymrCym42otOPNjBalMuNpUOcEhScjP31qHo jfVXSTc+n1/IWYwDkQrGFlLZCCBkY7Jz+egKPa+sClW5ckQyi5RF7JYA5UB8OT642itfsrLMhDhQ CULBKVJyDg9iD3+yrR9W9MrtWt7lGfiKRGkSFtHIG1XA+35mtL6+0S7AafusGO8Y3CztTlAHOfzU BwaW1M3CjOiQjCVq+DYkBI5JIAyMYyOBWZh3y6NS3YFwguRJTKQVsyWlIcTnkZSTkcEEfI1r2zXJ VtlNumLGlNoXuLUhoLSfXgjjP7hW9dunOqEVq6NXiFY7y0tSZKkM71PoOAN4yhSiAlO05OBkY54j K+mUqm6SXLN40rxvfWio0JTxTgmuOuDGRluulQebASOU8H99JLT+B7sGQTncVg/qxWAs2qC487Eu TLUN5gbVpcc2KChwoEK5BB8qyb96ZbbStAbWCMkh0YFazUtq1Oe1xO42mt6deWqqxrZXrznr8jqT rLMkOty/pGQw+ysONlh4pCCnkKHGQQfMVN+gk1xc/VDupLhLnmGI6mFy3lulPL2SCo8Z2p/NWtTq yS/PVHbYZDaFHkKJyAcVJ9BXNqLab7MQhTkqY4WlN7jsCUAlOAB3y4rOT5Dt5z2mKvCe2p0wcn8b 1dJubfz7PLnuw288/c111TuTN21lJmMLK0FttOceiRUacZdbShTjakpWMpJHcUkOOOvKW79c965p kxclmO0pCUhlG0Y8+391TZy46tSHSd4kwH2IygFRHHfiCskYVx64xWFhRnpchLEdlx5wgkIQMqOB nArYKZMGy6CTDc00x9KSGlIK5bOX2itSkhScpBBAAUPt++rValGrBxkZ2m39WwuI16Tw1/mPkZqF HivvNz2HC3nOWmyAhXccgdziue5zWoaNy0go9MDPnx+qoJpu6PN3W2WeVJXbmnXkNyZMhRIaStfL m34cJCSDgnnBOeeJnedNtSdYy7QNWxTYGkI931CtIREkrKUkoSVObNwKljAWT+LVx3xr70mq6nLy jr0P6hWFOzflxcaj5fHGe5jbjqaI0tkpcU2lIBUgEA4wRjGfXH5q68rVUGfEMQsvlCsZUUp8jnvu +VZBnpxYbr1Xs+jIOtmZcWfDU69dGEokoacSl1RSEIX5+GON2Rvzz5xLqDa/4J6pn6ZakJliEtKB KDXhKcBSFA7cnHf1rNWlUopeqNZqeP76pKak0oyXTGc9se2TF3l9DhWhpaxh04STwBzUq6U9OJ+s ZKpcpL0aztHDjwyhTpIWB4SikpVhSRu9AfWtsdA+ilvfsdu6gauX70iVGdXEskmMEpeUoqQ24sqJ KkFOXB8Kc/Cd2O+37ZZrda43uUG3xoUfcViPHQENoyckADipWnDZHBod3cu4quozqWm1txowUWlB RJyVelc6VNPKUGlJWlHCvMZroa21LFs9tkLDzKFJSQn4/PYSK4dEeP8AQiHZZV40ja4SruMpB/bm qzFIZ7SQLXS99LQ7vtg49M81lP8A2fNgiS7m5eXUtrdipkbMnkHLQHGP6VTDWWim9WaBv/vCVFiD BcljaB/IST6/KsN7Dku3WW63KE6tuMnMlGVrxwFtYP5k96A3h140Q7fIrepbapwXC2ME7EuYDiAo K+rg5IG/zFabl2Z7VcMOw0l0oBSUjgg+WRirXMvQ7hGX4LrEphYKVbFBaSD3BxxWg9XRHtGawns2 RtLcAJbUEKxgHYCrn/rvQGlTDjaXkb7u82yokpG5X3egrXftAans92tkOFa5zclQf3L284ASP312 /aU1g1PnRotteZO5S1uLaVyPiPFaQcWtw5WtSj6k5oD5pSlAKUpQF9mxn2NNNDOMxLdz/wDPbq0V VWmLDfsSWNanfCSm3QSV/wA38cjn7qtTVuHVlchSlKuFBrnpbEMXQ7sfCm0EtrJxkklKPX+z+uq9 e1LaHtPQtRwR/GIdxQnY4EkFtYCFDPlglRqzVqbNt0Mhbnd4NKHngFKT+6tadc4kvVnTbU78hqOy WIxcRsHxJWhxAAJPrtPagKMX+wOuaJgXtDwWWGksutgHKQFqGf1p7+tQqt0X2zzYtj8LdiJPgoQV IV/7wYzkZ55b/XWt7DZFOaut9tcWk+I8jJ+/P7qA6FqeXGewpIBB5CuD3FTCDEj3BpPiPY3IC8II zmtn9SunabnbW0xlNxnGwXAQkfEQDgEgfOtGy2bjp6epmUwgFtRQDuBz8+DWDdW0qnMHhm0aFrdK zfl3Ed0CbW+5x4kwWxZQlCE7QtTgB7ZrPMuMu7ShxKtw4woGtTxp6XbkJz+EhJ+IAHttKc+fy/PU ojatjMMNBOxezgJwoHtjvioW702aw4LL7/M6f4e8bWs1ONxNRjFvbnrt4x/nLJghCAtLqckn51xu Nh3ejkYVn9tQNzVrgfQhiO04lZxySM+n66yo1AUr8ZiKHvE5x4m3GefMVjy06vDDaJih4y0q53Rj Lp14b6+mFz9DN3tlchiO4jIUnI4TkYIH91fNzadnwRuSpKk/jAAknJAPH66wo1REjgNrZKSRzlZO CP8Aw/OpNGdaeQFNklDiM5+2rc4VaCjuXToZdrdWOqTqqlUTcktyXy49DqJZL1qaZdCkHYUdsH6o FQK0wpUW4JQ2y6QpSCSps8cmp5OcaYjrSpf889j6CoqXnkODAQSceX+dZllOSjJLozWvE9vQlXoS k/ih3XXoiVNkpbddUknhI448/wDOuuttEWPhKHD4wKfiP3ccfOv203CM4w6A6OSMfCa78qRGDba3 ndoRkkhJrCe6EtrRtFNUbigqsai6e3GXzz8jr2yMGoK+F7lKJ58uAP3V3YqClLQ5/N8zUVv2p24s lbMRlL+5IKVKJTnPHbFdVnUchUNbr8ZtGEqwkE8jHr9tX3ZV6kdzXUi4+J9LtKv4eMsuCw8Zxw/X GCdAZSsKUBlw107jNhRml5ksqcGElvxQFeVa9eviJakb0pb2uAkcnj/o0fvbYaLSGvPhRUTnn0xV 2OlTTWSPr+P7SUJKmkvRtt/pjj6k1gMPajjXEzc2W1Ro5eRcX2ytl1SHEI8MKJQnJJPmeU4x6QG/ SI0Sa5EgPiWhBwqQQAlw5JykAnA7Duex+wZS7X24L0VHtYSEw96lEFwk/E5v7dsZqLRmXJcpDLYy tZArYKFrTpRUYo5Dqmu3l/XlVq1MvouEuPojgr9NSu/aPVZrMubKklTn8gJSMdwOfz1E6ySEOVlo LaeWVEeGgKHHc7gMfr/VVn/ZrtUvVVk05plmOtuA1NEiTJ2qPIXuIBAxn4h3IrQmltJzLpa5F0cA RCa28hQ3LJUU4A+0Hv6VaPoZepmlbPpizxIpLC1tyZD/AImNu74lDAIz2AoDavWtDNq0vJsaFnYz B8BKjj4tqEYJz8sVSrpDdG06vlx30JxIYSgELxzvSB/9Rq2XXK+s3XTd0nstqWW0hKwrjsUc/m/Z VI9LyPctWMuncMLR9U/0kmgI7SlKAVLOlsgx9UxVjt4yM/rqJ1ltMOlq5srGeHEnINAeqnSGYJmi Y5H/ALtxxHOP5xP76xS9E2/+Hz8tbjqHZDKnWlpPCVFaioYPB4NRj2WL03K0zLiFS1FL25JOfMkV sbqBFccs4kshW9lQzsUEq2kgcH7cfroDWPWrSiJsN9uTJUqQl1LzKwnG7aBnj7CO1aLWwpFyXZJR Co61FpzI2naR61a7VBb1F07RMj8ulnIUofEDjCuT8x+qq2artrka6PZcJcQBuye49f8Ar0oDV/Uz oxGCFXexzXMOtlwtqSVp3A8jI7f+laSkxrjY5w3pUy6MFKyg4V2PG4c+VWoeMoRgwl1aUvJ+A7uN 3zHzOPz1r3VWn2L499GXPxor7a0ulDKknywMHBHn2oDX9w145fdPt2PUUVbsZp9MhDkRaW17koKM HIIIwo1x2i7xn0NQUrUEJQEqU4gIxjAGPiOe59KyF/6X3KG0X7fLaloz9RY2KSM8c5wT+aoKPFhS VtuIAcQdqgTnBH2VYr0VWjhkppOp1dPrqcHw+q9UTa726AiItaZKlLLZcHxpwTg8AYrFWPVkuxWi Vbo8RCvGeU4XVHtkJGMY+X66wzCpM+UEsNJW4EY25xxn5/bWxOm3Ru/65uceHCdaacdIJSop5+Eq IHPkEnmrdrQnSWJPJm65qlvqE1KlT2Y9O5r+yWqdf7qiNGbUpbikpUpKCQnJA8h86/dT2OVYboqB JO5QBIISRkbiPP7KspI6LX7p5dokYuMYfdQvIdBJAPP1QKyvtDdIHFQIV4jutJk+MttadqRwec5+ 3NZZrxWrQNtmvSX57DTmGWxtVsJTknHp8jWx7FpOdqK7Q5T0oI8MpSpIb/m8enzrc3s4K0dbdES7 dc0L97kxQhKvdUqwoLWe+PmOax2oLYzbr3Iu1tW4W2pIcHZI25AIwOaA6eouklrulpF6fuExmfDt TkFkI2lrapKxuUkjJI8U9lDsK1DZ+lH0w6iP/CNLSY6Q2D7lk4KirOPE9VGrWaXmsXjT6HU5JUna 4COyh9veulN0XAlK8bKErAxkNpz+yh6ng150v6Kr0ddpF5Y1O3ci7EMfwhD8PbuWhW7O9Wfq4xjz rMaP6PK05rC463g3B+TJUXXXWFp4yte8gYTkfEPzVLtOaNbs8t58XR/YrunyH5q/H9XN2I/Rcd16 Sta/DWSSkcnP396BtvqcsF+XPua35zXhuIQeySAPIDn7a4dQyUQLZNnEAhlpSuTjOBn99ZW2POPw VS1NBAeUXCM5Pl/dWtvaK1E3Z9CzIyQS9NYUlHB/nIB5H20PDRMvUUzVmo4NuAQlsvoW4QPLKU+X 2mrS6Zh744KyEhCQhH5vOqndA4SJesCtZ+oGwBjPdxP91Xw6aWNF0nueJhMdhHxdiST2wKAyGrLe 1pnofqKYtZLj9qUh3JwEhQKfP5LqnV011a7c7GgabaLUx1lLEp1biXEqKydxAycd0/mq5ntR/D7P +rgnjEJIGP8A4iKqR7GXTz+EuuXbpN8D3ZpD6hvbSs7kqbxwfmaAl0H+GXTjTll1tbr2xJZuaUyn UKipCW8qSkpBwc/6T1rYPXmzy/oCFc44Km5jbqMoSVFDiQRk8YwRj9E1EPaQgSrp1Z0n0ytzqEOO KaQk4KUBC3EKzjOBgIJxzVorxaWfoKNalYdHjYTuSMEnd3HPHxUB5J3ZqSzcHkSkkO71bspxnk8/ nrqVtr2nNFSdHayQzIeQ6HVOhKk+iXFAVqWgFKUoBSlKAvTeUJc9hi0tq+qq1Qkn7C6irX1VaYhT nsSWNtGNyrdBCc+peRVqatQ6srkKUpV0oITrKYiFaYUSUgNJbAKSVcKwNuB+fNdWJPs100Xe4WG1 umO+p0FIyokr2/aea8+r57QnWC9tMt3PWCn0s52AW+KjGcfzWhnsO9dWJ126qxWnWo+q1NpeGHMQ I2VD5nw8+dAWHvFgi3WzXG2pQ0hTUtwI3j6mFZ9eO9Rx3pNDNr/hFa22m7jbSXHW9pBWkYPHxEfV 3eVaLT1a6hJdedGoTufWVuZiMHco9zjZ8q5o/WXqSwy6y1qVQbeSUuJMNghQIIIOUehNAWFe1Fbh FRlZX4idoTtyUkjgV3rB0W+nzGl3+E2xbJCVOLlOMqUN3OMgEZyfnVVHNe6rXs3XNv4CCnERkdvs RzUwT7RnWRNpRak6wCYbYCUNi1wxgA5HPhZ/XQFpJ3s26NlR340e5WOYVjCWAksrGDkAKSsqB4Fc HTb2erHD1/Kh3Tpkyxa4cUriagN6W8iS8pKAWvdSvcnG9z4lDH4vI7iqms9a+qTMr3prWE1D2c7g 213+zbipBG9p3rlHZSy3rlW1IwN1shqJ+0lok/fQFturPR7Qtu0c6+9GjMraSTubiKUtfxI7AL4x ++qb9TNIRrWWV6YalO5c2KZbjuLJSc4VklXbAH31y372iOsV9i+63XWHvLOCNhtsRIwcZ+q0PQVF j1F1gdu65sqKexVBYJ/PszXjSfUqjOUHmLwdGVpy+IbQ49Z5YBzlxCC4Cc/0c4r8celQYaVxpSiF pSo7mdpGQOBkcj5+ddl3XuqnGfBVcm/DznamIyn9iKwDsyS6hKHHSpKEhCQQOABgCqXCMuGi7Tua tJ7qcmn6ptMzdrmPPsPuyZIwgZAKQM8HI4+6sa5NWp9tXZIIz+f7K6iH3UNqbSvCVZyMDzrj3H1q 3GglJsz62q1KlKEMvK6t855+ZnLq626+2sqH1cD/AK++uFT8ZAcbfaDynEEJO8p8I+Su3xfZWMW8 4vG5WcduK+XHFrVuUcnAH5uKqp09sUjHuryVetKpl4fYyERqW4Etw2HX/i2pWhBI3Hy/XUi0rpa/ XK+R0uxB4YV8ZLqAQcEp4Bz3AqOWq93K2I2Qnm0Df4nxMIX8XHPxA+grIwtbamhTPe4txS09kHKY zWMgY7bcVWoJcmPK4qSW3PHobq6f9Fur161dblP6TjnSyroyiY+6IXwxS4neQFKDhw2Sfh+L762l r/2PY77TsnSU6HGkFzcEKDoyCRnG5wgedV9tntKdarbFEaDrMMsg5CRa4Z8gPNr0Arsq9qLrqoYO uj91qhD/AP41UWSRI9l7Xl1amLiSoy3I/wBdLp8MZGQRkqwSSDXRi9Om9BxJK9QMLZu8dXhpUI61 pKiUjIUCU44NYBv2kOs7aVJb1iGwtW5ey1wxuOc84a57nv61iNQdaeo9/YcZvF6hS0ufWKrPCCj/ AOINAj89AbW1jY4mptFN26OQ5P8AjDACTlS1LTgcEfzRUUk+z3qawxY0zVCo0JuQsBDThIWoYBOA D91a9idRtZRH2n4942LZWFt/xZkgEHOcFGKzWrOt/VHVRjm/aoMv3YYZAgx2wn7kNgH76AsDNhW6 Hoti1wo7LLR8NsJQkAHbk9vuP56sVozSUGFpWNFfhshUZW8lKc7iEAgDn0/ZXm/I6na5faQ27fCU IOUgRWRg4I8kfM1K2vaT61tICG9bLCRjj6OiY7Y/7r04oC2vWzTz72j5jkRkoVKL4GEd0pWlKQfs ANUL1Mhy2al8RsFsjY4kZ9MfvFTe5e0N1guMIQ5urUvMDdhBtcQfWOTyGs8mtb3a5zbrJEic6lx0 J27g2lHH2JAoDp0pSgFdm3O+FMbJPwlQzXWr9BIOR3FAXa9mC8IjtPNJd2B1QH1v6eKtvDfbkx0u IWFcDdj1x2rya031H1ppzH0NejGxyP4s0vzz/KSfOpnE9prrfFbLbGtyhJOcfRcM8/ezQHolH02m BLlmLgRJYKFNAnCM9jgnyJ8q0z1ttLVvQJYa3PMPhLpCT8TRKwD6fyk/mqrJ9qPrqQQdc8H/APlM L/k1g7/136q35lxq7ap95Q4AFA2+KnP3pbFAb6hTUMOKSWg6hQCgjjkEZGP+vKpKzaGNRSlTWYbb LqGyCrAKyAR3xiqhI6j6zQUlN5OUgAZjNHgf+GspbOs3Uq2lRhakLe8YVmFHVkfe2aA35LbdYmkL U2ojJOOxGT3GPWvi5ab0HqCKlqfY225W7cl1DRys+YylQxnjvVd3+p2uHnFOOXsFSu590ZGec+SK NdTdbtLC0XoAjkfxRjj/AHKAtboz2f3ZyI86ysRYdtOSC9uSv7O5OO3OalFtkXXplqluzRJbDkQA OSksEEBW1Q5Kkk5qqlt9pLrVbbci3wtaeDGbTtSkWyGSB9paz+usFc+snUm5S3Zc7Uqnn3jlbhhs An8yOPuoC6Oqr/FvGrG5E9BebaQhIK8AA5zxj5V+dbbopMdlmNJ8VpwpcQEKyAcqz+oiqTudV+oD jniL1CsqyDn3Zny/8Fck/q71EnoQiZqJTwR9XdEY4/3KA3zZQMgttONEfFkIwM571J0tTVRveocY S5ASNoQApZI9E4xn5/OqqjqZrcElN725GCBFZA/+isnaetXUq1KSqFqBlBT232yK5/8AU0aAsfbL mWpbzLji7VcysFceQnwipWM7h5HPNSmDq23xYxFyWEPZyMEHNU8v/VjXl9non3K8R1ykdnWbZFYV 95bbTn766L/UTWT5BdvS1EdvxDX+GgLjXLVTElgphrU5yD8Pl9tYQyoQcbmTEpYSXU7lOY7fL9tV aa6ma2ab8Nu9BCflEZz+fZXHceo2tLgx4Eq9rW2OwSw0jH6KRQFurhrewtQ/d40xCtoCAQRjiq2e 0Nq36d1GzAjvLVHiNbFDIKSSQePzCoH/AAmv2c/Sj/fPcd6xsuS/LkLkSXVOurOVKV3NAbU9miI9 I1VKUjIbShsKI9d4r0Z6c2hVrsniOISlcopdGDzt2jAP5zXlVpHV2odJyFv2CemI44QVEx23c4OR wtJrY7XtQdc2mktt64CUIASkC0wuAOw/0NAXV9sO9wbP0Evzct1IdnBuPHRkZWvelWAPsSa0H7J2 v9OaL0Tcp89S0T3UyPd2m0gqWtSm9icepUn0qv3UDrB1H17GYjat1M7co7DnitsmMy22FYxnahCQ ePUetRm1ajvNrktyIUwIU04HEIU0hbYUCCPgUCk8gcEYoD036N2JNxYe6g32A2u83l8yorr7eXo0 bBS0gEj4fgJPAHChWw/dUG4++qJKwz4SBzhIzlXy5wn9H515oQ/ab63w4jMSLrRDLDDaW2m0WiEE oSkYAH4nsAK5fwo+u35c/wD+Jhf8mgJ57fmTqa3E44dfHBz/AC1VV2pRr7qBq7XkpuVqu7/SLrZU pKvd2msFRyeG0pHc1F6AUpSgFKUoC+nP4GGnMYz7lb8Z/wDjt1aSquIWGvYy026rsiHblH7n26tH VuHVlchSlKuFB4z0pSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoB SlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKU oC+qQk+xjpwK+r7nb8/Z47dWjqqlwS4v2IbKlr/SG2wQn7fGRirV1ah1ZXIUpSrpQeM9KUoBSlKA UpSgFKUoBSvtlp191LLLa3HFnCUISSVH0AFT7T/SXU89BkXMMWaMnB3S14UocHhI57Z74oDX1K39 Z9K6EtjXxWo3l5I2qV4SyknzIyoj/wBKl9u0rdpMYO2Xp0pMUDlaY6AAMZJ+r6EUBVePHekL2Mo3 KzjGQP21nY2idTyBlm2bh/8AHbH/AN1WQsOjtdajmSYmmtPGJ7q74Eh1aEoDa8Z/m+n7ak0boX1l fSlT91iMHPIMhJ4+4UBVBzp9q5ABVaRzyP4w1/irB3G1T7d/2xjwvi2/XSefuNXLd6J9XmEZEtDq gNxKZCec8Y/fWOuPTzqxbkF2fYVzmEd0pUheRj+zQFOKVZjU1jsbrK2dT6Fct0lQCXJnu23afI7k Y/bWubp0q9+DknS95gS0JTxHUShZV6DJOfOgNW0rvXe0XS0PeDc4L8VZzgOIwDg44PY10aAUpSgF KUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgL5OuBr2K9PultbgRBgK2IxuVh5vgZIGftNWmqr8dpx72N9MMspZU65FtyUB50N Nkl9sDcs8JT6qPYc1aCrcCqQpSlXCk8Z6UpQClKUApSlAKy+mdO3fUU9ES1xFulWdzhGEIA7kq7D GRXd6e6Vk6qv7UIB1uIn4pL6UghtHrk4HPat52m3It6I+idIxXpjrhDTzycrVlX1zjG0eVAY3TOk tJaYQhowxd76Vbm148ZKD/JHkBxz2rbGjelGvNYLLl8RIt8HIKQXfBBCsdhgk8Gtn9EujFv07Bbu F1TME5MguBpxQSOBhJOD/wBYrr+0F7Q2lNBWp+3Wmfb7vfXEOt+7IeUfAUErSCopSofXGMEigJPp /pB09sVn3T7dGfSUje9Ie+Ec8EK+HvkVitU9bOiOiLZJipv9ofcio2CFbmw+tZxgD4fhPbklX2mq Gau6rdQ9eWSPp+5y0y4UU+IhiPCQCMYGSUp3fr86gq4MxDhQuM6kg4OUnA+/tj50BbWB7XNqslzv hs2m0JjTJq32Ve4AKUCMJ34eHoPzmsdcvbS1etKRBs1naIOSVwl8j0/05qrbzDTSU7ngV/ykjn7s ipt040PH1CuQ9eHnbXCSkKQspyVkHBAyc+vkaA3NB9tDXbY/jVqsbp//AEJzH6nhWz+nPtgaMvLr UHWzDlqU4kBTyYJMcLz54cWrbj5VVS8dNmgpabVcVvvJwosqCSoJJOCeRjsfzGoRc7Fdra140yC8 0yVFIcIynP2jigPT+DK6P9T7Vst8vT14bk5SAypCHsj+jwsEZ8xWqtXdA7jCd960uyY4bSSluPKP xEY7gjnOSKorZZN1s8pi+W9K2lx3Apt4thSQoHjuMGro+z77WX09Oi2DqKLZClPyPCbuSVqZQQrc RvTtKRztTnckc5PnQGsdR2dmZ/8AqrqDZvcpoz4DpZ8NX80/EPnk1o/W2jLzpeQpcuKowluFLEhK tyVjuOR54P7a9QOo3TzTfUO3si6KeO1B8B6O7gYUDg+hHOaqlrWwPaYUrSep2nPo5Dmxt5wbNwT8 STlPB7igKj0qX9RdFytMzFPsodetTrhEeQQCCMAjJBPrjnHaohQClKUApSlAKUpQClKUApSlAKUp QClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQF+Yn+p1pft /wBmtvf/APSG6s/VX4x2+xxphRGQItuOPX8e3VoKogVSFKUqspPGelKUApSlAKzWi9Oy9UX1u1RF htSkKWpwoKgkAZ5x9w++sXDjuy5bMVhIU68tLaATjKicD9ZqwGjNOK05ERY7QhMm+ymx7y4cISgJ ysgK4J+sB38qAkOkNOrukiDoHTYDRawh+UUcrA5JIQO5JPerT9Jumtn0BZG5st5lyYhtT0mS62hA byAVHcRkAAdyfWuHor03jaCs30pPkoenOxQp9QaGGu6lYUOTwcfd51Wn2xfaAdvc27dN9MtuN29p xLEyWVrbW6tJVvQE5A2Z2jkHOPsoDK+1B7UKZMaXo7QaMsyY22TdUSviG4pOGi0vHYKByfPt61Aa akyn1FLb0h8q3KSEFZPPJPn3x+eueFbJEi6It4Whl87lErJ2pATuzlOT2B8qyLFyW3eLmLdGQ6qW +rwlr4UlBUrA8u+U/moDJpVA0u970C47McR4braVpCASQVYGMgZHGawKVS79eENhKwXVBOEAqCRn GSPTnk13V6Uvb0dmY3GcfQ+kLCy4gBQIBBGVZ5HqAayGnrY/ab+hqVPegyicoXHdILiNwAIIHYkH g47c4q3VqqlFyfYzbCynfV40INJy9XhHTk2yNayqOmSxIkZKSpKxubV2wB3BqQaQuE+2oTGuSJst Lyklh5tKnEt5JCgtSiNo+EHjyJNZuVZLY/4m9AVJdBUXlJBWpSv5RVjOc85qO6FuyLK/Li3NsvtL ZLhKju2DOM/trFs7+F02ksYJ7xF4TudCjCVSSkpZ6dmsf8kl1DdotlmeMhK3X3WUJeJWAhISV4+Y 5We/yrFnUwlKUz4SXIssbVIKsp+7y9azGn16Wl6buFsvNr1G65OWgrdiJihSNpSvCVLc9QfLtUK1 JDtVvlQhZJ9z8BLqiIk5Q8VvkEfUGznknBPBHnmsmNenJ4Uk38yDq6Xe0YKpUoyUX3cWl98HW1PE etb3jNDfAfKVhBBwgny9O4rBy2FSHHH4rDqmwNxKUZCQBzkjt2NTGfdUyLKiI5GdKDtS64raUpTn 4iOc5A7cd6wVrnqsD6i14M+C8ra5uSQF4HxJKT8lfYc1XGcZdGYtW3q0seZFrPqsG6PZW9odfTUS NP6jiSrlaJj7Sm3Uyfiin4UEgLO3btGccfVHOKuT1v0ejqN09RGtEqIXi61JiykjxErR57VJySCD nI74FeZ2s59iuLjTtoivRVpGFIKEhBHPbGPlxit5exl1ti6Hvz+ndVyJAssuNtYfBcc93WgrWBsG eDuUOBxx86qLR3tR6Wl2KTK0bqRZXGlo8Bt4pICF4CsgL4yN36q0F1E0u9pbUL0IKU9FVhbD2wgK SrOBntng9q9LOuPT+HrzR0jwV+FObR40d0ITycDuSMjgetU/1bpx+RDl9PruQ1OYV/FpHCwSnlGe +M80BW6ld6+2qZZbk5b56EofbAKglQUMEZHI+2ujQClKUApSlAKUpQClKUApSlAKUpQClKUApSlA KUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAX5iZHsdaXx392tv/ABDdWfqs dtf919kDSknwkPeExbF+Gv6q8SGzg/I1ZyqIFUhSlKrKTxnpSlAKUrMaMs7l81NAtyWytt19IeOc AIHKufL4QaA2d0t0+nTNqcvt4Yb9+lpSIDZB3pG0LCuexztHbyq33s1dL37Aj+Ft9aZcn3CKChK0 krb3Ek55x2x5Vqbo5pZnqDr4NPRA/ZLU2ghIOxIw6OPn8ArbXtR9ZoPSrRgt2nZsZGpVuNsxIxZ8 QNNp2KUVAkADYQB3+sPtAGp/bP6/W+Xan9BaIvEoSkSlM3WQx8CChIGWwojJ+IkHBH1T3qoMVb6A 7c1uZd3fAtY3lTm4Ek5+W45NcUiTIuVzdmz5BdfkOl191xXK1E5UT8zUlsFjMpn6RnpXH07HIL0g oUU5ylOeAScqUBwKA4k2mTdlQ27cy6/KSlSJc4OLUHVEnGArnhGE9h++s5a49ns7cN+WTDiSigmQ tpZKkHByAMqxjJ4rPt3vp/I0Pc4FsnStOXpSmjEkzG3Aj/SAr2mOHFD4QQcgZ3DvzjPddtMQ7FpL R9mdjtqurcQNvONH8UrwW0JWcnBOVLGOO2c4qiU1GO4yKNtOtVVJLlnW1BeW58K1taGgG0R2WVe8 e+xWCHQQjw9oBcxgBee3cd/KNSV2OG62qS09IlISB4m9a1DHzUfUV8a4kSrdaYv0asxyXAk+HgfC Enis3c9LXfS+irdq/U9oDlkuK0NQrkFtOpklaCtCko3eIkKShShvQkjzAPFa7Kdxdw8152+kf5Oy UbXR/D907FbVVwm51EmvX4emOpjI0x24zWnIbbyI7RSlW8pGeftJPFRPXjHuN9jBpJaacYSFpQr6 w3HINTaxz4E1kuW6GtDW/BUEJSM8eWc9sVhNWxmZep7Yl1IUj4EkKJ5BXyKs2dXyrjDWEk+O5J+J LBX2jKUainKUo4l/by8ccdMehlbzcGLYWPCa8MOFWdjafLH99c1zbszUllUyIxvJO1SmQr0+X2Vn en+m7fq3rhpTTl7imVaJaJZfYDy2txTHcWPiQQofElJ4PlUPsiX7vp1K57gdeVkNrPCgd5HccjsK t/h1GhCtnrlPHUzFq862q3Wmqmm4KMo7l8PCTf3b4x3Mo4zapMJ1tiLHWFJKUgNBPOPsFRa5WO5R ypxKFJtYIW8wl3II4CyEZxuwBz34HIwKyj01q1BuyMpdVcncFCh8ScqVxkqPoK7ynpK7eLTJUHbr M/EstAAb1rVtQM8JGSRyT9tV2861CadPlN9+rXr8jH1i20zVaE43iUZU4ttxwoxlj8rb/u9jXlrY hx3UruDBkQFvBBdSCnYoYyCRz2OSAcfbiszqrQsyDbXdQQJMGTanHSWW47ji1tIUr4ArKcduO55F Y2PabqufJjPMrVHiylqlMJeTgFJAXgZwTjjI/PXbtOqZsBiRYZ0ku2tWBscTvKCklQ5+04PetpjU jJ4T5RwavaVqMVUlFqMuja4fyfctd7GXtAx5zds6baumPiYhlEa1y31hSXSFK2tE4BB2lCU5Jztx W1vac0VDuGjJ2p7ezHZusXw3feAnCiASO4PP1gOxrzfjzX7Re0XOyTH4r8V8ORZDSylaFA5SoEcg 8VeX2QOpz3U3Tq9I65vDF2nMtOARpISHJDaC2UqV5ufWOc98ZPbNVmKV61vYkay0+7cWYxbvUFtQ KFIIW6MhQHkPqg4yK0dV6faI0lE0drCBcbLB91tUlke8oaT+LBCgg5GPRVVB6oaZc0zqVxgMluNI K3Y/ORs3qAA+7H56AilKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBS lKAUpSgFKUoBSlKAUpSgFKUoBSlKAvzDKR7HelysZSI1tyPUe8N1Z+qvxU7vY50wkecW2j/+4bq0 FUw6HsuopSlVHh4z0pSgFbh6I2xEbSN5v69yXyotR8nhWEEcDz5Wa1PbIjk+4x4TOfEfcS2nAzjJ xnFWQ03Y8XuyaPtiAtHjgKO3gla93ISPl6UBaf2dtLw9FdO3LnKWtj3plEuQt44ShAZSon7B8XPy rzz61a0ldQOpt81K+W/DlS1GOlA4S0kBCOcAn4EJ5NXb9t7Wrmh+kUGxW9OHryXIB2ubNjPu60KO AQTytPy4+yvPgpVFdG8fEUJUPLhQBH6jXjKopN8nzHQA+34jZWjcCpIVjcPTPlUhg2xN3mISxnxC gYz/ACEJTgDyBwABnz7107bZpU5TbyHmk+ICQFE8fqrYkWOu26XbaBSHW0DKkcd1Z7/fUTf33lpR g+XwdA8J+F/xk51rmLVOK3Z9cYeFzw2u533IUN9gxFo3JR5ZIxk5rCXHXwuyLJaZUFMZqw28wW1h 0qL2PDTuxt+H/Rjjnv3rrRn3Y+hhKbGXF917ylXDuO+KxWkbC3IDkmSppwKQkhKm92CrnzqPtkqN KqqkuM4+qNw1ypU1G/sJWdJKpt357KMlwn0zhI5+p65QbhlfDXiL29vlj9VSLoVe7povqfpufYZo ifSk2Lb5uWkr8WM6+2XG/jB252j4k4UMcEVlOn+hLz1n9/telXbVAXaVtqdNyeW0FBYcCQgNoXn6 hznGOO9Sn2VNIW9r2hNXaRurca7CwwpjcZ+TGSQl5iWy2l9CVbvDXwSCDkZIzUnY0asKME1jGfqa P4q1GyuNRuKlOXmblHD/APVpJPHBgdUT0TdfazcYxvOoJxwPUuqx3rUElFzk3Bpaj+Pb27Pq98nH y71tvqOIU7qdqOZEt7FsRa570R9DABMpxl5wqeOAnCl5HfJ47mtdq1U2q6Muhh7YCgEb+eFGsSEX TuKvlLd/BsV1Vp3ekWSv6jpdcJc7llc8dMYRldWiR/Bl4XNf47cNgwPq7kfzePWpXedJr0LrC76K cnfSX0WGCJPg+DvLjYe+puVjG/Hc5xnjtXNoPUts0j1OsfUK5ty3bdZ0vofYjtpU8vxWltJ2hSgn hTgJyRwDUO6eFH0M4lIwspPOPmusaSX4Bvvn7E1RlU//AF0Yv8qp4Tby5rnl/Xj6HUvrcga8jPxY /irajKkrRvA/FtJWtw5PohCj68cc133nI11tq9RQx4E2N+MaXyrats7gcHAPYdxWWe1fZbRpu56Y n6aZXc50gPs6ha2KlR2ihtJjBJSFeGvYtCsOAbXl/CrlKsS3HVdrGYkBfuzTqSnBG0D4jngVdqJQ p0Zp4axz7ehgWUql1e6hazipQk5NQaWXLopJ9v8As4unjz765bkhW9bji1qVgDJO3JwKa70/HcgP T46Nr5cSpRyeQeD3OPOs1qu4txND2y1wdM2e3S4CmVP3OIrY/KS20pKgvDYPxnCjlR5A796/dOXN u6W1hRbWFlvKgo57HHfzqzczlTq/iqMsx7klotvSvdPehajS2VEm4559srHpn1NPONqQ6ps90kip f0W1a/oXqjYNTNFsJiy0B8L+qWlfCvPBx8JPI5rl1hp1xMx+Wh1nY89kDbgjIzWBn2N2LZWrj7wl xKlFKkBJ+EhSk9/uH562C2uYV4qUWcg1rRLjSa8qVaPCbSfr6HqD1ftNu1h0vursd33hKYjq2HWV 5GUkE/byj9VUg6h2s6x0OzMaKlTbWDlLXO5IaJwR5cit6exXrmRqHo+/o2WhS32JT0RMhx0qwh7K +xyeCpVRTXmlXOmupm4bp8aPPB3KSjan65HoAeKySFKd0qQdQLMuyaqnxMHwvHUWlbcApOD/APcO 1R+gFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSl KAUpSgFKUoC/cAE+x7pUDuY9t/4hurO1WazlKfZG0gpYykNWskeo95bqzNUw6HsuopSlVHh4z0pX JHbL0htlJwVrCR95xQE/6UWQpkN39aifCC/DQMckYAP581cf2W+n4S05q+6P/j0SMtNgBQ+FKk5z yR3zWi+j1gcmuafsSI/jF7YXAlJ5Hc1dDWSI9k0M+1FX7m2ltfwJG0qyoZ4++gKTe3TrqLrPqJa7 ZbEueBao7jRUoEb1l5SSQD8kD89aHbMZd0npeQresq8ABIICt4PPoNoV288Vnzc7fK13drhMfSIi FPLTk8upDudqeRlRGcDNfcq9RJMJuyMW8x0h1Mv3gryV5SsgFOOMB3vk9vnVFR4i2ZNnT8yvCPq0 ZfR94ZeltWpMdSFNRwNwIwSAK7epmm1cXWfKg2t1wJceht+O8jAJGGlLbSrKgAfjGAc84welaNPR XoyX0SWC8tOc+ENyefXOflWWt9tkRpaErn+OykEFok8cemSK1SpUo06/mQfTs8n0BaWmpXel/g7l YU+FOLjwmsJNLrhdcM7bWjo1h6Yxde2i9P3exPpUXGJsQRX0YfLI2oS46hWVkk5UMAA8k4GBtUfx nnHba+tkKGSgp8MAE8fVJ7VhJLMyLqGRamJjzUGdt95joUUtvbEbk70A4Vg8jPY81LbZ4bK3Eojt xkpATuxt3fqrKv508KpTXMllmv8AhK1ut8rS7lmFGTjHs1xzhrt06mDvtyuljQ1eNPXOZZZqllpx 6C8phxSVjcUlaCCRlA48+KuT7SPWFnSTbmhbbBuCtQXuzlxmYiQGGobb3itB5K05WXULSFBICRjn eCMVTq5QLpqln6K0xbLhqCc2vx3GLcwqS4htOUlZSjJCcrSM4xkj1qyvthaLt03WMPVzetrBBulv tLbQsMyQGpElhDjzm9nBKlqUo7Eo2AEg/FxipC0dSNq8decGoeIYWVXXoKbzB7d2P+v4K8Wa0XWN LmTJ11cnPynvGkOOrUVurJJUpROSoqJJJPeo3qZCxe2IlufXFakNoQ6lHwJXlSh8QB57nv61KZ9v TcrcuR4SYzhbUSC38WSM/KoDbmWPAipckSkvJkqI93ZDisEIxn4k45Bx68+lYdhuqzlVc/iXsbN4 u8nT7Whp9Og/Kk8p7m3hNNpJrK6km1ayuPbBbo5H8YJUpX1BwUnsM57Vv3qY/pjrfbr5rzQzEix3 HRMRU27plwWmV3ZtbeUgvNOKJLbcZwJCwR8QTlIJI0+w+/GRvub7EcZOCqQSSP8AxAVtX2T9KOXn Xq+rFwffsdl062t1l+XF2x5yHGH2XVJfUoJSlopyojcBnB21VptSU80XHMecsseNLWjbOGo06u2q nFQhjlJc+vrzyucmg7ve4l2sDqiw6hwo25UArGFZ75rOWa72G4dMYmlIuloCLrhXj3xbaEvpUJCn cJwkqUPD2oyVp7kYwObOdf8A2hdDPaY1doe3x7vdpEm2uxWbnbkMvwHFOscEOpd5AK8KwOCFDBxV RNHToibcLc86GXnFqSAogdxx3OayakPwtGSo+v2ISzuf9f1GlLUljEVz03NPj0xnJk4E2NbUuQXC 9IUQUZKAB5D1PpWDTDkxrs7OXPdiQ3FqJMbKnEhRyAE5SDzjzFSq0RvdJuClCwo5C0jjuP7q4bxf l26W9/EnJCEq2/Cvj7e1RlKtJVGqcc56+5vF/ptOpZwnd1XTUG9uE248d2nlkamSY9zkNW+Jd7k8 +pwJHvTOxvOMZJDij+qsUu2S5FylRWVJW6ylXifFgKCODgnv2zzippFn2nUSkwjbvAkOZUVOMpUA R8+9RrUUV20S3Wo7obUhz4HG8oIChnHHbg4qUtrqLn5bhtl6Gia3odaNu7yNx51NvG7GOcZw03no bB9k3XK9Ka7btpZU6i5vt4wojapCHMH86h+arqdZrU9qzpgZ8lqO3JaSt1vZ5ILCj3POcmvOq1XO JZdf22829ZbiMSWnQUp24SCArv8AYfz16kaHXHvGjQN6XmH0bQTyNqm0/uNShoh5+9V7eb50/iXp GBKhrQHU4+sD8Hc/MA1pKrUz7I5b9R3/AEvLaAb8dQbSRlHwubuPuNVgu0NyBdJUJ1O1bDqkEfYa A6tKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKA UpSgFKUoC+7MtuB7G2mZzwKmo8S3OrA8wl9sn9lWhqqV2Tv9hu0I5+K1whx/8VFWtqiBVIUpSqyk 8Z6yOmWTI1BAZCdxU+kY++sdUg6dt79Y29WCQ254h+6gLhey5a/F6hQVusgtxYisZ8lBCcfrNbQ9 o/VESHpO5BiWP4pAeW4QOAopBH29hWM9lWEnwpVw8FQ3hxIXng/6Ktce17dkQtMagZZKP40222kE 5JGWQcfcqgKVBWUqCgCVKBKj38/7/wBVZK5P7Z29lK0FtlplXGPiQ2lCj9hKTXQ8ElsK5wTjtWes 7ceXew5LfS34pU4oJUE4JBOOc+tWK1RRi2yT0+yqV68YReG2ln5k6ht6XGlrRN0yrV30+QgzxdS3 9HFJbUHPC2fHnxNu3d/JznnFdxHiORvHcUG3SPiKDhOc4OKx14iQ51jbh+/tttpSjadwJ4GPWup0 +0+3K1zBss+/wrHa3/FCrrNaAZaw0txOSpxCfiUkIHxDlQ79jrsoq9luWFL0x/J2WlWl4YpKlNSq UmsuW5PDa5xFZaSx6ny1b4iLgbhJuAcdH1QXgfLHmKyLbikFxcpTKGSr4Cs4z3x347Vmep2nbnoz W72mb026IyAkxLi5HVHbl5bStewKKgraVBJwo4PfHasDqu1oukNhguqT4a85SM54xVurGUaihW4/ XC9jO0+vRq2dS601KeHnHTdJ9d2S0PsVO3KTb7nIu3TizadTDgw49su8ayKiv3RhYWVrW8r/AE2f DZWSnAJUCe4rSXWTUM3W/Wi+T7zDhoNhmSLJD93bI3MsSFlC17irK/iOSMD0Aq7HT+7ydRaA09fZ qWWZdytcWW8llJDaVuNJWoJBJITknGSTjzNUL6ixblA6t62hXOBIgKl3mfNie8sqbL7K5CghxAVj chWDhQ4ODg1O6ju/DtRZynwbKi9ZjKtHPXC9+x+ynmGY7viOISNhOFKA8qgOm1R3Lol1LKfDUtpK VFIwVBRyB8+R+cVw3a3S3Lghx6BLfYCj4iWUFKlpzyEqwoAkdjg49DWxOpd7h3zXrK4zjUhtpDJD rDoUkkkjHAPbb6+dRtC1hRt5yUstm66nr1xqOsW1GdDZGLfL5zx8jC3+5yrDqW3Xxi2W66oitrBh XKP7xFdKgU/G32Vjdkc8EA+VXQ00/atbdIL5pTRiLPEjT7NLiwRGaS1DQt1LiCfxQIA3qJVtBOc8 E1SbUUPUd+m+76e09cbt4LSVPe5xXHy3uUrG7YDjO04z3wfSr/8ASPQVp6eaViWGzyJchhhCwlyS tKnFb3FOHO0Ad1EcAcYqQ0uMo26Ukad48q0Kms1J0ZNvv88LoUS1DpSVoe/3DRF+dgv3G3qSl1cN RUyrxGkOp2laUqPwuAHKRyD9tYth18RH9PRrDYyxJlofNzch5ms48MlDbo+qj8X2x/LX61uP2utL XK09cDqmHaLq/arhDjPz56mFGLHf5jBHiBO1PwttHCiTlfzArWjz4K0uYHhAZKs8cfOo+6nO1rS2 f3I2/Qba117TKXnvmlJLhc8L1+p1pcpVvaabQwqQpGE/AjceP/SvxLsWS2pcmIApQyUrbGQcj1rK 6BtEbVt6kKvtya07pmMVJVd5AAYkPpUn+KIeWUtpeU2orA+IgJJ2kVB9SHScbVFxiRrTeZ0NiU60 2+zdmiH0pUQlxKhHIwQM8Z4I586UdNlOKlL4WVan41oW9eVKgvNS4xjCXbrh5JKxChAJkwkJafSS eABxyD2586xWuLaJFmVObyZCVoKx64+H0z6VinnbUWWxY7BeYkgKypyZOTJQUYOQEpYbIOcc7j2P HORmnJCIWmhJeea8ZSUpU2TtwSoHtnPlVudCdvVjKMsvP+IyrfVLfWbCtRq0PLjtbbzwml+ZLC5I RPkWxzTtrjx2VpuDK3/e3COFpJSWwOfL4vId6v57EmuntR6DkQLlKDkiGpltvIHI8JA75PORVAb/ AG5Vour0BxzeUpSd+3GcjPbJ+yrE+wdehFv94tq1IQncxISSeSd+0j9n562Y4gbH6/wHLV1LduLT YbaffcyQnAJLaT6euaqb1hgGFryetLRQ2+oOp9PiH+VegntO2pL2nodw3Ky3Ix24GQB+6qU9f4aV +BcU7iSloE447LFAagpSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKU pQClKUApSlAKUpQClKUApSlAXwlIU57E1iQnO5VvgAYHn4yKtRVVZ6pCfYisioagiSLdBLKj5L8Z G0/nxVqqtwKpClKVcKTxnqWdMEZvcl4kfi4qjz6lSRUTqbdKQS/cdoBUW20jP9v/ACoD0J9luIW+ mMOYopw6VdvsTz+qqh+1/d5KdS2+wllfgKjeOXys4WpSgNuO2R4QPf8AlDt53P8AZ5BY6O2kpAKk slWPniqFe1TIff6wLQ9H8NLENhtpW8K8VOCrdj+T8SlJwf5ufOvH0KoLMka4QyX3I8VGSVqSMpGS M8dqkj2lrHEisuTbi6l5ePESXUN7SRnsQSKw1imtW65pkupK8IyADjncD6H0qWXxiPedPJuKy4wX VBQSFBQ4OPT05qFuqtSFSMU2ovuvU6boGn2VxaV6soRqVorKjLOFFd89OXgjkyy2cJ/EXVPKuNzi Fcfdiua3gtSBZ5DzaIS2xtkkbfILHJOD6VmY2jL5bdHM342uRHsl+Wq3i5KmNLQ9scLm1LKcLSd0 bGVZHwn1TjrTVIixAAlTiIiEBJ3YKiAEenzNVVm44g5bs8/L0ZY0ynTrKd1GkqSi8PDbUkuZRaz0 x3X0NvyOrVx6sTXNF9R49qs1ouYSk3aI4qG3F8L8aN5eLiVbloQgcpxk9yRiYp9kuZK8RI141EYC 8tIXZy4tKfIKV4yQTg8kAZ9B2qCdAeiNx6p2g6h1W+9adNr/AOyGF4anp2FOIXtUVK8LYttOdzZ3 AnGO9ba6g+1FpaI1ETomB/C0vhwyj4zsH3XG3Z/pWfj3ZV27bOe4rPiswzXxlGqVaip3Dhpbk4y7 Y5/5N8aZtcXTumrVp6E4+5FtcJmGwp1QLikNICElRAAKsJGcAD5Cop1u6eW3qdpFNkn3CXAUxJEu M8yEqCXktrQnekj4kDxCSkFJOPrCun0d6mWTqHaluWx9SZ8VhhVxi7XD7q44Ffi96kpC8FCxlPBx njIr46/dQpXTzRUe6QbYzcZU6ciAyHnChDS1tuKS4oAZWAUDKAU5z9YVkOUduX0ImlSqusqcV8ee F7lVOsugJPSSXp6O7e2byLo46ARCMbwvCLQ/7xe7PifLGPPPEJ1K99GXq3lsBXilIVuPYBX+ZqUd TrjdOoOo4ep9RrhsSosNthKILCm29iFrc5C1rJOVq5B7Y4qNQ7xCvMsuKCmlsBOwJUSFEk8H4fkP z1rladKdTzKKzFdfr0Oz6Zb6hbWf4TUaihWm06eWm8R5l046ccv5HcjP32wa9tF/0tAXdLxHbdTH iCOt7xMoUlXwtkKOErUeD5Z7V6J6eLrlvjeKkhWwZzmvPzp7qg6T1zB1XN0zIukuClYhttXJEdob 21oX4gLayvhYxgpwRzuzxbj2e+r73VGVfoT2mDYHbKI54niR43ihw+SE7cbB653eWKldOklSUHLL NA8Y0Jz1CpdQpONNtLLT5ePc2PrjTzGqNI3XT8t1xtmfGcjLU0RvSlaSklOQRnBOODVVrz7Mq4Lp hRdRvG0Ep3MrhbpOzjeA6FBOTzg7OMjIOObGdbdZTdA9Lr1qqBCZmzIaWksNPLIQXHXUNJUrHJCS vcQCMgYynOR5+dSbrJ6garn6w1CuPEu9ySgrYhtKSyPDbS0nAUpR5DYzlXcntV+4nSjjzCJ0i3vq u92jxw03lJYfbn1LD+0farnF6CWCz2u1XO4RdPzom8obU4tqMxFeSXXClOEpAA3KwAM+VVfTJVLH iwyjxFc+F/pCB92K3j079pXWOk7Jb7JcdJ2e52q2W9uFF93dXGfUG0pQha1lTiT8KTkBA5OcjGDq KXJmXrXN61aqEIqbpNkzQwXAvw/FdK9m7jON2M4GcdhWNdSpSjvT5RN6DQv6VVWs6bUG8t46e+TA yYjDKy5IkpS4rlSchOCefOuBuJbZUkM+Mvcr+Ul1J8vTFbZ9n9hqd1006h74XlPyPgxkcRnsfqwa zXtJ9QZV51TcdHNt29WnrFPSpqS0w4iQqQ22WnUKKlYIS446OEDO1JBI5NNNt0t7fP8AJduowjfq 2jTTh1bfaOeW/T3NE6qmGVqFbtwZUVJSlK0tK8PPHGMhWO49e1TT2abi5bepbbYOPHQhojdjnxm/ 86jut7PNsFztki4R1R7jIQXJDPiJUlBSspSElOeCgJPc8k/ZXQ0NcVQNdWuYEgfx9kqGew8VJP7K kYbtq3dTTbrynXn5P5cvHyzx+h6Y9WIz0vpa0t9tC3G/BKkrzjuBnkHn++qX9cYod0H74FfVU0nA 57KPnV8Zr6ZmgUvFAIU02CF89lJH7qo91oBX09npKE5S9k47fWNVlgrdSlKAUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAvmtIV7FmnknODB t4OO/wDp26tLVWJDq4/sUWF9oZW3AgLSMZ5DyCKtPVqHVlcugpSlXSg8Z6nPR7m8SUk8bEqx64JN Qapt0aJVq7we4XHWcfYP/WgPRr2enzO6SQkDcCguMjd8v/WqBe0u7Ikdbrol1R8NCI6GDsAGzwUE 49fiKuf7qu77K90cd0s7aVKbKGXnlgD6w5R35+dUv9qOOY3W6S04hSQY8c4V3xsFUy6Mu0FmpFe5 rh2K+jwnEsrUjcn4gOKmk1dvuemIsA3FiM8hDZIWQMEDBHJFftot3vMFCHo6fd9gVvC8EHA+fp8q w90hRg68ylpSVJWQhwZycH17dqgJ1o15pZw4vsddtdOq6TbTqKKlCtHDUsr7NJfRkidu+oXumydG C6s3q2QlF61w2ksoMJ8vFS3tyficylbyNqiQPFz3SmuhpiaZMhux3G3yTM2FG1Da1KcKeQAlCf5o zx6V1LRbb5AbEu2tKWVpIAWpBBSTn5Gs3YffY92au78dyDc2tyhIZaKyFEFHY7kHKSfL81U1q0Z5 VWW5LuvzL6F3TdOr222pYUnSnJcxqJulJeu55eX81xk59G6h1Tolye50+1SxCaufhiWEMsO7/DCg j/S7iMb19sd+fKuzpvTVx1AsWLSFtbvFyaZ8d+OiWhspbbwhSiVqxwpaRjOeflUTuFpftylM2WW4 SnHwOraB8j2UAfM1N+l+u9RaJuSrjZ9E6XZuiofur0x16Q44+klCllSTJ2AlSEk7UpAPAAHFVpxq qLq1PhX0f1MapGvYTqwsLJqvPhtLdBeu189eephG9dXnRl19/wBLXE2O+toXDmoejoW42kqSVtqQ 8khJC2054BBGOOau/wBUtF2XqZohMG4pdbQ4171AW60625FkFpYbdU0ShW5HiEltePRQHlX32c9K aK6y37W1x11oK0MTospl9TkKbOa8V19b5dKgZCh9ZAwEgAZPyxKvaN663HR1yFo0y5DfuhdQpTEi OtSfAIWCoKSoDO5KRjP3VL21GNGnti8o53rOo19RvHWqwUanCeFjlJLp68GlerfT+5dLLlZog1A5 fPpFSxn6PEfw/DKB/OXuzv8AljHnmtda2h5u0FEfIWsbSMZx8XB5+01L9d9T71r36Ae1I1DYnQZj 2Ex2VIR4SvB2kkqVk5Svz8hWDu7K3NRQlPJKgPDAOePrnPao6s40rhSisLD+puWmxq3+jSo15OUt 8Vz/AG8/dZO460q1vNQozT06a/lTTTLSlLUAOcJSDnABNWh9kbTFx0nb9Q6mvy3xJukVt1+C1GLz jCWC6Bt8IqLqlJIISlOc8AE1WN65360a3tl9tNojznYbS9rUptRaUVpUk7sKSeAcjBHOKsV7OfXN hT1yj9SmrPpj44zVtcixH0tub1LDhdcKnENpSfD+JRQACSTgHFenKkoKcpLe/cx/Gcr+pXna0aUl bwfGIvHC5bffGWar9qfUNv1b1yE1DD8FizwmISffWnIzrvBf3qZeShbfL+0Ap5CQoHChUWtWnNWa uPgaN0zPvCCotmUygiM2tI3qQp5Q8NKtuDhSgfiSByRm2vVzp70o1No/VXVJmzW7Ulx+i5Exmazd H1xpLkdgpSPxLyUkDwgk7SOx5zk1Wjpv1y1rouwO2rSehtJW+3yZCpLjW2W7ucKUoKsuSVHshIx2 4q/Wt4Op5lWXHZEZpmsXVOydnp9F7n+ZpZfp9DWFyfuVuucu2v251mZDeXHlMpO/a4hRSsZAI7jH HFSKzWL6TZQNRagt+m7a80lbT7TjdxeKzghCozCi63kbiSpICSNpwSBWHdvTl01LebpdWY0afcJj 8tbadyEBbi9xSnKj8OSQMknHnXVWq5JeW9Ghx0nJw5v4Iz81Yqw/LjN4iuOmWSkFdVraMpVpfFxJ RjyvVcLh/Yk16maVi2eVYbNpwQlLUIs3UC5rh+lI6FhQPuzg/EeI42y7gHcnbt7E1jdNHTs6U7a7 q4li3sRnFRytSktyHgnCEleR4Y3K35Jwdm3+VWFXHu9xeCJS0KbPKkocb4wOO3PpWebsVvXZ/C8M l0IGcOEnO4Z/fVNS5jCUZVHn2XRe5cstFr3FCrRsouCaeZTWJS9uj4f3yY7qTan7NcIpu8dQuUhB W6tThVnBASfzYqNPMpg3CBIElp4uJbkK8P8AkEnJSfmMVJuoN1ueo4mmp13eS9Nkx3Q48W0t7sPr SOEgAYSAOBUMeDrcjwnh8bSthTnOMHtUyc3aaeGeqemXGpHTpJbVhSmGSVZzk7s1TTrC6j+CF2jb sqTlRPbPxGrjaAglfSyDIIcSt+IyoZVgfW8hVKOszqAL5EBI8NCsgnzzQ8NBUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgL4yFBPsUWB RGQIEA4/+cirT1Vh8KV7FNgSjG4wIAGe2fGRVp6tw6srkKUpVwoPGepR0ql+56+tSyoBLjhaVn0W kp/aRUXrmgyFRJrEpAyplxLg58wc0Bfv2XJzjOt5tuBSGnW3lDI5P+jP7q0f7f8Aa/o3rfEuCEKS iZbmiCex2EpOKlXs46gKNZ6TuywEplhEdeF4wpaSg81IP/aJ6bcmRLPqJCVEQ4jjZ+DPd5vzxx9a jWT2MnFpor1o+WqVbUsZSpGwAlPcfCkV9X62hEBbjSVlwLyFZHmfzedRWxXNqzuxVsKfdjOMo8cn BAdONyRggdh2PNbChSWbjbm5KWl+E6MhK0jPf0+6tSvaU7WtvS+Fs+hPDF/a6/pn4ecv9yMWvl7o xlpvFvjQGI0ua00+hO1QWcY+/t2rtSHnXYxlW6d4+eUpBQWyM4POP31gNSWRtb6n1JShpbg5Axj4 T8q71hm28x27Kyh4rbScqSE475PI/uq1OjTcfNp8vv6Iz7XUbuNZ2F3iEUsQabUm+i+659M9jjsL sW+JVJksluUrGVtpWBxkeeR2FfkZ1cNL0mQ8XVbwgeIBgZyT9UD0FZaSYVv2IQ5Agt85yoNn7vz/ AK6jM9ap0VMPT8e4XSQXUlwMI8YjggfU55J++rtGLuJtRTw+nt9TA1OvDSLeM69SLqQTy1xubxhu OeXl9WWM1LCuPs5dGY+q7CgydRanlW9q8xroUvMR3BHfdWGfB2Yw4SMlSxj89V/1WmZqZ9F3nXJ+ fekthByGkYAyrG1KQPrH9dXR9pLpvfuqfSyz2LTsu2xpTFwZmKXOcWhBQll1BAKEKOcuJ8sYB5qk t0jz45XJYQ9GmxFFEuK6FIcbcRkrSpA5BB4IPIPBqbvXKnsUHhfp9Tl/hiFC6depcw8yXD/+kucu PuuDDx7VdnZSfeoT/wCLWOdnGM/KsjraTKiXS3iGR4iuQkgHJ3DHeu9eLxLstst0p65WS4LuDW8s wny67GwlJ2vJJGxXx4xzylXpXB1NsV7smqbaxeHYSlkpCDEUsp4KSfrAfzhWPGjXlVUqsVhJ/sTN fUdLoWM6FlWk6lSUeXw1iXr8j4uVw1kkh1MJCQkc7EBXn9prHW27Xu5vC3zGN8Z0hLv4opwD8/Kp FqG4vQkofaQpbeCFggnzGPP51JYPT7XlylQrbbtAajt8me+iOJcuzvtMMb1hIccWlJKUJCslWDgA msWhmcFtpLno12JvVFC2uZeZfVMRWZRk87l6LhJ56Pg397NQE72ctR6Yush5NpRMl2pktlKVsMPx 21rCVbTk733VAqCvrY7ACtDdRNBXXSWr5NusNovlx04htLsS4e7KkkoKAV71tICUkOeIMFIIABxg gm0Vr0v/APhZ7MUiFq2TbkTbe3Il3GTAVxKWpxfhhKnAgrcKC02ndgkhKRwBVXZXUtM2A9Agr1Cm G6koBfi4IyOc4exipidGEqSjWfPqc5tdRuKWoVK+nQe1t4j2xnKXGDVt5LLt3aVDje8yG3AXW1pW MkKOUq5BHOAcYP2VzvNXV9lSWFIQtRz7s2ts+GM/VAJKsD5kn1NZ3Wt2t72pIU+02JyJGTBbYlH3 RKC69vUVunacEkY+InPHNLJZoMnU0KTJa1JLiSiS5FsgSuarLSlDw0kEcKAJ7/CFVhyaUo044a7N 88mxUqcp0qt5WUoy/vjF7cL1Wc5MWxEmNlMmUpNvHhpQUpcRgkADd8WTlWNx8sk4AGAMozJYbhOJ gylz5iUZU22nxSBuGSQgdhnvXUuVlbueopbNvfvca0tnCGrq5tlIUjCVJcAG0EL3ceQx513Y2mrZ aI5muy5KVBO1ag4kAgn1wPlWJcSpKe2rLnPRL9DYdIpahO3dWwpYg4/mqSfHH5sLC9yNaojz2m7F Z3W20zYkdaVIQ6hWFKeWscglPYjzrCt7Z+om0oSMPygkbc/FuX35+2uKRKeXNL6JDoUPqq3nI4x3 qV9E7EvUHUGBDRnKXmlg7d3PioA/bWxJ8HHKixJ85PReZeWdO9EbTIdUlsqix20B0474PpzwKoX1 Uu4MK4qKkePMfxt89pPfH2Jq4ntOzWhbLNpaMkBCHDn4hwENpKeB8jVAeoUwy9WTQD+LZcLSRnPC f+jXpQR+lKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKU ApSlAKUpQClKUBfJ5tT3sVWBpGdy4EBIx6l5FWmqsMI49jvSx9I1t/4hurPVap9WVy6ClKVdKDxn pSlAbw6I3ltnT8H8Y4HbdN8TCcg4Cwrv/wCKru9Y7Zbdb9I7hKU2VN+7rKApKTwHEhWdw/oV5t9M boLfqlht5xQjyQplQzwCrGCfvAq/vsvaljTNLStJ3eSHJiJCvDbd5DjbiSSkfIbVE/bQHnVc4q7d e5cH/un1tpyfRRAPH2VOtCX5EhqNa1jC0tHB2nkg+ufTNTn2xOmNz0pqtm8sQEpgyvFIcYSccOLU FHHbjv6cVoq1zZEOd73HcW2U55T5ZrDvrVXNJx79jZPC2vVNFvo1V+V4Ul7ZWTck6OiVGUysAg4P 2VDYLyLLqKU663uZTuQSkAqAJGPT5VJNM3ET7XHUtalvFvKyRjODiunq6A0IKpTLCd/iAvKHmnt+ 3bWr2z8ucqFTo+Du2s01e2tLVLRrdDEvXjGccemef3MRdFaZu0j3l69TtnmgBW0cAcAoPpXJFhlc cOaPukyC8lSFOqbdLBIwduSkDJBzXe01p+3ogh16My8V9jlRHBPka6Fxs03xVjT0sRdiyl5AeWnj +SPTjB7VmwrxjPZTm1t9cY+uDWLnSq9a3VzeW1Ofm8tQ3eY+62uTa937ZNiaV64dTdLzb3Jh3GDe 3J8lKlxLo7JeYgYLh2RklxIbRlWMZPCUelSf2nrDZ2fduo9ifhx7few0WUpZU23OD6XHt5bCArxl j+U5gYGFYNaDdg3exOKlTXfEQ8SklK9+Vd8nd99Zu66g1JKs8bT131P/APsrEfTcIMWZveAWhJS2 00QhS0fiyQlBKWxz2JzUlTrKpFwm1JPoaRd6bOzqRuLeEqM4P40+yfR/LHXPB0GmLe+42ZFpLZdI JV4bWOfPgmvzUke6zb5Ddl3GXP2lJ3SZBcI+LnGfkB+YVlYr8Wayl2J4eEoSAFN8g/LisWzKlJuL bdwyXCUbOE+p9Kj6dWom8cY7G43thZSp0963bmmppLH1a9e3B3NVSozVvLT2QtfKcJyO4rcvTzrZ 1H0FEe07qi62K4XCBDfzEuBmSrkt5xC3I+94K8BSApbalfHkNAgfGAmtBdREuifGYTn4miQAeO/+ VZq2MzrlFfuV0kPy7pICUqlyX1OuqAJQMrJJ4SAB8gB5VkW9aNraqa6yIjWNPq69r0reSxGkucdX hJ4zzy+xKepvVDXOvnxP1TdWoNvajpZetNsefRCf2LK0rWytSkqXuUOT/MT6Vr243N7LLEcMRGJU dT0ZxSSAvaVjHw5IJWhSBkDnBOBzXent25pRgy5Ky7geInxHSk55/Ziu7a7ZaXIrb3gpfSwFIbK1 KUEDJVgBXYZUTj1JNW3cx/8AJVTb7ccGVT0Wvn8Fp84U1/d8WZ+jXTjjr7mAtE+cwSq6MePHxk5I WQOPIq9M1M7NM1bp+Ez1NsVoiC1W0lTDktQ2L3Exinw0LC8grV6D4c57A4hq42Qy1W9xlsrKinln jGcY7Vl5EuKzZja3p0z6IOP4gJD3u5O7d/os7PrfF278968jcQhU3zpvPbBdraNdXVk7e2vIOGcS cnyvbKXT2OpHvkKbGXd576GZE15yTIDTatoW4tSyE8E4yfMn7TWD1jqGLMt5t8Bxaz4ifiKSnIAz +3FYTUUqMmQ6xb1rQyFjYjnAGPQ10w8wmEApIMjGd20fb3+yr9Kxi5qu8tt5wRV/4orxtpaZTcIw jHa5LPOFjCee/wAjpbWG3VJf3kbFfUx9badvfyzjPyzVn/YL0I7ddRTdQvuIbREUy4hJAUVbVpX9 2c1WezW+ZfLxEtkNsuSJDgaRgeZPc/Z+6vSvp9p+39Hemy0TAw3MkNjaWUHIKWRhJz6EH89TSWEc yqSUpZXQ0x141I2jqFdp6itaIz5aQFAkdkI45+VUzuTvj3GS/wD946tf5yTW3+tOqlOyJDapSnZS 3ypQ3DJOQea0xXpQKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKU ApSlAKUpQClKUApSlAKUpQF+oOB7HulSRke7W3/iG6s9VXWiU+xppojuIluP/nt1aKrcOrK5ClKV cKDxnpSlAckd1ceQ2+0cLbUFJPoQcirOdGNbGNd7ZqZiW4lDEj+MhI4KQSDuTn+aaq/Uv6aajVZ7 mmC94Yhy3AHVK425BHf7xQHpJ1b0jZup2g1CO3GnrDC3IywvvuaOE8Z5OU8H9VeYOprPcdN32baL hGdjvR3VNqQ4O+Dkfbxg1fH2bdexbSZNhu0mMxEcKDGcWrac8Ix8+MH7qxXth9EzqKL/AAmsTEt6 cuUVuhobwncEg5BV2OPljj7wKXWG8zLY+2rx3UxdqgkbQcj76n9tvdtuUJtLpK96MrS43wSPl271 qx9uTHkfRs5KmFMrKVIUMFB86/Fpeir8RGS32SvGQajbvTadw9y4Zuvh3xteaRHyZLfT9G37dPoS uJc5VvvSI7s50RRnCQokfVz9vc1nbi20p9wxpqmVLWVL2uOJzz8vtrX3iNSQvxjlzjaf/Ssvpi9R 4shz6RTvSG9qDkjnI/misW4s3jfHqlyl3JzSPEtNt21dry5yzGUm3s9vb2wZtqSqJKejakd94Yyf AyN+Ck4+3sa+Y8Ka2wEXRlMqGlXwEbTtTjjvhXbP56zM+LZb62kb9xSd+cLHf81fNmtMm2F5px3x YnhqAG0J9PmT2FR/4iKjnpLusY+3ozclo9eVdRz5lHnbNSUnzy1Uz+aKfT0MEm3TIt3jqhqUiG+t CkoDpxjd2IJ9CK79792F+hKKQkfBnj+maySZ8KNFecUceCpakjnyA+XyqIW6d76HFzF/HwlHHb58 D51dp+ZWbnJdOPnkjr38Jp0Y21OSbqPdjjEdvOPbLzglVxhxnpAnzG0OhpASjdk+Zzx28xXVjG6K AZQAgvENRWhsSS4v4U89h8RHJOBXDptEt+6+FPe8QITuCdoHcK8x9lckyxzbpc235qdrIWDtyDxx nkEHyq2tlOeypJYX+YRm1PPu6H4mypSU5NrHTno5Ta7LjC74OL6MEWXIYvyEi7ocAUkueJt+FJTy glB4OeCe/rWQiwZjjOyO8yiMSQpKfhJ9ewzX1JtFntcRyWpjKmhuHxr7+XmajFz1bIb3xoafDbwM DIPPfzTVaU7uTdBce/RfIxp1LfQKUVqjW9r+zO6XvJ+7znlnzqe1m2PiQlRS4VbtyXVE9yfP7KRG Z9yjoCHXdm0KUtx049PUnufSsBcJsya54kpzPGRwPn6fbX45dJC4YjFeW0gADA8vuqWjbVdkU2m/ U5/U1mxV1UnCEo030jwsv39Ptkyd2ctzDK4yW98xs4ccCcg478msCte8nbuyTwPlX0+WfCQUHLiu Vn0+VbS9nvpRP1/quChxqSiEVlaltpH1U4+IkkYHP7KzaVJU1jOTWL+/ldz3bVFdkv59X7m1/Y26 POXGdG1Re7MCxFl7luPqwEhKVYCR584z9oqfe0H1JYul1+j7fcXFQ4ocCgBtClhSh688VsnqVfrV 0z0H/Be0vtrlOtOFCXHMqTuWOSMHyUfTtVEOoOpXWlqZYcaU88VbzjJSDkfZ3q6YBB9Qz37jeJUp 90uFbqiCTxjPl9wFY+lKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgFKUoBSlKAUpSgL6oO32MtNqwDiHbzg/wDx26tHVWXSR7FenykZIgwMD/5zdWmq iBVLqKUpVZSeM9KUoBSlKA2JoHW3uikRLgnchGwIWXOeMDzPer8dHOp0O72mLbrmtlotx07H1vDC 8Z77lemK8w6nnTvqHK046liYlyRDCNqQlatye+O5x50BbP2pfZ5j6it0nVOlnUNy0KVJW0iOCFp2 p4HhpyRwT/fVJb3bLnp66vW24MvNLYcIUlSVJSo9sgECr19LOu8ZmBERIZck21xCUpIKw40MnyPB +welTLWejen3WuzPlhwM3CQxkOe5p3IUnzUVI5PxYyDQHm0GGXG/Ejv7TkgNLI3eXbH91TLpPerH aTdE3FBVJlNpQy4pKAG/rZGVHPJKTx/NH3TLrB7NmstAgS0vQ58BTZWHUvAKyNoIwceah5VpjcWH VNSmtxCvi5BIxwcHmh7l4wSqbp+5wxKnwZKvBLnCGtyQEknABHHFbB0r091hfNKRrq1dbKmNMjBa USbg6FhJHmPCI/XWp4d1ET4YM+SwD3S7naB/4T+6uzBVd3bh9JW6Yyw+UEuFha2y6M7iFeasnGee eKs1LelU/MiSs9ZvrJYoVGl/nqS3Vek7xaX0Wxx21POOI25jSVrGTxz8AqS6W6OzoWn7um8uW1yb LZAt5TuV4SwlwEqKkApBJRynPY+grU41Jd4d9Ex9fivNOhSkqWog4++p8nrK4tjL0J9LvbCXSR+f IqqnRhTWIos3eoXF3JSrSy17JfsY+FonUf0wYvvlvjvI3DxHZLiEnGRwdmfWszqXQ+p7NGZl3TUV vERxYQsw5rjriQfPapKAf0hUPuOortqxBhxobKWworc8Rwnd6Z/6NYubbpsCEtSpsaOnKd6Wt4J9 M4HOM/rqh21JvLiZENbv6cXCFVpMm9v0w2m+wn497ZvBUjJi3FrZHVlJGFELX27j4TyB2rB64s1v TdplylzrVbVq2qbgWpCVtgBCRxkowSck8etRJ91PhfFdpDxAHwBKsfnJrprS2t4Ij7yDgDfjJNXV FRWEiPqV6lWW+css5VrDqUs4SgBal+IoYUQcDH2DH6zXCvwzhLYUfUnz+6pXp/p/qO+SEtJQ20Tg AuOgkgkdsH51ZrpN7Kd1bUiZeJjMRtxnIcOx1ZJzwAO3l3NVFttt5Zp72euj0zW2q4KX3fCa4dWk slQQnPJVkf8AR86uJe9S6d6QaQRpSzMtyrsyylsuI8NHxLBypW0hXGBxj05rqaq1jpXphY5GltLw C5c0pVHfkqZDZHc7ipIBURvOPSql9UuopFwuCUqdeuEhRWSVKwkqJ5yeaHh99V9dzGnlpmPqmXCQ 1gL8YqDY4Azk57ZrSLi1OLK1qKlE5JJzmv15115wuPOKcWe6lEk18UApSlAKUpQClKUApSlAKUpQ ClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQF9CdvsXadV6 Qref/Pbq0lVbLKpHsX6dYQCpbkK3oSB3JL7Yq0lWodWVy6ClKVdKDxnpSlAKUpQClKUBndIamnaf uCXm3HXI5G1xncMKH3g1ubQnVmFDmxpttur1quSc4QsbRkjBG7BTz6VXyv1KlJUFJJSoHIIPINAe iOhfaGsEyIq362Ch4qykvKbS40UEHhQSkDHb171xaw6cdFOojaZViu+m4biQchSsbirJB2qWnHcd hVD7Zqm6xCht1/3hjd8SXU7jjzwe/wCupxaNb25obod4uFuc4+EqUE/qyKA2hqn2Vb2mUs2Q22Ww tRLbseTkFPHOCvitbTvZ/wBfxZyorMFx18ZKUhpQJHPPGeOKmVm6v6yjlKYerxIQ2jAT44VgfPmp nF643tLaXHCw9K27S8YySoD0Bz2xQGi3+hXU5DiQdOyXFKz2Qo9vuroS+j+v4iimVY3mTjOFgjP6 q35J60apW8h9iShJTnCSwjaQftP211ZfVe+zUD3tuC6pBylSozeT8jz2oDSdn6S68mlxuLCcSlHL gTv4/MKztr9n7W0iQEvQ3EpA3KyyrBHpk4rYjXVa9Q2Hfdha4zjisqUlhtOf11wyuteovB2i9w2l 8ctpSOw+RoD50v7Lt7nttyJSIEVsEhRlSCg5H9HdWzLF7PGkLE2ly86t07D2ErWlABKh9pcSa1VJ 663xNuMdWpyHCvJKFJH3d6hl86pSZ6yZeorg/wAYIC1EEenFAXO//Ero1oiKw1bnoUyU2yEBcKNu UopA7qPbJA8zWlOqXtBTbyhxpq6OWqEXiptlL6QrHGASlIJ+8+dVpuutJDm0QEBHfKnUZV93JqMS 5kqWrdJfW6c5+I8ZoCbax16/cm3mokmSpbivieUcZH7f2VBHXFuuKccWpa1HJUTkmvmlAKUpQClK UApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQC lKUApSlAegFlAV7JGj0qGQWrWCM/1lqrMVWW0gq9kXSIScEs2wA+n8ZbqzVWaf539P5Ls/yr6/wK UpV4tHjPSlKAUpSgFKUoBSlKAUpSgFc7EuQyAGnNoH9EGuClAdh+bJf/ANK5u4x9UCuAknvX5SgF KUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoC/wBalJR7ImklqOEpZthP2e8N1Zqqvxxn2N9MDGcxbd// AL26tBVmmvif0/kuT/KvqKUpV4tnjPSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpS gFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAU pSgFKUoBSlKAUpSgL8Rhn2OdMDOMxbdz/wD1DdWgqrZTv9i/TqM43QreP/Pbq0lWqfVlyfQUpSrp bPGelXS/A+0d+VV+/Ra/w0/A+0d+VV+/Ra/w1RvRVtKW0q6X4H2jvyqv36LX+Gn4H2jvyqv36LX+ Gm9DaUtpV0vwPtHflVfv0Wv8NPwPtHflVfv0Wv8ADTehtKW0q6X4H2jvyqv36LX+Gn4H2jvyqv36 LX+Gm9DaUtpV0vwPtHflVfv0Wv8ADT8D7R35VX79Fr/DTehtKW0q6X4H2jvyqv36LX+Gn4H2jvyq v36LX+Gm9DaUtpV0vwPtHflVfv0Wv8NPwPtHflVfv0Wv8NN6G0pbSrpfgfaO/Kq/fotf4afgfaO/ Kq/fotf4ab0NpS2lXS/A+0d+VV+/Ra/w0/A+0d+VV+/Ra/w03obSltKul+B9o78qr9+i1/hp+B9o 78qr9+i1/hpvQ2lLaVdL8D7R35VX79Fr/DT8D7R35VX79Fr/AA03obSltKul+B9o78qr9+i1/hp+ B9o78qr9+i1/hpvQ2lLaVdL8D7R35VX79Fr/AA0/A+0d+VV+/Ra/w03obSltKul+B9o78qr9+i1/ hp+B9o78qr9+i1/hpvQ2lLaVdL8D7R35VX79Fr/DT8D7R35VX79Fr/DTehtKW0q6X4H2jvyqv36L X+Gn4H2jvyqv36LX+Gm9DaUtpV0vwPtHflVfv0Wv8NPwPtHflVfv0Wv8NN6G0pbSrpfgfaO/Kq/f otf4afgfaO/Kq/fotf4ab0NpS2lXS/A+0d+VV+/Ra/w0/A+0d+VV+/Ra/wANN6G0pbSrpfgfaO/K q/fotf4afgfaO/Kq/fotf4ab0NpS2lXS/A+0d+VV+/Ra/wANPwPtHflVfv0Wv8NN6G0pbSrpfgfa O/Kq/fotf4afgfaO/Kq/fotf4ab0NpS2lXS/A+0d+VV+/Ra/w1wyfZE0g2tDbepr6txeSAfCAwMZ 52H1FN6G1lM6Vcr8EDTn5QXj/aN/8un4IGnPygvH+0b/AOXTehsZTWlXK/BA05+UF4/2jf8Ay6fg gac/KC8f7Rv/AJdN6GxlNaVcr8EDTn5QXj/aN/8ALp+CBpz8oLx/tG/+XTehsZTWlXK/BA05+UF4 /wBo3/y6fggac/KC8f7Rv/l03obGU1pVyvwQNOflBeP9o3/y6fggac/KC8f7Rv8A5dN6GxlNaVcr 8EDTn5QXj/aN/wDLp+CBpz8oLx/tG/8Al03obGU1pVyvwQNOflBeP9o3/wAun4IGnPygvH+0b/5d N6GxlNaVcr8EDTn5QXj/AGjf/Lp+CBpz8oLx/tG/+XTehsZTWlXK/BA05+UF4/2jf/Lp+CBpz8oL x/tG/wDl03obGSxrP4GmmtoyfdLdgf8Az26tFWgNa2FjTfs9QdNxpDjzNvehRUOOABRCJSE844zx 3H6u1b/qmn1Z7PoKUpV0oIpa9O6gbkqVdL7bJTGzCURrWthYV67lPrBHfjH318tad1EJE1Tt+tS2 VpIhoTanEqZV5FxXvBDgHmAEZ9RSlebUe7mctr09em1P/Sl5t8oKI8ARrctnYP6W55e7y7ba4oOn NRoiS0zb9anpKs+6uM2pxtDXpvSZCiv7lIpSm1DLOhYNC3W33G7XWZf4UmfcksJUpq2qaaQWgRkI Lyicg/zqz82wPOw3mo9wDDy21JbdLO7YojhWNwzg84yKUryUVLhnqk0RXT3Tq9WK4PPQ9VR5DEpx hUv323Ldfc8NhtrhzxxhSthUVFKvrduOc/K09e1XZh2Lerc1bkj8cw5bVreWf6LoeSlP3oVSletZ eSlcLB116c1OZji0ahs4ilK9jZs7hWkknYSv3jBAGMjaN2DgpzxzDTt++hPBN8tv0rj/ALT9GL8D Of8AufH3dv8A8z+6lK82o93M4ZukbrOtcKNJ1ElmQ1IbelPQ4imUvpQrcUBJdUUJVgA5UrIyPOul onQt409ao1nkahgTbdEipYYDdsW09kDG5Sy+pJHfgJH20pTauV6jLP3Q+hrvp22RLRI1BBm26HFQ wylFsW09uA+spZeUkjvwEj7a7sLTmpUe+e+agtD29JETwrQ434SvIuZkK8QduBsz6ilK9aTGWjif 01q4xdrGprGiR4pO9dkdUjw8DCdolA7s5+LOOR8I8+IaO1GnV/0unVcX6OXGbYdt6rc4RlJUVLQr x9qVKKsZKDwAOcZpSmEMvGD9Oj9Rx1TXIGqoniSpK3h77bnH0MpIQlCEJS+jCQEnP85Ss8cg9+Fp 6+InqXMvdueh+EAlpq2rbcDmBlRWXlAp7/DtyOOTjlSvNq6DLOmnR94kyYrt3u9mmJiyEPtBu0ON qQQhaVEEyFYUd3BxwMjBzkc7WnNSCTNU7f7SphaFCGhNpcStpX8kuK94IcA8wAjPqKUr3ahuZxu6 b1UYKkNajsqZZeyl1VmdU2G8fVKPeQSrP8rdj+jXLcNOajWpv6Pv9qYSEJDgftLjpUrncRiQnAPG BzjB5OeFKbUMs6mtNEXa+Q22bZqt6zOIbeBcaYUdyltlCVHa4k/DuKgM/WCT/J55GtL6qasLMVOp rSq5IICpTlndU0tA/wDy/eQrd/S3/dSlNqGWc0/TmpFlr3C/2lgBKPF8a0uO7lDO8pxITtB4wDnG DkqzxyMadvyffvHvltXvz7lsti0+DwceJl8+J5dtnY+vClNqGWdG4aMvN209eLRdb/AWmfFcjsuR rYtoshaSklQU8vf3B429q7EfS1+jzIjbN+tn0Wy0htbC7WsvrwnBIdD4SM/2DSlNqGX/AJ/nsdTT ejdT21q4NztWw7j47zr0VS7Y4lTClqKgkkyFbkJyAEjbwO9dr+Deqfobwv4RWb6T3594+hnfA2+n he87s/PxPupSm1Dc85PtzSdzkSLS/I1AEqhFa5KI0ZTTcpZQUjKfEJCBkq2kq5288VjLvoS/Xqxy bbdtSW1SlSY77DkW0raDYadS5tUFSF7s7AMgpx6GlKbVnIyzIfwQuc6yvQL3qAKddXnx7VHchKCA QdoPirUDxgkKHB4xX5pbREix++sK1HOuMN14ORW5hW87GTtAUjxVrKlgqBI3cjOKUphDLOeRp3UC rklyPfbW3B8QFTLlrcW6UYGQHA+ADnPOzjI4OMnru6FQu7qn+NbcmciXkwFFzKWS3nd4uN/PCsYC eNpPxUpRRSGWzu3jT93dYQLPd4MN0LBWqVAXISU+YAS63g/PJ+yulP01qxbEdMDUlkYeSFeOp+yu upWc/DtSJSSnA75Ks9+O1KV5tQ3My8WxPpjNJlTmnXwgB1bbBQhSsckJKlFIz5EnHqa5PoT+tf8A l/50pXu1DLH0J/Wv/L/zp9Cf1r/y/wDOlK82obmPoT+tf+X/AJ0+hP61/wCX/nSlNqG5j6E/rX/l /wCdcatP5lNv+9/USpOPD75I+fypSm1Dczs/RP8AWP8Ac/zp9E/1j/c/zpSm1Dcx9E/1j/c/zp9E /wBY/wBz/OlKbUNzH0T/AFj/AHP86fRP9Y/3P86UptQ3MfRP9Y/3P86fRP8AWP8Ac/zpSm1Dcx9E /wBY/wBz/On0T/WP9z/OlKbUNzH0T/WP9z/On0T/AFj/AHP86UptQ3MfRP8AWP8Ac/zp9E/1j/c/ zpSm1Dcx9E/1j/c/zp9E/wBY/wBz/OlKbUNzH0T/AFj/AHP86fRP9Y/3P86UptQ3MjuuNBnUukX7 ALt7oXZKH/G93342vB3G3cPTHf5/KprSleqKXQNtilKV6eH/2Q== --000e0cd1512a8aeab904a44576a2-- ========================================================================= Date: Fri, 27 May 2011 19:52:36 +0100 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: Masking with contrast images from other analysis in spm8 Comments: To: =?utf-8?B?PEVtbWE+IDxTbWl0aD4=?= <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----------=_1306521688-8454-67" Content-Transfer-Encoding: binary Message-ID: <[log in to unmask]> This is a multi-part message in MIME format... ------------=_1306521688-8454-67 Received: from [192.168.0.3] (5acc0817.bb.sky.com [90.204.8.23]) (authenticated user=cpernet mech=LOGIN bits=0) by lmtp1.ucs.ed.ac.uk (8.13.8/8.13.7) with ESMTP id p4RIfQRN016889 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-SHA bits=256 verify=NOT); Fri, 27 May 2011 19:41:27 +0100 (BST) Message-Id: <[log in to unmask]> To: "=?utf-8?B?PEVtbWE+IDxTbWl0aD4=?=" <[log in to unmask]>, [log in to unmask] From: "=?utf-8?B?Y3lyaWwucGVybmV0QGVkLmFjLnVr?=" <[log in to unmask]> Subject: =?utf-8?B?UmU6IFtTUE1dIE1hc2tpbmcgd2l0aCBjb250cmFzdCBpbWFnZXMgZnJvbSBvdGhlciBhbmFseXNpcyBpbiBzcG04?= Date: Fri, 27 May 2011 19:52:36 +0100 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_12_1306522356784" 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_12_1306522356784 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SGkgRW1tYSwKCkZyb20geW91ciAxc3QgYW5hbHlzaXMgd2hlbiB5b3UgaGF2 ZSB0aGUgcmVzdWx0IGRpc3BsYXllZCBjbGljayBvbiBzYXZlIGltYWdlICh0 aGVyZSBjaGVjayBpZiBpdCdzIGJpbmFyeSBvciBub3QgSSBjYW4ndCByZW1l bWJlciAuLiBJZiBpdCdzIG5vdCBvbmx5IDBzIGFuZCAxcyB1c2UgaW1jYWxj IGRvaW5nIGkxPjAgdGhlIG91dHB1dCB3aWxsIGJlIGJpbmFyeSBmb3Igc3Vy ZSBhbmQgcmVhZHkgYXMgYSBtYXNrKQoKRnJvbSB0aGVyZSAyIG9wdGlvbnM6 IGVpdGhlciB1c2UgdGhlIG1hc2sgd2hlbiB5b3Ugc3BlY2lmeSB5b3VyIGRl c2lnbiBtYXRyaXggb3IgaW5zdGFsbCB3ZnUgcGlja2F0bGFzIGFuZCB3aGVu IHRoZSBtZW51IGZvciBhIGNvbnRyYXN0IGNvbWVzIHVwIHlvdSB3aWxsIGhh dmUgdGhlIG9wdGlvbiB0byBtYXNrLiBTZWUgbXkgZW1haWwgZnJvbSBlYXJs aWVyICBleHBsYWluaW5nIHRoZSBkaWZmZXJlbmNlIGJldHdlZW4gdGhlIDIg YXBwcm9hY2hlcy4KCkN5cmlsCgpEciBDeXJpbCBQZXJuZXQKQlJJQyAvIFNJ TkFQU0UKVW5pdmVyc2l0eSBvZiBFZGluYnVyZ2gKCi0tLS0tIFJlcGx5IG1l c3NhZ2UgLS0tLS0KRnJvbTogIjxFbW1hPiA8U21pdGg+IiA8ZW1tYV9zbWl0 aDMwNzRAWUFIT08uQ09NPgpEYXRlOiBGcmksIE1heSAyNywgMjAxMSAxNjox NApTdWJqZWN0OiBbU1BNXSBNYXNraW5nIHdpdGggY29udHJhc3QgaW1hZ2Vz IGZyb20gb3RoZXIgYW5hbHlzaXMgaW4gc3BtOApUbzogPFNQTUBKSVNDTUFJ TC5BQy5VSz4KCkRlYXIgU1BNIGV4cGVydHMsCgpDYW4gc29tZWJvZHkgcG9p bnQgb3V0IGhvdyB0byB1c2UgZXhwbGljaXQgbWFza2luZyB3aXRoIGEgY29u dHJhc3QgaW1hZ2UgZm9ybSBhIGRpZmZlcmVudCBhbmFseXNpcyBpbiBzcG04 ICh0aGUgcHJvZ3JhbSBhbGxvd3Mgb25seSBmb3IgbWFza2luZyB3aXRoIGEg Y29udHJhc3QgZnJvbSB0aGUgc2FtZSBtb2RlbCk/CgpUaGFuayB5b3UuCgpF bW1hCgo= ------=_Part_12_1306522356784 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: base64 Content-Disposition: inline SGkgRW1tYSw8YnI+PGJyPkZyb20geW91ciAxc3QgYW5hbHlzaXMgd2hlbiB5 b3UgaGF2ZSB0aGUgcmVzdWx0IGRpc3BsYXllZCBjbGljayBvbiBzYXZlIGlt YWdlICh0aGVyZSBjaGVjayBpZiBpdCYjMzk7cyBiaW5hcnkgb3Igbm90IEkg Y2FuJiMzOTt0IHJlbWVtYmVyIC4uIElmIGl0JiMzOTtzIG5vdCBvbmx5IDBz IGFuZCAxcyB1c2UgaW1jYWxjIGRvaW5nIGkxJmd0OzAgdGhlIG91dHB1dCB3 aWxsIGJlIGJpbmFyeSBmb3Igc3VyZSBhbmQgcmVhZHkgYXMgYSBtYXNrKTxi cj48YnI+RnJvbSB0aGVyZSAyIG9wdGlvbnM6IGVpdGhlciB1c2UgdGhlIG1h c2sgd2hlbiB5b3Ugc3BlY2lmeSB5b3VyIGRlc2lnbiBtYXRyaXggb3IgaW5z dGFsbCB3ZnUgcGlja2F0bGFzIGFuZCB3aGVuIHRoZSBtZW51IGZvciBhIGNv bnRyYXN0IGNvbWVzIHVwIHlvdSB3aWxsIGhhdmUgdGhlIG9wdGlvbiB0byBt YXNrLiBTZWUgbXkgZW1haWwgZnJvbSBlYXJsaWVyICZuYnNwO2V4cGxhaW5p bmcgdGhlIGRpZmZlcmVuY2UgYmV0d2VlbiB0aGUgMiBhcHByb2FjaGVzLjxi cj48YnI+Q3lyaWw8YnI+PGJyPkRyIEN5cmlsIFBlcm5ldDxicj5CUklDIC8g U0lOQVBTRTxicj5Vbml2ZXJzaXR5IG9mIEVkaW5idXJnaDxicj48YnI+LS0t LS0gUmVwbHkgbWVzc2FnZSAtLS0tLTxicj5Gcm9tOiAmcXVvdDsmbHQ7RW1t YSZndDsgJmx0O1NtaXRoJmd0OyZxdW90OyAmbHQ7ZW1tYV9zbWl0aDMwNzRA WUFIT08uQ09NJmd0Ozxicj5EYXRlOiBGcmksIE1heSAyNywgMjAxMSAxNjox NDxicj5TdWJqZWN0OiBbU1BNXSBNYXNraW5nIHdpdGggY29udHJhc3QgaW1h Z2VzIGZyb20gb3RoZXIgYW5hbHlzaXMgaW4gc3BtODxicj5UbzogJmx0O1NQ TUBKSVNDTUFJTC5BQy5VSyZndDs8YnI+PGJyPkRlYXIgU1BNIGV4cGVydHMs PGJyPjxicj5DYW4gc29tZWJvZHkgcG9pbnQgb3V0IGhvdyB0byB1c2UgZXhw bGljaXQgbWFza2luZyB3aXRoIGEgY29udHJhc3QgaW1hZ2UgZm9ybSBhIGRp ZmZlcmVudCBhbmFseXNpcyBpbiBzcG04ICh0aGUgcHJvZ3JhbSBhbGxvd3Mg b25seSBmb3IgbWFza2luZyB3aXRoIGEgY29udHJhc3QgZnJvbSB0aGUgc2Ft ZSBtb2RlbCk/PGJyPjxicj5UaGFuayB5b3UuPGJyPjxicj5FbW1hPGJyPjxi cj48YnI+PGJyPg== ------=_Part_12_1306522356784-- ------------=_1306521688-8454-67 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. ------------=_1306521688-8454-67-- ========================================================================= Date: Sat, 28 May 2011 00:35:28 +0100 Reply-To: David Soto <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Soto <[log in to unmask]> Subject: Re: second level analysis affected by subject order Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear SPM list, I have replicated this issue in my data. I can't think of any reason behi= nd but it seems like a bug to me and any implications ought to be addressed.= .. hope this is not much of a hassle! Regards, david ========================================================================= Date: Fri, 27 May 2011 20:09:23 -0400 Reply-To: Lucy Brown <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Lucy Brown <[log in to unmask]> Subject: Re: second level analysis affected by subject order Comments: To: David Soto <[log in to unmask]> 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]> Do you mean the order that the subjects' contrast files were entered for the group analysis? Best, Lucy On May 27, 2011, at 7:35 PM, David Soto wrote: > Dear SPM list, > > I have replicated this issue in my data. I can't think of any reason behind > but it seems like a bug to me and any implications ought to be addressed... > > hope this is not much of a hassle! > > Regards, david Lucy L. Brown, Ph.D. Professor Department of Neurology Department of Neuroscience Albert Einstein College of Medicine 1300 Morris Park Ave. 125F Bronx, NY 10461 917-407-3266 [log in to unmask] ========================================================================= Date: Sat, 28 May 2011 14:10:06 +0900 Reply-To: Yuko <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Yuko <[log in to unmask]> Subject: Problem to decide the coordinate for eigenvariate and DCM MIME-Version: 1.0 Content-Type: text/plain; charset=ISO-8859-1 Message-ID: <[log in to unmask]> Dear all I would like to ask one question about DCM. I tried to extract time-series for DCM, therefor I used eigenvariate and decided a coordinate, but SPM8 changed the coordinate automatically. How could I extract the time-series from the VOI which I decided. Any help will be highly appreciated. Thank you in advance. Yuko ========================================================================= Date: Sun, 29 May 2011 15:32:14 +0930 Reply-To: Leon Petchkovsky <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Leon Petchkovsky <[log in to unmask]> Subject: The Jugain complexes and some help with DCM MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=00151759312a9c3d5304a463e92f Message-ID: <[log in to unmask]> --00151759312a9c3d5304a463e92f Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear all I need some advice and help with DCM. The study involved a replication of Jung=92s original Word Association test under fMRI conditions. The subject is asked to respond with the first word that comes to mind in response to a stimulus word from a standard list. Mos= t responses are bland and neutral, but some of them are not. Response time is prolonged, response are often idiosyncratic and affect =96laden. Jung infer= red internal conflict (his so-called =93complexes=94) from these unusual respo= nses. We ran 14 subjects thru 100 words, 20 second epoch for each . We compared complexed versus neutral responses for the group for the entire 20 sec epoch. (using SPM 5) I also split it up into 3 second bits (first 3 seconds, 2nd 3 etc etc) to get some idea of what goes on over time, to construct a functional hypothesis that could be subjected to DCM testing. . The complexed responses =93pattern=94 that survived FWE correction seem to = go like this. FIRST 3 SECS. There is strong activation (corrected for FEW) in (1) L Broca=92s pre Broca=92s, Z 5.58 (2) the L Middle temporal lobe Z 4.96, (3) L SMA/middle cingulate Z 4.97 and (4) L Anterior Insula Z 5.11 (all probably reflectin= g the conflicted word searching of a complexed response). But there is also homologous activation of all these areas in the R hemisphere, albeit more weakly. (???interhemispheric negotiation/chatter whatever). (R Broca Z 4.85, R Mid Temp Z 4.77, R insula Z 4.77) However, there is a SMALL area in R prefrontal region at 54,4, 47 (R precentral Area 6), which stands out on its own, 16 voxel Z 5.38, p FWE corr .001. This is the second strongest response in the entire result list. I don=92t know * what * to make of it. SECOND THREE SECS. Same as above , BUT, there is a brief bit of R supraorbital (43 29 -6) activity (p FWE corr .038, Z 4.14) probably to do with the executive effort of the conflictual nature of the =93complexed=92 response. And activ= ity shifts to from L to R SMA Z 5.28 ! The REST of the epoch achieves nothing that passes the FWE FDR barrier. So I suppose a model might say that there is some some antero-posterior (receptive-expressive verbal) activity; with inter-hemispheric negotiations which include SMA and cingulum; and cortico-mesencephalic-limbic (Bilate= ral Anterior Insula) interaction, with R supraorbital (executive function) region coming on line a little later. But this still doesn=92t account for the weird SMALL but very prominent = R precentral Area 6 region at 54,4, 47 . Does anyone have any suggestions on how I might model this in DCM? Or for that matter whether DCM could even do this in principle? Since my limited understanding of DCM is that you can only do about 3 ROIs at a time ? I=92ve asked my local colleagues, but they can=92t seem to come up with anything. . Leon --=20 *A/Prof Leon Petchkovsky Psychiatrist, Jungian Analyst Past President, ANZSJA Director, the Pinniger Clinic, HQ@Robina www.pinnigerclinic.com 0755809397 0420923885* --00151759312a9c3d5304a463e92f Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Dear all </f= ont></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I need some = advice and help with<span style=3D"mso-spacerun: yes">=A0 </span>DCM.</font= ></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The study in= volved a replication of Jung=92s original Word Association test under fMRI = conditions.<span style=3D"mso-spacerun: yes">=A0 </span>The subject is aske= d to respond with the first word that comes to mind in response to a stimul= us word from a standard list. Most responses are bland and neutral, but som= e of them are not. Response time is prolonged, response are often idiosyncr= atic and affect =96laden. Jung inferred internal conflict<span style=3D"mso= -spacerun: yes">=A0 </span>(his so-called =93complexes=94) from these unusu= al responses.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3"><span style= =3D"mso-spacerun: yes">=A0</span>We ran 14 subjects thru 100 words, 20 seco= nd epoch for each . We compared complexed versus neutral responses for the = group for the entire 20 sec epoch. (using SPM 5)</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I also split= it <span style=3D"mso-spacerun: yes">=A0</span>up into 3 second bits (firs= t 3 seconds, 2<sup>nd</sup> 3 etc etc) to get some idea of what goes on ove= r time, to construct a functional hypothesis that could be subjected to DCM= testing.<span style=3D"mso-spacerun: yes">=A0 </span>. </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The complexe= d responses =93pattern=94 that survived FWE correction seem to go like this= . </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">FIRST 3 SECS= .<span style=3D"mso-spacerun: yes">=A0 </span></font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">There is<spa= n style=3D"mso-spacerun: yes">=A0 </span>strong activation (corrected for F= EW) <span style=3D"mso-spacerun: yes">=A0</span>in (1) <span style=3D"mso-s= pacerun: yes">=A0</span>L Broca=92s pre Broca=92s, Z 5.58<span style=3D"mso= -spacerun: yes">=A0 </span>(2) the L Middle temporal lobe Z 4.96, (3) <span= style=3D"mso-spacerun: yes">=A0</span>L SMA/middle cingulate<span style=3D= "mso-spacerun: yes">=A0 </span>Z 4.97 and (4) L Anterior Insula <span style= =3D"mso-spacerun: yes">=A0</span>Z 5.11 (all probably reflecting the confli= cted word searching of a complexed response).</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">But there is= also homologous activation of all these areas in the R hemisphere, albeit = more weakly. (???interhemispheric negotiation/chatter whatever). <span styl= e=3D"mso-spacerun: yes">=A0</span>(R Broca Z 4.85, R Mid Temp Z<span style= =3D"mso-spacerun: yes">=A0 </span>4.77,<span style=3D"mso-spacerun: yes">= =A0 </span>R insula Z 4.77) </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">However, the= re is a SMALL area in R prefrontal region at 54,4, 47 (R precentral<span st= yle=3D"mso-spacerun: yes">=A0 </span>Area 6),<span style=3D"mso-spacerun: y= es">=A0 </span>which stands out on its own,<span style=3D"mso-spacerun: yes= ">=A0 </span>16 voxel<span style=3D"mso-spacerun: yes">=A0 </span>Z 5.38, p= FWE corr .001. This is the second strongest response in the entire result = list.<span style=3D"mso-spacerun: yes">=A0 </span>I don=92t know <i style= =3D"mso-bidi-font-style: normal"><span style=3D"mso-spacerun: yes">=A0</spa= n>what </i><span style=3D"mso-spacerun: yes">=A0</span>to make of it. </fon= t></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">SECOND THREE= SECS.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Same as abov= e , BUT, there is a brief<span style=3D"mso-spacerun: yes">=A0 </span>bit o= f R supraorbital<span style=3D"mso-spacerun: yes">=A0 </span>(43 29 -6) act= ivity<span style=3D"mso-spacerun: yes">=A0 </span>(p FWE corr <span style= =3D"mso-spacerun: yes">=A0</span>.038,<span style=3D"mso-spacerun: yes">=A0= </span>Z 4.14) probably to do with the executive effort of the conflictual= nature of the =93complexed=92 response. And activity shifts to from L to R= SMA <span style=3D"mso-spacerun: yes">=A0</span>Z 5.28 !</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The REST of = the epoch achieves nothing that passes the FWE FDR<span style=3D"mso-spacer= un: yes">=A0 </span>barrier. </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">So I suppose= a model<span style=3D"mso-spacerun: yes">=A0 </span>might say that there i= s some some antero-posterior (receptive-expressive verbal) activity; with i= nter-hemispheric negotiations which include SMA and cingulum;<span style=3D= "mso-spacerun: yes">=A0=A0 </span>and cortico-mesencephalic-limbic<span sty= le=3D"mso-spacerun: yes">=A0 </span>(Bilateral Anterior Insula)<span style= =3D"mso-spacerun: yes">=A0 </span>interaction, with R supraorbital (executi= ve function) region coming on line a little later.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">But this sti= ll doesn=92t account for<span style=3D"mso-spacerun: yes">=A0 </span>the we= ird<span style=3D"mso-spacerun: yes">=A0 </span>SMALL but very prominent <s= pan style=3D"mso-spacerun: yes">=A0</span>R precentral<span style=3D"mso-sp= acerun: yes">=A0 </span>Area 6 region at 54,4, 47 . </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Does anyone = have any suggestions on how I might model this in DCM?<span style=3D"mso-sp= acerun: yes">=A0 </span>Or for that matter whether DCM could even do this i= n principle? Since my limited understanding of DCM is that you can only do = about 3 ROIs at a time ? </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I=92ve asked= my local colleagues, but they can=92t seem to come up with anything.</font= ></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">.</font></sp= an></p>Leon<br clear=3D"all"><br>-- <br><b style=3D"COLOR: rgb(0,0,0)">A/Pr= of Leon Petchkovsky<br> Psychiatrist, Jungian Analyst<br>Past President, ANZSJA<br>Director, the Pi= nniger Clinic, HQ@Robina<br><a href=3D"http://www.pinnigerclinic.com/" targ= et=3D"_blank">www.pinnigerclinic.com</a><br>0755809397<br>0420923885</b><br= > --00151759312a9c3d5304a463e92f-- ========================================================================= Date: Sun, 29 May 2011 15:35:09 +0930 Reply-To: Leon Petchkovsky <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Leon Petchkovsky <[log in to unmask]> Subject: Trying again, the JUNGIAN complexes and DCM MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=000e0cd4d0880437bb04a463f40d Message-ID: <[log in to unmask]> --000e0cd4d0880437bb04a463f40d Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Dear all I need some advice and help with DCM. The study involved a replication of Jung=92s original Word Association test under fMRI conditions. The subject is asked to respond with the first word that comes to mind in response to a stimulus word from a standard list. Mos= t responses are bland and neutral, but some of them are not. Response time is prolonged, response are often idiosyncratic and affect =96laden. Jung infer= red internal conflict (his so-called =93complexes=94) from these unusual respo= nses. We ran 14 subjects thru 100 words, 20 second epoch for each . We compared complexed versus neutral responses for the group for the entire 20 sec epoch. (using SPM 5) I also split it up into 3 second bits (first 3 seconds, 2nd 3 etc etc) to get some idea of what goes on over time, to construct a functional hypothesis that could be subjected to DCM testing. . The complexed responses =93pattern=94 that survived FWE correction seem to = go like this. FIRST 3 SECS. There is strong activation (corrected for FEW) in (1) L Broca=92s pre Broca=92s, Z 5.58 (2) the L Middle temporal lobe Z 4.96, (3) L SMA/middle cingulate Z 4.97 and (4) L Anterior Insula Z 5.11 (all probably reflectin= g the conflicted word searching of a complexed response). But there is also homologous activation of all these areas in the R hemisphere, albeit more weakly. (???interhemispheric negotiation/chatter whatever). (R Broca Z 4.85, R Mid Temp Z 4.77, R insula Z 4.77) However, there is a SMALL area in R prefrontal region at 54,4, 47 (R precentral Area 6), which stands out on its own, 16 voxel Z 5.38, p FWE corr .001. This is the second strongest response in the entire result list. I don=92t know * what * to make of it. SECOND THREE SECS. Same as above , BUT, there is a brief bit of R supraorbital (43 29 -6) activity (p FWE corr .038, Z 4.14) probably to do with the executive effort of the conflictual nature of the =93complexed=92 response. And activ= ity shifts to from L to R SMA Z 5.28 ! The REST of the epoch achieves nothing that passes the FWE FDR barrier. So I suppose a model might say that there is some some antero-posterior (receptive-expressive verbal) activity; with inter-hemispheric negotiations which include SMA and cingulum; and cortico-mesencephalic-limbic (Bilate= ral Anterior Insula) interaction, with R supraorbital (executive function) region coming on line a little later. But this still doesn=92t account for the weird SMALL but very prominent = R precentral Area 6 region at 54,4, 47 . Does anyone have any suggestions on how I might model this in DCM? Or for that matter whether DCM could even do this in principle? Since my limited understanding of DCM is that you can only do about 3 ROIs at a time ? I=92ve asked my local colleagues, but they can=92t seem to come up with anything. . Leon --=20 *A/Prof Leon Petchkovsky Psychiatrist, Jungian Analyst Past President, ANZSJA Director, the Pinniger Clinic, HQ@Robina www.pinnigerclinic.com 0755809397 0420923885* --000e0cd4d0880437bb04a463f40d Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Dear all </f= ont></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I need some = advice and help with<span style=3D"mso-spacerun: yes">=A0 </span>DCM.</font= ></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The study in= volved a replication of Jung=92s original Word Association test under fMRI = conditions.<span style=3D"mso-spacerun: yes">=A0 </span>The subject is aske= d to respond with the first word that comes to mind in response to a stimul= us word from a standard list. Most responses are bland and neutral, but som= e of them are not. Response time is prolonged, response are often idiosyncr= atic and affect =96laden. Jung inferred internal conflict<span style=3D"mso= -spacerun: yes">=A0 </span>(his so-called =93complexes=94) from these unusu= al responses.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3"><span style= =3D"mso-spacerun: yes">=A0</span>We ran 14 subjects thru 100 words, 20 seco= nd epoch for each . We compared complexed versus neutral responses for the = group for the entire 20 sec epoch. (using SPM 5)</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I also split= it <span style=3D"mso-spacerun: yes">=A0</span>up into 3 second bits (firs= t 3 seconds, 2<sup>nd</sup> 3 etc etc) to get some idea of what goes on ove= r time, to construct a functional hypothesis that could be subjected to DCM= testing.<span style=3D"mso-spacerun: yes">=A0 </span>. </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The complexe= d responses =93pattern=94 that survived FWE correction seem to go like this= . </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">FIRST 3 SECS= .<span style=3D"mso-spacerun: yes">=A0 </span></font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">There is<spa= n style=3D"mso-spacerun: yes">=A0 </span>strong activation (corrected for F= EW) <span style=3D"mso-spacerun: yes">=A0</span>in (1) <span style=3D"mso-s= pacerun: yes">=A0</span>L Broca=92s pre Broca=92s, Z 5.58<span style=3D"mso= -spacerun: yes">=A0 </span>(2) the L Middle temporal lobe Z 4.96, (3) <span= style=3D"mso-spacerun: yes">=A0</span>L SMA/middle cingulate<span style=3D= "mso-spacerun: yes">=A0 </span>Z 4.97 and (4) L Anterior Insula <span style= =3D"mso-spacerun: yes">=A0</span>Z 5.11 (all probably reflecting the confli= cted word searching of a complexed response).</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">But there is= also homologous activation of all these areas in the R hemisphere, albeit = more weakly. (???interhemispheric negotiation/chatter whatever). <span styl= e=3D"mso-spacerun: yes">=A0</span>(R Broca Z 4.85, R Mid Temp Z<span style= =3D"mso-spacerun: yes">=A0 </span>4.77,<span style=3D"mso-spacerun: yes">= =A0 </span>R insula Z 4.77) </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">However, the= re is a SMALL area in R prefrontal region at 54,4, 47 (R precentral<span st= yle=3D"mso-spacerun: yes">=A0 </span>Area 6),<span style=3D"mso-spacerun: y= es">=A0 </span>which stands out on its own,<span style=3D"mso-spacerun: yes= ">=A0 </span>16 voxel<span style=3D"mso-spacerun: yes">=A0 </span>Z 5.38, p= FWE corr .001. This is the second strongest response in the entire result = list.<span style=3D"mso-spacerun: yes">=A0 </span>I don=92t know <i style= =3D"mso-bidi-font-style: normal"><span style=3D"mso-spacerun: yes">=A0</spa= n>what </i><span style=3D"mso-spacerun: yes">=A0</span>to make of it. </fon= t></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">SECOND THREE= SECS.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Same as abov= e , BUT, there is a brief<span style=3D"mso-spacerun: yes">=A0 </span>bit o= f R supraorbital<span style=3D"mso-spacerun: yes">=A0 </span>(43 29 -6) act= ivity<span style=3D"mso-spacerun: yes">=A0 </span>(p FWE corr <span style= =3D"mso-spacerun: yes">=A0</span>.038,<span style=3D"mso-spacerun: yes">=A0= </span>Z 4.14) probably to do with the executive effort of the conflictual= nature of the =93complexed=92 response. And activity shifts to from L to R= SMA <span style=3D"mso-spacerun: yes">=A0</span>Z 5.28 !</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">The REST of = the epoch achieves nothing that passes the FWE FDR<span style=3D"mso-spacer= un: yes">=A0 </span>barrier. </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">So I suppose= a model<span style=3D"mso-spacerun: yes">=A0 </span>might say that there i= s some some antero-posterior (receptive-expressive verbal) activity; with i= nter-hemispheric negotiations which include SMA and cingulum;<span style=3D= "mso-spacerun: yes">=A0=A0 </span>and cortico-mesencephalic-limbic<span sty= le=3D"mso-spacerun: yes">=A0 </span>(Bilateral Anterior Insula)<span style= =3D"mso-spacerun: yes">=A0 </span>interaction, with R supraorbital (executi= ve function) region coming on line a little later.</font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">But this sti= ll doesn=92t account for<span style=3D"mso-spacerun: yes">=A0 </span>the we= ird<span style=3D"mso-spacerun: yes">=A0 </span>SMALL but very prominent <s= pan style=3D"mso-spacerun: yes">=A0</span>R precentral<span style=3D"mso-sp= acerun: yes">=A0 </span>Area 6 region at 54,4, 47 . </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">Does anyone = have any suggestions on how I might model this in DCM?<span style=3D"mso-sp= acerun: yes">=A0 </span>Or for that matter whether DCM could even do this i= n principle? Since my limited understanding of DCM is that you can only do = about 3 ROIs at a time ? </font></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">I=92ve asked= my local colleagues, but they can=92t seem to come up with anything.</font= ></span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">=A0</font></= span></p> <p class=3D"MsoNormal" style=3D"MARGIN: 6pt 0cm"><span style=3D"mso-bidi-fo= nt-size: 11.0pt; mso-bidi-font-family: Arial"><font size=3D"3">.</font></sp= an></p>Leon<br clear=3D"all"><br>-- <br><b style=3D"COLOR: rgb(0,0,0)">A/Pr= of Leon Petchkovsky<br> Psychiatrist, Jungian Analyst<br>Past President, ANZSJA<br>Director, the Pi= nniger Clinic, HQ@Robina<br><a href=3D"http://www.pinnigerclinic.com/" targ= et=3D"_blank">www.pinnigerclinic.com</a><br>0755809397<br>0420923885</b><br= > --000e0cd4d0880437bb04a463f40d-- ========================================================================= Date: Mon, 30 May 2011 15:25:12 +1000 Reply-To: Mayuresh K <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mayuresh K <[log in to unmask]> Subject: Re: Power analysis Comments: To: Raphael Hilgenstock <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=000e0cd25d4afc8ec004a47782ad Message-ID: <[log in to unmask]> --000e0cd25d4afc8ec004a47782ad Content-Type: text/plain; charset=ISO-8859-1 Thanks Raphael and Alexa for your inputs! Raphael - Do you know of any documentation how the effect size is calculated using the "threshold and transform" option? I would imagine it is valid to use this option for fMRI T maps as well? Thanks, Mayuresh On Fri, May 27, 2011 at 9:05 PM, Raphael Hilgenstock < [log in to unmask]> wrote: > Hi Mayuresh, > > you might want to use the "threshold and transform spm-t/F-maps" option as > implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/software/) > which allows you to calculate effect size measure 'd' for the respective > spm-volume. I hope this helps. > > Best regards > Raphael > > Am 27.05.2011 08:21, schrieb Mayuresh K: > > Hello spm experts, >> >> Could anyone guide me how to do a power analysis using fMRI or VBM data? I >> have performed a random effects analysis comparing two groups of subjects >> using their fMRI and VBM data. I have found a number of regions with >> significant differences for both data sets. >> I can probably calculate the mean and standard deviation of the %signal >> change (or volume) for each group for the peak voxels say in a particular >> anatomical region. Then use these to calculate effect sizes and power - Is >> this a valid approach? >> Any other suggestions welcome. >> >> Thanks, >> Mayuresh >> >> --000e0cd25d4afc8ec004a47782ad Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Thanks Raphael and Alexa for your inputs!<br><br>Raphael - Do you know of a= ny documentation how the effect size is calculated using the "threshol= d and transform" option? <br>I would imagine it is valid to use this o= ption for fMRI T maps as well?<br> <br>Thanks,<br>Mayuresh<br><br><br><br><div class=3D"gmail_quote">On Fri, M= ay 27, 2011 at 9:05 PM, Raphael Hilgenstock <span dir=3D"ltr"><<a href= =3D"mailto:[log in to unmask]" target=3D"_blank">Raphael.Hilge= [log in to unmask]</a>></span> wrote:<br> <blockquote class=3D"gmail_quote" style=3D"border-left: 1px solid rgb(204, = 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Hi Mayuresh,<br> <br> you might want to use the "threshold and transform spm-t/F-maps" = option as implemented in the VBM8 toolbox (<a href=3D"http://dbm.neuro.uni-= jena.de/software/" target=3D"_blank">http://dbm.neuro.uni-jena.de/software/= </a>) which allows you to calculate effect size measure 'd' for the= respective spm-volume. I hope this helps.<br> <br> Best regards<br> Raphael<br> <br> Am <a href=3D"tel:27.05.2011%2008" value=3D"+12705201108" target=3D"_blank"= >27.05.2011 08</a>:21, schrieb Mayuresh K:<div><div></div><div><br> <blockquote class=3D"gmail_quote" style=3D"border-left: 1px solid rgb(204, = 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"> Hello spm experts,<br> <br> Could anyone guide me how to do a power analysis using fMRI or VBM data? I = have performed a random effects analysis comparing two groups of subjects u= sing their fMRI and VBM data. I have found a number of regions with signifi= cant differences for both data sets.<br> I can probably calculate the mean and standard deviation of the %signal cha= nge (or volume) for each group for the peak voxels say in a particular anat= omical region. Then use these to calculate effect sizes and power - Is this= a valid approach?<br> Any other suggestions welcome.<br> <br> Thanks,<br> Mayuresh<br> <br> </blockquote> </div></div></blockquote></div><br> --000e0cd25d4afc8ec004a47782ad-- ========================================================================= Date: Mon, 30 May 2011 10:20:38 +0200 Reply-To: Volkmar Glauche <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Volkmar Glauche <[log in to unmask]> Subject: Re: question regarding matlabbatch struct/class Comments: To: Martin Dietz <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="------------030907040504040401000207" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------030907040504040401000207 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit Dear Martin, dear list, for matlabbatch, all files & folders are cellstr's, even if it is just a single item. Hope this helps, Volkmar Am 27.05.2011 12:43, schrieb Martin Dietz: > Dear Volkmar & list, > > When running spm_jobman from the command line in 'serial' mode, what is the correct input format for dicom files/already imported nifti files, respectively ? > > Best wishes, > Martin > > > > On May 26, 2011, at 10:04 AM, Volkmar Glauche wrote: > >> To run a batch from MATLAB command line, SPM always has to be initialised. At least, >> >> spm_jobman('initcfg') >> >> should be run before. If one needs to rely on defaults which can not be set in a batch (e.g. in first level fMRI analysis), one should run >> >> spm('defaults','fmri') r 'pet' or 'eeg', depending on the modality >> spm_jobman('initcfg') >> >> Best, >> >> Volkmar > --------------030907040504040401000207 Content-Type: text/x-vcard; charset=utf-8; name="volkmar_glauche.vcf" Content-Transfer-Encoding: 7bit Content-Disposition: attachment; filename="volkmar_glauche.vcf" begin:vcard fn:Volkmar Glauche n:Glauche;Volkmar org:University Freiburg Medical Center;Freiburg Brain Imaging adr;quoted-printable:Breisacher Stra=C3=9Fe 64;;Neurozentrum;Freiburg;;79106;D email;internet:[log in to unmask] tel;work:+49 761 270 53310 tel;fax:+49 761 270 54160 url:http://fbi.uniklinik-freiburg.de/ version:2.1 end:vcard --------------030907040504040401000207-- ========================================================================= Date: Mon, 30 May 2011 14:10:57 +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: Batch processing using CLI in SPM5 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0023541882a09b427804a47a46cd Message-ID: <[log in to unmask]> --0023541882a09b427804a47a46cd Content-Type: text/plain; charset=ISO-8859-1 Hello, Is there any source to obtain Matlab scripts for batch processing in SPM5? Thanks in advance, -- Sarika Cherodath Graduate Student National Brain Research Centre Manesar, Gurgaon -122050 India --0023541882a09b427804a47a46cd Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div dir=3D"ltr">Hello,<div><br></div><div>=A0 =A0 =A0Is there any source t= o obtain Matlab scripts for batch processing in SPM5? =A0</div><div><br></d= iv><div>Thanks in advance,</div><div><br>-- <br>Sarika Cherodath<br>Graduat= e Student<br> National Brain Research Centre<br>Manesar, Gurgaon -122050<br>India<br><br> </div></div> --0023541882a09b427804a47a46cd-- ========================================================================= Date: Mon, 30 May 2011 11:24:44 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: second level analysis affected by subject order Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear Ciara, did you check via "Review Design" whether the data is assigned correctly = to your design? You should have a look at "Files&Factors" and check wheth= er the files for each subject are picked up by SPM in the correct order. = Issues might arise if you specify lists of files outside the batch GUI. I= n such a case, you would have to make sure that your file lists are colum= n cellstr arrays. If the are row cellstr's, they will be assigned to your= cells in a wrong way. Best, Volkmar ========================================================================= Date: Mon, 30 May 2011 12:32:33 +0200 Reply-To: Raphael Hilgenstock <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Raphael Hilgenstock <[log in to unmask]> Subject: Re: Power analysis Comments: To: Mayuresh K <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------050301050001070907010502" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------050301050001070907010502 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Hi Mayuresh, just edit the cg_vbm8_spmT2x.mat or cg_vbm8_spmF2x.mat and you will find the respective formula for calculating effect size within the first few lines of the files. Good luck! Raphael Am 30.05.2011 07:25, schrieb Mayuresh K: > Thanks Raphael and Alexa for your inputs! > > Raphael - Do you know of any documentation how the effect size is > calculated using the "threshold and transform" option? > I would imagine it is valid to use this option for fMRI T maps as well? > > Thanks, > Mayuresh > > > > On Fri, May 27, 2011 at 9:05 PM, Raphael Hilgenstock > <[log in to unmask] > <mailto:[log in to unmask]>> wrote: > > Hi Mayuresh, > > you might want to use the "threshold and transform spm-t/F-maps" > option as implemented in the VBM8 toolbox > (http://dbm.neuro.uni-jena.de/software/) which allows you to > calculate effect size measure 'd' for the respective spm-volume. I > hope this helps. > > Best regards > Raphael > > Am 27.05.2011 08 <tel:27.05.2011%2008>:21, schrieb Mayuresh K: > > Hello spm experts, > > Could anyone guide me how to do a power analysis using fMRI or > VBM data? I have performed a random effects analysis comparing > two groups of subjects using their fMRI and VBM data. I have > found a number of regions with significant differences for > both data sets. > I can probably calculate the mean and standard deviation of > the %signal change (or volume) for each group for the peak > voxels say in a particular anatomical region. Then use these > to calculate effect sizes and power - Is this a valid approach? > Any other suggestions welcome. > > Thanks, > Mayuresh > > --------------050301050001070907010502 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <meta content="text/html; charset=ISO-8859-1" http-equiv="Content-Type"> </head> <body text="#000000" bgcolor="#ffffff"> Hi Mayuresh,<br> <br> just edit the cg_vbm8_spmT2x.mat or cg_vbm8_spmF2x.mat and you will find the respective formula for calculating effect size within the first few lines of the files.<br> <br> Good luck!<br> Raphael<br> <br> Am 30.05.2011 07:25, schrieb Mayuresh K: <blockquote cite="mid:[log in to unmask]" type="cite">Thanks Raphael and Alexa for your inputs!<br> <br> Raphael - Do you know of any documentation how the effect size is calculated using the "threshold and transform" option? <br> I would imagine it is valid to use this option for fMRI T maps as well?<br> <br> Thanks,<br> Mayuresh<br> <br> <br> <br> <div class="gmail_quote">On Fri, May 27, 2011 at 9:05 PM, Raphael Hilgenstock <span dir="ltr"><<a moz-do-not-send="true" href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br> <blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Hi Mayuresh,<br> <br> you might want to use the "threshold and transform spm-t/F-maps" option as implemented in the VBM8 toolbox (<a moz-do-not-send="true" href="http://dbm.neuro.uni-jena.de/software/" target="_blank">http://dbm.neuro.uni-jena.de/software/</a>) which allows you to calculate effect size measure 'd' for the respective spm-volume. I hope this helps.<br> <br> Best regards<br> Raphael<br> <br> Am <a moz-do-not-send="true" href="tel:27.05.2011%2008" value="+12705201108" target="_blank">27.05.2011 08</a>:21, schrieb Mayuresh K: <div> <div><br> <blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"> Hello spm experts,<br> <br> Could anyone guide me how to do a power analysis using fMRI or VBM data? I have performed a random effects analysis comparing two groups of subjects using their fMRI and VBM data. I have found a number of regions with significant differences for both data sets.<br> I can probably calculate the mean and standard deviation of the %signal change (or volume) for each group for the peak voxels say in a particular anatomical region. Then use these to calculate effect sizes and power - Is this a valid approach?<br> Any other suggestions welcome.<br> <br> Thanks,<br> Mayuresh<br> <br> </blockquote> </div> </div> </blockquote> </div> <br> </blockquote> </body> </html> --------------050301050001070907010502-- ========================================================================= Date: Mon, 30 May 2011 11:45:49 +0100 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: induced power at sensor level Comments: To: Nico Bunzeck <[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 Message-ID: <4629769658632215911@unknownmsgid> Hi Nico, Yes, what you described is the way to get what some people call 'pure induced'. I think this is a rather artificial construct not corresponding to any distinct physiological process because in reality you get oscillatory activity with different degree of phase locking to an event so it ends up in both evoked and induced components. So most people just analyse the average TF and do not make this distinction. Best, Vladimir On 30 May 2011, at 10:31, Nico Bunzeck <[log in to unmask]> wrote: > Hi Vladimir, > > I am about to analyse MEG data regarding induced oscillatory power (at the > sensor level; SPM8). I am not quite sure how to get there and would > appreciate your help. If I understand the manual correct spm_eeg_tf followed > by averaging (spm_eeg_average_TF) results in the power spectrum averaged > over trials and this includes both 'evoked' and 'induced' power. > Alternatively, averaging followed by spm_eeg_tf results in 'evoked' power > only. Is that correct? Does that mean the difference between both would > result in purely 'induced' power or is there another way? > > Thanks and best wishes, > Nico > > > > ========================================================================= Date: Mon, 30 May 2011 13:21:23 +0100 Reply-To: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: =?ISO-8859-1?Q?Volkmar_Glauche?= <[log in to unmask]> Subject: Re: make a movie of statistical results Comments: To: Li Jiang <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> I must have missed that question. When you overlay your results onto orth= ogonal sections (either in Results or in CheckReg), the image context men= us contain a movie tool. This tool allows you to move through the data sl= ice by slice. The displayed images can be written to disk either as a mov= ie or as individual image frames. Volkmar ========================================================================= Date: Mon, 30 May 2011 14:46:47 +0200 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: VOI: mean vs. SVD (PCA) Content-Type: multipart/alternative; boundary="_1b5f9dfa-7631-403d-8532-d735737adf35_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_1b5f9dfa-7631-403d-8532-d735737adf35_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear VOI analysts=2C is there any strong opinion or even objective evidence [literature] for/aga= inst using SVD (vs. the mean) when working with data extracted from a VOI?= =20 My understanding is that SVD might help to protect against "contamination" = of the data either due to white noise=2C or due to a lack of functional spe= cificity of the spatially defined VOI. Thanks for any hints. Bw=2C Helmut = --_1b5f9dfa-7631-403d-8532-d735737adf35_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html> <head> <style><!-- .hmmessage P { margin:0px=3B padding:0px } body.hmmessage { font-size: 10pt=3B font-family:Tahoma } --></style> </head> <body class=3D'hmmessage'> <br>Dear VOI analysts=2C<br><br>is there any strong opinion or even objecti= ve evidence [literature] for/against using SVD (vs. the mean) when working = with data extracted from a VOI? <br><br>My understanding is that SVD might = help to protect against "contamination" of the data either due to white noi= se=2C or due to a lack of functional specificity of the spatially defined V= OI.<br><br>Thanks for any hints.<br><br>Bw=2C<br><br>Helmut<br><br> = </body> </html>= --_1b5f9dfa-7631-403d-8532-d735737adf35_-- ========================================================================= Date: Mon, 30 May 2011 13:58:00 +0100 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: Re: Time derivative in event related design 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]> Thanks for the reference. I've read through the article and played around with the Matlab scripts p= rovided. I have a few potentially stupid questions though: 1. The ratio value which is used to calculate the contrast weights, is it= always .44 and -.34 respectively if you want to keep the same physiologi= cal contraints as the author or do you have to calculate them for your de= sign? If so, how? 2. I'm not sure how to expand this method to cases where you want to comp= are activation between 2 experimental conditions. The scripts seem to be = designed for 2 regressors corresponding to the HRF and derivative for a s= ingle condition only. Thank you for being patient with me.=20 /Jonas Persson On Fri, 20 May 2011 17:00:39 -0400, MCLAREN, Donald <mclaren.donald@GMAIL= .COM> wrote: >See Investigating hemodynamic response variability at the group level >using basis functions by Steffener et al. > > >On Friday, May 20, 2011, Jonas Persson <[log in to unmask]> wrote:= >> Hello fellow SPM:ers, >> >> I am a bit confused with regards to using the time derivative in SPM. >> >> The issue is that I have an event related paradigm where I do post hoc= sorting of stimuli to 2 conditions. At the last SPM course I learned tha= t slice time correction should be avoided, so instead of using time slice= correction I wanted to include a second basis function to account for ti= ming differences in data acquisition between slices. >> >> To check for regions significantly more activated in cond 1 vs cond 2 = I did an F-contrast [1 0 -1 0; 0 1 0 -1] to look for voxels associated wi= th either the HRF or td. I masked this contrast inclusive with a t-contra= st [1 0 -1 0] to only look at voxels with activation corresponding to the= HRF. The problem here is that I'm telling SPM to look for voxels with di= fferent timing depending on condition, while I'm interested in allowing f= or timing differences between regions regardless of condition. >> >> Another variant I've thought of is to merely do a t-contrast [1 0 -1 0= ], controlling for any variance associated with the td, but I read that t= his may give biased results. >> >> The next issue is how to perform the 2nd level analysis. With the latt= er method it's straightforward, but from what I understand I can't do a r= andom effects on F-contrasts? >> >> Thank you for any suggestions! >> >> Bert regards, >> >> Jonas Persson >> > >--=20 > >Best Regards, Donald McLaren >=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >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 >=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D >This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTE= D >HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is >intended only for the use of the individual or entity named above. If th= e >reader of the e-mail is not the intended recipient or the employee or ag= ent >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, 30 May 2011 15:17:23 +0200 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: VOI: mean vs. SVD (PCA) Comments: To: Helmut Laufs <[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 Helmut, > is there any strong opinion or even objective evidence [literature] > for/against using SVD (vs. the mean) when working with data extracted > from a VOI? I searched the literature for this a while ago and found that two good=20 references were Saxe et al., NI 2006, arguing for the mean, vs. Friston=20 et al., NI 2006 (same issue, I think), arguing for the first=20 eigenvariate, so there seems to be a certain degree of uncertainty :) I=20 have not seen an exhaustive simulation definitely favoring the one over=20 the other. Cheers, Marko Saxe R, Brett M, Kanwisher N, 2006. Divide and conquer: a defense of=20 functional localizers. NeuroImage 30: 1088-1096 Friston KJ, Rotshtein P, Geng JJ, Sterzer P, Henson RN, 2006. A critique=20 of functional localisers. NeuroImage 30: 1077-1087 --=20 ____________________________________________________ PD Dr. med. Marko Wilke Facharzt f=FCr Kinder- und Jugendmedizin Leiter, Experimentelle P=E4diatrische Neurobildgebung Universit=E4ts-Kinderklinik Abt. III (Neurop=E4diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 T=FCbingen, 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, 30 May 2011 15:36:38 +0200 Reply-To: Maria Serra <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Maria Serra <[log in to unmask]> Subject: Is it correct to register binary images with DARTEL? MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016e6d7dfcb81e13804a47e603f Message-ID: <[log in to unmask]> --0016e6d7dfcb81e13804a47e603f Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPMrs, We would like to register multiple binary images. Is it possible to do it with Dartel (create template and create warped)? Thank you for your help, --=20 Maria Serra PIC (Port d'Informaci=F3 Cient=EDfica) Campus UAB, Edifici D E-08193 Bellaterra, Barcelona Telf. +34 93 586 8232 www.pic.es --0016e6d7dfcb81e13804a47e603f Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div><br>Dear SPMrs,</div><div>=A0</div><div>We would like to register mult= iple binary images. Is it possible to do it with Dartel (create template an= d create warped)?<br></div><div><br>Thank you for your help,</div><br>-- <b= r> Maria Serra<br><br>PIC (Port d'Informaci=F3 Cient=EDfica)<br>Campus UAB= , Edifici D<br>E-08193 Bellaterra, Barcelona<br>Telf. +34 93 586 8232<br><a= href=3D"http://www.pic.es">www.pic.es</a><br> --0016e6d7dfcb81e13804a47e603f-- ========================================================================= Date: Mon, 30 May 2011 10:14:58 -0400 Reply-To: Olivier Collignon <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Olivier Collignon <[log in to unmask]> Subject: Constrained VOI extraction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=0016364d22a79bc9d504a47ee9ec Message-ID: <[log in to unmask]> --0016364d22a79bc9d504a47ee9ec Content-Type: text/plain; charset=ISO-8859-1 Dear all, I try to use the batch editor in order to automatically move my VOI from a center coordinate (obtained from rfx analyses) to a Nearest local maxima in the FFX (to fit more closely with individual data). However, I don't want the VOI centre to move too much (and therefore change to another structure). For example, I want to constrain this search for local maxima from the rfx coordinate to a radius of 10mm. In the batch editor , I tried the expression .Sphere/Movement of center/Nearest local maxima/SPM index =1 (my spm.mat) and Mask expression = i2 (=a sphere of radius 10). I thought that it will move the centre coordinate to the local maxima of the sphere around my rfx coordinate. However this give exactly the same result as using a movement of center/fixed (what I want to avoid). Thanks for your help. Best, Olivier. --0016364d22a79bc9d504a47ee9ec Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable <div>Dear all,</div><div>I try to use the batch editor in order to automati= cally move my VOI from a center coordinate (obtained from rfx analyses) to = a Nearest local maxima in the FFX (to fit more closely with individual data= ).</div> <div>However, I don't want the VOI centre to move too much (and therefo= re change to another structure). For example, I want to constrain this sear= ch for local maxima from the rfx coordinate to a radius of 10mm. In the bat= ch editor , I tried the expression .Sphere/Movement of center/Nearest local= maxima/SPM index =3D1 (my spm.mat) and Mask expression =3D i2 (=3Da sphere= of radius 10). I thought that it will move the centre coordinate to the lo= cal maxima of the sphere around my rfx coordinate.=A0However this give exac= tly the same result as using a movement of center/fixed (what I want to avo= id).</div> <div>Thanks for your help.</div><div>Best,</div><div>Olivier.</div> --0016364d22a79bc9d504a47ee9ec-- ========================================================================= Date: Mon, 30 May 2011 11:21:52 -0400 Reply-To: simon <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: simon <[log in to unmask]> Subject: [Job] Postdoctoral Positions on Medical Image Analysis at UNC-CH Mime-Version: 1.0 Content-Type: multipart/alternative; boundary="=====003_Dragon314353248383_=====" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --=====003_Dragon314353248383_===== Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: 7bit Several postdoctoral positions in medical image analysis are available at the IDEA Group of UNC-CH (https://www.med.unc.edu/bric/ideagroup). Our current effort is to enhance understanding of the working mechanism of the brain via diffusion weighted imaging (DWI) and functional magnetic resonance imaging (FMRI) by automatically mining information latent in images for the purposes of growth and disease analyses. The positions will support our efforts in advancing novel technologies for analyzing MR for applications in neuroscience. Possible research topics include, but are not limited to, improving disease classification by combining information from multiple modalities, white-matter bundle segmentation/modeling, and structural/functional analysis of subject groups involving infants, and patients with MCI/AD, schizophrenia, and multiple sclerosis. We are seeking highly motivated individuals who have demonstrated academic excellence, including publications in first-class journals and conferences. Successful candidates should have a Ph.D. (or equivalent) in Computer Science, Applied Mathematics/Statistics, Electrical Engineering, Biomedical Engineering, or related fields. Competence in programming beyond MATLAB (good command of Linux, C/C++, Python, ITK, VTK, etc.) is essential. Experience in neuroscience, medical image analysis, machine learning, and mathematical modeling is highly desirable. The successful candidates will be part of a diverse group including radiologists, psychologists, physicists, biostatistician, and computer scientists, and will build upon the group's extensive foundation on medical image analysis. Applications should include a cover letter indicating the research area of interest, a curriculum vitae, and the contact information of three references. For more information or to submit your application please contact: Pew-Thian Yap Assistant Professor Department of Radiology and BRIC The University of North Carolina at Chapel Hill 3123 Bioinformatics Bldg, CB# 7513 130 Mason Farm Rd Chapel Hill, NC 27599 [log in to unmask] --=====003_Dragon314353248383_===== Content-Type: text/html; charset="gb2312" Content-Transfer-Encoding: 7bit <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"> <HTML xmlns:o = "urn:schemas-microsoft-com:office:office"><HEAD> <STYLE type=text/css>@import url( C:\Documents and Settings\shi\Local Settings\Temporary Internet Files\scrollbar.css ); </STYLE> <STYLE type=text/css>@import url( C:\Documents and Settings\shi\Local Settings\Temporary Internet Files\scrollbar.css ); </STYLE> <STYLE type=text/css>@import url( C:\Documents and Settings\shi\Local Settings\Temporary Internet Files\scrollbar.css ); </STYLE> <META content="text/html; charset=GB2312" http-equiv=Content-Type> <META name=GENERATOR content="MSHTML 8.00.6001.18975"><LINK rel=stylesheet href="BLOCKQUOTE{margin-Top: 0px; margin-Bottom: 0px; margin-Left: 2em}"></HEAD> <BODY style="MARGIN: 10px; FONT-FAMILY: verdana; FONT-SIZE: 10pt"> <DIV><FONT size=2 face=Verdana> <P style="MARGIN: 0in 0in 0pt" class=MsoNormal><FONT size=3><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma">Several postdoctoral positions in medical image analysis are available at the IDEA Group of UNC-CH </SPAN></SPAN><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: #333333; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma">(</SPAN><SPAN style="FONT-FAMILY: 'Tahoma','sans-serif'; COLOR: black; FONT-SIZE: 10pt; mso-fareast-font-family: 'Times New Roman'"><A href="https://www.med.unc.edu/bric/ideagroup/" target=_blank><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: #0066cc; FONT-SIZE: 12pt">https://www.med.unc.edu/bric/ideagroup</SPAN></A></SPAN><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: #333333; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma">)</SPAN><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma">. Our current effort is to</SPAN></SPAN><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma"> <SPAN class=apple-style-span>enhance understanding of the working mechanism of the brain via diffusion weighted imaging (DWI) and functional magnetic resonance imaging (FMRI) by automatically mining information latent in images for the purposes of growth and disease analyses. The positions will support our efforts in advancing</SPAN><SPAN class=apple-converted-space> novel technologies </SPAN><SPAN class=apple-style-span>for analyzing MR for applications in neuroscience.</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>Possible research topics include, but are not limited to, improving</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>disease classification by combining information from multiple modalities,</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>white-matter bundle segmentation/modeling, and structural/functional analysis of subject groups involving infants, and patients with MCI/AD, schizophrenia, and multiple sclerosis. </SPAN></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma"><SPAN class=apple-style-span></SPAN></SPAN></FONT> </P> <P style="MARGIN: 0in 0in 0pt" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma"><SPAN class=apple-style-span>We are seeking highly motivated individuals who have demonstrated academic excellence, including publications in first-class journals and conferences. Successful candidates should have a Ph.D. (or</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>equivalent) in Computer Science, Applied Mathematics/Statistics, Electrical Engineering, Biomedical Engineering, or related fields. Competence in</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>programming beyond MATLAB (good command of Linux, C/C++, Python, ITK, VTK, etc.) is essential. Experience in neuroscience, medical image analysis, machine learning, and mathematical modeling is highly</SPAN><SPAN class=apple-converted-space> </SPAN><SPAN class=apple-style-span>desirable. T</SPAN></SPAN><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: #333333; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Tahoma">he successful candidates will be part of a diverse group including radiologists, psychologists, physicists, biostatistician, and computer scientists, and will build upon the group's extensive foundation on medical image analysis.</SPAN></SPAN></FONT><SPAN style="FONT-FAMILY: 'Tahoma','sans-serif'; COLOR: black; FONT-SIZE: 10pt; mso-fareast-font-family: 'Times New Roman'"> <o:p></o:p></SPAN></P> <P style="TEXT-ALIGN: justify; LINE-HEIGHT: 18pt; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black"> </SPAN></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="TEXT-ALIGN: justify; LINE-HEIGHT: 18pt; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">Applications should include a cover letter indicating the research area of interest, a curriculum vitae, and the contact information of three references.</SPAN></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="TEXT-ALIGN: justify; LINE-HEIGHT: 18pt; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><SPAN style="COLOR: black"><o:p><FONT size=3 face="Times New Roman"> </FONT></o:p></SPAN></P> <P style="TEXT-ALIGN: justify; LINE-HEIGHT: 18pt; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black"><FONT size=3>For more information or to submit your application please contact:</FONT></SPAN></SPAN><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black"><BR><SPAN class=apple-style-span><FONT size=3>Pew-Thian Yap</FONT></SPAN></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></P> <P style="TEXT-ALIGN: justify; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN class=apple-style-span><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">Assistant Professor</SPAN></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="TEXT-ALIGN: justify; MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">Department of Radiology and BRIC</SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">The University of North Carolina at Chapel Hill</SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">3123 Bioinformatics Bldg, CB# 7513</SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">130 Mason Farm Rd</SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black">Chapel Hill, NC 27599 </SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><FONT size=3><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black"></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></FONT></P> <P style="MARGIN: 0in 0in 0pt; mso-margin-top-alt: auto" class=MsoNormal><SPAN style="FONT-FAMILY: 'Verdana','sans-serif'; COLOR: black"><A href="mailto:[log in to unmask]"><FONT size=3>[log in to unmask]</FONT></A><FONT size=3> </FONT></SPAN><SPAN style="COLOR: black"><o:p></o:p></SPAN></P></FONT><FONT size=2 face=Verdana></DIV></FONT></BODY></HTML> --=====003_Dragon314353248383_=====-- ========================================================================= Date: Mon, 30 May 2011 17:11:34 +0100 Reply-To: ddw <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: ddw <[log in to unmask]> Subject: Re: VOI: mean vs. SVD (PCA) Comments: To: [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]> For what it's worth, I've always extracted both the first eigenvariate an= d the mean time series and ran a correlation on them. I've never seen tha= t correlation dip below 0.9 and most of the time it's > 0.95. While I've = never formally tested an analysis (ppi) with mean vs. eigenvariate, I'd b= e willing to put money that, with the mean and eigenvariate being so high= ly correlated with one another, that the results of the analyses would be= near identical. But your mileage may vary, in our studies we concat across runs and have = nuisance variables to correct for this so maybe it's in the way you adjus= t your timeseries. If the timeseries is clean enough then the first eigen= variate should be nearly the same as the mean, or so I would expect. Cheers! ========================================================================= Date: Mon, 30 May 2011 12:45:38 -0400 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: VOI: mean vs. SVD (PCA) Comments: To: ddw <[log in to unmask]> In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=20cf3054a45765010704a48104ab Message-ID: <[log in to unmask]> --20cf3054a45765010704a48104ab Content-Type: text/plain; charset=ISO-8859-1 I suspect that it is dependent on the region size and homogeneity of the region. I've always preferred the mean timeseries as it weights each voxel the same way. In this way, when you extract from different subjects, your always using the same voxels. If you use the eigenvariate, then you might grab voxels from one part of the VOI in some subjects and another part of the VOI in other subjects. This leads to comparing region X to region Y across subjects -- but potentially functionally similar. I'm not sure if this is a huge problem, but it is something to be aware of when extracting data. All of the code I've written allows for the extraction both ways. 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, May 30, 2011 at 12:11 PM, ddw <[log in to unmask]> wrote: > For what it's worth, I've always extracted both the first eigenvariate and > the mean time series and ran a correlation on them. I've never seen that > correlation dip below 0.9 and most of the time it's > 0.95. While I've never > formally tested an analysis (ppi) with mean vs. eigenvariate, I'd be willing > to put money that, with the mean and eigenvariate being so highly correlated > with one another, that the results of the analyses would be near identical. > > But your mileage may vary, in our studies we concat across runs and have > nuisance variables to correct for this so maybe it's in the way you adjust > your timeseries. If the timeseries is clean enough then the first > eigenvariate should be nearly the same as the mean, or so I would expect. > > Cheers! > --20cf3054a45765010704a48104ab Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable I suspect that it is dependent on the region size and homogeneity of the re= gion. I've always preferred the mean timeseries as it weights each voxe= l the same way. In this way, when you extract from different subjects, your= always using the same voxels. If you use the eigenvariate, then you might = grab voxels from one part of the VOI in some subjects and another part of t= he VOI in other subjects. This leads to comparing region X to region Y acro= ss subjects -- but potentially functionally similar. I'm not sure if th= is is a huge problem, but it is something to be aware of when extracting da= ta. All of the code I've written allows for the extraction both ways.<b= r clear=3D"all"> <br>Best Regards, Donald McLaren<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC,= Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts Gene= ral Hospital and Harvard Medical School<br> Office: (773) 406-2464<br>=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D<br>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 a= bove. If the reader of the e-mail is not the intended recipient or the empl= oyee or agent responsible for delivering it to the intended recipient, you = are hereby notified that you are in possession of confidential and privileg= ed information. Any unauthorized use, disclosure, copying or the taking of = any action in reliance on the contents of this information is strictly proh= ibited and may be unlawful. If you have received this e-mail unintentionall= y, please immediately notify the sender via telephone at (773) 406-2464 or = email.<br> <br><br><div class=3D"gmail_quote">On Mon, May 30, 2011 at 12:11 PM, ddw <s= pan dir=3D"ltr"><<a href=3D"mailto:[log in to unmask]">[log in to unmask]<= /a>></span> wrote:<br><blockquote class=3D"gmail_quote" style=3D"margin:= 0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"> For what it's worth, I've always extracted both the first eigenvari= ate and the mean time series and ran a correlation on them. I've never = seen that correlation dip below 0.9 and most of the time it's > 0.95= . While I've never formally tested an analysis (ppi) with mean vs. eige= nvariate, I'd be willing to put money that, with the mean and eigenvari= ate being so highly correlated with one another, that the results of the an= alyses would be near identical.<br> <br> But your mileage may vary, in our studies we concat across runs and have nu= isance variables to correct for this so maybe it's in the way you adjus= t your timeseries. If the timeseries is clean enough then the first eigenva= riate should be nearly the same as the mean, or so I would expect.<br> <br> Cheers!<br> </blockquote></div><br> --20cf3054a45765010704a48104ab-- ========================================================================= Date: Mon, 30 May 2011 18:15:12 +0100 Reply-To: Max Hilger <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Max Hilger <[log in to unmask]> Subject: Realign&Unwarp: Artificial Movement in only one Condition Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-2RDSXC8sLJHmJAGA1n0WWTPAxlBAgEUpAU3BOAanwwhol" Message-ID: <[log in to unmask]> --part-2RDSXC8sLJHmJAGA1n0WWTPAxlBAgEUpAU3BOAanwwhol Content-Type: multipart/mixed; boundary="part-AYRAA5Xha8fLAAgo3WaUeGVhIAWilXiFAAX71hHL8jiEG" This is a multi-part message in MIME format. --part-AYRAA5Xha8fLAAgo3WaUeGVhIAWilXiFAAX71hHL8jiEG Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Dear SPM-Users, i am currently reanalyzing a fMRT-dataset in which the subjects did 4 con= ditions in the same succession. The first step of the analysis was realig= ning and unwarping the scans, using the following parameters: Estimation Options . Quality: 1 . Interpolation: 7th Degree B-Spline . Wrapping: Wrap Y Unwarp Reslicing Options . Interpolation: 7th Degree B-Spline . Wrapping: Wrap Y All other parameters were set to default. The graphs showed heavy movement (up to 15mm) of all the subjects, but ON= LY between the first and second images of the first condition and always = in negative-X-direction. It seems implausible that all the subjects reall= y did move in the same direction at the same point of time, so we do assu= me that there is an error in our data/analysis that causes this pattern o= f results. Interesting is also that all subjects and conditions were analyzed using = a batch, and that the 'movement' only appears at the beginning of one con= dition and not all of the conditions. Since the data was previously analyzed with spm2 (usingthe same parameter= s), we compared the results with the allready existing ones. As you can s= ee in the attatchmed image, if you discard the first image, the graphs ar= e virtually the same. The exeption of this pattern is the rotation in the first condition (Cond= ition 002), which does also have rapid changes both in positive and in ne= gative direction between the first and second image. We had the idea that we could maybe work around the problem if we simply = exclude the first image from analysis (and reduced the onsets of the stim= uli etc by TR), but we are not sure if this is an adequate solution. So I would be gratefull if you could tell me if there if you did previous= ly encounter this problem and can tell me it's origin, what your solution= was and if our way of attempt of working around it is adequate if we don= 't find another solution.=20 Best regards, Max Hilger --part-AYRAA5Xha8fLAAgo3WaUeGVhIAWilXiFAAX71hHL8jiEG-- --part-2RDSXC8sLJHmJAGA1n0WWTPAxlBAgEUpAU3BOAanwwhol Content-Transfer-Encoding: base64 Content-Type: IMAGE/PJPEG; name="Subject 24.JPG" /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRof Hh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwh MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAAR CAMxA/MDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAA AgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkK FhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl 5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREA AgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYk NOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOE hYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk 5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiq99f2emWcl5f3cFpax43zTyCNFyQBljwMkgfj QBYoqvY39nqdnHeWF3Bd2smdk0EgkRsEg4YcHBBH4VYoAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKAOH03wn4bt/iHqPkeH9Ki+zafYzwbLKNfKkMt1l1wPlb5F5HPyj0FR23inXJ/Cu iajcT6ba3ep263Qjt9Pub5lQohwIYyHIy2WfICbkTDkhz2iWNvHqM1+seLqaKOGR9x+ZELlR jpwZH/P2FZ8vhbRptOsbB7PNrYxCCBBK4/dAAGJiDl4yFXcjZVto3A4FAHLy+N9Yn06bVrO1 sYrG28P22uTRTF3kbzBMzQKQQBkRACQj5SPuPu+XYvdd1O18ULaSCC2sTLHHGJ7SZluFcKN4 ul/dxNuYosTqWZkAyPMUjQTwto0enTWC2eLWbT49MkTzX+a2QOFTOc8CR+evPXgVJN4e0y41 QajJBIZ96yMgnkEUjrja7xBtjuNq4ZlJGxMH5VwAcnaavdaboYis7mCGSbVdUY7rCe+kIW9l +7BCQ235hmQnCnaMEuCKbeItSvrLxFrcotJNPfwlbajFpc8TSIrSJcNtY7gGB2sGwq7gUHG3 5u0n8LaNcwJDLZ5jWWaXAlcbjK5klVsH5o3Y5aNsocDIwBiMeENDW3e3W0kED6YukvGLiXa1 qoIVCN2CQGYBvvDceeaAM+88RalDql5JELT+z7HU7XTZYGiYyytP5H7xZNwCBftC/KUbPln5 hu+WPTPEmsT6jaNeR2IsbzVbzTIUhV/MHkm4KysxOORAVKAdTu3D7g3JvD2mXGqDUZIJDPvW RkE8gikdcbXeINsdxtXDMpI2Jg/KuJI9E06L7Nst8fZruW9i+dvlml8ze3XnPnScHgbuAMDA Bh+Ltcm0O4luLe1tJJ7fQtSvY5JoyWVojAQmQQdjFssO+1eRitDR9S1KTWb7S9UW08+C3gu1 a1DBUWVpV8s7jlyphPz4XduHyLjm5qWiadq+/wC3W/m77Sayb52XMM23zF4I67F56jHBHNWE sbePUZr9Y8XU0UcMj7j8yIXKjHTgyP8An7CgDm4NKsfEWu6+2r20d29hepbWbv8AetU+zQSZ iYcxvvkY71w3C8/IuK+q+Jb6xt7yXTbu0u7TSNHi1SaeaPzG1BGEpAR42VIyRATvCsP3gIUB cHoNS8PaZq1ws95BIzhBG4SeSNZkBJCSqrASpy3yuGHzNx8xyal4e0zVrhZ7yCRnCCNwk8ka zICSElVWAlTlvlcMPmbj5jkAw7jxJrEGsakfLsf7MsdVtNP27XM03nrbjOc7U2NPuzht4+XC Y3NTk1LUtV8ReHbqVbRdPGu3lvEihhLG0MF5Fljkhw+xm4CbOB8+dw6yTRNOl+077fP2m7iv Zfnb5povL2N14x5MfA4O3kHJzXXwvpKaxHqiwzrcRSvPGgu5RCkjqys4h3eWGId8nbklmPUk 0AcHZazqR0HWNRlTTX09PBtnexaULVhbozRznZt3kbPkYEADK7BkbMt1F54i1KHVLySIWn9n 2Op2umywNExllafyP3iybgEC/aF+Uo2fLPzDd8ugPCGhrbvbraSCB9MXSXjFxLta1UEKhG7B IDMA33huPPNWJvD2mXGqDUZIJDPvWRkE8gikdcbXeINsdxtXDMpI2Jg/KuADD0zxJrE+o2jX kdiLG81W80yFIVfzB5JuCsrMTjkQFSgHU7tw+4JLnSdN13x1qNvq+n2moQW2mWbwR3cKyrEz y3IcqGBClgiAkddi56CtyPRNOi+zbLfH2a7lvYvnb5ZpfM3t15z50nB4G7gDAxHqXh/T9VuF uJ/tcU4QIZLS9mtmdQSQGMTqWAJYgNnG5sYycgHl+mQQ6p8M/EGvahFHd6xZaZE9pqFwokuL dhplvKCkhyykSMz5BHzMW6kmu01fQ9LbWrZdLsIB4ga7hvJr7ZmaGATBpN8xywV0V4lTPIJU DYrFdSbwhoczg/ZJIotio1tb3EsNvIqqFAeFGEbjaFXDKcqoU5AAoj8JaVDqk2oxNqSXE1x9 pl26pchHk4GWj8zYRhVG0jGABjAxQBuUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRUc88Nrby3FxLHDBEheSSRgqooGSSTwABzmgC SiiigAooooAKKKKACiiigAooooAKKKKACiiigDj9H0PS/EVtdanq9hBd6h/aF5Cl3In76FIr mSOMROPmi2qi42Ffmy33iSa/ge+uNT1FL+8k8y6uvDWkTTPtA3OxuixwOBkk9K6C+8L6TqN5 Jc3EM5aXHnxR3cscM+AB+9iVgkmVAU71OVAU5AAo1Dwxpup6ib+c30d00Sws9pqFxbbkUsVB ETqDgu3X1NAGHp/hvRdX8SeKrq/0u0nu01ONI7pogJ4gLO2I2SD50IJJBUgg8jBrU0AJ4m8A 6NJrcEF/9u0+3muUnhVklcorklMbfvc9OKG8F6I88krpfP52zz421K5Mc+1FjHmxmTbJlEVW 3A7gPmzk10FAHm+kWtn4Y8LXOraHomlJqb63NYq5hEW6N9TMIQui7goUgDggbV4OMVqal4p1 DSdZtrWae0uUFxa2k8dtp9w255mjQu04JjgIMgYRNuJUL837wY6T+xNO+w/Yvs/+j/a/tuze 3+u87z92c5/1nzY6dsY4qve+FtG1DUVv7qz8ydZY5x+9cIJYypSXYDt8wbVXfjcVG0nbxQBj 6Z4k1ifUbRryOxFjeareaZCkKv5g8k3BWVmJxyICpQDqd24fcGPq+u6nqHg3xTbamIIJjol3 IbNrSa3kgYRkMiu+UuVUtgyx7VBCnBEgx3EeiadF9m2W+Ps13LexfO3yzS+ZvbrznzpODwN3 AGBimnhDQ0t7u3+ySNBdW72rRvcSsscLjDRxAsRChGBtj2j5V/urgAy9Z8VXWmeIY7dHgltf tdtaPBFZTytmZ0Tc9yP3ULDzA3lsCSoXkeYNtPQb/Ukt9GXVJbTUJ7jxHqFuszQMDCqC75Tc 7FTmMqOeI22c43HpL3wto2oait/dWfmTrLHOP3rhBLGVKS7AdvmDaq78bio2k7eKkt/D2mWr o8UEmY72W/TdPIwSeRXV2UFiACJH+UfLlicZ5oA5M+NNYg8PNq0qWMn2zw/c63ZRLC6/Z/LS NhFId583PnKNwEf3Dx83y9Jo+palJrN9peqLaefBbwXatahgqLK0q+Wdxy5Uwn58Lu3D5Fxy J4Q0NLe7t/skjQXVu9q0b3ErLHC4w0cQLEQoRgbY9o+Vf7q41EsbePUZr9Y8XU0UcMj7j8yI XKjHTgyP+fsKAOTudc1KLWrmx0u102OefXRYtLLGwyv9nLP5r7Tl3BUADjKqFyv3h0Hh/Upt V0nz7hYxPHcXFrIYwQrtDM8RcAklQxTdtycZxk4yZP7E077d9t+z/wCkfa/tu/e3+u8nyN2M 4/1fy46d8Z5qxZ2Nvp8DQ2sflxtLJMRuJy8jtI559WZj7Z44oA5Pwzpumz+GtC8S3TR22sXl vaz3GpAqktxJKEzG7EfMjswURngfIEClUK2LPxFqU2qWckotP7PvtTutNigWJhLE0Hn/ALxp NxDhvs7fKEXHmD5jt+bYh8PaZb6odRjgkE+9pFQzyGKN2zudIi2xHO5ssqgne+T8zZIfD2mW +qHUY4JBPvaRUM8hijds7nSItsRzubLKoJ3vk/M2QDk7LxvrEHh6DV9WtbGX7V4fm1mOC0Lp s8lIiULsTu3+aDwo2YI/efeqO4udSsPE2qNqkem3k+zQ4ldIWEbq99Mu/wAtmJR1LHHzNyit nnaOwj8OaRFb2luLGNoLSybT4Y5CXUW7BA0ZDEhgRGg+bJ49zmvb+EdHtjMwju5nme3d3ur6 ediYJDJFhpHYgK5JwODk5zQBz+g3+pJb6MuqS2moT3HiPULdZmgYGFUF3ym52KnMZUc8Rts5 xuMZ8aaxB4ebVpUsZPtnh+51uyiWF1+z+WkbCKQ7z5ufOUbgI/uHj5vl6y38PaZaujxQSZjv Zb9N08jBJ5FdXZQWIAIkf5R8uWJxnmq6eENDS3u7f7JI0F1bvatG9xKyxwuMNHECxEKEYG2P aPlX+6uAA0fUtSk1m+0vVFtPPgt4LtWtQwVFlaVfLO45cqYT8+F3bh8i455P7BZ/2N/b32SD +2f+El8j+0PLH2jy/wC1fJ2eZ97b5X7vGcbPl6cV6Aljbx6jNfrHi6mijhkfcfmRC5UY6cGR /wA/YVn/APCL6T/aP23yZ93m+f5H2uX7P5md2/yN3l7t3z5253/N97mgDi9G0qxGi+A76K2j gv8AV0RNQvbf91cXQewmkffKmHYl1V8k53KG6gGug0bSdNtvFTXHh7T7Sx023t5ra8NpCsMd xcb49oAUAOYgkqljwpkKgk+YF0IfCOj27loo7tQEZIo/t05S3DKV/cpv2wkKSoMYUqCQMA4q TRvDGm6BsGnG+SNIhCkM2oXE0aIMYCpI7KuMADA4HHSgDYooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACqepTWNnbrfX4j2Wzh43aPeyuwKDYACS7bygC8nd tGc4q5VPUtJ03WbdbfVNPtL6BXDrHdQrKobBGQGBGcEjPuaAKeg2Nxbtqd9dR+RNqd2Lo25Y MYAIo4lVmHBbEQY44BYgFgNxy9J17xVrOjWOqW+gaMsF7bx3EayaxKGCuoYA4tiM4Pqa6DTd J03RrdrfS9PtLGBnLtHawrEpbAGSFAGcADPsKy/An/JPPDX/AGCrX/0UtAB9s8Yf9ALQ/wDw czf/ACLR9s8Yf9ALQ/8Awczf/ItdBRQBz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8 i10FFAHP/bPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLXQUUAc/9s8Yf9ALQ/wDwczf/ ACLR9s8Yf9ALQ/8Awczf/ItdBRQByd7r3iqxutOt5dA0Yvf3Bt4iusS4DCKSXLf6N02xsOM8 kfUXPtnjD/oBaH/4OZv/AJFrckghmeF5Yo3eF98TMoJRtpXK+h2swyOxI71JQBz/ANs8Yf8A QC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItdBRQBz/ANs8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ/ /BzN/wDItdBRQBz/ANs8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItdBRQBz/ANs8Yf8A QC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItdBRQBydlr3iq+utRt4tA0YPYXAt5S2sS4LGKOXK /wCjdNsijnHIP1Nz7Z4w/wCgFof/AIOZv/kWtyOCGF5niijR5n3ysqgF22hct6naqjJ7ADtU lAHP/bPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLXQUUAc/9s8Yf9ALQ/wDwczf/ACLR 9s8Yf9ALQ/8Awczf/ItdBRQBz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i10FFAHP /bPGH/QC0P8A8HM3/wAi1T1bXvFWjaNfapcaBozQWVvJcSLHrEpYqiliBm2AzgeorrKjnghu reW3uIo5oJUKSRyKGV1IwQQeCCOMUAYf2zxh/wBALQ//AAczf/ItH2zxh/0AtD/8HM3/AMi1 0FFAHP8A2zxh/wBALQ//AAczf/ItH2zxh/0AtD/8HM3/AMi10FFAHP8A2zxh/wBALQ//AAcz f/ItH2zxh/0AtD/8HM3/AMi10FFAHP8A2zxh/wBALQ//AAczf/ItH2zxh/0AtD/8HM3/AMi1 0FFAHP8A2zxh/wBALQ//AAczf/ItU5Ne8VRazbaW2gaN59xbzXCMNYl2hY2jVgf9GznMq447 Hp36ysfxNdf2R4e1TXILeCS+0/T7iWB5UzjCbyuRg7SUXIBGdo9BQBX+2eMP+gFof/g5m/8A kWj7Z4w/6AWh/wDg5m/+RaPsfjD/AKDuh/8Agmm/+SqPsfjD/oO6H/4Jpv8A5KoAPtnjD/oB aH/4OZv/AJFo+2eMP+gFof8A4OZv/kWj7H4w/wCg7of/AIJpv/kqj7H4w/6Duh/+Cab/AOSq AD7Z4w/6AWh/+Dmb/wCRaPtnjD/oBaH/AODmb/5Fo+x+MP8AoO6H/wCCab/5Ko+x+MP+g7of /gmm/wDkqgA+2eMP+gFof/g5m/8AkWj7Z4w/6AWh/wDg5m/+RaPsfjD/AKDuh/8Agmm/+SqP sfjD/oO6H/4Jpv8A5KoAPtnjD/oBaH/4OZv/AJFqnpmveKtVtXuINA0ZUS4ntyH1iUHdFK0T Hi2PG5CR7Y6dKufY/GH/AEHdD/8ABNN/8lVHDpniq2QpBq/h+JC7OVTRJVBZmLMeLrqWJJPc kmgCT7Z4w/6AWh/+Dmb/AORaueH9Vm1nSftdxbR2063FxbyRRymVQ0UzxEhiqkglM9B1qno1 7rH/AAkOpaTq1zY3P2e0trmOW0tXg/1jzKVIaR848kHII6mjwb/yA7n/ALCupf8ApbNQB0FF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFcv8RbG3vvh5r4uY/MWHT7iZFLHbvWJ9pI6Ng/MM5w wVhyoI6io54Ibq3lt7iKOaCVCkkcihldSMEEHggjjFAFPXdT/sTw9qereT532G0lufK3bd+x C23ODjOMZwaz/tnjD/oBaH/4OZv/AJFo8d/8k88S/wDYKuv/AEU1dBQBz/2zxh/0AtD/APBz N/8AItH2zxh/0AtD/wDBzN/8i10FFAHP/bPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yL XQUUAc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf/ItdBRQBz/2zxh/0AtD/APBzN/8A ItH2zxh/0AtD/wDBzN/8i10FFAHJya94qi1m20ttA0bz7i3muEYaxLtCxtGrA/6NnOZVxx2P Tvc+2eMP+gFof/g5m/8AkWtwwQtcJcNFGZ0RkSQqNyqxBYA9QCVUkd9o9KkoA5/7Z4w/6AWh /wDg5m/+RaPtnjD/AKAWh/8Ag5m/+Ra6CigDn/tnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCD mb/5FroKKAOf+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWugooA5/7Z4w/6AWh/wDg 5m/+RaPtnjD/AKAWh/8Ag5m/+Ra6CigDk9M17xVqtq9xBoGjKiXE9uQ+sSg7opWiY8Wx43IS PbHTpVz7Z4w/6AWh/wDg5m/+Ra3IYIbZCkEUcSF2cqihQWZizHjuWJJPckmpKAOf+2eMP+gF of8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWugooA5/7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8A g5m/+Ra6CigDn/tnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5FroKKAOf+2eMP+gFof8A 4OZv/kWqep694q0q1S4n0DRmR7iC3ATWJSd0sqxKebYcbnBPtnr0rrKjmghuUCTxRyoHVwrq GAZWDKee4YAg9iAaAMP7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+Ra6CigDn/tnjD/o BaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5FroKKAOf+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/ AIOZv/kWugooA5/7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+Ra6CigDn/tnjD/oBaH/ AODmb/5FqnHr3iqXWbnS10DRvPt7eG4djrEu0rI0iqB/o2c5ibPHcde3WVh+JbmbStOa/wBO jtE1Ce4s7Pz54S42yXCxjcFZSwXzXIG4ck+poAj+2eMP+gFof/g5m/8AkWj7Z4w/6AWh/wDg 5m/+RaPsfjD/AKDuh/8Agmm/+SqPsfjD/oO6H/4Jpv8A5KoAPtnjD/oBaH/4OZv/AJFo+2eM P+gFof8A4OZv/kWj7H4w/wCg7of/AIJpv/kqj7H4w/6Duh/+Cab/AOSqAD7Z4w/6AWh/+Dmb /wCRaPtnjD/oBaH/AODmb/5Fo+x+MP8AoO6H/wCCab/5Ko+x+MP+g7of/gmm/wDkqgA+2eMP +gFof/g5m/8AkWj7Z4w/6AWh/wDg5m/+RaPsfjD/AKDuh/8Agmm/+SqPsfjD/oO6H/4Jpv8A 5KoAPtnjD/oBaH/4OZv/AJFqnpOveKtZ0ax1S30DRlgvbeO4jWTWJQwV1DAHFsRnB9TVz7H4 w/6Duh/+Cab/AOSqjg0zxVa28Vvb6v4fhgiQJHHHokqqigYAAF1gADjFAEn2zxh/0AtD/wDB zN/8i1oaFqf9t+HtM1byfJ+3WkVz5W7ds3oG25wM4zjOBVPw7f6ldS6xaapLaTT6fei3WW1g aFXUwQy5Ks7kHMpHXsKj8Cf8k88Nf9gq1/8ARS0AdBRRRQAUUUUAFFFFABRWXqurz6dcW8Fv ompak8yO5NoIgsYUqPmaR0AJ38DJJw3oap/8JDqn/Qma5/3+sv8A5IoA6Ciq9hdfbtOtrz7P Pb+fEkvk3CbJI9wB2uvZhnBHY1YoAK5/wJ/yTzw1/wBgq1/9FLXQVx3gjXtIh+Hnh/z9StYR FptrG7SyhBu8ocAnAPKsOO6MOqkBNpbickt2djRWBqXi7TLJo7e1lS/vZlBigt3DDLAeWXYZ CK7NGoY8ZcHpkh9r4jhhs4F1z/iXXwjX7QkylY1bHJV8lCmcDO44LIpwzAVPtI3tcj2sL2ub lFc1c+J7gwW95Y6XcTWLNHmV9qGXzHRI1jBYckSB9x4AG04YnZcj8U6XIpG64+0KzK9stu8k ylSVY7UBJUMCpcZXcMbjR7SO1wVWF7XNmiuaQareXeoarpL2WJ1WC2e5VikkaKWWQbTkgyPK uehUqwyBh7iaxdWTSLrNi8EStxeQ/vIMEA/Ng7lC5YF2VV+TdldwUCmuoKoups0yOWOZS0Ui OoZlJU5GQSCPqCCD7isCTxfbi4e3t9M1S5nSN5TEsAjkMahMuEkZWIJfaMDJKtgHFVPDqalZ aXDqcKy39vqca3stp5qq1vLJ87+TuwChLj5WYbdpOWLYpe0V7LUXtk5WWp1tFYbeKrG3uFt9 RhutPmbG1bmPhuGJwyFlIVV3MwO1QwyQcgMPimOXUlgsdPvb+yEJlkv7VN0K4IGFP/LQjnIT J4wATkB+0j3H7WHc36Kyf+EggHL2OqIvdvsErYzyvCgk5GTwPl6NtbCk/wCEm0r/AJ7y4P3G +zSYlHYxnbhwTtAK5BLoBy6gvnj3H7SHc1qK5Kzvtduby+vrSPzodQt430xZmIghVTIN7kc/ MDG+MbjvC9EZl1/+Ehto/kuLW/huB9+3Fq8zoOxPlBxg9jnBww6qwEqonvoTGrFq70NaisB9 ck1ZY00BXmUzc3jLtgIjJLqGIOQSqpuAP+sLLu2MAQ6nqGlWMFzr/wBnS3kVDPOHCm2kfA2M OhQMdocNkArkHDPT9ovkP2sfl36G/RWTb+I7C5uDEouox5gi8ya1kjUSEA7G3AFThkxuADbw FJOQLD6zpiWMd79ut3tpW2RSRuHErc/Km3O9uCAoySRjFNTi+pSnF9S9RWXJ4k0SJgjatZFy qvsWZWbaQGDYBzt2ncW6BfmJwM0+213TbpJnW58ryN3mrco0DIFVWYlXAIADoScY+YUc8e4c 8drmjRUMV1b3DukNxFI6ffVHBK/My846fMrD6qR2NEd1bzXE1vHcRPPBt82NXBaPcMjcOoyO RmndDuiaimPLHG0avIitI21AxwWOCcD1OAT9AaI5Y5lLRSI6hmUlTkZBII+oIIPuKY7j6Khu rqGys57u4fZBBG0kjYJ2qoyTgc9BWZL4r0W3heS5u3t/LUuyXEEkThR/EUZQ20n5Q2MFiFBL ECpcordkynGO7NmisaLxRpSQouo39lp96FHn2lxcqrxP3U5xkejYwwwRwRVj+3tIN59kXUrV 7gSeW0SShmRs4wwH3fmwvOPmIHUgE549xKpB9TRopgljMzQiRDKqhmQH5gDkAkeh2n8j6U+q LCiiigAooooAKKKKACuf8d/8k88S/wDYKuv/AEU1dBXP+O/+SeeJf+wVdf8AopqANyeZbeCS Zw5SNS7CNC7EAZ4VQST7AEmsf/hLNO/59tZ/8Et5/wDGq3KK0g6a+NN+jt+jE7mH/wAJZp3/ AD7az/4Jbz/41R/wlmnf8+2s/wDglvP/AI1W5RV81D+V/ev/AJENTD/4SzTv+fbWf/BLef8A xqnweJ7C4njhS31YPIwRTJpF0igk45ZowAPckAVs0UOVHpF/ev8A5ENTH1++uNIih1YSf8S6 03tqMe0EiDaSZV7lkIU4B5QyYVm2CrGj/wBovZvPqfyTzSvIsA24t484RMr1YKAW5Yby207d oBqGmf2jeWDyzYtbSU3DQbciaQDEe45xtUkvjGd6xsCNuCaPpn9j2b2cc2+1WV2tkK4MMbHc I+uNqkkKAAFQKuPlycBnLya3rs3hrSdbtriCO31LULSUh0BeO1muIUjiVcYDGN8uxZsNuC8F SnQJfXFv4tbTZ5PMt7y0N1a/KMxGJkSVSRj5T5kTL1OTJkgBRWXpWiTT+DLDQLjzLaXSLi1i MjRkiZbWWN0de2JEjU8E7C5UklCK0BBNeeNReeVJHb6dZPbLIykCeSZo3YLnHCLDH8wyCZSM gowoAjs/+Sh6z/2CrD/0bd0eDf8AkB3P/YV1L/0tmos/+Sh6z/2CrD/0bd0eDf8AkB3P/YV1 L/0tmoA6CiiigAooooAKKKKACiiigAooooAKKx9Q1u6s9RNnbeHtV1DbEsrTW3kLGNxYBd0s qZYbckDOAVz1FU5/FGo21vLO/gvxAUjQuwjazdiAM8KtwSx9gCT2oA6SiiigDn/Hf/JPPEv/ AGCrr/0U1dBXP+O/+SeeJf8AsFXX/opq6CgAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/GX/IDtv8A sK6b/wClsNdBXP8AjL/kB23/AGFdN/8AS2GgDZvLuOxtXuZlmaNMZEEDzPyccIgLHr2HvWV/ wlmnf8+2s/8AglvP/jVblFaQdNL3036O36MWph/8JZp3/PtrP/glvP8A41R/wlmnf8+2s/8A glvP/jVblFXzUP5X96/+RDUw/wDhLNO/59tZ/wDBLef/ABqp7PxDZX10ltDBqayPnBn0u5hT gZ5d4wo6dz7Vq0UnKjbSL+9f/IhqYetald6Lew3zLJcaZKgt3gjCb1uGdVhK5K8OzGM5JAJi Pyr5jVIb640HwlNqWuSfaLiztHurz7MoxlVLusYO35Ryq7ucAbiTk1Je6JDqWqR3F/5dxaRW 8kSWckYZC0nDuwOQx2fIOBgPKCWD4Fe50Ca78FXXh2fUZJXmspbIXkqlnKspRXfLfO4Ujccj cQThc4GIzPkufEdhqPha3vbq0ZLu4aO/MS5Mkpt55SiZA2RI0ahTy7DG4jaS+ppl9cf25q2k 3cnmtb+XdQSbQD5E28KrYx8yvFKOB9zy8lm3Go44Jteh0HUrmKSwuLK4NzNaOpYrJ5MsLR7u MgNISHAIYKCOGBo0uCabxLrOrSRSQxOkNjCrqQZFhMjGXnBALzOgBHIjDAkOKAI/D3/Ic8Wf 9hWP/wBIrWjwJ/yTzw1/2CrX/wBFLR4e/wCQ54s/7Csf/pFa0eBP+SeeGv8AsFWv/opaAOgo oooAKKKKACiiigDL8S3M1l4V1e7t7qO0ngsppI7mRSywsqEhyAGJAIzjB6dD0rD0Sz8rWIH/ ALB8V2uN376/1v7RCvyn70f2p8+g+U4JB4xkdBrt5b6f4e1O9vBObW3tJZZhbuUkKKhLbGBB DYBwQRg9xXN+HPDd/pN/Zg6ZHa2duhRVj8T3t0sahCqgQSIEI6DBxjqOQKAO0rD8RXN2kuj2 FpdSWh1G9NvJcRKjSRqsE0uU3hlyTEAcqeCehwRuVl61pU2pCymtLmO3vLG4+0W7yxGWPcY3 jIdAylhtkfGGHODyAQQCv4dubt5dYsLu6kuzp16LeO4lVFkkVoIZcvsCrkGUgYUcAdTknG+H E3h2Hwxo+k6bqlld31pY5eJbiOSeIMVaQELyoDkAjHZc810Oi6VNpovZru5juLy+uPtFw8UR ij3CNIwEQsxUbY0zljzk8AgDlvB2p3OjeFdGGouTpQsLeQ3sxJKecqbAzE4wr+YpwPlVoe24 gcbR9o9lp9+v6GlKh7V+78S6d/8Ag3tZHdRxRwqVijRFLMxCjAySST9SSSfc0+say8VaPf30 1nDeKJomwA/yhwfLwynurGVAp/iP3cjms7R/GY1DSbK7l0y8BkaGG4kVERIppCoAwz7ip3ow IB+VxznIGftIbXNlgcRZvlta2+m+251VFVH1XTorc3El/apAsaSmRplChHOEbOcYYggHvjih tV05EV2v7UI0YlDGZQChVmDdehVHOfRSexq+ZdzH2U/5X9xboqG1ure9t0uLS4ingfO2SJw6 tg4OCOOoIqamQ007MKKKKBBRRVHSNXstb02K+sZlkikUEgMC0ZIB2tgnDDIyKV1exShJxckt F+peooopkhRRRQAUyWKOeF4Zo0kikUq6OMqwPBBB6in0UAMlijnheGaNJIpFKujjKsDwQQeo qvb6Vp1pcG4trC1hnMYiMkcKq2wAALkDOAFUY9h6VbopWQmk9RkcUcKlYo0RSzMQowMkkk/U kkn3NRTafZXMyzT2lvLKjIyu8YZlK5KkEjqNzY9Nx9asUUWQWRnS6DpEyIj6bajy/wDVskQV oztVQUYYKkBVAIII2rjoKrp4U0VGkc2jySyNvaWWeSSQsAArb2YsGUD5WzlcnaRk52aKXJF9 CXTg90jLTw7pIWRZbNLoyLsd7xmuGK5B27pCx25AO3OM84zRJ4d0mRg62aQOFVQ9szQMAoCr yhByFG0HqFLKOGIOpRRyR7B7OHYyR4a0wPG226xHIsoj+2zbC6sGDFN+0ncNxJByck5JJrWo opqKWyGoqOyCq8lhay2IsmgRbYKqrGnyBAuNu3GNpGAQRgggEYxViina42k9zGTwnocTSGCw S3WVt0sdu7RRy8AYdFIVlwPukEctx8xyz/hD9BWXzIbH7O4k8xTbTPD5bbdhKbGGzK4B243Y Gc4FblFR7OHYj2UP5V9xk/8ACMaKPmTT4o5/+fmLKTk9z5ykPk9zuycnOcmoYfCGixYL2nnu sawo87lykS5Cxr6JtJUgfeGd27JJ3KKPZx7D9lDsjDh8KacmEuDLe28caxQW9ztaOGNc4QAA bh93O/cSUQkkqCHy+HInheGHU9Xt45FIcJfSOxP8JDOWZSDz8pAPRtw4rZoo9nHsL2UOxk/2 G7/LNrOqSxtzKnmrH5h7HciqyYwOEKg45BJbMNt4T0y2iZFe/ZjI7iQ38wdd7FmAYMCASeR/ FtUtkjNblFHJHsP2cN2jD/4RiGS8+03epapdP5e3a9yUVWz/AKxRGF2Pt+XKYypIIO5ieZ+I XhZv+EM127tNYv4mh02YbZpWmHkhGd4gSdw3sFyxLHChfu8V6FXP+O/+SeeJf+wVdf8AopqP Zx7C9lDsdBRTJY1mheJi4V1Kko5VsH0IwQfcc1mf8I5Y/wDPfVP/AAa3P/xyqd+hT5uhrUVk /wDCOWP/AD31T/wa3P8A8co/4Ryx/wCe+qf+DW5/+OUry7f19wrz7L7/APgGtRWT/wAI5Y/8 99U/8Gtz/wDHKdFoFnDMkqzakWRgwD6lcMuR6guQR7Hii8u39fcF59l9/wDwCxqOpQ6YlvJc LJ5U1xHbmRQCI2kbahYZzguVXgHBYE4AJBa6lDeX99aQrITZOkc0hAC+YyB9g5ySFZGJxj5w ASQwEl/Y2+p6dc2F5H5lrdRPDMm4jcjAhhkcjIJ6Vl+DoJoPB2lG6ikivJ7dbm7WRSrfaJf3 kxKn7pMjudvAGcAADFUWblFFFAHCareaBafEPUP7c1mPTd2lWXk79Ta08zEt1u6Ou7HHrjPv Wp4Aa1fwqWsWhazbUdQMBgIMZj+2TbduONuMYxxip7P/AJKHrP8A2CrD/wBG3dHg3/kB3P8A 2FdS/wDS2alZXuKyvc6CiiimMKKKKACiiigAooooAKKKKAMPxdPNb+HZGglkjd7i2iIiYq8i vOiNGjDG13ViitldpYHcuNwp+G7XyNRkb+xvEdlmIjzNT1b7VGeRwF+0y4b32jgEZ5wbni8W j+GLq3vbaS5gunitPIS4eDzGlkWNVZ0O5ULOAxGfl3cN0NPw3o+o6fqMkt5Z+TGYiob/AISK 8v8AJyDjy5kCjofmByOnQmgDqKKKz9T1qy0jyhdNO0kuSkNtbSXEjAYy2yNWbaMqC2MAsoJy RkAz/Hf/ACTzxL/2Crr/ANFNXQVy/i2+t9R+GPiK6tZPMhfSrsAlSpBEbhlZTgqwIIKkAggg gEV1FABRWB4s8SL4d0iaWFEm1AwvJb27Z+cKVDnjqFDbiOuAegBI17C9j1Cxhu4ldVkXJRxh kPQqw7MpyCOxBFSppy5epCnFycOqLFFclN4/0qP7PfeZKNFfMbXjWsgDSncVCZALACOQNgHB ZP8Aax1tEZxl8LCFSM/hdyGG6huJbiOJ9z28gjlGCNrFVfHv8rKePWpq5XThrHl3Gp2L288V 1cvK8ci/NKiSsimM5Ay8Aj2sTt/dqed5Yami67Dqokhfyor6KSWOWBJC4UxyFDhiBnopIxkB 1yBuGZjUT3JhVTsnpc1qy/DmrLrvh2w1MFC08IMmxSFEg4cAHnAYEfh3rM13xjpdr4avbyy1 OJpzbym3aNfMO4FkV8AH5PMG3cRtJxzyMkIs/BMttZRRypo13IwRi5cWkgXcQckny2CuxPRS GJOG+VOoubR6f57EuqubR6dfnsdPRVHStY0/W7ZrnTbpLiFWCl1BwCVVsc98MM+h4PIIrOl8 Z6HDqV3ZSXTg2jRpPOImaGN3JAVpACqkEc7iBzjOQQLc4pXuaOpBJNvc36KxYvF/h2aOaRda shFCQskjyhEBJcAbjgc+Wx+gz0IJz/EPi86cyW+mWpvpp7YNDMnzRJNLlbUPjHySurruBwCF zgMDW1ClKvJRp6gpxaumdOkscjSKkiM0bbXCnJU4BwfQ4IP0Ip9eW+GNZ1iSz1C48P2sF4NS v2uysiMrW27Z5hwzIsqht8Y+ZMtExG4Z2ad0ZtOa01y31i31DW5meyKztJHF/pD7YgtuWJj2 SRRq2MHZHMW3OOChCNR8s5cr7ef6dn5taW1M4V1JXO/orzu+1fW7/R4l0+N7nV9JF0Xk8nBk mif7OJAuAuHRp3VRkbk2gtsYG5J4svLLXX8KtLjUI98/9qX8aLCLYKZN5QNHvI/1eFwPlZyf lZamMW6sqfbr008/67btIarx5rfidpPPFbQSTzypFDEpeSSRgqooGSSTwAB3qrNrFhBbalcS T7YtMz9rbYx8vEayHtz8jKeM9cdeK5aztbnxzLqKeIYZrSxsrh7J9IVw0Vwyqf3zOUVypEiM g+XBRXGSRhLXwLJp1zFpttqF7Jo9x+/1BpmR3uZFkdikhI+bzfNUM2MlLcISQwx2xw+HjeNS p7y6Lbz17pX8r21L5m9jqtI1a01vTLe/tHzHNFHIUYjfHvRXCuAThtrqce49avVzlzZwaLr+ jS2Mflfbrm4trhckhw6zXJY9ywkRtueFEsgA+bijrvjuCGwu/wDhHnstRu4LG4vtzzERCKIL lgVB8z5mAAGFJSQb1KkVH1SVWaVBXT79NWtXt0HzWWp2NFcjZ/21rB1AwypDp91qEiPL9qfz oEhfyXjjULjEghY7g6lDMxAJUFqtvpt/r1zHp13e3sNro1zP5hSVSzyiQPZ5YhjJ5cJSQ7uC zR7t5DgNYRK/NNK2/W39PT1t0YcxueJPEUGhWqt50PniWIyRtlisOWeVyByMQxTsPUx4GTwd iSeKJ4kklRHlbZGrMAXbBbA9ThWOPQE9q4O3+GMCWV3E93slntpbXdgvgLCba2k/h+ZYC+5e jNKx4CpjZg8O6hqIjuPEF+5vbZhJZvYTNGlu5T52C4AchmdQHDDy1UEEtJu0q0sKopQne176 a36WXa/ntr5CTl1R09Fcktx4g8NvbwXSJqGkrcSSXOqSz5nEbyfKGjCqqhDICWBKiOJzhflW tm18Q6dd3mqWyylDpuDPJINse0g/Or9CAySIeeGjYHGOeWph5RTkndb3Xa9te3o9dR8yNSis O98X6Fp9+lrd6hDEDw1w7qIo3zIAjOTgMTDMPYxkHBKhn3vinSLO2upku0uzaTxW9xDZsJpY nkkEYDIpLZ3HpjPBwCeKPq1bT3Xr5fId0bNFcxaeL2ZIbjVdIudHsXVzJdXzBFhYkGJG9CyH cSflViI9xfIEFj4+st9ta65bPpGoTQG4aCZshExG2ckK2AJDk7QFMM2TiMtWn1HEa2je3az/ AC/rbuhcyOuooqCe8gtprWGaTbJdSmGEYJ3OEZ8cdPlRjz6VypN7FE9FY3hvxHb+J9Pe8trW 5t0RkXZchAxDRJKrfKzDBSRT1z6gUzWvFdholyLeaG9nlHktKttbtJ5SSyeWjH1y4xtXLnkh SASNvq1X2nsuX3uwrq1zcornP+Epnnv/ALHY6JezSCLErS4RYbgniGQjOMKGdmGRt2Fd/mIG qaZ47iv0gml0y5totQg87SonINxeYO118vohGUYEsVKOHyAr7b+pV7X5fxX5Xv3fom9kLmR1 1c/4y/5Adt/2FdN/9LYaYfFscsgktbKZ7GK5itLqWdHt5I5ZWRUVYnUM3+sRmJ2gKwKlzkDN 1nxLo3iDw/bSaVqMNyP7V07AUkE4u7VjgHB4EsefQtg4PFRLDVox5nF2/L17fMfMjtaKhurZ Lu3eCRpVRsZMUrRtwc8MpBH4Gs//AIRyx/576p/4Nbn/AOOVzu/QTcuiNaisn/hHLH/nvqn/ AINbn/45R/wjlj/z31T/AMGtz/8AHKV5dv6+4V59l9//AADWorJ/4Ryx/wCe+qf+DW5/+OVL a6La2lwk8ct+zrnAl1CeReRjlWcg/iKLy7f19wJz7fj/AMAlutShs7+xtJlkBvXeOGQAFfMV C+w85BKq7A4x8hBIJUE07UodTS4kt1k8qG4ktxIwAEjRttcqM5wHDLyBkqSMggnP8YwTT+Dt VNrFJLeQW7XNosalm+0RfvISFH3iJEQ7eQcYIIOK1LCxt9M062sLOPy7W1iSGFNxO1FACjJ5 OAB1qiyxRRRQBz/h7/kOeLP+wrH/AOkVrR4E/wCSeeGv+wVa/wDopaq6Tqmn6fr/AIpS9vra 2eTU96LNMqFlSxtWcjJ5CjknsOTVrwJ/yTzw1/2CrX/0UtNxaSbW4HQUUUUgCiiigAooooAz 9YX7Vp13psFz5V9dWkwgCXHkydApdWAYrtLr8wVtpI4PAPN+HPDU2nX9ncXHg3wjYzxoRJe6 Y5EitsIJRDbqQCTjG/gE8nv0mp6Fo+t+V/a2lWN/5OfL+126S7M4zjcDjOB09BXL2Nh8LNTv I7OwtPBt3dSZ2QwR2sjtgEnCjk4AJ/CgDtIJ4bq3iuLeWOaCVA8ckbBldSMggjggjnNSVHBB Da28VvbxRwwRIEjjjUKqKBgAAcAAcYqSgArD8F/8iJ4e/wCwZbf+ilrcrD8F/wDIieHv+wZb f+ilreP8CXqvykLqal3YWt+IxdQLMsbMyq/K5KMhyOhBV2GDxzTrq1hvbOe0uE3wTxtHIuSN ysMEZHPQ1NRXPZF88tNdtvIxtP8AC+m6ZffbLeNhLudvmIIy2AuBjgIgKIBgKrMP4jVi20LS 7R5nhsot807XLs43nzGZWLDdnHzIhwMDKg9q0aKShFbI1lia0neUn95h33h1pbg3Gm6ldafI 28ukbs0TMwyG8snAIcK5xjd8wbIY1DDoeriIGTXJQ9xtN5GQXVPmLOkDAqYwdzruO5gu3BG0 V0VFL2cb3LWMrKPLe/qk/wA0cxaeFrnS7930vUvs1q/mMIxChC/OHji245jDPOflKthwM8Ai xeaTq2orFaXGorHaxTJL58KL58hjO5NwZSgO7aTgY/d9MSbU36KXs4pWQ3jaspKcrN97K/re 25zt9oGqal5cF3rmbFcq8MVtsa4Q4VllbdzlC4+UJhmDAfKBU2o6C2xZdEn/ALOvE2opRmEG 3bsJaIHaxVPu5HVEBO0Yrcop+ziJYysra6LpZJfNJa/MxksteLSXB1eBJZGyLZrUSQQrgfKC Crs2QfmLAHJ+QcYhks/EclyJvt9mhgVRGIkdVuQWDOHUlvLPyogYFyAZDjLALv0Uci/piWKk ney/8BX+X5/nqcleaN4okuLzVLHVrW1v7iOOJLRoxJAiKHH+sKhicuXB2jB+Uhhgi9caf4jl mgnXWbPdC2VhSzeONycKTJ+8YsFUuQoKgttJPFb9FL2S8/vLeNqO2kdP7q2ta22unz8zGji8 QwKT51nctKzSMsxKCDkkRqVX51xhdxAIILYbIQVLzWvES2MTW3hlluZWSI+dcoywM3BdgmSy AlenJAckJgbukopuDto2THERveVOL+9fk1/Xzvzp8WKLN5zpV/C8W2SeK5RUaGDAZpmwTwFL YUfMWRlA+VsZM3jGb/hI4WNlLFZy2DvpxknCrqUkjxCMAdFOc9eVV8kKM13FFS4Tf2jSnicP Btulffq+v9fd52aqDVLH+y49Te6ijsZI1kWeVvLXa2NpJbGM5HX1rObxdo6JPM00v2eHIM4g dkYhQxC4GWwhV8gYKksCQrFZrTw1pdlq82p29vtuJeW5yu8s7F8f3z5jDJ6LwMDIOtVe+12M 28NF6JyXql+j+/8AAxpPFvh6FQ0us2aZZVCtKA3JAB29dpyCG6bTuzt5q3perWurwzS2pYrD M0TblxkjBDA9CrKVYEcEMKmfT7KS2jtntIGt412pE0YKqNpXAGMAbSV+hIrF1LwnZzoZbJPJ uIpI7m3i3kQCeJVWJio6AKuwhcZUnqQpCftFroy4rCTXL70X30a/Q6KiubaPxPBqVrbw3sEs cyzNNNc2+9IlQqI9uzZ87hgWBOMhtuAMGW+0vVo7RobLVJ5Y5WWOQSBfNVXYCWRZARtZRl14 wDuABBQIc77E/VoppOotfXvbt/W5uSyxwQvNNIscUalndzhVA5JJPQVUstY0/UWRbS6WRnhW ZRgglGAIPPfDKSOoDrkDcM0ZfDFtLcWc7XF08kM6zzNJO5NxtBKhgGAAD7XCgbQQQAAxqxqf h/TdVtPImtlRlZnhnhASWCQtuLow5VtwDE9z1zTvPsJRwysnJ69bbfLr96/Q0Y5Y5lLRSK6h mUlTkZBII+oIIPuKDLGJlhMiiVlLKhPzEDAJA9BuH5j1rM/4RrSh8qQSxwf8+0VzIkBHceUr BMHuNuDk5zk1D/wjFpAkTWD/AGW6hnNxHMI0OTtdVRxgbo1WQqFBBCgAEYzRefYShh39p/Nf 5N/kblFYa6JqeyBpPEt/JPHgsfKhVGJUqx2qgPQkqGLAHaSGxgkGuJYebZ6vJKlxDIUSZoGC XI6psYLteQqQCi8lg21cAUc/8ysH1Zv+FLmflf8AVI3KKxh4o0xpmRWnZUUGRxbvhC2QqkYz uJV1wAfmUocMQplk16y3CK0LX9wyqyRWuGLggNkMSEwFKk5YYDJ/fXc+ePcl4WsnZxa/rr2+ ZqUVzqeKlfybcWMo1OWeSI2O9d8YXewLt91SyIWUE4bIwduXD7XxVazaqdNnVbe5gtpbi8Dy YFuEdV5JAyrZLAnB2hSQNwpe0h3LeCrq/u7fl39Oz2fQ36KZFLHPCk0MiyRSKGR0OVYHkEEd RWdJ4j0eG5FtJqMC3BmWFYi3zO5YLhR1YbjtJGQCGBI2nFOSW7MY0pzdoxb+RqVz/jv/AJJ5 4l/7BV1/6KatgX9qdSbTvPX7YsInMJ+95ZJXcPUZBHHTjPUVzPxO1a10n4da2botm6s5rWJV XJaR42A+g6kn0HrgEcklcFSnKSilqzrqKZKJDC4hdUlKnYzruUHsSARke2R9azPs/iH/AKCm l/8Agtk/+P0NtdAhCMt5Jet/0TNaisn7P4h/6Cml/wDgtk/+P0fZ/EP/AEFNL/8ABbJ/8fpc z7fl/mX7GH/Pxf8Ak3+RrUVk/Z/EP/QU0v8A8Fsn/wAfp0UGuiZDNqWnPEGG9UsHViO4BMxw ffB+lHM+35A6UP51/wCTf5FjUtW03RrdbjVNQtLGBnCLJdTLEpbBOAWIGcAnHsaNN1bTdZt2 uNL1C0voFco0lrMsqhsA4JUkZwQce4qvr+pTabpbGzWOTULhxb2UcgJVpn4UsAclF5d8chEc 9qsaVpsOkaXb2EDSOkKYMkpBeVurO5AG52YlmPcknvVGBcooooA5+z/5KHrP/YKsP/Rt3R4N /wCQHc/9hXUv/S2aiz/5KHrP/YKsP/Rt3R4N/wCQHc/9hXUv/S2agDoKKKKACiiigAooooAK KKKACiiigDD8T202r6JfaVp0lo+obIpPs9xMVQrvyBKFViYn2OpXHzgMuRkkZ+h6TDoNxc39 x4Y8MaFAluxkvLCcbgoIJDEwRgJgZJ3fwjjuNTWdC8N32/Udc0rSrjyIjuub63jfy41yxyzj hRkn0HNYem6T8MtZuGt9L0/wjfTqhdo7WG2lYLkDJCgnGSBn3FAHaVy+uX9npHjLR9R1K7gs rEafewG5uZBHGJGktmVNzYG4hHIHUhW9DXUUUAcE7IPhF4lndPMt5U1edCDxLE807oynurIw II4III61zcMGqNpkmqaq5l+xSTzXWm27tGunRKJ42uI3Zm8yctBlSScOzn5S5ceg+O/+SeeJ f+wVdf8Aopqx7i6tvCmvXGrXry21hLdvZnIcqVeMXAfAzuxKZx0/5akZAXFHtY05KU4prrft +nqdNDCxxUZU7Xluv8u7v2E8Pa5of9oXIvmnXV0lK3Fxd7zEJVN1kQsxIWNRFcYIwNpwTu3A XPE2laVaRG9uLu4to7idIpYkceW0blvPG0kbVZGZ5CpGPK8zqhJ4G10W01nwXPpt6ipc+F9N uEdUjQFZzMXGCBjIFsAzDO4StyDknQ0bSJ7fzYNUijWw1C2k0y41KaZTcJlLma5AY5AxMXBY 8EIvD/eGs5Ze5R5b8r6W82tX0/Gzdr9TWWTVI05xla8G+u+iendtO/mtTsZtR8P6l4htNJhu LdprC+E7RxosiNOyXJ2HaflkUxySHcByB3PGXcxeEdMs3tYr26eWx0V7+3NpId8dvgBp4XAC h32LnBweTjDvu5+8+H95qzaPG15b6ZfXE095q89q6pIl9hpoEUBiWaPz5OhBKLncMJjuLnwX 4ehVTBEumQtODLHbFY4pw5iDQshBXZIYogVABbGM/M27urYXLYST53K+9ultNX52ul2s+p5f snJ6xNzTZ7O60qzuNP2fYpYEe32JsXyyoK4XAwMY4xxWN4sudAihSLWraSctBNIDFE5eOBdv nPvXBRQrDdyCw+UBiQpwtPg8T3a3WjadJZadZ6dqbI00pd5ERZWnjjREYKYjE9smNy7V8xcd MdB4d8NLpE099PK8l7dNLI6GUyRQGSV5WWLdyoJZQxAUP5asVBzWdTDUKTftJXXRLqul3sv6 2uiviVrGbqh8H+EbfVftl1BB9p09ElsGuwJJookZFCKzBiSuV68lR3yT082qafbuEmvraNzO tsFeZQTMwDLHyfvEEEL1IINcUPh/9u8Eato+oRQzXMm6HTmn6wRwp5VqS6knovmHAAzNINoy QcrwDqX/AAnGv6vqF1ZY08yzzLBcxfJLHKsUELhSSGYJbXCMenzsBkFgN44DD+xlOnLSF+Z6 dbWt6vmFH3dErXLbeHr3WZ38QadCkLTXE1nbeXcsClo8rE3aNgEyGRmlADbWibaPvZNzU/BO sHUol03WfK06WOG2CfZUZ7RYC89vJuZv3myXam3byjfNkjfXWanMNF8NXk9lDCgsrN3hiC4R diEquBjA4AwMcVasL+11OxhvbKdZ7aZdySL0I/oexB5B4rjjjPZVb0YpaW2T0vpv1T1v33ua LC2h7W2l7X8/6/rQ5jS/CMd9BLd+IbY/bpLmSVYkuCUtSs8jI8DLtKFlKbiu0vtBYbi2dO68 IaFdWc9sNPit45Y2Qi3UIFyMEhR8ufunJB5RCclFxuUVz1pe2k5SXy6LyXkS6cHuhgijEzTC NBKyhWcD5iBkgE+g3H8z61knwvpjalPfNG5kmV1KghQoYxt8pABUh494IOQzu3U8bNFQ4p7j cIy3R5j4s8DXOnabqSeF4pIrHUbSZdQggc+YTHAPIEY6kFoyjjln89s54Kb1ppK+IdCmvyEJ 1O/+2qSxVWtiBACAOQXtR0PIZz90gbewqOCCK2gjggiSKGJQkccahVRQMAADgADtXZUxTqYZ UJLbr5ef3K3kkT7KO3QwbjwpZ272t3otta2t/ayZheRSyKjNJvXaDnGJpGCqV5CjOAMCxapr c8kd79q0lbbYUNnN96UOxJ3Ff3ibBGcFdv7xgQWX5eiorg9mumwvZR6aLscJ4l8PeIL290aW LWbj7S+oeXLJaI0UUFsYHDkKS4WThgsh53SAdNuLsPhOWxnY29vptzbx272MC3gkeU2rFXMU jkkMu4FFBU7VIOW+ZW66it+e0UoaW6rd379w9jG90c/bW2s6Dp1tb20Vlf2lskcCWtvG0EiR jC5VpJHDEKPusVz/AHuxq6d4n1Ocl5dGuLi3lubhYZbaMrsiRzGqurnIlLgDBCqA24kbWx1V FYyjJyvzMHTd9JWMn7ZrU/7y30qKFF+Uw31wEdj/AHg0fmAAdMdTk/d2jfWGu39ndtb6npNw Y0YL9rs43mSQFSVcIoLKMqwYclSU5YNuG/RRyvoxuEukjmtUMfiWGHRbiyuI0lmVr+3l+V44 Vy6ncpK4Z1ReCcjeBgq23ktZ8K+KNNvUt/DbWklu1o/mW7W6RQ3CQymRIn2IFUubh1KgqHCE k/MQvqVFa0pezbb1T3XR9r2JlRUtW9TkrDw7f+EtMmtvDkVjLAribyZlKSXB2qpXcpCI21Bh tpDEjcAQzvzupafq2l/Erw9e/ZBc2sdoyCz0tQgRYg0StsdgMZuh0PyBiOQpZvT6jMETTpOY kMyKyLIVG5VYgkA9QCVXI74HpVQqSUm5Sbumt/LT7tPuB0U9mzmdRurTxJpV3pOsaBfss8mE sGIWSdEYEvuDBVAdT/H02c/vFBmu9T0KeaRtU0t42FvI00l7YHaAiNuj3EYkOySbAQsCvmY4 PPSUySKOZQssaOoZWAYZGQQQfqCAR7is1Oqo8qlp/nb87L7kVyy7/geP6NexXnhQXviM6ml6 LFbq01G7aRoEni4hMMcjBGlKxxy8ElzJICQpK0zxNFep4cvNF0y7uLS28P3cj2GoW9w8ksrB GP2cAHKCOOfaWLk7YmIUgMF9mqvBYWttbWlvFAgitFCwBvmMYC7RgnnO0kZ64J9a7FmOKjNy i1a90mtv020tt5Gfsp2tzfgcLqV5/ZXj46Fbp9g0e602Ce6ltgyCFI2kjO3YpEZYeSpkJXak fDAhSvXabYaKtiYdPhtJbcypM5QiTfLhXWR25LOfkbexLHg56GjTfD+n6VZXNlbxu9rcNloZ 3MqgeWse35skrtQcEn06YAbdeGNFvHeSXT4hI0gl3x5jZZN27epUgq5IGWGCcAEkAVhUxNaa Wyt2vrbZvztYpKotdDTSWORpFSRGaNtrhTkqcA4PocEH6EVkW3iLw7fz208OoWTzSL5dvIzA M28qTGpPO7ITcg5HyZAyKU+FdHaZWezSSIKd1vL+8ikc4/eurZ3S8Ebz8xDHJPGL1vpdja2B sYbWIWpjEbRldwdQgQBs/e+VQOc8AVzpzv2K/eN9Crrd5o4iXSdWmRU1NGt1hYsDMGKxlQRz n94Ohzgk9ASOY8RDw7qun2dzp8dtJNb6xp1wssCFck35j3ZGA4LNceoyxbuCejtvCmiWk180 On24hvliE9sY1MJMedpCYwDz9OAeuSeL8U+C4dJm0e90hYoLQa7Z3F6jE5djdIsQQAYAXzXG BjgjOSKaq146Lbtd9dP+HIbqqSdlb8f66nptFQ3S3D27raSxRTnG15YzIo55yoZSeM9xWf8A Z/EP/QU0v/wWyf8Ax+m210OuEIyV3JL1v+iZrUVk/Z/EP/QU0v8A8Fsn/wAfo+z+If8AoKaX /wCC2T/4/S5n2/L/ADL9jD/n4v8Ayb/I1qKyfs/iH/oKaX/4LZP/AI/UtrDrKXCNd39hLAM7 kisnjY8cYYysBzjsaOZ9hOlBK/Ovx/yF1PXdH0Tyv7W1WxsPOz5f2u4SLfjGcbiM4yOnqKsW N/Z6nZx3lhdwXdrJnZNBIJEbBIOGHBwQR+FZfiWeaS3g0azlkivNUcwiSJirwQgZmmBHKlV+ VWwQJHiB4NbEEENrbxW9vFHDBEgSOONQqooGAABwABxiqMSSiiigDxTxL4cTX/GXiy3N5bNd XM8VvZ2t1ZTS7G+z2u6WJ1lVVbmMv8rMEjyQVyD6Z4Gx/wAK+8N7c7f7KtcZ6/6paTw9/wAh zxZ/2FY//SK1o8Cf8k88Nf8AYKtf/RS1pKtVnBQnK6Wy7enr1JUbNs6CiiisygooooAKKKKA MvxLZw6j4V1eyuLuOzguLKaKS5kxthVkILnJAwAc9R06isPRNe1G71iC3vNVgMcm4CKXw5eW DSkKTtSSaTaWGC20AkqrcYBI6DWIkvNOu7C406e9tbm0mWVInVd4IA8rJdSGYMcHgDByy8Z5 vSLbU59bspNVtfE8yW7tJDJqLacIoJCjLvxbkOTtZ1Aww+fJHAIAO0qnqV+2n26yR2N3eyu4 RILVVLMcEnJYqigAE5ZgOgGSQDcrn9U1TU9As7u8uk/tNZJVSyttP06bemSf9aymQlQMEuEG NpwrEqtAGhpOrJqsU/8Ao09rcW0vk3FtPt3xPtVwCUZlOUdG+Vj97BwQQKfgv/kRPD3/AGDL b/0UtHhcQPZXN1G13Jc3NwZbua5spbQyS7EXKxyAEIFVFGM8LyzNuY5/gDTrq38K6Jcy6zfX UMmmQbbSZIBHFlFI2lI1fgcDcx4POTzW8f4EvVflIXU6yiiisBhRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFMjijhUrFGqKWZiFGBk kkn6kkk+5p9FAX6DEijjaRkjVWkbc5UYLHAGT6nAA+gFZ2p+H9N1Ybbu2VlZmMoUAecCm0hj 1I4Q9cgxof4RWpRScU1ZmkKs6cuaDszmLPwpKQ0Wo38r2ce2K1tLeR41jijkZo8ndkuB5XzA Kw8sDJBbdvx2FrFYmyWBWtirK0b/ADhw2d27OdxOSSTkkkk5zViipjCMdi6uKq1XeT8+y+7v 57mBd+DtHuLbyIIWsF84zZsm8kh9oAIx0AKRvgYBaNSQcc8Z408Mao3hTXrK/nim0Cx025ud O8r5ZIHjRfLR8/eAXzFB5yCSSDtx6lRSdKD6G0MwxEFbmv662815nP8A/Cd+D/8Aoa9D/wDB jD/8VR/wnfg//oa9D/8ABjD/APFV0FMeKORo2eNWaNtyFhkqcEZHocEj6E1ocSML/hO/B/8A 0Neh/wDgxh/+Ko/4Tvwf/wBDXof/AIMYf/iq3Y4o4VKxRqilmYhRgZJJJ+pJJPuaI4o4VKxR qilmYhRgZJJJ+pJJPuaBuxhf8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVXQUUC OP1PXfhxrflf2tqvhS/8nPl/a7i3l2ZxnG4nGcDp6CpNN8S/D7RrdrfS9b8MWMDOXaO1ureJ S2AMkKQM4AGfYV1lFAHP/wDCd+D/APoa9D/8GMP/AMVR/wAJ34P/AOhr0P8A8GMP/wAVXQUU AcBD4v0s+Pb+506ZNUs5LKxhnubFxLFbASXZLyOuVUDKZyRwSecVs+Abpr7wot09o1pJNfXz vbuSWjY3Uu4HIBznPYVranp11f8AlfZtZvtN2Z3fZEgbzM4xnzY36Y7Y6nOeMZfgaNofDckT zSTump6grSyBQzkXk3zHaAMnrwAPQCps73uaucPZqCjr1f3/AHb/AIHSUUUVRkFFFFABRRRQ AUUUUAFFFFAGH4ugWfw7JuvY7IxXFtcJM8DTDfHOkiL5akM5ZlChVOSWAGTgVT8N6zfahqMk F7qUEuIi6QNodzp8jYIBZTPId6jIB2jgsuSMgHQ8QRG80y5tjp19ceX5M0TWjwq5lWTcpTzH C7kZFc7/AJTwPm5Wsvw7bXz6ybvVLXxA08du8cNzqjWO2NWZC6KLY5JYohywONnBGSCAdZRR RQBz/jv/AJJ54l/7BV1/6Kat2WKOeF4Zo1kikUq6OMqwPBBB6isLx3/yTzxL/wBgq6/9FNXQ UAnbVGHrHhu21vVLaa7TdapBLHKqSvGzscBMlCMgK0wwT/y0PHJpnijw/Brfh9tNS2XbJcxO fLCqyAygyOpPAbaXOepyeued+iodOLvpudMMZWg4NS+Db77nm3h7w1d31/cWviGDZv8AO1G5 t4rl0AluHMagbG5ASGTkt0lxjqa1LPwWNWtpbzxBLefb7pXjmj3oqY2+WG8tdyBmVIpDycOi 4wBiu1orOOHglZ6nXVzfETk5R92/bp3S7Jvf0WuhyWkeILfS3OhXdvdRPa3f2OJtgZPLZj5O CCSQEaBSTyDIm772amXxVDq2lXFxpcu1PtdvbRTFSGaOWREMqqwGMM0ijII3RnryKveINIF5 pN61jCqalt86CVFQOZkKMmSwxgtFGDnso9BiFvCWnrrej6pbboJdMhNuiAlleHYyqpyeo3Zz 9Qc8EFqi91bf1+RSqYOa9rNNSfTdXWr03956LdLXQr+EvEv9raIbm9uIsxQRStMRsBTZh3bs P3qTjtwgOMEE2Z7yw8F6JpNpNKVsIQtp58z8oqQswJwPmJ8sDAAyW49Kp3ng6y1LxYdSuIWS GG2jWERMFV3Msjyhl7hgQGzwRI3U8ixZ6BcSXFm2sNa3qWVpJaxs6l2l3hFZ33dysZJ6581l /h3O4yqqHJ/Wn9MVWOClV9onaNtY9rrRJ9dbX0Vn00JvEt1CbWHSS+Z9QkSPyQDmWHzY1mA+ kbsTjkDJHQkM0do9K1a50JLZoIWaS6slSPESw4j3gH182Rzt7D0G3MWk+DLLTLuG6e5nu7m3 mZ4LifBlCMr7kZsfMC8sj54JJXOdoq34h0FtchtRFetZXFtMJUnSMM2RyqkHgrvEbEHg7B7E Fp/HbUnnw6th1O8He7ae/R28kl976GnBdW915v2e4im8qQxSeW4bY46qcdCPSmpeRvqU9iFb zYYY5mJHykOXAx7/ALs/pWLb+F307y49L1a6s4W5uiVWWSd/mO/c4IDsWyxKnIVQNoFZNp4c vrOCy1q+82bVrKcMyI3mh0KRW8rnjc5McRkAGDlsYYjBHOatoRHDYeXNapp001v0uu2m6bSv r2OwtryO6nvIUVg1rMIXLDgkoj8e2HH45pzXUKXkdoz4nljeRFweVUqGOenBdfzrz+x8N6xp 1y/iHS7VTqOpaiZJI5pNhjspGWQrIGHyuCu07QxAdsZIGL0jeJZLy08Qw2GL6TdZf2VMzbIY lLu7eYON7mJAr4C/MoO7g1Kqytqv+GNp5fS5vcqJq1t0nzW9XZXu77W7O1+4ornbM33hqztb S9ll1CzT/XalLJ88QwSxkBJJG7GCOFUndgJuY1bxRp0dlbPY6rayPNdxRhoZFkGxXVps4yAF i3EscbQQcjitPaJK70OT6nOU1Gn7yezW3z7d2nqlqdFTBLGZmhEimVVDMgPzAHIBI9DtP5H0 qKe/tba7tbWadUuLpmWCM/ecqpZsD0AHXp09RXL6J4o03VPEmt3UEjC3gW0sfMIBWSQzTKpU qTlSXXB9+1OU0mlcmlhalSnKoouySf3uy/G/zR2FFFFWcwUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABXN+OZ4bXw3HcXEscMEWp6e8kkjBVRReQkkk8AAc5rpKKAOf/wCE78H/ APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KroKKAOf/wCE78H/APQ16H/4MYf/AIqj/hO/ B/8A0Neh/wDgxh/+KroKKAOf/wCE78H/APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KroK KAOL1LVvhlrNwtxqmoeEb6dUCLJdTW0rBck4BYk4ySce5q5Y+LPAemWcdnYeIPDlpax52QwX sEaLkknCg4GSSfxrqKKAOf8A+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qugooA5 fwhf2ep6h4pvLC7gu7WTVU2TQSCRGxZ2wOGHBwQR+FWPAn/JPPDX/YKtf/RS10Fc/wCBP+Se eGv+wVa/+iloA6CiiigAooooAKKKKAOf8b2WnXng3Vf7Ve+WxhtJppvsM7RSFRG24DBAbgn5 WypOMg1yei74/Gtrb3+jyRm1vZLaO5k8S3l6qXH2TzQFikUAkxSuNxwBtfnO0N3GttqUVv8A aLLVNN0+CFHe5kv7VplCgZzkSxhQACSTn8Mc83obiLxHZJJeaH5y2i26LB4fmtJVi2eYtuJW lIjYDEnkkbgoztA5AB3FFFFABWH4L/5ETw9/2DLb/wBFLW5XB/Bj/kkmg/7s3/o563j/AAJe q/KQup3lFFFYDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wb/yA 7n/sK6l/6WzV0Fc/4N/5Adz/ANhXUv8A0tmoA6CiiigAooooAKKKKACiiigAooooA5fx1p1l e6LDLefbi0V3brAlrfSWweV54xGHKH7u/ZlsMyjJT5sVh+DJPM8UFrnSpLacW93Dbzy69dX5 cRXKxTKqyrhRuSMk8EhkwD823pPE4v1s5pBqOlQaY0XlTwXumSXZlLHbtAWVd27cqhApJJwM 5Aqn4dlJ8S6iJr/Rri7kTE7WekSWrztGQmfOaVxMI8lGC52FgCVPBAOsoorL8Q6/Y+GdGn1P UGk8qJGYRxLvklKqWKovc7VYnsApJIAJABT8d/8AJPPEv/YKuv8A0U1dBXP+O/8AknniX/sF XX/opq6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKydY8OadrZV7mPZOsckQnjVRJ seN0K7iCcYkY49QDWtRScVJWZpTqzpS5oOzOKv8AwnqFtBNLo7wR3NrN/wAScBQotRK+Z2bj aQQ7ALtOFRcZYmsjV/C91otzqcOjWM4sL2GOSP7Kv+qkggmKLkHfu81YXDY5JwSSfm9MorGW Hi/6/r0+49Clm9eG9n/w6a+5rmXm5PqzL1DUZBHp66dLAzX83lxTsPMjUeW8m7AI3AhMDDD7 2ecYPMXcXjXTrHzI79bm/MJu5USEyQsYcKYl+XIMqsrYGPmjbbgMSuz4f8Kw6FezTJNK8aRr b2cbSErFFsTedoAAd3UsxHXg8Emuip8kpq8tGT9YpYeXLSSnHrdLXrbrbpfzX38UPFGvWgu7 aTQp7yTTW8uSeHDm8IRcEKg/dsxkhfHICmTuuK3PDdzI2j29teXSzXkLTQbycNMIZTEZCCSc nCknJ5b3rZritb0W40eaHWrKZruW2uZGtbaYKNklz5ilQRtyrSywk7jlQjYPOKTUoe9e6LhO hil7JRUJPtfV2dlrtdvva3okdrRXMDx1pcmkJdoJY7ueNmtbC5Xy5pztLIFHOQ+MKwyCSAOe KrT+Jr+WBLGOFrfVPtMFoZBbO8ZmDgzALjhPK/eAlhuR+CCpxTrQ6Mwjl2IbtKNtev4v0XVo 1NV1/wAvS7x9GWK/1CKcWccG7Aac4JXPfap3Ng8BWyRgkQxeMtNWWeO/Etg675IY7iNhJLCq sxl2Yyo/dycHnCqTjcoqjLoS2njPw39kkbbb21w9y0kh3ThAFVmA4L7rh2LYBO9jmtfX9Hj1 GO1mS1WS6gvbWZXB2sAkoJJPGQEaTg+pwM1N6ju+xvyYOPJB3alre9mtba9LaX9H91611K3u tIg1Td5NrLAtxumIXYhXdlucDA680+K/tZ764sop1e5tlRpo15KB87c+52k464we4rndO8L3 Au9Ri1K6a5019RluorSVFaNw6hgCOflDu+VPVkRht53Q3It/A1pY39w881rDbSwXc0as7zTM wkWR8k8F/NAJPDTAcAkh88kryVl1M3haM5uFKXNJ/Cl99vXpbu12aOworjdT1fxXYvBKbS1H 2mAx21rEplY3TMrBJORgKgkG8Nt+RmPVUrrLW6hvbOC7t33wTxrJG2CNysMg4PPQ1cZqTsc9 bCzpQU200+zv/X/AfYmoooqzmCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/w J/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/AKKWgDoKKKKACiiigAooooAz9d/s7/hHtT/t f/kGfZJftn3v9TsO/wC7833c9OfSub0WwlOrWsl5oniASrcSXcl5fS2W1pzD5QkcQyZyIh5Y CqF+bLAn5h0HiWSxh8K6vLqkMk+npZTNdRRnDPEEO9RyOSuR1H1FcXoM9za+MYrO602eBY7t 7IznxPe3iGb7Ms4URSKA2UckFsAbG5zsDAHpFFFcv4h8Z2WkaPfzLL9muopUtIX1G3kt4Gnk Yoh3uFDxggsxQnCKT6ZAOorg/gx/ySTQf92b/wBHPVz4ZyWbeDvIstS/tGO31C9iNy04meT/ AEmRlZ2HVmVlbPfcD0NY/wAFL2d/hxo9o2mXccCRSst6zReVIfOb5VAcvnk9VA+U89M7x/gS 9V+UhdT0iiiisBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/4N/5 Adz/ANhXUv8A0tmrQ1PUbqw8r7No19qW/O77I8C+XjGM+bInXPbPQ5xxnL8DSNN4bkleGSB3 1PUGaKQqWQm8m+U7SRkdOCR6E0AdJRRRQAUUUUAFFFFABRRRQAUUUUAY/ij7H/YMv23z9vmw +T9nx5n2jzU8jZu+Xd5vl43/ACZ+98uay/Ddh5WpRyS6JrNo8NvMqT38tqys8solmbEMjHfI +1jwFGzChMkNoeLhaN4dkS8tpLhJLi2jijS4eA+c06LEfMQ7kAkKEsuSACQD0PN+CryZtfkh uNPntd8V0sTyeILq/EpguPJlAjlAVcMFO44OHXGcuFAPQK5fxFZ6xL8PPEdreSwX99Np9ykI sbR4t2YiFUIXcls56HnIGPXqKKAOf8d/8k88S/8AYKuv/RTVoaZp11Yeb9p1m+1Lfjb9rSBf LxnOPKjTrnvnoMY5zn+O/wDknniX/sFXX/opq6CgAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKZJFHMoWWNXUMrAMMjIIIP1BAI9xT6KATtqjnYtDtz4ylvDa7Y LfTLe3gTywIhiV3wBjGVKRkY6ZHtWveWP2u60+bzNv2SczY2535iePHt9/P4VboqVBI3niKk 2m3srfhZ/eMMUZmWYxqZVUqrkfMAcEgH0O0fkPSn0UVRhcKKKKAGPFHI0bPGrNG25CwyVOCM j0OCR9Ca5uw1qz8P6d/Zup+bbf2fGUiZ4yfOhQsFKYyZCI1RnKjC7uccgdPVeWwtZ763vZYF e5tldYZG5KB8bse52gZ64yO5qJRe8dzoo1IJOFVNx8t7q9vz1+/yeLYeK7e/1uW1UbLTy8RT vhQ0iuFIznkMstuyEDDCTrngRaprOqXWtLpWgJELi2kc3ktyN0aDyQ0YYA7sO0igEEHMbcMA a1rjQtLurUWsllEIBn5Ix5Y5iMX8OP8AlmSvsMY6DBpWkQ6ZEGz5l28YW4n5Hmtudy23JC5e SRsDpux0AqOWo9Gzp9thY3qQjrayT2v3+XbvbzOVOo3Xiey0fRrLXGstVfTlv7y4hXcyho1X aVGAC3mlhzldqkDkEanhrX/7e1m+kVZYkSwsXMLNlUeRZJDt9flZATgZ2+wq9ovh+30uSe5a Nftk1zdTGRXYgiWQN0PGdqRA8dV46knz/wAf2reFrXVF0xJca7iS5lBYlQsrmQkjAUEzRIOo I3A8nJyk5017SXz+7Q9ChDD4yo8JS0b0i7LdyTk/NWWi+7rf1mmRyxzKWikV1DMpKnIyCQR9 QQQfcVz2teM9M0e8WGa6VVguUhvi0TkxB4ZHTGBySUXpngnOKy5PCWrzaJDcRajLDrKxs6RP MUgjllTEzEJnLhnndWXGC4HKqBWzq62jrY82ngvdUq75FK1m09f60d+zT6o7iql5ffZLrT4f L3fa5zDndjZiJ5M+/wBzH41w6eJb7w3dajd6zFEr3v7xYN3lJ5sUUyOY2YZYP9nhx6ecnXPz aetyxzfEDwe0Uiuoa/UlTkZEYBH1BBB9xU+2TWm91+LNf7NnCpaesXGTTW14wbt8np/mjsKK zhrdi11DAJf9b5wWRvlXdHKsTLzjne4A9fyzo1smnsedKnKHxKwUUUUyAooooAKKKKACiiig DL1LSry+uFlt/EGpaegQKYrWO3ZScn5j5kTnPOOuOBx1zT8Cf8k88Nf9gq1/9FLXQVheCI3h 8A+HInGHTS7ZWHoREtA7O1zdooooEFFFFABRRRQBn669xH4e1OSzvILK6W0lMN1cECOFwh2u 5IICg4JyDwOlY+nQ3lnrFi954f8ADmn7ovsMM9tflphGql1hjU26ZUbSdoYAAE44rY1nQ9L8 Q6c9hq9hBe2rZOyZM7SQRuU9VbBOGGCM8Gq9j4T8N6ZeR3lh4f0q0uo87JoLKON1yCDhgMjI JH40AbFFFFAFezsbfT4GhtY/LjaWSYjcTl5HaRzz6szH2zxxXF/Bj/kkmg/7s3/o567yuD+D H/JJNB/3Zv8A0c9bx/gS9V+UhdTvKKKKwGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAVz/AIN/5Adz/wBhXUv/AEtmroK5/wAG/wDIDuf+wrqX/pbNQB0FFFFABRRR QAUUUUAFFFFABRRRQBj+KEuJNBlitXsRJLLDGUv2CwzI0qB4WJVv9YpaMfKeXGOar6T9si1y dr/SNDsLq9i8x5bO+Ms9x5e1RuBhQsqhwN2TtyoxzVzWvDei+IkhTWNLtL4QOHi8+IMUO5W4 PUAlVyOjAYORxRpvhrQdGuGuNL0TTbGdkKNJa2qRMVyDglQDjIBx7CgDUooooA5/x3/yTzxL /wBgq6/9FNXQVz/jv/knniX/ALBV1/6KaugoAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqk+m291fwXky73gjZERgC vzPG+cEdQ0SkHt+WLdFJpPcqMnF3izmPBeiNY6JY3d9Ft1FoAGHzDy1KRIF2nGDthi3Z6MGx xxXT0UUoRUVZGmIryr1HUl1/DyRl6josepahaXMszKtupwijlj5sMoOfTMIBGOQ3UV5po8c3 h5r2PVJ8DwtdwrbTBBxDcyr5jFRnOYwSByR5jdwu31+s670Sxv57iS8i89LiOGOSJ+UIidnX jv8AMxyDwcAY65yq0eZ80d/6/Wx34HMfYwdKrrB29Vrrb1i5L5nlbXuqanLdC0l2TR7LizjS HzCpnV9QIx/E++GNAemAflya7Xw94gYWV7falctIt3qsaWaEjcsc0cLRIBwMqrgsBnox56no rHSLKwtrOGOFXa0hjhjmkUGTCKVX5sdcM3/fTeprzzxDeXtx4r1HSbbTJ5WguW1OG5jBYCaO xUIu0DBIcxHryWUEc84uMqKUm9WejGtSzGUqMIJRir30vo1f79/vC98WXthrt7ZhFxczXGoM 8bFGAtmKiPqeGS0IY/8ATXphcNrL47sdOieze5aS9XWHtZmlR/Lgja5fBZzhQBGDjk4wOMA4 0bPSV1bwNC6BRf3ulSgSuxC+ZcqHkJA4AL4PA47YHFWPBum29tokepIubvVY47y6kIGWkZFL dB03Fmx0BY4wOKcY1ObR7/1+ZnXrYN0nzQ1g7WT3a0V3Z6cvTujB1TXtQuvFUOnWBgkli1hV tJHw6BBakXAIUrzH5meWyS2B90ijRPE/iGW2Kz6W0+o6gqXkTRZNtawyKI4i3zEgF03Mo6Kz N1BFdVofh6x0LTo7O3hiKRTyTxts5UsWxyckkI2zdnJA/CtOKKOCFIYY1jijUKiIMKoHAAA6 CtI0p35nI5auOwyj7OFJNJJJvRu19fno7d79NDzYy6h4L+26nfSMZNQWG8liQhzbhZ4jPGoP GGa5lxg8AA5JYkdbpmtXE2pSx3ULLbXNzLDaSgqVDxl0aIgfNkiFpNxGPnK5+UZ2ZrWG4lt5 JU3PbyGSI5I2sVZM+/ysw59aqajo9vfaWtnGkUBg2vaOIgRbSJ/q3VeB8pxx0IyDwTTjTlD4 Xp2M6mNo4hL2sLSe8vLZWXlZeuvq6PivxBBovhW+1GK5UShWhgZCrHzslQADwSrAkjsFbjiq +v8AiWzW3utLguJYr66xZ2kygqrTyFk+Rx18tsFyPu5A+98tPbwRpMUN6tijW0lzpzaenzM6 QxnOSqk9SSCeeSM9SxOhpmkR2+mWkd7DBNeRrvll27gZmYSSMuRwDIN3GMEDgYGBqo276BGe Cpwi1eTT8k9lbvomrb9XsNTX7ddDfUbhfLkiglmmtlYM6mLiVVzjdtYbc9Dx2IqPwfKs/gnQ JlBCyadbsAeuDGprm/Ffh7VJLrUb6wWWbzI3RIIzjzzcRpbsh54EflJLkjBzjjbure8Cf8k8 8Nf9gq1/9FLVQcnJ8y2MsTClCjF0pX5ndrtbZfJPfr6po6CiiitTgCiiigAooooAKKKKACii svWtVm00WUNpbR3F5fXH2e3SWUxR7hG8hLuFYqNsb4wp5wOASQAaleY/CjxBpGlfCvQIL/Ur a2lMUrlZZANiefIA7f3VJG3ccDcQuckA+habPfT27f2jZR2lwjlSsU/nRuMAhkbCkjnB3Kpy DwRhjifDuytLL4eeH/strDB5+nW80vlRhfMkaJMu2OrHAyTzW9OpBQcZpu7T08r+vcTWp0cc 8UrypHKjvE2yRVYEo2A2D6HDKcehB70TzxW0Ek88qRQxKXkkkYKqKBkkk8AAd6wLXwJ4etIy qWOZPlAn3lZQiqFjTeuDtRUTbzwyK/LjdVqDwxp0U8ckhubtIWEkEV9cvcrDID/rF8wsQ3AA Oflwdu3c+5yjh09JP7l/mGpqmeJZ0gMqCZ1Z1jLDcyqQCQOpALLk9sj1qSuRl+HGgPBDHGLm 3eOeKUz28uyaRIgojhaQDd5ahI8AEHMatndljpf8IfoI+ZLDy5eonimdJQx+84kDBg7dHcHc 4ADFgBVShhklab/8BX/yQrs2IJ4rmCOeCVJYZVDxyRsGV1IyCCOCCO9MvL20061e6vrqG2to 8b5p5AiLk4GSeByQPxrDg8HwW0kkNvf3ttpXWHT7SUwJEWZnkAZMNtZijAZyu0gEIzJV6z8N 6TY3SXcNpm6TP+kSyPLI5xgMzuSXYKSqliSqsyggEgzKNBPSTfy/Bu+j76Ndrj1NWoLe9tLv H2a6hm3RJMPLkDZjfO1+P4TtOD0OD6Vz/wDwgHh10uILixS5spFKQWcyKYrMMWZ/IwA0ZZnJ JBzwoGAqgQab8P8AT7Oedry7udQtpVSJbC52tbJDGWECbCCWCKxHzEgt85G7BFqnhuV/vHfp 7v8Awf6fV7ivLsatx4r0O0vri0udQSKW2YJcM6sI4CVVl8x8bUDBhtLEBjkDJBAZ/wAJXp4+ drfU44Rw002nTxKHP3UAdAzM3IG0HJwv3mQNLLNaeH30bTLGwhhtr27e2SOACNIf3MsxYKBg 5MZGOPvZ+uvUc1BbRb+a/wAv1dvMeph/8JRBP8um6dqeoyD7yw2xiVfXLzFE3KflKbtwOQV+ VsH9s6xL/qfC97Ht+Z/tVzbpuUdQmx3y/oG2qecuvfcope0praH3t3/C35BZ9zn4I/F7QRie 60NJioWRo7eZ1Vsbi4BcEjPybcjP39w/1dSfZfE6fvjqumTSJ0gFi8Mcg6kM3muytwAGGQoL ZR+Nu5RTeIl/KvuQWOfj1rULB5bTU9OubqdGxFcWFo3lXG4ApwWbyyTvX5m2jy9zMgdMsbWN Y1fzP+EetrJbZJVia81BpVIPymTbCFBbaCUILoQ6spA289HRR7an8XIr/h936beQWZz4tPFM 07pPqun28KqqLLbWpLS5JLtsckRso2BPmdfvllOVVaq6P4rtdZa5h8Qw3UE0RiZLu3wkAXaY 2EaY3uf3oc70B3qQMIFrqqKaxU1so/8AgK/y+f8AwNBcpyurRT+GbOPVY9W1O5C3NvFNDNiZ ZUkmRGwipu3DzHcBMckLgoqIt7+09ZvuNP0f7PFJ/q7nUJAmF/vmJct3UqjFCcOG8shd1zTt T+332rW3k+X/AGfdrbbt2fMzDFLuxjj/AFuMc/dz3wNCh4hOK5opy7/8BW/G47HK6bP4zvYx Pc2llp8hijie3uSJAJAvzyp5bH5SznCs2cQAceaWS99l8SH/AEU6lZeU3zPeiA+aufvIkX3R jJKuxbACqyOQXbcoolibu6hFfL/O/wDW4WOYj0DXL1JYdc1xLi2af5obe2WOOaDIfawOWB3Z jI3MrRDBG5iwLfQfES6bbWVz4mRxarGUmhs2SWZ48bfOZpWLqxHzhdhbpuAJB6ein9cq7K3/ AICt/uFyow/svieP93HqumSBvmM01i5ZD3UIsoBUknByCoUA+YWLijL4uv8AT47ebVvDd7aR zRMqxQyLdTvcqu/ykSLdlSochyV/1Z3BcgnqqKUa8X/Egn+D/DT70/zHbsc/Br+o30Ed1p2h vcWcyieGdrlI/NgI4KqeRI3VVbau0jc6NlQyfxJf2ckdtd6FNHd3Hy2vlzLJDLJuVSpcDcvV pPuk+UjPjKsi9HWZ4hvYtJ0G+1mS0S5fTLeW8jRsA7kjbo2DtJBZc46MaFVpX1pq3q7/AJ/f oFn3KU994sigkkTQNJmdFLLFHq7hnIH3RutwMnpyQPcUQX3iZII3uNItppZ1EmyOcRfZiR/q nJLbypK5deCochQVVZOgope3ha3s1+P+YW8znLPxTc3tqkkHh3U7iRcx3IgaEJDOp2yRBpXT ftYEb1BU44PBAjn8aQRI4TSdWa4WeNPs5spGcwsU/f4RWKrtZiAwViY3XAZSB09FP2tHm1p6 er/Hf9BWfc5y38T3KYl1TRb20tp4kltWggmupCDnKyokWYnUbcg5GWIBO00XuqeJbiwuDpOg fZbmOJmT+05oz5jgEqqLFIwOSNpLOm3cD82CK6Oij21NPmjTXzba/P773+Ww7PuYfm+Kh/y5 aM235v8Aj7lG/P8AB/qzt25+/wDNu2fcTf8AJHJN4sgeIrZ6TdAt5TIszxDABPnlyGKgkBfK COQWB8wgGugopKuv5F+P+YWOfnk8XzwSCC10OymVS0byXE10rtjhCoSIqCf4stjH3TniT7f4 kP7waDZeU33EOpHzVz93evlbRjI3bXbADbd5ADblZ82p+V4hstJ8nP2m0nufN3fd8p4V24xz nzs5zxt754PbR25F+P8Anf7wt5lP7P4qk+f+09Gg3c+V/Z0suz/Z3+eu7HTdtXPXA6UfZ/FQ +T+09GOefN/s6Ubcfw7PP+bOc7tw27cYbdldyil9Yl2X/gK/yCxh/ZfFX/QZ0b/wUy//ACTR 5XipP+X3Rpdnzf8AHpLH53+x/rG8vp9/587vuDb8+5RR9Yl1S+5f5BYw/K8VRf8AL7o11v8A l/49JYPKz/H/AKx9+P7nyZz99ccn2jxPa8SWGmX8actLBcvbySDqQsLKyhuwBlwSMkrnjcoo 9vf4op/K35WCxh7PFU/PnaNY99vky3Xttzui6Yzuxzv24Gzc5/xVX/Hv/wASb/r/AP3v1/49 v/Hf9d/tf7NblFHt/wC6vu/pv53QWOYEnjSTUhaFdJt4FV3N6IHmR8eWEXZ5qMrE+ax+8qjY oZjk1b+y+Kv+gzo3/gpl/wDkmtys/RtT/texkufJ8rZd3Ntt3bs+TM8W7OB12Zx2zjnrTeJl paMV8l+twsZ8d34pjeW2fStPunRsJdm6NtFKuASdmJWQ5baBls7HJK/KGg8ASzz+FfOurb7N cyajftLBvD+U5vJiV3DhsHIyOuK6euf8G/8AIDuf+wrqX/pbNUTqKa+FJ+V/87fcvQEjoKKK KyGFFFFABRRRQAUUUUAFFFFABRRRQAUUVn6nc6pD5SaVp0F3I2S7XN15EaAY4yEdixzwAuMB skHAYAz/AB3/AMk88S/9gq6/9FNXQVx/iHU/7X+FXiS6MPkyLp9/byxhtwWSISRvtbAyu5Gw SASMEgHgdhQAUUUUAFFFFABRRRQAUUUUAFFZ82p+V4hstJ8nP2m0nufN3fd8p4V24xznzs5z xt7540KACiiigAooooAKKKKACiiigAorP0bU/wC17GS58nytl3c223duz5Mzxbs4HXZnHbOO etaFABRRRQAUUUUAFFFFABRRWfrOp/2RYx3Pk+bvu7a227tuPOmSLdnB6b8474xx1oA0KKKK ACiiigAooooAKKKKACiis+HU/N8Q3uk+Tj7NaQXPm7vvea8y7cY4x5Oc553dscgGhTDFGZlm MamVVKq5HzAHBIB9DtH5D0p9FAJ2Cq+n2cenaba2MLM0VtCkKFzliFAAzjvxViii3UfM7cvQ KKKKBBRRRQAUUVn6Fqf9t+HtM1byfJ+3WkVz5W7ds3oG25wM4zjOBQBoVz/gT/knnhr/ALBV r/6KWugrn/An/JPPDX/YKtf/AEUtAHQUUUUAFFFFABRRRQAUUUUAFcf4k8N3V41pcTSX2t2s GofapNOZoIz5Zimj8uPAjDLmVdyyOQyKQc5IbsKKAOX0FYPDmk6nd6hFBoWlPdiW3tZ5Io0s 4zHGm07WMabpQ74ViD5mT8xIqPSvCnhm50u3fR7/AFKXTwmyBrTxBdtEFX5cLtmxgYxgdMYq 54h/5DnhP/sKyf8ApFdUeHv+Q54s/wCwrH/6RWtAB/whul/8/Wuf+D29/wDj1H/CG6X/AM/W uf8Ag9vf/j1dBRQBz/8Awhul/wDP1rn/AIPb3/49R/whul/8/Wuf+D29/wDj1dBRQBz/APwh ul/8/Wuf+D29/wDj1H/CG6X/AM/Wuf8Ag9vf/j1dBRQBz/8Awhul/wDP1rn/AIPb3/49Uc3h TRbZA89/rMSF1QM/iC9UFmYKo5m6liAB3JArpK5fxxY29xp+nXUse+a21WwMJLHCFryAFgvT djIDYyAzAEBmyAU7/Q/B1tqlnZahrV3FqBcPaQXHiS6WUs26MFFM2cnLLkdcketSf2V4T/tj +yP7fvv7T/58v+Emu/O+7u+55+77vPTpzWfrX/IsfFL/ALb/APptgo/5kj/ua/8A3NUAdB/w hul/8/Wuf+D29/8Aj1H/AAhul/8AP1rn/g9vf/j1dBRQBz//AAhul/8AP1rn/g9vf/j1H/CG 6X/z9a5/4Pb3/wCPV0FFAHP/APCG6X/z9a5/4Pb3/wCPUf8ACG6X/wA/Wuf+D29/+PV0FFAH P/8ACG6X/wA/Wuf+D29/+PVn2uleE77UZ9Os9fvri+g3edbQ+Jrt5I9p2tuUT5GCQDnoa7Cv O/HUEOp+EtRi02KOHTdCsrxvMjUCNpBaTQeRGBxhBIdzDhSoQZO/ywC5p+leCLuK/vNN1+ea OLE97NbeJrlgny43yMs/HypjLdk9BVzTdD8M6zbtcaXrWpX0CuUaS18SXcqhsA4JWYjOCDj3 FZfj7/kZ/Dv/AAD/ANOWm10Fn/yUPWf+wVYf+jbugA/4Q3S/+frXP/B7e/8Ax6j/AIQ3S/8A n61z/wAHt7/8eroKKAOf/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeroKKAOf8A +EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//Hq6CigDl77w54f0yzkvL/VdVtLWPG+a fxFeRouSAMsZsDJIH41n3mleCJNDF5e6/O+kXe6ATTeJrkwTZyGTcZ9rdGBHsa6zUBCwhUyW kd4XYWL3KB9s3lvyq5BJ278hSCV3cgZrh9AEyfE+6S7kjlvFS9FxLEhjjkbytMIKISxQBSgI LNkgnIBCgA0Lyy8Hafb2txe+Jbu2gu0320k3ii6RZlwDlCZ8MMMDkeo9a1P+EN0v/n61z/we 3v8A8ern/AP/ACM/iL/gf/py1Kug8Cf8k88Nf9gq1/8ARS0AH/CG6X/z9a5/4Pb3/wCPUf8A CG6X/wA/Wuf+D29/+PV0FFAHP/8ACG6X/wA/Wuf+D29/+PUf8Ibpf/P1rn/g9vf/AI9XQUUA c/8A8Ibpf/P1rn/g9vf/AI9R/wAIbpf/AD9a5/4Pb3/49XQUUAc3N4U0W2QPPf6zEhdUDP4g vVBZmCqOZupYgAdyQKzzofg5vECWDa1dnWkRkS3PiS6+0KrAOwC+duAIVWI7hQe1Y/xC1KOX XLCG6tdS+y6Ve6fcxPHp08kctw91GCQ6pjKRbxtyQ5nwBuQCrn/Mkf8Ac1/+5qgDQtdK8J32 oz6dZ6/fXF9Bu862h8TXbyR7TtbconyMEgHPQ1of8Ibpf/P1rn/g9vf/AI9XP6L/AMix8Lf+ 2H/ptnr0CgDn/wDhDdL/AOfrXP8Awe3v/wAeo/4Q3S/+frXP/B7e/wDx6ugooA5//hDdL/5+ tc/8Ht7/APHqP+EN0v8A5+tc/wDB7e//AB6ugooA5/8A4Q3S/wDn61z/AMHt7/8AHqP+EN0v /n61z/we3v8A8eroKz9de4j8PanJZ3kFldLaSmG6uCBHC4Q7XckEBQcE5B4HSgDl7WHwRfef 9j8VT3HkRNPN5Piq5fy41+87Yn4UZGSeBRa6V4ITR59Rs9fnXTIpW865h8TXIhSR23NuYT7Q xZwTnklves/Vvt+g2mi+GrqSxntTLYvam0tpIfISC+s49h3yyF8iVecjGw53buLGrf8AJXtM /wC3T/0n1WgDYsfDnh/U7OO8sNV1W7tZM7JoPEV5IjYJBwwmwcEEfhW5pel2mjWC2VkkiwK7 v+8leVizuXYlnJYksxOST1rL8Pf8hzxZ/wBhWP8A9IrWugoAKKKKACiiigAooooAKKKKACii igAooooAK5fU/wDhINI06K00/wDtXWpriU+ben7GslpHgfdU+UjMT93IIBJZtwUI3UUUAc3K +g2fg77JrkUei6bdpJbSwaldojNv3bg0gkIZ3G5i28sckk5zRB4U0W6t4ri3v9ZmglQPHJH4 gvWV1IyCCJsEEc5qS8/5KHo3/YKv/wD0baUeDf8AkB3P/YV1L/0tmoAP+EN0v/n61z/we3v/ AMeo/wCEN0v/AJ+tc/8AB7e//Hq6CigDn/8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7 /wDHq6CigDn/APhDdL/5+tc/8Ht7/wDHqP8AhDdL/wCfrXP/AAe3v/x6ugooA5//AIQ3S/8A n61z/wAHt7/8eqOTwposLwpLf6yjzPsiVvEF6C7bS2F/fcnarHA7AntXSVy/iGxt/wDhLPCd +Y83Q1CSFXZidqfZLokKDwuTjOMbtq5ztXABTOh+Dm8QJYNrV2daRGRLc+JLr7QqsA7AL524 AhVYjuFB7VJa6V4TvtRn06z1++uL6Dd51tD4mu3kj2na25RPkYJAOehrP/5kj/ua/wD3NUaL /wAix8Lf+2H/AKbZ6AOg/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n61z/we3v/AMeroKKAOf8A +EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//Hq6CigDn/8AhDdL/wCfrXP/AAe3v/x6 j/hDdL/5+tc/8Ht7/wDHq6CigDn/APhDdL/5+tc/8Ht7/wDHqy7Oy8Hahb3VxZeJbu5gtE33 MkPii6dYVwTlyJ8KMKTk+h9K7CeCG6t5be4ijmglQpJHIoZXUjBBB4II4xXB+Jf+J3Np+u2/ Gn213Z2tvIf+Xvzb+0ZpF9Ix5ICt/HuJGFCs4BYtdK8EJo8+o2evzrpkUredcw+JrkQpI7bm 3MJ9oYs4JzyS3vWhY+HPD+p2cd5Yarqt3ayZ2TQeIryRGwSDhhNg4II/CsfVv+SvaZ/26f8A pPqtdB4e/wCQ54s/7Csf/pFa0AH/AAhul/8AP1rn/g9vf/j1H/CG6X/z9a5/4Pb3/wCPV0FF AHP/APCG6X/z9a5/4Pb3/wCPUf8ACG6X/wA/Wuf+D29/+PV0FFAHP/8ACG6X/wA/Wuf+D29/ +PUf8Ibpf/P1rn/g9vf/AI9XQUUAcnqWh+GdGt1uNU1rUrGBnCLJdeJLuJS2CcAtMBnAJx7G qepaV4It7O0l1TX547W62z2z3Xia5CS7Srq6Fp8Ng7GBHTg+ldRqKeZKi2c9jBq5if7NJcw+ aVj3J5mFDKxX7mcMBnYTngVx/gD/AJGLXAeZBEBKw6NINQ1EOyj+FS24hSSVBALMRuIBoXWl eE7HUYNOvNfvre+n2+TbTeJrtJJNx2rtUz5OSCBjqa0P+EN0v/n61z/we3v/AMerz/w9/wAk V8Wf9gqP/wBNFrXsFAHP/wDCG6X/AM/Wuf8Ag9vf/j1H/CG6X/z9a5/4Pb3/AOPV0FFAHP8A /CG6X/z9a5/4Pb3/AOPUf8Ibpf8Az9a5/wCD29/+PV0FFAHP/wDCG6X/AM/Wuf8Ag9vf/j1H /CG6X/z9a5/4Pb3/AOPV0FFAHNyeFNFheFJb/WUeZ9kSt4gvQXbaWwv77k7VY4HYE9qz7PQ/ B15rN1b2WtXc+qRpsuY4fEl006qjEYcCbcArORg9Cx9ax9V1KO8+KOh3Fza6lH9h1NrGy3ad OEZXtZjLKsmwKwZzGuMnAgLg7WY1l+If+SK+E/8AsFSf+mi6oA7DTNK8J635v9k6/fX/AJOP M+yeJruXZnOM7ZzjOD19DWh/whul/wDP1rn/AIPb3/49Ref8lD0b/sFX/wD6NtK6CgDn/wDh DdL/AOfrXP8Awe3v/wAeo/4Q3S/+frXP/B7e/wDx6ugooA5//hDdL/5+tc/8Ht7/APHqP+EN 0v8A5+tc/wDB7e//AB6ugooA5/8A4Q3S/wDn61z/AMHt7/8AHqjn8KaLa28txcX+swwRIXkk k8QXqqigZJJM2AAOc10lYfi+zm1HwxdWEN3aWpu3it3ku8+W0byKroQCCS6lkABUksAGU4IA MOGHwRcWdzeQ+Kp5LW12/aJk8VXJSLccLuYT4XJ4GetCaV4I0/Q7W8j1+e20g4gtpl8TXKQH bkBEbz9vG0jA6bT6VHdX+pf8JroOkapLaXE9tepdLcWsDQKVltL9dhRnc5BhJ3budwGBjJj0 n/kr2p/9vf8A6T6VQBuQeFNFureK4t7/AFmaCVA8ckfiC9ZXUjIIImwQRzmtywsbfTNOtrCz j8u1tYkhhTcTtRQAoyeTgAdax/Bv/IDuf+wrqX/pbNXQUAFFFFABRRRQAUUUUAFFFFABRRRQ BXvrCz1Ozks7+0gu7WTG+GeMSI2CCMqeDggH8KLGws9Ms47OwtILS1jzshgjEaLkknCjgZJJ /GrFFABRRRQAUUUUAFFFFABUc0ENygSeKOVA6uFdQwDKwZTz3DAEHsQDUlFAGfdaFo99qMGo 3mlWNxfQbfJuZrdHkj2ncu1iMjBJIx0NH9haP/bH9r/2VY/2n/z+/Z0877u37+N33eOvTitC igAooooAKKKKACiiigArHsfCfhvTLyO8sPD+lWl1HnZNBZRxuuQQcMBkZBI/GtiigDLs/DWg 6fb3VvZaJpttBdpsuY4bVEWZcEYcAYYYYjB9T61Y03SdN0a3a30vT7SxgZy7R2sKxKWwBkhQ BnAAz7CrlFABRRRQAUVx/j7QtH1DT7W8vdKsbm6GoafAJprdHcRteRBk3EZ2kMwI6HcfWqeu 3s2h2/iDStL0rRo9F0vQjfNbPbnbIzi5/deWuF2MYwSf94YO/cgB3lFcfrPiq60zxDHbo8Et r9rtrR4IrKeVszOibnuR+6hYeYG8tgSVC8jzBt0PBv8AyA7n/sK6l/6WzUAbF9YWep2clnf2 kF3ayY3wzxiRGwQRlTwcEA/hVOTw1oM2lw6XLommvp8L74rRrVDEjc8qmMA/M3IHc+talFAG XeeGtB1C3tbe90TTbmC0TZbRzWqOsK4AwgIwowoGB6D0rUoooAKKKKACiiigAooooAjmghuU CTxRyoHVwrqGAZWDKee4YAg9iAap/wBhaP8A2x/a/wDZVj/af/P79nTzvu7fv43fd469OK0K KAM+10LR7HUZ9Rs9Ksbe+n3edcw26JJJuO5tzAZOSATnqa0KKKACiiigAooooAKKKKAMuz8N aDp9vdW9lomm20F2my5jhtURZlwRhwBhhhiMH1PrRH4a0GHS5tLi0TTU0+Z98totqgiduOWT GCflXkjsPStSigCvY2FnplnHZ2FpBaWsedkMEYjRckk4UcDJJP41YoooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKAKepaTpus262+qafaX0CuHWO6hWVQ2CMgMCM4JGfc1YgghtbeK3 t4o4YIkCRxxqFVFAwAAOAAOMVJRQAUUUUAFFFFABRRRQAVHJBDM8LyxRu8L74mZQSjbSuV9D tZhkdiR3qSigDP8A7C0f+2P7X/sqx/tP/n9+zp533dv38bvu8denFFroWj2Ooz6jZ6VY299P u865ht0SSTcdzbmAyckAnPU1oUUAFFFFABRRRQAUUUUARzwQ3VvLb3EUc0EqFJI5FDK6kYII PBBHGKy7Xwn4bsfP+x+H9Kt/PiaCbybKNPMjb7yNgcqcDIPBrYooAy4/DWgw6XNpcWiaamnz PvltFtUETtxyyYwT8q8kdh6VcsbCz0yzjs7C0gtLWPOyGCMRouSScKOBkkn8asUUAFFFFABR XF+K/DWg3viTw5cXeiabPPdamyXEktqjNMos7ggOSMsAUQgH+6PQVHc6reW8uoWcdhpS6Rpe q6dp9tCYST+8a0IO3IVPL80lSM87eF2fOAdxRXHx+KroeL7bTXeCe1u7uW0RYLKcLEUjkfcb o/upG/dFWjUAqzEZPlndoeBP+SeeGv8AsFWv/opaANTUtJ03WbdbfVNPtL6BXDrHdQrKobBG QGBGcEjPuar3nhrQdQt7W3vdE025gtE2W0c1qjrCuAMICMKMKBgeg9K1KKAM+60LR77UYNRv NKsbi+g2+TczW6PJHtO5drEZGCSRjoa0KKKACiiigAooooAKKKKAI5IIZnheWKN3hffEzKCU baVyvodrMMjsSO9U7XQtHsdRn1Gz0qxt76fd51zDbokkm47m3MBk5IBOeprQooAz9M0LR9E8 3+ydKsbDzseZ9kt0i34zjO0DOMnr6mtCiigAooooAKKKKACo54Ibq3lt7iKOaCVCkkcihldS MEEHggjjFSUUAZcfhrQYdLm0uLRNNTT5n3y2i2qCJ245ZMYJ+VeSOw9KJPDWgzaXDpcuiaa+ nwvvitGtUMSNzyqYwD8zcgdz61qUUARwQQ2tvFb28UcMESBI441CqigYAAHAAHGKkoooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivL5 PFDrY6vcp43zrdtd3yWuh7rT960U0iww+X5fnNvCoMBtx3cEEigD1CisO48UW1tqj2htLt4I biK0nvVCeVDPJs8uNgWDknzYuVUqN4yRhttPRvFN7faDJf3GhXxuhqFzaRWkCxl3EcrqCT5h RcKhDFnUFlIXO5NwB1FFcva+LJb/AMTaVp9tp0/2O7tLuSeV9gME0EqRsh+f+FiyttDAlkKk ruIjh8a2UOjWV2INSvIP7Mh1G7uWSINbW7qSJZRuUEkJISsQY/IcLyoIB1lFc/J4us4tYudP azvttrdxWdxd+UPJjklWMxDO7LbjKq/KCVPLBVIY6Go3yWl9pMLyTo13dtCgiVSrkQyyYfPI XCE/LzuC9s0AWL2xt9QgWG6j8yNZY5gNxGHjdZEPHoyqffHPFV7zRNOv/wC0PtNv5n9oWgsr r52HmQjfheDx/rX5GD83XgYz9A8XWfiH7IYbO+tVvbT7ZaNdxBPPjGwOQAxI2mRB8wAbcChZ fmovZb3VPEM+jWuoz6bHaWkN089skbySmV5VC/vEZVUeSxPykksuCoUhgCxe+FtG1DUVv7qz 8ydZY5x+9cIJYypSXYDt8wbVXfjcVG0nbxWhZ2Nvp8DQ2sflxtLJMRuJy8jtI559WZj7Z44r Ln1q701LOzubCTUNWmSR/I04ooaONlVpf3zqFHzx5TcSC+AXClqp3HjzS4v3kNvfXNqNPh1S S6hh/dxWkm/96xYg8CMkoAXIPyq2G2gHUUVyfiHxU1rMLOwhu/Mj1Oxs5rtYlaJHkmhLRN1Y EwyZ3bdvzABt+BRYeKJ7nxRY6XBaXc9ncJqLSXcwiBR4LkRbRtYHYCSv3SSGiOSd5AB1lFcn D41sodGsrsQaleQf2ZDqN3cskQa2t3UkSyjcoJISQlYgx+Q4XlQbkni6zi1i509rO+22t3FZ 3F35Q8mOSVYzEM7stuMqr8oJU8sFUhiAdBRXP6zLe3PiHTdGtdRn0+Oe0ubp57ZI2kJieFQv 7xHXafOYn5c5VcEDIOHol/rHiPULvT31u7sRpyMvnWkUG+4Iu7qDdJ5kbLnbbIfkCjLvxjaF AO8org5dR1y58CweLRrEkFy+mQ3UGm21vF5E0zRKyxNvVpGLytsAR1JDKo+b5m7ygAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKp6rqtjoel3Gp6ncx21nbpvllfoo/mSTgADkkgDJNV9S8QafpVwtvP8Aa5Zygcx2 llNcsikkAsIkYqCQwBbGdrYzg4ANSiuX16WLUtJle706C/0h9klpNbq9+km6NsSTWyAeZGGK 4VTJnKv8m3cuxoUVxB4e0yK8WdLpLSJZluJxNIHCAMHkAAds5ywHJ5oA0KKKKACiiigAoooo AKKKKACiiubJ1DXtW1SCDV7vSoNMuFtQLSOFmmYwxyl2MsbgDEqqFUDG1iS24BQDpKK5PR9f vtR1zTLeZo1RrfU0uFjXCyS211DAJBnJUHLnbk434JbANSXSalqfjK+sINdvtOtbXT7WZUtI rc7nkkuAxJlic9Il6Y70AdRRWX4f1KbVdJ8+4WMTx3FxayGMEK7QzPEXAJJUMU3bcnGcZOMn g/D/AIoe60fQbuHxv/a2s3f2P7RpG60b/WMgn+SONZB5atI/3vl2ZbIBBAPUKK58eLIHvDBF p19JG8s1tbTr5QS6uIg5eFAXDBh5Uoy4VDsPzYKk19H8U3t94S0TVJNCvrm+1C0Sd7e0WMBR tBLbnkCBSWBVS+8hunyttAOoori5fGonfW5I4LtNHtdCi1WDUIEj3usiytuVXY87UG0Mg5R9 3BXOxceKLa21R7Q2l28ENxFaT3qhPKhnk2eXGwLByT5sXKqVG8ZIw20A1Lixt7ue0mnj3yWk pmgO4jY5RoyeOvyuw59fXFV5NE06X7Tvt8/abuK9l+dvmmi8vY3XjHkx8Dg7eQcnOfYeLrO/ 1FbVbO+iR7uexjuZogsclxCZN0a/NuPyxOwbGzAxuD5USa5r9todw0ty12yQ6Zd3zRQqhV0h MW484O/5wFAIBy2ei4AJF8LaMmsR6qLP/TIpXmifzXKxO6srlEztTfvYtgDc2GbLAEaFhY2+ madbWFnH5draxJDCm4naigBRk8nAA61T0rXIdVuLi3Frd2s8KJL5d1GEZ4XLCOQAElQxRxtb a42ncq8ZzzJqetatqkdnqkmmppdwtuiJDHKtw5hjlLS7hnZ+8VdqFDwx3/MNgB0lFc/qHilN Jyl5YzySW1ot3qLWjK8dlEd3zkuUZ1/dy/cUthD8oJUEk8XWcWsXOntZ3221u4rO4u/KHkxy SrGYhndltxlVflBKnlgqkMQDoKK5O48VNceItGsbKG7jtp9TntZLholMVwIoLjzEU8lSssaj 5gpbaSu5QTWfa+NZJ7LUpHg1JNMtfDlvqseoFIPtD+YkjFtoYpvwnA2BdyPn5SmQDvKKw7jx RbW2qPaG0u3ghuIrSe9UJ5UM8mzy42BYOSfNi5VSo3jJGG2x2Hi6zv8AUVtVs76JHu57GO5m iCxyXEJk3Rr824/LE7BsbMDG4PlQAdBRXN3h1DVPFV3pcGr3emwWdlb3Aa0jhZpWleZSG82N xgCFcbQPvNnPGOXtfEuuat4O1XxT/aclpLpllHcLZW8MRt5m+xQ3JD71aTBaVl+V1+UDGDli AemUVy+r/wBraXdW18utzzvcahDbx6aIIhC0byhWwNplLJFvkJ34yjNgICo6igAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiuXurQa14yvrC7ub5LW00+ 1mhS0vZrbDySXAckxMpbIiT72cYOMZObGgPceIPAOjTXl5Olxe6fby3E8BEbsWRWfBA+XdyM rgjPylSAQAdBRXJ+FHmj1nVLJotStII7e3lSy1O7NzOjM0waTzPMkGxgigLv4MbHau7LXD4z 0jzZo401WbyZXhd4NHu5U3oxRgHWIqcMpHBPSgDoKK5//hMtL/59dc/8EV7/APGaP+Ey0v8A 59dc/wDBFe//ABmgDoKK5/8A4TLS/wDn11z/AMEV7/8AGaP+Ey0v/n11z/wRXv8A8ZoA6Ciu f/4TLS/+fXXP/BFe/wDxmj/hMtL/AOfXXP8AwRXv/wAZoA6Ciuf/AOEy0v8A59dc/wDBFe// ABmj/hMtL/59dc/8EV7/APGaAOgorm5vHOi2yB54tZiQuqBn0S9UFmYKo5i6liAB3JAqT/hM tL/59dc/8EV7/wDGaAOgorn/APhMtL/59dc/8EV7/wDGaP8AhMtL/wCfXXP/AARXv/xmgDoK K5//AITLS/8An11z/wAEV7/8Zo/4TLS/+fXXP/BFe/8AxmgDoK58eGNmimzjvNt1HqE2oW1y IuYpHneYDGclcOY2AKlkZhld3B/wmWl/8+uuf+CK9/8AjNH/AAmWl/8APrrn/givf/jNAEd1 4XmuNUuJFv400+7vbfULmA25Mpmh8rZsk3gKn7iLIKMT8+GGRtrzeDpp9Em06W9tJkOpz30U dxZGSB1ld38qeLf+9CtKxBBX5ljOPl5uf8Jlpf8Az665/wCCK9/+M0f8Jlpf/Prrn/givf8A 4zQBT0HwW3h+bSPsuoRvBYJfRMjWqqZEuJllAXYVVCpRRwuCM4VeMV/+EEmj0aLS4NVjWCXR 4NGv2e1LNLDErqGiw4ETkSycsJB93jg7tAeOdFa4e3WLWTOiK7xjRL3cqsSFJHlZAJVgD32n 0qT/AITLS/8An11z/wAEV7/8ZoALjwx9o/tL/TNv23VbTUv9VnZ5H2f5OvO77P14xv6HHOhq Omfb77SbnzvL/s+7a527c+ZmGWLbnPH+tznn7uO+Rn/8Jlpf/Prrn/givf8A4zR/wmWl/wDP rrn/AIIr3/4zQAaP4Y/sr+wP9M83+yNKbTf9Vt83d5Hz9Tt/1HTn73XjmxqGk3jaidS0i9gs 76SJbec3NsZ45Y1LMnyh0IZS74IbGHbIb5Stf/hMtL/59dc/8EV7/wDGaP8AhMtL/wCfXXP/ AARXv/xmgAufD14kthd6bqu2/tIpoDPqEJuRKsrI7kqrx4bdGuMEKoyoXG3bTbwPCmjajpdv fSJBdaFDosbSRhmjWNZlEhwQGJE3TA+778XP+Ey0v/n11z/wRXv/AMZo/wCEy0v/AJ9dc/8A BFe//GaAK+o+Fry61Gd7bU4IbG61C11C6hltDJI0kJhwEcSKFUiCMYKscljnkASWHhebT9Xs b2K/jZIH1EyxtbnMi3U4nwp3/KUZVGSDuGeBniT/AITLS/8An11z/wAEV7/8Zo/4TLS/+fXX P/BFe/8AxmgDL/4QSaPRotLg1WNYJdHg0a/Z7Us0sMSuoaLDgRORLJywkH3eODu1Ljwx9o/t L/TNv23VbTUv9VnZ5H2f5OvO77P14xv6HHMcHjnRbq3iuLeLWZoJUDxyR6JesrqRkEERYII5 zUn/AAmWl/8APrrn/givf/jNAFjVtJvLrUbTUtNvYLW+topbcG5tjPG0chjZvlV0O7MSYO7G N3ByCMu08KalpEz3Wk6taR3dyhF413YtKjsZppsxqsqFBvuJeCz8bBnIJa5/wmWl/wDPrrn/ AIIr3/4zR/wmWl/8+uuf+CK9/wDjNAGfH4T1iwn09NO1qx+w6ZaR2tjBfac8zQ7U2NIWWZA0 jDjdtGFyFxufd2Fc/wD8Jlpf/Prrn/givf8A4zR/wmWl/wDPrrn/AIIr3/4zQB0FFc//AMJl pf8Az665/wCCK9/+M0f8Jlpf/Prrn/givf8A4zQB0FFc/wD8Jlpf/Prrn/givf8A4zUcnjnR YXhSWLWUeZ9kStol6C7bS2F/dcnarHA7AntQB0lFc/8A8Jlpf/Prrn/givf/AIzR/wAJlpf/ AD665/4Ir3/4zQB0FFc//wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8AjNAHQUVz/wDw mWl/8+uuf+CK9/8AjNH/AAmWl/8APrrn/givf/jNAHQUVz//AAmWl/8APrrn/givf/jNH/CZ aX/z665/4Ir3/wCM0AdBRXP/APCZaX/z665/4Ir3/wCM1HH450WZ5kii1l3hfZKq6Jeko20N hv3XB2spwexB70AdJRXP/wDCZaX/AM+uuf8Agivf/jNaGk61Za3FPJZNP/o8vkzJPbSQOj7V fBSRVb7rqenegDQooooAKKKKACiiigAooooAKKKKACiiigAoorz9rnVLbTvENvc6rPcTf8JB Y2jTL+62xTCyEiRAHMa4lcLg7hnO4tliAegVxeoXsyeN47ZL67XS2uLb7XtJ8uO4KSlIvM3Z QOVgLKMDPlDa/wBpcjQ0SI6d4q1XSYbi7ks4rK0uUW6uZLhlkke4VyHkZmwREny5wMEgAk5k PjPSPNmjjTVZvJleF3g0e7lTejFGAdYipwykcE9KAI/H8EM3gDxA8sUbvDpl08TMoJRvIdcr 6HazDI7EjvVe51bTdC8dajcavqFpp8FzplmkEl3MsSysktyXCliAxUOhIHTeueoq5/wmWl/8 +uuf+CK9/wDjNH/CZaX/AM+uuf8Agivf/jNAEnguCa18C+Hre4ikhni0y2SSORSrIwiUEEHk EHjFblc//wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8AjNAHQUVz/wDwmWl/8+uuf+CK 9/8AjNH/AAmWl/8APrrn/givf/jNAHQUVz//AAmWl/8APrrn/givf/jNH/CZaX/z665/4Ir3 /wCM0AdBRXNzeOdFtkDzxazEhdUDPol6oLMwVRzF1LEADuSBUn/CZaX/AM+uuf8Agivf/jNA HQUVz/8AwmWl/wDPrrn/AIIr3/4zR/wmWl/8+uuf+CK9/wDjNAHQVh3Oj6lBf3N3oeo2lobx xJdR3dm1wrSKioHXbIhUlVVSMkHapAU7i0f/AAmWl/8APrrn/givf/jNH/CZaX/z665/4Ir3 /wCM0AV5PC15Z3WnXOi6nBbzWsV1HK19aG489riWOWRyEkj2sXQnj5fmIAAAFH9h+JI9Wk1O HW9KW6ntIra4D6VIyN5ckrKygXAK8TYIJb7ueM4qx/wmWl/8+uuf+CK9/wDjNH/CZaX/AM+u uf8Agivf/jNAGppemw6TYLaQtI4DvI8khBaSR3Lu5wAMszM2AABnAAGBWXZeGPsGk+HbaK8x daLFHAtx5WBPGIxHIjLnO1gA2N2A6Rsd23BP+Ey0v/n11z/wRXv/AMZo/wCEy0v/AJ9dc/8A BFe//GaAI7XwvNb6pbyNfxvp9pe3GoW0AtyJRNN5u/fJvIZP38uAEUj5MscHdnv4Da48NaHo 95fWl0NMt/spM2nrJFImFUSLE7MqzqqAK53AbpPkIbA0B450Vrh7dYtZM6IrvGNEvdyqxIUk eVkAlWAPfafSpP8AhMtL/wCfXXP/AARXv/xmgDLj8CTRaNc6Wuqx+RceHIdEdjancGjWRVmH z4xiVspjsPmHfQuvC81xqlxIt/Gmn3d7b6hcwG3JlM0PlbNkm8BU/cRZBRifnwwyNsn/AAmW l/8APrrn/givf/jNH/CZaX/z665/4Ir3/wCM0AFv4Y+z/wBm/wCmbvsWq3epf6rG/wA/7R8n Xjb9o6852dBng8R+GP8AhIPO/wBM+z+bpV7pv+q34+0eV8/Ufd8rp3z1GOT/AITLS/8An11z /wAEV7/8Zo/4TLS/+fXXP/BFe/8AxmgDQh0zyvEN7q3nZ+02kFt5W37vlPM27Oec+djGONvf PFO50W+S/ubjSNTjsUvnD3ivbecxcIqeZESwCPsVR8wdfkU7fvb4/wDhMtL/AOfXXP8AwRXv /wAZo/4TLS/+fXXP/BFe/wDxmgCvq3hF777TFa6h5FrfaemmXyzxtPI8C+YAY5C4KyYmkyzi TJ2nHB3WLjwx9o/tL/TNv23VbTUv9VnZ5H2f5OvO77P14xv6HHJ/wmWl/wDPrrn/AIIr3/4z R/wmWl/8+uuf+CK9/wDjNAFeHwteQaxZSrqcB0yz1C41CO3NofOMkyzbgZfM27Q07kDZnAUZ PJNOPwJNFo1zpa6rH5Fx4ch0R2NqdwaNZFWYfPjGJWymOw+Yd9CDxzot1bxXFvFrM0EqB45I 9EvWV1IyCCIsEEc5qT/hMtL/AOfXXP8AwRXv/wAZoAjuvC81xqlxIt/Gmn3d7b6hcwG3JlM0 PlbNkm8BU/cRZBRifnwwyNslv4Y+z/2b/pm77Fqt3qX+qxv8/wC0fJ142/aOvOdnQZ4P+Ey0 v/n11z/wRXv/AMZo/wCEy0v/AJ9dc/8ABFe//GaAJL/R9S/tmTVNI1G0tZ57eO3nW7s2uFKx tIyFdskZU5lfOSc/LjGDnHXwJNaaRdaHp+qxx6Pe26W92lxamW4KiBLclJA6qpMca9UbDZPI IUan/CZaX/z665/4Ir3/AOM0f8Jlpf8Az665/wCCK9/+M0ARx6JryeJZtUfWNNlid9kcUmmu Xgt8gmJHE+AWxln2kscZBCoq9JXP/wDCZaX/AM+uuf8Agivf/jNH/CZaX/z665/4Ir3/AOM0 AdBRXP8A/CZaX/z665/4Ir3/AOM1HJ450WF4Uli1lHmfZEraJegu20thf3XJ2qxwOwJ7UAdJ RXP/APCZaX/z665/4Ir3/wCM0f8ACZaX/wA+uuf+CK9/+M0AdBRXP/8ACZaX/wA+uuf+CK9/ +M0f8Jlpf/Prrn/givf/AIzQB0FFc/8A8Jlpf/Prrn/givf/AIzR/wAJlpf/AD665/4Ir3/4 zQB0FFc//wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8AjNAHQUVz/wDwmWl/8+uuf+CK 9/8AjNRx+OdFmeZIotZd4X2SquiXpKNtDYb91wdrKcHsQe9AHSUVz/8AwmWl/wDPrrn/AIIr 3/4zWhpOtWWtxTyWTT/6PL5MyT20kDo+1XwUkVW+66np3oA0KKKKACiiigAooooAKKKKAMfU NBN3qJv7TVb7TLp4lhme0ELeaiFigIljcDaXf7uM7jnOBiRNDhg0tNNs7q7tLSG3igtkgkAN v5f3WViCWP3QQ5ZSFAIILbtSigDL0rRRptxcXc1/d6heXCJG9zdCMN5aFiiARoi4BdznGfmO SQABT8G/8gO5/wCwrqX/AKWzV0Fc/wCDf+QHc/8AYV1L/wBLZqAOgooooAKKKKACiiigAooo oAp6npsOq2qW87SKiXEFwChAO6KVZVHIPG5AD7Z6dauUUUAFFFFABRRRQAUUUUAFFFFAFOPT YYtZudUVpPPuLeG3dSRtCxtIykcZzmVs89h073KKKACiiigAooooAKKKKACiiigCnpOmw6No 1jpdu0jQWVvHbxtIQWKooUE4AGcD0FXKKKACiiigAooooAKKKKAOXsPF93qenW1/Z+Edcktb qJJoX8yzG5GAKnBuMjII61He6hfX11p1xL4P8QB7C4NxEFnscFjFJFhv9I6bZGPGOQPobngT /knnhr/sFWv/AKKWugoA5/8A4SHVP+hM1z/v9Zf/ACRR/wAJDqn/AEJmuf8Af6y/+SK6CigD n/8AhIdU/wChM1z/AL/WX/yRR/wkOqf9CZrn/f6y/wDkiugooA5//hIdU/6EzXP+/wBZf/JF H/CQ6p/0Jmuf9/rL/wCSK3Jp4bZA88scSF1QM7BQWZgqjnuWIAHckCs9tf01rK11CDUtNk0+ Z3BujdqE2ojsxRhkMRsORkYAc5+XBAKf/CQ6p/0Jmuf9/rL/AOSKP+Eh1T/oTNc/7/WX/wAk VJaeL9BufDVh4gl1O0tNPvUUxSXU6RgMQTsJ3Y3jDAgE4Kn0qSXxFZ23iGbSbyWC22xWzQyz TBfOkmeZVjUHGW/ckgAknPTjkAr/APCQ6p/0Jmuf9/rL/wCSKp2WoX1jdajcReD/ABAXv7gX EoaexwGEUcWF/wBI6bY1POeSfoOsooAw7DxFJdazHpd3ompadPLbyXETXTQMrrG0asB5UrkH MqdQO9R+Hv8AkOeLP+wrH/6RWtF5/wAlD0b/ALBV/wD+jbSjw9/yHPFn/YVj/wDSK1oA6Cii igAooooAKKKKACiiigAooooAKKKKACsefw3Z3EWpo0k6tf3cd6zqwzFNGsSxsnGPlMEbYYMC Qcgg4rYooAy9K0UabcXF3Nf3eoXlwiRvc3QjDeWhYogEaIuAXc5xn5jkkAAU/Bv/ACA7n/sK 6l/6WzV0Fc/4N/5Adz/2FdS/9LZqAOgooooAKKKKACiiigAooooAp6npsOq2qW87SKiXEFwC hAO6KVZVHIPG5AD7Z6dauUUUAFFFFABRRRQAUUUUAFFFFAFOPTYYtZudUVpPPuLeG3dSRtCx tIykcZzmVs89h073KKKACiiigAooooAKKKKACiiigCnpOmw6No1jpdu0jQWVvHbxtIQWKooU E4AGcD0FXKKKACiiigAooooAKKKKAOXsPF93qenW1/Z+EdcktbqJJoX8yzG5GAKnBuMjII61 He6hfX11p1xL4P8AEAewuDcRBZ7HBYxSRYb/AEjptkY8Y5A+hueBP+SeeGv+wVa/+ilroKAO f/4SHVP+hM1z/v8AWX/yRR/wkOqf9CZrn/f6y/8AkiugooA5/wD4SHVP+hM1z/v9Zf8AyRR/ wkOqf9CZrn/f6y/+SK6CigDn/wDhIdU/6EzXP+/1l/8AJFH/AAkOqf8AQma5/wB/rL/5Ircm nhtkDzyxxIXVAzsFBZmCqOe5YgAdyQKz21/TWsrXUINS02TT5ncG6N2oTaiOzFGGQxGw5GRg Bzn5cEAp/wDCQ6p/0Jmuf9/rL/5Io/4SHVP+hM1z/v8AWX/yRUlp4v0G58NWHiCXU7S00+9R TFJdTpGAxBOwndjeMMCATgqfSpJfEVnbeIZtJvJYLbbFbNDLNMF86SZ5lWNQcZb9ySACSc9O OQCv/wAJDqn/AEJmuf8Af6y/+SKp2WoX1jdajcReD/EBe/uBcShp7HAYRRxYX/SOm2NTznkn 6DrKKAMOw8RSXWsx6Xd6JqWnTy28lxE100DK6xtGrAeVK5BzKnUDvVHwbqEWp6l4vnhV1RNc aAhwAd0VtbxseD0yhx7Y6VU8Y3k9jr9rNZybL5tF1CG0OAf9IeazSIc8cyMo54554zWV8F7i 7vtA16/voPIuL3Wpbtk2FR+8hhcFQedpDZHXgjrXUsO3hnX7SS/Bv/Im/vWPSqKKK5Sgoooo AKKKKACiiigAorDv9Y1L+2ZNL0jTrS6ngt47idru8a3ULI0ioF2xyFjmJ85Ax8uM5OLFvq7a l4atdY0yzknN5bxT28ErrGcSAEbzyFADZbG44BwGOAQDUrn/AAb/AMgO5/7Cupf+ls1WNJ1a 8utRu9N1Kygtb62iiuCLa5M8bRyGRV+ZkQ7sxPkbcY28nJAr+Df+QHc/9hXUv/S2agDoKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooA5/wJ/wAk88Nf9gq1/wDRS1J/whfhX/oWdG/8AIv/AImo/An/ACTz w1/2CrX/ANFLXQVpCrOn8EmvRiaTMP8A4Qvwr/0LOjf+AEX/AMTR/wAIX4V/6FnRv/ACL/4m tyir+tV/5397DlXYw/8AhC/Cv/Qs6N/4ARf/ABNH/CF+Ff8AoWdG/wDACL/4mtyij61X/nf3 sOVdjl/iDD9o8JmD7NBdeZqFgnkXBxHLm7hG1zhvlPQ8Hg9D0qnbaRqc+tW2qyWElsk2um+e CWSMyQRDTmtgX2sVJLqMBWbhgTjkDsJoIblAk8UcqB1cK6hgGVgynnuGAIPYgGpKwGcHo1hr GkaX4Su5dEu5p9O0eTTbmyilg81XPkYcFpAhT9w3Rs/OvH3tpq3ha7ubfxE1vpNpFPceF49M s0gZNqSYuN0MbEKQmWh5IUHC8DHHaRX9nPKsUV3BJI3mbUSQEny2CSYH+yxCn0Jweakknhhe FJZY0eZ9kSswBdtpbC+p2qxwOwJ7UASUVXW/s38vbdwN5srwR4kB3yJu3IPVhsfI6ja3oasU Ac/ef8lD0b/sFX//AKNtKPD3/Ic8Wf8AYVj/APSK1ovP+Sh6N/2Cr/8A9G2lHh7/AJDniz/s Kx/+kVrQB0FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXP8Ag3/kB3P/AGFdS/8AS2au grn/AAb/AMgO5/7Cupf+ls1AHQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRV T+07P+1/7K87/TvI+0+VtP8Aq923dnGOvGM5pNpblRjKV+VXsW6KwH8V2o1+PSlt58tN5ZuX XEJAU5KvyCRJsjIODucDk8Vv0oyUti6tGpStzq11dBRRRVGQUUVk+Fv+RQ0X/rwg/wDRa0r6 2NFC8HPs0vvv/ka1FFFMzCiiigAooooAKKKKACiiigAooooA5/wJ/wAk88Nf9gq1/wDRS1J/ whfhX/oWdG/8AIv/AImo/An/ACTzw1/2CrX/ANFLXQVpCrOn8EmvRiaTMP8A4Qvwr/0LOjf+ AEX/AMTR/wAIX4V/6FnRv/ACL/4mtyir+tV/5397DlXYw/8AhC/Cv/Qs6N/4ARf/ABNH/CF+ Ff8AoWdG/wDACL/4mtyij61X/nf3sOVdjl/iDD9o8JmD7NBdeZqFgnkXBxHLm7hG1zhvlPQ8 Hg9D0qnbaRqc+tW2qyWElsk2um+eCWSMyQRDTmtgX2sVJLqMBWbhgTjkDsJoIblAk8UcqB1c K6hgGVgynnuGAIPYgGpKwGcHo1hrGkaX4Su5dEu5p9O0eTTbmyilg81XPkYcFpAhT9w3Rs/O vH3tpq3ha7ubfxE1vpNpFPceF49Ms0gZNqSYuN0MbEKQmWh5IUHC8DHHaRX9nPKsUV3BJI3m bUSQEny2CSYH+yxCn0JweakknhheFJZY0eZ9kSswBdtpbC+p2qxwOwJ7UASUVXW/s38vbdwN 5srwR4kB3yJu3IPVhsfI6ja3oasUAef+OJGbxz4RsooXlmu/MVApACiO6s53YkkcBIX6ZJOB jmug8NwRW2o+I4IIkihi1CNI441CqiiztgAAOAAO1F5/yUPRv+wVf/8Ao20o8Pf8hzxZ/wBh WP8A9IrWuhV7UnSS/wCHun+lhW1udBRRRXOMKKKKACiiigAooooA5u8GoaX4qu9Ug0i71KC8 sre3C2kkKtE0TzMS3myIMETLjaT91s44yadHqfhvwVZ6fDpcmo3+m6ZbwqkE0aJcSquwqrOQ QAVBJYDhhgMcqOkooA5/wwl0ftVxqOmX1tqEuwz3N2YP33XCRrFLJsjTJwpPG4nLMzsc/wAG 6Na+Xc6p5t99o/tXUvk+3z+T/wAfcy/6nf5fT/Z689ea7Cuf8G/8gO5/7Cupf+ls1AHQUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQBz/gT/knnhr/sFWv/AKKWrX2fxF/0FNL/APBbJ/8AH6q+BP8Aknnh r/sFWv8A6KWugpNXJcU9zJ+z+Iv+gppf/gtk/wDj9H2fxF/0FNL/APBbJ/8AH61qKXIv6bF7 Nef3syfs/iL/AKCml/8Agtk/+P0fZ/EX/QU0v/wWyf8Ax+taijkX9Nh7Nef3sw/Ft5fWOhLL ptxHb3b3tnAkkkXmKBJcxRtlcjI2sRwQeeCDyMOfU9Wtry8nGqzyR6bqtjpfkPFFsuVmFsHl kIQN5n+kMRsKr8q/LjIbtJoIblAk8UcqB1cK6hgGVgynnuGAIPYgGq8mk6bNqkOqS6faPqEK bIrtoVMqLzwr4yB8zcA9z61RZxfhn/kZ9P8A+5h/9OUVbjQrqHjrUYLgybLfR4UhMcjRtGLi WYS4ZSCCfs8POcrs4xk53IrCzglWWK0gjkXzNrpGAR5jB5MH/aYBj6kZPNV5tM3a5barDN5U iRNb3CbcieM/MucEfMjDKk5ADyDGXyADixBDZfCLXjaxRwjTrjVLm0WNQqwyQXc0kJCjjCtG h29OMEEcV6JWXqWiQ39gunp5dvZvcCW6ijjH79d5d0PbDt9/IO5WcEZbcNSgDk9e0yDVfHWi QXEl2iLpl84Nrdy27Z820HLRspI56Zx09BUnhCyi0/UPFNrC87xpqqYM87zOc2dseXclj17n jp0qxef8lD0b/sFX/wD6NtKPD3/Ic8Wf9hWP/wBIrWgDoKKKKACiiigAooooAKKKKACiiigA ooooAKjnnhtbeW4uJY4YIkLySSMFVFAySSeAAOc1JWH4vs5tR8MXVhDd2lqbt4rd5LvPltG8 iq6EAgkupZAAVJLABlOCAC5pmu6Prfm/2Tqtjf8Ak48z7JcJLsznGdpOM4PX0NZ/g3/kB3P/ AGFdS/8AS2apNHv9S/tm+0jVJbS4ntreC6W4tYGgUrK0q7CjO5yDCTu3c7gMDGTl+DZtY8u5 i+w2P9mf2rqX+kfbH87/AI+5v+WXlbfvcff6c+1AHYUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAVDPdW9r5X2i4ih82QRR+Y4Xe56KM9SfSpq4e/0mbU7jxBr4lluGt4zFpcCxghZYBkOB1Zx N5ijI6bh8ytUTk4rRHVhaEKsnzysl+bdrfr6JncUVgaJ4otdYkuyki+SNRextWUZ80pEHJyC Rg4cg8DAHetHWNWtdC0m41K9LC3gXLbFyxJIAAHqSQPTnnFNTi1zX0InhqsKipOPvO2nr/Vv UvVx8YkfxpBrCOrRz3M2lIWXkwrFvOORgrNDKOQchj2C1v8A9rRtrFjZQhZYry0muknR8rhG iAxjqD5uc57d88cxfX39leBbfxGkfmmC7bUoYi2NyXEr4VvQ+XOenRgOoHM1k4pSe2/3P/hz pwEZNyjH4pe5/wCBJ/qkWPKjPwyhvzGpvIdKF2kxGWEwVZt5PcmRFc5+8RzmuwrLi0WMeFk0 KaZniFkLN5UG1iNmwsAc4PfvVey8Rwumj2158uo3/mRtFGp2pLEp84ZJ6KylepzkYyOaUfdt fshVr11J09bSk/k1f8k2zcormNB8VQ6lrNxopl868t5LtpztK+UqT7Y16YOVYcg/w88mtHUt ehsvDyazbwS3sEnktGkKnfIsjqo2gjJOGyBxnpxVKpFrmuZTwdaFRUnHV2S+e339L9NR2t37 QeGtVvbKdfNt7adkkXDBZEVvqMhhgg9xiqnheO10vw08SlYLO1ubxQXf5Y41uJOpPYAdTWBc eDb600m4e0P2nVdR3x3REm2IedCEmb5jgDzFEuVXccBAAOlefxCsuj614fs9PvLq5ZtSWWSK IlUJlkwoxy7ZkhyAMASAkjBrB1Gpc0lbQ9OOEjUoeyoS5lzJvpZWe/prbvfQ9GorlfD+vXRF lp11ZTlY2/s9r6WTPn3MaOZCoPLJ+6b5yQSSBjqR1VdEJqSujycRQnQnySCiiiqMAooooAKK KKACiq8V/azXc1qk6m4hYq8Z4bIVGOAeoAkTkcfMKsUXuNxcd0FFFFAjn/An/JPPDX/YKtf/ AEUtWvs/iL/oKaX/AOC2T/4/VXwJ/wAk88Nf9gq1/wDRS10FJq5LinuZP2fxF/0FNL/8Fsn/ AMfo+z+Iv+gppf8A4LZP/j9a1FLkX9Ni9mvP72ZP2fxF/wBBTS//AAWyf/H6Ps/iL/oKaX/4 LZP/AI/WtRRyL+mw9mvP72Yfi28vrHQll024jt7t72zgSSSLzFAkuYo2yuRkbWI4IPPBB5GH PqerW15eTjVZ5I9N1Wx0vyHii2XKzC2DyyEIG8z/AEhiNhVflX5cZDdpNBDcoEnijlQOrhXU MAysGU89wwBB7EA1Xk0nTZtUh1SXT7R9QhTZFdtCplReeFfGQPmbgHufWqLOL8M/8jPp/wD3 MP8A6coq3GhXUPHWowXBk2W+jwpCY5GjaMXEswlwykEE/Z4ec5XZxjJzuRWFnBKssVpBHIvm bXSMAjzGDyYP+0wDH1IyearzaZu1y21WGbypEia3uE25E8Z+Zc4I+ZGGVJyAHkGMvkAHFiCG y+EWvG1ijhGnXGqXNosahVhkgu5pISFHGFaNDt6cYII4r0SsvUtEhv7BdPTy7eze4Et1FHGP 367y7oe2Hb7+Qdys4Iy24alAHP3n/JQ9G/7BV/8A+jbSjw9/yHPFn/YVj/8ASK1qnr0mpReO tEbS7S0uZ/7MvgyXVy0ChfNtOQyxuSc44x3PPHMnhB7yTUPFLX8EEF0dVTfHBMZUX/Q7bGGK qTxj+EenPWgDqKKKKACiiigArn/Fkeoy2tglla311bm7/wBOhsLlbeZofKkxtkLoR+88onaw JAI5BIPQVyfjWbTZtNhN3f6N5FpeqLiz1S6WK3uWMTEQysQwBAdJQCrcopwOGABc8MW/2f7V /wASvXLHds/5Cupfa9/X7n7+Xbjv93OR1xx0FcX4E/s2S41KfS5vD8MDJCjadoV2s8ETAyHz WKogDuGCn5ekK/Mei9pQAUVn6nruj6J5X9rarY2HnZ8v7XcJFvxjONxGcZHT1FaFABXDeDtV 1KWz1eK00yCa3s9b1CJ2N2Ulcm5kkO1Cm3OHAGXAJHJHWu1hnhuULwSxyoHZCyMGAZWKsOO4 YEEdiCK434bf8e/ij/sZL/8A9GVEr6JHRQUbTlJXsvPul0aOg+0eIf8AoF6X/wCDKT/4xR9o 8Q/9AvS//BlJ/wDGK1qKfK+/5f5C9tD/AJ9r/wAm/wAzJ87X7f5Xs7C8A482KdoSxPQ+WysF AJ5+cnAJAJwtH2jxD/0C9L/8GUn/AMYrWoo5X3D20esF+P6Oxk/btaj+WXRYnd+Ea2vQ6KfW QuqFR0+6rnrx0BPtHiH/AKBel/8Agyk/+MVrUUcr7/l/kHtof8+1/wCTf5mT9o8Q/wDQL0v/ AMGUn/ximRz+I3UxvY6dG+5gZjcuVUEkqVQJlwFKg5KEsG+6ME7NFLlfcfto9Ka/H/Myfs/i H/oKaX/4LZP/AI/R9n8Q/wDQU0v/AMFsn/x+tainyL+mxfWJ9l/4DH/Iyfs/iH/oKaX/AOC2 T/4/R9n8Q/8AQU0v/wAFsn/x+taijkX9Nh9Yn2X/AIDH/Iyfsuvtw+rWCjrmLT2ByOg+aUjG evGSM4KnBB/ZF9/0Meqf9+7b/wCM1rUUci/psPrE+y/8Bj/kZP8AZ+sQf8e2uebn732+0STH pt8oxY9857Yxzk+w61N8txrUUSDkNY2QjfPoTI0gx/wEHpz1B1qKORf02H1ifZf+Ax/yMn7L r7/K+rWCoeGaHT2VwPVS0rAH0ypHqD0o/svU4vnh8QXUkg6LdQQvGfqERGP4MOcdRwdaijkX 9Nh9Yn2X/gMf8jJ/si+/6GPVP+/dt/8AGaPseuxfJDrFrJGOjXViXkP1KSIp/BRxjqeTrUUc i/psPrE+y/8AAY/5GT9j12P/AFWsWr7vmb7RYlsN3CbJFwnoG3N6saP7P1g/uzrmIT951tE8 4Y6bWJ2DIAzlDklsbQVC61FHIv6bD6xPsv8AwGP+Rk/2XqcXzw+ILqSQdFuoIXjP1CIjH8GH OOo4J/ZOoNy/iK/U9MRQ24GB0PzRk5x15wTnAUYA1qKORf02H1ifZf8AgMf8jJ/s3VU/eJr0 rSjhUmto2hI6ZZVCuTjk4cDdyAB8tH2fxD/0FNL/APBbJ/8AH61qKORf02H1ifZf+Ax/yMn7 P4h/6Cml/wDgtk/+P0fY9di+SHWLWSMdGurEvIfqUkRT+CjjHU8nWoo5F/TYfWJ9l/4DH/Iy fs/iH/oKaX/4LZP/AI/R9n8Q/wDQU0v/AMFsn/x+taijkX9Nh9Yn2X/gMf8AIyfseuycS6xa ovrbWJVvQ8vI46EkcfeCk5AKsfY9d+4dYtfLb7zCxPmDPXafM2jHO3KtgYzuOSdaijkX9Nh9 Yn2X/gMf8jJ/svU4vnh8QXUkg6LdQQvGfqERGP4MOcdRwT7HrsXyQ6xayRjo11Yl5D9SkiKf wUcY6nk61FHIv6bD6xPsv/AY/wCRk/Ydaf8AdvrUSxHlnhsgswPXCszMgGeBlCdvBJPzUf2b qq8Jr0rD7mZbaMnYerfKAPMz0ONoGMoxyTrUUci/psPrE+y/8Bj/AJHP+BP+SeeGv+wVa/8A opatf2vff9C5qn/fy2/+PVV8Cf8AJPPDX/YKtf8A0UtdBTav1IhNR3in63/Royf7Xvv+hc1T /v5bf/HqP7Xvv+hc1T/v5bf/AB6tailyvv8Al/kX7aH/AD7X/k3+Zk/2vff9C5qn/fy2/wDj 1H9r33/Quap/38tv/j1a1FHK+/5f5B7aH/Ptf+Tf5mfrep/2Lo8+pND5sNttkn+bb5cIYeZJ 0Odibn2gZbbgckVl3filotLv9QitrRba3vWtEub29W2gITCu8jlSUAlDxABWJYKfutuGxqpH 9l3CtpsmpI6bHs0EZMytwRiRlQjBOQT0z16Vj2Phdl8K6NYXV3JBqdgiSm9tCrN9pKMsso8x SHL+ZLkupJ3k/ewRRgZ8PxBhn043MdnHI8tuxsRFch0vLhLg2zRo4XATzWgCyNgETA4G1sbm i+IbfX57h9PXzbCOKB0usld7yJ5mzYQCMRvC2e/mY4KkVXtfB2l2kGnQr58q2F3NdRtO/mu5 kdpCrswJZQ7K4yc7oo2JJXNaGj6NZ6HZva2SbY3leU5AB5PyrwB8qKFRR2RFXoKAOP1u51S8 8DeLdatdVnspE+2pEI+WhitRLGFjOcKzyRs5fbu2vsByqOvSalPNYeJ9GkSWRoNQeSxlgLHa GEbzJKM5AIEUikADd5gJJ2KKrw6A0ul+IPD12si6bfPcNDcRsu8pc7mlU56OsjyY+XG0x8sQ 2Ll9Y3F/4j0uRo9ljp2+68zcMyTsjRKoHPyhHlLZA5MeCcMAAZ+sW0t1490VIb2ezYaZfnzI FjLEebacfOrDH4Z4p/hSOaLU/FUc8/nyLqqAy7ApYfY7bBIHGcYzjAJyQAOBLef8lD0b/sFX /wD6NtKPD3/Ic8Wf9hWP/wBIrWlbW5XO+Xl/RfnudBRRRTJCiiigAooooAKKKKACiiigDn/F keoy2tglla311bm7/wBOhsLlbeZofKkxtkLoR+88onawJAI5BIJ4Yt/s/wBq/wCJXrlju2f8 hXUvte/r9z9/Ltx3+7nI644p+NZtNm02E3d/o3kWl6ouLPVLpYre5YxMRDKxDAEB0lAKtyin A4YV/An9myXGpT6XN4fhgZIUbTtCu1ngiYGQ+axVEAdwwU/L0hX5j0UA7So54Ibq3lt7iKOa CVCkkcihldSMEEHggjjFSUUAU9N0nTdGt2t9L0+0sYGcu0drCsSlsAZIUAZwAM+wrL8G/wDI Duf+wrqX/pbNXQVz/g3/AJAdz/2FdS/9LZqAOgooooAKKKKACiiigAooooAKKKo2epx3OnzX k223ihmnjdnf5VEUjIWJOMD5M+1K62KUJNXXp9//AAxeorlbTxPJrPh2z/s4M+qX1kWV4Ysx W833cvuztUOHIzncI2A3HAMJh1JdYfQ49QvIxNc/bGnmJcy2ixRrIit/AzSseE27BkjaNoOf tVutTsWAmm41HytX0flv/wADvZldb7/hMbyTRNQj8m1P2t18pubiFSiQygHIKESswPILxBge MV0+gWs1l4c0u0uE2TwWkUci5B2sqAEZHHUUR6JYw6vFqcMXlTxWn2JVThPK3BgNvQYI4xjq evGNGiEGneW4YrEwnFU6StHe3nrf8/yOS1TQYdM1PSdYs55Y0svs1iloWJjZWkESseckqk0u M55I9w0VhfW/jS80G/Mcq2q2l1NNaMwZN5IhAkHQggz7c4zg/wC0K3vEekrrvh2/00hS08JE e9iFEg5QkjnAYA/h3rM8L2s0Wra3JKmzZO0BUkEk+dNOG47FLiP3zkEDHMOFp8q2f5/0jqp1 1PCupJ3qRul/hat89ZN36W8zhLLVtSPiV9GYtB9h0N9Ls5EUp++aa3tzKG6lfNHUdAnTdkV6 vc6XY3enLp89rE9muzEG3CYQgqMDjAKjjpxjpXHa7YaRb/ETwjbSaWkr3RvZEm8wq0UilZ92 erDdv+UnA3n6V3ldUqU404KotOXTzXNL9dPkc+MxVOpNToaO930s7JffpzX7sK5jU/B8N7Pe yCTel1JCRDISogXf/pAjZeV81CQ2MbiTk88dPRWcoKSszloYipQlzU3b/h7nNxeF2sZrS60+ WCG/ZZI7+88gB5hJh3kA6b96grklVDNwQAKyIdJtfDmt6V4dtQy6deTRzhHbc00sSSu7k9QQ yWpxwDjgY3Cu7pjxRyNGzxqzRtuQsMlTgjI9DgkfQmodGPQ6YZhV1U3dNP79bP5N6dle1h9Z 2jab/ZlrcIyxCSa7nuHaMfe3ysyk8ckKVH4Y6CtGitLK9zjU2ouC2f6f8OYHjN7qPw1I9jGs t4tzamCNz8rSfaI9oPI4Jx3FP/4SvThLp6OJU+2Ru7M+0C12qzETc/IfkkGPWNx/Ca2ZIo5l Cyxq6hlYBhkZBBB+oIBHuK5DXPDUMGjSBJf31zfvvlKknFy0kIHJ4CfaS2BgEg9C5NZVOdPm id+EdCrBUau93b5q34NL7+23SarrGn6JbLc6ldLbwsxUOwOCQrNjjvhTj1PA5IFVPDd5qF1Z Txaqqi+tpgk2wAKC0aS7QBnhfM2dTnZnPNUfD8//AAkqtqN/FE2LSK28nblP3sUc0pweobfG u05x5fX5jVbxlGthJaXdkWt7/UZm03zkcjmWJgrHsCHSElwN2IwOQAKTm7c62Khhoc31WS99 9ez3VutrXv527HYVgN4x0caha2izMwuGmXz9u2JPLjWRizNj5SjKQwyCDnOOazIPCuqXU8t3 qGpym6ljKu2cIssL4tpljXjGN0jIW27m6emyPDOnkyQyxLJYtbW1stq4JUCB3Zckn5h8w4P9 3nOafNUlsrEKlhKTftJ83p93Xe262ulra5b0/VrXUbG2uoy0QuGZFinXy5BIu7chU/xDa2R/ sntzVTUNWZ/Dl9dWJaO53S2tvuUZNwJDCnqMGTGCeMHnHNZx8LtfeJb661CNZtOkuVnW3mId HZbdI1YIQRj5pd2ecpHjgc3tO8KadpV609oZY4PPa5S0G0RRSsmwuuBu+6SNuSoycAcYE6j0 t/XcJQwlNqSld6O1rr/C332V7dPkc7dac3hL+zL6JvtGsXV2+nfa53aQzJJ5phEp4zhhDuYD ICYGQAK2o/EEltrGn6FcyK9/JcyRsWTBlgWJnWUYwMn5FJwAWEgUYHGzqVj/AGhapD5nl7Z4 Zs7c58uVZMfjtx7ZrmvHVgqW8Gs20DC/t1lgS4hz5oEkMiIo28kmV4wMZILZ4GTUyi6abjtp /wAE6KVanjJRhW+J8yv52935XdrLy8jqrq6hsrOe7uH2QQRtJI2CdqqMk4HPQVytl4zkl1jU 2u7ZYdChZo7a96s7pF5rfKCSysm51YAAqB1LCqmhwSTXWn6Kt3PLa2c15c3YlTe0xW7YQF5M DDb0d+DzsORg1qeIdEsYrKykgi8jydWsrgJHwpYPHCBjsAmBgY6D3yOc5LmjpYVPD4ejUdCr 7zlon21sn5N2v1VmvMn8Cf8AJPPDX/YKtf8A0UtWv7Xvv+hc1T/v5bf/AB6qvgT/AJJ54a/7 BVr/AOilroK3av1PLhNR3in63/Royf7Xvv8AoXNU/wC/lt/8eo/te+/6FzVP+/lt/wDHq1qK XK+/5f5F+2h/z7X/AJN/mZP9r33/AELmqf8Afy2/+PUf2vff9C5qn/fy2/8Aj1a1FHK+/wCX +Qe2h/z7X/k3+Zn63qf9i6PPqTQ+bDbbZJ/m2+XCGHmSdDnYm59oGW24HJFZd34paLS7/UIr a0W2t71rRLm9vVtoCEwrvI5UlAJQ8QAViWCn7rbhsaqR/ZdwrabJqSOmx7NBGTMrcEYkZUIw TkE9M9elY9j4XZfCujWF1dyQanYIkpvbQqzfaSjLLKPMUhy/mS5LqSd5P3sEUYGfD8QYZ9ON zHZxyPLbsbERXIdLy4S4Ns0aOFwE81oAsjYBEwOBtbG5oviG31+e4fT182wjigdLrJXe8ieZ s2EAjEbwtnv5mOCpFV7XwdpdpBp0K+fKthdzXUbTv5ruZHaQq7MCWUOyuMnO6KNiSVzWho+j Weh2b2tkm2N5XlOQAeT8q8AfKihUUdkRV6CgDj9budUvPA3i3WrXVZ7KRPtqRCPloYrUSxhY znCs8kbOX27tr7Acqjr0mpTzWHifRpElkaDUHksZYCx2hhG8ySjOQCBFIpAA3eYCSdiiq8Og NLpfiDw9drIum3z3DQ3EbLvKXO5pVOejrI8mPlxtMfLENi5fWNxf+I9LkaPZY6dvuvM3DMk7 I0SqBz8oR5S2QOTHgnDAAHPeNrzULDX9Pm0pVN+2mXcMAYDG+S5skHXjPzcZ4z14rU8J3UN7 qHia7t33wT6lFJG2CNytY2pBweehpmq2cd18SvDUzswa10/UJkCngktbJz7Yc/jiqvw/gmtp vFkUsUkSrr0ohR1KgRCGEIFH90KABjjAGOKjXn/r+v8AhjqfI8PotU9/Xp8raerOyoooqzlC iiigArl/Fl1rdhPYXOn6vY2dn5uyWCXTJbuac7JOEWNwz/wttUAgIzFtoKnqK5fxTrOk2Op6 Nb38Wqm6F2JrWWysJZgjCOXIJVGDbkEilRlgH3ALjeoBH4L16XxA99df21HqMCJCqJDo81ik ZKl9wMrMXLK8Z4OAApx82T1lcn4Oh0yC4vItOg1kJDb28Ecuo2cluqQKZPLgj3ohYR5YliC3 7wZdu3WUAc/Z/wDJQ9Z/7BVh/wCjbuo/BcK3Pw08PQOZAkmj2yMY5GRgDCo4ZSCp9wQR2rU1 PQtH1vyv7W0qxv8Ayc+X9rt0l2ZxnG4HGcDp6CrgghW4e4WKMTuio8gUbmVSSoJ6kAsxA7bj 60Ac/wCBoIbXw3Jb28UcMEWp6gkccahVRReTAAAcAAcYrB+HUmpC88RRxWlo2nnxFqBlna5Z ZVbzDwsflkMPu8lx1PHHPfQwQ2yFIIo4kLs5VFCgszFmPHcsSSe5JNcr8P7G4s7TX3uI9i3O v380R3A7kMpGeOnKnr6VMt0b0tITfl+q/wAmddRRRVGAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHNweBtFtbe K3t5dZhgiQJHHHrd6qooGAABLgADjFSf8Ibpf/P1rn/g9vf/AI9XQUUAc/8A8Ibpf/P1rn/g 9vf/AI9R/wAIbpf/AD9a5/4Pb3/49XQUUAc//wAIbpf/AD9a5/4Pb3/49R/whul/8/Wuf+D2 9/8Aj1dBRQBz/wDwhul/8/Wuf+D29/8Aj1H/AAhul/8AP1rn/g9vf/j1dBRQBz//AAhul/8A P1rn/g9vf/j1H/CG6X/z9a5/4Pb3/wCPV0FFAHP/APCG6X/z9a5/4Pb3/wCPUf8ACG6X/wA/ Wuf+D29/+PV0FFAHNnw7/Y9wmo6JayX2oBGg/wCJprV0ypExDNtL+bglkToozjrxgx+EHvJN Q8UtfwQQXR1VN8cExlRf9DtsYYqpPGP4R6c9a6iuf8Pf8hzxZ/2FY/8A0itaAOgooooAKKKK ACiiigAooooAKKKKAOX8WXWt2E9hc6fq9jZ2fm7JYJdMlu5pzsk4RY3DP/C21QCAjMW2gqY/ BevS+IHvrr+2o9RgRIVRIdHmsUjJUvuBlZi5ZXjPBwAFOPmyZPFOs6TY6no1vfxaqboXYmtZ bKwlmCMI5cglUYNuQSKVGWAfcAuN6x+DodMguLyLToNZCQ29vBHLqNnJbqkCmTy4I96IWEeW JYgt+8GXbsAdZRRRQAVz/g3/AJAdz/2FdS/9LZq6CuV8PahFpnhqaeZXZH1y8gAQAndLqMka nk9MuM+2etOMXJqK3YHVUVm6bqEt5f6zBIqBLK8WCMqDkqYIpMnnrmRvTgCtKnODg7Py/FXA 5X4h+LE8H+E57798LmfdbWrxIreXM0blGYMcbQV56/Q1v6XqEWraTZ6lArrDdwJPGsgAYK6h gDgkZwfWuE+J9pbeINHNtJMZLNNIvdVhaFhgzQ+T5bA85G2RxjoQ+eoBHVeDOPBGhxniSGxh hkU9UkRAroR2ZWBUg8ggg9K9CrRpLAU6q+Jyd/Tp+RGvO0zcooorzSwqG6uobKznu7h9kEEb SSNgnaqjJOBz0FRSalbobDY3mpfSbIZIyGU/u2kBznoVQ8jPUVzsmqLq3haZTdRXAuL/AOzv sZTutpL0wjp2aMMoYc8Eg5GaiU0tjppYaUnFy0TaX33/AMmWdYt73UtdCWFwsF1plstzb+bl onklZl+dQRwEikTPJxMSMFeaNr4V1SbTILe41OW2hmtFS8gJ80u8ke2cegOVV1cE/O0pO4Ni tzSNG/sy8vZd+Y32xW6A8JCpZwuMDGGlkUAcBFQdQcy6xrmnaFZvcX91FFiN3SNpFV5doyQg JG49Bj3FZ8kX70zepj3QXJSa5VazaV9td/Nv0b0GWWix2Ov6pqqTMzaisO+NhwhjUrkH0II4 9QeecDUrOTW7GXWxpMUvmXRt3uDs5VVV/LIJ9d2RjttOccZqah4mt7HVtHtcRPa6lHPL9r84 BI0jjD7umCCD1yMda0UoRWn9X/4c8+riOd803fZfdZL9Ear3kCX8Ni0mLmaJ5kTB5RCgY56c GRPz9jU9eeaPrJ1v4q2F0kh+zzeFvPWFZd6Ru1wA4HbOVCk4B+UZ6VoT67qWqibUNCufMtYb C2vktAil5iZJfMi3ANglYyuACdwGCMHdviIxoqD35o3/AK+Ri60bXOrnv7W2u7W1mnRLi6Zl gjP3nKqWbA9AB16dPUVYrgbjw94ltTbapc6jFfz6R+8tljjbzHjWORZI+QSzygQ5YklSW29B uvS+NZLTULeO6sUFnes5tLhZOCiShGY8H5RHmffwu0gc43HlVa3xq39f5kqva/Orf1/n+hX8 Uf8AJVPAX/cQ/wDRC11tpqEV5c38EauHspxBIWAwWMaSZHPTEi+nINclqf8AyVTTv+3X/wBE anWx4cniuNV8UPDKkiDVQhZGBAZbW3Vhx3BBBHYgivZxME6UE/swX4zb/wDbrGqer9ToKKKK 8wsKKKKACiiigAqs6Wmp24BZJ4kmByj5AkikzjIPVXTkeoIPerNcP8MNY+1+D9Mhvp92rXX2 q7lQptY5m3liAMLkTxMBxkOCBit4YdzoyqLo0reqk7/KwKTjJWOi8OaZ/ZOlG18nyts820Ft xMYkYRZbJJxGIwMnIAA7VLrVrNd2EUcCb3W7tpCMgfKk6Ox59FUn8K0aK5uVcvKbOvN1vbPe 9woooqjEKKKKACs7WrWa7sIo4E3ut3bSEZA+VJ0djz6KpP4Vo0UmrqxdObhNTXTUwNEs408S +Jr4M3mzXMMLAn5QEt4yMe/7w/pW/WH4W1j+2LPUN8/nT2ep3dpL8m3ZsmbYvQA4jMfI/E5z W5VzoujJ05bpsdWs6zUn2S+5Jfocbf8AhfRdB0q2+z/2yIFntrSOGPXb2NY1klSIYAlwAobO PbHFW73wzomnWFxfXV9rkdtbRNNK/wDbl8dqKCScCXJ4B6VV+IV5PbQ+GoYZNsd14hsoZhgH cgcvjnp8yKePSo/i3PLb/C/WnhleNysSFkYglWlRWHHYgkEdwSK7aGEVR0Yv/l5K3yul/mYO Vr+Rq/8ACG6X/wA/Wuf+D29/+PVA/hnREv4bFr7XBczRPMif25fcohQMc+bjgyJ+fsa6euV1 nUfsXxH8K2/lb/t1tf2+7djZgQyZ6c/6vGOOue2K5qFL2s3Hyk/uTf6FN2LX/CG6X/z9a5/4 Pb3/AOPUf8Ibpf8Az9a5/wCD29/+PV0FFYjOD8XaNpGi+F9VuU1fVra8jsZprcyeILvO9Vwp Aabn52QdDyyjuK3P+EN0v/n61z/we3v/AMermJLNNY+OizyRzLbafphh5K7JpkeOXBXklVFz E4yB86gj7tej12YmlClTpxWra5n89l8rfiTF3bOf/wCEN0v/AJ+tc/8AB7e//HqP+EN0v/n6 1z/we3v/AMeroKK4yjn/APhDdL/5+tc/8Ht7/wDHqP8AhDdL/wCfrXP/AAe3v/x6ugooAx9P 8Mabpmoi/gN9JdLE0Kvd6hcXO1GKlgBK7AZKL09BUegytJrPidSBiPU0UY9Pslsf6mtyuf8A D3/Ic8Wf9hWP/wBIrWiw1JpNLqdBRRRQIKKKKACuf8WC4e1sIoGneOS7xPa2t0Le4uk8qQhI nLphgwVzh1ykb8kZU9BWfq2i2WtxQR3qz/6PL50LwXMkDo+1kyHjZW+67Dr3oAy/C9rfW9xe tJbalZ6e6RCC21O9+1TiUF/Mff5kmEKmIBd/BRjtGct0lZ+maNa6R5v2aW+k83G77Xfz3OMZ xjzXbb17YzxnoK0KACiiigArJ8Of8gyb/r/vP/SmWtasnw5/yDJv+v8AvP8A0plqX8S+f6G8 f4EvVflI1qKKKowCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wAP f8hzxZ/2FY//AEita6Cuf8Pf8hzxZ/2FY/8A0itaAOgooooAKKKKACiiigAooooAKKKKAOf8 WC4e1sIoGneOS7xPa2t0Le4uk8qQhInLphgwVzh1ykb8kZUx+F7W+t7i9aS21Kz090iEFtqd 79qnEoL+Y+/zJMIVMQC7+CjHaM5bU1bRbLW4oI71Z/8AR5fOheC5kgdH2smQ8bK33XYde9Gm aNa6R5v2aW+k83G77Xfz3OMZxjzXbb17YzxnoKANCo554bW3luLiWOGCJC8kkjBVRQMkkngA DnNSVz/jL/kB23/YV03/ANLYaANTTdW03WbdrjS9QtL6BXKNJazLKobAOCVJGcEHHuK80127 aPwFNax3PlSSaprM+1JNkhEEl5MjqQdw2ypCcjvgHg4Pb2f/ACUPWf8AsFWH/o27ryW6tH/4 T23hu75bqyutQvLKO0juwWgWa7cTb4wg2B0kKj5ix+9uAAUb4evTw9RVaj26Wvf08+urN6GH dd8q22+dnb8js9Lup9C1ZvElwITouqyzxXt9K4XyQt1KLWXcT86OkqIDjCqqHcFFdb4h8U6b 4Ztlmv2dgWClYgGZcq7KSM5AYxsoPr6AEjivFxu9b8PeJ9CsreH7DD9gt9Nt4lEYYi4MbL22 /vIyg6ABQRxyebF1rHxAkn+y3e2/a4N1EkboslpFbhmiXIKk73uNgc4I2k884WMxlOtyzirS ba/7dVkm7dr27WS8zTB4P2lCeIk7RjfTq2k3ZeqTa1/Q6H4WazBfJb+fcPLLHYw2kHmhmMbt JcyMik9MxxxZxwQij+EAaWleLILDQtUWH/X2WuXqTrIhb9ytyZZ5QFOdqQydTj5tq4JZQ3mW k6jdQeJL3xFDELWGKymu7RAQ5jVne2jVQeNqOVUAgAKhwMYB7j+1LK+1W8vLvTW06W5gW31X UJbbZFLbQ5WYRlsSbnkZYscsFCcBhgEMXSpOdKvF6cvKttEtL72unr5nTiMPHE2xlKfuyu5f 3W7Skkuqimv+ARv43t9U8TeD9cM8lraINWF2m1lKxJEsih1GdxCCNiBkbhxyONLS/E8uueD4 9Esry8h1qK00yKa7cB2c3EaMzod25iI/MZjwRgnPGRzGv6NLqaa/4kNsz6+9rbwJAqnEbyeb YzxgKx3jKsynrnb1GQYvC0DaPrPh/VdQVle3tpIJYlgHnzT4uIoYznaeIrUIF/v44y7NXdUq 4SrgIwi71LNdrWcpN97cra82vK5jTpctR4tK1ONpa66tJpffZPTRG5rH2qTxno+lWM62raPq LILqVtsQWcCWOIKGBxtV4wowCFUZG7A5jR9GbU9W0vw7e3TPHKt0kUkThrdo0EvlSJtILES+ efm6gjsa6K/ju/FXi5NU0y+iufKS+a3toblWQNa7FgPQLv8ANmLjcSNrjnBweX8L6+NQ1LVx pe+0QaNeWmlojFZFMt0otwTuOG3yhd2cDrngmvJpZTVrzjLaLf8AnZW81B6nZDNIYihKLhpC N13cnzRck/WpzJdHtsevtr1zLZXtgZLeLXI7lLUx27eZ5Xm4KSKGGXCxsWOQATFJ0AyMq+8K 2sT6TaajP9rkvtba5mbywFYiOeURBTuxHkN8pJ+85z81c7beK/D8+u6/4ztbWFTaJpaPcysZ SsczASttUkK6oxQhckGM9ckHpPFusfaL3w0dLnxJDrAeRynBjSYWcyDI+9uucdMYDEHgZ7Y5 VVlUUasWr3WvdRUtfW687HzfKpq89bflf9TTj8PxP4cttGW8S11W1tYmNxakb45PKMIkxwSC quoJwcDggqCMHTvB8WpaTqxguHQXd2LQA4DQRW08kBCNgnmAFOc55ySGOKhh13xyl7qujaj/ AGQLmxt7WWFZGDh/IF1FIsyjIw9wI2XHKbz1IAZomvXEeizaXPNcx6vFqFhcS26QusoEscV3 cDCjJLFbxinXqgHKqdpZPBw0d5K11r7qejb6bvft1B06btdGVqFtHaal4tvLa6hZhHqkJuFR dkn7qCc25x1ceZcc53fuucqm0ejeC7a0g8K2MttZw2klxEklzBFuxHOEVJFIJJBUptIJzlTn nNcBfaZcWPhTwnJqFukF/rPiiK71GFd4VjceZvjZX5A8tgjKeOCDnJJ6P/hNND8E6Xdwa3cv FdDUL5lgjiZ2cmUzqoIG3JjniPJA+fBIIOOnEZfTlCKw0Oad7aatqN02l26tr1Y4QhGXNY67 WtR/sfQdR1PyvO+x20lx5e7bv2KWxnBxnHXFVJP7O1vw0LCwuYYYNS09vsoVNuIWQAMsZwcA OnHGMgcZqr4tvILnwV4rhhk3SWtjcQzDBG1zb78c9fldTx61wfhx9Q0/xpp989o66bZtc+Gz cvG237NDJElu+e8jzPsJHHDfKu1iMKGAjXw8pPSSv8/dul8+nf7jWT1syHWfEt7Ya7ZeLNRS FIUt7GVLW2fflZXuQpYuF5ED3AwGxv2HkEgZnw78Rah4ct723urlrieS8ubqe2kYeaWitnMv mFhuXdIEwef9W/vn0fWdCg8S+INb0q6bFtNY6Y0owfnRLm4crwQRuClcg8Zz2pk0OmaRr2vP Jbu1tFZWd2IY2JeSc3V1KAmTyzSkbVzySF6HFE1OWFdGM25+69ltaNlf1btp63Od0al7qX9f 0jb8KCSPw3bW0ro7WbSWYdV2hhDI0QbGTgkICeeprTF1bm3juBcRGCXb5cm8bX3EBcHockgD 1yK8zsdRj8WeKLxNGnRtO1b7QtzMY2R2tBBbxiQK20nEgmjViCAS/Bw1bt6l1KLjwvp8iQ3E dybi1LjBihRFljcZBGxbgpGAAflUgD5SR5NSNTDydKpGzWnztf8A4fzKjVcVa22nq+huaRrU moahd280KRKjP5Bzy4SV4nHPUgorEjoJVBHGW2a8+09r9I9NtbKcz6uz6pFJdMiqoj+1hJZQ vTermNwvQqHHUrTZrS+8E6Pb+I9Qkl1DUIsxX5jnyjoVZEI3AYJZbUMwGf3YPPzbpbqU0vaR a6/16O/3Aq0ox95ebZ3dlex38DTRK4VZpYSGHOY3ZD+GVOParFeZ6Lr2oWum68sZcy3rLdaW gwzJNcCNinzHbtR7mHkgZ3McHkDqj448PpZvdvfbbddpVxGzF0IHzhVBYJuOwkgYcFTzShWi 1q7FU8RBr3nY1Dq1qNdXRsubw2xuiAvyrGGC8n1JJ4Hoc44z5Zb+X4I0bw74wWa5uBf2lpby Wm75V/0Jshc8AO0NsScbh5XUg7aveJ/CWp2Xw/mvbGWX+1fsIW+WaVWPlmOQzqGI+bc8rOcs eQCPuqKoeN9e1cWkGq6cBqEs14x05I4fPjhMUk1uJFUcbj50DKfmy/sFWu/BV6ij7COkqr5f S+ib8rtfcZTrStaSs+nlc7FfGtrqut6EmiXsNxplxJMt3MqHCkRIUUk9MtNGD33ELwciupi1 CyntEu4bu3ktpGCpMkgKMS20AMDgndx9eK8ak8M6loOk6XdQQXcdhLpTXWohAYZLEpJDcyvk 8NM21o1GFOFTORGSOx8S+H4oNOvdQ0u8Qf2PNHfQaeCFgt5Y1DupVf7yFWC8EEkggOayxVGd H95Be472u02raO9tN9u90UqlWKcmv6t/wPxO8ooorM6wooooAKjkniieJJJUR5W2RqzAF2wW wPU4Vjj0BPapKw9e/wCQx4Y/7Cb/APpJc1pShzyt5N/cmxN2MP4acx+KpBzHN4huZo2HR43W NkcHurKQwI4III611tpqEV5c38EauHspxBIWAwWMaSZHPTEi+nINcP4I1Oy0iHTbW4uEt47/ AErSnjD9JLp4pU69iUtowBwMqMctzAnjeLw78QvE9jqcaR6W95Z7bvcAUnlhiQK2TjbsjZy3 AURt1LKK9bE4OpXxNXljdpJrz1ivnuQpJJGl8UeNBtJBxJDLczRsOqSJY3TI4PZlYBgRyCAR 0rzvxLqGoa74a1RwrtNqcEU7WtuGKvMyaRtCpkkkF2A6n5iO9ega9I3ieC1k02F2fVPC+otb RSEKxMottqnnAPzAdce9cr4d0Oys/iFoslsrxpp89xZW8QbKiNpdU65ySR5SgHPrnNejl04U KCqT+KF3b0bdvK7SX/DET1du5PrvxNunuby50VXmRF1KysjE6hC0MdtIZ2ySrBR57LgHIwMD cateK/E9omraV4lK5Ogy38ckMThz8t1b2z5HGGMUhcAkYJXJI62/AXgNdCGsade2ji11PSrE XMRztEhSVJ4w4Y5OcsdpGN4xxg0Wfg7RpPh/PeXAs/7a1vTppJ769YKHnmQTMTxgKrIGGF+U ISOdxOc6+W0a0YR2jpf+ZSWt+llez311W+h71tf6selVm6/qEumaHd3VsqPdhRHapICVknch IlOCOGdlGcgDPJA5rhvD/ivxG9tdeH9XVz4ihtUQzLChEd1LJJsVivyECIwyYUHCrITkqRV6 wvo5tV8O6FGknmaVdSHewwpgRLy2T5u8n7sEqME5JAwrbfC9h7LEulKz5dW1s1urPs9NfMr2 qb5UbXh/wVpvhuaKWynvZDFE0Q+0TeZwyQIecZ6WyYHQZIAAwB0dMiljnhSaGRJIpFDI6HKs DyCCOopl1dQ2VnPd3D7III2kkbBO1VGScDnoKxq1p1Xz1HdmiskTUVk+HtSe90ay+2tt1Dy2 SeNyoZpIm8uUgKcY3jqOOR0zWmJYzM0IkQyqoZkB+YA5AJHodp/I+lZKSauKMlJJofRWda6v Dcatd6cw2TwSFUHJ8xVjidm6YGDMox+P00aE09hpp7BXP+Hv+Q54s/7Csf8A6RWtampSalFb q2l2lpcz7wGS6uWgULg8hljck5xxjueeOcbwkL433iWTUbeC3uJNUVjHBOZkA+yW4GGKqT07 qPx6ljudLRRRQAUUUUAFFFFABRRRQAVj+J764sNFElrJ5U013a2okCgmMTTxxMyg5G4ByRkE ZAyCODsVn61pn9r6YbUTeTIssVxFIV3BZIpFkTcuRldyLkAgkZAIPIAM/Rpb228Q6lo11qM+ oRwWltdJPcpGsgMrzKV/doi7R5KkfLnLNkkYAxNI8bWFnf3OifZ5ZrtL+8LLBcWzvt86RyRD 5vnNhcnAQkgcA8Z6PSdJvLXUbvUtSvYLq+uYorcm2tjBGscZkZflZ3O7Mr5O7GNvAwSXeHP+ QXN/1/3n/pTLRGSjNXV9H+hXtLRdPvr91/8AMh/4SG5++PDesmBv9XLshG/P3fk8zeu44HzK u3OX2gMQf8JPGeE0nWXZfnkX7C67Iv8AnplsBuMHYpaTnGzcGA3KK39pSe8Pxf8Awf0IszD/ ALY1a5/d2Ph6aOZf9Z/aNwkEa/7IaPzCzYKnKgrgkFgylaP7b1VPkk8K6m8i8M0E9q0ZPcqW lVivoSqnHUDpW5RR7WH/AD7X/k3+YWfc5iTxRqq6lFAPB+rLbhd08zyQkqTkRqgR2DlmGDkq EBDMQpzU83iW7tkEtz4W1yOHcqu6LBMUBIG7ZFKzkDOTtUnGeK6Ciqdak7fu197/AM/+Btpv cs+5h/8ACS+Z8ltoms3E/wB4xfZfJ+Ts2+UonIKnbu3jOCoIYLHD4nnuEMkXhrXDGzMkLPDH GZiCR915AyAgFgZAgIAHVlB6Cil7Sktofi/+B/X3os+5h/8ACS+X8lzoms28/wB4RfZfO+Tu 2+IunADHbu3nGApJUMf8JTbSf8e2m6zPt+aT/iWzRbE7t+9Vd2P7qbnPZTzW5RS56W/J+On5 X/ELMw/+EhuZfntPDes3MB+7Lshg3evyTSI45yPmUZxkZBBJ/wAJHJFxd6BrNsx5RfISbco+ 8cwu4G0c4Ygt0QM3FblFHtKe3Jp6u/8Al66elgs+5z8eq+IJHll/4R10hZvKhhluYllU4B82 QqzKIySVIXc67MhX34ST+3tR/wChS1n/AL+2f/x+tyim60H/AMu1/wCTf5hbzOfm8UEIFg0X VmnlZY4BJZyKjsxABdgD5agMrszAYBIwXR0WT+371Pmm8L6zFEOXkzbSbV7nakzM2PRVJPYE 8VuUUe1pdIfi/wANv1Cz7nOf8JHfXd/nS9Hvbmxgizcie3e1ld2OEEPnBFfbtYuCVwGUgk5U vj8Q6kzyo/hTVg6t8qLJbk7CBhixlCZLbxtVmICgnbuXPQUUOtT2VNfNu/zs1+QWfcw/7fvU +abwvrMUQ5eTNtJtXudqTMzY9FUk9gTxR/wkuz55tE1mKA/Mkv2XzNyf3tiFnXkqNrKH+bO3 CuV3KKXtKb3h9zf63/roFn3Ofh8WQSIQ+ka5DcKzK8D6bISoUkM29QY2GAWG1yWGAoLEKZP+ Eqsh8xstZER4WT+ybk7m7jaE3DGV5KgHPBJDBdyim50X9h/f/wAANTD/AOEptvuHTdZE6/6y L+zZjsx9759uxtoyflZt2MJuJUGOHxZBIhV9I1yK7DMn2Z9NkJLAkAeaAYsHHDb9uCCSBnHQ UUc9H+R/f/wNvx8wszD/AOEptjwmm6yzv/qF/s2ZfN7DkqBH82R+8KdNxwpDE/tTXoP+Prw5 53r/AGffJJ16Y80RdMNu9Mpjdltm5RS9pTW0Pvb/AEsFn3MP+29VTiTwrqbHqDDPasuDyAd0 qncBgEYwDkAsMMT+371eZfC+sxr0DZtnyx6DCTE8nAzjAzliqgkblFHtYf8APtf+Tf5hZ9zn 4fE88yFx4a1xQrNG++GNSsgJAXHmZYE7cOuY8MCWADFQeLIEneO50jXLdNqvFKdNklWZSTgj yg5Q8ZKuFYZGRXQUU/aUXvD7m/1v/X3hZ9zD/wCEs07/AJ9tZ/8ABLef/GqP+Eqsn+WGy1mW U8JH/ZNzHubsNzoqrn1ZgB3IHNblFLmofyv71/8AIhqYf/CWad/z7az/AOCW8/8AjVH/AAlN sOH03WVdP9ev9mzN5XY8hSJPmwP3ZfruGVBYblFHNR/lf3/8ANTD/tvVU4k8K6mx6gwz2rLg 8gHdKp3AYBGMA5ALDDE/tvVV5bwrqZDcoI57Uso6YfMoAbIJwpYYKnOSVXcoo9tD/n2v/Jv8 wt5mH/bepzcW3hy9DR8zrdTRRYHdEIZg7kFSOQnJBkVgQD+29VfiPwrqanqTNPaquByQNsrH cRkAYwTgEqMsNyij2sP+fa/8m/zCz7mH/b2o/wDQpaz/AN/bP/4/R/beqn5x4V1MRjgqZ7Xz CT0KjzdpXg5JYEErgNkldyij20P+fa/8m/zC3mc/N4lu7ZBLc+Ftcjh3KruiwTFASBu2RSs5 Azk7VJxnipP+Ejkm+aw0DWbyIcGTyEtsN6bbh42PbkKRz1yCBuUU/a09+RX9Xb87/j8gs+5z 8+u60IJJLfwjqDuqkLFLdWyM74+XpIwC8EMScjK4VsnbJ9q8Vf8AQG0b/wAG0v8A8jVuUUvb Q/59r/yb/wCSCz7mHv8AFTfP5OjJnnyvOlbH+zv2jP3cbtox5ucHysSn/FVT/L/xJrLPzb/3 tzs7bNv7vdn72/IxnbsON53KKPb9or7v8/1Cxh/ZfFX/AEGdG/8ABTL/APJNH2DxJN+7uNes ooj957LTTHKP91pJZFHvlDxnGDgjcoo+sT7L/wABj/kFjl9Slv8ARLdZNS8Y6dZ2cjiI3F9a pHJnBwEcuqB9q7uUYbtxxtwiweBLqO9l8S3MWpwamkmqqftduVMbn7JbZC7SRtByoGSQBySc k9fXP+Hv+Q54s/7Csf8A6RWtROrKas7fJJfkgsdBRRRWYwooooAKKKKACiiigAooooAKKKKA Co54Ibq3lt7iKOaCVCkkcihldSMEEHggjjFSUUAU9N0nTdGt2t9L0+0sYGcu0drCsSlsAZIU AZwAM+wrzO18JwroHinxAl3LFdSXWqyAxZVlMd2Xjw2eMNATkAE7+owDXrFc/wCDf+QHc/8A YV1L/wBLZqicFNWZrRqunK620uu9mnb70jjoPhvqWpQ2jareukdvNCDYsxMTW42t5YYMWBQy ToCSSQBgqOas/Crwrc6XbXGrakkKXsm60jijA3QokhDq7Lw5MgPPOAowcHA9IqG2tYbSJo4E 2I0jyEZJ+Z2LsefVmJ/Gpp0oU72W/wDX9f8ADmtfFOvFqfVxdkrJcqkl/wClf1oeUXnhqGz1 aaG2i36VB4g0+zS0MZdYYvLMrFmJJYNJdt97pkDOCAOw/wCEKsdR8R65qOsWcVxHdSRLbq45 VVgCMwIORkswxxgoG64I6K00+Kzub+eNnL3s4nkDEYDCNI8DjpiNfXkmrdb4lRrVfaPXSP4R t/X3nD7PTleyd0umqs/vR574o8KNdeN9CngMcdhdzFLuPywVcqTOUYZyyvsJ2/dDBmwSxzJ/ wgAsLK7v4FS71i3gT7Bvxy0DloQxOF+ZEhjbAXhWw3zE131FZRpxU+dq+t9fl+Gho3Jrlb0t a3lfms++uuvl2R5/4X0n+y/HV3YQJMbbTrYxh5hgmN4LJImzgBsm1mBK8Aoc4yM0rvwudO8d aKkFgkGmT3P2UTKwMgjggtpoFySWI32jjnPG7oWBr0KPT4o9WuNSDP508EUDKSNoWNpGBHGc 5lbPPYfjbr0546XtVUXSNvnytX+TbaCPuxlHv/mn+hxz+BoD40m1OK2sk0662T3cRBYzzCO4 iYFCNu1luASc8lDlTuLCpe/DbS9P8O3sOhQ3KXaWdwtpGlyU/fNIs0bZyMMrxxgMTnaqhiwA x3lFSsxxKcXzPS3zttcnkRm6Dodl4c0eLStOV1tIWkaNXbcVDuz7c9SAWIGecAZJPNQan4ei 1LxFoetGd45tJacqgAKyLLHsIPcEHaQfYjHORs0VzKvU53Uv7zvd+qaf33Y7K1iOaCK4QJNE kiBlcK6ggMpDKee4IBB7EA1yuseANI8Q61qF1qdjbSQ3UFqgdEAm3xSOz/NjIDKY0JByVUjj ANddRTo4irRd6crP/gp/ogaT3PILGO98O2Oo6BrkyGXXbzTZZRjMkkl42y7iYoAqjEM2CvQH hs4x3/i2CK18FaxLbxJE9tBLfRbFACzxkzK+OhPmKGOeCc5zk1h+PdDa813w7qoV1hsry3aR kYAPIbu3SJXHVgFkuCPQk8jcQe4ngiuYJIJ4klhlUpJHIoZXUjBBB4II7V6WKxMZ+yxHVu7S 7qy/JX+ZEVujnND1K11bxdql5Zu7Qtp9qn7yJo2DJPdowKsAwIZSMEdqw9d1CJvivY+H5FfO pQWc5dQPlW2kupwM54JdYx0OV3dDg10+i+H/AOytZ1m+8zMd7KvkRBsiOMbpGzkZ3GaadupG GUDGMVa1DR4r/VNJvzsWbTp3lVjGCzK8TxlA3VQSysfXYOO4yWIo068mtYuNl3T5Vb5ppXHZ tHA3/hl/DHj/AETUrGFP7HhXYIxEBsllumj2BguEUC+ZlUfeEWCOAwjg+JUth4xk0vUrEm5v NTmhtniiT95aRmSGKNW3A7zcIw+f5RvY5UEY7jxj8nhHUrof6yxi+3xg9DJARMgP+yWjAOMH GcEdao6Dp39qfDjw1b+b5Wy20643bd2fKMUmOo67MZ7Zzz0rphiaVWiquJhzO/Lfr3v6q/z2 J5bO0Tzw6Pqngjx5aa8N5m8RM81/EIwV0uE3EEk29/mVlCsyF/lA+9kVu+MPFc9hqPiENE7/ AGI2DWdt52ftRt5EuLgqMHYRHNHk4Pyru5Cnb32t6PFrOk31mdkU1zZzWi3BjDNEsi4OOhxk KSMjO0elcVofg3Wbe88KapqkvnanYXN4NRk3h/OV4TFHLuJ5+SGBcYyd2WwQ2doYzD4mKq4p JuKaa7296Py93ke+/d3E4NaIzPEN+NI+G3hvULW3gfVrdYZpbZXVJXaGICQMv3nEckcXmKOQ IuSu3ctHXtQ0zwtoqIXiguora/0OWF4n3yQ4laB+ON7FImYgci5DHaGQ1veFbeXUPH1/BqFs iS+G7y+kgIY5K3riSJuCQTs87IOMBk4JzjrtU0ZNU8RWT3dlDdactjcwzJOquhdpLdkBU9f9 Wx6cbfpXNOlgqNRUqsLp3k7O2lnypeVrNdbvyI9immzjde8Rab410TVNFabFjc32mRWlzH8n 2iGeWPOwtnLqyTgjbxswRlWx6VNBFcIEmiSRAyuFdQQGUhlPPcEAg9iAa8r1n4exeHrTwjJY T3M407WraOZmkCq0DXLtGXTOGZGm2hgAfnY4x09YrPMHQjCCw0rxvK3e/uq/zsn8zaF7u5Be 2cGo2FxY3UfmW1zE0MqZI3IwIIyORwT0rzTXNGn8H2ut3K3s09nqqXy/ZmcAGRrMStO+FHzl 7aQbR8oEg27cFT6lXMePNPi1Hw6kMjOhe8t4BJGRuRZ5BbyEZBGTHNIOQcZz1ANYYGrafsZ/ BPRr1/rdahOKaOE0D4nWOgaKn9orPe310YmcxzK7fLHHC29mIIkbymk2nqHX5ucje+EzqmlX ltHawwxOyXAaK584O3zW7nOMctbM42kjDgDpzr6N8OtC0rw3d6JJB9qgu7gzXEjEo8uJN8YY qc/KAo4wDgnA3Go/tCeHfFUWn2do4sUsbG3JwWEcfmzRIoYn726SM85yqueoGeOcqcE3duTe +ytu9F3dn5W+7lhCdNxlUf8AT3OxqOeeK2gknnlSKGJS8kkjBVRQMkkngADvUN5qNnp6k3M6 I2wusf3ncAqDtQfMxyyDABJLKOpFeb+IPEl5rsPinTtH1fTrm2SzujsysimD7PbchkOQcyT4 JyCQR2+W6dO65paRVrvyul+p01Kigrs62/8AFEieEptb061S6ZbnyYokfeJwLjyvlK92HIxn kjr3r+MtcsNJg0DW7idGsobySfejqfMH2O4KhSSAS3AHPJI9a4/Ttak0+G+skt3j1DQ7Easw nT5SyadFGIyMg9XJPTG3HfI5fxnJe3HhXUfDNxb6gJdL1QT28t2qon2JMW0ZXOMglh91cEkn nmtMvh+9hPEaQb5W+11v/wCTfI5frDS5penzX+f6HXWukW914H0rxcs0pbSdHgDWkigpJJZS CQMp/hY7JkDcnbL0HIbR0Hbpq+MtG8T38N3Y26W4uLtleNple2SJt3zMdxEa9DksTjqAK3hi 41PTvD4tvslpqGg/Z4rb7NKwEhnniWXByu0o7zrFz0BUngMTo6vpf2K7tre6eK6k1KRHvnMW Fn231vsBVi3CrM6AZPy4B4AxnPNcRUoJN67vT0tbrv8A1sJVJOCkt1v+X5/kVPCOhjTviJeu GtoUtIbuxitYQceW08d0jL8oAAW6VCvYqMZBO3Y8cRLoejJ4g02C0iutOuVuCGg4m3+ZGQxU g9bl3z6k/wB4msb/AIRm98K3EBgv7m6bVrySzbHMsXnOGe4EgAJcRQpnIwGBbOBgz6WbvWbW y0XVtLl8t7uSW/Cq4iJlilnKMOqbDNBjcQd/3clM1OJzGtXm29G1b71/wW/+CV7SVnC1m9vJ kviJNX8T6zNo9nc/ZbNN0kE8c5QtLAvJyEOR5s0QPJwYG6NXK6peyPrOmaHbKkWmm91DTwsY 2rHNLLJH93oQkc0bDAH3iM9cewWFlHp9jDaRM7LGuC7nLOepZj3Zjkk9ySawLjQdL/4SrTY/ sUW3y7u+zjnz/Ot28zPXOf046cVw1KMnrfV/5rQVXDya5r6u35rQxPE+lT+FtPvPE9k0mp6s l9FOiSxjdOzQm1jQiPAODMW+VQTjb/tCp4a02x8Za14n1yOVreNbv7BZ3NjcfPEIzud42xtA k3BsjJPmOM8kn02uY8D+El8HabqFlG6GG41Ca6hjTJEMbYVEy3LEKq5PqSOcZPrXoTwjhP44 vTzT3T9HqvP0RvKjFtLocpqb6x4W1e58O+GDcSGe2e/R3HmtCAEVU5ViQFgZFz/z1UdVBNm9 8X6pqZ1Dw9e6VFY3E1xDZSxibzmihuIyvmZXg7XKHPQ7wvB5r0msmz0bydevdUkfLyyN5Sqe NjRwKd3HXdBxg9D+XlujJfDLR9PIylh5p+7LRvbpYhvtKWxvI9X0ux8y8WQiaKMqGmicjeoL EKuGxJ2yyt0LsTzC2+o+MHnv7K++w3kemQxHETKIb0NMsi5PzIVV3XHJAlDdQpPodYfh3Tf7 MuteRVlEc2pvcI0g+9vijZiOOQGLD8MdRVzp3kl0NKlJOSXRnD6fa67p2r3Ovatp8vnvq5+z 2zymQoZIJVVRIAVCMz28e4cZQA42iuh0/wAZ3EYitdR0q/bUGt5r2a3ihLSQxmQeSoGBvyrB cjkFfmA+Yr2EkUcyhZY0dQysAwyMggg/UEAj3FPpRouHwyFChKHwyPPDrer+JNcdtClltIPl WBps/MqYDy+U20YK3iSLknPkrkcgDW8ER30N34qTUZA9x/bTPhZWkVFa3gdUDMAcAMB0HSut rn/D3/Ic8Wf9hWP/ANIrWrhTcXdu5dOk4vmbuzoKKKK0NgooooAKKKKACiiigAooooAK5H4f 31xeWniBLiTettr9/DENoG1BKTjjryx6+tddXB+C/t+m2utvHpkt3Fda7qFwHiljXC/aHj2g Mwy+5M4OF2nO7PymJaNMiejTO8orJ/tbUF5bw7fkNyoSa3JA6YbMgAOc9CwwRzkkA/tDWJPk i0PZJ97dc3aLHt7DKB238jI27chsMQAWfOv6TD2i8/uZrUVk/bNdj/1uj2r7vlX7PfFtrdi+ +NcJ6ldzeimj+0tVT5ZNBld05doLmNkYdhGWKknkZ3Kg4bBOAGOdf0mHtF5/czWorJ/tbUE+ V/Dt+zjhmhmt2Qn1UtIpI9MqD6gdKP7Yvv8AoW9U/wC/lt/8eo51/SYe0Xn9zNaisn7drTfO uixCNeWR70CUjrhVClScYHLKNwIztAcn2jxF/wBAvS//AAZSf/GKOdf0mHtF5/czWorJ+1a+ flGk2G8cljqDbCOwB8rOeDnKgcrgnJCn2jxF/wBAvS//AAZSf/GKOdf0mHtF5/czWorJ+0eI v+gXpf8A4MpP/jFH2jxF/wBAvS//AAZSf/GKOdf0mHtF5/czWorJ+0eIv+gXpf8A4MpP/jFH 2jxF/wBAvS//AAZSf/GKOdf0mHtF5/czWorJ87xE/wAv2DS4s8eZ9tkk2e+3yl3Y9Nwz0yOt H2jxF/0C9L/8GUn/AMYo51/SDnXn9zNaisn7R4i/6Bel/wDgyk/+MUfatfHynSbDeeQw1Btg HcE+VnPIxhSOGyRgBjnX9Jh7Ref3M1qKyftWvj5TpNhvPIYag2wDuCfKznkYwpHDZIwAx/aG sf6n+w/9I/56/a0+zev38eZ04/1fXjp81HOv6TD2i8/uZrUVk/btah+a40WKVDwFsb0SPn1I kWMY/wCBE9OOpB/aWqr+9bQZTA3CxpcxmcH1ZSQgHXpIx5HHJwc6/pMPaLz+5mtRWT/a2oLw /h2/Y9cxTW5GDyB80gOcdeMA5wWGCT+2L7/oW9U/7+W3/wAeo51/SYe0Xn9zNaisn+0NYj+e XQ98f3dttdo0m7ucOEXZwcHduwVyoJIU+0eIv+gXpf8A4MpP/jFHOv6TD2i8/uZrUVk/bNdf /V6Pap/EPPvivynoDtjb5xzuH3Rxhm5wf2hrCf6zQ93l/wCs8m7Rt+enlbgu7H8W/wAvHbdR zr+kw9ovP7ma1FZP9sX3/Qt6p/38tv8A49R/bN0vMnh/VEXoDmBuewwspPJwM9BnJIAJBzr+ kw9ov6TNaisn/hIID8y2OqGEdZPsEox6/KRvOCV6Kc7sjIVyp/bu7mPStUdV5kb7Pt8teobD EF8jnagZh0Khvlo549w9pHua1FZP9s3X3R4f1QyDlkzAMDsdxl2nPPAJIxyACuT+25xw+hao jd12xNjPC8q5Bycjg/L1bauGJzoPaR/pM1qKyf7W1BeH8O37HrmKa3IweQPmkBzjrxgHOCww Sf2xff8AQt6p/wB/Lb/49Rzr+kw9ovP7ma1FZP8AbF9/0Leqf9/Lb/49R/a2oNwnh2/U9cyz W4GByR8shOcdOME4yVGSDnX9Jh7Ref3M1qKyf7Yvv+hb1T/v5bf/AB6j+1tQblfDt+AvLB5rcEjphcSEE5x1KjAPOQATnX9Jh7Ref3M1 qKyf7W1A/MPDt/sHBUzW+8nsQPMxjg5ywPK4BySp/a2oJ8r+Hb9nHDNDNbshPqpaRSR6ZUH1 A6Uc6/pMPaLz+5mtRWT/AGtqD/Knh2/VzwrTTW6oD6sVkYgeuFJ9AelH9rag/wAqeHb9XPCt NNbqgPqxWRiB64Un0B6Uc6/pMPaLz+5mtRWT9o8Rf9AvS/8AwZSf/GKZFf69cQo66Fb27bR5 kd3fhWDd9vlo4K+hJBPPyjuc6/pMPaLz+5mzRWT9o8Rf9AvS/wDwZSf/ABij7R4i/wCgXpf/ AIMpP/jFHOv6TD2i8/uZrVz/AIe/5Dniz/sKx/8ApFa1a+0eIv8AoF6X/wCDKT/4xVDwo1w+ p+Kmu4oopzqqbkikMij/AEO2xhiqk8Y7CmpJjUkzpaKKKZQUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAVz/g3/kB3P/YV1L/0tmroK5/wb/yA7n/sK6l/6WzUAdBRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFAGV4ns59R8J6zY2sfmXNzYzwxJkDc7RsAMngckda0YIIraC OCCJIoYlCRxxqFVFAwAAOAAO1SUVbqNwUOid/vt/kFuoUUUVAFG30m0ttZvdVhTbc3sUUc+A AG8vftY4GS2Hxkk8Ko7VeooqpScneT/paAUdW07+1LOO383ytlzb3G7buz5UySY6jrsxntnP PSr1FFDk3FR6f1/kAUUUVIBXD/EvSGuNFXULKFHvxJDZugVN11BLMimEM4Kod5Rg5BKleMZN dxUc0EVwgSaJJEDK4V1BAZSGU89wQCD2IBrWhUVOopyV11XddV81oTKKkrM8laDxB4r1yXVN SltFuPDcck9pZ2MDMLiRZp48Ek7uZLVDgZyuANpJNZng23Ei3WiCx1BNbXRW0wQTIsYjR5pW aZ9xB2jzIenzEMcK3UeleHNPig8UeLLyNnBe8hgEWR5aKsCSkqMcFnnkY+pOepJOmNEhTxEm sRNsfyZklTBPmNJ5A3ZzxhYFGAOc5+u2IVNVeWLtBxWn/bt0v/Anuczw15KV/X+vuPG/DWmT J4N8Oa0PMuI9X2aDfRhDxA92VLu/XHlosA6EBlwwwBXqvjXRoNa0IW01l9p8y5tYZAqnf5LX MJlAI5C7VycEfdz2ro6K2rZg51lWhHlak5b93e3y2N401FWIbW1hsrOC0t02QQRrHGuSdqqM AZPPQVmeI9JbVYNPMYdpbPUbe6VVYAEK4DZz2CMxx1yB9Ds0V5jimrFSgpR5ehDNaw3EtvJK m57eQyRHJG1irJn3+VmHPrT0ijjaRkjRWkbc5UYLHAGT6nAA+gFPoqrDsgqu9lG+pQXxZ/Nh hkhUA/KQ5QnPv+7H61YooauDVwooooGFFFFABRRRQAUUUUAFc/4e/wCQ54s/7Csf/pFa10Fc /wCHv+Q54s/7Csf/AKRWtAHQUUUUAFFFFABRRRQAUUVHOZlt5Wt445JwhMaSOUVmxwCwBIGe +Dj0NAGfqXiDT9KuFt5/tcs5QOY7SymuWRSSAWESMVBIYAtjO1sZwcaEE8N1bxXFvLHNBKge OSNgyupGQQRwQRzmuPi1mHRvF97d+JrnTdIe60y0SPzL0eVIyTXRYJI6pvIV4ywxxvHqCdDw jBC3gXRtGv4ozOmj2yXdlOo3KrRbSJIzyASrrgjnaw7GgDY0vVbHWrBb/TbmO6tHd0SaPlWK OUbB7jcp5HB6jI5rL8G/8gO5/wCwrqX/AKWzUeDf+QHc/wDYV1L/ANLZqr2mh+JNMW4gsNb0 pbWS7uLlFn0qSR182V5SpYXCg4LkZwOlAHUUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8A BNN/8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8A wTTf/JVH2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/ 8lUAbF7fW+nwLNdSeXG0scIO0nLyOsaDj1ZlHtnnirFcnqeg+KtVtUt59f0ZUS4guAU0eUHd FKsqjm5PG5AD7Z6daufY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8A Qd0P/wAE03/yVQB0FFc/9j8Yf9B3Q/8AwTTf/JVH2Pxh/wBB3Q//AATTf/JVAHQUVz/2Pxh/ 0HdD/wDBNN/8lUfY/GH/AEHdD/8ABNN/8lUAdBRXP/Y/GH/Qd0P/AME03/yVR9j8Yf8AQd0P /wAE03/yVQBsJfW8mozWCyZuoYo5pE2n5UcuFOenJjf8vcVYrk49B8VRazc6ouv6N59xbw27 qdHl2hY2kZSP9JznMrZ57Dp3ufY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR 9j8Yf9B3Q/8AwTTf/JVAHQUVz/2Pxh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP /Y/GH/Qd0P8A8E03/wAlUfY/GH/Qd0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Y f9B3Q/8AwTTf/JVAGxYX1vqenW1/ZyeZa3USTQvtI3IwBU4PIyCOtWK5PSdB8VaNo1jpdvr+ jNBZW8dvG0mjyliqKFBOLkDOB6Crn2Pxh/0HdD/8E03/AMlUAdBRXP8A2Pxh/wBB3Q//AATT f/JVH2Pxh/0HdD/8E03/AMlUAdBRXP8A2Pxh/wBB3Q//AATTf/JVH2Pxh/0HdD/8E03/AMlU AdBRXP8A2Pxh/wBB3Q//AATTf/JVH2Pxh/0HdD/8E03/AMlUAdBVe4vre0ntIZ5Nkl3KYYBt J3uEaQjjp8qMefT1xWP9j8Yf9B3Q/wDwTTf/ACVVO90HxVfXWnXEuv6MHsLg3EQXR5cFjFJF hv8ASem2RjxjkD6EA6yiuf8AsfjD/oO6H/4Jpv8A5Ko+x+MP+g7of/gmm/8AkqgDoKK5/wCx +MP+g7of/gmm/wDkqj7H4w/6Duh/+Cab/wCSqAOgorn/ALH4w/6Duh/+Cab/AOSqPsfjD/oO 6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5Ko+x+MP+g7of/gmm/8AkqgDoKr299b3c93D BJvktJRDONpGxyiyAc9fldTx6+uax/sfjD/oO6H/AOCab/5KqnZaD4qsbrUbiLX9GL39wLiU No8uAwijiwv+k9NsannPJP0AB1lc/wCHv+Q54s/7Csf/AKRWtH2Pxh/0HdD/APBNN/8AJVWN B0m80xtTnv72C7ur+7Fy7QWxgRcRRxBQpdz0iBznvQBsUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAVXv7630zTrm/vJPLtbWJ5pn2k7UUEscDk4APSrFcv4rivL7TL+0vpYNN0U+SW1CKUyy7 RJGXWSFotixld6szMyhcllK7gADY0zWbXV/N+zRX0flY3fa7Ce2znOMeai7unbOOM9RWf4N/ 5Adz/wBhXUv/AEtmqv4W1Q32o6hb2+t/27psMULx6hmFv3rGQSRboVVDtVImxjcPM5OCuC00 PxJpi3EFhrelLayXdxcos+lSSOvmyvKVLC4UHBcjOB0oA6iiuf8AsfjD/oO6H/4Jpv8A5Ko+ x+MP+g7of/gmm/8AkqgDoKK5/wCx+MP+g7of/gmm/wDkqj7H4w/6Duh/+Cab/wCSqAOgorn/ ALH4w/6Duh/+Cab/AOSqPsfjD/oO6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5Ko+x+MP +g7of/gmm/8AkqgDYvb630+BZrqTy42ljhB2k5eR1jQcerMo9s88VYrk9T0HxVqtqlvPr+jK iXEFwCmjyg7opVlUc3J43IAfbPTrVz7H4w/6Duh/+Cab/wCSqAOgorn/ALH4w/6Duh/+Cab/ AOSqPsfjD/oO6H/4Jpv/AJKoA6Ciuf8AsfjD/oO6H/4Jpv8A5Ko+x+MP+g7of/gmm/8AkqgD oKK5/wCx+MP+g7of/gmm/wDkqj7H4w/6Duh/+Cab/wCSqAOgorn/ALH4w/6Duh/+Cab/AOSq PsfjD/oO6H/4Jpv/AJKoA2EvreTUZrBZM3UMUc0ibT8qOXCnPTkxv+XuKsVyceg+KotZudUX X9G8+4t4bd1Ojy7QsbSMpH+k5zmVs89h073PsfjD/oO6H/4Jpv8A5KoA6Ciuf+x+MP8AoO6H /wCCab/5Ko+x+MP+g7of/gmm/wDkqgDoKK5/7H4w/wCg7of/AIJpv/kqj7H4w/6Duh/+Cab/ AOSqAOgorn/sfjD/AKDuh/8Agmm/+SqPsfjD/oO6H/4Jpv8A5KoA6Ciuf+x+MP8AoO6H/wCC ab/5Ko+x+MP+g7of/gmm/wDkqgDYsL631PTra/s5PMtbqJJoX2kbkYAqcHkZBHWrFcnpOg+K tG0ax0u31/RmgsreO3jaTR5SxVFCgnFyBnA9BVz7H4w/6Duh/wDgmm/+SqAOgorn/sfjD/oO 6H/4Jpv/AJKo+x+MP+g7of8A4Jpv/kqgDoKK5/7H4w/6Duh/+Cab/wCSqPsfjD/oO6H/AOCa b/5KoA6Ciuf+x+MP+g7of/gmm/8Akqj7H4w/6Duh/wDgmm/+SqAOgqvcX1vaT2kM8myS7lMM A2k73CNIRx0+VGPPp64rH+x+MP8AoO6H/wCCab/5Kqne6D4qvrrTriXX9GD2FwbiILo8uCxi kiw3+k9NsjHjHIH0IB1lFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf/JVAHQUVz/2P xh/0HdD/APBNN/8AJVH2Pxh/0HdD/wDBNN/8lUAdBRXP/Y/GH/Qd0P8A8E03/wAlUfY/GH/Q d0P/AME03/yVQB0FFc/9j8Yf9B3Q/wDwTTf/ACVR9j8Yf9B3Q/8AwTTf/JVAHQVXt763u57u GCTfJaSiGcbSNjlFkA56/K6nj19c1j/Y/GH/AEHdD/8ABNN/8lVTstB8VWN1qNxFr+jF7+4F xKG0eXAYRRxYX/Sem2NTznkn6AA6yuf8Pf8AIc8Wf9hWP/0itaPsfjD/AKDuh/8Agmm/+Sqs aDpN5pjanPf3sF3dX92Ll2gtjAi4ijiChS7npEDnPegDYooooAKKKKACiiigAooooAKKKKAM PxbPNBoSiGWSIz3tnbO0bFW8uW5ijcBhypKuw3DBGcgggGq+iRHTvFWq6TDcXclnFZWlyi3V zJcMskj3CuQ8jM2CIk+XOBgkAEnOxqmmw6tYNaTNIgLpIkkZAaORHDo4yCMqyq2CCDjBBGRV fStFGm3FxdzX93qF5cIkb3N0Iw3loWKIBGiLgF3OcZ+Y5JAAABqUUUUAFFFFABRRRQAVzfj5 r6PwLrU+n38ljLBZTzGWJf3mFiZgEb+AlguWwTjOMEhl6SqerabDrOjX2l3DSLBe28lvI0ZA YK6lSRkEZwfQ0AYd1aDWvGV9YXdzfJa2mn2s0KWl7NbYeSS4DkmJlLZESfezjBxjJzx9hqF/ q/gDW/E13qN8dV03T45rZ4bqSGNXGnwXGWhQiN/3sjk7lOQdp+UAD0DUNBN3qJv7TVb7TLp4 lhme0ELeaiFigIljcDaXf7uM7jnOBjPbwLYJayWFpe31npU8SQ3OnwtG0dwixLDhmdGkGYkR DtdeFyMMSxAOoooooAKKKKACiiigArk9Y082epWc9lf6lLrNzexssTXkhj+ziVPP/cgiIIsJ K7iuclOTIyk9ZXPw+GJoNcudVj8RaruuZVeWEpbMhRfuxAmHeIwCcAMPvMc7mLEA4e/1C/0j wBonia01G+Gq6lp8k1y811JNGznT57jKwuTGn72NCNqjAG0fKSD2FraDRfGVjYWlzfPa3en3 U0yXd7Nc5eOS3CEGVmK4Er/dxnIznAwL4FsHtY7C7vb680qCJ4bbT5mjWO3RomhwrIiyHETu g3O3DZOWAYaGn6CbTURf3eq32p3SRNDC92IV8pHKlwBFGgO4on3s42jGMnIBsUUUUAFFFFAB RRRQBXvrX7dZyWxuJ4FkwGeB9j7cjIDdVyMjIwwzkEHBHncclyfEVroRbWdPsLu4gEtld6i8 l0AYLxy4nSV2VGaCIBVkBzE+VAc7/RL61+22cluLie3ZsFZoH2ujAggjscEDggqehBBIOGfB 8Ujm6m1fUpdUDo0WpN5IliCLIqqqiMREYmmHzIT+8PPC7QDn7OOW88Z3Phma+1I6XavcvEq3 8yy5WKwZd0wcSsAbmY4ZiPmH91cdR4Snmn0JhNLJKYL28tkaRizeXFcyxoCx5YhUUbjknGSS STVceD4o3F1Dq+pRaoXdpdSXyTLKHWNWVlMZiAxDCPlQH92OeW3bGl6bDpNgtpC0jgO8jySE FpJHcu7nAAyzMzYAAGcAAYFAFyiiigAooooAKKKKAOL+Jeq31n4VvbPSbmS11CWyubr7RHw0 EEKbpHGe5Yxx8EMPN3j7hqnrzTyWXjfV/tt9Hd6Jv+weTeSxxx7LOKZd0SsEk/eOxO9WyDg5 UAV0niHwhoPie3nTVNMtJp5bdrdbtoEaeJSDyjspKkFiR6Hmq9z4Msphc29veXdjpt4gS706 1WJYJ1EaxEHMZdAY0RMIy4CgjBySAYe6f+zf+Eg+2339of8ACQfYv+PyXyfJ/tH7Nt8jd5X+ q+XO3Ofmzu+avQK5/wD4ROD7Vn+0b7+z/tf23+zf3Xk+d5vnbt2zzf8AW/vMb8Z4xt+WugoA KKKKACiiigAooooA8nOp3mmeGb3W7+z8R6brVxaXt3ZyXt8XgWYxSzpCIBMw+RAQN8Sj91yF Yha1PEUcvh3VdP0/Tb7Ult9SRUuRNfzTsQb2zh+R5HZozsuJRlCp+YHqqkdBL4Ntr0eRqmpa lqViqSJFaXUibYg8bRHDqiysfLkdcu7H5iTlsMCTwfFdEyalq+pahcIgW2nm8lGtiJEl3II4 1UnfFE3zhh8gGMFgQA0SI6d4q1XSYbi7ks4rK0uUW6uZLhlkke4VyHkZmwREny5wMEgAk56S svStFGm3FxdzX93qF5cIkb3N0Iw3loWKIBGiLgF3OcZ+Y5JAAGpQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAYfi2eaDQlEMskRnvbO2do2Kt5ctzFG4DDlSVdhuGCM5BBANV9EiOne KtV0mG4u5LOKytLlFurmS4ZZJHuFch5GZsERJ8ucDBIAJOdjVNNh1awa0maRAXSRJIyA0ciO HRxkEZVlVsEEHGCCMiq+laKNNuLi7mv7vULy4RI3uboRhvLQsUQCNEXALuc4z8xySAAADUoo ooAKKKKACiiigArm/HzX0fgXWp9Pv5LGWCynmMsS/vMLEzAI38BLBctgnGcYJDL0lU9W02HW dGvtLuGkWC9t5LeRoyAwV1KkjIIzg+hoAw7q0GteMr6wu7m+S1tNPtZoUtL2a2w8klwHJMTK WyIk+9nGDjGTnj7DUL/V/AGt+JrvUb46rpunxzWzw3UkMauNPguMtChEb/vZHJ3Kcg7T8oAH oGoaCbvUTf2mq32mXTxLDM9oIW81ELFARLG4G0u/3cZ3HOcDGe3gWwS1ksLS9vrPSp4khudP haNo7hFiWHDM6NIMxIiHa68LkYYliAdRRRRQAUUUUAFFFFABXJ6xp5s9Ss57K/1KXWbm9jZY mvJDH9nEqef+5BEQRYSV3Fc5KcmRlJ6yufh8MTQa5c6rH4i1Xdcyq8sJS2ZCi/diBMO8RgE4 AYfeY53MWIBw9/qF/pHgDRPE1pqN8NV1LT5Jrl5rqSaNnOnz3GVhcmNP3saEbVGANo+UkHsL W0Gi+MrGwtLm+e1u9Puppku72a5y8cluEIMrMVwJX+7jORnOBgXwLYPax2F3e315pUETw22n zNGsdujRNDhWRFkOIndBuduGycsAw0NP0E2moi/u9VvtTukiaGF7sQr5SOVLgCKNAdxRPvZx tGMZOQDYooooAKKKKACiiigCvfWv26zktjcTwLJgM8D7H25GQG6rkZGRhhnIIOCPO45Lk+Ir XQi2s6fYXdxAJbK71F5LoAwXjlxOkrsqM0EQCrIDmJ8qA53+iX1r9ts5LcXE9uzYKzQPtdGB BBHY4IHBBU9CCCQcM+D4pHN1Nq+pS6oHRotSbyRLEEWRVVVEYiIxNMPmQn94eeF2gHP2cct5 4zufDM19qR0u1e5eJVv5llysVgy7pg4lYA3MxwzEfMP7q46jwlPNPoTCaWSUwXt5bI0jFm8u K5ljQFjyxCoo3HJOMkkkmq48HxRuLqHV9Si1Qu7S6kvkmWUOsasrKYzEBiGEfKgP7sc8tu2N L02HSbBbSFpHAd5HkkILSSO5d3OABlmZmwAAM4AAwKALlFFFABRRRQAUUUUAcX8S9VvrPwre 2ek3MlrqEtlc3X2iPhoIIU3SOM9yxjj4IYebvH3DVPXmnksvG+r/AG2+ju9E3/YPJvJY449l nFMu6JWCSfvHYnerZBwcqAK6TxD4Q0HxPbzpqmmWk08tu1ut20CNPEpB5R2UlSCxI9DzVe58 GWUwube3vLux028QJd6darEsE6iNYiDmMugMaImEZcBQRg5JAMPdP/Zv/CQfbb7+0P8AhIPs X/H5L5Pk/wBo/ZtvkbvK/wBV8uduc/Nnd81egVz/APwicH2rP9o339n/AGv7b/Zv7ryfO83z t27Z5v8Arf3mN+M8Y2/LXQUAFFFFABRRRQAUUUUAeTnU7zTPDN7rd/Z+I9N1q4tL27s5L2+L wLMYpZ0hEAmYfIgIG+JR+65CsQtaniKOXw7qun6fpt9qS2+pIqXImv5p2IN7Zw/I8js0Z2XE oyhU/MD1VSOgl8G216PI1TUtS1KxVJEitLqRNsQeNojh1RZWPlyOuXdj8xJy2GBJ4PiuiZNS 1fUtQuEQLbTzeSjWxEiS7kEcaqTviib5ww+QDGCwIAaJEdO8VarpMNxdyWcVlaXKLdXMlwyy SPcK5DyMzYIiT5c4GCQASc9JWXpWijTbi4u5r+71C8uESN7m6EYby0LFEAjRFwC7nOM/Mckg ADUoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigDm/HB1C18K6lqmnavd2E+n2VxcKsMcLrKyoWUP5kbHAK/wkdT 7YkTUDoWo2Oi3Nzqur3moeZLFK8MP7tEMStuMaoqqPM3ZI5wQCWKKdDXdM/tvw9qek+d5P26 0ltvN27tm9Cu7GRnGc4yKJtM83xDZat52Ps1pPbeVt+95rwtuznjHk4xjnd2xyAY8fi6C38P afqZs9Vu7F9Pjvri9aKJfIhKbvMl+ZQzYDFliDEY+6Ny50NGvri71XxDDPJvjtNQSGAbQNiG 1gkI46/M7Hn19MVy+q/DFNU8PWWkSajAy22lR6cJriwWZoyiECWAM2ImYkb/ALxZVQAoVDV2 GnaZ9gvtWufO8z+0Ltbnbtx5eIYotuc8/wCqznj72O2SAaFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHN+OD qFr4V1LVNO1e7sJ9Psri4VYY4XWVlQsofzI2OAV/hI6n2xImoHQtRsdFubnVdXvNQ8yWKV4Y f3aIYlbcY1RVUeZuyRzggEsUU6Gu6Z/bfh7U9J87yft1pLbebt3bN6Fd2MjOM5xkUTaZ5viG y1bzsfZrSe28rb97zXhbdnPGPJxjHO7tjkAx4/F0Fv4e0/UzZ6rd2L6fHfXF60US+RCU3eZL 8yhmwGLLEGIx90blzoaNfXF3qviGGeTfHaagkMA2gbENrBIRx1+Z2PPr6Yrl9V+GKap4estI k1GBlttKj04TXFgszRlEIEsAZsRMxI3/AHiyqgBQqGrsNO0z7Bfatc+d5n9oXa3O3bjy8QxR bc55/wBVnPH3sdskA0KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKAOTsrnxU/iq40u41PRngtbe2upGj0uVWkWR5VKDNwQpAh+9g/e6cc3IfGug3WnW9/a XU93BcZMP2WzmmeQAKWZURCxVdyqzYwrHaSG4q5Bps0XirUNUZo/IuLK2t0UE7g0bzsxPGMY lXHPY9O/Pt4W1lPCvh3SYbyMGwso7W5WO7nt18wIiiYPFtdwm1/3RKB9+SylRQBsXPjDQLS4 jgk1KNne3juwYkaRRA5YCYsoIEXynMhO1cjcRuGbE3iHTLfVBp0k8gn3rGziCQxRu2NqPKF2 I53LhWYE70wPmXPLweCtSi8K6hpbT2nn3Hhe20ZGDttE0aTqzH5c7Myrg4zweB31L3QtTfxQ t9ZGC2haWOSS5iu5kYqoUOj2wzFMzKpTzWKsqsuBmNSQCSz8WQLp3n6iJPPa9vLeOGztZZ3Z Ibh4t2yMM2AFTc2MAsOm4Co5vGdk15rFrDL5cNhpUepLqJt5JYCjiQ7htADqAisNr/PlgOUb FOfwtrJ06K0gvI1Q3t9NIiXc9uB59w8kcu+La7lFYgxZVWLn5htU1Th8F6xF4evdJ32J+0+F INH83zn+W4iSZc42cxnzs7s5G37pzwAdZN4h0y31QadJPIJ96xs4gkMUbtjajyhdiOdy4VmB O9MD5lzHZ+KdG1DUWsLW88ydZZID+6cIJYyweLeRt8wbWbZncVG4DbzWXeeHdSm1S8jiNp/Z 99qdrqUs7SsJYmg8j92se0hw32dfmLrjzD8p2/NJa+G7yD+yd0kB+x63e6hJhjzHN9q2gcfe HnpkdOG5PGQDU1PWrTSbjdeXkcMEdlcXkqGF2YxxGPc4YcAKH5XBLbhj7pzJpmt6drPm/YLj zfKwTlGTcrZ2yLuA3xtg7XXKtg4Jwax/Fnhu8177R9lkgTzNE1DTx5rEfvJ/J2HgH5R5bZPX kYBrUg02aLxVqGqM0fkXFlbW6KCdwaN52YnjGMSrjnsencApy6hrGoanfwaMbGOPTJVhnW8j djcyGNJdqsrDyl2yIN5DnLN8mFG+xdeJtO0/yF1Az20zxLNLGYWl+yqf4pmjDJEuQ3zswX5G IJCkivLp+safqd/PowsZI9TlWadryR1NtII0i3KqqfNXbGh2Eocq3z4YbM/XPDOqXn9ow2s8 FxHq2lR6XdXF3J5ckIXzh5wVE2yMfPY7f3YygwcN8oBsN4p0ZNYk0o3n+mRSpDKnlOVid1Vk Dvjam/eoXJG5squWBAp3Xi20HiLStIspo5Jbm9kt5t8bgFUgmdjE3CuVeNFbaW2k7WwSKjuv Dd5P/a22SAfbNbstQjyx4jh+y7gePvHyHwOnK8jnFe38Paxb6xpsWyxbTLLVbvUvtH2hxM/n rcfJ5Xl7Rta4xnechM4GcAALbxxa3EV/GJ9q2miQ6odTNhOIGWRXYuIzztAVWChyxyyg5Rsb k3iHTLfVBp0k8gn3rGziCQxRu2NqPKF2I53LhWYE70wPmXPJw+C9Yi8PXuk77E/afCkGj+b5 z/LcRJMucbOYz52d2cjb90541Lzw7qU2qXkcRtP7PvtTtdSlnaVhLE0Hkfu1j2kOG+zr8xdc eYflO35gDUs/FOjahqLWFreeZOsskB/dOEEsZYPFvI2+YNrNszuKjcBt5qve3usXfiGfSdJu bG0+y2kNzJLd2r3Hmea8qhQFkj27fJJyS2dw6Y5r2vhu8g/sndJAfset3uoSYY8xzfatoHH3 h56ZHThuTxmxe2WsWniGfVtJtrG7+1WkNtJFd3T2/l+U8rBgVjk3bvOIwQuNo654AOfh8aax qXhu/wDEtmljbWOnWkdzNYzQvLJPm1jumVZg6hOJQgJRsFd3Odo3NV8SzWfjPQ9BtreORLt2 N7MxIMCmKZ4go7l2gk55wIzkfMprDh8F6xpvhu/8NWb2NzY6jaR2019NM8UkGLWO1ZlhCMH4 iDgF1yW28Y3HQuPBd1/wktpq1tr19s/tU6hdW8og2f8AHu0ICYi3fd2Jgt93cc78NQB2FFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHNxX+vauZ73SZdNhs4Lia3W3u4HaSdo ZGjcmRXAiBZGA+STAAY5J2CTQvEn9vai3kR7LGTSrLUIN64k/fmbIbBI4Ea8DuTyeKjisNe0 gz2WkxabNZz3E1wtxdzuskDTSNI4MaoRKAzsR88eQQpwRvNO10HWPDmop/YdrY3timlWenr9 uvngkXyDNgnZC4bIkHPHIPFAFhL3xJf61rSWFzpSWunXaW6W89rIXm/cRSnMokwmTKVz5bYx nDdK1La/m1zw1a6lo8sdq97bxXEDXcBlCK4DYZFdcnaccNwfXvj2tn4qstR1WWCz0YJqVxHc GV72Vjbt9nhiYeWIR5gDRkj503DH3e3QaTpsOjaNY6XbtI0Flbx28bSEFiqKFBOABnA9BQBz ekeIb+20q51nxNq2lQ6bDdzWZMNlJDtdLo26uztK4Ckrk8DG7lsAk7A8UaSbyG1M06yS7Bl7 SVUjZwCkcjldsch3LhHKt86jGWGc/wD4Ru8/4Rz+zvMg87+2/wC0N247fL/tD7Tjp97Zxjpu 745qnr/hbWdX16K5S8jNpFe2l0m+7nQJHFJGzQ+QmI2JKu4lfccsE2gBXUA3LPxTo2oai1ha 3nmTrLJAf3ThBLGWDxbyNvmDazbM7io3Abeaz9T8Z2SeHtZvdLl826stPnvIBPbyJHcBEJDx swUSx525aMkYZefmGS18N3kH9k7pID9j1u91CTDHmOb7VtA4+8PPTI6cNyeM5Z8Iay+ja3pq PaWsF3pk9lFDHeTywSSOu1HEcgItUUbh5cZYYfGT5a5AOom8Q6Zb6oNOknkE+9Y2cQSGKN2x tR5QuxHO5cKzAnemB8y5z9H8WQa3b2k6CSz87U7ixWK5tZQ0xiEvC7gu0lY95JBAwyH5umfr /hbWdX16K5S8jNpFe2l0m+7nQJHFJGzQ+QmI2JKu4lfccsE2gBXWxYeHdShfThObRUstdvdQ BSVmLwzLc7eCow4a4AI5GFJ3c4oA0E8X6G9vd3H2uRYLW3e6aR7eVVkhQZaSIlQJkAwd0e4f Mv8AeXNzTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg1x58F6xP4ebSZXsY/sfh+50 SylWZ2+0eYkaiWQbB5WPJU7QZPvnn5fm6yDTZovFWoaozR+RcWVtbooJ3Bo3nZieMYxKuOex 6dwCvc+J9M02W5W/1CMbb0WcaJbybhKYFlEXGd7lSSNoGdyoAW66ljfW+o2cd1ayeZC+QCVK kEEhlZTgqwIIKkAggggEVz//AAjd5/wkf9o+ZB5P9t/2ht3Hd5f9n/ZsdPvb+cdNvfPFamga bNpWnS287Rs73t3cAoSRtluJJVHIHO1wD7569aAM/SdV1rU7ex1qOK0k0m/SOSK0RCtxDFIA VkaQvscgEFkCrgFtrOVAfQh8Q6ZcaodOjnkM+9o1cwSCKR1zuRJSux3G1sqrEjY+R8rYz9J0 rWtMt7HRY5bSPSbBI44rtHLXE0UYAWNoymxCQAGcM2QG2qhYFK9n4d1KHVLOOU2n9n2Op3Wp RTrKxllafz/3bR7QEC/aG+YO2fLHyjd8oBoab4w0DVrdrm01KM24tzdefIjRRtEACzq7gBgm QHwTsJw208Vnw+MoLzV763huI4La2SwGbi0lWVZZ55IzG8bbWQsFj2kgY8wOdy4qn/whF5P4 e0vSZ7qCPyPDVxo08qAviSVIFDqCBuUeUx5IPT1OJJtA17VdQvL2/h020eV9LCRwXbzDba3b TuSTEmCVfAGDyOSKANDR/FkGt29pOgks/O1O4sViubWUNMYhLwu4LtJWPeSQQMMh+bpYTxfo b293cfa5Fgtbd7ppHt5VWSFBlpIiVAmQDB3R7h8y/wB5c59h4d1KF9OE5tFSy1291AFJWYvD Mtzt4KjDhrgAjkYUndziss+C9Yn8PNpMr2Mf2Pw/c6JZSrM7faPMSNRLINg8rHkqdoMn3zz8 vzAHYaZrenaz5v2C483ysE5Rk3K2dsi7gN8bYO11yrYOCcGuf/4SHWPL/tffY/2Z/av9m/Y/ s7+d/wAff2Tf53mbfvfvMeX0+XP8VbkGmzReKtQ1Rmj8i4sra3RQTuDRvOzE8YxiVcc9j074 f/CPax5f9kbLH+zP7V/tL7Z9ofzv+Pv7Xs8ny9v3v3efM6fNj+GgA0jxDrFynh3ULx7F7HX8 eTbQ27pJa7rd513SGRhJgRlDhEyTu4xtNzQ/Es2s+Ktd09beNLCwSEW84JLTsXmjlJH8IWSF kAxzsLZIYVn6f4a1pLTQdKuJLS1tNDTbBfWs5knmK28lurGJ4tkZxJv+9IAVC4YHIseGfB8/ hrWZZV1e7u9PXTLaxt4rjytyiJpMA7I14VXAU5JO592cLgA6yiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooA4fWtT1ZLfxZqtvqs9svh/d5FnHFEYbjZaxz/v SyF+WkKnYy/KBjByx1PGmrTaZpdnFbXF3bXF9exW6T2tqbh41GZJCECOSTFHIB8jYJGcDJEe qeFry+bWLe31OCDTdaz9viktDJN80Swt5UgkUJ8iLjcj4bJ5B2jYvrbVJopPsWowW0wlDwl7 XzE2bQCki7wW5LMCrJj5RyA24Ar+G50uNOkZNWvtRZZSHa/gWCaI4B2NGI4yvBDDcuSHByQR Vf8A4Tvwf/0Neh/+DGH/AOKq5oulTaaL2a7uY7i8vrj7RcPFEYo9wjSMBELMVG2NM5Y85PAI Ap+BP+SeeGv+wVa/+iloAP8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq6CigD n/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq6CigDn/8AhO/B/wD0Neh/+DGH /wCKo/4Tvwf/ANDXof8A4MYf/iq6CigDn/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A 4MYf/iq6CigDn/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq0NR0z7ffaTc+d 5f8AZ921zt258zMMsW3OeP8AW5zz93HfI0KAOf8A+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16 H/4MYf8A4qugooA5/wD4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDiq6CigDn/APhO /B//AENeh/8Agxh/+Ko/4Tvwf/0Neh/+DGH/AOKroKKAOf8A+E78H/8AQ16H/wCDGH/4qj/h O/B//Q16H/4MYf8A4qugooA5/wD4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDiq0NO 0z7Bfatc+d5n9oXa3O3bjy8QxRbc55/1Wc8fex2ydCgDn/8AhO/B/wD0Neh/+DGH/wCKo/4T vwf/ANDXof8A4MYf/iq6CigDn/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq6 CigDn/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq6CigDn/8AhO/B/wD0Neh/ +DGH/wCKo/4Tvwf/ANDXof8A4MYf/iq6Cs/XdM/tvw9qek+d5P260ltvN27tm9Cu7GRnGc4y KAM//hO/B/8A0Neh/wDgxh/+Ko/4Tvwf/wBDXof/AIMYf/iq6CigDn/+E78H/wDQ16H/AODG H/4qj/hO/B//AENeh/8Agxh/+KroKKAOf/4Tvwf/ANDXof8A4MYf/iqP+E78H/8AQ16H/wCD GH/4qugooA5//hO/B/8A0Neh/wDgxh/+Ko/4Tvwf/wBDXof/AIMYf/iq6CigDn/+E78H/wDQ 16H/AODGH/4qj/hO/B//AENeh/8Agxh/+KroKz5tM83xDZat52Ps1pPbeVt+95rwtuznjHk4 xjnd2xyAZ/8Awnfg/wD6GvQ//BjD/wDFUf8ACd+D/wDoa9D/APBjD/8AFV0FFAHP/wDCd+D/ APoa9D/8GMP/AMVR/wAJ34P/AOhr0P8A8GMP/wAVUcHjnRbq3iuLeLWZoJUDxyR6JesrqRkE ERYII5zUn/CZaX/z665/4Ir3/wCM0AH/AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDB jD/8VR/wmWl/8+uuf+CK9/8AjNH/AAmWl/8APrrn/givf/jNAB/wnfg//oa9D/8ABjD/APFU f8J34P8A+hr0P/wYw/8AxVH/AAmWl/8APrrn/givf/jNH/CZaX/z665/4Ir3/wCM0AH/AAnf g/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDBjD/8VR/wmWl/8+uuf+CK9/8AjNZ+ja/pekWM lt5euS77u5ud3/CP3q486Z5duPKPTfjPfGeOlAGh/wAJ34P/AOhr0P8A8GMP/wAVWxY39nqd nHeWF3Bd2smdk0EgkRsEg4YcHBBH4VT0rxBp+s3Fxb2n2tZ7dEeWO6sprZgrlgpAlRSQSjjI z0NU/Bv/ACA7n/sK6l/6WzUAdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHD/ANp6t9k/t7+1 Z9v9t/2f/Z/lRfZ/L+3fZM52eZu2/Pnfjf22/LVzxhrE1nf6Pptve6lZG5eWeebT7E3Unkxp tKhRFJgmSWE5K4wG5BwDJ/wi15v+x/2nB/Y39of2h5H2Q/aPM+0faceb5m3b5vby87OM5+at S9tdakSNrHVbSCdXkDCayMsToWyuVEitvUADcHwcsSvK7QA0u9to/D63kmrSXVvGjvLe3gSJ lCk7vMAVAhTBUgqpXaQ3INU/+E78H/8AQ16H/wCDGH/4qs/xDpn9kfCrxJambzpG0+/uJZAu 0NJKJJH2rk4Xc7YBJIGASTyewoA5/wD4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof/gxh/wDi q6CigDn/APhO/B//AENeh/8Agxh/+Ko/4Tvwf/0Neh/+DGH/AOKroKKAOf8A+E78H/8AQ16H /wCDGH/4qj/hO/B//Q16H/4MYf8A4qugooA5/wD4Tvwf/wBDXof/AIMYf/iqP+E78H/9DXof /gxh/wDiq6CigDn/APhO/B//AENeh/8Agxh/+Ko/4Tvwf/0Neh/+DGH/AOKrQm0zzfENlq3n Y+zWk9t5W373mvC27OeMeTjGOd3bHOhQBz//AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D /wDBjD/8VXQUUAc//wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FV0FFAHP/8A Cd+D/wDoa9D/APBjD/8AFUf8J34P/wChr0P/AMGMP/xVdBRQBz//AAnfg/8A6GvQ/wDwYw// ABVH/Cd+D/8Aoa9D/wDBjD/8VXQUUAc//wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8A wYw//FVoaNpn9kWMlt53m77u5ud23bjzpnl24yem/Ge+M8dK0KAOf/4Tvwf/ANDXof8A4MYf /iqP+E78H/8AQ16H/wCDGH/4qugooA5//hO/B/8A0Neh/wDgxh/+Ko/4Tvwf/wBDXof/AIMY f/iq6CigDn/+E78H/wDQ16H/AODGH/4qj/hO/B//AENeh/8Agxh/+KroKKAOf/4Tvwf/ANDX of8A4MYf/iqP+E78H/8AQ16H/wCDGH/4qugrP1nTP7XsY7bzvK2Xdtc7tu7PkzJLtxkddmM9 s556UAZ//Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D /wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9 D/8ABjD/APFV0FFAHP8A/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FFAHP8A /Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFV0FZ8OmeV4hvdW87P2m0gtvK2/d8p 5m3ZzznzsYxxt754AM//AITvwf8A9DXof/gxh/8AiqP+E78H/wDQ16H/AODGH/4qugqvf31v pmnXN/eSeXa2sTzTPtJ2ooJY4HJwAelAGP8A8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8 GMP/AMVR/wAJlpf/AD665/4Ir3/4zR/wmWl/8+uuf+CK9/8AjNAB/wAJ34P/AOhr0P8A8GMP /wAVR/wnfg//AKGvQ/8AwYw//FUf8Jlpf/Prrn/givf/AIzR/wAJlpf/AD665/4Ir3/4zQAf 8J34P/6GvQ//AAYw/wDxVH/Cd+D/APoa9D/8GMP/AMVR/wAJlpf/AD665/4Ir3/4zR/wmWl/ 8+uuf+CK9/8AjNAB/wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUf8Jlpf/Pr rn/givf/AIzWfoWv6Xonh7TNJ8vXJvsNpFbeb/wj96u/YgXdjyjjOM4yaAND/hO/B/8A0Neh /wDgxh/+KrcgnhureK4t5Y5oJUDxyRsGV1IyCCOCCOc1T0nWrLW4p5LJp/8AR5fJmSe2kgdH 2q+Ckiq33XU9O9Z/gT/knnhr/sFWv/opaAOgooooAKKKKACiiigAooooAKKKKACuf8Cf8k88 Nf8AYKtf/RS10Fc/4E/5J54a/wCwVa/+iloA6CiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA 5/wJ/wAk88Nf9gq1/wDRS10Fc/4E/wCSeeGv+wVa/wDopak/4RPTv+fnWf8AwdXn/wAdrSCg /jbXor/qhO5uUVh/8Inp3/PzrP8A4Orz/wCO0f8ACJ6d/wA/Os/+Dq8/+O1fLQ/mf3L/AOSD U3KKw/8AhE9O/wCfnWf/AAdXn/x2j/hE9O/5+dZ/8HV5/wDHaOWh/M/uX/yQamhqOpQ6YlvJ cLJ5U1xHbmRQCI2kbahYZzguVXgHBYE4AJBa6lDeX99aQrITZOkc0hAC+YyB9g5ySFZGJxj5 wASQwFfX3mOlta29lHeS3ji1Ec0RkiCvw7yDoUVNzFSV3YCAgsKr+GoJtMt59GmikxYuPJum U/6XG43eazdDKW3h+clgXIUSKKwGR2f/ACUPWf8AsFWH/o27o8G/8gO5/wCwrqX/AKWzVBBe 2kHxN1G1muoY7m50qy8iF5AHl2yXZbaDy2BycdKn8G/8gO5/7Cupf+ls1NprcDoKKKKQBRRR QAUUUUAFFFFABRRRQAUUUUAFFFFAHP8Ajv8A5J54l/7BV1/6Kaugrn/Hf/JPPEv/AGCrr/0U 1dBQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/wCO/wDknniX/sFXX/opq6Cuf8d/8k88S/8A YKuv/RTUAdBRUc8K3EEkLlwkilGMblGAIxwykEH3BBFY/wDwienf8/Os/wDg6vP/AI7WkFTf xtr0V/1Qnc3KKw/+ET07/n51n/wdXn/x2j/hE9O/5+dZ/wDB1ef/AB2r5aH8z+5f/JBqblFY f/CJ6d/z86z/AODq8/8AjtPg8MWFvPHMlxqxeNg6iTV7p1JBzyrSEEexBBocaPST+5f/ACQa l261KGzv7G0mWQG9d44ZAAV8xUL7DzkEqrsDjHyEEglQTTtSh1NLiS3WTyobiS3EjAASNG21 yoznAcMvIGSpIyCCc/xLBNqdvBo0MUmL5z510qn/AESNBu81W6CUNsCc5DEOAwjYVY0B5hpa 2txZR2ctm5tTHDEY4iqcI8Y6BGTawUFtuShJKmsBlPw9/wAhzxZ/2FY//SK1o8Cf8k88Nf8A YKtf/RS1B4evbT/hKvFlh9qh+2f2jHN9n8weZ5f2O1G/b1254z0zU/gT/knnhr/sFWv/AKKW m01uB0FFFFIAooooAK4fV/Dfh+DVobKy8HaHqOp3/wBpvnk1BVUECRDITIY5GLF51wuMYzyM AHuK4vx9DCH0q8vk8PzafE8sc1vr94Ibd2ZQUKgxuDKNhwx6KZAAd+VALmieEdLtp4NQn8K6 HpWpWsrNA+mHOAUKElxHGTkOw2kEdD1xjqK5vwVJpsujTNpdp4ftoPtDBk0K5WeAttXlmWNA HxjIx0C888dJQAVn6tqyaVFB/o091cXMvk29tBt3yvtZyAXZVGER2+Zh93AySAdCuf8AE4eK 50HUPInlt7DUGmuPIhaV1RraeIEIgLN88iD5QcZyeASADQ0nVk1WKf8A0ae1uLaXybi2n274 n2q4BKMynKOjfKx+9g4IIGP8P7+1n8FaFZRTq9zbaRZNNGvJQPCNufc7ScdcYPcVY8MB5bnX tQ8ieK3v9QWa38+FonZFtoIiSjgMvzxuPmAzjI4IJ8+8GeANJlbwrd3rT31vcaILtLS4cmOG UGF/lA6p+9OFORySdxOQm7b7GsIKUZfzW07aau/yT+Z6bD4i0u41RNPjukMsgzC24bZiEVyE P8RCOjccENxnDYs6fqmn6tA0+m31teQq2xpLaZZFDYBxlSRnBHHvWHfeA9FvGSZY51uYQRC0 t1NKgzjeGjZ9rCTGJB1cFsnJJrNuvAVxqGvLeahfrc27AwzDb5ckiE58wFAAshUJA2B80Snk Z2jR+zUerforfn/VvPTkcqkelzuahjurebyfKuIn86PzYtrg704+ZfUfMvI9R61x5+G1kZ9S uJNQu7ma6nWaMXjGdE2GMxq6sfnClHXOVYpK6kn71S6do1hoHi7S8yRtqV7p9ytzcE7DdyiS J2fZnAJLSNgdBgdFABOCjG6km9NNfn06DcprdfidjRWDaeLtKvNVewV5EbZHJBNIAI7hHZlV 0IOdu5CoYgAlk2kh1J07rVLGzR3nuok2yCIruyxkK7ggUclyCCFAycjApVIunLlno/6ZfNHu W6Kw/wDhKbOS8+zW1rf3LGPzI3itiElXOMozYDDJQbh8v7xTnaGKw6f4ivPs8baxo91Y7Y1E 07KDGrglZG4Y7YwwXDE8ht2Nqswy9pEn2sL2udFRWBfeIzDqFgtlA97Yux+2XNtG8/kgxlow AgOS3ynIzgEZHzqaS88W2NlLZrcK0KSMy3TzsqfZMFFw/J5LyxAY4Ifdnbgk54t2D2sNdToK K5WPxVHBNLql9eW6aDO0kNrKP4XiyDkjO4ybZCuOMRqPvPirFv4kuYrKafVNFv7XbJMsZ2If Mw7CNAA5O9gAAThWYgA/MuUqsWJVoM6KisO18V6XNZwPNcxRXbRqZrNG82WByOUZVG4EN8nI GWIXG4gGsniW61RpDoWmvdQwXPkTyyt5RQqA0i7H2tuwQq5wNxO7aFBZ+0j3H7aHRnS0Vl6T rlrqqiMOkd6ikT227cYpFO2RN2MMVbAOOzKejKTce/tY76OyedBcyLuWPuRz+p2sQOpCsRna cUpJq6KU4tXTLFFMSWORpFSRGaNtrhTkqcA4PocEH6EUk88VtBJPPKkUMSl5JJGCqigZJJPA AHemtdiiSiq739rHfR2TzoLmRdyx9yOf1O1iB1IViM7TiV5Y42jV5EVpG2oGOCxwTgepwCfo DSuhXQ+iisPVPF2iaVZyzyX8M8iStbrbWriWaWcEDyUQHJfLKNvbIzgc1pCnOo7QV2DaW5uU VmyeINIieJX1K2USwfaUcyDYY8Fg277oBVXYc8hHIyFYiC68UaXbabY3yyvOl+qSWsMEZaad G25ZIvvsFVgzAAkKDx2qlQqtpKL18gujZorGg8T6dJPHFMLmy85glu19bPbrOScBVLgfMTkB DhztJClcMb2oapp+kwLPqV9bWcLNsWS5mWNS2CcZYgZwDx7UnRqJqLi7sLot0VlXmvwW109n b2t7f3iYzDaQFgCRna0rYiRtpDYZwcEYB3LmO38Uabc31vZhdQimuGKRfadNuIFdgrORudAu dqscZ7VSw9Vx5lF23+Xf089vuC6NmisfV/FGkaLDdvd3cYktomkMIYB5CF3bEyQGfGPlzn5l zgMM5fh3xWbnT9Uv9eddKij1F4II74C3McYRWQNuPUjLde5xxise1u9iHVipctzrKK5hNS1z XbyIaZGljpDrKTqLosrSgOqIYhvAUsBI6sVdCvlnncVBY2XiDWYIbrWLy50h/IjVrGzaLiYA lpC+GON5G1AxUqg37t7IOp4blV5yS8r6/cv6XWxXMdPWbrGvadoUHnahM6JtZyI4XlYIoyzl UBIVcjLEYGRk8isd7nxTpNlel4bO/hsfMkS6u7pYJLxMbguFQJFtDbd54JiOVUPuWzotjb3M 2svd6hDq88rizuiYAsaKqZMG3kFQ0sh5ycPtYsVzR7KnTd6kk12T1f8AlprqvLe9jm6G7NPF boHmlSNCyoGdgAWYhVHPckgAdyQKow+IdFuNSOmw6xp8l+GZDapcoZQy53DYDnIwcjHGDXKW Efhsa1fvb6O/9kpGlvFFbWDyWzylWMsgijUrlo5Yl8wqNykhSVznTu9U8LXlglg8MM9tDIkV pDEq+XMSAqLEQdu1tzR4JAIWVT8qvWKqYXq3+C/C7v8AejNVotXui9/wl2jS/JY3X9ozt/qo LJTK0o6blI42bvlMhIQNlSwIOI5/E0scEl7HoOrTWEak+YtuRNI2OAkBxJjIKksFwSpAKFnV fC8nhsWq2vh6OGOOC3iG1IWRhES5j3bgGOSXYZ5O/d/Fk6g1XTm87F/anyd/m4mX93sxv3c8 bdy5z0yM9a0dXDJ3hG683/l1+/5lqSavcyjr17qE6W+jac7RSqzxalc8WrICAWTaSznLAqpC CQBir4wTAo8V6oi3jBNFMSxPHY+bHMZ3BzKkz7GCqQAqtGcgMWIJwg2Ib7SbWP7JDdWUKW0J byUkRRFEhKE7R91VKlfQEY7VFpXiDT9amlTT5HmSOGObzdhCMH3AbSepGwg+h46ggH1mnF2h FfPV/wCXrp6WFzK9rlPwJ/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/AKKWrX2jxF/0C9L/ APBlJ/8AGK5m7FOSW5rUVk/aPEX/AEC9L/8ABlJ/8Yo+0eIv+gXpf/gyk/8AjFLnX9Ji9ovP 7ma1FZP2jxF/0C9L/wDBlJ/8Yo+0eIv+gXpf/gyk/wDjFHOv6TD2i8/uZrUVT1XTYdX0u4sJ 2kRJkwJIiA8TdVdCQdrqwDKexAPasPwp9s1SW41zVfIN4udPRIM+XH5LFJygbJG+dX5z8yRw 5AZTVFkN1pUGseONWgneaPZp2nSpLbyGORCJrvO1xyu5dyEgg7WYZGat+B4Irbw5LBBEkUMW p6ikccahVRReTAAAcAAdqpXGnXV/8Q9U+zazfabs0qx3fZEgbzMy3eM+bG/THbHU5zxi54Gj aHw3JE80k7pqeoK0sgUM5F5N8x2gDJ68AD0AqnOTjy30A6SiiipAKKKKACiiigAooooAK8/u PCvh+21H+xNE8C+HLuSztIZZXvwseEcuiYfyZGdv3T7i2OxyxJx6BXn/AI3is4PENteatB4U urGW08qOHxDfiHbIrkloVaJwMhgHI5bEf3dnzAHQaD4X0vTJYtSj8PaVpWp+U8Mg00fJsZlO NwRN/wBxDyvHIHU56Csfws9nJ4ctGsINKgtTv2R6TMJbZfnbOxgqg85z8o5yOetbFABWXqut DTbi3tIbC71C8uEeRLa1MYby0Kh3JkdFwC6DGc/MMAgEjUrm9blOneKtK1aa3u5LOKyu7Z2t baS4ZZJHt2QFI1ZsERP82MDABIJGQCv4n1KHVvhb4ju4VkQHTL2N45AA0ciJIjocEjKsrLkE g4yCRg1vQ6zptw18sV9AxsG23XzgCEgZO49h156ZBHUHHJXkMtt8IvEzXEDJ50GrXSRyqVYx yyTyJuXgqSjqSDhhnBAIIqpovw50fTvFt2LyW7vytpC9jJdTMHjxvR9hUjLKPLwwAKZTG3gl XV1c0jBShJrdf5q/5nXW3iTT5ry/t5biGA2geTMkgXdCh2PJzjCrIsiH02g9GGdGzvbTUbVL qxuobm2kzsmgkDo2Dg4I4PII/CsCfwJo738d/btd2t3GV2yRzmRVRSWWMRy70CKxDKoUBSqk YwKzrDwNcx+IZdSv9Q3pKg3C1Z7eQOowcMhGEkZmmdBj95t5bBJ1/dqNru/orem/fr2V99Dl 5qi0audpLLHBC800iRxRqWd3OFUDkkk9BQJYzM0IkQyqoZkB+YA5AJHodp/I+lcPB8NY7KzL WusXDamty91FeXkYuAsjGQbyjHltrpkggFoY228EG9o9jofhfxLq1tafZ7GEaba3EiPN0VDM jSHcegCrlvXk8kkk4KLtGXN6X/VLfoPnmmrr8TraKxdL8UWGq3txZKs9tdQy+X5Nymx2zGsi tjJK7kbIVsN8r5UbGxcudZ0yzVGnvrdA7Oq/OCSUOH4HZedx6KAScAGomnB2loXzx3uXqK5i bxZLPcXtnpuk37TxW+6KS4tXRXlYHyl2kBghKuC7bVGzryDViDxODPaR32l3unreTCK3e52K PmTcu/5vlZirqFGSCBu27gKy9rHuQq0G9zforAn8Qzx66LaHT7i508QyL9ot4mk3XKso8oEf KoAJyzELnIJBRqd/wlVh/bFrY7gsVwi7biRtg81mcJDtPIc+VLkEDBTb1OKpTi3YftYbXN2i uVsvFDQKk2qSJt1NVuNLjQBWkViFWEAn743RsSTjMrY+VCaYPGc1tpcf27Rb9NXe0WeKzEQH 2p+AwjwWPyk5Kn5lXnacGo9tAn28Or/r+tjraKyf+Ep0A/c1qwkP92K4VyB3OFJOAOSegAJO ACap23ih77Tm1Ow0m6vNP+dklhZd8iKSp2oSGL7gSF7rznd8lV7SPcr2sNrnRUVUsdV07U/M +wX9rd+Xjf5Eyybc5xnB4zg/lR/alj/aP9nm6iF32iLYLHG4gepC4JA5AKk4BGa5lvcrmVr3 LdFMjljmUtFIjqGZSVORkEgj6ggg+4pJp4rdA80qRoWVAzsACzEKo57kkADuSBTWuxRJRVdL +1kvpLJJ0NzGu5o+4HH6jcpI6gMpONwy5Ly2kuXtkuYWnTIaIOCy4Ck5HUYDof8AgS+ooWuw romoorAu/Gmg232by75Lv7QvmKbP98qxBwjyu6/KkaknczED5W6kYq6dKdR2gr/1/wAAG0tz forKv/EujaX9s+26jDB9ji82fcT8q8ce7fMnyjJ/eJx865jvvE2n2c8NvCXv7h2bfBY7ZZIk UsHkZAd21WRlOATuwoBYgVccPVlZqL1/4cLo2aKxofFGls5jupX0+Xa0ipqEZty6KCxZd+Aw CDcwBygOHCnIF671TT7Ce2gvL62t5rptlvHNMqNK2QMKCcsckcD1FS6NSLs4u4XRborGn8RR efJb2FhqGpTRMVcWsIWMYOGxLIUjYhvlKhiwORjhsSWPiLT9RvxYwi9juTE0ypc2M9vuRSoY gyIoOC69PUVTw9VK/K/679gujVorndU8a6RpSzrI7zXMc6wRWkJRprkllUmJNwLgMxU46FHH aovDXiXzvCNtqfiG5t7C5kMzTLckQeUFmKbSGPG0lF55yRk5NZKMnay32/Ij2keblOnorkZL /wAXanBdXVjYJp8KWaz2sFzGrTXMxDssZJcCIYESuGXILOFbgPV6PS9V1F5by71jULAyNvt7 O3MOLUYA+Y7GEjEbiQxZAX4yUV66ZYbl+OaXzv8Alf8Ay0a30K5joKzb/XtO028gtbqZxNM0 aKEheQKXfYm8qCEDNwC2ASDjocY63ni200+IXFvppdJEtDPNcYeYs4jFyUAVV7P5IbLB8BlZ cNcs9I06LRb9NUubfVYZ55Jr24u0j2SFCFG9R8gKLGinAAzHnAOaPZU6bvVldf3Xr69rf8N3 sc3Y2pJ4oniSSVEeVtkaswBdsFsD1OFY49AT2rOtvE/h+9877LrmmT+RE00vlXcbeXGvV2we FGRkniuU0d/CUzasJrS2Ni9yYra2eLzIUtlCbpEjGUSEzIzGQAKWALHIGNjWLnwjr0HkapJb 3Ee1kG8su5GHzYIxuUAJISOFAjk4ARqyhUwjesnb5flfX71+hmq0WrposTeLbKZAmjI+r3ZZ f9HteCEJGWZz8iApudC5USADaSCDRN4na1QXV3o+oW+nuyqlw6DIBIzJImcxRhTu3PggI4YK QofR0htKa1Y6SlskIYJIkCBNjKqqFZQAVZVCrtIBAAGBirN2LZ7V4rwQtby/unSbBR9527SD wck4x3zitPaYe+kLru3r+Gn4d/K2i1V7mFLrWs3vn/2Vo00P2eIXA/tCMJ9sU7tscWG+Rm2n JkwY8rmM7vlwvGkPiO58FeIdQluEtIVsbkDTMoyvbm3YEyPsLCYMS2EYp8irnkvXcf2hZCaW H7Xb+bCyLKnmDchfhAwzwWyMZ69q5nxTq1rqvw/8Zi1LsLOzvLWRmXAMiwktj2G7GfUHtgmf rMV7sYr8357/AJ7ro0K6va511FMlMghcwojyhTsV22qT2BIBwPfB+lZn2jxF/wBAvS//AAZS f/GK53JIbkka1FZP2jxF/wBAvS//AAZSf/GKPtHiL/oF6X/4MpP/AIxS51/SYvaLz+5mtRWT 9o8Rf9AvS/8AwZSf/GKdFPrpmQTabpqRFhvZL92YDuQDCMn2yPrRzr+kw515/czUorL1/TZt S0thZtHHqFu4uLKSQkKsycqGIGQjco+OSjuO9U/Cf+nac3iGTmbWdt0mesduR+4j9sIQzLkg SSSkcNVFmbZaHZazr/iNrtXIg1hSVRtolRrG2DRPjloydpKdGKLnIGK0vAn/ACTzw1/2CrX/ ANFLWXp+lXl94k8VS2/iDUtPQanGpitY7dlJ+x23zHzInOecdccDjrnU8Cf8k88Nf9gq1/8A RS1TnJpRb0WwHQUUUVIBRRRQAVxfjHU4dI8S6Hcvq+m6TLLb3dvHdajh4uTC5Tyw6HJ8vIfd tXbtIJkTHaVz5sfFiSzeVr+lNC0rvGJ9IdnRCxKoWW4UHaCFztGcZPOaALHhvUv7U06Sf+29 K1fbKU8/TE2xrwDtI8yT5uc9RwRx3OxWHbWXib7fbSXuuaa9pG5aWG10xommGxgFLtM+AGIb gZ+UDOCa3KACiiigArzuHTL3UfhT4Lk07abyzj025iR0LKxCKvzY5CjfuJHZT9a9Ern/AAJ/ yTzw1/2CrX/0UtTOKlFpm2HrSoVY1I7oxF8ValqMdxdW5axXSla41K1uLUrKYjLuRVDdHMMb knOMuMdcp0+ha9ZeIbJ7uxLGJWVSWx1MaPjgnkbwD6EEdqs2um29pe315Gv7+9kV5XIGflRU AzjOAFzg55Y+tcVquq2vgu+up3WdLd9YF3JDAdzSRzWzrnkgEGWNzgnjbnH3axvKnaUnp/X9 M9JQo4zmpUIWkldfO11566Rv0PQK47xVMdaOl2OkakIbhdY+yzTxHDRYt5WlCtggOIy2D2bH Qg4VfEupafYrY6lbM+uStGEKQloIzN/qzIVOFRZCYvvFjs3DOas+FLWbTbi7sp023E0FvqF3 kgn7TKHWXpxjMQOBxknHGANI1U5rS/8AX9XOSrl/LRnKo9rWt11WvpqiR/BmnrBHFZ3WoWnl wm2BFwbgeSQN0eycSIFO1ei5+UDOMg8d/wAIx4h0Dxtocdtq6TWlwHKyzW7uguhA3mvKocbm fLMCX3DJX7qAH1Sql5Y/a7rT5vM2fZLgzY2535iePHt9/P4Vq5vl5UluuivunvY8upRi0rLZ r8zO/s3xCeT4hhBl4mC6eNsY6Zgy5KNjkmQyjOCAB8pqX9j40uEgFrrek2rwzxs5WwdhcJn5 shpDsGCRtGSSgO9dxC9PRW8cVNO9o/8AgMf8jblOct9S1fTTMuoeHT5JkMofSXWaOJDgsXDF JHfdvYhIzkMANzZpLPQ4tV1m417WNLVLh4ha2tvcbHMUGDu3BSyb2Z5ASCfk2jglgekoqZVY uLSilf1/V/194uVPc5xNM13T47bStJuLKLS4MCO4m3NNDGqnZD5eMOoYKCxZWKZH3x5hgtfE Ou3OnTwf2FMmsCWeJCY2FohWRlRi7lSyBfKYlRlg52AlXCdVRWn1lNe/BN9/8+9/09Qsc/Pp niC2gkjsdce6edTGZb9Ila2JHEsflxAOR12MMNx8y4O5sPheYS3E1xr2pvJNIJf3LJCokVVR JCFX5m2ouVYmNjk7AMKOioqPrE+iS+S/y/IOVHIyeHdU1SJZruTToJY5JEFpLYLLbOpc5kKh wxaRgJMFyoyoKl131ShS6027u7TWdDm1S3PlJd6iVkmSWHDND5dv+8Ztj5RhncMrId24kd3R R7WDb54Jr0tb5rXbvdd0yfZxvc85tvCev3GvyPKlvYaRLcJfwKHE8sDoqKQ4OB5zkLJ5gMgV o2B3rI27eXwLYMlnHc6jqt3DAxeWCe5zFdtncDKgAU4YZ4A3HJfcSc9RRUzqKTVoRVuyX/D3 /wCH3EqMF0OV1TwRDdWN1a6Vqd3o0U5WXybJIhEJl27ZMFNwwUTIRlB29iSThWfww8+7W51e 9uHWVp3uLVLxpoxmVjEil0BICSS5bhgzFlIJJr0eil7V8qjZadbJPTpfqvJ32CVGEtWjzez8 M30PiGfw5BqupRafBAt19ojuNr7GCRwoxXaSw8u6xwRgQhtwUINqz8GG11mW7SSKNLOKOLRp CWlkt0CkOkhbl0JJABY4BO0oea66ioqcsklFWSS0XW3V/n93XUSoQXT+v6/HU5WfwFpU0MsA e4EF3L51/EZWK3T71cuVBCq5ZF+ZQOCwAHBWHQvBUvhl5p9MvbcSPGgeFbSOGO4YM7MZCoJH MjBduNgCghwuD2FFLXXV6+bH7GF7panP3Oi32v288OuSpbwbgbeHTrhwyMMFZGkwpLKwyo2g DgncQCubpXgm80iyW1TWIb6Nbb7Gq6lZ+cscGBujUB1wGbOc5yojU/cBPZUU4ylGPKm7eo/Z R3e5zkXheRLW3077ZCmlJJ5z2tvai3+fJYCNo2Xy0D7XHBbI5c5pup+ETqSRQvq15Jbbgs9v c4mjlgBB8vBxhgVGJeZBk5Y8Y6WiocU3d7+oOlB7mZb+HtItZJnisId00ZilLjf5gONxbOcl sLuY8ttXcTtGM2+8BeHtT16XV72yE88pjd0kOULx4Ct69AAVztbAypIzXS0VpzyTun/SKcIt WaMC18LR2pkMWoXsJlZlkEL7VaHexjiG7cY1RWKjyynrwcYr3Hh/UrBxPpGrXQtbbM8WmHa3 nSlizq0r5Yh9zD5icMwbPygV09FY+zjbQj2MLWWhgf2Fe3l9De6jqLnbMJGs4c+RsXayJyeW WRQ3mYBbkEbSAH3HhDRbhwy2n2dTbm1dLZzErwlixjIXGAWO44wT0JIJB3K43X/Fl9aXt5pW n2nmX0u62sQnLGYJC5Y5BXAWfdzgDyjk/N8szVOCvJHThsA8TPkgrvfX8Xr0W7OypkcUcKlY o0RSzMQowMkkk/Ukkn3NVLPWNPv7b7Tb3SmHdGu9gUGZFRkHzY5IkTHu2OvFc3rnjCZbD7Jp Ee/Xpbsw29qAH3Kk8iFmzj5CsL5I+7uGSOGqpVIxVzajgq1WfIo21s76Wv37Lq2b2raDa6wp WaS4iDqI5/s8mwzxA58tz3Xr0wQCwBAZsl74e0y+W0D2yRG0mhlhaEBCpiJKLx/CNzDHQBjj B5rk9G8S3ugaBJfeI4rwK1tFMkMikzgoy28n3yOCfKk5x/rm+9gmu6huobiW4jifc9vII5Rg jaxVXx7/ACsp49amDhPW2pOKy+VCTurra620tf7m7epn2fhfQrHH2fSrVdtw1ym6MN5chxlk znZ91eFwBgUal4Z0jVImSeyiR2yDNCgSTaWLOu8DID7mDYPIdvWq3iPxdpvhzTRdTSrK0jSR wxo4O6RA2QcZIG5QhODgsM1v1SVN3irGM8I4U1KULRle2m9t/uOf8Cf8k88Nf9gq1/8ARS10 Fc/4E/5J54a/7BVr/wCilq1/wkdj/wA8NU/8FVz/APG6tyS3ZUKU6nwRb9Ea1FZP/CR2P/PD VP8AwVXP/wAbo/4SOx/54ap/4Krn/wCN0uePcv6rX/kf3M1qKyf+Ejsf+eGqf+Cq5/8AjdH/ AAkdj/zw1T/wVXP/AMbo549w+q1/5H9zNaq9nY2+nwNDax+XG0skxG4nLyO0jnn1ZmPtnjii +t5buzkghvZ7KRsYngCF0wQeA6svPTkHr681h+DDqFzpb6neavd39vev5liLiOFSlvz5b5jj TJkXD4IBUMq4ypLUYEln/wAlD1n/ALBVh/6Nu6PBv/IDuf8AsK6l/wCls1ZN9ePp/wATJrpr v7PYixskuy+0RhCb4qWYj5fnCAHIyWxzkVo+A5/tXhhrjypYfN1LUH8uZdrpm8mOGHYjoRSU k3YlSTbR0tFFFMoKKKKACiiigAooooAK4PX9Zh0fx1Mv/CQaNo091pkB36oBIsyxyzcKoljM ZBk6sTv3fKB5b57yubg0/wAZQ28UT+I9GndECtLJosgZyB947bkDJ68AD0AoA1NEvPt+jwXP 9pWOpb93+l2C7YZMMR8o3v0xg/MeQenQaFY+n2fiCPURNqesWNxarEyC3tNPaDc5KkOWaVzw AwwMffOc4FbFABRRRQBz/jv/AJJ54l/7BV1/6KaoPFdrcJqWhazZ28txPYTy740QvmFomMmA Od5CBV5A3MAetT+O/wDknniX/sFXX/opq6CpnHmVjbD1nRqc6V918mmn+DOBTxffGw/4SCaT Zplh5UGpWa2+2U3GwiQJuPADyxDBOfkbB4+fs9N1K31ayF5aNvgaSRFfIIbY7ISCCQQSpIPo RRp+m2+mRTJbrjzp5LiRiBlndixJwOcZwPYAdq4FtY/4RBrHSbKCVpvMv7O2tFbKec8sclvv JYZGyReckjeR1zWPNKlrN6fqen7Kljm4YeNpJ6bfDZt39Gt/O3RX9JriNbii8Wa7oUFjqGdM nt70XjQOVM8CPErIrAdDIEBIIyu7B5qe513VvsyaHaxsNcdntVv5Y1+ziVF37m2/dZosSBdp ALAc7WxqeGbWHTYL/SbVNlpYXZjgBJJCuiSkEnrhpWA9gM5OSdYVU5rTT9en3dThrYHlotze t012cb6v5u1u+pV1Lwf9rt7kWmtalZ3M0QjMxZZ87CTFkSKSNhJIKFWJ5LFvmrl9K8Pa7pHx CuNPg1NYdOayleyYW5cQwGSMNGgJ2rKDt+c788MysWNenVUksfM1i2v/ADMeTbyw7Nv3t7Rn Ofby/wBfatHN8qSS08lf77Xfozzp0YuzS1Rnf2b4hXhfEMJDfO5k08FlfrtTDgCLIA2sGfBY eZkhlypdA8Wx3CSL4gs9QglllkvrK7s9sUyshURocsY0AVABzyzuxfOw9jRXRHFTV1aOv92P +Ro4pnOWmpazYWy2tx4WnkeLKqdMlt/s4TJ2KvmSI3C7QcooyDgYxUdl4StbtdUvNesbSXUd VylyIpGdEiGFRI2IVh8qIxIAJcZGMKB09FRKsmrRil6X/VsORPc5+a08TMgij1G2UWrK8cu0 b74Ag7JRs2xArlSyAkkhwEA8tqtjrms6zo1lFFpd7ZahPFEbq4mtxFHb5x5pUSHcW4lVBtb5 lUsNjKzdVRVrEK2sFfpp/V/y8raBY5+bTPECILS21x5IZWVnvZ0iFzbgEFlRVi8twwG35gCm S2X4UNtvC81lExg17U5LhJHmgadk8tHdiz7o41RZFYk5DZIH3Ch5roqKn6xPokvkv8vw28tX c5VucTF4LuLuC1vJbx7K6+yxRLYsiyw2ioCY1Qghi6E7TLv3ENJs8sspSO2t7jyJotW8O3d0 YLxjJdJOxluLrZsSeNMgLGyFMEOPKY4wojMg7qil7SDupU4v5Wt92/zuT7KHY8207wV4jkvJ I727tdMt4JppLSSw/emKKZmJiiJCeW6AuN+0hhKvGY129RB4Ps0nje5v9Tv4VgET2t9dtNDK wOfNdG4Le33R1CggGuhoqak4zd1CK8kl/X9W2SQlRguhxmp+AYzYJDoeqahpvkyJNFBHcfIX VPL/ANYytImY/wB38p2qMHYcEHD0j4TxK0kGr3MskBtIeLfYkfnlNspUbc5zHG28BSwwG3Dc D6fRSlUco8r/AC1fr3+fW3YUsPTk7tHnWk+HLzUtR1LSr/UL86dp03kvsuCoumkLSMG2FST5 L2wYkYLNKQCzeZW7Y+GLuPUb3VJ7qGLVJZDEl5bxqWe1CqoEilQpk+XduxwQo5QbK6iipq8s 5cyVttFt/V9fIaoQOVuPA9sdIGl2V21raLukWMwRyKspBBYKRtVG3sHjUBWBwNoLhotI8MX/ AIS40aaCewMhaaw8lYSyiNEDIw6y/uxncQrFj9zrXX0Upc0t5P72DoxvdHHeIfCuo+K9MlXU J47a4ik3WsFvcyGArt2uspARjvDOpIxhSMdW3MTwjq1vDqcUmo2OspeBNo1i08zasf8Aq4mw fmHRix6MGYKS5I7SiqjOcY8qk/v9P8l9yD2Mb36nPpol+ptdP+3tHpVspKPbkQzNxtETBFCB ApfldpHyYwV3NDN4OjuGtEutSu7y2hdJJFu3LyF4wwjeNwV8phuGWUZbaMnli3TUVny683Uf so9TJg8MaDbTrPDo9ksqMHjbyVPlEHI2ZHyDOWwuBuZm6sSaNv4E8Owape6g+nQ3El3I0rLc RrIqsxy5GRnk84JOMnbtBIPSUVcm5X5tb/pt93Qbpwe6OasvClxZaabSPxBqW6df9LnJVpJX IwXVmBZGwFUckBR03fOH/YdY0OD/AIlsn9oWcPyQaeypG6R7MKgkPXawXBPIUvne23HRUVl7 OK2J9lFbaHOpoV9d6jDcaxdRXUa+YXt0XEOQHjQKhB4aOWQvkk7goB2jFXB4b01Xj8pJYbeO RZVtIpWSAOrBlYIDgYIJwMAk5YEgEa1cPN4wvvNkgEcXyyC986MYC6es7q8mDnf8kanK4LCY FR8uTMuSG51YbASr35Fe3fzO4qFbW3RIES3iVLfHkqEAEWFKjb6fKSOOxIqJNTs5PJ2zZ86e S2j+U8yJv3L07eW/PTj3FcP4i8YX2o6NZ2vhuOV9WuYDPcwQjc0ETQBjzwQf3qFWHVlx1+Uu dWMVdm+GwFXEVORK3dvRLfV/c/mdfPocE93NK1xcC3uGD3NoCvlTsFC5bK7sYVQVDBSFwQct mvN4Q0KSW3ePT4rbyZDIFtVEIdtrKC23HK7iVb7ynkEZOcvTfEsmmwxQazFOLm9vYhBFty8S XOXQOWPRHEsfY4jHyjIrqra6hu4mkgfeiyPGTgj5kYow59GUj8KI8k+hhiMC6L9+N136GTB4 O8P29x9oTTImnO7dJKzSM+4OG3FiS2RIwOc54z91cY/jXQLG1+H+vG0+0Wyw6ROnlwzuqyKk JChhnkjavzfeIUKSVypu6v40sbF9Jhtm86TUvLmRsfLHb7lLyOCQVGwuQSMDac8A1P47/wCS eeJf+wVdf+imoiqbdopaE1MG6MYylCyd7adtGdBRTJZFhheVgxVFLEIhZsD0AySfYc1mf8JH Y/8APDVP/BVc/wDxutHJLdjhRqVNYRb9Ea1FZP8Awkdj/wA8NU/8FVz/APG6P+Ejsf8Anhqn /gquf/jdLnj3L+q1/wCR/czWorJ/4SOx/wCeGqf+Cq5/+N06LX7OaZIlh1EM7BQX024Vcn1J QAD3PFHPHuDw1ZauD+5mpVewsbfTNOtrCzj8u1tYkhhTcTtRQAoyeTgAdap+ISItGnu31e70 qC0Rria5tY43YRqpLAh43BGOeBngY9DH4Yt9Wt9DhOt3s9zfy/vZFmEQMG7kRZiVVbb0LY+Y 5PAIUUYFfw9/yHPFn/YVj/8ASK1o8Cf8k88Nf9gq1/8ARS1h2mqzaT4z8SyTyZ097psQpGN8 twLSy2IrE/M7KWCoME4br23PAn/JPPDX/YKtf/RS0lJNtdiVJNtdjoKKKKZQUUUUAFcv4p1G 8tdR0+2S41WzsZoppJbvTNPN1IsimMIhHlSBVYPIeVzlBgjkHqK5vxRa31xcWTR22pXmnoko nttMvfss5lJTy33+ZHlAolBXfyXU7TjKgFzw3N5+nSN9v1W9xKR5mp2X2WQcDgL5UWV99p5J GeMDYrD8L299bWVyLuO7hga4LWdve3HnzwxbEBWSTc+4mQSMPnbCsoyMbV1L69i0+zkupkne NMZEEDzOckDhEBY9ew469KALFFeZ6zbWOp6L481yWzjkv7FHfT7q4t9txaAWEMqbC43xFXZn wMEMSeDmuo8aabfaxpdnp9pp1pfwS3sT3kN1N5aGKPMoydr8GRI1I2NkMRxncoB0lc/4E/5J 54a/7BVr/wCilo8Jizgtb+xttGsdJms7vyrq3sAPJMhijkDKwRN2UdASVByCOQATn/DrUbq4 8G6BbS6NfWsMelW+27meAxy4jQDaEkZ+RyNyjgc4PFAHYVyV1a/a/iWsN1b+ZYvpPmAOmUeR XeM/XCTuCvT5xkdK62q72cb6lBfFm82GGSFQD8pDlCc+/wC7H61E481jow1b2Tl5pr+vmWKy bj/kb9N/68Lv/wBGW9a1Y17LHB4psZppFjij067Z3c4VQHtySSegont935iwyvNpdpf+ks2a K4ey1W48P29/cXFtdXN3qEaajDZHPnvNIWDQIDyREixA4GVGTjBCjp9O1ux1aeWOxl85I41k 81fuMC8ifKe/zRNz06EE5pRqKWnU0r4KpSTktYrr06f5289ezNGioYbqG4luI4n3PbyCOUYI 2sVV8e/ysp49awbXxVZQaez3t4sty810beKPG+dFkzGqDgMzRvEVHVgwIznNU5xW5nDD1ai9 1Xemnqm/usjpKK4+z8VSWmoWGiateWZ1h714blU4TYYzImzpgfPEg3ck7gMkZrqrq6hsrOe7 uH2QQRtJI2CdqqMk4HPQUozjJXXQdbC1aMlGS32t1V7XXr0JqKxrDXo5bLRRdlVvtRXaYU42 SLGWkBUnIClSp6kEgGtGG/tZ1Vo51w8zwLu+UtIhYMoB6kbG/BSelNST2IqUKlN2kv61X6P7 ixRWdpWt2Osadb31rLiC5keOAyfKZCpYHAPPIRjjrgcgc1U8SeI7fw4tlLcyRLHLJJ5iMwDs iRO/yAkZO4Iv/AgOpFDnFR5r6FRw1WVX2Kj72unpv+RuUUyOWOZS0UiuoZlJU5GQSCPqCCD7 in1Rg1bRhRRRQAUUUUAFFMiljnhSaGRZIpFDI6HKsDyCCOorG8Oa/wD2v4f/ALSvVitHTLzJ u+WFCBImWPB/dMjE+56cgS5JOxrGjOUHNLZpP53/AMvy7m5RVS11Ozu7JLyObbA0hiDSqYzv D7NpDAEHd8uCOtS3N1DaRLJO+xGkSMHBPzOwRRx6swH407q1yXTmpcrWu3z7E1FFV0vI31Ke xCt5sMMczEj5SHLgY9/3Z/SnclRbTa6FiimSyxwQvNNIscUalndzhVA5JJPQVgL4y003VhGw ljjvJJIVaSNgyyiXy0VlwSu8rIQWx/qz3ziZTjHdmtLD1aqvCLf/AAzf5ILHW4Vi8T6g0ss9 rYXcg2jOVEcEe9VBx/EH9iST3zXRV5NrGp/2Z4N1OWObyZtWsLScoFyrTXMk7y4GOMoCoJ52 ooySBXWeIfGVvpF7pREn+hyyXZuH4BPkIwMYDY5MmADkZKgcg1hCukve/q7Z6mJyypKS9kr3 ul/27CL/AB19WdbRVSfVLG2iikluogks4toyGzulLbNgx33Agjtg5xg1keIfEqafbm2sLi1O rS3cVnBDchtpkYoTnHOAkgJI4GQOvFbynGKu2eZSw1WrNQjHf+n93XsdFWH4btYZtKsdUZP3 88ctwnJ/drcSCZk9Dg7RnH8PbOK5K31fVNXgurZ9Glt4/EMksLEnP2Z9ghwS23JVbed2UD/n mMgtXpNRCSqO6OnE0J4SPs5PV9ntZNPbzbXy8zzmfR7rTJJPDWnXzK097HeW8z8+WPKmaGI7 gxKo1pHz1I4wO9vwZ4Nk0u2t5Ltla4sdRnMErQ4kaDa8YXnlVLM0gAJHOe+a10s5H+Jk98GX yodHjhYE/MS8zkY9v3Z/SukrOFGN+Z9NjqxWY1VT9lF/Ek5d7tO/3pmXqvh/T9antpr2Nna3 WVFCuVDJKhR1OOxB7YIIHPXPIaj4XTwrp17f2cl0umW9/bXosLd2kykZj3E7j/11Yg5ztjOV 2kH0OitJ0oy16nHhswrULRu3FW06Wvf5ddfNnn/hbw3rB1+S/wBeWCe2RriWGJ48rBcvcZYx BssFxEHDHH+sGM5Y1WsNR1fRf+KdtLS61Ofw9GZ5ZEkKi6VtwSLGDjEciuBzzEVAOA1ek0wR RiZphGolZQrOB8xAyQCfQbj+Z9aj2FkuV/1/VjoeaupKTrQTTtZbJWv212cr9277owvAn/JP PDX/AGCrX/0UtdBXL2HhC70zTraws/F2uR2trEkMKeXZnaigBRk2+TgAdaZqHhzxI1vIdN8c akk4jbYLm1tGUvkYyVgBAxuB69Qe2Du3ZHlxjzNK9jq6K4aPw543kMsh8cXca+dIscL2NoWE e8hHLiMgnbh8bRn7uVzkUV0zx7Jrgs18UXq2qXcvmTPa24Bth5JXa32fHmESOPTKH0NS6lt0 zojhXJtRnHTz7f10PR6K870CHxTqzXVreeJtasbyGyt5fntLQr5kock4MAJUbVBHBDB1ycZr GvPFV9pUso1DxNrg25jCW6WUg81J2jlTebYchNkgBC8HkjctS60EuZvQ1jlmJnUdKEbyVtE+ +q/D7up6L4n0m813Q5tMtL2C0W4/d3DTWxmEkJ4ePAdCNw4LA5AJxg4YWNMh1iLzf7WvrG6z jy/slm9vt65zulfPbpjGD1zxwt3qs1vqur2UPjbWrqWwtkdIYbe0LSTFyhjyLYjO5oV4HBY+ hx0Q02cwtMPH2qmJYROzgWG0RnJDk/Z/unaeenB9KqNSMtmYVMLWpJOcWk/8k/1QyfTbfVvG utWd2u+BtN0x2TAIbZcXTgEEEEEqAR6E1b8G/wDIDuf+wrqX/pbNVdLG78O6jNfrHrniO6vY o4ZH3WaeSkRcqMfuRyZX/vdO3GZPA0jTeG5JXhkgd9T1BmikKlkJvJvlO0kZHTgkehNVbW5z WV7nSUUUUxhRRRQAUUUUAFFFFABXH+IdTvIvELWb3uuafYpaRSxTaVpRuvOkZ5A6u3kSgbQk ZAG0/Oc54x2Fcn4is76bWRLJY6zqGn/Z0WCLSdR+yNFKGfzGk/fRbgymILy2NjcLn5gDc0ST zdHgf7VfXWd376/tvs8zfMfvR7Ex6D5RkAHnOToVl+Hob630SGPUTJ54eQqssnmPHEXYxI7Z O51jKKzZbJBO5vvGxqWpwaVbrPcR3bozhALW0luGzgnlY1YgcdcY6eooAuUV5v8AYLP+xv7e +yQf2z/wkvkf2h5Y+0eX/avk7PM+9t8r93jONny9OK3PGGj32t3+jwx6LpuqafbvLczxahP5 cZlCeXGCPLk3DbLKcbeqKdwxhgC547/5J54l/wCwVdf+imroK4fU5bOT4PeIVsdOg06GHT9Q t2tbdQI45I/NSTZgDKl1Yg4BIOSASRXUaZqN1f8Am/adGvtN2Y2/a3gbzM5zjypH6Y746jGe cAGhXJaXa+f498R/a7fdHD9mltvMTj50Tcw9fmt4+ecFOMHNdbVdLONNSnvgzebNDHCwJ+UB C5GPf94f0qJR5mvL/I6KFb2Uai6yVl/4En+SaLFZNv8A8jfqX/Xhaf8Aoy4rWrnby+/s3W9b vBH5rxaZbGOLdgyvvuNqDryzYUcHkjg0TdrN/wBaMeHi5qcY7tL/ANKib8cscylopFdQzKSp yMgkEfUEEH3FPritKvI/B9lPohVpxZMsigDZttRHEZrg5ySvmPKcDcScqOFO3odF1qPW47qS KFolgmERVz8wPlo5DD+FlLlSvOCppRqJ6Pcuvg507zjrDo/J7afM1KKo2Wr2V/pjajFMos1a UGZmG3EbMpbdnG35SQc9Oaw4fFKpoun2rTrL4hubaILbtGcmZgysXVQMKro+/H3Qp6HALdSK IhhK020lqnZ+W/5W17HVUVyWl+L7d7+00q8v7X7XDaTm/d2C4mhdUJB4AB2yvjGdoBworZ8Q aq2kaTJPCqyXkjLBaQsQPNmc7UXkjjJyeegJ7UKpFxcl0HPB1oVI0pLWW332+79DUorOt9bs bu8tLa3l8x7q0N7GRx+6yoBIPIzv447N6Uw69ZDQl1YlhE1kb5YTjzTGFDHC56jcB1xkjnmn zx7mf1erdLlf9X/yZqUVnWOt2OofY0ilxPd2gvY4G++Ijt5YDIHLAdeecZwazNS8XWtlrEFn DLBPGVcTkPjy2WWJWG7plEeR2XqAgJ2jmk6kUr3KhhK05+zUXc6SiobW6hvbOC7t33wTxrJG 2CNysMg4PPQ1NVmDTTswooooEFFFMlljgheaaRY4o1LO7nCqBySSegoBK+iH0VnarqX2Xw5e 6pZtFN5VpJcQtncj4Qsp4PIPHQ1Fpmp7fD0V1qE2ZocwXEgX/WTI5jbaoHO51O0AZOQMZOKn nV7GyoTcOdd7edzWoqpZalb3thY3iN5aXsavCkhAZtybwMZ67QTgZ6GpZrqG3lt45X2vcSGO IYJ3MFZ8e3yqx59Kd1a5DpyT5Wtf8tyaiiq9teR3U95CisGtZhC5YcElEfj2w4/HNO5Ki2m1 0LFFQ3V1DZ27zzvtjXGTgkkk4AAHJJJAAHJJAHNcxeeOrOO1lMA2zyaYb2zEqlg7iJpTG23h SqiNiN3IkGOxMSnGO7N6GFrV/wCHG/8AX9X7LUs6Priv4Zj1NHa6iuNReOJmYgmOS7MaHkZw FYYHoMcV0leZ3yLpMmn6FZyT+Tb+ILQLGAceSkUG4tgbQDLIjHplnz1rcvvGa2/iTS7eFGks L22haOQKQskk8yrHk7cqQiytgkbuRwRWUKqStLpZHo4jL5VJc9BXUuaS9NLLtf8ArZHYUVnT a9pFvLbxy6laq9xGZYh5oO6MKz7/AGTarHceOOtYeueLW82307w1PYXesSzyxm2nLfKIlcuD gjadygDOAc56cjWVSMVucFLB1qsklG3W70Vu7fbRm/qGpx2Wi32pRbbhbWGWQqr8MYwcrnnB ypB9DVez8PWdvpC2E6+fusI7Cd8lfNjRWA4B+X77dOeevArldGu9W1H7HptzpqwWt9MurQXE cyuBH5/2l8g4OcvDHjHUs2SBivQKmDVT3rG2Jpywn7tS1bvo76Lbbs76b336HnMWi3V9DJ4S /tNmksVuTNfNHvZpJfLYOyk5BZJ7hBlucM3Uca3gnwpHo2maRdlFjvBZSLcA2+yRzKySAOeu U2lef06Vo6Npklv4o8Sak+4LdzQRorJgERwr8wPcEuR7FT+G/UU6Mb8z9Px0/I3xmY1HH2MH o7Sei1bjeX4yemytoZOoeGtL1S/kvL23855bRrORGPyvGXDjjsQwyCMEZ9hjlZPDl74Yh0+a ynaSKDUZvs9q0haGHz/MiiLZIJUFocgcjdKctu49Aoq5Uoyd+pzUMwrUlyN3j2e2zXy0Z5z4 O8G6hDoWpQaiyxi805LS0d4QJYEdWeRSvXAklIwTklOwxWVrmt6xeeCvENtFYz3K6Tpk+mXk rXGFmcw4lnyw+YoYzgcsRLn5eh9bqhqekWuq6RqWmyqYotRheGd4QFch02Fs4PzbcAEg9B6V MaCikovY3q5pKtKpOtBNy26JPZvTuvx13L9Fc/8A8I9qn/Q565/35sv/AJHpknh3WCo8rxrr StuXJa3smGMjIx5A5IyAex5welbnlI6OiuJtPDvjOSzsHufG17HO+xryP7HafIMMXVCIiCc7 FBzgYZuchRlnSviL9pmiTxPdssFtI5LW1qpmk3TCIJ+5KgEJEWBbID9fSHO3RnVHC8zaU46e f9f1Y9Korg9M0/xVqEGqLL4o1K1vLS78mOIrZyIyhI35cWwPzBjg7flyMqSCDzdt4r1CbWNM sX8T6+v2+aHYyQWLL5ckQGA3kDLLMSpOMYVhjcKl1oRtfqa08txFVyUFflV3Zrtf5/I9E8Sa PqWspZR2Oo2lrFBcJcSx3Fm04mZGDRjKyIVCuA3B5KqDxuDaGmx6lFbsuqXdpcz7yVe1tmgU LgcFWkck5zznuOOOfPBrM041M2vjzVZltL2OJWW2tCph2B5Xz9nwSFS4Ix12DAORu6o6Rdrv 3eOtXGyRYnylj8rtjap/0fgncuB33D1qo1Iy2ZhWwlajZVItN/8AA/zRTstIh1bxBrbTn5LL xDFdhefmZbCALyCMYZg3f7uO9aPgT/knnhr/ALBVr/6KWq9vFd+GJ7tINO1zXpL6UXU95vs1 +fYsYXBeLGFiXovfqTnFjwJ/yTzw1/2CrX/0UtUkk7nMkk2zoKKKKYwooooAK8/8XolpqNsN R8OeDZNCXzjHc6xeLDidyjkDdCwVmPmsQN27buJUjDegVw/iafSNWvJIbka5Hi0u9MuPI0S7 kDxTBQ5RxEV3Bo0Ib5lxu4OQQAaHgV7OTSbtrCDw5Bam7OyPQJhLCv7tM72CqDJnP8I+XYOe tdRXP+EVH9nXc3nTyST3byyh9PmsY1chc+XDL8yqfvE5O52kOckgdBQBj33hfSdRvJLm4hnL S48+KO7ljhnwAP3sSsEkyoCnepyoCnIAFWL7RbLUYpI7lZ23SiZXS5kR4nChMxurBo/lyDsI zubP3mzoUUAU9N0u00m3aG0SQB3LyPLK8skjYAy7uSzHAABJOAoHQAVl+BP+SeeGv+wVa/8A opa6Cuf8Cf8AJPPDX/YKtf8A0UtAHQUUUUAFcl49+e10a3P3LvU0s5D3CTRSxMR7hXJHvjg9 K62snV/+QnoH/X+3/pNPWdRXjY68FP2dZT7Jv8GaElrbzXENxJbxPPBu8qRkBaPcMHaeoyOD isHSrOSy8d+IZpmULfw200AzywRWR+vUg7c4zgOuetdJXJeJ/wB/4o0PTDxHqMF1bSn/AKZ5 ikdf+BLGyZBBG7I5FKpZJS7P/gfqXgnKcpUr6Si7+SXv/wDtpUsfDLawkGom8lj0/UoJWurd ZGUyRSXBniXjGDtkZXOTwSo67qNO8AtFLFJfXEUjwfYJbcqGPkywKqShenDqijP0yPlFdxRS 9hDS5o81xPvKLsn/AJ3/AOB5pW2MO/8ACel6kbt50lE9xOlz58T7JIpEjCIysMH5RkgHIyxN c7BYvbapF4JuJJdStG8q+MsyqBFax/KkRXofniiBIHzB34B5rvqha1he8ju2TM8Ubxo2TwrF Swx05KL+VOVJN3RFHH1IRcZu6tp5NLRr0/q5xtxoWqRa9JPaJ5zWM/2qwMjbVIuZVM6FtuBt CTdnO2ZccgZwLLT9Y8QQaDZve3lpNZ3t1/ac9q2CkzvMSAU+UMAjAnsJkwCGxXq9YfhfTf7P tdRdllWS71O6uHWQYxmUqpHHQqqn3znpWcqCckun/Df8N6HZRzSUaMpNLmVrO2uqkn8tXL/E zmJ/DqeEJV8QXd750Nj9jSIkMBGNq205KAnOU2sCOcqByB81jw3o11rN9rWra7YtZXM2owPF blOYzb4KMrnO4HO0sMAjdg88dbrOmR6zot5psu0LcwtGGZN4QkcNjuQcEe4q9VKglLy/XUyn mlSVHXWo9L9orlaS6atNv/gu/FeATcaZHeaFOjfZYbm5/s2V2UtJCkpRwQo4KuQcnrv4GBXa 1nS6FpssSo1tgpJJIkiOySRtIxZyrghl3EnOCOOOnFcF4c1jUb/w4sVq+zV7+e2lhlmlZkby 02nzGHzfN9ilyAD99RnkkJS9klB6/wBf1Yc6Kx8p4mOmqv21b137JuXnfuekSXVvD5vm3ESe TH5su5wNic/M3oPlbk+h9KfFLHPCk0MiyRSKGR0OVYHkEEdRXn8EetReLVsvEM8Ur6lPbtA8 KBV2W6yXDKmOQFlKLl+WUn6g8XaVeaR4aheTUvM0ywgmtxCIAu1Wt5IYmZsli+5wpIwp3A7V 2kk9s7OVtv6/LUFltP2kaXtFeVmmrtO66efNeNtNfM7Wz1nTdRBNnfQTqJvIDRuCrSbN+1T0 Y7TnjPQ+hrL1XW5or+0j02WK4+02jmJRh0aV3jELMRzsx5rHB5VHIzt4br3hCPXb24u2v57e VraOKAxf8sZEkZ1lH+0N2BjBALjPzcVtD8PXCXFrdzz7EsZBbW8JgKsYYBcQoWYtyWWUNkAD gYHOabdRvlt8yKdPBxi63Ne32WurXe3+VrehmafLJ4e8Fan4cMjC+0+yu2W5hOFyI45SQeCC DcAD1254zity+8KLPdstncNaWFwqi7t42OyQKoj2BOiq0RZTjBBSIjlTmtrmgXF14ktbqFtl vcyRQ3IRSwaNRI8odemJPLt0LdSFCnjg9bShTveLWi2LxOLceWrTl70ruXq7fqr26HG3umap eXt9pVrDLbW8M7aja3LN+6aRkyiHBy2LgySkEEDYowQy4yG0nxB4m1w2+uzSwWk1pEL3TYZF 2QhvO2upBIJ8yFGHU/PtJIU59JopugnuzOnmlSnH3Yq9rJ9U+r9Xvfe9rPe/JWUXie+vbiw1 d4vsMV2c3Npvt2li2FgAQdw+Z4gNp/5ZSBmP8WNHqmsaDJfanfwTzXdzpzMwkTcsb20UQztU ghDNLMDt4OQwwuWPo1FN0ez1FDMEm+amnFqzS0/4Zt2fySOPm0vxJJHBA06y21re20flyOGa aCOVX88uRu83ARWBIHyOQDuXE2v+C7fUxPPZytb3TsJkG9lQTIk2xlK8ofMm3kjOSucZJJ6q in7GLVnqZrMa0WpQtH0Vr7b/AHHG3VrbzfEG30tbeIQRWEU5iKDy/JUXMJTHTrOnGMYB9gTw X4d/sqXWob29l1KZLuNDJOMj5VWdSAScHfMxznrzwa6FtIhPiOPWlO2dbR7Rxyd6l1Ze+Bgh u3O72q8kUcbSMkaq0jbnKjBY4AyfU4AH0ApRpe9d93+JrVx7dL2cXo4xT9Yv+tvnseeaT4M1 O10S5iuwstzpazx6KgVBl9/mrOTk4YuFGCQAFIIO41ueHvBVro1zHdTlbi5gXy7eVhmRVVpg pL8ZPlSIhGMDyxjoMdVRRGhCNn2FXzXE1oyi3ZS3tp6/fq33bdxkcUcKlYo1RSzMQowMkkk/ Ukkn3NPoorY81u+rCiiigAooooAKKKKACiiigAooooAKw7TQbQ3WsfbNNtXgubvzI1eJGDq0 UO4495IyTnqVB9DW5RScU9zSnVnTvyu1/wDO5x//AAiLQa1oUkMUDRQQv/ad1sCSXUimNo2Y j5ixkQPknsck5waK+HtU0eK30lFl1GzvI4rCafPKQIys2/J+QFZLpRjOAIgCG5PfUVk6Eeh2 xzSurKVmv1u2n6pv7kl0OBu5PEHhS9FxJqEt7pEck2IrplaacJZ71y4HA3ROOADnJO7dmtf4 fwzW3hMW9xcyXU0N/fRPPITukK3cw3HJPXHqah+IP/IEi/7ev/SK4q74N/5Adz/2FdS/9LZq KcbTkisZVdTD0ptJN3vZWvZ6N+er/pI6CiiitjzAooooAKKKKACiiigArzPxEttY6yItb8M+ A49Pjt0h02XVL9IWZEZ8ou6BsBVMZ2AYXdwzZ+X0yvP9Xu9L1K/uLyJtchkmithhvDt64Elt OZ4W/wBWPl3M4derAjDIQSQDpPCBhbwxatbx6NHAXlMaaK4e1VfMbARgACcfeOBlt3ArcrH8 LQRW3hy0ihed4xvI861e2K5djsWJwGjjXO1FPRAoyRydigDH/wCEX0n+0ftvkz7vN8/yPtcv 2fzM7t/kbvL3bvnztzv+b73NSXvh/T9QSNZ/tYeN5GSaG9milXe25lEiOH2E4+TO35V4+Vca lFAHL+LbG3074Y+IrW1j8uFNKuyAWLEkxuWZmOSzEkksSSSSSSTXUVz/AI7/AOSeeJf+wVdf +imroKACiiigArkvEf7zxx4Vtm5hm+0+YvrsEcq8+zxofwx0JFdbWXPFIfFNhMI2MS2VyrOB 8oJeAgE+p2n8j6VFRXjbzX5nVg6ns6rl/dl9/K7fczUrktM/4puDxUx/fzC/kvIoT8jS+aiF FXrnL7owRnLKQBniutrj7yNb74mJYTFvs66dDfFUcrvkimkCBsdVBk3Y9VX0IM1NLNbmuC95 ThL4bXfyaf5XM+4+G8jx3kKakzW82nQw+STsWW5ijMcbvgZCD5W25OW5P3QDuaX4NstO1OfU XZZbx9RmvUmWEKyiRSvlFuSVG4nqOcHFdJRRGhTi7pDq5pi6kOSU9NunZL8kYGp+DtH1Sxkt jC1szzSTG4tm2yh5P9YdxzkMCVIORjAxwMUdHaa58Sz6XcXEt1/YkjytNMRukecbomGB/CjT IRwPukDoF62mCKMTNMI1ErKFZwPmIGSAT6DcfzPrTdNXTWhnHG1PZyhUfNva/Rvd/NXVtru/ Q88TwtrdpZXg052a8slextpJZ2R5bfy5mQrJtHIM8Y28LugHIwMZlhokvjVNHC3F1FpMOimz JKOEadFTJxkAgOyH/aNuRjABr1msbwlYLpvhHSrUQNAy2yNJG+dyyMNz5B5B3E8dqxeHjdR6 f8MejDN6qpyq/burP1Urv79u135GHfWcfhHU4tVhZru4v72eCOKQ4P75TIsQbsDNGME8KJWy OrU/w14a+2eHFl1u3liur27uL+5ts7QGlR4yuOqjY+cZ3A9+1dPqGm2+pxQpcLnyZ47iNgBl XRgwIyOM4wfYkd6t1oqK5r9DknmE3RUV8fV9bK9vz166I5X4f3d1L4WtbS+2/aLaGLYQfvwu gaNsYHABKZ5yY25PNdVXN+LNH08+FtSmW1WKW306VIngJiYIqMRHlcZT/YPy+1YekzarfQ6X ZKkFrq0FzcX85njYxqX2sybMhshL3uRho8dDuqVN07QeppUw8MXzYmD5bt3VttL332v5aXO4 n1CytoZpp7uCKKBgszySBVjJwQGJPBO5evqPWnTXVvbZ8+4iixG0p3uFwi43Nz2GRk9siuC0 7Qr6XVbzw/rd1LdmX7RqEt9EMZEsf2aNSSMKdvnHbjA2rgkAipfENheafrulaheanLeJdz29 ksRQIsUpuY5SVA6IViYc5bhcluoPay5XKw/7Po+1jSVS7av117W09XrbSx1sWu6XPzHexFRH JKWJwoRNu5iTwBh0YE9VYMMg5rD8Q3V9f3GqaDYPE8l3aRwRCQZSJmEpmZyvIwnleuDJHxh+ bOreCdN1eS9lknvIJryZJZpLeUKxCxGIIMgjbtZs9zuPOOKdoejX0d6usanNEL6eN2mt4otq xs6QAqDubO3yOvfd2xTl7SXutf8ADE0nhaS9rCV2ls19rRq3daNN6fK9jMhvIz4ctdICt5um 3NjAzkfK4S98kMPQnyCcdsgZNaKeFFi1qSeG4aPT5Ln7bJbBiRLMSCxPoA8cLqQevmD7rACt /YFwnjVLgNtsJpJLqSNVLI2xYxGjdlPmyTyjHU/NyRletohC/wAS2DE4n2etCWk7t+V7aeqa dn0vpqcaNI1edpra2P2U6XI7WUkuTDKzyiVFUAghEiAhJA6SOo4UhsvQ/Cl1qHiPV9Q1S4YB rlZjDuzJbXIjjkiKtyGEYmkTB+U7QdpBwvo1FN0ItpvoTHNa0YyjFJcy/VN/e9fJ7aaHD2+k eJ9X05zqt3EtzHBcxQPGzwGO4BWOOQbQDjEbtv6kTMoXbwaL3N9af2npN7ZXhGtXMZlkZkkN stzPJDhiGIA8mNNuNwDcHggV6NRSdHzHHM3qnBWvdJaW1ucrDpfiSXVNIvtUv4JRFcyPNaWs QWGIGF0VlZvnJyec5+/0AXJfqPgnTbieK5tYfLnW7a4dWlYpJ5rx+fuHOd0aMu37vzHI5yOn oq/ZRtZ6mH1+spqcHy2VtNFa73S33a9Direzj1nxj4psZmZIo4fJcofmYXMEIOM9Cvke+d3b HMvhvw3aw+CNR0a1do1uZr23aZ/nbO94gxHAztVeBgce9dLDptvb6pdahGu2e6jjSXAADbN2 D0yThsZPYD0q3UxpK93vr+JrWzCbgqdN2iuT74rX11uzzyw8C3n/AAi8Q1M+drMnkweYjDda 2uBE8Sk/LxE0pOByzZ+YgGun8P8Ahq30MzTExT3k23zLryQsjYjRWy2STuZC556sep5O5RTj RhGzRFfMsRXUoyej/wCBouyVtEMiijghSGGNY4o1CoiDCqBwAAOgp9FFanA3fVhRRRQAUUUU AFFFFABRRRQAUUUUAFc3a+GIG8P3Ni1rBa3BmuTbTLEpa3BllaF0x0KiTK4IIyenNdJRUuKe 5tTrzpq0HbVP7r/5nJX/AIO+1apOtv8AZbTS30xraOKJMGKc+aocKABgLPJxnkt06EVI9F1S LWU0u6+1XWm3UiXE1wJMY8lXRS0gw4kOy0Y46sHI4yK7iiodGN7nVHMq6XK9dLea80973s/k eeTrr+mag1nc6hKLS6guLazXzWaSEyXkcaSsxJLlUlQryCANvynJPSeBmZ/h/wCG2ZizNpdq SScknylqj4r/AOQ3o/8AwH/0tsqu+BP+SeeGv+wVa/8AopaVNcsmvQrG1HUo06jSTfNe2nU6 CiiitjzQooooAK5vxTDr8jwPohkJ+z3ESbJFURXLqqwzSBiA0SDzNy/McspCMQCvSVxfjuGF bjTb3UU8P3GlxpNFJba7eC3gaZzGY3XdG6l1VJQOAQHbB5NAG54fsryxivobh5zai7b7ElzO ZpUhCqPmdixbc4kcZYkK6g7cbV2K5vwVJpsujTNpdp4ftoPtDBk0K5WeAttXlmWNAHxjIx0C 888dJQAUUVXvpriCzkktLX7VccCOIyBASSBlmPRRnJIBOAcBjgEAsVz/AIE/5J54a/7BVr/6 KWrnhrUptZ8K6Rqlwsaz3tlDcSLGCFDOgYgZJOMn1NU/An/JPPDX/YKtf/RS0AdBRRRQAUUU UAFcl4g/5KF4O/7ff/RQrra5LxB/yULwd/2+/wDooVlW+H5r80d2Xfxn/gqf+kSOtooorU4Q ooooAKKKKACiiigArLtPD+n2OpR31vGyyxWSWEQLkqkKkkAep6cnP3R751KKTSe5cak4JqLs nuVJLHzNXtr/AMzHkwSw7NvXe0Zzn28v9fan3tnHfwLDKzBVmimBU85jdXH4ZUZ9qsUUWWoe 0knF322++/5hRRRTICiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAxvFEUc+jJDNGskUl7Zq6OMqwNzGCCD1FQeDf+QHc/wDYV1L/ANLZ qv61azXdhFHAm91u7aQjIHypOjsefRVJ/CqHg3/kB3P/AGFdS/8AS2apS95v+upvKX7iMb9X +UToKKKKowCiiigAooooAKKKKACuX12y1261oR2jzixuIoIRJFOI1t1E265L4IbdJEERGUMy sGwY8lj1Fef+NIrO38QpeazB4UvLGa0SK1h8QX4g8qRHcytGrRODuDxAkYPyLntQB1nh6G+t 9Ehj1EyeeHkKrLJ5jxxF2MSO2TudYyis2WyQTub7x1Kx/Cz2cnhy0awg0qC1O/ZHpMwltl+d s7GCqDznPyjnI561sUAFFFYfi/V77QfCuo6np1nHdXFtbyTASvtjQIjOWfuR8uAF5JIGVGWU Aj8d/wDJPPEv/YKuv/RTV0Fc/wCO/wDknniX/sFXX/opq6CgAooooAKKKKACuS/5q9/3Af8A 2vXW1yX/ADV7/uA/+16yq9PU7sD/AMvP8D/Q62iiitThCiiigAooooAKKKKAGSxRzwvDNGsk UilXRxlWB4IIPUVXt9Ms7XUby/hh23V5s899xO/YMLwTgYHpirdFKy3KU5JNJ6P/AIf9F9xX SzjTUp74M3mzQxwsCflAQuRj3/eH9KLmzjup7OZ2YNazGZAp4JKOnPthz+OKsUUWQ+eV738v la35BRRRTICiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAxtcijm1Hw+ssauo1EsAwyMi3mIP1BAI9xUHgT/knnhr/sFWv/opav6lazXF /pEkSbkt7tpJTkDapglTPv8AMyjj1qh4E/5J54a/7BVr/wCilqYrVm9WV4QSey/VnQUUUVRg FFFFABXF+NX0E6pZ2mt6tHpiXumX9o0s0iRo0L+SsiqzEbZd3lspwwwsgI5BHaVz+o2fiyXX El03WNKt9M8pwYZ9PeVw/wAm0kiVd3STkFcZwQ33lADwibOTTruez1mx1Xz7t5ZpNPIFvHIQ u5UUO+zPDsCxJd2b+LFdBWfpNtqlvFP/AGtqMF7NJLvjMFr5CRJtUbApdyeQzZLH72OgFaFA BVOz0yCxuLqeGS7Z7l98gmu5ZVByT8iuxCDk8KAOnoMXKKAMvw1ps2jeFdI0u4aNp7Kyht5G jJKlkQKSMgHGR6CsP4daFo9j4N0DUbPSrG3vp9Kt/OuYbdEkk3RozbmAyckAnPU12Fc/4E/5 J54a/wCwVa/+iloA6CiiigAooooAKydX/wCQnoH/AF/t/wCk09a1ZeqxSSajojJGzLHeszlR kKPs8wyfQZIH1IqZ7fd+Zvh3ab9Jf+ks1KKKKowCiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAz9T0LR9b8r+1tKsb/yc+X9rt0l2ZxnG4HGcDp6CsvwNBDa+G5Le3ijhgi1PUEjjjUKqKLy YAADgADjFdJXP+Df+QHc/wDYV1L/ANLZqAOgooooAKKKKACiiigAooooAK4PxNc6Cnii8t7/ AF+00m7eyspwb50VHaG5klgK5dSwDrIJF7hkwynJPeVzcll4yOs3csOuaMmnsii3ik0yR2Q7 nznEy5O0oN2cHHCpg7gC54Wis4fDlolhqMGoWo3lJ7ZgYeXYlIwCQsanKKuTtVQuTjNbFU9L gvrawWPUr2O9u97s80cHkqQXJVQmWwFUheSSduSSTVygArn9T8NCTwbrOiadNOZL60niR768 mn2u8ZQZeQswXpwPc4ya6CigDn/Hf/JPPEv/AGCrr/0U1aGmaFo+ieb/AGTpVjYedjzPsluk W/GcZ2gZxk9fU1n+O/8AknniX/sFXX/opq6CgAooooAKKKKACsm4/wCRv03/AK8Lv/0Zb1rV lzxSHxTYTCNjEtlcqzgfKCXgIBPqdp/I+lTPb7vzN8O7TfpL/wBJZqUUUVRgFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQBl6l4a0HWbhbjVNE02+nVAiyXVqkrBck4BYE4ySce5qn4E/wCS eeGv+wVa/wDopa6Cuf8AAn/JPPDX/YKtf/RS0AdBRRRQAUUUUAFFFFABRRRQAUVHJPDC8KSy xo8z7IlZgC7bS2F9TtVjgdgT2qO+v7PTLOS8v7uC0tY8b5p5BGi5IAyx4GSQPxoAsVz/AIE/ 5J54a/7BVr/6KWtTTdW03WbdrjS9QtL6BXKNJazLKobAOCVJGcEHHuKy/An/ACTzw1/2CrX/ ANFLQB0FFFFABRRRQAUUUUAFFFFABRUck8MLwpLLGjzPsiVmALttLYX1O1WOB2BPapKACiii gAooooAKKKKACiiigAoqOOeGZ5kiljd4X2SqrAlG2hsN6HaynB7EHvUlABRRRQAUUUUAFFFF ABRRUc88Nrby3FxLHDBEheSSRgqooGSSTwABzmgCSiiigAooooAKKKKACiiigAooqMzwrcJb tLGJ3RnSMsNzKpAYgdSAWUE9tw9aAJKKKKACiiigAooooAKKKKACiio4Z4blC8EscqB2QsjB gGVirDjuGBBHYgigCSuf8G/8gO5/7Cupf+ls1dBXP+Df+QHc/wDYV1L/ANLZqAOgooooAKKK KACiiigAooooAKKKKACiiigAoqMzwrcJbtLGJ3RnSMsNzKpAYgdSAWUE9tw9ar6lq2m6Nbrc apqFpYwM4RZLqZYlLYJwCxAzgE49jQBl+O/+SeeJf+wVdf8Aopq6CuX8W39nqfwx8RXlhdwX drJpV3smgkEiNiNwcMODggj8K6igAooooAKKKKACiiigAooooAKKjM8K3CW7Sxid0Z0jLDcy qQGIHUgFlBPbcPWpKACiiigAooooAKKKKACiiigAoqOGeG5QvBLHKgdkLIwYBlYqw47hgQR2 IIqSgAooooAKKKKACiiigAooqOaeG2QPPLHEhdUDOwUFmYKo57liAB3JAoAkooooAKKKKACi iigAooooAKKKjE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSgCSiiigAooooAKKKKACiiigAo oqOCeG6t4ri3ljmglQPHJGwZXUjIII4II5zQBJXP+BP+SeeGv+wVa/8Aopa6Cuf8Cf8AJPPD X/YKtf8A0UtAHQUUUUAFFFFABRRRQAVXvriW0s5J4bKe9kXGIICgd8kDguyrx15I6evFWKKA OT1q2hm8SeDtUktZIrxr1o9szBmhU2dyxTglQc43FT821ckhVxc8V2V5qMWlWtnf2Nox1COV heIXE3lq8qKqqylmDoj4DLwjZyAVO5JBDM8LyxRu8L74mZQSjbSuV9DtZhkdiR3qvc6Tpt7b 3Nvd6faTwXTh7iOWFWWZgFALgjDEBEAJ/uj0FAFPQdQvLttTtL8wSXWnXYtnmgjMaS5ijlDB CzFcCULjcc7c8ZwMfQrfxhonh7TNJ/sjQ5vsNpFbeb/a8y79iBd2PsxxnGcZNXNViGkw6Hom i+XpNve3rW+6ygjUwqIZpiY1ZSgJaMA5U8M3fBFjw7c3by6xYXd1JdnTr0W8dxKqLJIrQQy5 fYFXIMpAwo4A6nJIBH9s8Yf9ALQ//BzN/wDItH2zxh/0AtD/APBzN/8AItdBRQBz/wBs8Yf9 ALQ//BzN/wDItH2zxh/0AtD/APBzN/8AItdBRQBz/wBs8Yf9ALQ//BzN/wDItH2zxh/0AtD/ APBzN/8AItdBRQBz/wBs8Yf9ALQ//BzN/wDItH2zxh/0AtD/APBzN/8AItdBXP8Aiu+1Swtr CXT5IIoW1C0iuXddzsklzFGUUdBkO2WJ4xgDLbkAM/Ubfxhf32k3P9kaHH/Z921zt/teY+Zm GWLbn7Nx/rc55+7jvkaH2zxh/wBALQ//AAczf/ItY+tanqyW/izVbfVZ7ZfD+7yLOOKIw3Gy 1jn/AHpZC/LSFTsZflAxg5Yn9p6t9k/t7+1Z9v8Abf8AZ/8AZ/lRfZ/L+3fZM52eZu2/Pnfj f22/LQBsfbPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLXQUUAc/9s8Yf9ALQ/wDwczf/ ACLR9s8Yf9ALQ/8Awczf/ItdBRQBz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i10F FAHP/bPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/AMHM3/yLXQVwfiXUdc8JWn29NYk1O5e3uppb Sa3iWCJYreSXzEVFEgQSrEnzO2BKASWKtQBc0638YWF9q1z/AGRocn9oXa3O3+15h5eIYotu fs3P+qznj72O2TofbPGH/QC0P/wczf8AyLWHrd/rHhzULTT01u7vhqKKvnXcUG+3Ju7WDdH5 carnbcufnDDKJxjcG3NGlvbbxDqWjXWoz6hHBaW10k9ykayAyvMpX92iLtHkqR8ucs2SRgAA PtnjD/oBaH/4OZv/AJFo+2eMP+gFof8A4OZv/kWugooA5/7Z4w/6AWh/+Dmb/wCRaPtnjD/o BaH/AODmb/5FroKKAOf+2eMP+gFof/g5m/8AkWj7Z4w/6AWh/wDg5m/+Ra6CigDn/tnjD/oB aH/4OZv/AJFrP1238Ya34e1PSf7I0OH7daS23m/2vM2zehXdj7MM4znGRXQazHcPpzm3v57J UzJLLbW4mm2AE4jUqw3E4/gfIyAMkEcfpmq6tq+sJoia1fQ24+0ypem2ijvHWNbXCyxyRbU+ a5k4MSsVWNhwcuAdB9s8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItYeiX+seI9Qu9PfW 7uxGnIy+daRQb7gi7uoN0nmRsudtsh+QKMu/GNoXqPDWpTaz4V0jVLhY1nvbKG4kWMEKGdAx AyScZPqaAKf2zxh/0AtD/wDBzN/8i0fbPGH/AEAtD/8ABzN/8i10FFAHP/bPGH/QC0P/AMHM 3/yLR9s8Yf8AQC0P/wAHM3/yLXQUUAc/9s8Yf9ALQ/8Awczf/ItH2zxh/wBALQ//AAczf/It dBRQBz/2zxh/0AtD/wDBzN/8i1nzW/jCXxDZat/ZGhj7NaT23lf2vN83mvC27P2bjHk4xjnd 2xzJ4v1++0670qx0poxLJe2jXzsu7y7Z7iOLb/su7PhcjBVJSCCoqn/aerfZP7e/tWfb/bf9 n/2f5UX2fy/t32TOdnmbtvz53439tvy0AbH2zxh/0AtD/wDBzN/8i0fbPGH/AEAtD/8ABzN/ 8i1j6LqerPb+E9VuNVnuV8QbfPs5IohDb77WSf8AdFUD8NGFG9m+UnOThh3FAHP/AGzxh/0A tD/8HM3/AMi0fbPGH/QC0P8A8HM3/wAi10FFAHP/AGzxh/0AtD/8HM3/AMi0fbPGH/QC0P8A 8HM3/wAi10FFAHP/AGzxh/0AtD/8HM3/AMi0fbPGH/QC0P8A8HM3/wAi10FU9W1KHRtGvtUu FkaCyt5LiRYwCxVFLEDJAzgeooAy/tnjD/oBaH/4OZv/AJFrP0a38YaRYyW39kaHLvu7m53f 2vMuPOmeXbj7Mem/Ge+M8dKx7PWdRttFh8/xJqs+rzfZLaaK801bVFaWeGKWWAPbxs+zzDjO 5RuXepyAblzf6xZ+J4fDA1u7lS6e3b7fJFB9oiDx3jlVxGI8ZtEHzITh35+7tANz7Z4w/wCg Fof/AIOZv/kWrHhjT7zTNFMF+IFupLu6uXWCQyIvmzyShQxVScBwM4HSo/DtzdvLrFhd3Ul2 dOvRbx3EqoskitBDLl9gVcgykDCjgDqck7lABRRRQAUUUUAFFFFABRRRQAUUUUAFU9SvZ7G3 WW30y71By4UxWrRKwGD8x8x0GOMdc8jjri5RQBy81jbwfE7T7uOP/SLnSr0SyFixIWS0CqM9 FGSQowMsxxliTJ4gsNSv/EGinT9S022NqlxcCK7haZmkwkYdUV0JCrLIpJbA8xflJIK9AYIW uEuGijM6IyJIVG5VYgsAeoBKqSO+0elU7zQtH1CzFne6VY3NqJWnEM1ujoJGJLPtIxuJZiT1 O4+tAGPL9s8W/D/V7L9xHfXEV9pu/lYzIjSQb8clVJXdj5iAcZbGTY+2eMP+gFof/g5m/wDk Wo9SWcazpPh7TbuTSbRrK4nD2UUW5RC0CJGodGQJiU8Bc/KuCBkG54Yvri/0UyXUnmzQ3d1a mQqAZBDPJErMBgbiEBOABknAA4ABX+2eMP8AoBaH/wCDmb/5Fo+2eMP+gFof/g5m/wDkWugo oA5/7Z4w/wCgFof/AIOZv/kWj7Z4w/6AWh/+Dmb/AORa6CigDn/tnjD/AKAWh/8Ag5m/+RaP tnjD/oBaH/4OZv8A5FroKKAOf+2eMP8AoBaH/wCDmb/5Fo+2eMP+gFof/g5m/wDkWugrn9Zv tUtfEvh6GCSCPTbq7eGcbd0kp+zzyAc8IqmNTxksT/CFO8Az5rfxhL4hstW/sjQx9mtJ7byv 7Xm+bzXhbdn7NxjycYxzu7Y50PtnjD/oBaH/AODmb/5FrH/tPVvsn9vf2rPt/tv+z/7P8qL7 P5f277JnOzzN235878b+235aNF1PVnt/Ceq3Gqz3K+INvn2ckUQht99rJP8AuiqB+GjCjezf KTnJwwANj7Z4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFroKKAOf8AtnjD/oBaH/4OZv8A 5Fo+2eMP+gFof/g5m/8AkWugooA5/wC2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCRa6Ci gDn/ALZ4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFrcnEzW8q28kcc5QiN5ELqrY4JUEEj PbIz6iuH1i/1jQNQi0+HW7vUBdpCs01zFAXszJdwQIy+XGqgsss5G8MCYeBhWBALmjW/jDSL GS2/sjQ5d93c3O7+15lx50zy7cfZj034z3xnjpWh9s8Yf9ALQ/8Awczf/ItYdzf6xZ+J4fDA 1u7lS6e3b7fJFB9oiDx3jlVxGI8ZtEHzITh35+7t6Dw7c3by6xYXd1JdnTr0W8dxKqLJIrQQ y5fYFXIMpAwo4A6nJIBH9s8Yf9ALQ/8Awczf/ItH2zxh/wBALQ//AAczf/ItdBRQBz/2zxh/ 0AtD/wDBzN/8i0fbPGH/AEAtD/8ABzN/8i10FFAHP/bPGH/QC0P/AMHM3/yLR9s8Yf8AQC0P /wAHM3/yLXQUUAc/9s8Yf9ALQ/8Awczf/ItZ+s2/jDV7GO2/sjQ4tl3bXO7+15mz5MyS7cfZ h12Yz2znnpWx4hW4WzE8eqX1hbxcynT7IXM7kkBQFKSfLycgIT0OVAOeb0O/1jxJdzWb63d2 KWdusizWsUHmzhri5iUzCSNgr7LdCVCoVdnBUYCqAbn2zxh/0AtD/wDBzN/8i0fbPGH/AEAt D/8ABzN/8i1ydr4l1zVvB2q+Kf7TktJdMso7hbK3hiNvM32KG5IferSYLSsvyuvygYwcsfTK AOf+2eMP+gFof/g5m/8AkWj7Z4w/6AWh/wDg5m/+Ra6CigDn/tnjD/oBaH/4OZv/AJFo+2eM P+gFof8A4OZv/kWugooA5/7Z4w/6AWh/+Dmb/wCRaPtnjD/oBaH/AODmb/5FroKKAOf+2eMP +gFof/g5m/8AkWs+G38YReIb3Vv7I0M/abSC28r+15vl8p5m3Z+zc587GMcbe+eJNV1++Xxz oelWDRiw+0NFqLlclna2mkjiVuQCBFvccEB4scMa5+68S65pPg7SvFP9pyXcup2Ulw1lcQxC 3hb7FNcgJsVZMBolX5nb5Sc5OGAB1n2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i1HZ nUNL8VWmlz6vd6lBeWVxcFruOFWiaJ4VAXyo0GCJmzuB+6uMc56SgDn/ALZ4w/6AWh/+Dmb/ AORaPtnjD/oBaH/4OZv/AJFroKKAOf8AtnjD/oBaH/4OZv8A5Fo+2eMP+gFof/g5m/8AkWug ooA5/wC2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCRa6CsPxhqk2j+E7+7tXkS7KLBbOkR lKTSsI422AEsA7qSAGJAOAx4IBH9s8Yf9ALQ/wDwczf/ACLWfoVv4w0Tw9pmk/2Roc32G0it vN/teZd+xAu7H2Y4zjOMmqcepzyy2On2fiXWZJb+9FvK99YxW9xaoIJ5Q0cbW6cO0O3cyMCF YLggkFtf6xeeJ5vDB1u7iS1e4b7fHFB9olCR2bhWzGY8Zu3HyoDhE5+9uANz7Z4w/wCgFof/ AIOZv/kWrnhrTZtG8K6Rpdw0bT2VlDbyNGSVLIgUkZAOMj0FR+GL64v9FMl1J5s0N3dWpkKg GQQzyRKzAYG4hATgAZJwAOBsUAFFFFABRRRQAUUUUAFFFFABRRRQBl61pU2pCymtLmO3vLG4 +0W7yxGWPcY3jIdAylhtkfGGHODyAQTRdKm00Xs13cx3F5fXH2i4eKIxR7hGkYCIWYqNsaZy x5yeAQBqUUAFFFFABRRRQAUUUUAFZ+s6Z/a9jHbed5Wy7trndt3Z8mZJduMjrsxntnPPStCi gDl9U8LXl82sW9vqcEGm61n7fFJaGSb5olhbypBIoT5EXG5Hw2TyDtB/wi15v+x/2nB/Y39o f2h5H2Q/aPM+0faceb5m3b5vby87OM5+auoooAKKKKACiiigAooooAK49PCesXMWqW+r61Y3 UGqxSwXkkGnPFOY2VlVEczOqKm84Gwj7xOWZmPYUUAcnd+FNS1eZLrVtWtJLu2QCza0sWiRG E0M2ZFaVy4328XAZON4zkgrqaTpN5a6jd6lqV7BdX1zFFbk21sYI1jjMjL8rO53ZlfJ3Yxt4 GCTsUUAFFFFABRXN+K7zXtNihu9LvNNjge4trVorqyeVt0s6RbwyzIMAODtx/CeeeC78SQaP b3Fhqmr2g1iCye8keKxl8tIgJCJTEGY7B5ZB+fk4GQXUEA6SisubxDplvqg06SeQT71jZxBI Yo3bG1HlC7Ec7lwrMCd6YHzLk0DUptV06W4nWNXS9u7cBAQNsVxJEp5J52oCffPTpQBY1KC+ nt1/s69jtLhHDBpYPOjcYIKuuVJHORtZTkDkjKnn4/CmpW9+2sW+rWg1qV5TPLJYs1uyukCE LEJQynFtDyZG538fMAvWUUAcnaeFNS0iZ7rSdWtI7u5Qi8a7sWlR2M002Y1WVCg33EvBZ+Ng zkEt0Gk6bDo2jWOl27SNBZW8dvG0hBYqihQTgAZwPQVcooAKKKKACiiigAooooA5fX/Aum67 dPeNPfW95LLbPJLFfXCqyQyq4TYsgUdGwQPlZyw+bmj/AIRa83/Y/wC04P7G/tD+0PI+yH7R 5n2j7TjzfM27fN7eXnZxnPzV1FFAHL6X4WvLFtHt7jU4J9N0XH2CKO0Mc3yxNCvmyGRg/wAj tnaiZbB4A2nqKKKACiiigAooooAKjnjaa3liSaSB3QqssYUshI+8NwIyOvII9QakooA5O78K alq8yXWrataSXdsgFm1pYtEiMJoZsyK0rlxvt4uAycbxnJBUk8KalcX66xcataHWoniMEsdi y26qiToA0RlLMcXM3IkXnZx8pDdZRQBl6LpU2mi9mu7mO4vL64+0XDxRGKPcI0jARCzFRtjT OWPOTwCANSiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAx9W0m8utRtNS029gtb62i ltwbm2M8bRyGNm+VXQ7sxJg7sY3cHIIsaLpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkg YBJPJ0KKACiiigAooooAKKKKACs/UdM+332k3PneX/Z921zt258zMMsW3OeP9bnPP3cd8jQo oA5f/hFrzf8AY/7Tg/sb+0P7Q8j7IftHmfaPtOPN8zbt83t5ednGc/NRpfha8sW0e3uNTgn0 3RcfYIo7QxzfLE0K+bIZGD/I7Z2omWweANp6iigAooooAKKKKACiiigCOcTNbyrbyRxzlCI3 kQuqtjglQQSM9sjPqK5O28J6wNMls7/WrG4Z5Y7v7RHpzxyyXUckciSSkzMGXMagooX5QFUo FAHYUUAcnJ4U1K4v11i41a0OtRPEYJY7Flt1VEnQBojKWY4uZuRIvOzj5SG2NF0qbTRezXdz HcXl9cfaLh4ojFHuEaRgIhZio2xpnLHnJ4BAGpRQAUUUUAFFcvr114ktNa0yCw1DSo7XUbs2 yLPp8kjxYgklLFhOobJiIxgY3d8c2H8TWUUr6bJqcB1O0ltobwraSFFeRogBtBOzzPNATLHG Sfm8t8AHQUVlw+IdMuNUOnRzyGfe0auYJBFI653IkpXY7ja2VViRsfI+VsHhrUptZ8K6Rqlw saz3tlDcSLGCFDOgYgZJOMn1NAEmp22qTeU+lajBaSLkOtza+fG4OOcB0YMMcENjBbIJwVw7 TwpqWkTPdaTq1pHd3KEXjXdi0qOxmmmzGqyoUG+4l4LPxsGcglusooA4tfAk1ppF1oen6rHH o97bpb3aXFqZbgqIEtyUkDqqkxxr1RsNk8ghR2lFFABRRRQAUUUUAFFFFAHL3HgXTZNetNXg nvoZ4tQN/Ov264ZJn8pox8pk2r1XoPupsxtJFU28CTXekWuh6hqscmj2Vu9vaJb2piuApge3 BeQuysRHI3RFy2DwAVPaUUAYdho+pf2zHqmr6jaXU8FvJbwLaWbW6hZGjZy26SQscxJjBGPm znIxuUUUAFFFFABRRRQAVXvobiezkjtLr7LccGOUxhwCCDhlPVTjBAIOCcFTgixRQBycnhTU ri/XWLjVrQ61E8RgljsWW3VUSdAGiMpZji5m5Ei87OPlIYj8Kalb37axb6taDWpXlM8slizW 7K6QIQsQlDKcW0PJkbnfx8wC9ZRQBn6Lpn9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYB JPJ0KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAorL8S6lNo3hXV9Ut1jaeyspriNZASpZELAHBBxkeorPF5r2l6tpcGqXmm3sGoXDWqi 1snt2iYQyS7yWmkDDERXbgfeBzxggHSUVy+peO9Js9D1TUbbz7prK0e6jj+zyoLlV/iiYpiS PJXMiblUMrE4IJ0LrxRpNj5H2maePzYlnbNpKfIjbo82F/cLw3Mm0fK391sAGxRXNw+LILq4 1eACSz/s3U7exMtxaylZTIYsBeF5LSFAQSB8rnKsM6EPiHTLjVDp0c8hn3tGrmCQRSOudyJK V2O42tlVYkbHyPlbABqUVj6N4p0bxBsOl3nnrJEJo28p0WVOMlCwAfaSFbbnYxCtg8UX/iHT tIurw6jqMFvb20Vs8geNl8rzpXjVmfO3azLjoNu0ljgjABJr+mzarp0VvA0aul7aXBLkgbYr iOVhwDztQge+OnWsvXvDd5qn/CT+RJAv9q6Imnwb2I2yD7TktgHC/vl5GTweOmdzTdUtNWt2 mtHkIRykiSxPFJG2AcOjgMpwQQCBkMD0INY8Wq61qBn1HTorSTT7e4mt/sToRcXBikaORlkL hEO5W2qVIYKuXTedgBn6/wCFtZ1fXorlLyM2kV7aXSb7udAkcUkbND5CYjYkq7iV9xywTaAF deg0DTZtK06W3naNne9u7gFCSNstxJKo5A52uAffPXrRN4h0y31QadJPIJ96xs4gkMUbtjaj yhdiOdy4VmBO9MD5lzHZ+KdG1DUWsLW88ydZZID+6cIJYyweLeRt8wbWbZncVG4DbzQBsUVx 9145sn1F4bK+gS1i0q8v5bia2kbYIjCElVflMkJDyEMmQ+z5W4NXIfFkF1cavABJZ/2bqdvY mW4tZSspkMWAvC8lpCgIJA+VzlWGQDpKKy4fEOmXGqHTo55DPvaNXMEgikdc7kSUrsdxtbKq xI2PkfK2I9G8U6N4g2HS7zz1kiE0beU6LKnGShYAPtJCttzsYhWweKANiiuP1fxDrFsniLUL N7FLHQM+dbTW7vJdbbdJ22yCRRHkSBBlHwRu5ztB/wAJDrHl/wBr77H+zP7V/s37H9nfzv8A j7+yb/O8zb9795jy+ny5/ioA7Ciubj8SzT/EFvD0NvGbOGyllmuSTu+0KYD5YHYCOdGJ5B3g AgqwrpKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorj/+Eh1j/hHv+Eu3 2P8AYn2T7f8AYPs7/afs+zfnzfM2+Zs+bbsxn5N2P3lAHYUVl6VqU19qOuW8qxhLC9W3iKg5 Km3hly3PXdIw4xwB9Tz6a/r0Wk6l4iuJtNk0uwuL0SWcdo6zmG3mkjJEplKl9se7GwAn5crn cADtKKx/E+oXmmaKJ7AwLdSXdrbI08ZkRfNnjiLFQyk4Dk4yOlU4dV1LStZSy16+02aCWynv PtMNu1qsCwtEG375XBBEwOcrt2HrngA6SiuX1Hx1ptlpn2yKC+lYXdtbvbvY3EcyCaQIHMZj 37cb8HbhmXYDu4rUm8Q6Zb6oNOknkE+9Y2cQSGKN2xtR5QuxHO5cKzAnemB8y5ANSiub0fxZ BrdvaToJLPztTuLFYrm1lDTGIS8LuC7SVj3kkEDDIfm6WE8X6G9vd3H2uRYLW3e6aR7eVVkh QZaSIlQJkAwd0e4fMv8AeXIBY1XTZr7UdDuImjCWF61xKGJyVNvNFheOu6RTzjgH6HLuvDd5 P/a22SAfbNbstQjyx4jh+y7gePvHyHwOnK8jnGxpmt6drPm/YLjzfKwTlGTcrZ2yLuA3xtg7 XXKtg4Jwap3PifTNNluVv9QjG29FnGiW8m4SmBZRFxne5UkjaBncqAFuoBj/APCLazJ4zsdW uLyOW3tL2aclrucmSN4pURVg/wBVEY96pkAlwCxKnKt0HhrTZtG8K6Rpdw0bT2VlDbyNGSVL IgUkZAOMj0FXLG+t9Rs47q1k8yF8gEqVIIJDKynBVgQQVIBBBBAIrD0nVda1O3sdajitJNJv 0jkitEQrcQxSAFZGkL7HIBBZAq4BbazlQHAOkorLh8Q6ZcaodOjnkM+9o1cwSCKR1zuRJSux 3G1sqrEjY+R8rYr6b4w0DVrdrm01KM24tzdefIjRRtEACzq7gBgmQHwTsJw208UAblFcnD4y gvNXvreG4jgtrZLAZuLSVZVlnnkjMbxttZCwWPaSBjzA53LirGj+LINbt7SdBJZ+dqdxYrFc 2soaYxCXhdwXaSse8kggYZD83QA6SisNPF+hvb3dx9rkWC1t3umke3lVZIUGWkiJUCZAMHdH uHzL/eXNzTNb07WfN+wXHm+VgnKMm5WztkXcBvjbB2uuVbBwTg0AaFFcf/wkOseX/a++x/sz +1f7N+x/Z387/j7+yb/O8zb9795jy+ny5/io0jxDrFynh3ULx7F7HX8eTbQ27pJa7rd513SG RhJgRlDhEyTu4xtIB2FFc3ofiWbWfFWu6etvGlhYJCLecElp2LzRykj+ELJCyAY52FskMK6S gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK4fWtT1ZLfxZqtv qs9svh/d5FnHFEYbjZaxz/vSyF+WkKnYy/KBjByxAO4orD8RXN2kuj2FpdSWh1G9NvJcRKjS RqsE0uU3hlyTEAcqeCehwRjrcam0Or28+vakkGk3ogNzaWEc93OGhgkXKrEy4BmcHbFnAQ5G 1iwB2lFYfh/VWm8Mf2hqVzGEhe4D3Eu2P91HI6q8o4CPsUF1IXa24FVxtEf/AAnfg/8A6GvQ /wDwYw//ABVAHQUVz/8Awnfg/wD6GvQ//BjD/wDFUf8ACd+D/wDoa9D/APBjD/8AFUAdBRXP /wDCd+D/APoa9D/8GMP/AMVR/wAJ34P/AOhr0P8A8GMP/wAVQB0FFc//AMJ34P8A+hr0P/wY w/8AxVH/AAnfg/8A6GvQ/wDwYw//ABVAHQUVz/8Awnfg/wD6GvQ//BjD/wDFUf8ACd+D/wDo a9D/APBjD/8AFUAdBRXP/wDCd+D/APoa9D/8GMP/AMVR/wAJ34P/AOhr0P8A8GMP/wAVQB0F Fc//AMJ34P8A+hr0P/wYw/8AxVH/AAnfg/8A6GvQ/wDwYw//ABVAHQUVz/8Awnfg/wD6GvQ/ /BjD/wDFUf8ACd+D/wDoa9D/APBjD/8AFUAXPEumzaz4V1fS7do1nvbKa3jaQkKGdCoJwCcZ Poaz7Pwhpui+JYNV0PTNNsUkt3tbuOCBYiy5DI6lV6hgQV43BwST5aipP+E78H/9DXof/gxh /wDiqP8AhO/B/wD0Neh/+DGH/wCKoA58+C9Yn8PNpMr2Mf2Pw/c6JZSrM7faPMSNRLINg8rH kqdoMn3zz8vzWPGXhjX/ABPp01vFcwQfadPMBi+3Txx20xDbjiML9oV8qv7zAQJuCtuZDsf8 J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQBTufDupT3GsqDaCC71iw1GB/Nbdth Nt5isu3AOLc7cE53c7cUWfh3UodUs45Taf2fY6ndalFOsrGWVp/P/dtHtAQL9ob5g7Z8sfKN 3y3P+E78H/8AQ16H/wCDGH/4qj/hO/B//Q16H/4MYf8A4qgCvoPhu80v/hGPPkgb+ytEfT59 jE7pD9mwVyBlf3LcnB5HHXBrHhu81DWLq8ikgWOX+y9odiCPs1288meO6sAPU9cDmrH/AAnf g/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDBjD/8VQBc0rTZrHUdcuJWjKX96txEFJyFFvDF huOu6Njxngj6DPi0rWtPM+nadLaR6fcXE1x9tdybi3MsjSSKsZQo53M21iwChlyj7Dvk/wCE 78H/APQ16H/4MYf/AIqj/hO/B/8A0Neh/wDgxh/+KoAp3nh3UptUvI4jaf2ffana6lLO0rCW JoPI/drHtIcN9nX5i648w/KdvzSWvhu8g/sndJAfset3uoSYY8xzfatoHH3h56ZHThuTxmx/ wnfg/wD6GvQ//BjD/wDFUf8ACd+D/wDoa9D/APBjD/8AFUAc2/g3XptGGkONNSCz8OXei2tw ty7NO0iwqkjp5YEYxDkgM+N2BnGTsXPh3Up7jWVBtBBd6xYajA/mtu2wm28xWXbgHFuduCc7 uduKuf8ACd+D/wDoa9D/APBjD/8AFUf8J34P/wChr0P/AMGMP/xVAFOz8O6lDqlnHKbT+z7H U7rUop1lYyytP5/7to9oCBftDfMHbPlj5Ru+WTQfDd5pf/CMefJA39laI+nz7GJ3SH7NgrkD K/uW5ODyOOuLH/Cd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFUAZ+r+HtYuU8RafZ pYvY6/nzrma4dJLXdbpA22MRsJMCMOMumSdvGNxP+Ee1jy/7I2WP9mf2r/aX2z7Q/nf8ff2v Z5Pl7fvfu8+Z0+bH8NaH/Cd+D/8Aoa9D/wDBjD/8VR/wnfg//oa9D/8ABjD/APFUAZ+l+C7r SPE1jqEWvX11ZwxXvmRXIgy0lxLHIcbIl+UsHY8gghAPl3A9hXP/APCd+D/+hr0P/wAGMP8A 8VR/wnfg/wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD /wDFUAdBRXP/APCd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+ hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+hr0P/wAGMP8A8VR/wnfg /wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFUAdB RXP/APCd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+hr0P/wAG MP8A8VR/wnfg/wD6GvQ//BjD/wDFUAdBRXP/APCd+D/+hr0P/wAGMP8A8VW5BPDdW8Vxbyxz QSoHjkjYMrqRkEEcEEc5oAkooooAKKKKACiiigAooooAKKKKACiiuH1rU9WS38Warb6rPbL4 f3eRZxxRGG42Wsc/70shflpCp2MvygYwcsQDuKKw/EVzdpLo9haXUlodRvTbyXESo0karBNL lN4ZckxAHKngnocEZ9hFrGpS6npp8Q3cKaZe+R9qjt4DcThoIZRuJQxgAyuPljBICcjDbwDr KKw/D+qtN4Y/tHUbmPyonuP9Mk2oskEcjrHOSMLho1V9wwp3ZAAIFR/8J34P/wChr0P/AMGM P/xVAHQUVz//AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDBjD/8VQB0FFc//wAJ34P/ AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUAdBRXP/8ACd+D/wDoa9D/APBjD/8AFUf8 J34P/wChr0P/AMGMP/xVAHQUVz//AAnfg/8A6GvQ/wDwYw//ABVH/Cd+D/8Aoa9D/wDBjD/8 VQB0FFc//wAJ34P/AOhr0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUAdBRXP/8ACd+D/wDo a9D/APBjD/8AFUf8J34P/wChr0P/AMGMP/xVAHQVx/8Awj2sf8I9/wAIjssf7E+yfYPt/wBo f7T9n2bMeV5e3zNny7t+M/Ptx+7rQ/4Tvwf/ANDXof8A4MYf/iqP+E78H/8AQ16H/wCDGH/4 qgCulr4k0zWtansNP0q7tb+7S5Rp9QkgdcQRRFSogcdYic571TTQNel0nUvDtxDpsel39xem S8ju3acQ3E0khAiMQUPtk253kA/NhsbTqf8ACd+D/wDoa9D/APBjD/8AFUf8J34P/wChr0P/ AMGMP/xVAFjxPo39v6KNOKQSRtd2sssc4yjxxzxyOpGDnKowweDnniseXwJZwReILHSLex02 w1zTzbyiC3CmGbayBwoA3KVcfLlQpTIBMjEaH/Cd+D/+hr0P/wAGMP8A8VR/wnfg/wD6GvQ/ /BjD/wDFUAZd/oGvakL7UZYdNh1CV9OEVqt27xFbW5M+Wl8oEFt7LgIcbQcnOAa/4W1nV9ei uUvIzaRXtpdJvu50CRxSRs0PkJiNiSruJX3HLBNoAV11P+E78H/9DXof/gxh/wDiqP8AhO/B /wD0Neh/+DGH/wCKoAp2Hh3UoX04Tm0VLLXb3UAUlZi8My3O3gqMOGuACORhSd3OKyz4L1if w82kyvYx/Y/D9zollKszt9o8xI1Esg2DyseSp2gyffPPy/N0H/Cd+D/+hr0P/wAGMP8A8VR/ wnfg/wD6GvQ//BjD/wDFUAXINNmi8VahqjNH5FxZW1uigncGjedmJ4xjEq457Hp3y/8AhG7z /hI/7R8yDyf7b/tDbuO7y/7P+zY6fe3846be+eKsf8J34P8A+hr0P/wYw/8AxVH/AAnfg/8A 6GvQ/wDwYw//ABVAFzQNNm0rTpbedo2d727uAUJI2y3EkqjkDna4B989etZ+k6VrWmW9josc tpHpNgkccV2jlriaKMALG0ZTYhIADOGbIDbVQsCkn/Cd+D/+hr0P/wAGMP8A8VR/wnfg/wD6 GvQ//BjD/wDFUAU7Pw7qUOqWccptP7PsdTutSinWVjLK0/n/ALto9oCBftDfMHbPlj5Ru+Wn /wAIReT+HtL0me6gj8jw1caNPKgL4klSBQ6ggblHlMeSD09TjY/4Tvwf/wBDXof/AIMYf/iq P+E78H/9DXof/gxh/wDiqAMubQNe1XULy9v4dNtHlfSwkcF28w22t207kkxJglXwBg8jkirF h4d1KF9OE5tFSy1291AFJWYvDMtzt4KjDhrgAjkYUndzirn/AAnfg/8A6GvQ/wDwYw//ABVH /Cd+D/8Aoa9D/wDBjD/8VQBz58F6xP4ebSZXsY/sfh+50SylWZ2+0eYkaiWQbB5WPJU7QZPv nn5fm6yDTZovFWoaozR+RcWVtbooJ3Bo3nZieMYxKuOex6d6f/Cd+D/+hr0P/wAGMP8A8VR/ wnfg/wD6GvQ//BjD/wDFUAZ//CPax5f9kbLH+zP7V/tL7Z9ofzv+Pv7Xs8ny9v3v3efM6fNj +Go9P8Na0lpoOlXElpa2mhptgvrWcyTzFbeS3VjE8WyM4k3/AHpACoXDA5Gp/wAJ34P/AOhr 0P8A8GMP/wAVR/wnfg//AKGvQ/8AwYw//FUAU/DPg+fw1rMsq6vd3enrpltY28Vx5W5RE0mA dka8KrgKcknc+7OFx1lc/wD8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQB0FFc /wD8J34P/wChr0P/AMGMP/xVH/Cd+D/+hr0P/wAGMP8A8VQB0FFYC+OfCLHC+KtDJ9BqEX/x VJ/wnfg//oa9D/8ABjD/APFU7O1wOgorn/8AhO/B/wD0Neh/+DGH/wCKo/4Tvwf/ANDXof8A 4MYf/iqQHQUVz/8Awnfg/wD6GvQ//BjD/wDFUf8ACd+D/wDoa9D/APBjD/8AFUAdBRXP/wDC d+D/APoa9D/8GMP/AMVR/wAJ34P/AOhr0P8A8GMP/wAVQB0FFc//AMJ34P8A+hr0P/wYw/8A xVH/AAnfg/8A6GvQ/wDwYw//ABVAHQUVz/8Awnfg/wD6GvQ//BjD/wDFVuQTw3VvFcW8sc0E qB45I2DK6kZBBHBBHOaAJKKKKACiiigAooooAK5fVPC15fNrFvb6nBBputZ+3xSWhkm+aJYW 8qQSKE+RFxuR8Nk8g7R1FFAGPq2k3motBPDewQ3Vld/abJnti6LmJomWRQ4L8SSEEFMZXrtO 6nBoWtWYuLm11q0XULy4M92ZLAtbufLjjG2PzQ6kLEn/AC0Iyz5ByuzpKKAOP8Q6Z/ZHwq8S Wpm86RtPv7iWQLtDSSiSR9q5OF3O2ASSBgEk8nsK5/x3/wAk88S/9gq6/wDRTV0FABRRRQAU UUUAFFFFABRRRQBnzaZ5viGy1bzsfZrSe28rb97zXhbdnPGPJxjHO7tjnQoooAKKKKACiiig AooooAKKKKAM/RtM/sixktvO83fd3Nzu27cedM8u3GT034z3xnjpWhRRQAUUUUAFFFFABRRR QAVn6zpn9r2Mdt53lbLu2ud23dnyZkl24yOuzGe2c89K0KKACiiigAooooAKKKKACiiigArm 7+90/QfFUl7dzXck+o2UcUVta2E1wwW3eQs58pWOM3KDkDtyc8dJXP3n/JQ9G/7BV/8A+jbS gA/4TLS/+fXXP/BFe/8Axmj/AITLS/8An11z/wAEV7/8ZroKKAOf/wCEy0v/AJ9dc/8ABFe/ /GaP+Ey0v/n11z/wRXv/AMZroKKAOf8A+Ey0v/n11z/wRXv/AMZo/wCEy0v/AJ9dc/8ABFe/ /Ga6CigDn/8AhMtL/wCfXXP/AARXv/xmj/hMtL/59dc/8EV7/wDGa6CqaalCb2KylWSC7mSa SOFwCWjidUZ8qSADvQgE5ww4BBAAMv8A4TLS/wDn11z/AMEV7/8AGaz9C1/S9E8PaZpPl65N 9htIrbzf+EfvV37EC7seUcZxnGTXSatqUOjaNfapcLI0FlbyXEixgFiqKWIGSBnA9RVygDP0 nWrLW4p5LJp/9Hl8mZJ7aSB0far4KSKrfddT071n+BP+SeeGv+wVa/8AopaPD3/Ic8Wf9hWP /wBIrWjwJ/yTzw1/2CrX/wBFLQB0FFFFABRRRQAUUUUAFFFFABRRRQAVy+qeFry+bWLe31OC DTdaz9viktDJN80Swt5UgkUJ8iLjcj4bJ5B2jqKKAMfVtJvNRaCeG9ghurK7+02TPbF0XMTR MsihwX4kkIIKYyvXad2Pd+E9YuLWZF1qxD3139q1JJNOdobnESRLGEEwZY9sallLNvOQfkJQ 9hRQBy/i1LyP4Y+Ilv54J7oaVd75IITEjfu3xhSzEcY/iPrx0rqK5/x3/wAk88S/9gq6/wDR TV0FABRRRQAUUUUAFFFFABRRRQBnzaZ5viGy1bzsfZrSe28rb97zXhbdnPGPJxjHO7tjnQoo oAKKKKACiiigAooooAKKKKAM/RtM/sixktvO83fd3Nzu27cedM8u3GT034z3xnjpWhRRQAUU UUAFFFFABRRRQAVn6zpn9r2Mdt53lbLu2ud23dnyZkl24yOuzGe2c89K0KKACiiigAooooAK KKo6rqaaVapKbea5lklWGG2t9vmSux6KGIHAyxyRhVYngGqjFyaitwLUc8UrypHKjvE2yRVY Eo2A2D6HDKcehB71gal4z03TdIN/IcYuZIPIdsSMkU/lTyKq5LKihpDgfdHOO3MeB/Feqapc +JQdGRtUj1VnvLRbgKII1jjiVVc5Ekh8pgPuqSrEsgK51fDHgaKyOnaxqe9tbiWKRmLBjG32 cxyRlskENJJNKxXG53yc4yfTlg6WGlJYl6q2ia10vbTZba9ttyOZv4TV0DxKurjVZp4nsoLX UBZwC7iMEjApEVLK/ILPJ8owCQV4zUGqajY+HfE739wb65uNStIYI7SysZLh1SCV98p2A/KP tS5zjpxuJxVHXfDWr32qfZraVE0m/wBVgvr6aOUpMixRIBGAMEAvBGfMVwwLfdwCTPFpFnpf xD0o2wmaSXSr7fLcXEk8hAltMLvkZm2jJIGcAsTjk558RCgkpwertpvbbd6a3v8A0xq5wdxr OteLdU1iTVdG1nTNPbSmgsLWbS7x9t28ToZCY42GAJZlJwMqUO0lfl6fwZ4mey0y6TV7HXI5 Zbk3KxjQ7shDKiSyqCsR+UTvMBkk4A5Iwa9Dop1sdOrT9nypR0ta+lu129+oKNnc5/8A4TLS /wDn11z/AMEV7/8AGaP+Ey0v/n11z/wRXv8A8ZroKK4Sjn/+Ey0v/n11z/wRXv8A8Zo/4TLS /wDn11z/AMEV7/8AGa6CigDn/wDhMtL/AOfXXP8AwRXv/wAZo/4TLS/+fXXP/BFe/wDxmugq mmpQm9ispVkgu5kmkjhcAlo4nVGfKkgA70IBOcMOAQQADL/4TLS/+fXXP/BFe/8Axms/Qtf0 vRPD2maT5euTfYbSK283/hH71d+xAu7HlHGcZxk10mralDo2jX2qXCyNBZW8lxIsYBYqiliB kgZwPUVcoAz9J1qy1uKeSyaf/R5fJmSe2kgdH2q+Ckiq33XU9O9Z/gT/AJJ54a/7BVr/AOil qDSL20sdX8VyXd1DbxnVVw00gQcWFux5Poqsx9lJ7VY8CjHw98ND00q1/wDRS0+V2vbQDfoo opAFFFFABRRRQAUUVz+o+LINPnvv+JdfXFpp3/H/AHsPleXa/IJG3BnDtiNlc7Fbg4GWyAAd BRWfq2rJpUUH+jT3Vxcy+Tb20G3fK+1nIBdlUYRHb5mH3cDJIBz08UmaJlt9C1We+ilMVxZI sIeAhUf5nMgi5WSMgByTu4HyvtADx3/yTzxL/wBgq6/9FNXQVyfifUodW+FviO7hWRAdMvY3 jkADRyIkiOhwSMqysuQSDjIJGDXWUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/ef8lD0b/sFX/wD6 NtK6CufvP+Sh6N/2Cr//ANG2lAGlqOk22qeX9okvU8vO37NezW+c46+Wy7unfOOcdTVH/hE9 O/5+dZ/8HV5/8drcorWNerBcsZNL1YrIw/8AhE9O/wCfnWf/AAdXn/x2j/hE9O/5+dZ/8HV5 /wDHa3KKr61X/nf3sOVdjD/4RPTv+fnWf/B1ef8Ax2r2naTbaX5v2eS9fzMbvtN7NcYxnp5j Nt69sZ4z0FXqKmVerNcspNr1YWR5nNaLceM9cNvp12+qLrti1veKjMkUSxWjXChwSISYtwbO 3zAVUF8bRY0TSFt/GOk311pEgn364i3LWbExlr3fFl9vyAxtKVJIBDsB97nvLext7Se7mgj2 SXcomnO4ne4RYweenyoo49PXNWKyGeXw6ZF/wrzV7ODQr5PEJ8PzQalMto8f2m6MWCGYgfaZ GfeQ6+Z/F8w8wb+s0TSl0jxVqsFnbSQae9laSA/MVlnL3AlcsfvylRFuYksfl3E8V0lFAHP+ Hv8AkOeLP+wrH/6RWtHgT/knnhr/ALBVr/6KWjw9/wAhzxZ/2FY//SK1o8Cf8k88Nf8AYKtf /RS0AdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz/jv/knniX/sFXX/AKKaugrn/Hf/ ACTzxL/2Crr/ANFNXQUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRUc88VtBJPPKkUMSl5JJGCqigZJJPAAHehK+iAkormIPFUuo+Ek1S 1sXtr+7nktLO0ugdwmErxr5ijBAG0u4GSqq/XbmjwdqdxJpUFrq9wkmryz37uI97IRFdMjbC 3IVSyBQecY9DjqlhKkISlLdO1uvW/wAlbcnmR09FFcj4g8dWukQ+H57O3udSTVmMkcFpbNJN LAIi+5F4wQTFkHnaW444zo0KleXLTV3/AMBv8kxtpbmzruuwaJYXczL5tzDY3F6kGSvmJCFL fNggcug/HocGsPw14F/sfWf7bvdQmub6W2QTQFt0CXJ3GWWMEDG5nkIAA2+bJjh8DmLvw5ce I9Q03UNesXtZ9cnm0++tZZ3byI4pVmjjjA42lLR8sCMtOXGRgD1iu6vbCUVTpy1lfm26O1k9 dLp+u+zsSved2QQWcFtNdTQx7ZLqUTTHJO5wipnnp8qKOPSp6KK8xtvcsK5+8/5KHo3/AGCr /wD9G2ldBXP3n/JQ9G/7BV//AOjbSkBpajpNtqnl/aJL1PLzt+zXs1vnOOvlsu7p3zjnHU1R /wCET07/AJ+dZ/8AB1ef/Ha3KK1jXqwXLGTS9WKyMP8A4RPTv+fnWf8AwdXn/wAdo/4RPTv+ fnWf/B1ef/Ha3KKr61X/AJ397DlXYw/+ET07/n51n/wdXn/x2r2naTbaX5v2eS9fzMbvtN7N cYxnp5jNt69sZ4z0FXqKmVerNcspNr1YWR5nNaLceM9cNvp12+qLrti1veKjMkUSxWjXChwS ISYtwbO3zAVUF8bRY0TSFt/GOk311pEgn364i3LWbExlr3fFl9vyAxtKVJIBDsB97nvLext7 Se7mgj2SXcomnO4ne4RYweenyoo49PXNWKyGeXw6ZF/wrzV7ODQr5PEJ8PzQalMto8f2m6MW CGYgfaZGfeQ6+Z/F8w8wb+s0TSl0jxVqsFnbSQae9laSA/MVlnL3AlcsfvylRFuYksfl3E8V 0lFAHmN54LbxP4n8U3LX7xmK+jtYYwAoiSW1tVuXBwdzNDlFBGFPPU5HXeBP+SeeGv8AsFWv /opaPD3/ACHPFn/YVj/9IrWjwJ/yTzw1/wBgq1/9FLW1SvUqQjTk9I7fP+r+t31Ekk7nQUUU ViMKKKKACiiigArz/XlnjsvG+kfYr6S71vf9g8mzlkjk32cUK7pVUpH+8Rgd7LgDJwpBr0Ci gDm/EUp+2aPerb3ckGl6mXuvKtpJGCtazICiqpaQbpowSgbGTnG1sY665caWdXvbfTdSb+2N TD2kjaXcyCKNbaCNpZY1TzFAaNgEIUucYIUl17yigDi9ZitIfhDr4svtZifTL6Rmu4HhleRl kaR2RlUqWcs2NoHPygDFdJpmnXVh5v2nWb7Ut+Nv2tIF8vGc48qNOue+egxjnOf47/5J54l/ 7BV1/wCimroKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAoorlYPFr65qcmk6HbTRTpF57X15as1sIt7KrJhh5u/blc MFKktuJUpWtKjOom4rRbvsJtI6OC8guZrqGGTdJayiGYYI2uUV8c9fldTx61z91450uGaC0i 3y6jPPNGlkFJlZIZWjmkAUNwBHIyjq+3aPm4GN4S0LxXZNrlvf6ziUam1wt8YNxvCYotuVYY WIAAFUOcgqrIEy3T6D4bstD0uztVRJpoFiLTsvLSJAsHmAEnYSigcdifU57J0cNQk1KXPta3 XTq7bXt5200e0pyZR03xfFLZ6zfavC+k2thqC2eLtQrJlIsFyGZcF5Dhgdu0qemTUl1PE3xK 0mASoZk0e9doww3KrTWoBI6gEq2D3wfSn3/hDT9R1iDUJ5rnZFeR35tVZRE9yibEkJ278hQo wGC/KMg85qx6Xp+k+O9Ig02xtrOFtMv3aO2hWNS3m2YzhQBnAHPtWNd4dpSp3v26Lb59/vXY av1N++k1GPy/sFraz5zv8+5aLb0xjCNnv6fjVT7R4i/6Bel/+DKT/wCMVrUVxNPuDi29/wAj J+0eIv8AoF6X/wCDKT/4xR9o8Rf9AvS//BlJ/wDGK1qKOV9/yFyv+Z/h/kZP2jxF/wBAvS// AAZSf/GKt2MmoyeZ9vtbWDGNnkXLS7uuc5RcdvX8Kt0UJPuNRae/5HH3niTWIbzXBHHYi1s9 QtdNtmZXZzJOLYB3GQNqGcnAOX4GU27mrvqF5d69otpfmCS607xA9s80EZjSXOmyyhghZiuB KFxuOdueM4HUS6Jp08V/HJb5W/lWe4IdgTIqoqupByjKI0wVwQVBGDzUdt4e0y0Ft5cEjPbX BukllnkkkMpjaIu7sxZzsYqNxOBgD7oxRRT8Cf8AJPPDX/YKtf8A0UtdBVewsbfTNOtrCzj8 u1tYkhhTcTtRQAoyeTgAdasUAcXp+lXl94k8VS2/iDUtPQanGpitY7dlJ+x23zHzInOecdcc DjrnU8Cf8k88Nf8AYKtf/RS1hw+LNN0LxN4ns5/OnvJdREy21sm99i2NtgkZGNz7Y1z953VR nnG74Fx/wr3w1jp/ZVr/AOilrSVKcYqclZPbzFdG/RRRWYwooooAKKKKACiiigAooooAKKK8 /wBeaeSy8b6v9tvo7vRN/wBg8m8ljjj2WcUy7olYJJ+8did6tkHByoAoA9Aorn/E5eW50HT/ AD54re/1BobjyJmidkW2nlADoQy/PGh+UjOMHgkHm9XfUrbwd44ttP1a7tk0l5fIlaRpp1jF lHNtWVyWBMjk7juIUkLt+UqAdJ47/wCSeeJf+wVdf+imroK5/wAd/wDJPPEv/YKuv/RTVoaZ rNrq/m/Zor6PysbvtdhPbZznGPNRd3TtnHGeooA0KKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAoqjqes6boscEup3sNnFPL5KSTttTftZsFjwOFbrj06kVaE8TTvAJUMyKrtGGG 5VYkAkdQCVbB74PpVOEkuZrQLklFFVNL1CLVtJs9SgV1hu4EnjWQAMFdQwBwSM4PrS5Xbm6A W6KqapqEWk6TealOrtDaQPPIsYBYqiliBkgZwPWuV8NeOTqWrLpWqfZoL0tJZqlusjLJdwNJ 9oCsRwoTyXGcf6wjLEHG9PC1alOVSCul/X4LV9hOSTsdrRXmOvazPr+o6tNaXuNK8NxR389q rBZJLq3uJsxtjPyOsJPzcg+UwHUHtdI8S6fqcFsr3NtBqErPDJZG4VpI54x+9iHQsUPUgYIw 3Qg1rWwNSlBSevddtL/kJSTZs0VzHiHxlFo73FnZWb6jqyKpgs0cKbhsM7opAJ3JGu8jHR4w OXUVRn1HXfDupanf6mlzqgvLMPY2WnWsskVvJFuzCWVWOXLriUgbtrZChVWlDBVZRvtfZdXt t8ne700avdA5I1df8UWumafdi2lR9SE4sLWGSNisl48QkijJGBhgy85AGeSK5/TTffEbQbC/ uJvI0q4iWC6ht5XjFwhVWmIwcj98nkgHHyCYhj5ika2jeB7Sx1P+2tTn/tLXJYjHdXbRCNJ8 OjRsYuVVk8pACuOmTk4I6qtZV6FCPLQ1l/N+dlvvqm9bbq+ys3ucxYeEltfGN7rzumyRm8m3 XJVQUjw2D8qMHN0cgZP2hiTyRV648K6NdX1xey2r/a52DtOlxIkiMFVMxsrAxkqqqxTbuAAb IrZormliq0mpczulb5IrlR5jH4n8V+JvB32Xw/pnn3T6ZCJdWln8hWmeFjIYdoAZlZSmQy7J GGRhSa7LQvCOjeGr/ULrSLX7L9v2GaFGPlhlLnKg/d++RgcYAwBzmPwPobeHPBWk6VIrrNDA GmV2DFZHJd1yvBAZmA9gOT1roK6cZio806OHXLC72v72qs3rbpdJaImMer3CiiivOLCiiigA rn7z/koejf8AYKv/AP0baV0Fc/ef8lD0b/sFX/8A6NtKANW+k1GPy/sFraz5zv8APuWi29MY wjZ7+n41U+0eIv8AoF6X/wCDKT/4xWtRUtPuS4tvf8jJ+0eIv+gXpf8A4MpP/jFH2jxF/wBA vS//AAZSf/GK1qKOV9/yFyv+Z/h/kZP2jxF/0C9L/wDBlJ/8Yq3YyajJ5n2+1tYMY2eRctLu 65zlFx29fwq3RQk+41Fp7/kcfeeJNYhvNcEcdiLWz1C1022ZldnMk4tgHcZA2oZycA5fgZTb uau+oXl3r2i2l+YJLrTvED2zzQRmNJc6bLKGCFmK4EoXG45254zgdRLomnTxX8clvlb+VZ7g h2BMiqiq6kHKMojTBXBBUEYPNR23h7TLQW3lwSM9tcG6SWWeSSQymNoi7uzFnOxio3E4GAPu jFFFPwJ/yTzw1/2CrX/0UtdBVewsbfTNOtrCzj8u1tYkhhTcTtRQAoyeTgAdasUAc/4e/wCQ 54s/7Csf/pFa0eBP+SeeGv8AsFWv/opay9P1+z0rxJ4qguIdSd21ONwbXTbi4XH2O2HLRowB 46Zz09RWp4E/5J54a/7BVr/6KWgDoKKKKACiiigDl/FOo3lrqOn2yXGq2djNFNJLd6Zp5upF kUxhEI8qQKrB5DyucoMEcg6Hhubz9Okb7fqt7iUjzNTsvssg4HAXyosr77TySM8YFPxRa31x cWTR22pXmnokonttMvfss5lJTy33+ZHlAolBXfyXU7TjK2PC9vfW1lci7ju4YGuC1nb3tx58 8MWxAVkk3PuJkEjD52wrKMjG1QDcooooAKKKKAOf8d/8k88S/wDYKuv/AEU1dBXP+O/+SeeJ f+wVdf8Aopq6CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKw9Y8U2GlW021vOvFuVsYbbDL5t08Ykji3YIXcGX5j8ozyeKxrDUNf8V6Lp dzCr2Nnf2YiupUHlurSRhnmiBO5QpUxocnJl34Kopfqhg6koe0l7se79L/PytuS5LY6OHX9L uNHOrQXiS2G5kWaMFg7BzHhABlyXG1QudxI25yMyaRqsGtacL62SZIjLLEFnjMb5jkaM5U8j lTwcH1APFc5pnhOe01+cD9xoEFz51pYK48sEKrqVQDAXzprhjnkNHDtwEFXpPB8P9p/b7bVt TtJE84wJA8eyFpnV5SAyHfuYZw+4DPygYXGtSlhVeMZ76p9l2duu1+m9uzE5HR1m6vrun6EL N9SuEt4bqfyFmldUjRtjvlmYgAEIR9SBXM2vizW/EHg4z6Jo80mozWKlLxXhS1FwYQzBNzlj tYlcFSN6lWIwxF6y8Babbarb3dxLNfw2G06Zb3R3LYEKFITGMrtSLAYEgoWyWYmhYWFFv6zK 1r6LV/8AA1a36X7WZzN7HMeI/EeteIJjpOlJbRWeuWd3Y2MV0jrLJNHKkczygqDGFj85lXr8 vILEIPTYIIraCOCCJIoYlCRxxqFVFAwAAOAAO1Mns4Lma1mmj3SWspmhOSNrlGTPHX5XYc+t T1niMRCpCFOnHlSv97f46JfO/TRCTTuwooorkKCufvP+Sh6N/wBgq/8A/RtpXQVz95/yUPRv +wVf/wDo20oA1b7UoNP8vzo7pt+ceRayzYxjrsU469+tVP8AhI7H/nhqn/gquf8A43WtRUtS 6M1i6SXvRd/Vf5Myf+Ejsf8Anhqn/gquf/jdH/CR2P8Azw1T/wAFVz/8brWootLv+H/BK5qH 8r+9f/ImT/wkdj/zw1T/AMFVz/8AG6t2OpQah5nkx3S7MZ8+1lhznPTeoz07dKt0UJS6smTp Ne7F39V/kjHn8SWdveTWbxz/AGqO7gtVhCjfL5oBEiLnLRgeYS3byZeuw1XvfFaWE8qTaPqp jHmrbyrCp+1yxo7tFEm7zNxEcm0sqq23hiGUtXmsIr74i214lpOVsrRvtEzxuiecOINrHAfC T3YOzIG75uQmK8fgRE8X22vm7gaS3u5bhWNkv2iUSRyKUknJ3MqeYAgAUKihSGIVloyNC48Z aPD9s8ub7V9mtEuk+zMj/ad/3Y4vm+eQ7ovl/wCm8P8AfFdBXH2fw906z/s/Y+fsl2ZTw37y Fdnkxfe42fZ7P5urfZ+fvvnsKAOc0OCK41nxWk0SSINYicK6ggMtpasp57ggEHsQDT/An/JP PDX/AGCrX/0UtHh7/kOeLP8AsKx/+kVrR4E/5J54a/7BVr/6KWi/QDoKKKKACiiigAooooAK KKKAOX8U6jeWuo6fbJcarZ2M0U0kt3pmnm6kWRTGEQjypAqsHkPK5ygwRyDoeG5vP06Rvt+q 3uJSPM1Oy+yyDgcBfKiyvvtPJIzxgU/FFrfXFxZNHbaleaeiSie20y9+yzmUlPLff5keUCiU Fd/JdTtOMrY8L299bWVyLuO7hga4LWdve3HnzwxbEBWSTc+4mQSMPnbCsoyMbVANyuf1HwnB qE99/wATG+t7TUf+P+yh8ry7r5BG24shdcxqqHYy8DIw2SegooAy9S0UakFZr+7hnhuBcWs8 Qj3WzeWYyEDIVIKtJneG++cYwu2v/wAIvbPoOraXcXd3cHVkkW8u3KCWQvGIt3yqEBCKqjCg fKCQSSTuUUAc/wCO/wDknniX/sFXX/opq6Cuf8d/8k88S/8AYKuv/RTV0FABRRRQAUUUUAFF FFABRRRQAUVU1TUItJ0m81KdXaG0geeRYwCxVFLEDJAzgetSWV5BqNhb31rJ5ltcxLNE+CNy MAQcHkcEdarkly89tNgJ6KjnnitoJJ55UihiUvJJIwVUUDJJJ4AA71ys/ibUYtWkkttPe+sJ 5zY2kUcqIzzRN++I3Y5I88YYhf8AROD+9FaUqE6t+X8dPxenn6JibSOurirr4r+DoLOC5i1V LkTrM6xxcOojRmO5X2lSSu1QcFiwxkZI5jxpe+IPEf2T+z7rU9E+y6xDpFz9mkkXbJJ/rJWx t3xcweWx2k72yBuWu8s/B2kW+iy6VPZW09rJO0hQQhFKfaHnjjIHVUL4A6HnjBIr0FhcPh6c Z4lttvZPZdbuzT3TVn/mRzNuyOgorjvDMV3oN/e2mr6vNcLa6PZTTyXNyXjjkzcCVwz4O0lN 2W5xgcKFUPHxJ8OJO6Xk1zYQ+etvDcXts8Mdw+SHCbhuwjKVcsAFOMnkVyywVVzcaSc0rapd 1f8Ar/IrmVtS9qcEV54v0uzuokntW0+9doZVDIW3W6ZKnjOySRc+jsOhNVdR0Z9A8u+8L2UM UxzbPaqreSRJgRtsToqS7WOMBUedgCzEmfwhpP2Ozn1KZJkutSlkuDHMNrQxPNLLHEVwMMvn MWzk7mYZIC46OqnWdGapxd4x0fZ6vp8/1BK+p5/r0/ijVLLUvDui3k1vqthl5L3ylQ3UDQsY gpOArvISm5MAGF2+TKoaOs6JfeFrlNL8IRbpNWsZIJnmlfzYUSRY4XR1Bb939rGS2cRwqARt yfTqK0p5i4WioLl6ru7bv0eqXTZCcLnmseuyzaLL4d1q4Q64uoWpu7YOW3QzXFs8iEZYCMC5 8kAsQyoccZA6e/8ACMB0q8g0Sf8Asq+uL7+0ReBDM0dwWBdwrN/EoKEAgYYjGCQdibS9PuHD zWNtI4nW5DPCpImUBVk5H3gAAG6gACrdRUxuzpLl1u10vp02tdbO/Yaj3MrRfDum6DGosYds v2aC1kmb78qQqVQvjALAEjOPQdAAJ59G024+1mSyhEl3s8+VF2SSFPuMXXDbl4KnOVIBBGKv UVyOtUlJzcnfuOyM3TdDstMeSaNXmu5WZ5bqdt8sjMEDHPRQRGnyqAo2jAAArSooqZzlN3k7 jCiiipAKKKKACiiigAooooAKKKKACufvP+Sh6N/2Cr//ANG2ldBXP3n/ACUPRv8AsFX/AP6N tKANW+1KDT/L86O6bfnHkWss2MY67FOOvfrVT/hI7H/nhqn/AIKrn/43WtRUtS6M1i6SXvRd /Vf5Myf+Ejsf+eGqf+Cq5/8AjdH/AAkdj/zw1T/wVXP/AMbrWootLv8Ah/wSuah/K/vX/wAi ZP8Awkdj/wA8NU/8FVz/APG6t2OpQah5nkx3S7MZ8+1lhznPTeoz07dKt0UJS6smTpNe7F39 V/kjHn8SWdveTWbxz/ao7uC1WEKN8vmgESIuctGB5hLdvJl67DVe98VpYTypNo+qmMeatvKs Kn7XLGju0USbvM3ERybSyqrbeGIZS1eawivviLbXiWk5WytG+0TPG6J5w4g2scB8JPdg7Mgb vm5CYrx+BETxfba+buBpLe7luFY2S/aJRJHIpSScncyp5gCABQqKFIYhWWjI0Ljxlo8P2zy5 vtX2a0S6T7MyP9p3/dji+b55Dui+X/pvD/fFdBXH2fw906z/ALP2Pn7JdmU8N+8hXZ5MX3uN n2ez+bq32fn7757CgDn/AA9/yHPFn/YVj/8ASK1o8Cf8k88Nf9gq1/8ARS0eHv8AkOeLP+wr H/6RWtHgT/knnhr/ALBVr/6KWgDoKKKKACiiigDz/wAXolpqNsNR8OeDZNCXzjHc6xeLDidy jkDdCwVmPmsQN27buJUjDbHgV7OTSbtrCDw5Bam7OyPQJhLCv7tM72CqDJnP8I+XYOetZ/i6 98J3Wsf2bqviexsLptPu7K4hknRWEE6x7uScRyZETLuzld/yn7y6ng24sbmy1CSx1i01Qtes 1xLYpstklZEYrEu5gAQQ7YZsu7kkEkAA6SvN/ENhZ3Wm/EHVbi0gm1LTPM+wXkkYaa022EMi +U55TDszjaRhmJ6mvSKx77wvpOo3klzcQzlpcefFHdyxwz4AH72JWCSZUBTvU5UBTkACgCn4 rghvbzw5YXcUc9ndamyXFvKoaOZRa3DgOp4YBkRgD3UHqBXL63psK+CfiFp9q0lnYWDzGG1t CIowv9nxSbAAPlQyOXIXG4k5yGYN3l9otlqMUkdys7bpRMrpcyI8ThQmY3Vg0fy5B2EZ3Nn7 zZjTw9piaNd6SYJJLS8R0ufNnkkkmDrtbfIzF2O3CglsgAAYAGACn47/AOSeeJf+wVdf+imr Q0zUbq/837To19puzG37W8DeZnOceVI/THfHUYzzjP8AHf8AyTzxL/2Crr/0U1dBQAUUUUAF FFFABRRRQAUUUUAFFFFABRRWVP4m0K1v7uxudXsoLmziSa4SWZU8pHOFLE8Dkj6bl/vLm4U5 zdoK/oF7GrRXK+IfH+leH/sn+j3uo/avK8v+z0WXPm7/ACsZYbt/lvjbn7vOMrnGNt8SdX8O yQi+0mzu2a6SSQxyxsf3jRqqEDKqFzIsg+Y4jBGNzN108BUcVOo1BN21/r+rolyWyPQ6Kw/D 3iVNf3AWU1sfs0F5GXZWWSGbeYyMHIbCEMCBg5ALDDHcrlq0pUpOE1ZoadzK1vXU0WOLFje3 88u8rbWSK8uxFLM+0sPlHA4/idAMlgKNO8Q2GqX8tnbmZZY4hIRPE0THkgrsfDBl+QkEDAlj PRxWbpF3Lr/iN9T+yvbwaat3p6s2SJnNwFYjIHCi3Q5GQTKVzlDnV1nQ7LXYLeG9VykE6zrt bGSAQVYdGVkZ0ZTkFWI966JQpU7U6itLq1rZ+nXS2z6sV29UTzapp9vpo1Ka+to7Aqri6eZR EVbG07ycYORg55yKytA8YaXr2ki9SdITHZxXd0HJ8uBXUk/vCArBSjqSOhRgcEEVVu/A9pqc 2sx6lP8AaNOvstbWflALZSMm2SRM5Bdm+cNtypLkffbL9f8AA2l+Iryxkut8UFqtwPKtmMRd pnRpCWUjhgsisMfMJWOQeauEMFblnJ3fVLbS9rdXfR7Wequt17xq6prEVhod5qUGy7MCuI4o 5B+9mUlRECM/MXGzGCdxxjPFcjofizVrW/mj8Rf8g+1lfTbjUJES3SK4jLMszqT8qTRPDhs4 DjAADqTb/wCEY1q2P9n2k9tLpAvLK4DXU7tcFo3jeV8hdoLGLO3B3PI7lxnaeuvLK01G1e1v rWG5tpMb4Z4w6Ng5GQeDyAfwq1LD0YcjSkpdeqWlvnq9O++gas88dNf8XG41NBcw28Vna6lp lsp2xXcyvPLArEuVxtMIlXgl1UhgqqW6c+Jp9NtZZNa0q9iitN4u76CINAoUnDhQxkKsu1vl Vwm/DNlHI6Ois6uMjU92UFyrZLdbLf8AzW/loNRt1OVvNa1nWI3i8M2e2PzQsWp3IAgbavmE 7Dhnif5Yw6Z+87DGxS8baJ4i0vUrzU9Mu7bUr3UIAlwdSmaKKB0/1XkoiMRGC8mUJycj587i eqggitoI4IIkihiUJHHGoVUUDAAA4AA7VJUfWlD3acVy+erfq9+idlZeQcvcw9G8J6bo119u TzrrU2iMMuoXL7p5kwgAcgAHAiQDjsT1ZidyiisKlWdR803djSS2CiiisxmV4a0KDwz4bsNG tm3R2sQUvgje55ZsEnGWJOM8ZxWrRRV1JyqTc5u7er9QStoFFFFQAUUUUAFY+raCdT1G0v4N VvtOurWKWFXtBCdySGMsCJY3HWJemO9bFFAHP/8ACPap/wBDnrn/AH5sv/kej/hHtU/6HPXP +/Nl/wDI9dBRQBz/APwj2qf9Dnrn/fmy/wDkej/hHtU/6HPXP+/Nl/8AI9dBRQBz/wDwj2qf 9Dnrn/fmy/8Akej/AIR7VP8Aoc9c/wC/Nl/8j10FFAHP/wDCPap/0Oeuf9+bL/5Ho/4R7VP+ hz1z/vzZf/I9dBRQBz//AAj2qf8AQ565/wB+bL/5Ho/4R7VP+hz1z/vzZf8AyPXQUUAcvbxX fhie7SDTtc16S+lF1Peb7Nfn2LGFwXixhYl6L36k5xY8Cf8AJPPDX/YKtf8A0UtdBXP+BP8A knnhr/sFWv8A6KWgDoKKKKACiiigAooooAKKKKAPP/F6JaajbDUfDng2TQl84x3OsXiw4nco 5A3QsFZj5rEDdu27iVIw2x4Fezk0m7awg8OQWpuzsj0CYSwr+7TO9gqgyZz/AAj5dg561n+L r3wndax/Zuq+J7Gwum0+7sriGSdFYQTrHu5JxHJkRMu7OV3/ACn7y6ng24sbmy1CSx1i01Qt es1xLYpstklZEYrEu5gAQQ7YZsu7kkEkAA6SiiigAooooA5/x3/yTzxL/wBgq6/9FNXQVz/j v/knniX/ALBV1/6KaugoAKKKKACiiuf8LeKovFEEs0djc2Q2xzQx3QAkkgcEJKQMgBmSQAZJ IUN0YVpGlOUJTS0Vr/PYV+h0FFZuu6uuh6X9ueF5kE8ETJGCWxJKkeQFBLEb87QMnGO9Qajr ipoKahpzJIZ54baNpFIEbyTLDl14bKMx3IdpypUlT0cKFSaTS0btfz/phdFHxJ4xtdIs763s Q95rcaultYxws7SzBI2wAMbgBNGzBTnbuI+6cZ0HjnVr68tIrPwlqeFuXiv43CCSFDD5sOCz KgZgylgW+QgqRlkJ2PC3hKy8K2MMFq7u6WcNrITwrlGkcuAckFnmckZIHAGMV0FdcquFp3hC HPvq21fTsunVK979baE2k92cdNFYeMvFFh51vDe6La6Yt/Gk5YpNJcMViYxEYO1IpeW5HmcA EZp+meINI8P3mqaLqupW1jNBeS3MTXcghE8c7tMGTdgEKzvGcE8x5OM4rc0vQ7LSJ7+a1V99 7OZ5N7btpJJKqTyF3s77egaRyMZxVtLOBL+a+WPFzNEkLvk8ohcqMdODI/5+wpTxFJp09XBJ WV7a9+vdjSe/U88tNLvfiFeQ+ITrDroJvHe2swdySRwuIhuQopxKpuw4J6SRg7guK7/TNPi0 rTYLKFndIlwZJCC8jdWdyAMsxJZj3JJ71borPEYuVZKC0gtl27XfV+YKNijq2nf2pZx2/m+V sube43bd2fKmSTHUddmM9s556Veoormcm4qPT+v8ijmPE3gbS/Fk5OpbzC0HlukbFWLqT5Ug bOAUEk3GCG8znO0V0F1ZWl9GI7u1huIxuws0YccqVPB9VZlPsxHep6K0liKsoxi5O0dvK4rI KKKKxGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFY+raCdT1G0v4NVvtO urWKWFXtBCdySGMsCJY3HWJemO9bFFAHP/8ACPap/wBDnrn/AH5sv/kej/hHtU/6HPXP+/Nl /wDI9dBRQBz/APwj2qf9Dnrn/fmy/wDkej/hHtU/6HPXP+/Nl/8AI9dBRQBz/wDwj2qf9Dnr n/fmy/8Akej/AIR7VP8Aoc9c/wC/Nl/8j10FFAHP/wDCPap/0Oeuf9+bL/5Ho/4R7VP+hz1z /vzZf/I9dBRQBz//AAj2qf8AQ565/wB+bL/5Ho/4R7VP+hz1z/vzZf8AyPXQUUAZei6KNGF6 zX93fT3tx9omnuhGGLeWkYAEaKoAWNe3rVPwJ/yTzw1/2CrX/wBFLXQVz/gT/knnhr/sFWv/ AKKWgDoKKKKACiiigDl/FL3EOo6fMsl9aWoimWS+02wF1co5MZWIL5chEbAOzHb1jT5hwGue F7i+ubK5N3JdzQLcFbO4vbfyJ5otiEtJHtTaRIZFHyLlVU4OdzSatoJ1PUbS/g1W+066tYpY Ve0EJ3JIYywIljcdYl6Y71c02ynsbdorjU7vUHLlhLdLErAYHyjy0QY4z0zyeemAC5RRRQAU UUUAc/47/wCSeeJf+wVdf+imroK5/wAd/wDJPPEv/YKuv/RTV0FABRRRQAUVBdXtpYxiS7uo beM7sNNIEHCljyfRVZj7KT2rn/CPia916BZNT09NPluoEvbOFZfM327DH3uNzAjcwAG1ZYge Sa2jQqSpuolov607/p1FdXsdPRWNretNZaXHcaciX073kdskETBmlPmhZVX5gNyoJTyQF2Et wpqreeLIP9EsLP5NavpWghtLhDuidNplL4OPkRvMxuG8Y2MQwNVDDVZpOK7/ACtu32S77aPs HMh/ifxjpHhfSby7ur22M8CsI7UzASSyBVIjAGTk70zwcBgTxVG3+JHh+7Fv5H9oSvPOYRHH YyvIo2NIjsiqW2ug3KcZIOcfK+294b8G6R4VRE0yN1CwCEeYQxzkl3zjO5zt3HoRHGMAKBXQ VtKWDiuWMZS872/Cz/MXvHK62sniq6i0Sx1PydKnsXn1Ca2VHeWKUFIkR2BUK+JWLAE/uxyu 4E3vDN7vtZ9JuLrzdQ0uU20yvJvl8vJ8mRz3Z4tjFuhYt0IIF7T9HsNLkupLODy5LqVpZWLs xLMzMQCxOF3O7bRgAsxA5OYL7wzoWqX5vtR0iyvbkxLCHuYVlwiliAA2QOXbp14z0GFKtSlH 2WvKtnZXv1v369ei7BZ7nKLrvivxh5dz4UWysdDllZYtSujvkkRd0bsIsd2YMgJHMB34DgV0 mmeHYIo7G81KGGbWIZZLp7lcnbNKpWQKxwSgU7FDZwqIDyoI2IYIrdCkMSRoWZyqKACzEsx4 7kkknuSTUlKti7rkpRUY+W7Xm+um/TyBR7nMa54Usp7aJ7DT0FwNatdUbY2weYskYkkxkDPl h8jucnBY5rp6KKwnWnOKjJ3t/wAD/IdrHP6roeo3WrS3mmas+mvPZiGSZI0kYPGzGLCupBX9 7KW5BO1MEfNlkXgnTLW6uLqyuNTtbm42tNMl/K7yOgxGzFyxbaCQFOUOfmVsLjo6KtYuso8s Xbp6+ve3S+3QOVEFlZwadYW9jax+XbW0SwxJknaigADJ5PAHWp6KKwbbd2MKKKKQBRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVDdXVvZW73F3cRQQJjdJK4RV ycDJPHUgUDSbdkTUUySWOFQ0siopZVBY4GSQAPqSQB7mn0Ct1CimPLHG0avIqtI21AxwWOCc D1OAT9AaiGoWRu2tBdwG5VgrQiQbwSpYArnOSqk/QE9qV0UoyeyLFFFFMkK5/wACf8k88Nf9 gq1/9FLXQVz/AIE/5J54a/7BVr/6KWgDoKKKKACiiigAooooAKKKKAOX8UvcQ6jp8yyX1pai KZZL7TbAXVyjkxlYgvlyERsA7MdvWNPmHAa54XuL65srk3cl3NAtwVs7i9t/Inmi2IS0ke1N pEhkUfIuVVTg53NJq2gnU9RtL+DVb7Trq1ilhV7QQnckhjLAiWNx1iXpjvVzTbKext2iuNTu 9QcuWEt0sSsBgfKPLRBjjPTPJ56YALlFFFABRRXL65YWer+MtH07UrSC9sTp97Oba5jEkZkW S2VX2tkbgHcA9QGb1NAFjx3/AMk88S/9gq6/9FNWsupWD29zcLe2zQWrOtxIJVKwlPvBznCk d89K41kD/CPxLblgkMaavbxgnCxRJNOiKP7qqoCgdAAAOBXKaV4ImeDRtU03R7qYWN0t3bR3 08Ucc0W0mNk8p/lchId5dPmfac7Q+bi6S/iN/JX0s/NdbL730s6ULq90v68r/wCR6BoWuXt7 rUyXLI1lfLNcaaEXlI4ZFiYlujLIGilQgdJGGcBSb2tahqEF9p1jpS2z3dw0ksiXIbb5Ea/M dwPyku0KZwxG/O1gDWRqWj+Lb2WEx6lpatZGW4trgwuHedkkRQU5CIFlIBy5ygJ3ZIrltA0z xD4M8XajF9ifxDGtpHHFdHYk6iSSRxyWJ2tM0m8nJGAx4UA7uvh3erJWstFa93qunZWd2rOz 1vvUqPLpzp/P/Pv23S3Ss7dXeaR4i8SJHDqk1tp2nszieztizvIoPlmNpMgSRyRmQ/dRkJjP JU1BrMi+F9QsY9JhRHTRZ7a1hYkrLIsttHbI5zkgPKVBJ48xjkZJqWPWPGEilf7BXbuYJdKE xImSVfyXmVkOAAULZy2cjbtbm77V7+/8beHrnULJxa2EF7LLsib/AEp1dUEQQF1EizRxlQZC CdhBDFVq8LjozqKNTSCUna1ls3bz7Ju/rcPq7k3Gm+aXl2ulft1237q1zprHwZLb+Ipr261W 5ubITreRwvIT505jVGM6kbWCmKN49oUIxIAAVayv7PiPxotbVmcJaWdzq9uEIVUaby4GTbjp lJJCRjLSn0JbsotYt1R1vnisriLiWKWUAD5WbcrHG5CqsQ3orZAKsBxF74k0mT4gWmo6bNap ObGeymvLyUxQeUJEZJEJOJQHEq4XBY55AAaiGZ2cnVlvFxX3W/4DfnqOOCqyvZaR1b6Jd77e nfpc9IrLTxFpMttJcW94tzEk3kZtlaYvJtDbUCAlztOTtzgA56HGDZ31rrtg0eq65utZIFku IDA9ojq6M3yyMFZo/llOR1WNcnhzJoHWNCj1V5rGz+16nNti821thmbMYkCidsIf3YDYL9AM dhXm+0vqrWHzYaC96XM/Lb8Vr12Xbe+mpDrWlXMLTQanZyxIrszpOrKoXBYkg9BuXPpuHrVu OWOZS0UiuoZlJU5GQSCPqCCD7iuP10Wd8/k674alnkngaSFLGQvO8UbKWSQrtAwXVtu5lLDg ltu6zpPh/T7qa6mljZIQwhOl7yIbSRMh9ijA2yAg8qCyuc8SFaSnJysVF4Sa0lJS7Wv263Wv XW2iOqqjqesafo8Pm390sQKsyrgs7BeWKqMsQBycDgcnism68P6bp0JeDUG0sFZYo2EoRIon y8qxg4Cn5WkDDlSo6ouysjQtE1CxbTb02M91JDZLBZrdXgEduCAWZ1ZC8TkDGFMmMlMhQKJT kna36lKOEjrKbfklq/nql079e2va2t1b3tulxaXEU8D52yROHVsHBwRx1BFV31rSo7mO2fU7 NbiRtqRNOoZjuK4AzkncCv1BFYd7ol+12L+dEuJA22VNOke1kmUr98HzBtbIjBG/BVMksRGq VNKstQg099E/4RyCzFyz3Rm80eTGWkLYBjG4vGGiC/czsbayhASOpK9rGSnhl8XNftb9db/h fyOnn1nTbW+ayub6CC5EPn+XK4QmP5ssM9QNjE46AZOKfd6rp2n5+239rbY2586ZUxuzt6nv tbHrtPpXK3nh29TwzqEckFud1sVltkzdSXW1Sd3mFVKyluVwpAdnch2f5a3hz4fS2R/4nM8V 04nN0Z4pHEksrRhfmPH3GLsrctltwKchl7SpeyiaXwlnZyuullrt16dd09u+h1t9r2l6dZx3 VzexCGSMyxlDvMiAAllVclgAckgHA5PFWbu/tbFd1xOqHaWCdWYZA+VRyxyyjABJLAdSKw7H wVptnqV/dTF72O8UF4boB0EhIaR9v3cuUjJ+XgpxgYAluPB2jzTQTQQvZSwNuja1bYB0YDby u0OFkC4xuGcHc26r1LbIVqCtdt+n5Xfbro9b20sakGqWNzZy3cV1EbeLPmSlsKoAzkk9tpDA 9CpBGQQaxr3xpplpfXVp51vuiZYY5JJhHG8x3F0LEYAQBCxGcbwMbsKXXXgrR793a+F1db4x E/m3LksgbKKWBBwvpn5j8zbmAYTaT4U0vS7OCL7NFLMlp9kkkK8SoQu8FSSMMV3EerMerMSn 7V6KyCMqCi3ytvz2XbbV9ntp5pXfoHiCPXG1GLy1huLC5NtLGJd5yAMsOAdpbeASOQueOQKL /EDw6NUOmw3v2m6OxYhbrvWZ26Irj5c9OSQBuHPBww+BdLuNUvru7iYRTMqxQW87xJ5YhSMB lUjkYlAxxtkIOc4G/BpWnWtnLZ29haw2sufMhjhVUfIwcqBg5HFC9q1rYJqgmrXe19lbTVLe 9nor9tbkM+vaXbaXbancXsUNnc+X5Msh2ht/3evI4OeegBJwAaim8R2EGo3Niwummt9obyra SXJI3EAICTtBQscYHmIM5OKrW/gnw3atdtDpECNdKyyEZyAwIYJz8gIYj5ccfQU+38MQ26GIalqjQnGY/tJTJ2gMcoA2WZQ7HOS27oHc M71fIlOjGOqcn93z6+lvO9zZiljnhSaGRZIpFDI6HKsDyCCOop9cVrHgBZr5LzRrtbJnVory KRC63Ub7Vfe2Q5JUEnJ5bkFWJYvTwjqM1xuv72K5SSCIsLpmuFilUHdGiMADEx8sksSx8sE/ PtdVzzvblBqha/M/S3/Bt+XodZLdW9u6JNcRRu/3FdwC3zKvGevzMo+rAdxVS/1ux06wivJJ fNjn4txD85nYoWVUxwSwU7eeTgDkisOPweZUmMgsNPYyK8MOn2sYjBC/KzsUEm9SzjcjIcYI 2EnAvw+04uzy6jqkriMpE3nrGYfmVl2bFXAVlBVOUU8haHKo9kEZYfdqT+5X/F2/E1JfFGmi C3ktZGvXumdbaK2ALTlHCPsLEKQCck5xtBboCavaXff2lpdteGPynljBki3ZMT/xIenKtlTw OQeBXGWPgqF9e8QWn2P7NoVxPC0kOCFuQI921CCCgWRtxIyDwo2hWBfD4WvWvYnm0uylJWdJ pJ5yYpXMikzyQgFXZyA4j4ChAC+QgSVOpe7X9f1/XUp1MK7xSkl3td+atponZJ9bN6KyNfT9 c1uXQrXVbnR4JoZLZJ2WyuWeZgVB+WNkAzz93fn0LHAMpGpazdre2OoLa2UDH7MphMiXZKge Y2GUlOXCqMAkB8sNoqHRrHW/D2i2enrFBqMVvCoZ2umWUHHzIgKbWAOduWQY2qcY3G/52v8A +tSzsGSTlbeadonhHozqrhye+AAvQF/vU1dpc1xPGQTcoU+V+jdk/J3V+mn3dVl3OpeL9OuU km0ez1C1lVx5dhIVkhkLYjDM5G5TlcsFGPmJAC/NbTUtV1FZL7S1gNlFNsEM0DCa4CkCTaWZ fLYHegV16pkkBuGSWOs6hrFs1832WzW3l3CwvHGHLRgK2Qu7KiTDYGN5xtZVc2Ro0mmTNLoQ srWJ1HmWbQbYnK5wV2EbGOcFtr8KvHy4IlK/W39f1/VhvGKSuqSuuvdej69Lq2nd6oTxEsjS Iuk6tvVsKGtCodSAVYMcLgkgYJDKSdwUKxFYeJJVv4XurX7Hpc0cwhknV1nklV1VU8sqCCw3 lVG5mABwMEVNB4hnn82NdEv3uoZCk8MbRfue6ZdnVWLIVbCFtucHtl5vtYtZlhn05Lx5VMqm 0OxIgMbkZnPLcjaeN5zlYwC1Pmb6/gSsTh9/Z/i/8t7+th//ABPbvj/RbGGT5t3Mk0a9NmPu b8fNvyyg/LtcDcT7Rr6/uxp1hK69ZjdtGjjsQvlsQeCSpyBlcM3OD+2bmT57fRL+aIcNnZE+ TypCyMuRt5PII3KMZDhDztf/ANa9nYKkfLW8M7SvMPRXZUCEdsghuhKfep3XRsn61F/YTXo/ z3/H8TOhv/FGpQCGPTItOnhjXz5rpxtkm2FtsaruzHu2AsWztLAfMMixBqXiG5u7pF0SCBLZ ljzc3RVZyVBLIyoflXpyvzbv4SpWmWtn4rTyI59VsHRLdWaZrcs7T/xKyjaDHyxBXY3C5zht 1lLTxCzSPLq1krK37sR2ZMbDA5ZS+4HJfID/AMMZBHzK0rm8/wAC3jIy/wCXKX36d/tfnt0H 7ddu/m82105Rx5flmd2Yd925RsJ7bdxXnKMcJDPoE2o+VBqt/wDbbFJBKYGhCGRl4UOVOGTo xXby4zwuEBbeFLCGJklnv7rfI8kgmvJAkhdizbo1IjIJY5Xbg9weam/4RfQl5i0q1t2/vW0Y hb25TB4OGHoyqwwVBFcra1X4kxxVaOsEo+m6+dr/AIla80eyjWJLzWbxI3mQW6TXQx54OYyp YZZhtGFJKkruZWbLHM0jU9d8TfZBLHLp1qI2luJY4ij+Yv7sRAsxwRKkrkYI2rGG+8wO8nh/ TwsnnRvcSuuz7RM5aZUBBVVk+8ApCkYOdw3EliWNSy8OyLCbS+u3lsLdtllbxHy1SMf6ssR8 xdMlQd2PlRsBxkS4SvpsXDG1IxalBSb2fbvp56a9LFn+wE76lqhA4Ufa2GB0I465XC5OSMbg Q5Zzi2fhfUhaWF3carePq9hM5Uyzkw3Khiil0O4KWi43L8w3knca2v7HuLf95YardJN0IumN xGw6KCpIIwOMqyknBcueudpniG4soorbxI0VvdNGRE4BzcvGxjcKuAWJIV1AA3CUBQdpJGo3 95WFHMq1P3X19PNW9HfVdWWBY+IZ9Sa6k1GC0ieED7PCDKqOhO3JYDerbm3YCN8qAHgmmSaF rcl7Cx8VXQtIpGcItrEJWym3DPjYQDkgbO/cgEa9vqunXdwbe2v7WacRiUxxzKzbCAQ2Ac4I ZTn3HrTxqFkbtrQXdublWCtCJBvBKlgCuc5KqT9AT2q+WL6/iUsbJaxUe3wxf5p9Pn8zFuZt Y0m8traO6+3x3mY45LqNA8c2VPOzYGQRiV8YBPlkbssop3gT/knnhr/sFWv/AKKWrOr61caU xK6Hf3lusXmSXEEtskcYGchvNlQjAGScYwevXFbwJ/yTzw1/2CrX/wBFLVRVupnUqqaSsk12 6/odBRRRVGQUUUUAFFFFABRRRQAVh6xf6l/bNjpGly2lvPc28901xdQNOoWJol2BFdDkmYHd u42kYOcjcrD1iw1L+2bHV9LitLie2t57Vre6naBSsrRNvDqjnIMIG3bzuJyMYIBka1qM+rfC fxLNOiC4jstRtZPLBCu0JliLAEkqGKbguTjOMnGTTg+KEM0qW50S9S4uLwWdsHIVfMYDCy7s OhUkB9quE45bK5vatY3Gi/CvxEkrx/ajZ6jdt5fzKjymWYqCQNwUvtyQM4zgZwNGz8EeG7Kx gtI9GsikQj3MYEBmKIUVpMAByAzdRwTkU4y5Wvdv9/3aff8AIuKha8m79l+d9fusumpz+meK I7fWoLy/8S2MkGoSXEU9nDdo8NrIuPKZWdVcJsiYMSdrPIpVcNmpIfFGleN9estMs764is44 pLmXyrw20ssm5kjjwjiTBVZJSuAQBETjJU9m+nWMhui9nbsbtQlzmJT5y424fj5hgkYPasm9 8HaPqGpXN/PC5muWgab5vlfyjkAg9VbChlPB2qcAjNbfWZr31Fc1rK2lvPz00XbR3dtXaja2 v4f1p+PZX0oHRvCTm11a91BL0wfvbW7vNRaRYgkiYKEttGGWNSw5bo5Ys26l4k1FL7xP4ett MvFJ1azvbOK7t5eEXfA0jK6nhgkcm0jOH25GM1vjwd4fGzdpkUm2RpW81mk81zn5pNxPmEbm wXzjJxiuWudDSy8ZqkbyxXtxE0Fndm5kLzxvl3ErZBbyhEy8ZbEkI3IQHCp46rTqKdb3kk0l fZtNLfpfoKXsNtVfrv8AKy/B336K+nZweH9Itr6O8h022jniUJEyxgeUAuz5B0UlAFJGCVVQ chVA5meyWz+LiX1xFCmnnSJ7tZpSuEuA0Mcr5PK/ulhXPAxnHVqsSaTr9uotNLkTTFZlCPZ+ U9smCN7tDIm6MsOiIzDd1IyWbDs/Cd3D49EGo6teXcz20l+t6YI9ok8yNcIkiuqsoVclecFA NoHOcMfVpt6N3Tjq+/b+rD5cPbmlN+SUdb+d7Jet2dt/wkdjL8lj5t9OfuQ268uO5DMQuMYb JIGHQ/xpuPtGuy/6P9htbWY/N9p80zwqvpt+Ry+e2AoBzuJG0sGj6jHM0kfiG9beoVvPiibA GfuhVVQcMxzgkkJnKqVaFPDUsk8h1DXNSvrZpvM+yyGNI2GwLtcIo3L1JXhTnlTyTh77/pf8 EXt4x+Cn97v+Vl+D+Y9dY1SK4ZLjSIpY1yjtp9357RyYUqjqyoRuDDnkDILYX5gReIZ5He3/ ALEvzeRfNNbo0WYkLMI2LM6q24KT8hbBBBORy+Xwtpctzb3CrcQyQQvCGt7h4mcMwY73Uh2O 4E8nksxOSc0yDTdR0jzYdJWw+wCQyQ2sgaPbu+8gZQdgDbnztbO/aAoUEr309dv68v6/KvrE V8VJfJy/FX/J7peab5fEcFnC8+pWV5YW8akyTXCKURuoUlGbJIxgjKkkLnf8tZdj4rvfEVvb jQrDY8toZ3u7yOQW8cgIHlAhR5h3ZBIIAAyN2Cta/wBn1244lvrW1X72baIswzwUy/B2jJD4 G5ipKgKVcHhbQNkaPothJ5caxq0tusjbVUKoLMCTgADk9qGqjej0KjiKaV1S183p92v4vt53 oweL4XmhtWtknup1LQLY3kMyT4znYSytgBWyWVRlGGScZdb+INSubKa/GjxQWkEkyTfaL1Uk AjdlYgBSnRf4nUZyM4wx1L7R9P1GForm1Rg0yz7kJRxIuArhlwwYAAZBzgY6VXtfDGi2XkeR p8QEG3ylbLBCv3SASRkHJB6gu56uxJy1L7/19xHtraezj98vyv8AqZd74zEEInt7S3lSSHzb aCS9SO5ug33Gjiwcq3GMkN1+QkAHOs/iDe6qCumeHHuLhptsds14kcvlbN4ldSPlUhlweVJJ GR8u7sH0uxezu7T7LEkF5v8AtCxrs80uMMSRg5I79asRRRwQpDDGkcUahURBhVA4AAHQUclR v4i1WjyuLpq/e8v87HNp4k1S8vFsrPQpUnWMSzPcvsQAlMbf4sNublgpGx/lZkaOn3viPU45 hBD4b1FCy8zyqkiRZ/iKxuxcBVclQQSQi9XBrpKKrkl/MN1advdpq/z3+/8Arrc5L/hK9XtL O5lv/DF+Z/mNrFaIZlbAQFGYDIO4v8xUKVXKl+C2da6p401aOXUrPSYreC6gjeFJbpSBEFJH lnJHmuZOrIFUIoO7t31FS6cnvJgsRaLUYpN9bX/Ntdnt63Tscbo+t6xaXWrQ6hpt/est3GsJ gKOwVoo/lYZCIQGQtg7SzuVG1WIsSS+N5GFzDbaTBEyqWtZJWmkQYBIGAil8lur7T8oGMFm6 dIo42kZI1VpG3OVGCxwBk+pwAPoBT6apu1nJhOrTbTUF07721dk1u9bbeSObm17XWhWO28LX gvAqPKss0PlAHOVV/M+YkrtzjKghiDwrVp/E+stLEbHw/dStJgraTQvE4G3dl5SPKTOemWI2 hSMufL62im4S/m/Ih1F0il9/+f4nJXfjyDSLi0tda0m/0+4uY9ylzE0RYDlRIHx1wMnGMgtt BzT28d2YaQLpmosiLGxkIiRSHBIKlpBuUbHy65XCM2do3V08kUcyhZY0dQysAwyMggg/UEAj 3FMjtbeHyfKt4k8mPyotqAbE4+VfQfKvA9B6UuWpf4vwH7WFvgV/V2+avf8AFdPnzF148tbV 3E1p5CRSBJjPdwAqQ3z7QrtvKKCWQfOCUG078h58V39wLRLLw9qLXLw+bNFNbPEgbY37oSuF AYNtyxG0qGxlsKekt7W3tEKW1vFChxlY0CjhQo6eiqo+gA7VNRyT6yCNaNtYL8f8zgZfFviC 4lWwj0+K01VJJEe1cqxlfaZIkifdtYBNvmk7cBvlwxUVr6f4qRdLj+1Q3VxdJbrN5iRqqzw8 7bgsSI4gwVm2uykYIweM78Fha213dXUMCJcXTK08g+85VQq5PoAOnTr6mqzaDpD3kd22m2pn ikeVG8ocSMVLPjpvJRfm68dalQqLXm/r/MJV29ORW+78d9eq9ErbvD0O11W98OWzxeI7+O/W BY5o7u2jfyZdg3BlKK5Izkbm5+UncDzegsE1G7uhqOptLexspa2sb2WFbVGUFUIRxuP3jvYA tnoAABe1LQdI1jcdQ021uXaMx+ZJEC4XngN1HU9DxmiTQdInt4YLjTbW4jh3eWLiISkFjljl skknkk8k8nJpqm1pv8xyxmIbbVl6K35K/wCL/FmBd2J0XVYJR4j1tpriGXdCYXuxJ86lnCKh WMqHAGFAzs4IDKz7X7ZfOiapfXWl628haGHzAsU0aNuULGrsGHyguA28ZI3BGXO/ZaLpWnTG ax0yytZWXaXggVGI64yB04H5VYurW3vbd7e7t4riB8bo5UDq2DkZB46gGhU2J4vEPVtX9F91 7GYbTxCJlC6rZmIKW3NZndv4+UgPynL4wVYbUyX+bNaKLVJrz7Xaa3a30kfmWs8A+SCB8qdw VdzF128o7c7jhkHBsp4X05GkYNegs2VK3kqGMYG4KVYEBiN7cksxy2SBixPoWm3HlbrbYIox Giwu0QCr90YUgHaeV/unlcHmnyP+mxLFVv5Y/cv8iL+xGm5vNTv5d3zSJDO0Cb/VdhDqAOAu 4jjJBbLGGSykguIbP/hJrqDzd32aE+S0r4HzDc6MXCjHbdySzNkYm/4RvT+pa/L9pDqNwXUd wG35APGQDg4XOcDDj4b0Q2NzZLpNlHbXShZ44oVQOB0ztA5HUHqDyKOV9vxYvrNfy/Br7rWR zej3lu+kI1947imgh2JHNFLFCWBXchl3gsJAQ3BbDBBuBy4Ntry1V1ll8bZiaMGCSIwZVNzB nkO0xkFgFDbVwVCg5Zt3T/Zbf7Z9s+zxfavL8rztg37M527uuM84p6RRxtIyRorSNucqMFjg DJ9TgAfQCkqbStf8/wDMqWKrybb5df7q/rX+tNDlf3En/H9/wlFxcN/qP9bD5i/w/wCo2Rpk 5/1m1h/FgYp8EcFpdw3Emh63DHbsXt4jKssULFSrFUjkYgbCcLjaAuEAZsN1VFP2ZLr12rc7 t22X3f5WOPSz0+7aQ2HhW4kjhbia6BtiMAH9wH+dXAKqpwgwijeAi4o6T5U9nAdTvdU1ORI8 Pb26TEROAozKVJCyqsavs3blkLldzMmO+piRRxtIyRorSNucqMFjgDJ9TgAfQCl7LW5SxWKS a59+/T0e6OYR/BEKyK9rpNokq7G+0WiwLKAQSuXUBirAblGSrAAgEVNpfhzR/wCybW2s50kF jNLLZ3NtJ89v5hLqA2TkbJF4bIYYJBBrpKxk8OwWRB0eZ9NypRliRWRlLs33WB2kGSQrjABY ZDKAtPks72TJ+t4qKtztrf8ABrq+za9AHhq1eZprq5vLqWRQJXkm2+YVzsOEChSm5iu0KMnc csAwH8KaM7RsbRgI23oizyKqNggMFDYDKDhWxlQFCkBVxDbWXieKG2M2r2Usxh2zh7X5Fk4+ ddpUsPlxjK8uzZwFQTR2mvSKZ5NSt4J2Zj9nEImhQZIUA/I5G05bJyWwRhQVZJR/l/IFjsR0 cl87fk/l6adBknh8x3ELWd9fxxPujulkvppN0ZGfk3ltr7lUbhghWfBB2kR+BP8Aknnhr/sF Wv8A6KWsnVvD11q95byazo3h68luCbcS/YPPktEDFgfMkBDDYJMZQDzGTggmtbwJ/wAk88Nf 9gq1/wDRS1cLa2ViZYida3P07nQUUUVZIUUUUAFFFFABRRRQAUUUUAFFFFABWfqei2Wr+Ubp Z1kiyEmtrmS3kUHGV3xsrbThSVzglVJGQMaFFAHL+LbG3074Y+IrW1j8uFNKuyAWLEkxuWZm OSzEkksSSSSSSTXUVz/jv/knniX/ALBV1/6KaugoAKKKKACuS1a1/wCKoisVt90erbZHkVN3 lrEVM+//AGJFS3i252nkkZ4braha1he8ju2TM8Ubxo2TwrFSwx05KL+VROPMrETi5LT+u/4G TL4U06Z0VzK1mv8Ay5PteL7yttG4FlTKJ8isE+QDbjOaNvpEemfEGBrOGC2sZdKmCW8C7FEg li3ttAAyQYxnqdvPQV1VFJ047oco3io30TuQy2tvcOjzW8Ujp9xnQEr8ytxnp8yqfqoPYUQW tva+b9nt4ofNkMsnloF3uerHHUn1qairstx2V7hWddaHYXdw9w8csc743SQTyQseMdUYHkBQ fXYmc7Vxo0UNJ7g4p7mdb6HYW7l/LlnfjDXU8lwVwwYbfMZtvzKp4xyqnsMaNFFCSWwKKWwU UUUxhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABTJIo5lCyxo6hlYBhkZBBB+oIBHuKfRQBljw5o4sbax/s63NrbKViiZcrg8k EH7wJAY5zlgG6gGnpoOlpZCzeyingG/5bkecTvfe2S+ScsAxyeoHoK0aKnkj2J5I9jmpPB1j Fe2c1irwqkxe43TOxkHmLNu+bOXMkcYLHkrkZ4XEvgT/AJJ54a/7BVr/AOilroK5/wACf8k8 8Nf9gq1/9FLRGKjsEYRj8KOgoooqigooooAKKKKACiiigAooooA5/wAd/wDJPPEv/YKuv/RT V0Fc/wCO/wDknniX/sFXX/opq6CgAooooAKrvZRvqUF8WfzYYZIVAPykOUJz7/ux+tWKKGri auFMMUZmWYxoZVUqrkfMAcEgH0O0fkPSn0UDCiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACuf8Cf8AJPPDX/YKtf8A0UtdBXP+BP8Aknnhr/sFWv8A6KWgDoKKKKACiiigAooooAKK KKACiiigAooooAKKKKAOf8d/8k88S/8AYKuv/RTV0Fc/47/5J54l/wCwVdf+imroKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf8Cf8k88Nf8AYKtf/RS1 0Fc/4E/5J54a/wCwVa/+iloA6CiiigAooooAKKKKACs+613R7HUYNOvNVsbe+n2+TbTXCJJJ uO1dqk5OSCBjqasX1xLaWck8NlPeyLjEEBQO+SBwXZV468kdPXiuD1KRpvCHxNleGSB3SVmi kKlkJ0y3+U7SRkdOCR6E0Ad5fX9nplnJeX93BaWseN808gjRckAZY8DJIH41HDq2m3OlnVIN QtJdPCM5u0mVogq53HeDjAwcnPGDWX4h/wCQ54T/AOwrJ/6RXVcv4ksbe+8MfEsXMfmLDLLM iljt3rpsG0kdGwfmGc4YKw5UEAHUeO/+SeeJf+wVdf8Aopq6CsvxLps2s+FdX0u3aNZ72ymt 42kJChnQqCcAnGT6Gqf2zxh/0AtD/wDBzN/8i0AdBRXP/bPGH/QC0P8A8HM3/wAi0fbPGH/Q C0P/AMHM3/yLQB0FFc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf/ItAHQUVz/2zxh/0 AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0AdBRXP/bPGH/QC0P8A8HM3/wAi0fbPGH/QC0P/ AMHM3/yLQBuGeFbhLdpYxO6M6RlhuZVIDEDqQCygntuHrUlcfNb+MJfENlq39kaGPs1pPbeV /a83zea8Lbs/ZuMeTjGOd3bHOh9s8Yf9ALQ//BzN/wDItAHQUVz/ANs8Yf8AQC0P/wAHM3/y LR9s8Yf9ALQ//BzN/wDItAHQUVz/ANs8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItAHQ UVz/ANs8Yf8AQC0P/wAHM3/yLR9s8Yf9ALQ//BzN/wDItAHQUVz/ANs8Yf8AQC0P/wAHM3/y LR9s8Yf9ALQ//BzN/wDItAG5DPDcoXgljlQOyFkYMAysVYcdwwII7EEVJXH6Nb+MNIsZLb+y NDl33dzc7v7XmXHnTPLtx9mPTfjPfGeOlaH2zxh/0AtD/wDBzN/8i0AdBRXP/bPGH/QC0P8A 8HM3/wAi0fbPGH/QC0P/AMHM3/yLQB0FFc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf /ItAHQUVz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0AdBUc08NsgeeWOJC6oGdgo LMwVRz3LEADuSBWH9s8Yf9ALQ/8Awczf/ItZ+s2/jDV7GO2/sjQ4tl3bXO7+15mz5MyS7cfZ h12Yz2znnpQB2FFc/wDbPGH/AEAtD/8ABzN/8i0fbPGH/QC0P/wczf8AyLQB0FFc/wDbPGH/ AEAtD/8ABzN/8i0fbPGH/QC0P/wczf8AyLQB0FFc/wDbPGH/AEAtD/8ABzN/8i0fbPGH/QC0 P/wczf8AyLQB0FFc/wDbPGH/AEAtD/8ABzN/8i0fbPGH/QC0P/wczf8AyLQB0FRieFrh7dZY zOiK7xhhuVWJCkjqASrAHvtPpWH9s8Yf9ALQ/wDwczf/ACLWfDb+MIvEN7q39kaGftNpBbeV /a83y+U8zbs/Zuc+djGONvfPAB2FFc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf/ItA HQUVz/2zxh/0AtD/APBzN/8AItH2zxh/0AtD/wDBzN/8i0AdBRXP/bPGH/QC0P8A8HM3/wAi 0fbPGH/QC0P/AMHM3/yLQB0FFc/9s8Yf9ALQ/wDwczf/ACLR9s8Yf9ALQ/8Awczf/ItAHQVH BPDdW8VxbyxzQSoHjkjYMrqRkEEcEEc5rD+2eMP+gFof/g5m/wDkWs/Qrfxhonh7TNJ/sjQ5 vsNpFbeb/a8y79iBd2PsxxnGcZNAHYVz/gT/AJJ54a/7BVr/AOilo+2eMP8AoBaH/wCDmb/5 Fq54a02bRvCukaXcNG09lZQ28jRklSyIFJGQDjI9BQBqUUUUAFFFFABRRRQAUUUUAFFFFABW fda7o9jqMGnXmq2NvfT7fJtprhEkk3Hau1ScnJBAx1NWL64ltLOSeGynvZFxiCAoHfJA4Lsq 8deSOnrxXB6lI03hD4myvDJA7pKzRSFSyE6Zb/KdpIyOnBI9CaAO8vr+z0yzkvL+7gtLWPG+ aeQRouSAMseBkkD8ajh1bTbnSzqkGoWkunhGc3aTK0QVc7jvBxgYOTnjBrL8Q/8AIc8J/wDY Vk/9IrquX8SWNvfeGPiWLmPzFhllmRSx27102DaSOjYPzDOcMFYcqCADqPHf/JPPEv8A2Crr /wBFNXQVl+JdNm1nwrq+l27RrPe2U1vG0hIUM6FQTgE4yfQ1T+2eMP8AoBaH/wCDmb/5FoA6 Ciuf+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgDoKK5/7Z4w/6AWh/wDg5m/+RaPt njD/AKAWh/8Ag5m/+RaAOgorn/tnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCDmb/5FoA6Ciuf +2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgDcM8K3CW7Sxid0Z0jLDcyqQGIHUgFlB PbcPWpK4+a38YS+IbLVv7I0MfZrSe28r+15vm814W3Z+zcY8nGMc7u2OdD7Z4w/6AWh/+Dmb /wCRaAOgorn/ALZ4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFoA6Ciuf8AtnjD/oBaH/4O Zv8A5Fo+2eMP+gFof/g5m/8AkWgDoKK5/wC2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCR aAOgorn/ALZ4w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFoA3IZ4blC8EscqB2QsjBgGVir DjuGBBHYgipK4/RrfxhpFjJbf2Rocu+7ubnd/a8y486Z5duPsx6b8Z74zx0rQ+2eMP8AoBaH /wCDmb/5FoA6Ciuf+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgDoKK5/7Z4w/6AWh /wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaAOgorn/tnjD/oBaH/AODmb/5Fo+2eMP8AoBaH/wCD mb/5FoA6Co5p4bZA88scSF1QM7BQWZgqjnuWIAHckCsP7Z4w/wCgFof/AIOZv/kWs/Wbfxhq 9jHbf2RocWy7trnd/a8zZ8mZJduPsw67MZ7Zzz0oA7Ciuf8AtnjD/oBaH/4OZv8A5Fo+2eMP +gFof/g5m/8AkWgDoKK5/wC2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCRaAOgorn/ALZ4 w/6AWh/+Dmb/AORaPtnjD/oBaH/4OZv/AJFoA6Ciuf8AtnjD/oBaH/4OZv8A5Fo+2eMP+gFo f/g5m/8AkWgDoKjE8LXD26yxmdEV3jDDcqsSFJHUAlWAPfafSsP7Z4w/6AWh/wDg5m/+Raz4 bfxhF4hvdW/sjQz9ptILbyv7Xm+Xynmbdn7NznzsYxxt754AOworn/tnjD/oBaH/AODmb/5F o+2eMP8AoBaH/wCDmb/5FoA6Ciuf+2eMP+gFof8A4OZv/kWj7Z4w/wCgFof/AIOZv/kWgDoK K5/7Z4w/6AWh/wDg5m/+RaPtnjD/AKAWh/8Ag5m/+RaAOgorn/tnjD/oBaH/AODmb/5Fo+2e MP8AoBaH/wCDmb/5FoA6Co4J4bq3iuLeWOaCVA8ckbBldSMggjggjnNYf2zxh/0AtD/8HM3/ AMi1n6Fb+MNE8PaZpP8AZGhzfYbSK283+15l37EC7sfZjjOM4yaAOwrn/An/ACTzw1/2CrX/ ANFLR9s8Yf8AQC0P/wAHM3/yLVzw1ps2jeFdI0u4aNp7Kyht5GjJKlkQKSMgHGR6CgDUoooo AKKKKACiiigArPutC0e+1GDUbzSrG4voNvk3M1ujyR7TuXaxGRgkkY6GtCigCnc6Tpt7b3Nv d6faTwXTh7iOWFWWZgFALgjDEBEAJ/uj0FEOk6bbaWdLg0+0i08oyG0SFViKtncNgGMHJyMc 5NXKKAOf1mW9ufEOm6Na6jPp8c9pc3Tz2yRtITE8Khf3iOu0+cxPy5yq4IGQbHhi+uL/AEUy XUnmzQ3d1amQqAZBDPJErMBgbiEBOABknAA4Bq2k3l1qNpqWm3sFrfW0UtuDc2xnjaOQxs3y q6HdmJMHdjG7g5BFjRdM/sjTBambzpGlluJZAu0NJLI0j7VycLudsAkkDAJJ5IBoUUUUAFFF FABRRRQAVz+s32qWviXw9DBJBHpt1dvDONu6SU/Z55AOeEVTGp4yWJ/hCnf0FZ+o6Z9vvtJu fO8v+z7trnbtz5mYZYtuc8f63Oefu475ABy/9p6t9k/t7+1Z9v8Abf8AZ/8AZ/lRfZ/L+3fZ M52eZu2/Pnfjf22/LRoup6s9v4T1W41We5XxBt8+zkiiENvvtZJ/3RVA/DRhRvZvlJzk4YaH /CLXm/7H/acH9jf2h/aHkfZD9o8z7R9px5vmbdvm9vLzs4zn5qNL8LXli2j29xqcE+m6Lj7B FHaGOb5YmhXzZDIwf5HbO1Ey2DwBtIB1FFFFABRRRQAUUUUARziZreVbeSOOcoRG8iF1VscE qCCRntkZ9RXD6xf6xoGoRafDrd3qAu0hWaa5igL2Zku4IEZfLjVQWWWcjeGBMPAwrA9xOJmt 5Vt5I45yhEbyIXVWxwSoIJGe2Rn1FcnbeE9YGmS2d/rVjcM8sd39oj0545ZLqOSORJJSZmDL mNQUUL8oCqUCgAAp3N/rFn4nh8MDW7uVLp7dvt8kUH2iIPHeOVXEYjxm0QfMhOHfn7u3oPDt zdvLrFhd3Ul2dOvRbx3EqoskitBDLl9gVcgykDCjgDqck58nhTUri/XWLjVrQ61E8RgljsWW 3VUSdAGiMpZji5m5Ei87OPlIbY0XSptNF7Nd3MdxeX1x9ouHiiMUe4RpGAiFmKjbGmcsecng EAAGpRRRQAUUUUAFFFFAGP4hW4WzE8eqX1hbxcynT7IXM7kkBQFKSfLycgIT0OVAOeb0O/1j xJdzWb63d2KWdusizWsUHmzhri5iUzCSNgr7LdCVCoVdnBUYCr1mp22qTeU+lajBaSLkOtza +fG4OOcB0YMMcENjBbIJwVw7TwpqWkTPdaTq1pHd3KEXjXdi0qOxmmmzGqyoUG+4l4LPxsGc glgDn7XxLrmreDtV8U/2nJaS6ZZR3C2VvDEbeZvsUNyQ+9WkwWlZfldflAxg5Y+mVxa+BJrT SLrQ9P1WOPR723S3u0uLUy3BUQJbkpIHVVJjjXqjYbJ5BCjtKACiiigAooooAKKKKAOT1XX7 5fHOh6VYNGLD7Q0WouVyWdraaSOJW5AIEW9xwQHixwxrn7rxLrmk+DtK8U/2nJdy6nZSXDWV xDELeFvsU1yAmxVkwGiVfmdvlJzk4YdJceBdNk1601eCe+hni1A386/brhkmfymjHymTavVe g+6mzG0kVTbwJNd6Ra6HqGqxyaPZW729olvamK4CmB7cF5C7KxEcjdEXLYPABUgGhZnUNL8V Wmlz6vd6lBeWVxcFruOFWiaJ4VAXyo0GCJmzuB+6uMc56SsOw0fUv7Zj1TV9RtLqeC3kt4Ft LNrdQsjRs5bdJIWOYkxgjHzZzkY3KACiiigAooooAKw/GGqTaP4Tv7u1eRLsosFs6RGUpNKw jjbYASwDupIAYkA4DHg7lV76G4ns5I7S6+y3HBjlMYcAgg4ZT1U4wQCDgnBU4IAOLj1OeWWx 0+z8S6zJLf3ot5XvrGK3uLVBBPKGjja3Th2h27mRgQrBcEEgtr/WLzxPN4YOt3cSWr3Dfb44 oPtEoSOzcK2YzHjN24+VAcInP3t2hJ4U1K4v11i41a0OtRPEYJY7Flt1VEnQBojKWY4uZuRI vOzj5SGI/CmpW9+2sW+rWg1qV5TPLJYs1uyukCELEJQynFtDyZG538fMAoBqeGL64v8ARTJd SebNDd3VqZCoBkEM8kSswGBuIQE4AGScADgbFZ+i6Z/ZGmC1M3nSNLLcSyBdoaSWRpH2rk4X c7YBJIGASTydCgAooooAKKKKACiiigAooooAKKKKACs+60LR77UYNRvNKsbi+g2+TczW6PJH tO5drEZGCSRjoa0KKAKdzpOm3tvc293p9pPBdOHuI5YVZZmAUAuCMMQEQAn+6PQUQ6TpttpZ 0uDT7SLTyjIbRIVWIq2dw2AYwcnIxzk1cooA5/WZb258Q6bo1rqM+nxz2lzdPPbJG0hMTwqF /eI67T5zE/LnKrggZBseGL64v9FMl1J5s0N3dWpkKgGQQzyRKzAYG4hATgAZJwAOAatpN5da jaalpt7Ba31tFLbg3NsZ42jkMbN8quh3ZiTB3Yxu4OQRY0XTP7I0wWpm86RpZbiWQLtDSSyN I+1cnC7nbAJJAwCSeSAaFFFFABRRRQAUUUUAFc/rN9qlr4l8PQwSQR6bdXbwzjbuklP2eeQD nhFUxqeMlif4Qp39BWfqOmfb77SbnzvL/s+7a527c+ZmGWLbnPH+tznn7uO+QAcv/aerfZP7 e/tWfb/bf9n/ANn+VF9n8v7d9kznZ5m7b8+d+N/bb8tGi6nqz2/hPVbjVZ7lfEG3z7OSKIQ2 ++1kn/dFUD8NGFG9m+UnOThhof8ACLXm/wCx/wBpwf2N/aH9oeR9kP2jzPtH2nHm+Zt2+b28 vOzjOfmo0vwteWLaPb3GpwT6bouPsEUdoY5vliaFfNkMjB/kds7UTLYPAG0gHUUUUUAFFFFA BRRRQBHOJmt5Vt5I45yhEbyIXVWxwSoIJGe2Rn1FcPrF/rGgahFp8Ot3eoC7SFZprmKAvZmS 7ggRl8uNVBZZZyN4YEw8DCsD3E4ma3lW3kjjnKERvIhdVbHBKggkZ7ZGfUVydt4T1gaZLZ3+ tWNwzyx3f2iPTnjlkuo5I5EklJmYMuY1BRQvygKpQKAACnc3+sWfieHwwNbu5Uunt2+3yRQf aIg8d45VcRiPGbRB8yE4d+fu7eg8O3N28usWF3dSXZ069FvHcSqiySK0EMuX2BVyDKQMKOAO pyTnyeFNSuL9dYuNWtDrUTxGCWOxZbdVRJ0AaIylmOLmbkSLzs4+UhtjRdKm00Xs13cx3F5f XH2i4eKIxR7hGkYCIWYqNsaZyx5yeAQAAalFFFABRRRQAUUUUAY/iFbhbMTx6pfWFvFzKdPs hczuSQFAUpJ8vJyAhPQ5UA55vQ7/AFjxJdzWb63d2KWdusizWsUHmzhri5iUzCSNgr7LdCVC oVdnBUYCr1mp22qTeU+lajBaSLkOtza+fG4OOcB0YMMcENjBbIJwVw7TwpqWkTPdaTq1pHd3 KEXjXdi0qOxmmmzGqyoUG+4l4LPxsGcglgDn7XxLrmreDtV8U/2nJaS6ZZR3C2VvDEbeZvsU NyQ+9WkwWlZfldflAxg5Y+mVxa+BJrTSLrQ9P1WOPR723S3u0uLUy3BUQJbkpIHVVJjjXqjY bJ5BCjtKACiiigAooooAKKKKAOT1XX75fHOh6VYNGLD7Q0WouVyWdraaSOJW5AIEW9xwQHix wxrn7rxLrmk+DtK8U/2nJdy6nZSXDWVxDELeFvsU1yAmxVkwGiVfmdvlJzk4YdJceBdNk160 1eCe+hni1A386/brhkmfymjHymTavVeg+6mzG0kVTbwJNd6Ra6HqGqxyaPZW729olvamK4Cm B7cF5C7KxEcjdEXLYPABUgGhZnUNL8VWmlz6vd6lBeWVxcFruOFWiaJ4VAXyo0GCJmzuB+6u Mc56SsOw0fUv7Zj1TV9RtLqeC3kt4FtLNrdQsjRs5bdJIWOYkxgjHzZzkY3KACiiigAooooA Kw/GGqTaP4Tv7u1eRLsosFs6RGUpNKwjjbYASwDupIAYkA4DHg7lV76G4ns5I7S6+y3HBjlM YcAgg4ZT1U4wQCDgnBU4IAOLj1OeWWx0+z8S6zJLf3ot5XvrGK3uLVBBPKGjja3Th2h27mRg QrBcEEgtr/WLzxPN4YOt3cSWr3Dfb44oPtEoSOzcK2YzHjN24+VAcInP3t2hJ4U1K4v11i41 a0OtRPEYJY7Flt1VEnQBojKWY4uZuRIvOzj5SGI/CmpW9+2sW+rWg1qV5TPLJYs1uyukCELE JQynFtDyZG538fMAoBqeGL64v9FMl1J5s0N3dWpkKgGQQzyRKzAYG4hATgAZJwAOBsVn6Lpn 9kaYLUzedI0stxLIF2hpJZGkfauThdztgEkgYBJPJ0KACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5fXrrxJaa 1pkFhqGlR2uo3ZtkWfT5JHixBJKWLCdQ2TERjAxu745sP4msopX02TU4DqdpLbQ3hW0kKK8j RADaCdnmeaAmWOMk/N5b4uarps19qOh3ETRhLC9a4lDE5Km3miwvHXdIp5xwD9Dl3Xhu8n/t bbJAPtmt2WoR5Y8Rw/ZdwPH3j5D4HTleRzgA1IfEOmXGqHTo55DPvaNXMEgikdc7kSUrsdxt bKqxI2PkfK2Dw1qU2s+FdI1S4WNZ72yhuJFjBChnQMQMknGT6muf/wCEW1mTxnY6tcXkctva Xs05LXc5MkbxSoirB/qojHvVMgEuAWJU5Vug8NabNo3hXSNLuGjaeysobeRoySpZECkjIBxk egoA1KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKAOX1668SWmtaZBYahpUdrqN2bZFn0+SR4sQSSliwnUNkxEY wMbu+ObD+JrKKV9Nk1OA6naS20N4VtJCivI0QA2gnZ5nmgJljjJPzeW+Lmq6bNfajodxE0YS wvWuJQxOSpt5osLx13SKeccA/Q5d14bvJ/7W2yQD7ZrdlqEeWPEcP2XcDx94+Q+B05Xkc4AN SHxDplxqh06OeQz72jVzBIIpHXO5ElK7HcbWyqsSNj5Hytg8NalNrPhXSNUuFjWe9sobiRYw QoZ0DEDJJxk+prn/APhFtZk8Z2OrXF5HLb2l7NOS13OTJG8UqIqwf6qIx71TIBLgFiVOVboP DWmzaN4V0jS7ho2nsrKG3kaMkqWRApIyAcZHoKANSiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKAP//Z --part-2RDSXC8sLJHmJAGA1n0WWTPAxlBAgEUpAU3BOAanwwhol-- ========================================================================= Date: Mon, 30 May 2011 14:30:08 -0400 Reply-To: "Smith, Jason (NIH/NIDCD) [F]" <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Smith, Jason (NIH/NIDCD) [F]" <[log in to unmask]> Subject: Re: VOI: mean vs. SVD (PCA) Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> I think this is a theoretical question as much as a practical preprocessing= question.=20 I am not a mathematician, so apply the appropriate amount of salt to this a= nswer. The implicit model is: Y=3DCx + Qe. where Y is the normalized signal from a few adjacent voxels, x is an underl= ying signal of interest, C is a mixing vector, and e is a matrix of some ad= ditive error mixed by Q. We're assuming we're looking for a single signal. = If you take the mean of Y as an estimate of x you are assuming that there i= s a single x represented in each y to the exact same degree (C is a constan= t vector) and that the e's normally distributed, uncorrelated in time, acro= ss observations (Q is diagonal), and uncorrelated with x, and all y's e hav= e equal variances. If the Y's are not normalized the mean assumes the eleme= nts of C are scaled by the variances of Y, but the error still have the sam= e variance. If you take the data projected on the first eigenvector you are= assuming the remaining eigenvectors with non-zero eigenvalues are noise. T= he signal x is not represented equally across voxels (C is not a constant v= ector) butt x is the largest unique source of variance in Y. The remaining = e's are all uncorrelated with equal variance though Q is not constant and t= he different noise sources can be shared across voxels at different levels.= If you take the first factor (from factor analysis) you are assuming that = the e' are normal and uncorrelated, but with potentially different variance= s. Q would be diagonal I think. Other options would be ICA which relaxes th= e normality assumption (or equivalently changes Cx to a nonlinear function)= , or some sort of linear dynamic system to relax the uncorrelated through t= ime assumptions (partitioning the state space into signal and noise). If you smooth, there is a second mixing matrix G such that Y=3DG(Cx + Qe) which will likely have the effect of making the PCA, FA, etc models look a = lot more like the first (mean) model. Still, the notion that all voxels rep= resent the signal x to an exactly equal degree seems a bit dodgy to me. Sin= ce the PCA and mean solutions are often correlated, the PCA solution is lik= ely better theoretically. Check the actual eigenvector to ensure that all v= oxels have positive values (I can't think of a case I've ever seen where th= ey don't). If they are all positive, the "different voxels for different pe= ople" problem is avoided. Take a look at Roweis and Ghahramani 1999 in neural networks for a better d= iscussion of the above. Just my 0.02$ ---------------------------------------------------- Jason F. Smith, Ph.D. Brain Imaging and Modeling Section NIDCD National Institutes of Health [log in to unmask] Rm 8S235B, 10 Center Dr. Bethesda MD 20892-1407 PH) 301.451.1647= ========================================================================= Date: Mon, 30 May 2011 21:14:44 +0200 Reply-To: Guillen Fernandez <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Guillen Fernandez <[log in to unmask]> Subject: Postdoctoral Fellow Opening: Cognitive Neuroscience of Memory MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_289559_725131480.1306782884233" Message-ID: <[log in to unmask]> ------=_Part_289559_725131480.1306782884233 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Post-doctoral fellow in Cognitive Neuroscience of Memory=20 Donders Institute for Brain, Cognition, and Behavior=20 Vacancy number: 010568=20 Closing date: Review of applications will continue until the position is fi= lled.=20 Job description=20 The Donders Institute for Brain, Cognition, and Behavior seeks to hire an e= xcellent PhD student in Cognitive Neuroscience of Memory in the laboratory = of Guill=C3=A9n Fern=C3=A1ndez. The Fern=C3=A1ndez Lab probes the brain bas= is of memory, emotion, and their interactions. It applies an interdisciplin= ary approach integrating cognitive neuroimaging, pharmacology, genetics, an= d diverse clinical disciplines.=20 The current position is financed by a prestigious ERC Advanced Investigator= Grant , which Richard Morris (University of Edinburgh) and Guill=C3=A9n Fe= rn=C3=A1ndez received jointly. This project is an interdisciplinary experim= ental analysis of the neurobiological mechanisms by which we acquire knowle= dge. Our approach builds upon recent findings of the participating laborato= ries that have each addressed key issues associated with the rapid acquisit= ion and assimilation of new associative information into existing neural = =E2=80=98schemas=E2=80=99 (see below for a list of selected, recent publica= tions of the Morris and Fern=C3=A1ndez lab on this topic). Rapid learning d= epends upon both novelty and prior knowledge . We will conduct a coordinate= d program of research concerning the mechanisms of both determinants. First= , we will test that novelty , acting via the release of dopamine, has a dir= ect impact on the mechanisms of cellular consolidation in the hippocampus. = Second, with respect to prior knowledge , the retention of new event-relate= d associative information requires the process of systems consolidation wit= hin declarative memory, as widely studied, but we further suggest that the = organisation of knowledge requires more than just stabilising memory traces= within neocortical networks. It also involves the integration of distinct = memory traces into neural =E2=80=98schemas=E2=80=99 that, once formed, have= a major positive influence on future learning. The studies conducted at th= e Donders Institute will involve fMRI using new cognitive tasks combined wi= th pharmacology (sophisticated dopamine manipulation), transcranial magneti= c stimulation, model-free analysis methods, and a translational project rea= ching into real-world education.=20 Requirements=20 Applicants should be able to demonstrate a consistently strong academic rec= ord, including publications in international journals. This individual must= be proficient in fMRI research and, most optimally, in performing pharmaco= logical studies in humans.=20 Organization=20 The Donders Institute for Brain, Cognition and Behaviour (http://www.ru.nl/= donders/) offers cognitive neuroscientists a unique, multidisciplinary work= ing and learning environment with opportunities for developing expertise in= a diversity of research areas and techniques. The centre is equipped with = three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG system, an EEG-TMS lab= oratory, several (MR-compatible) EEG systems, and high-performance computat= ional facilities. Website: http://www.ru.nl/donders/=20 Conditions of employment=20 Employment: 1,0 fte=20 The gross starting salary is =E2=82=AC 2.435 per month. In addition to the = monthly gross salary, you will receive two yearly 8% bonuses (holiday and e= nd-of-year).=20 Additional conditions of employment=20 The position is limited for three years (12 months probation period), but c= an be extended to maximally five years.=20 Application=20 We prefer online applications (link)=20 Alternatively, written applications including an application letter, a CV, = and the names (email addresses) of two persons who can provide references c= an be send to=20 UMC St Radboud=20 Mw. M. Korsten=20 [log in to unmask] Huispostcode 155=20 Concerns vacancy: 010568=20 Postbus 9101=20 6500 HB Nijmegen=20 Additional Information=20 Please visit our web page for further details about our lab, ( http://www.r= u.nl/donders/research/theme-3-learning/research-groups/cognitive-neurology/= )=20 or contact: Prof. Dr. Guill=C3=A9n Fern=C3=A1ndez ( [log in to unmask] nl )=20 Project relevant, recent publications from the Morris and the Fern=C3=A1nde= z Lab=E2=80=99s=20 1. Bethus I, Tse D, Morris RG. Dopamine and memory: modulation of the persi= stence of memory for novel hippocampal NMDA receptor-dependent paired assoc= iates. Journal of Neuroscience. 2010; 30: 1610-8.=20 2. Takashima A, Petersson KM, Rutters F, Tendolkar I, Jensen O, Zwarts MJ, = McNaughton BL, Fern=C3=A1ndez G. Declarative memory consolidation in humans= : a prospective functional magnetic resonance imaging study. Proceedings of= the National Academy of Sciences USA 2006; 103: 756-761=20 3. Takashima A, Nieuwenhuis IL, Jensen O, Talamini LM, Rijpkema M, Fern=C3= =A1ndez G. Shift from hippocampal to neocortical centered retrieval network= with consolidation. Journal of Neuroscience 2009; 29: 10087-10093=20 4. Tse D, Langston RF, Kakeyama M, Bethus I, Spooner PA, Wood ER, Witter MP= , Morris RG. Schemas and memory consolidation. Science 2007; 316: 76-82=20 5. van Kesteren MT, Fern=C3=A1ndez G, Norris DG, Hermans EJ. Persistent sch= ema-dependent hippocampal-neocortical connectivity during memory encoding a= nd post-encoding rest in humans. Proceedings of the National Academy of Sci= ences USA 2010; 107: 7550-7555=20 6. van Kesteren MT, Rijpkema M, Ruiter DJ, Fern=C3=A1ndez G. Retrieval of a= ssociative information congruent with prior knowledge is related to increas= ed medial prefrontal activity and connectivity. Journal of Neuroscience 201= 0; 30: 15888-15894=20 7. Wang SH, Morris RG. Hippocampal-neocortical interactions in memory forma= tion, consolidation, and reconsolidation. Annual Reviews of Psychology 2010= ; 61: 49-79=20 8. Wang SH, Redondo RL, Morris RG. Relevance of synaptic tagging and captur= e to the persistence of long-term potentiation and everyday spatial memory.= Proceedings of the National Academy of Sciences USA 2010; 107: 19537-42 ------=_Part_289559_725131480.1306782884233 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable <html><head><style type=3D'text/css'>p { margin: 0; }</style></head><body><= div style=3D'font-family: Times New Roman; font-size: 12pt; color: #000000'= ><!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:HyphenationZone>21</w:HyphenationZone> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>DE</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:DontVertAlignCellWithSp/> <w:DontBreakConstrainedForcedTables/> <w:DontVertAlignInTxbx/> <w:Word11KerningPairs/> <w:CachedColBalance/> </w:Compatibility> <m:mathPr> <m:mathFont m:val=3D"Cambria Math"/> <m:brkBin m:val=3D"before"/> <m:brkBinSub m:val=3D"--"/> <m:smallFrac m:val=3D"off"/> <m:dispDef/> <m:lMargin m:val=3D"0"/> <m:rMargin m:val=3D"0"/> <m:defJc m:val=3D"centerGroup"/> <m:wrapIndent m:val=3D"1440"/> <m:intLim m:val=3D"subSup"/> <m:naryLim m:val=3D"undOvr"/> </m:mathPr></w:WordDocument> </xml><![endif]--><!--[if gte mso 9]><xml> <w:LatentStyles DefLockedState=3D"false" DefUnhideWhenUsed=3D"true" DefSemiHidden=3D"true" DefQFormat=3D"false" DefPriority=3D"99" LatentStyleCount=3D"267"> <w:LsdException Locked=3D"false" Priority=3D"0" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Normal"/> <w:LsdException Locked=3D"false" Priority=3D"9" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"heading 1"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 2"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 3"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 4"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 5"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 6"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 7"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 8"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 9"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 1"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 2"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 3"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 4"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 5"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 6"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 7"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 8"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 9"/> <w:LsdException Locked=3D"false" Priority=3D"35" QFormat=3D"true" Name=3D= "caption"/> <w:LsdException Locked=3D"false" Priority=3D"10" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Title"/> <w:LsdException Locked=3D"false" Priority=3D"1" Name=3D"Default Paragraph= Font"/> <w:LsdException Locked=3D"false" Priority=3D"11" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtitle"/> <w:LsdException Locked=3D"false" Priority=3D"22" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Strong"/> <w:LsdException Locked=3D"false" Priority=3D"20" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"59" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Table Grid"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Placeho= lder Text"/> <w:LsdException Locked=3D"false" Priority=3D"1" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"No Spacing"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 1"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Revisio= n"/> <w:LsdException Locked=3D"false" Priority=3D"34" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"List Paragraph"/> <w:LsdException Locked=3D"false" Priority=3D"29" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Quote"/> <w:LsdException Locked=3D"false" Priority=3D"30" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Quote"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"19" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"21" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"31" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Reference"/> <w:LsdException Locked=3D"false" Priority=3D"32" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Reference"/> <w:LsdException Locked=3D"false" Priority=3D"33" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Book Title"/> <w:LsdException Locked=3D"false" Priority=3D"37" Name=3D"Bibliography"/> <w:LsdException Locked=3D"false" Priority=3D"39" QFormat=3D"true" Name=3D= "TOC Heading"/> </w:LatentStyles> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable =09{mso-style-name:"Normale Tabelle"; =09mso-tstyle-rowband-size:0; =09mso-tstyle-colband-size:0; =09mso-style-noshow:yes; =09mso-style-priority:99; =09mso-style-qformat:yes; =09mso-style-parent:""; =09mso-padding-alt:0cm 5.4pt 0cm 5.4pt; =09mso-para-margin-top:0cm; =09mso-para-margin-right:0cm; =09mso-para-margin-bottom:10.0pt; =09mso-para-margin-left:0cm; =09line-height:115%; =09mso-pagination:widow-orphan; =09font-size:11.0pt; =09font-family:"Calibri","sans-serif"; =09mso-ascii-font-family:Calibri; =09mso-ascii-theme-font:minor-latin; =09mso-hansi-font-family:Calibri; =09mso-hansi-theme-font:minor-latin; =09mso-bidi-font-family:"Times New Roman"; =09mso-bidi-theme-font:minor-bidi; =09mso-fareast-language:EN-US;} </style> <![endif]--> <p class=3D"MsoNormal" style=3D"mso-margin-top-alt:auto;mso-margin-bottom-a= lt:auto; line-height:normal;mso-outline-level:2"><b><span style=3D"font-size: 18.0pt;font-family:"Times New Roman","serif";mso-fareas= t-font-family:"Times New Roman"; mso-ansi-language:EN-US;mso-fareast-language:DE" lang=3D"EN-US">Post-doctor= al fellow in Cognitive Neuroscience of Memory</span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Donders Institute= </span></b><span lang=3D"EN-GB"> </span><b><span style=3D"mso-bidi-font-siz= e:11.0pt" lang=3D"EN-GB">for Brain, Cognition, and Behavior</span></b><span style=3D"mso-bidi-font-size: 11.0pt" lang=3D"EN-GB"></span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Vacancy number: 0= 10568</span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Closing date: Rev= iew of applications will continue until the position is filled.</span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US"> </span></strong></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US">Job description</span></strong></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US">The Donders Institute for Brain, Cognition, and Behavior seeks to hire an excel= lent PhD student in Cognitive Neuroscience of Memory in the laboratory of Guill= =C3=A9n Fern=C3=A1ndez. The Fern=C3=A1ndez Lab probes the brain basis of memory, em= otion, and their interactions. It applies an interdisciplinary approach integrating cognitive neuroimaging, pharmacology, genetics, and diverse clinical disciplines. </span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US">The current position is financed by a prestigious <b style=3D"mso-bidi-font-wei= ght: normal">ERC Advanced Investigator Grant</b>, which Richard Morris (Universi= ty of Edinburgh) and Guill=C3=A9n Fern=C3=A1ndez received jointly. This projec= t is an interdisciplinary experimental analysis of the neurobiological mechanisms b= y which we acquire knowledge. Our approach builds upon recent findings of the participating laboratories that have each addressed key issues associated w= ith the rapid acquisition and assimilation of new associative information into existing neural =E2=80=98schemas=E2=80=99 (see below for a list of selected= , recent publications of the Morris and Fern=C3=A1ndez lab on this topic). Rapid lea= rning depends upon both <i style=3D"mso-bidi-font-style:normal">novelty</i> and <= i style=3D"mso-bidi-font-style:normal">prior knowledge</i>. We will conduct= a coordinated program of research concerning the mechanisms of both determina= nts. First, we will test that <i style=3D"mso-bidi-font-style:normal">novelty</i= >, acting via the release of dopamine, has a direct impact on the mechanisms o= f cellular consolidation in the hippocampus. Second, with respect to <i style= =3D"mso-bidi-font-style:normal">prior knowledge</i>, the retention of new event-related associative information requires the process of systems consolidation within declarative memory, as widely studied, but we further = suggest that the organisation of knowledge requires more than just stabilising memo= ry traces within neocortical networks. It also involves the integration of distinct memory traces into neural =E2=80=98schemas=E2=80=99 that, once for= med, have a major positive influence on future learning. The studies conducted at the Donders Institute will involve fMRI using new cognitive tasks combined with pharmacology (sophisticated dopamine manipulation), transcranial magnetic stimulation, model-free analysis methods, and a translational project reach= ing into real-world education.</span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US"> </span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US">Requirements</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-bidi-font-size:10.0pt;mso-fareast-font-family: Times;mso-ansi-language:EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">Ap= plicants should be able to demonstrate a consistently strong academic record, including publications in international journals. This individual must be proficient = in fMRI research and, most optimally, in performing pharmacological studies in humans.</span><span style=3D"font-size:11.0pt;mso-fareast-font-family: Times;mso-ansi-language:EN-US;mso-fareast-language:EN-US" lang=3D"EN-US"></= span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><strong><s= pan style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Organ= ization</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">The Donders Institute for = Brain, Cognition and Behaviour (http://www.ru.nl/donders/) offers cognitive neuroscientists = a unique, multidisciplinary working and learning environment with opportuniti= es for developing expertise in a diversity of research areas and techniques. T= he centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel ME= G system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and high-performance computational facilities. Website: </span><a href=3D"http:= //www.ru.nl/donders/" target=3D"_blank"><span style=3D"font-size:11.0pt;mso= -fareast-font-family:Times;color:windowtext; mso-ansi-language:EN-US;mso-fareast-language:EN-US;text-decoration:none; text-underline:none" lang=3D"EN-US">http://www.ru.nl/donders/</span></a><sp= an style=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-languag= e:EN-US; mso-fareast-language:EN-US" lang=3D"EN-US"></span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><b><span style=3D"font-size:1= 1.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Conditions of employment</spa= n></b><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-U= S"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Employment: 1,0 fte </span><= /p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">The gross starting salary is= =E2=82=AC</span><span style=3D"mso-ansi-language:EN-US" lang=3D"EN-US">2.4= 35</span><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"E= N-US"> per month. In addition to the monthly gross salary, you will receive two yearly 8% bonuses (holiday and end-of-year). </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><strong><span style=3D"font-s= ize:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Additional conditions of employment</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-bidi-font-size:10.0pt;mso-fareast-font-family:Times;mso-ansi-lan= guage: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">The position is limited fo= r three years (12 months probation period), but can be extended to maximally five years. </sp= an><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">= <span style=3D"mso-spacerun:yes"> </span></span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <strong><span style=3D"mso-ansi-language:EN-US" lang=3D"EN-US"> </span= ></strong></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <strong><span style=3D"mso-ansi-language:EN-US" lang=3D"EN-US">Application<= /span></strong></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US">We prefer</span><span style= =3D"font-size:12.0pt;font-family:"Times New Roman","serif&qu= ot;;mso-fareast-font-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:DE= " lang=3D"EN-US"> online applications </span><a href=3D"http://www.umcn.nl/OverUMCstRadboud/werkenbi= j/Pages/Solliciteren.aspx?VacatureNummer=3D010568&EmailAdres=3DCBEG-sol= [log in to unmask]"><span style=3D"font-size:12.0pt;font-family:"T= imes New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US">(link)</span></a><span style=3D"font-size:12.0pt;font-fa= mily:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US"></span></p> <p class=3D"MsoNormal" style=3D"mso-margin-top-alt:auto;mso-margin-bottom-a= lt:auto; line-height:normal"><span style=3D"font-size:12.0pt;font-family:"Times= New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US">Alternatively, written applications including an applica= tion letter, a CV, and the names (email addresses) of two persons who can provide references c= an be send to <br> <br> UMC St Radboud<br> Mw. M. Korsten<br> [log in to unmask] <br> Huispostcode 155<br> Concerns vacancy: 010568<br> Postbus 9101<br> 6500 HB Nijmegen</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><b><span style=3D"font-size:1= 1.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Additional Information</span>= </b><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US"= > </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-fareast-font-family:Times;mso-ansi-language:EN-US;mso-fareast-la= nguage: EN-US" lang=3D"EN-US">Please visit our web page for further details about o= ur lab, (</span><a href=3D"http://www.ru.nl/donders/research/theme-3-learni= ng/research-groups/cognitive-neurology/"><span style=3D"font-size:11.0pt;ms= o-fareast-font-family:Times;color:windowtext; mso-ansi-language:EN-US;mso-fareast-language:EN-US;text-decoration:none; text-underline:none" lang=3D"EN-US">http://www.ru.nl/donders/research/theme= -3-learning/research-groups/cognitive-neurology/</span></a><span style=3D"f= ont-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">) <br> or contact: Prof. Dr. Guill=C3=A9n Fern=C3=A1ndez (</span><a href=3D"mailto= :[log in to unmask]"><span style=3D"font-size:11.0pt; mso-fareast-font-family:Times;color:windowtext;mso-ansi-language:EN-US; mso-fareast-language:EN-US;text-decoration:none;text-underline:none" lang= =3D"EN-US">[log in to unmask]</span></a><span style=3D"font-size:11.= 0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">)</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US">Project relevant, recent publ= ications from the Morris and the Fern=C3=A1ndez Lab=E2=80=99s</span></p> <p class=3D"MsoListParagraphCxSpFirst" style=3D"margin-top:0cm;margin-right= :0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;line-height:normal;tab-stops:21.3pt"><span style=3D"font= -family:"Times New Roman","serif";mso-ansi-language:EN-= US" lang=3D"EN-US"> </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">1.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Bethus I, Tse D, Morris RG. Dopamin= e and memory: modulation of the persistence of memory for novel hippocampal NMDA receptor-dependent paired associates. Journal of Neuroscience. 2010; 30: 1610-8.</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">2.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Takashima A, Petersson KM, Rutters = F, Tendolkar I, Jensen O, Zwarts MJ, McNaughton BL, Fern=C3=A1ndez G. Declarative memory consolidation in humans: a prospective functional magnetic resonance imagin= g study. Proceedings of the National Academy of Sciences USA 2006; 103: 756-7= 61</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">3.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Takashima A, Nieuwenhuis IL, Jensen= O, Talamini LM, Rijpkema M, Fern=C3=A1ndez G. Shift from hippocampal to neocortical centere= d retrieval network with consolidation. Journal of Neuroscience 2009; 29: 10087-10093 </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">4.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Tse D, Langston RF, Kakeyama M, Bet= hus I, Spooner PA, Wood ER, Witter MP, Morris RG. Schemas and memory consolidation. Science 20= 07; 316: 76-82</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">5.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:NL" lang=3D"NL">van Kesteren MT, Fern=C3=A1ndez G, Norris= DG, Hermans EJ. </span><span style=3D"font-family:"Times New Roman&qu= ot;,"serif";mso-ansi-language:EN-US" lang=3D"EN-US">Persistent schema-dependent hippocampal-neocortical connectivity during memory encodin= g and post-encoding rest in humans. Proceedings of the National Academy of Sciences USA 2010; 107: 7550-7555</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">6.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">van Kesteren MT, Rijpkema M, Ruiter= DJ, Fern=C3=A1ndez G. Retrieval of associative information congruent with prior knowledge is rela= ted to increased medial prefrontal activity and connectivity. Journal of Neuroscience 2010; 30: 15888-15894</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">7.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Wang SH, Morris RG. Hippocampal-neo= cortical interactions in memory formation, consolidation, and reconsolidation. Annua= l Reviews of Psychology 2010; 61: 49-79</span></p> <p class=3D"MsoListParagraphCxSpLast" style=3D"margin-top:0cm;margin-right:= 0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">8.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Wang SH, Redondo RL, Morris RG. Rel= evance of synaptic tagging and capture to the persistence of long-term potentiation and everyd= ay spatial memory. Proceedings of the National Academy of Sciences USA 2010; 1= 07: 19537-42</span></p> </div></body></html> ------=_Part_289559_725131480.1306782884233-- ========================================================================= Date: Mon, 30 May 2011 21:15:37 +0200 Reply-To: Guillen Fernandez <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Guillen Fernandez <[log in to unmask]> Subject: PHD Opening: Cognitive Neuroscience of Memory In-Reply-To: <[log in to unmask]> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_289573_670777790.1306782936965" Message-ID: <[log in to unmask]> ------=_Part_289573_670777790.1306782936965 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable PhD-Student in Cognitive Neuroscience of Memory=20 Donders Institute, Centre for Cognitive Neuroimaging=20 Vacancy number: 010567=20 Closing date: Review of applications will continue until the position is fi= lled.=20 Job description=20 The Donders Institute for Brain, Cognition, and Behavior seeks to hire an e= xcellent PhD student in Cognitive Neuroscience of Memory in the laboratory = of Guill=C3=A9n Fern=C3=A1ndez. The Fern=C3=A1ndez Lab probes the brain bas= is of memory, emotion, and their interactions. It applies an interdisciplin= ary approach integrating cognitive neuroimaging, pharmacology, genetics, an= d diverse clinical disciplines.=20 The current position is financed by a prestigious ERC Advanced Investigator= Grant, which Richard Morris (University of Edinburgh) and Guill=C3=A9n Fer= n=C3=A1ndez received jointly. This project is an interdisciplinary experime= ntal analysis of the neurobiological mechanisms by which we acquire knowled= ge. Our approach builds upon recent findings of the participating laborator= ies that have each addressed key issues associated with the rapid acquisiti= on and assimilation of new associative information into existing neural =E2= =80=98schemas=E2=80=99 (see below for a list of selected, recent publicatio= ns of the Morris and Fern=C3=A1ndez lab on this topic). Rapid learning depe= nds upon both novelty and prior knowledge . We will conduct a coordinated p= rogram of research concerning the mechanisms of both determinants. First, w= e will test that novelty , acting via the release of dopamine, has a direct= impact on the mechanisms of cellular consolidation in the hippocampus. Sec= ond, with respect to prior knowledge , the retention of new event-related a= ssociative information requires the process of systems consolidation within= declarative memory, as widely studied, but we further suggest that the org= anisation of knowledge requires more than just stabilising memory traces wi= thin neocortical networks. It also involves the integration of distinct mem= ory traces into neural =E2=80=98schemas=E2=80=99 that, once formed, have a = major positive influence on future learning. The studies conducted at the D= onders Institute will involve fMRI using new cognitive tasks combined with = pharmacology (sophisticated dopamine manipulation), transcranial magnetic s= timulation, model-free analysis methods, and a translational project reachi= ng into real-world education.=20 Requirements=20 As a candidate for the PhD student position, you should have a Master=E2=80= =99s degree (or equivalent) in a field related to cognitive neuroscience (e= .g. experimental/cognitive psychology, biology, neuroscience). Proficiency = in oral and written English, and programming skills (Matlab) are essential.= =20 Organization=20 The Donders Institute for Brain, Cognition and Behaviour (http://www.ru.nl/= donders/) offers cognitive neuroscientists a unique, multidisciplinary work= ing and learning environment with opportunities for developing expertise in= a diversity of research areas and techniques. The centre is equipped with = three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG system, an EEG-TMS lab= oratory, several (MR-compatible) EEG systems, and high-performance computat= ional facilities. Website: http://www.ru.nl/donders/=20 Conditions of employment=20 Employment: 1,0 fte=20 The gross starting salary is =E2=82=AC2,042 per month and will increase to = =E2=82=AC2,612 (fourth year). In addition to the monthly gross salary, you = will receive two yearly 8% bonuses (holiday and end-of-year).=20 Additional conditions of employment=20 As a PhD candidate, you will be appointed for a period of 4 years. Your per= formance will be evaluated after 18 months. If the evaluation is positive, = the contract will be extended by 2.5 years.=20 Application=20 We prefer online applications ( link )=20 Alternatively, written applications including an application letter, a CV, = and the names (email addresses) of two persons who can provide references c= an be send to=20 UMC St Radboud=20 Mw. M. Korsten=20 [log in to unmask] Huispostcode 155=20 Concerns vacancy: 010567=20 Postbus 9101=20 6500 HB Nijmegen=20 Additional Information=20 Please visit our web page for further details about our lab, ( http://www.r= u.nl/donders/research/theme-3-learning/research-groups/cognitive-neurology/= )=20 or contact: Prof. Dr. Guill=C3=A9n Fern=C3=A1ndez ( [log in to unmask] nl )=20 Project relevant, recent publications from the Morris and the Fern=C3=A1nde= z Lab=E2=80=99s=20 1. Bethus I, Tse D, Morris RG. Dopamine and memory: modulation of the persi= stence of memory for novel hippocampal NMDA receptor-dependent paired assoc= iates. Journal of Neuroscience. 2010; 30: 1610-8.=20 2. Takashima A, Petersson KM, Rutters F, Tendolkar I, Jensen O, Zwarts MJ, = McNaughton BL, Fern=C3=A1ndez G. Declarative memory consolidation in humans= : a prospective functional magnetic resonance imaging study. Proceedings of= the National Academy of Sciences USA 2006; 103: 756-761=20 3. Takashima A, Nieuwenhuis IL, Jensen O, Talamini LM, Rijpkema M, Fern=C3= =A1ndez G. Shift from hippocampal to neocortical centered retrieval network= with consolidation. Journal of Neuroscience 2009; 29: 10087-10093=20 4. Tse D, Langston RF, Kakeyama M, Bethus I, Spooner PA, Wood ER, Witter MP= , Morris RG. Schemas and memory consolidation. Science 2007; 316: 76-82=20 5. van Kesteren MT, Fern=C3=A1ndez G, Norris DG, Hermans EJ. Persistent sch= ema-dependent hippocampal-neocortical connectivity during memory encoding a= nd post-encoding rest in humans. Proceedings of the National Academy of Sci= ences USA 2010; 107: 7550-7555=20 6. van Kesteren MT, Rijpkema M, Ruiter DJ, Fern=C3=A1ndez G. Retrieval of a= ssociative information congruent with prior knowledge is related to increas= ed medial prefrontal activity and connectivity. Journal of Neuroscience 201= 0; 30: 15888-15894=20 7. Wang SH, Morris RG. Hippocampal-neocortical interactions in memory forma= tion, consolidation, and reconsolidation. Annual Reviews of Psychology 2010= ; 61: 49-79=20 8. Wang SH, Redondo RL, Morris RG. Relevance of synaptic tagging and captur= e to the persistence of long-term potentiation and everyday spatial memory.= Proceedings of the National Academy of Sciences USA 2010; 107: 19537-42=20 ------=_Part_289573_670777790.1306782936965 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable <html><head><style type=3D'text/css'>p { margin: 0; }</style></head><body><= div style=3D'font-family: Times New Roman; font-size: 12pt; color: #000000'= ><!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:TrackMoves/> <w:TrackFormatting/> <w:HyphenationZone>21</w:HyphenationZone> <w:PunctuationKerning/> <w:ValidateAgainstSchemas/> <w:SaveIfXMLInvalid>false</w:SaveIfXMLInvalid> <w:IgnoreMixedContent>false</w:IgnoreMixedContent> <w:AlwaysShowPlaceholderText>false</w:AlwaysShowPlaceholderText> <w:DoNotPromoteQF/> <w:LidThemeOther>DE</w:LidThemeOther> <w:LidThemeAsian>X-NONE</w:LidThemeAsian> <w:LidThemeComplexScript>X-NONE</w:LidThemeComplexScript> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> <w:DontGrowAutofit/> <w:SplitPgBreakAndParaMark/> <w:DontVertAlignCellWithSp/> <w:DontBreakConstrainedForcedTables/> <w:DontVertAlignInTxbx/> <w:Word11KerningPairs/> <w:CachedColBalance/> </w:Compatibility> <m:mathPr> <m:mathFont m:val=3D"Cambria Math"/> <m:brkBin m:val=3D"before"/> <m:brkBinSub m:val=3D"--"/> <m:smallFrac m:val=3D"off"/> <m:dispDef/> <m:lMargin m:val=3D"0"/> <m:rMargin m:val=3D"0"/> <m:defJc m:val=3D"centerGroup"/> <m:wrapIndent m:val=3D"1440"/> <m:intLim m:val=3D"subSup"/> <m:naryLim m:val=3D"undOvr"/> </m:mathPr></w:WordDocument> </xml><![endif]--><!--[if gte mso 9]><xml> <w:LatentStyles DefLockedState=3D"false" DefUnhideWhenUsed=3D"true" DefSemiHidden=3D"true" DefQFormat=3D"false" DefPriority=3D"99" LatentStyleCount=3D"267"> <w:LsdException Locked=3D"false" Priority=3D"0" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Normal"/> <w:LsdException Locked=3D"false" Priority=3D"9" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"heading 1"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 2"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 3"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 4"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 5"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 6"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 7"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 8"/> <w:LsdException Locked=3D"false" Priority=3D"9" QFormat=3D"true" Name=3D"= heading 9"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 1"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 2"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 3"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 4"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 5"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 6"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 7"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 8"/> <w:LsdException Locked=3D"false" Priority=3D"39" Name=3D"toc 9"/> <w:LsdException Locked=3D"false" Priority=3D"35" QFormat=3D"true" Name=3D= "caption"/> <w:LsdException Locked=3D"false" Priority=3D"10" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Title"/> <w:LsdException Locked=3D"false" Priority=3D"1" Name=3D"Default Paragraph= Font"/> <w:LsdException Locked=3D"false" Priority=3D"11" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtitle"/> <w:LsdException Locked=3D"false" Priority=3D"22" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Strong"/> <w:LsdException Locked=3D"false" Priority=3D"20" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"59" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Table Grid"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Placeho= lder Text"/> <w:LsdException Locked=3D"false" Priority=3D"1" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"No Spacing"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 1"/> <w:LsdException Locked=3D"false" UnhideWhenUsed=3D"false" Name=3D"Revisio= n"/> <w:LsdException Locked=3D"false" Priority=3D"34" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"List Paragraph"/> <w:LsdException Locked=3D"false" Priority=3D"29" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Quote"/> <w:LsdException Locked=3D"false" Priority=3D"30" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Quote"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 1"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 2"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 3"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 4"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 5"/> <w:LsdException Locked=3D"false" Priority=3D"60" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"61" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"62" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Light Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"63" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"64" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Shading 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"65" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"66" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium List 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"67" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 1 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"68" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 2 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"69" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Medium Grid 3 Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"70" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Dark List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"71" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Shading Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"72" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful List Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"73" SemiHidden=3D"false" UnhideWhenUsed=3D"false" Name=3D"Colorful Grid Accent 6"/> <w:LsdException Locked=3D"false" Priority=3D"19" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"21" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Emphasis"/> <w:LsdException Locked=3D"false" Priority=3D"31" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Subtle Reference"/> <w:LsdException Locked=3D"false" Priority=3D"32" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Intense Reference"/> <w:LsdException Locked=3D"false" Priority=3D"33" SemiHidden=3D"false" UnhideWhenUsed=3D"false" QFormat=3D"true" Name=3D"Book Title"/> <w:LsdException Locked=3D"false" Priority=3D"37" Name=3D"Bibliography"/> <w:LsdException Locked=3D"false" Priority=3D"39" QFormat=3D"true" Name=3D= "TOC Heading"/> </w:LatentStyles> </xml><![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable =09{mso-style-name:"Normale Tabelle"; =09mso-tstyle-rowband-size:0; =09mso-tstyle-colband-size:0; =09mso-style-noshow:yes; =09mso-style-priority:99; =09mso-style-qformat:yes; =09mso-style-parent:""; =09mso-padding-alt:0cm 5.4pt 0cm 5.4pt; =09mso-para-margin-top:0cm; =09mso-para-margin-right:0cm; =09mso-para-margin-bottom:10.0pt; =09mso-para-margin-left:0cm; =09line-height:115%; =09mso-pagination:widow-orphan; =09font-size:11.0pt; =09font-family:"Calibri","sans-serif"; =09mso-ascii-font-family:Calibri; =09mso-ascii-theme-font:minor-latin; =09mso-hansi-font-family:Calibri; =09mso-hansi-theme-font:minor-latin; =09mso-bidi-font-family:"Times New Roman"; =09mso-bidi-theme-font:minor-bidi; =09mso-fareast-language:EN-US;} </style> <![endif]--> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;lin= e-height: normal;mso-outline-level:2"><b><span style=3D"font-size:18.0pt; font-family:"Times New Roman","serif";mso-fareast-font-= family:"Times New Roman"; mso-ansi-language:EN-US;mso-fareast-language:DE" lang=3D"EN-US">PhD-Student= in Cognitive Neuroscience of Memory</span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span lang=3D"EN-GB"> </span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Donders Institute= , Centre for Cognitive Neuroimaging</span></b><span style=3D"mso-bidi-font-size: 11.0pt" lang=3D"EN-GB"></span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Vacancy number: <= /span><span lang=3D"EN-GB">010567</span></b><span style=3D"mso-bidi-font-si= ze:11.0pt" lang=3D"EN-GB"></span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><b= ><span style=3D"mso-bidi-font-size:11.0pt" lang=3D"EN-GB">Closing date: Rev= iew of applications will continue until the position is filled.</span></b></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US"> </span></strong></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US">Job description</span></strong></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US">The Donders Institute for Brain, Cognition, and Behavior seeks to hire an excel= lent PhD student in Cognitive Neuroscience of Memory in the laboratory of Guill= =C3=A9n Fern=C3=A1ndez. The Fern=C3=A1ndez Lab probes the brain basis of memory, em= otion, and their interactions. It applies an interdisciplinary approach integrating cognitive neuroimaging, pharmacology, genetics, and diverse clinical disciplines. </span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US">The current position is financed by a prestigious ERC Advanced Investigator Gra= nt, which Richard Morris (University of Edinburgh) and Guill=C3=A9n Fern=C3=A1n= dez received jointly. This project is an interdisciplinary experimental analysis of the neurobiological mechanisms by which we acquire knowledge. Our approach buil= ds upon recent findings of the participating laboratories that have each addre= ssed key issues associated with the rapid acquisition and assimilation of new associative information into existing neural =E2=80=98schemas=E2=80=99 (see= below for a list of selected, recent publications of the Morris and Fern=C3=A1ndez lab on this = topic). Rapid learning depends upon both <i style=3D"mso-bidi-font-style:normal">no= velty</i> and <i style=3D"mso-bidi-font-style:normal">prior knowledge</i>. We will co= nduct a coordinated program of research concerning the mechanisms of both determinants. First, we will test that <i style=3D"mso-bidi-font-style:norm= al">novelty</i>, acting via the release of dopamine, has a direct impact on the mechanisms o= f cellular consolidation in the hippocampus. Second, with respect to <i style= =3D"mso-bidi-font-style:normal">prior knowledge</i>, the retention of new event-related associative information requires the process of systems consolidation within declarative memory, as widely studied, but we further = suggest that the organisation of knowledge requires more than just stabilising memo= ry traces within neocortical networks. It also involves the integration of distinct memory traces into neural =E2=80=98schemas=E2=80=99 that, once for= med, have a major positive influence on future learning. The studies conducted at the Donders Institute will involve fMRI using new cognitive tasks combined with pharmacology (sophisticated dopamine manipulation), transcranial magnetic stimulation, model-free analysis methods, and a translational project reach= ing into real-world education.</span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= pan style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-= US"> </span></p> <p class=3D"ERCNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt"><s= trong><span style=3D"mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US" lan= g=3D"EN-US">Requirements</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">As a candidate for the PhD= student position, you should have a Master=E2=80=99s degree (or equivalent) in a field relate= d to cognitive neuroscience (e.g. experimental/cognitive psychology, biology, ne= uroscience). Proficiency in oral and written English, and programming skills (Matlab) ar= e essential. </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><strong><s= pan style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Organ= ization</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">The Donders Institute for = Brain, Cognition and Behaviour (http://www.ru.nl/donders/) offers cognitive neuroscientists = a unique, multidisciplinary working and learning environment with opportuniti= es for developing expertise in a diversity of research areas and techniques. T= he centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel ME= G system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and high-performance computational facilities. Website: </span><a href=3D"http:= //www.ru.nl/donders/" target=3D"_blank"><span style=3D"font-size:11.0pt;mso= -fareast-font-family:Times;color:windowtext; mso-ansi-language:EN-US;mso-fareast-language:EN-US;text-decoration:none; text-underline:none" lang=3D"EN-US">http://www.ru.nl/donders/</span></a><sp= an style=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-languag= e:EN-US; mso-fareast-language:EN-US" lang=3D"EN-US"></span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt;text-align:justify"><span styl= e=3D"font-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><b><span style=3D"font-size:1= 1.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Conditions of employment</spa= n></b><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-U= S"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Employment: 1,0 fte </span><= /p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">The gross starting salary is= =E2=82=AC2,042 per month and will increase to =E2=82=AC2,612 (fourth year). In addition to the month= ly gross salary, you will receive two yearly 8% bonuses (holiday and end-of-year). <= /span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><strong><span style=3D"font-s= ize:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Additional conditions of employment</span></strong></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">As a PhD candidate, you will= be appointed for a period of 4 years. Your performance will be evaluated after 18 months. If t= he evaluation is positive, the contract will be extended by 2.5 years. </span>= </p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <strong><span style=3D"mso-ansi-language:EN-US" lang=3D"EN-US"> </span= ></strong></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <strong><span style=3D"mso-ansi-language:EN-US" lang=3D"EN-US">Application<= /span></strong></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US">We prefer</span><span style= =3D"font-size:12.0pt;font-family:"Times New Roman","serif&qu= ot;;mso-fareast-font-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:DE= " lang=3D"EN-US"> online applications (</span><a href=3D"http://www.umcn.nl/OverUMCstRadboud/werkenb= ij/Pages/Solliciteren.aspx?VacatureNummer=3D010567&EmailAdres=3DCBEG-so= [log in to unmask]"><span style=3D"font-size:12.0pt;font-family:"= Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US">link</span></a><span style=3D"font-size:12.0pt;font-fami= ly:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US">)</span></p> <p class=3D"MsoNormal" style=3D"mso-margin-top-alt:auto;mso-margin-bottom-a= lt:auto; line-height:normal"><span style=3D"font-size:12.0pt;font-family:"Times= New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US= ;mso-fareast-language: DE" lang=3D"EN-US">Alternatively, written applications including an applica= tion letter, a CV, and the names (email addresses) of two persons who can provide references c= an be send to <br> <br> UMC St Radboud<br> Mw. M. Korsten<br> [log in to unmask] <br> Huispostcode 155<br> Concerns vacancy: 010567<br> Postbus 9101<br> 6500 HB Nijmegen</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><b><span style=3D"font-size:1= 1.0pt;mso-ansi-language:EN-US" lang=3D"EN-US">Additional Information</span>= </b><span style=3D"font-size:11.0pt;mso-ansi-language:EN-US" lang=3D"EN-US"= > </span></p> <p style=3D"margin:0cm;margin-bottom:.0001pt"><span style=3D"font-size: 11.0pt;mso-fareast-font-family:Times;mso-ansi-language:EN-US;mso-fareast-la= nguage: EN-US" lang=3D"EN-US">Please visit our web page for further details about o= ur lab, (</span><a href=3D"http://www.ru.nl/donders/research/theme-3-learni= ng/research-groups/cognitive-neurology/"><span style=3D"font-size:11.0pt;ms= o-fareast-font-family:Times;color:windowtext; mso-ansi-language:EN-US;mso-fareast-language:EN-US;text-decoration:none; text-underline:none" lang=3D"EN-US">http://www.ru.nl/donders/research/theme= -3-learning/research-groups/cognitive-neurology/</span></a><span style=3D"f= ont-size:11.0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">) <br> or contact: Prof. Dr. Guill=C3=A9n Fern=C3=A1ndez (</span><a href=3D"mailto= :[log in to unmask]"><span style=3D"font-size:11.0pt; mso-fareast-font-family:Times;color:windowtext;mso-ansi-language:EN-US; mso-fareast-language:EN-US;text-decoration:none;text-underline:none" lang= =3D"EN-US">[log in to unmask]</span></a><span style=3D"font-size:11.= 0pt;mso-fareast-font-family:Times;mso-ansi-language: EN-US;mso-fareast-language:EN-US" lang=3D"EN-US">)</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US"> </span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-fareast-font-family: Times;mso-ansi-language:EN-US" lang=3D"EN-US">Project relevant, recent publ= ications from the Morris and the Fern=C3=A1ndez Lab=E2=80=99s</span></p> <p class=3D"MsoListParagraphCxSpFirst" style=3D"margin-top:0cm;margin-right= :0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;line-height:normal;tab-stops:21.3pt"><span style=3D"font= -family:"Times New Roman","serif";mso-ansi-language:EN-= US" lang=3D"EN-US"> </span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">1.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Bethus I, Tse D, Morris RG. Dopamin= e and memory: modulation of the persistence of memory for novel hippocampal NMDA receptor-dependent paired associates. Journal of Neuroscience. 2010; 30: 1610-8.</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">2.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Takashima A, Petersson KM, Rutters = F, Tendolkar I, Jensen O, Zwarts MJ, McNaughton BL, Fern=C3=A1ndez G. Declarative memory consolidation in humans: a prospective functional magnetic resonance imagin= g study. Proceedings of the National Academy of Sciences USA 2006; 103: 756-7= 61</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">3.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Takashima A, Nieuwenhuis IL, Jensen= O, Talamini LM, Rijpkema M, Fern=C3=A1ndez G. Shift from hippocampal to neocortical centere= d retrieval network with consolidation. Journal of Neuroscience 2009; 29: 10087-10093 <= /span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">4.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Tse D, Langston RF, Kakeyama M, Bet= hus I, Spooner PA, Wood ER, Witter MP, Morris RG. Schemas and memory consolidation. Science 20= 07; 316: 76-82</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">5.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:NL" lang=3D"NL">van Kesteren MT, Fern=C3=A1ndez G, Norris= DG, Hermans EJ. </span><span style=3D"font-family:"Times New Roman&qu= ot;,"serif";mso-ansi-language:EN-US" lang=3D"EN-US">Persistent schema-dependent hippocampal-neocortical connectivity during memory encodin= g and post-encoding rest in humans. Proceedings of the National Academy of Sciences USA 2010; 107: 7550-7555</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">6.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">van Kesteren MT, Rijpkema M, Ruiter= DJ, Fern=C3=A1ndez G. Retrieval of associative information congruent with prior knowledge is rela= ted to increased medial prefrontal activity and connectivity. Journal of Neuroscience 2010; 30: 15888-15894</span></p> <p class=3D"MsoListParagraphCxSpMiddle" style=3D"margin-top:0cm;margin-righ= t:0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">7.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Wang SH, Morris RG. Hippocampal-neo= cortical interactions in memory formation, consolidation, and reconsolidation. Annua= l Reviews of Psychology 2010; 61: 49-79</span></p> <p class=3D"MsoListParagraphCxSpLast" style=3D"margin-top:0cm;margin-right:= 0cm; margin-bottom:0cm;margin-left:33.2pt;margin-bottom:.0001pt;mso-add-space:au= to; text-align:justify;text-indent:-18.0pt;line-height:normal;mso-list:l0 level= 1 lfo1; tab-stops:21.3pt"><span style=3D"font-family: "Times New Roman","serif";mso-fareast-font-family:"= ;Times New Roman";mso-ansi-language: EN-US" lang=3D"EN-US"><span style=3D"mso-list:Ignore">8.<span style=3D"font= :7.0pt "Times New Roman""> </span></span></span><span style=3D"font-family:"Times New Roman"= ,"serif"; mso-ansi-language:EN-US" lang=3D"EN-US">Wang SH, Redondo RL, Morris RG. Rel= evance of synaptic tagging and capture to the persistence of long-term potentiation and everyd= ay spatial memory. Proceedings of the National Academy of Sciences USA 2010; 1= 07: 19537-42</span></p> <p class=3D"MsoNormal" style=3D"margin-bottom:0cm;margin-bottom:.0001pt;tex= t-align: justify;line-height:normal;mso-layout-grid-align:none;text-autospace:none">= <span style=3D"font-family:"Times New Roman","serif";ms= o-ansi-language:EN-US" lang=3D"EN-US"> </span></p> </div></body></html> ------=_Part_289573_670777790.1306782936965-- ========================================================================= Date: Mon, 30 May 2011 22:34:43 +0200 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: Open phd position: Multivariate data-driven diagnosis of Parkinson=?windows-1252?Q?=92s_?=disease MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="------------020906010806030009010700" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. --------------020906010806030009010700 Content-Type: text/plain; charset=windows-1252; format=flowed Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by bifur.jiscmail.ac.uk id p4UKcifx023027 The Cyclotron Research Centre (CRC) at the University of Li=E8ge is=20 recruiting a PhD student with a background in engineering, biomedical=20 engineering or computer sciences for a project on multivariate=20 data-driven diagnosis of Parkinson=92s disease (see project description=20 attached) Applications are welcome from overseas as well as from EU nationals=20 (working languages in the laboratory are French and English). Employment=20 can begin immediately. Salary is commensurate with that of PhD=20 fellowships at the University of Li=E8ge. The project is initially funded= =20 for 2 years, with possibilities for further extension up to 4 years. The=20 position will be advertised until filled. The CRC offer an exciting and=20 friendly multi-disciplinary research environment with ample=20 opportunities for training and collaboration, and excellent technical=20 facilities. Application should be made electronically. Please submit your CV, copies=20 of all relevant degrees, a statement of interest, and the names and=20 email addresses of two referees to the following address: [log in to unmask] --------------020906010806030009010700 Content-Type: application/pdf; name="PhD_ParkinsonDiseaseDiagnosis.pdf" Content-Disposition: attachment; filename="PhD_ParkinsonDiseaseDiagnosis.pdf" Content-Transfer-Encoding: base64 JVBERi0xLjUNCiW1tbW1DQoxIDAgb2JqDQo8PC9UeXBlL0NhdGFsb2cvUGFnZXMgMiAwIFIv TGFuZyhlbi1VUykgL1N0cnVjdFRyZWVSb290IDI5IDAgUi9NYXJrSW5mbzw8L01hcmtlZCB0 cnVlPj4+Pg0KZW5kb2JqDQoyIDAgb2JqDQo8PC9UeXBlL1BhZ2VzL0NvdW50IDIvS2lkc1sg MyAwIFIgMjIgMCBSXSA+Pg0KZW5kb2JqDQozIDAgb2JqDQo8PC9UeXBlL1BhZ2UvUGFyZW50 IDIgMCBSL1Jlc291cmNlczw8L0ZvbnQ8PC9GMSA1IDAgUi9GMiA3IDAgUi9GMyAxMyAwIFIv RjQgMTUgMCBSL0Y1IDIwIDAgUj4+L1Byb2NTZXRbL1BERi9UZXh0L0ltYWdlQi9JbWFnZUMv SW1hZ2VJXSA+Pi9Bbm5vdHNbIDEyIDAgUl0gL01lZGlhQm94WyAwIDAgNTk1LjMyIDg0MS45 Ml0gL0NvbnRlbnRzIDQgMCBSL0dyb3VwPDwvVHlwZS9Hcm91cC9TL1RyYW5zcGFyZW5jeS9D Uy9EZXZpY2VSR0I+Pi9UYWJzL1MvU3RydWN0UGFyZW50cyAwPj4NCmVuZG9iag0KNCAwIG9i ag0KPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCA0NjcxPj4NCnN0cmVhbQ0KeJyVW9uO 28yRvjfgd+irZBzM0DyLXBjG2mP7xx/EWMNxshd2LiiyJXHNg0JSM1aeOI+x1VV9lERKsQGp RTa7m911+OqrGvb6C3vz5vXnx98/MP/tW/b+wyN7/+3li9efApZ7ecq+bV6+CJgP/wO28r0s jNkqDdm39uULn23Fx28vXzDf82O4WH6/+7bj7PFYNq+C8K6fhr4TDfaVj7wQraHc4YVH3k0D f/UP9u3PL198hAnFpGqeMPK9ONHzwDUxMpvtnXiZ6f397sfdq4fo7lF8fNWtH69eZTAEtArx MYkPpls7uMn1pb/Br040avHxJD7w5iA+Rn1jgm5H/VAvPjb0M7v7i+71b/Gx1WOIm7UeR/wa 9L1ST3KwJsGWXNBWT1eox7/Q+uHjA126vEvq7BLfC9W26kMbp0MFB8LE0TzX0w4bBVsX5U/R 2g69+Dp0Fas7xkW729YdNvgwdyyr2ItW9oTf7+puO9c79708cpeHS7tXx7ZWG4Eb3epNq/RW lXpfGvUQ9jA7p/eRW2d6eUGxn3pRfrJ8Gi27m3uLOAy9LHIfmjuQOMy8KHH79kocmH4f87Z7 LRiTktbhlhOPMi9Oz068rHlX8hGVcdMP8sT3Q/9/eKrlhHf6blbrgsjLcnd0HLnVy2zUWo0a FXrV4iiMJhrdq6hbRteLmdkjf4XCZWb/fvdw0vf1p/DchEVB6K3yy5vyxvez1PfzGL7fw3cG 3yv49uFfJO69DcI3dC8Tv3O6lwd0PU/kcwn1zwP4ndE98TsQz0Syr5jjg+zv0zOiP7SDNDPj +dHb05dasMthCt9yN6oaLO7I8Vx/3I2cWuJ8eSla08zOBhkMltpjXTG/QZZ6cXAyuRLQ0TVr tZZjbdZ6raFGyzlcb7TwP0vzPSOHWeKFuTu9vVT28fMjY5aXCywvd7qBQeL56S2DhAuD+JEX n1lYdItfH/EQ+s0GrKZQrwKs6S/RKmvUN7CPcK3CXpsBNFQ0qubI2oNoNVM9pw9SrO25cd5T pTAPgPUJ7Ae+W5bUHJs5sUYpcucq8lHZWuPHRt0qlEErlZc9MctPepJBH7iwsK3bbbrB0KV5 4KVnhg7dWdHucf8467Gx74fpgJtbw5YKezeiEcRDGIq6q8nHicPAgyrRATZNse6Hgo6q7+7J aMJ5yUPkDc0CnnQiLdt1dYm4p7m2eFA7Xx3EpijrRq/Mm1O8KPAi+8ELinomuNG84KZp5oU3 SX+8MEiSesnpGbzb7xvYB7VvI+3bwNkzQoiGdrflbDP0LanIEx/QN4ENYwV5Kdm5gd/QEXu1 7OPf8F5XzKpGHAReHjgLw0X1CgtoxDBqK0QI8llLJMrmz0tIbPlQ41jY0pPdaIpueyi2+Daj AFRojsE+NCgpIGG4PVM/HFmBbc4+DYS6SpBlMgrsI4loU4+7H6883IWP7Z5w97EFGSTBxTE7 tubbetaXq+VGkRcoqNO2vKrpyHhz9HCRfy2aYjjiuDWdyZxo5uCeI2tAuecniOZEyUfY24Pe bhsczJj+NHJnWYgRQLYhRrAX5FqIWViaXnyRSYNuDVXOgoAZwxv7Alfai7YRvDSQG70zjZJO 4wtH3V9a5znBXwkTb880LuPWW3ZSwla7q7sFZmvsgEqa9csBVWaFOsdb9CoMLkDafsP+Uv+b 9Mpj4HBRQhHxoHWm8KYWKidta9GAa0VfC6ENryQWRh8we3xhhDtlrwBnD2cfyHG/zAPzextF YC9WM6935MVcrBKBbbu4qLnzjtLwbFH3+rSelaEzR8k0DOjVkZk4eK0OsNGXJt0ygEAEqxvX pF47Zz/3Vqf7sDkMYCznDA/gmdx9EB8a1AJwOb/cuKNz3kZCkJnx01jgJTP+Ai5Oc3cp38G4 ZbSJUlGkUuOvWLeOFnrCjcpmzzGMAxEG2XN4epxvCnIZPbRP0D5j+eKZBcSf9fVGfzB12mZE XGJFWm0HpWZYCQYz6oUPHdRMkzuJMX21snqN3gx8fA4LxTnsQDZ7LmcIJplHMEme3RgEpAuD QFAUXYFBDF3orj80BPjXFKK1RcUZb6TNkgQaPAa2iizbFwKZIrgbMSxYtzXZtmN/GNijaP2d sGnZ72uAtmjSNgyGWHLaaumr2Eti+f4DLOSpkFii4luCI3y8Z4gr2DgRVuGtoo/ACte45IkP nG7LzoCTBcrBm0XLR4OeW2zVtLqiquDBUaLyfnPLksGcZercp+eeDXyD+zdwOc3Ui7mVgZ8D LYGXBe5gTdM/i6AMFygWhqBt/K8LcvHXPURzSjRWF0RDRKqxMz5dH7aq9fU3bbC2hDEzK9Aa 1K+DtmH/rX82GpN6qltJvx50UG2WbCbWi/KFBwHRz/0YRHYVwh6+fLH50wWxzy68W5TFAms4 76bI4StalC9oURJ4wSVAch7Uu9x1NMuRJFHuxWc+5fMBQusnAtsAekkRq2Kao5+kG3AGuxhu v/4Uny9FegbraaKe3mbIDL19iJBlou88w8vZSv706duPZLfU7Z6p67nuj6MG8mdyMnqih7O7 ZZk7SxC5o8MicVEfTkaTk8lB9aS0ZkFsnU56+g724+qV9aTmui3JIGb6bFdeHLEoiLw4h9PN kzzPtRzPsmdKds1x4KDXJG6BR0rClbe6TW5DR24vEJZqyCD1/FO5faNIxTlCURKJSCoaYtIm MpG0XMkxsK0Jy2D1CU+efq/8IFNE6AnJCedL461s4hPPl+ZP4fc7cz/75JKm4r7qm0kiVPSh fhYBm8g+7+h91T2QG/0szu9L4naFkkrzBETuqvcT5K0id/E7lnuQmvEUAfwfkbCJj9KkSdh+ qIjs06H+7tAWnfQoktYbPfa7Dk+IlJ/qFgzRclhqzSXtj8FYAl3u3Dgar0fiwxcf99TK6Jev e+gsh+EDDUtXKfxWaK8zOok1wwKaaxv6yKwsl0HflXrckItX3H2ch15yRjK95822PhB99Lzj 5KpZse4PhF6CRHz5Puv4M0NyDhCUoaIqigi3CE/6kVes6LB9MMjrW88qiXcI13Q9W9dEXhXD T74YOarFZ76go2S+Z8O+fGDPNTFArNhLjn4/1NY8bOTdKEPWp3o6auA07ilXg3c2dSnu7ejN 2Jqycx2gNoRXfUdiNcCgFUFPgYkQmU3X1gwOK0gUvtqJzSq2COn6URJBA28LIk1HiWLrDoJr 3OcGAm7a8MabJVqIxrdmuoXNDBbozDhZecltRniBz4zjxFudydoHeH/x7qTXsyghCgRKcIYw NFh5znaVOvxjigIv6JKdAzXKdZIj0fHtqGM9rmaq9WAmnNMz2ZqXXSRw7HBURmYm8z1qhS4w N/rgRv0PmhbLZjPuJ6kHM92oupm49ndln+w8+LL8RpEHSOCUh+27bT0dUKPrrmgoYrKEFS+M fKhFvEPhVPdEPOwE8Zvk9SkkGnkxlJQ3L4ea9HaoC7bFXk9c5lcrFBwct9Zx2S15S/UmIRI3 EjOGifCAofGGmfRi+P3R9coCKQgvnr4zKUmFFs48+Udov9deOQT0EKaPfvguP/XwYn75jPae yrMa76+RR+R6e4MM1JrOPG1yKYeLuSt7L+6Q0yuas628hPeILbMev2BqLrBlznyFllXNb5hf Z8mrzE4rPCrvOWpNxJ8/T1XOZlWN6no42rK8B76XnMk7yEn245XHPhPOAFEUja2x4fJrM2ei /VBw6M7ghlzDhf9Tx6OaWDOvYBgljU0MKWVsy0/HII0mRbxIyFqrWkh6Ehfr9DWcpb3/2alF kpylvH5SDcSU7eu0LWNKIE6qVQzj1Tpyc+VEIxGZn+fVmn/teN2SsfnjSOCE8v4EWBZS9uBs nUFvcLYLpFmUrbwgv8nZLrBm0SrxkjPa7Lf6CWDMtKNM2JpqkQqBfhRHhjCtZYCmVK89kT1d Px2plyKSvny4Z7WG2pQDbuqfnCCefL6YRGa+QDdQHmTCl187ojTyVisN/B2AILPIAEV1qpOQ Hq/qkoBd38kks6AD1xL3YXhQlnwcNwfKLR/lsnY1f5IZi8NIHmzLCvlCgspQAPLaqpPQ83Ui Zw/Ys5AVenX3REnvp/oKG26NYbNXTNk5phTZqPVWmzNj8C4UeLlWwWidt6QyEko6b7YskpfY OrU/Il13E6UcXOLF1Chh5qVniUdREyJwMm63kAViegW2sEV22oGk7inhDTJP1TugEwTiJYA5 gvU2cL5gLYmSYLUkFCFO9p4t0FrxaiWKaJzVLlaRxFnkRc7ryRDUVPvotMAtkV0UpF5+xs4V AM6OykmBkpugQkIwRS+vB7WbEDxvlVZU9OqFrKrpWxBtHKKuTJZ8lKVwpHJim0WYhHt+BtBO F+2DvKm33xVPYk7k6EVk18gVNg0q0zNDvRQT15ujqjKRRmENKFQlB8hwiJdtqJfIAFBECy/a WrEqn+qSzZbSxqsQVdSsUacG7dqrQrdKg8sX3zrMIy85Oyqx74ov/3H3+evvP17hoiFKbkkQ ZZSMwjrp6BuLDvu6mxw7DOrRj5NE2CIRQdhblOrIrcAvgN4ij7u83AzJfTqkE7urXcMpCSB2 VireuljXJHYQYyt5w1Prp35gf5DxwZYqh1A9yZpPKGp9yz32v9eMcQjfvkaYICJlvZd6azwT mAMVdgoXQhZbVu42FLPseLNna5yeBLAi74CWgpf1qN58IpaEU+FTb4cnHPaDNnm8tuokEykE WnXZt/tCmjImKitIaRcytkSGW4NoVJk5+DBzqnAMPny4krKVxeTOKufsmSwld/r+ZwDxITLF j7q6gbkqZpN06kmmUsMGTFfKHxrCjGnP2CscbSfTs4uTmKKbrQLlJzn6JwVFr510nHrpGQpF VNOS/fTYF2nCRD7wGVsy67jmwpaZbCZ5rU1PrZ6quyp22LOpRyke+Ia6c8wbkt0kwz3WKLx4 k8/nhGNRz+Ys+gaUu5CPEuJxG6QI/YVRIGIJz3bxKy/FliGjN/RblWlUpHGrYF/HWSP/lKIT 5fRia3ego9jiVJNPHB85vGdeNHgiO+FNyLARiSG80rYfJPnYXlXzIDBOfjzUYgppNgrJhRz/ xZk0d7Le8hfBDTQokyRbl0u0rGm0p3LUZUfinFlMkctanRDediFD5tQx2I8b3eu19hwcf6jD Opste7hc71Q6ztVw3b9MQLy410Gem+pru8BJKAKJCB+pOhjcI6dwrxhIRJhV5O2x/5EqVDZU RjkqdgtcZlXLVEO3PZBe7RQj3vYSm6CM4E0hIyhuV9aeWfUTa3BAlRJTs4RNLXMikleDG7Ml LkEubLE16hVMGoax+EsSZxl7Fb33+uQLJ+Q4qVwy3KYO0HXBuglMjPU2VMD9kiUelCxtXUs/ KkdhFyBlp9beMA+NNZ3NDB8syV8+o1V6oUa97QGJDSQ5XEMxWwDQEo1gbyShuea8k4Z5rCUc JVgEZosyX4sVa846buHgrAe+O97zgQqTzN9NNe7Wt+rwK3WqZhNN2dHNlWki8j2vNbdxJaiO LlIe+5aUkO7sDwBhyYZPh0EGHvxJbmXby5pfAaWoDOgXk0ks8SSh4OYg64d/gC2W+O8qUguS yBSZj/yfB/EnQFIPqbSn2O+kTqo/Ceq6fpJ5qEqEVaKx5aMgEkXz85JfuiA6mGrD97MFZnnV cWDq0ouyBLkiXLBVSGHaDfLvW0raadg3UZBEywGQgdat05F10UiULoh98l0tl6WgMMFhwEvl 8f4Wcxfmnv7LQwhg+LAn3z37Bzah70WZ/ZwUfdsiPVh/b2EskinRPCcbF/5YzNgr18I8WLXv Jsuz0R+1cmWmkLNTM+F6zmoNzzyqwcEb5bYHvUY76HxQAPRhJlvk+Fd88klthNF3s4XNjWFs EKwulOgTMNJCK6kHwapRiT6ppcBmcN4tKaLgCVAWufyrK5BKAqjMyQip4LHfEK1IWj+KSr+t NLkVJ9Kyq65qtJ/qMmwBiTU8KKQZAqStQ1gE2hx5UmIcQPuHjmD7tx15ZC7rn/k9m457NYSO yrmKOhuJ44VK3UaTABqPU1VXCHAWTQahxIJWIYJUIrOnHa6imgUFlIqwhrziPkI/EzSes4YT HKcxgknFmgjJYkUc7dAhnsGb+pchLyfX1axJgeyyB+Pbdc6k1jMd9c9O5VfdEg+zMoMWDGi5 ci5Z6K38+JzIEX8jVUiIWcoogYMA30tRkMK7PqqyheaozDLoxZ4QKfmqhjfMtswoe1MxyOL7 Au/UTW3qPVtOE/SuBmCc9f+vSR8EDQplbmRzdHJlYW0NCmVuZG9iag0KNSAwIG9iag0KPDwv VHlwZS9Gb250L1N1YnR5cGUvVHJ1ZVR5cGUvTmFtZS9GMS9CYXNlRm9udC9BQkNERUUrQ29u c29sYXMvRW5jb2RpbmcvV2luQW5zaUVuY29kaW5nL0ZvbnREZXNjcmlwdG9yIDYgMCBSL0Zp cnN0Q2hhciAzMi9MYXN0Q2hhciAyMzIvV2lkdGhzIDkzIDAgUj4+DQplbmRvYmoNCjYgMCBv YmoNCjw8L1R5cGUvRm9udERlc2NyaXB0b3IvRm9udE5hbWUvQUJDREVFK0NvbnNvbGFzL0Zs YWdzIDMyL0l0YWxpY0FuZ2xlIDAvQXNjZW50IDc0My9EZXNjZW50IC0yNTcvQ2FwSGVpZ2h0 IDc0My9BdmdXaWR0aCA1NTAvTWF4V2lkdGggNzQxL0ZvbnRXZWlnaHQgNDAwL1hIZWlnaHQg MjUwL1N0ZW1WIDU1L0ZvbnRCQm94WyAtMTIyIC0yNTcgNjE5IDc0M10gL0ZvbnRGaWxlMiA5 NCAwIFI+Pg0KZW5kb2JqDQo3IDAgb2JqDQo8PC9UeXBlL0ZvbnQvU3VidHlwZS9UeXBlMC9C YXNlRm9udC9BQkNERUUrQ29uc29sYXMvRW5jb2RpbmcvSWRlbnRpdHktSC9EZXNjZW5kYW50 Rm9udHMgOCAwIFIvVG9Vbmljb2RlIDk1IDAgUj4+DQplbmRvYmoNCjggMCBvYmoNClsgOSAw IFJdIA0KZW5kb2JqDQo5IDAgb2JqDQo8PC9CYXNlRm9udC9BQkNERUUrQ29uc29sYXMvU3Vi dHlwZS9DSURGb250VHlwZTIvVHlwZS9Gb250L0NJRFRvR0lETWFwL0lkZW50aXR5L0RXIDEw MDAvQ0lEU3lzdGVtSW5mbyAxMCAwIFIvRm9udERlc2NyaXB0b3IgMTEgMCBSL1cgOTcgMCBS Pj4NCmVuZG9iag0KMTAgMCBvYmoNCjw8L09yZGVyaW5nKElkZW50aXR5KSAvUmVnaXN0cnko QWRvYmUpIC9TdXBwbGVtZW50IDA+Pg0KZW5kb2JqDQoxMSAwIG9iag0KPDwvVHlwZS9Gb250 RGVzY3JpcHRvci9Gb250TmFtZS9BQkNERUUrQ29uc29sYXMvRmxhZ3MgMzIvSXRhbGljQW5n bGUgMC9Bc2NlbnQgNzQzL0Rlc2NlbnQgLTI1Ny9DYXBIZWlnaHQgNzQzL0F2Z1dpZHRoIDU1 MC9NYXhXaWR0aCA3NDEvRm9udFdlaWdodCA0MDAvWEhlaWdodCAyNTAvU3RlbVYgNTUvRm9u dEJCb3hbIC0xMjIgLTI1NyA2MTkgNzQzXSAvRm9udEZpbGUyIDk2IDAgUj4+DQplbmRvYmoN CjEyIDAgb2JqDQo8PC9TdWJ0eXBlL0xpbmsvUmVjdFsgMjgzLjAyIDU2MC4zMSAzODYuNDkg NTcyLjAyXSAvQlM8PC9XIDA+Pi9GIDQvQTw8L1R5cGUvQWN0aW9uL1MvVVJJL1VSSShtYWls dG86Z2dhcnJhdXhAdWxnLmFjLmJlKSA+Pi9TdHJ1Y3RQYXJlbnQgMT4+DQplbmRvYmoNCjEz IDAgb2JqDQo8PC9UeXBlL0ZvbnQvU3VidHlwZS9UcnVlVHlwZS9OYW1lL0YzL0Jhc2VGb250 L0FCQ0RFRStDb25zb2xhcyxCb2xkL0VuY29kaW5nL1dpbkFuc2lFbmNvZGluZy9Gb250RGVz Y3JpcHRvciAxNCAwIFIvRmlyc3RDaGFyIDMyL0xhc3RDaGFyIDExOC9XaWR0aHMgOTggMCBS Pj4NCmVuZG9iag0KMTQgMCBvYmoNCjw8L1R5cGUvRm9udERlc2NyaXB0b3IvRm9udE5hbWUv QUJDREVFK0NvbnNvbGFzLEJvbGQvRmxhZ3MgMzIvSXRhbGljQW5nbGUgMC9Bc2NlbnQgNzQz L0Rlc2NlbnQgLTI1Ny9DYXBIZWlnaHQgNzQzL0F2Z1dpZHRoIDU1MC9NYXhXaWR0aCA3NjMv Rm9udFdlaWdodCA3MDAvWEhlaWdodCAyNTAvU3RlbVYgNTUvRm9udEJCb3hbIC0xMzMgLTI1 NyA2MzAgNzQzXSAvRm9udEZpbGUyIDk5IDAgUj4+DQplbmRvYmoNCjE1IDAgb2JqDQo8PC9U eXBlL0ZvbnQvU3VidHlwZS9UeXBlMC9CYXNlRm9udC9BQkNERUUrQ29uc29sYXMsQm9sZC9F bmNvZGluZy9JZGVudGl0eS1IL0Rlc2NlbmRhbnRGb250cyAxNiAwIFIvVG9Vbmljb2RlIDEw MCAwIFI+Pg0KZW5kb2JqDQoxNiAwIG9iag0KWyAxNyAwIFJdIA0KZW5kb2JqDQoxNyAwIG9i ag0KPDwvQmFzZUZvbnQvQUJDREVFK0NvbnNvbGFzLEJvbGQvU3VidHlwZS9DSURGb250VHlw ZTIvVHlwZS9Gb250L0NJRFRvR0lETWFwL0lkZW50aXR5L0RXIDEwMDAvQ0lEU3lzdGVtSW5m byAxOCAwIFIvRm9udERlc2NyaXB0b3IgMTkgMCBSL1cgMTAyIDAgUj4+DQplbmRvYmoNCjE4 IDAgb2JqDQo8PC9PcmRlcmluZyhJZGVudGl0eSkgL1JlZ2lzdHJ5KEFkb2JlKSAvU3VwcGxl bWVudCAwPj4NCmVuZG9iag0KMTkgMCBvYmoNCjw8L1R5cGUvRm9udERlc2NyaXB0b3IvRm9u dE5hbWUvQUJDREVFK0NvbnNvbGFzLEJvbGQvRmxhZ3MgMzIvSXRhbGljQW5nbGUgMC9Bc2Nl bnQgNzQzL0Rlc2NlbnQgLTI1Ny9DYXBIZWlnaHQgNzQzL0F2Z1dpZHRoIDU1MC9NYXhXaWR0 aCA3NjMvRm9udFdlaWdodCA3MDAvWEhlaWdodCAyNTAvU3RlbVYgNTUvRm9udEJCb3hbIC0x MzMgLTI1NyA2MzAgNzQzXSAvRm9udEZpbGUyIDEwMSAwIFI+Pg0KZW5kb2JqDQoyMCAwIG9i ag0KPDwvVHlwZS9Gb250L1N1YnR5cGUvVHJ1ZVR5cGUvTmFtZS9GNS9CYXNlRm9udC9BQkNE RUUrQ29uc29sYXMsSXRhbGljL0VuY29kaW5nL1dpbkFuc2lFbmNvZGluZy9Gb250RGVzY3Jp cHRvciAyMSAwIFIvRmlyc3RDaGFyIDk3L0xhc3RDaGFyIDExNi9XaWR0aHMgMTAzIDAgUj4+ DQplbmRvYmoNCjIxIDAgb2JqDQo8PC9UeXBlL0ZvbnREZXNjcmlwdG9yL0ZvbnROYW1lL0FC Q0RFRStDb25zb2xhcyxJdGFsaWMvRmxhZ3MgMzIvSXRhbGljQW5nbGUgLTExL0FzY2VudCA3 NDMvRGVzY2VudCAtMjU3L0NhcEhlaWdodCA3NDMvQXZnV2lkdGggNTUwL01heFdpZHRoIDgy NS9Gb250V2VpZ2h0IDQwMC9YSGVpZ2h0IDI1MC9TdGVtViA1NS9Gb250QkJveFsgLTEzOSAt MjU3IDY4NiA3NDNdIC9Gb250RmlsZTIgMTA0IDAgUj4+DQplbmRvYmoNCjIyIDAgb2JqDQo8 PC9UeXBlL1BhZ2UvUGFyZW50IDIgMCBSL1Jlc291cmNlczw8L0ZvbnQ8PC9GMSA1IDAgUi9G MyAxMyAwIFI+Pi9Qcm9jU2V0Wy9QREYvVGV4dC9JbWFnZUIvSW1hZ2VDL0ltYWdlSV0gPj4v QW5ub3RzWyAyNCAwIFIgMjUgMCBSIDI2IDAgUiAyNyAwIFJdIC9NZWRpYUJveFsgMCAwIDU5 NS4zMiA4NDEuOTJdIC9Db250ZW50cyAyMyAwIFIvR3JvdXA8PC9UeXBlL0dyb3VwL1MvVHJh bnNwYXJlbmN5L0NTL0RldmljZVJHQj4+L1RhYnMvUy9TdHJ1Y3RQYXJlbnRzIDI+Pg0KZW5k b2JqDQoyMyAwIG9iag0KPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAyODIwPj4NCnN0 cmVhbQ0KeJyVWltv2zgWfg+Q/0DMw8JZJKypuzqDwW7dTLdFUxRpi3no7IMi07IQWdJIclz/ +zm8i9bFLgzINEUeHvKc79xo9Ooz+u23Vw+r92/R8vff0Zu3K/Tm6/XVqz8IinEcoK+b6yuC lvAhKFziyPFQGDjo6+76aoky9nh3fYWWeOlBZ/p9kZRrRF+S4oY4i33S5SVrZKjb0py1GlSz L9psqmaX8JcpbRF/V6IEtR1rwTz21XZ5mhTFkbVRW+358DU6JEfMu75U7GtHu3xH21vedfN/ 9PXD9dU9bIFt45Rzf4kdwfz3RQurtJskZfO6qjkizh9t9wXnoUWJ6EDVU5cIBuka8SXL4ogS Pgo2xveD6mZiadf1cBT2l4ZudlL5zZ27SNmDsge6iRYVa2zEL3isxcto8aJHFezBh9XskcPL kjUyRSLREzmJrR6fir7583Ej7AWSSSPSIquaXGx016JWSDDNNzk/O5AQ+zqibs8OiLVBuIgf z5aipObjCzm4y6sSVRt+dmlVpkIbylvUsla1o1wcTKDowNtFgZ654KtDeY59J4BvKV6miC+0 EcqT0ZITa/IUySW2XJJrEHPRUC7q9ZFJka30kq8p3wj9IRgsClp2iL80CoIRmmLIIwSHcZ8h KfT+BHT/sEKoB0DSA+Dp1oiPl4Hc2iwRZ4bI0sWecyrerxWiP+qCH0feaYVeJx0/lZZKONRN td6L41ij9V4cZ5lJQeetQGm3Xx9v+dkdKDrkUoJKCQTI8hLVSSNsREELrSrz0g1iggN1BLtk YnBEcBRYg/twKzUwOE5eRCsSLwvd/yR+RgqZ8NhpCHYCVxKtHGot/EIau43G3M6ezlZKNSoN Q1RNL/QiiSZUKvYkzqMF1lTf23uqezMjwacxMnu1J0P6Vs88WGwclGkpNFNIbFNt39DoTib1 D+icQCNQwoE6gqdADS04SF+SMuUNQHLaVcKB7LgdSbd5SUWbApbXQke7rTTLW667L3QM3aDF dC2pcpJHlFXV2th/bh/ac9wHEXaUOjKNBqa5uu/yMmmERWRo4ApPBTqqDXOEfKE22YlGd6yp cDWdBsLqmAo8dg1YS9Z4pC0VVNMt71iBQZoyPr4PGLAZbOjU4CDAcX+shMutcijfWONjz8fM n4ofYP9UpH8tPm/zoshrISTaIeE3sLOcoEYCCD4sandALow4OUKmZoFCsY33eBDr3zDOsdLU Txplxq82luIaV0u13jOMDwBjfvZtSKFWSnRXq22CeVHDsFqPOOqXDFJbm341ZK9RAjpBZSdm TkmbYDfqndD3xd28YozJE9z3xJwwZP5uIIDJNSIXuxNKA0CZmhXHJ5zNLuIvfeyOr5E03Tl9 9jymUtLpcEvTihCnqXYCyPuaNi8c5S0VAWoBUC1FxJjdIm5O8mfwqPx1KjwlC4O6hlIZapbC IDXQEEEt4iFSBfaoa4U/TXoR0RmmXRcTFQZBwLava2E7O2nxwJYaOyo8MliocZoOcXEU92iO RDL2+Bh7xOKhsvT1nVbVvQWuXPcbj/ZZOTMTwlKl8lMMuw4w7FoMGI/5oNHUKGLViCW4tbxa H+cjnrHvnw0dA8tcOe4+PqfAPi9Yh4zE53XVtvlTLvSsOwovUkF0vXuSspWZCviQgcc8yMAe veE+6DipB64TY9e3WTCB1YncSmWlOn1qlRLlsz4dPvSvhZ7/rM5I06hEY8IOMGw6fY5mLA2k X65nj221DtY96ctsK9dc8v71BeHMMsbhqWyyJqm3kMVy3LIQmjWeEpCWynA5HtGuWtMC/BT8 uEWHbS49/S4R+ctWAL9GXYWElWEBdCci9TTZt8KhCsuUMHj3rdQs234cmcwCMrIN5NkiaIHY XmS7lKU6Uz439NnB9qhckum400mKHwVgri9IdDyrXOFOliv80MO+dyqYR7qhDZX555TOE8fH EHRYFM5vzr+wjuIHDqsMXLDVYOa4fIIJOd3dKmmL5FkEmI+36KGSzgR9+CRU7J3oWIMX+rAS Xf8T8WWTt2h1L7r4G9CHlhZCt1YYrbi5SMqMtiqZ414vT7KyanNtVXgoe073XPAV6gQ2VVFU hylJQOoaWxPOeH0CaASkWyvsVcCFNMJLZcJPDJh5mSr7rqsxpTZsSHulSsRmxs5NOEcXQnPf 5spQ6pd/ol42Z7wIVYax6xnRaJh73Y1mhM/2zmRMKjdkDB7PVfGpOxooZTiilB4JsUtsMWml /DBLLprRcSfEofLln+i+qQrM9Zg1uenbNxn+zFtHGdAkHaR1kF9Aexn9ikLWiF8DonmXM8dJ PMKJuyRccIaTEf0bFnSWo6TgjEJ7UwSk4LATX7JHKAQwR3isVMQqjaFvE56vNzmvPgKQEbiJ uz8eb2YEQALIvi4i6V5KcukxTTlREZknimx59VEEvm+afVnx1sN/RZX3Ifl7L7PIz710cp4z b4QZJ/JZsGoxwxn5hYng8SQVjFTByNgGK5Z7UcNSDUU79N3p6YMykFzpFzXUWBWD1VSPr0To IiPh0g4625NYZXgU/rRcvNjB/iBRSosEYsxNLqq1KOGZfF3kst7bCfGk4FGfZF0PQbLEo56n qsjbnSx/yKpLBu+4g8hfZO2jhKyp4s74ma5neR/zhR7E+qD5Fu8XoHPUgoHpcvuUBpXDgV2O lFlWXa3A7zkpzFg8L1rieIAObvryHT+wjM4biDEjRlzC6r8W8fPhjDNmxIgbMH/cI/UdDFgk 7BeZJTdmuojvsrqERW6eyliZm/gRjm0i8zsbC0NJ4J0c0vfFrwqVPmsEMm2RJltUl15r/eDW OxatucVH7ZG7xJ5vLz6rw84YlGWUMa5E0TKaJTiGL8d3mJkcKs6sDjpj+HL8EEfkZ4QUXRjn e0GMiX8RyTFwKCrAnz84tPclv68Ulg2SJHFLmXZI3G/CS1604bdbrydjP0hBXXuBKfR9qZPS JEuXXsx6ns/MoLibZf1NplqP9i3tR/wn3dLymdvz4j+i5J3hJMW8ZNCvvBlKehWXxReEMHJA M3RQQ6+vNv8ey/NGwS7KtBa35ibZrj6/1a6tH8oqQ7ubCMllIC1rOPc9F668KCu7NNqdpioB MHcd99qkZ0MvbQpFuj/TM014sFa7WGmmdiqw32uO+9VceHxRDPUDfhNrzNU4eyfKjnBiKGFi tMeeSdOYe43jU0x8+5hN3cFBgBudTBuYi/4E8LahPeECVMxcdHqOw2pVFwAhxTUPOXk80/4k EJwlixovA8Koq5FA6HM7AMLUVUfALbKZOi1FEhNucs3YfqWAXTV1oi5VokdxUUWTxlwz8QBv qihIInYEFhuTV02ygmjxMVtKln+dsKj/SwPlrQL/uGUYRGXTliGyDIMF7VTTLs5XAD3QBH+A k/syy0tKG1VTU/HuquJRcb3vZFT9Jc2p/EfMlAvxAlaLt9bp20t4fAM2zW3dFOCcMMZh2Cd0 AeDGAha5czeGyOsiwGVZ0jTJ/gfbJ8Atw8KT4qdZqLlxwMKFGDy8cw5pYxGRvE+02BwgTSoF N9ZHlYKZP9wYzTBpGTPuj3b2Ja/vpXsxeZ6hbTushqvfLLx6bM+ARVx7WGMHYDF+NBq60bte aWlQj7pj96YSKHvFd2WfUCZO7s79aX30Yo/dP09udCjlmZKoC/6HXKzVY0GqIhT62Hcv0erD 4YB3lfw7AcXVlCUWWVOPbv9eu8lmIRC62ItRCBTOQmAssyReiMOTPZ1AYP7E4wvjcDdwmW25 pIJ/aWDrQv6xnMwW/gF72dnaDQplbmRzdHJlYW0NCmVuZG9iag0KMjQgMCBvYmoNCjw8L1N1 YnR5cGUvTGluay9SZWN0WyA2OC42IDQ0My4yMiAxODMuMDYgNDU0LjkzXSAvQlM8PC9XIDA+ Pi9GIDQvQTw8L1R5cGUvQWN0aW9uL1MvVVJJL1VSSShtYWlsdG86TC5XZWhlbmtlbEB1bGcu YWMuYmUpID4+L1N0cnVjdFBhcmVudCAzPj4NCmVuZG9iag0KMjUgMCBvYmoNCjw8L1N1YnR5 cGUvTGluay9SZWN0WyA2OC42IDQxOS44IDE4My4wNiA0MzEuNTFdIC9CUzw8L1cgMD4+L0Yg NC9BPDwvVHlwZS9BY3Rpb24vUy9VUkkvVVJJKG1haWx0bzpjLnBoaWxsaXBzQHVsZy5hYy5i ZSkgPj4vU3RydWN0UGFyZW50IDQ+Pg0KZW5kb2JqDQoyNiAwIG9iag0KPDwvU3VidHlwZS9M aW5rL1JlY3RbIDY4LjYgMzk2LjM4IDE3Mi4wNiA0MDguMDldIC9CUzw8L1cgMD4+L0YgNC9B PDwvVHlwZS9BY3Rpb24vUy9VUkkvVVJJKG1haWx0bzpnZ2FycmF1eEB1bGcuYWMuYmUpID4+ L1N0cnVjdFBhcmVudCA1Pj4NCmVuZG9iag0KMjcgMCBvYmoNCjw8L1N1YnR5cGUvTGluay9S ZWN0WyA2OC42IDM3Mi45NiAxNTAuMDcgMzg0LjY3XSAvQlM8PC9XIDA+Pi9GIDQvQTw8L1R5 cGUvQWN0aW9uL1MvVVJJL1VSSShodHRwOi8vd3d3Lm1vdmVyZS5vcmcvKSA+Pi9TdHJ1Y3RQ YXJlbnQgNj4+DQplbmRvYmoNCjI4IDAgb2JqDQo8PC9BdXRob3IoZ2dhcnJhdXgpL0NyZWF0 b3Io/v8ATQBpAGMAcgBvAHMAbwBmAHQArgAgAE8AZgBmAGkAYwBlACAAVwBvAHIAZAAgADIA MAAwADcpL0NyZWF0aW9uRGF0ZShEOjIwMTEwNTMwMjIxNTM0KSAvTW9kRGF0ZShEOjIwMTEw NTMwMjIxNTM0KSAvUHJvZHVjZXIo/v8ATQBpAGMAcgBvAHMAbwBmAHQArgAgAE8AZgBmAGkA YwBlACAAVwBvAHIAZAAgADIAMAAwADcpPj4NCmVuZG9iag0KMzQgMCBvYmoNCjw8L1R5cGUv T2JqU3RtL04gNjMvRmlyc3QgNDcyL0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggODYwPj4N CnN0cmVhbQ0KeJy1VsFOHDEMvSPxD/6DSZzESSTEocdSwQq4IQ4Ujaq2KiC6SOXv+zKe2WVn J2w4IK3kneT55dlOnDgmQy4MP6GI/5GswUci6/GVycZA3hJbQ94Re3x54mTJMzkr5IsnDKYN BssAIJF8xkiiwBjJFIQpGAoZI5bEeUyQYL3gsKpQ8BRBCNcYARFKFpBIqTglSklIDGWGk6WM EfDl7MEEuc6QeFjIkEDWGqAEFr6CcGyKJIiHyzwCcgZrMiw0R/i7GClaxGuLCFiQQBncGWpg wQNRNoAnglcYOPAIMoOfjZYpgS9CYQJfzNANvoQoE/hSCpTAV9Tjr80gT4nYGOAyLBIKajZw QtLYOuAEFmIzMs82InJYJMmV7zIIvwBxCJ0FOXQoTXQlwagNxJycdKsCNnTZXXWr7uyGzC11 qx/kytjp6fHRAsQehvBhiDsM8YchYQmCiink6unuYTPdnZGMkOvXp767+PL1srv4/gslH6b3 abhCE/dX+vbz4fcgecCUzQ/zIW2plgSrjKwm1Ih3vXJDGVtK3VBr21Bs21Bt21Buu1jvGUYa MLEBkxowDXnmhjyPEObDx+5dDDdgXAPGN2BCAwaXBSVCFzSbrfR+jC0BWHRPNEz0SLRZdF40 3dIf0QqLH5frB20PHQ/NriVnqQWUF0C7bYT91EZmTHFimo68zuPMO7MFzTtJ1AMf3fbAz3nn rWTLaythqJRyfy2TziIKtYikujJvQWEeUdTFUz2iUOV1tYhESXNbRFKJKNVr5LegeY2S1ijV a5TqNQqViFRKeRPUSGfnWQ6HHWthx6q87SWXZB52UoW1nL9x2eet3XMqpTxtGsNeOpF7fWy5 sXKe5PX364FMC+mCGt1TThU5jdZlvYCNGoV4hXiFeIUEhQStY1DqoA+D4NXoQkFZgrIEZRF1 F3UXdRd1F3UXdRd1F3WX0V1FaHMYT9S4Dcu7cjDql3WFzJrxtzvnav38cr++fu77y8fHdbe6 e+4fhs/ylBwKeTNmuuR3M3ve/1uf9a+bB9L5y5+/uNzKu7OsZKdXDJcX6JDUqSP56SCHaf/L uCNU2IdKNFJqrvTF9BkFu6Wi6+NVazC7hf18s7t1VLVeEmO7Hhvs2OrG5rTZT/nN0Z1vq+Oj /6wS9QYNCmVuZHN0cmVhbQ0KZW5kb2JqDQo5MyAwIG9iag0KWyA1NTAgMCA1NTAgMCAwIDAg NTUwIDU1MCA1NTAgNTUwIDAgMCA1NTAgNTUwIDU1MCAwIDU1MCA1NTAgNTUwIDU1MCA1NTAg NTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDAgMCAwIDAgNTUwIDU1MCA1NTAgNTUwIDU1 MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1MCAwIDU1MCA1NTAgNTUwIDU1MCA1NTAgMCA1NTAg NTUwIDU1MCA1NTAgNTUwIDU1MCAwIDAgMCAwIDAgMCAwIDAgMCA1NTAgNTUwIDU1MCA1NTAg NTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1 MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgNTUwXSANCmVuZG9iag0KOTQg MCBvYmoNCjw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMjYzNzAvTGVuZ3RoMSA0NTk1 Mj4+DQpzdHJlYW0NCniczHsJfBRF+vZb3XNPjsmdEEImDAmEJIQkBIgcmZyEOyckECCBBIKC RMMhIgIiAlFERBEQAVlERIRJOAyHIgooKirerOsREcUDBRZZRTLzPdU1hfd+u99/v9/+u+eZ 5+mq6jreeuvtmtAQI6JAfOnInlsycMCuxgnRRKvDiSLrBuTm5VOc8jLR8lKU6jCgcHjJ0Z6X NuB6Oq7LB5SUZR8Y8PkJXL9KZBo0vCQ59YYn1swgYluRXzVhanX9dZ/nXiAyrkMDdRNmTrdv O/nEAqLE7rg2TayfNJWt9nuNyEdHZA6bVN1QT1HkQH25uN82acrsie5XfR4i6t6baEDPutrq mo9eOIs8loj8nnVIsA5luJ/V4LpT3dTpt9w5fm4CkWIi0t8/ZdqE6s/uuNxANKse1/dMrb6l 3u9w2FmUX4Ly9hurp9Z+6b/gS6LbrsIIneunNUx3/0gr0H4nnl9/c239Temn7iQKaU8UFkzc VsCkOw/tHuff93syoxkcB05HduV86KU7RniM7njdJeN6XJpIIXHgHv2FtgtsrG6ix+i5WXdJ q+mXh46nKEepGioLUMhGyVRLZL0N7SrIVXUT2X2kJ5N+rT4NVUYLVk/SRIVMZsVqMCuKTlV0 G0n5zkn2wbLqoSV2OyHBfq/og7FSUexEG3ie+o0+ko+UmG4ilQOiM5wttJ4qaSodpxOUTdNo O+2ldfQlpdAsykBeBm1D6giU0FESBeKOafT7w+J5x/MO9fOelZ5Tf1CG0IbIz4aO/l3udq1m eJDHB+0n0fPe9Fk4f3tkeNahhvWeU2hpmzdtmnb/CJy/P6b+YX+IFtFGjHEqwZdpLPR2Wojx b6dR0JUAt8teyoTKpljKpz60+Q9r4aOJx9kPWO+5SDU0/nelZvxJH34e/3a6Hu0TWtxL8Z4G LaXem7vRi18flR4XdfamH/em8btXshjPIPQ78w/a+X0t4ijEufQ3aUu18/dpBTiJpVOxx4jv QvocacWeR7Q8EyzJj7HeHh332mI8fCnjT9r+04PtVrYzlya/pStsLVtEfZgJ53bWjDOZXsF5 hK1Cbh1q/5YymJ1VcNBy+BJHA2vFDC3yXOUnU9gynIlMp6FClKZNdEbDZtqNcxnmna+LL6kX es/9KhUzsQ4rgK+KHWhnBnKneT0tEFZOwApY5ZnqQczzbPP0x/chWGUjxpyJuhag9FLoBnzv hSdthCetgH8laF62HmUaNe/phRo7k/Ck3x0e+DDvO12h0/Sh1j/eQx867rmMNvgaWUeR6OE0 b/8qUVuG1r95iDClNAgRtxjpC6H5DH2J8YzyrubtxP2Gt3IRZRClaQ6+M4BFNBe9vEhhNMPj xtUOrWQM2hErhK+fsVgX8RjTalqMGodo7WxBb0o1C10E1ovatXYWegc0SvveiNKVsIlYobPJ idnaiJEeZgqZ0b4Pzusx2gjUlYbezsXJLdeLWmGFWKhy7VxMKzF7CdSMns+ANWr+NP0E7HeZ BdJbLJa+RTi+Ckts4Scrh//8+7nB107CiBfBD1dqvAh2X0onNTWDeHT0oZvQi37oO5/5eKQW Uxdt3KoX7cUTg5XiCkp1ko7hmUiJiN3cFkS+1BF3doPFB8HDRtKtqOkp2kX76KzdZq+3z7bf bl9qv9fj0er1xX1xuLs7ngxD0FqVVrr5Wunp9rn2+fZlKM083//C117wvIDvCdTETrCjnuOt wz711U6fT328z7QuqDWVeqJWflT+6QJWWZVSxK5TCtk8Nh9rbSyrVGqVOmWycr1ygzJFmarc qExT6tloNkrhnqcnAxkRQ8yYcytG7Mv6wmPuhA3vwlwugUUb6W66B2v0Xqzu+7CO7oe9H6AH aRU9BA9cQ2vpYfiegX2jtX/hd89ghueseGIr9M8P9vMIvAnecWhaGwsq+ZPRyEr+eFTI8Mez PwCrMwg6hEKxwsLh5e1+14n/t/H/q0fTv1zyP3mo/9HadIyvoi7aGjH9G+vjX10b147/y7r4 X74mnANqxo0dUzl6VEV5WWlJcVHh8GFDhwweNLBgQH5ebk52ljOzf7++fa7L6N2rZ3pyt6TE LnGxnRwdo8ODA2z+vlaL2WQ06LHzZJSY58ivsrviqly6OEdBQRK/dlQjofoXCVUuO5Lyf13G Za/Sitl/XdKJkhN/U9IpSjqvlWQ2e1/qm5Roz3PYXSdyHfYWNqqoHHpZrqPC7jqn6aGa1sVp F764iInBHfa88Lpcu4tV2fNc+TPrGvOqclFfk9WS48iptSQlUpPFCmmFcnVx1DexLv2ZJpQu edc1Yd/ty5t1qbF51TWuwqLyvNzImJgKLY1ytLpchhyXUavLPpn3me62NyU+13hPi43GVyX4 1DhqqivLXWo1bmpU8xobF7sCElzxjlxX/K2fhWPIta5ER26eK8GBygYXX2uAufSxNoe98XtC 5x3nvvl1SrU3xRBr+5645EO8ZibkS03oG3qI8cXE8L7c3eKk8bhwzS8qF9d2Gh/ZTM7khAqX UsVznpM5IWU8Z77MuXZ7lSOGT1Velfczsy7cNX+8PSkR1tc+sfgg3+5S46rGT6jjXF3b6MjN FXYrLXc5cyGc1d6x5jV1T0b56ioMYjI3Q1G5K9lR7wp2ZIsCSLDzOZhcUq7d4r3NFZzjwo9Q 712u5Lxc3i97XmNVruggr8tRVL6P0jyfNPWwR+5Kox5UwfvhCs3BpMTlNZbXTHRFV0XWwD8n 2ssjY1zOCpivwlFeW8FnyWFzxX+C5mK0FrW7MLbflJaF+ciNsSZ7uRKpVvDZQoI9H1+O7L7I sGG6tEs+o9l97eUskmQxtOItwdWv6sGFGptTwLNUfmtOQWRMRYw4/kmXIr190se6TL+oy4aE a30S7fxp10Rp3qF4e15t7i86+KtK9d4Oemv7434q3BbehnGHiU9ngcxSY7FykaagGi2Jz2K4 3UWF9nJHraPCAR9yFpbzsXFba/M7uMQxuGhUuTbbXi8p/dWVyO99Lc+rXEoOHDA/IVLOqXY9 QLu+dlnwm+yBMtveaHIMLmnkNTu8FZIdywcjNsQNrL67d2APrMt8hDZHfrUDT5P8xuoWz/zx jU1OZ2N9XlXddbwOx8CaRkdJed9IrWvF5XMjb+VNBdJgNrg0OykRgSe7ycGWFDU52ZKSUeX7 bHhkLSktb1aYklOVXdHUCXnl++xETi1V4ak8kV/Y+QWvqRgXJq185D4n0XwtV6claNcTWhhp aSaZxmhCiyLSbDJNQZpOpDm1NH5ghsLrYF/E2jx7DZ+b2yrqGqsq+MqiUMwjPszFHP3JpTj6 NzHF4OOyOGqzXVZHNk/P5OmZIt3A043wChbKYBwekBqrHAhS8KZyimTCD1Vepb3F4yktjzkR ea4iBn5WCYwqd5kTEPj1sYNQbgBHFZIHuOZPqOb9oLJyfq8xduCECvisrBBFBrrMqMHsrQEl 8rV7uC/ipgmYG0ygdv98XLjmV7gqEnij5ZMrNF+2uajAcR2mXdSpj+MNJVc0BjpStYWJdWCJ XczJjL5RSblIicQlGqsQRjL6oOcTHMiaUGWHtXU0oQR+LgKpJVKk1CIe6uJqNVgivZnEh6XG Wn0tLnM3VIgP19ZufD3qY40VFaLz2tVibwG0bXNZ0aO4X5jSewOsg6yBvC/4LEZXedHDvJqi Fip23IKwwjut1WREtss3dmA1Ir+434oUR295s4kHCKu3jiMi1chH7gO7q7GlLZ7HHbNjfnEk JTr4k4E7JkXug2NTReNvE1yjE5ISTb9N9dWSGxtNvn98g7CXyfca80R7Hh4Z2PDZFXW3OZwN srcoihRMCvIK5pHCLUWbFD9JcUWKH6X4QYp/SHFZiu+luCTF36W4KMUFKc5L8Z0U30pxTopv pPhaiq+k+FKKs1J8IcXnUpyR4jMpTkvxqRStUnwixcdSfCTFh1L8TYoPpPirFKekeF+K96R4 V4p3pHhbirekeFOKk1K8IcXrUrwmxQkpXpXiFSleluK4FC9J8aIUx6Q4KsURKV6Q4nkpDkvx nBSHpHhWimekOCjFASn2S7FPihYpnpZirxR7pNgtxS4pmqVoksIlxU4pnpJiuxRPSrFNiiek 2CrF41JskeIxKTZLsUmKR6XYKMUGKR6RYp0UD0uxVoo1UqyW4iEpVknxoBQPSLFSivulWCHF fVIsl+JeKZZJ0SjFUimWSLFYirukWCTFnVIslGKBFPOkuF2KuVLcJsUcKWZLcYsUs6SYKcV0 KRqkuFmKaVLcKMVUKaZIcYMU10sxWYo6KSZJMVGKWilqpJggxXgpqqWokmKcFGOlGCNFpRSj paiQolyKkVKMkKJMilIpiqUokqJQiuFSDJNiqBSDpBgoRb4U2VJkSeGUIlOKflL0kSJDit5S 9JKipxTpUvSQIk2KVClSpOguRbIU3ZyzuLqZZUVPU7Oib1Syoicn1ZVNSppYVptUUzYhaXxZ dWpVWXJVZpUyLnVsWfSoQ6OU+lGfjFJGJJWVZZax0qSSsswS9lwJ26B9ipOKygqThpfVD2fJ w9mGAlZfwJ4rYNMKmLOA5SflleUm5ZRlJ2WVtShqc4eO0Xg8CmLNUV1ApBHzCHILahN0VdBP ze0TQFcE/SjoB0H/EHRZ0PeCLjVHJoP+LuiioAuCzgv6TtC3gs4J+kbQ14K+EvSloLOCvhD0 uaAzgj4TdFrQp83teoNaBX0i6GNBHwn6UNDfBH0g6K+CTgl6X9B7gt4V9I6gtwW91RzRB/Sm oJOC3hD0uqDXBJ0Q9KqgVwS9LOi4oJcEvSjomKCjgo4IekHQ84IOC3pO0CFBzwp6RtBBQQcE 7Re0T1BLc3gW6GlBewXtEbRb0C5BzYKaBLkE7RS0Q9BTgrYLelLQNkFPCNoq6HFBWwQ9Jmiz oL8I2iToUUEbBW0QtF7QI4LWCXpY0FpBawStFvSQoFWCHhT0gKCVgu4XtELQfYKWC7pX0DJB 9wi6W1Bjc9gA0FJBSwQtFnSXoEWC7hS0UNAdghYImi9onqDbBc0VdJugOYJuFTRb0C2CZgma KWiGoOmCGgTdLOgmQfWCpgm6UdBUQVME3SDoekGTBdUJmiRooqBaQTWCJggaL6haUJWgcYLG ChojqFLQaEGjBFUIKm8OLQONFDRCUJmgUkElgooFFQkqFDRc0DBBQwUNETRY0CBBAwUVCBog KF9QnqBcQTmCsgVlCXIKyhTUX1A/QX0F9RF0naAMQb2bQ8aDegnqKShdUA9Bac0hhaBUQSki sbugZEHdBCU1ByOks0RBCc1BsaCuguKbA3lM7iKos6A4QbGCOglyCOooKEaQvTkgHRQtqIOg qGZbLqi9oEhB7QRFCAoXFCYoVFCIoGBBQYICBQUIsgnyF+QnyFeQT7P/YJBVkEWQWZBJkFGQ QZBekE6QKkgRxASR0wPmcANtwFXgJ+AK8CPwA/AP4DLwPXAJ+DtwEbgAnAe+A74FzgHfAF8D XwFfAmeBL4DPgTPAZ8Bp4FOgFfgE+Bj4CPgQ+BvwAfBX4BTwPvAe8C7wjl9x9NvAW8CbwEng DeB14DXgBPAq8ArwMnAceAl4ETgGHAWOAC8AzwOHAafnOXwfAp4FngEOAgeA/cA+oAV4GtgL 7AF2A7uAZqDJd3y0C9gJ7ACeArYDT/oWRm8DPwFsBR4HtgCPAZuBvwCbgEeBjcAGYD3wCLAO eBhY6zMteg2wGngIWAU8CDwArATuB1YA9wHLgXutjdHLgHsAWztW325+O6U+Yn6EkhyeGT48 XI0OSw7LDFM3hO0MU5xhkdH59cHzg98Idpo/CdbND2IbbazF89wuW2L3fLDTYYvumF/vzw75 s+V+G/x2+qk7/Q75KYf8Xvf72E91+vXPzlcPMu2NIWLsvqbSkoSEwS1GT/Fgl6lwtIstccWW 8G9n0SiXYYmLykaNLm9i7N6KJqbklLoC+J8etetFy5ZRVPZgV1RJebO6cWNUdsVg13yunU5N e7gmFKlIaJg+o2FGQkJDQwNLaJgxvaFhOiX8rz/Yf7sD/38ObnlMxHTMAsT06TMSpoMSGmR+ E/G/OGd5FJVqFAVgAFEN8wBuoA34CbgC/Aj8APwDuAx8D1wC/g5cBC4A54HvgG+Bc8A3wNfA V8CXwFngC+Bz4AzwGXAa+BRoBT4BPgY+Aj4E/gZ8APwVOAW8D7wHvAu8A7wNvAW8CZwE3gBe B14DTgCvAq8ALwPHgZeAF4FjwFHgCPAC8DxwGHgOOAQ8CzwDHAQOAPuBfUAL8DSwF9gD7AZ2 Ac1AE+ACdgJPAduBJ4FtwBPAVuBxYAvwGLAZ2AQ8CmwENgCPAOuAh4G1wBpgNfAQsAp4EHgA WAncD6wA7gOWA/cCy4BGYCmwBFgM3AUsAu4EFgILgHnA7cBc4LYs/j0HmA3cAswCZgLTgQbg ZmAacCMwFZgC3ABcD0wG6oBJwESgFqgBJgDjgWqgChgHjAXGAJXAaKACKAdGAiOAMqAUKAaK gEJgODAMGAoMAgYC+UA2kAU4gUygH9AHyAB6A72AnkA60ANIA1KBFKA7kAx0o5r/4uL8DxwV /+0O/M+OcCLjFLWX+7fvMBTTRGqgRlpFW+gdZmJpmP0G7S3KHXSYXqbzzMCi2JD/ycsc8tBH 8rdNPd+653l+8sTrL7rPuCsNYR6D/j1PsPqNyNMvIh/3RM9l9zz3KU+87gV3pYcMEz3xnvOK k0yyBt0cCkTaD/qJ+kX6rfqTGJf2fp329u6/ewyFDcZRLexwPTCF6sGVNIawgmgyrm6CPabT TJpNt9Icmks30izw7XQHLaS7aAmuG5AicufRAqQupqW/eHtjAUou0t7quNubshS8XCvL6xDv fPz6jY8VtBIzIt/z+PN3gh7RSv46fd0/Lb+eNmBuH6VNtBkzvpW2YZ5F2s8pT9J22klNSN+k peygd3C2kpt+oqv0HV2An1hYIGsHb+nHhiJm1FKdZqVKWO1GmkHTYK8GrR/zaD5GyMc2V7PB PM1m3D6ilwt+89bLzxa4X+v/avSC92slxsD7L/r+Fy1NjO/3o+O5j13L/6Pxb7pW5gmM1kXN tIt20156GiPfgbE342oP9OMY/RNeizyFHBesIsru0Upv/UXezt/lttB+OkAH6RmspBbaB8W/ ZdqzdMR7La4O0wtIOUrH6EV6lU7A4u9BvUSv0El6k97Srk/Rp/ytWfqYvsA8fIg5OUOf01n6 mr5B+nd0ni7QZczRVczVVaxcPk9JmKkIrOFYzFYGVrKOyol0flhLKhkpmlKoch852JrmJH9f /ndMm83UzvgsArhCQQj5Juxm05w2neK72mbrErGmneEhNcvfjl99u7voVrEcymz7qO01fJ0L zEg+x5I/bH231XbhWEBGcuvRt1tTurOAmAANwX6Kw+jonJaa3qOb4nCkp6V2UFhqaAhP79hN Se/RX9H5XR2klrfplBvs2ZMKdDWGm+/rOuQGpyN+8praFHdLbIpvmD0wMDrMzy8sWh955Yw+ 8qcs3fif1itnk8qyOq+/ujCpIC2yJq1oUtvXabHecoGB9nA//magBaOuxKiNUDXOQIuqM+v0 JoWZGdmNBrPFhN+XzvaKqpq7mA3LlQ0KDp3RkKXX6xjT4WYMNS05QPtkpmGwYwIzeqcl286l sojktLR24SdS5y4+coR5OaV7jEONUR0sLUhVdZXPj277pHIfa75U+eGHLMp9Wh959VHlQFse f3MOUUv9AT0LoA4UTzlNEXEHsGMxUCDbvMfXYLEYqIVtdgZEG7r6RrTGxhrCTxuz4k4b0J9z mZrVM5jt3bdbYfLADNj+KIzOjRoTEGMPCDYYO6iqZvGAtNT+SpCUPdUfBi4+NMO9jc1ke2pv Th9X0PVseN+Jw1qa+00Y0Dmha2FDwd6Da1axxWNX1PTQR7qfv351rKX7kDrW1mVAWgf3pHY9 i9oupRb26uDufzN+9VR6zqun9QnUnvrs8mvfIZT/7VIfBYcK2z3cj/kF8F/tlmw6wIJQJIL5 NSm58Jx3zwFjzqH7rbZ3YbJYg8NOAfCEtNTQsJC4OEdHPyUkOBC97aWefmS7+2v3e8Wr379r 9LqCAatHVT5Y1/u1F0vXDkjJYoNZ7iPupqpo+87O8XkLn5ntdrs7O/isT8XERcG2PhRKXfYR saO7bb7MN5hb1GIKCzptzgr+wiRMybvx9phWzXw6zXwkLWfXRUU5Jz5wZK77LCtiqSzpQI/r N05Zs4g9q6wbu/2B2SOSYaKj7p13Pzcz/WoI7HEcc+pCu1ZKaTJYeGvBWI06Yj6G02Yz059W sixn2M9TiLY/PBKYoc1eDJoO8UJ1tUUoX7S9okS3tSrp+shV7htWufvzN1lPoIUdaMFMXXer ZqNiaGFbnb5Gi/KpXm9kZ0xZRtR+rfILR21HMjAyUa0DFe9oe0fxa7u4V2nVLXSXP9i2H3Ux yvZ8q17SJ8EPc3cFx1DoAeybg8jEjjjNMV1DPgkKimsf38LCdw/n/7Olhfnvicv2/ap9Lh8H n8kAb3Otb184Fsjbk3PYQeHe1ysgJj0mwE+VC55Ps/rtwLWj6zZN69N94oZpQYmJ8Tb39+xA /OhxYxPveu+holF/OdNYvmGgrk/nhEGLn2245dk783VGi4FtXNlWE9Qh2FL62OVNj3qaxkR3 RI+meVeSldKaTDr+p2S9mXHj20jvY80yfaGqeqzoHOKmyeR9zWS2Y8kXjgm7wywwTRq+09Qf mts+aG5WYpuV8rat+si295R4bvXtaGG/1kKXPcxs1Zlg9i1Oq9VgPWPO0ROmFaZIxYclX3jn iO3DI9zm6d7pTI9hp9xvqX7ut1jS1YssSbdo1aqrQatXo969novqVNQbwr2Usced5sBgn9Cg L0KyrNzcPv5nDd4Fw8177F0tsnpDZ+f00FAskXQYV52aOnFtXeeiwdnh/R8aNHWm2x+BZnrp Q/XZ1qBI266kHovmKhfXor0vMY5TaE/P36nQ8faIGZQsndcn0caJVs0TYQ31VJttj9IH0XYu 7kzBikrXdmL1zihfH5PJavEPDFBV3K/39w8K1uvCjeGK0+z0b/G874zwNytGnWoy+hgM1sAA X58si8XKGFl5NPXG0jQeTQPDMnrzIy0gkGX0Q0ANP5KawQNruM0rtIiqGlWH2llVHUFpQWG9 gtJ06TvaWfsdntPSTxe21fmWK6PXvlNqMsvY2/YDu/Sye9/Vj7Ewg7ds4S+PM/5/KtTz6Lsv ZexmJr3FygMtdqbwEH+Db5ZqUS0mY5Yhh3EHSc3MTMvImJt8W3J4JktuTW5NDcjI4J1gagB3 ExYUAD85f9a98MYtZ9gNC7egXUPbVPdYmPyq+x3lIH7QIQJlYL+ajBUVRJ2p00EK1lrswMOP pUvEl/7ZjrP63GvhJ1mLPorBIFZIYM+e/DFp5FHoF8tIl1yy7JnJtx+Y07d42UHwbf32OYbN GVk6e1hcp6G3jii9dXicssrloceLRm//x8M7GETlk5cfbzjUOKTo7gOTpx9aCt6Pvsknjx+F UTceHV/Y5W8KNvIV42sKDz5jMJiCWs1Zpt9EkrdbvY8YB48kWgfj5EOl7+1HlmxqZvXVzYsL 9z32wN4t659U7y585Lah7gR9ZELV6hm339X2eSPa3gY/ikTbEdSJBjvNSgSjiFAz4UlxZFc7 S7RZi5mW2FDjEXuEPcLS4bQ1y5LDvEuX9yYjWTz5eId4tMmArwon6aZe6xnfXhhDEWW8PYTx Io+/ENotPbNzaTMbe/PmySmdBkzO7TW0y7CFs7bel1k7oDPb1D0z1sbjYeqoeUP7Ta3IsgWN G64YJte5C6MySrgfTUOUPIM5Taf8pjg81I5gdpPQb0v7nnH7mT919jzn9AkILejc9YukIH1M Nh4Aobutfl/pvWsYnW17N4Fb8wgi5RGvNa8t5f6Y+m4GESA7KOJBaMDkG9QzQ5YcaugxaVxp x8EHZo2YPdTRp2rO/DlVfXrP3Dc/ZujwoTFD1xasuc+byMaPuWdMN5OPzby9vT0yfUhK+uA+ KT36ldQPzbmjtr/B6mfaGBo2cUT6kL4pPfqX8af4CMyJTdsjRTWRwiObWa8y3RlDlqq5AKLa sQvHtMeIg4dync09d7d7rrpft/CnubqFq7ltpsI2V1BHGPd2greHkK/m7eFhWdaswE+Ft2dq D4nWn0eejtnT3BtxLE290m/mk9Nu3XZDSnNU9uQh9y5uZi9O2Tknxzljc6067eqGodOHddn8 oDqOt8e3ZuPRnolSnKGKTmUmshuwnzMoJp2axZQcQkjOTMvkUYbHlohkvlVLwweNM+zPWFAQ 04135553DzvO2hUWs0ixt7y63O1minoT2khCGxlazFuy21eHaN/CPt9j9NPZbDDSF3uZESfB Yx/eU29iJl1gC2twWkinCwq2WXV2y7Oe98nH8wkFeN4gf88nzkCrf4CPRWfw8zPyxzQTXdQe SN4oGOANg+Fvt9qwJdGc3daK/meIb3Sdx0EeDEUc5F+6jPIvNiUlr/p41NY0/8z1c/b20/Xg Q2EnH3zUjfB9dfGl19mZth+271b38ci0CDN1WHWTnZJp8O6QEDOL5v+i6xvTuYW97PQ3d/d5 ldmpI+vYsZ29KKSFIagHB/zYrjDxsq5YPPC5J/N9sHjkH0HXMpKxAjvL+NUzLS29B3deY3p/ lftySID6cyDDpi5WV/zEmBG3Do9t/dsbN88oe6w8pmjU+NTR99f23Plh1ti+UYFdnN36Pjxi cVFB4tC6fg89WV5xQ5zjEZ+wQGuX4ttK24aw4+2653SNSo8PHzgcI9rouQTfW4TVGId9lzX6 ANsJvyC2y2kLNnX20Ue0+ReaSzq+ZdJjAGnntDCi7Z1tPy/CuM4wvvHXgdcYIJaleiVv3t6b K/aP3mEa/OT4EXeUJzaHpQ7rGZM7sKhb2vXd+04tSVFMc48uHdgxVj/IPWd/bUnBwn23Dr9z bHpISlFfd0RwWFzFSvjTejztt6tXKIp6N/tTGP+Hd0MHvxZmdprn+TP/wB+thdjzG5+uj2JR 7a6oxaRtrq7tld/WtsqwMaX/dq8cqpl1e+7yId+6PYPu2l8/bEn/rMUFObNG9Wh6ZMCi/rHt Ipjyw6znGweHRmzpGJ1W1Thy7x67ne+UuTfAdsEUQz2afKzcFUyRmPZdzgDqaPLVh1+xFVuL LCXR3+sLtSWsBWE58yndg7x9gfkcXg/oJfoToAWwqfkL9kzrPiZp5zrDwG0TRiwcmdh8w/js lUUJdWn3bmCt819cOsDHl225MufZ6ycWLGyZs3fXzOnszeCQFu6rM9C78/BV/gvJucvHJwT2 2bsrOiQeu7ynnSEhXaNPLNcxna5rp5PtCn3f8TFethV790vcRbUHF7ZjrZqDMvwqignWZrRX iPRVEWyNPb0zrp5XlLbM7ZsSy4sH2wfsqlqwf1ZGvxnbpkzeNC1jj2rPqcnOGJvbVa8kxKaG rn3M6ONvXh4ckb/o8K3XP7O8NHv2U8U50wqTEgvrc7z/Kw3PuhXwy2DKbNLjp2azM9LPavUx UqhPiJ/+stXfZAqwBP1EyuWAUksRogG6zjfTGWkIurZjtndfw8UR7bcdbBkS4t3Fp8ek22JS w5gucumEr1mLu6DJ/QzLYZvqbv/pjO501GXXirZeyvEVW9iKcHcD/6051l2pC4Yd+9AQqqJl B8mP8f+qOpQ1PZ1C1kQ1pQ929U1O35GdUkbGp4wcmRKvxoQdYK9SLvVlrzjjYqptQe78gvzi V0yd8hNVU0/KZ/mmfFN1zz5vXze88pWehZkn24/Q9jOZfACIW2M4BWTYzgWk8R/LWtzHbi85 OZmHMzy5wxDUbPxREOr9a0Ac3AeuHKbvoP7854Gevbqp8ls4WkwIC/Y+LeM6x/qpQd7tUbp3 1aoN7aMz6laMzLzRHtRuYF9mGjxvdNp1s/fPv/XJG1NzB7SLC/fp1zUoKsSaMWlFeWxuOzal TX3gzpKb8jrUTHJfiUkIt6TbrxvevUdRryjJ6ljHmJ7DFlSmRQa3T42OS1UsSkfn2P45t4zu 2TlvXMbAG9N8OiV0D8ua0j0sMa1PHC9pMd17NWBAVoeUzJh+1+nNoV0TEtTo7oW9ox19h3Xl 3KnPMP4c24jpeRHrL5ySm/ytPHL5IHmn05/5RNis+p9CCv1KrEVUJLc/P8ct/G7pESfCFP/1 YuShNjREfXHHyCJHv+xhaTt2GOKHDy9OXLVZWTC9ISi5sG/bRP0i96T1KTldA59ugX/yXzUn 4Z9mRKSsg+TP2rAt08EP/MwMp8HYQWe+bCzCNqaL0xKMYFoQTN+rwzHNHyWcy9Qm9YR4RGmP dP4jT/u5E+TdgckVxi6732OnWMLVD9igflGpsSEhsalRXlYr77v62IoVegqJS4lqnxoXEhKX 2j4qJS4E/VvorlTr0L8Q/rv0IMWif5EUBJ8lS3DXgK4GPHrNeyz/h70vD2/iyPat6lZr3y1Z iy1LlizLli1rsyXLq7xhYwNeMTaL8W4DZt93SEJIQljMFiAMIYTkZRvAbDGBkGSGmJnkMbn5 SMJsJMNkZjJ5E7iQyTgJYHGrulu2MMlN3r3/vPe+95VRl4pS1zmnTp3zO6dKLdl33PpomkQT JtEU9S8QJpFZTThyusqQqGAVLSOOZEMnOIpWsixn3nOd5ZsDxtKKcQkLHgqpCO9okr+8mzjj uXlZMdoX5HqlYNuTZNU2+HyY7jAfOLpD69+G5lYICs+gYPrLEwIBEJ6HnyCgRaKJRhAG3grK IUckqCSEPJJD1XLYmUaLI5+d6Xf+hEzANToAoecZIReO7d9DqUeOwF/+LZRM6If+Rm24ux++ F3KHI9UP0JgCYD9Dz6WCnUshPZfgI7KWXqYRkxc5dZFTxRncekfc24vvOhlxIkb2wwHK+gxa fPJLL4H44rAij3E0qJFI0pzcQXtV1KC5ymDVx1TqqyVhrUV2gDELMOC8FcDQBvuNeMWwl7VE Vr0aRpHxq0JB9vBkGoXKbHOanuHJtAqlyeaKP7iBr3enZydU12nTMoL2N4gPnDkWqdbXWDC0 kjjvL06UqtInjxlaSX75ZqDKrZk5F6/h0NUha3i9IU40aL0R0Xi9aYAIrzexRksATbWoWn6H V4U1CPu68HqjHV2YMkXEwjv4NC+1Bi2yp5850jDJlFs43nWE/PJQeold8Vr/0Hxi/ZL5zMJD 2jwVRYRCNDIbESJ4gtxCHPKzOCL8TlZl+ZaqGUlIRWLksJMaHQ8KC1cdn9/zytL88PV48rjZ RRPmlsYnj+spxlcYWvzLTQj3v7V68dvouv6tR6ZtmubM7dxYjmIEfMXoKdRJ3kZ0qYAVZJ6Q CE18PLHCREBjADVfkijWD8mrxJWCiZbLDIn5jJFH4TGOw2hcGgZReAK/D0X5yNsVj5yc4Zru PvK0qAIBgftQlGumd/NBgv/orx4pFIpDU6lHX+x6AEO9idEKovVtWoZmhFb4QoxWxLEqBq1Y JIIE3W1ZjXCioMr04TBaYSzAKLTiR4T6WbDCgFIWrQSWvr7e256576hgy4WGhyalHNd5x6cj uJLUEdixD17r+cWOernCdrsbrrzybtkj/SvqHp3mwYAlSvsLJv/H4dP0efoI5VnYR2fR+oJy QZSKUPD5IKpafIcKqxd29PcrGJZcNM/iC6vXr5+hGn/esP9Z7jGytspSWlHnOkZ+eWZW87sX H1mq89fnDq0EdBbpBnkWjZoBas+AVHjiVExCTIIQzd+pEwqhz/I6BCABh6ZKXVmCbVCRyjNV IclRQeEzoqMiQiT9lheGmiMRKk4sI+sQTotim8mkm9Cs2kacsXok06eJI8mzCXk101qdna/U NBypX70syjNjSvHCOpet6eCy7G0Tanb4Chr9WnWgs6Zxxbh4qMyoK0iLkypVB3X64vyYFJs9 RuUd0xS0t9cHJPKnVVEqizMmJjU5WasPjGnAnOYjBP1rFIFrQEafXIItT7SQx+NrZHwtT/6N WCipBHc0qm9JFgR6L9F27kOcYqVRFA2fEzPofKSfzomy0PnX2WsK3ng99DnUIbMayl04z7Ug TyqTvNJHiLdDlSP05vYQ0TNLJscWBEn7IvkliAaOPgJgC6JCoe7RoFSoIYC6Ulwpu43kWR1h QK4xBs9iwynQRDrkZdKiXvLifq6juqra8fSBI0dMuUXIfNDG43Q/sW1ozefIduQSOG9XiPPZ aEw+CARlFBcCE59HkgLhChKS/ff+LSggefxqLpfCiuXxOPO9gcxpTNTLbFFc8mD1okNGaCkc GMzwf/1maMmvyC+Hug+9QOy8i5+VZEVjFNI7stuCNorD5YrFIp5QKBIJ+ByoUMhJDkHIZHK5 EkglOMaV0iEuDm//hMNbHOoGNSK5WKYQcngSCU/Ar+TyqglYraSpwrE4vYESmfHTjWT8cOxL 53LCmT8FU34o6i3cfrnXEL/pUu/LdcqU7at/5pBNQI5ZBQ939oRaEWP1ezagmSzasJk4ijVn DOIuGXEnApf7LPz+e38PxlkshMkCgYTkiAVCvBdUyeNxK0mSUw0hnj8sR+YPOr1Or3zAg/4q ahpOEpArJGUxQTFdEaJXSw2Up2yUXpCv3si/0AgycS8KcgUc3IuuCKjRvXAfPuAxfeiKAL1a asADdxIBHjMeXRGKRveiwQFEf178x0n+PHQr9PU1uCG0+VPIh+SV0Ea4NrQefgu/Dq2BD4Vw aAyyQ820RmlBVVCiitICk1KAkLhOr+6/96dTCJErqxG8IoMyoeQedHEgtz5KpdKy+uXNDwQy 2YxWJjuP2gtePaNoENJ6Hk5q+SEzYYbQyxnzHz3YprfrotMNrtryYlNo7kDIm/UPNG9rHh9Y m0MQB0gqNr+9FOvl/oPETkTnYeSvLyE6BcAZlJOAxycoKOSZeOQ3VCWBSD1J8L/Fk3Xdk4n/ 4dzaBc/VCzgjiMAdzsHDePLS3SuEf+gKcXvoMjFhNXF1+2NDTvw8CBxdUhuIRLARhWpcmAOw ZKrYqE0DxvXxKRyzWWRKiVIoVEpIOeDp5Fold5Avl6vFGhy4qeukwstimoJw9EYHb7+RfzLA xDvyUQEcDbSGg7jhGA5W3P0zNIcDOTaMu7B5M5FDB3KYsntfcdyIshRQfVwtFeGILcFoNpuA xGFKNYsHpUJLkkxnMiXpUu4AajCpLvo7tXJQVxPecqOJUwbQpI1QSIeXOAuSxqRo1CNGMRwj 06Sy9p7jNhUXBWOyW0sS4UehlLLx9iqTdVp6Xk+VM/Q7aMtd+EJ3xuN5nKt8iYAy5k7LPdYb 2jizTSraKRaax69shDt7X2jf3e6VyXB8/Diy5lfp+NgGxjDxBwVMyJQKDQZbXFJ8rEkDMLBX CIMKVZkw7htTpeo7W8KgJhzjI4yvYJIRFxC0pDHT6NAjggeWLcI5Cs/D96zT08uWTXKHXiWI KY32GjPVOxrN317KMEBYtw+1Y4Yi6TcCLxhzBjjh0HEr3rXtOxUtl6dnmDDtantQrimz6zXf RkcN6qt433HJQeHIjMhpFhAwHrg1wGC++0XPptEYRKUmR3FHbJ4y2V5tYsmfPNlea0lszihd 3uBuGs1jVeQszMJvdkjQm1WNxJUHIhcClKHIayHizAICYOw5kA7voRBMCftOWgT6rATMV9Ra 5zPOo843nRxnXMy3es1gXBXCjcKTAukdqj6CPToHg1jDzFlHRV80QIzgjo37hzEvWVaw4ujc 6j155gm1E5PX9MYXtgZzlnk9q4qn7l8QnG7wYta8BrHBnUDzqmSishdxVLblSWJhwZSAjuYz YeKWmSQHoj73MwogzEBz+CmTO+yLEmEgKeWrRHxSWAtuR0kHw0iCTXmFM3Bs4pqxb+Sn2U+V N+3s9B1ftMpQXFIcFx+tS2vZ3c3h3pU+vpEnicLPlBnRlfQ+WkeC0dEymSle8x3SCzkQ6uvu Uw0aiA1rxo/pxQ/rwX3zToRmtQ6/QTQ1IduKkX8MKDipUgmhEpMl1WqFek2sVnUZcqOxDxAF BcgHcKv0/0bH1Sx5NLC6gJYdm/2FTHAUBoQ82sp5FZYMeJmbUFHfnutqS3LNcWTUjitPhSlD E65cQXFSSlGaVsh/WRYV7R7vG/onsnUfPcHuon+J6NIBxwlJDIHizb7TOj3B01WL+6H4tER5 j1cFK5lsGYuwAvKB4QgN750w1oxB0KmT2xeVarJzMlWmMQV+ebBneq0Nw+iM8W4tJZDwj8hU Uq45t9Y9tBbb/1Y0U1gqLlB1SiTiOhK4WCxiVQwqbk8C1y5zYKEo04C9KvabGO2gsUr8nUgw KBtZ1flXaVtLbzwwKfwLjJBomvBUPjiTHppw/7AUSWdG3biK1CmTawstkzPwvNaPE2kkCqM8 Y/qEbDlhmzCpgxEq2a5xT/CF+me2aXYKROEFDonthDqtLJ1MG+NihAxIEEAepJPqRovZB0pB 7xmQD08FVZooj5tLqaiiRBvJKeQUqihOPOZZ5Ff5NahIkzkAxzLJwA5PB43SMrfNSXIFyYlR qiJOrAcAKuvb2FrnbUENVc2hY34m/xpA/g8hvkA4X3xrgEkN4u0MjO3k+MTMhQty+hXSiQgV l6ex2EaSs35bYmI4/qU39aDPBumIzY9QBkwP5781aOY5IN317ucNpUc7W3Z3ZiQVN7qjM/1p jQ/XVa+oSfbWzsqERCo5dMCcPyXwUGvoF+ZAsgYeqytLyrWrk1JjUsYF4omXH/544t3ZOfm+ lkerxy2dVhpnLNlSXLai0ZNWNSO7Yv4kBFnOL/kfofcyGvLNzXCLJj2vnNhRMVGflpfgm5Zp 0bqzS+m936/Ir5GU5SALNJ6wULKMfvjuqURHoiNOhb9mp5AhWPFeUBeXbQFRUY6sb1U6AJKz U+86XHeThXexw77uHHbX15HqKMIhLBOUMaA4IhWQS/oVEX7Bj4XmGxWfeYl8zImlbFbZ2Pnj k05lLT+7bu6ri3OthVN8k5uvXk6bnuLucHnr8yxJZW3ZxCFzSVeJv3lMcvL4nqKvd7zhHNo7 7fnVY7Nm7GgsWtFcJLE+0fXSWYp6iqL07rHO7Jp0Lc54k18S+5E1JQEHOPHO8tHjMEj2w2+D SuJN8n2SINethVvhM5CEHLSAP5mmvzQNOq/pr+K5t1i9kNh/JPTW3VA9Z8dXoS68Gm+EXiY+ gIXIFhjOABVBnAJC8L5O1g9T+rgYdOHDZwPo05poJgMcqSyWG97msamGBIM83muxFLgNuctP LbtERKcEk+3pqR67Oi1Ra8iZnFu7d8kY7M9vo7GuMnYHpJ0DRgIilUdjHgdC5PPsp/RVo4bO pwf/8IeGHw1Gbn8POTBgzLTrdPZMo8lv12rt/h+gj6qL6GPCn0EUw333bhCX2JySpY/S4W/0 yiwq/L2vuBoU49v7QBWTU0Iwb+BBeDQ6U0t4R1FjGvWeDI3+zyHLA4RBuAHR9QHMRnRFnwMU QQIZfghwH/ZuiJTvwWlEijnXodc7cs2WHHzNgdn6tFyLmXljtuSm6bG35sP3ySZiIdIvxXEU +p6BfKQiWAUuMUlMsmnoZaIevn/43j3IJ2pQ30FkMvBTMh9G0noVUXWS1k4NsOAM3KeoKoC3 j0cRnH6YeEJaA2qxV/kNIpEGYyjifEBC+2L2xc2McVnUaosrJtaFkEeCi9pw5zaHe2e+OsEd E/6vGARKEMWpCEm1Uh8ACdAcl/AJ/GgHIW82QBAJny7Bp98gffTNrwEKlVJDfBfyumefWNcR WnXhrdBU+ApBLb/11VdLu0LrQ2l/D9Wdx1LgoHtmRd7z5EkRDz/aN/Ke2FjafMoMOYSz4aXU mSce6/jlW6HV1AehiUP/6vn7P2+v6XobHv4b/CNcBZC0UkO/IFu5foIL14JO9J6D3mfR79eB TjTm2+Qx4gVadlq8rv9xGsXJFMTJ5Es0SmHFRbxgeDWW2hBKgR9h/STIY2Q7/SkeSDwHOPAL Ovf9RVAIAF9A8WqZO4Tv8TF7l3j0D34ae9hAtKIX5nb0LQkgQHFrA8JRZWAKmHsO1MJHQC6I hhODIktpeZl/ypSxsMzZD+uCFqFlavkUOJasH/fr4rJiqv7drHR/VjEqOtkxe12srs+EcepA /nUF45M88usevFel0ASgEyfAPl494PUo6N33W1qaNC53+JwGTm1y1HLlMCojyEQl7byjlWom z8PkmtLzOHTCkUdbYo46DBrFAkISq4712DQJE1Y3Fq+d3Zhv+cWxxn21NYcn3/sXAe2V87e+ OH3p0QV+mDJhbvGEcYkNeU1NEqNnbHswsVC2/rTAGhvnsuoUPK1ZrI0xRcE9wTpXVPbKc+u3 /flAbcnqox9+/UjofOiXycnrExPhGrjiKai++c6m8S2vhQZffvVf+8dWFY8v3bzHv2bt4kaf UmA8LI6yj2lsmpLQXCc0pNuRrDn3bnAcSNYKtCxq8ayfOF1ax+NG1Xr74YagMS45issjSeF3 tZW5lYVJlUmVaeTdwhrNd1k1lo/S6tjdQNrjYxkOMJJFch3weOVYnlEMqI2iM688KUlxaW/G Ce/k8Wzs8QE2AzlygtBPJ0o1/s+s1rTJHp8kzWBxK2Ic5ij4mgDKYxWxHrtZI0mqf6y5dNXM xoTlrsVPHGpae35NvjguvWbB+PqphD4rL99U0pihVtpLvKlLyhPH6TbC6nlTF66Dr0ujtIqY rvW9ivHtWVrXxIWP9pY9/Jst5fayZl/DmMUTnU19dw6UvvTygRVjWyf7ylKiMhqXFo578dkt 3lSXUmrF+60NSHJS+vRXPHCC8j6tGX9/nM6pbz6hEro0Z2EMSARWGBtUaM0qbSIqVMoNWZvh BtUBhsWGXuTX5Uw61iu/5vm+PYFEG8ThVB5hRSiIjeI50pwVp1bMeHVFYe7y08vx9RhFDl2R pRXUZhTPnWAfehmSRLIsLVjjxW+Jzbu/6WupPBw6svtbdH0+dNJcZAp1pDfkWwofenuTMc8A 93rrcs2FD799H282kA1yTvkdfocq7izcjM9a0+zlJPVDc1Dk8VA6xJJlhCXndTbbTS+5H9/h GO0lMF8MP8N8GcYsnIh5iCthrqGP9GnmqChzGvIbZpXK7Ihg7pu+Vsxc+5s7Jxc99PbajvM7 JiP+oFBlSdPrmN46fZpFhbFAJJf0bs1merdmM71bE8nVT96t+XHaf5RU5Ow2IvX6KxWPZB04 wYVCFJ+dD4rQKhSJudcpCiI/FnuC3ypAmEvfB9uZU6lY8LSB9V67cHWAzozFKyibFR/y/Ovd 92BV6GIQHtwHEw6Rr7+88w931qNxdtBnJh3ABAJ47/LcKZ1CpzAZ++FnQTGMl0v+yeEDVRth 6ocxfYAZB8esA1gcl9idA5yByyMz2DDfxgar2DHx4smbd5+DNbXdWSq9rz6n9rFAc/nb6yZt 7Q4kVa+su06s3A+rapa1NXhdkwoTy7MXTmhKb93SWLFu7WPVn+Os4L0b5BVEnQ9MPoNmZXtQ 4vRonAkap1OTQArQKusNGhQCv0d7UeE/qoEajd1vvZUQf9Pexv9aIL6Jpy4iJYaPMmGA/fE1 xfABhWsje1oZEUYff4GBPnkQebqJOW1/ZcyTH26esMqX90RZ66aGJNek5WW7nspuKbGuW1X4 ZJV5/Phxlu4DM30L271tlW64uf6p+QVC/n6R2FLYlO2r8sVsNWZN9M1o1uv3iKMkPEfdsop5 exx8VwXeO0wBgOOmYtCsS0BxUCDm8kRcSiRArn9LUC0hSUBxJRLBVxwRH00bj99KtuPDb3T4 4MVeDcE/PPseNqmOwTZ9jgSiNxDGc9yfHh/aRDhufxqaGwrAizA+9CmM30u23H2GODNUinH4 cSTx9xEF8aDgDIiF005r9KiIkKjPBiVRInP0jdhYc8wtbStCnbqTIumwjMPL/mNm0csjlsqI ZPEpTZx4xOIk36/b8atFhqLCfJ1vU2nVusmuo4e/Jd4a+suCpuObJsJDLQcXBpGr4ByQiBNL O/JXzCX27wn1miduRXJaiVYtoKnMAME+fSp+UpRQqRTSGqE2C32p1/W2AYPBp6Oibilb3ReH TROm8mp448s52tZyvF417ZFGnxALL25QsOylrp4X5mXpc9p3vbth/ZOLX12Ux+fkP9/ctqvN c9qQN70wcUJFcWxCaXdxYHqJDR6YfGBxUcnW3+9qP/fCE23+PZmd2xqTrY6srm0tOS1FVqnO pFz/8+5UaxkdBS1G0/oF4osL4l8jCYiPP+IVfpJqJfAi99JnNC8EkPyY040YCn8x9MeLRGAo j1zEWX5nI2f5IWS7l9IZbwftlYpAxzmQBLeiMNWM1Eiolxj0clSEKNZ/A7hAFjwX1AmLHUmG DJfElZVkVmdJJJT1H+q2/BGLrnMGmCO4ysA7KEjV0sZQEfZd1+T3GUafPzKYZ76iwp5xJTQ+ H/6SCvLxYYNJ3ixe8ULLrOfmZD765Lol6TMOzOrY252+dnFWe3ny+6vnz1sdl99UMH+OUpvX XVU8LaC1FTf5M5pKkmBZ7fqGNFf9ktJlR0oLnunKXzLVn1K1YMyCgzmGMfUzibbGSfX1tsKs TEPGwqGnrWOKixPiC4rKkhwlLm20owRpUTstbQed1805gUAfwI+EkTqt/cjSREWnO7+U2gfE CgXXdDGmjXeL2x52bwFGha596AmfOCO4HNZm+P33HzmLDN/9iHPyi6knQ4NHZi9ff251IdKc w81tu9s8hUtfaG95fllxyJc5vSTJkNtcaK2sKI5JLOsirrwZut5Xtye9o3caVpzurXVTDiwu Dm79C0ywlnYX5TYXJUi1WItmpIa9Nmcz8mdGZE0qT2sStBaOynQWnqHd9hnktlMRKtGDBGCB eoRKTCptAiqU7aasK+bmD6ESOTaZPwBLmMVNwxImC8GRBledXNzxyqoSfO1+ZWVRP0kO/btp SVPRrHHJQ6+THEJqWjK1uKciiShc8btD08t2fLJ7xZVnm8p3/PEZY74htLx8ckbPoVXGXAPc WD7ZP/s5cB9nGI+UngEO+NcTfj+GJCxvQRHCJMYkZ9LRJBJBEwsDTRBflpsR0GTaMDZx/gR3 bn0AmjzAW2zRvDrMS0zRvImYwdDV0dBkmMnlv2WYbHhuVTlia1b9s6vGIU7vYGTCdtbfh0xY fmlkcoZGJmdoZBLJ1E9HJj9K+o9SGkYmnPX/OTLp/C8jEz1CJnfjWWRyDfEfDzIxMnmDRibx CIj8JaggzHLJTQOHTwJVF2H6OgxMND8VmMiIePLa3eNwXHVHpioms9Zf/UigqeLteRN7Z2Sb K5ZMHDASzXugMNhaV+NOrcqxFGXOH1/nnvZIXd6SRUuDu6MBjU3iySuIvnRQxWATkUvr0CYk aB0sMtEpBBnWGwiNJLkQPtFoqKQu/i2MSmaEc//IkATkqBqBSZSByA2UH0QkYVYYRFK288/7 Mmf7cpbm9OyZnoIRybnzGJGsWTx2U2n+zyo6np6Rsag7vbPK/bvphxYX8qg9FD+xrLsgq86n 2xqfW++b0a3V7IqOcU1aUT4fg5EJM7EGptxL5rg5K4fRCA1GyEg0IhULuAgiingQkrxOshvQ p9xpMOLxMNt13lvvoGj7Au2yrP8pGlkfehE27COb7x4k+ofKaCwST76Pxo/H2BRhkVMYiwii MBRRAAENRSgERToFUkamAxGxh5JVgfv2oh6EIPXP/GmTs92u9mZmxWAPHglBbrS8uLqUIveT HA6RVjk7f+Vc4tie0GpL3QgC6aR9R7BPa8dPAOXL5XwTjUAAP93+jtY6EC2PiaEUN+UznSMI BKd6r4aJjEQghOKnApCPPjcUzXnuD1vXb9rwy4dKuJycg+0j8GP82KJYnNMdgR/QCuPaL772 s0WFT1U+/GKjMdEXnLu7JaerIgU7jsdP9Tgw/EDo414y+QWSNxeYGfRB0ejjFNVJdMNuOt+P 4MelH4If5O/uVHGOHQI0/viC/IraACwIn5WChedAKsIfSpAAtwWVsTJlttEdq4zNdse6s5Uc oe8sPA8AyIOvB2OEZW6j7aIvLyGVo5mVTRllMm70LG5H0b8z4svHSOQdZUCbr8H58uHTxrlO p5bG8zSkl48yht8PR9jTTHkkjUe43GE88lXJqlc6ep6dka7OndOwdnUYkTy+tmj2ePvllfP9 Dbnx+kBjvrchP0Gpy+0ct2KNtXh6VtLkCjesqdowPSOzaU1JSlNLq6vglaXBdR353sblpTNe KzRXNc8nnpo2x5A+1mErCebHG/MLS4ayksZXlFuLN1d5J2TEaFzlGJfQ87AZmFHck9On8GDd AgIBSKJxic7veUfhGJCpVALLxbgu4UVB92hcciFCr3j/G8hE763wTAzdObvx0Lrza4t5SK/a vgec5DUXWceXF+qtYzoJ/6SXnt7oa2rYWrHm+UZjsrdg7i4Gnmz5C7RgeIK0zI617LHTPQ5w 7x64RHSRe6ndBI94EinKBNTyGbGUnEttQS2b2ZZB1LKQ7rOFaYFK9KkmumUr2+cy6rOH2oBa trF9rKhPK93Sy/a5gVqW0i3b2T4AfaqZbtnB9rmL+syk77yTbfliePRd7Kca4FJyORd/am/4 zqhFSrfswy1ozi7d05F7iTLaW5uxt95Ne+vdQYFQ1y9rsbxGTWcT9BFb5D/sq8m9GdMfqytf 25SR3oyv03zHDZ6ChMQCdyx9LXTFnm7fN8PnaNkzq/1p+rqgujtHE1fQUYavxoIO7KM+Q5o0 lwgiC+7qw556ZzCK5EIeyT3J4SNfzW8WkB/DJjaqx4YJOr1Xma/LUnhbl4ddtJecO/T0jT9k wcxNv77SCy/3PLx/aAW++yC6+0LEcxy2zzz41msSaTQqhth++K+gDEh7JcLf8oCiBcb+AYQH wd7OyThohRdvqGT4sYdOfDBzoB78XdZYu6yo0DvVWu/bVZc/uzItNlDnXwDXX9n6oiPodcSk 5VhNM33ZxrwpOc6a8eW2mn0AQiWaiSZElQ+f1YiDTyEUqHFqEhM1ThKHsW8GoxSiREt/qkd7 Avnk1BaRtB/PDfIe9Jft5QhCOL/HG498ETfSG7MnG5gDw4xtafK0bG82ZKWnys1zc6b0qJwT smrGuQuTFFKTP8k3x6HNzi+I27m5MDsxN1WjyWwqshIcLudJPj+QrrXFyNYrDDZ1lFEjUUo3 80R8alZDaaNeGJeShT3yZSTxPYSf9sjBoJCHMBcl4ggA7IftQSWHwu74txB9iiJ5/GayKTI7 gByy8+qtd2hv7MG5AcqitmSw3pj0knseezbUDhv++ljoD199Naa3dxu8HtLBqaHDSKZWJNNW NK4RZJzQatUoctwNZEAPW4JygVp9Sq+ndL+PbhaIz7CSZJ8WMXDtYyZAug/QsBLEXlhNe+HW QNeWidaWdHeLq6s12j0h8whBhPaNKVnemB5f0FpkFvJ7BZJcn8GmFcL+vXuU7slYFjcQTUsR TTH4l9j6ohPww6b5UikfYDWXx/KNp6N1Okp2RtpsO8MuPtpE4tR0OPRn6aJXIet41czZXPbk N525Jpe6Jy0rO/Bc6oyfP7R4fcm0bAMndopz564jY8apUpLM4vLxxeOMea1FllePd7ywOLhZ k5JvEwpkq1a68iiekNMzpQDv5gA0d82IXi4wBWVh9/p7qplowisw7Fudkb61OfTieVgfaiaa 4fGhpcRibH7AXcT3TKTf8SAN/xLSOWCDbci3mmEbE9srUeGjGepEsX8AdgVlSLFSzRwq4bim JedEWBI4Rf9ACE+NiGPkizhhl/k9ATzzrbmZrrpFRbt/pncETIb0ZG1ixfyKJ/dW5Zuz7Joj jxeWauwBsy8oinXmmvROs2pCidFv0/Az6zJjZrbb8tw2udSclmNzjA+YOqcVTjXJEt1B2FRu tUZb4vTi6NzQz3S2OJ1YpDPZtI4UgdqM5/4L1vLEI/TlP8HjaQF+CCwdue8KyrX2M1JDVBTX /LqhhXeW2xQBvDDTngidDHvGyIcWxBFWM/c+x7gQw6bVvaXTcwwUIUtyONWBWbUe96SlpaVL 612hkyXl0TafSZZitwol5uxrW37eZNyqtucl8yk+l1T7m8pyW4oT4ms2zStwxyXrRPgJB8ml 6XFYKxpCyeRy0g/UIP64WiTvh7tfo0TCOWoTmAvy9bpL2nzdJah13vp4gD67R0+NhkmeaEjr wp8vzHLN6VtX1fJEY3LqlCeaQslpW155Y/qk147s8a9Pnrthd2X9/g2zE5kd9mRSyo4kUodH motGmvPgSMwRLbwxQx8Eh4VopIBrzrH1lS1PNOCRppF+NNL5pgdGwj7+3k1yL2lkfDy4gn08 Uty5pIbx8XTLIGpZSPfZwrQgu32TbKJbtrJ9LqM+e0gp4+PpPlbUp5Vu6WX73EAtS+mW7Wwf dCGb6ZYdbJ+7qM9M+s472ZYvhkffhVvoJ0WtZMuvoB0ugcfhZ4SUmE0cIN4lCVSM5GryNQ7F KULlWUpCOakuai+X8/9cmc19myfiTead4YX47wkEgkbBGcE/hXHCh4VvicSiVtFx0XVxq/iw +BOJXLJWypNOkT7+k8vO/8vKNdlY2UsPFnn095RNdHlzpCgEEWUiW3YyRUmw5VSUJ2rKf7n0 Rv0vlU/Visoy1XrV2f9W+ataptaojf+//B9bstRt0bro0z9eNAmapzSfaTXaTu0nOqNuge4T PaHfpb8X0xPzl1hXbHvsSYPV0GE4EWeIK4y7Zqw0NhhbjDONC42rjBuMW4xPGQ8aX2LLceNZ E9eUYVpnuhFvj18U/z/jb5oXmK8wxSK2FFjKLbWWqZYOyxzLUss6y+OW7Zan/7sF2WIHYQbh X51sp19J+sy+nH6H6wSQUqdB+JdYPdQFts6J6EMBLTXI1rkR7Txwlytl63xg5z7K1gXAxBOy dSFVM9xfBOp5drYuBnbeOrYuIfbwXmbrUtAjLBz+7VKP8Ahbh4Av/BNbJwBP8nj4V0qBTrKN rXMi+lBALHmJrXMj2nlgjeQUW+cDtST8O6kCIJd62bqQeGm4vwikSIvYuhiopXPYugSOkz7M 1qXAJ3sf/3ItiiAAyy2uM3Jm6oycmTojZ6bOiejDyJmpcyPaGTkzdUbOTJ2RM1Nn5MzUGTkz dUbOTJ2RM1Nn5PwSMAEPcKGSgWrjwQzQBhYgxLQQ/esEi1BbEaotAPPo1xbUMgPV5iDEbAIF oAcVE6hBbV2gG/3fQvpdB7p2oN5L0Gs76llEfwLfsQfdAfeZQb+2oOtsuo8JjYXvbwKL6c/i HnPQ6zyali565Nmo4NYu1N6BrkvQuwX0nWfT7xex95xD328uet9NU2FCHOGebejes+mnL/5H dd8B1kS29j9nEjoIAipKcUBFmjChCAgiYG+oFMWySEgGEglJTCY0G0QXEAuuBRUbgl3X3llU LKugrI3L1bVfXbB3ZdEV/2fOTADb7n7f87/f913z8E7mlPe85fe+pyRmmDYiJCWBpXGtKNSX gC0Yjoz+SngvaiWdHFmDlZyppRDfRPjHatliDxHkKUQyi2AfhjvThylLReOwkjFchEh2Rgop 5MGUM887lsG7ZFTOWEyMemSgEdMgL2kzTw8krxC21dlFimRk9EhCT+8Wc9IokF+pVnZWcjwY vYRIap3lEpAPGI6MhdSIA9NHhe6VqIe42YaM3tGcPow3WY+lcnYbg/iIYUka4tRiRzGypBIh IgONztiOacf2FKI2FJIkCWEhDWknaUYFi0gdHlk5ZchzrNdp+J5AvlQhK8lQGYWlo/Fp5A85 esd4Soy4S1vZ48+RoP7ETwzCNcg3zNg6m+hQrtNM3cr+KehKcdZN4coZbCbA1oxFmFpWLta3 TDwSnPwUsirFYUOnkwLpo0YRTKE2jCQjkK/lUCIWQ4wMFIpkDedTNtoY62kQV4KzTVKrsVWc vvLmMjmKJwpZTwa5BKGx2QiXINk8kA+ZSKSbvcpi7FOvZCIuCo6Hrg1TxyJdzuWdVC5qGOmU nOQ6ewqbJUrg/M/aS4cpJv6FXG6RQUo3R1FrBMtQ1CQ3926xrYhDSwIXwRoUH+JmnH0ZTzQa j0btE5BHU1FWyGi2oC4PfE3uBNS2dUZL4/IHIzGTY5NgLxlq9WXW7sVhsnXW7dUq04/mpJVy mBFAnkzNtzI1xcUe1WxrFZJAymmoarYFG0sU8q2Ks6Suz9drE/9L845uZtDZLgYhUoevKGR1 msMGazkvToJPZwQF8iDNxSxj02GwRIS5IL1duexDYAORVGxfBjNKaEcv+EpDL080J30quScX 0V5c1tbNX0rIIQOWMnNCSxb5lKuuPBHlARXKLzp+Y5HMbMbPaDVT0s2ZpiW7sr5jMZmC7KOz EItEnfX6Q/sNg/NWSxzpatgcK0Y2oZutnobGEqEs/LVxpV/JJi0x8mXOZ5GtRJrKuThjebEz OhObn+vN1LMZ0gX2ckXoZKNH/E2p5F9w/vs2auHekovZvMmiR/TJHPSl7i14/VSu1rmO0YTV hUbj6VDP8Gd1ZedQOcpQwm9qytpZ+IlNdVnl8xhgrMogT8PNxxRaUYk4TCnQLECh3/n+cw/9 /4qLlpjwQtIwMaBBc4An8pUSS99MeJOkHzFcKlIp1IpEmuirUCkVKiEtVcg9iTCZjIiUJklo NRFJqSlVKiX27KuQqxUyoZqQqgmhNIUSE4kKFaFRU4RUTihViiSVMCVFKk8iKHmqVKWQp1By 2F0oFxMKWkKpCJFUJdKkqGmhXESpiTRYRBFCIkUhV6iVQhFiJ6cZ5molJZImSuGQSA6RRKgS imhKpSYkwlSKgMwItTCFItKkYlriQcikyRShkIkJOkNJpamkTEsPIkWYzMgipeEYSQqFGLJR SEUUklkJWyjkQhkSLkGjlsoptZoQKVQqSq1UyMWMhJ5ENBxHmgIVg8oTY6RysSJNzcoolqqV MmEGIZTJFGmwUkiIKbU0SQ4loiWMKaAhGTtCnjIFtB5BKwi5QpUCR6SpdBpqIJQTtEooljKt YOlnRlCzOvVVaFRSSsVIwpicGUyN5E9RQNOJFCnwPS1MkGUQKgrygtoqEgnIn5KLISM0kkJO qEUqioIuHaGk5NHQQkQiJaQ1UFPoNpFMI6agVeVJqLcKjitn3sk1KZRKKFMHEWrocAkl9iDE CppmVIUW41TJpCByglCJUAaNLofYge5RS4RKipVTyDBKgPpDuRhLqURCiBYZRTMuYg0sUyiS mWokrQiaJQE6WCNn5Fe0+IkWqmmKSMggUoWqDEZABgMtvBOEKhZoaRAfas9IKkkjE6qaod2L 0EG3FwL9aMgW2p0QeJJka1BTUoRTITRnkhQOqGKkgF6iUoSqZALp0+o28euxwwQDI12MXMrY K4oW0hQSzgsy4AJBoZHT0LNqz2EakYtQ7QrhQwxUKWAtTSt7eXmlpaV5puiYe0JHe0FoM/Gl lGR4iWgEEa4p8z5RmKCSJjPtxio0EPgZKChpBjQIrlA7aMkUKXIgNCIjXv+YYWHIR8wNRKxY I6IZ0dMkUpGkVV9pM0yQR5qRD42tVElhAxFsBQPdk9CNrZBDQLpIXQkKukfcmpVc1/irEqHm CMUQm9A8IjaCmkdHduV4sahzkcJRaCqFMb1KCkeFESqXKYStB4UyC1lJGajoPKDQ0EoNjGMq lUkJsI2Ekik/U+jv+AJ5wktMJQo1MtpTqFam686HsKa32AJ00vL5PwBbGMOXFWbw8SNmjlqY wr/26CwG3uEn2V8YbT6rwWDraLjO5IkyVDLMOklFJWNuMiEtx3xhDYiKDCcwC4z5jAPjKL/5 HayPHDH88/oWvkd4kk/4jkZ86Wa+zqiHCaaHWptj1lgHzA6WOmLucI3L1ulDXiZwhHaYDWbP fNMA7lYEzTIYYwZQL1OsLdYRc4CzcxesB1w76+TqyrUxhFYxwyyxTlhnOHd3hfOUD8edOdVp w/xfPswWzphuWDc4p/lifiKYc0AoooMQHYloLKLxTKIBEkTliNKIZiKaJZYrUkAOonMRXYjo MkRXJ8LZBqxDdDuiBxE9jug5mUIkA7WIXkf0HsxDKvAQ0eeIvkX0D4biuAJecENE2yBqjWgn ROE8JqNxZ0Q9EQ1ANBTRQXBiSsRHIjoa0QmIJiAqUWsS1LgcURrRTESzEM1Ra5RqfC6iCxFd huhqRNchrPGRV//qCrhTzm9Roz+hPIgNA+bXUP/2O/Q0MxQHeKsI0P9TavonFIeoYk9Vde++ dWUw/N+nxt+kljBaPLGeWAg2AIuAURwHV2PMidt0LAebjxViq7ENGHsuC7AF3LWIu67jrltY TYEDew8GcNd0rryIu+7lyq9w13fsFY/krlu4aw3rO54he8/HuKsxd7VAz67AsUnYU+QJKfaE LcEH40OYEjAUDIMlsD/OfCcS4MzvdrhhlmAiSAAUUAAlUAMapINMMB1kAy34HuSA2SAfXMej GIsD5usFGBACIZSSeaIUDlKAHOOByUCF6YE0kIYZgAyQgRmCaWAaZgRmgCzMGMwEszBTkAvy sDbgGriGWeCRULe2zCkszByAeVIP0sAcybgMlMKxeGAT2AqLfwTbMT7YCXZCW+EwUxmBBWAx KIStVoNSsBFsAztgud6XrcElcAlKUwtqob44HMcYn4ZPx2fgWXg2rsVn4rPw7/EcWIPjFE5B QElwJYoOgNm0kon59S4ANAy2YX/2dB/AXNrSom2rOsgNaGBrDF+Br0c+YMZdga/EV+Gr8TV4 Mb4WL8FLYSR/Pi6OBWAd8Xx8ztek/GpZLp6Hz4b99JGVMcQNh9wmYzycxqdiZvhWfBvWHt+J 78RsOP7L8SJ8Lj4Pn48X4AvwH/CF+CJ8Mb7kq2WF+FJ82X+Bvx1mgk/AJ8M6DZ6Kp+HpeAae iU+BLaE38fH4eI4HQBpD/eGsZAA6AwI4giCwBxwGlcxo2FyM+aSfea4NAL1AL4iE3WA39Ooh cAj6+Qw4g2aun+GM5gt3XKEoPqOxcVg8eiaTCkuHMToLy4dRWYitxEpgFO7GDmPHMOb5LTAW gAmAqAdOwAVy9gTeoCe8MwXtITUDHSBtA6A2wBx0hNQCdIK0LbCF1BLYQWoF7CG1hjGNgy7A FdKuwA3SbsAdUmfgAWl30APy9gI+wB9eSeALAuBVAPxAILRgEp4IqRpXMf8zGcuDLwybA18A 5pX5sGwxfPGw41gVzOQXsBqY5+qxB5glsl07aN/JcE5FUY2w3xIxTFSvb0brt3GnwyrG5W02 V2G2UfBqzRbbDiW1tgP1jdxyBuU0mAEDvFhrGwCLfHEABCakkb6eexse3kkPI4X6xu76gA+0 /jjgF0eRo0iPViV2JQ5Zdlgweo3AErhjNQptnUOYF+nYihnf2jG1Q5uFu0L2Tc1LnTFi4Q/J 5wf7uRRr270jtbyT8K9HMQ8HOG4x8GjHJbfmRQ7o23AtZZCZYB1p1iwq0INCZc9BQvJi+PpW +LgwQTvSirkxtDIdQzEbBDnRF252BNakJVNsYGXST6NKEMLtr0xGCcwhN1hqbKUfLRGm0ZTA nrRlCkysrNkCoi8Ft4mJUhHaOAg6k/ZMNc+qPVcdDTfZcLucomQWxX3DSIcOZqSPwJv0JdG/ cR3MBMytj7ePX6Bf4DgyqpWwMVGCDmQ7dvw2cMMjjYL7Uw9isFzkKXAnXdmBnHQVaChmr8KO FQU3/FJmkw4H1QKn1lYBehhPC8wxWG6MawHANlftXneumthhPG32tlzN870RL25VmB9NEpaX iu1+LWus8tk6i5wdO33uteQbPVebH734OP1l2obpiuCji3aYHZa8li2uKo/ssXVQ7zf7//Hd RFt8zTuvZId1DaVFGzqdwe/MGBZ5t03841C76YfMbvY5vfdWbvnEzEkCT97ybKtNA4lfBGqz MT2q0319llgutzx0U+K1pe7u8fy5bifmOOYmls+MHaPQHA3e4pz7XZVFu+A1sx5GVxjLTzad GnLjkEHbpU5Tr4V0v+iQ/niNoPJFnVPHayf3DOxb1GliscOCe3Fvnk59MW1rAih4M9zk5gWn 0ZuWVG/PS93+9LDZq3vDrxa/lxRvtw7ak1tRhvMg8Euzr5HZV0hffUOIWOZROIDvQjqTXXX3 JMix4fYTCpFa6ZkK7c4cGDD7CYQdeysAPvINSX14wQFGhjFlnfm9yACyZ7FvsXcOyXUXqWSf 9PZisdIaKn3DPGErhFT7bnxT0lgnBc+QbMMUmjNj8WEE6EMJ4X1bPkTmuo5kBx2+eVam0VFh EGgBPQQ9/Hw+iwpedjY2JLnxYezxfnaC2RnL3QuPareBWrth1TvzY+W3DF1L485ULbKq50ea PRvY3QsL2HmvclFEUY1TQruGPv6OI5SCrBdzAnL33L+/FGs6H1MY0fXS5u4RmdsPCMNeuf1S X3k17kaZ+/ch+1btu3pnzMcje09Nf3PedPXzpU3ul4MibW0Dujf0GQJj+COpxeu5ODZ74P68 5oprno23nlFcUWre53H8b4mML8ORDGgdjmP+5qBeZA92UOe/GpSpo1R/GZK7R7oMunFZkjnL pl+i5rvpJw+uETl/7N135dS2ARbdYtRXNd2lHyIOERMuGzcW27o9iRntKLzicO3eTz7Jp5/d KPWn5tsuMt0f5TBhaqLfRL38/k2pEbeiskqyiVXb8yaUGDb8RjY+dfIfFm78y62fO5+sjXmQ 3WdfZKnHFpD5smTLPL+mNXXfTdJb0zv57tHCY03n4htD6w2K+z3KHiVf7/Zyf76Fy5OC6/rF OSOLpgwxNCPtqyxWJzc8iN3O3xy6fLfL/YL224LvRimGXvZbtU8htt9T6FHWuz7jUUpmY/s6 5x93PFsedSDUY8nBjC1NNZFbXenp4Y8DHUomta8bW9ZVcgXL6muRm5XMhWQVmX36vxmSps0h iZMY6cMGowfpRroUOxd3zXH6VjDSanUPkRCFX3sUfgyLP4lA/WN/KwJ9P49Axsu56cpfIyIB Mf52RqWWPPnhUMfC8h+wE+XV1T+/bnPlY+PwYz4JZNtTb2jbmoU3J64krHZN7X9kZPXM+qwO Mzd2X5RkNeB91cFlYbxzK0aN15szY5Pile1I266eL6XzZE4NZVXtlzwxpY9J0q4+Wp6QW6Fe 8PtsOrPL1tJlU5buaihwnTzcU2M7KOzX5/vMiOjatOKlWpH0g9H5/OeaMqMVVxvbxjgXCb2P ZOI7p+QcKTkxx8kj/aJf6k8L1RMaD9UNa2fc5dy9SzW+noND2wWbx2d2/Xl94rPC88pHIfWv zaZfvzi1NHWytGLliIGkn+Oukh2dEoLdr87f4mYw5YrNnglT/rVqvaIpePaPpJZvCVPAOzYF mGMV2Jzg4Ly2F0Peih7fCm1tMT7MAEpdbJtYOfVVKDNUzOE24SJyJQSBgf6fHeV5ChxIO7Zx u68e8gkcyc6sm2xa6iMVCpoI09AShUpKZzDpIdCfFAhI0p9LD96kgHkADHv7vyDRX07leHmF si7oZYSty5ql6XHkw5LN87pN/L1pybDSA02rSoiQqaNKVpQUxHsnXwwXZzzdlloZ/evLRytz 7ArWzErccyo5M6FLrX3wTXOw8H7hyaM9EouKJM7LL/TyOGq6L9a5YkC9cUhAocdml8BNjwfP DL87y7ysSBYj3Kaduja+R9qwB8v3ioOKRtoJDLtar9lc/4O7TV3vZSLr+Fg9ao29f2Ruw8Zn i/GfbS8fjem/Z3bW0V6PoxdHbP+wMTOFjthhc67QyMURG7MgXupfNtTSIHj0x/Hv1yUaG264 lD16zLP9QXHts9P4v749sj1rSdPO6hm1GzupJgRX/fTcsNSJ3KP/feUeIs3q+1tc3thEZq8n s0uYuAT87CIye2mWxfgLymdS1eouo6Zb7x4+/+PZtar/ef9p/wLjKCssuW9ybN6rpTZ+Tw6C rlfS2r6aEO+9ZrXJ2RC9H/IKKnvVOb58PmaRx77igWcSnv3xz3NBQeM294yWNnVN6VN5bstN vak3BPN6r7FQTiprshxhIz32x4W+d9uOI0Y8TJiyY0vHM+7+3XocodZa5nczF5U2RNs1OlbW tnsVuU3e19vgg7bD778lycxGvS1/EXm6vP4k+QchMMqzX+Laafg/7PH1L7Ju8/aOf73rxpkx T6nBpyOj9+/luVh+XFD73LBg+sGlp7b6e9zLvLcp7W5qMXZhUp+KSz3zb4dZbvKbZDvpmt+d Gjv+vU39+WfG+QTIh9uZJRwwLpl7+R/RfQZU28VsUF6z7JW7SLNm46VimBXOwsXBHm5hMMlk +YhjmOnWtlecnk2TZUz/P5EWSG/Sz9sHLu24RbyADPT2427J7A2fLhusyLbslsN4jFAtgcsB Go5jgaYRuOEwiKTEKQq5WCeZ8bck+5aa3nDQL9TsQjqyanRqXSOm0AKEWZGMRBsD4stsYsZk E0OUTU6cI+b9dOtjyMinmcdrunZ7m/qL48dqt9ERVSsPaHf7ZfTATm4y/Ieo8sD6tw8qKmp3 zS0sMXhnvl8bWfRI+3O5xalNx54mz5ofZVs28p0YzK5oX6OVYKHp/d5YBkS8F426/a73od/8 d90SGXQJmhzqO/B18vYBb7qrHZzOhnd0GLU/suhy6QWrnzv2mayf8nKJY7+J4U+OVS4XEwcr fP8o6Vc3Zbe918ENN1+vvbXC0bwpVhAWEzB9R2z9vcdjM7ptbXDzatsnID0kfMZGyb3pTpIO dUMWnkzvFzlw7YhZsxetOJY05aHR+xzetLfLJwe7b0xcdu5Wj3+5453MfQdRb4Itd7zItbN3 jlScg/jjlWqBG7SH89fW4rz/jBRjqW/EbcLbwYDBeTyMh7ap9m347fnW3X53H/rdGVX0j7+9 LXbr0P59RWNUNtmxuYs1zjd1MMaiMA3csvfFwkgTtPhBe48BpHnzIkuP5MHL19LZGvEQ+o60 +7jf9aO6Lz3tcDFbffGXw9mvOz1ctDR3x/WCXvi+ORZHL3vSNAjaG2r1dNTE4BPeK0bUPhhb 5YIVqto9muy1bNkx3h2/8AeGXgmC5X8IXl0P3359RMG5gJvKsIB4s2BiobvNSoOm0Ys2Xxqa V758iBP+vaLf4x/qOt50P9hu9wrlu1OPImZ69Jpc7B370i/y2g95oc8UtvWu4XuV1dNkik5n rq18dzEuv+GSRxE28err43vWTKkJaoqi3ZLss7uXaO6HLjOwmWNDmAxQ3XrW8LhnytPkysQo +3K/Lb+dtZ+ksuqf5XD1u9zEfDuPDVVnhTzeTaNKsGvssZ0f6hKXnn1nFDSV1OoNgCnNk01n xkLjhyQ6uo374qTiPyVtMPnPT+AtgBnQ198P7Zr84TYK3voxt2R23r9FESgwK5vr1z+1dRks Rx9hy4gYNUWMkMsyXP9yvXRiY/tLJjV9EhY8HRvttcRE8bpGekQ296jN+aj31ZWXJfcSDkyk TXh1WyoehXR/fGnZghXC0U3k082XZcMSr0zolj1/S21KF+C6JL/kYF6fm6NGdl0srv/pzGqs 3mvcitg/OuTUi4idGT3u77KYqBp2/3yngmHrDG+J4wNWRIiG3vPtt9jb76C5geqOfMr6g5er 9zRN+NF71J3EncXDD5jIeISl5JiH9e8F2VXzPH8Ch/WB1rXDRseqmfNPDxcTV9fPVBe+p6NT R3Y4sOfxoO75e7tF7PytrHIoVRIaePNG4+xut51XLossLad9e6vNybZJxOm+ucv7OO0SuWdT be6m6IX6/Hb8ZogkiF0vacECaJG5X2xsWrLEjin2kllPTjd6ds5JXxLuuu/FqfaLvpESNzOl XfjZa8ns1VlfTS9r6XX/G4nxy5XEEHZX2JcMI/sU9y4OyglstSv89BNjZbKUKfXiPmdXezFR wQTFSO6sJqLVLjWcDCVDmnepeI73Nz+IRmwp1Rf86K9tF0FwWMEvzgGxj4pFlyX1Wy+PUT4b /EwvJrfdidzKg2OEoaOfH2wgeYmZj+bM2ZpuM77m6cbwimNzP/z4zwvzG/NyjeJ3G09PlV7N OMkXvVkUvfq22ObV3LyhsgJ55W3DoC15I/ONK17V7X35+k3wFcEBo13PxpWPUE4KvXtgWvXD rh5V8d3vFO/e67Iluuuu1YvfBK5K8hGOGDJVVX2kQ35kQ9yLf/4rRHjB3zA4PiXnUpnoxv0z MbuJgDJLu5lF2T2j3S9ee3Db8sPZiP4173pOaqT9ap3TzO7tvqB/vIE2Xb+3dvvDdGFBuMVE jTwhZH3F9Qhh+qBDtWN+iX5cfWT91v472jgRJdumeN0RaPlHYSYtwwEgs/f9x+TLT3J+ywl3 cfZ10rp5nnUBAgOeHmrEzL6c6414AtPWh+pQ9JY7E0EbsnVtO7JLS0e+AEath/2u/pq4gYNt HJ7xt0pohcWa4pdkYqsupoJYcnSxR5bb3//K6VrnrK5/43sVny8s+VqA+QcMACtSd1uUH/99 hmPEnIOuSzVD+ddfL6oVLA3acOJCj7pXxYk1gpp9jeH9HSKchl2vmDvgzMCR/g3mpvWn+CaZ 12UJo8Pufp9MSANi9NcPuql/de2LuPDzsyxnefciV5/pLos7Y7p2Z5+wJ+fH5j+JG5e8wlkt WOTyr31HKk+c423Xmo2KD3wyf+2jGxe0gucu7yJX1LlVPV8/6deZQXv4J6PHThqVW/bL8ez7 wxzfx66zHfVhHVkQnbd10qDYqsQdKrOcbbL4Z+/PiWxqOsf+uuxkLhVrO6nbnv2LZPcPfrdw +7qBZxdaDQpZP+ex6MaU98nuh/YWlo66b78yJUQ2pP3YCzuSuxW9adT7uFYLV0ha8L7FS/oC LXgMi+4zkE76t5xxfuVk1VTfkBUAh5mleCxp0xpvJi2f9AAIt+YaPYE5N+0HCgK9/X0Dx8GM 2wpulnwLlwSjhPr7tnEfQy9fV63qkP8VCEzMtv3p4ibhAh+7Ffu87IvWP1u57smEukWn41In 9+4w9o/n1+RbSjqOKD+W9c8XRqmrKoPe9ExodE0+/sJWeYHeWnooVF/17qpW/dwaI+Inu6qG 2GEu+OwlB55uPVrUrrO7f83Eq/MWPq1eNd7tzvGbxzN99NoeKvsj/Hrtx4Jek13iFlvHvD13 Z8fcPhkTxlZbzMj77daBbddtn5NTx92znTETT5+GnZul3/3N4cMLLUzK+u3v4vhD4Js75ff8 wcLJUzyH9RykIVw/dNvmGFAeWn4Yq+2jfruTjwWmSrU34g74vOv98/HJwU5h/eZ5zby34oV+ aoahaKNDoKKjqX991W3Ae5Bp0b2sYV4h9v8A+PSaGA0KZW5kc3RyZWFtDQplbmRvYmoNCjk1 IDAgb2JqDQo8PC9GaWx0ZXIvRmxhdGVEZWNvZGUvTGVuZ3RoIDMzMz4+DQpzdHJlYW0NCnic dVLLboMwELzzFT6mhwjbgQASQspDkTj0odJ+ANhLilSMZciBv6+9RjSNVEtgze7M7MJueCrP peomEr6ZQVQwkbZT0sA43IwA0sC1UwGLiOzEtCB8i77WQWjF1TxO0JeqHYI8J+G7TY6Tmcnm IIcGnoLw1UgwnbqSzeepsri6af0NPaiJ0KAoiITWGj3X+qXugYQo25bS5rtp3lrNL+Nj1kA4 YuabEYOEUdcCTK2uEOTUnoLkF3uKAJR8yO+8qmnFV22QvbNsSjktECWIoggR87mYotOiWR3W gqmnpUe89gy16RlRxn3wiMEs8sEUr4Qvvv+0lnnDJHNatnciTtmCDh6d71tjj62x5OIsWEr9 R6Z/CkYPBXnMPI27Enzvu9wxj04exYgOmWfG9+Xd33ZLsY5S3IyxU8TNwfG5wXUK1uXSg3Yq 9/wAfqm7uA0KZW5kc3RyZWFtDQplbmRvYmoNCjk2IDAgb2JqDQo8PC9GaWx0ZXIvRmxhdGVE ZWNvZGUvTGVuZ3RoIDE4ODA4L0xlbmd0aDEgMzUwMzI+Pg0Kc3RyZWFtDQp4nMx7CXxTVfr2 e+9NmjTd0jZt2sbStKEt3ehGCxVK05VCge6QQsEWCpRNqgVREYERBKqoCIMbQmUUFRHSslhA AWUTFcfdcWZQq+M2goqjla3J/zn35CCgzvL/vu833708eZ57zrnnvOc971lSI0lEFIQPDVmL qocP29E2OYpodguRpXlYUXEJxcmvEDVtRak+wyrKq49k/7ARz6/h2TGsurZg37DPTxBN6Uuk H1FenZox8+mH5hFJTyG/YfLsxpaQ6dFniXTriZS1k2+aa93y5tNLiJLq0aB+asu02dKD/q8T +aQReZunNba2UCTZUD/Kk3HarFum1q3rP5wotYkoclLzlMamDw99WYT6k5Gf3YwEn1ES3peQ T32bZ8+9eemkhUlEsp5Ie/+sOZMbK4c57ica+TKe757deHOL/37zMZRfgfLW6xtnT/kqYMlX RFU1cEJ8y5zWua5ztBrt38byW26c0nJD1gdLiUylRGYTMV8BtulLbr8uYMiP5I1mcO371JLI +MDLvxvj1rkSND/oNuBRTzLxC+9oz/SekSZqprp17hs1P6g1XX5pWIp8hBpJS1ZomYyUSlPg mdvUdiVSNFOl+5Cr1z6szUSVUZyVN2mqTHpv2cfLW5Y1iqxpJ/lbO1nLRNWjqq1WQoL1Hm6D rl6WrUQbWZ5ySmthPSVJM5UcADeGsYE2UD3NpuN0ggpoDm2l3bSevqJ0mk85yMuhLUgdgxIa SqEgvDGHfnkZ3O+636Vcz13v/uBXyhDa4PkF0FG/yN2q1oyIcPui/RR6yZM+H/fVV457PWrY 4P4ALW3xpM1R3x+D+5fX7F+1h2gZtaOPswmxTBOht9Id6P9WGgddDzC/7KY8qAKKpRIaTI// ai2sNwm4c4EN7u+piSb9otS837Dh5/5vpRlon9Dibkpwt6opLZ7cdg+uvOrdTor3pB/3pLG3 10jR7hGwO+9X2vllLfyqwL3yqrSV6v3LtFLcJGVRlVuHzwr6HGlV7kfVPD08ya6JHouOe3wx CbGU8xtt/+Yl7ZS3Sk5VfkPnpYelZTRY0uPeKnXiTqVXcR+W1iG3GbV/QzmSVapjoHsRSwyt UjdGaJn7IrslWVqFO1nSqKjjpWkTfabicdqJexXGnc2Lr2ggrGdxlYGRWI8ZwGbFNrQzD7lz PJEWBC8nYQasc892Y81zb3EPxecBeKUdfc5DXUtQeiV0Kz53I5LaEUmrEV9JapRtQJk2NXoG osZ44pH0i8uNGGa203n6lE6q9jELfem4uwdtsDmyniywcI7HvnrUlqPatwgrTA2NwIpbhfQ7 oNkIfYX+jPPMZrbu71Zb+R5lNBAL8JkDLKOFsPJ7MtM8twtP29SS0WiHzxA2fyZiXiSgTw/S ctQ4Um1nM6ypUT30PbCB1662c4enQ+PUz3aUrodP+Ay9hewYrXb09EVJJm+074t7Bnobjroy Ye1C3MxzA6kbXoiFcqj3clqD0UuiTlg+D95o+s30E/BfjxREb0ux9A2W44vwxGZ2Sw7Ez3+e a7p0E3q8DHG4RuVl8PtKelNV84itjr50A6zIhe1s5BOQWkX91H4rHlzj2TH+jie2U7wHD2Df o/5YuzXkD+VHMeh1AvyfRUWIselYIW6C39ppjzXcGmmdZ73dusS6zHqP263W5Ic3Y9GKKN9I M+nGK8ovVMuvQnnJ/SPaXCqNlQfKA92H3IfQlnq7J0tHusu7R33ih9uX3R47E//tiaxIDXIl eUmn1Kczv9gZ2V7I91GZ/vklXVYfK/60mrhIWnxlMbnZw7P+aW2630gP/hdWsDheipG9ExG2 AuPcRnchbRXdgzXnPsxunEZoLf2e1tEDmBfseuRf1vjfvpSrE6QZQPP/sjYN7ScWISxyDf9R 1P77EfvvR+t/GKn2YU3XTZxQP35cnaO2prqqsqJ89KiRZSOGlw4rKS4qLMi35w3NHTL42pxB A7OzUvunJPeLi+1ri4kKMwUaA/x8DN56nZcWpzSJkottJQ1WZ1yDUxNnKy1NYc+2RiQ0XpbQ 4LQiqeTKMk5rg1rMemVJO0pOvaqknZe0XyopGa1DaEhKsrXYZnWeKLJZu6RxlQ7oVUW2Oqvz tKpHqVoTpz744SE6Gm9Yi8Oai6xOqcFa7Cy5qbmtuKEI9XX4GApthVMMKcnUYfCB9IFy9rO1 dEj9hkqqkPsVX9uBM6ofa9apxBY3NjkrKh3FRZbo6Do1jQrVupxehU6dWpd1OrOZ7rJ2JB9s u7vLSJMaknybbE2N9Q6n0oiX2pTitrblzsAkZ4KtyJlw69/C0OUpzmRbUbEzyYbKyqouNSA5 tbFGm7XtR4LxttOnrkxp9KR4xRp/JCZZFy+5CflCE2yDhehfdDSz5a4uO03Cg3NxpYM/W2mS pZPsqUl1TrmB5RwUOSG1LGexyLn0eoMtmg1VcYPn303NYc7Fk6wpyfC++i8W/5BvdSpxDZMm NzNunNJmKyrifqtxOO1FEPZGT1+LO9JSUb6xAZ2YztxQ6XCm2lqcJlsBL4AEKxuD6dUO9RXP a05ToRNf2DxvOVOLi5hd1uK2hiJuIKvLVunYQ5nujzsGWC07MmkA1TE7nKGFGJS44jZH01Rn VIOlCfE51eqwRDvtdXBfnc0xpY6Nks3oTPgYzUWrLapvoW9XlRaFWc91sXqrQ7YodWy0kGAt wYetYAgyjBgu9ZGNaMEQq0OykCiGVjwlmLqiHjwosYWlLEthrxaWWqLrovn1T0yyeGzSxjr1 l9VlRMIlm3g7v2kaL80MSrAWTym6zMArKtV6DPTU9ut2yswXnobxhp4NZ6nIUmIxc5Emoxo1 iY1imNVJFVaHbYqtzoYYslc4WN+Yr9XxLau2lVWOc6ij7YmSmiueeP6gS3ke5ZQLEYAlSRYx purzMPX50mPpVdnDRba1TW8rq25jNds8FZIV0wc99oob3njXoKABmJclWNpsJY02q9Fa0tbY 5V48qa3Dbm9rKW5ovpbVYRve1GardgyxqKZVORZabmVNBVGZVFZTkJKMhaegwyatqOywSyuq xzn2GLGNrKhxdMqSXNhQUNfRF3mOPVYiu5oqs1SWyB6s7IHVVIUHvVressdOtFjN1agJ6vPk LonUNL1Ik2hyl8zTjCJNRpqGp9nVNHZhhMKa4V+stcXWJjY2t9U1tzXUsZlFoRhH/JOckm0o OWXb0A5J9vJ1GmxTCpw+tgKWnsfS83i6F0vXISqkUAnOYQtSW4MNixSiyUEWicehwqq0drnd NY7oE5bTddGIs3pgnMPpnYSFXxs7AuWGMTQgeZhz8eRGZgfVOti7utjhk+sQs6JCFBnu9EYN 3p4aUKJEfYfFIl6ajLHBAKrvL8aDc3Gdsy6JNeqYXqfGstFJpbZrMey8Tm0cayi1ri3IlqFO TMwDQ+xyRt6wjaodPMWCRzRWx52k84Xlk23Imtxghbc1NLkacc4XUoOFp0zBeqiJm6LCYPFk EuuWEuvjZ3B690eF+Me0T382H7Wxuro6brz6tNxTAG0bnT6wKO4yV3pegHeQNZzZgn/LYSor +iKrprKLqmw3Y1lhRqs16ZDt9Isd3oiVn7/vgxTbIPGyni0QPp46DvNUHeu5L/yuxNZ0uZ+0 3RJ92ZWSbGM7AwtMsuxBYFNd29UJzvFJKcn6q1P91OS2Nr3fr7/A/aX3u8Qs0VqMLYP2kFVW dnqHSSOsXbIshCQEeYTkFsIlRK8QF4Q4L8Q5Ic4K8ZMQPUL8KMQPQvxDiO+FOCPEd0J8K8Q3 QpwW4pQQXwvxdyG+EuJLIb4Q4nMhPhPib0J8KsQnQnQL8bEQHwnxoRAnhfirEH8R4s9CfCDE n4R4X4j3hHhXiHeEeFuIt4R4U4g3hPijEK8LcUKI14R4VYhXhDguxMtCHBPiqBBHhDgsxCEh XhLiRSEOCnFAiP1CvCDE80LsE2KvEHuE6BLiOSF2C7FLiJ1C7BCiU4gOIZxCbBfiWSG2CvGM EFuEeFqIp4R4UojNQjwhxONCbBLiMSHahdgoxKNCrBfiESEeFuIhIR4U4gEh1gnxeyHWCrFG iPuFWC3EfULcK8Q9QqwSok2IlUKsEGK5EHcKsUyIpULcIcQSIRYJcbsQC4W4TYgFQtwixM1C zBfiJiHmCtEqxI1CzBHieiFmCzFLiJlCzBBiuhDNQkwTYqoQU4RoEmKyEJOEaBSiQYjrhJgo xAQh6oUYL0SdEA4hxgoxRohaIWqEqBKiUogKIcqFGC3EKCFGCDFciBIhCoTIF8IuRJ4QuUIM FiJHiEFCDBQiW4gsIQYIkSlEhhDpQqQJkSpEf/t8pm6U8qPmKPlR18v5UdNTmmunpUytnZLS VDs5ZVJtY0ZDbWpDXoN8XcbE2qhxB8bJLeM+HiePSamtzauValKqa/OqpYPV0kb1X1VKZW1F SnltS7mUWi5tLJVaSqWDpdKcUsleKpWkFNcWpRTWFqTk13bJSmefmChsj5ykzsh+IFJJcnNy cerldJHThc5rkkDnOZ3jdJbTT5x6OP3I6YdOSyroH5y+53SG03ecvuX0DafTnE5x+prT3zl9 xelLTl9w+pzTZ5z+xulTTp90RgwCdXP6mNNHnD7kdJLTXzn9hdOfOX3A6U+c3uf0Hqd3Ob3D 6e3O8MGgtzi9yekNTn/k9DqnE5xe4/Qqp1c4Hef0MqdjnI5yOsLpMKdDnF7i9CKng5wOcNrP 6QVOz3Pax2kvpz2cujrD8kHPcdrNaRennZx2cOrk1MHJyWk7p22cnuW0ldMznLZweprTU5ye 5LSZ0xOcHuf0B06bOD3GqZ3TRk4bOD3KaT2nRzg9zOkhTg9yeoDTOk6/57SW0xpO93Nazek+ TvdyuofTKk53c7qLU1uneRhoJacVnJZzupPTMk5LOd3B6XeclnBazGkRp9s5LeR0G6cFnG7l dAunmznN53QTp3mc5nJq5XQjpxs4tXCaw+l6TrM5zeI0k9MMTtM5NXOaxmkqpymcmjhN5jSJ UyOnBk7XcZrIaQKnek7jOY3jVMfJ0RlaCxrLaQynWk41nKo5VXGq5FTBqZzTaE6jOI3kVMZp BKfhnEo5DeNUwqmYUxGnQk4FnPI52TnlcRrKKZfTEE6DOV3LKYfToM6QSaCBnLI5ZXEawCmz M6QClMEpnSemcUrl1J9TSqcJS7qUzCmpMzgWlMgpoTOIrcn9OMVziuMUy6kvJxunGE7RnKyd gVmgKE59OEV2GotA13CycIrgFM4pjJOZUyinEE4mTsGcgjgFcjJyCuDkz8mPk29nQBnIh5OB kzcnPScdJy9OWk4aTgonmZPEiexuMIML6AUuAheA88A54CzwE9AD/Aj8APwD+B44A3wHfAt8 A5wGTgFfA38HvgK+BL4APgc+A/4GfAp8AnQDHwMfAR8CJ4G/An8B/gx8APwJeB94D3jXvyrq HeBt4C3gTeAN4I/A68AJ4DXgVeAV4DjwMnAMOAocAQ4Dh4CXgBcBu/sgPg8A+4EXgOeBfcBe YA/QBTwH7AZ2ATuBHUAn0OE3KcoJbAe2Ac8CW4Fn/CqitoCfBp4CngQ2A08AjwN/ADYBjwHt wEZgA/AosB54BHjYd07UQ8CDwAPAOuD3wFpgDXA/sBq4D7gXuMenLWoVcDdgjJBaIhZHyC3h i8Pl1LC8sPIwJcqcas4zKxvN282y3WyJKmkxLTa9YbJ7f2zSLA6W2o1Sl/vgDmNyWgnYbjNG xZS0BEgHAqR7/Tf6b/dXtvsf8JcP+P/R/yN/xe4/tKBEeV5Sf11DknRfR011UlJZl85dVebU V4x3SiucsdXs0145zum1wkm148Y7OiTpnroOSS6scQayPz2qz8tWraLIgjJnZLWjU2lvjyyo K3MuZtpuV7WbaUKRuqTWufNa5yUltba2Skmt8+a2ts6lpP/vL+m/bcD/m4t5HgMxF6MAMXfu vKS5oKRWkd9B7C/O+W5ZoSZZBiSAqElyAy6gF7gAnAfOAWeBn4Ae4EfgB+AfwPfAGeA74Fvg G+A0cAr4Gvg78BXwJfAF8DnwGfA34FPgE6Ab+Bj4CPgQOAn8FfgL8GfgA+BPwPvAe8C7wDvA 28BbwJvAG8AfgdeBE8BrwKvAK8Bx4GXgGHAUOAIcBg4BLwEvAgeBA8B+4AXgeWAfsBfYA3QB zwG7gV3ATmAH0Al0AE5gO/AssBV4BtgCPA08BTwJbAaeAB4HNgGPAe3ARuBRYD3wCPAw8BDw IPAAsA74PbAWWAPcD6wG7gPuBe4BVgFtwEpgBbAcuBNYBiwF7gCWAIuA24GFwG357HMBcAtw MzAfuAmYC7QCNwJzgOuB2cAsYCYwA5gONAPTgKnAFKAJmAxMAhqBBuA6YCIwAagHxgN1gAMY C4wBaoEaoAqoBCqAcmA0MAoYAQwHSoACIB+wA3lALjAYyAEGAQOBbCALGABkAhlAOpAGpAL9 qem/ODn/L1x1/20D/s+uMCLdLGWgq/6q3xFU0VRqpTZaR5vpXUkvZWL0W9VfHG6jF+kV+k7y kiKlkf/LX1dccWkt7JeZ7m9ci9wX3Ana712fueq9zG4v7ftuk3KK52mXka9rqrvHtcj1gTtB c8hV7yavqe4E93eynfSiBs0CCkLaWe1U7TLtU9o30S/1t2jqL13/02sUfHAdTYEfZgCzqAVc TxMIM0j9BcgN8Mdc9Xcgt9ICWkjX03zw7fQ7uoPupBV4bkUKz11ES5C6nFbSXXS353c2S1By mfr7m7s8KSvB96plWR381zn8tznindW0BiPyID1ED9Mjnl/qrLnslzo8fT09qpa8Mn39Py2/ gTZibB+jTfQ4Rvwp2oJx5mk/pzxDW2k7dSB9k5qyjd7F3U0uukAX6Vs6gzgxSEFSBKIlVxqF NWMKNateqofXrqd5NAf+alXtWESL0UPWt4WqDxapPmP+4VYuuer3ST974H7V/gdhBbNrDfrA 7Oe2/0FN4/37Ze9Y7hOX8n+t/5sulXkavXVSJ+2gnbSbnkPPt6HvnXjaBf0kev+0xyPPIscJ r/Cyu9TST12Wt/0XuV20l/bR8/QCZlIX7YFinyJtPx32PPOnF+kQUo7QUTpGr9EJePx9qJfp VXqT3qK31ecP6BP2C1P6iL7AOJzEmHxGn9OX9DWdQvq39B2doR6M0UWM1UXMXDZOKRipcMzh WIxWDmayhhxEGn/MJYV0FEXpVL+HbNJDnSkBfuzvmEajPkK3Hwu4TMFY8vU4zWbajRrZ70Gj sV/4QxFeDyj5AVZ869vZT7NOKqS83g97X8fH6aCc1NNS6snu97qNZ44G5qR2H3mnOz1NCowO VGHyl206W3xmRtaA/rLNlpWZ0UeWMkJDWHpMfzlrwFBZ439xhOLo1cgzrQXTSjVNXjfelzhy pt2WMP2hKemurth0P7M1KCjK7O9vjtJazn+mtVzI10y6sEH+MqU2P37DxTtSSjMtTZmV03q/ zoz1lAsKsob5s9/rzUavI9FrXwqlfnuIpCM7jX6Sn6lLetxu0JuDP/XON32hR39O552WjO91 vzOBWW/y19iiA6OtpKhWB2ZaNZGR9qlrDy90fSlVShlSyr4BM9pnPbRM2i+vn7h17S1jUrUW 1xHX9rsO3pR1MQTtYk1SzqJdfzJTf9buoR0BepOONeunDzN95uWlD+72zmct53EfGt87c8T4 Dm+dtWkLjIavdAPiWPsZ2crZIbcfXrGpU2pp7FxeseeJtbs3b3hGuavi0dtGuZK0lqSGB+fd fmfv521oe5n7G+VFxUVWSqWynSEh3lIU+5udX3R8l/SKPcA7zfc1yUoxUkxMhLUyBF+G7d6m wHMRFck9mirmiN73TufBpAlBOdwlh7tzMLDpadHxXl580LIzM7NgWIyXLmuowoY0JFCxxfjL IaY+MmwdqMRqqp6eMObW8tjuv75x47zaJxzRleMmZYy/f0r29pP5E4dEBvWz9x/yyJjllaXJ o5pzH3jGUTczzvaorznIp1/VbTW9I6XjEWmFiZFZCWHDy9GjdvcPynnsEsEUR+kdPlH7cAbU w6k77EaTPt5XG94bUOFdHfO2XosOZJ5WXQrT30FMHvY4NCYuPjAzUJednal6FcMKe3WBoaGZ GUNl5Xzxot031u0dv01f9sykMb9zJHeaM0ZnRxcNr+yfOSNtyOzqdFm/8MjK4TGx2hGuBXun VJfesefW8qUTs0LSK4e4wk3muLo1+Ba4wf29slU5T5E0qDOAzOxPq159/Lskb7v3ogApIOic TwV1SbrnWiKlyIjzCoztfW/CafWDezo9LZb5mNi8yMwINYfExXG/hqpu3Vp078hvXO4Rd+5t Gb1iaP7y0sL54wZ0PDps2dDYiHBJPjv/pbay0PDNMVGZDW1jd++yWtkMYNEA35komgZ0+Pqw UNBbMOw77IEUo/fThp03VvlUGqqjftRWsOFXvZcjRj49LdhjC9xn80TAQG5PILPOa3bJkl1z 0iakbF/vNXzL5DF3jE3unDmpYE1lUnPmPRul7sXHVg7z9ZM2n1+wf8bU0ju6FuzecdNc6S1T SBebn/Ng3XeI1T6UQPYdvr4h8M/uHVEhCRo4zx4Skhh14l6NpNEk9n0zosLvXV9dj1H12ml1 1qhuO3PYeLJbDVDJ5KWLNqkjOjBExCozHiOe7Rlx5TtZ7s3buinZUVVmHbajYcne+Tm587bM mr5pTs4uxVrYVJAzsShRKyfFZoQ+/ITON8D7XlN4ybIXb53xwr01Bbc8W1U4pyIluaKl0PNr YI1FuxpxaaK8Dq2+S+q0W/x9fHx1FOob4q/t8QnQ6wMNwRdI7gmsMVRissP0QFiek3k6QzIe Nb73Oh4OnzkC4+HLkJDokGg287Ois4zRGWZJY1k5+Wupy1Xa4XpBKpQ2Nd9+4TPNp5E9ztW9 A+XjqzdLq8NcrezXsRNd9RoT/DiYRlIDrXqe/CX2w/1RUsdz6eSTrKQPVrqkDrvf2L7pYxPS x45NT1Cizfvw9a2Ihkiv2uOiG43BrpLSkqpX9X1LkhV9NpVIJfoSfWP24HeuLa9/Nbsi781r xqjLZB7rQE5q6gRGgTnG04GZRtYZxEt3kBkZqcjAZgBt7DayBS3Us97HIXwQymZtH+XnDSB7 YH9FfPJAiw6R+CBijYmP9VeC1VWFPfIxVFqvicppXj0273prcMTwIZK+bNH4zGtv2bv41meu zygaFhEX5pubGBwZ4pMzbbUjtihCmtWrrF1afUNxn6ZprvPRSWGGLOu15WkDKgdGClYm2iZk j15Sn2kxXZMRFZchG+QY+8ShhTePz44vvi5n+PWZvn2T0sz5s9LMyZmD41hJg/6ei4HD8vuk 50XnXqv1Dk1MSlKi0ioGRdmGjE5k3HfwaPaL8HYMzzHMvzBK7QjwYSuXL5K32wMk33Cjj/ZC SIV/tU8lsdi4at2KtgUOiOPLlI2tXWypDQ1Rjm0bW2nLLRiduW2bV0J5eVXyusflJXNbg1Mr hvTiaOyatiG9MDHouS7E51b2f2chPr2xIuU/TwFSL4WTBnHg7y3h9tL10Xj36CoNXVI/u8GE xbTURD8q5RjmD5MwzmxQT7ChNnarGxPCMhABmhUdzJb8y7Zwqcf1vvSBlHTxL9KI3MiM2JCQ 2IxIDyv19118YvVqLYXEpUdekxEXEhKXcU1kelwIfLMb8ycevjFQwR7SS6d2eHuTYb/0IY4o ChylgaPO2I2Sxse7XDboFI22WuPxFIIrz+OpIx9jCnUzd0mqn3Di0cR/60retk069LkrQY7o /Vy77OJ66VVXOhsP7pNl8EniHtUXgR5fGFRf0LtKtRrml3X+8q5f3lVNz70XfFevZrWOQ098 Mf9SqLQjMoz9t7EIP4lRSixW3O12s59f/1SvnsSK4J6YisjYCEt5RKWfGHXMIz6tpJzUMzno 0zts3Y0OvLRL2S6XmWYeCOwzMFCZpQswB5pi4lOtG3UBYYFB1vi06PZl+oj0AYP7VtaE9c+y J74gv5k6xOYfll2X37tA3j+wKM7fNGBcSe8C5dSBnIp084w5bA64TvbGinhFT8yIVzmUxauZ fFi8+prDZDJX+lQaL+gqqJwdXJjpfBTUjUJYFnhZ4LY/okuuQpA+snGbY6w1t2BU2jbl1KYB xYmBz3X13iAvuekGHriI1np8xzOg5WCKp77Ps+0dy2of7FMGQ7/wcwEVtrPaqp8PauKkdPki 7/FSiGfB0BgKbuu8YdYz8/MEdyaMnF04es6w6ISRs4oYS655h+4aVfi7gwvnvQhecnDphLsm pOZOXT5iwt0qs9OHa6pyHnaZKJYG7fAzWPVsYA1xpO6hIXq/ON+IXmOFb7l3re1tbmIeXySl 1G52rAvKuewQwgbw104h2cr5sqU7p6ddl77tEZ8ybKRXnELSZmSuapf1d768tMDg66rX3vnU tF+cQQ5g9I6z/6tV9WFGhxy0T+qA0iPIjd7BJjlQr6fgSt8LWjF8bCO6cgCZZaE6W7YYvuMb tXXPOtY/5uVUqitsw8pq0pzKqT0zG145tnR++MAxub0LWPTvxi6+D61mUfUeSpZ27LL0tfQ1 wD+7dgQasm17JaK+7oN236Dw0r7xPYHJOmsF1hyt3bDRZ7uP7ON/VieOQnBa73tJp1WbAtns E8dxuC5LdRPbBeJ/3iwunZD6yOY+irKv79CqCZNSpz5T5dg2ZuHNwRnTxxe11qTFT2y/efB9 o6vWZOfXDQwLyZlaVXfryGgpKKsmv38f/yBTe3hEUZ4lKT7RYsosmWhPbBqT42d8xBRssqVa LMkJCWEROSUO1tM8nPCOa1MwJ7I6jH5sZocadDq9OUAfpjP+5GvwK6cLZtNZxXNIyTyhriPv HMaOqO7y6vEuLovt8IED2YoS6DnaHR98e/4Le11fSOFYtly5rS1pNw71D/B7pkP2vV8ypbgO 3O+SZ80MMLIZCm8fU07hG01Kh0xshprID5b4G8wyhZT7lgechz8rL5ug3XxBscX7K2rrmYEm 9ZAUmKkcW++VUllRmfLIhm3brLmFmJ7q5NzdJd/Xe/sXmJu58sNos4B9j0KbesqxB2i9JLLq dYribbhVkZQu9xt2b0Wnr/Ty0rLAyshIzcvMGTSBrWfhqZmZEWEnMk5ksPAKNg8MzpRsBUd7 sgb+cMB108vKqd7mTZvltRfZ/2E82NWgthFGFXY/U3AYWYO8cXYIjwjpcn+8C2eIoErvLkmx Bxj83FKaRvIaE2wyhXlazMzLyRnEv7nkDPK0HHY4M4I3LUlqz3kIoeOSaoom0rUl64Y72ydH JIaHDohMqx5RZHXNOerKvPZr5dTF21ceXTREljco2mvymoYxS9e3y2thZ4Xn1GemkR16LTvz 2QKC/IIMhiA/xUi6cGNYkFeP3mgM8TWzg19Ijb/hbV8Mx+kMcfpTD3+vGz88ys9LxqsOgOpG c+kQeOkMKJVd/ESKEQdBzzHw8KpV8hD1IMgsc3+vSYdlSVTZGeLvw058faNiYqzkl2JNjvHt 8TfY+gWEW639wpMukLanX03ouZCgnnBPsHqMC8qBC3+2UD2esm9R/flXvJCfg1acsVVTPfNR k24tKrRbBk8qjpPedSWVjkqssMZOGDB0VkWq6wMpPrd1c3PWyqGak3o/b21U7oRc52rX8hmT /X3W+hpiRi2ok9au3ty0rikzIICdr1ditp1Uz9fxVMLPL1qyItQNkZHxffpFX2M1Ezu7BBrs gaZSQ5+frOWmc/F9e8ziOwKOMYH8y8xhbK3qnnH10eWyPni6JadedYSRXo29bkDpzWPTXVtl eXxdYlWMdrU4xYhTzfn5vANy7P29TaxDl9sfRZlUsodSpd7OWC9Y3LEr1GgckGVltock2o3m 0sQI89nQ4J6ICt05L6XH8POIGNUu4GBw9MxRvudd6XrP13C+o4QoV/VOXjV+XGKl1WP+uHGJ 1ba4hqxhtzjSJ17dx4rLR2Eme1jjh4fb6uT3r+4rSVIWevYR/0beEezDNkJ/vclHrxiq6Xyw f49Y/zxfJMX3WmZwZqDnG5ry0eAHRkxcOzW7c+5tkUXFRX2iQ8P7N65r1nhd9F+5XOcXzP7f uJ89OKBD9Zw9NDQgwBptPgdvGckQUXOFw9Tt45K//pW3fts7V3hDds2cdOkBNk3ECYmdByyU v9NkMkhBzCz/sDBDhPmaMNPbklcoW6d87N5Yp7wqIt5gK5T4q4q6HRxGMHr+piLxI5PYxnTq 3McX7Szpba++ZWOactMm90u7PiWreuSIZCmpd/T77+P0lFTYP8yg3xIQHJo+Krv3H1gB3m3j e79yCnaFU8oOP4uMU2jH7vAIWRde6dsl+e72C3LrKqRy/h3Usy/kGI9eOrdhAw3hc5zv+8nj muYOMw8eMshkLckfaLTPuq46nm3+WaPSw7TefvptASZ/r5jc6vTeRWxnnISRYl5Jo4pdPj5e KX29mFt8TRbc6Rl9vRIDUphTgvpTYsU1P1nCeqIqfM/5ePcE/BzreSfVFSiHjZ968M45zJ2k 2sSG8pcjmaEaPvCSF5XUrJqRZcn/0953wEVxtP/P7N3Re5GuS6/CHoiAoBRrLKgUe9BrwMlx d94d1RLAgr1XNIqoscYC1hhrjIqRWF+iscVXY4mKLWps8J+Z3YOzJXnfz//9/8rnz3x4dnZ2 9nme+T5lZvZgb+iQ1CTvIZHYrgN6mztZ2raxiRyeHGtD+ScPlLGg8qROwuT2jbtHSpzmm5rr 3R5ScynH0B7teKHdGBZkQMFNTQ3UduTtPJTzvfHK9BqqmsJX1fYUfzf0q7FKAakY1x/Rgo8E Kc+b9/4eiapwq2g90o3xdnT0ZtzcGR9HRx9GMPH1K77R61GOPkI3/SU3oQ/eGzVQryi8d3MB TnuAA0XtRLukUy7WaSD+/AMYdhRJsW/F7on1y18irSFixGchHj4eNp4R3t6JQo+ORTsKeeMp x+CEoKB2IeGBrdr6OXvEDumYuiS/G8pQMUjTRiIlAoRsc/XZS+H/6HagQIKlGQh1rY0ITAO1 nFQiuJ486MWb+o8I9/1YI9UtOC0hwJV2tW7DeHrGhbrFqFYr3j4NSU18t3GNgreacgjo6O8f FsT4OwR5O7lHpcckz1ImlX68GdkBhjQO44kFZ4AlcKq2NKHwn86bGeeCAcgayBb1eN4Ctkib KCdg62DnRL1sjBDm1pTIGsccOdg4DG6kBEWPnzwpyGosbQy905i2H/PkI54dDHlu325ujF8z YsgTjczYv71dpA2EubAuZGTNZNl3BxvHCs40pr99prjz9NW4rENw9S14GY4BTU1Iz8M8sVEU ZQS/AJnonI/OO5DzEoD2PpDibeVJiYcZA79v0b70LtkD300wA8DEVGCcCvG2tC6+juS26/Wc k3miX3jNfbUHJUYEbf6D8aQL/4H8pxxlg18FnshrYmqMoBnKSPsTzNGazdzC6IFAAJHfuteY iE15u6HrNiglqxPyIJoIiLh+5AoysieeFAX+vhG2nrxf3/wA+zUeS4CVFdCnivfNhvmXXpci OdVoNXpK4AY8QeIe4A4/3+nkioo5ytV7Eyztzb1aNbi7e7k9dhajFb/LdnOrRwJZywM8vNCv Zxf6NgYbOv06LZI8/cYLDuxNvFNp847rPDonxbu0n9a9X8kQZsvqP6iDb29qMqqnpcMqUaU2 gScw4i+3tPDrLosvVlHLFjfO8UqfpUeDX/rnaGT+22i4IjTeeBI0PHmn+KMRGjEEjR0YDVN7 DIYtMCVgCBAYmaYIBznC4WgzDOev23H7HV9uqxjZmvcxEAas+GVamDTIMSK6g5tkgSTcEIQG 0bqx3QW8ZTw+nwrtmxs/WkVtXdw41jttVlMTuEEV8FSCmZQxNQOhkYy80JfKQtEzEbXM4VoG wQJekRFuWcK2gAbUYkVaKnALGuONpkCeikpAWDLbMJLzE+x5RtCYZ7Sdb4KwNBlhyquHGQRD spxEWSPiylEbvMYV4MnNGEMYwVO9XdpwqQOMnlb70xx4TjF+2dtiHH2+TS48MRWFZvzIGmdn R7S/WQisgSsUoT2so+MOV1eBy8+tRpha7BEMN8APJcR69iFwM3rNu0UWPUeCnjgma2a6r6id UMRkiVsJk6M3U1RjRbeuRYPbeSaKO3uZmcwxtezY3sPf2QzuXrLYTjgE6zSoMZBXxIsCjsCz 2tHcZjdcuEtgbqZ0pIEKxLu61DnHu9RB57DH9UfJOjmSJBwrHtmU8ny1X2s7MMptJf1EUwcH hgydmtEYGDpz477hA3dtXhxVGqiauLDvgGUTc/3wLNqAJFlxkswd9ZJUSJLyQ0nsY1Rj/048 simGSUhSDKPcWtpXNHUQlvQ5LwpJ2p/xgSTsDciaKp4T6w3gJ+wNTY94Yp4V6w3gJ7TUdkD7 iRTBPDLv+YNY0AukJpqDADgcxIEuiIajRDUc7ddoODzB3QpSgEfFV0d1r26b4l1t6lZtn8ZL pfrD/mRyxM9E0BzyAE8iYcj+eKbkZkvoDT98lNPywQdapP/pVX7Km8zVPBTqKOo7dRR19dUf 5yXEeLRtY8NSarG/4WUxupzYfJm2EUx8NRrlzzuJqv5hfr2UPRJUKaF+PZV9BqW5hsb7Evrm aqISN6rYi72UvQ0ukr8qGP1O2fWJ8ux/d4EZsBq+oXpRG6iHvG95r/g9UZnA3yJwEPQXfCl4 KnhqFGq0yuhX4zbG/UlZ87+4PDEZYvLNh8XU/5NlqEFZY1Aes8WM4cry/0C58G4xxz85f7ss MD9gfsz81P8v/43LH/9KsQjhyhfvlRq2WEKDYsYV+/eKu6UvKmlcWf5eOWdlgootV1w/KNv+ vYJycVvKC+jfGyQlFNchsCFnuE4BK8FOoH/DVbjgCFfnG/QRAGfBc65uZNBuDN4YWXF1ExBk NImrmwLa2IyrmwlSmvubgwHGQVzdAgQZl3B1S2qx8QaubgUUZknNb58KN9vM1SEwMfuFq1PA 2HKK/j1TwMVyNlfnG/QRAAvL9VzdyKDdGIyz3MHVTYCjpf5NV6bAxiqCq5tR65v7m4Ngq85c 3QI4Wim5uiXsbTWeq1uB9tan8BvB+KYczmydxZmtszizdRZnts436MPizNaNDNpZnNk6izNb Z3Fm6yzObJ3Fma2zOLN1Fme2zuK8HtBo/cKgEolqfYAcSIAGree06DcT6FBbZ1TTADWhItQi RzUled9ZIlCgQoMU1JYFstE1LTmToaMM9c5HVIp6diZ3YI4KxAH3kRMqQsdc0odGsjB/GuSR e3EPJaJqoksWkZyLCm7NQu0ydMxHZxrCOZec6zieSsJPhc6ziRY0GhHuKUG8c8lf6uE+EqIl DQq4XjJyL416YI54/Gp0LjHQTknQYDXHV2WEbyb6ZUfZgocE8RQRnSXoHswd34Pb8okcVjPM RUR0x1rIEQ/cHoLOFOgsh7RjxKTkjiIisQDxkjfzDCH64rdv6XGREx3xOLLIWxGlnDYqYleZ Ac5qjgcel4horUdOTGyAOWKEtIQDvkdDztXkDmkzhnjcadx4sDVZi+VzuA0kfKSopYBwasFR SpBUE48oItIxdrgfe6eI9JERTbKILxSQ0WU3ewXrkXp/ZPVUEMuxVtehOk1sqSEoKUibDBQS +TpiDyWpYUtJCXe5AR5/7gnad+yEPTyP2AbL1mOi93L9yLQG+OeSo4xDN5drx74pRr0xIvgq qxdrWxyPNKe/jKAq43xDPyYVGY+WRLCM9MGa9CW2ViKNWB/COshIJOdxNmWjDaOXR7jSHDZZ BrI13HiVzW1KEk8ygp4CcYklstkIzya6hRAb4kjUNVuV9bF3rVJMuKg4Hvo++Brr6Uou7+Rz UYO1U3Oa6/EUNWsk5uzP4qX3KRz/Ii63KBDVNUeRoQcrSNTkNN/dgq2E8xYxF8F5JD6kzX72 YTzpiDwd6S8mFs0nWaGoGUF9HviY3mLS1zCjFXD5A2uMc2wWuktBen2YtTtwPmmYdTsYZPoB nLZyzmeEiCe+8qlMLeNiT9aMtYZoIOdGqGnGgo0lGbGthkNSf8/Hr2b+S/OOfmbQY5dOPFLv X6kEdR3nGyxyYZwG784IKmJBHRezGNPeqEUCAsi4A7nsQ4PuRCv2XuwzaoRjGCoFpISSOeld zUO5iA7jsrZ+/lIjDkWoFc8JLVnkXa769kySBzQkv+j5DSY6sxm/yGCm1DVnmpbsytqO9clc go8eIdYT9eh1Rfj1RvNWSxzpr7A5Vkow0TWjXkBkSUgW/phc+UeySUuMfJjzWc9Wk5EquThj ebEzOo7N98eNr7MZMgDdFUi8k40e6Se1Un7A+e9j1MK9JRezeZP1Hsk7c9CHY2/x13f1Msx1 eCTsWHREnt7rMX92rOwcqiQZSvTJkbI4i97BVJ9V3o8BjCr2vDxuPpaRFZWE8ykVmQVk5H9C /txC/7fioiUmwog2OAbyyBwQSmylBoXr6XCGiaT7yCUalVaVqaM7qzRqlUakk6uUoXSiQkGn yLOydVo6RaaVafJl0tDOKqVWpRBpabmWFslzZVI6U6Wh87QyWq6k1RpVlkaUmytXZtEyZb5c o1LmypTodpFSSqt02TINLZFrJHm5Wp1IKZFp6QLUJKNFdK5KqdKqRRLCTqnDzLVqmUSeKUci iR6SbJFGJNHJNFo6W5QvoxEzWivKldEFcqkuO4RWyHNktEohpXVFalmBRo57htC5ohysi1yH ZGSpVFLERiWXyIjOatRDpRQpiHLiPK1cKdNqaYlKo5Fp1SqlFGsYSqchOfJcNDA0eHqgXClV FWhZHaVyrVohKqJFCoWqAF0U0VKZVp6lRBrpsjEUCEiMI+KpUCH0aJ2KVqo0uUiiTlaoQyMQ KWmdRiSV416o9T0QtOyYOqvyNHKZBmuCIcfCtET/XBWCTqLKRXWdSKwoojUyxAuNVpVJI/4y pRQxIpJUSlor0chkyKR91TJlGkKIzpSJdHlopMhsEkWeVIZQVWaRuzVIrhLXlHm5Mo1IoY2l tcjg2TJpCC1V6XR4qAgxbijFMuQ5saRFpECgK5HvIPNos0VqGaunCDMSo/EjvTBSGokIeYtC psMmYgFWqFQ5+DLRVoJgESMD5ymx/qoWO+lEWp2MFhfR+SJNEVYQ+0ALb7FIwzpaAfIPbWiK LCtPIdI0u3YHWu+6HYjTD0BsEe60MJRhDJ1aJid+KkJwZsmRQA3WAllJlivS5NBkPAanmR+P HRwMWLt0pRzjlaoT6WREuTDEgAsEVZ5ShyyrDe2dJwkQaQOR+9DdNSp0VadTdwgLKygoCM3V Mw9Fhg5Dro3jS51dFCbRERfhuuJ6pkiskefgfoNVecjxi0hQ6rDTEHdFo0NI5sqJARGIWL2u 6b0TiY3wCfJYaZ5Eh1UvyJZLsg3ulTe7CbFIs+cjsNUaOeogQb1QoIfSetkqJXLIAHkgLUPm kRqyUuo7f1Qj0p14MfJNBI+EjaBm6QRXjhfrdQFyJEUny8XQa+RIKopQpUIlMhSKdBaxmmJX 0VtAladT56E4luXjlID6ZMsU6vcG9HdsQSwRJpVlivIUulCRVl3Y/F7pxudgNgDNz15afiDq YYaKPTBuagLWpIcF+m1FnsWgM+oIovzmezE1I2+X50mKNArgkKWR5YAghUinBO3QFZiakkQD GwDYd6ATym+uoespffu8f72F735e9jt8BxC+uma+fuQOcyAgva2BA3AC7qjVEwSjNS57zQjx MkcSHIEz8AD+wAvtVoTNOpgBYzQuC2ALXEBrNDt7g7Zo7azXy4frY4JQsQR2wBW0QXO3D5qn IjjupuRd8PYIHzc0YwYBXzSntQOREpRzYAKhPQjtR+ggQkfgRAOzCVUSqiO0mNASqVKVCycR Op3QuYQuJnR5Jppt4GpCNxO6m9DDhJ5UqCQKWE/oZUJvojykgb8R+ojQ54S+wZSiVOhAmRBq RagDoa6EonlMoaP8CA0lNJrQBEJ7oIkpk+pH6ABChxEqJjRbmyfWUkpCdYQWE1pC6CRtnlpL TSd0LqGLCV1O6Gria3xi1b86Qu4p56eo6Z9QHvINY/x/VX+7Rt58QeKAMogAoz+lFn9CKeRV 7FNVfe1TR+zD/z41+yS1Q9ESCtqDTqAbSEZRnIFWY/iJ2zgwCcwEC8Fy8BXYzH0Lz2zuWMEd V3PHDexIYWv2HHbjjoVcewV33M61X+COr9gjlcIdN3DH86zteCbsOR9wRzPuaINQMUO/I0ED sYQcPGBbqM+onrgF9oK9UQu6n+qBbUbh/1AMAnZwOBRDGVRBNdRCHSyExXAcLIVlcCKcBKfA qfAylYoRhxkwAwkSQRHSEr99gIK5UAl4cBTUAAEsgAXAGBbBImACx8KxwJR844EZHA8nAAtY DicDK3gJXgI2VAoamy35/ghX9GvH+Yw10XExXIVk8eA6uBE1fw03Az7cCrcirCiUqUzhbDgf LkS9lsNVcC3cBLegdsGHveFZeBZpUw/rybcfuQIzaiw1jvqCKqFKqTJqPDWBmkhNwt/oQMko Gf4uBkpNogMCZwOd8N9ZQ5iHfRvdzz7dh+Q7MPQ9bA2uIW4wD/UG1FJqDbEBlruUWkZ9SS2n VlCV1EqqilqFIvl9uRSIBi7UVGrax7T8aFs5NZmagu4zYr9XgnCjELdRgEfpqDHAktpIbQKt qK3UVjQilv8SqoKaTs2gZlKzqNnUHGouNY+aTy34aNtCahG1+F/g7w7MqWHUKHQtj8qnCqhC qogqpkajnvjPAYZSQzkekIwYf5OGA7JPG0hDTxgLa+A3sBZLA9NRAeR/oCHsADsgT6iG1ciq e+AeZOfj8DiZuY6iGa0d2nElkPhMA0PACPL/+xpQiGJ0ApiKonIhWAaqUBRWg2/AQWQZU4hi AZpD5PXQCwYgzqEwHLZHZxawFaKW0AlRK4hGA62hC6I20BVRW+iGqB10R9QeeiDqgGKagt4w EFEfGISoLwxG1A+GIOoP2yLeYTACRqEjA9vBaHQUwkgYgxDMojIR1VIagL89ZjLA3/81DRWI 8spM1DYfFR44DE6gTH4anEd57ja4C+wIdo4I31FoTiVRTXy/JWJwVK9p9tZP+53eVwGXt9lc BdxS0dGBbXbrxZS5dTcyDZrUY9ILS2hMVZa5RaOmdhSEQnPG1EgQbMWjXAWAERmZBRtBPiyL oiC/MpXpz4QYtLhXtS5xB3Gk9CVfLqTiHj3KUGJFhfE0YMZ38Mx3spq7rdOOMZPzv+g7d07O qc8iAyrLHF8xZbwj6LdtJY+CFGXT/YDLgmszUrp1fnEpt4elcDVj2awqFCClSqcRJXnpfCN7 akii0JGxxycm9hYDZXiDoKQ7o82O0IGxw83G9uZd8jRiEdr+KhQyoTXihlrN7I3SskUFOpnQ g3HDDeb2DmwD3VmGtomZcgnZOAjbMB74Ms++FXc5DW2y0XY5V40XxZ0TmdZOlkyEMJxpx5Cf IU6WQnwaER4RGRMZM4RJNVA2PVXoxDiy8q3QhkeeivanIfRnSkmoMJgJZAV56S8QUXivwspK RRt+Od6kI6Fl0MsQFSgAvDJoDVC7GVUGIVh/onr1yTp6i9nYKZvK8x5tT3587ZD1gSzRvlVS 95/3vjwRsXECM2XQuOmXcq60X2594Mz9wicFX41TxR2Yt8Xym+zfFfNP7Etpu7FHx2c7//H5 cDdqxauwnNarX6yq+Mr1OHX9i94pN6xG3E9wH7fH8mr8se3XyvcNLx4pDOUtKbVf153+Uai1 HNi2rrBdxAK7JXZ7rmaHbbh14/DU6UHfTfMsz9w3ftBAVd6BuA1+5Z+fsHGMWzHht7RDZsoj jd/3vLLH2HaR15hLnfzPtC68v0JY+/iWl8ulIzXdO1e4Dq9sPftmxrOGMY/HbhTDWc/6mF89 7TVg3YK6zZPzNzd8Y/n0Zp+Lla+zKzc7xNaUH9pL8ZDjryq9xJReYNoZmSCPFQiMIeQHMH6M j/6cgZOcuf2ESqJVh+Yj3PEDA7yfIL7jYQ9hE9+EMUIHCgImEbe14Xdgopn2le0qwycx3O0S jeKdu8NYXzF0lc6JoagX8VQPX74FY6bXgmfCWOFGaywLf1+LEdIQndvykWeudmGc9P7Ns7dI S01EjhbdVtg2MuK9qOCVloKeOS9/G3S4i7twStGS4IUHyjbBevfedVunDlJeMwlclXH8xDz7 2/wUy4fd/cNA9NabtfOSK857iR1fxEd59lULSx5Piy6vuXNnEWg8lb4w2efsev/k4s27RIlP g368XXsx48re4Imddny54+L1gU37t38/7tkpi+WPFjUGn4tNcXOL9n8R3xPFcBNTRt3m4tjy bvCj8xcCJzuHC0wzKvInvx/H/5HI+DAcmWjDcBz4N4WGMW1ZoX5/JRRfk2n+MiSr+wX0uHIu u3iCc5fMvM/HHdm9QuLX1LHzsjG20Ta+6dqLef7yt8l76GHnzF5WugU9SB/gKbrQ+tLNbyNy jj28sipKNtNtnsXO1NbDxmRGDhdM7dqYn3wttaSqlP5y8+RhVSYvfmVeNnhF9U4y+/Ha0TZH 6tPvlsbvSFkVsgEWP6naMCOyccWtz0cKVnTMuXFg4cHGkyNeJtw2ruxyr7S/ck3Qk51TbQIe zLpsVDmpX8XoniaWjMcJm+U5L+4O2sxfn7CkOuDOrFab4m6kqnqdi/xyh0rqUbMwZG/H20X3 cotftrrl9/WWh0tSdyWELNhdtKHxfMrGQN24pPsxratGtro1eK9P9gVQ0tmmvCSHC8kTTOmx fzMkLZpDkmIAE8EGYwgTxARU+lX6TPL6VDDqtNq2EhEJv1Yk/DCLP4lAo4N/KwLbvR+B2Mrl heqfk1MgPfSXotoy5sjbPS4L980B3+2rqzv6u9WFppd9DkaIGdvvn+nczs+9OnwZbb9tTNf9 /erG3y5xGr/Wf16WfbfXJ3YvTuSdXNp/qGDaF+tUT936ufmEPpHPUHi92Hui1YIHFrqD2QUX 7y0Rlx/Szv5jiq7Ye+OqxaMXbXsxK3BUn9A8tx6JPz/aYUmn1RdULiqTyN+anpr6KG+v6dKL L23T/SpE4fuLqa2jJ+2v+m6aV0jhmcj8b+dqh73cc6u3o5n3yZtnz7cL/SzBMc56RLHP0TWZ DxeeUt/rdPt3y3GXz4xZlT9KfmhZ3+5MpOe2qi2u4rjgizM3BBmPvuBcM2z0P79co2qMm/I1 U8a3QyngFZsCrMEhMC0ubrLtmU7PJfevJRgixkcZQK2PbXN7r84qdZEGP9ymAySBtDAmJuq9 R3mhwtaMO9vZ8aMP+YSeTBvWTM4t11NUKh2dmKfLVmnkuiKcHmKiGKGQYaK49BDOCMMjhNzp f4FGfzmVU/sOqW/FPkl2C1ixqDCD+a1q/Qzf4X80Lui9alfjl1V0pzH9q5ZWzRoRnnMmSVrU sCm/Nu3nJ/eWTXKftWJCZs33OcVi73qPuKvWcO6dhUcOtM2sqMj2W3K6Q8gBix2D/A51u23W KXphyPqAmHX3PxufdGOC9d4KRbpoU9mYlSPaFvS+u2S7NLain7vQxMdhxfrbc4Kdb3VcLHEY MUggW+ERlVL+Yu3D+dRRt3MH0rvWTCk50OF+2vzkzW/XFufqkrc4n1xoGuAJBs4eIY/a28vO OG5A09DXqzPNTL46Wzpg4MOdsRmtSgv4Pz/fv7lkQePWui/q17pqhsWd+PaRySovpsZoYm0N XWA/8RqXN9YxpWuY0iocl5BfWsGULiqxGXpa/VCuWe7df5xDdZ+ZTT+s1Py/t1/ZX/g4yQoL 7pgfnPF0kXPkg93Q50KB7dNhI8JXLDf/oZNgzuRZtR1ueT55NHBeyI7K7sfFD9/8dDI2dsj6 9mnyRp/c+NqTG64KxlwRzui4wkY9cm+jXV9n+cE3pzvfsB1C9/1NPHrLBpfjwVG+bffLVtpN 9bWWrHqR5v7Ss7be8WnKJmXncOO3ZU5//JqlsOz/fN/jlGP7bh9h3tBC08keCwJd+/zDg1rz uOQX3vahv2+7cnxgg+yzYylpO7fzAuyaZtc/Mpk1bvei7zdGhdwsvrmu4EZ+JTg9Mv7Q2fZT f0m0Wxc50m3kpcjr5935N9d15R8fEhGt7ONuKd5lVjX93D/S4rvVuad/pb5k16F8Xt6KtWcr UVb4AS0OariFwUjzJX0PAouNthe8Ho5VFI37b5EWmHAmMjwCLe24RbyQiQmP5E6Z0q/eXTbY M7bslsNsoEibjZYDOiTHhkwjaMNhnCKT5qqUUr1mZp/S7FPDDEdCPximN+PJDsPV8IpURhYg eEXSj2wM6A+ziSXOJiYkm3x3kp7x7bWmTv0aig+f9/F9nv+jZ1Nd0IDkE8t2lVVHFrUFR9aZ /ENSu2vN87uHDtVvm76wyviV9c6ylIp7ZUf32Xy/7mBDzoSZqW57+72SwimHWp0vywYJhV2e 2UUnv5b0/+VVxz2/Rm27JjH2jh2V0K777zmbuz3z17b2+iHJpXX/nSkV51adtj/qEj/KKPfJ As8uw5MeHKxdIqV3H2r3pqrLrdHVHmG7v7r6+8prSz2tGwcJE9Ojx20ZdPvm/cFFvhtfBIXZ xkcXdkr6Ym32zXFe2U63es49UtglpfvKvhOmzFt6MGv0b6avJ/HGPl8yKi54bebik9fa/jOY crVu10P2LM5uy+Nydw+/FNVJ5H+8VWUwCOHh97G1OO9/RoqxMzLlNuGOKGAoHg/wyDbVw4rf iu/g+0dwr8+Pa9K+/vV5ZZBTq9eHXqaWMi7NtzhQfIvWZiAV5KEte2eQyJiTxQ/Ze3RjrJsX WQKGhw4fS2crpD111+X+Q/4wSvVfdKz1mVLtmR+/Kf3d9bd5i8q3XJ7VgdoxzebAuVCdDsZu T7Bv6D887rvwpX3r7w4+EQAWahzvjQpbvPgg73pk0l2TMLFwyRvh08tJmy/3nXUy+qo6MXqE ZRw9N9h5mXHjgHnrz/aavG9JTy9qoqrL/Tm3XK4G73asXqp+9f295PEhHUZVhg96Eplyac7k hIcqt9uBSdvVdWMVKtfjl5a9OpMx9cXZkAow/OLvh2tWjD4f25iqC8ryKPWvyruTsNjYeZoz bd5Nc+3hi/vtcxtyajNTPfZFbvj1B4+RGvuuJa0vfl6eOdU95KsTP4h4vKumtXDb4INb397K XPTDK9PYMUyZoBtKaaFsOjMTmf3GkEe3GR88qfifkjZw/osUhgtRBmwXFUl2TVFoG4VOI/Ep Uzr5PzIQpDCrW+DHP7UN+ExJPsJW0OlaGd1XqSgK/Mv10ndrW501Px8vnt0wOC1sgbnq9/Py /YrpB5xPpb6uqz2XfVO8a7jOnHdrw6F7nfzvn108e6loQCPTsP6confmhWG+pTM31Od6w8AF U6t2T46/2r+fz3zp7W+PLwe3w4YsHfTGadJtCb21qO2dbTbDNb3vnHKd1Xu1yTXpiOilyZJe N9t1mR8eudvaWHNdOXrN7nN1NY3Dvg7vfz1za2WfXeYKHm2XfTDE4Y9ZpSdmhH4LvzGCZYFO az1PjJ95rI+UvrhmvHbha11afj+nXTX3e/hP3e6bvPXXvbW9ZFUJMVevvJzi+4vfssUpq/bp 2nXUWjO2WfSxzuVL4r22SYJLZVY3cgUJEb8evtopO5ZdL5XB2QiR6R9sbFqyxJbRHtkTHhx7 GdpmUuGCpMAdj79vNe8TKXE9bvXml65kSpeXfDS9rNSt/q9IjB+uJHqyu8LOTCITX9mxMnZS jMGu8N1PjNU5ctwaxn3Org3DUYGDoh/3rCbZYJeaxCQwnZp3qdSk8E9+EE3YyjQf8NN9bLsI 4xJn/egXPehepeRc9u2N5waqH372UJBe7vhdee3ugaKEAY92v2B4mcX3pk3bWOg89HzD2qRD B6e//fqn0zNfTi43HVFtNi5ffrHoCF/ybF7a8l+kzk+nT+6lmKWs/cUkdsPkflPNDj29tf3J 78/iLgh3mW57OGRfX/XIhBu7xtb95hNyYoT/9crq7QEb0ny2LZ//LObLrAhR355jNHX7naam vMh4/NM/O4lOR5nEjciddHav5Mqd4+nVdPReO/fxFaXt04LPXLr7i93bH5K7nn/VfuRLXWS9 X4HlzerTRodf6CzWbK/f/FuhaFaSzfA8pbjTmkOXk0WFPfbUD/wx7X7d/jUbu26x8qKrNo0O uy4s4x9AmXQvBSFTuuN/TL58J+e3POGuLL3MODTPswFQaMwTkE549uVMb8oTWhg+VEeqt5yZ C60Yw6uOjHfLjXwhitoQj21d8zK6f+bc+iF/Y7ZOZbOi8gmTaXCLhXAQM6AypCTo7//J6Uq/ Ep+/8XcV7y8s+WUQREV3g0vzq232Hf7jC8/kabsDF+X14l/+fV69cFHsV9+dbnvraWXmeeH5 HS+TurZO9up9+dD0bse794t6YW1x+3u+efFlhXhA4o2JObQ8Ot1oTY+rRhdXPs5IOjXBbkJ4 B2b5cX9FxnGLlVvjEx+cGjz1QcaQnKV+WuG8gH/u2F/73Une5jLL/iNiHsxcee/K6TLho4BX KUtvBZ14tGbkz+Nja/hH0gaP7F++98fDpXd6e74etNqt/9vVzKy0yRtH9hh0InOLxnLSJsWI h69PSpzPtxn08+Ij5bJBbiN9a3bOU9zZ/fnczau7/zDXvkenNdPuS66Mfp0TvGf7wlX973gs y+2k6Nlq8OktOb4Vz14KmlaWoRVSGXzdYiUjYRm8j5ruYJfO+o884/zIk1ULIxNWAQpllsrB jLOhv5m3fNIDkbs1XxEIrblpP0YYEx7VLmYIyrgG7mbHtwkQm4pv33HLaEo4d1nzpdPUj7jA 8FK3b8+sE82OcF+6I8yjYs3DZasfDLs171hG/qiOToPfPLqk3FDl0nffwZKfHpvmf1kb+6y9 +GVgzuHHburTuo2r9iQYaV5dLNM+cgD0iFGBmp7uIICasmBXw8YDFY5tgqPOD784Y25D3ZdD g64fvnq4OEJgu2fvm6TL9U2zOowKyJjvkP785PUt0+OLhg2us/li8q/Xdm267PaIGTPkptsX 46nCseDkBCP/Z998M9fGfG+Xnd6ec2KeXd93MwrOHTU6tHf7Hnl04FvfTZ7R+xL2fQPq47XP t/JBTL687ErGrohXHY8eHhXnldhlRtj4m0sfG+UXmUjWto5RuVhE3T7xC+TdLbbx3/tixkLw fwBfpycuDQplbmRzdHJlYW0NCmVuZG9iag0KOTcgMCBvYmoNClsgM1sgNTUwXSAgN1sgNTUw XSAgMTlbIDU1MF0gIDEzMVsgNTUwIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwIDU1MCA1NTBd ICAxNDFbIDU1MCA1NTAgNTUwIDU1MCA1NTAgNTUwXSAgMTQ4WyA1NTAgNTUwIDU1MCA1NTAg NTUwXSAgMTU1WyA1NTBdICAzNTJbIDU1MF0gIDM2MFsgNTUwXSAgMzYyWyA1NTBdICAzODNb IDU1MCA1NTBdICA1OTNbIDU1MF0gIDYxNlsgNTUwXSAgNjIwWyA1NTBdICA2ODFbIDU1MF0g XSANCmVuZG9iag0KOTggMCBvYmoNClsgNTUwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDU1 MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCA1NTAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDU1MCAwIDAgMCA1NTAgMCAwIDAgMCA1NTAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAg MCAwIDU1MCAwIDU1MCA1NTAgNTUwIDU1MCAwIDAgNTUwIDAgMCA1NTAgNTUwIDU1MCA1NTAg MCAwIDU1MCA1NTAgNTUwIDU1MCA1NTBdIA0KZW5kb2JqDQo5OSAwIG9iag0KPDwvRmlsdGVy L0ZsYXRlRGVjb2RlL0xlbmd0aCAxNjU2Mi9MZW5ndGgxIDMxMzU2Pj4NCnN0cmVhbQ0KeJzs mwl4VNXZ+N9z7+yTzJZkZsJkmWFIQgghISGEBEgmK2HNDklESEgCgY8lEhYREaxFbACRVq0L Fau4VuskoAZBQAVcUSlutIpaa62tcau2NJCZ7z3nvRMCIrX9f8/zPf8+35y893fOuWd53/cs 99wkAwwAbHhRgbu4evKknZ2dPwWYGwngaptUXFIKidJLADMrsFTcpIry6iNjv9mJ6XZM102q ri3cN+mPxzD9AoBuSnl1WsbiE3dMBGAP4v3G5iVN7be8+e5xAO0OADm9edUK99/f/XIYwNA9 2OFt89sXLHHvSjwEYHgFQG9Z0NTRDk7wYns+rG9ZsHjN/McmnrgHIGkagAPaWptaTh3+Uwa2 PxLvj23DDON09iSmWzA9rG3JiiuXb1yIuks6APVPFy9rbtJPNScB5GF99ZYlTVe2G/9iLsLy N2B599KmJa0Lpj+9CqDoHXRCWfuyjhXBEfAc9u/m99uXt7b3HLvmJEDEuwCmd4D7CuVra0XP XPOEb0GP3eBn30eu4ZwHX/hRQ+DLQJTaqL0Ly+lAAvpgHfVX/V+xOar5gS+Dl6mNoqXBHxXP kY5AFsZ4XAILpEEjevUx0S8DWTVf2g9q0KlvV2dik3FE+XV4VgKdXjJq9ZKkkiXV3SB94QP3 1FDT06vdbsDgtpAO2tmShObt5Pfk02oXtxSYaj7MQCFl5guNroUqmAk9GNJgLtwKj2DOEUiF OZAAm1CuhXlQDrOwrAdtNUAbfPejCr4dfBuSYaIIVcGTFykDogcKAJHfuXsr9wP2BsEw7N8L dyn5czBc+EkIZoELNgVPYk/XKnnzMABqWn6RnmddVB+AJbAZNsI04J6YhvHtsBpuwet4jJeh TIM9sANTP8FpqUP7HajlxVqx4TVZCRvRtmkYLvzM/05OpPBCpBK2Y2jD/q2oAfYSXCtyapWy mxU5/1MWPAKxSv4eJW8Hhs1ME2xBvS/2+cn3+KIMw9oL8tZiWHNB3hoM2RiAOXCktXgthBcw ryr4C34veBpqRLkauFvwbpxf/FMF8ejBf/HDdkmb2FYRPQHvs/VsCc6Ms3CW3SZCNNyD4Um2 Eu9WoU4nIJupWC4X6MCZwaWMPYdjsjF4lgd4kXVgsMDHQkYzHRe4DU4K2YTa3g0NMEWsiyOQ g3O2DVeFB0fkWszjq2I7rom5WHI+zjQD6qWDJPAGjwTnBD04KyGYH/gSr1Ow7GbUKRm92iK8 WIVhLbZjwrouWI/58TgWhahbJuZHY7BiLPr7PBFE33Hd4X14GfYL/biGuKrQ47PhcuBrR4e9 zkX9mrCPKtQvSehXBxUwGoMB7bkW9c6CPMzndSvEmgExqx8RvXyN5finCSUB+NyeB+vgGO5T 84N9sBCuEaUt2A+3YL1YP7W4LqLRRyuxvRwMBsjH/rfidQaWfhn4zN0hWt2OsloxKF9cr8G2 yrB+kUjVQhqOFpZgD8NHoMccC4Zi9Fk0ejIFR2YOXIFri/v8JLyIHkxGa3joQB9uxtwdqO8i tLFMyV+LFm/B/EeUfL7TfQIfw2fwPM7cD+E0pj6E+3lgafD5v3F38GcGeqMN/cC5EXucD2Lu IQ+gDjrUfC3amoNzeBOu+EWon3iigKxIjPLE6MEUf1LswnHmnhuKe7dKPGfC0c+pkAuTYTqO cBO2vBCWwSqctY+6LcGgqB2OpQeXacYyi2H5QBkW/BY9PJp5gs/xgK0rIdgMXR/OUHSIu+TC lFkjaNhnIv7Vd55z/MlGT0UJLv1hA61d/PZ39/7Qncsu2W6YQstATtQly1+HshGux3G5QaRp T70RtsFNYtYC3Cz25Z/jPvH/x0f+H21NhauIzwkLtmv6p3Pw0vPv0nPvn847X+ncOZfPvqyh vq62prqqsqJ8xvRpU6dMLptUWlJcVFjgy8+bOGF8bs647LFZaaNSRw5PTBjmHRrvjLRazOFG g16n1ajxAMVgZIm3tNHtT2z0qxK9ZWWpPO1twoymQRmNfjdmlZ5fxu9uFMXc55f0Ycn5F5T0 UUnfQElmcU+ACakj3SVet/9Ysdfdwxoq6zC+tdhb7/b3ivh0EVclikQ4JjwerOEucbYVu/2s 0V3iL13V1lnSWIztdRkNRd6iVkPqSOgyGDFqxJh/uLe9iw3PYyIiDS/J7cLjYzjv1i8nlDS1 +Csq60qKXR5PvciDItGWX1Pk14q23Au5zrDZ3TXyUOeWHgvMa0wJa/G2NM2u88tNWKlTLuns 3OS3pviTvcX+5Kv+4ESTW/0jvcUl/hQvNja1aqAD5lcnWLzuzm8Blff2fnZ+TpOSo0mwfAs8 yk0ccBPeD8UBdUMN0T6Ph+uyuccH8zDh31BZR2k3zHN1gy8tpd4vNfI7h0J3omr5nQ2hOwPV G70ePlQljcrPqjanf8M8d+pI9L74ScAfvO/2y4mN85rbOJtaO73FxeS3mjq/rxgjvibF1pKu 9DQs39SIRizkbqis86d52/2R3kIqgBluPgYLq+tEFaWaP7LIj+9SSi1/Wkkx18td0tlYTAry tryVdXshM/hB1xi3a3cmjIF6roffXoSDkljSWdcy3x/f6GrB+TnfXefy+H316L56b11rPR8l r8Wf/AF25xE9ilpo2wWlQ4W55doEnbtOcsn1fLQww12KF2/hBLxhweESST6ihRPcdcwFoWLY i1KCx85rBxNyQlEZvyXzqkVlLk+9hz6XUMml6KRO8OsGtWXBjAGdqJ/vVY1Kc4WS3SWtxYMU PK9RtaKg0trF9ZS4L5SOsYaOD2dZ6JacgCsX8yRsRmTxUXS6/VDhrvO2euu9OId8FXXcNu5r Mb5Tq71TKxvqxGgrs6TmvBTdHzdwT4n5pSKcgKUprtCYivQkkR5Ill1we3LotrtT551a3clb 9ioNghuXD1qsSZzctHmcbQyuy1Lc2rylTV63xV3a2dQT3DCvs8vn62wvaWzL5W14J7d0eqvr JriEalV161xX8a5sMJVNrSlMHYkbT2GXl91Q2eVjN1Q31O214PPhhpq6bolJRY2F9V3D8F7d XjeAT+RKPJdn8oSbJ3hLVZjQifKuvT6ADeKuSmSIdHMPA5GnC+UxaO6RKM8SypMwT0V5PpHH PzhCzjb0L+61Je4WPjZX17d1NtbzlQV2HEf8YX7mzQO/5M3rYpImzG/wthb6jd5Cnp/P8/Mp X8PztTgrmJ2hc/iG1NnoxU0KZ1MduBjNQ5k36e4JBmvqPMdcvfUenGezURrq/PoU3PjVCVOw 3CQujZg9yb+huYnrAbV1vK42YXJzPc7ZUINYZLJfjy3olRawRKmow+ciVmrGscEBFPU3YMK/ od5fn8I7rVtYL+ayxQ9l3lwcdmpTncg7SqvvtHkzxMLEdWBI2MShR92guo5yXJjEzurJSdow 1LzZi7eaG93obRU0V+M8p43U4KKcVtwPVYmtQgwu5SZws+QEY7jBrx+FDeIPjxtH8fWoTtDW 15PyIrVJKYB9W/xG1ChxkCuVCugdvDWZ64I/m1BVXvQZ3kxlD1R5r8RthSstWtLibX94wuQm 3PmpvhFzvONClXV8gzAqbRymXC23PAz9LifU9AQf8K7xDPqkjvTyJwOfmODaixMb6jsvzPBf lpI6UndhbrjI7uzUhV+8AvlLFz5AzPSt0i9nBfHL5LT4pVJB/MLUttoFqfNrW1NbaptT59U2 ZTTWxjemNUpzM+bUbmtgFQ3siwZmaUhvaGyQZ6bW1h6qZRtqX6+ValKra1+rZv5qtq2aWarb qyVkI2dVamVtRWp5bWM5iy9nxalFtYWpBbU90tRutze+R5pCmEwo645PREwilBJKuuOSEcWE IkIhoYDgI+QT8ggTCRMI4wm5hBzCOEI2YSwhizCGkEnIIIwmpBPSCKO6Y0cjUgkjCSmEEYRk wnBCEiGRkEAYRvAShhI8BDchnhBHiCXEdMdkIVyEIYRogpPgINgJUYRIQgTBRrASLAQzwUQI J4QRjAQDQU/QEbQEDUFNUBFkgkRgBBBgQUKA0E84SzhD6CP8g3Ca8HfC3wjfEr4h/JXwNeEr wpeELwifE3oJnxH+Qvgz4VPCnwifEP5I+JjwB8JH3a7xiN8TPiR8QHifcIrwHuFdwu8IvyWc JLxDeJvwFuFNwhuEE4TfEI4TXie8RniVcIzwCuFlwkuEFwkvEJ4nHCUcIRwmPEd4lvAM4RDh IOEA4WnCfsI+wlOEvYQewpOEJwiPE/YQdhO6CV0EP+Gx7iFFiF8THiU8QvgV4WHCQ4QHCQ8Q 7ifcR9hFuJdwD+GXhLsJOwl3EX5B2EG4k3AH4XbCbYSfE24l3EK4mfAzwk+7o4sR2wk3EbYR biRsJWwhbCZ0En5CuIGwiXA9YSPhx4TruqNzED+i1LXdTo4NhPWEawjrCFcT1hKuIqwhXElY 3e2YhlhFWElYQeggLCdcQWgnLCMsJSwhLCb8F2ERYSGhjbCAMJ/QSmghNBPmEZq67Q2IRsJc whzC5YTZhMsIDYR6Qh1hFmEmoZZQ0x3VjKgmVBEqCRXdkfg4Y+WEGYTp3REJiGndthTEVMIU wmRCGWESoZRQQigmFHVbcddnhYQCgq/bMgGRT8gjTCRMIIwn5BJyCOMI2YSxhCzCGEImIYMw mpBOSCOMIqQSRhJSCCMIyYThhCRCIiGBMIzgJQwleAhuQjwhjhBLiCG4CEMI0QQnwUGwE6II kYQIgo1gJVgIZoKJEE4IIxgJhm5zKUJP0BG0BA1BTVARZIJEYATwBZFcAij9KGdRzqD0ofwD 5TTK31H+hvItyjcof0X5GuUrlC9RvkD5HKUX5TOUv6D8GeVTlD+hfILyR5SPUf6A8hHK71E+ RPkA5X2UUyjvobyL8juU36KcRHkH5W2Ut1DeRHkD5QTKb1COo7xuKo9/DeVVlGMor6C8jPIS yosoL6A8j3IU5QjKYZTnUJ5FeQbF99EhU0H8QZQDmHoaZT/KPpSnUPai9KA8ifIEyuMoe1B2 o3SjdIXPjvejPIbya5RHUR5B+RXKwygPoTyI8gDK/Sj3oexCuRflHpRfotyNshPlLpRfoOwI WxR/J8odKLej3Ibyc5RbUW5BuRnlZyg/RdmOchPKNmNa/I3GNfFbUbYYF8RDLDPHxsdui5X9 MYdipJ7gIV9DTGp6aXxMWoxkjomP2RazM+axGPV6F3vf9YVL8rlc8aU+l82OF0N4qW/I6DF4 SRqBF5cHLzYHXgym0vLoudFShbPRKYHT7zzklBud7U7e/BPO3MLSdAfjPUU4sB2//ZBdApvF 1m7bYFMZMH+3Lc5Tyu9bbY4hpW5LusVnkcFyk0Uy8buWjCxxN9+SnFpqNsebpXLzXPMyc9Cs Mpt3mh8zH8SIzzw2t9RsijdJBfx60PSa6X2TOt9UbpprkreZdpokeT8Tf9MGxm7qqqlOSZna ow1WTfXrKi7zsxv8CdX86qts8GtuwLezhsvquhi7sR5fhItq/Fb+WwWR3rh1K8QWTvXHVtd1 y3ffHVtYP9W/gcd9PhEP8jhgkfqOFSvndKTMQaTwK7+wjpQVKZjRkaJ8MLoiZUXHCkj5T/iw /20F/hc/OIIdHVxW4AfHWMRSBgbaOQdAu1jODsy+4C8AS+Ea2A4PwUH4kEWwcWwh2wSb4GbY B8/CUTgJH0GQRbIy1sh+/P/6Rw+1C+wAwT8HFgU2BpPVXwc+DszWOIIa9dvqj+XTdE+9ESIC HcFPsczJYLLqtcDsIGjmB5ODX0o5YA21oFoLdp6nXqTeqO5Wn5CnBcTfo7R3/TMdLvKZI/7e UgbXDfzF6iewFa6Eq2Adptdh7nb4Kdwi/lZ1J9wLu+BGuAM2wxbkdtiB19sxfSfW+zncDw+i Lx+GR+BR+DU8AT3wFHpyPzwNz6A/78b8X2OJX8FOjPOyD4ucx8AP3bAb9mCNJ0X5Azgeh7DO c3AYjsDz8CK8BC/DK3AMXoXXsM394t4RHKHnz7uzR/S5d6DXUDvPDrT0wgVtvQ7H4QS8CW/B 2/AOjvdv4XfwLrwHp+B9+AA+xPH/GP4If4I/w+fwJXwFf4VvRI03sA7V+FCU+Exp6Q2lrQtb +gh6cX7ZUFJYFkomy8bZlsPymY+1sXVsE7sB1qOvN2O4GX4B29DnN6N378Hr3Ri/D/31APqL vPYr9Ncu9FrIf12YDnnxcfQBt30f2kzW7xX+4j54Hj3G/cA9QPYfFl4854+XBmLH4TfCM+f7 h2wKee2cz95DC/8Af0E/9KKn3hIl3xf++wS99xmmz3n0D8Jjn8Cn6NVQDe7br9G7p0St3yul eN3Bpb4Q5b6Bv8HfoQ/OQgAfKRJTMTXTYV1M4b1vxd3T8A8scQbL9EMA1zAvJ4uSGqZjemZQ +rtU+VBpEzMzC7OK0fMyAwtj4SJuZ0OYi3lYAktiFWwiG83G4ohOZlNYGo5xNsZzWZ4Y4UJW xIrZJLwzg1WyKnYFjnk7W8FWso2g4v/XoIrGtS2DFtwwBmbvBw87CCkQzhr2WCy6IdoDbBZI EMkWgg6A1fmcKmlIl0tjSk9PtNwZHq7S7JALElU7WBHk97+a32vNsfTactJetZzqZWnv9Z7q tfQfteak9b7ROzqdWT1WIZEmyav1ypkZY7PGjJK83qzMjDhJ5rlDR0lZY/IknlZFn82Sc/pn Sg1JkxcWaDrD1k4ujM1vKsrJXn7fwozT1rgku314nNUaN9xuT4rDDarvY7XrzCKV9cwX0tPZ s4sSNzKpcFT8uGTnz0dXtPXvsifGWiyxiVg4xmqNSeJ/sb+V/z8aWm+Eav6wr617XNYYmF7d w/7hMxsMYeFqwwe6IpXxlI8VgDN/yPTejPzeDKuN5aT1v3HY8sbh0ekun/GihUan10dkeaI8 VpQsj/VWOTfwAXOfPcrcqqoXXzy75uWX5U2oQSr6Pwc1sMG9pIHPE6bVGi2SZDViMIBNb1Cr TKaISFuRtUClcctuPBi97rOYZI1WUhvVaoMlzFhgKMKe8zMz0fM2R864ceMy8/O5mtFpmZnO wxmHM6w5OWlp1kwKXGvDv9weWuSRZa3slZNk2RvhiXBk4wUPe/r87Xdcl621H/cxe0d29hrm KpA+YkkvBIrY0y8E3u93qF2BrcePsPV9H+NMuhYtzkKLHTAU1pPNT0Q5JEkDph4Jdju1sZoe 9q3PrnUURcRFSVrVO3Fx2pjf+3QF2iIQanEXo2I41XKsOfksrR9HovcwTrIMYVnUD6wo7ElM 9HqtXqsH51sUzj+t3e6IYGN4ZmZGHk7JniGX56f9ja1b7L+6aMS0hXnpFcOPB+656qrFLTmX Fw5juyZM7P9I7UqY2dlS1l6ZYTDU1wSmyHc01gS2OdMnobVLgp/KJ1UmGAaj4Wdk7e4hQ4ze Hsm428oSUnokhy/e6AoOGeKwvMKGQaIlUdKhVnHDKh3fxFWM6vdpqmhS5ffmo+5z51xuy+nl C4rhmso4zO3hRsf9O01wDySZ5IFFlylW41CNNmvsWL4Co6x4z4SOseNKzZZ1GndpTfP4Gasq kieu23fNRvvoGTkNj1VNPrjginsWZnxzS97MsdEVJZlLCzcOzRnhGFW1orTsqrqMiUn5I53J Cc/FJqbMvHZm/xT2jHNEdnxewZQSvgI3Bj+Xn1HpwAtryDvdXmv0PsmFm42F+Z4wW5lFtkby V/3wCtjHsrFgHBvT7ZPJpP63evP730KLhDuOoif2/ov10AcJGs25fcfuiMI5IayOk/gcyJaf KbujZs2h6ydN33JkTfOmsXnbC4oXT0kaMX1xoXvSpJK4pNiYwh8duuaqo1umRVl644eOqlpZ OnlFZYreag/HGTANZ8B7eLayQxI0k417wY7DH8HcDj784cbhLMpeGVXtPWupiOEqDx4wHDFm eUsMtdhsLl30whFFc3DBM2HK2GwrWSYbNO7iGfVjG7bMzSjfcuiKxOlx3/Qvc6QWjshrz4+t TRk/p2jYrUm+1OiyjQdWbjm2qcCgY1v71rJvalZP87bMYvuNpvS69fw/ouYHP1UBzm43JMMC ZXaHhTmA/7Lek6xC+GwOzzGVakTiX2MqwgNh+qDPdm4ExGLEgct4j09Hbp71nxRG+1ikRutR DIoabGqcpB1L01cFstzfkjx1oW/KQ5clzW6cN2pFz4bi4qu7liy+vz33NHNnTx4xe5VRmhJd 4S2aXzLMMeRZo82kLbz2wLqrX95eXnTl/b4J9bkxa9uV//pSudS3gQtiBuaoRR/eI4X7bGbc n83m2Dibvs8cERbmNLl6GHsS5H6fs8ZUybccobsVVc/JVB4ch9941dJ/+DDtwT+sHrdamZRR /JGC+1WWJzS+TOUauSY/ZcrYeLYtcMXfAidZUn7HPS2FqzPPfKx6whThyJievetAf5304IE7 Ft/ZPMoaFljH7dqOz76v1b+GCIgN7cP7wSBNQHudktVni42Jio2Ojo2KUcXFO2P7XJXmHjbV F25Wx6slp6yO7PPZy2nqneLriJ6LDnzKpKVZ+vnT3vX4D6smxvT8Rz/jj0zx4JQ+dKbkDh06 PsXpTBk/dGhuijPwdeBDKZolnN3JCtUmT25KdHRKrseTOzI6emRuwHjg7NEDB/jOQvZtBD2U k3V7JEmvk3vQNrNGrzcYVfo+bSX0+aTq0AqiATrWf1iZjYaLFOHaDmjHTpzTRfX8gTM52DXj /62sSlGFwQhYSz13Rdv5b/sjPfp97AE8bCSzB33RYDSmjBxRqTmbWGE964uriPbYHeX2SqMY flzKOcqqFjq9gceojDd6xfMt8gdU4vuAVdnMcbqci2U68mQ+a8QV9/c6VZgjMn5C4nFVmDNy iC/pxOMq+8iMnGG19RpnWub4YcsWSXsScxIjalr7l5yLyadvTvUl2+rL+fXO2/qT0OrN6O/3 cC+ww2WK1UbNPsmIG3mUZNwDOnuljBuWT++zVBh1+nKdUNlGCjNuIPetq/tSxciq8yzhDynp 06vzrsj/5oQmvmhGw7gZK2YkSbc+0DKzf518mm9jzszLr6vsn8nnfBnuV3GoYwQ+DqaTlgcx PwoiIQavBnCwjG5zhfgNs089sLMyOr7is+Xi9y8yg7VjEgcenXmSKq54w1Orr+i+urDk2qdW Lu++uiCweoWvIde1YdnEhnExkurK52+cUXjdkevWHN0yvejHz11/186MWSsKd/5y9KxVfC7z b59YUW8HLFN8G67fJ1m5wlJYNxh0/Nfs4S/huVXrM/hsFQ6HwVhuUFyXxpW8ut/Ze87Le0H3 Qypwu8ZeMHXQNDlaMnvS48d2lPS9p4/Onz5n3PTVlUmytCSnYoyzfj73+81JeanOrLZfLODa 78An4HHUfjTcqeyfQ6NhnzQMzDBciunWRXt6mOSzwFDLUMkmD034erhZHVehe4rlgj54yKc3 Wcv0YX8P+bv/rRR+jMkJPfYP49hYc4RRnn+7mYERTEzKstuV95KkUfJ5ZwINPxI44mT5+PT1 v6xp31MxZ19149zI7JbKqVdMSRi37P5ljTfnzLp96tTLbFlzZ9RdOdnNUmatne6Ninw3yTtx tCPRm+CIyiqb68tbWp0eGfZ8jGNsumPY0AR7dE7ZbL5vcD+pJ0J06Fm6xxqtN9v4juUI0xsw DHHpbX2mMHM5BKPtwcGHmcxjtHvh/ms53B96NTFfqvigkw9/nliz+Z5mVc56xwuvn9CyfW5q 4Atm7WObAysr5kxYnmYw20bM3DibfXmAqQoC+w4Ehs6qjwznY/wT1P138mmcofXKDA0z8NWv xZdZ427cpFS0qq1VaEmlvvy8xd9LO1v3pUrxEVKUwxONN3TIsbJ1efkTxep3F06/LHvGyhlJ 8un+RdW1rTOl687qxDkmc851ldLDqKUD11EVaqmD5crbVoRWJ0tuBmq13hCujlVLZpWavw8Z 8LVeK0vlwPiiyMRXIXwXGodTJfRWNcR5LOMYX0tP/qBqfOvyMHpnkp4LfBJYwiwn8vPeZBrU 9tBT3VIZ9yJeVAl41vDAlC6jmR8xIp2RkTYI99ptQ51hfSaD3R5nw+XCfAZQ98fVRFr6bJWA j6X8QccFZjl1FH8whTMB37pHyeeODvQ2Ouj4oEqIGpeXHzNq+rh4dntgAUs9+yozB46zNHGI uDZTtUcbrtfQISJgP3CA/Vk5Rpi4vrifFqO+SVD/RFKSOy451s1nqyHSoNVhGJ4cwVV1RDr6 LYa4PneMxaPTyZYa6E8adgZnI1ea7zc5Nq62RZigTOFXcA6/Kh7DYo4mZg3WOWrwYUiZsFek Lsy679HANyxsyoZHWyq3Z47cPGPUrKJk1hMoyytIrE1WGS3+Xey/SPso0xFTtGLUI3NmmE38 6bAW5/Bn4kSUBJP2Qpw0sTtSZ0SDHo/Qoi1efpCJMkezYDSLNmpk3RlDOfRHWAYswRNqzqnL r+ilY17/Yf7MGKS9VTm0XviokD9LXTRmf1fgeSnTkjB+BKoaeD+aH3roaCOIynfdy84e7J+d VpAcYTapJl9w8OH6r0H9Pxf6Z0L5XjzLTewGbwofEEvmkCFjsob1e919Sa6UJIc4k5nVPmd8 mTqpIrI/M/2MQzHiVAqfRzmhwQjZ8Z0TmmLX4BdGPE8oJkonLjSAuYSRloTcEUm1w3Gs9nUp xn7nDNf3pjB2AVn6jNHcdW/gDkrgGmEOYeVGiILhXQapRwrzmaOi7A5jn0EblJililUD3zRC +vfyFZBpveCFgTm+jc2uyJy8NcteVFE3Yvk9benqjWcjsmbmD421HdBHmPW+9tvqcIaTT++A eBjTZXZwX9rjNRq3x9JvDjujdcgROJnjYwZN5pxBjjs3/Bd3k/z59zlFjLf0dcgFmOg3KS5g /FtpqkgV/yZRzZMGgy7aEh6hQ82eNIVjGOLCw8DE3REv26P5ay//noHWZ7RXWM7Yyk2GvjDa Lfjbb+/VFnzGh1Ycrr+jdO5VXjW0Yq/ArTbL4xBqO1SRoxqG5cyrnTaMmfvze55iP9NOPbjJ Oz1BTgozx2aWJgfGHzggrT14a3W9Rod63o2dB8SpZXyX2cDXkQPfdaINXD/zyzbHPsa/16dm 2sdtFaZyrln+eWrhoz1jYPuiEwj9XsIhB+KLSwpcuU2oS99JDWoRV5Cfa5cNYVFmHddEnP1Q DYNJz/fVKtynuMdSYdqe1NSYyATurpgwjWZUWriLuykG4tFNtgRtpCw7Kwx91groTx1xbmXn 2xx8YFma2GNRr/O2Jn7WCw2uI5sQFfJiaKxLcXz3d/lKImM1YSkRG+/zqsl5IZ8OHnm2YM4M STogSfWzVNyZJxX/HlfmgMTWBz+VPlFJYq/ydqmj+b8pmr2R/P864qrQw9O7oBxHmp9d6SGg /IIrtIYjLkhLmYmF6TEx6YWJiQWjY2JGFyRekFZJMaMLE0KZCYWjY/rLMYY5/DbPSeff+TrL 9siLpK0gg7VbAmmvmIL8Jf/Y6HT++9Cz0lK25+TJYBDOStnyItkiacU3o65Dm25Dm17DlS3j jPHuB0lKwaheGtcdwfBcMHm3qUria7v/VZYmXjCZV/bIF9qx43gCc93ozhkRHT0ix410Okfk sFvlL87swIjHQxmC/NV+/feEQxRYzkDw/+cGyYehS46Xt8v9PKiWiPCwOkG9Wn2CB02G5qh2 mHa19vn/9KCL112v++N3g77+e8NNg8JvzwXDKCV0KOHd/6lgHC7CRGOpccP/hf8L/wkB9+JU aSiEvu3ZIq48zsAiUjwugU59CkLfMp6gfkKJq2CI+hUlrgan+mslroEhGoMS18JZzTAlroMR mh1KXA9urVuJG9RVA30ZYabWp8TDYIT2ViUeLt2mPaTETbDYUDfwneEMw2ElzkBn+JsSl0AV vjv07WCIC9+pxFUQHv6wEldDWPjTSlyD+S8qcS1cE/6WEtdBlMmmxPVgMdUocYP00EBfRkgx tSjxMCy/TYmHs2mmXUrcBGPNX/JvZav0ip8pTn6mOPmZ4uRnipOfKU5+pjj5meLkZ4qTnylO fqY4+Zni5GeKk58pTn6mOPn5IXBDBqRjyMLYdFgIzbAclkEHynxYgXlFGFsO7eLahDn8v0uW wii8UwCLMbihCvMWQBve6xCpVmQrll6F1xYsWSRq8BYXYwu8zEJxbUIuEWXc2Bdv3w0rRV1e Yile24UuC0TPSzDw3AWY34pchanlouUlIr1CaXOpaG8ZptuEFm7xDePleF2JJTswn5dpFlq6 YbVSqlXUdWMJ3iK3v118N/mcdkuFN0hzfrdVtMu/3UxWnvNHM7bZJHRuxjq8dV6H560S/ZBm vJUmoTvXYiG2wfNHYmoxpv5L5HOPtYgaa0SPq7GthQNtjhT6NmHZkF8WCh25HQuw7jJRl2uz TIxr6yA/tyttcLuahNYhz80TY8Bb5B7qEC3wOstFul3UaBnwIbe7RrGHjyaN2CrFb7NEOy2Y s1q0dM6PLcKT7WJGrBG9c9/xclSzSZRpFZosEHNhtbCubWBW0IwMzUfSc7EYORr1FRh3i7Fc Lry0WOS1wpWi/xViPJaKGB+pFtH6wkH+uPRM6DhvnPgMXynGhvcd8klolocs6xjk/yWCrYp3 lyj5fG7Ow9LcI/wu6UVjy9ejW9G/VXi1VZkbIZuWCXs6xApuFWW4JuVirJeiRjSHuA6tYiWv VMb0v6u5Drgokqzf1UMOkgVJNojk0IOgIIgEc0AliGmVYaZhRobpcaaHZAJ0ATFnRRcQjJgx 4booGAHFNR2naz4VTCtm1rD4VdcMQcXdvft999138OP1dFX1e6/ee/9XYehSoo21ngJxJVS2 SeggW6bqr6StTILwRCHriSGXACRbiXAh0s0d+ZBFItPmVWWMfe6VdMSFVvFobcPWKSNdoso7 ySrUsNpJVZq32pPXplGcyv9Ke7XGFIt/niq3iCFl2lDUMYLFCDWJbU+325avipY4FYIVCB+C tjj7Gk8Mkseg9nHIo8koK6S1WbA1D3Smdxxq2zGjpajyB6vx13m6ryoKP8+zoaoM0rdDlh+r 0lSkihcu5MfWfPm0R9vT38rflAqRVJsH2HhKQLWMKqsKOiCMQh6Xqezb+kzntfH/1GjUOl60 WjQaxWlr1EUiXzCqiFHa00ulwefjBI38yqiQzFp6BCzhY86o3y6qnERgg5FWymfZSJJCC7Mn V6WgX080Un2uuacK516qXN46qkkhhzRYyo4U7bnlc66t5fEoO8hQ1mnlNx7prBwH0jqMn0xb /mnPuUrfKSM1Cdmn1ULK+Gy13kBovxFwNGtHV2uNMvMKkE2YNqunIFl8lJs7kyvqJMe0I+fr kUAZ71LUU4kKfUpeynGeReyX/WbrlXnTGT7lgqJTiSnBN7WSfMX5r9uonXt7hlZmU2X08D8b mb7ue3u8fq5XxwzI9kTZFwbJa416GToxJU2VXVNQz2mEoc57qrQz7zObtuaaLzHAWpWNPIVq lKbQPIuviikajQ0UlCf9Ew/9b+GiHRNeSBsWAwo0MngiX0mx1G2EN0n6EiNFfBktp+MZIoyW SWkZjxHREk8iRCwmIkQJQkZORFBySpZMCTzDaImcFvPkhEhO8ERJlICIp2WEQk4RIgkhldEJ Ml5SkkiSQFCSZJGMliRREvg4TyIgaEZIyQi+SMZXJMkZnoRPyYkUWEQRPCKJltByKY+P2EkY lrlcSvFF8SIoEunBF/JkPD5DyeSEkJdMEZAZIeclUUSKSMAI3QmxKJEiaLGAYNKkVIpMxLZ0 J5J4iawuIgbKSKBpAWRDi/gU0lkKW9ASnhgpF6eQiySUXE7waZmMkktpiYDV0JOIgnJESbBj sPNEjEgioFPkSh0FIrlUzEsjeGIxnQIreYSAkosSJFAjRsiaAhqStSPkKaah9QiGJiS0LAlK ZKhUBvaAJyEYGU8gYlvB0i+MIFf2KYxWyESUjNWENTkrTI70T6Kh6fh0EvzM8OLEaYSMgrxg b+l4AvKnJALICEmiJYScL6Mo6NJRUkoSBS1ExFM8RgF7Ct3GFysEFLSqJAE9LYNyJewniSKJ kvHE8gBCDh0upATuhIBmGLar0GKqrqRTMHICUAlPDI0ugbED3SMX8qSUUk8eyygO9h/qxVpK xufBaBFTDOsipYHFNJ3IViNt+dAscdDBCgmrP93uJ4YnZygiLo1I5snSWAXZGGjnHceTKQMt BcaH3LMtpvsSbTEbCgOkLwr5sZAptDrB9STJ1moPtrpjfFMiFLI8aNkEEZQtYxWCDqOSeLJE AnWtw2185zBiccEqGi0RsaaLZHgMhfT0ggxUmKAVEgY6We45QsF35sldYCQRg2U0rGUYaV8v r5SUFM+kVuae0OdeMMpZqEmFaV58BkWLqin7OZ4XJxMlsu3G0wqIgTSET4aNHxS5sHfQqEki 5EtoT1a9gdEjQpC72BsYvAIFn2FVTxGK+MIOz4raIgY5pw0E0O5SmQg24MNWEPOeRKtsWgJj 01nkQlDQU4KOrCStjTvVCDVHAQ3DFJqHrwRTm3RkVxUvZQA6i6AUhkpiTS8TQakQrBIxzeso FOrMU2rKRk2rB2gFI1VASFPJbHaAbYSUWPpFh/6KL5AnvARUPE8hZjx5cmlq62lhn5yw4xjW tjfT/gNgCx342wXT/PQJM0At2DPWuqK9GniHs7s4am3PslQHzrrHYhx+mkyMmSbIqETMVcxj JJgPrAGREaEEezqb8sw6RNXaPsH6iFEjv6xv53uUI/yM71jEl2nj64ie0MXUUWsDzBQzx6xh qR3mBufByjoNyEsXSjDDLDAbzAmzh6sZbpsOOpgm7JceZoR1w2zhON0Dzpa92/RyULXRglbR x4wxS3QSoQMcsXqpuLO7Pux/THXFrODY6Yr1hKObD+bLh9kHBCM6BNHRiI5DNJZNOUCIqARR BtF0RDMEEjoJZCO6ANFliK5BtCAejjtgI6K7EC1H9Dii58Q0XwzqEb2B6H2YkWTgMaLPEX2L 6EeW4jgNL7gWol0QNUXUElE4ookZ3BFRT0T9EA1GdAgcouLx0YiORXQSonGICuWKODkuQZRB NB3RDESz5QqpHF+A6DJE1yBagOhG5b4g8uqfXYFqF/RbVPsPKAfGhib73sZf/oTek0Q4wDsg QOMPqd4fUBxGVRckofXTt65sDP/rVOeb1BiixRPrjQVhg7BwiOLJcF7G7sjNwrKxRdgqrADb jO1SnZS8RHXNV103qq6lyp4CW+U9GKS6pqrK81XX/apy1cnL4L3yikeorqWq6xWl7zhayns1 THXVUV0NoVV04N9U7BnyhAj7VVmCD8WHsSVgOBjB7tJCXkNYn+Fm8M4VMwZTQBygAA2kQA4Y kArSwSyQCbLA9yAbzAN54AYeyVocTGa/dQU8wINaCoAAckwCEowDpgEZpg5SQAqmCdJAGqYF ZoKZmDaYDTIwHTAHzMX00PtiXcB1cB0zxCNg34zQGZ+W8M9YFTMGSMc1oATK4oCtYDss3gl2 YWrst43QVjjMVNpgCVgBVsFWBaAEbAE7wG5Yrv51a3AJXILa1IN6dEK1JaaDz8Rn4bPxDDwT z8Ln4HPx7/Fs9pxOnMIpGFBCXIrQAaCUdp0MWTsBBRvb8Pls5b43Oqe0tYVRhzrIDShgawxf h29CPmDlrsPX4z/gBXghXoRvwIvxEojkL+XimB/WDc/D53emZadlOXguPg8+p4GsjCFuOOQ2 DePgDD4D08e34zuwrvgefA/skZL/WjwfX4AvxBfhi/El+FJ8Gb4cX4Gv7LRsFb4aX/NP8LfG dPFJ+DRYp8CT8RQ8FU/D0/HpsCX0Jj4Rn6jiAVCP2fNRTaF/ugMC2IEAsA/8CGpYadgCdNIm +04fAH1BXxgJZaAMevUwOAz9XA2q0ch1Go5oPnDtFYzwGYVNwGIhQtlTNlMhRudieRCVq9Db iKVYGfYjVgk9w75DhwNdAKMe2ANnyNkTeIPe8E4PdIVUH5hD2gXA3gAD0A1SQ2AJqRGwgtQY WENqAmwgNYWYxkEP4AKpA3CFtCdwg9QRuEPqBDwgby/2jUl4JYEP8INXLvAF/tCCCXg8pHJc BnurjeWi81bZdygBzCuLYNkK+MuBs5BamMkvYFdgnmPfOzRGtjOD9p0Gx1SEahT77YhhUb2p LVq/HXetsYqp8rYyV2FWkVjbqeRWw8ksq8Ea2q7ZQ7Kb9YEmXpRl5QeLfHAAuLqktoa6WxcO bqmOkTwNHTcNoAay+uBArSiSHEO6dyixLrbNsMYC0e8oLE61wUahRXQQ+0vadWCmZhrf/20P vNHkzfrg6Uu9Hhbs2kyUfSzKMntPZnFOwj+PIg4OcNxw8LFuK28vjBgU1nw9aYg+dyOp36Yq UIdKZc5HSnKi1TRM8AkhXDPShL3RMtGLodj1gYQIg8serilpzBZrmugOUMjieHAhLBZTXAPI DZbqmGhECXkpDMW1Ia3YAl0TU2UBEUbBBWO8iI/WDdzupA1bzTHpqqqOgsttuHBOkrJz4rAQ 0tZcn+zF9SZ9SPQzwVyfy9728u7l6+/rP4GM7KBsdCTXnDRTyu8CFz+iSLhSdSeGSvieXDfS RSnIvrUCiWKXKkpZkXDpL2KX61BoFrDvaBWgjnGygAEGy3XwLACwbbVlG8/VEbt1Zs7bkaN4 vj/8xe0qg2MJvIoSgfUvR97V9to+l5w3btaC64k3excYHLv4NPVlyuZZdOCx5bv1fxS+Fq+o rYjw2D6k35uDf/tuihVe+N4r0XZjc0n+Zstq/O7sERH3usQ+DbaedVj/Vv8z+2/nVExJn8r1 5KzNNNk6mDjPlevHeNSl+vRaabzW+PAtoVdpw73jeQtcT8y3y4mvmDMuhlYcCyx1zPmu1tAs sHDu46gqHcnJllPDbh7WNFptP+N6kNNF29SnhdyaFw323a6f3Dc4LN9ySpHtkvuT3zyb8WLm 9jiw+M1I3VsX7MduXVm3Kzd517Mf9V/dH3mt6IOwaJdpwL6cqiM4BwZ+SeZ1MvMq6aOhBSNW XV0TADVn0pF0aL0nQbaFajlB8+VSz2Rod3brgF1OoNixMQHgk5oWqQEvOMDIELasu1pf0o/s XeRT5J1Nqh7ny8SfPe2ljJWOoRIW4glboUi16ammR+q0asHRIruwhQasLPbgXg2oIbw3UoOR ubEbad4a3xwTvajIEBhofh5cD99eX6CCk5mJDUt893jc8QHW3Hlpa91WHcvaAeqtR9TtyRsn ua3lUjK5una5SaNahH7TYCcvzG/P/Zrl4flX7OPMmvv3sRsl5Wa8mO+Xs+/hw9VYy8/Rq8Id Lm1zCk/fdYgX8sr1fGPNtck3j7h9H3TghwPX7sZ8Orr/1Kw3P+sVPF/d4nY5IMLKys+puf8w iOFPZBbeqMKx/iO351euuuRaeKtrT85Pzv0Sx/8WZHwNR9KvIxxj/qJQL9JDKdTxz4SydZTs TyFZNtp5yM3LwvS5FgPiFd/NOlleyHf81C9s/QwjP8Oe0fJrCifR7+GHiUmXdd4VWbn+Gj3W jnfV9vr9n3olnmm6WdKHWmS1XO9gpO2kGfG+U9TzBrYkh9+OzCjOJH7YlTupWKv5AfnumX2f EaE652+f7n6yPvpRZv8DESXupSD9ZXHpQt+WwobvpqoX9ku8d2xVZcu52HfBjZpFA55kjpFs cn15MM/Q+dfFNzSKskfnTx+mpU/a1BoWJDY/GrdLbVvw2jLnh4u77gi8F0kPv+z7wwFaYLNv lfuRfo1pT5LS33VtcNy5u2lt5KFg95XlaaUtVyK2uzCzQp/62xZP7dow/oiD8CqWEWaYk5Go gmQtmXnmX4SkXhskcRIjeynB6E66ks5FjkUO2fbfAiMjl3vweQh+XRH8WBZ/gECNyr+EQJ8v Ech6OSdV+kt4BCAm3kmrySJP/n6426qKpdiJirq606+7XP30bmRlrzjS6NQbxurKsltT1hMm e2cMPDq6bk5jhvmcLU7LE0wGfagtXxPCObduzET1+bO30q+sRls5eL4ULRTbNx+p7bryVz2m Uphy7cnauJwq+ZLf5jHpPbaXrJm+em/zYpdpIz0VVkNCfnl+QJ+Iqk8pWp3FF/2u/XPec8UR 7XXX3hlFO+bzvI+m43umZx8tPjHf3j31om/yT8vkk94dbhhhptPj3P1LV3w8hwabBRrEpjuc 3hTftOpn6ZOgxtf6s25cnFGSPE1UtX7UYNLXbm/xbsu4QLdri0pdNadftdg3afo/fthEtwTO 20lmqRnDFPBemQIMsCpsfmBgrtHFoLf8p7eDO1pMDWYAaSu2dU3sw2hpmozd5iac+S4E19+/ zxc7eZ5cW9Ja2dis0z0+rh3ZXekmi/b6CJpmiBAFI6RlIiaNTQ/+fUgulyT7qNKDN8ll3xBX 3v4HNPrToRyvqJI2BLwMt3IuXJ06mXxcvG1hzym/tawcUXKo5YdiImjGmOJ1xYtjvRMvhgrS nu1Iron65eWT9dnWiwvnxu87lZge16PeJvCWAVj2cNXJYx7x+flCx7UX+rof0zswzrFqUKNO kN8q923O/lufDp0Tem+uwZF8cTRvR9aMDbEeKSMerd0vCMgfbc3VcjAt3Na41M2iod8avmns OHWq0KZPRE7zlqYV+Gmry8eiB+6bl3Gs79OoFeG7ft+SnsSE77Y4t0rb2Q6LWRIr6nNkuLFm 4NhPEz9sjNfR2nwpc2xM08GAyV0zU9R+eXt0V8bKlj11s+u3WMomBdb+9FyrxJ7cp/F9zT4i xeT726q8sZXM3ERmFrO4BGqZ+WTm6gzDiRekTSJZQY8xs0zLRi76dHaD7P/ef1l/EuMoK6x8 qFu58NVqC99fy4HD1RSjV5NivQsLdM8GqS/NXVzTt8Hu5fOY5e4HigZXxzV9/Pu5gIAJ23pH iVockvrXnCu9pT7jJndhv0JD6dQjLcajLESVHy+E3TOaQIx6HDd9d2m3arc+PT2OUhuM83oa 8Euao6zf2dXUm72K2CEJ89b8Pcv8twcJYv0xbyteRJypaDxJfiS42rk2K10sR/7NBt/0IuMO Z//E13tvVsc8o4aeiYg6uJ/jbPxpSf1zrcWzylef2t7H/X76/a0p95KLsAtT+1dd6p13J8R4 q+9Uq6nXfe9esVa7v3WgWvWEXn6Skdb6cYd0ihdc/ltU/0F11tGbpdeN++YsVxRuuVQEs8JZ ODnYp5oYTNVdO6oS09tudNW+aaY4bdb/i7RAepO+3r3g1E41ieeS/t6+qlsyc/Pn0wYT0ki5 5NCJ4cmFcDrAQDmGaBiBCw7NCEqQREsErZrpfEuzb3XTGwr9qps9SDtlNyw71ggoNAFhZySj 0cKA+Dqb6LPZRAtlkxPniIU/3f4UNPpZ+vErDj3fJp+3+1TnOja8dv2hrDLfNA/s5Fatv/Fr Dm16+6iqqn7vglXFmu8NDmZF5D/JOl1heGpr5bPEuYsirY6Mfi8A86q6XskSYsGpA94Y+4V/ 4I+5877f4Qd99t7ma/YImBbsM/h14q5Bb5zktvZnQ7vZjjkYkX+55ILJ6W79p2kkvVxpN2BK 6K+VNWsFRHmVz8fiAQ3Ty2y8yjffer3h9jo7g5Zx3JBov1m7xzXefzo+ref2Zlcvo/5+qUGh s7cI78+yF5o3DFt2MnVAxOANo+bOW76uMmH6Y+0P2ZyZb9dOC3TbEr/m3G2Pf7jhlgY+Q6g3 gca7X+RY2zhG0Odg/HFKsoArtIdjZ3Nxzn9HijHW0FYtws0gYHAOB+OgZapNF7WuaqY9f3Mb /l21LGrng7dFruZdP1S9i8wku7U9Yoqr6dnqYJGYAi7Zw7AQUhdNftDaYxBp0DbJUic58NJZ OisUDGPuipwm/KYR6bT6jO3FTPnF8z9mvrZ8vHx1zu4bi/viB+YbHrvsyTAgYH+wybMxUwJP eK8bVf9ofK0ztkpm9mSa15o1lZy7vqGPtLziuGs/cl/dCN11Y9Tic363pCF+sfqBxDI3i/Wa LWOXb7s0PLdi7TB7/Ht6wNOlDd1uuZWbla2Tvj/1JHyOe99pRd7jXvpGXF+aG9xEWzW6hO6X 1s0U05bV19e/vzg5r/mSez425drr4/sKp18JaIlkXBNsMp2KFQ+D12hazLcgdAfJbjc1P+2d 9CyxJj7SpsK39MFZm6kyk4EZtte+y4nPs3bfXHuWx+Hc0q4Be8dX7vm9IX712ffaATPILPVB MKV5KtOZDk/nMYm2bid/tVPx35I22Pzny/Xmwgzo08cXrZr6wGUUvPVlb8nM3H9LR6DCSt1c Ov/S1nmoBH2ZLSai5RQxSiJOc/nT+dKJLV0v6V7pH7fk2fgor5W69OsroqPiBccsfo78UFdz WXg/7tAURpfTUFr1JMjp6aU1S9bxxraQz7ZdFo+IvzqpZ+ai0vqkHsBlZV5xeW7/W2NGO6wQ NP5UXYA1ek1YN+6jeXYjn9iT5vFwr+EU2YiHP1suHrFR67Yg1m9dOH/4fZ8BK7x9yw00ZXcl 0zeVX67b1zJpp/eYu/F7ikYe0hVzCGNhpbvpb4szaxd6/gR+1ABZLuZb7GrnLDozUkBc2zRH vuoDE5U82vzQvqdDnPL29wzf8+BIzXCqONj/1s1383recVy/JqKkgvHpJzcgjRKIM2E5a/vb 7+W7ZVJd7iWpB/d6cPxWkDBAOV/KAkugRRZ8tbBpzxK7p9sI5/565p1n9+zUlaEuB16c6rr8 GylxG1vaQy1zA5lZkNFpetnAbPxPJMavZxLDlKvCMDKE7F/Urygg27/DqvDzL4yliSK21Ev1 Nbvci0UFC4rRqr2a8A6r1FAymAxqW6Xi2d7f/B4asaVkX/FjOlsugsCQxecd/cY9KeJfFjZu vxwjbRrapB6dY3Yip6Y8hhc89nl5M8mJT38yf/72VIuJV55tCa2qXPD7zr9fWPQuN0c7tkxn VrLoWtpJNf6b5VEFdwQWrxbkDhcvltTc0QoozR2dp1P1qmH/y9dvAq9yD2nvbZpQMUo6Nfje oZl1jx3ca2Od7haV7XcujXLYW7Dijf8PCb14o4bNkNUdNc+LaJ784u//COJd6KMVGJuUfekI /+bD6ugywu+IsfWc/MzeUW4Xrz+6Y/z72fCBV973nvqO8a13TNG/X3ZB43gzo7dpf/2ux6m8 xaGGUxSSuKBNVTfCealDDtfHnI96Wnd00/aBu7vYE8U7pnvd5WapHYOZ9AgOAJl54L8mX36W 89t3uIsyb5CmbeOsM+BqctRRI3b0Vblem8PV67ipDlVvv9PldiE71pqRPdofVONC1J6+UVLb vaApo3Ae9tvON5cn4w2Xysj4Do/occeRY4vcM1z/+j+fbnDMcPgL/1bx5cRSLQtg9OwJ3mdz GsVBVs/T788xK180u/LcBvyVuccVbsnI0HvnfEwdr9T79Pg4aujp3YfVUs8YxfkMFKX96D3m 47QT5UF3C08mnHAXjIv/NbxHin/3Ok/B0igr423JeltyWyrH+yednCeJlcTkPLt+9oXvVifO U7VBOW/1mn3Cgs/0r9hm7uy9wGXGsfvSqw64T68lDd2H/uP82VyJ3hh+5aWIqiFTny7o3/fa 8XKu7oNXsqmXmwYtzf3p7fZFDSs+HJnYVGMXXFCSLLpz9ZGWZeEjZptftQ92m/t27LPXvedh 4z2epLufws0sdxpbrMS0U6Mr/Hv6lY3cV10SKXh9cEqOVf2BLYdmSgtjTthv3JAFZ0hZ4EO7 lzS4WeApLHrIhnTCv2WPs5OdVT0NLaUCOMwsReNJi47xptv+TQ+A4dZWo841UA37/lx/7z4+ /hNgxu0QbsZqhotKoondH86MKp1JzoshL+R2EgI/6Cat2T1+z86K066fRM+uRl5n3HLfBcy5 PqT6ULcfY6k9ui9bdo/ociBjSMDw+lxZBn7xqeP8OcVe1iciHNeO1zZuin/jHx/cHKI7pze5 sSQ3dGdFgdkFnSExEXkfey2arpW/IgSfctP/qig8PH351NurcxcdPL2eHrZozezswHLPkdrn Zp4/mnl3x5gWp6rTh3znGQymi19P2OJu+3D67Mf6v1y/q1NjPSO7OV/2IHj+1hm4+Hjucyx1 RfUtr/JXC8hljruWNlz087slo9/MfWEc9criibnn/G7vuUbl31VPeW3/ZOXk3jcXmHserXe2 uplzq5i7/9bm+Sk1ZqlZjZc/ZIwbdKjoQ/WkEdj/AOM7wDENCmVuZHN0cmVhbQ0KZW5kb2Jq DQoxMDAgMCBvYmoNCjw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMzA3Pj4NCnN0cmVh bQ0KeJx9kk1ugzAQhfc+hZftIsImCcUSQmqSVmLRH5X2AMQeUkvFWMZZcPvanjSNUqmWAD3m e/PQDNm22TVGe5q9ulG24GmvjXIwjUcnge7hoA3hK6q09CeV7nLoLMmCuZ0nD0Nj+pFUFc3e QnHybqY392rcwy3JXpwCp82B3nxs26Dbo7VfMIDxlJG6pgr60Oips8/dADRLtkWjQl37eRE8 v8T7bIHmSXP8GDkqmGwnwXXmAKRi4dS0egynJmDUVX2Jrn0vPzuX6GWgGctZHRVHtUZVoip4 6nTy8J8O58CySFgpkF6daKzn14HlBjGBETtUm8uI/E+EYAkTHOmH5BUrfLlOj7v8/1xRIlZE Ly+iyhkXl7lxXHGr513Io3NhDWn1af5x8trA+e+wo42ueH0Dr9evWA0KZW5kc3RyZWFtDQpl bmRvYmoNCjEwMSAwIG9iag0KPDwvRmlsdGVyL0ZsYXRlRGVjb2RlL0xlbmd0aCAxNTk3NS9M ZW5ndGgxIDMwMjE2Pj4NCnN0cmVhbQ0KeJzsfAl4lNXV/7nvrJkks2WZSSbLOwyTELJBQoCE JUM2wpodkoiQIZksliwmAURUoEqxAbW0al2oUKu2WpdJRA2CgAruuBTF0iqWWmttxa3YUiSZ /7n3vBMCgrV9+j3f8+/zvZfz/u49995zz3bvfQNGYABgxZca5KKqObO39/X9EKB+B4CjdXZR cQkkSS8BVLyDoxJml5dVHZx8cju2z2C7dnZVTcHu2X88BFBZDaCfW1aVmbXi8B0lAOwX2N/Q 2O7tilRXLQfQbQNQQeOqXvnv73w2FmDMdbjgbc1dLe3yPUn7AQzYH2Ju8fZ0gR1cKP8VnG9u WbGm+f7haSkAyXGo5JOtPm/TsQN/ykL5adg/uRUZoQvYE9huwvbY1vbeK7o3tkUCSHoAzQ9X dDZ6d+4YfBEg99fY3tLuvaIr9CPTZTj+ehwvd3jbfS0LnloFkI/zraVdnT29gfHwLK7/IO/v 6vZ1DR665ihABLaNvwbuK6T7t7Q9ucw0/UsIwWXw2f2+YxzHfS98t374s+EoTajuLhynBwno wTmaz4c+Z0vVzcOfBS7RhApJox8150gHwYs1/khghkxoQK8+guvyR6VulvaABvSa2zXZKDKB UPU6PCOBPkQK1YVIklolqXeA9KkH5HlB0QuqZBmwyDGkg26JJMkA24XQUxoHtxSYuhkWIpEy zUKjDVAJi2AQSyYsg1vhQeQchHRYCm7YhLQBlkMZLMaxTrTVAK3w9UcdeDvwNqTADFEqA0cv MAbEClQAIr/Weyv3A64GgTBc3wV3KfylWM5/3IEccMCmwFFcaYPCW44FUNOyC6y8+IL6ALTD ZtgI84F7Yj7Wt8JquAXf07BeijQfdsI2bH0fbGi7E9+3XlCKFd8pStmIts3Hcv7T/DVOpPBC pFK2YmnF9S2oAa4SWCs4NcrYzQqd+5QGDkK8wt+p8LZh2cy0gSbU+0LP9y/ii1Isa8/jrcWy 5jzeGixTsACzYaR1+C6AF5BXGfgJ7wucgmoxrhp2CNyB+cWfSkhED/6LD7tH2sRuENXD8B5b x9oxM87AGXabKDFwN5Yn2ErsrUSdDsMUpmZ5nKAHM4NTKXsWY7IxcIYXeJH1YDHDB4ImMj0n uA2OCtqE2u6Aepgr9sVByMWcbcVd4cSIbEAe3xVbcU8sw5HNmGkG1EsPyeAKHAwsDTgxKyGQ P/wZvufi2M2oUwp6tUl4sRLLWpRjxLkOWIf8RIxFAeqWjfwYLBasxVzMEwH0Hdcd3oOXYY/Q j2uIuwo9vgQuBb539LjqMtTPi2tUon7JQr9aKIeJWAxozwbUOwdmIp/PLRd7BkRWPyhW+QLH 8ceL5Aae28vhajiE51Rz4DS0wTVitBnX4RasE/unBvdFDPpoJcrLxWKAfFz/BnwvxNEvA8/c bULqVqTVikH54n0NyirF+YWiVQOZGC0cwR6A9yEEOWYsReizGPRkKkZmKVyOe4v7/Ci8iB5M QWt46UEfbkbuNtT3MrSxVOGvRYu3IP9Bhc9Pug/hA/gYnsfMPQ6nsHUc7uOFZcIn/0bv6Gch eqMV/cBxI67YDCL3EPeiDnrUfC3amos5vAl3/GWon7hR8NIkilNujB3Y4jfFFozzdYJvRk4Y 1sLRz+UYnRaMxXegE+1eBTvkmEBAzAvHM573NoneDuge6WWBLzG6z8KY0SXQeLxMWdF+kbRT sQbQso9F/fOv3WcMbzC6/ST45oeNSPuffCL+6Yhrxft76P/rxUnIz046X34gshPgZjx/+XPb /4iG//lHdSEmu9At/W0eNfQCzwaeb/pvyLWL5dnFcuwb8stTsmzppUsuqa+rramuqqwoL1u4 YP68uXNKZ5cUFxUWzPLkz5wxfVpe7tQpk3MyM9LTxiW5x7rGJNojLWZTeKghRK/TavCDiEFa saukQfYnNfjVSa7S0nTednmR4R3FaPDLyCo5d4xfbhDD5HNHenBk83kjPTTSMzKSmeXpMD09 TS52yf5DRS55kNVX1GL9hiJXnew/IeoLRF2dJBrh2HA6cYZcbG8tkv2sQS72l6xq7StuKEJ5 /aGGQlehz5CeBv2GUKyGYs0/ztXVz8bNZKIijSvO68fPwXC+rF/lLvY2+csraouLHE5nneBB oZDl1xb6dUKW3MZ1hs1yf9r+vi2DZljekBrW5GryLqn1q7w4qU9V3Ne3yW9J9ae4ivwpV/7B jib7/GmuomJ/qguFzascWYD5NW6zS+77ElB514mPz+V4FY7Wbf4SeJWbOOIm7A/WAXVDDdE+ p5PrsnnQA8ux4V9fUUttGZY7BsCTmVrnlxp4z/5gT1QN71kf7BmZ3uBy8lAVNyh/VrXa/euX y+lp6H3xx41/sF/2q5Ialje2cvT6+lxFReS36lq/pwgrHq9ia3H/hEwc721AI9q4Gypq/Zmu Ln+kq4AGIEPmMWirqhVTlGn+yEI//mykzPJnFhdxveTivoYiUpDLclXU7oLswO/6J8mOR7Nh EtRxPfzRhRiUpOK+2qZmf2KDownzs1mudTj9njp0X52r1lfHo+Qy+1N+h8s5xYpiFtp23ujg YG65zq2XayWHqo5HCxlyCb5cBdOxw4zhEk0e0YLpci1zQHAYrqKM4LVz5GBD5S4s5V0qPrWw 1OGsc9LzDSo5FJ00br9+lCwzMkZ0onUuqhqN5gqlyMW+olEKniNUoyioSLuwnhL3hbIwztDz cJYGu1Ru3LnIk1CMYPEo2mU/lMu1Lp+rzoU55Cmv5bZxX4v4zqtyzauorxXRVrKk+pwW9U8d 6VNqfqkQE7Ak1RGMqWjPFu2RZul53XOC3XKf3jWvqo9LdikCQcbtgxZrk+Z4N0+1TsJ9WYJH m6vE65LNckmfdzCwfnlfv8fT11Xc0JrHZbjmNPW5qmqnO4RqlbVXO67kS1lhHptXXZCehgdP Qb+LXV/R72HXV9XX7jLjbXB9de2AxKTChoK6/rHYV7tLBvAIrsS5nMkbMm9wSZXY0Ivxjl0e gPWiVy0Yot04yEDw9EEeg8ZBiXjmIE9Cnpp4HsHjD0bI3or+xbO2WG7isbmqrrWvoY7vLIjG OOIf5meumeCXXDP7maQN8xtcvgJ/qKuA8/M5P5/4Ws7XYVawaIbO4QdSX4MLDynMplpwMMpD FRcpDwYC1bXOQ44TdU7MsyVI9bX+kFQ8+DXuuThuNqcGZM/2r2/0cj2gppbP1bnnNNZhzgYF 4pA5/hCUEKJIwBElYg7PRZzUiLHBAIr567HhX1/nr0vli9a21YlcNvuh1JWHYSeZmiS+UGZd n9WVJTYm7gODexOHENQNqmqJ48AmLlZHTtKFoeaNLuxqbJDR22porMI8p4PU4CCOD89DdZJP kMGhdAI3S+UODTf4QzJQIP7h9dAMvh81bl1dHSkvWpuUAbi22R+KGiWNcqUyAb2DXXO4Lvhn E6rKhz7NxVQMQqXrCjxWuNJCkg67/eHuOV48+Wl+KHJcU4OT9fyACFVkHCCujlsehn5XuasH Az93rXGOetLTXPxm4IkJjl2Y2FDXdz7Df0lqepr+fG64YPf16cMvPIH8pQ8fQWR6VoV0s1mJ narMxA5pVmJbemtNS3pzjS+9qaYxfXmNN6uhJrEhs0FalrW05qZ6Vl7PPq1n5voJ9Q31qkXp NTX7a9j6mtdrpOr0qprXqpi/it1UxcxVXVUSYgPHyvSKmvL0spqGMpZYxorSC2sK0mfVDErz BmRX4qA0l2AOQelAYhLCbIISguKBhBSEIoJCggKCWQQegnyCmQQzCKYTTCPII8glmEowhWAy QQ7BJIJsgiyCiQQTCDIJMgbiJyKkE6QRpBKMJ0ghGEeQTJBE4CYYS+AiGEPgJJAJEgkSCOIJ 4gbichAcBLEEMQR2AhtBNEEUQSRBBIGVwEJgJjARGAnCCcIIQgkMBCEEegIdgZZAQ6AmUBFI BIwABLAAwTDBEMEZgq8IThP8g+AUwd8J/kbwJcFJgr8SfEHwOcFnBJ8SfEJwguBjgr8Q/Jng I4I/EXxI8EeCDwj+QPD+gGMawu8JjhP8juA9gmME7xK8Q/Bbgt8QHCX4NcHbBEcI3iJ4k+Aw wa8I3iB4neA1glcJDhG8QvAywUsELxK8QPA8wXMEBwkOEDxL8AzB0wT7CfYR7CV4imAPwW6C Jwl2EQwSPEHwOMFjBDsJHiUYIOgn8BM8MhBbiPAwwUMEDxL8kuABgvsJfkHwc4L7CO4luIfg ZwR3E/yUYAfBdoK7CH5CsI3gToI7CG4nuI3gxwS3EtxCcDPBjwh+OBBThLCV4AcENxHcSHAD wRaCzQR9BN8nuJ5gE8H3CDYSXEdw7UBMLsJ3qbVhwM5hPcE6gmsIria4imAtwZUEawiuIFg9 YJuPsIpgJUEvQQ9BN8HlBF0EnQQdBO0EKwi+Q3AZQRtBK0ELQTOBj6CJoJFgOYF3ILoeoYFg GcFSgksJlhBcQlBPUEdQS7CYYBFBDUH1QFQjQhVBJUEFQflAJF5nrIxgIcGCgQg3wvwBayrC PIK5BHMISglmE5QQFBMUERQOWPDUZwUEswg8A+bpCPkEMwlmEEwnmEaQR5BLMJVgCsFkghyC SQTZBFkEEwkmEGQSZBCkE6QRpBKMJ0ghGEeQTJBE4CYYS+AiGEPgJJAJEgkSCOIJ4ggcBLEE MQR2AhtBNEEUQSRBBIGVwEJgJjARGAnCCcIIQgkMA6YShBACPYGOQEugIVATqAgkAkYAngAi p2GkIaQzSF8hnUb6B9IppL8j/Q3pS6STSH9F+gLpc6TPkD5F+gTpBNLHSH9B+jPSR0h/QvoQ 6Y9IHyD9Ael9pN8jHUf6HdJ7SMeQ3kV6B+m3SL9BOor0a6S3kY4gvYX0JtJhpF8hvYH0urEs 8TWkV5EOIb2C9DLSS0gvIr2A9DzSc0gHkQ4gPYv0DNLTSJ739xtnJe5D2outp5D2IO1GehJp F9Ig0hNIjyM9hrQT6VGkAaT+8CWJfqRHkB5GegjpQaRfIj2AdD/SL5B+jnQf0r1I9yD9DOlu pJ8i7UDajnQX0k+QtoVdlngn0h1ItyPdhvRjpFuRbkG6GelHSD9E2or0A6SbQjMTbwxdk3gD 0pbQlkSIZ6b4xPib4lX+uP1x0mBgv6c+Ln1CSWJcZpxkikuMuylue9wjcZp1Dvae41OH5HE4 Eks8Dms0vgzhJZ7YiZPwlTweXw4nvqw2fBmMJWUxy2KkcnuDXQK7377frmqwd9m5+MfteQUl E2yMrxRhQzn+6P3REljN1i7reqvagPxHrQnOEt5vsdpiS2TzBLPHrALzD8ySkfeas3JEb745 Jb3EZEo0SWWmZaZOU8CkNpm2mx4x7cOKxzQ5r8RkTDRKs/h7n/E143tGTb6xzLjMqLrJuN0o qfYw8W/UwNgP+qurUlPnDeoClfP8+vJL/Ox6v7uKvz0V9X7t9fjTWf0ltf2M3ViHPwgXVvst /G8VRHvjDTdAfME8f3xV7YBqx474grp5/vW87vGIeoDXAYfU9fSuXNqTuhQhlb/5i/Wk9qYi oydVebDam9rb0wup/w0P+99W4H/xwQj29HDqxQdjLGqpI4G2LwXQrVBNGV5y3r8AdMA1sBXu h31wnEWwqayNbYJNcDPshmfgOTgK70OARbJS1sCu+zf/TWPk0TggGiDw5+HLhjcGUjRfDH8w vERrC2g1b2s+UJ2iPs1GiBjuCXyEY44GUtSvDS8JgLY5kBL4TMoFS1CCei1Ec57mMs1GzYDm sGr+cBRfQXfXP9PhAs9SaINOKIVrYePIv0zdAFfAlXA1tq9G7lb4IdwCP4bb4E74GdwDN8Id sBm2IG6Fbfi+Hdt34rwfw33wC/TlA/AgPAQPw+MwCE+iJ/fAU/A0+nMH8h/GEb+E7VjnYx8Q nEfADwPwKOzEGU+I8XsxHvtxzrNwAA7C8/AivAQvwytwCF6F11DmHtF3ECP0/Dk9O8Wau0ZW Dcp5ZkTSC+fJeh3egMPwFhyBt+HXGO/fwG/hHXgXjsF78Ds4jvH/AP4If4I/wyfwGXwOf4WT YsabOIdmHBcjPlYkvanIOl/S+3AC88uKlMpykLLZFMy2XJbPPKyVXc02sethHfp6M5ab4Sdw E/r8ZvTu3fjegfV70V8/R3+R136J/roHvRb0Xz+2g158DH3Abd+NNpP1u4S/uA+eR49xP3AP kP0HhBfP+uOlkdob8CvhmXP9QzYFvXbWZ++ihX+Av6AfTqCnjoiR7wn/fYje+xjbZz36B+Gx D+Ej9GpwBvftF+jdY2LW75VRfO7oUZ+KcSfhb/B3OA1nYBivFImpmYbpcS62sO9L0XsK/oEj vsIxQzCMe5iPU4mRWqZnIcygrPdN44OjjczEzMwioudiBhbGwkU9msUyB3MyN0tm5WwGm8gm Y0TnsLksE2M8Bet5bKaIcAErZEVsNvYsZBWskl2OMe9ivWwl2whq/t8pqGNwb6tABzJMgiV7 wMn2QSqEs/qdZrM+VreXLQYJIlkb6AFYrceulmL7HVrjhAlJ5jvDw9XabapZSeptrBDyh17N P2HJNZ+w5ma+aj52gmW+e+LYCfPQc5bczBNvnpg4gVmcFkGRRsmlc6mysybnTMqQXK6c7KwE ScW5YzKknEkzJd5Wx5zJUeUOLZLqk+e0zdL2ha2dUxCf7y3MndJ9b1vWKUtCcnT0uASLJWFc dHRyAh5Qpz/QOL66TG356lPpqSlLCpM2MqkgI3Fqiv3HE8tbh+6JToo3m+OTcHCcxRKXzP/l fhNab0XrjeCAKn7d19T2a6N3s79zWyXwGE366ONarT7quCdklr4Q7Pn5sQu4eWjbm2jZgYkT HJ6wiwyZOKFOGOqyuCxOtEcjTLVkZ82U1Nai771w3dWn2Lp1z24sHh52FjaXbPi+Z3mhS7W2 +aFrSoaXaBxTLrtz5aSa6fKw01WwBJVpD3ykOqo2wliYCD8iXR+NjQ11DUqhj1qYO3VQsnkS Qx2B2Fib+RU2FpLMSZJelZSUMLbCdjKhPGPIo61E7VC5/BP5qN+ypZdac09YhC0n3sw6wHXm 5iT8OyLQVmeyUTUSvmwR1zFaXc7kyTyWURbsM0pRkdEY8ykqvVYuqW6ctnBVecqMq3dfszF6 4sLc+kcq5+xrufzutqyTt8xcNDmmvDi7o2DjmNzxtozK3pLSK2uzZiTnp9lT3M/GJ6Uu2rBo aC572j5+SuLMWXOLebDmo3/exTssGpKhkfyzC6LRORFMtnHnhIeOY1HRFVFVrjPm8jj+0+No c9AeZj4iHMG9EPrNQ8+3NyvaZsnGvE6QuH2WpCRurcqglYsW1k2u37Isq2zL/suTFiScHOq0 pReMn9mVH1+TOm1p4dhbkz3pMaUb967ccmjTLIOe3XB6LTtZvXq+q2kx2xNqnFC7jtvWHPhI DRh7GVKgRYl9WJgN+F+KOlPUCB6rzXlIrR6f9Ne48vDhsJCAx0oKDx1BlSljT2S9y4PFzbP8 k8Eid7U6p2JQ1GhTEyTdZAquGlSqoaaUeW2eufdfkrykYXlG7+D6oqKr+ttX3NeVd4rJU+aM X7IqVJobU+4qbC4ea4t9JtRq1BVs2Hv1VS9vLSu84j7P9Lq8uLVdyn9Fo3ZobsOdGAdryMYB c0j4oBTusZoMYDWZ4hOsIadNEWFhdqMDf1h9AlRDHnu1sYL2HOpuQdVzs09kWawsN/PAm6/i BhU71PAt53GrKXRRUc4oJ9+3Oc5gfJnakbYmP3Xu5ER20/Dlfxs+ypLze+5uKlid/dUH6seN EbasBVPu2TtUK/1i7x0r7mzMsIQNX83/G5X5w0vUbnUYzIByaIA/KtGbEJpdN8jeflQ1I8a4 mz2MB24FO+IJyR6XnYrFFbNbioASmClZPGkub/ULemvUX+fOnxs6QaWaOm6uSZ+ol0wq/Vz9 3KneGa9NL1vy0tTyWS95EhaN5Ci3KTeTdqjVlmvJNp/IMp8QGY45gD38nMZy5AjGO5c7afx/ bBXuxujoKHGcJyVrtVGYRTZbgipq1AE/JSlpFHD/OqMYPx1mSvzoSMbNFRFs0mbaGGeb9p1b 6nPqbRa7OTHV8XnFtUuzPRv2r1vbf/lk69jJ7onZ1vikKPfEGStuqU2vGsMahpyrVxQvmx4b kz7D/Xa8O0qXsqwmvTAtmt6qEteCcTXfrU2PMlpSYs1yjJmFSO4i78zZ11w6ObnEm+eaPmVS bFxt1phpk7Ps7raymo2XZIaEDpzJnjY1dvzUBIc7JlxSR451jVU5KpYlZBWMrVmWkF2EmbwV w/6F5mGIgHhYR/HeAwZpOma4HQNqjY+Lio+JiY+KUyck2uNPOypMg2yeJ9ykSdRIdpUm8rQn uoxcfAw9i17mCW1DP2dmmof4Pep47NtNC95Ao7Yuw4y2YG7nOKXj9tS8MWOmpdrtqdPGjMlL tQ9/MXxcimHuM9tZgcbozEuNiUnNczrz0mJi0vKGQ/eeeW7vXv7fs92COzUfT1oDtJF1nrAQ rV6nA/5po2V4IXmsHhUYKvRlujItU6mrVBUgdhuenLn4ZpmZuO8O4ImZjTsP3zz/TBcfz09b hrsRT1gnYyoY/v7Jk6yRTRxuYnvZmWG1ZuOZg+zh4YlDb6N2m9H77+JZGQ2XKDd6qHa3FIo3 epQUuhP00RUqPNA9IR5zeag+pEwvFrLmiowWtzr3m2Pgm4ZxfSyT+AnIlbLNVNEVJ3101czL 808e1iYWLqyfurB3YbJ068+bFg1drTrFj3l79qXXVgwt4mddKZ7nCahjBLhgAWm5D/lREAlx +DaAjWUNmMrF33R6NCM3D6PPKMeui/RfIN462j1RtJfUCUXrn1x9+cBVBcUbnlzZPXDVrOHV vZ76PMf6zhn1U+Mk9RXP37iw4NqD1655bsuCwuue/d5d27MW9xZs/+nExat45Hdi5C2otw06 Fd+Gh+yWLFxhKWwADHr+173hL2kGmc5j8FjLbTZDaJlBcV0mV/KqIfuJs17eBfpvM4HbpVw5 oxyuU8VIJueExMk9xaffDYnJX7B06oLVFckqqT23fJK9rpn7/ebkmen2nNaftHDtt+EXwhuo /US4U7lfxsTAbmksmGCcFDegj3EOMsljhjHmMZJVNcb9xTiTJqFc/yTLg5DAfk+I0VIaEvb3 oL+HjqTyj6DcoSN8t7HMA+JkFUY5/20xIxFMSs6Jjla+j5MzVMFDMopfT3SgJqhUbyxY99Pq rp3lS3dXNSyLnNJUMe/yue6pnfd1Ntycu/j2efMuseYsW1h7xRyZpS5eu8AVFflOsmvGRFuS y22Lyild5pnZUTUhMuz5ONvkCbaxY9zRMbmlS9BPNoxypeoU7phuZX9H6PQqSWag0YQYwjXx Gsmk1gwGXvcY8IcfnUoqA8ZDlp2fnY0beioawm/gmMzs7Fj7oaxDPNJPfKtpfGM5mTPCNiXC KT07/OFwOzMfzp/5FtOqTg3tf3JAKuVxxJfajV8KTpjbH2riHwiR9shIK4S7oq1j7GGnjYbo 6AQrBpN5DKAZSqiONJ+2VgAeiPmjLntmPvYc/sHWAf4dzzJUZy9+cUCOvvzV7qipM/PjMhZM TWS3D7ew9DOvMtPwGyxTfAJsyFbv1IWHaOkTYDh67172Z+UjwMj1xd1ehPomQ93jyclyQkq8 PIj3gCHSoNNjGZcSwVW1RdqGzIaE03Kc2anXq8zVMJQ89itVJXCl+W7ItXK1zcIExq9d84FX hg6YXxWfdG6tFpMmZ7TOUaM/ZZQP78vT23LufWj4JAubu/6hpoqt2WmbF2YsLkxhg8OlM2cl 1aSoQ83+e9h3SPso40FjjGLUg0sXmoz87FqLu+hjcbslw+xdkCDNGIjUh6JBj0Xo0BYXv5Si TDEsEMNiQrUq/VeGMhiKMI9Ywr83jl16+Qn6SBs6wE+0UdpblE/O8w8y1cfpl03a0z/8vJRt dk8bj6oOvxfDLzC6pgSi8v0/Y2f2DS3JnJUSYTKq55x3iXH916D+nwj9s6FsF97LMwbAlcoD Ys6OjZ2UM3bIJZ9OdqQm28T9atJ47ImlmuTyyKHsCV/ZFCOOpYpPn2AwgnZ87bZV7Br9w1B0 VNBE6fD5BjCHMNLszhufXDMOY7W7XzH2a/fx6beEsS1k6dOhpv6fDd9BDb5HqnGPRKr5bw9U P2Ew6GPM4RF6tPEJYziWWAce1DMejXg5OmY3mwL8v0XWeUKjy81fWcuMhtNhtFdOcEuvMuP5 G8w3zL7nDoh0Uz6TdWKnZFtcOU6bsM2mjsyoH5u7vGb+WGYayh98kv1IN2/fJtcCtyo5zBSf XZIyPG3vXmntvlur6rR61HMHLj4sbpRp/SYDzyIbfqfHGLh+ppettt2M/y6Phukes5Yby7hm +eeohcdu1sjmpduBfuK0qYYTi4pnOfK8qMvpo1rUImFWfl60yhAWZdJzTcS9jGoYjCHcY5W4 S7nH0mH+zvT0uEg3d1dcmFabkRnu4G6Kg0R0k9Wti1Sp7OWG05ZyGEoffzav8/ELGCssU5ww qNc5G5Pfw8EMsE0hiAp6UUkIVQmGfE+/pzgyXhuWGrHxXpeGnBf0qZIMlOctSxdK0l5Jqlus 5s48qvj3DSUJJHZb4CPpNfxOU6F3XXtAklKxGiJNHYhg6kE251FjpVSF/hx6lWWKz0rmUjlV /EtmVBZL295wM8eNcu74mJjxuTKi3T4+l92q+vSrbVhxOokhkHvxsPSs9IX4Kc7Gf/qe+ziE w3MOSzXkv4meeQ5XiYgWn/jq4B4Rqx2e3rogzRIbH2uJHj8jeeLCyXFF1798vWq5ZE2aPj4+ xZnoSpcTp2XEZcxvnHzpPVfOFn97vO5blbf+uwvziXJYKpb6VQ+oHlDHYmkTZbfGJsoqrUGU +v/6MqCL0a25QPn4Xy16j1I2KuWT/3wJGXdeufT/yv+V/x8LnsXp0hgI/kZWk3jzOgOzaPG6 BHrNMQj+xt90zeNKXQ2xmleUugbsmi+UuhZitQalroMz2rFKXQ/jtduUegjIOlmpG/AHquBa obBI51HqYTBed6tSD5du0+1X6kZYYagd+b2+LMMBpc5Ab/ibUpdAHf5o8Df4ICF8u1JXQ3j4 A0pdA2HhTyl1LfJfVOo6uCb8iFLXQ5TRqtRDwGysVuoG6f6RtUIh1dik1MNw/E1KPZzNN96j 1I0w2fQZ/w1JdYjiZ6qTn6lOfqY6+Znq5Geqk5+pTn6mOvmZ6uRnqpOfqU5+pjr5merkZ6qT n6lOfr4fZMiCCVhysLYA2qARusVv1nVCM/QirxBr3dAl3l7k8H8Z7oAM7JkFK7DIUIm8FmjF vh7R8iH6xG/g+VCjDCGhQ0hcgRL4mDbx9iK2izEyrsXly7BSzOUjOvDdJXRpESu3Y+HcFuT7 EFdhq1tIbhftXkVmh5DXie1WoYWMFvGRjSi7Hcf0ijGNQksZViujfGKujCO4RG5/F7YbR2nX IbxBmvNen5DbjERWnvVHI8r0Cp0bcQ6Xzudw3iqxDmnGpXiF7lyLNpTB+WnYWiF+h9EnrFgh 1u+FNWLF1SirbURmmtDXi2ODfmkTOnI7WnBup5jLtekUcfWN8nOXIoPb5RVaBz23XMSAS+Qe 6hES+Jxu0e4SM5pGfMjtrlbs4dGkiK1S/LZYyGlCzmoh6awfm4Qnu0RGrBGrc9/xcTTTK8b4 hCYtIhdWC+taR7KCMjKYj6TnChE5inov1mURy27hpRWC54MrxPq9Ih4dotat/N5oUBaN/eZM 6DknTjzDV4rY8LWDPglmedCynlH+bxfoU7zbrvB5bi7H0dwjvJf0otjy/Sgr+vuEV31KbgRt 6hT29Igd7BNjuCZlItYdqBHlENfBJ3bySiWmtNu491YKqbLim5ZRa3cr9naM8DrEfvIJ761A KdPE2rTDW4VuaSKGfCf2jkSVcuzcqFwppHQqMoJjeB9leody7qxSdg3XrkvRPOhP74hGy5X4 k7+COcX3v1c5W1bgu3dkF43O4BVi13xnZPZZ3zYq2bJc2cErxf5oGsmzr++nXrFerxi/XER0 lTgV1ox4MHgOXEjv5WLs6BNttXJ+cI2/fk7nKVl47jlboJwgeaNO+UWKpm1KvkxEebzn/Nnp I7Mvdn77lB3pG4kAz6cW0durnKpNo3aYT0S8W/FvcM6Fe5v/pdsoeF8EPVoj8jSYdVUiFr1K xpA/MxUNzr0nOkVce5WdzD09HzmNME7YnaKcSTLMFlrRXJ5JXehh/n+RWS1KhripztU8Q9nn mcpZHrzVulDCGuTym+Ls2XKu1CC/WZwO3eLUCcqrEzrTPbBm1P3ZO3L+nD1zKXaUqe3CP0EP UX4GvVeM/puPt9nZ3RXsoZO3Sfikd8Trq8VajeJsvtC6bRc4Y87unK/fBJTvXcLSDmX3kSy6 5/mOPd9u3k/n5jiclSKyk/ZU00W16via5G/vo7PSz57QdJpS9jSeczN93faz+XquXqNPQG4J 2dIr1gtmPZdPttLN2iHOLe9FLSU/e8/xafCsOX8PcK/yzFup3NI+8Z3VqORUp7gbfLhe1z+J 0H9qX5zdE5lCG74HVoqbIUPEqguuuF/OmjAhR17Q1tjd2dPZ3CsXdnZ3dXZ7e9s6OzLkWStW yJVtLa29PXKlr8fXvcrXlFHY2dHTucLbI7f1yN62dl+T3NzZLa/s8cltHXJXd2dLt7e9va2j RfZ1rGrr7uxo93XgdG9Hk9zZ2+rrlhvbuhtXtvf0ejsafT3yamT5ZK/c3tnR2dPlbRTiOnq5 8J4uX2NbcxsuKfRobPV2ext7fd09cqt3lU9GYXKPt90nr25r6v1/1VwHWFPZtj77JHSQXqQZ EOnlhCIgSLUXUIpgGSEkQSIhiUnoFoIOTQQbKKiAYEUFxYYyKFgBxVFhuDoo6BXBgmBnUAff PicRGcvMvPt99913yce/s8tZe+3Vzl47yYmyprBZ0UwKl82gCBN5zHg+Cx9pTYmhReO8sIRw jiVcLgOS4bLoTIJnHhzB5dDYBHMRsQIWhykQUOhcPp8p4HE5DJxDW0oQnIcVAxcGF08JYXEY 3HiBmEcGS8Bj0xIpNDabGw87aRQGU8BawoEcCaNwUUBB4nKENNlcKD2KkEvhcPkxcEYhM0EI V0DjUIR8GoOFj4KtXwhBIF6TLzeWz2LycU5wkeOTCQj+Y7hQdHRuDHwvpEWwEyl8JqQFV8uN pED6TA4DEiJm4nIoAjqfyYQq9ecxOUFQQpRIJk0YC1cK1UZnxzKYUKqcJcTVfDgvB3/HiY1h 8mlsgRtFABUexWRYUxhcoRBfKpSYZClJTGg5bkQLjQ2FzoG2A9UjiKLxmGI+aTihCLh+yBcu KT6dBq2FzRTiKhILmM3lRuPdBLd0KJYIqOBYDs4/97OehDSBkEmJSKTE0fiJOIO4DXymHUHj iw0tHtqHwHbYpidQhm3WBxrIBMLk50GiUOoUqi2Gfeq2wbtH2jeTRZgsDUp2CQvOzccZggpj xtD40RRiaSOqkd92I9wvcEaDOSxcdIFCmpBJ8GkHCUh8ghvLEUIlC2xnxdLNaQILaEmUqXwu 7BUKeRPs7OLj421jPhG3hTq3g1aOuxovKtGOLiSsRTIUfx9Ji+CzovFx87mx0AcSCf8U4vZD WC5cHRRqDIvQJZQnzt7k4FnehLrwCjReRixdiLMeH8WiR424ljVsMYRyhp0Ayp3HZ8EBdDgK +rwt5dPcXA60TXOWBYUJNcUYSYrzafA3OSKGEwYNzRSKhy52puHZCblKaIkN0JwFZxEyY3DR 81lwVuisHDaXNnJSyDNNzCluNZ80wI0V8mKhSzPj8OgAx0Qx2bwvFvR3dEFowo7BjKTFsoW2 NAEv4dMTfT6aIecQZPhs5vMfgCPk4WsUIvPxI6JMjMCfDaVFnNXAGoqf4pCHr8VRHu665yEk eiKfjWgs4TOjEUs2TchBHGEPCAzwoSAqcEbiKVIEkoffwf4A/9lf9n+me4YU9Qe68wi6wmG6 psQVCogUMVoZ0UC0EX3YaoRYwX2wuE8a0lKAM2giOogBYoYYw2yGOsyDPCID16WIqCKjEUN4 nx4Ld8v2w3yZSMbIQqkoIWqILvFUMBN4x3KQUMdPffBvmWghevDeaYmMg3c3R8SJDqMP8CJw GoFzCAwlMBwPOSCKQA6BQgKTCExhcLgxII3AbAI3EriVwKJIeN8BuwisILCawHMEXmVz6WzQ RuAdArtgROKDJwQ+J/AtgR9wRFEuLFBZAkcRqEGgLoHwjsYWoqYE2hLoQqAXgdPgLSoSnUPg PAIXERhBYJQgNkKAcggUEphEYAqBaYJYngDNJnAjgVsJLCJwl/hckNDqX5VAcgr6PZT7EyRB 25DBv4f8t98Rv3Ei/AAd4QHSf4qKf4IotKpRxAyf3n2vxG34X0f576Ia9BZbZDzigUxB/KAX L4b7MvxEbiWShuQg+UgRsgepkDy1dL2kLJSUuyRluXilwFBcB1MkZYKkvVBSHpO0S56CCt6J SzRAUpZLylax7kiy4joZkZTyklIFSkUe/i9F+ghNsJBn4hZ0OjoDbwEzwSz8lBbSmobrDNWE NUtEDYSBCMAEXMADAiAECSAJrAQikAp+BGkgE2SBO2ggLnGwGCyGE9EADXLJAAxIMQZwEBJY BviIFIgH8YgMSASJiCxYAVYgcmAVSEHkwWqwBlEkfusxCrSDdkQFDYBrUyWet6cL/9UkNqNM 8LgVlMG5SGAfOACbD4EKhAwOg8NQViiMVHJgPdgM8uGoIlAG9oKDoBK2S309GtwENyE3baCN eFqsLiKPrkBXoqvQFFSEpqKr0TXoj2ga/iw9lIkyoUFFoTzCO/Dnpn3mSQWXE4jFbRtenyY+ 9yaeGfhphOqIPkgNxMLRCLoN3U3oAJ93G7od3YEWocVoCboTLUXLoCd/OS+KuCCj0Sx07be4 /GZbOpqBZsLrpAkpIwQ1FFJbhpBQIbocUUIPoAcRLfQwehiuSEy/AC1Es9F1aA6ai65HN6Ab 0U3oZjTvm2356BZ06/+Cvj6igC5Cl8G+WDQOjUcT0EQ0CU2GI6E20YXoQgkNQKwYf4ahBtTP GEABRsANHAWnQSM+G5INXwjxexwAJoAJ0BKqQBXU6ilwCuq5ATQQd65L8I7mCHMvL8I/g5AF SDj0UDbMrBKgj65BsqBX5hO/JCpHqpDTSB3UDP4lURQoAGj1wBiYQ8q2wB6MhzVFoAVRCWhD HAXgaoAyGA1RBehCVAV6ENWAPkR1YABRA/o0CsYCC4gmwBLiOGAF0RRYQzQDNpC2Hf5rJ1hi wBG4wJIKnIArlOASNBKiAOXD1cohGQj+vGT8908AxpUc2LYZvkhwF9IEI/l1pBXGOfw3Q2qE 7DShfJfBeyrh1YTtf/YY3Kt3D1vr9+3uk60ikrgtjlWIXiAy/IRgvZlYqt5UaTnLtGlpA0pA Bi1J1XOBTY4oAFQFTE5aymoUCdWVQjCatLyVNCCDVGcUkEsCsbmY9YgW/VLDFH3EnXj5IxGS AzYmkUR74C/MaAQxskak59uxaI/6m+1eyRvsHhVV7KFUfShJ1XyHpZIuwH+bEhIKUFRl6tnR eZ3rAqb4DrTHTFOi7sKUhlkFUpAp0VqCSVIwWVodXeBN1cTU8YqsumIIE88POBRfmPZQNTA1 vFlGXWFSLD+CBhNhNptJVYbUYKu8unRQFC1eyKQaYHp4g4K6hriB4suECWMki07kDdQxmAHe TVLXknQHwXQbJs4xPHxP7OuNGWorYQ5Ue8wRI/4WaCtR8aqDvYOTq5PrAixwBLPBgVRtTFM8 /yiY/LACYaZqTZnOodtSrTAL8UTGnzqIqfBURTxXIEz9WXi6DidNBcYjpQKkEFIqUEZguzya CgCyv6lq19VmSqX8isyD6bHPj/m96KxXPruEVlvG0P+1ZrDJ4cAaLDN0ZXZ79N3xRcpnb/Qm vIzfs5LrfnZTpdLpqNfszU21ATYHpk18c+KXH8L00OJ3dtGGuwbKCvfoNqD3V80KeDAqvNdL f+UppQ7Py8c602vDkpZSbUkFIvV9UynXqAKlEJvmBEeHPLUCtVMdUXbl3Q/OZWVbnl9rlB5Z uzo0hBt71r3cNP2HJhVN9+I1T4Lq5TkXhi7OuHtKRnWL8fJ2D7Mbhgm9xdTGF93Go9svHJ3q W6gbVmK4vmvxm77lL1YciAC5b2YrdFw3nrcvr7kiI66i77TSq67Zt0veR5VUaLgdTa+vQUnQ 8MtE7ZjoFuYoLQstVkpKBgCyOWaKmXyqYyBNR5JOcOkCnm0clDt+dICnE4TtGKgD8JEsi0nD AgUI5o23jSFPwFyw8SWOJfZpmORyOp/9h6vtxLYy0lR8vW3hKMJSDcaRFTH5T1yQZLFReKMy Phf+0E1pyCGsq5KhZe4ajWl/sm+SumJQoDc0NBcbqo2TwxdeQRKJkBnRg09Cz03Sp2YmFljl n009CNr0ZzUfzgrldMpalC1uaNqk3kMOUOqfamaHuBzuatzkV9hqHKE54Ols5M+jprxY65J+ 9NGjLcjQz8H5fiY395v5JVWcpHm/srzW03h78d0aqx89ju84fvt+yMczxy6ufPOzYtHzLUNW LW4BenouZgOeM6APf8RS0R6JHys9tnreessiQ8deSm5xYVzGl378b/GMr90RcxnpjiF/c1I7 zEY8qelfTYr3Mfl/6ZJVc8yn3W2JSlqjMyky9oeVF6qL6aYfJ/puX67qojIuWHA71oz1u98p yqIW+cESPctnwfOMaLcM27t+coi+3H+3zJmZo7dJ8USg4aLlkU5hUlmTh+L8OgNTSkWUHRUZ i0plBx5ig33GzrN85K91XhpzoS34scjzeECZdTlIellavs5pqLj7h6VSxROjH5zNrxu6Gj7o 1SNTMumpaC5nt+XLE1kq5s9y70iXpM0pTJ4hq4QZNKkURQ88Dq0g7/cqqDJ/lKt10P1BIHdm i9OO41yGwdF865qJPYlPY5IGtbpND1X2FwSe9LLOq04sH2oNOGAhXOnT62pYulSre36NSdQt JMVXJT0lWuKSTZjo8r/okorDLoliCOYgdkZrzBIzLzEtMUkz/p4zCgUCGzqNcD8twv1wEn/i gdJ1f8sDHb/0QFzL6Qm8X/0CAGXhvcTGVOzC76dG59duQM7XNjdfej3q1sfB2XUOEZjqxTdC vdaNHWHbKepHlk8+M6d5dU+K9uq9ZpuWqE9531S91Zt0ddvchVJrV+3jvtKbo2di+5K1jm08 UNOklfdMUVgXFX/7aUFEer1g/W+ZwqSxB8q2Jm85MpBrsWy2bazeNO9fnx9XogS1xZdsSaWz fpf7Oet5bI3cttuDqsGmhTT7M0no4eS0M6Xn1xpbJ9xwivtpo2DR4KnuWZryY6923Wx1tJ3u pemuHJ5kcml3ZH/+z7ynHj2vlVbeubG8LG4Zq367/1TMyehIaaVuhLvV7ZxyS5nkWzpHFyX/ c8du7pB75iEslawGQ8A7cQhQRuqRte7uGao3PN7Sezu9RkqMDCMA75NvK6gb+3J5iXz8mJti TregUF1dnb84ybOlGmL64sGa3zzjoxphY8Rq0vncH8DlCinescIoLp8lTMTDg6szRqVimLMk PNhjVHsHqqT6H+DoL2/laG09r9vtpZ+eefGWhMXYk9L968aF/TaUN6vs5NCOUorH8rml20pz w+2jb/gwEvsOxjUG/fry6fY0/dziNZFHL0YnRYxtM3DvUAYbH+VfOGsTWVgYZVpwfYL1WcXj oab1U3rkPVzyrfebu+7rnb7a58Ea5ZpCdjDtYOryneE28bMeFxxjuBXO0afKmmgU7+/ZYKXT PXErXSM8VIpZbOAckD6wt38zekmv5Wzw5KOZKWcn9AZt9qv4fW9SjNCvUudqvpy5ERKyPpzl XDNTTcZ93seF73dFysvuuSmaF9J/wm2xliie/OvbMxUpeUOHm1e17dXlL3Jv+um5bJkxdlT6 x8ajlHj1HzslcWMfJtqNiUpxvwRkUSEm2pKisvA6r5/FLxo7d6VG1eycj1d28v/v9Zf6FzZO RIW8Rwp1615t0XF6Vg1MbsWrvloUbl9cpHDFQ2pDRm7jhG6jl89DNlkfL5naENH/4R9X3dwW 7B8fxBoyifFsvFreIbX8LnXdxGIV3tKaITV/HVbdh+u+D1QXUPyfRCRXlo9usHIeZ3OGuVMt a5wyvWwgSH/QqLFN81XAQY6vvczvqdq/PVzCVpr7tvZFwOXangvYBwpVLsMgz0J39i8G6O4X KfdIxxa+PnK3IaSPOf1yQNCJYyRztY/r257L5q6s3nLxgLN1V1LXvvgHcSXI9aWe9TfHZ93z VtvntFRvabvT/VZ9cte+yeSGBQ4unNn6ShEn5UuzW34J8pzSrB+8h9euNiF9U2zx3pslMCpc gZuDo5KNwVKFAv86RPGA6i3j/hXsxJX/L8ICZo852TvArZ1kE0/FXO2dJFVMtOeP2wZ1TFWc csiH0ARRcDsghPOoELcRmHDIBDAZMVwO4xNn8t/j7HvLtIeTfrXMsZiReBm6I3sYTGIDgu9I 5hCJAeXraKKERxNZIpqcv0pZ91PnR485fUnnWk3GvY27ZvSx2XKeX9P2k6lVTok2yIV9sr/Q G0/ufvu4vr7tSHZ+qcw75ROpAYVPUy/VqlzcV9cXvSYnUK9mzjsGyKzXak2NQrwSJr1Rc/F7 T597793EUw+dj3TSZca6LfNynPo6umLKGzOBofEVn9GGc08EFLaUXVe/NNpzmXTMyzyjSWE+ z+oaCxiU6nrHD6WTupOrDOyq93S83tm5zUh5KJTqHeyysjK0p6t3fuK4AwOWdqqeLgkePqv2 RnWtNI7S7p6x8ULCpICpO/3XZG7aVrck+Ync+zTSircFy9yt9kZuvdpp808rVFfZcRrzjbta 5Yt0fQPTAO5VaH+kslRgCeVh+q29OOm/I8SoSctJknBN6DAoiYSQiDTVYBRZi6wx7jermT80 8IMOPXxbYqmt9b5+MFCEjR6+RAMlKxrKI4FILEzZfRFvTIHY/BC5xxRMeXiTJYWRYPGtcFbM mCG8zzJb8Jt0oNmWy4Y3RIIb106LXus+2bQlvfJO7gT0+FqVsy22QiFwO+al3jc3zP28/Tb/ tsfzm8yRfL7m02V2W7fWke47+TyWtYugFnygvrrjU3HHP/eqSwfP2yVcyZ2y0Upnu8zQvE37 b87MqC2YYYz+yJ3Uu6F7dIdVtWbVNt67i0/9VltPWFZiH/rSKaB9Q4ZXP1evx8LnGK95BZur 29C+/d2NxVkDN60LkbDbr88dLU5udRsKFFouMRCZlcY+8toqo7NWh6Iwhd/ZP9A7PqYvujEy 0KDWqfzhFYOlfPXJKYa3f0iPzNK33tN0hUYidcg1giPz6w7/3h255co7ObflWKrUFBjSbMXh TJ4m/wQjjm4Xf3VS8d8SNvD450S1p8II6OjsRGRNzjCNglUnvIqJMv4tC4EMi3mz+PaHtubT OcSH2WxKsIBJ8eewEy3+cr90fq/WTYVWz4j1ffOD7PIUuK9bWWfY2Wd1fg5839zYEtUVcTJM qEDqLq9/6mHWe3Pr+m20eUNY3/4W9qzIW4vGiXLK22LGAou8rNLqDM+OuXNMNjN6fmooQnrs FmwL/aCd1kOnHE60eXREJYw/69HPurmzdsl2MsJdtvnRZ3Y5Ttps71StLMO/z0neXd3SfHRo 0SH7ufcjD5fMPqnAJlHUouqsNX7LFTWts/0JnJYGqRbae42aVudcns2g3N69WpD/XhgUN0f7 5NHeaWZZx8b5HX5Y0ziTWerl2nF3MHPcPdPtWwPKaoWOEwXKmOoSymXf9AJP4yN0KxFz1IMY KS+Hh+c6PKLcxPulVLAeSiT7q8Tmc5SoTDaIWvPs8qDtmLSEPB+L4y8uam36Tkjcj7eOJYt2 YqKilG+Gl53CXf+JwPj1TmKGOCv0xbwxz5KJJW5priOywj9+YMyLZuGtdpKP2QV2uFfgTjFH clbjNyJL9cG8MI/hLBVNs//u59AEWSb/K3rCb6WLwN0795qpS+jTEnpLVM+BlhBe//R+qeB0 zfPpjdUhNK95z6sHMFJk0tO1aw8k6Cxs7dvrU1+X/fuhf1zPGcxIlwuvkl8Zx7qdeIFMf7Mp qOgeQ+dVdsZMdi6n8Z6sW3nGnCz5+lfdx16+fuN+i3pS7kj/glp/3lKvBydXND8xsW4KN7tf UnXMvDzI5EjR5jeuO5Y40PxnLOc3n9HOChhY/OIf//SgXXeWdQ+PSbtZQ7/7qCG4iuJSo6a/ ulA0PsjqRvvje2q/X/Gb3Ppu/NJBoVObabxSV9V16XMDQsXdx9oqniTQcn1UwmI5ER676+/4 0RKmnWoLuRbU23xm94HJlaOMKaUHk+3uU1PJZ2EkrUEBwETH/2vi5R9i/ucT7hLRHUxj+D5r DqgyJCliEH73lahejkRVHHmoDln/XFOgjsJG9mpiYz9fSKZCr710p6xpTFF/SnEm8tuhNy2L 0e6bVVjkiEsUqaHYvBLrFMu//+XTnaYpJn/jaxVfbizJqQDhrlpgfyW9h+2h9zypa7Vmdc6q uqs70VfaNq3Ustk+D646api2tjmO/eA//VLlKXLCZdUIx8msxNP2cz8sO1/tcb/4wpLz1ozQ yGd+Y+NdxzTbMjYE6antj1PcmzFUN9815kImJ5wTkt7XfuWF0z4zUi95SvpbxQFHX6/LnrX7 tc3tsy2Wn+3i3TJBHR3Wd4+Z/s9rVzI4inPpdTcD6qct7c32nHD7XDVV4eEr/tKW/ikbMn56 eyCne/P7moX9jUZeRWVxrHu3HsvqFj8W7ndpcEQ6qW/n9b0en4nMt3maZH0R1dQ9pKaTh8gl BNe6jnOpmn20oSyQ8fpEWLpe2/G9J1fwikPOG+/amQp3SKng/WctSVNTQS9seoSb9JJ/yxnn N05WFaVlxQygMLKUzMd0RtqbwudPegA0t+EeKaqy5LbvSnW1d3Z0XQAj7ghzUyOr5JQFUyrf X/YvX4FlhmDXM75hAjsUYrZWzj98qPaS5UdW363AdqFVxqDb6vZpDSdHnw5nHlZ4OVQ5a9Tx lGluM9sy+CnojV7TtatL7fTPB5gWzJdT64984xrpNeCtsHo8tqssw+dQbZHmdflpIQFZHxxy kmULN3ujYXddb7H8/JI2Le3ckpFz4tJ27oycravS3KttZ8tdXXHtjOj+wblDZvWXTjplKk/l lr5esNfa8FHyqidKv7bfl2/UX542UMh/6LV233KUfS7jOZKwuaHDrvpVNrbRtGJD9w0Xlw4+ 982aF2pBr3SeatuuHf2Oqlr9Q0PYa+OneYvH383Wtj3TZq53N72jlHqsY8/a+EbNhNSelvcp oVNOlrxvWDQL+R/+l0NNDQplbmRzdHJlYW0NCmVuZG9iag0KMTAyIDAgb2JqDQpbIDNbIDU1 MF0gIDE5WyA1NTBdICAxMzFbIDU1MF0gIDEzNFsgNTUwIDU1MCA1NTAgNTUwXSAgMTM5WyA1 NTBdICAxNDFbIDU1MF0gIDE0NFsgNTUwIDU1MF0gIDE0OFsgNTUwIDU1MF0gIDE1MlsgNTUw XSAgMzUyWyA1NTBdICAzNjBbIDU1MF0gXSANCmVuZG9iag0KMTAzIDAgb2JqDQpbIDU1MCAw IDAgMCAwIDAgMCAwIDU1MCAwIDAgNTUwIDAgNTUwIDAgMCAwIDAgMCA1NTBdIA0KZW5kb2Jq DQoxMDQgMCBvYmoNCjw8L0ZpbHRlci9GbGF0ZURlY29kZS9MZW5ndGggMTM2MzkvTGVuZ3Ro MSAyNTc1Mj4+DQpzdHJlYW0NCnic7HwLfJTFufcz796SzW1JQm5LyJssCQm5knALKFkgCUFu ISG6QYrZ7G7IlmR33d0QgkUBRTCCCihFRaBKFdHqEluLl4rWS63VWnvRz2rVVj2254hVP7U9 Ctnzn5l3Q0C0Pf2d73zn933dyTP/mWdmnnluM+/ukkCMiFJR6Umtb10wv6RqUTnRgioia/f8 +oZGKlJ+QWSvx6zx85uXtj497ZMD6LvRd8xvbZv7qv7PPvQfJYoLLm2trF5zZMBGxA5jvMPV 6wwk/DjhFJEJfeWwa21Yvebw1R8SjYN83eyuwOpe9VDR40TmZqJ4y2pnKEC5hPX2KVhvWd0z 0PVSwpSPifICkH+s2+N0vznpj4shvwzj07rBSFoUD90Y12dCd294XUvjgi+wVxyRYX+P3+W8 JXPfHqLiYvT39jrXBeKOxw9g/jbMV33OXs+mjm9hv3LokLo84A+Fhz+gndg/nY8Hgp7AnTcd PUGU1oH9XyDuK5Dr+BNvX5Jy3qcUj23weuRtaynH489uXjX89nCJYYNpPykUB5IvrDF8dOoj tkrfhfE/GTbEEbuFRr/0fI7yM5qBFn8pZKFKwq6GK+ItgqPTd7EbyEBxhlsMNRCZJ1H3EnVh q3glwRivKHqzoj9Iyp/tpC6MiV7cqqqQnk9hqYNppaKoRAeE0PcNVm4pMX1X9GN9l6YMMHqI pmN/I3VSFzXBlkbEZSpdGf0ZpdH5GDuf6imZXsHYIcqP3ksHKS566BzFH/VDZDqVYEVHNEKp lBr9YLTp6HVi/HxQE9oTo09HPxDlk+jn0b9GP8TO3M1TiYb/SldSLa3UFqaRhUsScz/DzM/B Oz+6CpZOh4xHqV6bl0zJ0X+Dpi/L3TTZnwgtFoje56BXoyc1HkXfwx5NtAN1V3QvFWLPw/DH BfRZ9BO0mzFrJXR20xa6GNY3Rd+M7oH+X36tjL4HWbHXjugwtaCc8YKsruj70ffP4D0NbT6I FTqMUst9CQ2ayTH8Nn1GS+hObXItpzM9ilcT/GDW9u7UeG5yR/dTafQC6L1i9A5iF6KLwZPc PVqRIwm0jl48S/6LKI6zeA5yRG/nBREriYZQN5KZfogoXYx6kAoQI1HIJea76IjAI8gJ6zm8 97Uvlqpkww/81Uwr2RI2K/onGmbZbCVLZYtZOqz+Nr3DPmSl9FPagJjtZxOZlc1ilbQHeX2E wtFXWQlGdiKmcdFPWBzbze5i2fQNVk6XIptX0/fpMmA5cA7TQ+IWyqNecS6ujL4E+XEotbBx anS/OBVNyIFkjMYh1w4iivdi9tOYPw5xT4Ge46Iv8thiTXf0XboK/lpC19Bs6qNvIsLN0dcQ r03oNUd/i9YiugE7roNnsqMDkJoQyxcZLS1uH6I1DG/8WSmFF0poudCPa3gZdUZ3QB8LFk2N PggN46BfijjVr/DTBP2y6UHIeIduRP7NoCK038Q5vRL7dmIlIVKN2Msm9tuBOVvgq3SMn0+3 4wT0UTdkNSN/91ABsucDaP5odB8/ISIjd+A87YH2byJvj1BK9BTm22DRLOzzJ2i1HbSD3MKs w+AdEvvswx6EdbW4d5bgdC0DtxDa146Efi9NZyXMEW1Salhf9BXYr6I8TbuhWyJOvwUn9nrc CLdAxyfpyehjsGUlysXQoxw3xDB8eymkLIL8FlE4PxV65sOmLloU/ZgVsnyWiF2MyJhZzMGm s1o6ifIKi9D7yJoB6L2JVmHP3RSh6/HMWI5SyeJw6xyG1tfTvYjtYyiv0cP0aPRIrOB+X4T1 efDcIlqB0om5vI/cYo2IjhkxfJr6oy8hd94ja/Rt8lOxsFun0TjxHCIKo8efGh2Qyc9iKizX iWdOEixxkpd6yIc8j4rZZ/NY9FNWh7jFCjvHITMyeS999KVRhn3k002hr39pK1nH35j3f/p1 5Vn9a0V9HeiGUdw9/03a/M976XF/8Wclf9di/FL+nNH/uryxN16y6hsrL17R7mhb3tqyrHnp ksWLFl6woGl+Y0P9vLlz7HWzzz9v1szaGdOnTa2sKC8rLiqcYCvIy0ofY0lJSjDHx5mMBr1O YVTWYGvsUCNFHRF9ka2pqZz3bU4wnKMYHREVrMYz50TUDjFNPXOmHTO7zppplzPtIzOZRT2P zisvUxtsauSFept6jK1Y5kB7R72tXY2cEO3Foq0vEp0kdPLzsUJtyOquVyOsQ22INK7tHmzo qIe8ownmebZ5HnN5GR01J6CZgFak2BY4yopnM9FQihtmHsVbuCS+bURX2OB0R5qXORrqrfn5 7YJH84SsiHFexCRkqV6uM12rHi17fHD7MQt1dpQmum1u50pHROfEokFdw+Dg1siY0kiJrT5S sv6dLJjsiZTZ6hsipTYIW9gysgGLGAotNnXwU4LythPvn8lxahxjoeVT4k1u4oibMB5rE3SD hrAvP5/rcu0xO3WiE9m4zCH7KnVah8heWdoeUTr4yOOxkbFtfGRjbGRkeYctn4eqoUP7Wdud FdnYqZaXwfvipxA/GFcjuqKOTlc3R6dn0FZfL/223BGx16Nhd2q2NhytqsR8ZweM8HI3LHNE Km2BSLptrpwAhspj4G11iCXaskj6vAg+z2irIpUN9VwvtWGwo14qyGXZljkeoproW0enqNYH amgKtXM9IhnzEJSihkGHuyuS12F1Iz+7VIc1P2Jvh/vabQ5PO4+SzRIpeQvb5YsdxSrYdtbs 2GRuuakwTnUoVl07jxYYaiMq29zzMGBBuESXR3TueaqDWSk2DbtoM3jrDDno6ArnNfEhHV86 r8ma354vX1+jklXTyVAYiRslywLGiE5yn69UTc7mCpWoDZ76UQqeIdSgKahJO7eeCveFtjFW xPFwNsWGdIU4ueApECNYPIpZaoSaVYfNY2u3IYfszQ5uG/e1iO/CVtvCZSscItpaliw/oyfH Z4yMaa2IMg8J2FhqjcVU9OeL/ki36azhBbFhdTDOtrB1kEu2aQJJxfGBxcaiBc5rZ6ROwbls xNVma3TaVIvaOOg8Ft3YOXjUbh8MNHR0z+QybAvcg7ZWx3lWoVqLY4N1Pd8qlRayhcvnlpfh 4pl71Ma2LTtqZ9taVzgesuDT4LbljiGFKfM65rYfnYAxx0MqLnbBVTiXM3lH5R0uqQWdODHf +pCdaKMY1QuG6LuOMRK8uBiPkeuYInmWGE8BTy95dsHjL0Qoqxv+xV3boLp5bL7V3j3Y0c5P FmUgjvhhEWabTRHFNvsoU4yJEbPNMzeSYJvL+XWcXyf5Rs43IStYBoNz+IU02GHDJYVscpCV yTzUcZHqsWh0uSP/BeuJ9nzk2UrQCkckvhQXv6HwAsybz6kD7PmRjS4n14PaHHytqXCBqx05 GxOIKQsi8ZAQr0nAjEaxhuciFrkQGwRQrN+ITmRje6S9lG/q8LaLXLZEqMk2E2GXMg1FfKPK 9sFUW7U4mDgH5sKtHOKhG7U6JMeKLjZrl04yJUJzlw1Drg4V3taTqxV5Li9Ss1VyPLgP9UUe QWarNkjcLF1hQpI5El8Bgfjh7YQKfh4Nhab2dqm86G3VJmBvSyQBGhWNcqW2AN7B0AKuC362 QlU+9QkuZtkxarGtw7XClRaSTBiOJBUucOLml+sTwLHNiC2O4xdEgibjKck1ccsT4Xdd4fJj 0btsA/mjXuVlNv5k4IlJ1oeQ2NQ+eDYjcnFpeVnc2dwkwR4cjEs69wLpr7ikEQTTfkWQzcnz 6+bk+ZQ5ed7y7rbV5V1tnnJ3m6u8s81Z3dFW2VHXoVxSvaotb8XxFUpgxVsrlAvL29oeb2N1 bde3KcvLW9vqWtnjreyA+GkpX9bWXL60LbCUVS5lgQUbF9ywQHegiQWa2ONNzN/E7E2ssbyh rb58Xtvc8jltx9ieofEFecfYTUO5xYAbJeyWsEvCTgk3SLhewnUSdgyNKwVsl3CthEEJ10jY NmStBGyVcLWELRKuknClhM1DOTMAmyRslHDFUPYswOUSNkj4loTLJKyXMCBhnYR+CWsl9EkI SwhJCEq4dChrDiAgwS/BJ6FXQo+ENRK+KcEroVvCagldEjwS3BJcEjolOCV0SLhEwioJ35Cw UsLFElZIaJfgkHCRhAsltElYLqF1KHM+oEXCMgnNEpZKWCJhsYRFEhZKuEDCAglNEuZLaJTQ IKFewjwJcyXMkWCXUCdhtoTzJZwnYZaEmUMZbYBaCTMkTJcwTcJUCVMk1EioljBZQpWESgkV EsollEkolTBpaGwnoERCsYSJEook7JfJd5vMkH3SrbdKdW+RUm6WsFcu+LaEwqF0fpwmSLBJ KBhKKwTkS1CHUvmxyJMwXkKuhHESrBJyJGQPjZkKyJKQKSFDwlgJ6UOWekCahFQJYyRYJKRI SJaQJCFRQoIEs4R4CXESTBKMEgwS9BJ0QykLAYoEJoHsUSCnYdAp0EnQF6DPQf8O+ivoL6DP QJ+CPgH9b9DHoI9AH4L+DPoAdAL0PujfQP8K+hPoj6D3QP8Cehf0Duht0B9Avwe9BXoT9Abo d6DXQa+Bfgt6FfS/QK+AXgb9BvRr0K9Av0xuyXsJ9AvQi6Cfg14APQ/6Geg50E9Bz4J+AnoG 9DToKdCToB+DngDZo4+jPg56DPQj0KOgR0APgx4CHQP9EPQg6Aeg74MeAA2BjoIioPtB94G+ B7o3qTPvnqTmvCPAu0GHQXeB7gR9F3QIdAfodtB3QAdBB0D7QbeB9oFuBd0Cuhm0F/TtRH/e HtBNoBtBu0G7QDtBN4CuTxjMuw60A7QdZMlhgZyNOUoge2O2UplVl7U0S5eXWZlZl6k7kHl/ pmLPtOY1BtI3pv8i3R7/Vrp+Yxo7aGHHoo8/YCmragTabZa8gsZACjuewq5PPpB8f7Lu/uTj ycrx5BeT30zW2ZNnz23UPcrEP4gQYzccXd5aWrrwmCnasjAS13xxhG2LFLby2r5sRcS4DW8r VlzsOMrYde14BzdveWQMfzss+lt27KDcuQsjua2OId3Bg7lz2xdGNvK23S7aUd4mTGkvDYX7 Qn2lpaFQiJWG+sKhUJhK/8e+2P+1nblT4KMwHBRCKxzuKw2HgaWh2Iws8Q2PqUc3fXglnflq oS4K0SDtoTvpNyyO1bDF6G+hg3QfPUHP0YfMyHLZov+KL6oMVkrl338P741+ES0xfDz87vBK Y2bUaHglmq57X44ZtlDicFf0s+Erhl+NluifHF4ZJWNXtCT6oWKnuJgE/WWUCt5fDV2GTYbD hpdgV4mwcP8/oJaDLqHF8MM34YmV9A1aRW60vBSgS+GJtbSOBmg9XUYbyCc4/aLnA15FV9M2 upyuoE20la6h7bSDrqPr0d8IzmZ48WrBH6RrR43Jka1YeQ34nCdHrhRzd9Iu2k03Ih576Wa6 hW4Y4dwE3rc17q20j26j/YjS7XSIjmDWV83ZqfFj3H10Bz2ElQfE2jvpMN1L99NR0b6XIjSE uP+Gfg96ml6kj4j/+4qZpbIcGmalyA47W8yWkB9+CQo/XA4buYXboYH0g7T1ek3zm7DvfvoO 5N8BTb+LXXafoeU+YcXo8bug092w6XvQ5H5odBQ6PUDfpx/Qg/RDjB2he6DpuUeP0cP0CD1K P6LH6HHkL+8fF62nYdGz9Ct6j/4M6/4AfJ9O0jCdFPaVwsIsls1ymFVYuQgWrkeUdo6cAyJ9 9GNU9yIHdWSiAqqma46ThU2mXIpjxRRPRazbnlMRn1GRm1FRkZGrM5hMisGo0zMdIyXehncC 9nGlBTdaMs3mzFK9aZfCcDVm3JRpNOwmnY7mUF11peVU9YY3sk7U1YjWz8fUZlWm1p6wnPo5 q3yqtnZMzZiaalSpmbWVlZVjUmstJ3iZXMVYPmNp+Tpmm15TPVuZOqVCsfHmeGVserJiM1Vn CCwomjpltqK/d/jkyQuYXul685T+TbbPXbqqdmHpmIK69unlqRNXbr1oUsGi9e39hZUp2Wpq YWVydr7B+vm7BusXc/SdX+xnHSfV6pk5RbNKMtzFDRdXl3UsrflrjS1VzUquLkjLz0rm/7Kw Emd0om6Ysqic1vEnQJvDnpRXYIY3jPrEgoIJ2Xinbk9Pn1AwcdwfUloSjZSwTN888Qk7NVNW Xc7iE3V1qbW1dSfG1NayyhNPwfDqmjHC/MlVVnva37FsclU7q87InK5L1pls041GW0GFwo1n 6RnwkH769KIiW0GyXtk3Z+20/Zfq4xNTEw9lNzwdalrTWLBw1yvDJ+deUHphy4LzyxJLKl2T WtY7ZuQn6f46xjLx1Itps70Hwu+t21TV/q1FN5x6zHfKcUlPceMlvf2zfp6eOWNFaF0QHjgc /UD3qGEnpdF0uk164MHpVeVV1vFmfE6zp1Ph+PEzaoveLSx4vcRaNb5cX5JpwDtOuyXFbsls SilpTn93+pTX7ZktwrBTL5+oe7n0BA+8Be0xqay28plfn7C8UF1jeYa7JPkflMTdBK+MZAq8 xKSzpqdNQxIVGE1Tp03jwyadMT0Dvps2XcnLmDhlfG6VLU3g5II0dlvDzRfN76gxptUnsvSJ OfVX1Y8wEsYW8b5ByZtSlDHWVmXV8AtHUWnxRducrLl2cUXa1KJbisp4dzgycxG6hfzfnz6L fqzbgSdCGrVpOTTWHB+flmhKNOnMrfGt9C9plrfsutOG8eizypqnaitFnqR83TxYXhiztEZn NErbdDs6jrtXrTtPyfrJ0FOp8RlTyy74gb9+YmmF98hluldOlrQH5+ZMqoButbgLTMjwHFqj 6ZY/1pSZybJzEpNTUsbkGFiiaQzLNBhzlqUsS/u93dxsXIr9tSTN5GpWcj35cUYR+qZ+/RJo nDZbN73GxAOSYdLZ0qbxHEZkbHdqCdz4ZPDAlqJN923Krm22X+FblrroPiVP5OsfBzY9/sip Dcqm9b2OHRHH8BbYsCX6sb7UUE5W2qHZMCklNS09OT3dlJyUGB9nSkqymlAs5qTk1Hjz70zp upREy1Jr1hsjznymrqYGzjxRXf0MTHqhFvdSau2GrclPGZ56agyDYRbZFsf275IyOi750/PT puvyTfk643hFhKev7rKqrp3Lx2auHH4vl2XNODaXPZk7PHtVsnFS8+zCwNzklKTyzlv97MVd LL18+Piu4dIG5+xxKRacyBeRTVsM1+MdRBE1PUS5bLc9MS0xPjk+1WwyTSzOwsc+e3qB3ZLW VJBs0sX/LnEpvZZqeR061vGcOQEF3yjVjt5TNTDt1zg9o1XVGZFC088+TrotxR1TmtYusqZ7 h+/LVZRt6fYVy5qGh7KKp40fX21LFzh5Qro5MdHWeo1H0e86tcLVkanvzJtWnJFRONmqThUI CxxEup24U+LxyGn8vo708en8LhljjiczHmJmgyn+df3rpmUp/Pibs+2W9KZs+p1uKdWdgOZ1 J06MRAgPDnmZMq43HAxHn6W30jj8q1z2zj2sIPfkO7ls7r6zD71uxa6T3925U3/pWQebn9xG aLoNJzeeiu1pXNF4fGgwmPTx0I5+rGuFRnUnqrk6lbW1Z2uiXDf8yjj26ilWmnvytVx2gf6z 679I3LmTy/0hbtX18ICNGuwTbKZs8zjTGLMJZUJhWuq7Y5JfN40zZ+t1Y5czetemyti9caKa nx6OPHiw3/IM39Ly1KjwfdVdp1svLzJD2o1n32y7E9LEzVYn7zHl7dH3mOtUXuweUxiehrol 8IaOMqnyIXhiwQOKkkaPsM1gxbPtQ2mK/hgbhw+Z3DN1/GF/4tcn+NMdbsEx140KC3+G6Zb8 682RPeMqCtJ4fuRWAouqDVu++Fxv/OLSsbbKnNzJE8aOLajIya0uHIt3Ljf9s9BNLOOscowX xYjSPaq8+f9L0dnPUW76yvLE6aIf999cVv6z/LP8v1ZwL5crBSR/M4y036bjbUYW0eNthRIN b1DsN8jshge1tp7GG57X2gbKMnymtY003pistU100limteNokvGQ1o4n1VSstc2GlpG9EuhC U5PWTqRJ4rsU3k5S9pqe1drJ1GPuGPnNoWrz81qbUZx5WGsrZEx6RGvrSE26S2vrKSVpSGsb KDHpJ1rbCP6vtLaJLk/6g9aOo7HJ47V2PFmSY/ualbtH9kqg0uSA1k7E/Fu1dhJblBzR2sk0 LeUk/407fbzmZ9mWfpZt6WfZln6Wbeln2ZZ+lm3pZ9mWfpZt6WfZln6Wbeln2ZZ+lm3pZ9mW fpZt6ee7SaVqqkKZitZi8pKLguSnEKiLwuDNQytIAVE7wfGi5aMKjMyhHhSVWsBbTd0YC4me B+jB7LWo3Zg5T6zgEnsggc/xipr/1livmKNiLy5fpT6xls/woQ4IXVaLnXtROHc1+B7gWvSC QnKv6Ic1mT4hz49+t9BChUV8pguyezEnLOa4hJYq9WuzPGKtihlcIrc/gL5rlHY+4Q2pOR/1 CLn8Wzxp5Wl/uCDTKXR2YQ2Xztdw3lqxj9SMS3EK3bkWXsjg/DL0etBbI/jcY26xYkDs2A9Z 3hGZZUJfJ+bG/OIVOnI7VmOtX6zl2vhFXD2j/BzQZHC7nELrmOc6RQy4RO6hkJDA1wRFPyBW uEd8yO1ertnDoykjtlbz20VCjhucfiHptB/dwpMBkREDYnfuOz5PrnSKOR6hyWqRC/3Cuu6R rJAZGctHqWePiJyMehhtVcQyKLzUI3geWif2D4t4+ESLR8otpHtH+ePrMyF0Rpx4hveJ2PC9 Yz6JZXnMstAo//cK9Gje7dX4PDc7MZt7hI9KvWRs+XlUNf09wqseLTdiNvmFPSFxgj1iDtdk qYi1DxrJHOI6eMRJ7tNiKk8b916fkKpqvlk9au+gZq9vhOcT58kjvNcDKbPE3vKEdwvdykQM +UkMj0RV5tiZUVkvpPg1GbE5fExmuk+7d9Zqp4ZrF9A0j/nTOaJRpxZ/6a9YTvHz79Tulh7U 4ZFTNDqDe8SpWTOy+rRvXVq2dGonuE+cD/dInn35PIXFfmExv1NEdK24FQZGPBi7B86ld6eY O/pG69fuD67xl+/pmVoWnnnPLhBa9IjZM0fd9Bdq2nq1nJkMmXzkbAnlZ0j4qnvco51Mz0gk eF6tFqNh7XZ1jzppHhH5oObn2Jpzj3b9p55KsedGzLNtIl9j2dcqbAlrmSP9WqlpcObzwi/i G9ZONPf4InBcVCzsLtHuJpXmC63kWp5RAXi5EqVflArxxDpT8wrtvFdqd3rs6RaAhAFw+RPj 9B1zptQYv0vcEkFx+8TktQud5fNgYNRzNDxyD52+e2XsZMb2Cv/EPCTzNOa9BvhvEZ5qp09Z bETewG7hk/CI1/vFXi5xR59rX+857prTJ+jLTwSZ9wFhqU87hVKWfN7zk3u23Xxc3p/FWFUi slOeLfdXauX7kuS/30enpZ++qeWtKrPHdcYT6su2n87XM/UafRNyS6QtYbFfLOuD4rfuB7Rb tl9Y7hdn6NyWSj87z/Bp7M45+wxwr/LM69Oe1h7xfsul5ZRfPCM82C/wNyL0X3UuTp+JSqEN PwN94glRIWIVoHV3q9VVVVPVxV5X0B/yd4XVef5gwB90hr1+X4U6p6dHbfGu7g6H1BZPyBNc 63FXzPP7Qv4eZ0j1hlSnt9fjVrv8QbUv5FG9PjUQ9K8OOnt7vb7Vqse31hv0+3o9Pix3+tyq P9ztCaoub9DV1xsKO30uT0jtB8ujOtVev88fCjhdQpwvzIWHAh6Xt8uLLYUerm5n0OkKe4Ih tdu51qNCmBpy9nrUfq873F2m9njXeFR/j1sNDwQ8/UEvn1mm9jrXcF28Yeyx2u93Q4zf6/II nQOY4fc5e4RynX0hr88TCqkufzDoCQX8PjfXsEJdjn28vTAMxqsXeX1uf39I6uj2hgI9zgHV 2dPj78egU3V7Qt7VPmgU7uaugCO5HyGzxw/vqWG/6vMHe7Fj2LMuDAucPjUcdLq9fBa4Zzkh JG2a5+8Lej1Brgl3Od8sJPTv9cN1Ln8v2mFnZ8+AGvRAFqz1d6mQ7/G5IUjs5PepIVfQ40FI lwY8vuXwkNrlcYb7YCnC5urpc3vgVd9qsTqIfX285evr9QSdPaFZaggB7/a4y1S3PxzmpsJj minrPcicWYLj7IHTfcgdhCfU7Qx4pJ5OLqgT9kMv7qmgy4ls6fGEeYikg3v8/jV8WGjrgls6 EeA+H9fffzpOYWco7FE7B9S1zuAAV5DnwGnZnc6gTLR+5EeoYiSnZ6ojObsg7OzxumaKpL8Q YuF3dXJFVVVsQrmcMDrHPV6Rtk54d7UX+we5Ugiap9cZXKMK80Z1u859lPjZ4Mq2+bzcfa1h Z9gjdK2EAO1c+Pt8YQQ6VLGoz1XsDJUgm9T5QT9Gw+HAzMrK/v7+it6Y8ArEvRKZzo9boHug 0hUWGaNN5e0uZ2fQu4bPa/f34RwMiDMa5jkkshfWwbG9XhFP+JSr19C2aI4IGe8ggd19rjBX vb/b6+oetdY7kjUiQCMHAb4PBL2Y4MIsnPsKNba334f8LPaWqB5Eyz1alC82+ZwaiekiqZGq cI9LHqiR3YVfNVkyCYu92CXs6eWuD3qxKw6sr8fvHL0pdHZKTXnmxCLg7wsH+nCsPWv5DYE5 3Z6ewFkG/T2xEJGodHu6nH094QpnKLCOf0cT/ZRo+DPxt3HsS7/rwjDDjJJGpmiUUsSMRFCG +N4GPeU51PqRtbw24x34haRzDQR7KH110LOGJvU4wz6aghHW2jJX5X+pKv9WUdT6kRbGW5Yu Pnv8tNzHdN1nyL1QyA2PyC0SKxLIIGanUDpl0jhw86kU74flmBGyErDDWMqiXJpIBfhkM3lE BzOZ+Hc7NIayaTye1Ta8a64e0WuCNicOXkmiVMqhPDzJJ+CpVaNJ598AJcNbGWTF83MSFeIJ N4WmunADMbuom0TdLGqHqDv4tcO6Re0TdVjU60W90e3z97KrRb1d1LtEvVfU+7vw7GGHRH2f qI+J+seifr7H7+phL4v6dVG/g1spyP5V1B+K+jNRn+S1ovgBSpyok0WdLuocUeOp1hNWikRd IeoZoraLugmPqS6lWdQXinqlqDtF3R3q6wwpPlGHRb1e1BtFfXWoLxBStot6l6j3inq/qA+J XNOLqP4tZNo3ol9Vx39NrUNumMT/QPD3tsSvN4pzoIw6AcavrRO/plaQVfLb2Fjrq5CJvxL/ R2vzV9b837oraBrNpkZaglO8Cu/N+Ldz/Le2rqM9tJ/upPu0/x3jBg1v0fCQhs9KS9l5ss/c Gu7R+Mc0/KXG/0yikqVhQMNnNfxYxk5XJfv6Yg2rNZwBr5hB36QPRCS8dEJylKVKM+ewhWyR +H82DEqTskC5QFmsLOEZo4xVxsKlmXxnxabYSKcUKUXIJAUnN5VdwjqZh/lZgIVYmK1j69nl bBPbzLawq9k1bJC9rrTzeLBVbBXUcDIntxXWKqyX+UjHLmVBMrB+1k8mNsAGKI5tYBsonl3B NpKZXcmuokS2lW2jZPYae40sikNx4N7hf+mdA0rVMipFWLCX3YG9dOwwuwfs77H7SM8iLAJP KrjH4tkN7Ea2B7P2szvYXexedj/4hi/PZr+E103sZfay+OvfHDIrVypXKVuUq5WtyjblGmVQ uVbZzv/iW1mjrIFvfMpacXYYZY3SycIzn/XxzMf67fIbcvHX6rEZY0aNQRrrw2xSvqPcIyLE 9/2Ocrtyh3JI+a5yp3KXcli5WznypX0VmkHZyg3KznNpeU7eDuU65Xr+LwrCyySkKZDWj9iu VzZTknJUGaIM5UHlQVgk5R9QDiq7lN3KjcpNyh7l28pe5WblFuXWc/L2Kbcp+/8T8sdRgtKp 9GPsMuVbygblcuUKZaOyCTMRTcWpODUZTFisaNHMYyrLZ7PYA+xh9lNlocKz10Tbxf+j8Dy9 AN/PZDORDUNsCJF9iD2EWD/LcO4UVcnHTEbP4Mk3BZ/T7OIcL6cV1IGT3INPYetwlq+iQZze PbSPbqcjNEQP0+OIUTzDmWEJbCzqAlYM+RWsmk1DL5FloE5imaiTGexiKSwbtYXloB7DrKhT 2TjUaSwXdTobj9rGSlBPYJNQF7JS1EWsDPVEVg7ZlayGTQdWsSlsBnAym8pq4ctepQf1gLIO NsfTNuL/f8+1KAz3z3Xg3Yiiox/Tczinv6Bf4z58j/5EqcKLY+Hpfjx7xekXp+D02YmDj+8Z yduvzsBY1pJ2v8s7jaytxP9vG/GyLqzabJ1vjJ90ddPVf0liJuXgZusMsKYojE1OqIo3GkqT dUqOgaqcRnOpkenZ5ukK0x9srVpWVTaKM+728RvH0XmiLKVO7cs4j/jAPZuXqvxRwvTps44X 2lbXP2gtb5r3/g/ivvhZ9vMfWQ9uHvt51WbdU6DygzqFKYpl/vHsm97c0dI47y+v9TYlTT5U lTSiKjNAqU3XCiV1bXpjmrJizuSxVWm8E5eWeJGHf47wqfPwEWlyelUqZ5vSEur7gp1OfGju 6fFMToE0cM1pxuXdzv6wZ3JulZUzEtLSJUOd58GHyy6vS3y+mJxXlcuHdWkZ2vByfDTHh+ze AH/vPG9O1fjMpKqaydVVU6rEa0Vm0mTeramumVo7tXZFVesoZdtaJ2dWjZX7J+NjkrcVn2rL 1AU+V8Xk0qoSuVFBbEBsxT/SyL1aPUH+/jnEN93MCkZ7hRlIt5mlEPhmZTNjdPdzQ4eef0G9 37zhmnu39n34/SUfvflEyvHVzh/d4R7320f+/bmae66qusZx+fbX1vxu2v6U4y+9v+7j/jsv 9593fPf9SQ93f9Jz43M/aim/p+n8Tx/8zTcusSoHPq9cM/7QX+645c6cZ5XfX7Go5e3/qObK w6Hs/v6MGTvZl7I09p17hsnITqsYDKKSbdDYxdjlMVMhCiW7GCRLRbbosRcVUbZECj3WslYk FL8ZVJ6W93mu97qe9/c+f811zrnPfb7nnO/nc76f77mv2WYzpcUfcod5QPNB2WB4jXWgM1we kkzgyNsPewz3ZjaXa/NXUoxnT2a/M4BTKBgbvht5XvpelFC4Y81pC3MPnzq1AvHw4y2sXGoZ Z96YNjC4N6426b28Q8uWKBzcryHRIeg/lQFvfjsmvL2/sXS/bsoOa5Jg7IjVwkzw21PX7cAx CwaMA+3Ch/Pi2wojfAtnfmd+P2LQR1rBkQo5VUvDG6qoIGTHzyb0A4ReQImGjuyx1NS0YPLR C4gDol/KADiMd1N2eGC9PeV9yetOSTNQZMe67whwgMFrUDqAhvxDBQYB2pS6ndDdAArYRVIi IcKAze5YL9c/9VbY8JWtrqKrLU9+at1TBcSgTADDFysgdMA2SiULZSzKH0rQkC0kl9mgZM+8 uh3g+eLfEA4mUxNtsqOh5OBySMXvUAEhEEB6LktvLO7u4YefC0iWSagj3gD38Ou33Yq0cB+k k8q2etgSxzEOxTDP7pdQAKFujTTHoVO6he24FjWVhQw94aFvo1DhpRMTiaDVJ2YJaNHOfAl0 YGGFrfZ76cfjzX1WL6tkzmqUXynve2W+VlvWFLLwhCl9LnFVpksVw8eHkljU1CNjeA0gUo1v 4pj5tcxcd69UBC+Cmt4qxTfiexz/I8j4EY4Aaisczf/moAqA3Mag4n81KKXNwesvIVliJHng ZRcu8AzvHkef4yGNlRlY8TV13bRgNhSrmJl3n4+E02f0HZhlF8MSiU962uywkG2vYP9ItaLL g9mX2coO0XxxTLdNBC2DHZHW1JF7V33RgyahWQTYlcIIyyy6xVFgaUZYWV+H4fHg/Z2NPWav CZrlmGzZAnDgu6yCC8jVjLHjztQZ6i7DdQn1q602S1rjtKQ9kwRj9xzpd7cjWSWnY17QkMKM UoL06JgBgRbWdJfF1xaF0Hyt5BLJiRjuG2rDJh6HupBXyj3sBUoTZKvUxwMm3QKXuMfEbxbN JptUaMnGVwYUrHZjrkvhQ3SmVASznLnHjlSJ4npBobqs4aEum5BsAQgP/peQZPoKSSoABChu gFEWkAYkSeIk0TDhX4ER7+0th7Vdhx/3Ovwor/gfEEhT/7cQqPQ9Aim7HO7v+RyNAcOODQU0 E4HGz3e2J9RcBN2raWu7P7+td23JoF7RDmBrWsDzdV8asE6DcRQH7601ajs9HspzOlci7gTH vpWWyiRtSGuq8THqqN/yPN7zGfGJyr9zuuAqvFjVwh0/zYSvx/n1TSbbhTd4x348hw8UuZ6d FJRYvBgjddJA3ofvgPbzuXJmmGmPHymRiHX6TP8kcs6nij61b4nNTDzFFlEbSHUrKKw2616U sKx/B9K3+pK35dKdMX0uBpHWkc5uJfmDWlxqLDaBovdzHGcTnnhOaozPM4e86AjO9j3p1JBm uB9AChVnFe2wU5Ppiy6Qpg3q5S21DPrjSo7Hqtq5mwARyk6mgOUNCmABNYCi1NQi2Do0PmCn BrW2rhiUzACeX7DNyCGs6+EZ4EVJicMksVIwuIqK8ncZP3m4IMC/8TDXT3OBcCFg58Y28X5r x3h44GHaPnich5cTPoBCDyrKABwOAMqb9IAA4AhF+Gbxv2DRXx7lVDUNnmOq79B8khmJ/lbA m6z8C2LWH1fj9bMrVq9kwTSCjbNSs2JsEC4dOvYBMzd8m02fv5tMC+OPyTjjWNrkEmgn0iOg NsACvjSR0Fgn55iSghNPbt8tW8dUbiHesG+cQQOVIJsvqZI3dfC0zvAZlqoUVzPbG8TgTBs5 P/3XyWX2qilG/HA6Uc6M/PGLMrxj6klYThsLaocMAWVM+GLu7GWq+3xddWZ7S8+F1u2eMr2M LvycG+iGRxfxtibQSwqBzGNtnJSrDrHTqh1eO7Zy1ZGB7lon4bD57G1VK26CH/T5h9rC0PjV W22/9eTu8LJUa6meo8sWBkppzjaXwvw4zg5u8kYeQMgBCFkUXIKhhBSAkBjKeqzdc9bJK13E OISzxCB67VGm1//9/hH/wsfXWSF+grH+wvtEXuR0JVi014/tvaUNIiOd8ZEG9cWImObdY0Lv 5szjZMtJ+x/azX561qqqejR/l6nTqqibZnNrwQB18Ev4BfUMVk/nqlV2Q16n+k/tusNsR2GG b+yCigq2P5RRFpOrdchkjxRjwWYvmvIvCTX3cL3H3HDXRdB+JvJ8HD3hymz8oeYt5kHNeCPw CQanjxCIl9ph8FSAKudt6BCk7Nh88cuH5jMOBx9gTG+XQSTZ12J75uhiQioTm64ry44EjuT5 DfuSQO3Omg2duyKHtNnzkM58zv3IV9380JG8vdCHRxVR7gb8zHYVDFnnu56aau5r4ze75tnP vjs8zicjt5NEZoVH5OCgdDMwcGZMNqwHMV1n6xWePeUaEPL/ghYABIBEKJJDu80gHg6oIJCb RYBw7c9hAwfAtiE5GMxtvXHkcABPHod1/RghCw5ajIO9m4e7/RfLGH5l2a+miSAP+sM0RQCh jWns2Npi77AegFAiEqN1YQD7kU2YKWxCt84m91phF6oH1zSMZgLvdouKffB9LLTWJn0Y3ZJW QSxBBsiBGvPonmKbK3I+vG5o6Ck+n5BFu8xym4hJmSTer2FtyqufcTkTbcJXZbRsDz7XwN1N xIG0/PcssKPQK1jjoWX1O6PKxYNYWhHVk1pK++ddCvctSHgLCj/S2S5ofBuT0pXdznF/u+ZJ Grd38UJ7rHWm65uT7WGVDUqfsvaMBZUIKFReG5jPHEwVYlm1gGuboUKKLMZHpo4EiF1flFZg 00T5a+j8losbCRHG8YzpXWr034PZn2l45lxcav2JoDf0K2GQUx+ST6rJ5DomtQ7K/SFDtYNF 6YDDghp70dtwfgFxjEcr2f8g2USwNHk9xH8Wi0P+HRTDTkO/KcK5yIChgkBAkHWZKrANyg3l FPsoc+j4Qy/Tm6MfSNI83CsNSyYEYPvXLpxUUCZBBpAJyIcs2XVB2gDjevCzrj32ASxfgyxq AEL++RmdZdjr4V85SRz9SGMikfhAsIPg3fH4d8L8jjdxieFFL2J2U5VHsdZ1yePxYNUyLY4Z Y2u1e4hUw57XR1okQQleXJMnFZKS6iGvkDqv6RTs4Mmf4O9f6BS+MIxpRQ14aqNsmNVgl2R4 02hXD8fldx6KqEnWE6Y667Fn6uLY9gGZSq6SVM/lpkn0adndJ0kIi3dITP/FCK1ZD75xKZ0y z7ZTrh47HvanLXdYRS52yqaArPvm75ZmBHWrrprgpU8IECSyfCa0kmh5o3hhjPu8BmcXp3a5 zbg0O5oI1CALRh8JOHtx7A0V7Dse7hjJL3ut5ZEtBDJA3wwuPlJ/6/OYY+KjZXrVYIBIvY9M afIbdMZgy/AGWE/xWv2Qqfi30AaF/5BwBJzMgErKyHXVpEyWUeQiklIECBH/yETIBm/YJvXz y13Jg+7rF9+uMDNvB5ihu2uA1F/GS/dyuTsZuzXtYmeOmCrEM3rMdzvVup6v431istLW3IUb sauwxjNCxgoaJjUkpjqTYlNtD68CM/ldrvqOvZZihOiCHjcRsFR8ZFZlhOaAsZHoZfvx6ofp oHGFo6kWn3jCxrGwWwFyE8Ws1l76E092xOhfpRu0t0GlorGHRpT2XEYgK1lovV65B+VUdrWV rlreRBi/crxFMqhgdIXA2HH1spwfYwgtF+Srwb/TgIlSPLlCLaejHxjYw/pyTnsnrOBNfY14 KkqnDkhElomhb41WNR9yyNJSGXi5dE5sSDwtCZNdg1dS92YB2E7AHuiGJ2sKF2NlCA7bht2o tRRH7w5o4FQ34iUiOJa8Iud/EDbfWKIoSAB3ZvrBkvzOMP94Hanyt03ccb+gxHxKrQiUkAkQ 0kN/Si+Z+Kv/DWL8MZLQ21CFuoA2oElSJ6mGqWxRhX++WPZ0caLUKmxex3srUFBBAYXRZq4G vUWl6gBagMZXlUoVhvjlffX6ax28fngf/mdyEaymHfNYHGUxScJ24cavd5l7zh6cpTYL57oX 3lxpbqt1eK5yEYA4Bk5GRV335z3WPZOr01B//vPNZ+3RSxHh9DYlDCG+Tn0BjVDsQpxp+pA9 7/vzEYdcY9ybh+hUCyKMIhka3o+VvZtfUOuFV9AXzx6tMfR01hquONX2RlS2xUbiFamkTLLA VLQ4/fKCypUTiraGesFebbU8kZhFq7fP/tCwbVemU7NxC+uswr6ceGhWAkNVsfOfTiHsMpXp 6H89xP75EXpv9/Iu5yU8skfcj3mkpJ3m7iKeKaesp/CNv22MDqu1j7udRk7DC7St/4E7PeaP TafaanOu7y3aJgzLuhGk8ApOhNaRmbSKCgwGCOX/Gr78E+d/y3CTCC8Azq/nrCQYTguhXn+I cvpubj09BM60NalONv1biRG+DdjaygWIfOsIhZNR+6EdMZAfSN2/BDsvz3OyNzQ6NsMccNzS hQluARwmyYZK//0PVTPFQ0X/xucX3weWUCIYdPYQCoP3x2Hlh+bFM/w6UT2jFf52EU+4k64G J8HT9Np3c8sGQc0J2csYsxaMK0+UGYO49a4E+M7qC9WEOWra6Na4uU6DqLXpZ2cq8xACbPLV VUHn2jqGmTOGqW2LsZnUSMtIP8TyqSWLQvwjE1Vj2y60bwmNyXCmVUeq4vHWoybPnY9OB4fK o1lNb+U8Y/149RiNceHp/OHVk7Zp8MEYjoo89Nylmazy5djnGR/Oh/Q6359+BuLkYiDeR9pQ h8u0qzbfxGlxoGK0rogw7JA9R5jQcxMZXTA1VgZyYw+fVA1+Y/Bc97mIuxPd846L4voGq8zW 9ypeO+jrubNdMiriOcpZkUkkR0hE8Mq3XaKBE8FT5KoJikuf+EdynD/JrDLR0G0YQEVmFtIR gHervzF+u+kBk93taws1nGXz2FeBqyCUkfCjZMbd4m7sUFasAvIsr/4LPgLkmbYOa5P/z1zA SKnm2hnppzcrJFYiwnCxCvxsun80cB8KkjHmkZm7cpkVJxzk6hPWEj00ZRv69sFI+vJH3JDp dvkjx4VwqU06xpNZ4sNX9l1FjzYGmBkFjT8tjHtXfIsnEmLp6KrotW/liFnkYghsm6N6X0oe a+KQzCBdnRAW6Vfdb1U00pNx1bbNJelRVfR47WgNd9O8PmoefT9u98ESe/yxpqeShfmnITQd 3tmcoDvIm0VcNQs7Z1lo700NfqBX55i79pF98N7oea+G0rIj7HPoV4XXRA+NxazZLynsGa20 +1T+xIm/llRxX7q42u0z5MD0zt/0ItIfZxR5KnMF6Lpzq093OM2kxBrIWIH+A6uqDYQNCmVu ZHN0cmVhbQ0KZW5kb2JqDQoxMDUgMCBvYmoNCjw8L1R5cGUvWFJlZi9TaXplIDEwNS9XWyAx IDQgMl0gL1Jvb3QgMSAwIFIvSW5mbyAyOCAwIFIvSURbPDZBQkMxRDVFRkNDNDZFNERCRTk2 MjJDODNCM0FCODI1Pjw2QUJDMUQ1RUZDQzQ2RTREQkU5NjIyQzgzQjNBQjgyNT5dIC9GaWx0 ZXIvRmxhdGVEZWNvZGUvTGVuZ3RoIDI3Mz4+DQpzdHJlYW0NCnicNdK5TkJREMbxcxF3XBDu 5bKKuMIFVMR97SzsrW1ssLA20UQTK59BE2NJYW9iLKx8BTs79QksTMTD99dTzC8nZ2YyyRxj 7Gm1HBujxrQ5g0fhvIl4Q7gx4TlwCbcisQNXwj8RSR9eRIqe6V04FJkI7IvsHvyI3JfIuyKg oByGO1HZhldjQnbqglmHDViDv7dNm1l9/r85EIIOCEMn9EMX9EI39EAfRGAABmEIhiEKIxCD OLjgQQJ82IIkpCANGchCDkYhD2NQgGmYhHGYgCkoQwlmoAgBzMMsVKAKc1CDZViEBajDEqzA ql1OjS9VfxDXnri5F+8X4uNcfB63cUqnIvgWjSdxdABNY34BX10vKA0KZW5kc3RyZWFtDQpl bmRvYmoNCnhyZWYNCjAgMTA2DQowMDAwMDAwMDI5IDY1NTM1IGYNCjAwMDAwMDAwMTcgMDAw MDAgbg0KMDAwMDAwMDEyNSAwMDAwMCBuDQowMDAwMDAwMTg4IDAwMDAwIG4NCjAwMDAwMDA0 ODAgMDAwMDAgbg0KMDAwMDAwNTIyNiAwMDAwMCBuDQowMDAwMDA1Mzk1IDAwMDAwIG4NCjAw MDAwMDU2MzMgMDAwMDAgbg0KMDAwMDAwNTc2NCAwMDAwMCBuDQowMDAwMDA1NzkyIDAwMDAw IG4NCjAwMDAwMDU5NTIgMDAwMDAgbg0KMDAwMDAwNjAyNiAwMDAwMCBuDQowMDAwMDA2MjY1 IDAwMDAwIG4NCjAwMDAwMDY0MjQgMDAwMDAgbg0KMDAwMDAwNjYwMCAwMDAwMCBuDQowMDAw MDA2ODQ0IDAwMDAwIG4NCjAwMDAwMDY5ODMgMDAwMDAgbg0KMDAwMDAwNzAxMyAwMDAwMCBu DQowMDAwMDA3MTgwIDAwMDAwIG4NCjAwMDAwMDcyNTQgMDAwMDAgbg0KMDAwMDAwNzQ5OSAw MDAwMCBuDQowMDAwMDA3Njc4IDAwMDAwIG4NCjAwMDAwMDc5MjcgMDAwMDAgbg0KMDAwMDAw ODIxMyAwMDAwMCBuDQowMDAwMDExMTA5IDAwMDAwIG4NCjAwMDAwMTEyNjggMDAwMDAgbg0K MDAwMDAxMTQyNiAwMDAwMCBuDQowMDAwMDExNTgzIDAwMDAwIG4NCjAwMDAwMTE3MzcgMDAw MDAgbg0KMDAwMDAwMDAzMCA2NTUzNSBmDQowMDAwMDAwMDMxIDY1NTM1IGYNCjAwMDAwMDAw MzIgNjU1MzUgZg0KMDAwMDAwMDAzMyA2NTUzNSBmDQowMDAwMDAwMDM0IDY1NTM1IGYNCjAw MDAwMDAwMzUgNjU1MzUgZg0KMDAwMDAwMDAzNiA2NTUzNSBmDQowMDAwMDAwMDM3IDY1NTM1 IGYNCjAwMDAwMDAwMzggNjU1MzUgZg0KMDAwMDAwMDAzOSA2NTUzNSBmDQowMDAwMDAwMDQw IDY1NTM1IGYNCjAwMDAwMDAwNDEgNjU1MzUgZg0KMDAwMDAwMDA0MiA2NTUzNSBmDQowMDAw MDAwMDQzIDY1NTM1IGYNCjAwMDAwMDAwNDQgNjU1MzUgZg0KMDAwMDAwMDA0NSA2NTUzNSBm DQowMDAwMDAwMDQ2IDY1NTM1IGYNCjAwMDAwMDAwNDcgNjU1MzUgZg0KMDAwMDAwMDA0OCA2 NTUzNSBmDQowMDAwMDAwMDQ5IDY1NTM1IGYNCjAwMDAwMDAwNTAgNjU1MzUgZg0KMDAwMDAw MDA1MSA2NTUzNSBmDQowMDAwMDAwMDUyIDY1NTM1IGYNCjAwMDAwMDAwNTMgNjU1MzUgZg0K MDAwMDAwMDA1NCA2NTUzNSBmDQowMDAwMDAwMDU1IDY1NTM1IGYNCjAwMDAwMDAwNTYgNjU1 MzUgZg0KMDAwMDAwMDA1NyA2NTUzNSBmDQowMDAwMDAwMDU4IDY1NTM1IGYNCjAwMDAwMDAw NTkgNjU1MzUgZg0KMDAwMDAwMDA2MCA2NTUzNSBmDQowMDAwMDAwMDYxIDY1NTM1IGYNCjAw MDAwMDAwNjIgNjU1MzUgZg0KMDAwMDAwMDA2MyA2NTUzNSBmDQowMDAwMDAwMDY0IDY1NTM1 IGYNCjAwMDAwMDAwNjUgNjU1MzUgZg0KMDAwMDAwMDA2NiA2NTUzNSBmDQowMDAwMDAwMDY3 IDY1NTM1IGYNCjAwMDAwMDAwNjggNjU1MzUgZg0KMDAwMDAwMDA2OSA2NTUzNSBmDQowMDAw MDAwMDcwIDY1NTM1IGYNCjAwMDAwMDAwNzEgNjU1MzUgZg0KMDAwMDAwMDA3MiA2NTUzNSBm DQowMDAwMDAwMDczIDY1NTM1IGYNCjAwMDAwMDAwNzQgNjU1MzUgZg0KMDAwMDAwMDA3NSA2 NTUzNSBmDQowMDAwMDAwMDc2IDY1NTM1IGYNCjAwMDAwMDAwNzcgNjU1MzUgZg0KMDAwMDAw MDA3OCA2NTUzNSBmDQowMDAwMDAwMDc5IDY1NTM1IGYNCjAwMDAwMDAwODAgNjU1MzUgZg0K MDAwMDAwMDA4MSA2NTUzNSBmDQowMDAwMDAwMDgyIDY1NTM1IGYNCjAwMDAwMDAwODMgNjU1 MzUgZg0KMDAwMDAwMDA4NCA2NTUzNSBmDQowMDAwMDAwMDg1IDY1NTM1IGYNCjAwMDAwMDAw ODYgNjU1MzUgZg0KMDAwMDAwMDA4NyA2NTUzNSBmDQowMDAwMDAwMDg4IDY1NTM1IGYNCjAw MDAwMDAwODkgNjU1MzUgZg0KMDAwMDAwMDA5MCA2NTUzNSBmDQowMDAwMDAwMDkxIDY1NTM1 IGYNCjAwMDAwMDAwOTIgNjU1MzUgZg0KMDAwMDAwMDAwMCA2NTUzNSBmDQowMDAwMDEyOTMy IDAwMDAwIG4NCjAwMDAwMTM0OTcgMDAwMDAgbg0KMDAwMDAzOTk1OCAwMDAwMCBuDQowMDAw MDQwMzY2IDAwMDAwIG4NCjAwMDAwNTkyNjUgMDAwMDAgbg0KMDAwMDA1OTUyMCAwMDAwMCBu DQowMDAwMDU5NzU5IDAwMDAwIG4NCjAwMDAwNzY0MTIgMDAwMDAgbg0KMDAwMDA3Njc5NSAw MDAwMCBuDQowMDAwMDkyODYyIDAwMDAwIG4NCjAwMDAwOTMwMjQgMDAwMDAgbg0KMDAwMDA5 MzA5OCAwMDAwMCBuDQowMDAwMTA2ODI5IDAwMDAwIG4NCnRyYWlsZXINCjw8L1NpemUgMTA2 L1Jvb3QgMSAwIFIvSW5mbyAyOCAwIFIvSURbPDZBQkMxRDVFRkNDNDZFNERCRTk2MjJDODNC M0FCODI1Pjw2QUJDMUQ1RUZDQzQ2RTREQkU5NjIyQzgzQjNBQjgyNT5dID4+DQpzdGFydHhy ZWYNCjEwNzMwNQ0KJSVFT0YNCnhyZWYNCjAgMA0KdHJhaWxlcg0KPDwvU2l6ZSAxMDYvUm9v dCAxIDAgUi9JbmZvIDI4IDAgUi9JRFs8NkFCQzFENUVGQ0M0NkU0REJFOTYyMkM4M0IzQUI4 MjU+PDZBQkMxRDVFRkNDNDZFNERCRTk2MjJDODNCM0FCODI1Pl0gL1ByZXYgMTA3MzA1L1hS ZWZTdG0gMTA2ODI5Pj4NCnN0YXJ0eHJlZg0KMTA5NTg1DQolJUVPRg== --------------020906010806030009010700-- ========================================================================= Date: Tue, 31 May 2011 12:21:20 +1000 Reply-To: Seyed Batouli <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Seyed Batouli <[log in to unmask]> Subject: Segmentation problem Content-Type: multipart/alternative; boundary="_000_514CC4E8C864D744BED2869B02DA985333294FE6D1INFPWEC001adu_" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_000_514CC4E8C864D744BED2869B02DA985333294FE6D1INFPWEC001adu_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Dear All, I have two T1 scans of an elderly subject, done in the same session with ex= actly the same settings and orientations. But after segmentation one of them is good, the other one is very distorted= and unacceptable. Do you have any idea of why it happens? I have done normalization on both. both are ok. I can send you the scans if needed. Thanks, Amir --_000_514CC4E8C864D744BED2869B02DA985333294FE6D1INFPWEC001adu_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <html dir=3D"ltr"><head> <meta http-equiv=3D"Content-Type" content=3D"text/html; charset=3Diso-8859-= 1"> <style id=3D"owaTempEditStyle"></style><style title=3D"owaParaStyle"><!--P = { MARGIN-TOP: 0px; MARGIN-BOTTOM: 0px } --></style> </head> <body ocsi=3D"x"> <div style=3D"FONT-SIZE: 13px; COLOR: #000000; DIRECTION: ltr; FONT-FAMILY:= Tahoma"> <div></div> <div dir=3D"ltr"><font face=3D"Tahoma" color=3D"#000000" size=3D"2">Dear Al= l,</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">I have two T1 scans of an= elderly subject, done in the same session with exactly the same settings a= nd orientations.</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">But after segmentation on= e of them is good, the other one is very distorted and unacceptable. Do you= have any idea of why it happens?</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">I have done normalization= on both. both are ok.</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">I can send you the scans = if needed.</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">Thanks,</font></div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2"></font> </div> <div dir=3D"ltr"><font face=3D"tahoma" size=3D"2">Amir</font></div> </div> </body> </html> --_000_514CC4E8C864D744BED2869B02DA985333294FE6D1INFPWEC001adu_-- ========================================================================= Date: Tue, 31 May 2011 08:20:55 +0100 Reply-To: Cath <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cath <[log in to unmask]> Subject: marsbar for % signal change 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 two questions regarding using marsbar to output the percent signal= change.=20 First, how to output % signal changes of more than one coordinates at onc= e? (I know how to output % signal changes of one coordinate) Second, how to output % signal changes of more than one coordinates at on= ce at second level analysis? I can not find useful info in the posts. Or, I miss something? Thanks in advance! Sincerely, Cath ========================================================================= Date: Tue, 31 May 2011 09:23:43 +0200 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: multipart/alternative; boundary=Apple-Mail-5--45574633 Mime-Version: 1.0 (Apple Message framework v1084) Message-ID: <[log in to unmask]> --Apple-Mail-5--45574633 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=windows-1252 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_ne= uroscience_of_soci/211 -Deadline for submission of abstracts: 30 Sept 2011 -Deadline for submission of full manuscripts: 31 Dec 2011 Best regards, Leo Schilbach -- Towards a neuroscience of social interaction Hosted By: Ulrich Pfeiffer, University Hospital Cologne, Germany=20 Bert Timmermans, University Hospital Cologne, Germany=20 Kai Vogeley, University Hospital Cologne, Germany=20 Chris Frith, Wellcome Trust Centre for Neuroimaging at University = College London, United Kingdom=20 Leonhard Schilbach, Max-Planck-Institute for Neurological Research, = Germany=20 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 = =97paradoxically=97 represent the 'dark matter' of social neuroscience.=20= 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=92 the agent, it has been proposed that in = online social cognition the knowledge of the other =97at least in part=97 = resides in the interaction dynamics =91between=92 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.=20 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 =97at times=97 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.=20 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] --Apple-Mail-5--45574633 Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=windows-1252 <html><head></head><body style=3D"word-wrap: break-word; = -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear = colleagues,<br><br>this is to draw your attention toward and invite = submissions for an upcoming Frontiers in Neuroscience Special Topic = entitled:<br><br>"Toward a neuroscience of social = interaction"<br><br>Please find more details below and on the Frontiers = website:<br><a = href=3D"http://www.frontiersin.org/Human%20Neuroscience/specialtopics/towa= rds_a_neuroscience_of_soci/211">http://www.frontiersin.org/Human%20Neurosc= ience/specialtopics/towards_a_neuroscience_of_soci/211</a><br><br>-Deadlin= e for submission of abstracts: 30 Sept 2011<br>-Deadline for submission = of full manuscripts: 31 Dec 2011<br><br>Best regards,<br><br>Leo = Schilbach<br><br><br><br><br>--<br><br>Towards a neuroscience of social = interaction<br><br>Hosted By:<br>Ulrich Pfeiffer, University Hospital = Cologne, Germany <br>Bert Timmermans, University Hospital Cologne, = Germany <br>Kai Vogeley, University Hospital Cologne, = Germany <br>Chris Frith, Wellcome Trust Centre for Neuroimaging at = University College London, United Kingdom <br>Leonhard Schilbach, = Max-Planck-Institute for Neurological Research, Germany <br><br>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 = =97paradoxically=97 represent the 'dark matter' of social = neuroscience. <br><br>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=92 the agent, = it has been proposed that in online social cognition the knowledge of = the other =97at least in part=97 resides in the interaction dynamics = =91between=92 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. <br><br>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 =97at times=97 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. <br><br>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.<br><br><br><br><br>--<br>Leonhard Schilbach, = MD<br><br>Department of Psychiatry and Psychotherapy<br>University of = Cologne, Germany<br><br><a = href=3D"mailto:[log in to unmask]">leonhard.schilbach@uk-koeln= .de</a><br><br>&<br><br>Max-Planck-Institute for Neurological = Research<br>Cologne, Germany<br><br><a = href=3D"mailto:[log in to unmask]">[log in to unmask]<= /a></body></html>= --Apple-Mail-5--45574633-- ========================================================================= Date: Tue, 31 May 2011 10:33:07 +0100 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: Connectivity - sensitivity in small sample size Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Message-ID: <[log in to unmask]> Dear SPMers,=20 we used fMRI to compare differences between one brain-lesioned patient an= d a group of 12 healthy controls. Findings from a pooled 2-sample t-test = revealed two regions of higher activity in the patients within the task r= elated network. It would be an interesting question to investigate whethe= r these two regions were most strongly connected within the controls. Wou= ld a connectivity analysis incorporating only 12 subjects make sense? Are= there other methodological approaches to test our hypothesis?=20 Thanks in advance & best regards,=20 Ben=20=20=20 ========================================================================= Date: Tue, 31 May 2011 11:47:59 +0200 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: Connectivity - sensitivity in small sample size Comments: To: Ben Becker <[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 Ben, > we used fMRI to compare differences between one brain-lesioned > patient and a group of 12 healthy controls. Findings from a pooled > 2-sample t-test revealed two regions of higher activity in the > patients within the task related network. It would be an interesting > question to investigate whether these two regions were most strongly > connected within the controls. Would a connectivity analysis > incorporating only 12 subjects make sense? Are there other > methodological approaches to test our hypothesis? As a note of caution, we only recently tried something similar and found=20 interesting correlations between functional connectivity and a marker of=20 clinical impairment, which, however, were fully explained by looking at=20 the GM volume deficits in the patients. We felt that not taking into=20 account structural differences between groups may lead to potentially=20 erroneous conclusions. See http://dx.doi.org/10.1002/hbm.21227 Cheers, Marko --=20 ____________________________________________________ PD Dr. med. Marko Wilke Facharzt f=FCr Kinder- und Jugendmedizin Leiter, Experimentelle P=E4diatrische Neurobildgebung Universit=E4ts-Kinderklinik Abt. III (Neurop=E4diatrie) Marko Wilke, MD, PhD Pediatrician Head, Experimental Pediatric Neuroimaging University Children's Hospital Dept. III (Pediatric Neurology) Hoppe-Seyler-Str. 1 D - 72076 T=FCbingen, Germany Tel. +49 7071 29-83416 Fax +49 7071 29-5473 [log in to unmask] http://www.medizin.uni-tuebingen.de/kinder/epn ____________________________________________________ ========================================================================= Date: Tue, 31 May 2011 12:21:49 +0200 Reply-To: Bas Neggers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bas Neggers <[log in to unmask]> Subject: matlabbatch & dependencies 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 list, while developing some tools with the nice spm8 matlabbatch system, 3 questions popped to mind regarding dependencies: 1. when one concatenates multiple matlabbatch{..} cells (lets say cell 1 to 3 with cell 1 to 3 from another batch) referring to different jobs, would existing dependencies be spared? For example, when I combine a separate matlabbatch with 3 preprocessing jobs with a matlabbatch with 3 statistic jobs? 2. Is there a way to easily (re)create dependencies between in and output images from different matlabbatch{..} nodes by hand, that is, using matlab code? The only way I see now is in the batch GUI, making automation quite cumbersome. 3. Is there a built in method in the batching system to 'ask' a matlabbatch node what exactly its output images will be, adhering to its dependencies? In theory this might be possible as matlabbatch is an object rather than a cell structure. Thanks for any pointers. The matlabbatch system seems quite elegant but I have a hard time finding API documentation. Cheers, Bas -- -------------------------------------------------- Dr. S.F.W. Neggers Division of Brain Research Rudolf Magnus Institute for Neuroscience Utrecht University Medical Center Visiting: Heidelberglaan 100, 3584 CX Utrecht Room B.01.1.03 Mail : Huispost B.01.206, P.O. Box 85500 3508 GA Utrecht, the Netherlands Tel : +31 (0)88 7559609 Fax : +31 (0)88 7555443 E-mail : [log in to unmask] Web : http://www.neuromri.nl/people/bas-neggers http://www.neuralnavigator.com -------------------------------------------------- ========================================================================= Date: Tue, 31 May 2011 12:55:02 +0200 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Cerisa Stawowsky <[log in to unmask]> Subject: MEG-data: unpaired two-sample t-test/paired t-test MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_Part_2136808_11883007.1306839302199" Message-ID: <[log in to unmask]> ------=_Part_2136808_11883007.1306839302199 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable <html xmlns=3D"http://www.w3.org/1999/xhtml" xml:lang=3D"en" lang=3D"en"><t= itle></title><head><meta http-equiv=3D"Content-type" content=3D"text/html; = charset=3DUTF-8" /><style type=3D"text/css"> html, body {overflow-x: visibl= e; } html { width:100%; height:100%;margin:0px; padding:0px; overflow-y: au= to; overflow-x: auto; }body { font-size: 100.01%; font-family : Verdana, Ge= neva, Arial, Helvetica, sans-serif; background-color:transparent; overflow:= show; background-image:none; margin:0px; padding:5px; }p { margin:0px; padd= ing:0px; } body { font-size: 12px; font-family : Verdana, Geneva, Arial, He= lvetica, sans-serif; } p { margin: 0; padding: 0; } blockquote { padding-le= ft: 5px; margin-left: 5px; margin-bottom: 0px; margin-top: 0px; } blockquot= e.quote { border-left: 1px solid #CCC; padding-left: 5px; margin-left: 5px;= } .misspelled { background: transparent url(//webmailerng.1und1.de/static_= resource/mailclient/widgets/basic/parts/maileditor/spellchecking_underline.= gif) repeat-x scroll center bottom; } .correct {} .unknown {} .ignored {}</= style></head><body id=3D"bodyElement" style=3D""> Dear SPM user,<br><br>I have N-subjects and two conditions. I want to compa= re these conditions at source level (MEG-data, evoked).<br>First I used acc= identally unpaired two-sample t-test. Afterwards I used the paired t-test, = the correct t-test to compare both conditions. <br><br>The results a curiou= s: For the unpaired two-sample test I get significant results (corrected fo= r FWE, p<0.05) for the contrast Cond1 > Cond2 (t-contrast). For the p= aired t-test the significant results disappear (only uncorrected p<= ;0.001) for the same contrast Cond1 > Cond2 (t-contrast). The brain coor= dinates are nearly same for both contrasts. <br><br>My question: Why disapp= eared the significant results for the paired t-test? - What is the reason?-= I expected better results for the paired t-test. Is this assumption incorr= ect?<br><br><br>Best regards,<br><br>Cerisa Stawowsky<br><br><Bassfont size= =3D"2" face=3D"Verdana"></body></html> ------=_Part_2136808_11883007.1306839302199 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 7bit Dear SPM user, I have N-subjects and two conditions. I want to compare these conditions at source level (MEG-data, evoked). First I used accidentally unpaired two-sample t-test. Afterwards I used the paired t-test, the correct t-test to compare both conditions. The results a curious: For the unpaired two-sample test I get significant results (corrected for FWE, p<0.05) for the contrast Cond1 > Cond2 (t-contrast). For the paired t-test the significant results disappear (only uncorrected p<0.001) for the same contrast Cond1 > Cond2 (t-contrast). The brain coordinates are nearly same for both contrasts. My question: Why disappeared the significant results for the paired t-test? - What is the reason?- I expected better results for the paired t-test. Is this assumption incorrect? Best regards, Cerisa Stawowsky ------=_Part_2136808_11883007.1306839302199-- ========================================================================= Date: Tue, 31 May 2011 13:19:36 +0100 Reply-To: "Tayaranian Hosseini P." <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Tayaranian Hosseini P." <[log in to unmask]> Subject: Source Reconstruction-Epoched EEG Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 Message-ID: <[log in to unmask]> Dear SPMers, I am going to do some source reconstruction of EEG but I need some of my qu= estions to be answered first. I have an epoched EEG file with 27 trials whi= ch are all for just one condition. I would like to reconstruct the source f= or each trial first and I need to see all these reconstructed images separa= tely. Then, I will average the reconstructed images and would like to see t= he final reconstructed image. 1. When I press the invert button, it starts to reconstruct the sources wit= hout asking me if I want to average or not. Does it first average the epoch= s and then reconstruct the sources or first reconstruct the sources for eac= h trial and then averages the images? 2. In the next step (WINDOW), the question of power pops up. If I choose fo= r example "induced", does it start the reconstruction from the beginning? I= f so, why this question is not asked in the "invert" step? 3. In "window" step, even when I choose "trials" for power, I can not see d= ifferent reconstructions and I will just see "first trial". How can I see t= he reconstruction of other trials? 4. In "image" step, again, how is the final image computed? EEG epoch avera= ging or image averaging? Best regards, Pegah -------------------------------------------------------- Pegah Tayaranian Hosseini PhD Student Room 4077, Tizard building (13) Institute of Sound and Vibration Research University of Southampton, SO17 1BJ, UK Tel: 023 8059 2850 email: [log in to unmask] ========================================================================= Date: Tue, 31 May 2011 14:57:47 +0200 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: PhD position in functional neuroimaging MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----_=_NextPart_001_01CC1F92.56DF393C" Message-ID: <[log in to unmask]> This is a multi-part message in MIME format. ------_=_NextPart_001_01CC1F92.56DF393C Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable PhD position in functional neuroimaging in patients with neurogenic and n= on-neurogenic lower urinary tract dysfunction =20 Applications are invited for a doctoral research position in the human ne= uroimaging project "The bladder and the brain - supraspinal control of im= paired lower urinary tract function in patients with neurogenic and non-n= eurogenic bladder dysfunction", funded by the Swiss National Science Foun= dation (SNF, 32003B_135774/1). Starting September 2011, we will investigate supraspinal lower urinary tr= act (LUT) control in patients with neurogenic (i.e. multiple sclerosis) a= nd non-neurogenic bladder dysfunction. Patients with non-neurogenic LUT d= ysfunction will be investigated before and after therapy for their LUT dy= sfunction. The neuroimaging investigations will consist of a combination = of advanced functional (functional MRI for resting state functional conne= ctivity) and structural (diffusion tensor imaging (DTI) and voxel-based m= orphometry (VBM)) brain imaging methods. =20 The candidate has the chance to participate in an innovative and multidis= ciplinary research project that is based on the long-standing successful = collaboration and renowned scientific groundwork of the Institute of Neur= oradiology at the University Hospital Zurich and the Neuro-Urology Resear= ch Group at the Balgrist University Hospital. =20 The candidate should hold a Master's degree in neuroimaging, neuroscience= , neurobiology, neuropsychology, neurophysiology, physics, or a related f= ield. Fluency in spoken and written English and the ability to work withi= n a multidisciplinary team are essential. Good oral German language skill= s are not mandatory but are advantageous. Experience with human MRI, or o= ther brain imaging methods is an advantage. Familiarity with analysis sof= tware, such as SPM/Matlab or FSL, and experience with programming languag= es and statistical analysis is a great plus.=20 =20 Salaries are in accordance with the Swiss National Science Foundation (st= arting at around 40'000 CHF p.a.). For further information about the proj= ect please contact Dr. Lars Michels ([log in to unmask]) =20 Your application includes your CV with a cover letter including a descrip= tion of your motivation and research interests, a copy of your academic d= egree(s), and two academic referees to the address below. Reviews of appl= ications will begin on 1 June 2011 and will continue until the position i= s filled. =20 Dr. Lars Michels Department of Neuroradiology University Hospital Zurich Sternwartstr. 6 Z=FCrich 8091 Switzerland =20 ------_=_NextPart_001_01CC1F92.56DF393C Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable <HTML dir=3Dltr><HEAD> <META content=3D"text/html; charset=3Dunicode" http-equiv=3DContent-Type> <META name=3DGENERATOR content=3D"MSHTML 8.00.6001.19046"></HEAD> <BODY> <DIV dir=3Dltr id=3DidOWAReplyText62854> <DIV dir=3Dltr><FONT color=3D#000000 size=3D2 face=3DArial> <P style=3D"TEXT-ALIGN: left; MARGIN: 0pt" class=3DMsoBodyText2 align=3Dl= eft><B><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt; ms= o-ansi-language: EN-GB" lang=3DEN-GB>PhD position in f</SPAN></B><B style= =3D"mso-bidi-font-weight: normal"><SPAN style=3D"FONT-FAMILY: 'Times New = Roman'; FONT-SIZE: 12pt; mso-ansi-language: EN-GB" lang=3DEN-GB>unctional= neuroimaging in patients with neurogenic and non-neurogenic lower urinar= y tract dysfunction</SPAN></B><B style=3D"mso-bidi-font-weight: normal"><= SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt; mso-ansi-l= anguage: EN-US" lang=3DEN-US><?xml:namespace prefix =3D o ns =3D "urn:sch= emas-microsoft-com:office:office" /><o:p></o:p></SPAN></B></P> <P style=3D"MARGIN: 0pt" class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'T= imes New Roman'; FONT-SIZE: 12pt; mso-ansi-language: EN-GB" lang=3DEN-GB>= <o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt" class=3DMsoNormal><SPAN sty= le=3D"FONT-FAMILY: 'Times New Roman'; COLOR: black; FONT-SIZE: 12pt; mso-= fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3DEN-GB>Applicati= ons are invited for a doctoral research position in the human neuroimagin= g project “</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; CO= LOR: black; FONT-SIZE: 12pt; mso-ansi-language: EN-GB" lang=3DEN-GB>The b= ladder and the brain - supraspinal control of impaired lower urinary trac= t function in patients with neurogenic and non-neurogenic bladder dysfunc= tion</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; COLOR: black; F= ONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" la= ng=3DEN-GB>”, funded by the Swiss National Science Foundation (SNF, 32003B_135774/1).<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt" class=3DMsoNormal><SPAN sty= le=3D"FONT-FAMILY: 'Times New Roman'; COLOR: black; FONT-SIZE: 12pt; mso-= fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3DEN-GB>Starting = September 2011, we will investigate supraspinal lower urinary tract (LUT)= control in patients with </SPAN><SPAN style=3D"FONT-FAMILY: 'Times New R= oman'; COLOR: black; FONT-SIZE: 12pt; mso-ansi-language: EN-GB" lang=3DEN= -GB>neurogenic (i.e. multiple sclerosis) and non-neurogenic bladder dysfu= nction</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; COLOR: black;= FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" = lang=3DEN-GB>. Patients with non-neurogenic LUT dysfunction will be inves= tigated before and after therapy for their LUT dysfunction. The neuroimaging investigations will consist of a combination of ad= vanced functional (functional MRI for resting state functional connectivi= ty) and structural (diffusion tensor imaging (DTI) and voxel-based morpho= metry (VBM)) brain imaging methods.<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB><o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB>The candidate has the chance to participate in an innovative and mu= ltidisciplinary research project that is based on the long-standing succe= ssful collaboration and renowned scientific groundwork of the Institute o= f Neuroradiology at the University Hospital Zurich and the Neuro-Urology = Research Group at the Balgrist University Hospital.<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB><o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt" class=3DMsoNormal><SPAN sty= le=3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt; mso-fareast-langua= ge: DE-CH; mso-ansi-language: EN-GB" lang=3DEN-GB>The candidate should ho= ld a Master’s degree in neuroimaging, neuroscience, neurobiology, n= europsychology, neurophysiology, physics, or a related field. Fluency in = spoken and written English</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New R= oman'; FONT-SIZE: 12pt; mso-ansi-language: EN-US" lang=3DEN-US> and the a= bility to work within a multidisciplinary team are essential. </SPAN><SPA= N style=3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt; mso-fareast-l= anguage: DE-CH; mso-ansi-language: EN-GB" lang=3DEN-GB>Good oral German</= SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-US" lang=3D= EN-US> language skills</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman= '; FONT-SIZE: 12pt; mso-ansi-language: EN-US" lang=3DEN-US> are not manda= tory but are advantageous. </SPAN><SPAN style=3D"FONT-FAMILY: 'Times New = Roman'; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: = EN-US" lang=3DEN-US>Experience</SPAN><SPAN style=3D"FONT-FAMILY: 'Times N= ew Roman'; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-languag= e: EN-GB" lang=3DEN-US> </SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Rom= an'; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-= GB" lang=3DEN-GB>with human MRI, or other brain imaging methods is an adv= antage.</SPAN><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; COLOR: black; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-ansi= -language: EN-US" lang=3DEN-GB> </SPAN><SPAN style=3D"FONT-FAMILY: 'Times= New Roman'; COLOR: black; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; = mso-ansi-language: EN-US" lang=3DEN-US>Familiarity with analysis software= , such as SPM/Matlab or FSL, and experience with programming languages an= d statistical analysis is a great plus. </SPAN><SPAN style=3D"FONT-FAMILY= : 'Times New Roman'; FONT-SIZE: 12pt; mso-fareast-language: DE-CH; mso-an= si-language: EN-GB" lang=3DEN-GB><o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB><o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB>Salaries are in accordance with the Swiss National Science Foundati= on (starting at around 40’000 CHF p.a.). For further information ab= out the project please contact <U>Dr. Lars Michels</U> (lars.michels@kisp= i.uzh.ch)<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB><o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB>Your application includes your CV with a cover letter including a d= escription of your motivation and research interests, a copy of your acad= emic degree(s), and two academic referees to the address below. Reviews o= f applications will begin on 1 June 2011 and will continue until the posi= tion is filled.<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB><o:p> </o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB>Dr. Lars Michels<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH; mso-ansi-language: EN-GB" lang=3D= EN-GB>Department of Neuroradiology<o:p></o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH">University Hospital Zurich<o:p></= o:p></SPAN></P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH">Sternwartstr. 6<o:p></o:p></SPAN>= </P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH">Z=FCrich 8091<o:p></o:p></SPAN></= P> <P style=3D"TEXT-ALIGN: justify; MARGIN: 0pt; mso-layout-grid-align: none= " class=3DMsoNormal><SPAN style=3D"FONT-FAMILY: 'Times New Roman'; FONT-S= IZE: 12pt; mso-fareast-language: DE-CH">Switzerland<o:p></o:p></SPAN></P> <P style=3D"MARGIN: 0pt; tab-stops: 35.4pt" class=3DMsoHeader><SPAN style= =3D"FONT-FAMILY: 'Times New Roman'; FONT-SIZE: 12pt"><o:p> </o:p></S= PAN></P></FONT><FONT color=3D#000000 size=3D2 face=3DArial></FONT></DIV><= /DIV></BODY></HTML> ------_=_NextPart_001_01CC1F92.56DF393C-- ========================================================================= Date: Tue, 31 May 2011 14:29:11 +0100 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 it correct to register binary images with DARTEL? Comments: To: Maria Serra <[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 should be possible, but you may need to do a little extra coding in order to make sure your data are in a suitable format (all must have the same dimensions, voxel sizes, orientations etc). Best regards, -John On 30 May 2011 14:36, Maria Serra <[log in to unmask]> wrote: > > Dear SPMrs, > > We would like to register multiple binary images. Is it possible to do it > with Dartel (create template and create warped)? > > Thank you for your help, > -- > Maria Serra > > PIC (Port d'Informaci=F3 Cient=EDfica) > Campus UAB, Edifici D > E-08193 Bellaterra, Barcelona > Telf. +34 93 586 8232 > www.pic.es > ========================================================================= Date: Tue, 31 May 2011 15:02:30 +0100 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: Source Reconstruction-Epoched EEG Comments: To: "Tayaranian Hosseini P." <[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 Pegah, On Tue, May 31, 2011 at 1:19 PM, Tayaranian Hosseini P. <[log in to unmask]> wrote: > Dear SPMers, > > I am going to do some source reconstruction of EEG but I need some of my = questions to be answered first. I have an epoched EEG file with 27 trials w= hich are all for just one condition. I would like to reconstruct the source= for each trial first and I need to see all these reconstructed images sepa= rately. Then, I will average the reconstructed images and would like to see= the final reconstructed image. > > 1. When I press the invert button, it starts to reconstruct the sources w= ithout asking me if I want to average or not. Does it first average the epo= chs and then reconstruct the sources or first reconstruct the sources for e= ach trial and then averages the images? > What the routine does is slightly more complicated than that. It computes data covariance matrix based on all the trials and uses that matrix to calculate the MAP projector which basically encodes the information about which sources are part of the solution. Then this projector can be used to compute the source currents from the data. If your data is epoched the covariance matrix will be based on single trials and might be slightly different from when your data is averaged. > 2. In the next step (WINDOW), the question of power pops up. If I choose = for example "induced", does it start the reconstruction from the beginning?= If so, why this question is not asked in the "invert" step? > No, it will use the pre-computed MAP projector to compute activations for each trial and then apply wavelet analysis to those. > 3. In "window" step, even when I choose "trials" for power, I can not see= different reconstructions and I will just see "first trial". How can I see= the reconstruction of other trials? > You can see them in the images that are exported. > > 4. In "image" step, again, how is the final image computed? EEG epoch ave= raging or image averaging? > In the Image step, whatever was computed in the 'Window' step is saved as an image. So if you used the 'evoked' option that would be an image for the average ERP, if you used the 'induced' option that would be an average of induced power over trials. Practically, you have two options: 1) If you want to compute a reconstruction for each trial separately, you should assign a different condition label to each trial and then when as at the beginning of the 'Invert' step what condition to select, you select only that trial. That way the results will not be influenced by other trials, but if these are really single trials the data will probably be too noisy for any meaningful source reconstruction. Also if you want to do statistics across trials (e.g. regression) it will work very poorly because the sources will not be constrained to be similar. You can compute the average image and see what that looks like, but it'd be really a suboptimal way of doing things. 2) You can keep the same condition label and use the 'trials' option. Then you'll get an image per trial but the sources will be the same for all trials. That's the option to use if you want to do some statistical analysis across trials assuming common sources, but that'd not be the right thing to do if you think the activated sources will be different for each trial. Best, Vladimir ========================================================================= Date: Tue, 31 May 2011 17:13:46 +0300 Reply-To: Renate Rutiku <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Renate Rutiku <[log in to unmask]> Subject: two questions about MEG source reconstruction MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=90e6ba53b04020109a04a4930367 Message-ID: <[log in to unmask]> --90e6ba53b04020109a04a4930367 Content-Type: text/plain; charset=ISO-8859-1 Dear SPM community, First, I have a question about the cortical meshes available for MEG source reconstruction: Are the deep brain structures (e.g hippocampus, amygdala) modeled in any of the cortical meshes? Second, I would like to ask for Your advice on the following problem: When I do a paired t-test for two of my experimental conditions I find an extended cluster in the corpus callosum. Are there any possibilities to eliminate this artifact? Many thanks for Your help, Renate --90e6ba53b04020109a04a4930367 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Dear SPM community,=A0<div><br></div><div>First, I have a question about th= e cortical meshes available for MEG source=A0<meta http-equiv=3D"content-ty= pe" content=3D"text/html; charset=3Dutf-8"><span class=3D"Apple-style-span"= style=3D"border-collapse: collapse; font-family: arial, sans-serif; font-s= ize: 13px; ">reconstruction</span>:</div> <div>Are the deep brain structures (e.g hippocampus, amygdala) modeled in a= ny of the cortical meshes?</div> <div><br></div><div>Second, I would like to ask for Your advice on the foll= owing problem:</div><div>When I do a paired t-test for two of my experiment= al conditions I find an extended cluster in the corpus callosum. Are there = any possibilities to eliminate this artifact?</div> <div><br></div><div>Many thanks for Your help,=A0</div><div><br></div><div>= Renate</div> --90e6ba53b04020109a04a4930367-- ========================================================================= Date: Tue, 31 May 2011 16:25:32 +0100 Reply-To: Daniel Ferreira <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Daniel Ferreira <[log in to unmask]> Subject: how to refer the VBM longitudinal pipeline Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Dear Christian Gaser and experts, Please, what references should be used for the longitudinal pipeline or h= ow should it be presented in a manuscript? thank you very much in advance Daniel Ferreira ========================================================================= Date: Tue, 31 May 2011 16:45:32 +0100 Reply-To: "Tayaranian Hosseini P." <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: "Tayaranian Hosseini P." <[log in to unmask]> Subject: Re: Source Reconstruction-Epoched EEG 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 Vladimir, Thank you very much. Your answers helped alot. Best, Pegah -------------------------------------------------------- Pegah Tayaranian Hosseini PhD Student Room 4077, Tizard building (13) Institute of Sound and Vibration Research University of Southampton, SO17 1BJ, UK Tel: 023 8059 2850 email: [log in to unmask] ________________________________________ From: Vladimir Litvak [[log in to unmask]] Sent: 31 May 2011 15:02 To: Tayaranian Hosseini P. Cc: [log in to unmask] Subject: Re: [SPM] Source Reconstruction-Epoched EEG Dear Pegah, On Tue, May 31, 2011 at 1:19 PM, Tayaranian Hosseini P. <[log in to unmask]> wrote: > Dear SPMers, > > I am going to do some source reconstruction of EEG but I need some of my = questions to be answered first. I have an epoched EEG file with 27 trials w= hich are all for just one condition. I would like to reconstruct the source= for each trial first and I need to see all these reconstructed images sepa= rately. Then, I will average the reconstructed images and would like to see= the final reconstructed image. > > 1. When I press the invert button, it starts to reconstruct the sources w= ithout asking me if I want to average or not. Does it first average the epo= chs and then reconstruct the sources or first reconstruct the sources for e= ach trial and then averages the images? > What the routine does is slightly more complicated than that. It computes data covariance matrix based on all the trials and uses that matrix to calculate the MAP projector which basically encodes the information about which sources are part of the solution. Then this projector can be used to compute the source currents from the data. If your data is epoched the covariance matrix will be based on single trials and might be slightly different from when your data is averaged. > 2. In the next step (WINDOW), the question of power pops up. If I choose = for example "induced", does it start the reconstruction from the beginning?= If so, why this question is not asked in the "invert" step? > No, it will use the pre-computed MAP projector to compute activations for each trial and then apply wavelet analysis to those. > 3. In "window" step, even when I choose "trials" for power, I can not see= different reconstructions and I will just see "first trial". How can I see= the reconstruction of other trials? > You can see them in the images that are exported. > > 4. In "image" step, again, how is the final image computed? EEG epoch ave= raging or image averaging? > In the Image step, whatever was computed in the 'Window' step is saved as an image. So if you used the 'evoked' option that would be an image for the average ERP, if you used the 'induced' option that would be an average of induced power over trials. Practically, you have two options: 1) If you want to compute a reconstruction for each trial separately, you should assign a different condition label to each trial and then when as at the beginning of the 'Invert' step what condition to select, you select only that trial. That way the results will not be influenced by other trials, but if these are really single trials the data will probably be too noisy for any meaningful source reconstruction. Also if you want to do statistics across trials (e.g. regression) it will work very poorly because the sources will not be constrained to be similar. You can compute the average image and see what that looks like, but it'd be really a suboptimal way of doing things. 2) You can keep the same condition label and use the 'trials' option. Then you'll get an image per trial but the sources will be the same for all trials. That's the option to use if you want to do some statistical analysis across trials assuming common sources, but that'd not be the right thing to do if you think the activated sources will be different for each trial. Best, Vladimir ========================================================================= Date: Tue, 31 May 2011 20:15:53 +0300 Reply-To: [log in to unmask] Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Brigitte Bogert <[log in to unmask]> Subject: SPM8 truncating data at 2nd level analysis 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]> Hi, Has anyone run into issues with SPM8 sometimes truncating data at =20 2nd-level analysis (Results>Select SPM.mat>select Contrast, etc.) when =20 looking at different contrasts from an fMRI experiment? I use exactly =20 the same criteria each time, and this has happened multiples times =20 over multiple days on both Mac and Linux computers. Typically, the clusters of both the truncated and full data sets are =20 exactly the same, at least halfway or 2/3 through the data. However, =20 the last few clusters are omitted in the truncated set. It seems that either the truncated set of data or the full set of data =20 can randomly appear. Sometimes, I have found that if I switch =20 contrasts several times through the Contrasts menu within the Results =20 window, then the full data set will appear. However, this is not =20 always the case. I have thus far been unable to figure out a way to =20 manipulate this error consistently nor does it seem to be directly =20 dependent on Z score or number of clusters. Does anybody have any insight? Thanks for your help! Brigitte ========================================================================= Date: Tue, 31 May 2011 18:56:09 +0100 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-classification of gray matter using New segment tools 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]> I don't have any good ideas about how to fix this one. The image has tissue with similar intensity to GM, in a place that is fairly close to where you'd expect to find GM. Fully automated segmentation algorithms are simply not yet 100% reliable (largely because of a lack of good training data). Sometimes, it is worth trying various approaches to see what is successful for any particular dataset. Where one segmentation model fails, it is sometimes possible that another one will work. It depends on the modelling assumptions used, and whether your data are a good fit that particular model. Best regards, -John On 27 May 2011 18:42, Xin Di <[log in to unmask]> wrote: > Dear experts, > > I am using the new segment tool to run segmentations on several MRI images. > For one of the subject, the tissues outside the cortices are mis-classified > as gray matter and warped into the cortices after normalization (please see > attached figures). I use the default settings of number of Gaussians for > each tissue type. Can someone kindly give me some suggestions on how to > avoid this mis-classification? Thank you! > > Sincerely, > > -- > Xin Di, PhD > > Postdoctoral Researcher > Department of Radiology > University of Medicine and Dentistry of New Jersey > 30 Bergen Street, ADMC 575 > Newark, NJ 07101 > > ========================================================================= Date: Tue, 31 May 2011 22:07:22 +0100 Reply-To: David Soto <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: David Soto <[log in to unmask]> Subject: Re: second level analysis affected by subject order Comments: To: "<Lucy> <Brown>" <[log in to unmask]> Mime-Version: 1.0 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> Hi, yes, say you have 3 subjects and one factor with 2 levels at the highest level analyses you may specify the betas in different orde= rs subj1 beta1 subj1beta2 subj2 beta1 subj2beta2 subj3 beta1 subj3beta2 or subj3 beta1 subj3beta2 subj2 beta1 subj2beta2 subj1 beta1 subj1beta2 or any other combination you might see a similar set of clusters in both cases but order influence= s the stats in a funny way guess there may be implications to assess significance when the effects a= re borderline=20 but in any case it is weird and am curious to learn what is the reason of= this behaviour in the first place rgds. ========================================================================= Date: Tue, 31 May 2011 22:08:25 +0100 Reply-To: Max Hilger <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Max Hilger <[log in to unmask]> Subject: SPM8: Errors in Normalization to EPI-Brain Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="part-725WpPZCmosQAttp49BFulqr13FBABwp21WsCXZBDKTUP" Message-ID: <[log in to unmask]> --part-725WpPZCmosQAttp49BFulqr13FBABwp21WsCXZBDKTUP Content-Type: multipart/mixed; boundary="part-A6se0GkmWAPCAq2GdrNgbXAv3dSRlSBGzkYHCGBZcNH1c" --part-A6se0GkmWAPCAq2GdrNgbXAv3dSRlSBGzkYHCGBZcNH1c Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset="UTF-8" Dear SPM-Users, we are currently doing a fMRT analysis in SPM8. The preprocessing of our = images consists of realignment, slice timing, normaization to the EPI-tem= plate and smoothing. When we checked the results by overlaying the EPI-template with the activ= ation maps after preprocessing in MRIcro we noticed that for all the subj= ects there seems to be a consitent lack of activation in certain regions.= The images attached are typical for all subjects analized. As you can see= there seems to be a "hole" in the activation in the temporal lobe (bilat= eral, upper images) and in the inferior frontal lobe (lower images). The = size of these gaps in activation seems to differ slightly across the subj= ects, but the general pattern is pretty consistent. It would be very nice if you could give me insight what went wrong with t= he preprocessing and help solve this problem. IF needed I can of course p= rovide additional info regarding the parameters used. Thanks in advance for any input! Best regards, Max Hilger --part-A6se0GkmWAPCAq2GdrNgbXAv3dSRlSBGzkYHCGBZcNH1c-- --part-725WpPZCmosQAttp49BFulqr13FBABwp21WsCXZBDKTUP Content-Transfer-Encoding: base64 Content-Type: IMAGE/JPEG; name="MRIcro EPI.JPG" /9j/4AAQSkZJRgABAQEAYABgAAD/4QAWRXhpZgAASUkqAAgAAAAAAAAAAAD/2wBDAAgGBgcG BQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgy PC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCARrAxkDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEA AAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh ByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVW V1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5 usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEB AQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdh cRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RV VldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD5 /opcUYoASilxRigBKKXFJQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUuKAEopcUlABS4pKWgBcUbf rS4p1ADSo96Tb9al2im4PpQAzbSYp+2jFADMUYp+KCKAGYoxTsUYoAbijFLRigBtFOxRigBt FLRigBKKXFGKAEopcUlABRRRQAUUUUAFFFFABRRRQAUUUUAFFLijFACUUuKMUAJRS0uKAG0u KXFGKAExRilxS4oAbijFOxRigBuKMU6lxQAzFGKfikoAbikp2KSgBKKXFGKAEopcUlADsUuK AKdQA3FJinkZo2/WgBmKMU7FGKAG4oxTsUlACYoxS0UAJikxT9tLt+tAEdLipAnrmjZ9aAI8 UYqTZ9aNn1oAioqXYPejZ9aAIsUuKk2fWjZ9aAI8UYqTZ9aUJ9aAIsUYqbZ9aQp9aAIsUYqT Z9aNn1oAjxRipdv1o2/WgCLFGKk2fWjYPegCKipNvsaNvsaAI8UuKft+tG360AMxRin7frSb aAG4oxTsUYoAbijFOxRtoAbijFP2/Wjb9aAGYoxTwMU7aKAIsUhqQio2oASlFJSigB4py00U 5aAHUUUUAIRmjaKWigBNoo2ilooAZg+lGD6U+igCPbRinkZptADaMU6igBuKMU6igBuKMUuK MUANxRinYoxQA3FGKWjFADaMU7FGKAG4oxTsUYoAbS4pcUYoATFGKcBRigBuKKdijFACYoxS 4paAG4oxTqKAEAoxS0UAJijFLRQAAYopQB3pQMUANwfSjB9KfRQAyilakoAbRinUUANxRinU EZoAZikp5FNoAcKeBimCpKACiikNABgetGB60lFAxKKWkxQAU7A9aSigLCjApwApuKdigLC4 HrTuPWkC/WnBB70rjUWJx60U/wAr608QZ7NS5i1SbINuacFPoatpaZz8r/lVlNPzn5Zfy/8A rVLqJG0cJORmCMnsaXyj6NW2ulZz8k35f/Wpx0r/AGJvy/8ArUvao0+oTMLy/Y0bPrWw2m9P ll/L/wCtULWOP4ZPyp+0RLwU0Zuz60bPrV77H7P+VKLP/Zf8qfOjN4aRQEf1oMf1rSFj/syf lT/7P/2Zfy/+tRzoFhpGSUx2NN2/WtU6ef7kv5f/AFqiayI/hk/KjnQfVpGfim4HrVtrYjHy v+VQNER2ampEOi0R8etHHrQUI7GkxTuZ8rDA9aSlxRigVhOKQ4NLtpuKAsBHpSUtNphYcMHq aUYFMpQaAH0U2igB1FIKWgQ1qiapWqM0AIKUCkp2KAFFPWm0+gAooooAKKKKACiijI9aACim 7jRuNADqQjNJuNOoAZRStSUAFFFFABRRRQAUUoHrSYoAKKMUYoGFFGKMUAJgUtGKWgLCUUtF ACU7A9aSigBcD1ptLRQAlFLRQAlFLSYoCwUUYpKAsLRQBRigBQPWnU2igLDqKbRQFhTg02lo IzQFhKKSlAoCwYPpTtopaKBDSMU3FPIzTaAEFSVGKkoAKQ0tIaAEooopDCiilAoASnAUAU8C gYgFOC04LUgjJ7Gk2XGLY0L9alWInPDVKluTn5W/KtCGxJ3fJJ+VZynY7aWHcimlsWz8r/lV 2KwLZ+SX8v8A61bFtpLNuzHP2/h/+tXQWmhZ3/u7jt2/+tXPKskdyowhuc3BpTnd+6n7fw// AFq14NCJ3fu7jt/D/wDWrrrXQgN/yXHbt/8AWrcg0ZV3ZWft1H/1q5KmJ7EzxEYfCcSmg4z+ 7uPy/wDrUkmh4x8lx+X/ANavQxpiD/nr/n8Kry6evGBJ/n8K5/rDuc/1t3PM5tHxt+Sf8v8A 61Z0umsMYjm/L/61ekz6cPl4k/L/AOtWXLpY4+Wb8v8A61bxxB0RxCe5wP8AZ7f3Jfy/+tT1 01jn5Jfy/wDrV2P9kD+7N+X/ANapo9I6/JN+X/1q1+sIv2lNnIppbHP7ub8v/rVZXRyc/u5/ ++f/AK1dlFpI5+Wb8v8A61X49IXn5Zvy/wDrVMsSkS68F0PP/wCxW/55XH/fP/1qrS6KwxiK 4/75/wDrV6kujL/dn/L/AOtUMmhKcfLcfl/9aoWKJ+sw7HkM2lsu393N/wB8/wD1qy5rIjHy yflXrF3oA+T5Lnv2/wDrVzN3oe3ZiO45z/D/APWrpp4hMrlp1FoefvbkY+VvyqBoiOzV09xp jDb+7m79v/rVmS2ZXHyyflXSqlzmqYVoyCvsaTFXHgPHDflVdkx2NaKRxzpNEJFIRTyKaRVG LRHiinkZpuKZI3FKBiiigAooooEKKWkFLTENaozUjVGaACngYpAKWgBQPWnUUUAFFFFABRRR QAUyn0hwaAG0UpHpSUAFGcUmaKADNGaKKBhmjNFKBQAlLilAxRQAUUYpcUh2EopcUAUBYSin baTFACUU7bQBigBtFPxRigBuKMU7bRtoCw3FGKdto20BYaRSU/bRigBlFOIpMUAJRRiigAoo ooEFFFFAwooooAKKKKACilxSUAOooopkhTKfTKAEFSVGKkoAKQ0tIaAEoopQKRQAUtLTgKAE AqQCkAzU8cRbPBpNmkY3BIyc8Gr0VsTn5X/KpILMtu+WTt2roLPS2ff+7m4x0H/1qwnOx6VD D9WUbfTWbd+7l7dv/rV0dpoud+Un7dv/AK1a9lov3/kn7dv/AK1dLbaWF3fLL27f/Wrhq1zo nVjBWiZlnoy/P8s/bt/9auittLQbuJe3+elW4LQLu4f8a044guetcM6jZw1KrZDFYoufv1aE IHrU4AFITWcXd6nK22VmQDHWq7oOOtW3PSq7Cqm1YlGfLbg4+9VZrIHHD1qFc+tL5Y96yuzT maMc2C+kn+fwqQWSj+/Wr5X1pPLHvT5mHOyolooz9+rSQDnrUgFPWnHXcltscsQ96f5Cn+9T kPWpBg966OWFiTNmsUfbkvx6Vz15pKnZxN3/AM9K7BgDVWW3DY+9WXNyvQ2p1HFnmd5og+T5 Z+/b/wCtXN3WjldmEn79v/rV63cWCtt4k7/56Vg3Okg7Plm79v8A61dVOuz0KeI7nks+nsNv yS9+3/1qypbYrj5X/KvS7zRvuYSfv2/+tXMXmlsmzEc3Oeo/+tXdTrJmsoRqLQ494zxwaiZc eta89oV2/K/5VQeIjHDflXVGVzzKtFxKhFIRUpT61HitDlasMxSU8imUEhRRRQIUUtIKWmIa 1MNPamUAKKWkFOWgB1FFIaAFoptFAx1IaSigAooooAKCM0UUgEIpKdRQA2lxS0UDCiijFABS 4pQKUDFA0hMUuKdiikVYTFJTsUuKBDcUmKkoxTCxHS4p+360oXHrQFiPFKBUm32NIV+tAWG0 uKXB9DRt+tAWExRilooCwmKQinYpKBDSKbipKQigBlJtp5FNoAZRTqTFAhKKKKAFxRiijFAw oowfSlxQAlFLSUALRRRTICmU+mUAIKkqMVJQAUhpaQigAApcUAU8CkUAFPC/WlC/WrEcJOeG /Kk2aRhcbHDnPDVqW9kW3fLJ27U+3sS275ZO3aunsNJLeZ+7m7dvr7VhOdj0aGH6shsdLLeZ 8k3bt9fau00/SAPMys3bt9faptP0gDzMpMOnb6+1dZbWCru4k7V51asb1aqirIrW2nhd3Enb /PStJLcLn71WlhC5xnmnba4ZSucEp3IVTr1qwtRgU9ahmbZLu+lNJpKYxqSLDGPSomNOY9Kb imFhAtShPrQqdeDU6p9aQMh2+xqMr7GrZj+tMMf1oBFbpSg05lx2NMxTGTqaeDUANSZp8zJs PzSEUUuM1O4yBowcdapyWobH3q0jUZUVomaJnO3GnK23iXv/AJ6VzV9o4Pl/LP37fT2r0NoQ cdaz57BW28ScZ6f/AKq1hUsb06rR45faQV8vEc/ft9PaucuLIrt+WTv2r2K+0YHy/ln79vp7 VxeoaMV8vEc/ft9PavRpV7ndeNVWe557JCRjhqrlcetdBc2RXblJB16ismSEjHDflXbGVzzq 1BxZRK/WmEZqwy+xqIrj1rU43GxFRTiM02gkUUtIKWmSNamU9qZQA4DFOAxTafQAUhpaQ0AJ RRRSGFFFFABRRRQAUUUUDCiiigApaKcBQMQClApcUoGKAsIBmlApwFPApFJDMUYqQL7GjYfQ 0rl2I8YpQKkCH0P5U8RH0b8qLi5SILnsadsPoasJATn5W/KrC2pOflf8qTZcabZSERPZqd5B 9GrTWzJz8sn5VMtiTn5JPyqeY3VFmR5J9G/Kk8j2atsWB/uy/l/9al/s7/Zl/L/61LnK+rtm H5B9G/KkMJHZvyrcOnkfwS/l/wDWphsD/ck/KjnH9WZhtER2ammM+hrYezPHyyflVdrVuPlf 8qpSMpUGjOKfWmYPpVxoCMfK35VCYyOxqkzBwaIcUlPK47Gm4pmbQ2kIp1GKYiPFGKeRTcUA NxRilpcUAJtpaKKQwpCKWkNACUlLSUxC0UUtMgSmVJio6AEFSVGKkoAKXFApTQNABmpAKQDF TpHnPBqWzSKuPjiJzw1bFrZFt/yydu1RWtqW3/K/btXXabpZfzcpNxjoPr7Vz1J2PUw9BWux 2n6SX8zKTcY7fX2rttP0cL5nyz9u319qk03Sgvm/LN27fX2rrrWxA38Sdq82rXLq17aIr2+n hd3Enbt/9atNIAufvVaSEDPWnFAPWuKU7nBKpcqFfrTCKsso4qFl+tRczuQUuaUiozxQA4tj 0phb6UhNMNAwzmnouc9aRV69asInXrRuSxyJ14NTqn1oROvBqwqYz1rVQJIvL+tRsmexq5s+ tMZPrQ4Bcz3TOOtV2GK0HTp1qq6dOtZtWKTK3SpA30pjAjtSA0hk4NPzUCmpAaAHU0ilzRTK FC5p3kg/3qVR1qwgzmi4m7GXcWIbbxJ36VzGoaOG8v5Zu/b6e1d8yA461n3NoG2/f71cKjRp Cq4ni2paOV8rCT9+309q5G6syu3KyDr1Fe0anpgPlfLL37fT2rz/AFPTCvlfJL37fT2r0qFa 56MJqqrM4CSLGODVZhW1c22NvD96y5ExjrXoRlc4K9LlZTIphFTEVGRWpxtDRS0gpaZDGtTK e1MoEPWnU1aeKADFNanUjUANooopFBRRRQIKKKKBhRSilAoAAKMUtKBmgYlLilAp22kNIQL9 aeBmlCn0NSrH14NK5oo3GBc+tPVD6Gp0g68NVlbYnPyv+VS5G8KTZUERPZqkEB/ut+VaKWhO flf8qspYnn5ZPyqOc6lh2ZS2xP8AC/5VOloeflf8q2U088/JL+X/ANarkenE5+SX8v8A61S5 lrDGNHY5z8sn5Vej07Ofll/L/wCtW5DpZO75Jvy/+tWrDpP3vkm/L/61ZyqpGyoxjuc4mmE5 +Sb8v/rVZTS25/dzf98//WrrItJHPyzfl/8AWq6mlDn5Zvy/+tWLrFc8EcaNJJ/5Zz/l/wDW qUaN/sT/AJf/AFq7VdLHPyy/l/8AWqYaWP7sv5f/AFqzdch10tjhP7FJ/wCWc/5f/WqJ9FPH 7uf/AL5/+tXoY0sf3Zfy/wDrU19KXjib8v8A61T9YI+snmUukEY/dz/l/wDWrPm01hj5Jfy/ +tXp82kD5flm/L/61Y9xpH3fkm79v/rVrCuaKpGW55tLYkY+ST8qoSWxGPlf8q7u40rG35Ju /b/61Ys+n7dvyy9+3/1q6Y1LkToJ6o5N4cY4aq5THY1uzWhG35X/ACrOlgIxw35VspHBUotG eRSVOyEY4NRlfrWiZyONhmKCKcRSUCG4xTakpMYoGMopTSUAFFFFAhuKSnkZpCKYBSgUYpaZ mIaZUlR0AMFSVGKkoAcBTsZpAKfihjW45QeeDV+CLdu4btVWNSc8Gtuzt87+G7VlJnbQhzM2 NOsd3mfLJxjoPrXoemaYB5vyy9u319qwdHsAfOysn8Pb6+1eiWNoF8zh+39a8vEVD0K8+Rcq L1naBd/D9utbMaBc9arxJjPWrQNec3dnlzk2SYpCM0m76Uuc1SSMyFhUTL0qwaiYUNAVmFQE VZIqFhWb0KRCaTbmnlfrSqtA7j0Qc9atRp161HGnXrVpB1rSmiWPVevWplWmKMZqUV1KJIoF NZfrUlBq1ECoydOtVXTpwavsOlV3XpWM4AmZsidOtQkYq66dOtV2X61yNWLTIxUgpgFPHNA7 jqeBmmgVIooC49RU6jGajWng0hMlzUbDOKXNBNUIx7y1D7PvcZ6VxGraaD5PEv8AF/T2r0WZ M7etc9qFoG8vh+/9K0pTszpozcWeM6jY7PLwsnOeo+lczPDjbw1el6tYf6n5ZP4u309q4W8t 8bOG717NGd0d9WKqQ5jnyuPWoWFXJFxjg1VbtXWjx5qxGBikpaSrMXuNamgU5qaKBDwMU6lA pQMUAIBSEYp1I3agBmKMUtFIoTFGKWigBMUYpaMUAKBiijFOC0DADFOApcU4LSKSG7akCexp ypnPBq1HCTnhqTZrGFyJYjzw1W0tyc/K35VYjtCc/K/5VpwWJO75ZPyrJyO2nQbKUdmTn5X/ ACrQisCc/JJ+ValvpxO75Je3b/61bMGl/e+Sbt2/+tWE6ljtjCMFqYcWmk5+SX8v/rVoRaX1 +Wb8v/rV0MWmYz8sv5f/AFqvxacBniX8v/rVzusS61tjno9KHPyzfl/9ar8WlDn5Zvy/+tXQ x6eOeJPy/wDrVdjsBzxJWbrGbxBiRaaBn5Zfy/8ArVpRaeBniT8v/rVqLaAZ4era24GeGrCV U551mzPjsRzkSVajsxzw9X1hHP3qmWMD1rF1Gc7qsoC0X/bp/wBmH+1V7aB60hFTzsjnbKgt h/tUfZQf79WwKcBS5mJyZlyWSnH36y57AfLxJXTtGD61TlhzjhqqM2XCozibnTQdvEvft/8A Wrn7vSx8nyzd+3/1q9DmtQdvD1kXFiDt4k79q64VTup1mjzG604jZ8kvft/9asK4tMbflfv2 r0q7077nyy9+3/1q5W9sNuz5ZO/b6V206tzpajURxUsOMcNVRk6cGt65tsbeH79qypI8Y4Nd cZHl1qVmUsUhFSFaYa0OVobQaXGaSgQwim1IRTMUAJRRRQIKKKKAFpaSlqjMQ8VHmntUVAAo zmpBTFGM1IKAHAVLimoOtTbfrSZUdyeBCd3Brp9Ot8+Zw3b+tYNtHndwe1dlpVvu83hu39a5 6rsevg49WdtpFrjzuH/h/rXcW0eN3XtXOaXb483hu39a6uJcZ614taV2Z4iV2TrxTt30pvSm k4rmONku76Uu7HcVX349KPN9xT5mTYn3fSmFqi8z3FIZPcUczHYc3aoW7U8t9KaaTdxEdKBT sU4CiwEqd6nQ9arqcZqVWx6VpF2EWVNSA1XVuvSpgc10RmKxLmkJxSZppOavnACahbtTi30q MnFZymFiBx0qsy9KtN2qArXPLUoh20oFOxSVFgFFSrioqUN9KQycGl3VDvA7ijzPcUBYm3Ub s+lQeZ9KUP8ASnYLEjHOKzrqPds696vZqGVQcdaa0LWhwmqWgbyuH7/0rzjUrbb5XDc5/pXr 2oW4Pl/e7/0rzjV7bHk8N/F/SvRw0z08PLmXKcDMmNvWqLDpWvdR429e9ZUgxivVgzz68bMr gU01JjFMxWpxPcY1EfehqWLvQIlApcU8ClIoAjxTWHSpNoqNu1ADKKWikUJRS0YoAMUuKUDN OAoHYQCnYpQM08Ln1pFJCBfrUqx5zwackfXrVuKDOeGqWzaFO42OAnPDflWlDaZ3fK/5VJBa Z3fK/btW7aaeTv8All7dv/rVjOdj0qVC2rILewJ3fJL27Vu22l/eyk3bt/8AWrQtNL+/8svb t/8AWrorbTB83yy9u3/1q451jaVRR0Rl22l/e+Wbt2/+tWzDpwG75Zfy/wDrVrQaeBu4k/z+ FXkswM8PXLOpc5J1rmQlkOeHqwlr14etP7OB/eoEePWseYwcytHb9eGq0kAGfvVKiDnrU6qP eocjNyIBEPepFT61IBigCobM2wApaWg1ViRDTc0pptQNBTlNR05WNMZLULr061KGzTTzSEii 8WcdapS2+ccNWsy59ahaP61opGinY5e6swdnD965XULL/V/LJ37fSvQp4Pu8N3rmr+1z5fD9 /wCldNKep20Kmp5peWv3OH79q5+4gxt4bvXc39rjy+H7/wBK5i6t/ucN37V6lORviIXV0c06 YxwahYdK0ZYunBqk64x1roTPJnGxDSEU4jFNNUZiUhFLSGgQykpxppoEFIaWkNADhS0lLVGY 01FUrVBQA9akFRrUq0ASoOtWsVAnerOKT2KhuaVmmd/XtXeaNDnz+G/h/rXFWK/6zr2/rXoW hx/6/r/D/WuKu7I9zDq1Ns73ToceZw3b+tdAidetZVgmPM69v61tKK8WbuzzqsrsZioWGKsk YqJlzjrWbVjK5VY5qIv9KmZcY61XZcY600WhfMPtSB/pUR4oBNOw2iwHz6U8c1CKlWpaIaJA KcFPoacq5zwanER9GpxVySvg+hp9SGMjsfypm3HY1TTAep61Op61WWplNVFiJgcU1m+lN3fS mMc0OQCM30phOaDShSexqLtjGGmlPY1YEWexpWjPoarlYFIr9ajIq26YxwarOOlQwIiaYX+l DHpUJpJFJEvmfSjf7ioM0oye1VYqxMHPtUgbNQgVMooGSg0jjOKVRTmT60rk3MO9jzs4Pf8A pXnusQj9z97+L+lel3aZ2de9ef6yn+o6/wAX9K6sO9Tswr9480vE+5171jSjpXQXy/6vr3/p WDKOle1T2Jxa1KhFMNS4qIit0eY9yNqdD/FSMKWH+KgRaAxSE0CkJzQAVE3ank5pj9qAG0Ul GKRQ6lApKdigYopwFJTwM0hiqufWplTOeDSKuc9atxxk54NJs2hG46KHOeGrUt7XO7h+1Mt7 fO7hu1b1nZZ3/LJ27VzznY9ShSS1ZLZ6fu35WTt2/wDrV1Flpv3/AJZe3b/61JY2H+s+WTt2 +vtXUWlmBv4ftXBUqjq1uiFtNPA38Sdu3/1q3reyA3cSdqdb2wG7hu1a0UQGetccpnDObIY7 QDPD1KYAP71XFTHrTXXGKycjByM54+nWoMVck7VVNNFJjkGM1KKhBp+aGJjt1GajLY9KRW+l SS0TZopoNPFXe6IEppFPApKmwxhFJT8UmKWw0xAcU4UmPY0tIGJikKg+tOp22gChKnTg1hXc GdnDd66WVc4rIuos7OvetqbOilKzODv7UHy+H7/0rkru3+5w3ftXod7b58vhu/8ASuRvrfHl 8N3/AKV6dKZ6kHzxscVcQ/d4bvWVKuMcGuju4sbOG71hzp93rXbFnBXhYzmHSmNUrjpURGa1 RwSG0UUUEiU2nU2gBKQ0tIaAHUUUVRmNJzUNSmoqAHrUwqJe9SigCwnerVV071ZIpPYqHxGz YD/Wfh/WvRdEGfP/AOA/1rzvTx/rPw/rXo2h/wDLf/gP9a4MRse5S/hP+up6LYf8tPw/rWst ZNj/AMtPw/rWwteM9JHl1NxCKYV+tTYpCKbVzMqtHnHWq7xdODWgU+tRMn1qHGw0zMMfsaQJ jsaumL60wx+xouVzFcL7GrCLnPWjZ7GrEUec9aQrk8MOd3DVfS2HOQ1Lbwj5s57VcAroowuS yi1r0wHqpJARjhvyrbxUEsIOPvV1OjoTcxSmOxoHFWpIsY61WYVyzjYobmim8+lPVT6Gsdxh sz2NWEhJzw1Ojizng1fjhAz96uinTuJsgS265DU42w/2qu4pDXQ6dkTcxpYcY4as+RMY61vX EY+Xr3rIlXp1rgqKzLRnMvTrURQ+hq4U+tJ5Xs1QmUmUxH7GnrGfQ1bEP+9ThDjs1O4+YrLH 7GpVTrkGpxHj1p2z60tRXIwmPWnEYqTb9aaRihoky7tfufjXn+sr/qOv8X9K9Cuv4PxrgdZX /Udf4v6V04fc7cNuea3w/wBX+P8ASsCUdK6O+H+r/H+lc/KOle3TLxi1KeKhNWDVcit0eVLc jNEP8VBoh70yS1SEUA0hNADKY3an0xu1AIbSiilFIsMVIKatPpFJAtSqOtMAqdF60FJEsade DWlBDndw3aqsKfe4NbVtD97hu1ZSZ3UKdy5a22d/D9u1dVY2f+s4ft2+tZ1jbZ8zh+39a62z tfv8P2rgrTO6rLlVkX7K1xv4ftXQW9uBu4btVW1gxv4btWzFHjPWuCUjzZyuyxCnXrV6Mdar ovXrVheM1mzJk4NRyEcc0obHpUDv06VFjOxXkPSqp5qZ26dKgJqkWhQaC30pOlNJoGxCx9qU E00CpFTrwaTEyQVMKjA9jUoFOJmxwpCKcBS4qxERFGM05qbms5DCjaKWlxSsA0DFO6UUE0ho gk7VnTjO2tBz0qlN2rSJrE567iHyde9cnfwj9397v/Su1uU+7171y18n+r69/wCldtJnoUZH C3keNnB71z1yuNvXvXV3ifc6965q4T7vXvXpQYYqJjOOlQVakXpVcit0eTJDCKSnUhFMgQ00 inU00ANoNBooAWiiiqMxhqKpTUVAEi96nTvUC96sIM5oAsxjOatEVXjHWrm3NJ7F0/iNfT1/ 1nXt/WvRNEX/AF/B/h/rXBadH/rOvb+tejaNHjzuv8P9a4MQ9D3KelJnc2XHmfh/WtdTWTac b/wrTVuteNPc8ypuTZpah3emKfu+lJSMh9NK/WgH3FOzViImTPrUbL9anNRk0mBFtqzEvWoB VmHA3VKGasYxmpRUMZzmpga7KLSJY4UGkzQSB1Irs51YkpTIPl61RZenWtCU5xVVlzjrXBWZ SKoT61KkfXg08Rj3qZVxnrWVNXGyaGP73Bq3UUeBmpM13QsiRc0hozSE051FYLEUvasiUDit SdwNuCKy5D0rzqjuWiDYPenBPrS5py1mgARj3pdn1p4xRxVqwDdo96TApxOKYTSbASmsaUtn 0qNm6dKncaRQuv4PxrhNXH+p/wCBf0rurj+H8a4nVl/1P/Av6V00dzsw+55nfr/q/wAf6Vz0 wxtrptQX/V/j/SubnH3a9qmbYzcpmq7dqsVXftXQjx57kJpIu9KabGcZpkljdQTmmilwfSgB Ka3an4pjjpQCEpRSUoFIsetOpop60ikPWrMa9etQKOtXIlznrSZpEu26fe4Pat60i+/17VlW yfe69q6G0i+/17VhM9XDLU6Gwgz5nDdv611lpDjfw3asKwiH7zr2/rXU2yD5uvavMrMmu9TT t48buvatKIDmqUWBmraEc8iuWxx2uW1PWnh/pUAb6U7NPlDlJt30qBm6Uu76VExqeUlxInOc VFUrCo9v1pCsJ1pCKeB7GnhPrSbJbGKmc9anCexpyJ161OE+tCjczbItmPWnBcetSbfrTTxV PQQUhNITioy/0qXIYM3pTQaQmhagY8U6mqKfVIQU1hTqTFJjRXYdKqSjpV1+1U5e1XE1iY9z /D+NczfD/V/j/Sumuf4fxrmb0/6v8f6V2Ujvoo4+9X7nXv8A0rmrpfude9dTeDOz8a5u6X7n 416UDXEow5VxjrVRhV+YdKpsOldCPImtSAiinEU2mZDaMU4000AMIpKWkoELRSmkqjMYaiqR u1R0ASL3qxH3quverEfegC9EM5q6BVKLvV5aUtjSl8aOh05f9Z17f1r0fSEx53X+H+tee6cP 9b+H9a9G0r/lt/wH+tebiNj3GrUjrrbjd+FXd30qhbn734VcryZbnmT3JN30pwb6VFS59Kgz aJww9RS7/pVcNnrTt/0oTYrE2/6VGze4qMv9KiaT6UasEibd9KsRv16VQD/SrCP16UWHymzF L15WpxIP7y/nWWkmM8ipRL9K1hOxLReMo7FaY8pOOlVQ5PpUq81upsmwE5qM8VP5fsaikTGO tYVHdlRQxWznpUy1VWraDOamL5WVKNiUHFKZfpTcH0qN+MV1OehmTeaPVaYZsd1qqWPtUbSH 2rBzYx8smcciqTt06VI79OlVZG6dKybuykhd/wBKeHz3FVt/uKA+O4pWHYuB/cUu/wClVQ/u KcXz6UXYrExb3FML/So9/wBKYWzSCw8ufam7vpTSxppNNFIrTfw1x2qj/U/j/SuwnP3a5HVR /qvx/pXTR3OvD7nm2oD/AFf4/wBK5qcfd/Gup1Af6v8AH+lctcfw/jXtUzXGvUo1BJ2qwary dq6EePLcgNJGOtKaI+9MkmAzTqFGM06gBm0UxqlNRv2oGhlOWm0+kUKKcBTakXvSGiVB1q9C v3utU4x1rQhH3qTNoGraoPn69q6O0X7/AF7Vg2o+/wDhXRWuPn59K55nq4d2OpssDzPw/rXS W7Y3cjtXL2kmN/I7VvQzfe5WuGpC5VWF2bkcnXkVYWXryKyUn68rU6Tnn7tY+zMPZGssmc9K lDfSs+KTr0q2ppONiZRsTZpKUCl2/WsmYyIyv1pNmfWp9n1pQn1rJswbIRH7GpAn1qQL9aeE x60krkNjVTr1qTFLTSfcVtsiQNRM3TpQX+lQl8+lYyYwZ8Y6VEW+lNZunSmg5pFWJAc08U1R 1qQUhMcKM0maSquIfmg00GlJpNgRP2qlL2q4/aqMmeK0idEDIuW+7+NcxeH7n410t0MbPxrm r1fude9d1I9Gijlrr+D8a5+6H3Pxrorofc/GufuR938a74DxBiTDO2qTDpWhMPu1RbtW6PJn uQEU0inkYpuKoxGU2nkU00CGGm1IaaRQAUlLSGqMyJqjqVu1RUASr3qwneq696sx96ALkfet Be9UYu9X1HWplsaUfjR0mnf8tfw/rXomlH/W/h/WvO9O/wCWv4f1r0HSj/rfw/rXn11oe9vT OvgP3vwq8Kz7c53fhV9e9eVNHmzWo/bRinAUuKzMSI8U0mpWFRMKAIyx9qjJpSab1qkikhVN WUOM1XA9jUq96bRTRaV+vSpVf6VVDU8P7iosQ4l5D1q3GBzWbFL15FaELj5uRVqTW5DjYtYx UMi5xU2aawzSk76iWhREf1q1GOtJs9jUirjNJu5pJ3H4qGQYxU1QyEccitOayMio56VXZsY6 VJK2MciqrvnHSsm7lJA7dOlVnPSpWOahbJxQkWkQk0BvpQy0zpVFEwb6U4N9KgzTw1Jololz RTQacKmwrAaQ07FIaaBFSb+GuS1T/ll+P9K6yb+GuT1T/ll+P9K6qG524Zanneo/8s/x/pXK XP8AD+NdZqP/ACz/AB/pXJ3P8P417NMMY9Siagk7VPUEnauhHlS3ISMUkfelakj70EllaU0g NBoAaTio2p5OaY3aga3ClFNpwqSx1SL3qOpE70DLCDrWhDxuqgner8XekzSBs2x+9+Fb1q33 +R2rnrdsbvwrbtmxu6dqykj0KMjpLeTG7GO1bMUx5+7XPQPjdjFa0UmM9K55JHoLVGws3Xla spP1+7WOJvdasRz9eVrPlFyHQQyZ3citOM5zXP28/wB7le1bVvJndyO1c9RWOSqrGgoqUL9a ZGc5qdRXHJnBNibc+tKFHvTqXFZGI3aKDilqMtiqTEKW+lQs/wBKC30qJmq3IBGf6VCzfSlJ qMis9ykhM5p6rTQKkWnYqxKo604cUCgnFSQxuabu+lNZ/pUe/wClOw7E+acTUKt16U/OaQDX 7VUkHSrTGq7jpWsTWDMe4TO3r3rnL2P7nB711Vwv3cZ71z94n3OveuunI7qUjjLxMbOveudu h9z8a6e/GPL/AB/pXNXQ+5+NehTZvWV1cxJh92qDDpWjMfu1QftXSjyai1K5FNqQikxVHOyP FNIxUhFMIoER0hFONNNABSGnNTTVGZE3aoqlbtUVAEq96sx96rL3qxGetAF+LvV8VQi71eBx SlsXT+JHSaef9Z+H9a77S2/1vI7f1rz/AE9v9Z+H9a7nS2x5vI7f1rhrLQ96jrTO1t2+907V oI3XkVjW8w+ble1XlnHPK/nXmTjqcdSOpohh6in5HqKoC491/OpBOP7y/nWDic7iWCagc9OR SGbPdaiaTPcUkhJAeaUL9aQHNWI0zng1SLSEVPY07b9anEfsaPL9jTAhIxTCSKmZenBqJlPo aqKLih8Z61o27n5unaqEaE54NaEKY3daJRQTirGgDmlpid6fmskuhysMUuKKbu+lVypCFJxV OZzx0qyTmqkqnjg1O7KirsoyueOlVyxqaVTxwah2n0Na8uh0cqsHPpRtz2NSKnsakCexrNmb 0KjR+xqBkx61otH7Gq0idOtCBFItigP9KRxjFQ7selVYuxdDe4qQN7iqQkPtUokHqKVieUs7 h6ikLD1FV/NH95fzpDKv95fzpcouUZM33a5LVD/qvx/pXTTSjjlfzrltSYHyuR3/AKV1UVqd mHVmcDqH/LP8f6VylyPu/jXV3/8Ayz/H+lctcD7v4161Nhi46mdUEnarBqvJ2roR5EtyFqSP vStTV70ySdadTVp1ADKY1PprdqBrcSnCmU4Uix9SL3qOpF70gLKd6vxd6oIetXoe9Jm0DUg/ i/Ctq3P3vwrEgON1bFu33unasZM9CjG5uwN97p2rUV8Z5FYsEgG7kVe84Dutc0nqd700LxnA 7r+dOS668p+dZElxjHK/nUYu/dPzqkir2OttrvO75k7d66G0uQd/zJ27159bX+N3zR9u9dJZ XoO/5o+3euetE5q8bq520Muc8rV5W69KwLa5DbvmTt3rXjkznkV500eVNFwGjNMBzSk1mjER qiNSEZppX61VgITTCM9jU20+hpNv1oaArMn1phFWWA4quxAxyKImkRuKcCB3FQtKB3FRmcDu v51di+Uu+ZjuKY8vTkVTNyP7yfnURuc90/OlyC5GWWlzjkU3f7iqhmz3WnK/0quUrlLofPpU gaqqNnPSp1Oc5qGjNokJqGTnFSZqGRsY5FCHEozn7v41z1633OR3rbuZQNvK9+9czfTgeX8y 9+/0rqpo7KSuc5fMD5fI7/0rl7pvudO9bl9MP3eCvfv9K5y5k+7yO9ejTR2z0iZ0p6VRbnFW pWzjpVU11RPJq7kZptPpCM1ZzMYajNSGozQSMIptPNMPFADiM0w1KRmmkZqjMruOlRVOwqCg CRe9ToetQL3qZO9AF+JutXt30rOi71dzSexcPiOjsGx5nTt/Wu0sJceZyO39a4Sykxv5Haus s5/v8r2rlmrnt4eStZnZQ3ON3KfnVkXn+1H+dc7HddeUp5uj6pXM6NzZxgzolvf9qP8AOphe A/xR/nXMC8I6FKmS9PPKVlKgZyoJ7HTC590pyy57rWEl5nPKfnV2O4znlawdKxzyo2NqI5zW lEuc1j27j5uR2rYgcfNyKxkrGEo2LQT600r9akVvcUGoMyuVpnl57Gpyv1p6pnPWrTNIsakW M9auRx9eDSJH161aC4qZSvoZzmIBimbqe5xiq7P9KcY2MCbd9KaTUYf6Ub/pVNATLzmo5Fzj rT0PWnlayas7lRdjOeHOOGqAw47NWmY/rUTRdOtVzm3OU1jx2NSBPrU2zHrRgVDZDlcgKfWq cq4xV12HHIqnKwOORVRRUUZsw+7VJmx6VclcccisyWQccitkjdRJPOx/dpDce61Qe4xjlaqt d4x8yfnVqnc1jSbNb7UP7yfnTDdj+8n51ite+jR/nURvT6pWyoGyw5ry3Q4+ZPzrnb+4z5fK 9+/0qSS8PHKVj3dxnZyvet4UbGsYxgc/fN/q+nf+lc1cfw/jW9eSZ2cjvXPznO2uuCsceImm UDVeTtU5NV27VutjyZ/ERNTV70ppq0ySwvenU1O9SEZoAjIxTG7VI1Rt2oGhtKDiiikUPBqV e9QipV70DLKd6uxHrVFD1q5GcZqWawZqQt96tWF/vcisaFuvStGJ+vSsZI9GjKxsxy4zyKna 491rLWX6U5pc+lYuOp38yepPJcHjlarm5x3WoJJOnSqrSdOlXFGFSobEN2fm5SugsL4/vMmP t/WuLilPPStu0m+/93tSnC4U6ikrM9IsbvPmfMnbv9a6W3mB3cr2rz+wuceZynb+tdZa3P3+ U7d682rSsc9ejY6VGznpUwFZ8M2d3K1eV855FcbVmefKLQ/FG0U6kJq00ZjCKjOPWns2PSqs soXHK1LY0rjZJAMcis+W4Ax8y/nTbi6xt5Tv3rGuL3G35o+/etqdNs7aVFsuvd9PmT86qte/ 7Uf51kS3x4wY6qNetx9yutUDtVBG4b3P8Uf50gus90/OsD7WfVKkS668pTdGwOgjoBOD3X86 sxydeRWDHcg5+ZPzq/Hc9eU/OsZQsYTp2NpHxnkVOH9xWWlwDn5k/Ophcj+8n51zyicsosvG XHcVVmnA2/Mv51WkvOnzJ+dZtxfY2/NH370RgxxptsS7ugNnzJ371yl/d/6vDJ37/SrV7qH3 Pmj79/8A69cvdXhOzlO9d9KmejTgoq7Kd3cZ2cr3rEnkzt5FWLifO3le9ZkknTpXdGJz1q1y N2zjpUJNKTTM1qkcEpXCkpc001RmNNRmnsaY1BDGE000pOaRqYiSkNBNNJxTII27VDUrVFQA q1MKhWphQBbjPWrm7HpVBDjNWmfp0pMqLszZtZsb+V7V0dpc438p2rj4ZcbuRW1b3H3uV7Vm 4nbCrY61LjOeVqTz/dax4bn73K1YE3utLlK9uy/55/2aetwf9ms0zY7rQLj3X86lwNqddo3Y 7nGeUrRgux83zJ+dcst2Bn5k/OrKX2M/NH+dYyp3OyNSMtzura6HzfMnbvW3DcD5vmT864G3 1H73zRdu/wD9et631HO75ou3f/69clSiRUo32OuWcf3l/OphID3Fc/He9fmj/Or6XOc8p+dc sqdjklSsagOamjHWqUcuc8irsZHPNZPQwehbQYzUlRq49RSNL7ipizBiSydORVUv9KJH6cio S2fSr5xE2/6Ub/pUO6k3Uc4FxH69Ksg5rOWTGelWkl68rSchlimOBxzSeYPUVE8vTkVnuNIC R6iq7SAY5H51HJP05WqE92Bt+ZPzq4wbNYU2yeSdeMsv51nS3K8fMn51Tnvx8vzR/nWTNqX3 fni/P/69dcKLO6FAvzXS/L8yfnWTNdr8vzx/nWdPqZ+X5ovz/wDr1ky6j0+aL8//AK9dEaJu qSW5qS3ucfNH+dUnus45Sstr3OPmj/Oo/tWe6fnXRGlYbqRjsaRuMd1phuPdaz/Pz3WkM2O6 1qoHLUxDLUlyeOVrMuLjO3le9LJP05Wsqef7vK9605TmddlS5lzt5XvWPK2cdKuTy528is2R +nSmkZSnchJqu3apSagNWjmluRmmg4pxplMksJ3qWoRUuaAEqJu1S1E46UAhKUUg96dSLAVI KZTxQBPH3q5G3WqS96tRnrUs1gaEbdelXo369Kzoz1q3G3XpWbOuDsaCyfSnF/pVVX69Kk3f SpsdCmxXbOKrtUpNRmmiJu4qHrWtbPjd07VkL3rQgfG7p2qmjGMmmdXaT/f5XtXU2d19/lO1 cPazY3cr2robWfG/le1ctSFz0oe/GzO4t7n73K9q1ops55WuWtZ87+V7VtwS53dK86rCxxVq djZD/SkLe4qusn0oZ/pXI9DhaEklxjlay7i5+7ynfvUs8+NvK1h3Vz9zlO/et6VO7OmjS5mR XN3jb8yd+9Yc9yW2/dp885bb92s2WXp0r1KVKx6LcaSEefpytV2nP+zUMknTkVVabGOVrpUT jlXdy75/utKtzjun51lm4x3Wozde6VMom1OsdCl7jPzR/nV2O/HPzR/nXIi994/zqZL8jPMf +fxrnnTudLUZnaJf9fmj/P8A+vUn28f3o/zrk478nPMf+fxqUXx9Y6xdEz9gjfl1Dp80X5// AF6yLrUT8nzRd+//ANeqMt4eOUrNnuC237tXGkWqSjqxbq8LbOU71h3FwTt+73qWaU/L0rPl YnFdUY2MKtToitLITjpVNjnFWJO1VmFbI8+ZGTTc0pphOKtGDHZppakzTSaCRSc0w0uaYTim JiU00tNJzQSSmmE5p5IqOmSMao8VKaZQA1alFRLUgoAsKetTM3TpVVTjNPd+nSgaLscmM9K0 oLjG7le1Yiv9KtRzdclaRSZ1ENz97lKuLc+6VzUV1jPKVcF37p+dIpM2Tce6/nTGucd0rK+1 +6fnTGuunKUrFqRqG790/OlW/PPMf+fxrDa690pguj6pScTWNWx2FvqJ+b5ou3f/AOvW9bah 975ou3f/AOvXnsN2fm5Sty1vT8/MfaspQO6liD0GDUCd3zR9u/8A9etqC73bvmTt3rgre8+9 ynbvW/aXn3+U7Vy1KZ1SSmro7e3mzu5XtWrE/XkVzFpcZ38r2rdhk+9yK82rGx5taNjR8zHc VE0vTpURf0xUbN9KzjG6OO49nPtTd1N3UmabgA/dRu+lMzSZqeUCQP8ASpRJ9Kr5pd1PkAt+ aR6VC82McrTN30qtK/TpSitbGlPVkM9zjbyvesS5vcbfmj796s3c2NnK965m8uvucp3r0KVO 56lGmtwuL8/LzH37/wD16xZ7/G35o+/eq9zd/d5TvWFcXp+XlO9d0YFVK1tEXJ9Q+780X5// AF6zJL8nHMf51nTXZ+XlKovcnj7taqJwzxDNr7Znun509bv3SsEXPutSrc9eUq+U53VbN4XP utONwPVfzrGW668pSm690/OnYlyuXpbjGOVrLmn+7ytMkuc45SqMk2ccrTIbEkkzjpVR2zjp Tmk6dKgY0E3EDVFTs0wmqRD3GmmU+owaBEy1KMntUS1OKAEpj9qlqOQYxQCGUUUUihafTKVa AJ1NWkOM1UU1YQ9aTNYMvRnrVtT1qlGetWlOazZ1RLanGakzUCmpN1SbIfmkNN3UFqAkKKtR N16VTBqZGxnpVGPU3LaT73I7Vv2sn3+nauXt3+907VvW0n3uR2rKZ6NA6+0k+/07VvwSfe5H auVtJvv8r2rdgn+9ytefVRFZG8sg55FI82McrVAXA/vJ+dQyXWMfMn51ycl2cPs7sbdXH3OV 796564nzt5XvVi6u/ufMnfvWJNcfd5T869GhSsehBKnG42WTpyKzpZenK0s1yPl+ZPzrMmuc Y+ZPzrvjE8+rVux0s2McrVKScDHK/nUE11935k/Os+W76cp+dXY5nIuSXWMcpVVrrpylUJLr pylVzcZ7rSaNIVLGoLr3SpkuSc8pWGJ891q1HKeelZuJ106xvxznn7tW1kJ9Kx4Hzu6VpxHO azaO2nUuSs2aqyDpVrAqGQAYoQqj0MuVenWqMg6VpS445rNlbp0rVI4JyKj9qrOelTyN06VV ZunSrSOeUiNjUZNKxzjkVGTVIxkxc0hNNLUmaZFxSaQnNJSZoFcCaSjNIc0xD2NNoppNAgNN paSgBqHOafmo1OM0+gCQGlJzTBQxoBEgf6VIJPpVfdRupFF9Z8Z5Wphc+61mb8elO8z6UBc0 /tOe60huPdaz/N+lHmH2oC5dM/8Au0nmn2qn5h9qeH+lA0zSilxnpWtb3GN3K9q55JOvSr8M /wB7lalo3hOx11tdfe5TtXQWl19/lO1cRb3H3uV7V0Fpc/f5Tt3rGUT0KNex6BY3f+s5Tt/W uktrnO7lO3euCs7nG/lO1dDbXp+blO1cNWhc2qU1UV0dUs4PdfzpS49R+dYqX3X5o/zqQ33+ 1H+dcTpTjscE8NJM1d49RR5g9RWX9v8A9qP86adQ/wBqP8//AK9LkmR9Xka3mD1FG8eorI/t D/aj/P8A+vTxqH+1H+f/ANel7OYfV5GruHqKM1mi+/2o/wA6eb3/AGo/zotMn2Ei9v8AcVUm kxt5FQm8/wBpPzqlPefd+aP86unSk2b0qErlS9mxs5XvXJ3k+NnK962Ly7zs5Tv3rlr25+5y nfvXp0oWPQk1CNjMurj7nK96wbifO3le9W7q4+5yvesSabpytdSR5tSdyKWYnHSqpkPtSPJn HSoC/wBK0RyyZMJfpThOR/dqoW+lJv8ApTM7mgLn3Wj7SfVazfNPtS+Z9KAuXGnzjlagMmfS oC+fSk3/AEoC5KW+lRlqbux6UhNAADSZoBpKZLEJxTBTmpooETLUynOagWpVPWgCSo5O1PzU b9qAQ2imk5opFDs0tMBxTqAJVNTocZqqKmU0FRZfjfr0q5G3XpWYjdelXY269Kho6YSLob6U /f8ASq6t9KcX+lQdCZNv9xRv+lV9/wBKcG9xQDZPu+lSK3Wqwb6VKrdelMlGrA+N3StiCX73 K9q56KTGeRWnDNjdytZSO6gdfbXAG7le3eteO768pXI29zjdynbvWil515T864podRanTG9H 96P86qy3/T5o/wA6yDe/7Uf51Smvfu/NH+dRGOpEIalie+zt+aP86yZb4cfNH+dUZ777vMf5 1kTX33fmj/OvQpoWKdtDRmvh8vzR/nWZNffd+aP86zpb0nHMdUJLsnHKV0o8ebL0t5nHKfnV GS6JxylU3uDxytQGU+1MzuW2n91pnm/SqnmH2p4b6UDTLit16Vdibr0rNU5zV6NhzyKmxrGd jXgf73StSJwM8isOKTGeRV5LjrytRKJ10q1jWEg9RUEkg45FVTc+6VXkuenKVKiazrpoSaX7 vIrNlkHHIp8s/TlaoSSdORWqRwTqXEd+nSqzP06Upb6VCTmqsYuQE0wnNLyegzSbW9D+VAri YJ6Cl2n0NPjBGcin0xEBB9DSlAPWpKjY0CGFRRRSUAFNJzSk0lACUmaWmUAIKkqMVJQAtBOa KMZoASinbRRtFIdxtFO2ikxQAA4o3UACl2igBQ1OBxTQopwAoC5Ij9elWo5cZ6VVVF9TU6ov PNFhqRpwz/e5Wtu1u/v8p2rnYkTn5v1rYto4zuy/p3FJo0VSx19ne/f+aPt3roILzO75k7d6 5Wygtz5mZfT+Ie9dZZafZvvzOeMfxj/CodO50QxXKX45+vK1KZm9BWla6Jp7783EnGOjr/hW snhjT3z+/n49HX/CodG50LHx6nKNcMP7tRG5b/ZrsH8JWPGJbn/vpf8ACqcnhazGMSXP5j/C l7Af16BzX2s/7FO+1N/s1u/8Ivaf89Lj8x/hTh4YtD/y0uPzH+FL2AfXqZirdv6LUou2P92t 1fDFn/z0uP8Avof4U5vDdiv/AC2n/wC+l/wpfVyvr1Lsc+12eM7KoXF593lO/eugn0axXb+/ k/F1/wAKwLuws12YnPf+MVSoWE8dDoYN3ffc+aPv3rmry8zs5TvW1eQW42Yl9f4hXMXccXyY f17itFCxyVMVzGXc3GdvK96zJJc45FW50T5cN+tUHReOauxzupchZunSoi30qQqPWoygHrTs RzDM00tSkCkwKBXG5ozS4pAMUBcKKUDNLtFAXG0U7AppGaAuItLRjFFMQ1qaKc1NFAEgqQVG KeKAH7jTHOcUuaQjNADaTNO2il2j3pDuNp1G0U/YPegLjQcVIvejyx71Ksa88mgLjkPWrcbd elQJEvPJqyka88mk0aRmkThvpS5oVVHegqPWp5WbqvEbu+lKG+lMwPWgY9aOVh7eJOH+lSBs elQqB61IFX1pcrH7eBaSTGelXY5+vK1mDHrUgl29xUum2dFPGU47m/Dc9eUq4t3jPKVzC3jL 02U/+0pB2j/z+NYSw82bSx1JnTNedPmj/OqU1393lKw21STjiL/P41Wk1SQ44i/z+NJYaaJW NpItz3P3eU71kTXH3eVqKS9c4zsqk8xOPu10RptGNfF057D5Jycfdqs0ucdKYWz6UwgVtY8+ UkxS30pu40ECgAUEgDT1NN2inqo9aAuTo2M9KtRv16VURF55q0iLzz+tAXLiy9eRU63GO61U VEOct+tP2R/3v1osUpWLBufdahe4Jx92mlI/7361G0aHHzfrRYOdkby9OlQMc4qwYFPc00wL 6tTJuVyrf3T+VRlG/un8qvkZqI0CKqAjOQRT6eRmmUAJSGlptACE1G1PNR0AJSUtJQAhpKU0 lACUyn0ygBBUlRipKAFopKWgB1FIDS0AFBGaKKAG0oNKRmkFAC06m0tAEqmpFPWoVOM1KKAL UbkZ6VoQzkbvu1mKaso2M9KAOmtb0rv5TtXTWeq7d/zQ9u//ANevP47grn7taUN+V3cx0Aet 2Osf6z54O3f6+9dbZalv3/PFxjofr714tZaqw38xdv8APWu50vVC3m/NF27/AF96APTFkD55 HHpUckWcdao2V0H35ZOMdDWtwe9AFAxexpAhHY1dKAetQtgd6AIC23uPxrPub0Jt+ePnPU1N czhdvK8561yeo3+PL+aPv3+nvQAl7qoGz54e/f8A+vXK3eqZ2fND37//AF6qX2pn93zF37/T 3rnJ78nbzH/n8aALV1eltnMffvWFcTE7fu96WS5zjlKoyS5x0oAryP06VVZulTOelV27UAMJ qIt9KeTUJoAKaacaaRQAlFFFAADinU2nUAIaSgnNFACGig0UANamZxT2qM0ATKc08GoQ1SA0 AOzRmiigBaBRQKAHAZp4pgOKdQA4VIDiogaeDQBYVqmV+vSqoapA30oAuB/pQX+lVw+PSlLk +lAEhb6Ugb6VEWzSqaALAc+1ShvpVcHNSqTQBNv+lNL59KbSGgBTJ9KjaX6U01C56UAOeY8f dqu8ucdKazHioWNACu+cdKgJzSkmkNACU0nNOpMUAJRSkUAUALSg0lFAEqufaphIfaqtPDfS gC4JfpT/ADfpVMMakVqALPmH2p4JNQLVhV60AOxS4pwGaaaAGEYqEmpWbp0quxoATNRmlJzS ZoAKYacabmgBDURp5OKYTQAlITQTSUAFJRmkJ9KAEam7qCaTNACipKjFOBxQA6lpMj1ooAUH FOptAOKAHUUUtACUuKXFKB60ANxS4p20UAYoAUCpFpgp4oAmWpQ30qEU7NAE4k+lSrMefu1T 3Uqvj0oA3ra6I3cp2rtNKv8AHnfNH27/AFrzmGXG7kV0mn3mzzMFOcdfxoA9o03UAfN+aLt3 +vvXUQ3inPzx/nXlGnapjzPmh7d/r711Vvq33vnh7d//AK9AHZG5U/xJ+dVZbkcfMn51if2r /tw/n/8AXqtNqX3fni/P/wCvQBLf3oHl/NH37/SuF1O+/wBV80ffv9K077Us+Xlou/f6e9cT qN7u8vmPv0P0oAzLy8Pycp3rFkuDx92nXNwTt5XvWc0ufSgCczf7tRs/0qHdn0paAEY1CamP NRN2oAhNR4qY00igCPB9KTB9DUuKMUARYPoaMVLtpCn1oAiopxFMJoASiikoAKKKKAGtUZqQ nNRNQAoNSLUQqVTQBIKWm0oNADqKSloAWgHFJRQA8H1pwNRZpQcUATBvpUgf6VXDU4GgCzu9 xS7qgBp+76UAS5p6jOajBqRO9AEy96mUYqIVOBmgAxSEU+gigCuwxiq7jpVpx0qFl6UAVGHS oSM1ZYVGRigCAjNMIqUio2oAbijFLjNO20AMxRin7frSEUAMopaKACgUUUASA1IKhWpVNAFl B1qwtVlNTK/uKAJt2PSmM3uKYz+4qJpM45FAA75x0qBm+lIz9OlRl/pQA/d9KTNRbj7Ubj7U ASFvcU0t9KZmkLUAKTTSaQmmk0ALmkJxTSc03NADic0mabmjNABSUtJQA6nU0UoB9DQAtLuN Jg+lGD6UAOB9adTCPSnYPpQA4GnCmDI7UFmHQUASrTqgEjj+H9KXzX9B+VAE1GR61AZHPYfl SeY3pQBZFPFVvMb0FL5r+g/KgC2DinZqp5z+g/Kjzn/uj8qALROKTd9Kr+a57D8qDIx7UAXU fGela1rcFd/K9q54TOOw/KpkvpkzhF59jQB6DZ3xXfzH2rorfUj83MXbv/8AXryiPXruPOIo efVT/jVpPF2opnEFtz6o3/xVAHri6ifWL8//AK9Ry6ieOY/8/jXlo8baoP8Al3tf++G/+KpG 8a6m2P8AR7X/AL4b/wCKoA7W8vmOzmPvXL3dwTs5XvWRJ4ov5MZht+PRW/xqlJqlxLjMcfHo D/jQBLNLnb0qsX+lRtLI3VR+VR7n/u/pQBZBzTwaqCZx2H5UfaZB/Cv5UAXKYRmq32uT+6v5 GkN1If4V/KgCU0mKgNxJ/dH5Unnv/dH5UAWMUuKrC4cfwj8qPtEn90flQBaIzTSKhFw/dR+V BmY9hQA5qiNKZGPYVGWb0oAWim5PpSHPpQAu40E5puTRk0ABNManGmmgAFPFMzRmgCfNLUAk I9KXzW9BQBPmgGoPNb0FHmt6CgCxRVfzW9BS+c3oKAJ6Wq/nN6Cjzm9BQBYzTgcVW89vQUee 3oKALgOaeDVEXDjstL9qf0X8qANFTnNToetZQvJB2T8jThqEo/hT8j/jQBtCpwcVgDVZx/BH +R/xp/8AbNx/ci/I/wCNAG/SVhf21c/3IvyP+NH9tXP9yL8j/jQBstUDdqyzrFwf4IvyP+NN OqTn+CP8j/jQBfNRN2qn/aEn91PyP+NNN5IeyfkaALLVE1Qfan9F/KkNw57LQBYWniqguHHZ aPtLjstAFykNVftT+i0n2l/RaAJjSZqHz29BSGZj2FAE9Gar+a3oKPNb0FAFkGpVNUvOb0FO Fw47LQBoBsVJv+lZou5B2X8qPtknov5UAXzJ9KjL49Kp/an9F/KkNw57LQBOzZ9KZuqEzMew pPMb0FAExb6UbvpUBkJ9KN59qAJs0hNRbz7Ubz7UAPJzSZpm40ZoAdSUmaM0ALSUmaM0ALSU ZooAlUZzT6atPoAMUuKWgUAG2lAop4GKAECeuaTafQ1LRQBFt+tN2/WpjTGoAZto206igBMU u3607bTsUAMC560u3607FLQAgXFLtpaXFADdv1pcU6lxQAwClxS0tADcUbadRg+lADdtOCA+ tGD6U4DFACFAfWmlfrUtMagCEp9aYVx61MaaRQBDijFP20hHpQAwikxT8UlADdtGKdRQA3FG KdS4oAZigin4pCKAGYpKeRSEUAM2ijaKdikoAawqJqmaoWoASiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAJlp9MWn0AKDSikFBOKAH4qQC og59qcHPtQBJSE0zefak3n2oHYfTSc00sfak3GgLDqKbk04EigLEgpajDGl3n2oCw+lqPefQ Uoc+lAWJQMUVHvPtShyewoCxKB60u0UzefQUu4+lK4+VikelJg+lG4+lG9vSi4crHbRS1Hvb 0oEjegp3FyskopnmN6CjefQUXCzH0jU3zG9BSbz6ClcOVimmEUFyOwphc+lMLMcRSEU3efaj efagLC00j0o3GjcaAsJRSEmgE0BYWnbRTQSKXcaAsOpCM0m40m40BYSko3Gm7jQFhSKSlBzQ RQIbULjGKmNQv2oAbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFAEy0+mLT6AFFI1KKRqAFFOpop1IoKSlpKADGaSnCigAApaMUoFAwxShac BmpAn1pXKSuRbfrRj2qfZ7GkKexouU4EOKcBT9vsaAv1oJsGKcF+tKF+tSKvsaGUhgTPrRs+ tWFj9jTvJ9mqS+UqbPrSbPrVwwj/AGqaYfZqZDRV2exo2exqz5XsaBGfQ0NlRhcrbPY0mz61 b8r2NMMfsanmNfY6FQr9aZirLJ7Goyn1q0zCUbEO360hFTbPrSFMetMixDg+lGKl2/WmYoAb ikxTqKQCUUtJQAUh4paQ80wG02nGm0EirSmkWlNMkaahftUxqF+1ADaKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAmWn0xafQAopGpRSNQA opRTQcU4UihTSUtGKAACnAU4L9ads+tAxgFOxT9v1pQvsaRVhUXr1qdI+vBpETr1q0ideDUt m9OJH5XsaQx+xq4Ez1zSGP61CZ0OmrFAx+xpuz2NXTEPeo/K9jWiZyyjYgCfWpkTrwakWL2N TpF14aghCLFnsal8n2arCRdeGqytv7NSNkZ/2f2am+R7N+Va32b2ak+zezUXFymT9n9moMGO zVpmAejUwxfWokzppwM7ycdmqFounBrT8v61C8fTg1nc7PZpozGj9jUXlexrSMOezUww+zVa kclSgZ/l+xppj9jV8w/71RNH061omcsqdikUx61EV+tXGT61Cy/WncycSuRTcVNimFaZNiOi nY9jTaBCUlKaaTTEJTaWkoEKtFC0UyWIahftUxqF+1ADaKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAmWn0xafQAopGpwGKa1AIKcKYKkFI oUU9V+tIoqdFznrQUkCp7GniPHY1Mkec8GpxD7NU3NVAqeX7GlCexq35Ps1KIfZqls1jTIkT rwatIuM0gj9jUqr160mzWMbD1Wl2fWnAVIF+tQbLUrGL2NN8r2NXfL9jR5Ps1UmZzp3Kqxde Gq0kXXhqlSLrw1WUh6/eqkzncLDI4evDVcWHrw1Oji68Grix9etAIr+SP9qmtDj+9V/yx70x 06daC0zMeMDHWq7L0q/IvTrVJx0qWjWE7FcrTCmfWpTzSgVlJHbTncr+V7GmmH2argX60NH0 61HMdDSM54unWqrJ9a0XXp1qqyZx1raLOCtTsykydOtV2T61fZPrUDR+xrRM45QKRT60wpj1 q4YvY0wx+xqkzFxKhU+hqNl+tWynsaiZOnWi4uUrEUw1Mw6VC1UZtDaSg0UEirSkUi0ppkjT UL9qmNQv2oAbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFAEy0+mLT6AHU1qdTWoAQVIKYKlApFpEqL1qzGnXrUSL161ehj69ahs3hEmji69 atLFnPBpIo+vWriR9etQ2dUYFfyPZqPI9mq75f1p3lexqGzpjTKHlY7Gjy/Y1eMWOxphjPoa LlOmVwn1qdY89jTxF7GrCRdeDSchxpEYiz2NPEPs1Wlh68NUnlfWp5jb2WhUEX1qdUxnrUnl getKBWsWcNWNiRB1qyq9agQdatJ3rRHHIft+tROOlWQKgk7UyLmfJ2qg/atCTtVCQdKVilIg IpwFJinAVnJHZRmPAxTiKAKftz61zyR6cXdFJ06cGqxT2NabRezVXeEjHDflVKRM4KRnmP61 GYvY1fMXsab5X1rRSOaVIoGL2NRtF7GtExD3qNovrVpnLOmZrR9ODVZ06cGtN4+nWqkiYx1q rmPKZrr061Awq5IvTrVVhVJmM4kJpKdSVRhYFpTSLSmmSxpqF+1TGoX7UANooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCZafTFp9ACikal FI1AIcoqZB1qJe9ToOtSzWKLMa9etaEKfe61TiXr1rSiXr1rNnXBFuNevWrSioU71MGx0xUM 6oIlAFPAqEP9KlU1mztgkP2Z9aTys9jU6JnPBqyLfPZqhysW7IorD14arKQ9eGqyLf2f8qnS Drw1Q5k8yIhCPemlcetXfL9jVdx0pxdxplVhimVI3GKhzXTE4Kz1JlPWrUZ61SVqtI3XpWqO GRbBqCQ9KeG+lQyN06UzNlKXtVF+1XJT0qm1AIZinKKBUqr1qGdNNjlWpFXr1oVevWpVFYTR 6lJ3QeUD60x4OnDVbVevBp7R5xwa5W7MTlZmPJDjHDVCY/rWq8OccNVN48Y4NbRkaK0kUyuP WomXpVlhUTAVvFnNVgVHTp1qlInTrWi46VVkTOOtaHG0ZUi9OtU2XGOtaUq9OtUXGcU0ZTRU IplSsOlRmrRzSQi0poWg1RmxpqF+1TGoX7UCG0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBMtPpi0+gBRQaBSkUDQq96soOtQAVajHWpZr FFyIda0oh1qhEOtX4z1rNnXBloECnbqiBqRVJ7GpsaqY5SWzxVyNCc8H8qSG2Lbvlf8AKti1 05m3/JL27f8A1qznodtF31GwW7Hd8r/lWklmeflf8q0LbTD83yTdu3/1q1Y9NIz8kv5f/Wrg qVLGdWrYwlsjz8sn5VILTH8L/lXQiwx/DJ+VNayx2k/KslVMFWOdeDbjhqzplxjg10lxb428 N3rBukI2cHvXVSlc7aUuZGU/OKgzU0pxiqxNdsWctWOpKrYqwjYz0qkG+lTq/XpVpnJKJcD/ AEqJ2zjpSB/cVEz9ORVJmUokEjZx0qoTmppG6VWY07iUR6mrKjOapqetXI+9Q2b04llVz61M sec8GiJM54NaENtu3cP+Vc82epSVlcjSAnPDflU/2Y/3X/KtKGyzu+WT8qvLp/8Asyfl/wDW rinPUwqT1Oae1PHyv+VZ81uRt4b8q7N9PPHyS/l/9asm508jb8svft/9aiEx06hyEke3HBqq 1bNzaldvyv37VkyoRjg13QdzartcrNUDjpUzVE3NbI86W5QlXOOtUJF6da1JBnFZ8g6U0RJF FhnFQmrLjpVdhVo5pIaKSlFJVmD3ENQv2qY1C/agQ2iiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAJlp9MWpAKAFoNFLihjW5IverUY61XQZ zVuMdahnREuRDrVte9VU71bjBOeDUmiZYRSc8GtO3tS275X7dqgtoC+7huMdBXVWGn7vM+WT t2+vtUydjopQcmFlpZO/5Ju3b/61dXY6IP3nyT9u319quadpI/efLN27fX2rr7PTEXfnzR06 /wD6q86vXsdVWqqcbIxINGxu+Sf8v/rVeGl4/hm/L/61dEtsq55anGIe9efKTep5k6rkzmTY Y/hk/KqktpjHD/lXWPbocctWbPbAbcBu9TFhFnGXcGNnDd65a+Q/u+D3/pXd3tvjZw3euPv4 j+74bv8A0ruos9KhI5Ob+Gqhb6VduUxt4Pes1uMV3xZvUhck3fSpVfHcVV3fSnb8elaJnHOB b83HpUbS9OlQGT3FRtJ9KtM55RHu/TpUBb6UhfPpTM5pNlxhckTnNaEIzuqjGuc9a1raLO7h u1ZykddOiaNtDu3cN2roLWyzv4ft2qpZWmfM4ft/WuptLP7/AA/auSrM0qzsrISCxzu+WTt2 rSSwzn5ZPy/+tV+3sx82Q46VpJboM8tXDJnnyd2YDadn+GX8v/rVl3Wmfc+WXv2/+tXbeQp7 mqc9krbcb6cJWHCVmeV3+mY8v5Ze/b6e1ctdWhXZ8r9+1esX2nA+XxJ37fT2riNRsNvl4WTv 2+ntXoUZnoRanGxw8iYxwarNWtdQbdnDd6yXGMV2LU8+pHlZWftVOQdKtv2qq/aqM7lKQdKr MOlW37VWYdKaM5IiptOpCKtHLLcaahftUxqF+1MkbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEy1IKjWpBQA4UtJTiMUMa3JkHWrcY61V jHWrsQ61B0ItIvXrWjBGTu4NVIUzng1t2dvu38P27VL0NYK7NSxtM+Z8r9u31rutLsM+b8sn bt9fasPTbInzflk7dvrXoGm2m3zeH7f1rir1LI9LSlA2LGzC+Zw/b+tbaoFz1qCBNu7r+NWh XmR9+Wp5NWo5MXFNNOppqqlkjNEbHpVaQA4qd+1VnPSsYo2gjDvYf9X97v8A0rj7+D/V8N3/ AKV3V2udnXvXKX0WfL4Pf+ldVPQ7qWhwF5D9zhu9YsqdODXWXkH3OG71z08ONvDd67IyO6Lu tTMJpN30p7oRjg1AcitkyZ07il/pTC/0prE03k1akc0qQ7dn0p6rnNRhfrVqKPOeDSbNKdMs Qxk7uDXQWVt9/hu39azbaDO7hu1dNZQff4btWE5HQ9FZGzY23+s4ft/Wuqs7cfP97tWPZRff 4Pb+tdHANu6uWepx1NS2uFzzUob6VXBp6msWjnaLKmgrTVNSZrIyehj3duDs+93rjtRss+Vx J37fSu9nGdtc/e24bZ97v/St6U7HTRqWPKL60x5fyv37fSubnixt4avQ9Ss/9V8snft9K466 tsbOH79q9SnK5tXhdcyOdcYxVOTtWjNHjbwaz5B0roPNejKcnaq7dqsP2qu1BLZEaaacaaat HNLcaahftUxqF+1MkbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFAEy1IKjWpBQA4VIRUYqU0Ma3J4x1q/EvXrVONetaUCZ3cGoOhGjbQ53c N2rqbC0z5nD9u31rHsrfO/hu39a7XTbPPm8P2/rWM5WR6GGp63Zu6XZf635ZO3b612tnABv+ 92rI0+1C+Zw/b+tdHCgXd1rx8RUuycTVu9C4vFPD49Kg3fSjf9KmmrI85lnf9KMj1quJPpTw /wBKVTUEDjpVZx0q396oXXp1rGLNoSMy5XO3r3rnLuLOzIbvXVSpnHWsW7gzs4bvXVBnZTZx F5B9zhu9c9cW+dvDd67e6tc7OH71gT2f3flfv2rpizsjI5GWAjHDflVJ4cYwGrpJrT7vyv8A lWe9rjHyv+VXzGnOYjRnjg00Rn0NajWpP8L/AJU02h/uv+VUpj5kykkROeGrQgt87uGqSO06 /K/5VqQWf3uH7dqHMOZLYfa25+bhu3auls4Mb+G7VStbT7/yv27V0Nrbff4ft2rJyMpSNG0j I38HtW0g61TtofvZDdq0EXr1rGTOechQv1qQL9acq/WlJxWLkc7kOXApWf6VFu+lNLfSs2zK TEkOcVmXCA7evetFjnFVJFzjOacGVCRyOoWufL4fv/SuJv7XHl/K/ft9K9Mu4N2zhu9cfqFp /q+H79vpXo0ZnpU2pxsed3UGNnDd6xpkxjg11l7b/c4bv/SuduI8beD3ruizhrU7MxnHSqzd quyLjHWqbjGK0RxyIaaadTTVo55bjTUL9qmNQv2oENooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCZakFRrUgoAeozU2KiTvVjFJjjuWYV6 9a2LWPO/g9qy4V+91rfs4x8/XtWbZ2U43Oh0+D/WcN2/rXd6XbAebw/b+tcpp0Q/e9e39a7y wRV8zn0/rXDXnZHqS9ynodFaIF39e1aQNZ8LKN3I/OrPmj+8v515EnqeTUbbLBf3FN3/AEqA yD1FN83PcVop2MrFoPn0qUHNUw+e4qyhBzyKUp3HYtJ3prdqTfjuKYXHqKhJlxiyJx0qjNCG 29avM2e4qBhnFbQOmDsc/cWgO3h+9Y09j935ZO/auvmhB2/erOltgccNWvOb+0scXLp/T5Zf y/8ArVQfTc4+WX8v/rV2slmDjh6qNYrxxJR7QPanGnTj/cl/L/61NOnf7Mv5f/WrrzYL6Sf5 /CojYD0koVQFWOajsOvyyfl/9atGCy+98sn5VqiyA7PVmO1Azw9Vzle0uR21rjdw/atu3tsb uH7VHDbj5uGrWijC560uYXMOjjC5xnmrCjGajFPBqHqZyJxUTHGKcG9xUUhxis2jJoYX+lG/ 3FV2k6dKaJB6is2jKSLRbPpUbAHFMEgPcUu4eoqU7ExdilPHnb171zl9bD93w3f+ldTJg45r Gu4wdnXvXXSmdtGpY841C2x5fDd/6Vyl3FjZwe9egalAP3X3u/8ASuMvYsbOvf8ApXp05XOq tFSjc5adfu9aoSDpWrcKPl696zJB0rpR5E0VjTDUhFMrRHLLcYahftUxqF+1AhtFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUATLUgqNakFA Eqd6tAVWTvVvFJlR3LsC/e69q6GzX7/XtWDb87vwrorMff8AwrOR2UnqdZYjHmfh/WuxtJgu /le3euNszjfyO1b8FxjdytcdWHMeqrVI2OrjuevzJ+dSi790/OueW96/NH+dSLeH1SuGVA5Z Yc3/ALVnulKLj3WsRbvryn51OtxnPK1k6Ri6BtpLnPK1djk68isOObrytaMcvXkUvZiVIvmX 3FRmX3FV2k9xUZl9xWigaqmWzJ7ikzmqol91qVXB7iny2G4WJWAOKqSIOOtWS30qFj0rKTMZ MqNGOOtQNEOOtWm5xUeM1m5GTkVTCD/ephgH+1V7YD60zZ9aXMTzlEQgetSxxdetS7PXNSIm M9a0jI2jIlij69auYxUUYxmpia1TNkxM0u76VGW9xUZk9xVpXLUbk/me4pkj9ORUPme4qN5R xytHIPkI5JOnSoPOP+zTZJOnSqrS4xyKhwM5QL6zdeVp/n+61lfaMd1pftXun51HszP2RotN 05Ws+4cHbyO9RtddPmT86pzXX3eU/OtYU2bU6TuYuoAfu/x/pXGXy/6vr3/pXXXku7ZyO9cn en/V/j/SvRpxsdVVqMbHLXK429e9ZUg6Vr3P8P41ky9q6keRUZTIplS1Ea0RyS3GGoX7VMah ftQIbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFAEy1ItRrUq0AToOtWiKrR96tsKGNbl63/AIvwrobP+P8ACuft/wCL8K37Q43/AIVDOmDO mtmxu6dq1UlxnkViQPjdyO1XRL7is3E7IVbGmJ8d1p4uT/s1l+d7rThP7rWTgdca99zZS5PP 3auR3HXlfzrAWbOeVq1HP15WspU0bWjI6WKfOeVrRjuOvK1zUNz97la0o5uvK1hKBlKmbBn9 1qI3B9Vqn5v0pu40KIKJcWc/7NXI5Dz0rLjBOeK0IVPzcGomRUsi4CTTGPSpQh9DSNH04Ncc 2cE2VGz6UAVKUx2NIEx61kzBjcUhFSbaQrRckgIqRV69aXZ7GpVT61SZrF2FApGfHpU232NV 5F6da2gzogyF5+n3arNcdOVpJcjHFUnYjFdcUdsEW/tPutRtPnutUWlI9KjM/utacpq4Fp5u nK1TebpytQvcdOVqlJc9OUqeS5Psyy1zjHK1EbvHdKznuOnK1AZ/da0jSK5Irc02uzxylV5L jdjJWqJm91qNpunK1tGmZSrqOwlxL93kd65u8b7nTvWxPL93pWFdNnZ071uo2OGrWuYFzzt/ GsuXtWpP/DWXL2q0ckmVQKiNTVCatGD3IzUL9qmNQv2oENooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCZalFRLUwoAsxjOatOelVUOM1YY 9KGNbl+A/erdtm+907Vz8Lfe6Vs2743cjtUHQjooX+9yKtB/cVmQyfe5FWhJ9KRVyz5nuKBK faqpfHpSh/pUs2g2aCy9eRVlJevK1lLJ9KsI5OelZs7qbZuQSfe6VrxOTnpWDbkndx6Vu26E 7uD2rnmdMnoXlBPap1izng0Rxnng1fjh68NXPKdjmlUsRxQdeGrShhxu4aiOEc/eq9HGBnrX NOpc5alW4LD/AL1I8XTg1cVR7010zjrXO7s45SuZjpjHBqIr9avPH0wDUPl+xo5SSvt9jRs+ tWPKPo1Hlexo5QIBH7Gpli68NUix+xqykfXrRZjuQCL2aoJIenDVp+WPeonj6daqMrGkZ2MC WHpw1ZssPThq6OSDpw1Z81vjbw1dMKh2U6xz0iYxwaouxGK2Z4Pu8NWRNGRt4NdcZXO+E0zP kmPHSqMk3TlannyNvHrWXK546VsjRvTQc83TlaiMue4qs0h9qZ5n0rVHFUbLXm+4prSfSq2/ 6UFz7VojincSV845FY9w2ducd6vyP05FZdw2dvI71Zzsyp/4fxrLk7Vozn7tZ0namTYrVCam NQmqRjLcjNQv2qY1C/agQ2iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKAJlqUVGvepBQBYQ9anY9KrqetSOelA1uXYXxnpWtBJ97kdqwo3xn pWjBJ97pUmyZ0UMv3uVq4smc9Kx4ZPvdKvo/XpUtmsY3LZfPcUu7HpUANSDms3I7adK5MpNX YQTu4qpGhOcA1sWtrnfw/btWEp2O2MVBXZpWcOd/Ddq6K2g+9w3aqFnbY38P2robeHG7hu1c dSoc9WqSxQ9eGq/HH14NNjj68GraJ161ySmcM6g5E69asKBzUYFPWsWzFu5ZWlIzUampQa1g jMiMf1pog9mqyBmn4rZQC5WEA/2qaYPZqt4oxTdMVykIsdjUyr161IVpDxUuIxDxUR5p7N9K iJrCURkbLnHWqksIOPvVdNRsM0rNDjJowp7fO3hqxbi3xtwG711UsfTrWVPB93hu9b06h206 pxlzAfl4bv2rAuIyu3g9+1drdW33OH71zt1a/c+V+/auuFU7oVTnHyMcVGTV2a3I2/K35VUa M+hrpjMtxjIaWprP9KDmoXbGOlaxkc1WjYilfpyKy53+7yKtzP8Ad5FZU8n3elbI86cbFSZ8 7elUXPSp5H6dKqM3SqMmxmc1CalFRGqRjLcjNQv2qY1C/agQ2iiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAJ171IKjXvUgoAkTvUjHpUS05 z0oGiZG61oQt97pWYp61ehbrUs1grm1A2d3StFDnNZUB+9+FasXesZM9GjTLSDrVqOPOeDUM aZzwa1ra3zu4btWE5WPQSUUTW1pndw/btW/aWf3/AJX7dqjtbTG/h+1dBbWuN3D9q4qlQ5at UltrbG7hu1bMcYGetQQxY3cGryrjPWuWUrnFOdyVFHNWFGM1Eo61MKxZg2OpaBSUiR4OKeG9 xUOaTf8AStoaCLyMDnkVIKpLJjPIq0rg9xXRGQiSkNGaQsO5FaSmgEY1CzdORSPL05FQlvpW LkA8tTc0maKyaGKabS0hqWwIHGcVQljBx1rRbtVSQdKiLNIMxLiDO3hu9Yd1aZ2cP37V1Eqd OtZ81vnbw1bqVjpjKxxVxafd4fv2rLltsY4auyuLTO3h+9Yk9rjbw/5VtGobwqnMyR4xwapS nGK2p4du3AbvWLcDbt/GuynK52J80TMnbG3p3rJmf7vStG4b7v41kTt93pXZFnl1o2ZVkbp0 quxp7N0qEmtEcTAHNMNOXvTDVGbGGoX7VMahftQIbRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAFgVJUYqQUAOWlk7UAYok7UAhynGauQt9 7pVEVZjbr0qWb02blu33unatmDndXP27/e6dq3bZ87uR2rnkevQZs26A7uvaugtIh8/XtWFa 4O/n0ro7TA38+lcszqqbG/awj5+vatuFPvdaybUj5+R2rahx83NcFQ8uqWkGM1OKhQ9amBA7 1gzmZMDUoNQA1Ip61NhWJQaWmA06jqSxCajJp5qI1otCRwfHpVhJevSqWTTwxoUhmkJvdaia bp92q4fHpTCx9qpzEPaQ8dKQNn0qLNOFJMCYGn1GKfVMBaaaXNNJxWbYxjVVk7VZY1Wk7VK3 KiU37VC65x1qVu1MzWhuZ88I+X71Yl1APl+93ro5cHGax7oD5OfWqiVE5G7j27OveuauxjZ+ NdVfEDy+R3/pXKXjj5OR3r0KKPSpbXMO5P3fxrHmP3a07h/u9O9ZEpziu6B5+IepWftURp5O ajrZHnsVe9NIpy96aaZDGEVA/arBqu/agQ2iiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKALCnNSA1EvepBQBIDmh+1NBxTmOcUDQCpUOM1HS g4qTSJpwSY3city2m+9yvauZifGela1vPjdyvaspI9ChUsddbTY3cr2rorSf7/K9q4y1uPv8 r2robS4+/wAr2rmlE9NNSR21rN9/le1bkEo+bla5K0uPv8r2regmxuwV7Vx1IHLVpm4snXkV J5nuKz0l69KmEmfSudwOR0y+r+4qZW69KoK/XpVlH69KhxM5QLi081Ej9elSbs1n1MJCGojU ppmKpsRHinhaNuakVazuAm2mEYqYDFNIp3Ai20oFOIoAppgOFSUwU6q5gCo2b6U4nFQsc1G4 0gZvpUEnank0xj0ppFJFSQdKgZsVYcZxVSQ9K1RtEgmkxt5FYl3OBs5XvV+5lxt5XvXN311/ q+U79/pW9OFzppU7sx764/1fK9/6Vy11L9zle9ad5c52cp3rm7q4zs5XvXo04WOmrNQVkULm X7vTvWXIxOOlWppM7eRVJznFdUUeTVncjY02lNJVnMxU7000qnGaSmQxpqu/arBqu/agBtFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUATLU gqNafQA8GnE5plKaBrceKWmU+pLJFbr0q9DJ97pWcDVhGxnpSaNqcrHQW0+N3K9q6K0n+/yv auPglxu6dq6G0m+/yvasZI9GlUO0tJ/v8r2ro7aXO7p2rjrOXO/kdq6e0fO/kdq5aiOyequb 8TE5q0meapwHO6tBF69a5JOxxTdhy59KsITzTVTOetSBcetYyZhJosK3XpUyt7iqw4p4asmj GSuT5B7ikJHrUW76Uu6psRykoNSCoA1SKaLByktNalzUbN06UWCwhNANNzSZosHKSrinZqIN j0pd2fSlYTQ4mompS30qNm9KECGmomPSnk1CzdOlUi0RsaoTHGORVt36cis24kxt5HetImkT JvJcbOR3rkL+5/1fK9/6V0N/P/q+V7/0rir+fPl8r3/pXfQR6VFWVzJu7j7nK965+ebO37ve r11P9zle9YssnTpXoRRwV56kTvnHSoCac3ammtThbuJTDTjTSaZIimjNIpoNMhiE5qB+1TGo X7UANooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigCZafTFp9ADqCcUgNDUAh9Opgp4pFjwKkWmCnDikWmXYZMbulbdpP9/le1c8hxmtK3lxu 5XtUNHVTnY7ayn+/yvb+tdVZTA7+V7f1rgbO5xv5Tt3rqbG7A8z5k7d/rXLUiepTkpRsdxby A7uR2rVicc8iuXtbsfP80fbvW1Dcr83zJ+defUizlqwZsoRzzUoqlHODn5l/OrAlB/iX8652 mcjTJulGaj8weopPMHqKVgSZNS5qESD1FLvx3FOwcpMH+lPWTHcVUMgHcUw3AH8SfnVKBahc v+b7rTWk9xWf9rH95PzpRcg/xJ+dP2ZXsmXC/wBKTf8ASq3nZ7rS+Z7ijkJ5Cz5nrijzPcVU MvutN88eq/nUuAnAul/cUwyY7iqn2gf3k/OmNcD+8n51PIyfZsstKOOVqs84GOVqs90BjDJ+ dUZbvGMMn51pGmaxpMty3I4+ZPzrIubwDb80ffvUM99jb80f51gXeo/cw0Xfv/8AXraNE3hQ I9QvR+7+aPv3+lcbeXGdnK96v3l9u2cx8Z71zdzcZ28r3rvpU7G85KEbIo3EudvI71nO3Sp5 XzjpVZq64o8mrK7Iyc0UUVRzjTUbU+ozTEwWigHFFMhiGoX7VMahftQA2iiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAJlp9MWn0AKKRqKGo AcvenrUQNSA0i0TCnVGDUlIpEgOKsRyYz0qrmnq1KxSkb1tc43cp2robS9+/80fbvXFxS4z0 rVgu8buU7VnKJ20q1j0C1v8AG75o+3f/AOvW5b6h975o+3f/AOvXn1tfH5uY+3etuC9+980f 51hOkmd0akZ7newX2d3zR/nV6O7Bz8yfnXGQagRu+aP8/wD69acV+Ofmj/OuSVEUqCex0wuv dPzpftAPdfzrCW9HPzR/nUgvP9pPzqPZGfsTb+0+6fnS/aR/eT86w/tv+1H+dMN8fWP86pUS lQNp7scfMn51RkvsY+aP86y5L48cx1ny3vTmP861jRNVTjHc2/t/+1H+f/16ljvs5+aP865X 7Znun51Zhu87uU/OrdJFJRZ16XQOfmT86k+0Z7p+dc9Fe9eY/wA6sfax/eT86ydMiVI1WuPd aha6x3T86zWu+nKfnVZ7vpylCpAqJrNef7SfnULX3T5o/wA6xmu+nKVXa7xjlPzq1RQ/ZxRr yX3TDR/nWfNffd+aP86znu8Y+ZPzrPnvPu/NH+dWqSK9xFm4vz8vMfeufur37nMffvTbi9+7 80ffvWHcXR+XlO9axgZzrJLQS4uydvKd6yJpc46U+WXOOlU2bOOlbRiebVqtjWbpURpxptaH K3caaY1PNR5pkDSaZTmNNoEApaatOpkiGoX7VNUL9qAG0UUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBMtPpi0+gAoJzRSGgAp6mmUoNIZMD UgNQg08GgpMlFOBpgp4pFIlVuvSrMcuM9KpinBselItOxtw3ZG7lK14L373MfbvXJpKRnpV6 K6IzylS0bQqWO0hvvvfNH+daMV/1+aP864uK9PPKVfjvuvMf51k4ndTrnZJfdfmj/Opxff7U f51ykd91+aP86sLfdeY/zqOQ6lUR0n2z/aj/ADphvf8Aaj/OsE3v+1H+dRtff7Uf51SiYzqs 2ZL3p80f51QlvenzR/nWZJfdOY/zqhJe9OY/zq1E5pVmbQvP9qP86tw3n3vmT865UXn+0n51 aivOvKfnQ4jhVOvS7HPzJ+dWBef7SfnXLx3vX5o/zqyL3/aj/OsnE7Y1NDda890/OoHu+nzJ +dZBvc94/wA6ga86cpTSJnUZqtedOU/Oqr3vT5o/zrKe86cp+dVHvDxylaKJyyqs1JL3OPmj /Os6e9+7zH+dUHvOnKVQluiccpT5TJ1ixNdZ28pWZLOTj7tMefOOVquWz6VSRlKpcVmz6VGa M0VSMJajDTTTjTCaZmNJqM040wmmJjKQmlpDQSC06mrTqYhKhftU1Qv2oAbRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEy0+mLT6ACkNLQ aAEoFFApDHg08VGKeDigZKDTwahBp4NBSZNmjNR5pc0irkgb6VIshHpVfNKG+lAlI0EnIz92 rkdz15SscP8ASplkPPSpaN4TN1Ls88pVhbvHdKwVn91qUXHutQ4nVGobZvPdKja890/Osg3H utMa4PqtCRMqhoyXmccpVR7s8cpVF7g8crVdpj/s1okcspGiLo+qVajuzzylYQl+lTpNjP3a TRUJHRJeHnlKsi890rnknPP3asC591rJo7YVDa+1+6fnUT3WccpWZ9o91/Oozce60JDlNF17 k8crVZ7k8crVVp+nK1XaXOOlaJHJORO854+7VVpScdKYzk46VEWz6Vdjnchxb6UlMzTgaBJj qQmkzSFqBtiE4qMmnFqjNMzbAnNMNLTc0EiUhpaKYCLTqRaWgQlQv2qaoX7UANooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCZafTFp9ABQ 1FB5oASgUYPpS4PpSGKKdTQD6U7B9KAHUoNNwfQ0fN6UDJN1Lmmc+lL83pQO4+im4PoaXDeh /KgVx2408P8ASmYb0P5UYPoaCk7Ewk+lO833FV/m9D+VHzelKxaqFgy/SozKfaoju9Kad3pR YTmOZ/pUZb6UEMOx/Km7T6GmQ2O3VKr9elQYPoacAfQ0WGpWLaydelSCX6VTBYdqeGI7VNjZ VC15v0ppl+lV9zelISfSjlB1SYyfSmF/pUeW9KMN6H8qdjJyuLuNJRtb+6fypdrf3T+VMi4l FG1v7p/KkIb0P5UDuBNNJpTu9DTcH0NAmxM02nEH0NJg+hoEMJppp5Unsabg+hpgJRRg+lGD 6UAKKKVaSgQlQv2qaoWOcUANooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigCZafUYNO3CgB1FN3r60b19aAHg06ow4Hel8xfX9KAJKXcaj8x fX9KPNX1/SgCYHFG76VD5q+v6Ueavr+lAE4b3FODYqt5q+v6U7zl9f0oAsA0/d9KqidPUflT xcJ/e/Q0AWQaWq4uYx/F+hpwuof7/wChoAmNJUf2qD+/+ho+1W//AD0P5GgB7U2mm6g/56H8 jTftEH/PQ/kaAHGkpn2iH+9+hpPPi/v/AKGgCSlFR+fD/fP5Gl+0Q/3j+RoAkFLUf2iD/nof yNH2iD++fyNAElFR/aIP75/I0faYP75/I0ASU4HNQ/aIf7/6Gj7RAP4/0NAFgHFKTVb7VF/e /Q0faov7w/I0AWCajLVEbqP+8PyNMNxH/eH5GgCYmkJqHz09R+RppnU9x+VAE+aQmofOT1/S nedH/e/SgB9Mo86P+9+lM81PX9KAHUUzzE9f0o8xfX9KAHU2jzF9f0pu9fWgAJxULVIWBqM8 0AJRS4PpS7G9KAG0U7y29KPLb0oAbRTvLb0o8tvSgBtFO8tvSjY3pQA2il2t6UbT6UAJRS4N GDQAlFGDRigAooxRigAooxRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUYoxQAUUYNLtPp QAlFLtPpRtb0NACUUuxvQ0uxvQ0ANopdreho2n0oASil2se1Lsb0oAbRT/Lf0o8p/wC7QAyi n+U/939aPJf+7+tADKKf5Mn939aQxsO1ADaKXBpKACiiigAooooAlxijB9KUDNOoAZt+tG36 0+igCPFGKftFG0UAMxSYp1FADaKdRQAmKMU/aKAMUAN2/WnBQPWlpaAG7QfWmlfTNSkUmKAI sUmKkIzRtFAEeKMVJtFG2gCPFGKk20baAI8UYqTbRtoAjxS7frT9tLQBHt+tG361JRQBHt+t Lt+tPooAZt+tNK49amxSEZoAixRipNoptADcUYp1FADcUYp1FADcUYp1FADcUYp1FACYoxTg PWgjFAEeKQU80wUAPUVMBUaDrUwFADcUYp+KMUAMxRin4oxQAzFIRUmKTFAEZWk21IRSEUAR bfrSYqSgjNAEZFJipNtG2gCPFGKk20baAI8UmKl20hWgCPFGKft+tG360AMxSYqTb9aTb9aA GYoxT9v1o2fWgBmKXFSBM+tGz60ARYoxUuz60bPrQBEBS4qTZ9ads+tAEYX2NKEz61LtpwFA EXl/WlCA+tS4ooAYEx60bfrT6KAG7aTbT6SgBhFIRmn0hFADQMUtFFADqWmZxTqAFpabRmgB xOaY1Lmm5zQBERTMVMRmmbaAGYoxTsUY9jQA3FJT9p9DSY+tAEi06mrTqACiiigAooppOaAA nNJRRQAU4DFNp9ABRRRQAUucUlLQA6kIoFLQA3FGKdjNJgUAJijFLx60uRQA2lApcj1oyPWg BMUYpcj1oyPWgBCKSnZHrRketADaKdRigBtFGKKACiiigBKQjNLRQAyinEZptABRRRQAUUUU AGD6U7aKWigAooooAjNNFONNFAEq96lFRgYp4oAdTgfWmUtADsCjA9aQHFJQAUUUUAJSUtFA DSKSnUUANpQKWjI9aAExQRS5HrRketADaTj1oakAoAXj1oo2/WlCn0NA7AAPWlwKXyz6GnLG fQ0gsN2ijaKm8o+jU4QH0b8qLjUWQbcetKFHerItyf4X/KnfZT/df8qLlezZV2D1o2D3q2LT /Zf8qcLUn+F/ypcw1SbKWz60uz61f+yH+6/5U02f+y/5UudF/V5FHaKCPSrhtPUP+VN+zY7P +VHOg+ryKlFWvs3s9L9lH+1RzopYWTKlFWvsy/7VNMC/7VHOgeFkivRU3lAetNMY96fMZui0 RUnHrTyn1pPLHvTuQ4MZgUYFP2D3o2D3ouHIxmB60uR60pjHvTdv1ouLlFyPWjI9abikxQKw /I9aacUlFAWA4pMZpcZpVHWmFhu2lC4p2KXGKBDcU3aKkpuKAGLTqatOoAKKKTNAC0ynZpDz QAlFFJQAZpwYmm0ooGOzRmkooAXNKCaQCloAXcaNxpKXFAC7jSE5oxS4oCw2inbfY0uw+hpX K5WMop+0+hpNvsaLhysbRT9h9DS7D6Gi4crI6KeUPoaQrjsaLicWJuNG40YpKYrC7jS4ptLu NABQaM5pCTQIKKTNGaAFpCM0ZozQA2indetIRjpQAlFFKADQA6iiigAooooAjNIBSmgCgCQU +mgU6gApaMUUAGaM0ZpMj1oAWikyPWkLY6YoAdRTNxpdxoAdSMSKTcaCSaAE3GkpcUbfY0rl JCUVIEPoacISezUXKUGRYPpTgmexqwtuTn5W/KrMdmTn5ZPyqXIuNJsqLCeeG/Kp1tjz8r/l WnFYE5+WT8qvpphOfkm/L/61Q6iR1wwjZhC1P91/yqRbM/3ZPyrok0o8/u5vy/8ArVOukn/n nN+X/wBap9qjVYNnOCyP92T8qnSwPPyS/l/9aumTSG5/dz/98/8A1quJop5/dz/l/wDWrN10 aLCxjucmunn+5L+X/wBapBp5/uy/l/8AWrsBo3qk/wCX/wBanjR/9if8v/rVHt0aKjTOPGnZ /hl/L/61OGnf7Mv5f/WrsBo4H8M/5f8A1qcukDn5Jvy/+tSddDVOkjkBp7f3Jfy/+tTW05v7 kv5f/WrthpA/uzfl/wDWoOkf7E35f/WrN4hD56aOFbT24+SX8v8A61RNYN/cl/Ku6bRwcfJP +X/1qgfSOnyTfl/9aj6wg5qZxBsiP4JPyqJrU8fK/wCVdlJpHT5Jvy/+tVKXSunyTfl/9aqV ZMtODOUaA8cN+VQtHjHBroJrArtwkv5f/WqhJa4x8r/lW0ZXJnS0ujJZCOxqMr9a0Hgxjhqr mLHY1qmcc4FTb9aTb9asFPrTCn1qrnPKBFtHvSbfrUpT60FadyHEgIppFTFajIzVGTRGRSEU 8jFJTIGYpKfSUCG4pyjrRTkA5oEwxRinbRTgKYiPB9KbtqfbUWD6UAQLTqatOoAKbTqbQAUU UUhgRTadRQAgFLRS4oGJSgUuKXFACUuKcBS7aB2GgZp2KcF+tSCMnoDSbKULkezPrThEfRqs pATn5W/KraWhOflf8qhzOmFBsz1gPPyt+VSC3P8Adf8AKteOxJz8kn5VYXTmP/LOX8v/AK1Q 6h1RwpgG2J/hf8qabc/3W/KuiOmv/wA85v8Avn/61RnTm/55y/l/9ap9qW8IYYtz/df8qX7O 391/yrdXT2/55y/l/wDWqT+zm/55y/8AfP8A9aj2o1hDnTAR/C35VE0R/ut+VdE+nsMfJL+X /wBaqj2RGPlk/KqVQynhTEMeOxphXHY1pPakY+V/yqBoMY4atFI5JUWiltPoaTB9KsmM+hph X2NVcwcGiHFJUpX603FMmxGRSVJimkelAWG0UtJQKwUUUUxCYpcYoooAKKKKAHUUUUCIzUgW ozU4FAABS4pcU7aKAGUcetP2iosUADY4ptOIpKBhijFLijFIBMUUu2nbaBpDMU4A+hp4T61K sWc8Gk2aRptkKpnsamWEns35VZjts54f8quxWROfkk/Ks5TOynhmyitsTn5X/KrKWZOflk/K tiLTWbP7ub8v/rVpw6QTu/dz/l/9asZVUjsjhorcwItPJz8kv5f/AFq04NKY7v3c35f/AFq6 O30X73yT9u3/ANatq20YfN8k/bt/9asJ4hIq9OBzlvoud3yT9u3/ANatiPRRz8s/5f8A1q6a DSgN3yzdu3/1q0k01eeJf8/hXHPEMwniuxya6KvPyz/l/wDWqwujLz8s/wCX/wBausGnr6Sf 5/CpRZKP79ZOvJmLxLObTR1GeJvy/wDrVbXSU/6a/wCfwrdFuB/ep3lfWlecjCVaTMI6Yn/T X/P4U3+zgO0n+fwreMQ96aYR71DlJAqrML7APST/AD+FKLBfSStkwj/apPKHvR7Rle1ZlCwX /ppT/sCn/np/n8K1BEPel8v61DkyHNmOdPU/89P8/hUL6apxxJ/n8K3vL+tNMIP96jmYudnM vpi8cS/5/CqMmljj5Zvy/wDrV2BtwezVA1oDjh6tTkWqrR5/caX93CTd+3/1qwrjTSu35Je/ b/61emz6eDt4k/L/AOtWHc6Xnb8k3ft/9auqlXO6jiO55xNaFcfK/wCVUJIMY4au2utL+58k 3ft/9asOewI25ST8q9CFRM6JwUldHNtGR2NRFfY1qy2xGPlf8qpvERjhq3TOKUGiptpCKmZP Y1GRTuZOJEVqMipyKjaqTMZRISKbjNSkVGRirMGiMjFFOIzTaCRKcnem09O9AmPAzTgM0zOK eDTEOxmoqkJqHdQBXWnU1adQAU2nU2gAooopDCiiloGFOApAKcBQOwYpwFKBTwvsaVylG40L 9aeEJ7GpViY5+Vvyq3HaE5+V/wAqlyOiFFsrJATnhquxWZOflf8AKr0Gnsd3yS/l/wDWrbtd JZt+Y5+38P8A9asZVLHfTwyWrMeHTyd3yS/l/wDWrUg0ondmOb8v/rV0Ftov3vkn7dv/AK1b cOkAbvlm/L/61c06xveENjmYdH+98k/5f/WrRj0YHPyT/l/9auph0tRniX/P4VoR6avPEv8A n8K5pVjGeIOKOi/7E/5f/Wqu+jdPkn/L/wCtXoZ0xfSX8v8A61QPpSnHEv8An8Kx9uzD6wzh Bo4/uT/l/wDWqT+x/wDYn/L/AOtXaf2Yo7S/5/Cl/s8ekn+fwp+2ZSrs4OXSOnyT/l/9asmf SSNvyTfl/wDWr0qTTwccSf5/Cs2fSwdvyy/l/wDWreFY3hX7nmcumkY+SX8v/rVny2JGPlk/ KvR5tHHy/LP+X/1qyZ9G+78k/wCX/wBauiNdGn7uW5wMlqRj5X/KqrQEY4b8q7GfSiNuI5vy /wDrVkzWDLt/dy/lW8atzGeGT2OeMeOxphT61pyWuMfK/wCVVHiIxw1aqVzinQaKhFNIqYqR 2NRsuPWrTOaUbERGKbUhFNIqjNjaKKKBBRRRQIKKKKAHUUUUxEZqwKrmrAoAlAoxQDQTQAhq KpaioGgpcUUuKRVhMUuKWl2+xouNITFOCZ9akWPOeDVhLcnPyt+VQ5G0KTZEkROflb8qvRWp Oflf8qsQ2ZO75ZPyrbtNML78pNxjt/8AWrCdSx6dHDpaso2+nE7vkl7dv/rVu2ukZ3/JN27f /WrWtNI+/wDJN27f/Wro7bSgN3yzdu3/ANauKpXLnWUdEYdvo4+b5J+3b/61bEOkj5srN+X/ ANat+HTVG7iX/P4VoR2K85ElccqzOKddmJDpoG7iX/P4VpxWQGeJK0UtQM/fqcRAetYubZhK o2VktwM/eqwIh71Mq/WnhB71UYmVyHyvrQYwPWp8UFaqyQisUx60hX61OV+tMIq+YCHbSFfr Uu2jb9azlqBXKfWm7PY1ZKfWkKemaytYZX2+xo2+xqfZnsacI/Y0hkIT60uz61OI/Y07y8dA aAKxj+tMMX1q5s+tJ5f1reCJM9rfOOGqjPYhtvElbuz61G0IOOtJxa1RUZWOMudNzt+WXv2/ +tXPXelfc+Wbv2/+tXpE1mrbfv1kXGnKdvEnf/PStadZo7qOIaPK7nTiu35Je/b/AOtWPNaE bflf8q9Ju9LB2fLL37f/AFq5m604jZ8kvft/9avQp1kztTjURxzxEY4aqzJ061uz2hG35X/K s2SEjHDV0RlcxqUrGaRUZqy6dOtV2Fao4ZojNMNPNNIq0c0hhFMIxUtRkUzMbTk702lTvQJi 7jTgaZSg4piH5qKlJzTc0ARLTqatOoAKbTqbQAUUUUhhTgKAKcBQUAFPAoAqVUzng0i0rgqe xqzFATn5W/KnxQZzw1bFrYFt+EkPToKynOx3UKHMV4LJm3fJJ+VbNtpLNuzHP2/h/wDrVsWO jZ8z5J+3b6+1dTZ6IPn+Sft2/wDrVxVK9jtcoUzCs9E+/wDJcdu3/wBaukttFHzfJP27f/Wr ctdJA3/LN27f/Wrbh08Lu4k/z+Fcc69zmqYlswItKC5+Wb8R/wDWq6liBniStv7KPR6YYgPW svaXMfa3KCWwGfvVbjhHP3qeVx61KneokzOUgFuP9qkNov8At1bQDmpNv1rMxuZTWoGOHqFo Bxw1a7RjjrVSRcY600yoyMtoRx96oGtA39+tB1HFIF+taKRspGO+ng44k/L/AOtVKXSgcfLN +X/1q6kRA+tBtQf71HtGhe1aOAuNGHy/LP37f/WrAu9Gxswk/ft/9avU5bAHHElY13pYOz5Z u/b/AOtWsKzRvTxDR5Fc6WV24Sbv2/8ArVjT2ZXb8sn5V6leaQPk+Wbv2/8ArVy17pWNnyTd +3/1q7ada52RnGpucLJCRjhvyqsydODXQXFkV2/JJ36isqWDGOGrtjO5yVsPYziuPWmEVZZO nBqErj1rZM8+UbERGKbTyKbimZtCUUUUCCiiigQ6iiimIjNWBVc1YFAElFFFABUYFSgVHSGg FLilxTgKRokAWpUTOeDQqZz1q9Db7t3DflUSlY6aVJyY2K2znhvyrWt7Atu+STt0FWLXTy2/ 5Je3Qf8A1q6ix0j/AFnyTdu319q5alWx6cacaauzOtNJJ3/JN27f/WrqLLScb/km7dvr7VpW ek43/LN27f8A1q6K204Lu4l7dv8A61cVStcyqV+iM+30wDd8svbt/wDWrYisgM8PV6O0C54e rSwgZ+9XHKRxTncrR2454arAiA9anCY9acF+tSlcwbIBH9aXZ9am2fWjYPem4iIQn1pwWpdn 1pdn1qkhEW360FakxSEUwIitRlPrU+KaV+tNICEL9aUJ9ak2/WlC/Wq5QIin1o8v61Ps+tO2 fWk4BcrCP2NOCfWrAj+tLs+tRyBcgCfWl2fWptv1oK0KIEGz0zRt+tTbaTFbxiIrlfrTCtTs KYRRJAQMucdarSW4OPvVe20hQe9c0lbYpOxzlzYg7eJO9c5d6aPk+WXv2/8ArV3s0GcferIu bTO3h+9aQqWOqnVaPMrzTsbPll79v/rVztxakbflfv2r0y8sM7Plk79q5W80/wC58snft/8A WrupVT0adRSVmcPNDtxw1UWjxjrXRXNqRt+V+/asiaHGOGruhK5z16VjNIppqd06cGoCK2R5 s4jajNSYphFUZMYRilTvSGnR96CWIRSU+mUxCGmU81HQA1adTVp1ABTadTaACjFFPxikUFPC 0iipVX60ikhVXOeDVuKEnPyt+VNiiznhq2rWzLb/AJZO3as5SsdlCjzMktLEtv8Akk7dBXX6 dpH+t+Sft2+vtTdN0ot5uUm7dvr7V3Wn6UB5nyzdu319q8+vWsehOapqyGWOkqPM+Wbt2+vt XS2+nqN3Enb/AD0qe2sgu7h+3WtNIguetebKbkzzKlRtkKWyjP3qtiMe9KFxTwaagY3ImQe9 VJB0q4x6VUkPSs3oykysTT0PWoyaenOaGxtlyPvVkCq8ferSjrQiCJh0qnKucda0GXpVOUdK GgM1h0oXinSDGKjBppmiZZXvVhVB9aqRt1q4h60mTIGiB9aqTWoO379aI5prDNGwk7HLXWng 7OJO/wDnpXNXul52fLL37f8A1q9BlhBx96sm5sgdvD960hNo6ITaPJ7/AEvHl/LN37fT2rlb qzK7Plfv1FetahpufL+WXv2+ntXE6jp2PL+WXv2+ntXoUap6NKamrM4OWHGOGqowPHBrdubb bt4fv2rJkjxjg16EZHFXpWZRINNapnXpUWK1OFoZSUtFMgSiiigQ6iiimSRmrAquasCgCSlA oApaACmAYqQVGKTKiLT1UnPBpFGasxR5zwals2hG7JYYS2eGresrIvv+WTjHQfWqtpaFt/yv 27V2ul6Xnzfkm7dvr7VyValj16UFTjzMs6fpOfM+Sbt2+vtXX2elAb/lm7dv/rVPYaYB5mVl 7dvr7V01vZAbuJO1eVUqtnJVrNlKCwC7uJO3+elacdsFz96rSW+M8NUwix61g5HO53K4i+tO CY9asbcetNK1O5m2Q4pQKfilArSOhI3b9aXaPen4xRik5DI9uKMVJgU0ihSAjppFSYzSEU7i IyKTGexqTFKFrWIDNn1pdn1p4FOxW6RIzZ9adt+tPC4pQKpRQDdn1pNv1qSkpOKAiIpMVIRT SMVnyjGEUFadijFUtAIGHSoyKsFfrURWokwI8U7bmjGKUVgxkbID61TmhB2/erQIqF0zjrWe xSZzdza528P3rnLywzs+WTv2ruZoQdv3qxrq0HycP3raE7HVTnY80vbAfJxJ37fSubuLbG3h +/avSL6x/wBX8snft9K5K8siNnyyd+1ejSqHoxaqKzOOkiIxw1VHTpwa3J7f7vDVmyRYxwa7 YyOGtRsZ5FMqd1xjrUJFapnBKNiLFOQdaDSxjrTM2KRTGFSYpppkkJNRbjUxFQYPpQAq06mr TqACm06kFA0AFPAzSAU9RSLsOAqxGhOeDUaLnPBrRt4Sd3DdqmTsbUoXZatLYtv+V+3auu03 TC/m5SXjHb6+1Z+n2O7zPlk7dB9a9C0rSwPO+WX+Ht9fauGvVsetFKlEvabpQXzflm7dvr7V 2FrZBd/D9utR2VkF38P2rbSIDPWvJnNyZ5tWo2yNIguetSAU8jFMJqVGxgLmm5pM0hNbJ6CG s2MVVkPSpWbpVdj0rnluNEJqVO9R9amQdaSGy3GOtWwMVVjHWra96p6CEIzVOUdKvMKqyjpR e4GXIvTrVY1flTp1qoy4x1qSkOjPWrkZ61RQ4zVtG68imxyLG7FGai3H2pd30pNkCsM1A8Ib H3qnHNO2/Wmi07HPXlnnZw/euO1HTv8AVfLL37fT2r0qeANt+9XOX1lu8viTv2+lb052OmlU sePX9iV8v5ZO/b6VzVxAV28N37V6bqenf6r5Ze/b6e1cVe2ZTZ8r856ivUpVLnoSSqROVkTG ODVdhjFac8WNvBqg69OtdkWeTVhZlcjFJin4ptUc7Q3FJTjTaZDHUUUUySM1YFVzVpO9AEmM UUUuKAFAzUYqQUxRnNJlRJUTr1rTtbfdv4btVSGPdu4NdFY2m7zPlft2+tYzlY9TDUrs1tL0 /f5uVl4x0H19q9F0vTceb8svbt9fasbSNOH775Zf4e319q9C0+zC+Zw/b+teTiKlzXEVeiLl rZhd/D9q1o4QuetEUQGetWQtcO55jdyMLj1pcVJikxS5REZWmEYqbFMYdKrYCEj0oxinEYpm aTYJDqSijFIoSlNBpCaBMSjFJmjNWhBQBikzSbvpVqVhEgpRUYb1xTt30rVTFYkozUe76UFv pWqmFh+aTNM3UbqHMB9JjNJmloEJil25pacBmnYCIiomWrJWomXpWbiMrmkp5GKZWTQwpCM0 tAGaxkhld06daozQg7fvVqlfrVaROnWiLNIs5W7tc7OH71yl7Zf6viTv2+legXEGdvDd6527 tM7OH710052OylOx5vd2e3ZhX79qw54MbeGrvL2z+5w/ft9K5e6tsbOH716FOdzudqkTmJY8 Y4NVGFa08WNvB71muuMcGuuLPKrU7MrHmnR96Qilj71qjikh+KQilopkELjpUFWH7VBQAxad TVp1ABQKWhR1pMqI4CpQKYvep0XOetI1SJoo8561u2Vtu8z5X7dvrWZbxZ3cHtXXaZabvN4f t2+tc9SVkenhaa3Z0Wk6aP33yy/w9vr7V6Hp1iB5vEnb+tYmlWOPN4k7dvrXa2sAXf8Ae7V4 9epczxFW7LkEQXd1/GrVRrxRu+lYwRwMVj0qImlZs+lRlsU2IUtTSfpTd30ppNTzDGu3SoCc 1ITUdQ3cBAKsovXrUar161ZjTr1pxQEyr161OOKaq9etSFc+tOW40NzUUgzipttMYdKkbKEi dOtUpB0rScZxVGRenWjqJFYcVMjdelRYpQaossb/AKU9TUCmpVPWlYViZe9SjmoQalBpMTBl BxWdcW+7bkN3rTFRumcdaE7BF2OF1Gwz5fyyd+30rgtTsMeV8snft9PavXb21B2cP3/pXDar Zf6nh/4v6V20amp6FCrZnlV1BjZw3eseSPGOtdZf223y+H79fwrnZ48beDXq05XRWJp9UZbD FRkVYZenWoCK3PMZGaQ0ppKoyY6iiimQRmraDOaqGrad6AJKKKKAFFEa9eDSgVLCuc9amRrS V2aNpDnfw3au00myLed8sn8Pb61zlhBnzOG7f1r0fR7P/XcP/D/WuGvOyPbguSnc6fSrHHm/ LJ27fWuxtYMb+G7Vl6fb7fM4bt/Wuhhjxu6149SV2ebVldkqr161LQBRSijADTadTTWlhDaa adTWHSspDREajqXb9aaV+tSVoIKCaDxTCaaVyWOJqMmjdTCauyQhS30pN30phNJk+lIY/dRu poB9KcB7UALmlBpu360oFaIQ7NGaTFIabYDs0ZqPOKN30qeYCXNKDiot/wBKUPn0pqoFiwDm pAaqh/pUwf3FbRqIViQ1E46U/dn0pp5qnJMCBl6VHipjTCKxkMjxSgYp2KSsJDGtUD9qnaoG 5qUWilKmcdaybi3zt4bvW646VRnjzt4NaxZrF2ONvbT7nD9/6VyV7a48vh+/9K9EvIPucN3r kr62/wBXw3f+lddKR3UJnA3MONvDd6x5o8beDXU3dvjZw3esGeLG3g969KDHiYGOwxihO9Sy J061EneuiJ49RWHU0mnU0jFUYkTnpVfcakY9KhzQA9adSUtABS0AZpQM0mVEkUdasxrnPWq6 jrV2Fc7utSzogrs1bSHO/hu1d5o9pnzuH/h7fWuQsYs+Zwe39a9H0a3x5/Dfw/1rgxErI9Ze 5TOz063x5nDdv610kSYz1rMsYv8AWde39a2FFePN3Z5VR3YhOKjJpznpULN9KSkZji30phb6 U0sPUVGW9xTcrhYcW+lML/Sms2fSmbvpU2KSH7qBzUe7NPXvSaE0WEHWraDrVRO9XEPWt4Il llB1qTbUanrUw5pyiBGVxUTCrDVAx6VmojuVX7VTkA4q456VTc9KmQFRhTelPbtTDQi0OU1K rVBmpA2PSmMsBvcVIG+lVg30p4f3FKwmi0G+lO4Peq6t9KkDfSiwJFe5jDbeveuQ1O2B8rhu /wDSuzkGcVzuoQ58vg9/6VpDRm0HZnlOq2uPK4fv/SuPuY8beD3r0fVrf/U8N/F/SuDu4vud e9erQldHpfHTOfkXp1qqwxir8qdOtUmB44ruR5E1ZkJFNNPYUyrMGhaKKKZmRmrSd6qmrKnG aAJqUDNIKcBQA4DNW7dM7uD2quq9etaFomd/XtUTOnDK7Z0um2+fN4bt/WvTtIt8edw38P8A WuB0qL/Xde39a9O0pMeb17f1rzMQz2KukEjrLKPG/r2/rWsi4zWfa/x/hWkvevLlueTPceKS lorSKMhtGKdimmqYDTTDTiabWD1YxMUxhjFS1G56VaiBE1REinO2MdKhJpN2AC1Jye1KFJ7G p0gJz8rflS1YFcRk9jUiwk/wt+VXUtl5zuqYQqPWrUGxXKAgP91vyp/kezVe2D3owKrlsBQM OOzUhi9jV7aKDGD3NNMDPKexphX61faIcdarvH04NXy3EUzTM4qZ0PHBquw6VzzVig3e4pPN x6VETURf6VCRaRbE3utSrMOeVrL87/dpyz4zytXyl+zNgSD1FOzWck/XlatCXPcUXaM3CxP1 puKAQe9Oq1K5AzFNIqQimEUnG4ETVERUrCmEVm1YpMrsKgkXp1qywqFu1NGiMa7jHyde9cve wZ8vhu/9K7C4XO3r3rn7qHOzhu9dFN2Oqk7HB3tvjy+G79vpXN3UP3OG713d5a52cP3rl7u2 xs4bvXo05He1zxOSmTG3g1VAxWrcxY2cHvWbt+tdsGeNiI2G4prVJtFMIrQ4yo/aoKsuMYqt QBLS0gpaAFFOUZzSYxTk70mVEmjHWtCBfvde1Uo161pW6/e/CokdVLc6LTo8+Zwe39a9M0eP /Xdf4f6153po/wBb+H9a9M0jA87n+7/WvNxB6lX4Edparjf+FaINULcj5ufSre/6V5kkeXJD XPSqzt06VI79OlVnbpSSBREZ+nSmb8+lRs30pm41XKVykpf6U0mmZoyaVhWJAalQ9ahHNTKK lkNE6HGaso3XpVRTUyt9KqMrEWL6P16VOG+lUUbryKsB/pV84rEzN9KrO2MdKez+4qrJIOOR UOQ7Ebv05FVHfp0pZJOnIquz59KjVlJATmo80MaaTVJFJDs4pc0wHNBplEm/6U8GoakWnYdi wrVMpzVdT1qde9FgsOYVkXsf+r69/wClbNZt4M7PxpocTzzVoh+56/xf0rz2+T/V9e/9K9K1 YY8n/gX9K87vwP3f4/0r0cOelS+BnMyr061RcdK0ph92s5+cV6UTy6u5WNRmpDUZNUczYtFF FUZkZqwKrmrAoAnFSAZpi1ItAE0YzmtOzXO/r2rOiGc1rWSZ39e39azqHXhFqzs9KTHnde39 a9H0w4838P6157pgP73g9v616Bp5I8z8P615tc9er8J11scbvwrUQ5zWNbv97p2rUjbOeleZ M8qoWRS00GnVpEwEppp9MNUwIzSZpTUZOKxasMcWx6VA79OlI8gGORVd5enIocwsKzdKRUz2 NIvzZq9BADuzuqErsBI7frkMKuKgXOM0oGKcBXXTpkthilxS0V1ciSENNMJpxNVpZ1TGGXn3 rjrS6IpIl3D1FKG9xWa17jHMf50q3hOeUrBJmnIaWaY0YOOtRJPuzytThvcVpGo1uQ42Kckf Tg1SkTGOta7rnFUJUHHWnLURmOOlVXOMVdlGMVQlOMVitGaRZXd+nSmed7rTJD0qvuNdMFc7 IK5pRzZz92r8cuc8iseInmr8ZPPFTNETRqLJ15FThh6is5X69KsLJ15FY2tscziWSaaaYHz3 FLmtIyM2hGqM1JmmkZqJjRAwqBu1WWqu46UkaIoTDO2smePO3g962pF6Vnyp061rFm8XY5i6 t/ucN3rmL62/1fD9/wCldxcRZ29e9c5fQ/6v73f+lddOep3UZnn95DjZw3esRlxjrXU30WPL 4Pf+lc5KvTrXp0nc5MdCyf8AXYqE4ppOaVu1Rk1ueUQSdqrVO56VXoAmFLSCloAdTkHWminr 3pMqJai71p2/G78KzY+M1pQfxVLN6b1On0848z8P616NpEg/fcj+H+tea2L48zp2/rXd6TNj zslf4f61xVo3PZh78LHolvIPm5HarXmfSse2mzu5XtV/fn0rzZQscU6dh7P06VCxPFO600j2 rOxlaxDjNJtPoaseX9aURZ9alslyINvsaMYqcx47GmlfrU3JuNUdalC00CpRQIDkUK+PShj0 qAtjuKVgUbl1ZevIqYT47rWWJv8Adpxm91qlApUzQM/utVpJQccrVcze61A0vTlarkK9mPeT p0qPcTURkz3FKH+lHKHKS5phNLk0hpWFYUU8LmmgelTqmfWkK5Ht+tOGfSpSn1pNuPWmmNMF 4zU6N1qAZHangmnYqxZzWfdn7n41c3Y9KzrtvudO9XFFwjqcRq5/1P8AwL+lee34H7v8f6V3 2rN/qen8X9K4G9H+r/GvToR0O9+7TOcn521myDGK1J1+7WbIOldyPGqPUqN2qKpW7VGaoxYt FLSUyCM1aTvVU1bQZzQBKtTCogKmFAE8Q61tWK/6z8P61jRd62rI43/hWVU78CryZ22lj/W/ h/Wu7sTjzPw/rXA6YwHm8jt/Wu5sX/1nI7f1rzKzPTr6HU25+9+FasTZzWNbN97p2rXh/irz pnlVC2pzmpc1CvepK0gYDqaaWmmqAjNVXfGORVh26dKzZ5cbeRWD95lxjcjkn6crVfzs91qr LP05WoUnznlavkN/Zm9bDdu/CtdVC5xWNZODv5HbvW0DUx0ephJWHilpoNGa7IzSMx2aQmkJ pCaUqoJEM04Tbhl59TWLPcE7fu1auGJ28etZ5iJ7N+Vcbd3qaR0IWkPtUiOeeKcLbPZ/yqwl r14f8qrmRrzInhc/NV9WPNV4rfGchqtBPrUtomTTH5qlNzirmKoznG38aSkzOxnTH7vIrMmf 7vSrk7n5elZsmTjitIxub06ZXf5sU1YyexqYRE/wt+VW4rUnPyv+VdC0OtLlRHFAeeG/Krwi I7NVqKzxn5ZPyqZoOnDVzu8nocVSpqUsEdjS7qmMZHY1CU+tS4tbmakPWT6VKJPUiqp4pQ30 pWuVZMugg96DUCydelSB/pUu5LiI3aq79qnJqI00Uiq6g461RkXOOtaLgcVRl7VrE1iZE6/d 61z18mfL69/6V0c/8P41gXv8H41001qddJanEX6f6vg9/wClcxMuNvWuuvwP3f4/0rlp1Hy9 e9epRJx/wP8ArsZDjpUDnGKsydqrOOldJ4xWfnFRVM4xioaAJhS0gpaAHCnL3popyd6BouRd 604f4qzYv4q0oe9SzWJuWjff6dq7LTZj+9+72/rXD27Y3dO1dXp03+s5Xt/WsJo9XCyPRLOX O/le1bCHOa5rT5c+Zyvb+tdHCfvV51VGlaNicDNPC+xoUVMF+tccmcE2NVPrUnlAetSKvXrT 9v1rLcwbKzJ9agdcYq6y4xVSXjFJ6CRDnFShvTFVi2PSlEuO4q0jRIld+nSqbydORT3lzjla ovLnHIq4xNYxJTL6EUGXPcVU8z6Um/6VvGB0xploy/SojL7ioTJ9KhaQj0q+Qv2ZZMvuKmV+ vIrMMvuKnjm68rUOBEqZphvcUZqssuc8rTxJ7isnEwcS0nerijrVGNs56VejPWsZGEiXZ9aa yfWpV70EUkyUyuV9jQBUpFJt+taJm0WMNZt4fufjWo/asi+bHl8jv/StYm0Nzh9Xb/U/8C/p XC3n8H412mqtnyeR3/pXFXRzs/GvSo7HbU1gYUw+7WZJ2rVmH3azJR0rtR41RalFu1RNUzdq iNUYNC0lLSUyCM1dQYzVI1eTvQBMtPFNFOFAE8Z61rWrgb+R2rGQ9avW8mN3I7VnUVztwcuV s7fTrjHmcr2/rXcafcA+Z8y9u/1rzKzuMb+V7V2unXWfN+ZO3f6151aJ69Rc0bnodpIDv5Ha tyBs7ulcpY3GfM5Xt/Wultnzu6dq8yorHk1Y2NNe9OpinrUlVHY5xM0hakJqJ36ciplIaRDN JjHIrHuJcbeR3q9PJ93kVjXEn3cEd6unA7KUClLL06VXWfrytRzSH5elUjMR6V08h1+zOssr rG/lO3eukimV84ZT9DXAW11jdynauks78jfzH271z1IHHVpnRg0ZqvHMrZwy/ganFZKT2ORq w6mkZpaXFXuIz3hzjhqYLb2etHYD60vlgetNU7hcorb+zVYSEc53VPiih00guN20uKWilyoB hFUp0zt4PerxqFkzjrWezLizDktmOPlf8qrmyY/wSflXQeT7NTxbg/3q1U7HQqqRgx2HX5ZP y/8ArVpQ2Sjdw9XxbKP71SrHj1ptylsiJ17kawLzy1I1uDj71WAKdXVRpWWpytmVJb9OGqnJ D04at54w2OtUpYOnDVc6aaBMxmT61C3FaEkOMcNVORcY6158o8rNYSGCQ+1SLJ7iqjMRSpJn OSKLG/Lcuhs+lMpiv15FONTYi1iKTtVGU9KuyHpWfMw+XkVpFGsEZdwfu/jWBeN9zp3rbuH+ 7yO9c7eyfc6d666aO6jE5i+OfL/H+lcxNj5a6O8bOzkd65uYj5ea9KluYY93g/67GTJ2qBqn k7VCa6Dxiq69KhxVp16VX2n0NADxS0gpaAHUqd6SlXvSZUS7D3rUh/irMiPWtKE/eqWbxRpQ nG6uhsZceZyvb+tc5GcZrVtZdu/p2rKZ34fRnommTg+b8y9u/wBa662cHdyO1edaZdAeb8yd u/1rtrK4Db+U7d68+sdWIOhQZzU6r9arxMDnkVcUDmvOmeVMcBTsUoxSE1KMSJ+1Upv4auOe lUZj92jdjiii7dOlQ+bjutLK3TpVF5OmcVtGJ1QjcmefpytVXl6crULzdOVqu0vutdEIHVCm WvM9xSh/pVMS+4p/m+4roUDqVMsF/cVAz9ORTWk9xUDyZxyKvkK5UOaX3WnJPjPK1RaT6UwT Ef3aTpicEzcSfrytWFl68rWIk/XlavRSE56Vzzgc06ZswueelaUR61kQN96tSI9a5JxOKpEu qetLmmKaXNZWMbDqSkzTC49RVo0ihsrgY5Fc9qFxjy+V7/0rTubjbt5XvXJale/6rmPv3+ld FONzqpQuznNTnB8rle/f6Vydw2dvTvWvfXG7y+V7/wBKwpmzt6V6VONkdNZ8sbGdL2rMl7Vp ydqzZh92ulHlzRQftULVO/aoSM1aOaQUlLSGqMiM1eTvVJ+1XU70ATA5p1NWnUAPBqWJ8Z6V AKWNutTI2ouzOhtJvv8AK9q63T7nHmcp2/rXDW0hG7p2rpbKb7/Tt/WuapG57OHqX0Z6dptz nzeV7d/rXYWkv3+R2rznTLn/AFvK9v613FlOP3nK9v615FeNjmxMLM6ZH68ipN3uKpJIOeR+ dSNMBj5l/Oufm6I4UrkjyAY5FU5ZsY5Wo5LkcfMn51nzXQ+X5k/OtYU2zpp0bjpp87eVrKmk +7yKWW5HHzJ+dZ8s+ccrXZCnY9CnSsV5m+7yKoM+MdKnkfp0qmx6VvyHRyotxTYzytbVrdEb +U7VzKSYzyK0YJ8buVrnqQOarA7W2vPvcp2rYjuQ2csnHvXF291975k7d62Ibz73zJ+dcE1Z nmVInSB1P8Q/Onbge4rIS7zn5k/OrUdx15X86qLuYF+jNRpIGzll/On7h6it72Qhc00mml1H 8Q/Oqsl1txylc8pt7FJXLPmL/eH50eYv95fzrKa8PHKUi3ROfuVNmXyGvuB7inYqnHLuzkir inNVFczIegoFPC/WgCpBXo0MOiGxu360Yp9Ia7JUoom4yig0ma5pOwxaYyhsZp1Jms3MZSlh 6cNWZNF04at5gG61Qnh+7w1cdZ3KRzcvGKgWTHpV26jxs4Pesln246UQV0dsFdGgkvXlan8z 3FZKT9eVqYXHutDiEoFmSTOORWbPIPl5FSSXAGMFfzrLuLj7vK96uETSnEp3M33eV71zd7P9 zle/9K0bq5xs5XvXOXVxnZyveu2nE76asrmVeS/c5HesCVs46VpXUudnK96x3bOORXbTWp5m MleLKj9qjxmpSaQCtzzCBk6dag2n3q8y/WoNv1oAqilpBS0AOpU70lC8ZpMqJdiPWtOE/erJ jOM1pQt97pUM6YGpGetXY3256VQjPXpVlTWbOum7M6Cyu9u/lO3Wu206+H735o+3f615pDLj dyK6Sw1Ar5nzR9u/1965KsbndJc8T1S1uQd/zJ271rJIDnkVw1jqQPmfPF27/X3rpIL4Hd88 f515lSLPMqwaNwN7imlvcVUW6Bz8yfnSmcf3l/OsbHPyj2bp0qnIelSmUeq/nVWSQccitIxN YxKEx+7WZM+MdKvzuBt5FY08oG3le9dVOJ2UoEMk3Tlarmb3WoJZunK1B53utdkIHakorU0B L7rThN7rWcJvdacJvda2UTKVYvGb3WoWm6crVUze61C83T7tXymDrlhpenIqLzPcVWabpytR +b7rScRxramnHJ16VqW753dO1YUUg55FatvLjdyO1YTidd1NHRW5+9+Fa0XesO3mHzcr271r RTDn5l/OuCpE4KsDQU0pIHeq6zD+8v50jTD+8v51jY57EzP9KqyTAY5X86jkugMfMn51l3F6 Bt+aPv3qki4xIr28x5fzJ37/AEri9RvM+Vynf+lad9ff6v5o+/f6Vyd5cb9nK8Z6V20YnfRj bUoXEudvTvWdIc4qxI2cVVc9K74o5q0rspynpWdL2q/KelZ0p6VaOWWxTftUJNSuelRGrRyy CkNLRVGQxhnFXE71TaraHrQBODmnUwHFOoAcKbH3pwpkfekzSmacDfe6dq37OX7/AE7VzkJx u6Vs2r438jtWUkd1GdmdxplxjzeV7f1rubC5/wBZynb+teaWM+PM5Xt/Wuvsr7HmfNH27/Wv Or07nfVh7RXR3iXQ5+ZPzpsl50+ZPzrAXUP9qL8//r0yS/zj5o/z/wDr1zQoM5qeHdzSmvOn zR/nWbJeHjlKoyXZbHKVUe46crXdCjY6/cplx7nOOVqs8/TlaqtP05Wq7TZxytbqBnLEFl5u nK1A0me4qu03utRGb3WnykqtcseZjuKniucZ5T86y2mHqtM+07e6VjOFzpUlNWOphvAN3zR/ nWlFfYz80f51xMd/jPzR/nV+LUevzRfn/wDXrhqUjjq0TuIr7Ofmj/OtCO76/Mn51xMOofe+ aL8//r1rQX+d3zR/nXI04s4JwaOwjuuvKVY+1e6VzcN5975k/Ori3JPdaHJsjlNJ7jOOVqo8 hOOlReYT6UuM0loWtCEsfSnITzSlPrSqmM9aq5fMjQgb71acZzmsqLjNaULfe6U6S1MJFoU6 mCnZr1acrIzY4mmk0E0wnFVUqgkKTTdw9RUTP9KZv9xXDOoVYsZpM1Er/Sn5rGVQdhc1G43Y p9FYybYzAuovucN3rnblNu3g967C5iB29e9c3eQgbPvd61pSOujIwmmK/wB38aPteO6VBcjb t/Gs55yuPu12KFzuUEzTe76fMn51l3F393lO/eq0t305Ssue7zt5TvVxpmkaVhLq6zs5TvXP 3M/3eV71PPc/d5Xv3rFnuPu8r3rphGw6k0lZFaeb7vSs1nz6VLNLnHSqe7PpXTBHj4iV0KTm pAaipymtDiJc1BTycVDmgCqKWkFLQA6hT1opAcUiolmM9avwt97pWah61cibrUs3gzYibryK uKetZ0TdelXkPWs2dkSdWxV2C5Kbvu8+tZ9LvI9KzlG51U6ltzrrPVCu/wCaLt1P/wBeumtt W+/88Pbv/wDXrzSK5K5+7Wrb6kw3cx9v89a5alG5cqcZrQ9Qh1QfN88P5/8A16tLqKn+OL8/ /r159Bqx+b5ofz/+vVxdWP8Aeh/P/wCvXG6NjllQaZ2324f34/zqJ7wHHzx/nXKf2t/tw/n/ APXoOq5/jh/P/wCvTjTCNJmzPeA7fmTv3rEuLsfLhk796qTannb80X5//XrLlvunzR/nXZTg dsIqKuy3JcDjlfzqD7R7rWc930+ZPzpn2jPdPzrqjE5qla5rCf3WpBL7ispbjOeVqws2c8rW iRySncumX6VA03TlaiaX3Wqsk+McrVWMXImafpytM8/3Ws+S46crUf2nHdKGhxmb0VwOeV/O tOK6Az8yfnXKJd4z8yfnVtL/AK/NH+f/ANespRO6jWsdrDqAG754vz/+vWjHqI5+eL8//r1w K6j/ALUX5/8A16tx6qefmh/P/wCvXJUpnVKKmro71dRHPzxfn/8AXpG1EcfPF+f/ANeuOTVc 5+aH8/8A69OOpn+9F+f/ANeuR09TkdLU6ObUQNvzxfn/APXrFutR+580Xfv/APXrLm1InHzR fn/9esue+LbeY/wrSFM1hSJru9LbOU71iySZx0pZJy2Pu1VZunSu2ELG05qKshrnOKrSHpUz HpVWRunSt0efJ3ZTmP3aoSnpVyVunSqEhziqRlMrv2qM1K/aoqtHLJCmkpTSVRkMarUfeqrV aTvQBKKkqMU8GgB4qND1p2aiDUmXAvxsOeRWnby/e5HasRH69KvRS4zytS0bxlY6m1uMb+V7 V0Ftebd3Kdu9cTBdY3cpWvDe9fmj/OspQudtLE8qO0S/PPzR/n/9en/a890/OuZjvuvzR/nV lb0f3o/zpKmXLFaaG0bnPdPzqIzZ7rWaLr/aT86X7R7rWiickqzZcMvuKhMv0quZvdajM3ut OxnzsnMvuKhaX3FQNN05WoHm6crSaLjImefpytVnuMY5WoHm6crVN5+nK1DidMKti99qI7pU 8d+RnmP86wzP7rTRckd1rGcLnbCamrM6+HUPvfNH+f8A9eti31D73zR9u/8A9euCivCM8pWx a3v3+Y+3euOdEzqUD0S2u87vmTt3rYhl3bs4ri7K6z5nKdu/1rprefO7le1csoWOKcLG4jZz VgY9azkmAzytWVl68rWD3OWRawvrRgetQ+Z7inb/AHFJiuWkPWrsL/e6VnI/XpVuJuvStISs I1QadUMZJzUtd0JkNATUUhIxUpqN1zjrU1GwRVZ/pUYf6U6QEY4quWIrjkWWVbryKmV/pVMO PUVIJvdai4FvcPUUhYeoqr54/vL+dMN0B/En500Uotksm04yawb2Nf3fPr/StCW7HHzR/nWP d3IOz5k7961p03c6KNKVzm71R8nXv/SuenYjb+NdBdvu2cjvXPXA+7+NenThoerG0FqZk0mN vSsmefG3le9XbhsbenesK5l+7yveuhQMKlcrT3H3eVrJmmzt5WpJpvu/drPkk6citFE4p1rj ZHzjpUSnNNLUJ3rRI4qkrj6eKaKetUZCsKixVjFRYoAoilpKWgB1IKWkFJlRJVOKtRnrVUVM h61LNomnE3XpWjG3XpWTG3XpWjE3XpWbOuDLoNNahTnNKaEVN2GgkVOkpGelVzSBiKHEUKzT NNLkrn7tTC/Yf886yfM+lIZT7VlKnc7Y109zZGot6x/5/Gnf2kfWL8//AK9YHnH/AGaT7Qf9 mo9kX7SBsPfk45j/AD/+vVV708cx1mNcH/Zqu8+McrWsY2OerWujUN57p+dKLv3SsQ3B9VpR c47pWyR58pnRR3I5+ZPzq4lyDn5k/OuZjuuvKVbju+vzJ+dOxHMbzXI9U/OqUtyOOU/OqJvP 9pPzqrJdZxylAmyy9105SoDde6VQefpytQmYn+7TJuawuvdKlF4fVKxBN/u1KJz/ALNJo0jO xtC8J7pUwvSO6ViLN9KlWXr0rKUTuo17G/HfNzzHU324+sdYSzdfu1L5v0rB0zs9pF6mk90W x9yq5l9xVXzc+lG76VpGmZVMQlsSls+lNJpM01q1UbHFKo5DHPSqkjdORU7t0qlK3TpQK5Wk bp0qox6VNI3TpVZmqkjKTI2NRmnmmGqRhJimkpTSVRkMarKd6rNU6mgCxmlpimn0AKDioQal qEUDRKpxmp0kxnpVUGnhvpUl3NKObrytXo7rGeUrER8Z6VOspGelBVzoUvDzylWVvOvKfnXO LcHn7tWEuTzytAmzpFu+vzJ+dTC7/wBpPzrnVuuvKVKLz3T86ATN77UP7yfnTTde6fnWJ9sz /En50n2z3T86CjXa4HHK/nUD3HTlazWu/wDaT86ja66cpSKTLjz9OVqq83TlarPcZxytQNN0 5WlYtSLDTe60zzfpVUy/Sk8z3FQ0dEJ2L6TdeVrStrkjdgr2rn1l69Kuwz7d3K1nKJ2063c7 7T7zHmfMnbv9a6q3vPvfNH2715tZ3mN/Kdq6W31D73zR9u//ANeuKpSuFSlzbHbredfmT86u JdZzylclHfZz80f51rQXJbd93tXJOlY4qlCx0KzZzytThs1nQsTmtCMda52rHM1YsoetW426 9KpqKsxnrTjHUzZsQnO6pwKqWzD5uR2q3ketdtOy3JYtNNBYeoqJ5kGPnX86dWoktAimyKVV 45rMklC45Wp5rxfl+eP86wri9Hy/NH371zRpSnuddPDykXWvAP4k/OozqGP4o/z/APr1hyXh 4wUqqbxj/crphhTrjhUtzoW1Lp80X5//AF6rPqJ45i/P/wCvWGbkn+7UZnP+zXRHCou1KJrS agxx/q/8/jVGW6LYyUqk05/2arvOTj7tdEaCREsVGPwjppfu8rWNcyfd5HerUs3Tlax7m4+7 yvetlA5p4lszLuT7nI71zl1J9zkd61buf7nK96565lzt+73q0jmnVuUZX6dKpselSyNnFQkZ qrGLkNPNOj702nxjOaZLHinqaYKUUCJd30qLNDN9Kh3n2oAgpaSloAdSClpopMaJhT1OM1CD TwaRtFl6NuvStCKTr0rJjbr0q9E+M9Kho6ISNZH69Kkzmqcb9elWVbrUo1lqKaYaeaiY9Ks5 3uBb6Uwv9KaTTC30pFpsGb6VEX+lDN06VET70WDnYrP9Kru+cdKcxqFj0ppESncY0h9qb5h9 qa1R5qjBssrNj+7UyXBGfu1QzTg59qYrl83J/wBmo2nP+zVXfn0pN30oC5K0n0pu/wClRbjS 5oAmDfSpA2KgBqVTSGmWA1TK3XpVYGpA2PSk0aRlYtK/XpUok+lVA2PSnhvpU8pt7VlpX+lS qc1VU9anU9aqxk5tk2aRj0pN1MdsY6UmVEikfp0qhI3TpVmRunSqMjZx0pItsru3SoCaexzi omq0c8mNJpKDSUzNsUnNFFIaZA1u1SKetRMc05e9AFlD1qQGoFOKkBzQBJmoBUwqEUDQ7NOB plKKRRIDTw59qiFOzSKTJt/0qQSn2qrmjcfagRd88/7NO+0Ed1qjvPtRvPtTEXftB9Vo+0n1 WqO/6Um/6UDuXvtB/wBmkNwT/dqlv+lG/wClILlozf7tIZM+lVt30pd30oLTJt59qN30qLca Cak1UiYP9KnSXryKo7vpTw59qVi1Usb1vc43cr2retrr73Kdq5CGX73Stq2m+9yvas3A6qeI Z19vcn5uV7V0tm+d/TtXG2rZ38jtXV2Lj95yO39a5K0dDqn78bnWW/8AF+FaijrWPbyAbuR2 rRW4Az8y/nXlNO55E4u5cUipFkAzyPzrNa8Axho/zqB9Rxj5ovz/APr1ahKWw40JSOkiu1XP zp+Jq2b1P78f51xY1M+sX5//AF6mGps3eL/P41vHDSZ0xwfc6aTUQMfPF+f/ANes2bUs7fmi /P8A+vWLJescfcqq9wTjla6aeES3NlTpU9y/LfM2P9XWXNcZxytRyT4x92qbyZx0rsjSSIni 0vhHtLn0qMyfSo80oXNbKCOOeJkx240hagA+hpCMVVjB1WyN3xjpVSSUjHSrSwT3H+ohkl29 diFsflVCe1vV25tJh9YzTsQ5Mpzz428rWNc3H3eV71cuUul25t3HX+A1hXPnfLmI9/4TTsK5 QuZ87OV71jzB+PkP5VbnMny5Q/lTZW3Y6UCuZLK3Hyn8qUxD3q2wzioWoEQGJR3NIBt6U8nN M60AOoopCcUAMZsYqHNSOelQ0AMWn0xacKAHA0gpRShR70DQA04Gk2in7R60rDUh6HGauRN1 6VTAxVqPHNJo1jUSNGNuvSraN16VTiVTnmr6RrzyfzqOVm3t4CE1CzdM1c8iP+8fzqB4U45P 51XKyHVgyoz/AEqMvn0qR0UY5qBgB3o5WCqxEZvpURankA96ZtHrRysTqxI2NRMelTMo9ajK D1NOxDmiuaZVgxj1NMMSjuaZm2Q5oBqTyx70nlj3oFcZmlpfLHvTgg96B3GUZxUnlj3pfLHv QFxoNSA0giHvTxGPU0WHcepp+76ULGvqaf5SeposHMhA30qRWz6U0RqO5qRI155NKxXOiRG6 1ZU9abHbxnPzH86trbRnOWb8xRYOdEGajdunSrjW8Y6MfzqtJEgxyfzpcrNFViihK3TpVF2P FXpVXjmqD44xRyidVMgJqM1KVFMZRVWMnJEZpKeVFJtFBNxKSlpKYiM05aaaevegCQVKneoh Uqd6AJBUNTCk8oe9A0RinCpfKX1NHlL6mkO5EKWpfKX1NHlr6mgLkWaM1L5a+ppPLX1NAXIs 03NSeWPejyx70BcjzSZp+we9J5Y9TQK43NLml8se9KEA9aB3EFKDS7RRgUWGpBmgmjApMClY vnQmaeDTcClCj1osLnRZjbr0rXtZPv8AI7Vjxgc81rWyJ82W9O9JxKjUSOns5fv8jtXUWUuN /K9q5axji/efP6dx7111jaQN5n7w9u496xnRcj0KeNpJWkb0NzjdytWDeY7pSW+mwvu+aTjH Qj/Cr66FbvnLTcehH+Fc31N3EsTh76mRJetxjZVV7ljj7tdEfDVr/fuPzH+FNPhiz7yXH/fQ /wAK3jh7FPHUl8JzYuD/ALNTJdE5+7W6PC9if+Wtx/30v+FWYvCdhz++uP8Avpf8K2VOxzTx jl1MHzif7tMLn2rs4fBumtuzPc/99r/8TU//AAhGmf8APe7/AO+1/wDiatROaVVs8/kbOKrk k16HJ4I03jE93/32v/xNQnwTp3/Pa7/76X/4mnYzcrnCAVKFru08D6ac5mu/++1/+Jqb/hB9 M/573f8A32v/AMTTFc4z+x9T/wCgdd/9+G/wqCXTNRXGbC5H1hb/AAr15nC4yQM+prAv79U8 v54u/U/SgRxfh4zWf2nz4jFu248xSucZ6Z+tQX2oD9388Xfv9PepNR1Ufu/nh79/p71xl9qh Pl/NF37/AE96AHXupZ8v5ou/f6e9cvdXpOzlO9Jc3pbbynesmSYtjpQAyaQtjpVM5NTHmmMv 1oAgIqBu1WW7VVbtQBCaaDinE02gB1NJozmmk0ARsc1Dn3FSMah4oAVacKatOFADhThTRThQ AtOWkAzSgYoAcKsRnrVcVMnegDRifryK0I369KyYj1q/G3XpQBe3fSonPShXz1xTWOcUAVH7 VWY1ZftVZqAI9xp2R60zBooAQ0w04mmE0AFManUhGaAGGgilpKAEApaKKAFpwGKbT6ACnCkp RQA4Gn7vpUdLQA/dUyt1quKmXvQBdjbrVxW+lUUOM1YVselAE5b6VTlbp0qUv9KqSv06UAVJ W6dKpNVmRs4qq1ADTTSM040lAEZpKU0lADaSlpKAIzT170w05W60ATCpRUKmp1oAeBT1pAtP xQAUU7b9aNv1oAbRS7aMUANpG7U7FIRmgCM0UuKMUANxRilooAQjFJSmkoATNGaDTaAFzTN1 BOabQA7dT930qKjNAFpXxnpV6GYjd92spW+lWUkPPSgDrbK7x5nKdu/1rs9NvB+9+aPt3+te Z29zjdyvaun0+/2+Z80fbv8AX3oA9csJ1bzPmXt3+tdJAAd3Ned6bqQ/e/PF27/X3rtbK+Vt /wA8fbv9aANnygfWoWj6cGrCShs8rx7051Bx1oAoEYqaNjzSMnTrSLxQBqwP97kdq0EbOeRW LDIeelaUUmc8igCcjNRFcVNTGwO9AApx3pWmUfxL+dVnmxjlazbm/CbPni5z1NAEt7qIXZ88 XOep+nvXD6pq/wDqvng79/p71JqWrf6r54e/f6e9cHqOp7vK+aLv3+nvQBHqGqlvL+aHv3+n vXMXF6W28p3plzeFtvKd6zHmLYzigB0kxOOlRU3OaeMd6ADFMY4xUpbHcVWkcccigCF2xjpV Vm6U95M46VAzZ9KAGk0lIWHqKZuoAdu+lNY9Kbmmk0ADGoKkLVHQA9aeKYtOFADhThTRTqAH LTqatPFADhUi96YtPXvQBYQ9ato/XpVIGp1b6UAX1fr0pxNVlf3FSb/pQA1u1V2FTk1E3agC HFRmpjUDGgBpNMJoJpmaAHbqdUdOWgBdopuPan0uKAI8UYpxGaQjFACUoOKSigB+acKjBxTg aAH0opop4oAcF+tSgU0U8UATg1Kr+4qvnHcUu/6UASM/TpVaR+nSnGQ+1V3bOOlAELnpUBqV u1RmgBhpjdqeaY1ADTSUtNNACUlKaSgCM03NONMoAnVutW071Sj71cjPWgCyFqQJ9aatTigB uz60bPrUtGKAISg96YRipyKYRQBDikIqTGabQAzFN21JSYoAZtpCPSn0lAEdIRUm0U3FAEeK QipMU00AREU2pSKiNABRRRQACplb6VCKctAFyOTGela9reFd/KdqwQcVZilIz0oA9A0/VCvm fND27/X3ruNO1f8A1nzwdu/1968dtbwrv5Tt1rqbDVCPM+aLt3+vvQB7VZ6iH3/PFxjof/r1 spMrZ+ZePQ15lpur/wCt+eHt3+vvXX2uog7/AJ4u3f8A+vQBuPjjmoC2O4qP7UP7yfnUD3A4 +ZfzoAuxy4zytaEM33uVrnhdf7SfnVuG7HPzJ+dAHSLKOfmX86hlnC4+ZfzrPW8H96P86pXW ogbfni79/wD69AEt1fhNnzxc56muXv8AVh+7+eHv3+nvUGoasB5fzw9+/wBPeuKv9Xz5fzw9 +/096AJtR1XPl4eE9e/0965C7vC2zlO9Jc6gW2/NH37/AP16xpbgtjlaAFll3Y5FV2b6Uxnz jJFN3/SgCcMPUU7djuKrhh6ihpPpQBK8mMciqckvTkUSS9ORVR3zjpQANJ06VEX+lNLZ9Kbu oAdmkzTd30ppNADi2PSmlvpTS1NzQAE02ikzQBMtOplPoAXOKcKbTqAHU8Go6evegCQGpFPW ohUgoAlFSK2M9Kip4oAnDfSpN59qgTvUlAD9+fSmk0lFADSartUzVC1AERNMzSmmmgBQaeDi oxTxQBIKWkFLQAhooooAZTSaU02gB2actMFOoAeDUgNRL3p4oAmU5p+6o1706gB5am7/AKUl MoAcz9MYqEmnGmGgBjU3GaWkoAjao6laoTQAhpuc06m0AJTWp1MoAQ1GKkNRigCRDjNXIz1q mtWU70AXkbr0qcMPUVUTvU4oAnzS5qMUtADjTDTjUbUAJTaVqaaACkJoNJQAUUhooAKZT6ZQ AlNp1MNACGozUhqI0AFFFFACilWminrQA+nq2KZS0AWo5SM9K1rW6I3/AHO1Ya96v2/8X4UA d1p9+R5nMfb+tdpY6gf3nMfbv9fevNbD/lp+H9a7Kx/5afh/WgDtUv8AOfmj/OkkvM4+ZPzr Ki705u1AFs3uP4o/zqeK/wAZ+aP8/wD69YbVLH3oA6A6lj+KL8//AK9ZF7qp/d/ND37/AE96 jbtWJefwfjQBmahqjHy+Yu/f6e9cld35OzmPvWlf/wDLP8f6VzFx/D+NAEclyTj7tVjL9Ka1 RmgB7SfSkD/Somp1AEofHpTWk6dKbTG7UAMd846VXZulSP2qBqAEzTdxpKKAAnNJmkpDQAUl KaSgBKKKKAP/2Q== --part-725WpPZCmosQAttp49BFulqr13FBABwp21WsCXZBDKTUP-- ========================================================================= Date: Tue, 31 May 2011 17:39:25 -0400 Reply-To: Mobin Anandwala <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Mobin Anandwala <[log in to unmask]> Subject: Simulation of bilinear state model MIME-Version: 1.0 Content-Type: multipart/alternative; boundary=001636284801e8cdcd04a4993c86 Message-ID: <[log in to unmask]> --001636284801e8cdcd04a4993c86 Content-Type: text/plain; charset=ISO-8859-1 To SPM experts I am currently working obtaining z states for given data (this data is from DCM_MM). I have a discrete form of the bilinear state equation as follows zk+1 = (A*dt+ ukB2*dt + I)*zk + C*uk*dt where uk is the sampled version of the input (the input function being u2 based on the data as u3 was 0 and B1 and B3 were 0) I am trying to z as an m x n matrix with m rows being each z state scaled by dt, 2*dt, 3dt,...m*dt and each column being each state of z (z1,z2,z3...zn) I My main question is that is there a simulation of the bilinear state equation available that looks at each state? I was unable to attach my m-file Thank you Mobin --001636284801e8cdcd04a4993c86 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable To SPM experts<br><br><br>I am currently working obtaining z states for giv= en data (this data is from DCM_MM).=A0 I have a discrete form of the bilinear state equation as follows zk+1 =3D (A*dt+ ukB2*dt + I)*zk +=A0 C*uk*dt where uk is the sampled version of the input (the input function being u2 based on the data as u3 was 0 and B1 and B3 were 0)=A0 I am trying to z as an m x n matr= ix with m rows being each z state scaled by dt, 2*dt, 3dt,...m*dt and each column being each state of z (z1,z2,z3...zn)=A0 I=A0 My main question is that is there a simulation of the bilinear state equation available that looks at each state?=A0 I was unable to attach my m-file<br> <br>Thank you<br><font color=3D"#888888"><br>Mobin</font> --001636284801e8cdcd04a4993c86-- ========================================================================= Date: Tue, 31 May 2011 23:59:08 +0200 Reply-To: Bas Neggers <[log in to unmask]> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]> From: Bas Neggers <[log in to unmask]> Subject: Re: SPM8: Errors in Normalization to EPI-Brain Comments: To: Max Hilger <[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]> Dear Max, your problem is very likely with acquisition (check your raw data), and not image processing. Close to air-tissue transitions EPI suffers from magnetic field inhomogeneities, leading to increased dephasing of your spins and thus a fast drop of transversal magnetization. That is why you see it on top of the sinuses and over the middle/inner ear. This is a very well known problem of EPI fMRI. You could consult your MR physicist how to decrease the size of these dropout regions (short TE, thinner slices, SENSE or GRAPPA, etc), but they will always be there to some extent. Nothing much you can do about that. Good luck, Bas On 05/31/2011 11:08 PM, Max Hilger wrote: > Dear SPM-Users, > > we are currently doing a fMRT analysis in SPM8. The preprocessing of our images consists of realignment, slice timing, normaization to the EPI-template and smoothing. > When we checked the results by overlaying the EPI-template with the activation maps after preprocessing in MRIcro we noticed that for all the subjects there seems to be a consitent lack of activation in certain regions. > > The images attached are typical for all subjects analized. As you can see there seems to be a "hole" in the activation in the temporal lobe (bilateral, upper images) and in the inferior frontal lobe (lower images). The size of these gaps in activation seems to differ slightly across the subjects, but the general pattern is pretty consistent. > > It would be very nice if you could give me insight what went wrong with the preprocessing and help solve this problem. IF needed I can of course provide additional info regarding the parameters used. > > Thanks in advance for any input! > > Best regards, > Max Hilger -- -------------------------------------------------- Dr. S.F.W. Neggers Division of Brain Research Rudolf Magnus Institute for Neuroscience Utrecht University Medical Center Visiting: Heidelberglaan 100, 3584 CX Utrecht Room B.01.1.03 Mail : Huispost B.01.206, P.O. Box 85500 3508 GA Utrecht, the Netherlands Tel : +31 (0)88 7559609 Fax : +31 (0)88 7555443 E-mail : [log in to unmask] Web : http://www.neuromri.nl/people/bas-neggers http://www.neuralnavigator.com --------------------------------------------------