{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 4: Peak selection analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will use scATAC-seq dataset `10XBlood' as an example to train scAGDE and analyse the peak importance scores model have learned." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Train scAGDE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we train scAGDE on the 10XBlood dataset following [Tutorial 1: End-to-end scATAC-seq analysis](./Tutorial%201:%20End-to-end%20scATAC-seq%20analysis.ipynb)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import scanpy as sc\n", "adata = sc.read_h5ad(\"data/10XBlood.h5ad\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Note: Here, we specify the parameter `save_epoch=10`, meaning that the model will save the importance scores for each peak every 10 epochs during autoencoder training. These CSV files will be saved in the \"weights\" folder within `outdir`. As a result, upon completion of the training (with a total of 5000 epochs), there will be 500 CSV files in folder named as \"output/weights\".\n", "
\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "device used: cuda:3\n", "\n", "Cell number: 17736\n", "Peak number: 39565\n", "n_centroids: 9\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Training CountModel ##\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "CountModel: 100%|██████████| 5000/5000 [13:49<00:00, 6.03it/s, loss=12509.2070]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "## Constructing Cell Graph ##\n", "Cell number: 17736\n", "Peak number: 10000\n", "n_centroids: 9\n", "\n", "\n", "## Training GraphModel ##\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GraphModel-preTrain: 100%|██████████| 1500/1500 [02:59<00:00, 8.36it/s, loss=2591.9326]\n", "GraphModel: 100%|██████████| 4000/4000 [08:10<00:00, 8.15it/s, loss=6096.3760]\n", "R[write to console]: __ __ \n", " ____ ___ _____/ /_ _______/ /_\n", " / __ `__ \\/ ___/ / / / / ___/ __/\n", " / / / / / / /__/ / /_/ (__ ) /_ \n", "/_/ /_/ /_/\\___/_/\\__,_/____/\\__/ version 6.0.0\n", "Type 'citation(\"mclust\")' for citing this R package in publications.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "AnnData object with n_obs × n_vars = 17736 × 39565\n", " obs: 'orig.ident', 'nCount_scATACseq', 'nFeature_scATACseq', 'celltype', 'n_genes', 'cluster'\n", " var: 'features', 'n_cells', 'is_selected'\n", " uns: 'neighbors', 'pca', 'tsne', 'umap'\n", " obsm: 'X_pca', 'X_tsne', 'X_umap', 'latent_init', 'impute', 'latent'\n", " varm: 'PCs'\n", " obsp: 'connectivities', 'distances'\n" ] } ], "source": [ "import scAGDE\n", "trainer = scAGDE.Trainer(adata,outdir=\"output\",n_centroids=9,gpu=\"3\",save_epoch=10)\n", "adata = trainer.fit(topn=10000)\n", "print(adata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extract and save a subset containing only the peaks selected by scAGDE. Additionally, we save the names of the selected peaks as well as all original peak names, which will be used in subsequent analyses." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/haogaoyang/anaconda3/envs/torch/lib/python3.8/site-packages/anndata/_core/anndata.py:1230: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n", " df[key] = c\n" ] } ], "source": [ "adata[:,adata.var[\"is_selected\"]==1].write(\"output/adata.h5ad\",compression=\"gzip\")\n", "import pandas as pd\n", "selected_peak = adata.var_names[adata.var[\"is_selected\"]==1].values\n", "selected_peak = pd.DataFrame(selected_peak, columns=[\"peaks\"])\n", "selected_peak.to_csv(\"output/selected_peaks.csv\", index=False)\n", "all_peak = adata.var_names.values\n", "all_peak = pd.DataFrame(all_peak, columns=[\"peaks\"])\n", "all_peak.to_csv(\"output/all_peaks.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Further analysis (accessibility statistics and annotation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have developed several R scripts to perform downstream analyses, and the essential code for key analyses is provided below. To interactively execute the analysis within this notebook, we utilize the [rpy2](https://github.com/rpy2/rpy2) package, which allows running R code directly. Thus, please ensure that rpy2 is installed. Alternatively, you may extract these R scripts and execute them externally as standalone R scripts. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below initializes rpy2. After this setup, you can write and execute R code directly within notebook cells by starting with `%%R`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import rpy2.rinterface_lib.callbacks\n", "from rpy2.robjects import pandas2ri\n", "rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)\n", "pandas2ri.activate()\n", "%load_ext rpy2.ipython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To completely finish the following analysis, you need requires specific R packages listed below (`pkgs`), and you can verify and install using the code provided below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code will help you install all the required packages:\\\n", "% Installing with conda solves most of the dependency package installation problems: \\\n", "conda install r-signac\\\n", "conda install r-seurat\\\n", "conda install r-sceasy\\\n", "% The other packages can be installed just in R: \\\n", "install.packages(\"reticulate\")\\\n", "install.packages(\"hexbin\")\\\n", "install.packages(“dplyr”)\\\n", "if (!requireNamespace(\"BiocManager\", quietly = TRUE)) install.packages(\"BiocManager\")\\\n", "BiocManager::install(“ComplexHeatmap”)\\\n", "BiocManager::install(\"entropy\")\\\n", "BiocManager::install(\"ggprism\")\\\n", "BiocManager::install(\"TxDb.Hsapiens.UCSC.hg19.knownGene\")\\\n", "BiocManager::install(\"org.Hs.eg.db\")\\\n", "BiocManager::install(\"ggalluvial\")\\\n", "BiocManager::install(\"ChIPseeker\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1]]\n", " [1] \"ComplexHeatmap\" \"grid\" \n", " [3] \"circlize\" \"stringr\" \n", " [5] \"data.table\" \"ggprism\" \n", " [7] \"ggalluvial\" \"org.Hs.eg.db\" \n", " [9] \"TxDb.Hsapiens.UCSC.hg19.knownGene\" \"GenomicFeatures\" \n", "[11] \"AnnotationDbi\" \"Biobase\" \n", "[13] \"GenomicRanges\" \"GenomeInfoDb\" \n", "[15] \"IRanges\" \"S4Vectors\" \n", "[17] \"stats4\" \"BiocGenerics\" \n", "[19] \"ChIPseeker\" \"viridis\" \n", "[21] \"viridisLite\" \"ggplot2\" \n", "[23] \"dplyr\" \"entropy\" \n", "[25] \"SeuratObject\" \"Seurat\" \n", "[27] \"mclust\" \"tools\" \n", "[29] \"stats\" \"graphics\" \n", "[31]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \"grDevices\" \"utils\" \n", "[33] \"datasets\" \"methods\" \n", "[35] \"base\" \n", "\n", "[[2]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[3]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[4]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[5]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[6]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[7]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[8]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[9]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[10]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[11]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[12]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[13]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[14]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[15]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n", "[[16]]\n", " [1] \"hexbin\" \"ComplexHeatmap\" \n", " [3] \"grid\" \"circlize\" \n", " [5] \"stringr\" \"data.table\" \n", " [7] \"ggprism\" \"ggalluvial\" \n", " [9] \"org.Hs.eg.db\" \"TxDb.Hsapiens.UCSC.hg19.knownGene\"\n", "[11] \"GenomicFeatures\" \"AnnotationDbi\" \n", "[13] \"Biobase\" \"GenomicRanges\" \n", "[15] \"GenomeInfoDb\" \"IRanges\" \n", "[17] \"S4Vectors\" \"stats4\" \n", "[19] \"BiocGenerics\" \"ChIPseeker\" \n", "[21] \"viridis\" \"viridisLite\" \n", "[23] \"ggplot2\" \"dplyr\" \n", "[25] \"entropy\" \"SeuratObject\" \n", "[27] \"Seurat\" \"mclust\" \n", "[29] \"tools\" \"stats\" \n", "[31] \"graphics\" \"grDevices\" \n", "[33] \"utils\" \"datasets\" \n", "[35] \"methods\" \"base\" \n", "\n" ] } ], "source": [ "%%R\n", "\n", "pkgs <- c(\n", " \"dplyr\",\n", " \"hexbin\",\n", " \"entropy\",\n", " \"Seurat\",\n", " \"Signac\",\n", " \"ggplot2\",\n", " \"viridis\",\n", " \"circlize\",\n", " \"ggprism\",\n", " \"ComplexHeatmap\",\n", " \"TxDb.Hsapiens.UCSC.hg19.knownGene\",\n", " \"org.Hs.eg.db\",\n", " \"data.table\",\n", " \"stringr\",\n", " \"ggalluvial\",\n", " \"ggprism\",\n", " \"ChIPseeker\"\n", ")\n", "######## Install all packages using codes below ########\n", "# if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n", "# install.packages(\"BiocManager\")\n", "# BiocManager::install(pkgs)\n", "suppressWarnings(suppressPackageStartupMessages(lapply(pkgs,library,character.only=TRUE)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Statistical analysis of selected peaks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we selected 10,000 peaks from the preprocessed 10XBlood dataset using scAGDE, to determine the accessibility of human immune cells and analyze their accessibility-related statistics. Specifically, after identifying the peaks from scAGDE, we generated pseudo-bulk data by merging the accessibility profiles of each peak across different cell types, resulting in a summarized accessibility profile for each peak within nine distinct cell types. \n", "\n", "\n", "Based on this pseudo-bulk data, we calculated the median accessibility and the range (the difference between the maximum and minimum accessibility values across cell types) for each peak. Additionally, we measured the Shannon entropy of each peak and the percentage of cells where each peak is accessible. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ***How to read AnnData data object in R?***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we will convert Python objects into data formats required by R. Specifically, we will convert an `AnnData` object to a Seurat object and save it as an `.rds` file. Here, we use the `sceasy` package to perform this task; however, you may opt for any other method you are comfortable with." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " WARNING: The R package \"reticulate\" only fixed recently\n", " an issue that caused a segfault when used with rpy2:\n", " https://github.com/rstudio/reticulate/pull/1188\n", " Make sure that you use a version of that package that includes\n", " the fix.\n", " An object of class Seurat \n", "10000 features across 17736 samples within 1 assay \n", "Active assay: RNA (10000 features, 0 variable features)\n", " 2 layers present: counts, data\n", " 6 dimensional reductions calculated: pca, tsne, umap, impute, latent, latent_init\n" ] } ], "source": [ "%%R\n", "sceasy::convertFormat(\"output/adata.h5ad\", from = \"anndata\", to = \"seurat\", outFile = \"output/adata.rds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let we example the correct data object in R." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "An object of class Seurat \n", "10000 features across 17736 samples within 1 assay \n", "Active assay: RNA (10000 features, 0 variable features)\n", " 2 layers present: counts, data\n", " 6 dimensional reductions calculated: pca, tsne, umap, impute, latent, latent_init\n" ] } ], "source": [ "%%R\n", "obj <- readRDS(\"output/adata.rds\")\n", "obj\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ***How to calculate the statistics descirbed above in R?***" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%%R\n", "peakData <- function(atac_matrix,peaks,cell_types){\n", " # creation of data frame, including counts matrix and cell cluster\n", " peak_data <- atac_matrix[peaks, ] \n", " peak_data <- data.frame(\n", " cell_type = cell_types,\n", " counts = t(as.matrix(peak_data))\n", " )\n", " # data summary\n", " peak_data <- peak_data %>%\n", " group_by(cell_type) %>%\n", " summarize(across(everything(), sum))\n", " peak_data <- as.data.frame(peak_data)\n", " rownames(peak_data) <- peak_data$cell_type\n", " peak_data$cell_type <- NULL\n", " peak_data <- as.data.frame(t(peak_data))\n", " df.media <- peak_data %>%\n", " rowwise() %>%\n", " summarize(\n", " median_count = median(c_across(everything())), # meidan\n", " range_count = max(c_across(everything())) - min(c_across(everything())) # range\n", " )\n", " # Shannon entropy\n", " entropy_values <- apply(peak_data, 1, function(row) {\n", " entropy(row) \n", " })\n", " # accessible percentage\n", " accessibility_percentage <- apply(atac_matrix[peaks, ], 1, function(row) {\n", " sum(row > 0) / length(row) \n", " })\n", " # resulting data frame\n", " df.shannon <- data.frame(\n", " peak = peaks,\n", " shannon_entropy = entropy_values,\n", " accessibility_percentage = accessibility_percentage,\n", " count = rowSums(atac_matrix[peaks,])\n", " )\n", " return(list(df.media = df.media, df.shannon = df.shannon))\n", "}\n", "\n", "%%R\n", "library(Seurat)\n", "library(entropy) \n", "library(dplyr) \n", "cell_types <- obj$celltype\n", "atac_matrix <-as.matrix(GetAssayData(obj, slot = \"counts\"))\n", "peaks.scAGDE <- read.csv(\"output/selected_peaks.csv\",header=T)$peaks\n", "df.scAGDE <- peakData(atac_matrix,peaks.scAGDE,cell_types)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ***How to plot the statistics?***" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%R\n", "# plotting function\n", "densityPlot <- function(df,optionColor=\"D\",labs=c(\"Range of Chromatin Accessibility\",\"Median Chromatin Accessibility\"),theme=NULL){\n", " ggplot(df, aes(x = x, y = y)) +\n", " scale_fill_viridis(option = optionColor) +\n", " stat_density2d(aes(fill = ..density..), geom = \"raster\", contour = F,n=200, na.rm = FALSE,)+\n", " theme_light() +\n", " scale_x_continuous(expand = c(0,0), limits = c(3.2, 8))+\n", " scale_y_continuous(expand = c(0,0), limits = c(0, 1700))+\n", " labs(x = labs[1], y = labs[2])+\n", " coord_cartesian()+\n", " theme(\n", " plot.margin = margin(b=65,l=0,r=0),\n", " axis.text = element_text(size=14,color = \"black\"),\n", " )+\n", " theme\n", "}\n", "hexPlot <- function(df,optionColor=\"D\",labs=c(\"accessibility percentage\",\"shannon entropy\"),theme=NULL){\n", " ggplot(df, aes(x = x, y = y)) +\n", " geom_hex(bins=35,color=\"lightgrey\") +\n", " scale_fill_viridis(option = optionColor)+\n", " theme_light() +\n", " theme(\n", " panel.grid.major=element_blank(),\n", " panel.grid.minor=element_blank(),\n", " )+\n", " scale_x_continuous(limits = c(0, 1))+\n", " scale_y_continuous(limits = c(0, 2.3))+\n", " theme(\n", " panel.border = element_rect(linewidth = 1,color=\"black\",fil=NA),\n", " axis.text = element_text(size=14,color = \"black\"),\n", " )+\n", " labs(x = labs[1], y = labs[2])\n", "}" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(ggplot2) \n", "library(viridis) \n", "df.use <- df.scAGDE$df.media;df.use$x <- log(df.use$range_count);df.use$y <- df.use$median_count;\n", "p.den <- densityPlot(df.use,labs=c(NULL,NULL))\n", "df.use <- df.scAGDE$df.shannon;df.use$x <- df.use$accessibility_percentage;df.use$y <- df.use$shannon_entropy;\n", "p.hex <- hexPlot(df.use,labs=c(NULL,NULL))\n", "p <- p.den+p.hex+patchwork::plot_layout(byrow=F,heights = c(1,1), ncol=1)\n", "p <- p.den+p.hex+patchwork::plot_layout(byrow=F,heights = c(1,1), ncol=1)\n", "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result, the peaks identified by scAGDE exhibited low accessibility and large range of accessibility variability. Additionally, we observed an increase in the number of peaks with lower Shannon entropy and a smaller percentage of accessibility, alongside a decrease in peaks with high Shannon\n", "entropy, indicating that scAGDE tended to select cell type-specific accessible peaks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Peak annotation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To further dissect the identity of these candidate peaks, we annotated them with peak type using [ChIPseeker](https://github.com/YuLab-SMU/ChIPseeker) based on their proximity to the nearest transcription start sites (TSS). " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> preparing features information...\t\t 2024-11-11 06:25:50 AM \n", ">> identifying nearest features...\t\t 2024-11-11 06:25:50 AM \n", ">> calculating distance from peak to TSS...\t 2024-11-11 06:25:50 AM \n", ">> assigning genomic annotation...\t\t 2024-11-11 06:25:50 AM \n", ">> adding gene annotation...\t\t\t 2024-11-11 06:26:00 AM \n", ">> assigning chromosome lengths\t\t\t 2024-11-11 06:26:00 AM \n", ">> done...\t\t\t\t\t 2024-11-11 06:26:00 AM \n" ] } ], "source": [ "%%R\n", "library(ChIPseeker)\n", "library(TxDb.Hsapiens.UCSC.hg19.knownGene)\n", "library(org.Hs.eg.db)\n", "library(Signac)\n", "peakAnno.scAGDE <- annotatePeak(\n", " StringToGRanges(peaks.scAGDE),\n", " tssRegion = c(-3000, 3000),\n", " TxDb =TxDb.Hsapiens.UCSC.hg19.knownGene,\n", " annoDb = 'org.Hs.eg.db')\n", "peakTypes.scAGDE <- peakAnno.scAGDE@annoStat\n", "peakTypes.scAGDE$group <- \"scAGDE\"\n", "df <- peakTypes.scAGDE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the annotatation results." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "library(ggalluvial)\n", "library(ggprism)\n", "library(ggplot2)\n", "ggplot(df, aes(x = group,y=Frequency,fill = Feature,\n", " stratum = Feature, alluvium = Feature))+\n", " geom_stratum(width = 0.6, color='white')+\n", " geom_alluvium(\n", " alpha = 0.3,#透明度\n", " width = 0.6,#宽度\n", " color='white',#间隔颜色\n", " size = 1,#间隔宽度\n", " curve_type = \"linear\"\n", " )+\n", " scale_y_continuous(expand = c(0,0))+\n", " labs(x=NULL,y=\"Frequency(%)\",\n", " fill=\"Feature\")+\n", " scale_fill_manual(values = c(ggprism_data$fill_palettes$summer,\"#f06667\"))+\n", " theme_bw()+\n", " theme(panel.grid=element_blank(),\n", " plot.title = element_text(size=18,face = \"bold\",colour = \"black\",hjust=.5),\n", " panel.border = element_rect(fill=NA,color=\"black\",linewidth = .85),\n", " axis.text=element_text(color='#333c41',size=16 ),\n", " legend.text = element_text(color='#333c41',size=13))+\n", " ggtitle(\"Peak annotation\")+\n", " coord_flip()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Peak importance analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To illustrate our model's preferences on peak selection, we uniformly sampled 500 moments from the entire training process of our autoencoder model, and computed the importance scores of all peaks at each moment. To characterize the distinct preference patterns, we organized the 39,565 peak importance profiles into 6 modules by K-means clustering based on the elbow method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ***How to read peak importance scores in R?***" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 39565 500\n" ] } ], "source": [ "%%R\n", "library(data.table)\n", "library(stringr)\n", "\n", "file_path <- \"output/weights/\"\n", "# Get the filenames of all CSV files\n", "csv_files <- list.files(file_path, pattern = \"*.csv\", full.names = TRUE)\n", "csv_files <- stringr::str_sort(csv_files,numeric = T)\n", "# Read each CSV file in order and merge them into a dataframe\n", "data_list <- lapply(csv_files, function(file) {\n", " # read.csv(file, header = FALSE) # 假设没有列名\n", " t(fread(file, header = FALSE)) # 假设没有列名\n", "})\n", "# Bind all columns into a dataframe, with each file's data becoming a column\n", "final_data <- do.call(cbind, data_list)\n", "dim(final_data)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's annotate all the peaks using ChIPseeker again." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> preparing features information...\t\t 2024-11-11 07:18:12 AM \n", ">> identifying nearest features...\t\t 2024-11-11 07:18:12 AM \n", ">> calculating distance from peak to TSS...\t 2024-11-11 07:18:13 AM \n", ">> assigning genomic annotation...\t\t 2024-11-11 07:18:13 AM \n", ">> adding gene annotation...\t\t\t 2024-11-11 07:18:15 AM \n", ">> assigning chromosome lengths\t\t\t 2024-11-11 07:18:15 AM \n", ">> done...\t\t\t\t\t 2024-11-11 07:18:15 AM \n" ] } ], "source": [ "%%R\n", "library(TxDb.Hsapiens.UCSC.hg19.knownGene)\n", "library(org.Hs.eg.db)\n", "\n", "peaks <- read.csv(\"output/all_peaks.csv\",header=T)$peaks\n", "attention <- as.data.frame(final_data)\n", "rownames(attention) <- peaks\n", "peaks <- Signac::StringToGRanges(peaks,sep=c(\"-\",\"-\"))\n", "peaks <- Signac::StringToGRanges(rownames(attention))\n", "peakAnno <- annotatePeak(\n", " peaks,\n", " tssRegion = c(-3000, 3000),\n", " TxDb =TxDb.Hsapiens.UCSC.hg19.knownGene,\n", " annoDb = 'org.Hs.eg.db')\n", "peakAnno.df <- as.data.frame(peakAnno)\n", "peakTypes <- peakAnno@annoStat\n", "peakTypes <- peakTypes %>%\n", " arrange(dplyr::desc(Feature)) %>%\n", " mutate(lab.ypos = cumsum(Frequency) - 0.5*Frequency)\n", "types <- gsub(\"^(Exon|Intron)\\\\s*\\\\(.*\\\\)\", \"\\\\1\", peakAnno@anno$annotation)\n", "attention$type <- types\n", "attention <- attention[order(types),]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ***How to plot these score, alongside the annotation information in a single heatmap?***" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "%%R\n", "library(circlize)\n", "library(ggprism)\n", "library(ComplexHeatmap)\n", "\n", "set.seed(10)\n", "data <- attention[,-ncol(attention)]\n", "km <- kmeans(scale(data),6)\n", "cls <- km$cluster\n", "\n", "sorted_clusters <- order(table(cls))\n", "new_cls <- cls\n", "for (i in seq_along(sorted_clusters)) {\n", " new_cls[cls == sorted_clusters[i]] <- i\n", "}\n", "new_cls <- setNames(paste0(\"M\",new_cls),names(new_cls))\n", "\n", "\n", "mycols <- colorRamp2(breaks = c(-2, 0, 2), colors = c(\"#5100bd\", \"white\", \"darkred\"))\n", "cols <- setNames(ggprism_data$fill_palettes$summer[1:9],unique(attention$type))\n", "\n", "col_fun <- function(at){cols[at]}\n", "lgd = Legend(\n", " title = \"Peak annotation\",\n", " legend_gp = gpar(fill = col_fun(1:9)),\n", " labels = names(cols),\n", " at=1:9\n", ")\n", "data.ha <- apply(data,2,var)\n", "ha = HeatmapAnnotation(variation = anno_lines(data.ha), height = unit(3, \"cm\"))\n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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f9xWfvuk/PvbB973skvWYv/+Jl9/0qf133SMZGNFUHTtrsWLFinnz5l166aXTz3L33Xd///vfv+SSS7adVBYWOwfOPPPMVatW7bvvvjMtyK6OXWgn4S233PLWt751pqWwsLCwmC52IYK+6667Xvva1860FBYWFhbTxa5C0I8//vjuu+9uzxe1sLDYgbCrEPQtt9zylre8ZaalsLCwsNgC7CoEffvttx911FEzLYWFhYXFFmCXIOihoSHGWGdn5+aTWlhYWMwa7BIEfdttt9kDoC0sLHY47BIEvWbNmmOPPXampbCwsLDYMuz8BB1F0dNPP20PgLawsNjhsPMT9N13333QQQdtPp2FhYXFLMPOT9A333yzDbCzsLDYEbHzE/Tdd999yCGHzLQUFhYWFluMnZygH3zwwX322cd13ZkWxMLCwmKLsZMT9E033XTcccfNtBQWFhYWLwQ7OUH/5Cc/OfLIIzefzsLCwmL2YWcm6LVr186bN69YLM60IBYWFhYvBDszQf/gBz844YQTZloKCwsLixeInfn4zTVr1tx6660zLYWFhYXFC8ROa0E/+uijCxcuLJVKMy2IhYWFxQvETkvQ11577SmnnDLTUlhYWFi8cOycBM05v+OOO4444oiZFsTCwsLihWPnJOg77rhj+fLl9gVXFhYWOzR2ToK+/PLLTz/99JmWwsLCwuJvwk5I0E8++WSr1dp7771nWhALCwuLvwk7IUF/9atf/dCHPjTTUlhYWFj8rdjZCHrjxo3333+/3d5tYWGxE2BnI+iLLrroE5/4xExLYWFhYbEVsFMR9EMPPfTII4/Y98NaWFjsHNh5AtFardZZZ511+eWXz7QgFhYWFlsHO48F/eEPf/iUU06xwRsWFhY7DWYzQQ/ccd5bXn/UG4/8wH+to6nSRVG0atWqOXPm2NhnCwuLnQmzmKAf+saFz6xcc/tPPlO69Au/aE+W6g9/+MNRRx213377ffazn92e0llYWFhsa8xeH/Tw3feUlp9dhnfwYXt/7N4n8PrEdzE4OAhgYmLixhtvvO+++1784hdfdtll1rNhYWGx82H2EvTQ0FB1URUA6+hoDQ7GN9etW/eRj3wEwP333/+6173u2muvnUkRLSwsLLYlZi9Bd3V1jY6OAl00MuJ1dcU3Fy1adP311wNYsWJFX1/fjApoYWFhsW0xe33QnQcfXLvrrhra9/7ysb8/aI+ZFsfCwsJie2P2WtB4yVmfmn/qsUd91Vty9urD0nIuXLjw1ltvtSc+W1hsC/T393/yk5+caSkswIimDGGzsLCwsJgh5Lk46tceyxhjjHmHfn1d9usHz39J/HXhvWvkzdpfbvjK+avefeyhL92t4jHGvJN+sO2EtrCwsNgVMLmLw/O89t2rr3po1fn76rej31xx9UPM81jbCE1+fs1FH/3cffA6Fu27sG/Doxu2SIgH/vfu0Wd+Pc3E/pz9D3rF4QC++SlyGNoRiBBPAxwGx0Hoo1BAsYggoGqVql1U6aGgyCu9VOppe41aMLyJjfRjdAjNOkaHMTqMkVE0mhgcwfphjEdbJLyFxU6IvfbChRfOtBAWUxD0wqOP6br5pm+t/v2nLn41k3ebt6/+zjr/DW87/N6bbtMZuu9tX/7p4Yte8bI9+8Ifv7fwlm9PurEkD4uDxnDtuWkmroa1+OKZ/yHXRRiS76NSIT+E48Bx4BMFLsICBSDfIz/kfinyiuSWI6e/nzVqbHgjRgYwOoBGHeMjqI2hNo52C2wCPQ1UmogitNtIVBADEUAAEj3AGBhLPhIBDHH/OGI64jhgDGBwRMcxlgiXQlwOF+Vnq+AEoqR8WRHkR+0OUZIl+QgQ1z4SGEtuSpnlR85BAAhc1MXF/bgoEDhPhIFIKWsiUQWnpMCIg/OkFUzUpQuTlEwqIwPidLI0JrJl88Y9Gfc/iYu4IuYAUs5J6o2zpwXTGsVYzrNmos8hulqKSgQn88TlE2RIvk36XBPPcZIsBPWUXSfJFbcurk4WKMWT8icdxYwfAACHqWcdP7v4scq2MwcO09oiEjOGdgiLWYApFgn3Pm3l8jWrrll9xxdefUSQ3Bu5cfX3+ytvXXlS5d6bjMSVfQ974et1vutPP7HnJMJs2MRqDbRazHMxp4cWL6RFy3i1m+bvHvXt3u5a3ArHh4obn2b9z+HR51GvYWgThofQbGB0BGNjGK+h1cL4BMYnUGug2cZ4HWMNDDbRsn752QeHwQU44DP4TsKODoMjOIWLO1LNBC6YoGOHwXUUZ8UXnECAywBo9Ae4blIsCf3huUktnguXAQyRILtYFblSZyBJ6QitHBOovJAVOU6SEqKi+DrOmAgjCJQxuKKNhLQmiHk21rhMdo6oPdEfSCSEpGNuFCLnoQQ0W1vnqVn8bZgqimPhKSuP+ti7r1t901eOOLEMAFj/3SvWjPeeuvL4yvdWbh/5pkClRB0VlIrwPDgOWi1seMbpfx4bnnaKf/CKlTAIK8XqwlIHL3WSN5cK+/GwSq5PYZX7rZo/Mea0Gl5t2JkYR6OGdgv1GhoTaEyg3UKjjmYdjTrabTQbaDYQRWg10Wyi1QIR2m20I0QRiBCJa06IIkQc7TY4R8QTW7IdaeOZ1GCIIpA0k5lhiwHCPpJjmADAdeC6cJ2ER+Tf2PLyvGQoxsnSNCFseVcwV5xGN808P8kFZjCFtK0AMCep0WFgWuES2ZVn2TppDEpTTrf99XKklR1br0yavTINS4xlWXhSO09mMIYYpIxlaQsr61ifEon0xoSFkgKl2NnGyh5OfRVXl72WTQMzHrH+sJR4JIzlTKelZyiaMLIP1WyGJeycakjSClHUIhvYOiswZZhd1wlnnHD2j69ffX3/iaf1Anji6tU/by85e+WR4abvbVUpImKt4jTTyrCTgRFWDFEuUaFAvX3UN5/3LuTlLupd2u5e1KxseMIbH8bzT2PTc3jmeUzUMDSAoSE0GhgexdAoBkYwVscTo1u1JRaTI3AQMoSxUnHgMkWjTEz2XQdE8N0kS0wosTbSHURMaAtXOI6kl0nO+qUKyXU+JMpGXDDtwnEM0pT2KZCjihIV6BqkGZemJ47r0mlUaRphNSvT28k0Nk+BQTRQ/mVCYyVTBJ1/ueb10nQtREvjOyRqaW+Rk9JiW2HqOOjCMSvfteDay1Zf8+Rp5yylB6688j566adXHuRgzZTZthQDf+l++pnpmuSt1njPvgCw2xyKItTrqE2w5zey8K9OZwcVi+jp87vnhl1z9wvL1DGXV/aJyn1tv14Lhzf6wxvRmMBIP0YGMTaMZgO1MYyPY6KGVgv1CTSbqNfRjtBoot5AvYl2hIkGJppoRRirY6CJJt+cjLMDBQdVD56L0EPgIfDAGHwXvgfPTdjQdQEkNBdTpOPAc+E4iUmeEBZLvpVWWOI3cOC6SQLdTyq98AnrOYoxE9enZg7LxIBwBZgmpCxfZodGW7rVGYsdf5SzEMlcYKqohEw1apPlx0jzlzZ1SGzPDI1KSKplknadRGwpEqBsf5lH937ohK54Vpr1WqPUXcHOqVyMwXGThQS5oKLPD9K8D3TPSd+xmAlsZqOKt/yMU/f+xhev/NZD51yw/oqrHncOvOT0/ba6EI0lC9b93/ppJu5aMje+GJ8AAM8FYwgClIooFBAWyPWICK0GYw4mRpjjOYx5QaniVFpufdyJHRGxQV2vYaAf/QMYGEKtjvUDeHhgq7duxlDnqDdnWogdGY5y0sKLl9qAiFCI/UsMLktc2A4z2TZ2FgnvcDIzEP5xIrQjMAZO8ISycYTNG0N6tB1dPUiVI7Jw4fVSWgrJTbVIKyYfcb3Q/dqaz8Rh6isGvPTv8brjtk23WmwBNruT8O9Wrnz1v37iqivv3P+57z4bHPHZdy/Z+kIwJ6PAp5F4wTyqdqBSpbBIxTKVqlTtobBMnfOjytyoXJrwJ0bDwQ3O4AY8uwmNCYwOYWwYzSbGx1AbR30C7TbqDbTbKIQIfFSKWDYfLeE+JhG60Goj4uAklsLFMIqDH2QURJuDOCJKwhgiQpsnU/h4Mcpj6iMHOKFNu8qCZJ6b9G+CIygmEr7duIdJ3AEUb8bJ4m8JWghKBn7MlQAH2gQXYECb0CbVhCZPiDsufzs/wM32ZNyENsFl8AACIiAi+AyBUC0kfn4kGiiXH2NVVNyJLJUdGZvf6r3nqSsP+9Q/fPs9HxobKh/3vnf2bgehpoen1jntCJwjDDBvDi3YjffNp2KFGjXWqLHW/FJYLpbnFgp+6AUFNCZQLKNUSdwaE+Oo19Buo15Hs4FWC+14DbAl1vcitCO02wlBxxeSuInQ5mi1kzDsiKEdwWFgLlwzeszRbB8JudYv1+5jW8YTfgbSlp6SIDwxLmNryNUCxXRIW0xZc+Kj7qWVbkc5Gc+JKjPtNa2OdKWQngQzag1aCZnbqijl5DVFklLpK6WxbI4eH6ZJzrT1Nym24XwwV8ZUJ2j+ENWi+MJRRCx7I2Usp3tD6womXMzQ5RftlZFtMmRQuqFVP4uylM+aNK+RmSAlmOwBGWCn95WeJrX+ufuL042ymAlM4yyOeStWHvuRd924Dn2nr3xbdVsIMTgy/sTg89NMPH+oCMwF4Lro7KBqBZ5H5TKKJfgBuR4xBuLgbRa1iRcC7ofwA0RtOHGELEfURquJeh2tFiZqqNcTv3OziUYTjRaiCM02Wi002+AkYjCAKEoMZGgxqoBh0egRuESIdDoAICkyvmYgLVCMgIiDtEUbN+PljEnEEcNeeoehzU+VR1hkTMrR7iiRtEUw3ZWsmAIqsYzfQLYcc1lMaQLxtS5SiuV1ykjzkazInJWnBMiWKXtJLyFVKQDmKLdsimpT8uQWIpsGTUtJYeQvRPrEjXzM+EgZDSEpXmoRXUtBZ2TSEsdpSGXJBtVkG5uqVZwfaTGzmM5hSdV3XHx76T2b2G6vOjzYfOoXgGfGhn7X/9Q0Ey8crgB7AHAdii3gUgnlKvUu4L27RWGFqnN4uTcqdkV+oxb2b/SHN2FIxEEPbUJ9AkODGB7CyAjqDQyPYWgUm0Yx2kD/ru2xdQCPIXCENyZ2szqJ/vAcca2Rqeco2mUi/oEBrpNQubSO9WTQQjKkOlGLdVB50/pGEj1UYmjFApOkgWbOp8g0b3dJimf1C7nAptNlzqyCqQ0mqYC9FIyAOZnbVDzZ1cLNqyv9DlO7lvR5QPajxNj45BJbbD9M6zS7cPfXHLf7ZtI8dcMnL7j5OQBY9+sWQL/95nvjozr63vyZL79z2ZR5H3HWtw98ejqSAHiw0AG8GkBPD5pNcKDeYIMDCELH81Fq8LBElV4E9TF/fMgfeA79z2HweUxMYHgQYyNoNDA+hokGIoC5KBTQ5aBQQDvCbg3Umqi3wTkGW3+rbzFgCBwEInosNmNjyov/Y9DIC3lmrGmyMaZMVAY1zY8jMXLT6CSSw3GSHMUiUpJNnwvrZAcVigDBs8y05lSyDKekzGRdACAzwYdBbSn+zU4FEvkZGJIOSSfWeTnryRHf6g9d/0p6oiAmCrK7mNguKM18yeJSgFR8XtyNJNxlUqqUu0bLoJwh0HwyUlzHVYWI7tDiRsTuyskY3AizYSDCvEXplBYzga123OjA/Td++9uPqM9rf/HttQCApX0f2hxBv3/5Acs3zp9mRXv07BZffPYHUx9m3Qkswd5/N81iLSwsLGYb7HGjFhYWFrMUs+LA/mf76394bHCaiV+2rGP3+eVtKo+FhYXFbMCsIOhnnv9LsfmbaSZet2H/3ee/FsA5R/IwROAnTjN53oDjwPepUIDnoVShcpVKVQpCKndRqYuCApW6ebGbF7sjr1UPxwb80UFnfBjN+GS7UdRG0WqhXkO9hlp8RkcdjTqiCM1WchZHctxdpB21ESXxcEnwhnAvyp1daiVKX59hyck7EkysWUl3ZBKkIVbbZMBWal8f03bcyYU4ufKm9gE6iUNcebfNKLc4o1ymkwcFX2MAACAASURBVN/qG+fiIzggvaWOIYb0hKZ8qfF+aGgrdcl+xcx5INKZy4R7NOlJEV7mmN7VWAYZzJCIBJUmXbhotRYqYXyb8snKvJBeeFINlx5hedgQsm0xNwomPWCuHiqx45SZPYHGBsCMGx3i2UnI3ZLKcw2jRikJM0NuiNDRjf0Pg8VMY1YQ9DJ3YHRg7TQTl3qS5YtGk7XbNOHA91EIUShQpYKwQN191DOPdy/ghSrvXBh1zG2V+p916+Pu0PMY7sfIBJ4dxvgIJmpot9GsJ5zLOaI2Io6oDaLkFCR5fibEGpTvIQwAk330nc3QV+okH8Fgan3xSo5hdZyCzhRM0WWSxVzUkjdTq2cpz5VOx0BeVIB+Wwth1kMJDcoT2WU9inS09bqUwDnyxHvetIpS38oak/tRUr7eIimMZE+dEx0nKVyPdTPWCc3YQdkWeTO1ZKdalP2Waf2gL6uS0SdJN2tLlPknLmmRKmmZNXmMcpDT4YwhitLrinprGVNPOU7vTr3AY7GdMCsIOvRK0z+yKPQS/8bIOAAWB0R4LlyXVctULLIoIseB66PdQlCisOz5pQ5ijtOcYFGEsAg/QKmCVgs8QtRG1EYUiZMKoAUkkVpnV+eckTormUfJyE+ZNhA35RiRxMdE3FXqPARpycpvHG38SyrXKckYiqmwAZ0+pAmpR4wxZY2q9PJbjSCkjIwBk+uJvFGvyS9vpahES6NrFIMutQjfyaBrtVTi3FxG8IZsY0aFZG9C9JtWVlrzMcdg27SykT8PbdInpYKZzPhF6dUJBZBzX2uj3pZYCzKx/4WEqNknFwscFNL3LWYCs4KgXxj6usj3k/M1gwCBT2EBno+wQO0WxoZYc8Jp1dnI826h0ucFfWF1SbiIvJCCchRMjHr1Mafdcus1pzmBxgSiNloNtBpoNYU13UbUFtcRiINH4Bycg7iiaTn7jgPFyLyjILwf+ima8n58oQ6M1yxKSSLIMr5kJZ7cSdlWTOyXYXJYIv2tLC5LcMwkYpVRZ2ohlp7SiCozHR253gamNVNPQBkZlBmYikXLSELyEORM1SkDHDAvMrtCIAxhXU6jQ/LsWUidOkl64jnf5na4Pr0ADAdUVgBdEepiAKC2+IppPzyRSe+9tj0PelZgByZo30ejgfEaYwzlEhV70N1LpQr1zOdd83nHvCisUHVeVKptDAc3OPVxPN+Pv8a+5lHUxlAbS06wazTQbIJHiXM5Evwbg0kikA7f+K+buGLjoSK9lvIwtniAGG9REXPenHmrZmSluExnE5JOFY2eGAPz0hsolE8gZSdC1aUcApqECYSBpg4/04x+lUM6NJBcxNex3sqxXrVDjdMe0qmjiUSt6RemmEYoS+0K0RwOlGq+SKTrkuwm6RjxwUPQeVA4T7IyGFLLnw1AgKM5ymVpim3NaVUssDqWTyROOYKMurQ+0W3q2IJO+kcPiNZmUfqcCbAW9CzBDkzQY+MsDKi7Qr6PYpEKJfAIjTpGBxnnTmOc+QUa3eAUOxcUOuZ53RTsw/1mzWk3vcaE06w7rQbjEYvaLGoJj3MEHmnmsEkZytchX9ek3dFNUWMMaGCacyMhPjFOpP2rbB9tjElGk+UkpEPpcShSJPfljg8wg2oVR2vDUjKvrjNI42hmrgEC4FEy5lM2viIalmadxD+c6iLtLLfsnje9yVIXyoUBlnHgqgaalcpGMkfJBdHzhuQZM1beSSkno9kmsxvms6aZ5CNL66SsSSv+SakQJl7OonevrnVSYgNKMejNzK0rzlXuSBdlMRPYgQl6fAKNJhurwffQ3YlWi5oN5ges1SQekefz+MfpBuSXyC/wcGLEr404raZbH2eNGlqNxGrmsQ9aeifEC/oklUC378gYopLHFf0x436SUPOBKFqfzOsqs+himKYr8Uz5yBvzKQFSpJmF1ApICjTMuYw7OGt76jezhaeIIL5ULt2UaZnix8lIMNuEFLNrLchJyfIpPm2faulzEkiWzHhaksxk3pFei1SIhUivX+c8uCmbbzw7kZ7EXkfdOaZLngLtIIee7+zYgQk6DOKQCvJ9VKvU20c9c6lQoo4+Xu3j5V4eFKnUExXdWjja7wxPeGPDqI2g3UxebRW/xardSnzNijQpbW/KwaM7OmAuphmTXN1QNcdSaiKZb/VIu1VPqxvR2jlNMJ2eqYLi8pOouClPz8nyTko2PWPazSrEUILJZUyIztQ7AYmukjPurBhARkhNU6aSTcFToiUGGxoJ9LziSadcFkRGFn16YdaZfpqyx1SfE+AoWjR+QqqVmiRQIhlGgCmbamJW6wiLwZhJmJ2Q0rsAQuvimBXII+j6tccW3/1jAO4hX1v7m1XpTfkPnv+Sl37uIQDhqbfUrz4WAGhs7W9+fMMNP7r1rgce/euT6yf8vmUvO/SY9370vNMP6ttm4Tqj4+CEcoG5LoZH2dPPwHXJYXBdlIqoVCgIUO2kame50tUXFKhYpUKFXJ/CDgpKFJS5F8Avcb9IXqPmtFteo+Y0J9zGBItarNVEs4F2A5wnJB6HbcTrhMnyYOyqzryoImfkm2BiOS7xTgqVEA9XPegiTT3m8JNvMNJLhliNzLXoddeKQSgk/yguY7kPT8osa9TFNkXVdZWMnk4TZax7uNIoqZL1ZKrsrNtX863n+hlIe3e4vKm+ymojrTd0kWJRZTKV3eQ/0pfjpJ9aY1vZZFWRzMuTwlg2rhnm4oF8lHK2Z5YmvUBKQ5g9psxzDZVuWMwCTG5Be57Xvnv1VQ+tOn9f/Xb0myuufoh5HtNfWvbM6lNee85vEPTus/8rl7/sgOaGB3939w8u/s0Pr7vlyl/feNru24ajd5tLrngVnOEjBVotDA4xBgwMsCBAoUCeh3KFKh0UhKh080o3VXq4X6BSNyt1c1YpMCIAjHNGnCWDRwxj5sBx4TjgDsjTrGyejDH565fEYUzJYY4i00DTjeK45Jj6k9W2SMViy2/jXOqmKMfwz5DBxTK9bj4bJqqW0mB2ncNldVINiKWnpKWmMQhdFSFdVNxFxpRFJEtPEQBokqdakSx/aVKpeb0mXrKNSNizUqemK9IeorHMoDkx5E0V4wwjOlPejEuTXJ8qU9WsW/dMPX3lpmDJo+daLfHSJY8App3fpD9x0VEMcNzkDCkCuB5LLhCXHC9jEmHRHth/OSxmGpMT9MKjj+m6+aZvrf79py5+tXqOzdtXf2ed/4a3HX7vTbcphg4Wv+EfL7vgzJPfuHdncpDY2IOXr3j9B9bc/KFz/+vtP1zRObUQVGu7AwunKTEfS37UtTpjSLbyNVsohujpolKJ5syl3rnUNZcX4ncS9kWlHu4XqcBHg7Ehp9Vwa6OsUUNjAlGEZ+t4soV2AxFHvFoYtUEckTCWAe1CUKckQUANYLVUmDRLG4T6CIcxGnXjRZKj4kEYdKl6gRtZ5DhPTbp1u1hffNP+mYSvxR3lk5HqKtUi3cw3rXVA6SqmJVD3RVHZxsrZg0FhuibQPbNmkzPtUELqJaSdTqarN3n5umBYGWYHqHmA3lGyzJRdLwtJP/rMBEUpcs0qz/X/kFwshXophDGloHQYj3qbIjM6WT6y1KRqznTHo8U2xRQ+6L1PW7l8zaprVt/xhVcfIc6BHrlx9ff7K29deVLl3pu0pHPfceElZubKS/7hknOuWPPJ+375y99jxeGbkeL5JaX6dF916KxPXsbTbsfLg1QsoLubunqps4cHRVS6eLmLl3ooKPJyLy+HY8XB9e7AGBvehJEBNOsYG8b4KCbG0W6j0UCzgVYz2UPYbiMSTgzDnoqvmbGdOjXa9VOM1RsxtN8906f2AExq0r/TeUcf25JwUwa47nmEntJkw3R6J5lEM61A6AeWMqNMvRzjRa4icTZkzeiNrKEtiDjtHtE6QudHKbNOZBopqfJTXCbTpShSViELzKdFafyb7JZiXkMOrUD1pT7DEPIYtjm0QrQfgD4hM0CGLte3oZP2A9AvZCOY1IjMePrxpX1p7OzAVIuEC09ZedTH3n3d6pu+csSJ8e699d+9Ys1476krj698b/Mv4e7s7AQQhuFmU9ZLfU+1pntY0m6lObF7rBhSO2Ijo2xkFBv7mb+WAt9xXRQKKBapXCY/QLlKpWqxWOnzAhTKFJbJq1A4j4IyBSXu+gjK3A8jrz6e+KAbE25zgkVtNOti00qEdktsUYkSy5q48DzE/mh5jeSCm3tVckaLNMN1r4I2M4VmScUwiMOgds27apqEEGNbZ48sp2TNSZ0N0kY08tjWpEXFp5N5ilMlmDvd9WL1LJoUhvBGRq17MEljU6xkTAWypK9NHZhjJDDmHKaBrPtzZAZlsUIlytdMMp/2rW4xwCxfd7ao+9rTjA352HGnazIS8YUpSUJ7HtmswJRRHF0nnHHC2T++fvX1/See1gvgiatX/7y95OyVR4abvre5gumvV1/zK2DuW95y4GaFGHJaj7XGpilx6Bbi2ddEg/keOqpUCFEqUbGIUpn8ENUuXu2hjl4elKk6h1fmRKWw5jfGg+F+dzS2oEexaRyNutg92EIkzuJoS/7VPLzSX8kzKy3S3QGNhbO+ghQYM47vUJaszpJ6ejG/VutOUIkl3+WzGzTC1ZbRUsmUjTaJ8LrLkkj5+4VkIqvuTk2VoP2jvKsAIXkbWWrSoLOsZrnmzMdVW+J0iWPC9Bflday8H8sjV//0wmPNQeKlsdlnNGlvpyzfbIjL5EoxLZ78AQDQ3tKdlSH5Ckry+HMcSJpTXe6a9jR+wxbbBVOH2RWOWfmuBddetvqaJ087Zyk9cOWV99FLP73yIAdrNlNs7fefPenT97bmvv3Sz0zjNVnMn9KImCRxuURRhHodjQZGxxhjYIy5DgoFp1CgMITnISxQsUyVjqJf6C13Li51cL9A5R4eH2jnBTwcGwhGB73aCFpN1EZRH0c9dn1MoFFHs44oQquJdgucg7fAI7Tj05Q0Z0jyMll5Rgc3iRvmL16L3JCz0ZRfOE4GYe7pzKh4TLeL9TTScmImD2r6Q7kiKeevLCcx5wGCWqHSV8z0VbiUU0gxkVytgskFwvXJNKqK78iTemS7HAZHvDiGNCsY5kqX9CzpXhfovSGu5V+9o6QKVHMX6WPRVjtV9tRsQPftivtxAocpsck8BUVuJkpNKbIGuOsIA1zT07GmT218hd4QBpA6SlB2v15BIhiUUd+2cdCzApuJg/aWn3Hq3t/44pXfeuicC9ZfcdXjzoGXnL5ZZ3H9L994xzGf/b2z/3k3XLViui9KeQEgMKBYQLmMagdVO6jaSUGBqj1U7eWVPh4UqdwblarNcKTfbU54tREWk+9EA6MtrI1UbIPOTWEJIVDuMBfxhUHNOShKZosqvoIL/oqt7EjFDHAtgVxOVIuNWriFbnrzSUIC0tfQvChCfgO59pFJ8brTQxFQyq8aJ9+sha5l0Ukqi9yisjpGFZ5HLsy80P082TLlR90Ln8uDDDktyhrOqSgIWZrpfMrreZb/MeVy0Xk2BUPbxVnyaxO3SGyRZYq1pRbPlbCzJ1uQxfbHZjeq/N3Kla/+109cdeWd+z/33WeDIz777iVTpx+//+K3HXnuzxqv/Pit//2FQ7flflHXReDDcVCvo95gQ4MsDMn3USpTueKWO8gPqdxJpY6g1Fn2ClTs5KUF3CtSkY8GYwP+2BBrNZIDoCdqaLcSq7nVBG+j1UK7lRjO7UidQap4Vnc0a25lY9iY831laWpMGhNrMv61Yzx1toLgTRLuZsYMJ4M0lHSGheabdpwMNZiGZ9rs0pLpjUq7VmHsA4wFlWYjSW+DafTp5ibkmWr6Qujkpq7qWK32tEgsR2LGTAnz2NBIrNeY+lb7KtenbwgjalS/EKb9ZmSL81hSGbbZBpr38703oslqQjAJstnLm4m8stg+2PxOwj1PXXnYp/7h2+/50NhQ+bj3vbN3qrTDv/nM0cd89m4cfP5Pb7vw4G38iCfqLPCp2U4mxHWg0WCeh3YbnBMYC1vwPLge83xwDtdzHAdeg3ilozWn7Fd7nHbLq487jQm3WWdRG606mg2xBbyJdgtRCxFPLtQioTSKxeF2sXnCNS9H1h9tWMowDzU1HRTpEcnUHRljoPwGmVGnn5Gm/NTmsn6OGaVVp1O8QdZQXJO1fNUBF9qJGbo9yZDhFDKoLTW1Txl3igFNRZjkMpsgVUK2i3L0hOaklt4bR54xosWBxAlUiA5Ul+p9C9PyneIOTN6UYch6MkNyZPoRRrheSr9KyNUUXZ0oPZqpzu4knB2YxlbveStWHvuRd924Dn2nr3xbdfJ0m/77I0ced8kfi4d94ae3nLd/ZevJOAl8j9oRK4TkeygUUC5TqUyeh7CIsEBhkTwPfkiOm/Bh1EK7wZKdJdzlxaoTUjssus2G024yHjntptNusXaTcc6iNova4G1wQtRWK4fJTkJ56Kh2rn/s4jA8GMIZLbNDrDQSV2SdGnG6yyWHf1N3pFEGQBufhmFoJM9M6skcsVB0qVuFBnsylTuXQ3X7NEWIEBUZLmlTLGkhsjx9YIhvcpbuf9dkMtsPg9CzHTWVA0cndL1Rk9ViljOpJ8JMnzWop4Duq9FlS4qSTrNISyxkSE+PtH6udk1XAIttiemcxVF9x8W3l96zie32qkkX/PizP/rA4Sdd8XDXm756x41n71fcmiJOhlabAWi1EoPP91HpQKlCcxZFvYuj7iVRoYNXauvDgfVsZACtBkbG8HwNURutZrJ1OwYTby2JZ/rxpkHHgeMmP954j3LKtmVaJIbjgrnJOowKz2BJIbJMZpbMQGJXN+UbPiZzy/U6gOln2WgWHNOZnVKGvO7vFmmQ0Qe6WxwwzrckPQs0nsr6drTSIHmBhL1mLnUaDTHbm1OdrNG0W5EhNcqoN1m+/ijltWq1tjlV9htMzZdIoS0A6NvuVS6jeuXVMao2zWpZHUz9x4ysRpnMbJEszeBr8YNU9Qr7IAeE4ra3sCymgWkdlhTu/prjdp8qwWMXH3/iFQ+1nYV799z/5Q++1/juZadf/tHXbT4WesvRaKLVBsAYw6ZBcGKN2CERebEp4DIUw86Oyou6O6lQQGcXdXZRWKSOHqp082IH+SGFFQrKPCyT45NfIK9AXrPOorbbnHDaTbfVQBQ5UYu1W4x4EhAdaXF4KSqMIY1oHoGaeXSWXDORUWepxICKC5c2jjQnkXFTyFHKtC3FWWM2bcYKckgRWdpjEJlzYcmtGf+DpBsmEqQMtKyNLOks5dzIdUoY5WikmZnxT+LWMLtCCaBrEaY9JiT9z1zTV2NawVkvStoYl0pV6zHd8I93bKunmSuzJk9cnHzWUihRAjGm54j/YfrTERA/L5BRPgNjKFXsO69mA7bOaXbD/f1tAPyZX1/37V+nvnvTa76xbQi63kSzjVodbQ7XQU8Vi+dRpYI5c3nvXOqaw8MSdczhHfN4dV4UlHlxbFMw0u80JthIP8ZHMDGG8RaenUCzjmYDURvNpnp5SjtKNnwzGUxm7iF0HLiuMLdduK5mUEtj3NG2BU/iMlYwCVSl1IgMAGXin7JLjvK+onLHKJk0GmKiaY7pytSNQUAjkUylKpe22AiNYkiE66Xs5WwbpfAGW+XtrUiIReNukl1E2pndGcUmBZMXpFn0ehxxoiPlOiRTkjCdfGUQnuZ8l6QJGainvYZG9oDUZHHvJTHX3PRykPrtJVI7Rr0mszNZmqqKaQed62oiKZalEgPomoO9Xw6LmQajqShjO+HHax6+5Sf/60zD78aJLz94zxUnvwLAmYdRGFDEAYLrolxCuUyeB9+PaZMcB54PP0AYkushKJAfwnXJD+EF5PpwHDgeOcI5wRxKXl0djyaHHAY4anDFCZJftQvmkBwdKrBVc1cwscM5KdkDY8TcpPCkWB45PAJRfE4TiBhxJhzWjBNLjmQCi+9oHKSuAeMrEAgMyrMhDCiNwuL04qNIrJ03AsGhyl2u/8fNBDDf9aXEAMg4YjvlT1Ap0+IhR2ZoWSb/NuXHyDpDdG+DkV79k5crq5YyGXNGk6grx+MhqzDdFLqWnaxeSt3MbcIk11OVLLBgKd710fyvLLYjZsV50G6JNtVGpptY7EH9n/9jHKzgU0cR83oQBgTA81CpUrmDKp0UFKjUSeUuXuwkL6SwwsMKuT75Xstr1NxmnfG2E68QioVB8LbYexIpJwYnRLnDL+N7BZRDwLjHMgm0jzlpMsiuXOV8MmfouknK8nIlyFivshzSvBlMF1t3dOQilwum/NaAkF8yjj6jN76aVkmqWmXY5gqX8oRA6xxzfpP5YAgme9vookwn5xQga9EP8cikM/weme9FU2jSp6PXl8xE0nWVOzaf2WLbY1YQ9AvDgS/hjoMwRBhSbx9191H3XF4oU+c8Xp3Ly72RX6RCNBKMDXq1UdZusoEJrG8kMc5Ryzi1LhJxcuDJm1tT6zzGZDzDDikXp5p3p7aHwRznmtNATWC1jOqOWUjaywlDT+TbRJntLYkcudZWXvQIRJ/oZaQ4ZzOsOT3NlFJm6t9U15kJVL+ZeSct3Aw1S8vF8hJnZIg7MFumIVrmYcXJyFQ4abH1cszfVUrIfDnFh1TrJvHOs0xeRPalsbMCOzBBE9DdTX1zqFCizl7q6OWVHgqK8U7uqNDJvUYtGB/2xkec8WG0mpgYQ72GqIVGXb29ux0fMRqpyDmSq9t6zMPkSFOwfq2zrflRfpti8xQ7GOVoJSBviKZ1QIpNcqnHHN6Tpc+tLiVYutK8KvJnEnlZjPR5zZ+iFj1jutQpHtbkwhiJ5Vcmq04hWG4V6elXStS8j+miUlLJXHndmNW4UzwLAP7mT2iw2A7YgQm60WRPP8OeeApE8D10VKi7iwpFdPVSZy/v6KagWCx1dRc7qVDlXkiFDh4U2m6r4ddGvIkxtz7G2i3Ua2hOiIOTmuLYjSg5GFptRRHGdWqLtr4YhTyvX/yX5zk05Z3NLB7mDbnJk2ZZbhIDTbPIdOVhJMzlR/06Ex2Y1QGTCp+nbFI0kSNMto1aIEc6X7aKyVwZWek0ijdKFvSqf8wVe0t9VllFoopNT9nEHz1Pxt2mS7qZn1DmZ9OsT5neYjthVhD0eDuqlacryWg7OZcr8AkAYwgDdHZSZxd1dlNYos5eqvbxah8PSlTqiYpdPKgNu626Nzzmbhhn7RZaDbQayTbuZBMKwXEQhPCD/HUehc3yTgoZcgRyhmLOjamtzlRpkxuh+dJO7gA1Cpy86lwzTS/EuEyJJx3cUxNxnniTMXiqhJTOS4rN7X/kKaeUwUs5tednScexmQJkEptSaOknffqpViWyTaIORZiIuRoJRnoBKUlip0ex7MJi5jErCPopZ3z4RbVpJl7rj8cXrTYcBs7RaLLRMTy3njkOHAbfR7mMzk4qFKmrj3rm857dwrBC1blRZUnk8XpheJM/OsjGh9FqoFlHfRz1CURtNCbE+RukxTtrzuhUZBjpBrLmkSWkX4AUx7rGSE4v04KxgMTjrB94L8dwclCyVlr8QZ4kpx9GanCN+GiEGWjCqwJF4embBNIOt0zFS+SokJw1U8WV+iGZMr4tm1gvkIkshv0Ltbk5oWOoluodyPIUiQ5dGP0OoPocshxz02ZuD5B2vDJExBvMLgWQajfT/ofZOaSdBCD/ppZGsrWkUhoBdqkOMacesp/nLcHu+8JipjErCHq3OY15z62dZuLF8xbHF5vGGeco+Ah8FAvM8xCGFPioVKizkzp7qFBERx+v9lKpG34BXsCIOzwotqo93A+9UpVFLbcxgeYEmg3wKDmII3ZxyIXE+CjRZEWxLfzUSBYVJVtxMkmZjF9+eoGOqVz60px0iUgLR40xkTfHSRJpo06fwou6cmzb1BYP0s5z0CQn4YGRKbOhxFJy6cZRpAmhw7QCdX5IVh21Uzt0XQIRPa0yim2NUlHp8XZp6JEngsRJFzLlqDDtUE0dgHieRhGFK/GkkS4Tk7qTlC20KRMSpFSIysGUhJJY9eYYfZVry+sym0pXNdncjyIbaM/imB2YFXHQgxOjDz2/dpqJ9+lb0meP2rKwsNgFMCsI2sLCwsIii1nh4li3aeL+x4emmfilSzv2XGBfmGZhYbHzY1YQ9DP9E394dLovja0WvZigT9qfygUUQ3JdBAEqZXT3ULFEXb1U7ebFKvkBwgovVCgok+uTXyA3IObC9cjx4HjEHDg8ik8WZRRvleZMhtDBPDQOUFuiCdC3SkOuYMn4BHFb8ywbW6tFfB4TcXtM2zwtcpHIBfkxb78JTKcn0tfZqAaVJrXqhcQvqd9kmWNzzB0TJAUw17LUETzZsAfGDBetKlD5Ryk/8kFdp3fKKT8yM27Ga6cgUpsyoK3lGcuklApg1ztKF0O6d3OWAacE0958mI64EPWl93Azw6cf3zRWOMzF4Tw/u/p1iV7K22eonhQrlL1lL5pWiyy2JWYFQZf8Px37kjunmdgtHAAcC2C/vbkfJEecA3Bd8Ai1cdZqYnjADQvk+QiLTlhEUCDXgxeS55PjwvXheuT6YA5cjxwfrhuflZGcywGHxOGgBEccecSQnKTB1AIMcxJG1leJjHFnBC2oMSPJ3EhITFxoVGyO1uzNWGukQKbK0LOoEACu0RXLEclc0DNrzMpm8pXRriyybRf3c8TQsmjtytaUKXCyWlK5JmnmZvLqGTN9m1cES/cuI/0myy9yUpGMGnXDILXgKr5gWfEo9a9Coewunapai+2EWUHQu/FoeN3oNBNX9kk2oQ4OszBAEMDzqFxBpUrFErkewiIVyihWuBegUKGwTEGROx68kFwfjktO/NcFGByX5JFGCeTvW1mx0E8xMu7DYF45PNRI0wzEbCyyLCeGnkseLyxDxYQdqFlLZHKiaTgCcDROYE6GQDVz1ghaYJkhmyZDMlLJk5Dzgvc08mKpEAxkkqU0ihkE2ok96AAAIABJREFUyGRYh8E/8QdusLxQPEwvalJjNyszjMSkNRBSP5HRBLnfiMSEIqcWUxOQDF4xozf1OYBQ28azITltSc5zFXd0efS5HwOkMk49Sr1g7Q7fFudPWmw5ZgVB+84WxPT4TvI6gK7OZItfq8WGBjE2wlyP4tM5iiWqdLKwgEoXr/ZQpQd+kbyQ/CJ3A4TFtl8fSw5LajVZu+UYhyVF4AQuzh1VMWT6qWzaTkISr1yJxFto0+++EgZqMs0EIMYDN4c+NItImsq6IyUdiZxlUt1RIKqJ6dyY+Qq9wcT0Wbo14sBq9appGQNnnhwSFxRPLhJt4CpqYSIvUhdajYkvBWDxGw9EGiLx9izBVa6rzVmYUaCoLn71ATFHlkx6M+UZg/G1I6dCmreEISkBLCkQAGMk39sA0suhJDHETQfxThNNB8aJSYqhcXTy2+HM+D2o8wczyiA19dHVk55Sp2Zto6vsKprEtQH5uyNGhLCYTWUxA5gVBP3C0D8AAkoFhCGqVap2UkeXeKt3Dy91cb+AYhcvdPCwwh3e8urjXmOCTbSdkYSUQZxJFja4FcKSiQc8IXHDmmawHnAac1z8UU0pNUtMMSelnYZGafJao0KWrpf07MwhlhCbIq88H0DGZtb4IhsrDbkvIzmtlJj2QoDsdFxNsFN1CuIQhrrMTEw7P1q6R9NuDl1O40W0jISfWu/bbGP1ohhAYESMt4mpZNokJN/JQky1Oe2/Js1dLkziRDa50SZeS2DMMKMp00bTClZPR58R6K2WWjyn1UbDmXzZsayBZdMnacHAnDJgN6rMPHZggn52I/NcRBwM4MRcB74H10XoIwyoWIDnoVikchmlCnkeCqXusADHJT+EH5AfwnHJC+AF5AVgDrk+XJ+SI/jlQqI075g4OTo+3NnN/LYzk3RjTi1JhAAHjqMNqlzOz7wsG2kuIRCLrT3SmEF+G7O6coxICXXmZmKwMymh8Qbb5Ox4WS9LswFLNJkI2GQsdkYQc6QZmBijUvMZrZWmI2IWI8a0lVVZqTjtWlZJ3FzpUnN+dbZ1WuKkkNjyZcotwsgRzdOOxma6a0OTPD6YWzqeGJHWiaKvk2vO9Cbrq75xlrQnQlSROC2YZhOQUbKsKMngmA8G6tGqrs7Vo+INLFAiMSInCO1W79mAHZigl+5Gnpv83jwP5TJ1dFJYQEcXVXt4tZeCIlV6ebknKnZzr0Dh2JA/PuS0mm59DPUaWg1EbbSbyblIbUIzMvwJ8ncsX4zimPEMigLFbB1iHp31OktXgJqe6xmhjRmoBOq+GJyyZGbOjqXFbRjdmhkOcZKD7uPUvQQpJcD0MQxNY0Cbtosmm75wYkpmYa/FFeUdVWHYv7odakIZy7pJPh1kjMoUSSlpNes20RPQ75hl6npYkrjZihwrXtyULq8cORMh85pnkrhKn5acWKwhoaVBrIH0OQ3TVbcmA5tu31psW+zABD0yyro6qKeH/ADFIhWKKJTI81Eskx8mpNFuoD7mEDHXp1a525/b5VDkNibcVt1pNpI3eUctFrXBOZNv7CZKTlCSrmTdF6y2dCM9Spk55tPuVyfNsFouckyvqDxhI7bYRSHCW+qQ5swlrUzSOTfJqF79Qo5cK5Qn74hcSeEMmncVwq+qSkBipMb+St24VP5z3YVKmv3OtJQaJ6iPJAo36Usl0K1wMmcDWedENihRU3ZSYIPosiDFhLlhKnpRKbBJ9qIbcuonfJCW1mDdpDRDC8BojrIrNM3OZF0Zsc2eSGvJQtlduii3oRbbFTswQXPCWI2NjjMA8Ql3cryx+JWBDgKfSkVUKhQEqFSpXCU/RLnaUaxSqYN7IQoVCis8LJFToKBEXkhuQIwit9Vw2i2n3WIUsagdv4+KtVssaicvpuIROGdqGi9JDQZNpwZuag3QMLKMsaVNwKPY4tINutQrRkhOt0XJgk9lNRo7Gr7yycCUTsqZucuADE2ilL2W/UqVoPG0DHowfB2yczRJDLNUYzUVNqH1oYo11kqWLgXlGpZ1sbTMKXtZHk0lStJ61Wwy0++I/k896DijXgXPexaKxGUvaSKBEn++wffa9EiqPoKSJPXQZYtUpQQQeBdwaI5IFtsXOzBBj9bgOQh8+B7CgAohYr9zEJDnKR6QpmqrycZG4DiojTI/oCB0XA9+SH6IICTHg+dT7I92XHhhKX5vofRNMweuT26YROY5HrHkZYaUvLeQR/FWFAaKHXlMO00p+TaGuGDEGRevHFRWIlPvG4wTC0NIeyFhvLHFHG+y9vhTzuBL7iMexLEOkF4RfaVOeEWIqam0roEMmx0M2pIkQZJUMiFQsQ26RZd1VjBGzBEvhWVwtK0u0kOiakqznrLxU9wq0lMy7ZDfMFG1rF18q5Oe8BjrT0DNEgjSc6PTu+hdTby4m8RqdNbnIYpV75LU80J0m15aUpVwlRMBXKxDSnImI2OqTJa5E0seFlxrQM8G7MAE7TkIA4QBGEMUsdoEJurEGFyHBT4KBfg+KlXq6KRqJ/kFKndQoUJeQIUyhWXyi+S4iE3mmH8dF8xNIguS97rKgAgiULKspEfzMh6fbMcTKkzNsXXfguupJSlmWk9pyPhqmPQQ54a2YgZt8EmTVgnAIAeiPl51z6O2gSFZGTO5AWrgSvMc+kWSBVx3aBqWIzTrWInNeMx4sY8m16Oa8lAgacjmoOhImP/mhMOgK42ymWwOGYxL5rNgGd83E78QczYQa1DN4RuXISTRXRsp+RmLY2ZYkl6sYRo1qqLEcl4SZiOVWp5uztnUlAdWKAF7TielxTbFDkzQ8/qoEMJ1k3ERhlQswg9QqVK1izp6eVik6hxe7ePlXu4XeSFo+OPDTqvp1cecxgRrNRBx1Fra+2Fh0Fx6MQ3myhvU8DNcGcY6GCCtJi0qVRboOMIEky8AB6SKUDUK0ZgURrNeVRplb5L0OCtpHTkp1zY0SyNXiq/2eQvLV98oSaoifS6fT5xicq2aoLsjJMUkMcgigWbzOvIq5X0gZflKxmFqN6RQKSq2gZjYcsoc1ZNx2XoHJrEo0PpT0z2SY3Xviigna6XKztcj80imTN62ntnULu4lVeq9ZzzouKFMRe4kOTUSVrMEJKa13pOTajvmBoE/yXcW2xM7MEEPDDOHxYdB07y5NG8B9czjhTJVe3m5m4qd3AupUOVBlXxed6KWW6u7rQbjETku+SG5HhOkSXGchuOk/MjkuHIuT8yBw4i55Mg9EcJAVrYnIOfaki+Uj0KVrHs55H0SvlHl4U0W+pga5CZ36BolsZd1Q1iMSd1klsMyJ+5NWdBC4jgvkw5WzXtr2LzamSESJO5LgfVWkH5AhKYI9UKyK1+6E1+qMd0pIFUAQzrETT4ItSQnn0XcaTDangVpLgOpsZJIO02XZB0HiSEPGKsLJKRNnlQmwlpbAEh5zHV/lFmLkUbe4ZH8QtTLtVZqa+BxlkoX+ubld4LFdsQOTNAOQ+xrrjfYX9eyvz4JBheA48DzyPfgOvB9BAHCsOr7KJWpVIEfUKlChTKFJXJ9BEUKCuSLwzrcgBwPjivioKW72fB7UOxfZjxinAOccWLEKfY7J/vBYwKKEoKThyIhzhj7nYnx2FstNyhyJueh4g4Ag44VgZDa9iA1QYz04qQ0gVPz+6wlSMZ9PSbEKFbYvASzUBLGOFM3mfZXpY0tO1FUjisjlyQlgySmLKht5lKNNE++IIDBcSE5UK46In5nDUAgJlwi+jwpOz0wt+FTotWMqBjpWkkKgd7JWhC3UreqacLgFfpEKiGZUn8omlrS3S/KH5VIyHXdrHeZVri4w5gThvatsbMBOzBBe676kZZL1NmB3l4qlql3Hu+ezzvn8fg1V6XeKJwYdlpNt1FzWw1wLuLqRNSzNIsiwoRZh04r0KzXhFk0R4E+TU5C06CNYSOBWBODsM3VngDdPkwEMCjVRGLxTZFCa4jRBK2EFEcAWroc4iRp35nzAk1DZO5nCjGcJSmTU9LWpG1SmSm1nSdbn9IUWeeAqbQYM3tSzgkYKbKDUUgyRYg/8ISPtS5NKSWzEYa5SywtqLjIPpSUJKYiVG53yH529GmE8l9pZek6O267mznC0GImsAMTNI8X+wkE1BusuYn6B5jjsuBhJwwQhuR7KBRRLFGpWvF9FMoUFklGbnhhskjo+eR4Yvegq5YHkxMpmBaI7IhvEd9PuanVoQvqACYt3kFBODSFcZ2hsHjkx5dy/Ewy7Y6LMq4YgzbpjkWXxakhKrwCuoUNvSozCMGgZV0kMnNpXKH7HrTC4/4TlaTiFhhy2mvWmJ0epDuHabIZ048M6UGrV29sNlnq25wvtNrN7FlndXrJg29OxUJ6RAykq6J88dIuIl1gU6fFlyHchZsXyGKbY1YQNO9v++v3m27inggvBoB2BM7he/A9uA4B4ISoxZpNDI+COAMTEXgF+B5KJSqV4ftUKqNQpmKZvICKFYpPvHN9BA5YQH6BHI+8kLyQnDi6jrecOBSaR/F/iZ83is+4I8ajJDAuCe2INBczaYNGbmiGPpoTb6lxyA2EL1aztbMkZJ40ml0wTAhWutGlRZ9yXOjzem1ZSbnFIRYwY0IRa48yRiURQBaeGP7CMHTUQULJeUZxcIIsWWs4GIi5WpOZseoVpyPVl9lfiHJHk1F2UkHqK930l8lSxebOE8j4qK0yyoQA11wpspyUhkuuyTgkL1t1po1M17aG60ufY6hy4m7JHu6tOk2DF05hDlhsP8wKgnYaexf4dAnarSfv/+YcDkOpSIUQHVUql1GuUFBARzevdlO5mwcFlLp4ocrDKrkhBWXuNyecqOU2606r7rSajEcsamnnyBDjoAkGAKnTT5Uj1TRZkhU5ngRp6OMjvZKm0qv5qT6alYdXlcBgejlSNpr8wMTU2HHU10x9lWqI5AR5Uzt9STenxRKoCvbQZtNq4s9S5SdtTbrLWCc03DuiUtJ7WHqPRXZDl0CQq3ZbRrCl/N2kXcvpTCqeT1/RJWZWZHac0k+x0LHngyFRXaJ1QqsADOSKpWPRKFEGm6QWs0VxFt28lc8lbr/6aPSHkhmAQdjIJMnKQABcPwTse4tmHrOCoP2u3gbGppu4uy++qJYpihgR6g3UJhgIjsMcB2HglIqoVCkOuat0UqWLBwWUO3mpK4xDO8Iqhb3cDchv1rxGzW1OsChyWo347Spq1i3PCopPs4SD5GRLFcgBlgSBqAMqEy5w4DhJHEgS74G4LHKYsQlbG2kJp5hUKFeNNN9FbI1KGzuGiocjhvgoJWXgaQwhnAumHRdfCjtOt8x1ZFciFZGm0pBQEKQVk6ELGf+mvtITqFAUIb8pvM4wshy1jmiwccbPnDHf07pQzBCUcZqSYXpQD0HrPfXYkZFWu60tLmp5tc2VgLbPUfa2vpRIWrt0h9kkTxaAx9zidBtnsQ2RR9D1a48tvvvHANxDvrb2N6vSO4oePP8lL/3cQwDCU2+pX30sACB69MbPX/bDe+6978G16zcONgpzFi/d54Dj/uHcVSe8vGfzp2K129EIb01X4haPNfujT7PAQ3eFBT4Yg+ehHFLgo1ymchnlKgUBKp1U6eaVLvILVO7mxU7uF6jQyYNi5DVqznjbadWdVsOJ2uoUDuZIswfpdXkt3M1xNEcBi+M0iAFwEm4kDk6MR2LiL9gcDot3DjJA28kCCDvLdF0TY0wPCZAeEjW/VTYpOTCMYxLfau6TNPmKeLQE8pA8SUa6uWzOkZXFGfeBZnnr30KIRsS0sF3Rtyq4jNIEmgudQI3W662S1JV0QcLyeb5YZmTJI10y/mFGDwJSg24BY2fTJndYavFAV1n67ESbhZn9RdpkaLKWpE3trGZyZ4XlZjGFBe15Xvvu1Vc9tOp841jY6DdXXP0Q8zzW1gOcWr+78tNfu3P+i166z8v3OaDTHdvwyH2/vvYzd373P4675u4b3rV06gGHZ8cm7qsPTFPil46xblQAHLgfxZtTXBeeBz+gYpmCENVu6pzDq3N4UKRyT1To4qHfdFsNrz6WWMqbmvGpGsJGFgGhhsMyIeVk+DOW4ZuYQB3t1y4GhuOovQw6EnPYMXzHk63pqPXDrGlqGmNprmKS/TPmpQwfTpjdnEqrqGS1lU5vuKxUeWYoUU7Sn2AYv4k1bpz7bBAKgcCSEF3dZDVlhtCYikRIODqM3tPjx0WRSeQfS3WgMfHgpjULcK4angpANmSDGYgNIzY5i1yfhlG+VlqiWijvt5TeOiR2PxriiU3zSPSq8P4rG9ssUC17MIZyFZ2vzG+FxXbE5AS98Ohjum6+6Vurf/+pi1+tnnvz9tXfWee/4W2H33vTbRpDh0f/+9qh+Us7VHHNp64//XXv/M8f/ePn//uk/zhyaiualbcgpsepJIn//ChrRcxh8Fz0dGDJQlq2Bw+LvHdh1Pv/2XvzQNuK4l74V91r2OM5587MQxQFFcWBp/GhoIIiIKIimudAgIsQFRQTPzVIEKeoCWjURxwugkqUD0cSHGIUh6jBROMUBQWEKAjCZbrjOWfvver90VN1r7X3OZfB3CunxXvW7tVdXd1796+rq6ur9hz114zyNpfDTdnsZrVpLpzjKTUqO2hRUD4obU692AvFBkmVZnN1RVzMY6LUtiF6cEJuunEWN0XEnQAI5xkhx0WRrUu7LKvXzsei80jXVjoZU5sHs1umKG+MJYDUVERb/qaSnBaNU00K9gQjJYDgsUFwju9qBIxNdPuixVStLRmjgHuSeUvbXNCR/kHr+gFpjOewPajNfU9jiT0anzF9TEZSsi+POn0fbRNV+sp3pD7O0TIBtHjJF8d2kSbsZPY5Ye3Bl5/2sXVf/esDD3NG6xs+t+5Tt/eOXvvC3pWXybK0fLc949rFHsed/KxX/MP7119zzV14+or7mG0A2G0Nz86R8VpXFrx1K66/TmX/Tdf+XBclipIzjbLdbXWsG9Kyw60u68JEKeS8xTpnEwory1hl0Dmr0UAP58GVGg3VYE5VQyviGUSMkFW4rbBvEUu7fvOcSqNB78xSFo5FSzeyPscWZziZNMYe++i12Mo0bcWiRJq2FB0JQtB6A7UdANnmpe4FAAkKAOzNctgBMSgVPCVZjIlCt4TOsdH21G3yPGQmWGoZcF9Lw+tkmRGye2TiFjssTb8BAhEH11VNmxnbVtq4XX2tGBuAX7ARR/eWvWCOF4OkaYglX/JQE9+jhZn976I+COLZvFYJ9aX0P5MmqZp2ffHaw1/7kk+uu+y8w55v1L63fOLDl29ecfza5/QuWbsQ5bu++ZmvrQd2fdSj7g90BrB5CzHQyqAVawWloBS0RlGi2+NOl/MSnT63jRVdxlkJrRkERRgNaDQwkyNEwyACqQLUhcXSegxvcapHQoVgiIjgs7aMEgA1VudhUUB4YDaI6SAunioOEplYCGa2yKImFXHVBCeAFKSNgz3/RoqpkFgVi2dylars/8mtESkfkRGc77khbZTgQSJNUD4eDrNUSEJuUYwWMMln6oYictMKhPBUQSNMESVJ2H1oGH2h8iZIy3bX0YhQo2CfNOPWOUnc0mfLvzP2lufM5I+7mcO3Fo+o/z4YyLIlgN4u0sSzgJljTz729C9cuu7S259/wgoA11+07orhHqevfXq5/pLGCj//6Kvf+2+z1ezdt/7q+1//zrUbeo887YIzD7o/+AYwO0CuMRhgAJqdRz6LzjwXBTEzmMDIBw52ibOCTLArXXDZ4bzDeZt1xlmLdVHp+TlVDa0D6NGQuKLKuIG2bvupCsIy+VCw6TyS0hfFOTKwUHiIPf6QnM+mucRiGpCOdQJ4siggWpdVEwOC6EHcOUaCOhG3/m0APrl5TiIlRnTi7teAUtofhJuWDZwEHtJXyVITtdKw1bC7iogKouE1rDjUC12rI5eES25gzxAnz58cioaNgeAwdIoASI8fZCIOp19HFE/L1XLNEoAQ3CsQEi0GXhp7sZR+32nyYW3ryLUv2vni89d97L9POGNP/tEFF/yAH3722icoXN5c/tdfX/fBj262lHc7/M0fv+jMQ1bdxxz7tGY5A2iVyDK0WpznqCqMRpiboy1bsH49KYWy5E4XvZ7KS3T73OlzXnIrXE7homP+0ypH3ql0NdDDeRtpxYP1aIjRgKqKRiMazqMagSsb87tynn0lkpoZEus73c3e+ISdRMVgRxZvpCWdVJbimGDs8safyNUO08LqIlw1CbJudkeQJNr2TvJCBVNERezXJ3kkNQudcQLr49eSmhaCIw4Dw7V/wxcUD6NczPzCkBwMjjvIrWVB2KSMKSNTDc3T8g1rBiAjFia1vGGlBG7YDVpd2Tw+UaeH/mKvJiyl+y8tYE2THXzy8fu8/x0XfOSqM/7qlg9feK16/LtPnPC1HX7RJr6IBxt/9+ufffPiN7/uzU874Ft/+4XLznhM575l2qTNW2nrLEYVlLLwl2nkGab7vHwZL1/B7S6WraqmV1X9FVXe5s40l1OVLrjoVnk1p+e3WkXzYE5tGliReTSEN7nzbki9RjBVakpoiGdLJW6qWfCMbzj7o6JQxlBjp6oeM88bZEOPznVEq4FRtDyIiR2E2ag7sfDomIx2Bu5VyqRDkOYW4xSkYyD0R0JnSiSo191D4DYm25g4ATXZrKgfl5RkbcfHYKUdpTS/ZnMiNiWiYvMOIOpXirJCD8O+ragWYay23q8BAdZ1Xtwvk3YpbWNa0NzxkWvXHvjO1114wdcfc/Mnflscds5L9lioBuX9nR70hBecfdkj6HGPOPu1J5x7+I/Puj8CuBc5yoKLHFqj3Ua7xZ0O8oL7U9xbxlMruGxzf2XVWVa1Z6qs5LJX5XOb1XCg79qs501MwiENnT9oKXARQeXICq97ttbK5uKJCaOiNABn/qFZKRtU0O70FSBifKjIPSmTEnYjyvu58xcSvTYFsGKxffAfawIxJKpzvHrEkMVoBq4IQxKw4rRWjCceZNiwTS46t60tngPLFQV1qBdwYwFZnHFZw2wfGgHOiMy1YZmP5N0kqqEcLgeEvncNjSKuMmYrEn0SMnpiBpluQyjubK2k2+KEgfPDK8NvIem1oBngXywTk/n3Ga2u3mN5+mop/f7TwvboDzp+7SFvPOWjL33lpru6x5z0gsWf+OUPP/Tgnc/+yU++8e07z9pv2b1hsjlVFUYVzc6BGdlGaE1FzllGnTbabW61kOVotbjV4XYXOuOihazoKI0s5yyHzoyJHaxbUYL0lKSUsxx1V0xAUAxzHEUA+RstBKoABnlrWhUUtuQOt8gc9/kDQzORRkJaJeuCwi0KsGhB5haMR69gsie8fxgo8s9O9eElSwuYMWZAKFh8eXEnmcjP/si9csBbZw/IjOjQEu4tQAk0+sx4nQnRvAC4sGFWd1pfkaoKnp+qgve8ytEiRk4MJzCqyi2AHsmiv6kVReMoeSZJZoqFSvQxkPK4GIGo/3KTKslGQmZKOrGDK99KunLK3smw9GIZDB0MEjRoOXAoltL/dFrEhaE1f7L2qNe86HM3YuWJa5/d3wbaG6+77lYAeX7/3Eqa7jMArZHn6E9xr8edHme5dYTU6nJWoOxy2eWiUwV3zxoqd17r/CmdB9Zgd2HMJCrvzZmYUY3MgwW74CCJ7UcAFZs7hEjiEEa4yeQgT9zjs5Mw9TQPGwywYX/qpEm5wxXaCRVRIKupiMwSLIHwKtgthHWDCEwj9iXN/5zrJVXZjjRKnmDfupeWQ1/hJHN2W+yIiF9U5GkqwcJNsLgIBSzKOPfHqHXHDZ1UvteVts2uk2tbD1cvqFn8AYC4tS+PlO0uRWgwhAdni7N+bY+CABjbyvCxNs6x0ib5zQgHXl6cdl2AO+cIzENnWa+h/0vp950Wg53955375c5L19Mujzt0jBPv26/8zL+2n3zUo1Z5cqPbf/iBta//4gjFk488dFtQffFpwyaqKiiFinHreup1uN2GVpGcwSzUyOHnl/6rFIigNWsdzPW0hs6giHWGLIfSyDLWGXQGImjzrEHKPWcggjLPOSsFZaLKKhcEQNnIhzb4oZXZrTGfiwkgXJ5ScHAKJ+BHNg4W60OEWeIKVSXuIkcyrMWTyMLWA1O4ZR7A1xbxkAHXvENSGRDWtRGkscpxADS4nRNAnaBlpLiYsCsP7qQiixpmCraJvtlap1Nqpgw1FAnI77cxjltZRmrm4b8gX9JstsLo+JuigIHXaP8h31rjRUJFDWch0XbEozw42qNQosQLXMe7BMeOLkrM3A/b3qW0jWlRwm2590HH7D2pwJ3f+OvnvOFPlv/Rwx669+67LNMbbrnhZz/48W+3Qu18xPs/+Gf305Wk366HJpQFFEEp3LWR5gdgINPottDvcqvE9DQvW87Ty7nV5v5y7s5UeQvdmao1XbWmqqzkoj3Kt27Wc5vVaKjmZ9VgTg3nUVXWVMNY17Hbbhv1sBR4Kzlh5P7aJfKXFBgVUAEDg0xVqA6k1b3M5fWIkUydWrDanDheV3hN8RmmZ8mDyrjdscVrij5KOtGzlQ6T3XO0d7aWbTUewlEkohvq/rAxEeoV+a4Fu3TfBS8U+4UnYrgeMIEEUPrWo/F1htWuFQ6D7KhZC5bo9jzYONiyI2NzvQMWN06mUSM+y/UP4TuS+wDr7tWXD+EWjTl9rCQh+HiMBCmnuHGIrrbaxT3L9FJEle0h3Tfah12efda71n/6X77xvat+/M0f3Dmreyt23/+ZJx3x4lee/icHLGvaKt4Xac0ytFvc66LX4933qtbsWU2vrvLSRuzOWqwzztpsNRtgGg1UZWNQec/FPFLDsj1o9xhUjYgtMocHqWFkD601/Z7TeTqpxYOtPffzEa+MhYgtyaPgozS5881uaQAiwIcDFg9otXfuMcBmwJpUOR7hXnQmGtQ+Eh59sAKPxoKI0xclHvqCwxIPa8YzoOcTMUQb2Ax81lyiAvpNAAAgAElEQVSoik6ODXwqlCJuGyFxT6hKRJbHd8TaEtSKhT1E46liULx4/tguJ56eIo7WsybBns2AciyhSxlAysRyZfGvzA0VIres1IYsWtztvkYtOUvaPhJx08/r95x++cM7vv+1mxdZeP8nrtr/iasBvPwQ7vd4eoqLAr0+WzPnAibeYF6ANGc5VMY6D8oH86A0SLtDQm1v65GGEroFh03evajVSzgxw0lmTqYWdzGURXUHJ95OQzwDQvI2SegqRAHpWLLuuwOpyA5vwuFbgdMwEDd78DBcS8mXwluPiRaOOUaSiBYiKKmDiHNlF0mnss0IfMULqVsQnSIKY5NyFdlmWFHR3GCmGmt+fChpwr8a12M/Pl4FHwVRSDtpl6jkariT7ENbkgGErzjZGknK6cYlHujGZazhYNKlsqN3ftCSod3/fNouFsqbbr3zh7fcsMjCUzfr/bEawIplXFWYnaXBAMwYDmjrFuuvcjjEaARmGg7BDK2hFfKCixa05rKFvOCsgNbQxqIjZyLYwFca5pK3ohC5SoYVdG6fnRjrj3PcR0PBSI7KU1Di5q3c1gviAGQZiF24+WD8lTaUZJkTURPyac2m2VlsmXvAZOLYekMIuD2FAbDKnI5aLaq0iHAGGFUIJd6kZjVNmj/hZib8KiSBU8qKkD7q5MmnoGdXyJBvVxIX6KsO4UFBocKIBDfcMP0aEzJG2EKQqBVRJgiRPHLCBS+vcvRWdsv9zgKFsG0R6i4Wq4sZyCoMiVvdA2WuxiK13/mZlJeTlqWl9HtL2wVAb9Vz19956yILP1btbB42bkJRYNMWVCNUt6lRhZGzD1UOAbXiLENmI3xTnkNrLgrkOfKClUJeIC+Q5awUshxZzjqDUtC5OeuDUqwzKE2kYGygzWGdctK3meP+ZE8pkHbhwOVDxmQqGkPqjIngYmiZcOAjc4ncwCJZ6zHjVLoC2NqHGMySLu8ioJRWJYg/xjI5w81m/zHRfTvKgqwoH28BPGZ4OI5exQ9JivTsi0z1otsOKOOUI4ZaIs83KiKo8ZmSUmMI1lpvptDEZKqjqO87/GOTXJ2Uj4i5pvsz2OVJUSvrP3DIqj/7JgBQuWzXhx2y9m/PP/upOzVw15yufusj9jvrllP+Zf0Hmqz3Jr+9D9LPL33TpT/f64j/70//1461L9guAFqpbZhdvvCmLZTNIc85yzA9xVNTnOWoRti0EXfeTRs30bCCIuoQd9ooS0xP89QMl23uz3B3hrvTVVYaIzwTh9Da4emcyQSQJabRkGBCDrK3tzMqZAaEbEhS28jhJrQHU2euVzGs92MhzBiCpNwZvVdPWK1hZTSCOp7k8a3i9EobYtFMnmh5zXT07C/RUNBHevHeNiuMCOIAekFXM+6bFIBv9/heuIt2+03beYgmEKCGveEiAUoIgN6IzvNDwaSErMDoNc0ULiImV+1i7cQkZsYUjtRHiWmf6Gkog1QoBtCgmIpPo1M1uIfiRFUdNk5NnZJITijbY4LG7nbEX77qoOG3PvLuz73pecXDf33JsfeNidaK/732da/b+IS97hNiaRoOR9nPLz3nnM8cvPKVSwD9e0v9LmsF47BfKdx1F41GYEZVocixZhVrjXbbRVcpudPnzhTnBVr9qtV1gWI7Vd7mrG1PFPVoXg/maTBSW+fVaEAuViyMkOsPEBnOoiM5axIx7uTGVm5RPVbWcgIueyAOxhWGvLuQ4dS64bqwlL+8I1BRV6rIHZS7WWtROLTrjwIlxEdWySnK277bG5KhXwYPCd6VaMDQIDMbMxkrgleAs8f2nfZegoKALpwdVxV46GBWiuwcg5T8uhgCk0JmtAFg14DpHQsOLT238PgHoZKRSeJ+/dCHKBQIGxVEAMqS4fqORYKy5EoOReirqOK5Emwzozujdn1IyicArHnSyX/x+r1OXv2fM2u/euWVv8BR3U++/vS3XPrv/71let/DXvHu81/35FW48/OnHvLqT//q5o08tdujnvmG8z+49lEiftbG777u4EPf9avHvONr//y6x7qwh7d/Z90733nLKYee9eCL993/nPXPfe0J6y/58I/yw867+KTr3nDye38wfMSJF/7j3x1+x1sfsd9ZNx3+8hdt/uInfjL3kBe++5L/+4K99NarPvG6097y6X//9daZhxxy4rvef/Zhu9LVb33Efmfd/Mw/O27jlz/5m8cfgUs+A+Cbp62i0/b883+74cxbGji8+q2P2O+sm575ypdu/cLHf7Bp7+POv2zdsbth7prPnPXqcy757jW38eoDz7rsW39+wOxV9S7fb2kHBuj1d1FVodtGWfDyGaxcxStXV+0eL9+pmt6pmlo9KrrcXTlqbVxfbLhdzc/S5g3YugnDAea2YuMs7pxHVWE4RDVC5aBdioIUJEcE2FUBVYPRGHx58iVB5IFMeTtng4MKxlBMqZAvm/AbTxJWFE7jCQ+mMSinG1gP7jUVZ23jm2y9KS3mhduGXX9S11EgX5wS+hzRj6vYZrxhgW8XcCeM5Nc2oYD355eWZrCOCKsLwkYtXre8RtiFnQzJLIL+3odbbByRMCD29EN2R7QjPX+6wl4yTr4acj11BGtluD6ek9Q1UVWIhZn98HmR3vGpswwoGwjM33njDdfe9pUv/RSgXXdd84O3HPri9w6fd+5F71zzzbe/7PXHvGKfX136XL3ywD998zMfvlt387ffsfbsU1/52Gf+66usPD7/8/cc86Z3XfvIc77ypYDOtXT7F7/0uzNfeODVf/Pplx307YNffdpxG8658L2v/9CfHX4sANz1vavXXPiB8y579YkfPPGVT3jK5x/xnme/5H03P+bUd7xv7yve+oZzjrl7l19c8TIAwB1f+eJvz3zdO3Zevfeeu15/xLlXPuykj5xz+E4PfQj0cByHd33re+r/vv3P+JS3X/AX7z/12Lfptx39gr+5bs9jznr/MTvd8lNUGP3gLcfUuzyzuOHf9rQDA3SRYVRhfoC5edq4Gb/+LWmltEJRcKtAu408534f/en+1LK9ihb3ZrgzXeUl2surVp+LXqVzLrqc60E2t4VGQ+3soKka0WCeRgMMh9ZrkpHxfNzCVDmLho9eLookOoqBA5BT1+iCaSSmZXysI+2I5TmgpCPzPRvJAiDXGADJhKfEAg5BSK9vAmSybATR2BducJwU1UKQx+uv5XKSUBDbe2thI6tEC8qY/cRkdFPRVoNhLBDNRVAPoo2krIYqhmDXAaehEI42XLGwZNYHyo+UXLfEJiYsVa6RuGKNXvjdBHJEYBvdsiH99F1P2vtdAGjZY898+9rNX3rSLyumz772+Z8FqhH4iit+hOc+rtzynxe8/t1X3Xzn5q1bMPrpT68CLPx9/M9fM9zz5Vd8+a+eMEk1Uhz1xnVvfP6n//Nvvvq5vU4899y/nHrnpy784a+uv968bR9x2huf/Qx6+PHvufANX7/iymtv/OI1VfmiM9/3imOyo+++7LOv+eaXvrn1ZY8FAH34m/7hnD/tAQB/GMCqA5517LErAWway2F55Gv/5vjn33Ln+9/+ze9ef3117S//6epRfuzbP3nWC1qm9avf+oWGLh8yoTf3Ku3AAL33HlWrhdHIGGwgy9BuIy+42+feFHemOC/RnqrafS67rAsuvA9oYxw9mKNqpAbzautAWW+iQ2Jm5ezvuIwcRDiodXZ1blfp8Qtwf2tCpZBZUpmoUaLEmEkV+RwaAysJ6FvWxcIwWSIbLyOnxeK38GoTisswoiGSA+L+pp2KORRqFqRQ0phiPoNYHReIvjUkQ5KIqLV8inJSpUUYGQ49FMsW1+sEjYQoj6AUqulFGrOalNHjCzerYgAAudLN8u2DXvLBvz12j2W77nvAo/eaVlf/K4DyqHd/7+1P0Ybg9J644QOnnX7xTw558xc//cL5Dx75jPf9enbW1S7WrG7ddMM/X/yvtz7lWasb6QMA+itWFFBFoYFVq1YBQ62B0cic3mA0NzcE8rm5OYz/DQAAZnbbzV1Yj8pN4LC3bFkGZFkG8GgkYy3LlHb5/ks7MEBf+WNVObON5X2sXMbtFohoNMLWOWzeQnMDbJ0DGK0SZY7pPi+bQavF0zPcn+HedDcvuTPF7T6XPdY5F23O25y3WWnOStYtzoZzwTd0NVLDAVUV8QjGbb91teEmkD/iS3SHbvMdZC03D8gqX2G7IeOvwJi+sZ+oVhaz/m7kdQOmCAE8LMa/3bru090htq2nulvHYeiOQ0l2rXkjvLFNwJVMkCbZdvimjQgu1bi+LgVqvjBEN6MjuRpWcfxMJDwHIUigVM+RwyhLcBhqb4iSLkKJ6jcZn8SCojE/0uSI89O4rvzVyZi/nkp6QOkW1GSV9WW6fax8TANrMw97+jFH7+U+7fPMIx9y9o+v+OiH/n2vZ/Z++/1//NC1z/jmxQfOzgKctXvVLz761euAPNTuHPW+T7f/6unveeFRu37t629+wlgdx8Q0/4W3n3ru7Tv/y4euRueopz5+n0cc+ZCzf3zZW1/x3tv2+vp7r0T3kCMObmNTyvfMDHDtVy/4h5WPf8JzdxrPYZIefMSz9jv7R59/wwvOvuuYnW/9CT3zPWubuvySe9SRxaQdGKB3Wo5Wyb0O2m0sX8H9KW53OcvR7nG7X7X7nJnzwD6X/SoruOhxzrNqNNBzW/VgjoYD44zfhxwEA0Ngk51jLFQBRohjpUCq0pmZNs5ZBrEp7EL5mXxhM2sUoMqrVtmpEaKDKY5vqdTQzD46KGjEQpnGicUNJZMC9b11I4Um2RFwcD3ubZArORS2FRHVYdQwqOFjfYiSYhFB8ze+DxLJthwVrtdt/JgW88vxRCFXfkFBJVaj37Swir9cKw8kVh/J8uHLRYTjFkutd2ngPU36sWd9/h+2vPqtl55x3EewbK9HPfnl+5XY9+S3n/6FtR9581H/5ynP3XcfXHWDrDF1yLmXf/C6x5/4lmcdt9t3LnvZQ+4BAK047KlT/3Tu+T/ix7/sI+978Wqtz/r8xzee9uZPnfmqrTMPPfrsD75/7W7A1Umlg0567VP+5R1ffP2LL3va39/21Ukcxh189JmX/f+zrz7nk3/36q9g9f8661nQBzR0+f5L28VNwi9fcdW6f7hykYWPO/qA4579aABnPaeqKvuz1hqtFrc7yHPu9tCb5u40FyV3Zqp2n1s91gXnbS7arHIT5op1NbRmyNbo2IrAwpOcO5USOU5+NIlrwFYfTAJiRWR615YAcWM6pRk8upnnuGmhfAgHUOLokqXFBbv9vtAeCPk4GM9FzTulelPEAmah70kNpslXDzJusNIT1OLLFDYH8UjXWBJ1YfrE4pqlWU8BNFy8jJsIGxRfzKO2K+k/1uPP1MfEpXgdAvxuxA6XO2Nm2QtZZ/K8TI42fJuyYvxrTXRLDdRExfaUetBj2k1F/0fT/W4uvd2lHViC3rQZgyG0BhG4woaNVJbIM+p0uHc39aa4bHH3Lmr3ud1jnaNoc9G2kbzzknWRKc3WGbRx96zt9T8rGSvjeYZBUKMRnPtQf7mXzTaRObjTdH7g7IGV0hx7QwLkJjrqTl2ZZkynU8c6DvnI4zULlHGg4wzXyMGyUGEk15pht9HEYEeCavDkHVIzItgJMBxoOUY9IW+zJqPqsQBXeMMG9t0QfoucCXEo2bzGWE9Bzrmdxzs3amStn8VwRV+KX6uqsDI37x5kxXRJDdK3V27JAuy+FPJlREVfyy1RDUtX0mKAdW6AY0o4NfcTJSdR123JLNdNfV5Kv++0AwP0LbfbH6FSUIQiR6a5yNHuYHoZT62oyhZ6y6vudNWa4iznvMtFm1Vm3CdB64qqkRoNaTSkakjMNBxZq2fAXS2pwInqoWmykpRMrVbEVogPCQMupoeHUS1xMEUxKU8HTjaXKAshI6c3xALYOFiPTGtJEGQ5X/0Uj2PCulQhkrudsXPDFts0aptGaGLsuZZ9a0jpoHNwRCaoJsJjIvLX1Q4c4VfIbOKqLoa7VsgaTdSHIuqLoCa/8CZtw7jdQ0O/7CtqfBXljPn9Aum4tbp6ejv0NrrvG/+L3/g/zcTvNe3AAN0uoBS6bc4ztNvodLjbQ1Fwt8/dPreMvyTCaETDOXAFY+GqMmJmRoVCkSaAFY+ockKLnxwML9/5WyEp8ACxdYSbIQFJoysbxvM9OKAqJVPTSm7gWAkC8tDrJpi5seKBI7aTs11oNFnzHYx3vExBaRyepSwMr6ipY4NA+bpAWmMjWlRcnn1OnGWabEpKNlVMmmhqV/AbZPBoPfPSKUUt1UIOioF0gn/YAYjo5mKZqzHDKet1jB6XautE8FbSTGECLtdpMgBk+UQOFpFu//rZLzj1o9fNF3/0f86/9G2Hrpjw6jefO/2Esz5/9QYqVz/xjA+se+Xj7tnh4R9k2oEBesUyXr2KV+/ErS7PrKqmVladGc5LLrpctKusxUpDF6xyKM3ElfF6Ae/hfsAYODMGIiaj6chjK7rojMnDTrwhRrrhFFoMfxtOlBJCVrPhmi2Tooy0b5WtSKMxKQhHZONatbtqqQK6scDCOY6CvGsXmyqlAp38W5d2k8zGRpsIpvlR65Renm46SIzaGtfcwr2Iw8zEFSnp37iejmurQYgeT3DsUtDcYqunl62MX7/xjbjttuaatgnC296GFSsAgP/9bWsve9Lnf/HV/a5/+xOPeNuR15z3RKczqb/affmT/+pL73ry7sWtl73kMcef99SfnfWwSe08oNIODNBKYTDApg0YzBNXajBLc5uroo3OTNWZQYuqrORMI+P5bMsWNRyqwawazFM1UoM5Gs7TcICqgg3jbW4SuiuF9XsoQO3nX0dPf6UQ0Q5WvmrMDJqH6FKiy2kskBSrZbqcSKKXhSWIx3QmBOpGcswYF0h1IF45I9lzrAnhXTJcy6S0bsN2IZZ5fbWIB7GtQZrCaVqkkor59Eksul7I9wvfGMM1wYzdl6VqlIgx9hQajiaoxmqQ+mWOlN8pUq8EDrnWtJWgsxqKv+1tDT1K0itfaQH6R5ddtvxPrti/BPY94SW7//Fn/+O8Jz7Blqm/+tuDj90dALD6yQc99KYf/xZYAmiXtguAvm529td79MbEtIsT4Rdzc+bxd+vp7g00dTeXBdptbrfR7qi8QLvDRQt5wSpDnrMu2nkxpTR0jrxklXHeQt7ivGRVcNbirEBWMmnOClY0MpdW1HBA1VAZp6XG3oMZVeU8cLJVUhufc5V31CnBHXFwKS8yxbFLqGGep1t4OXXt3T/jN8/N4ZpCIwpJZ6e0QF7JD+AjeoTTK8ODp+wtkZvwQvhajbE4OmqkCFBCVBE7CF4Lz2QOyezFRQ5A4qMLGkvH+m/D34G0I8lmoFwASVMXwaVUuMNteGSh1AhKDBdBjKTcSsGxJ4mL5UHvzNH3zE03HqS1X2RDIi1MXMNR054UgxFFT681ERq1GrsxNwRDQwwARUv1p+ICb34z1qxBVeGMMxAudgAAXv967L03mLF1q8256abf7rqfuZu38+673/rdm4KWZfyr0S8v/Pgvj/rLx4/h8AGZtguALleqfOUdiyzcWmVDaA2HGChs2YK5OWzabGYxEaAVigJZZqeHcbBBhDxHq8V5gW7XevfPCnT63Opx2eUs57xtPNuVSrPxoJQVTKjUaKiGA7BBbWOZx+bBxKyy/uoqBoy/UOtLjZgxGpLQZZObuP4KdrOIBPI4Ir0kuPL2tIs5yFMODxwKG3RjINxHiEEfMKbZtr5BfEDIYrGUDVgjiQgmQplw67JJ6rQNJ6r1qnKGKAaWbQ/dB9uAj1NITixURN5RdcPQsYvdB7ciwkF/k+IgMizxOd5BhwnBYwaH/YIUNxsguunuCbNbGNJwwDF4O8sWEs4RCZK274hfL0J49brkTqEpqkbGGH+RKhVVtIBYFXz++eh0MBql6AzgIx9BrwdmfOhDkpplrYrFlHGv7vrGa56/bv8Pfeuo+yeE6Q6atguAnmrdvWbqhkUWnunsYR6m+2iVnGcgQquF6WnuT3PZ5qkZ7i3n3vKqaHNnpmpNVUWXdcF5MdLz7mbgaGAxdzSk4YBGQ3BFVUVVha0VmLFBAA0FwUPeXkHwXadYGZE2YzL+nglEbJxGg9jEiyUPZ2EDzh4ZSQkJl6OZxpyieZOwFKGPk50cIjijDT87vFBsi7FsiMStSJIla+gMwAcv92Z/xBWD4CIHprhsj9RMaEEEsZcETIjFhuHlSbMwVWaFM4euQRsgB8QZ3MnWRYTvWuA+q7hw7vrgtxfytnoQaqUeyYOp884RVNrxKzs4UmcWXZmJvvBI7PWMRoJ2KBxZo3s6qQGi7G5i+IyIKwBFpdPrF7+7JR4wkW67FbfdCiBcztxtt11uvPEmYC/g5htvXL3rrqFS46vZn7zz2adc/6ovfO6ZK2vUH9BpuwDoNb1tWDR3mrKFD30+mCnT0Bp5zkVJRUFKIy84zzkrtNJQ4OFW8BCKeD6DyvpEUJopY1UAJIOeWCNoMyUDigrrjPAczvXZ/cPwhk52MkdzjuIHMY256Sc/MXlZM8lCY4OSUfOmxnsiTXFMQWz8GbXS407YGttukF8b5ODonV+wohalXmF8GkfWvGwkDiF4yrccvx1POY1gOJG3cR1YoGMUl+CUO5rc9cZ38RqXFTVVyMoW9Dj9iKkY4kziUUcffccpF/3kjDftd/2FF//mmPMOBG79wT/+fOYZhzyo/mr0q4+84NjLD7/kKyc+eMn6OknbxU3CpbSUltL2nvZfvbAg8fefwv8+2Dyu/9pZx5160TWz5YNfdP6n3vH0lfjq2pk3HXD1t1+5U+3VFS9f/rSPql2XGX9xh7/vunXPuT9vT+9QaQmgl9JSWkqLSE/+o4Uke8Z5H8PjDvo98fPASNuFiuPHv7r7M9++aZGFn3ngTn+833IAn3ofa4WyACloFUIYVca8gtMDLWWKKWgTpVBDaeiMswwmDqHKoLWJ/G2ugEMRoCC0xEIH4lTKQp9cOyJq+j17terYlGxQual0okxIFKpuV17XSERX6aTxQEy8QUHRWBIuk5v2zbGqpKFujWZiehwpT4SmBa7XzfxDlgm6JhZ0WKh3PH05kq7uJF2trO7zx9kvh+9izFilX4InmChNmtQyvjvhpTQRqbXl/zYoQxgAesux/yGxQqPXXgCgmZGN9wu3lO5R2i4AenYw2obC87bwv13ORChycyCHPEe7zXmOVrsqSxQt1hnyAlnBWWbDtuoCipFp1oBWrAhasVYmsImF+KoCiHloHuwRYGREKx7CqV6SJhzCJMVi/WxdITvB9VqUYqVkUkyevdU58X1hgGpXlsNy0mRy0sCI07KPLWAbqinma5UIY0Y4WZAmtBSNQrPrCclVA/Ft2WHW72+GQ0yahJXjuYnOKCbr672ZINVL1n82fuFvItjqaiB2ltRpj7fRM3QaL68upXuVtguA1vlvDnjk93hcEAeRCFXWehSwCsDmLZRnrBWyHN0ud7rodDgv0O5yu8edPuclt427536V5ci7lQkOqwvWGatqCBsElsn54hHmt84MgxQ7x0PsAqoAZj64mNoms7Lyqbzj4it6G+iQD2tLbVPN/0MixsY2rRSOLlEz32gSr+p0nBAaXRSWlhTJ5Yakov2nAjNJCEmvRMhXgo2Uw0h0Rfy6ad1Kcyjt6cJVmlppai6VmmtE2K2I0YWPZPTEoevCUvZCbI/dykTVwmMjxNc4hC84s4Z22y+m3m4vjL9L+Hxfp+0CoHetfrP8hqsWWbjYZwp4PACtwaDBkJmxGbR1K26rqKowqkCA1tAaRcGtFlptZBmXbZQt1hpFi/MCWc6kraLDeXUWEaCETBfwyMMrHLTBzauqYe4H6S++X5Y6R0oudtQEc/sxvQou6XP8BoimojQpSWdrgu8R/kZLQq173vVaDGGNbUyiUyMb+jNhC8JA024gAaZxbzn+ZjFhb1GDyLTL8m0s+S4gwyabnhqn6fsJ625SM2miUakyLiXrq0/dRYTDzrcLPPlDStvFgHbVzNaFS7nC2gZoXH8X5gfQmrRCr41+j4scSqEoUBYoS85zdHvc6XG7x3mOVo9bPc5yLruct5CVrBTrHCozxsoyVh9LgGEAwtQ0ha1YILJCcTK9xPyXVVJtc6JtNFA1TpyUG3AJhyJ/AhqOlfVIsEfhVWgnCUHt6tiriLLn0h9QuigEXmVIqeAKNWU3GgHP5CSRTvrBTkbJF4hJ1ocxVbCMUXpE6pqYvmOSpcLFwr7ksLGnnhlBzeaZX6iKOfLLjmR+wXWxSWnW6uk00Eh7IffQDGTbBZ78IaUdeECnutAarZK1QpYh0zY+4dwc7h6hYgWAiDONIjfSNMqSswxFibLksoUs57KFosVFCaU5K6AzqMwEToF2rqLNRwBO7eEmgJey41sAVAcE8meYVL9x7XzYWahiABXJaWNyI/PlWGL1j9QoMcWrBdWffUeIXTcFwQmq58Rrm8V1Tgt4Sd4DGQXu420Bh1q1FS60IIjIQQ49lk6qpA49Zj7KTNCZY4hHDGRp04EH/xVY4hTqMqfjKdeYcJdFxG8wmy12t2jS0fCcJkDvmPe23lwBDK6iISAAym15xK0ZIi5aSFNZQE00U662SVBfSotKOzBAt0putzAzzXkeZD0xjZm9ghiAcRttDDMYw4G5ZkxbtyDLyCEyG8WIAWV/+89jtHJe/M2l2eCmn6DcQaIvb9DcUiAiX944T2gWlMK8r6N/OglJ4B1CFbKvGKgVqIuK41KyXYAf1dizpZTOEklNqnRjiwLbCRnIsEktm8rpHAvviHYYqYIiZTvmDQkR+5ak65FkKJoop9ymZYKiJtlQNXwL9e7Lohx/9YFEnY8mBoW5S6Mmp56mV9PK58ZZre4CvxviBW6yLKVtTzswQM8PaG4et9/lHRjYfAK0tv77yxJFjiyDUsgyNuFXdFVfyzYAACAASURBVIai4KKEzpAXnBfIS9YaWcF5CZ2z0shy1jl0ZgN8Gz21EagdaguHDRNSvI9On9zbhuOa2s60YScqEAp1B3xODo2IuzKRhC4F2QlIgRgX3ILhfXg0wkYUtVwI+HV50BGJclI9hhRdqdaa80cSbWFs76JFUeJUg4LCgP6YLzfhx3CYEkzrpMSBxm8UqHdZELEbgvhWv1+PXXWxaTEeWYy9oFCgUX2pE9OH2ag44rTwISGj0YnVUroXaQce0FbB/R7mBxgMMKqgyCo62m30ejyzjFsdXr6GZ3Ya9VdWRZtbU1x0KpUj71TZcE4P5owJByttsLZSmnXGSjGsNyS2wVWs+51qBB6F+IXMqIYUwVxNcKMQQ9naUAMCQzkYALhaUcgPax/i5FlCMPwIJ5Z+plVCVo2wUkxpYYOVSqZ1GInhQ26ZRSHZfZIfXV9iSxVXK5iyxP315l++QFJLth46YntBcmQ8h7ZYJcrH6xN7Pp16IeEtMsKxAyl3A2nhKLOGwnJJSPYofk2h2jdJ8hNFX19E2XPoNdSNewiZamvGsp1pj4fHZYrWwvKIXrqqfR+nHRigB0O6624wkGlut9Bpo9Vio8dgxvrbaDSia66m+UE2GgkgI3NjxQYz1BpKQWs2z+ajIaI127eZ1XtozUrDuPlUGsYVkn3WVgFin7XVe5jjR+WFbqctIZEjdSPGxo/Inv94HQucCzOVWSglafVRN1Ue6365KTUI+VIh0lCc0QQloUSTdFxvUUKdTzWFQNQqIvWIL9/Av8SjxnyHrwJMG+JwjyVS6woFrBdZomiCjw3ntLX8GgOBor1zVFOANJLyrlibqNUVWWj3VXpI2GoDEwFaVfJr24aIKvjlBS8+4V3//INfrnn9Vf/1pn0B4Bunrjri0+XyFoDucRf/4rxDJjX8h5x2YICenUerwGiE2TnatAUAqooY0BrtElM9brWwfAVPz3BvmvOS210u2qwzZAVnBbQxs3MngRbvVKS9JQoPzRtPAY7pjRKbGQt68FggiFjccehQwyeuwmxMefACldv/Jhtw2YWEZ782hB29LRy58o98LJN8DktF1CLS07BUcItvUiywbxYYF8hUoul69botdkIq3gSkewVOGyXfhWSvU2uiQao1RIT304am4zWJfYRDwGx9ZLMJT6lyjNOxongRjSV6bnCnxwCQ151htDoLfFUMKIcn2xRR5YnLDzz5vC++6p+fd4LQwuij1914yTGT2ntApB0YoGf6VqdMQJ6j2+VeD0XJvSnuTXNnivOSi7a1dw5aS5MYoyHRCKN5gMjfUEGAOsBtOf3c9ErPQK2+Z6QIIFj87sNsHHM+FrSEiRpBgKM8vk8pu7p28sYe89lTE/QnScFJt9z1HcjZbvUAceXgyxrRokJx64m0LlPjTlw27QaW5NtGUToxmUjFzQZeGlp2xBccJTnOCaQ3fKXit8Qht9YFz3y8EDas9J7buApzbCm0YC+A4UytUGshMzuiANDbFFHliU945MErce3XgZqn6Qd82oEBenaOhkMMhhhV5pyK/P+V2/wZcwsT9ltrznTI0e5ZK+u4OMvY5CuFLEeec14YX6bIclYKOre+O7wRXlBxKCjNoGDvAX+QKO6/BPMp86+Cj+lhX0mZFwAJB9TeHwhcSYjzNzTqBeJEDY+TsckXTsXMRSYBmlI9Kttm+0bQX9yV6EnKh0ZNxRj+U+uURqEYkfVgQxNSbrXyffSWZRl2Tq4bbxVKU/cmZpr3BzWGJVdBIeZU7aIL6U0lNLobLRfyxWEmnkn3KKJKlEZfPm2fvV5T7P20088975THPGCd+O/AAD03DyIUBZTCaAiG9d/fbpmIKmyAmMgBsVENA4DT/8JZ1GkopwLW2sWTUlY8h1AQg2k0YqpQjQAiJbBVKSKv8fDySn0iy1M+hCkaJjCLySkhtfYrTmda4/SpzfDkyMiBoxAlvW2JbzqCHoHtC3nnsCPjJWj/N1qEotXF6lh8mfrCJvITITqopxAVlm2FV/A0Zex1TypRCtkCQXtjVsf4Fqii0DW5e5BbtDB0ciwFV1LvFPhxtXzJSL0jqDV+I6TE78BJ7uH3FbUCAGVXA0VE4sCnod0DM75/BarYec7DDkR/mSBkf1v2B77IiCpRevQb/+P69+81M7j+8y9/xjFnPfyG9xz0ADXg24EBOs9R5txpQ2fINJhtdKuKMTsHzFpdhT9SrwSc2bljbJbdbLSStQIp1tp6vCOFTEOJU0SdERGUdguAN5F2RLxG2ym1I+0EkQk1BbM8+NlinzmUDCjmNqoOzrxQJ4aDEU6NDKVxPh/k7jjgO7NoOjwLpCCrO2ZfS2JEDWQDpqSQ4WFXCXa8yqKGWUH4jY9AwwqHcAu01sta7+UiKsVeYSdnrnVEZEQ3XTESUbFkez62FcFBs7Oiady3EKw6Igq24wZZVonWTXd7Je6ekL7tvSd3L4o9q7Z6NCYQM4UIvRVqlwfHlH/xQ1uuqOmnf/Uz+/Dog+3DtkZUSdP0bnsBQLb3c045+oy/+P5vcdBuDaUeAGkHBuits9i8le7aaOcwM8oCeYZ+j7sdtNusNYoSRcEGZw0Wm5SYkULE91RGgjM/WQG7gDTA4JBP3mYDIBZXWqAUSLMz4YgMqN1NFnG/XNlG7b1zCrw5gS4wbcpY9mVJCJhzSC0hJpIKG4c1lhxDdlzaS0FEDVPdj/ACSlsvXcqcBrm9oVb429hEDU8j7At9aK4V5YnMCbYW4yhES07zZr72llgK0OzbShiuf02ijtwiLIpb2R5QtGoSdLs7TqkiCLqTwG2KqFJPd/7q6sFu+64uRrd+7eIvze7/3p0XavgPNu3AAL16BRNBKzBQFuj1eGqaixLdPrd73OpyXrLxv1G0WWXICrY2aqBqhGoIrmg0wHCehgPwiIbzGA5oNI+qgolTaEJ4m9iw7oF4hIqpMna18liPEQVMpbEIkoKdQJAASk2TwZeMZNuEctOclIKn3yl7Xq1gLhT38HAhkU6IWknFOhti+5wqEOSwyL2FzVZi9x2x3TAOXk6sY5/cfJi3kUoH8c5ANpTgKQdW/cgkG53mJFca+ai8ip3k5sCxHRltABRiVUoZOrl/hISI05tVtqa0IKzvfuRomNRboVbvEXenaC1gZgcOEpB6/JkfftZxz3vw7rPlg1/0wU8dpIGf/P1LTUSV+qvbLnjuo8/+t423rudDdlv3xHO+/9k//tJrnvOOH21iVaz541Mu/cDTHrjm1dtFRJUNv/r2+p98ZpGFl+37jGX7Hg7g9KdV8wMaDMGwt1S0AjPm5jEcWUvnXodnpjEzw602Ty3j3gwXJbdNJO8eZzkXHS46nLVZZ6xL1gXrakhc0WioRkOqRmBW1RAVEyobC8BcyfAxvBnCQxJgIqg6NbPNtxWtU9PQGfdsoSOFXqdTIF84lW+tHt1Ma3M53Uw+UkzkvEARG40MbBxbF7NVg8hQYHegWUHxSAAH7GUZrggsLs5UYDj/q/46iXMpxaNgoMIVVUZjEFub2Os/8MUiBRQ4BObmKoxIuD+SKHDCuSJ5JsNLf8U52eNzBGohEyAl76qQ1dW4isJtRYPhc1gJOAQHl8ywayJUTHtReyXkd3lfxDMQfhexxY4fYXKW9WFMEBhONgr9FeqAwyMJevTj72KhpP/oYejPLFhsKS0+7cASdMXodrgsoDVaLS4KZDmUQqZZZ1btkGXB7kJpzG6muS206W72SEiJdBZPV4o/plqCSDJNTdAScSxUSgSucaoGl8K0pJRmxENTxVQ+bdwp17niWJYfVyXRFElSkxd9b4JW53yMNVvdzKCxxUjuliKhNCap161RDjKmI55ECb5PUsOVlgUqRNJ9IjpzXIbEmCQPjRSSpIs0p2p1Fug6Q6lsMf1YSotPOzBAD4e0dRYAtEamiQh5ZnbTNBxhOCR7bOg2p1oh00wKubGcy6xphwV05c4AtT0kVNqZ4mnrC0k4j7Z2df4w0LhDghBzyRkOm2dObA8SMwZpfhCbEEgzO0sQNf1ATeHgbp1wxE+4tcgRD0oyY20HQwElbCric0tSKT/S7Z/oXbxDj6qk+hbIkfGZLkkjmVg4DWYoJP8mcjsIYGFoZl8Rxqw2flOQYFvClivmpPdIF7Eo9I0VFiSWZbvQeElYMuD2Bcb/QNDi1BXnREyKlSIbpCJaR93NmLC/UGULiHQco7K7oLe6jBY4dFhK25p2YIDOMu520Oui0+FlK7jT5bwM1hTwIhTZYzoTdZAIOmN/OVtpVs5aw9g1232/fesAKxz0iWuHAeMcvMZCbjQ3x8uVUpIKG1KISWRnpfdG6rbJTXv2yHUcC7K+bmgY1szJGU2REBwR4CUBI4FP8s2ErcCiZ22jdj7NXyS1Rgmzse5Y6XohsjLRQgVqZajhYnlUUH5r6es4s3GgFth21JJsrj2lHhSfzA0bPJDW2FryZndfpx0YoLfO0tw8Nm5CltGGDbzTTrxyp6ps8dQK7kxXrT7rjLMSOmedMymoDEoxnHRpYcsolp0ulSuqRu6ZqRpZmHMP5LXNVjMLh4Mc/F6iJo0FfV+Clcm0MQrcum0o1x7lXcQm8A8CaxDlxCoipXX/PixpaftpTn35mZDG+8oIi1kikiYaCcFw0CaPYRVjROF7KNqREUtjl1hRY6mUHEFkFXfE1pDka4J+vJYQogXYl0/GIZBONhyCWKA6ZhUMPWKgFn9uWC7kzY65shcDltJ9lnZggC5ytEru95DnUIrvuINuu017a2hjyNztcq/P/WkuWugvq7oznLe4M8Vlryo6rDJkJRdFZRDcCsjkQg1G4FaFCWShooqkZZvCfHQHcQTIQIVOuRtyRGUHzRxYEHjtypt56m11IzHZ791rcnR66E/xjlmK6vAT2u3vHR3JjF9v6igjyAZZ0Id+DMyIMZNG2WHs45PAhlM+MXQNro78CJP13pF2NrkBLx7Dmscq3eLUOU8q1/uY5NSQNHx9srDXfXvgjmE+VPHsNt/VBMaZbaBhBAAonWLxcJAtKIVXC5h5LKVtTtsFQI+u6fZ/e+ZiC/P12BcABkNsnaM77gYBeU6dFnc7KArMzHB/invTXJToTHGrw3mLtYYuOCuYFJgxv4UGs+S0FsK5c2yYRWJ2mNRg2+TThF+/E4U8Int1hHRrKRWYDqqCVtMgsrluI1E4MYHwDIfZa2vZwtGFCMsMAZy61PAdkFBeL5KAFDeNIQXGfLGwwWfBjFS+N+1FXH6cK+wrokVTfHPkAU50h6zZu4C5piEIFiAIenaqf93j5cv46JgtI8ILlWQp6bKv2+iqxS5LbkMFpKtUWJ6FLUekwUbDpq27LIXa+dnIYL0x8eie7VOW0ti0XQB0a9f9Nl23aZGF27tZP7VEaJcoC1YEpVEWmJ7mdhvLVlYzq3hqRVW0uTPDrV6Vd1hnrAuozHiwY9KxWZL5bwTjA5oBHhlbaWsdZ4ymjfW0saOrRsG4ztRlZ1XmERNCPKmJlq5xrzUWwk7dSgyIbKGSAnLGRmWa9RUUwZZFLjdtE1UDA1XAtfrKxM5dveeEOWo6QEPMSVVZgCCKdwmi9fA3bXcMlHI0jH4QjCzfIKI6YhaiKwFnspGaSZ/XM3g+OayKkQZG4GOsBEND4TACckUU41AXFORwNQ2UTT5OW22nEidHcNnOlLiDHi4E0EzUoJ1bSvcubRcAfc/Shs3QCkVOilAWGI14OCKtsH69zq7lPNfO5xHygrWCieStNLIcWcba2d4pDaVZyct+7uivfjYVQZgQV0PGBOE6gVQW0CCTFwNlyRqBcfJaqv6Wji4ZAKoarZoWcuJMnDxLfQG5Zkwsf78LXXKvIJpLrA8JgEZDz8dUH/e9R4J/1De+J7+TCamm/q7xJPLI0R9HXJYn9GrWzIOtVI/lmxBo1P4vpXuTdmCA7rVR5Fg2zZ0Or1rNK9bw9MqqaHF7iosO5y1WGjpnEwcW1q0+kwJpd6UjllmcKO1uZFTRdQw4JaxXEPs7GibTHhuyFMRIFBAqVCc1BwHcv5L8mE8Mw6iUfWSxVCcrdR08Zk469hqOrqQ6Ytx8o4S9plQ1EEkXD7bE4HCkLj6ntbwXjjrnCAKggaRm3jjFXNRwp6GiZE7acXvD7YVs0JLNU+CCxQoxediT7UUjWjYsDK4H3qtJ09DJlZWAvJWSGGyl+kClLVeTCiyle5C2C4D+3R1b/mv2jkUWfugdvA86APbcjZXisoTWqCrMbkG5mZhRdjkvubu8ylrcmq4KzGZzW+zNwNEQYAwBOFGHyN64s9fttLloZx4qnYFUBTKX64IOhN1FOI+wVaTcSMDUInXt3M+oRJoQOU7yLMsjRX2L2nTr2nRUwpanCemHCBFsRUphx1VqAYLxMDGGGaE+NoSsEEhJYao9IIIPPwyyR5G1dW29SaiRbT3obmvsL0oebPyyxo5JDOKRuBwPe4MKqw6sbm2onVIa3p0mxZm3J8RirsRPhIGmiCoL6qBNlLhJJZbStqftAqDn+9hQDRZZeLZvfyZf/Z6a7nCrAJFVaOYZtEJZoN3idgmdod1Cq9UvW8gybnfQarPOUba4aKMoWWXIchtdRSlkBWv3rIzobVw8Z/ZOisqc8yN7Y6UKPpoNOlgnSs4cT1pjeB2kFP8oONZkFwPcHuEZAK3E9BsT50m4XqupX5y0nph8BEnKi9JSfnfoYE1XKiu/N0j6viLHlP3aA8lG6IUjRaF1hF5ER46+sOy7Eb0pqiX3JaZFSmDHUaN4ZWJB030tYYkl1PAxlHeASh4ZJUMwyiKrLgsNkWmC/HUUBkBOTW/PJF0x9vunaH2FYE+sVczWjy4Ey5w6XxKJGnIJGC6vmdltXeCMkGDch9qK9y7k1VKyabsA6HuWHrwLT/XYq/yMV34zH/IM7TaKgnt99Ke5N13lJTp9bnVZ51y0OSuRFaw0dMYqt6oPpW1QQSvTSRe6wbiNhkOAvVojEqLhlCTW4iLoRqSlHbgyN0ScWI0YFuFdXoTZk2CWfRSSVGOK9KHpbB5znBg3FzKRAkRNh8uwWLAtKeldE/+SybFJoBUQhH24O9qBeQKc42ZZPWIpzkn3Hxz/E/6y5JMI1tFNukfxJYT860TetEuAkfOjjcF4sT1aZsIDsxWlE9HZt0NJfv17nJ8df6/GV/QS9L0PebWUAOzQAL3zalYKVQWlMDXF0zPcneIsR1GwuTEYLvspEPFwgC0biBSpDVYHTcJ9MwjBbVgMDVGqiSDRK/k3oSOuekexo+QVcOPU1OGI8O9Ra3IxMBhXclOX/Cs525M9dQoEnOYkoqv7h+roljKVvOIan6J6xcmLWtPjiI/tC0V9j/lvQMBxZsXyY71HTXzWF5jEnMMR9xjXIP7XU731sSAOSc7WbbjtAoBD7Cqf5rcs/JsbeVhfCnl1H6UmgJ69+Kj2S74AQD/xvTd857TUU/bPz3rYw996FYDy+H+aveioBgJ3/NPxjzj6YzcDDz3zh1e/9YD7nGmTfreeqgq9DrdKOAEBSqFooztddWa4aHN3edWeropepXNkmNODeapGajQQUoxih9aVziqdV1kGptGQqmEwtquGxt0ojQZO12x8kDKhcjroIGi5JCZ9ZDzrnEeH63zJjz9xtOTEPatMHG9lYUUtAsK+W7wT0zMShxsEQdkLU5ND73i8pCylRY9f/mTPl0lE0Zp0n0BbwmkoK1/EHjjHAWKon/BM4Q3HVfxh5iKxL9qR1N4mOc1abKEzDoCdxKaSZBN7O445p7B0hhbkCFdpTnsqVXEs20VnBTHj5mtGiTndqj1V0Tb7S5d170NeLSUAkyToLMuG31134VWnnbWfzB5958MXXUVZRsPhmIq3ffbUkz+2sdfDpsWaNt+zRITpPucZQFi/nm67zUScQpajLNBqcZajLLlsoShZa2R5V+dQirMMRvvszey0iUaYQWWsc+O1g3WOrHD66NxqP/IWk2ZS0Jk1CDH55raLFdiNh81waBKjTuV0ukYrHSZT5c+5QpjbUNXgHAUhHz5HBUgW4ZtsrMOgWvHNBk2LUROH+SnsWMyc5wpgFmee3hMpe2oyeU20R+SAisYlD8fQQ8xMErvInd1FOc2/gOYVCEjhxjFHkL6ZRMVEyVMH8NqoRy3WuBC1pDfnpETaQNy5usrC7bekMUZ0NIoon6VHFw61UsWX4MJzV9QixM5uoqwAV5jfkq4Ts5uocrcHHC2xMbgnIa+Wkk3jAXrXI46c+cfLPrLuP9547oHh1zP/5XUfvzF/6rMPvfKyLzUi9O8uftkpn1IvOv+MG17+lu/c9/yKtMsaNmeDAOQXTYTBEKPNRIQsQ1mi1aIs51YbZYt1TqRYGRBhh0ojjCpUFWie7C+YiJKJM3GVDz7nrGfm5HU6P70aZJwupWHrWtNEm3OqxvvTzerpmmhZR4EGwdNX5eghUWEnYjK704EgdMuxrGOuA24ZCVfK6VY3Qc2ZsoNj8NW2AiCydmYnyU5gjyXQ+bNJSptzpLxiKrp9HqmAqRaDtpYJyxW5zZBfySdBvC9mLGTgljq/cDbK9fJfgBlTq2jNnlHBm34xFkxvvta+2uNhznb13oa8Wko2TdBB73PC2oMvP+1j67761wce5rzDbvjcuk/d3jt67Qt7V17WVOc3F5502ufzP738fc+65lkvvx/YlenqX1G3DSUua1VOHi0LTPW43UK3i5kVPL2sKlroTHF7qspylD3O25yXrDLOStbukJAyKOUuGZK7/G1SjE01nUBsBYHwQ0+1AbUjQV8sKEOc1I1AsHY+U9MDeOJB4PbZcm3gGrAmKYWIJlE0dJBCp1Dr0cRWxGJDvtg4fS7iAWlgLFEXOMgby79neyE+Q5FkZGQfXS/qnEd1k4FaJDJFrTTkyxWC45+GF/yjLQI3NR2v5d2ZVMUxt3WBQ0KWN0LvZcirpeTSpEPCXV+89vDXvuST6y4777DndwEAt3ziw5dvXnH82uf0LllbL883fOiEV32hd8Ll7zly2cb33D/8irT7Ttzrot3mLEe/z2XL2l1UFY2GGA6tscTmjbTpbm1/3E569Sd1ysUqJLIOoH18WOOP1LghDY6htQsqqK1rU5AJP8jWywGCT4+g+6NgQWU5qOsopSCT3CT0j0FfEImrMlEys2q2t+6GDjcL7zWp0Np3xRKfm+9OlpRdCK6ZG5zANZiOJDqCtD+1txQrDSbsQtx3DYqxm2rdlHUQvjXZycQYxq7fYngp6WZNricScm1tQBo6K5GUhN22YcbFBguLrrICs90dSsLuxxmRb/z9jPk25mcbRfeoZjgkvJchr07aaVJDD6Q00Ypj5tiTjz39C5euu/T255+wAsD1F627YrjH6WufXq6/pFa4uu7vjv/zr82c+M/vPnIa2Hh/MRxSpgFgbo62bsHGDVQUXJSklTWLHgwwGmEwoNEoKEAMaNro3T7kinbRuzPWGbQiE+9VZ9DG/E6R0lDkHEYr52Up/MtWJ+LnrWsuCK1mxvtpafTDQq4hsRXwkzY2AmFwIO6L+RYbIluL9UBkGiaUN7x1kGPjZzVUqQnLriGKClliAq8nJ7HMNJsTNCG7bDQ5vkvO9Op4IttqLCDb4hhzky2OJejWAOYa5nuhvh58YMzykII1ojH0S4UsE8nIhj0VC/7mSWB98xlmxAeGg/T13OyCvpIwEpcZVj7tLVdc8xbx8tB1dx065tWqkz5740kLEX9gpslmdq0j175o54vPX/ex/z7hjD35Rxdc8AN++Nlrn6BweVpydNW5L33Dt5ad8pV3P336fmM2Tr+4gVoFihxEyDQ6LbRbyHN02tzpYmY55zmKVpVlMG43CJHDNheEz2IxKeicsxxZzkTQOax/JeUfWJlTxMxcVGF7d9y789dWK2IUJkayTlGFrPzkPjZMMH+30AJCJaaWUANKkdBfavAdZA6XrclVDApUU6ty12HcBDZ4ZPxAhSY4NO3Pmjxee1tv1wUL+ZBmLbWbJoGUoexiEUhxVcbklaYgXrFryvgChjHznVZVoANOu5nwI8/cEI5GhQTNqXaCxTWc8P267nCsPJENRUdzicJBcGV8DEhhPyFY16fBYW4luil7lxyBpjol0UEGlu2UAvTWrRPFZ0lhKd13aQE76Ozgk4/f5/3vuOAjV53xV7d8+MJr1ePffeIj6sUGP33ni8/6t51P/crfHjZ1//DZkB7zcO73udW2N69GI4saijjLUZTQGZctlG0uO6wzFC3OSussSeesnMbZ3AwkEh5H4TakDlWDKCqlIUY1Ioz8TCab736oKRDfo8RjP6SZQtWQtlbHxwlvG8BFSmRW/rJiqiKClsWE3MZRdTSXMiU5fl3jKrwibkSCel7axKS3iHuXvhrTEfeqLqg3ZUzmZwyFgLTjyifC8hgeFtmc/9SdSa96z26ZSAFmCbzHP/Ol1JwWvKjyyLVrD3zn6y684OuPufkTvy0OO+cle9SK8M/++iVv+uEup17xN4f27hcmm9NNN1PrDhQFpXpGJmaMKph/qxEqJ0MpsvplrZFpKIUsM1oOjmISujiERget3WUWq5I2YVnCFZjwH+QzDC6bI3irQxDqDsOqLYbxW86mPX4tJ9J0N7HhDPAinuUNHWd5YgISko0A7jz8IQ73pYJZd3TpBlGL0UPglpP8uuJ1PJi4i8/xbj2MgKQQDzJRM2VhdhLzMMY7tsTKcZZqkOJtvbx9lSqCwhbKdihtJfSV4sK1AnVWARorvye8AQCyLD0knJ1dQMpgxljT26V0T9PCNwkfdPzaQ954ykdf+spNd3WPOekFK+olRj/9zx8PGH//lP7fJ29+8bZH09swfdKX7lp3+H3Dr0gGUMxvgghFgX6PyxJT09yf4f4M5yW3+1x2uWiz0shKNsbLSluRGfAbXqdgdW7nWF5CCft6VzjODG8R3gJJrTgHFH8Ub+0Hl4MGqVbMtJpms28WrQAAIABJREFUsFEGHPPKtjgiAKP0fYMqeYKYOfZ0KeG5+Z37IhBGIKLHcckmyo3l3RshizYpDcaacCQoljBTrxHfEEk5FGvGZIk2+hnUlNF1NhcgF381XLvDVE/DYU3FMbuQGE4YjSYWWErbnhZx1XvNn6w96jUv+tyNWHni2mf3GwqoBx120kkr47wtP7v8k1f+bvqA5xz72OWdJ+1+n/CapK2zGI5ops+tFqamuNfnbp/zEp2ejaJi7o8M5mg45/QOHlLRhIAyNaEhw1/TsCm2yqjZRdSxgNLqXJ+BbHeLRuwfN/0a8smtMXEvEtEsfdVE2gGltNw1FchsC0JvuOl6m2RK4jw3vIryBf6GczwxaOwlz7o0J+AvoGQM6JQUT6X7xg6EsVJix5AIuoErtwzQeJgmHwWlvgA4suat7EIjw2GpJjFOHLEBtz2KfvYcjVgynp3aRN+8ZeFDwgUQfClte1qML47+8879cuel62mXxx1aNL1XB75iXWrMeON7Dvrklb/b6ci/Wne/XfXea3een8eowtwcNmygqsJohDzH/CxabWp1OcvR6nHLSNAZZyVnuT3EMxS8RMz+unYVAqZUI6qG8M/M4IpDGFkTYGXkClRUjcgY9onyNkBiODiCPdeq7JmbUGi76UROheKT1R6IgOVeQaGM/ztlFDhBWxJrM+wVR5AwDXRELH1/AzHcuAHIGLEASGOZB6WKsmqceK2ypBwdBxcECPO+iFrSXxIKomQcpI7ItWjBSLTO7CkHIpTUQkPrsoDro1vfvN4maToh2ETKJrk8e4MZC9zSsVMo3LDqi1pwhj0syyctCj6TRs34pPoc4qLQQDTbN29dGH+rJYC+r9OinCWVex90zN73NyfbnG67HQAIqJg2bsaGjShL0spqlvOctUZRoiw4y52Nc7BQtiCYGDaRMKtQKoCOF0O87YdBLmXtN6AU68wiptJQuiJhN21kedO60uzjDzkVsFDmxnMsgg+ZA/jtew0BBYKIrtnk7zLIHAY3XRMbNyGJIsEtypQ7D9dO2LuId0lDLI1VgkBNoWzTdsG2KzNqWgtygvq47sjdQF374Qul4+YyE4COGGjsshDJ03wSHy0n0R2oiFK9O5Tme+WPocyNvUgSg4HeMrXsqVH2xi0LA/RwsT6Dl9Ji0w7szQ6Miqmq7AXCwZC2zIIIeca9LsoSZYmpGe7PcLvHWcZF2/rW8I4uEGNQMqOUsv6grRjlDsfIi42IchLohxRIKV4MnJ8MTzkWSwNPRNH8bZSw6kdV8LJ5rOLwdGyOMGqW5s/R4NQ2v/DCKdvVRVK2jZLrZlzLvGUBGYhbSEEswb6ky7F0OQE+6srfRp2GNyapk03KM0PeGg/iLMtvTPSEEMUkrF3p9thqEFk0VwnYlfhNfsuVqjXqZN2zM4Fv0pvJj4R2TcVx9+aGFSEiwJhfOiS8r1MTQLdefDm/eHK1oy7axBdNKrDbq7/Nr77nbC0mjSpSCstmuN1Cp8NFiXabswKdLrd71rQuLzkroDOO4dXBrkvimI4iCKhpq7mydqbkXamnqb6dFniNtI5UJgbJdJGpjl/bkiajWx3CGqBK9jES7AjWgQVJ9IkbkKS3ifHmsSIaM4Yx9PgcHldGtGLINgjC9SVFfqrJsL5xSaeJATtmBo4bl17Uv6wxuBw1LXsR7xKi1c7V7a9Uez0qauR3dy9w2AAsHRLe92kHlqCJUFXYsJG2bOENG0hpFDllGcqSs8xO1MoHA3TJi8k+UzlVqRJaC6upcPZn7iahuwvuSmrrxw7K6DFkRVPG6D3srRbzLLTG7j4LYK+YB1E9KDRCPoQSI3lGE6Qmw+UuC8feqBERSYT9IO7V7PaiB8C7z6uzF3TBNZ4RbSzcAkYO7gWfgTCJwuEjR698+XilHYt5UR4LCZOBCPIbbq/HVFzdIBKLV+y3FWQocKRk9whK4lm8jpE4UjO5jUHD8R8A0GhEXLH8ChH9lZsXBqmyBUQWtdfdgcnwS8DcknO6+zptFwB9zU2/++r1P1XRT7U5MUC7PnT/J64GMDAneAytyDjT0DZiFLGzM1WK8wxFDq2NWzvOcrRbXBrPdhmKFhclcnuBxYS/AinWGXTGKkT+Zq9rJh0wNBzoqYAXySEYEJ1TwRdzD6FwY1p8ASdBThJO62iIaHL6jwFnFhRvze6bIpkwxMRzn43Wxd5zS5hpuGootAATGOD4b3IfPanNNaEyIdYkeocxWMQ4TJLKvZ/VMXTkBqU2GnE9sehEKo4JrDniLPQnYyViRqun99o5ytu4CPXFlvnwvC0hr+o53zh11RGfLpe3AHSPu/gX5x2ycON/mGm7AGjdwYbZrYsubH9WVYWyQJ4zAYMBDYYAkGXodjA1xVPTXJbcm+Zun1s91jmKFucFkw/1bXDWicNOQgwGD8kJISDz/bYQMD/0kQhQFSsByP/ZJo1Efe/cLLHJD8KpfjTDCfXpDcd5PXMMDw07/Xp+eOsarbM9zqy7kcMmCuPKex1y89tG+kKknoR0Y/ruv9LxkFr7OsYK3/HfieOz4Oo1dogaK0bLMAHo1WISblvappBXf/yDWmFAH73uxkuOuVdM/CGk7QKg71ma7vPyGZ6aRlHy1AxPr+SplVXR5t7yqrOsak1XWclFPshmN+v5rVSN1HBAo2GwqrM2X8rrI5iUCeYNG9VbsdIu05qzVebckMEVGbM8NrJh5Y32xBEch4iF4l+7uwVCZpQDiTi1LbUs3FDe59fkNSkXJ4VRm8CT8Tcu4GhS/e24daXBLGGM9Jrm1FaUsFSOWXiaFo80J4Gt+tA1w6CQYEUXonGoA2JYpmvqhYXHoXEdTfKbOtggH0w0qim7NSKLSNpf+t+mkFftf6oVviet/0GmHRig79pAwyFuvxMV02gIBpnL2XmGVolWi/McnS46nWWtDmc5ihYXLShtjg1doG4RkxBGkdqk5E0VtckEW1xa+ACwPv3q8l2tJMZMYK/nnNBKQ1syvwnBJ7Y4pm7D64l5E+tOojwuGNi4Li+iOVlg4YWqsUCTmogdXKbS9yLYGNvW+HeLHBafP1+z4vjwh7HXXhgM8NznYjaOHXjeedh/f1QVcu+9Y5tCXjUUXoHRl0/bZ6/XFHs/7fRzzzvlMU0X5B4QaQcG6F6H5weEAYjQbnGr9PHqMRxi40Zi4I47oBQZC+g8t8bRWW4NpZWCcV+X5c6/aMZaA4m7Z7KWyxKU0xM2Nw8jsPPiFYsCNUl2bBL0Uw22NG5LTn3k0tKUwlQR53sTqNVJSWtxJAUcq1JcY7+e1eXupsUv2hZIMVOOg1Sk1qHdq5s4qh5VEfzUIbJuImIyiexdTdm19Dv1sMt2fJjjzZDnMVrynfliIMKuG5EFBbPooOh0fSVwJuAA21FNbC7lYWH46RJ0DRhOPjnN8ek1r7EPP/2pZNFJFwuGvGoo/Og3/sf1799rZnD951/+jGPOevgN7zno3qlcdti0AwP03ICqCmXB3TZ23pl33qNasXNVtLlsMyl7x280pNEAwwFxhWpofWNWFZn7frB3AlFVBEZVYTBH805NEYz8vTrV41cs0pqZZv81RiAEWIsO54NU2lAbxHcfvRmJPXKM7+z5Ks5cRORTzR6j0dAicAkSamrPeR34pGVFkpq3DvE891GaXBYBydFl0FA3HIuNTx5xAvJwaFu8jBcICnWak/gu6x1sNLuuKyiiUYUd2KhAxGfaSroQ1hY6ZwZX+wYbqwju/h97Xx5vRXHs/+2ZOdtdWcQNjLiDiIgrKijuRlzQqM9oNC6470ajee5x96nxKVGjoKjo8xmT6DOJxl/claCgMSauQUSDAVcQ8G7nzNTvj5nprl5m7mHRcOOtz/2cO9NTXV3dM/3t6uotz0g3oqTiSg3mSsJ6qKEhvVqqI68czK2DBgNAsM7+x+975tkz/4nR5tnV3xLqwQBdKlChACJ0duHdWd5b73gAhEDgU6mISgWFApqaqLGJGhoRFKnSSOUG8guoVKJiGUGRvABBkeIxwxgfubsjIbkxRdLEJ7aMciuDmcnQH8ndmWEyy0BudBsO1qQiZhh3RGxLYpeRC3e11H21sCpnRnLdw6jdM8gQKMW6uv66KHbLM0k2rFuolPzL1UFKM0jNGs6BdbeSVkchK2ljDkaG/Pp1zhYiD3vkaJ9cunUgtAwQgzZaphRjWqojr0YIM2TB7Lerg4asWgw/eXLqYx3Db1qj+xT/TakHA3R7h+joQnxubLJVZjqFOSJ0dqGrivZ28cUXolCIXRxULCJ2axQKVCjC9xEU4hl1ycb80rPh8Rl1uj0LSDsrmaGsrGOhgB6pI0LNumM+E27welKmoxvHpvdy+zftO8tb5kbX2xht/giZcSWPzs9S1E1vyZBeywbGdIZky9QyInsorCuQZl0BsQpntrC5yQbXORGgm8ZpX0GtZ5FJ6HGJXBk3yAC2VE+zlU3PUkjCSPDrZIW9vTSfA6JIGnLtqZ416f9RS/ZdxZjEiGDN+gR7DADF8rJY0IqW7sgrK+TNx87a/+rXlpBXXG3b4x+8bRe/+xT/TUnQ0rWMXws9/tRbk+6bXifzwftudvB+IwEcN4YoSnbeaG6iPq3U2odKZTS1UmMLVZooKFKxgQplCorwfIoPSUm3G03cyl66zTHUUkMy1xym0Bnvkqwm22l7N8Rk9WbNS75ag5ElRhrsnIc4M7ELLZzjE2OQJiGXk04Yd4tKeZQKKVuCTiwtDhmOvoKRtAy1Zpu452nwcs22T+3J1EZUQ4793hypu4x0WyalyZmBHMdZiuYn4JptYmbcEG6m7maOX5MxATxr6FjKbOzrDRtbcqXdS98o9WALekkbKiWAUKuhowNLfCEESh1Aak8V4q1bCDGOh1V4nkjrrCcrAN8DGuncOBnI0TCuhESs8tjLZ+GqJxmTvTJroOTqphusSUsp7rcSjLUels9UpW4nQVokE8iyVeJWXaaCWeRksr03OeVcHy27TeJEWKMdyoi4YhTIUCbjqUgta5LHRpi2c4aIWleWZ6eXvlHqwQBdKaGxgeI1hOUSGhupqZlKZTS2UEMLNTRHQRGlBiqUyS8AIO4EAD9exFNT65KecuoWkL5I2YPmg+Bq4rM2Jzq95VvE2TtQu7y63NubRhFAIjlm0I7pi8flo8SOT44HjONFiCICnzkQpg0SQRtXJyU8rfPaoX+8CmuL0LIsWUqKjjMTqRC7WeJOCcGxTzpAoEe0Wgj+jpRYmbrrqZG6VviuJJBhe3JHk/SNJIwkNO0Fku8wzUXSUUtTl5nj+YI0FHR3kGLWHU3qAwPFXzYlu3iDne7DXEbS/Oeta/ebP/fSN0E9GKD7thKQ1nwPtZpob0O1irCG9q9E8XPh+QiCxK2hpjx78NOdMbz0gFdPrd62Nj7WHaOy+snvW33u2Z+0NLchTZhs88fRbZeaECBP9YbDkrT6+HwGk8nOboWmD7FYDplGkm61dYGZuVOGnpkpSwgTlWWBdqODMMOFM1aWBACaM1foUgCtcU29K3pbaHRrHCmTpqmK57q3m7pccz5WXVi8DgnVzl4LeqWgHgzQn34hALQ0UUMFLc3U2ITGJgoKVG5AuYGKFfIDFEvkF1PXs6dcyRJh3QNZjkEtlrBlu2XdM1smL8bSETO1nI/4XSYAMfPcqZKNdA6+pcE11yiYyetApQx7OU9Vl2JWQyYxVWRJcGCfIVHIBtCyrLMUcN5n5M7dbDg5cyTxZxneIWdCTX2/pfOOVzbqwQA9fGjUtz/1HRCVG9DcL6q0RuVG8goolCkokV9ITWOPeA86tmSTkwOh+o+sm59WWm6s2ddQX7m5+bLNY7s4pDUtq4dt4bJHRooaTJuVTR+4czDwsjCRKHNMzMgX72bDgifDdW3NbbCzQyxEV1vHUGfGu12r7coX5WchY7cm25mjUd39BtkrymweDAnIyK/O6Xg1ACjDueTa3SkOCcq9FvRKQT0YoN/6u9dnPjVUPN9HqURNzejTLypV0Nw3amihciP5RZQaomJjcj6hX4x3p0t+k3OMoPA68eFGFONmFKZu5UjE44cJM9JPOcr4iHXoRwr3CRwTcxpakA27FtmSdYZ02ka6lyQDFo+hJffS8KQF0iUzWXjBiPlbXXzCvCRmtcWdEnu+AZ9caDqCbfQ0Ry2ZSjaRwZvqLp8KW8U6/B48BaawPbrgsKxNl7Sun9WwOW3zxOtlgK8zn6wEkk6hPqXPzmYc0tDiAQXzWS9949SDATrw8FWb6OyiUhGFAjxBFIkopK4O4XkIa8L3qWOx7wf6BqGJZ0N3Itvrx+ztQOFCAX0Uy7pJaRm2H8vgtwE6fZC3P2eWywKQrUW2OanHsm2uzKfOcANq3XGydchIXYM+R0TlZM/1l2Qa4AZPfeitceaqlxG7Hk6yLuuIlWkEpF9Cy4BeC3qloB4M0J6HSpn69aVKBf1WoT6rRC39qVihxj5RpYWKTZEfoFAmv0Re6oOGx9Z9JMaIOvNN+TpID48kcxLuiIIM9wifkJcYj2o7eI0BpoGs+C3jnT21ost8mSHZ/LFFTEh884ZRlj3HOVNzxc9m4Drl5IbronjS1uxpW5McCYwhQ22LWQG3e8v+fB+OCYiU7eLInbiZZfCa6QLaHrOkD6LY6um6ERBRL0CvFNSDAbpUpK4qPv5EQOAfc0Wp6DU0oFikhkY0NFClkYICKo1UaaJyEwVFlBqiQgV+QEGZgiL56Qnf/Dg+BqM6UkMFktzghRIvRzIdLQ6PQBxSIxDkDGskppycNqenmCRi1hbt2FDbStKQjsxbIGkSnM50ni8HUMZTwnVQ4JxOBJf5khAjRen5cqnKtbIeEVeelQOleVSBLjvZkRErisqI0IsiQ4itrf4/dSIZz7OA2ymZrAXZlmGeJYfSvDjZyJXBWHip2QLoz24bO+DEZwFAlPoO3HjshOtuuXjn1TOUt+ntyzcZeuH84//fZ7ftWnec5aQ3H7zkwTcH7/XjI7duqJPnX6BkN9SDAbqtXfg+GipUKqK1lVr7UmtfKpaooZmKDSgUSeiu1Y6vvM42JAZYOosuufAgIA9+TRjM8175dv7QvCL2NGpPft5ym/IEy8isogYwMchLnYwkkR2KV69pkeMRQ2TSU+Fjpm6LWGnpms+QhcuMNMOZCXLJ0YK66deb2rKImfyGA8dwrViOYHem7BS7Y7A9zO5i7I4SN7chQ0deTTZDf4Wy+b5vi1pWFerb5TRor/88fXTtuTt/9ptLvlcc9uEDB67EG4G++eCll/5qx1VOyQdoztN/+wnnnrt41OBvTMVuqQcD9MIl6KzCE8L3UAhEvIEcAF8eLQgEPopFKpdQCFCpoKGBggIqDVSuUKkML0CxSEEJhQIJH0FAXgA/EHzjJDUhWkB5MCTl4Bcju4PpfirMkBzfsV0hbbJV1S0pffqzSxo5A50pMulOjMtSxpFC3UBmr2vX7uqUY5vPOfzOudV2LJErymgq9HBtYnhWK2J/hLZ8Z2PjbGmsfkDZtqBjWm3MsWefN/jYVV/tM+GP06e/g70b/+e80y578OUP2lqH7Hbyz245d4cBWPDwCWPPeGj2vMXUMmjEd39yyy8mjKgoCYunnbvjrtfO3vzqJ/9w7hbpuQDOKG9fvsnQCz/67ilHtP/u3leWrHPwLY9MOnCQMxDtb91/7qmXPfTyh+19Nhx79LUTL95t3jmDD/oVgGdPHSBOXftHf5pz/nwriTfONngmvDjpmmvmH7/rheMHWgIHCnfSXy/1YIDu04R4N7taiFoI30OlRMUCmpvR0kLNrVQsUWMLNTRRqQF+geITvj0/OWzQCyjeFEl4DI7ZfkZ8gx7TWEYSDrjssnTksB4TzyZyeQzlI5WU7VXs9lzw7uqtm13Zwnkzz+x9gkxR2SCV1Q7Z3oz0UmRNJDHkLHUz5lIDWWa1nUQG5upClqJR6SZFF3+9chibzdvYJ2MWR9eCuXNmffrEY38FxMCBq71y2a4/uKn2veunXLPas1ced974kzeY/eAB/ipbHfnT7w4b1PjVC1dPuPiEU7b47vOnD0yiv3nj+EuunbXppU88ptAZQHaUhc+95P38yhPp+Csnnz3xhAOvbnIEXo7L9jv85nmbn3D1zes8dflPLh3/5Zrv/OqHt/7oxb2un77xMXdeuufqG20Iv2YlcYTJgz/G6tRm2gKfOs6pz5b1lfOyUg8G6M4qFrehECDw0dxIrS1obqZCEeUKFUvJfvzVTrGkJtqWkOeJeNe65EBYPz5vOz2cMF1DmCxmYbswA5oRLfRpYQnV0WfUzDQZ4pTDZowkHkyG/pmV06VDlqGmnnJIFay66oZbDoTJDR+cJjNvabK8zEkGOVIYjymVxjOTVeZCf5gypH0grZvCmxZjkTfxiHqOZKxuSW1c5/KxJFtkyCwImOWu+7VcCQCwhvNcijkGCS3JqhxysvbXa8escy0A0XeL86+c8NVjY96NSPz6nIN+DUQh6KmnXsMBW5baXp183s/emrfgq/Y2hH/961tAgrb3/uis2tonPfX4RaN014iXGaU07pz/+uFB8xdMvPLZae+/H2G4I3DWO7//e1Q67PybTx4f7PvlI78+69nHni0fd9iogQAGbLbPgQeuAmCJnUTfXb+r87ydqDPrcVtg+3FbuPTZ8utd0dODAXpAP2ptQUMDBQFK8eHcRYoPRgkKCIrk+yiUqFBCUEwO7fYDCB+eT14gd74ntVmd0GqvpLosLOOaLK7UdIqrIccO5VeVg29gKGYOoKmqntcHz+rdGxZTBmjqSViOHSu6SjF77YNbJbtFsbOm+80dRWdnYZkYnD5xAIS8AQAjL8bTrHkXzonnGeOfmek6IpL+P53LYXqTMrSSMfuuKTZyHgy43uG/uO7A7/QdOGSzkYNbvbefB1Da+2cvXbmTH8dtXRtzbjv1tKmvj/3p7x86pOsX4/a4+UN1QlZxtVXLH835w9TnP9lpn1WZ1JwoTX37BkAQBACFYZQTaJHWKmUkUU87q5GV9LcAoDvDqKNc75av7VEYX8yeKxrK6NuCYgGBL+KNoYWHYgGVCjU0olighiZUmqhcSU71LpbJC1Aowi8iKJDw4QfwEiM62aBD+LrrQ/d7ILWjNX8rkmuw8IRSq02ZmPojIfRwfRAyy9xW9iCvflajohmS8sYFLmpaXqSFw5p3qGZxMH618SqHqsgCUwKgZhlytJWtGiGRmY6Rao5m95iq4tRK0sD6eDKJfGQyWzJZGeUZlZpnyQmgzo4I20zK6H5oX5D9svTPr57emyxScECyMsWz0NTPc4NDn413H7/v4PRug++O2/Divzx19+0vD/5u0z9n/t/ts/Z4dupWHR0ABZWm6J27//ie5ilp2PvmhyoX7X7jIXsPfPLpn46SPo6O7Cj1UKzGI5effNOng5++aToax+61YwXo06cPMOuPk+9bZZtRB6zuTkLjGZsvcMnSabVCaKUA6Lk+fTC43tHgD1KVB69JYQQi1GrwPTQ3o6WVSiVqbEKlkUoN5BdQLFFsPrNDpwgCUQ3VUEBAeEKNBILiNXWQBo5RLSmpIVp9sAwcx8wBAyaMiEbt0qdzmI4RmYp2y1aus1hu+ZbmCQl3uA53pnApXzhruyXQzIhVwuq/o1hcMFmHgckkuMloPrXremTyMhLut8D15OhqildsrqFCq9gzc2epbb8mra3VI1ardVmW/hYXPnxf2xmXP3jmwXei7+ARO5w0tIQhx1552u8m3PnTvQ/d6YAhG+CtOTxGy9jrf/uL97Y5+rJ9Dh704iPHbRjX5two9alx7+JTf/rL809v77PRvhf/YuKEQQBGH3POTv/v6t+f94NHdrn10z+6k9B4xo7NFfi2O/WvlVaKDfunzHjz1mkz6mQ+ZLNNztxxCwAnjaUoQrFIgY9iAaUSKhUKAjQ0UkMTKo0UFKhYSnwdkIZwTLY5Iy80gCb1VDmmE+z24v37YZwLrmSCeZNVUuatCTtaw0CaHMcQZYYJZaerCWSOWkOgdoBIrp83FiV4+0Gakmbv0dJH9RWyzbokIrFrpE2fq8ANhR2trNBScRaUc+m2MwuaPHZaoGHgahGN7hQrZHd/OwMqHczSuObdguRR3NrYWdXDRbxnNEoVf/V1Gk3mXvrGaaWwoAv+JwP71ds8FQr944v5n6O5ARCCiAIfnZ3o7BRC4MsvRalE5bIoFFCuUKUBDc1UKFKlmUpNFBSp1EDFCgWl5JgVLzBnTKed+uQ3CpNN+nmvmSJQJDhnTNKeSrq9/MQp1tXlPorYAy5Xr0jivgWlWEoCqfuLb9mcTl9L9oVOzXBiYCoBMWLoINRh1WxxiuH8ZcagsU+00sq5LxIP0bFPZjOOTfpBhRI9jZnXSQESwJJTcoR2kfhwIiNR14JA3eDVGwWeoUSsPcZodLCU38ZuPylTH3XiAvRPSFdYm4upTZzXklJKIn37MMlQTwj0WV2svo7F10vfOK0UAN2vcSkO1xnQWI4vKiVUa4ja4QmxaAmIEEbxEd3wPFEpo1RAazM1N1NTsygWqdKIcoX8AIUiFYrwC+q8bbnpnRwtZNM51BlX0nA2KqppFKfDBtwQS0054sxJtUxsrrQGOb0raXJSlERXoW8bkmyQZCWaVOB0ZyWwhLQGhgS0ww0IVvdCpqVlnFttgvl3hGqZuPFOjs6EEqMCWVrE2jMl0Ljm7Z/Ok3RWFAQL6N0FzZo2bHauG2nJ2QmpiBKg9cxqw9E8UaNrwlomx1OhnVepiJCcfMiF6G/HyKPgn5MHAkplv3ezpJWBVgqAXrvP6tutPbxO5nX7J1Mq75uZ0ffTqB6eXuqlXuqllZFWCh90L/VSL/VSL9m0UljQbZ3hvC86uucDAKzet9xY95y8XuqlXloh9OGnbb964Z85DDuPGDBi3dZvTJ9vCa0UAP23OYt++9K8Opl3GjFgpxEDAJy+DzWW0drOB+TIAAAgAElEQVRMhUKyblAwd2FCAr6X7IWkkbHLTDrmk3j85PiMPvM3CU93ApKuvShSsVR0JpxfaJS7sNB+ZA8GEnvg7gplzy3L4tfmOVAu/7L2vgyfqiOD2TGXJU3LsS0X4MAewZORlj4lhwMd+puyni4t1bmyQk3nMBz92keUKWutDXDcZdrTMKQFi7tyUuzoCtVN9NwFo46d+kknqGGDg66+67/2HdQ5de/RcyfOPG+w5Jlz3Za7fjZp1tWbAQCmnz34jL6nD7n1+j8C4ZJPPw1bV28tAsP+85mjntjw2JfXXa0hCssbHnbjPZfvuiq+PbRSAPSyVbr2BWgX6FiEQgGFQESRGkTyPfg+PA++T76fjIYZ8zTivzhWFIFIROkYowyndNpAJMHaAVyJzJiMsSCRjonF/wS0VkSNracDR8ISwkopHQ9k1Z2vajFIG+W3ArOYsfTAZI/12Sny4ayUn8w11haQuQfBshXI5ydTtCOfsQRzVkMqNr9kHJNwLCXtl2KvJ+yWeFyjqM0hayM5vjAqvofWVslwP8oap66PvBGn/t9rl61eER3vXrnzqEv+8NmksXXEKu0/Ze6ZAObeOHr03OvmXDcKAGoPPFHc7/ZZU/emBY8eNfy46/adfe3Wy65YT6OVAqC9wj822/Qlou4XTQpEQXk4MADAgsWASDYd9eVabcS4DE8kkzF8D4EfH+adbHGXiJIj2q664cVT32yjmLRrFQiNU14rWxtmRFOIFZ7MgdDFOi4MIfyp7BNInqzkOLMBNDpzVopx22VrpbFpsUSdqCTNbXcvxFZVDzIDc7oUOd2F7mx8s03KNTriZl4Ix9fXTRvQXZBTbf4NqOkoliz2osWGofl0Kal1tXir6KhWiwRAEJW+qzQtl2tS9B2744i5/28u0AvQ3yyt6X3U+Omses5wEEBjYyuwLYBCgPZOEKEEtDRScxOCAABqNXRV0dWFMBSdnSKMTGsl3ozU9yAEBT58H7GVLe3uGNk9L5lj5/wDN9y4+em40p/akO3kTMHSWYH1X2G0CjnMbjaprcsSV/x638E2xvMWE2ZLzoouJbj7E0tp5tv65CBoVj9D6+7o/FmvIIf4V1RvW5XRGcoEXLsrk9GIGjRoHSz3DKgnfzTs2Htnz+v3g/95Zg8f4nv3Pr588sJ5jz4+Z/z3Ri+flB5GKwVAN0ctnV9V62RuiZKBCN9D32Y0NVCxiMBHtYqOThAhIgggCFAqUaWMcpnKZfg+iiUKCukcZ+briCJEobwQUYQoPUA2di4nzhNCmPo9gMQlwr0lxncvYdGoBtIejK+1mpmD1JadK/WPKbbaIluN9JeSWbPcIHaY55q2KYpxnfkts/R1p6fTftSN7qwGQyJInpXH9NEMfGuGb1zCtlfKcAXwQhNpiDsLyR501rtL05fhWimSq0nQodm+SLiYzWssh2Ev15ERrjY3Jnh7DD2iFNWxFCsTsmiX69+Yfd3CVy494PibX9rrilFF87kw20ErIKWuR08cssFxH81euPEl0/f7NnmgVxKAXjbq00xtHaKjU9RCeILCKEHMKF2xkuAvR9K0XylhOnaAxC6RwKfYPSIt6Ngl4qXrDD224DC9NXuYUbqpv2G9GiEJO98tSHdVKwYD61MsBpJFKaqWQp3kYtielCajwzfxdLMMK0qfOsadCGrINJWlEFav83EsjyVhtk1snJaXocyIeolpoBIejxOkEfm1zE4ykGAVe9wkw4JQksx6aVAqJ0qQWoCSpZvEUpFgbWM9z5eJ28zRREZeeOPBZEaGdGjFQkAYqZbbE4pBCoysYhcrCAZFny0O30OM++2bV4zazHzWv3//L975Ir37/PMvVhm+iltKcZ9b356655ev3/i9sYdfM/61i4Yvp23fg6gHA3RXTRQL8Lz4UxOFIMGcMBJhhDBMPm6KEPKaDBOgPS9xWEckKCL7c2dWYfJ1MxbHp6Iqv43F8hHHbiTYFHEccUGJEVdK5uk6bTUNVZneEvHNQSKdzbnkT8u/cPPbY6Q8bdnUyVE4IeAZ8lMJlBrp8leNIvBHpnrqrfNs8oaWayv0jMiIMglpezJpWmGr1IWmlRaop2XqnO1AE3DMvLA57YFZp2Sj/eNsa6wrlmsjzU/+NqN9va3WrtDCmfc+MnvoyYMdPE2jx6578ZSHPx47fjWa98t7/rjhjufn7f4RtG76o5vPvnfHa39/9r3jss+w+jejHgzQvgfPo0KAYhFNjQgkQIeIQmUyxHgHAU8w/7JIzorN+ohhVV0Aav9+ndO41uqkfq1S46ikQjMW7+aThG8mXg8Ev6GcEP2G6gjRHrnCnU9t5hwn7LI9cipRJzu5DOocscYry3JW2GT7XpRM41HaSXKcu1ZHQiaD0X2xpLV81Y3AbmjhS9ccOO7Fz0K/0H/kYffcemgfF9P6p99z2WknjN3w7CqKa+565T2nrN+NVDH01It22eDayR+OO/U7y6dfj6GVYiXhotkvfPb6r+pk7jtkj75D9gTww22oWkOfZjQ3UksLxeN7XjrQh9jSjFCrIQwRRajVRK2W+prZudqyx2r6Eyw/soInblDrlGyRpDkBENdEu6SdoKaZzEyIMHhgVjMjROtZu67zzXCZDGe2i8JIjvesiSXEI2ou77TfwKOn14IXhXQdGJ4KKTz2VMQhMU8UN9hslnpE7C9CBNRStxgBNUKNEBIiIPzXV4t/JW29NV56SQt5f/5Xdz3xQU6UcVuvvs2Qfl+vWt8+6sEWdLmIxgoaKiTiyXbp0F/go1iiUglBgGIDFUsICslm/PJg2QR89UHCMBRhDWEtQfOwhlpyjbAmIkIYohYirCXVPopITppOPN1AFIk4BNDRx0KofJNTI9kvZv16jxnymjGuR2EB5pXIsu716E6Hgy3abqwye9zSS6C3cZYnhFS4nh3HBBIkbWPWOJMWMcsRlIqywzWfvmUyO1WyoxucWhRVJnCUCcxCJvb6iNxRiN1Kk4X9y6NBG8JwcTRVgtHD+udEWbN/uXu5vbSU1IMBOghAhMVfiditIR3Kvk+BL3wfAiASYYywkYjPlqUIIakhL1/A9xH4EAJBQPGU6tgZwqfcyUHCkg8U9TqWjD06v3oy6i2fU5XjzdD7yMIOz+LnCeV1jVzDVtylw/31aQyVWSNWt+rlMBiDmXZgt5hr+BNsfikt0y3AIFkBt6t7oULYVY6DAmkXzdmiqNKG2T+SkEu6Mgpz2WQS+SEZXSKRZo2scu72TXVZawYbKovWW+fNnCgtjRsC3xrf8DdFPRigl7TB91AqwvcpKDM8FSAkbuhkOgeEEAh8+B77WGEaLLEPJCa7j6/IFZjhvhC2JZsVywQFd4Tsu5xHuel2r4CV30zfCOMjncEGRxvjlPPEkqmlzqMw4dwfwkMMzV16qrX7ZKnK5Wh5SRE/ihRbjJhynFmBLNcznfIR9+ecqmojxtZ3kvTPUjZemGGEWqQKR6Qtk5xd54nEnxPFzpz4mhVOlVAjbP0lztQLoStq++DLP5lFw6gUNLeWBuYw9NIyUA8G6IGrUbGIcok8P/EhxDPkANRqqFZFGKKzC9UuVGsURaiFyaKVeI5HqLwTLv8DOeoMJ1WvXDARyyGrVlPqjNbYjGtGppsinTwgfwF46UEenmQQybwUxZxexxMHVUgiwWS22WI0EUwNgNRcCOZ7kdd5cz+MLrnIK2rJpmGr3omJ+BxhaNIkm3xBytFCqaeFJS1fnNADLSNXGLH4LdibNVTm9qzm8hGKWXPTM+cP2LfEvWdGwRgfIY8Se+eTsmJ5MspnvQ0A8+tbKnrmhAF7PVTqVwbQePDUd24Y29G7F8eyUA8G6K4qvmqHJ4RcASgHCX2fgoAKBTQ2yjWBkFvv8+pi9z1VDecYbaC2/t3bRqIWYg3KER+IzwWmzF55fbS0/MLeHCPD2dqt20GYcJYCkOWBTTsxDIhc/iKHx4PYm8zQR/NsaNic3rkistcnuD4cT9X/jBRNX7aedzMJ6O74un0RnI2XjJGv/Pdl05qDxfIBNODvO2nuA+OXKkrvXhwW9WCAJkJzI/r0IT8dFSwUk4NRiBBFgiJEobbtEUWJtRU/VT3EbMAl0hFWGsVJ3NQiZgODTmRPdAZgDeNI4iNCdVUO1zIHPppkJFcPWVV7qeu2QTlYI8sHcd9CMQjFALOYyHHlfmQ+J4sn401YgpW3mFiQUxPOYHi3baGk3SmtuBXhhOgs4HYXjr70qR5plbzhwGWi3r04lol6MEB7HsIQn3wqKEKhICoVBAF5nggKycpAIF2okkyzQ7UqoghdVdSqqIXS3SG4aQyOa6mT2kvNRmnp5EyByFw4kGV76u4U0ylpuF+UhvIYKz0iRwQyLxxmvt4IcTkcQLlMiZsyXVtP4nJYQrx7LpPjEiLSIhIQbzZCWggonVen8YO1uEiX7MNUI4qS2XUAQkItSpzCQiQDyPE0u5CSiXffQtp6Mc5aThHh46duMPis4jq7nHb9Dcdv3lzq3YtjWagHA3RnFzoiBEHi3wgCam5GsUxNLdTYTOUm8gMERfLiadECFCGsgSKEVRHGs+UiEYUIawhDUCSSFS7pvhwUiTCKD4dN6jlRcvaotNATBja3mu9fSqmDOyJ1aCm32ROBYLeQIYJjimH9mfwcAaGhuQFPsHn0kSiOrSo6NFFOZu061V+Gx61SujZaaSIzJWewRa7mChmGvACCdAIZ96CQywctc0Rs0Mzg4T2PhI1SFzwPT90UMdxT6sfnikFoZR7Lj9K4QiDwkm/DSFqWVUxSslxyZdjmssQEP3gWySLbOPVCWtHlUnst+2weZ3w9dHVHaS8NjbxgxvsTB/epvv/wSXuMv3DYnBtHW+sSe/fiqIN6MEBThD6taG6mQgGNzVRppEIhCV+0QHz+sReFqFZRrYpaNZ3OHMZIKuKF4ApPKTGfTfPNsAFTg04LRBJXsx91K9jGyiyws61p0zh18aCOcJPNDNHw1NTKBetuaNZDyJCgtwqxwIgVGkGN2UaMP55yQKlhG8WTEFzbUPTSCqH21ZZTQOugwQAQrLP/8fueefbMf2L0IJOldy+OOqgHAzQE2jtQrQoA4hNRLqOxkQpFNDZRQxM1tUZBAcUy+TFqE8IqujpEWENnu+hoE50dqNXQ1SmqVQXcaspUOi84Hl2M5zDwwUY5OToeeNSGvJipBQZbziyAoyRjI1J2jRaYfavsj/SgbnuwXqbllGBIdiZnGOM8K7FTnlvf+fKNWzkMmDwVbj0ptWElTxJFOpoMz6804Y1jdMAKH6kC5Ch52WxLIUkZMlexPn6YMqfKO4pBRnf5qQ09PXaQuRKlN5mG/S4D7ZfltFMNCd/ZCMu1F8eC2W9XBw1ZtRh+8uTUxzqG37SGg6d3L446qAcD9JeLhdznqFhAoZ0WLRaegBCCALbGT1VRwTdIShekGJvVeel59XzhhjHSRaRmvyYMjqt6KRO+XQzdDtk5pZnQX7cCLiQVWU85fCTAlTVCaAyQCa2boqJkqKogkjnKKZ0dDFdHwVRDB00bxdSFlC8DCcYq/5xYhgTNq8DGD7SmQm/bTFEqVTODRhTSI8hWzzQLDCUJBFSW05Ew77Gz9r/6tSXkFVfb9vgHb9vFOTrYuxdH99SD9+I4elvyfRQLOqQCnofARxAk4IsUrGshalWEEWohwlBItzKYZaQm9qZIDekf1DdL4/ANFp5cO65cUwZYtXTiiNuShavG6sZgfCOnXRuUBeIcILTf7IhmLB5uKOnybttamRaiHi7SFE0jGgDb8zOnYIldJ79C4zc0s4UAZsmQbdgaDbWVLgR8QWbS8QOrncvSypAsFYvF8O/NHMHWvU8GTBOw8WaY/FstxQUdH/x53n3Ipg377z6oZcschl5aBlopLOiuOdXynN3qZK4GhCEA0FkFquiqohCIliZqbECxAOGpPZ0BIHZfplttRKXE+CUiEwRT+RxbtUrGnBhO+8h564xuPzIDncwufXLs6XgtiRWSoZpws2Vcu5ew58jMe0r2M/WIv5dlNCWcHgaDxdFcyFjZPYbswORDoDxOzYJWDKa4/Pa1m76R/OcqPqNB4rdrbQDDxdHQLrZtOtAUwSjwnFvW9dJy0UoB0JU+W1Ax78BgTuU+SXepfysVi2iooFBAsUgSlFMITvg9LzkKS3jqUC1tBIzbehEb2lKdbiFvyR5RJINZ2YlOdy05BgkFV0aFw2F1auahkQv7OkMlw0B2J2eFsxDhyJfOpuspDFtYKw2XEO0FZfAbZqwdIjNr2PLJe+QappTseEUsCaQvnWkew52ck1eLEBK8eJuB1JlbjRAC1QgAfJG8OylWAGUPAgihxj9XHtr6M5yih0Rh17wXb82JssqmBxSavl1TLL4BWikA2isWgXoB2islp/GEkfhyMRYvAQSiSCB1O3gCvkfCs8aF0qorYDqXnfOUlWeDb9nOSEqT/kTwE0O41cIR09EdNv1MyYQqWaX5U9UzEDJfTqIcCbIZsLrBQl0mPLEEwdZwk9JCxZXtgbSOSY07dW8Vchvc0w9YsKPYrZRydBi5pHRYjyG1tihc7/XbKXLQV06V1NkCpKvMOQ8rVUot5YgnR4jHnEGstHUjV0E58+NLTqErxpW3v1WS012sza3kC1X6CwhgvY0MGUtJtQfGF499ed3VK9X2sGXIrsdcdNVpOwzwgIWTdh0158a3L9/E4G+bOWXKkr1PGuuexmHHqj0wvum3Ezqm7m0z54tafsrKwtdDKwVALxt9/AWKAfq2oBBQIUChkPid46XegBr3i/+SqcpRvIOSkJVHAHAdDhsTr8ympUxJwxCxE7ZMTlblkrRSCOdVy6hOfEiFy+S/njARxfCEGjCRIprySmvjnOmsXq3ap0DJuwLgPLqZaShj3jJnsRC5qlqkGex8m01OOU4kC860FkZvpQwfl2Hdy0jQgdVdCIyb9Ji8tZOiSJ93kfDqux0paSmkyw6W4fWS706kjYHKkTALQbVeAASq6a5hy07x+mygY85j5//HAbsvfvKVC0d4zfvceH/nujZz28wpt8wftUJQtU5RFIbk+8swUSUrC18PrRQAXa1Fi6J6D40t1BJcKfjo6MIXX6JYEM2NFBRQLlOxiEoDlUqId1Cq1VCrorNThDV0doqODtRCdHaiqyqqNYQhqiHCEBGpTZzVGCCSKXQe2ypImtU8BPY6BaM6yQNnkVTmZIWbDt8G2eOQkAY7s145NHDfAt/YXpLhA4EdVw+JmOnHjUQJ5Zq9xkxRbWoaGCLomGgFJFzCWsVuTGTkzLys5IuTSz9s370ngEi9XC1RofiNvlSsjO853ku8t5SUL1iuuCg5RzO2hY0jLnlycuNy+REqLE1x1qEqX/Kq/ZF8TbyJkra8TWuuJ9wPlp7Kg7/7X7cet+Getz1/4a07Ln70jEPn3Pj25ZssfPbig8/89edeoUajLn1kz6dueO39riPGPtMw+vwnL2+9bvfTH/qso7OrstnZk+88cmgeSM2fOHrstDHfDT6YM+e9rn1u/dWJc3/KRY2d89A5J137wme1asOw034x6ZiNF0wcvdO07Xauzn6v3/5Trt9w6uETJn/YutbQbVZ7409bPPrCGau9Z/Nr8s/ZvCPNAqqzfnn2CZc/+Sk8f4NTXn3ouBVSXAatFAA9b0nbKx1fdM8HANh4kdgMTQA2XIdaWqhcJghQJKRHuNolOjuTSVfcsC0UkpUsAJAOcxkVQ1U8y5JSMR1X6Z3L/NMsLw5SFkTKCDaqOi+IEFu5lMGZL0SxGRoa6K+H8FxK242XibPl0C6kHN0e5Cmm2E48gwRLJaPTACtRPa08zQ2BLme9AKoppypGu8RYEpZ7PenBaB8bj5XeOl8TV5tLNnz0lhrpZgaJ70mVEmfjRTR0MY7ECiNvxBYjF0x8+3PsmPYNv3rkunuG//y967f10L54caF59FmbPTX/tmcuGQIAi4594Plz+wVon/Gj7c/75QGPfD9f+vv/WOP0Z68aTM+dttGVD5/80EVM1Jybjpm46m3PzxxS6nz94jEn3bH7MwcCf5/V/+Fp124U4K3Lh9+9/l2v/GZL8dp/jtoCWwBzbnXwG/L3TNOdPfGwcz87b9pf9l/d7/iiXvhaWlopAFo0LkVPw29OmBctRlubqFREoYCGBqo0ULGUTK2LItSqCENUu0RXF7q6EEWoVtMjr/RhPTC/Ibdc2D/m8pPHKbGhSJJLvaFOFif2F7HkiFy1KP1n1E8NViwAohQ1DGlm1zvDu6ok6/5Ph0ODaUV6TKlqlHpI46OqpObxVHTuwpabXsYy4s6ERCu5ewbi42lYjoi9KaUPqVdGae8kTCewRen5DBGpXo58KTILcvPlWG1PIPDUEkeZzZo87UHAE8mwXi1CF8EDPAFfoEYIBHwoHiJEQEcEHwiBokDJS5KQr4/S1ZK1VLGCSM6OCKNk8SQRaoQqwQNKXqJ2lSCEilL00BaiRvAFymmtCglhLFBApIsw5SuLEw0Eongwk+ALBCt2Y2eKjDWfDZvvsMqlpx9ZPGzvfQ7Yd7u10Mkftr3y8xOufvyDrqDw+T/mVmYDg3OFr7/zboM9AEOGfGfOnH/yJ0ueeWLGnI9P2OMJAGhv/3Ln9wFgvd322igAsPDFF9r2umzLBgCbHbLfRs9l8WfIX/zsHz/a57z9V/cBlPt9XWd9rRQAvWy0YJEIfHR0ke+jrU0Ui0LOek7+CFGEsIauGsIQtZoIU69rEKAQkO+hWESxgEIh2c0jCOAH8AR8H55PsUCnq4EkcmhYLPhyCeignIbxDT00Gz8+Los3HqQ3A7ZNh1QLeaXMMsO77dr6zrb7vLSbryQzT6XsSstb3klPoqRH6xrhsTZJkSa9fIB1ZXQhZoFLZYSMr/PIEo73oMh6atzyEUIitX+FEcW2dt1Di3JUEEkOKbWX2TtVsuJSsrWVChg2hO2hTvUUMAYVssnZr+K3RBhkTbNbHor+8upf+g09vT+wMAkRw8+Z9vJeT//+sYd/PPq6fX//8tGM+YmLjp42+sWn7/9OccmkPYe83dGd9ELSMfY8r1bTfOcRUcOeVz1z264qaP7rqFQqAAB7CYibP0M+Zb22FUo9GKDDCNUqABH46OxU7rbAR7GAUgnFAKX0cELhwRNRjAkaDkaIIsitOWo1dHYmm3VEkYiPyKqle5aGIcJIaPuXGpY4rz/WHo82+nA/qSfYil4kfkPp4IYOYZrrNqNuA26sUU+RWuVJXgRnMBoDjV834Q1r2mgGZNLSPkXc6U5yoWnvUDt97gmTLc4/k8kYWKnwmSfyBZnFqOMvB00+QwOWCS9095cQWtJmvsxHwtn2G2S0BLx4pZ78MyA22YMFJ/aKcpdb+eKaFFeYRUht7/3+gpNubz71Kb4NXdvc974cMGz3I4bt0PLaoP99O2pqbl787iIAQHXBAqwzZGAR9PFvHp4Wr3lYGmKiWsbuscH5P5v04zET1i1FC9/4y+frjWSLyftuP7py04MzL95qS/HnBx55B0d1w29Qy9jdB11+y2/O3Xb/Nfz2Tz+rDPhapo30YIBuqqSzViNUaxAeGkpUCNDUiHKZCoVk5kZ7O2o1QcT81AC0cRt1EUO87yWzql2VR7P4VCjDFAMWY9M4NpYlg2YU8946DyRNiMPpgdR+RwIM+uQIYWRWkkgdkryueoLhYVqNeWY9PnPRFKgb3eyBBh+kAEZd6q2XbfUbZJo9pNgoLRDpvtYipFuUGEMKETu+T4q0DdXkkfV++bWMqIRpLRzPGRllQlAzi4zcynRsrUQ6/cZoRFnZaCXB+zPyys6rv1w7NwMAOh8+ZvA65Wp71HeTPY759R9OH8EN8raZN46/8Kl2P0B5yOW/GOeVvzxy9xuO2GRkebcrXrninJPuPmP3g9dfrWn9QZsvfbrlXZWon510/61zT9h/xA1VhIU1D/vvR0dyvN/4jMmH/+CokSOa19pyx5Ebt7S0YJ1cfoPWPeneq987brdh58MvDT3jz788dul17Z5WiqXe7/75i5lPzquTefh2A4ZvtyqAk3ci3yNjoYrsCQYBPB/FAhVKKJbg+xQUEBTgeckGpCL+XthUqsQ0Tn5FFGm+CCJEYRoOtg0p3000np6cVhi5pEXKR1qFokiFSOBOf937DWX1RvmjrIpqxlLcjkCj6jotPHLdkA5M3NK01SYXLkjINgxzpMNrUm5GTz9DVT4XJZdfOiVsha1s8QQ0gzRmMPa8ln4PKcRs41NR/O0kRcqNZx1iDW3zHTsGOZ/Gaqy/CS6/V5PV/tmseS/8PEfaKpse0LLumLz0VjqqtbVRQ0MBne9cs+uBSyb+9bIR/2qNLOrBFnSxQLX41MEaurpEqYRymfwAlQrKDVSuUFBAqUxBEV6M3V7yTUcR4m2giRDWBL9OtoROXR+pN0Ok4JsgOAxLChpiGf1oIO3Me2ogK7ZQPJ9VP8gohHRITRpHysBM66qECYr0R8a1C5s4gwZA6Vi/dOMYfX/bBaEhKWuZeM/a8HvwwuTFJfsNIkahdMJ4evYCgRU7xzvowGSUgxCg+JBGqLi8PHOcDLaj2WLWVr0LfXqf3QQZ/hbegVN5YfEMGVKywQMritnLYbsz8nB31gXWXM18WGpZY9Au5znZYwrKzTlPV0r68tHT97z2z1HYgcGH3jpl5UNn9GiAXvyVCKNkpLuhQuUyggCFAvyAohDtXwkAi4j7FlLLy+qlJhMzIlX/nZ4Kfgu4+4ZpXGbl6YZwpM//k2lp05b1eSDptZqsHYeHMko6q1pLiPlPjG6v/EepwqpRQQ4YmdnUfJ0MJzhYqOkZBCLNny7DZY4MW1tijZ0v8PeSWq98BogsDW5+cvnpiWUUd1mIz+tgsWS4SGExihT8EYkoLTekm1xHlBzgm5S8AJBMhJcFK9IFkzF56ngcda5rnDUhVWLFG7vvuCUu0vBYYTkeHjKPMX0AACAASURBVDP4nsYQ6yOEWoMuoJqWiDC8CydBo655n3/6wEPIpr677dy0+WY5DCsf9f+PO2b8x79aiXzqwQAdExFIoKNT1BbQV23C91EIBJ9NBd2RmhCb4Z+sF4C0GGRNUUGchCBpaUodwBDE2eWUF7HtzH189gwBPYdaXz8nCUtPJkPvYneTossyrWe8mmM04Co8UoXbndpk2oCcB6lDWY9oqG0ypALt0pPdBTUBw/IV2ApnTb0wLjQhdqYoI5syPL3gXQ2ZoVwRTuU1ZmdRABi4PsxZHGFY+2JBTipRB5srl7XUewXS172kO6avHj9qr+fOePrKEUurfPTcBaOOnfpJJ6hhg4Ouvuu/9h0k8PnTF//HCXe/11Vc99BbHrxi1/6wQuZPHL3nwimvXSA3Xu3BAF0sAIDvkRAoFtHUiJYWKpbQ0EjlBio3kB+gUITnJ1ZHWBPVLkQhql2iVkW1KqJ4GWEoXcwidWtQauGSNGMBUDp1L7a5pG3O92YyKKkY1oqyPGYrUOTyOLv5phDodq6Mm1GzRWq7OpEui3i3I5Hu4qkvUDhcGZbTQPYGDD0dAllc0v5ZyriD9c4BR8+MFCnzRrN83bGMcb9uFBVGWrJj5EpO2K4X48URoWlRVqp1k3Op93JLVeRc0r3MC7gz6MNJV3140P1uvWtffNHer1+WX8cbcer/vXbZ6hXR8e6VO4+65A+fTdpj5hUTHhnz8Dt/HPr+ldvtdcW4v9+w7StmiLWEvAcDNAAhqLERxQLKZYrn0kUhOtpQ7RJtS4QQ8Pz4xMJkw+j4w4wihKGIQnWcYBiCSIQ1hFHqj5Z7Rid7d4AoOYFQzrBOXNV8rzuoMwYhf1OvhawCRFrtpowj8pIALsSSbJvAym3C4tpsbjTXEUGZugzIY6zXbTtNrDA8pJZP1vC6qsYDiYfUVs3w82qPjCs2O5ALJEOZVB81sAwtaV4Izvk8putZ61OxEkibFu7Q4JKtCO5swlXalIa6w5mQiLRwHhfQkxaAdR7F8pC21Bvtf73jhONvenUJFQYfdMOdF49ddO3WR5Yffu60NV88c60xf/nJp0+d1O/JEwfftcd7180da66xzl4dfso7O6oF3BNHvaAv1y69fI2xdnz+xNG7vjR66/nPTH938fBL7zlp7qXnPvDmJ60HTf7dVTvxTVM/eOC+rn1+bSzaocWznvqfOydN/t9Xx971zjU7ZOW7dbX4XMeoVosEQHjtkUf6ff+p4SVgyFGHr7Xtr2fcUHnUDDkbqL5xyxG7zXjjo0VrHXXn1HN6MECXS+T78Vnd6OwUlQpKIfk+CgXhh/BrBFW1YoM3OZywWkW1ilpNRDH4hunRrhEiYzIcADYxQ/OTMiAAv7BwGQwiyaon9q3q0sa3Aoh7mxZISeejdpv+GnjHwyGIoxIXAmEKNBJVt44r7Y7gwDs9247oTu56LHgJNW57nxSTwWC76bOI26T8Kn2/CoPJiBXvbspDRPJODf+YqTKZaRmXdtsfk9BdQPLrVT4WC9m53d3W7fKQpSG11Puz/z78vyo3zvjr2PLbV4zd/YLt371tzJiPbni+87Rtnp+5+hadz79QPWn9F17ccMxlAR4w1ljf3Zm9Onz+O2oB95ybxhrLtU8Z4lg7/vYblftenD554cRdhux763/PnPa3/jPOGXbu1DN3OkVtmdox7cW5m58tD1Ps+Gjar+6aNPm+55dssv+RR939ytQhfQC8eN6mhz2gdTiC/W+f9bPdATz5o2HH3jt7Xr8f/M8ze/j43Uf/HDg0Bvs11lrrk2kf0UdWCIA3/+xNfuX5UWL6OdscfcNeLoDumLp35fDfAfC3u2nOi6eahz2+eeHGwy5/C0Dph492TEn2+/vbBUOGX/GOJWq1k5+fP/FrOie9WAAE4n3sSiWSqzyq1XhttzJsY2SRp1sFAUol+D4JD75P8ZQ7CWeKXHWDjKdGt5fHZrfCcpgCqWVHit+cM6D6qsIMJ7OK8vSF3QYYdpOLsvwD5JKTdet0vGbxp4FpoRudDGEGZqVo+4tNIfUpr66lXe96v3YZEtIxyAw85UJIj6MxZvSHHNq69JHNv1RetyeELccWHksrFhxsy07pUu8vX3j2i70vHNsKYMiRh6y923Pv4sLRW87848uLO58v/fAnm9/z3MwF856Pxty0CmCusW4Yl7M6XC3gdi3X3tBcO746sMG4A0dUgMrmm7VuOmz/NTxg5Mihc179EFAA/cm8ef0GDEhu/nTWhmMmDzju53e+cPuI/gw3t7/69TlXu7O9y/VvzL5u4SuXHnD8zS/tdQWpl0Zsaq0WAmCdfQ8d1Qhg1A/Gt539QrYFHQRBbdqku9469cKhPDh88Y4pb4kgEDV7Q0Jv3V0OH6Pheeuo5T0dOJviMwkDnwB4ngj8ZGpzbDXHC/MKBQSBWrFNqaVcrUo0Ec5PNiaj/8eMHUGkhRgVya5XmhEtZ0aTClSebv2axzWqmdvvyUxF23mdGt0Eq2uvTGy2usS+gO0/ZYZYPWOJTieGg8vCViJHvqSr2tkSGH5YWBGz8FQFkImA8pFKUXf78tTl27A/EkMh3lBxddTHkGZDfZZ6N852JcuEYres+TRDcyJ0GPC3fKSWettUGD1m7UueuatrwXanjtv0hZ89fc/H742eMBT42FzDnbM6HIBcwG0v146eOM6xdryU7CvveV5yaS8Vr1QqHZ+kfYmRZ0yd1HTHnZcdvN/jBx151JEH77x+iwCAF3487JD7v+TRggMmz7lpj+RG9Nni8D3EuN++ecX3Bq05d+5HwGBg3ty5qw4cKAZZIZgHodWhbIAeuNe4Pv/3yJ2TZlxw/VYqRtfjk+6dW9h5v12nP/KYhdCFbc+YMsWxhXZ31NlVW9jRVidzR1eyMWmlTANWoT59qVBAUIQnEqdwGCKsJau31bgf1Fw00yFgdOptd4GqGrJSGSaHtKBSDr0mx1c2FnDTjKwL4zq9TSNlGID127Aap6ub7AoXNk8+MjoDsyAvqyjMYiQtikNzsqJkF2x3qZiGpztr2j8rO7ZWutu5HgMZ2fxZmkiurox3akePqbbCfNDaUm9v9I79rr33mYu3G1t+a8oDH+x4+UZAacyY2j7XTd/rzttLw8cEZ139/BZX3+FqwHNWh3Oyl2tvvMxrxwcMHdrx5KwubFYEUP7ODkf+dIcjL/nynT/cP/nW7292YmWHC26fcsSQ0de+MfdaO+4nf5vRvt5Wa1do4cx7H5k99OTBGLHvvl8cP+X1My8Z+v5dU/8x/oatMEKYIZiJ2Y/c96eLRm4rpt/7cOMOD+T4oDc4asKOvz31nkl/vGqr3YpJ2KLfTPrl5037Tjikafoj9eezO/r7/I+fev9vdTJXBmMrDAQgBL5YIBYvFsUiWluptR81NVNQoEIJwgNFiEJ0dYquDnR1irCGri5Uu0QUoVaLxwBjE0M4O+aSBOo0CzOF1OMiMADLCdZmeJ3XGbdGiKF7934JU4QLR6xbvtbZ6XWx+9o5KJxVVnVy5rM5eTRl4q6PraQeiz8yjFYJ0Ga6pnDVXdP4XTKJ2Lxylm4yQZvBLn8KNooYM3ct//mCzqXeG51+z4+OP27boYuotN4hd9+1SwnA8DHbL7p6/g7bCjSO2bZ8SueYMRWXvLzV4ecyPnu59j7LvHZ823G7nPiHaeGBY9WsWK91o++eeO13T7zy09d+/3aOI2jhS9ccOO7Fz0K/0H/kYffcemgfiG3Ov2Ofg7+3/lodpfUP+8UvR/uAFTIf2GT7yp37bXviP75ae8Kd9w1zLfWOfdBr/2jGa5tdssbhz+374Lz/PSjeM2T+bbuudeJrP3zso/EP9N/n7prpg5496thLxvjvfVarDFhn09H7HLjX8H51Lel//Km3Jt03vc4yO3jfzQ7ebySAw7ak5ga0NCfHqcT7kBHFG9ehVkuOi1UnBzIheb17/kgo5vTH7N3LcE5urKZcBtuGct1Td4EOPK3DYsq0EPWrDLjMtTF5XO6BdSEpv85CeUMrB4Z21wJ12/nIaq5If2Y3CWZCucpoH5Ihp563kBvoMMnrSEiybLQpbv6V9l13zH5/3i/uNFkZ9d9v75bttslh6IkUzjh3+1vGPH3X3s424xug3FkcfQ489sDTfvfgpAc/P+io/gDenzLpqdp3Tpuwe+mzB1z81el3nC9x9trzf7TJMbf/+rbvb/A1zRTp20KehzBMbvnSD89HuQDfQ7FIJbkXRwDfB0QyJCjJ8L5BzpZjawsTQ5ttDer8A2ntgTLP+RQO/VeznpgVA9moZHgJzV+mPJJYCW4qITqESZUcmrjStSNmAmsdEGmwZWK6Hj1LDQ39AZI7fOrZ0dnY5ByYcnhpyGNltH29eV7S9x5liIr3euZxBZRJEceVh8bG1/FJsvH20LXY/mWqft30pbWbndfU1Do2b6uN0sA1vkaF/kXkb/Xja0Y/+48IG67YVTZ1Uz54lsdNOGyNqbdMuueDo85cm16bPPkVGnbxhFEefmtytm7xw8vv3mrvMZsMXqM1+mT2nx+/7aILb3lx8uF7tgx644Yx5a9D9VIRQiR71zU1o1gkP4DnxXOfk7kZyQwNOfoSY6gE32RenYgX78bz7aIwBegomWAnMVdtipRWSyf+GuCV+LVTyMhaUpFj0qpA7V83j1RdttDQRmrogRx9uEoqis2shWg2dVYX3khdgzanPnqbYefFeOrkZMyk6WYkmlEOzibHYa5mNDCcmX8JbgVy5bt1Mzk1n5LQ20K3egAIQ63BfS/oV+qzvRnKGdLTnP+9qP+OxxzwL0y+G+s22PHYH24w8erJd7515kXz77hrlrfNz452nma71v4/OV/efGfYzsfdvP3I1m22u+IvE6+6/+IxR7euUKVj8jwMGEB9+1GphMbWqNKIQok8P1k6GO+dH2+EFNZEPHgYL06JV6AIAfIgInhRulzQV+dmpfYyJRaWsVNobFODMYNZagqj05pOGlppyGXAihKlTa3LAXSjWjrhycGWfWtgvRNuOJurnpues6zbzEbCEk4Zp5hngbVTZlaBOLOf1UplSshok9zqCbXDdVZbpcmx8pKfL60RUg/VFEwb5XnqlSIM1x21R21/bkc2eU1+YfUevK5i5aRuC3TTCRO2uubcuyY/vfm8+/9Z3O3Sw79Tp+TSVscctukVf3n15Zf/jKPHLpeSbmpvx7x54qOP4mE8r1BAuYQgQLHIvBkApSsAk4HBSISR8k2bB3K7rLBlI21XB+Z2JNeuDs646tZxZd85yJ0J24Aynmc8yDTquysrOyLpk7VzBLAJfBLZlGEoQGSVpiM567Rsp9o2nrqR0aLMbpArouxg5azaz4oLe+1PhtNDAq5gcxCdcG9dAoTG5ez01h4YXzjmhYF9YzH9Dn/g9au+puUQ/+bUfYu33g8njL3g+LuPOGXJwsbxx/yHcy6jm/r06QOgs3OFTqlUFEai7FO/viiVqKmJ/CC2kUVnF7o60faViI9cqakJG2bv0pjz64l0o3pj6K+OQcB8KIRVK5xGYhJA2RU+y+LLMkIT4SIzboZ9l2nKufAry7q3bbT8Rzk8HFl0ZpHH7AqJf3Qe97m0Mq4mJCMVW8OciJT6oGFHt93iLk0odamr69hPrd/KwI5405j4bEOA9JWNNm29/O6K0v5T5k5dhkm3vcSpji7Jat+fsPdZh/1mLlY5esJ+S7Hl66Jnn/0zgPXWW2+ZtculhgqFIT7+FJ4nyiVRKqJYTAYDgwKCgACEYbLzBul7G/HJ0XKXZwniST3h3oYcd6RV+QHT+DKRJYdZv830YxhJW/yZbNnNgwn6upSsFsWtGNwlYETpVg156WyxDJQ3QBYA6auQjHSzXoeENpsnB+41DLXeeOSS47EpcfyRhF2ljHxE+lP9WABAG6WssShFgQioASLmEUB3GL1iqXPGf259TO2Ol6/dOnz2pC3PXuuXf/rJJlVjX45V5k8cbey/8bWMXvUkqsdn1Py96x9vOOIzseaWuxbdHAtnPPJcabu9Nh2Qios+m3nrMSfftwCFUYcfsr470vJS4MMvIZ7+IoAoik8mFL4P3096xABiZ7RR5eJ9lEAgAV9/pFXX5EJVXxs6+a0rrvtCdtIzrU7j1sXsEutQz6FbGjmrYbCJtH8ZT3UGB6+zAWBK5qSYqbaN2uqKHEUHvRyMxiPjEVFmRO2FuqK4dXDNg+bvl4MyS8V84xHXJLWXecQ4PCQtFYnyxvTnmDkCWpffmdz58DGDB8fVc6uLpv/y6Esm77vNURc8cXXb6a8e8cubNwnwznXmvhybwNh/46FDGpZbj55Ndb2H0jqjx6+Tx7Dw2av2O+fV/uttMmSdQWu0hJ+8/7dXX/twCfkD9/35Pad9TfiMag2dXQgClIpobqZKBcVScpwVoObJRRERaSYwbPeFBY5a7bLqlYwr+AWbNM2WR5O2NFGyKd9qhkz7kUP7lEhflwYzsw6wMxoYV0yzPTCxRockF4Rp/7NaLJdW2rXB5mokGBJntjoZPA5gNcTm72VKRm6zNIRVOHKbPWfjlN2Y2XLMphdWSJa0NLrB+Z2NsLynepfGT56juTi2vOiufbba+uDypTNv3sR37suxibn/BvB1oUdPoRUz6rrKLif/5NjfvDD9tVmvvvvyolqpzxrr73r4PkecceZhm/etaxXeslA89FGrgSJ4nqCIqlXBZ9TxPzC/c7JDP3NDcygkuSAFCmoTaLaQIsv9Ki/k0Spap1VdCyNcj+uWDGcFJjNce2qFu3l4/nP4XAwiIwVrNYb5KCfBHBXcz8wRNFdEG8syAh046+pkZNnmIuPVSAke3xSAmwLGtgF1NE5WG+FoDxLZQt0qbLc4W5c4Elpe+vyNvy3p21L952cdQNZp2fr+G1+DEj2MXABd/sFv6Qf50faesoSmqNumkYdfefvhK1KvOujzhaJUQKFAYYTqYixaLCJCFIl4hgYAIRD4KBSoEMDzECSuD7mtHYmMFYOShPYvkxyzMurboT8fcDS++hjrZFshsSwpdWeGRUEdReAESsTzYTQLMl8re86E8b+7RN1iHTK7idpdhhmDdZVrmzuec2S3Nxd1tfEx9WvF0rSb9dC8KcddHFw28w/vf++Qc5+eMXGnVse+HM+t0BT/HagHz1v8fBE8gWIgAh+lIgq+WkwYz66LCF0Ruqq6zyDd604IIQSEIE+w6Rwpr3OvS60jLG8NV2Pi4FMTJ/ioYxzR6XdWiRgdWGi3lMFjMDgUNswrXbjpp2Yy89zrVoqmJ90yMLk+tgStxIwtA5dGvtOHazpzXZYvD9Sctq50HeWmH5nI1/4pzy8AwBMo+ma4pnYaKzSd3YjAlh3qOBoRarxsl5W2/hQnL58EdP7myEGDkkG+cbdMH3X75YWfTjt0rVXDKT/Y7oAfPzXj5ztb+3LMX84k/w3JtRfHN07LthfHMdtTPDboeWisUEszGhspPjc2PsYbqZml+Tp014ftcHQS38yeO0xg1RBXZNNycXucnVFyd+Y0kF17JLvMQrUNsGGarEC7qXDG1VqqRAtmyQrKlewM6c7J2010U21DpQwXuZTBBiNSfkMN/cqRuKutUtesdBxF4ZJotECsQTX95o4UdYFEVkiu8oOH4oJJ2qdX/ai68JEvkU1NY5oqw7/1sy5WNPVgC3qNAVQogAi+h4ZGamqmhkYKiihVqFhGUKB4kTcRolpynEqtK72oIqyJKF5qGLK1KpGQg+DQv2nzGoDRuTY+eSe2Cj1yGpApJ7/OW5TjE7ejOHjyfawOUdY8tu7gNb8xyExO+5cJcBoW65Elg+kP0dG2W/WMFHW9HF+LnZYeIPhN5ruGsgw0SS4Fum05DPkOVSlZOstpkVcLx+VtGVRtFv+qHYX+jakHA/Scj0S5iKZGaqygVEZnZ3KGAJEXRWpjjci9w5G5475NprtDOalJPz7KzSyjZNh5K5TsGugEo2x+p5+XAG2RSzaC5N/aWODggQU3WbHyH7nk6E3eUqCwnYQzLQ2gnXMqEjVYtwvKaFdNNpsuwoe7+XgGWFQuT7b0brWFCfFg/HYrEljAUKtFT/1yjhnKaMtd1mjp92+5Hce/knowQPselrSjForFS/DJ56JcosaKiF0cSE/aDpmBHKbnvSYz8EgASWAYguQCFkrPhI2UZ1CfW8pcFOlupXyfUu7R5oFJFPmPrPl5vILXj+uuo04NjOimncj2XLt4M6w57SrbxFt6yikNp9r2YK8rwNSQzzaz+jlafMeQsJyPkZVfF2RrszhckkkPdJrJ2gCLEcH6qIg0Ru1af+l9lnMWR+2B8U2/ndDhWknYNnPKlCV7G6dx91IG9WCAXn8wUZRsYidEskowOe/KU15jpEcUq66cmh+d7GYXm9Vyl7tIbjQq1xkaf0hscL4cMZYcV4NEoPR6S1Od6WAEqt+YrEEwWDVck5w95iZ3wgQLJ4JcaCdLjyS6WZZg3NjEDZVsEuJHEUEgHndVeqY7ARFvsaQxGEf02UTbON2YQejIpVTSIciYnoyUzfNUFLn5Sby3p91e8oKSMjWM03tIPERKi0/s5oly0gaf0yiQ7bf8KljrbhSLgeKefga80XuT79TQ2RPa2fbKOLBceQJYta9eCiuQ2mZOuWX+KAOgKQzJ9/9FW3quzNSDAfqtWaK5EcUg+RDjiXSej2IB5TKVy/ADlEpUKqNQJM9DUEC8jEV4ZHy+CuOi1PWR2tpI4QzaSCPbyg4Kc+WvCa+cAYkoQN0ammSR9lR2mQ2rTW8JjPAsNqeVqgnRn9Y/4mdIdAeb+cogSkA8Vweh33anUkZ4ncx1xlXtXf77dTGQdUWkg6dTh9wBEsrgk+GFjDXDS03m6u0T5/70htfe7zpi7DMNo89/8pR3dtxp2nY7V2e/12//KTdu+mjvym+TejBAD1qd4rNfPQ/FAoJArUAJQ9HWBgF85cW2g4CFyAyU9X2f9aeQxqk1W45fK7Dm9VOfIAX92n2BFHntGXVkRjGSM430HB6eL6aqlk0jC3rqzkTJpYOWIpes60P6rSorIB6KdMi0MmiEGE2R0SiSca3rzwtHdpt4C2H2SPRyRjwTzlaP7WcUn6KJdLSAD8sR3/s/vmXC5TQ7Svc/ijE2ZKkb5An9dXdHW3+O0+pm7oaM1dsXnbXZU/Nve+aSIQDmv4O/z+r/8LRrNwrwztWb9a78tqgHA/SChaJPCzU2IgioUEC8xajcHd/YAgns+3YsHhFkduf42VfWo+w7jRRA6GMxtvlsaAio2Y+kA0HWRcZTkcWThd1OUVnpGlHMdkimYjlwyMq1Az3TLoLaVtuQbzVddqNi5Gh52JyJSuszs4hghQOgZDK+VlauD8NM1OI0E8qdsAFACE2g9mGyAtloEFaYi8Ncva3TervttVGA3pXfGdSDAbqtE7RItHeSJ0SyPjB1QMd/QHKRLiAk30+XEabM8VQ8icXShtI6pNxPx/uD0uiOGVKgyer+Z7oFdFdyUvNjk4cEwDd+gjatAqySuciZrunhTaN7qQeQD7IJkfAK11CkvULSbvxUuwgY/RiDk89bkElzBytZ59Fo8rvDkyx3hPJ3pe9RQzRInzcE0rPhXUJc0h3ga+jDS9vm5G9Q6mm/UOdXZ/BAfZwOXQ0QX3N9N9uyUP7q7Uolb3Let37ldw8G6AF9qbUFhQLF43VCUAzEsSkdBBAefJ88H2qtoAcgRWcPAok/Whu3cdjXaijfbVBbsyBgGEHEeIwLy4sNIPZxx0HMeow5TXPStN2yDVLoFd7WBJBGq6a5Q072haZMBpgu1YVVdJaGy5SQGc70d+fOulaSLAuXM2Qlbc7iyEZzs2wNVZkCtt/ZmGotbWZDW9LlF/ri66Km5ubF7y6ygntXfjupBwN0RFjwJcpFUSyhbx9qbqVSGUJQFIlaF7q6EIaivU10dqCjU4QhOjvRVUUYia4qajXU0jkbMYkUuGNHth+b3l5iensu7HZeaMBuQL8RKBPOtn3IhokUYcmqsWS0BAyOEwZH2yDcPJSUMPTtK51OCWN/SxvKtSRsxaDprCnD8kUuNp6cKkjTDS378Wzyu17sKqcucOSdKq5z+pMEusAxUS+yZEICNEtaJqGGQ/Ryi5OJ8xNGSqw0kENCGKnZIPGUpJDgCxAQkXJGywk5cerJX7qj9JZdOA9fD5V3PXL3G47YZGR5tyteOZeFb9S78ttBPXip90/2iZqbqW8/KpbQ0ESFIuui8lOsZJXjFg0DOCJAP2BQRkkkMNhyglTsi5DgGCsAG2hcEd3q2WYXA1ObM1+yDXbmrc7giM4YmEChSbCS0yx6aDySTVNPB0fjOotBE8iuSNdf2JwcVV2OZl2eWytnFNL+WRKY78D2Gpnp2rlj90ag+31JB46rDMHLR4kVQ0bglt9obB//46sn/3eOS82EttxljQ1HWoeB99LyUQ+2oLuq6OoSHe0gIt8XRBSfQ4j4fO4QESGsibCGWhVRhLAm0gNW0sUp+jxo7Q8MUpF+uE7Ui8llWMHggYruvO2+u62uhSHcxAhHFM6qhWTCokuNeiO60u0GpjlPNrPBYAC9yeZ6X1mp85aGp8Lidn/CVv6tFC7SaeAOUfqYBDH1NLNavyb9GtJM5l0cnqnUXo4lh+muTCEhArpWhUF9VinvfXTeGF25oQeDyUpLPbhMifDZF/h8gSgEolRMRgKNVXzCgyfIY7uMAomfOh59SAbBmCWjPuJsezn5zbA6jXXkgEOOisgsU2TCsdkYJJKy4cxhaTKBPKH4v7BTTMRaLYFLmlNJWzczgxaCG7daRH6Tg+wuZiPETtrsr2QmZPx0l0oGM/GRyWw2I1O2WDOK1WhpC3xsP0yW2oSNBsIYJJzz0ec3TcpzCh+838gdt/2ajrf79lIPBuj3PxJNDSgX0VVFWztCPqMOHP7ScXK2UotN+SBfTfNIXM+CbeovdO+zppXILAAAIABJREFUiCfk2QOJKcU87qqlgtQEK0c9dwa6sMPQIWbodjKDk7hwoUG2QgIbzuzbPAMcGlg4mKHemhmiY6gQ3SfKUzfDWagF68JZzoYC5iOYOdUaPNe4nA3QNjrr/x1NndCvuyl8myizNFZpNXlrYfTxp4uzJAFoa+9i3A+MLx778rqrNURR4/AJP5/yn6NX7Kjjsq0X/+rxo/Z67oynrxyx1EsW3538g6Ou/cMr76523lt/i2dwTxy958Ipr12g+hR2yFe/++G46Wc/ddnw5Vgh2YMBOoxQCxNcDnyUS1QsxDv0xxM5SI6ccCdGHGI4KxJzmECEWph8qQZ/yiZs9M8ykNMfxWA/Naq9FO6w2aGqkN0UmVnrVmaOEKWeOV/CxEqmkp0LxZMBapYFqW4kzNmTZJwTafgUyRySAqUCvNVxzg60SSYko5vjh3A85WKlU1gue+ElKR8C5tvUREG7IiCMWMkL7XXLDKpKQaD4VKt0HyViX8UXy79mr7jf7bOm7k2f/frwTY/72d5v/nTTVI8VsarbuV7cJj2tDydd9eFB97vRufbFF+39+mWeid1vq2Nv+P3pf/jeUUtz0G7juFNHXXjVw+fef0DTUsTSqQcD9Gr9EIYIfBQDaqgkKwkRHzLfgTAFZSHU/GhzBgUAWeHjRwkmMdSG9quhNgBKXQ0csjO8k7CqIvT6k1xAVT6NOcOvarDZFd6Jzu5As2EgQ7ITyu2nctBV8fPylHCQMkQsXd4McDYjdfkupJtVznyIUvQhUtfxZIY4oXhL+1pGT6WXAKzRsYIEiVV222Xk9//fLMx/bnT+qu5dXxq99fxnpr+7ePil95w099JzH3jzk9aDJv/uqp36AO36+d9bTuPrxS8fO+ehc0669oXPatWGYaf9YtIxGy+YyNL6xWGrx6p88MB9Xfv8eqCuHy2e9dT/3Dlp8v++Ovaud67ZISsbq2y64yqY9TRglkv1vamHHjRlm0kPn70mUH3jliN2m/HGR4vWOurOqeds0YQt999lxjX/13bAocu8ArIHA7QnAB+1Gqo10VlFQ4UayigUUClRsZicrkKkRgWjCPIMQId9apgbUDaeAHdxmBcw1xpEzsUXORfGtQrhBpX9NDckjyFjNnema8SpQHphYlyu4ZnFQBnhyVNa6lun/asCs901WWKNwNgZkp+iGdKdl9wINPMFs9lGRpEazbYRbjf2hobxd7cCDo2Nqesfv3p05tDNrwP+2c2q7rffqNz34vTJCyfuMmTfW/975rS/9Z9xzrBzp5650ymrvvPf5vnffL34nJuOmbjqbc/PHFLqfP3iMSfdsfszB0KlJalj2otzNz97kLz9aNqv7po0+b7nl2yy/5FH3f3K1CF9ALx43qaHPaBN0g72v33Wz3Z3Z67z7UkHHvqr3e5+9JThFcwH3vyzN/mV50eJ6edsc/QNe/3lomEYueUm056ZgUN3XNby68EAXQiotQVNTeT7CAooBPB8Ui5j5+oSkVQs7TPl5m1q5bFaJ/hnDSsuXFVCq07dPeIkQ+QEbZBWnZwoY4uKlyCa9ZDrb8eyKq270YJ5686akZzNaV1LwzkrU0ql7KSZEA1ATfX0AV6lgF5Eee/OkaIexSmfcQqhPkVbsv3G60m9uzLpJgmkJRNmdvbrps6Hjxk8uOJV1hp94Z1nDMWSJ7tZ1b3BuANHVIDK5pu1bjps/zU8YOTIoXNe/RAo2fybqXSWPPPEjDkfn7DHEwDQ3v7lzu8DagW5ok/mzes3YEBy86ezNhwzecBxP7/zhdtH9Gd821/9+pyr68zgxw8eOn714x5+9OQh6S7Y6+x76KhGAKN+ML7t7Be+xLDW4qqrVv/5zypQWNriS6gHA3SphDBEV6eIN6urAn4kUlcGGXafbuSqW20Y0Fg/kpBtn7DLLBS2IdjlAzFaCPvXkGmDl+1nTC/IEU6gZAgzCRRCT9EJNFZChm7mr501HmIpb5Yk4zeeGo/Md2wQKT9sTFnfQD0kxdgxSA8n7kROnznT8YSrJ6IPzipvtSFZpyxvfv6IMWWtUAcGLv9uzaXxk+ew/aCXdLequ1SKcc7zvOSyvvXdEVHDnlc9c9uuKmj+6460KpVKxyeph2LkGVMnNd1x52UH7/f4QUcedeTBO6/fIgDghR8PO+R+7WCv4IDJc27aw5Vwn41HD3z9iRc+OH7IhgmMCrs4OzrCSmVZ0Rk9GqDn/f/23jzQkqK6H/+cXu72tlkZ9kVEUHDBgIoa3EBQNIjRYDSIKBo39Kv+1Gjc842KJpLEqFFANAQ1IRFUlOjXiPuexI24IaCAIiAMw8y8N+/d7vP7o7ZTS/e7wzAz94V7xHnd1adOnaq+/alTp05V3UwAygJEVOTqAgAq5dNgwN+gDtG3pIjaP1phUCsJ0sqWa89ij7NFYYjPhlOWGoyjNoZLzycryrJsbDZ6Yh/3rT6xYzfcvRqJhgpcwPYoA8//6wN6JZy/Q+HkVQG2wxhXJjSu9KCteOVOE769q7oT/NM/tOvFZx95wiF/fs55r/r9M+/RrTde+f3fHXzkVErK+nvfe+E/rlrEAzoAevsf+6y3HPusN93+089+5Pz3/fEDXtA/9nUf+NAzD3v4O668/h0jVqN7xMsufcU/nXDKadOfuehpB2QArv7ERd94w5HH0DcvvHTq2I/NAbjhZz878IgjtqdxAlrBAN3toK4x1Uen5G7XbbhhN0vSq7QbIufcImwj0KGeuIAwXZ3/OphFNCgZWLUJO1pKg28pB6ZowONZwYFYz1hOlJjodELjLtl1OaLWp5Yr5Qfn1ugIHdIXmLrkmLcralAJCeOLyT0aSUKTmQrvUcL9YlhGLCu0eykslP300IqPovcCczwM9ePol5D4Wai/dOBh8eO7jrZ3VXfMD7Fe/JwXfuR91z//lPu/awlVufcz/vZTRx6WlHLMSY95wWe/Xj3lkblNyuYOfdwL3vG4F7z15u995idtdu7N5z/5yDd+446bbuFH7nveQ9/83Y+fBACYPvpNl733FY994vMGnz33QcARD+t/8ORjXnDdlgPO/OBFhwO4/Yortpz0xh3Zgm8FL/X+owfwsNJfVAYUBXodlAV6Xe520Ck9pDbYzRbB1bJDuckG4JBIGr/wbyWk6uRg4tE3Vy2n+Zc4Bf3y3xDEg3LhmOEnBma7+Bu5IOWzQHjUf7QfI510gHrim50bQRaI7oSRQrrA8y6LM4/SE5jNLvukpyXIHvZ/ofTEEC18TX5eEtuNRsL8FjOP022bVI9DDXVnmeoUrGQWRRx6P5xzsQfQV/70xje+4/KwMoKe84yHPO7R925h2O1UfefVD3vv719xwRN21eG2N73/pKctvucLZx1450WsYAt6z3WcZ+iU6HYxNcVlB4XZD5rNcbHDoQ7hsL/4ugbXqAhLS9HKQ2FZJ0xs8zSzt+YC7Q7N8Ntmz1Ra7st3CcmvNAUTI94mUxrSuSHdEdkTraiRLTCH76Rt4JuTTTwtDIFjOsEpTNAUyidGHaGzu0Es+/Mfcd6kkk00+gu1T+2wpo0N2PcgBD/oudnekx5335ZcB+037htx5Ee/6uyHf+m6GvfaNWdrbf1V/xnvOuPAHZKxggF602bKzdmDRU6dkm1oXVVhWJFeyVKhqgHhP01EdzSQZ1tBWpQJnpCCne3gfvCp8BJ97boE8m/N90xRupRDsojUtSwdwSeYSombaLm5NfZKaSUBdo0cI+KsJzPu88yfuF9c1qyOxRqBxBFzcmQQDm5sUoSVSVs+zh530i2DFSk8yZZQFdgSjfe3dfLF/WbDVClkdvyP9F77iOc8edeVNjjqmU/fURkrGKCJsHqWp6aQZeh2ud9Hp8t5jryA2gPac7AK8zPhKQ58ysbJIHi8DZVSDN5/TjKscG8BS/BJBwP/wKfhNI9SvFyymvL7DIJGmrI0yHEZja+jFVMokOYufZlpLItxs7U7TLraOVn3SFRcU+naaoK5ltITRaQ6GFVoci+OwK0cdzZhYqONH2URznEOUpGoL0cr5m7duu2cL18ZyXbUL4t7b1jVwjChO0ErGKBnpxiEhQUQYX6ebtsIAhEhz1GWKEt9UGyR6735A/eb+5kKS1P+aL0rdgnK0QH57URwliJuvLFqcJgWwlwrqMnrhE0XoXNwHaewAVwhgZuYkymjjMFb0LyJIZiIa8+Sfk1x9oBBJCY7GDS0ZJo5erMk/MLpohNdCyUejVZ0W61TdVkb7cWxfTTue3FEe2tIuu6Sl5zx+kt/som6ezz0Zf9w3ouPmtpVO2/EtIIButfDVB8zs9zp8GAavQEXpfaE1hWGQ9QVhks0HKIaKr8H1WpDpToxpQPhd2bpfVb/6kbnEOithOTkC9x30GyHyU8omouLvi7PUmv6MmV6C2S34rhJ5Nan9sJo7ltk8a5GgQ+0rUcJoKehdl6DNNSlrbhIYHih5Sw7dGjKGKlnH/tdcpw3Iaeh6JZGcK3U4uKIBG7eih2lsd6Lo31vjTXHvuHydxy7X+emT5z2wNPf9egrXz+Kd/2u2HkjphUM0HmOrQvYtkhExGxOiBC73wL6QjklauuCQIiX6eXXiLF4ec9qi7Mi9dGSzNLm2fAlB19d7DDxPnJ/gp79dJsxlhYXF9bOZ0vWN9ECftESYtienCBk2hhtyy6La1Kb/VIIvv4UtaeRbtWuA7gMuhZjyAf61EKf2lfPCzcEcnJFSLFVrSPKLZv8tapzTzIgIxC8UPSh2OLZ5lUV37Y9m/wAeNBdFegwpntxNO6tAQD7PeIp+wEA9jj24Yfe8P1fAwagd/bOGzGtYIDevAVVRf0u9/tYu5anZ7koUNfYtoAtm2l+nqoKVQXl9MgydDrc6aAo0Olypwu1/jAv1H+cZchyZG5TaYZ/lqgiNt2AXhAoP3L9ZYvrIIuFRbsrCADpBK8BmJgTk8Ks/de1dH/bI2OsBAPx5tqd8AJOMcv0ANo4qhF5RSRDFyTpKlMyr1eQl0X9jV0Kok91vRe5dvNfUahVcGv7XeZkHxzqLONSgriLhP7NM89BV6FcHNSgCacCwL3wcMEfh4XIigTS5KxMw9sD7g57cYxA1c8uuPBnT3jtg4EtwC7ZeSOmFQzQs9MAOM9RlgxguKSP6x5MYTDFNTPXqGsdb6fgQO07urBAW7eCa2J9oore+CLYTYk5RBaJKZ7lJS3KJswC0BKAFbE28SQwMTkvxM0XSCgcJmqx7ouP5QSPkk3BUZYmfaTUpIEcGODL1CVVVqKOSahNad5oaPsym1op1l8BtFdWWBfyZYQVTLdqsuimakZaWX0Oxw7TWO/FMQJt/OLLn3refT/w5SfMAFt20c4bMY0FQC/V9VI5ane9ZIZwP/w5dUtM9VCW1O9xWVCem1NfM70KwFooarypjFO1aplr/Wv28BeJb9iStX0gft+SktCW+NptYuT3CD6kRnMyBVLBxxkM4WNj30oLqh9oIjXklBqOP0hU/gpfvSTKSCEcP20CUN+PIZk5KNGvQi3qUpvfhuJ3i+DNQnb1r1qtXjOI3Mp1pcN2Og9WBhW37rCIsd6LI6Qfn3PCE9/zC+C+r/nSJc/ZB1j4wdkn/+k1L/30JY8zXu5dsvNGTGMB0NeivvrgthBLST8n/crWzaFm9Lvo9zjPzYKUDEWGskSesz3jSlnQVaXnCYdD1EO90/+wQlVRZexofWEta4vsDLDYjMLAvbqtmFReZlS19iEyh85TvZrR/KdSMrOaUcV0E/Qh4plh1ovU4W5ByMQAWQZKh5HRhicDYEJZKOC3x4MFp4W5TbRZ35puzy7OdOswIyEASO0dGO7RyjKUG35kt7uNxu+yGVtuA0o+TXauCbYGhqYxRFNik8sluQLeMccDI5ciTYVE1xuXm5ybbbLBwdj3ENw1Lo4UjcFeHCHd+2Wfvepl5qa6+oOnPuWyEz/2uWff060K3yU7b8Q0FgDdnV5cPf2bEZl7s3qYUuQoS+52AKCu0e1AbQOd56yOsAIUKGM4JLUrdKWWEdbaK0eETgaUXmxG4wff/MEEW8fBfPtefEjywzas8YX7K+zxtBVspTXbyFJmGA9rzEx7EnmkodgWM9Y/VlhQ7BTVkCuHIYHflkRTREJY3AaNXPvFxyozm9XVcWxJrKtpisDvHIv1nNRCqyb8lwDNy202IgcrfuVdD9bSPsEvMJacLhGgHQyza6fdvxdHtLfGc/Z0jF/6q//vkzdk//kHh7wHAE589y/OOwbArth5I6ax2Ivj3374lYu+95kRmZ9w2COeffSJAF5xYp3n6JjQuixTh6pwWaIskRfqjEGmzOGjcXGQ2b/fuaflNbT9685+bTKRtmsrH0TfSYvltYxRBgGLPviGko2xFWvqKWPN2OhRHOKSfOpJGqFZ5GRdCyXR08LfKD/exncXibUvOo4ODK/9yYQmZkgM5BATvThoy+DnbHqqfqWWQXZ4yzdJFFUSe5D2Pwwvf7dnQX/nulue/bGvtEh97WPu/8cPvMeyhe9G2sl7cdwFO2/ENBYW9KCzHWpMGeZbNxJlmOpzWaDfQ1mg2+OyRK/PvQF6Pc4KFGL9d12hqohrVBWLa1RDVBW4xrCiqkJdoWblA+G6Qs0OzZUBXjsED41Wbe8kbT9Y89VLcOkRpY01X4zn0xgBi8kbHLeBKdlcErtj/4N/RUYZW0iwZK6pS6MoUkJeKOu+KbBBFyfgPvCTGGLr3mFfQiy2rWWaO6qWPttbqe93jeRDZEuvb+cV4bewFRX/Ymg5SyIYiCjasGeYYVV//tJnP6hRCjDo7Pg5hjuXdu5eHHfFzhsxjYUFffWtv/7atT8Ykfn39j3sPnscuDPVmdCEJhTSj3579Rs+d24Lw5kP+oPHH3rMLtPnbkJjYUHfY83e91iz9+7WYkITmtBdRGO+1PuGaDG3ZKu//LqHPPefbtoGHhzy1Ldf8M4/2Pe3u22p967Zd28Z2rR16cpfbhrxv1vvWNzd+k5oQhNajjonf+Cqq676xbffOPP3zztHjI+5qnY8MnHrdz/03i/esiybX9avznvbr576IrXU+9g3XP6z6391zTden7399Hf9j58ru/9Zn/zeNddee+1PL33U15/1ps+Opu7USWc95NNvu3TzdtRiBBoLC/rH193x6W+1z+M6etT91z/q/usBvPg4BmNYAeZ4N+WpzHL0OtzrocjQ7XG3i04HWaaXCxLp+UO9g5KdcCNvBy+2q/XMhIwX56DTzdb7Yp2eJuNqNBFpDD+eLJ6fUdNWbFO0n5Gcw5GMg7s5zsS7TM0yka2dmGiy2z/FE0y2Xo1OzGiz01DBwGMbxWHo0oOmQ3Qb8QCmddIeZ+N7ZV9ISnOXJpuCwspINdw6wIb6SpUCL7ByuLONzgmmQEeYO20hz3fvR4wwi599MElI5i0TAKzfjx7/whw7TuO51DtezH0fqfTcBpWlHg5rz68/Weq9HVQtgQjdkvMM3Q76ffR6XBQoS5QdViic5SgKfW1wWcd1eECZXKHbOlMEGAQUcVVuztAP/9CLYmoCPER2yA4/u4NpZr8gk05pCJYrQbxZfz8kLpq99KT5wQmBep60QIwILVDfu5QDcU45C7ZaKhaUm+ow/KqZIpCqdSpvMt1COVvO1pXQUmAynHmZ0tnNaoqnDT+10RZkQiCyxGV5ETMEbPbp9u7d0UjjvdTbLeYO6T9ecfhzL7z6N2v+5KNfPCHHb4G781Jvptv23+/no3LnANZDrQGrUNeU59i2iI2bwEwgFBnnOWVii1HSC0D0phx5jqLQK1lUxDSZlSDhf2qZhkjRi1/sesVMP7WbeJh0EDEpO51AhSqCAVDmOgaCWHLIwV/9XNq21rg2/wtRic0KSWslxeQsVkqxKfPNjBUCEAnjFkS35BKFzMA2d8ARbABrhdj3Re4WQVgFu0AaGRSRHFpIqKJMaCLxUZjYLA1tYTzVtdYhaKu4vs40Fk8pY8/AZ8i9XGKSfWFQEOLXyumWzDLX98RBMlBv2XZL9vdAALBqT9pR/+f4L/WWi7lDesxfX3n1X238zzc/+U/f/a3H/+WBd++l3hv6V3e73x6ReWqK1YCk39URKHWNmqnb4UEf3S7m5njVGp5ZXXd7GMxyZ8BFh7McecGZWcCiou64prpCXaFaorpGPUQ1pOGS9xRmIaLe2aOmeqhXiqt09UksbSO92tBu7mHCqK2EuiaLU3ZQ6bAVod0tE5OkZCgQJAGgQa4gtEuxOZ54nYjIzmbYmxFEDt8el0JSjggJAQgyIgwd8wBUmPAxOIYtEw2DagFJgXpOH19bp2DKxlS5yG+KhA6tWya5VxZ4NgJdXOfEst1tFxIP79JOJPODJyEhKCIufnHYMnIcjcZ8qbe/mDtc6g0AoFW/d9oJdNJl//OXL757L/VetVjwTaNuQDu3WrvGbr2del0QUDOGQzDotk0AQERFjiLPswx5zjBoSGbNtDKcc3PUd26MX8C7sKSMaPtUBvY6R6T/PbvIaGFYAcb6ayY53Dafk8UWhl+0JDdcCMzDaHhLYgFb/G88OtZ1sTwCT6Wpm2XCFCVvcBA3aeYbZ1KgxHp5oexf6bCWL0KY3qbvUOtDiY3qbFVt6b3ssqZkU3jvvcm+lCjLjWyuXGLlsJLdnu0XSWqSXjTIMt0bAfhlybkN+DxuoGPUWLOB1Fh1Z9DuX+odLeb2lnrf9KPvzB989AF93vjdCz9x9b1fdCDw1bv1Uu+ctqPTyQzz9ABEKEvOCEWut9EA1M6i6HbU0SpcFG7viNjAUdCgLXEW61Bqt+2GxVm1BBEGRuvaA2I582Z9wfbfeDDruRQhjFYA4vMR9pq/MbBv6gIeAsbOx7CPgd5bw/ptjItGH3xu0+01ZciIQW6KNbM+HDsdanw+8J1FgOOE2bUDapePTCQKFPY6S2LPbIw2G4FAGdlXQfKHXZrpQkx36PHETR0ktpB5NV7HE/E4g7eBwidN/pD4EcP28kYB/bOSA7Uoi7vuJ0b9dx3t9qXe8WLuU8SBihu/dfZTTvraLVVerj3yGf/4vqevUj7ou+9S701Xf/WWH/zbiMyrDzth9WEnAvg/x9eDPqanudtFtwvKdJBGlqsdOUAZspz1NSHLkWXsgENigZCfnIoJPoD4k4gtaN9QU8BNcd7EVF70NPGKBAA1uUES80gxDDXY/kZuCGqpYrzLGMpalEwnJn+Pvo2ZkDmCntY+DTUzcq3+jW0iB0MtRUl+6U6J2eQb8TW0esUWfZNwZs8TZbJpIWGjEcIXJq7X709PerlnQf8vWKgyWeq9S+na31CnQK9DmbQECWXJ/R4GfZQlDwbUH3C3jyJH2eWiJAXZWaFQmylHRsa4M/N+gGfBObubou/T2krWcdwEvi41DUyhhzqK7ggkKLe4uQ1PDwhuxexiYlMk1XnEnYHVJKiOlOaKAKwDxzCQN4zglBCngBt/WGlNnVmggKuCaR/3l/0L+chccZAlYg7Sg0cI+mPRLIHXKABu8kdESctATxh4Enykj6JN0haGLTcF9/E1Ryd0r+7NPP8hp4Spgu69/oCWp+NAK3Gp9woG6NUz2LaIzfMgQlmgUyDPAMLiEm2ZR3Y7CKS9zBmyjDslyhJZhiL3BvUArFHB/vZJ0r8B4cuLp90cSEGYRTAhH82+Y5ui/KeBg9u5F8i70N4J447IbAdjeGCcFbCuDOtJIH0L6T3I2O70BuMlMI4Ine4UDxwI6p8sdD3HFYQU6D9119KzLA3mxlDIsETPMW3eRWg+w1MjZAiGP1GhENW3vQtZpLPZzRSCcvsnZJL8y+y1seo4yFwTiNXZOrII108LCmc4M3MO57IbxRia24MAb1/OfebW7zO3HfvdjyWtfcRznryzZA+OeubT73qpKxigZwY83de/q6qmxSXtg2YCM9TtsNKbiwKUGfAqc5QFygJZhrLgskSRU5ajUP8VOvxO/aevCzZOEh1RZ/ERcOAYO3zhpxhj3AVdeY98afBAjS0ihP50UZaUFozlKTygo23sbB5EeXxLk1ryCjs3TGkwn0PR5prlWD5BwZQC4IO+hzwNbl9vFi4a+9suWZaZcuKI20AN46KRDFFX7d0zkzDAKWawmeLUYFLRnpJsV6kkmT2pqaImtOtpBQP00pCGFYYVMsLMFK9ZhU4HRKhrLC1hfh6LQ2ydpy0LWFwCM5ChIBQF+l30utzvoijQ6aDbZbWRtNr6TuFvbqxsEOuQg2DKyzeiHQkLNHxivzd9bDiIzBF8ZAa86stnh7wGy4RzUfpV1EPl3RYDc/tNwhj4Kj5ajuiZoSwyjrwQLD0S0pNQJ4CyaWyuqmzkhANzT0zgfhVmr4VmD0YEetZCT9inKT+SHAzJtiL/eEPpr0iM/V3LJJxCcUEyxaoUO7I934ssFICJ6FAT10H3EQcFyb6jNsa19XcHxSWJCPsfigfvNFtzQqPTCgbo4VBbsnWNjXfQ1nn0e9zpYGqAmRleu9Ys7yY3cHQuAmmBqp9uBLjMqCoAVJOHFwY6WVqgEpE18MF9Es0jZv9oV6T5WhBQSzGlSw9JkpkE7qdNVynZKE+2rfzIK1txV31TZemBVc1lryPsdlXwOjwByc4YJ5HXonYwNxCFXVMgp9VCJAr1lI/EjfkBGOPUXre/Ta2Q8Yo4n4+1081qF10Xdi2s+Gvzi7aONXmrJGT+z6CuzQqmwIPkjAZvKLB+/4kJPRa0ggF68zwYyDP9OVU1hhXl89g6j24HvS7yHN0ud7tUFO64JigLtcHRnBjMis/Sf7Qdv+AmQ7tREHl/OfFk51PcDjssbWcrbyE9AGjvsVSp2ZBk708ov4UnTGjG6Fg7apaJVrO36VHTJGHcUQWc3bkJQI8FrWCAJoLacZ8BZmSk3R1VrU7v5iJHXdNwaAI2UvNvcpIN8A3eYKTpnofp6d8yeRwk04VNJLN7E2jNGc1j0eEoi97nCXqFpGfGu5DsPIadAAAgAElEQVRB4uQnGgXIKuwysrsLTOAkAsoqk88YtTIZ3Vw7R84B33DXOrQP4dtIvFEKwyeicpMFpX42SloC4JeFQXZcYRNJVSWy+726zRWUFtgi0m3NwLoNNCZbXd7NaQUD9KatmJvCmjnudTAYoNPRzuLhEEtLtLSEusb8AjZvUX46hwl2gQaZGA8VNJ3ruUG2PmgdQK0nBtmyQe65AWSZ2HYjWKARx2+otW0kPyDxyUaj8nhA6kmL+hKV2UGtyI0A5KOyNGfkQvX7Gu+b9xBB7upgRvHqkXM/1CEmsR+O1jxj6duYjbUSvoJU4xAloMo+alIs9L2Y7pxSGdPqx3AsmojErfc6bCyNr6fzuZsOyQ0QWz0zXifYrHaZOll1QrueVjBA77UWvQ73e+iUKAr9HxF6PTCzOuR7cZGGQz1VYo3l3KxesRsnZTlbwJWhbMbe9FfHQUsTVqT/rz+RCBU/p1kB+MvtAMjTr/3YDJ0d7MlMmLF+0zREpAWXIarHJl6TEPbVi2I94lwUw5yvjze0F1Yxxfw+FifUs3c+roV5g5So/7Oe6CbgjlO8iQHz9mW9wrGCZG5p9qRzJu5+JUOcnaLfiQR9uAEBATPrJubzWNAKBmgChhUNh0xwIXE2IjgHiFAWDOhTvYfq7EFzGqwXqGAMWmkQScuXfDSRMOoMHAiDSBqnll+ICCzcoCzyIV48ZZcIPefjCaZwxtIj8VUnocfXMfFJ21s1WJFYY3sX2xqxbDnfJW05EjWlTAzJRU2DOrG80i/CzT5aCHN5I/ANEhPYGrdJhMs20V1TAn+RgvW4W4X9QZL3y2G/rbxmz/wIltYOgER2ll2yrwYB6/en+5+QqMWEdjGtYICem+WqwuIiti3StkXu9cie8A0ovOZOB7Oz6PXrvECni7LDWYaiw0WBvHBrwbPcLSMksegjMHJjnHWX1liGuDASliE/9C1I9FL0tdknT/oZ/J1APAmGAcqHEi16TkiDeMSCtwEIWvwSttAwOc4SLYoLDeqmjHEVWmQ2SWh5FCSy/9eu4G+QMKpDXLg7wip4jolUruYiVN4sb36/oseyNJgeTecJ7WRawQBNhKkBpgbaoQGgqpwRzYyqovl5bNmC4ZCGFbZtw7YlqiosLmFxiGFlgoXNSr/Mbg/k1h+CgExvfccZeT6QTNiMbkdpkx2wC/Y4i9zTMJiulhqqvNb8hzCv7Eq5MK91dLgWcbYzBaAmTVRhF7PYNY3hsguRUT/EursKTWxyQBqauqLvkfwerARjDj9vUy45tZXoL52U0IWSkd9QRlpoDttrcRBJOxG56ON0LtNMsc/HNqBtQzvOI+nWb9BBY3EWpgCoq9Sgxh9RBVkmNA60ggH6pt9Rp0RZAEBGPOhjZob7faxay6vW1rPruNvnwaq6P8u9mTrvcNnnvMNZjqxgZ9gqX0dFrJZy1KiHxDXqiuoaXIFr4sqs/66Ia9RDMFOtEitzXalvgPS20RWYwbUJc1b7QSsHS63k66LVHtMwT9kymx2l7a7TKpS11oezwK4xcRvsmdhBsNuKD+Yj154cEf5sL6S1Tn7oAsdoCABykZu+iK2wgKQ0MlNbFlj1EhiELprYemVh8cmnmRnFxwUFaiBlM0L2ail/SDhxanq1uIkyG3bilxL4W5pazOqctNxt9WMvTUCj8CRLZ8a2aC53QruFVjBAz0xxv4tuF0WBsuSyRKeDvOB6iK2bqa4oL7i8OSs7nNnDrsw3DOEzBZwxlfIniGG+Rjqz+s74Eyywmluzxszfj1RK89I52gRSbu7hw02mlopkYDF6l+ahwF93nmEwgA++/OSwNyROXJIQHLgI4iE/x09TBTWhvNeA7WR2wJCKSUd24DBgm9iyVqjJtxw0S8TRlEUWl+xCmnSIr+Xt9tq/HDcKAKBYwcDwv4pW8HuYnUFZoNtlNT0IYGkJS0u0bQH5JtLbjRIzYzikqlLhdzoyurb7PsP7hiF9C+IimGKKL9QZV+Ia8BcuWlcGhINCFZqYOxJeTefWyFx3onKxzS7cHdZD4tziFJbotleWrgP5l3whETUaZfH43Z/X0rYzNJvNJXkCAAW0Jwp+rsQkm2WQUYxJn48Q4tAN8HJJzIqcAx5LwBmjpF9A7E+Q7g72g2RkD+3KZyOVXJM2YbTzYqWGArGqBKzZhyZx0ONAKxigr/s1lQUGPWQ5ykKfkGK9wAYZCXBH2ClSjwrlJs6QZZyb/TcU1pPbq56h9kgi9yW4UszepHqZosJNGxANgXqZcCXL9BjmbEhD5HB0GBX7Iv2psARyBeY5AhAxHUL0AXNQhA9wCeCofX6EuRJmprO+IzXI5/GN6ISePm7aoY+RkVa7/dZrW4Y4L4zCNvGzeHCZwlmWGYX7O9Gqft0D30gwtrBypJ+Eff+VaqLA/261YWB+x4+8mtBdQSsYoNVh3sp2Vn5YtYSkzNHtslq6MphGf4o7Pc4LdLpcdpHlyEtWm/ojcwfCgrzZlXCuzP7Wzd4IaYQSnl/BIHbVkZ5iJPI6NqOBRpkYBURG99c3DHUqeZ+3rJFJSQn2vRbex29zBDES+jbcEjMoOulaVQYec4gscA2SUNMzYA0ohp0KTHtGdnHTAD/MbopgX7q7DC3whpr6YmVX7VWpyeORfEvsOuy0Be3XrmnoE7yddXtNLOixoBUM0PvtzWUJIlZnXG1bwNZ5GlZY2IbhkIZDFAW2bcPWLeh0KM9RlMgLJrNuMAhmsBeeZzncsoNCBoMd0k9tKQlJjQPMZECYoOT3HPhbPHeHjPkjn8c+9QO6Q9eHS48k+Nlh/SFxgIfv2PFqITVU0J4hEwxBkAPscvaWNmkMhWR3JzFr51mKHPwNKewX/N4i6BtGKpDDmnmddCRKGwHRU2bMrh210AntVFrBAL11HtVmAJRl6HbQ62FmRjsrLHpu24atW6mq3EKVmlGbTaI5tssE7shwOr0XvtjBAzCeZYNNygfttsZ36YDYs98lBkhHsCsGg6ch+EqcTaCYwJzg1hD718TaCzQKXCUwxet1BCImRwnRRSiPvYtAWjz4UGMUD9SS3pioUNd9RimB/o39a9SnxlkkaHq2MCUxU7JRrFJCh8RjA/XRXiLLms/2dt9D8LBTW0uZ0C6hsQDopZ9mg1+cNiLzsLoDhwFAv4dti24zDeVKLkv0B9zro9NltfykrlEtoa6xtI0Wl1BXWFqk4RDDIVhHucHGXVhrQgGudDqjAVtjGHWwCweRRP5ybUsyBjkihd0OVhgAap85RMx4wE7uEUmo4kYE18LYy+5ZeJGbohE0WMKTdZFEmvv3smRZs4DReWyCR4EoI47YdEPivG27l37aR8x6ejbuyyWsx2jYRLGXyX8cNk9Cq8DEXq6g0VTyetZ8Z53oPaHto7EA6MF+D9x8zeZRmfcfqIvNW1DXAKHIMegjz9Hro9PhXh9lh9USWK70B5YTsgF39YfqdrazM4rOdI0jHJAGsuCLbXI4elwMoPHoPHXTNLYNczUU3aJGeNkqPxQQwVCjBH1DKWGhHonSUsZsoEnSNrfSnGXaXoSvkdcBJPE9lpFs9mT3YzpOdgrq9ETHE/WyiR9MVJ20VuaCAiUTdfGe9fs7z/Uzoe2gsQDoO0d3bCG1gBDALbehyKnXRVlwr4tuF90uZxnKErkJ8NCrujNz7Lc5xQpZcBSQ/jFL/2bo55SBayZ4IzavPD+1G3ezvvXWhhkT3nyZSfNKO3mVvWNwngIL3CzlAATYyeE0wR2S3YqD7sIiWgiBfrqH42lUkxikFGe5MiXl+pCqxlJlivFuicZTrRSc3SUbRO79H8W3BZm49l0TI5ioqfRkb+nuYk+I7FO91k4J8aSZRuMo2jqhpH7LBEZZNjJPaFfSCgboqkKng9x4gYuc1bhscQlLS9iylcit0lb+YlIe5DznLEOWkQuny+06b9ZHDmZkjx+0R327naNNpJ0feMcIXBxBBHTKg+wNngm6Y4AA3DRY67ICmGh0Mlo2zcDNX6vhR8OXb9CtzYyNxMd2aNugO8JituVyyNlk3UYWrNeLNTqAU8my/wgYyBctO/WwwxZV4IDTL0X3iNEbkoK176thsbjQzGRJrSpsgexVe9IkimMcaAUD9Mw0E1AUyHOUBTodlCUXBbpddLqsYLGuMBzScAl1DWtuZ2Jb0Swze41KwIVzcTADNaqaACgBifAvfasdrHLu0a3D9pcgSgQMwSsy5r37AAsEFlHMGV8L4sRVOtHVN3XbDr4jWr5NGe11Ek2SKG/HJSQgraU7cEWg2R/v66lvBX+soX5XDQxpcKSG8pvShbS4D9M/YLgJEqO0+Cvlijbac37i4hgLGguAvvHWrT9ZuJVaOnRLjINuqQ/FAMDCAhUF8pyZUdVQ47JOB9MzPJjhwTQXJTp9zsx0h9oBo64xXKLhIoZDqitUQywtYrhIVY3hEqqKqkof1FL7MXaKvGgzH5esK8MeY2rD8tTCRR20B3EhBtcpmynyfae+Lvf5STs9MtXlbSb2YyIgecq4N+fp9wBJmXLoEIOCrIJFLl8Oa5wRbWsdNa6vaQbEgBp6pVAOjGPES6cEcI2CWAmV4lfGfvs0yRWleua7TRcdsO2tk0Mfr9P13wWbkVvQVr1Bg1YT2rU0FgC9NIPb6qURmfeZ0xdTA+52obYYVSbwwgItLGDTJmQZ6f34i3AOkHTEBZMXIQcQigJFyZRyR4RYE3xU8gtmyVbbqUjy4zpkXk5+gbFfImldhktFSH6N3JSr1VKOrS34CSzghhKpEbqllmaojgFeurkV+RtjHgJQCYJhggEERDtHjSlV9ToS8jWPINJDeb+miBpBmvP2MXPqV8F+ReQTjtMiUHb9ruOIvWGJ7ELuBKDHhMYCoO8cXf9bGlbod9EtMT3gwQDdjj4hpSyhDootcj03SJnCX2QZFyWKAnnJCsHzAlnOpDzRmVuWncCpaLRuvRbSEIbei04z26fuqO9oxJ0292xx0mAPdAgii9mwxP6B5DDfTlHKFBalh5yRALEAMgF99ql4YC1W31mkc9s9npxJ2NrTNKW0Mzs8FW6BsINcDsTt5BsHqx+jDsnU11sdHndsSXwPWoBNFg/SoxGY9bMlejjVmQVWNnsvhYtRBgwT2um0ggF67RyqWv/sti3R/G0oC3Q7vHoVzczy3Cru9Hgwzd0BFx1kGSjXGKA2/GS3b6f2flRDMjjhrSKoLWQoapjHV+SBdYB04htwX7KN37AS6tAWEyWLcikyymAGralANFm0VCyoSNz9yO8+sOAkziDRGAmyg3FTCQA6OoWZ9HPb3i7SXGUgGZXhmcyAF9bSVPpyHozwnfqipPKhTCnH76lki0KqTbEwX5OgXKTQVqjBYreNpIZxSaZXDJ+s3Zsmk4TjQCsYoBeXMOjz6jkMBjw7x/0Buj3OCxQl8lybc8MhFm/NlEO5WsKwIu1f9lzM8tRoNj4Qs+0RomgNAvSO7+HsIkReQKaw5NGrDbVkhlhDCHjga50zmlJLXYjgVsGkPkUNSlmYKkfBaQs0hK5G8uZLQ+vYAVhsHsZFB4NxadZZfLRdlMcW9FK+2yFuGDu4CRVyHLBnwYjwwYSqQYfnmqGh0dSjJJhGfIkOUj+iiCFlaKvn+gcZadSkQ3fi4hgPWsEAfeB+3Otxr6c3llvchvmtxEzDoZrucyNQG7CRF8gyFAXnOcoOsgxFyXnB2h9ScJbr0LosB2X6JFkSeAoYzDVBddYaAjyYBtznoSBDWevBNFfyyzcc/vBWm41ir2M3ZCYbhEDaFNUSYDUZJj5Q3wJVEiQCRQPtIH4u8G8EjhR/JCEr7lXWH1hIC1S3kBxk+AMLrn2BFpvsNSPOy8GQyMdfOwSRedOvKQmIUS4AYSA2RFNzYuGSrZcnJ25bcasrK+Ukt51arrtVzEuTDfvHg1YwQN+2Eb0uTU9zp4PBFHp9THVY+TGqIQ2Hbs8NhZt5rgCai1JvnKRO9c4K5MIHneUAmcMJpWnsrzNsoaQlG3v9Qmr6/OQI2UcHH4vkFUvAinkCF3C6FAfHrLfWbNMENUAxj+gBPOsvVS6JlehCCAHiJFnye5BMyBBAmYBIW3ctRtigDcyuDQl+e5Jkdo3J6S4QDJYeesNjeixGotYN5nOqjqHCiRS3n2IjPstuBuj1Yo4J7QZKAfTCPz2hf9qnAeQP/btrv3bWvsHj/3n9fQ7/vz8G0D39UwsfeoJ8tHT9f7zv7Hd/5PJv/PiGjcOpPfa/1wN//w9f9peveOT6naH6LbdRWWDLPDolel0qSxRqcaCPj/KobyIQyPdUsDLAAbPsWzo0zEmDOhDNLBo0tqqwl/0AajLeEqtJe0zYXUBJr2r8ISYTRxdNcVKCZAmxXstm3wXEyt60QxwAkXfFugWCjPF5A5LNg9pWc3X0n0SivO0jb1S0jCgGCNNzBEz249j91GxBF0Ux/Pp5F/z4rNffWyZXXzv3Qz+moqDhMMhw6xWvedzJb//2Hd31Rzzk2Cds6Gy5+fqrvvah8+77wp0E0FmGpSE2biIArLaRI8oIRYFOwR3lwcj9NSn6Qq0kBAAi8qLokLIuAWZKOn+DC3UtD0+RXg7vX4vgKZmBTwDxxyy91f5T9zEH0OhNfC6Dm1Ix4xbwRvQuY8On7oUE+GaylQYNlNLLkgY4O+qHdTS1BaUIzZPqkVPAA+UIr50Qcm0YGqpRQIWpl3sLJB7Z31vwIsgH/uRrsqM32xreexa9b+DckLq5Qpp7jvnF3d6HTghoA+h9Hn/Sqk9+4oPnfed1f320e1mL/37ehdeXjz75uG9+4nIPoa+74JmnvP3b2TF/9rmL33z8Ph2dWm+++bad1Q8fsA8ToSyZCN2uPpBQe5zNNww4xxzXqNkcDsuJ/8zOdmLyEB4QBG7WxnG0oCTcW3kSB+13qyb97KeY5LEpQlaIyTFDcOH4Kbpw2dKP7OBAT1FaDQ2z+2sGIpKkL56Im1AmWRHY9vTN+UR1RiY55LAt38gnS4X7PQR1b8wI13QSwQPZo1nhvi4pUyPBbLDb5grnaYGZuSjbhHYHtfigDznjzEdcdtY/nvf5tx19vAHcTZecd/Hvpv/gzKdNf/MTknfxC3/x2k/fPnXCBZe87fgNIj2bXr/Tdv7+5Q3U64CIhhVUQPTMFHe7mJ7mqSn0BpznKEouCmSFsaDz2guxsAav+DIDOAjiE1g6cIOwObV/W5goTDCEBk5gsrmbyHxyGNQA0JJV2GcmJUITewt4bl2KC48L4rRuoVYc4l1jT2aKbMRin8h0k8vjkTSNSdjLkbHpJIiFea56gUCYR4Hm7GS34LViViOt8BcSZZIGftR5Rb+Xhl+FcMexl9OW4bfkqg00cXGMA7VNEu7zJ2ee+MrTPnreJ951/FOnAAA3fuTcy7asPf3MU6Y/dqbkrL/wkX++EdPPeP4z5m74+j9//Iof3bitv+ehDz7hiY++1+xOGyrNTfOwokGfOyWmpnh6GlMz3OnyYBrdPhelXivIjHpItVrJPaTaLPh24cathkZ4kQp080y2wOAhZAAy/xvwUcMmet4JPwiMg4v4SxRGGaeex/VyJHyxbljgA30IDaKMOkoPE6SB6ivsZbHLRnw58fvhWmjLNgN5ZTVlbh3xhKobrGVR37bcAqBDUdC1CyA1btVQQ/FbauuE5F3MZn9R0Q8jyU/5TvtuJ7Q91BrFseopz33KSz79L+f9y++eesZaANd86LwvDPd/yZmP7d7yMY/xp9/5zibgIWt/fsb9nn7Rz+dNcrb2mJd/9JJ3ejb1XUdbFogAZhp2uNulmnlhHgtbs9/+Gps20e13YHGR5rcBQK+DsuCpAaan0enw1BT6U2yDpouOPocly5HlnHDemXhYaRF77pHaGdHsI740tBWSKS+K8zOyZ007e8qSb2XLcOZ0iAInwq30dqayCD9jwk7Ua53JCpGGnnQmsGFwqBhARgp2E/pbISTaR6IYexfkGbDhYiKkykwOC1yPFWXwgiZNfc2/QVcmNhmntAGuTXiRzuKRVNt5kCJtXUKTbW4N+OR2JWLfFdn9B8O43lRC+IR2PbWH2fVOOvMZe/3Te8/7x1+e8bID+Hvnn/+ffPgbz3xIhst8vptuugnAd97zmu/f89T3XvGmpxy1YfGaL533iue/5f/91ZOfduCPrnjRATtBdfU7Vpsl3bEZW+cpz5x9MjMFTLlvRtHSEoZD2roVdAvBfDBk1puomUML1nmGLGMD3FDxIZSbI8PtTKNcw+L2ydMrRxSDDSMBtExvJYv8quNqCkM7HnOHA2rZOhKR2U8313Zgjti+s4myMHnph+uRzBVn8WK6HehLiSyEuM5AqqFmCzmhCUS6jQ40VrbDbq1wbdrGtJodTllLk/2IdVmXaF6BQ7+WcSS490Us4529Divukp0cAA7T7bhBvsOWyDn5RkS/7g7PdaW7ptSPss7Egh4LWiYOunjEc08/5O/ffv4Hf/yyN9x47gVXZQ8+59lHxGxVVQGo6H5vuPTCFxyWAcB9n/jGS/rX3ev487/4zvd990VvP+quV73X5TzD0pCWhhhWmBpwniPP9aaj9nBCexqh2qlOL2CBZ3xlBIhDCM012xgPdV64xu7M5XKoak1LAz4WtQNKTvolRq4SdCWkwoctsk8in4kvNwHuEVIEMO99+ZFhniQv/kTo5o0VvAqlhMSl+MMItmamnxLn4aDp4N/67y7InHgDdmAReScSricjQtsCgXx/NNP0O1HZ9TSJWSflGIJ29CsiXx8JBtVheCNFpmBHw24/1GdCu4WWXahyvzPPPPrsV19w/hUP/M1Hft05/s2n7Z9gWr16NXAzDjv5lMPECHzquCcdP33+h3/53e/egqPW3YVKK6pr2rwFFYMZwwq33k5Fro6/otlpnpvlTg8z0zyY5t4AecFlV29WZy1iACTsWcAZgHoka37EYewzoi/B3bJ1dFjvB6QdxACDa7LGYNpzUjtlgulHpZ80JF0R8GFDLgXkEFQClWxWZ3mRQwFhDHoN5ciHVBdV4o/xXZO1An0bRVAbPfeUSngJuJHb60iCDiHKy3FbWan2l2N6Td3IYrIuHBUlt9GAAPqW8xnICfJKbKGox5b6z+0xsaDHgpZfSXjw6Wc+8nV/+uFnvnjzxqknPefUZFDGvQ49lPAzXrVqlZ++atUcsHlhYeEu0TWghW3odDA94KLA7AyvXs39KWQZmPVuG+pi00batBGAAk0dZldHkXaQRlC0FQbZJSpiQYojb/zuLM1YMvyPPR3LIR2dklJTkZ4JLJ64G/avbVkB4rgAbXlglNcaSTUCJWNADBkkLsRAmVpCGaBS4OUXzR6umfbu/HcE8VKWeV+SjR3O+szkMfi5AlHs16u2DK4/dmqz3BlRFOG6UluiLcJMmVjEdz8SoYDtiuJmYeCg++BBp2BCu51GWOq94Y/PfMLLn3HJ9Vj37DNPnkmyTD3muIfQp77xkx/+cAkb3Glmt/7wh78G+gcdtFNmCWvW49yMcMdmum0j1WYWrqrh9vYkZMRZhk5H70dalqz2I80yvQFpFq0eBElA80b3EoVDj4E1h1ngUzQpZ9nkbJKEIemPFraRAEQVfRzIjcFXPiT3r0yxKtkqu4GwfRqLJJHRKmYD9qRk2U2wn5oa13sa+plCW95L9hhltxT6HKLMXu2aDcfgRfgFx4ttvB4xLTBVfS223X5tCfSOV0jB9btuulhqa+ogad1+NAmzGwcaZS+OmT/8638fPPMW2vuo4zoNLPs846xT3vSNj1/4+r98wYPf9HszAFDfeNkrz/4CY90fPu24nXICJREWFpFlVJbc76LXQ5GraUPkGRelBuVOl8sOshxlybkF5ZzzAqT34uBc77+ht+BwVrMESt+KjEfooa0U21CRSaiMes+gk6tg/HSZUYqV8t3ua/A19OMfQkjziw74vTo2iUVror6nMHugP4XCU+xJPE48tXdJGz+pSVreaGx3gqRHnvyC2hwUTfCdfBc2i+s1mptNPMlX8CY9/6topPfQPejhTzqonWXtH7/7/Mv+66kfefNDD//cEx/7wA1Lv/zaZz7z/VvKg5593l+dtHNCdjJCxdi0BUVOGXG/j9lZ7nTQG3Cny0UJe+orDN6ptYLb5s2W0GZvaDWKrK3XGMZGdg5ic5wgHMbJoSJigBZwkxzwesxJavnkRC4ZbGAtpuTXTvKY0VbhASWitVrTmziTNQ2FBA6fnU9JxEu4g0WEHLW8tVTnvSP63CU9BMdBKa1UTeKgx4Puuo5y71Mu/M5XH/6Wt573ySs++t07MLvvESe//O2vfe1zjtpZSwnXreZeD7Oz3Otxtyfm/Qh1RYsVAB3CMVxEXdNwqF3PtThysK4pWuHtOTTgf26B+5gocSsjNPxrlulk4vB0KJ6fy0wlaReNm/yBNvCF19h3Cgs5Ujf9VPhPiHTlvLoIDzuJIgBRKKQE6xQKTwuT449QkyDRc90keAQb21GCimV0FLsv4BzEMiNsxQG3l6k/I0ouu7w3l+Q/dVq6ntspFQ2hrEg2QkLHlOjgWQK26OkpUdOI01Yt0wZKiM7CNyPHT2smG/aPBxHfJR30jtF/fu03X/jcVSMyH/PwAx5+/P4AnvZAzjOUBTJCr4telwHUNbYt0tYFLA1Rs/ZH18Zd4GAxCJZSAba+/9AykFkOnvkThpmQZr3YGTmPto6JNsxaSBjMp/2DAWonUd6BsvxXSEhkga5PiO/W4o5w3CG4V4oXhmVlBmUlpj1D/TnoAwKBntqSgrlT+aokF0XXAoIsBAckxx4ui5Xf7lUIMI5lJ8eJoG+zymZ5J0YwsWks9+DFAWEct0cEyN2jbLOI8V/gXgOwei961Ok7xTM5oe2isXA1Xbfxd9+6flSA3nBL7+HYH8CGNdztYGmIqkLNIMLUAGXJU1PcH3C3h7xA2eGi0Ma1iqiDj3SIrGAgtGHFjukAACAASURBVBO3j2Ifq58ov/q0P1f6XgO3SSA5tIaCXImohgAmlqHUGCJVkG/uIbQi/Vxkb91f31mU0BZBs5C39CPQqqkKzY05kuupqcUMLMatFN61tngLg+yJ2ivSlH/Ed23YqBrdezWhnUljAdC9/nbMF/cGWufpKQDodpFlKEu9XBvA4iKGQ9q6BVmGskPdHvf6nBfo9dHpIy+400PZ46LDWY6i5LxEVoAyVhv2wxwaC+hxsHFDa9zR03dspvhggp29KA4wk4plFo7sCMVgS2kloUycqw0XYuyQkCpSEgztXcJyiOadvZJCyabJz6bi0rVO9nDu2m+u5YR4L6hpmpTDRjC5PEQL3zVcp+U1gk2PAt3C7DAxGA0NEkr2L2K8DTo/v+m4bAoHmNCupbEA6DtH8/MAoddVsc+0tKRdbNYLwQAzqpoq5e6ozDSgtROgQ5uds8L4Fqw7IqDYp5yk2NsApEbfSX+CuUi4KWSicD5kmc8Zs8HxI/W5LmMwUfA3Bakp74EUK0EnXZwfakLek5BNmpMJO7q5z2uC3aRi9taD7KCukQLiL4U87CIaifRPNMjLwXWQVyjsGBvgmE1xmXwRDSAurzs7b5OzCW0PrWCAXhpiZgaDvt4DuiiQF8gIRYmi0FEceYE8Z+Pi8F2uVhB5H4O9SJpvSR5jDBLirwvwv9g2E3V5/0MLFqS+bfY2mmuTH32i5Nia65KOJlwOPho5U6Ka1G7xUYT6jyh8e6oQygwYpAfZPAosX9dzNzVv08ttro4uOiVEUuJ36OsGIE8veJjQrqYVDND9PgAsLFCeY3qaB1Pc62u/s7UoyezfT6QPhFWxd1muHRrq4EF1RLc8gRBotDeDODy4a7HjXTMKKNH223STbPaAJd8wDzzmMtG71eLMvzAp8jLWqnE0HQ+JvfpaCaEDx81rkXfyqR/fHXiNLBszuCavVUVe+JxOLOveKGp2gi9B9iheOwTviEPbNqi45G/xArc7iO1U7eh52ZzlGiK4EtgMyhStGIQB9Ni1zYw995lY0GNBKxigt27FzDSm57gs0e0iz8GMukI1RNZBp4ss57KLssNFB5QhL4QpLYxd52X2gcPGJJnEaMdRIyMAC8ife2B9WyEyl8xiCnL6pVAJ8q+/xLnR8JcZm/qP1EyXZ5Y1WGdp9Dfq+dqmqxYr5rl6k8dpI26KxG3wKJafhsJEcSYSQhjI7S0TN6ZH1Pg00bypKsScCXmtnUSTnLpu5JzQrqQVDNBVTZRxWaLbw9Q09/pcdpHlXJR6fSD5+yLVld6w0Vij4nsLwMUviMzGBibQSUc7kfmMyXj6LJQQm4OjjUQ2Gx7ZfwMTMk5HG9DDeVRSQOPhi1mB7bbhtEFXrCsI355yapsUT4K6FUJUc8pCXe3J/lVx4KYB4UYqtmrSqJTO98A7bEEy6fhuIdlcLtYwNYHmElmcXaBeqwzcE3+W7zPEtT8EimB9hI6w8XW3JobpDc6u6MTRCe0eWsEAvbCIrVvR7dBwyFmGLKcsZ2Yi4roGDeP5GY1od2LwlvitJ2IGQtD0vq6kIzsJzU3MiVzUyOxLbvJmePgSf/nRSN/Zki3meXCdLDS6HsWOTkLJssxRIoXpo5UY/wYaHcGSBwn41gBNiXTxJ2phyZacGm3QNuxvUup51QE6qzGhcaAVDNBbF3D7ZvrNLagqArB6FnMzXBTICEtDzC9gcYk2bcGmLdi8QEsVlioQkBHKAt2SuwWKHN0S3Q46BfIcRY6y0OF66rYo9HVGoIxzc0Y4bIRGBoJOVPt4aMe32TnaLT/RG0ybQBECINYQyjV7+pqdkSjnlGwsR6a+PB2y4vnNNQ97ea3x628zIssFRGiKKUieLQAZH6IzcqihZQ4ww+8YSQ447hKHpyxRzL8lyfOfRBI4fjQCWZkJNGSh3rKVDdiCSvmJBmGT0l0KSTcYh+LEK9Bvc3pNBnSXU3RCO51WMEDPTaOq9BrCmWl9vDcAZlQVTfVRM69fo5YRMgSCqGNT1Jb8ao9/faJK7tb+SbS1bmsZNidXqXnxbYkrTWpVsfLupabxAu7A2RA8jdijRJ1iIg69kbKEEouSKWXSw+RUMEnaVGy3MWM7GoRkoX4RXm3Y4VBYzUhCUFxCpeUSW9IVNS0RvHO5mij0+SyXd3u12usedL/jtkOfCe0kWsEAvX4N93rIc+YaVU11RWqtYJahKLS1q/E3Z8qQZybq2RxARTAb9luctUYl0pYONd7ob6DFGGz8iiSuNdyiBYL9JB+VKGbx2HzYstz2aRoFrKuajfVm8rLzIPjqj3YLcBrKkyls7D1Zh6TToOVRM3A3lR548IOnI/k9pJz2OiaqPCKz5/4apbOR19sW0/wT2sW0ggH6F9fpPfiJ0C3R66AotP1VqWOualQ1CCgK5Bl6He51URTo9dDtckftDd1BWUIHTZecqyA8c4CsPFEQiW0xxB5GYkYrQQFY2JtgzUXI7EI+YL+fJis1ikhj80w5oEUuimU24WYas5Lh2BRlN0QI5XtZR0v0Hjt7kMMokST/9ijQyNlupaZKCXMIHu2MasLNJlUbdAs0tOU0zUPGooMna9beJV6nCe0orWCA3nMdE9DtolOi3+dOB6XeYjQ8E0SRdBmTA2K2u5KaC3aheGZ7GsDgAkxIqfLsSa8rDGrD+zLctH+8Z1uQPSQO5SxHy4OLjURIonyTiSq//KRBx+HTNMT4eWOFl+2NAHg2uvq/j3TtJmc6vQmjKWyfmE/iJrdi4rJxFzK/aQEXgpPI0aqA6/ujwPxl1AA6k6Xe40ErGKA3b6Eix7DihQxLS9TraTd0XZM6HxZwWzwDZrov0yeqKDQvShQlylJt3s95jiwnO8unwqVJ7Ctt4S38ZbtvgDyLNTJC3XydfiTmaoyccGlM/BVZM4xSwK3322MgYeATOUCjxNds4u0CuUFBogq2eSWqWjZmDzIc3nGiz7FCPBc/TJukbGWOtnaD7US9XOkSw6zLdYOyLNtELkIxWlI0ihytWyqj2E7A/xGISnEoTYQCmip7qnIkJCwUcxtocqLKONAKBujf3Y5uBwQCIc+QZygKKjL0utzvY9BHUXC/j26PO13kOYqC80Lb0eZkbiDCgoj0htGelRZYVQ0uAkpdIAYKCv62je8ta1vkgzrprrbwtqy4tBA0OEkdQ+KyJUk8vHNPA4NRpDcN/0MbNqlbYnkOcdPTZaWNZimLDtEXTqmnTQZ+Qqntbnk3ThCP9tg6cXGMBa1ggD5oHx4OkeUockwNMDfH07Pc6aA/xZ2eWrSC3ITNwTqRAX3CfLR50EimKzw41j9rdoc56Yhjcx27DrQJ42Zv3JFXsUfYm3cKVjAmw2NhTPJobJ4O7w0+8gAI2pFdGmKBUdbsWHAU24CRGhFuRsWNQMuO7lNDEO+MQd2gYkbUZbSq+71sbBBznMohR/wcZgBhrpo0jshvaqdVVGiyPbu9EYqY0M6nFQzQRa6PqiJCWXKni16fO130prjb57KrLGWm+FwI4/dQc4zi2qCkHIbrf8hDjdQ3n3awxt9DA8kP2DkkSAggHxFSIKi6n6QyIV+zNRomtOgsHrnPPJYchww24MJIoWbt9nLqPm2Hxl1Xs3wWqaPEqynfWNoMD16N/KmknifVlo3nQiTjrs5etY63QmZguNTMP6FdSCsYoG++jcocUwMGsGULzS/g5ptydWh3r4f+gIsS/QF3B+j2OCvQ6XJRMuXI1b53Kvau4Mzs0aFSPG+dT96XoJdyK9vb5/O8yuRdBrNqtbm0vnJGXYu9OGrTl5hCvb6k1kKMoS32BmKvLM88N90Ps4ZOZ6cH6rGrWgJr2I+ujYxo0SBWihm0mInWeHBti5CILyP8vFsrZJRepKlPkpUK3l5KmnVwN5dIAEiZ4ewOOmckVPUsa9vsUvN4VWS8jDDqPDgpCinPWGLBJC0tTVwcY0ErGKCZsbAIBpUF5mZ4asD9AcoCnR53uuh2OS9Qdrnsouhwrvbm75hwugJZrpweDEZdAUA1lNvRkbWmLSZCQ2q4hT/gbq1ukryI1+avC0Bi56NkzHISO3wemB4FED5rdRSJxUziWGxso4kCbDcjbPsGyzSlIsd41KS8R6IgzbkcLlu5AfqEcwlJp1aqbw41anbQ21huCsv3FPMmPZetSFCW6MGCzswWLHRtxmW/5zPK8Kq1NDmTcBxoBQP0oAdSB6oCm7di67y1x0htD51l6JQqCNqdkKLARxgUrFcPklvYQvqCVUCeCo5WDhPNbDYpNStfWKUQQGb6kRAvjzbIJ2YmLYAKNpESV1tEC8SGu/cdGvQJHBHiSTiwdZdNqBJkMCEfy1NUZNzrpKnF49waQkeAQmPfuy3s4ztRdLNbxs5nhJJjfgGGCXcEIIASQNuQTvGxX7pl9hJVvA2P1BMA6EyWeY8HrWCA7nY4I0xNodfjwRQ6HS47KooOuQrYyPQyQjVPSLk5JyUXh7SKjSYa8dHux2b9GezdJqwTZ0prJpvLm6zzZ/yUy6IWbGq8KQ3z2GBPTyH6angCWVQp5pSSXeZQWizfy95EvlmXyBUZdCFz0JH4zznm5VRXNnr34CNdZICHscl6DsD3PrtnsnOKTN3GyUwERnLEzA2NGSgQdb2JAs1753zi4hgLWsEAffsdlGdYGjLdQXQzipw6HRQFBgOenqbBNBcFyi5nOS0xmFHXqCvUNaphVg1RVSaxJunVddcIf8JpvDPUtDrGXoRWM5ytra69jzaCJGco+UZoUKwbevvWk7oIdGAhwjNwUxEFqVGy+yMXyo9im8YSpCYeIsWNHMO3DE1rQbqgMlFLNs4cNkhLuVkSv4PwlyNOL7OJy0YQNdarIV2a3vbnSkl3h5OlcqK3agLQY0ErGKAXl7B+De+5gaemsHaPemYND2a57HBnwGVPbdKv12oDxlytwUx1Ba5Q1+Ca6kqjNtfEtd4z2oC1cEOb37f0PlsUs7NtSIG4Kh2+/ZLwR8cTdH66cI9SQlRUYsJcakCfeEjOwXXAL1MSJjPFAmM5o+oQ346SEtUiqUOshmIK0pp5LAT6f4PblBohQMs/0nXdZPDGbySZaNoh3blGHZVNLyaLVMaDVjBAz05j6zzd8BuUBW6+OZ+e5qlpLjvo9fWe/QAYFn9R11Qbq1naFMmP4c5Q7DVuSIH8OGMz2MeatEGaUpVTT9s/XS9xecQnce2JC4Wl8JGT1xFAhArYFmgG92RiiwUqzdUmxy6at5e7E46dRA6mJvltkvzO27O7R9CZU+8ipS+m7thu3Sa0M2gFA3Svy2DkBQiYn8e2bXTHHVQU6Pe539dhdt0+d/vc6YJyFGWtVhLKzToMQJO7tv9aBksRlIdsvknCYtk34HcJUUYJr14pzt8iJrgQCZE+aIiKCAaXvUl/x+Gc1EIxEYMheSNklLuYBjzScdEOahCc/hxvaBg2wY2evkvBViM4phy4rBUfgb9JPQRXZhYgYkrrZTuzJmNYtI90lcUyPAnNL2D1xMUxHrSCAbquMTOD6SkuCvT63Ouj2+M8R9FBUbLBYufoIIBrHYlrt3s2pxSaTUcztlOFcuYwtIXj78GzNM0nKiDS4Cl5iBwwxBl9w8fDzfCR+adFggNxCiRHc49sE2NnS1CWBKnQDyNaJuyKAiGCp82Wl0DGDTz6RizvdIkuL6c8yM0dMzvlk+q11ii8ZvHTSj3VXUKkfBz2EfdYVoFaZpawLQ/zjfRXVE3OJBwPWsEAvbhEG2/n4RL1eigKUJ8zvQhFbZkEggpwJjYxEtqtXKu5wXBWEEByzzC1ZRLERJ+H3RCJLs9IsWdNlLANW6be5NRc0pBsRSIPTXw5Xo6m7Mn+I8qY1C0JsgkBwdMG09uPr4hV9kCNgrZdTlWbLlepBJslBWrE+4MnZ+fc2MI+tUcgRhqk20micJzoJcQc6aI6ZbKkCe1qWsEA/cNrqF9iuo9BF3uux9o1mFuFTgeDGfQG3O0jz7nsoehw2eUsR166k2RVzDI08pL8Mp2h12C++cYmhU9TQoIs1uEQmmMNi1+8ziMy3yLjd4TJSVcxZ4KJJ+FH62GWg7cYAaU955USsfhPUzvhcYicbRCflhZxt3UEjCBohY20ls4PTTVqbQRFDt/Trvgwaft0SCc26Cru1P3sGposVBkHWsEAffRhNYBuB90u5uZ4ZpYHM1yW6PS57CAvGIRqiOESLWwW0Ga+Bm8PfnkkINxTfW1/xCkLJXmox3ZRYMbJ9DYnpqE4cs4T1WwLN/ZAfh5PoD/6DuPSbIulEKpxsG86J09BBhjeONvrbEy/SInqsIxW5sbWS/RnjsJ+N8yynEx77z9X8RlhlHT7+2304bRyBqUbc2S5rsv8FgdbJj7osaAVDNBVjYywuIS6xnBIt95KbvmJOgZFbeUsThS0KwbtISnqNFgE7otlvRb+r5f8FIoYHDXOQaUYktDAYYrM1RBg4CxiRpjFSw9KjEQtS7azkeoF17G/JS1K8lBKt9ig5sTtsmW1TSE29zR27tEw6/7RpJNriwbFgtnLJj0DnrYoZsPA7P88ZG2W7RIIYAwnYXbjQSsYoFfPYWkJAIjQ63FmvBZ5jqJQ+/Gz2heJUv5ifWEO27YLCx1DFjGbfUotxV9L+MkFO5FC+rsjDGoBr5SpaHVgjvGiQacGCsA6EQAgodca9tGQYhSUAcDs9WJhpoY+I5mYSBkhii4wGON+0asylqud/0zeNmI9J7DbFOVmkkNvkrj2ZimWUzI9+RlWwd3Ozk0s6LGgFQzQt9yKfg9lCTAWFqgo0OtxWWIwxYNp9Ke47HB3gE6Pyy5nGbICWWZOEUw6K9jdit8rSc4klMTkfzKpUkRiYEF7d03QmjSO4uKbxKYkmAfNq8CRADL3N0aHJENQdAqLw3O2llMgluDu2gG6SdRykkMJYU1lzESruZpusUjx5eqVVnLZrjmuprlZu8fEBz0WNBYA/fPFbT89bNWIzFcubVMXKlSjGjpvclUhyzBcosUFBmhxAdvmkRcUbzzG0RJBwHx1NuQDBj5qEwcSRUkj8i2E7g5Kp49Edr01ebdNFBeRdtH4OzQBehenJmbHGLhxfFPOPYhTRQpRQ0UCjPMpHZ7Q3JklBhNmKBNDcwLvUkjXbowvEyQnaqcGSUkbf9n+2JRFUuGg/4tfFqBjltTj5GxHUAT1Jhb0WNBYAPS6DbP44ajMG/aa01cMgt7/SIH1/DzNz+OOOwA4fzRSKBmsv/IoBR8C+0ygtBAViA1d4eaQrYxAxNYhLjmVRBL/ehqZs/VIai6OyZDO8RB84cw4Jzn+SgP4FnUMtxCJDzKHuHUZOWgWKcSbg407hrj0Jm3dLbvSM5ss/6QTQ92acIlDZSzI+vPJEDOkJDbC9iUTEGRP9HWpjCKdsgSgu7fgd4pqDS38Vo19ZZZm1mXA5ODY3U9jAdC3zf92r1VXjcq8MAvcG8DGO2jtKp6ZQVlyUQDGTKgqdW6sDnYeVrDXlswsotkj1NjgJEBTLRZ3X5HeaMb89t0BWpo/mMBRv/66BhGIdVw2WXT3x+/ShKnNGawxjNogXJa73hg1MtL/Bt1AloEy8noUw6myq21U7YhWYULcvWkdzL9WB8PgHXdgfeIaC2A9F4Dx7wfjA5jX4VojWh6pNNfH1Jq6s21YxTV0ApVM+WoCxYhM+InZYJrl+S/mTZk+kpVBEMzy+U1EDutNFGZirsI0dNxT2hJ1l5wlsFULEbtfhWaFaVX7294uGi5uH/+EdhKNBUAfsIane7eOyHzQWg20s9MAsLCAxSVSe7vYga0yV7MCROgi+s4NyY+WSM8BZgZlsswBkN6VFC6AWgO1h9F6wZk7ZlA4TyxZhAWi7w0AXMdgh8Ph7GJApnYVAH1ULDnkEhXUdUg0BVmEUpavxSzILThglrK5yvrak/mj1ukZwzY6hISgox0ICiLZcwh4/iKbIuIIrXXIFpqSYeDiFbtmNGz2key5bUFBDF8gyjYpi1VOtpHj16RMB78/Szs3PMMcoVURZyQK89qeOG6HhMUdAbuivQ6mo09OPZjQrqWxAOhse/r33DBXlfl1MhYX9fahgOdSkPYOzCekTvhWO/oXBYoCRclZrq/zQs8oqj37KUOmdvEntaO0M07JBvD5iRD/kjvwCMqGtCij0rVnUKF5DYlT6kp7vc2te6Izkvw+RbpX8dDpKRHOCo7wIoDVJCVsQ45ubYpklghoQ9PgWoCk/rXXZ/gaiELjPswOePy6eGESUjWFucpCD348Vlnbyfn9rv9mfQWWCce2PVv4oqXay0enmLxeWeQ+k0aXSCRwzT40mSQcBxoLgN5rZt399rrniMz7rtpDXfzDl8n/4ic0oQlN6H8VEbf1xROa0IQmNKHdRpNRzIQmNKEJjSndzQF6+Msr3v+6M078vUP2mh3MbDj4ASe+6H3fvKmSHHzTl99x2kPvsabfm97ziBNf9pEfb7WPln7xb2c9+pB1M6v2e+Cp7/rWbSLTLR958rqDX/rlhZ2o+S0f/cN1RDT9rMvGWNvFaz/z1tOOPWzDdK8/t+ehx5z8ti+5feDHTNX6xq+8+3nH3/fA9VP92b0OOfqU13z0R5vF412t7a8/f/ZL/uTxxxy6vp8RHfVX14bPW/TZDdpOaOcR363p5nc/otxw5JNe9Ja//eCHzn3Xq0651wD5Qc///CbzfPG/3nD/brbXY15z/iWf+udzTr/fFPZ48r/cqJ5de87Duged/i/XbLz5m295WG/v535+q850+2fO2GvP0z69cSfqfesnT9uwZq+9Opg6/VMudby0Hf7kH05ch/LA41/wpnPO/eD7z3nji5700o/dPJaq8uJ/vfn+Xaw+6nl/fdGln/y3c1//xHuUWPfki27Ybdp+5WUH7XHoMSf9ybMetR/we++8JlS3WZ+xa9sJ7QjdzQF64Zc/vXZB3N900ZPnkD3u/FvV7S0fekIX+7zwC+ZXfN17j+3gwFd8o2bmLf/4eDr4z/+bmZm3XngS7f+qbzMz8/zXzjp49SkX/XYnan3H58/cb/qEcz9w+pQH0OOl7TXvfngvO/ylV9yWejheqjJ/65UHgY59r5W99LWz9kN24nmbdpe2VVWpos55WAzQbfqMX9tOaEfobu7i6O5/rwO64n79Ix9xH9Q33HAjAGDhc5/83LZ9nnLao/r68b7PeOaj82svvfT7gI63tu3HaqlE9b23veDD+//fv3n6HjtN54WvvO75F+37pr9/zn5B+lhp+4MPvver9YmvftMjVwF1HUxEj5eqABYXF5GvXWvWqKJYu3YOrPXeHdoKkRG16zN2bTuhHaG7OUCHdPvXvvYjTN33vgcBAK668spFHH744e757H3vuz+uvvLKeWDwiMc+9Fcfe89lv956+3//7XlX7P3Y4w8HX/V3LzynfO17nr//TtNw8bt/8af/0P+z9730nmGE4Vhpe+tXv/pj3O8Bg4ue/eD9psq8M3fgQ5/5rq/8jsdQVQA45owXHlle9hcvvui719268aafff7s//Pen+x/5kufOjuO2rbqM3baTmjHaHeb8ONEv/v3Mw6gzpFv+eFQ3X/pBeuRn3qxZPnZWx8IPPTvfs3MvO2qfz3rMYdumFuz/1FPO+ebtzLf8IET5u7zZ99e3HkaDn/05iM7h7zsqwvMzJf7Lo6x0vaHf34oMDMzt+7hL3//Jz732X/96zPuN43uUW/9QTV2qmq67RvveOzeZhfkweFnXnxNpZ/sTm1TLo52fcaxbSd0p2kC0Ia2fu/sR6zCuhPO/fnQJH3x+etQPO1fJZf8rQd0y8Wnrtv/+Vds4eq6y//spCP2XrV6vwf84du+cnOC9U5S/ZN3PrS7z7M/p+cwA4AeK22//5pDAKx+xiW3m5Qb3vOoHlad8dnhuKnKzDz8yXlP2Lez73Gv+sCl/+8/Lv/YO551/5nOwWdcoiYJd6e2KYBu12fs2nZCO0ITFwcAYNv//P0px7/6e0ec/bmPn3lPe5jEmjVrMLztNhluddttt4HWrIn3Rt38+Ve/7Asn/O1bHzn47QXPfcqFU6/+6vW//MwZd7ztlJd+6o6I+c7Rby44600/euybX3N0tXHjxo0bN25ZBLC4ZePG2+eH46bt2rVrATzouONmTcrexx9/H2z83vd+OW6qArj9I696yWV05kcuO/u5Jx/36BNPfeUFn33nsddf8KKzv4Ex1LZdn3HTdkI7Rru7hxgDWvzp+5+wJ00f/cav3+4/mL/olA72fek3XMqm8x+X4+BX/3coYtvXX3Ho6pMu+A0zDz/+R511L/4iMzNXl5zaWfuiK+4iPb/xin3S73DqOZePnbaXP2cV8NgPbnIpP/3LI40xOF6qMn/nFQcAjzt/i0j67p/dE3jMubftZm1TFnS7PuPWthPaIbrbW9DDqz906qOf/x8bXnnZZ990zKz/rPfYPzi+e/3HL/qSCd6/4aILv1AdcPLJ9/f5qivf+cIPrHvLu0/fEwDYP+WI77K19Pd5wcVXSDr7hB56J7z1iisuf+WDxk7bR57ypFX41uWX26UQv/rMZ67EHkcfvf/YqQrsueeewA++9S23SOPX3/zmrzCzzz4zY6htuz7jpu2Edox2V88wHnTbZc85IEPvfs9778WCPvXfN+nni9/98/t2s72Pe+0HL/3Uv/ztGfefxvonfTT05V3zN8fOHvmWHxjX9W/ef/xg7z/68M833vr9cx47u+bUS3ZW8H/ggx4zbetf/P2j5rDqqOe966JL/u1Db3nqoT3MPuZ9V42jqsy//qcnrUa25++f9TcXXfrJfz3/zafeZwrlEX/+rcXdpW193dcvvvjiiy9+/7MOA+7xTPXz/Oq11Sj6jFnbTmhH6G4O0D9+4+GJTmuDGOBVN17xtqc/+IBV3c5gj/s89iUXXrk5EPGbDz9x1SEv/6ZY7jK89hMvP/7Q9dPTG4544pu/FdEZ2wAAAKtJREFUcCPvLIoAety0vf17577o+HtvmCo7U3sd8biXfvgHUpvxUpW3/uyTf/HHD7/P/msHvZk9Dz7y8S8591u3VO7xrtZ26aOJ3ZhPumB+NH3GrG0ndOdpspvdhCY0oQmNKd3tfdATmtCEJjSuNAHoCU1oQhMaU5oA9IQmNKEJjSlNAHpCE5rQhMaUJgA9oQlNaEJjShOAntCEJjShMaUJQE9oQhOa0JjS/w9uLe4zsuxGlwAAAABJRU5ErkJggg==", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "p <- Heatmap(\n", " data, name = \"Peak importance\",\n", " show_column_names = T,\n", " show_row_names = F,\n", " cluster_rows = F,\n", " split = new_cls,row_title_rot = 0,\n", " cluster_columns = F,\n", " left_annotation = rowAnnotation(peak = anno_simple(attention$type,col=cols),\n", " show_legend = T,show_annotation_name =F),\n", " top_annotation = ha,\n", " row_title_gp = gpar(fontsize=20),\n", " column_labels = c(rep(\"\",99),\"20%\",rep(\"\",99),\"40%\",rep(\"\",99),\"60%\",rep(\"\",99),\"80%\",rep(\"\",99),\"100%\"),\n", " column_names_side = \"bottom\",column_names_rot = 0,column_names_gp = gpar(fontsize = 16),\n", ")\n", "draw(p,annotation_legend_list = list(lgd),merge_legend = TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result, the identified 6 modules have similar internal distribution and are distinct from each other. Specifically, at the initial stage, these modules exhibited a nearly uniform distribution and as the training progressed, the first two modules (M1 and M2) received increasing attention, whereas other modules, particularly M6, garnered less focus. The variation in peak importance scores are greatest at the 20\\% stage and gradually stabilize as the model converged. As expected, M1 and M2 were primarily composed of peaks from intronic and distal intergenic regions, and near half of M6 composed of promoter peaks." ] } ], "metadata": { "kernelspec": { "display_name": "torch", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.19" } }, "nbformat": 4, "nbformat_minor": 2 }