diff --git a/.gitignore b/.gitignore index 7183ed4..b89dc0c 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,4 @@ vignettes/powsim_schematic.pdf powsimR\.Rproj Meta +doc diff --git a/DESCRIPTION b/DESCRIPTION index 3f58e0c..bbacdd3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,42 +1,47 @@ Package: powsimR Type: Package Title: Power Simulations for RNA-sequencing -Description: Recent development of very sensitive RNA-seq protocols, such as Smart-seq2 and CEL-seq allows transcriptional profiling at - single-cell resolution and droplet devices make single cell transcriptomics high-throughput, allowing to characterize - thousands or even millions of single cells. In powsimR, we have implemented a flexible tool to assess power and sample - size requirements for differential expression (DE) analysis of single cell and bulk RNA-seq experiments. For our read - count simulations, we (1) reliably model the mean, dispersion and dropout distributions as well as the relationship - between those factors from the data. (2) Simulate read counts from the empirical mean-variance and dropout relations, - while offering flexible choices of the number of differentially expressed genes, effect sizes and DE testing method. (3) - Finally, we evaluate the power over various sample sizes. The number of replicates required to achieve the desired - statistical power is mainly determined by technical noise and biological variability and both are considerably larger if - the biological replicates are single cells. powsimR can not only estimate sample sizes necessary to achieve a certain - power, but also informs about the power to detect DE in a data set at hand. We believe that this type of posterior - analysis will become more and more important, if results from different studies are to be compared. Often enough - researchers are left to wonder why there is a lack of overlap in DE-genes across similar experiments. PowsimR will allow - the researcher to distinguish between actual discrepancies and incongruities due to lack of power. -Version: 1.2.1 -Imports: bayNorm, baySeq, BiocGenerics, BiocParallel, broom, BPSC, cobs, cowplot, data.table, DECENT, DEDS, DESeq2, doParallel, dplyr, - DrImpute, EBSeq, edgeR, fastICA, fitdistrplus, foreach, future, ggplot2, ggpubr, ggstance, grDevices, grid, Hmisc, iCOBRA, - IHW, kernlab, limma, Linnorm, magrittr, MASS, MAST, Matrix, matrixStats, methods, minpack.lm, moments, monocle, msir, - NBPSeq, NOISeq, nonnest2, parallel, penalized, plotrix, plyr, pscl, qvalue, reshape2, rlang, Rmagic, ROTS, rsvd, Rtsne, - RUVSeq, S4Vectors, SAVER, scales, scater, scDD, scde, SCnorm, scone, scran, sctransform, Seurat, SingleCellExperiment, - stats, SummarizedExperiment, tibble, tidyr, truncnorm, utils, VGAM, ZIM, zinbwave, zingeR, zoo -Remotes: nghiavtr/BPSC, cz-ye/DECENT, mohuangx/SAVER, rhondabacher/SCnorm, statOmics/zingeR, bioc::bayNorm, bioc::baySeq, - bioc::BiocGenerics, bioc::BiocParallel, bioc::DEDS, bioc::DESeq2, bioc::EBSeq, bioc::edgeR, bioc::iCOBRA, bioc::IHW, - bioc::limma, bioc::Linnorm, bioc::MAST, bioc::monocle, bioc::NOISeq, bioc::qvalue, bioc::ROTS, bioc::RUVSeq, - bioc::S4Vectors, bioc::scater, bioc::scDD, bioc::scde, bioc::scone, bioc::scran, bioc::SingleCellExperiment, - bioc::SummarizedExperiment, bioc::zinbwave -Depends: R (>= 3.6), gamlss.dist -Suggests: rmdformats, knitr, mvtnorm, MBESS +Description: Recent development of very sensitive RNA-seq protocols, such as Smart-seq2 and CEL-seq allows + transcriptional profiling at single-cell resolution and droplet devices make single cell + transcriptomics high-throughput, allowing to characterize thousands or even millions of single + cells. In powsimR, we have implemented a flexible tool to assess power and sample size requirements + for differential expression (DE) analysis of single cell and bulk RNA-seq experiments. For our read + count simulations, we (1) reliably model the mean, dispersion and dropout distributions as well as + the relationship between those factors from the data. (2) Simulate read counts from the empirical + mean-variance and dropout relations, while offering flexible choices of the number of differentially + expressed genes, effect sizes and DE testing method. (3) Finally, we evaluate the power over various + sample sizes. The number of replicates required to achieve the desired statistical power is mainly + determined by technical noise and biological variability and both are considerably larger if the + biological replicates are single cells. powsimR can not only estimate sample sizes necessary to + achieve a certain power, but also informs about the power to detect DE in a data set at hand. We + believe that this type of posterior analysis will become more and more important, if results from + different studies are to be compared. Often enough researchers are left to wonder why there is a + lack of overlap in DE-genes across similar experiments. PowsimR will allow the researcher to + distinguish between actual discrepancies and incongruities due to lack of power. +Version: 1.2.2 +Imports: bayNorm, baySeq, BiocGenerics, BiocParallel, broom, BPSC, cobs, cowplot, data.table, DECENT, DESeq2, + doParallel, dplyr, DrImpute, EBSeq, edgeR, fastICA, fitdistrplus, foreach, future, ggplot2, ggpubr, + ggstance, grDevices, grid, Hmisc, iCOBRA, IHW, kernlab, limma, Linnorm, magrittr, MASS, MAST, + Matrix, matrixStats, methods, minpack.lm, moments, monocle, msir, NBPSeq, NOISeq, nonnest2, + parallel, penalized, plotrix, plyr, pscl, qvalue, reshape2, rlang, Rmagic, ROTS, rsvd, Rtsne, + RUVSeq, S4Vectors, SAVER, scales, scater, scDD, scde, SCnorm, scone, scran, sctransform, Seurat, + SingleCellExperiment, stats, SummarizedExperiment, tibble, tidyr, truncnorm, utils, VGAM, ZIM, + zinbwave, zingeR, zoo +Remotes: nghiavtr/BPSC, cz-ye/DECENT, mohuangx/SAVER, rhondabacher/SCnorm, statOmics/zingeR, bioc::bayNorm, + bioc::baySeq, bioc::BiocGenerics, bioc::BiocParallel, bioc::DESeq2, bioc::EBSeq, bioc::edgeR, + bioc::iCOBRA, bioc::IHW, bioc::limma, bioc::Linnorm, bioc::MAST, bioc::monocle, bioc::NOISeq, + bioc::qvalue, bioc::ROTS, bioc::RUVSeq, bioc::S4Vectors, bioc::scater, bioc::scDD, bioc::scde, + bioc::scone, bioc::scran, bioc::SingleCellExperiment, bioc::SummarizedExperiment, bioc::zinbwave +Depends: R (>= 3.5), gamlss.dist +Suggests: BiocStyle, knitr, mvtnorm, MBESS LazyData: TRUE Encoding: UTF-8 VignetteBuilder: knitr -License: Artistic License 2.0 +License: GPL NeedsCompilation: no Author: Beate Vieth -Date: 2020-02-21 +Date: 2020-06-18 BugReports: https://github.com/bvieth/powsimR/issues URL: https://github.com/bvieth/powsimR Maintainer: Beate Vieth -RoxygenNote: 7.0.2 +RoxygenNote: 7.1.0 diff --git a/NAMESPACE b/NAMESPACE index 39ddaf7..5d6d28d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -28,7 +28,6 @@ importFrom(BiocParallel,bpparam) importFrom(BiocParallel,register) importFrom(DECENT,fitNoDE) importFrom(DECENT,lrTest) -importFrom(DEDS,comp.FC) importFrom(DESeq2,DESeq) importFrom(DESeq2,DESeqDataSetFromMatrix) importFrom(DESeq2,counts) @@ -51,7 +50,6 @@ importFrom(MASS,rlm) importFrom(MAST,FromMatrix) importFrom(MAST,lrTest) importFrom(MAST,summary) -importFrom(MAST,zlm.SingleCellAssay) importFrom(Matrix,Matrix) importFrom(NBPSeq,nbp.test) importFrom(NOISeq,noiseqbio) @@ -133,6 +131,7 @@ importFrom(ggplot2,element_blank) importFrom(ggplot2,element_rect) importFrom(ggplot2,element_text) importFrom(ggplot2,expand_scale) +importFrom(ggplot2,expansion) importFrom(ggplot2,facet_grid) importFrom(ggplot2,facet_wrap) importFrom(ggplot2,geom_bar) @@ -209,7 +208,6 @@ importFrom(scales,math_format) importFrom(scales,percent) importFrom(scales,trans_breaks) importFrom(scales,trans_format) -importFrom(scater,calcAverage) importFrom(scater,isOutlier) importFrom(scone,SconeExperiment) importFrom(scone,estimate_ziber) diff --git a/NEWS.md b/NEWS.md index 5a71829..3944254 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,10 @@ +# Version 1.2.2 (2020-6-18) + +* Ensuring compability with R version 4.0 (e.g. deprecated DEDS Bioconductor package) +* Adapt log fold change model matrix addition +* Adding log fold changes to fraction of replicates per group (option `p.G` in `Setup()`) + # Version 1.2.0 (2019-7-30) * Downsampling of count matrices using binomial thinning implemented in `estimateParam()` (UMI-read ratio estimation) and `simulateDE()`. diff --git a/R/Evaluate.R b/R/Evaluate.R index 2a45d61..78eeb2c 100644 --- a/R/Evaluate.R +++ b/R/Evaluate.R @@ -594,11 +594,11 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fdr = simRes$SimulateRes$fdr ## calculate strata - tmp.ecdf.mean = stats::ecdf(log1p(estmeans)) + tmp.ecdf.mean = stats::ecdf(log2(estmeans+1)) tmp.quantile.mean = stats::quantile(tmp.ecdf.mean, probs=strata.probs) strata.mean = unique(c(0,unname(tmp.quantile.mean),Inf)) strata.mean = unique(round(strata.mean, digits=2)) - tmp.ecdf.disps = stats::ecdf(log1p(estdisps)) + tmp.ecdf.disps = stats::ecdf(log2(estdisps+1)) tmp.quantile.disps = stats::quantile(tmp.ecdf.disps, probs=strata.probs) strata.disps = unique(c(0,unname(tmp.quantile.disps),Inf)) strata.disps = unique(round(strata.disps, digits=2)) @@ -670,13 +670,13 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), # mean X.bar1 = mu[,j,i] ix.keep.mean = which(!is.na(X.bar1)) - xgr.mean = cut(log1p(X.bar1[ix.keep.mean]), strata.mean) - xgrd.mean = cut(log1p(X.bar1[DEid]), strata.mean) + xgr.mean = cut(log2(X.bar1[ix.keep.mean]+1), strata.mean) + xgrd.mean = cut(log2(X.bar1[DEid]+1), strata.mean) # dispersion X.disp1 = disp[,j,i] ix.keep.disps = which(!is.na(X.disp1)) - xgr.disps = cut(log1p(X.disp1[ix.keep.disps]), strata.disps) - xgrd.disps = cut(log1p(X.disp1[DEid]), strata.disps) + xgr.disps = cut(log2(X.disp1[ix.keep.disps]+1), strata.disps) + xgrd.disps = cut(log2(X.disp1[DEid]+1), strata.disps) # dropout X.drop1 = dropout[,j,i] ix.keep.drop = which(!is.na(X.drop1)) @@ -698,8 +698,8 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fix.keep.mean = ix.keep.mean[!(xgr.mean %in% lev.mean[strata.filt.mean])] fix.dekeep.mean = intersect(fix.keep.mean, DEid) # recut - fxgr.mean = cut(log1p(X.bar1[fix.keep.mean]), strata.mean[-strata.filt.mean]) - fxgrd.mean = cut(log1p(X.bar1[fix.dekeep.mean]), strata.mean[-strata.filt.mean]) + fxgr.mean = cut(log2(X.bar1[fix.keep.mean]+1), strata.mean[-strata.filt.mean]) + fxgrd.mean = cut(log2(X.bar1[fix.dekeep.mean]+1), strata.mean[-strata.filt.mean]) } if(filter.by == "dispersion") { lev.disps = levels(xgr.disps) @@ -707,8 +707,8 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fix.keep.mean = ix.keep.mean[!(xgr.disps %in% lev.disps[strata.filt.disps])] fix.dekeep.mean = intersect(fix.keep.mean, DEid) # recut - fxgr.mean = cut(log1p(X.bar1[fix.keep.mean]), strata.mean) - fxgrd.mean = cut(log1p(X.bar1[fix.dekeep.mean]), strata.mean) + fxgr.mean = cut(log2(X.bar1[fix.keep.mean]+1), strata.mean) + fxgrd.mean = cut(log2(X.bar1[fix.dekeep.mean]+1), strata.mean) } if(filter.by == "dropout") { lev.drop = levels(xgr.drop) @@ -716,8 +716,8 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fix.keep.mean = ix.keep.mean[!(xgr.drop %in% lev.drop[strata.filt.drop])] fix.dekeep.mean = intersect(fix.keep.mean, DEid) # recut - fxgr.mean = cut(log1p(X.bar1[fix.keep.mean]), strata.mean) - fxgrd.mean = cut(log1p(X.bar1[fix.dekeep.mean]), strata.mean) + fxgr.mean = cut(log2(X.bar1[fix.keep.mean]+1), strata.mean) + fxgrd.mean = cut(log2(X.bar1[fix.dekeep.mean]+1), strata.mean) } if(filter.by == "none") { fix.keep.mean = ix.keep.mean @@ -733,8 +733,8 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fix.keep.disps = ix.keep.disps[!(xgr.mean %in% lev.mean[strata.filt.mean])] fix.dekeep.disps = intersect(fix.keep.disps, DEid) # recut - fxgr.disps = cut(log1p(X.disp1[fix.keep.disps]), strata.disps) - fxgrd.disps = cut(log1p(X.disp1[fix.dekeep.disps]), strata.disps) + fxgr.disps = cut(log2(X.disp1[fix.keep.disps]+1), strata.disps) + fxgrd.disps = cut(log2(X.disp1[fix.dekeep.disps]+1), strata.disps) } if(filter.by == "dispersion") { lev.disps = levels(xgr.disps) @@ -743,8 +743,8 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), fix.dekeep.disps = intersect(fix.keep.disps, DEid) strata.filt.disps = length(strata.disps) - strata.filtered # recut - fxgr.disps = cut(log1p(X.disp1[fix.keep.disps]), strata.disps[1:strata.filt.disps]) - fxgrd.disps = cut(log1p(X.disp1[fix.dekeep.disps]), strata.disps[1:strata.filt.disps]) + fxgr.disps = cut(log2(X.disp1[fix.keep.disps]+1), strata.disps[1:strata.filt.disps]) + fxgrd.disps = cut(log2(X.disp1[fix.dekeep.disps]+1), strata.disps[1:strata.filt.disps]) } if(filter.by == "dropout") { lev.drop = levels(xgr.drop) @@ -879,7 +879,7 @@ evaluateDE <- function(simRes, alpha.type=c("adjusted","raw"), "DESeq2", "ROTS", "MAST", "scde", "BPSC", "scDD", "monocle", "DECENT", "edgeR-zingeR", "edgeR-ZINB-WaVE", "DESeq2-zingeR", "DESeq2-ZINB-WaVE")) { pval = pvalue[ix.keep,j,i] - meanexpr = log1p(mu[ix.keep,j,i]) + meanexpr = log2(mu[ix.keep,j,i]+1) if(MTC %in% stats::p.adjust.methods) { x = stats::p.adjust(pval, method = MTC) x[is.na(x)] = 1 diff --git a/R/Plot.R b/R/Plot.R index 2f172c8..19b8364 100644 --- a/R/Plot.R +++ b/R/Plot.R @@ -275,7 +275,7 @@ plotSpike <- function(estSpike, Annot = TRUE) { ggplot2::geom_point(data = cal.info.dat, ggplot2::aes_(x=quote(LSpikeInput), y=quote(LExpectation))) + - ggplot2::geom_smooth(method='lm',formula=y~x) + + ggplot2::geom_smooth(method=lm,formula=y~x) + ggplot2::annotate("text", label = paste0("italic(R) ^ 2 == ", round(Calibration$Rsquared, digits = 2), "%+-%", @@ -298,7 +298,7 @@ plotSpike <- function(estSpike, Annot = TRUE) { ggplot2::aes_(x=quote(LSpikeInput), y=quote(p_success))) + ggplot2::geom_point() + - ggplot2::geom_smooth(method="glm", + ggplot2::geom_smooth(method=glm, method.args = list(family = "binomial"), se=T) + ggplot2::scale_x_log10(labels=c("0.1","1","10","100","1,000"),breaks=c(0.1,1,10,100,1000)) + ggplot2::annotation_logticks(sides = "b") + @@ -443,7 +443,7 @@ plotEvalDE <- function(evalRes, rate=c('marginal', 'conditional'), quick=TRUE, A y = quote(value), group = quote(L1), color = quote(L1)), - fun.y = mean, geom="line") + + fun = mean, geom="line") + ggplot2::geom_hline(data = refval, ggplot2::aes_(yintercept = quote(ref)), linetype="dashed", @@ -515,7 +515,7 @@ plotEvalDE <- function(evalRes, rate=c('marginal', 'conditional'), quick=TRUE, A y = quote(value), group = quote(Var2), color = quote(Var2)), - fun.y = mean, geom="line") + + fun = mean, geom="line") + ggplot2::geom_hline(data = refval, ggplot2::aes_(yintercept = quote(ref)), linetype="dashed", diff --git a/R/Setup.R b/R/Setup.R index 77728d8..47bf28c 100644 --- a/R/Setup.R +++ b/R/Setup.R @@ -32,7 +32,7 @@ #' (3) a function that takes an integer n, and generates a vector of length n, #' e.g. function(x) rnorm(x, mean=0, sd=1.5). #' Default is \code{1}. -#' Please note that the fold change should be on \code{\link[base]{log1p}} scale! +#' Please note that the fold change should be on \code{\link[base]{log2}} scale! #' @param p.G Numeric vector indicating the proportion of replicates per group #' that express the phenotypic fold change. Default is \code{1}, this means all show the expressiond difference. #' For example, if \code{0.5} and \code{n1 = 10} and \code{n2 = 8}, @@ -49,7 +49,7 @@ #' e.g. function(x) rnorm(x, mean=0, sd=1.5). #' Note that the simulations of only two batches is implemented. #' Default is \code{NULL}, i.e. no batch effect. -#' Please note that the fold change should be on \code{\link[base]{log1p}} scale! +#' Please note that the fold change should be on \code{\link[base]{log2}} scale! #' @param bPattern Character vector for batch effect pattern if \code{p.B} is non-null. #' Possible options include: #' "uncorrelated", "orthogonal" and " correlated". diff --git a/R/utils_DEdetection.R b/R/utils_DEdetection.R index 979e207..8c5d2d9 100644 --- a/R/utils_DEdetection.R +++ b/R/utils_DEdetection.R @@ -952,7 +952,7 @@ # MAST -------------------------------------------------------------------- -#' @importFrom MAST FromMatrix zlm.SingleCellAssay lrTest summary +#' @importFrom MAST FromMatrix lrTest summary #' @importFrom S4Vectors mcols # #' @importFrom AnnotationDbi as.list #' @importFrom edgeR DGEList diff --git a/R/utils_estimate.R b/R/utils_estimate.R index c43e79d..d130e03 100644 --- a/R/utils_estimate.R +++ b/R/utils_estimate.R @@ -1,7 +1,7 @@ # checkup ----------------------------------------------------------------- #' @importFrom SingleCellExperiment SingleCellExperiment -#' @importFrom scater isOutlier calcAverage +#' @importFrom scater isOutlier #' @importFrom BiocGenerics counts .run.checkup <- function(countData, readData, @@ -577,7 +577,6 @@ return(res) } -#' @importFrom DEDS comp.FC .run.params <- function(countData, normData, group) { if (attr(normData, 'normFramework') %in% c('SCnorm', "Linnorm")) { @@ -606,8 +605,7 @@ # calculate log fold changes - fc.foo = DEDS::comp.FC(L=group, is.log = T, FUN = mean) - lfc = fc.foo(log1p(norm.counts)) + lfc = .comp.FC(X = log2(norm.counts+1), L=group, is.log = T, FUN = mean) res = data.frame(geneIndex=rownames(countData), means=means, @@ -618,6 +616,17 @@ return(res) } +.comp.FC <- function(X, L, is.log=TRUE, FUN=mean) { + if (is.vector(X)) X <- matrix(X, byrow=TRUE) + G1 <- X[, L == 1] + G2 <- X[, L == -1] + m1 <- apply(G1,1,FUN,na.rm=TRUE) + m2 <- apply(G2,1,FUN,na.rm=TRUE) + if (is.log) fc <- m1-m2 + else fc <- m1/m2 + return(fc) +} + # Fitting ----------------------------------------------------------------- #' @importFrom reshape2 melt @@ -689,9 +698,9 @@ disp = disp[sharedgenes] size = size[sharedgenes] - ldisp = log1p(disp) - lsize = log1p(size) - lmu = log1p(mu) + ldisp = log2(disp+1) + lsize = log2(size+1) + lmu = log2(mu+1) estG <- length(sharedgenes) estS <- ParamData$nsamples @@ -761,9 +770,9 @@ disp = disp[sharedgenes] size = size[sharedgenes] - ldisp = log1p(disp) - lsize = log1p(size) - lmu = log1p(mu) + ldisp = log2(disp+1) + lsize = log2(size+1) + lmu = log2(mu+1) estG <- length(sharedgenes) estS <- ParamData$nsamples @@ -879,7 +888,7 @@ cobs.predict.nonamplified <- as.data.frame(stats::predict(cobs.fit.nonamplified, cobs.sim.nonamplified)) cobs.predict.nonamplified[,"fit"] <- ifelse(cobs.predict.nonamplified$fit < 0, 0, cobs.predict.nonamplified[,"fit"]) cobs.predict.nonamplified <- cobs.predict.nonamplified[cobs.predict.nonamplified[,'fit'] < 0.05,] - g0.cut.nonamplified <- log1p(10) + g0.cut.nonamplified <- log2(10) nonamplified.dat <- cbind.data.frame(lmu.nonamplified, g0.nonamplified) nonamplified.dat <- nonamplified.dat[stats::complete.cases(nonamplified.dat),] nonamplified.dat <- nonamplified.dat[nonamplified.dat$lmu.nonamplified "3.6.0"){ + if(strsplit(version[['version.string']], ' ')[[1]][3] > "4.0.0"){ if (!requireNamespace("BiocManager")){ install.packages("BiocManager") } BiocManager::install(new.pkg, dependencies=TRUE, ask=FALSE) } if(strsplit(version[['version.string']], ' ')[[1]][3] < "3.6.0"){ - stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher.")) + stop(message("powsimR depends on packages and functions that are only available in R 4.0.0 and higher.")) } } if(repository=='github') { @@ -56,7 +56,7 @@ ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){ cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr", "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", - "gamlss.dist", "ggplot2", "ggpubr", "grDevices", + "gamlss.dist", "ggplot2", "ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", @@ -66,7 +66,7 @@ ipak(cranpackages, repository='CRAN') # BIOCONDUCTOR biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", - "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", + "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave") @@ -77,7 +77,6 @@ githubpackages <- c('cz-ye/DECENT', 'nghiavtr/BPSC', 'mohuangx/SAVER', 'statOmics/zingeR', 'Vivianstats/scImpute') ipak(githubpackages, repository = 'github') - ``` To check whether all dependencies are installed, you can run the following lines: diff --git a/README.md b/README.md index bdd9112..a06d43f 100644 --- a/README.md +++ b/README.md @@ -31,14 +31,14 @@ ipak <- function(pkg, repository = c("CRAN", "Bioconductor", "github")) { install.packages(new.pkg, dependencies = TRUE) } if (repository == "Bioconductor") { - if (strsplit(version[["version.string"]], " ")[[1]][3] > "3.6.0") { + if (strsplit(version[["version.string"]], " ")[[1]][3] > "4.0.0") { if (!requireNamespace("BiocManager")) { install.packages("BiocManager") } BiocManager::install(new.pkg, dependencies = TRUE, ask = FALSE) } if (strsplit(version[["version.string"]], " ")[[1]][3] < "3.6.0") { - stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher.")) + stop(message("powsimR depends on packages and functions that are only available in R 4.0.0 and higher.")) } } if (repository == "github") { @@ -51,15 +51,15 @@ ipak <- function(pkg, repository = c("CRAN", "Bioconductor", "github")) { # CRAN PACKAGES cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr", "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", "gamlss.dist", "ggplot2", - "ggpubr", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", - "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", - "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", "reshape2", "Rmagic", - "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", "stats", "tibble", - "tidyr", "truncnorm", "VGAM", "ZIM", "zoo") + "ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", + "MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", + "msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", "reshape2", + "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", "stats", + "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo") ipak(cranpackages, repository = "CRAN") # BIOCONDUCTOR -biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", "DEDS", "DESeq2", +biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave") @@ -173,12 +173,18 @@ library(powsimR) #> Loading required package: MASS #> Warning: replacing previous import 'DECENT::lrTest' by 'MAST::lrTest' when #> loading 'powsimR' +#> Registered S3 methods overwritten by 'lme4': +#> method from +#> cooks.distance.influence.merMod car +#> influence.merMod car +#> dfbeta.influence.merMod car +#> dfbetas.influence.merMod car #> Warning: replacing previous import 'penalized::predict' by 'stats::predict' when #> loading 'powsimR' #> Warning: replacing previous import 'zinbwave::glmWeightedF' by #> 'zingeR::glmWeightedF' when loading 'powsimR' sessionInfo() -#> R version 3.6.2 (2019-12-12) +#> R version 4.0.1 (2020-06-06) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 18.04.4 LTS #> @@ -198,147 +204,150 @@ sessionInfo() #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: -#> [1] powsimR_1.2.0 gamlss.dist_5.1-6 MASS_7.3-51.5 +#> [1] powsimR_1.2.2 gamlss.dist_5.1-6 MASS_7.3-51.6 #> #> loaded via a namespace (and not attached): #> [1] mixtools_1.2.0 softImpute_1.4 -#> [3] minpack.lm_1.2-1 lattice_0.20-38 -#> [5] vctrs_0.2.2 fastICA_1.2-2 -#> [7] mgcv_1.8-31 penalized_0.9-51 -#> [9] blob_1.2.1 survival_3.1-8 -#> [11] Rmagic_2.0.3 later_1.0.0 -#> [13] nloptr_1.2.1 DBI_1.1.0 -#> [15] R.utils_2.9.2 SingleCellExperiment_1.8.0 -#> [17] rappdirs_0.3.1 Linnorm_2.10.0 -#> [19] dqrng_0.2.1 jpeg_0.1-8.1 -#> [21] zlibbioc_1.32.0 MatrixModels_0.4-1 -#> [23] htmlwidgets_1.5.1 mvtnorm_1.0-12 -#> [25] future_1.16.0 UpSetR_1.4.0 -#> [27] parallel_3.6.2 scater_1.14.6 -#> [29] irlba_2.3.3 DEoptimR_1.0-8 -#> [31] Rcpp_1.0.3 KernSmooth_2.23-16 -#> [33] DT_0.12 promises_1.1.0 -#> [35] gdata_2.18.0 DDRTree_0.1.5 -#> [37] DelayedArray_0.12.2 limma_3.42.2 -#> [39] vegan_2.5-6 Hmisc_4.3-1 -#> [41] ShortRead_1.44.3 apcluster_1.4.8 -#> [43] RSpectra_0.16-0 msir_1.3.2 -#> [45] mnormt_1.5-6 digest_0.6.23 -#> [47] png_0.1-7 qlcMatrix_0.9.7 -#> [49] sctransform_0.2.1 cowplot_1.0.0 -#> [51] pkgconfig_2.0.3 docopt_0.6.1 -#> [53] DelayedMatrixStats_1.8.0 ggbeeswarm_0.6.0 -#> [55] iterators_1.0.12 minqa_1.2.4 -#> [57] lavaan_0.6-5 reticulate_1.14 -#> [59] SummarizedExperiment_1.16.1 spam_2.5-1 -#> [61] beeswarm_0.2.3 modeltools_0.2-22 -#> [63] xfun_0.12 zoo_1.8-7 -#> [65] tidyselect_1.0.0 ZIM_1.1.0 -#> [67] reshape2_1.4.3 purrr_0.3.3 -#> [69] kernlab_0.9-29 EDASeq_2.20.0 -#> [71] viridisLite_0.3.0 snow_0.4-3 -#> [73] rtracklayer_1.46.0 rlang_0.4.4 -#> [75] hexbin_1.28.1 glue_1.3.1 -#> [77] RColorBrewer_1.1-2 fpc_2.2-5 -#> [79] matrixStats_0.55.0 stringr_1.4.0 -#> [81] fields_10.3 ggsignif_0.6.0 -#> [83] DESeq2_1.26.0 SparseM_1.78 -#> [85] httpuv_1.5.2 class_7.3-15 -#> [87] BPSC_0.99.2 BiocNeighbors_1.4.1 -#> [89] annotate_1.64.0 jsonlite_1.6.1 -#> [91] XVector_0.26.0 bit_1.1-15.1 -#> [93] mime_0.9 gridExtra_2.3 -#> [95] gplots_3.0.1.2 Rsamtools_2.2.1 -#> [97] zingeR_0.1.0 stringi_1.4.5 -#> [99] gmodels_2.18.1 bitops_1.0-6 -#> [101] maps_3.3.0 RSQLite_2.2.0 -#> [103] tidyr_1.0.2 pheatmap_1.0.12 -#> [105] data.table_1.12.8 DEDS_1.60.0 -#> [107] rstudioapi_0.11 GenomicAlignments_1.22.1 -#> [109] nlme_3.1-144 qvalue_2.18.0 -#> [111] scran_1.14.6 fastcluster_1.1.25 -#> [113] locfit_1.5-9.1 scone_1.10.0 -#> [115] listenv_0.8.0 cobs_1.3-4 -#> [117] R.oo_1.23.0 prabclus_2.3-2 -#> [119] dbplyr_1.4.2 segmented_1.1-0 -#> [121] BiocGenerics_0.32.0 lifecycle_0.1.0 -#> [123] ROTS_1.14.0 munsell_0.5.0 -#> [125] R.methodsS3_1.7.1 moments_0.14 -#> [127] hwriter_1.3.2 caTools_1.18.0 -#> [129] codetools_0.2-16 coda_0.19-3 -#> [131] Biobase_2.46.0 GenomeInfoDb_1.22.0 -#> [133] vipor_0.4.5 htmlTable_1.13.3 -#> [135] bayNorm_1.4.14 lsei_1.2-0 -#> [137] rARPACK_0.11-0 xtable_1.8-4 -#> [139] SAVER_1.1.2 ROCR_1.0-7 -#> [141] diptest_0.75-7 formatR_1.7 -#> [143] lpsymphony_1.14.0 abind_1.4-5 -#> [145] FNN_1.1.3 RANN_2.6.1 -#> [147] askpass_1.1 sparsesvd_0.2 -#> [149] CompQuadForm_1.4.3 GenomicRanges_1.38.0 -#> [151] tibble_2.1.3 ggdendro_0.1-20 -#> [153] cluster_2.1.0 future.apply_1.4.0 -#> [155] Matrix_1.2-18 prettyunits_1.1.1 -#> [157] shinyBS_0.61 NOISeq_2.30.0 -#> [159] shinydashboard_0.7.1 mclust_5.4.5 -#> [161] igraph_1.2.4.2 ggstance_0.3.3 -#> [163] slam_0.1-47 testthat_2.3.1 -#> [165] doSNOW_1.0.18 htmltools_0.4.0 -#> [167] BiocFileCache_1.10.2 yaml_2.2.1 -#> [169] GenomicFeatures_1.38.1 XML_3.99-0.3 -#> [171] ggpubr_0.2.4 DrImpute_1.0 -#> [173] foreign_0.8-75 fitdistrplus_1.0-14 -#> [175] BiocParallel_1.20.1 aroma.light_3.16.0 -#> [177] bit64_0.9-7 rngtools_1.5 -#> [179] doRNG_1.8.2 foreach_1.4.8 -#> [181] robustbase_0.93-5 outliers_0.14 -#> [183] Biostrings_2.54.0 combinat_0.0-8 -#> [185] rsvd_1.0.2 iCOBRA_1.14.0 -#> [187] memoise_1.1.0 evaluate_0.14 -#> [189] VGAM_1.1-2 nonnest2_0.5-2 -#> [191] geneplotter_1.64.0 permute_0.9-5 -#> [193] curl_4.3 fdrtool_1.2.15 -#> [195] acepack_1.4.1 edgeR_3.28.0 -#> [197] checkmate_2.0.0 npsurv_0.4-0 -#> [199] truncnorm_1.0-8 DECENT_1.1.0 -#> [201] tensorA_0.36.1 ellipse_0.4.1 -#> [203] ggplot2_3.2.1 ggrepel_0.8.1 -#> [205] scDD_1.10.0 tools_3.6.2 -#> [207] sandwich_2.5-1 magrittr_1.5 -#> [209] RCurl_1.98-1.1 pbivnorm_0.6.0 -#> [211] bayesm_3.1-4 EBSeq_1.26.0 -#> [213] httr_1.4.1 assertthat_0.2.1 -#> [215] rmarkdown_2.1 boot_1.3-24 -#> [217] globals_0.12.5 R6_2.4.1 -#> [219] Rhdf5lib_1.8.0 nnet_7.3-12 -#> [221] progress_1.2.2 genefilter_1.68.0 -#> [223] gtools_3.8.1 statmod_1.4.33 -#> [225] BiocSingular_1.2.1 rhdf5_2.30.1 -#> [227] splines_3.6.2 colorspace_1.4-1 -#> [229] amap_0.8-18 generics_0.0.2 -#> [231] stats4_3.6.2 NBPSeq_0.3.0 -#> [233] base64enc_0.1-3 compositions_1.40-3 -#> [235] baySeq_2.20.0 pillar_1.4.3 -#> [237] HSMMSingleCell_1.6.0 GenomeInfoDbData_1.2.2 -#> [239] plyr_1.8.5 dotCall64_1.0-0 -#> [241] gtable_0.3.0 SCnorm_1.8.2 -#> [243] monocle_2.14.0 knitr_1.28 -#> [245] RcppArmadillo_0.9.850.1.0 latticeExtra_0.6-29 -#> [247] biomaRt_2.42.0 IRanges_2.20.2 -#> [249] fastmap_1.0.1 doParallel_1.0.15 -#> [251] pscl_1.5.2 flexmix_2.3-15 -#> [253] quantreg_5.54 AnnotationDbi_1.48.0 -#> [255] broom_0.5.4 openssl_1.4.1 -#> [257] scales_1.1.0 arm_1.10-1 -#> [259] backports_1.1.5 plotrix_3.7-7 -#> [261] IHW_1.14.0 S4Vectors_0.24.3 -#> [263] densityClust_0.3 lme4_1.1-21 -#> [265] hms_0.5.3 DESeq_1.38.0 -#> [267] Rtsne_0.15 dplyr_0.8.4 -#> [269] shiny_1.4.0 grid_3.6.2 -#> [271] lazyeval_0.2.2 Formula_1.2-3 -#> [273] blockmodeling_0.3.4 crayon_1.3.4 -#> [275] MAST_1.12.0 RUVSeq_1.20.0 -#> [277] viridis_0.5.1 rpart_4.1-15 -#> [279] zinbwave_1.8.0 compiler_3.6.2 +#> [3] minpack.lm_1.2-1 lattice_0.20-41 +#> [5] haven_2.3.1 vctrs_0.3.1 +#> [7] fastICA_1.2-2 mgcv_1.8-31 +#> [9] penalized_0.9-51 blob_1.2.1 +#> [11] survival_3.2-3 Rmagic_2.0.3 +#> [13] later_1.1.0.1 nloptr_1.2.2.1 +#> [15] DBI_1.1.0 R.utils_2.9.2 +#> [17] SingleCellExperiment_1.10.1 rappdirs_0.3.1 +#> [19] Linnorm_2.12.0 dqrng_0.2.1 +#> [21] jpeg_0.1-8.1 zlibbioc_1.34.0 +#> [23] MatrixModels_0.4-1 htmlwidgets_1.5.1 +#> [25] mvtnorm_1.1-1 future_1.17.0 +#> [27] UpSetR_1.4.0 parallel_4.0.1 +#> [29] scater_1.16.1 irlba_2.3.3 +#> [31] DEoptimR_1.0-8 Rcpp_1.0.4.6 +#> [33] KernSmooth_2.23-17 DT_0.13 +#> [35] promises_1.1.1 gdata_2.18.0 +#> [37] DDRTree_0.1.5 DelayedArray_0.14.0 +#> [39] limma_3.44.3 vegan_2.5-6 +#> [41] Hmisc_4.4-0 ShortRead_1.46.0 +#> [43] apcluster_1.4.8 RSpectra_0.16-0 +#> [45] msir_1.3.2 mnormt_2.0.0 +#> [47] digest_0.6.25 png_0.1-7 +#> [49] qlcMatrix_0.9.7 sctransform_0.2.1 +#> [51] cowplot_1.0.0 pkgconfig_2.0.3 +#> [53] docopt_0.6.1 DelayedMatrixStats_1.10.0 +#> [55] ggbeeswarm_0.6.0 iterators_1.0.12 +#> [57] minqa_1.2.4 lavaan_0.6-6 +#> [59] reticulate_1.16 SummarizedExperiment_1.18.1 +#> [61] spam_2.5-1 beeswarm_0.2.3 +#> [63] modeltools_0.2-23 xfun_0.14 +#> [65] zoo_1.8-8 tidyselect_1.1.0 +#> [67] ZIM_1.1.0 reshape2_1.4.4 +#> [69] purrr_0.3.4 kernlab_0.9-29 +#> [71] EDASeq_2.22.0 viridisLite_0.3.0 +#> [73] snow_0.4-3 rtracklayer_1.48.0 +#> [75] rlang_0.4.6 hexbin_1.28.1 +#> [77] glue_1.4.1 RColorBrewer_1.1-2 +#> [79] fpc_2.2-5 matrixStats_0.56.0 +#> [81] stringr_1.4.0 fields_10.3 +#> [83] ggsignif_0.6.0 DESeq2_1.28.1 +#> [85] SparseM_1.78 httpuv_1.5.4 +#> [87] class_7.3-17 BPSC_0.99.2 +#> [89] BiocNeighbors_1.6.0 annotate_1.66.0 +#> [91] jsonlite_1.6.1 XVector_0.28.0 +#> [93] tmvnsim_1.0-2 bit_1.1-15.2 +#> [95] mime_0.9 gridExtra_2.3 +#> [97] gplots_3.0.3 Rsamtools_2.4.0 +#> [99] zingeR_0.1.0 stringi_1.4.6 +#> [101] gmodels_2.18.1 bitops_1.0-6 +#> [103] maps_3.3.0 RSQLite_2.2.0 +#> [105] tidyr_1.1.0 pheatmap_1.0.12 +#> [107] data.table_1.12.8 rstudioapi_0.11 +#> [109] GenomicAlignments_1.24.0 nlme_3.1-148 +#> [111] qvalue_2.20.0 scran_1.16.0 +#> [113] fastcluster_1.1.25 locfit_1.5-9.4 +#> [115] scone_1.12.0 listenv_0.8.0 +#> [117] cobs_1.3-4 R.oo_1.23.0 +#> [119] prabclus_2.3-2 dbplyr_1.4.4 +#> [121] segmented_1.1-0 BiocGenerics_0.34.0 +#> [123] readxl_1.3.1 lifecycle_0.2.0 +#> [125] ROTS_1.16.0 munsell_0.5.0 +#> [127] cellranger_1.1.0 R.methodsS3_1.8.0 +#> [129] moments_0.14 hwriter_1.3.2 +#> [131] caTools_1.18.0 codetools_0.2-16 +#> [133] coda_0.19-3 Biobase_2.48.0 +#> [135] GenomeInfoDb_1.24.2 vipor_0.4.5 +#> [137] htmlTable_1.13.3 bayNorm_1.6.0 +#> [139] rARPACK_0.11-0 xtable_1.8-4 +#> [141] SAVER_1.1.2 ROCR_1.0-11 +#> [143] diptest_0.75-7 formatR_1.7 +#> [145] lpsymphony_1.16.0 abind_1.4-5 +#> [147] FNN_1.1.3 RANN_2.6.1 +#> [149] askpass_1.1 sparsesvd_0.2 +#> [151] CompQuadForm_1.4.3 GenomicRanges_1.40.0 +#> [153] tibble_3.0.1 ggdendro_0.1-20 +#> [155] cluster_2.1.0 future.apply_1.5.0 +#> [157] Matrix_1.2-18 ellipsis_0.3.1 +#> [159] prettyunits_1.1.1 shinyBS_0.61 +#> [161] NOISeq_2.31.0 shinydashboard_0.7.1 +#> [163] mclust_5.4.6 igraph_1.2.5 +#> [165] ggstance_0.3.4 slam_0.1-47 +#> [167] testthat_2.3.2 doSNOW_1.0.18 +#> [169] htmltools_0.5.0 BiocFileCache_1.12.0 +#> [171] yaml_2.2.1 GenomicFeatures_1.40.0 +#> [173] XML_3.99-0.3 ggpubr_0.3.0 +#> [175] DrImpute_1.0 foreign_0.8-80 +#> [177] fitdistrplus_1.1-1 BiocParallel_1.22.0 +#> [179] aroma.light_3.18.0 bit64_0.9-7 +#> [181] foreach_1.5.0 robustbase_0.93-6 +#> [183] outliers_0.14 Biostrings_2.56.0 +#> [185] combinat_0.0-8 rsvd_1.0.3 +#> [187] iCOBRA_1.16.0 memoise_1.1.0 +#> [189] evaluate_0.14 VGAM_1.1-3 +#> [191] nonnest2_0.5-3 forcats_0.5.0 +#> [193] rio_0.5.16 geneplotter_1.66.0 +#> [195] permute_0.9-5 curl_4.3 +#> [197] fdrtool_1.2.15 acepack_1.4.1 +#> [199] edgeR_3.30.3 checkmate_2.0.0 +#> [201] truncnorm_1.0-8 DECENT_1.1.0 +#> [203] tensorA_0.36.1 ellipse_0.4.2 +#> [205] ggplot2_3.3.1 openxlsx_4.1.5 +#> [207] rstatix_0.5.0 ggrepel_0.8.2 +#> [209] scDD_1.12.0 tools_4.0.1 +#> [211] sandwich_2.5-1 magrittr_1.5 +#> [213] RCurl_1.98-1.2 car_3.0-8 +#> [215] pbivnorm_0.6.0 bayesm_3.1-4 +#> [217] EBSeq_1.28.0 httr_1.4.1 +#> [219] assertthat_0.2.1 rmarkdown_2.2 +#> [221] boot_1.3-25 globals_0.12.5 +#> [223] R6_2.4.1 Rhdf5lib_1.10.0 +#> [225] nnet_7.3-14 progress_1.2.2 +#> [227] genefilter_1.70.0 gtools_3.8.2 +#> [229] statmod_1.4.34 BiocSingular_1.4.0 +#> [231] rhdf5_2.32.0 splines_4.0.1 +#> [233] carData_3.0-4 colorspace_1.4-1 +#> [235] amap_0.8-18 generics_0.0.2 +#> [237] stats4_4.0.1 NBPSeq_0.3.0 +#> [239] base64enc_0.1-3 compositions_1.40-5 +#> [241] baySeq_2.22.0 pillar_1.4.4 +#> [243] HSMMSingleCell_1.8.0 GenomeInfoDbData_1.2.3 +#> [245] plyr_1.8.6 dotCall64_1.0-0 +#> [247] gtable_0.3.0 zip_2.0.4 +#> [249] SCnorm_1.10.0 monocle_2.16.0 +#> [251] knitr_1.28 RcppArmadillo_0.9.900.1.0 +#> [253] latticeExtra_0.6-29 biomaRt_2.44.0 +#> [255] IRanges_2.22.2 fastmap_1.0.1 +#> [257] doParallel_1.0.15 pscl_1.5.5 +#> [259] flexmix_2.3-15 quantreg_5.55 +#> [261] AnnotationDbi_1.50.0 broom_0.5.6 +#> [263] openssl_1.4.1 scales_1.1.1 +#> [265] arm_1.11-1 backports_1.1.8 +#> [267] plotrix_3.7-8 IHW_1.16.0 +#> [269] S4Vectors_0.26.1 densityClust_0.3 +#> [271] lme4_1.1-23 hms_0.5.3 +#> [273] DESeq_1.39.0 Rtsne_0.15 +#> [275] dplyr_1.0.0 shiny_1.4.0.2 +#> [277] grid_4.0.1 Formula_1.2-3 +#> [279] blockmodeling_0.3.6 crayon_1.3.4 +#> [281] MAST_1.14.0 RUVSeq_1.22.0 +#> [283] viridis_0.5.1 rpart_4.1-15 +#> [285] zinbwave_1.10.0 compiler_4.0.1 ``` diff --git a/doc/powsimR.R b/doc/powsimR.R index 4bede8b..28577aa 100644 --- a/doc/powsimR.R +++ b/doc/powsimR.R @@ -45,7 +45,7 @@ opts_knit$set(width = 75) # cranpackages <- c("broom", "cobs", "cowplot", # "data.table", "doParallel", "dplyr", "DrImpute", # "fastICA", "fitdistrplus", "foreach", "future", -# "gamlss.dist", "ggplot2", "ggpubr", "grDevices", +# "gamlss.dist", "ggplot2", "ggpubr", "ggstance", "grDevices", # "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix", # "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", # "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", @@ -55,7 +55,7 @@ opts_knit$set(width = 75) # # # BIOCONDUCTOR # biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", -# "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", +# "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", # "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", # "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", # "SingleCellExperiment", "SummarizedExperiment", "zinbwave") @@ -95,8 +95,10 @@ knitr::include_graphics("powsimr_workflow.png") ## ----geneparams, echo=T, eval=F, include=T------------------------------------ # data("CELseq2_Gene_UMI_Counts") -# Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), -# stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +# batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +# Batches <- data.frame(Batch = batch, +# stringsAsFactors = FALSE, +# row.names = colnames(CELseq2_Gene_UMI_Counts)) # data("GeneLengths_mm10") # # # estimation @@ -120,9 +122,10 @@ knitr::include_graphics("estparam_gene_celseq2.png") ## ----spikeparams, echo=T, eval=F, include=T, warning=F------------------------ # data("CELseq2_SpikeIns_UMI_Counts") # data("CELseq2_SpikeInfo") -# Batches = data.frame(Batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1), +# batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1) +# Batches = data.frame(Batch = batch, # stringsAsFactors = F, -# row.names = colnames(CELseq2_SpikeInfo)) +# row.names = colnames(CELseq2_SpikeIns_UMI_Counts)) # # estimation # estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts, # spikeInfo = CELseq2_SpikeInfo, @@ -149,9 +152,9 @@ knitr::include_graphics("lfcdist.png") # n1 = c(48, 96, 384, 800), n2 = c(48, 96, 384, 800), # Thinning = NULL, LibSize = 'equal', # estParamRes = estparam_gene, -# estSpikeRes = estparam_spike, +# estSpikeRes = NULL, # DropGenes = TRUE, -# sim.seed = 5299, verbose = TRUE) +# setup.seed = 5299, verbose = TRUE) # ## ----simrun, eval=F, echo=T--------------------------------------------------- @@ -172,10 +175,15 @@ knitr::include_graphics("lfcdist.png") # target.by = 'lfc', # delta = 0) -## ----evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."---- +## ---- echo = TRUE, eval = FALSE----------------------------------------------- +# plotEvalDE(evalRes = evalderes, rate = 'marginal', quick = TRUE, Annot = TRUE) +# plotEvalDE(evalRes = evalderes, rate = 'conditional', quick = TRUE, Annot = TRUE) +# + +## ----evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal FDR and TPR per sample size comparison. (B) Marginal FDR and TPR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."---- knitr::include_graphics("evalderes_marginal_celseq2.png") -## ----evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."---- +## ----evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional FDR and TPR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."---- knitr::include_graphics("evalderes_conditional_celseq2.png") ## ----evalrocres, echo = T, eval=F--------------------------------------------- @@ -243,8 +251,10 @@ knitr::include_graphics("evaldist_smartseq2.png") ## ----thinning, echo = TRUE, eval = FALSE-------------------------------------- # data("CELseq2_Gene_UMI_Counts") # data("CELseq2_Gene_Read_Counts") -# Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), -# stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +# batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +# Batches <- data.frame(Batch = batch, +# stringsAsFactors = FALSE, +# row.names = colnames(CELseq2_Gene_UMI_Counts)) # data("GeneLengths_mm10") # # # estimation @@ -272,7 +282,7 @@ knitr::include_graphics("evaldist_smartseq2.png") # estParamRes = estparam_gene, # estSpikeRes = NULL, # DropGenes = TRUE, -# sim.seed = 5299, verbose = TRUE) +# setup.seed = 5299, verbose = TRUE) # # # run simulations # simres <- simulateDE(SetupRes = setupres, diff --git a/doc/powsimR.Rmd b/doc/powsimR.Rmd index 8f2e3f9..b6f077b 100644 --- a/doc/powsimR.Rmd +++ b/doc/powsimR.Rmd @@ -89,7 +89,7 @@ ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){ cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr", "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", - "gamlss.dist", "ggplot2", "ggpubr", "grDevices", + "gamlss.dist", "ggplot2", "ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", @@ -99,7 +99,7 @@ ipak(cranpackages, repository='CRAN') # BIOCONDUCTOR biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", - "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", + "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave") @@ -181,8 +181,10 @@ With the following command, we estimate and plot the parameters for the mouse em ```{r geneparams, echo=T, eval=F, include=T} data("CELseq2_Gene_UMI_Counts") -Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), - stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +Batches <- data.frame(Batch = batch, + stringsAsFactors = FALSE, + row.names = colnames(CELseq2_Gene_UMI_Counts)) data("GeneLengths_mm10") # estimation @@ -208,7 +210,7 @@ knitr::include_graphics("estparam_gene_celseq2.png") We have implemented a count simulation framework assuming either a negative binomial distribution or a zero-inflated negative binomial distribution. To predict the dispersion given a random draw of mean expression value observed, we apply a locally weighted polynomial regression fit (solid orange line in Figure \@ref(fig:geneparamsplot) D). To capture the variability of dispersion estimates observed, a local variability prediction band is applied (dashed orange lines in Figure \@ref(fig:geneparamsplot) D). -For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5\% dropout probability determined by a decrease constrained B-splines regresssion of dropout rate against mean expression (`cobs`). +For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5\% dropout probability determined by a decrease constrained B-splines regression of dropout rate against mean expression (`cobs`). For the zero-inflated negative binomial distribution, the mean-dispersion relation is similarly estimated, but based on positive read counts. Furthermore, the dropouts are also predicted based on a locally weighted polynomial regression fit between mean and dropouts. Of note, this fit is done separately for amplified and non-amplified transcripts and similar proportions of genes as observed are also generated in the simulations [@Ziegenhain2017-sf]. @@ -233,9 +235,10 @@ With the following command, we estimate the parameters for the spike-ins added d ```{r spikeparams, echo=T, eval=F, include=T, warning=F} data("CELseq2_SpikeIns_UMI_Counts") data("CELseq2_SpikeInfo") -Batches = data.frame(Batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1), +batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1) +Batches = data.frame(Batch = batch, stringsAsFactors = F, - row.names = colnames(CELseq2_SpikeInfo)) + row.names = colnames(CELseq2_SpikeIns_UMI_Counts)) # estimation estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts, spikeInfo = CELseq2_SpikeInfo, @@ -264,9 +267,10 @@ There is also the possibility to include batch effects, see the Details section knitr::include_graphics("lfcdist.png") ``` -The number of replicates per group (n1 and n2) are also defined in `Setup` which can be unbalanced. These numbers represent either biological replicates for bulk or number of cells for single cell RNA-seq. In addition, the user can choose to include the simulation of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation. +The number of replicates per group (n1 and n2) are also defined in `Setup` which can be unbalanced. In addition the user can choose to include the simulaiton of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation. -The distribution estimates and these settings are then combined to one object. +The distribution estimates and these settings are then combined into one object. +The following command sets up simulations with 10,000 genes, 20\% genes being DE, log fold change sampled from a narrow gamma distribution and parameter estimates based on Smartseq2 libraries in Ziegenhain dataset. Please note that I decreased the number of simulations for this illustration. The following command sets up simulations with 10,000 genes, 5\% genes being DE, log fold change sample from a narrow gamma distribution and parameter estimates based on CEL-seq2 libraries in Ziegenhain dataset. The spike-ins are only necessary if we wish to apply imputation, normalisation and/or DE-tools that can utilize spike-ins, see the Details section of `estimateParam()` and `simulateDE()`. @@ -279,9 +283,9 @@ setupres <- Setup(ngenes = 10000, nsims = 25, n1 = c(48, 96, 384, 800), n2 = c(48, 96, 384, 800), Thinning = NULL, LibSize = 'equal', estParamRes = estparam_gene, - estSpikeRes = estparam_spike, + estSpikeRes = NULL, DropGenes = TRUE, - sim.seed = 5299, verbose = TRUE) + setup.seed = 5299, verbose = TRUE) ``` @@ -289,8 +293,8 @@ setupres <- Setup(ngenes = 10000, nsims = 25, With the setup defined, the differential expression simulation is run with `simulateDE`. For this, the user needs to set the following options: -* **DEmethod:** The differential testing method. The user can choose between 20 methods in total. 10 developed for bulk, 9 developed for single cells (see the Details section of `simulateDE`). -* **Normalisation:** The normalisation method. The user can choose between 10 methods in total. 5 developed for bulk, 5 developed for single cells (see the Details section of `simulateDE`). +* **DEmethod:** The differential testing method. The user can choose between 20 methods in total. 10 developed for bulk, 9 developed for single cells (see the Details section of `simulateDE`). +* **Normalisation:** The normalisation method. The user can choose between 10 methods in total. 5 developed for bulk, 5 developed for single cells (see the Details section of `simulateDE`). There are also additional options: Whether to apply a prefiltering or imputation step prior to normalisation; whether spike-in information should be used (if available). For more information, please consult the Details section of `simulateDE`. @@ -302,7 +306,6 @@ simres <- simulateDE(SetupRes = setupres, NCores = NULL, verbose = TRUE) ``` -## Simulating gene expression of cell mixtures # Evaluation {.tabset} @@ -329,16 +332,23 @@ evalderes = evaluateDE(simRes = simres, The results of the evaluation can be plotted with `plotEvalDE()`. +```{r, echo = TRUE, eval = FALSE} +plotEvalDE(evalRes = evalderes, rate = 'marginal', quick = TRUE, Annot = TRUE) +plotEvalDE(evalRes = evalderes, rate = 'conditional', quick = TRUE, Annot = TRUE) + +``` + + * **rate:** The user can choose to plot the 'marginal' or 'stratified' error rates. The number of genes per stratum are also summarised. * **quick:** If this is set to `TRUE` then only the TPR and FDR will be plotted. -The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure \@ref(fig:evaldeplot1) and \@ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size Figure \@ref(fig:evaldeplot1). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure \@ref(fig:evaldeplot2)). In addition, the detection of true positives with small average expression needs far more replicates per group. +The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure \@ref(fig:evaldeplot1) and \@ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size (Figure \@ref(fig:evaldeplot1)). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure \@ref(fig:evaldeplot2)). In addition, the detection of true positives with small mean expression needs far more replicates per group. -```{r evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."} +```{r evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal FDR and TPR per sample size comparison. (B) Marginal FDR and TPR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."} knitr::include_graphics("evalderes_marginal_celseq2.png") ``` -```{r evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."} +```{r evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional FDR and TPR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."} knitr::include_graphics("evalderes_conditional_celseq2.png") ``` @@ -450,21 +460,27 @@ knitr::include_graphics("evaldist_smartseq2.png") ## Simulation Settings +### Replicates with partial phenotypic differences + +By default, all replicates per group express the phenotypic differences in gene expression defined by `pLFC`. If the user wishes for a more varied expression profile, i.e. only a proportion of replicates per group with a difference in expression, these patterns can be defined by changing `p.G` (see Details section of `Setup()`). + ### Include differences in library sizes -By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function. See the Details section of `Setup()`. +By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function (see Details section of `Setup()`). ### Downsample simulated count matrices by binomial thinning -There is the additional option to downsample simulated count matrices with the option `Thinning` in `Setup` in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as `countData` in `estimateParam()` and the raw read count matrix without UMI deduplication as `readData`. For more information, please check out the help page of `estimateParam()`. +There is the additional option to downsample simulated count matrices with the option `Thinning` in `Setup()` in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as `countData` in `estimateParam()` and the raw read count matrix without UMI deduplication as `readData`. For more information, please check out the help page of `estimateParam()`. Below you can find an example script using the CELSeq2 libraries from Ziegenhain et al.: ```{r thinning, echo = TRUE, eval = FALSE} data("CELseq2_Gene_UMI_Counts") data("CELseq2_Gene_Read_Counts") -Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), - stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +Batches <- data.frame(Batch = batch, + stringsAsFactors = FALSE, + row.names = colnames(CELseq2_Gene_UMI_Counts)) data("GeneLengths_mm10") # estimation @@ -492,7 +508,7 @@ setupres <- Setup(ngenes = 10000, nsims = 25, estParamRes = estparam_gene, estSpikeRes = NULL, DropGenes = TRUE, - sim.seed = 5299, verbose = TRUE) + setup.seed = 5299, verbose = TRUE) # run simulations simres <- simulateDE(SetupRes = setupres, diff --git a/doc/powsimR.html b/doc/powsimR.html index 2e108c3..cd4941d 100644 --- a/doc/powsimR.html +++ b/doc/powsimR.html @@ -41,7 +41,7 @@ font-family: 'Material Icons'; font-style: normal; font-weight: 400; -src: 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) format('truetype'); +src: 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ZEf8AERoaEVYBK/7VERkBFStVVStVVcAZEf8AERoaEQEAERlV/tUqGREBKwAEAEAAVQHVAYAADQARABUAGQAAATMVIxUUBiImNDYzMhcFNTMVNxUhNSUVITUBa2pAJjQmJhoIDv7Vq1X/AAEA/wABgCvAGiYmNCYEJioqgCsrVSsrAAMAKwArAdUB6wAHAA8AIQAAATUhFSE1MxUEMjY0JiIGFAMlFwczMhYVERQGIyEiJjURNAGr/qoBACv++zQmJjQmEAEODrD6EhgYEv6qEhgBAFVVKyurJjQmJjQBAm4kRxkS/wASGBgSAQAeAAUAFQBrAesBlQAHAA8AHwAjACcAACU1NCYiBhUVNiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzAREzERMzESMBC0I8QnMmHR0mHVAJDAwJ/wAJDQ0JAUAqKysrlRAWGhoWEMYdJh0dJlcMCf8ACQwMCQEACQz+1gEq/tYBKv7WAAIAQAArAcAB1QAIABEAACU1MxUhFSc3FTUVIzUhNRcHNQFrKv8AVVUqAQBVVZVWgEBVVUDWVoBAVVVAAAMAQAArAcAB1QAGAA8AGAAAJSM1IzU3Mxc1MxUhFSc3FTUVIzUhNRcHNQEVICArFVYq/wBVVSoBAFVVwFUWFatWgEBVVUDWVoBAVVVAAAEAVQBAAasB6wATAAABMhYVFAYiJjUzFBYyNjQmIxUnNwEAR2RljGUrS2pLSzVrawGVZEZHZGRHNUtLaktWa2sAAwBVAFUBqwGrAAYADQARAAAlFzcVIzcnNzMVJwEnAQcHJzcBPEMsdixDF3Ys/vQeAQx/Hm8e4kMsdixD53Ys/vQeAQxDHm8eAAIAgACAAYABgAADAAYAAAEzESMjERcBVSsr1bUBgP8AAQCAAAIAgACAAYABgAACAAYAABM3EQEzESPLtf8AKysBAID/AAEA/wAABQArACsB1QHYAAkAEQAZAB0AIQAAEzUzFQczFSM1NwYyNjQmIgYUNjIWFAYiJjQlByc3BwcnN8CATU2ATUt8V1d8V0WgcHCgcAGVG2Iby2IbYgEVKyZaKydZwFh8V1d8/nGecXGeTCFTICBSIFIAAQCAAIABgAGAAAMAABMhESGAAQD/AAGA/wAABQArAFUB1QGrAAMABwALAA8AHwAAJTUjFRc1IxUjNSMVNRUzNSUyFhURFAYjISImNRE0NjMBq9bWVirWVgEAERkZEf6qERkZEdUrK1UrKysrgCsrqxoR/wARGhoRAQARGgAFACsAVQHVAasABwATABsAJwA3AAASMhYUBiImNBc2NTU0JicHFhUUByYyNjQmIgYUByY1NDcnBhUVFBYXATIWFREUBiMhIiY1ETQ2M+8iGhoiGqQyHRUfJiV+RjIyRjIFJiUeMh0VASQRGRkR/qoRGRkRASsaIhoaIooyRwEdRxQfJjQ2JQYyRjIyRjcmNDYlHjJHAR1HFAEkGhH/ABEaGhEBABEaAAMAKwArAdUB1QACABIAGwAAJTcnNzIWFREUBiMhIiY1ETQ2MwcRIRUhIiY1EQEAgICrERkZEf8AERoaEVYBK/7VERnLYGBKGRH/ABEaGhEBABEZVf7VKhkRASsAAQBAAIABwAGAABMAAAE3FScVFAYjISImNTU0NjMhMhYVAWtVVQ0J/wAJDAwJAQAJDQEgVepVSwkMDAnWCQwMCQACACsAQAHAAdUADwAXAAATAQcnBiMhIiY1NTQ2MzMnBRUnMzIWFRVGAXobRAYG/wAJDAwJEDoBle+ECQ0B1f6GG0QEDAnWCQw6ReTvDAlLAAIAawBVAYsBqwAFAAsAABMzNxEnIyUUBzUWFmtVa2tVASA2FiABQGv+qmtAOxusCzMAAQCVAFUBVQGrAAUAABMzNxEnI5VWampWAUBr/qprAAQAQABAAcABwAACABIAHgAlAAABFScnAQcnBgc1NjcnFScjNTMnBTQmJzUWFhUUByc2JxQHJzUWFgEALXgBZRssIS0ZF1trVWVlAVU7L0FUFiALNQE0Fh8Bq1otQv6bGywcCywHEluQa4BlpTNODiwOaUQvKiEbHQkENC8LMgADAEAARQHAAbsACwARABcAAAEWFhQGBzU2NjQmJxcUBzUWFiUzNxEnIwErQVRUQS87Oy81NRYf/uBVa2tVAbsOaYhpDiwOTmZODo88GqwLMidr/qprAAQAKwBVAdUBqwADAAcACwAbAAAlNSMVJzUjFRc1IxUBMhYVERQGIyEiJjURNDYzAatWFevr6wFWERkZEf6qERkZEYDAwGtVVWtVVQErGhH/ABEaGhEBABEaAAQAQABAAcABwAADAA0AGQApAAAlNTMVJxUzMjY1NTQmIwc1IxUjNSMVMzUzFRMyFhURFAYjISImNRE0NjMBNStLVgkMDAmAICsgICvKERoaEf7WEhkZEuBAQGCADAlWCQyAgDU1gCsrAQAaEf7WERoZEgEqEhkABQAiADEB0gHPAAkADAAUABcAGgAAJTMVIzU3IzUzFQUzJyczFyMnIwcjFzMHEyM3AVCCtn59sf6cUyoRI2AnFG0UJ7ljMjNlMqgiG7ciG4JvLvQ0NCMyAWwyAAIAFQArAesBwAAXABoAAAEyFhURFAYjIzUzESERMxUjIiY1ETQ2MxM3FwHAERoaEVVV/oBVVREaGhFAgIABwBoR/wARGSoBAP8AKhkRAQARGv5rgIAABABVAEABqwHrABMANAA7AE8AACUUMzI3NzY1NSY0JiMiBwcGFRUWNxQHBwYjIgYjIicmJicmNTU0Nzc2MzI2MzIXHgIXFhUHIzUHNTczBzQ2MzUXBzUiBhQWMjY1MxQGIiYBGgoFAgQCAgkCAwMEAwMoAgYHBAIJAgkEAgYCCQIHBgQCCQIJBAIHBAQCWRQVJgOUZEdrazRMTGhMK2WMZbwHAgUEAisEBAUDBAQCKwQTDQQNBgICAQQBBRkPDQQNBgICAQQCDAYLNUYGDwwbRmRWa2tWS2pLSzVHZGQABABVAEABqwHrABMAJwBHAIkAADc0NjM1Fwc1IgYUFjI2NTMUBiImNxQzMjc3NjU1JjQmIyIHBwYVFRY3FAcHBiMiBiMiJyYnJjU1NDc3NjMyNjMyFx4CFxYVIzI1NSY0IyMGIhUVIzQ2MzI2MzIXFhUVBhUUIyIHFhcWFRQHBgYHBiMiBiMiJyYmJyY1MxUWFDMzNjI1NSY0IyM1VWRHa2s0TExoTCtljGXHCgUCBAICCQIDAwQCAioCBgcEAgkCBxACBAMDBgcEAggCCQQCBwQEAnEPAgQLAgQWCwcBCAEMDAgCBAIFCQIEAgEEAQQHAgkCCAIBCAIJEgIECwIEAgQN60ZkVmtrVktqS0s1R2RkGAcCBQQCKwQEBQMEBAIrBBMNBA0GAggBDAkIDwsGDQYCAgEEAgwGCw0EAgQCBAQIDwIGBA8HBAIEBQUDCAUJAgEGAQQCAgECAQUQBAIEAgQLAgQPAAIAVQBAAasB6wAyAEYAADcGFQcjNzMVIwcyNTQ2NTMzMhcWFhcWFRQHBgYHBiMiJyYmJyY1MxQzMjc3NjU1JycmIyc0NjM1Fwc1IgYUFjI2NTMUBiIm+gcCDQUzJQICAwQECAMBBgEJAgEEAggPCQIBBwIJEQ0EAgUCAgUEAqlkR2trNExMaEwrZYxl3gMBAy8PEwIBAQEDAQQBCQ4JAgEIAggCAQIBBQ4KAgQEAg0EBQINRmRWa2tWS2pLSzVHZGQABABVAEABqwHrABMANAA7AE8AACUUMzI3NzY1NSY0JiMiBwcGFRUWNxQHBwYjIgYjIicmJicmNTU0Nzc2MzI2MzIXHgIXFhUHIzUHNTczNzIWFRQGIiY1MxQWMjY0JiMVJzcBGgoFAgQCAgkCAwMEAwMqAgYHBAIJAgkEAgYCCQIHBgQCCQIJBAIHBAQCWxQVJgMXR2RljGUrTGhMTDRra7wHAgUEAisEBAUDBAQCKwQTDQQNBgICAQQBBRkPDQQNBgICAQQCDAYLNUYGDwyPZEZHZGRHNUtLaktWa2sABABVAEABqwHrABUANQB3AIsAACUUMzI3NzY1NTQmNTQmIyIHBwYVFRY3FAcHBiMiBiMiJyYnJjU1NDc3NjMyNjMyFx4CFxYVIzI1NSY0IyMGIhUVIzQ2MzI2MzIXFhUVBhUUIyIHFhcWFRQHBgYHBiMiBiMiJyYmJyY1MxUWFDMzNjI1NSY0IyM1NzIWFRQGIiY1MxQWMjY0JiMVJzcBHgsEAgQCAggCBAMEAgIoAgYHBAIJAgcQAgQDAwYHBAIIAgkEAgcEBAJxDwIECwIEFgsHAQgBDAwIAgQCBQkCBAIBBAEEBwIJAggCAQgCCRICBAsCBAIEDTNHZGWMZStMaExMNGtrvAcCBQQCKwEEAQIFAwQEAisEEw0EDQYCCAEMCQgPCwYNBgICAQQCDAYLDQQCBAIEBAgPAgYEDwcEAgQFBQMIBQkCAQYBBAICAQIBBRAEAgQCBAsCBA+1ZEZHZGRHNUtLaktWa2sAAgBVAEABqwHrADQASAAANwYVByM3MxUjBzI1NDY1MzMyFxYWFxYVFAcOAwcGIyInJiYnJjUzFDMyNzc2NTUnJyYjNTIWFRQGIiY1MxQWMjY0JiMVJzf8BwIPBTMlAgIDBAQIAwEGAQkCAQQEBQECCwkCAQcCCRENBAIFAgIFBAJHZGWMZStMaExMNGtr3gMBAy8PEwIBAQEDAQQBCQ4JAgEIBAMBAgIBAgEFDgoCBAQCDQQFArdkRkdkZEc1S0tqS1ZrawADABUAQAHrAcAACwAPACMAAAEVIxUjNSM1MzUzFRcRIREBMhYVAxQGIyMVIzUjIiY1ETQ2MwFVQCpAQCqr/oABgBIZARkRa6prEhkZEgErK0BAK0BAlgEA/wABKxkS/wARGSsrGBIBABIZAAYAFQBAAesBwAANABQAHgAuADIANgAAATU0JiMjFTM1MxczJzYHNyMHJyMXJzU0JiMjFTMyNiUyFhURFAYjISImNRE0NjMXMxUjJTMVIwHAEg5LIBkSIBMTsyYgFhUgJUITDUtLDRMBFRIZGRL+gBIZGRIgKysBFSsrAQsVDhKAKystCTaASUmAIEAOEoAS7hkS/tYSGRkSASoSGaBAQBUABAArAFUB1QGrABEAHQAnADcAACU1IxUjNSMVIzUjFRQWMzMyNic1IxUzNSM1MzUjNQc1IxUnIxUzNRclMhYVERQGIyEiJjURNDYzAbUaGBsYGw0JVQkMlVVVNTU1Nho2Gho3AQ8SGBgS/qoSGBgS1WtgS0tgawkMDFkbgBsXGxhlgEtLgEtL6xkS/wASGRkSAQASGQAEAFUAVQGVAYAAAgAGAAoADgAAJRcHJzMVIzUhFSEVIRUhAStqatarqwEA/wABAP8A1UBAgCrVKyorAAUAKwCVAdUBawAEABQAGAAcACAAADcnBycHNxUUBiMjIiY1NTQ2MzMyFhc1MxU1FSM1FyM1M+AwJRsltRoRgBEZGRGAERorqqqqqqrAQDAgMICAERoaEYARGhq8KyvWKyuAKgABAFUAVQGrAasABwAANjQ2MhYUBiJVZYxlZYy6jGVljGUAAgAVAFUB6wGrAAsAEwAAARYWFAYHNTY2NCYnBDQ2MhYUBiIBazhISDgjMjIj/qpljGVljAGlDlx2XA4sDUdKRw2/jGVljGUAAwAVAEAB6wHAAA0AEQAhAAA2NDYzMhc1MxUjFRQGIhcRIREBMhYVERQGIyEiJjURNDYzqyYaBw5rQCY07/6AAYARGhoR/oARGhoRpjQmBIQrlholFQEq/tYBVRoR/tYRGhoRASoRGgAEACsAKwHVAdUAAgASABYAGgAAJScVJRUUBiMhIiY1NTQ2MyEyFicVITUFITUhAVWAAQAZEf6qERkZEQFWERlV/wABK/6qAVarRYubqxEZGRGrERoaxCoqgCsABAArAFUB6wGAAAUACQANABEAAAEXByc3FyU1MxU3FSE1BRUhNQHLIJVhIEH+1apW/wABAP8AAQsglmAgQBYqKtUrK1UrKwADABUAIAIAAcAABQARACgAACUHJzcnNyczFSMVIzUjNTM1MzcyFhUVIzUhESEVIxUjNSMiJjURNDYzAgBgIEBAIItAQCpAQCqrEhkr/oABQCuqaxIZGRKAYCBAQCBLK0BAK0BVGRKqqv8AKisrGBIBABIZAAMAFQBAAesBwAADAAcAGwAAARUjNQURIREBMhYVAxQGIyMVIzUjIiY1ETQ2MwFVqgEV/oABgBIZARkRa6prEhkZEgErKyuWAQD/AAErGRL/ABEZKysYEgEAEhkABgAsACwB1QHUAAsAEgAYAB8AJQApAAAAFAYHNTY2NCYnNRYDNxYXFSYmJxYXByYnNwYHIzY2NzcGByc2NxcXBgcB1W5RP1ZWP1HuHiQwG0M2BR0eKQZNHAYrAxwQkDAkHjM/Kz8/QQFSpHoIKwhhgGEIKwj+jx4cBisDHKAvJB8zP34lLxtDFCIGHB4pBqUvLzEAAgBAAFUBwAGrAAMAEwAAJTUhFQEyFhURFAYjISImNRE0NjMBlf7WASoSGRoR/tYSGRkSgNXVASsZEv8AERoZEgEAEhkABQArAFUB1QGrAAkADQAZACkALQAAJTUjFScjFTM1FyM1IxUnNTQmIyMVMzUzMjY3MhYVERQGIyEiJjURNDYzFzMVIwGrGzUbGzaGICsSDksgKw4S6xIYGBL+qhIYGBIgKyvAgEtLgEtLgIBLFQ4SgCsTrRkS/wASGRkSAQASGYsVAAIAFQBAAesBwAADABMAACU1IxUTMhYVERQGIyEiJjURNDYzAcDAwBEaGhH+gBEaGhFrgIABVRoR/tYRGhoRASoRGgACABUAQAHrAcAAAwATAAAlNSEVATIWFREUBiMhIiY1ETQ2MwHA/oABgBEaGhH+gBEaGhFrQEABVRoR/tYRGhoRASoRGgADABUAQAHrAcAAAwAHABcAAAE1IxUXNSMVJTIWFREUBiMhIiY1ETQ2MwEAwMDAAYARGhoR/oARGhoRAWsqKlYrK6saEf7WERoaEQEqERoAAgAVAEAB6wHAAAMAEwAAATUjFSUyFhURFAYjISImNRE0NjMBAMABgBEaGhH+gBEaGhEBAJWVwBoR/tYRGhoRASoRGgACACsAVQHVAasAAgAQAAABFTM3FRQGIwUiJjURNDYzIQFAdSAZEf6qERkZEQEAAYt2FqsRGQEaEQEAERoAAgBAAIABwAGAAAsAHwAAJTUjNSMVIxUzFTM1NzcVJxUUBiMhIiY1NTQ2MyEyFhUBK0ArQEArgFVVDQn/AAkMDAkBAAkN6ypAQCpAQDVV6lVLCQwMCdYJDAwJAAIAFQBAAesBwAADABMAACU1IRUBMhYVERQGIyEiJjURNDYzAcD+gAGAERoaEf6AERoaEavq6gEVGhH+1hEaGhEBKhEaAAMAQABAAcABwAAKABgAKAAAJSc3Iwc1IxUzNRcnNSM1IxUjNSMVMxUzNTcyFhURFAYjISImNRE0NjMBgDAwJSYgICZbFSAgIEAgqhEaGhH+1hEaGhHAQEAwMIAwMCAgQEBAYCAg4BoR/tYRGhoRASoRGgACAEAAgAHAAYAACgAeAAA3NycHJzM1IxUzNTc3FScVFAYjISImNTU0NjMhMhYV1WsRWkI2XhfpVVUNCf8ACQwMCQEACQ3AaxFbQxdeNg1V6lVLCQwMCdYJDAwJAAUAKwArAdUB1QAHAA0AEwAZAB8AADY0NjIWFAYiJxcHFwcnFzcXNxcHNyc3JzcXJwcnByc3wCY0JiY0cCYmJiZLiSYmJiZMiiYmJiZLiSYmJiZM5jQmJjQmjCYmJiZMiiYmJiZLiSYmJiZMiiYmJiZLAAwAKwBAAdUBwAADAAcAEwAXABsAHwAjACcAKwAvADMAOQAAJRUjNTcVIzUXNSMVMxUjFTMVIxUDNSMVFzUjFRc1IxUXNSMVAzUjFRc1IxUXNSMVFzUjFRMzESERMwGAKysrVqsrKysrKyoqKioqKiorKysrKysrK6vV/lbVwCsrVSoqqtUrKisrKgEAKipWKytVKytVKioBACoqVisrVSsrVSoqAQD+1QGAAAEAQABAAcABwAAcAAAlMhYVFRQjIiY1NDMzMhYVFBcWBwcWFzc2MzIXFgGrCQwVldYZSgkMDAQJKS5lIQYJBQIkuAwJShnWlRUMCSckDQkkYDEqBgEMAAEAAACuAgABawAeAAABIgcVFAcGBwYiJycmNDc2IBcWFAcHBiInJicmNTUmAQA0LgwgGQYSBjUGBmkBImkGBjUGEgYWIwwyAUAPQg8FDxgGBjUGEgZkZAYSBjUGBhUSBQ5CEAABAFUAVQGVAZUACAAAEzMVIzUHJzcjwNUq+B74jQGV1Y34HvgAAgB3AE0BiQG1AAgADAAAEzcXIxUHJzc1Eyc3F6BgYEuAHnSASR5JAVVgYIiAHnN3/vhIHkgAAQBAAI0BwAFrAAoAAAEXBycVIzUzFSMXAaIewJUrq2J3AWsewJViqyt3AAEAawBrAasBqwAIAAABBzMVIzUzFTcBq/iN1Sr4AY34KtWN+AACAFUAVQGrAasACAAPAAATBxcVIzUnBzUzMxUnByc31TFxKmUx1oAxPh4+AasxcbSiZTGAgDE+Hj4ABAArACsB1QHVAAMABwALABkAAAE1IRUXNSMVNRUhNTcyFhURFAYjIQcRNDYzAYD/AKurAQArERkZEf7VVRkRAVUrK4ArK2srK5UZEf8AERpVAYARGQADAEAAlQHAAWsAAwAHAAsAABMhFSEHNSEVJTUhFZUBK/7VVQEr/wABKgFrK6srK1YqKgAEACsAKwHVAdUAAwAHAAsAGQAAATUhFQU1IRUFNSEVJREnISImNRE0NjMhMhYBgP8AAQD/AAEA/wABVVX+1REZGREBVhEZAVUrK0ArK0ArK9b+gFUaEQEAERkZAAUAKwAAAdUCAAAHAA8AHwAjACcAACU1NCYiBhUVNiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzETUhFREVITUBa0lESX8oHBwoHHsRGRkR/qoRGRkRAVb+qpUgGB4eGCDbHCgcHChXGhH/ABEaGhEBABEa/lUrKwIAKysABQBAAEABwAHAACAAJAAqADYAOgAAJTIWFRUUBiMiJjU0NjMzMhYVFRQXFgcHFhYXNzYzMhcWNzUjFSczFSMVIycVIzUzNSM1MxUjFTcVIzUBqwkMDAmW1QwJSwkMDAQJLxRTJi8HCAMEJCgWFUArFUBAKytAK1YWtQwJSwkM1ZYJDAwJASkiDQkvJlMULwcCDOAWFitAK0BAFhVAFRYra2sACgBVABUBqwHrAAcADwAXAB8AJwAvADcAPwBHAE8AABIyFhQGIiY0FjIWFAYiJjQ2MhYUBiImNBYyFhQGIiY0JjIWFAYiJjQ2IiY0NjIWFAQyFhQGIiY0NjIWFAYiJjQ2MhYUBiImNBIyFhQGIiY07yIaGiIaGiIaGiIamiIaGiIaGiIaGiIaZiIaGiIavCIaGiIa/sQiGhoiGhoiGhoiGhoiGhoiGpoiGhoiGgHrGiIaGiJmGiIaGiIaGiIaGiJmGiIaGiIaGiIaGiLEGiIaGiLEGiIaGiKaGiIaGiKaGiIaGiL+mhoiGhoiAAIAKwBVAdUBqwAFABUAAAE1BycVFzcyFhURFAYjISImNRE0NjMBq6urq6sRGRkR/qoRGRkRAVUra2srasAaEf8AERoaEQEAERoAAgArACsB1QHVAA0AGgAAARQGIyMHETQ2MyEyFhUXMhYVEScjIiY1NSE1AWsNCdVVDAkBFQkNVQkMVesJDAEVAQAJDFYBKwkMDAlADAn+wFUMCSvAAAIAawBAAZUBwAAGAA0AACUzByczNTMnFyMVIzUjAVVAVVVAKpVVQCpAlVVVlpVVlpYAAwBAACABwAHQAAgAEQAiAAABByc3FxYWBycVNScGFRQWFxYXFwcnBiMiLgI1NDY3JzcAAQAxHk95JhIWm2YaFhAm7QcbOi88HUcqHRYQOxsBKwGTMB5PeSZrL5rTZ2YiKxY0ECYbCBs6Jh0qRh0ZQBM7G/7VAAMAQAAVAcAB1QAVABkALAAAATY1NCYiBhUzNDYyFhQHBwYVFTM0Nwc1IxUTMhYVERQGIyMHJyMiJjURNDYzAUEUMkYyKhoiGg0aGSoZGSqqERoaEVVAQFUSGRkSASUUHCMzMyMRGhoiDRsbIQsiG5IrKwFVGRH+1REaQEAZEgErEhgAAgArACsBqwHVABMAJAAAExYXBycGBgcHLgQ1NDcnNxc3IgcnNjYzMhYVFAcnNjU0JvptRBtIECQKCgYUNCcgBEQbsggXEEUSQBo+VyRNER8BC2xEG0cYLgsLBxdFQVIfCxZEG7JnEkQTG1c+MEVODxgWHwACAGsAKwGVAdUABwAYAAASMjY0JiIGFCYyFhUUDgIHBy4ENTTqLB8fLB8JfFcfLCsPEAYUNCcgAQsfLB8fLKtXPh9QRj0SEQcXRUFSHz4ABAArACsB1QHVAAMABwALABkAAAE1IRUFNSEVBTUhFQEyFhURFAYjIQcRNDYzAYD/AAEA/wABAP8AASsRGRkR/tVVGREBVSsrQCsrQCsrAQAZEf8AERpVAYARGQABACsAKwHVAdUADQAAATIWFREUBiMhBxE0NjMBqxEZGRH+1VUZEQHVGRH/ABEaVQGAERkAAgArACsB1QHVAAQAEgAAJREhETcBMhYVERQGIyEHETQ2MwGr/qorASsRGRkR/tVVGRGrAQD+1SsBKhkR/wARGlUBgBEZAAIAMwAdAcMBwAALABIAABMBBycGIyMiJjU1JyUVJzczMhZOAXUcKAwI1hEZOAFi8jKWERkBrf6MHCkGGhHvOAP58jIaAAEAQABAAcABwAAcAAATFhc3NhcWMzIWFRUUBiMiJjU0NjMzMhYVFBcWB40wXS8KDCQoCQwMCZbVDAlLCQwMBAkBGl0wLwoFDAwJSwkM1ZYJDAwJKCQNCQADACsANQHVAdUAIgAyAEIAABMXAQcnIwciJjU3JwYVFBcHJiY1NDcnBhUUFhcHJiY1NDcnFyIHJzYzMhYVFAcnNjU0JhcnNjU0JiMiByc2MzIWFRRGFQFlG6ABBBEaASIJKhUdIxQeIS4nFTE5LCzVKyUfMj1YfR8gFWUxIwEyIwkEIxYaNUsByxb+mxuhARoRBCISFDEZJRE7IyYfHy42Lk8XJRxjOko5LQUUHx99WDwzHycpRmXbIwQJIzIBIwlLNRoABAAAAEACAAHAAA8AFwAfAC8AACUmNDczNycOAhQWFhc3Jwc1NCYiBhUVEiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzAX0ICCMgKhIjDAwjEiogdVhQWJo0JiY0JuoRGhoR/lYRGhoR1RUsFSorDjIqLCoyDisqVRUdJSUdFQEAJjQmJjRmGhH+1hEaGhEBKhEaAAUAAABAAgABwAADAAsAEwAjACkAAAE1IxUVNTQmIgYVFRIiBhQWMjY0NzIWFREUBiMhIiY1ETQ2MwUHJzUXNwHVqlhQWJo0JiY0JuoRGhoR/lYRGhoRAZVAQEBAAQCAgIAVHSUlHRUBACY0JiY0ZhoR/tYRGhoRASoRGmsqKhYrKwAEAAAAQwIAAdUABAAIAAwALAAAEyYnNxc3FSM1FwcnNxcWFAcHBiInJicmNTUmIgcVFAcGBwYiJycmNDc2MzIWiUoCHkxuKthMHkxVBgY1BhIGHB0MLmguDCAZBhIGNQYGaZE8kgEvSgEfTIhqaltLHkz9BhIGNQYGGg4FDkIPD0IPBQ8YBgY1BhIGZDsABABbACsBpQHrAAMAEwAdACUAACU1IxU3MhYVFRQGIyMiJjU1NDYzNzIWFwcmIgcnNgc2MhcHJiIHAUCAfQoODgp6Cg4OCj0oYB0eOJ44HkULLH4sHx9aH1Wrq9UOCs8KDg4KzwoPwCgdHjg4HkWCLCweHx8AAgAVAGsB6wGVAAMAEwAAASEVISU0NjMhMhYVFRQGIyEiJjUBlf7WASr+gRkRAYARGhoR/oARGgFr1tYRGRkR1hEZGREAAgBrABUBlQHrAAMAEwAAJREjERMyFhURFAYjIyImNRE0NjMBa9bWERkZEdYRGRkRawEq/tYBfxkR/oARGhoRAYARGgACABUAawHrAZUAAwATAAABIRUhJTQ2MyEyFhUVFAYjISImNQGV/tYBKv6BGREBgBEaGhH+gBEaAWvW1hEZGRHWERkZEQACAGsAFQGVAesAAwATAAAlESMREzIWFREUBiMjIiY1ETQ2MwFr1tYRGRkR1hEZGRFrASr+1gF/GRH+gBEaGhEBgBEaAAEAKwBrAdUBqwAhAAABFyMVFAYiJjU1NCYiBhUVMwcnMzU0NjIWFRUUFjI2NTUjAYBVQDJGMhoiGkBVVUAyRjIaIhpAAatWlSMyMiOVERoaEZVVVZUjMzMjlREaGhGVAAQAKwArAdUB1QADAAcACwAZAAABNSMVIzUjFSM1IxUlMhYVERQGIyEHETQ2MwFrKysqKysBFhEZGRH+1VUZEQEVKysrKysrwBkR/wARGlUBgBEZAAMAAACVAgABgAAHAA8AIwAAJDI2NCYiBhQGMjY0JiIGFCQyFhQGIyEiJjQ2MhYVFAczJjU0AWw+Kys+LOo+LCw+KwEvYkREMf7qMUREYkUbYBvALD4rKz4sLD4rKz6URGJFRWJERDErICArMQACABUAgAHrAYAABwAZAAA2MjY0JiIGFDczFSMVIzUjBgYjIiY0NjMyFoQiGhoiGaPdK1VdDUclNUtLNSVH1RoiGhoiPFZVVSMyS2pLMgACAEAAFQHAAesAFwAjAAABMhYVERQGIyMiJjU1MxUzESMVIzU0NjMXBxcHJwcnNyc3FzcBlREaGhHVERor1dUrGhFVVVUVVVYVVVUVVlUB6xoR/oARGhoRQCsBVitAERqaVVYVVVUVVlUVVVUAAwBVABUBwAHrAAcAHQA1AAATNTQmIgYVFTMyFhUVFAYjIyImNTU0NjM1NDYyFhU3MhYVERQGIyMiJjU1MxUzESMVIzU0NjPLExoTWwoQEQt1ChAQCiUuJK8RGhoR1REaK9XVKxoRARUgDBAQDCAQC0sKEBELSwoPIBYgIBa2GhH+gBEaGhFAKwFWK0ARGgAEACsAFQHVAesAAwATABoAIgAAJREjERMyFhURFAYjIyImNRE0NjMFFhQHJzYnNxYUByc2NCcBK9bWERkZEdYRGRkRASsUFBUSEkIoKBYdHVUBVv6qAZYaEf6AERoaEQGAERq8FTQTFhkYQiZrJRYfTxwAAwAqABUBqwHrABcAHwBdAAABMhYVERQGIyMiJjU1MxUzESMVIzU0NjMCMjY0JiIGFDcXFgcHBicnBgcHFCMjIjUnJicHBicnJjY3NzQmNDY1JyY3NzYXFzY3NzQzMzIVFxYXNzYXFxYGBwcUFhQGAYARGhoR1REaK9XVKxoRJiAXFyAWeBcEAhYCBRsICwQFLAYECwgbBQIVAQEBFwEBFwMBFgIFGwgLBAUsBQUEDhwEAhYBAQEXAQEB6hkR/oARGhoRQBUBKhVAERr+8BUgFRUgBhIEAyUEAgoFBRwFBRwFBQoCBCUBBQESAQcEBgISAwQlBAIKBQUcBQUcAgkLAgQlAQUBEgEHBAcAAwAVAEAB6wHAAAYACgAaAAATIzcXIxUjFxEhEQEyFhURFAYjISImNRE0NjPVKlVVKlbr/oABgBIZGRL+gBIZGRIBAFVVVUEBLP7UAVYZEv7WEhkZEgEqEhkAAgAVADUB6wGgAAkAJgAAJTUmIyIHFTYzMgMyFxEUBiMiJyYjIgcmIyIHIgYjIiY1ETYzMhc2AcAhKkE0NEEnJ0wqBwQDAik9QTQrSjYwAQMBBAcrS0orK3X2CiD1IAEgIP7JBAcBFiAgFwEGBAE5ICAgAAMAKwBVAdUBqwACAAcAFwAAATchATUHJxUBMhYVERQGIyEiJjURNDYzAQCr/qoBVqurAVYRGRkR/qoRGRkRARVr/wDVamrVASsaEf8AERoaEQEAERoAAgAAAFUCAAGrAAcAGwAAJTcnFQYHNjMXMxUhNTMiJjU1NDYzITIWFRUUBgEVVlZpFytVllX+AFUSGBgSAVYSGBnLUFAuD246eisrGRLVEhkZEtURGgAEAAAAGwIAAdsABAASACEAJQAANzY3JwYnAQcnITUzIiY1NTQ3JwEUByc3JxUGIgcnITIWFQMzFSOVHTEiIG4BpRs6/n1VEhgOIQG9FnYiVgIHAnABERIYEDsRwCgMIh/k/lsbOisYEtYTDCH+6hkMdiBPLQEBbxgS/wArAAEAQACNAcABawAKAAATNxc3IzUzFSM1B0AeondiqyuVAU0eoncrq2KVAAMAVQBVAaEBoQAHAA8AGQAAEzIWFSM0JiM1MhYVIzQmIxU0NjIWFAYjIiZVWHw9WT6JwzyfcRsoGhsTFBsBKXxYPlm1w4lxn+ETGxooGxsAAgArACsB1QHVAAcALQAANjI2NCYiBhQmMhYVFRQGIyInBiImNDYyFhUVFBYyNjU1NCYiBhQWMzMVIyImNOY0JiY0JhiwfSsfJxggWD8/WD8TGhNljGVlRmtrWH3AJjQmJjTvfVgfICwgID9YPz8sHw0UFA0fRmVljGUqfbAAAwBrABUBlQHrAAcACwAbAAAlIgc2NzUXBxcRIxETMhYVERQGIyMiJjURNDYzARFDIxRSRERa1tYRGRkR1hEZGRHmL1oLJEA/VgEq/tYBfxkR/oARGhoRAYARGgACAEAAQAHAAcAACwAoAAABFSMVIzUjNTM1MxUXMhYVFRQGIyImNTQ2MzMyFhUUFxYHBxYXNzYXFgHAQCtAQCsrCQwMCZbVDAlLCQwMBAkvL14vCQ0kAYArQEArQEDLDAlLCQzVlgkMDAkoJA0JL1wxLwkEDAADABUAQAHrAcAACwAbAB8AAAEXBxcHJwcnNyc3FzcyFhURFAYjISImNRE0NjMBESERATceNzceNzceNzceN8ARGhoR/oARGhoRAYD+gAFVHjc3Hjc3Hjc3HjeiGhH+1hEaGhEBKhEa/qkBLP7UAAQAFQBAAesBwAADAAcAFwAbAAABMxUjJzMVIwEyFhURFAYjISImNRE0NjMBESERARUrK1UrKwEAERoaEf6AERoaEQGA/oABVaqqqgEVGhH+1hEaGhEBKhEa/qkBLP7UAAQAQABVAdUBwAAFABoAHgAmAAABNzUHJxUXIyImNTU0NjMhMhYVFSYjIgYVFBYXNSMVNjIWFAYiJjQBAJWVlba2ERoaEQEqERobGiw/AZRVDD4rKz4sASBLKkpKKtYaEdURGhoRmQ8/LAIHAhYWViw+LCw+AAQAVAArAdUBvwAJAA8AFwAaAAATNjYyFhcHJiIHFzYzMhcHJzYyFwcmIgcBIQGLEDkuORAcHVAcGxEZGBEplz6yPhwzkTMBZv6rAVUBSxAXFxAcHR0bEREplj4+GzMz/sUBVQAFACsAKwHVAdUACQARABkAIQApAAAkIiYnMxYyNzMGBjI2NCYiBhQSMhYUBiImNBYiJjQ2MhYUFiImNDYyFhQBJUo7DSMZYhkjDaaMZWWMZVOwfX2wfZcaExMaE4MaExMaE4spISoqIV9ljGVljAEbfbB9fbBDExoTExoTExoTExoACABAAEABwAHAAAMABwALAA8AEwAXACYAKgAANzMVIzUzFSM1MxUjFzMVIzUzFSM1MxUjNzIWFREUBiMhIiY1ETQzBSERIZUrKysrKytWgICAgICAwgcMDAf+pggLEwFC/tYBKsArgCqAK4ArgCqAK4ALCP6mBwwMBwFaEyv+1gAJAAIAAAHcAdoAAwAHAAsADwATABcAHgAiADAAACUzJyMnNSMVFzUjFSc1IxUXNSMVFzUjFQMBBychEScFMxUjJxUnMxUzESc1IxUnMzUBAFUqKysqKiorKysrKys5AcAcQP6rKQFTKyuqPpPVKqs+E2sqKysrVSoqqisrVSsrVSoqAW/+QhxAAVUpqSqqEz5V/uwrvhM+KgACAGsAKwGVAdUAEQAbAAASMhYVFAcVFAYjIyImNTUmNTQTNTMVFAYjIyImwnxXQAwJgAkMQFWADAlWCQwB1Vc+Sy8xCQwMCTEsTj7+whUVCQwMAAEAawBrAZUBlQALAAAlIxUjNSM1MzUzFTMBlYAqgIAqgOuAgCqAgAACAEAAQAHAAcAACwAbAAAlNSM1IxUjFTMVMzU3MhYVERQGIyEiJjURNDYzAWtWKlZWKoARGhoR/tYSGRkS6ypWVipWVtUaEf7WERoZEgEqEhkAAgArACsB1QHVAAsAEwAAJTUjNSMVIxUzFTM1JjIWFAYiJjQBa1YqVlYqbbB9fbB96ypWVipWVup9sH19sAADACsAKwHVAdUABwAPABsAADYyNjQmIgYUEjIWFAYiJjQ3FTMVIxUjNSM1MzW6jGVljGVTsH19sH3qVlYqVlZVZYxlZYwBG32wfX2wE1YqVlYqVgADAEAAQAHAAcAAAwAKAB4AABMhJyETNyM1IxUjJRYVERQGIyEiJjURNDc3NjMhMhdtASYU/wCBdUpWSgErChoR/tYRGgodCg8BAA8KAZUW/uB1KyuQDA/+9hEaGhEBCg8MJAwMAAIAAABAAgABwAALABoAACUnNycHJwcXBxc3FxMyFhURFAYjISInJzc2MwGVTEweTE0eTU0eTUxeERoaEf7AFA5zcw4Us01NHk1NHk1NHk1NASsaEf7WERoTra0TAAMAKwArAdUB1QAIABEAGQAAJTI2NTQmJwcWJxQWFzcmIyIGNjIWFAYiJjQBAEZlFQ/wLW8VD/AtPEZlU7B9fbB9VWVGGD4T8CSrGD4T8CRlj32wfX2wAAEAawBrAZUBlQALAAABBxcHJwcnNyc3FzcBlXd3Hnd3Hnd3Hnd3AXd3dx53dx53dx53dwADACsAFQHAAesAAwATABwAACURIxETMhYVERQGIyMiJjURNDYzNxUhESMRNDYzAZXq6hEaGhHqERoaEar/ACoZEUABK/7VAVUZEf7VERoaEQErERlWK/7VASsRGgAFACsAKwHVAdUABAAIABAAGAA1AAABMxUHJwYyNCIGMjY0JiIGFDYyNjQmIgYUFwEVIycHFhUUBiImNDYzMhc3JwYjIiY0NjIWFRQBlUCVKyAWFoYiGhoiGhoiGhoiGnkBB0CVMgcyRjIyIxQPMjIPFCMyMkYyAcAVlitLFrYZJBkZJOcZJBkZJBH++BWVMg8UIzIyRjIHMjIHMkYyMiMUAAMAQAArAcACAAAHAA8AJQAAJREjFSM1IxESIgYUFjI2NDcyFhURFAYjISImNRE0NjMzNjYyFhcBlSrWKp4SDAwSDIARGhoR/tYRGhoRWQcgKiAHVQFWQED+qgGADBIMDBIMGRH+qhEZGREBVhEZExgYEwACAEAAQAHAAcAACQAOAAABByc3NjIXFxYUBTcXByMBuidQJwYSBjIG/oDsUOxQAWonUCcGBjIGEuDsUOwAAgArAFUB1QHrAAMAEgAAJTcnBwUVFAYjISImNTU0NzcXFgEAsLCwAYUZEf6qERkUwcEU625nZwTVERoaEdUZDHFxDAADAEAAgAHAAYAAAwAHAAsAADc1IRUlIRUhFzUzFYABAP7AAYD+gJVW6yoqlSvVKysAAQBrAEABqwGrAAkAAAEzFSMnIxUjETMBM3iWCHgqwAGA1SqVAWsAAQBVAFUBqwGrAAYAAAE1Fwc1IzUBAKurqwFVVqurVqoAAgA9AEABwAHAAAgARQAAJTI2NwYGFRQWJyc2NzYzMhYVFAcGBwYWMzI3Njc2MzIWFzMVIwYGIyImNTQ2Ny4EIyIHDgIHBiY1ND4DNzYnJgEoDhwEHiAKwCUKCBsfEyMcHAsGBQgJDxYbMDkqKQM1NAY+IRwoPDcBAQUHDgocOxENGAsjMgsSEA8BEQsHdCUmCCYNBwn5JAwIGx4fHignJBEYEhYkPDciNUU+JxshSAoIBg0GBUkVDxQDCzAkDyQiGxYBHAQDAAIAQABAAcABwAAJABkAACU1IRUzFBYyNjUTMhYVERQGIyEiJjURNDYzAZX+1VYmNCZVEhkaEf7VEhgYEsDV1RomJhoBABkS/tYRGhkSASoSGQADACsAlQHVAWsADwATACMAAAEyFhQGIyM1MzI2NCYjIzUHNTMVJBQWMzMVIyImNDYzMxUjIgFrLD4+LFZWGycnG1Zqqv7+JxtWViw+PixWVhsBaz9YPyknNicpgCoqMDYnKT9YPykAAgArAFUB1QGrAAUAFQAAATUHJxUXNzIWFREUBiMhIiY1ETQ2MwGrq6urqxEZGRH+qhEZGREBVStraytqwBoR/wARGhoRAQARGgACACsAVQHVAasABQAVAAABNQcnFRc3MhYVERQGIyEiJjURNDYzAaurq6urERkZEf6qERkZEQFVK2trK2rAGhH/ABEaGhEBABEaAAEAIQCrAdUBawAOAAABNxUjNyYjIgYHJzY2MzIBiUzATjA+M18QMhZ1SVUBHk3ATShEMRBEVgABAGsA6wGVARUAAwAAJSE1IQGV/tYBKusqAAIAKwArAdUB1QADAAsAACU1IxU2MhYUBiImNAFr1hOwfX2wfesqKup9sH19sAADACsAKwHVAdUABwAPABMAADYyNjQmIgYUEjIWFAYiJjQXMxUjuoxlZYxlU7B9fbB9atbWVWWMZWWMARt9sH19sEMqAAEAQABVAcABlQAIAAATFhYXJiMVJzfVaHESTZ6VlQFAD4FbbVeVlQACAAAAVQIAAZUACAAOAAABFhYXJiMVJzcHBxcVJzcBFWhxEk2elZWAVVWVlQFAD4FbbVeVlUBVVUCVlQADAEAAQAHAAcAAAwALABMAACU1IxUWMjY0JiIGFBMXFQcjJzU3ARUqChYRERYRbHBwoHBw64CAXBEWEBAWASBwoHBwoHAAAwBAAEABwAHAAAMACwAZAAABNSMVFjI2NCYiBhQTFxEUBiMhIiY1ETQ2MwFA1Xs0JiY0JqtVGhH+1hIZGRIBQFVV1SY0JiY0AS9V/wARGhkSASoSGQASAEAAQAHAAcAAAwAHAAsADwATABcAHAAgACQAKAAsADEANgA6AD4AQgBGAEsAABMVMzUHNTMVAzUzFQM1MxU3NTMVJzUzFQM1MxQGJzUzFQc1MxUDFSM1AzUzFRUiJjUzATIWFSMnFSM1BzUzFRM1MxUnNTMVJzQ2MxXAgKvWKysrKyorKysrKxoRK9UqVStVKxEaKwEqERorgCqrKyorgCsrGhEBQICAq9bWAQArK/6rKytVKyurKyv/ACsRGqsqKqsrKwGAKyv+1SsrVRoRAVUaESsrK4ArK/8AKyurKiqqERorAAEAKwBAAesBwAAFAAA3NSUlNQUrAUD+wAHAQJUrK5XAAAMAQACAAcABgAADAAcACwAANzUhFSUhFSEVNTMVQAEA/wABgP6AgOsqKpUr1SsrAAMAawBrAZUBqwACAAoADgAAAQczBwcjNzMXIycHIRUhAQAoUF0ULGUgZSwUygEq/tYBgGsmL+vrL1oqAAEAKwCrAd8BawAOAAABMhYXByYmIyIHFyM1FzYBC0l0FzIRWTg+ME7ATD8BVVZEEDRBKE3ATTcAAwArACsB1QHVAAcAFwAaAAAlMwMjAzM3MxMyFhURFAYjISImNRE0NjMXNxcBVC1tKG0tGHhvERkZEf6qERkZEX8sLHUBFv7qQAEgGRH+qhEZGREBVhEZ9XZ2AAMAQABAAcABwAAGABAAIAAAAQcnMzUzFRc1IRUzFBYyNjUTMhYVERQGIyEiJjURNDYzAVVVVSpWav7VViY0JlURGhoR/tUSGBgSAStWVkBAa9XVGiYmGgEAGhH+1hEaGRIBKhIZAAMAQABAAcABwAADAAoAHgAAEyEnIRcHMxUzNTM3FhURFAYjISImNRE0Nzc2MyEyF20BJhT/AIF1SlZKQQoaEf7WEhkKHQoPAQAPCgGVFnZ1KyvRDBD+9hEaGRIBChAMIwwMAAMAKwArAdUBwAAFAAkAIwAANzcnBxcHERUzNTUyFhUVMzIWFRUUBiMhIiY1NTQ2MzM1NDYz61VVFkBAVhEZVhEZGRH+qhEZGRFWGRF1VlUVQEABCioqKxkSKhoR6xEZGRHrERoqERoAAgAVAGsB6wGVABEAJwAAATIWFRUGBhUVITU0Jic1NDYzBDIWFRUUBiMhIiY1NTQ2MhYVFSE1NAGAERoTGP8AGBMaEQEvIhoaEf6AERoaIhoBKgGVGREuByAVLCwVIAcuERlqGhFrERkZEWsRGhoRQEARAAUAKwBVAdUBqwAHABEAFQAZAB0AAAEVITUzNzMXAzUzFRQGIyMiJiUzFSM1MxUjFTMVIwEr/wBAFVUWq9UZEYARGgEAgICVlVVVAZUqKhYW/uvV1REaGpErgCqAKwAEACsAVQHVAZUAEgAWABoAHgAANjQ2MzMVIyIGFBYzMzUXBzUjIjczFSM1MxUjNTMVIytROUtLKDg4KAtAQAs5r6qqqqqqqtJyUSo4UDgqQEArKyugK6AqAAIAawBAAasBqwAHABEAACU1IycjFTMXNTMVIycjFSMRMwGAaxVrgBaAlhVrKqrVgCuAK6vVKpUBawADACsAQAHVAcAAGgAeAC0AABM3AQcnIzUnIzUzJwYGFRQWMzMVIyImNTQ2NxcVIyc3MhYVFAcnNjY1NCYjIzUrGwFlG1YlMDoPLBkiJxtWViw+JhzoBCpELD46HxUcJxtWAaUb/psbVSUxKiwDJRkbJyk/LB46C04qKlY/LEEeHwUkFxsnKQADABUAFQHbAdsABwARABsAADYyNjQmIgYUFwcnByMnNTcnNxcVJzczFxUHJzX1FhERFhH3G00joHAjThu7UhegcBeUjxEWEBAWcBtOI3CgI00bcBRSF3CgF5M/AAIAQABAAcABwAAIABYAACU3FwcnNxc1MxczFRQGIyEiJjU1MxUhARU4HmtrHjgqgCsaEf7WERorASryNx5rax43zsCVERoaEZWVAAcAQABAAcABwAADAAcACwAPAB8AIwAnAAA3MxUjBzM1IzczFSMHMzUjASEiJjURNDYzITIWFREUBiczNSM1MzUjlUBAFWtrFUBAFWtrARX+1hEaGhEBKhEaGpFra2tr1UAVa4BAFmv+wBoRASoRGhoR/tYRGmAraisAAwArABUBwAHrAAIAEAAZAAABMyc3FxUUBiMjIiY1ETQ2MzcVIREjETQ2MwErdXUVgBoR6xEZGhGq/wAqGREBAHUggNURGhoRASsRGVYr/tUBKxEaAAMAQABLAdUBqwAFAA0AFgAAJSc3FzcXJiImNDYyFhQHFyM1NDYzMhcBSkoeLG0ex0YzM0YygEDAdTYPBktKHixuHikyRjMzRp1AKyYvAQADAEAAKwHAAdUAEAAUACgAAAE2MzIXFxYUBwcGIicnJjQ3NycHHwIVFAYjISImNTU3MxcjByEnIzcBEAYJCwRqBgaIBhIGagYG40xpS39AGRL+1REZQBIqKyYBKiUpKgHPBgZqBhIGhwYGaQYSBg9MaksCQFYSGBkRVkArKysrAAQAKwBOAdUBsgAdADsAWQB3AAAAMhcWMxUiJyYjIgcGIicmIyIHBiM1Mjc2MhcWMjc3FjMVIicmIyIHBiInJiMiBwYjNTI3NjIXFjI3NjIGMhcWMxUiJyYjIgcGIicmIyIHBiM1Mjc2MhcWMjcWMhcWMxUiJyYjIgcGIicmIyIHBiM1Mjc2MhcWMjcBTjoiGBMdIhoREhoiOiIaEhEaIh0TGCI6IhgoGH4aER0iGhESGiI6IhoSERoiHREaIjoiGiQaIjo6OiIYEx0iGhESGiI6IhoSERoiHRMYIjoiGCgYJDYkGBMdIhoREhoiOiIaEhEaIh0TGCQ2JBgoGAFTEQwqEQ0NERENDREqDBERDAxfDSkRDQ0REQ0NESkNERENDRG+EQwqEQ0NERENDREqDBERDAxNEgwqEQ0NERENDREqDBISDAwAAgBrACsBlQHVAAUAFgAANzcnBycHNjIWFRQOAgcHLgQ1NN+MHm4sHi18Vx8sKw8QBhQ0JyDVjR5vLR61Vz4fUEY9EhEHF0VBUh8+AAQAKwBVAdUBawALABsAJQApAAABFTMVIxUjNSM1MzUkFBYzMxUjIiY0NjMzFSMiBTQmIyM1MzIWFSUzFSMBlUBAKkBA/ugnG1ZWLD4+LFZWGwEzJxtWViw+/taqqgEAQCtAQCtAGzYnKT9YPylCGycpPywVKgADACsAKwHVAdUAAwAHAB0AAAE1IRUXNSMVEzIWFRUUBxUUBiMhIiY1NSY1NTQ2MwGr/qrrgOsQGhUcD/7WDxwVGhABa0BAlisrAQAZEUEYDPERGRkR8QwYQREZAAUAKwArAdUB2AAHAA8AFQAZAB0AADYyNjQmIgYUNjIWFAYiJjQ3FRcHJzUnByc3BQcnN8J8V1d8V0WgcHCgcMtVEGVDYhtiAUgbYhtVWHxXV3z+cZ5xcZ4bcDIaPIBjUiBSUiFTIAAFACsAKwHVAdoABwAPABUAGQAdAAA2MjY0JiIGFDYyFhQGIiY0NxUXByc1JwcnNwUHJzfCfFdXfFdFoHBwoHDLVRFkQmMbYgFIG2McVVh8V1d8/nCgcHCgGnEzGj6AYlEgUVEgVCAAAwArACsB1QHVAAUADQAVAAABFRcHJzUCMjY0JiIGFBIyFhQGIiY0AQtgEHAxjGVljGVTsH19sH0Ba3A5G0SA/upljGVljAEbfbB9fbAABQArACsB1QHYAAsAEwAbAB8AIwAAARUzFSMVIzUjNTM1BjI2NCYiBhQ2MhYUBiImNCUHJzcHByc3ARVAQCpAQCl8V1d8V0WgcHCgcAGVG2Iby2IbYgFAQCtAQCtA61h8V1d8/nGecXGeTCFTICBSIFIAAgArACsBwAHVAA8AGgAAEzcBBycVFxUnBzU3NQc1NzcXFScnNTQ2MhYVQBsBUBt7K0tKKqp/a6tEpxMaEwGQG/6wG3pPICAVFSAgdTUqUBtrKhWnTg0TEw0AAQArACsBwAHVABQAACUnFRcVJwc1NzUHNTc1NDYyFhUVFwHAqytLSiqqqhMaE6urNXUgIBUVICB1NSprdQ0TEw11awADAJUAKwFrAdUAAwAHABsAACU1IxUXNSMVEzIWFREUBiMjIiY1ETQ2MzM1MxUBFSoqKmMMEREMnAwREQwjVtVra1UrKwErEQz+uQwQEAwBRwwRKioAAgCVACsBawHVAAUAGQAANzcjNQczNzIWFREUBiMjIiY1ETQ2MzM1MxXrVStVK2MMEREMnAwREQwjVlWgdqDgEQz+uQwQEAwBRwwRKioAAQCVACsBawHVABMAAAEyFhURFAYjIyImNRE0NjMzNTMVAU4MEREMnAwREQwjVgGrEQz+uQwQEAwBRwwRKioAAQCVACsBawHVABMAAAEyFhURFAYjIyImNRE0NjMzNTMVAU4MEREMnAwREQwjVgGrEQz+uQwQEAwBRwwRKioAAwCVACsBawHVABQAGAAsAAAlNjU0JiIGFTM0NjIWFAcHBhUzNDcHNSMVEzIWFREUBiMjIiY1ETQ2MzM1MxUBMQ8mNCYgEhwSCRQUIhIPKGIMEREMnAwREQwjVvEPFRomJhoNExMaCRQUFxASYSkpASoRDP65DBAQDAFHDBEqKgADAGsAKwF6AdUAAgAFABMAACUnFREVNzMHFwcjNQcnNyc3FzUzAT0oKD1cXHoVYh53dx5iFaQpUQEIUSlcXHmiYh53dx5iogAFAEAAKwHAAdUAAwAGAAkAFwAbAAABFwcnBycVERU3MwcXByM1Byc3JzcXNTMHByc3AZUrKyouKCg9XFx6FWIed3ceYhVrKisrASsrKytcKVEBCFEpXFx5omIed3ceYqLVKysrAAMAVQArAasB1QACAA0AFgAAJTcnJwEHJwcjNQcnNycXFSc1MxcHJzcBFSgoogE4HjFcFWIed43AKhV6QR4ifCgp3v7IHjFbomIed40JRStreUEeIwAFAEAAKwHAAdUAAgAFABMAHAAiAAAlJxURFTczBxcHIzUHJzcnNxc1MxcWFRQHJzY0Jwc3FhUUBwETKCg8XFx6FWIed3ceYhXMHyEZFRVWMQoKpClRAQhRKVxceaJiHnd3HmKiZDI9PDUZKlgqVjEZGBkZAAMADwAPAfEB8QAHABcAGgAAJTMnIwczNzM3FwcVIwcnIzUnNzUzNxczBzcXATEpRSpFKQ9EiUZGZEdHZEZGZEdHZMQZGavAwCpyR0dkRkZkR0dkRka5Tk4AAwAPAA8B8QHxAAcADwAfAAASMhYUBiImNBYyNjQmIgYUJRcHFSMHJyM1Jzc1MzcXM91GMjJGMiBqS0tqSwErRkZkR0dkRkZkR0dkAVUyRjIyRqNLaktLanxHR2RGRmRHR2RGRgACAA8ADwHxAfEABwAXAAA2MjY0JiIGFAUVIwcnIzUnNzUzNxczFRfLaktLaksBK2RHR2RGRmRHR2RGgEtqS0tqEmRGRmRHR2RGRmRHAAIADwAPAfEB8QAFABUAACUyNjQmIxcVIwcnIzUnNzUzNxczFRcBADVLSzWrZEdHZEZGZEdHZEaAS2pLx2RGRmRHR2RGRmRHAAIAKwArAdUB1AASAB4AACUyNjcXBiMiJjU0NjcVBgYVFBYTFhYVFAcnNjU0JicBAB1EEzhAbFh9b1E2SldTUW8SOApKNmshFyFXfVhTeQhACFQ4PlcBaQh5UzAnIRwaOFQIAAQAVQAVAasB6wANABMAGQAnAAAlNTMVFAYjIyImNTUzFTcHJzcXBxcnNyc3FyUVIzU0NjMXMhYVFSM1AWsqGRHWERkqQB5iYh5Dtx5DQx5i/uoqGRHWERkqaypVERoaEVUqUR5iYh5EYh5ERB5ilSpVERoBGRFVKgADAAAAVQIAAasAAwATACAAACU1IxU3MhYVFRQGIyMiJjU1NDYzJRUzFSE1MzU0NjMhFQHVVWsJDAwJgAkNDQn+6tb+1SsZEQGAlZaWwAwJ1QkNDQnVCQwr60BA6xEaKwAGABUAQAHrAcAAAwAHAAsADwATACcAABMVIzU3FSM1BRUjNTcVIzUFESERATIWFQMUBiMjFSM1IyImNRE0NjOVKioqASrq6uoBFf6AAYARGgEZEWuqaxEaGhEBACsrVSoqVSsrVSoqwAEA/wABKxoR/wARGSsrGREBABEaAAMAFQAVAesB6wAHAB8AJwAANjI2NCYiBhQlMxUjBgYHFSM1JiYnIzUzNjY3NTMVFhYmMhYUBiImNMJ8V1d8VwFULCwHZD8qP2QHLCwHZD8qP2TbRjIyRjJrV3xXV3xTKj9kBywsB2Q/Kj9kBywsB2QBMkYyMkYAAgAVABUB6wHrAAcAHwAANjI2NCYiBhQlMxUjBgYHFSM1JiYnIzUzNjY3NTMVFhbCfFdXfFcBVCwsB2Q/Kj9kBywsB2Q/Kj9ka1d8V1d8Uyo/ZAcsLAdkPyo/ZAcsLAdkAAMAFQAVAesB6wAJABwAMgAAJScGBhUUFjMyNgE3AQcnBgcVIzUmJicjNTM2NjcFMxUjBgcnNjU0JiMiByc2NzUzFRYWAVvRDRJXPhU1/vYbAWUbLC03Kj9kBywsAhoPAVMsLAUQIAtXPh4bIB8lKj9kitERNRU+VxIBKBv+mxssJQYsLAdkPyoXOxJkKicdIBsePlcLIBAFLCwHZAADABUAFQHrAesACQAcADIAACUnBgYVFBYzMjYBNwEHJwYHFSM1JiYnIzUzNjY3BTMVIwYHJzY1NCYjIgcnNjc1MxUWFgFb0Q0SVz4VNf72GwFlGywtNyo/ZAcsLAIaDwFTLCwFECALVz4eGyAfJSo/ZIrRETUVPlcSASgb/psbLCUGLCwHZD8qFzsSZConHSAbHj5XCyAQBSwsB2QAAgAVABUB6wHrAAcAHwAANjI2NCYiBhQlMxUjBgYHFSM1JiYnIzUzNjY3NTMVFhbCfFdXfFcBVCwsB2Q/Kj9kBywsB2Q/Kj9ka1d8V1d8Uyo/ZAcsLAdkPyo/ZAcsLAdkAAUAQAArAcAB1QADAAcACwAPABMAAAEzFSMHETMRJTUzFRcRMxEnETMRAZUrK1Ur/tUrgCqAKwErVlUBAP8AVVZWqgGq/lZVAQD/AAABACsAKwHVAdUAAgAAJSEBAdX+VgGqKwGqAAEACAA2AfgBwAAJAAABFwcnMDcnNjIXAbQBtbUBRHn+eQEXAeDgAVRVVQADACsAKwHVAdUAGAAcACwAAAERIREzFSMVMzUjFRYVFAYiJjU0NzU0NjMTESERATIWFREUBiMhIiY1ETQ2MwGA/wBVKqpAFhoiGhYZEZb+qgFWERkZEf6qERkZEQGA/wABACuqqjAMGREaGhEZDDARGv7VAVb+qgGAGRH+qhEZGREBVhEZAAYAKwArAdUB1QAIABEAGgAiACcAMAAANxUzFSMiJjU1BTUzFRQGIyM1EzIWFRUjNSM1FhQGIiY0NjIHFzcXIQMVIzU0NjMzFVWWlhEZAYAqGRGWlhEZKpZWExoTExqDQCtA/wArKhkRluuWKhkRlpaWlhEZKgGAGRGWlip9GhMTGhOATzlVASuWlhEZKgACAEAAQAHcAdwADQARAAABFwczFSM1MycVIzUzFQM1MxUBY3l5XatOeKurq6sB3Hl4q6t4Tqtd/t2rqwAEABUAawHrAZUABwAdACEAMQAAExUzNTQmIgYHIiY1NTQ2MzU0NjIWFRUyFhUVFAYjFzUhFQEyFhUVFAYjISImNTU0NjPmNA8WDxEJDAwJGSQZCQwMCWr+1gFVERoaEf6AERoaEQErFhYKDw+KDAlACQwWEhgYEhYMCUAJDBbW1gEAGRHWERkZEdYRGQAEAGsAFQGVAesAAwATABsAMQAAJREjERMyFhURFAYjIyImNRE0NjMXFTM1NCYiBgciJjU1NDYzNTQ2MhYVFTIWFRUUBiMBa9bWERkZEdYRGRkRUTQPFg8RCQwMCRkkGQkMDAlrASr+1gGAGhH+gBEaGhEBgBEawBYWCg8PigwJQAkMFhIYGBIWDAlACQwABAAVABUB+gIAAAcAHQAnAEAAAAEVMzU0JiIGByImNTU0NjM1NDYyFhUVMhYVFRQGIwU3FwciJiczFhYlFhQHBwYiJwEmNTQ3NzYyFxcHJwcXNyc3AWZJFR4WEQkMDAkgLB8JDAwJ/vUcUQ5kkwggBUsBawoKiAkaCv8ACgqHCRwJNB4tefJ4Lx4BywsLDxUVmgwJVgkMCxYfHxYLDAlWCQz1HFEBiGM1ao8JGwqHCgoBAAoNDAqICQk0Hix48nkvHgAEAAEAAAH/AgAACQANAB8AKQAANzcXByImJzMWFhc3AQc3ARYUBwcGIicBJjU0Nzc2MzIXByc3MhYXIyYmoB1RDmSTCCAFSsyI/wCIngEBCgqICRoK/v8JCYgJDg2PHVEOZJMIIAZENhxRAYhjNWoQiAEAiJ/+/wkaCogKCgEBCQ0OCYgJGhxRAYhjPGAABABVACsBqwHVAAMABwALABkAAAE1IxUjNSMVIzUjFTcyFhURFAYjISImNRM3AYArFSsVK6sRGhoR/wARGgF/AVVWVlZWVlaAGRH+qhEZGREBAIAAAwAVAEAB6wHAAAMAEwAmAAAlESERATIWFREUBiMhIiY1ETQ2MxMiJjU0NjczNjMyFhczMhYUBiMBwP6AAYARGhoR/oARGhoRgBomIRgEFS4cKgQBFh8fFmoBLP7UAVYaEf7WERoaEQEqERr+6yYaGCUCKyUbHywfAAEAKwArAdUB1QACAAA3ARErAaorAar+VgADACsAKwHVAdUABAAIAAwAADcBFSMRMzUzFSc1MxUrAapVKyoqKisBqoD+1ioqVaurAAIAMwAdAcMBwAALABIAABMBBycGIyMiJjU1JyUVJzczMhZOAXUcKAwI1hEZOAFi8jKWERkBrf6MHCkGGhHvOAP58jIaAAIAKwArAdUB1QACAAUAAAERIQEBIQHV/lYBgP7nARkB1f5WAUP+5wACABUAFQHVAesABgAJAAATAQcnITcnJREnZgFvGyr+hb2HAXW3AaD+kBsrvYhm/pG3AAEACAA2AfgBwAAEAAAlAzYyFwEA+Hn+eTYBNVVVAAMACQArAgABwAAbACMAOQAAJRUHAzc+BzIeBhcXByYjIgYXNTQmIgYVFTMyFhUVFAYjIyImNTU0NjM1NDYyFhUBS0v3BgYNFhkgJCctLi0nJCAZFg0GBiwGEC09ihMaE1YIDQ0IawgNDQgeLh/LOF4BNgUECQ4MDwsKBQUKCw8MDgkEBTgCPU0gDRMTDSAOCFUIDQ0IVQgOIBceHxYAAgAIADYB+AHhAAoAEAAAExYAFwcnBwM2NycFByc2MzJGDAEbSBtHU/ghLSsBzXTdLSx/AeEM/uZKG0dnATUbFCxbkdwKAAYAKwBVAdUBqwADAAcACwAPABMAFwAAExUzNQc1IRUlNSMVJyEVIRcVMzUHNSEVVStVAar+qysqAar+ViorVQGqARUqKkBWVpYqKkBWwCoqQFZWAAEAZgAmAZUB6wAsAAABMxUjFRQGIyMVFhUUBiImNTQ3NSMiJjU1JjU0NjIWFRQHFTM1IzcXIxUzNSMBQFUVGRJAGhsoGxpAEhkaHCYcGUArQEArQBUBa1YqEhlBDhwTHBwTHA5BGRIsDhsUGxsUHA0sqlZWqioAAwAAACsCAAHVAAcAHQApAAAlNTQmIgYVFTMyFhUVFAYjIyImNTU0NjM1NDYyFhUnIgYVFQcBNiAXByYB1RMaE1YJDAwJawkMDAkfLCA2LD5L/wBwASBwOQarIA0TEw0gDQlVCQwMCVUJDSAWHx8Waj4sPWMBVVVVTAEAAwArADIB1QHAABcALQA1AAASMhYVFAYHJzY2NTQmIgYVFBYXByYmNTQFFAYHJzY1NCYiBhUUFwcmJjU0NjIWBjIWFAYiJjSosH05MRYnL2SOZC4nFTE5AVUjHRUqMkYyKhUdI0tqS5EiGhoiGgHAfFk6YxwlF08uRmRkRi9OFyUcYzpZWSM7ESUZMSMyMiMxGSUROyM1S0sLGSIaGiIAAgBAABUBqwHrAAgAIAAANzUHJzcjNTMVEzIWFREUBiMjIiY1NTMVMxEjFSM1NDYz1Xced0yVgBEaGhHVERor1dUrGhHATXgeeCqVASoZEf6AERoaEUAVASoVQBEaAAIAlQArAWsB1QAOAB4AABMVMzUjNTM1IzUzNCYiBhcWFRQGIiY1NDc1NDYyFhXrKhUVFRUMEgxVKz9YPysmNCYBlYArFSsVCQ0Nsx83LD4+LDcfqhomJhoAAgBEABUBwAHrAAUAHQAANzcXByc3JTIWFREUBiMjIiY1NTMVMxEjFSM1NDYzln4bmlEbATYRGhoR1REaK9XVKxoR4X4bmVEb1BoR/oARGhoRQCsBVitAERoAAwAgABUB1QHrAAoADQAaAAABIyc2MzMyFhURJwczJycBBycGBiMjIiY1EScBa8Q6Ch7WERkq1tDQWgGaGyUCGBDWERlLAZU6HBoR/ucrZ8+R/mYbJRAWGhEBJUsAAwBrAFUBqwGrAAMABwALAAATMxUjJzMVIwEzESPrQECAQEABAEBAAUDrgIABVv6qAAEAlQAVAYAB6wAnAAABMxUUBiImNRE0NjIWFRUUBiImNTUzFRQWMjY1NTQmIgYVERQWMjY1AWAgRGJFM0YyHywgIA0SDB8sIDNGMgGA9TFFRTEBCiMzMyPgFh8fFsvLCQwMCeAWICAW/vYjMzMjAAEAhwBAAWABwAAoAAATHgMVFAYHFSM1JiYnMxYzMjY1NCcmNTQ2NzUzFRYWFyMmIyIGFRT8Fh0gESkiQCErAi8EOx8bQGQqIEAhIwEvAjQaHwEXBgwVIBYfJwYuLgcqIS0XDyQQF0EdKQcuLwgsHS0WEh0ABQBAAEABwAHAAAMABwALAA8AEwAAATUjFRc1IxUnNSMVFzUjFQMhESEBlYCAgCqAgIArAYD+gAEVgICqgICqgICqgIABVf6AABEAQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAOwA/AEMAADcVIzUVNSEVJRUjNSU1MxUnMxUjBRUjNQU1MxUnNTMVJxUjNSMVIzUXFSM1JxUjNScVIzUXFSM1JxUjNRMVIzUnFSM1aysBgP6rKwFVKysrK/7WKwFVKysrVSsrKoArKyqAK9UqKyuAKisrwCsrgCsr1SoqKysrgCsqKyvWKytWKirVKysrK6sqKlYrK1UrK6sqKqsrK/8AKytVKioAFQBAAEABwAHAAAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAAABNTMVBzUzFQc1MxUDNTMVNzMVIwc1MxUzNTMVAzUzFSc1MxUHNTMVJzUzFSc1MxUHNTMVBzUzFQc1MxUHNTMVMzUzFSc1MxUHNTMVJzUzFSc1MxUBQCsrKysrgCqAKyuqKoArKysrKysr1SrVKysrKysrKysrgCoqKoArKysrKwGVKyuqKiqrKysBVSsrKytVKysrK/8AKyurKipWKytWKiqqKytVKytVKipWKytVKysrK1UrK1UrK6sqKqorKwADAAAAAAIAAgAAAwANABIAADUhFSEBByc3NjIXFxYUBwcjNTcCAP4AAboqUCoGEgYyBkXWUNZVVQGqKlAqBgYyBhJF1lDWABEAQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAOwA/AEMAACU1MxUjNTMVJzUzFTc1MxUnMxUjBTUhFQc1MxU3NTMVAxUjNRcVIzU3FSM1IxUjNSMVIzUTNTMVJzUzFTUVIzURNTMVAZUrgCuAKoArKysr/qsBgNUqgCurKioqgCuAKyorVSuAKysrQCsrKytVKyurKyuAK6oqKqsrK1UrKwErKytVKytVKysrKysr/oArK1UrK9YrK/7VKysADQBAAEABwAHAAAMABwATABcAGwAfACMAJwArAC8AMwA3ADsAACU1MxUHNTMVAxUzFSMVIzUjNTM1EzUzFRMzFSMVNTMVJxUjNSMVIzUzFSM1AzUzFTUVIzUTNTMVIzUzFQGVKysrq6urKqurVSsqKysrVSvVK4ArVSsrVSuAK5UrK1UrKwGAqyqrqyqr/oArKwGAK1UrK4ArKysrKyv+1Ssr1isr/tUrKysrABEAQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAOwA/AEMAAAE1MxUHNTMVFzUzFSc1MxUnMxUjETUzFQc1MxUTNTMVAREzETc1MxUnNTMVAzUzFTc1MxUnNTMVJzUzFQM1MxUHNTMVAUArKysqKysrKysrK4ArKiv+gCsqKysrKysrKioqKioqKioqAZUrK6oqKqsrK6sqKtUr/wArK1UrKwEAKyv/AAGA/oCrKiqqKyv+qysrqyoqVSsrVSsr/wArK1UrKwAHAEAAQAHAAcAAAwAHAAsADwATABcAGwAAExUjNRcVIzUXESERAyERISUVIzUjFSM1NxUjNcArgCqq/tYrAYD+gAErKysqKioBFSoqVSsrVQEq/tYBVf6A1SoqKipWKysAEQBAAEABwAHAAAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwA7AD8AQwAAEzUzFSc1MxUHNTMVNzUzFQM1MxUTMxEjJzUzFQc1MxUnNTMVBzUzFSc1MxUXNTMVIzUzFTc1MxUnNTMVIzUzFRM1MxXrKioqKiorKysrKisrVSuAKtUrKysrK4Aq1SsqKysrgCsqKwFAKytVKyuqKiqqKyv+qysrAYD+gKsqKlYrK6srK6srK1YqKqsrKysrqyoqqisrKyv+qysrAAgAQABAAcABwAADAAkADQARABUAGQAdACEAAAE1MxUlIRUhESMlNTMVBzUzFQc1MxUjNTMVMzUzFSM1MxUBlSv+gAGA/qsrAVUrKyvVKoAr1SuAKwFAKyuAK/6rqyoqVisrVSsrKysrKysrABEAQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAOwA/AEMAACU1MxUXNTMVAzUzFRM1MxU3NTMVASEVIQU1MxUnNTMVBzUzFSc1MxUHNTMVBzUzFSc1MxUXNTMVJzUzFSM1MxUHNTMVAUArKivVKisrKiv+gAGA/oABVSsrK9Uq1SsrKysrKyuAKioqgCsrK+sqKqsrKwEAKyv/ACsrVSsrASsrqioqVSsrqysrqysrVSoqqysrVSsrVSsrqyoqKiqrKysAEQBAAEABwAHAAAMABwALAA8AEwAXABsAHwAjACcAKwAvADMANwA7AD8AQwAAJTUzFQc1MxUDNTMVFzUzFSczFSMVNTMVBzUzFSMRMxE3NTMVATUzFQM1MxUHNTMVJzUzFTM1MxUHNTMVAzUzFQc1MxUBQCsrKysrKisrKysrKyvVKoAr/tUrgCsrKysrKisrK4ArKyvrKiqrKysBVSsrVSsrgCuqKiqrKysBgP6AVSsrAQArK/8AKytVKyurKioqKqsrKwFVKytVKysABQBAAEABwAHAAAMABwALAA8AEwAAEyEVIRczFSMHNSEVBTUhFSUzFSNAAYD+gFXW1lUBgP6AAYD+1dbWAcArKitVKiqrKyuAKwAFAEAAQAHAAcAAAwAHAAsADwATAAATIRUhFTUhFQU1IRUFNSEVBTUhFUABgP6AAYD+gAGA/oABgP6AAYABwCtVKytVKipWKytVKysABQBAAEABwAHAAAMABwALAA8AEwAAEyEVIRE1IRUlNSEVJxUhNQUVITVAAYD+gAGA/oABgID/AAEA/wABwCv+qysrqyoqgCsrqysrAAUAQABAAcABwAADAAcACwAPABMAABMhFSEXNSEVBTUhFQU1IRUFNSEVQAGA/oCAAQD+gAGA/wABAP6AAYABwCtVKytVKipWKytVKysAAwCVAIABewGrAAcADwAbAAAlMjY0JiMjFTUVMzI2NCYjFxYVFAYjIxEzMhYUASAOEhIOS0ANExMNOC4tIpeGJDG1ExoTQMBAExoTWxU0Ii8BKzJIAAIAKwBAAasBlQAIABEAABMhFSMHJzcjJycXAQcnByM3J4ABK3wiLQ8zPDoGATQbeSFANJQBlUBQLCQ8BAX+yxt5TnuUAAQAAAAAAgACAAADAA4AEQAiAAA1IRUhARYVFAYiJjU0NjclMycXFhQHBwYjIicnJjU0NzcnNwIA/gABlSsaIhkVCv7lzWeMCgp1Cg0MCnYJCW4zH1VVAQsvHBEaGhEMJgwtZlAKGgl1Cgp1CQ4NCW4zHgACAFUAPQGoAbwACwATAAATAQcnBiMiJjU0NycFFAcnNx4CcAE4GzklLzVLHEcBKwO3Og4rRwGQ/sgbOCBLNSI2R58PDbhLEDZ7AAMAAAAAAgABwAACAAoADgAAEzMnJzMTIycjByMHIRUhzWYzFSp1MBeGGDB1AgD+AAEAhzn+1UBAQFUABgBAAEABwAHAAAMABwALAA8AEgAWAAA3NTMVJzUzFSUhFSERNSEVJTcVFzUzFevV1dX+gAGA/oABgP6AVVbV6yoqVSsrgCv+qysrwFWqFisrAAYAQABAAcABwAADAAcACwAPABIAFgAANzUzFSc1MxUlIRUhEzUzFSUXBxU1IRXr1dXV/oABgP6Aq9X+gFVVAYDrKipVKyuAK/8AKyvAVVVrKysAAQCAAIABgAGrAAsAABMzFSMHMxUjNTM3I9WrPEgvqzxILwGrQKtAQKsABAAgAEsB1QG1AAMABwALABUAADc1IRUFNSEVASEVISMVMwcnMzUjNxfVAQD/AAEA/wABAP8AVTVKSzU1S0rrKiqAKioBKirWSkrWSkoABgA1AGABwAGgAAMABwALABMAGwAjAAATIRUhFTUhFQU1IRUkMhYUBiImNBIyFhQGIiY0FjIWFAYiJjSVASv+1QEr/tUBK/6IGhMTGhMTGhMTGhMTGhMTGhMBlSqAKiqAKio1ExoTExoBExIcEhIcbhIcEhIcAAYAKwBVAcABqwADAAcACwAVABsAJwAANzUhFQU1IRUBIRUhBzUzFQczFSM1Nyc1IzUzFQc1MxUjNTM1IzUzNZUBK/7VASv+1QEr/tVqQCcnQCYRFSoqQEAqFRXrKiqAKioBKipWFhQsFhQsQEAWVsAWVhYKFgoAAQBVACsBwAHVAB8AAAEzFSMVFAYjIyImNTUzNSMVFAYjISImNTU0NjMhMhYVAYBAqwwJKwkM1RUMCf8ACQ0NCQEACQwBq6vACQwMCetVFQkNDQlVCQwMCQACAGsAlQGVAWsABgANAAAlNyM1MxUHIzcjNTMVBwErKkCAKusrQIArlVaAgFZWgIBWAAIAQABrAdUBqwAHAA8AABM1MxUjFSM1NyEVIxEjESNAwEBAQAEVakBrAQBAQJWVq0D/AAEAAAMAQABrAcABqwADAAsADwAANzUhFSUhFSMVIzUjEzUzFUABgP6rASpqVmpqVtUrK9ZAQED/AEBAAAIAawArAcAB1QAGABUAACUHNSE1ITUnIiY0NjMzFSMVIzUjFSMBwFX/AAEAqyMyMiOrKysqK4BVQCpAVjJGMirr6+sAAgBVACsBqwHVAAYAFQAANyEVIRUnNzciJjQ2MzMVIxUjNSMVI6sBAP8AVlYqIzIyI6srKisrlSpAVVVWMkYyKuvr6wACAGsAQAGVAcAAAwATAAA3IRUhNiImNTUzFRQWMjY1NTMVFGsBKv7WympLNSw+LDVrK1VLNaurHysrH6urNQABAIAAVQGAAasACwAAARUjFwczFSE1Nyc1AYCVamqV/wCLiwGrQGtrQCuAgCsABABAAEABwAHAAAMABwALABsAACU1IxUjNSMVIzUjFQEyFhURFAYjISImNRE0NjMBaysrKisrAQARGhoR/tYRGhoRlVZW1taWlgErGhH+1hEaGhEBKhEaAAQAKwArAdUB1QADAAcACwAZAAABNSEVBTUhFQU1IRUBMhYVESchIiY1ETQ2MwGA/wABAP8AAQD/AAErERlV/tURGRkRAVUrK0ArK0ArKwEAGRH+gFUaEQEAERkAAgBVACsBqwHVAAIAEAAAATMnJzMXERQGIyEiJjUTNDYBFXZ2lauAGhH/ABEaARkBQHUggP8AERkZEQFWERkABQArACsB1QHVAAUADQAVAB0AJQAAJCImJzMGJiImNDYyFhQWIiY0NjIWFAYyNjQmIgYUEjIWFAYiJjQBJUo7DdoNnhoTExoTgxoTExoTsYxlZYxlU7B9fbB9iykhIWETGhMTGhMTGhMTGtNljGVljAEbfbB9fbAAAwBAAEABwAHrAAMAGwAfAAAlNSEVEzMVMzIWFREUBiMhIiY1ETQ2MzM1MxUzFxUjNQGV/tbqKxURGhoR/tYSGRkSFSuqFmtr6uoBgCsaEf7WERoZEgEqERorK8BrawADACsAlQHVAWsADwATACMAAAEyFhQGIyM1MzI2NCYjIzUHNTMVJBQWMzMVIyImNDYzMxUjIgFrLD4+LFZWGycnG1Zqqv7+JxtWViw+PixWVhsBaz9YPyknNicpgCoqMDYnKT9YPykAAgBAAEABwAHAAAQAFAAANwchJwcXFAYjISImNRE0NjMhMhYVtUoBKmBK1RoR/tYRGhoRASoRGuBggGA1ERoaEQEqERoaEQACAHcATQGJAbUACAAMAAATNxcjFQcnNzUTJzcXoGBgS4AedIBJHkkBVWBgiIAec3f++EgeSAABACsAKwHVAdUADQAAAREnISImNRE0NjMhMhYB1VX+1REZGREBVhEZAav+gFUaEQEAERkZAAIAQABAAcABwAAJAA4AAAEHJzc2MhcXFhQFNxcHIwG6J1AnBhIGMgb+gOxQ7FABaidQJwYGMgYS4OxQ7AACAGsAVQGVAasABgAKAAA3NxcjFSM1JyEVIWuVlVWAVQEq/tbVlpaAgNYrAAEAVQDAAasBQAAHAAABMxUhNTMVIQGAK/6qKwEAAUCAgFUAAgBAAIABwAHAAB8AOAAANxQzMjU0JiciJiYjIzUhFSMUFhcWFRQHBiMiJyYnJjU3NCMiBwYVFBcWFyM0JjUmNTQ3NjMyFxYVyD4xDREBBQQCzQGAUwMBB0MVGRAPJBQnrjMkCwMQEA5iBAggHzQ1Hh/oNiQQDggCAisrAQUBERI9EwYDBw8dMngtFgYIDwsKBQEDAQ0XJRoYGxwpAAIAVQBAAasBwAADAAoAADchFSElByczNTMVVQFW/qoBAFVVQCprK6tWVtXVAAMAVQAVAasB6wADAAoAEQAAEyEVISUHJzM1MxUDNxcjFSM1VQFW/qoBAFVVQCpqVVVAKgEVKqpVVVZW/tZVVVZWAAIAVQBAAasBwAADAAoAABMhFSEXNxcjFSM1VQFW/qpWVVVAKgHAK4BWVtXVAAMAVQBAAcABlQASABYAGgAAATIWFAYjIxUnNxUzMjY0JiMhNSUVITURNTMVAWsjMjIjK0BAMBEaGhH+5QFW/qqAARUyRjIrQEArGiIaKoAqKv7WKioAAgBXAEABqQHAABQAIgAAEwEHJwYHFSM1JiYnMxYzMjcnJjUnFyIHJzY3NTMVFhYXIyZyATcbLxMhQCEsAi8EPCUOS1NJtBMOHw4SQCAjAS8CAan+yBswEQcuLgcqIS0UShk7SSEGHwcFLi8ILB0tAAIAVQDAAasBQAADAAcAADc1IRU1FSE1VQFW/qrAKyuAKysACAAVABUB6wHrAAIACgAOABIAHgAiACYAOgAANzMnFyMHIzczFyM3MzUjEzUjFSc1MzUjNSMVIxUzFQc1IxURFTM1BSMVMxUjNSMVIzUzNSM1MxUzNTPkOBwlSw8jSR5IImArKysrKioq1ioqKisrAYArK4DWgCsrgNaA8FJtKsDA6iv+gCsrKyrWKirWKisrKwGAKytV1oArK4DWgCsrAAQASwArAbUB1QADAAcACwATAAABNxcHJTcXBzczFSMHNSEVBxUjNQFqLR4t/sMeLR5zKiprAQBAgAF0LR4uLh4tH4BAwGtrQGpqAAEAKwDLAdUBNQAbAAABMhYUBiMiJyMGIicjBiMiJjQ2MzIXMzYyFzM2AaAWHx8WJA0+DUgNPg0kFh8fFiQNPg1IDT4NATUfLB8gICAgHywfICAgIAACAFUAwAGrAUAAAwAHAAA3MxUjNSEVIVXW1gFW/qrrK4ArAAIANQBrAcsBqwAHAA8AAAEVIxUjNSM1JyEVIxEjESMBy0BAQNYBFmtAawFAQJWVQGtA/wABAAACACsAKwHVAdUAIgAqAAAlNjU0JyY1NDYzMhczJic1IxUGBhUUFxYVFAYjIicjFhcVMwIyFhQGIiY0AR5DWjgbGC0CKgI7OR0lWTkXHDUEKgNDOXawfX2wfX4NNj0XDhsPFCg5DyoqBiMbOBYNIQ0VKDkPKgGAfbB9fbAAAQBrAGsBlQGrAAcAABMhFSMRIxEjawEqdUB1AatA/wABAAAEAEAAQAHVAcAABgAQABcAGwAANzUzFSMiJgEyFhUVITU0NjMBNTMVFAYjJzMVI0BrQBEaAWsRGf5rGhEBAGoZEdZra2vA6xoBZhoRQEARGv6A68ARGurqAAIAKwArAdUB1QALABkAAAE1IzUjFSMVMxUzNTcRJyEiJjURNDYzITIWAWtWKlZWKsBV/tURGRkRAVYRGQEVK1VVK1VVlv6AVRoRAQARGRkABgArAFUBwAGrAAMABwALABUAGwAnAAATIRUhFSEVIREhFSEFNTMVBzMVIzU3JzUjNTMVBzUzFSM1MzUjNTM1KwEq/tYBKv7WASr+1gFVQCYmQCYRFSsrQEArFhYBFSpWKgEqKlYWFCwWFCxAQBZWwBZWFgoWCgADAFUASQGiAcAABwAPABcAACQ0NjIWFAYiAjQ2MhYUBiIGNDYyFhQGIgEiJjQmJjSdJjQmJjR8JjQmJjRvNCYmNCYBHTQmJjQmhTQmJjQmAAQAQABAAcABwAAHABMAHgAuAAAlNQcnBxU3FycVMzUjNTM1IxUzFTcVMzUXMyc3Iwc1NzIWFREUBiMhIiY1ETQ2MwGVgFVVVVWAVjY2VjY1ICskKyskK3URGhoR/tYRGhoR6zWAVVU1VVX6UCAQUCAQMIBAQEBAQEArGhH+1hEaGhEBKhEaAAUANQBAAcsBwAAPABMAFwAbAB8AAAEyFhURFAYjISImNRE0NjMBESERJSM1MwcjNTMHIzUzAaARGhoR/sARGhoRAUD+wAELKytWKipVKysBwBoR/tYRGhoRASoRGv6pASz+1CxWVtbWlgADAGsAawGVAZUAAwAHAAsAACUzFSMDMxEjJzMVIwFaOzt4PDx3QEDrgAEq/tbR0QADAEAAgAHAAYAAAwAHAAsAADc1IRUlIRUhFTUhFUABgP6AAYD+gAEA6yoqlSvVKysAAQArAIAB1QFrACUAADY0NjMzMhYUBiMjIiY0NjMzFSMiFDMzMjY0JiMjIgYUFjMzFSMiK0Qx4CMyMiO1FiAgFqCiCQm3ERoaEeAfLCwfy8sxxGJFM0YyHywgKxUZIhosPisrAAEAAABVAgABqwASAAABFhYVFAYjISImNTQ2NzY2MzIWAZ0pOj8s/us1S0MvFUwtNl0BKgM9Kiw/SzUvSwUnMEwAAgArACsB1QHVABIAGgAAJTI2NCYjIzQmIyIGByciBhQWMwIyFhQGIiY0AWAWHx8WCzIjGzAHAxomJhoDsH19sH2rHywfIzMmGwEmNCYBKn2wfX2wAAIAAABVAgABqwAFABgAADc3JwcnByUWFhUUBiMhIiY1NDY3NjYzMhbVjR5vLB4BEik6Pyz+6zVLQy8VTC02XZWNHm4sHkoDPSosP0s1L0sFJzBMAAIAAABVAgABqwAGABkAACUjNSMVIxc3FhYVFAYjISImNTQ2NzY2MzIWAWtAVkBrnSk6Pyz+6zVLQy8VTC02XetVVWuqAz0qLD9LNS9LBScwTAADAAAAKwIAAasABwATACsAABMjIgYUFjMzATcBBycjIiY1NDY3BRYWFRQHJzY1NCYjIzU0JiMiByc2MzIWpSUjMjIj0P7wGwFlGyv6NUtIMwEiKTotHyEmGiBEMRwaICcvNl0BKzNGMgEQG/6bGypLNTRKAisDPSo3IB8SJhomCzFEDR8ZTAACAAAAVQIAAasAEwAmAAAlMjY0JiMjNTQmIyIGByMiBhQWMyUWFhUUBiMhIiY1NDY3NjYzMhYBlRomJhogRDEoPgsPIzIyIwEdKTo/LP7rNUtDLxVMLTZdgCY0JgsxRDAlM0YyqgM9Kiw/SzUvSwUnMEwAAgAAAFUCAAGrAAYAGQAAJTMnBzMVMzcWFhUUBiMhIiY1NDY3NjYzMhYBK0Bra0BWcik6Pyz+6zVLQy8VTC02XetqalaVAz0qLD9LNS9LBScwTAACAGsAVQGVAcAAAwAKAAA3IRUhJQcnMzUzFWsBKv7WASqVlVWAgCvrlZWAgAACAGsAVQGVAcAAAwAKAAA3IRUhNzUjNxcjFWsBKv7WVVWVlVWAK1aAlZWAAAEAKwBVAdUBqwARAAATFzMyFhUVFAYjISImNRE0NjPVK6sRGRkR/qoRGRkRAasrGhHVERoaEQEAERoAAgArAFUB1QGrAAMAFQAAJTUhFQEyFhUVFAYjISImNRE0NjMzFwGr/qoBVhEZGRH+qhEZGRGAK4DV1QEAGhHVERoaEQEAERorAAMAKwBVAdUBqwAHAA8AIQAAJTU0JiIGFRU2IgYUFjI2NDcyFhUVFAYjISImNRE0NjMzFwGVOzQ7ZiIaGiIaQBEZGRH+qhEZGRGAK5UWExcXExarGiIZGSJaGhHVERoaEQEAERorAAIAKwBVAdUBqwALAB0AACU1IzUjFSMVMxUzNTcyFhUVFAYjISImNRE0NjMzFwGVQCpAQCpWEhgYEv6qEhgYEoAr1StAQCtAQKsZEtUSGRkSAQASGSsABAAVAEAB6wHAAAcADwAUACcAABMyFhUjNCYjFTIWFSM0JiMVMhYVIwEyFhURFAYjIzUzESEVIzU0NjMVYYorcU8+WCs/LBomQAGrERoaEZWV/oArGhEBK4phUHArVz4sPysmGgGAGhH+1hEaKwEqQEARGgAFABUAQAHrAcAAEgAaACEAKQAuAAABMhYVERQGIyM1MxEhFSM1NDYzBzIWFSM0JiMlFSMmJic1BzIWFSM0JiMVMhYVIwHAERoaEZWV/oArGhErYYorcU8BgHgUXz9WPlgrPywaJkABwBoR/tYRGisBKkBAERqVimFQcGvWP2AUI5ZXPiw/KyYaAAIAAABVAgABqwADABcAABMVITURMxUhNTMiJjU1NDYzITIWFRUUBlUBVlX+AFURGRkRAVYRGRkBgNXV/wArKxoR1REaGhHVERoAAgAVACsB6wHVAAMAGQAAJTUhFQEyFhURFAYjIxcVIzU3IyImNRE0NjMBwP6AAYARGhoRlSqqKpURGhoR1dbWAQAZEf8AERpAFRVAGhEBABEZAAIAFQArAesB1QADABsAACURIREBMhYVERQGIyMVMxUjNTM1IyImNRE0NjMBwP6AAYARGhoRlSqqKpURGhoRqwEA/wABKhkR/wARGisqKisaEQEAERkABgArAEAB1QHAAAMABwALAA8AEwAvAAABMxUjJzMVIzczFSMHMxUjBREhESUjFTMVIxUzFSMVFAYjISImNRE0NjMhMhYVFTMBAFVVgGtrgFVVgGtrAQD+1QGAKioqKioaEf7VERkZEQErERoqARWA1mtrQEBWKgEq/tbVKyorKyoRGhoRASoRGhoRKgADAIAAFQGAAesAAwATABcAACU1IxUTMhYVERQGIyMiJjURNDYzETUzFQFVqqoRGhoRqhEaGhGqwNXVASoZEf7VERkZEQErERr+KisrAAQAKwArAdUB1QAEAAkADgATAAABMxUjJwc3FxUjAxcHIzUlByc1MwFgdXVAYEBAgCBAQHUBFUBAgAFAgEBgQEB1ARVAQIAgQEB1AAEAQABVAcAB6wAZAAASMhYVFRQGIyM1MzU0JiIGFRUzFSMiJjU1NLCgcCYaQFVXfFdVQBomAetxT5YaJqsrPldXPiurJhqWTwABAEAAFQHAAesAHQAAEjIWFRUUBiMjNTM1IzUzNTQmIgYVFTMVIyImNTU0sKBwJhqAlVVVV3xXVUAaJgHrcU/WGiYrFasrPldXPiurJhqWTwAMACsAawHVAZUAAwAHAAsADwATABcAGwAfACMAJwArADsAAAE1IxUXNSMVJzUjFRc1IxUXNSMVJzUjFRc1IxU3FTM1JxUzNRcVMzUnFTM1NzIWFRUUBiMhIiY1NTQ2MwGVKioqFioqKiqqFioqKkAqKioWKioqlhEZGRH+qhEZGREBKyoqQCoqQCoqQCoqVisrlioqQCoqKioqQCoqQCoqQCoqQBkR1hEZGRHWERkAAQCAAKsBgAFJAAUAABMXNxcHJ55iYh6AgAFJYmIegIAAAQCrAIABSQGAAAUAACUHJzcXBwFJHoCAHmKeHoCAHmIAAQC3AIABVQGAAAUAADc3JzcXB7diYh6AgJ5iYh6AgAABAIAAtwGAAVUABQAANyc3Fwcnnh6AgB5itx6AgB5iAAEAQACAAcABgAAIAAABFSEXByc3FwcBwP7STB6AgB5MARUqTR6AgB5NAAIAgACAAYABiQADAAkAADc1IRUnByc3FweAAQCAYh6AgB6AKyvNYh6AgB4ADQArABUB1QHAAAIABgAKAA4AEgAWABoAHgAiACYAKgAuAD4AACUnMzc1IxUXNSMVJzUjFRc1IxUXNSMVJzUjFRc1IxU3FTM1JxUzNRcVMzUnFTM1NzIWFRUUBiMhIiY1NTQ2MwEAVapAKioqFioqKiqqFioqKkAqKioWKioqlhEZGRH+qhEZGREVVuorK0ArK0ArK0ArK1UrK5UrK0ArKysrK0ArK0ArK0ArK0AaEdURGhoR1REaAAEAKwCAAcABgAAKAAABMxUhFwcnNxcHIQGVK/68TR6AgB5NARkBa4BNHoCAHk0AAgAVAIAB1QGAAAMADAAAATMRIyc3FwcnNyE1IQGrKiq0HoCAHk3+0QEvAYD/AOIegIAeTSoAAgBrACsBlQHAAA8AGwAAATMUBgcVIzUmJjUzFBYyNgYiJjU1NDYyFhUVFAFxJEs1KjVLJENcQ1c0JiY0JgEANlEIRkYIUTYvPj4RJhqAGiYmGoAaAAIAAABVAgABqwADABcAABMVITURMxUhNTMiJjU1NDYzITIWFRUUBlUBVlX+AFURGRkRAVYRGRkBgNXV/wArKxoR1REaGhHVERoAAwAAAFUCAAHAAAMABwAPAAAlNSEVFzUjFSEzFSE1MxEhAav+qtZWAQAr/gArAarA1dVAFRUrKwFAAAMAAABVAgABwAAHAAsAIwAANjI2NCYiBhQDFSE1ETMUBiMhIiY1MyImNTU0NjMhMhYVFRQG9xIMDBIMlgFWVRoR/lYRGlURGRkRAVYRGRlrDBIMDBIBHurq/usRGhoRGhHqERoaEeoRGgACAAAAVQIAAcAAAwAZAAATFSE1ETMVITUzNSImNTU0NjMhMhYVFRQGI1UBVlX+AFURGRkRAVYRGRkRAZXV1f7rKysVGhHVERoaEdURGgAEAEAAQAHAAcAAAwAzADcAOwAAJTUjFSUjFTMVIxUUBiMjFSM1IxUjNSMiJjU1IzUzNSM1MzU0NjMzNTMVMzUzFTMyFhUVMwc1IxU3FSM1AWvWASsrKysZESsrKisrERkrKysrGRErKyorKxEZK6sqVYCV1taAKisrERkrKysrGRErKyorKxEZKysrKxkRK1UqKlWAgAADAFUAFQGrAekABAAMABEAABMVIzQ2AzUhFRQGIiYTFhYVI+uWV1cBVmWMZcA/V5YB6alBYP7fVVVGZWUBbwhgQQADAGsAFQGVAesAAwAHABcAACURIxEXNSMVEzIWFREUBiMjIiY1ETQ2MwFw4JtWgBomJhqqGiYmGoABK/7VQBUVAasmGv6qGiYmGgFWGiYAAwBrABUBgAHrAAMACwAbAAAlESMRFjI2NCYiBhQTMhYVERQGIyMiJjURNDYzAVXAUxoTExoTdhYfHxarFh8fFoABK/7VVRMaExMaAa0gFv6WFiAgFgFqFiAAAwAAAFUCAAGrAAMAEwAgAAAlNSMVNzIWFRUUBiMjIiY1NTQ2MyUVMxUhNTM1NDYzIRUB1VVrCQwMCYAJDQ0J/urW/tUrGREBgJWWlsAMCdUJDQ0J1QkMK+tAQOsRGisABAAAACMCAAHdABIAFQAiACYAAAEyFhUVFAYjIyczNSMVJzU0NjMlFTMBFgAXBychNTM1NDcnBSEnIQHrCQwMCQRALlUrDQn+6uX+71EBKyIbMv6GKwonAcf+5ysBRAFVDAnVCQ1Alm8rWQkMJeUBSFH+1CIbMkDrDwwnQisABgBAAEABwAHAAAMABwALAB8AKQAzAAAlNSMVIzUjFSM1IxUlMhYVFRQGIyEiJjU1NDYzMzUzFTcHJiMiByc2MzIXJiMiByc2MzIXAUArICogKwEVERoaEf7WERoaEdUrMREVISAVER4oKTEpMTApES09Pi2AKysrKysraxoRVREaGhFVERpVVYYRFRURHg0kJBEtLQADAEAAVQHAAZUAAwAHABoAACU1IxUjNSMVJRYWFRUUBiMhIiY1NTQ2MyElNwGV1SsqATsLDxoR/tYRGhoRAQz+1A+VKysrK4cDFw11ERoaEVURGm0oAAMAQAAVAcAB6wAJAA4AEgAAARcVFAYHJiY1NRcVNjY3IzUHFQEAwG5SUm7AO1IIlZUB61aAWZMUFJNZgJW/E2lDvEJ6AAcAVQArAasB1QADAAcACwAPABMAFwAlAAAlNSMVJzUjFRc1IxUnNSMVFzUjFSM1IxUBExQGIyEiJjURNzMyFgFrKysqKiorK9YrgCsBFQEaEf8AERqAqxEZwFVVKyoqgFVVVVVVVSoqKioBQP6qERkZEQEAgBkAAgBrABUBlQHrAAMAEwAAJREjERMyFhURFAYjIyImNRE0NjMBa9bWERkZEdYRGRkRawEq/tYBfxkR/oARGhoRAYARGgAEAGsAKwGVAdUABwAPABkAKQAAEjIWFAYiJjQWMjY0JiIGFBMiBhQWMzI2NCY3MhYVERQGIyMiJjURNDYz5jQmJjQmFFg/P1g/axIZGRIRGhpaERkZEdYRGRkRAQAmNCYmNIU/WD8/WAEXGSQZGiIaKhkR/qoRGRkRAVYRGQAFAFUAFQGrAesACAAQABgAIQAxAAATETMVIyImNREWNDYyFhQGIgYyNjQmIgYUNiIGFBYzMjY0NzIWFREUBiMjIiY1ETQ2M4DV1RIZoCAsHx8sDUYyMkYzZyIaGRIRGS8QFxcQsxAWFhABlf6rKxkSAVW2LCAgLB8gMkYzM0buGSQYGCREFxD+zRAWFhABMxAXAAIAFQBVAesBqwADABMAACURIREBMhYVAxQGIyEiJjURNDYzAZX+1gFVERoBGRH+gBEaGhGAAQD/AAErGhH/ABEaGhEBABEaAAMAQAAAAcACAAADAAcAFwAAJREhERc1IxUTMhYVERQGIyEiJjURNDYzAZv+ysZWqxomJhr/ABomJhprAVX+q0AVFQHVJhr+gBomJhoBgBomAAMAKwAAAcACAAADAAsAGwAAJREhERYyNjQmIgYUEzIWFREUBiMhIiY1ETQ2MwGV/sCTGhMTGhO2Fh8fFv7VFh8fFmsBVf6rVhMaExMaAdgfFv5qFh8fFgGWFh8ABAAVABUB6wHrAAUACwARABcAAAEUBiImNTMiJjQ2MxUyFhQGIzU0NjIWFQEARWBG6zBFRTAwRUUwRWBGAQAwRUUwRWBG60VgRuswRUUwAAIAFQBAAesBwAADABcAACURIREBMhYVAxQGIyMVIzUjIiY1ETQ2MwHA/oABgBEaARkRa6prERoaEZUBAP8AASsaEf8AERkrKxkRAQARGgACAFUAAAGrAgAABwAVAAASFBYyNjQmIhcUBgcHIycmNDc3MxcWgEtqS0tq4CYbFaoVQUEVqhVBATVqS0tqS4AjThV6ejKoMnp6NAABAEAAQAHAAcAAGAAAJTMVIzUnBxUjNTM3NSYmNTQ2MhYVFAYHFQFrVWtVVWtVVhMYJjQmGBOra0FaWkFrVUQHIBUaJiYaFSAHRAAEACsAwAHAAUAAAwAHAAsADwAAJTUzFSE1MxUhNTMVJyEVIQFVa/8Aa/8AamoBlf5rwCsrKysrK4ArAAUAFQBVAesBqwADABMAGwApADcAACU1IxU3MhYVFRQGIyMiJjU1NDYzBjI2NCYiBhQ3FRYVFAcVIzUmNTQ3NScRMxUjIiY1ETQ2MyEVAcBVaggODgiACA0NCHcaExMaE0oWFlUVFYBVVREaGhEBgICrq9UNCNUIDg4I1QgNyhMaExMaYiYUGxwUJiYTHRwTJoD/ACsaEQEAERorAAQAFQCAAesBgAAHAA8AGwArAAAAMjY0JiIGFAYyNjQmIgYUJzUjNSMVIxUzFTM1JTIWFRUUBiMhIiY1NTQ2MwGSHBISHBJCGhMTGhNAQCtAQCsBFREaGhH+gBEaGhEBABIcEhIcUhIcEhIcGSpAQCpAQJUaEaoRGhoRqhEaAAQAawAVAZUB6wADABcAGwArAAA3MxUjJjIWFRQGFSM0PgI1NCYiBhUjNBcRIxETMhYVERQGIyMiJjURNDYz7SYmDUArOCYRFhEWHhYmttbWERkZEdYRGRkRtSbiKx8ZMhQVHQ0UDA8WFg8f2wEq/tYBgBoR/oARGhoRAYARGgACABUAFQHbAdsAFQAkAAATAQcnBiMjNScGFRUzFSMiJjU1NDcnFyIHJzYzMhYVFSczNTQmMAGrGy4JCUDEEVVAGiYcR+ssJR8xP1BwaT5XAdv+VRsuA1XFHyYqqyYalTgsRxUYHiRxT5NpKj5YAAMAKwArAdUB1QALABsAJAAAATUjNSMVIxUzFTM1NzIWFREUBiMhIiY1ETQ2MwcRIRUhIiY1EQGVVStVVStrERkZEf8AERoaEVYBK/7VERkBFStVVStVVcAZEf8AERoaEQEAERlV/tUqGREBKwADACsAKwHVAdUABwAPABcAAAAUBiImNDYyBjI2NCYiBhQSMhYUBiImNAFAJjQmJjRgjGVljGVTsH19sH0BGjQmJjQm62WMZWWMARt9sH19sAACAEAAFQHAAdUABwAaAAAlNycnBwcXFxMyFhURFAYjIwcnIyImNRE0NjMBKFhYKChYWCiVERoaEVVAQFURGhoR7SgoWFgoKFgBQBkR/tURGkBAGhEBKxEZAAEAawBAAasBqwAJAAABMxUjJyMVIxEzATN4lgh4KsABgNUqlQFrAAEAgABAAZUBwAAPAAABMxUjFSMGBiMiJjQ2MzIXAQCVVQEENiUoODgoDhIBwEDrJDE4UDgGAA4AKwArAdUB1QAHAA8AFwAfACcALwA3AD8ARwBPAFcAXwBnAG8AACQyFhQGIiY0FzIVFCMiNTQGMjY0JiIGFBIyFhQGIiY0BTIVFCMiNTQXMhUUIyI1NCciNTQzMhUUBjIWFAYiJjQnIjU0MzIVFAcyFRQjIjU0FzIVFCMiNTQnMhUUIyI1NBYyFhQGIiY0NjIWFAYiJjQBIhIMDBINFgoKC2aMZWWMZVOwfX2wfQFACgoLCwoKCzULCwoTEgwMEg1ACgoLSwsLCkoLCwo2CwsKQRINDRIMDBINDRIM6w0SDAwSPgsKCgtLZYxlZYwBG32wfX2wIwoLCwpVCwoKC4ALCgoLIAwSDQ0SLAsKCguACwoKC0ALCgoLlQoLCwpKDRIMDBJiDBINDRIADgBAAEABwAHAAAcADwAXAB0AJQApADEAOQBBAEkATQBVAF0AZQAAJCImNDYyFhQmIiY0NjIWFCYiJjQ2MhYUFyI0MzIUJyI1NDMyFRQlIRUhBSI1NDMyFRQGIiY0NjIWFCYiJjQ2MhYUJiImNDYyFhQDNSEVAiImNDYyFhQGIiY0NjIWFAYiJjQ2MhYUAR4SDAwSDQ0SDAwSDQ0SDAwSDUALCwoKCwsK/ssBgP6AASsLCwqsEgwMEgxdGhMTGhMTGhMTGhNLAYD3EgwMEgwMEgwMEgxdGhMTGhOVDRIMDBJJDBIMDBJJDBINDRJXFhZWCgsLCnUr9QsKCgsLDRIMDBI+EhwSEhxDExoTExr++CsrAQAMEg0NEmEMEgwMEmwTGhMTGgASADUANQHLAcsABQANABMAGQAhACcALwBUAFoAYgBqAHIAegCAAIYAjACYAKAAADYyFRQiNRYyFhQGIiY0FzIUIyI0JjIVFCI1FjIWFAYiJjQkMhUUIjUGMhYUBiImNAM3AQcnFhUUBiImNDYzMhcnBgYjIiY1NDY3JxYVFAYiJjQ2MxcXMhQjIjQSIiY0NjIWFAYiJjQ2MhYUBiImNDYyFhQmIiY0NjIWFBYiNTQyFSciNDMyFDMiNDMyFAcmJic1NDYyFhQGIzYiJjQ2MhYUNRYWQhIMDBIMagsLCpYWFkISDAwSDAFKFhbpEg0NEgyLGwFbHFABDBINDQkEAjwCEgwNExALPAEMEgwMCQalCgoLaRIMDBIMDBIMDBIMDBIMDBIMtxIMDBIN4BYW9goKC0sLCwoPChABExoTEw0JEg0NEgzgCwoKQAwSDAwSPhYW6goLC0ANEgwMEgILCgpADBIMDBIBBxv+pRtRAgQJDAwSDAE8CxATDQwSAjwCBAkNDRIMAfQWFgEgDBIMDBJiDRIMDBJiDBINDRKfDBIMDBJXCwoKihYWFhaqARAKBQ0TExoTYAwSDAwSABgANQA1AcsBywAHAA8AFwAfACUALQAzADsAQwBLAFMAWwBjAGkAbwB1AHsAgwCJAJEAlwCfAKcArwAAADIWFAYiJjQWMhYUBiImNAYyFhQGIiY0NjIWFAYiJjQXMhQjIjQ2MhYUBiImNDYyFRQiNSYyFhQGIiY0FjIWFAYiJjQWMhYUBiImNDYyFhQGIiY0JjIWFAYiJjQ2IiY0NjIWFCciNDMyFAMyFCMiNCYyFRQiNTciNDMyFAYiJjQ2MhYUFiI1NDIVJDIWFAYiJjQGMhUUIjU2MhYUBiImNBYyFhQGIiY0NjIWFAYiJjQBHhoTExoTExoTExoTPxINDRIMCBoTExoTdgoKCwISDAwSDaAWFj4SDAwSDAwSDAwSDAwSDAwSDAwSDAwSDKMaExMaEykSDAwSDRYKCgsLCwsKlhYW9gsLCgESDQ0SDIsWFv6sEgwMEgw2FhZCEgwMEgwMEgwMEgwMEgwMEgwBSxMaExMaQxMaExMaTQwSDAwSwhMaExMa7RYWSgwSDAwSVwsKCsAMEgwMEkkMEg0NEp8MEgwMEmINEgwMEhcTGhMTGokMEgwMEj4WFv6WFhaVCwoK4BYWSgwSDAwSVwsKCmoMEgwMElQKCwsVDBINDRKfDBIMDBJiDRIMDBIAAQArACsB1QHVAAcAADY0NjIWFAYiK32wfX2wqLB9fbB9AAEAawArAasB1QANAAATMhYUBiMiJzY2NCYnNtVZfX1ZOjAxOTkxMAHVfbB9HBxjdGMcHAABAIAAKwGVAdUADQAAEzIWFAYjIic2NjQmJzbAWH19WCIeQlNTQh4B1X2wfQkUcY5xFAkAAgAPAA8B8QHxAA0AHQAAJTI2NCYjIgcWFhQGBxY3FwcVIwcnIzUnNzUzNxczAQA1S0s1GhshKSkhG8VGRmRHR2RGRmRHR2SAS2pLDA8/TD8PDMdHR2RGRmRHR2RGRgACAA8ADwHxAfEABwAXAAA2MjY0JiIGFAUVIwcnIzUnNzUzNxczFRfLaktLaksBK2RHR2RGRmRHR2RGgEtqS0tqEmRGRmRHR2RGRmRHAAIADwAPAfEB8QAFABUAACUyNjQmIxcVIwcnIzUnNzUzNxczFRcBADVLSzWrZEdHZEZGZEdHZEaAS2pLx2RGRmRHR2RGRmRHAAMADwAPAfEB8QAHAA8AHwAAEjIWFAYiJjQWMjY0JiIGFCUXBxUjBycjNSc3NTM3FzPdRjIyRjIgaktLaksBK0ZGZEdHZEZGZEdHZAFVMkYyMkajS2pLS2p8R0dkRkZkR0dkRkYAAgBAAEABwAHAAA0AGwAAARcVFAYjISImNTUXNxc3FScHJwcnNTQ2MyEyFgGAQBoR/tYRGkBVVpVAVVZVQBoRASoRGgEMQGERGhoRjEBWVt6MQFZWVkFhERoaAAIAKwBAAcABwAAJABYAAAEWFAcHJzc2MhcEMhYVFAYjIicyNjU0AboGBr87vwYSBv7eNCYyIzQhDxsBnQYSBr87vwYG5SYaIzIrFxMaAAYAKwArAdUB1QAIAA0AFAAcACEAKQAANzY2NxcGBiMiJzMHJiY3FyMmNTQ2BRYVFAYHJyc3IzcWFgcHJzY2MzIX0gROFU4YUB8YtM9PLkMgbKAEIQGFBCEXZgabz08uQ/QCThhQHxgWMAeII4cUHJWHEUf/uxgTIlRLExgiVBqwCxWHEUdPAocUHAUAAwArAFUB1QHVAAcAGwAjAAA2MjY0JiIGFBMzFzMyFhURFAYjISImNRE0NjMzFjQ2MhYUBiLUWD8/WD8rgCdEERkZEf6qERkZEUQjKDgoKDiVP1g/P1gBASoaEf8AERoaEQEAERrHOCgoOCgABABrAAABlQIAAAcAHwAnACsAABMVNDYyFhU1NTIWFREUBiMjFwc1IzUzNSMiJjURNDYzFiImNDYyFhQRMxUjlUlESREZGRGWQEBqakARGRkRfCIZGSIaamoB1eAYHh4Y4CsaEf7WERpAQCsqKxoRASoRGqsaIhoaIv7mKgADAGsAAAGVAgAACAAgACQAAAEyNjQmIgYUFjcyFhURFAYjIxcHNSM1MzUjIiY1ETQ2MxMzFSMBABEZGSIaGX0RGRkRlkBAampAERkZEZZqagGAGiIZGSIagBoR/tYRGkBAKyorGhEBKhEa/lUqAAcAKwArAdUB6wADAAcACwAPABMAFwAzAAABNSMVFzUjFSc1IxUXNSMVJzUjFRc1IxUTMxEjFAYjIyImNRE0NjMzNTQ2MzMyFhUVMzIWAasrKysrKioqKysrK1aqqhoRqxEZGREWDAlVCQ0VERoBQCsrwCsrwCsrwCsrwCsrwCsrARX+wBEZGREBQBEaFQkNDQkVGgAFAEAAQAHAAcAACAARABoAIwArAAAlNTMVFAYjIzUTMhYVFSM1IzUHFSM1NDYzMxUHFTMVIyImNTU2MhYUBiImNAGVKxoRVVURGitV1SsaEVVVVVURGp1GMjJGMmtVVREaKwFVGhFVVSsrVVURGivVVSsaEVWVMkYyMkYABgBAAEABwAHAAAcADwAYACEAKgAzAAA2MjY0JiIGFDYyFhQGIiY0FzUzFRQGIyM1EzIWFRUjNSM1BxUjNTQ2MzMVBxUzFSMiJjU17yIaGiIaCEYyMkYy6isaEVVVERorVdUrGhFVVVVVERrVGiIaGiJmMkYyMka4VVURGisBVRoRVVUrK1VVERor1VUrGhFVAAMAKwArAdUB1QAIAA0AHQAAEzMRIRUhIiY1NwchJwcXFAYjISImNRE0NjMhMhYVKyoBK/7VERnAQAEAVj+/GRH/ABEaGhEBABEZAYD+1SoZEatVak8bERoaEQEAERkZEQAFAEAAQAHAAcAABwAPABcAHwA0AAAAMjY0JiIGFCYyNjQmIgYUBjI2NCYiBhQGMjY0JiIGFDcyFhUUBiMjIgYVFBYVFAYjIiY0NgFoGhMTGhMtGhMTGhNXGhMTGhMtGhMTGhOVT3E/LCUOEhASDlBwcAEAEhwSEhxDExoTExoTExoTExpoEhwSEhyuZEcsPhMNCxQMDhJwoHAAAgBAAEAByQHAAAMAFQAANzcnByUWBwcXBycHIzU3JzcXNzYyF5SsKawBTw8PQykeHr9lvh4eKUMGEgZrrCms9A8PQykeHr5lvx4eKUMGBgADAEAAFQHAAesADQAQAB4AAAEyFhURFAYjIzUXESM1AzUHEzUzESM1IyImNRE0NjMBlREaGhFqampWamorK2oRGhoRAcAaEf7WERrAgAEVK/7AgIABQCv+KisaEQEqERoAAwArACsB1QHVAAcADwAbAAA2MjY0JiIGFBIyFhQGIiY0NxUzFSMVIzUjNTM1uoxlZYxlU7B9fbB96lZWKlZWVWWMZWWMARt9sH19sBNWKlZWKlYABAAAAEACAAHAAAcADwAcACgAACQyNjQmIgYUNjIWFAYiJjQHFBYXFSYmNDY3FQYGJRUzFSMVIzUjNTM1AQJ8V1d8V0WgcHCgcFUyIzhISDgnLgEqQEAqQEBrV3xXV3z+cKBwcKBQJ1AQLhRkemQULhJJKUAqQEAqQAACAEAAgAHAAYAAAwATAAAlNSEVJTIWFRUUBiMhIiY1NTQ2MwGV/tYBKhEaGhH+1hEaGhGrqqrVGhGqERoaEaoRGgACAEAAVQHAAasAAwATAAAlESERATIWFREUBiMhIiY1ETQ2MwGV/tYBKhEaGhH+1hEaGhGAAQD/AAErGhH/ABEaGhEBABEaAAIAFQAVAesB6wAQABkAADchFSMVIzUjIiY1NSM1MzUzEzUjNTMyFhUVlQFWVirWERlWVirWq6sRGZUqVlYZEdYqVv7VqyoZEasAAgBAAGsBwAGVAAMAEwAAJTUhFQEyFhUVFAYjISImNTU0NjMBlf7WASoRGhoR/tYRGhoRldbWAQAZEdYRGRkR1hEZAAIAQACVAcABawADABMAACU1IRUlMhYVFRQGIyEiJjU1NDYzAZX+1gEqERoaEf7WERoaEcCAgKsaEYARGhoRgBEaAAIAQABAAcABwAADABMAACURIREBMhYVERQGIyEiJjURNDYzAZX+1gEqERoaEf7WERoaEWsBKv7WAVUaEf7WERoaEQEqERoABABAAEABwAHAAAgAEQAaACMAAAEyFhUVIzUjNRM1MxUUBiMjNScVMxUjIiY9AjQ2MzMVIxUjAZURGitVVSsaEVXVVVURGhoRVVUrAcAaEVVVK/6rVVURGitVVSsaEVXVERorVQACAEAAawHAAZUAAwATAAAlNSEVATIWFRUUBiMhIiY1NTQ2MwGV/tYBKhEaGhH+1hEaGhGV1tYBABkR1hEZGRHWERkAAwBAAEABwAHAAAQACAAYAAAlFyM3FxcRIREBMhYVERQGIyEiJjURNDYzASpL6joqpv7WASoRGhoR/tYRGhoR+mVMM0MBKv7WAVUaEf7WERoaEQEqERoAAgBrAEABlQHAAAMAEwAAJREjERMyFhURFAYjIyImNRE0NjMBa9bWERkZEdYRGRkRawEq/tYBVRoR/tYRGhoRASoRGgACAFUAVQGrAasAAwATAAAlESERATIWFREUBiMhIiY1ETQ2MwGA/wABABEaGhH/ABEaGhGAAQD/AAErGhH/ABEaGhEBABEaAAMAKwCLAdUBiwADAAcACwAAEyEVIRUhFSEVIRUhKwGq/lYBqv5WAar+VgGLK0ArQCoAAgBAAFUBwAGrAAIABQAAExc3JSEDiHh4/sgBgMABgNXVK/6qAAIAQABAAcABwAAJAA4AAAEHJzc2MhcXFhQFNxcHIwG6J1AnBhIGMgb+gOxQ7FABaidQJwYGMgYS4OxQ7AAEAEAAQAHAAcAACwAOABIAIgAAJSM1MzUzFTMVIxUjFxEBExUzNTcyFhURFAYjISImNRE0NjMBNSoqICsrIGD+1hVrqhEaGhH+1hEaGhGrICoqICsVASr+1gEAICBVGhH+1hEaGhEBKhEaAAIAVQCAAZUBlQAGAAoAACUjNQc1NzMFMxUjAZUqQGQG/sCrq4DjFiQkgCoAAgArAIABwAGVAAMAIwAAEzMVIwUzFSM1NzY3NjU0JyYjIhUjNDc2MzMyFxYVFAcGBwYHK6qqARZ/uFkSDQcCChwuLhgZKwI7FAUEBwQRFwEVKkckIGESFgwQDQUaMSQYGSwLFA4LEwYbFwACAFUAgAGrAZUABgASAAAlIzUHNTczBxUzFSMVIzUjNTM1AasrQGQH1lZWKlZWgOMWJCQqVipWVipWAAIAKwCAAdUBlQALAC8AABMVMxUjFSM1IzUzNRczFSM1NzY3NjU0JyYjIgcGFSM0NzY3NjMzMhcWFRQHBgcGB6tVVStVVdZ/uFkSDQgLChQVDgsuGA4PEhYBOxQGEAwFDgkBa1YqVlYqVsckIGESFg0PEA8NDgsYJBgOBQYsDRIYGhQFEAkAAgCoAIABWQGVABUAKQAAATQnJicmIgcGBwYVFRQXFjMyNzY1NQc0MzIXFhUVIxQHBgcGIicmJyY1ASsLBAoHFgcKBAsZBwsVCwyEWEERBwEYDg4QKBAODhgBKCgRBgYEBAYGESg5OA8EERIoOQd0PhocLDsfEAUGBgUQGz8ABAAVABUB6wHrAAMAEwAZACIAACURIREBMhYVERQGIyEiJjURNDYzEzUjNTMVJREhFSEiJjURAcD+1QErERoaEf7VERkZEZYrVf7rAVX+qxEalQEr/tUBVhoR/tURGRkRASsRGv7VqyrV1f6rKxoRAVUABAAVABUB6wHrABQAGAAoADEAACUVIzU0NjMzNSM1MzIWFRUUBiMjFRcRIREBMhYVERQGIyEiJjURNDYzBxEhFSEiJjURAWuAGBIrVVUSGRkSK6v+1QErERoaEf7VERkZEVUBVf6rERrrK1USGSsqGBIrEhkqVgEr/tUBVhoR/tURGRkRASsRGlb+qysaEQFVAAQAFQAVAesB6wAYACEAJQA1AAAlFAYjIzUzNSM1MzUjNTMyFhUVFAYjMhYVJREhFSEiJjURAREhEQEyFhURFAYjISImNRE0NjMBaxkSVVUrK1VVEhkTDQ0T/tUBVf6rERoBq/7VASsRGhoR/tURGRkR6xIZKyorKyoYEiANExMNiv6rKxoRAVX/AAEr/tUBVhoR/tURGRkRASsRGgAEABUAFQHrAesAAwATABwAIQAAJREhEQEyFhURFAYjISImNRE0NjMHESEVISImNREFFyM3FwHA/tUBKxEaGhH+1REZGRFVAVX+qxEaAT9M6zsqlQEr/tUBVhoR/tURGRkRASsRGlb+qysaEQFVcWRLMgAEABUAFQHrAesAAwATAB0AJgAAJREhEQEyFhURFAYjISImNRE0NjMTNSM1MxUzNTMVJREhFSEiJjURAcD+1QErERoaEf7VERkZEatVKisr/tUBVf6rERqVASv+1QFWGhH+1REZGREBKxEa/tVVgFVV1dX+qysaEQFVAAQAFQAVAesB6wARABoAHgAuAAAlFAYjIzUzNSM1MxUjFTMyFhUlESEVISImNREBESERATIWFREUBiMhIiY1ETQ2MwFrGRJVVVWAVisSGf7VAVX+qxEaAav+1QErERoaEf7VERkZEesSGSsqgCorGRKA/qsrGhEBVf8AASv+1QFWGhH+1REZGREBKxEaAAUAFQAVAesB6wADABcAGwArADQAAAEVMzUHIiY1NTQ2MzMVIxUzMhYVFRQGIxcRIREBMhYVERQGIyEiJjURNDYzBxEhFSEiJjURARUrKxIYGBJWVisSGRkSgP7VASsRGhoR/tURGRkRVQFV/qsRGgEVKipVGRKAEhgqKxkSKhIZKwEr/tUBVhoR/tURGRkRASsRGlb+qysaEQFVAAQAFQAVAesB6wAGAAoAGgAjAAAlIzcjNTMVFxEhEQEyFhURFAYjISImNRE0NjMHESEVISImNREBFSpVVYBV/tUBKxEaGhH+1REZGRFVAVX+qxEawKsqKtYBK/7VAVYaEf7VERkZEQErERpW/qsrGhEBVQAGABUAFQHrAesAAwAHACUAKQA5AEIAAAEVMzUnFTM1ByImNTU0NjMiJjU1NDYzMzIWFRUUBiMyFhUVFAYjFxEhEQEyFhURFAYjISImNRE0NjMHESEVISImNREBFSsrKysSGBMNDRMYEisSGRMNDRMZEoD+1QErERoaEf7VERkZEVUBVf6rERoBFSoqVisrqxkSIA0TEw0gEhgYEiANExMNIBIZKwEr/tUBVhoR/tURGRkRASsRGlb+qysaEQFVAAUAFQAVAesB6wADABcAGwArADQAAAE1IxU3MhYVFRQGIyM1MzUjIiY1NTQ2MxMRIREBMhYVERQGIyEiJjURNDYzBxEhFSEiJjURAUArKxIZGRJVVSsSGBgSq/7VASsRGhoR/tURGRkRVQFV/qsRGgFAKytVGBKAEhkrKhkSKxIY/wABK/7VAVYaEf7VERkZEQErERpW/qsrGhEBVQAFABUAFQHrAesADwAfACMANwBAAAABNSERITUjFSM1IzUzNTMVNzIWFREUBiMhIiY1ETQ2MxczNSMXFAYjIzUzNSMiJjU1NDYzMzIWFScRIRUhIiY1EQHA/tUBKysqKysqKxEaGhH+1REZGRFWFRVAGRJAQBUSGRkSFRIZ6wFV/qsRGgFAgP7VgCoqKysrqxoR/tURGRkRASsRGqsVVRIZKxUZEhUSGRkSQP6rKxoRAVUAAwBAAEABwAHAAAMAEwAWAAAlESMVNzIWFREUBiMhIiY1ETQ2MxcHMwGVlZURGhoR/tYRGhoRlZWVawEqgKsaEf7WERoaEQEqERqrqgAFAEAAQAHAAcAABwAQABkAIgArAAASMhYUBiImNBc1MxUUBiMjNRMyFhUVIzUjNQcVIzU0NjMzFQcVMxUjIiY1NeY0JiY0JtUrGhFVVREaK1XVKxoRVVVVVREaAUAmNCYmNK9VVREaKwFVGhFVVSsrVVURGivVVSsaEVUAAgAAAFUCAAGrABcAKgAAJTI2NCYjIzU0JiMiBxYWFSM0JiIGFBYzJRYWFRQGIyEiJjU0Njc2NjMyFgGVGiYmGiBEMTokKTUrMkYyMiMBHSk6Pyz+6zVLQy8TUyg2XYAmNCYLMUQvC0UsIzMzRjKqAz0qLD9LNS9LBSQzTAADACsAKwHVAgAAAwAKAB0AAAEhFSEXESMnByMRATIWFREUBiMhIiY1ETQ2MzM3FwGA/wABACtgSkthAVYRGRkR/qoRGRkRVlVVAVXVKwErS0v+1QFWGhH+1REZGREBKxEaVVUAAQAVAIAB6wGAAAYAAAETITcXNycBK8D+KoBgIjwBgP8Aq4AZUQADABUAFQHrAesAAwATABwAACURIREBMhYVERQGIyEiJjURNDYzBxEhFSEiJjURAcD+1QErERoaEf7VERkZEVUBVf6rERqVASv+1QFWGhH+1REZGREBKxEaVv6rKxoRAVUACQAsACwB1AHUAAYADAATABsAIQAoAC4ANQA7AAA3NxYXFSYmNzY3FwYHNzY3MwYGByYUBiImNDYyBxYXByYnNwYHIzY2NwUmJzcWFycHJic1FhYHBgcnNjd5HiQwG0OILyQfMz9yHQUrAxwQZSY0JiY0wwUdHikGTRwGKwMcEAFOBhweKQZNHiUvG0OIMCQeMz9bHhwGKwMcDAYcHikGayQvGkMUoTQmJjQmVS8kHzM/fiUvG0MUcjAkHjM/kB4cBisDHAwGHB4pBgACADEAEQHPAe8ABwA+AAA2MjY0JiIGFDc2MzIXFAYHDgIHFhcWFhUGIyInFhUUBgcmJjU0NwYjIic0NjcnJiY1NjMyFyY1NDY3FhYVFN1GMjJGMpMjLiIeJhoCBwYDCQkaJh4iLiMCIx0dIwIjLiIeMCISGiYgIC0kAiMdHSOrMkYyMkaPHBEfQA8BBAMBBAUPQB8RHA4GIzsRETsjBg4cESVHCwkPQB8SHQ4GIzsRETsjBgAJABUAFQHrAesAAwAHAAsAEwAXABsAHwAjACcAADc1MxUnNxcHNzcXByYyFhQGIiY0FzMVIzcHJzcnFSM1BwcnNwcVIzXrKp0uHi6mHi4ehDQmJjQmq4CAHS4eLlUqJx4uHgGAFYCAgS4eLi4eLh7IJjQmJjQFKn8uHi5jgICRHi4ecyoqAAMAQAAVAdoB1QACAAoAEQAAATMnNxcjJyMHIzchMwczAzUjAWcyGRVFKQ9EDylF/tXVVVWVQAFdTirAKyvAwP8AwAACACsAKwGVAdUABQAOAAABByc1MwcnAQcnBzUjNScBayG11lbPAU8bWE1AagErObUuqpX+sBtZg8BPawABAJUAKwFrAdUABgAAEzMHMwM1I5XWVlaWQAHVqv8AwAAJAEAAFQHAAesABAAIAAwAEAAUABkAJwArAC8AACU1MxQGJzUzFSc1MxUTNTMVBxEzERMyFhUjITQ2MzMVIxEzFSMiJjUlNTMVAzUzFQGVKxoRK4ArKivVKoARGiv+qxoRVVVVVREaAVUrgCtAKxEaqyoqqisr/wArK4AB1v4qAasaEREaK/7WKxoR1Ssr/wArKwAKAEAAQAHAAcAADwATABcAGwArAC8AMwA/AEMARwAAATUhFTMVMxUzNTMVMzUzNRU1IxUjNSMVIzUjFQEyFhURFAYjISImNRE0NjMXMxUjNzMVIyczFTMVIzUjFSM1MxcVMzUhIxUzAZX+1iorKyorKysrKisrAQARGhoR/tYRGhoRKisrqysrVSorKyorK4Aq/wAqKgEVgIAqKysrKyqVKysrKysrAUAaEf7WERoaEQEqERqAKysrKysqKioqKisrKwAIAFUAVQGrAasABwAPABcAHwAnAC8ANwA/AAASMhYUBiImNBYyFhQGIiY0FjIWFAYiJjQGMhYUBiImNDYiJjQ2MhYUBDIWFAYiJjQ2MhYUBiImNBYyFhQGIiY0xCIaGiIZbyIZGSIabyIaGiIaOyIZGSIakSIaGiIa/sQiGhoiGhoiGhoiGm8iGhoiGQGrGiIaGiI8GSIaGiI8GiIZGSI7GiIaGiLEGiIaGiLEGiIaGiLEGSIaGiI8GiIZGSIACQAAAAAB5QHlAAIABwAMABAAEwAWACAAJAA9AAAlMycHNScjFSc1JyMVFzUjFREVMxcVMwMBBychIiY1EScFFTM1ISMnITIWFREnNSMnMzUjFSc1IyczNSMVJwFVHx8qDEoqDEpWVh9hH9kByhsr/rYRGSsBVVb/AB8rAUoRGSofK0pWKh8rSlYqVR8fSgxWgEoMVoBWVgEfH2EfARD+NhsrGREBSisfVlYqGRH+tisfKlZKKx8qVkorAAoAKwArAdUB1QADAAcACwAPABMAFwAbAB8AIwAzAAABNSMVFzUjFRc1IxUDNSMVFzUjFRc1IxUDNSMVFzUjFRc1IxUBMhYVERQGIyEiJjURNDYzAatWVlZWVipWVlZWVipWVlZWVgFWERkZEf6qERkZEQFVVlaAVlaAVlYBAFZWgFZWgFZWAQBWVoBWVoBWVgGAGRH+qhEZGREBVhEZAAQAHgAeAc0BywAUAB0AIQAwAAATFgEHJyM1JxUjNSMVIzUzFTM1MycXIyczMhYVFSc3FTM1ByMnNTMyFhUVFAcXIycjNWIBNhiiSCAgKyAgKwh19wggKA0TIGArKwgYSw0TExMgExgBy2P+zReiSSBpNTWAKyt1lSATDSkgCRUVYBdpEw0VFwctKwAFAEAAwAHAAUAAAwANABkAHQArAAAlNSMVNzIWFRUUBiMjNQc1MxUjNSMVIzUzFQU1IxUzFAcXIycjFSM1MzIWFQEVKioNExMNSkAgICsgIAFAK0sTEyATGCBLDRPgQEBgEw1ADROAKyuANTWAKwoVFRQKLSsrgBMNAAMAFQCAAesBgAAHAA8AFwAANjI2NCYiBhQ2MhYUBiImNCQyFhQGIiY0WiIZGSIaCEYyMkYzASFqS0tqS9UaIhoaImYyRjIyRl1LaktLagADABUAgAHrAYAABwAPABcAACQyNjQmIgYUNjIWFAYiJjQmMhYUBiImNAFIRjIyRjMhaktLakujRjIyRjOrMkYyMkajS2pLS2ogMkYyMkYABwAqACoB1gHVAAMACwATABsAHwAnAE8AACU3Jwc2IgYUFjI2NAYyNjQmIgYUJjI2NCYiBhQnNycHFiIGFBYyNjQXFxYUBwcGIyInJwcGIicnJjQ3NycmNTQ3NzYyFxc3NjMyFxcWFRQHAWNOTk0eEg0NEgxJEgwMEgwfEg0NEgwkTU1OuxIMDBIMZVUHB1wGCQoGVFUGEgZdBgZVVQYGXQYSBlVUBgoJBlwGBk5OTU56DBIMDBJJDBINDRIfDBIMDBIfTk1OIwwSDQ0SNFUHEAddBgZVVQYGXQYSBlVUBgoJBlwGBlVVBgZcBgkKBgACAEAAQAHAAcAABAAUAAA3ByEnBxcUBiMhIiY1ETQ2MyEyFhW1SgEqYErVGhH+1hEaGhEBKhEa4GCAYDURGhoRASoRGhoRAAYAKwBVAdUBqwADABMAFwAbAB8AIwAAJREhEQEyFhURFAYjISImNRE0NjMXFSM1IxUjNRcVIzU3FSM1Aav+qgFWERkZEf6qERkZEasrKivVKioqgAEA/wABKxoR/wARGhoRAQARGoArKysrVioqVisrAAQAQABAAcABwAADAAYAEgAiAAAlIzUzFxEBNxUzFTM1MzUjNSMVNzIWFREUBiMhIiY1ETQ2MwFra2sq/tYKKyArKyD1ERoaEf7WERoaEZUgSgEq/tb1ICsrICsrYBoR/tYRGhoRASoRGgABABUAgAHrAYAABgAAARMhNxc3JwErwP4qgGAiPAGA/wCrgBlRAAYAQABAAcABwAAHAAwAFAAcACQAKQAAJTQ2MxUiBhUzNDYzFSM0NjMVIgYVAxQGIzUyNjUzFAYjNTI2NSMUBiM1AStXPiw/KyYa64phUHArVz4sP4CKYVBwgCYaQD5XKj8sGiZAYYorcFABgD5XKj8sYYorcFAaJkAABQBAAEABwAHAAAUACwARADAANgAAATYzFSIHFyc2MxUiAxQHJzY1BzcBBycGFSM0NjcnBhUjNDY3JwYjNTI2NycGIzUyNzcUByc2NQFIOT8vKkIiGx4MiSIfFsAbAWUbPRMqEg0fKyshFzVCVh1HFh8pMiIbWAsiAwEJIisWQiILKgEVPzkfKi8bG/6bGz0bIhU1ER41RCRZGzU4KxkSHx8qE1geGyILDAABACsAKwHVAdUABwAAEjIWFAYiJjSosH19sH0B1X2wfX2wAAIAQABAAcABwAAYACgAAAE1NCYjIxUzFSMVMxUjFTMyNjU1NCYjMjY3MhYVERQGIyEiJjURNDYzAUAYElZWKytWVhIYEg4OElYRGRkR/tURGhoRASAgEhkrKyorKxkSIA4SEq4aEf7WERoaEQEqERoAAgAVAJUB6wGAAAsAFwAAEjIWFSM0JiIGFSM0NjIWFSM0JiIGFSM0n8KKK3CgcCuuelgqP1g/KgGAimFPcXFPYTVYPiw/Pyw+AAIAQABAAcABwAAJABkAACU1IxUjNSMVMxUTMhYVERQGIyEiJjURNDYzAUArKitVgBEaGhH+1hEaGhGV1lZWgFYBKxoR/tYRGhoRASoRGgACAEAAQAHAAcAAEQAhAAABNSMVMxUjFTMyNjU1NCYjIzU3MhYVERQGIyEiJjURNDYzAUCAVVVVEhkZEiqqERoaEf7WERoaEQFAK4ArKxkSKxIYK4AaEf7WERoaEQEqERoAAwBAAEABwAHAABMAIwAnAAABNSMiBhUVFBYzMzI2NTU0JiMjNTcyFhURFAYjISImNRE0NjMTNTMVAUBVEhkZEioSGRkSKqoRGhoR/tYRGhoRgCoBQCsZEoASGRkSKxIYK4AaEf7WERoaEQEqERr/ACsrAAIAQABAAcABwAAFABUAACU1IxUzFRMyFhURFAYjISImNRE0NjMBK1YrlREaGhH+1hEaGhGV1iurASsaEf7WERoaEQEqERoAAgBAAEABwAHAABQAJAAAATU0JiMjFTMVIyIGFRUzNSM1MzI2NzIWFREUBiMhIiY1ETQ2MwFAGRJVVSoSGYBVKhIZVREaGhH+1hEaGhEBFSsSGSsrGBJWKysYvRoR/tYRGhoRASoRGgADACsAKwHVAdUABwATAB8AADYyNjQmIgYUEjIWFRUUBiMjIiY0NxUzFSMVIzUjNTM1uoxlZYxlU7B9GRGrWH3qVlYqVlZVZYxlZYwBG31YqxEZfbATVipWVipWAAIAKwBAAdUBwAAbAC8AACURIxUyFhQGIzUiJjQ2MxUyNjQmIzUiBhQWMxUTMhYVERQGIyEiJjURNDYzMzczFwGrqy0+Pi0cKCgcHCgoHC0+Pi2rERkZEf6qERkZEUUmgCZrAQAWPVo+Jig6J4koOicmPVo+FQEqGRH/ABEaGhEBABEZKysAAQArAFUB1QGrABgAAAEzERQGIyEiJjURNDYzMxczJzMXMyczFzMBgFUZEf6qERkZERYqQCoqK0ArKytAAav+1REaGhEBABEaVlZWVlYAAQCAAEABgAHAAA0AAAEzFSMVFAYiJjQ2MzIXAQCAVTNGMjIjFRYBwFXWIzIyRjMMAAEAawArAZkB0gARAAAlFTMVITUzNSYmNTQ2MhYVFAYBFYD+1oA0SVh8V02oUyoqVAhWNT5YWD43VgACACsAKwHZAdIABwAlAAASIiY0NjIWFCUUBgcVMxUhNSM1NDYzMzIWFRUjFTM1JiY1NDYyFm4cEhIcEgFZTTdA/qsVDAlACQwVqzRJWHxXARUTGhMTGhQ3VgdTKmpWCQwMCVZAVAhWNT5YWAABAKsAgAFJAYAABQAAAQcXByc3AUliYh6AgAFiYmIegIAAAQC3AIABVQGAAAUAABMXByc3J9WAgB5iYgGAgIAeYmIABQBAAEABwAHAAAcADwAXAB8ANAAAADI2NCYiBhQmMjY0JiIGFAYyNjQmIgYUBjI2NCYiBhQ3MhYVFAYjIyIGFRQWFRQGIyImNDYBaBoTExoTLRoTExoTVxoTExoTLRoTExoTlU9xPywlDhIQEg5QcHABABIcEhIcQxMaExMaExMaExMaaBIcEhIcrmRHLD4TDQsUDA4ScKBwAAIAFQBVAesBqwAEABQAADcHIScHBRQGIyEiJjURNDYzITIWFbVKASpgSgEAGhH+gBEaGhEBgBEa9WCAYDURGhoRAQARGhoRAAIAKwArAdUB1QAHAA8AADYyNjQmIgYUEjIWFAYiJjS6jGVljGVTsH19sH1VZYxlZYwBG32wfX2wAAIAKwBVAdUBqwAXACEAAAEyFREUIyInJiIHBiMiNRE0MzIXFjI3NgcGIyInFTYzMhcByQwMAgRgxmAEAgwMAgRgxmAEHE5dWFNSWVhTAasO/sYOAiMjAg4BOg4CIyMCNxgY6BgYAAIAVQArAasB1QAIACAAADczJjQ3IxYVFAUWFRQjISI1NDc2NCcmNTQzITIVFAcGFIzoGBjoGAEFAg7+xg4CIyMCDgE6DgIjVVOwU1JZWGsEAgwMAgRgxmAEAgwMAgRgxgACACsAVQHVAasAEwAfAAASMh8CFhQPAgYiLwImND8CFiIHBhQXFjI3NjQns5pdEwYSEgYTXZpdEwYSEgYT8IxWDw9WjFYPDwGrEAMTQoZCEwMQEAMTQoZCEwMbDjpwOg4OOnA6AAIAQABAAcABwAAEABQAADcHIScHFxQGIyEiJjURNDYzITIWFbVKASpgStUaEf7WERoaEQEqERrgYIBgNREaGhEBKhEaGhEAAwBVACsBqwHVAAQACQAZAAA3IScHJycVNxc1NzIWFREUBiMhIiY1ETQ2M4ABAFJALkA1NpURGhoR/wARGhoRa21SN+6rICCrKhkR/qoRGRkRAVYRGQADACsAVQHVAdUABwAbACMAADYyNjQmIgYUEzMXMzIWFREUBiMhIiY1ETQ2MzMWNDYyFhQGItRYPz9YPyuAJ0QRGRkR/qoRGRkRRCMoOCgoOJU/WD8/WAEBKhoR/wARGhoRAQARGsc4KCg4KAADACsAKwHVAdUACAANAB0AABMzESEVISImNTcHIScHFxQGIyEiJjURNDYzITIWFSsqASv+1REZwEABAFY/vxkR/wARGhoRAQARGQGA/tUqGRGrVWpPGxEaGhEBABEZGREABwArACsB1QHVAAMADAAQABoAJAAwAEAAAAE1MxUnESEVISImNREXNTMVNzUjFTM1MzUjNQc1NCYjIxUzMjYnNTQmIyMVMzUzMjY3MhYVERQGIyEiJjURNDYzASsV6wEr/tURGZUV4EAgICA1Eg41NQ4SaxMNNSAVDRO2ERkZEf8AERoaEQELQEB1/tUqGREBK0sWFhYggCogFkBADROAEzcWDROAKhOtGRH/ABEaGhEBABEZAAQAQABAAcABwAADABMAGwAjAAAlESERATIWFREUBiMhIiY1ETQ2MxMVIzU0NjIWJiImNDYyFhQBlf7WASoRGhoR/tYRGhoR9cBCPEJMKBwcKBxrASr+1gFVGhH+1hEaGhEBKhEa/uUQEBYaGkAdJh0dJgADABUAYAHrAaAABwAPABkAABIyFhQGIiY0FjI2NCYiBhQ2MhYXBgYiJic25jQmJjQmFFg/P1g/HJ6AHByAnoAcHAFAJjQmJjSFP1g/P1jMWEhIWFhISAADABIAKwHVAfAAGwAfACMAAAAWFhQGBwYjIic3FjMyPgI0JicmIxUnNxUzMgcXNyc1FwcnAYYuISEXN1ExKyAeHxk+JBoaEiw+WloBIehOTk6KiosBiS5PQlAXOBgfDRokPzI+EixFWltFvk5OTjyKiooABABXAFcBqwHrAA4AFAAaACAAAAEWFhQGBzU2NjQmJxUnNwM3FhcVJicWFwcmJzcGByM2NwEVP1dXPy49PS5hYX4fFx4wOQUQHhwGQRIEKwYdAakIYIJgCCsIR15HCFNfYf6OHxEFKwaOHRgeJS5fGhstJgAEAFUAVwGpAesABQALABEAIAAAJTY3MwYHBzY3FwYHNyMmJzcWJwc1BgYUFhcVJiY0Njc1AWgRBSsGHHIeFx8kMJQrBREfHVhhLj09Lj9XVz+2Fx4uJRYFER8cBr4eFx4kRl9TCEdeRwgrCGCCYAhCAAMAQABAAcABwAADABMAFgAAJREhEQEyFhURFAYjISImNRE0NjMXFwcBlf7WASoRGhoR/tYRGhoRamtrawEq/tYBVRoR/tYRGhoRASoRGmtVVQACABUAgAHrAYAAEwAjAAAlNSMVIzUjFSM1IxUjNSMVIzUjFSUyFhUVFAYjISImNTU0NjMBwCsqKysqKysqKwGAERoaEf6AERoaEauqVVVVVVVVVVWq1RoRqhEaGhGqERoABAAYADAB2QHFAAUADQAlACsAADc1FyMiJjYyNjQmIgYUBRYVFAYHBwYjIiYnAyY1NDY3NzYzMhYXASYmNzcVfUofERoiEgwMEgwBQwMPC50GCgwXBWoDDwueCQgLFgX+yhAOBzRbh7Ia+w0SDAwSpgYKDBgEQQMPCwEABgoMFgVBAw8L/rIHIBB9wAACACsAVQHVAdUACQAdAAAlNycVIzUHFzUzNzIWFREUBiMhIiY1ETQ2MzM3MxcBQEtLgEtLgGsRGRkR/qoRGRkRRCeAJ7VLSzY2S0s2wBoR/wARGhoRAQARGioqAAIAKwBrAdUBlQAJAB0AACU3JxUjNQcXNTM3NxEnFRQGIyEiJjURNDYzITIWFQEVS0uASkqAa1VVDAn+1QkMDAkBKwkMtUtLNjZLSzZKVv7qVksJDAwJAQAJDAwJAAUAKwArAdUB1QAFAA0AFQAdACUAACQiJiczBiYiJjQ2MhYUFiImNDYyFhQGMjY0JiIGFBIyFhQGIiY0ASVKOw3aDZ4aExMaE4MaExMaE7GMZWWMZVOwfX2wfYspISFhExoTExoTExoTExrTZYxlZYwBG32wfX2wAAUAQABAAcABwAADAAkADwATABkAADc3FQc3FAYjIzcBMwc1NDYzMwc1JRYXASYnxvq9vRoRKlX+qypVGqM9+gFgGQb+oRcHQPo9vSsRGlUBK1UqERr6PbsHF/6iBxcAAwArACsB1QHVAAcADwAbAAA2MjY0JiIGFBIyFhQGIiY0JBYWFAYHBiInNzUyuoxlZYxlU7B9fbB9AR8gFhUQJWomWhZVZYxlZYwBG32wfX2wEiA0LDQQJiZagAAEAAAAfgIAAYIAEgAeAFEAWAAAJTU0JyYjIgcGFRUUFxYWMjc2Nic0MzIXFhUVFCMiNSUiFRQXFhcWFxYXFhcWFRQHBiMiJyY1MxQWMzI1NC4DJyYnJjU0NzYzMhcWFSM0JyYlNzMRIzUHARMKBxcKCBcCARAsCAYEe1MpExZSUwEcHhMGDQkTCA4IBQUsDBI5EQUpFhAhBgYOIwYIDBMqDBEjFBQqEgz+SWUGK0DmNiQRDwMONDUYCQUgEAwSR20YHDkpbm41Fw4GAgMCBgMJBQsKCyUPBCcMCg4QFwYJAwYJAgMIDRcmDgQREBoVBAM6Jf8AzRYAAgBZAH4BwgGCADMAWQAAASIGFRUUFxYWFxYXFhcWFRQHBiMiJyY1MxQWMzI1NCYmJyYnJicmNTQ3NjMyFxYVIzQnJgcWFRQHBiImNTMUFxYzMjQjIzUzMjc2NTQjIgcGFSM0NzYzMhYUAXYNEhQHFhIIDgcGBSwMEzgRBSgXECEGBgcFEBwUEioMESMUFCoTDKEqGBdKLysLDREqLhoZIAkCJhoKAyowERAmKgEhDAsBDAcDBAYDCQULCgslDwQnDAoNERcGCQMDAgQGDQwYJg4EERAaFAUDHg4sJRIUJiIUCApSIRcFCyYUBgktEwUkSAAEAEAAKwHAAesABwAYABwAIAAANjI2NCYiBhQlFhYVFAYiJjQ2MzIWFzcWFwc1MxUTFSM1wnxXV3xXASsRGXCgcHBQG0cWHhAOySorgFVYfFdXfLUWRRxPcXGecRoRHw0RrICAARYrKwAFACUAFQHAAesABwAUABgAHAAzAAAlMjcnBhUUFgMBBycGIyImNTQ2NycXNTMVNxUjNRcXBxYVFRQGByc2NTQmIyIHJzYzMhYXAQAnJMwUV4IBexs2MjhQcBMNO8YqK4DWHh4qEw0fFFc+KCIgMzccRhZVFcwiKT5YAVb+hRs2IHFPGD8TO1keSd8rK0weHzVCARg+Ex8iKT5XFB8gGREACAArACsB1QHVAAQACQANABIAFwAbACEAKQAAJTY3IxUXNjcjFRU2NyM1FTMmJycVMyYnJxUzJgMRBgYUFgIyFhQGIiY0AaUCApRwDAN/IB4+lAICkH8DDHA+Hko/V1cEsH19sH3VBhAWQBEFFj4EEMAWEAZAFgURPhQQ/rIBUghggmABdn2wfX2wAAIAKwAVAdUB6wAIAB8AABM1MzIWFRUjNRcjFTMHJzM1IyImNTUjNTM1IzcXIxEh1YARGiuAVStAQCqqERpVVStAQCoBKgFVKxoRgIDVK0BAKxoRqisrQED/AAAGAEAAQAHAAcAABwALABMAGwAfACMAAAE1MxUzFSMVFyM1MyUzFSM1IzUzFyM1MxUzFSMDMxUjFTMVIwFAK1VVVdXV/tUrK1VVgCoqq6vV1dWAgAFAgCsqK1UqK4ArKtWAKyoBKirWKgAMAEAAawHVAZUAAwAHAAsADwATABcAGwAfACMAJwArAC8AAAEzFSMVNTMVIzUzFSM1MxUjNTMVNzUzFSczFSMjNTMVFzUzFSM1MxUjNTMVJzUzFQGAVVVVwFbAVcBV61XAVlZqVRVWwFXAVVVVAZVV1VVVVVVVVVVValZWwFVVVWtWVlZWVlZrVVUAAwBAAGsB1QGVAAMABwALAAATIRUhFzUhFSE1MxVAAZX+a5UBAP5rgAGVgKqVlZWVAAMAAABVAfwBqwAHAB4AIQAANzMnIwczNzMlMwcjJwcjJwYGIyImNDYzMhczFzczFyU3F9woRCtEKQ9EAQgnLCUgICYCFVIxR2RkR1IzEBogIiD+1hkYq8DAKpbAgoIJKzRljGVAh4eHDk5OAAEAAABVAgABqwASAAABFhYVFAYjISImNTQ2NzY2MzIWAZ0pOj8s/us1S0MvFUwtOlgBKgM9Kiw/SzUvSwUnMEkABgAVACEB6wHgAAMABwAVABkAHQAhAAAlNxcHNzMVIycWFhUUBiImNTQ2NzUzBxUjNRc1MxUnNxcHAXAeJh4VQEBrHSNLaksjHYDrQNYqySYeJn0dJh7KK4QROiM1S0s1IzoRZ8ArK/8/P1MnHicACQAVACEB6wH0AAMABwAPABMAFwAbAB8AIwAnAAA3NxcHFzUzFQIyFhQGIiY0BTMVIwc3FwcTByc3JxUjNQcVIzU3Byc3TCYeJoEqSmpLS2pLAStAQDseJh4eJh4mgSqWQHseJh50Jx4nNT8/AWpLaktLaiAreB0mHgFLJh4mNT8/1CsreR4mHgADACsAKwHVAdUABAAUAB0AAAE1IxU3NzIWFREUBiMhIiY1ETQ2MwcRIRUhIiY1EQGrazU2ERkZEf8AERoaEVYBK/7VERkBAKurILUZEf8AERoaEQEAERlV/tUqGREBKwACABUAQAHrAcAABAAUAAA3IScHJyUyFhURFAYjISImNRE0NjNrASpgSjYBCxAbGxD+gBEaGxCVgGBAyxsQ/tYQGxoRASoQGwAOABUAQAHrAcAABAALAA8AEwAYABwAIAAkACkALQAxADYAOgA+AAA3MycHJychFSEiJjUTMxUjNzMVIycVIzQ2ATMVIxEzFSMFMxUjJTIWFSMVMxUjJzMVIxMUBiM1NTMVIxUzFSNA1UQ1J2ABK/8AERpWKipVKyuAKxsBOyoqKir+qisrAasQGysrK6srK9YbECsrKytrW0UuZtUaEQFVKysrKysQG/6rKwGAKyorgBsQKiuAK/7WEBsrqiorKwAPABUAQAHrAcAAAwAHAAsAEAAUABgAHAAgACcALAAwADQAOQA9AEEAABMVIzUzFSM1BxUjNTcVIzQ2ARUjNRMVIzUDFSM1AxUjNRMiJjU1MxUTMhYVIxcVIzUnFSM1ExQGIzU3FSM1FxUjNZUqgCuAKysrGwFlKioqKyvVKysRGtbVEBsrKyuAK9YbECsrKysBwCsrKyurKiqrKxAb/qsrKwFVKyv+qysrAQArK/7VGhFVgAGAGxAqKytVKyv+qxAbK6oqKlUrKwACABUAQAHrAcAABwAXAAA2MjY0JiIGFCUyFhURFAYjISImNRE0NjO5jmRkjmQBaxEaGhH+gBEaGhGAS2pLS2r1GhH+1hEaGhEBKhEaAAcATAAhAbQB9AADAAcACwAPABMAFwAbAAA3JzcXAzcXBwEHJzcHIzUzExcHJyczFSMHNSEVah4mHkQeJh4BQh4mHnkqKoEeJh6FKiqAASpWHyYeASQeJh7++R4nHno/AV8eJh5bP+qAgAAEAAAAAAIAAgAAEAAZACUAMQAANyEVIxUjNSMiJjU1IzUzNTMXNSM1MzIWFRUDMhYXIyYmJwcnMjYDNxciBiMiJiczFharAQArK6oSGSsrK6qAgBIZf2STCCAGRDUdUQMIXx1RAwgDZJMIIAZEqysrKxkSqisr1oArGRKAASuIYzxgGRxRAf42HFEBiGM8YAAFACsAKwHVAdUACAAQACQALAA0AAABMhYVMzQmIxUCMjY0JiIGFDczFRQGIyEiJjURNDYzMzczFTIWJzUyFhUjNCYCNDYyFhQGIgFVEhkcKh2BWD8/WD/WahkR/qoRGRkRRCeAEhkWNUscO8IoOCgoOAGAGRIdKhz+6z5YPz9Yl+sRGRkRAQARGitAGWccSzUpO/7/OigoOicABAAAACsB6wHrAAkAEQAkADAAADY0NjMyFhUUBiIGMjY0JiIGFCc1MzUzFzMyFhURFAYjISImPQMzFTMVIxUjNSM10SgcHSgoOg9YPz9YPitAlSdEERoaEf6rERorQEArQLg6KCgdHCgmPlg/P1iCQEArGhH/ABEZGRHWgEBAK0BAKwADACsAVQHVAasABwAPACgAAAE3JycHBxcXBzcnJwcHFxcTMxEUBiMhIiY1ETQ2MzMXMyczFzMnMxczAWksLBQULCwUZTs7Gxo7OxqrVRkR/qoRGRkRFipAKiorQCsrK0ABARQULCwUFCwaGhs7OxsaOwEr/tURGhoRAQARGkBAQEBAAAMAQABAAcABwAAHAA8AIgAAARcHBycnNzcXJyc3NxcXBxczFRQGIyEiJjURNDYzMxUjESEBGzo6Gxs6OhtrFCwsFBQsLBcqGRH+1REaGhHAwAErARsbGzo6Gxs6KiwUFCwsFBQswBEaGhEBKhEaK/7WAAQAFQBrAesBlQAEABQAGAAcAAA3MycHJzcyFhURFAYjISImNRE0NjMjMxEjAzMRI+vVRDYmtQkNDQn/AAkMDAlqKipWKyuVWkQuvAwJ/wAJDAwJAQAJDP7WASr+1gAJAEAAKwHAAesABQANABQAGgAfACQALAA9AEEAAAEHJzYzMhcnMxYVFRQGJxcjJjU0Nhc3FwYjIjcjNxYWBwcmJicWMjY0JiIGFCUWFhUUBiImNDYzMhYXNxYXJxUjNQEcOjEkKwpTPF0CFMtGZQITTz0tISsPiHMrFit5KxYpCDt8V1d8VwErERlwoHBwUBtHFh4QDnSAAWdkTBzYbQ4HARUznmsOCBQyw2lTGapMCS1rSwksFmtYfFdXfLUWRRxPcXGecRoRHw0RaisrAAMAQABAAdUB1QAEABUAIQAANyEnByc3MxUUBiMhIiY1ETQ2MzMVMzcVIzUjNTM1MxUzFWsBAFZAKqpAGRH/ABEaGhGqQEAqQEAqQGtqVUBVqhEaGhEBABEZQBZAQCpAQCoABAArACsB3gHVAAcAFQAaAC4AAAAyNjQmIgYUFxcHJwYjIiY0NjIWFRQHIzcXNzcXFRQGIyEiJjURNDYzMwYHIxEhATUsHx8sIIdCHkMZGig3OFA4S+s7KjpsKxoR/tURGRkRdgoBawErAUAfLCAgLB1CHkIPOFA4OCga20sySwcraxEZGREBKxEaFRb+1QACAEAAQAHAAcAABQAVAAABFSc1MxUlAQcnFRQGIiY0NjMyFzUnASsrgP7bAWUbejNGMjIjFRbAAWtEKm9VVf6bG3olIzIyRjMMBsAAAgBAABUBwAHrAAUAFAAANzcnBycHJTIWFREUBwcnJjURNDYz1cAeokweASoRGhOtrRMaEavAHqJMHtYaEf7sFQ50dA4VARQRGgACACsAKwHVAdUACwAbAAAlNycVIyIGFRUzNTM3FhQHBwYiJycmNDc3NjIXAStKSmsJDCpWpAYGwAYSBsAGBsAGEgbLSks1DQlVQA8GEgbABgbABhIGwAYGAAYAAAArAgAB4AAHAA8AJQAtADUAPQAAJDI2NCYiBhQ2MhYUBiImNCcXFSM1JyY1NDc3NjMyFxcWMxUiJycCMjY0JiIGFDYyFhQGIiY0JCImNDYyFhQBdj4sLD4rHVo+Plo9RS8qRQwMPAgWEw8pICw/LRHOPisrPiweWj09Wj4BXCIaGiIZSys+LCw+ij5aPT1aXjGEajwIFhIMPAwMKSArLRH++Cs+LCw+ij5aPT1ayRkiGhoiAAQAVQBAAasB1QADAAsAEwAxAAABNSEVFjI2NCYiBhQGMjY0JiIGFCc1NDYyFhUVFAcVFAYjIyImNTUjFRQGIyMiJjU1JgGA/wDSHBISHBKuHBISHBIrWKZYFgwJFQkNqg0JFQkMFgEVa2uAExoTExoTExoTExoD1TMiIjPVHBQmCQwMCRYWCQwMCSYUAAQAQABAAcABlQADAAsAEwAtAAATIScjFjI2NCYiBhQGMjY0JiIGFCUXFRQGIyMiJjU1IRUUBiMjIiY1NTc2MzMyawEqIOrdGhMTGhPXGhMTGhMBKSwMCRYJDP8ADAkWCQwsBhnqGQEVYMoTGhMTGhMTGhMTGsKAqwkMDAkWFgkMDAmrgBUAAwArABUB2wHrAAQAJAA4AAATFTcXNQEnJjU0Nzc1NDYzMzUzFTMyFhUVFxYHByMiJwYiJwYjBTMVIyInBiInBiMjNTMyNxYyNxaAgID+1CgBDxsaEUCAQBEaGxUHKAExJSVgJSUxAVYqKi0pKFooKS0qKi4oJ1wnKAGAVSoqVf7rjgIFDgYJYxEaQEAaEWMJBxSOKioqKisrFRQUFSscGxscAAUAVQBAAasB1QADAAsADwAXACkAAAE1IxUWMjY0JiIGFCc1IxUWMjY0JiIGFBIyFhUVFAYjFxUhNTciJjU1NAGAaz0cEhIcElVrEhwSEhwSLaZYLB8g/wAgHywBFWtrgBMaExMabWtrgBMaExMaAS0iM8sfKyALCyArH8szAAMAVQBAAasB6wADAAsAHQAAATUhFRYyNjQmIgYUJzU0NjIWFRUUBiMXFSE1NyImAYD/AG8iGhoiGoBYplgsHyD/ACAfLAEramqWGiIaGiIG4DMjIzPgHysgCwsgKwAFAFUAQAGrAdUAAwALAA8AFwApAAABNSMVFjI2NCYiBhQnNSMVFjI2NCYiBhQSMhYVFRQGIxcVITU3IiY1NTQBgGs9HBISHBJVaxIcEhIcEi2mWCwfIP8AIB8sARVra4ATGhMTGm1ra4ATGhMTGgEtIjPLHysgCwsgKx/LMwACAIAAFQGVAeAAGQAhAAATAzM3FxUzNSc3FjM1IicnJiMiBiMHFTM1NzYiJjQ2MhYU0TwtJywrLQ0uRz4dFg8VAwsDbysmYCIaGiIaAUL+06srgKArQDUqNCIVAi9kSA9JGSIaGiIAAQArACsBwAHVABQAACUnFRcVJwc1NzUHNTc1NDYyFhUVFwHAqytLSiqqqhMaE6urNXUgIBUVICB1NSprdQ0TEw11awACABUAVQHrAZUADgAWAAABMhYVFSM1IRUjETMVMzUGIiY0NjIWFAGVIzMr/oArK6s8NCYmNCYBazMjwEBAAUDAloAmNCYmNAACAEAAPwHAAdUABwANAAAlJiYnNxcGBgc3FwcnNwEACZEmwMAmkAqdI8DAI6sHcR2VlR1wP3sblZUbAAMAKwA/AdUB6wAPABQAGAAAEwEHJwcnNxc3JwcmJic3JwUGByc3Eyc3F0YBjxtQasAjnUseLQmRJkVaAZUbO6g+px8ZHwHr/nAbUVKVG3s7HiIHcR02WpAVLqgw/usfEx4AAQArACsBwAHVABQAACUnFRcVJwc1NzUHNTc1NDYyFhUVFwHAqytLSiqqqhMaE6urNXUgIBUVICB1NSprdQ0TEw11awADACsAVQHVAasAAwATADMAACURIREBMhYVERQGIyEiJjURNDYzEzUjNTM1IyImNTU0NjMzNTMVMxUjFTMyFhUVFAYjIxUBq/6qAVYSGBgS/qoSGBgSlitVQAkMDAkWKitVQAkMDAkWgAEA/wABKxkS/wASGRkSAQASGf7qFioWDAlACQwWFioWDAlACQwWAAIAKwBVAdUBqwAJACYAACUnNycnBwcXBzc2FBYzFRQGIyEiJjU1MjY1NCYjNTQ2MyEyFhUVIgFMF0ZaISFbRxdMqxkRGRH+qhEZEhgZERkRAVYRGRGaVzoFVFQFOlcxRiIaVREaGhFVGRIRGlURGhoRVQACAEAAQAHAAcAAAwAPAAATMzchIQcVMxUhNTM1JzUhn8Im/vIBR6tr/wBrqwGAAWsqwGorK2rAKwADAFUAQAHVAcAAAwAHABkAADchFSEBNSMVNzIWFRUUBiMjFRQGIyMiJjU1VQFW/qoBVisrEhgYEisyI4AjM2srARVAQGsZEkASGEAjMzMj1QAHAEAAFQHAAe8AAwALABMALQA4AEMATgAANyEnIxYyNjQmIgYUBjI2NCYiBhQlFxUUBiMjIiY1NSEVFAYjIyImNTU3NjMzMiYiJjU0Njc3FhUUFiImNTQ2NzcWFRQWIiY1NDY3NxYVFGsBKiDq3RoTExoT1xoTExoTASksDAkWCQz/AAwJFgkMLAcY6hjrGhMQCAggWRwSEAgIIFgaExAICCDrYMsSHBISHBISHBISHMOAqgkNDQkVFQkNDQmqgBYqEw0JHQoKJRUNExMNCR0KCiUVDRMTDQkdCgolFQ0AAwArAFUB1QGrAAkAFQAhAAABNSMVIzUjFTMVJzUjFTMVIxUzNSM1NzMRIzUjFSMRMzUhAVUVFRYrVUAqKkAr1UCqVqpAASoBAGsrK0ArK0AWFUAVFkD+6lZWARZAAAMAQAArAcAB1QADAA4AGAAAATchFxYyNjU0JicnBhUUAyEDBgYjIyImJwGHCf7gCW00JiAQEECAAYArAhgQ1hAYAgFVVlbqJhoTOhMTSCsaAUT+exAVFRAABABAACsBwAHrAAcAMQA3AD0AAAAiBhQWMjY0BzQ3JjU0NjMyFzU0NjIWFRU2MzIWFRQHFhUUBiMiJxUUBiImNTUGIyImFyImNTIWFTQ2MxQGARYsHx8sH74fHyAWDw8fLB8QDhYgHx8gFhENHywfDhAWIIlQcFBwcFBwAYsgLB8fLEYhDw8hFiAKBBYgIBYECiAWIQ8PIRYfCQQWHx8WBAkf5HFPcU9PcU9xAAMAVQBAAbUBwAAHAAsANQAAADI2NCYiBhQHNSMVJRYVFRQGIiY1NSMVIxE0NjMzMhYVFTMyFhUVFBYyNjU1BiMiJjU0Nyc3AXcSDAwSDGuAASYPHywfINYaEYARGhURGgwSDAkMFh8iLRcBKwwSDAwSDGpqOw8XyxYfHxZroAFVERoaEZUaEWAJDAwJmgQfFiQOLRYAAwAVACsBwAHVAAcAIwArAAAkMhYUBiImNAEzFyEyFhUUBwcGIyMHBxQzMxUhIiY1NDc3JyMSMhYUBiImNAFaIhkZIhr+1UYUATwJDANMDBmfEwEF9/8AERkFHU0rbyIaGiIZgBoiGRkiAW8qDQkBCYoWIwMFKxoRCgo1ov7VGiIZGSIAAgBAAEABwAHAAAsAGwAAJTUjNSMVIxUzFTM1NzIWFREUBiMhIiY1ETQ2MwGAVVZVVVZqERoaEf7WERoaEdVWVVVWVVXrGhH+1hEaGhEBKhEaAAIAFQBVAesBlQAOABYAAAEyFhUVIzUhFSMRMxUzNQYiJjQ2MhYUAZUjMyv+gCsrqzw0JiY0JgFrMyPAQEABQMCWgCY0JiY0AAUAVQArAasB1QAHAA8AFwAnAC4AADYyNjQmIgYUEiIGFBYyNjQ2IgYUFjI2NDcyFhURFAYjISImNRE0NjMTNxYVFAYiy2pLS2pLHhIMDBINMxIMDBINlRIZGRL/ABIZGRJEeBkyRlVLaktLagELDRIMDBINDRIMDBI3GBL+qhIYGBIBVhIY/sR5GSQjMgACAEAAHwHAAdUABwARAAAAIiY0NjIWFAc2MxUiByYjNTIBGjQmJjQmQFBwb1FRb3ABVSY0JiY0cUvqTEzqAAMAQAArAcAB6wALABEAJwAANjI2NSMUBiImNSMUNiIGFTM0FzIWFREUBiMhIiY1ETQ2MzM0NjIWFdRYPysmNCYrhTQmgFURGhoR/tYRGhoRKj9YP+s+LBomJhoslyYaGhoaEf8AERkZEQEAERosPz8sAAcAVQBAAasBwAADAAcACwAPABMAFwArAAABNSMVFzUjFRc1IxUnNSMVFzUjFRc1IxUBMxEjNSMVIzUjFSMRMxUzNTMVMwGAKysrKyuqKysrKysBACsrK6orKysrqisBQCsrVSoqVisrqysrVSoqVisrASv+gCsrKysBgCsrKwACACsAKwHVAdUABwAZAAASMjY0JiIGFAUWFAcHBiInJyY1NTQ2MzMyF2gaExMaEwF0DAyWDCQMwAwZEZYSDAFrExoTExp1DCQMlgwMwAwSlhEZDAACAIAAQAGVAcAABwARAAABMjY0JiMjFTcyFhQGIyMVIxEBGhEZGRFFQDVLSzVAVQEVGiIaVqtLakuAAYAAAgBAAEABwAHrAAsAGQAAJTUjNSMVIxUzFTM1NxUHFxUhNTcnNSE3FwcBVUAqQEAqqysr/oArKwEPHzIY1StAQCtAQMAqgIArK4CAKlYTQwABAEAAQAHAAcAAHAAAExYXNzYXFjMyFhUVFAYjIiY1NDYzMzIWFRQXFgeNMF0vCgwkKAkMDAmW1QwJSwkMDAQJARpdMC8KBQwMCUsJDNWWCQwMCSgkDQkAAwBAACsBwAHVAAcADwAUAAA2MjY0JiIGFCYUFjI2NCYiJjIXAwPvIhoaIhpAGiIaGiIi5k3AwMAaIhkZIqIiGhoiGUBV/qsBVQACACsAVQHVAasACQAmAAAlJzcnJwcHFwc3NhQWMxUUBiMhIiY1NTI2NTQmIzU0NjMhMhYVFSIBTBdGWiEhW0cXTKsZERkR/qoRGRIYGREZEQFWERkRmlc6BVRUBTpXMUYiGlURGhoRVRkSERpVERoaEVUAAgArAFUB1QGrAAUAFQAAATUHJxUXNzIWFREUBiMhIiY1ETQ2MwGrq6urqxEZGRH+qhEZGREBVStraytqwBoR/wARGhoRAQARGgAEACsAQAHVAcAAAwALAA8AHQAAARUhNQQyNjQmIgYUBzUjFTcyFhUVIxUhNSM1NDYzAYD/AAEMEg0NEgwrquoaJlX/AFUmGgHAVVXADBINDRKhamrqJhqAVVWAGiYAAgA6ADoB1gHBABEAFwAAAQcXBycHJzcmNjc2NhYGBwYGBycmNDcXAT0fkx6Tkx7QDBEZH0wuCR8ZQKhaGRmWAQofkx6Tkx7QGEAZHwkuTR8ZEBtaGUYZlQADACsAVQHVAdUABwAbACMAADYyNjQmIgYUEzMXMzIWFREUBiMhIiY1ETQ2MzMWNDYyFhQGItRYPz9YPyuAJ0QRGRkR/qoRGRkRRCMoOCgoOJU/WD8/WAEBKhoR/wARGhoRAQARGsc4KCg4KAAEABUAVQHrAasABwALABMAKQAAJDI2NCYiBhQ3IxUzBDI2NCYiBhQlFxUjFAYiJjUjFAYiJjUjNTQ2MyEVAXIcEhIcEkA1X/6oHBISHBIBS0ArJjQmgCY0JisaEQErdRMaExMarTWLExoTExrNVWsaJiYaGiYmGusRGlYABABAAEABwAHAAAMACwATADEAABMhJyMWMjY0JiIGFAYyNjQmIgYUJRcVFAYjIyImNTUhFRQGIyMiJjU1NzYzMzUzFTMyawEqIOrdGhMTGhPXGhMTGhMBKSwMCRYJDP8ADAkWCQwsBhk1gDUZARVgyhMaExMaExMaExMawoCrCQwMCRYWCQwMCauAFSsrAAMAQAArAcAB1QAJABEAHgAANjI2NyYmIgYHFhIiBhQWMjY0JjIWFRQGBwcnJiY1NN1GShMBWE5YAROHNCYmNCaQoHBRPjExPlF6KBwcJiUdHAEJJjQmJjRQcU9CZhExMRFmQk8AAgBAAEABwAHAAAMAFQAAJTUnFRMyFREUBwcnBwciNRE0NzcXNwFAgPULCHiAcgMLCHiAcmv9Lf0BKAv+vggCKS0sAQsBQggCKS0sAAMAFQAVAesB6wAHAB8AJwAANjI2NCYiBhQlMxUjBgYHFSM1JiYnIzUzNjY3NTMVFhYmMhYUBiImNMJ8V1d8VwFULCwHZD8qP2QHLCwHZD8qP2TbRjIyRjJrV3xXV3xTKj9kBywsB2Q/Kj9kBywsB2QBMkYyMkYAAQBgAEABoAHVAAUAAAETBycHJwEAoA+RkQ8B1f56D0BADwADAGsAKwGVAdUAAwANABoAADchFSETFBYyNjU0JiIGFxQGBwcuAjU0NjIWawEq/tZqGSQZGiIaq0AgIA4rR0tqS1UqASoSGBgSERoaESt1JSUPNHwrNUtLAAIAawArAZUB1QAHABgAABIyNjQmIgYUJjIWFRQOAgcHLgQ1NOosHx8sHwl8Vx8sKw8QBhQ0JyABCx8sHx8sq1c+H1BGPRIRBxdFQVIfPgADACsAKwHVAdUAAwAMABoAACU1IwcjMzc2JycmBwclMhYVERQGIyEHETQ2MwGAdStgNZMICCYIB5MBKxEZGRH+1VUZEdUrK5MIByYHB5PLGRH/ABEaVQGAERkAAgA6ADoB1gHBABEAFwAAAQcXBycHJzcmNjc2NhYGBwYGBycmNDcXAT0fkx6Tkx7QDBEZH0wuCR8ZQKhaGRmWAQofkx6Tkx7QGEAZHwkuTR8ZEBtaGUYZlQAEAEAAQAHAAcAABAAMABEAIQAANyEnBycnMjY1IxQGIzUVMjY1NzIWFREUBiMhIiY1ETQ2M2sBKmBKNko+Vys+LBom6hEaGhH+1hEaGhGAgGBAIFg+LD9rQScaKhoR/tYRGhoRASoRGgADAEAAVQHAAasAAwARABUAACU1IxUlIxUjNSMVIzUjNTchFycVITUBAIABQBUrVdYVFQFWFRX+qoBVVVWAgICAK2trqysrAAEAFQCAAesBgAAGAAABEyE3FzcnASvA/iqAYCI8AYD/AKuAGVEABABVAEABqwHAAAcAEwAfAE0AABIyNjQmIgYUFzY2NCYnIyIGFBYzFzY2NCYnIyIGFBYzNxQGBxUzFAYHFRQGIyMiJjU1JiY1MzUmJjUzNSYmNTM1NDYzMzIWFRUzFAYHFe4kGRkkGSsRGRkRARIZGRIBERkZEQESGRkSrCQcQCQcDQmqCQ0cJEAcJEAcJEANCaoJDUAkHAFAGSQYGCSEARkiGQEZJBlqARgiGQEZJBjAHi0IGB4sCBkJDAwJGQgsHhgILR4YCCweFgkMDAkWHiwIGAACAD4AFgGVAeAAGgAiAAA3JzcXNwcVIzU3MjYzMhcXFjMVIicHFxUjNSc2IiY0NjIWFNOVCGkiJypvAwsDFQ8VHT9HLg0tKy1JIhoaIhljHSsVrQ9JZC8CFSIzKzVAKqCAKssaIhkZIgACAGsAKwGVAdUACwAcAAABNSM1IxUjFTMVMzUmMhYVFA4CBwcuBDU0AVVAKkBAKlN8Vx8sKw8QBhQ0JyABKypAQCpAQKpXPh9QRj0SEQcXRUFSHz4AAwBrACsBlQHVAAkADgAfAAABNicnJiMiBwcXBzcnBxU2MhYVFA4CBwcuBDU0AT4GBhQCAwQCDx9QRx9HAnxXHywrDxAGFDQnIAFfBQYUAgIPH1BHH0cf1Vc+H1BGPRIRBxdFQVIfPgABAEAAQAHAAcAABQAAAQMjJyc1AcChFTiSAcD+gJI4FQADAGsAKwGVAdUACQARACIAACUyNjc0JiIGFRY2IgYUFjI2NCYyFhUUDgIHBy4ENTQBABYyDTs0Ox5JJBkZJBlpfFcfLCsPEAYUNCcg1RsTExkZEy7WGSQZGSRDVz4fUEY9EhEHF0VBUh8+AAQAQABAAcABwAAGAA0AFAAbAAAlFSM3JzcXByM1FzcXByc1MwcXByc3MxUnByc3AcCAMT4fPc+AMT0fPk+AMT4fPc+AMT0fPsCAMT0fPk+AMT4fPc+AMT0fPk+AMT4fPQACAEAAKwHAAdUABwAbAAABNDYzESM1Iyc1MxUUBgcVIzUmJjU1MxUzNTMVAVVBKjU2aiouIjUiLisqKwGAHjf+VqprlZUiMQLAwAIxIpWVlZUAAwBVAEABtQHAAAUADQA3AAA3NyM1BzM2MjY0JiIGFDcWFRUUBiImNTUjFSMRNDYzMzIWFRUzMhYVFRQWMjY1NQYjIiY1NDcnN6tVK1UrzBIMDBIMOw8fLB8g1hoRgBEaFREaDBIMCQwWHyItF4CVa6BLDBIMDBIvDxfLFh8fFmugAVURGhoRlRoRYAkMDAmaBB8WJA4tFgADAEAAQAHrAesADAAUACAAABMUFhcHJjURNDYzMwYWNDYyFhQGIgc2MzIXFRQGIyM1NPUYEdENGhGaECA/WD8/WEg0QCEfGhGVAYAYORHRDREBKhEaHk5YPz9YP0cnC38RGnUPAAUAKwArAdUB1QAWACIAJgAuADYAACU1NCYjIgYVFRQWMwcVMzczFzM1JzI2AxYWFREhETQ2NzYyBzMVIxY0NjIWFAYiNjQ2MhYUBiIBgEI+O0UhFxgkIDwgIBgXIQQrLv5WLisrorvV1QoMEg0NEokNEgwMEq2TJhoaJpMXIRgIICAIGCEBLhBELf7oARgtRBARlWszEgwMEg0NEgwMEg0ABQBVAEABqwHVAAcACwAPABcAMAAAJDI2NCYiBhQnMzUjBzUjFRYyNjQmIgYUEzIWFRUUBiMXFSMnIwcjNTciJjU1ND4CAVIcEhIcEitrayprEhwSEhwSgFNYLB8gKypRKjAgHywcNTWVExoTExqDVVVVVZYTGhMTGgEtIjPLHysgCysrCyArH8sbJBAGAAMAawArAZUB1QADAAsAKgAAJTUjFRYyNjQmIgYUNxQGIzMXFSMnIwcjNTcmJjU1NDY3NyM1MxUjBxYWFQFr1l0cEhIcErUdGgIgKypRKjAiFyBEOxFm1kYQP0HVa2tgExoTExoPHCYgCioqCiIFJBe0KR8CICAgIAIeKgAEACsAFQHVAeAAGQAhACgALwAAEwMzNxcVMzUnNxYzNSImJycmIyIHBxUzNTc2IiY0NjIWFBM1Fwc1IzU3MxUjFSc3ezstJS4rLA0tRx0xDxQLGQkHcComYSIaGiIZqzU1dTV1dTU1AUL+06srgKEsQDcqHRgiFAMuZEcQSRkiGhoi/rclNTYmIFogJTU2AAQAKwDAAdUBQAARABUAIwArAAABMhYVFSM1IxUjNSMVIzU0NjMHNSMVNzIWFRUjNSMVIzU0NjsCFSMVIzUjAcAJDCAVIBUgDAnrKjUJDCAqIAwJa4AwIDABQAwJa2BLS2BrCQxAICBADAlrICBrCQwgYGAAAwBAACsB1QHVAAMACwAOAAA3MxUjNjQ2MhYUBiIDFyNAq6vVOFA4OFBNdergqy5QODhQOAGqwAADAGsAKwGVAdUAEwAXACgAACU0NjU0JiIGFTM0NjIWFRQOAhUXNSMVAjIWFRQOAgcHLgQ1NAETOCw+LCYWHhYRFhEmJit8Vx8sKw8QBhQ0JyDtFDIYHysrHw8WFg8MFA0cFT0lJQElVz4fUEY9EhEHF0VBUh8+AAYAFQAVAesB6wAFAA0AFQAaACIASQAAARUXByc1BjI2NCYiBhQWMjY0JiIGFCczJjUjFjI2NCYiBhQBMhYVFAYHFRQHFRQGIyMiJjU1IxUUBiMjIiY1NSY1NTQ2MzIWMzYBYD0QTRdYPz9YPiccEhIcEsCtLYASHBISHBIBFT5YSjYWDAkVCQ2qDQkVCQwWWFMEEgQtAatbJBoua8A+WD8/WL4TGhMTGm0rP+oTGhMTGgFtWD44UwhCGxQmCQ0NCRUVCQ0NCSYUG9UzIwFBAAEAKwBVAdUBawAcAAASMhYVFAYHNTY2NTQmJiIGBhUUFhc1Fwc1JiY1NKiwfUc5Jy8oUmJSKDwvVVVCUwFrPywhNQwsCh8NDB0XFx0MDyMIOlVWRQs3JCwAAgArAJUB1QFrAAUAEQAANzcnBycHJTIWFAYjIyImNDYzmloOTCcPARQmNzcm8CY3NybMWg5LKA9pP1g/P1g/AAEAgACAAYABgAAIAAAlIzUzFTcXBzMBVdVAlSuSZ4DVZZArlQAEABUAFgHrAesAAwAKABQAJAAANyEVISUhNDY2FhYFNSEVFAYjISImBTU0JyYnJzM1MxUzAwYGIxYBQP7AAUD+v0JeX0L+vwFBDQn+6wkNAWw0HzwGaiprJAITDZUqVTFCEBBCxhUVCQwMDKs/Mh8RMldX/qENEgACACsAKwHVAdUABwAPAAA2MjY0JiIGFAY0NjIWFAYiy2pLS2pLVX2wfX2wgEtqS0tqI7B9fbB9AAIAKwBAAdUBwAAKABIAAAEiBgcnNjMyFwcmBjQ2MhYUBiIBABo/EmpZfHtaaiyUMkYyMkYBKRoSallYayy3RjMzRjIABwArAFUB1QGrAAMABwALABsAHwAvADMAADchESEnIREhJTUjFTcyFhUVFAYjIyImNTU0NjMHNSMVNzIWFRUUBiMjIiY1NTQ2MyMzFSNVAVb+qioBqv5WAUAWKwkMDAlACQwMCVUWKwkMDAlACQwMCVUqKoABACv+qoBWVoAMCYAJDAwJgAkMgFZWgAwJgAkMDAmACQyqAAkAVQBVAasBqwADAAcACwAPABMAFwAbAB8AIwAAJTUzFSc1MxUnNTMVNzMVIwc1MxUjNTMVBzUzFTM1MxUDNTMVAVVWVlbWVipWVoBW1lZWVipW1lZVVlaAVlaAVlZWVoBWVlZWgFZWVlYBAFZWAAEAVQBVAasBqwAIAAABFSEXByc3FwcBq/78dx6rqx53ARUqeB6rqx54AAEAlQDAAWsBKwACAAATMweV1msBK2sAAgArACsB1QHVAAIACgAAJTcjJjIWFAYiJjQBAFWqA7B9fbB91VaqfbB9fbAAAQCVANUBawFAAAIAADc3F5Vra9VrawABAFUAVQGrAasACAAAARcHJzchNSEnAQCrqx53/vwBBHcBq6urHngqeAACACsAKwHVAdUACwATAAAlJzcnBycHFwcXNxcCMhYUBiImNAFrTU0eTU0eTU0eTU2lsH19sH2zTU0eTU0eTU0eTU0BQH2wfX2wAAEASQBrAcABiQAFAAA3NxcBJzfA4h7/AHcep+Ie/wB3HgABAKsAgAFJAYAABQAAAQcXByc3AUliYh6AgAFiYmIegIAAAQC3AIABVQGAAAUAABMXByc3J9WAgB5iYgGAgIAeYmIAAQBrAGsBlQGVAAsAAAEHFwcnByc3JzcXNwGVd3ced3ced3ced3cBd3d3Hnd3Hnd3Hnd3AAEAgAC3AYABVQAFAAABFwcnBycBAIAeYmIeAVWAHmJiHgABAIAAqwGAAUkABQAAARcHJzcXAWIegIAeYgFJHoCAHmIABABrAGsBlQGVAAUACwARABcAAAEzFSM1Ixc1MxUjNSc1MxUjHQIzFSM1AStqKkBAKmrAakBAagGVakDWQGoqlmoqQFZAKmoABABrAGsBlQGVAAUACwARABcAAAEzFSM1MwM1MxUjFSc1MxUjNRU1MxUjNQFVQGoqKmpAqipqaioBVSpq/tZqKkDqQGoqqipqQAADAEAAgAHAAYAAAwAHAAsAABMhFSEVNSEVBTUhFUABgP6AAYD+gAGAAYAraioqaysrAAMAVQDVAasBKwAHAA8AFwAAEjIWFAYiJjQ2MhYUBiImNCYyFhQGIiY07yIaGiIamiIaGiIa5iIaGiIaASsaIhoaIhoaIhoaIhoaIhoaIgADANUAVQErAasABwAPABcAADYyFhQGIiY0NjIWFAYiJjQ2IiY0NjIWFO8iGhoiGhoiGhoiGjwiGhoiGqsaIhoaIpoaIhoaIkQaIhoaIgABAFYAVQGrAasAGQAAATcVIzcmIyIGFBYzMjY3MwYGIyImNDYzMhYBeTKWRSY0NUtLNSVHDSwOXDtGZGRGHkcBeTKWRSZLaksyIzhIZI5kHQACAJ4AVQFiAasABQALAAABByc3FzcDNxcHJwcBYmJiHkREpmJiHkREAY1iYh5ERP7IYmIeREQAAgCeAEABYgHAAAUACwAAJTcXByc3NwcnNxcHAQBEHmJiHkREHmJiHnxEHmJiHsREHmJiHgABAFUAVQGrAasACAAAEzcXBycRIxEHVaurH3cqeAEAq6sed/78AQR3AAEAawBAAasBqwAKAAATFwczNTMRIxcHJ+seTcQr700egAFAHk3W/wBNHoAAAQBVAEABlQGrAAoAACUHJzcjETMVMyc3AZWAHk3vK8RNHsCAHk0BANZNHgABAFUAVQGrAasACAAAAQcnNxcRMxE3Aaurqx93KngBAKurHncBBP78dwACAIAAgAGJAYAAAwAJAAATMxEjJQcnNxcHgCsrAQkegIAeYgGA/wAeHoCAHmIAAgB3AIABgAGAAAMACQAAATMRIyc3FwcnNwFVKyveHoCAHmIBgP8A4h6AgB5iAAEAwACVASsBawACAAABFScBK2sBa9ZrAAEA1QCVAUABawACAAA3NRfVa5XWawABAAAALQD5AdMABQAAEwcXByc3+a2tJtPTAa2trSbT0wABAH0AKwGAAdUABQAAEzcXByc3fS7V1S6pAagt1dUtqAAEAGsAFQGVAeEABwAPAB8AJwAAADI2NCYiBhQGMjY0JiIGFDcWFRUhNTQ3JzcXNjIXNxcBNSEVFAYiJgE3EgwMEgx0EgwMEgytPf7WPS0SMSBEIDES/uYBKld8VwFADBINDRIMDBINDRJXLUsWFkstLRExEBAxEf7bVVU+WFgABQBAACsBwAHVAAIABQATABwAIgAAJScVERU3MwcXByM1Byc3JzcXNTMXFhUUByc2NCcHNxYVFAcBEygoPFxcehViHnd3HmIVzB8hGRUVVjEKCqQpUQEIUSlcXHmiYh53dx5iomQyPTw1GSpYKlYxGRgZGQAEACsAVQHVAasABwARABUAGQAANjI2NCYiBhQ3MhYUBiMiJjQ2BTMVIxU1MxXEIhoaIhkqR2RkR0ZkZAEcKioq1RoiGhoivGWMZWSOZEBrVSoqAAMAKwArAdUB1QAHAA8AFwAAJTI2NTQnBxYnFBc3JiMiBjYyFhQGIiY0AQBGZSXvMHIl7zA5RmVTsH19sH1VZUY5MO8lqzkw7yVlj32wfX2wAAMAKwArAdUB1QAIABEAGQAAJTY1NCYjIgYHEzI2NycGFRQWAjIWFAYiJjQBhyRlRhg+E2kYPhPwJGUSsH19sH2XLTxGZRUP/s4VD/AtPEZlAYB9sH19sAAEAEAAVQHAAasAAwALABMALQAAEyEnIxYyNjQmIgYUBjI2NCYiBhQlFxUUBiMjIiY1NSEVFAYjIyImNTU3NjMzMmsBKiDq3RoTExoT1xoTExoTASksDAkWCQz/AAwJFgkMLAcY6hgBK2DLEhwSEhwSEhwSEhzDgKoJDQ0JFRUJDQ0JqoAWAAMAQABAAcAB6wADABsAIQAAJTUhFQEyFhURFAYjISImNRE0NjMzNTMVMzUzFQcHJzcXNwGV/tYBKhEaGhH+1hIZGRIVK6orH39EFy1oa+rqAVUaEf7WERoZEgEqERorKysrrH9EFy1oAAMAQABAAcAB6wADABsAJwAAJTUhFQEyFhURFAYjISImNRE0NjMzNTMVMzUzFQMnNyc3FzcXBxcHJwGV/tYBKhEaGhH+1hIZGRIVK6oruRc0NBc0NBY0NBY0a+rqAVUaEf7WERoZEgEqERorKysr/tUXNDQXNDQXNDQXNAAEAEAAQAHAAesAAwAHAB8AIwAAJRUjNQU1IRUBMhYVERQGIyEiJjURNDYzMzUzFTM1MxUHFSM1ASuWAQD+1gEqERoaEf7WEhkZEhUrqisV1tUqKmrq6gFVGhH+1hEaGRIBKhEaKysrK5UrKwACACsAVQHVAasACQAbAAAlJzcnJwcHFwc3NzIWFRUUBiMhIiY1ETQ2MzMXAX8RN0gdHUg3ET9rERkZEf6qERkZEYArlUcwBkNDBjBHJcYaEdURGhoRAQARGisAAgArACsB1QHVAAQAEgAANyEnByc3MhYVERQGIyEHETQ2M2sBKmBKNvYRGRkR/tVVGRHVgGBAoBkR/wARGlUBgBEZAAQAAABAAgABwAAHAA8AFwAmAAAkMjY0JiIGFAYyNjQmIgYUBjI2NCYiBhQlMhYVERQGIyEiJyc3NjMBiBoTExoTVxoTExoTWRwSEhwSATURGhoR/sIWDnNzDhTgEhwSEhwSEhwSEhwSEhwSEhzOGhH+1hEaE62tEwADABUAKwHrAesABwAdACoAACU1NCYiBhUVMzIWFRUUBiMjIiY1NTQ2MzU0NjIWFSciBhUVBhUVIQEVIiYBwBIcElUJDQ0JagkNDQkfLB81KDgV/uoBlgIHqyANExMNIA0JVQkMDAlVCQ0gFh8fFmA4KAYTHUABlsEBAAQAQABAAcAB6wAiACUAKAA2AAAlMhYVFRQGIyImNTQ2MzMyFhUVFBcWFRQHBxYWFzc2MzIXFicVNycVNwcnNyc3FzUzFwcXByM1AasJDAwJltUMCUsJDAwCBy8UUyYvBwgDBCQDFBQUWg87Ow8xCj0uLj0KtQwJSwkM1ZYJDAwJASkiBAMKBS8mUxQvBwIMsSgUcCgUeQ88PA8xUT0uLj1RAAIAQABAAesB6wAiACkAACUyFhUVFAYjIiY1NDYzMzIWFRUUFxYVFAcHFhYXNzYzMhcWJzUjNTM1FwGrCQwMCZbVDAlLCQwMAgcvFFMmLwcIAwQkA1VVa7UMCUsJDNWWCQwMCQEpIgQDCgUvJlMULwcCDGBAVkBrAAMAQABAAcABwAAHAA8AMgAAATQmIzUyFhUzNCYjNTIWFQcyFhUVFAYjIiY1NDYzMzIWFRUUFxYVFAcHFhYXNzYzMhcWAUAmGiw/Klc+UHAVCQwMCZbVDAlLCQwMAgcvFFMmLwcIAwQkAQAaJis/LD5XK3BQSwwJSwkM1ZYJDAwJASkiBAMKBS8mUxQvBwIMAAMAQABAAcAB6wAHAB0AQAAAATU0JiIGFRUzMhYVFRQGIyMiJjU1NDYzNTQ2MhYVETIWFRUUBiMiJjU0NjMzMhYVFRQXFhUUBwcWFhc3NjMyFxYBmhYeFVoJDAwJawkMDAkfLCAJDAwJltUMCUsJDAwCBy8UUyYvBwgDBCQBqwoPFhYPCg0JVQkMDAlVCQ0KFiAgFv8ADAlLCQzVlgkMDAkBKSIEAwoFLyZTFC8HAgwAAgAAAEMCAAHAAB8AKgAAJRYUBwcGIicmJyY1NSYiBxUUBwYHBiInJyY0NzYzMhYlFSM1MxUjFzcXBwH6BgY1BhIGHB0MLmguDCAZBhIGNQYGaZE8kv69IIBLYIAVlZwGEgY1BgYaDgUOQg8PQg8FDxgGBjUGEgZkO8ZLgCBggBWWAAMAQABAAcABwAADACYAKgAAATMVIxcyFhUVFAYjIiY1NDYzMzIWFRUUFxYVFAcHFhYXNzYzMhcWAxUjNQGVKysWCQwMCZbVDAlLCQwMAgcvFFMmLwcIAwQkGCsBwJV2DAlLCQzVlgkMDAkBKSIEAwoFLyZTFC8HAgwBC5WVAAQAVQArAasB1QADAAcACwAZAAABNSMVIzUjFSM1IxU3MhYVERQGIyEiJjUTNwGAKxUrFSurERoaEf8AERoBfwFVVlZWVlZWgBkR/qoRGRkRAQCAAAMAVQArAasB1QADAAcAFQAAJTUjFRc1IxUTMhYVERQGIyEiJjUTNwEVKioqlREaGhH/ABEaAX/rampWKysBQBkR/qoRGRkRAQCAAAQAKwArAdUB1QADAAcACwAZAAABNSMVIzUjFSM1IxUlMhYVERQGIyEHETQ2MwFrKysqKysBFhEZGRH+1VUZEQEVKysrKysrwBkR/wARGlUBgBEZAAMAKwArAdUB1QADAAcAFQAAATUjFRc1IxUTMhYVERQGIyEHETQ2MwEVKioqwBEZGRH+1VUZEQErVVVWKysBABkR/wARGlUBgBEZAAIAVQAVAasB6wAOAB0AACU1Fwc1IiY1NDcXBhUUFhMyFhUUByc2NTQmIxUnNwEAVVVGZRsfD0s1RmUbHw9LNVVVgEBVVkBlRjIpHxshNUsBK2VGMikfGyE1S0BVVgADAD0APQGrAasADgAnAC0AAAEHFhYVFAcnNjU0JicHNQc3AQcnBgc1NjcnBhUUFhc3FSM3JiY1NDc3BgcnNjcBqzMVHhogDxYQL+4bAU8bMhgYCAmsDxYQL4AzFR4aZgYKHxkWAaszFEcdLysfHh0WNBAvgB4b/rAbMg4GLAMFrB4dFjQQL4AzFEcdLysfAgYgDwUABABAAFUBwAGrAAMAEwAXACcAADc1MxU3BxYVFAYHNTY2NTQmJwc1AzUzFSc0NjcVBgYVFBYXNxUjNybrKqsyMkg4IzIVEDBVKtVIOCMyFRAwgDIy64CAwDMyRjtcDiwNRyUWNBAvgP7qKytrO1wOLA1HJRY0EC+AMzIAAwBrABUBlQHrAAYACgAaAAAlByczNTMVFxEjERMyFhURFAYjIyImNRE0NjMBVVVVQCpW1tYRGRkR1hEZGRHrVlZqaoABKv7WAX8ZEf6AERoaEQGAERoABAArABUBlQHrABQAHAAhACkAAAEyFhURFAYjIyYnMxEjFSYnNTQ2MwcyFhUjNCYjFTIWFSM1MhYVIzQmIwFrERkZES0DEUHWGREZEWphiSpxTxomQD5XKz4sAeoZEf6VERkrKgEVgAsDnREa64liT3GAJhqWWD4sPwAEAEAAVQHAAasAAwALABMALQAAEyEnIxYyNjQmIgYUBjI2NCYiBhQlFxUUBiMjIiY1NSEVFAYjIyImNTU3NjMzMmsBKiDq3RoTExoT1xoTExoTASksDAkWCQz/AAwJFgkMLAcY6hgBK2DLEhwSEhwSEhwSEhzDgKoJDQ0JFRUJDQ0JqoAWAAYAAABAAgABwAADABMAFwAbAB8AIwAAJREjERMyFhURFAYjIyImNRE0NjMTNTMVNzMVIwU1MxUnNTMVAVWqtQ4SEg7ADhISDvUrFSsr/msraytrASr+1gFVEg7+wA4SEg4BQA4S/tXW1quAK9bWK4CAAAIAKwArAdUB1QAHABUAACU1BzUjFTM1NzIWFREUBiMhBxE0NjMBgFWrq4ARGRkR/tVVGRHVq0REq0W7GRH/ABEaVQGAERkABAAVABUB6wHrAAoALwA3AE0AADc1IiY1NScGFRQWNzMWFRQGIyImNTQ2MzIXFRQGIyMVFAYjIxUzMhYVFTMyFzY1NDc1NCYiBhUVMzIWFRUUBiMjIiY1NTQ2MzU0NjIWFdURGWcEVv4rAX1YWX19WR8hGhErDAkrgAkNFR8KLC8VHhVZCQ0NCWoJDQ0JHywfQSoZERZmEBZBYbcHDll9fVlYfQo2ERoqCQ0qDQlAHS9EDrIKDxYWDwoNCVUJDAwJVQkNChYgIBYAAwArAIAB1QFrAA8AFwAeAAA3BiMiJyY1NDc2MzIXFhUUByEVIxUjNSMlFSE1MzIWmBEcGxITEhIcGRMUgAGqgKqAAar+68AjMv4TEhEdGhIUExIbGjwqKytqKoAzAAMAIABrAeABoAATABsAJAAAEwYjIiYnJjU0Njc2MzIWFxYVFAYHNwUHJxUjNSUHJTcXFhYVFJwKEhEiBwYVDwoSESIHBhWLDwGVDmGqATAP/vgtthciASYGFQ8KEhEiBwYVDw4OESIwKJIoIiJgBChfeUIILxkRAAIAFQCVAesBawAKABIAAAEyFhUVITUzFTM1BiImNDYyFhQBlSMz/iorqzw0JiY0JgFrMyOA1paWgCY0JiY0AAIAKwBAAe4BwAAUACAAACUWBgcHJyMiJjU1MxUzMhYXFzc2FiUUFjMzFSMiJjU1MwHnBwoMT0mVGiaASwsWBUgYDBn+dCYagIAsPiqQDBoGJJUmGquADgqVCwUIZRomKz8swAACAEAAQAHVAcAAEwAfAAAlMhYUBiMjNSMiJjU1MxUzMhYVFSUUFjMzFSMiJjU1MwG1DRMTDWCVGiaAaxEZ/tYmGoCALD8rgBIcEpUmGquAGhGVgBomKz8swAACAEAAQAGtAcAACwAhAAATFBYzMxUjIiY1NTMBFgYjIzU3IyImNTUzFTMyFhUHMzIWayYaVVUsPysBPwMTD2AVgBomgGsRGSoeDBMBABomKz8swP6mDxdAVSYaq4AaEZUPAAMAKwBAAdUB2AAbACcAMAAAJRcHJyMiJicnNCY1NDY3MzI2MzIXFxY3FQYnFxcVIyImJyczFxYWMwImJjY2FxYWBgFaeyBRkhYlBB0BGBABAQUBEQ0jMDQwPhZjliY+BioqKgQkGD8cBhQjDg4HFMBgIEAeFn4BBgERHAMBChslCi4IIldVKzQm0coXHwETFCMcBwoLIxwAAwBVADUBqwHWABYAIgAqAAAlBycjIiY1NTQ2MzMyFxcWFjMVIicVMwcUFjMzFSMiJjU1MzYmNDYyFhQGAasfS2waJh0TARMQHhE5Gj44Sr8mGoCALD8rLxoaIhoaVB9LJhp7Ex0QIRMYLy9PFRomKz8swBUaIhoaIhoABAArAFUB1QGrAAMABwALACgAAAE1IxUXNSMVFzUjFTciBhQWMxUUBiMhIiY1NTI2NTQmIzU0NjMhMhYVARUqKioqKuoRGRkRGRH+qhEZEhgZERkRAVYSGAFLKipgKipgKiqgGiIaVREaGhFVGRIRGlUSGRkSAAMAFQArAesB1QACAAYAHQAAExcHBREhEQEyFhURFAYjISImNRE0NjMzJzcXNxcHwJWVAQD+gAGAEhkaEf6AERoZEqJGD1VVD0YBK1ZVKwEA/wABKxkS/wARGRkRAQASGUYPVVUPRgADABUAQAHrAcAAAgAGABoAAAEHNQURIREBMhYVAxQGIyMVIzUjIiY1ETQ2MwFVlQEA/oABgBIZARkRa6prEhkZEgEVVavWAQD/AAErGRL/ABEZKysYEgEAEhkAAgAVAEAB6wHAAAMAFwAAJREhEQEyFhUDFAYjIxUjNSMiJjURNDYzAcD+gAGAEhkBGRFrqmsSGRkSlQEA/wABKxkS/wARGSsrGBIBABIZAAEAgABAAYABwAAUAAABMhYVFQcVIzUnNTQ2MzM1MxUzNTMBVhAaS2pLGhABKlYqAWsbEHVLQEBLdRAbVVVVAAQAVQArAcAB1QAHAA8AHQArAAAAIiY0NjIWFAYiJjQ2MhYUEyM1Izc2NjMzMhYXFyMFNSM1NDYzMzIWFRUjFQFyJBkZJBnZJBkZJBm1QEA2BBgNAg0YBDZA/vUgGhFAERogAYAZJBgYJBkZJBgYJP6SgKIMEhIMooCgdREaGhF1oAADABUAVQHrAaEACQAOABYAADc2MhcHJiYiBgcXNjIXByc2IBcHJiIHaz6vPSoSPzQ/EisbShtA62IBE2ErUOBQ6z09KxIaGhIrGxtA62FhK09PAAMAVQArAasB6wALABMAKwAAJTUjNSMVIxUzFTM1JxUzNTQmIgYXMhYVFRQGIyEiJjU1NDYzMzU0NjIWFRUBVUAqQEAqV4QnNifCERoaEf8AERoaERU/WD+rKkBAKkBA1SsrGycnRhkR1hEZGRHWERkrLD8/LCsABQAVAFUB6wGqAAYADAATABsAKgAANzYXBwYGBzMmJzcWFzcmJzcWFhchNjYXByYGByUyFQMVBgYjIiY1NDc3NmtAXBwVMg/WCw8MIBgrJzELHUYV/io7m08ZPXctARMLNAMYDxIZBW8D60ADPQMZDwsKPhAYKicUPAssFTsvDjkIKS2ACv7tAQ4UGRILCvgIAAIAQAAVAcAB6wAVACMAABMVJzY2MzIWFRUzMhYVFSczNTQmIgYBBycGIyEiJjU1NDcnN74nBTspLD8VERrddCc2JwECGhgIBv8AERoXLBoBgBonKDY/LCsZEbLcKxsnJ/6UGhgCGRHWGQwrGgAEAFUAKwHVAdUABgAKABIAKwAAARcHNSM1Mxc1IxUGMjY0JiIGFDczFSMUBiImNSMiJjU1MzUjFSc3FTMyFhUBa0BAq6sVVUkSDAwSDdYqqiY0JisRGpZWQEDrERoB1UBAKyvWQECADRIMDBIzKhomJhoZEUBAKkBAKxoRAAMAFQAVAdUB1QADABAAHQAANzMnIycBBycGIyImNTQ2NycFIyc2MzIWFRQGByczlUorH2UBoBw7NkNYfRYQPAFWSpo2Q1h9FhBjH+squ/5gGzwmfVgcRhc7n5omfVgcRhdkAAIAKwArAdUB1QADAAsAACU1IxU2MhYUBiImNAFr1hOwfX2wfesqKup9sH19sAACANUAQAErAcAAAwALAAATMxEjFDQ2MhYUBiLVVlYZJBkZJAHA/wBnJBgYJBkAAgA6ACsBugHAAA8AHAAAExYXBycHFSM1JzU0Nyc3FxcHJzUzFTM1MxUyFhWa7TMbXwtqSwFHG0TnCssqVioQGwFm7jIbXwpAQEt1BQNIG0WbC8s1VVVVGxAAAwAVABUB6wHrABEAFQAiAAABMhYVERQHJzUjJzMnNxc3FwcDIQEjJzcBBychIiY1ETQ2NwHAEhkII/UrQkYPVVUPRt4BH/8AHysbAZ8bKv62ERoTDgGVGBL/AA0LI/UqRw9WVg9H/tYBAEkc/mAbKxoRAQAPFwMABAAVAFUB6gHaAAQAFgAaACEAADc2MhcHAzcBByciBgcnNTY3JwYHJzY3BQcnFjcHJgcnNhbAG0obQNUbAWoblxk+EionNzAxKCsnMAEpFEw5fCpUejVRn5UbG0ABahv+lhuXGhIqAScPLxQnKycXkxVLDy4rVgc2ES4AAgBAAEABwAHAAAgAJQAAAQczFSM1MxU3BRYXNzYXFjMyFhUVFAYjIiY1NDYzMzIWFRQXFgcBrodZgBWK/u4wXS8KDCQoCQwMCZbVDAlLCQwMBAkBsYYWgFuGpl0wLwoFDAwJSwkM1ZYJDAwJKCQNCQADACsAKwHVAdUABAAJAA8AACUzBgYHERYWFyMnESYmNDYBFr8HbExMbAe/K1Fvb+pMbAcBqgdsTL/+Vgh6pnoABAArACsB1QHVAAQACgAPABcAACU2NjcjJhQWFxEGNxUzLgIyFhQGIiY0ARU2VweUwFZAP2mUB1OnsH19sH1XB1c2VoJgCAFSCAiUOlMzfbB9fbAAAwBVAFUBqwGrAAcADwAZAAASNDYyFhQGIgY0NjIWFAYiJjQ2MzIWFAYjIt47Vjw8VggZJBgYJNUoHRwoKBwdARlWPDxWO3AkGRkkGVw4KCc6JwABACsAdgHVAZUAFwAAAQcWFyMmJwcnByc3FzcmIyIHJzYzMhc3AdVBJQcrBhlWVYAgoFU9O1VJOx5IWmZHPQFsSTtIMy5hVoAgoFZGRTQeQE9EAAEAKwB2AdUBigAHAAA3JzcXNxcHJ0sgoFWXHrVVdiCgVqoezFYAAwBAACsBwAIAABsAMwA/AAABMhYVFRQGIicnBwYiJycHBiImNTU0NjMzNTMVFxYzMjcVFAYjISImNTUWMzI3NxcWMjc3JyImNTQ3NxcWFRQGAYAaJhkiDC4uDCMMLS4MIhkmGmsqTRYeFhQMCf6qCQwTFx4WFxcVPhUXSxEaByQkBxkBQCYaIREZDC4uDAwuLgwZESEaJisrlRYNYgkMDAliDRYXFxUVF74aEQsLPz8LCxIZAAwAKwBAAdUBwAADAAcAEwAXABsAHwAjACcAKwAvADMAOQAAJRUjNTcVIzUXNSMVMxUjFTMVIxUDNSMVFzUjFRc1IxUXNSMVAzUjFRc1IxUXNSMVFzUjFRMzESERMwGAKysrVqsrKysrKyoqKioqKiorKysrKysrK6vV/lbVwCsrVSoqqtUrKisrKgEAKipWKytVKytVKioBACoqVisrVSsrVSoqAQD+1QGAAAQAFQBrAesBlQAKABQAHAAkAAAlMhYWFRUjNTQnNiIyFhYVFSE1NDY2IiY0NjIWFBYiJjQ2MhYUAVUcQjiAKge5OEI3/tU4eDQmJjQlhTQmJjQm6xAkFzU1LB4BECQXNTUXJDomNCYmNCYmNCYmNAAFAAAAgAIAAZUABwAPABcAIwAvAAA2MhYVFSE1NCUWFhUVIzU0JiImNDYyFhQXIic2NCc2MzIWFAYnFSMVIzUjNTM1MxXtUFj/AAEOJThAkTQmJjQmKwoJExMJChomJu9AK0BAK+sjHSsrHR8GHxcrKyJIJjQmJjQmAxtEGwMmNCYWK0BAK0BAAAoAQABAAcAB1QADAAcACwAPABMAFwAbAB8AIwAsAAAlNSMVFzUjFQM1IxUXNSMVFzUjFRc1IxUnNSMVFzUjFRc1IxU3MxUhETM1NxcBlSoqKlYqKioqKioqVioqKioq1YD+gIBAQMArK1UqKgEAKipWKytVKytVKiqqKytVKytVKiqq1QErKkBAAAUAKwArAdUB1QAFAA0AFQAdACUAACQiJiczBiYiJjQ2MhYUFiImNDYyFhQGMjY0JiIGFBIyFhQGIiY0ASVKOw3aDZ4aExMaE4MaExMaE7GMZWWMZVOwfX2wfYspISFhExoTExoTExoTExrTZYxlZYwBG32wfX2wAAUAKwArAdUB1QAFAA0AFQAdACUAADYyFhcjNjYiJjQ2MhYUFiImNDYyFhQGMjY0JiIGFBIyFhQGIiY020o7DdoNIhoTExoTgxoTExoTsYxlZYxlU7B9fbB91SkhIWkTGhMTGhMTGhMTGtNljGVljAEbfbB9fbAAAgBVACsBqwHLABMAGgAAJRcVITU3NTQ2NzU0NjIWFRUWFhUHIiY1MxQGAYAr/qorMy0SHBItM4ASGVYaqysVFStqMkoLDw0TEw0PC0oy6hgSERkAAwBVACsBqwHLAAcAGwAhAAAlNTQmIgYVFTcXFSE1NzU0Njc1NDYyFhUVFhYVBiImNTMUAVUuTi7VK/6qKzMtEhwSLTNvIhpWlYApNzcpgBYrFRUrajJKCw8NExMNDwtKMuoZEREAAwBVACsBwAHLABMAGQAnAAAlJzY2NzM3MjYzNTQ2MhYVFRYWFQYiJjUzFAMWFhcHJyE1NzU0Nyc3AYC/AwoCAQYBBgISHBItM24kGVaEGMs2Gyv+2ysRPBvHyQEFAQMCDw0TEw0PC0oy6hgSEgE6GdA5GysVK2spIDscAAQAKwArAdUBywAFABkAIAAnAAAkIiY1MxQ3FRcVITU3NTQ2NzU0NjIWFRUWFhcmJic3FhclBgYHIzY3ARIkGVVWK/6qKzMtEhwSLTMqAiwdHlMF/s0dLQIrBVMrGRES0morFRUrajJKCw8NExMNDwtKJyRQFR5AZ4kVUCRnQAADAFUAKwGrAcsACQAdACQAAAE1IxUzBxUzNSMXFxUhNTc1NDY3NTQ2MhYVFRYWFQciJjUzFAYBNWo7O2o7hiv+qiszLRIcEi0zgBIZVhoBLyYmSSYmOysVFStqMkoLDw0TEw0PC0oy6hgSERkABABAAEABwAHAAAgAEQAaACMAAAEyFhUVIzcHNRMnMxUUBiMjNScHNxUjIiY9AjQ2MzMVJxcjAZURGmsWVlYWaxoRgGoWVoARGhoRgFYWawHAGhGAVhZr/tVWgBEaa0BWFmsaEYCqERprFlYAAwArAFUB1QHVAA0AGwAvAAAlMjY1NCcjFhUUBiMjFjciBhUUFzMmNTQ2MzMmNzIWFREUBiMhIiY1ETQ2MzM3MxcBACw/Ai0EJhpVITQsPwItBCYaVSF3ERkZEf6qERkZEUQngCeVPywJDA4HGiYr1j8sCQwOBxomK0AaEf8AERoaEQEAERoqKgAEABUAawHrAZUACgAUABwAJAAAJTIWFhUVIzU0JzYiMhYWFRUhNTQ2NiImNDYyFhQWIiY0NjIWFAFVHEI4gCoHuThCN/7VOHg0JiY0JYU0JiY0JusQJBc1NSweARAkFzU1FyQ6JjQmJjQmJjQmJjQABwAVAGsB6wGVAAcADwAXAB8AKQAxAD4AAAAiBhQWMjY0BiImNDYyFhQmIgYUFjI2NAYiJjQ2MhYUFzU0JiMiBxYVFSM1NCYiBhUVJTIWFRUhNTQ2MzIXNgFxIhoaIhoMPiwsPiz6IhoaIhoMPiwsPizgRyQaJgsgR0hHASsrYP4qYCsvMTEBdRkiGhoiXCw+Kys+SRkiGhoiXCw+Kys+oRoKHAwNDRoaChwcChpgJx86Oh8nFhYAAgBVAFUBqwGrAAcADwAANjIWFRUhNTQ2IiY0NjIWFMpsdf6qzkYyMkYy1S8mKysmWjJGMzNGAAMAFQBVAesBqwAHABMAGwAAJDIWFRUhNTQnMxUjFSM1IzUzNTMWIiY0NjIWFAEKbHX+qhVAQCtAQCvjRjIyRjLVLyYrKyaFK0BAK0BrMkYzM0YABABVAFUBqwGrAAkAEQAZACEAADYyFhYVFSE1NDY2MhYUBiImNBYiBhUVITU0JiIGFBYyNjTgQEw//qo/SUYyMkYygVhWAQRvJhoaJhrrEioaQEAaKtIzRjIyRrYhDBcXDOEaJhkZJgACAFUAgAGVAZUABQARAAABNxEjNQcHFTMVIxUjNSM1MzUBNWAqNmBWVipWVgF+F/7r4gsCVStVVStVAAQAQABAAcABwAADAAcACwAbAAAlNSMVIzUjFSM1IxUBMhYVERQGIyEiJjURNDYzAWsrKyorKwEAERoaEf7WERoaEZVWVtbWlpYBKxoR/tYRGhoRASoRGgADACsAKwHVAdUAFwAiACoAACU2NTQmJxUUBiMjFRQGIyMVMzIWFRUzMgc1IiY1NScGFRQWAjIWFAYiJjQBfi07MBoRKg0JKoAJDBUeiBEaZgVXBLB9fbB9jTBDNVYTCREZKwkMKwwJQFQpGhEVZhQSQWABdn2wfX2wAAIAFQBAAesBwAAGAAwAAAEXFSM1BycXFzcVBycBAOsrwOtWlZWVlQHAgKuUaYBZUlJWUVEAAQBAACwBwAHVACYAACUyFhUUBiImNTQ3JwYjIiY0NjMyFzcmNTQ2MhYUBiMiJwcWFAcXNgGAGiQlMiUBlxMZGiYmGhkTlgImNCYmGhgUlgICmBKpJRkaJSUaCgRYESY0JhFXCgUaJiY0JhJYCgoKWBAAAgBVACsBqwHyAAsAIAAANzI2NTQnBgcGFRQWExYWFRQGIiY1NDcVFBYzMjY1NCYn+is7DB9EPChDQEtkjmRFLCEgKQgEazsrLCopDg01HCcBhzSVVEZkZEZsUgghLi0iFDQPAAUAKwArAdUB1QAOABYAHgAmAC4AACUiByYxJic2MzIXBgYHJgYyNjQmIgYUEjIWFAYiJjQWNDYyFhQGIjY0NjIWFAYiAQAWFAsBChwkIh4CEwETXYxlZYxlU7B9fbB9ahMaExMagxMaExMaoAsMAQsTEwEVAgtLZYxlZYwBG32wfX2wMBoTExoTExoTExoTAAUAKwArAdUB1QAHAA8AFwAfACMAADYyNjQmIgYUEjIWFAYiJjQWNDYyFhQGIjY0NjIWFAYiBzMVI7qMZWWMZVOwfX2wfWoTGhMTGoMTGhMTGn6AgFVljGVljAEbfbB9fbAwGhMTGhMTGhMTGhNgFQAFACsAKwHVAdUACwATABsAIwArAAAlMjcWFwYjIic2NxYGMjY0JiIGFBIyFhQGIiY0FjQ2MhYUBiI2NDYyFhQGIgEAFhQTAx0jIh0MCRMvjGVljGVTsH19sH1qExoTExqDExoTExqrCxYDEhIQCQtWZYxlZYwBG32wfX2wMBoTExoTExoTExoTAAUAKwArAdUB1QAJABEAGQAhACkAADYyFhcjJiIHIzYWMjY0JiIGFBIyFhQGIiY0FjQ2MhYUBiI2NDYyFhQGIttKOw0jGWIZIw0ajGVljGVTsH19sH1qExoTExqDExoTExrVKSEqKiFXZYxlZYwBG32wfX2wMBoTExoTExoTExoTAAUAKwArAdUB1QAFAA0AFQAdACUAADczBgYiJhYyNjQmIgYUEjIWFAYiJjQWNDYyFhQGIjY0NjIWFAYildYNOkg6GIxlZYxlU7B9fbB9ahMaExMagxMaExMa1SYvL1pljGVljAEbfbB9fbAwGhMTGhMTGhMTGhMAAgArACsB1QGrABcAIQAAEzc2MzMyFhUVFAcHJyYnJjc3IyImNTU0JRUjIiY1NTQ2My45DRitFR0NiQkMBwMBFHYRGQGqKgkNDQkBE4MVHRWnEg2ICgwOBwdjGhEXCp/rDAnACQ0AAgArAFUB1QHVABcAIQAAJQcGIyMiJjU1NDc3FxYXFgcHMzIWFRUUBTUzMhYVFRQGIwHSOQ0YrRQeDYkJDAcDARR2ERn+VioJDQ0J7YMVHhSnEg2ICgwOBwdjGhEXCaDrDAnACQ0AAgBAAEABwAHAAAUAFQAANzcnBycHJTIWFREUBiMhIiY1ETQ2M9XAHqJMHgEqEhkZEv7WEhkZEpXAH6JMHsAZEv7WEhkZEgEqEhkAAgBAAEABwAHAAA8AEwAAATIWFREUBiMhIiY1ETQ2MwUhESEBlREaGhH+1hEaGhEBKv7WASoBwBoR/tYRGhoRASoRGiv+1gACACsAKwHVAdUABwAPAAA2MjY0JiIGFBIyFhQGIiY0uoxlZYxlU7B9fbB9VWWMZWWMARt9sH19sAADACsAKwHVAdUABwAPABcAADYyNjQmIgYUEjIWFAYiJjQ2MhYUBiImNLqMZWWMZVOwfX2wfalYPz9YP1VljGVljAEbfbB9fbATP1g/P1gAAQArAEAB1QHVAAkAACUHNyc3NxcXBxcBAIQjdJk8PJl0I5BQlmUNjY0NZZYAAgArAEAB1QHVAAUADwAAJRcnNycnFwcXJwc3Jzc3FwEAUBVHXiTVdCOEhCN0mTw8tzBbPghWQ2WWUFCWZQ2NjQACACsAQAHVAdUACQATAAAlFyc3JycHBxcHJQcXJwc3Jzc3FwEAUBVHXiQkXkcVASV0I4SEI3SZPDy3MFs+CFZWCD5btGWWUFCWZQ2NjQAFAAEAAAH/AgAACQATAB8AQwBNAAABMhYXIyYmJwcnFzQjIxUzMjc2NScyFxYVFRQHBiMjNQcWFRQHBgcGIyImNTMUFjMyNCMjNTMyNCMiFSM0NzYzMhYVFAc3FwciJiczFhYBAGSTCCAFRTUdUW8vFBMkCgIvNRMFFRYjMTEcBQYFDxkYHhsPDBwfEBAdGhkcDxAWGB1IHVEOZJMIIAVKAgCHYztgGRxR+zl7IgcRWS8MFggmFRaqUwscCgoMBAwYFwsONhYyFxEPDRgXGdccUQGIYzVqAAIAQAArAcAB1QALABMAAAEjESM1IxUjESM1ISYyFhQGIiY0AcCAKyorgAGA0SIaGiIaAUD+64CAARUrahkiGhoiAAUAKwArAcAB6wAEAAgADAAQABQAABMXFSE1BTMVIwU1IRUDMxUjJzMVI/XL/msBKkBA/tYBletAQIBAQAHraysrVZZqQEABAJaWlgADAEAAQAHVAcAABwALACUAACQyNjQmIgYUBzUzFQcVFAYjISImNRE0NjMhMhYVFSMiBhUVFBYzAUgaExMaEzXVFRoR/tYSGRkSASoRGsASGRkS4BIcEhIcR6qqKxURGhkSASoSGRoRFRkSqhIZAAMAQABAAcABwAAHAA8AHwAANxUhNTQmIgY2NCYiBhQWMic0NjMhMhYVERQGIyEiJjWAAQBYUFjAJjQmJjTaGRIBKhEaGhH+1hIZlRUVHSUldDQmJjQmlRIZGhH+1hEaGRIAAwArACsB1QHVAAkAEQAZAAA2MjY3JiYiBgcWEiIGFBYyNjQmMhYUBiImNN5ESxMBWE5YAROHNCYmNCaYsH19sH1mKB0cJiUdHQEHJjQmJjRmfbB9fbAABAAVACsBwwHrAB0AJQAtADkAADcUMzMVISImNTQ3NycjNTMWFxYWFzM2NxcHBiMjBxYyFhQGIiY0JjIWFAYiJjQ3NSM1MzUzFTMVIxWZBff/ABEZBR1NK0YUFAUkCpZLByVSDBmfE8AiGRkiGrwiGhoiGYBAQCpAQMUFKxoRCgo1oioqKwlNFYgOFZUWI0gaIhkZIhoaIhkZItpAK0BAK0AABQArACsB1QHYAAcADwAVABkAHQAANjI2NCYiBhQ2MhYUBiImNDcVFwcnNScHJzcFByc3wnxXV3xXRaBwcKBwy1UQZUNiG2IBSBtiG1VYfFdXfP5xnnFxnhtwMho8gGNSIFJSIVMgAAUAKwArAdUB2AALABMAGwAfACMAAAEVMxUjFSM1IzUzNQYyNjQmIgYUNjIWFAYiJjQlByc3BwcnNwEVQEAqQEApfFdXfFdFoHBwoHABlRtiG8tiG2IBQEArQEArQOtYfFdXfP5xnnFxnkwhUyAgUiBSAAUAIwArAdUB2AADAAsAHgAiADIAABMHJzcTJwYVFBYzMgMWABcHJwYjIiY1NDY3JwcnNycFByc3ByIHJzYzMhYVFAcnNjU0JqsSHxPS0iJXPjT2TAEZJRsvN0dQcBwTERgeGB0BshtiG3MbGCEqKlBwEyEJVwG6Dx4P/qDSKjU+WAF6TP7oJRsvL3FPHUoWERQfEx0uIVMgWAkgFHFPLCggGBw+VwAFACsAKwHVAdgABQANABUAGQAdAAA3NxcHJzcWMjY0JiIGFDYyFhQGIiY0NwcnNwUHJzfhaReARBYPfFdXfFdFoHBwoHBoYhtiAUgbYhvKaheARBaiWHxXV3z+cZ5xcZ5+UiBSUiFTIAAGACsAAAHVAgQAAwAHABsAJwAzAE0AAAE1IxUjNSMVNxYVITQ2NycmNhcXNjMyFzc2FgcWMhYVFRQGIiY1NTQkMhYVFRQGIiY1NTQXNSEVFAYjIxUUBiImNTUjFRQGIiY1NSMiJgFAFVYVizX/AB4WHAgQByAaHxoeIAgOB0EaExMaE/6pGhMTGhNVAQAMCRYTGhMqExoTFgkMAZUWFhYWPSZBGzwQHAgOByAODiAHDgiZEw2VDhISDpUNExMNlQ4SEg6VDcLV1QkMSw4SEg5LSw4SEg5LDAADACsAKwHVAdUAAwAHABUAACU1IxU3NSMVNzIWFREUBiMhBxE0NjMBFSoqKsARGRkR/tVVGRHAKytVgIDAGRH/ABEaVQGAERkABAAVAEAB6wHAAAMAEwAZAB8AACURIREBMhYVERQGIyEiJjURNDYzFxUjNTMVFxUjNTM1AcD+gAGAERoaEf6AERoaEVUqasBqQGoBLP7UAVYaEf7WERoaEQEqERqAQGsrQGsrQAAEAEAAQAHAAcAAAwAHAAsAGwAAJTUjFSM1IxUjNSMVATIWFREUBiMhIiY1ETQ2MwFrKysqKysBABEaGhH+1hEaGhGVVlbW1paWASsaEf7WERoaEQEqERoABQBAAEABwAHrAAMABwALABMAKQAAATUjFRc1IxUXNSMVEiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzMzY2MhYXAWvW1taWlnQSDAwSDIARGhoR/tYRGhoRWQcgKiAHAUArK1UqKlYrKwErDBINDRIMGhH+1hEaGhEBKhEaExgYEwAEAEAAQAHAAesABwAPABcALQAAJTU0JiIGFRUSIgYUFjI2NCYiBhQWMjY0NzIWFREUBiMhIiY1ETQ2MzM2NjIWFwGAWFBYmjQmJjQmNxIMDBIMgBEaGhH+1hEaGhFZByAqIAdrHh0lJR0eAQAmNCYmNHsMEg0NEgwaEf7WERoaEQEqERoTGBgTAAQAQABAAcAB6wAHAAsADwAlAAASMjY0JiIGFBc1IxUXNSMVEzIWFREUBiMhIiY1ETQ2MzM2NjIWF/cSDAwSDCoqKiqqERoaEf7WERoaEVkHICogBwGVDRIMDBLNgIBVKysBQBoR/tYRGhoRASoRGhMYGBMAAwBAAEABwAHrAAYADgAkAAAlNSM1Bxc1EiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzMzY2MhYXAVVVa2sJEgwMEgyAERoaEf7WERoaEVkHICogB8BVQGprQAEADBINDRIMGhH+1hEaGhEBKhEaExgYEwADAEAAQAHAAesABgAOACQAACU3IzUjFSM2IgYUFjI2NDcyFhURFAYjISImNRE0NjMzNjYyFhcBAGtAVkB0EgwMEgyAERoaEf7WERoaEVkHICogB4BrVVXVDBINDRIMGhH+1hEaGhEBKhEaExgYEwADAEAAQAHAAesABQANACMAADc3JwcnBzYiBhQWMjY0NzIWFREUBiMhIiY1ETQ2MzM2NjIWF9WrHo03HokSDAwSDIARGhoR/tYRGhoRWQcgKiAHlasejDce1QwSDQ0SDBoR/tYRGhoRASoRGhMYGBMAAgBVABUBqwHrAA4AHQAAARYVFAYjFSc3FTI2NTQnJyIGFRQXByY1NDYzNRcHAZAbZUZVVTVLD3E1Sw8fG2VGVVUBWykyRmVAVlVASzUeHkRLNSAcHykyRmVAVlUAAgAAAFUCAAGrAAYAGQAAJTMnBzMVMzcWFhUUBiMhIiY1NDY3NjYzMhYBK0Bra0BWcik6Pyz+6zVLQy8VTC02XetqalaVAz0qLD9LNS9LBScwTAACAFUAKwGrAdUABAAUAAATFTcXNTcyFhURFAYjISImNRE0NjOANTaVERoaEf8AERoaEQGrqyAgqyoZEf6qERkZEQFWERkAAQBrAEABlQHAAAoAAAEyFhURJwcRNDYzAWsRGZWVGREBwBoR/qtAQAFVERoAAgBrAEABlQHAAAQADwAAJREjETcTMhYVEScHETQ2MwFr1mtrERmVlRkRgAEV/usvAREaEf6rQEABVREaAAMAVQBAAasBwAADAAcANwAAATUjFRc1IxU3FSMWFRUzFSMVFAczFSMGBiImJyM1MyY1NSM1MzU0NyM1MzY3JzcXNjIXNxcHFhcBK1ZWVtYtAisrAi08ETtGOxE8LQIrKwItPA8YIx4vDx4PLx4jGQ4BACsrVSoqqioOCBUrFQcOKx0jIx0rDgcVKxUIDioZESMeLgMDLh4jERkAAQAOAA0B6wHuABYAACUWBgcHBicnBiYnJiY3FzcnNhYXFhYHAeQHAQgxDw/CJFEeIBAUXkBcJlggHhEPawQTBzEPD8IPER4gWCZcQFwSDiAeUSQAAgAVAFUB6wGrAA4AHQAAEzMHJzM0NjMyFwcmIyIGJRcjFAYjIic3FjMyNjUjgEBVVkBlRjIpHxshNUsBFVZAZUYyKR8cIDVLQAEAVVVGZRsfD0sgVUZlGx8PSzUAAgArAFUB1QGrAAIABQAAARMhEwchAQDV/lbViAEQAav+qgEF2gACACsAKwHVAdUABQANAAA3NycHJwc2MhYUBiImNNXAHqJMHj2wfX2wfZXAH6JMHtV9sH19sAAFABUAQAHrAasAAwATABcAGwAfAAAlESMREzIWFREUBiMhIiY1ETQ2MxczFSM1MxUjFTMVIwHAwMARGhoR/oARGhoR1ZaWlpaWlmsBFf7rAUAaEf7rERoaEQEVERrgIIogFSAAAgBVACsBqwHVAAQAFAAAExU3FzU3MhYVERQGIyEiJjURNDYzgDU2lREaGhH/ABEaGhEBq6sgIKsqGRH+qhEZGREBVhEZAAIAKwCAAdUBgAAFAAsAACU3JzcXBycHJzcXBwE3Y2MegICMHoCAHmOeYmIegIAeHoCAHmIAAwArAFUB1QGrAAMABwAXAAABNSEVBTUhFQEyFhURFAYjISImNRE0NjMBq/6qAVb+qgFWEhgYEv6qEhgYEgFVKyvVgIABKxkS/wASGRkSAQASGQAEAEAAQAHAAcAAAwAHAAsADwAAATMVIxE1MxUhNTMVJzUzFQEVq6ur/oCrq6sBwID/ANXVgICr1dUAAgBrAEABlQHAAAcAEQAAARUhNTM3MxcDESERFAYjIyImAZX+1koWahbLAQAaEaoRGgGrKysVFf7AAQD/ABEaGgAEAFUAKwGrAdUAAgAGAAoAGAAAATMnFzUjFRc1IxUTFxEUBiMhIiY1EzQ2MwEVdnZAqqqqgIAaEf8AERoBGREBQHXgKytVKysBVYD/ABEZGREBVhEZAAQAQABAAcABwAAHABcAHwAvAAASMjY0JiIGFCUyFhUVFAYjISImNTU0NjMSMjY0JiIGFCUyFhUVFAYjISImNTU0NjOEIhoaIhkBQAkMDAn+qgkMDAkvIhoaIhkBQAkMDAn+qgkMDAkBQBoiGRkiZgwJgAkNDQmACQz+qxkiGhoiZw0JgAkMDAmACQ0AAQBJAGsBwAGJAAUAADc3FwEnN8DiHv8Adx2m4x7/AHceAAMACQBrAfkBiQADAAkADQAANzcXBwEXASc3FzcHJzcJHnceAVof/wB4H1mHhx6H4h53HgEeHv8Adx5ZxIgeiAADAEAAQAHAAesAAwAbAB8AACU1IRUTMxUzMhYVERQGIyEiJjURNDYzMzUzFTMXFSM1AZX+1uorFREaGhH+1hIZGRIVK6oWa2vq6gGAKxoR/tYRGhkSASoRGisrwGtrAAIAQABAAcABwAAXACAAAAEyFhURFAYjISImNTUzFSERIRUjNTQ2MxM3IzUzJzcXBwGVERoaEf7WEhkrASr+1isZEmw3zs43HmtrAcAaEf7WERoZElVVASpVVRIZ/vM4Kjgea2sAAwArACsB1QHVAAMACwATAAAlNwcHEjIWFAYiJjQWMhYUBiImNAEvUa9RKLB9fbB9yxQNDRQN0a9RrwFVfbB9fbBBDRQNDRQAAQArACsB6wHrAC8AAAEyFhQGIyMVFAYjIzU0JiIGFRUjIiY1NTMyNjQmIyM1NDYzMzU0NjIWFRUzMhYVFQG1FiAgFiAZEVEiMCJRERkgGCEhGCAZEVYfLB9WERkBFR8sH1YRGSAYISEYIBkRUSIwIlERGSAWICAWIBkRVgAEACsAKwHVAdUADwAXAB8AJwAANjI2NTQnBiMiJicGBwYVFBIyFhQGIiY0BDIWFAYiJjQmMhYUBiImNLqMZQcWGy5lGiFPAVOwfX2wfQEKFhAQFhBwFhAQFhBVZUYWGgU1JVAjBgxGARt9sH19sFMPFhAQFg8PFhAQFgABACsAOQHVAcAAFAAAJScuAzU0NjMyFzYzMhYVFAYGBwEAHzUwOxZDMjomJjoyQzQ9RTkcMC5ENx0xRC0tRDEnVD4+AAIAKwA5AdUBwAAYAC0AACU+AzU0JiMiBgcjJiYjIgYVFB4CFxcTMhYVFA4CBwcnLgI1NDYzMhc2AQIwLjYVKyAZKwgoCCsZICsVNi4wAmAyQxY7MDUfH0U9NEMyOiYmdCssPC4WICocFhYcKiAWLjwsKwIBTkQxHTdELjAcGz4+VCcxRC0tAAMAKwArAdUB1QADAAcAFQAAATUjFRc1IxUTMhYVERQGIyEHETQ2MwEVKioqwBEZGRH+1VUZEQErVVVWKysBABkR/wARGlUBgBEZAAIAVQArAasB1QAHACEAADY0NjIWFAYiFyc2NTQmIgYUFjMyNxcGIyEiJjUTNDYzMxfAJjQmJjTFUhI/WD8/LB8cXgsO/wARGgEZEauA0TQmJjQmTVIcHyw+Plg/El8IGREBVhEZgAACAFUANgHKAasAFAAkAAAlFwcnBiMjIiYnBzUzBxYzMjY3MwYnIgYHIzY2MzIWFzcVIzcmAWNnH2goMAEZPhIsgDYfLSQ8CCsFjiQ+BysIVDgZPhIsgDYfvWgfZx0aEiyANh8yIyWlMiM2ShoSLIA2HwANAEAAQAHAAcAAAwAHABAAFQAZAB0AIgAmACsALwA0ADgAPAAAJTUzFQM1MxUFESEVISImNREFNTMUBic1MxUHNTMVBSImNTMTFSM1MzIWFSMHFSM1AxUjNDYXFSM1NxUjNQFAKysr/wABAP8AEhkBVSsaESsrK/8AEhkrVSqqERorgCorKxkSKysrlSsrAQArKyr/ACsZEgEA1isRGqsrK1UqKlYZEgEAKysaEdUrKwEAKxIZqyoqVisrAAkAQABAAcABwAADAAcACwAbAB8AIwAoACwAMAAANzUzFTM1MxU3NSMVEzIWFRUUBiMjIiY1NTQ2MxM1MxUBNTMVESImNTMnNTMVJzUzFZUrKyqA1dURGhoR1RIZGRKAK/7VKxIZKysrKytAKysrK4DV1QEAGhHVERoZEtUSGf6AKysBACsr/wAZEiorK1YqKgACAGsAVQGVAcAAAwAKAAA3IRUhJQcnMzUzFWsBKv7WASqVlVWAgCvrlZWAgAABACsAQAHVAdUACQAAJQc3Jzc3FxcHFwEAhCN0mTw8mXQjkFCWZQ2NjQ1llgAEACsAKwHVAdUABwAPABcAHwAAJDI2NCYiBhQmFBYyNjQmIgIyNjQmIgYUEjIWFAYiJjQBPywgICwfVR8sHx8sVSwfHywgM7B9fbB9ix8sHx8swSwfHywg/wAfLB8fLAErfbB9fbAAAwArACsB1QHVABUAGQAhAAABNjU0JiIGFTM0NjIWFAcHBhUVMzQ3BzUjFQIyFhQGIiY0AUEUMkYyKhoiGg0aGSoZGSpDsH19sH0BEBQcIzIyIxEaGiINGxshCyEbkSoqAWp9sH19sAADACsAKwHVAdUABwAPABsAADYyNjQmIgYUEjIWFAYiJjQFFwcXBycHJzcnNxe6jGVljGVTsH19sH0BDB43Nx43Nx43Nx43VWWMZWWMARt9sH19sAMeNzceNzceNzceNwACABUAQAHVAcAABQAeAAABMxUXBycmMhYUBiMiJic3FjMyNjQmIgYVMwcnJzM0AQAgSxBbOp5xcU8hTxceLD0+WFh8V0BWAlNAAVVaLRo31XCgcCEXHyxXfFdXPlYDU1AAAQArAFUB1QHAAAoAADcjNSM3FyMVIzUj1WpA1dVAalZVq8DAq4AAAwCAACsBgAHVAAQACQATAAABNzUjFRcnBxUzAyEVBxcVITU3JwEAVaqqVVWq1QEAVVX/AFVVAQtVS0vAVVVLAYCAVVWAgFVVAAEAgAArAYAB1QAJAAATIRUHFxUhNTcngAEAVVX/AFVVAdWAVVWAgFVVAAMAVQArAasB6wAHAA8AJwAAATU0JiIGFRUWMjY0JiIGFDcyFhUVFAYjISImNTU0NjMzNTQ2MhYVFQFCJzYnMSIaGiIaqxEaGhH/ABEaGhEVP1g/AVUrGycnGyvAGiIaGiKmGRHWERkZEdYRGSssPz8sKwADACsAKwHVAdUAAwAHAA8AAAE1IxUXNSMVAjIWFAYiJjQBFSoqKkOwfX2wfQFAKyurgIABQH2wfX2wAAQAKwArAdUB1QADAAsAEwAXAAATNTMVBjI2NCYiBhQSMhYUBiImNBc1MxXrKluMZWWMZVOwfX2wfcAqAUArK+tljGVljAEbfbB9fbDDgIAAAgAVAEAB6wHAAAYAHgAANzUjNTM1FzcyFhURFAYjISImNTUzFSERIRUjNTQ2M+vW1lWAEhkaEf6AERorAYD+gCsaEatAKkBVwBkS/tURGRkRVlYBLFZVERoAAgBVADQBqwHQAAYAEgAAJREHBhUUFjcWFAYjIiYnJjQ3NwEAWiZMrTJkRx1HFTIyeV4BNVomNTRM+TKNZB0VMo0yeQABAEAAawHVAZUADgAAARcHBiMjIiY1NTQ2MzMyAXhdXQ0W6hEaGhHqFgGDg4MSGRHWERkAAgBAAGsB1QGVAAQAEwAAJTcnIxUlFwcGIyMiJjU1NDYzMzIBVUxM6gENXV0NFuoRGhoR6haVa2vW7oODEhkR1hEZAAoAKwArAdUB1QAHAA0AFQAaACAAJgAuADMAOQBBAAAlMzY0JyMWFAc2NjcjBic2NCcjBhQXFzY3IxYnNjcGBgcVFhYXJicnMyY0NyMGFDcGBzMmFyYmJxYXJjIWFAYiJjQBXUgGBkgDKRk2Dj8KGQMDZAMDMhwNUg05DBIZNw0NNxkUClBIAwNIBqscDVINeA42GRIMrbB9fbB91RweHBUsiwgtFyhSFSwVFSwVfyksLNYrIQgtF6oXLQgkKCoVLBUcHrkpLCwsFy0IISuAfbB9fbAAAgBAAEABwAHAAAgAGwAAATMVIzUHJzcjEzUzFRQGIyEiJjURNDYzMxUjEQErlSvRHtFMaisaEf7WEhkZEpWVAcCVTNEe0f7WlZURGhkSASoSGSv+1gAGAEAAlQHAAWsAAwAHAAsADwATABcAABMhFSEVNSEVJTUhFSU1MxUHNTMVJzUzFZUBK/7VASv+1QEr/oArKysrKwFrK6srK1YqKlUrK6srK1YqKgADAFUAKwGrAesABwAPACcAAAE1NCYiBhUVFjI2NCYiBhQ3MhYVFRQGIyEiJjU1NDYzMzU0NjIWFRUBQic2JzEiGhoiGqsRGhoR/wARGhoRFT9YPwFVKxsnJxsrwBoiGhoiphkR1hEZGRHWERkrLD8/LCsAAwBVACsBqwHrAAMAIQApAAAlNSEVATIWFRUUBiMhIiY1NTQ2MzM1NCYiBhUjNDYyFhUVBiImNDYyFhQBgP8AAQARGhoR/wARGhoRwic2Jyk/WD9aIhoaIhpV1tYBABkR1hEZGRHWERkrGycnGyw/PywrwBoiGhoiAAQAVQArAasB6wADAAsAIwArAAAlNSEVExUzNTQmIgYXMhYVFRQGIyEiJjU1NDYzMzU0NjIWFRUGIiY0NjIWFAGA/wA+hCc2J8IRGhoR/wARGhoRFT9YP1oiGhoiGlXW1gErKysbJydGGRHWERkZEdYRGSssPz8sK8AaIhoaIgADACsAKwHVAdUAEAAYACoAACU2NCYjIgcHJyYjIgYVFBcXAjI2NCYiBhQFFhQHBwYiJycmNTU0NjMzMhcBcBAfFhcPEA8PFxYfD1utGhMTGhMBdAwMlgwkDMAMGRGWEgy6ECwfDxAQDx8WFw9bAQwTGhMTGnUMJAyWDAzADBKWERkMAAEAKwArAdUCAAAXAAABMhYVERQGIyEiJjURNDYzMzUzFSMVMzUBqxEZGRH+qhEZGRErq4AqAYAaEf8AERkZEQEAERqAVauAAAMAVQArAasB1QACAA4AHAAAATMnEzUjNSMVIxUzFTM1ExcRFAYjISImNRM0NjMBFXZ2QEAqQEAqFoAaEf8AERoBGREBQHX+9ipAQCpAQAEqgP8AERkZEQFWERkAAgBAAFUBwAGrAAYAHgAAARcjFSM1IzcyFhURFAYjIzUzNSEVMxUjIiY1ETQ2MwEAVUAqQOoSGRoRVVX+1lVVEhkZEgErVoCA1hkS/wARGivV1SsZEgEAEhkAAgBAAEABwAHAAAgAGwAAATMVIzUHJzcjEzUzFRQGIyEiJjURNDYzMxUjEQErlSvRHtFMaisaEf7WEhkZEpWVAcCVTNEe0f7WlZURGhkSASoSGSv+1gAEABUAFQHrAesABgANABQAGwAAJRUzByczNSUHNSM1MzUHFSMVJzcVNzUjNxcjFQErQGtrQAEWa0BAwEBra1VAa2tAwEBra0BAa0BWQEBWQGtrQBVAa2tAAAMAKwBVAdUBqwANAB0AJQAAJTcnNjU0JiIGFBYzMjc3MhYVERQGIyEiJjURNDYzFjIWFAYiJjQBZh4+DzhQODgoGhmDERkZEf6qERkZEYosICAsH3wePhkaKDg4UDgP8RoR/wARGhoRAQARGmsfLCAgLAADACsAVQHVAasAAwAHABcAAAE1IRUFNSEVATIWFREUBiMhIiY1ETQ2MwGr/qoBVv6qAVYSGBgS/qoSGBgSAVUrK9WAgAErGRL/ABIZGRIBABIZAAIAKwBAAdUBwAALAC8AACU1NCYiBhUVFBYyNjcyFhURFAYjIzU2NjUjFAYiJjUjFBYXFSMiJjURNDYzMzczFwErGiIaGiIagBEZGRGWLT4rMkYyKz4tlhEZGRFEJ4An61URGhoRVREaGrsZEf8AERotCEcvIzMzIy9HCC0aEQEAERkrKwADAEAAQAHAAesABwAPACcAACU1NCYiBhUVEiIGFBYyNjQ3MhYVERQGIyEiJjURNDYzMzUzFTM1MxUBgFhQWJo0JiY0JlURGhoR/tYSGRkSFSuqK4AVHSUlHRUBACY0JiY0ZhoR/tYRGhkSASoSGSsrKysAAwAAAAAB/QIAAAcASgBUAAAkMjY0JiIGFDcXFhUHFQcGJycGBwcUIyMiNScmJwcGJyc1JzQ3NzQmNDY1JyY3NzYXFzY3NzQzMzIVFxYXNzYXFxUWFRQHBxYVFAYnIgYVFBcjAQcmAYgaExMaE28XAgEVAwQaAw8EBioGBAgKGgUCFQECFwEBFwMCFQIFGgwGBAYqBgQEDhoFAhUBAhcBAU9CXgL3AasBDksTGhMTGgIRAgMBASUFAgsDBx0EBB0EBgsCBSUBAQMCEQIIAggBEgMEJQQCCwgDHAQEHAIJCwIEJQEBAQICEgMHAgiqXkIIDgGr9wIABABrABUBlQHrAAMAEwAXABsAACURIxETMhYVERQGIyMiJjURNDYzFxUjNTcVIzUBa9bWERkZEdYRGRkRgCoqKmsBKv7WAX8ZEf6AERoaEQGAERrWgIBWKysABABVAFUBqwGrAAkAEQAZACEAADYyFhYVFSE1NDY2MhYUBiImNBYiBhUVITU0JiIGFBYyNjTgQEw//qo/SUYyMkYygVhWAQRvJhoaJhrrEioaQEAaKtIzRjIyRrYhDBcXDOEaJhkZJgADAAAAKwIAAdUABAAWAB8AADchJwcnNzIWFRUUBiMhIiY1EzQ2MzMXBREhFSEiJjURlQErSzVL4BEaGhH+qxEaARkRgCv/AAGA/oARGsBgQGBrGhHVERoaEQEAERkqK/7VKhkRASsAAgBAAEABwAHAAAQAJQAAATMVIwcXMhYVFRQGIyImNTQ2MzMyFhUVFBcWBwcWFhc3NjMyFxYBAMCAQKsJDAwJltUMCUsJDAwECS8UUyYvBwgDBCQBwJVANgwJSwkM1ZYJDAwJASkiDQkvJlMULwcCDAADAAAAKwIAAcAAAwAHAA0AABMzNSMXNSMVEzIXAQE26yoqKioViHj/AP8AdgFVK9WAgAEVW/7GATtaAAMAFQBAAesBwAADABMAFwAAJREhEQEyFhURFAYjISImNRE0NjMFFSM1AcD+gAGAERoaEf6AERoaEQFVqmoBLP7UAVYaEf7WERoaEQEqERpVgIAAAQALAFUB9QGrAA0AAAEXByM3JwMjJzczBxcTAZVgYFVgOKhVYGBVYDioAaurq6tj/vKrq6tjAQ4AAgBAAEABwAHAABYAGgAAARYVFAYiJjU0NjcXBgYVFBYyNjU0JicnFSM1AXxEcKBwKBweFyBXfFcgF0kqAZI6WFBwcFAkVhgeE0QdPldXPh1EEk3V1QAEACsAQAHVAcAAAwALAA8AHQAAARUhNQQyNjQmIgYUBzUjFTcyFhUVIxUhNSM1NDYzAYD/AAEMEg0NEgwrquoaJlX/AFUmGgHAVVXADBINDRKhamrqJhqAVVWAGiYAAwArACsB1QHVAAUADQAVAAABFRcHJzUCMjY0JiIGFBIyFhQGIiY0AQtgEHAxjGVljGVTsH19sH0Ba3A5G0SA/upljGVljAEbfbB9fbAAAgArACsB1QHVAA0AGgAAARQGIyMHETQ2MyEyFhUXMhYVEScjIiY1NSE1AWsNCdVVDAkBFQkNVQkMVesJDAEVAQAJDFYBKwkMDAlADAn+wFUMCSvAAAQAQAArAcAB1QAZAB0AIQAlAAA3ERc3FzcXNxc3FzcXNxEnBycHJwcnBycHJyU1IRUFNSEVBTUhFUAgICAgICAgICAgICAgICAgICAgICAgIAEg/wABAP8AAQD/ACsBqiAgICAgICAgICAgIP5WICAgICAgICAgICD1KytVKipWKysABQArAEAB1QHVAAwAEAAYACAAQgAAJTUjFwcmJwYHJzcjFQU1IRUSIgYUFjI2NDYiBhQWMjY0FzIWFRUUBiMhIiY1NTQ2MzMmNTQ2MzIXFzc2NjMyFhUUBwGrbS0jQAgIQCMtbQFW/qp0EgwMEgx0EgwMEgxWEhgYEv6qEhgYEi8EJhohFAsLByAOGiYE1YA8GVcLC1cZPIBqKioBQA0SDAwSDQ0SDAwSHhkS6hIZGRLqEhkOBxomHA8PDBAmGgcOAAMAFQBAAesB1QADAAcACgAAJTUjFRc1IxUHExMBFSoqKtbr69VWVlUrK0ABlf5rAAIAFQBAAdUBwAAFAB4AAAEzFRcHJyYyFhQGIyImJzcWMzI2NCYiBhUzBycnMzQBACBLEFs6nnFxTyFPFx4sPT5YWHxXQFYCU0ABVVotGjfVcKBwIRcfLFd8V1c+VgNTUAACAGsAKwGVAdUABwAYAAASMjY0JiIGFCYyFhUUDgIHBy4ENTTqLB8fLB8JfFcfLCsPEAYUNCcgAQsfLB8fLKtXPh9QRj0SEQcXRUFSHz4AAwArACsB1QHVAAUADQAVAAABFRcHJzUCMjY0JiIGFBIyFhQGIiY0AQtgEHAxjGVljGVTsH19sH0Ba3A5G0SA/upljGVljAEbfbB9fbAAAgBAAEsBtQHAAAcAGgAANjI2NCYiBhQXFwcnNScGIyImNDYyFhUUBgcXo1A4OFA44GogagYmNDpRUXRQFA0G1ThQODhQOGogahEGIVB0UVE6FTUQBgACAC0AKwHTAdUABwA/AAA2MjY0JiIGFDcXFgcHBicnBgcHBiMjIicnJicHBicnJjc3JjQ3JyY3NzYXFzY3NzYzMzIXFxYXNzYXFxYHBxYU4T4sLD4s6i0HBSsECTUVDwgCCFYIAggTETUJBCsFBy0BAS0HBSsECTUVDwgCCFYIAggTETUJBCsFBy0BtSw+LCw+CiMFCUoHAxUPBjgJCTgIDRUDB0oJBSMHHAcjBQlKBwMVDwY4CQk4CA0VAwdKCQUjBxwAAwBAAEABwAHAADsASwBTAAAkNCc3NicnJgcHJicnJiMjIgcHBgcnJgcHBhUUFxcGFBcHBhUUFxcWNzcWFxcWMzMyNzc2NxcWNzc2Jyc3MhYVERQGIyEiJjURNDYzFjIWFAYiJjQBcAEgBQQeAwYlDQwGAgU8BgEGEAklBQQeAQIgAQEgAgEeAwYlDQwGAgU8BgEGEAklBQQeBAUgJhIZGRL+1hIZGRKEIhoaIhr2FAUYBAY0BQIPCgUnBgYoBwcPAgYzAQMFARgFFAUYAQUDATQFAg8KBScGBigHBw8CBjMGBBjPGRL+1hIZGRIBKhIZlRoiGhoiAAIAAABAAcABwAAXAB8AABIyFhQGIyImJzcWMzI2NCYiBhUzByczNBYUBiImNDYysKBwcFAbRBYeJjE+V1d8V0BWVUDrGiIaGiIBwHCgcBcRHhtXfFdXPlVVUD8iGhoiGgAGAGsAAAF6AgAAAgAFABMAFwAbAB8AACUnFREVNzMHFwcjNQcnNyc3FzUzEzUzFSM1MxUzNTMVAT0oKD1cXHoVYh53dx5iFUAr1isrKs8oUAEHUChbXHqiYh54dx5iov4AKysrKysrAAUAgAAAAYACAAADABMAFwAbAB8AACURIxETMhYVERQGIyMiJjURNDYzEzUzFSM1MxUjNTMVAVWqqhEaGhGqERoaEZUrgCqAK6sBAP8AAVUaEf6rERoaEQFVERr+ACsrKysrKwAEABUAQAHrAcAABQAVABkAKQAAARUyNjQmBzUnNzUzNxczFRcHFSMHJxcRIREBMhYVERQGIyEiJjURNDYzAQAaJiZvICA1ICA1ICA1ICDg/oABgBEaGhH+gBEaGhEBQIAmNCaVNSAgNSAgNSAgNSAgQQEs/tQBVhoR/tYRGhoRASoRGgAFABEAdQHvAYsABQAJAA0AEQAXAAABFwcnNycHNTMVNxUjNQc1MxUnBxcHJzcBe3R0IV1dbypWK6srGl1dIXR0AYuLixtwcIUqKioqKioqKoVwcBuLiwADABUAIgHrAesACwAcACgAABIyFhUjNCYiBhUjNAUVFwcnByc3NSY1NDYyFhUUJjIWFSM0JiIGFSM0n8KKK3CgcCsBAEkeQEAeSSAfLB9zfFcqP1g/KgHrimFQcHBQYZJGSR5AQB5JRg0kFh8fFiS5Vz4sPz8sPgAGABUAFQHrAesACgAWACIALQA5AEUAACU1MxUUBgcVIzUmAxUzFSM1MzU0NjIWFzMVIzUzNTQ2MhYVATUzFRQHFSM1JiY3NTMVFAYHFSM1JiYDFTMVIzUzNTQ2MhYBa4AYEysqViuAKwwSDKsrgCoNEgz+VYAqKxMYq4AYEyoTGFUqgCsMEg2rKioVIAdaWg8BV1WAgFUJDQ1egIBVCQ0NCf7WKiotD1paByAVKioVIAdaWgcgAT9VgIBVCQ0NAAYAFQAVAesB6wAKABYAIgAtADkARQAAJTUzFRQGBxUjNSYDFTMVIzUzNTQ2MhYXMxUjNTM1NDYyFhUBNTMVFAcVIzUmJjc1MxUUBgcVIzUmJgMVMxUjNTM1NDYyFgFrgBgTKypWK4ArDBIMqyuAKg0SDP5VgCorExirgBgTKhMYVSqAKwwSDasqKhUgB1paDwFXVYCAVQkNDV6AgFUJDQ0J/tYqKi0PWloHIBUqKhUgB1paByABP1WAgFUJDQ0AAgBrACsBlQHVAAsAHQAAExUzNTMVMzUzFTM1FzMVBxUjNSc1MzU0NjMzMhYVqyoWKhYqKxVAqkAVGhGqERoBq0AqKioqQECAgEBAgIBAERkZEQAHABUAFQHrAesABwAPABcAHwAnADMAOwAAJDIWFAYiJjQ2MhYUBiImNAYyNjQmIgYUEjIWFAYiJjQWMhYUBiImNDYUBiMjIiY0NjMzMgYUBiImNDYyAT4aExMaEz0aExMaE6WgcHCgcF/CiorCipMaExMaE6sSDkAOEhIOQA6DExoTExrAEhwSEhx9ExoTExrYcKBwcKABO4rCiorCoRIcEhIc1BoTExoTfRoTExoTAAYAFQBAAesBwAADABMAFgAZABwAHwAAJREhEQEyFhURFAYjISImNRE0NjMTBycnFSclFwcnFyMBwP6AAYARGhoR/oARGhoR6ysrVTUBNTU1gCtWagEs/tQBVhoR/tYRGhoRASoRGv7rNjaAVisrKyu2NgAEAEAAQAHAAcAAAwAkACgALAAAATMVIxcyFhUVFAYjIiY1NDYzMzIWFRUUFxYHBxYWFzc2MzIXFicVIzUjFSM1AZUrKxYJDAwJltUMCUsJDAwECS8UUyYvBwgDBCQYKysqAUArYAwJSwkM1ZYJDAwJASkiDQkvJlMULwcCDIsrKysrAAUAVQAAAasB1QADABkAHQAhACUAACE1MxUDFhUUBiImNTQ2NxcGFRQWMjY1NCYnJxUjNRE1MxUjNTMVAUArCkpkjmQrHx49S2pLJBotKiqAKysrAaE0WEZkZEYlUhUeJkg1S0s1HkAPU9XV/isrKysrAAQAWgAVAaYCAAAIABIAGgAqAAABMhcHJiIHJzYHNjIXByYmIgYHFjI2NCYiBhQ3MhYVERQGIyMiJjURNDYzAQBjQx44oDgeRQksfCweDS0kLQ07IhoaIhprCQwMCYAJDAwJAgBFHjg4HkWBLCweDRISDaEaIhkZImYMCf8ACQ0NCQEACQwABQBrAAABlQHrAA8AEwAXACMAJwAAARQGBxUjNSYmNTMUFjI2NQM1MxUjNTMVNiImNTU0NjIWFRUUAzUzFQGVSzUqNUskQ1xDMSuAKgU0JiY0JqsrASs2UghGRghSNi8+Pi/+1SsrKyvrJhqAGiYmGoAa/u8rKwADACsAQAHVAdUAAgAGABoAADc3JzcVMzUXMxEUBiMhIiY1ETM1NDYzMzIWFcCgoBVWKoAYEv6qEhiAGBJWEhiAa1VrKysr/usSGRkSARUrEhgYEgAEABUAKwHrAesAAgAGABoAJQAAJTcnNRUzNRczFRQGIyEiJjU1MzU0NjMzMhYVBRUhFAYjISImNTUBAHV1VStrGRL+1RIYahkSVRIZ/sABVRgS/tUSGcBVQGsrKyvqEhkZEuorEhkZEoDrEhgYEusAAwAVAEAB6wHVAAcACgAjAAA2MjY0JiIGFCczJxczMhYVBgYHBiMhIicnJjU0NjMzNzYzMhfvIhoaIhoVgEBvZgkNCigFCSD+6iAJNgENCWZdBgwNBZUaIhoaIpFeXgwJKJETHx/GAgQJDIwJCQADABUAKwHAAdUABwAjACsAACQyFhQGIiY0ATMXITIWFRQHBwYjIwcHFDMzFSEiJjU0NzcnIxIyFhQGIiY0AVoiGRkiGv7VRhQBPAkMA0wMGZ8TAQX3/wARGQUdTStvIhoaIhmAGiIZGSIBbyoNCQEJihYjAwUrGhEKCjWi/tUaIhkZIgAHACsAKwHVAdUAAwAHAAsADwATABcAJQAAATUjFRc1IxUXNSMVJzUjFRc1IxUXNSMVATIWFREUBiMhBxE0NjMBgKurq2trKisrKysrASsRGRkR/tVVGREBVSsrQCsrQCsrgCsrQCsrQCsrAQAZEf8AERpVAYARGQADADQAIAHrAcAABQAIABAAAAEXByc3FyczJxcnIwcjEzMTAc0ey2weTpdYLFUZeBgtbShtAQkey20eT7l24EBAARX+6wACACsAKwHVAdUACQARAAAlJzcnJwcHFwc3AjIWFAYiJjQBWhhQaSkpaVAYWliwfX2wfYBnRQlgYQhFZzYBH32wfX2wAAMAQABVAcABqwADABEAFQAAJTUjFSUjFSM1IxUjNSM1NyEXJxUhNQEAgAFAFStV1hUVAVYVFf6qgFVVVYCAgIAra2urKysABABVAGsBqwGVAAMABwALAA8AABMhFSEVNSEVNRUhNRcVIzVVAVb+qgFW/qrW1gGVKqsrK4ArK6sqKgAEACsAawHVAZUACgASABoAIgAANzIXBhUVIzU0NjYWMhYVFSM1NCYiJjQ2MhYUFiImNDYyFhTAFh0zlTdCl0pQ6hE0JiY0JnYsHx8sH+sGHC4wNRckEBYgGjAwGmAmNCYmNDsfLCAgLAACAEAAawHAAZUABgANAAABBzUjNTM1BxUzFSMVJwHAVZaW1paWVQFAVUAqQIBAKkBVAAIAawBAAZUBwAAGAA0AABMXIxUjNSMXMwcnMzUzwFVAKkDqQFVVQCoBwFWWltZVVZYAAwArACsB1QHVAAYADQAVAAAlIzUjFSMXJzMVMzUzJyYyFhQGIiY0AXU1KzVLoDUrNUstsH19sH3AVVVLy1VVS0p9sH19sAACABUANQHrAbUAFwAeAAABMhYVERQGIyEiJjURNDYzMxUjESERIzUDJzM1MxUzAcARGhoR/oARGhoRgIABgIBAVUAqQAG1GRH+1REaGhEBKxEZKv7VASsq/utVwMAAAgAVAEAB6wHAAAUAFQAAJTUjNSMRATIWFREUBiMhIiY1ETQ2MwHAq9UBgBEaGhH+gBEaGhFr1VX+1gFVGhH+1hEaGhEBKhEaAA8AFQBAAesBwAADAAcACwAQABQAGAAcACAAJwAsADAANAA5AD0AQQAAJTUzFSM1MxU3NTMVBzUzFAYBNTMVAzUzFRM1MxUTNTMVAzIWFRUjNQMiJjUzJzUzFRc1MxUDNDYzFQc1MxUnNTMVAWsqgCuAKysrGv6aKioqKyvVKysRGtbVERorKyuAK9YaESsrKytAKysrK6sqKqsrERoBVSsr/qsrKwFVKyv/ACsrASsaEVWA/oAaESorK1UrKwFVERorqioqVSsrAAcAVQBAAasBwAADAAcACwAPABMAFwArAAABNSMVFzUjFRc1IxUnNSMVFzUjFRc1IxUBMxEjNSMVIzUjFSMRMxUzNTMVMwGAKysrKyuqKysrKysBACsrK6orKysrqisBQCsrVSoqVisrqysrVSoqVisrASv+gCsrKysBgCsrKwACABUAFQHrAcAAAwAaAAABMxEjAzIWFRUUBwcnJjU1NyMiJjU1NDc3NjMBlVZWVREaDYwXCRWHERoDQQodAcD/AAEAGhHVEQ2NFwkNB2IZESsICJYaAAIAFQBAAesB6wAWABoAAAEVFAcHBiMjIiY1NTQ3NxcWFRUHMzIWBREzEQHrA0EKHcARGg2MFwkVhxEa/ipWASsrCAiWGhoR1RENjRcJDQdiGfwBAP8AAAIAAAAAAgACAAAXAC8AAAEyFhUVFAcHJyY1NjY3IyImNTU0Nzc2MycVFAcHBiMjIiY1NTQ3NxcWFQYGBzMyFgHgDhIJahEHAwsBbwkMAjEJFFACMQkUkA4SCWoRBwMLAW8JDAErEw2LDglpEQcKDjQHDAkbAwhxFFUbAwhxFBMNiw4JaREHCg40BwwABgBAAJUBwAFrAAMABwALAA8AEwAXAAAlNTMVJzMVIxU1MxUhNSEVJTUhFSU1IRUBlSsrKysr/oABK/7VASv+1QEr6yoqgCurKysrK1YqKlUrKwADAEAAQAHAAesAAwAHAB8AABMzFSMFNSEVATIWFREUBiMhIiY1ETQ2MzM1MxUzNTMVlWtrAQD+1gEqERoaEf7WEhkZEhUrqisBK2tV6uoBVRoR/tYRGhkSASoRGisrKysAAwAVAFUB6wGrAAsAEwAbAAASFBYXFSYmNDY3FQYWMjY0JiIGFDYyFhQGIiY0QDIjOEhIOCOZaktLaks6jGVljGUBJUpHDSwOXHZcDiwN7EtqS0tq4GWMZWWMAAEAKwArAdUB1QA2AAABFhUUBiImNDYzMxUWFRQGIiY1NDc1BgYVFBYyNjU0JzcWFRQGIiY1NDY3NQYGFRQWMjY1NCYnAZc+fbB9fVgVFhoiGhYcJDJGMhkeJktqSz0uP1dljGUdFQGXPllYfX2wfbAMGREaGhEZDC0ILB4jMjIjIRseJjQ1S0s1L0cIKwhgQUZlZUYdRxUAAwAVACsB6wHVAAIACgAkAAAlMyc3EyMnIwcjEwcHJwcnNyYnMxYXNjcjNTM1MxUzFSMGBgcHAVNFIxZgKxhlGCtgTRFCax5tKBgrFRwuFu+WKpY/Ci4XAZVdOf8AQEABAGwsQmoeayw1KB8zPysqKisgURoBAAEAKwCAAdUBgAAKAAAlNycHJzcXNxc3FQFVMWhVnh6AVYYxgDFoVZ4egFWGMYAAAQBAAKsB1QFVAAYAAAEHNSE1ITUB1VX+wAFAAQBVQCpAAAEAKwCAAdUBgAAKAAABMxUnBycHJzcXNwFVgDGGVYAenlVoAYCAMYZVgB6eVWgAAQBrAEABlQHAAAoAAAEyFhURJwcRNDYzAWsRGZWVGREBwBoR/qtAQAFVERoAAgBrAEABlQHAAAQADwAAJREjETcTMhYVEScHETQ2MwFr1mtrERmVlRkRgAEV/usvAREaEf6rQEABVREaAAIAQAAVAcAB6wAFAA8AADc3JwcnBxMXFRQGByYmNTXVqx6NNx6AwG5SUm6Vqx6MNx4BAFaAWZMUFJNZgAACACsAQAHAAcAADwAfAAABMhYVFRQGIyEiJjU1NDYzBTIWFRUUBiMhIiY1NTQ2MwGrCQwMCf6VCQwMCQFrCQwMCf6VCQwMCQHADAmACQ0NCYAJDNUNCYAJDAwJgAkNAAMAVQCAAcABlQADAAcACwAANxEzERMzESMhETMRq8AVQED+1UCAARX+6wEV/usBFf7rAAMAKwBrAdUBqwADAAcACwAAATMVIyE1MxUXETMRAYBVVf6rVRXWAYDr6+sqAUD+wAADAFUAgAHAAZUAAwAHAAsAAAEzESMhETMRMxEzEQFVa2v/AGsVawGV/usBFf7rARX+6wADACsAQAHAAcAAAwATABcAABMhFSEFMhYVFRQGIyEiJjU1NDYzAzUhFSsBlf5rAYAJDAwJ/pUJDAwJFQGVAcBAKwwJgAkMDAmACQz+60BAAAQAVQBrAasBlQADAAcACwAPAAATIRUhFTUhFQU1IRUlNSEVVQFW/qoBVv6qAVb+qgFWAZUqVisrqioqVSsrAAYAVQBrAcABlQADAAcACwAPABMAFwAAEyEVIRU1IRUlNSEVJTUzFQc1MxUnNTMVwAEA/wABAP8AAQD+lVZWVlZWAZVV1VVValZWa1VV1VVValZWAAYAVQCAAcABlQADAAcACwAPABMAFwAAATMVIyM1MxUXNTMVIzUzFSM1MxUnNTMVAVVra4BrFWvra+tra2sBlYCAgJWAgICAgICVgIAABABVAIABwAGVAAMABwALAA8AABMzFSMXNTMVIREzETM1MxXV6+uAa/6VaxVrAZWAlYCAARX+64CAAAIAVQCAAcABlQADAAcAABMhFSEVNSEVVQFr/pUBawGVgJWAgAADACsAawHAAZUADwAfAC8AAAEyFhURFAYjIyImNRE0NjMzMhYVERQGIyMiJjURNDYzIzIWFREUBiMjIiY1ETQ2MwEVCQ0NCUAJDAwJ1gkMDAlACQ0NCesJDAwJQAkMDAkBlQwJ/wAJDAwJAQAJDAwJ/wAJDAwJAQAJDAwJ/wAJDAwJAQAJDAADABUAYAHrAaAABwAPABkAABIyFhQGIiY0FjI2NCYiBhQ2MhYXBgYiJic25jQmJjQmFFg/P1g/HJ6AHByAnoAcHAFAJjQmJjSFP1g/P1jMWEhIWFhISAAEABUAKwHqAcAABQAVACYANgAAEzMyFhUVJwYVFBYzMjcnBiMiJjU0Nyc3AQcmJicGIyImJzY2NyYmFyIHJzYzMhYXBgcnNjU0Jv0DGiafDD8sFxghCAYaJgKXGwF6GwU1DisyT4AcCy8WDCvSFBMuJy5PfxwYMT4IPwFAJhoEMxgXLD8MIQImGgYIlxv+hhsFNA4SWEgaPxEMLDcILg9YSDsqPhMULD8ABQArAEAB1QHVAAwAEAAYACAAQgAAJTUjFwcmJwYHJzcjFQU1IRUSIgYUFjI2NDYiBhQWMjY0FzIWFRUUBiMhIiY1NTQ2MzMmNTQ2MzIXFzc2NjMyFhUUBwGrbS0jQAgIQCMtbQFW/qp0EgwMEgx0EgwMEgxWEhgYEv6qEhgYEi8EJhohFAsLByAOGiYE1YA8GVcLC1cZPIBqKioBQA0SDAwSDQ0SDAwSHhkS6hIZGRLqEhkOBxomHA8PDBAmGgcOAAMAKwArAdUB1QADAAcAHAAAATUhFQU1IRUBMhYVFRQGIyMVJwc1IyImNTU0NjMBq/6qAVb+qgFWEhgYElZVVVYSGBgSASuAgGsrKwEVGBLrEhlqKipqGRLrEhgABAArAEAB1QHVAAsADwATAC0AACU1IxUjNSMVIzUjFQU1IRUTFTM1FzIWFRUUBiMhIiY1NTQ2MzM1NDYzMzIWFRUBq0ArgCtAAVb+qmuAaxIYGBL+qhIYGBJAGRKAEhnVgCoqKiqAaioqAUArKysZEuoSGRkS6hIZKxIYGBIrAAIAKwBAAdUB1QADAB0AAAE1IxUzMhYVFRQGIyEiJjU1NDYzMzU0NjMzMhYVFQErVtYSGBgS/qoSGBgSVhgSVhIYAYArKxkS6hIZGRLqEhkrEhgYEisAAQArAEsB1QHAACIAACUXByc1JwYGIyInNxYzMjY0JiIGFTMHJzM0NjMyFhUUBgcXAWtqH2sGEDUVKB8gFBMoODhQOEpYUjVSOTpRFA4G1WogaxAGDRQTHwg4UDg4KFVVOVJROhU1EAYAAgBrAGsBlQGVAAIABgAAARchByEVIQEAjv7kBwEq/tYBldUrKgADACsAQAHVAcAABwAPACMAACUnJzc3FxcHBjI2NCYiBhQTMxczMhYVERQGIyEiJjURNDYzMwEAGzo6Gxs6OkdYPz9YPyuAJ0QRGRkR/qoRGRkRRJU7Gxo7OxobUD9YPj5YAQErGRH/ABEaGhEBABEZAAQAKwArAdUB1QATABsAIwAnAAASMhYVFAYVIzQ+AjU0JiIGFSM0FjI2NCYiBhQSMhYUBiImNBc1MxXdRjJAKhQYFBoiGioPjGVljGVTsH19sH3AKgGAMiMbORcXIQ8WDhEZGREj+WWMZWWMARt9sH19sNgrKwAEAEAAawHAAZUAAwAHAAsADwAAEyEVIRU1IRUFNSEVJTUhFUABgP6AAYD+gAGA/oABgAGVKlYrK6oqKlUrKwADAEAASwG1AcAACwATACYAAAEjFSM1IzUzNTMVMwYyNjQmIgYUFxcHJzUnBiMiJjQ2MhYVFAYHFwEAKxUrKxUrXVA4OFA44GogagYmNDpRUXRQFA0GASsrKxUrK2s4UDg4UDhqIGoRBiFQdFFROhU1EAYAAwBAAEsBtQHAAAMACwAeAAATMxUjFjI2NCYiBhQXFwcnNScGIyImNDYyFhUUBgcXlWtrDlA4OFA44GogagYmNDpRUXRQFA0GAUAVVjhQODhQOGogahEGIVB0UVE6FTUQBgAFABUAwAHrAUAAAwAPABcAHwArAAABNSMVNzIWFRUUBiMjFSM1BzUzFSMVIzUjNTMVIxUjNQc1MxUjNSMVIzUzFQHLKysNExMNKyB1YCAglmAgIFUgICsgIAELFRU1Ew0VDRMrgCAgIGBgICBgYAsrgDU1gCsABAArAEAB1QHAAAkADQARABkAACUjNTQ2MzMyFhUFMxUjJTMVIwU1IRUjNSMVAWvWGhGAERr+wEBAAWpAQP7AAVZA1uuqERoaEWpAQECrgIBAQAACADUAQAHLAdUAEQAVAAAlJicnNR8CNR8CFhYHBgYnBSEVIQErUnwiHxRqKTtxDQ0DBBYN/pgBlv5qzBcgCm4IMhywC8AeBBcNDQwDQysAAgAnAEAB2gG0ABEAFQAAABYGBwYPAiYnNxc3JzcXNzYFIRUhAdQGDQ18UnEiBzEfKmpYKZNyDf54AZb+agE/GhYEIRYeCgxUCCAcmQuJHgTiKwACAIAAVQGAAZUACwASAAA3MxQWMjY1MxQGIiY3MxUzByczgCsyRjIrS2pLaypLYGBL1SMyMiM1S0v1d2BgAAMAawDAAZUBQAAJAB8AIwAAASMVMxUjFSM1MyMyFhUVIxUzNTMVFAYjIyImNTU0NjsCFSMBlUArKyBg1QkMSiogDAlACQwMCXUgIAEgFSArgAwJC0AgKwkMDAlWCQyAAAIAQABAAcABwAADABMAACU1IxUlMhYVERQGIyEiJjURNDYzAWvWAQARGhoR/tYRGhoR6yoq1RoR/tYRGhoRASoRGgADACsAKwHVAdUABQAJABEAADc3JwcnBxc1IxUSMhYUBiImNNyPHnEpHtbWE7B9fbB91Y8ecSkenCsrAVV9sH19sAAGAFoAUgGvAacABwAPABIAFQAYABsAACQ2NCYiBhQWEhYUBiImNDYHNTMRIzUhFSMRMxUBNUREYUREblhYfFZWbFVVAVVVVYhEYUREYUQBClh8VlZ8WEBV/qtVVQFVVQADACsAKwHVAdUABwAPAC8AADYyNjQmIgYUEjIWFAYiJjQXIhUVFDMyNjUzFAcGIyImNTU0NzYzMhcWFSM0JyYnJrqMZWWMZVOwfX2wfdIoKA8UJhYVHigqFBgmIRMVJgMFAgpVZYxlZYwBG32wfX2wGzoGOhENGRMSMCoGKRcbExUcBwYKAgoABQBAACsBwAHVADgATABnAIAAkwAAJSMmJyYmNTQ2MhYVFBYyNjU0JiMiBgcGFRQXFgYnJjU0NzY2MzIWFRQGIiY1NCYiBhUUFxYXFgcGJxQWMzI2MhYXFgcGIyInJjU0MzIHIicmJyY1NDYyFhUUIjU0JiIGFRQXFhcWBwYDIjU0NzY3NjIXFhcWFRQGJyYnJiYHBgcGJSInJiMiBwYmNTQ3NjMyFxYHBgE+Ay4hExsmNicZJhpbQC5MEwwOAxQDEA8VVzRJZyc2JhomGSgcKgkCAj03KQIOCQkBAgsMDigaMwsKOQQDGxAXR2RHFjpSOhMNGwgIA4oLAiAwMngyMR8CEAQeKi5sLi0cAwEsBAE7PEE2BQoFOkdBQAkFBCsMIRNBGxolJRoRGBgRPFYvJxkjKCUKBgkpKyceLTZjRRokJBoRGRkROCgcCwEMCJwlNgIDBQsCAhMjOgujAxseKDQvREQvCgonNzcnMCIXHQcIAwECCgIELhgaGhgtBAIHBgcpFRcBGBcoBHABHh4DBgYHAyAgBQkGAAQAFQAVAdoB6wADAAcACwAPAAATFwcnNxcHJwc3AQclIRUhUng8efJ4PHlaPAEuPP53AQD/AAE2eTx48nk8eFo8/tI8GisAAwBrACsBlQHVAA4AIAAqAAAlNjU0JiIGFRQWFxcVMzUmMhYVFAcVFAYjIyImNTUmNTQTNTMVFAYjIyImAT0uP1g/GxMSVml8V0AMCYAJDEBVgAwJVgkM6SA3LD8/LBcyDg0xMflXPk4sMQkMDAkxLE4+/sIVFQkMDAADABUAQAHrAcAAAwATABcAACURIRElFAYjISImNRE0NjMhMhYVBxUjNQHA/oABqxoR/oARGhoRAYARGlaqagEs/tQBERoaEQErERkZEYGAgAAEAAAAKwIAAdUACQAkACgAOAAAEzMHFycHNyczNzcyFhUVIzUhESEVIxUzFSM1MzUjIiY1ETQ2MwE1IxU3MhYVFRQGIyMiJjU1NDYz/0E1FDQ1FDVBFcASGCr+gAEVKyurK5USGRkSAcBrawkMDAlrCQwMCQFAJj4nJz4mQFUYEmtr/wArKyoqKxkSAQASGP6AlpbADAnACQwMCcAJDAACAGUAAAGlAcAAHAAsAAAlFhUVBwYGIyMiJyc3NjMyFjMXNTQ2MhYVFTMyFycmNTQ2MhYVFAc1NCYiBhUBkhMQARIMkQ0JahEHCgEDAUkTGhMRAwhxKzhQOCogLB+tCRQEcQwPCWoRBwEP5Q4SEg6AAjIcNCg4OCg1G1AWHx8WAAMAawArAZUB1QATAC0ANQAAJTMGBiMiJjU0NjcVBgYVFBYzMjYnNDYXMxUWFxcWMxUiJxUzMhYVFSM1IyImNTQ0NjIWFAYiARIsBz4kLD4yIxMYJhoVITYqFgEHBhwkMjgyQBEZKmsRGhkkGRkkgCMyPiwkPgcsByEVGiYY0RgcDQEDBh8nKilJGhF1ahoR2SQYGCQZAAIAKwBrAdUBlQAGAA0AACUnNxUzFSMHNRcHNSM1AUBVVZWVgFVVletVVUAqVkBVVUAqAAUAQAArAcAB1QADABsAHwAjACcAACU1IRUBMhYVERQGIyEiJjURNDYzMzUzFTM1MxUHFSM1IxUjNSMVIzUBlf7WASoRGhoR/tYSGRkSFSuqKxUrKyorK1Xr6wFWGhH+1REZGBIBKxEaKioqKpYqKioqKioAAwArACsB1QHVAAcADwAbAAAlNjY3MwYGBzcmJic1FhYXJwYGFBYXFSYmNDY3ARUwSQdACGhQgAdJMFBoCOoyTk4yUW9vUWwHSDBQaQfqMEgHQQhoUH8IVmxWCEEIeqZ6CAADACsAKwHVAdUABgANABkAACU2NzMGBgc3Jic1FhYXJwYGFBYXFSYmNDY3ARUbDZgIaU8oDBxPaQjqERoaEVFvb1HDCCBNbAfqIAiYB2xNKAciKCIHmAh6pnoIAAsAQABVAcABqwADAAcACwAPABMAFwAbAB8AIwAnACsAABMhFSEXNTMVITUzFRc1MxUjNTMVIzUzFSM1MxUjNTMVNzUzFSM1MxUjNTMVQAGA/oDVq/6Aq6orgCuAKoArgCvqa/Vq9WsBq1ZVKysrK6srKysrKysrKysrVioqKioqKgAEAEAAVQHAAasAAwAHAAsADwAAEyEVIRU1IRUFNSEVJTUhFUABgP6AAYD+gAGA/oABgAGrVmpAQJYWFkArKwADACsAawHVAYAABwAUAC8AACQyNjQmIgYUJyc1MyYjIgYUFjMyNjcyFhQGIiY1NDY1BwYGIyImNDYzMycjNTMXNAFfLB8fLCCCPT0MJxYfHxYQH7woODhQOAEtBjchKDg3KK0qTV5WlSAsHx8sBQEgJR8sIBaAOFA4OCgCBgIbIS44UDgqK1YBAAIAVQAyAasBzgAGABYAADchNCcnBwY3FhUUBwYGIiYnJjU0Njc3gAEAJlpaJvkyMhVHOkcVMh0VedU3Jl5dJkgyRkcyFR0dFTJHHUYVeQAFACsAKgHVAcAAKwAzADsAQwBLAAAlHgcGFgcGBwYmIyMiBicmJyY2Njc2Njc2NzYzNjMyFzIXFhcWFjY0NjIWFAYiJjQ2MhYUBiImNDYyFhQGIgY0NjIWFAYiAXICDQQNAwwBCQMFAwsnB1McBBxTBycLAxQRFgohChITBAMGCwwFAwQTEgkiAx8sHx8sfx8sHx8snx8sHx8sfx8sHx8swwINBA4FDgYQBxEEKggBCgoBCCoUKhIWCygLFgYCAQECBhYLKFEsICAsH3UsHx8sICAsHx8sIDYsICAsHwACAKsAKwFVAdUADgAWAAAlFSMVIzUjNTQ2MhYVFhYmNDYyFhQGIgFVQEAqJjQmERmVGSQYGCTrVmpqlhomJhoHJpskGBgkGQAEABUAQAHqAdUABwAPABcAHwAAARYUByc2NCcHFhQHJzY0JwYyFhUVITU0NjQ2MhYUBiIBrD4+IywsIyAgJA4OuGx1/qpWMkYyMkYB1UGrPiIzgzAlI1ggJBMtE6ovJisrJoxGMjJGMgALAEAAQAHAAcAACwAPABMAFwAbAB8AIwAnACsALwAzAAABFSM1NCYjIzUzMhYBNTMVMzUzFTM1MxUDNTMVIzUzFQc1MxUHNTMVJzUzFQU1MxUHMxUjAcArJhpqaiw//oArKisrKoArgCsrKysrKysBKisrKysBVWpqGiYrP/6/KysrKysrAVUrKysrVSsrqysrVioqVisrKisAAwBVAAABwAHrABoAIgAnAAAlByc1JwYjNRY2Nzc2MzMyFhUVFAcnNQYHFzMCMhYUBiImNAMXIwcnAcBAQJcHDRo4Eh4QEwETHRRMFB2GIFEiGhoiGmA2K0sgQEBAIJcBLgEZEyEQHBR7GhRMMRENhgFrGiIaGiL++jZKIAABABUAgAHrAYAANAAAABQGIyMiJwcWFRQGIiY1NDcnBiInBxYVFAYiJjQ2MzIXNyY1NDYyFhUUBxc2Mhc3JjU0NjIB6xoRAQcDTAIaIhoCNwYKBmECGiIaGhEIA2EBGSIaATYDEANMAhoiAWYiGQFMBgURGRkRBQY3AgJhBgURGhoiGQFhAwgRGhoRCAM2AQFLBgURGgACAEAAQAHAAcAABQAlAAABFRcHJzUXIzcmJgcGBhQeAjI2NzY1MxQHBiInJjQ3NjYyFhc3AQtKD1vVkTsseywSGhokPjI/EisrODigODg4F09CTxc6AVVaLRo3ai08LAErEj0yPSQaGhIrPU83ODg3nTgXISEXPAACACsAKwHVAdUABQANAAAlNyc1IxUmMhYUBiImNAFaEWAgQ7B9fbB9phw6b4DqfbB9fbAAAQAVAAAB6wIAACwAAAERFAYjIyInJzYzNjMyFxc1NDYyFhUVMzU0NjIWFRUzNTQ2MhYVFTM1NDYyFgHrMyObJBmoGwEHCgcGXBMaExUSHBIVExoTFhMaEwGL/sojMhmrGgYDNP4NExMNlssOEhIOy7YNExMNtnYNExMAAQBAAEABwAHAACgAACUyNxcGIyImJyM1MyY0NyM1MzY2MzIWFwcmIyIGBzMVIwYUFzMVIxYWAUAzJyY2Sj5jFEtBAQFBSxRjPh5MFiYnMyNIEHuJAgKJexBIdSImMUg4KwYeBis4SB0UJiIsHysODg4rHywABAAVABUB6wHrABcAHAA3AEsAACURNCYjIxczNTMVMxUjBgcXBycXBzMyNicXFzY3BzI2NTQnIxUzBgYjIiY1NDYzMhc3JiMiBhQWATIWFREUBiMjJyMiJjURNDYzMxcByw0JwRwfG2EpDyRGFEIWKooJDbIREh8N0y45A2Q/AiAdHCcnHBsRHB8pLD4+AVcRGhoRwBWrERoaEZUWSwEqCQ1RGxscMihGE0JCNg3cMhgiKHM6LgEVJRMgKBwdKBEbHD9YPgEAGhH+wBEaQBoRAUARGkAABAAAAAAB5QHlAAcAEQAYAC0AADYyFhQGIiY0NychMhYVFAcHBgczJyMHBxQFBycGIyImNTQ3JyMiJjU0NzcnJzeEIhoaIhnhwAEfCQwDTAzHbCsyEwEBTBs9DRURGhIenxEZBR0vXhuAGiIZGSKFwA0JAQmKFisrIwMFpRs9EhkRFg0dGhEKCjVjXhsAAgBVACsBqwHVABgAJgAAJTI2NCYjIgcnFTMnNjYzMhYUBiMiJyMWFhMXERQGIyEiJjUTNDYzAQAsPz8sOiAbVSIIJxMfLCwfJxYlDDVMgBoR/wARGgEZEYA/WD4xHFUiEBgrPiwgHSMBVYD/ABEZGREBVhEZAAQAAAAGAdUB2wARABQAGAAgAAABMhYVERQGByczNSMnMzUjFScXMycXNSMVAwEHJyMHEScBqxEZGBGVaZQVqat+KSsrKytlAbobesBVKwHVGRH/ABEZAZUrFSspfsAraysrAQb+Rht6VQFqKwADAGsAQAGVAcAABwATAB0AAAEzFSE1MzczBxcHFzcXNyc3JwcnBxEhERQGIyMiJgFLSv7WShZqgS4tHi0tHi0tHi0tUwEAGhGqERoBqysrFb0uLR4tLR4tLh4uLrYBAP8AERoaAAIAQAArAcAB1QAHABcAAAAiJjQ2MhYUFxcGBxEjNSMVIxEmJzcWMgERIhoaIhqKCzJOKyorTjILTs4BgBoiGRkiGisOB/7rgIABFQcOKxUAAwArACsB1QHVAAcADwAVAAA2MjY0JiIGFBIyFhQGIiY0JRcHJzcXuoxlZYxlU7B9fbB9ATceq2oeTFVljGVljAEbfbB9fbAGHqtrHkwAAwBrAEABlQHAAAcACwAVAAABMxUhNTM3MwcVMzUHESERFAYjIyImAUtK/tZKFmqKqtUBABoRqhEaAasrKxWA1dXVAQD/ABEaGgACAAAAKwIAAdEABQALAAABFwEnNxc3BycHFwEBplr+tLRaWvLyWh54ARAB0Vr+tLRaWrbyWR14ARAAAQBAAZUBwAHAAAMAABMhFSFAAYD+gAHAKwABAIAAQAGAAGsAAwAANyEVIYABAP8AaysAAgArACsB1QHVAAUADQAANzcjNQczJjIWFAYiJjT1aEhqSk2wfX2wfVXQhtD6fbB9fbAAAwArACsB1QHVAAYADQAVAAA3NTM1IzUHNxUjFTMVNzYUBiImNDYywFVVS8tVVUtKfbB9fbCLNSs1S6A1KzVLLbB9fbB9AAMAVQArAZUBygAWACYALgAAJTIWFRUjNSMiJjc3IwcnNzYzMzIWDwIUBiImNDYzFSIGFBYyNjU2NDYyFhQGIgFrERkqaxcaCicvDikOCx1vGBoKJBg/WD8/LBomJjQmQBkkGBgk4BoRdWspFVchDCYaKBVOSyw+Plg/KyY0JiYa+CQZGSQZAAIAKwAVAdUB6wADABsAACURIREBMhYVERQGIyEiJjURNDYzMzUzFTM1MxUBq/6qAVYRGRkR/qoRGRkRFirWKkABFf7rAYAaEf6rERoaEQFVERorKysrAAMAQABrAcABgAADAAcACwAAEyEVIRUhFSEVIRUhQAGA/oABgP6AAYD+gAGAKyprKyoAAQBLAGsBtQGVAAkAADc3JzMyFxcHBiNLZ2fqFg1dXQ0Wa5WVEoODEgADAGsAQAGVAcAABgAQABgAACUzJwczFTMnIREUBiMjIiY1ARUhNTM3MxcBKypVVSpWqwEAGhGqERoBFf7WShZqFtVWVlXr/wARGhoRAUArKxUVAAUAKgArAdUB1QAKABUAHQAlAC8AACUyNjcmIyIGFRUWJzQ3JiMiBgcWFhcSIgYUFjI2NBYiBhQWMjY0JzIWFAYjIiY0NgEANVcTHjQdQQYoKBkPG0QPDjomFSoeHiodXyIYGCIYdlh9fVhZfX1VPTAYGRRXAVgjFgUTESY4DAETHiodHSoEGCIYGCKffbB9fbB9AAMAQABbAcABpQAGAAkAEQAAARcjESMRIwc1Byc3FQcVFxUnAYBAKyora2sq6y8v6wGlQP72AQqdUCgQZSwUahQsZQADAEAAWwG1AaUABgAJABEAADcnMxEzETM3Myc3FyMnIwcjN4BAKyorWFAoEGUsFGoULGVbQAEK/vZlayrqLy/qAAMANwBAAZ4BrgAGAAkAEQAAJSM3JzcXNycXNzcHJzcnByc3ATBaHr0fvB4jOC8qXx8TSy8f7UAevR69Hrs4aBLtHy9MFCBeAAMASQBAAbcBpgAGAAkAEQAAARUnByc3Jwc3JycXBycHFwcnAbcevR68Hro4aBLtHy9MFCBeATlaHr0evR4kODApXiAUTC4g7gADAEAAWwHAAaUABgAJABEAADcnMxEzETM3FTcXBzU3NSc1F4BAKyora2sq6y8v61tAAQr+9p1QKBBlLBRqFCxlAAMAawBAAbUBwAAGAAkAEQAAJQc1ITUhNSczJzcXIycjByM3AbVA/vYBCp1QKBBlLBRqFCxlgEArKitrayrrLy/rAAYAKwBVAdUBqwAHAA8AEwAtADUATAAAJDI2NCYiBhQGMjY0JiIGFDcHMyc3FxUUBiMjIiY1NSMVFAYjIyImNSc3NjMzMgQiBhQWMjY0NzIWFRUjNSMVMxUnByM1NyImNTU0NjMBohIMDBINnhIMDBIMHhbFFhUeCQYNBgirCQYNBggBHwUQmRD+whINDRIMgBomK8BrKyoWFhomJhqrDBIMDBIMDBIMDBJ0QEAHWHUGCgsGGhoGCwoGdVgOawwSDAwS4iYaFiuVawEsFhUmGqsaJgABAFUAqwGrAVUABgAAATUXBzUhNQFWVVX+/wEVQFVVQCoAAwAqABUB6wHWAAMAEgAgAAABNSMVBRUBNTQ2FzM2FhUVMzYWEwcnISImNTU0NjMzJzcBLFcBAP7XGBJWEhhWEhgVGyz+shIYGBIOOhsBgCwsKtwBKwcSGAEBGBIsARn+yBssGRLqEhk6GwADACsAQAHVAdUAGQAdACEAAAEyFhUVFAYjISImNTU0NjMzNTQ2MzMyFhUVBRUhNSc1IxUBqxIYGBL+qhIYGBJWGBJWEhj/AAFWgFYBgBkS6hIZGRLqEhkrEhgYEisr6uorKysABgCVAFUBawGrAAcADwAXAB8AJwAvAAAkMhYUBiImNDYyFhQGIiY0NiImNDYyFhQmMhYUBiImNBYyFhQGIiY0FhQGIiY0NjIBLyIaGiIaGiIaGiIaPCIaGiIavCIaGiIaGiIaGiIaVhoiGhoiqxoiGhoimhoiGhoiRBoiGhoiPBoiGhoiZhoiGhoigCIaGiIaAAMAQABrAcABlQADAAcACwAAEyEVIRU1IRUFNSEVQAGA/oABgP6AAYABlSpWKyuqgIAAAgBAAGsBwAGVAAUADwAANzM3JyMXFyE3JyEyFxcHBou1S0u1Smv/AGBgAQAWDV1dDZVra2uVlZUSg4MSAAUAQABrAcABlQADAAcACwAPABMAAAEzESMDMxUjFTUzFQc1MxUnNTMVARWrq9Wrq6urq6urAZX+1gEqKlYrK6oqKlUrKwAEABUAQAHqAdUABwAPACsAMQAAARYUByc2NCcHFhQHJzY0JycBBycWFhUVITU0NjMyHgMXJwYjIiY1NDcnFyczMhYVAaw+PiMsLCMgICQODucBZRs/AgP+qnU2CxsfHRwLXBUYIzINONVZBCMyAdVBqz4iM4MwJSNYICQTLRNW/psbPwUKBSsrJi8DBggMB1wNMiMYFThpWTIjAAMAQACAAcABgAADAAcACwAANzUhFSUhFSEXNSEVwAEA/oABgP6AgAEA6yoqlSvVKysAAwBAACsBqwHVABMAFwAjAAAlNDY1NCYiBhUzNDYyFhUUDgIVFzUjFQIyFhUUBgc1IyImNAELQDNGMisZIhoUGBQrKzaWa15NC0tq6xc4GyMzMyMRGhoRDhYOIRdLKysBNWpLSYclQGqWAAQAVQArAasB6wADAAcADgAVAAATIRUhNSEVISUHJzM1MxUDNxcjFSM1VQFW/qoBVv6qAQBVVUAqalVVQCoBACtrK5ZWVkBA/sBVVUBAAAEAVQBVAawBqwAWAAATJjU0NjMhMhYVFgcHFRQGIyMiJjU1JlsGDQkBKgkNAQZ7DAkrCQ14AYcHBwkNDQkGB52ACQ0NCYCZAAMAVQArAasB1QAJAA0AEQAAARUzByczNSM3FyUhFSERIRUhARVAVVVAQFVV/wABVv6qAVb+qgFAgFVVgFVVlSr+qioAAwArAEABwAHAAAgAEwAXAAATNwEHJwcjNTclFhUUBwcnNzYyFwc3FwcrKgFWK3h4UHgBAgYGJ1AnBhIGhSlQKQGVK/6rK3h4UHiABgkIBihQJwYGZylQKQAFAEAAQAHAAcAACgAaACAAMAA0AAAlJzcjBzUjFTM1Fyc1NCYjIyIGFRUUFjMzMjYHNSMVMxUBMhYVERQGIyEiJjURNDYzFzMVIwGgMDAlJiAgJmYMCTUJDQ0JNQkMdUAgARURGhoR/tYRGhoRaiAgwEBAMDCAMDAVVgkMDAlWCQwMDIAgYAEAGhH+1hEaGhEBKhEaoEAABwBAAEABwAHAAAMAEwAZACUANwBHAEsAACUzFSM3NTQmIyMiBhUVFBYzMzI2JyMVMxUzFzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MxczFSMBSyAgFQwJNgkMDAk2CQyLQCAgdioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEbUgINUga1UJDQ0JVQkMDHQgYJYgDQk1CQyAawkMDAlrYEBAYAFLGhH+1hEaGhEBKhEaVUAABgBAAEABwAHAAAMACQAPABsALQA9AAAlMxUjNyMVMxUzJyMVMxUzFzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAKQCAgakAgIGAqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVINYgYIAgYJYgDQk1CQyAawkMDAlrYEBAYAFLGhH+1hEaGhEBKhEaAAYAQABAAcABwAADABgAHgAqADwATAAAJTMVIzUyNjU1NCYjIxUzFSMiBhUVMzUjNScjFTMVMxc1MzI2NTU0JiMjFSM1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMBSyAgCQwMCUtAKwkMYEBLQCAgdioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEdUgiwwJIAkNIBYMCTUgFUsgYJYgDQk1CQyAawkMDAlrYEBAYAFLGhH+1hEaGhEBKhEaAAYAQABAAcABwAADABUAGwAnADkASQAAJTMVIzc1NCYjIxUzFSMVMxUjFTMyNicjFTMVMxc1MzI2NTU0JiMjFSM1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMBSyAgFQwJS0ArK0BLCQyLQCAgdioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEdUga1UJDSAWFRUgDHQgYJYgDQk1CQyAawkMDAlrYEBAYAFLGhH+1hEaGhEBKhEaAAYAQABAAcABwAADABEAFwAjADUARQAAJTMVIzc1IzUjFSM1IxUzFTM1JyMVMxUzFzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAqFSAgIEAgi0AgIHYqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVIHYgQEBAYCAgYCBgliANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABgBAAEABwAHAAAMAFQAbACcAOQBJAAAlMxUjNzUjFTMVIxUzMjY1NTQmIyM1JyMVMxUzFzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAVYEBASwkMDAkrS0AgIHYqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVILYgSxUgDAkgCQwWICBgliANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABwBAAEABwAHAAAMAFwAdACkAOwBLAE8AACUzFSMnMzI2NTU0JiMjNTM1IyIGFRUUFicjFTMVMxc1MzI2NTU0JiMjFSM1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMXMxUjAUsgIDY2CQwMCStASwkMDDdAICB2KgkNDQlKKwwJYAkNIBYgFbURGhoR/tYRGhoRtSAg1SBWDAkgCQwWIA0JVQkMgCBgliANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERqAIAAGAEAAQAHAAcAAAwAMABIAHgAwAEAAACUzFSMnNzYmIyMVMwcnIxUzFTMXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgIAsfAwwLSzgdRkAgIHYqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVIFZkCxEgYIAgYJYgDQk1CQyAawkMDAlrYEBAYAFLGhH+1hEaGhEBKhEaAAgAQABAAcABwAADAAcACwAbACEALQA/AE8AACUzFSMnNTMVBzUzFTM1NCYjIyIGFRUUFjMzMjYnIxUzFTMXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgICsgICAgDAk2CQwMCTYJDItAICB2KgkNDQlKKwwJYAkNIBYgFbURGhoR/tYRGhoR1SCgICA1ICBVCQ0NCVUJDAx0IGCWIA0JNQkMgGsJDAwJa2BAQGABSxoR/tYRGhoRASoRGgAHAEAAQAHAAcAAAwAPACEAJwArAD8ATwAAJTMVIxc1NCYjIxUzNTMyNgc1NCYjIyIGFRUzNTMVMzUzFQMVMxUzNRc1MxUHFTMyNjU1NCYjIyIGFRUUFjMzFTcyFhURFAYjISImNRE0NjMBSyAgQA0JSiAqCQ2LDAlgCQ0gFiAVSyAgSyBASwkMDAk2CQwMCStVERoaEf7WERoaEdUgCjUJDIAgDS1rCQwMCWtgQEBgARYgYIA2ICAqIAwJVQkNDQkgCQwVlRoR/tYRGhoRASoRGgADAEAAQAHAAcAACgAQACAAACUnNyMHNSMVMzUXIzUjFTMVEzIWFREUBiMhIiY1ETQ2MwF1MDAlJSAgJXBAINURGhoR/tYRGhoRwEBAMDCAMDCAIGABABoR/tYRGhoRASoRGgAEAEAAQAHAAcAACwAWABwALAAAJTUjNSMVIxUzFTM1BzMnNyMHNSMVMzUHNSMVMxUTMhYVERQGIyEiJjURNDYzAaAgFSAgFVsmMDAmJSAgQEAg9REaGhH+1hEaGhH1FiAgFiAgNUBAMDCAMDCAIGABABoR/tYRGhoRASoRGgAHAEAAQAHAAcAAAwAYACgANABGAFYAWgAAJTMVIycyNjU1NCYjIxUzFSMiBhUVMzUjNRc1NCYjIyIGFRUUFjMzMjYHNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzFzMVIwFLICB2CQ0NCUpAKwkMYEDKDAk1CQ0NCTUJDCoqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHKICDVIIsMCSAJDSAWDAk1IBUgVQkNDQlVCQwMoiANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERpVQAAGAEAAQAHAAcAAAwAJAB4AKgA8AEwAACUzFSMnFTMVMzUHMjY1NTQmIyMVMxUjIgYVFTM1IzUXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgICAgIIAJDAwJS0ArCQxgQIsqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVINYgYIBLDAkgCQ0gFgwJNSAVyyANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABgBAAEABwAHAAAMAGAAtADkASwBbAAAlMxUjNzI2NTU0JiMjFTMVIyIGFRUzNSM1IzI2NTU0JiMjFTMVIyIGFRUzNSM1FzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAVCQwMCUtAKgkNYEBgCQ0NCUpAKwkMYECgKgkNDQlKKwwJYAkNIBYgFbURGhoR/tYRGhoR1SCLDAkgCQ0gFgwJNSAVDAkgCQ0gFgwJNSAVyyANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABgBAAEABwAHAAAMAFQAqADYASABYAAAlMxUjNzU0JiMjFTMVIxUzFSMVMzI2JzI2NTU0JiMjFTMVIyIGFRUzNSM1FzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAqDAlLQCoqQEsJDKAJDQ0JSkArCQxgQKAqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVIGtVCQ0gFhUVIAwpDAkgCQ0gFgwJNSAVyyANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABgBAAEABwAHAAAMAEQAmADIARABUAAAlMxUjNzUjNSMVIzUjFTMVMzUnMjY1NTQmIyMVMxUjIgYVFTM1IzUXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgIEAWICAgQCCgCQ0NCUpAKwkMYECgKgkNDQlKKwwJYAkNIBYgFbURGhoR/tYRGhoR1SB2IEBAQGAgIBUMCSAJDSAWDAk1IBXLIA0JNQkMgGsJDAwJa2BAQGABSxoR/tYRGhoRASoRGgADAEAAQAHAAcAACgAfAC8AACUnNyMHNSMVMzUXJzI2NTU0JiMjFTMVIyIGFRUzNSM1NzIWFREUBiMhIiY1ETQ2MwGAMDAlJiAgJoYJDQ0JSkArCQxgQOoRGhoR/tYRGhoRwEBAMDCAMDA1DQkgCQwgFQ0JNSAVyxoR/tYRGhoRASoRGgAEAEAAQAHAAcAACwAWACsAOwAAJTUjNSMVIxUzFTM1BzMnNyMHNSMVMzUnNTQmIyMVMxUjIgYVFTM1IzUzMjY3MhYVERQGIyEiJjURNDYzAasgFiAgFlslMDAlJSAgQA0JSkArCQxgQCoJDcoRGhoR/tYRGhoR9RYgIBYgIDVAQDAwgDAbIAkMIBUNCTUgFQ2+GhH+1hEaGhEBKhEaAAUAQABAAcABwAADABgAJAA2AEYAACUzFSMnMjY1NTQmIyMVMxUjIgYVFTM1IzUXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgICsJDAwJS0AqCQ1gQFYqCQ0NCUorDAlgCQ0gFiAVtREaGhH+1hEaGhHVIIsMCSAJDSAWDAk1IBXLIA0JNQkMgGsJDAwJa2BAQGABSxoR/tYRGhoRASoRGgADAEAAQAHAAcAACgAcACwAACUnNyMHNSMVMzUXJzU0JiMjFTMVIxUzFSMVMzI2NzIWFREUBiMhIiY1ETQ2MwGAMDAlJiAgJnANCUpAKytASgkNqhEaGhH+1hEaGhHAQEAwMIAwMBVWCQwgFRYVIAz0GhH+1hEaGhEBKhEaAAQAQABAAcABwAALABYAKAA4AAAlNSM1IxUjFTMVMzUHJzcjBzUjFTM1Fyc1NCYjIxUzFSMVMxUjFTMyNjcyFhURFAYjISImNRE0NjMBqyAWICAWNjAwJSUgICVlDQlKQCsrQEoJDcoRGhoR/tYRGhoR9RYgIBYgIDVAQDAwgDAwFVYJDCAVFhUgDPQaEf7WERoaEQEqERoABQBAAEABwAHAAAMAFQAhADMAQwAAJTMVIyc1NCYjIxUzFSMVMxUjFTMyNhc1MzI2NTU0JiMjFSM1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMBSyAgFgwJS0AqKkBLCQwWKgkNDQlKKwwJYAkNIBYgFbURGhoR/tYRGhoR1SBrVQkNIBYVFSAMoiANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABABAAEABwAHAAAsAFgAkADQAACU1IzUjFSMVMxUzNQcnNyMHNSMVMzUXJzUjNSMVIzUjFTMVMzU3MhYVERQGIyEiJjURNDYzAasgFiAgFjYwMCUlICAlUBUgICBAIMoRGhoR/tYRGhoR9RYgIBYgIDVAQDAwgDAwICBAQEBgICDgGhH+1hEaGhEBKhEaAAUAQABAAcABwAADAA8AHQAvAD8AACUzFSMVNTMyNjU1NCYjIxU3NSM1IxUjNSMVMxUzNQc1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMBSyAgKgkNDQlKFRUgICBAICsMCWAJDSAWIBW1ERoaEf7WERoaEdUgQCANCTUJDIC2IEBAQGAgILZrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoAAwBAAEABwAHAAAoAHAAsAAAlJzcjBzUjFTM1Fyc1IxUzFSMVMzI2NTU0JiMjNTcyFhURFAYjISImNRE0NjMBgDAwJSYgICZwYEBASgkNDQkq6hEaGhH+1hEaGhHAQEAwMIAwMGAgSxUgDAkgCQ0VoBoR/tYRGhoRASoRGgAEAEAAQAHAAcAACwAWACgAOAAAJTUjNSMVIxUzFTM1Byc3Iwc1IxUzNRcnNSMVMxUjFTMyNjU1NCYjIzUlMhYVERQGIyEiJjURNDYzAasgFiAgFjYwMCUlICAlZWBAQEoJDQ0JKgEKERoaEf7WERoaEfUWICAWICA1QEAwMIAwMGAgSxUgDAkgCQ0VoBoR/tYRGhoRASoRGgAFAEAAQAHAAcAAAwAVACEAMwBDAAAlMxUjJzUjFTMVIxUzMjY1NTQmIyM1FzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MwFLICAWYEBASwkMDAkrVioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEdUgtiBLFSAMCSAJDBb2IA0JNQkMgGsJDAwJa2BAQGABSxoR/tYRGhoRASoRGgAEAEAAQAHAAcAACgAeAC4AMgAAJSc3Iwc1IxUzNRcnNSMiBhUVFBYzMzI2NTU0JiMjNTcyFhURFAYjISImNRE0NjMXMxUjAYAwMCUmICAmcEsJDAwJNQkNDQkq6hEaGhH+1hEaGhFAICDAQEAwMIAwMGAgDAlWCQwMCSAJDRWgGhH+1hEaGhEBKhEayyAABQBAAEABwAHAAAsAFgAqADoAPgAAJTUjNSMVIxUzFTM1Byc3Iwc1IxUzNRcnNSMiBhUVFBYzMzI2NTU0JiMjNSUyFhURFAYjISImNRE0NjMXMxUjAasgFiAgFjYwMCUlICAlZUsJDAwJNQkNDQkqAQoRGhoR/tYRGhoRICAg9RYgIBYgIDVAQDAwgDAwYCAMCVYJDAwJIAkNFaAaEf7WERoaEQEqERrLIAAGAEAAQAHAAcAAAwAPACMANQBFAEkAACUzFSMVNTMyNjU1NCYjIxUnMzI2NTU0JiMjNTM1IyIGFRUUFhc1NCYjIyIGFRUzNTMVMzUzFRMyFhURFAYjISImNRE0NjMXMxUjAUsgICoJDQ0JSkA1CQwMCStASgkNDR4MCWAJDSAWIBW1ERoaEf7WERoaEYogINUgQCANCTUJDICWDAkgCQwWIA0JVQkMlmsJDAwJa2BAQGABSxoR/tYRGhoRASoRGoAgAAMAQABAAcABwAAKABMAIwAAJSc3Iwc1IxUzNRcjNzYmIyMVMwcTMhYVERQGIyEiJjURNDYzAYAwMCUmICAmkB8DDQtKOB7wERoaEf7WERoaEcBAQDAwgDAwZAoSIGABABoR/tYRGhoRASoRGgAEAEAAQAHAAcAACwAWAB8ALwAAJTUjNSMVIxUzFTM1Byc3Iwc1IxUzNRcjNzYmIyMVMwcBMhYVERQGIyEiJjURNDYzAasgFiAgFjYwMCUlICAlhR8DDQtKOB4BEBEaGhH+1hEaGhH1FiAgFiAgNUBAMDCAMDBkChIgYAEAGhH+1hEaGhEBKhEaAAUAQABAAcABwAADAAwAGAAqADoAACUzFSMnNzYmIyMVMwcXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzAUsgIDYfAw0KSzgdWyoJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEdUgVmQLESBgliANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERoABQBAAEABwAHAAAoAGgAqAC4AMgAAJSc3Iwc1IxUzNRcnNTQmIyMiBhUVFBYzMzI2NzIWFREUBiMhIiY1ETQ2MxczFSMVMxUjAYAwMCUmICAmcA0JNQkMDAk1CQ2qERoaEf7WERoaEUAgICAgwEBAMDCAMDAVVgkMDAlWCQwM9BoR/tYRGhoRASoRGpUgFiAABgBAAEABwAHAAAsAFgAmADYAOgA+AAAlNSM1IxUjFTMVMzUHJzcjBzUjFTM1Fyc1NCYjIyIGFRUUFjMzMjY3MhYVERQGIyEiJjURNDYzFzMVIxUzFSMBqyAWICAWNjAwJSUgICVlDQk1CQwMCTUJDcoRGhoR/tYRGhoRICAgICD1FiAgFiAgNUBAMDCAMDAVVgkMDAlWCQwM9BoR/tYRGhoRASoRGpUgFiAABwBAAEABwAHAAAMAEwAfADEAQQBFAEkAACUzFSMnNTQmIyMiBhUVFBYzMzI2FzUzMjY1NTQmIyMVIzU0JiMjIgYVFTM1MxUzNTMVEzIWFREUBiMhIiY1ETQ2MxczFSMVMxUjAUsgIBYMCTUJDQ0JNQkMFioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEYogICAg1SBrVQkNDQlVCQwMoiANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERpLIBUgAAQAQABAAcABwAAKAB4ALgAyAAAlJzcjBzUjFTM1Fyc1NCYjIyIGFRUUFjMzFSMVMzI2NzIWFREUBiMhIiY1ETQ2MxczFSMBgDAwJSYgICZwDQk1CQwMCStASgkNqhEaGhH+1hEaGhFAICDAQEAwMIAwMBVWCQwMCSAJDRUgDPQaEf7WERoaEQEqERqVIAAFAEAAQAHAAcAACwAWACoAOgA+AAAlNSM1IxUjFTMVMzUHJzcjBzUjFTM1Fyc1NCYjIyIGFRUUFjMzFSMVMzI2NzIWFREUBiMhIiY1ETQ2MxczFSMBqyAWICAWNjAwJSUgICVlDQk1CQwMCStASgkNyhEaGhH+1hEaGhEgICD1FiAgFiAgNUBAMDCAMDAVVgkMDAkgCQ0VIAz0GhH+1hEaGhEBKhEalSAABgBAAEABwAHAAAMAFwAjADUARQBJAAAlMxUjJzU0JiMjIgYVFRQWMzMVIxUzMjYXNTMyNjU1NCYjIxUjNTQmIyMiBhUVMzUzFTM1MxUTMhYVERQGIyEiJjURNDYzFzMVIwFLICAWDAk1CQ0NCSpASwkMFioJDQ0JSisMCWAJDSAWIBW1ERoaEf7WERoaEYogINUga1UJDQ0JIAkMFSAMoiANCTUJDIBrCQwMCWtgQEBgAUsaEf7WERoaEQEqERpLIAABACsAQAHVAcAAFQAAASM1IxUzNTMVIzUjNSMVIzUzFTM1MwHVlSsrlZVVK5WVgJUBFUCqQKtA1UCrQEAABQArAEAB6wHAABIAHgAiACYAKgAAJTUzFRQGIyEiJjURNDYzIRUhEQEzFSMVIzUjNTM1MwczFSM3MxUjJzMVIwGAKxoR/tURGRkRAQD/AAFWQEArQEAr1isrVioqqysra2pqERoaEQEqERor/tYBACtAQCtAQNVVVZWVAAIAQABAAcABwAALAC4AAAEVIxUjNSM1MzUzFRcyFhUVFAYjIiY1NDYzMzIWFRUUFxYVFAcHFhYXNzYzMhcWAcBAK0BAKysJDAwJltUMCUsJDAwCBy8UUyYvBwgDBCQBgCtAQCtAQMsMCUsJDNWWCQwMCQEpIgQDCgUvJlMULwcCDAACAEAAFQHVAesACwAeAAAlFSM1IzUzNTMVMxUHBgYHJiY1NTcXFRQHJiMiBhUUAZUqQEAqQLsCEwVSbsDABhogNUtVPz8rQEArNwEHARSTWYBWVoAbHQ5LNSsAAwBAAEABwAHAAA8AGQApAAAlMxUUBiMhIiY1NTMUFjI2NzUhFTMUFjI2NTcyFhUVFAYjISImNTU0NjMBQIAaEf7WERqAJjQmVf7WVSY0JlURGhoR/tYRGhoRq0ARGhoRQBomJq9VVRomJhqAGhGVERoaEZURGgADAFUAKwGrAdUABgAYABwAAAE3NCYiBhU2MhYVBzMyFhUVITU0NjMzJzQTNSEVAQBAJjQmFFg/a4ARGv6qGhGAa+v/AAEVVhomJhpqPiyWGRGAgBEZliz+6SsrAAMAFQAVAesB6wAMABQAHwAAJTYnJyYHBwYUFxcWNwIyFhQGIiY0BTUXBzUjFSM1NDMBpQ4OmQ0MmQUFmQ0MbsSJicSJARZKSlYqFfQOCpoKCpoICAiaCgoBkYnEiYnENzVLSjVAVRYAAgAVABUB6wHrAAUADQAAJTcnBxc3AjIWFAYiJjQBTAhUVAhMYcKKisKKlQjOzggiATSKwoqKwgACAEAAFQHAAesACgAYAAABMhYVEScHETQ2MwERNCYjIzQ2MzMyFhURAUARGpaVGhEBKhkR1hoR1REaAZUZEf6qQEABVhEZ/usBFREaERoaEf6rAAcAFQAVAeoB6wADAAcADwAXABwAJABMAAABMxUjNTMVIwYyNjQmIgYUFjI2NCYiBhQnMyY1IxYyNjQmIgYUARYWFRQGBxUUBxUUBiMjIiY1NSMVFAYjIyImNTUmNTU0NjMyFjM2NgFAKysrKxdYPz9YPiccEhIcEsCtLYASHBISHBIBFT5XSjUWDAkVCQ2qDQkVCQwWWFMEEgQTRwErK6trVT5YPz9YvhMaExMabSs/6hMaExMaAW0BWD01VghCGxQmCQ0NCRUVCQ0NCSYUG9UzIwEbJgADACsAKwHVAdUACgAUACQAABMVIRQGIyEiJjU1JTU0JiMjIgYVFSM1NzMXFTMVFAYjISImNTVVAVYZEv7VEhgBKg4HQAgNKytqK1UZEf7qERkBQOsSGBgS60AVCA4NCRUrKior6xEZGRHrAAMAKwArAdUB1QATABkAIQAAJTUnNTQmJzU0JiIGFRUGBhUVBxUWMjY1IxQCMhYUBiImNAFrFiEfDBIMHyEWXRwSQDiwfX2wfasVFTgjNAcKCQ0NCQoHNCM4FRU2Ew0NAU19sH19sAAEAEAAVQHAAasAFwAvAD8AQwAAASM1IxUzNTMVFAYjIyImNTU0NjMzMhYVByM1IxUzNTMVFAYjIyImNTU0NjMzMhYVNzIWFREUBiMhIiY1ETQ2MwUhESEBgCArKyAMCUAJDQ0JQAkMlSArKyANCUAJDAwJQAkNqhEaGhH+1hIZGRIBNf7AAUABFQtACxYJDAwJVgkMDAkWC0ALFgkMDAlWCQwMCYAaEf8AERoZEgEAEhkg/uoABQAVAEAB6wHAAAcADwAUABgALAAAEzIWFSM0JiMVMhYVIzQmIxUyFhUjBREhEQEyFhUDFAYjIxUjNSMiJjURNDYzVUdkH1I6LD8fLR8SGSsBa/6AAYARGgEZEWuqaxEaGhEBVWRGOlEhPiwfLSIYEhYBAP8AASsaEf8AERkrKxkRAQARGgACAEAAQAHAAcAACwATAAAlJzcnBycHFwcXNxcRFxUHIyc1NwFrUFAbUFAbUFAbUFBwcKBwcLBQUBtQUBtQUBtQUAErcKBwcKBwAAQAQABAAcABwAALAA8AEwAXAAAlFTMVIxUjNSM1MzUhMxUjEzMVIyczFSMBgEBAK0BA/uurq9Wrq9Wrq+tAK0BAK0CrAYCrq6sAAwAAAAAB6wHaAAIAFwAlAAA3MycXIxUzFSM1MzUjIiY1NSc3FgQXFwc3FAYjIyczESEnITIWFUDT0/4TKqoqlREaFRoYAQBDZRwtGhEVK0D+wCsBaxEaq9P+KyoqKxoR/hUcGP5EZByrERorAQAqGREAAwArAFUB1QGrAAgADAAeAAABFwcnNyM1MycXNSEVATIWFRUUBiMhIiY1ETQ2MzMXAQBVVR8iWFghyf6qAVYRGRkR/qoRGRkRgCsBQFZVHiIrIqLV1QEAGhHVERoaEQEAERorAAMAQABVAcABqwADAAgAFAAANzczFSU3JwcVARYUBwcjNTY3NjIX61WA/sS5GrgBHg0N81bwAw0iDVVWViu5GrkaAQQNIg3zV+8DDQ0AAwArAFUB1QGrAAgADAAeAAA3NxcHJxUjNQcXNSEVATIWFRUUBiMhIiY1ETQ2MzMXq1VVHiIqIuL+qgFWERkZEf6qERkZEYAr6lZWHiJZWSJM1dUBABoR1REaGhEBABEaKwACACsAKwHVAdUABwAVAAAlNQc1IxUzNTcyFhUVFAYjIiY1NDYzAWtAlpaAERmAW1R7fFnAgCsrgCvqGRGrWnt8U1uAAAMAFQAVAdUB1QA3ADoAcAAAARQHFRQHFAcHBhQHBgYHBgcGBgcGBgcnNwcnNjY3MjcyNT4FMzY2PwIyNzYzMzI2MzIWBTcnJwEHJwYGByIHIhUGBwcGIgcHBiMGIyMGIyImNTQ3NTQ3NjU3NzY2NzQ2Nzc2NjcyNjU2NjcnAdUBAgIDAQEBBAECBQEFAgEFAW9Aim8BCAEBAgICBAMFBAYCAgoDBwwDBAgFBwMMBFh9/quKSpABoBw7AQgBAQICChAPAQUBDAQDCAUGBg5YfQECAgMCAQMBBAEDAQMBAQIBBQE8AQAOBgYFCAMEDAEFAQMLAgYKAgoCAQgBb4pAbwEFAQIBAQICAgIEAQMBAgMCAgF92EBKxv5gGzwBBQECAQcFBgEBAwICAX1YDQYHBQgEAwwHAwoCAQcCBgIGAgQBAQgBOwACAGsAVQGVAasABQAJAAA3JzcXNxcBIRUhzWIqOJ4q/tYBKv7WumIoN54r/wArAAUAKwBAAdUBwAADAAcACwAPAB8AADcHIzcXByM3NwcjNzcHIzc3IQcjNyMDMwcjNzMTIwcjvweAB3IGgAZtB1UGYwdVBmwBFBc5DzcqMgmbCTIrNw846ysrVioqqysrVSoqK5dg/u43NwESYAAIAEAAQAHAAcAAAwAHAAsADwATABcAGwAfAAAlNSMVJzMVIxM1IxUnMxUjBzUjFSczFSMTNSMVJzMVIwGVVSurq4BVK6urVVUrq6uAVSurq2tVVYCrAQBVVYCrqlVVgKsBAFVVgKsAAwBVACsBlQHVAAMAHwAnAAA3MxUjAQYGBxEjNSMVIzUGBwYVFSM1NDc2MzI3NjU1MwYiJjQ2MhYUVUBAAUACLCcrKisJBB4qLS07LR0hKoQiGhoiGquAAaIvRQz+3oCA/gMEGCoLCz8nJRYbLwpVGiIZGSIAAQBVAEABqwHAAAgAAAEXESM1IxUjEQEAq2uAawHAgP8AlZUBAAABACsAFQGrAdUAEQAAARUhNSMVMxUzFSM1MzUjNTM1Aav+1SvWKoAr1VUB1YArVWurq0CrKgACACsAQAHVAcUABQARAAABByczMhclAQcnISImNTU0NycB1UDqqhYN/s0Baxsq/wARGg0iAQBV6hJC/pYbKxkR1hENIgADACsAKwHVAdUACAAOAB4AABMRIRUhIiY1ERc3JwcnBzcyFhURFAYjISImNRE0NjNVASv+1REZ34sebSwe6xEZGRH/ABEaGhEBgP7VKhkRASurjR5uLB61GRH/ABEaGhEBABEZAAIAKwBAAdUBwAANABYAABMRMxUjIiY1ETQ2MzMVFxcHJzcjNTMnVaurERkZEatramoeN9nZNwGV/tYrGhEBKhEaKyprax83KjgACABAAEABwAHAAAMABwALAA8AEwAXABsAHwAAATMVIyczFSMnMxUjNzMVIyczFSM3MxUjFxEhEQMhESEBQCsrVSoqVisrqysrqysrVioqqv7WKwGA/oABFSoqKioqgCsrKysr1QEq/tYBVf6AAAMAKwBAAdUB1QAFABUAIwAAATUHJxUXNzIWFRUUBiMhIiY1NTQ2MzcjJwcVIiY1NTQ3NxcWAauLi4uLERkZEf7qERkZEf04eosRGRGkoQ8BByRHRyRHlRkRwBEaGhHAERkWQEnNGhGnFQhRUQkABABAAIABwAGAAAUACQANABEAACUHJzcXByUhFSEVNTMVBzUhFQHAHmtrHkz+zAEV/uvV1QEVsx5rax5NgCtqKiprKysABABAAEABwAHAAAMADwAhADEAAAE1MxUHNTMyNjU1NCYjIxUnIgYVFTM1MxUzNTMVMzU0JiM3MhYVERQGIyEiJjURNDYzAUsgICoJDQ0JSqAJDSAWIBUgDAmqERoaEf7WERoaEQEAICBAIAwJNgkMgIAMCWtgQEBgawkMgBoR/tYRGhoRASoRGgAEAFUAFQGrAesAAwATABwAJAAAJREjERMyFhURFAYjIyImNRE0NjMTIyImNREzETMnIgc2NzUXBwGAq6sRGhoRqxEZGRGA1REaK9UeMhoOPjQ0qwEA/wABQBoR/tURGRkRASsRGv4qGhEBVf6r5SNCCRwwMAAFAEAAQAHAAcAAAwAHAAsADwATAAABMxUjJzMVIzczFSMXESERAyERIQFAKyurKytWKiqq/tYrAYD+gAFrKysrKyvVASr+1gFV/oAABAAVACcB6wHVABwAIgApADYAAAEWFRUUBwYHDgImJicmJyY1NTQ3Njc2NjIWFxYnIgc2FyYDMjY3BicWJTUmBxUyFjMWMzI3NgHgCwsKFBZQXFxQFgoUCwsUChlpgGkZCsxVNIiKNlMjUBaLhzUBHcrIAQQBTnVMNTIBSgYLcgwGBQgxPxERPzEECgUMcgwFCgQ4RUU4BFtDKChD/qInHCgoQ39gX19gAicQDgADABUAJwHrAdUABgAMACkAADcWFjI2NwYTIgc2FyYXFhUVFAcGBw4CJiYnJicmNTU0NzY3NjYyFhcWdxZQRlAWiwJVNIiKNo0LCwoUFlBcXFAWChQLCxQKGWmAaRkKlBwnJxwoAUNDKChDZQYLcgwGBQgxPxERPzEECgUMcgwFCgQ4RUU4BAAEAAAAAAHrAdoAAgAVABsAIwAAEzMnFycjFSM1IzUzNSc3AQcnIzU0NgUjJxYWFSQ0NjIWFAYigBMTczU+K0BAVRoBwBxV1DYBIBSANGD/ADJGMjJGASsTczVAQCs+VRz+QhxVKxkna34DLiKyRjMzRjIAAgAeAB4BxAHEABYAJwAAJQYGIyMiJjU1NDYzMjc2Fxc2Nyc3AQcnJzY3JyY3NjU0NjMzMhYVFAE2KIQ0AQkMDAkoJAwKLx4T+R4BiB40HxMNLwkEDAwJSwkMjiAuDAlLCQwMBQovEA/6Hv54HqweGRkvCQ0kKAkMDAl+AAEAQABAAcABwAAcAAABJyY3NjU0NjMzMhYVFAYjIiY1NTQ2MzI3NhcXNgFzLwkEDAwJSwkM1ZYJDAwJKCQMCi9dARovCQ0kKAkMDAmW1QwJSwkMDAUKLzAABABAACsB1QHAABEAGAAfACYAACU3FyMVFAYjIxUnNxUzMjY1NQUiJjU1MxUDNTQ2MzMVMzUzMhYVFQErVVVANyciVVUiFR//ABEaa2saEUAqwBEa61VVIic3QFVVQB4WIqsaEcDrARVAERpraxoRQAAFAAAAAAHaAdoAAwAPABQAHAAjAAA3MycjAwEHJyM1IzU0NjcnBSMnNSEWIgYUFjI2NAcnMzIWFRWrqGo+kQHAHED+VSQaaQGAwEABAB4SDAwSDRTCwBsla2oBBf5CHEBVgBklAmtVPheVDRIMDBKJwCUbgAAGABUAFQHrAesAAwAHACUALQA0AFAAAAEzFSM1MxUjBxYXMxczNzY2NTQmJyIPAhUGDwQGFRQWFxYGMjY0JiIGFCczJjU0NyMFFAYHFRQGIxcVITU3IiY1NTQ2MzMyFzY2MzIWAUArKysrBwQFBgMVBCY1PiwcGgEKDQUIBAIDBiseAYYiGhoiGlWbGwKCAatLNSwfIP8AIB8sWFMEHQsSPhk9WQErK6trUQIBAQEGPiUrPwEPAQYBCgcMCAMIEhIgOwoBhBkiGhoifCYvCgwWNVcHNx8sIAsLICwf4DMiARIaWQACACsAKwHVAdUAFgAeAAABNTQmIyM3NTQnJwcGFRUUFjMzMjc3NiYyFhQGIiY0AYAMCWIMBg9iCRkSdxIILQLYsH19sH0BAxIJDUgFCQYOagcNbBIZEWkE132wfX2wAAQACAA8AfgBxAADAAcAEQAVAAABByc3Fyc3FyU3AQcnByc3FzcnFwcnAX9OHk4NH4se/jgeAWoeaTl3Hlkb7XcedwFrTh5OqR+KHjse/pYeaDl3HlkbPncedwACABUAFQHbAesABgAVAAAlJyc3FxUUFwcnBgcmJjU1JzceAhcBpLlgdcAbG0k0Q1JuKxs0do4onrlgNFaAPagbSTkQFJNZgCsbNHaNKAADABUAFQHrAesACAARACEAACUjNQcXNSE1IyczFTcnFSEVMyUyFhURFAYjISImNRE0NjMBa9ZVVQEAKtbWVVX/ACoBKxEaGhH+gBEaGhGVQFVVQICAQFVVQIDWGhH+gBEaGhEBgBEaAAQAFQAVAesB6wAGAA8AGAAoAAAlNSMHFTMVFyM1Bxc1ITUjJzMVNycVIRUzJTIWFREUBiMhIiY1ETQ2MwEVFSsgdtZVVQEAKtbWVVX/ACoBKxEaGhH+gBEaGhHAgBUWVStAVVVAgIBAVVVAgNYaEf6AERoaEQGAERoAAgArACsB1QHVABIAGgAAJDQmIzUHFzUyFhQGIiY1IxQWMgIyFhQGIiY0AYBLNVVVIzIyRjIrS2qNsH19sH3LaktAVVZAMkYyMiM1SwFVfbB9fbAAAQAVAEAB6wHAACQAAAEyFhUjFRQGIyMVIzUjIiY1ETQ2MyEyFhUVIzUhESE1IxUnNxUBwBEaARkRa6prERoaEQGAERor/oABgKtVVQErGhFrERkrKxkRAQARGhoRQED/AGtAVVZAAAQAQABAAcABwAADACUALwA/AAAlNTMVIyM1IxUUFjMzMjY1NTQmIyM1MxUzNTQmIyMiBhUVFBYzMzcVMzI2NTU0JiM3MhYVERQGIyEiJjURNDYzATUrlSsgDAlACQ0NCTUrIA0JQAkMDAk2SlYJDAwJKhEaGhH+1hIZGRLgQEALFgkMDAkgCQ0VCxYJDAwJIAkNS4AMCVYJDIAaEf7WERoZEgEqEhkAAQBAABUBwAHrAAkAAAEXFRQGByYmNTUBAMBuUlJuAetWgFmTFBSTWYAABAAVABUB6wHrAAYADQARACEAACUHFwczNQcDFwEXARc1BycHFzcyFhURFAYjISImNRE0NjMBPB5DLHYsSiz+9B4BDCzJbx5v/BEaGhH+gBEaGhHiHkMsdiwBDCz+9B4BDCx2b28eb80aEf6AERoaEQGAERoAAgArAFUB1QGqAAgAKAAAJSImNTQ3NwcGNxYWFRQHBiMhIicmNTQ2MzIWFwcmIyIGFRQXITY1NCcBABEaDbV5DKEOFBwNGP7YGA0cfVkaQxYoJChGZRcBKBcSqxkREg14tQyeFkQaOy8WFi87WH0UDhoSZEcuJycuKSQABgCAAFUBlQGVAAMABwALAA8AEwAXAAATMxUjFTMVIzczFSMVMxUjAzMVIxUzFSPrQEBAQGpAQEBA1UBAQEABQEAVlpYrFVYBQFUV1gAIACsAKwHVAdUABwAOABYAGgAhACkAMQA5AAASNDYyFhQGIhMHJiYnNxcHBgYHJzY2PwIXBycHJyc3FzMSNDYyFhQGIiY0NjIWFAYiJDQ2MhYUBiLVGSQZGSS4HxJCCB4HehJDCB8SQwhyXh9ech4HVR4GAVQZJBkZJMMYJBkZJAE9GSQYGCQBmSQYGCQZ/voeEkMIHggXEkIIHhJCCHJeHl4fHgZXHgf+pCQZGSQYwyQZGSQZGSQZGSQZAAIAKwAVAdUB6wARACwAAAE3FSM3JiYiBgcXIzUXNjYyFhMVBwYGIyMiJyc3NjMzFzU0NjIWFRUzMhcXFgGtKGosEVJoUhEsaigYXXBdEBACEQyRDApqEQcKBUkTGhMRAwhhEwGtKGosFh4eFixqKBwiIv7cBHAMEAppEQcQ5Q0TEw2AAjAJAAQAKwArAdUB1QAHAA8AHwAoAAAlNTQmIgYVFRIiBhQWMjY0NzIWFREUBiMhIiY1ETQ2MwcRIRUhIiY1EQGrWFBYmjQmJjQmQBEZGRH/ABEaGhFWASv+1REZqyAcJCQcIAEAJjQmJjRQGRH/ABEaGhEBABEZVf7VKhkRASsAAgBVAFUBqwGrAAMAHwAAJTUjFTcjFTMVIxUjNSMVIzUjNTM1IzUzNTMVMzUzFTMBK1bWVlZWKlYqVlZWVipWKlbVVlZWVipWVlZWKlYqVlZWVgADACsAKwHVAasACQAhAC0AACUiJjU1NDYzMxUFJyYnJjc3IyImNTU0Nzc2MzMyFhUVFAcHNzU0IyMHFTMyFgcBqwkNDQkq/wAJDAcDARR2ERkDOQ0YrRUdDXZYB603dhMbBcAMCcAJDeuVCgwOBwdjGhEXCgeDFR0VpxINOVeoB34XHxQAAwArAFUB1QHVAAkAIQAtAAATMhYVFRQGIyM1JRcWFxYHBzMyFhUVFAcHBiMjIiY1NTQ3NwcVFDMzNzUjIiY3VQkNDQkqAQAJDAcDARR2ERkDOQ0YrRUdDXZYB603dhMbBQFADAnACQ3rlQoMDgcHYxoRFwkIgxUdFacSDTlXqAd+Fx8UAAIAKwCVAdUBawAHABMAADYyNjQmIgYUJTIWFAYjIyImNDYzezQmJjQmARYsPj4s1iw+PizAJjQmJjSFP1g/P1g/AAIAKwCVAdUBawAHABMAACQyNjQmIgYUNzIWFAYjIyImNDYzAVE0JiY0JkAsPj4s1iw+PizAJjQmJjSFP1g/P1g/AAMAKwCAAdUBawAHADMAOwAANiIGFBYyNjQ3MhYUBiImNTQ3JwcjJwYGIyImNTQ+AjcnIzUzFzczJyM1Mxc3FSMXPgIWIgYUFjI2NIYiGRkiGusfKys+LBkJO0AWBSobHysIDRQLFCBOLUhBGio9Gj4tHAUJCRYiGhoiGfUZIhoaIjkrPiwsHyEWC2IUGyQsHwwXEg4EERIcHCAWIRgtJQEDASAZIhoaIgADAFUAKwGrAdUACAAOABwAADc3FwcnFSM1Bxc1IzUjERMXERQGIyEiJjUTNDYzq1VVHiIqIrdrlauAGhH/ABEaARkRwFVVHyJYWCFN62v+qgGAgP8AERkZEQFWERkACAArACsB1QHVAAgAEQAaACMAJwArAC8AQgAAJTUzNTMVFAYjISMiJjU1MxUzEzMyFhUVIzUjJxUjFSM1NDYzEzc1Byc3JwcHFzUnNxYVFRQHBwYnJyY1NTQ3NzYyFwFrQCoTDf7gSg0TKkDWSg0TKkDWQCoTDcpWVhVVVVUWVlbwEBB1EBB1EBB1CBAIKypASg0TEw1KQAGAEw1KQCoqQEoNE/67MWIxJTEyMocxYjE7ChKHEgpECgpEChKHEgpEBAQABQBAAFUBwAGrAAMABwALAA8AEwAAEzMVIzczFSM3MxUjBzMVIwEzESOVQEBAQEBWQEDrQEABQEBAAStWwFVrQICWAVb+qgAEACsAVQHVAasAAwAKAA0AEQAANzUjFTcXIxUjNSMFNRcnFyMnwCsWgCurKgEAQECqNXXVVlbWgNbW1kBAq6t2AAUAKwBVAdUBqwADAAcACwARABUAABM3Fwc3MxUjByEVISUjNjYyFic3FwdMHi0ecioqwAGq/lYBU/wIR15HDC0eLQFXHi0egUDAVoAuPT1ELR4tAAIAFQBAAesBwAARABoAABMhESM1MzUjNTM1IzUnNSMmJwcXFSM1IxUjNdUBFoAqKioqKxsiLieSa1VrAcD+gFUrKyonHRIWHxph5aur4AADACsAFQHrAcAACgAYAB4AACQyNjQmIyIGFRUUNzIWFAYjIiYnBzU3JzUFFRcHJzUBKVg/PywrP2o+WFk9M08Nm8DAATUwEEBAP1g/PiwCLMJYelk9MEKVKyuVy0UgGytVAAEAkABAAW4BwAAOAAA3IzcjIjc2BzY3MwczMgfrFhZLEAgDAjNJFhZLDgVAlQ4FAlmBlQ4AAwArABUB6wHAAAYACgAdAAAlJzM1MxUzFzUjFTcyFhUVFAYjIyImNTUHNTcnNQUBYEsrQCsVwMARGhoRwBEaqsDAAVVVVkBAa8DA6xoRwBEaGhFLS5UrK5WVAAMAKwBAAdUBwAAHAA8AFwAAJDIWFAYiJjQCMhYUBiImNAYyFhQGIiY0AV1GMjJGMk5GMjJGMk5GMjJGMuszRjIyRgEIMkYzM0ajM0YyMkYAAwBVACsBqwHVABsAIQAvAAABFRQGIiY1NTQ2MzMWFhUVIzU0IhUVFBYyNjU1ByERIzUjNxcRFAYjISImNRE0NjMBVTJGMh8WBhUaKhYaIhqrAQBVq8BrGhH/ABEaGhEBK2sjMjIjixYfAiEViIsKCosRGhoRa9YBAFYqav7qERkZEQFWERkABgArAEAB1QHAAAcADwAXAB8AJwAvAAAkMhYUBiImNDYiBhQWMjY0AjIWFAYiJjQ2IgYUFjI2NAYyFhQGIiY0NiIGFBYyNjQBXUYyMkYyZiIaGiIazkYyMkYyZiIaGiIazkYyMkYyZiIaGiIa6zNGMjJGCBoiGRkiARoyRjMzRgcZIhoaIpEzRjIyRggaIhkZIgAFACsAVQHVAasAAwAMABUAHgAnAAABFSE1FxUjIiY1NTMVJTMVFAYjIzUzJSM1NDYzMxUjJTMyFhUVIzUjAYD/ABVAERkqAVYqGRFAQP6qKhkRQEABFkARGSpAAVWqqtUrGhErKysrERor1SsRGisrGhErKwADAEAASwG1AcAACQARACQAADc3JzM3FzMHFycGMjY0JiIGFBcXByc1JwYjIiY0NjIWFRQGBxecEi44EhI4LhIuJ1A4OFA44GogagYmNDpRUXRQFA0G9TUgNjYgNSFBOFA4OFA4aiBqEQYhUHRRUToVNRAGAAYAJQBAAdsBwAAQABkAIwAsADYATgAANyE1IyInBiMiJwYjIicGIyMnBhcWMzI3NyMXFBYzMjY1NSMHNxUUFjMyNicnNwcXFhYzMjc2JzcWBwcVFAYjISImNTUnJjc3NjYzITIWF2sBKwUeEhQcGxYUGx4UEh0FFQMJBw0VBA0qSQ8LDBAqDGAQCg0RAgxUKQ0BDgoMCAkDKQgVBhoR/tYRGgYVCBYDGA4BKA4XBGuAFBQUFBQUTQ4LChlnYwsSEQtkYGBkCxETDWABAWcKDwoLDgojGgaUERoaEZQGGiNeDhISDgADAEAAawHAAasAAwAHAAsAAAEzFSMlMxUjEzMRIwGVKyv+qysrVdbWAYDr6+sBFv7AAAQAKwBAAdUBwAAIAAwAHAAlAAATFTMVIyImNTUlNSMVNzIWFRUUBiMjIiY1NTQ2MwcVMxUjIiY1NVXAwBEZAYCrqxEZGRGrERoaEVXAwBEaAQCVKxoRlRVWVqsaEYARGRkRgBEaa5UrGhGVAAEAKwBAAcABwAAoAAAlMjcXBiMiJicjNzMmNDcjNzM2NjMyFhcHJiMiBgczByMGFBczByMWFgFAMycmNko+YxRgFUEBAVYVSxRjPh5MFiYnMyNIEJAViQICnhV7EEh1IiYxSDgrBh4GKzhIHRQmIiwfKw4ODisfLAABAKsAQAFVAcAACQAAARUzByczNSM3FwEVQFVVQEBVVQFr1lVV1lVVAAIAQAAVAcAB6wAHACUAADY0NjIWFAYiNxUUByc2NTQuAiIOAhQeAjMyNxcGByYmNTU3wCY0JiY02ik+EhIaLSQsGhMTGiwSIBtCNElSbsDmNCYmNCbVgExCPhsgEi0aEhIaLSQsGhMSQz0SFJNZgFYAAgArAFUB1QGrAAYADQAANzcVIRUhFQEHNSE1ITUrVQFA/sABVVX+wAFAq1VAK0ABAFVAK0AABQAVADUB6wGgAAkAEwAdACcARAAAJTIXFSYjIgc1Nic2MzIXFSYjIgc3Igc1NjMyFxUmFzUmIyIHFTYzMgMyFxEUBiMiJyYjIgcmIyIHIgYjIiY1ETYzMhc2AXUaHBQiPCQmJio2GhwUIjwkYDwkKDgaHBgtISpBNDRBJydMKgcEAwIpPUE0K0o2MAEDAQQHK0tKKyvOBSAFFSQRKBEFIAUVThUjEgUhBqv2CiD1IAEgIP7JBAcBFiAgFwEGBAE5ICAgAAIAVQBAAasB1QAHABsAACU1IycjFTMXNTMVIycjFSMRJjU0NjIWFRQHFTMBgGsVa4AWgJYVayoWGiIaFoCVgCuAK6vVKlUBRgwZERkZERkMGwADAFUAQAHVAcAAAwAHACkAADchFSEBNSMVNzIWFRUUBiMjFRQGIyMiJjU1MxUHBhUVFDMzMjU1NCcnNVUBVv6qAVYrKxIYGBIrMiOAIzNWJwQLVQsEJ2srARVAQGsZEkASGEAjMzMj1TMfAwVbCwtbBQMfMwAGACYAKwHbAdUADwAWAB0ARwBPAG4AACU2JicmIyIHFzIfBBUHNicnBhcWJxY3JyYHBjcWFzMVIxYHBxcWFRQGBwYjIicGIyInJjcmJjc2NjMyFxc3NjMyFjM1MzYyNjQmIgYUNxYHBxcWBiMiJycHBiMiJjc3JyY3NjMzNzYyFxczMgEECwIMDRAPDCIBAgYBAQENFAcZGgsIcx8yRBMHCLEmDSIdAxsMDAMYEgsLIhIJDyIZIw0PEQgGJBMMCR4JGSMBBgEVdxIMDBIMaQcTEggGEQ8MBxERBwwPEQYJExMHBxQXBgUuBQYYEskNHwwNCg0BBgEBAgGUBxRDMh8VgAsaGQcUE10NJxYoGwkeCQwTJAYDGwMZIzAKKxURGQMMDBkBHRYMEgwMEiEUDAsaDRsGDw8GGw0aCwwUExQWFhQAAgBVACsBsgHVABMAGwAAARcHJxUjNSMVIxEmJjUzFBYzMzImNDYyFhQGIgFTXx5UKyorLzwrPiw3GmYZJBkZJAFTYB5V/4CAARsOTzIsPi4kGBgkGQAJAD8AKwHNAdUACQAUACwAOgBCAEoATgBWAFoAADcUFjMyNzcnBwY3IgYVFBcXNzYnJhcXBxcHJwcGIicmNDc3JyY0NjIWFAcHFzYiJjQ2MzIXNTMVIxUUFDQ2MhYUBiImNDYyFhQGIgc3FwcnNSM1MxUjFSczFSNrBwMEBBcQFgMZAwcDBwgICANHHh4eHh4XDy4PEBAXBxAfLCAQBw+vLCAgFhIOVUASHBISHH0TGhMTGh+mHqanQKtAa6urYAQHBBYPFgNvBgQFAwgICAcDKB8eHh4eFw8PECwQFwcQLB8fLBAHEIggLB8LYCpgFuMcEhIcEn4aExMaE1qmHqbiVioqVsAqAAkAKwBAAdUBwAADAAcACwAPABsAIwArAC8ASAAANzMVIzUzFSMTMxUjBzMVIzc1IxUjESMRMzUzFRYyNjQmIgYUBjI2NCYiBhQ3BzMnNxcVFAYjIyImNTUjFRQGIyMiNSc3NjMzMmsqKioqaisraioqwIBrFWqrYhIMDBINnhIMDBIMHhbFFhUeCQYNBgirCQYNDgEfBRCZEGsrgCsBACpWKlVra/8AARVrgKsNEgwMEg0NEgwMEnNAQAhYdgYJCgYbGwYKD3ZYDgAFAEAAQAHVAdUAAwAHAAsAFwAqAAA3MxUjNTMVIzUzFSMlFTMVIxUjNSM1MzURNTMVFAYjISImNRE0NjMzFSMRlaurq6urqwEAQEAqQEAqGRH/ABEaGhGVlcAraytrK8BAKkBAKkD+kZqVERoaEQEAERkq/vsABAAVAFUB6wGrAAkAFwAfACcAADYyFhYVFSE1NDY3Iic2NTQmJzYzMhYUBiY0NjIWFAYiFxYWFRUjNTSgQEw//qo/7A0PHBAMDw0jMjL4MkYyMkbHNVJW6xIqGkBAGionBSEvEzAOBTNGMjJGMzNGMhgIKyBAQDEABAArACsB1QHVAAcADQAVAB0AAAAyNjQmIgYUBjI2NyMWNiIGFBYyNjQmMhYUBiImNAE+GhMTGhNPSDoN1g0gGhMTGhMtsH19sH0BFRMaExMaqC8mJqYTGhMTGpN9sH19sAADAEAAQAHAAcAABAAJACkAAAE1IxU2BTUjFRQlMhYVFRQGBwYGBxUzFSM1MzUmJicmJjU1NDYzMzUzFQGVKir/ACoBKhEaNigJLRdW1lYXLQkoNhoRKtYBVRZSDw9SFi1tGREWKDwFFiQFQisrQgUkFgU8KBYRGSsrAAQAXgArAZUBwAAIAAwAEAAvAAABNycHJwcXFTMXNSMVFzUjFRMyFhUVFAYHBhUVFAYjIwYiJyMiJjU1NCcmNzY2NzYBCycPIyMPJxYgVlZWKz5XHRUOGREGDDIMBhEZDj8PCEEsDwENJhAjIxAmOEAWFioVFQFVVz4BHEESDBMxERoVFRoRMRMMOFQsQggDAAgALAAsAdQB1AAHAA0AEwAbACMAKwAxADcAADczFSYmJzY2NzMGByYmBSYnMwYGNxYWFwYGBzUnJiYnNjY3FTMjNRYWFwYGByM2NxYWFzY2NxYXvywfTRcfM7NnBRwZKP7bHQRnBSjbBTMfFk0gVgUzHxZNIFYsH00XHzOzZwUcGSjfBSgZHAXrvwMlFxFMIzIsDDdDLTEbN1IjTBEXJQO/KiNMERclA7+/AyUXEUwjMiwMNxsbNwwsMgADACsAKwHVAdUABwALABsAAAA0NjIWFAYiBzcXBycWFAcHBiInJyY0Nzc2MhcBQCw+Kys+Oh5bH0sGBjwGEga2BgY9BhIGAWw+Kys+LLseWh7EBhIGPQYGtQYSBj0GBgAEADMAawHQAZUABwAPABsAMwAAJDI2NCYiBhQmMjY0JiIGFAc1IzUjFSMVMxUzNQUWBiMiJycjBwYiJjU0NjU3NjYzMzIWFwFiEgwMEg0eEgwMEgxAKxUrKxUBDAQhGRYQMIAwEC4fARcEMCDCHzAF6wwSDAwSNAwSDAwSIhYqKhYqKmwZJRAwMBAfFwEGAaMfKiseAAQAPwA/AcEBwQAFAAkAEQAXAAABJzYeAgc3Jwc3FwYGByc2NgcXBi4CAcBzIzsMCu54HnhioxBoWaMQaH5zIzsMCgFNcwEKDDq8eB5456NZaBCjWWj3cwEKDDoABgBrACsBlQHVAA0AFQAdACUALQA1AAA3NTMVIyIGFRUjNTQmIxI0NjIWFAYiFjQ2MhYUBiImNDYyFhQGIjYiBhQWMjY0BiImNDYyFhSV1isRGioaESsMEgwMEh4NEgwMEmIMEg0NEmBYPz9YPy18V1d8V2sqKhoRFRURGgEMEgwMEgwfEg0NEgwMEg0NEgxrP1g/P1jBV3xXV3wABAArAFUB1QGrABIAGQAhACgAACUXMxUnIicnJiYnJzMXNjY3NzMTMhYVFSM1IzcXBwYjBzUHNDYzMxUjASIeVWQNBh4HGANXSDECCQElSEcJDCrrEiISBg1kQAwJFSruQ1YBC0IONge9bwUTAlX/AA0JQFYpSikLAVYWCQ1WAAMAawBAAZUBwAADAB0AJwAAATUjFTYyFhUVFAcHBiMjIicnJjU1NDYzMzIWFRU0AzUzFRQGIyMiJgFAq+ISDAERBBDeEQMRARkRqxEa1tYNCaoJDQErQEBADQk7BgNVERFVAwZ7ERoaEUAJ/vdAQAkMDAACACsAVQHbAa8AFQAeAAABFgYjIyImNTU0NzMyNjU0Jyc2NzYWBxQGIyM2NxcWAdQHZUvWERkErh8uLmY2SUhwzRQOphMpdxUBEUxwGhERExwuHzMUKycEBVlJDhRGLjIIAAMAMgA2Ac4BygAGAA8AIQAAJTY3BgYHNicGBzY2NzY3BjcWFgYHBgYjIicmJjY3NjYzMgFaQQQjgE1pckEEDzoaQE1pzREIJy4qczhEFREIJy4qczhEpkFqTYAjA/ZBaiBTGkAjAxkRZokuKicVEWaJLionAAcAKwArAdUB1QAJAA4AFgAhACkAMQA5AAAlNjU0JjUnBwcXJzcnBxcXNycjBxcWMic3JycHFAYVFBYXEwYGBxcXNzUzFRc3NyYmJyYyFhQGIiY0AYEqARUeHxE5HU1NHWUPDW0ODxk4hREfHhUBGRFPGTcOCR1VKlUdCQ43GYqwfX2wfZEwPwIEARAKXR0tVjY2VmIgFxcgCT4dXQoQAQQCGkEUARIHKhYdCTseHjsJHRYqBzJ9sH19sAAEACsAgAHVAYAABwAPABUANQAANjQ2MhYUBiIGMjY0JiIGFCYyNTQiFTczFSMiBhcWBwYGByIGIyImNTU0NwYjIiY0NjMyFhc2wBkkGBgkCDQmJjQmVhYWm+VWCg0BAwQIPikCCgI1SwIIBRYfHxYSHAUn7iQZGSQZFSY0JiY0WgsKCjVWDgoXFSg4BQFLNQYHCgIgLB8VECUABgArACsB1AHUAAQACgAPABQAGQAfAAABJzUWFgE3FwYjIhMXBgcnBxcHJic3BzU2NwcVByY1NAHUv0ps/uG/QkBtLEG/BA6tFUC/GRWYQB8ha0ITARluTQhp/tluJ1gBK24hHmMlJW0SHYgl3A4DKtsnKy1qAAQAJwAVAdUB8QAIABQAHAA6AAAkMhYUBiImNTQ2IgcGFBYzMjc2NCcmFj4CJgYGNhYGBwYGIyoCDgMHByc3PgQ0NDU0Njc2AV1GMjJGMmckDA0aERIMDQ3bMlVEDTJURKpKDC8ZQRsDGAsXDhMQB1ofWwcLBgQBHRkvwDJGMzIkIwcMDSIaDA0jDZcyDURVMg5EfUx3LxkdAQQGCwdaHloHEBMPFwsYAxpBGS8AAwB2AAIBkQHqAAoAIQApAAABJjU0NjMyFgcGBhcWFgcnNjU0JwcnNycHJzcmJjcXBhYXPgIWFgYGJgEQEBIOExIJBxo3LhEbJQ4VhiVAJSAkbSEMGSUWFiYNEiMeCRIiHgGvCRIOEiAPCwiGJnErFhkcIB/oFW8VNxW+JGIsFiZVFi8eChIjHgkSAAQAAQArAgAB+AAdADkAVABcAAATFjMXIicnBxcVIzUnFQcnNyc0JiY1Jjc3NjMyFxY3JiY3Njc2MzI3MjMyFjMyFhYHBgciBiMGIyImBSM1Jx4CFyMnBxUjNTY3JwYHJic2Nzc2FxcmNDYyFhQGIvAhLAM+Lg8yLSsbWx5OFwIBAg9HDREPCQYmEBYEAxcGAwICAgECBQEKGAENBggBBQIDBQEGARQrJgkUGActJywrEhsNEhseFzIWFhQkbMsZJBkZJAEfISwsDzMtgG4ZL1oeTT8BBQQCEg9HDQcGBAMeERcJAgEBGCIOBgMDAQFKSA8tZHoiqyuAoBEaQBQPAxcJKCMgDy1UJBkZJBgAAQBVAFABqwGrABQAABM2Njc3DgIHBgYnNjY3BgcmJjQ2gSGVOjoBCSwhIl4pCUQmbTASGhoBVCErBgUZT6EhIg8UMnUgOHISPzI+AAIAKwArAdUB1QALABgAACU1IwcnIxUzNRc3FTcjFTMVITUzNSM1NxcBVSorKyoqKyuqKir+Vioq1dWAlUBAlVVAQFWVwCoqwCuVlQADACsAKwHVAdUAEQAjACsAACUzFSM1BiMiJiczFhYzMjY3IycjNTMVNjMyFhcjJiYjIgYHMxY0NjIWFAYiAVWAKkBrTXUPLA5cOy5PFj6qgCpAa011DywOXDsuTxY+FSY0JiY01YArVWFJN0kvJ4CAK1VhSTdJLydvNCYmNCYAAwArAEAB1QHAAA4AHQAxAAAlNyM0JiMiBxc2MzIWFSMHMjcnBiMiJjUzJwczFBYTMhYVERQGIyEiJjURNDYzMzczFwFgNSo/LBoYDw4VIzIqKxoYDxATIzIqNTUqP9cRGRkR/qoRGRkRRCeAJ7U2LD4NDwcyI2sNEAgzIzU1LD8BFRkR/wARGhoRAQARGSsrAAMAFQAAAgABwAALABMAIwAAJRcHFwcnByc3JzcXBjI2NCYiBhQ3MhYUBiMiJicHJzcnNwU2AYYPJiYPJiYPJiYPJjFiRERiRHVCXl5CPFoIrAHAwAEBMBLVDyYmDyYmDyYmDyaERGJERGLRXoReTztKlSsrlYICAAwAQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsANwAAJTUjFRc1IxUDNSMVFzUjFRc1IxUnNSMVFzUjFRc1IxUnNSMVFzUjFRc1IxUlMxUjNSMVIxEzNTMBlSoqKisrKysrKyorKysrKysqKioqKgEAVasqq1XWwCsrVSoqAQAqKlYrK1UrK6sqKlYrK1UrK1UrK1UrK1UqKqrVVVUBK1UAAgArACsB1QHVADAAOAAAJTMVFAYjFAYjISImNSImNTUzNTQ2MzIXFxYXMzU0JiMiBwcWFRQHJzYzMhc3NjIWFQQ0NjIWFAYiAasqGRENCf7WCQ0RGUAdExQQHQYMkgoIBwUbAgc7DAsFBhsRMiT+wBgkGRkk64ARGgkMDAkaEYAQEx0QIQcIrgcLBRsIAwsMOwcCGxEjGUAkGBgkGQADACsAKwHVAdUACgAVABwAACU3FwcVIzUjFSM1JxczFSM1IxUjNSclIxEjESM3AZcRKRErKivoEVcrKisRAabAKsDVq1UIXXBVVYBVVYBVVXBdSP7rARWVAAMAKwArAdUB1QAdACwAMAAAJREhETM1MyYnJzQ0PgY3BgYWFxYVFAczFScWNjc2JicGFx4CBwYGAyERIQGr/qorMBkCAQMECA0UGSMVAgIWHCgWK4MUMQMCKAQFAwEEAQECE94Bqv5WVQFW/qorICAGBQgRDxUTFxYYCwYVNRMcLCMdK0ERExsTKAgODQQPBwgKFQE7/lYAAgArAFUB1QHAAAUAFAAAEzM0JiIGFxcjFSM1IxUjNSM3FzUz1VYaIhrAQEBqVmpA1VVAASsRGRkCOquAgKvATTgAAwArAGsB1QGVAAMABwAhAAABNSMVIzUjFSEyFhUVIwcjJyEHIycjNTQ2MzU0NjMhMhYVAYBrKmsBKxEZHA4WDv7yDhYOHBkRGhEBABEaAStAQEBAGhFrKioqKmsRGkARGRkRAAIAFQAqAbIB1QANACgAADcyFhQGIyMiJjQ2MzIXNxYWNwYGIyInNjU0JicmIyIHNCY1NDY3BgYWlREaGhFAGiYmGi0PXCF1Lx1eNyIhLiQaITcXEwF4Vg8UAasaIhomNCYqU0A4BS00CxoxGy8ILgkBBgJWfAMXNl4ABQBrACsBqwHVAAoAFQAgACgATgAAATYnJjczBhcWFgcjNicmNzMGFxYWByM2JyY3MwYXFhYHFiIGFBYyNjQHIiYnIwcGIyInJjU0NzcmJjUhFAYHFwYHJwYiJwczNjYzMhYUBgEzBxAZBhUFDxEGBEsGEBkGFQUPEQYESgYQGQYVBQ8RBgSWEg0NEgwVFSAHiBUFDQgECQNVKDABKjMkDBQOFQUcBSpsByAVGiYmAWsbEBolGRITGxEbEBolGRITGxEbEBolGRITGxHrDBINDRJJGBMiCQMFDQgEgRJJLShQEBMIEiEBAUETGCY0JgAEAFUAawGrAZUAAwAHAAsAJQAAExUhNScVMzUXNSMVFxUjByMnIwcjJyM1NDYzNTQ2MzMyFhUVMhaAAQDVQGpAlh0OFQ65DxUOHRoRGhGqERoRGgEAQEBrQEBAQEArayoqKiprERpAERkZEUAaAAIAVQBVAasBqwACABwAADczJxcXISImNREXBxc3FwcXNxcHFzcXBxc3FwcXlXt75DL+1REaMhYPFioXDxcqFw8XKRcQFioXD5V7iTIaEQErMhcPFyoWEBcqFg8WKRcPFyoWDwACAFUAawG1AZUABQALAAATFwcjNyczFwcjNye1a2tga2v2ampgamoBlZWVlZWVlZWVAAMAKwArAdUB1QAQABgAIAAAARQWFwYiJzY2NCYnNjIXBgY3FhQHJiY0NiUWFhQGByY0ASs8Kj6mPi44OC4+pj4uOIQmJiczM/7JJzMzJyYBAC5bEzk5FFRoVBQ5ORRURjeGNwxEVEQMDERURAw3hgADAD8AKwHBAdUAOgBnAHQAABMmJjY2FzY2NzY2MhYXFhc2MzIXFgYHFhQHFhYGBicGBxcjJwYHBgYiJicuAicHIzcmJwYmJjY3JjQ3BhUUFxYXFgcWFzcmNTQ2MhYVFAcXNjcmNjc2NTQnJicmNyYnBiInBgcWBwYXNjIXNjcnBiMiJwcWUg0GEB8ODzUWAhggGAIzJgYLGQwIBg0HBw0GECAODw0cJxUNEAIYIBgCAw0KBBUoHQwPDx8QBg0HIwUHFwoKCAkKIBArPisRIAcMCBgTCAYYCgwMHiwMMgwqIAwMClgNKA0IEB4MDxAMHhABNAofHAoGEB4FEBUVEAwnAxUOHwoWPBYKHxwKBg8JQC4GAxAVFRABAwMCLkAJDwYKHB8KFjwNEhYZFQITEhQICEgTGx4qKh4ZFkcFDBQlARgWEhYCExQWIQsWFgshFhQTvRAQAgZEBQVEBgAFAD0AoAHDAWsACQASABsAKAA2AAAlIycmNjMzMhYHBxcjJyY3NzYWFzY2FxcWBwcjBwYjIicnJjc3NjIXFwUnNjc2MhcXFgcHBiMiASZMEAEMCEYIDAF+DiYtChIiCBKRAhAJIhILLCbVBQcMBgUFBhIFGAUVAQ8nBw4FGAURBwUFBQ0HwJMKDg4KG3hXEwsTBQoLCwoFEwsTVx0DDAoMCRsJCSYdHQ0ZCQkbCgsKDAACACsAQAHVAcAAAwAXAAAlNSMVEzIWFRQHFRQGIyEiJjU1JjU0NjMBK1arIzIqGhH/ABEaKjIjwFVVAQAyIzEZthEaGhG2GTEjMgAGAGsAKwGVAesABwAYABwAJAAsAEIAABIyNjQmIgYUNzMVIxUjNSMGIyImNDYzMhYHMycjFjI2NCYiBhQGMjY0JiIGFDcyFxcVFCMjIjU1IxUUIyMiNTU3NjOiEgwMEg1SrhUrbg8tGiYmGhMjW/Qcu6wWDw8WD6UWDw8WD9IOBiMKFgrWChYKIwUPAZUNEgwMEh4rKioqJjQmGf1Wqw8WDw8WDw8WDw8WsQ9qkgoKICAKC5FqDwAFAFUAKwGrAcAABwAnACsAMwA7AAA3IRUjFSM1IwEWFxUUIyMiNTUjFRQjIyI1NTY3NDY1NzY2NzMzFhcyBwczJwYUFjI2NCYiFjI2NCYiBhRVAVaWKpYBGwMiEAoQ1hAKEB0EBAEECgMDtgwIAc4c9B3RDxYPDxa0Fg8PFg+VKkBAAUgGZosRERkZERGLVg4BBQIBBQYBAQsJVlaGFg8PFg80DxYPDxYABwArAEAB1QHAACcALAAwADQAOAA8AEAAADc1IzUzNSM1MzUjNTMWFhczFSMGBicjFTI2MzIXNjYzMhYVFAYVITY3NSMVNjc1IxU3IxUzJxUzNQcVMzUDIQchVRUVFRUVgBAZAtXVAhkQKwIHAhIXDUAgMUQB/pkEORUPRisrKytVFRUVVQGqKv6qoLUWFRUWFQEZERURGwGWAQkeK0QxAggBECuapAiyFRVAFhYWFisVFf7rKwACAEAAKwHAAdkADgA2AAAlMjU0JycHBhUUMzM1MxU3FhUVFAYjIxUjNSMiJjU1NDc3NSYmNzY2NzYWFSM0JiIGFBYyFhUVAYwJBZCQBQkh1jYfHhYh1iEWHh+MFhgEBBwTHi4rDBIMDBIM1QkHAkBAAgYKFhY5DSIBFh2AgB0WASENPw0IKRgTGwQFJh4JDQ0SDAwJHwACAFUAQAGrAcAACQAXAAA3MxUUBiMjIiY1EzMVIycVIzUjNDYzMxXAgAwJVgkMwCsrQIBrPyyA65YJDAwJAWurQEBALD9AAAYAQAArAdUB1QADABAAFAAwADQARAAAJTUjFTUVMzUnJiY1NSMVFAc3FTM1FxYVFRQGIyMiJjU1NDc3NjU1NDYzMzIWFRUUFyUVMzUHNTMVFAYHFTMVIzUzNSYmAauWlhUSGRYrKxZNHRkRlhEZHRQPDAlACQwP/scqVYAYEyuAKxMYVSsrzCEhBwYkEwYGLg+DFhZhCh/MERkZEcwfCgYFEFsJDAwJWw4GBEBAgKurFSAHRCoqRAcgAAMAJgBVAdoBqwAJADoAQAAANyEVFAYjISImNTciBwYjNTI3NjMyFxYzMjc2MzIXFjMyNzYzMhcXFSInJiMiBwYjIicmIyIHBiMiJyYlISY2MhYrAaoZEf6qERlHCw0WGQsNFxgXFw0MCw0XGBcXDQwLDRcYFxcZGBYNDAsNFhkYFg0MCw0WGRgWDQFX/lYFfrh+qysRGhoRYAgNKggODggIDg4ICA4OCCoNCAgNDQgIDQ0ISzRMTAADABUAVQHVAZUADQARABsAAAEzFSMVFAYiJjQ2MzIXJzcjFychBxUzFSM1MzUBa2pAJjQmJhoNCZQelR5pASuAK4AqAZVAwBomJjQmA24rK1XAVSsrVQABAEIAKwHAAdUACgAAARcjFSM1IzcjNxcBa1WWVJRTKZSVAQCAVVWA1dUABQArACsB1QHVABMAFwAbAB8AIwAAAQUVIRUhFSEUBgcVIzUmJjUzNSUFFTM1BxUzNRc1IxU3NQcVAdX/AAEA/wABAEc5qjlHQAFq/qsVFRUrFRUVAcAeIhVrOl8XJSUXXzqrKj0YGi9ra2tra4AfAh0ABQArACsB9gHhAAwAGgAnADQANwAAATY0Jyc3FxYVFAcHJxc2MzIXFwcnJiMiBwcnJzY0Jyc3FxYVFAcHJxcnNzYzMhcXBycmIgcBExcBQQcHIhciERFNFncTFhcTIhYjCAsKCCIXcwcHDRcMEREMF2wXeBMWFxMMFg0IFgj+fmrAAYMHFggiFyITFxYTTRczEREjFiIICCIXjAcYBwwXDBMXFhMNF2wWeBERDRcNCAj+vwEqwAAHACsAKwHVAcAABwAPACAAKAAwADYAPgAAADI2NCYiBhQGMjY0JiIGFCczFRQGIyInNTM0JiIGFTUjBiIGFBYyNjQ2IgYUFjI2NAYyNjUjFCc1MxUUBiImAYwSDQ0SDF4SDAwSDSrqRDEbGmofLB9AdxINDRIMXhIMDBINYSwfakDqRGJEAVQMEgwMEgwMEgwMEmCLMUQNPg0TEw01VQ0SDAwSDQ0SDAwSaRMNDRiLizFERAAGACsAKwHVAdUAAwAHAAsAEwAbADUAACU1IxUXNSMVJzUjFRc1NCYiBhUVNiIGFBYyNjQ3MhYVFRQGIyEiJjU1NDYzMzU0NjMzMhYVFQGAVVVVFioVLCgsThwSEhwSyxEZGRH+qhEZGRFrGhEqERrgICBAICCga2vAEA4SEg4QgBIcEhIcfRoR6xEZGRHrERpAERkZEUAAAQArACsB1QHrAA8AAAEXFSM1JwcVIzU3NTMHFyMBFcCVP0GVwGoVFUABhobVaisratWGZSEfAAIAVQAVAasB1QASAB4AADcmJjU0Njc2NjIWFxYWFRQGBwcnFzcmJicGIicOApIZJB8XBENcQwQXHyMZbkVFRAEFAR1CHQECAusCJRkXJAQtPj4tBCQXGSUC1uKEhAEEARISAQMCAAMAFQArAdYB1QARACMAJwAAJTIWFQcnNTMXFhUUBiMjJwcXEzYzMhYVFAYGBy4CNTQ2MzIFMxUjAasSGauWKpsRDws8JQcsQBcjHSkqKiwsKioqHSL+11ZWlRkRQCrAOQcRCg8OFBABJhopHRY9KSgoKT0WHSnA6gAEACsAKwHVAdUACAASABwAJAAAJTY1NCcHFhQHJzY1NCcHFhUUByc2NTQnBxYVFAc2MhYUBiImNAE5HBwdGRkmFRUcEREmDAwdCQkRsH19sH2OOTk6OA8xZDIOJS4vKA4kJSQfEhobGBsNEhQVE/x9sH19sAAFACsAawHVAZUABwAPABMAGQAxAAAkMjY0JiIGFCYyFhQGIiY0JzMVIxYyNjUjFCUVByMUBiImNSM1NDYzMxUzNzUjNTMyFgGMEg0NEgwFNCYmNCbqamohEg0rARVgYCY0JioyI1VLS0BAERmVDRIMDBJJJjQmJjS7K8ANCQnJSHgaJiYaQCMya105KhkABAArACsB1QHVAA8AHgAmACoAADc1MxUzMhUVFCMhIjU1NDMlJyYmNTUzFRQHBxUzFSMjISI1NSEVFBMzNSOVVmAKCv7qCgoBSwkOFIAgCipVNf7qCgEqKysrqyoqCxULCxULAgkPMhTKyzEjC1YqCiAgCgEqVgACACsAVQHVAasACQANAAATMxc3FwchJzcXFyEHIcB/ViIeO/7MOx4iBQEgD/7+AatIIR47Ox4hTsAAAQArACsB1QHVACsAAAEVIxcHJyMVFwcnFSM1Byc3NSMHJzcjNTMnNxczNSc3FzUzFTcXBxUzNxcHAdVZRR5jK2QfRSpFH2QrYx5FWVlFHmMrZB9FKkUfZCtjHkUBFSpFH2QrYx5FWVlFHmMrZB9FKkUfZCtjHkVZWUUeYytkH0UABgAVAGsB6wGVAAMACwAPABcAGwAvAAABMycjFjI2NCYiBhQnNSMVBjI2NCYiBhQnMzUjJRcVIxQGIiY1IxQGIiY1IzU0NjMBQGtWFSgaExMaE0BVThwSEhwSIFVVASuANiY0JnUmNCYrGRIBFVbgExoTExp3VlaKExoTExp3ViqAahomJhoaJiYawBIYAAEAAACNAgABcwAtAAAAMhYUBiMjIiYnJzcXFjI2NCYiBwYHBwYiJjQ2MzMyFhcXBycmIgYUFjI3Njc3AV1gQ0MwARMvDxsgGRY8Kio8FUcTPCFgQ0MwARQvDhshGBY8Kio8FUcTPAFzRF5EFA4YHBUWKjwqFT4SNSFEXkQUDhgcFRYqPCoVPhI1AAIAQABAAcABxwAXABsAAAEmJgc2FhcHJiY3BhYXByY0NzQzNhcWFwc3FwcBdDKDPjBzMXoxMQYVIDI9Pz8BRmJROJkfiR8BRDIgFQYxMXoxczA+gzI9P7M/AUYIBji4H4oeAAMAKwBAAdUBwAADAB0AKwAAATUjFTMyFhUVFAYjIzUjFSMiJjU1NDYzMzU3MxcVBzM1MxUUBiMhIiY1NTMBK1bWERkZEYBWgBIYGRFVK1UrgFaVGRL+1hIZlQFrKioaEUARGisrGRJAERoqKysqwBVVEhkZElUABgBAAEABwAHAAAcADwAXAB8AJwA3AAAAMjY0JiIGFBYyNjQmIgYUJjI2NCYiBhQmMjY0JiIGFBYyNjQmIgYUATIWFREUBiMhIiY1ETQ2MwFSHBISHBISHBISHBJOHBISHBJOHBISHBISHBISHBIBFREaGhH+1hEaGhEBQBIcEhIc0hIcEhIcThIcEhIcThIcEhIc0hIcEhIcAS4aEf7WERoaEQEqERoABQAVAEAB6wHAAAUAJQBFAE0AVQAANzMGBiImFjI2NzIWMzI2NCYjIgYjJiYiBgciJiMiBhQWMzI2MxYlBgYHBgYjIicmJyYmJyY0NzY2NzY3NjIXFhcWFhcWFCQ0NjIWFAYiNjQ2MhYUBiKgwAw0QDQhZk8NAQQBERoaEQEEAQ1PZk8NAQQBERoaEQEEAQ0BawQiFRZfOUc4Hw8VIwQCAgQjFREdNpI2Hw8VIwQC/sUQFg8PFlsPFhAQFtUdIyNNPC8BGiIaAS88PC8BGiIaAS9LFigGLz8xGyIGKBYIDAgWKAYmGDAwHSEGKBYIDBsWEBAWEBAWEBAWEAAEACsAKwHAAdUABwAPACkALgAAJDI2NCYiBhQGMjY0JiIGFCUWFRQGIyImJyMGBiMiJjU0NycjNTMXIRQGAzIWFSMBXhoTExoTrRoTExoTAREZKx8cKgQtBCkcHywnLS9KFAE3FZZHZKtVExoTExoTExoTExpFFyEfKyUbGyUrHywWXisrGD0BFWRGAAEAKwArAdUB1QAbAAAlFwcXBycHJwcnNycHJzcnNyc3FzcXNxcHFzcXAbceLR4eHy0fHh9Mt0weHh4tHh4fLR8eH0y3TB7DHy0fHh4tHh4eTLdMHx4fLR8eHi0eHh5Mt0wfAAMAVQBAAdUBwAADAAcAGQAANyEVIQE1IxU3MhYVFRQGIyMVFAYjIyImNTVVAVb+qgFWKysSGBgSKzIjgCMzaysBFUBAaxkSQBIYQCMzMyPVAAIAVQArAcAB1QAPABcAAAEHFRYWFRQGIiY1NDcVMxESNDYyFhQGIgFrgC48S2pLQCvAEhwSEhwBgkLBAhgQERkZERkMJQGA/n0cEhIcEgAIACsAKwHVAcAADgAeACIAJgAqAC4AQgBKAAABFgcHIzc2JyY3NzMHBhcXFgcHIzc2JycmNzczBwYXEzUjFSM1IxUjNSMVIzUjFTczFRQGIyEiJjU1MzU0NjMyFxcWJjQ2MhYUBiIBOSEGASkDBRUiBgEpAgUTVyIGAigCBRMCIgYCKAIFEwkqKysqKysqg+cZEf6qERlAHRMUEB0EdRgkGRkkAYMgLwkMGxUiLgkNGxQBIS4JDBsUASIuCQ0bFP7RgICAgICAgICrqxEZGRGrEBQcECEFZCQZGSQZAAUAVQArAasB1QADAAcACwAPAB8AABMzFSMRMxUjFzUhFQU1IRUBMhYVERQGIyEiJjURNDYzqyoqKirV/wABAP8AAQASGRoR/wARGhkSAQBrAQBAFWtr68HBAYAYEv6qERkZEQFWEhgABAArAEAB1QHAAAcAHwBRAIEAAAA0NjIWFAYiByInJic3JyYjNTIWFxcGBwYjIicmIgcGBSInJiMiBwYjIicmIyIHBiMiJyYjIgcGIzUyNzYzMhcWMzI3NjMyFxYzMjc2MzIXFjMVIicmIyIHBiMiJyYjIgcGIicmIyIHBiM1Mjc2MzIXFjMyNzYyFxYzMjc2MzIXFjMBKx8sHx8skQwNBAxFFSA1KTQYiQYDDQwLDRYyFg0BERcXCw4NCxcYFxcLDg0LFxgXFwsODQsXGA0LFxgXFwsODQsXGBcXCw4NCxcYFxcLDhcXCw4NCxcYFxcLDg0LFzAXCw0OCxcXDQsXGBcXCw4NCxcwFwsNDgsXFxgXCw0BdSwfHywgVQgDBUUWIDUTGIgEAQgIDQ0IYA4HBw4OBwcODgcHDisHDg4HBw4OBwcODgeLDgcHDg4HBw4OBwcOKwcODgcHDg4HBw4OBwACACsAawHVAZUADwATAAABFhYXITY2NyY1NDYyFhUUBSEVIQEnP1YE/oAEVj8EGSQZ/wABqv5WAVoNYUFBYQ0ICRIYGBIJzSoABgArABUB1QHVAAIAGQApAC0AMQA5AAAlJzMnIiY0NjMVIgYVFBYzMzIWFRUjNTQmIzcWFhUVIzU0JiM1MjY1MxQHMxUjNzMVIyU3AQcnIzUzAWs/PzYdKiodERYXECEeLCAZETweJSAxJBAXICcgIDUgIP52GgFrG5XVlaw/Wys6KiAVEBEaJxwiGxQXUg46JCwsJDMgFxEewkBAQNUb/pUblkAABQArAGsB1QHVABYALAAwADQAOAAAATIWFRUjNTQmIyMiJjQ2MxUiBhUUFjM3FhYVFSM1NCYjNTI2NTQmIzUyFhUUBzMVIzczFSMlIRUhAVYfKyAZESEdKiodERYXEF0eJSAxJBAXFxAdKicgIDUgIP52AUD+wAEmJh0jHBQXKzoqIBUQERo1DjojMDAkMiAXERAXICodHsVAQEBAQAAEACsAKwHVAdUAAAABAAoAGgAAAQc3BgcmJzY2NxYFMhYXNjYzFAYHBgcmJyYmAZXflCogICoDLBxD/udEcCEhcERfTBEZFRVMXwEiIjMXIiIXJ18cQ2dCMzNCVIYbBgUDCBuGAAIAFQAVAdAB0AAIABcAAAEnMxUzESc1IwcjFTM1FwcnFSM1MzUnNwErtbVqKkBAFivQHInrK1YbAQu1Ff71K7VrKhXQG4pfK/RVHAACAEAAQAHAAcAAAwARAAATFTM1NxEjNTMRMxUzETMVIxHVKyvrK8BqK1UBFSoqa/7AKwFVFf7AKwFAAAEAAAArAgAB1QBOAAABFhQHBwYjJwcGIi8CBxcUIyMiLwIHFCMjIjU3IwcXFiMjIicnBxcWIyMiNSc3NSY1IRY3JjcWFxY2JyYnIicmNzQzFhYXFx4CBxQXAf8BARkCAg8HAgkCDhcvEQQUAgIUChkEFQQXhyMIAgYUAwEcIgYCBhYEDRUXARIwLwgOHAgHCQIJJAECDAIEHSkHBAMHBQECAT4BAwEgAgMWBAQeBXCIBANREWIDBIdCRAUCZic8BQI4UpAJFQEiFg4UBQQLBxwOAQMFAwQlEQMECA8IBQIAAgBAAJUBwAFrABYAKAAAJTMVIzUzNCYjIyIGFRUUFjMzMjY1NSMFMxUjFTMyNjU1NCYjIzUzNSMBaypqlRkSahIZGRJqEhlV/tVra2sRGRkRQGqV6yuAEhkZEoASGRkSVSorKxkSKxEZKysAAwBrABUBlQHrAA8AEwAXAAABIyIGFREUFjMzMjY1ETQmAyMRMwczFSMBa9YRGRkR1hEZGRHW1sCqqgHrGRL+gBIZGRIBgBIZ/oABKhUrAAQAFQAVAesBqwANABEAFQAhAAAlMzUzNSchBxUzFTM1MwcjNTMnIRUhATUjFSMVMxUzNTM1AUArFRX+wBYWwFWAa2uVAUD+wAGAK0BAK0CVQCtrayuAgFVV1iv/AEBAK0BAKwADAFUAFQHrAesACwAXACgAAAEVMxUjFSM1IzUzNQMyNjU0JiMiBhUUFjcVMxUzFhUUByY1ND4CMzIBq0BAK0BAgBIZGRISGRk9QD4Cq6scMD0iFgHrQCtAQCtA/wAZERIZGRIRGdBQQA4MapKSaihALhkABgBVABUB6wGrAAsADwATABcAGwAfAAAlNSMVIxUzFTM1MzUDMxUjJTMRIxMzFSMVMxUjFTMVIwGrK0BAK0BrKyv+1SsrlioqKioqKoBAQCtAQCsBK8DA/qoBVlYqVipWAAQAGQBAAesB1QAEAA0AGQAiAAATBxc3JxcjBzMmJjU0Nhc1IxUjFTMVMzUzNScnIzEXNjYzMqSLSIxJeU1KsA8SBJIrQEArQDFxkoMSLxoTAbX1gPWA9YARLBgMFQtAQCtAQCtlxeYSFAAFAEAAQAHVAdUACwAeACIAJgAqAAABFSMVIzUjNTM1MxURIREzNSMiBhURFBYzITI2NTUjIxUzNQczNSMHNSMVAdVAKkBAKv7WgIASGRkSASoSGStVK4AqKisrAZUqQEAqQED+1gEqKxkS/tYSGRkSgFZWVqurgIAABABAAEABwAHAABUAJQAxAD8AAAEyFhc1JwcVFB4CFzY2NyYmNTQ2NhciBgYVFBYWMzI2NjU0JiYHMhYVFAYjIiY1NDYXIiYnNDY2MzIWFhUGBgFrBQsFoKAYKzsiCREICwwiOiQYJxcXJxgXJxcXJxcKDg8JCg4OCg8ZCBIWCAcXEQgYARUBAWdGRmkkRDkoCAIGBA8mFCQ6IioXJxgXJxcXJxcYJxceDgoJDw8JCg5yDQwHCwUFCwcMDQAGABUAVQHrAcQAHgAuADoARQB5AIkAAAEyFhc1NCYjIyc3JwcXNxcVFAYjIxYWFRQGBzM+AhciBgYVFBYWMzI2NjU0JiYHIiY1NDYzMhYVFAYlMzQmIyMiBhUUFhcnNyYnByc3JiMiBgcXBycGBxcHJwYVFBYXNxcHFhc3FwcWMzI2Nyc3FzY3JzcXNjU0JicHBiYmJyY2Njc2FhYXFgYGAaAPGwsZEYcWHg9MEB4WGREMDxIBAUMDGikZFSEVFSEVFSEVFSEVDRMTDQ0TE/6oaxkSQAgNDYUEFA8jCAoJExQIEQcHCggiEhMEEwgDAxMEFA8jCAoJExQIEQcHCggiEhMEEwgDA0wRIRsHBwUUEBEhGwcHBRQBAAkHZRIZFx4PSxAeFjESGRErGQULBRgmFxUVIRUVIRUVIRUVIRVrEw0NExMNDRPAEhkNCQgNZwoIIhITBBMIAwMTBBQPIwgKCRMUCBEHBwoIIhITBBMIAwMTBBQPIwgKCRMUCBEHWwcFFBARIRsHBwUUEBEhGwADAEAAKwHAAdUABwARADgAABMHJiYnNxYWNycHMxYXNyYmNSEnBzMOAgcGBgcmJicHHgIVMDAxMRUzNTAwMTQ2Njc+Azcz0R8LFAcqBQ4iVlVAAQMqAQIBFVVWQAEQGA0IEAcFDQYeCg8IKgoRDAsXFA0BQAESHgseFQsOFWZVVRsTCggSClVVJy4cDAcQCwgNBh4JEhoUamoWGhMLChgjMyUABQBAAEABwAHAAA8AEwAXABsAHwAAASEiBhURFBYzITI2NRE0JgMjNTMXIzUzNSM1MxcjNTMBlf7WEhkZEgEqEhkZ5ysrVSoqKipWKysBwBkS/tYSGRkSASoSGf7Va2tAKyuW1gACAEAAKwHAAdUALwA7AAAlFw4CBzUzNSM1NjY1NCYmIyIGBhUUFhcVIxUzFS4CJzcnFRQeAjMyPgI1NScyFhUUBiMiJjU0NgFrIQslLxhAQBMYER0SEh0RGBNAQBgvJQshVSM5QyEhQzkjwAkMDAkJDAzAIRIeFAS+KxkHIRQSHRERHRIUIQcZK74EFB4SIUBAHzYpFxcpNh9Aqw0JCA0NCAkNAAUAKwArAdUB1QAZACgANwBHAFYAAAEiBgcGBwYGFRQeAjMyNjc2NzY2NTQuAgMiJiY1NDY3FB4CMwYGNyImJjU0NjcUHgIzBgY3BiMiJiY1NDc2MzIWFhUUNzQuAiM2NjMyFhYVFAYBQCtHEyoWJCwXKTYfK0cTKhYkLBcpNp8dMR0MChcpNh8NIS4dMR0MChcpNh8NIVISEh0xHQcSEh0xHSoXKTYfDSESHTEdDAHVLCQXKRNHKx82KRcsJBcpE0crHzYpF/6AHTEdEiENHzYpFwoMQB0xHRIhDR82KRcKDEcHHTEdEhIHHTEdEhIfNikXCgwdMR0SIQAFABUAFQHrAesAAwAJAA8AFQAbAAABByc3NRc3JwcXBzcnBxc3NwcXNycHBycHFzcnASsrKystNmNjNlMtNWNjNdMtNWNjNVMtNmNjNgEAKysrVS01Y2M1Uy02Y2M2LS02Y2M2rS01Y2M1AAQAawAVAdUB6wAPABgAIQA5AAABIgYGFRQWFjMyNjY1NCYmBzQ2MzIXByY1FyInNxYVFAYjByMRMxUzNTQmIyMiBhURFBYzMzI2NTUjAYAYJhcXJhgYJhcXJk0fFg0MSAY1DQxIBh8WFdbWKhkR1hEZGRHWERkqAVUXJhgYJhcXJhgYJhdVFh8GSAwNNQZIDA0WH0sBABVVEhkZEv6AEhkZElUACABVAFUBwAGrAAMABwALAA8AFAAeACMAJwAAEzMVIwczFSM1MxUjNTMVIxc1IxUzNycmIgcHFzc2NAcVMzcnJzMVI9VWVoBWVlZWVlbWVjS0GQIHAxMmEgPVJY4lJFZWAatWqlbWVtZWXjRWOhkDAxMlEwIIkiaPJaJWAAMAawAVAdcB6wBAAEwAZAAAJSc1NzYnJyYjIgcHJicnJiMjIgcHFCIVBycmIyIHBwYXFxUHBhcXFjMyMzcWFxcWMzMyNzcyNjE3FxYzMjc3NicHIiY1NDYzMhYVFAYHMxUUBiMjIiY1ETQ2MzMyFhUVIzUjETMB0RERBgQSAgYBAhQDAQMBCCQIAQMCAhQCAQUDEgQGEREGBBICBgECFAICBAEHJQcBAwEBAhUBAgUCEgQGUQ0TEw0NExMiKhkR1hEZGRHWERkq1tbwDgQOBAcfBQEIAQEWBwcVAQEBCAEEIAYFDQUOBAYgBAgBARYHBxYBAQgBBR8HBBATDQ0TEw0NE0tVEhkZEgGAEhkZElUV/wAABACIAEABeQHAAAUACwAcACgAADcXNzcmJzMGBxcXNyc0Jic1IxUGBhUUFhYzMjY2ByImNTQ2MzIWFRQGiAUjOxYQdhAWOyMGORgTKhMYER0SEh0RQAkMDAkJDAxvLyGiBRAQBaIhL+YVIQYvLwYhFREdEhIdBA0ICQ0NCQgNAAMAKwArAdUB1QATACgALwAAATIeAhUUDgIjIi4CNTQ+AjciDgIVFB4CMzI+AjU0LgIjFzUjFSMXNwEAIz4vGxsvPiMjPi8bGy8+IyxNOyEhO00sLE07ISE7TSwVKkBVVQGrGy8+IyM+LxsbLz4jIz4vGyohO00sLE07ISE7TSwsTTsh1VVVVVUAAwArACsB1QHVABMAKAAvAAAlIi4CNTQ+AjMyHgIVFA4CBzI+AjU0LgIjIg4CFRQeAjMnFTM1MycHAQAjPi8bGy8+IyM+LxsbLz4jLE07ISE7TSwsTTshITtNLBUqQFVVVRsvPiMjPi8bGy8+IyM+LxsqITtNLCxNOyEhO00sLE07IdVVVVVVAAQAQABAAcABwAAPABMAFwAbAAABISIGFREUFjMhMjY1ETQmAyM1MzcjNTM1IzUzAZX+1hIZGRIBKhIZGXyWlkDW1tbWAcAZEv7WEhkZEgEqEhn+1SsrKisrAAMAFQArAesB1QASABgAOgAAATU0JiMhIgYVAxQWMzM1NDY2MwcnNRc3FRcVFAYjIiY1NTQ2MzIWFRUzNTQmIyIGFRUUFhYzMjY2NTUBwBkS/qsSGAEZEusRHRKAq6uqKxkSERkGBAUGKyAWFh8XJxcYJxcBK4ARGRkR/wASGWsRHRIWaytrayurVRIZGRJgBAcHBGBgFh8fFmAYJhcXJhhVAAMAFQAVAesB6wAHAA8AFwAAATc3JycHBxcHJwcHFxc3NxcHBxcXNzcnAZUbOzsbGjs7hjU1dnY1NXYqGjs7Ghs7OwFAOxobOzsbGkZ2djU1dnY1QDsaGzs7GxoAAwBAAEABwAHAAAkAEAAXAAATERQWMzMRIyIGJSMVMzU0JgMzMjY1NSNAGRKAgBIZAVWAqxmSgBIZqwGV/tYSGQGAGRmrgBIZ/oAZEoAAAwArACsB1QHVAAgAEQAhAAABIyIGFRUzNTMXIyIGFRUzNTMXIyIGFRUUFjMzMjY1NTQmASvWERkq1lXVEhkr1SurEhkZEqsRGRkB1RkR1tYrGRLV1SoZEqsRGRkRqxIZAAQAFQAVAesB6wAHAB0ALQAzAAABIycjByMVIRciBzUhERQWMzMWFjMyPgI1NC4CAyImJjU0NjYzMhYWFRQGBicjFRc3JwFASxVrFUsBKxUWFP8AGRF6EUksHzYpGBgpNh8dMRwcMR0eMB0dMBMgTRE+AdUWFiprBlv/ABEZJjAYKTYfHzYpF/8AHTAeHTEcHDEdHjAdwGssGSQABQAVABUB1QHVAAcADwAXACgALQAAExcnNwcnFwcFJxcHNxcnNxEHJxcHNxcnByYiBwcGFBcXFjI3NzY0JycHJzcXB6A1Hh41NR4eATU1Hh41NR4eNTUeHjU1HoQHEQf4BwcxBxEH+AcHMRYuNC40AYkeNTUeHjU1tB41NR4eNTUBAB4eNTUeHjU8Bwf4BxEHMQcH+AcRBjJ1LjQuNAADABUAFQHVAdUABwAYAB0AAAEHJxcHNxcnByYiBwcGFBcXFjI3NzY0JycHJzcXBwHVNTUeHjU1HoQHEQf4BwcxBxEH+AcHMRYuNC40AdUeHjU1Hh41PAcH+AcRBzEHB/gHEQYydS40LjQAAwArACsB6wHrAAcAFgAnAAABBycXBzcXJwcXBxc3NjQnJyYiBwcXNwcnJwcXBwYUFxcWMjc3FzcnAes2NR4eNTYelC00ETcGBjIHEQY3ETURUJIbkowGBjIGEgaMkhuSAeseHjY1Hh41Ty00EjcGEgYyBgY3ETSOUJIbkowGEgYyBgaMkhuSAAMAFQA1AesB6wADABcAMQAAAQcVNyURFBYzMjYzNjYzMhYXESYmIyIGAREmJicRJiYjIgYGBxU+AjMyFhcWMzI2NQGVamr+gAcEAgIBFjgYHz8XFz8fID4BvgoVDBIlFBIrKg4OKisSGzQXAgMEBwHra+tgi/7HBAYBCg0OEgFLEg4O/s4BIAcKBP7gBgUIDwkrCQ8ICgwBBwQABQBAACsBwAHrAAwAGAAcACgAOwAAARUjJwcVIzU3NjYXFyciBhUUFjMyNjU0JhMhNSE3MjY1NCYjIgYVFBYnNCYjIxUzFRQWMzMyNjU1IxUjAStAOhxVIgYiElhJEhkZEhEZGQQBAP8A4A0TEw0NExN+DAlAKxkRKxIZKysBVSoZVMXVYxIPCCeWGRISGRkSEhn+gCoWEg4NExMNDhJVCQwqFhEZGRFAKgACAFUAKwGrAdUAFQAbAAABERQGIyEiJjURNDY3NTMVMzUzFRYWBRUzFTM1AasZEv8AEhklG0BWQBsl/tXVKwFV/wARGRkRAQAeLQgtKiotCC1zKypVAAUAKwArAdUB1QAIABgAHAAgACQAAAERIRUhMjY1ESchIgYVERQWMyEyNjURNCYDIzUzFyM1MzUhNSEBq/7VASsRGYD/ABEZGREBABIZGadra5Vqav8AAQABgP7VKhkRAStVGRH/ABIZGRIBABEZ/tZqamorawAFAGsAKwGVAdUADwATACYAMAA6AAABIyIGFRUUFjMzMjY1NTQmAyM1MzUjNC4CNTQ2NjMyFhYVFA4CNyExNDYzMzIWFScjMTQ2MzMyFhUBa9YRGRkR1hEZGWcqKioQFhAVIRUVIRUQFhBr/wATDcANExXWEw2WDRMBVRkR1hEZGRHWERn+9iAVDBgaHRAUIhQUIhQQHRoY6Q4SEg5ADhISDgABACsAKwG5AdcAEwAAASYOAhUUHgIzMjY2Ny4DNgEHLlA8IiE7TSwoRjgTME84GwgB1QIhO04tLE07IRswIAEoQlJYAAQAKwBrAdUBlQAGAA0AFwAjAAABNTMyFhUVBzI2NTUjFQMRIyImNTU0NjMXNCYjIgYVFBYzMjYBVVYRGSoRGYAq1hEZGRF2Ew0OEhIODRMBFYAZEVaqGRFWgAEq/tYZEdYRGZUNExMNDRMTAAUAAABAAgABwAAWADQASQBZAGUAADczJyYmIyMVMxcwMDEGBgcjFTM1NDY2NyMnJiYjIxUzFyMXMwYGByMXMx4CFxY2NjU0JiYHIiYmNTQ2Nxc3JzA2MzIWFhUUBgYnIgYGFRQWFjMyNjY1NCYmByImNTQ2MzIWFRQG1RApAxgOU1MeIzEGV4AXJ9cRHQQWDlRUGHkKWAcKAjwKMgQdKRkeMx8cMR4SHREMCxQoFAEBER0SEh3SER0SEh0REh0RER0SCA0NCAkNDdW0DxMrhQs2JSoVGCYXgE8NDytAKgoVDCsXJhcBARwyHh4wHKoRHRIPGQg4DjkBER0SEh0RFREdEhIdEREdEhIdEVUMCQkMDAkJDAADAGsAQAGVAdUAIgA1AEEAADciBhUhNCYjIzUzMjY1IyImJjU0NjcmNTQ2NwYGFRQWFjMVEzIWFRQGBxc3FzcnNycHJwcXBwc0NjMyFhUUBiMiJpURGQEqGRFWQBIZqxEdEhINCQEBHSUcMR0MFh4JCQ0UBygHFDoVBygHFBUTDQ0TEw0NE2sZEhIZKhkSER0SER0JDREEBgQMNiEdMR0qAR8fFgwUCCIHFA8UB6EIFA4UCFYOEhIODRMTAAMAKwArAdUB1QATAB0AJwAAATQuAiMiDgIVFB4CMzI+AiUXBiMiLgI1NAUUByc2MzIeAgHVITtNLCxNOyEhO00sLE07If6l7ys+Iz4vGwFWJe8rPiM+LxsBACxNOyEhO00sLE07ISE7TZXvJRsvPiM+Pj4r7yUbLz4AAwAqACkBwAG5AAoAFAAYAAABFRc2NRE0JiMhFycHFxEUFjMhFzcnIzUXAZUqARkS/vYqcBsWGRIBBywbedXdAYDgKgYEAQASGSs5Gxb++BIZLBs83d0AAgArACsB1QHVABMAKwAAASIOAhUUHgIzMj4CNTQuAhMHBiInJwYmJyYmNxc3JzYWFxYWBxcWFAEALE07ISE7TSwsTTshITtNPR4ECANJFCoPEgYLMh4yFTESDwkHSQMB1SE7TSwsTTshITtNLCxNOyH+4R4EBEgGCBARMRUyHjILBhIPKhRJAwgABgBAAEABwAHAAA8AGwAfACsALwAzAAABISIGFREUFjMhMjY1ETQmBzcXNxcHFwcnByc3JzMVIxcjFSM1IzUzNTMVMxcjNTM1IzUzAZX+1hIZGRIBKhIZGZEXHh4WHh4WHh4XHq9ra3AqICsrICqLa2trawHAGRL+1hIZGRIBKhIZVxceHhceHhYdHhceECCQKysgKio7IBUgAAUAKwBVAdUBqwADAA8AGwArADIAAAEVMzUHFhYXNjY3JiYnBgY3JiYnBgYHFhYXNjYFIgYVFRQWMzMVMzUzFxEHFzQmJxU2NgGAVYAQJRAGDQYQJQ8GDVcGDQYQJRAHDQYPJf6yERkZERYqFmpqoBIODhIBFSoqjQscDAkRCAwcDAkR+AgRCQwcDAgRCQwcPRkSKhIZVVVAAQBAQBUlDY4NJQACAEIALQGxAeAADQARAAAlAQcXBhQXFxYyNzc2NAcnNxcBpf7wU64NDR4MJAxbDIUeWx7QARBT+QwkDB4NDVoNI2weWh4ABgAVAEAB6wHAABIAGAAjAC8ANQA5AAABISIGFRUzNSERIxUzMjY1ETQmARUzNCYmJxUyFhYVMzQuAicVMh4CFTM0LgIXFRc3NQc1Bxc3AcD+gBIZKwGAlZUSGRn+Q0ARHRIeMB0rGCk2HyhGNB4rJEFVpUpLS3V1dgHAGRJAQP7WKxkSASoSGf7AQBIdEVUqHTEdHzYpF1YrHjVFKDFVQCUYKikpKiiVQEBAAAMAawAVAZUB6wAFABUAGQAAAQc1IzcVJzMyFhURFAYjIyImNRE0NhcRMxEBNUAqQHbWERkZEdYRGRkR1gEVgFaAVtYZEv6AEhkZEgGAEhlr/wABAAACACoAVQHWAa4AJQAoAAAlJzU+AicmJicmBgYVMzQ2MzIWFRQGIyIGFRUHBgYWOwIyNiYlNxcBzbgSGgwEBB8VGCoaKxMNDRMTDggMuAgBDArAwAoMAf6rgIB8iRQFGiMUFSAFBREmFw0TEw0NEwwJJokGEw4OEwpgYAABACsAKwHVAdUAEwAAASIOAhUUHgIzMj4CNTQuAgEALE46ISE6TiwsTjohITpOAdUhOk4sLE46ISE6TiwsTjohAAIAQAAVAcAB6wAVADwAAAEjNTQmIyMiBhUVIyIGBhUVITU0JiYXIzU0JiMiBhUVIzU0JiMiBhUVIzU0JiMiBhUVIzU0NjYzMzIWFhUBVRUZEioSGRUeMB0BgB0wIioNCQgNKwwJCQwrDQgJDSoRHRKqEh0RARWrEhkZEqscMR2Wlh0xHNVACQwMCUBACQwMCUBACQwMCUBrER0SEh0RAAIAKwArAdUB1QAGAA0AAAEHFyM1FzcBNxc1IxcHAdVxR6tGcf6ScUarR3EBt3FGq0dx/lZxR6tGcQADAB4AHgHEAcQAHAApADsAABMhMhYVERQVJzY1NSMVIyc1MxUzNTQmIyMiBhUVEychIiY1ETQ0NSc3AScnIxUjNTMnBhUVFBYzMzI2NZIBAxIZSQkgBCcrIA0IQAkNkTj+/RIZIh4BiNkSDisEGwkNCEAJDQGrGRL/AAECSAYKFgsnGQsWCA0NCAT+9zcZEgEAAQEBIx7+eJ0SC0AbBgpWCA0NCAACACsAKwHVAdUADQASAAABISIGFRE3ITI2NRE0JgcnBzUzAav+qhEZVQErERkZJzU1agHVGRH+gFUZEgEAERnqICCqAAIALQA8AdMBygADAC4AACU3FwcnMjY2NTQmJwcnNyYmIyIGBhUUFwcnNyc3JiYGBwcXIwcXNzUXNxcHFzcWASYtgC0xFSIUBQQ5IDkHEQoUIhQEJyYPHi0MISEMTB48D0sPHw8mni3zDLwugC7vFCIUChEHOSA5BAUUIhUMDCgmDx4uDAgIDEweD0wPPR4PJp4t8gQACAArAEAB1QHAAAUACQANABEAFQAZAB0AIQAAATUjESERASM1MzUjNTM1IzUzNSM1MxMjNTMHIxUzFSMVMwEA1QGq/wCAgICAgICAgNarqytVVVVVAWtV/oABK/8AKisrKisrKv7W1SsqKysAAgArAFUB1QGrACMAJwAAATU0JiYjIgYGFTM0NjMyFhUVIzI2NTUjFRQWMyMVMxUhNTM1ByM1MwGAER0SEh0RKwwJCQyqERmAGRJVKgFWKsAqKgErQBEdEhIdEQgNDQhAGRFWVhEZK6urK6uAAAQAQABAAcABwQAZACQALAA2AAABNycHJyYiBwcGFBcXBxUzNxcWFjY3NzY0LwI3MDAxFwcXNxcXJzcXBxc3Fzc2NCcnJgYHBxcBWiJQIlgMJAwpDAxYZVBmWAoXFQYpDAzvWCgbGR4ZH4BYKB8ZHhkbJgYGMggSBCdQAQoiUCJZDAwpDCQMWGZQZlkKAwcGKQwkDGJYKBsZHhkf0FgoHxkeGRvWBhIGMgcDBCdQAAQAHgAeAdUB1QAOABwAKAAsAAATNzEXBxc3NjQnJyYiBwcnBxcHBhQXFxYyNzcXNycnNxcjFTMXBxc3Fyc3FwfJN6s3HjcMDKsMJAw3bx5QNwwMqwwkDDdRHsSrNyIZRCAXHhcgThdrFwF0N6s3HjcMJAyrDAw3Mh5RNwwkDKsMDDdQHhmrNyIqIBgeGCDGGGsXAAMAKwBAAdUBwABeAHIAfgAAJTMRIREzNjYnJg4DJyYmPgInJiYGJicmPgI1NiYmNDc2NhceAzc2LgI2NzYeAhcWPgMyFxYOBBcWMjY2FhcWDgMXFB4CBwYGLgIHBhYXEzIWFREUBiMhIiY1ETQ2MzM3MxcTIiY1NDYzMhYVFAYBFJf+qpsCBAoFCgkLDwkJAQgKBgQBEBIPAQEKDQoBBgYGBAwDBwwNDggHBAoIAwwNDQkLCgsRDg4ODwoJAhAXFQsDAgsODw0EBAsVFhABDxIIBggRExEPBgYGA5cRGRkR/qoRGRkRRCeAJxwICgoICAoKawEA/wAPHgcDBg0MBgUHCgoNEQwHAQEHDQgIBAYGBgkJCgYEAgEDDAsFBQQNDw4MAwQNFRMDAwwTFQ0MDRENCgoNCgYHBAULDAsDAQMFBwcHDA0MAwgKAwgJGw0BKhkR/wASGRkSAQARGSsr/usKCAgKCggICgADABUAHgHrAdUAKgAuADIAABMXFgcGIyInJwYHFxYUBwYjIicnBgcXFhQHBiMiJycGBgcGBhUVMzcXNwEBJzcXJwcnNx6oBwcDBAUDJAQGHgMDAwUEAxsICRwDAwQEBQIeCxYJDBDLR38e/ngBTx5fHy5fmGABpqkHBwQEJA0LHgMJAwMDGwkIGwMJAwMDHQcLAwUWDjhHfh4BiP7tH18eLV+XXwAHAB4AHgHEAesACAAcACQALQBBAEkAZAAAASc1NDYzMhYVFzU1NCYjIgYVFSM1NCYjIgYVFRcDNCYjIgYVFxcnNTQ2MzIWFRc1NTQmIyIGFRUjNTQmIyIGFRUXAzQmIyIGFRcTAQcXIxUuAjEmIyIHFA4CMRcWMzMyNyMXARU1EAsLD5YQCwsQFRALCw+A4BALCw81SjUQCwsPlhALCxAVEAsLD4DgEAsLDzX5/ngeeAEMGRIGBggHBwoHkRMbgRMQARkBJzV0CxAQC+AlewsQEAt7pgsPDwupgAETCxAPCjVLNXQLEBAL4CV7CxAQC3umCw8PC6mAARMLEA8KNf7KAYgeeFsGDwoDBQEHCQeZFAsYAAMAQABVAcABqwAFABUAGQAAAScHJwcXEyEiBhURFBYzITI2NRE0JgMhNSEBYh5bLR5LrP7WEhkZEgEqEhkZEv7WASoBGB5aLR5MAQwZEv8AEhkZEgEAEhn+1dUAAgArAFUB1QGrABEAGAAAASMnIyIGFREUFjMhMjY1NTQmAzUjNTM1FwGrqyuAERkZEQFWERkZkVZWagGAKxkS/wASGRkS1RIZ/wBAVUBqAAMAFQAVAdUB1QAQACEAXAAAAScmNzcjBwYXFxYHBzM3NiY3JyY3NyMHBhcXFgcHMzc2JicHBhUVFBYWOwMyNjU0JiMjNTMyNjU0JiMjNTMyNjU0JiMjNTMyNjU0JiMjPgIxNjU0JzAuAjEBTgITBQIoAgYiAhMFAigCAw9FARQFAigBByICEwUCKQEDDvGaFBIdEYYlewsPDwt7pQsQEAuluwsPDwu7kAsQEAvTBw8KAwYHCQcBmAIVGgwJLSIBFRoNCRYpEAIVGgwJLSIBFRoNCRYpDZESHIERHRIQCwsQFRALCw8WDwsLEBUQCwsQDBkSBgYIBwcKBwAFACsAVQHVAasABgAQABQAHgAiAAAlNSM1MwczBxUjIiY1NTQ2MxcjFTMTFSMiJjU1NDYzFyMVMwFrK5UqKpXrERkZETAgIJDAERkZETAgIFXAlmtVlhkSQBIZOyABG5YZEkASGTsgAAEAKwBrAdUBlQAIAAABBxchFSEHFzcBQB5i/qcBWWIelQGVHmIqYh6VAAcAVQBVAesBqwADAAcACwAPABMAHgAjAAABIxU3JTMRIxMzFSMVMxUjFTMVIyUnJiIHBxUzNzY0ByM1NxcBgCsr/tUrK4ArKysrKysBDBgKGglxRXEKiRZJFgGrqSt+/qoBVlYqVipWnhkJCXFGcQoadBZKFgAFAAAAFQIAAesALgA/AEUAWgBgAAABIycmJiMjFTMXIyczNSMVMxcjLgInJgYGFRQWFjMyNjY3Mx4CFxY2NjU0JiYFBgYjIiYmNTQ2NjMyFhcjFTcjJzMGBhciJiY1NDY3FzcnMDIxMhYWFRQGBgcjFzUzJwGVESQFFg43NyBnCBhrJiYOBB0pGR4zHxwxHhosHQVaBB0pGR4zHxwx/vQHIBUSHRERHRIVIAc8wh4QQQcKZhIdEQwLFCgUARIdEREdvFaAVoABa2QMECtVFSsraxgmFgECHTEeHjAdFicZGCYWAQIdMR4eMB2AExgRHRISHREYEyoqKwkWYREdEg8ZCTkPOREdEhIdEWtAK0AABQBAABUBwAHrABsAJwAzADcAPQAAASYmIyMiBgcHFRQWMzMyNjU1IRUUFjMzMjY1NQUiJjU0NjMyFhUUBjMiJjU0NjMyFhUUBiU3MxcBMzUXIxUBlAQQC+oLEAQsDQgWCA0BAA0IFggN/ssOEhIODRMT3Q0TEw0OEhL+6CDqIP8AVoBWAdUKDAwKgKoJDQ0JFRUJDQ0JqlUTDQ0TEw0NExMNDRMTDQ0Ta2Bg/uorQCsABgArABUB1QHAABsAIgAmADYAQgBIAAABNCYjIxUzFQcjNSMiBgYVFTMUFhYzMjY2NTM3BSImNTMUBiczFSMFIgYGFRQWFjMyNjY1NCYmByImNTQ2MzIWFRQGBTM1FyMVAZUZEUBAS0tVGCYXKhIdERIdEWBg/wAIDSsNM2pqASoRHRISHRESHRERHRIIDQ0ICQ0N/vdWgFYBlRIZKzhdaxcnGEARHRISHRF5jg0ICA3rK2sRHRIRHRISHRESHRFVDQgJDQ0JCA1rK0ArAAcAFQAVAesBwAAfACIAJgAyADoARgBMAAABNTQnJyYjISIGFRUUFjMzFhYzMjY3MxYWMzI2NjU0JicjNSUzFSMXIiY1NDYzMhYVFAY3NTM1IzUzFRciJjU0NjMyFhUUBgUzNRcjFQHAClQNFP7/EhkZEgQHIBUVIAezBiEVEh0RGEo0/utVVUAJDAwJCQwMN0BAa4AJDQ0JCA0N/uJWgFYBESAPDGUPGRKqEhkTGBgTExgSHREVITU9GFWADQgJDQ0JCA0rKitVqisNCAkNDQkIDWsrQCsABQAoABUB1QHrACIALgA+AEoAUAAANzM1NDY2MzMnJiYjIxUzFzAwMQYGByMmJgcGBgcGFhYzMjYnIiY1NDYzMhYVFAYlIgYGFRQWFjMyNjY1NCYmByImNTQ2MzIWFRQGByMXNTMnp5kXJxcQKAQXD1NTHiMxBnAIKBkTHAMEEB8UFCE1CQ0NCQgNDQEiER0SEh0REh0RER0SCA0NCAkNDbNWgFaAqxUYJhe0DxMrhQs2JRYZBQQcExQjFRgTDAkJDAwJCQxVER0SEh0RER0SEh0RVQwJCQwMCQkMVkArQAAEAEAAVQHAAasACAARAB0ARgAAJTQmIyMVMzI2ByMVMzI2NTQmJyMVMxQWMzM1IyIGJzQ2MzMyNjY1NCYmIyMiBhUUFjMzMhYVFAYjIyIGBhUUFhYzMzUjIiYBwA0IKysIDRUrKwgNDbMrKxkSQEASGZUZESAVIhQUIhVKCQ0NCUoOEhIOIBcnFxcnFysrERnVCQ0rDTgqDAkJDEBVEhmrGQQRGRUhFRUhFQ0JCA0TDQ0TFycXGCcXKxkABQBAAEABwAHAAA8AGwApACwALwAAASEiBhURFBYzITI2NRE0JgcyFhUUBiMiJjU0NhcjFSM1IzU0NjMzMhYVFyczJzcXAZX+1hIZGRIBKhIZGfILEBALCw8PQRZAFRkSFRIZYDZrazY1AcAZEv7WEhkZEgEqEhlAEAsLDw8LCxCrVVU2ERkZEXZWKlZWAAcAFQBAAeoBwAAOAEkAVQCRAJ0AygDRAAA3Ig4DFRUhNTQuAyU2NDU0Jzc2JycmBwcmJycmIyMiBwcGBycmBwcGFxcGFRQXBwYXFxYzNxYXFxYzMzI3NzY3FxY3NzYnByImNTQ2MzIWFRQGBycmBwcmJycmIyMiFQcGBgcnJgcHBhcXFBUUFQcGFxcWNzcWFxcUMzMyNTc2NjcXFjc3NicnNDU0NTc2ByImNTQ2MzIWFRQGJTMyNjU1NCYjIzQmJxUUBiMiJjU1JiYjIgYHFRQGIyImNTUGBhUjIgYVFRQWFzI2NyMWFsARLS4nGAFWGCcuLQEGAQEQAwIPAQQSBQcDAQMeAwECBwYSBAEPAQIQAQEQAwIPAQQSBQgCAQMeAwECBwYSBAEPAQJHCxAQCwsQEAILAQINBAUCAQIVAwICBQINAgELAQILCwIBCwECDQQFAgMVAwEDBAIOAgELAQILCwI1BwoKBwgKCv7otgQHBwQGFxMHBAQHBQoGBgoFBwQEBxMXBgQGBl8eLQekBy3ABw4UGxErKxEbFA4HrgIDAgQEDAIDGQMBBwQDFAMDFAMEBwEDGQMCDAQEAwQMAgMaAggEAxQDAxQCBQgBAxoDAgcPCwsQEAsLD1QSAwEGBAEPAgMOAQICBQECEgICCQMCAgMJAQITAgEGBAIOAgIOAQMCBgECEwIBCQQBAgMJAiEKBwgKCggHClwGBAEEBhgnCxQFBgYFHQECAgEdBQYGBRQLJxgGBAEEBlUkHBwkAAIAQABAAcABwAAPACEAAAEhIgYVERQWMyEyNjURNCYHIwcjIiY1NDYzMzczMhYVFAYBlf7WEhkZEgEqEhkZPCVqRw0TEw0lakcNExMBwBkS/tYSGRkSASoSGYDAEw0NE8ATDQ0TAAMAQAArAasB1QALABcANAAAEzIWFRQGIyImNTQ2FxQWMzI2NTQmIyIGFyMiBgcHJyYmIyMiBhUVMxUzNRczNxUzNTM1NCaLERkZERIZGdISDg0TEw0OEkA9CREFE00GEwtAEhkgazYvEFUWEwHVGRESGRkSERmgDRMTDQ4SEkMKCB2ECgwZEoCV3V0Xl2pLDRMABAArACsB6wHrAAwAQwBPAFcAADc0NjMyFhUUBiMiJjUlBxYWFRQOAiMiLgI1NDQ1NjY3FhYzMjY3LwM3NyYmIyIOAhUUHgIzMj4CNTQmJwcHNDYzMhYVFAYjIiY3BycnNzcXF6UQCwsQEAsLEAEWFAICGy8+IyM+LxsqQBEdVjIIDgcNEyk8PA8ULBgsTTshITtNLCxNOyEKCQeWEAsLEBALCxCSFxc0NBcXNOsLDw8LCxAQC14rBw8IIz4vGxsvPiMBAQEQPSkkKgEBHCkTGxsHCQohO00sLE07ISE7TSwYLBQPXgsPDwsLEBCpNDQXFzQ0FwAFACsAQAHVAcAADwATABcAGwAhAAABISIGFREUFjMhMjY1ETQmAyM1MzUjNTM1IzUzFyc3FzcXAav+qhEZGREBVhEZGedqampqampnPB4eRB4BwBkS/tYSGRkSASoSGf7VKysqKyurPR4fRB4ABgArACsB4AHVAAsAGwAnADMAQQBPAAABNDYzMhYVFAYjIiYTNTMnJiYjIyIGBwcWFhUVJzI2NTQmIyIGFRQWJzI2NTQmIyIGFRQWEzUzNTQmIyMiBhUVMxUzNTM1NCYjIyIGFRUzFQFVGRISGRkSEhlWNTYFFg4CDhYFEhEWYA0TEw0OEhKIEhkZEhEZGTwgGRJAERkg4BUTDSsNExYBqxEZGRESGRn+koCiDhAQDjcJIxWq4BIODRMTDQ4SdRkSERkZERIZ/quVgBIZGRKAlVVVDRMTDVVVAAoAQABVAcABqwAYAB4AJQApAC0AMwA3ADsAPwBDAAABNSM1JwcnBycHFSMVMxUjFTMVITUzNSM1JxcVIzU3JxcXFSM1Nwc1Mx0CIzUnNxcXFSMVMxUjFTMVIzMjNTM1IzUzAcArQCorKypAKysrKwEqKytAFisJSQ0IKggIKipWFgwJKysrKyvWKysrKwEAK0BAKysrK0BAKysqVlYqK24VLi4JDAwJLi4JjSsrKisrrhUMCS4rKyorKyorAAEAVQBVAasBqwAdAAATHgQxFRQWMzMyNjU1MD4DNzYmJiMhIgYGWw0gIRsRDQkqCQ0RGyEgDQYCDAn+2AkMAgGIECoqJBWACQ0NCYAVJCoqEAcQDAwQAAQAawArAWsBwAAJAA0AJAAwAAA3MxUUBiMjIiY1NTM1IzcVJzAxFhYXIzY2NyYnJzU3NjYzMhYXBzQmIyIGFRQWMzI2ldYZEoASGdbW1kQZIwbSBiMZCwZdXQgeEgsUCBIMCQkMDAkJDGsWERkZEStr1YAOCikbGykKCAwTFhMPEwgGMgkMDAkJDAwABABrACsBawHAAAkADQAkADAAADczFRQGIyMiJjU1MzUjNxUnMDEWFhcjNjY3JicnNTc2NjMyFhcHNCYjIgYVFBYzMjaV1hkSgBIZ1tbWRBkjBtIGIxkLBl1dCB4SCxQIEgwJCQwMCQkMaxYRGRkRK2vVgA4KKRsbKQoIDBMWEw8TCAYyCQwMCQkMDAAEACsAKwHVAdUABwAbACcAMwAAJTEnNxc3FwcDIg4CFRQeAjMyPgI1NC4CBxc3FwcXBycHJzcnEyImJzcWFhUUDgIBLDQXHTUWSywsTTshITtNLCxNOyEhO02PHh4WHh4WHh4XHh56Iz8Y9BYbGy8+iTMXHTUXSwFMITtNLCxNOyEhO00sLE07IVseHhceHhYeHhYeHv7yGxb0GD8jIz4vGwADAFUAQAGrAcAABAAYACAAAAEHESERBxQGIxUjNSImNTUzFTM1MxUzNTMXIzUjNTQ2MwEAqwFWoBMNFg0TFgoWChY1FRYZEgHAgP8AAQBLDRNVVRMNQEBAQEC1S0ARGQADACsAFQHrAasAEwAWAB0AAAEhIgYVERQWMzM1IzUXNxUzNTQmBychBxcHNSM1MwGr/qoRGRkRwMCrqyoZvKsBVhZWVlVVAasZEv8AEhkr1WpqapUSGZZrwFVWQCsAAwArAFUB1QHAABYAGgAeAAABMycHMxUjFTMVMzUzFTM1MxUzNTM1IyE1NxUzNRcVAZVA1dVAKysqVipWKisr/wBWKlYBAMDAQCtAQEBAQEArZ0yzs0xnAAYAVQBAAasBwAADAAcACwAPABUAGQAAEyEVIRUhNSEVMzUjFTM1IzcnBxc3JwEVITVVAVb+qgFW/qqWlpaW9B4ePGIf/skBVgFrK1UqgCuAKxEfHj1iHgEAKysABAArAFUB1QGrAA4AIAAtADwAACUjNTMxJiYnNjYzMh4CNyYmIyIGBxYWFxYVMzM1IzY2JzY2Nw4DFTAwMTYnJiYnFhYVFAYVFhYXNjYBANV6C0ItChULJD4uG9UKFQsvThYFBwIEK4B7DEJaDC0eIjwtGh8rDjQjFxoBBw0GBA1VKy1CCwMDGy4+gQMDLiYKFgwVFistQiYhNxMCGy49IylFIzYPHkkoBAYDBAkFEyMAAgArACsB1QHrAAsAMAAAJScjBxUXFjI3NzY0JzcXNjYmJycHNScHFzMHFwcnNScHFzMXByMHBhQXFxYyNzc1NwHOcRU2cQYSBi0HYx4tDQgIDUseD0wPPR4WPVlAPEAeWBItcQYGLQcRBnFufHE2FXEGBi0GErEeLg0hIA1LHj0PTA8eFz1YHkA8QVgScQYSBi0GBnEtbgAJAAAAKwIAAdUADwAfACsANwBGAEoAVgBgAGQAAAEyFhYVFAYGIyImJjU0NjYXIgYGFRQWFjMyNjY1NCYmByM1IzUzNTMVMxUjFyMVIzUjNTM1MxUzIxQGBxcjJyMVIzUzMhYVBzUjFSc1MxUjNSMVIzUzFTcyFhUVFAYjIzUXNSMVAQAjOiMjOiMjOiMjOiMYJhcXJhgYJhcXJgMqKysqKyvrKyAqKiArgAsIEyATGCBLDRMgK+ogICsgILUNExMNSkoqAdUiOiQjOiMjOiMkOiIqFycYFycXFycXGCcXlisrKior6yoqICsrCRAFLCoqgBQMFhYWCyuANTWAKysUDEANE4BgQEAABAAeAB4BxAHmABcAJQAtAFUAABM2NjMyHgIVFAcUBgcnNjU0JiYjIgYHFxc2NjU0JicHFhYVFAYnIgcXNjU0JhMBBxcGBhUzNDY3FwYGBwYGBwYjIiY1IxQWFjMyNzY2NzY2NzI0MxeBEioYIDYpFxcBAR8NHDEeDhsMzx8XGyUgHhoeFKwGBkACINn+eB4tBQYrAQGNDhoICBEMBgoRGSsXJxcTEBYbCgUSDQEBjQG8DA0XKDcfKikBAQEgHBoeMB0IB88eHkopMVUgHhpGKCA7kAFBBwYWH/7HAYgeLQ0dDwYLBY0KHBcYFQYEGRIYJhcHCyQdEBIKAY0AAwBAAEABwAHAAA8AGwA5AAABISIGFREUFjMhMjY1ETQmAyImNTQ2MzIWFRQGNwYGBwYGFSM0NDc2Njc2JiYjIgYHJzY2MzIWFxYWAZX+1hIZGRIBKhIZGacLEBALDA8PNAwVBgMCJwcHGggHAhERERIDIwclHRchCAcEAcAZEv7WEhkZEgEqEhn+wBALCxAQCwsQnhIRCwULDwgYCw4TDQgYEhULDxUgEw0MJgAOAEAAQAHAAcAAAwAMABAAFAAYABwAIAAlACoALgAyADYAOgA/AAABIzUzAzM1FzcnMzUjNzM1IxUzNSMHMzUjAzM1IwMzNSMXNSMUFgEVMzQmBzM1IwczNSMTMzUjJzM1IzUzNSIGAWsrKysrNx43N4BVKysrK6oqKlYrK1UrKysrGQE8Kxm8KiqrKytVKytVKysrEhkBlSv+gDc3HjcrgCuAKtUrASor/tUrgCsSGQGAKxIZKyuAK/7VK4AqgCsZAAMAHwBVAcABqwAYACUAOAAAExUmIyIGBxczFRYWFxUjFRQWMzMyNjY1EQU1Iyc2MzIXFwcGIyIXFAYjIiY1NSM1Njc3FzM1JzUzwBsdHDcWRxcOIxJAGRLVEh0R/ugwFxMULCAeBBEYD98MCQkMgBMOBD0egKoBqx4LFhZHGA0PATBAEhkSHREBFokuFgcfHgQRhggNDQgrNwgOBDwegA0ADQAsACwB1AHUAAUACwARABcAHQAjACkALwA2ADwAQgBIAE4AACUXBgc1NicjFhc3Jjc3JicVFhczJicHFgcHFhc1JhM1BgcXNhc3JicHFgUnBgczNgUGBxc2NyMHFzY3JwYDJwYHFzYHBxYXNyY3IxUXNycBQhUfIxjWKwMPJQnoFR8jGHwrAw8lCegVHyMYGCMfFRW1JRUcFRP+6SUPAysDAU8DCSUPAytCFhwUJQ7hFR0UJQ4OJRUcFRKOKlseT2MlDwMrA5EjHxUVyiUPAysDkSMfFRXKJQ8DKwMBTysDDyUJPxYcFCUOOBUfIxhCGBUVHyNzJRUcFRMBAiUVHBUS4BUcFSUO5XRbHk8AAwArAFUB1QGrAAsAJwArAAAlIzUjFSM1IxUhNSM3IzU0JiMjIgYVFSMiBhUVMzUzFTM1MxUzNTQmIyM1MwGAK6orVQGqVStAGRKAEhlAERlVK6orVRl8gICrFRUVa2uVKxIZGRIrGRFWKysrK1YRGSsAAQBVAOsBqwEVAAMAABMhFSFVAVb+qgEVKgACAIAAKwGAAdUACQAOAAAlNSc3NSEVFwcVEzUzFQcBgFVV/wBVVSuqVSuAVVZ/gFVVgAE1S0tVAAMADwAPAdMB1QALABYAGwAAEzMVBxc3MTE1IRUXJwcXBxUxFSE1FzcnIzU3F6uqPBpN/wArfh6+TQEANR5+qjxuAatLPRpMgBkqQR6+TAF/GTUeKEs9bwACAIAAKwGAAdUACQAOAAATFRcHFSE1Jzc1AxUjNTeAVVUBAFVVK6pVAdWAVVZ/gFVVgP7LS0tVAAQAKwBVAdUBwAAKABAAEwAXAAABMycHMxUzNTMVMyczFxUjNTcjNwc1MxUBlUDV1UAq1ir7zAXWom43a9YBAMDAqysr1gQnJy4y3CoqAAYAQABAAcABwAAGAAwAEwAZACkAOQAAJTI2NyMWFiczNjcjFjciBgczJiYHBgczJic3ISIGFREUFjMhMjY1ETQmAyImJjU0NjYzMhYWFRQGBgEAEB0LcAsdOZMIAqgCUhAdC3ALHVkJAqgCCUz+1hIZGRIBKhIZGacjOiMjOiMjOiMjOqsLCgoLKg8REXELCgoLKg8REQ+VGRL+1hIZGRIBKhIZ/sAjOiMjOiMjOiMjOiMAAwAPAA8B0wHTAA0AEgAYAAAlJzEnMScHFxEUFjMhFyU3FzcXFwEhMhYVAdO10SAeMRkSARkx/rZKNhlAfP68ARkSGS210SAeMf7nEhkxcWBAIEAEAUQZEgADABUAVQHrAcAALwA3AD8AAAEiBgYXByYHJzYmJiMiBgYXByYGBhUUFjMyNjYnNxY3FwYWFjMyNjYnNxY2NjU0Jgc3NycnBwcXBzc3JycHBxcBwA8VCQRMCws2AwgVEA8VCANhDBgSGRIPFQkEYQsLNwQIFRAPFQkETAwYEhmSFCwsFBQsLOEKKysKCysrAVURGQtMAwM2DBkRERkMYQQJFQ8SGRIYDGEDAzYMGRERGQxLAwgVEBEZFSwUFCwsFBRXKwsKKysKCwAEAEAAQAHAAesAIAAmADIAOAAAASMmJiMiBgcjIgcGBwYHBhURFBcWFxYXFjMhMjY1ETQmBwcnNxcHNyImNTQ2MzIWFRQGFyc3JzcXAZVZByAVFSAHWQUEDAkGBAMDAwcJDAQFASoSGRm8Hk1NHi9EBwkJBwcJCSweLy8eTQHAExgYEwECCgYHCAn+1gkIBwcJAgEZEgEqEhnuH01NHy6lCgYHCQkHBgryHy4uH00AAgBVABUBqwHrAAgAIAAAAQcnFSM1Byc3FxUUBiMhIiY1NTQ2MzMVIxUhNSM1MzIWAVUeIioiHlWrGRL/ABIZGRJAQAEAQEASGQGVHiLu7iIeVsDrEhkZEusRGSrr6yoZAAMAVQBrAasBlQADAAcADwAAJSE1IRUhFSEnNzUHJwcVNwGr/qoBVv6qAVZra2trgH/AK1Yqqkw0S0tOMk0AAgBUACcBrAHhAB4AVwAAJQYGBwYmJyY2NzY2NzYmJyY2NzQyFx4DFxYVFgY3JiYnJiY3NiYHBgYHDgIXFBYVFAcGJyYnJiY3NiYHBgYXFBYXFhcWFhcWNjc+AicmJicmJyYmAToFDwYSIwwCAQITFgQDCAICAQQEAQYTFRECAQEMMQwaDBgPCgIHBQwVCh8kBQ8BCAgIAgEQCwQBCwQUEgIDAwcME0AlJ08eFhkCDAEBAQkLBAmIBQgCBgsKAgQBBhoPDhoOCxUKAgENFBIVDgYFDx6iCxIMGEAhBQcCBQ4HGkdRJwIEAgkEBAcDAhQzGQYGBRo/IAkTCRgUICsFBRIbFTc8HAIDAhEOBgoAAgBAABUBwAHrAA0AFwAAAQcVFB4CFz4DNTUHFycHNyc3NxcXAQDAHTRGKSlGNB2LFElJFEBUISFUAetWgCxTRjEKCjFGUyyAolIrK1I3CE5OBwACAGsAKwGVAdUAJQAxAAABMSIOAhUUFhcWFhcWFhceAjMxMjY2NzY2NzY2NzY2NTQuAgciJjU0NjMyFhUUBgEAHzYpFw8PDyUPCAsGAwYKCAgKBgMGCwgPJQ8PDxcpNh8WHx8WFh8fAdUXKTYfHDQXGSwZDBcNBg8LCw8GDRcMGC0ZFzQcHzYpF9AgFhYfHxYWIAAEACsAKwHVAdUABQAlAC4APgAAJRcHJzUzFxQOAiMiJicjIiY1NTQ2NzU0NjYzMhYWFzYzMh4CJTM1JiYjIgYVBTQmJiMiBgYVFBYWMzI2NgE1PhFNIKAaLz4jITkXZRIYGREaLBoZKhoCDAsjPi8a/qtrAh8VFh8BKyM6IyQ6IiI6JCM6I9EkHC9rViM+LxoWFBkSwBIYAQobKxoXJxkCGy89cg8VHCAWoCQ6IiI6JCM6IyM6AAIAKwBAAdUBwAAIABYAABMHFyMVMwcXNxcjFTMyNjURNCYjIxUz6x432dk3HmpWq6sRGRkRq6sBax44Kjgea5UrGRIBKhIZKwACABUAFQHVAdUAFgAiAAABIg4CFRQWFwc3FhYzMj4CNTQuAhcjFSM1IzUzNTMVMwEALE07IQsJKo8VLhksTTshITtNKUAqQEAqQAHVITtNLBkuFY8qCQshO00sLE07IepAQCpAQAACACsAKwHrAdUABQAdAAAlJzcXNxcHND4CMzIWFzU0JiMhIgYVETczMTQ3JgFyTB4uWh/rFyk2HxIgDhkR/qoRGVWAAQFVTB4tWh45HzYpGAgHjxEZGRH+gFUFBgUAAgArACsB1QIAABQAJAAAARUUBiMhBxE0NjMzBgYVFBYWMzI2JxQWFjMyNjY1NCYmIyIGBgHVGRH+1VUZEdgBARwxHRIhcxIdERIdEREdEhEdEgFrwBIZVQGAERkFCwUdMR0MXxIdEREdEhIdEREdAAMAKwArAesBqwAWABwAIgAAJTQ+AjMyFhc1NCYjISIGFREUFjMzJgMXNxUHJwEnNxc3FwEAFyk2HxIgDhkR/qoRGRkRrQKrq6urqwEdTB4uWh9rHzYpFwgHjxIZGRL/ABIZDAEfa2sramr+1kseLVoeAAIAKwBVAdUB1QAdAC0AAAEVFAYjISImNRE0NjMzBgYVFBYXBycVFzcWFjMyNicUFhYzMjY2NTQmJiMiBgYB1RkR/qoRGRkR2AEBExBOq6txCRIJEiFzEh0REh0RER0SER0SAUDAEhkZEgEAEhkFCwYXKQ8xaytqRgMDC18RHRISHRESHRERHQABACsAFQHVAesAQQAAAQcnNyMGBgcOAiMiJiY1NDY2MzIWFzY2NyMGBiMiJiY1NDY2MzIWFzMmJicGBiMiJiY1NDY2MzIWFhUWFhczJzcB1VUeInAFOSsBER0REh0RER0SDxoIHykFQgchFBIdEREdEhQhB0IFKR8IGg8SHRERHRIRHRErOQZvIR4BAFUeIjJPFhEdERIdERIdEQ0LEDokExgRHRISHREYEyQ5EQsNER0SER0SER0RFk8yIh4AAwArACsB1QHVABkAHQApAAABIzU0JiMjIgYVFSMiBhURFBYzITI2NRE0JiczFSMXIxUjNSM1MzUzFTMBq1YZEVYRGVYRGRkRAVYRGRnnVlaAQCpAQCpAAYArERkZESsZEv8AERkZEQEAEhkrK8BAQCtAQAAEAB4AHgHEAdUAEQAbAC8AOQAAJQEHFyMXMxQWFjMyNjY1NRc3JxQGIyImNTM3FzcVJzU0NjYzMhYWFRUnNTQmIyIGJxQGByc2MzIWFgHE/ngeUBkWFRcnFxgnF3sexBkSERkVDDQrKxcnFxgnFysZEhEZVgYFWBATEh0RPAGIHlHVGCYXFyYYGXseRBIZGRJ4NLxuKkQYJhcXJhjuKsQSGRkDChEIWAsRHQACAFUAKwGrAdUADgA0AAATIyYmNTQ2NjMyFhYVFAY3MhYWFREjETQmIyIGFREUBgYjIiYmNSMnMwcjFBYzMjY1ETQ2NsVfCAkSHRESHREJiRgnFysZEhEZFycYFycXFRaAFRUZERIZFycBawgWDBIdEREdEgwWYhcmGP6rAVUSGRkS/wAYJhcXJhjV1RIZGRIBABgmFwAHACsAVQHVAasAGAAxAEEARQBRAF0AYQAAEyc2NjMyFhcWMzI2NxcGBiMiJicmJiMiBhcyFhcWFjMyNjcnBgYjIicmJiMiBgcXNjYlERQGIyEiJjURNDYzITIWByMRMzc0JiMiBhUUFjMyNjU0JiMiBhUUFjMyNjUjFTORHgUbEg0TBwoFBgwCHgYaEg0TBwUHAwYMEgMHBQcTDRIaBh4CDQUFCgcTDRIbBR4CDAE2GRH+qhEZGREBVhEZqtbWagwJCQwMCQkMDAkJDAwJCQwqKgEeHgYTCQUHCgMeBxMKBAQECmEEAwUJEwYeAwoHBQoTBx4DCsD/ABIZGRIBABIZGRL/ACsIDQ0ICQ0NXgkMDAkJDAx0KwACAJUAKwFrAdUAEgAXAAABNSMVFBcXBwcXBzcXJzcnJzc2BwcnNTMBa9YLWRVJNxE/PxE3SRVZC1YVFSoBIbS0DAY1MgcvRyUlRy8HMjUGGg0NxQAEACgALAHXAdUAOQBFAIIAjgAAJTc2Jyc2NTQnNzYnJyYHByYnJyYjIyIHBwYHJyYHBwYXFwYVFBcHBhcXFjc3FhcXFjMzMjc3NjcXFiciJjU0NjMyFhUUBgUnNDY1NCc3NicnJgcHJicnJiMjIgcHBgYHJyYHBwYXFwYVFBcHBhcXFjc3FhcXFjMzMjU3NjY3FxY3NzYHIiY1NDYzMhYVFAYBLh4DBR8BAR8FAx4DBiUMDQYBBjwGAQYOCyUGAx4EBSABASAFBB4DBiUMDQYBBjwGAQYOCyUGbxIZGRISGRkBBhUBARQEAxMBBRcICQMBBCYEAQMFCAMYBAITAgMUAQEUAwITAgQYBwkDAQQmBQQECAQYAwIUAl8MEREMDBER3DMGBBgIBwcIGAUFNAUCDwkGJwYGKAUJDwIGMwYEGAgHBwgYBQU0BQIPCQYnBgYoBQkPAj8ZEhIZGRISGaMPAwQDBQQQAwMhBAIJBgMZBQQaAQUDCgEDIgMDEAQFBQQQAwMhBAIJBgMZBAQZAgQDCQIEIQMBEQwMEREMDBEABABAAEABwAHrABMAFwAlAEAAACUUDgIVIzQuAjU0NjYzMTIWFgcjFTM3FAYHFzY2NTQmJwcWFicnFTAwMSIOAhUUFhc3JiY1ND4CMzAwMRUBSxAWECoQFhAVIRUVIRU2KiqAEg8eFRklHh8ZHkBVKEU1HhkVHg8SFyk2H+AQHBoZDAwZGhwQFSEVFSGVIKsbMBQeGUAkLEsbHxQ8h1ZAHjVGJyRAGR4UMBsfNikXQAACACsAQAHVAcAAFQAZAAABISIGFRUUFjMzBxUhNSczMjY1NTQmAyE1IQGr/qoRGRkRQBUBABVAERkZEf6qAVYBwBkS6hIZFSsrFRkS6hIZ/uvqAAUAKwBrAdUBlQAbACIAJgA2AEIAAAE0JiMjFTMVByM1IyIGBhUVMxQWFjMyNjY1MzcFIiY1MxQGJzMVIwUiBgYVFBYWMzI2NjU0JiYHIiY1NDYzMhYVFAYBlRkRQEBLS1UYJhcqEh0REh0RYGD/AAgNKw0zamoBKhEdEhIdERIdEREdEggNDQgJDQ0BaxEZKjldaxcnF0ASHRERHRJ4jg0JCQ3rK2oSHRESHRERHRIRHRJWDQkIDQ0ICQ0AAwArACsB6wHVAAUALQA5AAATFRc3JzUXFhUUDgIjIi4CNTQ+AjMyFzUmIyIOAhUUHgIzMj4CNTQnNzUjFSMVMxUzNTM11WURVokCFyk2HyA2KRcXKTYgFhQVFShGNR0dNUYoKEU1HgICK0BAK0ABVYA+GjNxVQsKIDYpFxcpNiAfNikXBiwFHjVFKChGNR0dNUYoCguVQEAqQEAqAAQAKAAoAdUB1QAPACYAPgBJAAAlAQcXBgYVFB4CMzI2NxcnIi4CNTQ2NxcGBhUUFhYzMjY3FwYGAzY2MzIeAhUUBgcnNjY1NC4CIyIGBxcUBgcnNjYzMhYWAb3+hhspEhQhO00sIj0aKKEjPi8bDgwgBwgjOiMQHg0fEy6SGj0iLE07IRQRHwwOGy8+IxkuE9oIB6wNHhAjOiNDAXocKBo9IixNOyEUESgtGy8+IxkuEx8NHhAjOiMIByAMDgFbERQhO00sIj0aHxMuGSM+LxsODJEQHg2sBwgjOgADACsAKwHVAdUAKQA5AEUAABMXBhUUHgIzMj4CNTQuAiMiByc2NjMyHgIVFA4CIyIuAjU0Nhc0NjYzMhYWFRQGBiMiJiY3FAYjIiY1NDYzMhY9IQkbLz4jIz4vGxsvPiMdHCAULRgsTTshITtNLCxNOyEJTCM6IyM6IyM6IyM6IxUSDg0TEw0OEgFYIRscIz4vGxsvPiMjPi8bCiEJCiE7TSwsTTshITtNLBgsRCM6IyM6IyM6IyM6rg4SEg4NExMABQArACsB1QHVACkANQBFAEkATQAAARQOAiMiLgI1NDY3FwYVFB4CMzI+AjU0LgIjIgcnNjYzMh4CJSIGFRQWMzI2NTQmFxQGBiMiJiY1NDY2MzIWFicjFTM3IxUzAdUhO00sLE07IQYGKQsbLz4jIz4vGxsvPiMeGw8RJBMsTTsh/qANExMNDhIS/SM6IyM6IyM6IyM6I5UrK1UrKwEALE07ISE7TSwTJBEOHB4jPi8bGy8+IyM+LxsLKQYGITtNfxMNDhISDg0TqyM6IyM6IyM6IyM6HYCAgAAGAEAAVQHAAasABgASAB4AJQAxAD0AAAEXBzUjNTMjIgYVFBYzMjY1NCYjIgYVFBYzMjY1NCYXMzUjNQcXNzI2NTQmIyIGFRQWMzI2NTQmIyIGFRQWAWtVVVZWlggNDQgJDQ1eCQwMCQkMDAxWVlVVlggNDQgJDQ1eCQwMCQkMDAGrVlVAKw0JCA0NCAkNDQkIDQ0ICQ3WK0BVVkANCQgNDQgJDQ0JCA0NCAkNAAMAFQArAesB1QAQABwAOAAANzM1IyYmIyIGBhUUFhYzMjYnIiY1NDYzMhYVFAYlJxUjLgMjFTIeAhUUDgIjFTI+AjczFZFaWgYhFREdEhIdERUhNggNDQgJDQ0BjVZWBCQ5SikjPi8aGi8+IylKOSQEVusqExgRHRISHREYEwwJCQwMCQkMFUArKUY0HSobLz4jIz4vGyodNEYpKwACADwAPAHiAeIAAgANAAABNwcXAQcXBxUXFzM3FwEAwEdp/ngebGiXPB4nbAF5R8CmAYgebCcePJdobAACACsAKwHVAdUAEwAmAAABIg4CFRQeAjMyPgI1NC4CFyM3JiYjIgYGByc+AjMyFhc3AQAsTTshITtNLCxNOyEhO01UazEPKhgZKx4FFQckMx4dMRIrAdUhO00sLE07ISE7TSwsTTsh/zARFBUmFwYcLBoYFCwAAwBVAEABqwHAAAQAEAAfAAABBxEhEQcyFhUUBiMiJjU0NhcjNSMVIzUzFTM1MzIWFQEAqwFW2wsQEAsLEBCmFqoWFkpLEhkBwID/AAEASw8LCxAQCwsPdSAglWBLGRIAAQBnACsBgAHWAB8AAAE0NjY3NjYmJyYmBw4DBwYeAjMyNzY2JicuAjUBAB0zIQoFCQoRJRQkPzEfBAUdPFEvFxYLCAUJIzMcAQAmQzINBBERAgQCAwQhM0AkMFVCJQUCEBIEDjNCJQADAB4AFQHEAesACwAYABsAABMnNjYzFzIWFREnNRMnBgYjIyImNREnNwEnJxW8TwUWDdYRGSo7EwUWDdYRGU0eAYiArwGATwwQARgS/ucrrv6eEwwQGRIBGU0e/nhErq4AAwAeAB4BxAHEAAcAEwAWAAATIRUHJzM3IxMnFSE1MzUnJzcXAScnFXwBRIRqjybg/yb/AGserx4EAYRrRAHAK5RqKv6JJgQraiGwHgT+fC9DQwAEABYAFgHVAdUAEwAuADYAPQAAJScHFwcjIgYVFRQWMyEyNjcXNycHIiYmNTQ2NjcXJiMiBhUUFjMyNjU0JxcOAjcnMxczMhYVNzMHNSM1MwEp9R5xBDYOFBQOAREMEgMuH0yrFycXEiATIgkJFh8fFhYgBCIEGCOWmSUcNg4UMyJAFVXX9R9xBRQOyA4UDgwvHkwrFycYFCQYAyEDHxYWICAWCAkhFB8TZ5keFA50dFZqAAMADwAPAesB6wAHABEAIAAAEyczNTMVMwMFFRQWMyEyNjU1FwEHFw4CFTMXIRUhNRfyB2orax7+SA0JARUJDH7+Wh55ITQe7yr+5wFAYAFKS1ZW/tovFQkNDQkVEwGmHnkIITMgKyoDXwAFAA8ADwHTAdUACQAZABwAIAAnAAAlNTQ+AjMRJzUXJwYGIxUjNSImJjU1JzcBAScVNyMVFxc1IxUXNjYBVREdJxYrIN8GCgYrFycXMR4Bpv6xGVUrK1UqJwIB1asOHhoP/qcrLsbfAgHAwBcmGEQxHv5aARMZGZUuKzyVgygGCgADAA8ADwHVAdMAEwAgADUAABMnNzMXMzIWFREUFSc2NTQmJiMiEychIiY1ETQ2Nyc3AScnBgYjIiYmNTQ2NycGFRQWFjMyNulJIIAnRBEZbQMdMR0LwDH+0REZCAYqHgGmniAFCgYSHRECAiAPHTEdDxoBU0ojKxkR/wABA20LDB0xHP66MRkSAQAJEQUrHv5aYiACAhEdEgULBSAZHB4wHQgABAAeAB4B1QHEAAsAHwA2AD4AADcyFhUUBiMiJjU0NgEiBgYHBxc1NjYzMhYVFTM1NCYmBycHFwcGFhYzMxcGBhUUFjMyNjcXNycnNjY3JiYHF4ASGRkSEhkZASASGRIHS2wJDwsMESoTIL6oHqo5BgILCY4ZDA8ZEQ4WBCkeZj8FCgYnWCdagBkSERkZERIZAUAPFAhYa6gLEBINCwsUIhSkqB6qQwcQDBkEFg4RGQ8MKB5m8wUMBhMBEVsABAAeAB4BxAHVABoAJgApADoAACUBBxcVFBYXFRQWMzMyNjU1MxUUFjMzMjI3FyUiJjU0NjMyFhUUBic1FzcnNjYzMh4CFRUUBgcnMzUBxP54HjcMCgwJFQkNqg0JFQEBASP++g0TEw0NExMtLg5BGEUoIz4vGwMBgFk8AYgeOMMOGQkmCA0NCBYWCA0BI3cTDQ4SEg4NE4AvL2tBDQcGESIc1QYKBX9rAAEAawArAZUB1QAIAAATFzcRMxEXNydrHmIqYh6VAUAeYv6nAVliHpUAAQBVAFUBlQGVAAgAABMVMwcXNxUzNcCN+B74KgGVKvge+I3VAAEAawBVAasBlQAIAAA3MzUXNyczNSNrKvge+I3VwI34HvgqAAQAHgAeAcQB1QAQABwAJgA+AAABJzY3NhYXFhYXFxYWMxUiJicyNjU0JiMiBhUUFicHFxUUFjMzFzcnIiYmNTQ2NzUOAhUUFhYzMjY2NyMGBgErSQsPCAwGAwYEHA8vGBw6PxIZGRISGRmyHrcZEi54Hu8RHRIYExgnFhwxHRosHgUsByEBFEkMAQEDAwIEBB8RFioYfRkSERkZERIZRB64LhIZdx4ZEh0RFSEHLAUeLBodMRwWJxgTGAADACsAKwHVAdUAEwAXABoAAAEiDgIVFB4CMzI+AjU0LgIDIzUzFzUXAQAsTTshITtNLCxNOyEhO01BKysVawHVITtNLCxNOyEhO00sLE07If7WqqqqVQAGACsAaQHVAZcAEgAWACQAMgBAAE4AAAEUDgIVIzQuAjU0NjYzMhYWByMVMzc0JicHFhYVFAYHFzY2JTQ2NycGBhUUFhc3JiYlFAYHFzY2NTQmJwcWFgc3JiY1NDY3JwYGFRQWAUsQFhAqEBYQFSEVFSEVNioqwCEdFxkcHBkXHSH+dhwZFx0hIR0XGRwBKhIQFxQXFxQXEBLfFxASEhAXFBcXAQsQHRoYDAwYGh0QFCIUFCKUIJUsTh0XGEMlJUMYFx1OLCVDGBcdTiwsTh0XGEMlGCsQFxU2Hx82FRcQK4IXECsYGCsQFxU2Hx82AAEAQABAAcABwAAJAAABNSMXBycVMyc3AcCrR9ZGq0fWARWrRtZHq0bWAAMAQABAAcABwAAPABwAIwAAASEiBhURFBYzITI2NRE0JgMjFAYGIyImJjUjNSEHMxUzNTMnAZX+1REZGREBKxIZGRJVER0SEh0RVgEr6ipWKlUBwBkS/tYSGRkSASoSGf8AEh0RER0S1YBAQFYAAwArAEAB6wGVABUAHQAmAAABMjM1NCYjISIGFRUUFjMzJiY1NDY2Byc1Mxc3MxUXBxcjFTMHFzcBiwUFFxH+5hEXFxHPBgcgNYyJBYSHBioeIllZIh5WARVYERcXEdoRFwwbDiA2HypRL09PLlIeIisiHlUABAArACsB1QHVABMAIQAqADgAAAEiDgIVFB4CMzI+AjU0LgIHIiY1NTQ2MzIWFRUUBhcjNTQ2MzIWFTcUBiMiJjU1NDYzMhYVAQAsTTshITtNLCxNOyEhO01sCQwMCQkMDGJWGRISGSoMCQkMDAkJDAHVITtNLCxNOyEhO00sLE07IdUNCEAJDQ0JQAgNgCsRGRkRaggNDQhACQ0NCQABACsAVQHVAasAHAAAASIHBgYjIicmIyIVERQzMjc2NjMyFxYzMjURNCMByQMDL2MxZF8DAwwMAwMvYzFkXwMDDAwBqwIREiMCDv7GDgIREiMCDgE6DgABAFUAKwGrAdUAHAAAJSYmNTQ3NjU0IyEiFRQXFhYVFAcGFRQzITI1NCcBqRESIwIO/sYOARISIwIOAToOAj0vYzFkXwMDDAwDAy9jMWRfAwMMDAMDAAEAKwBVAdUBqwAbAAABIgYPAgYVFB8CFhYzMjY/AjY1NC8CJiYBACxSLBMGEhIGEyxSLCxSLBMGEhIGEyxSAasICAMTQ0JCQxMDCAgICAMTQ0JCQxMDCAgAAwAVAFUB6wGrAA8AHwAoAAAlNTQmIyEiBhUVFBYzITI2JyImJjU0NjYzMhYWFRQGBiUVFAYjITUhNQGVGRH+1RIZGRIBKxEZwBEdEhIdERIdEREdAQQZEv6VAWvVqxIZGRKrERkZJxEdEhEdEhIdERIdEYDrEhkr6wAEAAAAVAIAAasALwBAAEYAWwAAAScmJiMjFTMXIyczNSMVMxcjLgInJgYGFRQWFjMyNjY3Mx4CFxY2NjU0JiYjIwcGBiMiJiY1NDY2MzIWFyMVNyMnMwYGFyImJjU0NjcXNycwMjMyFhYVFAYGAYQkBRYONzcgZwgYayYmDgQdKRkeMx8cMR4aLB0FWgQdKRkeMx8cMR4R3QcgFRIdEREdEhUgBzzCHhBBBwpmEh0RDAsUKBQBAREdEhIdAStkDBArVRUrK2sYJhYBAh0xHh4wHRYnGRgmFgECHTEeHjAdgBMYER0SEh0RGBMqKisJFmERHRIPGQk5DzkRHRISHREABAArACsB1QHVABMAHwArADcAAAEiDgIVFB4CMzI+AjU0LgIHIiY1NDYzMhYVFAYzIiY1NDYzMhYVFAYzIiY1NDYzMhYVFAYBACxNOyEhO00sLE07ISE7TZcNExMNDhISXQ0TEw0NExNeDhISDg0TEwHVITtNLCxNOyEhO00sLE07IfUTDQ0TEw0NExMNDRMTDQ0TEw0NExMNDRMABABVACsB1QHrAA8AFQA1AEEAAAEiBgYVFBYWMzI2NjU0JiYXJzUzFRcDIyYmIyIGByMiBhURFBYzMyYnIxEzFTM1MxUWFzU0JgciJjU0NjMyFhUUBgFrHjAdHTAeHTEcHDEGLhUoHUQHIBUVIAdEEhkZEoITC2QrqisWFRmSCQwMCQkMDAEAHTAeHTEcHDEdHjAdnS5EOygBThMYGBMZEv7AERkSGAFAQEBsAwp5EhkrDQkIDQ0ICQ0ABQAVAFUB6wGrAA8AGwAqADUAQQAAATQmJiMiBgYVFBYWMzI2NicUBiMiJjU0NjMyFgcVITU0LgMjIg4DFz4DMzIeAhc3NTM1IzUjFSMVMxUBFRcmGBgmFxcmGBgmFyoZEhIZGRISGdYBVhgnLi0RES0uJxgrAhcmKxYWKyUYAmtAQCtAQAFVGCcXFycYFycXFycXERkZERIZGecrKxEbFQ0HBw0VGxEGDg4JCQ4OBkBAK0BAK0AAAwAVAFUB6wGrAA8AGwAqAAABNCYmIyIGBhUUFhYzMjY2FxUzFTM1MzUjNSMVBRUhNTQuAyMiDgMBFRcmGBgmFxcmGBgmFytAK0BAK/6VAVYYJy4tEREtLicYAVUYJxcXJxgXJxcXJxMrQEArQECrKysRGxUNBwcNFRsAAwArAFUB6wGrAA8AEwAiAAABNCYmIyIGBhUUFhYzMjY2FxUzNQUVITU0LgMjIg4DASsXJxgXJxcXJxcYJxdAgP5AAVUYJy4tEREsLicYAVUYJxcXJxgXJxcXJxMrK6srKxEbFQ0HBw0VGwADACsAVQHrAasADwATACIAAAE0JiYjIgYGFRQWFjMyNjYXFTM1BRUhNTQuAyMiDgMBKxcnGBcnFxcnFxgnF0CA/kABVRgnLi0RESwuJxgBVRgnFxcnGBcnFxcnEysrqysrERsVDQcHDRUbAAQAKwArAdUBqwAPABwAMAA8AAATNDY2MzIWFhUUBgYjIiYmFyYOAxUVMyYmNjYXNjU0JiYjIgYGFRQWFjMyNjcXNyciJjU0NjMyFhUUBoAXJxcYJxcXJxgXJxddEi4wKRnLFA0BBsMMFycYFycXFycXDBYJNx6AERkZERIZGQFVGCcXFycYFycXFydpAQYOFRwRKxYtJRdUFBcXJxcXJxcYJxcHBTYeNxkSERkZERIZAAIAQABAAcABxQA5AD0AACU1IyYnNycHJiYnNDY1NCc3JwcmJgYHJwcXBhUUFhUGBycHFwYHIxUzFhcHFzcWFjMyNjcXNyc2NzMHIzUzAcBBAgM3FTMFCgYBDiQeJRIfGAclHiQOAQwJMxU3AwJBQQIDNxUzETchITcRMxU3AwJBqyoqwCsNCyAlHQcOBgQHBBoVIx4lCgEHBCUeIxUaBAcEDQ4dJSALDSsNCyAlHR0jIx0dJR8MDSuAAAIAKwBAAdcBwABNAFkAACUnNiYmIyIOAhUUFhcHJiY1NDY3JiMiBgYHJiY1NDY2MzIyMzI2NTQmIyMiBhUUFjMzMhYVFAYjIiIjIgYGFRQWFhcVFBYWMzMyNjYmByImNTQ2MzIWFRQGAcczBRMlFgoZGBAMCg8NDxwWGBkZLB4GFBoSHREPGA8WHx8WIAkNDQkgBAYGBAEVIB0xHBYnGB0xHr0PFQoFUgkMDAkJDAyNLhkpGAcSHRQQGwoPDSMUGywNDBYlGAYiFRIdESAWFh8NCAkNBgQFBh0wHhksHgUBHjEdEBkaGAwJCQwMCQkMAAMAKwBAAdUBwAATABcAHAAAATIWFREUBiMhIiY1ETQ2MzM3MxcTESERNwcnByEBqxEZGRH+qhEZGRFEJ4AnRP6q1kArQAEAAZUZEf8AEhkZEgEAERkrK/7WAQD/AJVPOlYABAArAFUB1QGrABMAGQAlADIAAAE3FScVFAYjISImNRE0NjMhMhYVBzUhESE1ByImNTQ2MzIWFRQGBzIeAhUVIzU0PgIBgFVVGRL/ABEZGREBABIZK/8AAQCAERkZERIZGRILHRsTqxIcHQEgVepVYBIZGRIBABIZGRJPT/8AsTEZEhEZGRESGRUGChALFRULEAoGAAQAVQArAasB1QANAB4AIQAtAAABIyIGFREUFjMhMjY1EQcnBiYnJiY2NzY2FhcWFgcXJzUXBzQ2MzIWFRQGIyImASurEhkZEgEAEhlqKBYwEg8KCg8OJicOEgYLKEp2thMNDhISDg0TAdUZEf6qERkZEQEA9CgLBhIOJyYODwoKDxIwFijBdXV1DRMTDQ4SEgACAEAAQAGrAasAAwAMAAATNRcHFy8CBxcVNxer6jZMbZNQG2txdAGRBJUjgm2TUBtrukhzAAMAUgAvAbAB0QALABUALwAAAScmIgcHFzcXNjYmBRYyNzcnBwYUFzcHBycmIgcGFBcXBwcGFBcWMjc3FjI3NjQnAZstDCQMPS4tSw0ICP7NChoJNS01CQnMDw9EChoKCQlED4cNDQwjDcQGEgYGBgGXLQ0NPC4uTA0hILAJCTUtNAoaCoAPD0QKCgkbCUQPiAwkDA0NxAYGBhIGAA0AKwArAdUB1QAPABMAHQAnADUAQwBRAF8AbQB7AIkAlwClAAABIyIGFRUUFjMzMjY1NTQmByM1MxMhIiY1NSEVFAYnJiYjIyIGBwchJSMiJjU0NjMzMhYVFAYnIyImNTQ2MzMyFhUUBicjIiY1NDYzMzIWFRQGFyMiJjU0NjMzMhYVFAYnIyImNTQ2MzMyFhUUBicjIiY1NDYzMzIWFRQGFyMiJjU0NjMzMhYVFAYnIyImNTQ2MzMyFhUUBicjIiY1NDYzMzIWFRQGAWvWERkZEdYRGRkR1tZA/qoRGQGqGTEFFQ3IDRUFSgGq/vYWBAYGBBYEBgYEFgQGBgQWBAYGBBYEBgYEFgQGBjwWBAYGBBYEBgYEFgQGBgQWBAYGBBYEBgYEFgQGBjwWBAYGBBYEBgYEFgQGBgQWBAYGBBYEBgYEFgQGBgHVGRErEhkZEisRGVUr/oAZERYWERn8Cw4OC6crBgQFBgYFBAYqBwQEBwcEBAcrBgUEBgYEBQZVBgQFBgYFBAYqBwQEBwcEBAcrBgUEBgYEBQZVBgQFBgYFBAYqBwQEBwcEBAcrBgUEBgYEBQYABQBAAEABwAHAAA8AEwAfAC8AOwAAASEiBhURFBYzITI2NRE0JgMhESEHFAYjIiY1NDYzMhYnIgYGBx4CMzI2NjcuAgciJjU0NjMyFhUUBgGV/tYSGRkSASoSGRkS/tYBKnUTDQ0TEw0NEyAdMyYKCiYzHR0zJgoKJjMdFh8fFhYfHwHAGRL+1hIZGRIBKhIZ/qsBAIAOEhIODRMTSBYmGRknFhYnGRkmFosgFhYfHxYWIAADAEAAFQHAAesADgASABYAAAEHFRQeAhc+AzU1JwczFSMVMxUjAQDAHTRGKSlGNB3AFSoqKioB61aALFNGMQoKMUZTLIBWgCsrgAADAFMAQAGrAcAACwAlAGIAAAEiBhUUFjMyNjU0JiciDgIHBwYWMzMVFBYzMxUzNTY2NTQuAhcUFBUXFgcHBicnBgcHBiMjIicnJicHBicnJjc3NCY1NDY1JyY3NzYXFzY3NzYzMzIXFxYXNzYXFxYHBxYBFQwSEgwNEhINHjQpGAIpBAYHIBkSFZUmMBgpNiESAgERAgQVBggDAQQiAwEDCAcVAwIRAgMSAQESAwIRAgMWBwcDAQMiBAEDBwcVBAIRAgMTAQFJEgwNEhINDBJ3FiczHjYGC0ASGUBkEkgtHzYpF5UCBQIOAgMeAwEJBQMXBAQXAwUJAQMeAwIOAgUCAgQCDgIEHQMBCQYDFgQEFgMGCQEDHQQCDgMAAwAeAB4B1QHVABkAKQA2AAATJzY2MzIeAhUUBgcnNjY1NCYmJxUUBiMjEwcnBgYjIi4CNTQ2Nyc3EyImNTUnBgYVFBYWF+thGjshLE07IRIRHwsNHDAfGRIq2R4wGjshLE07IRIRMB6vEhlmAgMoRCoBUmAREiE7TSwhOxogEiwYJD4vDQkRGf7RHjAREiE7TSwhOxowHv68GRIVZgkTCixIMAUAAQBrACsBlQHVACQAAAE1MzI2NTE0JiMjIgYVMRQWMzMVFAYGIzEVMxUXNzUzNTEiJiYBVRYIDQ0I1ggNDQgWEh0RfxUWgBEdEgFAawwJCQwMCQkMaxIdESuVFRWVKxEdAA4AQABAAcABwAADAAcACwAPABMAFwAbAB8AIwAnACsALwAzADcAABMzNSMXMxUjAzM1IxczFSMTFTM1ByM1MxEzFSMnMxUjMzMVIyMzFSMzMxUjNzMVIzUzFSMzMxUjQKurK1VVK6urK1VVqqsrVVUrK4ArKysrKysrKysrKysqKioqKisrARWrK1X/AKsrVQFVq6uAVf7WK6srKyorVSqAKysAEgArACsB1QHVAAMACAAMABEAFQAaAB4AIgAmACoALgAyADYAOgBAAEYATABSAAATFSM1NyMVMzUHFSM1NyMVMzU3FSM1NyMVMzUHMxUjMzMVIzczFSMHMxUjMzMVIzczFSM3MxUjFTMVIxMjNSM1MxE1IxUjFSEzNSM1IxEVMzUzNctAYICAIEBggICKQGCAgIAgICAgICAgIEAgICAgICAgICAgICAgYCpAaipA/sBqQCoqQAF1QEAggIDKQEAggICKQEAggICqICBAICAgIEAgQCAgIAEAQCr+VmpAKipAAUBqQCoAAgArABUB4AHVAAwAEwAAATQmIyEiBhURNzM1MxcjNyMVMxUB1RkR/qoRGVXAlQsvJGoqAasRGRkR/oBVq4BVgGsABAArAJUB1QFrAAMABwALABIAAAEzFSMVMxUjNzMVIycnFSMVMxUBFcDAwMBAgIBAaoCAAWsrgCuAKhVrVipWAAYAQAArAcAB1QAWACEAJQApAC0AMQAAAScHJwcnBycHJxEjFRQWFjMhMjY2NREDFAYjIiY1NSM1MwczFSM3MxUjBzMVIzczFSMBoCAgICAgICAgIEARHRIBABIdESsMCQkMwOrVgICVKyuVgICVKysBtSAgICAgICAgIP7WQBIdEREdEgFq/pYJDQ0JQOoqKysrFSsrKwADAFUAKwGrAdUADQAtADAAAAEjIgYVAxQWMyEyNjURByMVMzIWFRUUBiMjFSM1IzUzNSMiJjU1NDYzMzUzFTMnNRcBK6sSGAEZEgEAEhlrVUAIDQ0IFiorVUAIDQ0IFiorK2AB1RkR/qoRGRkRAQBVFQ0JQAgNFRUrFQ0IQAkNFRUqYGAABAArACsB1QHVABUAGgAjACgAAAExNC4CIyIOAhUUFhYXFTM1PgInIzUWFicVIzU2NjMyFgc0NjcVAdUhO00sLE07ISA6JqomOiAqVicvgFYLFQsLFcsvJwEALE07ISE7TSwnRTUPJSUPNUUnlBdOdqWlAwMDqC9OF5QAAwArAFUB1QHAAAMACAAUAAAlIzUzNyMVMzU3MTUjFTEnBzM3FzMBFSoqK4CAVUBV1UCVlUCAKyqAgGVxOE3Ah4cABABAACsB5wHAABEAFQA0AEAAAAE1MxUzNSM1IxEjFTMmJjU0NiczFSMXNycHJicnIwcGBycHFwYXBxc3FhcXMzc2Nxc3JzY0ByImNTQ2MzIWFRQGAStAKmrAK8YNDiM5Kyv6GBUfCg0HKgcNCh8VGAICGBUfCg0HKgcNCh8VGAFQEhkZEhIZGQEQcFWAFf6rKxIrGCZAGipIFSUKCAUgIAUICiUVDQ4VJQoIBSAfBQkLJRYICzAZERIZGRIRGQAEACsAVQHZAawABQAJABUAGQAAASc3FzcXByMVMxcnBycHFwcXNxc3JycjFTMBYUweLloe7sDA1R43OB44OB44Nx43nsDAARVMHi1aHiMrXh43Nx43OB44OB44FSsAAwArAFUB1QGrABEAFwAjAAABIycjIgYVERQWMyEyNjU1NCYFJzcXNx8CBycHJzcnNxc3FwGrqyuAERkZEQFWERkZ/us8Hh5LHmMiHiIiHiIiHiIiHgGAKxkS/wASGRkS1RIZ1TweHkwfKSIeIiIeIiIeIiIeAAMAKwArAdUB1QATAB8AOgAAASIOAhUUHgIzMj4CNTQuAgcyFhUUBiMiJjU0NhciJicHFxUjNScHJzcXNwcVIzU3NhYXHgIzAQAsTTshITtNLCxNOyEhO00MCQwMCQkMDD4LJA8IHBYXC1EEPBQhFUYIDwQJFRIEAdUhO00sLE07ISE7TSwsTTshVQ0ICQ0NCQgNgBIUMx5VTBo4EBUMaAwjMhoDBwcSEwcAAQBUAFUBrAGrABUAACUnNTc2JiMjIgYXFxUHBhYWMyEyNjYBpnscBQYHfgcGBRx7BgIMCQEqCQwCd6VZJAYMDAYkWaUHDwwMDwAGAAAAAAIAAgAAAwAXABwAKAA7AD8AADczFSM3MjY1NTQmIyEiBhUVFBYzIxUhNSU1IRUVJzQ2MzIWFRQGIyImFyc2NTQmJiMiBgYVFBYWMzI3FwEhESHNZmbeERkZEf6qERkZEVUCAP5VAVbpFxEQGBgQEReWNQsSHxMTHxMTHxMUETX+vAIA/gCaJw0ZEtUSGRkS1RIZKysr1dUBghAXFxARFxdKNREVEx8SEh8TEx8TCzUBQ/4AAAIAEQBLAbUBwAAoADQAACUjJzY2NTQuAiMiBgYHMz4CMzIWFhUUBgYjIiYjFTIzMjY3FxUXNyUHJwcXBxc3FzcnNwFLEQYQERUmMxwlPSYCKwIbKRkaLBoaLBoDBQMFBhkvEgZqIP7VNTQQNTUQNDUPNTXVBhIvGR0yJhYiOiQYJxYaKxsaLBoBKxEQBhFqIK41NQ81NBA1NRA0NQACAEAAVQHAAasACwBCAAATNDYzMhYVFAYjIiYXNSImJycmIyMiBwcGBiMVMjY3FQcGBhUUFjMzNTQ2MzMyFhUUBiMjIgYVFTMyNjU0JicnNRYW1RkSEhkZEhIZ6yQ8Fx0NFBYUDRwYPCQsTB1SCw0WEC8fFkAFBgYFQA0TmhAWDQtSHUwBgBIZGRISGRnDKh4cIg8PIhweKiQhMCEEFAsQFwsWHwYEBQYTDQsXEAsUBCEwISQAAgBVACsBqwHVAAkAFQAAASEiBhURIRE0JgciJjU0NjMyFhUUBgGA/wASGQFWGUcOEhIODRMTAdUZEf6AAYARGfUTDQ0TEw0NEwAEAFUAKwGrAdUAAwAUABgAIAAAAREhESUhIgYVERQWMyEyNjURNCYjAzM1IzczFTM1IxUzAYD/AAEA/wASGRkSAQASGRkS69bWQFZA1kABq/6qAVYqGRH+qhEZGREBVhEZ/paAQBaAgAAEACsAQAHVAcAAAwAHABcAMgAAJQUnJRUhFSETFRQGIyEiJjU1NDYzITIWByIGBy4DIyIOAjEwHgIzMj4CNxYWMwHB/oECAYD+gAGAFRkR/qoRGRkRAVYRGSobJwMFFiIwHiY2IxERIzYmHjAiFgUDJxuJFCAUSSABVZUSGRkSlRIZGSccFAYUEw4UGRMUGRMNFBMGFBwABABVACsBqwHVABgAIQAoADEAAAEjNCYmIyIGBhUjIgYVERQWMyEyNjURNCYHFAYjIiY1NTM3MhYVIzQ2FxQGIyImNTUzAYArFyYYGCYXKxIZGRIBABIZGb0MCQkMKisSGVYZZwwJCQwqAYAYJhcXJhgZEv8AERkZEQEAEhlVCQ0NCSpWGRISGYAJDQ0JKgABACsAKwHVAdUAAgAANyERKwGqKwGqAAEACAA1AfgBwAAQAAAlEy4EIyIOAwcTFTUBAPgDGSw7SisrSjssGQP4NgE1AhIZGBAQGBkSAv7LAQEABQArAGsB1QFrABcAHwAnAC8ANwAAARUUBiMjNTM1IRUzFSMiJjU1NDYzITIWBzc3JycHBxc/AicnBwcXBzc3JycHBxc/AicnBwcXAdUZERYW/qqAgBEZGREBVhEZoBgzMxgXMzNNDR0dDQ4dHSgYMzMYFzMzTQ0dHQ0OHR0BQIASGSuAgCsZEoASGRnnMxcYMzMYFzceDQ0eHg0NiDMXGDMzGBc3Hg0NHh4NDQADACsAVQHVAasABAAWABsAAAEXFSM1NxUUBiMhIiY1ETQ2MzMXMzIWBycjFTMBUyJAoBkR/qoRGRkRgCurERlANUuAASAjSGs11RIZGRIBABIZKxlcNasABgAVABUB1QHrADoARgBTAF8AbAB4AAATBwYVFRQWFjsDMjY1NCYjIzUzMjY1NCYjIzUzMjY1NCYjIzUzMjY1NCYjIz4CMTY1NCcwLgIxFzIWFRQGIyImNTQ2NyIGFRQWMzI2NTQmIxcyFhUUBiMiJjU0NjciBhUUFjMyNjU0JiMnIgYVFBYzMjY1NCbDmhQSHRGGJXsLDw8Le6ULEBALpbsLDw8Lu5ALEBAL0wcPCgMGBwkHaAYKCgYHCQkHFBwcFBQcHBR6BQYGBQQGBgQRGRkREhkZEkUNExMNDRMTAZWREhyBER0SEAsLEBUQCwsPFg8LCxAVEAsLEAwZEgYGCAcHCgcaCgYHCQkHBgogHBQUHBwUFBwQBwQEBwcEBAcgGRISGRkSEhlAEw0OEhIODRMAAwArAFUB1QGrABEAFQAZAAABIycjIgYVERQWMyEyNjU1NCYHIzUzNyE1IQGrqyuAERkZEQFWERkZkaurVf8AAQABgCsZEv8AEhkZEtUSGdUqKysAAQBrACsBlQHVAAgAACUnBxEjEScHFwGVHmIqYh6VwB5iAVn+p2IelQABAFUAawGVAasACAAAASMVJwcXIxUzAZUq+B74jdUBQI34HvgqAAEAawBrAasBqwAIAAAlNSM3Jwc1IxUBQI34Hvgqayr4HviN1QADAEAAQAHAAdUAIABAAEQAAAEjNjY1NCYmIyIHJiYjIgYHBgYVFBYXFTM1MzI2NTU0JgUiJjU0Nj8CNjYzMhYXFzA2MzIWFTAqAjEiDgMXIzUzAZUhBgYXJxcKCw0hEx4xDBkhJBzrKhIZGf7uERkQDREIBxwRDRMFERINEhkTGhMVHhcTFfIqKgFACRYMFycXAwsNIBoILBweLQi4KxkRgBIZIBkSDRYFBhAPEgkFDQYZEQ8XFg+LgAACAEAAQAHAAcAADwAfAAABISIGFREUFjMhMjY1ETQmByMVIxUjFSM1MzUzNTM1MwGV/tYSGRkSASoSGRknNDc3XjQ3N14BwBkS/tYSGRkSASoSGWtHR0crR0dHAAIAKwBAAdUB1QAJABMAAAEnJwcHFwc3FycHBzcnNzcXFwcXAdWZPDyZdCOEhCNhUBVHXiQkXkcVATsNjY0NZZZQUJYfMFs+CFZWCD5bAAEAKwArAdUB1QAJAAABJwcjFwc3Fyc3ATQ0NKGEM4SEMoMBK6qqXqJkZKJeAAQAQABAAcABwAANABEAFQAYAAABISIGFREUFjMzNzU0JgUzFSMXIzUzFzUzAZX+1REZGRLVgBn+7tbWa2trK3UBwBkS/tYSGYDVEhlrKlYroHUAAgArACsB1QHVAAMAGAAANzM1IzciDgIVFB4CMzI+AjU0LgIjq6qqVSxNOyEhO00sLE07ISE7TSyrqoAhO00sLE07ISE7TSwsTTshAAQAVQArAdUBwAALABcAMgA6AAAlNDYzMhYVFAYjIiYnNDYzMhYVFAYjIiYBNTQmJiMiBgYHBwYWFjMzMjY1NTY2MzIWFRUnJiYjIgYHFwErGRESGRkSERnWGRISGRkSEhkBgBMgFBIZEge8BgILCqESGQkPCwwRehQvGR85GGhVEhkZEhEZGRESGRkSERkZAScLFCIUDxQI3AcRDBkSugsQEg0LPgsMExJoAAIAfQBVAesBqwAUACIAACUjFTMVIzU0NjMzNSM1MzIWFRUUBiEzNzMXMyc3IwcjJyMXAdUqQFYNCSpAQAkNDf6fOUkCSTlkXTlCAkI5XIAVFisJDBYVDQgWCA10dJuQa2uQAAQAFgAWAdUBzAALABcAGwAgAAABIRczFSMXNjURNCYlFwYVERQWMyEXNwEXFSM1FxUjNTMBq/7nqm9EaQUZ/loaBRkRARk/H/5od1bW1sQBq6sraQoKAQASGQIZCgr/ABIZPx4BmMwrK2cZKwACAH0AVQHrAasAFAAiAAABIxUzFSM1NDYzMzUjNTMyFhUVFAYBMzczFzMnNyMHIycjFwHVKkBWDQkqQEAJDQ3+nzlJAkk5ZF05QgJCOVwBaxYVKwgNFRYNCRUJDP7qdHSbkGpqkAAGACsAKwHVAdUAEwAbACsAMwA7AEMAAAEiDgIVFB4CMzI+AjU0LgIXByYmJzcWFgciJiY1NDY2MzIWFhUUBgYnFwYGByc2Ngc3FhYXByYmFyc2NjcXBgYBACxNOyEhO00sLE07ISE7TXM7CCEWGCIzkhIdEREdEhIdEREdTxkWIgg7DTNAOwghFxkiM88YFiEIOw0zAdUhO00sLE07ISE7TSwsTTshmBgWIQg7DTOfER0SEh0RER0SEh0R3zsIIRcZIjPPGBYhCDsNM0A7CCEXGSIzAAQAKwBAAdUBwAArADcAQwBTAAAlNC4CIyIOAhUGBhUVFBYzMzU0PgIzMh4CFRUjFTMyNjU1NjY1NTQmBTQ2MzIWFRQGIyImNzQ2MzIWFRQGIyImNy4CIyIOAhc2NjceAgHAHzZFJiZENx8KCxkRFhcpNh8fNikXqqoSGQkMDP7iDAkJDAwJCQyADAkJDAwJCQxVBSQ2IBgwJhUCKDgIDi47+yxINRwcNEktBRQMKhIZgh82KRcXKTYflysZEhoFEgwxCxILCQwMCQkNDQkJDAwJCQ0NMx4xHBMkMyARQiscKxgAAwBAAGsBwAGVAAIABgAJAAATFSc3BxcRMxE3tUholZVWlQFIkEiVlZUBKv7WlQADAEAAawHAAZUAAgAGAAkAACU1Fwc3JxEjEQcBS0holZVWlbiQSJWVlf7WASqVAAMAKwArAdUB1QADAAcACwAAASE1IRUhFSEVIRUhAdX+VgGq/lYBqv5WAaoBa2qgajZqAAkAQABAAcABwAAPABMAFwAbAB8AIwAnACsAPgAAASMiBhUVFBYzMzI2NTU0JgcVIzUXNTMdAiM1JyM1OwIVIwczFSMzNTMVJSMiJjU1NDYzMzIWFRUjNSMVMwGV1RIZGRLVEhkZEtVVKysqKyuAKiqrKyurKv7rFRIZGRLVEhkr1RUBaxkS1RIZGRLVEhkrKyuAKysrKiorKysrKioqKhkS1RIZGRIVFdUAAwAgABUB1QHrAA8AEwA7AAABNSMVFBYXFSMVMzUjNTY2JxUjNQcUBiMjFSM1IyImNTQ2MzM1IyImNTQ2MzM1MxUzMhYVFAYjIxUzMhYB1aokHCuAKxwkKlZKIBYqKysWHx8WKysWHx8WKysqFiAgFioqFiABK8DAHi0ImCsrmAgts0BAtRYgwMAgFhYfFSAWFh8rKx8WFiAVHwAHABUAFQHrAe4AJAApADUAQQBhAGUAaQAAATQmJicmBgYHIxUjIgYHBxUUFjMzMjY1NSEVFBYzMzI2NTU2NiUzFhcjFyImNTQ2MzIWFRQGMyImNTQ2MzIWFRQGNwcHBg8DBicmJzQ1NTc1PwM+AhceAhUUBgcnMxUjFTMVIwHrHjUiIkI0Dzo1ChEDLQ0JFQkMAQANCRUJDCcv/nVhAymtIA0TEw0NExPeDhISDg0TEzEEBAUEBQUELiYmBQEBAQIBCSQwGhkoFx0aSSsrKysBVSM9KggHDyogKgwKgKoJDQ0JFRUJDQ0JpBJJITknaxMNDRMTDQ0TEw0NExMNDRN4AgICAgEBAQodHC8FBQoEBgQFBQMYIg8EBR4tGR4yDrNrFSsABABAAEABwAHAABEAFQAZAB0AAAEnJiMjIgYVERQWMyEyNjU1NCUzFSMXIzUzNSM1MwGzZwwSwxIZGRIBKhIZ/tWWltbW1tbWAU1nDBkS/tYSGRkSwxIrK6srKyoAAwArAFUB1QGrABEAFQAZAAABIycjIgYVERQWMyEyNjU1NCYHIzUzNyE1IQGrqyuAERkZEQFWERkZkaurVf8AAQABgCsZEv8AEhkZEtUSGdUqKysAAgBrACsBwAHVAAgAFAAAASE1IxEzNSEnIxQGIyImNTQ2MzIWAcD+1SoqASsrVRkSERkZERIZAasq/laqaxIZGRISGRkADAArAEAB1QHAAAMABwALAA8AEwAXABsAPABAAEQASABMAAABMxUjBzMVIyczFSMXIzUzNyM1MxcjNTM3IzUzBwcmJzc2JycmJiMjIgYVFhYXFhYXFhYXFjY1NTQmJycmJzMVIyczFSMzIzUzFyM1MwErKioWKysqKiqVKysVKiorKysVKiqdNlEvNggCEAEMCFgJDQMaFxlHLCZVLwkNCgdPCxUqKkAqKqoqKkAqKgGrKxUraytAKxUraysVK942L1E2CAtPBwoNCS5WJixHGRYbAgENCVkHDAEQAlYrKysrKysAAwCAACsBgAHrAB4AIwAoAAABJzU1NDYzMhYVFTM1NCYjIgYdAgcnExYWMzI2NxMHFzcHNQc3NxUnATUgBwQEByofFhYfIEtsAgwGBgwCbGUREypBEQYqAWwZKAoFBgYFDAwWHh4WCigZE/66BwcHBwFGNA0Ff4sRDAWLfwACAKsAVQFVAasAAwAKAAAlFSM1NxUzNTMnBwFVqkAqQFVVgCsr1qurVVUAAgAVACAB6wHgABMAGQAAASc3JycHJwcHFwcXBxcXNxc3NycHJzcXNxcB6zQHTShJSShNBzQ0B00oSUkoTQfgUSAxfSABADxOEkQfH0QRTzw8ThJEHx9EEk4pUh8yfiAABAAVABYB/wGrABIAFQBNAFkAABMhFTM1NCYjISIGFREUFjMzNSMlJxUFNic3NicnJgcHJicnJiMjIgcHBgYHJyYHBwYXFwYXBwYXFxY3NxYXFxQzMzI1NzY2NxcWNzc2JwciJjU0NjMyFhUUBkABgCsZEv6AEhkZEsDAAQCAASQCARcDAhYCBRsHCQQBBSwEAQQFCAMcBAIWAgMXAQEXBAMWAgQbCAgFBSwFBQQIAxwEAhYDBGYNExMNDhISAYBraxIZGRL/ABIZK4BVqjQKCBIDBCYEAQsFBB0FBR0CBAMLAgQnBAITCggSAwQmBAELBQQdBAQdAgQDCwEEJgQDBRMNDRMTDQ0TAAQAKwBVAdUBqwADAAcACwAPAAAlIREhFzM1IxEzNSM1MzUjAVX+1gEqK1VVVVVVVVUBVlZW/qpWKlYAAwAVABUB1QHrAA8AHwBaAAABMjY1NC4CMTAOAhUUFgcyNjU0LgIxMA4CFRQWJwcGFRUUFhY7AzI2NTQmIyM1MzI2NTQmIyM1MzI2NTQmIyM1MzI2NTQmIyM+AjE2NTQnMC4CMQGLFh8QFRAQFhAgVQ0TCgwKCgwKE1CaFBIdEYYlewsPDwt7pQsQEAuluwsPDwu7kAsQEAvTBw8KAwYHCQcBVSAWDSEeFBQeIQ0WIBUTDQcSEQsLERIHDRNVkRIcgREdEhALCxAVEAsLDxYPCwsQFRALCxAMGRIGBggHBwoHAAIAKwBVAdUBwAAGABYAAAEHMxUhNTMHIiY1ND4CMTAeAhUUBgEA1UABKkDVEhkNEQ0NEQ0ZAcDAq6tVGREJHB0UFB0cCREZAAEAKwBrAdUBlQAIAAA3NychNSE3JwfAHmIBWf6nYh6Vax5iKmIelQAEAEAAKwHiAdUACwAeADYAPgAAEzQ2MzIWFRQGIyImFzU0JiMjIgYVFTMVMzUmJjU0NhcUBgYjIiYmNTQ2NzUGBhUUFhYzMjY2NTcjNSMVMxc3YBkSERkZERIZdRkRQBIZK0oUFimsER0SEh0REg4gKx0xHR0xHRZhK3U1IwGrERkZERIZGYMqEhkZEoCVAhQ2HipEbhEdEhIdERIdCC4KNyQdMRwcMR1AgKpPFwACAEAAQAHVAcAACwAsAAABLgMjIg4CBxcXIicmBwcmJic3NicmNTQmIyMiBhUUHgMzMjY1NTQmAdUBEh8qGRkqHxIBdUsoJA0JLy1JFy8JBAwMCUsIDSVCW208CA0NAZYCDQ8MDA8NApZLDAUKLxdJLS8JDSQoCA0NCDxtW0IlDQhLCQwAAgBfAEABoQHAAA8AHwAAATcjFTcWFhUUBgc2NjU0Jgc0NjcGBhUUFhcHMzUHJiYBZDGqMR8mCgkmLSHhCgkmLSEcMaoxHyYBjzGrMRdHKxUnERhQMChGMxUnERhQMChGGTGrMRdHAAIAgABAAYABwAARABkAABMVFBYWFxUjFTM1IzU+AjU1ByM1MzAcAoAcMB9AqkAfMBwrqqoBwIAgNSQFVysrVwUkNSCAa0ATGRQAAwBVABUB4QHhAA8AGwAnAAABNSYjIg4CFRQXNjU0JicHIiY1NDYzMhYVFAY3JwcnBxcHFzcXNycBKxYVIj0wHKurAQGpEhkZEhIZGc8eLi0eLS0eLS4eLgErjwYZLkAoapKSagcMB0AZERIZGRIRGdgeLi4eLi0eLS0eLQAEAEAAQAHAAcAADwATABcAGwAAASEiBhURFBYzITI2NRE0JgMhESEHIzUzByM1MwGV/tYSGRkSASoSGRkS/tYBKirW1laAgAHAGRL+1hIZGRIBKhIZ/qsBAGsrgCoAAwArAEAB1QHAAAMABwALAAA3IxEzNyMRMzcjFTOgdXWbdnaadXVAAQCA/oDV1QAAAAEAAAXvANIAGAAAAAAAAgAAAAEAAQAAAEAALgAAAAAAAAAAAAAAUgAAAFIAAABSAAAAYQAAAHAAAAB/AAAAjgAAAJ0AAACsAAAAuwAAAMoAAADZAAAA6AAAAPcAAAEGAAABFQAAASQAAAEzAAABQgAAAVEAAAFgAAABbwAAAX4AAAGNAAABnAAAAasAAAG6AAAByQAAAdgAAAHnAAAB9gAAAgUAAAIUAAACIwAAAjIAAAJBAAACUAAAAl8AAAJuAAACfQAAArcAAAMFAAADNQAAA6IAAAQLAAAEWgAABOwAAAWRAAAFwQAABhQAAAY3AAAGWQAABqQAAAdFAAAH0QAACC8AAAiEAAAI+wAACXcAAAnFAAAKMgAACqUAAAsaAAALfAAAC9QAAAv6AAAMMwAADIMAAAybAAAMyAAADQsAAA1WAAANwwAADhkAAA6FAAAPAwAADz8AAA+NAAAPzgAAEBcAABA7AAAQYQAAENAAABDrAAARTQAAEfQAABJQAAASkQAAEuEAABMTAAATMgAAE7EAABQHAAAUYQAAFNgAABUzAAAViQAAFmcAABfNAAAYjwAAGW0AABrYAAAbnQAAHAgAABypAAAdPgAAHXkAAB3dAAAd/wAAHkcAAB6vAAAfCgAAH1EAAB/FAAAgIQAAILQAACD5AAAhewAAIb4AACIDAAAiUgAAIpYAACLTAAAjLgAAI3MAACPmAAAkQgAAJLQAACVfAAAltgAAJhoAACY/AAAmdQAAJqAAACbFAAAm/wAAJ1UAACeIAAAn5AAAKF4AACkAAAAp6wAAKjUAACqKAAAqvAAAKzIAACu1AAAsKQAALHgAACzUAAAtCAAALU4AAC2TAAAt6wAALq8AAC9AAAAvwgAAMFAAADDFAAAxCgAAMU4AADGTAAAx1wAAMjcAADKLAAAy+AAAM0YAADOzAAA0QgAANLUAADXAAAA2GgAANo0AADbjAAA3OQAAN7YAADfgAAA4MAAAOK4AADkKAAA5fwAAOe0AADpNAAA6wgAAOyYAADuoAAA8KAAAPMEAAD0UAAA9PAAAPY4AAD3MAAA+IAAAPoUAAD7iAAA/OQAAP20AAD/KAABAawAAQN4AAEEbAABBXwAAQZAAAEG3AABB2AAAQp8AAELxAABDWAAAQ6IAAEPsAABEIwAARD0AAERrAABErwAARNcAAEUTAABFWQAARa8AAEaVAABGtgAARuYAAEciAABHWgAAR7UAAEgbAABIfgAASOYAAElZAABJtwAAShAAAEpKAABK0QAASy0AAEt4AABL8gAATEcAAEyWAABNFgAATlwAAE6oAABPHgAAT3oAAE/hAABQSAAAUJYAAFEIAABRYgAAUaYAAFH7AABSSQAAUogAAFLHAABTRwAAU48AAFP0AABUSQAAVL0AAFUWAABVewAAVcgAAFYQAABWcwAAVuwAAFdMAABXyQAAWEAAAFihAABZPgAAWdsAAFo8AABaiAAAWqIAAFrNAABbUwAAW+EAAFwdAABcqgAAXTgAAF36AABeiAAAXtwAAF9VAABfbgAAX6AAAF/lAABgDQAAYD8AAGBdAABg+wAAYUEAAGGVAABiDAAAYocAAGMmAABjhAAAY90AAGQ3AABklAAAZMUAAGU0AABlqAAAZfEAAGa8AABnsQAAZ/gAAGjAAABpbAAAajwAAGqdAABrZwAAa9MAAGyhAABtbQAAbbcAAG4DAABuTwAAbpwAAG7xAABvNgAAb6kAAG/zAABwLgAAcIAAAHDTAABw/QAAcUsAAHHBAAByOAAAcpEAAHLDAABy+QAAczIAAHN2AABzugAAc/kAAHQnAAB0fwAAdNsAAHUaAAB1kgAAdfEAAHZYAAB2nwAAdtUAAHcKAAB3RwAAd3MAAHeWAAB4MwAAeGAAAHiiAAB4zwAAeSUAAHmVAAB5uAAAelgAAHqjAAB6+AAAexsAAHtSAAB7zgAAe/EAAHxKAAB8mgAAfREAAH1hAAB96AAAflIAAH6CAAB+swAAfxcAAH9YAAB/rQAAgAEAAIBTAACA2gAAgU0AAIGgAACBzQAAgfkAAII0AACCfQAAguMAAIM5AACDrQAAhDYAAISAAACE0gAAhSgAAIW0AACGAQAAhkwAAIaVAACG5gAAh5UAAIe2AACH1wAAh/cAAIgXAACIQQAAiG0AAIklAACJVAAAiYoAAInfAACKKQAAimMAAIrNAACLGwAAi7EAAIvzAACMQgAAjJwAAIz8AACNegAAjg8AAI5lAACOqgAAjyEAAI9lAACP4wAAkHUAAJC9AACRDgAAkWoAAJG5AACSCAAAklMAAJKeAACS2QAAk3UAAJPzAACUcgAAlOAAAJVNAACVngAAlfcAAJYeAACWVAAAl38AAJikAACaXwAAnEcAAJxpAACcnQAAnNEAAJ0wAACdfQAAncUAAJ4qAACehQAAntMAAJ9fAACfywAAoEgAAKC2AAChSgAAocYAAKJXAACiuAAAo1IAAKOiAACkAwAApFcAAKTRAAClEwAApVoAAKWkAACl5wAApikAAKZwAACm1QAApxgAAKdvAACnswAAp/oAAKgsAACoUQAAqI4AAKj5AACpJQAAqZAAAKnLAACqUQAAqs8AAKtBAACr0gAArG8AAKzhAACtWwAAregAAK6EAACu9wAAr7YAALBTAACxBQAAsVIAALHOAACySwAAsq8AALLVAACzNgAAtAEAALS1AAC1OwAAtX4AALW6AAC12wAAtm4AALcxAAC37wAAuKoAALlJAAC51AAAuk4AALqdAAC67AAAu9sAALwiAAC8lAAAvP4AAL0kAAC9ngAAvkgAAL5rAAC+3AAAvyUAAL90AAC/1QAAwEgAAMCPAADA9wAAwVMAAMHZAADCJwAAwlgAAMKSAADC/gAAwyAAAMNBAADD2wAAxCMAAMRcAADEwgAAxSMAAMWIAADFzwAAxiUAAMaRAADG8gAAx6cAAMgaAADIcQAAyOQAAMlYAADJyQAAyhsAAMp8AADLCAAAy2QAAMvBAADMOQAAzJgAAMz0AADN6wAAzt0AAM9GAADP5gAA0HEAANDMAADRNQAA0cAAANHxAADSXQAA0p4AANMNAADTlgAA0/YAANQ+AADU/gAA1ccAANYUAADWfgAA1w4AANeoAADYKwAA2K4AANkgAADZfgAA2kwAANquAADbPQAA24kAANvTAADcLAAA3OMAAN1wAADd9gAA3pkAAN8XAADfdQAA3/MAAOBaAADgngAA4OYAAOEhAADhgwAA4ccAAOJWAADiyQAA4wIAAONTAADkMgAA5JAAAOTpAADllQAA5ioAAOauAADnAAAA50gAAOfWAADoFAAA6IYAAOkFAADpWQAA6ZYAAOnmAADqPgAA6ogAAOr7AADrRQAA66MAAOv8AADsaAAA7OQAAO1yAADt1wAA7iMAAO6aAADuvQAA7xYAAO9lAADvwAAA8BkAAPCCAADwyQAA8O8AAPHDAADyLAAA8oEAAPLpAADzCgAA83UAAPPWAAD0KAAA9MAAAPUlAAD1xgAA9lQAAPbQAAD3XwAA99MAAPgLAAD4ggAA+VEAAPmqAAD56gAA+g8AAPqHAAD6vwAA+wMAAPuaAAD8CQAA/DMAAPxKAAD8dwAA/I4AAPy4AAD9AgAA/SQAAP1GAAD9ZwAA/ZsAAP29AAD93wAA/icAAP5xAAD+owAA/vIAAP9AAAD/kQAA/8gAAP/9AAEAJgABAFIAAQB+AAEAqAABANYAAQEEAAEBHAABATIAAQFTAAEBdAABAfMAAQJnAAECugABAwsAAQNjAAED6QABBFIAAQTOAAEFOgABBZUAAQXYAAEGUQABBssAAQdtAAEH5QABCHUAAQklAAEJpwABCiUAAQp5AAEKxAABCxgAAQtjAAELwQABDFUAAQzQAAENJwABDaEAAQ4nAAEOmQABDuAAAQ+pAAEQBwABEH8AARC+AAERIgABEXwAARHeAAESdgABEvEAARNnAAETzgABFCkAARR4AAEUtwABFTkAARWMAAEWAwABFpQAARcBAAEXfgABF+EAARgPAAEYPQABGJcAARkPAAEZigABGfsAARo7AAEajwABGuIAARs1AAEbWwABHBAAARy7AAEdKwABHbQAAR4+AAEetgABHy0AAR+BAAEf5wABIGMAASDiAAEhTwABIbsAASJEAAEitAABI2sAASOiAAEj9QABJF0AASSYAAEk8AABJWoAASWfAAEmEQABJnUAAScFAAEndQABJ/4AASh/AAEo9gABKVwAASnCAAEqDQABKlQAASqNAAEq3AABKwkAAStKAAErmAABLG8AASyxAAEs/QABLWsAAS3NAAEuJAABLsYAAS8tAAEvnwABMEUAATCuAAExhgABMc8AATI0AAEyjAABMwwAATOVAAE0CQABNHoAATTpAAE1WQABNbYAATYJAAE2TwABNnwAATa5AAE3VAABN6gAATgFAAE4KwABOGEAATjFAAE5CwABOUAAATmTAAE5zQABOg4AATpkAAE68AABOxIAATtSAAE7sQABPBYAATxeAAE82wABPVYAAT2aAAE+IQABPmwAAT7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DBYQFhQWHBcsF3wXoBUMF4AXQBYgFMwXlBcUFNgW+Bb0FsgUFBbUFfAXZBTcFVAXDBbYF7QXGBagF3AXEBd0FtwWrBcAFAwWQBSkFLQXWBdcFkwWJBYYFZgUqBTgFigWLBYwFxwXIBckF5wXrBdoFvwWABUwFygUbBbMFRwUuBSMFTgW0BV0F5gVyBVAFrgAMAAAAAA7gAAAAAAAAATwAAAAwAAAAOQAAAAMAAABfAAAAXwAAAA0AAABhAAAAegAAAA4AAOAAAADgBAAAACgAAOAZAADgGQAAAC0AAOAbAADgIQAAAC4AAOAjAADgJAAAADUAAOAoAADgLAAAADcAAOAuAADgMQAAADwAAOAzAADgOQAAAEAAAOA7AADgUwAAAEcAAOBVAADgdAAAAGAAAOCvAADgvAAAAIAAAOC+AADgvwAAAI4AAODDAADgxAAAAJAAAODGAADg8AAAAJIAAOFFAADheQAAAL0AAOGQAADhlQAAAPIAAOGcAADhnAAAAPgAAOGjAADhwwAAAPkAAOHIAADhyAAAARoAAOHNAADh0AAAARsAAOHYAADh2wAAAR8AAOHgAADh4gAAASMAAOH+AADiAgAAASYAAOImAADibAAAASsAAOK8AADixAAAAXIAAOLGAADiyQAAAXsAAOLMAADizAAAAX8AAOMHAADjCAAAAYAAAOMKAADjGAAAAYIAAOMaAADjOgAAAZEAAOOdAADj4AAAAbIAAOPiAADj7gAAAfYAAOPxAADkEwAAAgMAAOQVAADkLgAAAiYAAOQwAADkQAAAAkAAAOUtAADlNgAAAlEAAOU5AADlfQAAAlsAAOXDAADl4QAAAqAAAOYOAADmIAAAAr8AAOYjAADmSQAAAtIAAOZcAADmXAAABP0AAOZfAADmYQAABQ4AAOZjAADmZgAABRIAAOZ1AADmdQAABTUAAOa4AADmuAAABWIAAObEAADmxQAAAvkAAObdAADm3QAAAvsAAObfAADm3wAAAvwAAObhAADm4QAAAv0AAOccAADnHAAABQQAAOcpAADnKQAABPoAAOfpAADn6QAAAv4AAOfuAADoAQAAAv8AAOgLAADoDgAAAxMAAOgRAADoFwAAAxcAAOg0AADoOgAAAx4AAOhNAADoUQAAAyUAAOhTAADocwAAAyoAAOh1AADotgAAA0sAAOi4AADozgAAA40AAOjQAADpAAAAA6QAAOkCAADpBgAAA9UAAOkIAADpDwAAA9oAAOkRAADpQwAAA+IAAOlFAADpRQAABBUAAOlHAADpfQAABBYAAOl/AADpfwAABE0AAOmCAADpggAABE4AAOmIAADpiQAABE8AAOmLAADpiwAABFEAAOmPAADpjwAABFIAAOmSAADpkgAABFMAAOmUAADplAAABFQAAOmWAADplgAABFUAAOmYAADpmAAABFYAAOmaAADpmwAABFcAAOmdAADpnQAABFkAAOmhAADpowAABFoAAOmlAADppQAABF0AAOmoAADpqAAABF4AAOmqAADpqgAABF8AAOmtAADprQAABGAAAOmwAADpsgAABGEAAOm0AADptAAABGQAAOm2AADptwAABGUAAOm6AADpvQAABGcAAOnAAADpwgAABXkAAOnDAADpwwAABGsAAOnFAADpxQAABGwAAOnIAADpzwAABG0AAOnRAADp1AAABHUAAOnWAADp2QAABHkAAOndAADp3QAABH0AAOngAADp4QAABH4AAOnkAADp5AAABIAAAOnmAADp5gAABIEAAOnpAADp6QAABIIAAOnsAADp7QAABIMAAOnvAADp7wAABIUAAOnyAADp8wAABIYAAOn1AADp9gAABIgAAOn5AADp+QAABIoAAOn8AADp/AAABIsAAOn+AADp/gAABIwAAOoAAADqAgAABI0AAOoJAADqJAAABJAAAOomAADqOQAABKwAAOo6AADqOgAABR0AAOo7AADqOwAABQkAAOo8AADqPAAABSwAAOo9AADqPQAABUEAAOo+AADqPgAABVYAAOo/AADqPwAABXMAAOpAAADqSQAABMAAAOpKAADqSgAABaoAAOpLAADqSwAABbkAAOpMAADqTAAABREAAOpNAADqTQAABZsAAOpOAADqTgAABSsAAOpPAADqTwAABU8AAOpQAADqWQAABMoAAOpaAADqWgAABaQAAOpbAADqWwAABVwAAOpcAADqXAAABVoAAOpdAADqXQAABXgAAOpeAADqXgAABQ0AAOpfAADqXwAABSEAAOpgAADqaQAABNQAAOpwAADqdAAABN4AAOp1AADqdQAABeMAAOp2AADqdgAABbsAAOp3AADqdwAABWgAAOp4AADqeAAABbwAAOp5AADqeQAABQAAAOsbAADrHwAABToAAOsoAADrKAAABXcAAOspAADrKQAABZgAAOs7AADrTAAABOMAAOtOAADrTwAABPUAAO84AADvOQAABPgAAO86AADvOwAABPsAAO88AADvPQAABP4AAO8+AADvPgAABQIAAO8/AADvQQAABQYAAO9CAADvQgAABQwAAO9DAADvQwAABRgAAO9EAADvRAAABRoAAO9FAADvRQAABRwAAO9GAADvSAAABR4AAO9JAADvSQAABSIAAO9KAADvSgAABScAAO9LAADvSwAABTEAAO9MAADvTAAABTQAAO9NAADvTQAABTkAAO9OAADvTgAABUQAAO9PAADvTwAABUgAAO9QAADvUAAABUsAAO9RAADvUQAABVIAAO9SAADvUgAABVUAAO9TAADvUwAABVsAAO9UAADvVAAABWEAAO9VAADvVgAABWQAAO9XAADvVwAABWcAAO9YAADvWAAABWkAAO9ZAADvWgAABXAAAO9bAADvWwAABXYAAO9cAADvXAAABX0AAO9dAADvXQAABX8AAO9eAADvXgAABYEAAO9fAADvXwAABZEAAO9gAADvYwAABZQAAO9kAADvZAAABZkAAO9lAADvZwAABZwAAO9oAADvaQAABaIAAO9qAADvagAABaUAAO9rAADvawAABa0AAO9sAADvbgAABa8AAO9vAADvbwAABbgAAO9wAADvcAAABboAAO9xAADvcQAABc8AAO9yAADvcgAABdIAAO9zAADvcwAABdQAAO90AADvdAAABdsAAO91AADvdQAABd4AAO92AADvdgAABeIAAO93AADvdwAABekAAO94AADveAAABewAAO+nAADvpwAABW4AAO/sAADv7AAABSQAAPBvAADwbwAABcwAAPCSAADwkgAABWAAAPCoAADwqAAABcEAAPCwAADwsAAABcIAAPDFAADwxQAABUUAAPDPAADwzwAABXUAAPDRAADw0QAABY4AAPDSAADw0gAABZIAAPDcAADw3AAABakAAPDiAADw4gAABdUAAPDrAADw6wAABY8AAPDsAADw7AAABc0AAPD1AADw9QAABRkAAPD6AADw+gAABaAAAPD7AADw+wAABeEAAPD8AADw/AAABeoAAPD9AADw/QAABaEAAPD+AADw/gAABY0AAPD/AADw/wAABSgAAPEAAADxAAAABVgAAPEBAADxAQAABdgAAPECAADxAgAABT8AAPEEAADxBAAABVMAAPEGAADxBgAABZ8AAPEHAADxBwAABaYAAPEIAADxCAAABVkAAPEJAADxCQAABW8AAPEKAADxCgAABS8AAPELAADxCwAABVEAAPEMAADxDAAABXQAAPENAADxDQAABawAAPEOAADxDgAABV4AAPEPAADxDwAABTAAAPERAADxEQAABdEAAPESAADxEgAABdMAAPEUAADxFAAABeQAAPEWAADxFgAABV8AAPEbAADxGwAABWMAAPF9AADxfQAABVcAAPF+AADxfgAABacAAPGBAADxggAABQoAAPGEAADxhAAABQEAAPGHAADxhwAABU0AAPGJAADxiQAABWsAAPGKAADxigAABW0AAPGLAADxiwAABWoAAPGMAADxjAAABWwAAPGbAADxnAAABRYAAPGdAADxngAABSUAAPGfAADxnwAABTIAAPGgAADxoAAABUAAAPGhAADxoQAABUIAAPGiAADxogAABUYAAPGjAADxowAABUoAAPGkAADxpwAABYIAAPGoAADxqAAABYcAAPGpAADxqQAABcsAAPGqAADxqgAABd8AAPGrAADxqwAABegAAPGsAADxrAAABUMAAPGtAADxrQAABeAAAPGuAADxrgAABdAAAPGvAADxrwAABYgAAPGwAADxsAAABTMAAPGxAADxsQAABeUAAPGyAADxsgAABcUAAPGzAADxswAABTYAAPG0AADxtAAABb4AAPG1AADxtQAABb0AAPG2AADxtgAABbIAAPG3AADxtwAABQUAAPG4AADxuAAABbUAAPG5AADxuQAABXwAAPG7AADxuwAABZoAAPG+AADxvgAABdkAAPG/AADxvwAABTcAAPHAAADxwAAABVQAAPHBAADxwQAABcMAAPHCAADxwgAABbYAAPHDAADxwwAABe0AAPHEAADxxAAABcYAAPHFAADxxQAABagAAPHGAADxxgAABdwAAPHHAADxxwAABcQAAPHIAADxyAAABd0AAPHJAADxyQAABbcAAPHKAADxygAABasAAPHMAADxzAAABcAAAPHNAADxzQAABQMAAPHOAADxzgAABZAAAPHPAADxzwAABSkAAPHQAADx0AAABS0AAPHRAADx0gAABdYAAPHUAADx1AAABZMAAPHVAADx1QAABYkAAPHWAADx1gAABYYAAPHYAADx2AAABUkAAPHbAADx2wAABWYAAPHcAADx3AAABSoAAPHfAADx3wAABTgAAPHgAADx4gAABYoAAPHjAADx5QAABccAAPHmAADx5gAABecAAPHoAADx6AAABesAAPHpAADx6QAABdoAAPHqAADx6gAABb8AAPHvAADx7wAABX4AAPHxAADx8QAABYAAAPHyAADx8gAABUwAAPHzAADx8wAABcoAAPH0AADx9AAABRsAAPH1AADx9QAABbMAAPH2AADx9gAABUcAAPH3AADx9wAABS4AAPH4AADx+AAABSMAAPH8AADx/AAABc4AAPIAAADyAAAABU4AAPIBAADyAQAABbQAAPICAADyAgAABV0AAPIDAADyAwAABeYAAPIEAADyBAAABXIAAPIFAADyBQAABVAAAPIGAADyBgAABa4AAPIMAADyDAAABe4AEP/9ABD//QAABPcAAAARAUQAAAAHAFoAAwABBAkAAABgAAAAAwABBAkAAQAcAGAAAwABBAkAAgAOAHwAAwABBAkAAwBSAIoAAwABBAkABAAcAGAAAwABBAkABQAaANwAAwABBAkABgAqAPYAQwBvAHAAeQByAGkAZwBoAHQAIAAyADAAMQA4ACAARwBvAG8AZwBsAGUALAAgAEkAbgBjAC4AIABBAGwAbAAgAFIAaQBnAGgAdABzACAAUgBlAHMAZQByAHYAZQBkAC4ATQBhAHQAZQByAGkAYQBsACAASQBjAG8AbgBzAFIAZQBnAHUAbABhAHIARgBvAG4AdABGAG8AcgBnAGUAIAAyAC4AMAAgADoAIABNAGEAdABlAHIAaQBhAGwAIABJAGMAbwBuAHMAIAA6ACAANQAtADMALQAyADAAMQA5AFYAZQByAHMAaQBvAG4AIAAxAC4AMAAxADcATQBhAHQAZQByAGkAYQBsAEkAYwBvAG4AcwAtAFIAZQBnAHUAbABhAHIAAAADAAAAAAAA/4MAMgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAf//AAIAAQAAAAwAAAAAAAAAAgACAAMAJwABACgE9wACAAEAAAAKAAwADgAAAAAAAAABAAAACgAeACwAAWxhdG4ACAAEAAAAAP//AAEAAAABcmxpZwAIAAAAAQAAAAEABAAEAAAAAQAIAAGinAAhAEgA5gFIAZIBuAHmAgwCMgJYAn4RGhk6JfQuejSCQUBDBEd6SwRM1FbCXjhjgGUWdAx1PHvWkcCYrpl6njiiKqJYAA0AHAAsADYAQABKAFQAXgBoAHIAfACGAJAAmAQsAAcAGAANAB0AGQAiACAEKgAEAAwAGgAdBCkABAALABoAHQQoAAQACgAaAB0EJwAEAAkAGgAdBCYABAAIABoAHQQlAAQABwAaAB0EJAAEAAYAGgAdBCMABAAFABoAHQQiAAQABAAaAB0EIQAEAAMAGgAdBCAAAwADABgEKwACABgACAASACIALAA2AEAASgBUAFwEMwAHABgADQAdABkAIgAgBDEABAAHABoAHQQwAAQABgAaAB0ELwAEAAUAGgAdBC4ABAAEABoAHQQtAAQAAwAaAB0ENAADABoAHQQyAAIAGAAFAAwAJAA0ADwARAMlAAsAEQANAB8AHAAhAA4AIQAWABwAGwQ2AAcAGAANAB0AGQAiACAENwADABoAHQKZAAMACQADBDUAAgAYAAMACAAYACAEOAAHABgADQAdABkAIgAgBDkAAwAaAB0AfQACABgABAAKABIAIgAoBDwAAwAaAB0EOwAHABgADQAdABkAIgAgBDoAAgAYBPgAAgAUAAMACAAYACAEPgAHABgADQAdABkAIgAgBD8AAwAaAB0EPQACABgAAwAIABgAIARBAAcAGAANAB0AGQAiACAEQgADABoAHQRAAAIAGAADAAgAGAAgBEQABwAYAA0AHQAZACIAIARFAAMAGgAdBEMAAgAYAAMACAAYACAERwAHABgADQAdABkAIgAgBEgAAwAaAB0ERgACABgAfgD+ARABJAE+AVgBeAGUAawB1AH8AhYCLAJEAloCdAKMAqYCrgLOAvgDIAM0A14DhgOqA8oD6AP+BBQELgQ+BFIEbASMBKQEugTeBPoFFgVEBWQFggWiBcYF5AX6BgoGJAYuBkAGYgaEBp4Gpga6BtQG6Ab4BwYHGgcyB1IHZgd2B5IHpgeyB8QH2AfsB/gIGAgoCEgIagiWCL4I9gksCWYJngnUChAKQgpmCn4KqAq2CsgK4gr+CyQLPgtiC3QLnAu4C9oL9AwGDB4MNgxcDHIMhgyYDLIMwgzWDOoNAg0KDRINJA0+DVwNdA2iDcIN6A3+DiIOPg5WDnIOjAAuAAgAIwANACEAFgAaABIAHwM6AAkAIgAhABwAHwASABsAEgAkBRUADAAiACEAHAANACAAIQAcAB8AFgASACAFFAAMACIAIQAcAA0AEwAWACUADQAcABMAEwUTAA8AIgAhABwADQATABYAJQANABsAHAAfABoADgAZBRIADQAiACEAHAANABMAFgAlAA0AFQAWABQAFQURAAsAIgAhABwADQARABIAGQASACEAEgUQABMAIgAhABwADQAOACQAEgAgABwAGgASAA0AGgAcACEAFgAcABsFDwATACIAIQAcAA0ADgAkABIAIAAcABoAEgANABoAHAAgAA4AFgAQBQ4ADAAiACEAHAANAA4AJAASACAAHAAaABIBtgAKACIAEQAWABwAIQAfAA4AEAAYBMwACwAhACEAHwAOABAAIQAWABwAGwAgAXIACgAhACEADgAQABUAGgASABsAIQEsAAwAIQAhAA4AEAAVAA0AGgAcABsAEgAmASsACwAhACEADgAQABUADQATABYAGQASBQ0ADAAhACEADgAQABUADQASABoADgAWABkClQADACEAGgG1AA8AIAAgABYAIAAhAA4AGwAhAA0AHQAVABwAIQAcBFAAFAAgACAAFgAgACEADgAbACEADQAbAA4AIwAWABQADgAhABYAHAAbBE8AEwAgACAAFgAgACEADgAbACEADQARABYAHwASABAAIQAWABwAGwG0AAkAIAAgABYAIAAhAA4AGwAhAzkAFAAgACAAFgAUABsAGgASABsAIQANACEAIgAfABsAEgARAA0AFgAbAzgAEwAgACAAFgAUABsAGgASABsAIQANAB8AEgAhACIAHwAbABIAEQM3ABEAIAAgABYAFAAbABoAEgAbACEADQAfABIAIQAiAB8AGwM2AA8AIAAgABYAFAAbABoAEgAbACEADQAZAA4AIQASAzUADgAgACAAFgAUABsAGgASABsAIQANABYAGwARAzQACgAgACAAFgAUABsAGgASABsAIQMzAAoAIAAgABIAIAAgABoAEgAbACEDMgAMACAAHQASABAAIQANAB8ADgAhABYAHAUMAAcAHwAhABYAEAAZABIAawAJAB8AIQANACEAHwAOABAAGAK1AAwAHwAfABwAJAANACIAHQAkAA4AHwARBBIADwAfAB8AHAAkAA0AHwAWABQAFQAhAA0ADgAZACECvAALAB8AHwAcACQADQAfABYAFAAVACECuwAKAB8AHwAcACQADQAZABIAEwAhAr4AEQAfAB8AHAAkAA0AEwAcAB8AJAAOAB8AEQANABYAHAAgAqUADQAfAB8AHAAkAA0AEwAcAB8AJAAOAB8AEQKkAA0AHwAfABwAJAANABEAHwAcAB0ADQAiAB0CowAWAB8AHwAcACQADQARAB8AHAAdAA0AEQAcACQAGwANABAAFgAfABAAGQASAqIADwAfAB8AHAAkAA0AEQAfABwAHQANABEAHAAkABsCuAAOAB8AHwAcACQADQARABwAJAAbACQADgAfABEFCwAPAB8AHwAcACQADQAQABYAHwAQABkAEgANACIAHQUKABEAHwAfABwAJAANABAAFgAfABAAGQASAA0AEQAcACQAGwK9AA4AHwAfABwAJAANAA8ADgAQABgADQAWABwAIAKhAAoAHwAfABwAJAANAA8ADgAQABgAwQAHAB8AEAAVABYAIwASBQkADAAfABAAFQAWACEAEgAQACEAIgAfABICoAAEAB0AHQAgBE4ACAAdAB0AHwAcACMADgAZBQgAEAAdAB0ADQAgABIAIQAhABYAGwAUACAADQAOABkAIQUHABAAHQAdAA0AHwASABQAFgAgACEAHwAOACEAFgAcABsFBgAMAB0AHQANAA8AGQAcABAAGAAWABsAFAUFAAMAHQAWBMAACQAdAA4AHwAhABoAEgAbACEDMQAMABsAGwAcACIAGwAQABIAGgASABsAIQUEAAkAGwAWABoADgAhABYAHAAbAzAABwAbABEAHwAcABYAEQUDAAYAGwAQABUAHAAfBQIACQAbAA4AGQAmACEAFgAQACAEmgALABoAHQANACAAIQAcAB8AFgASACAAsgAPABkAIQASAB8AGwAOACEAEgANABIAGgAOABYAGQUBAAkAGQAhAA0AHwAcACIAIQASA90ABwAZABkADQAcACIAIQTlAA0AGQAZAA0AFgAbABAAGQAiACAAFgAjABIETQAJABkAGQANABYAGwAPABwAJQAtAAUAGQAPACIAGgMvAAgAGQAOAB8AGgANABwAGwMuAAkAGQAOAB8AGgANABwAEwATAy0ACQAZAA4AHwAaAA0ADgARABEDLAAFABkADgAfABoE5AAPABYAHwAdABwAHwAhAA0AIAAVACIAIQAhABkAEgBgAAcAFgAfAB0AGQAOACYA9wAPABYAHwAdABkADgAbABIAGgAcABEAEgANABwAGwD2ABAAFgAfAB0AGQAOABsAEgAaABwAEQASAA0AHAATABMA9gAVABYAHwAdABkADgAbABIAGgAcABEAEgANABYAGwAOABAAIQAWACMAEgD3ABMAFgAfAB0AGQAOABsAEgAaABwAEQASAA0ADgAQACEAFgAjABIC5gAbABYAHwAZABYAGwASAA0AIAASAA4AIQANAB8AEgAQABkAFgAbABIADQAbABwAHwAaAA4AGQLlABoAFgAfABkAFgAbABIADQAgABIADgAhAA0AHwASABAAGQAWABsAEgANABIAJQAhAB8ADgLkABwAFgAfABkAFgAbABIADQAgABIADgAhAA0AGQASABQAHwAcABwAGgANAB8AEgARACIAEAASABEC4wAbABYAHwAZABYAGwASAA0AIAASAA4AIQANABkAEgAUAB8AHAAcABoADQAbABwAHwAaAA4AGQLiABoAFgAfABkAFgAbABIADQAgABIADgAhAA0AGQASABQAHwAcABwAGgANABIAJQAhAB8ADgLhAB0AFgAfABkAFgAbABIADQAgABIADgAhAA0AFgAbABEAFgAjABYAEQAiAA4AGQANACAAIgAWACEAEgLgABgAFgAfABkAFgAbABIADQAgABIADgAhAA0AEwAZAA4AIQANAA4AGwAUABkAEgARAt8AEQAWAB8AGQAWABsAEgANACAAEgAOACEADQATABkADgAhBQAACwAUAB8AFgAQACIAGQAhACIAHwASBP8AFAARABoAFgAbAA0AHQAOABsAEgAZAA0AIAASACEAIQAWABsAFAAgAbMABgARABcAIgAgACEE/gAIABEAEQAQABUADgAfACEAZwAMABEAEQANACEAHAANAB4AIgASACIAEgGyAA0AEQARAA0AIQAcAA0AHQAVABwAIQAcACABJgASABEAEQANACEAHAANABUAHAAaABIADQAgABAAHwASABIAGwT9AAwAEQARAA0AIQAcAA0AEQAfABYAIwASAysAEQARABEADQAgABUAHAAdAB0AFgAbABQADQAQAA4AHwAhBPwACAARABEADQAfABwADgARAk4AEwARABEADQAdABUAHAAhABwADQAOABkAIQASAB8AGwAOACEAEgRMAA0AEQARAA0AGgAcABEAEgAfAA4AIQAcAB8E+wAQABEAEQANABkAHAAQAA4AIQAWABwAGwANAA4AGQAhAokADAARABEADQAZABwAEAAOACEAFgAcABsA8AAIABEAEQANABkAFgAbABgESwALABEAEQANABYAEAANABAADgAZABkBawALABEAEQANABAAHAAaABoAEgAbACEAwAASABEAEQANABAAFgAfABAAGQASAA0AHAAiACEAGQAWABsAEgC/AAoAEQARAA0AEAAWAB8AEAAZABIESgAJABEAEQANABAAFQAOAB8AIQC0AAgAEQARAA0AEAAOABkAGQT6AAwAEQARAA0ADwAiACAAFgAbABIAIAAgAL4ABwARABEADQAPABwAJQArAAkAEQARAA0ADgAZABIAHwAhAPUACQARABEADQAOABkADgAfABoCSQALABEAEQANAA4ADQAdABUAHAAhABwAvQADABEAEQK/AAMAEQAPBPkACAARAA0AIgAbABYAIQAgBEkADAAQABAAHAAiABsAIQANACEAHwASABIDKgAOABAAEAAcACIAGwAhAA0AEAAWAB8AEAAZABIDKQALABAAEAAcACIAGwAhAA0ADwAcACUDKAAWABAAEAAcACIAGwAhAA0ADwAOABkADgAbABAAEgANACQADgAZABkAEgAhAycADwAQABAAHAAiABsAIQANAA8ADgAZAA4AGwAQABIEBQASABAAEAASACAAIAAWAA8AGQASAA0AEwAcAB8AJAAOAB8AEQPlAAoAEAAQABIAIAAgABYADwAZABID/QARABAAEAASACAAIAAWAA8AFgAZABYAIQAmAA0AGwASACQDJgANABAAEAASACAAIAAWAA8AFgAZABYAIQAmAPQACwAQABAAEgAgACAADQAhABYAGgASAPMADQAQABAAEgAgACAADQAOABkADgAfABoAIADyAAwAEAAQABIAIAAgAA0ADgAZAA4AHwAaBOMABwAQAA0AIgAbABYAIQBMAJoAugDMAOAA9gEQARwBMgFMAVgBdAGgAboB3gH8AhwCPAJWAnACigKkAr4C2ALyAxQDOgNaA3ADigOkA74D1gPwBBQELgRIBGQEegSOBLAE0ATiBOwE9gUGBRgFMAVMBXQFmgXCBeIF9gYSBh4GLgZIBlQGZgZ2BpAGsAbIBuIHDgcqBzoHXAdwB34HmgemB8AHzgfiB/QE5wAPACIAIAAWABsAEgAgACAADQAQABIAGwAhABIAHwCAAAgAIgAgABYAGwASACAAIARSAAkAIgAgAA0ADgAZABIAHwAhAkwACgAiAB8AIAAhAA0AGgAcABEAEgUgAAwAIgAWABkAEQANABAAFgAfABAAGQASA0AABQAiABYAGQARAz8ACgAiABQADQAfABIAHQAcAB8AIQL7AAwAIgAPAA8AGQASAA0AEAAVAA4AHwAhAcMABQAfACIAIAAVBOEADQAfACIAGwAQABUADQARABYAGwAWABsAFAUfABUAHwAcACQAIAASAB8ADQAbABwAIQANACAAIgAdAB0AHAAfACEAEgARAcIADAAfABwAGAASABsADQAWABoADgAUABIBBAARAB8AFgAUABUAIQAbABIAIAAgAA0AGgASABEAFgAiABoBAwAOAB8AFgAUABUAIQAbABIAIAAgAA0AGQAcACQBAgAPAB8AFgAUABUAIQAbABIAIAAgAA0AFQAWABQAFQEBAA8AHwAWABQAFQAhABsAEgAgACAADQAOACIAIQAcAcEADAAfABYAFAAVACEAGwASACAAIAANAAoBwAAMAB8AFgAUABUAIQAbABIAIAAgAA0ACQG/AAwAHwAWABQAFQAhABsAEgAgACAADQAIAb4ADAAfABYAFAAVACEAGwASACAAIAANAAcBvQAMAB8AFgAUABUAIQAbABIAIAAgAA0ABgG8AAwAHwAWABQAFQAhABsAEgAgACAADQAFAbsADAAfABYAFAAVACEAGwASACAAIAANAAQEzgAQAB8AEgAOABgAEwAOACAAIQANABEAFgAbABYAGwAUAHYAEgAfAA4AGwARABYAGwAUAA0AJAAOACEAEgAfABoADgAfABgBOAAPABwAHwARABIAHwANACMAEgAfACEAFgAQAA4AGQE3AAoAHAAfABEAEgAfAA0AIQAcAB0BNgAMABwAHwARABIAHwANACAAIQAmABkAEgE1AAwAHAAfABEAEgAfAA0AHwAWABQAFQAhATQADAAcAB8AEQASAB8ADQAcACIAIQASAB8BMwALABwAHwARABIAHwANABkAEgATACEBMgAMABwAHwARABIAHwANABYAGwAbABIAHwExABEAHAAfABEAEgAfAA0AFQAcAB8AFgAnABwAGwAhAA4AGQEwAAwAHAAfABEAEgAfAA0AEAAcABkAHAAfAS8ADAAcAB8AEQASAB8ADQAQABkAEgAOAB8BLgANABwAHwARABIAHwANAA8AHAAhACEAHAAaAS0ACgAcAB8AEQASAB8ADQAOABkAGQRRAAkAHAAcABgAGgAOAB8AGAAgAz4AEAAcABwAGAAaAA4AHwAYAA0AHAAiACEAGQAWABsAEgM+AA8AHAAcABgAGgAOAB8AGAANAA8AHAAfABEAEgAfAz0ACAAcABwAGAAaAA4AHwAYAzwABAAcABwAGASSAAQAHAAZACEBugAHABkAIgAfAA0AHAAbAbkACAAZACIAHwANABwAEwATAbgACwAZACIAHwANABkAFgAbABIADgAfAbcADQAZACIAHwANABAAFgAfABAAIgAZAA4AHwEAABMAGQAiABIAIQAcABwAIQAVAA0AIAASAA4AHwAQABUAFgAbABQA/wASABkAIgASACEAHAAcACEAFQANABEAFgAgAA4ADwAZABIAEQD+ABMAGQAiABIAIQAcABwAIQAVAA0AEAAcABsAGwASABAAIQASABECwAAPABkAIgASACEAHAAcACEAFQANAA4AIgARABYAHAD9AAkAGQAiABIAIQAcABwAIQAVBR4ADQAZABwAEAAYAA0AEwAZABYAHQAdABIAEQDDAAUAGQAcABAAGAUdAAcAFgAcACEAEgAQABUFHAAMABYAGAASAA0AIAAQABwAHAAhABIAHwUbAAUAEgAbACEAHAJRAAgAEgASABsAFQASAB8AEgUaAAcAEgARACEAFgAaABIE5gAMABIADgAQABUADQAOABAAEAASACAAIAD8AA8ADgAhACEAEgAfACYADQAiABsAGAAbABwAJAAbAPsACwAOACEAIQASAB8AJgANACAAIQARAPoADAAOACEAIQASAB8AJgANABMAIgAZABkA+QAVAA4AIQAhABIAHwAmAA0AEAAVAA4AHwAUABYAGwAUAA0AEwAiABkAGQD4AA0ADgAhACEAEgAfACYADQAOABkAEgAfACEEwQAHAA4AIQAVACEAIgAPBRkAEAAOACEAEAAVAA0AHQAfABIAEQAWABAAIQAWABwAGwFwAAkADgAfAA0AEAAVAA4AHwAhAOoABgAOABkAGQAcACEEzQANAA4AGAASAB8AJgANABEAFgAbABYAGwAUBNsABQAOABEAFAASBRgADAAOABAAGAAiAB0ADQAhAA4ADwAZABIDOwAGAA4AEAAYACIAHQDCAAkADgAQABgAIAAdAA4AEAASBRcACAAOABAAGAAdAA4AEAAYBRYAFQAOAA8AJgANABAAFQAOABsAFAAWABsAFAANACAAIQAOACEAFgAcABsAeADyAQoBIgE+AVoBeAGMAZ4BsAHCAdQB6AHyAgoCLgI8AlQCcgKGArYC0gLwAwwDJAM+A1ADaAOIA6QDvgPYA/IEGgQ8BE4EYASIBKYEtgTGBOAE8AUCBRgFQgVaBWQFfgWWBaoFyAXeBfgGBAYqBloGeAaaBqYGugbGBuoG9gcgBy4HVAdyB4gHpAe+B9IH/AgWCEYIWghmCI4IpgiwCNII8AkUCTwJUAloCXoJoAm+CcgJ1gniCfYKDgouCkoKYAp2CqAKyArWCugLAAsYCzILUAtmC3QLkguoC8QL7gwGDBwMMAxCDEwMcAyODKIMrAHbAAsAHwAcAB0ADQAgAB4AIgAOAB8AEgJHAAsAHwAcAB0ADQAfABwAIQAOACEAEgHaAA0AHwAcAB0ADQAdABwAHwAhAB8ADgAWACEB2QANAB8AHAAdAA0AHAAfABYAFAAWABsADgAZAdgADgAfABwAHQANABkADgAbABEAIAAQAA4AHQASAdcACQAfABwAHQANABMAHwASABIB1gAIAB8AHAAdAA0AEQAWABsB1QAIAB8AHAAdAA0ACgANAAgB1AAIAB8AHAAdAA0ACAANAAcB0gAIAB8AHAAdAA0ABgANAAUB0QAJAB8AHAAdAA0ABAAJAA0ADAHTAAQAHwAcAB0DRwALAB8AEgARABYAIQANABAADgAfABEBfwARAB8AEgAOACEAEgANABsAEgAkAA0AEwAcABkAEQASAB8AyAAGAB8AEgAOACEAEgUuAAsAHAAiABsAIQASAB8AIQAcAB0AIAUtAA4AHAAfAB0AHAAfAA4AIQASAA0AEwAOAB8AEgPeAAkAHAAdACYAHwAWABQAFQAhAdAAFwAcABsAIQAfABwAGQANAB0AHAAWABsAIQANABEAIgAdABkAFgAQAA4AIQASAc8ADQAcABsAIQAfABwAGQANAB0AHAAWABsAIQB/AA4AHAAbACEAHwAcABkADQAQAA4AGgASAB8ADgDHAA0AHAAbACEAEgAbACEADQAdAA4AIAAhABIAxgALABwAGwAhABIAGwAhAA0AEAAiACEAxQAMABwAGwAhABIAGwAhAA0AEAAcAB0AJgCLAAgAHAAbACEADgAQACEAIATfAAsAHAAbACEADgAQACEAGQASACAAIAQbAA8AHAAbACEADgAQACEADQAgACIAHQAdABwAHwAhAJsADQAcABsAIQAOABAAIQANAB0AFQAcABsAEgCcAAwAHAAbACEADgAQACEADQAaAA4AFgAZBSwADAAcABsAIAAhAB8AIgAQACEAFgAcABsEVgAMABwAGwAbABIAEAAhABIAEQANACEAIwLnABMAHAAbABMAFgAfABoADgAhABYAHAAbAA0AGwAiABoADwASAB8C5wAQABwAGwATABYAHwAaAA4AIQAWABwAGwANABsAIgAaAYIACAAcABoAHQAiACEAEgAfBBwACAAcABoAHQAfABIAIAAgAp4AEwAcABoAHQAOACAAIAANABAADgAZABYADwAfAA4AIQAWABwAGwPmAA4AHAAaAB0ADgAfABIADQAOAB8AHwAcACQAIAHOAAcAHAAaAB0ADgAfABIEEQAHABwAGgAaACIAIQASBSsADAAcABoAGgASABsAIQANAA8ADgAbABgAigAHABwAGgAaABIAGwAhAc0ACAAcABkAHAAfABYAJwASAcwACgAcABkAHAAfAA0AGQASABsAIAJBABQAHAAZABkAEgAQACEAFgAcABsAIAANAA8AHAAcABgAGgAOAB8AGAHLAAsAHAAZABkAEgAQACEAFgAcABsAIANGAAQAHAARABIBeQAMABkAHAAiABEADQAiAB0AGQAcAA4AEQF4AAsAGQAcACIAEQANAB4AIgASACIAEgF3AAkAGQAcACIAEQANABwAEwATAXYADgAZABwAIgARAA0AEQAcACQAGwAZABwADgARAXUACgAZABwAIgARAA0AEQAcABsAEgF0AAwAGQAcACIAEQANABAAFgAfABAAGQASAXMABQAZABwAIgARBFUAEgAZABwAIAASABEADQAQAA4AHQAhABYAHAAbAA0AHAATABMFKgAXABkAHAAgABIAEQANABAADgAdACEAFgAcABsADQARABYAIAAOAA8AGQASABEALwAOABkAHAAgABIAEQANABAADgAdACEAFgAcABsFKQAQABkAHAAgABIADQATACIAGQAZACAAEAAfABIAEgAbAqoABQAZABwAIAASAIkACQAZABIADgAfAA0ADgAZABkAxAAFABkAEgAOAB8FKAARABkAEgAOABsAFgAbABQADQAgABIAHwAjABYAEAASACADRQAFABkADgAgACAEVAAUABYAHwAQABkAEgANABsAHAAhABYAEwAWABAADgAhABYAHAAbACAFJwAGABYAHwAQABkAEgNEABIAFQAfABwAGgASAA0AHwASAA4AEQASAB8ADQAaABwAEQASBOoADgAVABYAGQARAA0AEwAfABYAEgAbABEAGQAmBOkACgAVABYAGQARAA0AEAAOAB8AEgKpAA0AFQASACMAHwAcABsADQAfABYAFAAVACECqAAMABUAEgAjAB8AHAAbAA0AGQASABMAIQUmAAkAFQASABAAGAAfABwAHAAaA/4AFAAVABIAEAAYAA0AEAAWAB8AEAAZABIADQAcACIAIQAZABYAGwASA0MADAAVABIAEAAYAA0AEAAWAB8AEAAZABIDHwAXABUAEgAQABgADQAPABwAJQANABwAIgAhABkAFgAbABIADQAPABkADgAbABgDHgAJABUAEgAQABgADQAPABwAJQKnAAUAFQASABAAGACXABMAFQAOACEADQAPACIADwAPABkAEgANABwAIgAhABkAFgAbABIAlgALABUADgAhAA0ADwAiAA8ADwAZABIAiAAEABUADgAhBSUAEAAVAA4AHwAUABYAGwAUAA0AIAAhAA4AIQAWABwAGwNCAA4AFQAOABsAFAASAA0AFQAWACAAIQAcAB8AJgHKABEAEgAbACEAEgAfAA0AEwAcABAAIgAgAA0AJAASAA4AGAHJABMAEgAbACEAEgAfAA0AEwAcABAAIgAgAA0AIAAhAB8AHAAbABQAuAAJABIAGQAZAA0AJAAWABMAFgTZAAsAEgAZABIADwAfAA4AIQAWABwAGwKWAAgADgAhABIAFAAcAB8AJgUkABIADgAgACEADQATABwAHwANABIAEQAiABAADgAhABYAHAAbAYEADgAOACAAIQANABAAHAAbABsAEgAQACEAEgARAYAABAAOACAAIQToAAYADgAgABYAGwAcBFMABQAOACAAEgAgBSMACQAOAB8AHQASABsAIQASAB8DzAALAA4AHwARAA0AIQAfAA4AIwASABkDywAPAA4AHwARAA0AGgASABoADwASAB8AIAAVABYAHQPKAA0ADgAfABEADQAUABYAEwAhABAADgAfABEE0AAKAA4AHwANAB8AEgAdAA4AFgAfBM8ACgAOAB8ADQAfABIAGwAhAA4AGQS/ABQADgAbABAAEgAZAA0AIAAQABUAEgARACIAGQASAA0AIAASABsAEQC1ABMADgAbABAAEgAZAA0AHQAfABIAIAASABsAIQAOACEAFgAcABsCpgAGAA4AGwAQABIAGQUiAAgADgAaAB0ADgAWABQAGwHIAAsADgAaABIAHwAOAA0AHwAcABkAGQHHAAsADgAaABIAHwAOAA0AHwASAA4AHwHGAAwADgAaABIAHwAOAA0AEwAfABwAGwAhA9AADgAOABoAEgAfAA4ADQASABsAFQAOABsAEAASAcUACgAOABoAEgAfAA4ADQAOABkAIQHEAAYADgAaABIAHwAOAHcADgAOABkAGQANACEAHAANAA4AEAAhABYAHAAbAIcACgAOABkAGQANACAAHQAZABYAIQCGAA0ADgAZABkADQAfABIAEAASABYAIwASABEAsAAUAA4AGQAZAA0AGgAWACAAIAASABEADQAcACIAIQAUABwAFgAbABQAhQALAA4AGQAZAA0AGgAWACAAIAASABEAhAAKAA4AGQAZAA0AGgASAB8AFAASAIMACQAOABkAGQANABoADgARABIAggAIAA4AGQAZAA0AEgAbABEAgQAEAA4AGQAZBAcAEQAOABkAEgAbABEADgAfAA0AIwAWABIAJAANABEADgAmBAYADgAOABkAEgAbABEADgAfAA0AIQAcABEADgAmBSEACQAOABkAEAAiABkADgAhABIC/gAEAA4AGAASA0EABgAOABAAFQASABEATACaALQAzgDWAN4A+AEAASgBXAGMAawBwAHeAfYCBAIeAjYCTgJoAnoChAKsAswC2gLkAv4DFgM6A2ADhgOkA6wDzgPiA/gEGAQ+BGAEhASiBMgE5gUIBSYFRAVkBYQFmgW2BcYF3AX4BggGJgZKBmAGfgaeBq4GzgbmBxYHNgdOB24HjgeoB8YH5AfyCAAICgggCDYIXghyBTcADAAmABsADgAaABYAEAANABMAHAAfABoEmwAMACYAGwAOABoAFgAQAA0AEwASABIAEQEIAAMAIwAfBF0AAwAiABwE0gAMAB8AJgANABAAGQASAA4AGwAWABsAFAU2AAMAHwAmBFwAEwAfABYAIwASAA0AEwAcABkAEQASAB8ADQAiAB0AGQAcAA4AEQRbABkAHwAWACMAEgANABMAFgAZABIADQAfABIAGwAOABoAEgANABwAIgAhABkAFgAbABIEWgAXAB8AFgAjABIADQATABYAGQASAA0AGgAcACMAEgANABwAIgAhABkAFgAbABIFNQAPAB8AFgAjABIADQATABYAGQASAA0AGgAcACMAEgLEAAkAHwAWACMAEgANABIAIQAOBBUADgAfAA4AFAANABYAGwARABYAEAAOACEAHAAfAWIACwAfAA4AFAANABUADgAbABEAGQASAMkABgAfAA4AEwAhACAEygAMABwAIgAPABkAEgANAA4AHwAfABwAJAPpAAsAHAAbACIAIQANACAAGgAOABkAGQPoAAsAHAAbACIAIQANABkADgAfABQAEgQAAAwAHAAbABIADQAcACIAIQAZABYAGwASA00ACAAcABsAEgANAA4AGQAZA0wABAAcABsAEgU0ABMAHAAaAA4AFgAbAA0AIwASAB8AFgATABYAEAAOACEAFgAcABsAuwAPABwAGgAOABYAGwANABEAFgAgAA4ADwAZABIAEQL/AAYAHAAaAA4AFgAbAYYABAAcABAAGAUzAAwAHAANABsAHAAhAA0AIQAcACIAEAAVBTIACwAcAA0AGwAcACEADQAgACEAEgAdAvMAEQAcAA0AGwAcACEADQARABYAIAAhACIAHwAPAA0AHAAbAvIAEgAcAA0AGwAcACEADQARABYAIAAhACIAHwAPAA0AHAATABMCwgASABwADQAbABwAIQANABEAFgAgACEAIgAfAA8ADQAOABkAIQLDAA4AHAANABsAHAAhAA0AEQAWACAAIQAiAB8ADwNLAAMAGwAgAsIAEAAbABEADQATABwAHwAkAA4AHwARACAAGQAOACAAFQLBAAkAFgAgABAADQATACIAGQAZBTEACgAWAB8AIQAmAA0AGQASABsAIAJaAA8AFgAfABIAEAAhABYAHAAbACAADQAkAA4AGQAYAlkAEgAWAB8AEgAQACEAFgAcABsAIAANACEAHwAOABsAIAAWACECWAAQABYAHwASABAAIQAWABwAGwAgAA0AIQAfAA4AFgAbAlcAEQAWAB8AEgAQACEAFgAcABsAIAANACAAIgAPACQADgAmAogADgAWAB8AEgAQACEAFgAcABsAIAANAB8AIgAbAlgAEgAWAB8AEgAQACEAFgAcABsAIAANAB8ADgAWABkAJAAOACYFMAAOABYAHwASABAAIQAWABwAGwAgAA0AHAATABMCVgAQABYAHwASABAAIQAWABwAGwAgAA0AEwASAB8AHwAmAlUADgAWAB8AEgAQACEAFgAcABsAIAANABAADgAfAlQADgAWAB8AEgAQACEAFgAcABsAIAANAA8AIgAgAlYADwAWAB8AEgAQACEAFgAcABsAIAANAA8AHAAOACECUwAPABYAHwASABAAIQAWABwAGwAgAA0ADwAWABgAEgJSAAoAFgAfABIAEAAhABYAHAAbACAE0QANABYAGwAbABIAHwANABEAFgAbABYAGwAUAI0ABwAWAA4AGQAdAA4AEQCMAAoAFgAOABkAEgAfAA0AIAAWAB0BrgANABIAIwAWABAAEgAgAA0AHAAhABUAEgAfAQcABwASACMAFgAQABIAIAGwAA4AEgAjABYAEAASAA0AIgAbABgAGwAcACQAGwEnABEAEgAjABYAEAASAA0AIQAVABIAHwAaABwAIAAhAA4AIQGsAAoAEgAjABYAEAASAA0AFQAiAA8BBgAOABIAIwASABkAHAAdABIAHwANABoAHAARABIBhQAPABIAIwASABkAHAAdABIAHwANAA8AHAAOAB8AEQHdAAcAEgAhAA4AFgAZACABhAAPABIAIAAYACEAHAAdAA0AJAAWABsAEQAcACQAIAGDAAsAEgAgABgAIQAcAB0ADQAaAA4AEARZABcAEgAgABgAIQAcAB0ADQAOABAAEAASACAAIAANABEAFgAgAA4ADwAZABIAEQUvAA8AEgAgABYAFAAbAA0AIAASAB8AIwAWABAAEgAgA0oACwASACAAEAAfABYAHQAhABYAHAAbApgADwASAB0ADgAfACEAIgAfABIADQAPABwADgAfABEE4AAPABIAGQAWACMAEgAfACYADQARABYAGwAWABsAFADkAAwAEgAZABIAIQASAA0AIAAkABIAEgAdA/8ADgASABkAEgAhABIADQAcACIAIQAZABYAGwASA/wADgASABkAEgAhABIADQATABwAHwASACMAEgAfA0kABgASABkAEgAhABIB3AAGABIAFQAOACcAEgTCAAQAEgAQABgD5wAKAA4AIQASAA0AHwAOABsAFAASAQUACgAOACEADgANACIAIAAOABQAEgRYABMADgAgABUADwAcAA4AHwARAA0AEAAiACAAIQAcABoAFgAnABIDSAAJAA4AIAAVAA8AHAAOAB8AEQRXAAkADgAbABQAEgAfABwAIgAgADkAdACIAKQAxADkAQIBIAFCAWQBdgGOAZ4BsAHIAeAB7gIGAhwCMgJIAmgCdAKKAqICrALQAuQDAAMMAyADSAN4A5ADugPWA/AEDAQmBE4EZgSABJ4EqgS8BOQFBgUqBUgFYgV+BYoFngWwBcwF7AX2Bf4DUQAJACUAIQASABsAIAAWABwAGwHkAA0AJQAdABwAIAAiAB8AEgANACcAEgAfABwB4wAPACUAHQAcACAAIgAfABIADQAdABkAIgAgAA0ABQHiAA8AJQAdABwAIAAiAB8AEgANAB0AGQAiACAADQAEAeEADgAlAB0AHAAgACIAHwASAA0AGwASABQADQAFAeAADgAlAB0AHAAgACIAHwASAA0AGwASABQADQAEAeEAEAAlAB0AHAAgACIAHwASAA0AGgAWABsAIgAgAA0ABQHgABAAJQAdABwAIAAiAB8AEgANABoAFgAbACIAIAANAAQB3wAIACUAHQAcACAAIgAfABIEXgALACUAHQAZABwAHwASAA0AHAATABMDUAAHACUAHQAZABwAHwASADEACAAlAB0AGQAWABAAFgAhAqwACwAlAB0ADgAbABEADQAaABwAHwASAqsACwAlAB0ADgAbABEADQAZABIAIAAgBB4ABgAlAB0ADgAbABEDTwALACUAFgAhAA0AIQAcAA0ADgAdAB0D1gAKACMAEgAbACEADQAgABIADgAhAscACgAjABIAGwAhAA0AGwAcACEAEgLGAAoAIwASABsAIQANAA8AIgAgACYCxQAPACMAEgAbACEADQAOACMADgAWABkADgAPABkAEgNOAAUAIwASABsAIQKPAAoAIwANACAAIQAOACEAFgAcABsD9wALACIAHwAcAA0AIAAmABoADwAcABkEnAAEACIAHwAcBUMAEQAgABAADgAZAA4AIQAcAB8ADQAkAA4AHwAbABYAGwAUBUIACQAgABAADgAZAA4AIQAcAB8AKQANAB8AHwAcAB8ADQAcACIAIQAZABYAGwASACgABQAfAB8AHAAfADAACQAeACIADgAZABYAJwASAB8C7gATABsAFQAOABsAEAASABEADQASABsAEAAfACYAHQAhABYAHAAbA9AAFwAbABUADgAbABAAEgANAB0AFQAcACEAHAANACEAHwAOABsAIAAZAA4AIQASBUEACwAbABQAFgAbABIAEgAfABYAGwAUBKYAFAAaABwAFwAWAA0AIQAfAA4AGwAgAB0AHAAfACEADgAhABYAHAAbBKUADQAaABwAFwAWAA0AIAAmABoADwAcABkAIASkAAwAGgAcABcAFgANAB0AEgAcAB0AGQASBKsADQAaABwAFwAWAA0AHAAPABcAEgAQACEAIASjAAwAGgAcABcAFgANABsADgAhACIAHwASBKIAEwAaABwAFwAWAA0AEwAcABwAEQANAA8AEgAjABIAHwAOABQAEgShAAsAGgAcABcAFgANABMAGQAOABQAIASqAAwAGgAcABcAFgANABIAIwASABsAIQAgBKkADgAaABwAFwAWAA0AEgAaABwAIQAWABwAGwAgAI4ABQAaAA4AFgAZBUAACAAZABIAIwAOACEAHAAfBT8AEwAZABIAEAAhAB8AFgAQAA4AGQANACAAEgAfACMAFgAQABIAIAU+ABAAGQASABAAIQAfABYAEAANACAAEAAcABwAIQASAB8FPQARABkAEgAQACEAHwAWABAADQAfABYAEAAYACAAFQAOACQFPAAOABkAEgAQACEAHwAWABAADQAaABwAHQASABEFOwAMABkAEgAQACEAHwAWABAADQAQAA4AHwU6AA0AGQASABAAIQAfABYAEAANAA8AFgAYABIDzwAFABcAEgAQACEFOQAJABEAFgAhAA0AHwAcAA4AEQQfAAgAEQAWACEADQAcABMAEwKKAA0AEQAWACEADQAZABwAEAAOACEAFgAcABsCmgAPABEAFgAhAA0ADgAhACEAHwAWAA8AIgAhABIAIAHeAAQAEQAWACEEuwADABAAHAU4AAQADgAgACEAcADiAPYBFgEsAUoBYAGCAZYBrAHCAdIB3gICAiQCXAKUAr4C1gLyAwwDJgNYA4IDrAPUA/AEHgRMBHAElgS6BNQE7AUSBTYFYAWIBZwFuAXWBfIGCgYYBjQGTgZuBpYGoAa+BtYG5Ab2BwoHIAcsBzgHQgdgB3YHjAegB7oH3gf2CBAIKghICGwIhAikCLwI0gjuCQgJMAlOCWQJgAmSCaQJtgnICdoJ7An+ChAKIgowCkgKYgqICqQKuAreCvILBgsuC0ILVAtgC3ILkAu2C9gL+AwKDBwMNAxODG4MhAy0AU8ACQAiABsAEAAhABYAHAAbACACrgAPACIAGQAZACAAEAAfABIAEgAbAA0AEgAlABYAIQKtAAoAIgAZABkAIAAQAB8AEgASABsE7AAOAB8AEgASAA0ADwAfABIADgAYABMADgAgACEFTgAKABwAIgAbABEADgAhABYAHAAbBU0AEAAcAB8AJAAOAB8AEQANACEAHAANABYAGwAPABwAJQBjAAkAHAAfACQADgAfABEADQAIAGIACgAcAB8AJAAOAB8AEQANAAYAAwBhAAoAHAAfACQADgAfABEADQAEAAMAzAAHABwAHwAkAA4AHwARAI8ABQAcAB8AIgAaAU4AEQAcAB8AGgAOACEADQAiABsAEQASAB8AGQAWABsAEgARAU4AEAAcAB8AGgAOACEADQAiABsAEQASAB8AGQAWABsAEgFNABsAHAAfABoADgAhAA0AIQASACUAIQARABYAHwASABAAIQAWABwAGwANAB8ADQAhABwADQAZAUwAGwAcAB8AGgAOACEADQAhABIAJQAhABEAFgAfABIAEAAhABYAHAAbAA0AGQANACEAHAANAB8BSwAUABwAHwAaAA4AIQANACAAIQAfABYAGAASACEAFQAfABwAIgAUABUBSgALABwAHwAaAA4AIQANACAAFgAnABIBYwANABwAHwAaAA4AIQANACAAFQAOAB0AEgAgAUkADAAcAB8AGgAOACEADQAeACIAHAAhABIBSAAMABwAHwAaAA4AIQANAB0ADgAWABsAIQFsABgAHAAfABoADgAhAA0AGQAWACAAIQANABsAIgAaAA8AEgAfABIAEQANAB8AIQAZAUcAFAAcAB8AGgAOACEADQAZABYAIAAhAA0AGwAiABoADwASAB8AEgARAUYAFAAcAB8AGgAOACEADQAZABYAIAAhAA0ADwAiABkAGQASACEAEgARAUUAEwAcAB8AGgAOACEADQAZABYAGwASAA0AIAAdAA4AEAAWABsAFAFEAA0AHAAfABoADgAhAA0AFgAhAA4AGQAWABABQwAWABwAHwAaAA4AIQANABYAGwARABIAGwAhAA0AFgAbABAAHwASAA4AIAASAUIAFgAcAB8AGgAOACEADQAWABsAEQASABsAIQANABEAEgAQAB8AEgAOACAAEgFBABEAHAAfABoADgAhAA0AEAAcABkAHAAfAA0AIQASACUAIQFAABIAHAAfABoADgAhAA0AEAAcABkAHAAfAA0AHwASACAAEgAhAT8AEQAcAB8AGgAOACEADQAQABwAGQAcAB8ADQATABYAGQAZAT4ADAAcAB8AGgAOACEADQAQABkAEgAOAB8BPQALABwAHwAaAA4AIQANAA8AHAAZABEBPAASABwAHwAaAA4AIQANAA4AGQAWABQAGwANAB8AFgAUABUAIQE7ABEAHAAfABoADgAhAA0ADgAZABYAFAAbAA0AGQASABMAIQE6ABQAHAAfABoADgAhAA0ADgAZABYAFAAbAA0AFwAiACAAIQAWABMAJgE5ABMAHAAfABoADgAhAA0ADgAZABYAFAAbAA0AEAASABsAIQASAB8FTAAJABwAHAARAA0ADwAOABsAGADfAA0AHAAbACEADQARABwAJAAbABkAHAAOABECyAAOABwAGQARABIAHwANACAAHQASABAAFgAOABkBfgANABwAGQARABIAHwANACAAFQAOAB8AEgARAX0ACwAcABkAEQASAB8ADQAcAB0AEgAbAXwABgAcABkAEQASAB8DWQANABkAFgAdAA0AIQAcAA0AEwAfABwAGwAhA1gADAAZABYAHQANACEAHAANAA8ADgAQABgEvgAPABkAFgAdAA0AEAAOABoAEgAfAA4ADQAWABwAIAS9ABMAGQAWAB0ADQAQAA4AGgASAB8ADgANAA4AGwARAB8AHAAWABEB/AAEABkAFgAdA9gADgAZABYAFAAVACEADQAhAA4AGAASABwAEwATA9cACwAZABYAFAAVACEADQAZAA4AGwARAlsABgAZABYAFAAVACEB+wAIABkADgAgABUADQAcABsB+gAJABkADgAgABUADQAcABMAEwH5AAoAGQAOACAAFQANAA4AIgAhABwB+AAFABkADgAfABIFSwAFABkADgAYACYAywAEABkADgAUBOsADgAWACEAGwASACAAIAANABAAEgAbACEAEgAfBJcACgAWACEADQAgABAAHwASABIAGwK5AAoAFgAfACAAIQANAB0ADgAUABIEwwAJABYAHwASAB0AGQAOABAAEgVKAAwAFgAfABIADQAVACYAEQAfAA4AGwAhBUkAEQAWAB8AEgANABIAJQAhABYAGwAUACIAFgAgABUAEgAfA98ACwAWABsAFAASAB8AHQAfABYAGwAhA1cADAAWABsAEQANAB8AEgAdABkADgAQABIDVgAMABYAGwARAA0AFgAbAA0AHQAOABQAEgH3AA4AFgAZACEAEgAfAA0AIwAWABsAIQAOABQAEgH2ABEAFgAZACEAEgAfAA0AIQAWABkAIQANACAAFQAWABMAIQH1AAsAFgAZACEAEgAfAA0AGwAcABsAEgQdAA8AFgAZACEAEgAfAA0AGQAWACAAIQANAA4AGQAhAMoACwAWABkAIQASAB8ADQAZABYAIAAhAfQACgAWABkAIQASAB8ADQAVABEAHwHzAA0AFgAZACEAEgAfAA0AEwAfAA4AGgASACAB8gAMABYAGQAhABIAHwANABEAHwAOABoADgHxABMAFgAZACEAEgAfAA0AEAASABsAIQASAB8ADQATABwAEAAiACAB8AAOABYAGQAhABIAHwANAA8ADQAOABsAEQANACQFSAAKABYAGQAhABIAHwANAA4AGQAhAe8ADQAWABkAIQASAB8ADQAMAA0AHQAZACIAIAHuAAgAFgAZACEAEgAfAA0ADAHtAAgAFgAZACEAEgAfAA0ACwHsAAgAFgAZACEAEgAfAA0ACgHrAAgAFgAZACEAEgAfAA0ACQHqAAgAFgAZACEAEgAfAA0ACAHpAAgAFgAZACEAEgAfAA0ABwHnAAgAFgAZACEAEgAfAA0ABgHmAAgAFgAZACEAEgAfAA0ABQHlAAgAFgAZACEAEgAfAA0ABAHoAAYAFgAZACEAEgAfAXsACwAWABkAEgANACIAHQAZABwADgARBJUADAAWABkAEgANAB0AHwASACAAEgAbACEEXwASABYAGQASAA0AEQAcACQAGwAZABwADgARAA0AEQAcABsAEgF6AA0AFgAZABIADQARABwAJAAbABkAHAAOABEA6wAJABYAGQASAA0AEAAcAB0AJgBtABIAFgAPABIAHwANACAAGgAOAB8AIQANAB8AEgAQABwAHwARAHUACQAWAA8AEgAfAA0AHQAWABsAaQAJABYADwASAB8ADQAbABIAJABsABMAFgAPABIAHwANABoADgAbACIADgAZAA0AHwASABAAHAAfABEAaAAJABYADwASAB8ADQARACMAHwTcAAgAEgAgACEAFgAjAA4AGQVHAAUAEgAbABAAEgNVAAgAEgASABEADwAOABAAGAB5AA4AEgAOACEAIgAfABIAEQANACMAFgARABIAHAB4ABIAEgAOACEAIgAfABIAEQANAB0AGQAOACYADQAZABYAIAAhA1QAEAAOACMAHAAfABYAIQASAA0AHAAiACEAGQAWABsAEgNUAA8ADgAjABwAHwAWACEAEgANAA8AHAAfABEAEgAfA1MACAAOACMAHAAfABYAIQASApwACAAOACAAIQATABwAHAARADMACwAOACAAIQANAB8AEgAkABYAGwARADIADAAOACAAIQANABMAHAAfACQADgAfABEFRgAPAA4AGgAWABkAJgANAB8AEgAgACEAHwAcABwAGgVFAAoADgAQACEADQAQABUAEgAQABgFRAAXAA4AEAASAA0AHwASACEAHAAiABAAFQAWABsAFAANABsADgAhACIAHwAOABkDUgAEAA4AEAASABgAMgBIAFwAaAB8AIwAngCqAMAAzADcAO4A+gEKASYBOgFSAVwBZAF0AYQBkAGcAawDXAAKAB8AHAAiAB0ADQAkABwAHwAYAwEACQAfABwAIgAdAA0ADgARABEDAAAFAB8AHAAiAB0EYQAJAB8AFgARAA0AIwAWABIAJAIAAAcAHwAWABEADQAcABsB/wAIAB8AFgARAA0AHAATABMFUAAFAB8ADgAgACABDgAKAB8ADgAdABUAFgAQAA0AEgAeAf4ABQAfAA4AFgAbBU8ABwAfAA4AEQAWABsAFAH9AAgAHwAOABEAFgASABsAIQNbAAUAHwAOABEAEgELAAcAHQAgAA0AHAATABMBCgANAB0AIAANABsAHAAhAA0AEwAWACUAEgARAQkACQAdACAADQATABYAJQASABEE7QALABwAGQATAA0AEAAcACIAHwAgABIE9wAEABwADgAhA9oAAwAWABMDWgAHABIAIQANAA4AHQAdAM0ABwASACAAIQAiAB8AEgPgAAUADgAjABIAGQA0AAUADgAaABIAIAGHAAcADgAaABIAHQAOABED+AALAA0AIQAfAA4AGwAgABkADgAhABIALwBgAGoAdgCAAJgArgDIANQA8AEOAS4BVAF2AYIBkgG0AdQB6AIQAigCMgJYAnACgAKiAr4C2gLuAwgDIgM6A0QDUgN0A4QDlAOsA8QD1APmA/wECgQaBEIESARaBGwFXgAEACMADgAQA2MABQAhACEAHQAgA9UABAAhACEAHQDtAAsAHAAkAA0AIQAcAA0AIwAcACEAEgDsAAoAHAAkAA0AIQAcAA0AHwASABQFXQAMABwAIgAgABIADQAgABYAEQAWABsAFATEAAUAHAAiACAAEgVcAA0AHAAiAB8AFAAZAA4AIAAgAA0AIQAcAB0DYgAOABwAIgAfABQAGQAOACAAIAANABMAIgAZABkDYQAPABwAIgAfABQAGQAOACAAIAANABIAGgAdACEAJgVbABIAHAAiAB8AFAAZAA4AIAAgAA0AEQAWACAADgAPABkAEgARBVoAEAAcACIAHwAUABkADgAgACAADQAPABwAIQAhABwAGgJcAAUAHAAhABIAGQTuAAcAHAAhAA0AIQAiAA8EFgAQABwAHwAWACcAHAAbACEADgAZAA0AIAAdABkAFgAhBVkADwAcAB8AFgAnABwAGwAhAA4AGQANAB8AIgAZABIEkAAJABwAGgASAA0AJAAcAB8AGAVYABMAHAAaABIADQAfABIAHQAOABYAHwANACAAEgAfACMAFgAQABIEYwALABwAGgASAA0AEwAWABkAGQASABEDYAAEABwAGgASBVcAEgAWACAAIQAcAB8AJgANACEAHAAUABQAGQASAA0AHAATABMFVgALABYAIAAhABwAHwAmAA0AEgARACIDXwAHABYAIAAhABwAHwAmA14AEAAWABQAFQAZABYAFAAVACEADQAfABIAGgAcACMAEgNeAA0AFgAUABUAGQAWABQAFQAhAA0AHAATABMFVQANABYAFAAVABkAFgAUABUAIQANAA4AGQAhAWQACQAWABQAFQAZABYAFAAVACEANgAMABYAFAAVAA0AHgAiAA4AGQAWACEAJgPRAAwAEgAZAB0ADQAcACIAIQAZABYAGwASBVQACwASABkAHQANABAAEgAbACEAEgAfA10ABAASABkAHQSdAAYAEgAWABQAFQAhBVMAEAASAA4AHwAWABsAFAANABEAFgAgAA4ADwAZABIAEQA1AAcAEgAOAB8AFgAbABQCBQAHABIADgAZABYAGwAUAbEACwASAA4AEQAgABIAIQANABwAEwATAYkACwASAA4AEQAgABIAIQANABoAFgAQAYgABwASAA4AEQAgABIAIQIEAAgAEQAfAA0AJAASAA4AGAIDAAoAEQAfAA0AIAAhAB8AHAAbABQCAgAGABEAHwANABwAGwIBAAcAEQAfAA0AHAATABMFUgATABEAHwANABIAGwAVAA4AGwAQABIAEQANACAAEgAZABIAEAAhAF4AAgARBNMACAAOAB8AEQAkAA4AHwASBVEACAAOABsAEQAmABoADgAbBGIABAAOABYAGQAeAD4ARgBaAHwAoAC8ANABAgEUAS4BRgFqAYoBrgHMAfgCEgIeAjgCQgJyAn4CogK+At4DBAMeA0YDbAN4AggAAwAgABwFYgAJABwAIAANACAAFQAOAB8AEgNnABAAGwAjABIAHwAhAA0AEAAcABkAHAAfACAADQAcABsAkQARABsAIwASAB8AIQANABAAHAAZABwAHwAgAA0AHAATABMDZwANABsAIwASAB8AIQANABAAHAAZABwAHwAgAPEACQAbACMAEgAbACEAHAAfACYFYQAYABsAIQASABQAHwAOACEAFgAcABsADQAWABsAIAAhAB8AIgAQACEAFgAcABsAIAVgAAgAGwAgABYAFAAVACEAIAFWAAwAGwAgABIAHwAhAA0AHQAVABwAIQAcAVUACwAbACAAEgAfACEADQAZABYAGwAYAVQAEQAbACAAEgAfACEADQAWABsAIwAWACEADgAhABYAHAAbAVMADwAbACAAEgAfACEADQASABoAHAAhABYAEAAcABsBUgARABsAIAASAB8AIQANABEAHwAWACMAEgANABMAFgAZABIBUQAOABsAIAASAB8AIQANABAAHAAaABoAEgAbACEBbwAVABsAIAASAB8AIQANABAAFQAOAB8AIQANABwAIgAhABkAFgAbABIAEQFQAAwAGwAgABIAHwAhAA0AEAAVAA4AHwAhA2YABQAbAB0AIgAhA2UADAAbABMAHAANABwAIgAhABkAFgAbABIDZAAEABsAEwAcA9sAFwAbABEAEgAhABIAHwAaABYAGwAOACEAEgANABAAFQASABAAGAANAA8AHAAlAM4ABQAbAA8AHAAlA+MAEQAaAB0AHAAfACEADgAbACEADQARABIAIwAWABAAEgAgAJAADQAaAB0AHAAfACEADQASACUAHQAcAB8AIQCsAA8AGgAdABwAHwAhAA0AEAAcABsAIQAOABAAIQAgBGQAEgAaAA4AFAASACAAEgAOAB8AEAAVAA0AHwAcABkAGQASAB8CTwAMABoADgAUABIADQAgABIADgAfABAAFQVfABMAGgAOABQAEgANABsAHAAhAA0AIAAiAB0AHQAcAB8AIQASABECBwASABoADgAUABIADQAOACAAHQASABAAIQANAB8ADgAhABYAHAIGAAUAGgAOABQAEgTdAAgAEAASABAAHwASAA4AGgAOAB4ASABwAJgAvgDiAQYBKAFIAWYBggGcAa4BwAGNABQAEgAmAA8AHAAOAB8AEQANAA4AHwAfABwAJAANAB8AFgAUABUAIQGMABMAEgAmAA8AHAAOAB8AEQANAA4AHwAfABwAJAANABkAEgATACEBiwATABIAJgAPABwADgAfABEADQAOAB8AHwAcACQADQARABwAJAAbAY8AEgASACYADwAcAA4AHwARAA0ADwAOABAAGAAgAB0ADgAQABIBjgARABIAJgAPABwADgAfABEADQAOAB8AHwAcACQADQAiAB0BkAARABIAJgAPABwADgAfABEADQAQAA4AHQAgABkAHAAQABgCsAAQABIAJgAPABwADgAfABEADQAQABwAGwAhAB8AHAAZAZIADwASACYADwAcAA4AHwARAA0AHwASACEAIgAfABsBlAAOABIAJgAPABwADgAfABEADQAjABwAFgAQABIBkQANABIAJgAPABwADgAfABEADQAVABYAEQASAZMADAASACYADwAcAA4AHwARAA0AIQAOAA8ExQAIABYAGwAUAA0ADwASABEBigAIABIAJgAPABwADgAfABEE7wAHABYAIQAQABUAEgAbAGAAwgDcAOwBBgESARwBMAFEAVQBZAF0AYQBkAGeAaoBxAHYAe4B+AIeAjgCUAJqAowCsALMAuIDAAMUAzYDVgN4A5wDtgPMA+QD/AQaBDYETgRoBH4EmgTGBN4E/AUkBUgFZAWQBagFwgXyBhAGJgY6BlwGcAaMBqoGugbOBuAG6gb4BxQHJgcwB0oHYgd4B5wHsAfMB+gIDAgkCC4ISghiCHQIjAimCLQIwgjWCPQJCgkuCTwJTgliCX4JkgnCCeIE1QAMACIAGwAQABUADQARABYAGwAWABsAFANwAAcAHAAmAA4AGQAhACYA5QAMABwAJAANAB0AHwAWABwAHwAWACEAJgIUAAUAHAAiAB0AEgA3AAQAHAAcAB0CEwAJABwAHAAYACAADQAhACQAHAISAAkAHAAcABgAIAANABwAGwASAhEABwAcABwAGAAgAA0ACQIQAAcAHAAcABgAIAANAAgCDwAHABwAHAAYACAADQAHAg0ABwAcABwAGAAgAA0ABgIOAAUAHAAcABgAIARnAAYAHAAUABwAIgAhBWgABQAcABQAFgAbA28ADAAcABAAGAANABwAIgAhABkAFgAbABIDbgAJABwAEAAYAA0AHAAdABIAGwVnAAoAHAAQABgADQAQABkAHAAQABgDbQAEABwAEAAYAQ0AEgAcABAADgAhABYAHAAbAA0AIAASAA4AHwAQABUAFgAbABQFZgAMABwAEAAOACEAFgAcABsADQAdABYAGwCUAAsAHAAQAA4AIQAWABwAGwANABwAGwCTAAwAHAAQAA4AIQAWABwAGwANABwAEwATAnwAEAAcABAADgAhABYAHAAbAA0AFQAWACAAIQAcAB8AJgEMABEAHAAQAA4AIQAWABwAGwANABEAFgAgAA4ADwAZABIAEQMCAA0AHAAQAA4AIQAWABwAGwANABAAFgAhACYCewAKABwAEAAOABkADQAhAA4AJQAWAnoADgAcABAADgAZAA0AIAAVABYAHQAdABYAGwAUAnkACQAcABAADgAZAA0AIAASABICeAAQABwAEAAOABkADQAfABIAIAAhAA4AIgAfAA4AGwAhAncADwAcABAADgAZAA0AHQAfABYAGwAhACAAFQAcAB0CdwAQABwAEAAOABkADQAdAB8AFgAbACEADQAgABUAHAAdAnYAEQAcABAADgAZAA0AHQAcACAAIQANABwAEwATABYAEAASBWUADAAcABAADgAZAA0AHQAcABkAFgAQABICdQAKABwAEAAOABkADQAdABkADgAmAnQACwAcABAADgAZAA0AHQAWACcAJwAOAnMACwAcABAADgAZAA0AHQAVABwAGwASAnIADgAcABAADgAZAA0AHQAVAA4AHwAaAA4AEAAmAnEADQAcABAADgAZAA0AHQAOAB8AGAAWABsAFAJwAAsAHAAQAA4AGQANABwAEwATABIAHwJvAAwAHAAQAA4AGQANABoAHAAjABYAEgAgAm4ACgAcABAADgAZAA0AGgAOABkAGQJtAA0AHAAQAA4AGQANABkAFgAPAB8ADgAfACYCbAAVABwAEAAOABkADQAZAA4AIgAbABEAHwAmAA0AIAASAB8AIwAWABAAEgJrAAsAHAAQAA4AGQANABUAHAAhABIAGQJqAA4AHAAQAA4AGQANABUAHAAgAB0AFgAhAA4AGQJpABMAHAAQAA4AGQANABQAHwAcABAAEgAfACYADQAgACEAHAAfABICaAARABwAEAAOABkADQAUAA4AIAANACAAIQAOACEAFgAcABsCZwANABwAEAAOABkADQATABkAHAAfABYAIAAhBWQAFQAcABAADgAZAA0AEwAWAB8AEgANABEAEgAdAA4AHwAhABoAEgAbACECZgALABwAEAAOABkADQARAB8AFgAbABgCeAAMABwAEAAOABkADQARABYAGwAWABsAFAJlABcAHAAQAA4AGQANABAAHAAbACMAEgAbABYAEgAbABAAEgANACAAIQAcAB8AEgJkAA4AHAAQAA4AGQANABAADgAfAA0AJAAOACAAFQJjAAoAHAAQAA4AGQANABAADgATABICYgAJABwAEAAOABkADQAPAA4AHwJhABAAHAAQAA4AGQANAA4AIQAhAB8ADgAQACEAFgAcABsCYAAJABwAEAAOABkADQAOACEAGgJfAA0AHAAQAA4AGQANAA4AFgAfAB0AHAAfACECYQAOABwAEAAOABkADQAOABAAIQAWACMAFgAhACYC6AAHABYAIwASAA0AIQAjAJIACQAWACMAEgANABUAEgAZAB0AugAIABYAIAAhAA0ADgAZACEDbAAEABYAIAAhBNQABgAWAB4AIgAcAB8CSAANABYAGwAYABIAEQANABAADgAaABIAHwAOAOcACAAWABsAGAANABwAEwATAM8ABAAWABsAGAFlAAwAFgAbABIADgAfAA0AIAAQAA4AGQASA+sACwAWABsAEgANACQAEgAWABQAFQAhA+oACgAWABsAEgANACAAIQAmABkAEgPhABEAFgAUABUAIQAPACIAGQAPAA0AHAAiACEAGQAWABsAEgC8AAkAFgAUABUAIQAPACIAGQAPAD4ADQAWAA8AHwAOAB8AJgANABoAIgAgABYAEAA9AA0AFgAPAB8ADgAfACYADQAPABwAHAAYACAEZgARABYADwAfAA4AHwAmAA0ADgARABEADQAQABUAEgAQABgAPAALABYADwAfAA4AHwAmAA0ADgARABECDAAEABIAGwAgBWMADQASABQAEgAbABEADQAhABwAFAAUABkAEgILAAsAEgAOABgADQAfABIAGgAcACMAEgIKAAgAEgAOABgADQAOABEAEQXuAAsAEgAOABEAEgAfAA8AHAAOAB8AEQJeAAwADgAmABIAHwAgAA0AEAAZABIADgAfAl0ABgAOACYAEgAfACADawAGAA4AIgAbABAAFQK6AAkADgAgACEADQAdAA4AFAASAZgADgAOAB0AIQAcAB0ADQAkABYAGwARABwAJAAgAZcACgAOAB0AIQAcAB0ADQAaAA4AEAGWABEADgAdACEAHAAdAA0AEAAVAB8AHAAaABIADwAcABwAGAGVAAYADgAdACEAHAAdA2oACAAOABsAFAAiAA4AFAASAgkACQAOABsAEQAgABAADgAdABIDaQANAA4ADwASABkADQAcACIAIQAZABYAGwASBGUACQAOAA8AEgAZAA0AHAATABMEFwAXAA4ADwASABkADQAWABoAHQAcAB8AIQAOABsAIQANABwAIgAhABkAFgAbABIECAAPAA4ADwASABkADQAWABoAHQAcAB8AIQAOABsAIQNoAAUADgAPABIAGQBKAJYArgDQAPIBEAEoATwBUgFgAYIBngG+AcQB3gH8AggCJAIwAkYCcAKSArYCygLeAvQC/gMKAxwDJgNKA1oDbgN6A5oDuAPMA+YEDgQkBEQETARwBJ4EsATMBOAE8AUCBSIFRAVMBXAFhAWUBaoFvgXSBdwF6gYEBiYGOgZMBnIGiAasBswG7gcMByoHOAdKB1IHbAJ+AAsAJgANABkAHAAQAA4AIQAWABwAGwA+ABAAJgANABkAFgAPAB8ADgAfACYADQAaACIAIAAWABAAPQAQACYADQAZABYADwAfAA4AHwAmAA0ADwAcABwAGAAgADwADgAmAA0AGQAWAA8AHwAOAB8AJgANAA4AEQARAG4ACwAiACAAFgAQAA0AIwAWABEAEgAcAlAACQAiACAAFgAQAA0AHAATABMCFwAKACIAIAAWABAADQAbABwAIQASBLwABgAiACAAEgAiABoBDgAQACIAGQAhABYAIQAfAA4AEAAYAA0ADgAiABEAFgAcBXwADQAiABkAIQAWAB0AGQASAA0AIAAhABwAHQL8AA8AIgAZACEAFgAZABYAGwASAA0AEAAVAA4AHwAhBGsAAgAdAkoADAAcACMAFgASAA0AEwAWABkAIQASAB8CFgAOABwAIwAWABIADQAQAB8AEgAOACEAFgAcABsAOwAFABwAIwAWABIA4AANABwAIwASAA0AIQAcAA0AFgAbAA8AHAAlAZoABQAcACIAIAASA+wACgAcACEAHAAfABAAJgAQABkAEgV7ABQAHAAhABYAHAAbAA0AHQAVABwAIQAcACAADQAdAA4AIgAgABIAEQV6ABAAHAAhABYAHAAbAA0AHQAVABwAIQAcACAADQAcABsFeQARABwAIQAWABwAGwANAB0AFQAcACEAHAAgAA0AHAATABMCsQAJABwAHwASAA0AIwASAB8AIQV4AAkAHAAfABIADQAhABYAGgASArAACgAcAB8AEgANABUAHAAfABYAJwLKAAQAHAAfABIFdwAFABwAHQASABEDBAAIABwAHAARAA0ADwAOABEDAwAEABwAHAARAhUAEQAcABsAHAAQABUAHwAcABoAEgANAB0AFQAcACEAHAAgBXYABwAcABsAFgAhABwAHwFhAAkAHAAbABIAJgANABwAEwATAp8ABQAcABsAEgAmAWgADwAcABsAEgAhABYAJwAOACEAFgAcABsADQAcABsFdQAOABwAEQASABkADQAhAB8ADgAWABsAFgAbABQBWQAJABwAEQASAA0AEgARABYAIQFYAAwAHAARABIADQAQABwAGgAaABIAGwAhALMAEwAcAA8AFgAZABIADQAgABAAHwASABIAGwANACAAFQAOAB8AEgEpAAoAHAAPABYAGQASAA0AHAATABMBKAAPABwADwAWABkAEgANABMAHwAWABIAGwARABkAJgLJAAMAGgAgAH4AEQAWACAAIAASABEADQAjABYAEQASABwADQAQAA4AGQAZBXQAFgAWACAAEAASABkAGQAOABsAEgAcACIAIAANACAAEgAfACMAFgAQABIAIAQCAAgAFgAbABYAGgAWACcAEgVzAA0AFgAZABYAIQAOAB8AJgANACEAEgAQABUFcgAJABYAEAAfABwAJAAOACMAEgA6AAcAFgAQAA0AHAATABMAOQAIABYAEAANABsAHAAbABIFcQAPABYAEAANABIAJQAhABIAHwAbAA4AGQANABwAGwVwABAAFgAQAA0AEgAlACEAEgAfABsADgAZAA0AHAATABMAOAADABYAEACXABEAEgAgACAAEgAbABQAEgAfAA0AHAAiACEAGQAWABsAEgCWAAkAEgAgACAAEgAbABQAEgAfAJUABwASACAAIAAOABQAEgFXAAoAEgAfABQAEgANACEAJgAdABIEagAJABIAGwAiAA0AHAAdABIAGwSgAAkAEgAbACIADQAPABwAHAAYAq8ABAASABsAIgGZAAYAEgAaABwAHwAmBPYADAASABIAIQAWABsAFAANAB8AHAAcABoFbwAQABIAEQAWABAADgAZAA0AIAASAB8AIwAWABAAEgAgBW4ACQASABEAFgAOACEAFgAcABsEAQAIAA4AJQAWABoAFgAnABIDcQASAA4AHwAYACIAGwAfABIADgARAA0AGgAOABYAGQAPABwAJQDRAAoADgAfABgAIgAbAB8AEgAOABEFbQARAA4AHwAYAA0AEgAaAA4AFgAZAA0AIgAbAB8AEgAOABEFbAAPAA4AHwAYAA0AEgAaAA4AFgAZAA0AHwASAA4AEQVrABAADgAfABgADQAQABUADgAhAA0AIgAbAB8AEgAOABEFagAOAA4AHwAYAA0AEAAVAA4AIQANAB8AEgAOABEEaQAOAA4AHwAYAA0ADgAgAA0AIgAbAB8AEgAOABEEaAAGAA4AHwAUABYAGwVpAAgADgAdACAADQAiABQAEAJ9AAMADgAdAK0ADAAOABYAGQANABwAIgAhABkAFgAbABIA0AAEAA4AFgAZADIAZgB+AJoAxADmAQoBMAFaAXYBpAGwAcIBzAHkAgwCKgJIAl4CdAKAApgCsAK+AtwC/AMOAx4DMANMA2ADcAOIA6oDvgPaA+ID9gQKBCQEPgRcBHgEkgS0BMQE2gT2BRYFMgVAARMACwAcACQADQAkABYAEQAUABIAIQAgARIADQAcACQADQAkAA4AGQAZAB0ADgAdABIAHwMJABQAHAAhABYAEwAWABAADgAhABYAHAAbACAADQAdAA4AIgAgABIAEQMIABAAHAAhABYAEwAWABAADgAhABYAHAAbACAADQAcABsDBwARABwAIQAWABMAFgAQAA4AIQAWABwAGwAgAA0AHAATABMDBgASABwAIQAWABMAFgAQAA4AIQAWABwAGwAgAA0AGwAcABsAEgMIABQAHAAhABYAEwAWABAADgAhABYAHAAbACAADQAOABAAIQAWACMAEgMFAA0AHAAhABYAEwAWABAADgAhABYAHAAbACAALAAWABwAIQAWABMAFgAQAA4AIQAWABwAGwANABYAGgAdABwAHwAhAA4AGwAhAXEABQAcACEAEgAgA3IACAAcACEAEgANAA4AEQARAHoABAAcACEAEgWOAAsAHAAhAA0AIAAhAA4AHwAhABIAEQKXABMAHAAhAA0AGQAWACAAIQASABEADQAZABwAEAAOACEAFgAcABsAQAAOABwAIQANABYAGwAhABIAHwASACAAIQASABEFjQAOABwAIQANAA4AEAAQABIAIAAgABYADwAZABIFjAAKABwAHwAhABUADQAkABIAIAAhBYsACgAcAB8AIQAVAA0AEgAOACAAIQWKAAUAHAAfACEAFQWJAAsAHAANACEAHwAOABsAIAATABIAHwWIAAsAHAANACAAIQAfABwAGQAZABIAHwCYAAYAHAANACAAFgAaBYcADgAcAA0AHQAVABwAIQAcABQAHwAOAB0AFQAmBPUADwAcAA0AGgASABIAIQAWABsAFAANAB8AHAAcABoFhgAIABwADQAaABIADgAZACAFhQAHABwADQATABwAHAARBYQACAAcAA0AEwAZAA4AIAAVAvAADQAcAA0AEgAbABAAHwAmAB0AIQAWABwAGwWDAAkAHAANABEAHwAWABsAGAAgBYIABwAcAA0AEAASABkAGQTGAAsAFgAUABUAIQAgAA0AIAAhAA4AJgWBABAAFgAUABUAIQAZABYAFAAVACEADQAfABwAIgAbABEE1gAJABYAFAAVACEAGQAWABMAEgWAAA0AFgAUABUAIQANACAAFQASABkAIQASAB8BEQADABMAEADiAAkAEgAlACEADQAkABIAEgAYBX8ACQASACUAIQANAB0AGQAOABsAPwAMABIAJAANAB8AEgAZABIADgAgABIAIAEQAAwAEgAhACQAHAAfABgADQAkABYAEwAWAssADgASACEAJAAcAB8AGAANABkAHAAQABgAEgARAu8ADQASACEAJAAcAB8AGAANABAAFQASABAAGAEPAAwAEgAhACQAHAAfABgADQAQABIAGQAZBX4AEAASAA4AHwANABoAEgANABEAFgAgAA4ADwAZABIAEQKLAAcAEgAOAB8ADQAaABICfwAKAA4AIwAWABQADgAhABYAHAAbAhsADQAOACMAFgAUAA4AIQASAA0AGwASACUAIQIaAA8ADgAjABYAFAAOACEAEgANAA8AEgATABwAHwASAhkADQAOACEAIgAfABIADQAdABIAHAAdABkAEgIYAAYADgAhACIAHwASBX0AAwAOACEADwAgADwASgBmAIIAkACkALwA1gD2AQYBKgFIAWQBfADmAA0AIgAhABkAFgAbABIAEQANABMAGQAOABQFkwAGACIAIQAZABIAIQWSAA0AIgAhABQAHAAWABsAFAANABoADgAWABkExwANACIAIQARABwAHAAfAA0AFAAfABYAGQAZBZEABgAiACEADwAcACUDdQAJAB0AEgAbAA0AJAAWACEAFQN0AAsAHQASABsADQAWABsADQAbABIAJAWQAAwAHQASABsADQAWABsADQATACIAGQAZA3MADwAdABIAGwANABYAGwANAA8AHwAcACQAIAASAB8D7QAHAB0ADgAQABYAIQAmBY8AEQAbABkAFgAbABIADQAdAB8AEgARABYAEAAhABYAHAAbAukADgAbABEAEgAaAA4AGwARAA0AIwAWABEAEgAcBGwADQATABMAGQAWABsAEgANACAAFQAOAB8AEgPcAAsAEwATABkAFgAbABIADQAdABYAGwQDAAwAEwATABkAFgAbABIADQAPABwAGQAhAH8BAAESASIBOAFGAVwBdAGQAa4BugHKAegCBgIsAkACWAJkAnYCiAKsArYCxgLQAt4C+gMMAx4DOgNgA3oDlgOyA9oEAAQiBDgETgRaBH4EkAS2BMoE+AUeBTwFbAWcBc4F6gYEBioGTgZoBoAGjAasBsoG5gcEByQHOAdSB2wHhgegB7wH3Af4CBYINAhkCIAIjAiWCL4I2Aj2CRIJOglWCXoJkAmuCdYJ+AoWCiwKOgpYCnYKjAqoCtgK+gseC0oLbAuMC6oLwAvOC+4L/gwUDCYMNgxcDIYMrgy6DNAM2g0QDTgNag2ODcYN8A4mDk4OcA6UDqYOuA7IDtoO5gWsAAgAIgAgABUADQAdABYAGwFaAAcAIgAPABkAFgAgABUFqwAKACIADwAZABYAEAANABwAEwATAxMABgAiAA8AGQAWABAFqgAKACAAJgAQABUAHAAZABwAFAAmBakACwAfABYAIwAOABAAJgANACEAFgAdAvQADQAfABYAHAAfABYAIQAmAA0AFQAWABQAFQR0AA4AHwAWABsAIQANABEAFgAgAA4ADwAZABIAEQODAAUAHwAWABsAIQWoAAcAHwASACMAFgASACQAqwAOAB8AEgAgABIAGwAhAA0AIQAcAA0ADgAZABkD7wAOAB8AEgAUABsADgAbACEADQAkABwAGgAOABsDggASABwAJAASAB8ADQAgABIAIQAhABYAGwAUACAADQAbABIAJAL1AAkAHAAkABIAHwANABwAEwATAa0ACwAcACQAEgAfAA0AFgAbAB0AIgAhAusABQAcACQAEgAfBKcACAAcACAAIQANAA4AEQARAicACAAcAB8AIQAfAA4AFgAhAJoAEQAcAB8AIQAOAA8AGQASAA0AJAAWABMAFgANABwAEwATBPAABAAcABwAGQOBAAcAHAAZACYAGgASAB8DEgAEABwAGQAZBJ4ABgAcABkAFgAQACYFpwANABwAFgAbACEADQAcABMADQAgAA4AGQASAxEACAAZACIAIAANABwAGwASBaYACAAZACIAGgAPABYAGwAUAGoADQAZAA4AJgAZABYAIAAhAA0AHQAZAA4AJgBwABIAGQAOACYAGQAWACAAIQANAA4AEQARAA0AEAAVABIAEAAYAEcADAAZAA4AJgAZABYAIAAhAA0ADgARABED2QANABkADgAmAA0AEwAcAB8ADQAkABwAHwAYBaUADQAZAA4AJgANABEAFgAgAA4ADwAZABIAEQBGABMAGQAOACYADQAQABYAHwAQABkAEgANABwAIgAhABkAFgAbABIARQASABkADgAmAA0AEAAWAB8AEAAZABIADQATABYAGQAZABIAEQBFABAAGQAOACYADQAQABYAHwAQABkAEgANABMAFgAZABkARAAKABkADgAmAA0ADgAfAB8AHAAkBaQACgAZAA4AFAAWAA4AHwAWACAAGgKBAAUAGQAOABAAEgRzABEAFgAjABwAIQANACEADgAPABkAEgANABAAFQAOAB8AIQKAAAgAFgAbAA0AEQAfABwAHQL6ABIAFgASAA0AEAAVAA4AHwAhAA0AHAAiACEAGQAWABsAEgARAvkACQAWABIADQAQABUADgAfACED4gAWABYAEAAhACIAHwASAA0AFgAbAA0AHQAWABAAIQAiAB8AEgANAA4AGQAhA4AAEgAWABAAIQAiAB8AEgANABYAGwANAB0AFgAQACEAIgAfABICJgAOABYAEAAhACIAHwASAA0ADgAgAA0AHQARABMCRAAXABUAHAAhABwADQAgABYAJwASAA0AIAASABkAEgAQACEADQAgABoADgAZABkCQwAXABUAHAAhABwADQAgABYAJwASAA0AIAASABkAEgAQACEADQAZAA4AHwAUABICQgAYABUAHAAhABwADQAgABYAJwASAA0AIAASABkAEgAQACEADQAOABAAIQAiAA4AGQIlAA0AFQAcACEAHAANABkAFgAPAB8ADgAfACYCSwAMABUAHAAhABwADQATABYAGQAhABIAHwWjABIAFQAcACEAHAANABAADgAaABIAHwAOAA0AEwAfABwAGwAhBaIAEQAVABwAIQAcAA0AEAAOABoAEgAfAA4ADQAPAA4AEAAYAiQADAAVABwAIQAcAA0AEAAOABoAEgAfAA4CIwALABUAHAAhABwADQAOABkADwAiABoCIgAFABUAHAAhABwAqgAPABUAHAAbABIAGQAWABsAGAANACAAEgAhACIAHQCpAA4AFQAcABsAEgAZABYAGwAYAA0AHwAWABsAFAGeAA0AFQAcABsAEgAZABYAGwAYAA0AHAATABMAqAAOABUAHAAbABIAGQAWABsAGAANABkAHAAQABgApwAPABUAHAAbABIAGQAWABsAGAANABIAHwAOACAAEgGdAAkAFQAcABsAEgAZABYAGwAYAtEADAAVABwAGwASAA0AHQAOACIAIAASABEC0AAMABUAHAAbABIADQAaABYAIAAgABIAEQLPAAwAFQAcABsAEgANABkAHAAQABgAEgARAZwADAAVABwAGwASAA0AFgAdABUAHAAbABICzgANABUAHAAbABIADQAWABsADQAhAA4AGQAYAs0ADwAVABwAGwASAA0AEwAcAB8AJAAOAB8AEQASABEEcgANABUAHAAbABIADQASABsADgAPABkAEgARBHEADgAVABwAGwASAA0AEQAWACAADgAPABkAEgARAvgADgAVABwAGwASAA0AEAAOABkAGQAPAA4AEAAYAswAFwAVABwAGwASAA0ADwAZACIAEgAhABwAHAAhABUADQAgAB0AEgAOABgAEgAfAZsADQAVABwAGwASAA0ADgAbABEAHwAcABYAEQCZAAUAFQAcABsAEgPuAAQAEgAhACAFoQATABIAIAAhAA0AEAAcABsAIQAfABwAGQANAB8AHAARABIAGwAhBaAADAASACAAIQANABAAHAAbACEAHwAcABkC6gAOABIAHwAgABwAGwAOABkADQAjABYAEQASABwFnwANABIAHwAgABwAGwANACAAEgAOAB8AEAAVBZ4AEwASAB8AIAAcABsADQAfABIAGgAcACMAEgANAA4AGQAhAA0ABAWdAA0AEgAfACAAHAAbAA0AHwASABoAHAAjABICjAARABIAHwAgABwAGwANAB0AFgAbAA0AEAAWAB8AEAAZABICfAAKABIAHwAgABwAGwANAB0AFgAbAxAADgASAB8AIAAcABsADQAcACIAIQAZABYAGwASBHAAEwASAB8AIAAcABsADQAOABEAEQANABEAFgAgAA4ADwAZABIAEQWcABAAEgAfACAAHAAbAA0ADgARABEADQAOABkAIQANAAQFmwAOABIAHwAgABwAGwANAA4AEQARAA0ADgAZACEDDwAKABIAHwAgABwAGwANAA4AEQARAw4ABgASAB8AIAAcABsDfwAOABIAHwAaAA0AIAAQAA4AGwANACQAFgATABYDfgAOABIAHwAaAA0AHQAVABwAGwASAA0AGgAgABQDfQAKABIAHwAaAA0AGgASABEAFgAOA3wADQASAB8AGgANABYAEQASABsAIQAWACEAJgN7ABcAEgAfABoADQARABIAIwAWABAAEgANABYAGwATABwAHwAaAA4AIQAWABwAGwN7ABAAEgAfABoADQARABIAIwAWABAAEgANABYAGwATABwDegARABIAHwAaAA0AEQAOACEADgANACAAEgAhACEAFgAbABQDeQAVABIAHwAaAA0AEAAcABsAIQAOABAAIQANABAADgAZABIAGwARAA4AHwN5ABAAEgAfABoADQAQABwAGwAhAA4AEAAhAA0AEAAOABkDeAAPABIAHwAaAA0AEAAOABoAEgAfAA4ADQAaABYAEAMNAA4AEgAcAB0AGQASAA0AHAAiACEAGQAWABsAEgSoAAoAEgAcAB0AGQASAA0ADgAZACEDDAAGABIAHAAdABkAEgWaAA8AEgAbABEAFgAbABQADQAOABAAIQAWABwAGwAgBZkABwASABsAEQAWABsAFAWYAAoAEgARAA4AGQANAA8AFgAYABIFlwAIAA4AJgAaABIAGwAhACADdwAHAA4AJgAaABIAGwAhALYAEgAOACIAIAASAA0AHQAfABIAIAASABsAIQAOACEAFgAcABsAQwAUAA4AIgAgABIADQAQABYAHwAQABkAEgANABwAIgAhABkAFgAbABIAQgATAA4AIgAgABIADQAQABYAHwAQABkAEgANABMAFgAZABkAEgARAEEABQAOACIAIAASAwsACgAOAB8AIQAmAA0AGgAcABEAEgTXAAQADgAfABgFlgAaAA4AGwAcAB8ADgAaAA4ADQAkABYAEQASAA0ADgAbABQAGQASAA0AIAASABkAEgAQACECIQATAA4AGwAcAB8ADgAaAA4ADQAkABYAEQASAA0ADgAbABQAGQASBZUAGAAOABsAHAAfAA4AGgAOAA0AIwASAB8AIQAWABAADgAZAA0AIAASABkAEgAQACECIAARAA4AGwAcAB8ADgAaAA4ADQAjABIAHwAhABYAEAAOABkEbwAbAA4AGwAcAB8ADgAaAA4ADQAdABUAHAAhABwAIAAdABUAEgAfABIADQAgABIAGQASABAAIQRuABQADgAbABwAHwAOABoADgANAB0AFQAcACEAHAAgAB0AFQASAB8AEgWUABoADgAbABwAHwAOABoADgANABUAHAAfABYAJwAcABsAIQAOABkADQAgABIAGQASABAAIQIfABMADgAbABwAHwAOABoADgANABUAHAAfABYAJwAcABsAIQAOABkCHgAQAA4AGwAcAB8ADgAaAA4ADQATABYAIAAVABIAJgASAh4AEQAOABsAHAAfAA4AGgAOAA0AEwAWACAAFQANABIAJgASAh0ACAAOABsAHAAfAA4AGgAOA/YACAAOABsADQAhABwAHAAZAhwABwAOABkAEgAhACEAEgN2AAgADgAUABIAIwAWABIAJAMKAAUADgAUABIAIARtAAcADgARABEAFgAbABQACgAWACwAVACAAKAAuADEAOQBAAEgBa8ACgAiABYAEAAYAB8AEgAdABkAJgCcABMAIgAWABAAGAANABAAHAAbACEADgAQACEAIAANABoADgAWABkAmwAVACIAFgAQABgADQAQABwAGwAhAA4AEAAhACAADQARABYADgAZABIAHwBxAA8AIgASACIAEgANAB0AGQAOACYADQAbABIAJQAhAEkACwAiABIAIgASAA0AGgAiACAAFgAQAEgABQAiABIAIgASA4UADwAiABIAIAAhABYAHAAbAA0ADgAbACAAJAASAB8DhAANACIAEgAfACYADQAPACIAFgAZABEAEgAfBa4ADwAfAA0AEAAcABEAEgANACAAEAAOABsAGwASAB8FrQAHAB8ADQAQABwAEQASAEEAhACYAK4AxgDQANgA6gD4AQYBJAE+AVYBggGcAb4ByAHYAfACBAIeAkQCVAJ0AooCnAK4AtYC7AL6Aw4DGgNEA1YDagN+A4wDqAO+A9ID4APwBBoEOARaBH4ElgTCBN4E7AT8BQYFFAU4BUwFaAWCBZIFpgW+BdgF9AYiBkIGZAaOAvEACQAjAA0AFQAcABwAGAAiAB0FuAAKACIAGwANABAAFgAfABAAGQASBbcACwAiABkAEgANABMAHAAZABEAEgAfBbYABAAiABkAEgRgAAMAIQAhALEACAAgACAADQATABIAEgARA/IABgAcACQAFgAbABQBnwAGABwAIgAhABIAHwPxAA4AHAAiABsAEQASABEADQAQABwAHwAbABIAHwIrAAwAHAAhAA4AIQASAA0AHwAWABQAFQAhAioACwAcACEADgAhABIADQAZABIAEwAhAikAFQAcACEADgAhABIADQAMAAMADQARABIAFAAfABIAEgAgAA0AEAAQACQE8QAMABwAHAAaAA0AIAASAB8AIwAWABAAEgW1ABAAHAAcABoADQAdAB8AEgATABIAHwASABsAEAASACADigAEABwAHAAaBbQABwAcABwAEwAWABsAFACdAAsAFgAbABQADQAjABwAGQAiABoAEgWzAAkAFgAQABIADQAPABwAJAAZA/oADAASACAAIQAcAB8AEgANAB0ADgAUABIECQASABIAIAAhABwAHwASAA0AEwAfABwAGgANACEAHwAOACAAFQOJAAcAEgAgACEAHAAfABICgwAPABIAIAAhAA4AIgAfAA4AGwAhAA0AGgASABsAIgKOAAoAEgAgACEADgAiAB8ADgAbACEEfAAIABIAIAASACEADQAhACMFsgANABIAHgAiABIAIAAhAA0AHgAiABwAIQASA4gADgASAB0AHAAfACEADQAdAB8AHAAPABkAEgAaAOgACgASAB0AHAAfACEADQAcABMAEwDYAAYAEgAdABwAHwAhANcACQASAB0AGQAmAA0ADgAZABkA1gAFABIAHQAZACYEewAUABIAHQAZAA4AJgANABAAFgAfABAAGQASAA0AEwAWABkAGQASABEAZgAIABIAHQAZAA4AJgANAAgAZQAJABIAHQAZAA4AJgANAAYAAwBkAAkAEgAdABkADgAmAA0ABAADAE4ABgASAB0AGQAOACYEegANABIAHQASAA4AIQANABwAGwASAA0AHAAbAE0ACgASAB0AEgAOACEADQAcABsAEgR5AAkAEgAdABIADgAhAA0AHAAbAEwABgASAB0AEgAOACED0gAHABIAHAAfABEAEgAfA/kAFAASABoAHAAjABIADQAgABUAHAAdAB0AFgAbABQADQAQAA4AHwAhAigADgASABoAHAAjABIADQAfABIAEQANABIAJgASBHgAEAASABoAHAAjABIADQAaABwAEQASAB8ADgAhABwAHwByABEAEgAaABwAIwASAA0AEwAfABwAGgANAB4AIgASACIAEgR3AAsAEgAaABwAIwASAA0AEQAcABsAEgDVABUAEgAaABwAIwASAA0AEAAWAB8AEAAZABIADQAcACIAIQAZABYAGwASANQADQASABoAHAAjABIADQAQABYAHwAQABkAEgDTAAYAEgAaABwAIwASArIABwASABMAHwASACAAFQDSAAQAEgARABwDhwAGABIAEQASABIAGgPwABEAEgAQABwAHwARAA0AIwAcABYAEAASAA0AHAAjABIAHwR2AAkAEgAQABwAGgAaABIAGwARAEsADQASABAAEgAbACEADQAOABAAIQAcAB8AIAWxAAwAEgAQABIAFgAdACEADQAZABwAGwAUA4YABwASABAAEgAWAB0AIQWwAAkAEgAOABEADQAaABwAHwASAoIACwAOACEAEgANAB8AEgAjABYAEgAkBNgADAAOABoAEgAbAA0AEQAWABsAFgAbABQEdQANAA4AFgAZACQADgAmAA0ADgAZABIAHwAhAyAAFgAOABEAFgAcAA0ADwAiACEAIQAcABsADQAiABsAEAAVABIAEAAYABIAEQMhAA8ADgARABYAHAANAA8AIgAhACEAHAAbAA0AHAAbAyAAEAAOABEAFgAcAA0ADwAiACEAIQAcABsADQAcABMAEwMhABQADgARABYAHAANAA8AIgAhACEAHAAbAA0AEAAVABIAEAAYABIAEQBKAAUADgARABYAHAC8AXoBnAHAAdwB9gISAiQCLgJIAmICegKWArQCwALqAwwDIANOA2QDegOYA7QDxAPqBBgEMAQ+BFoEbgSKBJ4ErgTgBRAFHAUuBU4FZAVyBYgFngXIBdQF5AYIBiAGKgZGBnIGoAbMBvoHBgcaBzQHTgdiB3oHhAeSB7YHzgfyCA4IKghECGoIgAieCLoI2gjyCRIJMAlOCXIJkgmoCbYJzAnYCfQKGAo0ClAKYAp0CnwKkgqoCrQKwgreCugK8gsACx4LNAs8C1gLbguEC54LwgvWC/IMBgwcDDoMTAxsDJoMvgziDQoNNA1iDboN4g4ODjoOVg5sDnwOkg6oDsQO5A7+DxAPGg8oDzQPUg+ED6QPwg/gEAQQMBBYEIoQvBDqEQ4RMBFMEXQRmhHKEfYSCBIaEkwShBK0EtwTABMuE0oTYhOEE44TsBPGE9YT6BP+FAwUIhQyFDgUUhR+FJ4UyBTyFR4VKhU6FUgVZBV2FZAVoBW6FcwV1gOrABAAJgAgACEAEgAaAA0AIgAdABEADgAhABIADQAhACMDqwARACYAIAAhABIAGgANACIAHQARAA4AIQASAA0ADgAZACEC2QANACYAIAAhABIAGgANACIAHQARAA4AIQASAtgADAAmABsAEAANAB0AHwAcAA8AGQASABoC1wANACYAGwAQAA0AEQAWACAADgAPABkAEgARBJ8ACAAmABsAEAANAA4AGQAhAtYABAAmABsAEAIwAAwAJAAWACEAEAAVAA0AIwAWABEAEgAcBdcADAAkABYAIQAQABUADQAfABYAFAAVACEF1gALACQAFgAhABAAFQANABkAEgATACECLwANACQAFgAhABAAFQANABAADgAaABIAHwAOBIQADgAkABYAIQAQABUADQAOABAAEAAcACIAGwAhBIMABQAkABYAHQASA6oAFAAkAA4AHQANACMAEgAfACEAFgAQAA4AGQANABAAFgAfABAAGQASA6oAEAAkAA4AHQANACMAEgAfACEADQAQABYAHwAQABkAEgOpAAkAJAAOAB0ADQAjABIAHwAhBAQAFgAkAA4AHQANABUAHAAfABYAJwAcABsAIQAOABkADQAQABYAHwAQABkAEgOoAAoAJAAOAB0ADQAVABwAHwAWACcAowAKACQADgAdAA0AEAAOABkAGQAgAFUADgAiAB8AHwAcACIAGwARAA0AIAAcACIAGwARBdUADQAiAB0AHQAcAB8AIQANAA4AFAASABsAIQXUAAcAIgAdAB0AHAAfACEDpwASACIAHQASAB8AIwAWACAAHAAfAA0ADgAQABAAHAAiABsAIQQKABYAIgAdABIAHwAjABYAIAASABEADQAiACAAEgAfAA0AEAAWAB8AEAAZABIF0wALACIAHQASAB8AIAAQAB8AFgAdACECkQAGACIADwAkAA4AJgXSAA0AIgAPACEAFgAhABkAEgAgAA0AHAATABMAVAAJACIADwAhABYAIQAZABIAIABvAA0AIgAPACAAEAAfABYAHQAhABYAHAAbACAF0QAJACIADwAgABAAHwAWAB0AIQOmAAcAIgAPABcAEgAQACECtwAYACIADwARABYAHwASABAAIQAcAB8AJgANAA4AHwAfABwAJAANAB8AFgAUABUAIQK2ABcAIgAPABEAFgAfABIAEAAhABwAHwAmAA0ADgAfAB8AHAAkAA0AGQASABMAIQIuAAUAIQAmABkAEgXQAAgAIQAfABwAGQAZABIAHwFcAA8AIQAfABYAGAASACEAFQAfABwAIgAUABUADQAgApAACgAhAB8AEgASACEAIwAWABIAJASCAAYAIQAfABIADgAaAi0ACgAhAB8ADgAWABQAFQAhABIAGwSZAAoAIQAcAB8AEgATAB8AHAAbACEChQAUACEAHAAfABIADQAaAA4AGQAZAA0AEQAWAB8AEgAQACEAHAAfACYDpQAFACEAHAAfABIBIgAHACEAHAAfAA4AFAASAK8AEQAhABwAHQANACAAEAAfABIAEgAbAA0AIAAVAA4AHwASBc8ACwAhABwAHQANABAAFgAfABAAGQASAFMABAAhABwAHQXOAA0AIQAWABAAGAAmAA0AGwAcACEAEgANAAUAogAVACEADgAmAA0AHQAfABYAGgAOAB8AJgANAB0AHAAfACEAHwAOABYAIQChABYAIQAOACYADQAdAB8AFgAaAA4AHwAmAA0AGQAOABsAEQAgABAADgAdABIAoAAVACEADgAmAA0AEAAiAB8AHwASABsAIQANAB0AHAAfACEAHwAOABYAIQCfABYAIQAOACYADQAQACIAHwAfABIAGwAhAA0AGQAOABsAEQAgABAADgAdABIDpAAFACEADgAfACAFzQAJACEADgAfAA0AHwAOACEAEgMkAAwAIQAOAB8ADQAcACIAIQAZABYAGwASBcwADAAhAA4AHwANABwAIgAhABkAFgAbABIDIwAJACEADgAfAA0AFQAOABkAEwMkAAsAIQAOAB8ADQAPABwAHwARABIAHwMiAAQAIQAOAB8FywAGACEADgAWAB8AIASBABEAIQAOABAAGAASABEADQAPAA4AHwANABAAFQAOAB8AIQTJAAsAHgAiAA4AHwASAA0AEwAcABwAIQS3ABEAHQAcAB8AIQAgAA0AIwAcABkAGQASACYADwAOABkAGQS4AA0AHQAcAB8AIQAgAA0AIQASABsAGwAWACAEtQANAB0AHAAfACEAIAANACAAHAAQABAAEgAfBLQADAAdABwAHwAhACAADQAfACIAFAAPACYEswASAB0AHAAfACEAIAANABoAHAAhABwAHwAgAB0AHAAfACEAIASyAAoAHQAcAB8AIQAgAA0AGgAaAA4EugAOAB0AHAAfACEAIAANABgADgAPAA4AEQARABYEsQANAB0AHAAfACEAIAANABUAHAAQABgAEgAmBLkADwAdABwAHwAhACAADQAVAA4AGwARAA8ADgAZABkEsAALAB0AHAAfACEAIAANABQAHAAZABMErwAPAB0AHAAfACEAIAANABMAHAAcACEADwAOABkAGQSuAA4AHQAcAB8AIQAgAA0AEgAgAB0AHAAfACEAIAStAA4AHQAcAB8AIQAgAA0AEAAfABYAEAAYABIAIQSsABEAHQAcAB8AIQAgAA0ADwAOACAAGAASACEADwAOABkAGQTLAA8AHQAcAB8AIQAgAA0ADwAOACAAEgAPAA4AGQAZBcoACgAdABwAHwAhACAADQAPAA4AHwS2AAYAHQAcAB8AIQAgA6MACgAdABIAGQAZABAAFQASABAAGASAAAUAHQASABIAEQCeAA0AHQASAA4AGAASAB8ADQAdABUAHAAbABID+wARAB0AEgAOABgAEgAfAA0AGwAcACEAEgAgAA0AHAATABMDogANAB0AEgAOABgAEgAfAA0AGwAcACEAEgAgAaUADQAdABIADgAYABIAHwANABQAHwAcACIAHQGkAAcAHQASAA4AGAASAB8BWwAJAB0ADgAQABIADQAPAA4AHwT0AAMAHQAOBckACgAcACIAIQAVAA0AJAASACAAIQXIAAoAHAAiACEAFQANABIADgAgACEFxwAFABwAIgAhABUFxgAGABwAIgAfABAAEgBfAA0AHAAfACEADQAPACYADQAOABkAHQAVAA4A3AAEABwAHwAhBcUABAAcAA4AHQBSAAYAGwAcABwAJwASBcQADgAbABYAHQAdABIAIQANABMAHAAZABEAEgAfAtUACgAaACAADQATAA4AFgAZABIAEQLUAAMAGgAgBPMADQAaABwAGAAWABsAFAANAB8AHAAcABoAIATyAAoAGgAcABgAEgANABMAHwASABIBowAKABoADgAfACEAHQAVABwAGwASBcMADAAaAA4AHwAhAA0ADwAiACEAIQAcABsAcwARABkAHAAkAA0AGgAcACEAFgAcABsADQAjABYAEQASABwCLAAJABkAFgARABIAIAAVABwAJABRAA0AGAAWAB0ADQAdAB8AEgAjABYAHAAiACAAUAAJABgAFgAdAA0AGwASACUAIQTIAAoAFgAbABQAGQASAA0ADwASABEC0wAOABYAGgANABAADgAfABEADQAOABkAEgAfACEBogAIABYAGgANABAADgAfABEBIQAPABYAFAAbAA4AGQANACQAFgATABYADQAcABMAEwEgABYAFgAUABsADgAZAA0AJAAWABMAFgANAAcADQAPAA4AHwANABkAHAAQABgBHwARABYAFAAbAA4AGQANACQAFgATABYADQAHAA0ADwAOAB8FwgARABYAFAAbAA4AGQANACQAFgATABYADQADAA0ADwAOAB8BHgATABYAFAAbAA4AGQANABAAEgAZABkAIgAZAA4AHwANABwAEwATAR0AFAAWABQAGwAOABkADQAQABIAGQAZACIAGQAOAB8ADQAbACIAGQAZARwAFgAWABQAGwAOABkADQAQABIAGQAZACIAGQAOAB8ADQAbABwADQAgABYAGgEbACsAFgAUABsADgAZAA0AEAASABkAGQAiABkADgAfAA0AEAAcABsAGwASABAAIQASABEADQAbABwADQAWABsAIQASAB8AGwASACEADQAHAA0ADwAOAB8BKgATABYAFAAbAA4AGQANABAAEgAZABkAIgAZAA4AHwANAA4AGQAhARoAFQAWABQAGwAOABkADQAQABIAGQAZACIAGQAOAB8ADQAHAA0ADwAOAB8FwQAVABYAFAAbAA4AGQANABAAEgAZABkAIgAZAA4AHwANAAMADQAPAA4AHwJNAA0AFQAiACEAIQASAB8ADQAgAB0AEgASABEEfwAKABUAIgATABMAGQASAA0AHAAbAE8ABwAVACIAEwATABkAEgL9AAoAFQAcACQADQAQABUADgAfACEBZgAKABUAHAAfACEADQAhABIAJQAhA6EADQAVABwAHQAdABYAGwAUAA0AEAAOAB8AIQOgAA8AFQAcAB0AHQAWABsAFAANAA8ADgAgABgAEgAhBcAADAAVABwAHQAdABYAGwAUAA0ADwAOABQDnwAIABUAHAAdAA0AIQAkABwDngAEABUAHAAdBH4ABgAVABYAEgAZABEDFQAFABUADgAfABIDnQAOABIAIQAhABYAGwAUACAADQAjABwAFgAQABIBGQAYABIAIQAhABYAGwAUACAADQAgACYAIAAhABIAGgANABEADgAmABEAHwASAA4AGgOcAA8AEgAhACEAFgAbABQAIAANAB8AEgAaABwAIQASA5sADgASACEAIQAWABsAFAAgAA0AHQAcACQAEgAfA5oADgASACEAIQAWABsAFAAgAA0AHQAVABwAGwASA5kAEQASACEAIQAWABsAFAAgAA0AHAAjABIAHwAgABAADgAbA5gAFQASACEAIQAWABsAFAAgAA0AFgAbAB0AIgAhAA0AIAAjABYAEQASABwDlwATABIAIQAhABYAGwAUACAADQAWABsAHQAiACEADQAVABEAGgAWA5YAGAASACEAIQAWABsAFAAgAA0AFgAbAB0AIgAhAA0AEAAcABoAHQAcACAAFgAhABIDlQAYABIAIQAhABYAGwAUACAADQAWABsAHQAiACEADQAQABwAGgAdABwAGwASABsAIQOUABYAEgAhACEAFgAbABQAIAANABYAGwAdACIAIQANAA4AGwAhABIAGwAbAA4DkwARABIAIQAhABYAGwAUACAADQASACEAFQASAB8AGwASACEDkgAQABIAIQAhABYAGwAUACAADQARABYAIAAdABkADgAmA5EADQASACEAIQAWABsAFAAgAA0AEAASABkAGQOSABMAEgAhACEAFgAbABQAIAANAA8AHwAWABQAFQAhABsAEgAgACADkAASABIAIQAhABYAGwAUACAADQAPABkAIgASACEAHAAcACEAFQOPABcAEgAhACEAFgAbABQAIAANAA8ADgAQABgAIgAdAA0AHwASACAAIQAcAB8AEgOOABUAEgAhACEAFgAbABQAIAANAA4AHQAdABkAFgAQAA4AIQAWABwAGwAgA40ACAASACEAIQAWABsAFAAgBb8ACAASACEADQAaABIADgAZAxsAGAASABsAIQAWABoAEgAbACEADQAjABIAHwAmAA0AIAAOACEAFgAgABMAFgASABEDGgAbABIAGwAhABYAGgASABsAIQANACMAEgAfACYADQARABYAIAAgAA4AIQAWACAAEwAWABIAEQC5ABcAEgAbACEAFgAaABIAGwAhAA0AIAAOACEAFgAgABMAFgASABEADQAOABkAIQMZABMAEgAbACEAFgAaABIAGwAhAA0AIAAOACEAFgAgABMAFgASABEDGAARABIAGwAhABYAGgASABsAIQANABsAEgAiACEAHwAOABkDFwAWABIAGwAhABYAGgASABsAIQANABEAFgAgACAADgAhABYAIAATABYAEgARBb4ADQASABsAIAAcAB8ADQAkABYAGwARABwAJAW9AAsAEgAbACAAHAAfAA0AEQAcABwAHwSTABAAEgAbABEADQAOABsAEQANAA4AHwAQABUAFgAjABIA2wAEABIAGwARBbwAEAASABkAEwANABYAGgAdAB8AHAAjABIAGgASABsAIQDaAAoAEgAZABIAEAAhAA0ADgAZABkEGgAHABIAFAAaABIAGwAhAaEACAASABAAIgAfABYAIQAmBbsACgASAA4AHwAQABUADQAcABMAEwOMAAYAEgAOAB8AEAAVARgACgARAA0AIAAhABwAHwAOABQAEgLSAAcAEQANABAADgAfABEEfQACABEArgAMABAAHwASABIAGwANACAAFQAOAB8AEgW6ABUAEAAfABIAEgAbAA0AIAASAA4AHwAQABUADQARABIAIAAYACEAHAAdARcADwAQAB8AEgASABsADQAfABwAIQAOACEAFgAcABsBFgAUABAAHwASABIAGwANABkAHAAQABgADQAfABwAIQAOACEAFgAcABsBFQAUABAAHwASABIAGwANABkAHAAQABgADQAdABwAHwAhAB8ADgAWACEBFAAVABAAHwASABIAGwANABkAHAAQABgADQAZAA4AGwARACAAEAAOAB0AEgFuAAUAEAAcAB8AEgW5AAcAEAAWABIAGwAQABIDFAAGABAAFQAcABwAGQSRAA0AEAAVABIAEQAiABkAEgANACAAEgAbABEDiwAIABAAFQASABEAIgAZABIBbQAMABAADgAhACEAEgAfAA0AHQAZABwAIQGgAAcAEAAOABsAGwASAB8EmAAMAA4AIwASABEADQAgABIADgAfABAAFQDpAAgADgAjABIADQAOABkAIQDZAAQADgAjABIChAAJAA4AIQASABkAGQAWACEAEgBJAJQArAC6AMAA3ADwAPoBAgEaATIBVAFwAYwBoAHEAdgCDAIWAiICMgJOAlgCYgJ2AoIClAKeArICyALUAtwC6AL8AwwDHgMqAzwDUANsA4oDrAPGA9gD/gQcBDIERARiBHIEggScBMIE6AUUBUQFbgWMBaQFvAXMBeIF7gYIBiYGOgZCBlgGdgaEBpoGsAbIBuYEigALACQAHAANACQAFQASABIAGQASAB8C9gAGACMADQAcABMAEwGqAAIAIwO7AA0AIgAfABsAEgARAA0AFgAbAA0AGwAcACEDugAJACIAHwAbABIAEQANABYAGwI6AAQAIgAbABIF3wADACEAJgKdAAsAHwAWAB0ADQAcAB8AFgAUABYAGwO5AAsAHwASABsAEQAWABsAFAANACIAHQO4ABAAHwASABsAEQAWABsAFAANABsAEgAiACEAHwAOABkDuAANAB8AEgAbABEAFgAbABQADQATABkADgAhA7cADQAfABIAGwARABYAGwAUAA0AEQAcACQAGwO2AAkAHwAOABsAIAAZAA4AIQASApsAEQAfAA4AGwAgABYAIQANABIAGwAhABIAHwASACUAFgAhAjkACQAfAA4AGwAgABMAHAAfABoClAAZAB8ADgAbACAAEwASAB8ADQAkABYAIQAVABYAGwANAA4ADQAgACEADgAhABYAHAAbApMABAAfAA4AGgKSAAUAHwAOABYAGwKHAAcAHwAOABMAEwAWABADtQANAB8ADgAQABgADQAQABUADgAbABQAEgAgAakABAAcACYAIAXeAAQAHAAiAB8D5AAJABwAIgAQABUADQAOAB0AHQXdAAUAHAAdABYAEAI4AAgAHAAbAA4AGQAWACEAJgO0AAQAHAAZABkEiQAJABwAFAAUABkAEgANABwAGwSIAAoAHAAUABQAGQASAA0AHAATABMDswAFABwAEQAOACYDsgADABwAEAFpAAUAFgAhABkAEgI3AAkAFgAaABIAHwANABwAEwATAjUABwAWABoAEgAfAA0ABgI0AAgAFgAaABIAHwANAAQAAwI2AAUAFgAaABIAHwPzAAgAFgAaABIAGQAWABsAEgIzAAkAFgAaABIAGQAOAB0AIAASAtsADQAWABoAEgANACEAHAANABkAEgAOACMAEgOxAA4AFQAiABoADwAgAA0AIgAdAA0AEQAcACQAGwSHABAAFQAiABoADwANACIAHQANABwAEwATAA0ADgAZACEDHQAMABUAIgAaAA8ADQAiAB0ADQAOABkAIQOwAAgAFQAiABoADwANACIAHQSGABIAFQAiABoADwANABEAHAAkABsADQAcABMAEwANAA4AGQAhAxwADgAVACIAGgAPAA0AEQAcACQAGwANAA4AGQAhA68ACgAVACIAGgAPAA0AEQAcACQAGwOuAAgAFQASAA4AIQASAB8AIATaAA4AFQASAA4AIQASAB8ADQAQABwAGgASABEAJgIyAAcAEgAlACEAIgAfABIApAAHABIAJQAhACAAGgAgBdwADAASACUAIQANACAAGwAWAB0AHQASACEEEAASABIAJQAhAA0AHwAcACEADgAhABYAHAAbAA0AGwAcABsAEgQPABIAEgAlACEADQAfABwAIQAOACEAFgAcABsADQARABwAJAAbBA4AFQASACUAIQANAB8AHAAhAA4AIQAWABwAGwANAA4AGwAUABkAEgAiAB0EDQAXABIAJQAhAA0AHwAcACEADgAhABYAHAAbAA0ADgAbABQAGQASABEAHAAkABsEDAAUABIAJQAhAA0AHwAcACEADgAhABIADQAjABIAHwAhABYAEAAOABkECwAOABIAJQAhAA0AHwAcACEADgAhABIADQAiAB0A3QALABIAJQAhAA0AEwAcAB8AGgAOACEBZwALABIAJQAhAA0AEwAWABIAGQARACAChgAHABIAHwAfAA4AFgAbBdsACgAOACUAFgANAA4AGQASAB8AIQXaAAUADgAdAA4AIALaAAwADgAdAA0ADgAbABEADQAdABkADgAmBOIADgAOABgAEgAcACIAIQANABEAFgAbABYAGwAUAjEACQAOABQADQATAA4AEAASACAEhQADAA4AFAGoAAoADgAPABkAEgAhAA0AGgAOABABpwAOAA4ADwAZABIAIQANAA4AGwARAB8AHAAWABEBpgAGAA4ADwAZABIAIQXZAAoADgAPABkAEgANACMAFgASACQF2AAKAA4ADwAZABIADQAfABwAJAAgAWoACwAOAA8AGQASAA0AEAAVAA4AHwAhA60ADgAOAA8ADQAiABsAIAASABkAEgAQACEAEgARA6wAAwAOAA8ACgAWAB4ANgBGAFQAbACEAJwApgC6ASMAAwAgAA8EiwALAB0AGQAcAA4AEQANABMAFgAZABIF4QAHAB0AFAAfAA4AEQASA/QABgAdABEADgAhABIAtwALABsAIAAiAA8AIAAQAB8AFgAPABICtAALABsAEwAcABkAEQANABoAHAAfABICswALABsAEwAcABkAEQANABkAEgAgACAA3gAEABsAEQAcAOEACQAbAA4AHwAQABUAFgAjABIF4AAIABoADwAfABIAGQAZAA4ALABaAGwAfACiALYAzADkAPwBEAEuAUQBYgF4AYoBngG2AdAB5gH+AhICKAJEAlYCcAKGAqgCwALcAvIDCgMqA0QDVgN0A5ADqAPKA+AD9AQSBDgEZASQBKwC3gAIAB0AGwANABkAHAAQABgApgAHAB0AGwANABgAEgAmBN4AEgAcABkAIgAbACEAEgASAB8ADQAOABAAIQAWACMAFgAgABoAXAAJABwAGQAiABoAEgANACIAHQBbAAoAHAAZACIAGgASAA0AHAATABMAWgALABwAGQAiABoAEgANABoAIgAhABIAWQALABwAGQAiABoAEgANABEAHAAkABsApQAJABwAFgAQABIAGgAOABYAGQQZAA4AHAAWABAAEgANABwAIwASAB8ADQAcABMAEwLdAAoAHAAWABAAEgANABAAFQAOACEDyQAOABYAIAAWAA8AFgAZABYAIQAmAA0AHAATABMDyAAKABYAIAAWAA8AFgAZABYAIQAmAkUACAAWABQAGwASACEAIQASA8cACQAWABIAJAANACQAEgASABgDxgALABYAEgAkAA0AIAAhAB8AEgAOABoF5AAMABYAEgAkAA0AIAAWABEAEgAPAA4AHwPFAAoAFgASACQADQAeACIAFgAZACEDxAALABYAEgAkAA0AGgAcABEAIgAZABIDwwAJABYAEgAkAA0AGQAWACAAIQSMAAoAFgASACQADQAWABsADQAOAB8DwgANABYAEgAkAA0AFQASAA4AEQAZABYAGwASA8EACAAWABIAJAANABEADgAmAjwADAAWABIAJAANABAAHAAaAB0ADgAQACECOwAKABYAEgAkAA0AEAAcABoAEwAmAjsAEAAWABIAJAANABAAHAAaABMAHAAfACEADgAPABkAEgPAAAsAFgASACQADQAQABwAGQAiABoAGwO/AA0AFgASACQADQAQAA4AHwAcACIAIAASABkDvgAKABYAEgAkAA0ADgAfAB8ADgAmA70ACwAWABIAJAANAA4AFAASABsAEQAOAa8ADwAWABEAEgAcABQADgAaABIADQAOACAAIAASACEAWAAMABYAEQASABwAEAAOABoADQAcABMAEwBXAAgAFgARABIAHAAQAA4AGgXjAA4AFgARABIAHAANACAAEgAhACEAFgAbABQAIABWAA0AFgARABIAHAANABkAFgAPAB8ADgAfACYAfAALABYAEQASABwADQAZAA4ADwASABkAVgAQABYAEQASABwADQAQABwAGQAZABIAEAAhABYAHAAbAHsACgAWABEAEgAcAA0AEAAOABkAGQLcAAkAFgAPAB8ADgAhABYAHAAbBBgADgASAB8AIQAWABAADgAZAA0AIAAdABkAFgAhAV8AEgASAB8AIQAWABAADgAZAA0ADgAZABYAFAAbAA0AIQAcAB0BXgAVABIAHwAhABYAEAAOABkADQAOABkAFgAUABsADQAQABIAGwAhABIAHwFdABUAEgAfACEAFgAQAA4AGQANAA4AGQAWABQAGwANAA8AHAAhACEAHAAaA7wADQASAB8AFgATABYAEgARAA0AIgAgABIAHwXiAAgAEgAfABYAEwAWABIAEQAqAFYAZgCEAJgAvgDiAPwBDgEYASoBSAFyAYQBmAGyAbwBzAHoAgwCHgIoAjgCTAJUAloCdgKIApoCtgLWAuoC+gMGAyYDQANYA2QDbgN+A5IDrgPSBe0ABwAmACAAFgAkACYAFAXsAA4AHwAcABsAFAANABkAHAAQAA4AIQAWABwAGwFgAAkAHwAOAB0ADQAhABIAJQAhBJYAEgAcAB8AGAAgAB0ADgAQABIAIAANABwAIgAhABkAFgAbABIElAARABwAHwAYACAAHQAOABAAEgAgAA0AEwAWABkAGQASABEEFAAMABwAHwAYAA0AHAAiACEAGQAWABsAEgQTAAgAHAAfABgADQAcABMAEwPNAAQAHAAfABgF6wAIABYAGwASAA0ADwAOAB8BJQAOABYAEwAWAA0AIQASACEAFQASAB8AFgAbABQF6gAUABYAEwAWAA0AHQAfABwAIQASABAAIQASABEADQAgABIAIQAiAB0C9wAIABYAEwAWAA0AHAATABMBJAAJABYAEwAWAA0AGQAcABAAGAXpAAwAFgATABYADQAQAA4AGQAZABYAGwAUAu0ABAAWABMAFgETAAcAFgARABQAEgAhACAA7wANABUAEgAfABIADQAhABwADQAjABwAIQASBegAEQAVABIAEgAZABAAFQAOABYAHwANAB0AFgAQABgAIgAdAxYACAAVAA4AIQAgABUAHAAhBecABAASACAAIQDjAAcAEgASABgAEgAbABEAdAAJABIADwANAA4AIAAgABIAIQBdAAMAEgAPAuwAAgAQBI8ADQAPAA0AIQAkABYAFAAVABkAFgAUABUAIQJAAAgADwANACAAIgAbABsAJgSOAAgADwANACAAFQAOABEAEgJGAA0ADwANABYAHwAWABEAEgAgABAAEgAbACECPwAPAA8ADQAWABsAEAAOABsAEQASACAAEAASABsAIQI+AAkADwANABAAGQAcACIAEQAmAj0ABwAPAA0ADgAiACEAHADuAAUADgAjABIAIASNAA8ADgAhABIAHwATAA4AGQAZAA0AEAAVAA4AHwAhBeYADAAOACEAEgAfAA0AEQAOABoADgAUABID9QALAA4AIQAQABUADQAZAA4AIQASAB8BqwAFAA4AIQAQABUF5QAEAA4AIAAVACoABwAOAB8AGwAWABsAFAESAAkADgAZABkAHQAOAB0AEgAfA8wADQAOABkAGQASACEADQAhAB8ADgAjABIAGQPLABEADgAZABkAEgAhAA0AGgASABoADwASAB8AIAAVABYAHQPKAA8ADgAZABkAEgAhAA0AFAAWABMAIQAQAA4AHwARAAEABAPOABQAHAAiACEAIgAPABIADQAgABIADgAfABAAFQASABEADQATABwAHwADAAgAIgA0Ao0ADAAcABwAGgANABwAIgAhAA0AGgAOAB0D1AAIABwAHAAaAA0AHAAiACED0wAHABwAHAAaAA0AFgAbAAIABAAEAAwAAAAOABYACQAYACQAEgAmACcAHw==) format('truetype'); } .material-icons { font-family: 'Material Icons'; @@ -64,15 +64,15 @@ return new Za.prototype.init(a,b,c,d,e)}m.Tween=Za,Za.prototype={constructor:Za,init:function(a,b,c,d,e,f){this.elem=a,this.prop=c,this.easing=e||"swing",this.options=b,this.start=this.now=this.cur(),this.end=d,this.unit=f||(m.cssNumber[c]?"":"px")},cur:function(){var a=Za.propHooks[this.prop];return a&&a.get?a.get(this):Za.propHooks._default.get(this)},run:function(a){var b,c=Za.propHooks[this.prop];return this.options.duration?this.pos=b=m.easing[this.easing](a,this.options.duration*a,0,1,this.options.duration):this.pos=b=a,this.now=(this.end-this.start)*b+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),c&&c.set?c.set(this):Za.propHooks._default.set(this),this}},Za.prototype.init.prototype=Za.prototype,Za.propHooks={_default:{get:function(a){var b;return null==a.elem[a.prop]||a.elem.style&&null!=a.elem.style[a.prop]?(b=m.css(a.elem,a.prop,""),b&&"auto"!==b?b:0):a.elem[a.prop]},set:function(a){m.fx.step[a.prop]?m.fx.step[a.prop](a):a.elem.style&&(null!=a.elem.style[m.cssProps[a.prop]]||m.cssHooks[a.prop])?m.style(a.elem,a.prop,a.now+a.unit):a.elem[a.prop]=a.now}}},Za.propHooks.scrollTop=Za.propHooks.scrollLeft={set:function(a){a.elem.nodeType&&a.elem.parentNode&&(a.elem[a.prop]=a.now)}},m.easing={linear:function(a){return a},swing:function(a){return.5-Math.cos(a*Math.PI)/2}},m.fx=Za.prototype.init,m.fx.step={};var $a,_a,ab=/^(?:toggle|show|hide)$/,bb=new RegExp("^(?:([+-])=|)("+S+")([a-z%]*)$","i"),cb=/queueHooks$/,db=[ib],eb={"*":[function(a,b){var c=this.createTween(a,b),d=c.cur(),e=bb.exec(b),f=e&&e[3]||(m.cssNumber[a]?"":"px"),g=(m.cssNumber[a]||"px"!==f&&+d)&&bb.exec(m.css(c.elem,a)),h=1,i=20;if(g&&g[3]!==f){f=f||g[3],e=e||[],g=+d||1;do h=h||".5",g/=h,m.style(c.elem,a,g+f);while(h!==(h=c.cur()/d)&&1!==h&&--i)}return 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strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.7",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return e=a-d&&"bottom"},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return 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@@ -91,7 +95,7 @@
  • - + @@ -116,6 +120,12 @@

    Page not found (404)

+ + @@ -126,7 +136,7 @@

Page not found (404)

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 39c540d..6c895ea 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +
@@ -91,7 +95,7 @@
  • - + @@ -212,6 +216,12 @@

    NA

+ + @@ -222,7 +232,7 @@

NA

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/estparam_gene_celseq2.png b/docs/articles/estparam_gene_celseq2.png index 1687f4b..c0f45fc 100644 Binary files a/docs/articles/estparam_gene_celseq2.png and b/docs/articles/estparam_gene_celseq2.png differ diff --git a/docs/articles/estparam_spike_celseq2.png b/docs/articles/estparam_spike_celseq2.png index 2fdb757..879ce8c 100644 Binary files a/docs/articles/estparam_spike_celseq2.png and b/docs/articles/estparam_spike_celseq2.png differ diff --git a/docs/articles/evalderes_conditional_celseq2.png b/docs/articles/evalderes_conditional_celseq2.png index 3b1d1bb..5810907 100644 Binary files a/docs/articles/evalderes_conditional_celseq2.png and b/docs/articles/evalderes_conditional_celseq2.png differ diff --git a/docs/articles/evalderes_marginal_celseq2.png b/docs/articles/evalderes_marginal_celseq2.png index 5ee9cb9..0e504f9 100644 Binary files a/docs/articles/evalderes_marginal_celseq2.png and b/docs/articles/evalderes_marginal_celseq2.png differ diff --git a/docs/articles/evalrocres_liberal_celseq2.png b/docs/articles/evalrocres_liberal_celseq2.png index 38846d1..6f8c919 100644 Binary files a/docs/articles/evalrocres_liberal_celseq2.png and b/docs/articles/evalrocres_liberal_celseq2.png differ diff --git a/docs/articles/evalsimres_celseq2.png b/docs/articles/evalsimres_celseq2.png index beb3b4f..e35fe3d 100644 Binary files a/docs/articles/evalsimres_celseq2.png and b/docs/articles/evalsimres_celseq2.png differ diff --git a/docs/articles/evaltime_celseq2.png b/docs/articles/evaltime_celseq2.png index 0021834..4df0dc7 100644 Binary files a/docs/articles/evaltime_celseq2.png and b/docs/articles/evaltime_celseq2.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 242599f..a98ddfd 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +
@@ -91,7 +95,7 @@
@@ -130,7 +135,7 @@

All vignettes

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/powsimR.html b/docs/articles/powsimR.html index 0f03ded..f447f5b 100644 --- a/docs/articles/powsimR.html +++ b/docs/articles/powsimR.html @@ -6,19 +6,19 @@ powsimR • powsimR - - - - + + + + + - - + - +
-

powsimR

+

powsimR

Source: vignettes/powsimR.Rmd @@ -97,75 +97,76 @@

Installation Guide

For the installation, the R package devtools is needed.

-
install.packages('devtools')
-library(devtools)
+
install.packages('devtools')
+library(devtools)

powsimR has a number of dependencies that need to be installed before hand (see also the README file on github). I recommend to install first the dependencies manually and then powsimR. If you plan to use MAGIC for imputation, then please follow their instruction to install the python implementation before installing powsimR.

-
ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){
-  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
-  # new.pkg <- pkg
-  if (length(new.pkg)) {
-    if(repository=='CRAN') {
-      install.packages(new.pkg, dependencies = TRUE)
-    }
-    if(repository=='Bioconductor') {
-      if(strsplit(version[['version.string']], ' ')[[1]][3] > "3.6.0"){
-        if (!requireNamespace("BiocManager")){
-            install.packages("BiocManager")
-        }
-            BiocManager::install(new.pkg, dependencies=TRUE, ask=FALSE)
-      }
-      if(strsplit(version[['version.string']], ' ')[[1]][3] < "3.6.0"){
-        stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher."))
-      }
-    }
-    if(repository=='github') {
-      devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE, dependencies=TRUE)
-    }
-  }
-}
-
-# CRAN PACKAGES
-cranpackages <- c("broom", "cobs", "cowplot", 
-                  "data.table", "doParallel", "dplyr", "DrImpute", 
-                  "fastICA", "fitdistrplus", "foreach", "future",
-                  "gamlss.dist", "ggplot2", "ggpubr", "grDevices",
-                  "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix",
-                  "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", 
-                  "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl",
-                  "reshape2", "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", 
-                  "stats", "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
-ipak(cranpackages, repository='CRAN')
-
-# BIOCONDUCTOR
-biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", 
-                  "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA",
-                  "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", 
-                  "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", 
-                  "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
-ipak(biocpackages, repository='Bioconductor')
-
-# GITHUB
-githubpackages <- c('nghiavtr/BPSC', 'cz-ye/DECENT', 
-                    'mohuangx/SAVER', 'statOmics/zingeR')
-ipak(githubpackages, repository = 'github')
+
ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){
+  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
+  # new.pkg <- pkg
+  if (length(new.pkg)) {
+    if(repository=='CRAN') {
+      install.packages(new.pkg, dependencies = TRUE)
+    }
+    if(repository=='Bioconductor') {
+      if(strsplit(version[['version.string']], ' ')[[1]][3] > "3.6.0"){
+        if (!requireNamespace("BiocManager")){
+            install.packages("BiocManager")
+        }
+            BiocManager::install(new.pkg, dependencies=TRUE, ask=FALSE)
+      }
+      if(strsplit(version[['version.string']], ' ')[[1]][3] < "3.6.0"){
+        stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher."))
+      }
+    }
+    if(repository=='github') {
+      devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE, dependencies=TRUE)
+    }
+  }
+}
+
+# CRAN PACKAGES
+cranpackages <- c("broom", "cobs", "cowplot",
+                  "data.table", "doParallel", "dplyr", "DrImpute",
+                  "fastICA", "fitdistrplus", "foreach", "future",
+                  "gamlss.dist", "ggplot2", "ggpubr", "ggstance", "grDevices",
+                  "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix",
+                  "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir",
+                  "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl",
+                  "reshape2", "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform",
+                  "stats", "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
+ipak(cranpackages, repository='CRAN')
+
+# BIOCONDUCTOR
+biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel",
+                  "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA",
+                  "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq",
+                  "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm",
+                  "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
+ipak(biocpackages, repository='Bioconductor')
+
+# GITHUB
+githubpackages <- c('cz-ye/DECENT', 'nghiavtr/BPSC',
+                    'mohuangx/SAVER', 'statOmics/zingeR',
+                    'Vivianstats/scImpute')
+ipak(githubpackages, repository = 'github')

To check whether all dependencies are installed, you can run the following lines:

-
powsimRdeps <- data.frame(Package = c(cranpackages, 
-                                      biocpackages, 
-                                      sapply(strsplit(githubpackages, "/"), "[[", 2)), 
-                          stringsAsFactors = F)
-
-ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F)
-
-ip.check <- cbind(powsimRdeps, 
-                  Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"]) 
-
-table(is.na(ip.check$Version))  # all should be FALSE
+
powsimRdeps <- data.frame(Package = c(cranpackages,
+                                      biocpackages,
+                                      sapply(strsplit(githubpackages, "/"), "[[", 2)),
+                          stringsAsFactors = F)
+
+ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F)
+
+ip.check <- cbind(powsimRdeps,
+                  Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"])
+
+table(is.na(ip.check$Version))  # all should be FALSE

After installing the dependencies, powsimR can be installed by using devtools as well.

-
devtools::install_github("bvieth/powsimR", build_vignettes = TRUE, dependencies = FALSE)
-library("powsimR")
+
devtools::install_github("bvieth/powsimR", build_vignettes = TRUE, dependencies = FALSE)
+library("powsimR")

Some users have experienced issues installing powsimR due to vignette compilation errors. If that is the case, you can leave out building the vignette (by setting build_vignettes to FALSE).

Alternative, you can try to install powsimR and its dependencies directly using devtools:

-
devtools::install_github("bvieth/powsimR")
+
devtools::install_github("bvieth/powsimR")

@@ -199,33 +200,35 @@

The user can choose between multiple normalisation methods (see Details section of estimateParam). Furthermore, a number of methods are group sensitive (e.g. batch labels can be provided in SCnorm). For single cell data, it is important to filter out genes and/or cells that are outliers or deemed undetected (see options SampleFilter and GeneFilter).

The estimates, sequencing depth and normalisation factors are plotted with plotParam.

With the following command, we estimate and plot the parameters for the mouse embryonic stem cells profiled with CELseq2 (Ziegenhain et al. 2017) (Figure @ref(fig:geneparamsplot)). Firstly, we can examine the quality of the data set using the metrics in Figure @ref(fig:geneparamsplot) A. But most importantly, we see that the variability (i.e. dispersion) and dropout rates are high (Figure @ref(fig:geneparamsplot) B). Furthermore, the dispersion depends on the mean and does not level off with higher mean values compared to bulk data (Figure @ref(fig:geneparamsplot) D).

- +
data("CELseq2_Gene_UMI_Counts")
+batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1)
+Batches <- data.frame(Batch = batch,
+                      stringsAsFactors = FALSE,
+                      row.names = colnames(CELseq2_Gene_UMI_Counts))
+data("GeneLengths_mm10")
+
+# estimation
+estparam_gene <- estimateParam(countData = CELseq2_Gene_UMI_Counts,
+                          readData = NULL,
+                          batchData = Batches,
+                          spikeData = NULL,
+                          spikeInfo = NULL,
+                          Lengths = GeneLengths, MeanFragLengths = NULL,
+                          RNAseq = 'singlecell', Protocol = 'UMI',
+                          Distribution = 'NB', Normalisation = "scran",
+                          GeneFilter = 0.1, SampleFilter = 3,
+                          sigma = 1.96, NCores = NULL, verbose = TRUE)
+
+# plotting
+plotParam(estParamRes = estparam_gene, Annot = T)
Estimated parameters for CEL-seq2 libraries from Ziegenhain et al. 2017. A) Quality Control Metrics: Sequencing depth; Library size factors with median (black line) for the filtered data set; Detected genes; Ratio of gene to spike-in counts (if spike-ins were provided). Outliers are marked in red. B) Marginal Distribution of gene mean, dispersion and dropout rate per estimation set. C) Number of genes and samples per estimation set. Provided by the user; Detected = number of genes and samples with at least one count; All = number of genes for which mean, dispersion and dropout could be estimated using non-outlying samples. -Filtered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene.

+Filtered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene." width="755">

Estimated parameters for CEL-seq2 libraries from Ziegenhain et al. 2017. A) Quality Control Metrics: Sequencing depth; Library size factors with median (black line) for the filtered data set; Detected genes; Ratio of gene to spike-in counts (if spike-ins were provided). Outliers are marked in red. B) Marginal Distribution of gene mean, dispersion and dropout rate per estimation set. C) Number of genes and samples per estimation set. Provided by the user; Detected = number of genes and samples with at least one count; All = number of genes for which mean, dispersion and dropout could be estimated using non-outlying samples. Filtered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene.

We have implemented a count simulation framework assuming either a negative binomial distribution or a zero-inflated negative binomial distribution. To predict the dispersion given a random draw of mean expression value observed, we apply a locally weighted polynomial regression fit (solid orange line in Figure @ref(fig:geneparamsplot) D). To capture the variability of dispersion estimates observed, a local variability prediction band is applied (dashed orange lines in Figure @ref(fig:geneparamsplot) D).

-

For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5% dropout probability determined by a decrease constrained B-splines regresssion of dropout rate against mean expression (cobs).

+

For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5% dropout probability determined by a decrease constrained B-splines regression of dropout rate against mean expression (cobs).

For the zero-inflated negative binomial distribution, the mean-dispersion relation is similarly estimated, but based on positive read counts. Furthermore, the dropouts are also predicted based on a locally weighted polynomial regression fit between mean and dropouts. Of note, this fit is done separately for amplified and non-amplified transcripts and similar proportions of genes as observed are also generated in the simulations (Ziegenhain et al. 2017).

We and others have found that the negative binomial (NB) distribution is particularly suited for scRNA-seq protocols with UMIs (e.g. SCRB-Seq, Drop-Seq, 10XGenomics) (Svensson 2020) whereas non-UMI methods like Smartseq2 show a considerable proportion of genes with zero-inflated count distributions and the zero-inflated negative binomial (ZINB) is then more appropiate (Vieth et al. 2017). In addition, we recommend the NB distribution for bulk RNA-seq and only recommend the option ‘singlecell’ in estimateParam() and specialised single cell tools for e.g. normalisation if the data is extremely sparse.

@@ -240,22 +243,23 @@

\end{equation}\]

The number of molecules \(Y_{j}\) for each ERCC spike-in species is the product of the molar concentration \(c_{j}\) (attomoles per microlitre), the dilution factor \(1/D\), the volume \(V\) (nanolitre), Avogadros’ constant (\(6.02214129*10^{23}\)) and conversion factors between unit scales.

With the following command, we estimate the parameters for the spike-ins added during library preparation of mouse embryonic stem cells cultured in standard 2i+lif medium (Ziegenhain et al. 2017). Descriptive plots of the spike-ins can be drawn with plotSpike (Figure @ref(fig:spikeplot)).

-
data("CELseq2_SpikeIns_UMI_Counts")
-data("CELseq2_SpikeInfo")
-Batches = data.frame(Batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1),
-                       stringsAsFactors = F,
-                       row.names = colnames(CELseq2_SpikeInfo))
-# estimation
-estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts,
-spikeInfo = CELseq2_SpikeInfo,
-MeanFragLength = NULL,
-batchData = Batches,
-Normalisation = 'depth')
-
-# plotting
-plotSpike(estparam_spike)
+
data("CELseq2_SpikeIns_UMI_Counts")
+data("CELseq2_SpikeInfo")
+batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1)
+Batches = data.frame(Batch = batch,
+                       stringsAsFactors = F,
+                       row.names = colnames(CELseq2_SpikeIns_UMI_Counts))
+# estimation
+estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts,
+spikeInfo = CELseq2_SpikeInfo,
+MeanFragLength = NULL,
+batchData = Batches,
+Normalisation = 'depth')
+
+# plotting
+plotSpike(estparam_spike)
-Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells.

+Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells.

Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells.

@@ -273,20 +277,20 @@

Examples of Log Fold Changes following a gamma, normal and uniform distribution.

-

The number of replicates per group (n1 and n2) are also defined in Setup which can be unbalanced. These numbers represent either biological replicates for bulk or number of cells for single cell RNA-seq. In addition, the user can choose to include the simulation of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation.

-

The distribution estimates and these settings are then combined to one object.

+

The number of replicates per group (n1 and n2) are also defined in Setup which can be unbalanced. In addition the user can choose to include the simulaiton of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation.

+

The distribution estimates and these settings are then combined into one object. The following command sets up simulations with 10,000 genes, 20% genes being DE, log fold change sampled from a narrow gamma distribution and parameter estimates based on Smartseq2 libraries in Ziegenhain dataset. Please note that I decreased the number of simulations for this illustration.

The following command sets up simulations with 10,000 genes, 5% genes being DE, log fold change sample from a narrow gamma distribution and parameter estimates based on CEL-seq2 libraries in Ziegenhain dataset. The spike-ins are only necessary if we wish to apply imputation, normalisation and/or DE-tools that can utilize spike-ins, see the Details section of estimateParam() and simulateDE().

- +
# define log fold change
+p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2)
+# set up simulations
+setupres <- Setup(ngenes = 10000, nsims = 25,
+                  p.DE = 0.05, pLFC = p.lfc,
+                  n1 = c(48, 96, 384, 800), n2 = c(48, 96, 384, 800),
+                  Thinning = NULL, LibSize = 'equal',
+                  estParamRes = estparam_gene,
+                  estSpikeRes = NULL,
+                  DropGenes = TRUE,
+                  setup.seed = 5299, verbose = TRUE)

@@ -299,15 +303,11 @@

Normalisation: The normalisation method. The user can choose between 10 methods in total. 5 developed for bulk, 5 developed for single cells (see the Details section of simulateDE).

There are also additional options: Whether to apply a prefiltering or imputation step prior to normalisation; whether spike-in information should be used (if available). For more information, please consult the Details section of simulateDE.

- -

-
-

-Simulating gene expression of cell mixtures

+
simres <- simulateDE(SetupRes = setupres,
+                     Prefilter = NULL, Imputation = NULL,
+                     Normalisation = 'scran',
+                     DEmethod = "limma-trend", DEFilter = FALSE,
+                     NCores = NULL, verbose = TRUE)
@@ -319,46 +319,48 @@

Differential Expression

The results of differential expression simulation are evaluated with evaluateDE. We have separated the evaluation from DE detection to allow the user to evaluate power in a comprehensive way as advocated by (Wu, Wang, and Wu 2015). In this function, the proportions and error rates are estimated. The rates can be stratified by mean, dispersion, dropout or log fold change. Furthermore, the user can choose between different multiple testing correction methods (see p.adjust.methods(), ihw() in and qvalue() in ). Also, the genes can be filtered by mean, dispersion or dropout. To define biologically interesting genes, a cutoff for the log fold change with delta can be set.

With the following command we evaluate the marginal TPR and FDR conditional on the mean expression:

- +
evalderes = evaluateDE(simRes = simres,
+                     alpha.type = 'adjusted',
+                     MTC = 'BH',
+                     alpha.nominal = 0.1,
+                     stratify.by = 'mean',
+                     filter.by = 'none',
+                     strata.filtered = 1,
+                     target.by = 'lfc',
+                     delta = 0)

The results of the evaluation can be plotted with plotEvalDE().

+
plotEvalDE(evalRes = evalderes, rate = 'marginal', quick = TRUE, Annot = TRUE)
+plotEvalDE(evalRes = evalderes, rate = 'conditional', quick = TRUE, Annot = TRUE)
  • rate: The user can choose to plot the ‘marginal’ or ‘stratified’ error rates. The number of genes per stratum are also summarised.
  • quick: If this is set to TRUE then only the TPR and FDR will be plotted.
-

The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure @ref(fig:evaldeplot1) and @ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size Figure @ref(fig:evaldeplot1). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure @ref(fig:evaldeplot2)). In addition, the detection of true positives with small average expression needs far more replicates per group.

+

The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure @ref(fig:evaldeplot1) and @ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size (Figure @ref(fig:evaldeplot1)). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure @ref(fig:evaldeplot2)). In addition, the detection of true positives with small mean expression needs far more replicates per group.

-Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error).

-Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error). +Marginal Error Rates. (A) Marginal FDR and TPR per sample size comparison. (B) Marginal FDR and TPR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error).

+Marginal Error Rates. (A) Marginal FDR and TPR per sample size comparison. (B) Marginal FDR and TPR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error).

-Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum.

-Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum. +Stratified Error Rates. (A) Conditional FDR and TPR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum.

+Stratified Error Rates. (A) Conditional FDR and TPR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum.

In addition to the classical evaluation of power analyses, powsimR also includes the option to evaluate the simulations using summary metrics such as the Receiver-Operator-Characteristic (ROC) Curve as well as accuracy, F1 score and Matthews Correlation Coefficient.

With the following command we calculate these metrics with evaluateROC() and plot them with plotEvalROC(). The classical ROC curve as well as precision-recall curve, which is more appropiate for imbalanced number of true positives and negatives, can be helpful to identify the optimal sample size setup (Figure @ref(fig:evalrocresplot) A and B). Most importantly, we can check if the FDR is controlled at the chosen nominal level (Figure @ref(fig:evalrocresplot) C). If that is not the case, then the chosen pipeline, particularly the normalisation and DE testing, might be an issue (Vieth et al. 2019).

-
evalrocres = evaluateROC(simRes = simres,
-                         alpha.type="adjusted",
-                         MTC='BH',
-                         alpha.nominal = 0.1)
-
-plotEvalROC(evalRes = evalrocres, cutoff = "liberal")
+
evalrocres = evaluateROC(simRes = simres,
+                         alpha.type="adjusted",
+                         MTC='BH',
+                         alpha.nominal = 0.1)
+
+plotEvalROC(evalRes = evalrocres, cutoff = "liberal")
A) Receiver-Operator-Characteristics (ROC) Curve per sample size setup. B) Precision-Recall (PR) Curve per sample size setup. C) TPR versus observed FDR per sample size setup. The filling of the point indicates whether FDR is controlled at the chosen nominal level. -D) Summary Statistics per sample size setup rounded to two digits.

+D) Summary Statistics per sample size setup rounded to two digits." width="570">

  1. Receiver-Operator-Characteristics (ROC) Curve per sample size setup.
    2. @@ -376,20 +378,20 @@

    Pipeline

    We can also check in how far our pipeline choices are affecting our ability to correctly identify differential expression in evaluateSim().

    With the following command we calculate these metrics. Of note here is particular in how far our estimated log fold changes deviate from the simulated fold changes as well as how the normalisation is able to ensure comparability across sample groups (Figure @(fig:evalsimresplot)).

    -
    evalsimres = evaluateSim(simRes = simres)
-
-plotEvalSim(evalRes = evalsimres)
-
-plotTime(evalRes = evalsimres)
    +
    evalsimres = evaluateSim(simRes = simres)
+
+plotEvalSim(evalRes = evalsimres)
+
+plotTime(evalRes = evalsimres)
    Pipeline Evaluation. A) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and robust Root Mean Squared Error (rRMSE) for the estimated log fold changes of all (ALL), differentially expressed (DE) and equally expressed (EE) genes compared to the true log fold changes. B) Median absolute deviation (MAD) and robust Root Mean Squared Error (rRMSE) between estimated and simulated size factors. -C) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error.

    +C) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error." width="670">

    Pipeline Evaluation. A) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and robust Root Mean Squared Error (rRMSE) for the estimated log fold changes of all (ALL), differentially expressed (DE) and equally expressed (EE) genes compared to the true log fold changes. B) Median absolute deviation (MAD) and robust Root Mean Squared Error (rRMSE) between estimated and simulated size factors. C) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error.

    -Computational Run Time in seconds per simulateDE() pipeline step.

    +Computational Run Time in seconds per simulateDE() pipeline step.

    Computational Run Time in seconds per simulateDE() pipeline step.

    @@ -411,25 +413,25 @@

    Access to raw read counts stored in online data bases

    We have provided a number of exemplary single cell RNA-seq data sets for parameter estimation. Nevertheless, you might not find a data set that matches your own experimental setup. In those cases, we recommend to check online repositories for a suitable data set. Below you can find an example script to get count tables from recount2 (Collado-Torres et al. 2017). For a single cell RNA-seq data base, see conquer (Soneson and Robinson 2018).

    As before, the user can then estimate the negative binomial parameters with estimateParam(), view these estimates with plotParam() and use it as an input for Setup().

    - +
    # Install and load the R package
+BiocManager::install("recount")
+library('recount')
+
+# Download the data set
+url <- download_study('SRP060416')
+
+# Load the data
+load(file.path('SRP060416', 'rse_gene.Rdata'))
+
+# count table
+cnts <- assay(rse_gene)
+# sample annotation
+sample.info <- data.frame(colData(rse_gene)@listData,
+                          stringsAsFactors=F)
+# gene annotation
+gene.info <- data.frame(GeneID=rowData(rse_gene)@listData$gene_id,
+                        GeneLength=rowData(rse_gene)@listData$bp_length,
+                        stringsAsFactors=F)
@@ -439,17 +441,17 @@

The evaluation is then plotted with the function plotEvalDist() which summarizes the best fitting distribution per gene based on goodness-of-fit statistics (Chi-square test), Akaike Information Criterium, comparing observed dropouts with zero count prediction of the models and comparing the model fitness with Likelihood Ratio Test and Vuong Test.

As noted by other developers, goodness-of-fit tests are not an objective tool and heavily depend on sample sizes (Delignette-Muller and Dutang 2015). A graphical evaluation of the fitted distribution is considered the most appropiate way but for high-throughput sequencing this is an unrealistic recommendation. Bulk RNA-seq experiments are usually conducted with a small number of samples. We therefore recommend to rely on the goodness-of-fit validation by (Mi, Di, and Schafer 2015).

With the following command, we determine and plot the fitting for the embryonic stem cells cultured in standard 2i+lif medium profiled with the Smartseq2 protocol (Ziegenhain et al. 2017). Note that the results shown in Figure @ref(fig:evaldistplot) will differ from the executed command since I reduced the data set and evaluate the fitting only for a fraction of genes.

- +
data("SmartSeq2_Gene_Read_Counts")
+evalDistRes <- evaluateDist(countData = SmartSeq2_Gene_Read_Counts,
+                            batchData = NULL,
+                            spikeData = NULL, spikeInfo = NULL,
+                            Lengths = NULL, MeanFragLengths = NULL,
+                            RNAseq = "singlecell", Protocol = "UMI",
+                            Normalisation = "scran",
+                            GeneFilter = 0.1, SampleFilter = 3,
+                            FracGenes = 0.1,
+                            verbose = TRUE)
+plotEvalDist(evalDistRes)
Distribution Evaluation. A) Goodness-of-fit of the model assessed with a Chi-Square Test based on residual deviance and degrees of freedom. B) Akaike Information Criterion per gene: Model with the lowest AIC. Model with the lowest AIC and passed goodness-of-fit statistic test. C) Observed versus predicted dropouts per model and gene plotted without outliers. D) Model Assessment based on LRT for nested models and Vuong test for nonnested models.

Distribution Evaluation. A) Goodness-of-fit of the model assessed with a Chi-Square Test based on residual deviance and degrees of freedom. B) Akaike Information Criterion per gene: Model with the lowest AIC. Model with the lowest AIC and passed goodness-of-fit statistic test. C) Observed versus predicted dropouts per model and gene plotted without outliers. D) Model Assessment based on LRT for nested models and Vuong test for nonnested models. @@ -459,55 +461,62 @@

Simulation Settings

+
+

+Replicates with partial phenotypic differences

+

By default, all replicates per group express the phenotypic differences in gene expression defined by pLFC. If the user wishes for a more varied expression profile, i.e. only a proportion of replicates per group with a difference in expression, these patterns can be defined by changing p.G (see Details section of Setup()).

+

Include differences in library sizes

-

By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function. See the Details section of Setup().

+

By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function (see Details section of Setup()).

Downsample simulated count matrices by binomial thinning

-

There is the additional option to downsample simulated count matrices with the option Thinning in Setup in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as countData in estimateParam() and the raw read count matrix without UMI deduplication as readData. For more information, please check out the help page of estimateParam().

+

There is the additional option to downsample simulated count matrices with the option Thinning in Setup() in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as countData in estimateParam() and the raw read count matrix without UMI deduplication as readData. For more information, please check out the help page of estimateParam().

Below you can find an example script using the CELSeq2 libraries from Ziegenhain et al.:

- +
data("CELseq2_Gene_UMI_Counts")
+data("CELseq2_Gene_Read_Counts")
+batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1)
+Batches <- data.frame(Batch = batch,
+                      stringsAsFactors = FALSE,
+                      row.names = colnames(CELseq2_Gene_UMI_Counts))
+data("GeneLengths_mm10")
+
+# estimation
+estparam_gene <- estimateParam(countData = CELseq2_Gene_UMI_Counts,
+                               readData = CELseq2_Gene_Read_Counts,
+                               batchData = Batches,
+                               spikeData = NULL,
+                               spikeInfo = NULL,
+                               Lengths = GeneLengths, MeanFragLengths = NULL,
+                               RNAseq = 'singlecell', Protocol = 'UMI',
+                               Distribution = 'NB', Normalisation = "scran",
+                               GeneFilter = 0.1, SampleFilter = 3,
+                               sigma = 1.96, NCores = NULL, verbose = TRUE)
+
+plotParam(estParamRes = estparam_gene)
+
+# define log fold change
+p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2)
+
+# set up simulations
+setupres <- Setup(ngenes = 10000, nsims = 25,
+                  p.DE = 0.1, pLFC = p.lfc,
+                  n1 = c(500, 1000), n2 = c(500, 1000),
+                  Thinning = c(1, 0.5), LibSize = 'given',
+                  estParamRes = estparam_gene,
+                  estSpikeRes = NULL,
+                  DropGenes = TRUE,
+                  setup.seed = 5299, verbose = TRUE)
+
+# run simulations
+simres <- simulateDE(SetupRes = setupres,
+                     Prefilter = "FreqFilter", Imputation = NULL,
+                     Normalisation = 'scran',
+                     DEmethod = "limma-trend", DEFilter = FALSE,
+                     NCores = NULL, verbose = TRUE)

@@ -515,7 +524,7 @@

Session info

Here is the output of sessionInfo() on the system on which this document was compiled:

-
R version 3.6.2 (2019-12-12)
+
R version 4.0.1 (2020-06-06)
 Platform: x86_64-pc-linux-gnu (64-bit)
 Running under: Ubuntu 18.04.4 LTS
 
@@ -535,26 +544,27 @@ 

 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] qvalue_2.18.0    IHW_1.14.0       rmdformats_0.3.6 knitr_1.28      
+[1] qvalue_2.20.0    IHW_1.16.0       rmdformats_0.3.7 knitr_1.28      
 
 loaded via a namespace (and not attached):
- [1] Rcpp_1.0.3          plyr_1.8.5          pillar_1.4.3       
- [4] compiler_3.6.2      formatR_1.7         highr_0.8          
- [7] tools_3.6.2         digest_0.6.23       tibble_2.1.3       
-[10] evaluate_0.14       memoise_1.1.0       lifecycle_0.1.0    
+ [1] Rcpp_1.0.4.6        plyr_1.8.6          pillar_1.4.4       
+ [4] compiler_4.0.1      formatR_1.7         highr_0.8          
+ [7] tools_4.0.1         digest_0.6.25       evaluate_0.14      
+[10] memoise_1.1.0       lifecycle_0.2.0     tibble_3.0.1       
 [13] gtable_0.3.0        pkgconfig_2.0.3     png_0.1-7          
-[16] rlang_0.4.4         lpsymphony_1.14.0   rstudioapi_0.11    
-[19] yaml_2.2.1          parallel_3.6.2      pkgdown_1.4.1      
-[22] xfun_0.12           dplyr_0.8.4         stringr_1.4.0      
-[25] desc_1.2.0          fs_1.3.1            tidyselect_1.0.0   
-[28] rprojroot_1.3-2     grid_3.6.2          glue_1.3.1         
-[31] R6_2.4.1            fdrtool_1.2.15      rmarkdown_2.1      
-[34] bookdown_0.17       reshape2_1.4.3      purrr_0.3.3        
-[37] ggplot2_3.2.1       magrittr_1.5        splines_3.6.2      
-[40] backports_1.1.5     scales_1.1.0        htmltools_0.4.0    
-[43] MASS_7.3-51.5       BiocGenerics_0.32.0 assertthat_0.2.1   
-[46] colorspace_1.4-1    stringi_1.4.5       lazyeval_0.2.2     
-[49] munsell_0.5.0       slam_0.1-47         crayon_1.3.4       

+[16] rlang_0.4.6         lpsymphony_1.16.0   rstudioapi_0.11    
+[19] yaml_2.2.1          parallel_4.0.1      pkgdown_1.5.1      
+[22] xfun_0.14           dplyr_1.0.0         stringr_1.4.0      
+[25] generics_0.0.2      vctrs_0.3.1         desc_1.2.0         
+[28] fs_1.4.1            tidyselect_1.1.0    rprojroot_1.3-2    
+[31] grid_4.0.1          glue_1.4.1          R6_2.4.1           
+[34] fdrtool_1.2.15      rmarkdown_2.2       bookdown_0.19      
+[37] reshape2_1.4.4      purrr_0.3.4         ggplot2_3.3.1      
+[40] magrittr_1.5        splines_4.0.1       ellipsis_0.3.1     
+[43] backports_1.1.8     scales_1.1.1        htmltools_0.5.0    
+[46] MASS_7.3-51.6       BiocGenerics_0.34.0 assertthat_0.2.1   
+[49] colorspace_1.4-1    stringi_1.4.6       munsell_0.5.0      
+[52] slam_0.1-47         crayon_1.3.4

@@ -603,50 +613,11 @@

- @@ -657,7 +628,7 @@

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/authors.html b/docs/authors.html index b577b4f..367afcd 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +
@@ -91,7 +95,7 @@
  • - + @@ -116,7 +120,7 @@

    Citation

    Vieth B, Ziegenhain C, Parekh S, Enard W, Hellmann I (2017). “powsimR: Power analysis for bulk and single cell RNA-seq experiments.” Bioinformatics, 33(21), 3486-88. -doi: 10.1093/bioinformatics/btx435, R package version 1.2.0, https://github.com/bvieth/powsimR. +doi: 10.1093/bioinformatics/btx435, R package version 1.2.2, https://github.com/bvieth/powsimR. 

    @Article{,
   title = {powsimR: Power analysis for bulk and single cell RNA-seq experiments},
@@ -127,7 +131,7 @@ 
    Citation
    
   volume = {33},
   number = {21},
   pages = {3486-88},
-  note = {R package version 1.2.0},
+  note = {R package version 1.2.2},
   url = {https://github.com/bvieth/powsimR},
 }
    @@ -154,7 +158,7 @@

    Authors

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('
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    `, then `

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    Please also consult my Github Page of powsimR made with pkgdown!

    -
    +

    -Installation Guide

    +:arrow_double_down: Installation Guide

    For the installation, the R package devtools is needed.

    -
    install.packages("devtools")
-library(devtools)
    +
    install.packages("devtools")
+library(devtools)

    I recommend to install first the dependencies manually and then powsimR. If you plan to use MAGIC for imputation, then please follow their instruction to install the python implementation before installing powsimR.

    -
    ipak <- function(pkg, repository = c("CRAN", "Bioconductor", "github")) {
-    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
-    # new.pkg <- pkg
-    if (length(new.pkg)) {
-        if (repository == "CRAN") {
-            install.packages(new.pkg, dependencies = TRUE)
-        }
-        if (repository == "Bioconductor") {
-            if (strsplit(version[["version.string"]], " ")[[1]][3] > "3.6.0") {
-                if (!requireNamespace("BiocManager")) {
-                  install.packages("BiocManager")
-                }
-                BiocManager::install(new.pkg, dependencies = TRUE, ask = FALSE)
-            }
-            if (strsplit(version[["version.string"]], " ")[[1]][3] < "3.6.0") {
-                stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher."))
-            }
-        }
-        if (repository == "github") {
-            devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE, 
-                dependencies = TRUE)
-        }
-    }
-}
-
-# CRAN PACKAGES
-cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr", 
-    "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", "gamlss.dist", "ggplot2", 
-    "ggpubr", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", 
-    "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", 
-    "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", "reshape2", "Rmagic", 
-    "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", "stats", "tibble", 
-    "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
-ipak(cranpackages, repository = "CRAN")
-
-# BIOCONDUCTOR
-biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", "DEDS", "DESeq2", 
-    "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq", 
-    "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran", 
-    "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
-ipak(biocpackages, repository = "Bioconductor")
-
-# GITHUB
-githubpackages <- c("nghiavtr/BPSC", "cz-ye/DECENT", "mohuangx/SAVER", "statOmics/zingeR")
-ipak(githubpackages, repository = "github")
    +
    ipak <- function(pkg, repository = c("CRAN", "Bioconductor", "github")) {
+    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
+    # new.pkg <- pkg
+    if (length(new.pkg)) {
+        if (repository == "CRAN") {
+            install.packages(new.pkg, dependencies = TRUE)
+        }
+        if (repository == "Bioconductor") {
+            if (strsplit(version[["version.string"]], " ")[[1]][3] > "4.0.0") {
+                if (!requireNamespace("BiocManager")) {
+                  install.packages("BiocManager")
+                }
+                BiocManager::install(new.pkg, dependencies = TRUE, ask = FALSE)
+            }
+            if (strsplit(version[["version.string"]], " ")[[1]][3] < "3.6.0") {
+                stop(message("powsimR depends on packages and functions that are only available in R 4.0.0 and higher."))
+            }
+        }
+        if (repository == "github") {
+            devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE,
+                dependencies = TRUE)
+        }
+    }
+}
+
+# CRAN PACKAGES
+cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr",
+    "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", "gamlss.dist", "ggplot2",
+    "ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr",
+    "MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments",
+    "msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", "reshape2",
+    "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", "stats",
+    "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
+ipak(cranpackages, repository = "CRAN")
+
+# BIOCONDUCTOR
+biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", "DESeq2",
+    "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq",
+    "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran",
+    "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
+ipak(biocpackages, repository = "Bioconductor")
+
+# GITHUB
+githubpackages <- c("cz-ye/DECENT", "nghiavtr/BPSC", "mohuangx/SAVER", "statOmics/zingeR",
+    "Vivianstats/scImpute")
+ipak(githubpackages, repository = "github")

    To check whether all dependencies are installed, you can run the following lines:

    -
    
-powsimRdeps <- data.frame(Package = c(cranpackages, 
-                                      biocpackages, 
-                                      sapply(strsplit(githubpackages, "/"), "[[", 2)), 
-                          stringsAsFactors = F)
-
-ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F)
-
-ip.check <- cbind(powsimRdeps, 
-                  Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"]) 
-
-table(is.na(ip.check$Version))  # all should be FALSE
    +
    +powsimRdeps <- data.frame(Package = c(cranpackages,
+                                      biocpackages,
+                                      sapply(strsplit(githubpackages, "/"), "[[", 2)),
+                          stringsAsFactors = F)
+
+ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F)
+
+ip.check <- cbind(powsimRdeps,
+                  Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"])
+
+table(is.na(ip.check$Version))  # all should be FALSE

    After installing the dependencies, powsimR can be installed by using devtools as well.

    -
    devtools::install_github("bvieth/powsimR", build_vignettes = TRUE, dependencies = FALSE)
-library("powsimR")
    +
    devtools::install_github("bvieth/powsimR", build_vignettes = TRUE, dependencies = FALSE)
+library("powsimR")

    Alternative, you can try to install powsimR and its dependencies directly using devtools:

    -
    devtools::install_github("bvieth/powsimR")
    +
    devtools::install_github("bvieth/powsimR")

    -User Guide

    +📖 User Guide

    For examples and tips on using the package, please consult the vignette after successful installation by

    -
    browseVignettes("powsimR")
    +
    browseVignettes("powsimR")

    Some users have experienced issues installing powsimR due to vignette compilation errors. If that is the case, you can leave out building the vignette (by setting build_vignettes to FALSE) and read it on my Github Page of powsimR or download it as a html file here.

    @@ -175,201 +180,210 @@

    -Citation

    +📜 Citation

    Please use the following entry for citing powsimR.

    -
    citation("powsimR")
    +
    citation("powsimR")

    powsimR is published in Bioinformatics. A preprint paper is also on bioRxiv.

    -
    +

    -Notes

    -

    Please send bug reports and feature requests by opening a new issue on this page.

    +:incoming_envelope: Notes +

    Please send bug reports and feature requests by opening a new issue on this page. I try to keep up to date with new developments / changes of methods implemented in powsimR, but if you encounter run errors while using a certain tool (e.g. for imputation), then I appreciate if you can post this as an issue.

    -R Session Info

    -
    library(powsimR)
-#> Loading required package: gamlss.dist
-#> Loading required package: MASS
-#> Warning: replacing previous import 'DECENT::lrTest' by 'MAST::lrTest' when
-#> loading 'powsimR'
-#> Warning: replacing previous import 'penalized::predict' by 'stats::predict' when
-#> loading 'powsimR'
-#> Warning: replacing previous import 'zinbwave::glmWeightedF' by
-#> 'zingeR::glmWeightedF' when loading 'powsimR'
-sessionInfo()
-#> R version 3.6.2 (2019-12-12)
-#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 18.04.4 LTS
-#> 
-#> Matrix products: default
-#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
-#> LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
-#> 
-#> locale:
-#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
-#>  [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    
-#>  [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   
-#>  [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
-#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-#> [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-#> 
-#> attached base packages:
-#> [1] stats     graphics  grDevices utils     datasets  methods   base     
-#> 
-#> other attached packages:
-#> [1] powsimR_1.2.0     gamlss.dist_5.1-6 MASS_7.3-51.5    
-#> 
-#> loaded via a namespace (and not attached):
-#>   [1] mixtools_1.2.0              softImpute_1.4             
-#>   [3] minpack.lm_1.2-1            lattice_0.20-38            
-#>   [5] vctrs_0.2.2                 fastICA_1.2-2              
-#>   [7] mgcv_1.8-31                 penalized_0.9-51           
-#>   [9] blob_1.2.1                  survival_3.1-8             
-#>  [11] Rmagic_2.0.3                later_1.0.0                
-#>  [13] nloptr_1.2.1                DBI_1.1.0                  
-#>  [15] R.utils_2.9.2               SingleCellExperiment_1.8.0 
-#>  [17] rappdirs_0.3.1              Linnorm_2.10.0             
-#>  [19] dqrng_0.2.1                 jpeg_0.1-8.1               
-#>  [21] zlibbioc_1.32.0             MatrixModels_0.4-1         
-#>  [23] htmlwidgets_1.5.1           mvtnorm_1.0-12             
-#>  [25] future_1.16.0               UpSetR_1.4.0               
-#>  [27] parallel_3.6.2              scater_1.14.6              
-#>  [29] irlba_2.3.3                 DEoptimR_1.0-8             
-#>  [31] Rcpp_1.0.3                  KernSmooth_2.23-16         
-#>  [33] DT_0.12                     promises_1.1.0             
-#>  [35] gdata_2.18.0                DDRTree_0.1.5              
-#>  [37] DelayedArray_0.12.2         limma_3.42.2               
-#>  [39] vegan_2.5-6                 Hmisc_4.3-1                
-#>  [41] ShortRead_1.44.3            apcluster_1.4.8            
-#>  [43] RSpectra_0.16-0             msir_1.3.2                 
-#>  [45] mnormt_1.5-6                digest_0.6.23              
-#>  [47] png_0.1-7                   qlcMatrix_0.9.7            
-#>  [49] sctransform_0.2.1           cowplot_1.0.0              
-#>  [51] pkgconfig_2.0.3             docopt_0.6.1               
-#>  [53] DelayedMatrixStats_1.8.0    ggbeeswarm_0.6.0           
-#>  [55] iterators_1.0.12            minqa_1.2.4                
-#>  [57] lavaan_0.6-5                reticulate_1.14            
-#>  [59] SummarizedExperiment_1.16.1 spam_2.5-1                 
-#>  [61] beeswarm_0.2.3              modeltools_0.2-22          
-#>  [63] xfun_0.12                   zoo_1.8-7                  
-#>  [65] tidyselect_1.0.0            ZIM_1.1.0                  
-#>  [67] reshape2_1.4.3              purrr_0.3.3                
-#>  [69] kernlab_0.9-29              EDASeq_2.20.0              
-#>  [71] viridisLite_0.3.0           snow_0.4-3                 
-#>  [73] rtracklayer_1.46.0          rlang_0.4.4                
-#>  [75] hexbin_1.28.1               glue_1.3.1                 
-#>  [77] RColorBrewer_1.1-2          fpc_2.2-5                  
-#>  [79] matrixStats_0.55.0          stringr_1.4.0              
-#>  [81] fields_10.3                 ggsignif_0.6.0             
-#>  [83] DESeq2_1.26.0               SparseM_1.78               
-#>  [85] httpuv_1.5.2                class_7.3-15               
-#>  [87] BPSC_0.99.2                 BiocNeighbors_1.4.1        
-#>  [89] annotate_1.64.0             jsonlite_1.6.1             
-#>  [91] XVector_0.26.0              bit_1.1-15.1               
-#>  [93] mime_0.9                    gridExtra_2.3              
-#>  [95] gplots_3.0.1.2              Rsamtools_2.2.1            
-#>  [97] zingeR_0.1.0                stringi_1.4.5              
-#>  [99] gmodels_2.18.1              bitops_1.0-6               
-#> [101] maps_3.3.0                  RSQLite_2.2.0              
-#> [103] tidyr_1.0.2                 pheatmap_1.0.12            
-#> [105] data.table_1.12.8           DEDS_1.60.0                
-#> [107] rstudioapi_0.11             GenomicAlignments_1.22.1   
-#> [109] nlme_3.1-144                qvalue_2.18.0              
-#> [111] scran_1.14.6                fastcluster_1.1.25         
-#> [113] locfit_1.5-9.1              scone_1.10.0               
-#> [115] listenv_0.8.0               cobs_1.3-4                 
-#> [117] R.oo_1.23.0                 prabclus_2.3-2             
-#> [119] dbplyr_1.4.2                segmented_1.1-0            
-#> [121] BiocGenerics_0.32.0         lifecycle_0.1.0            
-#> [123] ROTS_1.14.0                 munsell_0.5.0              
-#> [125] R.methodsS3_1.7.1           moments_0.14               
-#> [127] hwriter_1.3.2               caTools_1.18.0             
-#> [129] codetools_0.2-16            coda_0.19-3                
-#> [131] Biobase_2.46.0              GenomeInfoDb_1.22.0        
-#> [133] vipor_0.4.5                 htmlTable_1.13.3           
-#> [135] bayNorm_1.4.14              lsei_1.2-0                 
-#> [137] rARPACK_0.11-0              xtable_1.8-4               
-#> [139] SAVER_1.1.2                 ROCR_1.0-7                 
-#> [141] diptest_0.75-7              formatR_1.7                
-#> [143] lpsymphony_1.14.0           abind_1.4-5                
-#> [145] FNN_1.1.3                   RANN_2.6.1                 
-#> [147] askpass_1.1                 sparsesvd_0.2              
-#> [149] CompQuadForm_1.4.3          GenomicRanges_1.38.0       
-#> [151] tibble_2.1.3                ggdendro_0.1-20            
-#> [153] cluster_2.1.0               future.apply_1.4.0         
-#> [155] Matrix_1.2-18               prettyunits_1.1.1          
-#> [157] shinyBS_0.61                NOISeq_2.30.0              
-#> [159] shinydashboard_0.7.1        mclust_5.4.5               
-#> [161] igraph_1.2.4.2              ggstance_0.3.3             
-#> [163] slam_0.1-47                 testthat_2.3.1             
-#> [165] doSNOW_1.0.18               htmltools_0.4.0            
-#> [167] BiocFileCache_1.10.2        yaml_2.2.1                 
-#> [169] GenomicFeatures_1.38.1      XML_3.99-0.3               
-#> [171] ggpubr_0.2.4                DrImpute_1.0               
-#> [173] foreign_0.8-75              fitdistrplus_1.0-14        
-#> [175] BiocParallel_1.20.1         aroma.light_3.16.0         
-#> [177] bit64_0.9-7                 rngtools_1.5               
-#> [179] doRNG_1.8.2                 foreach_1.4.8              
-#> [181] robustbase_0.93-5           outliers_0.14              
-#> [183] Biostrings_2.54.0           combinat_0.0-8             
-#> [185] rsvd_1.0.2                  iCOBRA_1.14.0              
-#> [187] memoise_1.1.0               evaluate_0.14              
-#> [189] VGAM_1.1-2                  nonnest2_0.5-2             
-#> [191] geneplotter_1.64.0          permute_0.9-5              
-#> [193] curl_4.3                    fdrtool_1.2.15             
-#> [195] acepack_1.4.1               edgeR_3.28.0               
-#> [197] checkmate_2.0.0             npsurv_0.4-0               
-#> [199] truncnorm_1.0-8             DECENT_1.1.0               
-#> [201] tensorA_0.36.1              ellipse_0.4.1              
-#> [203] ggplot2_3.2.1               ggrepel_0.8.1              
-#> [205] scDD_1.10.0                 tools_3.6.2                
-#> [207] sandwich_2.5-1              magrittr_1.5               
-#> [209] RCurl_1.98-1.1              pbivnorm_0.6.0             
-#> [211] bayesm_3.1-4                EBSeq_1.26.0               
-#> [213] httr_1.4.1                  assertthat_0.2.1           
-#> [215] rmarkdown_2.1               boot_1.3-24                
-#> [217] globals_0.12.5              R6_2.4.1                   
-#> [219] Rhdf5lib_1.8.0              nnet_7.3-12                
-#> [221] progress_1.2.2              genefilter_1.68.0          
-#> [223] gtools_3.8.1                statmod_1.4.33             
-#> [225] BiocSingular_1.2.1          rhdf5_2.30.1               
-#> [227] splines_3.6.2               colorspace_1.4-1           
-#> [229] amap_0.8-18                 generics_0.0.2             
-#> [231] stats4_3.6.2                NBPSeq_0.3.0               
-#> [233] base64enc_0.1-3             compositions_1.40-3        
-#> [235] baySeq_2.20.0               pillar_1.4.3               
-#> [237] HSMMSingleCell_1.6.0        GenomeInfoDbData_1.2.2     
-#> [239] plyr_1.8.5                  dotCall64_1.0-0            
-#> [241] gtable_0.3.0                SCnorm_1.8.2               
-#> [243] monocle_2.14.0              knitr_1.28                 
-#> [245] RcppArmadillo_0.9.850.1.0   latticeExtra_0.6-29        
-#> [247] biomaRt_2.42.0              IRanges_2.20.2             
-#> [249] fastmap_1.0.1               doParallel_1.0.15          
-#> [251] pscl_1.5.2                  flexmix_2.3-15             
-#> [253] quantreg_5.54               AnnotationDbi_1.48.0       
-#> [255] broom_0.5.4                 openssl_1.4.1              
-#> [257] scales_1.1.0                arm_1.10-1                 
-#> [259] backports_1.1.5             plotrix_3.7-7              
-#> [261] IHW_1.14.0                  S4Vectors_0.24.3           
-#> [263] densityClust_0.3            lme4_1.1-21                
-#> [265] hms_0.5.3                   DESeq_1.38.0               
-#> [267] Rtsne_0.15                  dplyr_0.8.4                
-#> [269] shiny_1.4.0                 grid_3.6.2                 
-#> [271] lazyeval_0.2.2              Formula_1.2-3              
-#> [273] blockmodeling_0.3.4         crayon_1.3.4               
-#> [275] MAST_1.12.0                 RUVSeq_1.20.0              
-#> [277] viridis_0.5.1               rpart_4.1-15               
-#> [279] zinbwave_1.8.0              compiler_3.6.2
    +R Session Info +
    library(powsimR)
+#> Loading required package: gamlss.dist
+#> Loading required package: MASS
+#> Warning: replacing previous import 'DECENT::lrTest' by 'MAST::lrTest' when
+#> loading 'powsimR'
+#> Registered S3 methods overwritten by 'lme4':
+#>   method                          from
+#>   cooks.distance.influence.merMod car 
+#>   influence.merMod                car 
+#>   dfbeta.influence.merMod         car 
+#>   dfbetas.influence.merMod        car
+#> Warning: replacing previous import 'penalized::predict' by 'stats::predict' when
+#> loading 'powsimR'
+#> Warning: replacing previous import 'zinbwave::glmWeightedF' by
+#> 'zingeR::glmWeightedF' when loading 'powsimR'
+sessionInfo()
+#> R version 4.0.1 (2020-06-06)
+#> Platform: x86_64-pc-linux-gnu (64-bit)
+#> Running under: Ubuntu 18.04.4 LTS
+#> 
+#> Matrix products: default
+#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
+#> LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
+#> 
+#> locale:
+#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
+#>  [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    
+#>  [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   
+#>  [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
+#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
+#> [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
+#> 
+#> attached base packages:
+#> [1] stats     graphics  grDevices utils     datasets  methods   base     
+#> 
+#> other attached packages:
+#> [1] powsimR_1.2.2     gamlss.dist_5.1-6 MASS_7.3-51.6    
+#> 
+#> loaded via a namespace (and not attached):
+#>   [1] mixtools_1.2.0              softImpute_1.4             
+#>   [3] minpack.lm_1.2-1            lattice_0.20-41            
+#>   [5] haven_2.3.1                 vctrs_0.3.1                
+#>   [7] fastICA_1.2-2               mgcv_1.8-31                
+#>   [9] penalized_0.9-51            blob_1.2.1                 
+#>  [11] survival_3.2-3              Rmagic_2.0.3               
+#>  [13] later_1.1.0.1               nloptr_1.2.2.1             
+#>  [15] DBI_1.1.0                   R.utils_2.9.2              
+#>  [17] SingleCellExperiment_1.10.1 rappdirs_0.3.1             
+#>  [19] Linnorm_2.12.0              dqrng_0.2.1                
+#>  [21] jpeg_0.1-8.1                zlibbioc_1.34.0            
+#>  [23] MatrixModels_0.4-1          htmlwidgets_1.5.1          
+#>  [25] mvtnorm_1.1-1               future_1.17.0              
+#>  [27] UpSetR_1.4.0                parallel_4.0.1             
+#>  [29] scater_1.16.1               irlba_2.3.3                
+#>  [31] DEoptimR_1.0-8              Rcpp_1.0.4.6               
+#>  [33] KernSmooth_2.23-17          DT_0.13                    
+#>  [35] promises_1.1.1              gdata_2.18.0               
+#>  [37] DDRTree_0.1.5               DelayedArray_0.14.0        
+#>  [39] limma_3.44.3                vegan_2.5-6                
+#>  [41] Hmisc_4.4-0                 ShortRead_1.46.0           
+#>  [43] apcluster_1.4.8             RSpectra_0.16-0            
+#>  [45] msir_1.3.2                  mnormt_2.0.0               
+#>  [47] digest_0.6.25               png_0.1-7                  
+#>  [49] qlcMatrix_0.9.7             sctransform_0.2.1          
+#>  [51] cowplot_1.0.0               pkgconfig_2.0.3            
+#>  [53] docopt_0.6.1                DelayedMatrixStats_1.10.0  
+#>  [55] ggbeeswarm_0.6.0            iterators_1.0.12           
+#>  [57] minqa_1.2.4                 lavaan_0.6-6               
+#>  [59] reticulate_1.16             SummarizedExperiment_1.18.1
+#>  [61] spam_2.5-1                  beeswarm_0.2.3             
+#>  [63] modeltools_0.2-23           xfun_0.14                  
+#>  [65] zoo_1.8-8                   tidyselect_1.1.0           
+#>  [67] ZIM_1.1.0                   reshape2_1.4.4             
+#>  [69] purrr_0.3.4                 kernlab_0.9-29             
+#>  [71] EDASeq_2.22.0               viridisLite_0.3.0          
+#>  [73] snow_0.4-3                  rtracklayer_1.48.0         
+#>  [75] rlang_0.4.6                 hexbin_1.28.1              
+#>  [77] glue_1.4.1                  RColorBrewer_1.1-2         
+#>  [79] fpc_2.2-5                   matrixStats_0.56.0         
+#>  [81] stringr_1.4.0               fields_10.3                
+#>  [83] ggsignif_0.6.0              DESeq2_1.28.1              
+#>  [85] SparseM_1.78                httpuv_1.5.4               
+#>  [87] class_7.3-17                BPSC_0.99.2                
+#>  [89] BiocNeighbors_1.6.0         annotate_1.66.0            
+#>  [91] jsonlite_1.6.1              XVector_0.28.0             
+#>  [93] tmvnsim_1.0-2               bit_1.1-15.2               
+#>  [95] mime_0.9                    gridExtra_2.3              
+#>  [97] gplots_3.0.3                Rsamtools_2.4.0            
+#>  [99] zingeR_0.1.0                stringi_1.4.6              
+#> [101] gmodels_2.18.1              bitops_1.0-6               
+#> [103] maps_3.3.0                  RSQLite_2.2.0              
+#> [105] tidyr_1.1.0                 pheatmap_1.0.12            
+#> [107] data.table_1.12.8           rstudioapi_0.11            
+#> [109] GenomicAlignments_1.24.0    nlme_3.1-148               
+#> [111] qvalue_2.20.0               scran_1.16.0               
+#> [113] fastcluster_1.1.25          locfit_1.5-9.4             
+#> [115] scone_1.12.0                listenv_0.8.0              
+#> [117] cobs_1.3-4                  R.oo_1.23.0                
+#> [119] prabclus_2.3-2              dbplyr_1.4.4               
+#> [121] segmented_1.1-0             BiocGenerics_0.34.0        
+#> [123] readxl_1.3.1                lifecycle_0.2.0            
+#> [125] ROTS_1.16.0                 munsell_0.5.0              
+#> [127] cellranger_1.1.0            R.methodsS3_1.8.0          
+#> [129] moments_0.14                hwriter_1.3.2              
+#> [131] caTools_1.18.0              codetools_0.2-16           
+#> [133] coda_0.19-3                 Biobase_2.48.0             
+#> [135] GenomeInfoDb_1.24.2         vipor_0.4.5                
+#> [137] htmlTable_1.13.3            bayNorm_1.6.0              
+#> [139] rARPACK_0.11-0              xtable_1.8-4               
+#> [141] SAVER_1.1.2                 ROCR_1.0-11                
+#> [143] diptest_0.75-7              formatR_1.7                
+#> [145] lpsymphony_1.16.0           abind_1.4-5                
+#> [147] FNN_1.1.3                   RANN_2.6.1                 
+#> [149] askpass_1.1                 sparsesvd_0.2              
+#> [151] CompQuadForm_1.4.3          GenomicRanges_1.40.0       
+#> [153] tibble_3.0.1                ggdendro_0.1-20            
+#> [155] cluster_2.1.0               future.apply_1.5.0         
+#> [157] Matrix_1.2-18               ellipsis_0.3.1             
+#> [159] prettyunits_1.1.1           shinyBS_0.61               
+#> [161] NOISeq_2.31.0               shinydashboard_0.7.1       
+#> [163] mclust_5.4.6                igraph_1.2.5               
+#> [165] ggstance_0.3.4              slam_0.1-47                
+#> [167] testthat_2.3.2              doSNOW_1.0.18              
+#> [169] htmltools_0.5.0             BiocFileCache_1.12.0       
+#> [171] yaml_2.2.1                  GenomicFeatures_1.40.0     
+#> [173] XML_3.99-0.3                ggpubr_0.3.0               
+#> [175] DrImpute_1.0                foreign_0.8-80             
+#> [177] fitdistrplus_1.1-1          BiocParallel_1.22.0        
+#> [179] aroma.light_3.18.0          bit64_0.9-7                
+#> [181] foreach_1.5.0               robustbase_0.93-6          
+#> [183] outliers_0.14               Biostrings_2.56.0          
+#> [185] combinat_0.0-8              rsvd_1.0.3                 
+#> [187] iCOBRA_1.16.0               memoise_1.1.0              
+#> [189] evaluate_0.14               VGAM_1.1-3                 
+#> [191] nonnest2_0.5-3              forcats_0.5.0              
+#> [193] rio_0.5.16                  geneplotter_1.66.0         
+#> [195] permute_0.9-5               curl_4.3                   
+#> [197] fdrtool_1.2.15              acepack_1.4.1              
+#> [199] edgeR_3.30.3                checkmate_2.0.0            
+#> [201] truncnorm_1.0-8             DECENT_1.1.0               
+#> [203] tensorA_0.36.1              ellipse_0.4.2              
+#> [205] ggplot2_3.3.1               openxlsx_4.1.5             
+#> [207] rstatix_0.5.0               ggrepel_0.8.2              
+#> [209] scDD_1.12.0                 tools_4.0.1                
+#> [211] sandwich_2.5-1              magrittr_1.5               
+#> [213] RCurl_1.98-1.2              car_3.0-8                  
+#> [215] pbivnorm_0.6.0              bayesm_3.1-4               
+#> [217] EBSeq_1.28.0                httr_1.4.1                 
+#> [219] assertthat_0.2.1            rmarkdown_2.2              
+#> [221] boot_1.3-25                 globals_0.12.5             
+#> [223] R6_2.4.1                    Rhdf5lib_1.10.0            
+#> [225] nnet_7.3-14                 progress_1.2.2             
+#> [227] genefilter_1.70.0           gtools_3.8.2               
+#> [229] statmod_1.4.34              BiocSingular_1.4.0         
+#> [231] rhdf5_2.32.0                splines_4.0.1              
+#> [233] carData_3.0-4               colorspace_1.4-1           
+#> [235] amap_0.8-18                 generics_0.0.2             
+#> [237] stats4_4.0.1                NBPSeq_0.3.0               
+#> [239] base64enc_0.1-3             compositions_1.40-5        
+#> [241] baySeq_2.22.0               pillar_1.4.4               
+#> [243] HSMMSingleCell_1.8.0        GenomeInfoDbData_1.2.3     
+#> [245] plyr_1.8.6                  dotCall64_1.0-0            
+#> [247] gtable_0.3.0                zip_2.0.4                  
+#> [249] SCnorm_1.10.0               monocle_2.16.0             
+#> [251] knitr_1.28                  RcppArmadillo_0.9.900.1.0  
+#> [253] latticeExtra_0.6-29         biomaRt_2.44.0             
+#> [255] IRanges_2.22.2              fastmap_1.0.1              
+#> [257] doParallel_1.0.15           pscl_1.5.5                 
+#> [259] flexmix_2.3-15              quantreg_5.55              
+#> [261] AnnotationDbi_1.50.0        broom_0.5.6                
+#> [263] openssl_1.4.1               scales_1.1.1               
+#> [265] arm_1.11-1                  backports_1.1.8            
+#> [267] plotrix_3.7-8               IHW_1.16.0                 
+#> [269] S4Vectors_0.26.1            densityClust_0.3           
+#> [271] lme4_1.1-23                 hms_0.5.3                  
+#> [273] DESeq_1.39.0                Rtsne_0.15                 
+#> [275] dplyr_1.0.0                 shiny_1.4.0.2              
+#> [277] grid_4.0.1                  Formula_1.2-3              
+#> [279] blockmodeling_0.3.6         crayon_1.3.4               
+#> [281] MAST_1.14.0                 RUVSeq_1.22.0              
+#> [283] viridis_0.5.1               rpart_4.1-15               
+#> [285] zinbwave_1.10.0             compiler_4.0.1
    -
    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/GeneLengths_hg19.html b/docs/reference/GeneLengths_hg19.html index 3200adb..1169dcd 100644 --- a/docs/reference/GeneLengths_hg19.html +++ b/docs/reference/GeneLengths_hg19.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
    @@ -93,7 +96,7 @@
    - @@ -148,7 +148,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/GeneLengths_mm10.html b/docs/reference/GeneLengths_mm10.html index 0a82b37..f30f085 100644 --- a/docs/reference/GeneLengths_mm10.html +++ b/docs/reference/GeneLengths_mm10.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
    @@ -93,7 +96,7 @@
    - @@ -148,7 +148,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/Setup.html b/docs/reference/Setup.html index 69ec618..31a1dff 100644 --- a/docs/reference/Setup.html +++ b/docs/reference/Setup.html @@ -6,27 +6,31 @@ -Setup options for RNA-seq count simulations. — Setup • powsimR +Setup options for RNA-seq count simulations — Setup • powsimR - + - + - + + + + + - - + + - + - - + + @@ -35,14 +39,13 @@ - - + - @@ -60,7 +63,7 @@ - +
    @@ -98,7 +101,7 @@
    • - + @@ -116,13 +119,13 @@
      -

      Setup options for RNA-seq count simulations.

      +

      Setup options for RNA-seq count simulations

      Source: R/Setup.R
      -

      This function generates the settings needed for simulateDE(). +

      This function generates the settings needed for simulateDE. Firstly a set of differential expressed gene IDs with associated fold changes for a given number of genes, simulations and fraction of DE genes is generated. There are also a number of options relating @@ -131,13 +134,13 @@

      Setup options for RNA-seq count simulations.

      Setup(ngenes=NULL, nsims=25,
-p.DE = 0.1, pLFC = 1,
+p.DE = 0.1, pLFC = 1, p.G = 1,
 p.B=NULL, bLFC=NULL, bPattern="uncorrelated",
 n1=c(20,50,100), n2=c(30,60,120),
 Thinning = NULL, LibSize='equal',
 estParamRes, estSpikeRes=NULL,
 DropGenes=FALSE,
-sim.seed, verbose = TRUE)
      +setup.seed, verbose = TRUE)

      Arguments

      @@ -168,7 +171,14 @@

      Arg (3) a function that takes an integer n, and generates a vector of length n, e.g. function(x) rnorm(x, mean=0, sd=1.5). Default is 1. -Please note that the fold change should be on log1p scale!

      +Please note that the fold change should be on log2 scale!

      + +

      + + @@ -187,7 +197,7 @@

      Arg e.g. function(x) rnorm(x, mean=0, sd=1.5). Note that the simulations of only two batches is implemented. Default is NULL, i.e. no batch effect. -Please note that the fold change should be on log1p scale!

      +Please note that the fold change should be on log2 scale!

      @@ -204,7 +214,7 @@

      Arg

      - - - + + @@ -275,7 +285,8 @@

      Examp 
      if (FALSE) {
 # estimate gene parameters
 data("SmartSeq2_Gene_Read_Counts")
-Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts), "_"), "[[", 1),
+Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts),
+                                    "_"), "[[", 1),
                      stringsAsFactors = F,
                      row.names = colnames(SmartSeq2_Gene_Read_Counts))
 data("GeneLengths_mm10")
@@ -291,7 +302,8 @@ 
      Examp
 # estimate spike parameters
 data("SmartSeq2_SpikeIns_Read_Counts")
 data("SmartSeq2_SpikeInfo")
-Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts), "_"), "[[", 1),
+Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts),
+                                    "_"), "[[", 1),
                      stringsAsFactors = F,
                      row.names = colnames(SmartSeq2_SpikeIns_Read_Counts))
 estparam_spike <- estimateSpike(spikeData = SmartSeq2_SpikeIns_Read_Counts,
@@ -309,19 +321,13 @@ 
      Examp
                   estParamRes = estparam_gene,
                   estSpikeRes = estparam_spike,
                   DropGenes = TRUE,
-                  sim.seed = 52679, verbose = TRUE)
+                  setup.seed = 52679, verbose = TRUE)
 }
      - @@ -332,7 +338,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/SpikeCounts.html b/docs/reference/SpikeCounts.html index 5f82c24..711365c 100644 --- a/docs/reference/SpikeCounts.html +++ b/docs/reference/SpikeCounts.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +
      @@ -94,7 +97,7 @@
      - @@ -159,7 +159,7 @@

      Contents

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/SpikeInfo.html b/docs/reference/SpikeInfo.html index 6c00ef2..feccd1d 100644 --- a/docs/reference/SpikeInfo.html +++ b/docs/reference/SpikeInfo.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      - @@ -152,7 +152,7 @@

      Contents

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/estimateParam.html b/docs/reference/estimateParam.html index 72a8a7e..0dbdf22 100644 --- a/docs/reference/estimateParam.html +++ b/docs/reference/estimateParam.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -39,7 +43,6 @@ - @@ -57,7 +60,7 @@ - +
      @@ -95,7 +98,7 @@
      • - + @@ -252,13 +255,13 @@

        Value

        Details

        -

        Normalisation Methods

        +

        Normalisation Methods

        TMM, UQ

        employ the edgeR style normalization of weighted trimmed mean of M-values and upperquartile as implemented in calcNormFactors, respectively.

        MR, PosCounts

        employ the DESeq2 style normalization of median ratio method and a modified geometric mean method as implemented in estimateSizeFactors, respectively.

        scran, SCnorm

        apply the deconvolution and quantile regression normalization methods developed for sparse RNA-seq data -as implemented in computeSumFactors and SCnorm, respectively. Spike-ins can also be supplied for both methods via spikeData. Note, however that this means for scran that the normalisation as implemented in computeSpikeFactors is also applied to genes (general.use=TRUE).

        +as implemented in calculateSumFactors and SCnorm, respectively. Spike-ins can also be supplied for both methods via spikeData. Note, however that this means for scran that the normalisation as implemented in computeSpikeFactors is also applied to genes (general.use=TRUE).

        Linnorm

        apply the normalization method for sparse RNA-seq data as implemented in Linnorm.Norm. For Linnorm, the user can also supply spikeData.

        @@ -310,17 +313,10 @@

        Examp plotParam(estparam, Annot = FALSE) }

      - @@ -331,7 +327,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/estimateSpike.html b/docs/reference/estimateSpike.html index 1177cba..72ab630 100644 --- a/docs/reference/estimateSpike.html +++ b/docs/reference/estimateSpike.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -205,7 +208,7 @@

        Value

        Details

        -

        Normalisation methods

        +

        Normalisation methods

        'depth'

        applies the depth normalization method as implemented in computeSpikeFactors.

        'none'

        No normalisation is applied. This approach can be used for prenormalized expression estimates, e.g. TPM/FPKM/RPKM estimated by RSEM, salmon, cufflinks etc.

        @@ -216,9 +219,10 @@

        Examp 
        if (FALSE) {
 data("SmartSeq2_SpikeIns_Read_Counts")
 data("SmartSeq2_SpikeInfo")
-Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts), "_"), "[[", 1),
-                       stringsAsFactors = F,
-                       row.names = colnames(SmartSeq2_SpikeIns_Read_Counts))
+Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts),
+                                    "_"), "[[", 1),
+                     stringsAsFactors = F,
+                     row.names = colnames(SmartSeq2_SpikeIns_Read_Counts))
 # estimation
 spike_param <- estimateSpike(spikeData = SmartSeq2_SpikeIns_Read_Counts,
 spikeInfo = SmartSeq2_SpikeInfo,
@@ -229,17 +233,10 @@ 
        Examp
 plotSpike(estSpike = spike_param, Annot = FALSE)
 }

      - @@ -250,7 +247,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/evaluateDE.html b/docs/reference/evaluateDE.html index 09b227b..e790998 100644 --- a/docs/reference/evaluateDE.html +++ b/docs/reference/evaluateDE.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +
      @@ -94,7 +97,7 @@
      • - + @@ -229,7 +232,7 @@

        Value

        Details

        This is the main function to compute various power-related quantities, -using stratification and filtering.

        +using stratification and filtering.

        Gene stratification

        We recommend to compute and visualize error rates (especially TPR) conditional on expression characteristics like mean, dispersion and/or dropout rate. It is likely that the power to detect DE genes is strongly dependent on @@ -295,18 +298,10 @@

        Examp plotEvalDE(evalRes = evalderes, rate = "conditional") }

      - @@ -317,7 +312,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/evaluateDist.html b/docs/reference/evaluateDist.html index 4006cf3..d1c63d7 100644 --- a/docs/reference/evaluateDist.html +++ b/docs/reference/evaluateDist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      - @@ -255,7 +252,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/evaluateROC.html b/docs/reference/evaluateROC.html index b9d1681..3ea84ad 100644 --- a/docs/reference/evaluateROC.html +++ b/docs/reference/evaluateROC.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +
      @@ -94,7 +97,7 @@
      - @@ -257,7 +253,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/evaluateSim.html b/docs/reference/evaluateSim.html index 4bd1d6b..71621b4 100644 --- a/docs/reference/evaluateSim.html +++ b/docs/reference/evaluateSim.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ - +
      @@ -94,7 +97,7 @@
      • - + @@ -192,17 +195,10 @@

        Examp plotTime(evalRes = evalsimres, Annot = TRUE) }

      - @@ -213,7 +209,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/index.html b/docs/reference/index.html index da7aa32..8111bdb 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -53,7 +57,7 @@ - +
      @@ -91,7 +95,7 @@
      • - + @@ -127,6 +131,11 @@

      + + + + + - + + + + + @@ -208,8 +218,7 @@

      Arg

      Value

      -

      A list of list objects complex list where the list named "SimulateRes" contains the results of simulations: -SimulateRes: Results of DE simulations

      +

      SimulateRes: Results of DE simulations

      pvalue, fdr

      3D array (ngenes * N * nsims) for p-values and FDR from each simulation. Note that FDR values will be empty and the calculation will be done by evaluateDE whenever applicable.

      mu,disp,dropout

      3D (ngenes * N * nsims) array for mean, dispersion and dropout of library size factor normalized read counts.

      @@ -221,33 +230,38 @@

      Value

      elfc is for the DE tool estimated LFC; rlfc is for the LFC estimated from the normalised read counts.

      time.taken

      The time taken given by proc.time for each simulation, given for preprocessing, normalisation, differential expression testing and moment estimation.

      -

      SetupRes: Simulation specifications

      +

      SetupRes: Simulation specifications

      +
      DESetup - ... - estSpikeRes

      Reiterating the simulated setup defined by Setup.

      +
      Pipeline

      A list of chosen pipeline tools defined by above arguments.

      + +

      Counts: Simulated Count Matrices

      +
      Counts

      List of lists object where Counts[[Sample Size Setup]][[Simulation Run]] containing simulated counts. Note that this will only be returned when Counts is TRUE. In addition, if DEFilter is TRUE then the filtered/imputed counts are returned.

      + + +

      Details

      Here you can find detailed information about preprocessing, imputation, normalisation and differential testing choices. For recommendations concerning single cell RNA-sequencing pipelines, -we kindly refer the user to (Vieth, et al (2019). A systematic evaluation of single cell RNA-seq analysis pipelines. Nature Communications, 10(1), 4667.).

      -

      Prefiltering prior to imputation/normalisation

      - - - -
      +we kindly refer the user to Vieth, et al (2019). A systematic evaluation of single cell RNA-seq analysis pipelines. Nature Communications, 10(1), 4667.
      +Prefiltering

      CountFilter

      removes genes that have a mean expression below 0.2.

      -
      FreqFilter

      removes genes that have more than 80 percent dropouts.

      - -
      - -

      Imputation prior to normalisation

      +
      FreqFilter

      removes genes that have more than 80 % dropouts.

      - +

      Imputation

      +
      scImpute

      apply the imputation as implemented in scimpute

      +
      DrImpute

      apply the imputation as implemented in DrImpute.

      +
      SAVER

      apply the imputation as implemented in saver.

      +
      scone

      apply the imputation as implemented in scone, defining 'house keeping genes' for the FNR model estimation as those that have less than 20 % dropouts and small variance (i.e. in the lower 20th quartile). If less than 25 genes could be identified, the genes with less than 5 % dropouts are used. If spike-in data is provided then these are used for the FNR model estimation.

      +
      MAGIC

      apply the imputation as implemented in magic. Please note that the python package MAGIC needs to be installed to use this implementation.

      -
      +

      Normalisation

      TMM, UQ

      employ the edgeR style normalization of weighted trimmed mean of M-values and upperquartile as implemented in calcNormFactors, respectively.

      MR, PosCounts

      employ the DESeq2 style normalization of median ratio method and a modified geometric mean method as implemented in estimateSizeFactors, respectively.

      scran, SCnorm

      apply the deconvolution and quantile regression normalization methods developed for sparse RNA-seq data -as implemented in computeSumFactors and SCnorm, respectively. Spike-ins can also be supplied for both methods via spikeData. Note, however that this means for scran that the normalisation as implemented in computeSpikeFactors is also applied to genes (general.use=TRUE).

      +as implemented in computeSumFactors and SCnorm, respectively. Spike-ins can also be supplied for both methods via spikeData. Note, however that this means for scran that the normalisation as implemented in calculateSumFactors is also applied to genes (general.use=TRUE).

      Linnorm

      apply the normalization method for sparse RNA-seq data as implemented in Linnorm.Norm. For Linnorm, the user can also supply spikeData.

      @@ -258,23 +272,17 @@

      depth

      Sequencing depth normalisation.

      - - -

      Differential testing using raw read count matrix

      - - - -
      -
      T-Test

      A T-Test per gene is applied using log2 transformed and normalized expression values (i.e. CPM or TPM).

      +

      Differential testing

      +
      T-Test

      A T-Test per gene is applied using log transformed and normalized expression values (i.e. CPM or TPM).

      limma-trend, limma-voom

      apply differential testing as implemented in lmFit -followed by eBayes on counts transformed by voom or by applying mean-variance trend on log2 CPM values in eBayes.

      +followed by eBayes on counts transformed by voom or by applying mean-variance trend on log CPM values in eBayes.

      edgeR-LRT, edgeR-QL

      apply differential testing as implemented in glmFit, glmLRT andglmQLFit, glmQLFTest, respectively.

      DESeq2

      applies differential testing as implemented in DESeq.

      ROTS

      applies differential testing as implemented in ROTS with 100 permutations on transformed counts (voom).

      baySeq

      applies differential testing as implemented in getLikelihoods based on negative binomial prior estimates (getPriors.NB).

      NOISeq

      applies differential testing as implemented in noiseqbio based on CPM values.

      EBSeq

      applies differential testing as implemented in EBTest.

      -
      MAST

      applies differential testing as implemented in zlm for zero-inflated model fitting followed by lrTest on log2 CPM values.

      +
      MAST

      applies differential testing as implemented in zlm for zero-inflated model fitting followed by lrTest on log CPM values.

      BPSC

      applies differential testing as implemented in BPglm on CPM values.

      scDD

      applies differential testing as implemented in scDD on CPM values.

      DECENT

      applies differential testing as implemented in decent.

      @@ -285,15 +293,16 @@

      See also

      -

      estimateParam, for parameter specifications;
      +

      estimateParam and estimateSpike, for parameter specifications;
      Setup for simulation setup;
      - evaluateDE for DE evaluation.

      + evaluateDE, evaluateROC and evaluateSim for evaluation of simulation results.

      Examples

      if (FALSE) {
 # estimate gene parameters
 data("SmartSeq2_Gene_Read_Counts")
-Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts), "_"), "[[", 1),
+Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts),
+                                    "_"), "[[", 1),
                      stringsAsFactors = F,
                      row.names = colnames(SmartSeq2_Gene_Read_Counts))
 data("GeneLengths_mm10")
@@ -309,7 +318,8 @@ 
      Examp
 # estimate spike parameters
 data("SmartSeq2_SpikeIns_Read_Counts")
 data("SmartSeq2_SpikeInfo")
-Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts), "_"), "[[", 1),
+Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_SpikeIns_Read_Counts),
+                                    "_"), "[[", 1),
                      stringsAsFactors = F,
                      row.names = colnames(SmartSeq2_SpikeIns_Read_Counts))
 estparam_spike <- estimateSpike(spikeData = SmartSeq2_SpikeIns_Read_Counts,
@@ -317,7 +327,7 @@ 
      Examp
                                 MeanFragLength = NULL,
                                 batchData = Batches,
                                 Normalisation = 'depth')
-# define log2 fold change
+# define log fold change
 p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2)
 # set up simulations
 setupres <- Setup(ngenes = 10000, nsims = 10,
@@ -341,21 +351,10 @@ 
      Examp
            quick = TRUE, Annot = FALSE)
 }
      - @@ -366,7 +365,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/man/Bulk_Read_Counts.Rd b/man/Bulk_Read_Counts.Rd index 5484832..9879a0b 100644 --- a/man/Bulk_Read_Counts.Rd +++ b/man/Bulk_Read_Counts.Rd @@ -4,7 +4,9 @@ \name{Bulk_Read_Counts} \alias{Bulk_Read_Counts} \title{Parekh et al. 2016: Gene Expression Matrices} -\format{An object of class \code{data.frame} with 57773 rows and 10 columns.} +\format{ +An object of class \code{data.frame} with 57773 rows and 10 columns. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75823}. } diff --git a/man/GeneCounts.Rd b/man/GeneCounts.Rd index b753ae0..7d2db00 100644 --- a/man/GeneCounts.Rd +++ b/man/GeneCounts.Rd @@ -4,7 +4,9 @@ \name{GeneCounts} \alias{GeneCounts} \title{Ziegenhain et al. 2017: Gene Expression Matrices} -\format{An object of class \code{"data.frame"}, with genes per row and cells per column.} +\format{ +An object of class \code{"data.frame"}, with genes per row and cells per column. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790}. } diff --git a/man/GeneLengths_hg19.Rd b/man/GeneLengths_hg19.Rd index b370d1d..92bd620 100644 --- a/man/GeneLengths_hg19.Rd +++ b/man/GeneLengths_hg19.Rd @@ -4,7 +4,9 @@ \name{GeneLengths_hg19} \alias{GeneLengths_hg19} \title{Parekh et al. 2016: Gene Lengths} -\format{A named vector of gene lengths.} +\format{ +A named vector of gene lengths. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75823}. } diff --git a/man/GeneLengths_mm10.Rd b/man/GeneLengths_mm10.Rd index c9f84f4..0b86634 100644 --- a/man/GeneLengths_mm10.Rd +++ b/man/GeneLengths_mm10.Rd @@ -4,7 +4,9 @@ \name{GeneLengths_mm10} \alias{GeneLengths_mm10} \title{Ziegenhain et al. 2017: Gene Lengths} -\format{A named vector of gene lengths.} +\format{ +A named vector of gene lengths. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790}. } diff --git a/man/Setup.Rd b/man/Setup.Rd index f85148f..d76315f 100644 --- a/man/Setup.Rd +++ b/man/Setup.Rd @@ -32,7 +32,7 @@ it will be sampled with replacement to generate log fold changes; (3) a function that takes an integer n, and generates a vector of length n, e.g. function(x) rnorm(x, mean=0, sd=1.5). Default is \code{1}. -Please note that the fold change should be on \code{\link[base]{log1p}} scale!} +Please note that the fold change should be on \code{\link[base]{log2}} scale!} \item{p.G}{Numeric vector indicating the proportion of replicates per group that express the phenotypic fold change. Default is \code{1}, this means all show the expressiond difference. @@ -52,7 +52,7 @@ If the input is a vector and the length is not the number of total genes, e.g. function(x) rnorm(x, mean=0, sd=1.5). Note that the simulations of only two batches is implemented. Default is \code{NULL}, i.e. no batch effect. -Please note that the fold change should be on \code{\link[base]{log1p}} scale!} +Please note that the fold change should be on \code{\link[base]{log2}} scale!} \item{bPattern}{Character vector for batch effect pattern if \code{p.B} is non-null. Possible options include: diff --git a/man/SpikeCounts.Rd b/man/SpikeCounts.Rd index 4b29c57..c0890e4 100644 --- a/man/SpikeCounts.Rd +++ b/man/SpikeCounts.Rd @@ -4,7 +4,9 @@ \name{SpikeCounts} \alias{SpikeCounts} \title{Ziegenhain et al. 2017: Spike-Ins Expression} -\format{An object of class \code{"data.frame"}, with spike-ins per row and cells per column.} +\format{ +An object of class \code{"data.frame"}, with spike-ins per row and cells per column. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790}. } diff --git a/man/SpikeInfo.Rd b/man/SpikeInfo.Rd index 42b487d..6d1af6d 100644 --- a/man/SpikeInfo.Rd +++ b/man/SpikeInfo.Rd @@ -4,7 +4,9 @@ \name{SpikeInfo} \alias{SpikeInfo} \title{Ziegenhain et al. 2017: Spike-Ins Information} -\format{An object of class \code{"data.frame"}, with spike-in IDs, number of spike-in molecules and lengths of spike-ins.} +\format{ +An object of class \code{"data.frame"}, with spike-in IDs, number of spike-in molecules and lengths of spike-ins. +} \source{ \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790}. } diff --git a/vignettes/estparam_gene.png b/vignettes/estparam_gene.png deleted file mode 100644 index b2253b0..0000000 Binary files a/vignettes/estparam_gene.png and /dev/null differ diff --git a/vignettes/estparam_gene_celseq2.png b/vignettes/estparam_gene_celseq2.png index 1687f4b..c0f45fc 100644 Binary files a/vignettes/estparam_gene_celseq2.png and b/vignettes/estparam_gene_celseq2.png differ diff --git a/vignettes/estparam_spike.png b/vignettes/estparam_spike.png deleted file mode 100644 index 3a853d6..0000000 Binary files a/vignettes/estparam_spike.png and /dev/null differ diff --git a/vignettes/estparam_spike_celseq2.png b/vignettes/estparam_spike_celseq2.png index 2fdb757..879ce8c 100644 Binary files a/vignettes/estparam_spike_celseq2.png and b/vignettes/estparam_spike_celseq2.png differ diff --git a/vignettes/evalderes_conditional_celseq2.png b/vignettes/evalderes_conditional_celseq2.png index 3b1d1bb..5810907 100644 Binary files a/vignettes/evalderes_conditional_celseq2.png and b/vignettes/evalderes_conditional_celseq2.png differ diff --git a/vignettes/evalderes_marginal_celseq2.png b/vignettes/evalderes_marginal_celseq2.png index 5ee9cb9..0e504f9 100644 Binary files a/vignettes/evalderes_marginal_celseq2.png and b/vignettes/evalderes_marginal_celseq2.png differ diff --git a/vignettes/evalrocres_liberal_celseq2.png b/vignettes/evalrocres_liberal_celseq2.png index 38846d1..6f8c919 100644 Binary files a/vignettes/evalrocres_liberal_celseq2.png and b/vignettes/evalrocres_liberal_celseq2.png differ diff --git a/vignettes/evalsimres_celseq2.png b/vignettes/evalsimres_celseq2.png index beb3b4f..e35fe3d 100644 Binary files a/vignettes/evalsimres_celseq2.png and b/vignettes/evalsimres_celseq2.png differ diff --git a/vignettes/evaltime_celseq2.png b/vignettes/evaltime_celseq2.png index 0021834..4df0dc7 100644 Binary files a/vignettes/evaltime_celseq2.png and b/vignettes/evaltime_celseq2.png differ diff --git a/vignettes/powsimR.R b/vignettes/powsimR.R deleted file mode 100644 index 4bede8b..0000000 --- a/vignettes/powsimR.R +++ /dev/null @@ -1,286 +0,0 @@ -## ----knitr_init, echo=FALSE, results="asis", cache=FALSE---------------------- -library(knitr) -library(rmdformats) -## Global options -options(max.print = "75") -opts_chunk$set(echo = FALSE, - cache = FALSE, - prompt = FALSE, - tidy = FALSE, - comment = NA, - message = FALSE, - warning = FALSE) -opts_knit$set(width = 75) - -## ----DevTools, echo = TRUE, eval = FALSE-------------------------------------- -# install.packages('devtools') -# library(devtools) - -## ----dep, echo = TRUE, eval = FALSE------------------------------------------- -# ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){ -# new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])] -# # new.pkg <- pkg -# if (length(new.pkg)) { -# if(repository=='CRAN') { -# install.packages(new.pkg, dependencies = TRUE) -# } -# if(repository=='Bioconductor') { -# if(strsplit(version[['version.string']], ' ')[[1]][3] > "3.6.0"){ -# if (!requireNamespace("BiocManager")){ -# install.packages("BiocManager") -# } -# BiocManager::install(new.pkg, dependencies=TRUE, ask=FALSE) -# } -# if(strsplit(version[['version.string']], ' ')[[1]][3] < "3.6.0"){ -# stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher.")) -# } -# } -# if(repository=='github') { -# devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE, dependencies=TRUE) -# } -# } -# } -# -# # CRAN PACKAGES -# cranpackages <- c("broom", "cobs", "cowplot", -# "data.table", "doParallel", "dplyr", "DrImpute", -# "fastICA", "fitdistrplus", "foreach", "future", -# "gamlss.dist", "ggplot2", "ggpubr", "grDevices", -# "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix", -# "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", -# "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", -# "reshape2", "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", -# "stats", "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo") -# ipak(cranpackages, repository='CRAN') -# -# # BIOCONDUCTOR -# biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", -# "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", -# "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", -# "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", -# "SingleCellExperiment", "SummarizedExperiment", "zinbwave") -# ipak(biocpackages, repository='Bioconductor') -# -# # GITHUB -# githubpackages <- c('cz-ye/DECENT', 'nghiavtr/BPSC', -# 'mohuangx/SAVER', 'statOmics/zingeR', -# 'Vivianstats/scImpute') -# ipak(githubpackages, repository = 'github') -# - -## ----depcheck, echo = TRUE, eval = FALSE-------------------------------------- -# powsimRdeps <- data.frame(Package = c(cranpackages, -# biocpackages, -# sapply(strsplit(githubpackages, "/"), "[[", 2)), -# stringsAsFactors = F) -# -# ip <- as.data.frame(installed.packages()[,c(1,3:4)], stringsAsFactors = F) -# -# ip.check <- cbind(powsimRdeps, -# Version = ip[match(powsimRdeps$Package, rownames(ip)),"Version"]) -# -# table(is.na(ip.check$Version)) # all should be FALSE - -## ----install1, echo=T, eval=F, tidy=T----------------------------------------- -# devtools::install_github('bvieth/powsimR', -# build_vignettes = TRUE, -# dependencies=FALSE) -# library("powsimR") - -## ----install2, echo=T, eval=F, tidy=T----------------------------------------- -# devtools::install_github("bvieth/powsimR") - -## ----schematic, fig.cap="PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data, which can be either single cell or bulk data. The plot shows the mean-dispersion estimated, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration.", echo=F, eval=T, include=T---- -knitr::include_graphics("powsimr_workflow.png") - -## ----geneparams, echo=T, eval=F, include=T------------------------------------ -# data("CELseq2_Gene_UMI_Counts") -# Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), -# stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) -# data("GeneLengths_mm10") -# -# # estimation -# estparam_gene <- estimateParam(countData = CELseq2_Gene_UMI_Counts, -# readData = NULL, -# batchData = Batches, -# spikeData = NULL, -# spikeInfo = NULL, -# Lengths = GeneLengths, MeanFragLengths = NULL, -# RNAseq = 'singlecell', Protocol = 'UMI', -# Distribution = 'NB', Normalisation = "scran", -# GeneFilter = 0.1, SampleFilter = 3, -# sigma = 1.96, NCores = NULL, verbose = TRUE) -# -# # plotting -# plotParam(estParamRes = estparam_gene, Annot = T) - -## ----geneparamsplot, echo = FALSE, eval = TRUE, include = TRUE, fig.wide = TRUE, fig.cap="Estimated parameters for CEL-seq2 libraries from Ziegenhain et al. 2017. A) Quality Control Metrics: Sequencing depth; Library size factors with median (black line) for the filtered data set; Detected genes; Ratio of gene to spike-in counts (if spike-ins were provided). Outliers are marked in red. B) Marginal Distribution of gene mean, dispersion and dropout rate per estimation set. C) Number of genes and samples per estimation set. Provided by the user; Detected = number of genes and samples with at least one count; All = number of genes for which mean, dispersion and dropout could be estimated using non-outlying samples. \nFiltered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene."---- -knitr::include_graphics("estparam_gene_celseq2.png") - -## ----spikeparams, echo=T, eval=F, include=T, warning=F------------------------ -# data("CELseq2_SpikeIns_UMI_Counts") -# data("CELseq2_SpikeInfo") -# Batches = data.frame(Batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1), -# stringsAsFactors = F, -# row.names = colnames(CELseq2_SpikeInfo)) -# # estimation -# estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts, -# spikeInfo = CELseq2_SpikeInfo, -# MeanFragLength = NULL, -# batchData = Batches, -# Normalisation = 'depth') -# -# # plotting -# plotSpike(estparam_spike) -# - -## ----spikeplot, echo=F, warning=F, eval=T, include=T, fig.height = 7, fig.width=10, fig.align='centered', fig.cap="Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells."---- -knitr::include_graphics("estparam_spike_celseq2.png") - -## ----lfcs, echo=F, eval=T, include=T, fig.cap="Examples of Log Fold Changes following a gamma, normal and uniform distribution."---- -knitr::include_graphics("lfcdist.png") - -## ----simsetup, echo = TRUE, eval = FALSE-------------------------------------- -# # define log fold change -# p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2) -# # set up simulations -# setupres <- Setup(ngenes = 10000, nsims = 25, -# p.DE = 0.05, pLFC = p.lfc, -# n1 = c(48, 96, 384, 800), n2 = c(48, 96, 384, 800), -# Thinning = NULL, LibSize = 'equal', -# estParamRes = estparam_gene, -# estSpikeRes = estparam_spike, -# DropGenes = TRUE, -# sim.seed = 5299, verbose = TRUE) -# - -## ----simrun, eval=F, echo=T--------------------------------------------------- -# simres <- simulateDE(SetupRes = setupres, -# Prefilter = NULL, Imputation = NULL, -# Normalisation = 'scran', -# DEmethod = "limma-trend", DEFilter = FALSE, -# NCores = NULL, verbose = TRUE) - -## ----evalderes, echo = T, eval=F---------------------------------------------- -# evalderes = evaluateDE(simRes = simres, -# alpha.type = 'adjusted', -# MTC = 'BH', -# alpha.nominal = 0.1, -# stratify.by = 'mean', -# filter.by = 'none', -# strata.filtered = 1, -# target.by = 'lfc', -# delta = 0) - -## ----evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."---- -knitr::include_graphics("evalderes_marginal_celseq2.png") - -## ----evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."---- -knitr::include_graphics("evalderes_conditional_celseq2.png") - -## ----evalrocres, echo = T, eval=F--------------------------------------------- -# evalrocres = evaluateROC(simRes = simres, -# alpha.type="adjusted", -# MTC='BH', -# alpha.nominal = 0.1) -# -# plotEvalROC(evalRes = evalrocres, cutoff = "liberal") - -## ----evalrocresplot, echo=F, eval=T, fig.cap="A) Receiver-Operator-Characteristics (ROC) Curve per sample size setup. \nB) Precision-Recall (PR) Curve per sample size setup. \nC) TPR versus observed FDR per sample size setup. The filling of the point indicates whether FDR is controlled at the chosen nominal level. \nD) Summary Statistics per sample size setup rounded to two digits."---- -knitr::include_graphics("evalrocres_liberal_celseq2.png") - -## ----evalsimres, echo = T, eval=F--------------------------------------------- -# evalsimres = evaluateSim(simRes = simres) -# -# plotEvalSim(evalRes = evalsimres) -# -# plotTime(evalRes = evalsimres) -# - -## ----evalsimresplot, echo=F, eval=T, fig.cap="Pipeline Evaluation. A) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and robust Root Mean Squared Error (rRMSE) for the estimated log fold changes of all (ALL), differentially expressed (DE) and equally expressed (EE) genes compared to the true log fold changes. \nB) Median absolute deviation (MAD) and robust Root Mean Squared Error (rRMSE) between estimated and simulated size factors. \nC) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error."---- -knitr::include_graphics("evalsimres_celseq2.png") - -## ----evaltimeplot, echo=F, eval=T, fig.cap="Computational Run Time in seconds per simulateDE() pipeline step."---- -knitr::include_graphics("evaltime_celseq2.png") - -## ----online_repos, echo = T, eval = F----------------------------------------- -# # Install and load the R package -# BiocManager::install("recount") -# library('recount') -# -# # Download the data set -# url <- download_study('SRP060416') -# -# # Load the data -# load(file.path('SRP060416', 'rse_gene.Rdata')) -# -# # count table -# cnts <- assay(rse_gene) -# # sample annotation -# sample.info <- data.frame(colData(rse_gene)@listData, -# stringsAsFactors=F) -# # gene annotation -# gene.info <- data.frame(GeneID=rowData(rse_gene)@listData$gene_id, -# GeneLength=rowData(rse_gene)@listData$bp_length, -# stringsAsFactors=F) - -## ----evaldist, eval=F, echo=T------------------------------------------------- -# data("SmartSeq2_Gene_Read_Counts") -# evalDistRes <- evaluateDist(countData = SmartSeq2_Gene_Read_Counts, -# batchData = NULL, -# spikeData = NULL, spikeInfo = NULL, -# Lengths = NULL, MeanFragLengths = NULL, -# RNAseq = "singlecell", Protocol = "UMI", -# Normalisation = "scran", -# GeneFilter = 0.1, SampleFilter = 3, -# FracGenes = 0.1, -# verbose = TRUE) -# plotEvalDist(evalDistRes) - -## ----evaldistplot, echo=F, eval=T, include=T, fig.wide = TRUE, fig.cap="Distribution Evaluation. A) Goodness-of-fit of the model assessed with a Chi-Square Test based on residual deviance and degrees of freedom. B) Akaike Information Criterion per gene: Model with the lowest AIC. Model with the lowest AIC and passed goodness-of-fit statistic test. C) Observed versus predicted dropouts per model and gene plotted without outliers. D) Model Assessment based on LRT for nested models and Vuong test for nonnested models."---- -knitr::include_graphics("evaldist_smartseq2.png") - -## ----thinning, echo = TRUE, eval = FALSE-------------------------------------- -# data("CELseq2_Gene_UMI_Counts") -# data("CELseq2_Gene_Read_Counts") -# Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), -# stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) -# data("GeneLengths_mm10") -# -# # estimation -# estparam_gene <- estimateParam(countData = CELseq2_Gene_UMI_Counts, -# readData = CELseq2_Gene_Read_Counts, -# batchData = Batches, -# spikeData = NULL, -# spikeInfo = NULL, -# Lengths = GeneLengths, MeanFragLengths = NULL, -# RNAseq = 'singlecell', Protocol = 'UMI', -# Distribution = 'NB', Normalisation = "scran", -# GeneFilter = 0.1, SampleFilter = 3, -# sigma = 1.96, NCores = NULL, verbose = TRUE) -# -# plotParam(estParamRes = estparam_gene) -# -# # define log fold change -# p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2) -# -# # set up simulations -# setupres <- Setup(ngenes = 10000, nsims = 25, -# p.DE = 0.1, pLFC = p.lfc, -# n1 = c(500, 1000), n2 = c(500, 1000), -# Thinning = c(1, 0.5), LibSize = 'given', -# estParamRes = estparam_gene, -# estSpikeRes = NULL, -# DropGenes = TRUE, -# sim.seed = 5299, verbose = TRUE) -# -# # run simulations -# simres <- simulateDE(SetupRes = setupres, -# Prefilter = "FreqFilter", Imputation = NULL, -# Normalisation = 'scran', -# DEmethod = "limma-trend", DEFilter = FALSE, -# NCores = NULL, verbose = TRUE) - -## ----sessionInfo, echo=FALSE-------------------------------------------------- -sessionInfo() - diff --git a/vignettes/powsimR.Rmd b/vignettes/powsimR.Rmd index e299dbd..b6f077b 100644 --- a/vignettes/powsimR.Rmd +++ b/vignettes/powsimR.Rmd @@ -89,7 +89,7 @@ ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){ cranpackages <- c("broom", "cobs", "cowplot", "data.table", "doParallel", "dplyr", "DrImpute", "fastICA", "fitdistrplus", "foreach", "future", - "gamlss.dist", "ggplot2", "ggpubr", "grDevices", + "gamlss.dist", "ggplot2", "ggpubr", "ggstance", "grDevices", "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix", "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl", @@ -99,7 +99,7 @@ ipak(cranpackages, repository='CRAN') # BIOCONDUCTOR biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", - "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", + "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA", "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", "SingleCellExperiment", "SummarizedExperiment", "zinbwave") @@ -154,7 +154,7 @@ In this vignette, we illustrate the features of `powsimR` by assessing the power The basic workflow of `powsimR` is illustrated in Figure \@ref(fig:schematic): A) The mean-dispersion relationship is estimated from RNA-seq data, which can be either single cell or bulk data. The user can provide their own count table or a publicly available one and choose whether to fit a negative binomial or a zero-inflated negative binomial. The plot shows a representative example of the mean-dispersion relation estimated, assuming a negative binomial for the Ziegenhain data, the red line is the loess fit, that we later use for the simulations. B) These distribution parameters are then used to set up the simulations in which the user can freely choose the magnitude and amount of differential expression, the sample size configuration as well as the processing pipeline for DE-testing. C) Finally, the error rates are calculated. These can be either returned as marginal estimates per sample configuration, or stratified according to the estimates of mean expression, dispersion or dropout rate. Furthermore, the user can evaluate the analytical choices (e.g. imputation and normalisation). -```{r schematic, fig.cap="PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data are estimated , which can be either single cell or bulk data. The plot shows the mean-dispersion relation, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration.", echo=F, eval=T, include=T} +```{r schematic, fig.cap="PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data, which can be either single cell or bulk data. The plot shows the mean-dispersion estimated, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration.", echo=F, eval=T, include=T} knitr::include_graphics("powsimr_workflow.png") ``` @@ -181,8 +181,10 @@ With the following command, we estimate and plot the parameters for the mouse em ```{r geneparams, echo=T, eval=F, include=T} data("CELseq2_Gene_UMI_Counts") -Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), - stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +Batches <- data.frame(Batch = batch, + stringsAsFactors = FALSE, + row.names = colnames(CELseq2_Gene_UMI_Counts)) data("GeneLengths_mm10") # estimation @@ -208,7 +210,7 @@ knitr::include_graphics("estparam_gene_celseq2.png") We have implemented a count simulation framework assuming either a negative binomial distribution or a zero-inflated negative binomial distribution. To predict the dispersion given a random draw of mean expression value observed, we apply a locally weighted polynomial regression fit (solid orange line in Figure \@ref(fig:geneparamsplot) D). To capture the variability of dispersion estimates observed, a local variability prediction band is applied (dashed orange lines in Figure \@ref(fig:geneparamsplot) D). -For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5\% dropout probability determined by a decrease constrained B-splines regresssion of dropout rate against mean expression (`cobs`). +For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5\% dropout probability determined by a decrease constrained B-splines regression of dropout rate against mean expression (`cobs`). For the zero-inflated negative binomial distribution, the mean-dispersion relation is similarly estimated, but based on positive read counts. Furthermore, the dropouts are also predicted based on a locally weighted polynomial regression fit between mean and dropouts. Of note, this fit is done separately for amplified and non-amplified transcripts and similar proportions of genes as observed are also generated in the simulations [@Ziegenhain2017-sf]. @@ -233,9 +235,10 @@ With the following command, we estimate the parameters for the spike-ins added d ```{r spikeparams, echo=T, eval=F, include=T, warning=F} data("CELseq2_SpikeIns_UMI_Counts") data("CELseq2_SpikeInfo") -Batches = data.frame(Batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1), +batch = sapply(strsplit(colnames(CELseq2_SpikeIns_UMI_Counts), "_"), "[[", 1) +Batches = data.frame(Batch = batch, stringsAsFactors = F, - row.names = colnames(CELseq2_SpikeInfo)) + row.names = colnames(CELseq2_SpikeIns_UMI_Counts)) # estimation estparam_spike <- estimateSpike(spikeData = CELseq2_SpikeIns_UMI_Counts, spikeInfo = CELseq2_SpikeInfo, @@ -264,9 +267,10 @@ There is also the possibility to include batch effects, see the Details section knitr::include_graphics("lfcdist.png") ``` -The number of replicates per group (n1 and n2) are also defined in `Setup` which can be unbalanced. These numbers represent either biological replicates for bulk or number of cells for single cell RNA-seq. In addition, the user can choose to include the simulation of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation. +The number of replicates per group (n1 and n2) are also defined in `Setup` which can be unbalanced. In addition the user can choose to include the simulaiton of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation. -The distribution estimates and these settings are then combined to one object. +The distribution estimates and these settings are then combined into one object. +The following command sets up simulations with 10,000 genes, 20\% genes being DE, log fold change sampled from a narrow gamma distribution and parameter estimates based on Smartseq2 libraries in Ziegenhain dataset. Please note that I decreased the number of simulations for this illustration. The following command sets up simulations with 10,000 genes, 5\% genes being DE, log fold change sample from a narrow gamma distribution and parameter estimates based on CEL-seq2 libraries in Ziegenhain dataset. The spike-ins are only necessary if we wish to apply imputation, normalisation and/or DE-tools that can utilize spike-ins, see the Details section of `estimateParam()` and `simulateDE()`. @@ -279,9 +283,9 @@ setupres <- Setup(ngenes = 10000, nsims = 25, n1 = c(48, 96, 384, 800), n2 = c(48, 96, 384, 800), Thinning = NULL, LibSize = 'equal', estParamRes = estparam_gene, - estSpikeRes = estparam_spike, + estSpikeRes = NULL, DropGenes = TRUE, - sim.seed = 5299, verbose = TRUE) + setup.seed = 5299, verbose = TRUE) ``` @@ -289,8 +293,8 @@ setupres <- Setup(ngenes = 10000, nsims = 25, With the setup defined, the differential expression simulation is run with `simulateDE`. For this, the user needs to set the following options: -* **DEmethod:** The differential testing method. The user can choose between 20 methods in total. 10 developed for bulk, 9 developed for single cells (see the Details section of `simulateDE`). -* **Normalisation:** The normalisation method. The user can choose between 10 methods in total. 5 developed for bulk, 5 developed for single cells (see the Details section of `simulateDE`). +* **DEmethod:** The differential testing method. The user can choose between 20 methods in total. 10 developed for bulk, 9 developed for single cells (see the Details section of `simulateDE`). +* **Normalisation:** The normalisation method. The user can choose between 10 methods in total. 5 developed for bulk, 5 developed for single cells (see the Details section of `simulateDE`). There are also additional options: Whether to apply a prefiltering or imputation step prior to normalisation; whether spike-in information should be used (if available). For more information, please consult the Details section of `simulateDE`. @@ -302,7 +306,6 @@ simres <- simulateDE(SetupRes = setupres, NCores = NULL, verbose = TRUE) ``` -## Simulating gene expression of cell mixtures # Evaluation {.tabset} @@ -329,16 +332,23 @@ evalderes = evaluateDE(simRes = simres, The results of the evaluation can be plotted with `plotEvalDE()`. +```{r, echo = TRUE, eval = FALSE} +plotEvalDE(evalRes = evalderes, rate = 'marginal', quick = TRUE, Annot = TRUE) +plotEvalDE(evalRes = evalderes, rate = 'conditional', quick = TRUE, Annot = TRUE) + +``` + + * **rate:** The user can choose to plot the 'marginal' or 'stratified' error rates. The number of genes per stratum are also summarised. * **quick:** If this is set to `TRUE` then only the TPR and FDR will be plotted. -The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure \@ref(fig:evaldeplot1) and \@ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size Figure \@ref(fig:evaldeplot1). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure \@ref(fig:evaldeplot2)). In addition, the detection of true positives with small average expression needs far more replicates per group. +The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure \@ref(fig:evaldeplot1) and \@ref(fig:evaldeplot2). As expected the power (TPR) to detect differential expression increases with sample size (Figure \@ref(fig:evaldeplot1)). On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure \@ref(fig:evaldeplot2)). In addition, the detection of true positives with small mean expression needs far more replicates per group. -```{r evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."} +```{r evaldeplot1, echo=F, eval=T, fig.cap="Marginal Error Rates. (A) Marginal FDR and TPR per sample size comparison. (B) Marginal FDR and TPR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error)."} knitr::include_graphics("evalderes_marginal_celseq2.png") ``` -```{r evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."} +```{r evaldeplot2, echo=F, eval=T, fig.cap="Stratified Error Rates. (A) Conditional FDR and TPR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum."} knitr::include_graphics("evalderes_conditional_celseq2.png") ``` @@ -450,21 +460,27 @@ knitr::include_graphics("evaldist_smartseq2.png") ## Simulation Settings +### Replicates with partial phenotypic differences + +By default, all replicates per group express the phenotypic differences in gene expression defined by `pLFC`. If the user wishes for a more varied expression profile, i.e. only a proportion of replicates per group with a difference in expression, these patterns can be defined by changing `p.G` (see Details section of `Setup()`). + ### Include differences in library sizes -By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function. See the Details section of `Setup()`. +By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function (see Details section of `Setup()`). ### Downsample simulated count matrices by binomial thinning -There is the additional option to downsample simulated count matrices with the option `Thinning` in `Setup` in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as `countData` in `estimateParam()` and the raw read count matrix without UMI deduplication as `readData`. For more information, please check out the help page of `estimateParam()`. +There is the additional option to downsample simulated count matrices with the option `Thinning` in `Setup()` in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as `countData` in `estimateParam()` and the raw read count matrix without UMI deduplication as `readData`. For more information, please check out the help page of `estimateParam()`. Below you can find an example script using the CELSeq2 libraries from Ziegenhain et al.: ```{r thinning, echo = TRUE, eval = FALSE} data("CELseq2_Gene_UMI_Counts") data("CELseq2_Gene_Read_Counts") -Batches <- data.frame(Batch = sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1), - stringsAsFactors = FALSE, row.names = colnames(CELseq2_Gene_UMI_Counts)) +batch <- sapply(strsplit(colnames(CELseq2_Gene_UMI_Counts), "_"), "[[", 1) +Batches <- data.frame(Batch = batch, + stringsAsFactors = FALSE, + row.names = colnames(CELseq2_Gene_UMI_Counts)) data("GeneLengths_mm10") # estimation @@ -492,7 +508,7 @@ setupres <- Setup(ngenes = 10000, nsims = 25, estParamRes = estparam_gene, estSpikeRes = NULL, DropGenes = TRUE, - sim.seed = 5299, verbose = TRUE) + setup.seed = 5299, verbose = TRUE) # run simulations simres <- simulateDE(SetupRes = setupres, diff --git a/vignettes/powsimR.html b/vignettes/powsimR.html deleted file mode 100644 index 65c2499..0000000 --- a/vignettes/powsimR.html +++ /dev/null @@ -1,1711 +0,0 @@ - - - - - - - - - - - - -powsimR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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      Abstract

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      This vignette describes the R package powsimR (Vieth et al. 2017). It aims to be at once a demonstration of its features and a guide to its usage. Statistical power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. powsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

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      For our count simulations, we (1) reliably model the mean, dispersion and dropout distributions as well as the relationship between those factors from the data. (2) Simulate counts from the empirical mean-variance and dropout relations, while offering flexible choices of the number of differentially expressed genes, effect sizes, DE testing and normalisation method. (3) Finally, we evaluate the power over various sample sizes and visualise the results with error rates plots.

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      Note: if you use powsimR in published research, please cite:

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      Vieth, B., Ziegenhain, C., Parekh, S., Enard, W. and Hellmann, I. (2017) -powsimR: Power Analysis for Bulk and Single Cell RNA-Seq Experiments. -Bioinformatics, 33(21):3486-88. -10.1093/bioinformatics/btx435

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      Installation Guide

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      For the installation, the R package devtools is needed.

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      powsimR has a number of dependencies that need to be installed before hand (see also the README file on github). I recommend to install first the dependencies manually and then powsimR. If you plan to use MAGIC for imputation, then please follow their instruction to install the python implementation before installing powsimR.

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      ipak <- function(pkg, repository=c('CRAN', 'Bioconductor', 'github')){
-  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
-  # new.pkg <- pkg
-  if (length(new.pkg)) {
-    if(repository=='CRAN') {
-      install.packages(new.pkg, dependencies = TRUE)
-    }
-    if(repository=='Bioconductor') {
-      if(strsplit(version[['version.string']], ' ')[[1]][3] > "3.6.0"){
-        if (!requireNamespace("BiocManager")){
-            install.packages("BiocManager")
-        }
-            BiocManager::install(new.pkg, dependencies=TRUE, ask=FALSE)
-      }
-      if(strsplit(version[['version.string']], ' ')[[1]][3] < "3.6.0"){
-        stop(message("powsimR depends on packages that are only available in R 3.6.0 and higher."))
-      }
-    }
-    if(repository=='github') {
-      devtools::install_github(new.pkg, build_vignettes = FALSE, force = FALSE, dependencies=TRUE)
-    }
-  }
-}
-
-# CRAN PACKAGES
-cranpackages <- c("broom", "cobs", "cowplot", 
-                  "data.table", "doParallel", "dplyr", "DrImpute", 
-                  "fastICA", "fitdistrplus", "foreach", "future",
-                  "gamlss.dist", "ggplot2", "ggpubr", "grDevices",
-                  "grid", "Hmisc", "kernlab", "MASS", "magrittr", "MBESS", "Matrix",
-                  "matrixStats", "mclust", "methods", "minpack.lm", "moments", "msir", 
-                  "NBPSeq", "nonnest2", "parallel", "penalized", "plyr", "pscl",
-                  "reshape2", "Rmagic", "rsvd", "Rtsne", "scales", "Seurat", "snow", "sctransform", 
-                  "stats", "tibble", "tidyr", "truncnorm", "VGAM", "ZIM", "zoo")
-ipak(cranpackages, repository='CRAN')
-
-# BIOCONDUCTOR
-biocpackages <- c("bayNorm", "baySeq", "BiocGenerics", "BiocParallel", 
-                  "DEDS", "DESeq2", "EBSeq", "edgeR", "IHW", "iCOBRA",
-                  "limma", "Linnorm", "MAST", "monocle", "NOISeq", "qvalue", "ROTS", "RUVSeq", 
-                  "S4Vectors", "scater", "scDD", "scde", "scone", "scran", "SCnorm", 
-                  "SingleCellExperiment", "SummarizedExperiment", "zinbwave")
-ipak(biocpackages, repository='Bioconductor')
-
-# GITHUB
-githubpackages <- c('cz-ye/DECENT', 'nghiavtr/BPSC', 
-                    'mohuangx/SAVER', 'statOmics/zingeR',
-                    'Vivianstats/scImpute')
-ipak(githubpackages, repository = 'github')
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      To check whether all dependencies are installed, you can run the following lines:

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      After installing the dependencies, powsimR can be installed by using devtools as well.

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      Some users have experienced issues installing powsimR due to vignette compilation errors. If that is the case, you can leave out building the vignette (by setting build_vignettes to FALSE).

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      Alternative, you can try to install powsimR and its dependencies directly using devtools:

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      1 Introduction

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      In this vignette, we illustrate the features of powsimR by assessing the power to detect differential expression between two groups of mouse embryonic stem cells cultured in standard 2i/LIF culture medium GSE75790 (Ziegenhain et al. 2017).

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      1.1 powsimR Workflow

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      The basic workflow of powsimR is illustrated in Figure 1.1: A) The mean-dispersion relationship is estimated from RNA-seq data, which can be either single cell or bulk data. The user can provide their own count table or a publicly available one and choose whether to fit a negative binomial or a zero-inflated negative binomial. The plot shows a representative example of the mean-dispersion relation estimated, assuming a negative binomial for the Ziegenhain data, the red line is the loess fit, that we later use for the simulations. B) These distribution parameters are then used to set up the simulations in which the user can freely choose the magnitude and amount of differential expression, the sample size configuration as well as the processing pipeline for DE-testing. C) Finally, the error rates are calculated. These can be either returned as marginal estimates per sample configuration, or stratified according to the estimates of mean expression, dispersion or dropout rate. Furthermore, the user can evaluate the analytical choices (e.g. imputation and normalisation).

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      -PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data, which can be either single cell or bulk data. The plot shows the mean-dispersion estimated, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration. -

      -Figure 1.1: PowsimR schematic overview. We want to investigate the statistical power to detect differential expression in our RNA-seq experiment. Firstly, key expression characteristics of the RNA-seq data, which can be either single cell or bulk data. The plot shows the mean-dispersion estimated, the red line is the loess fit, that we later use for the simulations. The expression of spike-ins can also be modelled. Secondly, we define our desired simulation setup: the number and magnitude of differential expression, the sample size setup as well as the tools to use in our DE-Pipeline. Last, we evaluate the simulated experiment using the error rates of the confusion matrix. Particularly, the power (TPR) and false detection (FDR) are calculated per sample setup configuration. -

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      2 Estimation

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      2.1 Single Cell Gene Expression

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      The parameters of the (zero-inflated) negative binomial distribution, i.e. mean and dispersion are estimated by the function estimateParam. In addition, the dropout probability, i.e. the fraction of zero counts per gene, is calculated.

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      It is very important to supply the count matrix of a RNA-seq experiment profiling one group of samples or cells only. For example, for bulk please provide the count matrix containing the expression of the untreated samples / wild type ONLY; for single cells please provide the count matrix containing the expression of one cell type sampled from one patient / mouse ONLY, e.g. the expression of monocytes from peripheral mononuclear blood (PBMC) extracted from one patient.

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      The user can choose between two estimation frameworks:

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      • Negative binomial distribution (NB)
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      In both cases matching moments estimation of mean and dispersion are based on normalized counts.

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      The user can choose between multiple normalisation methods (see Details section of estimateParam). Furthermore, a number of methods are group sensitive (e.g. batch labels can be provided in SCnorm). For single cell data, it is important to filter out genes and/or cells that are outliers or deemed undetected (see options SampleFilter and GeneFilter).

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      The estimates, sequencing depth and normalisation factors are plotted with plotParam.

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      With the following command, we estimate and plot the parameters for the mouse embryonic stem cells profiled with CELseq2 (Ziegenhain et al. 2017) (Figure 2.1). Firstly, we can examine the quality of the data set using the metrics in Figure 2.1 A. But most importantly, we see that the variability (i.e. dispersion) and dropout rates are high (Figure 2.1 B). Furthermore, the dispersion depends on the mean and does not level off with higher mean values compared to bulk data (Figure 2.1 D).

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      -Estimated parameters for CEL-seq2 libraries from Ziegenhain et al. 2017. A) Quality Control Metrics: Sequencing depth; Library size factors with median (black line) for the filtered data set; Detected genes; Ratio of gene to spike-in counts (if spike-ins were provided). Outliers are marked in red. B) Marginal Distribution of gene mean, dispersion and dropout rate per estimation set. C) Number of genes and samples per estimation set. Provided by the user; Detected = number of genes and samples with at least one count; All = number of genes for which mean, dispersion and dropout could be estimated using non-outlying samples. -Filtered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene. -

      -Figure 2.1: Estimated parameters for CEL-seq2 libraries from Ziegenhain et al. 2017. A) Quality Control Metrics: Sequencing depth; Library size factors with median (black line) for the filtered data set; Detected genes; Ratio of gene to spike-in counts (if spike-ins were provided). Outliers are marked in red. B) Marginal Distribution of gene mean, dispersion and dropout rate per estimation set. C) Number of genes and samples per estimation set. Provided by the user; Detected = number of genes and samples with at least one count; All = number of genes for which mean, dispersion and dropout could be estimated using non-outlying samples. -Filtered = number of genes above filter threshold for which mean, dispersion and dropout could be estimated using non-outlying samples. Dropout Genes = number of genes filtered out due to dropout rate. D) Local polynomial regression fit between mean and dispersion estimates with variability band per gene (yellow). Common dispersion estimate (grey dashed line). E) Fraction of dropouts versus estimated mean expression per gene. -

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      We have implemented a count simulation framework assuming either a negative binomial distribution or a zero-inflated negative binomial distribution. -To predict the dispersion given a random draw of mean expression value observed, we apply a locally weighted polynomial regression fit (solid orange line in Figure 2.1 D). To capture the variability of dispersion estimates observed, a local variability prediction band is applied (dashed orange lines in Figure 2.1 D).

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      For bulk RNA-seq experiments, dropouts are less probable but can still occur. To include this phenomenon we sample from the observed dropout rates for genes that have a mean expression value below 5% dropout probability determined by a decrease constrained B-splines regresssion of dropout rate against mean expression (cobs).

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      For the zero-inflated negative binomial distribution, the mean-dispersion relation is similarly estimated, but based on positive read counts. Furthermore, the dropouts are also predicted based on a locally weighted polynomial regression fit between mean and dropouts. Of note, this fit is done separately for amplified and non-amplified transcripts and similar proportions of genes as observed are also generated in the simulations (Ziegenhain et al. 2017).

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      We and others have found that the negative binomial (NB) distribution is particularly suited for scRNA-seq protocols with UMIs (e.g. SCRB-Seq, Drop-Seq, 10XGenomics) (Svensson 2020) whereas non-UMI methods like Smartseq2 show a considerable proportion of genes with zero-inflated count distributions and the zero-inflated negative binomial (ZINB) is then more appropiate (Vieth et al. 2017). In addition, we recommend the NB distribution for bulk RNA-seq and only recommend the option ‘singlecell’ in estimateParam() and specialised single cell tools for e.g. normalisation if the data is extremely sparse.

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      2.2 Spike-Ins

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      Some normalisation methods can use spike-ins as part of their normalisation (e.g. SCnorm, scran, Census). We have found that using spike-ins when comparing expression across cells with strong asymmetric differences is important, e.g. comparing resting cells to very transcriptionally active cells (Vieth et al. 2019). To use spike-in information in the simulations, their distributional characteristics need to be estimated. We follow the estimation and simulation framework presented in (Kim et al. 2015) where the variance is decomposed into shot noise and mRNA loss due to capture and sequencing efficiency. In short, the parameters for a Beta-distribution describes the RNA molecule capture efficiency and the parameters of a Gamma distribution describes the sequencing efficiency, which we can then use to simulate in silico spike-ins given a mean expression value. We assume that biological variance does not contribute to spike-in expression.

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      The user needs to define the spike-in expression table and the spike-in information table (IDs, molecules, lengths per spike-in) in the function estimateSpike.

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      The following formula can help the user to calculate the number of molecules of spike-ins:

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      \[\begin{equation} - Y_{j} = c_{j} * V * 10^{-3} * D^{-1} * 10^{-18} * {Avogadro}, \quad j=1,\dots,92 -\end{equation}\]

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      The number of molecules \(Y_{j}\) for each ERCC spike-in species is the product of the molar concentration \(c_{j}\) (attomoles per microlitre), the dilution factor \(1/D\), the volume \(V\) (nanolitre), Avogadros’ constant (\(6.02214129*10^{23}\)) and conversion factors between unit scales.

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      With the following command, we estimate the parameters for the spike-ins added during library preparation of mouse embryonic stem cells cultured in standard 2i+lif medium (Ziegenhain et al. 2017). Descriptive plots of the spike-ins can be drawn with plotSpike (Figure 2.2).

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      -Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells. -

      -Figure 2.2: Estimated parameters for the spike-ins added to CEL-seq2 libraries in Ziegenhain et al. 2017 (A) Sequencing depth per sample with median sequencing depth (grey dashed line). (B) Library size normalisation factor per sample with median size factor (grey dashed line). (C) Calibration curve with mean expression estimates and average R squared over all cells. (D) Capture efficiency with binomial logistic regression fit over all cells. -

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      3 Simulation

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      3.1 Setup

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      For simulating differential expression between two groups, the number of genes, number of simulations, percentage of differential expression and effect size are set up with the function Setup. The effect size is here defined as the log fold change which can be a constant (e.g. 1.5), sampled from a vector of anticipated fold changes (e.g. derived from pilot experiments) or function. The uniform, normal and gamma distributions are possible options and illustrated in figure 3.1. Depending on the settings, these distribution can be broader or narrower. If using this option, we recommend to choose a distribution that closely resembles previously observed or expected fold changes. -There is also the possibility to include batch effects, see the Details section in Setup().

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      -Examples of Log Fold Changes following a gamma, normal and uniform distribution. -

      -Figure 3.1: Examples of Log Fold Changes following a gamma, normal and uniform distribution. -

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      The number of replicates per group (n1 and n2) are also defined in Setup which can be unbalanced. These numbers represent either biological replicates for bulk or number of cells for single cell RNA-seq. In addition, the user can choose to include the simulation of dropout genes so that the resulting count matrix has a percentage of dropouts equal to the rate filtered out during estimation.

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      The distribution estimates and these settings are then combined to one object.

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      The following command sets up simulations with 10,000 genes, 5% genes being DE, log fold change sample from a narrow gamma distribution and parameter estimates based on CEL-seq2 libraries in Ziegenhain dataset. The spike-ins are only necessary if we wish to apply imputation, normalisation and/or DE-tools that can utilize spike-ins, see the Details section of estimateParam() and simulateDE().

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      3.2 Running differential expression simulations

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      With the setup defined, the differential expression simulation is run with simulateDE. For this, the user needs to set the following options:

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      There are also additional options: Whether to apply a prefiltering or imputation step prior to normalisation; whether spike-in information should be used (if available). For more information, please consult the Details section of simulateDE.

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      3.3 Simulating gene expression of cell mixtures

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      4 Evaluation

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      The results of the simulations can now be evaluated. Primarily the differential expression in terms of power can be examined with evaluateDE and evaluateROC. In addition, we can also check in how far our chosen pipeline tools perform in terms of log fold change estimates and normalisation with evaluateSim.

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      4.1 Differential Expression

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      The results of differential expression simulation are evaluated with evaluateDE. We have separated the evaluation from DE detection to allow the user to evaluate power in a comprehensive way as advocated by (Wu, Wang, and Wu 2015). -In this function, the proportions and error rates are estimated. The rates can be stratified by mean, dispersion, dropout or log fold change. Furthermore, the user can choose between different multiple testing correction methods (see p.adjust.methods(), ihw() in and qvalue() in ). Also, the genes can be filtered by mean, dispersion or dropout. To define biologically interesting genes, a cutoff for the log fold change with delta can be set.

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      With the following command we evaluate the marginal TPR and FDR conditional on the mean expression:

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      The results of the evaluation can be plotted with plotEvalDE().

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      The quick marginal and conditional power assessment for the Ziegenhain data is plotted in Figure 4.1 and 4.2. As expected the power (TPR) to detect differential expression increases with sample size Figure 4.1. On the other hand, the ability to control the false detection (FDR) at the chosen nominal level depends on the average expression of DE genes (Figure 4.2). In addition, the detection of true positives with small average expression needs far more replicates per group.

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      -Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error). -

      -Figure 4.1: Marginal Error Rates. (A) Marginal TPR and FDR per sample size comparison. (B) Marginal TPR and FDR per sample size comparison with dashed line indicating nominal alpha level (type I error) and nominal 1-beta level, i.e. 80% power (type II error). -

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      -Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum. -

      -Figure 4.2: Stratified Error Rates. (A) Conditional TPR and FDR per sample size comparison per stratum. (B) Number of equally (EE) and differentially expressed (DE) genes per stratum. -

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      In addition to the classical evaluation of power analyses, powsimR also includes the option to evaluate the simulations using summary metrics such as the Receiver-Operator-Characteristic (ROC) Curve as well as accuracy, F1 score and Matthews Correlation Coefficient.

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      With the following command we calculate these metrics with evaluateROC() and plot them with plotEvalROC(). The classical ROC curve as well as precision-recall curve, which is more appropiate for imbalanced number of true positives and negatives, can be helpful to identify the optimal sample size setup (Figure 4.3 A and B). Most importantly, we can check if the FDR is controlled at the chosen nominal level (Figure 4.3 C). If that is not the case, then the chosen pipeline, particularly the normalisation and DE testing, might be an issue (Vieth et al. 2019).

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      -A) Receiver-Operator-Characteristics (ROC) Curve per sample size setup. -B) Precision-Recall (PR) Curve per sample size setup. -C) TPR versus observed FDR per sample size setup. The filling of the point indicates whether FDR is controlled at the chosen nominal level. -D) Summary Statistics per sample size setup rounded to two digits. -

      -Figure 4.3: A) Receiver-Operator-Characteristics (ROC) Curve per sample size setup. -B) Precision-Recall (PR) Curve per sample size setup. -C) TPR versus observed FDR per sample size setup. The filling of the point indicates whether FDR is controlled at the chosen nominal level. -D) Summary Statistics per sample size setup rounded to two digits. -

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      4.2 Pipeline

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      We can also check in how far our pipeline choices are affecting our ability to correctly identify differential expression in evaluateSim().

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      With the following command we calculate these metrics. Of note here is particular in how far our estimated log fold changes deviate from the simulated fold changes as well as how the normalisation is able to ensure comparability across sample groups (Figure @(fig:evalsimresplot)).

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      -Pipeline Evaluation. A) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and robust Root Mean Squared Error (rRMSE) for the estimated log fold changes of all (ALL), differentially expressed (DE) and equally expressed (EE) genes compared to the true log fold changes. -B) Median absolute deviation (MAD) and robust Root Mean Squared Error (rRMSE) between estimated and simulated size factors. -C) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error. -

      -Figure 4.4: Pipeline Evaluation. A) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and robust Root Mean Squared Error (rRMSE) for the estimated log fold changes of all (ALL), differentially expressed (DE) and equally expressed (EE) genes compared to the true log fold changes. -B) Median absolute deviation (MAD) and robust Root Mean Squared Error (rRMSE) between estimated and simulated size factors. -C) The average ratio between simulated and estimated size factors in the two groups per sample size setup. All values are mean +/- standard error. -

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      -Figure 4.5: Computational Run Time in seconds per simulateDE() pipeline step. -

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      5 Additional Functionalities

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      5.1 Estimation

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      5.1.1 Count matrices of single cell RNA-seq experiments

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      We have uploaded count matrices of 5 single cell RNA-seq experiments on github. In powsimR, we have included subsampled versions of these data sets, see description on the help page GeneCounts. -The user can calculate the negative binomial parameters with estimateParam(), view these estimates with plotParam() and use it as an input for simulations.

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      5.1.2 Access to raw read counts stored in online data bases

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      We have provided a number of exemplary single cell RNA-seq data sets for parameter estimation. Nevertheless, you might not find a data set that matches your own experimental setup. In those cases, we recommend to check online repositories for a suitable data set. Below you can find an example script to get count tables from recount2 (Collado-Torres et al. 2017). For a single cell RNA-seq data base, see conquer (Soneson and Robinson 2018).

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      As before, the user can then estimate the negative binomial parameters with estimateParam(), view these estimates with plotParam() and use it as an input for Setup().

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      5.2 What is the best fitting distribution for my data?

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      It is important to validate the appropiateness of the chosen simulation framework. The function evaluateDist() compares the theoretical fit of the Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial and beta-Poisson distribution to the empirical RNA-seq read counts ((Colin Cameron and Trivedi 2013), (Kim and Marioni 2013), (Delmans and Hemberg 2016)).

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      The evaluation is then plotted with the function plotEvalDist() which summarizes the best fitting distribution per gene based on goodness-of-fit statistics (Chi-square test), Akaike Information Criterium, comparing observed dropouts with zero count prediction of the models and comparing the model fitness with Likelihood Ratio Test and Vuong Test.

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      As noted by other developers, goodness-of-fit tests are not an objective tool and heavily depend on sample sizes (Delignette-Muller and Dutang 2015). A graphical evaluation of the fitted distribution is considered the most appropiate way but for high-throughput sequencing this is an unrealistic recommendation. -Bulk RNA-seq experiments are usually conducted with a small number of samples. We therefore recommend to rely on the goodness-of-fit validation by (Mi, Di, and Schafer 2015).

      -

      With the following command, we determine and plot the fitting for the embryonic stem cells cultured in standard 2i+lif medium profiled with the Smartseq2 protocol (Ziegenhain et al. 2017). Note that the results shown in Figure 5.1 will differ from the executed command since I reduced the data set and evaluate the fitting only for a fraction of genes.

      - -
      -Distribution Evaluation. A) Goodness-of-fit of the model assessed with a Chi-Square Test based on residual deviance and degrees of freedom. B) Akaike Information Criterion per gene: Model with the lowest AIC. Model with the lowest AIC and passed goodness-of-fit statistic test. C) Observed versus predicted dropouts per model and gene plotted without outliers. D) Model Assessment based on LRT for nested models and Vuong test for nonnested models. -

      -Figure 5.1: Distribution Evaluation. A) Goodness-of-fit of the model assessed with a Chi-Square Test based on residual deviance and degrees of freedom. B) Akaike Information Criterion per gene: Model with the lowest AIC. Model with the lowest AIC and passed goodness-of-fit statistic test. C) Observed versus predicted dropouts per model and gene plotted without outliers. D) Model Assessment based on LRT for nested models and Vuong test for nonnested models. -

      -
      -
      -
      -

      5.3 Simulation Settings

      -
      -

      5.3.1 Include differences in library sizes

      -

      By default, there is no difference in library sizes between the samples. If the user wishes for a more realistic, i.e. more variable distribution of read counts across samples, the library sizes can be sampled from observed, vector or function. See the Details section of Setup().

      -
      -
      -

      5.3.2 Downsample simulated count matrices by binomial thinning

      -

      There is the additional option to downsample simulated count matrices with the option Thinning in Setup in order to know in how far a shallower sequencing depth affects the power to detect differential expression. For example, the estimation was done using 500 cells and in the simulations we would like to compare two groups with a total of 1000 cells but do not want to increase the sequencing depth compared to the pilot data. Thus we can set the Thinning parameters to 0.5 to half the number of counts per cell. Please note, that the thinning assumes that the count matrix is based on reads, not UMIs. For UMI-methods, please supply the UMI count matrix as countData in estimateParam() and the raw read count matrix without UMI deduplication as readData. For more information, please check out the help page of estimateParam().

      -

      Below you can find an example script using the CELSeq2 libraries from Ziegenhain et al.:

      - -
      -
      -
      -
      -

      Session info

      -

      Here is the output of sessionInfo() on the system on which this -document was compiled:

      -
      R version 3.6.2 (2019-12-12)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 18.04.4 LTS
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
-
-locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=C              
- [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] stats     graphics  grDevices utils     datasets  methods   base     
-
-other attached packages:
-[1] qvalue_2.18.0     IHW_1.14.0        rmdformats_0.3.6  knitr_1.28       
-[5] powsimR_1.2.1     gamlss.dist_5.1-6 MASS_7.3-51.5    
-
-loaded via a namespace (and not attached):
- [1] mixtools_1.2.0              softImpute_1.4             
- [3] minpack.lm_1.2-1            pbapply_1.4-2              
- [5] lattice_0.20-40             vctrs_0.2.3                
- [7] fastICA_1.2-2               usethis_1.5.1              
- [9] mgcv_1.8-31                 penalized_0.9-51           
-[11] blob_1.2.1                  survival_3.1-8             
-[13] Rmagic_2.0.3                later_1.0.0                
-[15] nloptr_1.2.1                DBI_1.1.0                  
-[17] R.utils_2.9.2               uwot_0.1.5                 
-[19] SingleCellExperiment_1.8.0  rappdirs_0.3.1             
-[21] Linnorm_2.10.0              dqrng_0.2.1                
-[23] jpeg_0.1-8.1                zlibbioc_1.32.0            
-[25] MatrixModels_0.4-1          pcaMethods_1.78.0          
-[27] htmlwidgets_1.5.1           mvtnorm_1.1-0              
-[29] future_1.16.0               UpSetR_1.4.0               
-[31] leiden_0.3.3                parallel_3.6.2             
-[33] scater_1.14.6               irlba_2.3.3                
-[35] DEoptimR_1.0-8              Rcpp_1.0.3                 
-[37] KernSmooth_2.23-16          DT_0.12                    
-[39] promises_1.1.0              gdata_2.18.0               
-[41] DDRTree_0.1.5               DelayedArray_0.12.2        
-[43] limma_3.42.2                vegan_2.5-6                
-[45] pkgload_1.0.2               RcppParallel_4.4.4         
-[47] Hmisc_4.3-1                 ShortRead_1.44.3           
-[49] apcluster_1.4.8             RSpectra_0.16-0            
-[51] fs_1.3.1                    msir_1.3.2                 
-[53] mnormt_1.5-6                digest_0.6.25              
-[55] png_0.1-7                   bookdown_0.17              
-[57] qlcMatrix_0.9.7             sctransform_0.2.1          
-[59] cowplot_1.0.0               pkgconfig_2.0.3            
-[61] docopt_0.6.1                DelayedMatrixStats_1.8.0   
-[63] ggbeeswarm_0.6.0            iterators_1.0.12           
-[65] minqa_1.2.4                 lavaan_0.6-5               
-[67] reticulate_1.14             SummarizedExperiment_1.16.1
-[69] spam_2.5-1                  beeswarm_0.2.3             
-[71] modeltools_0.2-22           xfun_0.12                  
-[73] zoo_1.8-7                   tidyselect_1.0.0           
-[75] ZIM_1.1.0                  
- [ reached getOption("max.print") -- omitted 254 entries ]
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      References

      -
      -
      -

      Colin Cameron, A, and Pravin K Trivedi. 2013. Regression Analysis of Count Data (Econometric Society Monographs). 2 edition. Cambridge University Press.

      -
      -
      -

      Collado-Torres, Leonardo, Abhinav Nellore, Kai Kammers, Shannon E Ellis, Margaret A Taub, Kasper D Hansen, Andrew E Jaffe, Ben Langmead, and Jeffrey T Leek. 2017. “Reproducible RNA-seq Analysis Using Recount2.” Nat. Biotechnol. 35 (4): 319–21. https://doi.org/10.1038/nbt.3838.

      -
      -
      -

      Delignette-Muller, Marie, and Christophe Dutang. 2015. “Fitdistrplus: An R Package for Fitting Distributions.” J. Stat. Softw. 64 (1): 1–34. https://doi.org/10.18637/jss.v064.i04.

      -
      -
      -

      Delmans, Mihails, and Martin Hemberg. 2016. “Discrete Distributional Differential Expression (D3E)–a Tool for Gene Expression Analysis of Single-Cell RNA-seq Data.” BMC Bioinformatics 17: 110. https://doi.org/10.1186/s12859-016-0944-6.

      -
      -
      -

      Kim, Jong Kyoung, Aleksandra A Kolodziejczyk, Tomislav Illicic, Sarah A Teichmann, and John C Marioni. 2015. “Characterizing Noise Structure in Single-Cell RNA-seq Distinguishes Genuine from Technical Stochastic Allelic Expression.” Nat. Commun. 6: 8687. https://doi.org/10.1038/ncomms9687.

      -
      -
      -

      Kim, Jong Kyoung, and John C Marioni. 2013. “Inferring the Kinetics of Stochastic Gene Expression from Single-Cell RNA-sequencing Data.” Genome Biol. 14 (1): R7. https://doi.org/10.1186/gb-2013-14-1-r7.

      -
      -
      -

      Mi, Gu, Yanming Di, and Daniel W Schafer. 2015. “Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data.” PLoS One 10 (3): e0119254. https://doi.org/10.1371/journal.pone.0119254.

      -
      -
      -

      Soneson, Charlotte, and Mark D Robinson. 2018. “Bias, Robustness and Scalability in Single-Cell Differential Expression Analysis.” Nat. Methods, February. https://doi.org/10.1038/nmeth.4612.

      -
      -
      -

      Svensson, Valentine. 2020. “Droplet scRNA-seq Is Not Zero-Inflated.” Nat. Biotechnol., January. https://doi.org/10.1038/s41587-019-0379-5.

      -
      -
      -

      Vieth, Beate, Swati Parekh, Christoph Ziegenhain, Wolfgang Enard, and Ines Hellmann. 2019. “A Systematic Evaluation of Single Cell RNA-seq Analysis Pipelines.” Nat. Commun. 10 (1): 4667. https://doi.org/10.1038/s41467-019-12266-7.

      -
      -
      -

      Vieth, Beate, Christoph Ziegenhain, Swati Parekh, Wolfgang Enard, and Ines Hellmann. 2017. “PowsimR: Power Analysis for Bulk and Single Cell RNA-seq Experiments.” Bioinformatics 33 (21): 3486–8. https://doi.org/10.1093/bioinformatics/btx435.

      -
      -
      -

      Wu, Hao, Chi Wang, and Zhijin Wu. 2015. “PROPER: Comprehensive Power Evaluation for Differential Expression Using RNA-seq.” Bioinformatics 31 (2): 233–41. https://doi.org/10.1093/bioinformatics/btu640.

      -
      -
      -

      Ziegenhain, Christoph, Beate Vieth, Swati Parekh, Björn Reinius, Amy Guillaumet-Adkins, Martha Smets, Heinrich Leonhardt, Holger Heyn, Ines Hellmann, and Wolfgang Enard. 2017. “Comparative Analysis of Single-Cell RNA Sequencing Methods.” Mol. Cell 65 (4): 631–643.e4. https://doi.org/10.1016/j.molcel.2017.01.023.

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

      p.G

      Numeric vector indicating the proportion of replicates per group +that express the phenotypic fold change. Default is 1, this means all show the expressiond difference. +For example, if 0.5 and n1 = 10 and n2 = 8, +then only 5 replicates in group 1 and 4 replicates in group 2 express the phenotypic fold change.
      p.B
      bPattern
      Thinning

      Integer vector specifying the downsampling. +

      Numeric vector specifying the downsampling. It has to be the same length and order as the vector n1. This is an implementation of thinCounts. The vector entries should be a proportion between 0 and 1, @@ -245,8 +255,8 @@

      Arg Default is FALSE, i.e. no gene expression dropouts.
      sim.seed

      Simulation seed.

      		setup.seed

      Setup seed.
      verbose
      @@ -156,7 +165,7 @@

      Setup()

      Setup options for RNA-seq count simulations.

      Setup options for RNA-seq count simulations
      @@ -264,11 +273,10 @@

      -

      Contents

      -       + @@ -279,7 +287,7 @@

      Contents

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotEvalDE.html b/docs/reference/plotEvalDE.html index fabc9b7..405fc5b 100644 --- a/docs/reference/plotEvalDE.html +++ b/docs/reference/plotEvalDE.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -187,16 +190,10 @@

        Examp plotEvalDE(evalderes, rate = "conditional", quick = FALSE, Annot = TRUE) }

      - @@ -207,7 +204,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotEvalDist.html b/docs/reference/plotEvalDist.html index 971f42d..8423cd8 100644 --- a/docs/reference/plotEvalDist.html +++ b/docs/reference/plotEvalDist.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -154,16 +157,10 @@

        Examp plotEvalDist(evalDistRes) }

      - @@ -174,7 +171,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotEvalROC.html b/docs/reference/plotEvalROC.html index 6258f11..92354f7 100644 --- a/docs/reference/plotEvalROC.html +++ b/docs/reference/plotEvalROC.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -184,16 +187,10 @@

        Examp plotEvalROC(evalRes = evalrocres, cutoff = "conservative", Annot = TRUE) }

      - @@ -204,7 +201,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotEvalSim.html b/docs/reference/plotEvalSim.html index c5c3214..8b0862c 100644 --- a/docs/reference/plotEvalSim.html +++ b/docs/reference/plotEvalSim.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -179,16 +182,10 @@

        Examp plotEvalSim(evalRes = evalsimres, Annot = TRUE) }

      - @@ -199,7 +196,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotParam.html b/docs/reference/plotParam.html index 522355a..febf62f 100644 --- a/docs/reference/plotParam.html +++ b/docs/reference/plotParam.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -170,16 +173,10 @@

        Examp plotParam(estParamRes = estparam, Annot=TRUE) }

      - @@ -190,7 +187,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotSpike.html b/docs/reference/plotSpike.html index 4f25938..0552309 100644 --- a/docs/reference/plotSpike.html +++ b/docs/reference/plotSpike.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -157,16 +160,10 @@

        Examp plotSpike(estSpike = spikeparam, Annot = TRUE) }

      - @@ -177,7 +174,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/plotTime.html b/docs/reference/plotTime.html index 13f5783..09786d7 100644 --- a/docs/reference/plotTime.html +++ b/docs/reference/plotTime.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +
      @@ -93,7 +96,7 @@
      • - + @@ -185,16 +188,10 @@

        Examp plotTime(evalRes = evalsimres, Annot = TRUE) }

      - @@ -205,7 +202,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/printEvalDE.html b/docs/reference/printEvalDE.html index faf9b3c..175bad2 100644 --- a/docs/reference/printEvalDE.html +++ b/docs/reference/printEvalDE.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ The results are marginalized, meaning that they are averaged quantities over all strata and simulations. This provides a quick view of the statistical power analysis per sample size setup." /> - @@ -59,7 +62,7 @@ - +
      @@ -97,7 +100,7 @@
      • - + @@ -151,17 +154,10 @@

        Examp ## for example evaluation result see \code{\link{evaluateDE}} }

      - @@ -172,7 +168,7 @@

      Author

      -

      Site built with pkgdown 1.4.1.

      +

      Site built with pkgdown 1.5.1.

      diff --git a/docs/reference/simulateDE.html b/docs/reference/simulateDE.html index 8abbbdf..3ffa2b1 100644 --- a/docs/reference/simulateDE.html +++ b/docs/reference/simulateDE.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ The user now needs to specify the RNA-seq Analysis Pipeline including preprocessing, normalisation and differential testing method. The return object contains DE test results from all simulations as well as descriptive statistics. The error matrix calculations will be conducted with evaluateDE." /> - @@ -59,7 +62,7 @@ - +
      @@ -97,7 +100,7 @@
      • - + @@ -142,6 +145,7 @@

        Simulate Differential Expression Pipeline

        "ROTS", "baySeq", "NOISeq", "EBSeq", "MAST", "BPSC", "scDD", "DECENT"), DEFilter = FALSE, +Counts = FALSE, NCores = NULL, verbose = TRUE) @@ -193,6 +197,12 @@

        Arg

      A logical vector indicating whether to run DE testing on filtered and/or imputed count data. Default is FALSE.

      Counts

      A logical vector indicating whether the simulated count matrix is also provided as output. +Default is FALSE since the output can be quite large. Note that if DEFilter is TRUE, +then the returned count matrix will countain the filtered and/or imputed count data.
      NCores