{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "import scanpy as sc\n", "import anndata as ad\n", "import numpy as np\n", "import pandas as pd\n", "import anndata2ri\n", "import gdown\n", "import scipy\n", "import scipy.io\n", "from rpy2.robjects import r\n", "\n", "anndata2ri.activate()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%load_ext rpy2.ipython" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%R\n", "suppressPackageStartupMessages({\n", " library(SingleCellExperiment)\n", " library(Seurat)\n", " library(SeuratDisk)\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Expressions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download the data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=1mXikpKDZ6Nqh42kDHHbwVeMJWc1V9mdB\n", "To: /mnt/home/icb/anastasia.litinetskaya/multigrate/example/gayoso2020-expressions.h5ad\n", "204MB [00:05, 38.8MB/s] \n" ] }, { "data": { "text/plain": [ "'gayoso2020-expressions.h5ad'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdown.download(\n", " 'https://drive.google.com/uc?id=1mXikpKDZ6Nqh42kDHHbwVeMJWc1V9mdB',\n", " 'gayoso2020-expressions.h5ad'\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 30293 × 4000\n", " obs: 'batch_indices', 'n_genes', 'percent_mito', 'leiden_subclusters', 'cell_types', 'tissue', 'batch', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts'\n", " var: 'n_cells', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'batch_colors', 'cell_types_colors', 'neighbors', 'pca', 'tissue_colors', 'umap'\n", " obsm: 'X_pca', 'X_umap'\n", " varm: 'PCs'\n", " layers: 'count'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata = sc.read('gayoso2020-expressions.h5ad')\n", "adata" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "adata_ = ad.AnnData(adata.layers['count'])\n", "adata_.obs_names = adata.obs_names\n", "adata_.var_names = adata.var_names\n", "adata_.obs['cell_type'] = adata.obs['cell_types']\n", "adata_.obs['batch'] = adata.obs['batch_indices']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "An object of class Seurat \n", "4000 features across 30293 samples within 1 assay \n", "Active assay: RNA (4000 features, 0 variable features)\n" ] } ], "source": [ "%%R -i adata_\n", "rna = as.Seurat(adata_, counts='X', data=NULL)\n", "rna" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Protein" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download the data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=1Y4wyzhaXrmW1anoByA5clmzrY6OurL4R\n", "To: /mnt/home/icb/anastasia.litinetskaya/multigrate/example/gayoso2020-proteins.h5ad\n", "57.4MB [00:01, 39.5MB/s]\n" ] }, { "data": { "text/plain": [ "'gayoso2020-proteins.h5ad'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdown.download(\n", " 'https://drive.google.com/uc?id=1Y4wyzhaXrmW1anoByA5clmzrY6OurL4R',\n", " 'gayoso2020-proteins.h5ad'\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "adata = sc.read('gayoso2020-proteins.h5ad')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "adata_ = ad.AnnData(adata.layers['count'])\n", "adata_.obs_names = adata.obs_names\n", "adata_.var_names = adata.var_names\n", "adata_.obs['cell_type'] = adata.obs['cell_types']\n", "adata_.obs['batch'] = adata.obs['batch_indices']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Warning:\n", "R[write to console]: Feature names cannot have underscores ('_'), replacing with dashes ('-')\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "An object of class Seurat \n", "110 features across 30293 samples within 1 assay \n", "Active assay: RNA (110 features, 0 variable features)\n" ] } ], "source": [ "%%R -i adata_\n", "cite = as.Seurat(adata_, counts='X', data=NULL)\n", "cite" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Integrate RNA batches" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Calculating cell attributes from input UMI matrix: log_umi\n", "\n", "R[write to console]: Variance stabilizing transformation of count matrix of size 3996 by 16845\n", "\n", "R[write to console]: Model formula is y ~ log_umi\n", "\n", "R[write to console]: Get Negative Binomial regression parameters per gene\n", "\n", "R[write to console]: Using 2000 genes, 5000 cells\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Found 113 outliers - those will be ignored in fitting/regularization step\n", "\n", "\n", "R[write to console]: Second step: Get residuals using fitted parameters for 3996 genes\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Computing corrected count matrix for 3996 genes\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Calculating gene attributes\n", "\n", "R[write to console]: Wall clock passed: Time difference of 5.294546 mins\n", "\n", "R[write to console]: Determine variable features\n", "\n", "R[write to console]: Place corrected count matrix in counts slot\n", "\n", "R[write to console]: Centering data matrix\n", "\n", " | \n", " | | 0%\n", " | \n", " |============ | 17%\n", " | 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"stderr", "output_type": "stream", "text": [ "R[write to console]: There are 1 estimated thetas smaller than 1e-07 - will be set to 1e-07\n", "\n", "R[write to console]: Found 95 outliers - those will be ignored in fitting/regularization step\n", "\n", "\n", "R[write to console]: Second step: Get residuals using fitted parameters for 3997 genes\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Computing corrected count matrix for 3997 genes\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Calculating gene attributes\n", "\n", "R[write to console]: Wall clock passed: Time difference of 5.17424 mins\n", "\n", "R[write to 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"name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating " ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Running CCA\n", "\n", "R[write to console]: Merging objects\n", "\n", "R[write to console]: Finding neighborhoods\n", "\n", "R[write to console]: Finding anchors\n", "\n", "R[write to console]: \tFound 31900 anchors\n", "\n", "R[write to console]: Filtering anchors\n", "\n", "R[write to console]: \tRetained 19749 anchors\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13m 26s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Merging dataset 2 into 1\n", "\n", "R[write to console]: Extracting anchors for merged samples\n", "\n", "R[write to console]: Finding integration vectors\n", "\n", "R[write to console]: Finding integration vector weights\n", "\n", "R[write to console]: 0% 10 20 30 40 50 60 70 80 90 100%\n", "\n", "R[write to console]: [----|----|----|----|----|----|----|----|----|----|\n", "\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: |\n", "\n", "R[write to console]: Integrating data\n", "\n", "R[write to console]: PC_ 1 \n", "Positive: Trbc2, Trbc1, Cd8b1, Ms4a4b, Cd3g, Cd3d, Tpt1, Nkg7, Trac, Dapl1 \n", "\t Ms4a6b, Rps29, Lck, Il7r, Rplp0, Skap1, Rpl12, Fxyd5, Cd8a, Arl4c \n", "\t Thy1, Bcl11b, Tcf7, Lef1, Lat, Cd3e, Igfbp4, Fyb, Ccnd2, Saraf \n", "Negative: Cd74, Igkc, Cd79a, H2-Aa, H2-Ab1, H2-Eb1, Ly6d, Iglc2, Cd79b, Ighm \n", "\t Ms4a1, Iglc1, Ebf1, Iglc3, Mef2c, Ighd, Fcmr, Vpreb3, Bank1, Fcer2a \n", "\t Siglecg, Ifi30, Napsa, Tnfrsf13c, Mzb1, Gm43291, Bcl11a, H2-DMb2, H3f3a, Gm43603 \n", "PC_ 2 \n", "Positive: Tyrobp, Fcer1g, S100a6, Ifitm3, Alox5ap, Lst1, Lyz2, Cst3, Ifitm2, Lgals3 \n", "\t Psap, Cd300a, S100a4, Ccl6, Msrb1, Mpeg1, Rgs2, Anxa2, Csf1r, Cebpb \n", "\t Sirpa, Gsr, Ctsb, Plbd1, AF251705, Apoe, Ifitm6, Tgfbi, Igsf6, Ms4a6c \n", "Negative: Igkc, Cd79a, Rpl12, Rps27, Rps29, Tpt1, Rpl13, Cd74, Trbc2, Iglc2 \n", "\t Cd79b, Cd8b1, Rpl39, Rps7, Ighm, Rpl37a, Ly6d, Ebf1, Rps21, Rpl19 \n", "\t Rps3, Rps4x, mt-Atp6, Rps5, Ighd, Iglc3, Trbc1, Ms4a1, Rplp0, Cd3d \n", "PC_ 3 \n", "Positive: Msrb1, Ccl6, Hp, Csf3r, Gsr, S100a9, S100a8, Cxcr2, Mmp9, Igfbp4 \n", "\t Tnfaip2, Pla2g7, Lst1, Slfn1, Gcnt2, Gda, Ifitm2, Slpi, Cd300ld, Retnlg \n", "\t Pilra, Il1b, Cebpb, Hdc, Hacd4, Emilin2, Trem3, Rnf149, Pygl, Clec4d \n", "Negative: Ccl5, Nkg7, AW112010, Id2, Klrk1, Klrd1, Ctla2a, Fcer1g, Lgals1, Klrb1c \n", "\t Ahnak, Tyrobp, Klre1, Ly6c2, Ncr1, Gzma, Il2rb, Klrc2, Ctsw, Ccr2 \n", "\t Cd7, S100a4, Tmem176b, Cd74, Tmem176a, Serpinb6b, Efhd2, Il18r1, Samd3, Klrb1f \n", "PC_ 4 \n", "Positive: Psap, Aif1, Cst3, Ctsb, Adrbk2, Ms4a6c, S100a4, Apoe, Tmem176b, Ccnd1 \n", "\t H2-Aa, Hfe, H2-Ab1, Cd68, Hpgd, Tmem176a, H2-Eb1, Rps29, Igfbp4, Cybb \n", "\t Ftl1, Csf1r, Mpeg1, Tbc1d4, Nav1, Grn, Cd74, Ppfia4, Batf3, Plbd1 \n", "Negative: Nkg7, Ccl5, Ctla2a, Klrk1, AW112010, Anxa2, Klrb1c, Klre1, Ctsw, Il18rap \n", "\t Klrd1, S100a9, S100a8, Igkc, Gzma, Ncr1, Mmp9, Cxcr2, Il2rb, Klrc2 \n", "\t Csf3r, Fgl2, Pglyrp1, Fcer1g, Retnlg, S100a11, Anxa1, Ly6c2, Hdc, Samd3 \n", "PC_ 5 \n", "Positive: Plac8, Ly6c2, Cd8b1, Cybb, Nkg7, Ly6d, Cx3cr1, Ahnak, Iglc1, Clec4a3 \n", "\t Zeb2, Ccr2, Pld4, Igkc, Csf1r, Ace, Lrp1, Ctsb, Ighm, Klf4 \n", "\t Cd8a, Cebpb, Vpreb3, Ms4a1, Clec4a1, Hpgd, Ms4a6c, Ifitm6, Psap, Gngt2 \n", "Negative: H2-Aa, H2-Eb1, Tbc1d4, Marcks, H2-Ab1, Cd74, Adam23, Mxd1, Marcksl1, Bcl2a1b \n", "\t Basp1, Cd63, Apol7c, Tmem176a, Mmp9, S100a9, S100a8, Il4i1, Cxcr2, Il1b \n", "\t Fgl2, S100a11, Tmem176b, Cxcl16, Ankrd33b, Fscn1, Slc40a1, Gadd45b, Slpi, Asprv1 \n", "\n" ] } ], "source": [ "%%R\n", "rna.list <- SplitObject(rna, split.by = \"batch\")\n", "rna.list <- lapply(X = rna.list, FUN = SCTransform, variable.features.n = 4000)\n", "features <- SelectIntegrationFeatures(object.list = rna.list, nfeatures = 4000)\n", "rna.list <- PrepSCTIntegration(object.list = rna.list, anchor.features = features)\n", "\n", "anchors <- FindIntegrationAnchors(object.list = rna.list, normalization.method = \"SCT\", \n", " anchor.features = features)\n", "integrated <- IntegrateData(anchorset = anchors, normalization.method = \"SCT\")\n", "\n", "integrated <- RunPCA(integrated)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Integrate ADT batches" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Renaming default assay from RNA to ADT\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "An object of class Seurat \n", "110 features across 30293 samples within 1 assay \n", "Active assay: ADT (110 features, 0 variable features)\n" ] } ], "source": [ "%%R\n", "cite <- RenameAssays(cite, RNA='ADT')\n", "cite" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Normalizing across cells\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+ | 0 % ~01m 58s |+ | 0 % ~01m 33s |+ | 0 % ~01m 35s |+ | 0 % ~01m 33s |+ | 0 % ~01m 34s |+ | 0 % ~01m 35s |+ | 0 % ~01m 48s |+ | 0 % ~01m 45s |+ | 0 % ~01m 43s |+ | 0 % ~01m 46s |+ | 0 % ~01m 43s |+ | 0 % ~01m 42s |+ | 0 % ~01m 41s |+ | 0 % ~01m 41s |+ | 0 % ~01m 42s |+ | 0 % ~01m 42s |+ | 0 % ~01m 40s |+ | 0 % ~01m 40s |+ | 0 % ~01m 41s |+ | 0 % ~01m 40s |+ | 0 % ~01m 41s |+ | 0 % ~01m 41s |+ | 0 % ~01m 39s |+ | 0 % ~01m 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% ~01m 38s |+ | 0 % ~01m 38s |+ | 0 % ~01m 38s |+ | 0 % ~01m 39s |+ | 0 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 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|+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+ | 1 % ~01m 38s |+ | 1 % ~01m 38s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 38s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 1 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 36s |+ | 2 % ~01m 37s |+ | 2 % ~01m 36s |+ | 2 % ~01m 36s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 37s |+ | 2 % ~01m 36s |+ | 2 % ~01m 36s |+ | 2 % ~01m 36s |+ | 2 % ~01m 37s |++ | 2 % ~01m 36s |++ | 2 % ~01m 37s |++ | 2 % ~01m 37s |++ | 2 % ~01m 37s |++ | 2 % ~01m 36s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 2 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 36s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 3 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % ~01m 35s |++ | 4 % 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| 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 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34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 4 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 34s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 5 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 33s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |+++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 6 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 32s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 7 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 31s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 8 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 30s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 9 % ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 29s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |+++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 10% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 28s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s |++++++ | 11% ~01m 27s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 27s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 12% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 26s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 13% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s |+++++++ | 14% ~01m 25s 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~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |+++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 14% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 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15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 24s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 15% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 23s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s |++++++++ | 16% ~01m 22s 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16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 16% ~01m 22s |+++++++++ | 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17% ~01m 22s |+++++++++ | 17% ~01m 22s |+++++++++ | 17% ~01m 22s |+++++++++ | 17% ~01m 22s |+++++++++ | 17% ~01m 22s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++ | 17% ~01m 22s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 17% ~01m 21s |+++++++++ | 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18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 18% ~01m 21s |+++++++++ | 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"stdout", "output_type": "stream", "text": [ " |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 18s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s |+++++++++++ | 21% ~01m 17s 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| 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% ~01m 16s |++++++++++++ | 22% 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| 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 16s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s |++++++++++++ | 23% ~01m 15s 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|+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 14s |+++++++++++++ | 25% ~01m 13s |+++++++++++++ | 25% ~01m 13s 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|+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s |+++++++++++++ | 26% ~01m 13s 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26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 13s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s |++++++++++++++ | 26% ~01m 12s 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"name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 12s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ | 27% ~01m 11s |++++++++++++++ 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|++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s |++++++++++++++ | 28% ~01m 11s 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|+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 28% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s 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|+++++++++++++++ | 29% ~01m 10s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s |+++++++++++++++ | 29% ~01m 10s 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|+++++++++++++++ | 30% ~01m 09s |+++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s |++++++++++++++++ | 30% ~01m 09s 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|++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% ~01m 04s |++++++++++++++++++ | 35% 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|++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% ~01m 03s |++++++++++++++++++ | 36% 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~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 36% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% 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|+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s |+++++++++++++++++++ | 37% ~01m 02s 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~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |+++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s |++++++++++++++++++++ | 38% ~01m 01s 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85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s |+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s 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91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s 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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++|" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s |++++++++++++++++++++++++++++++++++++++++++++++++++| 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 13s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 14s |+++ | 5 % ~01m 13s |+++ | 5 % ~01m 13s |+++ | 5 % ~01m 13s |+++ | 6 % ~01m 14s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |+++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 6 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 13s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 7 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 8 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 12s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 9 % ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |+++++ | 10% ~01m 11s |++++++ | 10% ~01m 11s |++++++ | 10% ~01m 11s |++++++ | 10% ~01m 11s |++++++ | 10% ~01m 11s |++++++ | 10% ~01m 11s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 10% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 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10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 10s |++++++ | 11% ~01m 09s |++++++ | 11% ~01m 09s |++++++ | 11% ~01m 09s |++++++ | 11% ~01m 09s |++++++ | 11% 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12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |++++++ | 12% ~01m 09s |+++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s |+++++++ | 12% ~01m 09s 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|+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 13% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 08s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |+++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 14% ~01m 07s |++++++++ | 15% ~01m 07s |++++++++ | 15% ~01m 07s |++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ |" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 20% ~01m 02s |+++++++++++ | 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~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 22% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s |++++++++++++ | 23% ~01m 00s 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|++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s " ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s |++++++++++++++++++++ | 39% ~47s 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|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s |++++++++++++++++++++++++++++++++++++++++++++++ | 91% 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92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s |+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s 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| \n", " | | 0%\n", " | \n", " |======================================================================| 100%\n", "R[write to console]: \n", "\n", "R[write to console]: PC_ 1 \n", "Positive: ADT-CD90.2-A0075, ADT-CD43-A0110, ADT-CD5-A0111, ADT-TCRbchain-A0120, ADT-CD11a-A0595, ADT-CD73-A0077, ADT-CD278(ICOS)-A0171, ADT-CD357(GITR)-A0193, ADT-CD4-A0001, ADT-CD127(IL-7Ra)-A0198 \n", "\t ADT-CD45-A0096, ADT-CD28-A0204, ADT-CD8b(Ly-3)-A0230, ADT-CD8a-A0002, ADT-CD86-A0200, ADT-CD62L-A0112, ADT-CD103-A0201, ADT-CD117(c-Kit)-A0012, ADT-Ly-6C-A0013, ADT-CD45.2-A0157 \n", "\t ADT-CD183(CXCR3)-A0228, ADT-CD134(OX-40)-A0195, ADT-CD184(CXCR4)-A0444, ADT-CD122(IL-2Rb)-A0227, ADT-integrinb7-A0214, ADT-TCRVb8.1-8.2-A0235, ADT-CD304(Neuropilin-1)-A0552, ADT-TCRVb5.1-5.2-A0354, ADT-CD36-A0555, ADT-CD25-A0097 \n", "Negative: ADT-CD19-A0093, ADT-CD45R-B220-A0103, ADT-CD38-A0557, ADT-IgD-A0571, ADT-CD24-A0212, ADT-CD79b(Igb)-A0561, ADT-I-A-I-E-A0117, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD23-A0108, ADT-CD55(DAF)-A0558 \n", "\t ADT-CD200(OX2)-A0079, ADT-CD71-A0441, ADT-CD102-A0104, ADT-CD20-A0192, ADT-CD54-A0074, ADT-CD150(SLAM)-A0203, ADT-CD49d-A0078, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-IgM-A0450, ADT-CD196(CCR6)-A0225 \n", "\t ADT-CD62P(P-selectin)-A0229, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD48-A0429, ADT-CD106-A0226, ADT-CD29-A0570, ADT-F4-80-A0114, ADT-Tim-4-A0567, ADT-H-2KbboundtoSIINFEKL-A0231, ADT-PanendothelialCellAntigen-A0381, ADT-CD172a(SIRPa)-A0422 \n", "PC_ 2 \n", "Positive: ADT-CD62L-A0112, ADT-CD55(DAF)-A0558, ADT-CD23-A0108, ADT-IgD-A0571, ADT-integrinb7-A0214, ADT-CD45-A0096, ADT-TCRbchain-A0120, ADT-CD150(SLAM)-A0203, ADT-CD79b(Igb)-A0561, ADT-CD21-CD35(CR2-CR1)-A0107 \n", "\t ADT-CD90.2-A0075, ADT-CD19-A0093, ADT-CD45R-B220-A0103, ADT-CD5-A0111, ADT-CD127(IL-7Ra)-A0198, ADT-CD8b(Ly-3)-A0230, ADT-CD103-A0201, ADT-CD28-A0204, ADT-CD102-A0104, ADT-TCRVb8.1-8.2-A0235 \n", "\t ADT-TCRVb5.1-5.2-A0354, ADT-CD15(SSEA-1)-A0076, ADT-TCRVr3-A0210, ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD140a-A0573, ADT-CD137-A0194, ADT-TCRVr2-A0211, ADT-CD197(CCR7)-A0377, ADT-Notch1-A0442, ADT-CD8a-A0002 \n", "Negative: ADT-CD29-A0570, ADT-CD172a(SIRPa)-A0422, ADT-F4-80-A0114, ADT-CD54-A0074, ADT-CD106-A0226, ADT-CD11c-A0106, ADT-Tim-4-A0567, ADT-CD14-A0424, ADT-CD41-A0443, ADT-CD115(CSF-1R)-A0105 \n", "\t ADT-CD48-A0429, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CX3CR1-A0563, ADT-MERTK(Mer)-A0565, ADT-ESAM-A0596, ADT-CD304(Neuropilin-1)-A0552, ADT-CD36-A0555, ADT-PanendothelialCellAntigen-A0381, ADT-CD49d-A0078, ADT-CD86-A0200 \n", "\t ADT-CD301a(MGL1)-A0551, ADT-CD63-A0559, ADT-Ly-6C-A0013, ADT-FolateReceptorb(FR-b)-A0564, ADT-CD169(Siglec-1)-A0440, ADT-XCR1-A0568, ADT-CD117(c-Kit)-A0012, ADT-NK-1.1-A0118, ADT-CD64(FcrRI)-A0202, ADT-CD366(Tim-3)-A0003 \n", "PC_ 3 \n", "Positive: ADT-CD8a-A0002, ADT-CD8b(Ly-3)-A0230, ADT-CD103-A0201, ADT-integrinb7-A0214, ADT-CD54-A0074, ADT-CD183(CXCR3)-A0228, ADT-Ly-6C-A0013, ADT-CD62L-A0112, ADT-CD122(IL-2Rb)-A0227, ADT-CD48-A0429 \n", "\t ADT-CD49d-A0078, ADT-CD11a-A0595, ADT-CD36-A0555, ADT-CD86-A0200, ADT-CD73-A0077, ADT-TCRVb5.1-5.2-A0354, ADT-I-A-I-E-A0117, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD29-A0570 \n", "\t ADT-CD90.2-A0075, ADT-IgD-A0571, ADT-CD38-A0557, ADT-CD102-A0104, ADT-CD55(DAF)-A0558, ADT-CD45-A0096, ADT-CD23-A0108, ADT-CD326(Ep-CAM)-A0449, ADT-CD43-A0110, ADT-CD11c-A0106 \n", "Negative: ADT-CD4-A0001, ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD28-A0204, ADT-TCRbchain-A0120, ADT-CD357(GITR)-A0193, ADT-CD279(PD-1)-A0004, ADT-CD25-A0097, ADT-CD134(OX-40)-A0195, ADT-CD335(NKp46)-A0184, ADT-SiglecH-A0119 \n", "\t ADT-CD150(SLAM)-A0203, ADT-CD304(Neuropilin-1)-A0552, ADT-CD223(LAG-3)-A0378, ADT-CD137-A0194, ADT-TCRVb8.1-8.2-A0235, ADT-CD69-A0197, ADT-CD206(MMR)-A0173, ADT-CD192(CCR2)-A0426, ADT-TCRVr3-A0210, ADT-CD127(IL-7Ra)-A0198 \n", "\t ADT-CD140a-A0573, ADT-CD5-A0111, ADT-CD62P(P-selectin)-A0229, ADT-CD300LG(Nepmucin)-A0416, ADT-CD15(SSEA-1)-A0076, ADT-CD278(ICOS)-A0171, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-anti-P2RY12-A0415, ADT-CD169(Siglec-1)-A0440, ADT-CD135-A0098 \n", "PC_ 4 \n", "Positive: ADT-CD150(SLAM)-A0203, ADT-CD45-A0096, ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD45.2-A0157, ADT-CD11a-A0595, ADT-CD200(OX2)-A0079, ADT-CD49d-A0078, ADT-CD357(GITR)-A0193, ADT-CD4-A0001 \n", "\t ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD48-A0429, ADT-CD62L-A0112, ADT-CD278(ICOS)-A0171, ADT-CD29-A0570, ADT-CD86-A0200, ADT-CD23-A0108, ADT-IgD-A0571, ADT-CD304(Neuropilin-1)-A0552, ADT-I-A-I-E-A0117 \n", "\t ADT-CD54-A0074, ADT-CD5-A0111, ADT-integrinb7-A0214, ADT-CD73-A0077, ADT-CD25-A0097, ADT-CD38-A0557, ADT-CD106-A0226, ADT-TCRbchain-A0120, ADT-CD55(DAF)-A0558, ADT-CD28-A0204 \n", "Negative: ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-CD335(NKp46)-A0184, ADT-SiglecH-A0119, ADT-CD192(CCR2)-A0426, ADT-CD223(LAG-3)-A0378, ADT-CD206(MMR)-A0173, ADT-CD301b(MGL2)-A0566, ADT-CD300LG(Nepmucin)-A0416, ADT-CD137-A0194, ADT-CD8a-A0002 \n", "\t ADT-TCRVr3-A0210, ADT-CD8b(Ly-3)-A0230, ADT-CD140a-A0573, ADT-CD197(CCR7)-A0377, ADT-IgM-A0450, ADT-CD15(SSEA-1)-A0076, ADT-Notch1-A0442, ADT-CD135-A0098, ADT-CD62P(P-selectin)-A0229, ADT-CD103-A0201 \n", "\t ADT-Ly-6C-A0013, ADT-anti-P2RY12-A0415, ADT-IRF4-A0249, ADT-CD36-A0555, ADT-CD195(CCR5)-A0376, ADT-CD16-32-A0109, ADT-TCRr-d-A0121, ADT-TCRVr2-A0211, ADT-MAdCAM-1-A0232, ADT-CD201(EPCR)-A0439 \n", "PC_ 5 \n", "Positive: ADT-CD48-A0429, ADT-CD102-A0104, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-CD25-A0097, ADT-CD20-A0192, ADT-CD357(GITR)-A0193, ADT-CD71-A0441, ADT-CD24-A0212, ADT-CD304(Neuropilin-1)-A0552, ADT-CD73-A0077 \n", "\t ADT-CD29-A0570, ADT-CD184(CXCR4)-A0444, ADT-CD183(CXCR3)-A0228, ADT-CD54-A0074, ADT-CD279(PD-1)-A0004, ADT-CD122(IL-2Rb)-A0227, ADT-IgM-A0450, ADT-Ly-6C-A0013, ADT-CD278(ICOS)-A0171, ADT-NK-1.1-A0118 \n", "\t ADT-CD134(OX-40)-A0195, ADT-CD43-A0110, ADT-CD69-A0197, ADT-CD36-A0555, ADT-CD5-A0111, ADT-Notch1-A0442, ADT-TCRVb5.1-5.2-A0354, ADT-CD45R-B220-A0103, ADT-CD38-A0557, ADT-CD19-A0093 \n", "Negative: ADT-integrinb7-A0214, ADT-CD62L-A0112, ADT-CD106-A0226, ADT-SiglecH-A0119, ADT-Tim-4-A0567, ADT-CD172a(SIRPa)-A0422, ADT-F4-80-A0114, ADT-CD41-A0443, ADT-CD23-A0108, ADT-CD11c-A0106 \n", "\t ADT-MERTK(Mer)-A0565, ADT-CD14-A0424, ADT-CD169(Siglec-1)-A0440, ADT-FolateReceptorb(FR-b)-A0564, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD150(SLAM)-A0203, ADT-IgD-A0571, ADT-CD200(OX2)-A0079, ADT-CD63-A0559, ADT-ESAM-A0596 \n", "\t ADT-CD103-A0201, ADT-CD366(Tim-3)-A0003, ADT-MAdCAM-1-A0232, ADT-CD62P(P-selectin)-A0229, ADT-CD300LG(Nepmucin)-A0416, ADT-CD55(DAF)-A0558, ADT-CD223(LAG-3)-A0378, ADT-CD115(CSF-1R)-A0105, ADT-CD45.2-A0157, ADT-CD301a(MGL1)-A0551 \n", "\n", "R[write to console]: Centering and scaling data matrix\n", "\n", " | \n", " | | 0%\n", " | \n", " |======================================================================| 100%\n", "R[write to console]: \n", "\n", "R[write to console]: PC_ 1 \n", "Positive: ADT-CD90.2-A0075, ADT-CD43-A0110, ADT-CD5-A0111, ADT-TCRbchain-A0120, ADT-CD73-A0077, ADT-CD278(ICOS)-A0171, ADT-CD4-A0001, ADT-CD357(GITR)-A0193, ADT-CD127(IL-7Ra)-A0198, ADT-CD11a-A0595 \n", "\t ADT-CD28-A0204, ADT-CD8b(Ly-3)-A0230, ADT-CD8a-A0002, ADT-CD103-A0201, ADT-CD86-A0200, ADT-CD117(c-Kit)-A0012, ADT-Ly-6C-A0013, ADT-CD134(OX-40)-A0195, ADT-CD184(CXCR4)-A0444, ADT-CD45-A0096 \n", "\t ADT-CD183(CXCR3)-A0228, ADT-CD45.2-A0157, ADT-CD62L-A0112, ADT-TCRVb5.1-5.2-A0354, ADT-TCRVb8.1-8.2-A0235, ADT-CD122(IL-2Rb)-A0227, ADT-CD304(Neuropilin-1)-A0552, ADT-CD279(PD-1)-A0004, ADT-Notch1-A0442, ADT-CD223(LAG-3)-A0378 \n", "Negative: ADT-CD19-A0093, ADT-CD45R-B220-A0103, ADT-CD38-A0557, ADT-IgD-A0571, ADT-CD79b(Igb)-A0561, ADT-I-A-I-E-A0117, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD24-A0212, ADT-CD23-A0108, ADT-CD55(DAF)-A0558 \n", "\t ADT-CD200(OX2)-A0079, ADT-CD71-A0441, ADT-CD102-A0104, ADT-CD20-A0192, ADT-CD150(SLAM)-A0203, ADT-CD54-A0074, ADT-CD49d-A0078, ADT-CD48-A0429, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD93(AA4.1-earlyBlineage)-A0113 \n", "\t ADT-CD196(CCR6)-A0225, ADT-Ly-6A-E(Sca-1)-A0130, ADT-IgM-A0450, ADT-CD29-A0570, ADT-CD106-A0226, ADT-F4-80-A0114, ADT-H-2KbboundtoSIINFEKL-A0231, ADT-CD68-A0560, ADT-Tim-4-A0567, ADT-CD83-A0562 \n", "PC_ 2 \n", "Positive: ADT-CD45-A0096, ADT-CD48-A0429, ADT-CD11a-A0595, ADT-integrinb7-A0214, ADT-CD62L-A0112, ADT-CD49d-A0078, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD102-A0104, ADT-CD150(SLAM)-A0203, ADT-CD45.2-A0157 \n", "\t ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD86-A0200, ADT-CD357(GITR)-A0193, ADT-CD43-A0110, ADT-CD54-A0074, ADT-CD5-A0111, ADT-CD90.2-A0075, ADT-CD73-A0077, ADT-CD29-A0570, ADT-CD127(IL-7Ra)-A0198 \n", "\t ADT-CD55(DAF)-A0558, ADT-CD278(ICOS)-A0171, ADT-Ly-6C-A0013, ADT-CD200(OX2)-A0079, ADT-CD8b(Ly-3)-A0230, ADT-TCRbchain-A0120, ADT-CD8a-A0002, ADT-CD4-A0001, ADT-CD20-A0192, ADT-CD21-CD35(CR2-CR1)-A0107 \n", "Negative: ADT-SiglecH-A0119, ADT-CD206(MMR)-A0173, ADT-CD62P(P-selectin)-A0229, ADT-CD134(OX-40)-A0195, ADT-CD169(Siglec-1)-A0440, ADT-CD370(CLEC9A-DNGR1)-A0556, ADT-CD192(CCR2)-A0426, ADT-CD300LG(Nepmucin)-A0416, ADT-CD279(PD-1)-A0004, ADT-CD301b(MGL2)-A0566 \n", "\t ADT-CD140a-A0573, ADT-FolateReceptorb(FR-b)-A0564, ADT-anti-P2RY12-A0415, ADT-CD335(NKp46)-A0184, ADT-TCRVr3-A0210, ADT-CD63-A0559, ADT-CD16-32-A0109, ADT-CD137-A0194, ADT-CD301a(MGL1)-A0551, ADT-CD223(LAG-3)-A0378 \n", "\t ADT-MERTK(Mer)-A0565, ADT-MAdCAM-1-A0232, ADT-CD201(EPCR)-A0439, ADT-CD15(SSEA-1)-A0076, ADT-CD41-A0443, ADT-CD197(CCR7)-A0377, ADT-CD366(Tim-3)-A0003, ADT-FceRIa-A0115, ADT-CD45.1-A0178, ADT-IgM-A0450 \n", "PC_ 3 \n", "Positive: ADT-CD29-A0570, ADT-CD172a(SIRPa)-A0422, ADT-F4-80-A0114, ADT-CD54-A0074, ADT-CD106-A0226, ADT-Tim-4-A0567, ADT-CD11c-A0106, ADT-CD14-A0424, ADT-CD115(CSF-1R)-A0105, ADT-CD274(B7-H1-PD-L1)-A0190 \n", "\t ADT-CD41-A0443, ADT-CD49d-A0078, ADT-CX3CR1-A0563, ADT-CD48-A0429, ADT-MERTK(Mer)-A0565, ADT-ESAM-A0596, ADT-Ly-6C-A0013, ADT-CD86-A0200, ADT-CD11a-A0595, ADT-PanendothelialCellAntigen-A0381 \n", "\t ADT-CD304(Neuropilin-1)-A0552, ADT-CD117(c-Kit)-A0012, ADT-CD36-A0555, ADT-NK-1.1-A0118, ADT-CD64(FcrRI)-A0202, ADT-XCR1-A0568, ADT-CD38-A0557, ADT-CD223(LAG-3)-A0378, ADT-CD63-A0559, ADT-CD169(Siglec-1)-A0440 \n", "Negative: ADT-CD62L-A0112, ADT-CD55(DAF)-A0558, ADT-TCRbchain-A0120, ADT-CD23-A0108, ADT-IgD-A0571, ADT-CD150(SLAM)-A0203, ADT-CD90.2-A0075, ADT-CD5-A0111, ADT-CD79b(Igb)-A0561, ADT-CD127(IL-7Ra)-A0198 \n", "\t ADT-CD103-A0201, ADT-CD45R-B220-A0103, ADT-CD140a-A0573, ADT-CD19-A0093, ADT-integrinb7-A0214, ADT-CD28-A0204, ADT-TCRVb8.1-8.2-A0235, ADT-CD8b(Ly-3)-A0230, ADT-TCRVr3-A0210, ADT-CD137-A0194 \n", "\t ADT-TCRVb5.1-5.2-A0354, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD279(PD-1)-A0004, ADT-CD15(SSEA-1)-A0076, ADT-CD197(CCR7)-A0377, ADT-IgM-A0450, ADT-Notch1-A0442, ADT-CD102-A0104, ADT-CD335(NKp46)-A0184, ADT-TCRVr2-A0211 \n", "PC_ 4 \n", "Positive: ADT-CD4-A0001, ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD28-A0204, ADT-CD357(GITR)-A0193, ADT-CD150(SLAM)-A0203, ADT-CD25-A0097, ADT-CD45.2-A0157, ADT-CD304(Neuropilin-1)-A0552, ADT-TCRbchain-A0120, ADT-CD278(ICOS)-A0171 \n", "\t ADT-CD5-A0111, ADT-CD69-A0197, ADT-CD127(IL-7Ra)-A0198, ADT-CD200(OX2)-A0079, ADT-CD279(PD-1)-A0004, ADT-CD134(OX-40)-A0195, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD45-A0096, ADT-TCRVb8.1-8.2-A0235, ADT-CD29-A0570 \n", "\t ADT-FceRIa-A0115, ADT-PanendothelialCellAntigen-A0381, ADT-CD137-A0194, ADT-ESAM-A0596, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD184(CXCR4)-A0444, ADT-IgM-A0450, ADT-CD20-A0192, ADT-IRF4-A0249, ADT-CD223(LAG-3)-A0378 \n", "Negative: ADT-CD8a-A0002, ADT-CD8b(Ly-3)-A0230, ADT-CD103-A0201, ADT-integrinb7-A0214, ADT-Ly-6C-A0013, ADT-TCRVb5.1-5.2-A0354, ADT-CD62L-A0112, ADT-CD122(IL-2Rb)-A0227, ADT-CD54-A0074, ADT-CD183(CXCR3)-A0228 \n", "\t ADT-CD36-A0555, ADT-CD49d-A0078, ADT-CX3CR1-A0563, ADT-H-2KbboundtoSIINFEKL-A0231, ADT-CD115(CSF-1R)-A0105, ADT-CD301b(MGL2)-A0566, ADT-CD86-A0200, ADT-CD300LG(Nepmucin)-A0416, ADT-TCRr-d-A0121, ADT-CD90.2-A0075 \n", "\t ADT-CD48-A0429, ADT-TCRVr3-A0210, ADT-CD11c-A0106, ADT-XCR1-A0568, ADT-CD370(CLEC9A-DNGR1)-A0556, ADT-CD117(c-Kit)-A0012, ADT-CD55(DAF)-A0558, ADT-I-A-I-E-A0117, ADT-CD366(Tim-3)-A0003, ADT-TCRVr1.1-Cr4-A0209 \n", "PC_ 5 \n", "Positive: ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-CD102-A0104, ADT-CD71-A0441, ADT-CD20-A0192, ADT-Ly-6C-A0013, ADT-CD24-A0212, ADT-CD183(CXCR3)-A0228, ADT-CD48-A0429, ADT-CD29-A0570, ADT-NK-1.1-A0118 \n", "\t ADT-CD122(IL-2Rb)-A0227, ADT-CD25-A0097, ADT-CD36-A0555, ADT-CD184(CXCR4)-A0444, ADT-CD279(PD-1)-A0004, ADT-CD357(GITR)-A0193, ADT-CD54-A0074, ADT-CD335(NKp46)-A0184, ADT-CD73-A0077, ADT-CD43-A0110 \n", "\t ADT-IgM-A0450, ADT-CD304(Neuropilin-1)-A0552, ADT-CD69-A0197, ADT-Notch1-A0442, ADT-CD301b(MGL2)-A0566, ADT-IRF4-A0249, ADT-CD137-A0194, ADT-TCRr-d-A0121, ADT-TCRVr2-A0211, ADT-TCRVr1.1-Cr4-A0209 \n", "Negative: ADT-integrinb7-A0214, ADT-Tim-4-A0567, ADT-CD62L-A0112, ADT-CD45.2-A0157, ADT-CD200(OX2)-A0079, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD150(SLAM)-A0203, ADT-CD23-A0108, ADT-F4-80-A0114, ADT-CD106-A0226 \n", "\t ADT-IgD-A0571, ADT-CD14-A0424, ADT-MERTK(Mer)-A0565, ADT-Ly-6A-E(Sca-1)-A0130, ADT-FolateReceptorb(FR-b)-A0564, ADT-CD11c-A0106, ADT-CD169(Siglec-1)-A0440, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD4-A0001, ADT-TCRbchain-A0120 \n", "\t ADT-CD172a(SIRPa)-A0422, ADT-CD117(c-Kit)-A0012, ADT-SiglecH-A0119, ADT-CD103-A0201, ADT-I-A-I-E-A0117, ADT-CD55(DAF)-A0558, ADT-CD366(Tim-3)-A0003, ADT-CD86-A0200, ADT-CD63-A0559, ADT-CD64(FcrRI)-A0202 \n", "\n" ] } ], "source": [ "%%R\n", "features <- SelectIntegrationFeatures(object.list = cite.list)\n", "\n", "cite.list <- lapply(X = cite.list, FUN = function(x) {\n", " x <- ScaleData(x, features = features)\n", " x <- RunPCA(x, features = features, reduction.name = \"pca\")\n", "})" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Computing 2000 integration features\n", "\n", "R[write to console]: Scaling features for provided objects\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~00s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Computing within dataset neighborhoods\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~13s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=23s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Finding all pairwise anchors\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating " ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Finding neighborhoods\n", "\n", "R[write to console]: Finding anchors\n", "\n", "R[write to console]: \tFound 19649 anchors\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=32s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Merging dataset 2 into 1\n", "\n", "R[write to console]: Extracting anchors for merged samples\n", "\n", "R[write to console]: Finding integration vectors\n", "\n", "R[write to console]: Finding integration vector weights\n", "\n", "R[write to console]: 0% 10 20 30 40 50 60 70 80 90 100%\n", "\n", "R[write to console]: [----|----|----|----|----|----|----|----|----|----|\n", "\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: *\n", "R[write to 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"R[write to console]: *\n", "R[write to console]: *\n", "R[write to console]: |\n", "\n", "R[write to console]: Integrating data\n", "\n" ] } ], "source": [ "%%R\n", "anchors <- FindIntegrationAnchors(object.list = cite.list, reduction = \"rpca\", \n", " dims = 1:30)\n", "integrated_adt <- IntegrateData(anchorset = anchors, dims = 1:30)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Centering and scaling data matrix\n", "\n", " | \n", " | | 0%\n", " | \n", " |======================================================================| 100%\n", "R[write to console]: \n", "\n", "R[write to console]: PC_ 1 \n", "Positive: ADT-CD90.2-A0075, ADT-CD43-A0110, ADT-CD5-A0111, ADT-TCRbchain-A0120, ADT-CD11a-A0595, ADT-CD73-A0077, ADT-CD278(ICOS)-A0171, ADT-CD357(GITR)-A0193, ADT-CD4-A0001, ADT-CD127(IL-7Ra)-A0198 \n", "\t ADT-CD28-A0204, ADT-CD8b(Ly-3)-A0230, ADT-CD8a-A0002, ADT-CD45-A0096, ADT-CD86-A0200, ADT-CD103-A0201, ADT-CD117(c-Kit)-A0012, ADT-CD62L-A0112, ADT-Ly-6C-A0013, ADT-CD45.2-A0157 \n", "\t ADT-CD183(CXCR3)-A0228, ADT-CD134(OX-40)-A0195, ADT-CD184(CXCR4)-A0444, ADT-integrinb7-A0214, ADT-CD122(IL-2Rb)-A0227, ADT-TCRVb5.1-5.2-A0354, ADT-CD304(Neuropilin-1)-A0552, ADT-TCRVb8.1-8.2-A0235, ADT-CD36-A0555, ADT-CD25-A0097 \n", "Negative: ADT-CD19-A0093, ADT-CD45R-B220-A0103, ADT-CD38-A0557, ADT-IgD-A0571, ADT-CD24-A0212, ADT-CD79b(Igb)-A0561, ADT-I-A-I-E-A0117, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD23-A0108, ADT-CD55(DAF)-A0558 \n", "\t ADT-CD200(OX2)-A0079, ADT-CD71-A0441, ADT-CD102-A0104, ADT-CD20-A0192, ADT-CD54-A0074, ADT-CD150(SLAM)-A0203, ADT-CD49d-A0078, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-IgM-A0450, ADT-CD196(CCR6)-A0225 \n", "\t ADT-CD62P(P-selectin)-A0229, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD48-A0429, ADT-CD29-A0570, ADT-CD106-A0226, ADT-F4-80-A0114, ADT-Tim-4-A0567, ADT-H-2KbboundtoSIINFEKL-A0231, ADT-PanendothelialCellAntigen-A0381, ADT-CD68-A0560 \n", "PC_ 2 \n", "Positive: ADT-CD62L-A0112, ADT-CD55(DAF)-A0558, ADT-CD23-A0108, ADT-IgD-A0571, ADT-integrinb7-A0214, ADT-TCRbchain-A0120, ADT-CD45-A0096, ADT-CD150(SLAM)-A0203, ADT-CD90.2-A0075, ADT-CD5-A0111 \n", "\t ADT-CD79b(Igb)-A0561, ADT-CD19-A0093, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD45R-B220-A0103, ADT-CD127(IL-7Ra)-A0198, ADT-CD8b(Ly-3)-A0230, ADT-CD28-A0204, ADT-CD103-A0201, ADT-CD102-A0104, ADT-TCRVb5.1-5.2-A0354 \n", "\t ADT-Ly-6A-E(Sca-1)-A0130, ADT-TCRVb8.1-8.2-A0235, ADT-CD15(SSEA-1)-A0076, ADT-CD357(GITR)-A0193, ADT-TCRVr2-A0211, ADT-CD137-A0194, ADT-CD8a-A0002, ADT-CD140a-A0573, ADT-TCRVr3-A0210, ADT-Notch1-A0442 \n", "Negative: ADT-CD29-A0570, ADT-CD172a(SIRPa)-A0422, ADT-F4-80-A0114, ADT-CD106-A0226, ADT-CD54-A0074, ADT-CD11c-A0106, ADT-Tim-4-A0567, ADT-CD14-A0424, ADT-CD41-A0443, ADT-CD115(CSF-1R)-A0105 \n", "\t ADT-CX3CR1-A0563, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD48-A0429, ADT-MERTK(Mer)-A0565, ADT-ESAM-A0596, ADT-CD304(Neuropilin-1)-A0552, ADT-CD36-A0555, ADT-CD49d-A0078, ADT-PanendothelialCellAntigen-A0381, ADT-Ly-6C-A0013 \n", "\t ADT-CD63-A0559, ADT-CD86-A0200, ADT-FolateReceptorb(FR-b)-A0564, ADT-CD301a(MGL1)-A0551, ADT-CD169(Siglec-1)-A0440, ADT-XCR1-A0568, ADT-CD117(c-Kit)-A0012, ADT-NK-1.1-A0118, ADT-CD366(Tim-3)-A0003, ADT-CD64(FcrRI)-A0202 \n", "PC_ 3 \n", "Positive: ADT-CD8a-A0002, ADT-CD8b(Ly-3)-A0230, ADT-integrinb7-A0214, ADT-CD103-A0201, ADT-CD54-A0074, ADT-CD183(CXCR3)-A0228, ADT-Ly-6C-A0013, ADT-CD62L-A0112, ADT-CD49d-A0078, ADT-CD48-A0429 \n", "\t ADT-CD11a-A0595, ADT-CD86-A0200, ADT-CD122(IL-2Rb)-A0227, ADT-CD36-A0555, ADT-TCRVb5.1-5.2-A0354, ADT-CD73-A0077, ADT-I-A-I-E-A0117, ADT-CD45-A0096, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD274(B7-H1-PD-L1)-A0190 \n", "\t ADT-CD90.2-A0075, ADT-IgD-A0571, ADT-CD29-A0570, ADT-CD23-A0108, ADT-CD11c-A0106, ADT-CD38-A0557, ADT-CD55(DAF)-A0558, ADT-CD102-A0104, ADT-CD326(Ep-CAM)-A0449, ADT-CD43-A0110 \n", "Negative: ADT-CD4-A0001, ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD28-A0204, ADT-CD279(PD-1)-A0004, ADT-CD25-A0097, ADT-TCRbchain-A0120, ADT-CD357(GITR)-A0193, ADT-CD134(OX-40)-A0195, ADT-CD335(NKp46)-A0184, ADT-SiglecH-A0119 \n", "\t ADT-CD304(Neuropilin-1)-A0552, ADT-CD223(LAG-3)-A0378, ADT-CD150(SLAM)-A0203, ADT-CD137-A0194, ADT-CD192(CCR2)-A0426, ADT-TCRVb8.1-8.2-A0235, ADT-CD69-A0197, ADT-CD206(MMR)-A0173, ADT-CD140a-A0573, ADT-TCRVr3-A0210 \n", "\t ADT-CD127(IL-7Ra)-A0198, ADT-CD62P(P-selectin)-A0229, ADT-CD300LG(Nepmucin)-A0416, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-CD197(CCR7)-A0377, ADT-CD5-A0111, ADT-anti-P2RY12-A0415, ADT-CD63-A0559, ADT-CD15(SSEA-1)-A0076, ADT-IgM-A0450 \n", "PC_ 4 \n", "Positive: ADT-Ly-6A-E(Sca-1)-A0130, ADT-CD150(SLAM)-A0203, ADT-CD274(B7-H1-PD-L1)-A0190, ADT-CD45-A0096, ADT-CD45.2-A0157, ADT-CD11a-A0595, ADT-CD200(OX2)-A0079, ADT-CD4-A0001, ADT-CD357(GITR)-A0193, ADT-CD49d-A0078 \n", "\t ADT-CD21-CD35(CR2-CR1)-A0107, ADT-CD48-A0429, ADT-CD278(ICOS)-A0171, ADT-CD29-A0570, ADT-CD86-A0200, ADT-CD62L-A0112, ADT-CD5-A0111, ADT-CD304(Neuropilin-1)-A0552, ADT-CD54-A0074, ADT-CD73-A0077 \n", "\t ADT-I-A-I-E-A0117, ADT-IgD-A0571, ADT-CD23-A0108, ADT-CD25-A0097, ADT-TCRbchain-A0120, ADT-CD38-A0557, ADT-CD106-A0226, ADT-CD55(DAF)-A0558, ADT-CD69-A0197, ADT-integrinb7-A0214 \n", "Negative: ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-SiglecH-A0119, ADT-CD335(NKp46)-A0184, ADT-CD192(CCR2)-A0426, ADT-CD206(MMR)-A0173, ADT-CD8a-A0002, ADT-CD8b(Ly-3)-A0230, ADT-CD300LG(Nepmucin)-A0416, ADT-CD301b(MGL2)-A0566, ADT-CD223(LAG-3)-A0378 \n", "\t ADT-CD140a-A0573, ADT-CD137-A0194, ADT-TCRVr3-A0210, ADT-CD103-A0201, ADT-CD62P(P-selectin)-A0229, ADT-CD197(CCR7)-A0377, ADT-CD15(SSEA-1)-A0076, ADT-IgM-A0450, ADT-Ly-6C-A0013, ADT-Notch1-A0442 \n", "\t ADT-TCRr-d-A0121, ADT-CD135-A0098, ADT-anti-P2RY12-A0415, ADT-CD16-32-A0109, ADT-CD36-A0555, ADT-CD370(CLEC9A-DNGR1)-A0556, ADT-MAdCAM-1-A0232, ADT-CD201(EPCR)-A0439, ADT-CD195(CCR5)-A0376, ADT-IRF4-A0249 \n", "PC_ 5 \n", "Positive: ADT-CD48-A0429, ADT-CD102-A0104, ADT-CD93(AA4.1-earlyBlineage)-A0113, ADT-CD25-A0097, ADT-CD20-A0192, ADT-CD357(GITR)-A0193, ADT-CD71-A0441, ADT-CD24-A0212, ADT-CD304(Neuropilin-1)-A0552, ADT-CD73-A0077 \n", "\t ADT-CD54-A0074, ADT-CD184(CXCR4)-A0444, ADT-CD29-A0570, ADT-CD183(CXCR3)-A0228, ADT-CD279(PD-1)-A0004, ADT-CD122(IL-2Rb)-A0227, ADT-IgM-A0450, ADT-CD278(ICOS)-A0171, ADT-NK-1.1-A0118, ADT-CD43-A0110 \n", "\t ADT-CD134(OX-40)-A0195, ADT-CD69-A0197, ADT-Ly-6C-A0013, ADT-Notch1-A0442, ADT-CD38-A0557, ADT-CD45R-B220-A0103, ADT-CD5-A0111, ADT-TCRVb5.1-5.2-A0354, ADT-CD36-A0555, ADT-CD86-A0200 \n", "Negative: ADT-CD62L-A0112, ADT-integrinb7-A0214, ADT-CD23-A0108, ADT-CD106-A0226, ADT-SiglecH-A0119, ADT-F4-80-A0114, ADT-CD172a(SIRPa)-A0422, ADT-CD41-A0443, ADT-Tim-4-A0567, ADT-CD14-A0424 \n", "\t ADT-CD11c-A0106, ADT-CD150(SLAM)-A0203, ADT-IgD-A0571, ADT-CD21-CD35(CR2-CR1)-A0107, ADT-MERTK(Mer)-A0565, ADT-CD200(OX2)-A0079, ADT-CD63-A0559, ADT-CD45.2-A0157, ADT-CD169(Siglec-1)-A0440, ADT-ESAM-A0596 \n", "\t ADT-MAdCAM-1-A0232, ADT-FolateReceptorb(FR-b)-A0564, ADT-CD300LG(Nepmucin)-A0416, ADT-CD55(DAF)-A0558, ADT-CD366(Tim-3)-A0003, ADT-CD140a-A0573, ADT-CD62P(P-selectin)-A0229, ADT-CD115(CSF-1R)-A0105, ADT-PanendothelialCellAntigen-A0381, ADT-CD206(MMR)-A0173 \n", "\n" ] } ], "source": [ "%%R\n", "integrated_adt <- ScaleData(integrated_adt)\n", "integrated_adt <- RunPCA(integrated_adt, reduction.name = \"apca\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run Seurat v4 WNN\n", "Following https://satijalab.org/seurat/v4.0/weighted_nearest_neighbor_analysis.html." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Calculating cell-specific modality weights\n", "\n", "R[write to console]: Finding 20 nearest neighbors for each modality.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~22s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=43s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Calculating kernel bandwidths\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~05s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Finding multimodal neighbors\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~01m 09s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 26s\n", " | | 0 % ~calculating |+++++++++++++++++++++++++ | 50% ~16s |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Constructing multimodal KNN graph\n", "\n", "R[write to console]: Constructing multimodal SNN graph\n", "\n", "R[write to console]: Computing sPCA transformation\n", "\n", "R[write to console]: Creating h5Seurat file for version 3.1.5.9900\n", "\n", "R[write to console]: Adding counts for RNA\n", "\n", "R[write to console]: Adding data for RNA\n", "\n", "R[write to console]: No variable features found for RNA\n", "\n", "R[write to console]: No feature-level metadata found for RNA\n", "\n", "R[write to console]: Adding counts for SCT\n", "\n", "R[write to console]: Adding data for SCT\n", "\n", "R[write to console]: Adding scale.data for SCT\n", "\n", "R[write to console]: No variable features found for SCT\n", "\n", "R[write to console]: No feature-level metadata found for SCT\n", "\n", "R[write to console]: Writing out SCTModel.list for SCT\n", "\n", "R[write to console]: Adding data for integrated\n", "\n", "R[write to console]: Adding scale.data for integrated\n", "\n", "R[write to console]: Adding variable features for integrated\n", "\n", "R[write to console]: No feature-level metadata found for integrated\n", "\n", "R[write to console]: Writing out SCTModel.list for integrated\n", "\n", "R[write to console]: Adding data for IADT\n", "\n", "R[write to console]: Adding scale.data for IADT\n", "\n", "R[write to console]: Adding variable features for IADT\n", "\n", "R[write to console]: No feature-level metadata found for IADT\n", "\n", "R[write to console]: Adding cell embeddings for pca\n", "\n", "R[write to console]: Adding loadings for pca\n", "\n", "R[write to console]: No projected loadings for pca\n", "\n", "R[write to console]: Adding standard deviations for pca\n", "\n", "R[write to console]: No JackStraw data for pca\n", "\n", "R[write to console]: Adding cell embeddings for apca\n", "\n", "R[write to console]: Adding loadings for apca\n", "\n", "R[write to console]: No projected loadings for apca\n", "\n", "R[write to console]: Adding standard deviations for apca\n", "\n", "R[write to console]: No JackStraw data for apca\n", "\n", "R[write to console]: Adding cell embeddings for spca\n", "\n", "R[write to console]: Adding loadings for spca\n", "\n", "R[write to console]: No projected loadings for spca\n", "\n", "R[write to console]: Adding standard deviations for spca\n", "\n", "R[write to console]: No JackStraw data for spca\n", "\n" ] } ], "source": [ "%%R\n", "\n", "integrated[[\"IADT\"]] <- integrated_adt[[\"integrated\"]]\n", "integrated[[\"apca\"]] <- integrated_adt[[\"apca\"]]\n", "\n", "integrated <- FindMultiModalNeighbors(integrated, reduction.list = list(\"pca\", \"apca\"), \n", " dims.list = list(1:50, 1:30), modality.weight.name = \"RNA.weight\")\n", "\n", "integrated <- RunSPCA(integrated, assay = 'integrated', graph = 'wsnn', npcs = 20)\n", "\n", "SaveH5Seurat(integrated, 'gayoso-seurat.h5seurat', overwrite = TRUE)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "%%R\n", "integrated[[\"apca\"]] <- NULL" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Warning:\n", "R[write to console]: Overwriting previous file gayoso-seurat.h5seurat\n", "\n", "R[write to console]: Creating h5Seurat file for version 3.1.5.9900\n", "\n", "R[write to console]: Adding counts for RNA\n", "\n", "R[write to console]: Adding data for RNA\n", "\n", "R[write to console]: No variable features found for RNA\n", "\n", "R[write to console]: No feature-level metadata found for RNA\n", "\n", "R[write to console]: Adding counts for SCT\n", "\n", "R[write to console]: Adding data for SCT\n", "\n", "R[write to console]: Adding scale.data for SCT\n", "\n", "R[write to console]: No variable features found for SCT\n", "\n", "R[write to console]: No feature-level metadata found for SCT\n", "\n", "R[write to console]: Writing out SCTModel.list for SCT\n", "\n", "R[write to console]: Adding data for integrated\n", "\n", "R[write to console]: Adding scale.data for integrated\n", "\n", "R[write to console]: Adding variable features for integrated\n", "\n", "R[write to console]: No feature-level metadata found for integrated\n", "\n", "R[write to console]: Writing out SCTModel.list for integrated\n", "\n", "R[write to console]: Adding data for IADT\n", "\n", "R[write to console]: Adding scale.data for IADT\n", "\n", "R[write to console]: Adding variable features for IADT\n", "\n", "R[write to console]: No feature-level metadata found for IADT\n", "\n", "R[write to console]: Adding cell embeddings for pca\n", "\n", "R[write to console]: Adding loadings for pca\n", "\n", "R[write to console]: No projected loadings for pca\n", "\n", "R[write to console]: Adding standard deviations for pca\n", "\n", "R[write to console]: No JackStraw data for pca\n", "\n", "R[write to console]: Adding cell embeddings for spca\n", "\n", "R[write to console]: Adding loadings for spca\n", "\n", "R[write to console]: No projected loadings for spca\n", "\n", "R[write to console]: Adding standard deviations for spca\n", "\n", "R[write to console]: No JackStraw data for spca\n", "\n" ] } ], "source": [ "%%R\n", "SaveH5Seurat(integrated, 'gayoso-seurat.h5seurat', overwrite = TRUE)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "R[write to console]: Validating h5Seurat file\n", "\n", "R[write to console]: Adding scale.data from integrated as X\n", "\n", "R[write to console]: Adding data from integrated as raw\n", "\n", "R[write to console]: Transfering meta.data to obs\n", "\n", "R[write to console]: Adding dimensional reduction information for pca\n", "\n", "R[write to console]: Adding feature loadings for pca\n", "\n", "R[write to console]: Adding dimensional reduction information for spca\n", "\n", "R[write to console]: Adding feature loadings for spca\n", "\n", "R[write to console]: Adding wsnn as neighbors\n", "\n", "R[write to console]: Adding scale.data from SCT as a layer\n", "\n" ] } ], "source": [ "%%R\n", "Convert(\"gayoso-seurat.h5seurat\", assay=\"integrated\", \"gayoso-seurat.h5ad\", overwrite=TRUE)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 30293 × 3994\n", " obs: 'cell_type', 'batch', 'nCount_RNA', 'nFeature_RNA', 'nCount_SCT', 'nFeature_SCT', 'integrated.weight'\n", " var: 'features', 'SCT_features'\n", " uns: 'neighbors'\n", " obsm: 'X_pca', 'X_spca'\n", " varm: 'PCs', 'SPCA'\n", " layers: 'SCT'\n", " obsp: 'distances'" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "integrated = sc.read('gayoso-seurat.h5ad')\n", "integrated" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "latent = sc.AnnData(integrated.obsm['X_spca'])\n", "latent.obs = integrated.obs\n", "latent.obsp['connectivities'] = integrated.uns['neighbors']['distances']" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "... storing 'cell_type' as categorical\n" ] } ], "source": [ "latent.write('../data/integrated/seurat/gayoso-seurat.h5ad')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "!rm 'gayoso2020-expressions.h5ad'\n", "!rm 'gayoso2020-proteins.h5ad'\n", "!rm 'gayoso-seurat.h5ad'\n", "!rm 'gayoso-seurat.h5seurat'" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R version 4.0.3 (2020-10-10)\n", "Platform: x86_64-conda-linux-gnu (64-bit)\n", "Running under: CentOS Linux 7 (Core)\n", "\n", "Matrix products: default\n", "BLAS/LAPACK: /mnt/home/icb/anastasia.litinetskaya/miniconda3/envs/signac/lib/libopenblasp-r0.3.10.so\n", "\n", "locale:\n", " [1] LC_CTYPE=C LC_NUMERIC=C \n", " [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \n", " [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \n", " [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \n", " [9] LC_ADDRESS=C LC_TELEPHONE=C \n", "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \n", "\n", "attached base packages:\n", " [1] parallel stats4 tools stats graphics grDevices utils \n", " [8] datasets methods base \n", "\n", "other attached packages:\n", " [1] Matrix_1.3-2 SeuratDisk_0.0.0.9018 \n", " [3] SeuratObject_4.0.0 Seurat_4.0.0 \n", " [5] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0\n", " [7] Biobase_2.50.0 GenomicRanges_1.42.0 \n", " [9] GenomeInfoDb_1.26.0 IRanges_2.24.0 \n", "[11] S4Vectors_0.28.0 BiocGenerics_0.36.0 \n", "[13] MatrixGenerics_1.2.0 matrixStats_0.58.0 \n", "\n", "loaded via a namespace (and not attached):\n", " [1] Rtsne_0.15 colorspace_2.0-0 deldir_0.2-10 \n", " [4] ellipsis_0.3.1 ggridges_0.5.3 XVector_0.30.0 \n", " [7] spatstat.data_2.0-0 leiden_0.3.7 listenv_0.8.0 \n", " [10] bit64_4.0.5 ggrepel_0.9.1 fansi_0.4.2 \n", " [13] codetools_0.2-18 splines_4.0.3 polyclip_1.10-0 \n", " [16] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.1 \n", " [19] png_0.1-7 uwot_0.1.10 shiny_1.6.0 \n", " [22] sctransform_0.3.2 compiler_4.0.3 httr_1.4.2 \n", " [25] assertthat_0.2.1 fastmap_1.1.0 lazyeval_0.2.2 \n", " [28] cli_2.3.1 later_1.1.0.1 htmltools_0.5.1.1 \n", " [31] igraph_1.2.6 gtable_0.3.0 glue_1.4.2 \n", " [34] GenomeInfoDbData_1.2.4 RANN_2.6.1 reshape2_1.4.4 \n", " [37] dplyr_1.0.4 Rcpp_1.0.6 spatstat_1.64-1 \n", " [40] scattermore_0.7 vctrs_0.3.6 nlme_3.1-152 \n", " [43] lmtest_0.9-38 stringr_1.4.0 globals_0.14.0 \n", " [46] mime_0.10 miniUI_0.1.1.1 lifecycle_1.0.0 \n", " [49] irlba_2.3.3 goftest_1.2-2 future_1.21.0 \n", " [52] zlibbioc_1.36.0 MASS_7.3-53.1 zoo_1.8-8 \n", " [55] scales_1.1.1 promises_1.2.0.1 spatstat.utils_2.0-0 \n", " [58] RColorBrewer_1.1-2 reticulate_1.18 pbapply_1.4-3 \n", " [61] gridExtra_2.3 ggplot2_3.3.3 rpart_4.1-15 \n", " [64] stringi_1.5.3 rlang_0.4.10 pkgconfig_2.0.3 \n", " [67] bitops_1.0-6 lattice_0.20-41 tensor_1.5 \n", " [70] ROCR_1.0-11 purrr_0.3.4 patchwork_1.1.1 \n", " [73] htmlwidgets_1.5.3 bit_4.0.4 cowplot_1.1.1 \n", " [76] tidyselect_1.1.0 parallelly_1.23.0 RcppAnnoy_0.0.18 \n", " [79] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0 \n", " [82] generics_0.1.0 DelayedArray_0.16.0 DBI_1.1.1 \n", " [85] withr_2.4.1 mgcv_1.8-34 pillar_1.5.0 \n", " [88] fitdistrplus_1.1-3 abind_1.4-5 survival_3.2-7 \n", " [91] RCurl_1.98-1.2 tibble_3.1.0 future.apply_1.7.0 \n", " [94] hdf5r_1.3.3 crayon_1.4.1 KernSmooth_2.23-18 \n", " [97] utf8_1.1.4 plotly_4.9.3 grid_4.0.3 \n", "[100] data.table_1.14.0 digest_0.6.27 xtable_1.8-4 \n", "[103] tidyr_1.1.3 httpuv_1.5.5 munsell_0.5.0 \n", "[106] viridisLite_0.3.0 \n" ] } ], "source": [ "%%R\n", "sessionInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualize results" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/icb/anastasia.litinetskaya/miniconda3/envs/multigrate/lib/python3.7/site-packages/scanpy/neighbors/__init__.py:121: FutureWarning: This location for 'distances' is deprecated. It has been moved to .obsp[distances], and will not be accesible here in a future version of anndata.\n", " adata.uns['neighbors']['distances'] = neighbors.distances\n", "/home/icb/anastasia.litinetskaya/miniconda3/envs/multigrate/lib/python3.7/site-packages/scanpy/neighbors/__init__.py:122: FutureWarning: This location for 'connectivities' is deprecated. It has been moved to .obsp[connectivities], and will not be accesible here in a future version of anndata.\n", " adata.uns['neighbors']['connectivities'] = neighbors.connectivities\n", "/home/icb/anastasia.litinetskaya/miniconda3/envs/multigrate/lib/python3.7/site-packages/scanpy/tools/_umap.py:147: FutureWarning: This location for 'connectivities' is deprecated. It has been moved to .obsp[connectivities], and will not be accesible here in a future version of anndata.\n", " adata.uns['neighbors']['connectivities'].tocoo(),\n" ] } ], "source": [ "sc.pp.neighbors(latent)\n", "sc.tl.umap(latent)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "latent.obs.batch = latent.obs.batch.astype('category')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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fO+aQSiYcERERERERcWgTpbV6edOP8cd6A0aM/gwTuXoJ8JH5c8e8AaCltf09QMf8uWMiH9WIiIiIiIiIlx2RhfVlRktrexXGB/VsYEHpczsmPcQvMKb5/Py5Y+4/QF2MiIiIiIiIiHhJiQTry4yW1vaJmKjESZh8hA4mQnE1Ztj/76X/NUDt/Llj/vYsm4qI+K8sWdY2GuOn1Y+JiBXAnTOrOQrSi4iIiIjYb0QuAS8/WoGJQBHwMddw0fy5Y65vaW0/Bjh//twxX2xpbR/Hi0ykHPHKYsmythFAZs6s5nRpehSmAsktGHeTTZigvddgAgzalyxrW4WJjP0O8MU5s5r79nvHIyIiIiJeMUSC9WVCS2v724ANwFhM/rfTMSLicEzqKjCJeL8CMH/umJeqeknEIcaSZW1fAb45Z1Zz/5JlbUdhyiM+uWRZ20pMNZWJmLyVV2CC+CZiKtJ0YmpW/w8mj98DpenlS5a1nTRnVvMz8ilGHPqISIAZ5RFMdZwPq+p9pXl3Yso0/lNV95hcXUR+AZyJseIngN+p6pf2Q9cjXgJERIHfqurbStMOJtj3gWe75qXljgFGlfLf7ot+bcL8fgWADXxOVf+8L/YVsX+IXAJeBrS0tl+OKf93BKYO7ycxGQF+jqkIMgAsmz93zIYD1smIg5Yly9oSmOTRnwEewrz4vAmoxiRzrwc+jPGBDkt/ikmR9mtMAugBoIxSyT1MPfcqTKq0yzBi1gPmY2pR/27OrOYz9/3RRbwQvvD1xQuBqzH147cAV3z50/P2KnG8iKRVdai61GuAK1T1zNL0WZgCJu//L4L1dlW9WUQSwCrgLFXduKflI148nSuXP+P6N8yYudfXH1gHzFTVnIi8FpOZpv2/CNaLgRNU9cMvYF+C0S3h81h2U2n7XSJyOLBYVcf/l9UiDmKiSlcHKS2t7fUtre12S2t7JTASE1T109L/i4GtwEMj7NC30M0Y94CIiH+zZFnbUUuWtcWBHwA7MMLyfRhB+mngGkwJzT7gbkp+z6Vls5jqJOOBP2NSp3kYwTofUzVtA6YU33Wlbf8ZU5giXdpnxEFESaz+BHNNpfT/J6X2l4pKoHdoolQ7/oVY3odqlEfuTC8xJbH6jOtfat9bWoDXlT5fBPxuaIaInCQiy0XkURG5T0QOF5EY8GXgzaWqV28WkStF5JPD1lspIhNKf0+KyK+AlcBYEblcRB4UkcdF5PlY4//jvox4eRIJ1oOXn2PKpZ2KGbIdhREbtwF/AE4CfizIOWcm9dHIBSBiOEuWtc0DvovJ03sucDzGQv86jLX1UYxlNY2x3tsYa2oVpoTeCOCo0nQGI0wdTDnHeOnvZIwv9QzgbZiyexdjXqresWRZ21lLlrWN3OcHG/F8uRpj7RxOqtS+NyRLomMN5qX6Ky9iG/8rIo9hMp7cpKq79rJPEc9kX11/MJUT31KykB+FcRcaYg1whqoeC3wBuLpUCekLwO9V9RhV/f1/2f5hwPWq2oxxgzsM8ww8BjheRGY9y3pLRGQl5oX8cy/u0CIOFiIf1oOMUtqqX2JqA98OnAWMwdTcHYepkXwY0Dl/7pj/B0RZAF7BLFnW5mD8/+7G/JCvx1Q4m4qpdz4BY7XqxvhypUrtazD3VAXGVaATI04fxzwEujFCdWNpvZ9iSvxWYCxmRSCJyVRRgbHYPFxa/oOl7X8BU1v60n10+BEvjHEvsP35klPVYwBEZCbwKxGZoS/M3+zykktAOXCXiJw65Acb8ZKxr64/qvp4qYLSRRhr63CqgF+KyGEYVyP3Rexis6oOpWqcV/p7tDRdjnkmLtvDenNKLgGTMffVUlVNv4j9RxwERBbWg4SW1nanpbX9vZhhkvGYB/50zBezArgLuB+4ByMgoiIAEdTkBs9IFXI/xNwXDwGPYNxFajA+qSMx4nIAYxUtB7ZhfFfPwojKtQAj0j1vxzxQFNiO+X04sbTsAOYFqq3oB9vTeT+GcRtoxDyQAsw9+i7MPbwZqAM6lixr+8O+PAcRz5tnG4V5yUZnVHU5xnLf8CLXTwNLMUGlES8t+/r63wZ8k2HuACW+AiwplQI9l6fdPnbH5z81yfDlhruICPC1kmX2GFWdoqo3PFfHVHU95uV6+n8/jIiDlcjCevDwGyAGfAQjPG4AdmGGyDowvoVfmD93zD8wkdkREfQmK6qAWzHi08FY43cB78a4BAxi7qsZmIC9lcAYVCvQYDOWk8KkrXJ3pqrvBF6FeYCNxgjZBowY3YJJcRW6thXrz3p95Ql2YqyxIeb+fDfQhAnWmgT8lpKld8myNgsjbpvnzGq+a1+ek4hn5QqMD+PwYeFsqf0lQUSmYdxLul/k+g7G1eR7L1WfIv7Nvr7+PwP6VPUJEZk9rL0K85IMxmVoiEGMMWaITcA5ACJyHCY7yZ74G/AVEfmtqqZFZDTgPZcbiYg0lra3+fkeTMTBR5Ql4ADT0tr+FozYaMNYyc4Hmi3CwRCrERPYMgczZPthzFvnivlzxxQOUJcjDgKWLGs7vSqfvrU+n373+uqRPYTh37CsGMYdIMTcN2uAozHDZR7GeuEBrmiQTnrFp7Kx5GTMA2Un5kHmYASvhxGbReAxjIXDxlhNR2MstVpa/kRMMYsRpfWKmECuXcC3gGaMqD4JOA348JxZzVEAxAFgH2UJGEprBcb6dYWq/rU07x6MT3Q5RsReoqp/2239X/B0WqsYxlJ/2Qt0KYh4HuyrLAFDWSKGtc0GPqmq55TcRH6J+Q35K/A2VZ0gIrUY8elisgrchgncHI3xgZ2JSacHJovEjGHb/yjwntJkurTN9bv1YRNPp7VygWtV9Wd7c6wRB5ZIsB5AWlrbRwC7QLcAHSCfAC4EPbMqlmvuL6Y+hPnCPokRFFXACcBP5s8dc+sB63jEAWHJsra5GBG5DRNEdQbws5hffF1tPv2qHeW1mzDicC3Gn9XFWOprJAzOb8wO7NxZXtMJNKN6sxsG53m2Y2GEqIMRursw7gQ25kGwkqeHZ4ulbQ6W5m/BuK/8AyNImjFuB9swlt4QI5I7MfmD/4gJ+PrZnFnNURR4RERERMTzJvJhPQC0tLaXtbS21wL/BNyqWP7eMeV9jwA/BlyQ5f3F1ObS9C5MIMsHMJHY7wIuOEBdjzhAlNJTfQlj4TwcU373e8DOohM7e0d5LZgAqxhmSD4dK+YFkzs1r5bNzrLqWlTHo+oiMseznQd42koqpV2NwAjSLszvw5EY3+mhqmo+xkXlMYxobcMM652JGfLPYobdrujP+lswAnUCRsReCHyMyI8sIiIiIuIFEgnW/UxJqN6EERZzgdr+YnJKe7rmw5i0G1sxvjyfwlyfLwGngvqg38REar9kPmcRBz9LlrV9RVTvwCRSryYIPozqlZjgptsx91IFQTDo+l4/sBrIFd04wJsxLgEWIjHCIIZqgAmMOQpjjR0KtPoTZvisHiNcyzEuBiN5WtQ+UZo+FVNh7SjM8H97adlKjBvC175285ba/ox/JMbCamGG/D7EM4MyIiIiIiIinpNIsO5n5s8d0wM8BXxy/twxmzG+fWMwgqEF4w/4eoy16hPAj4B/jinv/2uFm/80prLVdQei7xEHjExjtl9HZvragFOcMNiGGa6/RQJ/V1kxR8z3FMvq8i2rHGMVTWNKJnrAcZjIaxvbSWJZO4BFsSBbRIPpmJegvtK+1mCG8nswLgApjItAL8YH7ViMqH0QE8RliwZnVvmDVZhqN4OYezfx9XdMqqkqc7Zh/NQE4zKQA/6y705VRERERMShSOTDuh9oaW2fjgmm+r9jGre9pjNb1jiqfPDhB3eMPRwzzF8PzAI+WB3LfKAYOF42iN+DSVH0YYz16gHMA387sATjy/ojYHD+3DE79v9RRexrlixra5wzq3nXsqWP/3H8QOfXuxMV16Td+FEK60PLno5IDtU6x/e2+W4sxETo+xg/0iGxmcP4QNdjghlymMC9DMYy243xS63H5FGtKK03qrSNfozYTGGG/48trRdiXAIGALU0DEKxyjH36miMcI1jXorXl/bxJeBPc2Y1R1XZIiIiIiJeEJGFdR/T0tp+LqasZQH4UFvniFd358tGFgL7SExpzFqMtezPQCEbxD8et72NmKHc6Rg/1i9jEjLfgPEffATjF/hp4Ov794gi9gelNFC/W7Ks7asT+nclBtzkDzNu4oxArEQ88But0LPRsErCsHtkrn+8hGGdaFjEpJLKA/2EobpesRvzojMCIzz7McK0DyNIGzDD+IOY34Mcxk+2B/gG5v7swiTlnokRq/2lZXsxAnkwFItS+1DA1lKMa0IWEyj2ECaAcOOSZW2n7avzFhERERFxaBJZWPchLa3tlU2p/vs6shWDYCXq4um+7kL5PcCbMGk+pgGzMSk9Po6xRDkY94BLMcEuVwIfBe0fX9GzZfNgXe7EkVuvfHTnqKyv9uuBivlzx3xz/x9dxL5gybK2ptGD3e8v2M6urmTlmHjgnZ0s5icmQ//GjvLaPkoCstIf+EARJ5+3k42oaiwMLEEHC04sg8kmEUM1jkgWIy4zrlfc6dnOYViWGd5XrbU1LA8su4en01mNx1hgBzCW/ONRHSEaiooliKQxIjUG2KgOCoiK2BhhOxIjeodKunbwdBaBD2NStB0xZ1bzt/fLCY2IiIiIOCSILKz7iJbW9lcBV3Zkq/4OVhnQnnD9nwM/xAzrX4xJ5v7/gEvmzx0zrdR+KcZH9btAK6ZU5p9AL6uK539dHcteuGWgalHMDk7FWF/9ltb26Dq+zOlYtHioiEfZYCzZA3zYCYN3B2J9Nu/GEwNu8hJM4v4O4HWDkorn7cRIRPqwrJ8UHdcrODHFDO0XUY05oR9gIvafAvKBZU/CWFY3ASNAM0mv8ATGzxWM0BzuTvB6oByRjlSY+7sQrC4ttwIzxO/G1KuIadHCjBIoRvT+ABNAOOS3eg7wTuDnGNeX+iXL2l7zkp/EiAOCiIwUkZtEZL2IPCwiLSIyVUQmiEhORB4VkdUi8i8RuXgP658oIr6IvGm39joReaz0t0NEtg2bju23A4x4TkREReQ3w6YdEekUkdtL068XkU+/hPvbZ0HHIrJJRJ4o/a0Ska+KSGLY/Kml+/spEXlERP4gIiP2VX8i/pPIwroPaGltvx44/6iG7d2PdzZ1g2wCrpo/d8zaltb24zCuAK3Ae0eX9f0l5Xq7nuprOAn46vy5Yx5uaW2fCVzXkBxsH10+8PhjnaOvwoiEN2JSFl2MSStUjhnSPRcTme3Nnzuma/8ebcRLQceixTdvqGpsK9runcDZQIUTeB8IkXxoO5WEYR7YhmWlrDBMxINiTc52d9gaeoHtNmJePi2M9TNLGLqIFDBJ3YsYy2cGI0qfwrgITMUI2gkYN4IiRnjWopooBW0FGH9VD2NV7cKI1aEKWB5G5CpPp7jqBJJBsTjRjsUWYdwLTsH4a/cDnXNmNUc/PAeAJcvanpE4fs6s5hedOF5EBLgP+KWq/qjUdjTGzWQrwxK+i8gkTCaK61T156U2G/g75v77mare/Cz7uRJIq2o0mrQX9C/+1jOuf9W8j+914QBMwOVMVc2JyGsxo4btqnrOi9ieo6rP6ue+p0IF/2V7gtE64fNYdhNwgqp2iUg5xijkqeo7S8L1CeDjqvqX0vKzgS5VXfl8+xPx4okscy8hLa3to/921+bPYx76p8Xt4HiQpZhh/Q/f0br1nSeO3JrCFAEYCdy1LVO1oTqR/1/gq6PK+q5e9/AjR2NcAe7vL8RTXmidBLwPE2ldhvEL/DUmo0AjJi3RKOAS4BMtre1VLa3tf2tpbR9e8i7iIKdpwbw3FW33NoywWyWqs6oK2e2xwHeADkF7gMaq3GCt6xXKY4Gfs1XrbNV6y/fWozqAqSJTBMoSXqHCCoNyVBskCDqBGGHoEwZDpS+nYSyg9Rix2YlxBUgAdlVusF00DDGuAUXMvZfBuCSUYSy9Q78fKzH37FSMBbdRw3AtfhhifFenYQTyujmzmndFYvXAUBKrP8G4fUjp/09K7S+WOZgH+o+GGlR1hares/uCqroB4/p02bDmjwC3YCzzEfuQklh9xvUvte8tLcDrSp8vYljqOhG5WES+X/o8WUTuL1kwv1oSu4jIbBG5R0RuA1aV2haVLPZtIvK+UtvXgWTJyv7bUtvHRWRl6e9jpbYJIvKkiPwK8/v0eRH5zrA+vVdEntMtSVXTmPznC0pVuRYCy4fEammZpaq6UkSaSyMIj4nI4yJy2Is+kxHPSiRYX0ImVHbXjUgNXp50ipNPHLl1woM7xlZjLE7dQM6WsDJdjL0PE5ByI/BVkFc9uGPsD4DPDRQTy9b11r/btJNNut6Obemqx4CfYoTMF4Hj588d82eMhXUAeO/8uWN84P7pdTvW1yfTfwMumD93zGBLa/tZLa3tQsTLhdnVufRrRvd3fnhKb8eRGcupyDtu4HhFLxZ4P0MkHlj2/3qOG/Ynyu8JbOfWohN7MrTs6RgRORJTZCLIx5NbQ9sZQFWSfmEyqkVEPMSqBiAMdWS6BwmD2NiBrhzmfoqjWmMHnt+fqhyjYvVi3AcsTNCggxG0grHu76z3elaJBmsxVtShjAKNYllnkoi/X1W/gCkecFwkVA84V/OfdeQpTV+9F9ucgQnie748gnmBoVQD/nyMm1TEvmdfXP8hbgLeUrJCHoXJarMnrsNY2I/EZBQZznHAR1V1amn63ap6PCYjzmUiUqeqnwZyqnqMqr5VRI7HFNM5GfOMfK+IHFta/zDgelVtxqSDPFdE3NK8dwH/tUyrGkPAxtK2nute/0DpuI4p9Xf3Y4t4CYgE617S0tpe3tLa/tuW1vayTQN1UwO1nrDFb3hi14h3YaKtp2CE5gxf7fet7hmRwuShLM6fO2YrJrAlDlhpL3F5iPUXjMXh1/2Fsq/3FVINGItEG6ZK0Dktre2T5s8dEwJfAPItre3fAx4uc71FGS/2W2B658rlCUE/DFzW0tr+mZbW9oktre2H79eTE/GcdCxanOpYtPhrG2/7xy8f/dsD37MC/7LKQqbGCYO+zZX1Vir0KxDL921nGyqflsC30rHkhyu8vAuMJQzOJwzGYlk/QCSHeTE6MlHMB3bgjwUqbA1VlGogRKQRiKUK2dsR7utMVe1Uy9b2itoU8GpUCwS+F4qdwLig1PN0qdcERqSuxbgGOEBtl1PTrmLPcUNvWqU/WIMRzVtDz1/ne8FvP/nz9T8srXPOkmVtH9+f5zfiGYx7ge37guEv0N8BPvV8hmojXhL22fVX1ccxrkUXYaytz8ZMTIlmMEab4fxLTXGUIS4TkRWY0ZuxGNG4O6cDt6pqpmQR/ROmZDXAZlW9v9S/NCb48xwRmQa4qvrE8zy852P0WQ5cISKfAsarau55bjviBRAJ1hdJS2t7qqW1vQaYiBkmPRVY2JNLfTDtpb6bD2OVmC/vkxgr1ecwb3SXYIbyp7S0tl+D+XKdihEDT2CGZ6/FpAzKYlILXYwRI2/E+Kq+taW1fQom24CF8bPpa5gxs2vO7Enfmz93zP3AqSeMbF+Msc6ehnmTnrdPT0rEC+U8YKAzWeHlnNjRoViZdCwxoi9RdhyqYc5270KkGpGTHA2dWBisBU32J8ofQMOJINnqYq6ivJA9D0gCZxCGTt6JVYaWVQQ2B5Z1l2/bYXdnGiBEdWc2ljgPsU4KLHsk4KhYEIZxNAhcQq3ODvioHkUYemgYs0NvBqqKqsXT6bHWArsQOQ3Y6llu1YBT8U2MyN1quc50BgZ+c+27p7wLY3m9Zc6s5m/t/1McMYwtL7D9+dAGHP8Clj8W49YExhJ1U8lv8E3A9SKyYC/6EvHc7IvrP5zbgG/y4ivZZYY+lHxD52L8Yo8GHsW8NL+o7ZX4KeZZ+i5MAOh/RUQqMEJ8Lc9xr6vqjZgg1RzQIiKveoF9jXgeRIL1BdDS2n5sS2v7qNLk6Zj8qJ/BDBPcB8Q8dS4CTpo/d8x5wHXT63Z8xiKsw6SvOg5jUZ1e7hbKaiWcMScZfh/zJbgZuAOTKeBdGPH6FoxAuKEylvtyXSKzC/gsxkL7/tL/e4D4/Llj/j3c2tLa/psHd4xtenDHWGv+3DEZjEieBjzS0tr+3n12giL+Kx2LFs/qWLT4tUuWtcma2tHbgAfHZnoPH5vuOrwhO/BQznY7++NlxaLtrMk5sZpUkHkCkesysURfZSGbSnoFk35K1QHK++JlO9NuvAhsRrWcMFTQQRXLK+SKh9lBMKv3oVU89GSfi6oixAhDB2MlTWP8xYq2hmE8DJ371gwWVSy3IdO7E7BCJSiqlQHyqHqoOo76GzEvXaMxL0JPApVhqMflM7mu8iDTCzza45Tln9iUXg/4c2Y17/7wiNj/XIF5CR5Olr0r9fwPID7kYwggIkeJyBm7LygiEzCC5nsAqjpRVSeo6gTM79+lqrpoL/oS8dzsi+s/nJ8BX/ovlsv7MYYXMM+3Z6MK6FXVbMkiesqwed6wof17MD6mKREpw7iYPMN/GkBVH8BYahfyPER1KejqemCRqvZiLMKnisjrhi0zS0RmlAIKN6jqdzE51Y/6b9uPeOFEgvV50tLaPh5jJW1saW2/e/7cMYuBP2D8W64tCcPXYwKsftjS2v7/gPu9wD6/OpE7DjjTEf/HwNnTanf2TanpenufSrgkJ1cAvwE2YIY8PgW8A3jIluCEMif7SaA/ZgcnuZZ/DsZ9oPvI+u0/EPQk0KNsCd5W6uP7Wlrbf4mpL38YcHZLa3stxkdpLCZF1pKW1vZL9sc5i9gjHcDhU3u2XTd6sPsrBcv+7KDlHrW5ssGOhV6+wis0AptTvufEwqAmL7HpwMWIPNlZUVvIuYkBICjziquBf40a7L5ZTPBEDSKbcBzFssuBZDzuhIFlS/lJR8prTh+TR8QBcVOBV0R1CybBfyOgge04BSe287gjarUvVdHRl6ysxbI6ezO+7Oz3KhEpYFm3g7puWJyI8Xm9HxNUtRzY4gV6TGvbYCJjpcbfePeOu5MU7xtfH+vDWCciDjClbADvxWSG0NL/9+5NlgA1aWbOB+aW0lq1YSLEh6rvTR5Ka4X5vfzuUIaAiP1LKRvAM67/3mYJGEJV20uC7bn4GPBxEXkc4y7X/yzL3Qk4pfvm65jfmiF+DDwuIr9V1UeAX2D85B8Afqqqjz7H/v8A3FsSoM/GEhFZWdrmFoxxiNIw/znAR0pprVZh0lB2AhcCK0XkMYyv66+eY/sRL5IordXzpJTrdBTmjettwCcxw1ujge/GbP8+Vd6QdLzXqbIl48XvdqxgZjF0lp04sn01sHZtb92GuOX9rT6ZnRuzg8U5P/bGJ3sbfw4sbkimC7aE7o5sZQ/w7sOqd+0a9GKza+J5BoqJbNpzy2oTubptg1WBI8GdVfH8+weLiR+kYsWjahO5HXHbT67oHJ0EGqdW77y9PV19ctaPr8WUw7SPqN1ZUx4rdg0WYzXr+uqP8kO7Alg2f+6YDQfifL5Sue8fj13VNNB1aoKw3hcrnbftQEI9eWdZ1d2eEysk/eKoMNQRiFTlnVgey/okJrL3YQnDY1Rkh2hY3pAbdHaVVXtxv+gXbLcWke3AGEwKKgvj9F+Gar3re77nxgAexLiUDGBcUHZhhrnmoeoiUsQMo3mYtFVm2D8MjrPDkMBxuzDiuA1j+a8G8uvaE8tH1hZPK0+F92AsZe/8xp+2bP7i2RWddjy2KW2X/XHOrOaofHBExCscEUlhgqZURN4CXKSq5+3H/d8OfFtV79pf+4x46YgE6wugpbU9iclzmcJE+ndh/BB/OiI1cFhNPLe9K586IWF7mxpTmZr1fXVTRpYNrqiMFy2gKl102d5bWzGpflcyUN3g2kzYNFDTOViIf/3oxh1Z4EfZotX5VF995YiywX4vcEcEKvHxlf1kPNq2DDbsCAM92bZ066Tq3olxJ9w4WIz1JGy/fWXXiLPHVfTeu2Gg4X9OGLH1BwqN29NVD9QlM3VJx7+0Lx9fkXKLT8Vs/cNgMba+Px+/rCNb9dj8uWM/07ly+WuBu4Hyhhkzo/Qy+4iORYvflnVi3/jhvX25dx2TvGNX0xhvWs+2z/c6ifU2rNpeWbceOAINp9tB4AW2U43Ih4BvAaEV+DuAinjgZXKxZBVhGE7q3zliS2XDUt92pmJEpsPTOVNzBEEekQosKwCQMMgrUoFlBlckCNapyBg39NOeE6vAWF37StvZBoxDtQLVdCHQhG1J3rEtF9huaTgKNLelS29vqnMc22IypnxwO+DOmdV82/47uxEREQc7JVeR72P84PswmQDW7Yf9VmMspitU9YJ9vb+IfYPz3xd55dLS2h6bP3dMcVjTtbaE3YFaqyZUdq8v+M4UhF+PSA0c1V9IlsVt/5jxFX19CCcI3K9IlWv5k4GMk09lEm52zIC6ZZsHqvrGVvYfFYYaaKjWhMqe7wNrVKnw1aqoieeDhO1vq0/m6gfysXUiVJe5HFUTz00PQg0SdjBlV7bMcW2mjSwbfKAnn7ig3C1kEo5/YtIpTniytyEZKleGyo9s8QtuKrjFQr9nCV9Y31e7phjY8xK2d9rxI9o7V/2r50siqYUDhcTSSdU9DcCCA3KyD1E6Fi0+F6hoWjDvRuD0jBv/4DlnT3lkl0hl3PeuAkZ0lVV91EK/iOpRtu9/HVs2B447EfioE3gtPtKO7UwIbeeHqL4+L9ICfAbLKttQ1ehj2YqxeKYxadTagTXAfEQyiBRQjce8wpaiGw8Jw9swPs1HpIq5R3KxxBbPiR1HGK5FZBqmilAMGOP5YdwSBoOQst60/3htuTO+vEg6HWeHjT9a0OrWJ3reOP+4ug2N1bEy4HJM4MFYoHbJsrbb5sxqfv1+Pu0REREHIaX8vEcfgP32YfJER7yMiSysz0JLa/uxloTvsUR3Hdu4/csP7hh7QXU8O76pbGB+IbBn1CXz29JFO+VYjPMD7ojZwastwVbIOBZlImwHkkFIuW1RQKkFFKE7VMIwpMJC7E2D1fGJVb0CBAXfTjtWmHRstQCr4Et3zNaYCL2hMjbrOZ1lrp8UYXmoHJ/3bd+x9O+WhBdkPXdrRcyr90JyjkUlyk5frdG9ucQNCTec5Vr+Ea7lS6jW32xLZ1houcLf1vbWj55U3Z3fmS4b01useMekqu63tQ9W9846c/JXDuwVePnTsWhxBca/760YN5Ljn6wZZY/v3/VZz7YdRTZWevltPnJeT6KiKuc42dp8ZnTecgayscSNoWVdUrAd19bwqU/+enP9Zy8Y/+bKlHMrxvdsWqxYbC86dgaTh3UnxhVgJSYnYS/GXcWkDDKVshIly2pQnx14JO4XJ3aU11SFRvDmCEMXSGFZd2N8oCUMdZQIOcAWkThAvECu4JLBwu0Z9FKA1la4BYxgXlf6/1ZM4ER6zqzm7n1+siMiIiIiDmkiwboHOlcuT2zsrzm2Jp6bOlCMHTOucqDgB1K7PVM2sSpevKsqXqwE3tebj5WjVi7m+KvKXN8GphcD4ih2zIGiL7/vKyRObSzLlWH8/bYDEioVBV/iih2qgmv5gyKkNg/UVUys6vYFfMsiUfTRQG0r0DBTHtNCqNihUulY7AS04FthMbDqs777LS9wEmMqBy9MF91thcA+rCaer93YX9M/qbq3W4TaYmBlbQm7bYtRGMfzi4CqvG9nXQniCF5/IXHSQDHx7fpkenx5zPcxJWQfBWiYMfPxA3EtXs50LFpcDbwW+EfBcpYHlvXHlF/8EvDbNdVNvRZaM6Vvx13tZTWnW6rNVV7usH4nfp+KHJ5zk15o2wVUM1YYzPCRvGXbPZio/lcD92OSaq9HZCTgEIYDCb+4IR9LnIpxV6nFBOl1YXIC59GwH8SxNey1wrDas53/QeRSTHDBGzE5FM/GlMpc7/u4+aKUlac0hcmE8VqMGC5XVfnLg927jp9cYY+uixef2JzObd6VX3LOifUfw1hR5s2Z1fz1/XO2IyIiIiIOZSLBugc6Vy7/3K5s2UmFwO5qKhs8tr+Q+GVlLHeVJcQUQsdiXRBSr0qtbeGt7amP1SfThdpkPglk/JBNjkXftsGKo8pjhaRtqZuwvadcm7FAmPdtcSXYIRajLZOSOATioZJf09OYOKx610ddmy8HIRVeaBViVmgFyiLXpg6YjsljeMaq7ob1R9R2TgmUDSAjvJAwZqntq9XnSjgyXXRyFXE/JUI30Lihr3LphMqB4wLFE4g7NrEw5MltmYoZYysGfwKcBAwCU3dmksWk4z1QHvNP3ZauWju2or8VY73rA7Y2zJj5UuXuOyToWLTYxlhRW5oWzOvsWLT4rZhh+mnAmLSbODztJr4xMtt3BrAM+GVXouLT9fnB8gBqdqaqJmfdhOfbTiNGXA5gUps1pYrZG3NOfLKKXCxh6Knt1GICn8ZjXoS2AVtRPRUN27HsJoxbwBhAJfQCtVwBBFUn7hWeKMQSEzH+2EmMqN0IpCoK2V2DseQxiDwYqs5AhYGMFKsrNAHci8npm8dEGScHcr5WJh3L80NbRIJ3fGf1sbd/cOTP8omKaaOqiv878diTrtoPpz8iIiIi4hAnEqzDaGltnza9bse0Mteb3ZNLXmtJeG/c8YOkEzwFzPRD6Xqqt6H+iLpdXmc2MVDm+mOSjj8wWHTjtoRxxwqwhbRlkVLwbcEGKPhIzMYS4fPAJwu+dINMiTthpyoNgIrgA0VVROTfCY+rPQ0zllqVfshDcYeTMp6DoNmEE3iqJAHXtsh355KfSzne15OuHxZ8K+ZYYTYIrbhjh25PLrm2JpGbaFs8CBw/ULDDuB04rkW/CHWqoBCIEJQEdA9GmB6myuInexuWTavt/CYmX98o4Bjgu4A0zJj5kqREeTnTsWhxDfBXTE7KHU0L5n2/Y9Hioeooo4AAIwy3AodjBOLnMflLJ3XHUvXdqcpTRg/27My48UxPvGwKIquxrMsxVu40MBZVtcLgb/EwmOaEQXEwnpqDeXmZgUmLNkk0tFSsnB/whGPpkYSBa4EV2s5qTB7BCzAitwoTYFUJFIHFQGAF/vxQrOyuAT+zcku66VVH1f4OeHNpGQ8jVhuBgqpmQ2VjGGq+vTt/fNy1m2++r/MEz49/mHDyjbd/Z+y/68tHRERERETsDa9owdq5cnkFJsmwAGG66PSkHP9oy6IqCHlksOhOTxdj60ZVZJotIQ3Eg1D/2J8J5u4qjqAqlqt37HBHfTJfDELGdOVSYcwOktWJwpJQGXAsTsM86H2M6BhKtB6o4gK2H+I7Fm5JsNoYP0TNe7Kr4OQaVstOOVnHZ4FAgTU9Demx5b2NKdfPhyGhQkIEd8tgTXFSVa/nh9ZTfYXEMXXJbCFbtCyEmGL19RcSblNZOm5bJqVRpsiWmMUkx8YKwd/YX2tPquoB8EuitSUIsbcOVp3SkMp0lbn+45jkzQWMcNkB3N4wY+a399PlOujoWLTYwgjKWzFW0dWY4fd+THWzbwM/wvixXoUZJr8Nk1j6w8A3FMbuTJS/fyBRfoci54xOd2/YVl43ubKYLfQnyv+Cifw/DlPy9ImKQrYxEIl7Ymc9N9YAJOJ+0fbFag9spx7IFH2p7Bu0ncYavwvVBtv3OgI3FsO4E7Rh7rNjgamO75X5thNi8gdOwQhrwdy3u4DJmJeXBODW5QbCrBPXnBvnpnt2/vyR9emKK940bupjmzLjf7Vkx5pvvmvyl+KOdck5845auA9PfcRBhBiXlO8AJ2LulZ2YfJtFzHdiDeb+GcTUdv9Fab0qTA7qcZgA4G8Oz9EqInXAUPqhkZgXv87S9EmqOjwgNuIAISIKfEtVP1Ga/iTGZehKEbkSSKvqN0UkgSl2c6+qXnnAOhzxsuUVK1g7Vy6fjxEb0zBfIgdjDTsCiPkBRSARqASurVsFqtQEUW3f3u2V1VWnKi0CEm6YEvhhxnPe2J0vH1Hm5L2aRP6+7emqkywriDeVpbd2ZpKjbYue+lRuyMLmBSHlItgoecsikfcJ4zaOCHkvIGZbEITSC1rnhVYh6YTxkqh1vEA0UAqupR2B0l0M7PpyNxiLoEWPtGNThRBaAgMFd7A85sXDkKxtUZv1ZCDpaMKyKHgB5bYQWBZOGKKBUtiVTQ2Orsg+CrwK2BWENIiQVaUyVJ5wbcZgkiLfCExrmDHzN/v/6h14OhYtPh3jz3k0EG9aMO/Vu80/F/gC8BGM5bUVuNcXORd41FE9GahZUzv6SOA8J/A/Pq5/V2pjZUNCHXc0sALoljAsQzhFldWIdFUVsmfkbEdULNtz3O2AKZkqIhixmcD4So/G+JzOwbwsKSaDwFCe1WogiHmFhzzLmaS2vQFTcW07RiQsLG2rUFo3DiwXDacrshWRf6jqhWGo3ZYlvbnA+1cQ895SEZYdGwVZHbysvuvuhZhCIuMwfstXHHHWmS96lETMfXcf8EtV/VGp7WiM5X4rcLuqzii1T8LUer9OVX8uIlcAVar6KRFpwIw4jNyTEB0ufF5sXyOg70fJZ1z/6g/k9mqUTETymIIoJ6pq154EK2ZE7lbgCVX99N7sL+KVyyuu0lXnyuUTO1cuvxbzYP4DJoDkDMyDfTrGqvRnxyZpWWRB05YSeD4pVcSxmDC+0X2gPOY9moqFcUso+Mr7E7ZfPr6yr1iXzNt+wIjKeHagOpZvV6WhJpm3K+P5eoylLI6xFCCAZWEDnYUgJqqEQOBYiCVIznfKvEB81wodVdrzvrW26EubY6k6ojHbwo3ZbC53g5FOPpXNFqXXsbG9UPycZ2WBXGXcaysGdry3kKwVIZd0tUqEAMgXA5F00c2HIXdbFoNZzx0cVZ6tD5WzMFbhetvCs4QNtkWPbVGR9awcJqF8F3Bs58rlH9mPl++goGPR4kbMi049JgH/wx2LFl/WsWhxomPR4s8DNC2Y9xdMveqFwCIg5fcMfChvOUeJ6mRMgFMPquePTPd8eMRgT3XOTdSVB16IeVkQKwzsskLu9LrsQHzcYFeuupC+d7BzcPXanvBLnuOuwFiyRmFyDDZi/F53YHxXh/AwaaYewAznr8X4TG8ApOjE0rYGcYz/stoeD1SnuRgjNr6LyUjwM4xozapYSUSqQtUPDmb9hg0787pqS8b7+8P9R1en3NsisXrwUhKrP8H4Pkvp/09K7S+WOYA3JFYBVHVFKX3Rf6CqG4CPA5cNNQEVJdFbjnFF8veiLxHPQUmsPuP6l9r3Bh9Tfer/Pct8B/g98FQkViP2hlecYMXUsn4ncALmIV2NedOUMPz38P1iYFAVYjbV2cCeEOAkbDN0X8DUCb4fc/6KjlCwLWIAIhTjLisHCqlMwvEf9wPLcUV9Cw1U2YkRCinbIqMKqjhAeVW8aFkWHrBGhAFVqIh5iaSrnmujvspoR8JptqVTRfAcm15MecyxIXircwlitlZZFmVxRzvLYmGAeQDM9IrZwM92hICriq9GNJdZYhXK455YFp1AvCLmNYSKhfkxGwDuD5VdoTIZqEVpTDjhaODI0t8K4OzOlcvf3Lly+Sf27WU7OOhYtPgeTPndDcAtmHylczHn6xRgzpJlbXWlxf8PE3H/e+ASqa2sSceSRRt+ihGyzrTe7cXqYs6Ja9Dihr5dnx/84fj+XT8A/qDIk0XbCXyxiluqGo/pS1S8L93YMLapNvYFzMtVO+aFa01p/9sxli0L85A4DnMt/wY8hSl2MQ3VctcvVgDbETndd2IpjDDNYNHp2RQwltVjGzJ9E1F9C/D79q7C7O//ddvyMERyhaDjmj9t6akpd3Zs6y5MOe/kho/MOu7YD++r8x7xknA1xiVpOKlS+4tlBvDwC1j+EczLHpgE8kdg7tsngI+qargXfYl4bvbF9R/iB8BbS24eu/M/QFFVP/YS7CfiFcwrRrB2rlz+u86Vy48GuoEPYPJE1mGsTZ1hyCMIlSh3E3KdKl7ed36lsDHlBk7M8b/L01Wu8piUQTkglvXsbKgqqupi/ANHVMYL4wROsS2lrxC3iqGzzQvJAy6loVs1Ka76MJYGAAvlaEIq1fisBkMzenOJUARsCytdcDQIxc567hGqTLKE8rGV/T0iuF5AgIlOjwNjwxDxglDrqyusvoK9whLWC0jGc2IxO7AtwQXmhwoibLQtBi3BKh3nuExB4gJlQMGysCwhjxFbPwhDPozxsZzL075lhzRNC+adgbFaXoVxi3gvRixehEk3NXlEumfD9kWL1wPvV9jsiXXV2qqRNw3EUndZ8D/frLZPfzTGI8DnMMmsvxHT8Nv9sVRLxomN2lFWfSuwUDV8d9Fxnb5UZRrACsPGVMzy466dw9x7c4F7XN87CnONZmIsVUXMfV2JEa6vxdx3q4A4Ini2W4ZxG0hixGkdEA9szsskiWP8cO3eRHkxVpT+shxvqq90WTircUpVMBiLC3VXvX3yRfWVsT/PO67ukjmzmtcuWda2+8Mw4uBi3Ats3xfIsM+vwbx0DwVyfl9EKvdjX15p7LPrr6oDGFexy/Yw+5/AqSISJe6P2CsOecHauXK5dK5c/kWMP973MWLjx8AUfGsaxqfvDMviVEvIo0xSiK3rqysvi/nvzHn26Jxn77SFj2QLoYahdqJMRbkA8L0A25bQRbH8QHcCfjoXtqfsPMXQrrYttSrjhSDl+qmYzZ+BGiAoeGFcodu2qAhDkqHiURIJwPqBnHXro09tz4jpd1CfyniWKEBQDN2c7cdW9OaTk/K+neoYLA/78vGyQmBnLKGIeSgMuR54lWVJtSw7n7SDMSKMFEGSjp/rK8QJQizA68vFHg2Vph3pskTetxQjzqsrElotAgPFWH9nLhEvWaFVlbL+YmIcxkJSDryrc+Xy4Q+jQ5n7MJain2KG9qcDEzDitVBTzL1X4C3AxECs+zdWjVgytX/Hx6qK2TX1uYHim9PBhUcVuRaYB9zZtGDercBFjbn+iwZiyfOqC5mP2GGwuswr3GVp6GOsub+oyae98mJuwPWLNsYloxw437NsByNMBRPQ5WN8ysAM7e8CTscEev0F6Ld83YCPx9NBdA5m6P/b5UFmFBqeZHnFyb5lazFGTSZOdyJmLa6tcIuDTvlqJ+58Hvge8PM5s5r/sWRZm4MJKIs4eHm2VHR7k6KuDeP7/Hw5FhOIBfAu4E9qWIdJrTbtWdeM2Fv2xfUfzneASzAvz8NZhgnCu0NEml6ifUW8AjnkBSum1OgkzMP8kwyl9VFqEOKYh3sv4IUhiYxvN2R9Jzehsvsz6YLEwjBwLAnqgOK27iBd8DUJWITYhcCS3lwycCzN2LYUXcf6Zlc2mdieqT5+R1/4VMFHA8VGIes5dcAsjAuC2CJ5x0TsO15IsRTklVMIcoE9MR8m31BdVStF3+pTJbAtizCUXBji1yRyqViQmDUyNdBvWUGyIpajNpFNhWHQIehajAgJAduyGHQsQtsiEXdIYIbeyPt2POkUrZI/a6o6WTzZtmBEWQY0bA+ValV8VYJQ6Stzig0VTtFTI2wSIvg1iXwC+C1GVJ8G/KRz5fKa/XJVDyBNC+b9HnNfnY+x1j+IEYUu5lx8DuNfOsbR8FVnzj7yI8BUC/5qQd9Yn9Oeqh39hu5E+Qrgyo5Fi0cDtTbcnHcTn+6Pl3UHYm3NxFNHNqV7vgocV/DChevCJIFl53zLsTHW0R1AiGVlMS86WYx1fRBTNxuM5XQU5jtQwLgobMSmGVvjmCwZDka0zgG+lcNd5oSBG7qxThUpC4VyLJ4CHgLGer4eni8GX63JFd6fKHLVkmVt4zH31YX76pxHvCRcgblHhpMttb9Y/gHEReR9Qw0iclSpZvx/ICITMC5Z3ys1bQHOKs0bgQlI3bAXfYl4bvbF9f83qtqDiQu5ZA/zbsFc+zvF+NxHRLxgDmnB2rly+flZz52+M1N2OJD2An5S8GVrEJIOFMEOMwFhLpaurUfJ5gNHdmYrppfFfNexuCYIZW3nINf7oZMNCO+pHpV3EjHpU9E8YSHmZHfFGxOFvGMxEiMQr6hN5MJJ1QO9dVVuVWU8cBwLFYtuP7AGMdYtANtxxAHGqyIxm0TMwcIMr4eWaLYmkeuaPCLh5wKnTJUYEFOh7B7ZFKSD4HfFZH/ntmyFY0Eu5QY4Nm53vzepZzA8GiBfDGyMCAmBWBjiBwFZ4KSCb/muFWxzLV1ZSmElKEEYEgOsuMMIgVBNUFFMlXLbYklHpsLvL8QTaqzBaUwKm7Mw7hF3YCzYA/v+yh44OhYtntGxaPEYzHD7WIxIHXLD+DUm0GoD5ryvBL5YWvVm4LNNC+bd3rRg3magOu/EAuCTTQvmbQOuA3YlvMJHC5Y9Ang3kNtRXrsQ6G8spONVKSeec+PNallFIOkHNOULPIARnCeW9jkTE3T1JuB3GNeBe0v/twPrgebQsjYg4iUK+ZQEQRzjRmADGljugCVhCFTnXe4MbHYCn31k/eB5y9r6lo8ZkKwqOxzJ3zGhM/TjowevB96fzVuLr75+y3n75sxH7C2lbADvxfgra+n/e/cmS4CaNDPnA3NFZL2ItAFfw7xMAUwWkUdFZDVGzHx3WOqqr2CGip/AZKb4lKp2vdi+RDw3pWwAz7j+e5slYDeuxTw3noGq/hCTKeC2UoqriIgXxCGd1qpz5fJfAser4odKtx9aJ8XssCxQBrNeLFkRK2paCuzUwdwUqbeDgLhYqCX0EeIAFVj0YoaqTgC2dJHJ1pI6QgILy3O8IOZttiwqMZbaEaX/WYzlcTYmQXsS2LRyczFsrLaPb6ywcYsVlpcYDHYN0NNQQYMIgR8S2EJSxERk9+bceMr17Fw+lI7ewJs62kn7BO2VuXotJAemBYQ7bIt/hMpbBbb6IRO8QCTpaqiqGcsSB+PyUF8MyLoWqVDJl/xW7dJ+BjHW55QqVqgUbIuzgCn4ckNWbTvl+mFXLqm9ufhAU/ngjpQTTEXwLWFge7pikaDvrkvmtsfsYHRvPnHfur6GS+fPHdO2Hy/1fqFj0eJKYB3GmlqJud5LMOVrzwW+0bRg3q86Fi1+HUbE/wtY27RgnnYsWnwSRrA+p6DrWLT4uwUspzNV8fZ0onwNRkROdgIv6dvujzHDqH8B5gcBK22bHkyu1AcxwqEMY0ndhXlhuRUzHDcIvBcNvw3ahdhTAY8w6ESsyYgUVSEIUNvmARHGYVxUvocJMHz7W7+16tc7+zyr9ctHvwvzwjIXOCMxdrB25sRTuq++fksZ4F9x6bjC3p/tiIiIiIiIpzlkLaydK5cvxAS0PCFCJlTrzN5CyhMhdCzutDTsJbC6EkFCx4f1GSBu26glhEAOIef5on5ILTAtCAmDkPH1Wlb3pN9fyHn2rwphoidUJqtiq9JDIJk12ulu14GaIOQYVWrzHop547RH1CZPcBy3IwC8xGAB6Is7tKuiwLZcUR9+Ov6KsCwW/MKxGIi5+FVlKL5d0zfAYQ9m43fnQ/muF1pdGKEU+CHVjgVJV0WEvGWJDYQBYblPmI3ZpBTY0F+bFEHFHOdOjMBxgIEd2TIpBlYItPoB30YtLfh2mC461CVyzsjybGXCCR8PlI2hUkwX3aUF3/lWmVOwbAlqgcEyJ78dWNvS2v6hltb2Q+r+alowbwAzvJ7CWISqmxbMOxtjRa0ArulYtPg04EyM9Xlq04J5Qxf0TIwPKR2LFjd0LFr8qdLnSR2LFv+yY9Hi/+lYtPi1wM98x3l9OpZaB/wp7C9uRbXCt90HMRbbInAT0G/bnIR5KSrDDMfHMBbuGMbHdhLwidrcYCEWeDHgu44GT4kRtH8FerHsB3oG3c88sT6+5ou/20ioFOxi4bhC2osDbrYQnHzyWn3n9HYWb190wepg6cI2YEFHT2Eu8NE5s5p15sRTugFmzhj0Zs4YLK6+6+4Prb7r7tp9chEiIiIiIl6RHFKCYojcvVumuIWySZgh0rlAr2uH2ZFl6TagJwj1nHKb+phftrUjU/EVq1RCtbS8FSqNOc8a6brqWEJQ8HHIl++wfScARlaGCYnFi2/1rHxrLIj7WU9qg5Cpljo6JTd2bW1Ydl8hkBOA0a7NyCBEgfKuQQ2zfnxkznc9p1BWCIqWHUjiSAXpyiYnrO+MnbCpN/EQRnS4MTt8tW0hyZg4TTWOLU7Q31jlbBpZ3nvShv66N9gSTlGl1hJirk2VCCJCMVRSQCoI1BXFtZEy4A5L0MNqeu7FZEoYKv/aC1iDBee2xmSGgi8ZPyDemSuvDe1gV9L1CpsG6kSEYtaL5bzAOkeVsZawMx+4iYnVvZdUJrx1toW3ZaCyzLE5/8SRW68rd3NxhqnvQ4GORYtnYipYtWBeQn5dapsNvNdbtem24sNPbsCkidmC8R8FoGnBvP9tWjBva2myn6cr+JzcF0vm+93EsYDbtGDeY2V+8bDJ/Tt3hiEj1w9UnIvqk8lC7qGyQnZ6d59lq/LHunRfD6rbUL2bMKzHWPa3Y15C/olx1SgAQTqWsDzLbgP6fMt9QMUeC8zHjAJ01Fb6O46YUEh941Vj+2MO26yY/ZdEmV0OJBdcvbLnc+2dy1eN5rVLlrXVAgxk/Uuu/XP7ekz+4uFcgfHTbrv9weX7M/I8IiIiIuIQ55ATrJ0rl9flE71HB7Z3vCo1Wc/eiAlQyQEZQuvbsXx5LLCLudAuHj4hlTvVEipUsYMQAdZbwpqkGw4QWqElxBxL3KdyyX7PLVwUSOiNlmSOwNq8pq/htYHlPxlzvXCw4Mn9G9JVTio/LWHbp2a8hIZKQQS74EsOWEZQuK7cyZxXHvNi6YKfyPt+ZU0ivy7txe92rWDwmJHh8oTrTlZlyL+nOgwJE7mavKVODyIxEeofTDxxwriK3m2hWuWDRSf0CQYxqbaywLbObGqDKostC9cPLdvzJecF1hFAhyonhCF3A3aojNyZTVQDibKY/3YgkwviftZ3vtxUnt6GUONaoTWjfme+rxC36pJpO2YH5fnAiVvC2qRVmBkql2OEWaGpLO2WxP8Hjqjr+vKJI7fuKcXJy5KORYuPA87DnOOjMVH6FZjcqx8ETnAOGzPWPeawN4fZ/NuCnT0fblow7xf27BtvtWffmOxYtPjfGRTGfKcrGPOdrqcAmhbM+11VMSeVXv6ppgXzbgNYUzva31pZXz+9b9uKw8YW3mBp2J5z4+/wbMeb6PdkUn5hY0+yYlw88OIpr3CDFIMAY9F1MdbWkzDC9W/Ap4q2e7qKNQqYCPT6hElF88A7MKMQzRXknESNl0X4zZZev8wL2Qa0Lf7S0d8/91WNpyL8AvNyw3lnH333YxvTDXO/sOKHw8/RnFnNV86Z1Xz37+9Z8lAmn3/7vrgOERERERGvTA4pwdq5cnkl8F0/nt0eOsW+ULkr7SW6MP593121pZjrGvQqg1SmaFtWLB/q4m5Pji4ExIB1Wc/ZQihT8a3DECoCCdPbBlOyk8F8dfXmqYUi7+vx8rYvYWVHtnJ1YyrtFwWrPDNiW3XcsY6fEtuI0gOsqXBzth/qYBhi9xQqkqqcfcRYd0R1mfU6YAfJrDgxBrtyifKE459eDOyyfL58em0i37u6pzFWCmzyECrT8T5RK6gq+ipA5jXhNE3ZwbFxO9Ru9dd1FKzVGItpUZXyrB8fWwzsE0Qk4wfSVQxsK+s5o4AaLyAmwuuBtAD1ibyGIev8EN+2SDSVZ3dUxv0vAimBbCmLQLxL7O3FwOp7qrfOr4z7q4FZSTeoRUn7IU7et1zXDu/E+Fx+EJON4fTOlcsr9u9dsM8oYiq5LMdUiLkb+CjGT3VD04J5V4vrfEts643i2nlN5/7SsWjxNQ+/p3Z9+8fqR2OCsYY4NuFaQy4Bbx6IJV7zZO3obwzNnDOr2ZvUv+s1a6qbdgAXlHv5CYhsjwX+ybnqVK9nW92pMO8l/WJFnyT/b113eSzfk98kge8DVfE8H+wZsNOdvbbnqN+FKZTRD+ywAt7wz0Iu8cRA/hcYC2sTcEbOSvxmIBa/BFiyYmMmf/aXnrgQE0yWxgSMOcBZS5a1/RwgWLpwfrB04R59Va+88sr0lVde+YooJBERERERsX845IKuOlcuPwtjTa3HVBq6sfR52dpt3mXbe/3KM6YnemxLqooBjQor4zaTMRaquu0DZVUNjt7lx4orErb/TlVqOzLJYkXMi23Y4eRGVhdStakg5zjWYBjKCEtVLJsuPHeX7xSCIuGMOHb/0u1dycluTWxsnSuWnxSNZbXgh4NpL+VVaqxtV3LrpB47UzYjHNXZnU1W1aRyyV15J9bohJu2ZqsOm1Td27ttV2CNrLNssaRS0I4/3Z+rPf/kVN4qJiqLVgHbVqsQ2gMJO0hZFt2YqPCPYlwKFEh6AaFtEv6nQ7TKQlyeHqoPi776MUe2YgJ30phh5KF0X50YQRMCsQDitmlrA073Q/7lWDR0ZlOjFVKNqexaIFfwrXVxJ3wUk58xBZzfMGPmyzYQp2PR4isxVaPOxVgth4KvTsdYXRX4gBfKhQ8P1v/1lKrO8qYF8y7oWLS4HriwacG866++fst7gZVXXDpuOcCSZW13AvOn9WzTrkSFdeTZM4PSvqox4nZLxon/dGtl/eWApIq5L7lh8MusE/8VGh4nom8vOomdqG5zAt/xfUZ4flCTSCS+HPP4yoBDRvPeX2tSxeaMXbYd4yKQbuoh0VFLgCm7+lpM7tS7Mf6xt2Luoddics1+qzTdhXFhaP7srzfMf+CpwR8A7wuWLjxzX573iIiIiIiIIQ41C+t44HdurvI6y4+9GhOp/RZgHr71+um1NTtnN6cW2SZ6fmzMpjNuMxUj0Canyafqygd/Grj5MzL5xAVBKDUWtDa5DJbh5g8fVfDry7VXhJgqtV5o+ZbF+lCpz4s/dgeDlcvzudwW6a09s6nOmjDCKdq2Li1KNhcQZO/ZPFBRyKbdeGLw5FF2hX1EMColwrjG8lyba1FZn8rFwlghnFTd+7one+qrN/XatYtlXbUlFESk4Y0zUztFSIbxfOiHfkFENeUGFb5aTqg84AW8pYhvpSlsBKww1MC1cS3hQaC6mwwe/pAQvR146o6HcxqqbgayoYIXWJUY/8siJj1SDmM17d9hO52Zou1scWRmPmSVY1EFjKxJ5O6oS+T6VJniB4wcLMYnqHEVWILJkHDcfrwN9gX3YSLtAV6FEfBHYsTexcDbgCoL/ezaTM1nMZXAwFi9R5c+t1DKgQswrWfb/Gk92/6vacE8HRKrAI8N1n357p6RfWtqRxe3VtYLJvJ/YzaWbO1PlB/vOe6xnhsfUXQSn54zq3kicJ0KzTE7HLlpy+DGSj/39Zoc3SNyFGIV7jkZu6wZM+y/DKjpqOVhTGqbb2HSCt0OzP/4Dese/NQv198+9wsrrIIXtv7wjm0f29lXvOnzv92QAtxMPtAtnXn/jOaqSy84reEhTE7jiIiIiIiI/cIhJVgxJeZWecmB94dOsYhJRN0BdPkazi7Y2TNjhfJvByH/HMi7/8DUYP8ysBhYa2Nl41hv7c6VxesSmcaYH1+DxdEFy2/sJJuP2XQXi4m8qtgCbtIO4lj8vDtX9mChkNRqTVWelarY5Irt2bbkglC7Hl5fmLlmez7rYOtrJjRkx9Q5FQXLC0FHeKHjFHzL68omm3N4D7nYVhy3pi8fP3VydZdz8mF4zemj0kArJn9mnQKWYKUStm1Z0g/kUTZuyznz8kqVjdVVTnzKlsJgz31sKQP+mi/qSfliONBA+ZN26OQxAv0WIHXeyakrLJHTgG1BSI8XQujbo7szqeUYa+ITGAti3ejA/3vSCSpHF3UgJhwdmuwGKxxL32hbWuWHrAuRxvpU7igRshiL3eE8LfZeljQtmLe4acG8DcCngRWY4gBPYXxGr8MEOY23LUZ96tIJ25sWzPM6Fi2OAROaFsz7LMAVl47bdsWl49LDthlichb+m45Fi52jy7unbshXfwi4EnOddgFvUuVv+SKdGEvnq4AvL1nW9gAixxbUvSYr8czUI+rv7IvHH95ei/RU4mFSqhWAp0Q53vX5KTDTUf+NY/tb64H3YNJWfbE/55/38Pp0NfD2uGvdmcmHN3i+blr+5GCy6If3LX+y/+h3f+9J7+zj6tz3v2bUG1u/fHTDb2595FP27BvfuE9OesTLBhEZKSI3lfKwPiwiLSIyVUQmiEhuKA+riPxLRC4ett40EVkuIgUR+eSw9rEiskREVolIm4h8dNi8/xWRNSLyuIjc+mKT0IuIKyJfF5GnROSRUj9eW5q3SUSeKP2tEpGvDs8bKiLfKPVrtYh8V0Rkt23fKiKPicg6EekvfX5MRE59MX092BERFZFrh01/UkSuLH2+cujaikhCRP7+Sjs/ES8dh5RgbZgx8x5MAvsvAj3AF4CjgLsdl1vsWOgWE4P3WMICFZP4PlSm7cyUO8DyJLGbxUtsbEjmnsQO/UIsOwL4UmUY3zY2rEzYlhYT8XyjZau/sSe4pTcT9j2+qTgvbmUaKxO5RIVX8TsvCCtGa5Xzz7Z8Lp/X0PN1y1ET4itEyKntJbYykImL8ycbWyz0/rgdbk+5nuUEbirrBz296bC+0govcL1YemuXJ+MqcxmMNa8OeHJXtqwDuDsI2Z4uOjFgg2OHIwgS8YQdWDZWCogVu+Pu9MzonCrnbOn0c8WipFAmpz27iLEMfhRjPf0wxldximKNGCjEH1crsGKONwojmPrDUJNBqA7wasvCti2ut4QtltCZLsaO7SskAFa5NnfEbF2BcSdowoi6GqDrUCjb2rRg3jXAORih6WDO4ft4Op3UouGLA6//L9tbs9u0L8Ibr7h0XHbOrOYMJr/rV4HRRY97d/XGVgEno/qrEe2bn1z17i/eB7xle5ezIAyotgNfVawEJoVWL8Zv9XygOG4nN8S7rdfZAbMcDWal+m5fBFwFfGDOrOZgwx/euCZYuvCLwdKFZ9zxcPflzWNTFybjole/fWLP677yRGzu0bUTgqULr50zq/lBzEvMmzsHindjrM0RLxNaP33VwtZPX7Wp9dNXhaX/C//7Ws9OSazdCixV1cmqejzwGUyOYoD1qnqsqh6BGe36mIi8qzSvB1N7/pu7bdYHPqGq0zFBjR8SkemleX8HZqjqUcDa0r5279OVw4Xxs/AVzHd0hqoeh6lcN9zffo6qHokJYJyEcS+jJKpOwzxXZmAKdvyHa4yqnq+qx2BeCO9R1WNKf/f9lz7tc6ofu3th9WN3b6p+7O6w9H+vrn+JAvAGEdljwQAAEYlhjCQPH8znJ+Lg5pASrJ0rl18EvBq4oGHGzKt6csmTQmUlZsj2hxgr5VIRuqvi3gPAUyBHZrxYHGOJPc938/4qu33sU2F/33rZ9SBwmRdPX6sS9ANZx49l/NDqrk7Yx4SqMqbenpaKhZY4uiKI5d9mizU2VDLHTY6PSJCKTx4Zm5jz7GOBP1mBu62qWNXXK9nDgVVJJ7jZshjdW0jaltAUU6fKtoj5opN6PWtXTzp0chT7gQcIcAiZNrIs81dgjCXU5/zYD4B/WUIgWIGNncEIp2DKqNg9lSlnix9iHzbKTVSWiYewqTIeVJXOx5E8XX/+28DDjhXa9ancDNviHRVx7xhMuqZuVYod3f4GTO36POYhUQekXCvoillBJ2bIe0oQkiz4VgL4ESbF0dcw/rEf6ly5/K377urvH5oWzNuJEfo3YB6Y38b4gP6macG8FR2LFn+sY9HiwzBW6Z+9iO0PpRtjzqxmf86s5p8Dr4nHeGTciOI64HZEzso1NKzc8ZHLBoCb6quDP4zK6k7fcSf5AYepcjNwY2effXcY0gvc9GDcWb65O7b5pA0MrnHsm58c/52PYe6VNwAsWdZ21kmX3DbZnn3jTTXlzs8njkxcU1cRW33r8q7i606oawd+tGRZ25AICYHL/987T7k/WLrw1hd1IiP2OyVx+hNM0KCU/v9kL0XrHMBT1R8NNajqClW9Z/cFVXUD8HGMSEVVd6nqg5gCF8OX61DVR0qfB4HVlFxrVHWxqvqlRe/HuC29IEQkhXGL+YiqFkrb3amqf9hDn9OY8ssLRKQW871OYF5S45hRlp0vtA8HgpI4fcb1fwlEqw/8GBOUuicc4PfAU6r66b3cV8QrmENKsALjGmbMnNswY2YRIFDrDksoR+lwC2XfAx79a27Dm3J4o51C2fvi2ertlujXJlX3TMNYzoJiYG8fF9RrzI81TdORcyWfeBAhIVgxK5+comiZi70rEXNG1VXaubpEvCKtfk0Q6lF5vPKdXj5TkR7xlzLH7UgSz9Qk4m48V18TKm8XddY/4m66KheG41frzm43X/6RdC7srbYzD8eLqTWOLW2d8XTQr8Wi5YblE8ezPQzsepSzCe1dEjhLgpC5Wc+eFKr+OZcZmAO8I5MPEiOS/QOWUBYqdRhBsTUfuHX9hWTXlsHqOEIeIzhvHSjYq72AfmBsby4+alN/zUpg0BK6CfX/MFaNX2N+mN9i27J1TIMLJtp8GeZHOgXcEXeCppTrFTEPgC22xY6YHWb8kOMxD5oJQCMmUGn5/rgJ9gOHY6z4n29aMK+79HdTx6LF5RirUTumROrVL8XO5sxqvn/OrOaVwBTgMeCkgXgqXl2ZfMPylRXbztiu6UldkgBuvaevcv2Py8ZMtULOG9chUybv4lfAnY01/tF10/MXfKtQsfDHmmvMEo4EeufMah7q4/9c887JJwK/e2hdeutho1JvuWdV37EKl33s3DELMcKgbsmytjGYIKyPLlnW1vhSHF/EfuNqzPd2OCn27j6dATz8ApZ/BJj2fBcWkQnAscADe5j9bsyI2gtlCrBFVZ9XGenSchuBw1R1OcY3v6P09zdVXf0i+nAg2BfXf4gfAG8Vkao9zPsfoKiqH3sJ9hPxCuaQEay5e7dIw4yZ1wxNd65cfnlDKnMN8AaQHi+eSTvF5HJ/zcSuhLrrQ8uvLcbTM818qguef7961oqkyIxsrmbnpPyEvpxnpwuON68YyJWFeHp96BSyFvbOWBBr8CWfCH1Zo7bvVNuximIgf9ipuXuC0IplU51HYgUNeT826KkMDJZv6dbQctI5PaGqq+Gae9hwX5m4K29LPXRNwddfl1mOLqJwUqiSLBfRVUFf0J2P/bBS4g0py9I+zVda6k61sKeLMCXnW49awowR1c6xUoyFYrtpsSQFxAYKbraUx7WxzPXspFPcML6ybytQjRGs53am45V9uVhalWJlvPDA+MredwJxVd3elwnfE4R8qhjIO7anK3KDRTeF+YGeAPwGY8EOgbCvEDsv4zmrgJEYi8M7gWP9ELsY2HGMq8HxGEvEGcAl+/5O2C/8HjPk+a2ORYt/37Fo8V9L7SmMmD8C8yCt71i02H6Wbbxg5sxqfs2cWc19mGwO3yrkRpw8WvyWWMDiDY0UCbj2xHz2yS+1bX8sVCpGV/vxpIdfM8jNqJ6NhpOAX19ZrPxaCutdmGA6lixrq8Rcozf//LLDf3jh6Q0f++eqPqez3zv5oXWDOmdW89uBN2KKDXwL4+bxKCazRsTLh2cr5rA/izw8b9cgESnHDCN/bHdxKSKfxVj2fluaPnLIFxJjEf3yMN/IOvYeKe1nCub7PQZj9X2ViOxeQONgZZ9d/9L1+RUl6/lu/BM4VUSm7u1+Il7ZHDKCFfh15+P3/65z5fK5pek4cDnpqlviuepK4NjAtz7/uumOpDL1F8fzlbcIxMr6m3bs7OJax7bf25UvzC6q3l5b3j8pE0rt6s15q+h5ayw/vkJyqQJO6BSswr1OoaL4pNPxaDaf3DYY+mFHgceSLm8aaSfGWrFCJ3a4pfXx/OMdRfsh7OIqR6xYslhxeXc82z+mMj54ljW1frTWLhijVV+vq7A/ErjFMZOy7pZ8IMkKr1xnutWx2rKeyx3BzeTDysAXb7vT+5fALfgDhfj2vlzC2dohY+LYmrXydhm2WkIf0F+d8OIiDACzRLDKnGAioSSANX5IVxCytqJu16VYzieBAdvi46BnF7xwrIiMra+y77SErzqWbhyZGnQxVtLDgSIhfVYh4WOS52uFWzzaQjdjfpB2YiyLv+jOl/9iydfv+uTvZ31uLMZXbFWo3MZuQUYvV5oWzNuEEe4J4GzgRx2LFv+sacG8XU0L5t2AsQjVYyqKHQmQv6HNyt/QdvpzbTd/Q1tD/oa2D+VvaDuuY9Fi59mWmzOrWWc+xZP/Lz74l/FH5NZsq+YrT6WdN43ezl/rBmS9Wx6uxmLkhkY+v6mRH9VmiDUVen6QCHpO/capG3tec9b0tuUrK94/Z1bzztL2BubMap798LrBz/6zra+tsSp2+pkzau59w8yG+a1fPnr6kmVtj2PcB6yH1w3e/+Xfb5I7H+m5bu4XVlz4EpzOiP3HlhfY/nxow7yUPl+OxfymPCci4mLE6m9V9U+7zbsYMyL2Vi3lZVTVJ4Z8ITHuSF8Y5hvZvdvm1wHjRKTy+XRYRCowL+xrMT7h96tquuQucAdmNOXlwL64/sP5DsYoUbZb+zJM4O0dItL0Eu0r4hXIISNYk6eNexuWtmDKU1LeP/resv5RhScLsXgu2fsn4OxN6f7i3W25P/pu/ue5sq6Foe1/+cmwePmISvfkRLHypnzW7a7I1V9SNzjiK1vj7X+MJWP9/YNuM252vOcU+zSkzhZr9notbDrSnyjlqdwRA0Vu8QrVRxOy3FKrdqvT02AF7opJ5dXhQGC1DzrZUTuDfHkxMfCmB1cPfqXGiVm9u+o2ZQrJpqP8cXHLi91f8DiMzGB9bzq/ebXXafUFQbzMdsj5stNSu6dcYstHWslpKJPKbL+sLpW7uzOhqzwn0KTGBuxiMuX5VGPyd67ApKRa7wdaWQypLvjsBCpznlNYvs79YFuu+7q6VPr7IiSBs1R5zDbyqBI4T4Q3WUKtZfFARcx7M0Z07cIiHcbyj2OsqX1eKLcnnGA+JuhgLMb5/s0jy9JPHb7g2Pee8ZdrHywG8kbgRC/gGi+w3rP/7oh9S9OCeWsxgjQG/BXjEjE074amBfM2NC2Y956mBfMeKzXHMdH9z8XJgD9Y2dmIyV7xrHyrUDHq/sP4y/ZeZ+lAikrb1bdoIPWnvn/qZ0b1UThmM2efOaf55mycu9aPZN60ebNuem0tTwAXAti2XrdkWduXAOzZN46yZ9/4jk/9asPGRNz+KfAJ4MnS/1+Hqrf4gd4chrp+W0/h8MOakh/87d0712OGdyNePlyBedkcTrbU/mL5BxAXkfcNNYjIUXuyOpaG97+JyUzxrJQCuW4AVqvqt3abdzZmiPn1qrr7sTwvSuvdAFxXCgZCRBpE5II99KUcuB5YpKq9GHF3pog4JVF9Js9DgB8k7Ivr/29UtQf4A3sYSVPVWzDX/s4Xm9khIuJQLBzwHkyaqtPcfMVnPDf9ZSy9xvZjlwa2d30sW50SeIcdxv+ZrdzxfQ2sd4iEG1UJtvY1ZC07N3qU668sxr0RCSf4JN31N3clOr6biiVf7Qaulql7+E7P/r+yVN8HfbuwOpdNHWm7hScfjm0qf03u6NGh7f0zmavLPda1a/7oMcUdqbjlSWCvLkix/J7tvWPPqpnQtEtyO2JWMKLcsXzPLgzavh0O5MOfDlT0+70+F00Oqkdt7s8uP7q+6mxCAlR7cNUOQ6otlW5svXNj2H9RvL/MGVXjPBLLVh5VTA5+HdFajOWhDOhe+kR++1ETYzt39frnThsbywJ1m3b4O0fW2E0xl5xlyVZgFNCaw3tVDNu2se7P+9ZS0FjC0VcBuVA5S5WdtsVrtw5WfWFEarAyZoenZT17kyWhJ1Aed3Q0JjK9A2jyQ5a0D9ZkRpX3nhyzyQDXAFbDjJl/2sNle1nSsWjxLzHuERMxQ5O/a1owL/1c6+RvaKtOXNLc93z3cfX1WyqAyisuHbdtt/bv11Z61/k+/7QsbrItjqqp9D/UUO2vdn3e4DmswDyMflWT5uvTOliWuKS5CLBkWdvbMX6oq+bMat5sz76xCnhzzJE3eIH+TJVbgqULg8Zz/9Dy+8ubk3O/sOKPX3rL+K+NqI594qZ/7rqmvauwet2O/EeCpQsffQGnK+IgoBRgdTVmGHgLcMXcr3/2xr3ZpoiMwljXjscEZW7CWNQ8jJhbgxmNGASuV9VflNYbCTyEeVEOMa4u0zER+PdgslEMZaG4QlVbRGQd5uVvyGp6v6p+YLf+XAlsGtrPs/Q5hsnA8YZSnzMYq+zfRGRTqa+CMercCnxFVfMiYmME7CxMANadqvrxZ9nHbOCTqnrOs/Vjf1MKsPqP6993zJl7e/3Tqlpe+jwC4+/7DVW9snQt0qr6zdL8KzEv7vMwsRIH1fmJOLg5ZARr58rlFiYA4Grg1vL+0Q/4YXjtjx/e0PSeU0e6TpD4UDa161VOMfUOJ0jYoe1/1nOzM1XCtyHBIg3ss7NqS0dXJkEheduWgfD4k5uDB8ssK/avYOekesee1uBX9qkfj6eSXvldrB90scrOTh+/oSO1o9jW3z+ps7prx4X+8XfftSKfOnZi/LjK2sIECZz1FtbPgnxq3E636+Jq7P+LxeSd29OFh0eVxRM7rIHHmqi8RFWKjhevz0oQOpnybRv7e0eMr6nuiZX3b7f9xJhVfT3FpupYTcJPFIJErrNdBwO7u6Guocq6rSLunUFo1VmB/a/A8WaL0IlJ0WJjHgJlmB9f1/LdVX2Z8Hgr7nWWJ6yFmHyfN2EeMPVAKuc5j4poQ8IJNgAjMlqcmsTps8Ta0JNPjquK5UfalmaAHQUfidlMFqEBSK4rEx2f1V+4ShkmIvj/MOlj7m2YMfMZFoxDgY5Fi1djHqKbgVc3LZjnD5+/5GdP3NxdTG0+x81fAdyUuKT5/GfbVv6GtpmJS5r/HZx29fVbTo2jR38onq5MXNJ8DcCSZW328pXlq06Ylr5/xbqyStvSafVV/vfaO+NTT50+OFYtWjBZG7qBkybu4tqR/Xw4cUnzg6X164HCnFnNg8P3vfgfT3zha7dsCZe19Q+0fvno7/38ro6Zv/rS3Pvs2TeOvuC0hitnNVf9+iM/Xvd/ltAdKnOCpQv/I7o7IiIiIiJiX3FICNaW1vb5h8e8L9VU9HYGbqENGFXeP/qjwGu6Yx0jy6xEjZMvv7BQ1jtZLT/vDtaEj3TvXNw8otpzRRrXe+68CVW933dz5QtErbpVnQM3jBoZvsuJ6ZaEl+qOFctPXpNal2+gYpdrSSrhJbbtypZ1qJM+LuYWQj9UN+6QtgLrllTcelVvR/ldtdXhyZrMHqYhtRmKO0YMjq/JpjpjaSleZYXWh9busK2jxwVXSmCfSWgfWbCL6awvR1e7ujFf1Gwm9MbE/NqkU967UYTR6aD4UJ+VO31y/2GFoLx3QyyMN/twVZfnzGosy3yPUMZlA+scxwpOiNlsxviWvQUjWtcBUwr4hSWsC85mWjsmmv3otdo5kPal6ji3fiWwFLioGEghW5QnqpPhGcAtAxRfm8DpjmE1Y1I4TcFUsKoH/pkuOn1x2x9wbY7rjPFnwGkoMgBMR3Ukxa4y4g01mBKtL5fhs+dNKTvAGRifuy7ggqYF8/4d1bzp9/d8uneg7vZj3zt95bNtI39Dm5u4pNnL39B2beKS5k/kb2h7FZBPXNJ8X/6GNhs4PXFJ891XX7+lcnRt4ac1QdBZMTZ4ZM6s5hsW/2NV0xNPlv38Q7H0l/tSfGdNEzeoxaPAg3NmNf/7C1792N31wIV9x5x5/VCbPfvG5Ptf03TSBac1bpozq3lzabnjZhXjb/5/+YpXz5nVfJw9+8YET0cXX/7bjx9xwVtef+yUl+4MRkREREREPDeHimCtm+iEoya4TM8luxsktLr9RHYz8DggqYGRbxXldZ6Tv8dS+zN5yWzACcY+YLX/S7IV28Pe0W89fUThU2qFl9nFRJ2XGqyKZ2t2PV6wRvh1K//cSFl/o1+9wCmU2xpYlsZz/YiuVj++Om/n3mW5BbdD+u8eY1WdAXzlkTapSFYWPhzGwj9MHeGev9Ub3NRlp6snWtUdPWF+TPWucX5lXa6pP+19r7HK+UixyECf4y3f6BUPO84unxJ3dMCTIBOEVpjAasLSDX2SayTnhgRSX52yd3XlU/W1yaxYwpcwPqazMVa1OkwgUAF4qoBf3kOmtomqjpBwioW1HOMysb0zW3ZOVTx3/1MddE2s93+QSlhPAnWZoi6969Hc215/cmolULbRieVtGD3OL34b+BAmr94TmCpMqwaL7qllrneKJXRghgHTwEdynrM4YeX/0dHVe1JjQ0OFY3FBw4yZd+3HW2O/UnIRuAhj2bwGuLVpwbzNz2fd/A1t3wd+lLikeWX+hjbBlFP1Epc0bxha5urrt0wEbi4j2Fwu2jRpeu7XM9dxDrC8X+V1W8bqvQ0DbNxVxS8zCT4HHD1nVvPZq++6+0jg6Jl13AKc2HfMmcvs2TfGg6ULC/bsG5svPK3hQ+97zaib5n5hxdnAZyu+M7oOqDvqR4PXfGnhxP+b+4UVOeADlnBKY5U7fkefN6n1y0dvK23/wZfuDEZEREREROyZZ41EfrmQu3eLdeLIbUcBdydnzHyCe/ljSKCCPc7xEmkV/3ZL7Y/nEl33ucWKCwup3p5UuqG5EOtdd7I74g07wqAmV154Y68X/06+kBvsl1hikpRvzpd3rTk8qD/m77vUGVmdnHHzvwaXvm5q+RnxWLhNpVAu8eC2dJh/+2BsMGz0qm6p1/rZJLw1YagXO8ni2KQm3K3J7bO6SG3Ku/lwEjWxUMOyqVK3fV0wMCHmS279Ltvv6nF3jmsqjB/puOfGHC/bod1OUYKaydSWW3a4GGQ10FSusVTWc1Jqh9uxONfzuXtTR5BtGOV9uoJEGpNEOwCOI2AFNvVAbRxnQxNVI4Hazr7wmoZK+bRlyVigImZ7Oy0JJzSPYQZYGUzqq0xZTE4f3+jkglCLtiWpiX6xEZMsvwJY358JXBE5sTJlfQqQScedcHbnyuVJTCnX6zHVYX61vq82MbGq16qpi9dAkBgoxC5uMKVFD0maFsx7Z8eixe/FpP+aD1zSsWjxaTXdo+uAgcQlzd1g/FiBkxOXNP9taN3EJc0fHrape4AzEpcY62j+hraG7lC+BeUTErFgoL7W++Goeu+Nrk8rcNKGOl5bnddL0kk+mE5yIfDzObOaPwWwZFnbJNupeVcqzLX2HXNyDlhmz77RwrzcnBUsXdgGXGrPvvFV5QlrwqIrjqyZc0xzF9A1YtsffwuUBUsX3mHPvrEjVFoGc0EnJkH4VzEBJ5FgjYiIiIjY57ysswTk7t1yOKZyx8nJdP2E7L2brvfczE35su4aUfmpqk5F7W8GUhxU4USB9RI4FfnYwO1W0a1KZGqfXF+75beJ2s7byxPZX3X6maUN8V1hzh14V/nA6IGEZQ1O8hvOGOnVHXXyxIrZg8mubQOkE7syfn06F1z4+Kac10RFLOsFYVc6u6ZnMGi59f7c/ceMLXtgfG1y1ymVDXUjqBjd0F/v/7El/D1eXLfLQNO4JkmXxSVWW67TKuwKunrLb/EJBu5jY64iqO5JD45c8QBbHusLiq8jsKaEqn0tD8vOpU8W+1SCJ4Gm+nj24fpKGXSwfsrTpVDP3tLp9QykaSxNP4DJF/oB4PGymHwyNBb1WiBfFS/WOxbrMC4DQymnxgMXTx3t/su2RHb1+51rthXrMJbTS0N00LapBFZhRMvszpXLJ2PKlCqmqpUD7JjRsKujLOaNSDqBOBadlfHi6zpXLn/Hvr8zDhxNC+YVmxbMuxDjE/wUxlJ6MjC1Y9HioWH1WHtoz3i2bSQuaT59SKxeff2Wud8qVHz6t15Z7cfjg7MnNhWubwqDE6Z0cPIJG3GB7voM9dUZPlad4VpgTqms6xCbArG/OWiXn7NkWVsSIFi6MAyWLjxrt90u/eXHjrjjomtX/d6efaMN0DXo3dw96B1pz77xFNvieuATmUK4Flg2Z1bzrjmzmncvqRkREREREbFPeNm7BOTu3dKISZPyr3yy56nAKY4oGxx5UbZs13Gh+muT+fpGDam3cR7x3dyUQqLvxmVsPvd4f+yDjbkxd66rWtMysf+wKj8x+KdYoeLwzsQOay09mdNy09+0tGzFlOn5cZ/ptTL3TsyNOX79YL/TXFN3X4B//GBy5/R8ENwgudRUtcMjHTcQ37aSPX3WnU31+bN35Qu+V5Zb15ivq13tbq/YeH+lnDLDLjhVxT+NzTf91otlfhoo4wmshyw3PF7U+l2/Xzw24TLe9xPd2+2OhkG8imn5qX9JJgenrdgcpCY2JXsed9duPD4cf2/KsT6KqSAVEkqsPyz+0HX0XZp3VqTikhaRXBAye2e24k+jygdPA7b35hNtjhW8pyLm3Zb3WRuzucwS3DVd1Y+WS/fjY+rsszAJsfuBbVJ0j3CDZKGQ6HdFxAcWDlK43idojOHEkrjdFvJbjHvAcozP7FeBVz/Z7s3tzwb2SVMTb8BYftcDbwXSDTNmvqh0NC9nOhYt/hWwIJWu+r/ADm7+Ue+YdwEfuuLSccGzrXP19VvuBX5QRfidS+KZn2B8hz/WHlr/bHTCnrxPUCEci+B3l7GlPsMpiUua+/e0LXv2jeXAT4KlCy96rn7as28cC7wOUxzABt4OFD74qnGHlVl9y77ZOnAk8Ptg6cJnK8MYERERERHxknMoCNa5mNySazG+k48Dv0hXbO+wQicVz9U4duiOzSR3Lot7lZc6fvII4DQvln7fusTmBzbT13T2wGmrgK8DtxbddJuofaQXHxwsBhyW9cOKB3LbbjzWGjPiqeqNI6ZmJ4wYEzTclkvtel8Bf1tvsdi2vaw7c1w4elMx1PNu3bij6bjD7F9bWNPX0p19bTA9lUg3bOhxOmfHY+GjrsYfvN9Z/z+HUVvhabijUSvACbsp2gXHKx/3eNna70zXEfN3FYJ8rWPPCbDau5zeVSPDymmWSL3jJ1ZJaI0quhlyRXdcBXauwxpYudPtocMaWPMaPfwXGNFY5QVy68b+uiem1nb9EvhHqBxjBe5o10t8o99Jn+ZY2BvtXc5Yr67WEamKOXIvJtXI/cCvxXPfJkhX+0B21ag650xMCqybgeOeZNe5I6i4voD/+Agq7sC4AlQBrwWe2Nbt/xQ4e3SdMxYTALYceLJhxszb99e9cTDRsWhxHDg5lk8e67vFTaEd3IFJAzShacG83wHkb2gbBci3ChXnA3+No00Xudm3/MOPH/6mWO4jmPyTJ4TKUVvrKCb62FAJkyzBcwIet2HOyOO7FpxfSH7orX9Z2ofvLZz79c9mlixrOwE4fe4XVvw6WLrw30nUqx+7+3rgusGPbbs87tpXfe78mVVXXDruMXv2jede846Jn+jPBl97fHPmQ7c/2B07Zozbe+FxZbuuuK2vHFiJSQf0mmDpwoPu5WPz574bG//Vy4oHuh8RERERES8dL3sfVuA+zAP0AuCdmfIdV8WzNX8vS4+8JxSvK22n70hJ/D51/HNCL3xVLtmdtdReaHvJBxPqXnyCjDluwOodWJVsf/8pmSNPcr3UMZmqjtX3+NsWzGDUj0Zq3Z9PVPf0YDBePKV63IOrEjt3Nnrxw8VLeCmsjngye+ZIEqufsDoy06zGwYkVZdVH9E18l1/VvXyyP6I8QJPbk9t6VLU+KYmJgv5jZuEwv8cd2NDv9LpVQeq9u+i/aqxbfdQTO7u6RhaaLrfK3fhojW3znME7HcKxfeRnVIVVTQmhXZx8MYRH0hTmJsNE2g3KE255z0NH0TTpSB25NfDtEbYdlEto17qEH5ha2+Xki2EhU9BCXYV9C7b/Bs9O1/TLwMQRlO2oJpFMuVZfOhfS1R/876g65xRMHsQ3q+u17uoPmkJTreqzmJeCScDjh9O4FDj1QbZ+YwQVczDZA3ZhfFTvHF3n3AlcjMnPeCFGsO6pHvgrgqYF8wqYii/LADoWLR5je+6n7SA2Hvhdx6LFqbVOw61rMlVjgJb3xNIrY+ilvyyWlTlw7h+r3MvPwHu8qo+TuwJ5eFy3eiJUFGwey9usGPWLFb8HTqs4fvQtRwTu4qYzTrjOcuwqIPP28u6mj+cqFg0XqyU+3nfMmXmbGy/73Pkz48C7gMeCpQv/MvcjLZ84cnzqO41V7gAQnDY58cm4K/+fvesOj6rY2++ctr2kbDpJgFATICDVIBFBLNixXLGLFRWxcwU7IHZuVKzYe2+oKAhRIh1CCaEFkk1Cenaz7eyeNt8fs9ybi2DHcr+8z5Mnu3POmTPnzOzMO7+aBuB0ffnkx6bf/Wm/qhatH35ZHvnDhppZJQQsrJ0GYFrNrJJrwcbfWzmzp4VrZpVkAmjNmT0tVjOrZCSAfjmzp734W+9bubT0TrCxHQAwvN+44h8Niv+/CEKIDqZl2Y+3KKXzfsH10wE8uz8RQOe4nocbhJBCABmU0s//iPv9L4IQQgE8Sim9Kf79ZgB2Sundv6IuN4DJlNIFP3Xuz6wvF8BnlNIfmGARQu4F8C2ldAkhZDlYTNh1v8d9u3B48LeXsO6HXOYdCeADA4Zq8LEvOEMS33Isvz6dOvzDkLkaIHt4xXKWpNgbeSpOVKTQU4oUSDVHkmid2mb/NmHLB2erw97hDPFlxRzg7R2ZfcO2plVm2b04avEdGfRbhq7hd8cmCgOMqNkvbm+WgzQ5vLV7KEvXbB3Zleb6l+tIxx6Hz/3CkaYsy+7G2Pt9uvND9xmhjLz2vks71Kg70cl9LwvBFouccEbM1ZIPQs8GxXM7SDPJQzLvDchvpfD24Q6TYDEEtaEp6BiVag9KugEZBD6eQ55mgBCDqFuDFj3XquxwUBESQV5UDFtquPbKJk3ZeySyexHQTZQzPgBHg4ZBZxGCjwkhJ0VU0VkXcq3rndB6LICvwZypHtzbpE5vS27unkeS69yc6XQwB6t7KKXWJr9+cbKTb9xepw4oyJHqAbyjG7hSNyBLAr4Dy641Hsxu9kmwQPo1YLazUwEMAvCBp2BU6x8+MP7CiC6sSDSIIVkvHdDY8NFXV0QN/hqDwvJ2Uy8TgJFWGO8P4ZRoPYSpdYR792Ip3OxXuWfshD6VwNFRANpW5eFszsBXY+/ZRAFQffnkvfzRb5Dzh9v6vrYm7ALgdszPzALwrr+wuCO6sMIC4CzzlPxXAGDZtxUzAXwy/s5Nu8AkuLMBXNsr3XycQWGpaoy+BmBp4xPdswCUewpGhQGgcmnpaQC+7TeuuP1wvJuaWSV3gWk9VADFObOnLYuTUgHMVGEH2EaoEMC6YGFPs3XL3jN73HPtDbufe9sl1DR9RVjw9yWRsYN28w3t55u2194ZGTsoYF22aZKa6l4ltAYGqhlJi7W8jJp4vbeAhSXrBeAJAD36jSteWrm0dDxY9jhbv3HFX+xvY+XSUjOAU8E2Ia/G23oJgLR+44rLD8d7+a14zDT6B4Hjb4it+N0Cx//IOTyl9KDmL/FA/UMppa2/oD6BUqr92Dk/B/E0r0Mppdf+1Ln/C1i4t/IH/T+le7/f2v9RsIQxwyilrb+RsObi0ATzF/f5j9V3wHnL0UVY//L420pY5TJvMoD3LEXZR8tlXgGMKLVy4L6PSuGTiB4LTA6MO0XnlFWEciFCua81Qc6JWTpe5A3BLEVdtQBtUaXIRx4rd9xgZNCoFHpDAP+wOZREQ676222BtEcJ5ddSXhdNJsNbXSEd5R/eGPbp6sp1Wds+OyY8uC+1Bq+nFHXdjaTr1i+zLz5haHqMmMJ8jt05SkLsgY4ay5XVjrpl6cnSvT4hppbv1Ebraigyxm5btprUPnek1hOJ0aR1FbwPe5wNe4+L9R/j95kHOjyKJ8EcrDMo7QjKdGRTu7G4T7ZQG4maezglNbePLUyDJNqX423VOiXfe7l2W3ckZfZTEgaqpmAi5XU32KRUzXEEAK4AUGXiNT3FGmpRQtHEJtkxJiNJ1TY0d7tr1bptzm6F9jtTnCmLFWetkQxHJQfyPCHki7QE4UxFo0d6XNz9AJ4F8DaAc0QeuWDEYSOYreNTYI58D4NljzHASLERv6YLnWCekt8OAHMXePO7mTKb7Jxir5STym6fmn1hw0dfDdMpcu1t6dcMJ+peAE/AwFwHb9wOIBMsdNnkkbtRC6BZXz75/U5VZ7y2JnwlgEcB+PyFxV92OmaA9c1+LNRLlwjnZwqx1+oTloOl903d1RC9Gixz2ZH68sk1LVtXTgCwBywjEPqNK/7olz5vzaySHjmzp+05SPkaMA3JcLA0kR4wM5LbADwE4OSaWSUULJ97Jlj+9sXx9vkfGtH9xrNt9kHVYwdoyvufOasTzBNdfkva9yn2D8/c1fI6gCl6euJ8bK81uEDkPUMS7qd2yxTVZt6nZad8CrapMgE4DkBjkKDoaStOuiWMNZVLS0Nt26vfentwDqbKpKVyaellzas2fuHs1X2sOcktgJHba8CyO6WDkYGJlUtLLwCwst+44v9KzvBnIk5Wn8N/YurmAHjuMdNo/FbSejDEiejbAI4F8D4hZBKldEj8WK/4sZfATI2WEUJaKaVj48fngGXtkwGcSiltIoS8BDZ2BwMoI4S8AuDp+PNUAbiUUuojhOTFyz1g9vNnAbgLwAeU0o/i9b8Olkb0XgAWQshoAPcD+AzMJ6IAgAjgbkrpx4SQfLDfhgQ2x02ilO76vd/Z4UScrP6g/xfurcRvJK0a2LpwA5gm7t8ghHjA+iI7XjSdUlp2kAxYW8H6ex6AnoSQcrC1YxGYhs8HoC8hZCDYOjM0ft8bKaXL4huP08HM0jIBvEYpvSd+T54Q8hyAIwHUg40nOT6ePqOUvtepvTxY2t6hYE7EL1BKH/sN76YLvyP+NhLWOClNCLnqB1oDqTdzVCgBMNJSlH1X/HgvAHMojHoCbgXY4P1ItrbmmSOJrxBwoyjohQCVDU4/NWbxnSPFnFEKYziFkUkpNzpka/napjqLJNmZoInRGaJqKYrYWrq9I2x8IT+We3aqIO5yctKFCYFu/SP25h7UIE/sIS398ozUpoDYQdb7/E8V6wOO0pOahu/S21x5QuIWHpxNUKyJdWLzejdnGretRv2oV5J1ZCJxemI6Tzinb2Msyp3QpIf2ZdusoaWytz1NcRT2cjmIQIU3YooxZlut4hmVmulUHG2vqBqZ3NgWC6cnC/UUXIHA4VtCUAX2Q+S4qOlMQ4rJIEjkVNMN35i23X0kzUmWINwO5khzikGhrnt59SchMXl0n1MG8U0Rp8tm7HupTzryYaBoF9fy6ZEFp8xv2bpyIYBj9rVrwsY9ysaCbCknJ0VoMAxkNkXsjnR76CgA8zwFo85r2bry9AYTjkhQcbLZwKlgoZ0mgWXRuhbAWZ6CUXf/wcPmL43owgobgDerdL7Px5p1GYDZN5qCHgBDfUn1XgBv2oKJp38ecf3zZEEmBLBx4PuAxdF9BkAa2OJ5lHlK/jUAsOzbim5jx+TXHuReNwH4wjwlf1t0YcVbAM43T8nXAOCRqx/55jlv4tZdYdPFAOZYOT0WMfh3wWL5lgIYoC+fbBxY56FQubR0MICEfuOKvwGAmlklHhCMAcU/wUKnPQPgQrDNzrsAnqRANQEawSTzdQDOB3AzAD+AXKVHWra0p7Fvzuxpl1Tf/eQseUgeCdjNS5wUhdc4cNGjQXxpAp6Hon7+boevPCGsjBN6pG+f4+JfCHH4dGUbzoKq6SBkMgTeJ1XUZCr9s7eAkGf6jSsur1xa+jiAPgD2AUAEONnKtAxfcY3to8vSnB8kccKQdAP9A4FgbLPTxDeZpS8vjiIMFjHDBRYNYgQYQZoAQOw3rnjqz31vhxuPmUZXg5GUA1FzQ2xF7q+t9yAmAfdTSt+OE9YFlNIH4+ctA3ADpbScEDIXQAOl9PGDSFgpgFMopZ8SQh4EEKCUzo4TjGQwwqETQjYDuI5SWhpX7zoppdMJIasBzKOUfkgIMYP9RobF730aIcQFoBxss3E+OklY4+3aRil9La6eXgNGkOeBpYF9PZ7WlaeUyr/2nf0ZWLi3shqH6P8p3fvl/tp6CSEhsE3HZjBt2uWIS1gJIW+AjYEVhJBsAIsppf1+hLACnSSi8fS2iwAUUEr3EkJuApBPKb2UENIXbC7sDebwez/YRiMCFm7vYjCNyW6wPi4nhLwD4JN4/74Uv9d7+yWsYCR1HqX02Pj93ZRS/699N134ffF3CmvVH8CNoMRlcNoisMX0arnMK8pl3gmU4vHdCobrnHoe2ORycsTafKU5kuiJmvwf63zsQQJyB0BkGKTUGk4Jfb2j7bq9Lcp5uqD4DJP8jVW1z4kg5u9wNFAK/SFFCh0nRR3jz4uMPqMgmrM6O9TjBmco5VsAt5pDSfMF1dQ9j6bstshJH7Rr+kZXgn5SK/Ub64KtJBLiZF3nUkXF+rlmipyfxtmdZoiWITnWBqvdUFToMmfrMAN4mJfUjiyr2QOgbaA5yZPk4u2yiuBGn/NITec+tZu5JYqjLQhg2NtlssWTwoUFntslxwyqanT0ZjRsAXAyKKZx4F3gUMnp4iuGLjzVXUvuiMj2tWAe+pcAKDYoceScNfaSYyb3/DjTHooOSd3X2icd5wBIEGVnfoaacfrnS+rywqo4wKD4MiNReGN4L9Nos0T6A2iIaOILNlHZBRYKy96ydWWup2DUhwRY/sXKyO5Fa+UTASwD2xlbwaRPz/2BY+VvgTcUS0G1zmd8oVkuAHN2ewhscl2aftqExQltmZmSYjmll6uppTY9YDWgG4oUsWqcMihs9d3lS6xv0zntOI1XAgCw7NuKpAAxZrnLS187yO3eB7AnurBCBJBlnpKv8Ue/kcgf/UbfWyvTg7vCpjEwE2PwlZaLPhzuzdp6R+bFW+/IFAEM1JdPNiqXlh5sofs34skJ9uMfJBhx1cwqub9mVsktANZxKYlvcx53DEALWC7xcrD88oUAlqrZHjdlEtwFYJudFWDZ1BYAgJaTekpk7KDplV8srdMlwVnD08HL/P55N/LRiVRV+1jag0sQUx+GJLYd73Q/o0kCd7RGNp0Yw8jF7Xif+EJTYRgFXEObgw81FOrd9uxp4sg+AA9XLi29Aiz2LYFh9JM2VU22Ao1qOKJQSscaaYmu4RBS8gy4CLAw0+kQJ0DacWEUfQEMijS3bQILHzcajGD3ADAOwPmVS0sLK5eWvvrzR8VhRfYvLP+5kCmlhZ3+OmtTOn9+HsAlcSnWOQAOJdVTwCSdALORzu107N04WXUBcFNKS+PlLwMYQwhxAMiklH4IAJTSKKU0Ej+vV1zidy6A9w+hXp4AYEZcwrccbIxmg9kp304IuQ1Azt+NrMZxuPoflNIAgFcATDvg0HgAT8Tf5ycAnISQX2qfvIZSujf+eTTY/ABK6XawDW7v+LGvKaVt8b75IH4uAOyllJbHPx84ng7EHgA9CCGPE0KOB7NN78JfBH8LwiqXeU1gdmuCPZAxVtDNmyxF2d9YirJTZEv7VNnc/gAheCZMyWyDV++hoDk6URQD+j5NiObwhpRFiVFHYdykcfKnFEYmALVPD/2jzCQuICk2URejyQavPsdrpmE1XNtnhqg8q1qC/+SouIlQbpAVFoOAFBucVmRwmocDP7MBoa2IWdp8ptZbEjXXZ7311DpikUd3d1oEe4LhqzNCq1QpcqwK/arV8GZGoRIDNLSRNry5Rax1b/cFt6ysjtyxDg3fBklMBsUQj2FzeKjdlxJNVYfZleuS4DquT5a0HUAZgB0XjnI3bKrgYl49pNcEqOIL6ysaSOA0sIVhnUlxXi8rxogYYrN0S9Dq0dx2E0czAcwCU5uVg9O2pdoiBMBLxOCPb/BpozWN9gYwTLUFPmkMOxY6pciTdc1yn4rmhB4dUfGYxveXfyB0BB8EQFqC9Nrvt4V6ASgCC4N16bayFa8u+ix06ukjrMsnDrMoYHasJgAXeQpGhT0Fo+r/2FHz18PcBV7n3AXe1PhnvpEKyz/QLFcT0EfApNAXPBpzdHs05rj+iQXVw/cZXPajMcfM7b70JhJ1jaBUaiQG51OlaHdCuWJTMOntmCmcE7S3Xdb69rffjh2T3za8reGmtwJJ7vVfViRGF1Ys339v85T8avOU/Kh5Sr4KYEy8+H4ws4FmAJmI0iW7+yWOBHBHy9pNYwFcufWOzLzKpaVtABZWLi0dWLm0NK1yaemBixIAXF25tJSPf37PvG7XgwAGU+B+3Sy+oTe1f623+BWwFLYKgDsB3ASmZs0S6tq8am7q4FCC9dXI2EF5AO4BIwnzwBbUydZlm6L8vlZeT3Wd02/lzpbTy70rb6DmxgV1kUYYxmJ0hMYDsLrX7XzvWJt9px4InXrj2uokJ8VIKnArIIpVYhKXrjXKrjWJxeI6EZPBFjwK4IFmgguudXGfKAN7aAB6ClZLiBBCoRv1vEGP44BUO3ACQrKPN4yeHNCLGkY3wWb5FExivAvM5OKW+OcwgMlgquW/Ary/sPz3QOeYwO+DRRE5CcB6SumBToD7odL/qP50/LfpWvgg5/9cvAImUb0EwAuHOIeAqfv3k+9sSmklpfQNAKeAmSh8Tgg55je048/C4e7/+QCmALB1KuMAjOz0PjMppSEwdX5n/mH+kXp/bp8fqC7e/z3WqezA8fTfF1DqA5MSLwfzwXj+Z967C38A/haEFSz+5P1gpOvBmLnjxpatK48NrKk8HjBSRMUSAPDqIBM9V1Rt12lc7FqOCqJFTjqZM4TnBdXymqBZno6ZAmfoonKPAMnXwvkb7IKjQ4aQKovB8d8ZNW++Iqz53KHb1yUatmBYDK0hunCNYg40UGJkAXjHH1X2fO/1GVFzu8VSlL35C/u60iCN9WuSWiJern1Sc8BzktssJGbKmd0KOwZcP1DuUw6AOoJp4wcqud/tQfs2nRpZaW0pF6dykj2W3DG4b4ZARpIsI1lO/dQUTtwAQGwkwTXrrTtS9XDS2ztV7m4A1ZoBe1jlhywStyQOyTN9HuKi1U5PZI3HJajH0T4P7fEn3hKLmRJf3lJ1rlnkQqLANTTq8us+MdTdIsUywNQ0xygauACNXQhgCwgUyuvLPE7eKwjkbLDdZH53V/vodHuwplc6T/t7fMWEkG3OHE/6juSWQFQnp6Y5jZwe2cmZgZgk6Qa6AXAlOTnrmUXWkwGkewpGPQ/gn2CEI/SHjpS/NoYCmDV3gbcaTMK4cxiv7B0tKPvAxvgIE4xbBnGKaiN0tp3QCwEoPXh939om6wsBSjJEzfylJeqknGJWNYFrEjXTq7Jsj+mcJjZ89NV1Ysz8/PAa8khGtD5onpJ/NABEF1YM7dwI85R8AwD05ZOvBFuEp+rLJ3u23pF59so2DB0/b6acNKj/IgBXA/gITNr5wY0OXD3JjQKwsGf/hX7jiqdal22aUDOr5BGxquFuynMiBQZpyc6vwJE+8thBY+QhPdfLw3qXGFZTUqxPVs/I2EFyZOygf6i5qbdGxxSkqKnusK+9vcny7ZaLwSSrZgDXKrmp3cFsDXv59ta26G1BW3hA7hn6UQVDuhlwEJHfZNitMXjcqwH00osKFvAuezMEIQUe98mkpYODw3oxOHJzzJyQvLhfrqe/xvU6tapFg274wWyuS5MonpkdxJ0g5GsApYQQA8AGob7VxDf5NoARz2xIQioYsWklHGeSbNY2ABvAtAnrwOIjjwNL0DEYwHOVS0udv2nk/D64HUxd2hmRePlhB6U0CmZ7/BTYRmU/gmDmF7+krg4APkLIUfGiCwCUUkqDAOoIIacBACHERAjZb7P5ElhCD1BKtx3i3osBXEcIIfHrB8f/9wCwh1JaAuBjsEgqfzcc1v6nlLaD2QVP6VT8FYDr9n+JR2UAmNZtvz3zEDABB/DTY+E7MG0hCCG9wTazO+LHjiWEJBJCLABOAxPy/CIQQpIBcJTS98H4xpBfWkcXDh/+0oRVLvM65TLv+bK1rQrADDA7mTRBsX5vCaT05zXpQUGxDhIMSx8A34A5oqSCow9R0Gc5KlQQg79KNQcXa4KcaoixZaaYK82Anl9urupt8OqtlI9ttsYSEo4Ljnz8H7Hhx6lSeHtHWByyY5vlpX1GMMorZqfOK+0AhqePy4s5BVPVXi183sLNL3x/oTb86ATO8mie3D1QAE96tiOYGuRiCYql472wo+GLkLN+apSoqY1co5osp17QH6kvNQYTehDelZyueXyFkbyaBFHyGZQmfFnh6+s3tbdovO52RpKOHBzps1S2N32eZfc/BeBSnqCXSOjaE0ivjVEORZvK7GdkyEndlJh5eEQV3+jh8FsdMA3s3ztLXxemVbqB792661WtNXG1qopt9UHHJACfSTwSE2F9G8CbAJ4HhcukW3oBqG7yaX3kmMFxBPbtpt01HVQ1dEoedJqUftnHDBg23NzzzohBXxU4Y12yJdos8XobIfjGoNjOEfKYy8ol6TptvmveV0+CSV9Hgi3Y/68xd4H3xbkLvANun5r9DdiE/CKAywB0rNVNI5Zq5idun5r9Ui9OfYwHrAoQaKD8jSmX9Z95+9RsYzCv5p0pRU9L4mglWErVapMubrPLvElUza3piq2E14UdAF61h5L2SZryGDqpWze2Re7mj37jpoO1TV8+WdOXT1bjX88A8Fbl0tJTOUk8DsyJIgTgTACZZ0bx7j4eK/qNK16z//qaWSUpNbNKJgCAPCTvfgr0Fb3NNYbLykWH9b5Czc95OdY/Jw/Ad7CYTuabfeOiI/ou1TOSBDA1+mtq97TVIOQZPhJLzuKtFxDdWJgze9ppsbyMBfLQXvr2DPcT70k4NTJ20FmWdI/fHIm5Q6GQw1DVHCjasVwwehLMYhU0/Rgo6mYwx48gb7d+Qz2ulTTBXgZGNnNBuLJjI7pmAbqBEBcIpoGpM//JA2e7mWR0DFjki6UAvtayU5r19MTPwSRR7RCFPeC4CjAp0c54n2aDzU9P6qpKdEUJx88fBOBGMFvdPxVxx6rL8R+pcg2Ay38HhysLIaS809+PhbR6HUwK/VWnsmcBfBm3cf0luAjAQ3Fb1kKwTQ3AyOu0ePn3YLbeoJQ2AajEf5PlZQD6x9t9DpgZkwhgMyGkIv4dYBEptsZV2wVg0tq/FeKOVT/o/98aJeAAPAJmZ7wf0wAMJYRsJoRsA5NaAkzanhh/x9eC/Y4Ql7qXEUK2EkIeOkj9CwBwhJAtYFrFiyml+yWoa+L1bgYz+fg1Hv+ZAJbH+/k1MMFLF/4i+Es7XcllXofGx2boQqxQlcK32oPpAPPEXaFz6tcxiz9mDXveBltcbtX52GuyGPinSbUP0UW53BxNrIpJASdHBcUg6nBJca4mIDt1og5v5gMvf2wvO21KaPxAg9cAio8kxXEygN2qTstXNzb17tlbMTwxz4mqFL79X4vbBp4z2nplmt38SaSDkA3i7rxj6YjFBFzUgGHnwG3ROOVen7WRuqNJM2JW/1MgqO6gUXd9qx7qbkm57D3nt8uGh4c8nKhJZ2Vytt26EKOKJCfZwknCe9vq96YOb0lKp868RGoLWzmhO2LSHpmPNtlg4gxBvaGZ0hccCskJyFrU4TbOt1IpQZLdJVVC/QuJgnSCjRN1AO+HI2JBsppsjlrae2qymGOWyNWqJXiaqtE0QcAKAzSZB/cR2GTcCkp8IPS9mGqUmAi/FzzNaCNhRdCFFidnEghBDaV0TDBGeKtIRI3yFRGVz3aZY7N2tnv+6ZCi32Q5guvApA4X1/rN3hSH+l7WoOF3tmxdebWnYNRTf84I+nMRV/8XA7iKUhxBCKaASSuDt0/NDs9d4B0HZs9ZCKCXC1p7CPz9OogGNunefZEY/rCNoz22JsjnnOEz3wJmmyWAJR14AECeeUr+vyWo0YUVR6hi1FH06e7uH5/jPs8skNr00yZc8nPbXLm09I14/Z/1G1f8SuXS0uR+44pbK5eW2gBERiVhCgB503ubvgaTilwJpnI7BUAFBbLV9IS3l/FGj5FNoUSXqp+jJzq+iRXkfg+eW67LsZfUUPiawf84/fUtn36ZJFgt3cCkyx9Yl20yRftnf8nJMauWm7aD39tYoKcnioauQw9Hk9q37w6nDexXH4qEk60Ox6fEYV0PprLbDqAPKCXQ9DYaklPbvfVPClZznatX7pMA9sV8HW/6Kne1px059EolFPEasdgd5qSErQCuFKr23aD1SFdASBWY49XTYIvhBDCyqQNwgtnfUmh6Kgh48LwEYI9/xx63OSkhYk5OSAHQTilNBWAQQhaBLd5RAPkAsvuNK/7NoZj+zoiHPHJRSu/4E+5tBXMOGxKX0HbhfwT/38KT/X/FX5KwymXek8E8/PLA1Grzo6aO6zmDs0qqoxVMJaMDONaAnqOYgkvMMfdWVQw/UCWbV/QSDIkQbqvGR0dTork0k/w1KEm3RJLrOMrfBuDqiK0pUePV3lLUXgjKvWxS7WdSYpzBUeEUhSKtCgFPecKqpwdq2aNqhBbFUpVRs6NnxcTz6RHZn59dsjBnxJB/jLjsnEQAWRFLa8gqJzc2C63O763bGieo/cZHDMNrNmuZrUZYaSPyq2v4GplqiKbqzptPRMH7e0nbST6qNbnl3EiOvaN0Mb/91UKaMTtdTZQI5QbpUixlE214JltPPsuiWavduuOKFSbfjKxmaYAoElVNaN/mIdZi6Jy0dHvgveF5zojbapQTnc80RxKHLqqqu794gOkcAfxZmiI9tpu0nO2SzQlaQji9nnRs7U9TWqyQlphjjhelqHNx2NWwoR7+Xpl6YiJAfeDpYqKKA6mgQlCtkipE+vhipiQzr1gDqs0fUcWM7i5f/Z6OxFhPd7sfzEs6AYASUYWYyBlVIm+8DUD3FIw6mBPQ/zTmLvDawOzkRusGQjEVTrdNUVo4ssKliAqAO26fml03d4HXkwQ9gwP9UAG3uAPcSwB9+0opfPELiuXcq6SI/qZqsd99RP2re7YkzgDQDcBuCixd3x3/GLoXX5in5N8JANGFFf0AXGCekn87f/QbV5w3wPzOfZ7GsVC0OwCMBQvJkwtAzpk9bevB2l25tHQ2mBRkYL9xxfs6H6uZVZKtMvu/aoUnl3+UmzjkvKq2u8CkHiYAV1OCs6LD+rxxViLZOqopKD87adKy3U+/cYvaM52DbphM63b1i43stVeUtwViYv+7wHFVHM8pACRLWcWFUPXbYoU9NlGT+Dq1mAbzext9hsf1nljV8Cy1mW2awzxZN0smqT3YrnVPJwCSwOaCGIAOyMpGBELZ0fqmBNotNWbxJN4KTX+A6kaLqsaCkt1+XLixRRJktULUjc1cXpYBYACAVDUacwuSGCIc1wA2lqvB7PGCYGF1xuA/jjhWMMldiqHrbo7nNTCTmkSwkFw98B/b1b1gRNjcb1zxfmn2/zsQQj4E0BPAMfsjAvyB9x4PFq7oMUrp/D/y3l04/OgirP8/8JcyCZDLvDa5zPsumM3LCWAOPSkAdlLoEFSrG0CaKoangRnulxFwNinmyNUoMkXVNiRTjPE+LnwfR/kbdF6RBc0im8OJRZZIkrwb0feapI7VihiazulSphR1pvKa+d5KrsmqQX8kYm/WNUFu4YDemcQyyE+iyIxk9aIUG4bn2tInB8YNBSXfjHvrmg07b0pIiWn6o1FVe/rdlaH317c2Wixm7bbRRo9lhqR80x7UulHQBtEwKUmKY3is1tE/j0vcPl7oqelirO8+vu3OenHfXX5nhdbAdZwxjuZlZ8XSyihHK3QxtgeU5OSFeg5NDKSZXLo9gRJj0zCOe9XHdThszqicxtmLPlkTcdRE3JdMGGh/c6MRcS5RG4+t5ls12dFS3iHrt0QU42LK688s3OhdHDUHm9JcQnuW4fIVqt2ra1Q1V4cxXDWFnwy7GjYCsLiozepXhdrv+KqdtfAfQzmtJ4ATiCrZ/FHJ64va3EQXtRRLSOnh9pHt7R4pxRLaCqZmS6MUVQCcVlFLEHkjB8xebEXL1pW2H3T2/zDmLvC+BuDCmAqbP4wGnsO9VhPeHk9UR1nv5qvBAsy74qffzYHO5QncEqE5AE65WAxvMYGeR0EyRYKvLpTkh/ZsSTwJTI3XCKCQANcOqsFuMAnnfuwA8Gh0YcWx4QsGvfbK42f4oWjfAVj7Zs+k/mDEcimA52tmlTxdM6vkv37/NbNKbADuJOHoDfy+Nq1yaWlO3++/drnLS2+omVXyIYBZIlAmAmfZdPrWOXvawgA6ACyPFeQOk4f2SiMUN/c95bgHt4ye8PmzkyYtq5lVYhLqWv/J17fGIAo7e8y66lyi+bOIEb2GE4VNHM/tJYGwE5Sul4vy75SPHphg2C0tMOhOAM8bKe63qNX0kjKoR4PaI204ddtdnMtu1bqnV4PF2VwGRsS3AJhEwtE0JDqTzH175Fo8ibkAPkIo4hIa2o+WLNZTYRjNFk8iJWYpJ2SoZ3NN7ecB6ANFlUSDCoTjbNQwMgFkIqbUkq17FgNoj9+rCUC/eN8dBaYy3B5r8+8BYAdwpRqWNSUU5uLv5XNKaQNYyJ3X8DeOe/17gFJ6OqV04B9NVuP3XkIpzekiq/+boJS+1EVW//fxl5KwymXeowxO+0CRgo+bou6dFLSKA3cCgCMpaAEAjwGtXjPJLVLM4QDQTkB6aRSlDTqO7CZg5wbTTmsvwVngCKf1VUyBBzWi9jTF7C2UUE0Roj1MilPggM+iUscluln22gMZpe1i+yQLlV7STeFacPqd9mDG02AxRI8GcFnY3ljL66ZbzXLCEwZRV6mm8H0NpuZXP26oO+p4c7/zed7Y+O1G2XTuyIR2g9OKKNH3bKmPDR+QI50InX8/HFPty7bJ4ZShrXVDaFY6B65tMd394hja/WQLz3XTFPFdSTDG7IsaQrOp+fHBRrebl5M9C9r4jusm0r67rYqzQJfCX0Pn8blYcf6Jet8N4JDhi3C29fXivsG925PtML1TB38eDH5EnuH5Jrlw2DktW1eeYFD6nKaTNBAEJUMIhyJ0ndMs2tuitjv8aphWNVvvG9+/fS+Ao1WdRLRIYoFgCpS3S+1TE3TXvRzRh3/vjZX3TEo6SlZN/u6u9lKehxsG9LDGjbRJhgIW8/YiWePONfGGyBHIYIv8/WDqUL+nYNSnf8aY+qMxd4E3xaA4oy2I+y0SXgpFcXqaGxyAOWAG/EkAbr19avae+PkngJHQyWB2kNfwoNsni5HbPJzxMpjaPxWMjN4ARoyeX6yKZzSnBqQL2i1nm6fkL+nchujCiis0NXJ+W93K6Tmzp22omVXieWxA2psv9Uk9c9N7m54FC531DzAHhsvApK8X606rldrMe4SG9v6UgKrpiXl6k+/F97onkQt2t34O4PLrRuWc9/jKmiCYSUIJmLd/U2TsoLBUWVsgNLYrObOnVQBA5dLS8wFYpS3VdSBIVQpy+4BJQ08HcDdYbMyxiETzYDV7ARBo+ieIqdfBZt4BZjv7FJgD0x4ATXxDe5busrpgNa+Jt38GWKxfRoJUdRIEAZTSFAq4OY5rA8CTQMTO1bVAM4t7SPf0VHF7rRGxCETISbcTQgQSjMhE1VcoZqEOHHehYDap0PQlUZ//SFNy4rp4TM7e8fusAnPqMMX7zgdms/2ioevjCSEq4TgBQE2wpj5gSU1OEMymVf3GFe+33+tCF7rQhS78QvwlCKtc5r0BQIiC3qsTtZYK6mqdVzN4XSwSVdslsqX1SLOcdBEB8apCBKJmLdCJ5icUNgJeojCaOfAeAJxBEeMI3GALfA5YiJnzDRivgBincVTIp6DfaVz0ZENQnzUpzvU6iU2iPD2e04SnQDDZdmSPLLnMOxFsgZoIYCIFvQ6gE2VbW4pJdn0XcjS17ZON1N562kuU0LyX9ZWnJdiF1BF6luoSpO4giACoh4FcULIGUfsJujVgW42ajkJkritDzfeZxHFeLyQJomYSVvHV7+SoqaZ0SZoXQmzV12TXRyES23FF4PiIbGsZ30GirgTZc4xqCkZDMl+5z7bveDT36pVtl+9yEKHWsMijwDL13CLIjmRBlz6L2tuugoEWAzT9vbXBooHu5AeCCB8xrJeYBoKHQwpfGoryi9NcykiwYOeDwbyfP1y1I/Zx96xEs8jHZNXsI6lwGOZgst1rbqrz8Lyd6DyooDcSgm66Tht4nrSDkbEVYE4JX4CpTF/2FIxa+QcOpz8Fcxd4u4GFXnsHwOv+CPq4rTgewBIA026fmh29++nqZIU3Jsy9vMcb8Ws+v31q9onxzy8BaLnRFHx4hSoNWmOYvu5G1FvOEKMTeII7K1Tu3Cze6OviYAYQ8htItBMq2C8rOOJg7am/94VhmhJKzZk97bOaWSU8WIzXAFi4qAFg4XmuBOurGID+lOMylPzsS0xbqs8DU2mHwAhmGIwUpoGZBLwPFl/xTQDn5syeNmz/fSuXliaayqsu4n2hTyJjB40Ek0qu7jeuOFi5tPQOMKeGGjDitxxMYjmYtAWuhW7soSnubFAaACECWJKBJuhGumnLXicl5C09xT2Hb2z36Sbpa6VnWh5vkt4AI7P/BEtyYIAlHjhSpZQKhKQSgKqhsI03EORUTYLbISGmhAxJ4AnPpxBCvlLCkZ68JCXwoqCDSbJLSDByJTVLGyEKYwE4DF1v4njeC5aK9RQwJ6tVYDbIG9RQpF6wWdoMTbuagLRwopAA4A4A4/qNKz7tFwynLnShC13owgH4000C5DJvOpjE5TwK+rUuxNYSneN41dQmqrYLABxl8OpagCqWouzRzhF9RwOYQCjnVsXwBzoMkTJiGokJQWujBjuALwF8YCnKdoDZuw7lwJ0FSnppUFwEZC4IadGF2Bchx76hiik8RCcqojZfftTafiUAKFKoJ4Vxbsi577mQq/4f1qKch6PW9g4par/UPirvqgWfhd/U99lFXjddZPDq1mPsWSVjkLM9Rc50hXXNASALBgaBIt2nmEZQa9AeIbGXLETc/R2qR44jPS7vj9Q7RAh+8Ab6kORwssQXmUOJUyQqrNKpNvQC44h3dCE6HSAWF8zPfe9t/VIwxA8qbXs3myAMGmZynO4w4V+bW/TczY1K2rKg+tyr3PqXl5PK86NSx5NSxJVKNHEoR/lRQ9Od7/fqrt84srsjBxxuBkHYbtKTFCWS9vZ3Yc7ekTnY3pH5tCWULIHijYx0QyF6UBEAmgrHcwBE2dLeZpBYaltUcoM3bELMstIwILYEtIR4d+aBORnFwAhOxf86WZ27wOu58QHvrZqOZ8Bimj7nj2Cks4eyyTUkej2AptunZkfnLvAmfdynOT/ZEbkxurDCNHeBdzSAh+cu8L4yd4G3O5j5xGwAR48WlVtun5pN2ylfQ4FVn6umWb0F40ory6I0BMBQDhjSQckh091m3nnpWgD7amaV9MyZPU0HcNPaZNtxjw1In5oze9rmnNnTdoGlq1yXM3vaqQB0Yhg39j7nlHfAUpWuA+vDMWDRN5aCSc2ngXlI05zZ0x4FiywAAKhcWnokgO56ktMFINRvXPHr/cYVLwEwuXJp6clgySMsYGPkegD3QNdPNW3eM5gQIlC7OQeABFXLhKo1cf7Qq1wg8gp47sXYoB5fUJv5BT09kVMG5xVrLus7YpOfA/PeHoOwDGjaeDDnqJEA0r6WSEYlDBsMI02wWUXYzE7qsFp9lVUi5bmnTbv28cSgFJo+TFL0RE7gEymlKWCbthGUEBME/ngAtQA0NRg2g0mUe4IFIF8DFhZoNYD0YHXtBD2mTOJ43gyCDwBsBfBJF1ntQhe60IXfjj+csMpl3mFymbdz1qMPAfQ3oD/JgVtiUh1HRu2+u3hD2gPm7NDTFkpfR8Atlsu8D8ll3jsAXEZh1BuCelbE4D8O6fxDFLRB1KyLMkQ8BxYmpSy0avfnCh+eLltatypi6FsO/CwB0hIA/xR182KA3EcU80kA3SupdsUcTnrH4LVRcpk3SVSssxRzcLEtkHaSJeAxWraulIjOm6NW//sAcMOonlfmZhnHALhHUKz3pBkJ15g5fn3M0cJbOaGUi5mWgpI2EAStYixCKb5RoD+fqycN6h3rJrXRyPb6WPSRFiOcLSiWKB+RLtrdGl3yrW3z5ADkI6zE1B+UXKeKsg+ExihFxCwRV5jSiuE0e3IPJF0akzr2Grx6to8mXLqpMRBN47m8SUrh+pEktwaiBkUMc7qkrI112O5cG2gZ+O7qwBfNbcRuCiV++c3m6IsGpfelk+RIboqQHTP5a0KKdv6K6vZTAMz5Ypf//vaA9ojTrBEAH4lhZ40GKnk4ayzZosQ4Xdiqm+RzOIIVaQliGdgmYVv87zIwKdovjoP3d4K7vHToSwPrzzUJ6KdTUEVD33AM98gxtFCB1jbWkEcN0P3ZVjadVZl29LEBYXuzwS0Cc2wiYF7kSQA6bp+a3bFBExOejdliS57etf1KU/juBsqf05dTRjQQNFRbtHIw++4EB0eFoFm7L7qw4vrowopjD9FEF+JBvHNmT6MD2sJPX7Kj+eWaWSW31cwqOSJn9rQwgJdqZpUQACMGnTnonfh1EQCtObOn/eu1vOT7v01zvDHozEH1n2YnSADW5syeVgngs5pZJRKAO2tmleyPm9gfQIPWzfN0ZOyg5APaMRwsDm0ZgNvACDEPjrtYzfIcQzT9TTCpr8q1Bq4AIXdA1XuTkJwJ4DkQcr2YvHaCtfmVB8N1jS9ySU47nNZKMIemDhh0KQjZp4YiGbF2f08A9EQVI/tFlEnQjZ2EkCZCuJW8P7wzMTkJXE3T4mi2xwwCNSbLQkzXQpGWtm8opWrM15EA4FjYLSkgJBry1tvC+5q2Sy4HRw3jfjD7+lPiz/YCgCsA7Ess6EMEsymTcNxqThDGgZlvcJVLS4+rXFqa9QuGVhe60IUudOEA/KFOAHKZ9yUAQ1VBXhHautJt78g8Gky99pYmyI9Imt0A8Lk9kLEGLE7fNjBP2+fAYrW9oXPKMIPTFoqadZEUc64yc/RpjYsWGlRoMHhlJ9Wky3Qheg0oqTZEba6gWubrIE+KqvVDAKPAQuC8BOB6cyQh2yd0fBC2tV+hGXJjBmd9Too6VJ0o9TK09WESKE4SxDLZUKZKsmOKaglexOnCnS1bVy6zCqlzzFHPPQB0XYi9qAux8wCsX7xR3jwwV0xKd2O8GHGFRcPshaNpmxR19ek7bNSqV5a+92Yfm+e8bmruETE+tlUTOwbA4KolnhT0SDBf7ScOnwvmaCK1Pqe2Obh6n67IFvlDzpQ9q1dmSxNHqCmskW9VMZTuttBbOJBnjsppspCw/V+9BPE2BGya4A4+C4IdHCG3fUOqUnppvU+0ZkbSBnjcH3o7Qid6kgxfzzQh8t7OxtVZefXNY80Dpvo5fyxmVZ/YFw7ddgSxtlx5RPpiMPVtFMBGcNQvcthtwOgDwt1uiOq1YHEQ7wSQ4ykYdQb+EwdxPzYcxuH0V8CuZrvS7rBgNVgc4HkCh3G2BJz9fHLL92dWpnZooj597gLvt2DkZfenmlUfwitL04nedq4UyQLQtFYTg9/p5p0PLaiZaRaE+0HovhbKXbNT507yU+6CYbz+hQY6govxg8FU7I0AkuskozpXwT8BuKILK+6ctMX1HoDqRfOzKADkzJ7277iW7vLSc952mknfjqgBFofSHz90DYB7Tzlt0LREHVe6y0s3cpMGvjSmISB/BOChwsy7wYh1yqzh2Z5rr7hgf3aitQC0nNnTLt9/j37jip8HgMqlpYPBohFUxLNffQggrd+44u8ql5Zmg0l2zwIQBCFvUg57jGTnCeA4G4BtRkbSrYau28JqNMWSmpzLMalprewYe03S7lsuzzDqR3ZkzphOzdJ6sHzwn8Jh/RBAKWeWeArKg0mDj/VV1TzgtjmiXFZKEpEE2Uhxm7kmX1jW1P5GVA5bbJadJod9JBwo48KRYVRVWxR/h8WU4OJ1VZU4QXDqimZ3ZqYhvK/peclpv1hy2FeCOWGJYCr/PPwng86xYPbIR4GZThi/fnj9PUAISQXwGJhk2weWyexBSumH8VzwH4OZbVjBxu+DlNLPDqjjEjCpO8A2PjvA3umXlNIZf8BjdOFXghCigzk9CmAOuBdRSiOEkBCl9JemYf2921YNJvjSwUy2ZlFKP/4z29SFX48/zIZVLvMOA/CoASMr4mxoB8E4e0fmXDA7ubfAYh+qYB6/CyxF2S9GymrKdC7aUzAsm8HSinY3iBYmlB9DQFoM6E4C4gBgB4hBYVwXs/iHCKq5D6+ZguAgcIbwIpga9VyDqNsrxNpWjRhJg2N5SzRezgLllHZzq9sle5YQXrtJsfgoCPbtikUSdaLVDIn1GvVGef0Xk/pnjbSI/AQAz4Zc9TsArBMUy/1mOXFYq6saAcRGpMCeEYkZN0gCaRF4Mh4UK/1hvb/dwtkJwQ6eI9M/MLaeOFLtcV6umrkvbG/to1Btk0N1djfEmN+gtLth0M0xWZLMDk//lZvr1SSzWLJkp//Oa4pTl2237h3Ri3hW7dQCvXyiv3Ev3zbpAuOIQaGo8Vwwprc1uJqU3EiGNVmwvCcLkSu9kRi2OqqUsTSvfRVqnjoefQcASBRl+3t7xJZH6/1G2clHj5/UtPX75ZVoNOfB85YQdl2RNmJg/9rVq58VzLom8GTe458F9wGgV40zbVwz93130X3nCmAOL01gxOchT8Go4B8ykP4imLvAeyWYiv5fYORsIJgN8CkA2t/u3zD3rG1pkzmQdWASua9vn5p9xdwF3qphXOzTKEiNQBAbK8S2hSgZu1CxnURAtStNoV51urD1S8389Rmi7ErnjFYwAtQLLNTSpwDeAyNKLWA2oP8yT8m/beL0uscAzFk0P+u/vLDd5aUvgcUqtk7e2ZJf2B5+9YqrLvqi0/HrRQPD315Tm/GvbOfzpRmu7QBEf2HxDzJaAUDNrJIhADbmzJ5G3eWlbwG41l9Y/F/3rFxamgqWqrEV7Le7gRpUbF2/padn2ECWBCQc3QaCHFAYhq6t0Qhukhz21wBs0BX1RE7gJcJxOpi5xKVCfWs+CUV5PdkZ4tsCUqxnehrH834wUkgByFo0FuFNko0QshjMWSsIVR1IZJWjZjGsE5ioqgUEq+VFsAgk31JKnyKErAcg6Yo6hBCicKLwFaX0eFDaRDhOAZOCOwHE1LDs582SheN5G9jmYRmYrbsD/0kkULjvm+/HiA674Bk28K5+44qP/4kh9Ydg4vS6yQDmgjn4eQHcvmh+1q8OHB/PCPU9gJcppU/Hy3IAnEIpfTxOWG+mlJ4UP1YIlj1tCqV06SHqrAYLUfSHRxP4X8e2lsYf9H9/T9pvShzQmZgSQl4HS7v76F+IsA6llLYSQvoA+IpSmvNntqkLvx6HlbDKZV47mD3cEjBJ3PtgGV9UMOkEBXPkqAdzcLoVwARVDCu8bjI4Q3hBJ8rxPJVSwRaDG8B26bcBsFIYimxvqbOGUgWwDCr3GkSboPNqmi5EXZoUSbMHMimAY0Ku+rM4TfrcHE7q0cp1XJBiJDSH7Q3vWUKesCZFlhAqPByz+kpsHek1BNxsA/SeFdYtA5I05+f5Su5L8bY9bCnKDgJAy9aVb4AtpNGwoa5SONWSAOsRAB6CxgG8cTYICID3O8L62RwhnMPCyRFDNyK60eDR3Z/wHL2pylQf6hZL81ZHTT1rSOXuXH/mc8mJuCKBJmRtd+yS8wyPvLpKLq807RtyalqmheP4pxOo5QKD178gBkmNhs0nVzQHmv3ZtUvqLW1Pyls9F1zRP9vWbsi7dsT83uGW1GN4cE9tQ9PKbtFkn8PEt4Oggvenv6eTyBnf1DSuCwfEuwb05MzJHuNoXqCFkiG019Rz97hc2uYkJz/1/e8jt2R7+IJhvUz7U+69AyapbvUUjJp52AbQXxhzF3iPBOCsrEbG3T3DaW/rNiuYajgGoOb2qdmj5y7wfmWDQY8Tohs/0Kz3gxGkNjDPd3N3og49RYyqZapEy6m4RQPXi4DWXiiGFY0iIZWnn5qn5M9rfX7bNzKled0uz8+OLqx4FsBGAI+DSbP2xe+5zDwl/7aDtdVdXroGjEgNFjW9HsCXqsDfAGabWQBGLN/c9N6mvTmzp0V/6tlrZpXMBTA3Z/a0kLu81OIvLJYPPKdyaekRYJLc88DsXqdRSqu0sGwW7dYXAUzRmtvnRf2BC+y9c938jlqzbhb3IieNB/BmcG/tdE4Qrk/wRyPeQHhyysh+MSEcO8cwS6vAc/UkGLkgosYMiycpBCa9DgKoppQWBGvqd1lTkroJVosVwG5qGJRwnNtQVREgAuG5DYZhjIBB1/CS2N5WsatPYv88nRDiCTc0e0wJLuhytEN02AOcwO8EcCSYxDYMFvReIIQ0gpk6mMGc2baDbVisYJuJ6fVfr5gMQB0/b2b5T73TPwJxsvocWBv3IwLg8l9LWgkh4wDcSSktPsTxo9GJsMbLLgVwMqX09ENcU40uwvq7I05WD9r/v4W0HkBYrwIwkFI6dX85IcQOJmVPAFv3Z1FKPyaE2MDWkiww6ed9lNK34/3/JthGXwObV+8H02Q8RCl9+lB1HqRt1fgPYR0G4DlKaeGvfdYu/Lk43IT1VDDpUB2YBMoAm9yjYCq1s8FUixYwR442AJaYqSPIK5Y7eSrYDCAUQqTMCetmAs4D5pm7FyysjNsgehlH+QKwYN21AMaG7U25kuwqEXXzXrB86NXxa3qBqQbGl5uqvnMZ1pXd1XQBwF4KepImytt51XRcjdhEGk3N3gw49IxQTk8RwgiD6HUG0e4lIN1jFv8QazjleEtRdrhl68oMMWZbonHKQ1RUj1++RR46Jt+SZg0nI2ptrZapwHOgHosh1BpSbBfYD5NoVD+Z08XdnGCoAFytEd61TNm+8Cxn75HE4DNFxbpbB1VE3bSLaOI/qon3c9Ua69nEh/oOJRkPU86oCCq4tblNzwj4+dXlWVtfvnXU5V+1bF1JalrV79bUB5WzBiVOFRRLcVgInQid+8oeTrlpmbcxIyFNax0sdf+4LtR2fo3ms2yoNBpPH2Fr3qcGE3skm51vfBX7dnKxLdNp5Z54/LPgNwBuvnfGhBtatq48DczhZD6Afp6CUQcNPv//AXMXeIcBkDfsJkOe6hOMPKfbjwWzCb8AwMbUE8L3tn1vvlPr4Hw8ENJBYmCh0hwAJg0gyh0VVNx5thB5cJkmzWqFcJQO8iAYOaoVYCRPM4VvA1OnG5QinxBcs04TzxAIXVLIa3eBbdzuBiOwF5in5LccrK3u8tIBYCpbBUzqKIAtBI8AeBXAHn9hcexg1/4c1MwqmZkze9qcA8srl5aOAotI0AT2m38VLFf76QA+VEORNeDI1aLVkgVAj/kD33I8f5LosG02NK2AcFwVicTcEV9QlSxSAp/kssYleh0wDBs4juqKEuUlqRZALnTDp0ZjkUBVTY/Egt4a4TgAaPBvr8qwZ2dEeMIJsai83pzgzqSUZhNCSkDpVcGa+rdMCc5cyeXUiS90tO6wNMrNrVZzUoKFCPxmqhmDeJMYBdMmZIDNMx+AbRY6wAjqFwDOAZvftH7jivv92vd5uDBxel01mIPqgahZND8r99fUSQiZBqA7pfSGQxw/Gj8krIUA3qSUHvQddRHWw4NtLY3VOET/9/ek5f7aejsRUwFMKPVlXGvRudxKKQ0QQpLBImv0AnPYPJ5Senm8HheltCPe/w/E63gMbM4qAtscbqWUph6qTnoAoelkEkDAIp+cfaA5Shf+PjjcTlf1AL4Gc/wgAEaDLdjfgqnS/gVgBwVtojAujrenyRRz3cxT8WYC7hIv1zTTRMQsTZRvANtp5YOF1nEDsHOUHwMmYdoMZkczm1AyTNTNFGwHdgQAnYIODTn2yWAxKI9yGBZvipbwD02QP1XF0OlRS9v8mMVnA2cs60YTvxwW7ftQTqjnWSKEWwBYVCnIqWL4It6Q1lnDKbkAlshl3jMtwZTzVVP4DipoDcTg2kf2McU4DnLU1iqCEI9bTrSpGqcZYiwTTHU8oQKNH+hE/yCqaxmVwY7XauFvJJryxQi1e6Y5mHq2LZx6qmIJfKmbQ+aoo/W0Krnplu8rlSMTfLlDByC1jXLGg2sbu63e5fPoG3fp23rncscca845Z9fHH21Y/OratziOOjILgrUALtWIcka1Xx65oUo59oXEL4dpBXs39EwUEsPm5n5paQYGZJg/njDYFNsl+1t6SznCuQqCV53gcDmt3AueglGvXnOivUf3VGEBAHgKRn0E4FFPwSj9/zNZnTi9rnfZTm5D2U6uTTbILs+V/d8D0xRkAtgXFrS8vV+bXqUargRIrQ7yGJja9GwAIwE6roIKogEsNyhu68bT1iToi8BsrL3TpWBkmim8C0zlbwVwFyG4D8DQdbpUR6ieDkY4XWDZe542T8lviS6smB5dWHHWQZq8I35eD7CUozKYiQD1FxZXHkhWa2aV/LNmVkn+AWV31swqSTvEKyk/WGG/ccUr679e0dC+efsgAJcauv6s0hEkYPaMxxGRP12To93AiN+nvCgOBc81ANjKCUI14bhVMEuiNSPZCZs5BgBqKFxBDSNqADKl1FBDEau+u7ZftKnVRDpCrlhHgEsa2FcjHKeBaXJUd9+ehmAyycQwJLPbNRIACCENACaIG3ZXO3KzbJLLORhAseGyqpQaG2wZqRbeJEmGpu+XomoAJGoYxND0bmC2v0dEmlpmxPyBBLCNylwwUj61cmnpoRzh/kxk/8LyXwxCyJOEkE2EkLU/dtrvdb8u/CIcrv63EELKwRwpvWBzTWcQAHMJIZvBtK2ZYLGltwA4lhDyACHkqAPS5X4S/78FwGpKaZBS2gIgFo+JfKg6D4axlNICsM3zE3HpbBf+hvgjogTcCeYhnAY2yBxgjgmjKWiCDmWnJsgNMUvHUgp6tsrLAgVtJCBCzOy/P81kWkOk2AucLj0NZr+3HWwh8gLooDCEGJQeOlF6URgPUNDrreGUbmAk9k2wXO0gILoUc7wAJgWJ9VQzNtuoeYhiCvXSBUXbXYcLt9WqPaMWf1XU2pYdM/tnANAtRdlfAujWodBXpJg7BCYtrgSwmYIeK9tbjgUwUlDNZ1pDKX6zxKUD2AgOIXC0KuZo/oIzRF2jUAA8p0KvqSX+882adaRVsy8bavRLcUQc6etN1eMsJnLtZ/aVX+wx19xdQ3wTFvHbmnajbbcsRU4a3kdKXcKti6i8mrYp2BZdU1l+RdnaPSWj+vPPh6h+SkTh3avyfD2ViclFn+9tFeo2OY/cEwme1UwCw928SUpP4IZOVoe8W6T13tRSb3lyWb2/756ovnB1rTvLYeGWuDnz1o3ttatf4IUA0YRT2zR5e8lTS77iOJJ30jBLeH9negpG/fmBe/8ATJxe13fi9Lof/D4mTq+zgxGTL8GcTDzxQ3eDjTVJ0jiJ0yHoYf4RAHW3T81eefvU7Kdun5r9GQBHKvTegzi1CSBXvaPbCqsM4ZuzJDkJTHL98XuqRVEoloCpoD8GcFqU4po9On/2KE4+vR9PJ4JtAqeAbeD8AGCekj/fPCX/XQBwl5f+e1L2FxYrYCS1AizjUh6A6/yFxT8w54h7/r8KwF4zqyStZlZJYfzQ+wAuq5lVIh54Tc7saYt+5FWucOblSAAyDVWboEYiwwBUaZHYUo7nMwmFoivqagCnCzaL1dIerjOv3u4FkwLnQ+BLKCFNaiDcYahqO2eSskGIxVDVAAnJYVOCK2wkuXTB5eBoorPNnpGaAjav+cGkn7uhqDp4zgFBELh97So0rSeAJEPXM9UjeuUYmjYu2u53Aojoqror1t5RBCYRjgkcV2FqaIuBqR4jIe++bYGqmkfj75/jTVIVbzZFwFSrswCU9BtXvAxMIvRXg/cXlv8cVID5CAAAKKXXgEnEPIe8gplOVP6Ge3bh1+Fw9D/ANo+F8b/rKKXKAcfPAxsPR8TV8U0AzJTSnWBjhwmaCLmz0zX7N9FGp8/7vwuHqvPHGkkprYqf1/+XP2IX/go43IT1RrBdzyowdf9eMGnP1QCeM4i2XpPkIlGzrjTLCZ+oJJLN69LDFPoOAE1EEzfyqmklKC7T+di1BvQJChcaAOBzsAWlPiqGt6y2VBoG5U4M8sHPAXxoEK1/xN50gc6pBMyObhyAByXF0QNsgjUDWBty7LvYJLurzHJiqZ2zbMsgHpVTpTtE2cnZQmkrwDLZwFKUXb7TXMdFEN0MtjClAdhAQDbaAmmJnCaepklyn7CzMVsKud8kmvC4NZB2IyjuALBdMMm7DYoFNc3aIEPlvjie9nVRTltfZt0u1jp3n6QGLLy6KXN1o7ltczfe5lFM4bke2N8bTDNOUaHmtyQ3ure2hap5s2HYICXlWZ0b+hT7qq87yXGn00bG7vLSlzv83U60785qGuvOqJF8Ds0FkwSd8OtqomsTHLySnSKom72xN5ftCIz1GfIZAZOx3YLoUcU9A7cnu3gjP90yJ8Nurl6+Wq7hO5Lff6/c92labrTwk9WR8wFEW7au/P8WludiMJJyIKaBqdfPBvDpovlZ+yUBA8BsU9slkH3JlIuA7fovn7ugZvHcBd6Fcxd4ywF80Aw+u9IQe90+NTsfgOCjXOg5xc4DGOWGfksD5VMrNOGDjxXTMkpxGYACHXjfCt3SXzCcHBv7Cph5Rol5Sv7yzg10l5cWgCUI6IyVYPbkbWARN65yl5cemJo1A8CLObOn1QGYAOZINLhmVokFTBuyBSzzFgBgyYw5ny6ZMedHF4nx82b6BKslEcD9gtmk2tJT3zdUzRppap8UrmsMcybpWvDcsYaumwHoWlbyBdERfdvACGGbGorcILe0WXmLKZETBDvH83ZdUZcJJpMEm1mihiHquhY0NM1LDSOLUsoBeCbS2h7zb6/yAagzVdbK0A0BJrGVJjv/JW2t3hjzB2Jtm7YTSqmFE4Q3zYluA4CdanoG1Y2VABoAcBCFIq2bx0cprTR03eXIzUq0pnsu7Nhd/aih60NNbtdawWz6HMBnAMb3G1fsB4B+44rv/rH38ifhdrD5qzMi8fJfi28AmAkhV3cqsx7qZELIQDATiid/wz278OtwOPr/58AFoJlSqhJCxiJulkAIyQAQoZS+BpbYZMiP1PGz6vwxEEJSAHQHC7nYhb8hDrcN63QA94HZdwXAFsAPwSSlT4Htsj8Ek061U1CnbG7nTTGHm6fS9WBOTe1gDlYpAEyUDVSJAzcNwDwAizSikHZbo9UT6jaUgNwNoBsFnRS1ttstkaRPwWzOngCQRCn9IqRoyQ6TmKmKkV2CallHQC6Ura3HCTHbaNUcvNoUSWjYKzT3HDB8lAEAvg2bMn1ccN233J6l5wSP/hTM1OGcT12la/t15J7YE9l+SozWPSqZnMERW8TlbRQNSeF5IxsEswHcwSvmusZWkhRNbcpI5iwf7CFtBWla4rVBVXnSaHT7e3TXzmxDuKSU7Dkyjdp3DEdOmgG6Ug5butstip/nQBfv8n2Z6CLDBnjsjg2oP20UyW4rr1JJRNUTGtqNEAHRQI0NqkpycxJsKT0KQrZtEf/rUVt0XN9Aln2Facemvu3dGxAxDV61Q5mT7eEbTx1hLfMUjFJbtq68GMCF63bHylduV+7ulSFsHdxdXPrBSlk9Ik+qGd7bVOUpGPXWYRssfxFMnF5XDGDVovlZB7XpnDi97nqwcGtnL5qfdfnE6XXJACKL5mdF5i7w8gA+7s2pT1KKJ3ZR8XsO9KhT+cj6D3VbCtikPBDMdisK4JjxvPxqFRU/2WsIywEEehLl8TAl8lFirFc6oTs26kKPfE4bZOVQCWbfqoOZ07gAPH8oR6vOcJeXDgYQ9RcW/6RUq2ZWiQcsAcI2AN1zZk+7omZWyWIAJ4GpxfsC0HNmT9u5ZMYc6/h5Mw9cAH+AyqWlDjAnsXqwcE9XALCE6xvf5M3m98xJ7pupbqwjPCdqwKBPTGg7xR91Cxaz19D1HlQ39hKeSyUcZ6OUghBCQWkd4bhUMOnLGwC8uqrerwRCMS0UoYTniC0xQSCUyobZZOV4rgws2H9ae8VOs7tvz2/D9Y13mpISv5dslm5gJkXDdFW1ERCFEwWLHlNS2rfsaPcU9vcACOjUEHhRXK8EQlz71h29rameL5w9sx8FcC6YidK0fuOKtZ96H38mfu8oAQBACEkH28SNADNjCQN4Ou5AczT+O6xVM1hYq0Omau6yYT18ONxRAg5WHrcx/RTMOXIdmGbqBAB9wOZEA0xrejWldN0BjlIXxz9fG6+zGiyeMw5WJ6W0+oA2VOM/Ya1EAI9QSl/4Lc/bhT8Ph5uw9gNwKZjq0gVgicbFlupCbIJJcQ4HcDNYIHRLpVjTLU9LbeIon0TANQJGC+XpG4oUnC/G7AFi8DYegkZhPE6BozhwPJjX9VNhe0NYjDmukFS7BOA5CnptzOTziDGHwkOsUKRgmajYNhJwxwIYXR+IfJp3Qt9b5DLv6WBZfF4IueobedVsN8nuMW85lrd08OHRBTQtdTDNbLYG0tJlW0vAFkq7Csxb8Y6Y5K/w0lCrEDZndJeSPwVQEBF9AcMcMeugg3lwkky1mEmzODiTshcsbaS7XVErE0XxA4MYT4iKNbkjoq35blfoQ9ewxj45RuI1y3YHbcmc5c1xmRmrbNR8RbXVu0qBNigzlrKCRKzjPpPWt42zZXsCbfwoi0Tecli4sR0kqpZt1Pmh3c0fhYJcv72NNFXhYtW98xV9FVezL8OXGTa1Ok4blJ6w4wth03CZqleGv++2h3dHO/SgdA107u3sZL7PqSOtHWWVsReaO7QnxuSbL0py8O8//llwPoBEAI/dO2PCcYdtsPzBiKv27wYwE0wauWjR/KzvJk6vmwbg1UXzs3zx83IAzFo0P+vy+PczAJwP4KxF87P0idPrrgSwY9H8rOWd65+7wGsBcya8BozoBW6fmv1k/FgegKMBTD6Sj71ZpfMrmyC8AWDasUL0fpEafXdS8c1BRLnITgxRIPjYSSBRIIUjqABTqXoB+M1T8i/Zf093eakbwIX+wuKSzm1xl5ceCeApf2HxIHd56cf+wuJTD3wflUtLMwFE+o0r9sUTAcwG8GzO7GkVNbNKzPujB8SlsFfnzJ52x8Hea82sEiFn9rQfkLbKpaUpYHF5y8L1jZs4URwruhyNhOdSeEEoAvA8AGIAF1c0Nmt9LJZGyeXIBvCtFpG7K4Ggy5LqSae6HqYUJmNLVbNY2Duka1qWJkefNoKRUyxJrm2GKI7mBF5Ww5EMohk+TuCF1oqdNslh1119uguGrmuEcBHCc9tBaY+or8NlTnBt1eRYfyLyKiHERng+CMAKCgUEhDMMP2LaNbCZnwAzrZhjaNpT4brG+3mTJFjTUz7qN664/GDvowtd6EIXuvD74LAR1nhIq3fBSGkGgKaYqeNiMWazUt4Yx+vSpWAL+SIAjQYMEwcuBcA+CmM9BR3OgV+uctGjOXAuzhAlAuIFU4XawHZkeQBqDaKXEUpOJOAoWC77RYoUKhQV2wACMjXsaPyHNZhiJuAuiF/vBbNlORosQcECAJstRdmheNtn1AhNmyrsO7kxtEetp2DUZrnMe4FBtAAlxi28IT1NYZxMwMkAmijoSVGzby04I0vnlDbwtFVQLK9tEWtuT4JFSSGOIWBhhd4AS+P4ECh6gyLX4IyYQfGEAO6Wcm8sJKe2qMOlzJsU3Xj+zS0tzZcNyFHWoPYsTow9NFDPkl7cW/1dMmc59uTcFFuM07p9iopFEoSanPru07/fSKf17aNf2KpFvbF9tm400583xJ70ZUUVXZNg585q6zDgVxUtvaj5E//6lCL0btmotVrGKjXuK8YNNM/qny2+8v326FlBmb62e5/24jUTHSd7CkbtBYAH53/18SXjHed5CkaFDsuA+Z0xcXodD6YCHw6manoALP7jfWA79Dww6f2rYAT0qkXzs749RF1Ji+ZntU2cXucG8MSi+Vnn/17tnLvAez2YmcoysI3BJAC8ANp0BBd7ppBTEywctABF6U5DsA4XtAww+8xvAXxhnpL/5f663OWlJgBH+guLOycMGAGgj7+w+JX49+QDY6YCQOXS0n8AaOw3rnj5b3memlklJQBeyJk9rfyA+vsAuBDAi2pYFoPVdYbJ7VwmOu1mSRS+4SLKbpmnx2ohOQe76zrQMzPNlJLYwfH8HkLIcEPTdnOi2DvWEawWrJaMaGOLyZ6dUaUEw2m8Wdop76nPCbW2uqwZqUFn927tYKZHowA8HN26+0RTckKiYjNnihbzd5zAx8DGRRhAY8uGrYMFq8XkyMlqVgLBPRZP4pG6pmmEEA4A4UVxG4A5AJ40dP3t/AnHXL/hjfcvVYKhVeakhLdEh/36/OOOWYYudKELXejCYcPhtGGNAngZzCnkKQCXiDH7JAJuH69L9wMYBOATnaiiDjWdAxcF8zYWCTg7B34dAE40zG7ekIIqF9pgQE8EU72lg9msfKhz6uMGr4oE3LdgTg81AHZLiv12AlID4GlbMM1CwL0O4EEw1UA9GEFYBkZwLwcwWi7zni2XeTkAT+VoqV9Pyj9vkadg1Ob486S1csGbW0kwGcCRBFwPABED+hEGp1WD0K8oaL0p6v6cU6VLNUm+vL+eOThFd3/NR606KNYC2AKD/AuqUK8RPWvJlkhFWWTfajViFaI6t8WWHlYLzSmOEBcVqsXWtrR+wbKkwUN6DhVTcu0wr3+5fXulHhLP6PBx+/bq/nuX7vF9aybihVpZj1PWV2qNTpNwia939bMJvUXT0af3flWjujMM9YjjjzAHAzD5bT2NCFWFktT29Ad6Z/OZkfXpC6jGfZTRJ/LJyp3qRY9/FvSu363ajhtsMd9924SC/WT1znlfjRzUXdr13Feh/5Lc/cVhgKkfzwQLrv8imLryOjDV5U4wwnorAHdnsjpxet0DcQnsfjwxcXrdsWCS1vMnTq9Lip83aOL0utyD3Xzi9Lofte3shB4Aht4+NVsH28xclQg9cZopxBeJ6jsGhzoKlLk52IcLmgTgLfOU/MHmKfnXdyarAOAvLI51Jqtx7AAjt3CXl04AcGX8863u8tJh+0/qN674LeuyTZf8SCSAf6NmVolUM6sk8SDlHIAHDiSr8fp39BtXPBPAWYLFdI/i63DbMlN7SQ7bSURWmpVw5ELf1p0dajisiYW9s0WXnSgtPpeyr6WAAj5ektoBfCo5bE2CJH5uSUveEqius4s2SxlV9YxYVOZFt9Nvy0gVo/6ANeYPvCXuqv+Maw+4xR5Zw2iSq3estV3t2LKjGLLyphqWt8rNbQkALMmD859w5eUqRORhTk6wB737ymhMjcnNbWv1mBo1dJ1GGlsWGLpBfFt2XLDpg89usaQmn+zKy420rtvyccOylU0/9c660IUudKELvw2HLTWrpShbk8u8vcEcrOoAjOHAawCKwSREpwGo4aiwhYBUgEUOyALL4nOBAUMiICAgnwL4h2BYF+pi1MSpts/BbFLvAtCdGNxJFIY/JgWppNgtBKRDNrffa/DqPFs49UgwZ5PxYBEKfGCk9tl4u4JgUphZYBLbfwHYbSnK3tCydWVqaEv9JHsgcyEAYinKfmjZlmVvnhooehMsjmSupSg7Fli94wxeFy/jdelSxRT4OmZTTKDwAXiHCioALNH5iBcE9wmKZahBDMng1ON9kL8Z1cei6KYkTwdpHRgUknrlaQmfBxHL4UAerCcdQh9LwlmPbntiHDiMD36fcTxvcTxhIbztnCEJZ3xOtnsyklLSy7/IGT9plPmrj1dix9F5iUet2ZT17O5GrqnxyK/u1jVXoyamt5YlbJ1wzInjplQ2rliVbsq7L5q4q7rW3HTMKG7g183pjea6SovJUPTNVpvxjZHV/p4C6+mPbnviohv7X3vZnfO+mgMgu6xSmQKW9/5vgXh60rcBvD1xep0AFlaqP5gdE8Ck7CawLEzfT5xetwDMbOVSMEn4PydOr3ti0fysBgBP1A5svyyUHFvFhigWgdlM7fdIrwaA6MKKY95rNs17s8l8JoDHJk6vm7Jofpb/x9p5+9Ts6zt93TOQiykNhnBrvUbM6TztcBD4wWy11wA41jwl/51f8h78hcV+/CcN69dgTjIA8AKYXXlnXJMze9pPStDb9NjNZsLnAbi0ZlaJO2f2tP315wK4DD/uxLEw1u5/WnTY3gKbCwRZV28XUxLOSMlIHqbJUSNSXV/l1OAW++Umw6BE8QdNpkRXVmv5tgJiUFPyEQVfxNo7qgkhAUM3ipVgCPbcrJ28ScyiwGaT1TKgbevOMwyns9BiNT/BmcRGNRROFqzmqE0y12ixqEtT1NFyU8vnlpSkEZG6xjxLRqosN7amgRpuZ27WgwBGiQ4b17J+6+bkIfkPKP7ghuDeulOs6Z53JJejo9+44ocAoN+44oOaRnShC13oQhd+XxxuG9aHwdT+T4FJMt1gkQJywYhpIZhEdFi83AoWU62agv4LQD0BWQQgi8LQVDHSKqn288AkZ41gaV0LAUQoqJuAmMDUfJPBUoeaLEXZHXKZtxjA0RT0SINXr6QGncNTMdValDs+broQBnMmGW4pyn5ZLvM+pvOxZynoNEE3J8Wf4VGwiActYI4eL+M/AdFjIVf97WDmBdUAvKLsaDIpzjcsRdmbAKBl68qTiM7zABIprwNMwnx+O8Jte9BmHoKs4hCURWWkuraY9nCvRM093eS8izkxNABCbMeGWNPQUID0j+7wXDtuJKk02q2zM1O4pDe+UNtVHcfcO2NC053zvhJdR3n3RPdZV8Wqks8dObT/CINokypdX4wzgiacm5Z/X5UQmL3sfXqnZWBTsT25x4j29W3dqEGOkKKmOZlJ3Kg9HbF12af0uMRqaJvHy6GTdYPeu+Dz0JfxPptz74wJ6u84RP5QTJxelwimCr4GLJbvM2BS/WawcTgVLAXnUWAOATsWzc8yAMBdXmoGoBa91NMAMGPR/Kz743WOBOBZND/r0+jCCnO1zNlv2u34AExqOzB+jhtA5qL5WRU/1cboworLWw0yilD4rql0CMckKJ9fkRktNU/JP2QGKnd5aaK/sLjdXV5KAGT7C4trAGDc2tJjHQaSPxpR/ObPfUc1s0quAfBtzuxpWw51zpIZcxIAkF6CIwzgrZzZ0w6asegg1/UBe/9jE/J7pVszUq8P1zUc5a+sSvWMGjxW6QgM1IIROPNy2zmOeNRIlOdNkg2guq5qQU4SnXo4yoPglUhLe7HZ7YhR3YgKNkv/jp3V+ySX3eADkdU20TyR9sve1by6vIctM+1bqhtuLRodaO+W7gjV1E909+/1ltzcJnGi0Cg67YYaCJWb3M4jtUhUbl614UJHj+w5ju7d3iCEXAFgcr9xxXvi7Z8M4IPx82b+ZDawLnShC13owu+LwyZhjcMGYCmY7ZoNjGAKAMwGtDMpAcdTwQlGLs8ECzg8jILeSqH7OQi2+LEnCLi7JNV+M5g37zMALgIjj30AfEZAUsEkufulqE4AiXKZ92IADwO4JOzc942gWt/ThdgblqBHlcu8rwEYA+BksNBXp4MR0bd43XQVmGT2WTBJ7LNgEQ3ywo6GAksoZR1H+U1gXopvwyAjwdGPAFxrCaRcp4vRlJgpIIa2rnz18c+C158xynJSZhJuBDBe0+ktAk8EAA0EpLYA6cdw4MbaYeoZQPSZGLSLolAfq6GBdQXRrBnp9mA3nylSafKIgzelekduWN73WTm/2idy8qOq3vsVAN+0bF05DoDU8V32QgJMKOpnClRWb2nUR1VpPMGn3WxpjlByjjvBiD1/4iQ6p3ydY+G+LYFyUHMzgLvNdhSIDmkybTQeqXm7NvPyk523bdqrVH+3LfZY/J3fAdCFj2574tob+197oGTub4FF87PaweKnfgkAE6fX/WPR/Cw1/jkHzLZ1AwAJwEsA/gFg78TpdQlF6PnoovlZl2A+AOZ4tx9tAO6ZOL2udNH8/EBfIIrpdU1gCSr2IwNsU/aThNU8Jf+5LJY+Eer0umum3dlzcbx9FgDJi+Zn1XY+311emg7mNHYFmMT3erBwcjhPhttGkXGw+7jLS5MBBOIxWjvjSwCNNbNKeufMnrbzYNeOnzfT1+nrQcnqkhlzigF0HJCWNAz2Xlb6KnYt8FXs2h1vdy9eki40JyQM7GjreEHpCGSIDnuFf3tVT3uPbis7Knble4YPcsZa29foMWUAJ0onh/d413M9syeYU5Kv5U3SRdb0FJ03m/r76hvHiQMybojs3Hu1q1f3RLmx5RglFGlx9+tpjvkCm5x5uRQGrRSs5urg3rp+ktOuRxqak129cl8ggnCVLTvr5WCVd5e9W/rJRBRfj7b5WpbMmDMMwFYAS7rIahe60IUu/Dk43HFYXwHwFlgIjX1g6lhCQQEQEEo+BrMjHAYmwbwnfk4dYVEA7gPTwa4E8LXOqUt1orrBgpi/CmbHqoGRUx9YoPBbAUyPWJs/VMTQWrAUii2WouzN9kCm3ywnmGzBtE9sRd0fByO2vcG8xUsBLJXLvK+COen0BVOBJ4AR4XWWouz7AHxtC6av5CifBxbD7j0AdZZI0hQuZm4KaXqHag4NlRTHEyGzLwYgdPUJ9g8+WCl/4ykYFQGwYmm53KPBp4UBJCTAmmKG2CcK9YjvsGcHAQbuRuueDhJNy7Eq4hLn8p6vcutf9xL/0ARYLknZ0cM1rJf0vuiRx3sah/zT04Mf77aR28BCx6gAAn1ySM4eV21dxNUxTQ+JR9/Y/9qb9tE9N/X3pD0vcaYly77Y85bNJZzhPnnXe9ed5LAm2AnVwKUZlvBLDiuyAKQ+92lA/rYilkspzgGTJPfkkyL7QmVZO+6c99Xh3uj8IdhPVuOgYER0GZhj3kIwwop4xIBDhY9yigg7AJw8cXrdvInT6+YDWA5gzcTpdRdNnF531KL5WdsWzc966ee0aeL0uqmR5yvuiS6sKMB/x0zsi4OQQ39hcYO/sPgKd3mpC0ArgAXu8tJJAHDpmOJ3r3Th6UPc6iIAQyqXljoql/4nHmvO7GlVObOnhQHcXjOrJDdul/qzUDOrxFYzq+TCmlkl34GZG4Q7Hx8/b2bd+HkzV4Il77gewDPJwwfuEqyWFjUYXtpUti4Qa/OFDU2vClTXNTq6Z1na1299wZqV+pKharBW1m+O1jW3Km0+Krqda0EREUzShoZlq7iwt76HGgi+mzioX5+WtZu/IRxZTCndaEnzvOHKy+Ej+xpj0Za2bHDcJ7F2/y7JYb9EcjlWaZHoEi0U6QmQfwgW81qqKMdxkng2J4q+fuOKH2/bUPEpmCPcEAAHyyLWhS50oQtd+ANwuAnrajBV//4g0W8D6GZAryAgDQRIpzDWgBHQ68AWuS8JyMUE3EawAP/JYMTURgzO0ET5bjCV7okASgCMNaA36ERrAou1ej+AFEvE4xFVWykYGa2Sy7xWMLWvFcA1cpl3EJhd6xFgZNW1XfKSHVLtGjASmwgWjisTjLC1ymXee8CkZUcB2GIpyl4IRk6uk+2t17QI/t3reK9XkyLqB+5lR73DbToTwPUCT6687iTHgJatK1cB+K4gV3rTaeF6x99JCwBihmjxEv/TjppuF3qJKNlh8jWQwJGgqDei/D1tOx3O5XTX0t31xiM+PfZ831352/btCfMdpOVUf5gOevyz4Bn3zpigAHTTjkaVZrZnvkZF/RFCydN3zvsqw9Lh2txUvnplxZJKKke08/bU1zUA6LVPC231heiYUMhobd5n9Eiwc1vApIs3g5lnHAXAN3mMdYIUtPFGjJPBHJj+p7Bofpb3jn/ULb3jH3VPLpqfZWyaWPfpmrOru3c6RTpY5isAp9j48BGS5HtQlbTprVlBE4ALCbRHHVzrPQDsE6fXTZg4va5t4vS6a/dfNHF63RkTp9fdM3F6XWH8e8LE6XU3AQicW+Ecf8k2RwTMxnN/+zYump/1Y05vi8GSHTwEtoHbj2fc5aWT3eWlR7jLS7Pd5aVnuMtLzwHwub+weBXYBm/wgZXlzJ52MZiJxL/THS6ZMSdvyYw5B00gUTOrJBcsFuexAKaMnzdz4/h5M3cdeN6SGXPm8RazRgT+A3d+rxO1YHiaZ2Sh35zkvhPAPkeP7BoQkmfIUU/bxm1vWtNTMjheOEpXlDYjO+V03mn7SglFzGogeGtwj7eled2Wm1x5uUFBMi0ddsm5V7Ss3nQFgG2hmn12/7ZduXJT6z/MyQnLBLO52pyckNz47ZqAf0dVRFe1teHahrMj9Y0BU6K7VgkELYauD480NE909+9VBuCRJTPmiGDmIm+OnzezbPy8mV3B7g8CQkio0+fehJDPCSG7CCEbCCHvEEJS48dGE0LWEEK2x/+u6HRdH0LIckJIOSGkkhDy7CHutZwQMvQg5UMJIT/qFEoIySWE/GhKaULIzHgbygkheqfP037qPfx/Raf3tJUQ8mk8deovuf53izxDCKmOx33924IQcjEh5Ik/ux1/RRxuG1Y3mCS0Gmzx2wumtqwC8KLOxyYZnK6LqvVZsLAxj4CpJJeCefKLYJKvajCiOAgsNetKMEL1OoBlFMY5UUub0ywnVRJwdjCSaQJbxI8DU/E+EnLVvw4Db9mDmV+AEd6BYOYA1QC+6uDCTo3qy5OoMwhglqUoW5XLvE1g4bmuB/Po7g3meX0SmOPOCAClIVf9qwBqTRH3SsUcXE85/R8Arni3LDzBHzKWXX6cQwdz1qmIP/92MGJhgGUeaouBnLaLUzbU07qdHUZQPzKaX9tkacjZQ9qCshyNka+9I3Fqj3kcJeekyQkL9lnbk3WZ3xf8umd291S+5aRh1mULVjRHTd0Cz7rr0/voBpL8VHamWUzNAb/oLMg0x6paAlyzT92l8dLDzmOrsmN7EobGqhKPADD/qP6mq3SDzvx+u/I2mBd9DzBST1JtpneHZiR0X+StzYdBdkIVcgHccO+MCR//9pHy18CqVatuAvDByJEj98btQXv4C4urAGDi9Lo5AJ5fND9rb+drJk6vewHA41GbujqQKCvJtY5yDqQHh2gTwPUxIK0GS0f8Dliw7I8A1I/p7rv3270JMwBsXjQ/q3ri9DoJwPBF87NWdKpbPEAKfEi4y0sLwcwTisDGVwQsMcd7YCYtDgBXgTme+QHU+wuLW37J+1kyY86JYGr+soMdr5lVsgrMfrwO7PeyKWf2tK86XZ8A4KiM8UVuLRy5oKPKK0wdkj3oX8srfJwgfqaFwqWCzXKuq2/eCb6KnYQaxgOJA/tewHGctXX91hZLShI0Va0hlAaVcCRKo4phTk5clOJwHW0o6qj6psZFyUPy7W2bKo9NHNDHZ0pwHQ+moRlraFprrCM4BsCkcG1DMgzjW1u3jFD7psp3QGkCmE1zn/HzZiYvmTHnBTBHNz+A1elHjxzEicJ1/cYVz/kl7+uviLkLvD8IHH/71OzfJXA8IcQMlg3txv2JAeKJA1rjf2sAnEYp3RAnFYsB3EkpXUQIWQxgAaX04/h1AyilP7CjJoQsB3AzpXTdr2hnLoDP4nnlf/ZzHVBGwNZN45fe/6+AkLf1B/1vz07+3RIHEEJeBrCTUvqzfyuHSjzwK9tSjb9Q0glCiEAp/UXJRA5MltCF/+CwSlgtRdl+MI/h3mAE1AnmLJUEYAyvm0RRtb4AFmD9wqjZt0kVw+/pRNsKoMqAPk8nSgUYkbWBhZ9aCiZh9YCZAGwl4KhZTt5LwP0LgNdSlD0MTJpKwDL23Acg6CkYFTFF3VeBSVkfASO1BMzjW3AZtvok6rwXwCU6HzPkMu8HlqLsVAAzANgtRdm7wFJw9gaTCp9lKcq+LeSqPxksXJZLlULjKTFOjz9jzZh888WTi23nASjbWqPsqG/TjgIjwHmaTk/UDXoZGLnwr9gkF5rkkIfSGIlxauL35ooN27imR6fmX3VKdz5zo/WkfpKhcD0C61IX77O2SwBOtRLhUTE5bE0Y3nxNAwJTHGNqn+E9kQ1NRrg9XFB9bMKw5g0p3TS/L6rWflcV1AMBmaeGrAJwB77ueRkFnWTq32S2jaoNtwT0md9vVx4Hs+V8Byw7zfMAtjeFYyMX7WocBY0k8DZ1fwimE3+/0fLnY+TIkY+MHDlyLwD4C4vpfrIKAIvmZ808kKzGceOi+VkbzWFxUEqt82EOZCCAiAFzigFJABu3vQdYlt1yQY8P+gF0MIATeyRFpQm92xeBjT+UXVx1Q9nFVbvd5aU93eWlQvyePyCr7vJS4i4vvc5dXiq5y0vL3OWl+xMHvHvv3sdHXNjwcQ4MfNq3xXo/2LhKBcsy9Z2/sLjZX1i83F9YXP5LyWrNrJJ7ewmO1s5ktWZWySQAWDJjzhgA2KUF/7lLC+7NmT1NAdN+LDmgGgGAad+SspXh2oaXlXb/Lc9ubfhOstubtFB4IIALtLBc5NuyPeLKy9FEu+0aQ1FWt6zd9ARnMSXIzW1mpdXXLdbmT6JRZY+7oHcBpcY67969l9bV151NqeFu21R5QfLg/FtNCa6vAJweaWyZFm313c0Jwv3RprYT1UBosrt391Pc/Xu9pUdj+0Dp7WCb3xUAei6ZMedOsN/nZePnzWwG8HFT2boU/Ce6wt8WcbL6HJjGisT/Pxcv/z0wGcDKzlmsKKXLKaVbwebslyilG+LlrWDS/RnxU9PBNjr7rzuk0x+As+KS2p2EkKMARowJIZ/FP3sIIV8TQioIIc8TQmo6Sd14Qshz8WNfEUIsP/VQccnsDkLIK2C2zN0IIbcQQtYSQjYTQu7pdO4d8XNXEELeJITcHC+fRgjZFj//T8kYGCerP+j/ePnvhZVgAiMQQnoSQr4khKwnhHxHCOkbL+9OCFlJCNlCCJl9qIoIIS8RQp4mhKyL9/VJ8fL/kkASQj6Lb4w6X5sbl+K/HpfYv0cI+UHK4LjE/rH4PSoJIcMIIR/ENQSzO533Ufw5Kg7QDBwf1yRsIoQsjZfdTQh5lRBSBuDVeFu+iff9UkJI9o89XxwZ8Xe3ixDyYKf7PRU/v+KAcXdi/HnXE0JKOv0WbISQF+K/l42EkFPj5fnxsvJ4u3r9SJ/+ZXDYbREtRdlbABwnl3lvBsvjWwTmWW8Cy7/NgUk6rzBFXTGDV3dzlD8CwEwC8iooJ4MRo3owB6tLwOxWC8CcgSYDeIqAhMFIqFsu854KRqiWgQWFnw1gmVzm/VKEbSIYcU4FcBNYKCMXmNPVBQCaLEXZKa2bV2+xBlO/kMu8yfE228Ekug9airL3767Pbdm6ske8rsUABhqCRsFCZvkBZKa6+e/BHMpa8rPF3vG2Pw2gz/rdyuY1u5TZ153kqAJw8bZaVfDm7GsVLNF2ABtivFqYaJizK7cuebPa1DQJwEV6q7UABpmTHkp6OWQNJXaXEr7VRmrfmqnFRX3W/rZEaXuyxdazfkSj4jBMZ7ZE1Rw+NTSj/yjesXMPOSvUYkmGaO5LTFoKjQn3weCOUetcJLbNXLLLGToTMHsBfAImDbHYjqztLW9KyTPCJgJgF3RB0f1CbwC1986YcOXvMET+1tgftmrR/KxKAHdPZA5XW8AcA6eDJaUwbZGPltpbZRAghQLSS+vS1248tdY7eGdiubu89CQA5978fW7f1wc0uBscscrUFWXLupUntO4e3dIXwDv+wmI9fsvzwSTfU8FCwyW7y0tHA/CnKj7fc+lnHskB3wbMehTAnWDjcK+/sHgdALjLS5PAfm8Rf2Hx6l/wqA806LK8a8Yc0/h5M2M1s0oImJQGACYsmTGnDMwcYMXyGXNHuDhxapuhPJjDojAAAMbPm9myZMacvgDC4brGPABnRdt8SYTjNoNJNOcCeNFQNU9wb93JnNlUJTkdOih26qGInYjCFkKEf1FNewoA9W/duQ9sTrjWnJw4joj8+Xo0Oqt51cbXOIvZZPEkvGNJSRZDtQ3JkebWKCGkJLi7xhVtab8fQHc1FL4WQBJE8URC6YNU08aCJZR4CMCpS2bMsY2fN3N6vPmdnej+rpgLtlnvDGu8/DdJ2eIoAEt/ezDkgzm0dsa6eDnAzEm+IYR8DxZt5UVKqf8QdQmU0uGEkBPBwhuOP+D4XQC+oZTeTwg5Hsy0az96ATiXUno5IeQdMPvk13760dALwEWU0lWEkAnx78PBiN8nhJAxYFnQJoFpAkUwB87972MGgO6U0hj5hSrz3xGHtf8JITzYOrowXvQsgKsopbsIISPA5sJjwMJHPkUpfYUQcs1PVJsL9p57AlhGCMn7BU3qA2AKpbSMEPIC2Jz58EHOUyilQwkh14P5ghwB5ltTRQh5jFLaBuBSSml7fIOzlhDyPhh3eQ7AGErpXkJI59jU/QGMppTKhJBPAbxMKX2ZEHIp2Gb+tJ94vkIwU60YgB2EkMcppbUAZsbbwQNYSggZCKYNfaZTOzpHhZkJ9lu4ND7u1hBCloBp2/5FKX2dECIB4H/Be/3TcLhtWDtjEJjjyuVgP+JssB32VQZRT6IwzARcf143jSEgCoDpBFyMg9AMpkbvB2As2A53G4DzwBbDVjC13xgAa8FU9OUAXrEUZZdZirKvthRld7cUZV8KJpVNArALTKqaCEZmTwfbFSYCuDT8/d6oJZSyjrDMWw9ZirI/thRlvw4AncgqAMBTMGoPWOcHwBzMzGBS5WKwgXli/L53EWbr4wOzizNG9DFdce+MCW+DEe/7750x4TMhMXoMGJGfAKBvOxc9OYRYVrzNETE1UmQf0VASMIdzdcPgTRC7FyLjnr6G5wxvQB4ogNvbsdeWTyUtvcMcvir0Xc6oYehW63bTNiEh+mpiEr3MddLuV13H7TkPgFfZm/CwETD7OKuy1Tqk8d74vV8DcAuAPepOz9F2p0kAm5i/B3P+SYq/8y4cgEXzs55aND9rxaL5WfmL5me9G3EoVaqoxQAi1QesPgqyHsym+85u5QnDAewqeqlntr+wuJADuazBEZsEpr6/uCU7eC8Y6TS7y0t7u8tLZwK4F4xw7vEXFrec1/RZdEn55XcAuPaVlS/vSP9w1PNDGp179jli1/gLix8EMwc4wV1eehcApISinos37HkSbEL9USyZMSd9yYw56QCQM3taOES1cWAkHDmzp9Gc2dMeWzJjjsNC+PvyePtuJxGKAdxtgFYHDDUK9lv/L6Rx5o6evL1h/LyZd4+fN/NUXY5N1sJyd7CFc5itW8ZJnNl0mhaOePWI3Cvc0HI0gCfBkXJravIR1qy0J3mTZAFQSUTBZUlNLgGQHm1tnxhr71jp6t3zRACVhhxtjzS2TuQkaXC0qTUkOqyfRRqa7wawTe0Ivqh2BNdZPEl3EoFfI0jiq1TTngTbyG4DM9N5cPy8meED2/83R/YvLP/DQCl9EWyOfxcsA+EqQojpEKd/EP+/HmzBPxCjweZiUEq/BHPI3Y+9lNLyn7j+YKihlK6Kf54Q/9sIRkr7ghHYIgAfU0qjlNIgWK77/dgM4HVCyPlgQos/A4er/y2EkHIw7WkqgK8JIXYARwJ4N37sGTApOsDe035S9epP1P0OpdSglO4C0/j1/QXtqqWU7tcIvQY2Lg6GT+L/twCooJQ2UEpj8ft1ix+bRgjZBGBVvKwXGIf4llK6FwAope2d66SUyvHPo/CfDcGrB7TjUM+3lFLaQSmNgs1JOfHyswkhG8DGXj7YPN4XwJ797cB/3i3AxumMeB8sB+Mn2WCS8NsJIbcByOnU1r80/jDCainKvgCMGM4HW3TdYHZuCw1O+9aA3pPCMANoiXHBTArjWTCp5lYwcnQb2IDfHW93M9hOqAhMnXc3mEf/fmeh4+Qy72C5zDteLvPeH4+3egSYRNYJRnANsMw/KQDaDaLnALhFlULLdSFmBYty8O9J5zHT6ITHTKP/LYbfD0/BqGYAr8lQj3mLbHw9/ow3gZkOPATgq/aQ/kxlreKOP9NpAHp4CkZtiV+veApG1ceruwzAUYnUmjLEyMwHsOU7bm8WT7lXLzCOWB1v/8ORMJICfs6zlqtdVI32/M/5Su+u7jtLOkh0ma97XYZO6BqD0Ofcp+y89lVu/dPbxMZLbb38J0waYS8hHO4FcLX7lJ1t7lN2yvfOmDCTc0VJtMYVIaJuA3CiY8LuVHDGJiqq74c6FD8YoRgIJrluA3DGz+/9/024y0sd7vLSbj92zsZJtcfXDPX1AvMwbwdQBraLr0uusX8BFrbNO3F6nbtsJ3eJv7DYAPBozK6dX3PKiJMBvPH1pitshBpzOKqvA/C4EOGHgOI5d3npka+nnvT+qQXzFwBYWDOkLUuxauasgHksgKzun64dnlBrvQ+s745zl5d2b7abd71TkH0vT3W4y0s97vLS/5oD2p62XTT/w3OOjn8tRCeHrPHzZn49ft7MBw54xAtlqn9CCOk1/P7bigAsMYDzVdAdCZA+XDHj/tolM+b8W5Pj4MRSjpC2TnV6ARwP5jCmRxqa1xtytAEA9GjUpvg7ZM4kPZI0OL+AM0mrtVBkvR5T1gO4CLrOG5qeBvYb3mHElNNbVpc/Itis77j65znMie5Y8/frH0gePugbk9NRD91YAcA5ft7MEgBmubFlN9X0VUffccMNYFKmVAAfj583MzZ+3sw9P9avf1N4f2H5L0UF2Bx7MGw7yLEj0CnUG6V0H6X0BUrpqYhr0QghL8bVlp93ui4W/6/jl2sJY50+/5LrO29eCID7KaWF8b88SunCQ10Yx0SwjeoQMAndnxFp5XD1v0wpLcR/TA2uAVuj/Z3eUSGltF+na37gPEMImRPv6/IfOY+CjY3O89ahsgoe7NqDYf+YMPDf48MAIMTNDcYDGEUpHQRGFn8qk+HP3eweqo0/GKeEkO5g/GYcpXQgmE/MT7WDAJjUqQ+yKaWVlNI3AJwCphX4nBByzM9s75+KP1LCCjCGPxksXNDblqLsly1F2QsF3VLOQ3yfgGsDkGcyHHYC7jqw8EKNYAv9JDC1ezOAL8B22QPAVC19wOxg7wMjnw+CSTWvApuQhoGpn6aBTZADwNQTEpi00KeKkUxVCp0GwGSKuT4SVeuXYM4rozq1PwDmNPMDeApGrdyHwJkean8FjFwfB6YCWwAgaBZJpdPK+QDc4ykYFfUUjDqUDeFaABndaSJfTvZJYDuom3sgsbEN4XwAqtpimROLcKIeFQEgcz2pf8xPovudECKEx2fh9WkTqEquBNvlJgJo14mhvcttLr6x/7WRG/tfuwosocPQR7c98Y3W4DzO2r8lS0wPHQfgBAJyHBGMwWqDw2NEJAEsu5gfLM3nhHtnTPjdPDv/xugP1s+HhL+w2L/jnMFNi+ZnvY//bKqmLZqfJQM4YdH8rPfjWbliAOrc5aUvA2jzFxZTd3npwwD8ebL3ode3zdhTuea0HgDOH1e9/bkbvS/dASbBvLR+6IkfA+DrBvqPLj+17pqP+jZP9BcWV+49ediacFJsE9hGbxEAR2pIOi5HrbnrzOavbgWb/Ea6y0v/neJ1uXtow7MZZ54W95BfP37ezM5EAUtmzOGXzJjzzP7vcc/5aTmzp2nx78vATAMeipq4C0wWiwLg2SUz5qwHgF1asHGXFmxbMmNO4ZIZc65YMmNOD7DN40oAN1FNew1Myi+CIqS0+fOMmHKmoagvRZvbCmKt7YlgqmdnIhW/IRyncpJYIbocZ4NJSC5NPXLIy1TVp4p2qwRgkm/rzmjLms2jwQj4/kxnEwDsHD9v5rp4uzePnzfzofHzZlb+ZK//fXE7/jtUGuLffywz2S/BGwCOJIRM3F9ACBlDCCkAI2sXE0IK4+VJAB4Am6v32wKK8c9pYPNyPaX0kvhC+0vs5csAnB2vawLYb+73xGIAl8aliCCEZBJCUuL3PZkQYo4f229zyQHoRildBrY2uMAEF380Dmv/U0ojYGvsTfF69xJCzgKYsxohZFD81DLEQwaCaUr3Xz9zP7HqVO1ZhBCOENITzBRqB5iTdGG8vBuYSv1gyCaE7F+/J4MJtn4NXAB8lNIIYXa4I+PlqwCMiRNJHGAS0Bnf47+f97tOxw72fIeCE4wIdxAWeeOEePkOAD0IcyoEWNjA/VgM4DpCCIm3cXD8fw8wqWwJmABl4I/c9y+DP3SXZynKrgUAucz7EICn5TJvNzB7IxWMYBrxzxzYwnINWCdGwBa198AcgjLBOu99MOmRD0ya2Qtst7gCbJCFAFxrcKqPM8QkMCetL8AkOleDDbxrAOSLqrUIzKGrAGwHd0H8vv9W39wQW6GD7a5+gJatK7meSNrWE0lzwcwS8sDsR54CcKbVxFVZTdzLYCkxD4kb+1/71qPbnnh/PVe3Amx3JABo20Vae+8irecBmC565P0e3/nA/7F33+FRVF0cgH9nZks2PSEBQgkbeuhNWIqAEopGwV7AhlhQsIAtAsqKorGBBRQFxIpgxRJEBAFpK1ICBEJPCCWB9LrZNvf7404+I4JSAknkvM/DQ3Z2ZvbuTLJ75s655+IKF3kvgSz/ZRzfZux703bOiLK0P2YhoyiErH87EbLX98ni1Y3XPvvjklFTnhySAmDU+DZjDwP4Ud4gHtTh2aSl4VMSBpU8m7g0Sz8HTSDzJn8EcMeUhEEbAGBndlYjANltIutXvhK8qOg5oKedB5r0RqMvT3h8pNLPTgBLQpP3by7o1K9iYgYLgGUxtiXfAyja8/tVHxg1z5tbm4V2OeDr3wCyosbrocmr3gDQf7/jinkte/y4yUeqMzR5VSqAHfCHCzJvLDy8zLi/z8GwaV+3bTYkNaDZ85C/i50AHApNXjWgoFO/5aNaTwkD8D2wpx3k30nlSRIQlzjRtyxh6psnLPv/h6zem3plXOJELXX5qqczf9uwG8CtkYp57fqnE7sCmBPQKGpl2bHsSOHxeiBv/+5Q/f0+95WVJ0J+uHcHkBUGY4kbmqEUvuD87bsHQ/4thENevC73i+s+M/eXNWsApAc1jTYXFhYvgkIbC/YcGGMM8O9dsDMtDsC3vjLnYcgA4XsA6anLV13ecGCf47ED+j16uufuv2DCg9HzX3wnA6jiKgEV9Hy9qwC8QURvQH6WbwPwiBDimH47fDYRBUF+tr1RaYDWIABvElHFxAxPCCGyzrIpzwH4nIhuh7wQyoJMjamSIFEIsZSIYgGs1+OAEgC3CSH+IKLvId9zRS57IWR+4KdEFAL5vt/6h/zc8yYwOmJ+SUYOUMVVAioTQmwhom0AboUMzt4lokmQ3yULAGyFvLM6X78d/W9VZjIgx1MEQ+bDlpMczJQG2WufCpmWcTK7AYzR81d3Qn4Xn40lAEYTUaq+T4f+XrNJDsD6Rr8oOQ55sX6ihwDMI6InIMtYjqz03Mne30kbIYTYSkRbICsMHYIM/Cv+7h4EsISISiE7vSo8D/ndv01vYxrkhdRNAG4nIg/k38eLZ3ZIqsd5LWv1b5xrM4ZC/gEHAGgsICYDOE4gf8jeTw0yGF0M+QFQBPnLvgXyxJshA9qOkB8OA/XlIwXEiwTaBiCsJPjoEf/ieqoiVIIMWN+EzKnpAhn0rob8wGwL+Ye8BrJH4BUAX1h6R5842vmkslPWT4D8gFoGmQN6CWQuqyWyXc/Mf9t+2s4ZfQFsGt9mbOm0nTNW6+/vK31/CmSd2R0AGl2mNX94P+V+kEH5mZCDFGIgez+bQ35QvAL5S6jox+bN8W3G/jZt54xhkH/kj45vM/bBU7Xl2cSlgyCPaznkHPSNAKybkjCoDAB2ZmeNh/xgfhzAzDaR9X841b4uRqHJqy4HYCjo1G+p/jgGgFYxbeoJ615f0Knf1ydZbk53DIlaG9xx2og2L7/dqDzrscN+9T+FPBf+kB9M10AOqkosmGUJCR3tLAxNXvUxZI8qIMvF1YXs/WpqcXvbdcrKD14fHZkI+bv+IGQQ+FZBp363hCavagBgQkGnfmdcUmVZwtSGAF6LS5x4a6VlBgC/GUjJ8Cm0U/h8uQ0H9ikGsPzIL2s6ApjhFxm+iAxqC2dm9jHIL58bIQcSdDNY/Pb6PN63hNcbDXk3xQ3gOhBthaqWwOt9EcDjQU2bHAxu1niou6jkFmdWdvuSg0eehLzI/QRAkjE0uLunoMgMYF5omxblzmPZw1y5BXfEJU48DPafoue++oQQXr2H7d0Teu3O52sHCiFKSI5I/w3AfRWVEdiZIaIPIcuQfXUW21pxBiXMqsO5vL+T7Kvi944gY5e9Qojp57rfmqZaZyyy9I7+vvLjwg07GqkevwAVxk2Q+a7hkF86YyBvbW8TEA9oqmeO6jN1hrwlOgoySCux9I52OddmNAHQwmtwNlR8hu2qMMUEFjVIh0wDCIYcxZgDGegehjy5mZBXHoGQX5hL9ZJc9znXZvR1rs0YZ+kdfTonvwdkN31JZLuelW8PFJzmIWkFeQVXChlILIPMab1MPw4vQAYq766mA57WqPssZFB5DWQuby99P27Isl7rILv6e0Ev9j++zdjvAGDazhmP/VNDpiQMWgoZCFfYVfn5NpH1p+3MziLI8/QA/jrAgMlBcpVTblpAH9mv13j9FcDQgk79igE0Dk1eRb0/bDYGwJ6kNxotBYCCTv1cBQ4c6l20dXxBp37pAFaEJq9qCXnF3LagU7/c0ORVOQCSQ5NX9YRtyR1IXvUNZArAbsgBWz9C/j5FApjvNBnmOBpF7IOsP1wMWWt2T2jyqnH6ax4NTV712cne0LKEqX0AXBGXOHHiyZ6PS5x4JHX5qntPWOZdljD1KiU08EWTxa9VWWZ2aOyAfvEAEDug35FlCVNvLs/OuxXy4qcr5IXjNMg7CPdrPu2HcoNiCg4OWSCEaOvOL/IEN7euI4NSXLjrQDbknRCft6x02pFf1pghUzSGB8U0jijLPP61r9zVB8BtYbHN7z++fnOWYjQsBsRHoa2azWg/dAgHq/9N0QC+0HuU3DjJ4L/z6H0iagOZW/gRB6vsArmXiO6E7OjbAtkh959TY6bYdK7NiDIhqAH0mqeQt+YPWXpHlzjXZuRBBphHNcXbyBmQ81lke1uuc21GAGTpqhIAC51rM7YIiAmAGKBohi8Iyq2QowNvgOyhJcgv6XDI7vtRkF9wsZCjRWMgc6f2ONdmfG7pHX0rZPf/P9UEBABkp6wPgQwKnJCDys7Y+DZjZwPAtJ0z2gEYBpmicAxyYoGjkHkwGQB2eEl7IwVZ70EOFEmCHIzWETJQug5yjvYcyJypewC8Mm3njN/GtxlbpL/WOY8KbBNZX0BeTPzfjuOZU7wlZV90bNrsH2eUuQj0BfBNaPKqepAJ+2vwZ/5YDOQI5aahyavCAfRIdwxpcJ/yx0elWuhfUiystiV3A9hZAKSHJq/qD9ljH90gJeTS+A8Pryp4o98fehA7C7JXvQvkRU3FQK++kLdmH4a8oMoUCk2D/F25+oVlW+9Ap36TCjr1ywxNXhUIoLygU7/1ldsQmryqPoD8gsSJa5YlTC2sWJ66fJUFQMPYAf0ql3yanbp81djYAf1yU5evehyyjq+/O79olTu/6EBEt/bfpCz+JTzrtw0u1c9sA9EbwU2j9wkIS/H+jBLIvG8B2Ys8JF949/vI2DOsuDzUDU0AKCraf9AfQlBIbLOvnUePj3EXFqc7s3I2AwgmVWnqF1knvTjt0EwQbTcGBy7xFJWEHV+/+RMAmYrJNCugYf2vYgf0O3o2J5XVfPqI67/N3naBXrsqa5pe1IQQd53DtumQMUSNdS7v7yT7mg5ZGu4/rVpTAk6Xc22GCnl73A8yaJtl6R2tOddmGCBv738HmVh/WFM8e72G8smq1+wvFK2Nwev3G2SuamPI5PJRkGUldkOmIiRDfsHbIHtce0HeMl8C4H1L7+gNp9vO7JT1jSLb9TznXptpO2dEQgYgwZC5qash0x2OQ+bh3ALZq7wRMtgug6y6EKFv8w5kDtE4yF62y8a3GVtwru06HeuWrp6/f9oXeTkrtlwC4KZxrjV/uwV+Mbh2xSsPbQxq26PYEPg6gDoFnfotC01eFVbQqV9+aPKqeyBLkayE7P15as/vV405ZK7/Ste79h6q2Edo8qrmkMHb55CBnPPDrx2pOf4my/v+Q16ITAu6be1d+ztDBrF1ALwbkGN61xnicTXcfqDusZbNSt2Bmpi92374wRYTLR7FuErxaY/enpz2+kddm31S0KnfvmUJU++E7O09PPKaHs8SxPoM28DKPesITV71LYAXCjr127QsYeosAGPiEif6UpevioW8OPsudkC/T/UAdgtkD/AhyPSY32MH9Pv/BAj6JAM+ANcENm18wJmVPU5zeUaAyCi83nGQaTQq5IXYEgCdg1tYN1BBaUhpdo7HCzEQMs3BBfn3fAxyEIYKoKviZ0pUzebrPIXF0QAOQ1U+gE/7ArIndn5c4kQOVBljrBaqFQHrmXKuzYiDHOFPlt7RB/SeWBWyB/RjyFJTQyCDYCfkwK9VkLfg74cMAD2W3tF1q6H5AIBpO2dYIYOaVyDbWQRZXHgvZA/xMcjcxCOQKQ0hkIntZZC3gP+AHFDz8/g2Yz/HBTbd3OdVyHSB4+Nca3r92/o1VcEsy2TI3shnAAwJHe18/YTn1dDRTt9JtnvIRYa0bl0XtCo2BB6D7Nnfjz9zr1sCWJOy4dpLn276yOwfIvq/AeBgQad+34YmrxoFGaA+d1meo2hFuC0LsuB1i5u3H7T93DxqdIHFdDvkhVeXgk79poYmr7oUQnza1dJFDAABAABJREFUfE3da/f1zh4YfCx/VNsyX8zv1vCSwQVrN60N6flEkcEw8qZtB9/4PrbhO+VGwwLVp91Zt9TV7OnVOxwv9W2b/XbSptSfWkT55nZtNhTAyIJO/bIAIDR51a0Fnfr97XdoWcLUcACmuMSJ/x8ck7p8lT/k72XYkV/WHDsxQFyWMHU8gIJgMjxZLrR9bmglkBeiJZBVFNZCBulGyAsyQ2CThs+UHDzyM+Rgy+8h7yh0hkxrWK/4W5opquL1FpdGQ1YUOQD596LqxygRMjUiNS5x4kU7SJAxxmqz/2TA+k+cazPI0jtaVHrc1NI7+kClxxMgbyXYLb2j91RHGwFg2s4Z90HmrIZD9vyGQQaiYZCVCyrKSm2H/PL2h/yi/gBAxPg2YydO2zkjcnybsWc0BWdVmm7uY4AMzPpD5iVeBuCPca41/1o4e7q5z9UAFuuVGS64glmWPpCjO62QQVK0I6h9W3uT0dm3ZC9pecvxn59XoN1vEt4tAH622pYcggyOPJDB5ubZu+y/vtXw1vAX0t5Ondb4jvGrwrqvhRzBfByy7uj6dMeQwEebPTF1UeSA+ypmngpNXjUCQEagp+TtbsU7QlaG9+gOmY8cCdnb/jFknuol+ms1BmAILHdPfX759gfHxXfNgwz4EgBkPbPloFEN8G98oLTE+XHnmDHzv1h7o0kTy265sdeResXOl0rNxpFzvtvwC2T6wFUT4jr49kQEryro1O9vpcv0PNa1kL+X3wB4Jy5x4sKTHcNlCVO/hOzZbAnZ0/8g5MQD/c1QDvgg6nkh0iADymsB1IfMxe4CmTJRoPr7tQ1t1Wx37pYdGyFLtIRDzqBzBEBrxWI+FtSkYURpRua73jLn3ZAXbpmQaUU99ePQ+MTyXIwxxmqXiy5grQ2m7ZwRD/nlPBLyiz5Ef+otyMEo+yGDFSdkb9Ru/V8JZH7k6PFtxu6/wM0+penmPmGQ+TVXQ/aafT3Oteb9U6wbA9kbth3Ah5CTFHQY51ozt9I6jQCYxrnWnLfi7gWzLE9C9rb/CDlo7iEAPxw1RgzMMtWp83VkXHZLZ8b2O4/98E3oaOfsS9fMHj9n1+QBvbp+Ot5HakfIWoQPALg/tnT/W+/vmbLv0s4fmQo69SsOTV71NWSpMFe6Y4ja9pJvkkpVf3/Ic/cDZGC3sGVpWk+naqZDfg1ehiw83h2yDnCP9iV7rswy1tmeba7zAoDveh48Hv9HwzrXeA3qqwWd+n0fmrzqV8g0gaj6Rc5nyo2K4lWUeiVmI2KPF37w/K/bD91wS594o0/r2jq7aOz2+qEtAATHHi987flftz8QlzgxF5WkLl9VF4Alc6XjXs3jDQAwHoA5LnFiuf58ryO/rDkKmbJyHMDuuMSJLy5LmLoa8vd3L2TFj276/1GQv8dvQNazdUMGq9dBVqY4BjnjVylkz2s7/FnfMR8yrWA3CC4oyn74tMGQFUCuhSw19mxc4sSLMh2FMcb+izhgrYGm7ZwxYXybsS/qP78KWXeyHmTeXgBkuZTekIOqAiHLXbWA7IG7e3ybsVU1c02Vmm7uo0Dm2bYc51qzZrq5jxWyx3HSONeaQ9PNffZDBm1bANwB2YN3DYAJ41xrbq+0n74A/Me51ixBFSuYZUmATMP4AbK00gOQ5dVUyJJixZCB4ALIIOpJfdNn9liiRXz7mdnZXeJOOor+VEKTVwVDDiocDRnIPgoZnC2HLLVmgKxiEWvyel/rfTCnk8Xrq/9T8/pXC1UtjCx2lgV4vK0KLOY5BRbTm5C5oxEBLs+CUrNxhb6PtyEHVP0UmryqHYBrJq1MaTWna7O3d106ZENo8qorex3MHncwxN9+JDSgZ0Gnfq8tS5hKAHrFJU5cm7p8VV/I36/e+Tv2bi47eswPMv/bTQb1gDk0ZI0Q4pgrN/9TyN9NG2Su6fOQFSbckL/DRZCVOO7X39dtkBdlXSAv0kyQlS1+hrxr0BgyFWAiZBrPUMhe7Pv1Y9QAf6YUfAoZMGfHJU78yxTK7PwhohIhREUR/Vchp6NeDHlBUqbPGX8XgKVCiKP6enMhL14I8u/sLiHE33r0K+/7hOWjK/b9D+26C0A3IcQ/lmgjopWQF1BOyHSd6UKIk15Qs7/SJ4BYrj+sD3khWXFXr7sQwl2FrzUFcirUZUT0KID39ckKoM+ENrwq69sSkR1AiRDitZMsvxfyffpBDvweI4Tgz5zzjAPWGm7azhlTID/UlwN4HfLL/3rIXqYsyJ4obXybsWcyE0yNoAewKQB+Guda89h0cx9/ABjnWnPibCznVcEsSxBkT+puyF7gUsjSOKkA2pTDsFOFr44Rou4W/5bHY8qP1HeRcV89X8FiyALMuwGsCR3tnFwV7QlNXjUTMqhrVNCp33OhyaumXJu9/KGpaW8l9G+zcOvoP/YNyPY3HfugS1PFZTS8SZqmBLi97hI/06aW2UWRnY/mzYhLO7Z4cYuoHd+2aTwLRGmQgd7zBZ36rQlNXjWpQ2beJelhgVTkZ/oOwFd+Hl+zyw8c67a4ZVRzEH331YI16wF871Yof0XTeubB+7J2QFaq2A9ZdaMv5ICrNpBVLGAMCnSTqnjcBUXrIatZ2CB/P7/Qt+sBGZyuBjAb8oKkIp0gHnLwZHPoPcP68W8PWR6MIL8M/fXzUwR5698Jedv/c8gvkGVxiRMrJl5gJ1i87PBwnFA4/sq4RudUOP6EgLUQQLgQwnfCOisBPC6E2Kg/DhZCFOk/TwNwXAiR+E/7Pot23YXTD1gfF0Js1Gcr2g+gXlUGWzWFc23G386/pXfVTBxxsgCPiAxCiH9NATuL10qHPLc5Vb3vSq9hx6kD1hIhxGt66bTfADyjz2TGzqMaU9aKndK7kF/ETSB781ZC9sTVAXB0fJux7mk7Z7SsvuadvXGuNRqANtPNfS6fbu4zcpxrzbzqaEfoaGdxwSzLVwDuhLx9bYa89dwAQJkKrcVRU+SyHGP4oK6lu8JcwFELDFbIXs+vIfMpP6mq9hR06jcGAEKTV3UNTV71AoDJu/2ts/w1V86ePoPd6DPYEZq8ygbZc2qHwD0Wr29FYLHz/TuT0ya0yi0+DOD2rEDLKqNPG+UxqG80zS22DNt1pG4o8MVXC9bcfNzf/OgX7aItv1nrbn5iTerwtLCA4d0P5y66e8uBjLjEiWuXLVjTCQCKzUaUGg0tIKc2nAwZhDogq1Isikuc+MWyhKk9IKf5m+8pLF4GGWTuhgxwW0LOllVHP15DIHunv4bsCY6F7Jnxhyy/thcyUBX6ti7Ii5pL9edug6wxuB6yZ65c/9crLnHiI1V1Dv6L9GB1NuSxBuRnyuzFyw7jXINWANBneQoEsImIXoI8tyWQVRy6AfiMiJyQc7JXBKsEOaPbKXtOiGgq5Ow8TgDD9Fmz7PgzaLgEwFzIOyG/ALiiUsH4BkS0BHLA6rdCiCf//gp/EQh5QVQtufPnkx6s/u38O9dmoKqCVuD/BfHLIcdWrCWiBZDVfPwgz+FIIcRu/YJiqN6e/58fIlIhz2c3yN+LD4QQ0ysK7UN+LjcAsIKIcoQQl1UOYIloPIC79ebMEUK8oU8k8BNkecFekJ/Zw/RZou6FLANpgiwPeXtFz+1pMOnvK/8sDhU7Q9zDyqrddHOfIACWca41x6eb+3wC4O5xrjWef9uuKhXMsrwKYKQbavk3kQMPxOWt7fBpvfhDRuHTxmR+aQXwylFjxNUNPDltIdMCQiG/INsAOBQ62lnlf0ihyasUAP4nG/x0KssSpnaOS5y4ZVnCVPMHnWPinQZ19LfX3zxowZRXX4koc88AkJUSGfzsZx2tkdemHvZ2P5I3HjIFY9rCNo1uTW4Q3i7A7W016bedkyFnWQsB0CUuceILyxKm2gH0EoBnS1TY9i6Z+f6Q1TV6ALjG5/J4nceK6gRG13FBBprrIFM6PoD8kugImd/6OWTptcmQAwhjIT/0G0P2yBoge1pzIb8Q8iGDWgNkqbaxkCkGxyFLbNWYfO2abPGyw+mQQcqJDl4Z18h6tvs9oYe18s92/BlUrkSlHlb9+XmQ6QM7AcSfLEggIgFgqBDiByJ6BUCREOKFE/adAuBeIcR6IkoEcJUQop0eED0LGTi5IC+i+gghDp3wGishe/RdkKlVjwoh/nOF151rM9JxivNv6R1tPdf9V5wTyFSpCMiA0EdEwZDpG14iigPwgBDi+lOdH8gqI4lCiIH6fkOFEAWVZ4Y6sYe14rH+/j6EvLAmyHz22yA/Q/bp2yQT0RcAvhdCfEpEdYQQufp+XgBwTAjx9mmmBDQB8BPX370wuIeVVbtxrjXFkEEgAEy80MGqriGAVW7F2CvKffySXENo9g7/FpGz9z1fAlkqqbkerC6HnM3sE8hb0dGho53nJWe4oFM/DX9WgzgtcYkTt+j/u5Aw1QugFNcDEWXuLyAnEujQPK+4+9BdhwNmdm8xYEKPOBeANssSpr50887Dxht3HnYpMl/0GOSH/V4Avy9LmPot5Mxng1dZ67b6qUXUa10y8z+D7BmJU0zGSM3jK9C83kzIahWXQfaK/gD5RfIO5G1/I4CbIXt6giBzgw9An8IYsirDesje2QWQ5cTyIIPZPZBpCCUAlsQlTnz8zI7oRS/6DJefV0KIkXpv2tuQvxMnu8PihuxVA2TliL/M1U5EoQCChBAVk13Mh+yNrbBcCFGor7sTMsD4S8CqG6GnBEQCWEdES4QQ/7VBexfy/H9ZKS0kBMBHRNQCf5asq3Cy87MDQFMiehuyhN1f6kL/iz6QPbWl+j6/gfwc+h5AmhAiWV9vE+RnDQC00wPVUMge9p9P43Wm6xdLRgBfEdEtQogFZ9BOdhaUf1+FsQuqWC9p9X/ZKeuV7JT1H56PFyuYZalTMMuyCPJD7cpArdzbr3CzoZXrUL1Z+57/EvL2dwDkYLBjkB9y0wBMCR3tvDN0tHPN+WhXVYhLnPh9XOLEa/WH+yB7O91+PvF0z8N5Oz/69vdGyxKmrliWMPUJAO0UP3OdgIb1F0L2MvWBzJfeCjmI6iiASQDS+qcf//nlX7YehQzcfwKRL7x962zFqG4LblovETIF4HXIEmw7IScmeBiyJFUrAPlxiRP3QU6OsRfyYiEU8ossADJAjYZMH5gLWR3jQFzixA5xiRPrAricg9WzcqoLq2obpKkHNQsAXE9EKhEl6/8qZgv0iD9vA/pw5p0slevu/uv2QohsyPzxHmf4OrXBhTz/pZV+fh7ACj1N42rIuykV/nZ+hBD5kHdjVkKmv82pojad6nfhQwBjhRDtIQcB++E0CSE8kBOc9K2iNrJ/wAErq2kUyBzS/4ts11OD/NA7HxLxZx7VHsiZwgiAospbVb0hg73XIQOvbqGjnU+fr17V8yUucWIBZOWFuQBsuRZTgyKT4UHI2/HRAEIMAZYE55Gs+yC/LOIgA0kj5K33lyF7QwnAQX1fOwD8CCH252zaXpGfZoKcmGMmZE9XI8hbfUGQtwo9ALzLEqZOgbxt54HMxd0BGahuhky1WAf5ZfVDXOLEzyArB1S8l/SqP0IXhQn4c3rgCmX68vOtGPJ3ACQ1r/gZ8u9vlxDCJ4TopP979nR2qo8KLyaiigDzlnNpJBH5Q/7d/xfTTKrr/IdA5owCsv70PyKiCACKEOJryIvkLidZ7f+/TydYDeAaIvInogDIMner/+UlgwBk6r2lI/6tfSe0lSC/I/6Lvy81DgesrEYZ51qTCyBsurnPTZWXR7bruR8Appv7/NugidNWMMvyOmSPnhcyYG2vPy6D/NvwhyyS/wlk0PVh6GhnlY94vVDiEiemxSVOdADou71uSOPddYK66k/dDCDUnVsQBXksPoK8Tf8dZCmqupA9rbsgv3zqQn5h3A55qz4HMljtBdkbcl9c4sR8yEFWQyFzTsMhe12aQOanDoUcRGiCvO23D/I43xmXOPG2uMSJI+MSJ+bGJU5M1ttea497TaEPrLoX8oJD6P/fWxUDrk7DhwBmEVEy5N/VR0S0HbLechRkqsfZGgVgtr7vAMjBfGfqM337TQA+FEJsOof21Ej6wKq/nf+qHHB1Cq8AeImItuD0esgbAlipn49PATx9knXeB7CEiP4yMl8IsRnyd20DZErTHCHEln95vWf0dddCfsadjnF6+1IgL+TfOc3t2DngQVesxplu7mOCHGTzPGQv2xfjXGuuBoAZI55Osrxwx1WjYmLP+Re3YJblYfw5J30w5If4YshgaoX+2jdATnH7QuhoZ9q5vmZNsCxhqhFAV8jJJ66CzDd9EjIojYHsTV4AmTv4PWQAMB+y90GFLPZ/CWQvbA7kqP3xkLfSJkB+wVwDeZusM2SeazDkrbNmkBcGX+r7LIasqbo9LnHi/yeHYOx0EVFgRQ1XIkoAECWE4IoRjP3HcMDKaqzp5j6Wca41zunmPv7jXGvK1qc5/FPunHW05Pd9PgBPjHOt+aBi3YJZlmYADpzpaP2CWZZ6kLfJ60MOELoVMlj7LHS0c2/BLEsjyN7B1NDRzuoYDHZeLEuY2hWyR3Qw5CCUOQCGQQ6AWQEZpLaGTM/IhEwbSII+KhYyeB0LWbw/ATI4vQZywMLlkD1drfHnNKm/QubBlkPmrnYC4NB7Yhk7a0R0M+RFkgGy1/AuPReVMfYfwgErq1U+vuq+Ue5dh98oPlT0KYCXx7nWpANAwSzLawBeDh3tPKcvqoJZllcAPHU+ylTVRMsSplbMPjYWslbqF5Clo3Ig81SLIQP5KMiZrSpG8a6BzEHdBVmeahNkEDoVcgrZiQDUuMSJ/7ni64wxxi48zmFltUreyt29A5vXXQxZ2/NwxfLQ0c7HzzVY1ffz5MUSrAKAPoWpAqBNXOLEMXGJE1dBTkd7BWSwmgkgLi5x4mDIQHUlZH7ryrjEiS9BBrb3AtgclzixK2R5qrlxiRN9HKwyxhirKtzDyqrd+jRHfQC5PWNsHv3x1QCU4q0HC3bc/Hb/ca41z1WsO93cJ2Sca83/B1UULp3WCkCzkEHjF1/whv9H6b2ufeISJ/62LGGqBUDduMSJ/7WalIwxxmoRDlhZtVuf5ngHwEjIEePbIXMe/bIXb9OOLNgYGdG9UfNGYwe1hCyvNAzABz1jbFkAULh0WiMAjUIGjXdUU/MZY4wxdp5xwMouuPVpDou30HSXGuz2I0JLyFHq9SHzIT+DDFrvF5p2iUp0NNAL/yIj/QxZkP52yFvTdwN4v3mp2BTZrqerYJZlNIDc0NHOL6vnXTHGGGPsfOGpWdkFsT7NMQJyNPrzAC4xGMgDWbD5IOSI8nwAj/SMsf2ur381KcozpIlmJpd3/8/zl38x+M4hlwD4omeMLXd9mmOf4sVbpS4lK1IOGJoLWXCeMXYBEVGJECJQ//lVAFdClofbDzmH/Mf6vPFLhRBH9fXmQk7ZS5ATdtxVUZrqVPs+Yfnoin3/Q7vugpw7fuy/tN8I+bl0PWTetgvAFCHET/oc9RXTRqsAvgHwghCivNL2wZAzui068bWI6FvIUnGBACIhaxMDwINCiHX/1K7agIjqQE5XDchOBx+AirEE3YUQnMfOqgwHrKzKrU9z9KgUeI6BLIn0AOQHvgeAxxjg1QjGLW54SgC0dzldx1Z+sWJnT7sNANAzxvbY+jTHjT6Fcj7/dkUoZD1UgkwLyO0ZYzu65t2Rqcmidb3QP/qPBHDb14M3D0Raqm1UTCynBzB2EpmLlg4H8CLk32QGgAlR1wyqysLx9wEIrzSPfIW7IIusH9UfjxNCFAEAEU2DvOhMPN0XEULMOvem/t/zkFUw2gkhXERUD3LwYIXLhBA5RBQIWbD+PQB3nrD9b6do57UAQET9ATwuhLiqCtt9xsrn7vjb+fcb1fasz78QIheyOgiIyA6gRAjxWsXzRGQQQvCkH6xKcMDKqtT6NMfvABqsT3O8C1nncyCAIsgvqvqQxeUneeBrD/geBlB+ZN+RZ9zl7hGhkaHXr09zWHrG2N612+3pAAYPvnPITQNvH/Rxzxjb/wf9FMyyvPpmw+GLHqEF+SnODtlZxvr1Wj+UNgBpqQEArpiblrodwLBRMbEXYgYfxmoFPVidDTnTFCBLlM3OXLQUVRG0EtH3kD2Jm4joJchpf0sApEP2pn5GRE4APSsFqwTAAjlpx6n2OxVyggsngGFCiGOVgyMiugR/3mH5BcAV+rz1ANCAiComrPhWCPHkCfv2h6xyESOEcAGAEOIYZHm3vxBClOg9u4eIKFwIkUdEXSFng1uiv8caSw9W/3b+y+fuwLkErSciog8h6y13BrCWiGZCTtUcCTmL4L1CiF1E1AwyBSwAcla9R4UQgUQUBWAh5GQjBgAPCCH+bXpVdhHgslasqoVDzh//POStsEcBNM5IPfhuXlbeSgBbIGc6yuoZYyvsGWNzZR44KsLqhW3ucaXtS6/bu9Vut7cAcMnjd489fOSBBfv2Xv/O+oJZlggAKJhlaeMTyjfFKU0X/1o64KBbmAYuyL3zZwAYFRNbOiomdjLklx9f1TP2Vy/iz2Clgr++/JwJIYYCcAohOgkhFlZa/hWAjQBG6M85AYCI5gHIgpxg4u1T7DYAgEMI0RGyF/Pek6wzD8D9QohOkLekK+sEOfVwewA3E1HjE55vDiCjIoA+jfdYBHlbvwURKZCzwj1+OtvWAOf1/J+gEYBeQojxkL3SDwkhukIeq4ppTN8E8KYQoj0qlSgEMBzAz/r57AhZ/5kx7mFlVWN9miMCwNUAjkHOBT0HcvajiQDeP5ZxbJ/BZGgMYBWAlT1jbP+f3/mh+x96puJnu92eAeAeu91uL8nIWdpkkK3R+slvvgW/Bl9+M/+LzzNz7uw/IuKjNwJdnt7bXN0O2e32ry+X2z1jt9ufB4BRMbFldru9o13mVqmQsy8lKIoyOiwsjHJzc9MBfASgnd1u/8ThcCQAWG2z2dae9wPFWPWJPsPl55UQYiQRqZDB6s2QgeeJ3JBTBANycoqBlZ8kolAAQUKI9fqi+ZC9sRWWCyEK9XV3QvYqHjrHppP+/4MAFgshDsuO4hrvQp7/L4UQPj2NoheALysdI7P+f0/I2fEAed4qUgn+APCBnlu8SAiRfB7ax2ohDlhZVfFAFpmvB/mB1B7AbZBz1s+8ZHD3YgDHe8bYpp9s4+yU9SEAWtrt9j8A2AEgMDpi0HJX2ldX//LOYUODxj+0zz62ZtuenbNDRzuFvdK2drvdAkCz2+1bIXtsPobsyc0H0ALAVgC/BQQEDKlXr1633NzcIMgC+Bl2u/3efv36XW2xWEqr9nAwVuNkQAZsJ1teLfSgZgGAJ4noY8igFAC+F0I8C8Aj/ixl48OZf2e5Kv18su33AYgmouDT6WUloiAAVsiBYg8DuJSIHoRMhTDpg8QSzrCNF8qFPP8Vn6cKgAK9t/S0CCF+I6K+AOIBfEhE0/5pcB27eHDAyqpEzxhbIYAl69McEwBMAtAKcs74XwHsBtAGf94KOpkwAO0gr67/b+yIe28EkJ9fWPDrZz98eaXdbo87ybZPA3gEsurAVZBfHvl2u13TX/sufb1xFRvY7fbOAGIMBsNMh8Mx2+VyvWiz2ZLP7F0zVqtMwF9zGAGZUzjhArx2MeTfZ0XeajMhxD7956EAdukDtTqdyU6FEAVEVExEPYQQvwO45Qy3L9MrFrxJRPcLIdxEFAmgvxDiLyXy9N7CdyB7/fIBjKj03F2QFQlqarAKVMP5F0IUEVEaEd0ohPhSP98dhBBbATggKzMsRKXzRkRNABwWQswmIjOALpCdEOwixwErq1I9Y2xfrk9zHAJwK4CnAAzqGWP7viQjpw7+nj/1f5HteqYDmFe4dBoB8A8ZNL4UAF77YMaQFk2a7sk8fmzIo3c+sL4kIyc0MDqi4ITNv4YccNHWbrf/7Xafw+Ew5fibth0LsiwE8OqomNgSu92+xW63bw8ICOgUFBSUdM899yRXwdtnrMaKumbQ/MxFS4HzWyXgVD4EMEsfdNUbwEd6OSiCvAPywDnsexSA2USkQaYcFf7L+ieaBOAFADuJqByyd/DZSs+v0AMtBcC3kPn5tY7fqLbzy+fuAKqwSsBpGgHgXSKaBMAIYAHkOX8UwKdENBFy0FrFeesP4Aki8kAO2rvjPLeP1RI8cQA779anOVqGU/CjDZW6qwKjIxb+07qFS6e1BzDxaJF38cKUslUAuo+8bvhbx3ft26aAWrayXdI6MDri/7f57Hb7IMgPxHsgR6GGAsiy2+15lfc7Ny1VgSy589OomNiDlbZXAcy12+13napNDofjJgApNptt5xm+dcbYeUZEgRU1XIkoAUCUEOKRam4W+xd6hQanEEIQ0S0AbhVCDKvudrGaiwNWdt6tT3OYATTuGWPbpz9eAuDGnjG24hPXtdvtPYLM9KXQhFbiQQYA57UDryoTeUXLjiSvOuyKMHXadszztd1uT9HXfwJyROo1kIOrbJBpAHa73b7jxP3PTUsNBRA8Kib2tPO2HA5HJwBHIG9pfgAgStO0uS6Xq4We+7oTMl/2d5vNVm35gIxdjIjoZsi0IAPkRCR3CSGy/3krVt2I6FIAMyB72QsA3C2E2FetjWI1GgesrMaw2+1+kCVjAiDL4PymVwt4ds5Xn2QOaeL55VChb8jaDNePdrv9sL6NClkzsQsAKwEuAYyy2+2fVd63w+EIAnDdjnohewG0BTANsj5so1ExsX/7IyhcOu0BAAtCBo3Pr7SPByGrDrxUUFCwyGQy3SqEWB4QENBV0zSfEMJfVdWJkLUgS222P2vHMsYYY+zsccDKqlXh0mmXAiibvq5oH+TV9rWQSfi7AeQAyLgmLv72n3ZsbOXKPJYCWYrKBgB2u/0mAN0hKxG0DTHT5ZfF+G1dtMspANxut9s/r3gdh8PhD2CgzWb7DgDmpqVmQ+bUho2KiXU7HA4VgGaz2YTergEA1qYG9woG8L7NZrtG30/70tLSmVlZWeV+fn6XWiwWQ3h4OHJycgzFxcUiJiaGvF6vz+VyOQMCAvoCyLLZbJnn+zgyxhhj/2U8cQC7oAqXTrMULp02uvIiACV2u70QsmZrXwBf2+32V+x2+wcturQMPu5fkNLL1ntPUEDge0NX56Sl9B+j6tuWQ85+8wyAXwtdYt6iXc50yELTLSu/rs1mKwPg0ANT9DvsGB3uLPABuN3hcJgAPATgGrvdbnY4HPVSg3s1Sg3uFQ0ZNL/pcDhiHA7HAgD3E5EhMjKyXsOGDTd7vd4cAN84nc78BlFRBJcbiqJQRkaGxePxPAJgksPhqHs+jiVjjDF2seAeVnbBFC6d1hnADZC9qF2mryvyAnjObrdPPHHd6eY+AwBEj9izIJOAl75bvDhCUZVW3edviWy3cuZBu90eA5lT2sFut39qt9sVyPqIt0LWWP0Jcu7yPnp5KzgcjucBzLfZbKmFS6e1T/dv94jTEDwPwGSbzTbIbrebzWbz2ssuu+wXyGB4MGRB8xGQha2HQ06I4AXwA4CS4uJiLSgo6Eufz/caebz7aW9me2pv/RUyL+sNm832y3k5mIwxxthFhANWdt4VLp12N+Q0rXUhJxb4OWTQ+HcBwG6397bb7X+ZYaokI8fg83i/+aDNDaPu3bsoCcDHhTsW3D/795w+ek8s7HZ7b8i5poPsdvvf5v222+3zAIyw2+0mvQ1tAFwaMmj8exXrOBwOBbJerMlms23Vl5khJzzYBFn25T3ICREesdlswuFwPAJZUzILcpDXLsic1Y8BJEKWyDEDOGKz2Xad88FjjDHGGAes7PwrXDptA4Dwcq/4/t0NxTvtdvucf9tGr9u6UfO5LCDSfl7507wd6YfWmd1aYYvDZbaBtzUYCOBKAH0A2EIGjX+58vZ2uz0KMpicbrfb7YVLpwUCaBgyaPzuyus5HI6uRWZDg0Gdu/1wqrY4HI6uNpttk/5zQwD1IVMRUjVNmyyEIFVV6wF4FUCazWbTzuDwMFar6bM7Beo/vwr5d7kYwH4AZUKIj/XC+kuFEEf19eYC6AZ5J2IP5Mj+kn/a9wnLR1fs+x/adRdkMf+x/9J+I2Rt1eshL0ZdAKYIIX4ionR9GSAHXH4D4AUhRLm+7SuQMzIpkBeuj1SamQtE9C3kxXogZNm9NP2pB4UQ6/6pXbUZEdkB3AsgG3IQ7XYAk4QQO/Xn/+mYT4WsvRp2snPPLl48cQA7rwqXTrsxp9TnKyrX0huHGrJwBvN4r54009Bx9FXHA6PqtRnStWnjge7HlQ/2jCkPNyv1AVwfMmi8r3DptLUAgguXTnsuZND4yYVLpykAYu12+w673Z4NYLDdbp9it9tLIAdy/YXNZts0Ny314Z+SN20LK/cMt9lsLwGAw+G4FcB3NputrFKweiWAm/BnesAht9v9cGZm5lcxMTFTbTbbgXM8XIydVwcnvTUcJxSOb/LCw1VZOP4+AOH6rFWV3QWZonNUfzyuYipUIpoGYCzkHYrTIoSYde5N/b/nAUQBaCeEcBFRPQD9Kj1/mRAiR5/p6n3Iuy53ElEvyEkQOujrrdG3W1mpndcCABH1B/C4EOKqKmz3GbPb7X87/3a7/XxNHDBdCPEa8P/SY78SUXu95Ng/HfMfIAfg7j1P7WK1FA+6YudN4dJpnQA0sxhpab0gdV3EFY+9Yrfbfz7Jev6FS6f9WHlZYHREboc7BzwZ3KjJfEX188GvweBSpR5a9ezeruuN9Z2/7HNeZrfb+01fVzQ+ZND4H0IGjZ+sbxoOmccKyBlw6gFoUXnfDocjzOFw1K94PCom9s6wcs8RAMsqrTYMwPeVt7PZbIsB3G2z2ZZC1n3N9fPzy42JicnkYJXVdHqwOhtyPnnS/5+tLz9nRPQ9ZE/iJiK6mYjsRPQ4Ed0A2Zv6GRElE5GlUrBKACwATnmrj4imEtFWInLogQ0q9q3/fAkRbdP3/SoRpVTavAERLSGivXpv6In79ofsCXxICOECACHEMSHE39KM9B7g0QCuIaJwvc1+AEyQaUBGAMfO8LBdMHqw+rfzry8/J0R0h34OthLRJyc+L4RYCGApgOH/dsyFEA4hxN8qqxDRjUSUor/Gb+faZlb7cMBaCzXOxLzGmQir7nb8k8Kl06IBfAWgc4OrHp/c4KrHn9WXqyeuGzJofBnkYKyKbTsULp3WPNC34Qrfro/nCG95jnAXBgVcvvLRltamI0MGjX8m5binE+QXxk8n7CsnZND4SfrDTZA1Wrfa7fYn7HZ7RZDaFXL6v/+z2Wxem832R6VFMwBcUXkdh8NxY6WHQyFTDl602WyTwFjN9yL+Pj2yv778nAkhhkLOXNRJD1Aqln8FWVd5hP6cEwCIaB5kLnhrAG+fYrcBABxCiI4AfoMMdE40D8D9QohOAE7s2e0EOXCyPYCbiajxCc83B5BREUCfxnssgryt30IIsR7ACsgc90wAPwshUk9nP9XkvJx/ImoLmbt/uX6eTjXL2GbIc31Gx7ySZwEM1l9j6Nm2l9VenBJQOz11KAr5/77ahVW4dFosADuAqwFMhux9EJWebwBgfuHSaYmQeUtXAdgC4MeQQePLCpdO+wpy1qqlAL4LGTT+DgAoychJz/REh9bxU7OjIusXlWTktIDMj/MH8Lc8MD0wLQDQ0m63J9jt9gUANqiqOnHmzJmTsrOz19vt9mUnbleZzWZbc5LFEZAXeRqABgBuB5B+ekeHsWoXfYbLzyshxEgiUiGD1ZshA88TuQFU3H3ZBGBg5SeJKBRAkB48AjJdp/Jt9+VCiEJ93Z2QvYqnnZZ0CqTvrzmAWMjPLAD4hYguFUKsPsf9ny/n6/xfDuBLIUQOAAgh8mTH+d+cdOEZWAvgQyL6AjKXmF1kuIe1FjoUhePV3YZTeA0yib4Q8nb6pQDGVHo+C8BHkF88xyGvyh+AvOoGgBsBXBUyaPyzIYPGb6q03X3+JvIrKir2ibJDD2nZK+97/O6xv0D2mphP0o5bGjRoEB8eHv6Ew+F4DsDORo0aHfH5fMHZ2dlvARhmt9uvtdvtkWfy5mw227s2m82rP/wF8oKvqcPhuO9M9sNYNTnVtMHVNp2wnuu6AMD1RKTqt/WTiWiKvoqn0iAmH868k8VV6eeTbb8PQDQRBZ/OzogoCLJ83h7I8nwOIUSJni7wE4CeZ9i+C6m6z39nAKk4w2NeQQgxGvI7ozFk2kmdqm8iq8k4YK2FGmfirupuwyn0AlAK2evZPWTQ+DQALxUunVZxK74/gMcgg9lhIYPG+0IGjb8sZND4zQAQMmi8CBk0PufEnQZGR+xYn5K9zuzNvFxLm7tElB8fA6DP43eP3fD43WONJ65vt9vf6NChQ9vu3bsnAJhht9vd7dq1awpZhsoHYBxkqsCjdrvdz2631zuL95oAoB3kNK+/nsX2jF1oEwCUnbCsTF9+vhVD1k0GSc0rfoaeXiOE8OkpA52EEM+ezk6FEAUAiomoh77oljNplBCiDMBcAG8SkUlvUyQR3Xjiuvqgq3cALBJC5EMGev2IyKCPeu8HGZDVVOfr/P8K4MaKAFLP7/0LIroewCAAn5/JMT9hH82EEL/rvxvZkIEru4hwwFpLNM6E0jgTQY0z4YdT5whVi8Kl08yFS6etPlTgKT9a7B0L4N2QQeM/AYCQQePvA9BEv90/BLKX9Y6QQeNfO5PXuMJWPyG4XqtQanRjH/g12B8YHfEt5AfWpSUZOUEnrm+z2abYbLaDNpstW38sANwD4HHIntyXAHyIP3Pc/pHD4Yh2OBx+lRZ9BfkFTABmORyOr8/k/TB2oenVAO4FcBAyVecggHuruErAqXwIYBYRJUOm8nxERNshyx1FAZhy6k3/1SgAs/V9B0De4TkTkyADoJ36gK0fAVTOr1yhL98AGaTery//CjI1aTuArQC2CiFOWR6vuunVAP52/s+1SoAQYgeAqQBWEdFWANP0p8bpveV7IWtbX65XCAD+4ZgT0StEdBiAPxEd1ktkAcCrRLRdX38d5DFnFxGuw1pLNM5EZ8jSMMMAbDgUhZuqt0V/0mew2qwJUawQZYcMGt+s0nPfQpay+Rqyl6U4ZND4syqoX/BRy/fUS5d1IIN/EwDxgdERW0oyckYB+DkwOuLwub6PlP5jBgFo227lzOknPudwOB6DzFtdBTlA7BnIMlkmAJ8AeMJms9XUVA3G/rOIKLCihisRJQCIEkLUqIt6xti540FXtYcT8gp0GwBqnIlbAYQeisK71dmowqXTGkLO8lSgEO0HEFO4dNpnAN4LGTT+NwD3hwwaXyWBnNpjwVGofg0BeAB0LMnICQZQ72yDVb1iwYqQQeP76ot+hayl+Dc2m+11h8NBkKVrIgC8BSAPMmD1crDKWLWJJ6KnIb/PDgI1NmWKMXYOuIe1hmucCSOAlgDWQ5ZT2Q15GyoKstj2pYei/lra6UIqXDptGoCHAHghR88/B5knOiNk0HjXP217Nkoycp4GcIn+MBHA9sDoCGdVv86pOByOKMj3+B6ANwHYAJQAmA7gFZvNdsHawhhjjF0suIe1BmuciSYAXoHM1fSHLMnSHHJkvArACCF+dzh+FwAybTZbdeT0NIbs/Z0CYFrIoPHne1rS7yCLd4cCuP9CBqsAYLPZMiFn8wGAPg6H4zoI8RmEuBeK8rfC5Iwxxhg7d9zDWoM1zsT9kMWSoyB7LQXkRQYBQBNXidujqOqTx3f1buh1ZdpstmorT3MhlWTkLIEcvDUvMDpiVXW3x7F81WYQYuBvngrgE5vNVmNnu2GMMcZqI64SULPlAlgEOSLyEGRPpgagHID7oDnQdNRoGfdow877L5ZgVdcHsgbr30pananw9T3b/njT3Y+n9B/T52z3YRvQr0vglC/qA2gF4IxquzLGGGPs33HAWoMdisJXh6IwBsDzkAObZkKWDDkGIBlytPpxAA2rq43VpARyBpxtVbCvju9ccWQjZG7wufgSXl8w5EAsxhhjjFUhTgmoZRpnohnk9ITzDkWhygc11QYlGTlbIadenRMYHfHJue4vpf+YrgCubbdy5qRz2Y/D4TDbbLaL8pywixcRlQghAk9z3Zsgp28WkHVLh59knQ8B/CiE+OqE5Q0AvCWEuKGq2sPOD7126r2QtVYDIGvVThJC7NSfN0J2xFwPWe7QBTkOYhWALwE0g0yD+0EIkXCh289qJh50VcscisJ+yCoBF7MkyLsD353rjlL6jwmGrK+aeK774mCV1XQp/ccMB/Ai5PzxGQAmtFs580JMHAAiagHgaQC9hRD5RFT3TLYXQhyFvKvEzpLD4fjb+bfZbOfr/E8XQrwGAER0M4Bfiai9PnnA85BjM9oJIVxEVA9ypjAAeE0IsUKfBWs5EV0hhKi2Sjis5uCUAFYbfQFgMYAZJRk5Z/Sld6It1uK3fuyW8zqAbSn9x5z2BdzctNTYuWmpzc/ltRm7kPRgdTZktRHS/5+tL68SRKQQ0V4iiqz0eJ/++F4AM/VpTSGE+KfaxX2JaB0RHSCiG/R9WfVZjkBE/kT0BRHtJKJvieh3IupWqR1TiWgrETn0YOjEdtqJ6CMiWk1EB4noOn2Gpe1EtETvAQQRDSCiLfryD4jIrC9PJ6LniGiz/lxrfXk4ES0iom36a3eokgNbBfRg9W/nX19+TojoDv09byWiv931EkIsBLAUwHAi8of8XXhICOHSnz8mhPhCCFEmhFihL3MD2Aygkf4aNxJRiv4av51rm1ntwwErq3UW+rL3L/RlXxEYHXFHYHTEORXsb3cooEuXfYHXARjVbuVM7xlsWg9AxNy01Mi5aalnXc4qpf+YJSn9x/Q/2+0ZOwMvQpbHq8xfX14lhBAagE8BjNAXxUHe+s+GrCfdkojW6sHckH/YVRTk4MqrcPK7Hw8CyBdCtIGcda5rpecCADiEEB0B/AYZHJ1MMwCXAxiqt3mFEKI95ODWeCLyg5xS9mZ9uQHAA5W2zxFCdAHwLuSUz4Cs0bxFCNEBwATIsQc1xXk5/0TUFnKq1cv1Y36qWcY2A2gNWZoxQwhRdIr1KvYbCuBqAMv1Rc8CGKy/xtBzaTOrnTglgNVGJQAWnssOwtf3bP7h9Nad1vUpKv2pa+7TW4euHPHvW/1pVEzsSgCYm5ZKAN4HgJT+Yz4BMLrdypml/7RtSv8x/QAEAhgHoBvkbFmMnW/RZ7j8bH0Ama7zBoC7AczTlxsAtADQH7LX7Df9FnHBSfaxSA9+d56shxQymH0TAIQQKURUeQCmG3JuegDYBJnzfzI/CSE8RLQdsq71En35dgBWyKofaUKIPfryjwCM0d8XAHxT6TWuq9Su6/V2/UpEdYgo+N+CswvkfJ3/ywF8KYTIAQAhRB4RnWy9ky486YpEBgCfQ+YsH9AXrwXwIRF9gT+PPbuIcA8rq3VGxcSKUTGxyWe7ffj6npcpGqatiS1YcCS8PFrR6N3w9T1vCF/fs05K/zELU/qPuetM2tJj5Iws/eHjACam9B9jOdX6Kf3HWITZkCkMyh2QJbAKAQSdYt0Te0MYOxenKn1XpSXxhBCHABwjossBdAf+PxPfYQDfCyE8Qog0AHsAtNBv3ycTUXKl3VTOBz/tQEfnEX+OJvbh1B0zFbejtRO20f5hm79t/y+vUZNckPP/DzoDSAWwD0A0EQX/w7rvA9grhHijYoEQYjRkT25jAJuIqM55bCurgWrDHxljVabhyt79uqUHztnYqiT008uO7/Fz0Z4gl+Fno4e+I9nL8iqAzMrbpPQfUwfAUyPG71jx8ofNhjXK88tut3LmM/pzTQE8AXmrUAXwA2SdXKT0HzMeQFvIv7M3IUuQveftGBPis9bN9Pti7YuQs3aNSOk/ZgSAXwFMhJzFayaA+gBuO79HhF1EJkDmMFa+ECrTl1e1OZC32T8RQvj0ZYsA3ApgHhFFQKYIHBBCTIT8vT8TawHcBGAFEbWBrMtc1XYDsBJRcyHEPgC3Q45i/yerIdMhniei/pBpAzWhdxU4f+f/VwDfEtE0IUQuEYWfuAIRXQ9gEIDHhBBlRDQXwJtEdL8Qwq3nOPcXQnxJRC8ACAFwzwn7aCaE+B3A70R0BWTgmnuObWe1CAes7KIRvr6nRTXgRZdJiwJA5WbN5+8ytM8OciPhqyYtyvx8nfq+tLk8r+f6jfr6jZplWpqMah01s2Na4OEpnzUN8Hcp/gACUvqP6Q3gTgCvA1BT+o+JBXAtgO/brZxZ0VPzJoCXIb+YoyB7Uq3GDXt3Gjfs7QeZ7wbIW6QWfb1IyDsfN7VbObOqb9Wyi1i7lTPnp/QfA1R9lQB/Ijpc6fE0AG9DpgLMq7T8ZwCDiGgnZK/kE0KIsw043gHwkb6vXQB2QN6tqDJCiHIiGgngS/0W9R8AZv3LZnYAH+gpCmWQnxE1gs1mm+9wOIAqrhIghNhBRFMBrCIiH4AtANIBjCOi2yBzilMgc1yz9c0mAXgBMuWjHEApgGeJqBHkxcsuAJv11IIZQog5AF7VK00QZF5rdUxFzqoR12FlF5Xw9T2LAORAfuCNqFOolkbkG/yv3BJ5dIu1eOPKzoWXALgRwAEAM4xuyr3294ihYxc3Xg/ZY7QawHTID9W2AMyQOXKLAayqGLilB7DN2q2c+aP+mCAD0csge1CHQ+Z+vav/bICsTVkXwFcAnm+3cmbqeT8gjJ0H+oj96UKIS8/T/lUARj2obAZgGYBW+shyxth/EPewslpnblqqAkAdFRPrOYvNi+vmGkyd9gd5l3bPfyY3xGfpcDCoRCi4oTDI29fgwafWbMuKQ3WcW11mGB/+sdHQYX9EvgE50OJ6AI8C8IPsSWkHeVuqDoAnAexP6T/m7XYrZ74OoAhAWEr/MfXbrZyZBZm3tQhADIC57VbOvC6l/5ifIKsNhEH2sCa3Wzmz0VkeFsZqBCJKgEyROaOBjGfIHzIdwAjZ4/YgB6uM/bdxwMpqo6EALp2blopRMbGPnc4GKf3HKG9ddagHeiOoINDrv7JTwV2Q06i60uuWqz12B7u77Q1+q82hwJgyP+3pAVvDEt+74ugWRZDxWIirb71C8zIAvQDMB1C/3cqZY/W802zIkjrPQ44yTtNfMhgyH/bllP5jZgGYC9mj2hLA6pT+Y1pAVgjwQvbmBgM4rffCWE0mhEhEFUzE8S+vUQz598MYu0hwSgCrdeampdYdFRN7RvVXU/qPiTlUp3zyiMd3FkBeqC0GcBBAUGCZ8pL1uKV+nWLjMJ8i5q1pW3gPZCBZN/qYeeinb7Sd0G7lTF+lfZnarZzp1n+eAuBbyGkFNwAYCZmztRtyBq0YyDJcGZC9sWbIqQiNkAOrtHYrZz6b0n9MawB7K78OY4wxxiQOWFmtMzct9RsAN4yKidWqap/h63v2yuu5ft3Zbp/Sf4wfgK8h82OvgqwUkAnAA6AAshh5U8jBJp9A9qgmVgS+jDHGGDs1DlgZq2Ip/cdEtFs5Myel/5hPARyCLHX1NOSsO33brZx5dbU2kDHGGKtlOGBl7DxJ6T/G2m7lzPTqbgdjjDFW23HAyhhj7KwRUYkQIvA0170JssKGALBVCDH8JOvYAZQIIV6rynayC4uIVkLWn3ZBTj+9DMCkiql4iag+ZPWVSyDTpo4BeLTSVLiM/QVXCWCMsYtE+Pqew3FC4fi8nuvPdeKA06IXfX8aQG8hRD4R1b0Qr8v+1DgTfzv/h6JwPs//CCHERiIyAXgJwHcA+pGcEeBbAB8JIW4BACLqCFnmjwNWdlJKdTeAsdMxNy11yty01KjqbgdjtZUerM4G0ASydmkTALP15VWCiBQi2qtPtVnxeJ/++F4AM4UQ+QAghPjXSh9E1ImIHES0jYi+JaIwIqpLRJv05zsSkSCiaP3xfiLyP2EfdiL6iIhWE9FBIrqOiF4hou1EtESv5QoiGkBEW/TlHxCRWV+eTkTPEdFm/bnW+vJwIlqkt81BRB2q6jieD3qw+rfzry8/a0RkJaJdRPQZEaUS0VcnngO9Ru6TAKL1wPQyAB4hxKxK62wVQqwmoigi+o2IkokohYjOy+QTrPbhgJXVFiUAXpybllplX66MXWRexF/nkYf++MWqegEhhAY5I1zFpAFxkLf+syFrELckorV6gDfkNHb5MYCnhBAdAGwHMFkPdP2IKBjApQA2AriUiJoAOC6EKDvJfppB1kEeqrdvhRCiPWT1jngi8gPwIYCb9eUGyMkPKuQIIbpAzkz3uL7sOQBb9LZN0Ntak53P898KwDtCiFjISVMePHEFIYQPcjrV1pCTrmw6xb6GA/hZCNEJQEcAyVXQPvYfwAErq/HmpqUSZI3TXwHsm5uWmjA3LbVZNTeLsdom+gyXn60PANyh/3w3gHn6zwYALQD0B3ArgNlEFHqqnRBRCIBQIcQqfdFHAPrqP68D0Ft//KL+/6WQUyefzE9CCA9k0KtCTvIB/bEVMuBKq5Q/Wfm1AOAb/f9N+voA0AeyRB2EEL8CqKMH0TXV+Tz/h4QQa/WfP4U8NidDp7GvPwCM1HOZ2+uTRDDGASur2eampSZCJuXHj4qJ/WRUTOwGAF9BlotijJ2+jDNcflaEEIcAHCOiywF0B/CT/tRhAN8LITxCiDTIXMUWRDRVv/2bfAYv8xtkgNoEMi+yI2SQdKqA1aW3TYO8FV0x2ljD6Y3lcOn/+05z/ZrofJ7/E0dv/200NxGpANoDSAWwA3KGwL/vSIjfIC8WjgD4kIjuONl67OLDASurseampTaFLMA/DMAzFctHxcTuGxUT69bXsc5NS/3b7SfG2N9MAHDi7fIyfXlVmwPZ0/alfisYABZB9q6CiCIgUwQOCCEmCiE66beA/08IUQggv1IO4+0AKnpbVwO4DcBePQjNA3AlgDVn2d7dAKxE1Pwkr3Uqq6GnPhBRf8i0gaKzfP0L4Xye/2gi6qn/PBwnnAc9T/glyJ7YbZB3y8xEdF+ldToQUUVqxzEhxGzI36MuVdA+9h/AASurdnPTUlX9/5C5aan95qalhs5NS20DYCLkF1wOgB6n2HwOgC369r3npqX20H8OnJuWWlt7Qhircno1gHshpyQW+v/3VkGVAH8iOlzp33gA3wMIxJ/pAADwM4BcItoJYAWAJ4QQuf+y7zsBvEpE2wB0gpwCGUKIdMjby7/p660BUFAxoOtMCSHKIadV/pKItkP2vM76561gB9BVb1ui3tYaS68G8LfzX0VVAnYDGENEqQDCIHN9AeAz/fikQE5ZPQwA9B7uawHE6QPldkAGtFmQn/lbiWgLgJsBvFkF7WP/AVyHlVWbuWmpIZBfQDcDCAKwEnKQxjQAYyE/VCcDMBgKSle0nv6DOSAjJxDATQAe2PzGyMFRP266IWNE3zTIoPVKABGQwa0LwMxRMbHTLvDbYuyiR0TdAEwXQvAI7/84IrIC+FEI0a6628L+27gHilWnWyBv9zghe2MGA/DqyxUAiyEHQ/QURrXvrvFXX+4NtGxs8OPGHQD8PIGWwKzBnRoB8ED2vvQBkA+gPuSgDL8L/H4Yu+gRUQLkCPsR/7YuY4ydLu5hZdVmblrqEwCeBVAKwAE5iEKF7G3Ng7wttxay17QL5ECrYAC5kLcDwyHLsgQCOA4gBPKW0pJRMbFjL+R7YYwxxtj5wzmsrDq1hvwdLIGsjxgC4CjkSFx/AJEABgCIhSwfswEy70oFkAYZnObq/xdBJvI7AWTNTUu98UK+EcYYY4ydP5wSwC64uWmp7SHzUCMhb9vnA9gG+fu4EMAvkIMo8gH8DiAGwMpRMbEL56alrgHwCAA3ZBHq20fFxM6Ym5Y6CsC8UTGx2oV+P4wxxhg7vzhgZdXhUsgAdTKAnpCjQG2Qg6weHRUT65ibltoachTuBwCuhux9BeTI0rxRMbEVpXJmAMComNi5//SCc9NSW0JODTgeQKdRMbG//dP6jDHGGKs5OIeVXXBz01KDALhHxcS65qalBgBoPSom9lTT9FXl66oA6kKWVpkMYD6AJysFv4wxxhirgThgZReluWmpl0IO5LICaAtZaWDFqJjY16qzXYzVNkQkAHwmhLhNf2wAkAngdyHEVUQ0D0DnSpuEArAIIeqdsJ+7ALwKOcOREXJGpDuEECcWu2eMXYQ4YGUXtblpqUYAj0PWg1UBJIyKiX2lelvF2PkxbeeM4QBehJw/PgPAhPFtxp5T4XgiKgGwD0BPIYSTiK6ALAJ/WAhx1QnrKpD1lj8WQsw54bm7AHQTQozVH88H8IsQovLkA4yxixRXCWAXtVExsZ5RMbEvjYqJNQJoB+Cb6m4TY+eDHqzOBtAEsixcEwCz9eXnajGAeP3nWwF8for1JgDIPjFYPZHeSxsAOfCSMcY4YGWswqiY2J2jYmL3VXc7GDtPXoQsF1eZv778XC0AcAsR+QHoAFnd4y+IqDuAeyCnBz2Vm4koGTItIBzAD1XQNsbYfwAHrIwxdnGIPsPlp00IsQ0yH/xWyN7WvyCiQACfAhglhMj7h10tFEJ0gpytbjuAJ861bYyx/wYOWBlj7OKQcYbLz9T3AF7DydMB3gbwnRBi+ensSMjBFT8A6FtFbWOM1XJch5Uxxi4OEyBzWCunBZTpy6vCBwAKhBDbiah/xUIiugFAR8hay2eiD4D9VdQ2xlgtxz2sjDF2EdCrAdwLOb2x0P+/91yrBFQQQhwWQrx1kqemQs5qt4GIkiv9s5xk3Zv157ZBlsJ6viraxhir/bisFWOMMcYYq9G4h5UxxhhjjNVoHLAyxhhjjLEajQNWxhhjjDFWo3HAyhhjjDHGajQOWBljjDHGWI3GAStjjDHGGKvROGBljDHGGGM1GgesjDHGGGOsRuOAlTHGGGOM1WgcsDLGGGOMsRqNA1bGGGOMMVajccDKGGOMMcZqNA5YGWOMMcZYjcYBK2OMMcYYq9E4YGWMMcYYYzUaB6yMMcYYY6xG44CVMcYYY4zVaBywMsYYY4yxGo0DVsYYY4wxVqNxwMoYY4wxxmo0DlgZY4wxxliNxgErY4wxxhir0ThgZYwxxhhjNRoHrIwxxhhjrEbjgJUxxhhjjNVoHLAyxhhjjLEajQNWxhhjjDFWo3HAyhhjjDHGajQOWBljjDHGWI3GAStjjDHGGKvROGBljDHGGGM1GgesjDHGGGOsRuOAlTHGGGOM1WgcsDLGGGOMsRqNA1bGGGOMMVajccDKGGOMMcZqNA5YGWOMMcZYjcYBK2OMMcYYq9E4YGWMMcYYYzUaB6yMMcYYY6xG44CV1XhElE5Ecef5NexE9On5fA3GGGOMnR0OWNl/FhGtJKJ7qrsdjDHGGDs3HLAyxhhjjLEajQNWVltcQkQ7iSifiOYRkR8RhRHRj0SUrS//kYgaAQARTQVwKYAZRFRCRDP05W2J6BciyiOiY0Q0odJrmIjoYyIqJqIdRNStGt4nY4wxxk7AASurLUYAGAygGYCWACZB/v7OA9AEQDQAJ4AZACCEmAhgNYCxQohAIcRYIgoCsAzAEgANADQHsLzSawwFsABAKIDvK/bFGGOMserFASurLWYIIQ4JIfIATAVwqxAiVwjxtRCiTAhRrC/v9w/7uApAlhDidSFEuRCiWAjxe6Xn1wghFgshfAA+AdDxvL0bxhhjjJ02Q3U3gLHTdKjSzwcBNCAifwDTAQwBEKY/F0REqh50nqgxgP3/8BpZlX4uA+BHRAYhhPcc2s0YY4yxc8Q9rKy2aFzp52gARwE8BqAVgB5CiGAAffXnSf9fnLCPQwCans9GMsYYY6zqccDKaosxRNSIiMIBTASwEEAQZN5qgb588gnbHMNfA9QfAUQR0aNEZCaiICLqcSEazxhjjLGzxwErqy3mA1gK4ADkbf0XALwBwAIgB4ADcjBVZW8CuEGvIPCWnuc6EMDVkLf/9wK47IK0njHGGGNnjYQ48a4pY4wxxhhjNQf3sDLGGGOMsRqNA1bGGGOMMVajccDKGGOMMcZqNA5YGWOMMcZYjcYBK2OMMcYYq9E4YGWMMcYYYzUaB6yMMcYYY6xG44CVMcYYY4zVaBywMsYYY4yxGo0DVsYYY4wxVqNxwMoYY4wxxmo0DlgZY4wxxliNxgHrRcSakGSu7jYwxhhjjJ0pQ3U3gF04MTi6uH3CF0eKEbDkS6M9+xJ1z05r+fxLAMTcqf7c/SPf4MT0xPit1d1OxhhjjLHKOGD9L7OH1C0Vxk8AJf8tzzU3/mj+lt73XOlUFUTlI6Dnt95ev7akQ73rIi/ncXVh0498gxcA4ICVMcYYYzUKpwT8h93nHv/ABM/INma4r/FBPZAtQrR01C9ShdbvKc/oPSB6v6+ybdsa0fGOIKU8ND0x/rvqbjNjjDHG2IlICFHdbWBVxJqQ1F+Fr9P3pkmj490vJfegVCXR+H6/DFH3617KzvaxrnkHvTD0uFVdHv65b8Cz6YnxM6u7zYwxxhhj/4YD1lrMmpCkAOi11PTEPcdEyNCmdCzUAC9GeCa9sU80fHat6aHNROJYA8qbA+Bra/n8ZgAs6YnxjmpuOmOMMcbYaeMc1tqtLoCRq7T2Nw5W/rB85+uR9oOvj2MfGuanJ8aXWBPQHUA5gJsA9E5PjP+5epvLGGOMMXbmOGCtpawJSd1Xmx7pGILi28Z7HvjuenVNOxNp8amwtgCQCQDpifEF+rqLAbiqsbmMMcYYY2eNUwJqGWtCUtv6yHvhVeM7HWOVw3W3+5ooDZU89xXuFxf6YPQCWA+gFMBSAKb0xPj86m0xq+2sCUnm9MR4vuBhjDFWbbiHtZZpiOz755leGQYhyj1CySaioOe9t7/ng3EOgIYAbOmJ8a9YE5KGAIgCMK96W8xqG2tCkgrgHgArAHQD0An2kAbbtJgpQ91T2wMISk+M/1Bft3N6YvyW6mstY4yxiwH3sNYS1oQkU7rfcN/Q8ufzNYJ3pPqzZZ5vyLpU0aS1D+qn6YnxT1kTkkYB2JSeGJ9c3e1lNZg9xAp7YXrFQ2tC0kMAUgH8CqA5gFgAz92hLrF87BvSBIBvnLow44iIpC+0y5cGoezq3sr2vCVaj/shA9sAAA+kJ8aXXPg3wxhj7GLAPay1gDUhqTGAL69z2dVZpuniWe/Iux7zPtgVwO0AtgP4Xa8YkA5gfzU2ldVA1oQkA4AJAD5NT4w/8KW37+/apGHdb37hu4PWhKTVAHZAppCMBTAIgLUPbVv4pGHhUwdF/bWkiU7XKWvCi+Bn7qLsG/KLr3PBnepSU2falxSv/r6kj/stqghWrQlJTQAcBfBYemJ8YjW9ZcYYY/8xHLDWDi8BKDeT56PVWochy7Suj+jLrgFwLYDxAHLSE+OXV2MbWc3VHUBrALdbE5I0YHQIgNlPJSS1NMO9vT9tyb5cTX5ngveeDtcpvxWONvz4hxHeUde77KnfmJ9dZIKvvZG0FULgRpPP62cxuNLzRaBxqLr2vQ1aq7ueNCx4wZqADwCEAtgDYCbkhRRjjDFWJTgloIayJiQ9DyAFwB8AZgB4HcAsANMArANQfrWy7p5+6tbrH/c80D09MT6n+lrLahprQlIEgLkAVijwPRpHG01PGxe4R3vGvVqHikLXae2sbxreuuKwqOsdom4w/uTrHpgtQtJGGZZY12ttjv7q63jIbvzk0lVax6xbDCuXArB/7I37rZ+SHBJF+ZHFws/tT+7jGVpk5HKtC7xQN27XmuJX0WXtBvODAV1d77UHcBWnCTDGGKsK3MNaAxVOjuo41dBjzFxf/JaHDN82Khem1tu1mCfma3EjAeQC6A+gztsvTn3io0k3TElPjC+u3hazmqYRZT/2mOGLS8Z5xhR/aHwlvB7lUQCcpS3o8LMTDZ+FL9J6vTZA3UJ2z+1H/OHqdJ26xthAyXtkobfvHAvKQzJEvcBFvl4fvewbEf6Vr2/2RtEyNQq5pQ0N2W5/xVV+h/uJI08Zvog0kc+3TzQ8MsbwXesQKt0eLkqWh6LUCWBoQ2R/C3vIDtgLH63u48EYY6x244C1JrGHdAHQ0yBMdgvcJc8b5u0f43l4xFWqw6+XuqP/fC0uGzJg7QbgAwC484WvOFi9iE2e+HCLkeqSb/q7p/841TDnjqW+bp/1V5MNzxjy3utA+26+hHYNDaUi9UH3I7teNr6Xs0c0osMiomdHOtDvM1/cw6rQxodRUc5yX5cdBDF3vm9A8Vcme6NDIjL3bsOSW/aKRrv3isYtu9Ge/NGGH8ryRPCv7/iuGXa/8SdrMQJzt2jNsocqa9s6hfnYZO/dn6ea73pug9byufrIzTyKiBynMM2KTUjqkp4Yv7m6jxVjjLHai1MCaohWCd/eeoe6tM1E4/zVj7jHtB2l/jipBJbclVrnUg1KVDQdf/Yt73WX5SDkdsh8xMz0xPjc6m43q17WhKQxAG6uhzxDOBV3u1f90ZcrgnJ+0HqVHhURH44xLHrYpqRq0TgW6YYxf4toXtSO0gItcNc7itBfrZQzYK9o8GUYSq/IF/5Hl2g202B1Q2CKz7r5ZsOqGAL83vfFh27VmnruU384WIdKmq71tS0+hLqBM33XqlbKNL9hmKl+5+vla0kZO68y/NFpn2iwIRL5DZ733vn104bPhhQiIKKjklYP9kLNmpBkTE+M91T3cWOMMVa7KNXdgIudNSGpizUhacB680P3Lfb1CLGWz+/8ndY7N1Y5pGRqdcqsdCz7R1/PrQdFvYffNM54FoCWnhifwsHqxc3xrE2dPemW9x3mMWsADFhjfrjtB8aXfc2UI9tbKEfUwcofdXMQWviK95aARd5edRf7ugk/ckd+6+udv0c0xHERUvil9/LkbVqT4y3oyMBMEbbTCF9pR9pXt1RY8rqqezs/47lL2SmiQ77x9T7QQ9k910Q+v0Mi8kgPdWfgrepy3zPqx8VdaY/xNs/ELwerG307hbXFx97+2g1uu7ZHRJua0pHBVuX4gx2VtJsBmKwJSb0BzLImJFE1Hz7GGGO1DKcEVL9SK2V+9KF38KYjiPwAwOUt6PDz7/iGGab7blwOOc3qsLm+K38rhd/NQ5QNXa0JmJWeGP9zNbebVQNrQlLdD4yvFHdXDjwTpeQMftVz81fN6MiEYviLz72XZd9qWNmpsZJ94KhWx7jdNPLVt7zXPT/S8PNoAqKf99yW/oThy+Ylws8w1zcka6zhhzFLfV3QkdKMwSjt3IBytHKYDrRUjpjvdj+mDVCSG5cJv+IEw4I27Wl/h1Lhl95COdIwU4QKHxR3b3W75yZa4TvmDlt3HGGbHjT88NJhEeEahcXNDos67hZ0JMhW/vZLP5ufjPrMG1cAIA1ABoA6AHiQIGOMsdPGPazVxR5ihD1kcbrf8DSnMEccQ5gBwE0ELaxMmDdG03E3gPeuUtYP7qns+PkFw9xuo9SkfreoK2YCsFRz61k18E0OXf+k+vmsOlT01FFRJ22oe+rUr7W+l3ak/Ves9bXb3lo9HJLoHe7ZorWwtlYOmS3k8jsq6lz2jm/YH3N8VxZniXAcFnVc9Sg38BZ1ZUGmCHfM8V2pKAQyw6OAyLhJa27NFiFhQ9SN5u+13r7HvA/syxUhJX5w+xoqeU0EhFokLFu8MJZG03GlTFhSrZTVbLCy4ZVUrUFhPSrI/VXr8rsP6g+XK1sO9Ve3Nnzfe3WkH7li1pvGDG5NGZcB8KvuY8kYY6x24R7WC8yakBQK4Ml0PyQC2Gwtn+8B8OxC32V7ADzblg4GfWxK/GO91mZsemL8HtiHJ97inhS3UuukLfZ1L1gn2qcAeADAoup7F+xCsiYkdQNwfSOafvSoiPgiBz8/OUj5w7za/EjWle6XIneLxuUx4kh0B6Qd7K9srrvR13Lr48YvWmVpoZYHjT90Xa51KVaFJ3im6YuQ2d74XTbDrqhyUdShgZJf/JHpZbcGYYpScmmj1nLjYHVj10gqMsQpWwyN6Xhpc8ps8oj7gc1Xm9cNKBdGpxOmP2KUnK5rtHaewyLC7ytfvzpjDIuUo1p4uU3dW2eNr035C4Y5PQtEoFMjBelaZHFfdWtIlgg7kotgpxuGjQAWAuhdzYeVMcZYLcI9rBdeEYAm1vL5E2EvnLTPPOJTBd7nADjSE+MHvGN888kurvciD4p6rWAPGb5Va2pzaG3qLtO6Hl4n2sdA9q6ur963wC6w+x5Sv+l4qbI9a67x1QmrtPbNy2GKMcBXGABno8cMC8U9hp+iu6l76gohgoPI2el3rZWfhVzaWl8bSz3k+q5Xf9tTLozOhpTT4g3vtftf9I7Yc1wE5ReJgNL1vjYHd/qaHGyhZLr3aI3XlWgm8oHyVYiAQHJGTDHOu/SoCM85JCKNhQjs8aznjqL1vjaiPvLKxhgWuZd5O1rKYXZOcI881lk5IHxCISfM9bO0UN+npsT6EVT4UV9la3kDyi0+IBq8B+BYdR9QxhhjtQsHrBeCPcSc+my7CP1RNIBEAG8CQJ4INkxWPy5JT4x3WROSRji02E8ANNuuxTh+87Xf84O357AXDHODHla/SZtk+KQRgI8AjIc9JA72kJDqeUPsAmuYh+C3R6lJSonwO5om6if6k6tsqmd4/S+MUwJ3aNbCz7wDfujpmlFiIOEZpPzhH0FFPiMJUzlMRpcwWo6L8GCC5jdI2ahdq65uaCRvnWByNrDSsbB0Ud/QXk1vGABncFdlb71AxaPVp/ywAhHoydLCCjO0ehn1qMDfRJ7CMBQbJxg+bzBA+cPnhRKaJcJMV6sbGijQtPsNSeUzPVft6KbuCY9TN5ds1ZrVu8P9VPKvWpew3aJJ5FZfMweAAwAmVvcBZYwxVrtwWavzzJqQFPOW8e1L80TQKLv3rncgS1INApD2rOHja9zCkN1X3Yrr3c997YRfOoBeVyiOy6YYPgy8xD1rZQhK/J4xfjprkmfkm+UwvwHgms60966FpikPjvI8sXa11uHS9MT4R6vvHbLzwZqQpAD4HMDLY9RFg7opu+M70IEOn2kDlBnea8s/Nz3vXw95mleo5CKTfz3K82WKOlnbtOY5Q9W17Zf5Ohd3ot3B+0XjDAu5IyKpsHSXFl1nh9b4YCclzS+MioPaU5opByGaIjTFSF7j3e4nvn3RNK+9Wyh1QqnEaPfcufxV43t99opGBxpTTthP3m4NbzEsM5rh1kwE8x6tYVEmIn7qpuy5JsnXXWtBR00RyC/8Suur9lFS1jSi482yRGTzGb5rc1dqnZIB7AVQH8BT6Ynx6dV4eBljjNUy3MN6nvWgnZM9UGO7Kbtf6Uq7Ozai7B7t6cB3AF6b4r3js3ZKmhpLhza8aJgz/BfTE3eHoCR5vdbm861a0wUPqd92ulTZ9kVvJWX0VvO9pSPUX7wAZn5rnnz8qIjIXK11uBtAcvW+Q1bVrAlJjQH4A0gCMPd9X/zD73mvmv2C97asad6btkdR7gIrjnlXaJ0oSCkzRdNx4RZKgUsY/SZ57zbO8l69rwj+R6KUIhKgwCaUZcnRgpQCBHrXae3rKwR/u/fONfvRUHPDYNqHBqXzvFcU3WJYGTvcPdFbLsyhO7XoA/cZklZ/5BsU0Ywyu33muzwi3rA+wAejJ100yCoVptIgcqpNcfS6KZ7b8+KULa4myrHVqcJq8CPNt8A3wJqPUOVj36D9LeiwB8CrAD5oRRnZHxhfia3WA8wYY6zW4YD1PLImJN3xkOGb1p2w58FS4fe5C6a4VaZHZzxr/Piuhsh+eZS6OOg7X69PiYBx3rHxIz1PfleIwMMFCN4x3jtmZZy68eNlWle/Xq63m83wXvvEZ76BPQG8m6bVW2RVji0GcDWAkdX8NlkVsiYk9QUwpy7yd92qLp8A4O3fTI9GjFG/ffdHradvsuGjZTcpKzso8BhjkGn+3DugfJGvlyNNNAz1QfEbq35lvF1d0qCHsruNC0Ztk2gRGEFFniZKjmGJ75KjL5jmGQuF/44pxo/67tei9nmgHnzJO4Js6g5DF9qd9Z7xtTbNlCNKR9oXG4nCF3MQduAHny1/pLrEaYFn2xbRwmcmX4QfeQO8UP12i+j8KYYPzB5QeaGwNOiq7Dq+T4v6tgkdV1vTwWY9lR3WG9Tf9gaibDGAXn2UFEeWCLNZE5LaVPOhZowxVotwlYDzoHvCp4PvMSye86jBfCyK8hY+5Hmk2U4R81Z6YvxO2Ce2jhbHVxTBv9EYw6K4L7z9XwVQCKDFYRH5LYDdAK4LoZIHjoswyy6/kbfDXvj298/M2Hy1WHf7D1qv4rVaO2OMcuySwcofhp+1S2ZYE5K+BTAyPTG+oDrfNzt71oSkBADLIHvMnwih0r6Zos7LdVD4cIHw9xnJI2y0Y/8vvs7DhqnrwxVSjB3VA6WRorCgHvJ8KkRpCfwCBiqbmznhX7bR18IVTA3LZ/humD5ASW68Q2ty40zTW402+FqvSxf1tzk1v+hCYWlVVxRkv2J4z1yXCssjqLDDYu8lRZtEC+U+4+IAryilXpTS2KG1OVACs0mFX4tDoq6wKbs2rvW1veRrXz9lgLrRYiDN/xtPHxGjHF8TSGVtnzAsDB3leaJ0i7vZpoOiXmsvDE1L4O8FUG+u78pSAD8C4CmFGWOMnTbOYa1K9hADgJiY8s9ef87w4fI7DL/kJXpu6fmjZltxWNTNAOBL9xu+8zXPDYsfNHyfYYG7K4B1RJhlLZ/fG8BQAFdCVgJwDFPW/JGPoK6/aR2fuVFd2bxU+L22WLNFA3jlR9OELYneW3qs0Tr8CCDqI2Pi2kuVbZcozxW+DHvIIwDeh73QWV2Hgp0Za0JSD8ge85UAhgG4dpiyJq2nsrNFBPL9o5Dra60eCX7Zc1PpdYbV/tGUo/zha+nqqaZ6S4U5TSXR6IhWJ6e5crRxvgj0hFJRwB6tkTMLkRZ/lGfv0hpHHEfowWjKDq5PeaHdlV0KILDK1z5noJpcJ0cEFajCq/nIaAmjEnOZUEsLEOQXDLcpydfddaXqMBchoCAALm+2CPYDYAmnEuVlz81orWSUXqpsL4+mbKNCvoIVvs7qr1qX0mHq2tZ3e566DEAIgNcBtExPjOcPHMYYY2eMUwKq0BzvFZ3f98avEqC5z3pHfgJ74SezfEPrHxZ1lwOY3oyOhruF4Yc9ovFWu/fOJUS4rASWI31d0xun+w3vDmAngCnpfsOvfMkw+9EftZ6N92kN3wSw8Utf/7ieys6Ze8x3XJ2eGH/TVe4XXysW/mGrTI++lp4Y//xoz7ihd3iezrAmJL0PYA8Anq+9FklPjP893W/4Z2a4n1eg7VhueiyzEeW0cwqTUoigfU3U7Ffm+K7MusPwy5EIFJeXwbQyiMpWrPS1/8MHtY0F5RYnzBudwrTIBK+3UAQVW+DK6qmkYJG397HPtIHq1crvlu7KrrBGdNx1RAsHCaAuFfgfFhGeD7xXbAgkV51DWoTzoKgrtoqWylxv/FEjeZMt5FYneUcVlQmLMR+BdYthcTdWcoo+8g70tFCPbtsnGt0WTOVbF2iX40b3c8WTvSPFw4Zv85pS1pMAvgHwAoCPOVhljDF2triHtQrovWMbADSAvN2pAPgCwIqPjS/NfNc39OP1WtsYAB0N8KZ7YXgAgCvdb7g6x3vFzWu1ti3nGF/r0Mw1vzmAZ9L9hu8d4Ho1/g71l5w6VHTzZM+dz+QiJB5AwRbzvRRGpZ/CXpgFe0hrlzC80cr1sR3AHwDiANwN4P30xPjl1oSkTQCehZwSc096Yrz3Ah8a9i+yJ0f3nuO9Un3Pd7XnFcN7z/3s61ayRrS3rTE9XPc17w3eOwzL8sd5xhhKhZ+xr7pN8Qj154cN31yzW2u8+Bvt0m5j1EVKEfzz2isHGj3ufmDJK6b3hyb7mlEHZb+XAJcqtJBCCqQnPfc5x6tf5DZWjjXYLaJXGyDqNKDcphGUZyoTFhKkFrzvjbc8aFgUsNzXVSsQltLr1HUBxcJy4A7P0wV240eXJPua+QqFJfMO47LIT71xapy6pWifaLhtiLqh5+/e1l+1UI/ceIf76eMTDPMNTehY8iFE9h7nefBNAeUtyHJu4emJ8b9U9zFnjDFW+3AO6zmyJiS1BzAXwDUAPoG8pes/WNkw6CHDIuf97nE/d1H2rgPQBMDXXhjWpvsN7wWgL4BPv/T1u+0pw4IW20WMgMzrq2Mtn/8IgEF3vvCla8akO/Z+ZHp53gfeK37/Rrv0gwmee17eLmK2rgGyrOXzuzysftMIMpVgZ3pi/M/WhKTVAFzWhKTLAHyNP+tefmRNSBqUnhj/xAU9QOyUrAlJ1z2sDrz8d6116+Hqcv9CBDR+zfSuGgBXxjzPQHOR8N8VgtIuo9XvCnxQQw6Kup47DMsGeIEDgShtP1D5o44LJt8kz6g97ZW0iDAq6lWi+ZlaKoc8u7VGhrpKccEREZEeRcfbTTB8ojWivIaFCNAMEG270J7QdBGlZIoIXzTlGA6LiDoPqd+WaQKllylbyjdqLSyZIqyshZLR7A5lSUoTytIMikcYSPPVQ4HhXsNi51EREZApwnLzRPCmNERdtdhr23+pst2aISI/fsV7800hVFoqoPSDLGU1B0BWNR9yxhhjtRSnBJwla0LSQGtC0vi+yta+YShaCyD0V9P4/Dhl00YASxWIR8Ko+Csjed9/2zTjKcig8S4AY6zl8/dby+e/dVyEvLJbRE+62/PkS9e4pz4J4Nf0xPjZzejIU28b31pnTUhK8EJdd1Cr+/oTxoWfAXg2VTT5KV8EmWAPmfucYd6Wtkr6AQAfpifGFwFAemJ8GYD+ADIhe3sPQPb+LgfwljUhqfUFPlSsEmtCUqg1IaliEokVb/mu25gsWjgJwtqCDm84poVmlwpjnd6GVH8vGcxZIszZhI4dG6au9RWKwDQFPtPr3luczZRMa18lpTwfgVnXGtaaYikdzxg/bRChFCqpWhNlubhE1QC1kZLdvC6KjK2VIwHHRGihQ4stOqBFRRYIf8Mc3xWe+z2Pa1/4+uXUp1zhD6flgFYf+0XDiB7KHjSknIBsEe67zbi8NQGih7rHMNN3TVQJ/NzrtNgdbZUDahiKhjq02OMumNTLleQ5bSi9RAM1v05dU7xbRM/vrWx3pfsNfyk9Mf739MT47dV68BljjNVaHLCeJmtCkr81IemDdglfXQF7yAQAawHk3qyu7PCtafKn6YnxG5/w3D9lmdY1A0BZf2VraUPKfc0jDE32a1FNIlGQDGAy5O35dgB+6O56tzOAfgCOAyjtpySHfDrpuvf2i4b41nfpoW3me5ona82+7q7uat/TNSMNgGuleXzkDr9RRQCeuNPwy+7WlJGzwfxAL72N/awJSUMADAcwGsBsAAKyYPsKAPUAdIA9JPwCHjr2V70ADLEmJEUDWAKgXnva12ac+iVN8ozMeMk7QjWTL3ir1izNAk95Cfy1LaLF0Q1a613hVHy0HEbvWMOiAy96b//9kIj026K12DJS/alHMQJyl/s6l3mhiq7qXl9/2lJYB0V13/fGO3/WunuStWZl0XQ8xK0ZfDtFtAhSXLhV+dX4onH2mo7q/nCPUJCFiGPl5FeULwKzV2vtxTbRtOABzzhkiHr0i9Yte4rn9p+HKWsVD4woE/7Bu7Vo1/u+YVn9la0b12ttd9dFXsJlSrLfPtEo5x3fsAUAygPhrFsujEHVe8gZY4zVdhywnr5AABvKYG6QJwK3AmixyXz/kCJh6d3fPT0PADaJVkEPqN91Sjbf2+dT34AX3UJtMUxdm3Gd+7lh2QjtBWBKfeT+sdl8/0v3qD++ttw0PswIz02QPa/RB0X9dCtlfQjgxV+1zoff8Fzn15YO1r3ENatBNLIm36CssiV47rG1LP/Q8LB7zIYC4b84WwT7XvCM6G9NSBr+sfGl2NHqd69fqmz7EMA2ANkA6gBomp4Y3y89MX5jut/wlQBesSYkRVoTkuiCH0XmhcwzXpdoeD87CtlPzjK+Eb1TNEmrRwVl16jrxFuea3f0VFJMPZRU03u+q7K70t4Be7TG/ncZllxWh4rL8zX/Zp2Vfe3n+a5Y6YQpf4vW3D1c/bWRTdlJTmHMLRFmtYOaFjjRO6rkWePHdQcpG4ydlP2WPATlBVJ5yHjjwj3LfB13u8j4RyvlsPUbb28RRC51obdvGAFKBiJpu4hRJnnuKUsRzfIOivpqond48LWG1aHtKN1XKAKwVTTz2yxaFtZHTtY73qEduiq7m7ZWDu1/wPPwlsWaLQ5A/MfGl+qFUklKa9dHpmo+5owxxmo5HnR1hqwJSTfOM778KwE/vO+98o+Oyv41P2o92640jeudIert8UK9JcFzz+Zeys5f12rt7hdATrJo4RGgbAAdHzN84btZWVH/Q0/cvpZqZsCPmu3zwcof79xoWH2ZUxhuuNr9UmEjOh4bKJxBjxm/bPK57/KyWMo4ViACjt5gWGX42tcvKllrVjxUXd/lkIj8oj7l9fne1+sTKx27rQUd3uWG4bcOyoHrX/DeFjjP+OpyI/k2A/h4s9ZsRxdl/0pr+fwMAEv7K8kPTjJ8mtZ8Supka0KSkp4Yr1Xzof3PsyYkXQKZ5+wPYPtAZWOdtpQ+NZhKvwiC8/Aarb3vRnVlRBM6Rj5QcH/3m+pu8223rdPaz8kWIZlmuELCUIyGSp7rsIgsJ2imTsr+Blt9Tbe5YczupaQMUCHISWafU5jV2d4ry28xrFAFBHyagnCl9OgarX341craAIWE8pOvu7cR5Swzwtd3r9bgmE3dEV2CAJEuoo77wRWxzNflmw0itvs3psnWud4hZcPU9QFbtObeBO99Re8Y38iJFHkN/RSvKVU0eS9Za371HF+8P4AnAfwEoPxr02S1q7K3FPZCrljBGGPsnHDAegasCUmPA1gFYNOzhg9/a0zZuQ94xk1+Uv380bsNP7XxCiXaQMK9U2sS0lTJ1A6K+iH5WkDph9oVfyzTujoHKJt6Jhpmh37qvfz4vYakcAMJNU2rv2+nsJZepa4/bIDvKhdMcAqTL1lr/vte0bB+V2VvZDRlGZ0wG43Qyt7zXWmJVzZ4GlB2eY4IMX7gveKt+42LH0zT6uUOVDeHtHZ91Kc9HbjzWnVN1npfG1ucurHdQHXzK4+6H4y8xbByohG+wS3p0LAZ3mHjHjV8fXdDJe+Xm1yTfn7KuOD1691TrklPjB9R3cf5P8Ue0giAAnthhjUhaSyA5pC/Q0cB9E9PjH/580nXTL9JXdHmCvfL3qmGOcvyRdCtViXL9Zr3loWhKOrWUTlwRQs6bF6udZm1WzRqt1LrEvWV8dlgA3z1AQRkoU56hMhvnI2QQzF0PHqT1jyrDhUG91D2BB4UdZ1NlaOKU5jdDq1t6iPeh/C18ZmWFlG+30eGtmu1dodDURI+xLBBfdEzYlcU5V7SV9lWXgy/8u7KnuDDWmSRHzlNIeQK6OF6O78cFhOA8h+NCfub0ZGuh0VddzCVGe5xP75nO5oZAfwOoA+AoPTE+HrWhKQb0xPjv6yuw88YY+y/gVMCTsGakNTCmpD0AOwhdQEA9pDW7xtfL/jQmPjeXvPwI3cblh7apLWIedUw65drDGuHbtWaNXPCL0wDzOEo1MzwHMoRwSt+F60zI5Efu9r00JDXDO8G7tbqpw9UN3kMpKklwpzfWjnccrCyoeNqX5seRcJfKPB6TORW2ijp7TNFuOFt3zXaJq15uleo2mTP7cc9MJZYyJ2dpkUFGyDEa6b3b41Gprmdkl5vvxZlGq788sL96g83TfHeYbne8NvNc71XOj/xDpzxpmnmE8WaOawtHViYKcLuvcuwxEDAk695blzfS9mJAJQPA/CCNSGpmTUhKaZ6j/5/xzER2iHBc89M/aEbwHELXC0BXA7gpqzJ1hlCUPCNrsm99opGTWIoa4JHkHu/1sBxmbJ5zNOG+XcMUv9wHxIRZWFUsmG64d0uVynr/tinNWxSAov2h69VYTSyApoox7US+EfP9sVvc4i25RoMe71CiHVaa/MRLYKM8Pn1V5K7rDON6WaGpyRQcVlM8MANo98nvoGm73x9Cp0wt4tAkRZEZSk9lV3ese6xn5TCFKySsGSIyNLpxncDoumYmmK+y88Fk+lJz/3iCCL9D4u6pa+a3nNZ4FKuVBwNo+lYFoCX9ffcqDqOO2OMsf8W7mE9BWtCUiCAe1eYxt12mXv6tA+NiQElwi/AjzypceqWKSXCHOkUpvp5CDb9rsV+cpW6fiAJYdwtGoa2oiOKCd4jJvIcdmhtOtZDnqaRGhCBgiIVmkkl4fzO18s/Xlnvmua5tvwR4/dhGSLS4Aev8Ec5/MkpQqnU8JvWwesUfuXlMBouod1GDVS2Wmuv/K61Tmqs5F7igSG0BR3Ku0Fd9fsyb+clLdSs17/x9slsr+xvuk80DLxS3VBigFjzja9Pi+vU1dGbtOZ5eQhOLhfmAxHIv76zul/52DvYeJWyvigdUQ0meO/pD+B9AMd+ME3YPs7z4LxlL92fXK0nohazJiQ1BjAVwMsRKJiSg1AvgOHLTI8fHOh+dZMAJY9UFy90w7TYKUzHLle2tPGD09lN3Rs+yv3knq7KXt/V6lqPW6hNFJD3Xs9j3z6oLrq+CBayKbt3mOCJCqESXxTltvQJ9Yg/ucq2C6s3EsUtIig/VxNUP0PU9UQrOe7ffS1NzZXDniRfr+JL1e2alY6X7dYaGlb5OjofMP4Y1cr18eaZxjf6/qZ1ONCGDjYapGwUXqiF/iirG0plZQUisGyt1q50p7A2akNppd2VXf6pWnTmERFx5AbD6i5zvFc6Z/quKblS2XD0CnXDg/d4HvcCGJGeGP909Z4Fxhhj/wXcw3oS1oSka543fPDVJvP9ZQ0od4MRntJYOmjvryYnpmhNFrzlvba0TJgjA1FMkcjP76nsuOUHb49CEzzhbZRD2CUae43ki1IhmnVR9hgaUrZ/oeaXEYiygCOijuuAFqW96r15rxsmZbzpu7pbtGbooKRpEcjXjiNM3Od5YlGxCCjqTSnqlaoj0Axv+XLRdQqALTcbVvoFkqtnvOIIG6H+sq8DHYjZ6GvZo5GSP6MecsWjxq9DTPDtvMewGKXCTzSmY10vVzabv/D198zzDkm7RV05IICc/Qaqm4IjUBTYRdmdFa1kNt6hNf41DIX3hKNopQpftw+8V/TsTPuuX/+MbUm3hPlzrAlJcdaEpDb69LPsZOwhEbCHEABYE5K6AWgK4OPLlc1XzjW92v8n01MbADwb535tmwBNBXDlA4YfFjSjoz9eq6wWA9WNOX3UHfkGaNvzEZj/vu+qkCxRp365MGdEKXl5Dxu+HhxKxWl3GJaFpYt6lgAqb/ilt2/DLBGxD0BunghsslOLEUZ4jVmiToYHam6y1lR1CcXcVdmnajBaChBcZ7XWcZ9TGKMaUF5UQyV3n0eoZetNY6yvem8WJcJSMkxZbUnydctO0mzB830DDx4V4X51lJKIPASWXan+blyoXb7nkKhHS7TuTab7buz6tvfa0k99A9Xb1aVRX2t9597jefyKOGXTB3bDRzuq8Wwwxhj7D+GA9SQGKhuj7d47X9+vRT1dJCyDE9TPhgVTWcgLntuKYpVD6ufey1q7oB4QZDRsEK1Lw1BINxhWtzSRh371dUpupWRkf+W9dGsZzKGHtQjNj7zURT1Qz0BiSxTlKcXCr/Q306Ptj4o6/hZR7g2iMlIgjPkIPqqSpvrB7SqDyQgiUSZMm/qo24PX+trcul9EdTLCp/SilLy53kGeUmG2tlCOuOtSQU4zOvwjgMBiYQlpqRzqvllrUdCYjisuYQxvTMfct6rLDI0oO8MDQ3a2FtrgoKhv8IOLetFOazCcpTHKMW2e6dXrfjY9MfIn41Np9xu+D2iqZD7ygOeRbgtNU7oCKAIQVCACnn914r0v66kDdav5VNUI1oSkIGtC0kQAiQAG64uLAZQDaPer1kUMc0997gr3y18B+Co9Mf5KAC0BvOWG8YEGyHG1Ug53nu+LCxrlfiJVADEzjW8aAbS91/N4ygjvM/NvcNuTV2idzXbv3b4tWvOsNVrbiEA4lVAqPXpchDaf6xsSDiimlpTR0gyXc6dmDd2ktQ7rohzw5ohg93Tvjbv6ud9Mfdc3DJ/64nr8pNly3/NdVRivrrvKn1z1o5T8nFvUFRsGKRtbG6G5VeBoDDLVq1WHZ43W/uDX3t77N2ixFIqS4+PVL21NKMvdTkkvKIa/y0rH8i9Xt4QMVjd9DmCPTdl5xROGBa7mdDi1Os4HY4yx/56LOiXAmpCktqaM+CjK6XiNui5womdUQH3KHjTP+GrwEl+3R1vSkWktlCOby4S5fwiVej0waIne4XuuUddad/sa5N9uWNo0B6FFRSLQYqWjXgu5zbt80YYY9bgnQ4tw/qp11cKpiFojTbRTD/oDtKlQBLQ6pEX4OqppkV6g0CuMoTkihDxQYVWOkwJBPkDL1MIX11Xy4wkKfvO2d7rI8OsgdfOVAuRT4VMVQBzR6sALUhorOdimNd8TQM6wSOSrgVQWelirWxJJhUEWuMhDan6ZMFMAuYyaIAOIjF6hFu7RGqKtcjCsGH7kgUEc0uohnIq8gshbB8VHZ/vi8xWI1d/5euUcReT3s4zTfi0RlsLHvQ8QgGUAfOmJ8WOq+zxWJ2tCUiRkcDr3acNni+83JOXCXrikRcKihL1+d87XB1s1ATA+PTH+EWtC0s8A9kDmsI7/zjjxpc2iRb0C4f9jvPpHq/qU1/Vbb2985BtccAANDwIoA9BlpuGNIA/U3a94bz66yPRsuBN+S4Ko5KG9WiNqq6QHLfFesqqnsqP3HjRwt6cMlUDmrSJmTxfa2zJNq+cb53soy0pZjw5Xl2eEUuk1b3mGXfek4Uv/HBEQ3kLJ8tugtf62p7Jj0PveeO9uEW1sQLkIQUnR91rveivN44xXu14Ub5veEh5BZWt97QNuNKymJz337e6nbt3uhaHRi94RHb83TRrxme/y/Dhl86MD1c03cXUAxhhjVeXiDVjtIUGHfOH3RipFieM8DyY74VcUQ5mtJhg+q+sSBjUHIa4QlFp+1rq5TfC6X/He7PzMlKgVCsvBGb5rG71nnB7hhNHwtvc6T29KeaqXuuN1BZpSIizaAt/lj+UheIRbGDolGD8vP+SL8IYopVmFIjC0GP71wqjY1YiOmws1f4CgBaFcKYPJF0QuY57wdzuFv1EhAVW4suopxVGlws9zCOGmVnTU5xRmykQ40rSoA/d7H6/zm+khtQ4VwgBfUIoWU1KAoF29lR3dcrXAglwKtmzxNTfliLDyuwyLC8LI2WCXr+GGKCW/gweGEqdQ60RQiSD4vGbSjC4YcFirW5SBet44dcuRw1odCqOiljs0a145zJGawM61okPkbN9V0yEH00xLT4w/WN2nsjpYE5KiADQEsBDAH+mJ8bdUfj7u6fc3jjUsErF08MGtWrPvd4no4lHqYvs0740PPmX8/MnvfL2+PCAaeOpRfswurfH3bSitVVvKKHzTd93qXsrO4TeoK41HRN1p93oeu/pV46yWh7XIo8laU+qlpja2oNz/JnWVkqzFHCuFf0AH5YDZD86iI1pdcxiVqGkiShjhMQfBubkOFXb9TWuf1lFJt27Wmnnj1T/cmSJsZSQVXekURqUIgV4Lyst/8nVfPVjd2PEN7/V1vtH6er8zTfRrgizDDmH1hFBJoVNYgteKdsXdaHfweO8YVzMcXjze+LU6wTMqPEU0VQhaxmzj650TPPfes9HvQSfshTyrFWOMsSpz8aUE2EMssId0BXBTHaVodLKvmbcn7Sx7Wv201UDacPxnb9c0L9T8ulRgzkFI0WDlDxFCzk8aUh62adbgCBR17EPbM+b5hpRu1FrldadUY0PKfvFbX+9tO3xNXP7kUn/XWj12lbq+bYLxc2W+7/LspmpW+BGtTssi+Ac3oUxvfeQVu4QJwUo5LfZd4spGiAggt6lUWMRRrY6vFGaKQi4FkTvCK0gzwGeoi+LSLBG6U4HQmuA4tVUONP3cOCXzcvf0hSXC3+gThjIfDOpH3kGdVvg6agohJEIU0pXqH3tHGZKMgVQelSEiy2LVI90JIr9ABBgiqQgZoq7bB0NRvvBzWuA9Up/yzJcpW6hEM7UNp8IWbphM/2PvPuObOtZ98f9m1lqSXOXejYVtTO+99xJEgEAIYEogEHooCRDRTRc1ARJKAqEbCIQueu+99yrcuy1XSWutmf8L2Pefu+8+55az984+2fq+wZKXZkZrng8ejZ6ZoQAiSWbWc17uzW9q8yS8P/p1KoBfDCaLxx/ZnX+EDwcuxAJYAuBlHfLCigR9RQBAgj4QAE4tHFZvvDz6dEfn4qi5yoALedzrhgB1uAIhigMbuwuX++9Tmz3+Xvn04070RnZV8i5TQ5QKEcgZkQev5y945P1jav3BQciJa03u6MNpthxK87Z7k9IikzJ8TifngqwQUnBf5AqKuRt3cC29yyvYflVbFb9hIRm+KC6khFN3IgvPeZTfJVbtRCncWCb3KVIgdj6l1i48y2rZWzq/23CVVdI1FR43v8EqholQH+2XpmftU5o/pQTJ4SRX9YKjOJJkieuVzpfmKAPtCqd3ewtnW19hVbvqIGsFqDQMufps7hNxy9zvhmuw6uLi4uLy9/ZvN2BVOZl6Sa3yq8GeeH+t0m2OB7UX9RNP1Qwheb6NhOfVaguvhEVK369zuTfXo5i/40HCIyVyxPfSD4E27i4E0fzShsLzqF70/G+t6IOAcjTb5k4cUnt6t4YHLZNKuZQ9SdqddUKtn7xe+WidALVcFtcXRdEsoSq1uklQkQtv3yz42lXOWG/xgu4iq2l/x4K4ABmxQkZhFtezfHhBIqpUAnenQGTmR4rcy+BWqRRiaSHcnL6kpKw6eaN9rP2i5T0Wk/yah2VlcD86UdjNJMLyM7hfMYhQlMP1AW95iOCExAmgeaCWUwSw4HCajSJ4lGRzH7uGKKc0gMiAcAGKyAH9brXF00us+mERys0QZBcHID+4p3C+0xHNlAavtP0W4n2u5n0A9w0mS1WDydLsD+7afwqDyTIJQCcA3XZp5qwYLewv704cxiXyZ6ORoPcBcOt3l38H4NsiuKecYzXO+5Ci8vHCKc9Gjh93b1PaV54iJsZaNFOcLYSHsV6k7NVUZUjmK4SPaUnvb77GKq9pL9zq9bWwRycQzrOYn/iMlfPNZPqsSGR8lcEDsq+ySrKelIirla5XM7mfWgiP/b3EC88q0eRIlRCP1s7v9LUdP1m3qe3S3/GQ9TE0/f4keYS2m3Oe9SGPfmagWUpN8rptPfoq76pahYXSfKcXKZsfRnLLdROvaNcrnVfJoC/e8JBnm9VOrw5rpjTsSS9klifpFXREti9WesvdhMuhXiitkYrAa33n7Xcd+evi4uLi8g/xb7Pi22CyNLuqHX3LD4LzIqtx6Y02fkkyD/JKZ76aNOK/+CmPahVD0to94VEOAF+7EVnJZL6Fz3iUOkQ6qi3luvzewrlwAaqvBOduLZEz07hfiRZOmYMU5zOtphzNcVcgBIaS3GtjxH0dd6qtP6tPX9B7LNZTQ1jOXSXa1oA+O6wnpSMZR04oFaNSmJ9dhGoLo7keh5WGRdE006uh8Iw+Y+Wg4XrIEORibtBGknTouJO6U8U7i/uqS+VeZX6kZP9YcW/t1sL9VrdZTFo7epvnwVsnQBEqkFS5lGvcg0iekMO97FooogySYiduTCAsWgNFm8bdaSPhqa6XY0an3dq5YhkXMiTCQxxcYp8Kl8Iz4Rs41TnEuUxaHahAIO7EKRM4NMVw39eEPDxUkaZcSuMBNIDY+m1X253+o/v4n+QFgMEXNOPOZsLn85usYsJ9HtN4q3YhA/DrE1bueE/Tb7fKoDsJYC0A9/Oa8Y2+kUfIZ9S6UgTNOvFY+8UKd+L0zmQ+qTOUwaoGSqlJ2nE5mQcbAER7wFFXB5lFkqzGOuIpPWcRihvsVX7WLK+UxAIdMgTeXzxTSYJTYqDpWlWpqidF2p7kwohM5lOqhcx9SKm4U5pdLoUHaXVEvj5SnvDdNVbliQ+KPPdoEnzushjdXrmp7jfNrAqlXKcYxavFHJQOViev6CJcEzYondRpUmLfVyysigdxhO1Rm2v7iyc8Ymja027kis8NVqnqD9KqRaPk8VUApFjNRvMf2CcuLi4uLn9y/xY5rAaT5ZNl0pqZrcndm3d4hQABrEkgCgpLofVUQYNDSZ4zhORJ99XoklOs9mGjcKOvDs50LXGmDnROqbpFWiTkwLuUg2tiSYaQqLYknnAkV6IpgVXoWwmc5ANqWAG81ZcsLO8ljzzWVzjT7TirT6NJuhBG8nSfOOfkqEDRPk1CuSKuRQWaJp5U6xVF0zREkGxPlVM1h3vDQLKEfHg6+8gzf+xBL3QfKh6JOag0fhtD0yIiSDZxQEMUiKXrlY/s06VEvyNKvbR2wp0QBYJdC9ljjDxG+UrcT6vQd0QDRlUQlsu9eQAppJvVdm+Pq418YkmK75eiRbayYBJOsrkPKaVBtHB/PvNoy8F0WiLrNsgfFTQQXoo+KBQr0DSdAmQ9ZtH+D1j0OwJV31647xVAbGSB0tcRivxjd3hc3HFWv47VbPzTB5TBZJk9W9xY6zqr5HGfxY6pRJOm1yfPUjPgR4u4+ycHWNNiBWIGAB9fFGYd1EwLbO5cZQYQAuDGXe2XF71R8q4Ebq9rODacspqNK/dO7+xzjVV5c4fFph7STvfq6UwQpgnbWH3hecQLFrl/ijJUWCWt6vqGBb0OoEUB59Va72qTl9XiaHJWIfcILIK7k4FI73hwdgTNxTGljmcPekmbQgLT1ird+PfS6pu53KtKEXTiWrVbyhDhaLPyJF3DwUge17/arbRwGywdezNJHqULILbDr1nopFQEyX3pqWwQUjpYOFb5jFqrqD59oXIgb4oydMdLHhkF4Cur2ej4cF9+/HdfhOfi4uLi8o/x559hTdDXOaIp1+Aeiz3zPetZpnCq/1baqU9jfuQBq6Rfq3ZLWSct05Zwjb6e8NIrTkjy94Y9c6/a7HV1vGlwTPutSMGYG7e7v2LhjhJo5d7CRX0G94kIIfkOcOKmQtCeVOvLKhE3a7hjkJOLTSUobk3pI+qBMtrLObskguSU/ih9v/wWqzCtNn0ZYuOeTAd7QRBskdfUSk4DzZLv8dh7elLaWAEcBzTTxz9hUUjn/o76wstyL1moPYaki5nc1/mLahTmSBt91ysd0E84EwkQfofFFgmEJJajOcVxNG2cAIZiril+yctpIklWaR738O5Eb/tRrr76XDxT/xUPQwPhqUhBIEApsXHdJ+6kzFHE3JDCAwt7iZd8GZCuJ6WeCuhzClSIIFmshvgmpJB7aA6yJnaRK2cni782GOycJBXDPRrApwaT5Ter2cj+6G7/B1uby73Ni6WfKy9Tep3pRG96uxF7rkVtGNBTvOhIkQM379LOWytzIb6CY6tPJM1dCmAxgGIAVds6lg26oxvxeqvcLhnAZIPJEvJWe/loRZr8KF3xW/aYlZ/UiDwdFUrzerxhoSQfnhPGC3sy87nnuzQEZXtyR0Qt+sr/KYuUy/HM4EiajV1KK7euwhVu55qJr1lI0z7i+VSRq7OrkSQPI73mKEcyW+tgd1uo9MNDFh0hCapQyN1YBZpeTEA96govAynI21+kxTGF3C0gVMr3GuEcbx0gnqw41DlJ3qG2vQ0gDSq6tqe37rek9ztmqn4/3dEOr2YwbXtwQztquS8WbfxDe8XFxcXF5U/rT53D+mFxTKcqNKl6K3q3TijJs40QD7dwcIGrEDwjSHbKOe3XGjc4xHVK50IHF3kR92zLQQIrkLTKp3h9xx6lZe5DFl3shCbNQDNyLrGqShr3tZdyiSWzADZN/iL1PKue3UJ4SC+o1WIaCM/FZyiXX8W5OSOb+3ANUckQ8Yi2HMmMyoDf8Lc8NFQHhR5j9RyJavvATO7NmwuPhdVKN+kBi6mXxv3VYu6hyeeeDg48B5i6Q22ZXY5maSWial/zMPEBM6Ql8wDmTRzPdZALSqBzGEh2UF364stJ4q9fv2JhTAUlbkT2Oseqq1rI3gxUecYjRA/iqJzBPB0OLqVdY9VK8rmHPZ+5u8mc0hzuo/GhJW4cxO0pi3C6QQ45q1R3I0BcAXcrLuM63FDiiI7I6nMWaW8oPG/7iodvv86ruq+QfnQCqA3A54/t9X+KnCiS8VEW9/Y9qdZhveWZAwY6pxQfZY3urlM+TtYRuSGAop9VIwCkIsFWAmA8gOdWs3HsHXPfPQAqRtOMpQCSAWSVdyQONzrNYRdZzTmfygnhFtZwPwEmbGXt+5Yj2ZIMQVeFWssRzmr7oshRjbwN6Suc0bzmoTlmue/dCjTt59c8NKcqta7/TLgwOpd7JdzhcXdN8tDiYq4tus9ifN7xUGGwcCTZLP6UGkeSXseSNOEL56TEAu4ZUJu+LuacpRDO/DLg26uQe7AF0i8eh9QmBas1K6gGzhMAZABFS8W1dT4TzlbdrZk9j4PPBjBikdzHMx9e4wwmS4U/pktcXFxcXP7M/tQpAbGmAzcmCTuv9hXPtr6mVt7XVHhkuqxWFXxRqNYQrHjLQtQokiUVcjc4iZRDoQbbuVQWTvPUYq4Ti7lbWTKCL6Vz/3bRJIVRQh8mKm18pojbK3oQOyngHlxD1JQrahX/KJLp9ITD/RaPozoi565Wuh0fJhxqNk+J950m7sik4NbrrNLJSiR56afiGV7CPeljbiASFKUcMoUC4skkyPDgpTIDFezErUABlTzg8PaFjRbBXbVxDwcBSakqJBXvVpr7TVWGBIwWDrzoJ56qVcjdX0SSzAq5zBsFRE9jSCpEMMdLFm53EEkfgWyug8OZzfWlWjgldyq7vWARpVWp9V0hdw93Jw4956BPeKSSxf03lSNZbQu4R2Q9+lx2QnLMUT5/OEQ4WjOGpnrmcS/lklq9pDp9WxpHU1OesEjvxUqfU36kqGJ5krlxpdrjmNVstP3R/f/3ZjBZ3AFcnyZuOxGEvIFxJMUvkmZnfuKck7FfM0PvQZy7DPbEOQAWALgEIBtAstVstBpMFj2A7Vazsct/VkdV056LnihzL4Cntqtw5dRs8ZdeVhbq5oDo/pRFPWov3qkNoOANC/WuTV6kFxOP2woXOisQuoWQvEOZXE8PqM2K2wu3lJ/VLo+KuEfZMPFg0xKuU6w8dFB34dLnedw71MFFw20WV7ZG7Zq9WzO7LgUvO6A2VgGiXaAOOCZDbC9BSZMhLm9N7147y2rrAXwE4PUUYZtvJZqi/KB0b2MUrg1NUAbFW83G5QaT5XMAD6xm491/bE+4uLi4uPy7+dMOWA0mS12TmNhmkHBs0FW1krUyTWmvgzM3FQGBNu5xXQtHo61KhxwvWuqbwf1fDBUs0U9ZlFCJJtF18scPftB87+uAGFYMN/6YRac1Fx5G7FRaIRMB94J4dvVWwv1UTyJH5HAvyY8UFZZBd9kD9g5LlN5sirhN9iHFxwY5TR3Lk/Ssb6VdkRuUj4qjSIZbNMkolCD73GYV5Z9Zl0xfFOUsltZVnCwPy33EDW4vtJ+HFzEtf8rL525j7dCEPNB3Fa4Ld3gsa0ieySdZXWpFyI+Eo+sP6ic2O7T2ieLOuj3pOepDSkUBKn2oGvJqC9YSgOekct+whyzGJ4DYUJu+1HJOOCeEv2Dh0KNUDSCFNJd7MyuCSXXymj1lUWX7WfMnk8RdNzj4GF9SQp2cOu1w0xTDzS5D+DqH6U21hVd+h5TGO3pKl2/nM/fVZ9UayXXpy/BUBN6Nl2f2seri85FgK/ij4+DvyWCyCADGmoTtY9oI9/JGO8euOan79leDPdEZjLzsn6WlezeoxhkHWNNKAGoCeG01G/cDABL0a5fJnx76Zv4Gy4dyYqxm4wsk6LUA2hrsiRkAsqy6+HqvWNiSXO5d7h6PfabjZVsHiKcXG50LXj7j5Q5WICk9TiwcGfNqZlyWL0p8n/AoxyVW4+0I8WAFkSv0NKutVqXJ2n1K47Qx0n6fAu7NFim9NZPFnSSb+96rQFNjt8ltSgOpLf2sWtvQS7zw+BSrHaFHsdyAPg/To8QjkmZnHlfr3TApw2sBOLJXM/NSgvy578GFY9d8uA/bARgArLOajVv+xn0KB5D275DP7OLi4uLyz/HnTAlI0BM/FKpDhSPTc7k+1IeUWr9TehbPUQaoXih11KEvq1UgySVfiMeE8cLeh9OFLf7lkaaNo8lCDEmnszWbYglBqDexa9whU3fiDOGcyu3pHTpQOF6nkfBcK4HzT50zlgeRgjIO4h6E/DZ6UuKoSJLIOxbizOI+LbZoFnnVpc8js7m32Iree1WbvNYVwc03imZLT1kk+00zy2+HZm51A0nXd6OXDKvFFc4basVUhWiUikKK93DRUnJIbZIpEkZjSIpCCNcaSPrrZUqf3AHiKUmEXGGvNKNROLI1N1jlJCsLRgH3UmsIb/WvWEhEGver6A67WzvhtoaCl8kQ1UJ4KA4u5nMI3AaPi2dZrWINkRUNlC3exKGpQt+VTRO3NfQmpV85oLNlc29FS5jGDU4mgIGCz/EmxYIEVtRTuvxpBvd9LIIpnYVbkRqobK/avDyAegC2IkEv/NGh8HcW25w+qHyANTvbwblEeInIYIM9kVvNRmcm/DpUpClVdbDrAPgCuIn3KRJ/MeGb+RssH34OBPA5ADR3fN9/u9L2CwCTAXwM4OAWtf30IfLENcPFw8s/Ey8syubeTgD2KuTdg2niNv/TM1qVPmAx19PgkxOE/PQm5H6AyBVyitU+8YxF7e/jnFbaVHiUrYPiHkBsnpPFXfZC7pEUSTIjqjo2blnC+u58x0PKB1Lbw4HyFN09VmH+QOFUmifKyuYqA59qITsrkXddADwCUGLnGv9Ykmb9MNAGgODK5F2AVRff6Pc3x2CyDDKYLLUATMT7BWYuLi4uLi5/F3/OGdYE/adblLbzewnnTzDQeuuUrmpn4Vq9Ps4Za03ijrw29PZUD9j5T0qXG0k86PpSzbrhD1k5d4kQJZt5X7zIqpcfK+6NJGBOLZE9VUjKeqXzwZOsTuwBzczqTpBMgSNwE+tUUMq12rrkhS6OJisP1fJKOSFL9SUl+jssTjYgOdtO3ALWyN2U6oJ1vQ+K2+5SW9mmiIl1q9O30hMWztJ5gOqL4qRkHujvS4oLGwtP3E+rdTVTlSGeHOTONe3oGm9ZiEoBrRvsqeEk1+sga+JTgaQc9UFRp3CSK1xiVZ2RJJvG0HSqcKq6EVl4ywML/VDq9ZKF0aqCFSk86IQPitrqScnruyxWCiN54V4o1d5hFVKaCg8iHVwsy0SAGEgK+TM1/GIAKawXSAocxdw9K5jmRtu5VquCFurg8HYQrfCWhV69yyq4dxBuxsqghxgn4RE0rwkDSDoPKMzkvsU+pOTLynMeHfujw+HvwWCyhABo+lg76KdXLPy0QNid26xiXRt3vzlW2t8LQAmAdABTDfbEUgDlAdyx6uJ7AtiNBJvyH5Tr3Zw+6H+R1TAAmG01G0sMJosE4LhJTNySrvp+cYlXv/saEc0A2K9rRtaaI/cv0xGnapZ+0d5icZPq0edrMplPyguUCyzmWlt94bnuoNL4/mDx+O1FSp86T1j55JHigTANZL+f1C6fzhC3Pr7DKpBQknehifA02WBPPDhH2DC1l3ghrprjl61RJDOop3Ch8RKlz92vxd0LRwkHBsc6tqUDWGk1Gws+tPu7CeLumePmbSr63XuJw/tZYhXABiTYPvuHdYiLi4uLy7+VP9UMq8Fk8TaYLCtzuHdBICk8/YqFV/UgzlgFgucLHnFhvPhblVbC3Rp2rhFe8ghNb/Fc1Jei5XMO0Ko0BbOcA5wNhafNqtO32iQeSEQCncwFHHbWT65MrI1a0fubVIA5oQ3erbb8xUivpcSS1PvlaXphKZckK0KEW6yi5265xYtc7pVTSjz93bhT+F7zo26l0v2TqcrQLf2E0xNDSJ5yWq1ZGkSK+Bq169Wa9I13LfrG16QM91Eg6n1R5LFI+lmoQ15UL+TuOoC6L1V65W5TO0ScYbXcOtCbzB1lzSWiOreqbfPOsdo3AqmtdJnSy1FM3DJvsrjHySzExsGcefBStijt7G5wtu7rnJF/UqkbW41ai0JJ9iMHFwrDSXaYBMYUrpXCkZszXR78NJjaGkbRTC6A+4TR3Mo/yp+IEmFOxnkJCFXusegzEpTyHwuXKwSRAvcQ5LfREDXqolo1T4EEHXcUC1DXViLJY/7omPg7orulhEkSlHUypAZDnRMbfi6e2DRW2n8XwB4k2FqdV2u8qmLfcBHvdwSYCKDZLRZXqZ1jcZjBZBn5P0pK0NdAgn7Ch0cx48XfxgPYaDUbSwDAajbKAJaYlfiUzfyj10PEY30niTtDO9OrvzkhZT7kMffqkReaUmjFeXL/L++y2MK3POT8WaV6cgvhUfBvSstb7rBXPanWbbJB7XLzHQ9Or0qtTTO5X5UgUrBnvWoUpipfbM/mPklTnENKAKyxsMaOr+SxZ1QI51N4QLli7jYbQMPlSq8HImEzrGbjHKsuXo8E/eAP7d72+8Hqh3a/sJqNBUiwFQEY+g/sCxcXFxeXfzN/uhnWD6uUtQC6RZO00G+EnZuN4q3wafLgtO708s4a9PWbTUqHCjE03ccbpa+CSAEJIzlVbrCKaTvVNr6fCJe9vFBW9oPSLX2M+FtoNZLkkQO9ksl9hRLobI3pE593LMjmRpxFYSQ37DGLcvqSkjcKaNhtFlekR0lwS+G+KoAJE+RRwiDhCD2iNiITpL3KZbXqWy9i11xg1fY9ZuXjm9JHxTd4ZfevhL3B93msukbp6nRCfFSbvq5/iVU7s1OaUyed+7OHPNorH55kjLCfOyHl+JP8EAmMPWDRYnmSnpvEQ3TnWK3SPWoLi56UDPxO/LHIykOcKgipSJI8w0guzedeqaXQRsTRNA0ArgCQuQaFcFP1pOzabVahYlP62KsUYtpZtU5IE/pELIRHsQZOz2LuBg1RivxQqLeyYKsfLQ5wQnI/rtYrSObBGWPEvRWCSL62CB5nspl3SEUhfSmAz9s7Fj85qJkuuBHneCTYHH9oYPxXJOjLJbPADUHIbZvK/Q++RbjPaqXb/HHi3rleKJ1YW3hND6qNfe6y2B7FXHfGLG2gwuz8TQaTpT+AlgDG4f3X/aetZmMOEvS6Tg7zT894uUEAKuD9YqYfrGaj8qG+7gDqTZSH396jtgx6pP2i2xm19rXq9PXwzk7znWrEqiySfurym9qcrVa739wrzaxWgSSnO6AJyoF+axb36SFy1aMatV5MQ8DDXs5ZGT6keHoSD3bc0Q7zu6RWc7YW7t1bIPcr8iRllX5Wu3wlQFVe6wbEGeyJXgB6P9EOMlZxbCq26uLj8f5kr4cn1Lplhdy94WVWbeI+1ryx1Wzc8c/vDBcXFxeXf0d/qhlWJOj9rbr4Y1PF7d8DCBkuHBpRlVh3FDPN0pni1m+CSd7ZlUqPtGiSkWIgmTyJB0X/qrYM3Kx0zKxK3vmOE/fytUqXS76k6MxazfJrIaRQUgl57gZHiS8pdnqhxJ7N9c6Vak/PecoAn8dqlCOE5Ann1RqV9qnN0+3Q2GrR12odxzq3RLWN0w9FKOYeJf2Es/ZHLFpJQ0D0aqWrHEoK+vuSYiWGZoSm8YCU46z+ojb0Lm8uPBSqUWvF06zOytOabxr7kELfAGLza0nvis3JAyfhrPiFGhKYxIKIA5LgDjtXIPjUoK/cxot7Hh7XTGq1QVpEVyifpO5Vm0tN6BO/KJouqaDvtqjty+dxD7WQa/LzuWe6Aql0l9rKVggPZz73dFQjb/xvsgqljAtBDYQX5B0Pyj+p1nE+4DGZ93ms08GlIg/iIOE0N1wFTdbBkZsJv/w+whnvbO57tYS72wnQCiAeZ9Wac9KYr7UDvXlGC+e5PzYo/i4Wr1G6zHzAoyES3srBpar16fPvq9B3xZ/Ic2kK89e0oXe7ylysl8v1wQJhhR9etxdAmNVsLAVAAJQDACTY7M94uaVWXXxjq9n43Go2fv+XwarBZJlyUG08YKxz9KurapWr9zRfLs3jnhfPsNo+51ktTWd649xNXul8V+e8g0XwqMlBXkxWvtQWwb38brWFXYTaX+aC3o06pM1q++QyLn1Vkbz7eqRwUJ0o7goY4ZzwNozm3fEi9iE7WNt1I8TDLay6+CETxD3NUri/BOCXHdLcke7EeebDYLW1lQWce8QMS1rTe1Vb0gdPk3hQHQD/6W4HLi4uLi4uf09/qgFrI/uq6iOcY6UFSr+lAMJ8SbFZIgrVEDVAhNI5mOTF58Frwy0eV/qARb+9xSsdHSIey8mEr5CEYLdzai26SFpfyYOXddZC6fOERdraOZYF3WUxmuNq3eLjrFFGH+fMW8OFw6hLnrlHCVnuWfCdkcb9Xx1T6+kr0NRKS5Rez3ZrZnOJK2IX4WpSKgKtCernEgORgpDP2gh3Y2cqg/QOaLzLIEm96LlqehT3P63Wye5JL2gmir/eEaDuucNitRIYr0ysJI6mldWmr1ka/MRgWsQZJMicFAeioPgtC2ElXPdmhHNc5DNWLsTBNUpP4VLYbGmjwgGez30u6ojiW4NaUw00h6gQfR6w8v6Dnd/+WEd4/WKr0uGsBKVuHtc/k5nIBTA3i1Lvp3T4vegnnhZa0XtCBZKsEwgPbuj48YYWiuDgUqCeFAd2ote9M7j3s1CS0/BnpbM0wTkyPYvrIwKQlyeC9QkjOSQP3uv7O0xr/ujY+C9JsPW5xqs+PK3WSZegFGfBN+2A2vRsS8d3L6NIRh134hy4Ue1UmILA4b8snLEECba9APBhoPrJh593Ws3GO38p0qqLf4T3s66/q0dPt0oL3MbKX60/yJrmpSIwYJoyOK2Lc8GAUJLb+gULK3CHHcvE1fNnilvKb1E7DAKwM40HnjqkNlEDSOGzfO4pFsE9e6488EoH4c6YPO6t6yFczg0juW+vqFUeBJEC/0+dCX2RYHsHoEddx9qxedxraFP6qFoQ8gfPEzfcayw8rQUgB0CFW6xCh11qm7Z+KFwuEfVBILXt+0072wBggsFkqWUwWTz+CT3g4uLi4vJv7k81YM2A/7VjrFGfTvTG0JXSqsPD5G/ENzzcnUJdyIFMFVRzS604Jl446+mE5OcGe5o/KToAkLR45/SDy9TP7nZ2LpTP8ZrFz1hkyl7W0valaFnUWHimK+TuuVEkw2O3ZnaDPO5ZqoP8OJkHydVoUueu4lVlqbTW56xae31r4a4SiALOCX1yWa3yriZ9HTFW3Ldpgjwyb5nyWXEVYs3vSc+nRpEM90zupzggvfEgDvfl6qe3j7H6uW9ZSLNBwrGlJ1k9Nk8ZkLlbbX1tlfLJwwVKvFOGZm8feUbuJGX4oZe8HEtGsBhG88qusGrlV2l+rJABf/6Gh5UGEJv7KxbmUDjlI+VxTbO5j5s7ygLSeEDZJVYjMw0By8aJv907pDSAQoSkTO6juhF7bCnRFh1Qm74eIJ4ZW58897DB88FLHpnykoU5c7iXNFb8LaMEOvatPCxfhoalc787NWhSwwBS6BEvnmaTxZ236wqvbeVJZo0daivxI3pj7m6lpZSCoJw/Ojb+q84s/LJ4LetePpQWrOwnnF54UDu9dgnc1p3Xfv36NquwapnyWdNzrFaHv1xvMFkqfDiq1Pk3C0ywMSTYTH/9dHPh0Tur2XjUajYeBFBmYU0KC+FJjfTahhlSYkAZNK+7C5endBau+3igzNcHRV+GIyvrE/GSdqIyaudX8tjLHYTbmyvRpAoH1cY356iff3uVVXmhgGbWpK9DvEnppbniL+/4LP1xAEUA3tVxrCutPedOHw1hDfuLp9sa7InPhji/4TdYRfqpc/bCNWr39W2cy8oB2AbgMYD9l7VfLalG3nQCsPwfcb9dXFxcXFx+7091NKtVF689ptb/zsqDTV2FqxmN6NMWN9SKxXdZ3ISv5RHjl0lrt3YUbnl7ojS7KX28oQ89N/SIUu/+KPEAbU3vPTnHaja9wSrJAlQWSvOEQTiG+vT5+I7OxWv60NND/EjRfjsXAy/x6kI34XJaBHLiZE6ra+H0CCOFsgLStozrYu/zGN5DuFjrDKld6gandrHcp54NnoXdhcvlgkkBy4f3nUokJWyeMuDb/ZoZy56wcs4zmm+MOfBWdNwpqaAxngi2fipciLvGKsnHlfqWCiRFUTl6ntRMOvuERzVmoLoQkvPsS/mb0hbkflR5mhm6SOnNI0iuWxIPovOk9e8korrPFTfqs7le20x4rDq5KFci7/Qaoky2Q6Om8MDkTWqnT+PpKdnBtfAmZSGcCEJbx7L1GzRLPi9l2mtpPGBGD/HyYScXlWBS0OEGqySOkA5rdqstc430WmkwybfvU5raOoq3T7kR55Mzaq3SdWqXziYxkcgQX+1lzW9aeUiMwWTpCKDUajZe/KPj5P/V+8GnbbGUoF8XCNs4AJ9kcp8zN1glEcALq9k4y2CyVALwzmo2vjSYLJP+rypIsDEAG35X32sA9Q0mi6WLvLA4HNmj3GCvY4c0qRS6i92FS5e/EvbNXKd+fO+KWiVnlripfD3yvNl6xXgtELbbnwnnGk6U9lzapHSou19ten2x9HNVEeosiahJAC4CeAXgi7/sl/ohf7WZF0rqjBP3eQaR/PA29E7kGVbnkh3aAUiw5b1vp35MOMnddlg7/RQSbH+uJHgXFxcXl39Jf6pFV+VNhy+/1fUbjgTbIwC4OKPJuwo02cfGvcnPqnHlAnHDR5uV9vJWtX0dLVEut6L3xvysdpk8QjgY9jG9wm6yuNqfiRfeFHL3KlYe7FGfvkhr7vhuexqCLlzXjvx5q9Je20W4qg2CzepJyipfUKud8yJlTfapzZ60oA+crYQH9To4Fpe1ovdWTxW3D1+h9kyrS55XjKJZJJvrryfzILE2fXVagdBmiHPSj5nwGwqwaqvFFWdnKwOS29G73b8QjkSlc98SN6rwQqYTYml6ejHcxQLuHqhHSXElmvI4k+ubPOdRl7O5j9hZuF7fxt3FJUrvounSdr0WjtLrrIqjKX1066paOfYpjwpsS+962uD2Zrvcvvhj6Yp3JfJOyuL+EX7EVlzC3TXJCK7+jTxi40ppVe1R8rgVDmiPPdQO2aRwbnjLwzKq0XdeM5yDXtqJ9kEcSWneWzhT3gHJyUBXeKF0wChlgncB93wzRtifVZu+qKWFrPcmJVtiHYlzAPyC98ePXgRwymo2Jv2RMfJ3kaDvuFLutmC52rsAwMcfvvoHABhMlnEAjlh18R3vsli59pzb6/5e1RpMln0AvgJQfFrzzQ99nDO6ZMOnmQ+KKlzWfrVWB2XOExYxMofryzcWnq3K5L6dw0lWmjjb1slgslS8p/1SbeT4waebcGXdl4JlXDvn0ssA9FazsWDd9PjbPiie860yHFaz8cCH97FVj+L6V7Vfjaji2PgSwEGr2XjJYLJQqy4+BsAbJNjUv9f7c3FxcXFx+Y/8qQas/5MEfXkk2N7aZobW8yBl+zarHU/XpK837lJbnwpB3p369Fml06zuukBSkHtTrTh6lrTpgMylDvtYkzcPWAV3L1KSNkfY2OEIa1jmTpxSE/owJY/ry1Wnb73vs+gH51nV6HbCAySzgJJgUhB+iDXOK0/Sda94hFAOmTeOs3rVyuCWnMO9ay6V1jAOwifLI4o/F449jSFpfJQyvrwMacU5zYQh/sSmK2ba4MVq34IvhYP+/qSYHWWNToeQvHrZTC8RqG516GuSjKDzrem9Fi9Z6KP7vMLbIq7r2I1eEnOIn1rCdQo412Ywb2c76aH7COe4DiZp5+7x8pj8FeKqEB9SKFHQsnTuSwGIN1jlO0bhSq3HvPyzRKXtmWc8slx9+rJqKMn170XPeUtELvBGSZAvKRVSeMBlT5TVvssr5KQwf+8e4iVfHRwkm+lPepKyWoXwkl7wCBKCvBIbPG55o7SRE2JW7bl3a8SYDk6qTV4Nu8UrngEgW83G//ZbXRXNCulxXq0xJhlBAcfU+jPu81h3q9m4w2CyRAOYYzUb+387baKnlQVt2rVw8qd/r3oNJktzAFcAuAOYDmA3gNgg5Hue0X6z8haLS29MH1kpEDFdHvTkM+G8UIe+rkcITCOd44YeZQ3H4f2uBbfx/vjYEVaz8bHBZPGMI8lrDSS9iIA3vcRqJJbAreGHa6tadfEAEG2Sh7rtVNu0AVByQDO9Sk36ZhISbGl/r/fn4uLi4uLyH/nT5LAaTJYxD2bW6okEfeiHp+afn9H8q4HylGm/qq3e7VRbd/FF0ba29O7+NsLdOS2ER9evs0pdKpN3CCG5Ly6ymsNPs7pCd+GqfoqU2DReOPPZLRaX0YQ+vuVFSouf8vJyJMkSirnu5F7WPKKHcM37Jos71VR4Em7j7tZ29Pa7BUr/U1vUjmo0SY+eJm5n1ejbouXS6iOxJD1zjjzwYSP6pCiZB1arLzz37U3Pbrqr/XJCOve7d1GtvlMhkn2qmPg2mBbwy6xaOgGr1pLeD2hD7xI34hTv8Jj02fKAWgly/9QiuPs2Jo8rdhauu0mE0VIuPX3Bwos9SJnoK9g1ycw/PR3+G0u4zm28sEdHCMqsPNR5Qq2T/YqHP5+jDGyQDv8XGdzfvknusHaZtGbsZs3ipjdZxdxhwmF+U40rOaY29JCgOm+rsaXxzum/lkLrmcW8fbuLl31/k5vlFXKPEi1VW+TDe+8ZVuv1GbUmiaFpHhqiNlijdi09y+osN5gsbu5w9JotbUrVo5gAmPlHxsjfS3XHBotJ+TIlhfsra6TvVvajJ2ohQd/Paja+sZqN/Q0my6pdausKu7Tzo5Cgr/F/U7bBZIkymCzef+t3VrPxotVsVK1mY5FVFz/rlbZ/RwDTs+BrNyt9J0yUh7+p6NgWGufYovcgcgN/UlS6WOl9vIH9x5X3WOwxANWsZuP3AG4BGG01Gx9/KLr0BY/8YY60+fFscXPOZe3YSgBKAXQEoN2itB8NYP4CcX1rAPVmiZt/uKhWT8Of6P8PFxcXF5d/bX+mPzi396nN+n3lHNMEAH5Uuq0/x2ryCeLuun2Es96rpFUWb5R4cc5D1yofT87jHo3niJs+cYNzkI27740jyckfC5fDGCeV+zhnPH7Go4ojaU5EEgviRdzdPlEe/iYTft4XWfVKAnh6V+fcwTEkLWecPOpybeFNlIOLAXe0w9pvleZLP7MuiZQgf4/aQjaQ9DoEavBUKfHEQdZkVzHczz1j5Z7FkrTJ99RozSW1WseHvPxoKw/m11hl7U65LSh4oI176HK515unvJxyRq39eLoy7HgW/JRkhPrY4HUiG/oyAqglcEd1Yq3clD722sI++u0+i00PoQX6IcLRbAmKJoDk68KQrW1En6VriVx8i1e6fZfHHTvLalWOolnaHTrzOS9SSq+rFX0SNfOaW3lQvpNqzj3lUUmT5BHP+yozx8yRNi24zioXhdACRYXwRiGStzcpoRnc74WDi7070xu1C7ln9hL5s8UxJHXZYdakzkq1R0wIco/ZIc0NREHYAnF9s0vasW3/6CD5e7CajY5H5l4DBwono7xQFjJfs2kZgOzfXTLOajbeBdANwDMAQILeEwn6o/9pwQn6fvPFDTMAfGQwWQb9b5rh3KB+xEKR69gsmRccURtG5EEfajUbq7zV9R85VLSsauX8ftAatdsXWfD1TYf/hkRpnoIE/TCr2VgGYK7BZGkAAFZdPKy6+DchJH9jCC0ozudeqQAcJzUTNW3pbVMx3KoBWEAJTm6SFh2oTV+tDiZ5H+OvdzlwcXFxcXH5B/nzpQQk6CMAZJxRa3U6zer0esnC0+dLvxgq0NRRCsfLUfL4ew3p8/VN6UNpojwiaaaw+UQ0zci6ySude8nCpO7C5SZXWZVffElJI2+UHKtCrUsfsvJ3z7C6fndYbOEMaauXN0qvHlIbtx8gnhRvqXFkl9pSrEKTkxuRR0FhNO9FNvwSJ8rD53ejV5ZzsKgjaqPGoTT/XgHzqLVVa47r6UhYM0H8dUhD+lx+y0NexJGUitd45YNt6L2b59Ua02Yrg3wWSj9lF3KPIBt3VxQI96vRdxVDSZ6+o3NRtgT5xmbN4o4evJQGUltGFvcJWat0fbKXtShdKq4J6SFcdC/kbncd0MTdZ9GFtegr5z0WWzmE5Osu82q7m5DHxj1qC81YcV9JIC3knOPpJqW9trd4rq4CQSnkHo+e8CgyQ/6ibJ20/LYexSNB+IMoknN6h9I6KZqmTWpInwUmqm3fNqBPPZ6r4SkfibebPmJR9liSyovhrjR3rng0Tdweu0ttxeeIm0r6y1NzVNArr8zdxv/RIfL3cnpGq6TyJA3RNHMwgHtIsOX+py9I0Av/ac5ngl4DgBjsiW4Awn83A/q/MJgsOwBsBbDvE3qhWVvh3pJW9N50z9mZl5Jmxn4mQzwfQ9PzPnPM+OUGrzzDajZakaB3B6Az2BMDAEQBqGA1G1cjQR+XzfVfnVNrFvYSL1xGgu0IAIyZOv2nZB7Y8T6PnfYxvTS0r3Bu7zfyyPEF8LxdBu3WDzsZuLi4uLi4/MP9mWZYAQC53GvWFqV9i1CS9yaN+8uTpZ3ub1lwTQADn3DDL4OE440KuMfk3s6ZGaHI/tKPFirLlV4v5sv9evQXTnYKJzkBPYWLfefIAwJiaLrhIYs+cprV3c7BC7vRy0Fv1FDdR86FrZ1c8PFBsaMefZ4+Ujw0PQAF2nI019HVuUDeobRu25le1/YSzn1djVobWhF26xN6kcyXNhiWKp89e4eQoT+oPTOmK4MfzpUHyIVwZ27c0S6Le8+Ppak+w8WDT2bKg0rT4ZdXmSSpHYVbtdeoH9+fq/R/Pl7cI66Vvmc9nLNvP+QxcmvH8jMjnBNKPxUuaEKQd+I8q5F1TK2X/4gZDKdYXeJJ7HoPOGql8IDsdO6feoVVa8EBjzc8LOMSqzZM5jTtilppfS/xXA0B6r46jp+ehJI8azXyNjOU5GYVwj0WYNQdjuCezoT94TRnRhIPUgvh/u4+M+TcV6OV9uLdhgD61hLeNiwinlV8SXFiQ/JUSlAGBS+QfnmwT2maW4kkb/0zDVYBoK1wt3I0zYw7rdbcm8L8LyNBv/hvXZc+09AECfpmBnuiaDBZVv+HBSbYnEiwOaxmY8FfBqsGk6WtwWT5xmCyGH5/abxw6hEA+qs02/K1uGfPOVbzcDXHL20NJsvYFs4V1ds6l0UDCF6vWZpl1cXvRIK+PhJspR9W+lcAUGQ1G1d/qPdFfceaBT+q3fwsSoNYJOi7AcBh1jjtPo/t+qO0ImO0eLC6gWQ4t2oW6GuQN1F4v72Vi4uLi4vLP8WfY8CaoA+fNfWrrxyz/PcWcfeQtcrHdT9ymj0vsBqVa5FXvj6k6PB91ZBDOR9Sk762p3H/iKliYoXZ0hbtaxahJLJ241MQdGK/2izdBnf+iBmObNQsrtnOseTjH9Xu78aLe5Z/KRypdIvFFWiI4jWQnqAVaer1ByzGZ5PS0SeQ2JaUo9l+C5X4O8ukNYFl0JUkqm1uHWf1PVvRB5Xuaoa6+aKoggqq7lTb9NgsLTzngWL7U2a42U88ffYmq/Qum3vfKOOS2zy5/6sHLMZLgRh2RG2oGaBMPRLvnPZ2rPBb9R+klXFOLtqvs0pVzmkm1DnG6s/Oh7f/G4R/bFK+PDVO3FNtrrix5iHW9GYO8S2rTl4HPVbL+fRyznxUkaZEtBTuh/Wlp/IT1XZKHrwLc+Az8pJazeMobzQ1mYfklsC98UHNtMqFcPM+rtYtmSollhsoTw1RiVh6hVXzmi1uvPiYG+7WoG/DHqqG8GHi0aZpJCD9CYs8WN2xwTbeOWoYBeuoJYq6TWt++7W42yJwpXSctFdqQe5VNJgsf454+4sEWwkSbPZFSvynC5R+p3KZ58At0z5p/9fvc6/a/Gc7F9tZzUYHgJX/eZn68wBgMFn0BpPFE8B5AIkAJv/lEoPJ0qcDvf2pDo7yelKyZLXSdVkT+lgAcLknvdDqmfbzMqvZeBUJthTv2RnfABgO4D4S9AQJ+gir2Wixmo3Xfl+t1WxMt/LQRVqiWG6yuNcGk6ULgPZWs/G+ce6JU5XmPPYPm/N27UR5+LYewsXAD1tuubi4uLi4/FP8WfZhraglcr26jrXiI93QQoko0daFxiUAmiKBt5YhNTWQzLUSVLvMRU28cDLnJKs/P5b7p8XRlH27NQmzj6oNbnwsXOs4wTlKai3c/4Iwnv6leMjvGqva/hULK6xIUoRe4vmIEOQVB9NccRX7NPASq75rvrihj0VtnBtAbJtiSWrTEfLXywFEn9J803iB3HdxHJLGnOF1m/ihKK0K3tw5Kk0+LYKxH6VVQQ9YzA0DTZ9nUZt4DRMPBUtQc/xJocdYcb9aKMc7h4lHVCeE5l6wFwSTgrSDSqNX3YVLtc+otXOymWdOVWqdfoLVPxOK3G5HNSbjMOfXrwSo56aLm/tooBRJUEolQfX4kfXIlsBKbNzT4xGPfvwraz0SQMMCfrdLuJD19K0c4l9IdVkVSfEuiSijbqhxaCPc67hdacfuaIffEbh6OAR5NfaoLQ7f4zEdHVw83Vm4Ub+Hc7YSSbItLcUHoy5pvjKeYnWLzHLvWt9p1j0B8HNl8q6jO3HG7VJa6nuL579Y5ez5EMDfbZunfxUnFo44aTBZLvbjJ/v2E88c/0i4dfr76e06jZ+3UQWA0fO3Vv3LtVaz8dnfKsNgsowGcMSqQ7sPT3UBUGw1Gw8ASAcw6sNirKVRJKPVXRaT/Uw3uCuAx5Vp0vM87rV8gHAi7aDaRNeTnd/eYJbvkVjHtotWs3EhgJcAgj6UOx7AxN/VW3eWuHnuYPH4DiDxWru5Z18aTJbGAGLrkue7DSaUt+riywFoW8pFr580HsP9UdT+73n/XFxcXFxc/nf+dDmstlmhmz9xzvFfLy3Tblbb79ysdto9UDhx/jqrtHSrZmGLQu7+8T0WTTaona/NlraMu6ZUrm2gGb+eZnWKOUCmi9vSflC6+VzktXhv4Uxwffpi3UsWprujxlRtKdyv2kZ4oJ0qD0nax1osB9A9kmRV+Fr4VR9Octzf8RDnAdYkZ4hwLDgI+e7z1P6PFS5kliMZlYeIx/wIWKknyk6NdI7tsEGzXNaT0qv7lMaNOwh3/MbI47KniVu8gqnNfZ/StLS9cFc8ota/1Fm4Hh1K8vI2qR1je9CL+lc8ND+ZBa9oJ96ZOd/ZN9lJdLYm9LHhI3r14a9qq7O9hAu9KNQIifCSJyzKY7g84b4XKWtIwC+m8YC8gcKJTsHIu2cUrrs5IAaUp1k3rqmVKkeRzIoMeJsPvWaX0jxzmHgkbo/a8iKFUmmweAISFB8V9OcnLGqULymStygdcyrQtPEXWI3Q9Zplv6Uz36pNnasa1SHPhT3auc0B2BVOeh9Qm172JYU1qxOr5ihrOGzgvD1b/ugY+UcqnBUy8AqrsrIasSaVwM1Scc7jKX/zwgS9iASb8peHBpOlBoBXVrOx1GCyzAEw639s6G+y6OJIcufDmqmtDqhNox7y8hOLuds3e1mLRT9JS69WJMkrWzpXnHupHdB7rHOMx1HecOxq6Xs2Sh7fCMCtieKu3mPEA1ok2D6kAOgpgKVIsH1tMFkIAA+rLj5ovHPU6v2s2Ud/qTd1VvT5po5VY626+Bf5zKM/gHm58PJJ4wGrB8pTlwMwW83Gfv/A2+ni4uLi4gLgv3lKgMFkiTCYLCORoD+OBL0/EvQtuznnvXrDw+Q2zmX3PxPOL3isHZQWiXTvFzxi8VT5iwh/UkQrkDRlvrSxfkWSNHM5+yzajTj3lycZRdEk/Y2Ne/jZuOfK7Zr5k3oLZ9/VJi+HehB73195O6UCzXjTzzntaSl0IQAqByNPXCctLzdfiZ8dSnIGVKevZ38unMzwIGVFA2UTGy4cpM3og8pexH51kdzHPEP+wloGXaNYkkZyubfvCqXHszbifZ+LrEbxaul7/7NqzYxXLLi4o3CbHFPr2ZJ4cMM7LM7tolpd52BC7gW1OtcS1dFbulAic6HkG2lPSHP6sNTKgo7kQ1/uKGs04A6LK2jvXJpjURv6LFV6HbFop3qvklbuWSz9VH2XNLt1b3pGPqg2iktiAXoB3C2fu9eNJukRqdx/1Q61XclPitHjFq+y9xSr87YafVs1hQeW5HC9IZkHe1xm1b/wIvYNItjxQeJx/QMePd0J8QuDPdHR2PmjyECn1aRvvO6ymBdvWHDIQbXxysOs4e4KJFWngxNhJHt6LdPOhn903Pwjec/O2NJp7hmfpzzq5wiS9SUS9K0+5KB2/h8XJegrAljz+9dZzcYHvzuA4PzvBqu+AGok8aAvNEQ94UVKFaNw/aO9rMUTACnD5ImVL7KaU8uRzKYSUe+4E8cBAGWd5560W83GcwC8liq9qcGe2Mpgsri9r9/GAOz8UC+3mo3FALgfKXoOYMZf2hQ++01Lqy7+RwCqJ7FXMat952mgXGghPHprNRuTAXz+j7iHLi4uLi4uf+2/9QyrwWTxAFBljyYhsB59kQFgCIDXtew/tdmqWRjdz2nyv6Yd43tIaZxhJ9obs5TBNQEwAEoPev7KQmlDg4qOLYnLpdWPdiitx2bBt/Fi8SfPLOifNaePdq9Wutp9UDzgPKuV+pxH1nKDIy2I5D9rTB9XqUteVNysdrxACS/fiD5x70Bv+vqS4pKhzknfdaLXJ/jDVtxTvOz/tXO47Qyv6xwpHixrR295fuqc/aQOeTF3peaHDWncP3yX2mbjRrVTg270sqMCTY6Np2fc3Yn9WioPiMnivjHV6ZuDV9SqtJnwqKmNezyarQxMb0kfLP1UOG+2wb0yA3UXOHd7wcPWV6apw2bLA+4OEE+m31LjgorgUVGAeqyfeCrJnxQ3fM0CmRecbZapn130Q6Gms3Ct5nNe7vFVtfLODO4nL5F+mkGhUh9S/OAHpYe3L/JZF+Fm1HG1vu4+j0m+zePutqO3u6bwQPdYkprWWbgRWMzdljYQnmcY7IkKgI+tuvhZx9W6Qe6wf/2cRYYMEY9V7uKcX7BMWnP5JquUtkjpu/mR+dPrf2Tc/NMk6HUAnAZ7ouCJst4XteOGe6JsiETU1wZ74n4A3a1m43+4a4DBZBkKQBWgTlQh/ADAE8AvEST75E5pTkwBPHcvU3oN7ClcLB4jj0sCYLSajan/QVmBAJpazcb9/0l9WgCTAKyz6uJjdiqtvM+xmn1Oszq6juRGQ5WIj1rRe3V7i+cHI8F28v/5vri4uLi4uPxf+m89w2o1G0sA8GNqvUkA5tWxr537hEWeXadZrpchuG+SFp9aLXe9Mlsd5OcOxxkACwEMBXBmL2vZ6T6LGfA5PXa1h3Cpb0fh5oGl4lrrUPmbozIX8Y1zRGxVau29WO0bcp1XDj6omfZ6mrSVBqIgtrdw9l1lmkRa0vv0Aqv5YI3S9XUv5+ztpVxX1lc4Pf4Kq2rLgT4rH55zD/BmZ9dpluffVWOSU3lgRhfh6quGwtN50+XB0Wk8sLQefdahGnnrt481f7VYiac/qV1uZnMf3V01pqAiTVJ/UT6qOVL5utpOtY0sQm0USvJivFC6M497GZJZUOB1taKQjMCSVyyiPQPu2eD5fT73unSYNc54xsv9eI/HMg/Yv0xn+psFXK8tgQ453Pu0Fyl9Xcp1ahDyfQrhOSkP3qa9arMcNzh9kllgOQr1RXfhanVvUopAUnAMQJ6Vh2prk1fjOtDb5zLhV26cPDqlgfAcAC73pOeTYpCiGOyJd71QNry58PhSG+HeySXKZ28iSNbtq6zqkRnKFyv+bQarAJBgsyPBxqxmo1wMtyKL2pCrID8A+BZAf6vZqCJB/78cEmAwWWINJosGQHMAfns1swwAEIy8op+lpQPPacZV9yVFqQny5x9/RG+qmdyHANAAyEaCvrp52vBvUmbFxCNBL34ozx+AAUDoX9XzicFkCTCYLD0NJouP1Wx0WM3GeQCWHFPrNftUuOCzSlrVYq64sX0b4Z7cVbhyrTm9vxHATCToJ/zjbpyLi4uLi8v/7M+w6Eper3ZZs1Nt89YBzbrOzkWzAHh4oXTHKmkl7yVe6LbZ2bFws9rh+6Oabx92cS64oEJYCyDADunXj4Wr5QY5Jw1O5/6Pm0hPbEXwePi1MrqcFk7fz3Hi46bkofNraffj+zwGtcmrwCrSu6onWb1jFUhK+w7CjfDPxPP1SqDLaeNY+tiXFCfNV/pHThe3ve4lXqjn5KTNRmlx+FR56MEQkmd6y0Pvjhd+y0pigUMGS8dKhzgne1/kNe4BqNKIPu6WzII07ejtECekoK7itbI87vUyimSSoxqTWgJtyERl+M1IZB+JFtKba4nctIi7ixE0z72rc8GR1vTu7mQ56PENVrHjMOHQt63p3cdr1S4tvhV/PZPK/N13sjZtAXK7hGvqNCWPanWm15toiXw3n3u6zRJ/cfcjpU534lxS0b6pgQzRMEg4tryu8+fpvYUzdZ1cWrOPNd8CIGWK8mWVTsKNXVdZ1a2TxZ05HzkWrpojbbR/LFztOE7cm9zCueLreHl6tWt09Lo1StfZX4qWdZEku/e38pf5+G/+Aem/4v3iKeMBJOgJAFh18eWRgB0A9ACaAkBN064VQaQgRkLwFRnSHQ3k65PFXVHdnPMqAChzQtx4jtWqXYEkK3YuRd3lFU7OoltbvuYhub4oHJkPb3cA3AlR8kVROwBHABRIUIJkiLFWs3HNXzVLBcABIEHc5IeE+HVIsPW2mo2DsmeNKADY02J4FD3nEbc2qp1veqOk4g7NvM5hKDDMlgfs2WiynLaajQ/+Sbfw/4jBZInC+5nrFQaTRbSajcrfuMYPgJ/VbHz1d6jvIwBv8X5hnPeHVAkXFxcXl7+z/9YpAb93fkbzaQTqmN1q61s3WaW7B7TTr30rD3u7Tlr+5CmL2PeER1cu4VrDKVZPuc4rDzyomdbkEStfI5971t2odtIe1k5LbeT4sQPe5/ZlATB3oDcPe6Ds6ixpS83XLDTkgNr0zhbWSVktfVcxnGR7f6/03BOKfJ/x0h5Ne8eS832Fs8patevcndIc6oXSlbE0dWh7xxLs18662M05Z1syD265WvxuQ0fh5o971OZRAB1Uh75qpoOjXyl0gVfUKgft0NTTo0Rvh7TuFKtXIYDYqs+TfjF87JiXtUMzL6SAe9AbvHKOB+wez3m5wiDkyQXwCKxB3gi58EnvLly+XMR1NYOJLTSLe19+wg1KNvcRn7KoK+8Q3GAwPeqXB+9rYSS7m5OLV2sJbzs4ufjNCx4xLxWBJ9YrnR/MkTYOfceCo75RRu1rTB7bB4onhoyTx7wdJhxiRuFG8UdO870okvHJJHHX3U1Kh4dzpM0Xh8lfl2tH7wTfYnE5ezSzX7zlIQgnOTu8SVl8B8eiVi945D6r2Xj7Dw6Tfy0J+h4AKiHBtuDjKavaBSNv/VxpIymAp89OtU0cA7ZVI++c3yrDxgG4DGDULHFTtc+E819fYtW+GS5/Ux5AeQB7ABAAzGo27vtQ9uCNSseTs5XPS626+HUAxiLBlv7hd15IsBUBgMFkaQjghdVszO86ZWXVgwvHPjaYLEFByP+mA73Vf4608SEluA1gxofcVwCAPMvvJ4moi5Fg+y8P+v6DexOJBNt/OPgzmCxLrbr4aQDcAJTgfbrEOoM98XO832c2drKwY8Eo6dAMgz1xD4ClALZZzca7w6YmtD/B6gcCOA0g63f5wh0BFAPgVl38NQAaJNjsBpOF/OWav9GOZgDeAfgKQCsATf7WINnFxcXF5b/mv+0Mq8FkcQcwxGo2rjKYLIKBDHImSnNLGtCn1UaL+yr9onTqk8b9uzHg9gR5TIhA+G6JO0fMlLatajz32r5HM6cMnqP0rzlH3Lzak5RpGjl+fL1UWvvLVHmIVoFwjIGKWdx3pj+h+71ROtsbpfoh4tGPtjg7laxWutsf8fI7ASAMOZ/UU5+/7ijc+mKt2nUBgJwS6AJnKwM7rpJW9Roonlg1Sf7yai3yelMyD166XW134DqrbB0gnso5ptbrXso1/QrgcawWfRnpJxY+7OJY0DgTvlJN8rp5Cg+s3k84lbNc7lGiQngpQvU10Az3YG4L2aK0dzQVHioH1SYZnwoX0hkndfxIystU7p+1X23ivlrtkWbVxZ9vYp8keqNkygXNeJLMA72rCMnV7Fx8dZ9Fa2sI1jp1HOtuPTd/sucr07YiB6SoRdLPk+qSl4FupIwAmH+DVxrRnt/uG4CCj3sIlz4fI4/dCWBlBverF03SO1WiKSFveKh2lzSnVxZ8X77jwTt0RPapTJLTAMwFMOFnaUmDls6VS/64aPkXlWDb+5cfvxQtkc9ZBEJp/oVQ5K+OYNlia3qPtHMu5QDyZ4hbe91iFU7/qHRfeI7VHrRZs+iMu2yvWQqdBOCh1WzM+v/L1YcDqD5b+TwDQAASbL1+9ztfvF/w1efDMxUAZBlMFjsQMxYJ+ludyNiex3ijTdtY+43bHO1fW81G2WCybIbJMs9qNr4EAGl23rC/121YP73PR/OUAScBhAHoO0ncGd6NBtTpYtqx5J5uxGkAewF0M9gTqwCoBOAVgG5JLPBGOZrd6T6LfhpK8vq2dSxJw/tZ/JhJ4s5No8RDcz9UcQPAbQBWg8ly4o32+/Tmju9fOCCtPaf92h0J9ulOLmrXSLUH3GOxz0Uo3TOZ/nYwtdkATIomaacNJkvoQ+1gtHMsG7VRs+TpMHlCxRQe5KGBPNsJ6dVMcXNRe3r7kIYove/O9B9Tm75u+vsBvouLi4vLf81/vxnWBL1ksCd+CeA+gMlWs7GbwWQhM8XNC3oKF9q3dyy1fC3+GisSljVRHnk6HNmTUxE4FEAm3q+AtjWlD0dfZtVXA1gNoBGArgBCBgonHNvVtu+uasd0aeBYY36p7R+4QunprEtfpFWh70atVHo83K62q4/3MykAcKIxfezmg2L/weJRPsQ5cbMTmlrLpdU1R8vji1aJK+VmwoPi2yzOUQx3dZI8vGULet+WxX3da9A3D/rSM1XXqR8fyuNevZoLD9PfspCXHwtXo2Yogw1TxB1qZZJksagNI6NpWv1C7mFfqfZYlcIDewwRjkR1pNcKfXipjzt15nmT0huexG49qtStG0Fy6ruTMmcJ3O2Z3O+7L+WJPccKv737WvotKY35ndMSOXyiPHxIEClgs8XNETUcP19xQlMEIAnA5h70wqblmrWFAEwGe2JdXxQNnyVtuhOMvFsexGGKILmP6zjWUbzPidwHIMRqNo6uZtq9vyZ9feAei/2yBG6tNkqL4lsL9x8BOP5EjZjfWV585a83q3cBZk4b15wDP2xVOyzWwWl/phuUDCD/AYtuWZEkrdASJcZgT6yxR5PwWRxJ9mvuWNHMBs8FVl28vr/T1OoSq3EFgJfVbBz7Pwp9n3YQhgRb6u+eqwHgLRJsRUjQdwFgQYLtLzOL2wBsfKwdPEQFvelNyroCmGywJxYA2Iz3i7X+z/+jeL/YTESCrfj3TxtMlkOhyLl5VTd2dTHXDkpU271ZoPS7cEwz+d1PSpct7sSRtE1t33qkcEDjzstCuotX9zR3rlh4TTu6fRDyDTLEjyo6toy5pR1BWzuWOYrgkQIgPI4k/5bB/XIL4bH8N82srsOdE6QF4vroQriLn4qXjO8PeNAHjnaOjczkvkNv8YovTmu+mWx0zl/1TPfFQwDna9h/HlSbvGz6o+b7gstq9Vg7NDWb0QfuF1nNy83pw7adnIverZJWFsSR5NiLrMYdgNewqI3ufadZE7xI6XPlM+FcQBbX1w0ghT6eKLtgIFkFm9QOq7+Y9+uV/5e4cHFxcXH5n/23mWE1mCzVAXS16uDTgd6MuM9ici5rx0YggYlWHfRdHXNvvFTDy62Rvvu8tzxzkQLxewCeqQg8TsAGE6AXA90OoNsI4dCjciRr7WDh6M4flO4V03jgm9Hifr/B8rcjQ5A7aKI8QqdHUbvbLK5hNtcrGdzvu8mO4Rsnizunb0e7ZwA+BtAEQOQqaVVZPcfakYvJuiFXtV/VqubYqDmqNnibKM3xDyA2Vsrd7OdZrW0TxN9a/SwtKxGh7ggleR/1cCYYDqhNn00Xt2VHkGyhmOsCLLzR60Z4mv+LuKhSPvEq/Vz+1u0dDz6pqkJ0T3ouZYa49ZMM7lOps3AzzwfFdzl4u7tqbFkJ1c6f5hzcebh4VLtVaZ/xhXTskQ6ymwfsvQGQ2vSVekKt0+Cg2qRDM/qoUmt67/5htfE7HZH7PdIO2facR57o6pxXfqu0cP9BtXG5O2rsrB7ynFd9hdPDapHXFRuSZ94S5E8ZhMCNSvutAlRdCPKiyqBNy4O3p8Fkcbeae3W/N7OO+R6LkbxJaft87vkpAAeAtGnKlwcBtATgGrD+zldTp1X9Ujy/J597fbNV7fCpHZo8vD8Jq3kN+mYLgDwAawHU3qG0vlcKbb154gbHLGVwr7NqzanbFkyZbTBZvgbwFAAMJksAgAVA4iGr2Xjor6prBKDMYLKUrpIaffmxcO0oANVgskh4f7DAoj1qi0qM87jB0qmNAMqsunirwZ74/f/VYPW9wQCABP01AOpxpe7cdO7foQct/6qtcLf6HRbbWuZC8C61Vf5uTYLxJ6VLciF3q7ZMWtv4iNpwZX363N6MPprWyWleD+DHRo4fz7Wjt79YKa1q80rbf1QBPEI8YW9WBI/HcSSZbNUs9B/snNjxGTd02ah0Ks9Apz7gMde2qu2X1aWv9k6ZuuTIt2KMMYTkFQSTvLiKPCl0ntJ/hh263gZ7YncAtrb0zp1BwtHRSTxYzYSvZCAZqQXwqpLC/YQ7PDarCn23f7byed8fxe/K9KSwSR36urgefaF4EMeGOdLm/a0dS43xwulae9WWSVdZlZ3DhMPT69IXRwwmSx0Ara1m44b/+whxcXFxcfmL/zYzrB/+sCbg/deBiQAUqy6+zwsWfqmQu084w2r7txPu1uXgb2zcs8MX8mQbgAsAynWll6PaCnezuglXNhnsiakt6P21WzSL5v+mNte5o6x6E/pkt56UegGo/pYFjz7Paj4phcajjOuOnmD1GjcmT/Rp8N8eSbJL7rGYTt9LPz5v7lyZPUQ48qkbHHyb2o4WwGsVhdqUQWg8Ujjg051epEHEdmScMua38iQ9+i6r0KybcKlOICn8satwNSOPe8zeq7ZwW6z06b9XM3M056xaCLX5tncsOXtTO/KT+yym1MpDfqxDX/bdoHYabRJ37ryiVv3xMY/s85JFHu8mXqndmt6rk8u97V7EnnaDVbr/q9oi0ixtsH+vfOLegd5yW6d87DlKOqAms+CAmcqghMrk3YBvxR0RlWjyxA6OxSMvacdFATzlPKvlOUX+Us6Gz24vlDQ0Sz+Lt1ncMQPJmJWgDBr7g7SibXmSUes6qxzUmt5N3qG2FirTZL8g5Ov7ydNvMdDNI4X9Od9Kv5YB8PxZ/qhdA/q0bk3Beh7AMSTYXAPVv/Jhw/6DeD9L3Q5AIYAVeD/T3dhqNp4CACToParYf6l7Uzvy+ACnqccOzfzdL1h4UmWaHPuEl7PmMa+rWiK3vcqqjv16/i97DSZL1Axx62x32EdOUb4caTUbl/+NertbzcZ9BpNlcUt6P5KBhI8QDtYdLY+7cVM78tciuF94zAwzmguPNgO41N0xZ84+zcwAQjAG73M8I5Fgs/5VuUcAfPxh54OpAMLvsZgohZMaVaj1Ugl3q7dNbX95hfrpzwC2AxgHoLI/bM/y4TVim7QgvBhuaUVwv/iNPHIjgMoAPPxha+5PCvNOaL9VAKxZKXfP4oTk/KR0uXlG+030EOfE6wD6TxG3Py/hbhUOsqbZ7YXb+fdZ+ewG5Kl+jdp9px2aoc95uWfjhT0rWtPbv1akqW6l0F3P5j68o3Px3obkyd1d2nlHrqmVxiXzwOp+pHhKE/Iw/xUPTw8nuXHveKA4QJ6WOlvcJDghrqxLnw+5olYT2wm3w0JJXtmvSguxipBEViufFGRz/ZufNMsyHjHD/RnyoMkS4fXf8LA+VrNx9j8mklxcXFz+Pfy3mGEtmBUWe0MrLm7gWLMYQNVq5O2Zw9ppnxnsiYlXNaPnZMK3bTVq1Qx2Tlq3TbOwxmx5wMuTmm/K71Wb116jdh97kDUddJVVvdeB3pzWj568tZ21v2ewJ/4IwGO+uP7BbHlg3HLN2p8BXO/lnFU3Bz6JAI4CmETBfo4m6W1/0nz3i8GeyPxR0HOAPCXHajbOMpjQIJJkps6Xfuk1Wh4XCZDyg4Wjb7oIV2sJQEtfWrLoO+nHtTrIj/o4pr/xQbHPKvmTl5PlYRNmCJtIFtcnjRV+W6mF81o0TZcfMcPrSeKuzv2dU8be5JVmAyTuja6/cT7dNKKQa2fVF573aIfbgUk8qGF5mvXkuFo3NIv77tqitH+8UvNDr1cs4kojxw9XH2u/WPGchQcykCQ37lTbCrdTJikjciZLuyJr4M1txumSNsLdszbuUcsJ8d1GpdO7bPg86iOc6VmJJF0/qdbbX5u+NEeTdPd14rL16xXj0+u8SgmAzkbN1Z+9eFmtJuRx6S615fFa5FX1OvSlx2fCuVH7lKYoT9NfzFcHDL8nfhkHYDIA7R8XOf9aDCZLZwFqpeniVqkZrRMyUjjU7jBr9PUOte1YvE9P8QIwGoBbn6lLpi4R1z1v7kycKEEe/JqH5UwRE7ecZzVSXrLwuGyuJ+d5TWc5ku2ZrvoFGIVrs9ZNj88B+jUeIh49k8IDGBT8LwuiPsyW7vvw8OfPhHNRRuF6fmP7Kv8CeBVLs/Ov1DFZpL7C6Vd16ItxAK+3QFw/8KDa6MRZVufb7zWr9wLoht8dMPBB7/HiHnH+tEN+W9WNubc1w0dkMm8ljqYFpPBg7oGyojVqt5wb2pFJDi6tj6Q515FgO2gwWeoCuBYvT6/oidKriZp5c8ORnX9ZNy7IYE9c1lO42KqfcNL7DQs51Ma5vEJN8vL0x/Rqjfr0WXpzx4oNz7WfL0hUWj+qSJLrlRC3jX3ImVZ58HrZgj4wSFyNWiT9NCGfewZmcr+QSJIVqoUiZnO91wseIdxmcSEARramdwPtXLjdSHjmUYGlDJ6sDLtSW3xRuwCeMbnc+3k0ySB96Glvo3DFF5zMlQgTz/CaxQBRtijtNR3oTe4JO4sXTnv7k8KqHihtdJNVar5Bs0xbwL2OfCbPOvAPCyoXFxeXfxP/+jOsCXpvgz1R4w77+CfmntMNJgtdJa2cIENM/loeNRrvVwlH1SSv0+/zmMUd6fVVs8Qtjyi4bZ7Sv+ph1uQ2gDM/ScsuN6MP537snBv1mkfqAMw/qZk47xPnnPuLpJ/UTvSGWzHcb1nURtf2qs2+6CWcy7zJK0dWIdaac5TPnQD6WHXxtwu4+9MHLAbfKZ8u2aedlT/GObrqHGnzEG8UK2k8IGin2lpbl7y495yXy7SwxpcPa6aWK4FU0ZPI4UOc31w7z2pWOa6Z7G0gGZGveVhWNEk/eJdV+MyPFO4ngDsB/2SA01RoltZ7zlMGXMvnntW/FA+fqknedHEnjqTnLJwuVvr+Olnc2fAZL1e+DnnhmQU/a23yvHYgKXQ7yeoe7CFcdJxi9arqUbzoHotZdpPFlSyS1hOTPDRzrWZ5DIOoTJaHf/eWh/TYo0m4MEceUOUqr9ZnmrBt/QxlcNh+7czoL51fjx8uHjY1Iw+FK6xyeh36yk8LOeoFDy+rTV9HpXJ/epzVd7Qgj/L2qU3vW1ijW6PEg6MTlEFjrLr4twZ7oj+AQQC+s5qNN/64APrjGUyW+ni/6Ef0QNnK2eKmxnE05WlX5/xiAOMB+OD9DGtnAFOCkH+PAYm3dKOvI8FWgAT96VKuydXBSeIcW/b/Ii0Zb+XB4XqUKOOUr75+oR3wmYaojWzcfQgDGeeB0nKpPHhT+TnPl3+o/waA6Vaz8cT/pp0t72uHbtaT0noL5b6je9MzPUJIrrc7VYwGe2L4Mc23EZVo8i0k2B7+rdenzow+nsr9Ax/x8tou9FqhA+IlT2K/c0htMqknPa/uUNvoI2iO50G1yaXVmpW3ALwx2BNDAOyPF07Nes3CeuTBu+EbHvrZa92Ak20dS5pXIe/a+pDiY6k8YOZqacVn3ys9ln0hHKvawbkYdejL1EfM4Jgr/pL6BmHWDvRWP7PcN70atZJAUrCmJb0/c5Q8XrzPY21ThK1uIlG1/rzgogTeVEeVsvr0maOUa+xWHkKusyr9xkn7ItO43y/3WfTrEJK/uiJJju/rnKZZIG2oAc4vaYmzRjmS7Z/MAnML4fGgCO5xNehrdz3K8s+xGmcDUPDRPrV5rQHiqXvfK594tKN3sl7y8Jcr1V5PAFitZuPav1NIubi4uPzb+deeYU3QhwFYadXFX0CCbbrBZOkCoMpX8thLsSR16UZpkW2w/O1vAKo0po+xXFh9e5nS69FjbqiVz7yuDxKO/ZzLvX2iaFboGsWY2Fpzr3CauMNrvDxaKITHvfbOpXtjSUp7PS/2z+I+V6w8dGBN8rJXItpmdRZutFvv7PJ8D2+pA2BdJy0vb7An3hOhaoz0qviOB80HcPAHzY8PX6shEw7yJmk7lDa/5cFLeUSjzaulFbsNakZDBxd1OdDrPEnOwQ0LZg5+OrPaN8fU+tMGiScyHrPyz2KFtP6Z3HdXf3lqbwI8WS7+KKQgWCqDNqYZffhzCg98tlLpER1L0tZpiDxoobhePq2dOPM8q7H5iNLwnZ1KH3sRe1oUzS43X+6jS0KYskHtHFbGtamjxAOfNqcPpMHC8XMMCG1N772drQzW7VZbqQBujBX21H/HgzuNEQ9EV1aTojsKN3sJUHL9UfhwtfTd/Jc8IiQX+oM2eOYFkYIBMgQ/d+603mVxO31QVONz4WRqHvfSR9Gsc+3J7d171ebDa5KXq7KZ9yurLj7NYE+cjvd7ff7b+nDC1Nbp4pa8Iu4+YIX66cGJysgNVrPxpnl6t4kBxLYqn3sVh5C8gm/kkaOy4Nv7hm50XQDFBnuiz+NZQYIHwbdF3N1iYY2+Xiyui9qutgtL4QHzvUnpYAADNUTtA4DpZ6fbkaAvd16tXu2w2khekqAfCSALSOyP9wcL/O88S+H+uU9Z5Jp8eL2JFjJr4/12WE9gskR3ci46adXFhyNBXwcJtjsfdhyYY7AnNtghzXnTWMi9FMZzW79QIxeeYbXmdRJu3Tup1r2dzvy0HqLzTijPPZrHvb49whplIGHuYgCw/qXmhPilV9XKP/aVZ3QF0NhgT3xbjmQOmiTu8mrhXHEVgGOV8smYk6xeQSm0tyh4RAd661pj8qRHeZoROdw5sb5GVDMu8RopucyrQQH3io3XnfU5sHCcYjBZttuhq1iNvC21Q1OvKn2Xt0z5bGxb4W6lY2rdfusVo980KXFsT8es7dE07UIu04saojxbKa2K10AZ1805r+ikZlJQAffwOMqi8tsJ97zcub2lG+xCLvd+6UPLAmQm9HGjTvfJ0q6DDIioS1/lVKPvkpqTRy0OsybP3vBws8Fk2WA1G+W/b4S5uLi4/Hv4l5xh/bCxd6/Rwj7PbWq7lve0w70dEKRTrO7jMfL4tOrkzZE9mlmLejjnJqVw/5vDxUPPnjLDxKb00bcmZVhib3omYLz428E8eDUwkIzy65XOKRE013ux3DuzEX066Ftpxy9NHD/srEreRoSTnPim9JHlGqtSp79wcvkQeZLNDm1jAPsB+OH9DNhuqy4+Nsa+9bQKwbhQ/PmtBnL7cJJTxQ0OaZPaCU2Fx0nrZGOtTsLNtNHigYgfZeOTZ8yQH0ALKkSRnFkjpMMR71jgyyHy5J8A7B0sHG2SyzwVmUikPn1x3iR/eTqOpiyOIan+G1SjDCAQwBOLZkp4Egu8OVL5ujuAo0OopbZRuJ62We2wcpH006AnLKrKbOXz623ondh48cwhJ6TSBXK/GAtrtMyiMXUtT9KGXmC1hoSR7P5aKC1WyD1soTQ/c4a0zc44Auo51iy5oxtZ7Yxai/iT/CY16TumcoQn8eCMx7y8PRzZry6wGsH9hJMN76ix7vWFl/Q5j8yKIWnl5in91swRN1W8yqq61RVeBLzjQVefsnJtutDr4mlWZ+oR1vDcGVZnndVs/OiPiaQ/lsFkGS1B+XqUsO9hO3qnVSb8fpokj8jMh1cKgINWXTzH+9nVJnOc/Zc/5IY1T3j5rA701sB9rHlXAA9niZvXpHG/npPFXfenK0P2tCR395SnmTdCSN6EFzyya2/nzAyr2bj+Q30+AGpazcbzSNAfBHAK748iDgKwCAm2kv+DNl+oR57bNmoWj/GanfHuf7kgQT8GQFIT+8pOaQgwfkyvTDvEmpzcp5kxpzxJbeUGJaoYbtde8fCnDekzdwCD87n7hotqjcNNhUdRx9X6g6cqX35m1cW/PanWebxTbf08Qdwc84JHHmwr3P11l9Jqw7fKsBK8X2wW+blw/H5/4eRvb3jojeHyNwKAir9JMz0z4Wex8uA3FLxvffos46JaLbMhfWa4wSpqR4sHgwFyKtaxrQmAani/sf9vBCxpv2bG5+Ekx2+H2mbzMqX3ZgCTf5aWNm9CHnqOUL7GRmmxqIAev8Kql/2mNm/wlbg/5DKr+qaAe6amc9/wGdJ2WYLywg3OZi94hI+BZEDhVCqEu2LlwQWxJM19v9o4I5zkhzagz4R0+MtPWFRxMdwChszb9X/yocHFxcXF5W/4l5thNZgsTQDMPKSZEhVBciKOqg205R2J1VaIK6/Voq/q96Jny74Qj43+Uel+ryF9EtkLmQM+FS6ke4qOKwZ7YgqA/Kc8aoOOyPlX1Kodw2h2WlP6+O59HhMSRnJHexB7v0Vyn3wPlHUMJDavQGK7dUBteusbcbdSkaacs0P7Qw3y+m0JdLte8/Cf8SHlwGBPLAdgJoCnh1mj203po4E7ldZbhomWqIHiicwA2Gr7SbZ6bYT7tczO3kMnSrta5HNvslNt7RCJOnGg89tfBgtHg1eL39H7PCYskBRwldJ0iSv2CJLdNQ0BJz6j5wY1o48QSvK+7SFcDJwjD7Sncb+STWrHKgDiAJzzpUVVoml62TByeEQu947qLc/c5oeiRdd41ZZXnNVir/Mq9wHkn9BMmvacRVZ8DMNCAcxWjuZUtnH3m32EU5VFkN3FXDfsGSu3qRAe0hsWXFYMN10ALwhWOJVFwtaWJ5ktdsstwg7xRpVWSytCGPi69uK9MRa1fnoj+iwgj7vnfipc/OQ0q11yjtW+X4kmRUYho0YcTQnKgm9eCdfN5SD3AfT6j/r6z8pgsnyB9yv3R40SDqQOFY850rnfnghkj48kmfH53KstgLtIsL0wmCztq5E3XdZLS7yesqgBc9WBU6vTt9ICacOh+o7VB2crn0sAOj7i0YEKF05F0qxri+W+cenc/9gYcX8SgEgA6z9U7QEgAgCQYOv6/l99WwB1AEgGk2XDR/R6yxusUn4u9AlWs9HyN5r/2S1e0VLdsUFj/cszCfpIAB1q2X9qsEMTeaUyTe5xVGsqv1ntyJYrvdyniNun16av5yezwG0mpd+ztQtmZ/v///dC8730Y73nPOJxO9yRpypfdglEwS9X1EpV9Ch+k8SDqq1TuqT7kaLqw+UJL/1RuLEHvbiuGG43c7i31x1WgW9WO36lR/Hlk5qJUw+pjWv+qrYsE8DqveThLUYJB21hyIkbKh6pfIdVWF+ZphQdVRs2qCc870KhJl/RfnWjs2Ph05b0/id+pEjgnBCRKGISC+p7XjNu4DlWs3CL3P5ylCZD6UKvtDzB6mvtTGj2kXjTPYakbXjNw9Y3oM+mf+xcoPtR/N5NgfDuLQ9tUcK1+jCS23eEPL5FJvdt97lwMjGOJIUR8MGdhNulT3m53B1q64INamcSQAq/9UPR9rkmi95qNtr+3vHm4uLi8u/gX+qoTIPJUkWAevRr4ddrVcg7+y/KR5urUetbADsnKiMLvVGGWvRlUTDJl+KF0x7RSHvZSHh6Y4/aYp7MqS0QBb0ABMQLp4fWdvy0JZTkaVUIvmncv0oAbKmVydu5H9Hrg+rSl7BopsadY7UqWtSGmeVJ2hRPUna9nWNJn1+kxbn+pLBZeZKh/XCuereX2gGJv0iLz+D98YtzbrDKeaPEQ2tu8so3okhm+5csvOAsq1n8hfztnDNqrcUMJBQgkEHmCFCLRS6HfC4cn75LbVUujOTSHO4dVAZd/Qb02dq2wu3qbZzLlwFoWBOvpr5gEf3zuWfUYOfkyFnS5ncPWXlnN+GqBkBsD3rB2ZHedr+uxr2LQHbYKx5eKoBF/qqZ8+sMcUt4V+FKh4faL1YDKDmkNp7wgEdfj6Op08uRjF/LuFRog0dlDyIXBtH89p0cCzePkccIw4TDQSVwryNzsbJFbXDfyoPDf1E6jryqVt45Vtx7+mN6tWGMY/vbUu42XQU8UhAUepg1MkfSPM1tNTatlXDfuVLz48EdapsT+dBX3KB+pE6Xv9CfYbXfnWW1rwLoZTBZPv7DguoPsFuTEPdaG79rkrDz2FDRUtvGPQwyRO0UeejdBzw2CsAVAH8ZuHxkIJm35ioDC8cqX91+w8NIDfKaNXKs2lMM900RJMs5Tdw2aZ64YfMMaYt7W+FO8870xgsGeusHpXsOgE1/qddqNqZazcbtfJZ+V5+pS8YBABJspwGcb+VY+gOAlQukn/l8cf3qk5pJa87MaPnV+2v07jdmNqgUYzo43RvFsRc1Y3fPEzf0iZ+66AoS9AMBmO6yWFGGUFCZJvcG0FFPSjuVcm0qgOdt6Z3XAHyvsiofHWMNjH9pz4fDPXyPq/Uur1U/rlLFsclzkHCMZ8Mn7ZDa5EFt+tohQb2QgqDKV1mVtxulxV9XJ2869xLOplciSQFRJHNfEMk/MUHcvawFfbBjnDy62mlWp2SIeMytCnl3YL60MTcP3iSZBxW2cKystkE17tmvNh2cigCfTx0Jpde0YyKLuHvYAnF90BQxUXeB1RDWKV32THcOdnDwwjNqrfTqeOO7Qbu0mz9sXUUwNkoe/7CakCSeVWs6lyi9G4Ug98Ba5eM7FMxXJGruXRZbe6DT9NoTpSXLlJ6Le9CLX/QSLvh8qwxb+JxHlmRyX2blIdFPWZT/j+onBwrgnQSQITd45VeuwaqLi4vL/7t/iZQAg8lS8YRmEpspD1ruRUrpfGnjgwaO1Q/wfj/HA94oyd+iWbjcHXZNX+d0tQwaaZO0+Pl6pXNIb/GcvF1pkzRX2lTPyUXBpHyZOUw84pOgfH4/mQf5RJDsTRPEPYPsXHrwhodV0MIp1qMv/Bcq8SEvecTuj+mVpGr0TbsqJMnwtTzyp6rUOukcqx20SlrZuwF9JjV0rF44X9zQ7rDacOtCaUNmK+d37Kzm66qtncvflCOZTgcXDy6X1n7iTUps29V2Hha1UfUl0upjbnB8fIrVW5XDvMdEkFyhu3gJWVyv0UBRw0lOng8pTf1B6V5hhHAAe1grbSF32zVGPBDoTpwjjqt1Dw+Xv3k9RDgSe4rV6eeHwmUN6bOCZvRBxzCSo25VOrycpdm2fZI8rGM683PrJZ4vCCc5DT51zu4iQE2sSJIzj2in9gfwwysWUlrGNY1FqGGr1B7769EX1ZvTh76PePk+nwiXn+1QWl+bonw59oH2i4l53CuTgnfM5d4jfEjRYAPJbp3D3dNf8Sgxl3vamwsPIwknvwqEffqMRaQxUH0ptKk/K11St2rM1S6qVZzlaHZxH+eMkpnSttBR8vgTADYAeGY1G/+3X0f/d2cwWSiAXzRw5j/QDq23UIn3Oq3W2d2O3i7cxD5KBCDEkpTqr3jERavZ6DSYLH2tZuOOD6/tjfcHAKxXZ/lsPcYafPOzYnRfrfl+exjJ2wUgcZPS/ughtbF2neb7B7vUVq0kKB4LlP6VrGZj5l+1gwD4DkCu1WyciwS9lM89r9R2/DTdqot/BmDYHTXmeXVqbVnBsfX5c+3Azsncv3YkySm8yGqcy+eesenM19FLvCCG0oKLx9V6PR6x8uOr0LeVPxJuzQLwrJRrLj/j5QJ7OOd4W83Gbn9Vvy+AIrw/5jgZwA9Ws/F1rOnAorOabzo2d66QADS26uIbjnOOHnWANa0UgtzIJdLa46Ekz2et2pXtUVs2B3AdQOAg4VhUZfKOfasMnwggphO9kfe1uKvfVVYtNYLkGC6wGtvcUTaqlfDgzOfObx0DhRPlOYj7LRZXYbK44/I6pUvrnsJlZwr8xQH0lC6FB+TqiOz5gEWRj4TbbgudfTLqCy9EA814c06tea+HeHlQG8eyLWe1Xzd1cPHJz6qxUT/hdLg3ikvXqd1SU1jA11OlbZNDSX6zRyxqxQj5m1a50P9wTjM+QgO5dQApLP+URzp03Bm2We20p5Nws/VAeUoFq9n4b53P7eLi4vJf8S8xYO0yZdVck5gY6keKG29V25Mdatt8vE9XsD7UDhle3bFhgR7Fn93XDatvsCcW9KJnh0wWd438zDnz6WJpbfOKNNVzh9omtwZ5jZ+Ujz3fIIyncv9JCkR/APcjSebWKeKORaPk8cO2S/PDpitf1HvLQ88A+PQ3zaxUA9JO3OCVg0bKX68DkGM1Gxc8nFnLb7I8bNJTHrUdALGajQ9fzazUqZ1zmcFqNq41mCzuN7UjlwQSG0WCbaTBZBm8UlpZty29E0PAWz7lUS8zma+2Nn0RIoKfUEDb5nOvHB0cBzrISz9RICZNEndd80RZD29Skr9Ajrdkw3dYa3p3Yn/h1Joh8qRxVrNxu8FkyYgkmUlbpYUbCHgPBWLVCfKoNw94zOcApJOaidMHOKcaVmlWhNxmcZPMSr/Du6VZ2R6k7E15kpn1mJWLrUXfGNK5/+vjrH6IWen7WoH4EEAugF33tUPP6EmpTxvH0h4zxa2p+9SmbrXIq9ndhYt1tUQW3KDgiFL/ehjNrSdwNTuapoUl8eAiO3RSeZp+k4O0uKxWtn0s3rxi47qGt1jFsgLuhW+UUY0B1AJw1Wo25v0xkfXPcWpGK8+h8qSfqpE3hx/x6KY3tSO2r1O6VPOAveIK9dP7VrNxCwCsmv75R03o45l159xqbDBZtABGADhlNRsfG0yW4QBKrbp4HYCPkWDrajBZfgVwF0DEr1LC8CcsMkdLGN2kdHDs1M5z26q0/37s/M3z/qN2fRhAc6vZyA0mSzkAswFstpqN55CgFwFUyONe63K5d98KNHU13h/j2tRgsgw5o5kwM5pmPgWwfp/a9MwEeXRjD5QFPtYNiVE5rvV0zl56j1doCqCW1Ww887s6mwJYAGDS/9gdIkGvBeA02BMr1iYvwzO5b0gaAsYBOA7g3hrpu9cj5Qk/Aagwhv6mBtMCWxb3PbRK7eEHoNQPtgYt6YNy+1jzIwCGA1A9UDqqJ724qJbwuvA5i9jt5OIXXqSUaYh628pDwszizyEAdzi54KYSUZol91e7CdeyPFHmVDjVVBFSMp+zcEc18qZWPrztz3gU/IjNpzwyIBJVdify21b2JXSUeCBEhkb0RwFaC/eSKzm2nLiqHdPVySU3J4T8KJLllcwClbHKV+lrpO+1IuRkPSnpdJ/F/rRI6VOhi3D9m3nKgOy//lDh4uLi4vJ/55+eEmAwWbQGk6XqXx5vmN57x6+aOaZnrFzAbHngmh1q23d4v8fj2hfagSs8UXbNqov/9or2q5cGe2IZAO+J0q9hWVzvuVMzL1oLpfAbx/Cjp9S6yU5Ibunwf9qG3olVIDbH+xN8iMzFx9dYlQAAN/rJ0+5MFnd2qkqs+19r+w9bJn96+QWPzAhBvtYbJYZl0hqLwWTxGimPf1nI3T2qEmsDAFUA4BWP2CpC6fGh6T0bOn7oUcK1y6/OaPxrK3qv81h5bGYRd3dfqxgflHAd26B29szj3vAmpfUfsmiPIui2pCNgzHfij34CVLclSu9G+5VGj+qRZwGDxGNNt0kLvn3DQyfsU5uemSVu/sVgsswAsCuZB9830Cw/BqHaWVZr1C/SEh+rLv4HADPbO5eezIDfth+Vbh5BxLayDb3zVAFJrkRS4tyIHF2FJpe2cyw90Ny5UrjCqqZVI2/vNyUP9e3oLc1Lbb++elKaDKBcMHI9nBD9BovHf9jPmj4Yp4x9tEbpdrmMS/ZzvObeWJqWXVlICU3j/unBpEAIIbmOHK4Pu6pW/qWx8PRFIdPpbdxTf4fFFXQXLi/E+1Xpdf7Mg1WDyaLfMb17R/L+gAr/aeL2FQLUGp4o+2G6lGj4QjwahvcDLBhMltPLlM/eVSJJTiTop7ak92cBUMcJvx1Bgr6X1WxcZzUbt37unHzkW+fQiwaTZRWAl1Eko2st8qoZ53j4kXjLGoLcX1dIPwR5ocS/MkmaYDBZgj8cxfq3jAPQHQCsZmMS3h9nXGgwWeob7Ik+SLA99SNF7SvMeZIK4Abeb6sFAJv6O6fFA+j8q9KSBMDWz2o2Wkrg1shgT9wS40g8do9X2PGhb/sbTBYvAECCfqNVF5+D9yc7/X4rs+PX1YoHADRsTJ/M7yOeaVyRJGWJcIasFpdvK4P2EgD/z4SzbJh0hBxQm+blwyvTDY7yAELyoJ+zjzVvaKRXV/WgF3YuENenViCpvXxJsfMqqyJw0MY50OdqIOcJXLl9Uq2r/9Q5w57MAlftUVucFaBikrg7xcY9vV/wyNBLvGpwG8fSAfvUZvUVImqPqA1EFVQ6qjZ6lc192ETn0NzhznHSYe3UuFb0gWpRGz4ogDcv41r/Z9rP1RBSkBVM8q4FE9tRibBPo4XMMRPE3RGPWVR4BvdvrUDiB9SmYTncJ3qeMuA1ANuHWW8XFxcXl/9H/9QBq8FkaQxgHoCfAAAJ+sp9hTMpR9UGfYdKx/J3auZ9Xpm824/3fzg/jnNsSS3vSLw/xvnVl0OcE/uFIefaOc34hwvlvuow+ZuUYFpwpoZgvZyg2WwYKJ7QfC5POdlDuGhvTe489UBZHoCORnrt+1HiQbpF7fAIwHwAG+vT5/emiNtfP+SGdmPE/buqUWuN7Wqb7QaSUTUYeScFqLW/E3/cE0tTm6+RvhvshdJWBpNlxgh5QncFYqjBZPkUQEsGoW9Vx0bNNGVQynxxPflW3PEUALnLKhYcU+tX+kbcpY2jyU+0REluLdw73XDurfnTlCGprYX7PrPETdpvxR3hX0kHPk7iwfZ+wul2kSRjTgvcdZsqbm9xl1WYvkladHWLtDAOgLvBnih2dC667oPiuGOsrncHx6Lfjmsme/cSzilWXfz6meK253dZrK6ncCHyMquh48CvG5SOjiqOTa8mSb/6XdSM87/Poldsksyffyet7jZb3DzgMqtuu6dGsynykE+WS2u71qUvwro755a7zys4LrCajTPgP+sIa/xQQ9iWHK7XznH2fRFDMwOSEHToNq94LZfr71zk1Y1OrvH2npPZ3J04Y2vSN92F2QVrrGbjW6vZOPefGV9/gNgpypehrei9JU+0n8d8JX81W4Xg39lpfvuz0nneNHloDQC/GEyWtni/OGrRMqVX1hNWLmSzZlEYgDsr1J7TC7jHsRczqywDgCfMcCSM5vYHoA1Cfpfdmtn1H/Mov2c8qhzlvNZJXo8G0Tx+TK5rr0zfeQwRLAd+VVqkJs2M7frXjbOajd9Zzcb3hwQk6L0A1AWQAiAagO+Hy9YhQe9nsCfuf8rKbWk5ZUN/ABFpCPgECTZ2ntUsOc9qZiNBH2HVxQtWs/Hlh6+2KxhMFje839+16H0dtsFIsD0H0Mlgskz/XVPavOSRfQEkTpZ2tR0t7D++UzO3cn96qkUz4XHJ1/KoQwBOHVCb3qjrWKsz0MxXUSRz4DJpTWgt8krfmt6Nserix2Vzn1PTpW2X3/AQOlbc53WdV9pbnzwr+FI8/PMh1rRfU/pE86V4dOzP0lLve7xidmv5+zMK0Qir5W7XC+AdeZQ1PHuDVb7xkXDH0U640/U31gpH1UZv2wl3bConlzmIvxctVWZLW4MWiOtDFUhc5oK3Dymq7gaHbZPS6V4m1w/JZF6RIlgHL1KmG+8Y2WmP0uzbUu72xEAz3bLge/IUq7Nrovhry6b0kR1Afbz/AF7/7xl4/0oIIZ0IIc8JIa8IIaY/uj0uLi5/Tv+8lIAEfchsecCxjepHkQCeA4hvRB5/87W4+10D4YWHgwu9bqoVr/RXprcHkA8g/rW239B7PKZLCXcrWK58uqqUa8ft0MzzfceDfc6xWhk1yavkevRFizc8VLNR/UhhoENvsbiEw9qpkQ0dq5+qEI65wV79pGbSL5dZ9Ql16IukX9SPrDvUtl4VSZKSyz1DOQR5qGip0oLeRxl00bPkwWXp3O/xUc23VW+yStYxyrh8AL5TxW3qMPHIRwZ7YiKAsInirtEZ3C9hm9r+M6suPgTA4Nr2Nfe+EfdMCCC20rb0btsc7pH0gMcQG/eUQ0jelhbC4wXV7OvXVSTJbT8RLsoSVPatMjyrIXlSWo2+dQwRjrTfpzYr7CjcshdyD78a9BU2Kp1+m68OnB5Bso7+Ii4OdSfOq+94kPKD2r1WJZK84itxX3Qv54zaVcg76yJpfcuDapOyNzw0ob9wqttkeViT9sKtjPr0hdjbOf3QBe0Et1Kuce/hnKMkSvPb72RtXmxUPxoOYC8AgvcnU1kAhFt18b0BFH7qmHVsz8Kviw0mS7l44dSiE2rdNjOlrcldhWvn1ypdSDjJqbhC6fnbqYXDfvnnBNK/BoPJ0hBAGIAzVl28/oJabdt05QvnYvGnxoXcnSUog86kIUCL9/fWjPe5nHUBbLmqHZMeSvJOGOyJbwCYPFG6wyyuMx9hjfRnWJ2AVvReyjHWcFtN8spzh2ZezbqOdT7XtKMqnlFrj6lF30z/Xulxabh4+It7rPzZZvRx++nyF4+3aBfHG+yJEwBMtZqN2f9TYxP0VQB8A+C3Ac5vS/8/9u46vIls/QP498xMpELTFkqLNlDc3d0huC7F3d2CDx7c3a0Li0twd3crlDJQaCmlkmqSkfP7I+X+uHtZX5bde+fzPPs8NBk5M8zuvj3znve9qBR//K9teENGB9XkWiK1GHdKKVU8lOa8DFcaQkbBYprwo+N4gbcl/ug+BAPYL1hMqf9xk3iDEUBVuCptGMDb5oI3ZLwsF9pTjnlW+Zhc9oIfscXyYpdSzxBoB3CKg+Q+gN0XHU0zttqh1AoCEA1gWiB5nyk3ogaO0ux8cU4uqvVGcnEfJoUkUzcaSD5EjhF7RY/gfshlBwd/kqChYM/NkdpcGs7tGl2ChGfKQNK0sdQrLhZe3Ha5jtiHPZx7v1y5ZWX2cbH8eDPMjdjPK2CL35XzeGYi8ZEHlSopJuZavgASK15SijgKMhEeGZGESWInZbDm4NMlUvNsI9idPoQwqSLYcxykarPEYK48+/ywDg7GSKIrfSdOyrVZY3Grzj7oA94273c/bH9jhBAWwHMAdeH6ZegmgPaU0iffdGAqleq/ztcva8UbchntISzP1R09WbP1xEa54VMAUYLFJByZuGh7SeZlHvC2bY8mlWHciaP1Li0f/lIOeOAk2u/Wy43CNkt1vdZoF7KDuP2zeoijBl+hhRtEKplKdWBPvUuGtuI+pcr1e0re8gVIxOPMJG5qKM3Rq4xjtaMlc6FTS/ZiNgnc+A/w6RdJM64sCm5yZiRUyICUlcd15oTuzpFVziilpNlS8PV++sODajjmH0umbhvi4HXdn7G1b8xc9xonpRRPhMeATuypygBEuFpnfhzIHVh4W857aPqMRSl5zZvbDeP2FDqtG3nAgNQsjRwzb1XRPhQfKbnFZVKLhxM12xoWZoTxABrf1vXJNkgc6HdKLrXHolnT7LBmLBHBZfNGctIwcUCaJ9LWV2Ifd85I4hP6i4N17dgLhRrRaw1f04BYDZHy31dyyalwq8lR+bKAgBeH5Ir1O7Cn/PxhS2ahzE6mbqOasVe6faSGoAHc/qiq7OPcA52DpozkfhjnjWTCEA+3C7qh10PkOuduyPl8BH1w0AslCxo7Z1IHdIUAmAFkvygXtlRlHz/frZvy/Oak7ysO5wpnr8Q8fh6qZD+wW6puDiDxokUKrliEhNc+Nau39NWfo78fAqAogFehSvY8hZg3l+Zwa9qkQfdmldw0JhKZOgDYA6A1gF0ANgoWk2Q0W8Pe0MzV+jmHNtXBmak1e2FvAIldXo55/qYWez+ps3Osm5nbId5wFsx+n+YpUsixqe4abu4sBUyhFtOP7jKarZObM5ci8pAI6iSkfHbyMWaqZnOE0R6SGV8KVgEkUI/zk8SuC+uxt1qPYncWaMVeXLNtwrrA19TfVoipXHOx1PJdJeaxNZTm3ATg0k+mcaQHq0azdR+ALoLFlAhXndf/YDRbG93VuW/1Ian9AbQA0HHUuJH3R3AGv27imLdZESMrhHVWIQ/rT9Fs0lxXCr4pyzzLvExuWaYdd/7Caqkx44+4adHw9RnI7ksLkWtXqso98PZCim8c9fIM1pzTbRdrKlWZ+y+1hBqLMK/c8zDv3mQjsflPyyXEysyTaj9op7d2UubtJaUYHlNjci3mrns4DUjaLVeTe7OHY2Qwya2d/L6MsN2ar1kp5SXvbgaQj3WzkPicV5VCiXrY4yklKZ25E54pcHuWSrVeAzUHfROoB8qRJzZP4vB2J/ZUB3Q1kqn+ZX7mrW8B8rpIb3HEtSS4+QHo2lscnhbKdr37B5+1P036LxgzAeQE8AauZybkDxyyHIAwSmk4ABBCdsDVulcNWFUq1Z/q686w8oaSAGZ+oIZt5+TibEkmTHCANRZhIhQARa8rBabeVvIt7c8d9IGrUH9eiZLgNOiSLiuFjBPEntJ37NkbIXLtwIPa8VFWpcLKA3Jl7+bs5aF6pGUuRV5o39LMj6dJnY+Y2Kv3MiFx3hal/rPO7Ilq95SgGXHwun9NKbQyH4mIPKEbs7ikfdXgTuypV8vlZlWLkfDJ2UhMwT7c4fxNnDOjurNHpRc0W6GR3A/lmzmnh0zlNviWYl74LZeaX16pXTwrjno+HSv2anFcKQsAtQV98DG4am3miqcel+NphpBazgXHTmiHN/BFSncb3KmDckpB5h2NpR54qWSza4hiK8WGPQAghMpZe2YmCfoEmiHpHg0CAc3UhL127hnNwWYnMcVfKwFEBHcrkLwX3lPfUqeU0o6y5OlrjihFcjIxHys6lh3erpnesxQTmoeCee5OxJPgbSMAYMg484jF2pUvwdv2gzcYEhSPwES4b9si1c2Uh4mUPipe6/bQ6sUXcCuqvFQC5Cbcde1tJa/ugZL7ih9JNORioq4+VXJkqcfeiijrWD2pJnM3WAMp8oRSNhKuWUUtgIoAxv+vrnw2mq294XoT0B6AJOiDz2yTavVswlwt/ozmvN1OnNwRwHi4mk8I4brgFx2d40aGzBpjLG7e+V0i3Cu/1HUMPiRXXJWXRAQRoMZa2fR4nOZ7v8qOJVF7tHy2CWK3+v4kod40bkNfb5KUK59jW6FwXXCf99TnJkPlLb5MyokYaih/UinN8FK3aZ8Wd/1YefPWxjlIzIhC5FWhftx+OlPqxBYmQgZKyOlYatCtk02TBYvp8m+4dr1gMdk/+zmbYDG9+/F2dLJhASF4AiDBaA85eFE76M176qsNo9nenlTKnIynGdaa2KtjLitFm3ZkT344KFd8eFCp3LQ2uX1+tnZdnbbOSatjqHexfuyBLMnQp7RkLxbRQbI/U7JfLkje1FonNXCM1e5Uzskl2DfIjK1S3ZcrNItzaSAluxG7IZr6cgflik+/Y89lnSW1d/+OPWt/QzO/v6XkD1ugWVnhB7nGtEDmw9QwmvXaO5opZxbEKc3Yi0Es6EcfkqiPVzKkvkXmhclUPzkZ7mIp8jyGIQhyQ2qqF7FfYQn2ihQZE+FRZKA49NYGzZyFJ+RSYhk2bEdlx9KpgsX06jc+Vl9VerC6FoD7Zx+nAuj1e4NWQkhrAA0opT3Tf+4EoDyldOAfHa9KpVJ97qsGrJGTc5V6rfiX3ydXLjBDs6FDAnV3PyOX2t9OcyG5r3Moe0wpd6EhuVZtoXaFu55I3Em55IZe4qhQbyTNb85e1t1U8j+swDwp0oq9kGOF1NzwlvplfUpzPB/AHnjvBkfp8sxT/VGlfOQH6u2tJdL77+Xa4wAYc5PIJoe0Ez72FYduvagUM5zWDqssg+XOKyXCZ0gdgwFEb9dMZzKSRL/9cpXWq+SmZedpVsY/UYyeG+SGQQDqwbWC+QGAkQDqdGePRNxV8r5+SHPFH9aOL9xHHHbiNQ2YCCAwN4kcb6fau1u0lvYx1OAZgI8aFrJPAJOQ9FTJGZuV+ZjxiWJ8N1bsdfKKfkgVAIv3SRUt5ZhQdrbU/lp55mlYXuZt9/bOid+/1HdaB0CWKDNwudzUlEZ1CXo4XxVhBDYneV/5nZIptJs0ttJR7RjP/CRi3Typ7S0WSpcUuI3dIDd0BuK9NInbPLg2d79gPPVc9L1Uo1Zl9kndZs7pXcuTJ7b85M28vtyhQlE04yADSZmeDJ02TvFKrMQ+Tb2gFE0LIPHMRqnB1aNKuRx26FrB9T+3toLFtNJotuYC4C1YTH+bGaNvzWi2EsFiouAN1iNy2Xd3pdytG3G34h8pxgsT5R4KgNC27FnHHM3aCq+UzLlyMR+qW8R2wVlI3PjdcvWiD2nune5IK1GChDmnaLbcauC03BnB7UroyR5xSGBurJcbnXuhZHvWjzvo0dA5Wz6sHfv0mFw2IYhEdjqsVOqxVLOo7CMlaH+5aTdupjcJiAdvu/Nvg+QNOROpW78nSk7v4kx4rSjqm6+Hc+TjY7qxD3VTYtv/x0XxhtIAXnz++t9othYH8ORLrUWNZusuwWL6coMI1y+tUkvH5EGbtbPLZyD2GafkEuvtVCflY94+fqwEljiplP6YDHf3TuxJNo56OPVESilMXmW6r+R5VYe5U1xDJPmanFeuwT7W3ZFzJ+RjojwoqKSFrHOAgwSkAYRzJxLX3DH15CatpW4KdVNuKXkSizMCLcRGuJ+ViySUYF4aPOBU1ksNYlqzFzLNk9qFjNWElIug/mujFa/Oldkn+aMUH+aSUvhxHiayeEaSrLsiF05twl7xWiC1vutHEnM2ZG4gieqdPiTFkYWJu+kGR+ZI6mus4lzmWZO5O78/d+DNQql1m5CZY1r8kefqazCarQKAwC989VqwmIy/55hqwKpSqf4qX3XRVSXHsmyvqX/jZzQwZazYc00iPGUvJq3cBGc376Hc7qx3dL17V2QeuykgxQGUkSgp15E9UeuSbnCxwdyeST3YIxk2yQ0sA8Shp7KTmBPusO8pi1CxD3ewQhLVReRhIlk3ajdM5LZwjZmri2Zwa6cS0ObhNGuzh4pxUSHyOgsAqwccDd7RTNVasRdynNCO1u3UTm3eQZzwqLFz5vLv2LMVB7D7R0wXO7Y/LFdoBUAP4CUAX8FiOiJYTLUA1HxGc76lwHMJXOcs+Hh6h3Zaxiu6gcsBzDqjGznnin5wuB9J2EAp1d+jQYlaItudYB/kY95tmih2i8yAtMBxXIgmUvHxlCjTdbXUJPE9Mn44qFQ+0ZS90uu5kv2sDPYmgP6vFb8xB+SKF0uRsF5N2asXa7D335dgwmq8VvyjKzBPi/Hcpqv9nUMqHlfKXlohN/e4S/M+TKK68Su4hfvqsTe3+JDkXAnUPXyl2Lh/T+5ojRVS06T6zPUxq7ULy25T6nFhNPsNDaQRD5Vc29o7Jx8RifZlAcfmJ2eVErY9crUc+5Sql+3QdVqrmVcDwFwAZwAgfSGVGqzyhl3gDZM6j5vZFK6FfAAwHRSthmn3vc1PXvk3Ya9814k9EVmAvMn6g1xzE4COuaa+qABg6BhuZ49g9nTUPu2kN/nx2j+YPV13pXbxzMHiwDsyWK/zcrH8y6Rm8e5TYp4WIq8va4m0Y6g4IGUSu/GMGxxFWrHn89Vjb2n7cgeXPFTytJontf1UrD8GQMJ/jtf2xoukHT0iV2hZ0LH5Yi3nwp6vkG2bjki9f+IKiwPI9KPPGs/g1hf90sY/Gay6eAPYtFE7t8ZDJVdoInVbekwuv3qT3GBLC+dU74NKJXt/7uDlcuTpTYmSFD/YpGbs1bkTnN2TCjCv344Tu/ffIDeMHiv1ef5EyRHnRewad5IW8UAx2uZIbePfUH/RQNLc9ETSvae+SQRKtSNKeQDKu3LsC4DgqkQZbW5EGpKoh0cq1XC9uSNGb5JEq7IP6imUCQCV+5Zln5ZIUbTuudkPXCvuUlEN5JQHSu7nXbgTUzOQtNjJmq3FspKPwkuaxf4MOSLj4JljkDjA95xcPMgNzowLuWXuczWrO5dlnn8MmTmmBXjDlZ+5J99Kzt/4+a/xDq4FhZ9kT/9MpVKp/lRfdYY1cbL/0LdKpll6IsX6kQTRRj0ytHHyKVu1s3rcUfK2vawUDpulWdvKnYjPACxIUNwv3qb5EosQ4bw/k3AagCVZ0QhuRIw9pZTOG0u9fAJobExh9jWbmdi8l0tNlhdhXvUPIlG+adCnaCGKL2nW8OFi/2sWzdrqFCTtHc1E2rFnqRucZzVEvt7UMW3wAxq0FkCAoA/eXMa+oqIIrvYMzfq3UTRjuYNypQ+hNEeSE5pqABp8KnhvNFsvwrUAZP0PWn7mDbngqN1K9QqjuR15S5LQfKeV0raO3JlQB+UCJkndju2Ua5Zuw56LbsjcKOuDxHJhSrZTF2jxY4eUSuMNSE7tyh7P+hGGezKYWxflwr14zZbk0iTUSsF0eaDkmlOCfdXATjXFNsgNSHby8XID9kbASbl0UBv2wrCHSq5x6+WGffpzBys1cc6skIe8vXFIO74/oNyToMk6X2qd1o45n+22EvSwKXc10El1WT5Sz4++JPn7cs5VWeB6nZ99uWbx+1NyqVX7lKqlAYwDMAhANgBPe7OHmtdlbvu1EfkOgsV08as9JH9TRrN1M4CRP84JNZqt/cewIfH9NIf9ARwAb3sFAI3HLuvfmj0/woDk+LzkbaY0aB05SYzgTVJEHZF2npGL3z6mlKs+gt1Z05PY672iWWgR5nXZeOpxkIO4bpnUqt1jaly0RjPfyYAOfkRzBS6Vmi3coJnbIZZ6Rb+nGfMWZIR8AJWfKMbYoozg94gGyllIfFe/KREhPxpjXgDNBIvp/xf68IYG6X+KAFDHaA+pBaCFYDEpn+3nB9fr4ZngDWtmi+1OrJSbVRb0wWMA9Ierxesa8LZL6cfsAmA7eNvP5zDzhk5DnP1hICnjPiiGZf01h9o2dc4o64+4y524k8w8qV2mnRq+oBvsqaeUci0zkYQCfiRhXBbEXQZQ6LpSMLYhe6PSAbliSm/O6kUpOeYAV+oRzSVdlQtnikBG2pM9pnFAm5iHidSFKQExucl7xSIHR41gf2Ac0JSQQBh3OB1+xPY+Ae6ZvGDXHpdLM4EkRsnBfLhLqVLymZI9TccgOpnq75diXhR/IOfKQ3tKV/UAAG6tSURBVAkjFWfCPqZQvZ9AA5CTRCccl8uczkwS7Q3ZmxlFMEfjYBjYwznyyFbtLDdfkjwCvO3bF7f+gq80w8rBteiqNlyB6k0AwZTSx79zmCqVSvVFX3WG9a6St3gmksjkINH9x4o9H+iJSM7rhk7Kw0TGt+XON5itWdu2jmPehWNymXiZkk02uIc+V7I5UqAtPE7s/mGbVPvjTaVA/jTosp2SSxJPpHxwZ9J8riqFA4aIA3o2Ym+O9YDzSn3nnNhhYv/nR5QKkT3E0etkkAceSHMWJK/zLpOaRX+g3oF5HVs3xFAvBwF8M8JWHcCy63KBqy3Zi+Vt8AwZKA6JuaoUyrRJYxkQzJ7qAKDa592Z2jJnzk/ktmh9kPQhAPHCPLndOBNzdUs95tZLL5LmNkHqeeWenDs1nmaob5XLt27KXKnUhjmXsQB5MzcrE3f3DC0pZYJtxm7NZLeazN2bhZjXV7bLdfZmIx+HGkjq3VrM3Yk+JLWbCO7dAaWykAGpCgPlxg651qvbSj7DGbkkMhPb/qHigMvFmFdcWSb0h3WSqV8d5iY3jNs9OpL66mw0Q/VZzu+eVCKPNRlIikd77nzt90rGCBkkhSNK1urORYGFiFBntWaB+1rNvKK82HnBPqXqQQDDAQRxkGht5vaheszNBy3Yi7mOKWXTAGwKnVS4E3jDoK/5rPwNDfrSAiYAN4oxr24BSPgUrALAI5orZJ1sqpSTiXGbKnU5QUADI6mP1xvFrwCAgaWZFweaMVe+YwiqbJHqWNdIpl3gbWGx1OvyHDH44mq5ycRAEv38oZJ7zgslW0oC9UjuyJwuL4EJ3SnXqFyMfZUEII1SogSS6Ctp0CVG04wvTsmlT35hjBEADvzrJ97AAjgB3nYMQDiAgwC6fx6spkvIRyLOgTe0ymPfErdSbtYFruoCndP3GwCg1mfb/6rALFwJiLyqFK7djT0+7Sot0qSpc8YRAOczkLSgeVK7VQCCtsj137sTybZNrr14ktS9/lzpO88LSrFq+Zi3BR8quUpvlup9aMDe9FQokdLA1XqPjJ6p0Gd9Tf1TR3J7oyxS+4kyYEhRNLQQeSOtlU05y5GnRXTE6emFFEkDJW201GdPkCNk8hSx261zcvE3vkgmu+Sq0j65SlIydX+XhAyJ56RimrtK3qqP5UC7HxPPZSIJlzWQs7yiWZ8pIMoG2RRpJDH1GND6Z5XiubREyVDBsTzuMc1VuZRjzdy/a7CabhxcOaufS03//HehlEoABsKVQvUUwA9qsKpSqb6GrxqwdhHHjtRC2sKCrp2m2VgrjnqmRdKMDU7KperPFVsnNXbOWF2AiShagoR1S6T6bIfkijk8YPf0I4mBIXKdmveUoO2vEZD4kmbNNUWzMTA/8y7PfSXIzQ0OzW0l78DuzpERQUwkHcHt6pwEdxpB/bhy5Kl+t3Zqv87iuILtnJMebdDOqzhMHNBM0Ac7PeAomgQ37RTNJhMAaYXUdOMwbndWQR/8BkDidaXg99vkuvfTqG53uC64Tfdx0xoZzdb6rcfOX1aNfTigHnN7fDwyzM85NawFgAc1mXub9spVxtxT8o4CYFopNe0tg9y7o+tdZ65mZWAQiex5Xim2+Y6cx+cJDcxelb1/4QXN5nZFKfz2rhIU6oWU7zIg9b4XSbOzBGHH5DJ7HinGjNWYR+bTcslzZ5SSMUnw2GzWhPhvkutf6C2OyFSDuX/srFL8Rhv2vNcdJc/ThuzNPLkRKWUnsfZzcvFdAzUHCpVhX2ieKzkX13HMSbpP87w/IZe5IYNJ6MseTJ3EbU3tLw5ZWYV51OymfsBTQR9M4WqjmVCQvAlarlmyJIr6vHuo5L7kDscEAD1bOSdr4aqQ8F/DaLYSo9laOv3PGqPZqk//826j2ZolfQU8jGarwWi2Nv9s196lmOcz4UobAXjDAPCGWoLFlHBpVtfo0syLBj/MGtk7kPlYpRgjzA5i3juGOfttLu5Yd6WDOL5uWceqAsnwjNERqYTRbF2SAjdNJmLLIuiDu8/QbPBPhPteBfC8I+c9Wol9WNJJuXYZSMoyAJdZ0PdPaI5j75Axrx3aR73EkQfbT9//4xJW2QWLyS5YTC8++7TXEqnFDwBgtIc0Am97JVhMsT++J4LFJJ7QjbkPwP2ernf8Vs3MCelBrRPAYfC2JAAbwRtWu85l2/KLs6sAck8NPV2AebM4jGZdZYNnRgBnVmkWHFmhWRgPoFs95qbOqlQ8vlpqtGmhZkUenttUJ5xmZYKYKO1QsX90USb8UijN6u2PBITTAOWZkpPMEDscnCZ2utmTO0KSoGu3WTtbb1Uq3tqjVCfujJi5M3Oc+pM4xChePnE0g/20VPLVTG59s4Ma87Ie3JFi+5SqEwD6yKz5/kMz9gr1YBweviTR2F1zPHc37lhiDuZDHnfqjPZDQnkGlPOAXeuLpDc92CPZ5sutj+RgPvh2F8fcALCM5zY1BNBGsJiifulefEvpC6t6AXgN1y8br/EHFlx9Qik9QinNRykNopTO+OU9VCqV6rf7qmWtCpNXSwQEXCmCcP8dUo3M7dhzbY8oFUqmQTerPBOasFxu+aQne2SYnjiuc5BK+ZMEcZTUz78YK0SXJ0++G8ttP+NGnNkey0ZhOW2R7ZBSOeIN9V88kdtSegS3q2gR5lX2s0qJF9WZ+401VPJdKrfQFmLe2Bs4ZzsBCB/gk6+Vc0pbAMsclIsfKfaOm8ZtcK/APNkequtc/5US0OgxNYbqqZhVsJjSZ6RarwdvMAHI1oY93/qMUurxLVpg1S2xwNBS5DkHIPnBpBJVlmgCqrcWp7xlIfPntUPHAdhynJarU0e5/bYE8/JYXibS6wnN3OSRYswwmNtnrEnvLwGY15lI4qgP8G2bCv3YJsxlFsB7T6Tu/KhkOOZO0vQcqPiBagceU8pu9CKp4QCGL5Fadd6j5fvIYAPqO+ZMDCJvTSdQOjQTSSh2T8lztAQT9uq2kmdAPDIc8CRpOXSQMmZlPvbap5080ptJjQbg90bxCyzBvHRfIzdKkMHaCzo23QGQ9ZB2/AIA45dqlnRrwl5bfkiukKEQE1HvpFKm4m0l32nBYtoF3nAewKSv+ax8Fbwho9EeIhcgrz2O6cZ+AG8TG5hXZK3J3vOxaq8NPysXy2Q04we4GllMALBQsJha/+goBK6qCABvMAh6pAKY9dns6nYAaf9/TlsEAPhNeXMLwC0A+/eZrYUA7BMsJicAJ2AaCgC7zNYyxZnwdsWZ8AS4qmScPCaXrdiXPZChi+ZEQBp1y89App3Y06MBnIui3meLMhH110mN0o4q5a+lj+3z680MVy3dwZ8+MpqtPkDIcQBh6R9+6ZUwjGbrAAARgsV2EMBWT95QsCr76FX6Nf1/9QHeFgHeMPpn7nkfAKfA215+/vEFpXjKMyXnVgBHAVR7ogRmKsy+Ur7XTKvwkOYOO6GUPcISjDynlEi7rBRhTmuHx80V24gADW/Fna/Rkj331oOkBeVGVEocyaAfSX5od00p9MEOjTZK8VkQwCaU681aaRo0zgTFbWci8WyfC+9JhOL3OifzIc9r+Gf1RrKPG0l9rQF9lpdEbIiBTzilbJYrtGDOuswdLjeJfDpS7Oe9XLO48D0ll7MUG+bxXMnu8EHKuCbizPlW7djkSJpx0SHdpClX5YKZAWx/pmSP7cqdMHedvmvlT96Tv5H04PQPBagqlUr1LXy9HFbeYHBQbq5uSmzvHuOm7r6sFDn/SNfdvEWqe3+q3NXztHZE/4NypcWN2GtVZEriVsuNE4oRIfUGLfB6qWbZjVAlu38+5u3LnXKNYblJZKYxYp+4NGjDC5LXuQszrzOelEvHr9Auti+RWn5Mgf5wNeZ+rTCa7XUj5kaZzuLYQXD9z3wpgIOCxTRRmJRPW8O58PwMbl22APIxOjtibavlJvnOKCWXbdPOzFGEeb3HaA+5IVhMqeANTQE8AW8L+3Q548cPOzGA3YcI6h+UCPfD+cmbalOkLmIL5lJyMSY8Z23nvHPrNPPKrJEbn7msFO0GYM5Wzazxp+US04xMdNNdcvVHj2muZzu1Uz03i/UyTNZsbr1caq5PoB5pWiId+o490/kNzZxUl73jO1nsOi4feTvzLfwStsl12z3TdXbXE6lWHPVI1ED+bqzY80Un7pTjuZK97lKpxZDW7Plm9ZkbDfMykefPKcXf5yWR+fMy7w7ZKTfnHc301kk1M72ZlFb3lNx+sdTbWpO50+exkiu+tzSidyXysHw39qgSjYzjfUlSQH9x6EnBYqpqNFtLCRbTv1abWyfWezhAHLJAsJg2fp0H5k/GGziZkl1pVNvEjThYO9Xa06C9TMFU0BBR/0AOeliBfeKdBp2gg1PST4mr+9m+JQHc+9frXd5gtlPN5gKOzdkEfbANvO3FT5z1NzOaresAJAgW00ij2brnqa5LITci7jbaQyY91nVrtlJqvI2FHDGE2//qlFzCvwb7wL2Bc07/cJo1GsArwWJK+4XjlwOQX7CYtoI35AeQE7ztpNFsLQtA+JT2kD7D3ATAkc9TYX4z3lAcwEvwtuQfjYMFkFnQB3fZKdUIbcedO3RaLjFxodg6pw+TLEdR3zphNPs7DlJJL6Q4QjTT2Yc0SKOBM6oWez/TcGef+/XZ27EckbUNmOuFoxXfTAohSd5Iff8B3pk3SvUMUzRbGEARbdSLre5cOG8ouyfXSbn0mW7cseUFmdeyFs4oA0mN8SL20yFSzdrZSUy57OTjFoCmZCUfusRSw9vWziniKs1CvzAlm8d9mhveJEXvT+ITM5JkRQ8HeUSN7teUwt9dUor2ATDTD/FNY+CzTbCYnv7ue6ZSqVSqX/S167DOTqL6rjvkWqmpVLttAHegGkfoY4WS45flQtuKMi+TdUROc1LuxVv46V/TgCA3pF3zgL16ZiRo7yFvZF68vR6HDC2jqe+6m0q+lk3Ya8+3yvUcx5WyhTQQl49nt9Uoxb4otl+uaqvF3N08SBwUHY8MNQGsALC+HnMrUzHmpWGe1G4agDetmPOVDymVyj+3NC//YlLB6TOlDh6hSo47SXB7mASPhXDlLj4ymq0MgKyCxfT2s+sZs16qX4+D7OjCnZoE4O00Z4fg07R0ZYEGZPJB4jYFzNXizMtdF5Tip+Eqqr6/MXO1QxYSV+euEjR/hWZRp0S4Z/dCqruBpJ5Ngtv1FKrTZiHxA+4oeeMuKEU8jeTDw4JMRNUVUtPURZrlp3REmgGg7Cm5lP9DJVfZ8szjivmYyGMtnFMLXtQNrR6jGMJjkSFqrtjW+yO8HdM0G8l1ueAAGcyu3My7yHc087pu3PHIt0rGZYPFgbu/107vOl3sWNeHpNTuzh4pJBKmUl/nCDqAO7C3uzhaB6AEgJmCxXTMaLY2BDAGQEsA7QWLafnXe2D+fEmT/auck4vly0XeL8xC4jwd0DCp0Ct+SHhb2bmUWaZZ9K4G++ik0R5yEICXYDGdBm+YBddM4DXwNid4w1A71eTv5Byr7Jo1YgB4w2bwti5Gs3UEXEHjXgAwmq3ZAXSB69kbAWDOp9SCn2I0WzMDSBUspuRFE7q978ieio6kPj4RNLNPI+YmnSe1ebRcbnEoVNfZOULs13zZzOlVf/fNcHWeygredsVotrYBcF+wmJ5/NpZgAIf+1Wr1p8fcEkBRwWKa8qPjBwHoA972kzOwc8f3KrpGbrxfBJcsWEzFjWar5rR2RB2z2GtHIfI6rQ57y3GH5nN7rBgjO7Cn8hUhgtaTpEIDOXa/XNn9HfzobSXv/GTq5rtGO7+dJ1K543JZelIu8/EVzZpps9bijKFetK80YlZL9mKnYuTljdIktG4kzZR7q1znQz/uUICeiJYk6j4iB/nwqK84VP9SyXpmq3Zm3VhqyCcT1v5G8ZtamgmzVHYufTKd25CxFXvh9ADnoCdjNTs898hVI3fINW8mIAMrWExXjWZrVgAfBIvpf7F5hkqlUv1lvmrAmjrZL48IrnYn51jle+30GVGKt1sQE30VQPmzSrGEisxT6ZaS71155mlQE8eMqIrsk48x1DshmD3z6I6SZ1IgeR9fgX325rxc7EUe8vZOPuZd2zfU32uPXG3fOtmUY41mXv2qzP0MOsixdZ1zXm3RzAy00QwkWJwwOwEZvgfQDsDKw9pxug7OcVts8CwGVw7Xbg2ktSK4RQACBYvpVnpf9OIA7gHIAyCpGXN5+kBu/426zrkGwWKaDgBGs7XSYe249o2dMyHog4UK9qWVNmtn69fJjSb3ZK1jXijZvCKRqeRMqWM1AJ4AHgj6YO04sXvlD9Sn3zrt/Oh5YpuklXLTAtd1/Wr5Ipk8oznvFyBvzu+Rq3lVZB4X5iAXHiIOPKMhUv2GzI33YUrWkD7c4a7v4Femi3PM7rWa+YbhYj+fqZpNmYeIA98rIJWfWVpGPZ5ULJdVrhB6Vilx8CkN7FSXuRWcETa/LtyJwQWZiFVhSgChYIKvKIWbT5a6+R/Vjp6bh0S6a4gyY4tUe0Q18jBvDXHRyUJE6N6avbC+O3eszzG5zPjMxPa8FPPiDXjbka/2sHxlRrO13B7NpJKb5HrtLJp1lRjQQAe0LaaKnbO2ZC9wHcXxBQDkXqBZMb7lNKsVvGEUgE3gbTHgDQRATbhWQT8HcBO8rQwAV7tSQA/e9sFothYD0LI1e94Zofjlv04LdfvCwqafxhuyXZYLzWCgVF4mNdet0C460tAxRx+JTHMEi+k3dw4ymq0eADjBYrL9hn3yA4j8uaDVaLbWh+vZPgxXGkX/9PEzAPzA26K/uCNv0ByQK3kPEQdWuaIbNKOVY/KQKGSqDOAOB3lxZ/a44bRSMnmjZk5KIvXI7kdsOh1xxrnDnsxB8RbBsP2dQ8l5WnImB6lnJebRw77swboAtIRi1wmldMVenPWGAakNXiPgUSTNWMwDaXuykw/lj8vlMh5WKng/pEF1lmuWzJ0ntc4azJ4JbcjcqMqAhmckthzR1Of5G+q/rDjzsnsf5/DkRzRXUjwyjBcsppfp170LQG/BYor/tfdTpVKpVH+Or7ro6j31PXlHyZP3NfW/elfJjQwkNUUBKZVGOVKBeSLukGpqCzBvPA/IlZ9s0VqKdWRPl2xBLtwryzyd7EVSIouSV3JmYhuclcQ23yNXL3lKKW3zRrJHeeZpowHs/ibvqN/3jZ2WkWeUkmldmGNFfEmS/iHNnViLuZuxHHky9Zh2TG4Aup1yjcP39b2PbtfM6HtL17dYNsTcvKQbXPmFrlMJABOMZmuBYdyuhgGI7ZnewWdwG+ZsnQ7syQL9xSF1mzJXXoI3nGg6dvGZx7pu49KghQfS/G4q+aJs8FxPQUZ7Ii0hkLwP8iO2XJE0Uy8AqwqQN1kFfXAr8Lbk6dyGoeu08zsDEPpzBxps0syWMpHkF1eUwiuzkLiSm6V616ozd3u8oZnnjZb6LLtL84Y1Y648fUyN1zMxSaV3ydWTWjmn+CfD/U1F9mn947oxSSukpodToW9rh64bABSe+uDVHrlqi2yI8erGHtmfTPVlcjAxpXdINdbckYMqdXaOu7xYarmThbLZHWk9p4sdH72lfq+N9pAyO+VaS5fLzSt2Y4/uzUXeO2Kpl/8csZ22AXvrWCnmxV4H5UY0Gru859d8Xr4mwWK6UXra7dVLZ86q5TElRu9GRJs3SYneq1Td0VEcvxyuFqrvGNB1dceu1hjtIe5Ge8inHG8NXEFpKHgb/Vew6lIOQB/wBiJYTA8Ei4kfwO5vWowJ13wKVo1mayWj2Wr5xUHytneV2Se9BomDK12mxYpI0Fy4oh885meDVd7gD94wJ/3PAeAN/8qlnMZtGL1Cs2jZb7xV1fELdTkFi+m4YDHtESwmB4Aln41f+RSsGs1WD6PZqvvRrpUqME/6Ayh5TSl4/Kqly0kARgAlJLBDNsiNTndnj2VIpVpqVSoca+KcYZsvtjFclIv5A9BdkQpry7GhHru0PO+LJK/5mhUNVklNO+Ui758WZV418CHJma8oRQo0EWdEXlEKPS1Lnrk9VgLrPlJyv67D3sk4jguxheq6pP4gV7cv0SzPOFXqUqi+c873z2m2/RyhR3xJYp4RYr/+D2ju/E9oYPRW7ax8fdmD7GfX3UYNVlUqlerb+KozrNcnlc8jKAFnn9Kcbwazu/N5EKdPHefciOPaMdlTqE5JgturK3Kh+A/IZHMn9jSLFDzqqGbU5dxMlEFQsjgS4HkwL/PuOAvluy1yvQFG8n7VI8VYarXctIAH0vxT4NYVwGQfJG4/px3aXEekyO7i6ENZSaxHdvIh6R31K7ZLruEB4JFgMXV9OalA9slS58fbZo4zgDcwlGJiLkdINADxlq4vM1TsX+OSUmwJgBevdMEbp4odKkzWbt8AIAVAHfC2GiXMO4c0Yy9X2C7XLiCBuwngMYB9ADb4wnb1gHbiyKrOJVsBLGnBXEwuzTzvV5gREls4p2UXLKZ+AJA2OdPuSMX3w1q5UfNB3P6EqVKXk4HkQ1hWEtOLl7pdbcueNc3i1mVt55zI36IFtggWk2A0W7cAkAAUAbBX0AffXyM2Kv4S2ZbtlGvWBlAGwIIC5M3aSdzmaqnQa2zUY3cadMJhpcK5a0phFkCMoA9uTilyEIJCRntIYwB+R7Vj+nOQBm6QGu4ZrdnZpKRjbV3BYrphNFt9AWwF4HZZO2j1XKltkZwkJmi4ZneHv3n5nt/MaLZWBrASrlnUlnAtgpogWEw3wRs8AHQFb/vPdAjekMVJ2bNaIveTKdEHObanWbg1FQoyb0oXZ8I7AlDA20Sj2cr+mja2RrPVAKAoXL3YdwoWU930z8sDqCdYTNN+dH4GQK5/LXLiDVkBOMDbYtPLWXHgbY4vnGc4gMuCxXT9iwPhDYfB2xr/0ni/sF8Goz2kN4BEANGCxXTwC9vkUSjq5HdsPiNCk7ZEs9RilcsnH1fKbajKPNiVn7yRbyv5XhRjXuXbLNefuFyzOJ+Nug9qwN6s9ELJdiwbYtjxcq8iGzRz4uaLrYeM1OzqDMD7ilI4tKM4/h5cCwTPTeHWF9ZAeV6XuZ1XRxzSOLFnVGfulE9b5+T9AEYBuJIJCWk7tNPc/BG/rqNoLnef5rss6IO3H5fLtKg/7fT3RrN1HoACAOYAKC1YTAt/8z1RqVQq1R/2dctaOcckx1IvrwbMjTG+TGq4jkjDujFHs6ZSTcJbmilqpdw8+gMyZqjB3JX0cFYVLKawguy7kTqijNmlVJP6icPOlHKsabpHrvY+N4kig8TBT1fLTQ8I+uASBcnrbQBKvdR1qHxMOyZAR6SDeiKdns5teL9brt6jAImI90f8W7hWeMsAEMRE+U7nNsUazdYJ4G0KmWKbAuARgIplHKu2X1KK3QUweBi7a9cH6lWkGvvoFAAHgI0AYt5PNlZOgOeNWsxd+2Xd4JcAhlchD/TntUNFACMPa8ffsEMrAggAwO5TqjacIPU4l5e8iwWwyWi2jjKarTv1EMtcpwUbxdMMnj4k+V4M9ebXyI3r2aF7No9b0WEWt27+PSXoREkmbDCA/rnNhwYB8AEQC+BpFsTmHOHsUyYFbmP9ETf1lS64BgCrYDHFx1DD3gc06GFPcdTASVK3QreUfJ47Zo6+/EzXpaugD64KYD0hqHtUKq2BKwWiBgV8cpIPl8oyoaZzSon7hYhQIa95/53W7Pl1BcnrFZ5IzR1A4iwt2ItpuUlkVbgW5/xXESymy4LFVEywmBoKFlNaThI9oAN7qi0AgLelfDFYBWC0h/R9TTPnBND3AzVYKpEHPWqzt4sHItoLwA0AIz9OzqEP13VYZzRbM/xrP7M1V69xU4qCN3wqp0WMZmsuFnLIZo1lMFwNHDZ8dqpbABb/xwBcs5ovjWbrGqPZWkumJBiudsIAb5ON9pDRLccuDARv0BrNVia9TJc/gJ0A7hnN1iFGs7XIFy6ts9FsXWE0W7P9qhvIG7zAG4IB7MiGmEjBYlr7pWDVaLY2iKeeE8o7lufIS96dBXB8ldTEcUUprANw/6JS7NJ2uQ6JohntRcirqPWauY9Gi7076uHU31XyXM5JPuwXqL+zJHkeNVbs8aQq++hxR+e414+UwDNV2MeT2rJnT37Hngndo528vzLzNLI4E57rlFL61TvFTzOJ21LkmZzNszt7JAGuJhn2eGS4sUpq8iEZbourk4cVwnXBbhPErotrM3dKpg/ZAVed4mtw/XdA9SOEkA2EkA+EkEffeiwqleq/11cNWJ/pu8V1546u6SiOyxmleBd7Kmeb0Ik7lSqD20tAlk7lNmnDaRbxrFLiey+kpJY2h3QAEP1MyV4vM7HdrMI87BWA2Lz75cpV39GM7W/p+nbUw6H0dQ5tuU47vz2AId3E0WlaOB8+VXJ4xVOPvRykXs90ne/kJB9anVFK7gPwfDj7Q/1LEys1M9pDnsyT2u5vz56JOTyx3u59ExpuPawd9xbANADXDUgeDMDtMc01uZtoHt5NHDPcIn7Xr5Fj5lgAUzdIDcoA6BqPDB/HiT22AMjZmzs8RAQ7+YRm5CZC6Khkqi8H4A2AegCuA7hexLHBCqDsbG5108u6QRWCHNtGehDnwYXaVWfcifg8J96P18F5LTMShjZmrzFPlRxN1kiNPdbLDY8v5xZlv6AdWh/AAxYyBH3woav6QR6FmNfVFsut1iyRWyUSgoeCPjgDAOgg3oujGUov0KyY35O1+gSS6O+mjR/w/XapVpt3iu+oF0qWGWmUS4mF9wRBH5xnlWah+xG5fL2P1OvDRrmBUpo839mfPTCvDAl1G81+X2WNZsGu/dqJmQF6IQeJqXhfyeXo4hw9Y/OE1kPBGzRf8/n5qxnN1qVGs7UgAFzQDYudodkQ89l3c41ma9CP91msWZrCgB6iFG38SULBzVqL3gdJ8kfqUT6R6rONcfawxlOPkzeU/PkA3DGNXfap5Wm3YPZ0DwA10n/2AzDopb5T4+rsg3Vw1cj8Vx6pYDHJv7CA6zoAbozUuxKAxekzr5jNrSnWnzswN0zJMhiuxXQjALQHkC39lf5BfKop+zneFgdgsmAx/avNptFsHWw0W9v+xPnZlVKTRgAOX9YPuW40W6sazdY8X9qupGPN6xj4tO7P7WeGsHtyPKHGDJ6wLwNQvzh5meu8dlj2vuzB8hLYzRWZR1vGcSF3h0sDJsyQOj5OhLufgCwx5ZjQCxbNOkYL56C27NmcRiamltEewt5T8uSPod5sK+cU0zWl4PbvnBPXj5V6fZ+NiRvjR5KeV2EfPytA3vQvRZ73BuAtg505XrNdHC31uS7A358hCDkoV36Tx7FtjGNypuy7tHwzAO8BGAWLKeFn7v//sk0AGvzSRiqVSvVHfNU6rAC460pBTT/24PCHSm7pETU+8lJSdEFMZLtbSsGLJdjwc/M0K98zgEOgAfNLMi8u3FXyjH2tZE70RnKe3CTqpJ512uZo1triqUf/C3Kx1Xbo6p9USnscc5QbBuDtBaX4xHs0X9Eo6vumECLK7lOqtOjOHVeaOqb310CuBODNIG5/KCH4ABHksFLxlQfSGs7SrNvzVsm44i7Na4YrMHh9QDsh2w651sRiTPid/uJQPYDmq+SmnQGEgrcJ44CH43hDXQDvhooDcgDoUW3a5axGs7XWDxq+TBfHaO95mjWXipBX8x/RXN3hep1+FcBcbySVqsfezljWseKOAib2mlxwWzP2ijuAPRbtetYpasKHS/13SJRINdn7Gdtw531fSwEHdio1XxuZ6MhMSJi4TjOvhkIhymAO7pWrplRn7o+vwdyLq+uYYxvPbSteZHKOwGgsD81NooLGij3PLtIsP7xRqt9pKre5kQPcIxAU8aD2zrulakcmyj00RZnwEuuk+g2KMQI9oZQ7+YN26pP31Cf3RKnbgHh4BURR326dxXHxszRrpawktqYfEux9uUM5N8v1H8RSw6ji9jWd7gOlv/Iz9Fca/a8yUbwtHq7XwJ/MBfAfna+asVejAPSZ7WzzJguJLd+Cu1JNQ0XPFLgLsRRBozU/XDwgV3x3WK6Y3I49s7MS8+Roj3FxylHtrsL9xcGBNadd+FTA3xfA6PRUixOC67PDXxwlb/CB65V7PwCHwNteN2Bu9JqpWVerj3P402NymQNB5N2VteNGVI1B2bNFyKvbOkYakd6q9c7nhxIspldfOkX6d/9/vbyh9jOdZrd+ysfIzz5jwNuUT/drttm6p9/0bfsAAGZrDvyoTmz6gq6mAIYCSB4oDrVu08xoVV2513GNdsGh/I4tByKpb5aXNOB9c+5yxqHO/pOnSh2djdnr2f0Qb96mmRn7hmb2rcve+dDOOen6e+rzwg7dWAOS1z2np0aVIc+qZSGx3Q8plXeWJC+Ejtzpo/eUIGa4ZvcxgfonNXXOYMZz217fkfPul8EeALDSF7bz48XurwjoR09in2i0h2QuSsK992knLeemxPfvZN77Cq48265wzbT+s7lmwWfClaf8BsA48LY/2jjgAiHE+CeMTqVSqX7S1y1rlc5othYcxO5dDSDTIG6f/3MlmzU785HxJqkfAaSlUW2OcBrgwUAhH6hPMQe0XkYS1cgs9hrxiOaqUJgIu7pzRyvGUsMBXupaaiK3pVlH9lRNHZGSBSVz4BrJ5LFbqd7ICW3UOG5bT1+SXMNOte3rszeSyzpW9QSQANfCmTIA8gHII+iD3wHobrSHHACQG8DrcF2Hjgyh3dZIpmwzpQ6jADCCxdTHaLY2A+ApWEzbwRvKAfhgtIe8BVBc0Ad/OCyX31icvPQiULwykSShkGPjbgWMU7CYthjN1kYAhuzS8GU2yfWvj+J21vhAvbcnU325WtyDHAB67ZMrxR2RK3RdplnyfKTYZ/hlpWh4JebRmdtKHhoHb1sv1lp3mdyiuwZiUQ/Y60zWbOkzW/xuoAwmt56I7d7RjKMMSOmTCA8c1Zoz5yAfUvfJVRJasJd32aGd4I60ETHUJ+iREuhOCRrmJ28DX9GsJ4uQV+/jkKFrEnU/lJ+J0BpI6uAIJdOr3XK1LcHcqejbSoERXkgdOFdqM3MIu/dKEeZl2QzE4VnNsfhCDLxDABxTy/l8hjdksYplDmdj48/GUEOru0oe5yuaRRvMnPDQEtlQjAnXMKDbx4i9b/TlDrZ8TnO8rMPc8XcnzqaH5AqnCpI3u31IUs1ZUvCeeTPm7PyJcxQCsKyEfXXHe/o+neH6ZSskRKp5JTeJ2mWHtqEvkmwTpW5RizTL6zR2zjyUArdRv6VSwE+ctxGAR+Btb9J/JgAugLdVBW84B6C20R5yEkADQR+sHewcUGTJzJnXfnwYo9laAEBluN5A+DdjLmXNQWKuhdMscx/Q3DM2a2aTy3Ih35bcpWKPlFz3u4pjEu7req28KwctuUKLRK2Umy6ox9xaWph5HV+YCImFGaFfGceqDb3Zg5Nbsxc23FQKhC+WWtnqMbfy9NccmOSkmpFvacbsfcXhT5PhfhDArVPa4XN3yLVyr5Mbj/qOPdOnGAnPqIAZNUHqzmkgtRPBDanMPCx9WSl6GEB2AO9+Tf7x354rWF0LwP2zT1MB9PqjQWt6wHqYUvql9BKVSqX6w/6SgBUAjGbragCWudzK+gaS0j8b+Wh3Uo0xDxO5dbtcu6AnTdF11JwNj6beDoky9R8oQQ+d4BqslU2p5Zmnjnwkwn2O9N3TGZoNhsVSq8AomnH7PX3vywBqA/goUxIV5Nie9ETX7VwlxxJDAjLkFSym7UazlQNQF8AJQR9cLJ569ivpWLP5oHZCwWJMeOx9OVcDSgjT3Dl9DYDvAKzKifeLYuATlgadL4A4AOMBsF/8nz5v4ADUT6Ru/TSQwt2I2BTAbACht5S8VeNphkHvqW9obhI535OkTXSnaf4aIjsywJ7TnTjauhHnRQBeACqDt21eMqHrtPbsKZIJSQ1jaQb9KKmvVzXmwdUpUpfNAAyrNAsqv1Sy1NwsN7iwWLOszXCx3/3TupF7kqhb6/FSD/eC5M3UJuzVzjbqXt2HJCeOF3t8f58GfcxGPnrM0qwrMVrsHdOVPeb5g1xTf0Q37gqA5+ulBu06sCeaXFOKPu4qjln/XNd5opZIk3Pbt7ULZk6V9CBp+mDmtE925uOx8c7ulXtqjmrWyI2j58yYl/frPzn/LEaz9dOsYhPBYjooTMp7KRs+lpfA4JGS49UQaWikRbPuowOawzZ4VslBou+VZ0I3AqgCgC4UW6UtlluVANAdwEe4XuPnESwmG3hDuWVi075p0D9aLjcvIlhM3Y1mq34gu29qE/bq6/zM2xU35XyVBosDx0ch00bBYtplNFuXD+N2HR/C7XOAtx1PnexX4aZSYFEX0XxPsJj6/sZrKw8gn2Axbf23L3gD66Tsmj7icI+zSsnxV3SDCp6SS46ZLHWLf2Vp3PRHx1gMwAJXPrbPNG6DSQdxeFnmWd/H1LhhoDgkuj5zI3NN5q5Ujgn1OCKXe1qHuWXTEbmWExzdKdd4WJu565dM3eYtl5u1WqFZEiWDOSeCy/lYCWw8RBoUsV0zvaQn0nIvkVo4+3OHLviQxFJ1nfNuyWDLASj1QNdj9jWlYIXe4sjXh7Xj9m6S6w/aLVdfCmD7C12nXRoitzLaQ4YAWPBLjRn+UXiDgC93OnsN3mb8I4dWA1aVSvW1fe2UgM/NAsCMkvpJAOrP41aebsBcP+dJnNkrM4+KE6p42ah7dVA58pZSwK0a8/CjCJZIYJY0Ym/UeKQYPQ9qJ2Z8RzO9maFZf9MbKYnH5LJkoDgoTynyfFZr9qIewMFo6v3xnr5PnSqOxeb07j06AHcFi0k2mkMe7NdOsANo+4b61SpKw496k5REBopW0AdHplJtjnFiz+kTua21OcjeF2jxucuk5s+Oz+qbDADpzQQKCRbT/y8ucPVSt9Yxb8u9STs7qBB5s+8jzUBGOfs0Hcbt6lCTudcjRK5pKcK8WnlaKpGURvSJTqqtM1Xq1IGCOZyeR/gBQBgALJDaPNdAbOCgmnzPaM6PszVrrmSe8qZdN96wzGgP4fuKw5MBbFqpWWB9RQNio5ApJpG6j7WDS+nCnjhukdrHtmPPVsnNRB4bKg4o50nSVvvQ5G6ndaMq75UqS+s187zPKiVWLNUs7QbethQAppmtd7qwx/xrsPffQURvDvJWADuf6bqMjqbeTEaS5C7QzC+PK2WMI7W7XlnE9ux0zcYewLy/7OH5pxAspk+/AR4EACPzYctbJePlV9S/SQx8DJHw615t2qXw9BzT3HAtpnMHbzsO3uA3bMaGmMWuwLA2AG8AAYLFZDOarUuXa8q/Pa6UyzOZ29zopFL6GQCE6TpsWCE1f56TfKgC4F5Z9nnDnWRaoWrOxfeeTSoStEnjq9srV3Ef7BzQ4qDZegXYdAPAUsFi2v7FC3BVGfgI3uY0mq2dARDBYtqc/m0oXM/q59t33yrV8ejEnXqSlcTeAMBXcizdfFk36GUgibbsn7h8RPNpR+d/tsdTAEUEi+kkgGjAtMFoth5fpVnoM1Accg2A7o6Sr1hz9vKjweKg3KlU61GJfRLbwDk1KgBx4bnIO7k9e4ZuluvPrMw8tp9UStecI7XLckQ7LnCINKgLgIIpcPOpyDzR5WfeaIozYcJ62ZRTB/Hudi1PSjIvcwEYu0OuzS/VLM1ZiLx+X4KE1d6N6h0AfJ/XsXWBYDElC8CM3/0Q/H39VLmyny1jplKpVH8Hf9kMKwAYzdbvAJRlIct3dL3rGUhaBQCS0R4ybTa32sMBrrmJvR7gjlTyRDFOykE+Tt0lV7+WhcS+KsK8XtLEOWP8as38DflJRIOKzhWxACoB2A3AR7CYZgHAm0lBk3IyHxuvlRqGz5A6PQXwVg/HwGPaMbfnSm3LlGTCXjVgbyVOErtkOKOUjhD0wRYAQ3ZKNZbnJu+OeJNkTU4S0+0HubqzE3c6TqLMLG5K/Hfp4/cBMFKwmMb/x8W5Sh/1AhBttIc8bslcyNubs9YMp1lqlmZCl/giKU8s9Wp7Ril5Onj6/u4/c492AAg7px3W7baSxz07iUkozz5/AtdM73zwtnvgDdUclDHbqa7EBVq8VzXmfjUWytUtcv2I/tzBZJEym/uLQ7zmaFY/sVNdmdrO+XI15sGcR9QYtV87KTrTlIjr6WNeBuAKXCvSXwKYtlZqtKcje+pqEcf6OnM0ax5TiqJ1mZut7dBx7sSxNwOx50pPhxgqWEypf8Zz8U9lNFtrAignWEyzf+X2pwDsAPBB0Acfg2smfjZ423vwhiwAJhntIYcBnBAsJtFotrYH0CZM1zFiqtjR2JE7s80Jtkc09cm2QGy926qfUAnAdgCHqjgWvXhLM88FQHyQtDceGV51ZY9leKwYD9ykBU4AyA9X7qg2O/lw5ajWrC/hWLtGBrsDQFXBYloNAJGTc083iz2Dtswc1z797QR+nPZhNFsrChbTVQAAb8hcyr7KfMfSfvhn388CsKY5c2FGD/aoabTUt/lTGlhSsJgWfLaNF4DMgsUUBt5AJMrsyOPY5g3X8/gOwLQu7HHuilLY5wXNvmipZkmNY3LZ97M064IiqF9CZ+fYorEwbAwk79uP5UJsc6Tv3iVS95u39P2zAugjU4wOp1kH1HXO2+KLREMl5jH6cwdayGDqFp16/4bRbB3IQQoO03eu/N9Wou0nqTOsKpXqH+wvDVg/MZqtGQGsFSymlkaztcFRrTlXHvJ2gEAD/EXKMPmYd4bzSjHbfSXPsg1yQzcDkstd1A2tGkO9+3mRlIJJ1N1QzrlyNYDwu7reaeE0i2dp5kUxmZKSQY7tJQWL6Tuj2Rrgh4SS+ZgIn9tKviETua1ibebOzZc0a5KBpGCM2PvxY5rrUHXmfs+azN2xb6lf+2jFsGykZlfGnXLN0Qxo82VyiwkAhgkWU+9fc12Txg+e3Jo9f/+SUnT9HKl9dgAH6jM3igSRyJS7St6svbnDT7uJY9oLFtPP9qI3mq1tH2i7jecIDbRRj5N9nUMfTddubKeHc1NeJvKNk7JmDvIphiBivLNrHbPm+2wS5TLdpXlTak07X3DW+L4jg9mT5gjFT94i159wgpbLDKAtgMdntCOu5mai9oK3vQNvGAlX7dFq4G1HwRvKlrcvqxwN35X1mZt+zdjL6x4qucRW7LkTmUjSGA1kiwdxbE9fkPQ/z2i2sgB0vyVwN5qtWQB4CBZT2E98PxDAesFiSjOarSMBHBL0wTIAR3/n4AYXlaJeszTrArWQDLWYOw/yOLY3hCvlpds+7cSMGZE4sJpzcXkA3eCqNNCFhUxksFp/xJ0ar9neeYbYYVF37mjaLKnDSQDV4MonnShYTBFGs9UIwE3QB+sB1Advs/xofCxcbXvH/Mw1dhIspq0HJzao05i5uu+oXI6cpSUPzdOs7pTLvn0fBdkHIAsAPVxl5/IA4OHKMy8JoBMAoRVzvngYzWZgoZyLhVem1zSgLIDL9ZkbkaeU0h1ksLmv6QasoSCjKzqW9QbQNDeJ3BxOs96Ea5Hc/kbMtfrjue3OduJEIQeJufC9dsb3RnuIDoATgOZ/6peur5TDSgj5Hq5qF5kARAOYTCld/wdGqlKpVP/hmwSswL+C1jYAijJQAn2Q6JyrWVmlPPNciqS+Xs+UHKlexC52Ec3vAdy/re1dxqpUmFmHubPwhFLGzktdHz7RdYMbHNIRpZxxsDhoz1rN/AXvaKY7E6Xu8wF07MCevNCcuVwsA0nTzRA76EZodnmNcfbakIuJ6jyY2+feyGmZ15Y9e2cwu698duZj41VS4wNP5ZzfzdeuescR5brRHtIDwGnBYhr3K66HqcPcDlugWdFyt1yNmyp16T+J21Jro9ygzTbNzKaBU19M/C33Z8C4CZciqJ9bTea+slhuNRNAzAB2f6NRmh9Kn5WL+cyR2nc9oh37w2a5HleJebTcGyll45DhVV9x+Lx1mrlPN0iNXo3TbMvAgOao4lhyNQ5eewWLaUPfcZPPBZC47pvkBsUEi2n/z1zPfADlRnI7NaukJgsCSfSgxzRXj3/1nucNk6Ko76mKjmV6wWI681uu7X8Cb/AFwIC3ffylTY1mK/OlFq5Gs7VRQ+b6fXc4Ru1Rqp0FMDJcF/zyqFzu6EypQ9538HsHV+5rDsFiqgfe0B/ASgflGhZ0bBqrgDkNIFuYLrhRXce8+BxMzJWO7Mni06VO4hvq7w3XrHpXuNIP/AWLabnRbG0JwF3QB+8E4APe9uHH4/qZ6/AD0E2wmP6/ugJvGDpbbNtiOLfHi4W85BHNXaCpc3o2AMcBNIPrLUk/AEUNSA7WwzkmGr49AKTO4NaViaSZOA7yxac0Z+ZyzLOEJ0pgxF6l2lR/xL22Q7vtvr73GrgaBdzNa98cKkKTFUBjAF0Fiyl6y4TWUlHy8uwjmpu5oBQrclIp0x1AYbjyxmf/XAva/0pfoUqASqVS/RW+WcAKAOmvHKkrv9Tqv10z/UEKdLFPlcDLZ+UShTMSm3yalt0BwA7ADQDblLniVpJ5UXCK1GWDJ1IHPNL3XDtfbNNtqdziLoCFA9l9i5bJLbbmJNEHMiD10Frt/IbvaMaBXkiblkTdHibDLacvSchejHkTMU3sGNWaPV+Hg3y9lXPK/Y7siTr3aZ5Tl5WiYYLFtC49B9YXrpqVN3/pekaMGz13ILe/Hy91DjuvlGws6IMLgbed+A33YxuASYLFFF7AvLexibneZ49S7XyorrNDR6QxC8UWx4Zp9t0AcBGuRSsdAAhDnAPasJDKZSMxh3bJNWtkJElLH1NjyEtdh5sjxT5PDyqVc8lgawoW06dc3IxwvQbe/9v+xj7DG7xrOBZwzZhL1/pwh9Pcp8QU/d3H+m/EG5oA0IC37QUAo9naswt7PMeUGUsmp/88HMDK9JnU8wBqwjW7GCtYTMJnx8maRjVtCzo2L4Gr41LMLG7t0WVS84Hv4HcHrpnJxXDNVlYU9MGHAewB0PetkrHLeaVYuQ7c2QAAoxaJLbwKEWFbASbC3k8cttQBzeMwmn03XLnsQ9LLXsFotq4TLKb/aMNrNFtzw5XD/cVyW0azVQug+I//XTGarXUB9NykmZ11tdyYfj9zdLXPvuMBUA5S5AHNhHLHlbLFl8itNgHwLkZeKr3Ywz29SNriLqL5kJG8fxFPPdGMvfx9XryttFeplr0Ld9xSk7nXxkBSr26W6g6bLHVrDODkDu00xhOpNzZKDRscUcqV1EJqc1k3+OJGuUGRA3Klp2E0eyW4gus/Vj1BpVKpVH+Jv3LR1X/4UW6co4M4YYqgD35bj73rv1BuW3efZqKyfiq/3Gi2jp/Bre+yR656aTq3fk48MnTsZvmhS/LkzB4AcqyUm1wEcACAnJkk3NytmTyCgKbtVqo1XyU2PulOnOt+kGs8X62ZbwrC2wgZHLdJrLtmNLdj8gBx4KUKTOi1JLjJhcib105oW11G0cLgDbUFi+10eqvMgF+6FqPZynVh0mq5E7s+IxLTBIvpLWB7+xvvR0cAAG/w2KTJL6dBl/uQUrEAAc0WpXg/zUxseY7Jpb0asLcNYUqWenWc88+81AUXb82et4fTLHIwezpwGLdXekBzz2/unK5JgMfJZuwVqSF7s6SNelwBH1wJvC1ZsJhi4Wo9+vvxtoRzAIAeedLrgn66DwYASV+aMfxfYrSHnBEsppT0qgE+j3XddnsQxzpgCdJfu5cBIKZO9vMT9M4Q8DYl/RcJx4+OkwHAVsFiUoxmqwNAtUR47L6sH3I9PfdyHADYJmdZeFEpGpPbvm3rJG7Ls6tKoRyrtYsedGDO7gVQAcD6Ptyhg9eVgpIHHCfqMTezduJODy3lWO0hWEyb8NkKui8Fq+nsAH4ywBMsJid4Q0vwCANvizearbkACII++IODcrYwmm33Ss2iUeBneIG3JaYHuFJpEvpdAOI8RcK9ysW8fw8ZNRkoLVOgaxFBM3sfkCpNqkru5+rMnWSuKAVjS5OwlvPlNu8aMjcGclReckEpZmvCXlv4lAZyZ7XDxmYhceUKODavBiB4IG2kRbOu9jixR+kijg20JXPhkgfsRz8F5yqVSqX6Z/imAeuP2AE8B287BQAwW9eXnHrnU9Czz6qU//iEBn7wYtKWeyGtPAB4EoeyQmwybr920onTSqn8C6XWT/IxEfpjcpkSJUjY1urkQbvq3P0cq6QmEXHwKnpaKb1mILe36X05j18B5k13B2U91mkXSUZ7SLOF3PLc1dn74QMdQyoA2HpBLurR2WyNFCymp3CtbP4lus1Kg+2bHQ2GA7i64Bc3/1l5KrChLQGEPmO6+MRTT/0KuVlgXeb2YV+SbAMg5SZRxSozD289pLlqjhT73KzP3HzNsUptQiA8k7KfsHCrR62Qmq1bL5uiAcy7pBucF0DKHxvWT/j3nNZbAA61Grvg+p5Zw79cS/R/w3qj2XoWwD0AbT2IYwZcv1Thvq6n/pJSZNMQcWDvCo7lpdZp5x1pa7YuAWARLKbIHx2nNIBqRrN1FYDMWfHR9lzJvn6/XOlZc97Q2GgPieYgrw7Tp5ZszF5v1ZC54R1DDY15qWuxg3LF7VlJTMh9OWiLB3Gsr8A8yTZd6qRRwMw7oxtZD0ArAKPh6lQE8IZeAB6Ct/1b/VTb5CxTDCT1kGCx3TKarWuNZuuV9LciJeFqA7tEsJiugzdoXyhZs4wRe2XZyxsoEDIIwCzwtvs6oPeYsUu5bORjx/NK8bx2s7UcgGIAqs3VrOmWjXyU8zs29wOQ96pugH6c2MPelj0/IhNJdOZmol66w9H5hZzleQP2Vg6z1Gvle+pbX6LM2OLs81M1nYt1HMTGM7j1bbZJtReVZMKm9mP3V42ivqkt2cultst1zqfArTCAqwtmzi78Ff6uVSqVSvWVfdOUgD+D0WzVChaT898+5A0zZortP6yRm+Q4oh07L5L6Nu8pjooFkFCOPBk+XbPRSwtRDqPZuUNyxQUHlMqpZcmzlRWZx9Ef4JNolSvkIqC2RHhU/yY5brxBC1cg8fKBHNh2stTNtlqz0DuGGqr2FoePu6AbOu2kXOZZcSY83w65ZlogeR9anX1Q3ANpSgrVpfFS1zM5SEzBXXL1BTHwmQig8l9VTzI9jzHLVs3M3cPF/i9i4H1SsJgW/RXn/hrSK0PUSl/VHwje9uRX7cgbpiwQW31cIrcKBxAB4LlgMdnTv+sIV97oBKM9pDyAsgAEAC8Fi+mU0WzNIFhMSUazdS5c3dIWAPAHMETQB+cHUGG7VHtSB+70WwAXjPaQxoI+uMphuVzPOsydSBls9CmlVM/a5Jb/OLHn/a6akw5PpB3PQyKr5XdseiVCUz0AcZ0u6QaXOq6UDSjLPMvT1zns3F4dH3BRLhzRSRzPAtj4aZa8nHnbie3amaa8U5+IRrPV61OL2PTZ49IAbILF9AK8gd0rVzG7wxHZgL3pDt62/Av3JVcdx1xvEdycpZol+36Qa+TfJtc1FCavkh/TXLd8YZs3XbNeYgG3sWLP43Hwqt6EudI9HxPRSQtpQRPmytEQufbIAuRN7crMw/ZuRIwp5NgUtkc72S+IvDPWd8wWPsA3sQd7xO879gzzimaJGSf2HBgN3zoANgkWU8TvehBUKpVK9U394wPW3yI9Z7aBoA8+Nl3sELhONmUA0AjAIgAVVmsWvNss17uYjXxMLkrCrZ2n7xn0zQbLG8qvERstb8eeyasjEqMnUkK0YtA1dM42bNHOSr4sF45vzl1iqzkWL+zMnhg6gvvBroHyfJXUZM4upcZIgQZ0hGvhyYpvMXyj2aoD0B9AQUEfvPiZkn1EgamPf7Kc19+N0WzNA9fq+VEALtVg7glVmQcVp0mdVxtJlCzQLKfgyiFOBfBIsJhuG83W0nB1R7vZ2Lx04EtkzZUGfRSA+MyIH9GQvXFzs1w/RLCYjhvN1mpwLXopvF4zd1UE9evCS13vCRZTnNFsPXBAO2FQfhIxcbA4sNUJpWw+uMphcb5I3HdH3/fZdqmWWJO9NyIriesOV/6qM1LxvdVPHNrxgG5SawDJTsoOnC51jN4i138HIMwfcVevWzqtMZqtPf0RF35QN6HCaLF3vxrM/foVmSeDCjARE1o4pvjcpXknAeAFiyn8Z+6PO1y1Sp8KFtOan9hmh2AxfSoJ1wLA/tWaBVsfKUZKQTITKPSGUpB5TrObz+mGD+knDt14R8k7rDR5Xr0Le+JFH2m42wHteAdA3l5TChbcJdcoeVI3unsa1Q4Mo1mfZ0Z8jY8wvPFEmrGraN5WmTwM7sYe48ZLPZ4t0Szz7+AcZ1mtXdisvnN22AtL8z5/xnOhUqlUqm/jfypg/TXSZ42yfOHV7F+LN5SeInZcNo4LKRtB/W77kqTSD+XArU6ia1mKPFco4OlDUukdJSjVk9hTtos1PXppjr1+ogS+rM/d/h68bdc3Hf9npMnerZ4qgdt7iiM/9uCOrOrNHdkI3vbup7bvOW5ql4/U0OcezVMn/SPu06zeX8VotlbnuU2DZkod3unhDD+tHVFvlNTndGXy2FyGDfVdLLV6n49ERN5QCmy8R/PuhmtGPAWu3GDvwezeHaWZ56O7iOZ+ANYWJ2HZ92knP2CmJOwHb9hhtIesA/AKrrJOKwFcA5ARwELBYnpyf2KJ4ZlJ/NQDcuXTPbmjE/M4th09ojVrJopdd7dhL2TXEul7iTJd23IXvgfAAiibSN3blHCs6RtuabJLnOyz7ppSMKkoI/jPkdreD1Oy56YgAbt0U+N6OkccP6WUvgrXgsL+qzUL8tdnby0AcLync8T2U0rp0T9Tdus4gLFwpck0B7D/0+y90WzVwFX54w1cM8YEQOfiJKxaFebhu+VyixMMlN47tVM1bZy8BEAW9MFnY6jXibKOVd4BiB12WDeudpicNS43G2U/JpeTQcHtVqoveECD/AEMyIqYk5XZR7UfKHmEMuSZfEYp6VWL3E3MxCSSvUpVn4HsvoAZUseIEkzY7i0zx034s58LlUqlUn0bf6cc1r+F9E5F3zRYdeUI2ipN5g3VNkt1Vjdirpumip1SurLHy+YnoZqe4vBla7ULhn6kHokJ8KCn5LIHB2sPPh7l7J1xo26eBGDftxz/j3FTEvYUBfbkGzerfz3mtjuAQQDMRrPVA0BpwWK6AAD1xq5aW5V52D6G5pOmaDYNL8GEhxntIYPgWlhU+tPxjGZrMACDYDGt/LPHajRbAwSL6X1d5lZ9Hez0snZgq3tKEM1A0gJuKAXLezJpR/LRiBLVyP3tpZnn3uM0338w2kMcAB7mJNGTT2lHZsvn2DppidyyljDD9ARmqxVA3IFZQygw5NNpRgsW05vPzrkWQHW4Zvs7GM3Wmjd1se2bOqZ1W65Zun6NbBoCYMYmqf7UVOjvmqXezCRu86s6zK2qiVTvN19qdz8AsQ0asjcvKWBOAMB1pcCVEKnWuFnaDWlpVP9qo3bOFg/icANw7ZRSmoGrkUBZANaHSq4b9dlbL+/JQf3XaeefAyD/eG1VermrbACOwRWc9wTwQ3ouKwFwHcBOuEpGVYEreG/clT1WrQh55esBu64ze2JBZebR7jZO/n0OEj24J3v0pdEesuuQdtzJKdxGn8lSt/vhSta0rEysLRMSd7dhzwe1dU7KP4HbZnqg5L6Zl4n8OF7s3kwLKSyU5rgVSnPUnMBt1b1WMoeWZZ7lfUUDpo6R+iQAeLJl5rifrXWsUqlUqn8WdYb1b67TuFnd12vmDn1Cc94sRN60ua3ksWUlcf7x1DPipFLa6YQmYK3cZN9tXZ+t3zknZjg5q8/Bbz3mX8totmaGa2Zxn2AxjShu3jmlMCPUu6IUCRYsplfgDUHgbS8/5XR+th8B/q0N6h8dh64MCR26WzclcqNUf0wT5tLGjCRl6lKp2dPKzKNSQSQq5ZJcWKzAhroNFgdcnKrZVICC7ntNs2Q9Ipd7v1ep/gBAZgCzBH2wF3hbwu8cBwvgOwDVpnIbC86X2mS6r+9dyWgPWVmEhFccwu3t10scWQBA+77swcRu3JH4/VKlug3Y2/bZUvsMjxTjjJLMy4JHlHL7Xui7XJ0udghbJ5t6eiO51G1dnyosoXvA2+Y9nlRs0HG5dFAgeV+sLnt3i9eU95vAG3LMEr9b0Zc7dMGHpGQBbxtezryt6RXdoEEVHMuafYR3Nbh+kePg6pY0Hq5fjAqmf1a1KXM5Qxp0q04qZY7ClU6RoQt7PGs4zbLholLsQ1v2rD9Dlc3uxJFwTSm0Pz+J6LBPqXqtPnN9yVRu87YJYrcea3ULC8ZSz6EyZX3vKHku64lYNid5r8lJPuyYJXZw3KT5W0/SbKFmsfcjb6TEPKS5njigHVyUhD8+pJvwEgAP3qbmqapUKtV/GTVg/Zszmq1dAJwHEFSfudFstXbR2lHOXvVPKaXzxMPrGFwzW4MFi2nstx3p72M0W2v3Yw/Yxmh2JoG3hX6jMWgCyfsBx7RjOBby1GTqxhBQ5qxcIqoq+9CTgyJ9RIaHgeR9pWNKebkIBIUliltWEtv3Pc04uqpz8U0AJwWLacufNJ5ccL1W94NrxjKqBnPPvEKzaIX7lJhr6dvUAlDBqjV31cKZbZA4JPEZDTy+QLOi4DSxozUeXik/aKd09kaSb1YSey2a+ryt7VywGkDNJ7pup0eJvXYu1y67AeCc0R4SBmBaZebh9L7sIaXqtMtX0nN4jxQnYRf7coeD+4lD68I1y50ZQDiAZoLF1NRotnpauLXPN8n17jZmr21/KBs3duTOJHYSx54FUC4AsWGDuH3iLSW/lwexJ5UmoefToPd8p2SsWo59WoJSZu0upUYnN+LIkAtRTEfu5PUSjnXJufEuapduaoMuzlG20uRFXHfuaJnLSlFlidTy6HzNijgP4sick0THlXSs8wVwA676tav+jPuvUqlUqr8fNWBVfXu8oSyArOBtB4xmaz24gr+/9MEsYN675rpuQBcNRPsMZ/t932kutNNRZ4QExpFAPX0qss8i4qhH7kHiYE0L9lLMW5opcK3cJCEV+upwtfh89GePyWi2ZgdQUrCYDn3hOxaAXrCYUm5PKlO+NPPimdEe4gQwDa5X9iX0cLTryR5pPlKzq9paqdGsGVLHdQC0gj74HoDMBewb19ihawBXFYMNAKoNYvd5jtDsKmO0h/QB8AKudqnv0heVXU3/OQOAM6XI8+N7Zw3b32js8lGjuB195krthIHs/jJ5mMiHi6RWDyozj8IdlGvWhLuWMZ56ZrmoFH1bmnkhPFECuWtKodQSJCwPBYkpwoS/faoY88pgihdgIuTiTFjYEyVnES2ktwEkXpeADF5aODMkwT26OPOqWCJ1E7ZI9U6UYMLcqrCPpxvtIfcA5BIspod/9t+BSqVSqf4e1IBV9bdiNFvHw9UyU/rFjf9EI8eNGtObO1Q2HxNlohTcBxjSXiv+sYWZcA8Oirsd+tUKJd12yDXjn9JA/6c08PULml0LYLJgMf0tW1sazdaGABYJFlN+o9k6Y7VmwbEg8q6EExpzDkTbPYhjMDPFZjWardkA9K3G3C8wkdtaa5nUvMMBpUo3AF2Pas3WMWIvjwc0aBxcC6m4LuxxZST3w1wJbGpT57TM3djjSefkYiX9iC21M3tSO1HqlpiPeZurEF5FlmVCMxZmXi87p5TY6o3ke9vk2o8PKFV8ZLDf3dP1mvpAyZUpN4ny7eocw1RhHxr9Ebv2Bc1RqSt7okgidbtbhn1ecLtc+1B58rTVXSWIa8jeTsjEJL4UKRub17F1smAx3f6W91ilUqlUfw01YFX97RjNVp1gMf1bxyfwhhrgbee+wrlIC+ZiCYtm7SknZewZGOdDhaLOEblcvAL2bhB5xzygufO2Zc9dj6CZq9qhczZwzvYE0BXA7W9eTeJnGM1WLwBugsUUDd5gECm78wHNdWuj1CApB4lpu12uXTMRnuUAPFulWdihnzi0dTf2aOojJdeT1dqF++ZLbXy2yXXnb9TMDu0mjskMIBmutsDFMsLGxsKwD8Dx8ezWzeXYZ5mbOWdcBJALQI+s+Lh2KLc76xa53iF/El96GrcxDwUZ/BZ+XYc6B9ijkLEjgIU3dP0id0g12FToe2ciNo0ezgAdpMGjpL4fAHhoIAUFIDZfReZxHnfirFiChNVqzl2pA2ApeNvrb3RrVSqVSvUXU6sEqP5WjGZrXrhqn/b+14e8gYGr0P65P/t8R7TmFjnJ++8lSq7oiDOnQpFLBqRybGjG14r/Rwls4BmllG977twVjsq+Q8X+vGAxXfqzx/E1pJcC+1QOLHGC1P1wURKe14ckh6+UmxlykA9tsyBubijNeSQZ+gws5Psb5Eb5buv6dLkt5+7Ykz3s1p/df36K1FVbm7ldP5Z6XblH8/YZzO5925s7GDBd6nSyAXOzvx8SpONyGQ7Aq/malWNqMnd3DxYHzbxN84/MRj6u0EAuKILjv5dqhHThTia1Yi9EL5NbVABw/o6St0Jp5kXra3IBbX7mjV8qdU/zIUkTAxCnew9fswguNQL+HSIU/3bpDUKOpf+jUqlUqv8h6gyr6m/HaLaWAWD/Yl4ob/AHb4v+k84TNIzbvaYquVszN4lK46DoNUSCTBlEwVfJRJJe3ZTzv6nD3ZsCwAtAIfC2uX/Gub+l+5NK7tojV63PUQky4eQ7St7ogdx+/Xqp4eDJms07XypZOgWSD6tzMtFJGZB2MESuU0QDKToOnqXnSu11ZcmzdxO4rbmfKIHWOuztrmvkxgdzkfcl3tDMcQTItUWuN+uxpfVc8IZ8cdTz/gyxw4a2zNmqxdhXSqic1asQ+zbTGsl0qjF7Ld8Rpfzinuzh7iUda2/e1PWr6k7EbeBt87/1PVKpVCrV3wvzrQegUn0BAyCD0WxtazRbOaPZ2vmz7/6Ulfjp1h2Ry+11I1IkAbTuxKlQMFdTiO5WKA1MdFDtgjrcvSNn5eJljfaQM/8NwSoAiOA275er7PxeqbN+s1w/Zp5mVc4JYte5N2jBqBdK1pMVmCd9i7OvYn+QqlsADCzHPKkcTb1LJyv6hKPa0dme0Jw3zinFU+7QPJ0c0EjZ8fFSPhLh04y9UuKaUvBaCtyqGM3WAKM9RD4pl57Qjz1YNBX6gOdKNqcPk+YWRrNoQuTammtKobA5UnvDY5r7bSP2RnQhx+ZyarCqUqlUqi9RA1bV345gMd0A8AjAv0//8wYOQDnwhi3gDeX/hPPUbMDcfLVGauxVy7kweY9c9fBzmo11h5TxrFJyYOapb1YBiN4nV/EHkOmPnu/voszUm4cT4bEhDbpYACF75SpCDHwlAI2ac9d6ZWaSKqdQ7dkXNEerl0qWd2nQvW3A3ERt9l7AIyX3ntPakcoQzX6yS6m1wiz1OdVFc/IuAQ6/pX6Hn1DjFQA5X+o6bC1Nnp3JRGwTFZAiB5VKzsc0d+w2uc6zRs45ja/oh7z8jjs3WrCY5rVwThuxS66xTrCYxG98a1QqlUr1N6WmBKj+OXiDllIcVoAyLMEIADvA2z61BV0HYLxgMf2udAGj2eozl1uleUGzXclF3hdoP33/X1ql4K9mNFs5AB5wdaW6I+iDMwFYC2BOuJLZ0sc53L5IszwogMRrZ4ntTzVkb9bKwsQdauS0FGnCXFm2VLusrEKJb13nnGMntaPqbZHrad5TX8MquelYAN0EffAek2NGtYLMm00m5ppXN3HMDbha7Dq/4WWrVCqV6h9KDVhV/yj3J5Xom5tEzctA7OsAzAVvewcARrM1o2AxxX7j4f0z8YbiAI4AMAKQezmHZwpXAvaO0ewo31scGQ5AEiymwkazdSAAXwDlATQV9MHUaA8Z1YU9rtks1y8EYPlV3cCuk8WuZ9bMnPz9N7selUqlUv3XUQNW1d9SemF8AiBQsJhe/usL3tABgPVT+1Oj2aqBq3RT4peOo/qVeAML3iYDgNFszQBgJ1wzrh4AKgoW0wCj2eoHgPnSLLbRbCWCxUTBG7IDeAfepv6HRaVSqVR/GjVgVX17vGFilOJdjVCaN554TdZCnGRyzoq1QxcDoAyAvILFlGg0Wz0AZBEsprBPuxrN1moAygkW07xvNXyVSqVSqVRflxqwqr4Zo9mqBeBThdw/tUSzrEAM9cJL5BAqkCdH9ilVm0+TOmUczv2Qdk/Js/yMUqoqgCEAJgLYBVcR+5OCxfRpVrA8gFS1PadKpVKpVP991CoBqr8Gb2ABwGi21ixk3nXu8sTytn2aCUmbNZZn4TSb93K5RWR9cX7DRtNO5vVlkodapPZ3ATR6oWQfdlEpWhNA8/S6rB0A7AVQEMBFo9k6IP0MSvo/KpVKpVKp/suoM6yqr4s3eAPw+6h4XtgpV4vxRqqxFXfJ7YpSOKUwEZiP1PDitFIqiBBEzJfaVhcspjgASH/9zwPgBH3wOQBJRntIUwAh6WWvYDRbOwAokYe8W3NKN6o9eNvUb3KNKpVKpVKpvip1hlX15+INQ2uOXXN90vghHuANnaIUn1cflQznX9Bs6MUdL1yHvcstkVqM6S6OSWEI7RjERI5ZJzeayUJ++0TXrbrRbM2ZfqQZAOYA4OaJbWIBPBYspqGCxXTDaLZWM5qt7gD6AEh9SbP0B3ARvCEDeMNh4F8Br0qlUqlUqv8CasCq+rP1PqkZVXoStznhhFyqoydJi10mNV8yXBywY4dUY2B550qpHnu7bSny/KDflIiD+imxp5Zolre6Jhcs0NlpDgXQM/04WgBiXvJ2JQul/Y/asRYCcAfAoNWaBaWu6AZfBm87C96WBKBF+mr2NX/tZatUKpVKpfpa1JQA1Z9m14Qm2WKo15E+3OGir5RMMd8rDd5ukeuFieDaC/pgC4CWRnvIAV8k7ouD15NPr//BG4jRHpIhvRKAEcAYwWLqZzRbVw5h9+Ruyl7OnI3E3g+jWecVYV4PAXAqPT1gCdIXWhnN1hyCxRTxaSxGs7UUgPuCxSQbzdbGAO4CyAcgDECKYDHFGc1WnWAxOcAbKgMQPtV0ValUKpVK9feiBqyqPwdvCAIw2E652rflfAUyMYkz+olD/TZo5pYwMtH9Xyn+PjWdC+MAvAaQ9Gl1/5cYzVaDYDHZjGZrMbiC0u41mbttHdC8D9HODAVvu/qFfXYAOHxT18/gR2wHy9hX9PkI7wVPdV2L7JKr75skdWuQh7zbNoDbnzVCyZxSi70b39g50wmgtqAPLg8gFLwt7MfHValUKpVK9e2pAavqT5UwOeD4RaVYhSyIu1uYEc4NFAdvXqJZtnGN1PjlYrnVTcFiWvWl/Yxma3EAfQSLqf+PPi8F4ACAUEEf/Ay8beAX9u0YzJ6eH0MNmYZwewcUYQQFwHPwtnP/cSLeoAPgXtm+aOs7ZOY4SLUlcH3hKpH15o/fAZVKpVKpVH827lsPQPXfpYRjfSMAww9pxw1PgXb7VaXwwsKOjS3gKv5/46f2Eyym+wD6f+GrhwAkACU3SA3Wdf/y7trDcoVwESw56Syz9j9mb3kDB2A+gO3gbTcAOC4DjcEbdK+VzHerOxfpAST9jstVqVQqlUr1F1BnWFVfndFsdd+omd2jJnsf4G1Lf3Zj3pAPQBsAa8DbYoxm6yIALb2RdFMC2ygZ7oGCxfThR8evAUAAUEOwmDZ94ZgEQL8OznE1LitFpggW0+PPvmMB/ADe1upnxu8PwCZYTPZfdcEqlUqlUqn+VOoMq+qvENBHHI7nbJc1AGA0W8cAWCpYTKk/3vCIXO4dpch6j+Y50XJS0TjAcmMSt0XORmIyJFKPpTJYDnywDrzNkX6s4yzkp4913SMKODZfvzmp7M6yzPNQACvB26IAIL2v/YrLZusqwWL69+YCvE0Gb+j7C+P/DsA1o9l6G0B3ADoAhwR9cAyAAgBeAEgGb1MbF6hUKpVK9RWoAavqqxMspnAASwHbp49uAnB8adv+4tBtAJrM5lYLEdRvLYC7s6Xv/I0kan4oDfSYxm1YD2AugDPpuzRTwFwfJ/bIvVsz2f2V4m8vyzz/AcCXZkO9wBvKAyjxTMnuPUHsfmj3rBFXwNti/mNL3pDlU8ArWEyLAcBoto4FEDSR29LpvFy8oZ1y9WUwe54qgVVLMC9TMNn7I0dosx+V4FKpVCqVSvUHqSkBqm8qvQGAAlee6ncANgG46onUZynQJ1MwkwEcfKzrBg/iWAAgP3jbXAAAbzAA8Mxv38SP5nYEcpAM7dkzZROpR1wZ5+r8gsUU+9l5/AHMEvTBvQDo3ykZEzUQSWYmUQPe9p8VC3jDEgCzjPaQVACdBYtpqdFszQDAuxt7tFdf7mCZ94qhnpYoNErxsVdnH3kQUFBAIkAUQxADoC54W/xXvH0qlUqlUv1PUBsHqP5yRrNV+9mP7QA0gus1ewCAhQBOJcN9OAUzDkAnAFumSZ00CkXVi3KRT52wsEuqNv6Okmd/M/aK20WlaIn5UrvLCkiYO9K0GzSzZ3x+TsFiihYspu6uFABbylWlYJdwmsWWXs8V4A3NwRsqpf+ZAJiWPsNqB/A4fczlASzORGzDkqh7Nj2RjryTM0aeoOXj7JSTFIAwgEYBcigUxQGMMJqtS41ma+avcydVKpVKpfrfoKYEqP5SRrM1C1z5oPkFi8ku6IOvgLeFpn8977PtsgLwgqvg/0ANJG1L59Sp92ieYTBbGcFiUtpMPzTaaLZ6ChZTstFsLVCQvA7WEzlmo1R3VEYmeSV4QxSAqek5rDCarVMAfC9YTM9ac5e4hWKrKxlhiwJvsABYC0AEb1gJ4AiAJuAN+SowE5pdUwqN2amdamegTGvj5BumUH3GN0rmk3oiVizCveFeyDl5Stg5VqlkUi32nqcM1nZBKSYZSbQMYPaPF4mpVCqVSqX6bdSUANVXl/4q3e26rv/kKCVjo+bitAeCxdQMvEEDYBl4W59/24E3kNNyydE9xFFPAfQC4LFJY8lRg31QCLxNTN+m4wfqHVDOsaK0YDG1/2xfndEeomzmZi6tzj2qCd6WP30MHgCKArj5qeyV0WytCiA0VNc5SDcl9mr6/h3Gi90busFxfIJmux1AbQCrwNvupX8/FK5SWzoAd52UWaslSko7x/iTRRmhUE3mHh0iDkj9CO/HAHYLFpP0Ne6pSqVSqVT/S9SAVfXVGc3W63o4sgUgdtI5/cgT4G1vf3YH3sAAGLhbrpbpgFyp9Dhu++uCTIQvgB+M9pAjcKWytAWwWdAH5wCQA7zt8o+O0V+hmLtJqlen+4xdV41maxEA1QWLafmPxhYMwOvzhgZGs5X9Vy1XV5mtwQAGgbdR8IZMACoA8ANv2+ic7Hugrzhs4RmllP9EbmuFHtzRheBtagMClUqlUqn+RGrAqvq6eEPBHVKNGUUYwaMII/gBuAbe9qUGAf/GaLb6ruIWPHJAo5RnnmbyIbbJOkI3GO0hzQH0AbASwAZBH5wHQE0AUeBthz4/xomJNcM3yfVvh8w0t/mFMa4BMAJA1n+lJ/AGd/C2fyu7ZTRbCwHoAeA2ALk0CX23RWv5/pJS9E4fcfh1wWKa+etuikqlUqlUqt9CXXSl+mqMZitp4JhVJzuJCUik7p7gbaW+FKwazVaN0Wzt9vlngsUU95Dmvn5CKv0yA0nTJVKPYTIlgwSLaS2AEwAuAeCM9hAOwGEAkenHymw0W7cCwDKpxaJC5E2g0Wxlf2GoMwDoAXT99EEc9fyh/tiVY360XSqA3QB21mTuzrxN8/e8LhfwKU6e31eDVZVKpVKpvh510dU/kNFsNQE4KVhMzm89lp9iNFvd53Erd2cgqYw/Saiel3n3c88aBfD/18IbCHgbHaX54QU0mP1R8XhwVS5EGnM3YsAbIoGQknBVF5ABGMDbZiI9YBUspg9Gs7UfAPTkrNdyk6ixEzTb34JHF/C2E+ljawYgQLCYVrvOZ3udfuaxn4ZQyrHmJYCIH43zrWAxCQBwfqLlRQ/26Mkq7KNEACG/7y6pVCqVSqX6NdSUgH8go9naH8CmL3WK+tbiJ2fJv0pqMsadOH94rfivna9Z9YhMsTUEAKPZqlunmTeyDnvnOIA4AEUAnARvS3Ptm63/JblwlibcdXcA1wE8ABB6X8n1ypcm+uVgY+8AWA3gmNEesgzAIMFi+s+i/z+iTDZ8f0YuXqwOdz8OQC+jPSQcACtYTGm/5dqMZutJAI0Fi8kB3tAylnp5dXGOfn141qCzv+U4KpVKpVKpfhs1JeAfSLCYVvwdg1Xwht1eSL3Vlj3f6R31y7hXqbY7Bt49P9vCsE2u7QHgNYA0APEAzoE3FAOANzTzm6tKYSeApeBtP4C3PQNvo1PELouzMbG6D4rHSwC2vs6hRgAdBH3w5vTmAT8exxjwhgrgDefAG6o0cc4Y9INcI1ChqGxTdHcEffBqAOJvvTzBYqorWEwOAFgotrrV1zlk/nRuwwLwhqq/9VgqlUqlUql+PTVg/Qcymq3kW4/hJxhFyroFO8ff/EGu4S5YTMPKOVYMTe8yBQDsCs2iZnntW1YZ7SGjwNsuAhgG4BUAFJ969/DMmQungbcJnx9076xhC2WQZA5KwcdyDkzhNli3aWbWA29rBN5mw39aC1c+6hQAOa2zBn48Qct3ilB80zjI+ijZq/wqdl5EAfPelr/3Qr1Iart27LlTJdjwG/g8nUGlUqlUKtWfTg1Y/2GMZqseQNS3Hsd/4A15Aej0jLzzom7YdQA70r9ZCeADeMN3CzQrmqdCT+Zyq3YBmOTaz3YFvC3plw6vIXSBF0krUZCJ2O3PJHpWYR/dMZqta4xma+H/HIstDsBo8Laz4G0hACBYTPvqiguLDxEHX/NmktJqc/fcKjJPAsEbKoE3jPytl7tWavRgo1S3sURJq/L2ZW3AG5r/1mOoVCqVSqX6ddQc1n8K3lAYQIPvpZppY6VeYvpq+b8Fo9la4phm9J3sJEbuLQ47GzJrbL1/fckbWpS0rxpwW9f3A0PwBMB7ABvA25TffCLesEGhqMIQCADagbfFgzesBbAQvO3Jrxzr/9dYdR1TC8Aj/Vi5AfiDt139wn5T4VrYdRmuBgSXH2m7PdESUX9GLjmpAXfbDt42/zdfk0qlUqlUql+kVgn453hpU9weVGYe7QRwyWi23gHgJlhMl771wDZrZlXyJ3GUEnJjtXZRRaO5WG0ALwSL6Q2APPHwasZMsaX80fPYKed9W87jUZl7VgRAGaPZ+hwI2SZYTL8qWAWAz4NVo9nKACFDBItpbvpHWgBuP7HrfAB5ASTDlX/bZpncPKozczxnVfZBc/C2sr/vqlQqlUqlUv0SNSXgn4A3eLVyTL4dhuw7Gjtn/gDAE0AQgCRX0PXtGM3WieWZp8vCaUDMVrlO2VKONeEA5gBwBwDwtrmCxfSHg1UA0BOpd2XuGZ9MtT9UcywcV4O55wVXkPl7UQDP/vWTa5HXmS9tKFhMNsFiupW+z4RSJNRjGLcnqxexp4wU+zrBG9z/wDhUKpVKpVL9DDUl4O+MNwQA6PBUzhGph3PNKaXk07fUz2uMZqenQP0zz5I6tLmoFPtO0Af3BeAAb7P/1UMMnVR4XVZ86BBGs39oIU6fBmC9YDF9vYeKNzRLo9zwg3KlXO24C8HgbX/5DLPRbNWll7bKsEOsdo8hCAwk0THl2dAi4G2xf/V4VCqVSqX6b6cGrH9nvCHzebnI9nLM01phSk7iQVKpB1KpJ9JAQZjnNHuchlAUIG94DVFCwdtOfoMxllkpNinSb8a2TX/R+TROSiJiqSE+C5NQOX2B1Tc1aNy422O574tmZeLcwdukbz0elUqlUqn+26gpAX9vy+KoVw4GkLRwJrdwTo7MSJIZFpToiQQ/YvO9Jed5mtexbdU3CVYBgLfd+suCVdf5xE1SQ7sD2ncAav0ZhzSarUU/K731mx1VyvfZLVer+1gJDG06dsmfMiaVSqVSqVT/Tw1Y/854W9sW3JXmOiL3yM++O8WAcb+oFHtS3LnOeUop/ThKyXhvi1zvCoAa33qof6UG7HXRn8QlALj5R4+Vvvq/BYDfHbCGWZrdGqzZX7cw83r6Qd3EW390TCqVSqVSqf6dWiXg7463PQNveAfgxF1L+w9Gs1cnAD0MSJmam42+9krKbhcspt9eIuofLI56OTWQCrgRWz24mgT8EVMAFARQB65WsL/XAQA5APxP/V2oVCqVSvVXUHNYVf84iZMDdsVSz/u5mJjFv6bpwC8xmq1uAAIEi+nVnzA8lUqlUqlUfzI1JUD1j7NCanZjt1wzo9EeMs5otmb6I8cymq3DATwEMP1blwhTqVQqlUr1ZWpKgOofx6zZMQ9A8eVycweAP1QlYKVmYYeh4oAtDmhLw/UL3K96pW80W/sDSASwE0BpwWK69nvObzRbywB4LFhMab9nf5VKpVKp/heoAavqn8gTQDvBYhr7Rw90TSn4AYAgWExTf8t+gsW0AgCMZqsvgGoArhnN1sYAjv5b69cvMJqtPgA0gj44eJ8maNZ34sQJcHXSUqlUKpVK9QVqwKr653Hlrf7uYNVotrozUHbe1/WsU5Ep8vgd9XN3FQoAjGarCa4ZT+HXHEuwmOLg6uwFAEGXdYOKAaa7v7BbeQAV90hVsuQnbx6H6rue+HR+lUqlUqlU/0lddKX638IbmHL25asT4R64Szul1GmpePQQzQEmlyPECWABgLIArgkW07ZPuxjNVg5AaQCPrukG5Akg8angbS/Sv8sBoLNgMc1IP/4iAGPB29KMZmtNAMkAXgkW08f07We1Zc8GBCDezUje6wNJZEppNnyf0R4SD+DqEHb3tArMYw+L1HH2fRrURbCY+L/s3qhUKpVK9TelzrCq/mcYzVZyWpvl+3O6oS2OyeXIeLH74wa4+uyZkr3BNG7D+rrs7aiAKcLAH+9XiXk0IJm6FWzPnnG3Ke4lz9LicWPN1u4AigE4DOCj0Wz1BjAMCNnwWT5q0jntsI13lDxZjWYsykmiHz7QjavdyzlCKsc+zZiDfOhYjBGO2BR90zPaYTQriUsgoAFJVM+lQlcfQLO/7OaoVCqVSvU3pgasqv8lWh+SVGeHVP1dbib6XivmYqNa7J08qdChNAkNbu6YWrHO+KHLps9YlA8AjGZrN3/EFZ2oOf1dbhL5rhATwa+UmoyeLbWfDSB5s8ZSsjr7wN9oDykFIArAUgDx6ftqAYQZpz4vagTQEgB4g8c+qVL1G7RA1E5u+pg9UuUxUbJvv9rM7Z6BJDrwHfWBH0nSPKHGhNLM800nZ/T5I3VhVSqVSqX6r6GW8VH9zxAsJodImaR67B2GAbXFwstS1zlv6Rqp8eXcJCrFi6TkDCKRAnjD04rmLZsAjI2Gb5uB4pDobCR23GSxi8dsqX2FEuTFbUEfPKqLaNZNE4Nrh+o61QdwCsAs/H/XsQoAun06dy7zobWF7evr1GLvrqzOPFgEIH8+5m2t5zRbVQd0VexUE0TAZR3mHNCmGvvI1zJj/pS/9u6oVCqVSvX3pc6wqv5xjGZrVQD/197dx1Z113Ec//zO7b19Eg6lQIGgXEAeNh5XYDBhCoHNsOIcAzfSBYKBsGxIiokkbaJ4LCAXMvbQscCUKSGui5gNYd5EEKIgDpBNRZnlye2WB9cWaHspbW/vwzn+IYnZ4pIh0Htr369/z+/c+/2d3D8++eV7v2e8pKZIqOT1z3yjY5sab+yVB62/jR9kNc580DoVKzKNdg+1/2Jrcm6Py17fq4NMww8lvbTA97v7T3uDz7Uob6wl98z4ju2xE9nPzOirpkPZJjFYUpWkHZOss/UtXl5qoK6O9GSCH6kwIUmRnNKaD92iV4PlikRCJbur/Ft8U63359mm/Sc7Apt+I6lirFV7ZqxVO9/11Bbz/A373OL52zZU/lm6pYEFAAD83yOwois6LSmqW/n9OvYQSS/0NdeHNXv57QVqbbOMju9OTkusD+xYfp/OR7/t/2VVnVtQ/XR81SP1Xu+KnwY2JT/yCoqPuaN3vuOOOdHXROu/5d97XVKjnGhtRJoZLNe6+03NxucDW7fda2rr5nas+1HdmsFH/+6NOz3dOpV40jpY/cfvff/dr/nOXXE9zYl5WeYf7oAB/Uz0SI6Jne9h4vsto815JrFqmbUvdnceFwAAXRtTAtD1OPZOSUvkRD/TkP+b91iSiiTVyYl6n7iWFfd82wI/aFwWLA9PlHQ6EippvXnNJyf6qXNVg+XhIklXIzmlw8+7A59zEouCm/3bBvQ0rfGAks3H3Hu+MMq62LgvNWlXnVcwcXnWr4YGlLrWpkD+hsRTT24MvBaVE/3g1h8CAADdB4EVXY9jD83IkOfYIybHXomNsi6UveR/5cgx997JZYkVMxLyH89X+/SfB9ZaO5OzL20KbJ93S2EbAIBujsAK3CXB8vAaSRsioZJEsDw8WFKDpEmRUMnv01waAABdCoEVAAAAGY2xVgAAAMhoBFZ0CcHy8MSbw/gBAEA3Q2BFl1Bszs4PByoelWMPT3ctAACgc9HDiq7BsZ/dmZw98z135ME97rSjks5FQiVt6S4LAADcfbw4AJnNsUdIikn64uJ1b35jsaQ95eGVkpol1aazNAAA0Dk4YUXmcmzT4PV8c3pHVc3ZnCUvy4nWfXLJzb7WQZFQSebNZQUAAHcEPazITI6dJWllH11v+kN2WVTSiE9ZuU5SaecVBgAAOhstAUgfx/ZJmtLs5i2NKn/oYOvKLkmVq+PL1i71DVw4yvfP3FCytO2kO/TtEdalmrWSguXhIUVq7H88tOioHDsgVZ8qNmff3vHdHQEnueTXkZxSryrxWPUud8bQS16//ZFQyaE07xIAANwmWgKQPo49TtJ3LrqFC4rUFA9YbpakQNzTLp/0mM8oJumbu5JfWTDROtMyzKpbcDg1Zv0I69LC/qb5kXYvUFnv9XqiOjWr0Se3cZI5Y/lNsqBd2aNeT826cdid8FYkVLIkvZsEAAC3i5YApNMKSQs+b107HLDcJkluylONJS1scHvFJb0jafQTWYdmDtTVeRfcwpaRpnZtf9Psl/TCX9xhA+fFK68GlGi94BW1nfBGTj6SGhN4Mfn44WLrfKGklencHAAAuDMIrEinvZI6PE8P1Luf65X0lGtJI42UyjYdftfTw56nSteTsYy3v1k9rf2pyQdSngpveNnDtiS//tBz/lfznsnaM2miVVOwNzXtQIG5kXzev+3oqqy3xkVCJS3p3iAAALh9BFZ0PsculGN/ucXzVyY9Y3vS5T6mtUe7l500Rj/2Gf22p2nPjXsmuTc19WKrcnpmm9TlR+Prix/2vTehXdk/G9vx2qkJ1geze5vrN/JNIs82sfw+Jrp1Y6p09T3WxWpJTeneJgAAuDPoYUXnc+w5koadcoMnJbdiiKnPyzcda8KpKdaKRNmaSE7pV2+4/u0p4ysJKLk61yTLJB2XE31ajr1I0mE50VubwerY/STNkhN94y7sCAAA3EUEVnQ+x54tqUFO9K/B8vDw+8y55bs3rFr9X9bl6d8vDbAkuXKi7m18Zy9JUyRFJFVUJed17Ew+VPVu6Kn3/+fPBAAAnYLAiu7HsXO3J+eEH/cdye1tWjZXJJZG30jNOhjJKS2QE72W7vIAAMDHEVjRvTn27KmxlzdX+beMG20+jOdb8QfkRP+U7rIAAMB/8KcrdG9O9MCx0OLxzybK5rYr+6SkL6W7JAAA8HGcsAIAACCjccIKAACAjEZgBQAAQEYjsAIAACCjEVgBAACQ0QisAAAAyGgEVgAAAGQ0AisAAAAyGoEVAAAAGY3ACgAAgIxGYAUAAEBGI7ACAAAgoxFYAQAAkNEIrAAAAMhoBFYAAABkNAIrAAAAMtq/AE9PXozto0lIAAAAAElFTkSuQmCC\n", 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