From d108a37fcebabe6fe3135082658cc69db7d4a4e4 Mon Sep 17 00:00:00 2001 From: karleg <32402869+karleg@users.noreply.github.com> Date: Wed, 13 May 2020 14:19:05 -0400 Subject: [PATCH] Updated figure3 This should generate the data for the figure for a single tissue and create the plots. I removed the linear regression since we are removing the plots that show the relationship between expression and splicing. --- jupyter/figure3.ipynb | 2516 ++++++++--------------------------------- 1 file changed, 454 insertions(+), 2062 deletions(-) diff --git a/jupyter/figure3.ipynb b/jupyter/figure3.ipynb index 609e469..4f71dcb 100644 --- a/jupyter/figure3.ipynb +++ b/jupyter/figure3.ipynb @@ -25,36 +25,41 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Warning message:\n", - "“package ‘visdat’ was built under R version 3.6.3”\n", - "Warning message:\n", - "“package ‘patchwork’ was built under R version 3.6.3”\n", - "Warning message:\n", - "“package ‘ggsci’ was built under R version 3.6.3”\n", - "Warning message:\n", - "“package ‘gridExtra’ was built under R version 3.6.3”\n" - ] - }, - { - "ename": "ERROR", - "evalue": "Error in library(report): there is no package called ‘report’\n", - "output_type": "error", - "traceback": [ - "Error in library(report): there is no package called ‘report’\nTraceback:\n", - "1. library(report)" + "Loading required package: StanHeaders\n", + "\n", + "rstan (Version 2.19.3, GitRev: 2e1f913d3ca3)\n", + "\n", + "For execution on a local, multicore CPU with excess RAM we recommend calling\n", + "options(mc.cores = parallel::detectCores()).\n", + "To avoid recompilation of unchanged Stan programs, we recommend calling\n", + "rstan_options(auto_write = TRUE)\n", + "\n", + "\n", + "Attaching package: ‘rstan’\n", + "\n", + "\n", + "The following object is masked from ‘package:runjags’:\n", + "\n", + " extract\n", + "\n", + "\n", + "The following object is masked from ‘package:coda’:\n", + "\n", + " traceplot\n", + "\n", + "\n" ] } ], "source": [ - "Sys.setenv(TAR = \"/bin/tar\") \n", - "\n", + "Sys.setenv(TAR = \"/bin/tar\")\n", "# dataviz dependencies\n", "library(ggplot2)\n", "library(visdat)\n", @@ -62,7 +67,9 @@ "library(ggsci)\n", "library(grid)\n", "library(gridExtra)\n", - "library(report)\n", + "library(coda)\n", + "library(rstan)\n", + "\n", "\n", "# BDA2E-utilities dependencies\n", "library(parallel)\n", @@ -71,53 +78,6 @@ "library(compute.es)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Figure 3a\n", - "\n", - "This figure is a diagram to explain the modeling and therefore not included here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Figure - DBDA2Eprograms plots\n", - "\n", - "code from: [dimorphAS/figures/oldFigureDrafts/figure3b.R](https://github.com/TheJacksonLaboratory/sbas/blob/master/dimorphAS/figures/oldFigureDrafts/figure3b.R)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "*********************************************************************\n", - "Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:\n", - "A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.\n", - "*********************************************************************\n", - "\n" - ] - } - ], - "source": [ - "source(\"../dimorphAS/DBDA2Eprograms/DBDA2E-utilities.R\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieving the required data" - ] - }, { "cell_type": "code", "execution_count": 4, @@ -127,37 +87,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "Downloading GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz \n", + "Downloading GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct \n", "from https://console.cloud.google.com/storage/browser/_details/gtex_analysis_v7/rna_seq_data/ ..\n", - "\n", - "Done!\n", - "\n", - "\n", - "\n", - "Unzipping compressed file GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz..\n", - "\n", - "Done! \n", - "\n", - "The file GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct can be found in ../data/\n", "\n" ] } ], "source": [ "# Download GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct from Google Cloud \n", - "if (!(\"GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct\" %in% list.files(\"../data/\"))) {\n", - " message(\"Downloading GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz \\nfrom https://console.cloud.google.com/storage/browser/_details/gtex_analysis_v7/rna_seq_data/ ..\")\n", - " system(\"wget -O ../data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz\", intern = TRUE)\n", + "if (!(\"GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct\" %in% list.files(\"../data/\"))) {\n", + " message(\"Downloading GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct \\nfrom https://console.cloud.google.com/storage/browser/_details/gtex_analysis_v7/rna_seq_data/ ..\")\n", + " system(\"wget -O ../data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz\", intern = TRUE)\n", " message(\"Done!\\n\\n\")\n", - " message(\"Unzipping compressed file GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz..\")\n", - " system(\"gunzip ../data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct.gz\", intern = TRUE)\n", - " message(\"Done! \\n\\nThe file GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct can be found in ../data/\")\n", + " message(\"Unzipping compressed file GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz..\")\n", + " system(\"gunzip ../data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz\", intern = TRUE)\n", + " message(\"Done! \\n\\nThe file GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct can be found in ../data/\")\n", "}" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Change local paths: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +129,32 @@ " 'Thyroid',\n", " 'Cells - Transformed fibroblasts',\n", " 'Artery - Aorta',\n", - " 'Skin - Sun Exposed (Lower leg).Skin - Not Sun Exposed (Suprapubic)')" + " 'Skin - Sun Exposed (Lower leg).Skin - Not Sun Exposed (Suprapubic)')\n", + "\n", + "tissue<-tissue.list[1] #can be replaced with a loop or argument to choose a different tissue\n", + "\n", + "file.with.de.results<-paste('/Users/karleg/Dimorph/other/',tissue,'se.txt',sep='') \n", + "\n", + "rbp.table.name<-'/Users/karleg/Dimorph/splice-relevant-genes.txt'\n", + "\n", + "events.table.name<-'/Users/karleg/Dimorph/fromGTF.SE.txt.1'\n", + "\n", + "inc.counts.file.name<-'/Users/karleg/Dimorph/rmats_final.se.jc.ijc.txt'\n", + "\n", + "skip.counts.file.name<-'/Users/karleg/Dimorph/rmats_final.se.jc.sjc.txt'\n", + "\n", + "metadata.file.name<-'/Users/karleg/Dimorph/SraRunTable.noCram.noExome.noWGS.totalRNA.txt'\n", + "\n", + "expression.file.name<-'/Users/karleg/Dimorph/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Figure 3a\n", + "\n", + "This figure is a diagram to explain the modeling and therefore not included here" ] }, { @@ -179,463 +163,349 @@ "metadata": {}, "outputs": [], "source": [ - "all.genes<-data.table::fread('../data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct',\n", - " sep='\\t',\n", - " header=TRUE,\n", - " skip=2,\n", - " colClasses = c(rep(\"character\", 2), rep(\"NULL\", 11688)))" + "## Load the skip and inclusion count matrices, and the list of RNA binding proteins that are annotated to either mRNA splicing, via spliceosome (GO:0000398),\n", + "## regulation of mRNA splicing, via spliceosome (GO:0048024), or both. The table has the Gene Symbol, the Uniprot ID (uprot.id), the NCBI Gene ID (gene.id) and boolean columns for being S=mRNA splicing, via spliceosome (GO:0000398) and R=regulation of mRNA splicing, via spliceosome (GO:0048024)." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
  1. 56202
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A data.table: 6 × 2
NameDescription
<chr><chr>
ENSG00000223972.4DDX11L1
ENSG00000227232.4WASH7P
ENSG00000243485.2MIR1302-11
ENSG00000237613.2FAM138A
ENSG00000268020.2OR4G4P
ENSG00000240361.1OR4G11P
\n" - ], - "text/latex": [ - "A data.table: 6 × 2\n", - "\\begin{tabular}{ll}\n", - " Name & Description\\\\\n", - " & \\\\\n", - "\\hline\n", - "\t ENSG00000223972.4 & DDX11L1 \\\\\n", - "\t ENSG00000227232.4 & WASH7P \\\\\n", - "\t ENSG00000243485.2 & MIR1302-11\\\\\n", - "\t ENSG00000237613.2 & FAM138A \\\\\n", - "\t ENSG00000268020.2 & OR4G4P \\\\\n", - "\t ENSG00000240361.1 & OR4G11P \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.table: 6 × 2\n", - "\n", - "| Name <chr> | Description <chr> |\n", - "|---|---|\n", - "| ENSG00000223972.4 | DDX11L1 |\n", - "| ENSG00000227232.4 | WASH7P |\n", - "| ENSG00000243485.2 | MIR1302-11 |\n", - "| ENSG00000237613.2 | FAM138A |\n", - "| ENSG00000268020.2 | OR4G4P |\n", - "| ENSG00000240361.1 | OR4G11P |\n", - "\n" - ], - "text/plain": [ - " Name Description\n", - "1 ENSG00000223972.4 DDX11L1 \n", - "2 ENSG00000227232.4 WASH7P \n", - "3 ENSG00000243485.2 MIR1302-11 \n", - "4 ENSG00000237613.2 FAM138A \n", - "5 ENSG00000268020.2 OR4G4P \n", - "6 ENSG00000240361.1 OR4G11P " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "dim(all.genes)\n", - "head(all.genes)" + "events.table<-read.table(file.with.de.results)\n", + "\n", + "events.table=events.table[abs(events.table$logFC)>=log2(1.5) & events.table$adj.P.Val<=0.05,]\n", + "\n", + "annot.table<-read.table(events.table.name,header=T)\n", + "\n", + "rownames(annot.table)<-annot.table$ID\n", + "\n", + "merged.table<-merge(events.table,annot.table,by='row.names')\n", + "\n", + "rbp.table<-read.table(rbp.table.name,sep='\\t',header=TRUE) \n", + "\n", + "inc.counts<-read.csv(inc.counts.file.name)\n", + "\n", + "skip.counts<-read.csv(skip.counts.file.name)\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Filtering out duplicate transcripts ids" + "#Read sample info" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "all.genes <- all.genes[!duplicated(all.genes$Description),]" + "meta.data<-read.csv(metadata.file.name,header=TRUE)\n", + "\n", + "meta.data<-meta.data[meta.data$body_site==tissue,]\n", + "\n", + "inc.counts<-inc.counts[,colnames(inc.counts) %in% meta.data$Run]\n", + "\n", + "skip.counts<-skip.counts[,colnames(skip.counts) %in% meta.data$Run]\n", + "\n", + "sd.threshold<-quantile(apply(inc.counts,1,sd)+apply(skip.counts,1,sd),0.95)\n", + "\n", + "skip.counts=skip.counts[rownames(skip.counts) %in% merged.table$Row.names,]\n", + "\n", + "inc.counts=inc.counts[rownames(inc.counts) %in% merged.table$Row.names,]\n", + "\n", + "if (nrow(skip.counts)>100)\n", + "{\n", + " select.events<-apply(inc.counts,1,sd)+apply(skip.counts,1,sd)>sd.threshold\n", + " \n", + " inc.counts<-inc.counts[select.events,]\n", + "\n", + " skip.counts<-skip.counts[select.events,]\n", + "\n", + " merged.table<-merged.table[select.events,]\n", + "}" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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A data.frame: 6 × 6
Geneuprot.idgene.idSRomim
<fct><fct><int><lgl><lgl><fct>
1AAR2 Q9Y31225980TRUEFALSEn/a
2ALYREFQ86V8110189TRUEFALSEn/a
3AQR O60306 9716TRUEFALSEn/a
4BCAS2 O7593410286TRUEFALSEn/a
5BUD13 Q9BRD084811TRUEFALSEn/a
6BUD31 P41223 8896TRUEFALSEn/a
\n" - ], - "text/latex": [ - "A data.frame: 6 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & Gene & uprot.id & gene.id & S & R & omim\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t1 & AAR2 & Q9Y312 & 25980 & TRUE & FALSE & n/a\\\\\n", - "\t2 & ALYREF & Q86V81 & 10189 & TRUE & FALSE & n/a\\\\\n", - "\t3 & AQR & O60306 & 9716 & TRUE & FALSE & n/a\\\\\n", - "\t4 & BCAS2 & O75934 & 10286 & TRUE & FALSE & n/a\\\\\n", - "\t5 & BUD13 & Q9BRD0 & 84811 & TRUE & FALSE & n/a\\\\\n", - "\t6 & BUD31 & P41223 & 8896 & TRUE & FALSE & n/a\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 6\n", - "\n", - "| | Gene <fct> | uprot.id <fct> | gene.id <int> | S <lgl> | R <lgl> | omim <fct> |\n", - "|---|---|---|---|---|---|---|\n", - "| 1 | AAR2 | Q9Y312 | 25980 | TRUE | FALSE | n/a |\n", - "| 2 | ALYREF | Q86V81 | 10189 | TRUE | FALSE | n/a |\n", - "| 3 | AQR | O60306 | 9716 | TRUE | FALSE | n/a |\n", - "| 4 | BCAS2 | O75934 | 10286 | TRUE | FALSE | n/a |\n", - "| 5 | BUD13 | Q9BRD0 | 84811 | TRUE | FALSE | n/a |\n", - "| 6 | BUD31 | P41223 | 8896 | TRUE | FALSE | n/a |\n", - "\n" - ], - "text/plain": [ - " Gene uprot.id gene.id S R omim\n", - "1 AAR2 Q9Y312 25980 TRUE FALSE n/a \n", - "2 ALYREF Q86V81 10189 TRUE FALSE n/a \n", - "3 AQR O60306 9716 TRUE FALSE n/a \n", - "4 BCAS2 O75934 10286 TRUE FALSE n/a \n", - "5 BUD13 Q9BRD0 84811 TRUE FALSE n/a \n", - "6 BUD31 P41223 8896 TRUE FALSE n/a " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rbp_filename = '../assets/splice-relevant-genes.txt'\n", - "if (! file.exists(rbp_filename)) {\n", - " error(\"Could not find RBP file\")\n", - "}\n", - "rbp_df = read.csv(rbp_filename, sep='\\t', header=TRUE)\n", - "head(rbp_df)" + "#Prepare expression of genes and RBPS:" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A data.frame: 6 × 6
Geneuprot.idgene.idSRomim
<fct><fct><int><lgl><lgl><fct>
1AAR2 Q9Y31225980TRUEFALSEn/a
2ALYREFQ86V8110189TRUEFALSEn/a
3AQR O60306 9716TRUEFALSEn/a
4BCAS2 O7593410286TRUEFALSEn/a
5BUD13 Q9BRD084811TRUEFALSEn/a
6BUD31 P41223 8896TRUEFALSEn/a
\n" - ], - "text/latex": [ - "A data.frame: 6 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & Gene & uprot.id & gene.id & S & R & omim\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t1 & AAR2 & Q9Y312 & 25980 & TRUE & FALSE & n/a\\\\\n", - "\t2 & ALYREF & Q86V81 & 10189 & TRUE & FALSE & n/a\\\\\n", - "\t3 & AQR & O60306 & 9716 & TRUE & FALSE & n/a\\\\\n", - "\t4 & BCAS2 & O75934 & 10286 & TRUE & FALSE & n/a\\\\\n", - "\t5 & BUD13 & Q9BRD0 & 84811 & TRUE & FALSE & n/a\\\\\n", - "\t6 & BUD31 & P41223 & 8896 & TRUE & FALSE & n/a\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 6\n", - "\n", - "| | Gene <fct> | uprot.id <fct> | gene.id <int> | S <lgl> | R <lgl> | omim <fct> |\n", - "|---|---|---|---|---|---|---|\n", - "| 1 | AAR2 | Q9Y312 | 25980 | TRUE | FALSE | n/a |\n", - "| 2 | ALYREF | Q86V81 | 10189 | TRUE | FALSE | n/a |\n", - "| 3 | AQR | O60306 | 9716 | TRUE | FALSE | n/a |\n", - "| 4 | BCAS2 | O75934 | 10286 | TRUE | FALSE | n/a |\n", - "| 5 | BUD13 | Q9BRD0 | 84811 | TRUE | FALSE | n/a |\n", - "| 6 | BUD31 | P41223 | 8896 | TRUE | FALSE | n/a |\n", - "\n" - ], - "text/plain": [ - " Gene uprot.id gene.id S R omim\n", - "1 AAR2 Q9Y312 25980 TRUE FALSE n/a \n", - "2 ALYREF Q86V81 10189 TRUE FALSE n/a \n", - "3 AQR O60306 9716 TRUE FALSE n/a \n", - "4 BCAS2 O75934 10286 TRUE FALSE n/a \n", - "5 BUD13 Q9BRD0 84811 TRUE FALSE n/a \n", - "6 BUD31 P41223 8896 TRUE FALSE n/a " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "spliceosome_genes <- rbp_df[rbp_df$S=='TRUE',] # GO:0000398\n", - "cat(sprintf(\"spliceosome_genes: n=%d genes (from a total of %d)\", dim(spliceosome_genes)[0], dim(rbp_df)[0]))\n", - "head(spliceosome_genes)" + "num.events=nrow(merged.table)\n", + "\n", + "event.to.gene=c()\n", + "\n", + "gexp=expression.mat[rownames.expression.mat %in% gene.names,]\n", + "\n", + "rownames(gexp)<-rownames.expression.mat[rownames.expression.mat %in% gene.names]\n", + "\n", + "gexp<-gexp[order(match(rownames(gexp),gene.names)),]\n", + "\n", + "gexp=log2(gexp+0.5)\n", + "\n", + "gexp=gexp-rowMeans(gexp)\n", + "\n", + "gexp[apply(gexp,1,sd)>0,]=gexp[apply(gexp,1,sd)>0,]/apply(gexp[apply(gexp,1,sd)>0,],1,sd)\n", + "\n", + "rexp=expression.mat[rownames.expression.mat %in% rbp.table$Gene,]\n", + "\n", + "rownames(rexp)<-rownames.expression.mat[rownames.expression.mat %in% rbp.table$Gene]\n", + "\n", + "rexp<-rexp[order(match(rownames(rexp),rbp.table$Gene)),]\n", + "\n", + "rexp=log2(rexp+0.5)\n", + "\n", + "rexp=rexp-rowMeans(rexp)\n", + "\n", + "rexp=rexp/apply(rexp,1,function(v){ifelse(sum(v==v[1])\n", - "A data.frame: 6 × 6\n", - "\n", - "\tGeneuprot.idgene.idSRomim\n", - "\t<fct><fct><int><lgl><lgl><fct>\n", - "\n", - "\n", - "\t7C1QBP Q07021 708FALSETRUE617713\n", - "\t8C9orf78Q9NZ6351759FALSETRUEn/a \n", - "\t16CELF1 Q9287910658 TRUETRUEn/a \n", - "\t17CELF2 O9531910659 TRUETRUEn/a \n", - "\t18CELF3 Q5SZQ811189 TRUETRUEn/a \n", - "\t19CELF4 Q9BZC156853 TRUETRUEn/a \n", - "\n", - "\n" - ], - "text/latex": [ - "A data.frame: 6 × 6\n", - "\\begin{tabular}{r|llllll}\n", - " & Gene & uprot.id & gene.id & S & R & omim\\\\\n", - " & & & & & & \\\\\n", - "\\hline\n", - "\t7 & C1QBP & Q07021 & 708 & FALSE & TRUE & 617713\\\\\n", - "\t8 & C9orf78 & Q9NZ63 & 51759 & FALSE & TRUE & n/a \\\\\n", - "\t16 & CELF1 & Q92879 & 10658 & TRUE & TRUE & n/a \\\\\n", - "\t17 & CELF2 & O95319 & 10659 & TRUE & TRUE & n/a \\\\\n", - "\t18 & CELF3 & Q5SZQ8 & 11189 & TRUE & TRUE & n/a \\\\\n", - "\t19 & CELF4 & Q9BZC1 & 56853 & TRUE & TRUE & n/a \\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 6 × 6\n", - "\n", - "| | Gene <fct> | uprot.id <fct> | gene.id <int> | S <lgl> | R <lgl> | omim <fct> |\n", - "|---|---|---|---|---|---|---|\n", - "| 7 | C1QBP | Q07021 | 708 | FALSE | TRUE | 617713 |\n", - "| 8 | C9orf78 | Q9NZ63 | 51759 | FALSE | TRUE | n/a |\n", - "| 16 | CELF1 | Q92879 | 10658 | TRUE | TRUE | n/a |\n", - "| 17 | CELF2 | O95319 | 10659 | TRUE | TRUE | n/a |\n", - "| 18 | CELF3 | Q5SZQ8 | 11189 | TRUE | TRUE | n/a |\n", - "| 19 | CELF4 | Q9BZC1 | 56853 | TRUE | TRUE | n/a |\n", - "\n" - ], - "text/plain": [ - " Gene uprot.id gene.id S R omim \n", - "7 C1QBP Q07021 708 FALSE TRUE 617713\n", - "8 C9orf78 Q9NZ63 51759 FALSE TRUE n/a \n", - "16 CELF1 Q92879 10658 TRUE TRUE n/a \n", - "17 CELF2 O95319 10659 TRUE TRUE n/a \n", - "18 CELF3 Q5SZQ8 11189 TRUE TRUE n/a \n", - "19 CELF4 Q9BZC1 56853 TRUE TRUE n/a " - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + " [1] 1 1 0 1 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 1 1 1 0\n", + " [38] 1 1 1 1 0 1 1 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1\n", + " [75] 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0\n", + "[112] 1 1 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 1\n", + "[149] 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 0 0 1 0\n", + "[186] 1 1 1 1 0 1 0 1\n" + ] } ], "source": [ - "splice_regulation_genes = rbp_df[rbp_df$R=='TRUE',] # GO:0048024\n", - "head(splice_regulation_genes)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "summary.tab<-matrix(ncol=7,\n", - " nrow=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "colnames(summary.tab)<-c('Event',\n", - " 'Gene', \n", - " 'Sig. RBPs',\n", - " 'Sig. Gene Expression',\n", - " 'Sig. Sex',\n", - " 'Tissue',\n", - " 'Dimorphic')" + "#Run stan:" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "top.rbps<-rbp.names" + "dataList = list(\n", + " as = round(skip.counts) , #skip event counts across experiments\n", + " c = round(skip.counts+inc.counts) , #total counts for event, i.e. skip+inclusion, across experiments\n", + " gexp = gexp, #read counts for genes (from gtex, take the raw counts) across experiments\n", + " rexp = rexp, #read counts for RBPs (from gtex, take the raw counts)\n", + " event_to_gene = event.to.gene, #the gene index for each event (1 to the number of distinct genes) \n", + " Nrbp = nrow(rexp), #number of RBPs\n", + " Nevents = nrow(merged.table), #most varying AS events in \n", + " Nexp = ncol(expression.mat),#number of experiments such that we measured each event, gene and RBP in each experiment\n", + " Ngenes = nrow(gexp),\n", + " sex=sex\n", + ")\n", + "\n", + "\n", + "modelString = \"\n", + "data {\n", + "int Nevents;\n", + "int Nexp;\n", + "int Nrbp;\n", + "int Ngenes;\n", + "int as[Nevents,Nexp] ;\n", + "int c[Nevents,Nexp] ;\n", + "matrix[Ngenes,Nexp] gexp ; \n", + "matrix[Nrbp,Nexp] rexp ; \n", + "int event_to_gene[Nevents];\n", + "int sex[Nexp];\n", + "\n", + "}\n", + "\n", + "\n", + "parameters {\n", + "real beta0[Nevents] ;\n", + "real beta1[Nevents] ;\n", + "matrix[Nevents,Nrbp] beta2 ;\n", + "real beta3[Nevents];\n", + "real beta4[Nrbp];\n", + "\n", + "}\n", + "model {\n", + "\n", + "for ( i in 1:Nexp ) { \n", + "\n", + "\n", + " for ( j in 1:Nevents ) if (c[j,i]>0) { \n", + "\n", + " as[j,i] ~ binomial(c[j,i], inv_logit(beta0[j]+beta1[j]*sex[i]+dot_product(beta2[j,],rexp[,i])+beta3[j]*gexp[event_to_gene[j],i] ) );\n", + "\n", + " }\n", + "}\n", + "\n", + "for (k in 1:Nrbp){\n", + "\n", + " for ( j in 1:Nevents ) { \n", + "\n", + " beta2[j,k] ~normal(beta4[k],1);\n", + " }\n", + "\n", + " beta4[k]~normal(0,1);\n", + "\n", + "}\n", + "\n", + "\n", + "for ( j in 1:Nevents ) { \n", + "\n", + " beta1[j] ~ normal(0,1);\n", + " beta0[j] ~ normal(0,1);\n", + " beta3[j] ~ normal(0,1);\n", + " }\n", + "\n", + "}\n", + "\"\n", + "\n", + "stanDso <- stan_model( model_code=modelString ) \n", + "\n", + "stanFit <- sampling( object=stanDso , data = dataList , chains = 3 ,iter = 8000,warmup=6000 , thin = 1,init=0, cores=3 )\n", + "\n", + "mcmcCoda = mcmc.list( lapply( 1:ncol(stanFit) , function(x) { mcmc(as.array(stanFit)[,x,]) } ) )\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "136" - ], - "text/latex": [ - "136" - ], - "text/markdown": [ - "136" - ], - "text/plain": [ - "[1] 136" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "*********************************************************************\n", + "Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:\n", + "A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.\n", + "*********************************************************************\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading required package: coda\n", + "\n", + "Linked to JAGS 4.3.0\n", + "\n", + "Loaded modules: basemod,bugs\n", + "\n" + ] } ], "source": [ - "length(top.rbps)" + "source(\"/Users/karleg/Downloads/dimorpAS-master/DBDA2Eprograms/DBDA2E-utilities.R\")" ] }, { @@ -647,79 +517,16 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "df <-data.frame(coef=NULL,rbp=NULL,tissue=NULL)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "# Refactoring needed to not rely on hard coded by position id of tissue\n", - "tissue <- tissue.list[[1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "'Heart - Left Ventricle'" - ], - "text/latex": [ - "'Heart - Left Ventricle'" - ], - "text/markdown": [ - "'Heart - Left Ventricle'" - ], - "text/plain": [ - "[1] \"Heart - Left Ventricle\"" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "head(tissue)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## `{Missing files!}` Dimorph/McmcMostVaryingMoreSigs_'" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "load(paste('/Users/karleg/Dimorph/McmcMostVaryingMoreSigs_',tissue,'.Rdata',sep=''))\n", - " \n", - "mcmcCoda<-mcmcCoda[,which(grepl('beta2\\\\[101,87\\\\]',varnames(mcmcCoda))),drop=FALSE]\n", - "\n", - "diagMCMC( mcmcCoda , parName=c(\"beta2[101,87]\") ) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using cached `.Rdata` until the files Dimorph/McmcMostVaryingMoreSigs_* are located" + "df <-data.frame(coef=NULL,rbp=NULL,tissue=NULL)\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -741,17 +548,22 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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smUKAAQG8v1HaTvn6+Hjr03mFbmhrpWWaeH6jq5yiaJorivZfDn6ztW4giiGgrR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jYoczo0gdlFE2c0hkTwuBkcrX1n2O5D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\n76R0tzQspG2aoxX6maz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Pw+vBoPhsRQ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\nvqoiB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"text/plain": [ "Plot with title “”" ] }, - "metadata": {}, + "metadata": { + "image/png": { + "height": 240, + "width": 360 + } + }, "output_type": "display_data" } ], @@ -860,220 +672,80 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Figure 3b \n", - "\n", - "code from: [dimorphAS/figures/figure3/figure3e.R](https://github.com/TheJacksonLaboratory/sbas/blob/master/dimorphAS/figures/figure3e/figure3e.R)" + "##Collect coefficients for RBPs whose 95% HDI does not contain 0:" ] }, { - "cell_type": "raw", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "tissue.list<-c('Heart - Left Ventricle',\n", - " 'Breast - Mammary Tissue',\n", - " 'Brain - Cortex.Brain - Frontal Cortex (BA9).Brain - Anterior cingulate cortex (BA24)',\n", - " 'Adrenal Gland',\n", - " 'Adipose - Subcutaneous',\n", - " 'Muscle - Skeletal','Thyroid','Cells - Transformed fibroblasts',\n", - " 'Artery - Aorta',\n", - " 'Skin - Sun Exposed (Lower leg).Skin - Not Sun Exposed (Suprapubic)')\n", - "\n", - "\n", - "all.genes<-read.table('../data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.gct',\n", - " sep='\\t',\n", - " header=T,\n", - " skip=2,\n", - " colClasses = c(rep(\"character\", 2), \n", - " rep(\"NULL\", 11688)))\n", - "\n", - "all.genes<-all.genes[!duplicated(all.genes$Description),]\n", - "\n", - "rbp.names<-unique(gsub('_.*','',list.files('/Users/karleg/Dimorph/RBP_PSSMs/')))\n", + "rbp.names<-rownames(rexp)\n", "\n", - "rbp.names<-rbp.names[rbp.names %in% all.genes$Description]\n", + "df<-data.frame(coef=NULL,rbp=NULL,tissue=NULL)\n", "\n", - "summary.tab<-matrix(ncol=7,nrow=0)\n", + "hdi<-HPDinterval(mcmcCoda) \n", "\n", - "colnames(summary.tab)<-c('Event','Gene', 'Sig. RBPs','Sig. Gene Expression','Sig. Sex','Tissue','Dimorphic')\n", + "s <- summary(mcmcCoda)\n", "\n", - "top.rbps<-rbp.names\n", + "m <- s$statistics[,\"Mean\"]\n", "\n", - "df<-data.frame(coef=NULL,rbp=NULL,tissue=NULL)\n", + "beta2.mat<-matrix(nrow=nrow(merged.table),ncol=length(rbp.names))\n", "\n", - "for (tissue in c('Heart - Left Ventricle',\n", - " 'Breast - Mammary Tissue',\n", - " 'Brain - Cortex.Brain - Frontal Cortex (BA9).Brain - Anterior cingulate cortex (BA24)',\n", - " 'Thyroid',\n", - " 'Artery - Aorta'))\n", - "{\n", - " \n", - " load(paste('/Users/karleg/Dimorph/McmcMostVaryingMoreSigs_',tissue,'.Rdata',sep=''))\n", - " \n", - " hdi<-HPDinterval(mcmcCoda) \n", - " \n", - " #diagMCMC( mcmcCoda , parName=c(\"beta3[101]\") ) \n", - " \n", - " s <- summary(mcmcCoda)\n", - " \n", - " m <- s$statistics[,\"Mean\"]\n", + "for (rbp in (1:length(rbp.names)))\n", " \n", - " #hist(m[grepl('beta3',names(m))])\n", - " \n", - " #names(m)[grepl('beta2',names(m))][abs(m[grepl('beta2',names(m))])>1]\n", - " events.tab<-read.table(paste('/Users/karleg/Dimorph/EventsTable_',tissue,'.txt',sep=''),header = T)\n", - " \n", - " beta2.mat<-matrix(nrow=nrow(events.tab),ncol=length(rbp.names))\n", - " \n", - " for (rbp in (1:length(rbp.names)))\n", - " \n", - " for (event in (1:nrow(events.tab)))\n", - " {\n", - " \n", - " var.name<-paste0('beta2[',event,',',rbp,']')\n", - " \n", - " low<-hdi[[1]][rownames(hdi[[1]])==var.name][1]\n", - " \n", - " high<-hdi[[1]][rownames(hdi[[1]])==var.name][2]\n", - " \n", - " beta2.mat[event,rbp]<-m[grepl(paste0('beta2\\\\[',event,',',rbp,'\\\\]'),names(m))]\n", - " \n", - " if (low<0 && high>0)\n", - " \n", - " beta2.mat[event,rbp]<-0\n", - " \n", - " }\n", - " colnames(beta2.mat)=rbp.names\n", - " \n", - " \n", - " for (rbp in top.rbps)\n", - " \n", - " df<-rbind(df,cbind(beta2.mat[events.tab$adj.P.Val<=0.05,colnames(beta2.mat)==rbp],rep(rbp,nrow(beta2.mat)),rep(tissue,nrow(beta2.mat))) )\n", - " \n", - " \n", - " beta3.vec=matrix(nrow=nrow(events.tab),ncol=1)\n", - " \n", - " for (event in (1:nrow(events.tab)))\n", + " for (event in (1:nrow(merged.table)))\n", " {\n", - " var.name<-paste0('beta3[',event,']')\n", + " \n", + " var.name<-paste0('beta2[',event,',',rbp,']')\n", " \n", " low<-hdi[[1]][rownames(hdi[[1]])==var.name][1]\n", " \n", " high<-hdi[[1]][rownames(hdi[[1]])==var.name][2]\n", " \n", - " beta3.vec[event]<-m[grepl(paste0('beta3\\\\[',event,'\\\\]'),names(m))]\n", + " beta2.mat[event,rbp]<-m[grepl(paste0('beta2\\\\[',event,',',rbp,'\\\\]'),names(m))]\n", " \n", " if (low<0 && high>0)\n", " \n", - " beta3.vec[event]<-0\n", + " beta2.mat[event,rbp]<-0\n", + " \n", " }\n", + "\n", + "\n", + "\n", + "colnames(beta2.mat)=rbp.names\n", + "\n", + "for (rbp in rbp.names)\n", " \n", - " #par(mfrow=c(1,2))\n", - " \n", - " #hist(beta3.vec[events.tab$adj.P.Val<=0.05],main=paste('Expression in Dimorphic',tissue),xlab='Posterior Mean')\n", - " \n", - " #hist(beta3.vec[events.tab$adj.P.Val>0.05],main=paste('Expression in Other',tissue),xlab='Posterior Mean')\n", - " \n", - " write.table(beta3.vec[events.tab$adj.P.Val<=0.05],paste0('/Users/karleg/Dimorph/',tissue,'_Expression_comparison_sig.txt'),quote = F)\n", - " \n", - " write.table(beta3.vec[events.tab$adj.P.Val>0.05],paste0('/Users/karleg/Dimorph/',tissue,'_Expression_comparison_other.txt'),quote = F)\n", - " \n", - " \n", - " beta1.vec=matrix(nrow=nrow(events.tab),ncol=1)\n", - " \n", - " for (event in (1:nrow(events.tab)))\n", - " {\n", - " \n", - " var.name<-paste0('beta1[',event,']')\n", - " \n", - " low<-hdi[[1]][rownames(hdi[[1]])==var.name][1]\n", - " \n", - " high<-hdi[[1]][rownames(hdi[[1]])==var.name][2]\n", - " \n", - " beta1.vec[event]<-m[grepl(paste0('beta1\\\\[',event,'\\\\]'),names(m))]\n", - " \n", - " if (low<0 && high>0)\n", - " \n", - " beta1.vec[event]<-0\n", - " }\n", - " \n", - " #par(mfrow=c(1,2))\n", - " \n", - " #hist(beta1.vec[events.tab$adj.P.Val<=0.05],main=paste('Sex in Dimorphic',tissue),xlab='Posterior Mean')\n", - " \n", - " #hist(beta1.vec[events.tab$adj.P.Val>0.05],main=paste('Sex in Other',tissue),xlab='Posterior Mean')\n", - " \n", - " write.table(beta1.vec[events.tab$adj.P.Val<=0.05],paste0('/Users/karleg/Dimorph/',tissue,'_Sex_comparison_sig.txt'),quote = F)\n", - " \n", - " write.table(beta1.vec[events.tab$adj.P.Val>0.05],paste0('/Users/karleg/Dimorph/',tissue,'_Sex_comparison_other.txt'),quote = F)\n", - " \n", - " \n", - " #read events table and compare dimorphic and non-dimorphic for each RBP\n", - " \n", - " if (sum(events.tab$adj.P.Val<=0.05)>1)\n", - " {\n", - " out.tab<-rbind(colMeans(beta2.mat[events.tab$adj.P.Val<=0.05,]),colMeans(beta2.mat[events.tab$adj.P.Val>0.05,]))\n", - " }else{\n", - " out.tab<-rbind(beta2.mat[events.tab$adj.P.Val<=0.05,],colMeans(beta2.mat[events.tab$adj.P.Val>0.05,]))\n", - " }\n", - " rownames(out.tab)<-c('Dimorphic','Other')\n", - " \n", - " write.table(out.tab,paste0('/Users/karleg/Dimorph/',tissue,'_RBP_comparison.txt'),quote = F)\n", - " \n", - " for (i in (1:nrow(events.tab)))\n", - " {\n", - " \n", - " next.row<-rep('',6)\n", - " #Event, Gene, Sig. RBBs,Gene Expression,Sex,Tissue,dimorphic\n", - " \n", - " next.row[1]<-as.character(events.tab$geneSymbol)[i]\n", - " \n", - " sig.rbps=''\n", - " \n", - " for (j in (1:length(rbp.names)))\n", - " {\n", - " if (beta2.mat[i,j]!=0)\n", - " {\n", - " \n", - " if (next.row[2]!='')\n", - " \n", - " next.row[2]<-paste0(next.row[2],',')\n", - " \n", - " next.row[2]<-paste0(next.row[2],rbp.names[j],'(',round(beta2.mat[i,j],2),')')\n", - " }\n", - " }\n", - " \n", - " #if (beta3.vec[i]!=0)\n", - " \n", - " next.row[3]<-round(beta3.vec[i],2)\n", - " \n", - " # if (beta1.vec[i]!=0)\n", - " \n", - " next.row[4]<-round(beta1.vec[i],2)\n", - " \n", - " next.row[5]<-tissue\n", - " \n", - " next.row[6]<-ifelse(events.tab$adj.P.Val[i]<=0.05,'Yes','No')\n", - " \n", - " next.row=c(events.tab$Row.names[i],next.row)\n", - " \n", - " summary.tab<-rbind(summary.tab,next.row)\n", - " \n", - " }\n", - " \n", - " \n", - "}\n", - "#diagMCMC( mcmcCoda , parName=c(\"beta4[12,2]\") ) \n", - "write.table(summary.tab,'/Users/karleg/Dimorph/summary_hbm.txt',sep='\\t',quote = F,row.names = F,col.names = T)\n", + " df<-rbind(df,cbind(beta2.mat[,colnames(beta2.mat)==rbp],rep(rbp,nrow(beta2.mat)),rep(tissue,nrow(beta2.mat))) )\n", "\n", - "summary.tab<-summary.tab[summary.tab[,'Dimorphic']=='Yes',]\n", "\n", "colnames(df)<-c('Coef','RBP','Tissue')\n", - "\n", - "df$Coef<-as.numeric(as.character(df$Coef))\n", - "\n", + " \n", + "df$Coef<-as.numeric(as.character(df$Coef))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Display a violin plot for some selected RBPs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "labels<-read.table('/Users/karleg/Dimorph/labels.tsv',sep='\\t',header=T)\n", "\n", "df$Tissue<-as.character(df$Tissue)\n", @@ -1084,911 +756,102 @@ " \n", " df$Tissue[i]<-as.character(labels$X[which(as.character(labels$tissue)==as.character(df$Tissue[i]))])\n", "\n", - "#df$Tissue[which(df$Tissue %in% labels$tissue)]<-labels$X[]\n", - "#df.plot<-df.plot[df.plot$Tissue %in% c('Heart (LV)','Cortex','Breast','Thyroid','Aorta'),]\n", - "library(\"ggsci\")\n", - "library(\"ggplot2\")\n", - "library(\"gridExtra\")\n", - "library(grid)\n", - "\n", "\n", "df<-df[df$Coef!=0,]\n", "\n", - "#top.rbps<-names(sort(unlist(lapply(lapply(split(df$Coef[df$Coef!=0],df$RBP[df$Coef!=0]),abs),mean)),decreasing = T))[1:10]\n", - "\n", - "sum.pos<-sort(unlist(lapply(lapply(split(df$Coef,df$RBP),'>',0),sum)),decreasing = T)\n", - "\n", - "sum.neg<-sort(unlist(lapply(lapply(split(df$Coef,df$RBP),'<',0),sum)),decreasing = T)\n", - "\n", - "hnrnp<-c(\"HNRNPA1\", \"HNRNPA1L2\", \"HNRNPA2B1\" ,\"HNRNPC\",\"HNRNPCL1\" ,\"HNRNPF\",\"HNRNPH1\",\"HNRNPH2\",\"HNRNPK\", \n", - " \"HNRNPL\",\"HNRNPM\", \"HNRNPU\")\n", - "\n", - "srsf<-c(\"SRSF1\",\"SRSF10\",\"SRSF2\",\"SRSF7\",\"SRSF9\")\n", - "\n", - "sum.pos<-sum.pos[order(names(sum.pos))]\n", - "\n", - "sum.neg<-sum.neg[order(names(sum.neg))]\n", - "\n", - "pos.rbps<-names(which(sum.pos/(sum.pos+sum.neg)>=0.75 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", - "\n", - "neg.rbps<-names(which(sum.pos/(sum.pos+sum.neg)<=0.25 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", - "\n", - "cs.rbps<-names(which(sum.pos/(sum.pos+sum.neg)>0.25 & sum.pos/(sum.pos+sum.neg)<0.75 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", - "\n", - "df.counts<-data.frame(type=c('Skip','Inc','CS'),counts=c(length(pos.rbps),length(neg.rbps),length(cs.rbps)))\n", "\n", "df$RBP<-as.character(df$RBP)\n", "\n", - "sort(sum.pos,decreasing = T)\n", - "\n", - "pos.rbps\n", - "\n", - "df1<-df[df$RBP %in% c(\"HNRNPH2\", \"HNRNPU\",\"U2AF2\"),]\n", + "df1<-df[df$RBP %in% c(\"BUD13\", \"GTF2F1\",\"CTNNBL1\"),]\n", "\n", "pn1<-ggplot(df1,aes(factor(RBP),Coef)) + geom_violin(aes(fill='red')) + scale_fill_manual(values = '#4DBBD5FF') \n", - "pn1 <- pn1 + theme_minimal() + theme(text = element_text(size=20),\n", - " axis.text = element_text(size=20, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=24),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\") + ylab(\"\") + labs(title=\"\")+ylim(-2,2)+ geom_hline(yintercept=0)\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "sort(sum.neg,decreasing = T)\n", - "\n", - "neg.rbps\n", "\n", - "sort(unlist(lapply(lapply(split(df$Coef,df$RBP),abs),sum)),decreasing = T)\n", - "\n", - "df2<-df[df$RBP %in% c(\"YBX1\", \"SRSF9\",\"MATR3\"),]\n", - "\n", - "pn2<-ggplot(df2,aes(factor(RBP),Coef)) +geom_violin(aes(fill='blue'))+ scale_fill_manual(values = '#00A087FF')\n", - "pn2 <- pn2 + theme_minimal() + theme(text = element_text(size=20),\n", - " axis.text = element_text(size=20, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=24),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\") + ylab(\"\") + labs(title=\"\")+ylim(-1,1)+ geom_hline(yintercept=0)\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "df3<-df[df$RBP %in% c(\"HNRNPK\", \"HNRNPA1L2\",\"SRSF7\"),]\n", - "\n", - "pn3<-ggplot(df3,aes(factor(RBP),Coef)) +geom_violin(aes(fill='green')) + scale_fill_manual(values = '#E64B35FF') \n", - "pn3 <- pn3 + theme_minimal() + theme(text = element_text(size=20),\n", + "pn1 <- pn1 + theme_minimal() + theme(text = element_text(size=20),\n", " axis.text = element_text(size=20, hjust=0.5),\n", " axis.title.x=element_blank(),\n", " axis.title.y = element_text(size=24),\n", " plot.title = element_text(hjust = 0.5),\n", " legend.position = \"none\") + ylab(\"\") + labs(title=\"\")+ylim(-2,2)+ geom_hline(yintercept=0)\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "pn4<-ggplot(df.counts, aes(type, counts)) + geom_bar(aes(fill = type), position = \"dodge\", stat=\"identity\") + \n", - " theme(axis.text.x = element_text(angle = 90, hjust = 1))+ scale_fill_npg() + guides(fill=FALSE)+theme(axis.title.x=element_blank(),axis.title=element_text(size=18))\n", - "\n", - "\n", - "\n", - "grid.arrange(pn1,pn2,pn3,pn4, nrow = 4,\n", - " left = textGrob('Coefficient',gp = gpar(fontsize = 20), rot = 90,vjust=1))\n", - "\n", - "\n", - "rbp.tab<-matrix(ncol=3,nrow=0)\n", - "\n", - "colnames(rbp.tab)<-c('RBP','Total','Mean')\n", - "\n", - "rbp.coefs<-split(df$Coef,df$RBP)\n", - "\n", - "for (rbp in names(sum.pos)[which(sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))])\n", - "{\n", - " \n", - " dist.mean<-mean(unlist(rbp.coefs[rbp]))\n", - " \n", - " tot.int<-length(unlist(rbp.coefs[rbp]))\n", - " \n", - " rbp.tab<-rbind(rbp.tab,c(rbp,tot.int,dist.mean))\n", - " \n", - "}\n", - "\n", - "write.table(rbp.tab,'RBP_summary.txt',sep='\\t',quote = F,row.names = F)\n", - "\n", - "save.image('/Users/karleg/Dimorph/RDATA/figure3e.RData')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NOTE:\n", - "\n", - "Because the files `'/Users/karleg/Dimorph/', tissue,'_Expression_comparison_other.txt')` are missing we will reproduce using the stored Rdata" + "\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "### Loading relevant .Rdata for figure 3b" + "##Create barplot of the number of RBPs that tend to promote skipping, the number of RBPs that tend to promote\n", + "#inclusion and the number of RBPs whose effect is context-specific, for the two RBP groups" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "load(\"../dimorphAS/figures/figure3/figure3e.Rdata\")" + "spliceosome_genes = as.character(rbp.table$Gene[rbp.table$S=='TRUE'])\n", + "\n", + "splice_regulation_genes = as.character(rbp.table$Gene[rbp.table$R=='TRUE'])\n", + "\n", + "for (RBP_set in list(spliceosome=spliceosome_genes,splice_regulation=splice_regulation_genes))\n", + "{\n", + " sum.pos<-sort(unlist(lapply(lapply(split(df$Coef[df$RBP %in% RBP_set],df$RBP[df$RBP %in% RBP_set]),'>',0),sum)),decreasing = T)\n", + " \n", + " sum.neg<-sort(unlist(lapply(lapply(split(df$Coef[df$RBP %in% RBP_set],df$RBP[df$RBP %in% RBP_set]),'<',0),sum)),decreasing = T)\n", + " \n", + " sum.pos<-sum.pos[order(names(sum.pos))]\n", + " \n", + " sum.neg<-sum.neg[order(names(sum.neg))]\n", + " \n", + " pos.rbps<-names(which(sum.pos/(sum.pos+sum.neg)>=0.75 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", + " \n", + " neg.rbps<-names(which(sum.pos/(sum.pos+sum.neg)<=0.25 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", + " \n", + " cs.rbps<-names(which(sum.pos/(sum.pos+sum.neg)>0.25 & sum.pos/(sum.pos+sum.neg)<0.75 & (sum.pos+sum.neg>quantile(sum.pos+sum.neg,0.2))))\n", + " \n", + " df.counts<-data.frame(type=c('Skip','Inc','CS'),counts=c(length(pos.rbps),length(neg.rbps),length(cs.rbps)))\n", + " \n", + " pn4_new <- ggplot(df.counts, aes(type, counts)) + \n", + " geom_bar(fill = \"#00008B\",color=\"black\", position = \"dodge\", stat=\"identity\") + \n", + " geom_text(aes(x = type, y = counts + 10, label = paste(100 * round(counts/sum(counts), 3), \"%\", sep = \"\")), size = 3) +\n", + " guides(fill=FALSE) +\n", + " xlab(\"\") + scale_y_continuous(breaks = c(0, 20, 40), limits = c(0, 60))+\n", + " theme_minimal() +\n", + " theme(\n", + " axis.text = element_text(size = 8), \n", + " axis.text.x = element_text(angle = 90, hjust = 1), \n", + " axis.title = element_text(size = 10),\n", + " axis.title.y = element_text(vjust = 5)\n", + " )\n", + " show(pn4_new )\n", + "}\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 25, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "
MATR3
124
RBM6
96
MSI1
88
ZNF638
88
ESRP2
84
KHDRBS3
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YBX1
84
FUS
82
HNRNPC
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FXR2
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TIA1
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72
PTBP1
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SAMD4A
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RALY
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RBM28
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G3BP2
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RBM24
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RBM41
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SF1
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SRSF7
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HNRNPK
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CPEB4
54
PABPC1
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ZC3H14
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RBM4
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SNRPA
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PUM2
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QKI
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TARDBP
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A1CF
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DAZAP1
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FXR1
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HNRNPA1L2
46
HNRNPA2B1
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PABPC5
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HNRNPL
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MBNL1
40
PCBP3
40
RBMS3
40
RBFOX1
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RBM5
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ZCRB1
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36
SRSF2
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ENOX1
34
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32
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32
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30
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28
SFPQ
28
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26
TUT1
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LIN28A
24
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24
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22
ANKHD1
20
CNOT4
20
CPO
20
RBMS1
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PCBP2
18
ZC3H10
18
IGF2BP3
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PABPC4
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RBP1
10
\n" - ], - "text/latex": [ - "\\begin{description*}\n", - "\\item[MATR3] 124\n", - "\\item[RBM6] 96\n", - "\\item[MSI1] 88\n", - "\\item[ZNF638] 88\n", - "\\item[ESRP2] 84\n", - "\\item[KHDRBS3] 84\n", - "\\item[YBX1] 84\n", - "\\item[FUS] 82\n", - "\\item[HNRNPC] 78\n", - "\\item[HNRNPCL1] 78\n", - "\\item[KHDRBS1] 76\n", - "\\item[FXR2] 74\n", - "\\item[TIA1] 74\n", - "\\item[PCBP1] 72\n", - "\\item[PTBP1] 70\n", - "\\item[RBM42] 70\n", - "\\item[SRSF9] 70\n", - "\\item[PABPN1] 68\n", - "\\item[RBM45] 66\n", - "\\item[SAMD4A] 66\n", - "\\item[RALY] 64\n", - "\\item[RBM28] 64\n", - "\\item[SRP54] 64\n", - "\\item[YBX2] 64\n", - "\\item[RBM38] 62\n", - "\\item[SRSF10] 62\n", - "\\item[G3BP2] 60\n", - "\\item[RBM24] 60\n", - "\\item[RBM41] 60\n", - "\\item[SF1] 60\n", - "\\item[SRSF7] 60\n", - "\\item[HNRNPK] 58\n", - "\\item[CPEB4] 54\n", - "\\item[PABPC1] 54\n", - "\\item[ZC3H14] 52\n", - "\\item[RBM4] 50\n", - "\\item[SNRPA] 50\n", - "\\item[PUM2] 48\n", - "\\item[QKI] 48\n", - "\\item[TARDBP] 48\n", - "\\item[A1CF] 46\n", - "\\item[DAZAP1] 46\n", - "\\item[FXR1] 46\n", - "\\item[HNRNPA1L2] 46\n", - "\\item[HNRNPA2B1] 46\n", - "\\item[PPRC1] 46\n", - "\\item[PABPC5] 44\n", - "\\item[HNRNPF] 42\n", - "\\item[HNRNPL] 42\n", - "\\item[PABPC3] 42\n", - "\\item[RBM3] 42\n", - "\\item[RSF1] 42\n", - "\\item[HNRNPH1] 40\n", - "\\item[MBNL1] 40\n", - "\\item[PCBP3] 40\n", - "\\item[RBMS3] 40\n", - "\\item[RBFOX1] 38\n", - "\\item[RBM5] 38\n", - "\\item[SRSF1] 38\n", - "\\item[ZCRB1] 38\n", - "\\item[CPEB2] 36\n", - "\\item[KHDRBS2] 36\n", - "\\item[SRSF2] 36\n", - "\\item[ENOX1] 34\n", - "\\item[SNRNP70] 34\n", - "\\item[SART3] 32\n", - "\\item[U2AF2] 32\n", - "\\item[HNRNPA1] 30\n", - "\\item[HNRNPM] 28\n", - "\\item[SFPQ] 28\n", - "\\item[IGF2BP2] 26\n", - "\\item[TUT1] 26\n", - "\\item[LIN28A] 24\n", - "\\item[RBM46] 24\n", - "\\item[FMR1] 22\n", - "\\item[ANKHD1] 20\n", - "\\item[CNOT4] 20\n", - "\\item[CPO] 20\n", - "\\item[RBMS1] 20\n", - "\\item[HNRNPH2] 18\n", - "\\item[PCBP2] 18\n", - "\\item[ZC3H10] 18\n", - "\\item[IGF2BP3] 16\n", - "\\item[PABPC4] 16\n", - "\\item[HNRNPU] 12\n", - "\\item[RBM8A] 12\n", - "\\item[RBP1] 10\n", - "\\end{description*}\n" - ], - "text/markdown": [ - "MATR3\n", - ": 124RBM6\n", - ": 96MSI1\n", - ": 88ZNF638\n", - ": 88ESRP2\n", - ": 84KHDRBS3\n", - ": 84YBX1\n", - ": 84FUS\n", - ": 82HNRNPC\n", - ": 78HNRNPCL1\n", - ": 78KHDRBS1\n", - ": 76FXR2\n", - ": 74TIA1\n", - ": 74PCBP1\n", - ": 72PTBP1\n", - ": 70RBM42\n", - ": 70SRSF9\n", - ": 70PABPN1\n", - ": 68RBM45\n", - ": 66SAMD4A\n", - ": 66RALY\n", - ": 64RBM28\n", - ": 64SRP54\n", - ": 64YBX2\n", - ": 64RBM38\n", - ": 62SRSF10\n", - ": 62G3BP2\n", - ": 60RBM24\n", - ": 60RBM41\n", - ": 60SF1\n", - ": 60SRSF7\n", - ": 60HNRNPK\n", - ": 58CPEB4\n", - ": 54PABPC1\n", - ": 54ZC3H14\n", - ": 52RBM4\n", - ": 50SNRPA\n", - ": 50PUM2\n", - ": 48QKI\n", - ": 48TARDBP\n", - ": 48A1CF\n", - ": 46DAZAP1\n", - ": 46FXR1\n", - ": 46HNRNPA1L2\n", - ": 46HNRNPA2B1\n", - ": 46PPRC1\n", - ": 46PABPC5\n", - ": 44HNRNPF\n", - ": 42HNRNPL\n", - ": 42PABPC3\n", - ": 42RBM3\n", - ": 42RSF1\n", - ": 42HNRNPH1\n", - ": 40MBNL1\n", - ": 40PCBP3\n", - ": 40RBMS3\n", - ": 40RBFOX1\n", - ": 38RBM5\n", - ": 38SRSF1\n", - ": 38ZCRB1\n", - ": 38CPEB2\n", - ": 36KHDRBS2\n", - ": 36SRSF2\n", - ": 36ENOX1\n", - ": 34SNRNP70\n", - ": 34SART3\n", - ": 32U2AF2\n", - ": 32HNRNPA1\n", - ": 30HNRNPM\n", - ": 28SFPQ\n", - ": 28IGF2BP2\n", - ": 26TUT1\n", - ": 26LIN28A\n", - ": 24RBM46\n", - ": 24FMR1\n", - ": 22ANKHD1\n", - ": 20CNOT4\n", - ": 20CPO\n", - ": 20RBMS1\n", - ": 20HNRNPH2\n", - ": 18PCBP2\n", - ": 18ZC3H10\n", - ": 18IGF2BP3\n", - ": 16PABPC4\n", - ": 16HNRNPU\n", - ": 12RBM8A\n", - ": 12RBP1\n", - ": 10\n", - "\n" - ], - "text/plain": [ - " MATR3 RBM6 MSI1 ZNF638 ESRP2 KHDRBS3 YBX1 FUS \n", - " 124 96 88 88 84 84 84 82 \n", - " HNRNPC HNRNPCL1 KHDRBS1 FXR2 TIA1 PCBP1 PTBP1 RBM42 \n", - " 78 78 76 74 74 72 70 70 \n", - " SRSF9 PABPN1 RBM45 SAMD4A RALY RBM28 SRP54 YBX2 \n", - " 70 68 66 66 64 64 64 64 \n", - " RBM38 SRSF10 G3BP2 RBM24 RBM41 SF1 SRSF7 HNRNPK \n", - " 62 62 60 60 60 60 60 58 \n", - " CPEB4 PABPC1 ZC3H14 RBM4 SNRPA PUM2 QKI TARDBP \n", - " 54 54 52 50 50 48 48 48 \n", - " A1CF DAZAP1 FXR1 HNRNPA1L2 HNRNPA2B1 PPRC1 PABPC5 HNRNPF \n", - " 46 46 46 46 46 46 44 42 \n", - " HNRNPL PABPC3 RBM3 RSF1 HNRNPH1 MBNL1 PCBP3 RBMS3 \n", - " 42 42 42 42 40 40 40 40 \n", - " RBFOX1 RBM5 SRSF1 ZCRB1 CPEB2 KHDRBS2 SRSF2 ENOX1 \n", - " 38 38 38 38 36 36 36 34 \n", - " SNRNP70 SART3 U2AF2 HNRNPA1 HNRNPM SFPQ IGF2BP2 TUT1 \n", - " 34 32 32 30 28 28 26 26 \n", - " LIN28A RBM46 FMR1 ANKHD1 CNOT4 CPO RBMS1 HNRNPH2 \n", - " 24 24 22 20 20 20 20 18 \n", - " PCBP2 ZC3H10 IGF2BP3 PABPC4 HNRNPU RBM8A RBP1 \n", - " 18 18 16 16 12 12 10 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
  1. 'ESRP2'
  2. 'FUS'
  3. 'HNRNPC'
  4. 'KHDRBS3'
  5. 'MATR3'
  6. 'MSI1'
  7. 'PTBP1'
  8. 'RBM6'
  9. 'SRSF9'
  10. 'TIA1'
  11. 'YBX1'
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 'ESRP2'\n", - "\\item 'FUS'\n", - "\\item 'HNRNPC'\n", - "\\item 'KHDRBS3'\n", - "\\item 'MATR3'\n", - "\\item 'MSI1'\n", - "\\item 'PTBP1'\n", - "\\item 'RBM6'\n", - "\\item 'SRSF9'\n", - "\\item 'TIA1'\n", - "\\item 'YBX1'\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 'ESRP2'\n", - "2. 'FUS'\n", - "3. 'HNRNPC'\n", - "4. 'KHDRBS3'\n", - "5. 'MATR3'\n", - "6. 'MSI1'\n", - "7. 'PTBP1'\n", - "8. 'RBM6'\n", - "9. 'SRSF9'\n", - "10. 'TIA1'\n", - "11. 'YBX1'\n", - "\n", - "\n" - ], - "text/plain": [ - " [1] \"ESRP2\" \"FUS\" \"HNRNPC\" \"KHDRBS3\" \"MATR3\" \"MSI1\" \"PTBP1\" \n", - " [8] \"RBM6\" \"SRSF9\" \"TIA1\" \"YBX1\" " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
ZNF638
231.755298649641
U2AF2
103.318146577871
MATR3
101.66940336896
G3BP2
97.4908041695574
PABPN1
82.3909695769502
HNRNPH2
80.885843462961
PCBP1
73.8045184632622
RBM46
73.2485299893158
KHDRBS1
73.0123778614421
HNRNPCL1
67.2813031156986
CNOT4
66.499615786642
FXR2
66.0969334263465
RBM38
65.9472269479394
HNRNPK
65.6779136240446
RBFOX1
55.1872113398685
HNRNPU
54.8101427702097
PUM2
53.9744350311024
HNRNPA1L2
53.8925846431266
SART3
53.0321785033932
ANKHD1
52.7111926246497
SFPQ
51.3325094208499
SRSF10
50.7726490718131
PCBP2
50.7609581071295
MBNL1
50.2314492544127
RBMS1
50.1953958265368
RBM5
48.4095282541054
KHDRBS3
47.706765547535
RBM28
45.8579591659952
TARDBP
45.2461386236215
HNRNPH1
44.9741313837708
SF1
44.624316837804
RSF1
44.6155199710813
PPRC1
43.8998387426906
RBM8A
43.7702692115768
FUS
43.0827978173706
HNRNPA2B1
42.0419780140703
DAZAP1
41.6098970741066
FXR1
40.6862386448845
FMR1
40.5100277003227
HNRNPM
40.0252744832019
RALY
39.5679943760387
SRSF7
39.5562892848236
PTBP1
39.5285892144038
SRP54
39.5144496107468
YBX1
39.2067197595424
HNRNPF
37.8878315906491
SRSF2
37.2971488676168
TIA1
37.2521545850092
ESRP2
36.6345614169498
SNRNP70
36.3956261505067
SRSF9
36.2660052934321
SRSF1
35.2414125606478
SAMD4A
34.8428291676424
RBM42
34.7555345969212
ZC3H14
34.5664765778173
MSI1
34.5246031072578
RBM41
33.9447204797353
RBM6
33.6598083196647
ZCRB1
33.1059120907759
PABPC1
32.8388178368039
HNRNPL
30.4998258779072
RBM4
30.0682655362262
YBX2
29.5869324574711
TUT1
29.4417218436294
KHDRBS2
29.009331432412
SNRPA
28.9779021734349
RBM24
28.2420136910499
QKI
28.2299915735165
A1CF
27.7983533743637
PABPC5
27.014611870491
RBP1
25.2764563675069
HNRNPA1
24.3676888810533
IGF2BP2
23.8104692341667
CPEB2
23.5154325253519
RBMS3
23.1020888329447
ENOX1
22.7507010433425
CPEB4
21.4706190576409
RBM45
21.1014871969819
ZC3H10
20.3662227991457
PABPC4
20.057415053595
CPO
19.2450429682424
HNRNPC
18.7241077418792
PABPC3
18.6801539708497
RBM3
18.2373187904472
IGF2BP3
15.9604426932137
PCBP3
15.32275183353
LIN28A
8.7321864444122
\n" - ], - "text/latex": [ - "\\begin{description*}\n", - "\\item[ZNF638] 231.755298649641\n", - "\\item[U2AF2] 103.318146577871\n", - "\\item[MATR3] 101.66940336896\n", - "\\item[G3BP2] 97.4908041695574\n", - "\\item[PABPN1] 82.3909695769502\n", - "\\item[HNRNPH2] 80.885843462961\n", - "\\item[PCBP1] 73.8045184632622\n", - "\\item[RBM46] 73.2485299893158\n", - "\\item[KHDRBS1] 73.0123778614421\n", - "\\item[HNRNPCL1] 67.2813031156986\n", - "\\item[CNOT4] 66.499615786642\n", - "\\item[FXR2] 66.0969334263465\n", - "\\item[RBM38] 65.9472269479394\n", - "\\item[HNRNPK] 65.6779136240446\n", - "\\item[RBFOX1] 55.1872113398685\n", - "\\item[HNRNPU] 54.8101427702097\n", - "\\item[PUM2] 53.9744350311024\n", - "\\item[HNRNPA1L2] 53.8925846431266\n", - "\\item[SART3] 53.0321785033932\n", - "\\item[ANKHD1] 52.7111926246497\n", - "\\item[SFPQ] 51.3325094208499\n", - "\\item[SRSF10] 50.7726490718131\n", - "\\item[PCBP2] 50.7609581071295\n", - "\\item[MBNL1] 50.2314492544127\n", - "\\item[RBMS1] 50.1953958265368\n", - "\\item[RBM5] 48.4095282541054\n", - "\\item[KHDRBS3] 47.706765547535\n", - "\\item[RBM28] 45.8579591659952\n", - "\\item[TARDBP] 45.2461386236215\n", - "\\item[HNRNPH1] 44.9741313837708\n", - "\\item[SF1] 44.624316837804\n", - "\\item[RSF1] 44.6155199710813\n", - "\\item[PPRC1] 43.8998387426906\n", - "\\item[RBM8A] 43.7702692115768\n", - "\\item[FUS] 43.0827978173706\n", - "\\item[HNRNPA2B1] 42.0419780140703\n", - "\\item[DAZAP1] 41.6098970741066\n", - "\\item[FXR1] 40.6862386448845\n", - "\\item[FMR1] 40.5100277003227\n", - "\\item[HNRNPM] 40.0252744832019\n", - "\\item[RALY] 39.5679943760387\n", - "\\item[SRSF7] 39.5562892848236\n", - "\\item[PTBP1] 39.5285892144038\n", - "\\item[SRP54] 39.5144496107468\n", - "\\item[YBX1] 39.2067197595424\n", - "\\item[HNRNPF] 37.8878315906491\n", - "\\item[SRSF2] 37.2971488676168\n", - "\\item[TIA1] 37.2521545850092\n", - "\\item[ESRP2] 36.6345614169498\n", - "\\item[SNRNP70] 36.3956261505067\n", - "\\item[SRSF9] 36.2660052934321\n", - "\\item[SRSF1] 35.2414125606478\n", - "\\item[SAMD4A] 34.8428291676424\n", - "\\item[RBM42] 34.7555345969212\n", - "\\item[ZC3H14] 34.5664765778173\n", - "\\item[MSI1] 34.5246031072578\n", - "\\item[RBM41] 33.9447204797353\n", - "\\item[RBM6] 33.6598083196647\n", - "\\item[ZCRB1] 33.1059120907759\n", - "\\item[PABPC1] 32.8388178368039\n", - "\\item[HNRNPL] 30.4998258779072\n", - "\\item[RBM4] 30.0682655362262\n", - "\\item[YBX2] 29.5869324574711\n", - "\\item[TUT1] 29.4417218436294\n", - "\\item[KHDRBS2] 29.009331432412\n", - "\\item[SNRPA] 28.9779021734349\n", - "\\item[RBM24] 28.2420136910499\n", - "\\item[QKI] 28.2299915735165\n", - "\\item[A1CF] 27.7983533743637\n", - "\\item[PABPC5] 27.014611870491\n", - "\\item[RBP1] 25.2764563675069\n", - "\\item[HNRNPA1] 24.3676888810533\n", - "\\item[IGF2BP2] 23.8104692341667\n", - "\\item[CPEB2] 23.5154325253519\n", - "\\item[RBMS3] 23.1020888329447\n", - "\\item[ENOX1] 22.7507010433425\n", - "\\item[CPEB4] 21.4706190576409\n", - "\\item[RBM45] 21.1014871969819\n", - "\\item[ZC3H10] 20.3662227991457\n", - "\\item[PABPC4] 20.057415053595\n", - "\\item[CPO] 19.2450429682424\n", - "\\item[HNRNPC] 18.7241077418792\n", - "\\item[PABPC3] 18.6801539708497\n", - "\\item[RBM3] 18.2373187904472\n", - "\\item[IGF2BP3] 15.9604426932137\n", - "\\item[PCBP3] 15.32275183353\n", - "\\item[LIN28A] 8.7321864444122\n", - "\\end{description*}\n" - ], - "text/markdown": [ - "ZNF638\n", - ": 231.755298649641U2AF2\n", - ": 103.318146577871MATR3\n", - ": 101.66940336896G3BP2\n", - ": 97.4908041695574PABPN1\n", - ": 82.3909695769502HNRNPH2\n", - ": 80.885843462961PCBP1\n", - ": 73.8045184632622RBM46\n", - ": 73.2485299893158KHDRBS1\n", - ": 73.0123778614421HNRNPCL1\n", - ": 67.2813031156986CNOT4\n", - ": 66.499615786642FXR2\n", - ": 66.0969334263465RBM38\n", - ": 65.9472269479394HNRNPK\n", - ": 65.6779136240446RBFOX1\n", - ": 55.1872113398685HNRNPU\n", - ": 54.8101427702097PUM2\n", - ": 53.9744350311024HNRNPA1L2\n", - ": 53.8925846431266SART3\n", - ": 53.0321785033932ANKHD1\n", - ": 52.7111926246497SFPQ\n", - ": 51.3325094208499SRSF10\n", - ": 50.7726490718131PCBP2\n", - ": 50.7609581071295MBNL1\n", - ": 50.2314492544127RBMS1\n", - ": 50.1953958265368RBM5\n", - ": 48.4095282541054KHDRBS3\n", - ": 47.706765547535RBM28\n", - ": 45.8579591659952TARDBP\n", - ": 45.2461386236215HNRNPH1\n", - ": 44.9741313837708SF1\n", - ": 44.624316837804RSF1\n", - ": 44.6155199710813PPRC1\n", - ": 43.8998387426906RBM8A\n", - ": 43.7702692115768FUS\n", - ": 43.0827978173706HNRNPA2B1\n", - ": 42.0419780140703DAZAP1\n", - ": 41.6098970741066FXR1\n", - ": 40.6862386448845FMR1\n", - ": 40.5100277003227HNRNPM\n", - ": 40.0252744832019RALY\n", - ": 39.5679943760387SRSF7\n", - ": 39.5562892848236PTBP1\n", - ": 39.5285892144038SRP54\n", - ": 39.5144496107468YBX1\n", - ": 39.2067197595424HNRNPF\n", - ": 37.8878315906491SRSF2\n", - ": 37.2971488676168TIA1\n", - ": 37.2521545850092ESRP2\n", - ": 36.6345614169498SNRNP70\n", - ": 36.3956261505067SRSF9\n", - ": 36.2660052934321SRSF1\n", - ": 35.2414125606478SAMD4A\n", - ": 34.8428291676424RBM42\n", - ": 34.7555345969212ZC3H14\n", - ": 34.5664765778173MSI1\n", - ": 34.5246031072578RBM41\n", - ": 33.9447204797353RBM6\n", - ": 33.6598083196647ZCRB1\n", - ": 33.1059120907759PABPC1\n", - ": 32.8388178368039HNRNPL\n", - ": 30.4998258779072RBM4\n", - ": 30.0682655362262YBX2\n", - ": 29.5869324574711TUT1\n", - ": 29.4417218436294KHDRBS2\n", - ": 29.009331432412SNRPA\n", - ": 28.9779021734349RBM24\n", - ": 28.2420136910499QKI\n", - ": 28.2299915735165A1CF\n", - ": 27.7983533743637PABPC5\n", - ": 27.014611870491RBP1\n", - ": 25.2764563675069HNRNPA1\n", - ": 24.3676888810533IGF2BP2\n", - ": 23.8104692341667CPEB2\n", - ": 23.5154325253519RBMS3\n", - ": 23.1020888329447ENOX1\n", - ": 22.7507010433425CPEB4\n", - ": 21.4706190576409RBM45\n", - ": 21.1014871969819ZC3H10\n", - ": 20.3662227991457PABPC4\n", - ": 20.057415053595CPO\n", - ": 19.2450429682424HNRNPC\n", - ": 18.7241077418792PABPC3\n", - ": 18.6801539708497RBM3\n", - ": 18.2373187904472IGF2BP3\n", - ": 15.9604426932137PCBP3\n", - ": 15.32275183353LIN28A\n", - ": 8.7321864444122\n", - "\n" - ], - "text/plain": [ - " ZNF638 U2AF2 MATR3 G3BP2 PABPN1 HNRNPH2 PCBP1 \n", - "231.755299 103.318147 101.669403 97.490804 82.390970 80.885843 73.804518 \n", - " RBM46 KHDRBS1 HNRNPCL1 CNOT4 FXR2 RBM38 HNRNPK \n", - " 73.248530 73.012378 67.281303 66.499616 66.096933 65.947227 65.677914 \n", - " RBFOX1 HNRNPU PUM2 HNRNPA1L2 SART3 ANKHD1 SFPQ \n", - " 55.187211 54.810143 53.974435 53.892585 53.032179 52.711193 51.332509 \n", - " SRSF10 PCBP2 MBNL1 RBMS1 RBM5 KHDRBS3 RBM28 \n", - " 50.772649 50.760958 50.231449 50.195396 48.409528 47.706766 45.857959 \n", - " TARDBP HNRNPH1 SF1 RSF1 PPRC1 RBM8A FUS \n", - " 45.246139 44.974131 44.624317 44.615520 43.899839 43.770269 43.082798 \n", - " HNRNPA2B1 DAZAP1 FXR1 FMR1 HNRNPM RALY SRSF7 \n", - " 42.041978 41.609897 40.686239 40.510028 40.025274 39.567994 39.556289 \n", - " PTBP1 SRP54 YBX1 HNRNPF SRSF2 TIA1 ESRP2 \n", - " 39.528589 39.514450 39.206720 37.887832 37.297149 37.252155 36.634561 \n", - " SNRNP70 SRSF9 SRSF1 SAMD4A RBM42 ZC3H14 MSI1 \n", - " 36.395626 36.266005 35.241413 34.842829 34.755535 34.566477 34.524603 \n", - " RBM41 RBM6 ZCRB1 PABPC1 HNRNPL RBM4 YBX2 \n", - " 33.944720 33.659808 33.105912 32.838818 30.499826 30.068266 29.586932 \n", - " TUT1 KHDRBS2 SNRPA RBM24 QKI A1CF PABPC5 \n", - " 29.441722 29.009331 28.977902 28.242014 28.229992 27.798353 27.014612 \n", - " RBP1 HNRNPA1 IGF2BP2 CPEB2 RBMS3 ENOX1 CPEB4 \n", - " 25.276456 24.367689 23.810469 23.515433 23.102089 22.750701 21.470619 \n", - " RBM45 ZC3H10 PABPC4 CPO HNRNPC PABPC3 RBM3 \n", - " 21.101487 20.366223 20.057415 19.245043 18.724108 18.680154 18.237319 \n", - " IGF2BP3 PCBP3 LIN28A \n", - " 15.960443 15.322752 8.732186 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning message:\n", - "“Removed 30 rows containing non-finite values (stat_ydensity).”Warning message:\n", - "“Removed 2 rows containing non-finite values (stat_ydensity).”" - ] - }, - { - "data": { - "image/png": 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qv78uXLy5YtUygUAr8A+aBRovadMdvvWG9uRPY9RcJhXdZdoVC4fPnyoUOHcl3R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\nAQBSowgBAFL7P5L2HJc3jYBoAAAAAElFTkSuQmCC", - "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stderr", "output_type": "stream", "text": [ - "Warning message:\n", - "“Removed 4 rows containing non-finite values (stat_ydensity).”" + "The files lv.txt or mt.txt are available in the folder ../data/! \n", + "\n", + "The 'perl parseMT.pl' command will not be re-run \n", + "\n" ] - }, - { - "data": { - "image/png": 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GPV1dWXXnrpwIEDKysrq6urkyQpPEZ4/PHH9+/fv3CzTZs21dfXdz1BtG/fvkuX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\nIQChCSEAoQkhAKEJIQCh/R/BHNSbfN5/7wAAAABJRU5ErkJggg==", - "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "options(repr.plot.width=5, repr.plot.height=12)\n", - "\n", - "pn1<-ggplot(df1,aes(factor(RBP),Coef)) + \n", - " geom_violin(aes(fill='red')) + \n", - " scale_fill_manual(values = '#4DBBD5FF')+\n", - " theme_minimal() + \n", - " theme(text = element_text(size=20),\n", - " axis.text = element_text(size=20, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=24),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\") + ylab(\"\") + \n", - " labs(title=\"\")+\n", - " ylim(-2,2)+ \n", - " geom_hline(yintercept=0)\n", - "\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "sort(sum.neg,decreasing = T)\n", - "\n", - "neg.rbps\n", - "\n", - "sort(unlist(lapply(lapply(split(df$Coef,df$RBP),abs),sum)),decreasing = T)\n", - "\n", - "df2<-df[df$RBP %in% c(\"YBX1\", \"SRSF9\",\"MATR3\"),]\n", - "\n", - "pn2<-ggplot(df2,aes(factor(RBP),Coef)) +geom_violin(aes(fill='blue'))+ scale_fill_manual(values = '#00A087FF')\n", - "pn2 <- pn2 + theme_minimal() + theme(text = element_text(size=20),\n", - " axis.text = element_text(size=20, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=24),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\") + ylab(\"\") + labs(title=\"\")+ylim(-1,1)+ geom_hline(yintercept=0)\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "df3<-df[df$RBP %in% c(\"HNRNPK\", \"HNRNPA1L2\",\"SRSF7\"),]\n", - "\n", - "pn3<-ggplot(df3,aes(factor(RBP),Coef)) +geom_violin(aes(fill='green')) + scale_fill_manual(values = '#E64B35FF') \n", - "pn3 <- pn3 + theme_minimal() + theme(text = element_text(size=20),\n", - " axis.text = element_text(size=20, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=24),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\") + ylab(\"\") + labs(title=\"\")+ylim(-2,2)+ geom_hline(yintercept=0)\n", - "#pn <- pn + geom_dotplot(binaxis='y', stackdir='center', dotsize=0.5)\n", - "\n", - "\n", - "pn4<-ggplot(df.counts, aes(type, counts)) + geom_bar(aes(fill = type), position = \"dodge\", stat=\"identity\") + \n", - " theme(axis.text.x = element_text(angle = 90, hjust = 1))+ scale_fill_npg() + guides(fill=FALSE)+theme(axis.title.x=element_blank(),axis.title=element_text(size=18))\n", - "\n", - "\n", - "\n", - "grid.arrange(pn1,pn2,pn3,pn4, nrow = 4,\n", - " left = textGrob('Coefficient',gp = gpar(fontsize = 20), rot = 90,vjust=1))\n", - "\n", - "df1$facet = 1\n", - "df2$facet = 2\n", - "df3$facet = 3\n", - "df_c <- rbind(df1, df2, df3)\n", - "p <- ggplot(df_c,aes(x = factor(RBP), y = Coef, fill = factor(RBP))) + \n", - " geom_violin() + facet_wrap(~facet, nrow = 3, scales = \"free\") + \n", - " xlab(\"\") + ylab(\"\") + ylim(-2,2)+ geom_hline(yintercept=0) + scale_fill_npg() +\n", - " theme_minimal() + theme(text = element_text(size=20),\n", - " axis.text = element_text(size=8, hjust=0.5),\n", - " axis.title.x=element_blank(),\n", - " axis.title.y = element_text(size=8),\n", - " plot.title = element_text(hjust = 0.5),\n", - " legend.position = \"none\", \n", - " strip.text = element_blank())\n", - " \n", - "pn4_new <- ggplot(df.counts, aes(type, counts)) + \n", - " geom_bar(fill = \"#00008B\",color=\"black\", position = \"dodge\", stat=\"identity\") + \n", - " geom_text(aes(x = type, y = counts + 10, label = paste(100 * round(counts/sum(counts), 3), \"%\", sep = \"\")), size = 3) +\n", - " guides(fill=FALSE) +\n", - " xlab(\"\") + scale_y_continuous(breaks = c(0, 20, 40), limits = c(0, 60))+\n", - " theme_minimal() +\n", - " theme(\n", - " axis.text = element_text(size = 8), \n", - " axis.text.x = element_text(angle = 90, hjust = 1), \n", - " axis.title = element_text(size = 10),\n", - " axis.title.y = element_text(vjust = 5)\n", - " )\n", - "pn4_new \n", - "p_grid <- arrangeGrob(p,pn4_new, nrow = 2, heights = c(3, 1), \n", - " left = textGrob('Coefficient',gp = gpar(fontsize = 10), rot = 90,vjust=1, hjust = -0.2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Figure 3c\n", - "**EDIT**: \n", - "This code comes from `dimorphAS/notebook/figure4a` but corresponds to the figure **`Figure 3c`** of the publication." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This script creates figure 4a. Please run the following command first:\n", - "\n", - "`perl` [`parseMT.pl`](https://github.com/TheJacksonLaboratory/sbas/blob/master/dimorphAS/notebook/parseMT.pl)\n", - "\n", - "This creates the files needed for figure `4a` and `4b`, namely `lv.txt` and `mt.txt`.\n", - "The input file for [`parseMT.pl`](https://github.com/TheJacksonLaboratory/sbas/blob/master/dimorphAS/notebook/parseMT.pl) is a `.tsv` file name `summary_hbm.txt`. Here is a preview of this file:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "summary_hbm <- utils::read.table(file = \"../dimorphAS//notebook/summary_hbm.txt\", \n", - " header = TRUE, \n", - " sep = \"\\t\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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A data.frame: 2 × 7
EventGeneSig..RBPsSig..Gene.ExpressionSig..SexTissueDimorphic
<int><fct><fct><dbl><dbl><fct><fct>
121228TANK CNOT4(-0.21),CPEB2(-0.11),DAZAP1(-0.14),ENOX1(-0.06),HNRNPA1(0.07),HNRNPA2B1(0.22),HNRNPCL1(-0.16),HNRNPF(0.22),HNRNPH1(0.14),HNRNPH2(0.24),KHDRBS1(0.23),KHDRBS3(-0.06),PABPC1(0.12),PABPC5(0.08),PPRC1(0.14),RBFOX1(0.19),RBM4(-0.16),RBM45(0.03),RBM46(-0.13),RBMS3(-0.07),RBP1(-0.05),SAMD4A(0.13),SRSF1(-0.23),SRSF7(-0.12),TARDBP(-0.17),U2AF2(0.25),ZC3H14(0.27)0.290.16Heart - Left VentricleNo
228196UBE2E1FXR1(-0.16),FXR2(-0.19),HNRNPA2B1(0.19),IGF2BP3(-0.04),KHDRBS1(-0.19),LIN28A(0.02),MSI1(0.05),PABPC3(-0.07),PABPC4(0.11),PABPN1(-0.16),PCBP2(-0.15),RBFOX1(-0.13),RBM4(-0.1),RBM45(-0.03),RBM5(0.11),RBM8A(-0.14),RBMS1(-0.19),RBMS3(0.07),SAMD4A(0.12),SRSF2(0.13),SRSF9(-0.23),U2AF2(0.14),ZC3H14(0.2),ZCRB1(0.13),ZNF638(-0.24) 0.680.00Heart - Left VentricleNo
\n" - ], - "text/latex": [ - "A data.frame: 2 × 7\n", - "\\begin{tabular}{r|lllllll}\n", - " & Event & Gene & Sig..RBPs & Sig..Gene.Expression & Sig..Sex & Tissue & Dimorphic\\\\\n", - " & & & & & & & \\\\\n", - "\\hline\n", - "\t1 & 21228 & TANK & CNOT4(-0.21),CPEB2(-0.11),DAZAP1(-0.14),ENOX1(-0.06),HNRNPA1(0.07),HNRNPA2B1(0.22),HNRNPCL1(-0.16),HNRNPF(0.22),HNRNPH1(0.14),HNRNPH2(0.24),KHDRBS1(0.23),KHDRBS3(-0.06),PABPC1(0.12),PABPC5(0.08),PPRC1(0.14),RBFOX1(0.19),RBM4(-0.16),RBM45(0.03),RBM46(-0.13),RBMS3(-0.07),RBP1(-0.05),SAMD4A(0.13),SRSF1(-0.23),SRSF7(-0.12),TARDBP(-0.17),U2AF2(0.25),ZC3H14(0.27) & 0.29 & 0.16 & Heart - Left Ventricle & No\\\\\n", - "\t2 & 28196 & UBE2E1 & FXR1(-0.16),FXR2(-0.19),HNRNPA2B1(0.19),IGF2BP3(-0.04),KHDRBS1(-0.19),LIN28A(0.02),MSI1(0.05),PABPC3(-0.07),PABPC4(0.11),PABPN1(-0.16),PCBP2(-0.15),RBFOX1(-0.13),RBM4(-0.1),RBM45(-0.03),RBM5(0.11),RBM8A(-0.14),RBMS1(-0.19),RBMS3(0.07),SAMD4A(0.12),SRSF2(0.13),SRSF9(-0.23),U2AF2(0.14),ZC3H14(0.2),ZCRB1(0.13),ZNF638(-0.24) & 0.68 & 0.00 & Heart - Left Ventricle & No\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A data.frame: 2 × 7\n", - "\n", - "| | Event <int> | Gene <fct> | Sig..RBPs <fct> | Sig..Gene.Expression <dbl> | Sig..Sex <dbl> | Tissue <fct> | Dimorphic <fct> |\n", - "|---|---|---|---|---|---|---|---|\n", - "| 1 | 21228 | TANK | CNOT4(-0.21),CPEB2(-0.11),DAZAP1(-0.14),ENOX1(-0.06),HNRNPA1(0.07),HNRNPA2B1(0.22),HNRNPCL1(-0.16),HNRNPF(0.22),HNRNPH1(0.14),HNRNPH2(0.24),KHDRBS1(0.23),KHDRBS3(-0.06),PABPC1(0.12),PABPC5(0.08),PPRC1(0.14),RBFOX1(0.19),RBM4(-0.16),RBM45(0.03),RBM46(-0.13),RBMS3(-0.07),RBP1(-0.05),SAMD4A(0.13),SRSF1(-0.23),SRSF7(-0.12),TARDBP(-0.17),U2AF2(0.25),ZC3H14(0.27) | 0.29 | 0.16 | Heart - Left Ventricle | No |\n", - "| 2 | 28196 | UBE2E1 | FXR1(-0.16),FXR2(-0.19),HNRNPA2B1(0.19),IGF2BP3(-0.04),KHDRBS1(-0.19),LIN28A(0.02),MSI1(0.05),PABPC3(-0.07),PABPC4(0.11),PABPN1(-0.16),PCBP2(-0.15),RBFOX1(-0.13),RBM4(-0.1),RBM45(-0.03),RBM5(0.11),RBM8A(-0.14),RBMS1(-0.19),RBMS3(0.07),SAMD4A(0.12),SRSF2(0.13),SRSF9(-0.23),U2AF2(0.14),ZC3H14(0.2),ZCRB1(0.13),ZNF638(-0.24) | 0.68 | 0.00 | Heart - Left Ventricle | No |\n", - "\n" - ], - "text/plain": [ - " Event Gene \n", - "1 21228 TANK \n", - "2 28196 UBE2E1\n", - " Sig..RBPs \n", - "1 CNOT4(-0.21),CPEB2(-0.11),DAZAP1(-0.14),ENOX1(-0.06),HNRNPA1(0.07),HNRNPA2B1(0.22),HNRNPCL1(-0.16),HNRNPF(0.22),HNRNPH1(0.14),HNRNPH2(0.24),KHDRBS1(0.23),KHDRBS3(-0.06),PABPC1(0.12),PABPC5(0.08),PPRC1(0.14),RBFOX1(0.19),RBM4(-0.16),RBM45(0.03),RBM46(-0.13),RBMS3(-0.07),RBP1(-0.05),SAMD4A(0.13),SRSF1(-0.23),SRSF7(-0.12),TARDBP(-0.17),U2AF2(0.25),ZC3H14(0.27)\n", - "2 FXR1(-0.16),FXR2(-0.19),HNRNPA2B1(0.19),IGF2BP3(-0.04),KHDRBS1(-0.19),LIN28A(0.02),MSI1(0.05),PABPC3(-0.07),PABPC4(0.11),PABPN1(-0.16),PCBP2(-0.15),RBFOX1(-0.13),RBM4(-0.1),RBM45(-0.03),RBM5(0.11),RBM8A(-0.14),RBMS1(-0.19),RBMS3(0.07),SAMD4A(0.12),SRSF2(0.13),SRSF9(-0.23),U2AF2(0.14),ZC3H14(0.2),ZCRB1(0.13),ZNF638(-0.24) \n", - " Sig..Gene.Expression Sig..Sex Tissue Dimorphic\n", - "1 0.29 0.16 Heart - Left Ventricle No \n", - "2 0.68 0.00 Heart - Left Ventricle No " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dim(summary_hbm)\n", - "head(summary_hbm, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The files lv.txt or mt.txt are available in the folder ../data/! \n", - "\n", - "The 'perl parseMT.pl' command will not be re-run \n", - "\n" - ] - } - ], - "source": [ - "if ( (\"lv.txt\" %in% list.files(\"../data/\")) && (\"mt.txt\" %in% list.files(\"../data/\"))) {\n", - " message(\"The files lv.txt or mt.txt are available in the folder ../data/! \\n\")\n", - " message(\"The 'perl parseMT.pl' command will not be re-run \\n\")\n", - "}\n", + "if ( (\"lv.txt\" %in% list.files(\"../data/\")) && (\"mt.txt\" %in% list.files(\"../data/\"))) {\n", + " message(\"The files lv.txt or mt.txt are available in the folder ../data/! \\n\")\n", + " message(\"The 'perl parseMT.pl' command will not be re-run \\n\")\n", + "}\n", "\n", "\n", "if ( (!(\"lv.txt\" %in% list.files(\"../data/\"))) | (!(\"mt.txt\" %in% list.files(\"../data/\")))) {\n", @@ -2005,477 +868,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Left ventricle data from `lv.txt`" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "dat <- utils::read.table(\"../data/lv.txt\", header=FALSE, sep = \"\\t\", col.names = c(\"RBP\", \"Expression\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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  2. 2
\n" - ], - "text/latex": [ - "\\begin{enumerate*}\n", - "\\item 37\n", - "\\item 2\n", - "\\end{enumerate*}\n" - ], - "text/markdown": [ - "1. 37\n", - "2. 2\n", - "\n", - "\n" - ], - "text/plain": [ - "[1] 37 2" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - " RBP Expression \n", - " Min. :0.970 Min. :-1.940 \n", - " 1st Qu.:1.860 1st Qu.:-1.370 \n", - " Median :2.970 Median :-1.110 \n", - " Mean :3.506 Mean :-1.042 \n", - " 3rd Qu.:4.530 3rd Qu.:-0.830 \n", - " Max. :9.570 Max. : 0.520 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dim(d2)\n", - "summary(d2)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "0.94% of rows in the dataframe were filtered out because they contained 0 values\n", - "\n" - ] - }, - { - "data": { - "image/png": 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ZYUGRkpjYrdvHnz4MGDFy9e/O233z7//HONRtOkSRNpCEStVvOzDoC1SMIwgySs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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width=8, repr.plot.height=3)\n", - "\n", - "dat <- dat[order(dat$Expression),]\n", - "with_zeros <- visdat::vis_expect(dat, ~dat$Expression != 0, show_perc = TRUE)\n", - "no_zeros <- visdat::vis_expect(d2, ~d2$Expression != 0, show_perc = TRUE)\n", - "both <- with_zeros + no_zeros\n", - "\n", - "message(paste0(\"\\n\", round((nrow(d2)/nrow(dat)), 2),\"% \",\"of rows in the dataframe were filtered out because they contained 0 values\\n\"))\n", - "\n", - "both + labs(title = \"Comparison of data before and after removing expression rows with 0 values\") + theme(plot.title = element_text(size = 10, face = \"bold\" , hjust = 1.2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are checking above if our expectation of having none 0 Expression values is true. We can also verify this by the initial and final row count of the dataframe that contains the `RBP` and `Expression` values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fit a linear model (`expression ~ rbm`)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "lm_fit <- lm(d2$Expression ~ d2$RBP, data=d2)\n", - "LM <-summary(lm_fit)\n", - "rsquared <-round(LM$r.squared,digits=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A report_table: 8 × 10
ParameterCoefficientSECI_lowCI_hightdf_errorpStd_CoefficientFit
<chr><dbl><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl>
(Intercept)-1.52418060.13064990-1.7894140-1.2589471-11.666144351.296862e-13-1.5241806 NA
d2$RBP 0.13749660.03146206 0.0736252 0.2013679 4.370235351.056443e-04 0.1374966 NA
NA NA NA NA NA NANA NA NA NA
AIC NA NA NA NA NANA NA NA45.7960906
BIC NA NA NA NA NANA NA NA50.6288443
R2 NA NA NA NA NANA NA NA 0.3530374
R2 (adj.) NA NA NA NA NANA NA NA 0.3345527
RMSE NA NA NA NA NANA NA NA 0.4143047
\n" - ], - "text/latex": [ - "A report\\_table: 8 × 10\n", - "\\begin{tabular}{llllllllll}\n", - " Parameter & Coefficient & SE & CI\\_low & CI\\_high & t & df\\_error & p & Std\\_Coefficient & Fit\\\\\n", - " & & & & & & & & & \\\\\n", - "\\hline\n", - "\t (Intercept) & -1.5241806 & 0.13064990 & -1.7894140 & -1.2589471 & -11.666144 & 35 & 1.296862e-13 & -1.5241806 & NA\\\\\n", - "\t d2\\$RBP & 0.1374966 & 0.03146206 & 0.0736252 & 0.2013679 & 4.370235 & 35 & 1.056443e-04 & 0.1374966 & NA\\\\\n", - "\t NA & NA & NA & NA & NA & NA & NA & NA & NA & NA\\\\\n", - "\t AIC & NA & NA & NA & NA & NA & NA & NA & NA & 45.7960906\\\\\n", - "\t BIC & NA & NA & NA & NA & NA & NA & NA & NA & 50.6288443\\\\\n", - "\t R2 & NA & NA & NA & NA & NA & NA & NA & NA & 0.3530374\\\\\n", - "\t R2 (adj.) & NA & NA & NA & NA & NA & NA & NA & NA & 0.3345527\\\\\n", - "\t RMSE & NA & NA & NA & NA & NA & NA & NA & NA & 0.4143047\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A report_table: 8 × 10\n", - "\n", - "| Parameter <chr> | Coefficient <dbl> | SE <dbl> | CI_low <dbl> | CI_high <dbl> | t <dbl> | df_error <int> | p <dbl> | Std_Coefficient <dbl> | Fit <dbl> |\n", - "|---|---|---|---|---|---|---|---|---|---|\n", - "| (Intercept) | -1.5241806 | 0.13064990 | -1.7894140 | -1.2589471 | -11.666144 | 35 | 1.296862e-13 | -1.5241806 | NA |\n", - "| d2$RBP | 0.1374966 | 0.03146206 | 0.0736252 | 0.2013679 | 4.370235 | 35 | 1.056443e-04 | 0.1374966 | NA |\n", - "| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |\n", - "| AIC | NA | NA | NA | NA | NA | NA | NA | NA | 45.7960906 |\n", - "| BIC | NA | NA | NA | NA | NA | NA | NA | NA | 50.6288443 |\n", - "| R2 | NA | NA | NA | NA | NA | NA | NA | NA | 0.3530374 |\n", - "| R2 (adj.) | NA | NA | NA | NA | NA | NA | NA | NA | 0.3345527 |\n", - "| RMSE | NA | NA | NA | NA | NA | NA | NA | NA | 0.4143047 |\n", - "\n" - ], - "text/plain": [ - " Parameter Coefficient SE CI_low CI_high t df_error\n", - "1 (Intercept) -1.5241806 0.13064990 -1.7894140 -1.2589471 -11.666144 35 \n", - "2 d2$RBP 0.1374966 0.03146206 0.0736252 0.2013679 4.370235 35 \n", - "3 NA NA NA NA NA NA NA \n", - "4 AIC NA NA NA NA NA NA \n", - "5 BIC NA NA NA NA NA NA \n", - "6 R2 NA NA NA NA NA NA \n", - "7 R2 (adj.) NA NA NA NA NA NA \n", - "8 RMSE NA NA NA NA NA NA \n", - " p Std_Coefficient Fit \n", - "1 1.296862e-13 -1.5241806 NA\n", - "2 1.056443e-04 0.1374966 NA\n", - "3 NA NA NA\n", - "4 NA NA 45.7960906\n", - "5 NA NA 50.6288443\n", - "6 NA NA 0.3530374\n", - "7 NA NA 0.3345527\n", - "8 NA NA 0.4143047" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lm <- report(lm_fit)\n", - "lm$tables$table_long" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save predictions of the model \n", - "Save predictions of the model in a new data frame named `predicted_df` along with the variable we want to plot against." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "predicted_df <- data.frame(expr_pred = predict(lm_fit, d2), RBP=d2$RBP)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9LISZ7URnEvm2VZw2ebrKDb9Bjx66YTAQEPjcvs0Na9Z4if4XFqaqp9B38AYEt1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GKc3u8LLEsSvnHzhwYNmyZYPWDVpfD2xt\nQXo6cHcHQUFgmNWFvnPnTlFttdWpfb3ulTLUI1pN8fLyCg8PRziwvjQ0NJiamlbyoqxunMER\nJRnbW0vLo7zP5FlbP3/+HDecHhFAzCF0Zefr6xsQEKCg8M8FzoYNGwoLC9etW7d+/fqUlBR/\nf39kwkBSZGSk2Dh1JkXAR1mbVtRUp6enD875qqqAuTlITwfz5w/DTAcAePjw4Sgrk15TP538\nNKvo6OjOzk4ko2Ji8+bNFSiKkc+e7pkOAIAfJTP5xIHsyrL9+/ezKzZoABBKdrdv33Z1dT15\n8iQA4MePH0+fPl2+fHlAQMD58+eXLl167949ZMJAUkFBAUFZnkkDHixWQIZYUFAwCCerqABW\nVuDTp45Fiw6NHj3J2FhaWlpRUXHGjBl3796lUqmDcIrf9u3bt59u538ipKLY3t7+48cPxEJi\nori4OOjWrfEeq9E9VsIDAGAEcFobl587d66hoQH52KCBQSjZFRUV2dra0l/HxMTQaDTGfNiJ\nEycWFRUhEwaSMBgMrdszyl7RKJRBWISouBiYmoLPn79Pmybz6NGZkDsdk7WVt66SXj0vWwC9\nZO1qMzOz7kMa2YWHh4f5D4TWRQEA9Fxmky2io6MJKgpCfWdnCZ1xaCFBWJ2MgyCU7LqPsYiL\ni8Pj8YzV1+nzPJAJA0kaGhoNOcyu2sjNLa1llZqamr91mu/fgaUlyM8vnjtXPSFBfskcE39v\npRn2kvo6xMn6GqsWWN08l0dqtbOzY/sDdQ0NjYav+UwaNHzNFxER6XdIJjKKi4sFR8kya4FC\n4eVlv3//jlRE0O9CKNkpKiomJSUBACorKyMjI21tbfn4/nl28/Hjx+FcBWDAXFxcOkrK6rJy\n+mpQ9DBGc+zYcePGDfwcubnAxAR8+0bZts08LU15gavSDLufmvAK4vUPbS9qaTx69OjATzQY\n5s6d+yP+Jampua8GhRFPZs2aNUzW28ThcJROEvM21E4S7KDgIAglu/nz5wcHBxsbG+vp6bW0\ntGzevJm+/ebNmzdu3BiR5WHl5OQ2b978/ph/Z10vj3VqM7Nzb4f5+voO/ARfvgALC1BaCg4f\nDp80qaK5UdWt9x8jD5Zv7Ip5Fy5cIJH6+d87pJycnCZP1H//1zlqbxfyheFP2rPzh88jf21t\n7fovubS+H3dSOjob8wt1dHSQjAr6HQglOw8Pj6VLl3748KG1tfXs2bPm5ub07bt27VJXV9+9\nezcyYSDs6NGj1gaGSet3lb14zXhc1dXWnn/nwZudR7y9Dk+b1vt6YP3LzAQWFqC8HBw/Dvbu\nTUxMlJo0Ad33NZGkvm5LR/v79+8HeLrBgEKh7t+/L97c8WrL/u4XvB3VtR9PXfp27d69e/cY\n/fVsZ21tLYrFlcQk9tWgMOKpioKioaEhgkFBvwWhWwZ+fv5r165du3btp+3h4eH6+vrD5M5l\n0GEwmPDw8FOnTv3111+ZJy8KKo6ikEgt33+MVlZ+GBbu5OQ0wOO+fw9sbUFtLfDzA1u2AACq\nqqr4e6s8zIDG8GBFhSsrKwd4xkFCJBJfv369e/fuq9sPowl4ARkiqam5paTMwtw88vVrbW1t\n9obXHQ6H8/HxWbl+nZCyQs+ltavfZX69ef9ReAQsFcNB2JxljIyM2BvAUOPh4dm+ffumTZte\nvnxZWFjIx8enqak5ceLEgc+KTU0F9vagqQkEBID/T64SExMjVRQx+RCNRiM1NouLiw/wpINH\nWFg4ICDA29s7OTm5rKyMQCAYGhqqqqqyO65eLFmypKCgwHvrgdHzZio4TOUXFwUAtFdWFz2M\n+Rb+5JSvr6OjI7tjhH4BQsnun6JDfSCRSPTui5EKi8VOnTp1EA708iVwdAStreDvv8GyZYzN\nkydPvr3nIY1G6yuH1n36wgtQEyZMGIQYBoOIiIizM7O1H4cJLy8vIyOjvXv3xt64jxUVBlRa\nZ0OjsbHx37GxjEcxEKdAKNnFx8f3tYtAIBB6lFobqVpaWmJjY7OzsykUyujRo+3s7H7haisp\nCTg5gbY2cPUqWLKk+x5XV9cdO3aUPE1QcOglpdKo1JzAu4sWLRIQEPj9r8BtHBwcHBwcioqK\ncnNzeXh41NXVR+TgAW6AULLrOZKORCIVFhZev349LS0tMjISmTDYiEaj+fn5HT58uIMHJaym\nguJBN18uoaxYsXnzZi8vL8ZAnD4lJgJnZ9DeDm7cAAsW/LRTSEjo7NmzC5cuwYqJEo30uu+i\ndlE+nb4s2Nh65MiRwf1GXEVJSUlJSYndUUC/BaFk17MLAoPBjBs3ztfXd/fu3Tt37rxw4QIy\nkbAFjUZbvnz5nQcR4zevlDE1BP+/2az5kOV/6nJ6evqTJ0+Y5bvHj8Hs2YBKBaGhoI+ib+7u\n7vX19Vu2bJGcYjDK1lxQXpbSSarL+votLEqaD/coOlpSUrLXD0IQl2B/X9KMGTOYLQg0Ily8\neDE4ImzKuaMyZkag22M1CV0tE/+jb75m79mzp88PR0QAV9d/XjAtb7lu3br3799bjVLJP3Ep\nfuEfL1Z6Up69/HPthg8fPmhoaAzal4EgzsT+MR/Nzc0jezY1mUw+ePDguLVL8LK9rMfKJ0TQ\n3b7+3I4jnp6evSzEcf8+WLgQ8PKCBw+AjU2/59LU1Lx58yYAoKWlBYvFDpN5phA0HCCU7HpN\nZ2Qy+fPnzzt27FBWVkYmDLZ49epVXUuzgdWUvhqIaY3lk5F6/PjxihUr/rPj7l2waBHAYsGj\nR8DK6pdOOvLqA0LQb0Io2TFfXyYoKAiZMNgiLy+PoDiKyfQGAICQqlJubu5/Nl29ClavBgIC\nIDISWFgMaYQQxA0QSna9Dr/k5eWVkZGZNWvW4IxBG67QaDQLpef/O0Tu8mWwbh0QEgJPn4KR\nPu4agpCBULKLiopC5kTD0JgxY1q+l1JJZDRfn0/QGnO/jV34/3vYCxfAhg1ARATExAADA4Si\nRFxra+v58+fDwsKys7OpVKqamtqMGTM2b94sJsZs3hsEDdgQJrvS0lLWG4/ggZqTJ0+WEhUr\niX2h6Nj7NJKad58oNfX/XPz6+oIdO4CUFIiNBcNprujg+vDhw4wZM+pRVEUnG50Fzig0uqmw\n+Oz94ICAgJCQEAt42w4NgSFMdvLyzIqS/2QErzGGwWCOHDmyZtNGMa2xBMWfc3pnXcPHkxc8\nPT0lJSWBjw/YtQtIS4PYWKClxZZoEVBYWGhjYyNkNsl83RLU/yfSi2qOUXSYmn/3oaOj48uX\nL4fPzDZoxBjaRbKH7uCcZdmyZe/evbu0ee+4dUtHWZuieHgAADQarepNxqczf1sbGR84cAAc\nOAC8vIC8PIiPB2pq7A55CG3evJlXc/S49UvBTzN5UajR81w6autWr16dlpY2VCuIQ9wKoUWy\noXPnzmlra+/du/fzhevCqsooHnRTYTEfmbJ3166dO3fyHDgAjh4FioogPh4MyxIgg6W4uDgy\nKsrqxtmfM93/qS91i527+s2bN5MnT0Y4NmhkQ3RQ8efPn4lEooSEBOMtiUTinhuWVatWLV68\n+MWLF1++fKFSqcrKytbW1oJ4PPDwAGfOAGVlEB8PRvSQQwDAy5cvBeVl8XI9hk//H68gXkxr\nbFJSEkx20OBCaLoYmUxesWKFlpZWVlYWY+Pz58/19PSWLVtG6W8VrhEDi8Xa2tpu3rzZw8PD\nxcVFEI8HGzeCM2eAujpITh7xmQ4AUFNTwy/GbNAlAAArLlpdXY1MPBD3QOjK7ty5c4GBgY6O\njoqKioyNNjY2bm5u169f19XVZaxKMeKVlpYmJiZWVFSIi4jMfPJEJCICaGiA+HgwPFbVGmri\n4uIddfXM23TW1sOyBdCgQyjZXb9+3cnJ6adSTurq6nfv3m1ubvb39+eGZFdWVrZly5bQ0FC8\nvKyguJhfQaFIU0seHt9x/vx47sh0AAATE5OWkrLWsspeZwoDAMgtrXVZOaZnTREODBrxELqN\nzc/Pt7S07HWXhYUFNyy+mZ+fP2nSpBdFeRaBftZXToYR8O5NLbmjleaYTTJycnr27Bm7A0SI\noqKik6Pjlyu3+mrw9cZ9nXFaI75eP4Q8hK7shISEioqKet1VVFQ04gfNd3V1zZw5E6WpOmnn\nH3xdXTsPnZiUkp4zTv3gX38q4AW6lBTc3Nw+f/4sK8t0VeaR4syZM5MmTfp84YbmmkWo7gvW\n0Gj59x9VPH0e+vIlXMgGGnQI/Uo5OjpevXr1yZMn3TeSyeQrV65cvnzZ1tYWmTDY5datW/kV\nZdoea7Ak8p59xyelpGfpaO732duGFwAAqMx24lVR8Pb2ZneYCFFRUXn27Bk59eOLVduKHsbU\nZ+c2fC0ofprwctPeqrCnUVFRenp6/R8Fgn4RCpmpC+Xl5RMnTiwvL1dQUFBXV8disQ0NDdnZ\n2XV1dTIyMm/evBk+C4b2FBgYKCQkNHv27AEfwcnJKVcEqzvfdc8+H513nz7qaR85sqMTi2U0\nqHiVVnz+ZkVFxWDEyxlaWlrOnTsXGhpKH4ijqqo6c+ZMDw+P4bAEGsSJurq6XF1dmVQCRijZ\nAQAqKysPHjwYEhJSW1tL3yIpKeni4nLgwAE5OTlkYhiY3092ampqsnMcAh7HjcvMTjfUO3Zw\nG+m/RQHaq2vj3NfW1dUxr4UFQVBf+k12yA0qJhKJFy5cCAgIKC8vb29vl5aWxuPxiJ2dvQQp\nFP9boeNKylKnGBzf50HuUUCYPjWKSqWyIzoI4gpIl2VHoVBc8hj+X3V192prxzQ1vTKffOLP\nzRQMT88mjflFEhISI76jBoLYCKFkR6PRQkNDb968WVpa2nNZRQBA95kVI0p1NbCxGdPUdA/H\nH7xzA623TAcA+B4V6+LiAqe+Q9DQQSjZnTx5cvv27QAAAVOHPzQAACAASURBVAEB7loF5vp1\n8PEjZfHiAykpnZdujd+0omeTkujnLZlf9gSHIB8dBHEPhJLdmTNn7OzsAgICVFRUkDnjcLFt\nG5CX55k7N+TzZwsLi7S6erUV84TlZOjjyLraO/KDI4rDHt++fRuuwQxBQwqhZFdZWRkaGsp1\nmQ4AgEIBd/d37955eXm1trZ2JqdWvkpDyUjhJMX5AarxS/7Y0aNjY2NNTeHsKAgaWgglOyKR\nOIJrETN38eLFLVu2EKeaTDp1kKCs0PTte2lcUk1GZktlzeGDB3fs2MHD0/uDPE5BoVA+f/5c\nVVVFIBC0tbVxOBy7I4KgXiCU7ObNmxcUFMSFEx4fPXq0YfMm/QPbiEb/zAoQ1VAT1VADAHx/\nHHf48GEbGxt9fX22xjhwnZ2dJ06cOHv2bE1dLa8Qoau1DcuDWbBggZeXVy8LfkMQWyE0qLil\npWX27Nni4uKLFy9WUFDo2UcxevRoBMIYmAEPKiaRSGPGjBF0sFCZ7dRrgyz/QGJ5/evXr387\nRjZoaGhwcHDILC4au9ydOFmfB8tHo1LrPuXk3gzhq6qLjo4eP348u2OEuMhwGVRMIBDoL4KD\ng3ttMCJvcuPj48vqam1d7PtqoLZgVuzc1V++fNHQ0EAysEGxcOHCr011pheO8eIF6FtQaLS4\njqbRif1ZZ/92dnb++PGjsLAwe4OEIAbkbmP5+PgwGKTHMLPX27dvxcePRff9rbGiwgRlhbS0\nNI5LdjExMTEJ8ZY3zjIyHQMKhdLauCJ53S5fX98jR46wJTwI6gmh7NPXBd3I1tzcjBHsZ0oc\nr6BAU1MTMvEMoqCgIDlrM37x3mfyotBo1bnOt27dgskOGj6GMNlVVFRgsVj6zPZ+63mMyOfZ\nsrKybTFVzNu0lVcN8zoIvcrMzBR37r0aK52Y1th33882NDSIiIggFhUEMTGEyU5GRsbOzi46\nOpr+mnnjEfnMzsbGxnP79vbqWpxk72WL6rNzu+obLSwskI1rELS1tYl1K1HVEw8/P70ZTHbQ\nMDG0i2Tr6uoyXg/diYYtLS0tW2vrj/6BBge39VwmlUomfw64vnz5ck6c/6+oqFheWsakQUvJ\nDxwOJyUlhVhIEMQcQotkc+2C2ZcvX540adKH4wFam1ZgcPyM7aTG5vd/nSXSeHx8fNgY3oBN\nmzbtcMA5VbcZfRUvKI1LtrW15bYuKWg4g5X+h5aCgkJycrJoRV3Coj8+B1wveZZY/DQh0+9S\nwqI/xgmKJiYmcujgjFWrVmFb2r+FRvW6ty4rp+Rpwu7duxGOCoKYgMluyKmpqb179+56wEUD\njCA69jU2KcNcTDbi3v2EhAQisfflBIc/YWHh4ODggmv3cm+GULu6uu8qS0xJ3e3tdfCgoaEh\nu8KDoJ44/i6DQqHcvn07LCxMVVX11KlTzBvHx8efOXOm5/YFCxYM6VNFHh4ed3d3d3f3oTsF\n8qytrZ89e7ZkyZL4x3FEo4k4oiSpsakmIxPUNvif8luzZg27A4Sg/+DsZFdSUnLq1KmyMmZP\nyrtrbW0FAJiZmf204LympubgB8cFzMzMcnJyIiIi4uPjy8vLxcTEDD13uLm5SUhIsDs0CPoZ\nBye7trY2Dw8PBQWF06dP//HHH6x8hJ7sXFxchvNUXM6CxWJH3kUrNCJxcLKjUCgODg5Llixh\nvURSS0sLAGDEL/RTVVX1/ft3HA6nqqoKCy5BEB0Hd1AQCITly5f/UjE4+pUdHo+nUqk1NTWc\nOE+LuQcPHhgYGBCJRCOTKdra2hISEosWLSosLGR3XBDEfhx8ZTcAbW1tAIBHjx49efKEfpUn\nJyfn7u5ubm7evVlXV1f3QjFVVVVCQkIIh/qraDTapk2bLgZeHe02w3rrchxRkkah1H/Ji7v/\nKHLChLCwsKlTp7I7RghiJ+5KdvQru6SkJFdXV3Fx8ZKSkidPnpw8ebK9vd3e/t9CTCQSydvb\nm/F24sSJw/8Zn4+Pz5VbQSbnjhKU5OlbUDw8YlpjxbTGFtx75Orqmp6erqamxt4gIYiNOCDZ\ntba23rhxg/FWRkZm5syZAzuUm5ubo6Ojnp4eP/8/kxksLS23bNkSFBRkbW3NGO7Px8f3559/\nMj6Vm5s70NgRUlFRcfjwYd39HoxM152q2/SG3IKdO3eGh4czP059fX18fHxJSQkOh9PV1Z00\naRJ9YSAIGgE4INm1t7fTqwnQaWhoDDjZaWtr/7RFXl5eX1//9evXhYWFjAsfDAbj6urKaBMY\nGDiw0yEmJCSEV5YoZaDbVwO1Ba6R63bV1dX1NQ+3ra1t9+7dFy9e5BERwstJUzpJTQVFqopK\nfn5+Dg4OQxY4BCGHA5KdhIQEk1LLv48+Yaujo2PoTjHU3r9/L67NrPynkIoiCofNzMzstcJK\nU1OTlZVVXkOtwfG9YuP/OU5Xe0dhxFPnmS5nTp5icWQPBA1nHJDsBktHR8fz58/xeLyZmVn3\n7cXFxQCAn4YZc5aWlhYegX6GmGAEBJqbm3vdtWrVqm/tzSZnj/Dw/1u1CYPjV5s/U0RNefPW\nrRMmTJgyZcpgRgxBiBvJT2RIJNK3b98YdUOxWOz9+/f9/f1LS0sZbVJTU7Ozs1VUVDi6euio\nUaPayiqZNKB0dHbW1isoKPTclZGRERoervfnpu6ZjkHSQFdphh2c0g+NABx8ZZeVlZWRkUF/\nTaFQamtrGf0Yrq6uBAKhvLx8y5YtOjo6hw8fBgCgUKh169YdPXp069atpqamYmJixcXFb968\nERAQ2LhxI9u+xmCws7Pzv3KZ3NrWc0UIurKk10RJyV6X+woLC5M00BWQ6bMkgdIM++eLN1ZU\nVHD03wMI4uBkl5OTExYWxnhbX1/PeGtra8tYz6y7SZMm+fj43Lt3LyUlpaOjQ1hY2NLS0s3N\nrd9CysOcjY2NltqY7EtBOlt7mX7f2dCYE3j3rz/39tq1+vXrV2E15bKysvLy8vb2dhQKJSgo\nKCsry7ivx8sSMXiB3NxcmOwgjsbByW727NnM13JVVFTs2bMxduzYAwcODGVcbIBGo2/dumVs\nbPwJw6O5elH3G9LmopIMr1MWEw36unptamr6XttEzhTilxDFiAkDGrW6pa3kbZqEmLienh4f\nHx8AAKBQVCoVme8CQUOEg5Md1J2mpubLly/d3NziF26QMZssKC9LIZHqPuVUv32/Ytnys2fP\n9jqvrrCwMCUlpUtZTkJvPOr/1304IhBUkGvMK0xJSTExMSHXNXS1tMIByRCnG8kdFNxGS0vr\n48ePQZeuWIhKi37MUyqpXWJq9S494/Lly4xB1D9ZuXKlwFhVam4Rtf4/04TRvLwiY0d3UCk5\nOTnfo2INDAw4cQk0COoOXtmNKBgMZs6cOXPmzGGl8bt37xKTkqYGB3w8ebEh4JbI7nUozL9X\nfyg0mqAk/z3+FQiPjY56PGQhQxBC4JUd94qLixMbP5ZfXHTCzj/4mlrrvc51lZT/u5tC7Ur9\nSA17Nn+um42NDQCATCZ///49NzeXowdgQ1wLXtlxr7KyMgFpKQAAnzDB5NzRrPPXSrcfwyjK\nYmSkaJ0kcl4RHx+fIFFyypQphYWFXl5e4eHh9KJYWCzWxsZm3759kyZNYveXgCBWwWTHvQgE\nArm1jf6aVxA/YecfY5e6Vb392F5VzYPFCs+eLjFhfNLaHYWFhTo6Ovjx6uP2bREZOxrFw9Nc\nVPLpacJkE5OTx49v2bKFvd8CglgEkx33MjAw8PU/R6NQUP/vqMURJRWdrBkNOmrrm4t/nDt3\nTnW5u7Lrv+UARNRVRdRVZcwMt+3aKSsrO3fuXKRDh6BfB5/ZcS8bGxsJvOD3qNi+GuTdChUR\nFpa0mtI90zFI6mlrrl2yZcsWeklUCBrmYLLjXjgczs/PL/tSUHX6x557ix49q3iW1NDYOGbh\nrL6OoOhkXU/q6F6AC4KGLXgby5GKi4tjY2OLi4vxeLyurq6FhcU/Ux1+0Zw5c8rLyz22bZO3\nt1R0mEpQlqdRqfVf8gojnrZ8yN68ebP/rRs4qT7XRUSh0eLamm/fvu1e/g+ChieY7DhMfX39\npk2bgoODBZUV8LJESkdn/WEvaVGxU6dOMZ8815dNmzZNnjz50KFDsZv3kUgkAICgoODMmTMP\n3riTmJjIK9jPSmwYQXxjY+NAvgkEIQsmO05SXV1tampazYsyu+xLUP6nXhO1q+t7ZKz7wgXH\nioq2bds2gMMaGBhERUW1t7cXFRXx8vIqKCjQrxNzc3PbKqtpNBoKherrs20VVXKGZn3thaDh\nAyY7TrJo0aI6Av/ko7vQmH//4dAYjPLMaUIqCjt3/mlgYPDTSmmsw+FwGhr/KXdsYmKC7iDV\nfvgsMUGr14901jfWfvxs439pYGeEICTBDgqOkZSUFJeYqLtjQ/dMxyCuM07ZddrevXsH8YyC\ngoJr167NvniT0knqtcHngOsmRpPh0GKII8BkxzEiIiKIkyfyi4v21UDRyfbVq1eVlcxKFv+q\nw4cPKwoQ0vb81Vn/nwdzlM7OTL9LHR+/DP/ViCCIDt7Gcoz8/HwhVUUmDfCyRB4cf35+PpHY\nZ9nhXyUoKBgfH79gwYKERX/IWpmIjh2NwvA0FXwve/5KhSjzJClJVVV1sM4FQUMKXtlxDBQK\nBWj9NWLamTAwEhISMTExUREPpkrJ4159QD1LmYjCXTl99uPHj5qamoN7LggaOvDKjmOoq6un\np71k0qClpIzaSRozZsxQnN3Ozs7Ozm4ojgxByIBXdhxj1qxZlW8y2qtq+mpQ9CjGwsJCQqLP\nMcAQxM1gsuMYRkZGztMc3nmf7bVvtCr1Xdnj+KNHjyIfGARxBJjsOMm1a9fk0XyvNu+rz85l\nbOxq78i7Hf72wAk/Pz8jIyM2hgdBwxl8ZsdJREVFX758uWvXrstbD/KKiwjKy3W1dzTmfRut\npPz40SN7e3t2BwhBwxdMdhxGUFDQ39//0KFDcXFxJSUlOBxOT0/P0NCw1zVhIQhigMmOI4mL\ni7u5ubE7CgjiJPByAIIgrgCTHQRBXAEmOwiCuAJMdhAEcQWY7CAI4gow2UEQxBVgsoMgiCvA\nZAdBEFeAyQ6CIK4Akx0EQVwBJjsIgrgCTHYQBHEFmOwgCOIKMNlBEMQVYLKDIIgrwGQHQRBX\ngMkOgiCuAJMdBEFcASY7CIK4Akx2EARxBZjsIAjiCnB1sSH0+fPn27dvf/r0qa2tTVFRcdq0\naTNnzsRg4M8cgtgAXtkNCRKJtH79+vE6OlcTYvKJhEoNxee1PxatXT1+/PjMzEx2RwdB3Ahe\nZQw+KpU6Z86chPfpZhd9hFQUGds11y7+8newiYlJSkqKlpYWGyOEIC4Er+wG38WLF5+9Sp58\n8mD3TAcA4MFitTYsE7cynj9/PoVCYVd4EMSdYLIbZDQazcfHR32pG7+4aK8NNFYu+Pq96PHj\nxwgHBkFcDia7QZaTk1NSWipjPrmvBhgBHHHyxJiYGCSjgiAIJrtBVlpayidM4MULMGmDl5Mp\nKSlBLCQIggBMdoMOj8dTOjoBjcakTVdbu6CgIGIhQRAEYLIbdFpaWmgKtSH3G5M2tZnZenp6\niIUEQRCAyW7QCQkJubi45N990FeDmvdZ7d+K3dzckIwKgiCY7Aaft7d328cvecERPXe1lJS9\n/+vs7t275eXlkQ8MgrgZHFQ8+FRVVSMiIlxdXeuzc0e7zxDVHINCozuqa0vjkvOCw5ctWLh3\n7152xwhBXAcmuyFhaWn5/v37/fv3h+3+q72zE82LoXR06urq3rlxc/bs2eyODoK4EUx2Q0VJ\nSenmzZuXLl3Ky8trbm5WUVGRkZFhd1AQxL1gshtaOBxOW1ub3VFAEAQ7KCAI4g4w2UEQxBVg\nsoMgiCvAZAdBEFeAyQ6CIK4Akx0EQVwBJjsIgrgCHGfHBu3t7cXFxTgcTlZWFi42BkHIgFd2\niHr16tW0adOEhYXHjh2rqKgoKSm5atUqWMgTghAAkx1yjh07ZmZp+RWHmux/1PFp8LTIm2N3\n//EoM0NbWzshIYHd0UHQCAfvoRBy7dq1fV6HjI7vFdfWpG9B8/FK6utI6uvk33vo4uKSlpY2\nduxY9gYJQSMYvLJDQlNT0/bt28dvWsnIdN2NdpshbDjB09MT+cAgiHvAZIeEyMjIdl60nI1Z\nXw3GLJ4dHR1dXl6OZFQQxFVgskPC27dvxXXGoVCovhrg5WSw4qLp6elIRgVBXAUmOyQ0Nzfz\nCuKZt+EVxDc1NSETDwRxIZjskCArK9tWUcWkAY1Kba+qkZOTQywkCOI2MNkhwdrauubdJ3Jz\nS18Nqt9+4EfzGBkZIRkVBHEVmOyQYGZmpjdeO/tSUK97u9o7si8Fbdy4kZ+fH+HAIIh7wGSH\nBBQKdf369caUjCz/QEonqfuujpq61F1H1SWl4ZJjEDSkOHtQcUtLy/3791+9elVfXy8mJqas\nrDx79mx1dXUmH2ltbQ0ODk5NTa2trRUSEtLX11+4cKGoqOhQh6qhoZGUlDRnzpyEJZvkLKcQ\nlOWpJHJ9dm5Z4msHW9ubN2/icLihjgGCuBkHJ7vm5mYPD4+qqip9fX0rK6vKysrk5OT379+f\nPHlSUVGx1490dXXt3bu3oKDA2NjYzs6uvLw8ISEhMzPTz89PUFBwqAPW1tbOysr6X3t3HtbU\nlTYA/GQDAig7CYwxslgURAFpEQRDUQcFAQehggpVpIXYAW19cMH5BMZngNbWBdGplVFcRhi0\nFREqKAoUeCoqKqigbLLIhIop+05yvz/u9E6GXQzekry/v3LPPZy8Jze8OffcLSkp6dq1azU3\nf1ZWUvrA1NT3x/9zcnKa6rcGAEzjZHfx4sVXr14FBQW5urriJba2tjExMWfPnt2/f/+If5KR\nkVFdXb1582ZPT0+8xMrK6quvvkpJSQkICHgHMTMYDH9/f39//3fwXgAASdN4zo5Goy1atGjV\nqlVEyZIlSxQUFOrr60f7k5ycHCaT6ebmRpTY29vr6enl5ORgGDa14QIASDWNR3aBgYFDSgYH\nB0UikZaW1oj1+/v7a2trzc3NGQyGZLmpqemtW7d++eUXNps9VbECAMg2jZPdcJmZmSKRaNmy\nkS9Bff36tVgs1tbWHlKuq6uLEJJMdmKxWPLKrba2tpkzZ05NyACAd0R2kt2TJ0/OnDljamoq\nuWMrqaenByE0/Fw2vKS7u5so6e3t3bZtG7G4ePFiDocj/YgBAO/QNEh2XV1dZ8+eJRb19PT+\n9Kc/Danz008/HT16lMvl7tu3j0ajvVH7+Gyd5FX6DAbj448/JhaFQuFk4gYA/J5Mg2TX09OT\nmZlJLM6fP18y2WEYlpSUlJycbGVltXv37jHOVlNWVka/je+GtI8QkvxDBoMREhJCLJ4+ffqt\nOwEAINk0SHba2tppaWkjrsIw7NixY9nZ2WvWrAkMDKRSxzq4rKOjQ6PRmpubh5Tjd5HT19eX\nVsAAgN+haZDsxpCQkJCdne3v7+/l5TVuZTqdbmRkVFFR0dfXp6ioiBdiGPbkyRNtbW0dHZ0p\nDhYAQKZpfJ7dzz//fO3aNTc3t9EyXX9/f01NTVNTE1GycuXKvr6+H374gSjJzMz89ddf//jH\nP055uAAAUk3jkd2ZM2cQQhiGSR6+wK1bt05VVVUgEOzYsWPRokUHDhzAy1euXJmbm5uUlFRT\nU2NkZNTQ0FBQUMDlcocf8QAAyJhpnOzwIVt6evrwVatXrx7xWlcqlRoREZGUlFRYWHj//n11\ndXUXF5cNGzYQe7UAAFlFgcukxnX69OmZM2dOZFoQAECWwcFBT0/P0Q5momk9ZwcAABMHyQ4A\nIBem8Zzdu/To0aOxT+IDAJBLLBaPXQGS3fisra1pNFpHRwfZgUyJO3fuKCkpWVhYkB3IuzAw\nMHDv3j1NTc158+aRHcu70NHR8fjxY319/Tlz5pAdy7sgeZXncHCAQt7Z29tzOJykpCSyA3kX\nhEKhs7Ozo6Pj119/TXYs70JpaWlAQMDGjRs///xzsmMhH+yaAQDkAiQ7AIBcgGQHAJALMGcn\n7zo6OqhUqoqKCtmBvAtisbizs5NOp+P3+5J5IpGoq6tLUVERrhFCkOwAAHICdmMBAHIBkh0A\nQC7AScVyqqmp6fvvvy8pKXn9+rWysvL8+fO9vb3fe+89suOaQsXFxZcvX66urqZSqUZGRj4+\nPubm5mQHNVVevXqVnJz84MGDtrY2TU3NpUuX+vr6jvHQAnkAc3byqLGxcdeuXT09PfgzwgUC\nQX5+PkIoJiZGVi8tyM7OjouLY7PZPB6vv7//9u3bXV1d0dHRMtnfX375ZefOnR0dHXZ2dnPm\nzCkvL3/w4IGJiUlsbOybPo5KlsDITh6dPHmys7MzOjrazMwML7G1tY2JiUlLS5PJf/62trbv\nvvvO0NAwNjYWf3LmqlWrtm/fnpubK5P9PXfuXHt7+5///GfiFtynTp26du1aVlaWi4sLubGR\nCJKdPDIxMTE2NiYyHULIxsaGRqM1NjaSGNXUuX37dm9vr7+/P/HUYD09vX/961+Sz8+UJcXF\nxZqamitXriRKNmzYkJWVlZOTA8kOyJeNGzcOKWlpaRGJRCwWi5R4plpJSYmCgsKiRYsQQgMD\nAwMDA8rKyrKa6Xp7e7u7uw0MDCQ7qKKioq+vX11dLRaL5fb+PZDs5F1fX19FRcWpU6eYTOZH\nH31EdjhT4uXLlywWq66u7uTJk8+ePcMwjM1mr1+/fvny5WSHJn2Kioo0Gq29vX14+eDg4K+/\n/qqtrU1KYKSDZCfXfHx8uru7EUKOjo7h4eFsNpvsiKYEfnuuqKgoHo/n4eEhFApTU1OPHj1K\np9N5PB7Z0UkZhUKZN29eWVlZXV0dl8vFCxsbG6uqqhBCvb29pEZHJlpkZCTZMQDSdHd3czgc\nBoNRXFxcXV29YMGCER9UNN0lJyd3dHQEBwd7enpyOBwTE5Nly5ZlZmY+efLEw8ND9vZnWSzW\nrVu3iouLdXV1KRRKSUlJXFyciopKZ2fn2rVrZXITTwSM7OSav78//uLx48cHDhyIjo4+evSo\n7P3zKykpiUSipUuXEiUaGhqLFy8uLCxsaGgghj8yw9zcPCgoKDExMTo6GiGkpKS0adOmqqqq\npqYmuc10CJIdwJmbm9vY2OTl5b18+ZLD4ZAdjpSxWKyamho6/X++7Wpqagihnp4ekoKaWq6u\nrk5OTtXV1RQKxdDQkMlkfv755xoaGnJyx4cRyelxGXkmFApDQ0MPHz48pLy/vx8h1NfXR0ZQ\nU8vExEQsFldXV0sWCgQChJCsztaLxWImk7lgwQIzMzMmk9nc3FxTU2NpaUl2XGSCZCd3tLS0\nOjs78/PzKyoqiMLGxsaHDx8qKSnNnj2bxNimyPLlyykUyrlz5wYGBvCSqqqqR48ezZkzRyaT\nXWJi4rp16yorK/FFDMMSEhIwDFu9ejW5gZELLheTR3fu3ImNjaVQKHZ2dnp6ekKhsLCwsLe3\nNygoyNXVlezopkRCQkJaWpqhoeGSJUuEQmFOTo5YLI6KipLJy2Nra2vDwsLodLqTk9OMGTPu\n3r1bVVXl6em5efNmskMjEyQ7OVVRUXH58uXy8vKOjg4mk2lsbOzm5vbBBx+QHddUwTAsKyvr\n+vXrjY2NdDrd1NTU19d37ty5ZMc1VZ4/f56UlFRZWdnX18fhcFxdXVesWEF2UCSDZAcAkAsw\nZwcAkAuQ7AAAcgGSHQBALkCyAwDIBUh2AAC5AMkOACAXINkBAOQCJDsAgFyAZAcAkAuQ7AAA\ncgGSHQBALkCyA+A/7t+/jz+RA8gkSHYAIIRQdXW1o6Oji4sL5DtZBckOAIQQMjIyCg4OzsvL\ng3wnq+AWT0BGdHV1vf2XOSws7Ntvv+XxeD/++KOysrJUAgO/E5DsgIzQ1tYWCoXSau2zzz6L\nj4+XVmvg9wCeLgZkhJ+fH/4w7LfR0NBw48YNKpVqb28vlajA7weM7AD4j7q6ug8//LCuri4x\nMdHPz4/scICUQbIDACGEGhsb7e3t6+vrz549u2nTJrLDAdIHR2MBQAghLS0tU1PTc+fOQaaT\nVTCyAwDIBRjZAQDkAiQ7AIBcgGQHAJALkOwAAHIBkh0AQC5AsgMAyAVIdgAAuQDJDgAgFyDZ\ngYm6ePHirFmz6HR6WFgY2bFIwfDuyFgHJ83Hx4dCoTQ1NUmxTTqdvmTJEik2OAmQ7MgkFosv\nX768Zs0aAwMDJpPJZDKNjIz8/PxKSkrIDm2otra2wMDAzs7OAwcOODs7j1jnwoULlGEUFRUN\nDQ0/+eSTFy9ejFaTSqXq6OhYWFjs2rVL8jZNwxuk0WgsFsvT07OgoEC63ZlIB99UbGxsVVWV\nVJp6lywsLJydnRUVFfHFadqL4eAWT2Ty9fVNSUnhcrleXl5sNrutra24uDgpKenKlSvXr193\ncHAgO8D/qqys7Onp2bJly969e8euuXTpUsn7I7W0tNy7dy8hIeHSpUsFBQULFiwYXhPDMKFQ\nmJOTc/DgwatXrz548EBFRWXEBnt6ep4/f3716tXU1NTExER/f39pdWfiHZwggUCwd+9eCwsL\nY2NjqTT4zuzZs2fPnj346+nbi+Eg2ZEmJycnJSWFx+NlZ2fT6f/dENeuXXN3dw8NDX348CGJ\n4Q3R29uLEJoxY8a4NVesWBEZGTmk8Ouvvw4LCwsPD09LSxutpkgkcnZ2vnXr1pUrVySvxh/e\nYH5+vpOT044dO9avX08MQN6yOxPv4ATdu3dPWk2RSDZ68R8YIAl+I9z4+Pjhq86fP3/z5k2R\nSIRhmKurK0KopaWFWDswMIAQWr58Ob7o6+uLV/j00091dXWZTKaNjU1RUVFXV9f27dv19fVV\nVFRsbW2Li4vHjqe2tnbz5s36+voMBkNLS8vNza2oqAhfNWS3LigoaMQWzp8/jxCKiIgYvqqv\nr09BQUFTU3PsmocPH0YIHTp0aNwG8ZCICIdramrawNWUoAAAC6JJREFUtm3b7NmzGQyGtra2\nh4fH3bt3R+vOaB0coxGcQCDYunWrvr6+srLywoULjxw5MjAwgP221Qj5+fkjBjm5bVdUVLR2\n7VotLS0Gg8Hlcjdt2vTixQvJCunp6e+//z6TyWSxWKGhod3d3bNmzbK0tJR8046Ojl27dnG5\nXAUFhVmzZh06dEgsFuMV1q9fjxASCAQj9mLcLySGYRkZGVZWVkpKSjo6Olu3bm1paaHRaDY2\nNhPZOlMHRnak4XA4CKGbN28GBQVJjuwQQm90lyEFBQWEkLe3t4ODQ2ZmZmlpaXBwsLe398KF\nC83MzNLS0mprawMDA11cXBoaGhgMxoiNNDQ0fPDBB93d3Xw+38zMrLGx8cSJE8uWLcvOzra3\nt4+IiODxeOHh4Z6enn5+fgYGBm/aWQzDxGKx5M7piMrKyhBCixcvHrdBLS0thNBoT8Zpbm62\nsbFpbW0NDg5esGBBQ0PDiRMnHBwcsrKyeDze8O50d3cP7+DYjeAVrK2tOzs7/f39uVxubm7u\njh07Hj9+nJCQ8Je//EVTU/P8+fP79++3tLQ0NTUdMc5JbLvi4mIej6epqbl9+3Y2m11TU3P8\n+PEbN26UlZXhn8lPP/3k4eGho6OzZ88ebW3tS5cu+fj4dHR0/OEPf5B8Uy8vLwMDg+TkZLFY\nHBUV9cUXX6irq2/ZskUyvAn2YoiCggJ3d3cWi7V//34dHZ28vDx3d3cq9b+HB8b9YKfKVGdT\nMJr+/n5LS0uEkIWFRVxc3NOnT4mfVknj/pBu3boVIcTn84kKH330EULIy8uLKNm+fTtCqLCw\ncLRgPv74Y4TQDz/8QJSUlZXRaLQlS5bgi/n5+Qih3bt3j9GjMQZiUVFRCKGAgADJmiEhIZW/\nuXv37u7du6lU6ubNm8dtsL+/39DQkEKhCASCESPh8/l0Ov3evXtESX19/YwZM6ytrUfrzvCS\ncRvh8/kIoaysLKICvrGePHmCYVhMTAxC6Pr166N+XpPadidOnLCyssrJySEqHDt2DCF07Ngx\nfHHlypUIISLswcHBDz/8ECFEDKzwN/X19SVaqK6uRgitWbMGXyRGdiP2Ytwv5KpVqxBCkiO1\nbdu2SQYw7gc7RWBkRxoGg5GbmxseHn769OnQ0FCEkJaWloODg5ubm4+Pz5s+2srT05N4PXfu\nXISQh4cHUWJiYoIQEggEI/4thmGpqaksFmvt2rVE4fz5821tbQsKCoRCIT5kmKDc3FzJKbbW\n1taioqI7d+4YGxv/9a9/lax57Ngx/B8VR6FQgoODY2Njx2i8t7e3srIyMjKypqbG19eXzWaP\n2J1Lly4tXLhw1qxZxPkTDAbDzs4uKyurs7NTVVV13F6M24iKikpKSgqHw8GTCy4uLm7nzp0s\nFmvc9iW90bbj8/l4kkUIDQwMiEQifMBVW1uLF+bn58+bN8/a2hpfpNFou3fvzsnJGfKm+M8b\nztDQUFlZ+eXLl28U9ojEYnFeXp6RkdH7779PFH7yyScnTpzAX0tl60wOJDsyzZw5Mz4+/ssv\nv7x9+3ZhYWF+fn5GRkZqauqePXsuXry4YsWKiTdF7KQghPCdYskSfA8I/wUerqmpqa2tbfHi\nxRQKRbLcxMSkoKCgoqLC1tZ24pHk5eXl5eVJlujq6oaHh+/cuVNTU1Oy3NvbGx/IIITa29uf\nPXuWmJh45cqVlJQUySPRUVFR+MBQkru7+8mTJ0cM4NWrV69fv379+rWent7wtfX19RPZHRu3\nEXV1daFQaGVlJfmhGRoaGhoajtv4EG+67c6fP5+QkFBaWtra2koUDg4OIoRaW1t7e3uHHDm1\ns7Mb/qazZ8+WXGQwGKN9Pd6IQCDo6ekZ8iHMmzePeC2VrTM5kOzIp6Ki4ubm5ubmhhBqaWm5\ncOFCWFiYl5dXVVWVtrb2BBsZPhk32vTccF1dXXgYQ8qZTCaxduIiIiKIkV1PT4+pqalQKOTz\n+UMyHULI1NTUy8tLsuSzzz6ztLTcuHFjZWUlcZiVx+M5Ojrir6lUqpaWlr29/aJFi0YLAH/G\nmIWFBb4LNoS+vv5EejFuI/j5gJM7FjzEG2278PDwmJgYa2vrw4cPGxgYKCoqPn36NDAwEF+L\nRzVkt2DGjBk0Gm3ib/E28FlUJSUlyUIlJSXiJ0EqW2dyINn9vmhoaISEhNTV1X3zzTd5eXnr\n1q0bXqe/v1+6b4rvOAxPanjJ25yNwWQyjxw5snbt2pCQkCtXroxbn8vlOjk5ff/990+fPrWy\nssILHR0dh5/LMgYiYHzyaHLGbQRPFpJjq3egt7f3yJEjHA4nJyeH2N1ra2sbEhV+Gg2hu7tb\nJBJNXVSSX0j8B3JIAJ2dndhvj3+QytaZHLiCghwikYjP57u5uYnF4uFr1dXVEUKdnZ1opL0Y\nyUsRpILNZmtqapaXl2P/+0CSsrIyCoWCzxlNmoeHx+rVq1NTU1NTUydSH//lH/Lf8kZYLJa2\ntvazZ8+GZKLm5mYpNqKioqKjo1NeXi65aZ4/fx4fH//06dNJBz+2pqamnp4ea2tryYktyUkD\nNptNpVLr6uok/6qoqEiKMYz9hWSz2QoKCkO+oqWlpcRrqWydyYFkRw4ajfbixYv09PS9e/cO\n+dWtrq4+efIknU7H993wqY3y8nKiwrlz56Qej6enp0AguHr1KlHy6NGju3fvOjk54Zn3bRw9\nelRRUTEkJGTch1jfv38/Pz9fVVV1jL3UifD29u7t7T148CBR0tzcvHDhQnyuQFqNeHh4CIXC\ns2fPEhUiIyNDQkL6+voQQvieY09Pz9t0ZAgWi0WhUIhjEQihR48e4d8H/OdBQUHB2tq6tLT0\n2bNneAWRSPTll19O+h2H92LsLySdTrezs6uqqpI8G/n48eOSbUpl60wC7MaS5tSpU46Ojl99\n9VVSUpKrqyuLxers7Hz+/PmNGzcGBgYOHTrE5XIRQv7+/n//+9+/+OKLgwcPKisrX7169eef\nf5biif64qKio9PR0Pz+/0NBQExOT2tra48ePq6qqHjp06O0bnzt37s6dO6Ojo/ft2xcXF0eU\nZ2dnEyO4vr6+6urqzMxMkUh0+vTpcc/IG1tkZGRGRkZ0dLRAIODxeP/+97+//fZboVCIH/WW\nViMRERHp6el8Pr+kpITL5ebl5aWnp/v7++M74PgkfWxs7IsXLxwcHCSPTk4ak8l0dXVNT08P\nDg52dHQsKyuLj4//5z//6e7unpGRkZSU5O7uHhYW5u3t7eLism3btpkzZ164cMHQ0HDSc4vD\nezHuF3LXrl15eXlr1qwJCAjQ0tLKy8vr7u5WU1MjKkhl60zGlJ7YAsbW3t4eGxtrZ2enqalJ\no9GYTOZ7770XEBAgeQoShmGJiYmmpqb4CfGffvppa2urvr6+vb09vhY/baqyspKoHxERgf73\nrP1Tp04hhJKSksYIpr6+fsuWLXp6enQ6XVdX18fHp6ysjFj7lufZdXV1cTgcKpWKX/OA15Sk\npKRkbGzs7e0teTLgGA2OSyAQ8Pl8DodDp9PV1dXd3d0lL7eYyHl24zaCYVhtbe2mTZt0dXUZ\nDIahoeE333wzODiIr+rv71+3bh2TydTQ0Lh06dKIQU5i27169WrDhg06OjpqampOTk54zaio\nKFVVVTabjZ8c949//MPExERBQYHL5e7bt6+/v19BQcHOzm60N8UwTE1NzczMDH8teZ7diL0Y\n+wuJYVhycrK5ubmCgoKOjk5AQEBLSwuHwyEu4ZjIBzsV4LmxAMi49vZ2NTU1d3d3yWkKOQRz\ndgDIlDNnzjg6OhYXFxMliYmJCCHJW9HIJxjZASBTioqKeDyehoYGn8/X19d/+PDhd999p6+v\nX1JS8vbHmqY1SHYAyJrCwsK//e1vxcXFLS0turq6zs7OBw4cmNLzdacFSHYAALkAc3YAALkA\nyQ4AIBcg2QEA5AIkOwCAXIBkBwCQC5DsAAByAZIdAEAuQLIDAMgFSHYAALnw/wN1VZSuRWe6\nAAAAAElFTkSuQmCC", - "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width=3.5, repr.plot.height=5)\n", - "mypal <- ggsci::pal_npg(\"nrc\", alpha = 0.7)(9)\n", - "\n", - "\n", - "p<-ggplot(dat, aes(x=RBP, y=Expression)) + geom_point(shape=21,fill = mypal[3],size=3) + theme_bw()\n", - "#+ scale_fill_npg() \n", - "p <- p + theme(axis.text = element_text(size=12, \n", - " hjust=0.5),\n", - " axis.title.x=element_text(size=12),\n", - " axis.title.y = element_text(size=12),\n", - " axis.text.y = element_text(size=12),\n", - " panel.grid.major = element_blank(), \n", - " panel.grid.minor = element_blank()) \n", - "p <- p + geom_hline(yintercept=0, linetype=\"dashed\", color = mypal[4])\n", - "p <- p +xlab('\\U27F6 \\n Sum of RBP effect magnitude')+ylab('Expression\\ninclusion \\U27F5 effect \\U27F6 skipping')\n", - "p <- p+ geom_line(color='red',data = predicted_df, aes(y=expr_pred, x=RBP))\n", - "mylabel<-paste(italic(r)^2~\"=\"~rsquared) \n", - "p1 <- p+ geom_text(x = 5, \n", - " y = 0.45, \n", - " label = as.character(paste( \"r^2==\",rsquared)), \n", - " size=6, \n", - " parse = TRUE)\n", - "p1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (3c) Predicted effects of gene expression vs. RBP levels on exon inclusion in 100 sex-biased SE events in the left ventricle. \n", - "\n", - "The Y axis shows the mean of the posterior of the coefficient that determines the effects of gene expression on exon inclusion. \n", - "Negative values favour skipping and positive values favour inclusion. \n", - "The X axis shows the sum of the absolute values of the posterior of the coefficients of the 87 RBPs. \n", - "The higher the value, the more the predicted effect on exon skipping. \n", - "In the left frame it can be seen that for 61 out of 100 sex-biased events in left ventricle, \n", - "no effect of gene expression was predicted (flat line at y=0.0). \n", - "\n", - "For the remaining genes there was a correlation with **`R2=0.35 (p=7.98x10-5).`**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Figure 3d\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This should be run after the `figure4a.R` script corrsponding to the figure 3b in the manuscript (see above code chunks) " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "dat <- read.table(\"../data/mt.txt\", header=FALSE, sep = \"\\t\", col.names = c(\"RBP\", \"Expression\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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l++3LBlcbFnh1qqL1++TJgwwWDGhG6eHhWZDgAoNHGtIX37H90ZEf/Zw8Oj3o0W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jdsgN27oVkzOHnyPzHPH6EvGQa7T+Xp6VkSl1BLAUVJ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LJ4nOXGF4e0jKhNl3HmqUytQzl0aOHFlt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\nufnFcQndunWrz1YhhKqFwe7jGRkZjR8/PnbnAY1KVc1ukozd8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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width=3.5, repr.plot.height=5)\n", - "mypal = pal_npg(\"nrc\", alpha = 0.7)(9)\n", - "d2<-dat[dat$Expression!=0,]\n", - "lm_fit <- lm(d2$Expression ~ d2$RBP, data=d2)\n", - "LM<-summary(lm_fit)\n", - "rsquared<-round(LM$r.squared,digits=2) \n", - "\n", - "# save predictions of the model in the new data frame \n", - "# together with variable you want to plot against\n", - "predicted_df <- data.frame(expr_pred = predict(lm_fit, d2), RBP=d2$RBP)\n", - "\n", - "\n", - "p<-ggplot(dat, aes(x=RBP, y=Expression)) + geom_point(shape=21,fill = mypal[3],size=3) + theme_bw()\n", - " #+ scale_fill_npg() \n", - "p <- p + theme(axis.text = element_text(size=12, \n", - " hjust=0.5),\n", - " axis.title.x=element_text(size=12),\n", - " axis.title.y = element_text(size=12),\n", - " axis.text.y = element_text(size=12),\n", - " panel.grid.major = element_blank(), \n", - " panel.grid.minor = element_blank()) \n", - "p <- p + geom_hline(yintercept=0, linetype=\"dashed\", color = mypal[4])\n", - "p <- p +xlab('\\U27F6 \\n Sum of RBP effect magnitude')+ ylab('')\n", - "p <- p+ geom_line(color='red',data = predicted_df, aes(y=expr_pred, x=RBP))\n", - "mylabel<-paste(italic(r)^2~\"=\"~rsquared) \n", - "p2 <- p+ geom_text(x = 15, \n", - " y = 3.2, \n", - " label = as.character(paste( \"r^2==\",rsquared)), \n", - " size=6, \n", - " parse = TRUE)\n", - "p2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (3d) A similar correlation was found in mammary tissue, with R2=0.33 (p=3.6x10-12).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8+PlojtQ4NcIrpYB7QmFNW/f/979+6xLJucnNy3b19CUTZtfF0iuth3CKQl4oCA\ngBMnTnCHx8bGtmvXjhaL7ELauUR0sWnrR2i6Z8+et27dqvT+nj17Njg4mBaL7Nq3dYnoYtPO\nnxLQ3bt3v3nzZpWfh6KiounTp9M0bdnSwzm8s2OXUJGlhVgsBkKs/LxdIrrYdwwWyKTe3t4H\nDhyo+aNVSUpKysCBAwlFWQe0donoYh8aJJBKfH19jx49+lTnadKys7NHjRpFCFH6eDmHd3YI\nay+0kLu7u+/YsaPK/gzD/PDDD9bW1hJba4WHK6FpUCpIKw95oJ/MxVEqlX788cc6na6GR7x2\n7dr8+fPr59k0GbGxsZ9//jnfUTQ9xsEO7mJ0k1Dll1uxWGzes9KldlPW1tZVDjQQQpYtW2Z+\nqqftX18ePmQ7dmQB2M6dWZWq4R63Kbh///6sWbPqdiz/I6MAsGrVqi+//DIlJcW00c/Pb9Gi\nRWPHjq3bOf/666/k5OQpU6YY97QICQn5+uuvt2/fPm3atOd1SG389NNP77w313/6+P7DB1IC\nAQBocvJOv/mRSkRgZhTVuiUrk7BCAQCQY2fZnYeBosDaEjJzIcAbcvJBLBa9N431cNYVlzD5\nhVBYzN66z568QLw92MxcsLOGuZPBwRZUOaDREqUFe+o8nLxIt2u9acsWSi5r++6M27pSPcuC\nQS/0cGEfZul/3Q7qMlDIoKUbtHCFA6dheB/oHQZrouFhNggEENUfenUCmgKKQHY+/H0PTl+C\n/ALB7IkGpQLKtXA7hT0cxyanqXNyPfr0kHq6vfP+PImNFTUtkvV0IVaWAgs5xYLhcKx+xyFw\nti9/kAlCgXDcEJtBvYCmANj8hFu6lHT2xHmi0cLbE8HKkmi17J0UuHEXTl2k+3RmHG1ZQmi1\nJvXOg65dux44cKBHjx4AcPv27S5dujwqLJSEh+nTMgwsK5o3jbR005eWGVTZbJHaRk+9/cG8\nW7dude7cefLkyZ5jhg5YtoCWVHxp1uTkJaxc16lTp9WrV0+bNk3UtnXfLSsldjbcvfoyTfK2\nPwcMGLBx40aRSDRmzBi34QP7f/m+QCblOpTnFyT+uqFTp04xMTHGgc9Dhw6NGDHCeXCffp+9\nK5RXDMqWFxT+vWpTly5djhw5EhYWZvp5KCgoiIiISNOWdv/5v5beLQBAnZEV+84nBk9n0jnI\nPjjQz88PAAzl2ns7/xo8dOhvv/wyderU2nzSkpKSIiIiaN+WfTb/LLWvGMwzlAruYr8AACAA\nSURBVJcnb987ePDgNWvWjB8/vq6f4iZDpVJ169at2FrRc+0PCncXrpHR61P3HB4zYfy3D76a\nM2dOpUNef/3137dtaff2jIyT8dk3k5TzZohD2uiKS4rupVpKpD5ixfc/rLp8+fKePXt4XsgQ\nNUfcdaGmRa/X17JnDcstPe0TbxQv1JUrMGwYPHgAY8bA2rUglfIdUPNRMS+YR8btQHv06FFp\nO1CWZdetW1e3HZjmzJmjUqk2btwoFAqNja+99lppaen69eur/IJVh0MA4ODBg7dv33777ber\nvPfs2bPdwsPDPv/AvmNwRRPLxr7zSSEwzNCerKsjsABSMdA0oQgAsNdvw/L1YGCgZxhk50Gx\nmsydIrK11mq1AMCWaeBRETwqImIh+906sLWCT98CmgaGITTNpmYQnY7Y2zDnb8C2AyCXwphB\nImslKxbpSstEfl5EIi5PTYesPFi5Ddp6w9De8OWv8MZYCPKD2MuwcS8wDMyZBG18gAAYGNDq\n4E4quDqAUAir/oAyDZk/kwgFwAJ77wEciiV3Utnycmn/Hnp7a+3anYKJQw2hbUQWCuM8bv35\n64YVm4GmyOhB0KmdhaVSKpWWqrKKU9OJtSXrYAs/rAeRkMyeyN66Bw62xMGGPXUR1u8Rfjpb\nX1gklEhond5d9ahg/4lbt24pFIp27drduX9POrSPPiuvPF0l+s9rIK649spodbqMLGGROsyr\n9bn3FlN6Q5v3XnPrG175LWHZa9/9X+bRM84DegbOmWn+lj04fOLWj2sIId6vTfR8uZ95h5sr\n1zEXExITE6VSaWZmpp+fn9vESK/Il8x7Jq3eXHryfFJSkkKhMDaOHj362N83unyzkBIJAYBl\nmFOzPtQ62yvfnKgvLc1PuBXaoYPT45LMzDPnr3/544ULF9q1a1flB8xIp9MFBweXeDoFvfe6\n+Tz6h8dj/172y5UrV3x9fWs+T1PXs2fPJK2646fziNngTe7lG+f+s/TU8ePdunUzNq5Zs+b1\nOe90/98XmXEXbu3Ya7PkPZq7PgDAGgz5CUluDo6+7p6n3/xo7oxXP//88yof9Pr161u2bFm6\ndGk9PakmIS4uLiYmZsGCBXwHglD9MJYrLVyI5UpVSklJ+frrr3/++ec6HMv/0k7cdqBZWVlH\njhz5/fffV6xYsXHjxnPnziUnJ3t7e3/11Vd1OKdWq01JSWndurVpWgkAAQEBhYWFWVlZz+WQ\n2li8eLHn4D7/ZKIAWeeuPLqfZgjvQHm6gkQMwALLcpkoAEBbH/B0AQcbaOUGf9+DycNZitJp\ntSzLEgFNCAFbK5CKWYEAugaDphxoGigCFGFZhrg7sRotUBTxcgNCSN/OlKuj1mBgtFqBqyOR\niA2acsgrhLJykIigewfYEwPd2kN7f2AY2H4QLOTQMwza+gABYAEoCnLyQSEDWytQSGH8S/Aw\nC/5OBiBACPF0YbsEsTQlaNu69FicICSADOiuPxInkMn++RVlWaZ1C5BLoWsw6+5ESstLSkpY\nlil58BAENHF3BrEQpkXC38lwJRHEIuJgC4SQbiEQ5GvYEyNwd9apS7XACrq0L7dSLF++fMuW\nLXfTUomzg7hLe03sJeGro42ZKABQIiFlb2MQ0NmMVtG/u4Gm3Pr0qOItIUTp46WViJRD+1T5\nlrn370m8PRmZpMpMFAD8Z07M0ZevWrUKAL755htBC7cqM1EA8J06tkhIrVy50thy7dq1nbt2\nBX/4FpeJAoDq1NmSnDzLGaOBEIFcLndzTkpKMvZ36h7mENHl008/rfL8pjZv3nwvO7Pt7OlV\n/ol07dXNulP76nKpZuPIkSOxF84Hz5tlnokCgF1IuxZD+y9cuNDYwjDMwoUL/WdMkDrY3t0U\nbTk1ypiJAgChaaV3i7S0NINYGDj3tWXLljWKsRmEUAPjdlcaMwYIgZ07YfFizESfO/6T0frY\nDjQ3N5dhGPPdmxwcHACgysyyDoc8UVFR0bFjxzwG9zVtzIw9Lwj0pWysWJGQUBQIBGCsAeRG\nqTVaCPCG26nQyh3sbYCmWJ2eUBQAsAYGhAKwVkJpGYQFQlEJpKmAECAUMCwIaLCyYAuKIa8Q\nJCLW3RkYBqRig1bH/RNrKCwGsQhu3oXOQSASQsId6BkGQCAlA8q18KgIeob9+wmowVYJAEDT\nYGkBQX5w8WbFXQIBsbYk7f0NjwqhTKO5/4DqGQYPs0hBkfFoRm9gCwqhRA0+LYiNEkrUDMOU\n5RewDEuslUARAAK2SghszV66CbZWwP12C2joHsJc/ZuyUABFRBaKzKwsz8F9d+3atXv3blos\nkvbpUn4pgfLxJK6OlV5wWixiLWQqlUrt4WTQ6YrupVb5vmTFXxJ2CsrKza3ujdP7t9SrS6Ga\niwaUgPYY2JsrvNu1a5fny32r7AYAhKI8BvU2LdHbvXu3Xfu2cpd/Is88c17SrQN5PJFA6mBX\nXFxsOj3f4+W++/fvN66lV53du3e79Q2nxdXW6Hi+3G/Pnj21v77WFO3atcupe5jIyrK6Dp4v\n9zt58qQxpzx//nxmbq5bvx65VxIMAlrcMbBSf4FcTsukmZmZ9h0CiZXlwYMH6zF6hFAjpNHA\nxIkwfz64uMDJk1g4X0/4nwJVH9uBcstAcCXepriW0tLSZzwkMjKyoKCAu+3q5qkFi0c//GsT\nKU9XmymjuqWkpBgYxqKFO9fIlhlKz+RZ6X3kzu3YLCu2iAIgwIpv+ZdVdOD+96gQ7K1bPXIW\n+MnhbylQBFgAmiYAKW5EI6dAIgYDA3aWYG8D2XkujJtFAQGGAEWg3A60OigRZrfu8EgkZMu1\nIBYRsRAoCgCsssEhzwWkHcDOBrJpCBkNea7FLMnIyQMLORQUg6sDF4K0FDySCBQ4QZmioprK\nING79ki+se+f74MyCWtrBRdu+HadCCnWpNSa7TqRukaDkgWAtEBSzhpIXiErk4JCBgIBq9F6\npknkxWAoc4MyCWQSAAIsUXn4F12/QKQmr7yHi71DgN1ZPat1owpoVquzlDvly0pTs4r1pWVy\nT9eyE+eIhzPXV14AjneNWSPNaGygVKMRyNLtbNQPMy1btTCeldUxpSdyAcBe2t7e3gFui9SF\n2QAg6+NATL6UGQwGrVLu1bpv8f4M4/hlxYch1Iq2FgGApXeL2/uP63S61NRUd4G7+kh2pY+N\nyFsubCmv6Ll9H9d48XpqfGK5h2c/0/5SrX2x3z/l/5RQSIlE6pxiiKvIR0UGS492wxd8E21v\nZ/3B6wPBzNIV+7U6Q4bayc7ayXhmaVdbSv6vXyvLVp5WrqELvomW/nue09B+Qe3beFQ654n4\nWyfP3a7UGNLWY0jfyosJJKfmbNx1tlKjvY3ijUm9KjVqdfqlKw6Yx//hrEESceU/RCs3nsjO\nLa7UOH54J58WDpUa9x27fulGxbeOxIcSF/tu6iPZwpYykbeiUk+9SkNuCXw6jv30h702NjYA\nkJKS0jJkMC0Wqx9mCtydjaMdAg1rd1XH3bYtcRLn6dR3s31CRt65c8c8/m9/PZSeei9P9cj8\nLoRQ05aRAcOGwcWL0Lkz7NqFa9rXH/6TUW470M6dO5vf9dy3A2UfL3j2jIcoFArjMhMikYhh\naKnkX1mLWCQEAIqigGX/NcAmJAyrM7AClmJYmgXCjYb+Ox5CgAWG0RkYHdAMUBSwAAKWgLEj\nW/FflgVCGAFroAEoFggAxQLFADGwDAMsVLqUwFLEQDHA6AAMQAjoy4FmGZoAIRVBsix3CAtg\noFkgBhCwFWcGlgHDP9MJjFEQYjBogRgIzbKGcpZiiOCfO/8VAAGGsAwNBmIAmgW64hFZlgGK\n/OtVYlmW0RtolqUYoCiWYoBiGYOW0GzFO0IRYCr6swQYwT8HGiiGUAxDGONz+VfAQgIABkYL\noCeUgPuRgNlbwIJBrwMh4Tr88wyMT59hKIqq+FQQqNQNAIA29mSNu9jRNKEIw4DetD/LsMbn\nYmwjYHJOQgz6colYUOkzZiQRC2maAlYPhDEeVcVnnAXGoDM/j6Cqr4JCYeWPNNdo3pOiiHlP\nsbjqUKt5ClWMQEtEQvPONFXFb67IJFQCBhYMICSkqq+3hCIgIAZ9uVhUcYhQQBhGD9wvuMm7\nwMI/HyqGYhia+83VV7khoVgkFItpfnZ/QQjVH2O50tixsGYNlivVK/6T0SVLlgwfPjwlJWXs\n2LE+Pj4ymUytVicmJq5Zs0ar1XLbgRo7u7m51eac3EqT3GCnKa5FWtVH6qkOWb9+vfF2RQFT\nVUNWLVq0EAqFhXdTrHxbAQCR0vKe9sVXH2Q8zDQEdAFvDxZYtlxrTBy4uZpgo4TsvPuQB4kq\nGDgZCCECAYgIoSmmkAUDA2XlQNPwqBByHoGzfaY7gDsLBgMRCtjUPEIRUOWw+y5BeEsis2QZ\nhi3XgcEANF1ozxawKtAnQJYA2gfAjT3wkgN4eIDBDorVQAAeZIKnCwBoZHA3CCAxB1wpsLIA\nQqBEA/Gnicn1ZSjVkJx8cLS9e3Yb9cEMoqQM57aLxn1MbB4/HT3N2lmBugyK1aDTEYn4gX25\npUhUdEtF7KyJmyPDsMAY4PjfYG/DlmmI/PGLnJKem3+3uKNAm3BPYm0tY1hhdpaCuefl4nVD\nLtPfeyBwdiyPv1wRhRJSgh/vGqc36DMKFBpdeWGRISdf4eFq+nYQESXvaQ8ABceS8lR3bQeE\ne3e0N3/XaJoWFRQ/TDkd2K/q+ZcAUHD7nr+/v0Ag8Pb2Liq7796/Z5XdAKDgzj1/f3/udvs2\nHt3aWfywc6vrrK7GDuXxj/T3/5mJyGh1jFYnd7CUtagYLs27lph5+/An72ypbpnMd2f0A4AL\nx1ZfLspsM2FydZEU3k4uzb35yTvDarPmaLdQ726h3k/sBgAt3e2qHK81JxIKatkTAKaO7vbk\nTgAA0D+8Tf/wirVjH/59YGdCbMi0qveGph3FBnVZyrU/Pz76Gzcv6NKlS6t/nOtbGqVwd9Gn\nPQQDw10HMEhIdmjFq513PSuglbfcwy7+/3b6TVlpftrZU3pfv263Zcv9WgaMEGoCtm+HqVNx\nd6UGw/+cURcXF24v0KFDh/r7+3t6egYEBERFRe3fv//o0aM+Pj7uJmp5Tnt7e5qmc3JyKrWr\nVCp4vAHpsx/yRAqFYtCgQSl7Dpk2uoR31t+4xeY+ojRa1sCATg/G5ID7uAtoSLgDvi0hNQPS\ns4BhKZGQZRgAIDQFOh3kF4JcCnFXwUYJro7AssANLur0UFBErCzAyhJ0epKSwRICpRpaLDLk\nPAIAWmkJOh208YGz10FTDsH+cDQegAV3J1DIwNoSjsY/jgQAACwVkPsIAEBvgPwiuHYLwh7X\ndGt1bH4BeymRVijAQi7xcGGOxkELV9ZSbnymlEBALC3AQg5/32PzC0Aho2lKaqWkaIrNLwQD\nAywLmXlw8w7p2BZyCyoGyPR6OHmR6tCGKSwmQLRFxc5OTql7D0dFRUVFRelLy8qOxoo7tGXu\np7P3//mWwjFoykmx2sXFRZacLhCLLDyr/uri0LmD7vx1J9tqZ4DQCXcFEnF1f30YrS7tQExU\nVBQAREVFpfx5qLrZpYxen7bvCNeTExkZmX/j7+KUB8YW5/DOmthLjLpiHkhpZraVlZXpuv33\ndx8YNmxYzQu2c5GkHz2lL638bcrkPAcjIyOb9+r3UVFRmbHnNTl51XW4v/tgv379jDPUQ0JC\nWri7px2IsQ1uI6BpTdylSv11RcVMmcbR0TEz7qJAox0wYEA9Rl+fSkpK1qxZM3369MjIyBkz\nZnzxxRe3bt2qof+xY8eGVmXbtm0NFjNC/ODKlcaOBYrCcqUGw//IaH1sByoQCFq1anX79u3y\n8nLjyVmWTUhIsLOzs7evYjysDofUxmeffdapU6eMjsEuPSsGw+xC2tm19cs/fs6glIO7C7fQ\nPcs8Lqi/cAMeZIKBgeu3INAX1u0m86YJhEJWp2N1epZhIa8AtDpKq2POXQdrJWjKQSwChiU0\nxaY+JHIpW65lb98HA8MejQMbS6mzo56mdKpsYiGn5VKDnTVbqgKdDmLOwbjBsOT/IPYKdAmG\n8S/Dr9sh7iq0aw0d2wEBYBiwt4E7qZCVByIhbNhDfDzBxxOABYZl76eTU5cooUB/Pcly5MDS\nuMvs8XOCaSP16lKhpYXxkiV1LclQWgbnrxM3R8bVUamwAEIpPN2L7qWyqRngYAO/7YCQAGjX\nGm6lsBnZxNmePRIPt+7Tn76tf6ASSKUihlUfPGXNkDfffFMmkwV/9dXVmwmlh07J+nQt+2Wb\naMEsUFTkbYZyLZudJ2LBWqO/cyRWIhDcj97f0qzOnWWY3EvXpQybs+VPt4/eJmYXXu/t/Euk\nyiWE3Nm8y2d85bnqLMve+PG3FjZ2U6ZMAYD33nvvt99+u7X+D9/Joyu/9yyb8NNaZ5nFzJn/\nLCDl7+8/+ZVJO5f+2PW7T7lFSZ26hlq19Cj6eZPV3Gk6dWmpKqtzp07G/g8OHi+6dGPRb+sr\nn9zMqFGjvv3226vf/NxhwRzzWvKUPYdLb97+ZOvOJ56nSevRo8fAvv3OLv2x09KPzWu5VKfP\nZR09vTs+3thCCFm6dOmEKZNtA/19J4+5uXaL0NuTdq6YlsrodIXJKV5eXmxh8Y3lqz5dsMDS\nstrSqMasuLj43Xffzc7ODg0N7d27d1ZW1unTp69cubJs2TJPT88qD+FK6MLDwyv96QsICGiI\niBHii1oNkyZBdDTurtTA+E9G62k70H79+q1YsSI6OnrcuHFcy8GDB/Pz843rfmu12vT0dJlM\nZlzT8YmH1EFgYOCqVatmzJhRlJzaauwwLv8I+ejt029+VLphH/TtTAX6shRhhUJWq4MDp+Hg\nKSAUONrB2Wvg7QECGr5ezUwaRvl56YvVkFdIikrg+i0m9jLV3p95mA2fr4TxL4OnM6vKAQMD\nIiG7+xicvSZo462/eZdsO+g1a1KKmGGEAt3t+wJnB/pBln7jXiAUqLJh/Z8wqAf8vgvSs2BA\nV+gWArGX4f+2QWoGDOoBUjEIBWBnBZcT4fQl0OkEn7xhIMAUlkDCHTgaT1Q5jMHQauRgIhLf\n2bhDbG2lL9VQOY90eoPAUkFp9fq9x5lDpykneyYjm40+ItIZxMP7A4DU3laTk6e9eQd+vkiE\nAhg9iDUYiKcze/Muu+soXEmkp0Tq8vJpmiY5+bLrd4tu3T98+DC3VOeuXbu6dOmSceSMMNBP\nIJdpF/9PMGk4aetjKFEbVDl0calrsfbCD18uXrAgNDR05MiRJemq1pOixFYVI2HF99MSfv5d\nllt48ODBiRMnnpv/RZs3pxoHUMsfFd5atz3n2Jno6GiRSDR06NDSjEzfqWMlthVbpJQ8yLi5\nch2dptp1/Dg3VGljYxMdHf3SSy+VZmb7TxsnebzUvPqhKvH/1jN30/6KialUFffTTz/d7t//\nzFv/aTt7mn37dkBIh4VzY9/5JHfBd0zX9v7hXbmiPW1R8Z1N0Rn7jm7atMnHx+eJnzSKonbu\n3BkREXH2gyVt3ppq2bKiJklbUHR7wx+Zh05u3769usyjOVm/fn3Pnj1j53zSbvZ064DWXKNO\nXXrvj713t/65+tdfK23TOnr06KtXr37z7iL/V19x694p/ZPvLSaNkHTrUF5UXHw/zdZCaaHK\nO73wu8gBg+bNm8fHE3oONm/enJ2d/dprrw0ePJhr6dKly9KlS9etW2e60JUpLhkdPny4t3et\nZmsg1Bw8fAjDh8PFi9ClC0RHY7lSg3r2DaDqQKVS5efnG2/XrG4PYTAY5s+fP2TIkCVLlmzZ\nsuXrr78eOnToW2+9pdFouA4pKSlDhgxZsGBB7Q+pUm22Az1x4kS7du0ogcDa38cupJ3MyV4g\nEPj7+1MUBUoL8HAGZ3ugKSAEqMf/hcdX7eVSIAQUMnBzotycQCggFEWLhEBT4GgLFnIAAIUM\n3J3A2R4ENNA0CGiBQPD+++//8ssv9vb2QoVc7OUObo4gFQMAsbIApQUQAAENAKCQg0gIFAVO\ndmCjfLwlKQUuDv9sN8oF42gHni5gowQAoChKKJC7OdNikY+Pz7Zt22bMmEHTNG2tBHdncLYH\niuLOT2ja0tJSJpNx61gRNyfi4Uws5BRFVexnY62EFq7gaAcUqXjKdtbg4SxwtANCBg8enJyc\nbPpi5uTkjBgxglR6cdydBB4utETs6en5xx9/cD0vXbrUpUsXQlHK1l72IYFyV2dCyPjx47Oy\nsliWValUY8eOJYQo3F3sQwKVPl6Eorp27XrlyhXu8Bs3boSHhxOKUnq3tA8JVHi4EkKioqLS\n09Mrvb+JiYm9e/cmhFi2amEfEmjh6QaEDB8+PDU1tcrPg0aj+fDDDyUSicTOxi64rU0bX1os\ntrCwIITIXRztQwKtfL0JTXfo0CE+Pr7mj1Yl2dnZEydOpChK7upsHxJo5duKUFSnTp0uXrz4\nVOdp0oqKit544w2hUChzsrcLaWft70MJhW3atImJianukI0bN7q6ugpkUrmLEyUQgERM3Bxl\nrVuKLC2sra2///57hmFqeMRGvh3oqlWrFixYoNfrjS0Mw4wcOXL69OnVHfLrr78OGTIkIyOj\n9o+C24Gipi0+nnVyYgHYsWPZ0lK+o2mSnmU7UH52YCKEDBgwgFu074lVqHWOUKPRbNmyJTY2\nNi8vz8rKqnPnzuPHj7ewsODuTU1NnT17dlBQkOlK4DUfUqWad2AyfRaXL1++dOlScXGxu7t7\n79697ezscnNzDxw4cOLEidzcXIPBUFZWplarpVJpSEjIzJkzVSrVqVOn7ty5QwhRKpVWVlbO\nzs7u7u7FxcWZmZmFhYWEEEJIZmZmQUGBRqPJzc2ladrJySkiImLGjBnc0J1Wqz19+nRiYmKR\nCVdX11atWh0/fvzGjRvFxcXcjm3cSpYKhUKhUBQVFen1eqVSGRUVNXv27JSUlP379yckJOTm\n5trb2/v6+orFYqlUKpPJgoODO3XqxFUZ5+TkxMTEXLly5eHDh0VFRRYWFr6+vj169OjevTvD\nMKdOnTp48GBaWpqVlVWvXr0GDRokl8v//PPPffv25eXlCQQCGxsbjUbDMIxcLnd0dOSObdmy\nZZWvZ2Zm5vbt2+Pj4zUajVwub9GihbOzc2BgYNeuXStNi7x58+a5c+fy8/NdXFx69uxZafpv\nRkbG8ePHVSqVjY1N586dza9C3rp1Ky4uLjc319nZOSIiooaJy3fu3ImNjc3JyXFycgoPD3/i\nMCS3DG1KSopEIgkKCurcuXNubu6xY8cyMjKUSmXHjh2Dgiqvo1RLmZmZMTExGRkZ1tbWYWFh\nT9y9qVnKy8uLiYl58OCBQqEICQnp0KFDzX9q9Hp9bGzsjRs3ysvLy8rKpFKpUChs3bp1z549\nzVd8q6TJ7cCk0+lGjx7dunXr6nYV+eGHH2JiYjZu3KhQKPLz80Ui0ROnKOAOTKgJ27YNpk4F\njQYWLoTFi/mOpql6lh2Y+ElGx44dGxwcPH/+fO52zZ23bt3aIEHVUS2TUYRQc9XkktG9e/eu\nWrXK9MJ9JV9++eXZs2dHjx69f//+kpISAHB1dR07dmxERIRpt3v37hm3Y7h161ZCQgImo6iJ\nYVn49FP47DOQy2HDBhg+nO+AmrBnSUb5mTNqml828lwTIYSak4SEhLVr1wYEBAwcWO1KW9yc\n0VOnTkVGRtra2j548GD//v3Lli0rKyszPer9999PTa3YcSAgIKB169b1HTxCz5NaDa+8Art2\ngZsb/PknhITwHdCLi/8Cpg0bNrzyyitV3vXo0aNXX331jz/+aOCQEEKoWTp16tTy5cs9PT0/\n/vjjGtb5GjNmzODBg0NCQoxTFHr16jVnzpwNGzb07du3YrY3QL9+/Yx7qxJCjFuBINQEPHwI\nw4bBpUvQpQvs2gWOlfeXRg2J/2R08uTJ2dnZ7733XqX206dPT5gw4cGDB1UehRBCqPZYlt2y\nZcvWrVtDQkI+/PDDKvf+MAoMDKzU4u7uHhoaGh8ff//+fePyDq+//rqxAzdn9LmHjVC9OHsW\nRoyAzEwYNw5Wr8bdlXjH/6L3I0aMmDdv3gcffGCcvWowGBYuXNirV6/8/PxVq1bxGx5CCDV1\nLMv+73//27p168svv7xw4cKaM9HqcJsFaDSa5x0dQg1r2zbo3RuysmDRIti8GTPRxoD/kdE/\n/vhj3rx533zzTVZW1urVq9PT0ydMmBAXFxcaGrp58+barLCIEEKoBr/99tvRo0cnTZpkuh9Y\ndTQazfHjx+VyeXh4uGl7WloaANR5BxCE+GdarhQdjeVKjQf/yShFUd99952Xl9ecOXOSk5MT\nEhKKi4vnz5//2WefCYVCvqNDCKGmLT4+fu/evUOGDKkuE620A4hYLN6+fbtarfby8nJzq9gS\n4ty5c4mJiV5eXk64EjhqorBcqRHjPxnlvPXWW56enuPGjVOr1bt37x42bBjfESGEUHOwdu1a\nAGBZdt26dZXuGjlypEKhUKlUc+bMMS66TAiZNWvWF198MXfu3B49etjY2KSlpZ09e1Ymk82e\nPZuHJ4DQs8NypcaNn2Q0PT3dvLF9+/abNm165ZVXfv755w4mG8Iav5ojhBB6WpmZmQCwb98+\n87sGDRrEbbRbSVhY2FdffbVt27a4uDiNRqNUKnv16jVmzBhnZ+d6Dxeh5w7LlRo9fpLRGrax\nAYDDhw+bduBlWX6EEGoe9uzZU3MHT09P8z5+fn6LFi2qt6AQaihbt8K0aaDRwKJFuLtSo8VP\nMjpmzBheHhchhFAzwDBMVlaWQCDAgipULdNypV27AKf/NWL878CEEEII1dLNmzeXLFny119/\nFRcXA4CDg8OoUaP+85//uLi48B3ac5OVlZWQkFBaWurp6dmuXTtCCN8RNUElJTBpUkW50p49\n0L493wGhmjSWAiaDwWDcDqS8vPzq1asikSg4OBh/CRFCCHFWr1795ptvKRz25QAAIABJREFU\n2nUPa7t4roWnO8swBUl3t+8+uLlt2x07dvTu3ZvvAJ/VtWvXPvjgg6NHjwosFbRIVJ5f4OLk\n9NFHH73++usUxf+64E2GsVypa1eIjsZypcaP/2TUYDC8/fbb2dnZ3LafKSkpffr0uXfvHgB0\n7979wIEDVc6vRwgh9ELZu3fvq7NmdfhkjlO3MGOjU7eOTt063t26e/jw4WfPng0ICOAxwme0\ne/fu8ePH2/fq2nPtD3I3ZwDQl5ZlnIib+8nHx44d27Ztm3EjVlST+HiIjKwoV1qzBh5vaYsa\nM/6/aX3zzTc///yzh4cH9+Obb755//79WbNmvfHGG3FxcT/99BO/4SGEEOJdeXn57Nmz/WeM\nN81EjbzHDrfq2uGdd95p+MBqoNPpsrOzdTpdbTonJCRMmDCh9RuTg957nctEAUAgk3q81Cd8\n5VcH42Pnz59fn8E2F1u3Qp8+/+yuhJloE8F/Mrpp06bIyMhly5YBwMOHDw8cODBt2rSff/55\nxYoVU6ZM2bZtG98BIoQQ4tmRI0dUBfkthg+sroPv5NFHjx1LSUlpwKCqxjDMpk2bunXrJpFI\nHB0dJRJJt27dNm3axDBMDUd99NFHtj3CPF7qY36XxM4m5ON3/ve//yUnJ9db1E0fy8LixTBu\nHNA07NqFhfNNC//JaEpKSv/+/bnbhw4dYll23Lhx3I8dOnRoDH9ZEEII8ev8+fO2gQFU9dep\npY72Cjfnc+fONWRU5tRq9bBhw6a9OSvfz7PHL1/33/lbj1++zvf3nPbmrKFDh5aUlFR5VH5+\n/sGDB1uNGlLdaa39fSz8vLdv315vgTdxJSUQGQmffgru7nDqFBbONzn8T0AxLVE6evSoXC7v\n0aMH9yPLsrW8wIEQQqgZKygoEFo8oX5AZGnx6NGjhomnOhMnTjyVlBCxapnE1pprEVspLb08\n3Qf0OvOfpZMmTYqOjjY/KjExEURCi5YexhaGYTIzM/Py8rRarUAgsLGxsQrwuX79egM9jaYl\nPR2GDYPLl6FrV9i1Cxwc+A4IPTX+R0Y9PT1PnToFAFlZWXv37u3fv79IJOLuunbtGm6/hBBC\nyNnZuSw7t+Y+pVk5/C7wtGfPnn2HD3X64iNjJmoksbUOW/Lh3kMHq9yDQKPR0I//4QOA3Nzc\nmJiYKzeuZ5WWFFJsrrbsRtLfKenpd+7cqd8n0BTFx0NoKFy+DNOmwfHjmIk2Ufwno+PHj9+8\neXPXrl1DQkJKSkqMM9DXr1+/bt26oUOH8hseQggh3vXu3TvveqK2oKi6Do8SbxsKi8PDwxsy\nqkp+++03z5f7SexsqrxXYm/rOaTfqlWrzO/y9PTUFhZpi4oBIDs7+9y5c7S9jX1IO8tWLRTu\nrhYtPe3at6XKyi9fvbpixYr6fQ5NC1eulJsL//0vrF4NJgk9alr4T0bffffdKVOmXL16Va1W\n//jjjxEREVz7/PnzfX19P/roI37DQwg9Z/fugcHAdxCoscjMzNy8efO3337766+/Xrlypbpu\nnTp16hzaMfHXDVXey+j1N1eumzZtmpWVVb1F+mRnz561Dw2qoYN9aFB8fLx5u4+Pj4+PT/rR\n01qt9vLly3JPN7mrM5B//oFmikv1iXd9J4+eO3fuzZs3n3/oTY5puVJ0NHz4Id8BoWfCfzIq\nkUjWrl1bWlpaUFAwe/ZsY3t0dPTly5etrStf7EAINVV6PSxfDoGB8MMPfIeC+JeXlzd58mRX\nV9dXP5j39ZYNH37/TWhYx5CQkNjY2Cr7r1mzpvjc1Zs//87o9abtuhL1xcXL7PWwdOnSBgm8\nWgUFBSLLmia2ii0tCwoKWJY1v2vBggW31/9x58JlEItkTv/e45Rli9fusGrp4TN+hH2PMN6f\nJv+M5UpeXnD2LOAV1KaP/wKm6nTu3JnvEBBCz8/FizBzJly9CnZ2gHPBX3gPHz6MiIgokIt7\n/N9Xlq1acI26YvXdLbv69OmzYcOGUaNGVTrE19f3+PHjI0eOPD75Hbd+4ZZenoxeX3ArOf3w\nydC27f44fpz3wQtHR8ey7Dylj1d1HUqzc52cnKrcWfCVV145duzYhm9/kUwaDvBPB6ZYXbzm\nDzYxOWTFl0CIe7+I/V/9XC/RNxXGcqVu3SA6GieJNg+NNxlFCDUTZWXw6afw7bdgMMCoUbBi\nBdjbP/ko1HyxLDt69Gi1k02XRe+RxxtBA4DQQu7/6kSLFu6TJ08OCgpq3bp1pQODg4MTExPX\nrVu3e/fu+xf+oigqJCDgq9VrRo4c2Rj2ju7Vq9eJM+edunWsrkPm6XO9evWq7t41a9bs3Lmz\n9P+26A6cEvp6EalEn56pvfa3tXfL9iu+lDk5AIDCw/XRo0eFhYVKpbJenkMjFx8PI0ZAVhZM\nnw4//4yTRJsNTEYRQvXp1CmYORNu3wYXF1ixAoYP5zsgxL89e/ZcuHGtz4YVppmokVv/iKxz\nlxcvXrx582bze8Vi8auvvvrqq6/Wf5hP7Z133tnUuVOLYQOs/LzN7y1Iuvsw5vScs9WuhEpR\nlL29veXIgfrSsqK7KUx+scTTw2H0MLvgtsY+hnItIUTyYm4stGUL/D97dxkX5dIFAPxs093d\nSEmKoIAYCAJioSjiNUHBFhUbUa/dCgZ6DXzVq6iAXSCKGBggJSXdHQtsvx/2Xi7uAiosoc7/\nE8zOM89ZfwJn55k5M28e0GiwezdaJPqLQckogiC9o64OAgKAvXfYxwf27wdh4f6OCRkQbty4\noThyOEFYsLMOahMcozbtpVKpxJ9q6svc3Hzd2oADm3ZbbF0tYTio/Us1KZ/fbd2/bm2Aubl5\nFyMMHjw4pbBY32dWZx1qUj4PGjSIRCLxLOifAosFQUEQFATCwvD332iR6K8HJaMIgvSC27fB\n1xeKi8HAAM6cAbQEHGknIyND1Nasiw5iOppkMrmwsFBTU7PPouKJHTt2EAiEnau3SVqaylqb\n80mKt1bXlr9+X/3m44YNGwIDA7u+fObMmV4L5mvPmNRhhX8mnfEl/E6A96LeiX2gamoCLy+I\njAQNDYiKAgOD/g4I4b3+302PIMgvpbQUpkwBNzeoqICAAHj/HmWiCAcWiwVdL/HE/NvtZ4PB\nYLZu3ZqYmDjN3Arz5FVx8EXMk1dTTYcmJiZu3br1mwtb3d3dhxqbvN9xmEGhcLzEYjKTj4TK\n4Elt1bh/C0VFYGcHkZEwfDi8eoUy0V8VmhlFEIRHWCwIDYU1a6ChAYYNg9BQ0Nfv75iQgUhL\nS+v1l/wuOjTk5PPz8/+8J/Dp6+sfOXKkGxdiMJjr1687OjrGLd4waIGntIUJFo9jsVi1KZ8/\nn7vKX90Q9fChkNA3jkX9dcTHw+TJaLvS72AAzYySyeQlS5ZcvXq1vwNBEOTHZWfD6NGwcCHQ\n6bB7Nzx/jjJRpDOTJk0qjo6jN7d01iHv9iNnZ+ffc5uOrKzsy5cvF0+fmbH3xAO3P57OXPxg\n/B8f1u+abDn8/fv3+r/Pj9WVK/+drnTmDMpEf20DZWaUTCa7uLjExsaeOHGCRqPNmtXp8m0E\nQQYWGg0OHoTAQKBQwNkZTpwAFZX+jgkZ0KZMmbJ79+5Ph0+brl/G/eS69MWbqudvAt++7ZfY\nBgJBQcGdO3cGBQV9/PixrKxMQkLCzMxMQECgv+PqK+23K127BuPH93dASK8bEMloWyYKAEwm\nc86cORgMxsvLq7/jQhDkWz5+hAUL4MMHkJGBffvgjz/6OyDkJ4DFYsPDw21sbBI27TZcMk9A\nXpbdzqBQv9y4k3kx/MypU0ZGRv0bZL8jEAiWlpb9HUXHaDTa06dPX758WVFRISsra2trO2rU\nKFxHhbp+GNquNACQyeTg4OBr166lpaUxGAx1dfUJEyasWrVKVla2l+7Y/8loWybq6+t74sSJ\nsWPHZmVlzZ49G4PBzJw5s7+jQxCkE01NsGkTHD8ODAbMng0HDoCkZH/HhPw01NXVExIS/Pz8\nomYtFdXRFJCXoZGba1Mz1BWV7t2+7ejo2N8BIp169uzZwoULc0uLpUwMiaIilLTE3YcOaimr\nnDlzZtiwYT0aurAQJkyAjx/R6Ur9KDk52c3NrYpJU3NzNJ8zGYvHN+QVnL1/59SpU5cvX3Z2\ndu6Nm/Z/MpqWlpaQkLB69erAwMATJ04oKyufPn3a3t7+ypUrKBlFkAHqwQPw9YW8PFBXh1On\nwMGhvwNCfj4KCgoRERGZmZlPnjwpLi4WFRW12G1hZ2eHx/f/HyakM1FRUdOmTVP1cHOYHogj\n/bOOk9FKyfrfjdGjR0dERHT/g0R8PEyaBBUVsGABBAejRaL9orCw0MHBgd/KxH7x3LYzKUR1\nNJTH2udFPpgyZcqTJ0+GDx/O8/v2/8/8kCFDkpKStLS0mpqa2C2qqqovXryQRgcGIsgAVFUF\nK1fCpUuAw8GqVbB9O/w+S9mQXqCjo8N97CcyMJWWlnp5een6zVZ1/erzJ46PNGi+J1FcbMaM\nGZmZmVJSUj889OXLMH8+Ol2p3/n7+4O6ktHS+dzF19QmOLVW1Xh7e3/69InnnxgHxG56LS3O\nk9OUlJR+uxMmEGTgu34d9PXh0iUwMoL4eDhwAGWiCJKYmLhv377ly5dv2bLl1q1bLS2dVgn4\n2R04cICkqcqRibbRmOyMUZA5dOjQjw3KYMC6dTBzJhAIcOsWykT7UXl5+Y0bN/QWeHZWBlh7\n5pTsgvyYmBie33pAJKMIggx0JSUwaRJMmwaNjRAYCO/ewUDdWoEgfaagoGDs2LHmQyz2Xr4Y\nkZVy9vmTGfPnampq/qo1CqOiopTHjeyig7LTyNu3b//AiE1NMHky7NkDmprw+jXaON+/4uPj\nSdKSwuqdlkPB8ZEkTQzi4uJ4fuv+f0yPIMiAxi5lv3o1NDbC8OEQGgp6ev0dE4L0NTqdXlZW\n1tzcrKyszM/PDwA5OTnDhw/H6qiN/l8In5QEuxuLySy499Rrzpzi4mJ/f/9+DZnHmExmXl6e\ntZpyF32E1ZQ/5OR874hfvoCbG6Smgo0N3LwJaG1ef6uuriaJi3bdhyQhXllZyfNbo5lRBEE6\nl5ICw4bBwoWAxcLhw/D8OcpEkd9NUVHRwoULZWRklJWVdXV1xcTExo0bFxcXN3XqVMJgXYtA\n/7ZMFAAwWKyqq4PlznVr16+Pj4/vx7B5DovF4vF4Fp3RRR8Wg0EgEL5ruJcvwdoaUlNhwQJ4\n+hRlogOBlJQUpaau6z6UmlqZXqhygJJRBEE6QqPBnj1gYQGvX4OrKyQnw/LlgEW/MZCfWHl5\n+YYNG0xMTMTFxRUUFMaOHXvhwgUGo6vsKjY21tjY+NaHNzoBfk6R553v/c/q6PYMfozdyJGf\n0tOMli3ocHWdlImh6niHrVu39tY76Sf6+vq1n7O76FCbnmXwPZVBz56FUaOguhp274bQULRx\nfoAYNmwYtbq2ofOjeuktrVUfU2xtbXl+a/SnBUEQLvHxYGIC69aBmBhcuAC3b4NyV8/mEGTg\ni4iI0NbWPnUngjXayiDIX3XF/Hx5sYWrVlhbWxcXF3d4SWZm5oQJE2QmOlrt2SQzxIQgJIgj\nEUW1NQyXzJNaOIPBZJa+eNPZ7VTGjYqJiWloaOi1N9QPPDw88iIfsDpJ35k0Wl7UIw8Pj66G\nYG9XWrAASCSIiEDblQYUGRmZqVOnpp++xGKxOuyQGXZ9kIamvb09z2+NklEE6XX37t2bPHmy\ngoICPz+/urr6/PnzP3361N9BdaK5GdatAzs7SE+HWbMgNRUdqoT8Ah49ejR9+nQNn5nDD29T\ndRkjYThI2sJYd/a0UReOFhJYDg4OtbW1YWFhTk5OSkpK0tLSZmZmmzdvXr58uZCZofbMydwD\nMmQl+aeNSzt5kUZu7vCOwqpKDCYzP7/TSaaf0eLFi2XwpOSjZ4ErWWGxWJ8OhyqLiC1cuLDT\n6xsbv9qu5Orau+EiP27//v24ovLkw6eZXOsxvty4Wxz5KDQ0lDdHbX2NBxuYWCxWeHj4xYsX\ni4qKaDQad4eUlJSe3wVBfkbNzc2zZ8+OuHtHxWWMyrK5WqLCLeVVD1+8CbOw2Lhx45YtW7gP\n5u5P9+6Bry8UFICGBpw6BWPG9HdACMIDra2t3t7eGn+4qziP5ngJL8BvvmXVi8Ub9PX1aykt\nKi5jlBb/gePja8wvOn79al1GtuHejR2OicFg8BaDqY9flr9MUBo7grsDi8VisVi/WAF/AQGB\nqKgoBweHN+t36nnPFNFUY7fXZ35JC73EV1Eb+eRJp2UZ0Xaln4GiouKTJ08mTJjwbN5KNbex\nYoO0MFhsY35R4f1oVklFRESElZVVb9yXBz8nBw4cWLNmDQAICAh878plBPkNMJnM6dOnx6Z+\nsj93mF/6n6MyxXS15O2salI+79yyF4/Hb9zY8Z+6vlZRAatXQ1gY4PGwbBns3AmCgv0dE4Lw\nRmRkZEVz02j3jssGMTEY2lCj8r/vjb0eShQVZjdKGusLyEm/3XU0raxYuEqJu4q7kJBQObmJ\naKBTl5HdYTJal5EjwM+vpqbG07fS/wwMDBISEtasWXPNdx1BQowkLkqprqXXN86YMWPv3b1y\ncnIdX/byJUyeDBUV4O0NwcGAUoUBTF9f/9OnTydPnrx27VpS2A06na6pqblowpSVK1d25ziD\n78ODZPTIkSOOjo4hISEaGho9Hw1BfhlXr159GPtsxJkDfJLiHC9JGA6yCPTftm6bu7u7rq5u\nv4T3n+vXwc8PqqrA2BhCQ2HIkH6OB0F46vnz5zJDTLD4jp8tZmZm0lXlAY8jF5e2JaMAQG9u\nwQkL8inJJyUljRw5Evv17j0FBYWCN2/4+UidPabPvXlv/Pjx7CJQA19FRcWnT5/IZLKysrKJ\niQm2y62KioqKly9fPnr06KtXryoqKmRlZa2trSUlJTu94OxZ8PMDBgOdrvSz4OfnX7ly5cqV\nK/vsjjxIRsvLy8PDw1EmiiAcgoODNdxduTNRNkljA3HzwadOnTp48GAfB/af3FxYtAgePQJ+\nfggMhI0b0YwF8uuprKwkdfJjyGQy8/PzhbXVG0SFKbX17V/ikxRn1NQJykpXllWUlZUpKCi0\nf1VKSkpaSqoq9wXJSJ972IJ7T+veJm77eI6H76KXpKSkrF279uHDhwQRYRwfsbW6TkZScu3a\ntcuWLet6aaCUlNT4b9aoZzBg40bYsweEheHyZbRIFOkMD5JRWVnZzjZeIchvi0qlvn792nbO\npC76yFqbP4993mchfYXJhDNnwN8fmprA1hZCQ6HfJ2gRpHdISEhQizuuxF5fX89gMokiwsyG\npvbTogAgNkgLh8FSkj6TxESrq6s5klEAMNIZFP2lsLSeLDPUTMrYgF3jqbW6NvvyzdIHzy5f\nvqyjo9NL74hX7ty54+HhIWlrOeLsQSEVRQBgUCilz99s2PXn48ePb9261aNzuevrYcYMuH8f\ntLUhKgoGDeJZ3MgvhwfJ6IwZM8LCwnppTSuC/KRqamqYTGZn8zFsfJISZb1wlMW3JSfDggXw\n9i2IicGpU+Dt3dlJxAjyC7CxsQlbc5PFZGK4nj5TKBQsgUD9nINlgai2evuXsASC+mTn3P9F\nEv08KRQK97BZ566a6BvY29uf2LiHxU8SVJCjNjaRC0usra2vx8WZm5t3O+Ds7OyQkJCYmJjy\n8nJxcfFhw4YtWLBg6NCh3R6wQ58/f54+fbrmAk+1CY5tjTgSScnBTtrC+MWqwJUrV4aEhHRz\n9JwccHODtDQYNQquXwcJiW9fgvzGeJCMbtmyxd3dfebMmX/88YeKigr3HiYtLa2e3wVBfi4S\nEhJYLJZSW08S6/R0NUptXe+tB+9Yayvs3g27dgGVCq6ucPIkKCr2aQAI0jNlZWXXrl179+5d\nfX29oqLimDFj3Nzcut60PnHixNWrV+ffftw+62IjEolMKrXp1hNlxxE4rllAbc/Jle+SGk7/\nLTF3Wvt2JpWWfuZ/dS/e3n/1Sl9fPygo6Pnz5yUlJQICApaWltra2j15g/v379+4caO4maHc\nGGtVaUlqfcODd0l/2dj4LFhw9OhRHu4S3rhxo9hQU+5/EwAgiYuab155xm/94sWLv6uIPYe2\n7Uo+PnD8OFr8g3wTD5JRYeF/Hm1cvny5ww7oIT7yGyISiZaWlhWvP4ioq3TWp/z1+2k2Nn0X\nU1wc+PhAejrIy8OxYzBlSt/dGkF4Yf/+/YGBgQRFOSlTQ4KkQGJexl/zL6vKyF66dGlI5xvv\nhISEQkJCps6YThIXlbf76iGesIAAPIzDVtXqzp3OfSGORBy6a8OT6b4lO0Mwo5LE9XRwREJD\nXkFJ9EslMYnY2Fh9fX0AEBYWdnFx4dUb3BC01XxHgLT54LZGxdG2Wh4TwjbvaWlpOX/+PE9u\n1NDQcOfOHasj2zvrIKKhKm5icPXq1e3bO+3TsTNnYPFitF0J+SG8eUxPJBJ/sWpqCNJzixYt\nWrhiufK4kR1OjtakZlS/+bjwVJ9scaivhy1b4PhxYLFg1iw4fBg9NUN+OmvXrj1y6qTJxuWy\nVmZtjQwKNfPCNXt7+ydPnlhbW3d27eTJk8+cPOXr61v05LnyWHshFUUGhVKTkpF7654ouRVk\npQhCHdcyK4mJlxITO3v2bEREROrrTy0tLSaampv27vf09CTy+hDL7OzsjRs3cmSibMJqykN3\nb/rfwjXu7u6uvNgGlJmZSWexOFYmcBA30E1OTv6BQdu2K4mIwOXLwKMEHfkd8CCD7GxCFEF+\nc7Nmzbpy5cqbDbstdwSQJMTav1Sf+eXd1v0BAQHsmZXedecO+PlBYSFoasLp0zBqVK/fEUF4\n7fHjxwePHh1+dLuo1lf5E45E1PPxwhDw06dP//z5cxellObOnWtnZ7dv3767oVeKioqIRKKh\noeG2VWumTp1qY2OTtP/E4FULsV/PqpTFvU09eeHa5SsuLi68mvvsQkhIiLiZIXcmyiaoKKc+\nadzhw4d5koy2trZiCfiuF4vjSMSWltrvHbGxEWbOhNu3QUsLoqJAT6/nQSK/D15OZ1ZVVWVl\nZZHJZGFhYV1dXTExsW9fgyC/LiwWe+3aNU9Pz0dzV6hNcJS2MCaKCLdUVJW+eFP0KHb1qlVB\nQUG9G0F5OaxZ808p+4AA2LoV+Ph6944I0jt27typPmkcRybaRmfW1JincWFhYT4+Pl0Moqmp\nefLkSQCgUCjt94k/evRo/PjxsQv81Sc5SxjqYonEpoLiosexNW8TT4ecmPKDC1qam5sjIyOf\nP39eXV0tISFha2s7ceJEwe84ReLZs2dyo7rapSQ33PLFykAGg9Hz8xjV1NTozS2t1bWd1Z4D\ngKaCYnX171v/2rZdydYWbtxApyshP4o3Z9PHxcVZWVlJS0sPGzbMwcHByspKQkJizJgx6CBQ\n5DcnIiJy+/bty+fOq1c2JW/Z/2z+qtxDZ6yFpeLj4vbs2dN1ZekeYbHg4kUwMICwMDAxgdev\nYfdulIkiP6nGxsYXL14ojbHtrAMWj1McOez+/fvfOSBHxSJdXd3ExMQtS1dgYxNe+q2PmbO8\n6ESYi65RcnLy/PnzfyjUu3fv6ujozF++9F5hdiKJ+aD4i7f/Sm1t7YiIiG9eW15ezifdeel4\nAD5pSSqVWlNT80MhdUhJSWnw4MFFj2I760AjN5fGvf2u+eC4OLC2hrQ08PGBp09RJop0Aw9m\nRt++fTtmzBg6nW5jY6Orq8vPz08mk9PS0qKjo4cPH/727dv+OmCGTCZfvnz5zZs31dXVIiIi\nFhYWXl5e4uKdfgp8+vTpkSNHuNtnzpzp4eHRm5EivzIMBuPu7u7u7g4ATCazFxPQNl++wMKF\n8OQJ8PPD7t2wejX0eB4FQfpRSUkJg8EQVJLvoo+gskL+o/hu30JAQGDNmjVr1qxhMpkUCqV7\nJyddu3ZthpeX3gJP00njMP/+0LGYzLzIh1OmTTt/9uysWbO6uFxcXJxa39BFB2pDIxaL5dVT\nx82bN8+Y/Yfc8CHsCqMcUo+fM9LW+XYyeuYM+PkBk4m2KyE9wYNkdMeOHdLS0o8fPx70dUnb\njx8/Ojk5BQUFfeeiUgwGIykp2bY3v4fodPqmTZtycnKGDRvm6OhYWloaHR396dOnQ4cOCQkJ\ndXgJmUwGADs7O+mvP9j1xao+5PfQ65konQ7BwbBxI5DJMGIEhIZCz6rMIMhAwE4NGRQqtvMi\nQYxWqoiAwHcOWFtbGx0dXVBQwM/Pb2JiYmlp2fazicViu5eJFhcXz5s3z3jVQo6j6jFYrPqk\ncSRxUR8fH1tb2y5Oq7e2tn6QkKQ0xq6zDpVvEy0sLHhV3cnd3T06OvqvlYEmAYtlLE3b2qkN\njSnHz1GS0q/Gx3f1K4tOB39/OHoUxMXh2jUYM4YnUSG/Jx4ko/Hx8f7+/oO4DlcwNTX18/M7\nceLEd44jKChYVVXV83jY7t69m5OTM2fOnMmTJ7NbzMzM9u7de+3atXnz5nV4CTsZnThxIiqM\nivyUkpLA2xsSEkBcHA4eRKXskV+GoqKilJRUTcpnWatOK8nXpKTbGht/c6jm5uYNGzacOHEC\nJyYiqCjHoFAbcvI0VFQPHTrUwy1Khw8f5tfV4MhE2yjYDyt6HLt///7jx493NoK3t/dfw4dr\nTZ8g3FE9OGpDY0747ZB9B3oSJIfg4GBVVdVt27bh5WUkB+vh+fmaCksq3yVZmppdfP1aU1Oz\n0yvr6mD6dHj4EHR1ISoKBvxZU8gAx4N5mvr6eiUlpQ5fUlNT48nqlm6IiYnh5+dvf3KujY2N\nvLx8TExMZ3VPm5qaAOB7lpkjyMDS0gJr14KFBSQkwIwZ8Pkz+PigTBT5ZeBwuOnTp+dci4JO\nfnuTi8tKn7/x8vLqepyGhoYRI0acux1hsWfjmCsnrPcH2hz70/HmX3h7S7fJk44ePdqTIO/e\nvavk0OmkJgAoOYy4e/duFx0sLS0X+fi83bSHXFTK8RK1oTFh896ZEfHkAAAgAElEQVRhxqaz\nZ8/uSZAcMBhMQEBATk7OrlVrRkkoWLD4ZlmPeHT3XlxcXFeZaE4ODB8ODx/CmDHw6hXKRJGe\n48HMqIyMTHp6eocvpaWlycjI9PwWP4pKpebl5RkZGXE8ztDX13/69Gl5ebmcnBz3VeyZUUFB\nQSaTWVNTQyQSRURE+ihiBOm26Gjw8YGcHFBRgZAQVNsP+SVt2rTpuvH19DOX9RZ4kkvKyuIS\nmgqLAUBQWUHSSP/TkdMzpk3ros4om7e3dw65fvjRHXj+/zbz4fhIWtMnimprrFy92tTU1Na2\n021SXcvPzzdT4jy/vj0hFcXEwsKu98IfPny4paXl4sI16hOc5Gws+WWkqPUNFQmJX8LvDDMx\nCw8P7/k+em5ycnJ+fn7f2zsuDiZPhspKdLoSwkM8SEbHjh177NixoUOHurm5Yf6djGGxWBER\nEcHBwTNmzOj5LX5UVVUVk8nkPmiRnRl3low2NzcDQFRU1L1799izpIqKitOnTx8xouPHLgjS\nz2prYfVqOHcOMBhYsgR27gQeLblGkIFGVlY2KirKxcXlydMXrdW1eB11groSi8UqiXmRfuay\nvKzsoUOHuh7h48eP4Tdv2p873D4TbSNtPlh98rj169fHxcV1L0I+Pj4mjdZFB0YrhUgkdp1N\nEgiEv/76a+rUqYcPH37mH0SlUrFY7JAhQ04cODR79uy+2P7YtdBQWLwYWCw4cgSWLevnYJBf\nCA+S0cDAwHv37k2cOFFOTk5fX19QUJC9m76srExeXj4wMLDnt/hRLS0tAMDHVciG3cJOOrmx\nZ0afP38+efJkSUnJwsLCe/fuHThwoKWlxcnJqX3PgICAxsZG9teioqLy8l3t8USQXnH7Nvj6\nQnExaGvD6dNgb9/fASFI7xo8eLC6unpicT7GazxdSpyBxzFpdD6TQWr8QpWXbo0fP569Oquz\ny2/cuCFtYSyoINvWwmAwyGQyg8EQEBAgkUhqbk7RXotLSkoUFLqa4OwivLKUDEnjTk9yr03L\nNP6OVa0AMG7cuHHjxrFYrNraWhERkQFxwGHb6Uri4nD9Oowe3d8BIb8UHvwXV1NTe/fu3ebN\nmyMiIqKjo9mNEhISCxYs2LZtW28namQy+cKFC23fysvLT5o0qbPO7NWimE7W0nl4eLi4uJiZ\nmbVlsSNHjlyxYkVYWNiYMWPa/zr4+PFj21pYY2NjlIwifaq0FJYuhRs3gECAgAAICoKviyYi\nyC9p06ZNnytKR5/ch+Uj1dfXMxgMPj4+ISEhDAajbW76cunGdevWdViejy0jI6Pt9EsymZyR\nkVFWVsZgMjEYDIvJFBMT09bWJggLZWRkdC8ZnTlz5tL1ARpTXHB8Hfw8MijU3Fv39mzc8v0D\nYjAYid4/tpfFYt2+fTs8PDwzM5NOp2tpaU2aNMnd3f2rGdzGRvD0hDt3QEsLbt8Grv3KCNJD\nvPm8paysfP78eRaLVVZWRiaThYSEOnwO3htaWloePHjQ9q2ent6kSZMEBATg3/lRjs7wb5UQ\nboMHcx7CpqysbGFh8erVq9zcXO12JXJu3LjRtgvq2bNneXl5PX0bCPI9WCwIC4OVK6GmBqyt\nITQUDDqdhkGQX0ltbe3x48fNd60nCAoAAEeWxsLjNBd6hWzau3z5cg0NjQ5HYDKZgMEBQEVF\nxfv37/GiwqJ62gQhQcBgGBRKa2X1uw/vsQwGk8nsXoRz5sw5ceLEh11HzTev5DhWlElnJO0L\n0ZCS7fqAqL5XUlIybdq0t58SFUcOF7U1w2CxCTl5Eb4Ld+3aFR4e/k9hmZwcGD8e0tPBwQH+\n/hs6r9WNIN3WzWS0rKyMRCKxC8iXlZW1tWMwGHYVz/aNXSemcXFx+vr6HX7+e/v2bWFhYddH\nsUlJSUVFRXE0SktL43C4yspKjvbS0lIA+KFPvaKiogDQ2travrF9MVQikfj9oyFI9+XkgI8P\nREeDgAAqZY/8bp48eYIVE+F+CF5TU5ORkVFdXQ0ALGEBXV1dFxeX7du3GxkZcfTU1taOj4tp\nbGx89+6dgLKCgPx/z+txJJKgkgIBg60lNz979mx0t55B4/H4iIgIBweHl8s2DZo3Q8rMCIPF\nspjM6sTUz39dkaAyIx89GlB/L6qrq0eMGNEoLTY67DhB+L/y27pzpycfPWNra/v27VvlvDyY\nMgVtV0J6WzdXQ8vLy7ftTJL/lq6HsrW1ff78eYcvvXjxwtvbuxvh4fF4TU3NzMxMCoXS1shi\nsVJSUqSkpKQ7OqystbX1/v373JEUFBQAQIeXIEgfodNhzx4wNIToaBg3DtLSICAAZaLIbyUv\nL0+Y66CgnJyc+Fevmgk4icH6MlZmJC1VRaeRH1rrLS0tuQ9bmTRpUkXCx+T41wQJsfaZaBvq\nyw+CSvKHDh2qqKjoXpDKyspv3ryZ6+yWsv3wfdc/nnotue/6x6etB7zGOCUkJHQ2ZdtfVq9e\nXcOHt9i6un0mCgB4fj7TtYtBUznC2RlGj4baWjh6FE6dQpko0nu6OTPq4eFhYmLS9nU3RsjO\nzs7OzmZ//fHjR+7NRi0tLdeuXWufTf4QBweH4ODgmzdvtiXNDx48qKmp8fT0ZH9LpVKLiooE\nBATYE7ckEunatWtkMllDQ6OtbOqbN2/S0tI0NDT6bNUBgnBKTIQFC+D9e5CQgCNHYIA95kOQ\nvkEikRjUr/aqFxcXp3/+LKanRfy3Bh+LRhdQkNXymFA8xGTevHkqKio2NjZt/YcOHTrGfuSj\nK1GSgUu5x6dl5jZHPR26IyDt5MUbN274+vp2L05RUdGDBw/u2rXr48ePFRUVUlJS7fchDBwV\nFRVhYWHDg3di8R18rMWyWIclxT1eJjBERXE3bqDtSkhv62YyevXq1Q6//n7h4eHr169nf71t\n27bOurFP9O4GBweHZ8+eXbly5cuXL5qamoWFhXFxcaqqqm3bm0pLS1esWGFsbLx9+3YAwGAw\nvr6+f/7556pVq2xtbSUkJAoKCl6/fi0gILB0aQe/uZBfD4PBaG1tHUCnHrS0QFAQ7N8PDAZM\nnQrBwYBm6JHflaGhYX3WFyaNxj4RlMlkpqWlCasptWWiwGDScwpEPCYBgOLI4Q3ZecuWLfvw\n4UP7QRYuXPh42tSGvWdEvD3wKgptF7ZExzeGReh6TZG2MJY01ue4qgtFRUWVlZVycnIczwBJ\nJJKVlVWP3nAve/bsGUlGSlS7g8lageaW1X8etnj9IZdIjFu8eBbKRJHex7OCEe0L+VIolMTE\nRCKRaGJi0tnW9XXr1s2ePTshIWHChAmzZs3iPv8dh8NpaGi4ubl1Lx4sFhsYGHjlypWXL1++\ne/dOTEzM2dnZ09OT1Pm+Y0tLyz179vz999/x8fGtra2ioqIjR4708PBAm+V/ba2trcHBwZcu\nXUpOTmYwGBISEk5OTmvXrv3OIiy9JTYWvL0hKwvU1ODkSXB07M9gEKS/2drayklIFtyLVpvg\nCACVlZVUBl1U5r+PZy2xbwgEgpSJIftbrekTHrkvSEpKav+DjMVi+aSlpNRVC9fuwaso4OVl\nWFQqLSuPSCSa+vsqjrYBALwAP7vOdBeam5sDAgIuXrzY0NDAbpGVlV27du3SpUt5dXB8byst\nLRWQ6+DDrWJR6aZNuxULSz5aGM8UFBhPp/d9bMhviAfJKIPBWLZsWUVFxfXr1wEgLy9v9OjR\nX758AQAbG5v79++ztzRxk5eXd3Nzc3Fx8fPz640PkXx8fHPnzp07d26Hr6qqqnLvfBo0aFC/\nFEZF+ktubq6rq2t+fY3GFBebxV44Eqm5pOx5dNxVC/PdO3etWbOmH2KqrYV16yA0FDAY8PGB\n/ftRKXsEIRAIe/funTVvrpiuptggrfr6eoKwUNuZt7TcwsaLN42XLcAS/8kFCcJCwuqqHz58\naEtGS0pKWltbKdW1Rsu9ded4VL5Lai6vxJGIou5uUqZGbReSi8uULW24A2hTUFBgbm5eVVND\nMNQWMhqJ5SfRSyoq3ySuXrMmLCzs2bNn7G2vA5ywsDCtibPktvGHTwHbDgo1ku9MGnfWd3b1\n5r0/xXtBfgE8SEb37dsXEhKyatUq9reLFy/Ozc319fXFYDAnT548fvz4unXrurj8zp07AJCa\nmiorK9t2ZlJqaiqVSjU1Ne15eMivJzk5OTc3F4/HGxgYqKqqdnucuro6R0dHsqK0/YFNbX+K\nBBXlpIeYKDvab9gcKCYm1r0tdN13/TosWQIVFWBoCGfOwNChfXp3BBnAZsyYkZGRsWPVVm2v\nKRRtZQwOBwDMpuaW6Hhy+AOtKS7KY+3b98cL8Dc2Nra2th49evTkyZO5ubns9qdeS7RnTlZ1\ndcDicU1NTY2NjSUV5QICAuLi4vQmckVCotOWHZ3FQKFQjI2N63EgsXsNQfW/DVXCXhOawh8k\n3Xo0bty4+Pj4Xnn/PGVpadnwJZ9a10AU+2edg3PkQ+/gc4CBkJU+D1wdmFRabcpnyx2W/Rsn\n8pvgQTL6v//9b/LkyQcOHACA4uLi+/fvz5s3LyQkBABaW1v//vvvrpNRGo22aNGiv/76KyYm\nxv7fU2RiYmKWLl06Z86cM2fO9MZRvMhP6sKFC0FBQfmFBSRJCRaD0VpdazV06J49e+zs7Lox\n2o4dOyqxzOHrl3Ev4ZcyMTT2X7R69eqJEyf2US2FkhJYsgRu3fqnlP22bTCQqsAgyECwdetW\nMzOz9evX55+7CqJCVUQio6pGSFHeYsNyORvOtIlcUiYqKmpnZ5dWUqjtOXmspSlJQizj/cfs\nJ7Fpl28UPH3OcrVvpFHxfCR2qVECDi/w4oO50eCRI0d2FsD8+fPraBSpHQE46a/LEeJwQh4u\nLCbzdeST8PDwbu926DOGhoYWZmZZl28a+M3BMRjeweecIx82igjvDlyVbGIIAF9u3FWWlUOn\nYSN9gwcH3ebl5Y0dO5b99cOHD1ksVtsGdnNz828WhD927Nhff/3l4uLSforLwcHBw8Pj/Pnz\nx48f73mEyC+AxWItWLDAe+kSofGjxt78a8zlEIe/TzlcOVGlKjtyzJiTJ0/+6IB0Ov2vv/7S\n+WNqh5tJAUDBfhjISHAXiOE9FgtOn4ZBg+DWLRg+HJKSYPdulIkiSIfc3NxSU1NvR0VBXaOB\n52T70/tHnjvMnYnWJKcz6xuDg4PzGBS7U/uUnUaSJMQAQMfMRHKEFcxya2gmN0c9lTYzkjQx\nlDQ2kDbUg7gP9a8/CgkJMRiMzu5+48YNPic7zkz0X0Lu40BUaPfu3Tx8v73n2LFjJXef1t28\nt3XdTufIhyWK8gFHtrMz0bKXbzMuXDt+/PjPsgQW+dnxIBltv0XpyZMngoKCtra27G9ZLBaN\nRuvkun+cP3/e1dX1zp076urqbY26urpXr151dnZGySjCtmfPnks3w22Dd6pNcGIfwQIAfNKS\nej5eFltXL162LCYm5ocGTE9Pr6uvlzbnPHarPZkhJr3+xC0rC0aNgoULAYOBw4fh+XPQ0+vd\nOyLIz8/FxcXNza3sZYIQV/FRAGBQqMnH/7Kzs0v+km2+1R/P/19lJQwGo6mpycRgwNGGkVfc\neOpq871nDaFXq5Zv5yuvsT4Y9CotZcOGDR3eNDc3t7W1ld92SGdRYQh4oqlBenp6z99gHxg6\ndGjUwYOhJy4Yf/j0Vldz+YHAfFmpmpTPiXuCP/15NPTkyXHjxvV3jMjvggfJqKqqKrtWfHl5\n+e3bt8eOHdt2yERSUlJbzc7OZGdnd/ZMxN7ePj8/v+cRIj+7qqqqP//803i1n6BiB5UNZK3M\nNKeNb1u1/J1qa2vxgvzYLj/3k8REa2pqfizW70ejwZ49YGQEz56BiwukpMDy5YDlwY8kgvwO\nTp48SSqter/9ELWhsX17a2X164Ad6oKizc3NahPHtX12bfP582dBBVlJKzOClQkrNYuYmCFN\n4DNbtcju1F7JwXqm65ceOXKEfdwJh7y8PCAScOIi3C+1wclKUqnUnr+7vvDkicPGjbpMZoy2\n9riSivDpi+45e31Yt9NSWPLdu3fz5s3r7/iQ3wgP1ox6enpu2LAhNzc3Pz+/qalp+fLl7PaL\nFy9euHCh7dvOiIiIdPYoPy8vr8NjQpHfze3bt0FcRNbKrLMOGlNcH11ZkJaWxl0jrDMyMjL0\npmZ6S2v7WRMOLZXVsrIdnNTCA69egbc3pKaCnBwcPQpTp/bKXRDk1yUnJ/fy5cvp06dHey2R\nt7MS1lAFFqsuPassPsHF0en8+fOKiormM1w4rmpubq6rq5PWNMYSCULWZs2fMoYd3Nq+g4SB\nroCGys2bN1esWMFxraSkJNDoDHIzrvMi9oxGMj8/P4/eYm86fRqWLAEWC44dG7lkSRmNVlxc\nzGAwlJSUuiiA+EMoFEpFRQUWi5WVlcXjeVZHEvkl8WAaZuXKlXPmzElMTCSTyUePHm1b77xu\n3TpdXd22yvadcXFxOXv27L1799o30mi00NDQ06dPt61GRXoDi8WKjIycPn26oaHhoEGDxo4d\ne+zYseZmznof/S45OVlcX4dKpWZlZcXHx0dHR8fGxiYlJbEPpAYAoqiwoJJ8cnLy94+po6Mj\nJydX/updZx1YTGb5q/dtm+p4prERli4FGxtIS4O5cyEtDWWiCNI9ysrKcXFxEdeuOylrSafm\nymUUTjIwjXn8JDIyUkhIqLW1Fc81LdrY2IgjEdnVMzCC/PTmFu5hxfW0U1NTudt1dXUJeHzz\n+5QuQqKlZhkZGfXgPfU+Oh2WL4eFC0FYGB4+hCVLAIBAIKipqWlqavIkE01KSnJ3d5eQkFBR\nUVFSUpKWlp4/f/43N5AgvzMefFjh4+M7d+7cuXPnONpv3rxpYWHxzc9DO3bsuH//vouLi4qK\niq6uLolEqqurS0tLq6mpkZeX37Gj0xIbSA9VVFRMmzYt/v07pbEjxCeM5icSC/IK1+/dtWfP\nnuvXr1tbW/d3gP+hUCgUOj06OhpIRD5JcYKUGJPBqGxoKnz9WlxMTEpKCovFMrGYurq67x8T\ni8UuWbJkZ8hxWStzvEAHMxl5kQ+F6MzunXbbqXv3wM8P8vNBQwNOngQHB14OjiC/HwwG4+zs\n7OzszNFOIBBkZGSaSyuEVb9aKsZisdqqkzLKq/ilJbnHxOLxHT5qJ5FIrq6utx6/pFmZEkQ6\nqP5LfpPIzCveE9adUwn7SE0NTJsGT5+Cnh7cvg2amjy/Q2ho6NKlS2XsrU13rRfRUGXS6fWZ\nX+5GPLg+eDB7KwjP74j8AnrxBCYSifQ9VZnk5eU/fvy4devW69evP378mN0oLS3t7e0dGBio\nqNjB4nSk5xobG8eMGVNGwo4KO0b897eqvO1Qbc9JmWHhY8aMiYuLGzh1XpubmysyskUmjOSX\nlQJg/yFhMQX4gI9QU15V19hI4CdRSspXrVpVW1u7du1a7PetvFy1alVUVNTbTbsttvi3Fdtj\nK3z0LO1UWMSNG8K8KjhfUQErVsCVK4DHw5o1EBQEP8WzPAT5aTk4ODyLjedY3iMgIMCkUFkM\nBgaHa43/oNDRFsaGvEJNV4sOxwwODn6so1N76rLofA/29vw2zSkZTaevurq62th0VTO/2xgM\nRlRU1L1794qKikgkkpmZ2fTp03V0dH5giIwMGD8esrLAyQmuXoVeKGgfGRm5aPFii8BVstb/\n/QNKWxhLWxjnRjyYOnXqgPrLggwcPHhMz2AwFi9ePH36dPa3eXl5+vr6VlZWZmZmdnZ23zxX\nDQBkZWVPnDhRWVlZXFycnZ3d1NRUUVFx+vRplIn2nqCgoIKWpiHb1hK//nyPweF053jIOtnP\nmTOHyWT2V3jtNTY2RkZGQkkFETBtmWhdXT25uRkvJkocpMkCFraglMjHp792cdD+vZ6ent8Z\nOR8f3927d02lFaJnL0s7FVYW/64qMSX/9qOXK7ZkB1+4cunS+PHjefMeLl4EfX24cgVMTeHN\nG9i7F2WiCNLb/P39S6LjapK/2tsuIiLCx8fXWlVN+ZBKS/qsPsWV46rmsorqjymdnUQtLy//\n7Nkz4czC+j9DqiMeNeUWtJRVNiamVoWENe45bW9hGRkZ2RvvJSUlxdjYePq8OY9KcgtUpD+L\nkY5FhhsaGq5atYr+nSd2Pn4MVlaQlQUrVsCdO72RiVIolGXLlukt8GyfibZRn+gkM8Zm2bJl\nPL8v8gvgQTLKPoFJRUWF/W3bCUx+fn7x8fHfX5upqamptrZWUlJSUFCw51EhXWhpaTl58qSe\njxeORAQAFotVWlqamJj48uXLV69epaSkyE50TM/JfvLkSX9HCgBw6dIlmrCA3LAhDaevAoMB\nAGQymUqjEoSFsQQ8Bo/HCQm23HikNWOivK2lTfCuyCePDh069J2DS0pKPnr06OqFi0ZUbHno\nlezdIbjYdwtdJ2ZlZU2bNo0H0eflgZMTzJ4Nzc0QGAivX4NZp9uwEAThIRMTk6DArW837yl/\n/aF9u66ubuPjl/VHzhn4zRZU+GqHIpPOSNp/ws3V1cTEpLNhzc3Ns7Ky/hjtSIh4Sl63v2HF\njuZdpxS+lF0IPRMTE/Odj2V+SEpKio2NDVlLafSlYGP/RZoebjp/TLXet8XqyPZTVy97enqy\nWKxvDHH6NLi4QFMTHD8Ohw5B7xwl8/Tp07K6WrWJTp110PZyf/nyZWZmZm/cHfmp9f8JTAAQ\nGxvr7+///v17ALh//76TkxMAuLm5LV++fPTo0T2PEOHw5s0bKgakTA0BgEwmv3v3rqmlmU9S\nHCcqBCxWU2N9Xn4+SUP5/v37A2EDWUxMjNywIZrT3OKWbqjddVJ4oQeZxcALCmKwGABg5hQw\nQq5AI1l+hDUA8EtLGi6Zt337dl9fXwEBzr0LHcJgMJMnT548eTKP42Yy4cwZ8PeHpiawtf2n\nrD2CIH1o06ZNQkJCGzZsyNZSkx1qRhIXba2uLX/1Dp+dxyISBOS/ykTJxWVJB05KNFPPnDnT\n9bAyMjLnz58/c+ZMXl4emUxWUlKSlOxg7SlPMBiMGTNmiI8YarRsAcdLYrqaww4FRS0KOHv2\n7IIFnK/+g06HVavg2DGQkIDwcOj8cKmeS0hIkDDSw3a+UYRPUlxITfnt27c/troA+Q30/wlM\nb9++HTt2bGZmpqOjY1tjZWVlQkKCs7MzO0NFeKusrIxPUhyDxTY3N8fFxdFIBClTI2F1VQE5\nGQF5WVFtDSlTQ6ogf1hYWEtLB1tN+1hpaSm/jBRRVNjm2E5xIeHqFTtZJ/9m3nhIvxRFCwqm\n7zjBbzkYQyBQa+vZ/eVsh7Zi4enTp/0ZdHIyDBsGCxcCDgeHD8OzZygTRZB+sWLFioyMjBVT\nZygXVOKi36iV1K79Y15BQcG2jZtSdhyJmbsicV9I8tGz8asCY+auGKmt9+rVq+/MLPF4vJaW\nlrGxce9logBw9+7dzIJ8PW+vDl/ll5bUmT1t165dHV9cUwNOTnDsGOjoQHx8r2aiAFBfX08Q\n+sYUAEFIsL6+vlfDQH5GPJgZ7eEJTNu2bWOXi8Pj8fLy/5Q0l5aWTkpKGjJkyPbt2yMiInoe\nJNKemJgYtbEJABITE7HCQiJaav+uxfwHlkgkEomNNGpQUFC/H20nJiZW1dgEAERRYcsdAclP\nnxXGJxAamjE4LN7alLRiLlZEqPlODEH4n9UdGAxGREO1354E0Whw8CBs2QJUKri6wokT8K1z\nHxAE6VXKysqbN2/maFy/fv2CBQsiIyNTUlJoNJqKtb2rq6uBgUG/RNiFhw8fylp3XPGDTXHk\n8IdHz2RmZnJON2Znw/jx8PkzODrC1asgJtbJADyjoKDQHP2o6z4tZRUKCgq9HQny0+FBMso+\ngWnhwoXdO4Hp9evXq1evVlJSKisra98uIyOzaNGiffv29TxChIOFhQW9vrE4KbWmtlbKzIgj\nEwUAYDJpaVnqLqOPHTu2ZcuW73ze3UuGDh366voVba8p7G/5VBTxJJzoIK22DpSET0Qxka+e\nuLG++ozUd16+BG9vSE8HOTk4dgzc3fshBgRBvo+0tHSnT7d7QVNTU3h4+IsXL6qqqqSkpOzs\n7KZMmSIkJNT1VYWFhR2ePNeGICxIFBUpKCj4Khl9/BimTYO6OvDxgeBg6JOa86NHjw5Yv661\nupZPUrzDDrVpmfS6Bt4Xb0Z+fjx4TO/p6Xn58uVhw4aZmZlxn8DU2Z7ENvX19crKyh2+JC8v\n/z2b8ZEfJSUlNWHChKyL1wkiQh2eh9nyNB7PYGp5TqbjcXFxcX0fYXuzZs1qysip/PCJ/a2Q\nkBCN3Azw74J9BqMp/IGK0yjMv/sGWExm/Ze8QX38WLy+Hnx9wdYWPn+GhQvh82eUiSII0ubq\n1avq6uqLN6yLqSnJlBaMqSnxWx+gpaV169atri8UEBBgtLZ21YPFore2frXxt/12pVOn+iYT\nBQBTU1N7uxGpIeehow1VTDo9NeT8vHnzxMU7TlWR31n/n8AkJyeXnp7e4UvPnz9H8/m9ZN++\nfdT0bGZsAvdvDWpieuPFW4ZL5hEE+AXkZYqKivolwjaqqqpbtmz5+OfR+uxcAJCWlsYymZTa\negBg0Rn1If8j0RhanpPa+pfEvhLC4kf28uqor0RGgoEBnDwJ2trw7BmcPNkbZVMQBPnpkMnk\nnTt3KigozJgxo6qqilJT11JVI2VqZLh0/qiLR2U8XN2ne1y6dKmLEUxNTas/dfwnkq0u8wuO\nyfpngQGdDsuW/XO60qNHsHgxb9/ON4WGhlJTMpMOnWZQKO3bqQ2NCVv2yTCx/b7uCxmY+v8E\nJmdn55CQkMmTJ7fPO2tra/fv33/u3Dk/P7+eR4hw09DQcHd3v3L9Wk1JpYCzPVFHHXBYenF5\na+wbStx7A9/ZCvbDAIDW1Myzqu89sH79+tra2oNLN6q5OSqOGq4ur5DzMVWQBS33YvkwWMtd\nGwj/HvrXXFqeevzc3qBtfXQ8dFkZLF0K4eFAIMCGDbB5M+xCwqUAACAASURBVHR+aDWCIL+V\nrKwsV1fX4hZys5GG8EJ3PjlZellFfdz7lys263i568xyV5vgRBQV8fHxsbW1VVVV7XAQDw+P\nLVu21KR8ljDs+GlP9pVbEydOFBERgZoamDoVoqNBRweiokBXtzffXMc0NDSeP3/u7u4ePWup\n0hg7YXUVFotZl55VHP3S2sz872d3xXp/6SryM+Ll7H1jY2NBQYGioiL7f5uVldX3XBUUFHT/\n/v2hQ4cOHjwYANavX79+/fr09HQKhaKiorJlyxYeRoi0N378+BsP7ikY6ZdcvFVfXQsAOD6S\n7FAz7ZBdIppqANBaWU0uKbOw6Pgkkr6EwWD27dvn7Oy8Z8+emBWB7JP6mnBYBfvhg1f64Pn5\nAIDFZJY+f51y7K/pEyYuXbq0L8K6fh18faG6GkxM4MwZMDfvi5siCPIzqKmpcXR0pGkpS4+z\nK22sFxikDQBEcRGinha/3ZDMPacIQoLqk8Yp2A8rfPjswIEDR48e7XAcNTW11atXH9px2Ppg\nEEdVVADI/juy+dPnnef+B1lZ4Ob2z3alv//ux4czenp6SUlJV65ciYqKyrj7jEAgDNfT87h8\nxdXVtX+W8iM/A94koz0pFConJ/fu3butW7deu3YNABITEwFASkpq3rx5W7dulZGR4UmECDcX\nFxfhpXgRDVUD39nUhkYmjU4SF8W0q9ic+b+bI+zs1NXV+zHI9kaOHDly5EgajVZeXi4oKHjm\nzJk///wzetYSEU01YLHqc/KEsPi9QduWLl3a67/yvnyBRYvg8WPg54fdu2H16l4qIo0gyE9q\n+/btdXz44Wv8YmJj+b5OIgm6GqJ+XunHLiqMsCZJiCmNsbt/7W4XQ23btq2oqOiKX4COl7vi\nGFuSmCiLxapLz8q+GtmSknHjxg3NnBzw8Ojj7UpdwOPxs2bNmjVrVv+GgfxEePBfll0olEQi\nOTo6Pnz4kN3YVig0Pj7e/FszRjIyMiEhIcHBwRUVFY2NjcLCwrKynJ//EJ4TEhLau3evt5+v\noKIc9wOg/DtPKp68uPXyZb/E1gUCgcAu0bBmzZrFixc/fvw4Ozsbg8Ho6OiMHj2615/O0+kQ\nHAybNkFTE4wYAadPA6rejCDI12g02vnz53UD/DA4XEtLizg/iaMDycKIrCBbHB2n4e4qpKyQ\nUlDAYrE6+xSNw+EuXLgwduzYnTt3PjpxAS/Az6TRiTjcpEmTdly8ovHkCSxZAiwWBAcDWtiG\n/Jx4kIx2o1Cov7+/i4vLqFGjAGDRokW+vr7GxsYYDEZWVhaloX1pzpw5xcXFW1YHqU90Uh43\nSkhFkcVg1mVk5968V5+QdPXqVVNT0/6OsSsCAgITJkzou/t9+gQLFkBCAoiJwalT4O0N6KkT\ngiBcPn/+XN/QIGVmBAA4HI7FYHL3IRoPqknN0HB3ZVAo/Pz833yeM3PmzJkzZ+bn5+fn5wsI\nCOjp6QmSSLByJRw/DpKScP16b9e0R5Dew4Pd9K9fv/b19eWuJ8ouFPr8+XPuSw4fPvz27Vv2\n16dOncrNze15GEj3bNy4MfrxY5Wqphfeq+85zbjrNONDwJ92ciofP378Zlmu30hLC2zdCkOG\nQEICuLpCair4+KBMFEGQDtXV1eH5+dgHYwoLC7MPGeGAFRakNZEBoDr5s7Gx8XeOrKqqamdn\nZ2FhIUihgKMjHD/eN6crIUiv4sHMaDcKhcrKyu7Zs6ewsJC9UzssLOz169edjY8qQfS2ESNG\nREdH19bWFhYW4nA4DQ2NPtqK/rN48QJ8fODzZ5CXh+PHgeen2CMI8muRlZWlNbfQm1vwAvxK\nSkopnz8LyMu2X5EPAIzqOpK4GL2lNS/ywaqdP/hnLisLxo+HjIx+366EIDzBg2S0G4VC9+7d\n6+3tHRISwv725s2bXYyPktG+IS4ujmoRc6qvhy1b4PhxYLHAxwf27QMRkf6OCUGQgU5bW1tJ\nUbEs7q3S2BHKysr5+fn1WV/EdDQA828+ymBS3iVLek7+uOuYgar63Llzf2D0R48G1HYlBOk5\nHvwn7kahUC8vL1dX1+zs7NbWVltb2507d7YdZ48gA8Xt2+DnB0VFoKUFp0+jp2AIgnwnDAaz\nfPnywAP7ZK0tCMKCQ4YMef36dU3yZyEVRaKoCGAwzfefYZtb8yIfKZL4bz18+M2C3P85cgT8\n/QGHg7NnYd683nwTCNJ3eJCMdq9QqJiYGLuApaOjo729vbW1dc8jQRDeaF/KPiAAgoKAxLkZ\nFkF+ImQy+fLly2/evKmurhYREbGwsPDy8ur6SUg3Lum20tLSgoICQUFBLS0tvh85NqK2tjY3\nNxeDwWhoaIiKigJAbm5uRUWFqKiotrY2rjcLrjU0NOTm5lKpVA0NDUlJSe4Oy5Yti4yMfLNh\np8XW1fyS4jY2NllZWQXZeXQGA1KyWE9fC/LxLZwyddOmTSJfP2+h0WhZWVmNjY0KCgpfLYGj\n0WDxYggNBWlpuHED0AwO8gvhwQYmdqFQb2/v/Px8AEhMTExMTBQWFvb19U1ISOhwd7y/v390\ndDT7azU1NQEBgZ6HgSA8wGLBxYtgYADh4WBqCq9fw+7dKBNFfmp0On3Tpk23b9/W0tLy9PQ0\nNzePjo5eu3Zthwv6u31J9/z999+mpqYKCgrDR4wYPHgwu8J0YWHhNy989erV2LFjpaWlhwwd\namFpKSUlpa+vr6ioqKGhMdx+hJ6enpycXEBAQF1dHW8DBoBPnz5NmDBBWlra1NzcavhwGRkZ\ne3v72NhYjm5EIvHOnTs2mroxs5clHz1b+TJBqrFVm0wXuh0r8DJx3+7dNTU1e/fubZ+JVlRU\nLFmyREZGxsDAYLj9CBUVFT09vbNnzzKZTKipAScnCA0FQ0N48wZlosgvhjdrTX60UOjhw4el\npaXZpZ1OnTrl5OT0/XsJEaS3ZGaCtzc8fw5CQnDkCCxZAlgefFpDkP519+7dnJycOXPmTP53\n752ZmdnevXuvXbs2r5PnvN245EcxmUxvb++wa39rzZjosN6XT0qCxWDUpGTcvRoRZWoaERFh\nY2PT2bXHjx9fvmqVqusY29P7hNWU6c0tr9Zs+1xSBEMHG27wU9fTZVCole+STl0Kv3nz5oMH\nDzQ1NXkSMwBcuXJl3rx50iOsrI5uF9VSBwyGXFSaf/fJqLEOfwZtW7duXfvOoqKiUVFRDx48\nuHDhQlL4g4aGBlVV1emz5vr5+XFPpn769MnZ2blFQmTQusWSpoZYPJ5SV18SE794tf/7q1eD\nCwowmZng5ARXr6LtSsivhwfJaFRUlKampoGBwfcXCkW76ZGBhUaDfftg+3ZobYVx4+DECejk\nnGgE+enExMTw8/OPHz++rcXGxiYsLCwmJmbu3LkdlrfsxiU/avPmzZejImxDdgkq/lOdGoPD\nSRrrSxrrZ1y4NmHChA8fPnR4XHtkZORy/1WW29dKDzFht3zcfZxMpUod2ECj01Izs4WkJKSl\npeWGD5EZapa497irq+v79+958vwtLi5u7ty5Bv6LFEf/lygLKsnrL5wlN8xiU8BWFRUVT09P\njqucnJzYpxJ2obq62sXFhWBhaLp0fts/L0lMVH3SOFdR4XW7jmGYTFi2DA4eRIe9Ib8kHiSj\nHh4eW7duNTAw+P5L0G56buXl5Z8+fWpublZRUTE2NsaiObk+k5AA3t6QlATS0nDmDMyc2d8B\nIQjPUKnUvLw8IyMjAoHQvl1fX//p06fl5eVycnI9v+RH5ebm7t+/f8jeTW2ZaHu6s6fVZ+Vu\n3Ljx0qVLHC/RaLQVK1YMmjejLROtfJdU/i5R8tAmrLAgCUBQUT4lJcXe3h6DwWDxOJM1frE+\na44cObJ+/foexgwAK1asUJro2D4TbSNhpGfgN8ff33/ixIndSHx37txJFhEYvmQeR6LvduPu\nvJMXmVjMfMAunj3bDGWiyC+KBxmPjY1NbGwsk9nBCROd8fLyKi0tTUhIePHiBQDs3LnzRed6\nHuEAl5SU5OjoqKCgMH7a1BkLvS0sLZWVlY8dO/ZD/6RId7S0wLp1YG0NSUkwdSqkpqJMFPnF\nVFVVMZlMKSkpjnYZGRkAKC8v58klP+rq1atCupoSRnqdddD2nHTjxg3uJaovXrwoqihXn/Df\nRGPho1g+O0ucpBj7WwEFWXJzc21tLftbLIGgOc3twoULPY85NTX1Q2KilsfEzjqoOI+qpbQ8\nevToR0dmMpn/+9//tKZPaF+IFE+nLz54akHI+UYRoU0Htt63sbx48WI3Q0eQAY8HM6OXLl1a\nuXKli4vLH3/8oaOjI8q1nEVLS4v7KrSbni0yMnLGjBnS9tb25w4LKskDAKOVUvL81dqgwMeP\nH4eHhxOJxP6O8RcVGws+PpCZCQoKEBwMEzv9G4MgP6+WlhYA4N6izm5pbm7uySUnT56sqan5\n5yoKrbS89uaDD+0vERHiG2OjzzEOjc6I+1CkZGBPSalv307UF8Fg/5kXFNPTpjIZ6enp9RTB\nhqbWtj5Pn75Xt3QBCsC/vxcbsnOJE0YDAH8lg1jPAgAiVbY1uY4iiQMAvAyf5GC96P0nyGSy\noKAg+5L84ur3yfkcUclICtsM0eZobCJTHr1IZX+dkJCgZuHEKmRSihtI+h3UG6ZmkNUtXW4/\nTWbyqbQ1jh6uJyrMeYbIqw85pRX/vffa2lqskIa4is5//271jeuC9hsmpeWpKG+Zu7gcpBS0\nhr1Pr2D/8yorSAwZrMYxZlVN0/O3mRyNgvwkxxGcDy1ZLLj18ANwcR45mI/EmRI8e51RU0fm\naLQy1VCQFeNoTEovzMmv5GjUVpMxGsR5OmNxWe2bRM5jFyXFhUYM1eFobGml3n+Wwh3qZCcz\n7sb7z1JaWqkcjfZWuhJighyNbxNzi8pqORqNBilpq8lwNObkVyalc26nU5AVszLV4GisrW+O\nefWZo5GPRHAeacQdasTDj0wWi6PRcYSBID/nZtkXb7Mqaxo5GocYqynLS3A0pmQUZ+ZyflDU\nUJY2MeA8k6isoj7+Qw5Ho6gw/+jhnB8OqTT6naefuOOf4GCCw3FOZT56kZqfl1/X0MGvlO/B\nm6L37C8ePHjQYQcW1z96e21XNTY2FhQUKCoqiolx/i//VaWmpnp6emr7/qHqMqatEcdHUh5r\nL21uHL0ycM2aNUeOHOnHCH9NdXUQEAChoYDBgI8P7N8PwsL9HROC9Cn2r+UfWv3Jfcnjx4/Z\nRVQAQENTm8IUePt1kiErJcKdjDIYzGa6IB+eQCtoad9OHCTS9qwOg8Hg+fkaGxtTcuvLKv/L\n20qr6UJCiqxWJvz7I0tvpZD4SQBArGcJljEAgEjmx9FZNHILAGBIWLyUAAA0NTW1JaNVNU1v\nuZIhLTUZ7mS0hUJt65lfQhYRU6MVtGBwmA6TUXpRiyCfXEUdq/3gVqYa3Mlo5pfy9OzStm+b\nmprEZXUwtH8ewavkFW7atEeutPyttcXuGQvq8mnQ1EKgCzNwdPbINBqDOxltJLdyvykJUcGO\nklEmd08AcLDV505GUzKKi0o58zZdDTnuZLSguIZ7WD4SgTsZrart4N9fRVGCOxml0hgdhtph\nMpqYWlDf2MLRaGGkxp2MdphiSokLcSejZZX13AEY6ChwJ6PkZgp3T2Ehvg6T0bdJuUwmZ15k\nb6XLnYymZ5d+KeBM8dWVpbiT0aLSWu4AcFgsdzJa29DM3VNBVow7GaXTO/6vMn60MfeCkeT0\norz8InIL5+eB78SbNaNEIpFAIHR7VXtsbKy/v//79+8B4P79++y13m5ubsuXLx89enTPIxyw\n1q9f/3/27jsQyv8PAPj7pnn2puyZKKWBzFAi7UVTSbSXvqWotKU0NDSptLSHEkoSIpIoys7e\nZ925c78/9JM4Qscpn9dfep7P8zzv5+LufZ/n83l/hAx0W2eiLTiFBUdsX3tqlZuzs7Oqqmrf\nx9ZadnZ2fn4+Hx+fiopKm2Fkf5+HD8HJCfLzQVMT/PxgzBh2B4Qgvah5/GJzZ2drzVuYLvzb\n9UN8fX1pNFrzz4mJSdExMeucfpmpg8My+VDgIBIURGrCC3PEjLVab8fgfjZuJNdQq2sGDx48\nYqQUvdXH9rVr5R7HAmSE9Fu2cIkKNxaWAgBZDlcrgwOA8uQMDRUVHglRAAACpuJLGhcXl6io\naMshmqrS8oPaDkIgEJgMxxQW4N38/zsKCQl5tNp7kIsXg9lNAQCXgXBy8LkZc+c7Ov58Efja\nZaIAMG2CTiON3vJPMpl8/sgaMcwwIpB03iVu3n2Eu7bu4TSrc86LGHTgkW8CgILrb5SJtOZg\nOIhMPrhlJAU3O7WdJoVlFioGg2nfEgB4uJnUsJs/bSyd3nbAGC8Pk5Zm+ur6I9s+BeXkYPJ5\noa4k2T4APJ7J60/i4WAaKlMr5hu3z/BIvEzK1k42126fo3O3SwQBYMRQOXWltsOaiQQmr7+E\nGH/7UDtKijYut4R2fXQCfEyGGs+ZPIrW6lelGdPX33C0yqhh8m02crT7dgEASrJi7UNl+qfK\nyUlg+vrj8UxGeC6da5iVleV7gkmne1ewIBm9fv36nxweGxtrYWHBwcFhaWn57Nmz5o0lJSXv\n3r2zsrKKiooaMWLEnwfZD1VUVDx9+lT/VIfTs/iVFfiHqt24caOjhQN6G51O9/PzO3z48Nev\nX/FcnLQGigA/v729/Y4dO1q/s/81Cgpg5Uq4cweVskcGDlFRURwOV1LStnOloKAAAJgu19z1\nQ1rXTsnOzuYg4tv3QrWHwYDVBLOrSx3UnefiOvgb/B72RlFBof0Qr8nWlqtXLifn5pHkfvT3\niI7UzoiM5rYybsJjmvBAq61toNUJS4tiOHD/P1Wkubl56ymhHEQ803yuPSwW03JHVhPMaJVl\nZWmfRYZrMm3cUFVW8jFp8lXL374IbTIkIQGeEcM181+9Wf1J2OnYOQYGs81Y70JhMXXNdiI/\nSWiourTZuNxXr1b959bJmQl4XFdefADAYDBdbAkA7bt1O8LNReTm6tKgMiIBLyTQxdcf2/VQ\nmSZzTPFwczDNvNvj5MC37y1mCo/rRqhC/F1tyccsmWaq668/gdDVXxVsN39V+Elc7R/fdxEr\np2wXFBQkJCSEh4cnJSW1fyPryK5duyQkJFJSUi5dutSyUVRU9MOHDxISErt372ZhhP3K58+f\nGXgcn0JnJYSEhqh+/Pixz0JqraGhwdbWdp3bVq5JJpZ3Lkx8FDAp+JraFpdrL1/o6OiwK6oe\nYjDg7FlQU4M7d0BPDxITUSl7ZIDA4/GKioppaWkUCqVlI4PBSE5OFhERYfqtsgeHdJetra28\nhOTn84FM9zaUlKX532pTs7NZc+2kjz7nmv7fXSRnY8HIL64PewsAwGiqzsgZNGgQx///uitS\n03Meh7q6uv55zCQSydnZ+ZPvJXoDpf1eBoPx0eeczaRJGhpthyV0xZaNG9cF3nU5coZMJFji\ncYdycslSojS94WQp0fSQl6F2znhy3cKFC//4JhCkn2JNMurn5ycvLy8lJaWjo2NqaqqtrS0m\nJqaurt6VTtPo6OgVK1bIyLQdViImJubk5BQREcGSCPuhhoYG3O8mJ2GJxPYPy/qGk5NTxKck\nw9MH5SZbEPlJAIDF40V1h+kd2YnX0bC2tm6Zr9rfff0KZmawfDnQ6bB/P7x+DT36tECQv5S5\nuTmFQmldQS84OLi8vNzCwqL5n1QqNSMjo7CwsOuH/CE8Hn/t2rXiZ68+nbpMp/wyyKzqa2bU\nBo9JpmYODg5Mj/Xx8RGso77bfoBSUQUARAG+YZtdyBdu1T4Kq0hJ5wBMSzpYHPM+duu+rVu2\n6OnpsSTs3bt3KwuKRG/eXV/0S28LtZocv8ubo6D0zJkzPTlvWZn1sWNONHoyFqtDp8cvnyu0\nbxPvzIlc4/V4Z0zgcLFnWBrUUBrOnz/PkrtAkH6IBY/pT5065ezszMHBMX78eGlpaR4enqqq\nqvT09Hfv3s2dO5dKpS5YsKCTw6uqqn5ZfrcVSUlJlq8+13/IyclRyTWUyioOgQ6X06jJyZNX\n1epob++JiYkJuHrV+Lw3h1DbUeoYLFZzlUPkym379+8/cOBA38fWDY2N4O0N7u5AoYCVFZw6\nBYMH//4oBPm3mJubv3z5MjAwMCMjQ1FRMTc3NzIyUlZWdurUqc0NCgoK1q5dq62t3fIk6reH\n/Lnhw4dHRETMmTMnbP5KyXGjeWSkaPUN5R9TKxKSV6xY4eXl1dF4O2Fh4cjISDs7u7D5KyUM\nRvErKwCDIaSuUnblPk5YUNRE73t1KKWyuiTuQ0Nm7j5Pz/Xr17MqZm5u7tDQ0MWLF99ftEZC\nT1dATQmDw1V/yyp8HTNq2PDAN296UoE1LQ1sbCAtrWbcOMM3byqtjQn8PPS8AiweR2ugUMor\niFjc2AVzmgz0N23aZGFhoaamxqrbQZD+gwXJ6NGjRy0tLW/cuNGmqFNmZqaFhcWBAwc6T0Yl\nJCRSU1OZ7oqIiGA6pOnfIC8vr6GunhcSoTjThmmDRnJN0dv4SetZUKu5uwICAiTHjW4uNdUe\nBotVmmMbcDZg3759/bc4//v3sHQpJCSAuDgcPAid/hIiyD8Mi8W6u7sHBga+efMmLi5OQEDA\nyspq3rx5HB2PVOnBIT0wfPjw5OTkoKCgp0+fZiV9JZFI08ZPtLt45bdPusXFxV+8eBEaGnrn\nzp20tDQMBmM8zmTSfq+cnJywsLDCxHQBAYElC5bY29tLS0uzMGAA4Ofnv3PnTlRU1M2bNz9/\n/kylUg1UVGxXb5owYQLT7JlCocTGxubn5/Py8urq6jbXav3p2TOYPRuqqmD16o319ZxANV1s\nn5eXV1lZSatrIHFyiqprSElJ4XA4EBYWHqNz+PBhPz8/1t4RgvQHLEhGs7KyLly40L68qLy8\n/Lp16377rdTKysrX13fatGmt886KigovL6+LFy86Ozv/eYT9lpub20LHZRJjRzJJ+xiMj8cv\n6AzRtLS07PvAEhIShHQ7W1JLWHtIXEFBQUEBy9/rWaCuDnbtAi8voNNh5kzw9YV25bsRZEDh\n5ORcvHjx4sWLme6VlZV98OBBtw5hFQKBMGfOnDlz5vTgWDMzs/blVpYvX86KuH5DT0/vt4/+\n6+vrPT09T5w4UUdr5BITaSTXUCuqJk2a5OXlpaKiAgBw9iy4uAAOBxcvwqJFzxUUpOZP5ebm\n/rG3HWkT/ecXb7H8XhCkP2BBMsrPz4/rYI0yHA7XfhmPNnbu3Pn06dPRo0draWkBwH///fff\nf/+lpqZSKJTBgwezayJ535g7d25YWNiV9e7DXFeKjvj5OJ5aRU4+fp6anHYtOpoly0B3V21t\nLZ67s0l8zXv74yCKp09hxQrIzgZ5eTh9Glg0xA1BEKTrKisrLSwsPhcXqLs6i+kOw+BwAFCT\n8/29/y1dXd2HQUGGgYFw4QKIiMDt22BkBADfv38fJCneyTl5pCS+f//OYDDY8qGAIL2KBcmo\njY3Nw4cPxzCr1/jo0aOZM2d2friEhERcXJyHh8fNmzcBIDExEQBERESWLFni4eHR9qHGP+fs\n2bOysrJ73fcSZSSFhqrhOTlrcr+XxH0YM2Kkf0yMvHzbsmF9Q0ZGJv17YScNavMKcDhc/+oW\nraiALVvg7FnAYsHREQ4fBl5edseEIMhAZG9v/62ebHByH57r57d63sHSOm5ryy4EEiZOBBoN\ntLTgwQOQ/VFQhZeXt5HZglgtGuvqeHl5USaK/JNYkIx6enpOmTIlKytrzpw5ysrK3NzctbW1\nKSkpFy5coFKpLi4ueXl5LY3bz5oHADExMV9f35MnTxYXF5PJZBKJ1Lp83b8Ng8G4ubktWbLk\nzp0779+/b2hoGDTWaOKu/cbGxmyMytLS8pXXAZX5MzAdDAn9HvZGX1+ft/9ke7dugYsLlJSA\nlhb4+cGoUewOCEGQASo0NDT4xQvTgOOtM9Fmspk5fqGR4jTaB1lZ7TdvWn9h1tHRyfmQIjKM\neRFTACj7kPKvVt1GEBYko81jPWNjY69du9Z+r7LyLwusdbI0KAaDERcXHzhpaGuSkpIaGhpZ\nWVl5eXmFhYVxcXEKCgqD/2Dqd3p6+q1btz5//kyj0WRlZSdPnjx27NiuH75o0aI9e/Zk3Hqk\nOHty+73kzJyMoMc+d+/2ODxWysqCFSsgOBi4uGDfPti4EfAs+K1GEATpmevXr0uZ6HEKC7bZ\nrhsdv3GPD1ddfYDR2HVJnws5OVu/VS1cuNBhlYvCtEkEEpMy443k2qx7wduOHe/NwBGEbVjw\nsT1lyhTWTrEcaDIzM+3s7N59SBTTHcY7WJpWV/7wot+2bds2bty4a9eujsbjdoRCoaxfv97P\nz49PQ0VATRFLIIS/Dj10xHu8scmlS5e6WHmERCJduXLF2tq6iU5TnGWLbbVQW2lC8vu9PiuW\nLZs4cWL37pPl6HQ4cQLc3KCmBoyMwM8PlNsuLY0gCNLHPn36JDim7YrkU28+WOh3lY7DHdmy\n6rneyLLJC79//y4r+3PRk3nz5p09ezZup5furs147l/WPaLV1cftOjxqqJadnV1f3ACC9DkW\nJKN3+0kP2d8pIyNj7NixBE0Vsysnm2vLNytP/nxkj09WVtaVK1e6PkiIRqPZ2tq+SU0ee2IP\nv9LP8aYNpeUJh0/r6elFR0d3cRju+PHjnz59umDBgvDHoRIGo7glxRuryaUJyTVfvrm5uW3b\ntq1bt8l6Hz/CsmUQEwMCAnD2LCxdCmgoFYIg/QCFQsESfq7JjqfRnI+cHR8cXinIv3fnps9D\nVHHUxuZmrY/CYrFBQUHW1tYRTptV7GeIjx1BIPE2kmuKot+nBdzSkBp0+/bt/ltKD0H+DAt+\nsyMjIzva1dTUdOTIkT+/xL+KwWDY2dkRNFV0tq5unYkCgJCmmt6RXbcfP7x48WLXT3jo0KGI\nxPf6R3a1zkQBgFNEaNSeLTXCfI6Ojl0/m4mJSVpaBpK4SAAAIABJREFU2om9+8cQ+YSTvqqU\n16+bNe/z589ubm7sHERPocCOHTBiBMTEwNSp8OkTLFuGMlEEQfoJeXn5muwfMyX4K6s9N+wc\nHxyeqSi3wXf/5yGqAEDOyiUSie1nUIiKir569Wr7qrVlgQ+Cpyx+PGFe8JTFpVfvbV+1NiIi\n4p+fzosMZCxIRo2MjNavX99+1cr09HRDQ0MWrn7x7wkLC4tL+qC5agnTXIpbQlR14WxPT89O\nBtq2RqVSDx06NMRpAVGAr/1eDBartW75w8ePkpKSuh4hFxfX4sWLr169+vLlywcPHmzbtk1O\nTq7rh7NeZCQMGwa7d4OICAQFwZ078O8ui4AgyN/I2tr6e1hkE7VRNjPnsMsWjeTPMfq6rj67\nS8R+FDrMfRZuZmbGzc3d/lhOTk5XV9fs7OyMjIzYt28zMjJycnJcXV05OTurtYcgfzsWJKOW\nlpZHjhwZPnx4dHR085ampiYfHx9tbe24uLhdu3b9+SX+VU+ePBEbNZzIR+qogbSZQVZ2dkcr\nVLURHR1NpjSI6+l21IBbQlRIU/3p06c9iZXtqqvBxQWMjODLF1i2DFJSYNo0dseEIAjS1rx5\n86T5BaV2ex9c7SZaVHp77pS9Ozc1/H9mfVlSSvbj0O3bt3d+Enl5+REjRrCruh+C9DEWJKNP\nnjy5fft2XV2dvr7+5s2bk5OTjY2N165dO2bMmI8fP/72T24gy87O5h3UWccegZeHQ1gwOzu7\ni2fjlhRvPdmoPd7BUllZWd0Ksl948gSGDgVfX5CTg+fP4exZEBBgd0wIgiBMEInEV5MmnY6K\nwzZQDq119F9qx8BgAIDBYOQ+fxm7dd/e3bu7Vd4EQf55rCmCM3369AkTJnh4eBw5cuTQoUPC\nwsIXL15ctGhRt05CpVJ37NgxatSoaQOmx4uLi4tOre68Db2BwsXF1Xmbn2f7dUQ8k7NRqF08\nW39RVASbNkFAAODxsHo17N0LPEzqniB96f379xcuXIiJiamsrJSUlDQzM3N0dJSUbLekLYIM\nQBQKODlJXbpEFxRcJSZ28Yy/aPwHbnHRxpra0oRkrkb6uVOnFy5cyO4oEaR/YdnUPDwez8PD\n01yHCI/H9yDjoVKpBw4cePLkCatC6v+0tLQqkr900qAm53tTXb2mZodlkNucre57IaWiqpM2\n5clftLW1uxclG926BZqaEBAA2trw9i34+KBMlL0aGxtXrlypO3rU3U8JjUYjBexsyzUVjt2+\nrqysfPnyZXZHhyDsVlYGlpZw6RJoaeESEs6kpj5/9HiRvokep4Ct0pDTB70yMjJQJoog7bGm\nZ/TFixfOzs5fv35dsWKFg4PDypUr58yZExAQ4Ovr+yeV2/8cnU6/evVqUFCQoqKit7f3b9vX\n1tZeu3YtJiamrKyMj49v5MiR9vb2goJtaxezysyZM93c3ArfJwloqHBwcLSfov71+j1LS0sR\nEZGunE1FRWXEiBEZtx6qO9ozbVAYGYsj19rY2Pxp3H0gMxOcnOD5c+DiAnd32LYNWpVKQdjF\nwcEh6MXzcacO8Cn8rI+oMMP6e/gbh+XLAQB90CIDV1ISTJ4M2dkwdSoEBAAPDwbAxMTExMSE\n3ZEhSH/HgmR03rx5gYGBcnJyoaGhzX91kZGR3t7e27dv19DQ8PT0XLt27Z9fpQdyc3O9vb3z\n8/O72J5Go7m5uX379k1PT8/S0rKgoCAsLCwpKenIkSO9se5lenq6p6cnBoN5t9sbZkzAC/FL\nSEioqKjw/L/zL+dJaNnr2AMxMV0/p7e3t7m5uaCGioRB2/Uwydl5H7xP7/fYKSQkxLJ76A1N\nTXDuHGzYADU1YGgIZ8+Cqiq7Y0IAAO7evRsYdNvwzCEeqbbLpEmb6AOAi4uLhYUFel6PDEQP\nHoCdHdTWwtatsHs3oIKgCNIdLPiDuX79upOT08ePH1u+/2Gx2I0bNyYmJmppaa1bt+7PL9ED\ndXV169atw2AwR48exXdtfcjHjx9/+/Zt0aJFW7ZsmTlz5urVqzds2FBUVHTz5k2WhxcUFDRs\n2LCQrLRhuzZJDB2Cvf2MWFBaWlH+6tWr/Pz8mpzviYd800/5X716tYvP6JuNGzfu1KlTSXuP\nJZ+8WFdY3LyRWkX+duth5Mqty+wXsOuLQVclJcHYsbB8OeDxcOYMvHyJMtH+48iRIwrTJ7XP\nRJtJm+hzyA86c+ZMH0eFIOx34ABMnQo0GgQEwJ49/TYTpVAoiYmJ4eHhnz9/7mLFQATpGyzo\nGQ0JCTEzM2u/XVVVNTIykl1F7+l0upWV1cKFC7u+nGZ4eDgXF1frp9gGBgYBAQHh4eGLFy9m\nYZn3yMhIOzs79TVLB1kaA4DYCK2cp2HpV4MaSsqxJJ74hmtYaqOlpeXBmJhuZaJ0Oj0mJgaL\nxW7evPnhw4dh9iuJAnwYHI5SXqmqonL14qVZs2ax6hZYr6EB9u+HffuASgVrazh9GqSl27fK\ny8t7+/ZtRUWFmJjYuHHjhIWF+z7SgamhoSEqKspgwYFO2kgajgkNDfXw8OiroBCE3SgUcHQE\nf3+QkIC7d2HMGHYHxFxxcbGHh8eVK1dq6+twXJyNNXXSUlLr1q1bvXo14f8joGJiYi5duhQf\nH19VVSUtLW1ubu7o6IjeY5G+0fNktLS0lIuLi4eHh2km2iw0NLR9Mfy+QSKRlixZ0vX2VCo1\nKytr6NChhF/HJmpoaISGhhYVFXVxVfffYjAYK1euHDR1YnMmCgCAwQy2MhtsZUbOyq0rLM4P\niyTllz169KhbK79dvnzZzc2toKSEd5AUYDE1Od9FRUXt7e1NTU1VVFSU+3DR9rq6OqbFnDsT\nGQnLlsHnzyApCcePw/Tp7Zukp6evW7fuyZMn3FISRD7ehtJyanmlvb39wYMH0cIkfaCkpIRO\np3OJdjZ8mVtctKDgdZ+FhCBsVlQE06ZBVBQMHw7378OgQewOiLlPnz5NmDChTpA0ZMdaEe0h\nGByOVldfGBm7w+vAw4cPHz58SCQSV6xYcTkgQNJorIjRSBIXZ2Fh8cHL5w8cOHD+/PnpzN6Q\nEYS1ep6MioqKrlmz5ujRoy1bPDw89PT0LCwsWrY8fvzYx8fHzc3tj2LsE6WlpU1NTe2nCjUn\nOixMRuPi4j6mpFjsZrIwFUluEElukLCWxrPpDrGxsWO6/CV77dq1vuf81JfZaVkY4ziIANBE\nbcwLfX38zFkymdw3T07fvXvn5eX14sWL8vJyIpGoo6OzaNGiJUuWEDqfeFRZCZs3w7lzgMHA\nihWwfz/wMVk+Kjo62srKiktb3cT/eMtj4qqvmY9P+Yfr6oaHhysoKPTGTSEt+Pn5AaCxpoZA\n6rCgAZVcI4DqvyIDRMt0pWnTwN+/3xb6qKqqsra2xg1T01uzDPP/Dg48N5eMhZH42BHRrnua\nJx2GxMcanTvcuu610mzbnKdhs+bODbp5c8qUKeyJHhkwWDObvtnOnTs3bNjQOhn9izT34LZf\ncq15S11dXeuN58+fb9nS0NDAwcHR9QvFxsYKqCoSeDrsO8RzcwmqKcfExHQxGT137pzv+XP6\nPrtJ8j8LF2CJhMETTYWHql9evW3o0KGrVq3qeoQ94OHhsXuPp4y5kYqrM7ekGK22vjQhee32\nbRcuXLh3716HM1ru3gUXFygoADU18PMDAwOmrcrKyqZMmSJiZaK2ZG7r7fxK8mMObU/Yd3zq\n1Knx8fFdHBmM9AwfH5+6unpJfJKstXlHbUriPkztr48pEYSVnj6FOXOATAZXV9i7t98OEgUA\nLy+vCjxm3OqlmHZBEki8I3duvG+/EsfNaXzWi1P01yfyGMxgKzMGnb506VJjY2P0PRPpVX/9\n53dtbW3rAoeSkpJTp05l4fmbR3m3GTB648aN8vLy5p+1tbW7VbmzqqqKwPub79AEEm9VVWfl\nQltQKBQ3N7chzotaZ6IteGQkh65e6u7uvmTJEp5e++Lu5eW11/uw3pFdghoqLRv5VRRkbczj\nd3lbW1tHRka2rTtbWAibN/8oZe/qCh4e0PHKy4cPH6YK8aktntN+FwaL1d7oFLZgtb+/f7dG\nZSA94ODg4O59SGa8IY6Tybev6m9ZhZExSw4f7/vAEKRP+fjA+vVAIMClS7BgAbuj+Y2AgABF\nO1tMB3MnuESF8dyc3EZj2mai/ydrbZ714PnFixfZNRcZGSD67/e5Lqqvrw9uJTo6umfnaR7m\n2H6Ea/OWNrnU/v37ff/P0NCwWxeSlJSsLyrpvE19UYmUVGfLhLZ49epVeV2NtNm4Di9nNLYO\nwwgJCelWkF2Xk5Ozffv2EdvWtM5Em+G5uUZ6bPxSVODj4/NzK4MB/v4/StkPHw4xMbB/fyeZ\nKADcvHlTbsoE6GACGY6DY7CV2Y0bN/74VpDfcHFxURGXit/tTW9ou9BX7ffCd+5ezk4rdHR0\n2BIbgvQFCgUWL4a1a0FcHF696v+ZaGVlZXZ2ttBQ9Y4aUKvI1OoamnzHHzcYjITBqLCwsF6J\nD0H+76/vGRUREXnw4MGfn0dUVBSHw5WUtE0TCwoKAKBNatj6E7e8vLyLvZjNTE1NyQ4ONbn5\nHa1KX/u9oPpbVifTwlpLTU3lV5TvZD16DAYjoKKYkpLSS4N+Ll26xKOqIKo7jOleHCeHiv30\n06dPb9myBQAgIwOWL4cXL4CbG/bvh40b4Xe1DigUSkZGhrGKYidtBFSVUsICenoHSFdxcnI+\nevTIxsbm5dINSnOmiI7UIvDy1BeWFETGZAQ9njdjZlfWlUCQv1Xr6UoPHoCMDLsD+r3a2loA\nYPoooxmlvAIwGAZvZ1NOuSXE8pPfsj44BGnlr+8ZZRU8Hq+oqJiWlkZptbw7g8FITk4WERER\nFRVl1YVkZWWnT5+efPIi0zJvDAYj+cTFqVOnysvLd+VsNBoNg/vNfyIGh6PRaD2JtQvevHkj\nNmp4Jw3ERg3Pzs7Oy8oCHx/Q0oIXL8DYGBITwdX1t5koANBoNAaDgel0PCgW34s3iLQmJSX1\n5s2bXRs2NQZHhNq5BNsuinTeIp5VdPta4OXLl38zWQ1B/l4fPsDo0RAVBdOmwevXf0UmCgBi\nYmIcHBy1eQUdNcDz8gCDwdlpJtCIJiYivW/gJqNUKjUjI6OwsLBli7m5OYVCuXPnTsuW4ODg\n8vJylk/JOnbsGEdBacLeY7S6X0YF0OobEvYdJ3wvPnHiRBdPpaioSM7KhU7LF5Mzs5WUlHoe\nbqfKy8uJ/Ezmv7cg8pNGYjDCEybA2rXAxQX+/hAeDl0uNcXDwyMhIUHOzOmkTXVmTu/dINIG\nJyfnhg0bUlJSqqqqsrKyampqIiIibG1t2R0XgvSaJ0/A0BBycsDVFW7d6rcT59sjEAjjx4//\nHhbZUQNOYUEMHsdVUtnJSUrik0aPHt0L0SHIT3/9Y/qOJCcnx8fHN/9Mp9PLyspa5jlNmzaN\nRCIVFBSsXbtWW1t79+7dzdvNzc1fvnwZGBiYkZGhqKiYm5sbGRkpKyvL2hlRACApKRkRETF9\n+vSw+atkLIz4FGUxGEzV16y8kFfqsvK3X73q+oKKZmZmuAZqcWyC2GjmY/XKPnyil1dZWlqy\nLvxfiImJZZRVdLSXg0KZdcp/KoOB//IF5s2Do0eh+33Mtra2956ESujrMt3LaGrKDQ5f7LK6\nu6dF/hAfHx8fszpcCNI3iouL8/PzeXl55eXlu764Sbe1TFe6fBnmz++tq/QaV1dXYzMzabNx\ngupMugAy7zzhInJUvYhsmmGDZfYAqvzTl4qE5IX+gb0fKTKg/VEyGh0d3Wa1laioqNZbejyd\n6M99/vw5KCio5Z8VFRUt/7SwsCCRSO0PwWKx7u7ugYGBb968iYuLExAQsLKymjdvXrcqN3WR\nvLz8u3fvbt68eefOnc8PwxkMxjA1tV0nfGfPnt2td1USibRx48ZDx06NO6lEFGibGTSSaz94\nn1m9enXvraJhbGwcdf6Msj2TqshDklJWep+Rzs0vxuPFbt2Cng5adXV1DdDUzAuJkDFnMlcs\nzf8WiUp3cnLq2ckRBPm7MBiMwMDAQ4cOJSYmYjAYBoMhLCxsb2/v5ubWvlD0H2logGXL4MoV\nkJSEu3fh7+wdHDdu3FZX1wP/7Rm2eaWE3siW7U00+reb9zOv3AkMDPzvv/8S958Y5uqC/XWk\nDTk7L36X96ZNm1TRssxIL8P0eIHari+P2cVL1NTUkEgkBweHc+fO9SwktggODk5LS1u9mm09\nczQazcbG5vXHxGGbnFvPmqxISUs85KurqBIcHEwkEnvp6sXFxUpKSsqrlkib/awSylNTu8jv\nisXjUAC4wEFs2rN32XomRf677ubNmwsWLJCbO0Vpti2W+OPtspFcm3r+Wll41LNnz/T09P7k\n/AjyJ5KSkgIDA/ft28fuQNgpKioqLCyst5c4odFoCxcuvPXwgfLcqZJGY7klROkNlNLET18D\n73JX1jx+/FhLS4s1VyoqgqlT4e1b0NGB+/f/lkGiHTl69KibmxtBRkJ0hBaBl7euqKTobZwI\nB9elS5dMTU0zMjImTZr0vb5Gac4U4eGaBB7u2vzCgldvM+48cVy85MSJE91aDhAZsLKysg4e\nPOjr69uDY3veMxoQgOYv9wt4PP7Bgwdubm4+rns4pMT5lOUxgKn6llWf893FxWX//v29l4kC\ngJiY2LFjx5Y6OeE4iRL6owBg3MuoZScuCFRUZQ+SWorFNimqPPvjTH3WrFnCwsJOTk7Pgx4J\naw3hEOSrLyotS0rV0dJ68OYNyz5+/kB1dXV5ebmYmFi3l0JFEKTLNm7ceC881PD0AW6JH4sA\n4zg5xMfoiI0ennz8wqRJkxISEljQP/rhA0yeDDk5MH06+PvD3/9HvXbt2tmzZ1+/fj06Oroy\nq0hSUtL00OGZM2c2Vy1UUFCIi4s7cuTIxYsXEw6cAAAcDjdu3DifO3cnTpzI7tiRAaHnyai9\nvT0L40D+BIFAOHDgwNq1ax8/fpySksJgMNSmzbG2tpaWlu6Dqy9atIhKpa5evbpRXfloTY3R\n1ywqHndUQ2VbVq6Z2fgrV66wZG0kMzOzlJSUsLCwqKio0tJSydFGxt7G+vr6Xe+h7w0UCuXU\nqVN+fn4pKSkAgMViR48evWbNmlmzZrE3MAT593z69MnX13fsiT0tmWgLDAajuXJx1Nodnp6e\nrRep7ol792D+fKitBTc32LWrowrHfx1JSclOCtfz8PC4ubm5ubmVlZWRyWQJCYn26xEiSO/5\nZycwDUCSkpJLly5ly6Udly6dXlTE4+nJSaXG4PFbhIWF1DSv7/eysbFh4VUIBIKlpWXvTcbq\nrqKiIhsbm0+52UpzbM22uRB4eRpKywvfvJu/bOnt27cDAgLQuzmCsFBAQIDQCC1+JeZl7zBY\nrNK8qVe8/by8vHr4BZjBgP37wc0NODjg6lWYO/f3h/xzhIWFe2+OAYJ0BCWjA11paembN2+K\nioqEhYXHjh3b+cpPBQUFUVFRZWVlYmJiBgYGPx6HpaaCo6NwZCSQSODtPXrFivABMMCosbHR\n1tY2i0E18vPCc/9YoIvAy0OSGyRjbhjs6rlixYqLFy+yN0gE+Ze8f/9eWFujkwbC2kNiy8qy\nsrJ6UuuNQgFHR/D3B0lJuHcPRo3qeaAIgnQTSkYHrqKioo0bNwYGBhKFBDiEBRuryXUFxTY2\nNkeOHFFQUGjTODMzc926dQ8ePOCSECPykyjlldSyCrtZs07IyJCOHQMKBaytwdcXBg1iy730\nPT8/v6SMr8YXjrRkoi24xERG7Xb1X7rBwcHBwMCA6eEIgrRgMBiJiYlfv37F4XAqKiqamppM\nm5HJZDx327em1vCcHBgcjkwmdzuCwkKYOhWio2HECLh/H/pkgBOCIC3Yn4ySyeSZM2e6u7sP\nHTq0eQuDwXB0dNTT01u8eDF7Y/uHffv2zcTEpF5UQN93X8tjr9q8gnfnr+nq6gYHB+vq/qzr\n+f79e0tLS7yagvHFoy2rmEqHRKz28SPVN9CFhXGXLsGcOWy4DfY5f/68wqzJ7TPRZjwykjLj\nx50/fx4lowjSOX9/f3d395zv37klxRhNTXUFxWoqKvv37588eXKbltLS0h8Kijo5VV1RKaOp\nSaa7M98TE8HWFnJyYOZMuHTpH5iuhCB/HfY/Tk1PT4+KipowYcLbt2/h/5nouXPn7t+/z+7Q\n/lnNj5gZqvJjDri1HoDFIyM50n2DkPm4KVOmVFb+WJOjurra1taWz3iMrsfG5kyUg0JZ6Hf1\n5MGT6vUNjyVEDYWFqdOmsedO2IRKpSYkJIgOZ95/00xEZ2hMTEyfhYQgfyMXF5elLs6kyWaW\nd86bXDxqevmYZdA50NeZNmtmy3IkLSwsLApeRzPo9I7Olh/+Rmf48O6t3nz3LhgYQG4uuLvD\njRsoE0UQtmB/MqqjoxMcHMxgMKZMmQIAgYGB586ds7a2vnnzJrtD+2ddunQpo7hQa/1yDLPB\nnWrL7Gp5OL29vZv/6ePjU82B13Ba0DyrVOdd4skl66Zfv1ciJrLj4PZTl3wSqyv+rtKwf45M\nJjMYDAKJt5M2RBJvS0KPIEh7x48fP3clwOD4HjnbCT8HXpN4leZOGevlvnPPntu3b7dub29v\nz8/Afr3xgOnZ6gqKvl6/t3nz5q5ensGAvXthxgxoaoLAQPDw+GcmziPIX4f9ySgA6OnpBQcH\nN688VF9fb21tHRQU1KvVMQe4GzduDJ40HsfB/BXGYDDyUya0fBm4efOmnK0lBoPhqyKv23/c\nY8sekeKy+zOsV533ThyhhSUQZK0tBto3B0FBQQ4Ojvri0k7a1BWVdD4bDEEGMjKZ7O7uPnTN\nUt7BTAZoCmqoqC2dt3HjRnqrflBubu6AgIDMK0Hp1+626R+t/PLt7cads6dMnT17dpcu39AA\n9vawbRtISMCrV9DFoxAE6R39IhmF/+ejJBJp0qRJKBPtbampqYJqnc02FVBTSk9Pb2xsbGpq\n+vLli6CqklFo5Mkla01CIrIUZDed2HN+xcIGzh+rpAqoKjZX2Rw4sFisiYlJQURnq90WRESb\nmZn1WUgI8nd59uxZPQ4jZTS2owayk8bnFRU2D99qYWpq+vTp0+qnL8MXr009eyX7Uci3mw9i\n/tsbvWa707z5Xa1fUVgIxsZw7RqMGAGxsdBqfDyCIGzB/glMLfT09CorK9GyY32gsbERg8N1\n0gCLxzMYDDqdjsFgpOn0AycujP6YSiUSriyZGzTblo7/5VgMHtfY2NjLIfc769evn2hjPWiC\nCUmOSQGBwjex5E9pLnce9n1gCPJX+PTpk4CaUidPxnEcRH5FueTk5DazAE1MTNLT069cuRIS\nEpL3NolEIk02tVxw6aq6unpHp/pFQgLY2kJuLpquhCD9Rz9KRgEAZaJ9Q1FRsTorV0RnaEcN\nqjOypaWlOYlEOHnyEwD3x9RPWhon1i//PojJc2dyZk5Pqvr95czNzR2XOFzcskfXY6PAr93M\nBRHRCQdOnDzqM3jwYHaFhyD9HJVKxf6uND0Gj6dSqe23c3NzOzo6Ojo6dvuqQUGwcCHU1YGr\nK+zdC+gTB0H6h/6VjCJ9w9bWdp/fafmpEztasjLnSaizkRGYmsKrV1gODjdR4Y/eHgxmjRkM\nRs7T8I0LBmIRruPHj5NIpCNrdwiPGi6mq03gIzWUlBVExlIyck4fP8Gu1bAQ5K8gLy9fE3il\nkwYMBoOcmV1WVhYSEqKpqSkpKflH12uerrR9O3BywvXrMGvWH50NQRCWQt8LByJnZ2eu2ob0\nK0FM9xY8CV3+IWXHrVvw6hVYW1e+fu1TWZ31JJRp46+B94iV5JUrV/ZmvP0UDoc7cODA+/fv\n547S547+WB34UPhT5qpps9LS0lAmiiCdmzhxYl1uflVaBpN9DEbO07CQmY7UmrqDp07azJ4p\nLS2toqIyevToQYMGycrKTpgw4ezZs0w7TZlraAA7O3BzAykpiIhon4k2NTUVFhZWV1f/2T0h\nCNJDqGe0v2AwGPHx8dHR0VVVVeLi4iYmJoqKir10LT4+vlu3bk2YMIFaVa26eA6B58eoKTqF\nynf8vH9wuDqDAeLicPAgLFggAXDlypXZs2c3lJQpzZ2C4/gxb4lWV//l4vXC4JePHz8WEBDo\npVCZqqurCw0N/fLlC41GU1ZWHj9+PD8/f18G0JqmpmZLGSwEQbpIWlrawcHh+uHT+j67cf+f\nDQnNqzEdPJkfFQd6w5QnmKppa1VXV8eGhH2NSYT4OIVpk4S1NbLSM9du33bkyJG7d++qqan9\n5koFBTB1KsTEwLBhcP8+/Dp4Jj4+ft++fcHBwbW1tQAwePDgefPmbdq0SUhIqBduGkEQ5lAy\n2i+8ffvW2dk56dMnAVVFIj+pvris2tHRxsbmxIkTg3pngc1x48ZFRkY6ODiEzHIUGTaEU0QI\nW1a5Iv7DemojDoOB+fPhyBEQFm5ubGtr++zZs+XLl4fcfSqsPYRDkK+htLzsQ4qmqtrNiIgR\nI0b0RoRMMRiM48eP79y5s7aJzq8sj8Fhq79lY+saNm7c6Obmhv/dELT+o6mpqbi4mE6ni4uL\n/0VhIwgLHT58OM7IKGq9+7DNLi0TAdMDbufHJmDm2wjJSKsM1aytrY2KiiLISooZjmqITsw8\neYV3sDSBl2fwBJPihI+Ghobv37/vbMmlDx9g8uSOVlfy9vbe5OoqY26o7bmZd5B0U2NjefLn\n00G3AwICHjx4oKOj06u3jyBIC/QpyH4PHjyYPXu29GQLi51rCbw8zRvrCopifS+NGjXq5cuX\nqqqqvXHd4cOHx8XFRUZGvnz5UjQmZt6nNH5qI0NREfz8wMSkTWMjI6Pk5OTw8PDIyMiSkhIJ\nXQnDfYcNDQ37eM7ZsmXLAoJuDV3lIGk09seAVwajOO7DoaOn3r17d//+/f6f2OXm5u7Zs+f2\n7dtlZWUAwMvLa2Nj4+bmpqGhwe7QEKRP8fCPWmOKAAAgAElEQVTwvHz5cuXKlf6OmwQ1VPiV\n5Gh1DXnPX8J0Czl1NXV1dSwWm5SUhOMnkeQGAwB2iCIMU/3gfYaopoDh5aGTybTS0jFjxsTF\nxUlISDC5QFAQLFgA9fXg4QE7drSZue/v779529bR+7a2nsopbWogZaKfctp/4sSJ8fHx3V5Z\nFEGQHunvn9z/vKysLDs7O5UVC2Wtx7fezi0prrtr84fDp6dNm5aYmEggEHrj6lgs1lBNzfDU\nKXjyBPB42LwZ4+EBXMzXW8fj8ebm5ubm5r0RSVecOnUq4PZNg+N7eKRbTWXAYMR0hxmc3Pty\n5TZ3d/c9e/awK7yueP78+axZszhU5JU2OI5QUcRgseSsnIjHoSNGjPD19V28eCDOA0MGMl5e\n3kuXLm3duvX+/ftpaWmfSz6XyEgaLLLj4uICADKZXFpWJqozFAAaSsurv2Vhx+tD3Cc+l/k4\nUSEAoGblfve9qqOjExUVJScn9/O8DAZ4eoK7O3BxwY0bMHNmm+tWVVWtX79+6Jql7YuKYDCY\nIU4LYrLz/vvvv4CAgN68ewRBfmB/MspgMG7fvu3v75+Xl8e0XGVycnLfR9Vn9u7dy6Op2iYT\n/QGD0VzlELZwdUBAwJIlS1h/bQYD/P1h/XooLy+TlY1ZtkzGxkarg0yU7RobGz08PIY4Lfwl\nE/0/DgF+7Y1O3tsOrF27tnsrU/ehxMTEqVOnyi+cqTDDumWjsPYQYe0hBa9jli5fLi4ubmVl\nxcYIEYQtVFRUNm3aBAArVqzIE+Lm+v+7UGlpKYGXB0sk0uvrq79l4WWlsUL8TUL8tJz85mSU\nKDcIP8+6Pix25syZMTExPx7U1NfDkiVw/TpIS8P9+8BsHNHdu3cbOAky5kbMA8JgVBfPvrnW\n/cSJE2wcj44gAwf7Z9MfPnx41qxZjx49SktLy2OG3QH2IgaDcffuXVmbDvsacRzEQZbGd+7c\nYf21MzMZlpawaFFDRcVuAkGNiJ1z/uzwETqamprPnj1j/eX+WGRkZEVtjZSpfkcNRIZp4kWF\nHj9+3JdRdcuaNWtETfVaZ6ItJMeNVl08e+XKld2YIIwg/5z6+npsq+X3KBQKjkgAgJq8AowA\nH1aIHwCASGC06rbAcXCIz5uS9DUtKCgIACA/H4yN4fp10NWF2FimmSgAREdHi+hodVTbDgAE\nVBQZHIT37993JewnT57Y2dkNHTpURUXFzMzs8OHDaGI+gnQL+5NRHx8fS0vLb9++1dbWVjLD\n7gB7UUVFRWlpKb+iXCdt+JXkv3z5wsqrNjXB2bOgpYUJCXlDINi7LIp9HDD29EHjc4ctbp+n\n6Wpa2dgcO3aMlVdkhbS0NF5Zmc6rZPMryaelpfVZSN2Snp7++vVrlfltHxe2UJhmlVdaEhrK\nvIQWggwEcnJyNbn5Lf8kEolNjTRgMCgVlThRQQAAaiOjtAInKtzSpolG4+QjyZgaBAUFQXw8\njBoFsbEweza8egVSTBbpaFZZWUkk8XYWCgZD5CNVVFR0HnBFRcWECRNsZ86Iqi0jTjYVmD8l\nX15i18ljysrK6G8ZQbqO/Y/pi4qKbt++raCgwO5A2Kfjb+es9/EjLF0KsbH1HByuAvxpfl5E\nIYGWyxNIPCrzZwhqqKzbuFFTU9PU1LTvAvvXxcbG8srKcIp0WC8GSyAIa6nHxsZOnDixLwND\nkP7Dyspq9769DaXlzX8pgoKCjamptPoGBr0Jy8UFAPSYJCwvN0Hhx9T7psbGxto6QUFBjLK8\n7M0nYGgI9fWwcyds3975+6qEhER9SkInDZpo9Iayis4r7dfX10+YMOFrPdnU/ziH4I+n+RL6\noDR3yrebD6ytrUNCQtqsZYogCFPs7xkVFxdnMBjsjoI9BAUFRUREqr5mdtKm6msma2bTNzSA\nhweMHAmxsXWmphpNTYl7thCFmNQHFR2hpTjLZvPmzSy4KOuoqKjUZOc10WidtGHZa9ULqqqq\nWkoldIRA4v23HwUgSOdGjRo13tgk6ahf84eCgIAAiZe3rrAYAAAYjIoq+s2nPFMsWhLNmtx8\nIUFBPl5exzfv9mdmAgDcvNl+4nx7ZmZmxbEJddXk8vLy8vLy9sNjSt4l8HJwdl637uDBgymF\n30fv/a8lE22GwWCUZtsOnmm9aNEiNPAGQbqC/cno3LlzB+yMRQwGM23atOyHzztqQKdQc5+9\nnD59+p9eKSICtLVh504QFYV7985YW1cpy7dZUb01+SkT379///Xr1z+9LusYGBgI8vB+D4vs\nqEFpQjKtpLzfTgCSlJSsLy7tvE19UYlUxw8WEWQguHjxIjGvKG7HwYayCgwGo6WlRSkpw2Cx\n9OSvjbtPEVXkuS3HNbesyy+klpaPVFXd6Hl0RWRsBTc3vH4NM2Z05SpcXFw4Ki10j3fU27dR\nb98+f/787du3LQ/l6Q2UVL+rK1euJLYawNoGjUY7fvy42uI5eG7mkz6V503Lqyh78OBBN18A\nBBmI2J+M7tix49u3b3Z2ds+ePUtNTf3aDrsD7F1bt26tTU7LfvSCyT4G4+Oxcwqi4vb29j2/\nQFUVODmBsTGkp4OTE3z6BLa2qampAqqdLe/EISTAJS6SkpLS8+uyGoFA2LlzZ8op/9rvBe33\nUiqrPnidWr9+fb+dSm9oaNhYXln5pcPfZ0p5ZfnHz2ZmZn0ZFYL0N1JSUtHR0UO4+MPmr3y3\n41DRvWdin3MY15/QDl8gDlMnrZhHp1IbysorPn1pyC+aqKRycofXuJdR73C4qKNHoWtl6g8e\nPGg5yUpYXxeb9IUnv1Rs1HBh7SF1BGxUVFRWVhalsip2235lYbGtW7d2cpKkpKSK6mrxMR12\nnWKJBPHROmFhYd1+CRBk4GH/mFESidT8w7Vr15g2+Lcf4svKyl69enX27Nm1efnK9tObn+TW\n1dXlJqfmXb2Lycxz2b69urpaWFj4t6di4t49cHGB/HxQVQU/Pxj3o0ehsbERg8N1figGh2da\naYuNnJyc4uPj/Vdt01y5RMpE/2fR+3eJH338jHV0d+7cye4YOyQsLLxw4cK7vpfHHvbA4pm8\n+J9OXTY0MECLviCIlJTUixcv3rx58+jRo+zsbIKIDJeN8rlz5+icxNIPnwCAk5NTRkpqIuB2\nbj8kXFp+T0zk8CDZVw4OXTn5zZs3t+7YMWa/m7C2xmArs3jPI2WR8ZzjRhJlJDDVtR9PXEj5\nkmWqb3Dt2jXuX5draqOwsJAowIcldlYBmktctKCAyZdnBEHaYH8yOnfuXCKR2P8Xzuk9kydP\nDg8Pd3Z2fj5jGb+KQgMW6otKobiMS0yEX0dz9wmf3bt3b9myZevWrd1Y7qiwEFatgtu3gUCA\nbdvAzQ04OVt2KigoPH7Q2eB9Wl19fVGJklKHz/HZ5ezZs9ra2u7u7p9OXuJXUcBgMVXfsnF1\nDZs3bdq2bVs//y06ePDgq9Gj43d6DXNd2Xr8aBO18dOpS/WJKediY9kYHoL0K/r6+vr6P0u5\nGRsbL1q0SHbaRMU5thy8vAYvo9Yc9CVSqfukJM7gia+Cgrry9kilUjds2KCx3F5YWwMAhLU1\nTC8fy3kSWhQdX/cyFkck8OGwJEGh4ODg355NQECAVlPLYDA6qQ9FrSYLCIh1+Y4RZOBi/4d3\nRx2iA8qYMWPi4+Nfv35tb29fX1+rYmUmY27EIyXevLf4XeIeryOpqalXrlzp5I3vBwYDLlyA\nTZugogJ0deHcOdDSatNk8uTJHrt21RUUcUuKMz1HXsirwTIyWu0OZDsMBrNy5colS5aEhoZ+\n/vyZRqMpKyubm5v/FYWphYSEXr58OXPmzLD5q2TMDflVFDBYLDkzJy8kYrCQSEREhKJiZ2Mn\nEGQgmzNnjpSUlIuLS2iQ4x4B/k3FpfUYzAwAwjijGB8fcXHmb2VthIeHF1VVaE/6WdoZz82l\nMMO6pfovvYHybNqSmJiYsWPHdn4qbW1tPAMqPn0R0lRj3oLBKI1PGrt1e5duD0EGNvYnoy1K\nS0vT09Nra2tJJJKqqqqAAJOJ3v8wDAZz8eJFMjeH6Zl9OA6O1rvEdIcZHPMMcv5P/9QpZ2fn\nzs7y9Ss4OkJ4OPDwgLc3rF4NzB7Ha2trT5k8+dUh3zEHt7ev3FmbV/D54vVzJ3x/n/iyCTc3\nt42NjY2NDbsD6TZpaek3b97cuXPn1q1bnx6GNzU1qSkqbtl/cP78+b204iuC/DMMDQ0/vH1b\nMWWKcGhoNT9/+Nq13osXy8rKdv0MiYmJgmrKTMfJNMNxcvArKyQkJPw2GeXh4Zk7d+6jy7fG\nHHRjOnn/e9gbXFXNjK5NqEKQAY79E5gAIDIycsyYMaKionp6eubm5mPGjBESEho/fvy/vRBo\nG9++fbscEDBs04o2mWgzLnFR9eX2u3btonVU24hGg4MHQUsLwsPBwgI+foR165hmos38/PxE\nKU1vN+xsXWIaAArfvHuzZvuiOfMWLFjwZzeEMIfBYKZPn379+vWPHz9++vTpwYMHS5YsQZko\ngvze9+9YExPh0FAYPZrv82dbD49uZaIAUFdXh+Pi7LwNjouztra2K2fz9PTEF5Qkn7zIaGpq\ns6vsQ0rS0bNeXl5CQh2WFkYQpAX7e0ZjY2PHjx9Po9EMDAxUVVW5uLhqa2tTUlLCwsL09fVj\nY2P7beVI1nr06JGAqiJJfnBHDaSN9ZOPnY+OjmZSRTkpCZYuhXfvQEAAjh6FZct+W2ZPSEgo\nKirKxcXlusN6QXVl3kFSTTRaxacvWHLdHnf39evX//kd9Y2Ghobi4mI+Pr6B1pWOIAPLu3cw\nZQrk58O8eXD+fOtB8F03aNCg2u+FnbepzSsYPLjD9+HWpKSknj59amNj89rlP8WZNkKaalgi\noTavIC/kVW7wy72eng5dm1OFIAj7k1FPT09RUdGQkBA1tV9G3iQkJEyYMGHnzp0DZFDpt2/f\nSHKDOmmAJRJ4pCW/ffv2SzJaXw8HDsC+fUClwsyZcOIEiHV1vLygoOC1a9fc3d2fPHmSmZlJ\nJBK17B2sra3/lq/yT58+9fLyioiIaO4tVlJSWrx48Zo1a3h4flNbHkGQv8yNG7B4MTQ0gKcn\nbN3a41XrLCwsyI6O5Mycjr72V6SkNZaWjx8/vosn1NbWTkpKOnjw4LUr195nZwMAFxeXpaXl\nragoXV3dngWJIAMQ+5PRqKioDRs2tMlEAWD48OHOzs6nTp1iS1R9D4/HM+j0ztsw6PRfJoy/\nfg3LlsGXLyAlBSdOwNSpPbiuqqrqX9f33NTUtGrVqjMXzstPnah3Yg+3hDitrq40IXnfGd8r\nV648fvxYXl6e3TEiCMIKDAZ4eMDu3cDNDUFBPXuXazF48GA7O7snPufGerm3HzlKp1A/+pxz\ndHTsVik9AQGBvXv37t27t7Kysq6uTlxcHPe7wnkIgrTB/jGjVVVVMjIyTHfJycmVl5f3cTzs\noq6uXvnlWycNGmvranLzNTQ0AAAqK2H5cjAygrQ0cHSE1NQ/fI/+u7i5uV24ETju5D71pXb8\nygoEEg+XuOigCSZGZw9ViQtaWVmRyWR2x4ggyB+rq4PZs2HXLpCRgcjINu9yDQ0NFy5cmDVr\n1tixY01NTTds2PD+/fvfntLHx0eEQo/dtq+hrKL19vqikujNu+V4+Pbv39+zYAUEBKSkpFAm\niiA9wP5kVExMLDU1lemulJQUsS4/dP7bTZ48ubGwpDShwzlb2Q+fKykoDBs2DB4+BE1NOHsW\nlJQgLAzOnAE+vr4Mlb3S0tK8vLxGuG9oP6oBSyAM37Iyn1p/6NAhtsSGIAjLfP8OhoZw6xaM\nGQOxsTBsWOud0dHRampqK7dueY+h1I0ZWqwuG/juje7oUQ4ODg0NDZ2cVUhIKDIyUkdYImz+\nqvjdR9Kv3U2/didu5+GwhWuMlNVfvnzJy8vbyzeGIEhb7E9GLSwsjh8/fv/+/dYrLTEYjLt3\n7548eXLixIlsjK0viYuLb9q0KfHgyYaSsvZ7yz99SfO/dXzrVszMmTB5MhQXg6srfPwIxsZ9\nHimbXb58WWDYEKEhzIcWYAkEZbtp58+f/7cX7kKQf9y7dzBqFMTHg50dhIeDhETrnbGxsWZm\nZkSDEab+xzSWz5e1sVCYPmmkx0Yjv8O3Qp/PmDGD3umQJ1FR0eDg4LDnz2cNG6VYWKVURJ6n\nq/8mIiIoKCgyMnLZsmVGRkbGxsbOzs4vXjBbqBlBEFZj/5jR5gk0U6ZMkZCQ0NDQ4OHhaZ5N\nX1hYKCkp6e7uzu4A+467u3tKSspjl//UHO0xKnIVVVVUKhXPgKaElMK7wXesbSzWr4fychg7\nFvz8YMgQdsfLHrGxsaIjOqvGL6IzNH73kdzc3C5OiUUQpP9oamqqPHNGYN06bGMj7N0LW7a0\nma7U2Nhob28vNdlCbcncNsfyDpYe6+Ue6rT5zJkznZdkzs/P//LlCw6HU1NTU1dXnzRpEo1G\n09fXj/uYJGk0lk9LCRiMR19T/SbbGOsbXL16deA8o0MQtmB/MionJxcXF7d9+/Z79+6FhYU1\nbxQSElq6dOmuXbskJSXZG15fwuFwN2/eXLZs2aUDJ5sIOKyYCNDpTcVlSgwIw2LHBQUBNzfs\n3w8bN3ZSQPSfV1lZSeBV7qQBgcSLwWAqKytRMoogf5GKiooD+/eL+fquq6mpA1iAwXy/f3+T\nisr06dNbN7t3715OWYnZgplMT8IhJKC6aPbBgwdXrFjBdNkOCoWyZcsWX19fThlJPkVZAKi6\nc8vZ2ZmTk5NbS83sygkC6edjevVl9gn7jxsZGb19+xYVj0OQ3sP+ZBQABg0adOnSJQaDUVhY\nWFtby8vLK/HrQ5mB49q1a5cCAtQd5/MpDq4rKiVgMEvTMxY/CSVSG4MxGMbhwxOdnNgdI5tJ\nSkp+KS7tpEF9USmDwZCSkuqzkBAE+UOpqanTJ07cW142paamRETIc/fmSh7uqvCouQsXzHv4\n8Pz58y0Tg0JCQiT0RuI4iB2dStJobJL3mbS0tPZ1QqhUqpWVVezXL6MOuwtqqLRsj1rvXl5d\nzTlhHJ73l8JwHIL8ozxdo9Z7bNq0yc/Pj3W3iyDIL9gzZrSwsLCioqLl52ZFRUUYDKZ58Hhh\nK2yJkC2ysrKWLVs2fMtKhZnWIjpaZgqDbwU9Xn4vuJ6b6/DWNf9tXDFr06aCggJ2h8lmpqam\nha9joOMhoQWvo7W1tUVERPoyKgRBeqy8vHyJhcWNyvIp5JovGiobTx3IVFHkkZZUtp8+7tSB\nm8FPtmzZAgC1tbXnzp17/PhxSfzHd9sPpF+9U19U0v5sBB5uAj8pLy+v/a4dO3bEpKUaHPNs\nnYnW5heVJaUKuswvI1enp6e3OQRLIGiuXHLx4sXi4mKW3jSCID+xp2dUUlLS0tIyODi4+efO\nGw+cmSheXl78wzSkjPU4Gyh2F6/b3HmCbWoKNzc857yIzEcaBJAXEuHt7T3Ap4ovWrRo9+7d\n2Y9CZG0s2u9tKCn7GnjX79iJvg8MQZCeubJmzb3CQnEa7eV4wxMbnKjEn6vj8khLjNyx3mft\nDjU1te3bt1djmjDKcjRBUg2BUP42Ni3gtrLdNBX76a3HlTIYDFpdPV+7GiPl5eU+Pj7DPV1b\nP4gHgNL4DwT5QYRBknwk7q9pXxUUFH4p5wwgoKrIKSEaFhY2Z86cXrh7BEHYlIzOnj172P/r\ndMyePZstMfRDT548kbGfMjzug8uRM2KFJcUSoifXLU8Yqd3SQMbC6MmjJwM8GRUQEDh37tyM\n2bMxONxgK7PWu2pyvr9zPzTJdPz8+fPZFR6CIN3CCAx0vHqVCODvMC9o7hRGu4GeAmpKPENU\nli9fruowb8SMSRlZWem52Tya6jwzJlI/paf7XGqsJg+aYFJXWAwYLO8gKWpVNQcOP6TdFM/n\nz59jBfhEhmu22V5fWo4TEwYAogAfA4ctLS1tP06MW0oiNzeXpfeNIMhP7ElGr1+/zvTngayp\nqakuN/dUSIRldHwTFvtg+qQrS+Y2cHK0bsM7WDo+M5NdEfYfU6dOvRkY6ODgkP3ohZTRWG5J\nscbautKE5IJXbxctWHDy5EmmExcQBOlfGAxwd8d4etIYjINbV8eZjWPaqrGxkSzIyyklrjh7\nMgBISUl9/vyZWlVN5OcjDlHmdrHLOHQu414wTkwY6HR6WSWek8NYX5+bm7vNeb59+8Z0FVA8\nF2fTj9KkGDw3V21tbfs2tNo6Eon0Z3eLIEiH+sUEJgCg0+kt49MpFEpiYiKRSBw2bNjAySqw\nt259pNNFo+OzFGRPbHBKU1Nq36aJSuXi4ur72Pqh6dOnGxkZnT17NiQk5Ht4NIlEstbVXbzn\n0OjRo9kdGoIgXVBbC4sWwe3bjZKS4woLZUwNOnqvz83NZXBzErh/vPVxcXGpqKikpX8T1FCp\nLyqpo1Cw40biC0oEPdYAMMip6ZQXb8MiXh04cMDV1bX1eTpacplfRaHx6h1GIw1DwAMD2n/o\nNNbWVaVnjhw58s9vGkEQptifjNLp9NWrVxcXF9+6dQsAsrKyzMzMMjIyAMDAwODp06f//noY\n37+DkxM8esSPxR7RGXp1hX0dnY7PyhISEmoz7Kk8+YuWVmclNgcUERGRrVu3bt26ld2BIAjS\nVW/fvj179mxiZOTlrCwtGi1DQqL41KnEqVOFi0q4JZjX8iwuLsZTGjlFf64Xr6ys3NDQkP0x\nlYHBEJTlMIL8VPdjlJLSuuIyDIU6du3yptyCbVt2qKmp2drathylrq5elZ7BYDDapJvCWkO4\n+Hjrn0VwTzJh2gP67cb9IWpqI0aMYN3LgCDIL9i/AtOhQ4d8fX1bSkK6uLhkZmauWLHC2dk5\nKirqxIkBMBOFQoGwsIaRI6fKy6//mpn0NT2jMP9zVsariIjIyMjq6urmVvQGStaDZ/PmzWNv\nsCxXXV2dk5PT+Qp+CIL87RobG52dnQ0MDUMLsnBzbb7qDnusqWamImc4c6aEuHj+q7ctLcvL\ny1NSUmJjY9+9e5eamlpbU0P7mCY6Qrv12dTV1XFYLE5cuJFKoXISgN5U8yFVSkjYyMhIUFBQ\nWEtDdeHsDRs2NDU1tRwyfvx4HgwuP/xNm8CweNwQlyU11x+TX8UQcP9j7z4DojjaAADPXq/0\n3psgKIKggqJYwYIogoq9YomJscQWMRpiEk2MLRijkdiwIMHeUFEUxYIFC6BSFKSX44DjuOPa\nfj8u34XQNAi3gO/z65id3X13xL2X2Z0Zsq6ubt2tBTfvvou5GB4e/uk8pgNA/YjvGT169Ghg\nYODWrVsRQvn5+ZcvX54zZ87u3bsRQmKx+MSJE8pJPTozG5v0gwcHLFpEdrRl19bI7yRrLpqG\nyCSFVCrMK7hz506fPn10tLSSf9rlaG45e/ZsosNtHSKRKDw8fP/+/a9fv0YIkcnk/v37f/XV\nV/7+/kSHBgBofZ999tnxi+e99/7MtTJHCB329lSQSM4Imaa+TlrzY+nRkzoePSsUsuzsbKFI\nRNPkUjlsDCF+eVlt4mOsjG/U718dkyUlJQoSSd/SHMdxKY/PR2iwry9D55956a3GDr9y6ERS\nUpKnp6eyhMVihYWFfbVurWYXG475v+YhNurXyy54TMYfJ3S9Papt7bnWFgjHKzOzs8/Glt66\nv2/fvoEDB7Z9CwHw6SI+Gc3Ozv7iiy+Un69cuYLj+OTJfy/y5u7ufurUKeJCUxORSDRi9Wr2\ngN7dv5gjLCi+v/K78rBfOZNG0xxtudaWZCrt4clz7KevDRHlzLVr9eYc6aDy8vL8/PyyeKW2\nwWOGrv+SymbXFJcU3H4QOCl4evCkP/74o3NcJgBA6erVqwePHBn4xxa22d9z+SlIfz+X0+nm\n0HPd0oehmxKXf4uNGYwsjElWphIMyUkkLoeDPX1dezcZ6Wo/CN3stWMjhclQ7iUQCGhcNkIY\nhmGK9Bymvi5DW7PuGckMOtfaIiUlRZWMIoQ+//zzlJSU/YtDneZPMxvmTaJREULyWknetVvZ\npy9PDAoSCARxn6+VyeUIx2k02ogRIzYmJTk7O6ujjQD4hBH/lV/32UdcXBybzR4w4O8xlTiO\nS6VSguJSn3379pVIxIMWzkQIsU0MB/y++fWBqNzNexQkjKTBUVRWI4nU1tX1+vXrnWM9OpFI\n5OfnV8KlD9z8i2oZFU2utaadtbnPwKhVG7VWrdq2bRuxQQIAWlF4eLilv48qE61LzOOnhP+J\nWZmSq4SyoxdIPZ1ITjYkNlORX1Lx8AWlRtxt9edpAn7NuRsv9x11/nKuci+FQvH33KJyhfBs\nnLXvQNTgMTqZRqv3/g+GYXv27Ondu/c333yTuvsgx8IMIVyQnWesrx+x+/cZM2YghKqqqnJy\ncjAMs7Ky6vwjFgBoH4hPRi0tLRMSEhYsWFBcXHz+/HlfX18a7e8E5dmzZ2ZmZsSGpwanTp2y\nGDWERPl7MgGaBtd5yTynhTMqM97W8ivp2prCgqLyExc7RyaKEPr111+zykvrZqIqbDPj3mEr\ndi1eN2vWLBiqBUCncfPmzR7frWx005Pvd8hN9Nljhwk27NRaNluSni19mIqLa6m6WuRBHmI9\nLe2e3U2zswsH9s45ct5h5kSaJhchxGKx5IUFSKGoijhBE0tsg8fWOyyO49Xv8m1sbBqece7c\nubNmzbp7925GRgaGYV26dOnbt69qOhcNDQ3oCgVAzYhPRqdMmbJ27dq3b9/m5ORUV1cvWbJE\nWX748OFDhw6pfuzE0tPTLUfVn2CPTKfrdO+q/EzT4D7LyamtraXT6Q327ngiIiLsJgU0tbS0\nZhcbPU+3/fv379ixQ82BAQDaQk1NTXV1NUNPp+Gm0odPyzPe6IVvqLmdhEz06R6udA/Xf9V4\n++7169e9e/eWSCQlXFbaviNO86bRNLl62jrS1Axe1CVqtajPprVUdv1ZRUvuP6ZIZarVVeoh\nk8kDBgxQPYUDABCL+GR02bJl6enpJzAJK7oAACAASURBVE6coNFov/76q+o98TVr1jg4OHz9\n9dfEhqcG71/vFMM+qFpHwOPxMjMzh/Va0UwdffceD+4/UFtIAIA2xWQymUympLKKbVp/ZaPC\nO0kMDxcSly0v42P6uo3sa6hf9jxNoVD06dPnlvmFmqRnVy7foLCYMpGYRqVibFb/PT/VS3PF\nPH76oeh3l2/gCoWpqamuru64cePWrl1rbW3dhhcJAPgIxE/txGAwDhw4UFNTU1FRsXjxYlX5\nqVOnnjx5oq2tTWBs6mFvb1+Zld1MhcrMt5aWlgwGQ10RtaHKykqEEJXDbqYOlcupqKhQV0QA\ngLaFYVjfvn1LkpIbbhIWFFEsTBBCcrkck8pEIlFFBZ9XVlZWVlbB54tEIjKTgRCqqanBMIyK\no7CwsOzs7FvX4lJTUoqKirpaWD4K21qdW6A6YNmTF/GzlrxLSeNOGj3kyC6fqD22y+dfePms\ne/fuyqmsAQDtEPE9o02pOwSycwsMDAz95Sfb8aOx/7+0VE/O+aszAwPVHFUbMTAwIJFIopIy\nrmWTbwOListMTEya2goA6HAWLFgwLWSO1ZjhdJ1/vfuOIQzhSC4SydgM/E2uoLKCxGSSGHQM\nQzKZXFItqBEKlY+E5OLayoy3PXv2tLS0tLS0VO6ekJCwcOHCEyHLdXo4aVhb1FZUFcQnov5u\nVkF+Tk5OJBIJIcTQ1zX0dMu7emv69On6+vqDBg2qF1tVVVVqaqpYLDY1NbW3t1eWnDp16u7d\nuxUVFcpdxowZ0znekgKgfSI+GR02bFgzWyUSSUJCQosPLpfLjx49evLkSVtb2/cO0L5+/frO\nnTsblk+dOjU4OLjFMbzX/Pnzt2/fnrY3stuiWQ23vom5oHhXuGrVqrYLQJ04HI6Hh0dhwn3u\n9PFN1Sm8fX/K1JnqjAoA0KYmTJhw+PDhO6Gb+ny/hqH7z/MutrlJVVZOjakeydxEQaOSH6WS\nffsrN5GoVMSgS/mVCMdJJNKbU5dsLCzqdVJoaGgcO3YsNDT03LlzSUlJ1+4/IfdwoHq58/n8\nV69eWVpastl/P4Qx8x0oLCj67LPPUlJSVGOVMjIy1q5de+7cOTmGUZiM2soqWxubwYMHnzlz\nRkQj6/dyoWpwa9++2n/iuNGqVfv37x88eLBaWguATw7xyej169eb2sTlchuuzPbhcnNzt23b\nVlBQ8P6qCCGEhEIhQsjb21tfX79uuZOTU4tj+BBMJvPMmTNDhw59Ul7htGC6atU7SZUg/VB0\n8dWEM2fOGBoatmkM6rR8+fIps2eZ+Xg3uvpf3rUEWV7RvHnz1B8YAKCNYBh2/PjxyZMnX527\n3CZwlEGfnjQtDXFZuaSqqvbhc4Z7N4W2BmOSn/DASczcmORo+89uAiGJSn129lLl0bPnT58h\nkRp5tczJyen06dMXLlyQyeXcIF+yno5CKssr5719+7ZLly7Kzk6EkN3kgKsnLyYkJChzyri4\nuKCgIE7Pbp47N2ra2yCEpALh8537Ig4e0ArwHbRglupcCpksK+rsyJEjT506NWrUKDU0FwCf\nGuKT0YYziUokkrdv3x48eDApKen8+fMtO2xNTc2yZcssLCx27NihmlS/ecpkNCAgwM7OrmUn\nbTFXV9cHDx4sWrQobsoiLQdbhr5ubXlFxavMXm5uMbdvu7m5tenZeTxeXl4elUq1trZmMplt\nei6EUFBQ0ITTp8+u2tg7bCXX2qLupry4hOfb9x76c7+BQeOrVAMAOigul3v+/PkTJ07s3bv3\n7vENEomEyWT2799fzNUoP3IWOXeR6mqj3t2lW/7EhnhSfLxIhrqKiipFVi7tXRH/5oMN69Y1\nlQhu2LDhp/CdVhP8sxMfsOz/TmSZhvrSKkFGehaO4w4ODuj/U5Tcv39/8ODBWVlZQUFBZpPH\n2k4cozqOuJxfdPuB5tJZAgrp5cuX3bp1U5aTKJQu04IobNa0adMyMjLqrRcKAPh4xCejDdfa\noVAo3bp127Jly9dff7169erff/+9BYeVy+WjRo2aOXMmuYkXMRuqrq5GCKke66iZnZ3d1atX\nX758efPmzeLiYl1dXS8vr549e7bpgshXrlzZuHHjvXv3EAlTyOQsFiswMDAsLKzRyflaC4Zh\nBw4cWLp06d6Fq429PfXcnGlcTk1RScGte9J3BQcj/pw6dWrbnR0AQBQMwyZNmjRp0iSEUEVF\nRVRU1JIlSyQYQvraKLsAf/oKSWSk3s6K1EzplTuIQUcKBZLKOD0cqSaGxsaNTJiPEEpOTv5h\n04/9tn/Hf5VJ4v7r7k3V4Go52GWkvjYyMtLU1EQI0TS55eXlCKHQ0FC2q1PdTBQhlBl1htan\nB6O3C7lamJ362srKqu7XgXXAiLy4hN9++239+vWt3jIAfOKIT0abMXbs2KCgoJYlo1wud86c\nOf9pF2XPKJvNVigU5eXlNBpNQ0OjBaf+GI6Ojo6Ojuo519dff71l+3abIL+B87ZzzE0UMlnF\ny4y4mAvnevaMjo4ePnx4252aRqPt3r17zpw5+/fvf3DjAb+qytjYeGLw1AULFnSmFxIAAE35\n7rvvdv6+G/Ppy/YbLMLlGL9KXsZHuYWKuHuYTI5TKJhE6jA72GLUULqW5qNvfyksLGz0ODt2\n7DAZ7KXtZF9TXCovrz8LB5XLoevpvHnzpmfPngghUUmZsbFxdXX1mTNn+mz7tl7lkvtPWJ9P\nRQhROWwKl5Ofn696xI8QQhhmNsz74sWLkIwC0OradTIqEAjUOcVPTU0NQujcuXOXLl1S9pKa\nmppOmjRJNfWpSkJCgkQiUX7Oy8tTW4StKDw8fOtv4V47vlO+LIUQIlEoOs6OOs6Ob05eHD9+\nfFJS0gemxSUlJdeuXSsoKKDT6a6url5eXh/YG92rV69evXq1/BoAAB+muro6Ojo6MTGRz+fr\n6OhYW1uPHz9e+fC6UW09mjMyMvLXPb8rJgxndrWVlPJwhJN1tckmhri1Gd7NXr7rKLNXD1l+\nkVQgpGtpIoQklYKmpvmLj483njcJIaTbw0lRVCbLLaSY/6sPlaGjXZaThxCqLa/gp74ePHjw\nq1evpAqFloNt3WoykVhSJdAw/vuPYSqXo5yHri6Opemb6Asff/kAgHqIT0YbTTelUmlqauqq\nVavUOU2xsmc0ISEhMDBQV1c3Nzf30qVLW7duFYlEI0aMqFvz+++/Vz7rQQi5uLi4uLioLchW\nwePx1q1b57J8oSoTrcsmyK8y482KFSsuXrzY/HEEAsGKFSv2799PN9TnmBvLasQV6VmWJqY7\nduzw9/dvm9gBAP+NQCBYtmxZSUlJr169hgwZUlxcfPv27eTk5K1bt6rmSKqnTUdzyuXyZcuW\n4V49kZGeRKFANCourpW+zcOoFIqVGbmLJbYwWLxlv0bIhLwTlxznT5PViPivMvr27dvo0YqK\niqwN9BBCDF1tk8FepYdPa3+9ENUZ50Sm02praxFCqXsOe/cf0LNnz4SEBDKdVm8texKVimEY\n/v8xDCQySV5bfzyDolaihrfqAfgEEZ+MNj+tfWRkZPO7C4XCQ4cOqX40NjYeN25cyyIJDg72\n8/Nzc3NTTS8/ePDgpUuXRkZGDhs2rO67rXPnzhWLxcrPfD6/Zacj0JkzZxRaXGPvJmdy7TI1\nKHbOsqKiIiOj+iumqPD5/IEDB+ZKRH1/3ajl8PeQL3mtJPtMbMD4oO1bfvnyyy9bP3QAwH90\n7NixkpKSBQsW+Pn5KUv69u27adOmQ4cONfXEuU1Hc65Zs4ZXWUka0ItiYkBm0BFCuESC4zgq\nLZemv6XaWZIcbZGBjqJGXFteUVte8SbmQg+nbr179270aJqamhJBtfJzt89m3l60pvK3Ixrz\nJ2H/X3BYIZNRSeTU3w7UPEn548EDhJCFhYW0WlhbUansdlUiUcgcC1Pp6zcUMyOEkEwkZrHq\njx8oT3nd4boeAOgQiE9GVffHuqhUqrGxcVBQ0NChQ5vfXSQSxcbGqn50dHRscTLao0ePeiXm\n5ua9evW6d++ecooQVXndB1WxsbHp6ektOyNRHj9+rNujuR4OjrkJTVszOTl55MiRTdWZPXt2\nASb32vEdiUZVFZLpNNvgMRp2VstWrHBzc+vfv39rxg0A+O/IZLKLi0vdxzuenp40Gu3du3dN\n7dJGoznLysqWLl169OhRZGqg0OYq5HK5REImk8lUqlQiwYz0MTJZ9jaX1s2eZGUqLy1HCOXG\n3sg/d/XGjRtNDeX08PB4/ei5vlsPhBBdR6vf9rCH67eULdnIHOxBtbVEGFbzLE3+MAXT1o2P\nj1fexq2srBwdHfPjbtuMH133UKbDBmReimcO8sARquVXGFj+67mcVCB8d+n693/sa902AQCg\n9pCMXrjwUa/g6OnpnTt3rrWCaUg5BlPVD9o5VFdXU1jvedhEYbMEAkFTW+/fv3/+8qWB+7fz\nKivEYjGZTNbU1FR9dem797AaOzw0NPTWrVutGTcA4L8LCQmpVyKTyeRyeTNTFLXFaM60tLSR\nI0cWVVZQbMzlOI5RqTiGIRJJJpeRFCQqlSqVSjFdLby8UlbKwzFMxuMjDHtz5NSQIUOOHTv2\n+PHjgIAAM7P6K7eFhIRMnDbVOshPOZc+y9jQe89PeddvF966V33/mVwqk5bz580N2blzZ90V\nlb/++uu5ny8y8urNMv5nxKT1uFHvLt2oiohGQ/tymKy6gynltZLHP+zwcHGdMGHCR7YDAKAh\nYpLR/zTop+Hdpy2IxeL4+Hg2m+3t7V23XNl5UO/FqY7OzMys5k5zvbkKmUxUUmZubt5Uhejo\naJZTl4TkxziGkek0hVwhF4t1dHS6d++uTN+txgy/OWtJcXExjI4HoL2JjY2Vy+X17nV1feBo\nTqFQKJfLlZ+V72U2paKiws/PDznbK27eZfbvJYi+RMaQTColMWgYiaKQyZFCQaPR5HK5XIsr\n51Wgt7lSCgXhOMva/BUdPU95UhETvWzZMn9//4iICB0dHdWRAwICRgwZmrBus8ePa+namggh\njEw29x1k7juoll+ZFLrJt6/X3r1768Uzffr0a9euxSzb4LZuqU73rspCCpPh/s2yu8u/VWRk\nd/3/engKmbz00dNXEcesuFp/nf+r0Vn3AQAfiZhktJkspyEcx9siBolEkpeXx2KxlK9F0un0\n6OhooVBoY2OjSn8fPHiQlpZmY2PTzKuTHZGPj8+WHdulgmoql9NoheK7jzRZ7KaGuldVVR08\neLDG2VbT3oampYEQhhBSSCTC/KI7d+64u7sbGRmxTY1IDHpGRgYkowC0KykpKQcOHHBycqo3\nLrOuDxzNOWPGjJycHOVnJyenf02E9G9btmzh00iecye/uxhH7+MiPBuHHqdhPRzwWglGp5Mo\nZIVUhpNJFAqFxGHLUzIUhWU4CXNZuUjDyjzltwP8lxkUU0NkaXL66pWz+vqLFy/+/vvvOZy/\nb19Hjx6dOHHijZDlthPHGHq6M3S0xeX8kvtPsqLPDfbse+TIkUZDOnDggPn69b+s3MhxsNV1\ndiQz6ML8wqK7j7ra2jo6Ol78bscLDNE0uLX8Cg6Dueizz0JDQ1VnBAC0LmKS0TZd6l0pJSXl\n8ePHys9yuZzH46nGOQUGBnK53MLCwqVLl7q4uGzcuBEhhGHYZ5999sMPPyxfvnzAgAE6Ojrv\n3r27f/8+i8VavHhxW0erZoMGDXJz7pG6+5Dr6s8bbpUKhGl/HFm7fDmVSm24FSE0e/bsapmE\naahPq/v6P43GtbagsFlPnjzx9vbmcDgYhrXRHxIAgIY+ZDRnQkLCzp07LS0tQ0NDm5mC7QNH\nc/bo0UP116bykUijcBw/fPiw3ewJZDodIYQUCs6EkYKoi2Q7SxmOIwWO6DQMwxRyBYlCQjUi\nxeXbiITZBIykcdiJy9YzhvbTXzKDpKWBEFJIJLxL8XtPRd+8efPGjRvKLlIul3vx4sXIyMjf\nfvvt1r6jOI5jGObu7r53x87p06c31ZdJJpN/+OGHefPmRUVFPX36tLq4ysLGccSiZaNHjyaR\nSEKhMDk5uayszMjIyM3NjUajvaf1AQAfgZhkNCoqqq1P8erVq5MnT6p+5PP5qh99fX0bXfK+\nT58+P/3004kTJ+7evSsWizU1NQcPHhwcHNzUyh8dF4ZhkZGRnp6ez7fvdVo4k8L851UqYX7R\n4++29bbvumrVqkb3vXfv3pkL582GDSx+18gc1EwDPUll1atXr5xMzWU1ImVPybt375RjvOzt\n7S0sLBruBQD4eM2P5sRx/Pjx41FRUW5ubqtXr25+iqIPHM25YcMGVYW7d+/euHGj0aOVlpbm\n5eU5OjuSGXSWkYE0/S1zmJc4NUPy/e9owgjcxQGVlaPULHkxT1FRhb/ORsIa68BRln7DEhau\n5swezxzyz6ROJBpNw7uP0FAv98bDGTNmqIYckEikmTNnzpw5UyQSlZaW6uvrf+AcTFZWVmvW\nrGlYzmazYfwlAGpD/AAmpdTUVENDQz09PdWPEolEuWZGy4wfP378+PHNVLC0tGw48qlr1651\nb6+dmL29fWJi4sSJE29M/8LYuy/HwhSXycpTXxfffTRl0qQ9e/Y01S164sQJo369zUcMyl0R\nJudVkHW16lVgGRsUp6YzH6V5eXk9ffo0NDT08ZMndC0NhFBtRVUvd/cffvjB19e3za8QgE9M\nM6M5cRwPDw+Pi4sbPXp0SEhIy158bPFoTuVDf+UfvaZD+mdfulVroC3xcEYMKjp2Hh07j2ql\nyEAHGejgCoS4LCSsUUgkr/ZHUd271c1ElejaWlUUstVn02OXfhsfHz948OC6W5lMJvzFC0CH\nQ/y72FKpdO7cud27d09JSVEVxsfHu7m5zZ49W/V2PGh1jo6OT58+jdy7z1vTQCv5tXFG/iT3\nvvfv3o2MjGxmSpe0tDStrnY63RwM3Jyrdh/BpbJ6FahstuJdYfaZy127dh01xl/gZO0Ttcc3\nJsI3JsInak+Vo9VI/9GbNm1q44sDAPwjIiIiLi5uxowZ8+fPf28mKhaLL1++nJCQUK+8xaM5\njYyMKBRKTUExQsh24hhFfrH4bBxmakSaMRbrYoU4bDR/Ilo6A031R2MGo8l+5Lnj81+8LEpM\nYg7t18jhMIzMZEhpFGNvj+jo6P8aTPNwHD958qSfn5+BgQGNRrOyspo7d27d7yYAQFsgvmc0\nPDx8//79fn5+ddcC8fHxCQ4OPnjwoKur65IlSwgMr3Mjk8kTJkz4T5OVSKVSEpmMEHJd/cXd\npev5Yb9qhARTrEyVW3GpTHTtNjoTN8J3+IHIyL5b1qtGqiKEGHo6DrOC9Xu5fLMqzM7ODiZJ\nAUAN7t27d/78eX9//6YeFrX1aE4mk+nt7f3uxh0NOytBdq5cKkVpWejIOVxPGy8swdZ/jjTY\nuECICkoxiRSjUOQabO2VIfxvfxXff0rr3tigKBwnkUhaXbukPUv7r8E0o7KyctKkSdfv3Lb0\n97FdtZDKZouKS68k3I90c/v222/Xrl3biucCANRFfDJ68ODB0aNHnz9/vm6hg4NDVFSUQCDY\ntWsXJKMfr6KiIjc3l0wmW1lZsVisjzmUjY3N25x3CCGaBtcr/Ie03w/lfr2FbGpINtLHayXS\njGw6i4XJFc+ePXOcP61uJqqi072r47ypX331VUBAQFMvAwAAWsuBAwcQQjiO1x3epBQUFMTh\ncNQwmnP16tWjxowxHTYg7Y9I5OJAmx4gi7qouH4PBQzDC0pQjhThiKyvTTbUl7zMRGSStLYW\nDesr+usKJ2g4SftfQ6NwhUJWI+JyuVVkkkxW/8lMi8lkssDAwOTC3MEHd9L/f0YtB1tjb0/e\n87Rv1//IYDCWL1/eWqcDANRFfDKamZk5a9asRjcNGjTo2rVr6g2ns0lISAgLC7t165YCx3GF\ngslk+vv7h4WFde3aSJr4IcaOHRs5barj/OlUNovKZrms+MxhVnDp4+c1RSUUFlNj+oSSpGTO\n63cvszJdRw9r6iCW/j5XD0QlJCS8d4UtAMBHKioqQk0sLzJy5MhGpytq9dGcvr6+XyxcuGfl\nd5Kqamz2OExfm+zdS/EyizTEA5fLcRKJqsEhUSgIIbK2ppxfKa0WYsb6yEi3Njmt3mujoqIS\nFpOppaWV9/ada+utVvrnn3/efZY8MGIrTaP+8FbdHk5uoUtDQ0MDAwOtrKxa64wAABXik1EN\nDY3s7OxGN2VnZ9ed3Bj8V5s3bw5dv95q7PABe3/mWJnjckVlelbi6cvu7u6RkZGBgYEtOObo\n0aN72Du82Bnh9vVihGEIIYaejvnwQcqt5amvs89eWRgyr4CCkyhN/naRKBQtB7tnz55BMgpA\nW3vvGnXqGc25fft2hUIRHh6OdDXlYrGisAQZ6yvYDAxhVApF9SYrxmRg5RWSSgGFw5bpacuL\ny+oeRCqors4t6OXuLheJC27eHbv/YGuF99tvv9lNCmiYiSoZ9HblOHWJiIj4/vvvW+uMAAAV\n4gcw+fn5/fnnn5cuXapbKJVK9+3b98cff8Cw6xaLjIxcF/at58/run02k2ttgWEYiULWdrJ3\nC13SZdHMqVOnPnz4sAWHJZFI0dHR8tSMR2FbxTy+qhzH8dwrNx+s/j7sm/Wmpqbk903LR6LT\nRCJRCwIAAHREGIYpn/JjYilZriAhhBQKCplCo9H+NaYKxxGOtDQ1ZTUivEYs51X8XSyTCfML\n+WnpDvb2RoaGL3ZGdLftEhAQ0CqxVVRUvHjxwrCvezN1DD3dGw7qAgC0CuJ7Rr///vvLly/7\n+flZWFg4ODjQ6fSKioq0tLTy8nJjY2P4M7RlhELhihUrun8xR7eHU8OtFiOHVGVlL126NDEx\nsQUHt7GxefDgwdy5c69P/VzX2ZFlaiSrqSl//pKDSBG/75k5c+bRo0er8wqaP0h1bj488ALg\nk2JhYcHhcGrL+AwZTrK35cdcJcsV6N9z7yuqhUihcHNz45WVPdv3l/hNnoRJw7t3wRUKDMPo\ndHp1YfG9gzHkt/nRiYmttThnWVkZQoiuU3+iuroYutqlpaWtcjoAQD3EJ6PGxsbJycnffvvt\nX3/9pXpDVF9ff968eRs2bDA1NSU2vA7q8uXLVXJpn/8/PW/IbvK4uOAF6enpzazg1wwLC4tr\n1649fvz4ypUreXl5HA7Hfd4Xfn5+yvfPfHx8JLPL+C8ztB27NLo7Py1dUlzm4+PTglMDADoo\nOp0eEBBwLu2pwNRQt5sDmcOWJzwkD/tn/iZcIlVUVBno67NYrMrsAi4iWTg6plxLxO49pXSx\nIrOZsmJeXlYOpsBXrFhhY2PTWoEpp7iuLa9gGTe5fLG4vKIF01oBAD4E8ckoQsjQ0PD333/f\nvXt3YWGhSCQyMjJqZp5L8CGePHmi4+yINd1twNDVZpubPH78uGXJqJK7u7u7eyMPtgwMDEJC\nQo7tjPDa+d3fCwDWIa+tfbEzYt68eQYGBi0+NQCgI9qwYcOZnj25liY8DGOOGyY8cpZka4FZ\nmyGEFFKpNDOHSqa4u7sL84te7IxwsLJ6W8UfvH9H9bv8iow3smohw7GrwVeLpDU1Ozf8oqmp\nOXPmTAqFYmBg8JFdpFpaWt27dy++/8R63Mim6pTcfzx2+OiPOQsAoCnEvzOqgmGYiYmJra0t\nZKIfr7q6msJ6z2p4VA5bIBC0UQA///yzDUfz3orvhPlFdcuF+UX3Vnxnw9X6+eef2+jUAIB2\ny87O7vjx49IrdzTuPhOTMJJbN8kPe6Rnr0ty8qWv3tDkCu9+/Qqu3rrzxVovN/eXb7M8Nody\nLEyN+vfpOntS98Vz7YLHathZUVksrpXZunXrzM3NjY2NDQ0NFy1alJ+f/zGBLVq0KDPqjFRQ\n3ejW0kfPqlJeh4SEfMwpAABNIb5nFMfxmJiYw4cP5+XlSaXShhVg9YsWMDMzq7kT30wFHMdr\nCorNzc3bKAAOh3Pz5s358+f/NWeZnpuzpr0NQqgy/U3ZkxcTx4/fu3cv/MkBwKdp9OjRiYmJ\ny5Ytu7nvL7qhnoLLlp68ik7HUQ10GSzWzZ2Rhrq6v23ddvjwYZtAP6a+br3ds89dTd19kO7l\nTp0TZGpvZ2/XpeJ1ZsypS1HOzjExMUOGDGlZVPPmzTtx4kRS6Obe362iaWnU3VSe8urx9zt+\n2LjR2tq6hdcMAGgW8cno1q1bV65ciRBisVgwBXpr8fHx+To0VFxWztBrfG6ssuQXpFrJgAED\n2i4GDQ2NqKioNU+fnjx5Mj09HSFk7+sXtHe/q6tr250UAND+ubq6xsfHv3379tGjR1VVVSYm\nJlwuNzs7W6FQ2NnZeXh4KBSKhQsXek2rv25w4e0HKbsPan41l97TSVRcyudV0LU1DT3dDT3d\nM6POBAQE3L9/38mpkVGb70WhUE6fPj1p0qQbs5ZYjR2u7+ZM5XJqCksKE+4X3Ez8dv0G5fcU\nAKAtEJ+M7ty5c/jw4bt3727Ft9FBz549hw4e/CL8z97frlDOBlqXXFybuvvQ559/3uh8163L\n1dUVsk8AQEPW1tZ1+xr79++v+szj8eRyOUP3X39Ly2slKbv2c6aNpfd0QgiRaTRRba1qq92k\nAEFO3rJly65cudKyeLS1tWNjY6Ojo/fv3/8gbLsySx46dOjyR7+6uLi07JgAgA9BfDJaXFwc\nExMDmWiri4iI8PDweLIp3HlJCJX9zxKgolLek+93dNHWCwsLIzA8AABoipaWFolEqq2opNdZ\nC7T00VOJVKrp83fOKpdJaf+ez9hh5sRr07549+6dhYVFy86LYVhwcHBwcHCLIwcAtADxyaih\noSGO40RH0QlZWFgkJiZOnDjxxrQvjAf25VqZ4wpFxcuMosQk/5GjDhw48JGL1AMAwMfIz8/f\nvXt3XFxcXl6epqamu7v77NmzlS990mi03r17lyQla1j/k1ZWvMqkOdkhMgkhJJfLRGXlbBLp\n7du32traWlpaCCGWkQHLxDApJmPYjAAAIABJREFUKanFySgAgBDEj6afPHlyZGQk0VF0TjY2\nNklJSScOHR6iZ6qV/NogLXuck+vNuOunT59W3rsBAIAQ+/fvt7Oz23XmL55LF7MvZrLGj7hd\nUeQ7amRwcLBQKEQILViw4M1f5yWV/8z4IRXWYGyWQi6v4PPL8guklYIaXPH6XfadxMRbt27x\n+XyEEJXLqaysJOyqAAAtQnzP6Pr168ePHz916tQZM2ZYWFg0HMNkZ2dHSGCdA4lECggIaK1F\n8wAA4CNVVlZOmTLl0tUr2PD+km5dBDJZTnmxhYWF4xdzukwJjP3m5wkTJpw/f37GjBnHjh1L\nWre598ZVdC1NhBBDR1uencMr5yGZHCvhs4wNOZZm6O+VQovu3r3r7uYmKi41MTEh+hIBAP8N\n8ckol8tVfjh27FijFeAhPgAAdA55eXlDhw7NyMxkzxzHGe6NMAzhuKSyKje3oLi42NPT0/On\ndTfmrzhw4EBISEhMTExwcHD87GXW40bq93JhmRlLjsRguUW4VMoy1OdY/L0+H0ahcCzNyAz6\nkwuxZKGo7kAoAECHQHwyOnnyZBqNRqEQHwkAAIC2I5PJAgICCiUiiqUJZ8TAv0sxjKalqaOp\nUZnx9uHDhwMGDOgyedyOHTtCQkI0NTUvX74cFRW1b9++eyc2isVihBDpaqLmomlUDW69gzN0\ntAUJjxzt7VUdHACAjoL4FLCpDlEAAACdyaFDh1Kz33CtLeRanOoTF+Ql5YiEUUwMGZ6uZGMD\nDTsr3tOUnJwcowEecb8dKCkpMTAwwDBs8uTJkydPRgiNGDHiNR3l37gjPn2NMmUMRv3n+0sh\nEFbuOsyQKQoLC3EcxxrMZwcAaM+ISUaLiorodLq2trbyc/OVjYyM1BIUAACANnT06FHz4YOy\nz16R1taSuttj5kZILpc8e1n91yXm4L7cWYEsI4P8/HwrLysMw4qKigwMDOrunpWVpTctwHL0\nsEdhW3kPnzMG9KKYGiEFLs3MFt15pGNn3f371bfmrywvL9fVrb9uEwCgPSMmGTU2Nh4+fHhs\nbKzyc/OV4Z1RAADoBJ4/f16TiskNdChTRpO7/jO3NJ5bKN59XL5pD2fJjMq8QmlVNY7jOjr1\nV49T9ndq2FoN2r897+qtoruPah6nYWSytrV5tzVfGvV1F/P4qmoAgA6EmGQ0ODhYtSoPTC8M\nAACfgsrKSmRtxgwZLxLWkOuUY+bGtNCFkg3hoou3FDamJUnJFhYWpqam9Xa3t7dPz3xrPMCD\nRKGYDOpnMWpo/eNnvNHX12+YxQIA2jliktGoqKhGPwMAAOiUMjMzZXI5Z9IohpFhzdMUhUBI\n4rIRQrhcjuM4xmJQpo0R/3aU8dnkjGOnVs9b2LCDMzAw8LOvllVlZvOep8lqRCQKWatrF4uR\nQ8x8B2IkEkIo++yVoKAgAq4NAPBxiB/ABAAAoNOLjY1lWJpKSCQ2g842Mxa+zcXMjHAShuM4\nyitGecWYuBbHMPmpq3aaesuXL6+3u0KhePbsmUQg5GFy7oq5ZD0dhbBG8vz1831Hcq/c7BW2\noiD+ruhV1tqT5wi5OgDAx4BkFAAAQJvLzs7WtrUqqRaKy3gMXe2agmJFdj5WKUCxd1AJD5kZ\n4TQqolEk7wpdpg1VKBT1dv/mm2/+OBrZe+uG54W5NWSMq8mlGupRbSyYPl4VW/bdmrcCVdfE\nxMSYm5sTcnUAgI8BySgAAIA2x2QyMZncxcUl+elTjExCWlwsPRs/dhEN64uG9EFMBiKRUF4R\nllMUdePqi/794+PjlTOuIITS0tK2bNnise1bbSd7rS7WL168KEp+QWGzyHSaQiqVDe4jP3rh\nq8WLR40aRew1AgBahvi16QEAAHR6zs7O/LR0UxMTE2NjBYYpqoX48YtowUQUPBLpaiMWk8Rk\noFopcnOUjR/+Rlw9e/Zs1b4RERG6vV20newRQgwGo3fv3kOHDu1m18XawMjW1NzUxpri6bpl\nyxYajdatW7ewsDBYnh6AjgWSUeLV1tYmJSVduXLl0aNHUqmU6HAAAKD1jRw5kiFT5N+8W1FR\nQTfQQxduIS83rFc3DCGMTCZRKXgZH6PTaIZ6mLaGzKfvhdjLiYmJyn3v3r1r0Kdn3aOxWCwL\nCwtNTc3MzMySqkp6354IIc9tG6ijBm4/etjR0TEpKYmAiwQAtAgko0QqLy9fsmSJnp5eP+8B\nAdOmeHj1MzAwWLNmjUAgIDo0AABoTVwud+PGjS927BPm5EkkEvTqLWlYP4xMwchkjERCAiFe\nVEYxN0YYRtbSEFFIen16/vXXX8p9y8vLaZoa9Q7I5/MfPnzIMjfR7ubAtDJDCNF1dS1GDhnw\n+2b2II8RI0ZkZmaq+yIBAC0C74wS5s2bN76+vuU0Uvf1y/Rcu2FkskImK3307Pf9URcuXLh6\n9aqJiQnRMQIAQKv5/PPPX7x4sffAftTXFSGEzI0QQkgixcvK8ZJyipkRSYODEMKoFIyEYUZ6\nL1++VO5oYGBQWVZe91A4jj9//pxpbMA0MkAIyXkVGIbRtTQQQhiGOYZMfVxY8uWXX166dEmt\nVwgAaBHoGSWGWCz29/cXWxr327FR370HRiYjhEgUiqGn+4BdP5Zx6YGBgXK5nOgwAQCgNf32\n229kuQI9TkUIKVIzFSnpitQMrFpEtbciG/y9hicursXIFKlMphpTP2jQoKK7D+seh8/nC6qr\n2aZ/L+BX++iFVlc7MoOuquA4b+rl2Njs7Gw1XBQA4CNBMkqMvXv3ZvN5risXkSjkeptINKpb\n6NKn6a+OHDlCSGwAANBGyGRyjx49MFsLpFBQGHSqlSnN2YHa1YbEYavqKMorSTSKpKC4S5cu\nypL58+dXv8wsvv9YVYfH41E1OMo/4xXllTUXbliP+9dQepaRAdfCVPXWKQCgPYNklBhHjx61\nHjeSRKM2upXCYlr6+0IyCgDofBYvXqxIyaBYmeGPUkgaXOzft0F5aTmSSPAqYc3zVwEBAcpC\nCwuLn3/++ckPO0sfPlWWSKVSEpWKEJKX8Pg/7jbo0c10iFe9E9G1tcrKytr+ggAAHwveGSWA\n8m2nPrMDm6mj49z1eexetYUEAADqMWvWrFWrVvEKivHcAlJPR5Kj7d8bcFxewpMVFLMNDWv+\niOrn5u7r66vaa/HixXK5fPXq1druPUy8PWUyiSwvr+rWQ/HNByYDPFy++gw1WD5UVMYzMDBQ\n23UBAFoMekYJIJfLpVIpmU5vpg6ZTq+pqVFbSAAAoB4Yhh08eBDJ5BiOpFv+lJ68Ii8skeUW\nSlIzFCU8Dodbs/uINr86Ojq63o5Lly5NSUmZ2Kuv5EI8f1+ULPqylkjq+eNat7VLyHRavcrV\n7/Jr8ou8vb3VdVkAgJaDnlECUCgUExOT6twCrlWTK9cJcwusrKzUGBQAAKiJn5/fLz//vHLN\nGq65cfWlBNnVRJKFCYXLVhTzqnMLbayt76Y9MTQ0bLhjly5dfv31V4QQjuMeHh6l2pp6Pbs3\ncgIcT9t7ODAw0NTUtK2vBQDw8SAZJcbIkSPPxyUYD/BoqkLetYTZI0eqMyQAAFCb5cuXOzs7\nr1q16pksj85h44Vl0vS32lraa376aeXKle/dHcOwffv2eXl5peto200Zl3/9dv6121Vvc2Q1\nIqahPolCpgtEO/46q4YLAQB8PEhGibFy5crDzs5FiQ+NvHo33Jp79aY4M3vpxaXqDwwAANTD\nx8cnOTn59evXr169kkqltra2rq6uWINXP5vi4uISGxsbGBgYG3VGQaUwBnvSBvUiyeU1WTlY\nWpZEILh58+a0adPa9BIAAK0CklFidOnSJTw8/LPFX3RbNNvCb6jq/ovL5W9PX34ZcSzq6FGY\n9B4A0Ok5ODg4ODi0bF9XV1d9fX2RoQ7Zf3CNTCrHMI6Whq1zN8slloW37s2cO4fNZo8bN651\nAwYAtDpIRgkzb948TU3NL774Iiv6rEGfnnQdLXEpr/hBsg6Fdv7MmVGjRr3/EAAA8AnbtGlT\nnqja+7dNDQcwmQ72klZVL1y4cNiwYVwul5DwAAAfCEbTE2nixImZmZm/fvfDIC0j+1LhUH3z\nvVu2pqenQyYKAADNk8lke/fudZgd3DATVbL09xFSSQ1H5QMA2hvoGSWYhobGnDlz5syZQ3Qg\nAADQkaSmppZXVHj26dlUBYxEMvRwS0hImDt3rjoDAwD8V9AzCgAAoOMpKyujcTnKdZiaQtfT\nKSkpUVtIAICWgWQUAABAx6OrqyutrlbIZM3UqS2v0NPTU1tIAICWgWS0PSopKfnmm2969+5t\nZGRkY2MTGBh46tQpHMeJjgsAAIhUXl6+ceNGT09PY2PjsWPHkjHSqyvxTd0bcRwvSUoeMGCA\nmoMEAPxX8M5ou3Pq1KlZs2ZRLUxMhw6wNh0hr5U8f/Fy0qwZA37ziI6O1tXVJTpAAAAgQGxs\n7JQpU3ADHTOfgVbjhilksqojJ7OOnizn0Hp5eDAYjHr1c2Pj6ULxxIkTCYkWAPDhIBltXy5d\nujRh0iTnJSEWI4eoCo28ettNHvcobOvIkSMTEhIa3nMBAKBzS0hICAgIsAuZYjNuJPr/xMw6\nPZxuf7amMubyPYl0wOBBFMo/32hFdx+lhP8ZeeCglpYWQSEDAD4UPKZvR4RCYUhIiGPIlLqZ\nqBJNk9vn+9Uvi/K3bNlCSGwAAEAUiUQyZ84cq0ljbQJHoTpLNFHZrL5bvuGIZcLdR5P2HOS/\nzBDk5BUm3H+4Ycvz73fs/jV88uTJBIYNAPhAkIy2IzExMZW43Hpc40vSU1hMh1nBu3btksvl\nag4MAAAIdPHixbzyMrtJAQ03MQ31B/y+2Wpw/4pzcY9WfHdr7vKiP0+McXJNSUmZP3+++kMF\nALQAJKPtSHx8vGFfd4xMbqqCYV/30rKytLQ0dUYFAADEunnzpn5vVxKt8VmcSBRKt89nYTTq\ntWvXpFJpbm7u3r177ezs1BwkAKDFIBltR0pKShh6Os1UINPpNA1ucXGx2kICAADClZSUMJu9\nN2JkMl1Hu7i4mNz0H/MAgHYLktF2RFtbW1IpaKaCQiaXVgt1dJq7KQMAQCejra1d2+y9EeG4\npKoK7o0AdFCdeTR9dXV1dHR0YmIin8/X0dGxtrYeP368g4NDM7sIhcJjx449ePCAx+NpaGj0\n6tVr2rRp2tra6gnYy8vr/C8/Oc2fVvcN/bp4yS+4bHb37t3VEw8AALQH/fr1O3Q6BlcoMFLj\nHSj8lxkkiaxXr15qDgwA0Co6bc+oQCBYunTpmTNnLC0tg4KCnJycHj16FBoampOT09QuMpls\n3bp158+ft7OzmzJliru7+40bN1atWlVdXa2emIODg0nllfnxiY1uVcjkrw6emD17No1GU088\nAADQHgQEBHAU2LtL1xvdiuP4qwNRU6ZM4XK5ag4MANAqOm3P6LFjx0pKShYsWODn56cs6du3\n76ZNmw4dOrR+/fpGd7l48WJWVtasWbMCAwOVJW5ubj///HN0dPScOXPUELOuru7WrVsXLv6C\nrqWp5+Zcd5NCKn265XddKd5U8AAA0FlxOJzw8PApM6YzdLUN+/6r+xOXy1/8+ie9uPzHH38k\nKjwAwEfqtMkomUx2cXEZMWKEqsTT05NGo717966pXeLj45lMpr+/v6qkf//+kZGR8fHxs2fP\nxpp4dN665s6dW1lZuXL1KpMh/c2G9OdYmMpqROUpr7JiLphzNM/HxqrtnQEAAGg/Jk6cWFFR\nsXjxYr1+vcx9B3IszeW1tfzU129OXdJD5NjYWCMjI6JjBAC0UKdNRkNCQuqVyGQyuVze1HKa\nEokkOzvb2dmZSv3X7CFOTk7Xr18vLi5W251u+fLlQ4cO/emnny5v/q2iogLDsO7du2/4ctkX\nX3zBZDLVEwMAALQ38+fP9/b23rRp08Wtf/B4PISQo6Pjqjnzli5dyuFwiI4OANBynTYZbSg2\nNlYul3t7eze6taysTKFQ6Onp1Ss3MDBACNVLRgsLCxUKhfJzW7xR6uLicuzYMYSQQCBgMBj1\n8mMAAPg0de3a9dChQwih6upqGo0GL9AD0Dl8KsloSkrKgQMHnJyc6j64r0skEiGEGi77riyp\nqampWzhz5szy8nLlZxcXFxcXl9aPGCGEELyPDwAADUFXKACdSYdPRoVCofIPZSVjY+Nx48bV\nq5OQkLBz505LS8vQ0ND/OiUyjuMIoXovjHp7e6s6ROl0ekviBgAAAAAAnSAZFYlEsbGxqh8d\nHR3rJqM4jh8/fjwqKsrNzW316tXNvHPJYrHQ//tH6x0fIVRvx3Xr1qk+x8bGpqenf9xFAAAA\nAAB8ojp8Mqqnp3fu3LlGN+E4Hh4eHhcXN3r06JCQEFITsyUr6evrk8nk0tLSeuWFhYUIIRMT\nk9YKGAAAAAAAqHT4ZLQZERERcXFxM2bMGD9+/HsrUygUW1vb9PT02tpa1ZN3HMdTUlL09PT0\n9fXbOFgAAAAAgE9Rp12B6d69e+fPn/f3928qE5VIJG/evCkqKlKV+Pj41NbWnjp1SlUSGxtb\nXl7u6+vb5uECAAAAAHySOm3P6IEDBxBCOI7XHd6kFBQUxOFwCgsLly5d6uLisnHjRmW5j4/P\nzZs3jx8//ubNG1tb29zc3Dt37lhaWjYcEQUAAAAAAFpFp01GlV2eFy5caLhp5MiRjU4LQiKR\nNmzYcPz48cTExEePHmlpaY0aNWrKlCkwXh4AAAAAoI1gyqmLQIvFxsZGRUW5u7sTHQgAgBil\npaVSqXTTpk1EB0Kku3fvhoeH9+vXj+hAAADEqKysLCgo2L17dwv27bQ9o2rj6upKoXxazZic\nnJyWlubr6/uJj+s6e/asTCYLCgoiOhAiVVRUXLx40c7OzsPDg+hYCOPo6GhoaEh0FARzcHCY\nO3duU1tfvnz55MkTb29vc3NzdUbV4cTExNDpdH9/f6IDadcyMzMfPHjg6elpa2tLdCztmvq/\npHR0dFq246eVRbUFIyMjtS1b306kpqa+ePFiyZIlrq6uRMdCpF27dgmFwmHDhhEdCJEyMzM3\nb95sZ2f3ibcD0NXVbeZ3oKCg4MWLF9OnTx8yZIg6o+pwfvzxRy0tLfjf1LyqqqoXL16MGzcO\nGqp5HehLqtOOpgcAAAAAAO0fJKMAAAAAAIAw8Jge/Gd0Ol1DQ4NMJhMdCME4HE7zy3p9Ckgk\nkoaGBoPBIDoQ0K7RaDQNDQ0qlUp0IO0dl8ttdLIXUBf8On2gDvQlBaPpAQAAAAAAYTpGygwA\nAAAAADolSEYBAAAAAABhIBkFAAAAAACEgQFM4D8oKio6efLks2fPysrKWCyWo6PjhAkT7O3t\niY6LAI8fP46JicnKyiKRSLa2tpMmTXJ2diY6KLUqKSmJiop68uRJZWWljo6Ol5fX5MmTmUwm\n0XGB9kUoFB47duzBgwc8Hk9DQ6NXr17Tpk3T1tYmOi7iyeXyo0ePnjx50tbWdtu2bfW2Qrsh\nhKqrq6OjoxMTE/l8vo6OjrW19fjx4x0cHFQVoJWU3vvV3P4bCgYwgQ+Vn5+/atUqkUjUv39/\nY2PjwsLC27dvI4Q2bdrUtWtXoqNTq7i4uF9//dXIyGjgwIESieTGjRtCofDHH3/8dNqhuLj4\nq6++EggE/fr1s7KyUq6v4+DgsHnzZphmAajIZLKVK1dmZWX169fP1ta2sLAwPj5eT09v+/bt\nn/iY8dzc3G3bthUUFIjF4obJKLQbQkggECxbtqykpKRXr162trbFxcW3b98mk8lbt261tLRE\n0Er/996v5o7RUDgAH+abb74ZM2ZMSkqKquTu3bv+/v4//fQTgVGpX0VFxYQJE5YsWSISiZQl\nBQUFEyZM+P3334kNTJ1+/vlnf3//K1euqEr++OMPf3//ixcvEhgVaG/OnDnj7+9/8uRJVcnt\n27f9/f3//PNPAqMinFAoDAoKWrZsWUFBQWBg4LJly+pVgHbDcXzPnj3+/v4XLlxQlSi/ccLC\nwpQ/QispvferuUM0FLwzCj6Ug4NDUFBQt27dVCUeHh5kMjk/P5/AqNTvxo0bYrF4xowZqsk1\njY2NT5w4sXDhQmIDU6fHjx/r6Oj4+PioSqZMmUKj0eLj4wmMCrQ38fHxTCaz7krrys6b+Ph4\n/BN+KCeXy0eNGrVlyxZjY+NGK0C7IYTIZLKLi8uIESNUJZ6enjQa7d27d8ofoZWU3vvV3CEa\nCpJR8KGmTp06Y8aMuiV8Pl8ulxsaGhIVEiGePXtGo9FcXFwQQlKptKamBiGEYRjRcamPWCyu\nqakxNjaue9VsNtvExCQrK0uhUBAYG2g/JBJJdna2vb19vcnJnZycKisri4uLiQqMcFwud86c\nOU290ALtphQSErJx48a6rSSTyeRyua6uLoJWqqP5r+aO0lAwgAm0RG1tbXp6+r59+5hM5sSJ\nE4kOR63y8vIMDQ1zcnL27t376tUrHMeNjIyCg4OHDh1KdGhqQqfTyWRyVVVVw3KZTFZeXq6n\np0dIYKBdKSsrUygUDX8ZDAwMEELFxcVGRkZExNXeQbs1JTY2Vi6Xe3t7I2ilJjT8au4oDQXJ\nKPjPJk2apOwOHDRo0Nq1a9vJr7LaCAQChFBYWNjAgQPHjh3L4/HOnDmzc+dOCoUycOBAoqNT\nBwzDunbtmpaWlpOToxxJgBDKz8/PzMxECInFYkKjA+2FSCRCCDVcKlZZoryHgIag3RqVkpJy\n4MABJycn5YN7aKWGGv1q7igNBcko+M9GjRolEAhycnJu3bpVUlKydOnSTyoflclkyqseMmSI\nsqR///4LFizYv3//gAEDOspCwB9pypQpoaGhGzduDAkJMTc3f/PmTWRkpL6+flFRESwYDZqn\nfE3tk3qzpVV8yu2WkJCwc+dOS0vL0NDQ5ufr+JRb6T99Nbe3hoJkFPxnqtdTXrx4sXHjxh9/\n/HHnzp3t53e6rTEYDLlc7uXlpSrR1tZ2d3dPTEzMzc1V9RR2bs7OzgsWLDh48OCPP/6IEGIw\nGNOmTcvMzCwqKmpHc4UAQrFYLPT/jpm6lCUwJW1ToN3qwnH8+PHjUVFRbm5uq1evVl0+tFJD\njX41d5SGgmQUtJyzs7OHh8etW7fy8vLMzc2JDkdNDA0N37x5Q6H86/+OpqYmauw/fCfm5+c3\nZMiQrKwsDMNsbGyYTOayZcu0tbXZbDbRoYF2QV9fn0wml5aW1isvLCxECJmYmBARVAcA7aaC\n43h4eHhcXNzo0aNDQkLqPneCVmpG3a9mY2PjDtFQn8QjRfDxeDzel19+uX379nrlEokEIVRb\nW0tEUMRwcHBQKBRZWVl1C5X/sT+pgTsKhYLJZHbv3r1bt25MJrO0tPTNmzc9e/YkOi7QXlAo\nFFtb2/T09Lr3BxzHU1JS9PT09PX1CYytPYN2U4mIiIiLi5sxY8b8+fPrvQEFraT03q/mjtJQ\nkIyCD6Krq1tdXX379u309HRVYX5+fnJyMoPBsLCwIDA2NRs6dCiGYYcPH5ZKpcqSzMzMp0+f\nWllZfTrJ6MGDB4OCgjIyMpQ/4jgeERGB4/jIkSOJDQy0Kz4+PrW1tadOnVKVxMbGlpeX+/r6\nEhhV+wfthhC6d+/e+fPn/f39x48f32gFaCX0YV/NHaKhYDlQ8KHu37+/efNmDMP69etnbGzM\n4/ESExPFYvGCBQv8/PyIjk6tIiIizp07Z2Nj4+npyePx4uPjFQpFWFjYp7M8fXZ29sqVKykU\nypAhQ7hcblJSUmZmZmBg4KxZs4gODbQjCoUiNDQ0NTXVw8PD1tY2Nzf3zp07FhYWv/zyC51O\nJzo6wqSkpDx+/Fj5+fTp05qamqrRkIGBgVwuF9oNITR//vyioqLRo0c3vOSgoCAOhwOtpPTe\nr+YO0VCQjIL/ID09PSYm5uXLlwKBgMlk2tnZ+fv79+nTh+i41A3H8StXrly+fDk/P59CoTg5\nOU2ePLlLly5Ex6VWr1+/Pn78eEZGRm1trbm5uZ+f37Bhw4gOCrQ7YrH4+PHjiYmJPB5PS0vL\n09NzypQpXC6X6LiIFBMTc/jw4UY37d27V7ksE7TbmDFjmtoUERGhnCYTWknpvV/N7b+hIBkF\nAAAAAACEgXdGAQAAAAAAYSAZBQAAAAAAhIFkFAAAAAAAEAaSUQAAAAAAQBhIRgEAAAAAAGEg\nGQUAAAAAAISBZBQAAAAAABAGklEAAAAAAEAYSEYBAAAAAABhIBkFAAAAAACEgWQUAAAAAAAQ\nBpJRAAAAAABAGEhGAQAAAAAAYSAZBaD1PXr0qKamhugoAACASHAnBB8IklEAWllWVtagQYNG\njRoFd2EAwCcL7oTgw0EyCkArs7W1Xbhw4a1bt+AuDAD4ZMGdEHw4DMdxomMAoH0RCoUf//9i\n5cqVe/bsGThw4KVLl1gsVqsEBgAAagN3QqA2kIwCUJ+enh6Px2uto33++ee7du1qraMBAIB6\nwJ0QqA2F6AAAaHemT58uEAg+8iC5ublXr14lkUj9+/dvlagAAECd4E4I1AZ6RgFofTk5OYMH\nD87JyTl48OD06dOJDgcAAAgAd0LwgSAZBaCV5efn9+/f/927d4cOHZo2bRrR4QAAAAHgTgg+\nHIymB6CV6erqOjk5HT58GO6/AIBPFtwJwYeDnlEAAAAAAEAY6BkFAAAAAACEgWQUAAAAAAAQ\nBpJRAAAAAABAGEhGAQAAAAAAYSAZBQAAAAAAhIFkFAAAAAAAEAaSUQAAAAAAQBhIRgEBjh07\nZmZmRqFQVq5cSXQsraDh5XSyC2yxSZMmYRhWVFTUisekUCienp6teEAAiNLJbhRwJ2wK3Anf\nC5LRTkKhUMTExIwePdra2prJZDKZTFtb2+nTpz979ozo0OqrrKwMCQmprq7euHHj8OHDG61z\n5MgRrAE6nW5jYzNv3ry3b982VZNEIunr67u6uq5atYrH4zVzQDKZbGhoGBgYeOfOnda9nA+5\nwP9q8+bNmZmZrXIodXJL+QkkAAAPGUlEQVR1dR0+fDidTlf+2EGvAnQgcCeEO2E7BHfC96IQ\nHQBoHZMnT46Ojra0tBw/fryRkVFlZeXjx4+PHz9++vTpy5cvDxgwgOgA/5GRkSESiWbPnv31\n1183X9PLy6t///6qH/l8/sOHDyMiIv766687d+507969YU0cx3k8Xnx8/JYtW86ePfvkyRM2\nm93oAUUi0evXr8+ePXvmzJmDBw/OmDGjtS7nwy/wAxUWFn799deurq52dnatckC1WbNmzZo1\na5SfO+5VgA4E7oRwJ2yH4E74XpCMdgbx8fHR0dEDBw6Mi4ujUP75Nz1//vyYMWO+/PLL5ORk\nAsOrRywWI4S4XO57aw4bNuzbb7+tV/jLL7+sXLly7dq1586da6qmXC4fPnz49evXT58+XXdZ\n5IYHvH379pAhQ5YuXRocHKz6s/UjL+fDL/ADPXz4sLUORaDOcRWgPYM7IdwJ27/OcRWtDwcd\n365duxBCu3btargpMjLy2rVrcrkcx3E/Pz+EEJ/PV22VSqUIoaFDhyp/nDx5srLC/PnzDQwM\nmEymh4fHgwcPhELhkiVLTExM2Gx23759Hz9+3Hw82dnZs2bNMjExoVKpurq6/v7+Dx48UG6q\n97BmwYIFjR4hMjISIbRhw4aGm2pra2k0mo6OTvM1t2/fjhDatm3bew+oDEkVYUNFRUWLFi2y\nsLCgUql6enpjx45NSkpq6nKausBmDqJUWFg4d+5cExMTFovVo0ePHTt2SKVS/P//aiq3b99u\nNMiW/ds9ePAgICBAV1eXSqVaWlpOmzbt7du3dStcuHChd+/eTCbT0NDwyy+/rKmpMTMz69mz\nZ92TCgSCVatWWVpa0mg0MzOzbdu2KRQKZYXg4GD0v/buP6jp+o8D+HuMTQYoMBib1OJYGgZF\ngiuTg7a4ukpleMg6rKREK2Y37eQMw06gLsU8zfhVphmlhSdXYgeXmBcs4AqLSykGhihCNU/k\nmDgBJ7D+eN/38/00xjZwOcHn46/P3p8378/783l/eO39+bw/e38IMRgMNvfC4QlpsViqqqpi\nYmK8vLxEItHq1av7+vq4XO7ChQudaR240yASIhIiEk7RSIg7o9OBVColhHz33Xevvvoq+34A\nIYR9NewQn88nhKjV6vj4+GPHjjU3N2dkZKjV6qioqMjIyG+++aazs3PNmjWLFy/u7u7m8Xg2\nC+nu7n7kkUcGBgY0Gk1kZORff/1VUlLy2GOPnThxIi4uLicnR6FQZGdnJycnr1y5MiwsbKI7\na7FYRkdH2UNONun1ekLIggULHBYYGBhICBkYGLC5tqenZ+HChUajMSMj44EHHuju7i4pKYmP\nj6+urlYoFGN3Z2BgYOwO2i+EZpDL5SaTKS0tLTQ0tLa29vXXX//tt9/27dv31ltvCYXCAwcO\nbNmyJTo6OiIiwmY9J9F2TU1NCoVCKBSuX79eIpGcO3euuLj4+PHjer2eHpMffvghKSlJJBJt\n2rQpKCiovLw8NTX16tWrd911F3ujKSkpYWFhhw4dGh0dzcvL27Bhg7+//6pVq9jVc3IvrNTX\n16tUKrFYvGXLFpFIpNPpVCqVh8f/n3R3eGDhjoJIOBYiISKhM5twP3f3hsEFzGZzdHQ0IWT+\n/PkFBQUtLS3MBRmbw8uv1atXE0I0Gg2T4dlnnyWEpKSkMCnr168nhDQ0NIxXmRdffJEQ8vXX\nXzMper2ey+U++uij9GNdXR0hJCsry84e2bl8z8vLI4Skp6ezc2q12vb/OXnyZFZWloeHx0sv\nveSwQLPZLJPJOByOwWCwWRONRuPp6fnzzz8zKV1dXTNnzpTL5ePtztgUh4VoNBpCSHV1NZOB\nNtbvv/9usVi2bdtGCPn222/HPV6TaruSkpKYmJiamhomQ2FhISGksLCQfnzyyScJIUy1h4eH\nH3/8cUIIczlON7pixQqmhI6ODkLI0qVL6UfmfoDNvXB4Qj799NOEEPb1/dq1a9kVcHhg4Y6C\nSIhIiEho88De/nBndDrg8Xi1tbXZ2dn79+9ft24dISQwMDA+Pj4xMTE1NdXb23tCpSUnJzPL\nc+fOJYQkJSUxKeHh4YQQg8Fg828tFktFRYVYLF62bBmTeP/99y9atKi+vr63t5deaDqptraW\n/WCT0WhsbGz86aef5syZ8/bbb7NzFhYW0vBBcTicjIyM/Px8O4UPDQ21t7fn5uaeO3duxYoV\nEonE5u6Ul5dHRUXdfffdzKwcPB4vNja2urraZDL5+vo63AuHhfj4+Bw+fFgqldKQRxUUFGRm\nZorFYofls02o7TQaDQ39hJAbN26MjIzQy/TOzk6aWFdXN2/ePLlcTj9yudysrKyamhqrjdIv\nXUomk3l7e//5558TqrZNo6OjOp3u3nvvffjhh5nEl19+uaSkhC67pHVgOkEkRCSkEAmnXCRE\nZ3SamDVrVlFR0fbt27///vuGhoa6urqqqqqKiopNmzZ9+eWXTzzxhPNFMUMPhBA61MVOoeMa\n9LptrIsXL165cmXBggUcDoedHh4eXl9f/8cffyxatMj5muh0Op1Ox04JDg7Ozs7OzMwUCoXs\ndLVaTS9/CSH9/f1tbW2lpaVHjhw5fPgw+/ezeXl59HYCm0ql2rNnj80KXLp06fLly5cvX549\ne/bYtV1dXc4MsjgsxN/fv7e3NyYmhn3QZDKZTCZzWLiVibbdgQMH9u3b19zcbDQamcTh4WFC\niNFoHBoasvq9Z2xs7NiN3nPPPeyPPB5vvNNjQgwGw+DgoNVBmDdvHrPsktaBaQaREJGQIBKy\nTJVIiM7otOLj45OYmJiYmEgI6evrO3jw4MaNG1NSUs6ePRsUFORkIWMfgRrvoaixrl27Rqth\nlS4QCJi1zsvJyWHuBwwODkZERPT29mo0Gqv4SwiJiIhISUlhp7z22mvR0dHPP/98e3s78+NQ\nhUKhVCrpsoeHR2BgYFxc3EMPPTReBa5evUoImT9/Ph1YsRISEuLMXjgshM4COLlfsFqZUNtl\nZ2dv27ZNLpe///77YWFhM2bMaGlpWbNmDV1La2V1M2nmzJlcLtf5TdwM+uyal5cXO9HLy4v5\nonJJ68C0hEjIQCQcL4WBSHg7QGd02goICNBqtRcuXNi5c6dOp1u+fPnYPGaz2bUbpcMBY0Mt\nTbmZOT4EAsHu3buXLVum1WqPHDniMH9oaGhCQsJXX33V0tISExNDE5VK5dgZUuxgKkwf2Zkc\nh4XQEMa+Ir8FhoaGdu/eLZVKa2pqmEGcK1euWNWKTs7CGBgYGBkZ+e9qxT4h6de2VQVMJpPF\nYqHLLmkdmPYQCREJ7UAkvE3gDUxT3sjIiEajSUxMHB0dHbvW39+fEGIymYitsQn2CzxcQiKR\nCIXC1tZW5v+E0uv1HA6HPqkzaUlJSc8880xFRUVFRYUz+en1otX/8ISIxeKgoKC2tjar+NjT\n0+PCQnx8fEQiUWtrK7tpzpw5U1RU1NLSMunK23fx4sXBwUG5XM5+nIg9FCiRSDw8PC5cuMD+\nq8bGRhfWwf4JKZFI+Hy+1Sna3NzMLLukdWDaQCQcDyKhHYiEtwl0Rqc8Lpd7/vz5ysrKN998\n0+paraOjY8+ePZ6ennREhj5Q0traymT4/PPPXV6f5ORkg8Fw9OhRJuXUqVMnT55MSEi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- "text/plain": [ - "plot without title" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "options(repr.plot.width=7.5, repr.plot.height=4)\n", - "\n", - "p1 + p2" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -3054,7 +1446,7 @@ "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", - "version": "3.6.2" + "version": "3.6.3" } }, "nbformat": 4,