From 5856644177f60043667135830da848f2bf9b81fd Mon Sep 17 00:00:00 2001 From: Gonzalo Benegas Date: Thu, 16 Jun 2022 09:55:04 -0700 Subject: [PATCH] Add warning about restarting runtime in Google Colab --- differential_splicing_example.ipynb | 4429 +++++++++++++++------------ setup.py | 2 +- 2 files changed, 2434 insertions(+), 1997 deletions(-) diff --git a/differential_splicing_example.ipynb b/differential_splicing_example.ipynb index e70a207..f61e8e7 100644 --- a/differential_splicing_example.ipynb +++ b/differential_splicing_example.ipynb @@ -1,2050 +1,2487 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ZqEj_KcGSpyV", - "outputId": "ab5ad42c-5d18-48e9-d723-b774b0d9b4e4" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Install scQuint\n", - "!pip install -U git+https://github.com/songlab-cal/scquint.git" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ypi1bs2OTEeV", - "outputId": "75266b3e-8d95-4f7a-fbf3-e7b862d6a646" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Download processed intron count data\n", - "!wget https://figshare.com/ndownloader/files/27696714 -O adata_spl.h5ad" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# Download gene annotation\n", - "!wget --no-check-certificate https://ftp.ensembl.org/pub/release-102/gtf/mus_musculus/Mus_musculus.GRCm38.102.chr.gtf.gz" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pQMBiHyKSnCw", - "outputId": "ad44cf61-464b-417a-9ebf-e4d4d7c9a22c" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/software/sl-7.x86_64/modules/langs/python/3.7/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", - " import pandas.util.testing as tm\n" - ] - } - ], - "source": [ - "import anndata\n", - "import scanpy as sc\n", - "import numpy as np\n", - "import pandas as pd\n", - "from umap import UMAP\n", - "\n", - "from scquint.data import add_gene_annotation, group_introns, filter_min_cells_per_feature, filter_min_cells_per_intron_group, calculate_PSI\n", - "from scquint.differential_splicing import run_differential_splicing\n", - "from scquint.dimensionality_reduction.pca import run_pca" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SW-KOMk0SnCz", - "outputId": "b545eee4-8556-486c-8ba9-df2866662bfe" - }, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/software/sl-7.x86_64/modules/langs/python/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:8: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " \n" - ] + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZqEj_KcGSpyV" + }, + "outputs": [], + "source": [ + "# Install scQuint\n", + "# In Google Colab, it might ask you to restart the runtime after installation\n", + "!pip install -U git+https://github.com/songlab-cal/scquint.git" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering to introns associated to 1 and only 1 gene.\n" - ] + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ypi1bs2OTEeV" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# Download processed intron count data\n", + "!wget https://figshare.com/ndownloader/files/27696714 -O adata_spl.h5ad" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/scratch/projects/fc_songlab/gbenegas/projects/scquint/scquint/data.py:126: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", - " adata.var[\"gene_id\"] = adata.var.gene_id_list\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "cVDApFoxXf-a" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# Download gene annotation\n", + "!wget --no-check-certificate https://ftp.ensembl.org/pub/release-102/gtf/mus_musculus/Mus_musculus.GRCm38.102.chr.gtf.gz" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering singletons.\n", - "Filtering intron groups associated with more than 1 gene.\n" - ] + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pQMBiHyKSnCw", + "outputId": "f3c7a35a-5e49-4813-c0ac-8f9bc9ae8c5c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ] + } + ], + "source": [ + "import anndata\n", + "import scanpy as sc\n", + "import numpy as np\n", + "import pandas as pd\n", + "from umap import UMAP\n", + "\n", + "from scquint.data import add_gene_annotation, group_introns, filter_min_cells_per_feature, filter_min_cells_per_intron_group, calculate_PSI\n", + "from scquint.differential_splicing import run_differential_splicing\n", + "from scquint.dimensionality_reduction.pca import run_pca" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/scratch/projects/fc_songlab/gbenegas/.local/lib/python3.7/site-packages/pandas/core/generic.py:5516: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", - " self[name] = value\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SW-KOMk0SnCz", + "outputId": "1e51409a-a22e-49db-aac4-c6dc2527f66c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Filtering to introns associated to 1 and only 1 gene.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/scquint/data.py:126: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + " adata.var[\"gene_id\"] = adata.var.gene_id_list\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Filtering singletons.\n", + "Filtering intron groups associated with more than 1 gene.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5516: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + " self[name] = value\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 44518 × 29924\n", + " obs: 'FACS.selection', 'age', 'cell', 'cell_ontology_class', 'cell_ontology_id', 'free_annotation', 'method', 'mouse.id', 'sex', 'subtissue', 'tissue', 'n_genes', 'n_counts', 'louvain', 'leiden', 'cell_type', 'plate_id'\n", + " var: 'chromosome', 'start', 'end', 'strand', 'annotated', 'gene_id_start', 'gene_id_end', 'n_genes', 'gene_id', 'gene_name', 'intron_group', 'intron_group_size', 'n_genes_per_intron_group'" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "# Load preprocessed intron counts\n", + "adata = anndata.read_h5ad(\"adata_spl.h5ad\")\n", + "\n", + "# These steps only needed because this data was prepared with older code\n", + "adata.var = adata.var[[\"chromosome\", \"start\", \"end\", \"strand\", \"annotated\"]]\n", + "adata.var.end -= 1 # to match the coordinates from this gene annotation\n", + "adata.var.index = adata.var.chromosome.astype(str) + \":\" + adata.var.start.astype(str) + \"-\" + adata.var.end.astype(str)\n", + "adata = add_gene_annotation(adata, \"Mus_musculus.GRCm38.102.chr.gtf.gz\")\n", + "adata = group_introns(adata, by=\"three_prime\")\n", + "\n", + "adata" + ] }, { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 44518 × 29924\n", - " obs: 'FACS.selection', 'age', 'cell', 'cell_ontology_class', 'cell_ontology_id', 'free_annotation', 'method', 'mouse.id', 'sex', 'subtissue', 'tissue', 'n_genes', 'n_counts', 'louvain', 'leiden', 'cell_type', 'plate_id'\n", - " var: 'chromosome', 'start', 'end', 'strand', 'annotated', 'gene_id_start', 'gene_id_end', 'n_genes', 'gene_id', 'gene_name', 'intron_group', 'intron_group_size', 'n_genes_per_intron_group'" + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 588 + }, + "id": "sOt5Va6JXf-d", + "outputId": "04085537-b3f6-420b-b458-4b0477d55551" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " FACS.selection age cell \\\n", + "my_id \n", + "A10_B000126 NaN 3m A10.B000126.3_39_F.1.1 \n", + "A10_B000127 NaN 3m A10.B000127.3_38_F.1.1 \n", + "A10_B000166 NaN 3m A10.B000166.3_56_F.1.1 \n", + "A10_B000610 NaN 3m A10.B000610.3_56_F.1.1 \n", + "A10_B000633 NaN 3m A10.B000633.3_56_F.1.1 \n", + "... ... .. ... \n", + "P9_MAA001887 NaN 3m P9.MAA001887.3_39_F.1.1 \n", + "P9_MAA001888 NaN 3m P9.MAA001888.3_39_F.1.1 \n", + "P9_MAA001889 NaN 3m P9.MAA001889.3_38_F.1.1 \n", + "P9_MAA001892 NaN 3m P9.MAA001892.3_38_F.1.1 \n", + "P9_MAA001894 NaN 3m P9.MAA001894.3_39_F.1.1 \n", + "\n", + " cell_ontology_class cell_ontology_id \\\n", + "my_id \n", + "A10_B000126 bulge keratinocyte CL:0002337 \n", + "A10_B000127 myeloid cell CL:0000763 \n", + "A10_B000166 basal cell CL:0000646 \n", + "A10_B000610 bladder urothelial cell CL:1001428 \n", + "A10_B000633 endothelial cell of coronary artery CL:0000115 \n", + "... ... ... \n", + "P9_MAA001887 hematopoietic stem cell CL:0002035 \n", + "P9_MAA001888 CD4-positive, alpha-beta T cell CL:0000815 \n", + "P9_MAA001889 B cell CL:0000236 \n", + "P9_MAA001892 endothelial cell of lymphatic vessel CL:1001567 \n", + "P9_MAA001894 astrocyte CL:0000127 \n", + "\n", + " free_annotation method mouse.id sex \\\n", + "my_id \n", + "A10_B000126 nan facs 3_39_F female \n", + "A10_B000127 nan facs 3_38_F female \n", + "A10_B000166 basal cell facs 3_56_F female \n", + "A10_B000610 nan facs 3_56_F female \n", + "A10_B000633 coronary vascular endothelial cell facs 3_56_F female \n", + "... ... ... ... ... \n", + "P9_MAA001887 MPP Fraction B + C facs 3_39_F female \n", + "P9_MAA001888 BM CD4 T cell facs 3_39_F female \n", + "P9_MAA001889 B facs 3_38_F female \n", + "P9_MAA001892 Lympatic facs 3_38_F female \n", + "P9_MAA001894 nan facs 3_39_F female \n", + "\n", + " subtissue tissue n_genes n_counts louvain \\\n", + "my_id \n", + "A10_B000126 Telogen Skin 2552 339047.0 10 \n", + "A10_B000127 Fat SCAT 1785 254953.0 16 \n", + "A10_B000166 Mammary_Gland Mammary_Gland 4713 2246909.0 22 \n", + "A10_B000610 nan Bladder 4182 1486054.0 28 \n", + "A10_B000633 RV Heart 2001 448241.0 3 \n", + "... ... ... ... ... ... \n", + "P9_MAA001887 KLS Marrow 2919 227378.0 8 \n", + "P9_MAA001888 T-cells Marrow 1960 171978.0 7 \n", + "P9_MAA001889 EPCAM Lung 1622 427794.0 1 \n", + "P9_MAA001892 Endomucin Lung 1805 438322.0 37 \n", + "P9_MAA001894 Cortex Brain_Non-Myeloid 1466 122397.0 30 \n", + "\n", + " leiden cell_type plate_id \n", + "my_id \n", + "A10_B000126 9 bulge_keratinocyte B000126 \n", + "A10_B000127 16 myeloid_cell B000127 \n", + "A10_B000166 20 basal_cell B000166 \n", + "A10_B000610 26 bladder_urothelial_cell B000610 \n", + "A10_B000633 3 endothelial_cell_of_coronary_artery B000633 \n", + "... ... ... ... \n", + "P9_MAA001887 8 hematopoietic_stem_cell MAA001887 \n", + "P9_MAA001888 7 CD4_positive,_alpha_beta_T_cell MAA001888 \n", + "P9_MAA001889 1 B_cell MAA001889 \n", + "P9_MAA001892 36 endothelial_cell_of_lymphatic_vessel MAA001892 \n", + "P9_MAA001894 39 astrocyte MAA001894 \n", + "\n", + "[44518 rows x 17 columns]" + ], + "text/html": [ + "\n", + "
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FACS.selectionagecellcell_ontology_classcell_ontology_idfree_annotationmethodmouse.idsexsubtissuetissuen_genesn_countslouvainleidencell_typeplate_id
my_id
A10_B000126NaN3mA10.B000126.3_39_F.1.1bulge keratinocyteCL:0002337nanfacs3_39_FfemaleTelogenSkin2552339047.0109bulge_keratinocyteB000126
A10_B000127NaN3mA10.B000127.3_38_F.1.1myeloid cellCL:0000763nanfacs3_38_FfemaleFatSCAT1785254953.01616myeloid_cellB000127
A10_B000166NaN3mA10.B000166.3_56_F.1.1basal cellCL:0000646basal cellfacs3_56_FfemaleMammary_GlandMammary_Gland47132246909.02220basal_cellB000166
A10_B000610NaN3mA10.B000610.3_56_F.1.1bladder urothelial cellCL:1001428nanfacs3_56_FfemalenanBladder41821486054.02826bladder_urothelial_cellB000610
A10_B000633NaN3mA10.B000633.3_56_F.1.1endothelial cell of coronary arteryCL:0000115coronary vascular endothelial cellfacs3_56_FfemaleRVHeart2001448241.033endothelial_cell_of_coronary_arteryB000633
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P9_MAA001887NaN3mP9.MAA001887.3_39_F.1.1hematopoietic stem cellCL:0002035MPP Fraction B + Cfacs3_39_FfemaleKLSMarrow2919227378.088hematopoietic_stem_cellMAA001887
P9_MAA001888NaN3mP9.MAA001888.3_39_F.1.1CD4-positive, alpha-beta T cellCL:0000815BM CD4 T cellfacs3_39_FfemaleT-cellsMarrow1960171978.077CD4_positive,_alpha_beta_T_cellMAA001888
P9_MAA001889NaN3mP9.MAA001889.3_38_F.1.1B cellCL:0000236Bfacs3_38_FfemaleEPCAMLung1622427794.011B_cellMAA001889
P9_MAA001892NaN3mP9.MAA001892.3_38_F.1.1endothelial cell of lymphatic vesselCL:1001567Lympaticfacs3_38_FfemaleEndomucinLung1805438322.03736endothelial_cell_of_lymphatic_vesselMAA001892
P9_MAA001894NaN3mP9.MAA001894.3_39_F.1.1astrocyteCL:0000127nanfacs3_39_FfemaleCortexBrain_Non-Myeloid1466122397.03039astrocyteMAA001894
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44518 rows × 17 columns

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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "# Metadata associated to each cell\n", + "adata.obs" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Load preprocessed intron counts\n", - "adata = anndata.read_h5ad(\"adata_spl.h5ad\")\n", - "\n", - "# These steps only needed because this data was prepared with older code\n", - "adata.var = adata.var[[\"chromosome\", \"start\", \"end\", \"strand\", \"annotated\"]]\n", - "adata.var.end -= 1 # to match the coordinates from this gene annotation\n", - "adata.var.index = adata.var.chromosome.astype(str) + \":\" + adata.var.start.astype(str) + \"-\" + adata.var.end.astype(str)\n", - "adata = add_gene_annotation(adata, \"Mus_musculus.GRCm38.102.chr.gtf.gz\")\n", - "adata = group_introns(adata, by=\"three_prime\")\n", - "\n", - "adata" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
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FACS.selectionagecellcell_ontology_classcell_ontology_idfree_annotationmethodmouse.idsexsubtissuetissuen_genesn_countslouvainleidencell_typeplate_id
my_id
A10_B000126NaN3mA10.B000126.3_39_F.1.1bulge keratinocyteCL:0002337nanfacs3_39_FfemaleTelogenSkin2552339047.0109bulge_keratinocyteB000126
A10_B000127NaN3mA10.B000127.3_38_F.1.1myeloid cellCL:0000763nanfacs3_38_FfemaleFatSCAT1785254953.01616myeloid_cellB000127
A10_B000166NaN3mA10.B000166.3_56_F.1.1basal cellCL:0000646basal cellfacs3_56_FfemaleMammary_GlandMammary_Gland47132246909.02220basal_cellB000166
A10_B000610NaN3mA10.B000610.3_56_F.1.1bladder urothelial cellCL:1001428nanfacs3_56_FfemalenanBladder41821486054.02826bladder_urothelial_cellB000610
A10_B000633NaN3mA10.B000633.3_56_F.1.1endothelial cell of coronary arteryCL:0000115coronary vascular endothelial cellfacs3_56_FfemaleRVHeart2001448241.033endothelial_cell_of_coronary_arteryB000633
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P9_MAA001887NaN3mP9.MAA001887.3_39_F.1.1hematopoietic stem cellCL:0002035MPP Fraction B + Cfacs3_39_FfemaleKLSMarrow2919227378.088hematopoietic_stem_cellMAA001887
P9_MAA001888NaN3mP9.MAA001888.3_39_F.1.1CD4-positive, alpha-beta T cellCL:0000815BM CD4 T cellfacs3_39_FfemaleT-cellsMarrow1960171978.077CD4_positive,_alpha_beta_T_cellMAA001888
P9_MAA001889NaN3mP9.MAA001889.3_38_F.1.1B cellCL:0000236Bfacs3_38_FfemaleEPCAMLung1622427794.011B_cellMAA001889
P9_MAA001892NaN3mP9.MAA001892.3_38_F.1.1endothelial cell of lymphatic vesselCL:1001567Lympaticfacs3_38_FfemaleEndomucinLung1805438322.03736endothelial_cell_of_lymphatic_vesselMAA001892
P9_MAA001894NaN3mP9.MAA001894.3_39_F.1.1astrocyteCL:0000127nanfacs3_39_FfemaleCortexBrain_Non-Myeloid1466122397.03039astrocyteMAA001894
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44518 rows × 17 columns

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" + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 661 + }, + "id": "wCbolVLhXf-h", + "outputId": "47416fda-846b-4b8d-991e-8e38f3d80b8c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " chromosome start end strand \\\n", + "Sox17_chr1:4492669-4493099 chr1 4492669 4493099 - \n", + "Sox17_chr1:4492669-4493771 chr1 4492669 4493771 - \n", + "Mrpl15_chr1:4774517-4776409 chr1 4774517 4776409 - \n", + "Mrpl15_chr1:4774517-4777524 chr1 4774517 4777524 - \n", + "Mrpl15_chr1:4777649-4778747 chr1 4777649 4778747 - \n", + "... ... ... ... ... \n", + "Arhgap6_chrX:169036867-169220197 chrX 169036867 169220197 + \n", + "Mid1_chrX:169685373-169926953 chrX 169685373 169926953 + \n", + "Mid1_chrX:169879910-169926953 chrX 169879910 169926953 + \n", + "Eif2s3y_chrY:1012170-1014633 chrY 1012170 1014633 + \n", + "Eif2s3y_chrY:1013474-1014633 chrY 1013474 1014633 + \n", + "\n", + " annotated gene_id_start \\\n", + "Sox17_chr1:4492669-4493099 True ENSMUSG00000025902 \n", + "Sox17_chr1:4492669-4493771 True ENSMUSG00000025902 \n", + "Mrpl15_chr1:4774517-4776409 False ENSMUSG00000033845 \n", + "Mrpl15_chr1:4774517-4777524 True ENSMUSG00000033845 \n", + "Mrpl15_chr1:4777649-4778747 False ENSMUSG00000033845 \n", + "... ... ... \n", + "Arhgap6_chrX:169036867-169220197 True ENSMUSG00000031355 \n", + "Mid1_chrX:169685373-169926953 True ENSMUSG00000035299 \n", + "Mid1_chrX:169879910-169926953 True ENSMUSG00000035299 \n", + "Eif2s3y_chrY:1012170-1014633 True ENSMUSG00000069049 \n", + "Eif2s3y_chrY:1013474-1014633 True ENSMUSG00000069049 \n", + "\n", + " gene_id_end n_genes \\\n", + "Sox17_chr1:4492669-4493099 ENSMUSG00000025902 1 \n", + "Sox17_chr1:4492669-4493771 ENSMUSG00000025902 1 \n", + "Mrpl15_chr1:4774517-4776409 1 \n", + "Mrpl15_chr1:4774517-4777524 ENSMUSG00000033845 1 \n", + "Mrpl15_chr1:4777649-4778747 1 \n", + "... ... ... \n", + "Arhgap6_chrX:169036867-169220197 ENSMUSG00000031355 1 \n", + "Mid1_chrX:169685373-169926953 ENSMUSG00000035299 1 \n", + "Mid1_chrX:169879910-169926953 ENSMUSG00000035299 1 \n", + "Eif2s3y_chrY:1012170-1014633 ENSMUSG00000069049 1 \n", + "Eif2s3y_chrY:1013474-1014633 ENSMUSG00000069049 1 \n", + "\n", + " gene_id gene_name \\\n", + "Sox17_chr1:4492669-4493099 ENSMUSG00000025902 Sox17 \n", + "Sox17_chr1:4492669-4493771 ENSMUSG00000025902 Sox17 \n", + "Mrpl15_chr1:4774517-4776409 ENSMUSG00000033845 Mrpl15 \n", + "Mrpl15_chr1:4774517-4777524 ENSMUSG00000033845 Mrpl15 \n", + "Mrpl15_chr1:4777649-4778747 ENSMUSG00000033845 Mrpl15 \n", + "... ... ... \n", + "Arhgap6_chrX:169036867-169220197 ENSMUSG00000031355 Arhgap6 \n", + "Mid1_chrX:169685373-169926953 ENSMUSG00000035299 Mid1 \n", + "Mid1_chrX:169879910-169926953 ENSMUSG00000035299 Mid1 \n", + "Eif2s3y_chrY:1012170-1014633 ENSMUSG00000069049 Eif2s3y \n", + "Eif2s3y_chrY:1013474-1014633 ENSMUSG00000069049 Eif2s3y \n", + "\n", + " intron_group intron_group_size \\\n", + "Sox17_chr1:4492669-4493099 Sox17_chr1_4492669_- 2 \n", + "Sox17_chr1:4492669-4493771 Sox17_chr1_4492669_- 2 \n", + "Mrpl15_chr1:4774517-4776409 Mrpl15_chr1_4774517_- 2 \n", + "Mrpl15_chr1:4774517-4777524 Mrpl15_chr1_4774517_- 2 \n", + "Mrpl15_chr1:4777649-4778747 Mrpl15_chr1_4777649_- 2 \n", + "... ... ... \n", + "Arhgap6_chrX:169036867-169220197 Arhgap6_chrX_169220197_+ 2 \n", + "Mid1_chrX:169685373-169926953 Mid1_chrX_169926953_+ 2 \n", + "Mid1_chrX:169879910-169926953 Mid1_chrX_169926953_+ 2 \n", + "Eif2s3y_chrY:1012170-1014633 Eif2s3y_chrY_1014633_+ 2 \n", + "Eif2s3y_chrY:1013474-1014633 Eif2s3y_chrY_1014633_+ 2 \n", + "\n", + " n_genes_per_intron_group \n", + "Sox17_chr1:4492669-4493099 1 \n", + "Sox17_chr1:4492669-4493771 1 \n", + "Mrpl15_chr1:4774517-4776409 1 \n", + "Mrpl15_chr1:4774517-4777524 1 \n", + "Mrpl15_chr1:4777649-4778747 1 \n", + "... ... \n", + "Arhgap6_chrX:169036867-169220197 1 \n", + "Mid1_chrX:169685373-169926953 1 \n", + "Mid1_chrX:169879910-169926953 1 \n", + "Eif2s3y_chrY:1012170-1014633 1 \n", + "Eif2s3y_chrY:1013474-1014633 1 \n", + "\n", + "[29924 rows x 13 columns]" + ], + "text/html": [ + "\n", + "
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_group
Sox17_chr1:4492669-4493099chr144926694493099-TrueENSMUSG00000025902ENSMUSG000000259021ENSMUSG00000025902Sox17Sox17_chr1_4492669_-21
Sox17_chr1:4492669-4493771chr144926694493771-TrueENSMUSG00000025902ENSMUSG000000259021ENSMUSG00000025902Sox17Sox17_chr1_4492669_-21
Mrpl15_chr1:4774517-4776409chr147745174776409-FalseENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4774517_-21
Mrpl15_chr1:4774517-4777524chr147745174777524-TrueENSMUSG00000033845ENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4774517_-21
Mrpl15_chr1:4777649-4778747chr147776494778747-FalseENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4777649_-21
..........................................
Arhgap6_chrX:169036867-169220197chrX169036867169220197+TrueENSMUSG00000031355ENSMUSG000000313551ENSMUSG00000031355Arhgap6Arhgap6_chrX_169220197_+21
Mid1_chrX:169685373-169926953chrX169685373169926953+TrueENSMUSG00000035299ENSMUSG000000352991ENSMUSG00000035299Mid1Mid1_chrX_169926953_+21
Mid1_chrX:169879910-169926953chrX169879910169926953+TrueENSMUSG00000035299ENSMUSG000000352991ENSMUSG00000035299Mid1Mid1_chrX_169926953_+21
Eif2s3y_chrY:1012170-1014633chrY10121701014633+TrueENSMUSG00000069049ENSMUSG000000690491ENSMUSG00000069049Eif2s3yEif2s3y_chrY_1014633_+21
Eif2s3y_chrY:1013474-1014633chrY10134741014633+TrueENSMUSG00000069049ENSMUSG000000690491ENSMUSG00000069049Eif2s3yEif2s3y_chrY_1014633_+21
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29924 rows × 13 columns

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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 6 + } ], - "text/plain": [ - " FACS.selection age cell \\\n", - "my_id \n", - "A10_B000126 NaN 3m A10.B000126.3_39_F.1.1 \n", - "A10_B000127 NaN 3m A10.B000127.3_38_F.1.1 \n", - "A10_B000166 NaN 3m A10.B000166.3_56_F.1.1 \n", - "A10_B000610 NaN 3m A10.B000610.3_56_F.1.1 \n", - "A10_B000633 NaN 3m A10.B000633.3_56_F.1.1 \n", - "... ... .. ... \n", - "P9_MAA001887 NaN 3m P9.MAA001887.3_39_F.1.1 \n", - "P9_MAA001888 NaN 3m P9.MAA001888.3_39_F.1.1 \n", - "P9_MAA001889 NaN 3m P9.MAA001889.3_38_F.1.1 \n", - "P9_MAA001892 NaN 3m P9.MAA001892.3_38_F.1.1 \n", - "P9_MAA001894 NaN 3m P9.MAA001894.3_39_F.1.1 \n", - "\n", - " cell_ontology_class cell_ontology_id \\\n", - "my_id \n", - "A10_B000126 bulge keratinocyte CL:0002337 \n", - "A10_B000127 myeloid cell CL:0000763 \n", - "A10_B000166 basal cell CL:0000646 \n", - "A10_B000610 bladder urothelial cell CL:1001428 \n", - "A10_B000633 endothelial cell of coronary artery CL:0000115 \n", - "... ... ... \n", - "P9_MAA001887 hematopoietic stem cell CL:0002035 \n", - "P9_MAA001888 CD4-positive, alpha-beta T cell CL:0000815 \n", - "P9_MAA001889 B cell CL:0000236 \n", - "P9_MAA001892 endothelial cell of lymphatic vessel CL:1001567 \n", - "P9_MAA001894 astrocyte CL:0000127 \n", - "\n", - " free_annotation method mouse.id sex \\\n", - "my_id \n", - "A10_B000126 nan facs 3_39_F female \n", - "A10_B000127 nan facs 3_38_F female \n", - "A10_B000166 basal cell facs 3_56_F female \n", - "A10_B000610 nan facs 3_56_F female \n", - "A10_B000633 coronary vascular endothelial cell facs 3_56_F female \n", - "... ... ... ... ... \n", - "P9_MAA001887 MPP Fraction B + C facs 3_39_F female \n", - "P9_MAA001888 BM CD4 T cell facs 3_39_F female \n", - "P9_MAA001889 B facs 3_38_F female \n", - "P9_MAA001892 Lympatic facs 3_38_F female \n", - "P9_MAA001894 nan facs 3_39_F female \n", - "\n", - " subtissue tissue n_genes n_counts louvain \\\n", - "my_id \n", - "A10_B000126 Telogen Skin 2552 339047.0 10 \n", - "A10_B000127 Fat SCAT 1785 254953.0 16 \n", - "A10_B000166 Mammary_Gland Mammary_Gland 4713 2246909.0 22 \n", - "A10_B000610 nan Bladder 4182 1486054.0 28 \n", - "A10_B000633 RV Heart 2001 448241.0 3 \n", - "... ... ... ... ... ... \n", - "P9_MAA001887 KLS Marrow 2919 227378.0 8 \n", - "P9_MAA001888 T-cells Marrow 1960 171978.0 7 \n", - "P9_MAA001889 EPCAM Lung 1622 427794.0 1 \n", - "P9_MAA001892 Endomucin Lung 1805 438322.0 37 \n", - "P9_MAA001894 Cortex Brain_Non-Myeloid 1466 122397.0 30 \n", - "\n", - " leiden cell_type plate_id \n", - "my_id \n", - "A10_B000126 9 bulge_keratinocyte B000126 \n", - "A10_B000127 16 myeloid_cell B000127 \n", - "A10_B000166 20 basal_cell B000166 \n", - "A10_B000610 26 bladder_urothelial_cell B000610 \n", - "A10_B000633 3 endothelial_cell_of_coronary_artery B000633 \n", - "... ... ... ... \n", - "P9_MAA001887 8 hematopoietic_stem_cell MAA001887 \n", - "P9_MAA001888 7 CD4_positive,_alpha_beta_T_cell MAA001888 \n", - "P9_MAA001889 1 B_cell MAA001889 \n", - "P9_MAA001892 36 endothelial_cell_of_lymphatic_vessel MAA001892 \n", - "P9_MAA001894 39 astrocyte MAA001894 \n", - "\n", - "[44518 rows x 17 columns]" + "source": [ + "# Metadata associated to each intron/splice junction\n", + "adata.var" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Metadata associated to each cell\n", - "adata.obs" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_group
Sox17_chr1:4492669-4493099chr144926694493099-TrueENSMUSG00000025902ENSMUSG000000259021ENSMUSG00000025902Sox17Sox17_chr1_4492669_-21
Sox17_chr1:4492669-4493771chr144926694493771-TrueENSMUSG00000025902ENSMUSG000000259021ENSMUSG00000025902Sox17Sox17_chr1_4492669_-21
Mrpl15_chr1:4774517-4776409chr147745174776409-FalseENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4774517_-21
Mrpl15_chr1:4774517-4777524chr147745174777524-TrueENSMUSG00000033845ENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4774517_-21
Mrpl15_chr1:4777649-4778747chr147776494778747-FalseENSMUSG000000338451ENSMUSG00000033845Mrpl15Mrpl15_chr1_4777649_-21
..........................................
Arhgap6_chrX:169036867-169220197chrX169036867169220197+TrueENSMUSG00000031355ENSMUSG000000313551ENSMUSG00000031355Arhgap6Arhgap6_chrX_169220197_+21
Mid1_chrX:169685373-169926953chrX169685373169926953+TrueENSMUSG00000035299ENSMUSG000000352991ENSMUSG00000035299Mid1Mid1_chrX_169926953_+21
Mid1_chrX:169879910-169926953chrX169879910169926953+TrueENSMUSG00000035299ENSMUSG000000352991ENSMUSG00000035299Mid1Mid1_chrX_169926953_+21
Eif2s3y_chrY:1012170-1014633chrY10121701014633+TrueENSMUSG00000069049ENSMUSG000000690491ENSMUSG00000069049Eif2s3yEif2s3y_chrY_1014633_+21
Eif2s3y_chrY:1013474-1014633chrY10134741014633+TrueENSMUSG00000069049ENSMUSG000000690491ENSMUSG00000069049Eif2s3yEif2s3y_chrY_1014633_+21
\n", - "

29924 rows × 13 columns

\n", - "
" + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tD23kYcGXf-j", + "outputId": "aac3b949-2345-4687-b9e5-3b56f672a306" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "filter_min_cells_per_feature\n", + "filter_singletons\n", + "filter_min_cells_per_intron_group\n", + "filter_singletons\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(4433, 3044)" + ] + }, + "metadata": {}, + "execution_count": 7 + } ], - "text/plain": [ - " chromosome start end strand \\\n", - "Sox17_chr1:4492669-4493099 chr1 4492669 4493099 - \n", - "Sox17_chr1:4492669-4493771 chr1 4492669 4493771 - \n", - "Mrpl15_chr1:4774517-4776409 chr1 4774517 4776409 - \n", - "Mrpl15_chr1:4774517-4777524 chr1 4774517 4777524 - \n", - "Mrpl15_chr1:4777649-4778747 chr1 4777649 4778747 - \n", - "... ... ... ... ... \n", - "Arhgap6_chrX:169036867-169220197 chrX 169036867 169220197 + \n", - "Mid1_chrX:169685373-169926953 chrX 169685373 169926953 + \n", - "Mid1_chrX:169879910-169926953 chrX 169879910 169926953 + \n", - "Eif2s3y_chrY:1012170-1014633 chrY 1012170 1014633 + \n", - "Eif2s3y_chrY:1013474-1014633 chrY 1013474 1014633 + \n", - "\n", - " annotated gene_id_start \\\n", - "Sox17_chr1:4492669-4493099 True ENSMUSG00000025902 \n", - "Sox17_chr1:4492669-4493771 True ENSMUSG00000025902 \n", - "Mrpl15_chr1:4774517-4776409 False ENSMUSG00000033845 \n", - "Mrpl15_chr1:4774517-4777524 True ENSMUSG00000033845 \n", - "Mrpl15_chr1:4777649-4778747 False ENSMUSG00000033845 \n", - "... ... ... \n", - "Arhgap6_chrX:169036867-169220197 True ENSMUSG00000031355 \n", - "Mid1_chrX:169685373-169926953 True ENSMUSG00000035299 \n", - "Mid1_chrX:169879910-169926953 True ENSMUSG00000035299 \n", - "Eif2s3y_chrY:1012170-1014633 True ENSMUSG00000069049 \n", - "Eif2s3y_chrY:1013474-1014633 True ENSMUSG00000069049 \n", - "\n", - " gene_id_end n_genes \\\n", - "Sox17_chr1:4492669-4493099 ENSMUSG00000025902 1 \n", - "Sox17_chr1:4492669-4493771 ENSMUSG00000025902 1 \n", - "Mrpl15_chr1:4774517-4776409 1 \n", - "Mrpl15_chr1:4774517-4777524 ENSMUSG00000033845 1 \n", - "Mrpl15_chr1:4777649-4778747 1 \n", - "... ... ... \n", - "Arhgap6_chrX:169036867-169220197 ENSMUSG00000031355 1 \n", - "Mid1_chrX:169685373-169926953 ENSMUSG00000035299 1 \n", - "Mid1_chrX:169879910-169926953 ENSMUSG00000035299 1 \n", - "Eif2s3y_chrY:1012170-1014633 ENSMUSG00000069049 1 \n", - "Eif2s3y_chrY:1013474-1014633 ENSMUSG00000069049 1 \n", - "\n", - " gene_id gene_name \\\n", - "Sox17_chr1:4492669-4493099 ENSMUSG00000025902 Sox17 \n", - "Sox17_chr1:4492669-4493771 ENSMUSG00000025902 Sox17 \n", - "Mrpl15_chr1:4774517-4776409 ENSMUSG00000033845 Mrpl15 \n", - "Mrpl15_chr1:4774517-4777524 ENSMUSG00000033845 Mrpl15 \n", - "Mrpl15_chr1:4777649-4778747 ENSMUSG00000033845 Mrpl15 \n", - "... ... ... \n", - "Arhgap6_chrX:169036867-169220197 ENSMUSG00000031355 Arhgap6 \n", - "Mid1_chrX:169685373-169926953 ENSMUSG00000035299 Mid1 \n", - "Mid1_chrX:169879910-169926953 ENSMUSG00000035299 Mid1 \n", - "Eif2s3y_chrY:1012170-1014633 ENSMUSG00000069049 Eif2s3y \n", - "Eif2s3y_chrY:1013474-1014633 ENSMUSG00000069049 Eif2s3y \n", - "\n", - " intron_group intron_group_size \\\n", - "Sox17_chr1:4492669-4493099 Sox17_chr1_4492669_- 2 \n", - "Sox17_chr1:4492669-4493771 Sox17_chr1_4492669_- 2 \n", - "Mrpl15_chr1:4774517-4776409 Mrpl15_chr1_4774517_- 2 \n", - "Mrpl15_chr1:4774517-4777524 Mrpl15_chr1_4774517_- 2 \n", - "Mrpl15_chr1:4777649-4778747 Mrpl15_chr1_4777649_- 2 \n", - "... ... ... \n", - "Arhgap6_chrX:169036867-169220197 Arhgap6_chrX_169220197_+ 2 \n", - "Mid1_chrX:169685373-169926953 Mid1_chrX_169926953_+ 2 \n", - "Mid1_chrX:169879910-169926953 Mid1_chrX_169926953_+ 2 \n", - "Eif2s3y_chrY:1012170-1014633 Eif2s3y_chrY_1014633_+ 2 \n", - "Eif2s3y_chrY:1013474-1014633 Eif2s3y_chrY_1014633_+ 2 \n", - "\n", - " n_genes_per_intron_group \n", - "Sox17_chr1:4492669-4493099 1 \n", - "Sox17_chr1:4492669-4493771 1 \n", - "Mrpl15_chr1:4774517-4776409 1 \n", - "Mrpl15_chr1:4774517-4777524 1 \n", - "Mrpl15_chr1:4777649-4778747 1 \n", - "... ... \n", - "Arhgap6_chrX:169036867-169220197 1 \n", - "Mid1_chrX:169685373-169926953 1 \n", - "Mid1_chrX:169879910-169926953 1 \n", - "Eif2s3y_chrY:1012170-1014633 1 \n", - "Eif2s3y_chrY:1013474-1014633 1 \n", - "\n", - "[29924 rows x 13 columns]" + "source": [ + "# Filtering to heart cells\n", + "adata = adata[adata.obs.tissue==\"Heart\"]\n", + "adata = filter_min_cells_per_feature(adata, 100)\n", + "adata = filter_min_cells_per_intron_group(adata, 100)\n", + "adata.shape" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Metadata associated to each intron/splice junction\n", - "adata.var" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "filter_min_cells_per_feature\n", - "filter_singletons\n", - "filter_min_cells_per_intron_group\n", - "filter_singletons\n" - ] }, { - "data": { - "text/plain": [ - "(4433, 3044)" + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 337 + }, + "id": "rND8w5yyXf-k", + "outputId": "6966f142-ba75-4b95-be13-986e30960894" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n", + " warnings.warn(problem)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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XGkn9B3L8Vdez45uvCYuJY9fyJQBU5u9FyfXSGzhu8liSQqI4556H+eiBf1K6R/UfKtyykZDIaFrra7EEh9De1IiUCkLoMIcE42xpCThedz4jncGAs7UlcP/dscfFI3Q6Vr3/Fhs/+xCDyYCQ0Lu2ibLIELxmEzFxx7Du4XrySxbgMe4n9eJgJo9OZ//qb0gZOITY3hms3NNIS4eXqMoI6uO/weQpp9D+Pi8en8nVi/LQSQcAXn0cXiAn/zL2XGVk0Kzt6BfchG3ESBIfe/QnfV40NDR++2giSEPjMLgdHXz11KMIIUjo25+dyxbTXF0Z6C/avgWh63Krc7a1Bl6vePtVlG75dDodl4Ps4az+4J0eAkAqCpIuy8mok09n3JlzqS8rYe861fdn9f/eYvIFl1FfVoI5KBhXexuttTWEREWTMmAwTZUVdDQ3YwkKYtrFVx50LyvffZ2O5iZsoXYmnXcxHqcqAtZ+9C5fP/sEXrcbn9dD9f4CWutqQQgUrxedXk9C3yymT50NQFtjA611tYRERNHaUAdAUtYAclYvx2S1svzNlwEQOoGzRbVYJQ8cQn1JEe3NTT2eUXfSho0kJi2dY045E4Adi+cD4NXr2dy/N1GpaYRnZ5NW3Uhd/STafVlEma6nJvl+pDWULctXs+uD1wmOiOTKF97Ep6g+VdW79Eji8RjjuTqxF+Wn7ibuJIlt7SYqvllMhyuBRts5SJ1Nvd7OVciKSlqq5mM46372vGOg17HQmC8ISYY+Z2hulBoafyQ0EaSh0Y3qwgLmP/MYSVkDSBsygv3b1CSfhVs3HXJ8p4XjQOIy+1FdsC9w3FBRxuDpMwHIXrqIuuL9h16AEJTvzaGlvpaIxORAgsFd335D4bbNdDT19LkZctyJtNTWEJfRB51Oh8flwmg+eEtn5Ow55K5ZSe/ho3jnjhvRm8x4XU7amxoD/kUmq43UwcPYvWIZ4QmJRCYmkz7iGPpPmtbt+eTT1E0IRvdK48S/3sLwE0/m4wfvDrR3F3mNFWV4PJ6Az09nnz0ugaaqCgD2b9vM/m2bqS7I4/Q77iMmNZ3SPdkAtEdH0FBbQbrbw66UWMyuBdjbIxl0WjpJCeegdBQRFWlmlxDEpKUDMLF/OE1tHkpzBdWRkqA4wdhZsVij42AARBx7LKn+5a6500fNdsiYAwkjzqH9vVpCxg9n92cG6rKh2ebCF+5FedWK9EmkIuh7lkDoe27BaWho/P7QMkZr/Ckpy92N1+0mdfAwnO1tWIKCKcvZxdfPPUlLrZpMMC49k6qCPAxWK16H4zvnE3p9IO8OqILC7egIOB73HTeJ2X+7leaaKgwmM58/8SCV+3IPmEQd6z/ghGtvxGix8vWzj6MoEp/H3WN4SGQU7c1NKN6ubbT+k6ZzwrU3Hnadhds2Me/hewHVH6gzhH7o8bOZfulVSL9ztsFsPmQIv+LzsfK9N2iprcEWHoVv4Cx8Qk/JO/fRUV0KEmIzMlEyJmGMSKD8/QfAbxETer0qGjvvUQjiW52EtLaxL17NX6SzhYDXg+J2drtPdbtNJyWK3/dH6HSceuvdzHv4HgBOvP7vpAwcgtFkxmS1ArBhbxUVjYLkSMnIzLjDPpNOGvdKll6jIPRg7wNxIwVV2xVazq4HAXztxrtSDc0fe6+OpEkCn1uy93+SkCRIntbNKtgoaa+UhPcR6Aw/XixpGaM1NH5eNEuQxp+Gkr17+fThezDZo3FUFAKQPnIMBZvXkzxwMKW7sgNju9e0OlAADZh6HLmrl+PzeEBCVPOdBBn60Jb8HLqwOmqL9wfC4ju/8FMGDmHLgi9Y/ubLmKxWUgYNxWA243W5AvMKoUPKTiEl2TDvQzXLspSERsfSUlvdYx2t9XUH3eOelUuxBAdjMJoYf875BwmZtCEjmHDuRRjNZoYeP4vCbZtoa2hgxKxTWPHOa2yZ/xlTL76CocedSFN1FWExsT2cjnV6PeW5u6nK38fQORfQ4VPnD9KHENLQQl1sNJkzLqApt4QOeyw6kwXFHwYvfT50QWEoHS3qc5ESW1s7GTVNtNqsVIbZUDp6bpcJnY6g8Aha62tRhCBr0lRyVn6LVBSWfPxZQDjuXr2CFW+/isfp5LyHniQiIYm6pkoggYqKAtZs/YZhM0/CFmY/xCdDpW63RCogFWjYDU37JOmn6LCGGairqMe1vys3katJwefSkf2KQv4nalvJch/tFdBRB57OTAJCMvFRQdxIbRtNQ+O3iCaCNP4UdDQ3sX/XLnwdrTi6fdFW7y8AoL60uMf4ltpqWg8TyVS2Z5cqgAAdodjcxyDd4C6Po6N1q1pkVErQ6bAGBeNsbyMsJpZP//0vANwOB/kb1ZpdBpMZr1sVQgfW5GqoKOuxnkORNuIYfC43Lkc7jpZmgiOi2Lrgc/89N3L81TcExjbXVFO9P59RJ52GTq+no7mJyry9ABRs3kjxzu1YkvqRu2c/+9bfQdmeXRgtVlIGDgYEDeUlnHzznegNRgDMuBjUK5js7fnEpo0gYek3bBMC7wN3klxSiGfaiQy99W52LZlPweb1eN1urEl9ad+7MbCmoqhQ9sfYkUJgyxhJR35Pi7FUFKry9waOc1Z+G3jt9SmqyHI5CLFHUNzYELjPiIQkekdVkF+2lrKPt5BXU0N7cxMzrvjrQc9QKpK6XZD/mSpYQ3uDq1H9V/CFJKbEjn6/DWOHwAPoTLD1P1C2XMHVlReSilWHeIMkVK6VxGm2HA2N3ySaCNL4Q+Fsb8NgNGEwmQJt8595nNzVy+k3YQpRfQaheDw07Fe3otrq1WzFnUkAIxKTaSgv/c5rdDpHW4JDcLa1UB/yLCZvKm2WJUivtysaSlFwtrchFYX6spIefjKd22edAkhnMKA3GvH4rU46g6HHNpfeaEIqPhSfr4dfTUJ6H+Iy+uB2OugzejzO9jZevuYSPE4HjVUV7PhmAT6vl46WZjZ9/jGKz0dIZDSTzr+EvmMmkDVxKnWlxfQePpKYvoPYUOKj+JVbAltYHqeDgs0bAusozt7Oabf/i9riIhL69KOxw4cIj6N9ZBxt65diNDvQtQIlhURH2ogfMIDUAQNY/var7Pjma2ypg7AkZFD/7XsA+Axdv4IOFEA9EDrVROMnJrU3bQ0l6Gx2QqLjmH7plfQeOhy300GaP39QVvos+vVWWLjzP+TU1pHUb8Ahp97zpmTPWxJLJBhDIPNUsEQKSpaC9EnKVgB0fZ5MIeCsh9YysB0hcXVEFmSdr1mBNDR+q2giSON3j5SS6oI8PC4nn/77HqxhYVz8+HOYrDZ2LV9C7urlAIGfGceMpeEwfsnpI47h+Kv+hqOtlc8eufeg/u7FQzsjndqt3/TI+ycVhf4Tp1JZmEdLbQ0ZI8cgpaTX4GFd4fRWG71DI9hbUojXoEfxenuInu6vjWYzRqst4BQdGhOLPS4Bj9NJ9f78gF/PrL/dyqbPPyEiIZGEvv1JyOzH/KfVMG+90RgQTq31tWz6/BP6jZvEidfdDMDOZYspzN6Odcy5GEMi8bXVk5Q1gBL/FuHYs86jo7GBAVOmY7LaSOzXX53XWY+7aDtejw/vtBGcc+nltNXVs/qhf5FjgdmODlyODjqaGrEGh1C/8gPM4XFYQ8NwtbeBEOh0enR6PZbkfrQU7gKv6vsU2zsDhy4IS9YEWnetxLF/Z+CZ1JYU+Z3Sm0g7ZjYGo5HM0eN6vFcdzU0YzGZOuO5mjr/6Bhr36tj4kELGHEF+aDVhHgd9U9NQ/I/aEg7HvaJnydU+GnMlg64QJE3S0VGj0JDTNa+zHgxB4KhV/x2OY+4Q9DpOE0AaGr9lNBGk8bulo7mJ7CULaW2sJ/ubr7HZw/F63LTV1+Pq6MBgMrPjm68POq+7pSc0Ohap+JBSktCnH/0nH4vRZCQmtTdBEZG0N9QjhAiUsfC4nITGqP5CrtZWpBC4/dFVOoMRxevBbAti7JlzefX6ywHYu24Ve9euZMiME9GbjBRu3kh8QQm9qrYTYTayql/Kd96nx+XC2i0/UFNlBeFxiSAl+ZvWB9rLcndTU6Ru740/+wJie2cQGh2Dz+tl+AknU5azi7j0PpTs2s6wmSf1uMay11/C63YxLDyCy//veVA8mCwWNn72MaHRMQyYPP2Qa1v8wn8o25VNUv9BjL/7IQAaq8vZXV4E5UVU7M2hLHcPOauXI4Rg3JnnMXDaDILDIwDV0bq1vpbabdl8/trTQJeVbPCxJ1Bc0UjprpUYLEFMu+QqyvfuIX3kaBY88zgASf0HMv3SqwDY85ZC0SLJsOt0KBG5fHjvP7DZ7VzyxPOYrDZ2vOCjfhd8K6px7ruRoI42XBdfRfyoEymcD8EpCmseaqVxr5qTydMOwYmCyU/oyJ+nkP8FOPy7kt4Ds10DhmCIOwaG/1UgFYElQose09D4raOJII3fLavef5Nd336D0aKGhHc0NTL2jLkk9OlHSGQUG+Z9GPAnMZot9Bs/mbFnzmXtR+/SUF6GwWSmtb42EOa+b/0a8jauQ6fXc+rf7ybEL4L0RiNet2qd8DideJyq0/PEuReT/cV23HI7CFD8dbMMZjP22HjCExJprCgPOEebLFaaqtStNJM/jL3716Q5KARX+8F5dGz2cDLHjGPnkoW4HQ50Oj0+j5vy3N09xu1YNL/r2bz3BrYwOyNmzWHr15+z/pP36TNmIuPOnMu4M+cedI1hJ5zE/q2b6Dd2AlabBVDXN/aMc3uMa2ts4P27bkGn03PuA48THp9Iya5s4tIzAw7UCX2z6Dd+MlJKkgYMxhoaRt6GNcRn9qWutJitCz4PhOaX7dmFq6OdXtVNEGsHIDq1N3VFheRvXo/ZFhSwAC3L2UBwfAqmiRdyyr1P4aouImvCZEC1BhbOV2ipr6J4SRz2aTUoPi/tjY201zkwJdtImS5or5SEpTWh7HIjgJr778MzfTDu5kRqo5/H0GcHhvrzUHZOYcAl6v3sek0h7+ODHlkPTHY4Zd6PL4iroaHx66CJII3fHQVbNqD4fAHxYrRY8TidCJ2O9uYmkrIGAmANCQ2c43E5KduxlbaEVLWchMGATq9X/WwQAadkqSj4FIVPHrorcG6nAAI1Okrxh3kXrqgkrPBeTIbt1NofBp3qzxPtL39htgYBoDdbiIiLZ8v8zwJbUnvtVmr18bRYunxNolN6UZGX22MrDFRxt+XLeVhCQgEHMb0zqOpWquJADCYztf48RKV7dgbmy9u4lpnX3HDIcybNvZhJcy8+7Jyd1JeWBFII/O/uW5l8wWWMnnM2IZFRAOxZ9S21xfvZu24VRrMZZ2sLsb0zOP+Rp1j74bts/vLTQ87rMelJr2qgIDactvo6FJ+P0l3ZnPr3fwa2MUHSVllCS1Mrzsg4Bk7qDcD2whaKah24+n5E5d6FhIecSFZrb47fU0GtZRprbrYz6wNJxqk6Mk4FyGJe0WjC531EbHM7RrkMuABdlJqzyJBUQXJvmH+WQlA82OIP/zyihkDdDogZcsRHp6Gh8RtEE0Eav3kq8vbSVFlBWFwsXzzx74BvjM1uBwjU0pKKQvY3C8hbv5oJ515I8qAhPRyMG+tree/tFwPVyj+fcBLHrvgc4wF1rrr7/RxIv4lT2eMvK9HaXI7e8g1Cdv03ikhKZvpl1zDvkXup8idLNBj0tDU29MwSDdSH2ALHQgjKcnaROWYC+RvWcKj8XZ332T1aqvtWXa+hI5h6wWV88u9/0VpXi95kxmS24GhtJiw27pCZpAHcTgftjQ2Exycesr87Ha3N6I0mfB43jZXlZC/5mjm3qVFv9WUlfP3sE4GxHpcLr8dDZd5e3v/XrUifD3NQML0GD6O+rJT2pobAPYWOHce+3TtACBwtzcRl9GH82RfQa/AwjplzJhvnfQSANSWLXskx9I7renY1zW6khGaXGuHX3F7B6g+X09vjIELZRl0tNOVDeGbXfcy5/Tbap0zGsXMn1hPOZPcucM+7lrS/5DLwrxMo+0aQ97HE2aD6ZB+KiAEw5T86WvZDSPIRH90fBiGED9iJasj0AddJKdf+xDkvBkZKKa8TQtwDtEkpH/+pa9XQOBKaCNL4TdNQXsb7/7z5kH1ZE6dRnrOLhMx+5KxZgaNFjfBytLawffEClr/1ak+ripQBAeTV6WkMDe8hgExWK4rPh6ujHUtQCKHR0dQUFQb6kwYMYv8WNbw7MqkXHc0ltIQ+C6jRXBc/8TL2mDh8Xi/7t28JnOdqVx1IrCGhOFp71tLq9H8ROj3S5yVv/WqOmXMWOSuXHTIPUA+E4Iy7H+Sje+8AwOdxs23hV2rZC0BvMOBobcZmDydt6Agik3p+U/u8XhTFx7t33ERDeSknXHtTj+zQh2LdR+8GkjaGRscyaPrxrP3oXdKGjSQ8PpGwmFhc7e1MvugvRCYmY4+Nozh7ayCRZGRSMlHJKexb1zOevLC4gMTBQyndne0Pi98XsOgF2yMC40L0Lob2smA0dW09jcwIJa+wiqKacgAWB6WQm6xn7sA2BrjvJCrt0CIlaMwYgsaMAWD2hxJIRm9S/bNSpkvaKyEoFupzoG6nJGGCehycBL1m6DDa1M9SWO/vfGR/RBxSyqEAQojjgX8Dk3/VFWlo/Eg0EaTxm+arZw7/x+DOpYvoN24So+ecTVnu7oAICg6PZNRJp7Hw+f/0GG+0WANJDA2Kj1O3LAOjCfxf6tGpvSnPUf1shE7XUwBlDWTk7NMCEWOKz4ujtStJTHh8AjqdnlX/e4vti74iulca9eWl+PxbaUKnI2viNHJWL2PQtOPZ8vUX+FwupM9HVEoqepOZar+Fp7mmitb6OsITkmms6HLi1huNgfxEAMERkXz60L8IjY5B6HSU7d5J2e6dmGxBuDvaA2Klo6mR7Yvm42xrI2vCFArefYv0yFiWlOXjbG9H569/1n5ASY5DMXL2aWxb+CW9RxzD2DPm8u3rL5G9dCG7Vyzl8mdf49z7H8dktfYo3TFgynE4WlvwuN2MnD2H2qL96E0mTBYrluBggsLC6Td+MkOOO4GmqkqWv/1fEvpkYTCq+Yj6jp1IzuoVVOblUrO/gLriIuIz+wbmjwgxMSgtjO1WE6WW3iyrSgQS2Ty3H9edNv6I9wSgN4mDjgdeqralzpRkni4ITeF3WSoj9fb5c4GHgBSgBLij6OFZ7x2l6UOBQ35whBAXAregGj6zpZQXCCGigRf9awG4QUq55iitRUPjB6OVzdD4zVKwZSOfPXrfEcfFpKYHoqL6TZjCzKv/Rk3Rfla881/Kc/YcNN5oseLzenpYiYReT1K/AZTuzj5oPEBweARTL7mK9Z+8j95opN/EySx//RWieqVhCw2jZOd2TFYbPo8Hn99B+sBSGp3bbDZ7uLrd1u3/XnSv3tQWFyL0BkwWSyB8vPsYvcmMz92VYbo7EQlJgeSK486YS03xfvI3qQkZdQYDQgiCI6Np9tfq6lXbRHG0HYBpl16NNSSEPqPHo9P/MOfe3SuWsvCF/yNr/GT6jpvI5489SGRyChc+8nSP4rIHIqXskYn6SHjcLr59/SWMZguTL7zs0CU9FB+N7W7OfGkDLq/Cp9eMIzb04Dpqvyd+atkMvwB6BbB1a+4ALv+xQqjbdpgFiAemSSm3HDBmADAPGCelrBNCREgpG4QQ7wHPSylXCyFSgEVSyixtO0zj10KzBGn8ZuhoaUZvMGC2BdHW2MDnjz/wvc4LiYoic/Q42psamHTeJXz1f48EQseFTofOYFAtMn5R0VlBHcDq9uDRCbxwWAEEalTUl0+qIeA6gyGQNbmltiZQDFVRfPh8XcKquwACcDrUsP0Di6CGRsdQW1zoP8erCiAAKYlMSiEyOYXqwnyaq6sOu77OrbOIxCSGzjyJr59/EluYHVtoGKNPO5v5Tz0aEEBISbQliGqTGafbTWRSMikDBh927gMp2r4FZ0c7/cZNYsDk6fQbPwm9wcjmr+YhpUJTdSU+nw/Dd4igHyKAAIwmMzOuvP47x+h0eiJDrCy7ZcoPmvsPzkP0FED4jx8Cfqw1qPt22FjgLSHEQNnzL+ppwEdSyjoAKWWDv/1YoH+39z9UCBH8I9ehofGT0USQxm+CTV9+yqp338Bos3HZf17EZLUGCmd2+s9YQ8JwtDb3OC++Tz9iUtNJGTgYV0cHRrOFir1qZjuDyczAqcfR3tRIXWmRGq4OGEwm0keMpuzbpSTVt7A3MeqQa+q0zhyI4vUGIqQ6cwQBTL34SqzBIXzz8jM9tso6Mzwb9Aa1qOoBGK0Hfkd1kTFqDBEJSexbtzpwT0Kvx9N9HiEC23wN5WUsevEp9vur3nc0N7Hohad6TioEm0OM6HxeQAaSPn4fGirK+eThe0BKzFYbacNGBgThsJknYbJYie6VFtjK0vjVOVwSqu9OTvU9kVKuE0JEAdFAzfc4RQeMkVI6uzf+UFGsoXG00ESQxm+CdR+/h5QK7vY2nO3tRITZOe/f/+G1v12uOhYLwfATT2Hdp++DooAQKF4vlftyqdyXy7qP1T9qQyKjsISG0tGiRkSFREaxfdFXB11v1t9uZZPBTNO776oNfqdpoUikTv2FXFtciDU0FEeLKmiSsgYSnpCIQJA6dASvf/gB6RmZTB83jnUfv8+Sl59l8nmXHCQqgiOjaampwu3oIGviVPQGA/vWrwkIovqSIoBA1BWoQi0kKoawmDj2ru1yIu4ss9HduZoDtrTbGno6VHvd3Yu0CowWK0azmZnX3IjP6yV9xDHf700CLMHB2ELDcDscBEf2FI96g4HBx8783nNp/CKUAL0O0/6TEUL0A/TAgYX2lgHzhBBPSinrO7fDgMXAX4HH/OcPlVJuPxpr0dD4MWg53TV+Eww+9gRsoWFMOu8SIhLUUG2jyYTi9W8pSUnZnmwUjwfF5zsol04nrfV1NFdV0X/SNKZfciUZo8YSkZCE3mjEGqrmDTJaLDx90Rms37SaioRYghwuEIIRhZUcu6eI3vVdVhxHS0sgoiwsJpamygqSsgZQkzmQ1068iPuzJrCvvILqwnykVFj78XsHhbf7utcA0+uZccVf+esbH3LMnLN6+M10CiBQcxNd+p8XWfraCxRu23TQfYZ1q3KfmDUQncGAyW9Rqj5EDiHhF0xSSuIz+3LF828Qn9mX0OiYQz7Hw2ELDWPqxVdgs9sp2rH1B52r8atwB6oPUHc6/O0/FqsQYrsQYjvwAXCRlLLH3q+UcjfwILBCCLEDeNLfdT0wUgiRLYTYA1z1E9ahofGT0SxBGr86UlGISU2j/4T7iUnrijeuLszHZLMFtnps9vBAX3BEFG0NdQRHRtHWUE9sWjqttbV0tDaT2K8/J1x7E/VlpexYPB9bmJ2GijIcHg99x02mZOc2vC7VOlJvAAxmdU6XG6NPoe+I0RQWdRWLGjxtFiHREVTl76N0z07aGhuwFRZy89efs2bcTNZtW0Wwx409LuGQfjvt3Swzu5YvoXVHX7yGGsprPzpobKeFRwjBsjde7hEN1h2X00H/SdMo27OT6sI8FK8Xt9eLwWTCaLEGIuUAhISEpnYsw4ZRUJxPTFo6AC9dfTEep4OkrIHo9DpmXHk9YTFxR3y/9q1bTUtNNTuXLmLUSacdcbzGr0fRw7PeS719PhzF6DAp5ffynpdSvgm8eUBbHXD2Ica+Abzhf33Pj12bhsYPRRNBGr86Xz/3JDn+rMBn3/MISVkDKMvZxQf33B4YM3zWqQyfOZvy3D2YbTaa/T45bfV1GM0WqgvzCY2KYcxx57L+0/f58v8ewdnaQsmuHUQm9ULnr1a+d+2Kg65vCQlBKpKc6eOJsEcw8457cX+ezdZXKzD6kinY/DTtzTVE987AGhpGe3MTcusGdFLhjJZKjKm9qC0uoqnT8fi7UKz49o1DJ60QPQ+hVxh/1gWs/fg9FK8nsMUlpaTQn5PoUPQaNJTkAYPZs3JZoE3o9HjdbqyhYeh9CiaPF4fFhBRQHhHCGSkZHH/3/ViDQ6jM34fH6cDmdFOWswuA3DUrGT3nrCPewpjTz0EIQf/D1BPT+G3hFzxHKyReQ+MPhSaCNH5xfF4Pi954jeKCYuhopqO6yz1h0dtvk3XhbYR2+FQfHSGQisLW+Z+RNX4ylz/7Kh89cGegLAQQsBS11NWw4bMPANi3bpUqBoxGbGFh1JcVH3Y9ztZWkgYMpmx3NjUVZQzbX0D/EwbQmhdHZdVCWttVq1Ftt20mj9NB6pAR2MLs7Fm59DvDwfUms1qew+slLnYChjYL7cp2EF56Dx/D6DlnYrSY+faNlwEICo/A63LTXKNalYIjowiyh1NdkAdAbHoGuWtWBJyfdXo9Fz72HLXF+1n30bsoPoWJe0uxebys6ZNEs1W1dC3YsY4LvecBsOz1F4lq6WDU/kryesXjHj+GrAlTjvzmATGpvTnppn98r7EaGhoav2U0EaTxi7Phi8/I27QBb9PBwSShx8xi34qFGDoamHv/Y+zfvjXg9NxUXUlceiYRCUmU7NwBQEKfLFKHDKOhspy9a1f18Mfxupzc8M485j12f49rnHDdTYAgd+3KgJDoDDEPi4klIjGZN2+/lpaa6q6TdHpQupW9UBSKdmzpcXwgsRl96DVwCFHJvYhMSiEmtTetDfV88uCN1Jepwi9rwmSyly5izYfvYDRb8LicdDQ3B2qZAbQ3NuB2dIX1h0bFUl2Qj8sfmWYODmbH4vkMnn48x199A//7580Y/OsJTk6huU69j46WZtqbGgmyh2M0WzCGRiOoJLOqkb7X34reX4ZEQ0ND48+ClixR4xfF2dbGp088TFX+XqS764sdoePc+x+j2Wdmwb+uA9T8OQMmHwtIhE7PwKnH8sXjD2Kzh9Nr4BCMFiuDps3oMf/iV55l55KFAITFxhGblkHJrh0421qxhdpJHjiE2X/7OwC5a1ey4JnHyRw9Ho/Twf5tmxl1yhkMn3kSL1190fe+J73RGCjoGjZ6Nu7qIvSWYNpy1wfGGM0Wzrjzfj687x8BR+m4zL7UFBagdMstFJOWTnxmX/auWx2oq3UgI086DZs9nJXvvHZQZJjJFoQ1OASb081xJ5+JHDSArQu+wCoElpx9DJx7AUFjxuBxOtm+t4qGzbvx2qM4Yc4x6HQ9w5R9Xi96g/Z30q/JT02WqKGh8d1oIkjjF2XL/M9Z/tYrIHQYbcH4nB0oPi+Zo8dz8k2qQPj03/+iMn8vHqe6zTXypNMZNO04qvL38fVzapDJxU+8cFAtLID1n37Amg/e/s41GK1WLLZg5j74BLbQMHR6PR63i4ayUrYtmk91YT51Jep2W1BEJB2NDYcsaAqQ2Lc/J930D3YsXsC6T94n/oy/Y03uR+mb/8TTUNk1UAhMFuvBeYL8CRwtIaFExCdwzr2PInQ6qopLePfWaw57D1G90gJJGg8kNCYOnV6gE3rOvvcRbKFhlF3/N1oXL8bUuzfpC+YD4PYq5JS1ERFsJDnKCnRlcv762SfIWb2C46/+GzqdDqPFSsaoMd/5XDWOPpoI0tD4edH+zNP4RUkdMpywmDhCo6I5864HD/Kl0RsMnHnXg1Tm7+W9f94CUrL5y0/IXbuCAZOmER6fQGK/gXz11MPEZ/ZjxhV/7XF+Z+mIAwmPT6CjpRlXezsehwOPw8HKd16ncOtGkvoP5NS/30VHSzO7l3/T47zErIEUbt6A1+Vk+ImnULhlI03VXeKmz9gJBNnDiUxWU7FUfv40BlsY3mZ1qy8yKUXd+pLykIkSkZKsSdNIHTwMj9NBU00VRnsMGyqNBGUOx1WyOxDJ1p0DBdCoU04nKCyCNR+9Q0tNV4Tamg/e5rjLryNkxgw6tmzBO3kCz/1lLuFx8Zx9z8MMSQ0NjF3y6gtkL/ma4664jrLcPUipkLtmRSAU/qLHniUqJfWQz1dDQ0Pj94gmgjR+UUKionC1t1FaU8W+DWvpO3bCIcfFZ/TljDvuZ8eSr8nbsAZbqJ0N8z4EwGA0U1dSTF1JMZPPvwyzrSvj8sApx5G3cW1AOBhMZrxuF62Njei71cWKS88kZ/W3ABRs3oDX7SYyKSWQ3VnodAifmcb5EwnTDWfMP0zkrFsWyBlkMJmZ++ATfPPSM2xb+CVNVaowig4R1Db4fZ2EYOY1N6L4vMx79D6cra0gBEHhEUSl9KJyby5uRwc5K5eR0y3KKyo1g8a6eoTeeEgB1IktLJyOZrUEx6bPP8EeF0/0lInkrFhGTZiRxHoXit83KGz2LMJmz2LHN1/j3LqGytYWnG1tGC0WTBbVClSycztSUSjdvZPZN9xK4dbNpA4ZRsW+HAwmM7Yw+/d7kzX+sAghIoGl/sM4wAfU+o+PkVK6D3nid885BbhFSjm7ew2xn75aDY0jo4kgjV+UjqYmvP6kgM72NrYvXkDmMWMJ6pYDSCoKZTm7iOmdzsk3/YPmmiqsoXYWPf8fmmuriU3PpLZkPyartYcAAkgZOJjzHniCzx57gOaaKrxuF0Knw+t00D29Yv/Jx1JXWozP40FKydLXXqS5thrF5yMusy9VeXuRwoHDtJXwtsvxtmdT6HeiHjHrVPpPmobP46HSX/m9k6bWbqJFSpBSrYhuMgOtGE0m2hvqaW+oxx4bf0jrUH1xwSG33zodp81BIVz29MvUlRTx8QN3BXyKmqoqmTbsKpp6x5FhDWJQvYLL0cHzl5/H+LPOZ8hxJzBg8nTaGhsIj09g1XtvsHvFUoxmC8kDBnHCtTeRt2kdw46fTUhkFPEZaqX2K194E6HT9agMr/HnREpZDwwF0AqdavwR0ESQxi/Kxw/ehdftRuj0bPnqUxorK9i/bRNzbvtXwB9l3Sf/Y93H7xEcHklwRARTLrqCsJi4QFi2ovhIHTyMmFQ1saLX42HDp/9D8fnoaGkhIjGJSeddwvynHkVRfCQPGEJIpJpcsWZ/AaPnnMWw42fRf+IU3vvnzTSUl7Hr28WBNTo7C5gCbbYFZI6YzPaVb6M3Guk9fBRjz5xLQ3kpUcm9SOzbn/K9XZXqPZ4u8TLypNOIy+gDQFhMHG31dXhcLpL6DyRl4BAswSEse+3Fg55R1sQp7Fn57UHtneUvXO2tvHfnzTRXV/YQS2Gxccx7+D6k4sOckETGXQ/w5ZP/xtHSzN51Kxly3AkYTCbGn3Ueis/H6vffUtfscrJ/2xZm33Ab8Zl9D7qu6Ttqm2n8DrgnbC4HJEvknuafNW+QEGKm/5p6oE5KOV0IEQQ8AwwEjMA9UsrPf851aGgcCU0EafxiSClx+OtqScVHY2WF/7XCi1ddSHtjA33HTyYiPgGA9uZG2hrr2fXtYhL7ZrFz2WKWvvoCmaPHccK1N7H2o/co2bWd3iNGs/7TD3peS6fHazRhdDkIiYxkzOnn8MZNV+HzeCjctpmtX3/B9Euvpu/YSWz+6tOAE/agY2cSk5LGU9+8RVqti6zMQQy7LJxtN6g5gqJqGnn9qotpd3ZgsgYRFnPoshNBERGkDh5O/qb1RCYlkzp4GOX+pITO9nYKNm8gsd8AzrzrQZa//Rq1RQUA9J88nf3buoIIDCYTQujwuJzExUyjqnI9Utd+yMSMeoMRnU6HT/HRWFHGstdfIjoljcr8fZTu3knO6uUE2SMw22zkbVxLa30ttjA7GaPGktx/IA3lZRgtFiISkn70e6zxG0MVQK/QVUm+F/AK94TxcwkhIUS0/5qTpJT7hRAR/q47gWVSykuFEHZgoxBiyc+xBg2N74smgjR+dqSi4HE58Xo8PaqudxLfN4v929WcO/vWreKvb3xEUtZAaouL2L99M/3GT2bNh++wY/ECfF4PuWtWkLt2ZSA8PColleCISAiJRDEE0VGwDafBSF5afwYU57B7+RIaKsoCJShKdm4H1BD5hvJSPE4neqORtGEjmXH5dWytLmBdRQTrgGdGzuDzx+/HEhyCvayS8M++wtO/FxgNuB3t2Ox2QlqjcLS3ERRmp6Wuhl6DhlK0YyufPHSXaqkROrImTA7cb31pMVJRqC7MZ8SJp3DKzXfw+k1X4/O42bNiaWCcOSiY6ZdehclqpWCejuY1w4k1nEptzE34fN6DwuPTho1gyHGzWPDM41Tl7yV/47oe/Uv++zxuRwc6vYFhM2cDahqC4y6/lrKcXbzzjxvQG41c9tQrhBxQHFXjd8tDdAmgTmz+9p/LGjQGWCml3A/gL5wKMAM4WQhxi//YwlGqZq+h8WPRRJDGz86H999Bec4eTrrxdkbMPo0tX30KgDU0jFGz59DULZpJCIHi85IycAgpA4cwYtYpvHztJbTW1fac1C8AYtIyGHXy6Uy77Dq+WFeGMJioXfw6tjEj2agPwvNJGXpHB9UFPYuKJmUNYPScs8jbsJb2pkbaGuop270TgHR7HP3DErDUt1A//xvqy0oB8IYGs62X/9L+qvPFO7YF5uzM8FyUrbYFJIpUyFmlbm8JvR7Rrc/V0U5VQV6P4qmdjJ5zFstefwlnWyuhHWdiZzg6xYzP27Oe2JSLL8dksZE2dAT5m9ZRdYCfUied/keKz0tcRh/Ovf8xIpNSOh+8P0O3DsQhT9f4fXI4kfFriA8BnC6l7PEBFULEHma8hsbPjiaCNH52avYXIKVCTXEhUy64lMjEJHJWL6d0905WvvcG/fxWkuhevTn2L9dgtgUdcp6QyGic7W14nGqSRYdRR82oTGyRsewqbgEpkVLB52jjhLhI3I8/0BVdJaAm1EhMiwePThVgb992fcA6FJ/Zh37jp7Bh3od0tDRzWbOF3DUbcCb3Quh0WENCOfv/XuK5Sw6q/XgwnRaaQzg3Ryb3oq6oMHC86MWn1Er1h6ChvBTFnzm6xfoJbn0hWSdmMWLIP6gvK2XjZx8SFB5Bn9ETee1vl+N1u9AZjEdeH1BTVEi/cZMAKNqxleCISC54+CmMFgshEZoV6A9ECeoW2KHafy7WA88LIdI6t8P81qBFwF+FEH+VUkohxDAp5bYjzKWh8bOiiSCNn53T77iPnUsXg1SdcAdNm0H6yNG8cdPVuJ0ORs6aw5g552CPi8Pr9qAoPnS6rnD2uQ88Qe7qFWRNnILZFsSa5etZ/v7/4dJL1udsIKb3KIJdvfC5nVR9+Aie+jKswRczaOoMti38Ep3eQN9Zs/hv+yZmbKsnpkOSt2FtjzUqPiVQuwu6KtbXl6o1x5KyBvLCX+Zij0sI+OPYwux0NDf1mEfSZUgxmM0HhbgbjaYex9Xd6pF1Jk7spLGqgr6jJ7Dz28UgFIwJxUy65J+U5+wmdfAwolN68dVTj/HKtRcHynYo3kNXnQcwBwXham8nJDKKkbPnAJC7ZgXzn34Mg8nMFc+/jjWkK2+Qx+mkLHc3SVkDtMiw3y930NMnCKDD3/6zIKWsFUJcAXwqhNABNcBxwP3A/wHZ/vb9wOyfax0aGt8HLWO0xs+Ox+Xk2UvORvH5mHTeJYw6+XRA3Qryut24nQ6kotBQUc4XTzxIaHQsEfEJJGUNZNTJp7Ppy09Z9d4bhMXGMf3Sq1n5zmvUlJWgk5K6UCNJfUfRe+pfiAg20S8GhNdNaLTqsNzR3ERVYT7xmX15bdUnON/4+KD1pQ0dSU1RAe1NjVjD7DhamnuIEaPZjC0snOaaKnoNHkpx9vbvdd+WkFB0Oj0dzY3oDAZ0ej0n33QH6z95n4p9uQeNTx44hPKc3Sg+L3qjMWCl6uRv78xjy/x5gaiupP6DKNuz83utpdfgYSAEZbuzGTJjFkhJRGIyy15/EcWnWpsMJhNCpydrwmSOu/w6vnzy3+zbsIa+Yycy+4bbvtd1NI4uRyVj9K8QHaah8XtBswRp/OwYjCbi0vtQU1SINTSMF644n7CYWE79+11s/Pxjti/6CoCsiVORikJzdSXN1ZXs376FjV98jCUoRJ1IwvynH8PV3oZOPSSixYNz01r6HXs8yWmD+OzR+yjZnc0J191M1vjJbFs0n/WfvE9kci8ibDYqAKHTIRWF4KhoopNT2b9tU2CtQWHhOA6w7iT0HcCAydMoyt7G+LPO54N7bqelthqDyYTXrfryRCanUl9a1HXPJhPTL76S9FGj+fTf91CWs0sN///4fYLCwwmOiMQSEkpjZTk+/xy9h45g5lV/Y8fShWz0J4bsxBYWjk6vY80H7wTaWmq7Crza4xIYf9Z5LH3tRTxuF/bYeDV7tpRIRaHY76cU36cfpXt2UltUiC3MHhBAQOBespcs5OPCtfStVkWYrluSSY3fIarg0USPhsYh0ESQxs/OlvmfYTCZmPvgE1Tl76OjuYmO5ia+feu/5K5erg4SQo3w8hNkj6C9qQFXezuu9nZGnXw6bY0NAQfjvuMnM2DSdMpzd7F90Xw+/fe/CIuNp9lf0mLrgs/Zt25VIOqsvrTYn7Cwq+J7W10tbQc4XNeXH+wq4XE7Wfj8/6EoPobNmEVbg1pxXulWOb6+rIS0oSOpKtiLo7UVr9uNzmhg64IvaPWP93k8VOZ1WYB8Xg+WoGCcshXF5yMyuReh0TG42tp6XN9gtnDu/Y/SWFHeo1p9e5OaLTp95GgKNm9gwbNPMPaMueSuWU59aTFDj59F+ojR7F2/ml3L1DxIlftyA1mv1a28Ljdtc1AwOquF1qZKbpi3jYYgE45rrmLKBZcd7q3V0NDQ+F2jiSCNn5WWuhpWvvs6Ukreu/MmzEHBgb5961YhdDoik5Jpqakhxy+IDCYzf3n2v2z8/BMq9+XS1lhP+d7d9B03mZzVy9HpdKQNG0Ha0OHs+vYbXP6w++ZuNb2q8vcFXnf6wngPiMCyx8bTVF1JkD08IChkN8tIJxW5e9QaZxK+/L9HiEvvQ8W+HBSvF73RpEZ2SYX92zfTf9I09qxcRq9BQzHbbKz+31uBeQwmM0Kn63Ls9jtERyQmc+79j2HxP5vO6LJOgu3hvHr95QyadjwhUdG0NdRjtFhJ6jeA0t3ZGM3+4qeKwtoP3+lxvabqKlUAHeBv1HWstk256HIik1KISe3NV5fNBcDm8uESAoOppx+ThoaGxh8FTQRp/Kxs/PwTf64cgdftxutuCPQpPh9xGX0wWazUlRTj9ouDtGEjaWtsYNvXX+BxqUkMfR4PsWmZTDznAla9/xaLnv8/7DFxgdpZh8IWZsfZ3saok04nMrkXpbt3sHXBFwDM+cc99B46ErfTwd51q1n84lOHnUdnMJA1fgq7Vyyhpba6xzaUTq/H53fdEUIwZMYsIhIS2TDvI1a++0aPeRSpkNJvEC211TTXVGMNCaGtoZ6G8lJevvpigiOjmP23Wxlz2tkseuH/AJg492LWfvguAMXZ2xBCIBWFY04+nb1rV+JxOdm7buVBaz7ltnv4ts1Oy871AFhDQnF1tKN4D84vBLDp849pb2okbegISgW4UuNotZgYZNEcojU0NP64aI7RGj8rRdu38OX/PYLi8wZ8Tr4PMWnp1BbtR0qFQdNm0FhVwbSLr8TtcPDJQ3djstlob2xA6PRIpdN60y0Dj9/SMf6cCxkz5ywAWmpr+Pq5J4lITOLYv1yLo6WZ/du3kD5yNEU7trLstRdxtLYg9AakzwtCEJGcRkOJGtIeEh1LazcB1B2DyUTasFFU5uXS1lAfaO8syHooUoeOoCp/H05/Fm1Q/ZXm3H4PbkcHC55+DMXnI7pXGrXF++k3fgo1RQU0lJcy5cK/EJmaQc6KJdQWFVDrrypvtFg59vLrWN1k5aHVtQgBr0/Q4a0sYsc383s8mx7rN5rwetwMPX4WjZUVSClJ6JvFyFmnHjZlgcbPz1FxjNbQ0DgsmgjSOGo42lqZ/9SjmCxWZl53E8tefZGWuhpc7e3U+MtCfF+C7HamXnwVOoMenU7P6g/eZsSJpzBwyrFIKVnwzOPkrlkR+EIPCBc/ncVGAYLDIxk+6xSGn3AyekOX8fOjB/5Jyc7t6A1GwmJimX7Z1Xx0/50AhI8/DWtKFu37NtOxawWKz0uv4SPZuXMnQY6Ds153IvR6pM+HyWpTkxPqdAyfOZui7O14Y7No2bKo53ihI2vS1B6Zom1hdk6++Q7+d/etAKSPHEPBZtWiM2LWqeRvWsfQuddSKhKw2wxEVG9iyX+fQ6c3cPGTz7P01RfYlFtKrTmGMLPggcfupTZvDwuefQJrSCgdzU09hBeo+YumX3IliVkDeqQn0Ph10USQhsbPi+7XXoDGH4OynF189uj9FGdvI2/jWoq3b2X3iiWU7s6mobIMS3DIQecI/5etyXZwgU5XRwdfP/s4oVExLHvjJeqK97PohadoKC9FKgrHXX4tBrMZpMQcl0bMrKsD5/YaPJSLHn+OuPRMANoa61n5zmtsW/oNoOa/2bV8CWaL6kvj83poqCijvryUQdNmEJfRF9lai9Eei6exGkXx4fN6+axd8t6pV7Bm+BTc3ZIShsTEB17bQsOYc/u/GH2aP6miolC9v4Bz7n0E+/BjESZrYKzBZGLAlGkMnHIceqMRU5BqcXE7HDhauxIolvlrjgEUbNlAc001OzduAQSNLQ7279gGqJm237vjJtp37mR6wW4GtO4hqW43H/7zRpa+/hKOlmYayktxOx1c/MQLnPmvfxOT2pv4zH7Mue1ukgcM1gSQxo9GCPGduYeEEAv8NcO+a0yREOI3l61TCGEXQlzza69D4+ij+QRpHBXmP/M4bfV1geOq/flkTZxGzqpleF0udDp9l3UEiOmdwcAp07EGhyL0Br76z78D5xpM5kDF9I6mRoLs4bTUVAOS12+6GoQgc9RY4tMzaWloxJCQhrG1q6BocfZ2WhvqsMclUFWQDzodQqdnb7WHoW43nz5yL2V7dhIcGcXUS6/i29deJCg84oCK7ntpz9uCx9GBTq+nz9iJhGQMZIk1jJz+o5jW2oLRHIy3eBvx592HsvA1lOp8pl54Kb2HjSJ1yHCyv/ma5poqynN2s33RfCZOm8M+/S3sfPcpnK0teN1uCrdtIXftKnweTyAvkNftYvvCr5h43iWsevd1XO1txGX0YfD0mQSHR7Bl/meUbFiA19GOp76ciPGnk3rMebSvfJvanesJa2ojprmDsAgnzUEWGspLe7xX1pBQIhISiUxK5oJHnqbh3Xdxf7UALrnkR733NUWF5G1Yw6DpMwmNiv5Rc2j8IbgDNR9RD4QQAnXX4cRffklHDTtwDfD8r7wOjaOMJoI0jgppQ0ewc2nXVo8QAtFVPSsgfjqpKcxnWWE+OoOB6ORUjGYLUkq8bleP2lhF2duYdN4lfPCvbsn6pCRv07ouv5aqcmKbHiYmKQFP6DzCE1NoNERTVFQJSExxaVgT+1A1/2W+KN9Cxb4cABwtzQw/fjZDjzuBitwcPrj39h5r9PjXnDJoCJGJSex7+wX+GRuP1xSEqzQf4jNQFB15z1wDCMZe/FcSbaG0Ll9OyJQpHDPnDL556VkAmioriAs3EzN5JElhNzHvkXsAVeQB9B0/ifxN6wM5g4p3bmf8ORcw7dKrWPH2a1Tl7yM+sy/FO7dTWbAPk9lEy7Yl6CxB6EOjaN29BkxB6PR6qu3BmLw+wtsdNAdZeuQzAkjsm4XQ6WhrqKcjO5v6+x8AoKyxlmE33fq93/M9q76lcOsmaosKaagoo6GinJNuvP3IJ2r8ovR7/eqDkiXmXvLCj84bJIT4DEhGLYD6lJTyZSHEw4BVCLEd2I1aMX4RsAEYAZwohFgBjJRS1h1qjiNcsw14ATgRqEQVXI/67+kGKeUXQoiVwPVSyu3+c1YD1wKlwGtAb9Rs2VdIKbOFEMHAM8BIVGfCe4EwYLCU8gb/HJcD/YF4IN1/f99IKf8uhPg7cBZgBuZJKf/1ox6oxq+K5hOkcdRQFB+FWzfT0dTI4GNnsn3xApa++t1/OAmhQ8qu3DdxGX3oPWwkBVs3UV2QB8Cxf7mWvetWUbo7OzBOp9cTFB6B3mjBURxOdNOd6DAz/jWFZoObTd98g7Mij9adauSU0BuRPg9x6Zk429tpqqrokQm5OHsblQX7aKmpYf+2TVhDw2hrqMfR2tLDggWgMxhRvB7MCZm4KvIC7Uavj+m5Jeh8Con/eZINpQVkL10IgMli5cLHnuPD+27H2d5G2pCRKIqXlIFDEQLcTicr33lNncdioc/oCZTs3oHJasMaHExF3j5soaEBp+vhs07FbLWx7uP30IdE4Gvtiro76BnrdJxw7U0seuH/8Hm99Bk7kYnnXsSbf78WqSj0KaqkLsRKbIebKa++iTkj44jvdU1RIe/ecSOKz4dOryeiqZUhp53F0L9cdcRzNb4/P9UnyC+ADlU24/IfK4Q6a4EJIazAJmCylLJeCNEmpQz2j0kFCoFxUsr1/rYiukTQ4eYIjDngmhI4UUr5tRBiHhAEzEIVKG9KKYcKIS4ChkkpbxBC9AHek1KOFEI8A9RJKe8VQkwDnvSPfwQwdxM84YAH2AH0k1J6hBBrgSuBVuArKeVA/9gZwBn+PgF8ATwqpTw4VFPjN41mCdI4KjTXVLHxs4/pPeIY2hobqCrII23oiIPGpQ4dSVX+3oBjbqcAiuqVxpBjT8DrdtJUXc24s87j62eewNXRgT02jn7jJxGblsFmfwV6W5idK557HYC9H/nIfkliDJdExxpQ2tzULn4NpAyUn5D+OPaw2AROu/RKaov3k9x/EIrPx951q1nwzGMAnHTTHUw45wJsYXbWfPA2WxZ8TkhkJPVlHQEnbMXrISg8guMvPA+EYO1H79LW0ICztgafwYBOetGHheHY0+XX4/V6eO+umwOWn6yJk0kfMRqAstzdfHiP34IiBDOuugG9TsfuFUsC5w+aPpOdfkEVHp/I6FPPpKW2hp3LFqPT62np6eccQG80EpWcSubocSx45nEA2hrq8DgdqnVISooSo3AAXp2OhjffIv7++474fq94+79q1JsQgeg32xvvgiaCfms8RE8BhP/4IX58FunrhRBz/K+TgUyg/hDjijsF0E+YoxM3sND/eifg8ouUnUCqv/0j4C6/heZS4A1/+wTgdAAp5TIhRKQQIhQ4Fjin8wJSykYAIcQyYLYQIgcwSil3+kVdd2b4/3Um9Qr234Mmgn5naCJI46iw7uP/sXvFEnavWILP62Xdx+8xeMasQH+QPZzew49h6kWXs/qDt9m28EuCwuy0NaoWjIyRo9m28Csa/BmbdyxWw7ktwSEsf+u/1JUWq87DVhtB4eGce/8TXXMPykWO/ZDeUyazc3swuzbvwJ6Sgbe1gZGzT2P526+CX2xVF+ax6r03yDhmLAtffIo9K5Zi7lY0dNfyxXz55EOMP/sC8jatw+N0YjBbOenG29m9eAuFu1XnaqmoAmvHNwtI6JNF2rCROFpbqNmZjexoJyEjnWlZ/RA6gT0ukY2ffUhHUyN9x01C8fmY/9RjRKX04tRb78bjcCC7VZ7fvHojcdPPZcCss9i/ejEdzU201dcxYPKx7F6xhMbKcuY9ch/JAwaq2auFYNzZ57Pt6y9xtDT3SBtw8i130nuoakiISEymobyUlAFDiO6VxvRLr2bpq8/jVCR6KWm2mmkc2Jdld9yI3mjitNv/hcl6sNM6qBFrFfv2YtbpsNU10re8DkXoqbjzTuIfeAAhxCHP0/jFSfmB7d+JEGIKqngYK6XsEEIsR93SOhSHDKP8gXN04pFd2xYK4AKQUipCCIP/dYcQ4hvgFNRtqoP/Cvt+/Bd1uy0XeP0wYwTwbynlSz/yGhq/ETQRpHFUSB95DIVbN2IwmWmtV0tR7FmuCgY1dPsFLEHB1JeV0H/iVKZccBlv/+OGgAha/8n/Djmvs601YDXqdB72ul2YbV1RVlu++ozSfVsozdvaI//Nidf/nZbaGpAKOoOBgVOPo3JfLjuXLSZ/84bAFpfLH4mVOmwEFbmqv9COb74OlMeoLtiHELqAABJYmX3DrXz75ivUFqk5hLYvmk9QeDit/jIcu3ZuY+yZc9m3fg1C6Jh68ZU0VJQx/uzz+fiBf+JxOanM28sXT/6bObfezSl//ycr33mN1vo6RPJgWt06QgafwIgwG6vee4P92zdz3JXXB6xDVfl7GXr8LPqMnUi4NONoteP1qUKvK28SlGRvD4igzmfTUlcDQPKAQRiMJoROhzkoCL3RiEhMpOrT9wGoLd5PUG4ehpgY9KGhmNPT0VnV5z78hJOpLS6i5aOPGFiuPieJh+ZPPsVduJ/Ep/4PY0zMkT42Gj8/JUCvw7T/GMKARr/g6AeM6dbnEUIYpZSew5z7feb4qfwX+BJY1WnZAVYB5wH3+wVYnZSyxS+YrgVuAHU7TErZKKXcIIRIBoYDg/1ztALdQ1wX+ed7V0rZJoRIRBVqNUfxXjR+ATQRpHFUyDxmHJnHjMPtdPLC5efhdbvQG4x43e7AlldzTRVv33Y9Pq9aJV3xdfkCWYJD8LicPSqnW0JCcba2EBIVQ2hkFPb4BExWG0lZAwDByvfeQG8w0n/iFAo2r++ypvixhYRhNJqIz+xH3zHj2btudSCpoFcRmBL64ijuCj8v2t4lotoae1rm6ytKiU0eTHVpNhIHtcVF9B0zUZ1PShSfNyCAQA27r8jbi9FsISq5FwOnHIveaEBvMDL1ostZ+Pz/+SPHdrF98XxGn3omGSPHkLdxLYs//BSdwciQMYMIjx1LzurlRCQkEWWPREiJRM1H1GvgEKKNFuou/QtCStouupa87QtBCIwmMx6Xk7Kc3YE1zfrbrRRu2cig6ccDEJmYzGXP/BchBEH2cP+6vVTlFdK6Yhq77gkhPvsajIr/97peT+qHH+BraMC9v4jhx89iQ0UFvuVrsFhs6EKCce3eg2PbNloWfE3kxRf9mI+SxtHlDg7tE/Sd4ezfwULgKv9W0V6g+3bXy0C2EGIrqmP0j5njJyGl3CKEaKGnBece4DUhRDbqvXd+MB8AnhNC7AJ8qI7Rn/r7PgSGdgopv7/SGv/Yr/2O0VnAOr/Vsw04H9BE0O8MTQRpHFVqi/cHwtunXXYVrbW1RCb3whIUTFX+voD/SHexA6B4vZhtQbja2/B5vYG2rIlTGXLciST2zaLD5ePbt19j4XP/wePqCqm3hoYFBFBIVAwzrr6esKhY3B3tfPzgPwGo3p9P2pAu67guNIq4OTdQPe9JOor3qI3+OYROR+Yx40geMJjlb76Cz+vBYDAy6eJz+Oj+bIQQxPfpS0h4JGs/egfF5yMsLo7mqioAkgcOpnRXNvvWruTiJ55HZzDw8rUXo9MbOPueR0jKGshlT7/CvIfvoaogj+T+gwLryjxmnLrlZw8nKimC8nonA696kHRnFXXXX8u06irajQYyn/o/giMiKampxKfToQMUR5cIi0zuRWLfLLImTgVg4fP/oTx3D9Muu5r8zetJH34MwRGRBIdH9Hgf9AYDI6ZdwqK3FFxAqG0cdtd88HjA56P6gQdxZGeDz0fcffdywt33s6Hvu4h5X2Ks63LOdpb1DMvX+HXIveSF9/q9fjUcpegwKaULOOEwfbcB3cI4GXhAf2q3w8PNkXqY9uBur+85XJ8QIgE1/93ibv0NwKmHmLONLkF0IBOA/xwwfu4Bx08Bh6+3o/G7QBNBGkeVmLTeZIwag8flovewUViCgvnmleeY//SjRCal9LDWGC1WjCYTHS3NeN1u3E4HmaPHk9CnH3kb19Fn9DhGzDoVt9NBfW0Dq/JdKP1nIbK3Q3VxYB5HSzPxffrR0dzM6bffQ3hCIqBWhO/M4ByZmMzsG2+juboKRUoK22w4fXpm/Ot+Vr35PMXZ22n3W3+kotBaX8fS116g9/BRZB4zjqwJU9j1rd8fSEqWvPIcHqeTlIFDaaqqoKmqkvjMvgTZIxh/1nkse/0lgiOjCI9PpHDbJlztqnvEB/fcxpUvvIneYOC0f9x70PNTFB/xmX1Z87+3cTk6aE0ejyEqBdPyRRjKyzEDSddfT9SkKQD0nXUyG+vrCAoJY/KAQfj+W0dReTF1xfuZe/9jSI+HXff+i927N4MQfPWfh3E7OlhpfY2/3PhPWhYvJuL88zHGdyV8tBoq6J/+Gc74Exlw1a2sv3gUUaX3o8eJp74ec+803CWlmDMyeX/NOl5v03G5U2FIVRUOox4kNBYXErp0KSHTpx/Nj5fGj8AveH50SPzvBSHEhcCDwE2ye8jpD5vDDmwEdkgplx5huMYfAE0EaRxVjCYzp9zyTwq3bmLRi09TX1pMY2U5AHUlxQSFR9Du9wPqPXwke9euAlTLRUxab8aefi5hMbGMnK0GjjRWlPPuP29GMdpIuvB+hN6AzmhBmIPRW4OIGzCcrAEZDPZv8XRn37o1SJ8PW1gYI2fPoaW2hsikFGqL95MVDcHhYej0ek645kbamhpY//H75G9aT3tTI/XlpSAljRXlDJxyLAA7vw38cUlDeSlet5vGynKCwyMBSOw3gMnnXwrAWf9SLVUlu3bw1X8exhISgrO1FenzBYRgS10t2UsXERIRiTkoiIUv/B8+t5vYfkOozt0BgM64ktQrHscw+UT2b9lBnqGe8fFRdKbU1RuMjL30SqTXy97xU+jTUk+SLQZz7ACqN3nQb3wF70efwIBUQLVyASiKQsVtt+MuKsJbU0vMA/dhNKu+qUW33YZu8xYiR66jaNXr2Kq+RI9aggQpwWQGg4Gaxx/n7WtuZXefwbx+koGn33wOU2Mje+Mi6L9qHWWr1qGPjSXlxRewZGX9hE+VhsaRkVK+Bbz1E+doAvoclQVp/C7QRJDGUSd76UK+efnZg9p9Hg9XPP86q99/Cykl486ci7O1labqKsITErGF2TGFRQbGr99aQPYX7+NqbwPaqJ73JIqicOoVl+OxRdLo1NE/LRKz8dDVX2xhYYCai+jr557EFmZnwJRj2fT5x4HCptP/ci1561dRsisbEET07kd7UyMWWxAjTjyF9JGjA/M529oCr70eDyaLFa/HzdSLr8BktZIycMhBa6jZX4DP60V2ODj5ljuJSu6FwWikvryUt279q1rVHQiLiQskSqwrL0NvCcLnbEfx+fApsHtjJFXeaLy6avI3raf3iNHYY+PQ6f1lLoRAKjoMEkLba6Cwhk0352IfNIYo73Nk1jTRPnY6kWmZGDqqyMneSlloEEFmO76waD646Cx6pWeSOWka+0sL6QPU1Q+i5p0a0jxdLhveykq8pepWl2PrVi6wW2ncXcCs1ctQmpvRS4m9zaH6LQG+6mr2zzmNxKeeIvT4GUf66GhoaGj8omgiSOOoIaXE6VFoKC87qC8kKppjTjkDnU7PpPO6yjOc8c8HyF2zgvlPq3l6KkIGEhcsSc3qT6U7GMWfPdoQGsXQi25hQJye0Gg16ij1COvJPGYcQq/H63Hz7WsvYQ4KZtPnHwOgeAVIM8vfeA2f19F5BzSVq9tsHreLsWecGwj1Ltuzi9a6bj6PUuKVoPh8LHzlecKjIjn55jsIi4nrsYYhx89CSkl0rzRShwwPtLsdHQEBZLLaaK6pCvTZ0gbSa8godr39OCg+PA2VmIcEEf7VpaQ2XUv4rvnM37iJ4KlTOPnmO9EbDHidOnTXf0nlu3cRUbKVVvNUnIYsqnL1FKdkMLCthW/L91JRsIWw6FREeCK1SXdTU2vDkFuDlCuozNlNeFkV+XERlMdGEV1zHQgLEj0C1ZfLEB6OIS4OT00Nvupqjrn6L6x44QU84WdRmZtNq8uJotejYEWHg85AeW9tl7+ShoaGxm8FTQRpHDU25zdTVu8iLn0EacPKcLa1YY+NY8aV12MwmQ57XvKAwcT36UeTQ2AKj6N03yaqKmoxp48gYtLZmKKSMITHU1tWirnPMKSisG/DWtptcYTHJZAWqwa+VDW6CLLoCbGqH+sP77uD+rISxp4xl8ueegWP28Xbf/8bUkqsjnE4bCvxeSEiIQmvT0EXmULa+ONIDPIQk5rWI9dNye7sgFN3uNFM1mlns2nRIhSXA09bMzVtzeSsWs6Y09Xcaz6vF73BgNFkZtTJpx90z/EZfTnzrofQ6XWU793D6vffIsgaRNrgYQy74Eoi7DaszftpcoI3Ph2d9NG/7RosrerWYni7k71bNzHvkXs548772fGCZP/8YILsdxGrTCIkKI/oIQJ73bM4LZHojUkIRxMAbY4WDE1WiHMDNoJDIuntCaexsgKjaOeY4gqCXB6CLsqlqSwYWalTRZDRiM5uJ+nZZym59FJ81dX4amtpfPttmj7/Akw22k0Gkppa8WELCCBCYwidPeugZ6ChoaHxa6OJII2jRqtDFQlb3vw/XM31GExmTvjrzd8pgEBNpHjufY/x0WOPUr/qQ6wp/TGnDwPAGBrJxNknsKPUhc8azJoNOUR7q1i5cAkJp91MUWELNU0uwoKM5JS1Y9ALThgejUEvAltFOr0ee1w8Ukpab7yP/bmtDF74JEjIPGY6/c69ghCrgfhw8yHXV1XXTocX+qT1IXTpCsLbnfS+OpGUex5n46Y91O/JxlNaRL9xk5FS8sE9t1FVkMdpt99zyC0ygP3VHRCdQUqsWvoiWdown38/3o527HYbBqOBCXMvZk9JK/uKHXg/C6N60HMMTvoEYTXiaamFfXtoqq4EIFj1BSdsQATJN72MOSMDV/4GSi9/ETNgePY1+nwVS07lTfjaGohq/Ac6fRBJ57iwb7qLr1obkcFWNuPj9OGj0IeGEn/9IJo//ZSqhR7Q68HjwZ2Xx7rLL+GY556j5v4HsI0aSeuKlYBAuNsJt2dCWx4IF1KqW2KypYbSy68g7aMPf9gHSkNDQ+NnRhNBGkeNockmvn37HdxtTYBaDX3Ll/M49i/XAKB4JNkvSwxWGHCJoLbFTWuHj/ggH+/843pa62pJHjAExarmJFN8PkwmA/bwMJzLF2LtO5qoMCveRjPho2YFHIwrGt1UNKr+NAadoNOAc+bdD1FfWkxivwEAFDpcvOCQhIc6GCT9BUUTerO7pA0BnDgyGpOhy79o+duvsnvVckxpw2nZvhSh09E/NBiPyUjup+9xyv2PMWZ0f77+YD/mholse6OCiTdHUpm3D8XnpTJ/3yFFUH2rm+371QSQwRYDyf0HU1LnRQkOwx0chssLJiNUNrrIq3Qg80zIbRZaycB8wz+I7C+Y7XSSs3o5Sf3VKOR+5+pImSaxREFryQSK1khCYkwUJHyGVWynvd1MkPIOCc1X4FP09K16FFmlozr4QSxrv0bXvxc+vZ7wlnZKXTOxpB9PHEbsZ52FPiICpb2d8iefxFvfwH6fi/jtcbQNeZn48wWhM2ey8dyPsbRvp8M5jN4nZNDy9dcBS5AAjElJR+ETpvFHoHuNsV/yev7w+aellGd8x9hUutUI+wnXvAdok1I+LoR4wz/nxz9lTo2fB00EaRw1ijeuoGjNN4HjdlsII2adGjiu2gh5H0t8ugb2FL1Imz2G8HGnUli5OZBoUOgEVV8+hzUli9jjL8Hrk3zz2stUb/6W+KIN7A8Lo3zfXhxtrcSffjPWpAyQOhCCAclB9IqxodepX7/W4BCSsrp+l6VazZwRG46vcFugrXLbGmLTRmNsN9KwE+KGdd3P9kVf4fN4EC11CIMRnV7P7qgQQgafhN4aTF1pCQVb1tPo/RhCoHE7pOXez6m33kV1YT7DZ550yOcUZNZjMeqQQLBVz5jTz2HUqWewb3cZ1vCwwHZeeJARq0lHR0w1TvN+bOEW7BlqlJXRYmHwsTMB6GhppqpgH70GDaPG2criGwTWlhDsGXYc0o5DpMM7z1Hm0aE3P0tS7V04jAMIcX1LUtgqakL+waTcR3EbdJiUdAoiT4aFED3cQ8pxJhCCyjvuxJyURPPdt3NMUALbnzQCElsc6Jwmqg0XEW1UCG1fgvRkoLPaUJwuREQK0ZedReTFF/7Yj5WGxvdGCGGQUnoP1SelrEAteqqhEUATQRpHjcikZKyhYewLCqcoKZ0NwyZzntFMuL8/oj+E94F630pK925QG/UG5JBJxGX0RfF6OPHamynN2YmztRV9Qiil9S7cXrW8RVXBPqSiIIROrQXmaKL4pVvQma0MOflc+ow59jvX52uDCz9LxhRzBqUjy9m3eT1txXuI+XgLHVuOZ0ucZNb74NyzB6lI+k+Yyc5vv8TscHHhS++x8JmHqSipwD7yBNpy11NYUExx9jYQAqHTIQRYgoKJS888ZPHYTiwmPTOHq0HunX5Her2BrMGpPcbZzHomJkQhUyyUR20ndUhf9CZByRKFfR9J4uO/JSjnv2wLM1PkaGDg2FPYNzCFyLbJAIT08aEz6GnYK6DuOqKBpvCbMGf2obrkGJo9M8n47FwSFS8SHWYvFEQ9BNKLyVtM882XsdfQAUYDSImvvp4xc86i4d33CXEuw2nMYt87NpytsWCAmpDrqAm5nvjT64i/x4L0+TDGxv6wD5HGUSenX9ZcDkiWmJWb86PzBgkhzgeuB0zABuAaKaVPCNGGmjxwNuAATpFSVgsh0lDzFAUDn3ebRwCPoiZOlMADUsoP/H23oWZgVlAzNN8uhLgcuMJ/3XzgAn/pjTcAJzAMWOOvGn+o66Xit/L4X7+NWo0e4Dop5doj3Peh1pQOPAdEo2ajvlxKmftDnqfGr4smgjSOGvOfegxHczPRMp41cakMyt3Corf+i7ujhZjE4Zx632Uc+1IYzTXj+fTf39BQUUbj2nmE9x3GOfc9hl6vbkX1GzcpMOegVHBl3cT+7VuozNtL0Y6tZB57GvkldZh6DSFxcD6V29eR3jv+MKuCkqUKu16TRPSD0mUANobE1mIuqcGZMYDM0VPJ3SNIHC9w5eWRff55+AToshaTWHs2xhYbVougPGcXPgXqFr2OozyXTWvNSFwER0RywcNP4XW7cVVG01woCeutiptFLz5NVcE+Zv31FqJSUgNrOlyBUZdHCYT8l6+WrL1bISjexsy3JuBqglX3+WjcB65GaN8dTHR7B8ZL0uiTpaP0y3wi82Yh/BtR7lo9Yb2hIRdAgrmNKXeej29PEI2vgc8Uiw8LOtpQhAmfCMNlyAAhiGp/G4O7DsXdtbaExx9DZzYTOm0Kfb+6GZclk50VD4AAIbxIYQIpIDQRQ5RWQPW3gF8AdS+b0Qt4JadfFj9GCPlLRZwNjPdXcX8etS7XW6iCYr2U8k4hxKPA5ailKZ4CXpBSviWEuLbbdKcBQ4EhQBSwSQix0t92CjDaL3I605p/KqV8xb+OB4DLgGf8fUnAOL8Y++Iw1+tODXCclNIphMgE3gdGfsd9n3CYNb0MXCWlzBNCjAaeB6Z991PU+C1x6AQrGho/AkdrCwiIqk7nhncyeSQ9HoezBJ+uieZCheKd23G7nLQX25l1zT0E2cPRGU0Uf/I0C74+/B9hZlsQ/cZNYupFl3PJky8QHW6l/tv3KH3pb8QnxHD9mx+R5Pf7ORRFCyXtFdC8H+x9ICwTan0nkNjkxZLvoHn+m/SZ8RfST95HS0szqzMSWJuRSG1lNnoZjIzcyVPnn05UUgoGncBWl6auyzWIiMQkRsw6FVuYHUdJNMv/prDkKoX2GoXymhZ2r15BXUkRBVs2HvH57SppZcGWWnYWq/5CjjoJUhU80gtlKyRVG9RjtzmfsugvWZ9poOWLq1FqU7CE2QmrjSFhhOoQXr0JWstgwMWC8Q/qOOWTEII9I0mcYiZ1Flgc22kyn4gA9NJJRdi/1Pvy5mJ3fA46HaEnn4zObscyaBBBY8ciFcnm15PJsX5I1B0PkThwP9GtL9KnZiaT7m9l4iM6YkdoAug3xEP0rBuG//ihHznfdNTq7JuEENv9x739fW7gK//rLXRlsRiPKjJAtb50MgF4X0rpk1JWAyuAUagV5l+XUnZAoOwFwEAhxCohxE5U4dX9P/1HUsrOysGHu153jMAr/rk+Avof4b4PWpMQIhgYB3zkfxYvAYf/a0zjN4lmCdI4KhRu3cSgY66nsXYAuv4CxwfVRCWnMHTM1ZRsKSRiiA7MIXz8+XrcNRXYV00jtE8s7ftywVNH1daVMHtCjzmb2j3YzPoezsqAv7yFRPF62fLVPCacff53rm3AJTqMQQqpxws2PS5xNUAzx5HfK4/60K8ZZP4va9bFE5N3NzP/+TQ6owmf14tijKVNfEuDRy0PFJfRh/Me+g/VWyQb/nMGrZZlBMXrGXr8bAD0JkAHOiNszm+hQeei14X/xrr3CwZOPe6Iz7ClXXVlaPb/TD9ZYA6FkBSB3iyIHyvZ/gxga8Vy7iM4v6wBBFLnItiThbXxJAzR4PTXzhZ6SJokyDxdh7exkeJlIWx9UsEYBL3nlqAIM/XBF2Dx5mJQWnCYBoAQuAz9aD51BQPO7SBsSGKPNbqaFMpXqq/r98CwK50Ur30NU1oi0aOC0Jk0AfQbI+UHth8JAbwppfzHIfo8sqsujo+e3y/yEON/KG8Ap0opdwghLgamdOtrP2Dska53I1CNaoXSQWdK9B+EDmiSUg79Eedq/EbQRJDGj6a+vJRlD21A39qXko5niW98Hp1iAVMHLenv0dEym+k3zqJshcRRK9mTvwBr6iisKVl4NtUxcMpxNFVVYgoJZ9pZZ/aYu7Cqgx1FrYRY9Rw7JKpH39CZs/H5FAq3biRr/OQjrjOyv2DsPXo8bRJXU9fvxojRMbQW2+nwNgFgDo9lQ5mOuIGP4ltqwORLoSH8dUJDjsVedxUti3S4TpeULJW0uvZQpXsXNkNjZTnRKalEDRIc/7oOgxW++RLoD8IVwYl/veWQ69r3oULpCsnQa3VE9hcM7R1KWb2TpEi1fIXQCZKndYmK4Hgdw29QKN8GrZEWep1sJ8Z3NkMnZ1D4VTp5u7vubcQtgsRRrRiDjTS89xnV992PJ2ka8DQ6I+x9JRHM6vdgccJdRLWuw6frytZdviEUa0IYw7oFt/lckm+vl+gMED8W0k4QmEIG0Xf9OjAYAiU5NH5TlKBugR2q/cewFPhcCPEfKWWNf1soREpZ/B3nrAHOAd5BteB0sgq4UgjxJhABTAL+jmpRulsI8W7n1pPfGhQCVAohjP55yn/g9boTBpRJKRUhxEWA/gj3/c2h1iSE2C+EOFNK+ZHfx2mwlHLHEebS+A2hiSCNH83OpYuQe87HhxG7+WJQQKLgdK3HbnNjtFhor5Ksu0etZZh+bRZ5FfswOByc+MSx7KxPZNgtxzAyIwyjXkfu+wrlqyTDrtfhCVG/0B1uhbyKdjITgvA1NVF67XUIo5ERzz3LqJPm/KD1GoMFvWdB4ZfqcYzxTGa+dAobP/uIBFsQ/aaeyNLdzZiywtFvtmOMrsFWNg59WQwt+i/QOSfTXBJFyTcemq1pNEXZiWlIY+lVdvqfrDD4Kh2hKapoSfEEU/CmCUNUCUstPpretdNnvJFBl3cJhZx3Je4W2L9AEtlfYDPr6ZMQdKil43VKKtdJEicK0k+xU7rlXlwhHWRMUL/fEidA6bcSbweE94W4XvvYP+Nc9CEhBE1QLWxKZT7EgCUKXE3q73xTtJcT/jsAU+hA8j5RaMiVCB1UbYL4sT2tOu42aC0HFEgYJzCFqP3iCHmgNH5V7qCnTxCoDrx3/JjJpJR7hBD/BBYLIXSAB7gW+C4R9DfgPb9j8efd2ucBY4EdqJabW6WUVcBCIcRQYLMQwg0s8K/3LlRH7Fr/z5AfeL3uPA984i+6upCDLUkH3vfh1nQe8IL/mRiB//nvR+N3guhe1fvnYuTIkXLz5s0/+3U0fllqS4r45pYiaEhH4sbkS0XioyJ+Lj6fE1tIPJc/8zKLr/DhGtNO2BgfoweEEWTRU9fiZtUedd9mXD87sXYzn8324WmH3icJht8oyClrZ2+5+rtpysAIjFvXUXrFlQD0eudtbCMP68d4WKSUrLpNoXoT9L9IMOBiXY++snoHm2tyGGDsxZf33wOuIn8IvsKWjFAudLjotWAPKyenUeEyklp9JcHO4zEEeRj3dBmxvTNwKwoeRfL6Xy4iYup1WCyZ4IPgpXamP6+j4EtJZJagqUBStkIy5God9ozv3kba8qRC4ZeSiP4w/blD/9FasUaS+76CPVMQE7mZ1gcuAiFI/eRjauZtZMfCsarjM9DvPDCHCTJOFeiM338Lq2KtpL1Kkn6KQKfXtr5+boQQW6SUP/yD3o2jHR2mofFHQrMEafxoolNSOevdRBa/9DSOzeNRilPRG3X4fC4ADN4oDFbBtP8Kvt7mpNkDqxYvp09SKKlDR9Ir2oKzo4Oydd8SPmUSA/9ioXxDB6HH7mR3cSoKQQRZ9Bh0gmCLHsOYMYSffz7CaMQ6bNgRVteF9En2fSwx2yH1eB0TH9bhqAdbdM8v8d0lbezb4KZheTz/SPiAyVYzHheAQAItoUZidhYAMLKqgVum9MdQsYN+RYOo9LzJe3duYeo5/+XKmGpq3F5uO2YO+ncH4vNYCZnsYeQtOvZ+KNn9mkRvkcz5Skf6yd9vC8nk/5vXYIWdryhEDxHEHaOuv3qLxBwGuf9TqN8N9bslhfoRTH/4MYyRYVj798fcloVriWqRix4G/ebqMNp+uIhJGCcATfz8nvALHk30aGgcAk0EafwkDEYjJ153Mz6nwqezJIpHENl2I43Bz9Pi2Mm+DWvoM3o8mfE2CoprKc3Zzb5PVnH9mx8xPD2Mt89dRnZoOF/t2MwN14+lLfM98qrXozSk47H8hfFZdmLCOstZmIj7552HXUt5g5Md+1vpFW1hQEqXpbxsFWS/qFo8I7MkISkCW7Tat6FyH09snseczDFkGoaiLLYRVmBncs2JxDXG45t5J3oRirLoaqbuKqNu7mMEr6+jMWkU5y25gbTUSHr9LZfPn92IzhPK+td1lP7NjQIU75tIpMcKQOZwExFZAkc9CJ0krDeIH2BJGfgXQdIUQekyhdz3VFF32kIdVRtg9T8UdEYYfoOguUDidYDQgf2UWYFQ/NhRMOxvAoMFUmdqvjsaGhoaoIkgjaNEWY0b9eMkEO7hbO9lo1etF1e7up1lNupQTEFETT6bFtmVfKYg2syXwz4AYEp5L6KNajZ9gyEYq81AmM34vddQ2eDC5VEoq3P2EEH23hDJAiz6Csxhl6DmWlN5a8dn7KzbT1FzFR/MzuKL9K2kVA+iIaSG1OM/wZDeCDTiLM4ned8gKr86AaXdRtCOkwlDT8MeiNyRxTm3vcyGeywERYTwet80Nn5eR9Y2dSctJWUJtpgZfHu9j8zTdZzyueo8/UMQQhCeCa5GHfnzFGJHqG0Gmwwkush5RzLqdkHVRknKsaJHLiIh1K0vDQ0NDY0uNBGkcVTYXtCK/lqBbpeFBaYP2REbRMOgVP7e5zjWPuTlM2MrEWM99DWYiO/7F/I+Veg9W3DK1YP4bN1iQkxBDEqMItx6DkkJEwm2xaPTfffH07lnDzuu+4pqw8UMvCGKftOC0OkEiRGq5eizx+6nLGcXJ190FfGVtwJQ9XoMbZGnkHm6YG/5R8wy5TI5QY9J30xJXTYfx31M+qy3Gbi1kepiC4npVqTDilKciSHlRXrt+B81oUH4YmPpPXgcNVshYawgZlgccz6TfuERhmeBm+Ta0/Hp7CSfNoM9Hx5L3U7wuRWSJh8pEOVgmvdLct5Rxc2cr3UBgRM9WHD864JFl0jaK9XEiCNv+eHza2hoaPwZ0USQxlHBWGDG26HgyTcQnxHPDraRnpTMhseaWTJ4JW/1TkHngrfLM2l42cJ2RSJ9MOzMZDZkPoZep8OgU7+8Q4OTv9c129asoc41BZeMpGiBj94nGhneOxRQi6/u37YZxeejrLyYuL598VRWsvOLfnRISVNzK8sTVzA0DIIMPnDasKyO596m22gt+YTG5uW0NHuIm2BG1sViHbuV9LL9yJYOItuc7JKZCAQnfdy1tdTd8jLqX1FULnqL1FHVRMwYTZ/1Ep9boc+ZP24rKucdSekySUOOJGFsT5ETmqJj5C0KNdsgY45m7dHQ0ND4vmgiSOOoMO30EL480wdmyfjSE9l1wm4WspWBY1tJiKnDIBOIERaqXjVhUgABoanqF7bZ8P23vLoTfuaZpO/5lJrWWPpf2TP3W2OujqG9XsAU8yUjh8ZjmvsZUlGoulXi2AHFbWtYWhRH7PIr6IhoIiPSSskWNTGgVX8B3qGbsCQIZFl/vDvGM/mmcZiaY6koraRVTsHQFk1T4eEjK2NHCmJHptKZNDd+jCB+zHdbaPI+UVB80OdMcVBZjZRpgvrdktQT1PZWh5eKBifJUVZsZj1pJ+hIO+GHPT8NDQ2NPzuaCNI4KljCBZHjFPLqqwnaF4EiJCk1vVkbZea0FiePlW2mctMq4ge8gqPaTL+zYftzCsEJMO5e3Q8K0+5Eb7fT7z+X0u8Qfe+/msNX0V8zoyiU8R+eBWe+iRhwKvxtF29v/owZob05/rU0QluiCG2JwhYFbf5zDb5I0pIuI2NaHFtuHIBErddqHjCEbO/HeB3quNjD10j9QbRXSdorJdufVUWVPV0cNHfCeEHCeD0Ot48N+5pobPPgcCs0tnkZ09d+dBaiofELIIRok1IGf0d/KmodsPf8xxcDI6WU1/2AaywHbpFSbhZCLADmSimbvmN8kf8add/3GoeYY4r/mrN/6JqFEGZgPmoNtX93FpL9s+B/du4jFbH9OdBEkMZPQvFJct6S1MpGlsyYz7KSbCITkrlsyXWY3DbWDKulbXsUXkMDISYLVWXVTLo7ldpshdZiaC2GvE8kfc/54SKounYbu/e9Q1L8RPr0PrVH37pB31JEPovCdNxUAxjVXHEf7F3F3qZyPNLHvOfvZv45Cj4n1HRLY2WOEDR9Pp0dywCDB0PiHhRrDO3ViQEBBNCU/8Of14EUzlfY8rjEnglBCSB94GqW1GyDmGHqM1GkxOOVmI06SmodVDS4AueHBxuQUtLY5mWHqwObQc9o+2G/XwJ43W5y16wgPrMfkUnfb/tRQ+MXIhWYy1EK65dSnng05vmZGQZwNEtwCCEMUkrv0ZrvgLn13Wq1/dS5DKglUNqA7y2Cjtb9abGyGj+K9RW5zHj2IR59ZA173pLUvm1n/wKJ0+umICEXzB0A2OLa+fyidVg8kdjbL8CopGCwqnWxDDZAgCFaUl7vRPkBiTtbyyQ7l23E6WqkvGrNQf3XzJjG0OjeXDPpZLh6HfSZAcDlg49nYvwArhtwEuYwQXifg+d2+cs1elpBuo10FEfw2X1P96hGZLLDkKt/+n+fjmr1p7MBTnxXz5i7dWy4X7LiZoWmfPWCK3c3smBLLeX1TuIjLNiDDKTFWjlpVDR9E4PZVdTMit0NrNvbzJxt+eS1H7kM0vpPP2DRi0/x4b2389I1F/Pm36/D1dHxk+9H47fHR1N9cz+a6iv6aKpP8f+c+1PmE0KcL4TYKITYLoR4SQih97e3CSEeFELsEEKsF0LE+tvThBDrhBA7/dXfO+cRQojHhBC7/H1n+7seBib657/R35YghFgohMjzV6jvnGOGf+6tQoiP/EVND1xvkRAiyv/6MyHEFiHEbiHEFd/jXmf6594hhFjqbwsSQrzmfwbbhBCn/IBnF+FfQ7b/GQ0WQsSglvgY5b/n9APOyRBCLPGvYasQIv1wz04IMcVfZPYLYI8QwiKEeN0/ZpsQYqp/3MVCiE8P80xfEEJs9j+jew94jo8I8f/tnXd8XMXVv59zd1e9N/feC+4VGzDY9E4IhBogLwkpQELgF0IIgUAIgTeBUAIJhJLAS+g1YIPBxmDce+9yk2T1Xnf3/P6Yu9JKliw3YSzNw4fP7s6dmTv3St771TlnzpHlwJ3ua+jYgPDPYe03isgSd+1viUiM2/6iiDwjIouA14GbgF+413+SiKS7/Ze4/09xx90rIv8WkfnAv0VknphM3qHzfSUiI5uu40BYEWQ5ZPJ37+TzJ1/GVxLDhuLdqKsOhuwbxq1jzufhqOuJ7rGd7MErOfm8XWyqfYfcxGwCEkC9ASISIKGXwwXvOJz/psPW5EIWbympzw59MGx+TSl+41zqVk+iV+z+5YGmdBvCq+fezoAVp7Pu48Fo0KxxsLc3Zz/2Yyp+MpK8NQGm/cVhUEuPBDFlQHzBrsRUnoIvThHXdlpbDJ6o1te589Mgm98M1p+/KUOuFsb/SjjlL+afYkQ8eCLN/75Yk8W6rMr8sVNW5Sch2supJ6Qyqk8CXo8Zs3HJEgC6lpcS43GI87b+zzoh3SRKioyNo7wgn/xdmZTm7Wv9gizHFa7geRZTP0zc12cPVwiJyBDgcmCKa7UI0FCfKxZYqKojgXnAjW77X4GnVfUEIDtsukuAUZgipjOAR0SkC3An8KWqjlLVR92+o9zzngBcLiI9XGFzNzBDVccAS4HbWrmEG1R1LDAOuEVEUlvqKCLpmHv3HfeaQgUOfwN8rqoTgFPddTdf72Z/7gNWqOoITNmNf6lqLvA/Yde8rcmYV4Cn3DWciLmHLd07gDHArao6EFPSRN17fwXwkoiEvrlG0eSehq7PzVI+AjhFREaEraVAVceo6h+AkjABcj3wQjPX+7aqjnfXvgH4Qdix7hi35yXAM8Cj7vV/ifmdeVRVxwPfAZ4LGzcU8zO/AvgncB2AiAwEog61dpt1h1kOiYL1Qb64N8DuzpFsG7CJHZ23cN6SywCY7BnN6cM9FEcpb761i7+f+xCeryL55eab2NhjG2nrOiF+h+JtSuJAmL+liOraIB43aWDTavEtruHFfxH1wlOkxPyYgvd+QE2amK+CJuSvhnXPG/GRNlzoNA4K1oGb0Jq5t8KJvw8SqBLEq+xnWFUQ9++EAaPOwl8pqN/M54kyIuVAlO1SFj9o+sekC92bqfXqiRB6n9XgCozvIZz9ioMIRKWY9qlDkiksr6N3RvPJhfIXfwwr5hKT1pkFN99EqT/AxooqBsVEUVYdIC7Kg9Mk0HrE9LPodcJoXlmxnfmrFwDwoy7dD3xBluORB2lcNwz384McnrtpOjAWWCLmdyoayHWP1QIfuu+XAae776dgHmQA/wb+5L6fCrzqulX2icgXwHigtJnzfqaqJQAish4j5pIwD8T57loigAWtrP8WEQkVHewBDAAKWug7CZinqjsA3CKuAGcAF4hIqDJyFKYkycEwFfdeqOrnIpIqIgktdRaReKCbqr7jjql22w907xaH1uye7wl37EYR2QmE7N/N3dPdwGWulcwLdMHc49XumPBYpeeA60XkNoyYmtDMJQwXY/1LAuKAWWHH3jiAS20GMFQavrcSwqx876tqKDDhDeC3InIHcAPwYgvztUirIsj9AaU3VaciMkJVV7cwzHIcUVy6ndiYLviaZPCrLVcW/zGIL0YY//9Mjal5tyvBql6cWHQryFz65AzEF29cRzEZUJ6llO9V0gYJXlGG7BpBzLIBjGEAvun/pVvP0+gyOYaV/wiQuysC3RRB36uDjJqhfLBzPtmBrkztNhSAvNWKxwcpQ9zYGL9SsBZK/zsbx19KXM18CmKvYfdcZdD3FMfb+EGf2AeS+oO/BlY8HsQbDanDwzooLP8rVOcrCb1NSYqKfQ3uMMD87azQZSLkLFG8MZAyGCbdLUQmHziOKSoVIhKgrgIq8oLUljpEJBx4THFOHl88+hXde09lzE1pJklinI/kuMY76PJ2ZRKfkoYvJpZhV9xM1sZ1nDj9JHZv8HNh4WYCKH/t3A1vSRAn2qEgIsgVA9OI9jTsUCvSBDbHZPD1uNMQDVIeVFqPJrIcZ7T0cD7Yh3ZTBHhJVX/dzLE6bShGGaDx8+VIi1TWhL0PzS3Ap65FoFXEBN/OACa7leDnYgTMoSIY69CmJvN3Ooy52oKDNanvd09FpA9wOzBeVYtE5EUa36Pwud8Cfgd8DixT1ebE5IvARaq6Skyw+LSDXKcDTAqJvhCuKKof5/4cPwUuBC7DCPRD4oB/eovIZcBGTLXddSIyPuzwi4d6Msu3j62ZH/LVgvt54Vc38NIdP6O8sOH3OHc5ZH8Nu2YrxdsgUKs47rO4U+9YTlt9Ln1yB5AxGib+Rpjwa+HznwZZcK8ydHR33jzz59x8zigSegeI9OWRuCSRkf0+YvNrsP01QRdGQaGHigU+Pt69kD8teYtfv/UaS56sZcfHQebeGuSznwUp3WW+P1c+ocz9RZB9qXfAuO+yL8OU0CjZBjmL9/+OjUgQTn/WQ6fRQtluKNoMJe7fR754Y8mpzgfxQO+zhG4nSX2NLjDtoa/usj1SX6U96IfIZAd/lVKetf95A7VK6c4gteWmSrwGYPVTMPumIGVVfoItuMYAPvzfx9ma+U8WznqCkqZGcZd1X3zGv+74GY8/9z7/92YheQUxRPYbz9Y1yqKF5QRdB2VWjtno4q8MkFwCHy3J479Lc1m31+yDU5T5AfMH1akJscR7bZLFdsiuQ2xvjc+AS904llCMS69WxswHvue+D/ddf4lxw3hc19PJwGKgjJYrxIezEJgiIv3dtcS6LpGWSASK3AfnYIylp7X5T3aFASKS4rbPAm4W94ksIgdfyNBc81XuuGlAvqo2Z/kCQFXLgD0icpE7JtKNq2np3h3ofAMx4ndTM/1CJGBERokr6lpMvOEKlFnA0zTvCgPzc8wWER+Nf/ZNafoz/wS4OfQhPO6nGZ4DHgeWqGrRAfo1S2v+h7uAsa7v93pMIFLIlGizsh1HrC/YTWFVGUXV5QQCtWza9ha7s+YRCNRSW+KnbE8Z+bsyydqysX5Mp3HQ/RShzzlC0gDY8ZF5qCMw7c8OXaeANwb6nufQc4aDN8YNdgYyZykrrxxA9u9GUrrToaYunfzSqRTk9CPtBMHxQeow4yIafr3D4JTuDMkezvfn/oTMtzxs+o+Jv/FEmP8Bgq7h1B8/lF6P3seQn/YgprNpW/iAUlPSvLgo2mLaY7vApHuE0bcKZzznEO9+dQ++Qsj6WlnzD6VsJ/V9I5Mb5qjIVkb8yKHXGcLImxyKtwf58LIgH18VZPfnwfp+5VnKu+f7mXVtOYsvfoGk1B0Nc+Qrs1cVsGRryX5rzF+rfPaTANEe45ZPTOtJfAubtmoqK6npewL9J0wnrqefyhUmQ7a32iEjysNvItO4vSpI4Pl7yJ35HNWl+agqtarcWb6P8zZvY2NFFV3Toqh0zL2ZW1bFSYs3UuE/Khs+LN8e7gKaRrxXuu2HjKqux8ThfCIiq4FPMS6TA3Er8FMRWQN0C2t/B+NmWYWxJvw/Vc1x2wJuMO0v9putYS15mHiQV921LIBmM2aEmImxdmzABF8vPNCi3fl/CLwtIqtocAXdD/iA1SKyzv18sNwLjHXX+xDw/YMYcw3Gjbcas3uqMy3fu6b8DXDce/8acJ2q1jTTDwA3nmYFxvjxfxgBeyBeAYIY0dIcvwUWufNsbKEPwAfAxaHAaOAWYJyYAPL1mMDplta8DOMGbEmIHRDRA+zIEZE1bkBV6HMXjM/3JczNHHMwJxk3bpwuXbq09Y6WI2Llvu28u3URvpo9TIopY0lxBfsihzA6oy8PLn6DSImgTv3c3e9UkvwfAXDK5D9RXrGXrXNW4a8JMPV71+BpIXlh/lpl3u1B4nvA9Ked/dxPAKueDrD5dYy8btAGZIyuIy69mp7nJrD2OaX7NOgyyWHvV0pVvlK0CQrWK+oXECNMek4HPJDYy2j1QK2StxJSh8JHVwWpLQXHaywzCKQMgYl3OUTEG4tP2ghjPs1brWx/X+l/iRDbGRbcFySumwk+rsyDfYvcOUIIJA2A4s0NTZ3Gw8kPN1hKvvhlgFx3L0S/i8AbKfQ6E778lVKVZ9oTKj6gImoyIuB30ogZW0ftpSUkx/mYNjyFcBY/FGTnLCUyGSY/VMKaxxOJ7yGMu0MQp/F9VlU+X7SB3IpUIn0QuT6O066PZFVmGdlFtfgcYVIfhzd+83MCeOh2g9n4UeAJcHe5Cd94eGB30iK83LA2M/yyGZsQw+8HdGNMwsHGeVraEhFZ5gapHjZuEPSDGCvALuCu787x2KryliPGjYtKVNXfHsM1dAXmAoNVNdhK9/3HtyKCvgauCY8HcgO13gWmqmpkS2PDsSKobVmQtZFbPvs7ZX7jPj0xLsClqeap/vs9EVw85HReWDsbT8BDwBPgoswLOP30T4mJTmfi6DsOWKOrqLqcuIhofG5Ji2CdsdCING8I/OynAQrXG3cTaqqZx3aHovXQ5UQo2QqVuSZWJr6HCVQOEZUCtWUw7Hqh+6nwyY0Bqmrq2PSTj/j9RZc2Os+b0wM09+s++GrY8SHUFMPQ62HYtY1dPMv+HGD7h/uPa4rjg2AdRKVB95MgbSTEdRWS+griEba8HWTNP5SuUyBvpdni3nki5CwKm0QVUNKSlzHigYnE9Q2SU1ZLRmIE0ZGN11W0WVn99yA9ThNqS5U1/zDt573uEJ2+/72uqQuycEsRTqWXk8YlAvDpynzKqwM4AhdO7ERdbQ1F1bX8cXU+ncXDoN5x5AT93L81C58Ir4/qxy827ibO47CnppaCOmMF8gAvntCH09MSW79RljblaIggi6UtEJF3gH7AaUeSZPII13At8AfgNlV943DmaC0w+sc0cXupapmInIUJQrJ8QwSCAbIqCukelwbAZ7tW8dqmL8kqL2R7SU6jqMMNVUKNeilyUrlq+In8ZNS5jO/Un6xZXlYtzOO87icydWrrNRY+3bmSW+f8g6GpPXnz/DsB9svsvDB7E48ue5fvDJjCZYOm4oaYoAEQgdpSqF1v2vYtMcICIH0kRCabUhAh0kZC1gJY8w9l2/sQKHeIIJJFGzMpzKkhLi6CiDghGND638qYDCOqcIzg2vxawzkqwjfj1p9D2P6hIh7j7opOMwImsTfkLKE+Big0R3U+FG2FrWZvBn3OhXG3C10mCrs+Uwo3QkJfI956nSEk9obM2UpNAeAIY34h9JoxEW+0AB56RTe/wyt5oNB5vJD1lZK9yE9QauhxtQOJ0RhZYgLVBfDFCZE+h1OGmt29WYXVbNpbQc/0KPJKaunTyZxjl195Na+MswYnE+k4TEtJYHlJBQFMAsYEn4f/G9mXmfklfJJXzPwS4zUJANes2UGq18PSE4cR7bGZNCwWS2NU9eLWe7X5Gv4F/OtI5jigCHIjui9yg7LWqOost70O4wu0tAGPr/iA59d8yhW+/mRuKKUsoRNLU5aDwPQeI/B6PMzKXLHfOC+gdQFO2+nhomv+1shac1rPkXAjXHKBEp12cOvYVpxNUJXtxTkEgkE8TsPDUFVZ8kflMd8XrErLpLC6nMsGTaX3OcKqp0wAcWJfqMgFv1uPIiQsYjrDyB8LTpSy81MIVBnLS85CCLp7ASpzIOPsKhYmLOHSAeP57Eovnsgg574qRCY5THnAoXCDMvAyoTJP2fiKsmu20TDRaVBVBDtnQnVBoJEbq9cMh6R+ii/ObFsHqC0J8sFl2rB/xYNRAi4Fa8Ler1fevySAOFDtxpDXFMF3PvFQV65Ep8Dw/4GcxZDYD2I7NRYQGlAqciCuW8PPJmuBUrQpyPqXGn6SDh6y5tdRNKKAs8ekU7UPPrkxiABn/NMhplPD+O05VRRX+BGgpJPD9uoKrtEofr15D/OKyrkiLpFbe5mNK2MSY3lrVD98IgyOjeb8ZVtYUtr8Jo0Cf4DNldWMjG+6w9pisVjaBwcUQSLyN2AYJhjrfhGZoKqHEgRmOQTyK0t4e+sCXlv/BTFFFXi//oT+wHuTMkCM53FJzhYGpTTkc/GKww9OOINRGX05tccJ5GzbQlLnLi26q8Ifnq1x/bAZxEdEMzytVyMBBGZL/M5PlXHp0wicW8HVo04ETBXz6nzI/EQp2W76KoqEGRSr8uGjKxUnqkH0BOsaRFKIwi+i+X8fTmPLm8oqVQLVsOZ5Zdj31S1IauZMjBVT3AslrieM+6Uw91ajaPa5XthAreKJcPv3Ma+7PguSOUtJGWLcfPXmpWZig8UL6ofSHfsf81dB3iplzXNBCtbC4CuFE2500KCy4eUg4oFB3zNFUZf8Sdn5qSkTMuJHDjUlyvy7g43ip4Lk4ZCOZnvd+wfVReB3tUp1McSEbcYd2C0GVWV7WRm/Wmc2R3SO9DE1OZ4dxdWcHIhhxfYyoiM8dEqKZExCbL11p/YALnQHGBHXvOXKYrFY2gOtucNOBkaqaiBsW54VQUeRuoCfVzfOY3H2Zmbvbkh0GedzqPUY6VDtc4itjqUmooY/nXwdk7oM4i/L3uPEboM5scuQRlXYO/cbcNTWFun1cdWQac0ei0gQRv5Y6LWtPzdf9HMiE42AcDxCXDelJmyjojTZSBhKShgSQI0Is8IEquHznwZJ6NtweMcHkLcyyNn/ahxTM+YXQueJpnp7ZCJ4Y9WIBoU5Pw+Qvwrie8Lke4VFDyhVBcZVh8K+JWECCONWC9cGKUOgcEPz98jxmaGRScYFCA272HJXwNp/NiRrTDsBKvaZz6FyGZX7lPjuULaHeiEUU5dDz2nZxF88jC7DU/E4QupQOPF+B3EgZVDDWnOLa9hTWEPKvA/YOeoMUsRDDUEKZn/IVVNP4sYThzB3TSElfj+3b9tNWVWAhXVV/KBbGn8Y2J3dVbXNXxjwP93TWxTTFovF0h5oLTB6efgOsKafDxYbGG0IBE0GF6/j4YNti7l7/svUBOqa7esTiPcrVw2eTueMblzYfyJe5/jI41Jbrix9JMjeeUZc1FuCBPqcawKXQ3hjwN9kA2/XqZC9oEFUNCWxL3SZJGx6XRn1U6HfhcLeL02gdcjKU7A+yNJHlLQTMIHQ7q95jxmwe7Y7kZsIEQm5wqR+/op9xvISnQan/Nlh3q+CVLobUJMHQ8/ThMS+QvIAI3qikk1Ac+EmyBgDRZtg3/IgO2eB44Fpf3WITBSq8pWshUG6TBA2vaZsfRsiEqH3mZhddUCvM2DCrw/uZz1zeR5VtUF8FaU4X+7G11XZuvMzKreupsfQE/ju3Q9w78ad/D23cSqSEbHRfDJhEBev2MKCYmNi8gBeR/jPiL70i4kiI7L5XYKWbw4bGG2xtC2tiaBKIFQrWzCR4Fvd9+rWP2kVK4JgX95Wfjv7cb6qriM+IoaS2v2LVXpxQGBGz1E8MPVq4iKOb1fE9pkBtnxWQ3VGEbGZGRRtdMyW9jDjQ2QK9D0HNr7mWoiU+u313miT7Zkg9D4b+p4rIII3SvnsJ0qgxuw46zpZWPZnxRsN57/luEHIhs9+EmhsxQkJH9z5qwAUJ0kJFjd2+UWlwpifO3SbKmx7P8Dyx4woOvN5B1+cUBcIsn59JRnJEXTpHlE/TgPKu+cH8VfB8B8IQ6526tvn3BqkeBukjzIxUPX4oMfUACx7hdTSV+j1wnPUJSWy7KP36DtqHN2HDm+0tsKyWlZllhEIKmVVRi36H0vCWyMUnfoyVbs2MnrKBLp/OJMbJ5zGqoFD68dOiI7mkeE9GRQXTWGdn4/zShidEE26z4eIkBZhq+l8W/g2iiARuQWzaWY5JvfMUFV9yM0u/KGqvnmY894LlKvq/zZp7+3OO7y5ca3MeZeqPniIY74L/B7IUdVTD/Wch3CeclWNc7d4P66ql7Y66MDzXQRsdnM5ISK/x5T9mH3AgR2c1r7thnwjq2inlFaV8srCl/g0exvjI+DL6joUKKmtxOd4qHP9Jp1jEvntpO8xvdeoY7reo83OmVCyKgKVDJaOWsiI4ImNBBCYrewbXjaioOuJwqq/ab1byF8Fvc+C3XMge6EpXdH9FGHHR1pf/6tkG5TvMapGfGareeowZfljULxVSewHhRvNTjU3bAg8MP1JYeu7ys5ZAEKweH+3T3UBrHspyLqXXKuUQlUeLHkkyIn3eVi6sJwcTxXb8yo5LzkdX6yZQzxCbBeTqyihl2mryFE8kUrhBrOOgrVNTlYHufP2kpG/gsraAEWr17Esawcb5sxk/bzPueF/X8IXbeYGWLylhKraINRBcE0kHr8D+zxIAiT2H0XyhHOpjHQo2fQ6d2z+B+9f8UOKRoxnbLd4ftarE14391CKz8tVXVusIWmxNMdPMAUs97if3z/YgSLiVd2vSl9bchcmR9Kh8APgRlX96mgt4kDXrapZwBEJIJeLMHn81rvz3nMU5mz3tLY7bGdz7W7xtiswFWotTViyfhZv7VzFuzkNUbTra+DkKC8Lqv04Hh9PnPYjfvjpkwDcPOaCdieAwIgAQRAVem0cSkJvKM1s0skVPEWboTSzsVVSvFC0xRQ8DdTAgt8rF/9X6T5N2PaBUrSxIbYGIFgDc3+u9D0fdvzXtJXuhJMehkV/gKR+JqePBiB3JUy404MGA+z6tGEOb1zDbjYweY3MYiC2G1Tspb60RtkiLzoJdI8XnWBKd3z9uyCpw6B0l4kXSuwLOz4yrrmUwTD5PocNrwQpCsudGspJVBPoye7kR4mKKWZP1wCFRYrjRJO65yHeOz9IdBpMe1yI6+IQ4XWoqg0iS6MIvh+Hut6z2lI/5ZuXkzypG6WZUBTxf5wwbS6//+54YpLjD7pIraX98OfLz9svWeIvX/vwsJIlisgzQF/gYxF5HigCxqnqz9wuM0TkTkz5hdtU9UO3ZtQlmAKaHrfqwPPuPJXAD8PqUI4UkQVAGvCwqj7b5Py9MUVYQ9k8f6aqX7uJfF9zz+vFWKrOBaJFZCWwTlWvajLXFRiRJMB/VfVXInIPpujoP0XkfVW9o8mYXwFXY765PlbVO0XkRkxm6QiMp+QatzTHi0A1MBpT5PUJTBbmOOC9Jtf0oaoOb+n6Wjp32BwnAhdgqr7fjSnS+lt33jdF5CH3uB/4RFVvdy1ev8NEYZao6snuz6r+5ykiHwL/q6pzReQM4D4gEtgGXK+qYd+WxycH/Y0oIqNF5BERycQERx8oBXaHI7+ikKc+eZjJL/2E3y9/n/fCBFAMcE60j3+kxzF/6kWsvOavnNh1MKPS++ARh42Fe1qe+DjmlEccojKMyumWnsjQq5vvJx4TF1RTbD57XC+gYKq1ixt8nNALNr0eJFBtdmCFcFxPVMg6JB6hy2TqJ9m3DGqLIXdZQ5xRziIjuHrNEGK7gTfWuNxm/E3wNJMCtPMkkwsonKTqSAL3paIvJVBTqqydW02g2lSv14DJQh2obRBqFfug21ShyUY7gnXuNYauJymRQBDiBo3n/N/9CyrMVrCqfJj1faUk38+EgYmM75/I0N6m5KkGoOtJsHBiEbMrJrJq9kcEXogFFSLGnEZSeqIVQB0QVwA9i6kQLu7rs277IaOqNwFZwKmq+mgzXXpjqomfCzwjIqHim2OAS1X1FMyDdIUbTnEXjfO8jABOAyYD97iuonBygdPd2NTLMTWjAK4EZrklnkYCK12RUKWqo5oRQF0x1exPA0YB40XkIlX9PbAUuKoZAXQ2plDnRFUdCTzsHnpbVce7bRswlqQQ3YETVfU24K/A024VhmaymLV8fQc4NwCuUHofuMO93vAEx6nAxcAw954/4B66BzjTne+CFtYTmiMNUy5lhru2pcBtBxpzvNDaFvmBGIvPFUA+RmlLW/pJjxvcbMD/XDubveUFvL15PtWue6soqEyK8LC8LsiA+DReOPs2EvZ+DWU5JPSYaOJaxEOX2BRW5u1g3p51/Gbisb2ctiB5oHD2S14K1kHR1iALH2g4Fm5xaRoAHagCXxzUlUPhOhh1s9DvfGHW/wRZ/wJsfCXIxN9CQh8jnMJ3osV0glE/ERyfQ+4KJTodHK+y+T8NVibHBwO/K3x+c8BkrHYPZc402/59sUZQ9T4Xdn3ibt1XqHNji4td61DfC4Sdn5rhOVl1lI4tQyqiGX5KJKkZPhwPZO1cyoJlz9H1tBmMPvM7AIz8mbDoD0pkkrk+MO46POZeeCOEk09Ioao2QEZiJMHbAqx5SgnWCEEnyCc/CxJ5fiWjeiSw7W1T/mPwFcqmpZ8T6RnJxYuGsi+lCxFjXmXsqTfQ7eQj/1lajlsexPwdFk6M294WpTNed0sXbBGR7TTU8vpUVQvd91MxlgpU9XMRSRWRBPfYe6paBVSJyByMoFoZNr8PeNItqBkAQgVTlwDPu4U631XV8DHNMR6Y69YHQ0ReweyGfvcAY2YAL6hqpbv20PUMF5EHgCSMlWdW2Jg3VOu/4aaErhtj7flTM+do6fpaOvfBUIKxSP3TteyEtqbMB14UkdeBt1uZYxIwFGPRAmP1WnAIa/jW0lpM0EbMtvjzVHUrwIEK2nUYaitg8ZP4/bW8vCub7EDDAzbD4zA4PpWbx1/KCd3D4sYHnLPfNLeMOZ+4iCjO7fOtins8qnijhE5jYcnDjdv95TQKUgbIj6wgxR+NE3Coc3PiiNdkl3Z8Jhtz+S4TWL2ghUo1sV0gfy1kjIaM0SFrkZAy1JTzABOTs/B+k3eoEWqCs2vd+qa5yxtyF0WGVZCIckNo0oY7DLwsgL8Weo7wkrPVi/M9P5HZMZTtVjqNgS9+M5sK314qcmZSOedCuo3OoaKyGxV7jWsNx8xXndcwd6+zYPfrDjGdHJ4bnsfdcXuZfkcsFy/OQD+Jg30ONc/HsyhsG3/+3ky25z5Oku9nwOkkxtdx+m2XEpNgt7h3cHoeYvuR0nSnTehz8xk5D358iF8A+zDWHgfzcEdV54nIyRgL1Isi8hc3m/A3wYvARW5y4euAaWHHml53yzuRDM1e35Ggqn4RmQBMx8Qe/QxT6uImEZmIuWfLRGQsxl0WbjIOWfIEI2SvONL1fNtoTQRdAnwPmCMiM4H/0AGrxweCQZ5e9RGRHh83jjjTZK6rLsYLnJXegy+qavmfE06noraKK4ZMO+it7H0SO3H/lBZ8RO2MHqcK299XOo2HvfPcxrCvgxrHz9+HLKEkspoHck8hYofxiYk0ZHZO6i9kL1SSBjRYUHITskkv7Vy/Bb94K3xxW5BxtwvBAFTlKQl9oCKr4VwagMBBFEuvDDNYa9iPNHmgSb646fVg/bb2TqMcJk9IZs5tQZZsNBc3+CohoeIygpFKbM3JqHrYszy8iDYQNKU5wNRTO+f/HHZ9pix9xtyclfeZOiSbvTXEn1VLl3TYPT9IdZY0CjJPGxKBZ72PuLjXOfWZU0jp23m/EieWDskujAusufa24Lsi8hLQBxPzswkTExPOl8BVmAS804B8VS11LQwXisgfMTEx04A7MVaHEInAHlUNisj3cWvKiEgvt/1ZEYnEuN/+BdSJiM+tchDOYuBx181ThPF2PNHKtX2KcdG94sb8pLgWmXgg27VCXQXsbWH8fMzz9GW3X3M0e30HOHc4Ze5aGiEicUCMqn4kIvOB7W57P1VdBCxy3W09gEzgJyLiAN0wljiAhcBTItJfVbeKSCzQTVU3Nz3f8UZrgdHvAu+6F3wh8HMgQ0SeBt5R1U/afIXHgNyKYt7ZupBz+46ne3wqX+1dz5MrTaTthC4DGZneB4ZcAoFaftXjRH5lE8q1ysibHEbeZN4XbQmy4D41lhCXgogqqn11dArG4tlhgnI8MdD/IuMaA9gzVwnWNk5cGF+ViLi5iFKHCKVuKH/BemXHR0dn7Z4o0LCMBlHJwqf/EzQJDl0Us20/PPC7auNWfHV9Sff/KiwjtBKVLkTEhWWfdsWgLw5WPa10Hm/yJ0Wlwq9GdaZHvo+zUxMYFh/DxpVKZaZT/6dIZCKMv9PBm9yNktjLKIoJktDfh+Oxv5MWwMTcPEtjl1il294W7MIIjATgJlWtbibh5r0Y19Vqdy3fDzu2GpiDCYy+X1Wz3GDhEH8D3nILZ86kwdIyDbhDROqAcuBat/0fwGo3x1298FDVbDeAew4NgdH1wcrNoaozXTfVUhGpBT7C3MffAouAPPd1PyHicivwf26Ac0vnavb6DnDucP4DPOumMAjfbRYPvOfGZwkNsTyPiMgAt+0zIJStdwdmh9kGTBoEVDXPtXK96opMMDFCx70IOmCeoGYHiCQD3wUuV9XpBzPmeMgTtD13M3t3LWVKek8mfPkW5XXVpEbFM/+Kh8mrLOHamY8S5fHx0lm/ICHS1lI6UrIXBfn6biXoN7XEYjKgJDtITZ7gNDE2ZoyFU/7Xw6IHG+/kAiiPLCOuJh4cuORjh1nXB6nIMsIloacpfhpekiLEGS8KOYtg9dOt//6PuhlWhv5GFJhyPyy4r3GZj/F3Cksf0fr4pjjfOqQ0i7Lo0wETvB1uufFEmKDp0LH47sZNV5oJKYPNtn4cOOdlh9guQrBOCQagaCPM/22Q2M5m95032sQ41RRBdvIe/nb+Q/zzjFuY0s1mt2gPHI08QUdzd5jF0t5oLTA6pYVDb7r/H/9seBfNWsKvc0pYVRvgl4lR9XUPYnxG8KbHJPLxJfceuzW2M/xVyroXjQAC4/LKXw3gNLtdMXcZvHVmwIiIsDiijLEwZlo8y/8MBGHlU0FSBhvXV6Da7PhqTgCBKb8x5Bphx4dK2e4Dr7e2FGK7ui41hcJNwsn/K+QsUja9oXijYMnDaoSNK4LK64aRUfsJZdHTAYdgrdkFFxFvgrnDE4UHa83OsT7nQNmeAJWpX+H4TsIX67BnXpDyPZC9xARmn/akw+nPCZ/dpCgmeDxEYjCByV0GMTK994EvyNKhcAWPFT0WSzO0FhOUD+zBBEtB43ggxfh8jyu+ztpAj/g0esSnQ8FW2LsIAUqD5slapA7vnnML8/N3c0G/drhl61tA9iLq8+REpUFFtqm9hQOpw4wgqi2BVf2WMHLbeCDMihJmuBGBuC5CZJJSUwzb34eM8UZsaADyVoSd1IGoZIjuBEUbwBOlrHnuwAJo2PWCeJX1LxlrS0wXiEqCvucJMRlCaaaidVDnCppArbme6nxAlW63XMykCxyWPwF75po1hdIANFyH4vhgxI3Cztmgfg91pREM/tNMauadw+pnGq9pzXNBspvuyfAEWddjJVXTM3l6xk+I8kZgsVgsltZpTQQ9DpyKCeh6FfhKD9V/9i1gybavidsxm7hAFRnBAL8oCfDKpQ8RGZtWXzvh79278XlsP64a+x18Hi+Xp/U51stut2SMhvTREBEHPU4Ttr6jVORAVS5kfWWyR+etgPljZ1HlqaB/1lDSyjP2m6euCr66Sxu5mXKXGPfSfnXHgiYDdMYYKFoPm/5jXGYtEZUKRVuV/NVGgAVrTS0xT4SpjRaZBL3PEipylD3zzE4vX1xDkDMirHipN8WFRgA1j6mtFqwzBWlj0qEAJbFrPOkRI/ni3ca9nQj2F0AAAYdOBd35a93znJk1kNN6jmz5wiwWi8VST2uB0T8XE9U2DbgGeEJEPsEkfNpxoLHHElXlwS+eZ+6uFZRpgHTH4YMuCSbO3uNhcpSY6thRSXDK3QD0FIfrjuWiOxCRicK0vzRst+oxDVY8EWDr2+B4YdwvhfIsoVP6pbw/dBGRr/d3E8GHIdDnLFgWyrPjMWMDNexXmgPcZIRB2DXb9I1Op34bfr8LIK67UFupbHjRtNUUQ9aXjc8HUJkDn/5AcXzKtMccshdSH+Dd3KbAHR80fw/Sk+aQV2zSbfl6+ek6JYJuJxvrU3yPkfirlJTBQaoKjTiE5q8rRHl0KROSBjOu04CWO1ksFoulEQcdGC0iSZjtffcDdzVNZ34gvqnA6GDQz1OfPsaL2dvo5XVYX2cCQuIE7k2OYXSkl+jkvujQS0iNT2/z9VgOjZIdpghqbOf9dzbtWx5k3QtKyXZTxX3Crxz2fKGm1hgw+CrY+MrBnSdlKEz7i0N1EZTvhU5jhbpy5d0Lgy3GEOFghFCo/hjQ5zzY+2VDXqH4vlC2vfXzi9SgGonHU8nAq2PoMlHY+wX0u0ioKYF5dwRJ6GVinvyVsOWNZuZwXX4xXSCxt7GeDbrs4FIzWI4fvo0FVC2W9kRrgdGhrfGXA+mYrJJjVbWtckwcFou2zKdy2yf8IjuXKIEKhfV1QZIcIUGgWpUuA86g8+DT8TStWWD51pDYp+Vt3Z3GOHQa07jNE2nUSFQaDL/BIfPjINWFENfN7DjLXWb6ieMWT3UpXA8L7w/ir4ZJ9wggeGNMUsaC9a7FxXiqGmJ3QuPFJHBUv3Hd1ZYYF9vUBx32zlcW3d/KHxUOJKdlUpg7iKiEOoZ93+G/VwSp2gc5S5SKfcbtVrDWLbIaWrg0/N4GRYnPMEVaR/zQIXmQ3Q5vsVgsh0NrMUG5wBZM/oEtmEfCOBEZB6CqraXabjNyK0vIXvkqT2auY2q0lxMjvdQBp0Z6mVXtZ1yEh6e7pPF52kTOHjAZb3TSsVqqpY3od4FDQm8lrhuII5zyZ4c9Xyp9zhYik2Hd88qOmUqNm1IstqvZlRWdAlnzTduC3ynTHjXjx9/h8NGVYWqpiZ7JGAtdp8DWd6B8D3SeCPsWQ9cpgr/aiKsh1xqLVHhMkvhA6zDWpCAM+NFg4rqDI0kUrGvYbVCyE5MoPxyPs591ylFBFU75s7X8WCwAIjIXuF1Vv925WCzfOloTQW4+XAbSUMMk9J2ttF5v5KiTV1nC4ys+YNX2BaR7hPk1Ab6s8fNyRhzXxEaQHZXBfybNYEQfs7Pr/G96gZZvlPQRDVaQhN7C0N4NnytyGgRQ8mCY/jcHEUEDyszrzNbzihxY/liAqnyoq3StPAF3S3y2SUZYU2SSFxZuMNalPudBXBezPb26CLa+o1QXwJa3mrEChRtpXDGTuwwWP6QQAA0qfc6FzFnGuhSi5wwTv9RIAMUH2DCynCHJkUy70uaqsrRPRMQTVm/LYmlTWhNBa2lwDOC+z8PsEvtGA6M3Fe6lc2wSv/v6//h892oAdvohEkh0hNJAkDvP/jXEd/4ml2X5lqJBZc+8BlESnQaVeUFEHGLShbG3Cztnwb6lyrZmcreGgp2jko0I8rsCCWCHW37Q61a7j0yETuOELW83X4+sHoEhVwu5K83W+hA7mwgggF1zGn+OSIDzXvfhiWwpdZfF0jx77vxyv2SJ3R866bDzBrkZnGdiSimciCle+gKmOnwGDSUh/oqpPVUFXK+qm0TEgykcehZG4j+rqk+ISCamQPfpwMPuhpy7aMjm/Cv33OWYDNhnADnA90JFUDElO/6GKWT6A1X90l3rvzFlOAB+pqpfu2UhnsRUkd8N1AHPq+qbbg2tv2CKoeYD16lqS1XfLcc5rYmguGbaegG/EZF7VfU/bbCmev459xleyFxNvhu83T0ulTN7j4HdkOCN5OK4SC4aPIPeKV2J7jSsLZdi+ZaSu1xZ/WyQPmcL/S5oiJsRR5h4l7D6H0pljonf2fW1Hy8eTvmzw5d3KsEaIy6aEp7dOWUolO0xn6OSYcB3hNVuXS+/K3g6TxAyRgsjboIVj7Of+0rrICLRxA/lrVET60NDrFKwiQAC6t1iToR5Cgz9PngibeyP5dBwBVB42YxewLN77vySIxFCQH9M5YAbMCLoSkx1+Asw4uVa4CS3eOcMjAj7DvBDoDcwyj0WruoLVHWMiHTFCKyxmLpen4jIRW4Zp1hgqar+QkTuAX6HKQgK4FXVCSJyjts+AxPScbpbvmMAJtXLOExdzN6YyugZmBIRoSr0TwAXuqUiLgf+4F6npR3S2hb5+5prd39xZ2NihdqEvVmrkMJN9QIIoKy2itvGXsS1w04jIzqRZmrSWDoYW98LUrQRakuVfhc0PtbjVIc1zzdY1T1Bs8Urd7kRQAD++tIVASS+lEBBcqOt6BFx0ONUY61JHQZ9z4Ot75qt8iikDDP1zWqKlayvaXF3WV2FEVw9p0HxZmNZ8sVDt5OF3GXaqMBriOgMOPvfgifCBvNbDpsHaVw3DPfzgxxZFukdqroGQETWAZ+pqorIGoy4SARecoWHAj533AzgGVVj+2xSBPQ193U8MDdk4RGRV4CTgXcx/8JC/V6mcUhG6P0ydw24533SrbsVoCGsYyrwhqoGgRwRCdleBwHDgU/d54sHsFagdkxrlqBmUdVCaWMFUlZdyYgID108QnFQGd15MPeeeCUex6FTTFJbntpynFBXoeS6WaGdCHj7nADJ/eGUvzg4XvPrGV6kdWmvhYzcPYANL6eCW6Aj6FpzIk76BGfC+1Q99iha1ZBFcdfncNZLwoBLjEtt3UuuAHIpXAfvXag4EXrAPD7qhy6Tod+FHgo2Bdj5sbEMZX6sTP6dsP5fSrDOJFyMTIJuU4TeZ1nxYzlieh5i+8FSE/Y+GPY5iHmu3A/MUdWLXZfU3IOYs6L1LvsR7nAOrSFAw7PtF8A+YCTmH31Th3VTBFinqpMPYy2W45DDEkEicirGTNlmDO47mU9Lcrk2chNXT/8lvuYy0Vk6NPlroa7MvC/LdNvWwDvnBkkeAKNvlUZfkdM3lVMatX9+KMcHvSalUZrYDU93KNvScKwqF945VxGPyc0TrKZ+l1c4BxJAobpjO2dBvwuUXtOFnR+bhXkiTHD36f+wgsfSJuzCuMCaa29LEoHQnyDXhbV/CvxIROaE3GFNrEFgqtA/LiJpmOfMFRgXFZh/fZdivBBXAl8dxDr2qGpQRL6PseyAqYLwfRF5CZP+ZRrGMrYJSBeRyaq6wHWPDVTVdYdw7ZbjiNbyBK1hv43CpABZGJ9vm3L66Avb+hSW45iM0ZDYD0q2NW4P1pp8P5W5jdsj67aTEHyf0hjXb+bmARp2nTB4xiRgEoWJQeb9UhsVJiVoYndCwcyeCPYPgG6BiEQYfYvw9T2K+o3g6jTUYfydJqdRn7OFiATr1rW0GXfROCYIoNJtb0sexrjD7gb+G9b+HMYltVpE6ty1PRk+UFWzReROYA4NgdGh7QsVwAR33lxMDrsD8TfgLRG5FhPMHbI2vQVMx+Si3w0sB0pUtVZELsWIsETMM/IxwIqgdsoBM0aLSNO/IBQTvHZIZstvKmO0pWOiAWXl34Ls+MiIk4yx0PtModfpDvuWKf5qc0zqymHjZxRFnEFVgdnaNfoWod9F0ii+rHKf8tVdQUpayP7c+xwjvIo2AQKDr4boFCEiziRpXPU3pXyviRUadr35w7M8Swn6IaGnFTyWg+doZIw+2rvDjiUiUq6qzW3YOZy54lS1XERSMdanKaqa09o4S/vioMtmHAlWBFm+TVQXKnN+HsTjg2mPOUTENy9Mds8NULABtrwFjgP9LzFip99FgidCCNSo3bFlaVNs2YzGHGURNBeznT4CeFhVXzwa81qOL6wIslhaobpQcbxYt5XlG8eKIIulbTmswGiLpSMRlWLFj8VisbRH7JYUi8VisVgsHRIrgiwWi8VisXRIrAiyWCwWi8XSIbEiyGKxWCztAhG5zq09ZrEcFFYEWSwWi6W9cB1gRZDloLG7wywWi6Ud89HnN+yXLPGc054/7GSJbi2wmZhK7ydiqsi/ANyHqch+FbAVeB7oi8lQ/UNVXS0i97rr6Ou+Pqaqj7vz3kZDtfbnVPUxt/1a4HZMst7VwE/c14GqWiciCcAq4P9hKsS/IiJVwGRMlfi/AHFAPnCdqtqCqJZ6rCXIYrFY2imuAHoWUz9M3Ndn3fYjoT/wZ2Cw+/+VmMrst2NKctwHrFDVEe7nf4WNHQycCUwAficiPhEZC1wPTAQmATeKyGgRGQbcDZymqiOBW1W1DFOQ9Vx3vu8Bb6vqG8BS4CpVHQX4MTXHLlXVsRhR9ocjvG5LO8NagiwWi6X98iCN64bhfn4QUzD0cNmhqmsARGQd8JmqqltvsjdGbH0HQFU/F5FU12IDphZYDVAjIrlAJ4yAeidUkklE3gZOwlh/3lDVfHeuULHV5zCWn3cx4unGZtY4CBgOfOqWxfEA1gpkaYQVQRaLxdJ+6XmI7QdLTdj7YNjnIOa5UneQYwMcxnNIVeeLSG8RmQZ4VHVtM90EWKeqkw91fkvHwbrDLBaLpf2y6xDbjxZfYmKDcIVKvqqWttL/IhGJEZFY4GK37XPgu26RU0QkJWzMvzDWrBfC2sqAePf9JiBdRCa7Y32ue81iqceKIIvFYmm/3IUJTA6n0m1vS+4FxorIauAh4PsH6qyqy4EXMdXcF2ECo1eo6jpMHM8XIrIKE+Qc4hUgGXg1rO1F4BkRWYlxf10K/MkduxITyG2x1GMLqFosFsu3lKNRQPVo7w77tiAilwIXquo1x3otluMXGxNksVgs7RhX8Bz3oiccEXkCOBs451ivxXJ8Y0WQxWKxWI4rVPXmY70GS/vAxgRZLBaLxWLpkFgRZLFYLBaLpUNiRZDFYrFYLJYOiRVBFovFYrFYOiRWBFksFovlW4ebEfrKsM/XiciTx3JNTRGR8jace5qIfOi+/9Zde3vBiiCLxWKxfBvpjSnMarG0GVYEWSwWSzum85yVV3aeszKz85yVQff1sIWFiMSKyH9FZJWIrBWRy932TBH5o4isFJGlIjJGRGaJyDYRucntIyLyiDtuTdjYZtsxmaZPcuf8hdvWVURmisgWEXm4hTUezFrqrSzu5ydF5Dr3/UMisl5EVovI/7ptnUTkHfe6V4nIfpmnReQOEVnijruvhbWdJSLL3Tk+C7unz4vIYhFZISIXHurPxXL42DxBFovF0k5xBc+zNFSS7wU823nOSnJOHXU4CRTPArJU9VwAEUkMO7ZLVUeJyKOY8hVTgChgLfAMcAkwChgJpAFLRGQeppRFc+13Arer6nnuua5z+43GFGHdJCJPqOruZtbZ2lqaxa1RdjEwWFVVRJLcQ48DX6jqxSLiAeKajDsDGABMwBRufV9ETlbVeWF90jE/i5NVdUdYHbTfAJ+r6g3u+RaLyOyW1mg5ulhLkMVisbRfHqRBAIWIcdsPhzXA6SLyJxE5SVVLwo69H9ZnkaqWqWoeUOM+3KcCr6pqQFX3AV8A4w/Q3hyfqWqJqlYD6zGirjlaW0tLlADVwD9F5BIa6q6dBjwN4K6zpMm4M9z/VwDLgcEYURTOJGCequ5w5ykMG3unW+9sLkas9TzAGi1HEWsJslgslvZLSw/Tw3rIqupmERmDKVfxgIh8pqq/dw/XuK/BsPehz0frWRM+b+AA87a2Fj+NjQBRAKrqF5EJwHRM8dWfYQRQawjwR1X9+0H0bW7sd1R1U6NGkU6HMZflELGWIIvFYmm/7DrE9gMiIl2BSlV9GXgEGHMIw78ELhcRj+saOhlTNb6l9jIg/nDWeRDsBIaKSKRrGZoOICJxQKKqfgT8AuOiA/gM+LHbx9PEDQgwC7jBHY+IdBORjCZ9FgIni0gft09K2NibRUTc9tFH7zItrWEtQRaLxdJ+uYvGMUFgXDx3HeZ8JwCPiEgQqMMVBgfJO8BkYBWgwP9T1RwRaam9AAiIyCpMXE/RYa55P1R1t4i8jokR2oFxY4ERXe+JSBTGQnOb234r8A8R+QHGAvVjYEHYfJ+IyBBggatlyoGrgdywPnki8kPgbRFx3GOnA/cDjwGr3fYdwHlH61otB0ZUtc1PMm7cOF26dGmbn8disVjaEyKyTFXHHckcbnD0gxgX2C7grsMMirZY2h3WEmSxWCztGFfwWNFjsTSDjQmyWCwWi8XSIbEiyGKxWCwWS4fEiiCLxWKxWCwdEiuCLBaLxWKxdEisCLJYLBaLxdIhsSLIYrFYLG2GiJQfg3NeJyJPuu/vFZHbv+k1WI4PrAiyWCwWi8XSIbEiyGKxWNox7yzcd+U7C/dlvrNwX9B9vfJw5xKRh0Tkp2Gf7xWR20UkTkQ+E5HlIrJGRC5sZux/ROTcsM8visilbhmKR0RkiYisFpEftXDua93jq0Tk325buoi85Y5dIiJTDvfaLB0TK4IsFoulneIKnmcx1dbFfX32CITQa8BlYZ8vc9uqgYtVdQxwKvDnUC2s5saKSASmXtd/gR8AJao6HlM9/sZQfa0QIjIMuBs4TVVHYspYAPwVeNQd+x3gucO8LksHxWaMtlgslvbLgzSuG4b7+UEOI4u0qq4QkQy3kGo6UOTW4fIBD4rIyZhK7d2ATkBO2PCPgb+KSCRwFjBPVatE5AxghIhc6vZLBAZgamiFOA14Q1Xz3XUUuu0zMIVQQ/0SQkVMLZaDwYogi8Viab/0PMT2g+EN4FKgM8a6A3AVRhSNVdU6EckEosIHqWq1iMwFzgQuB/7jHhLgZlWddRhrcYBJqlod3ri/EcpiaR7rDrNYLJb2y65DbD8YXgO+hxFCb7htiUCuK4BOxbjdWhp7PXASMNNtmwX82LUmISIDRSS2ybjPge+KSKrbJ8Vt/wS4OdRJREYdwXVZOiBWBFksFkv75S6gsklbpdt+WKjqOiAe2Kuq2W7zK8A4EVkDXAtsbGH4J8ApwGxVrXXbngPWA8tFZC3wd5p4Kdxz/gH4QkRWAX9xD93inne1iKwHbjrc67J0TERV2/wk48aN06VLl7b5eSyWtqCgaBPrNv+bLhnjGdBnv00vFkubISLLVHXckczhBkE/iHGB7QLuunhSJ1tV3mLBxgRZLK2SlbOA8oosMvd8ZkWQ5bjDFTxW9FgszWBFkMXSCinpp/HbWR4ciWDUqBrS4yOP9ZIsFovFchSwMUEWSytsz6+guErZkBfBhyvWHOvlWCwWi+UoYUWQxdIKPZNj2FmSQpU/gj3ra1sfYLFYLJbjAiuCLJZW6JnRk+sqk7lzwBqSqlaTmf+N14O0WCwWSxtgY4IsllYQEU4dvYTyXpsB2LQxi64TfsW+/GV4/X66JA6CxG7HeJUWi8ViOVSsJchiOQg6O91x/OZvhkDtHlase4Y1G19kxdaXKXt2KmStPLYLtFiOM0TkOrf8RkvHfy8iMw5z7kwRSTv81R17ROTnItK05InlKGNFkMVyEESkfErQ42/U5nUiiaqpJLK6HKqKjtHKLJbjluuAZkWQiHhU9R5Vnf1NLEREPN/EeQ6Rn7N/3TfLUcaKIIulFVSD5CclAgoKfXqcic8bjS8igYSkS3ly4M94ZescqqqtELJ8C5n96yuZ/etMZv866L4ebgV5ROQhEflp2Od7ReR29/0dIrLEzd58n9vWW0Q2iMizIrJORD4RkWi3WOo44BURWem2ZYrIn0RkOaZExouhoqoiMl5EvhaRVSKyWETiXUvSk2Fr+VBEpjWz5ndFZJl7/h+GtZeLyJ/dDNSTm4yZKyKPishSd/3jReRtEdkiIg+4fX4vIj8PG/MHEblVDI+IyFoRWSMil4f1+ZXbtsq9l/3c6w0dHyAiy0XkFoxAnCMic9xjZ4jIAvf4G7ZQ7NHBiiCLpRV2bHmT3Lp8ECGpNJ/kL5+kYsdsqqrz+CSrhMFpO0n27WFn9spjvVSLpTFG8DyLqeUl7uuzRyCEXgMuC/t8GfCaWwl+ADABGAWMdSvK47Y/parDgGLgO6r6JrAUuEpVR6lqldu3QFXHqGqouCoiEuGe91ZVHYmpHB/qfzDcoKpjMaLrllD9MSAWWKSqI1X1q2bG1brZup8B3gN+CgwHrnPneB5TIgQRcTD11F4GLnHvQWitj4hIFxE5G7gQmOhex8Oqug0oCat5dj3wgqo+DmQBp6rqqa5r725ghqqOce/dbYdwDywtYAOjLZYDoKqUbnwH4qLx+OsYvmU5UbU1DCnNZnv3gVyc/29mVY4iNbqKUdvvZWG3FymPT6ZH56kM7PcdW83acqx5kP1dKjFu+yFnkVbVFSKS4cbypANFqrpbRG4FzgBWuF3jMOJnF7BDVVe67cuA3gc4xWvNtA0CslV1ibuGUjikSvG3iMjF7vse7roKgADw1gHGve++rgHWheqkich2oIeqrhSRAhEZDXQCVqhqgYhMBV5V1QCwT0S+AMZjaqa9oKqV7nUUuvM/B1wvIrcBl2OEZFMmAUOB+e51RwALDvYGWFrGiiCL5QBk5y4hKy4aVOm/fQ0LRp2KOg7pBdmM2riECH8tSwJpjJeNdHV2kxvrpTDSw7ZdH+H1xdKv19mHdL7q6mJUIDoyqW0uyNLR6HmI7QfDG5gK8p1pEC0C/FFV/x7eUUR6AzVhTQEg+gBzVxzCOvw09mZENe3gusdmAJNVtVJE5ob1q3aFSkuE1h2k8TUEaXh2PoeJbeqMsQwdDm8BvwM+B5apakEzfQT4VFWvOMxzWFrAusMslgNQW1dW/37TgNEEPF6CjoeynmPZl9yZT4LjSNAKFupgNnYbTFF8Sn1/n/dA3/UNVNeUsGTloyxd/QRzv7yVL768lcJtHwPg91dTW1fBlh3vk1+4IWxQCexddnQu0tKe2XWI7QfDaxjXz6UYQQQwC7ghFKciIt1EJKOVecow1ehbYxPQRUTGu3PHi4gXyARGiYgjIj1o3oKSiLFWVYrIYIxF5WjyDnAWxtIzy237ErhcRDwikg6cDCwGPsVYfGLc60gBUNVqd+zTwAthc4ffn4XAFBHp746NFZGBR/laOiTWEmSxtEAgWMee7K8IKtQGPER5AuCa4Cur9vGKczrP1E4nyqnjL1PeZHv0MAA65+4mKnUoIh5qakuIjEhsNG9x6Q6y9y2mR9dTiIvtzMLlf6SyKtccdBwQoXrta+TEZ7Bi3dOoKiYoWxk17Cd07TweXjgX9q2BMx6AE2/+Jm+L5fjiLkxMULhLrNJtPyxUdZ2IxAN7Qy4iVf1ERIYAC1x3TTlwNcby0xIvAs+ISBVNApObnK/WDS5+QkSiMfFAM4D5wA5gPbABWN7M8JnATSKyASOmFh7KtbaGu7Y5QHGYVekdzPWsAhT4f6qaA8x0Y3+Wikgt8BENP4dXgIuBT8Km/4c7JsuNC7oOeFVEQsUL7wY2H83r6YiI+YJtW8aNG6dLly5t8/NYLEeTquoC5nx9R+NGVZyAn6A3ktU56TyzeipJkZXcN/VjfO4m2567t1CWkExRYhqREUlMn/oXM1/mPDbtnkV+5W5qfebvj17dprMvfwXVNYXExXalZ1ER5KwhP7kTuUmDIJAFqvXiiyDExGYw6avXiKoohGm/hml3flO3xPINIyLL3ODcw8cEQT+IcYHtAu5ixh9tVfmjgBsQvRz4rqpuOYJ5bgcSVfW3R21xloPCiiCL5QAsX/M0OXlLGrUlxfUmLW0UWzPfY19FLPHeSuJ9fgIixFdWEltdQU6q8QR4nGhOGHwNCRWlrFn5OEXJrocgTNikpZxAVFQyqUlDSE7sx+oNz1NYvJHQP01HA6jjwVtTTcDrRT1eImuqmNLtQqKGfxecb2OKE8vR4KiIIEubICJDgQ+Bd1T1l0cwzztAP+A0Vc0/WuuzHBzWHWaxtEBJ2W6KS7fv164ieD0RgNIptpyhW1fSM2s7OaldWDlsMmWxsQCIeAgEq1i5/h/mc2Jq2CQNIii/0FSm35M1jwhfAlFRye740Pk8oEE8CN17nYOqn+joNKJ6ntXsusvK9yKOh7iYzkfjNlgslmZQ1fVA36Mwz8Wt97K0FVYEWSwu5RVZlFdk0yl9NCIOK9c+TXWNu1EjTLSUlO2gsiqXtOThFJZsocYXiYOS1blPo/mSEvpRUradYNBkmlbHi6Ao4HijcBwfglDnbyjIWltXSm1dKSI+VOsaJhOHmshIEhN60a1zi+ETlJTt5Oul9yM4nDTxfmJjOh2dm2OxWCztELs7zGIBgkE/Xy/7A8vXPsXOvXMASEke1NChSU6SOn8F+UVrGdj3YmLO/BOBH8+H/tMb9enW+URSEgeHtRgBZM5Xy8ghNzB2xM/wevbPjK9at58lJzamM2kpQw98IaqoKqH/AJZ+8DbP//xHbFu2+MBjLRaLpYNhRZDFAog4+LzGjRXpi6e6pojiilIeXXoGD3w9g5Ka/VKQALBx6xts2vYWJZGRnDDsRhLje2NSesD6za+QX7S2xXNW1RQRH9eD1JTB9ec2a/EQG9OZfr3Ow8RdQmJCX06Z9OB+O82akpjQmynj72Hq+N/Vi6hVn35MUfZe1s/7/GBvh8VisXQIrDvMYsGIoKkT7qWmtpS4mM5s2/kxa3ftYEuhcfnvKElhdKe8eheVzxdPXV0ZEKC2rozla55k3MhbKavYC64FJhjuzgIiI5OJjEym1I0z2rLjXSJ8cezLC9/Z66AaoLIql1UbniMkqOJiuhz0tSTG92r0+eRrbmDDvDlMuOi79W01taUEArXERB/XhbYtFovliLAiyGJx8Xlj8HljKMhaROp/7+asqGjW9+tEVcDHCWnZqCrDB11LSdlOdmd9AUCEL4naumJq68r4eukDB5y/pqaImpqGIqt1deUkqo/01BEUFm3C441iQO8LKCzexL78FagGASUqMoVO6WP4ctE9BAK1jBnxUxLierR6PapBysr30G/seAaMb4gjqqkt5YuFdxEI1DB5zJ3kF22kqiqPwQMuP+gEjxaLxdIesCLIYmlC3rKnGJy7lW1DJnJ2+qZGx+r8VXROH1MvgryeCGobG3yIrK4kOqAUx4ZcXALsn4oiuryEkg9+zPj/WdAo5qhX91OpqNzHstVPUlmdy9CBV5KTu4Syij0A7M2eT8KA77V6Heu3vMrOPZ/ROWM8CXHdiY/rQae0UQQDtQQCNagGKC/PYvN2Uz4pMaE3PdPGwTs/Am8UXPgU+Jp3A1osFkt7wMYEWSxNiBtxNfu6DCDJl7zfscLiTWzY8jpeTxQiXmKa2YY+tBJOXDaTKUtnuy3N5+KqiktkZb+hFJY05FjbsesTFq14BL+/mvLKLILBOkrLdpGRNoqQa6yk7OAqHtTWmV1npWW72Lz9HZaveRK/v5ro6DQmjLqd0cN/StfVH9Ipfy/xdQHSUoZB5lew8UNY+yZkrWjlDBaLxXJ8Yy1BFksTuvc6A350Bp2A5OItLF/7NLW1xUT4Eiko2kQw2FBLsaBwLR5PNIFAVX3b5oQY6rr0IzVtJF0yxhMMBtiX31xGf0OEL57dWV+yJ/tLSkozCaqfPTlfMXLojRSXbKV39+lERMRTULSB3VlfEBWZRCBQy7xFvyUQqGby2Lua3Qo/fNC1dEobhccTyfI1T+HzxrJh6xskxvdi07Y38AdqmOr3Mnb9Quh9EkSnQ5+TYNgl4I2EbmOP6n21WCyWbxs2Y7TF0gqFRZvYu28BvbufzldL7kU1QGREEh5PREPNrxZcXgP7fof+vc9l/pL7KSnbgfm7w+QN8jjR9Oo2jdSUwaxe/zw1dSXExXbD541mYN9L2JP9FbExnejf+3wAgsEAZeW7iY/rztrNL7Mnax4APbudSnRkChER8fToevJ+aygt38NXi+9p9triYroQHQjStedFPLYwiugID7dPT2PrjreJje3KwD4X1O9Qs3zz2IzRFkvbYi1BFksrpCQPqs8ZNG7ELRQWb6Zvz7MoKdvF4pWPuL0U888pQLgY2pe/AhBXAEFIAAEEglVs3/0x23ebivE+bzxD+l9Oeupwduyezd6crwHo1nkK0VEpOI6HxITeAMRFN7jhRHxscuN6khL6AYrPF0tUZDJ+fxULl/+xxWsrr8yhHCV70z/5atM04qvLWBC7mICjkAdokEH9Ljm8G2exWCzfcqwIslgOgfTUE0hPPQGA/MKmOYD89Ow2HQcPmXtNMeiS0u2UNFN6oznq/GVk7p7NstVPENQ6fN5Y0lKHExWZtF/fvr3OIimpP7W1pSxf8xQA0VFpZOcuZWvme4h4mTL+XqIOkFdI8KHU0TlvD+lF+zivczLnl8xhjzO8oU+TJJEWi8XSnrDuMIvlMNm64wM273inzeYX8eD1RJGSNJjRw3+E43gpr8whGKglIb4nAGs3vsyuLJMEceKoO1iy+q8Eg7X14yN88YwbcSv78lewNfP9Zs9zxvz38Ab8ZKV2pdYXQdDro6DTAPpMuYfU5EHWHXYMse4wi6Vtsd9uFsth0rfX2XTpNPGg+nrDMkI3cODq76oB6vwV7MtfRnbuUqqq8vlq8T18teQ+ikq2AdApYwxeTzSpyUNJSOhVL4BC42tqi9mW+SFbMz9o9hyOv5biuCTqPF66FmSRVFVJxPQ/MOLMv5OWMsQKIIvF0q6x33AWy2HiOF76tlDJPYTHE0n/Xufj91eEtQpDBlyFiR8yn1tja+YHVNeWhoUbmTc7ds3EH6iisioPryeafr3ObzRfQnxvcvKXER6n5HGiiIxMAWDk5uWUxcSTk9bNXJO/htQu44iMSGh1TRaLxXK8Y2OCLJYjIDG+FycMvoEdu2dSUZmLqh+PJ4pAoBoRhynj7iEu1pS82LtvARERCQwbcBVKEBBEvEweexf78pZSXWMyT0dHpZGSNJD1m1/G44miqjqfispsKqv2MXXCfQSCtfWlMWpqSwCoqs5jx66ZbNv5AY5EElSzjb+0LHO/NUdFJTNl/D18tfh3ZHbuR8/VqymN7c7WEWeiA85gQFTqN3LvLBaL5VhjY4IslqNEeUUWVdWFpCQNpKhkG8mJ/fF4fC32Lyvfi8fjIyY6o8U+qsqm7W9RW1vK0AFX4vU2zuBcUpLJwpUPE+GLR8RDZVVOs/OkJg2lLlBBadlOunc5iRFDricY9FNZXkzu1h30HD4Sb0TE4V24pc2wMUEWS9tiLUEWy1EiLrYrcbFdAUhLGdJq//i4bq32EREG97u0xeNVNQUEAtVUBapb7BMT3ZmJY26nuqaYwuJNdEobBRh3XlxCGnFjbBFVi8XSMbEiyGI5jklPG0Gv7tPxOBHkFaynrGJnkx7CqKE3AhAVmUTXgwzktlgslo6AFUEWy3GMx/ExbOBVAAzuD2s3vUxh8WZGDv0BsdEZBIN+IiLij/EqLRaL5duJFUEWSzti+KCrj/USLBaL5bjBbpG3WCwWi8XSIbEiyGKxWCwWS4fEiiCLxWKxWCwdEiuCLBaLxWKxdEisCLJYLBaLxdIhsSLIYrFYLBZLh8SKIIvFYrFYLB2Sb6R2mIjkAU1T2VosFoulZfIBVPWsY70Qi6W98o2IIIvFYrFYLJZvG9YdZrFYLBaLpUNiRZDFYrFYLJYOiRVBFovFYrFYOiRWBFksFovFYumQWBFksVgsFoulQ2JFkMVisVgslg6JFUGW4xYR6S0ia5u03Ssit4vIiyJSKSLxYcceExEVkbSwtovctsFN5q0SkZUisl5EnhERxz02U0SKReTDb+IaLRaLxdJ2WBFkac9sBS4EcEXMacDeJn2uAL5yX8PZpqqjgBHAUOAit/0R4Jq2Wa7FYrFYvkmsCLK0Z/4DXO6+nwbMB/yhgyISB0wFfgB8r7kJVNUPfA30dz9/BpS12YotFovF8o1hRZClPbMZSBeRZIyl5z9Njl8IzFTVzUCBiIxtOoGIxADTgTVtvViLxWKxfLNYEWQ5nmmp5kt4+9sYK89E4Msm/cKF0X9o7BLrJyIrMdaj/6rqx0e8WovFYrF8q/Ae6wVYLEdAAZDcpC0F2BH2+TVgGfCSqgZFBAARScHECJ0gIgp4ABWRO9xxoZggi8VisbRTrCXIctyiquVAtoicBvXC5ixMoHOoz07gN8Dfmgy/FPi3qvZS1d6q2gMjnk76RhZvsVgslmOOFUGW451rgd+6rqvPgftUdVt4B1X9e9M2jOvrnSZtb7H/LrFGiMiXwBvAdBHZIyJnHsniLRaLxXLsENWWwiosFovFYrFY2i/WEmSxWCwWi6VDYkWQxWKxWCyWDokVQRaLxWKxWDokVgRZLBaLxWLpkFgRZLFYLBaLpUNiRZDFYrFYLJYOiRVBFovFYrFYOiRWBFksFovFYumQ/H8X6CvcniWBBwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "# Obtaining a low dimensional visualization based on alternative splicing\n", + "adata.obsm[\"X_umap\"] = UMAP(n_components=2).fit_transform(run_pca(adata, 10))\n", + "sc.pl.umap(adata, color='cell_ontology_class')" ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filtering to heart cells\n", - "adata = adata[adata.obs.tissue==\"Heart\"]\n", - "adata = filter_min_cells_per_feature(adata, 100)\n", - "adata = filter_min_cells_per_intron_group(adata, 100)\n", - "adata.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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ksbKc+KxswuMS2L92ZXCtrsNH0WP0eL5/6VmaqxWhM+eJhxh37sWMPvM8di9fwqDpJ1FTlM+yWa/THpj9czGYLUy9+kZ8SX1Y++23iLwtdB0/ldbY3jRvWQhAWGwcUUlJCCGQUrJp7mxqivJJ6t6rkwgCWP3Je6T26kNSt56/4On/OKV7dqI3mUnIyv6vrvP7fBTt3Ep8RldCIiJ/832pqKj8M1FFkMr/DPNffIqiHdtw2JQg39z1q0nrPwhnaytDZ57KziULsDcoWVMIKN65DRBoDEb8Ti/Z2RmcduoZSCl5fvESkBKf10trQz3v33YdA6fOYM0XH+G0tTDguBOYdPGVzH3+CXLXreq0j27DR1Odn4fD1oIQkNan/2GtOACaa2swh4ZhCQvH2Wpn4HEn8MO7byD9RxBBQhCdnHpEd5lGqyVryHDsdgf1PyiWrd0f7aP3hGNJz0xjR0EKDeVlrP7kA0JjYmltaqRk1w4aK8uoLy3mpNvv45snHlSW0WjQGQxYwjsLDbejjW+e/A9et4tRp51L0a5tpPTohTHESnKPXmgCGXM/RumenXz+4N0IjYZLnn2diITEI46z1ddRmrOL7GEjgxWu1335Meu//ozolC6c++iz6I9QekBFRUXlv0UVQSp/S9yONgq3b6Fw2xbqSotI7tmHvauXA+DRgEBQHRcCgWrKrY0NXPDkS8x99lFKdu+kcHt7LztJSveeDD7hVDL7KQ1NhRBkDBrCgU3rATiweT1Ou43Vn74fLCGU1K07QgiMFsthe9t5oJaw2Hiaa6oAyFn1Q6eq0aBYb3qMGc9n990OKPWCTr3nIboNH8P+dSsPmxMpDxNA7YUS2/c25qwL6Dv5OPatWYnH6SAiLo5B004kJDKaVR+/G9zPsVffwP41q2isLMPtcFC8cxu9xk7E6/Ew4YLL0BtNmKzWTmvVFhcFawt99eh9AGyb/y1+v49hJ57G2HMu6jTe61EKNur0elxtrThaWtDqDSAEOoMRncGgzFtShCUsHI/LhTk0FIPZwleP3Ed9WQkVx07nmEuvBlCuBZqrq3jh/FMZNO1EJgbqJqmoqKj8UlQRpPK3ZMlbrwRFDyhtLdpZ0zOS0jgzw9tMUGXHEh5BzzETMFtDmX7D7Wyc8yUxySlsfvdtHGYjY8889zD3zIm33sveVT+wae5seowcy6a5szulrEfEJwGHx+YA2Hb8QLPrYEB1+b49h41xO9r45vEHOx3T6fVBK9YRCQip9qrQHufBFh+Kaw9Gn3EejpYWQqOjMYSE8Ma1FxMWE8uxV93A7McewOt2sejVF5R6R4Fgar3RyKSLrwTA3lCP2+nAZLXSXFPF6s8+oPJA7mEFF4VGg95kwtXWSn1550axtvo6PrjjBhCCcx9+ms8fvJuWulql6KSUDD/5dKxR0RzYvIE5Tz6EwRKC29GGNSqaiLj4oNgLCY+gIncvqz/9gF7jJnHWQ0/y6f/dBkDh9i2qCFJRUfnVqCJI5W9HS20NdaXFRzwXnZrGvWddSq6niZPShtB2ci3xGVnB85bwCCZccBk5U6YwvLSMwv49+ejumzjmsmvpM/GYgMDwsnHOF4RERHLB4y9QvGv7QQGk0WCyhBAaFwdAev/B7FuzEq/bFVxDp9fhdkHW4GEU79mF13nklHeNVsuUK66jYOtmZX4hCItV5hVCg5R+Erp2o+pArnJBwJIUmZRMXWkxUh4UYJbwCAD2rl7OgU3rOq1jq6vF1dZKxsDB5G1YCyiWMVCKP25bOI/8LRs57qp/8flDdyN9Ps64/zG+evj/gun6zSixTEKjIbV3X0648U4Wvvo8+ZvXU1vSualsc211sDZRU3W1UoNIymAbkJpCZbwn8Fx8HjdIib2+rlP6fv6WjeRv3UR1fi7NNdVMuOBSALQ6Hf2OmcrsJx7EXl9P1uBhHNiygR6jxjHsxNOC1ztsLbha7YRERKE3dW4c67C1sHHOl6T07E3W4OGoqKj8M1FFkMpfHo/LyYFN69j4zZfYmxpJ7NpN6ebewcXUHvAbmZSCZn8RY7ukE2oNI9Qaxr41K1j92Qek9OiNVq8nvf9g3JWVCK0GV2MDRFpZOutVtsz7hguefIm8DWtY+7kSWxORkMicpx4+uBm/H6fdRkNpCfbaWlJ79+VfH3zF14/dT+G2zSRm9+DEW++hYNsmpSJ0B0uR3mzh9Hse4rP778Tn9ZA1eBj9Jk/lh/fexOty8d2zj5GY3T3YJywkIgqfx8OhNFSUYQmPUNprBNLi8zdvwNlqx2BW3HPWyGjsjUr8k0arZeVHs2iqPDz7rF0M1ZeVsPm7r/G6FDHncTgPtuDoQM+xEzn+mpsA6HfMcTRUlNF34pTg+bbmJmz1dUy5/Dp0RiNpffuTkNWNspxdwTHdR45R5hozAZM1lHVffUJtUSFetwt9RDw+px2/s5XqgjwAdAYj/Y89njWffQAodY9WvP9WcL6aonwAGivK8LrdjDz1LKoK8vjyP/+H29GGMcTKxc+8iiU8ItgCZcu8b9j83dds+/47LnzqZWoKC+k2fOQR71lFReV/F1UEqfylqTqQy4JXnqEh4HKxdB1EszZKOSklMV0yqS8vJj4ji6oDuThbmln54Sw0Wh3XzvoEg8nMnpXLaK6uorlaiYkRQkNRv2xsXjdSo1VEh8dNU1UFDltLp9pAO5cuDFos2uk5bhI1RQWs+OBtzKFhmEPDlK7yQOWB/WyeO5sDm9YH+pB1JjG7O91HjSVn5TKKd20HQG804XW5MBhN5AfikABamxs7WXtAscT4vV7amho7Hfd5POSsXEZbs+JOaxdAoGRWNVVWEtp2AkZ3PxpD30bq60jomk1FrpI9ZwqxkrthDQDDTz6DtH4DuOCJFynasZVVH72LM1A1u3xvDvvXrkSj05ExYAiXPPtap31899xjlOXspte4SRx/7c0A9Js8lbKcXQiNhtCYWJJ79AqOj8/IojKwB0tGP/TRSbTmbsbvdoLfF9z/qo/e48ey5nQGA163m3VffkzF/r0U79oWPOdqtfPBHf+itbmR3uMmM/mSq0ju3guD2Uxyjz7MuvFKkBJzaBjXvPXxUddQUVH530MVQSp/WfavW83SWa/iCLR0iE3PxDhgIhVzlS/e7GGjKN61HenzYbKGcvGzr9NSW0NF7l6iklOCwbejTjsHgzEQv1JWQo/R4xg68xRm3XgV0udV3DEo1qRZN15BaGQ0eqMJj8vJ/jUHg5T1JhPDTz6T4SedzsuXng0obpWObSl0BiObv/sagMjEZBorywEwh4bT75ipALjbFJFlNFvw+32cetcDuJ0OjCFWFrz4FHVlJSAlg46fSf8px/POTVcF5+894RiMZgt7Vy/HYbcFLUFCoyGt30CskVFotBrWf/1Z53glCRH2SxBo8TjKaNa+j8ft5pS7H+TAxrUMnDqD7195jtamBrqPHs+2hfNorq4kY8AQrp31Ke/ddh11JUU011Qy9/knAOg76Tj6TDyGuIyu6AItR8yBPmu1JUU0VpYTmZhMzzHjSe8/EIPZjFanD26prqyElR/Oov+UaexduwKNKQTbrpX4XW2gVf5pUnqtdRCTQhCTmkZdSVHweWt1OlxtrcHzHQVQWJcsWkryaW1SLF4HtmwgZ9UydGYrWVc+TePC14LWRIethQ/emcNpZ0/FbFKzz1RU/gkIKf+7miS/hCFDhsjNmzf/9EAVlQ48f/6peN0uNDodg6edxKjTz+HDxx6jft9W8Hk7uXwQgnHnXkx1fh77160iJDKKq157PzhX1YFcQmPjKNi6kYj4RHYtXQhC0FRdRXV+buf+X0C/KdPYuXh+p2PdR41l2nW3Ul9Wwvu3Xx88Hhodi62+FqHRHBYoPfiEk/G4XRjNFuIzsvC6PXz/itK1ftIlV1O2dze561Yx6oxzGXnq2Thb7bx57cW4HQ5CIiKJ6ZJO/ynTmPvCE/g9HizhEWgNemy1tQcXEYIeY8bTY+RYknv0xhRipXjXduY9/wQOWwsRiUlYwsJp2zoco6sPDaEvoY2sJywuHktYOCfedg86nSIYpd/PxjlfKplwgNBoue6dT5l141W0drAuAUQmp9BYXkZIZBQR8QnMvOUejBYLz19wGtLnwxweweUvvX3UdPY3rrkIW31dsKksgCE2FU9zHdLd2fomNFqk33dYlt20629j2TuvBfu8HYoxIRONOQRH4S50BiNxmV2pCASqm1J74CzPhQ4/s9jjLmXScWNISY494nx/NEKILVLKIX/2PjoihPABu1ByJX3AdVLKtb9yzouAIVLK64QQ9wN2KeVTv3avKio/hWoJUvnL0nPsBAq3bmLa9bcGu6ufcNEF7FwSR1VhPpW5ew8OlpKVH71z8AuywxfllnnfsPz9t7CEh9PW3IxGpzuiq6ojxTu3HnZs/9pVdB85ltj0LDQ6PX6vB41Oz6gzzmXR6y90EkDtgshps+F2trFz0UFBFZuWgcflJKP/IPYsXwwQdNWZQqyc/9gLbF88ny1zZ9Pa1Ehpzm78XiU2qK256fDNSsm+VcvZt2o5yT16ceyV/6K2uJBT7ryffetWEZ+RRc8xE1g661V2/3APfSYcQ/qAwcHaQF88eC+JXbuxffF8fG53p95hsV3S0RkMtDU3HrZsSFgEjeVltDY20NrYwJvXXkx8VnbQOuVobmLt5x8y/rxLg9c0VVex6LXnSezWA3NoGLb6OrQGA/5AALa7thSNVneY40sGXGMdf64IwfevPHOYgO2Ir6mapMn/IuWEU+nVO5P1X38WFEHO0n2d7yezP7EJsX8ZAfQXxiGlHAAghDgOeBQY/6fuSEXlF6KKIJW/LMdecf1hxxqrK5GCzgIISOrei4aKMpw2GyBJzO4RPNcee9MeL+P3+w+z2ugNWvTmEBwtNqSUhERE0tbUhMd1MA3dEBJCVX4eaz//CBlw0Wi0Gha++lynvQiNFnNoGG3NjexdvZze4yd1Oj/5kqtJ6t4T6fczcOpMmmurGXT8DLYvms+2Bd8SGhtL8Y5t6IxGvC5XUAC1E5uWQWNlOXEZWVTs7/wcakuKmPvcY9QWFxKV0gWnrYUtc2fjam1l8iVXM/kSpe7OjqXfB60rFftzqAik2AM0VpbTe8IUxp1zEaZQK9IvsUZGY6uvJWvIcGx1tSRkdSOpR0/K9u4GwBwWjqOlmcrcfVgiIoMxSzVFBzPHbA11fHTXjThb7ZTm7CIiXimW6DmkYWx8ZhaVefsBMIWGBTPzNDodoVExhERFUbEvB6T8UQEEoNUKRo3qQ5NTg6Otmd0/LDrq2NaCHeQU7KBiYSKhUTHMvOWew+ol/R1Jv3PeOcAjQBegBLi76LHpv1XwUxhwuEIGhBAXALeiBHPtlFKeL4SIBV4L7AXgRinlmt9oLyoq/zWqCFL5S1FTVEDpnl30nTQlmOnUjrPVzndPP4qUfpK698QSFsHYsy+kraWJb595FEtYGNP/dTt1RQX0HKv08/K63dgDGVDtCOBQN3B0hIFzLz+Bz7/ZTemenUSndGHs2Rfy+UP3oNXp6DFqHLnr17Dxmy8OmUvJNjKGWIlOTqUidy/S70MfiEfy+7zsXb0i6MaxRsew8LXnaGtuDsaxaPUG6kuLyd+8Hp/XGwyybs/UOhRLRCRDZ55C6Z5dxKRnUZW3j/qyUnxuFyk9+2C0hFBbXEhDWUkwLkqj1SKlRAhBRe5elrzx0lF/BhqtlpyVS6kpyOO8x59n9afvY6uvJSo5FbfDQU1RAfXlZTgCwdJavYEz73uU3SuWkNyjN12HDOfTf99B+b49dOnTPzhvxf59wQBrgKbqDtlqHdxc7QIIITrVZvJ7vTTXVNFjzATCYuPYt3pFZ8sQYLSE4HY6ggLX1WpnzgvPET50GhUfP3TQovQjNFVV0lRVSUXuXjIHDf3J8X9lAgLoTaD9lykNeDP9znn8CiFkFkJsB0xAIjDp0AFCiN7AvcAoKWWdECKQzcDzwLNSytVCiC7AQuC378+iovIzUUWQyl8KpfZLHbb6GiZc0LkYnsFsJqFrNnUlxYw/75Jgb6vakkIcLc04WpoJi44lve8AAFqbGnn/9us7ffFChwKHgXRppMRqtUDiAGbePJPy/XtI6zsQncHA8dfehNPeyvaFcztljbVnI0WndiEqOZWi7Vuo6GCdaq6tDr72ul1MuuQq8jaspaogr1MtHFDq5OSuX/2zn5FGCOa/+DQAjqRornvsFVa+/Dx5G9bi83jQGU1BUeF1u0nq1gOh0fDcuSeROWgYY8+9KDiX1mDE16HGEUKQ3LMPpbt3UFtSxJf/+T8SunYDwGQNJbFrN0r37MTncRMRn8CQGaeQMWAIUckpjDrtnGA9ntPueYjmmiqiU7oEp47tko4lPBKNRkObrbmzS1JK8EsQYNTpyRw1lkmXXMH3rzxP/uYNncTOhq8/PeqzyR4xhvqyMipzDxao9NSWID3uTgJIZzCQ0LUbZTm7O927KTQca0QEcRlZdAl8jv7mPMJBAdSOJXD8l4qgju6wkcD7Qog+svNfFpOAL6SUdQBSyva/RI4BerWXKgDChBB/f3Obyt8WVQSp/KWITU3DXl9HdGoaoKRHa7RaNnzzBRX7czjhX3dgjY7u1Ksqf8sGdEYjCVndCA8UMVz/1ads/PbLYFVlg9lC5uBhZPQfRNWBXLYtnKvUFgoIotTxp/P8zf+HTq/nzAefYP3sz9i/duVRO7trXG4QSsB1sJghHBa4qxzSsHPp99QVFx31vi3hEUeM99Hq9ESnpKLR6phw4WVsnPMVcZlZFAWy4ip9bSzYvJQocwigCLxdSxZ0mqOutJiS3Tvw+3wc2LSO8Lh4xp93CeX7ctBotcHUeGUCedASg9Lvqz07KzIxibHnXERYXDw1hfkMOeFk9EYjOauWM//Fp3DYmpl5y91kDR6OzmDoJIAAindtC8YWGcwW3IfGZWmUL0aXz8uoicdhsoTSd+KxncoGBJ9poNp1p8t1OnLXr8bd1oYptSfOUkWUuhurqfjiceKzsolLy8BgCWHgcdN595Zrg9dGDJ1GxIgZFL10Dc6WJnqMGhfMePub0+W/PP5fIaVcJ4SIAWKBmp9xiQYYIaV0djzYQRSpqPyhqCJI5S/FSXfcx/L33mT/2lXsXbWc0j076TF6PPvWrAAgPrMro04/Nzje7/Oxd9UKQFKWs4ucVT/Qd+Kx5G5ci8fpRGc04XU5cTvaiEvLoNe4SfQaN4mUXn3ZPHc2TdUVGCwh7Fg0D6/bhdftYv6LT1FbVHD45jQ68Ctf3G4BsS2t1IWFdA7iDQggc3gkjsAXvpR+nPbO1iih1YLfH3TLdYw96ojP68EaFc2ki69i++L5FGzZQMGWDco5AenVDipeeAN7TBznP/Y85rBw3rjmYkAitDoi4xMwhYbRY9Q4CrdvwdVqx+fzEpWSypb5cw4WYxSCgcfPJGfFUlyHWM7K9ynxQsU7tzH78QdI6taTKZdfR0tdLe/cdGWne6suyD9qBebuo8ZRkbsPh60l0Lz2yETZHDR53ESgVIc+Eqm9+lK6ZyfS7w8WyvR7vUFh5XfYMCRk4a7KR0qJNTKCoTNPofuIsQC42lrxBUWYQOiNCK0ea9cB+GsKSe7ej3UPKCJr2J0atMa/7Zd0CYoL7EjHfzVCiB6AFqg/5NQyYLYQ4hkpZb0QIipgDVoEXA88Gbh+gJRy+2+xFxWVX4IqglT+MjRXV/HtM49QExAgQijVe/etWcHg6SdRkbeP7qPG4ff5KNu3m+9ffo62liZiu6Rha6jH7/MRn9EVgAkXXMYXD96Nt11cCIHf56OtpRlLWDilOTupzFOygxwtB+NOLBGRWCMi6ZCAHkQXnYi3thQQSCDUJanz++GQKsNJPXoxZPrJfPu0Uml64NQT2Pb9XABiumSQ0rM3o04/l/duvYbWpkbQaDr1AWtHaDQYLSGExsTx1vWXBp9JaGwcLTVVaDuor5b6Wr55+mGSsnvSXlRQ+g7GF33zpCJktDodOxbNp2j7VuwN9egC6etGs4VdS74nPqsr5Xv30HPsRCxh4WxbODfotgoJj6Rg6yYKtm7C2dbKvjXLgwJIazDgc7uD64FShfrLR+4jJDwCKSWJ3Xow+ozzsDfWU74/h7j0LAYfP5NVn75PU1VF8LqGUDOrP/+AOU8/TFjMkTO1eo+fTFxaJjuXfR+su9QRd11gH0JgtoZib6hn+btv0m34mEDj2xBOu+dB6stK2LdnNxXr5tDUWMGwG29jTEwYhQv8lC1XnmPKOEnqxL+tCLqbzjFBAG2B47+U9pggUELsLpRSdjLLSSn3CCEeBlYEUuq3ARcBNwAvCyF2onz/rASuQkXlT0KtE6Tyl6G99QRAVHIqDeWlgGJBOOFft9NSW0PBtk1sXzjvkI7qghve/wKd3tCp7cGcp/4T7ATfjjUqmviMLPK3bkKj0SClPLwJasClFdMlHZ/HEyx42I7DaObts24kvrmO0795E1CCiY+98gZCIiPZtWwxrjY7xTsUa8epdz/AhtlfUFtSiKtVCYY+4/7H2DTny+D9BtHq0YdF42msOupz6pWQytCzzmPLji0/mu10JIL1djoQnZJGfZnSi23EqWeROWgo8ZldyduwlrnPPQ5CMHTGqXQfNYZls17H7/NRla+4AIVWy7Rrb6Z41w52/7CI8PgELntBaWmxc8n3LH7zpUPW13DWA08Ql5nJD++8SfHObSR160FtSTF1JYWEREYr9YjaA8mjYpT4oaDFSkNCVlfGn3cJn91/Z/DZp/UbROH2zUFLXFTXATQU7gafN1iHyBIeQXhCIo3lZXQdOoLJl16D3Q2+5joWfPkR1n6DOWmCElBfsdbPmnuUuSa/KggNq0IYjeiio/+r5/1r+S3qBP3O2WEqKn9rVEuQyl+GzEFDKdy2Gb3JxMQLL2feC0+ClLgdbVTl5/H1Y/cHq0e3I7Raplx+HbXFhcSlZwUDloVGMHjaSeRv2dTpS9/eUI+9QbHcHzW9OvBFagkLp2T3jk7F/ADqIuMYuWU5Hq0Sl5SQ3YPjr7mRqKQU9q5eTu66VZ2mW/P5x1jCw4lNywz20CrZte1wAQSkHn8hTr+G2vlvHHZObzITUtdA+sLl1CxeSX73VDDogtWtTW4P8c2ttBn1NMZEdWrq2k72sBHkru+ckdwugPpMnMKQE07BaAn0H4uKQWi06PR6knv0Ij6jK2c/9CRrv/g4KIKQkqwhw+nSpz9hMbHBbKrWpka8Hhf9jpmKRqenrrSYsoD7qqm6CiEEOwOxS801VUQmpnR69khJ9ojRhMcmsPm7r4INZU+9637i0jNZP/tzTNZQvB43E6adRHxBKXadkVqPk7Buw4iYfiWmqmLqP/kQXVgjrY2NRMVnUrZfqf+0+4fFGLr0oSmqD9GhRs674Va8DonfJ9FoBUmjNIz4Pz86s8DszeHAcWejMZkQNyyhYHEIfS7TkDLu72EdCggeVfSoqBwBVQSp/GUYcOx0LBGRfPf0I3z92P3EZ2ZTdWA/hds2U12Qf5gAAggJj6Aqbz+LXnuebsNHM/bci3n/1mvx+/1kjxjNyXfcR0qvPlQXHOCzf9/xs/aR3L0XI087mxUfzQLo1AQ1Ij6RcL+L5t3rkEBYXDxnP/h4MFA7rd9A0vsPoqW+joaAtarqgBJo3BAeQ7hGQ2rXbuxatviwdeMyupIe5mXVx+8ecV8ajQYpBH7Aj8Qf+A5ujyfqU15PXEsrB7okYDeZgiJoyMxT2Tp/Dn6vl+JdO8kaMpyGinIaO7iutHo9ky+5Gp3BQG1xId89+yhxGV3pOXYCOSuWsuj1F7j6jQ8BGHna2Wh1OlZ/+j7m0DCE0GAJj2Dg1BkYQ5QA7UWvv0DB1k3BwpQpvfoG13LabcSkjSQ9EKSe0qsvY8+5kJLdO7E31LNh9mdEp6aRv2kDBosZgPC4OE646S4aKspY9en77F520AK29OtPOXZXIZkWI4kGHSExTqoBfXg0obGxdJswkrxPDWhWn0SEZTa+kYVEjz4ZGWbC29hE4arvidh3LHmvdyMkCaa8oUFrEKROUqyKrWubwePB7/dTvMiArQyKFvhJGXcwOF9FReXvieoOU/lLUbB1E7Mff+Anx7W7vU656wH2rFjKvtXLMVmt6E1mbHWdI3qyho4gLi0TvcnEyg9n/ax9tBfp05tMaPWGYL2aYJFFIYhNy6C5pgrp93POw88QE8iGam1qpKYonyVvvUJLbQ1avZ4fBk5gw6Dx9K0oYMbCj48YCH35S29jjYrh0/tupzIgnGLTM6ktKkCj1QWztCwuN36hwWnQoTeZ8bqcSED4JWa3mzajgfC4BOwNdZjDIzjt7gfZuXQhW+fPaX96XP3mhyAlDRVlbF80nxGnnElEfCJrPvuQrQvmBC1fMWkZNFaU0WPUeKZec2On/dYWFxISEYklPIJl77zOtu+/o9vIscy48Q6WvP0qOxbNC44Nj0+g2/DRNFSUM+XyawmJiDzqs3fa7bxx7cXBxrUjTzuH7OGjmP/iU9SVFBGblkFt8cEijEhJ16oGQtxeItxeLA4XtdNPpSkznhFTpxKf2ZVVd/qo2gBh2W1wUQ1tGitRVj3Nqz4hb/kCInwzCKu/DKGFGV9pMIZ3tvLYV69Baw2hwd6LvFlVdJ1UjtVUTujU49FaQ370s/Rr+Cu2zVBR+V9CtQSp/Km4nQ5Wffwu4XEJDDnhZMo7VC7+MaTfjzk0DL3RRN7GtQiNBqfd3iFTSdAeIJy/aT35m9Zjsob+7H05bS1Yo6OJS8tk+ClnseL9N2ltbgq2t0BK9EZTMCh32/ffMebM85n73GNU5O7D63YTmZgEKB3e7SFKY9G28MgjCqDo1C6K+0kIZt56D9sWzsNktbL+q08AMFmtwRT6NqMheF32sJHkrFymbEkcPJfUvSdavZ7dyxbx7i3XAEpBRyXzS/LqFecx/fpb6TF6PJGJyRhDrGz85nM2z/26077qigsZMuMU9q1ZwebvvmbIjFOC52LTMgDYuXQh277/DoDyvXt477brgm1OAFJ79+PYK64nIiHxqM+77rXXaPz0M+Lvuouw444la/Aw9q9bxZDpJzPq9HMAxT0JdBZAAEJwIDGaAUWV1I4ZjmvnTkoLN3P+rbOCaw66UUPxYkmXSVY84SYKqtvIiDPT5B5GycaVJA1xkZ0ksKaIwwQQgHXMaOUZvvoq8WtfoGlDCLUiiYSdu0h88AFqtkn0Fojs/vdwkamoqCioIkjlT8Pj87N3zQq2L5wHWh0FMgkZPgC034BPCYQVGi3WyEh8Xi+9xk9m87dfBa8PjY6lOj8Xn9sNQgT7eSkctHBG9RuDp66U6NhoqgvzAYmjpYX4rGyOu/pGCjZtYO2XH2MJj8DecLCQYVtTEwX1m4hOTePsh57iq0f/TXN11cF2E3kHe09pNFrKcnZTsnsnToOJ2TMvZeL670kAXDrBMesW0KWigLSyAx2egPKFOeC46dSWFPL1Y/dTU1SAo6WZ5B69sdXVKjVvrKHBgo9GqxVXh5T0+kDweEfC4xPI37IRd3tn9QCdUt8DVqDc9av57rnHiU3LYMgJJweeuQZjiBWnrYXIpBRqS4qwN9STs3p5JxHUTntVaoPZjMftpLWkgcbKchKystEbTZx0+/9hMJkPu64jzbO/wVtVRcv8+YQddyzTb7iN6Tfc1mnMSXfcR976NXz/6nNIv5/YtEx6jB7H6o/fxeTyUBVuZdCiFZTGRmBKTMQcFha8NiRB0Ov8doGiZ7BVEVRRg4Zy3Tuf/ejeOqLv0gWEoCj6HRzaXmBfR8Fru6ne5MNf1IfovtD/ag1RqhhSUflboIoglT8cKSWrcxqps3kIiehPSEQUTh/oY9MBsHYfhj1HCd4VGoGtvg6dwUDB1k3EpWdRU5QPwHHX3EhkYhIel4vo1DTWfv4RtcUFnQsWCg3mPhMxuR0Ufak0pR4681SKd21n9BnnMe+5x6kvK6HvMVPpP3kqH951Y3Cf7S4hj9PBB3fcEOyD1R5oHRYVg72hHr/fR3KPXqQPGETv8cewya/FbTCyqe8oEss2Yte1MGFPI332H1obR2LN6IOtvpbyvXs6nSkPNPkMi42n94Rj2L5wLo6W5k4CCGDQ8Seyc8kCYrqkU1OUT2XuvoPWKjisR5pWrw/WBtq9fCnp/QeBlNQWF1JXWhx0u5lCrJz570eJTulCTVEBlrBw+kyYgqutDen3Y7Jaqcjdx97Vyxk4dQbnPvIsodEx/PDem+xfu5KErG6c9cDjP/szEX/vPTR/9x0xl19+1DGF2zZja6jnkudeh9Y2wtIz0Gg0dBs5ho/uuolWfx1ejSArJYPJz71xWI2hXW/6yf9OMuhfgi6TA/E+Th+7S2zEhhnITDi0sPJB6nZJbKWS9KnTCBk2jILrI6AaqvzJyG4PYeoGjll3Urcji6VX+5n8MkT1VGOGVFT+6qgiSOUPx+OT1NmUL2K7w0t4XDytuXspmXUn2pAIXFUH3R3tNWq8bncw0LgdR3MTVQf2s/bLj4lJTScsNpba4gJ0Oj1ejxuAHmf/C3d8Oi27VgJKOnXWkOH0HDsRR0tLMNV+15LviU3NOKzDvN5kUixVR2DYKWeQlN2DtuYm0gItFqZecyPDamswXn8pWikpT7CSOngI06YMZ/ui+cHsMACNJZyQPuPxlKwLihUhNKT3H0jhjq2YLCFc9NTLzLrxik5B4e3FAQEWvPw0SEn5vhx6j59EXUkxWr2O7iPHodFo2LFkAXrTQbedOTQsmB1nq6shZ8VSQiKjaG1sYNOcLzFYQnC3eWmqqsQSHoEQgviMLKZddwv2hnreuu4SfF4v5z32PEvefClgJarjxFvvBeCEf93O9Otv7VSq4OdgHTsW69ixhx2ve/VVvHX1hFx+Gd89+xhIiWfDBqJnz8N5zjkk3Pd/1BYX4rTbcJqN2I0G0i+/7IhFFstWSDw2qFgLXSYrxwqr26hocFHZ4CIj3typcvGOV/3kfSXpd5Vg1xsSvwcKF0haK6IQerCmQGuRBbPLCEIiHYHYIAnLboATPpOYov63LEJCiGhgaeBtAuCDYFmtYVJK9y+YcwJwq5TyBCHERcAQKeV1v363Kio/jSqCVP5wDDoNvVJC2L1xM5WrvsFVcQCh0eJtrsXbXMu48y5h1cfvHbXZZUxqOhqtli3z52Cvr8Pv9VJTeIC6QKyI1+MmMikF6fMxZlQfmhqacfVLwdvjFlJ79uHT+++kpbZasYJ0YMWHb3USQO3i4EhotFoyBw4hNCrmsHPS6UQbEClpTV56OoxkDBhMQlY28196hpCICDIHD8cdmUFTwR627VDStkOjojnvseeZ+/wTICUuhyPQSqLzF2m7AGoXQxqdDp3ByJ4VS0nu0TtogZn7wpP4vV68gf0Ch/VR83k9GEOstDY2oNFqGTrzFNqamkjM7h6MwWnHYWsJXr9s1mtEJadSW1J0SM0m/msBdDRceXnUPv8CAMZ+fYhNy6CpqpLQ+iblfK6Spp85cAgDxk7CPvsbwr1+9MkpwTmaWj0U5pQi8+KIH2rGk93AnZGvMmRFCk+Nv4SUGBM1zW5iww2HtW6o2iSRPtj1ukRjAOmHhr3QuSxgDM7XHyUiW9LWcND9Jr3QVg2mKP6nkFLWAwMAhBD3A3Yp5VN/5p5UVH4NqghS+VPonmKlS/ggfijfzN6KA50ET/9jjyetb38+uONfAESnptFYXYnRbKHrkGFkDR7BN088CEVKBWSD2YLJGkpLh6alZmsoQ2eeSlNlJV8+fC96o4lzH3mGkMioYCPU5O69CI2OIXfDWqWdRMBNNOC4EzCHhRGbms6iN18iJCKCkIgoSnZtxxweQXRSCqm9+h4mgDwuJwtffb6T+8nrdLJr2UJiuqQz6PgZnP3gE+xdvZylb7+K1+3GF4xhAltDPV/8517qSooA6Df5OPQGI+c++izfPvMolYEGrQaLBY1GS79jjqd4x1aqCw+gt4bhbmulqapCsShpNBRu3Qgobr2bP/kWIQSvXHbOwe70QmCNjGbMWeej1xv46tF/s+bTDxhw7HSyh48O7mv/utXUlxUz9MTTMFosuNraKN61jczBwwBorqnB5/Wg1f22vbb0XboQMno03vp6rMNHcMGJJ+H3+/DV1dMybz6hU6YASn+1ydfdjOeUs5A+H4bU1OAcuW9/TOgrj9GaMZiy1scIFW/zoE1HXko1zv5+GlZraXo5An1/4LHO6w+9Q8O25/007AUEnPClYNtzkpptirfVY1PGWcJCyRgPDYckwPr9kkMF7J/C/eGHFUvk/ubftW6QEGJqYE0tUCelnCyECAFeBPoAeuB+KeWcH5lGReV3RxVBKn8a5tAwJlxwGU3VlZ2adpbs3EHXoSM495FnsTfW03XICN649mJsdbU0VVdzYNO64Fif10tkUgoDpkxjyduvBI9X5O5lztMPc+wV1wOKQPnonpu5+JnXOPfhZ6gvLyVz0FCEEGQPH82e5Uvw+Xwkd+8ZDBD+9N934LS14LS1UF9aQmh0LNnDR7J1/rdU5O7F0daKrbaa6f+6gzWfvs/ClfOIsR0UNaAUODRZrXTprdTJ2b5oHkvffrXTGJ3RGBQm7W0nhFaLRqth9/Il9BwzgbFnnc+8x9+m1ZUfdG1t+vZLhsw4hZqiAmLTM2jd3oDTbsPr9VCWsxu3Q0kxj0pOQQiBs9UetNK0u98ctmZqiwroN3lqMOB7+6J55G/ZwAk33YXBbGbuc4o6MJjMjD7rQjYsXoq/rZlhJ51OQmY2CVnZv7kAAtAYjXR5+63OxzRaNHFxRF980WHj9UlJhx0Lry/HDxiqS4lNXIOzpgvV4RcQ0dTIvJOa8AvF2lW1mWChxHaiugvGPKIh5wNJbH+BKUJQsdaH3wNZJ4HfDYXzFcvQlqchNB1sRQfXXnWXJDzNx+CbNIRn/kliSBFAHdtmpAFvcn84v5cQEkLEBtYcJ6UsFEK028PuAZZJKS8RQkQAG4UQS36PPaio/FxUEaTyh1KyZwf7Vq/A3thAdcEB2pqb0GiVj2H7F3NVfh5dh44gISublrpw9qxYSt9Jx7Huy48p3b2D9nwojVZLaHQsvcdPoqnmoBWoPTBao9EQFhPH5EuuYums1/C63LQ2N7Jz8QIs4ZFkBSwZGQMGkzFgcKd95m/dGAxObsdWX0vB1s2ExcYTHhfH9gXfAvDaFefR7HOytXckY/c0ovdKdH4JQjDzlrvJWbWMH95/i4kXXUFNYX5wvtFnXcCwmafidjjYsWQBqz95D7/Xi85gJHvYqGC/sS3zv6GuuIhQxwkIYzlC7wMh0RuMhEZFc9Mnc3Da7exZsQSTNZQ5T/6H2C4ZaLRa9EYTZ973KACOluZgmr01KgZbXQ0+j4d1X36CzmDk+GtvYuXH72Kvr8NWX8fnD9zJwONOCO537ZefENJ9BPGn3YHFqCUpO5rkbj1/3Qfidyb7jhtp7pGBefBg9AkJbLpkAQ314NdYQSjCTeO3MeiOUAKt6mj84gsaPvyIuH/9i9rW8YSlQco4wZKrFAGkC4Hk0YLixTDoJqjdIWmt6CyAALw2qN8NRQsl/a/+0yxCj9C5bxiB94/w+1WRHgGslFIWAgQapwIcC8wUQtwaeG/iN+pmr6LyS1FFkMofyteP3N/JBQQEiwC2u5EaKhSZU7RzK18/ej/S7yc0OqaDm0mAAI1OT3NNFWs+/RCvx01seiZCaKgpVNLQTdZQvnz4XtL6DuDYK28gMjGJ+rJSdgWqDfccM57oDgUO8zaspeuwkVQeyOXbp/5zcINCQ0R8Ak1VFbha7ZhDw7A1HIwVcjvaMAPJ9U6+HJ3AxXs9eKtqyRgwhK8e+bcSTAK8d8s1RKWkktKzD5FJKQw/6XSEEJisVkIiDxYPDIuJY+iJp1K0YwsRiUnUBrLSvNixOqaTMMiBOUayc8kClr3zOmV795C7YQ1TLr+OA5vWU7xzG8W7tnPZ829iCAnBbA0lb+NaKnL3Ks1e/X5sdTWdfgarP32fuPRM7A31hEbHYquvxef1EhIZFWwb4nE68AcqY3t9h/Rb+4uisViIPPvs4PsRX55J9MdN7H3bjsufiNm3m5TTsyl+5jvq7vuB7q/dSP1rr+Epr6D0rkfYYxkHQFuNj8Y8ZQ5vK5QslRQvgrIVkHR4PHcQcyxkTP9TXWJHExl/hvgQwKlSyv2dDgoR/yfsRUUFUEWQyh+IlJKfU6E8b/1WXj/zAZzGnUHhY6tX6vdkDh5GRHwSW+d/g9flxBoZTZtNyZyqLSogMjEZAJ3BGLR6FO/aTn11NWNue5ZtWw7W1bHV1wVF0MLXnqdw22bWf/0ZLkfn+jp6o4Fp193Cx/fegsPWgiNQPfqYK66lZNdOWpsaaaqqoFeDi+zySrw+yflPvIjRbOHtG6/oFEjbUFaKO9pBY1UFb167mR5jJlC6eweTL7masNh4WuprmXzpVXhee5OJq7cRevUwviwsAMBh3ASaVmzblVT3iPgErFExFO/aDlJSsnsH0Smp5G9er4ir0FCMlhC8bjffPfvY4Y1iO/5s/H6qCxTxKJGYIiNxNjay8uN3kT4fSd17KUKoNpd0bTXd+vQ+LJD470L2ORFknBxO7VYPMQP70JyvQffKo2hlC3WzIjD17YenvALRWoU20ofPpSX3S0ifCkXzlTmaAgmMPheU/ohDZ8D1ENblT31OJSgusCMd/71YD7wihMhod4cFrEELgeuFENdLKaUQYqCU8tC6ESoqfyiqCFL5wxBCcM5/nqK64ABb5s8JdonvhASEE6/Th1+nqAed0YTX5URoNCR370VDRRkhUdG0NtQTGh2DvbE+eHl0ahqpvfuyZ4VSRbnd8uHyQmHOXoyJWWhNIfhdbZTs2UV0lzTmPPEQtkDaeGvTQQuP3mzG63TicTpZ/dkHhzVSXf3JB8F2GgjB9GtvZv3sz4mITyA6ORWtTseZ/36UT++7vdMt2usPFmTcPHc20udj7ZcfkTFgMD6fl7C4eGoPKGaHvD078QXS/dEcFGdavZ5uI8awcc6XwWO561aR0rMPfScfR58Jx2C0hNBQUc7Xj/0bY0gITput0z4iEpJoqqo47Edgr6/DJ5SIVhm4X1OIlZm33H3E1PO/IzqzIHG0UuQxpi/UTzgL/+4lJFx4AsYePTCkpWEeOICYSB2bn5IkjxZkTBcUL/KDH6K6QdP+o89vjoOB10PymD+9VtDddI4JAmgLHP9dkFLWCiGuAL4WQmiAGmAK8BDwHLAzcLwQOOGoE6mo/AGovcNU/nDqSot5//brO1kmIhKTaaosD77X6UPweloxh4XjdbuDfaTa08LD4xNprq5UqinbbQiNBq1OhzUqijFnXUjB1k3BdhLtGEOs9L34Lja//H9BF1VHUnr17VTH58Rb7mH5B2/TXKMUHxx+8hnEZ2WzbNbrnSpLtzPhgssYPP0kAFxtrXx0zy3Y6mrwujuXTrHGxNLW1Ijf66XPpGOpys+lrrgoeF6j1WIyhzBz+HjqkxNY/P6bwXNavQGjJYS25sajPl+9ycSVr7xLwfYt1BUXsXHOFwBMuvgqlr3zGqA0a41OTWX/mpWACLokAXxagdYnQUriumRQW16C9PsZd+7FDJ156lHX/SdgK5VIPzQXwvoHjmBZC3RrGXCdIPvUX18q4DfpHfYnZIepqPxd+N/4s07lb0NjZQUtdTVIf2fx3dbU+Uvd61GsHj6vJyiAQHGpGUOsTDj/UuY+/zhOu2LdkH4/Xrebpqoqlr33FlqN4oLoaL1xtdrZ/NI9R91bl779cTvagsHLm+bO5qTb7mXOM4/QUlNNbFom81986mCKOWAIseJ1OQkJj6T/lGnB47b6uk5d2hEaNFoNfq8Xe4cGr3EZXdm78odO+/D7fLTZWzBNn8aGR//d6ZzP46atpXNMlVZvxOdxBQPLPU4nX190NhUaic5goN8xU4lO6cKA46ZTV1pMY1UFjuam4Lpag0EpeRdAh5ZuFdVY3B6KwyKCYtV1SBuOfyKhqcrnKiRR0vUUOPAN0FELSRh8myBz2m9TK+k3QRE8quhRUTkCqiVI5Xdn/7pV7Fu7krKc3UHREh6fQHRyFyrz9hGX2RWdXk/+5g2HXTvi1LPYvnAuHpcrWMcHoNuIMTRUlFFXUoTBZEZvsWAKj8bulkQMOZ5IRykNeTsZedo5mEKsfHb/HUfdX79jplJTWEBVfi7m0DDSBw5hb8CKlN5/ECfdfh/15SXsWrqI7Qvndrq224ix5K5fBcCGc2/g/WmTMAVcRrt+WERzVRV6k4meYydiq6tl9hMPdu7h1aHR66GYrGF43E58bjdxGVnY6mqD8UiW8AgiEpOp6JDBpjMaiY5PpL4gn67VDexPiSM0Jo6Ln30NnV6Pw26juuAAc556GG+giavWYGDatTcr1ZgDWMIj6Gtz48nPZ1tmUjCOS6vTcd27X6BxuxEmE0L7p7t6/nRaiiW5X/opDHwshBaOfUtDWPpvEwekdpFXUfl9US1BKr87y955PRik3E5Lsx2SwnHY7Qw4biZdBw9h+6L5LO1Q68caHUPmoKGMPO1sKvbv6yRkctevZvgpZ9FUVYnb6cDtdGDsM4mkQcchhKBfyiBSL70UAHtjA8dcfh07lywIWnli0zNJ7dWXtL79yRw0jPduU6r0u9paGXXq2VQdyKWxoozIxGQ8Tief/fvOYJHFdsxh4eSuX4UmPJKd8WmsDI1j8Z59zOjfB4C+E4+luaYaS1g4epOJsJhYLnzyJT67/86gi+1QASSEBoPZjKutFae9JXi876Rj2TD7c0KjY2lraSZ7+Gh2LOrczsPrctFtzAQyM3vjqahk9OWXYY6JZuM3X+Bxu9m7almnCtixaRmcctcDWMLCO7XicNrtFGVkUu1vC/ZgS26wkdnioPSSS3Fu3YqpRw/Sv/j8N6sO/XclLE0w5BYt0T39NOZKsk8ThKb8PQPGVVT+iagiSOV3pTRnV7DhZq9xk2ltaqTK5sfa/xikx0XLntXMeeJ+4rv35dhLLueCJ19i3nNPUF9egkaj5eN7bmHAcdNJ6zuw07wanY4NX3/a6Zi3uY5Qs46+aaG07N/EgUod9sYGlr79CtEpXagvK0EIDam9+zL9X3dgCXQZl1Jy2r3/YeWH79Br3EQiEhK5+JlXsTfUY42KxtXaigzEEIXGxOFxOXDabEE3nb+5kT7NjTiS0phwwdkseesVDmxej8FkprGynLDYOC578W2EEIRGx+BxOoN7Th8wiKaqSpqqKkEIjr/+ZrbOm0NVfl7QvaXR6tixeEGw5xdAwZaNne49JCSUISU1ON/7APHwoyT27AVA/paNrPtS8YRoOlhusgYPY+at92BvqKemqIDMwcOClji/z0tTVQXh8QlEJaVQm59HdEk1uREWbLl7SPL5cOXlIT0ehNH4yz8c/0NkTNOQMe2nx6moqPy1UEWQyu/K3lU/4LC1YAoNY+KFSofwmnobucUNeIWBGqMFr9tB9f5dfPPkQyR164kjYAFpFx61xUVMvPCK4JwRiUlEp6TS1thEfWUFPqElsd8wskeMIyXWjbNyf9C9032UUuelPXjaYLaQ3n8QG77+lAFTZ/DVw/fi9Xg45z9Pc/y1NwXXaBcsALaGuqBw6Vhfp+eYCegMRnYsWYBA8NopMwgNsZKz6gc8TgftETQttTVIv58fPniLvA1r6TV+EjkrlqEzGUnu0Zui7UrvsOjkVCpz95PUozeJ2T3Z9r1SjNHv81JXUoTOaEQIgcfpxFZ/MK4IIZg8/WRyn3uWfXozux6/n6vf/RxQrD3tYqrH6PF0GzGanUu+J3/LRpa8+QoH1qwkvLoWbe/ewenaA8RdrXY8Thd6k4nSrC40elzUhoUg/XpG3n4PGlUAqaio/M1RRZDK78rg6SfjtNvpOmxk8FhcdCgW4eb7V58jvGtfHHY7bXmbsdXVsr+uNlh/JqFrd2x1tZTv20NzTRWjzziPop3bKN+3h6bKCiZfeg07Fs+nrqSIup3rKFurFEGMSk5BazCg0Wgo2rmNtP6DiE/PZMv8b3G12ln50TsA+KWf5kCl6YbyUsJiYtmzYqlSk0gIdv+wiAkXXI414mAhQ73RRGrvvky65CrCY+OpLyth2/ffAfDtM48SFhND9vBRVOzPQWi0NFaUEZ+ZjcftYtsCZdzm777muKtvpMfo8Xz+wF3BuevLSoLNSLOHjURvMuN1uxQ3lZQIRCcrks5gxO92EWl3suqdN9CFmAAwRkQEx4TFxHLxs6+xf+0q8jdvoKawgKZqxRXnamulW1E5yTWNlLU6KU9WRJ9WpyMkIpKoVidhu3PJSYomqXdfGvftwa/RoJsxg5Dhw37tR0NFRUXlT0cVQSq/K9Epqcy8pXNJkrbmJj644wbaWpQih+FxCXQdNpIDG9eBEEQkJNFcU01Gv0GU7NyGwWymrqyEgcfPJC49k616Pc7WVoRWi8MYTUi8F+k8WAOnoVzJyopMy6SuuICynF0UBzq1gxLLo9Vq6TP+GGJS0vC63TTXVDP3ucfZv04Jctbq9Pi8HjYsWszx19/OSbf9H3tXLyd3wxraWpoJj41HSsn2xQvQ6PVIP9QW5VNTlB9smTn8pDMYNG0mhTu38dJFZ2IOi8DR0gTAwlefY+WHs/B5PMSkpNGl/0B2L1sUjDsq3buHa978iNwNa1jw0tMAGCwheFxOhFZLeGwczdVVSKDBrKebR8OBCD2Dj51OtxGj+fbpR8geNpKeYycSmZCEw9ZCVX4uNcUF+L1KOnxKrz5Et7ho/eRT0jO7ccDkp625iZJd25UsPLuDvg0txLe0sjkiklPvegBTaDgJWV1/08+Iyv8GQoi7pZSP/Mj5+cA5UsqmHxlTBAyRUh5eg+JPJNDr7Bwp5Ss/NVbl74UqglT+UJa8/So7Fs2nY0Bwc00VJ952D3qTibSBw8nqN4DP/n0762d/yun3PcLH997Kt089HBwvNBqssQls356LJaM/hoQM8HkJ9zjxtzbTkrMWR+FO6ooLEFotiec+QMu2xXgKtxIaFcOki6/Eml+IodVJ+MixtDY38e7NVwFgiYjE3dqK1+PGGJ+Otu9U1u2qoHnBFzRVKh3a60qL8ft9NJSXsT1gBYqaeC6tu1fwbYqDsbsbsXj85Kz6AWerjYq8/YDE0dKE3mTG43SgMxqDmV5ZXc+jaO3WToHXTlsLuRvWEJeeeTA2KGAhkz4fWpcnGMgsNRoSL7yAsu/nsmXeNxTt2Ep9WQlle3fTpesENAaITEzGZA0le8QYqg7sp7GijIaKMqJPO5mMM87AkJXFwqsvUBaXkgi7A7+AHamx9CmtJdxipXr/XsKSklURpHI07kapR9QJoZh2hZTy7xw1FQFcA6gi6H8MVQSp/K64nQ4MJnPwfenuHYAkNDoWc1h4sM9XQ3k5e1f+wN41q+g29QzqAm6hxsryQH2dg3MKrQ5bdQW+kFjCknrSvHUhcVMuDp43pfWmefsymtbORhsSgT4smrA+Yxh7xgnUlxRR9uILhC1dATodq4b1we5opUvfAbTUVDPj5rvwul0sev1FksfPxB6bStXSd2nK3QdAZEISx11zExqNlsjEJLKHjaK1uZFhx01g6e4l1IcZKEi00KfEjq2+lh2LF3R6Hh6ng8jEZLr06UddaTGpPQdR/voQqqOfQWrBp9Gi8ytFe3xeLzGpaVz/3pdIv5/vX34G20blD2RNXR1a6cOn0yI0WrLHTmLtt0r16Jb6OqKSU8nImsmC8/1ojdDWcz1Ou43aQqVprdftZvv3c9n+/Vx6jplAZE4y2kAjWyklZo+XyshQmkLM9D3zPLo3N6H798PUhVoo+up7kpKiMegOZobtXbOC/M0bGH3meUQmHN7NXeXPo8c7Vx9WLHHfxa/+4rpBQohvgFSUBqjPSynfEEI8BpiFENuBPSgd4xcCG4DBwDQhxAoCVp4jzfETa9qBV4FpQCWK4HoicE83Sim/FUKsBG6QUm4PXLMauBYoBWYBmSjVsq+QUu4UQliBF4EhKH+VPQCEA/2klDcG5rgc6AUkAlmB+1sspbxNCHEbcAZgBGZLKTsX9VL5W6DWCVL53Vj1yXts/OYLhp54GuPOuQiAqvw89q9bRd9Jx+F2tPHNkw8Rn9kVoyWEvauU4n1Cq0f6POiNRizhkRxz+TXsWb4UodHQZ+IUDrSYyP/hO8L7jEEfl46nqRZ9RKxyrQCj34lTmPA01aA1h+JpqqZu+Se4KhTB1a+4mpQmpVbPkr5ZuDUw46Y76TZiDADNNdXsX7+aXmMmYImM4o2rLqA1UMxxwLHTmXzp1Z3u0y8lmzflsO2bj6gt2U1ecgg9i23oj/KrFZ+ZTXVBoBunBK0vkbJEDYm15Xh0eiZefBU6ZxuDpp14WAr67hVLEELDD2++jMutFG3MGjqCk269l83zvmHF+2+RPXwUM2++m5qtkhW3+BFayL5hEzvXfoLeaKJ83x4QAiEEGq02WH+pPYPOYLYQGhNLeGwc5rBwplx+HaWPPoLjo0+oOvsaGiadiHvXEoaP6EvmoKEAvHzJWThb7fSfcjzHXHbtL/q8qBzOr60TFBBAR2qbcfkvFULtvcCEEGZgEzBeSlkvhLBLKa2BMelAATBKSrk+cKyIgyLoaHMExxyypgSmSSkXCCFmAyHAdBSB8p6UcoAQ4kJgoJTyRiFEN+BjKeUQIcSLQJ2U8gEhxCTgmcD4xwFjB8ETCXiAHUAPKaVHCLEWuBKwAXOllH0CY48FTgucE8C3wBNSypW/5Jmq/HmoliCV34XC7VuCfa2qDuQGj0clJZOzchlb5s1h5s13ce7Dz7B39XL0JlNQBJnDwmhrrMfjctFcU4Wtvo7pN9wWnCPR56d3z+vw+iRb8xrwh0Tgaa5jSP8MtBpBU+4Byuud6MNT8BiMNKyZjbu68ODe4iKIcHmImzCRU6+5kg1ff8aeFUvxeb3sWbGUxqoKWmqq2TD7M65582NO/7+H2bZsCaaETEZMHHXYve7OKaXcH0Ps9Gtpe+Nm+hUdjE8SGi0yYNmxxsRhTOqKs7Hq4MUCfLpK7JYeLBo7k7bwSG6aNAXjUerv9Bl/DAD71qygaPsWAPxeZf4h008K9gwDiOrrZdxTGvQhGqJ6jKD/zBFsXzSP8v05aL1ehjkkKTNOZHlBDubQUCZfeg1le/fQbcRoPBoTDXYPiRoHbcuXk3LtdTQlJOHsP4GiDctoWP0136yczXXvfo7BZKbfMVPJ27iW7iOVluotixfT9OlnxFx1JZahQ3/OR0bl9+EROgsgAu8f4ZdXkb5BCHFy4HUqkA3UH2FccbsA+hVztOMGvg+83gW4AiJlF5AeOP4F8H8BC80lwLuB42OAUwGklMuEENFCiDDgGOCs9gWklI0AQohlwAlCiL2AXkq5KyDqOnJs4L/2BrDWwD2oIuhvhiqCVH4XKvP2KxlNGg3HXH5d8LjTbg8WTtz03VcIBOX7c5QigSYzCV17Yio+H7sun/SZDlyuenoE0tw9Xj/lDS7iwg3ERyjp2bHhCXz+yANU79mCadrZNLRK6lco9YOEEPSZfDzDx48gqcelbJrzBXkb1mIzG1nZLYXTL70Ya2QUBVs3KXsuq8RZX027/nA7HHhcTupKiymqbsOzbynG2BSGDjwYE7Nz6fdsL3Vj7T4Mv6sVv+dgSw2AiPh4/H5Jc3Ul1sx+WEafjbOqgJbPHguKI4Ds4v3Een1c89BjRxVAHTnuqn/x9r+uwOd203v8JPavW01cRiY6gwEhBLUlRXz1n/8jKjmVM+95lIbP5mLp30+pNSQlmbXNRFY3Yn/uBaZ9P4+Y9Ez8DgeW5DRMIVaWba7G5YXs/1yF9sA+Is48g4T778eaX0BDnIUGFLdZQ3kZCVnZjD3nIsYGrH32lSupffoZ3EVFoNXQRRVBfyZd/svjP4oQYgKKeBgppWwTQixHcWkdiSP2Wfkv52jHIw+6LfyAC0BK6RdC6AKv24QQi4ETUdxUg3/WTR3OWyjutn3AO0cZI4BHpZSv/8I1VP4iqCJI5XdhyIyT0Wg0JGb3ICrxYIxIWGwc3UeOpWjHVsLjE4PtKaT043Y6yOp9LCt3PIxPW4coP4Vh55yDTmekdJmfMk0rVaY2IkJ0TOwbDYBBp8HXoljOWwvq8IUYgmtJKdm1ZD7WqGiOS+nCzJvvpnj3DvavXUlEfCKpvfvicbswhYbhamvF6xdIn4eQmGR6jR1HYtdumEKsVBUcoHnLIpB+StcldxJBbocDv9OB9DhpO7AVU0wyzupiAMyhYTRWVqDRKYJN6q0AhEfH0NxtCK37Am1ChBakj4iKA8Tofto97fL4KSxvwud2I6Wf1Z9+QFNVBaYQKy5HGyHhEQyafhJej5umAj9zTwWtcxDd3JfTb+5n1JeXknSgBEfTeuosRvZ/9C4zrrie8ltupTw0Gd+M03AKHSI8HndzI2Ygr7CIi+euYPSCbzlb46K6W0/MVisxqWmd9tb83XdU3HY7GAwYu3cn8qyz/5uPjcpvTwmQdpTjv4RwoDEgOHoAIzqc8wgh9FJKz1Gu/Tlz/FreAr4DVrVbdoBVwLnAQwEBVielbAkIpmuBG0Fxh0kpG6WUG4QQqcAgoF9gDhsQ2mGdhYH5PpJS2oUQyShCrQaVvxWqCFL5TWiwe6hocJIVb8Fs1GIwmRlx6llHHJvcoxc+rweN3kTyOfehMZjwbZ1NatdMBpw4kq0Lv6S5qQ6nxcyOIhu1XzVS8mUCGpMZcXcbRqml6Hs/TtN2ygs2Mvmya1j7eAly1RhCUrfTpPmekaeeyf6li2hoqMPeUM8P777BjJvv4utH7kMIwQVPvoQQgvK9e3AGsrRCuw7AG5fMlNNOIr13n+B+R55yJlX5ebTUNzB8ymS2FrRg1Al6pVoZPO1ENlx1AfU/fEIw401o0GgEDlsLGq2W0JbTMXq6E0tXhvQ2EmaJZZdxJlvMYXhra+kecjn+tPkkZGWjN/74H8Rl+/Yw++nHMab2otdFd+LOWU7ehrWA0jvM2WrHYWuhR8AtJWr6U/ihEZ8ug31xXekZG8eMa25m/+Ah5MWEU5YcQ/9vF5D32RxEXBzVDz4GGg2Gmr2UfPgGTRE6fOHJzBkxju3WCAqOP5nzPnyZc156udO+fE1NOHbtRpiVIHhtWBjpn36CxmxG5U/lbo4cE3T3kYf/JN8DVwVcRfuBju6uN4CdQoitKIHRv2SOX4WUcosQooXOFpz7gVlCiJ0o935h4Ph/gJeFELtRWgg/AHwdOPc5MKBdSAXildYExi4IBEb3BNYF6prZgfMAVQT9zVBFkMpvwpYDzdidPpxuP6maWop2bKXflOMxW0M7jZNS8sO7byKln/6nXoQxXvkjddw1d5IQqVhMLnz+MSrKKtlc7KI1fxsl++pAnI0lWjB6UDR5bwg2fSupiH8Wr2zE63LiIZbq6MvolnoO1975EZSXY73vYXKSomlITaTn2Im0NTfj9/sRQFtLM6V7dtLa1BTc25hxg8kcODjYgb4dg9nCmfc9QmtTIzs2bqPSYkaj05MSYyLcoqf3mAlsmfeNMlijI85yD/qEFZTnr+C4q26kcOtQ3CNasTc4cbr1RIdqGDSsPz369cZVqSG8i0Cj7RxsfTQKtm7C3dKAe+864s69ivj0CKoLDhCf0ZXjrv4XxTu3YY2KITQmlrCYOCobV9Ac/gNuWYNDV4nH6URnMhEyYQLdVq+mm92JbFPaf2zWeYg+sAlD/1GE9xyAJ64nYbNfYP/+zQzYthqHzsLkzeuIu/nmw/ZVetXVOLZvJ+qii8icPw9tRIQqgP4C7Lv41Y97vHM1/EbZYVJKF3D8Uc7dAXTsVNznkPPpHd4ebY70oxy3dnh9/9HOCSGSAA2wqMP5BuCkI8xp56AgOpQxwLOHjD/nkPfPA88f5XqVvwmqCFL5TYgNN9DqdBATZmDOAw9jq6vF1lDPMYdkUgkh6HfaxTRqoknt1QURZsXnk8RHHHRj6U0m0rpmYCtdzKYtcxhy1lmkZmkwhoPOLLDE+gFJlHUEDZ7l5G/bgqOtETRQVP4xJuuJeKOjMUVGMrDJQcxTd/Dpi0+y9vMPQUp01nA27y6nYuNKQjWO4LrOloagAFr58bvkbVjDsVdcT2pvxSL+zRMPUpWfR9zQY+g64yKsJh15Fa1EjjuD0eHRNBbVwc6zsCSbqDsuhQzfaXQZlkJ+hRIoLXQSu/Ngrr/FpMOS8fOfcUXuPnqMHo/TZkOb2I3iWhfa+BQuf2lWcEx7hpurrZV5zz+BlH6EWQnOjnZdjpBK642k/zxE3qjRSrgp4IyOpDrcSnh9Dsf3n8naXUpbjpDyevwaQWp1GSe8+jgxqWkYe/RAShms7A0gtZrAPWoxZmb+/JtS+d0JCJ5fnBL/d0EIcQHwMHCzbO+589/PEQFsBHZIKZf+httT+YuiiiCV34QBGWH0Tw9FCEFCVja2hno03cayKa+JgZnh6LQHvzBNvSaga3FT6dExISnkqHPGRk6ke9hEoqyCA7u+JzwunuSevel+to6U8RpCEq7htdvzcARS30FxCfn9PpbP+QLX6TOYfP5lLP/8Q5x2G2Gtp2F2DcXf+wAypT/xCb0w7/icEaecSdkyPa4tA3ENlBgjBDsXL8DV1sr+dauDIsgYovzBmZkazcjuEdidXnaXKKn2g0ZOJcFoZPN3kvBsiVarQaO3oBEC46YQ3AVaoqJ1dD3m51tHnG4fm/KaMRk0mMo2s+i15wiJiOSKV95lS4GNlnoXtS3uI16r02qJs4ZRZ3Nisc3E7O6DydMfe6WfiEwtuqgo4u64A1deHuGnnIwmMxNr/n7S+g4AILtsK7p3PsG6cwPdokLRCg1hDheu3Fzyhg8nbNrxJD/zDHVvvImztBSn04lEg23FSrRRUURddNE/vsO8yh+LlPJ94P1fOUcT0O032ZDK3wJVBKn8ZrRbBqZdfxulJVVsrzXQXO8i1NxKj5SgxZr0ODMOt4/0+EMzdw9SuG0LS+/bhfSbKMvtQpl4E+l1IzQatHo9lzz3JkIXiQxUUTRGxeNprmf8eZdQU1jAjkXzAMgeMZrMgYPZu3Ip4W1nIKQRTQVoBIRp2igrzFcqQa+/jsbQeymoSuH0J69l8mXXULBlI0NOODm4pxNvvYeG8jLiMrIAsBi0xIUbaHP5iAnTs2c7SB807RIcc2M0GiGwGLVM+LeGxlw9VVF2VuxuYFBWGBEh+p98njXNbupsSoypr0yxJvn9fqSU9O4SisWoJSmqcwzRgc0bWPv5h/TsksXg1VvYH7uQNoPAH55LRO8tGG1GpL8vQqMh+uKLaKuWrHqsAEP0PvpnFFHx9As48/ORNhuhKFFOKQ02difHYLWYKA8PQev303PnLprmzaf2mWcAqAi9gyRycOflUfPEkxjS0gidPPkn71FFRUXlz0QVQSq/mKqNEnu5JHOmQNPB0vPVw/9H2d7dxB57CaG9Rx8WY5MSYyIl5ugBwB63i2+ffhivRbFymMZeQcbAVyn/5FFcVQfwulzsW7OcXUu/JyI2hYzpJ7PtjadBgL2hnpSBI+g2YgyutlZS+/THVlfL0KkXYtu/HU1LJsNv7kF4hob8zYVsL1UyuZK6LcFVv5vS0t3YG86i5+jx9Bw9HoCNTXbO31XI4DALH/c/mBmm0QhG9zzYXLXvZRJzNCSNFlhNB+/ZECaIGyxZu0Fpi1Fe7zyqCPJLSZvLh9WkIzHSSGqMieZWDy3Zo+hyVgzHje2BVqfD11LHlpcfJCc0jJNvvw+dQXEn7lg8n9riQqTHw5jMTLKMr8Hx/ya2bT7NH8+i6IsWoi+7lLhbbwUgd14Te8pvI3tLJdU1TQf3odWh8XmRGi0av4+Q8AhKsjLR5R3Ao9Ugm5qovOUWAFzmEJrM0wlzLcfgK8EU0oqxq9paQ0VF5a+PKoJUfhGuZsnqu/xIP+zZsgnRo5jjT5+J3miioUJpYBqvbaJbRihpcT/tApJSsuddP0150OcyQUhUFM2BbueWWKXOTHTfi6ioupfUXn2xhCnp51RWMDG7G5F7ivBFRaLpO4klu5pJnn4Vw7pFAPD+rddhb1TqsEVEZXFcphLLmDFwCENnnorBZCZj/DGctyoVvcnE6K2byZk3m1FnnEuPUeNY19RKs9fHhkY7+ytbSY4wYjFq0XQQd2V1TvIqW9k2zk2d8HG3NxGrThs8L4SgX3oodS1uMg6xgDW3ethZbCMpykS9zU15vYvuySH0SrUypGs4tc0udpfYSRkxiLAYxX1YtmcXpi3bcei1NFZVENslHYDhJ52uPPuMriz3raT3hP6MOMXIvr4vIwOVoW0V5Xx7w2Wk9OzDsGMvRbPYSJshIMq0WmRyFwqvfQhtm53oOe8Run0dmTkH0MbH46tuwI9SqKWd0LRUzL4N1PsvYMD9XYifmIHQalFRUVH5q6OKIJVfhM4Moalgq/Dj7ZOJJj2d3TtzcYd3Ifakm0jwlDN86rE/mfLdTmMu5Lzvpjb8MbbdZuO8lx9Eq9VhDgtj7k21+BIjaM3ZQ3qfsfRIuY2YCB9DTywjJDwCU72NZinROZzYAxVK7M6DhQhjuqQHRZCpxyDyq9rIjDej1ekYd67Sc2xlg41dEfEAzNu0jojKcnb/sJgeo8ZxcUoMLT4fKTYNOcV29pfa8QVUQGKiEcJ1NO5yUm1y85yzln5aI18Y67k4La7TPWYlWMhKONwFeKCyjboWD02tXkLNyq9km8uH2+tn5dZKXGXlRNNGdt8JAHjKy4ncupO+ZbVIIYjQG4NzpfTsQ0rPPsx74UmaqivZtWwhI045k8hrb6Vl+Tqip49mr8ZD87df0VxdTUN5GVI4aOuaid4UgedAHrKqEpPfg9dgIP2Mk6jfsR6kxJCSjKO6Gp3Vit9uRxsXh6+mBve+fWRwG14RhdO3CqFVY4FUVFT+HqgiSOUXoTUIprytwdbsYdmeVvDpSclOZ80BB9qoFKKSu6M3msipL2VtxV5O7zaacOPhQdDb87fQvLOElm8n0xCWg9OotIKY/ezTnPvvB9EbTRzzUDjvX3c3Lorp6nqTnPcl5vkaTvjiIiDQVT0iHGPXrmgyw4kKcwazzea/9DQ1xQWccN3/4fJHkG+MYGeRjRCjlrgIAwLFStPTbefs+Ag0Gg0XHHssORofQ2acAkCYTsuM2AierCjnNH0YXr9EIJBSct/uvey0mDlm735mhI3l2rQouukNWJp/nhCobXZTWudEI6BbkoUuMWb278mlcPZXiDHTsIlkSEqD1x+GUyZgL7ZTftop+G0toNejMRrJP2EGCXffhWnGSWzJb8Fi1DLsxNPx+/30GD0Ot02ycfE5uBrPpOeCx+j76IXYWpop2r6F+hw/EW3XEVX6IZ7aPCQafKPv4JiTR7D2Ph/rd8PAf2cQZi0hbPo0mnLsbHpMYPLsIb6kc6q8TjbgLtgL9P5lHyqVfwQde4z9kesF0udfkFKe9iNj0+nQI+xXrHk/YJdSPiWEeDcw55e/Zk6V3wdVBKn8YjRaQXiUiRNGdcHvB6NewzBhoN7mISvBgpR+Plj3JI6GFuJf/ZBx088kfPp0DjRV4vX72JbnxdzyGoZwHx8O2UrPDaWE+QTSJ2ks2s/2pUtIiZyJR1+P21uP1Pip3tWCRsQQ00/gapKUrZLE9BGEz5wZ3FdmwNoi/X72r12J3+ejtnw/Q089j8rd9ThaHXxx1zVEDDmO+CGTsDTmseG1RxjWvSdnP/gEPpnK/SKCB+sdfGB30M1spLHcSZpGzyxnA5eYooJr+fw+puusjO09jBqdD5BU+D1saLPx7OYGXurVha6Wo1vDbA4vEpAStBqB2ailfPVcCjavp7a4iP4nX49j4wYSuqXSWilZfLkkuxW0QNxNN1Hz1FPg91P3+htoxxxPTbMSR9VtQCozblRKtrRVS1xNIKUO+55a9t23F6/zeiyep9C2TMPg64VTl4bkRFy6DFpTp/DZ3Xch1j2AQMuBF1aR6H0X6XJS5TyZ5jJJM0OIanTh0yRi8FciEUgktteexX3msxiiQo96zyoqvxdCCJ2U0nukc1LKCpSmpyoqQVQRpPKLqXO0MGv3YsYk92JUUk8ADGY3X+2by1BfNsPCTYy12IlYWgbrhrJiVxZx7lwub3kBn/QzVtudUZFGYi1tXNCjnPrCmURO/Q5Hg4+CJc18snEnw9YOBaEhUfM0vshdhNZWEpO1l8E3TWL9v51UbFUCk4fe4ccZuop9a1cx+oxziU3LQGg0HH/tzZTt3cOg42di1Gs4dkAM799+PaI6CvdWF619fLSaMwntNx5b+R4A8vP2k/XCQ1jiUpkXfQNNJgs7GlsZrDERqdMgazV4v7Aiwvz06bKHhBFJfCFcTERHN62R7Q4npRo3BQ4PC2qbuT7t6CKoS5yJPaV2vD4ZLCPQe/xkaooK6D1+Mt36xLN2rw3noOF4neD1WTgQ8xVZJ6+naNazWPyKXy78pBMJizJR3eTGYtRiNihiUmgFlnjBuCcFex7fQ63mfBxFvUEDcY6eNFuUzgJSxJCb+B19Ri7nQPwSynbswhL2DAZPdwz+b0hosFF5z73EPZ1E6sThhEbXof8shGLnlUS3fYLZux+AUNcaXNs2YlAzw/4y7O3R8xwOKZbYc9/eX1w3SAhxHnADYAA2ANdIKX1CCDtK8cATAAdwopSyWgiRgVKnyArM6TCPAJ5AKZwogf9IKT8LnLsDpQKzH6VC851CiMuBKwLrHgDOD7TeeBdwAgOBNYGu8UdaL52AlSfw+gOUbvQA10kp1/7EfR9pT1nAy0AsSjXqy6WU+/6b56ny56KKIBUAmm3F+P0eIsOVrJ42Ry37mpt4cetCTog+iTCjifF9ojDoDrp5Xt2xgI/2Lmd23nrWnfMkAA+t/4yFRVv5IncNm856CIs5DmdvO/at5+LSZFM+dw+jpnnZURnFkIgaTFrljzYhwDxsPVqNwBitw9g7hIyVZej9SpyOXb+YkRN74H9uGqJBcmCClUiHh4bIj3Dqe2ArhbVbZ+Gs0aN1rGHGfUoVwh6jx9MjkOUF4JeQOfYaqjZnQ5MG/4FmNF099ItPpOvpMwBo3LOTcHszGr+fymo75/hqcAJmBC4kZ9ijGV2qWIN2T57AumYXJVYX5X4PF4tI/M0abkiIplrr5eSEg1ajI6HTaDimfzStTh8xYQac+/eTmpbJxc+8CsCSt15h76ofKNi6ketmjWPSCxoKt9awaO67aBMjmFTfSMq99xB17rl4nE5EziIMUdl8dWkPkFoyzDeijy4k697XqKtR3FROw37MliSkJhm9sODzgVaG4SGMhrmrSQgNozZpCG7DVhzGVWTVBZp7awTePasZcd9IarbHkl+9mOaVbbj1mSS2PEz0oAi0UZGEjPgtW0Gp/BoCAqhj24w04M29PXryS4RQoFXEmcDoQBf3V1D6cr2PIijWSynvEUI8AVyO0prieeBVKeX7QohrO0x3CjAA6A/EAJuEECsDx04EhgdETvsv0ddSyjcD+/gPcCnwYuBcCjAqIMa+Pcp6HakBpkgpnUKIbOATYMiP3PfxR9nTG8BVUso8IcRw4BVg0o8/RZW/EqoI+puzq7ZI6ZYec6QeidDsasUvJZEmK5X2BtZW7GN7TQG1jmYu6DWRnXXFJJhMGCs+RkofIwbdRX71DmrL59MgIqi3J6OJ0mN3+mh1+jBYD4qgkYnd+Sp3DeNSlC/XMls9S4t3ABBlsmIxRTGq96OsWrwfxpSiq5XoRs7j4i5TKXtjGtIjCJn+Pt7+a9AK8K3vSWV0C22+MAw1dTi1K7GZ5qP1RyOlZO8XoWTro9HSgr+tDeH30/fcOjwJkHG8wG49leK3j8X5g541MXYGTKxF1pZiHTcuuOeSWge1phikwQcePx53FWmvvUr4+iW4cqbBM08T2nc8YXtL+HzgGIp9bbTngDkCvcHmxjfQNF5LW6iXPREO+u8JoyXFT02Ih0f9dVznigWgp7QQb/zpekBmgxazQUvznDlU3HEnuqREui5ejNBq6TZiNEU7tpA1YCLVWySxA6DZ5YN5AmNEOBmffEzogAE0F0p2rJjDpgUfYG09gSi/UuCxwHoK1os+pnTNM2zIPpkBhUMxeLsiW7TYGQBARF873lo97tIKrO6NhNb76e6+nYaQOzDIXKwTzYT53qRl7lwaZs0i6rxz2flKPI15WhBWHLquGAeMIe29W3/251blD+MROvcNI/D+EX5ZFenJKN3ZNwXqgpk52C/LDcwNvN4CTAm8Hg2cGnj9AfB44PUY4BMppQ+oFkKsAIYC44F3pJRtEGx7AdAnIH4iUKw8Czvs64vAPD+2Xkf0wEtCiAEofcN+qkDiMYfuSQhhBUYBX3Sonm48yvUqf1FUEfQXR0pJmb2ORoedz3MWcKClBVu9ZHBqOgO7pHPXmvcRwJyT7qVbZHKnayvsDZw45z/4/H6+nnk31y59lfzmquD5LdX52DwOBIKHumgJ0Ui0Gj3z8n5gmAXMuLF4tJRuq2VwcgaljnK+Kyng9G5jcNU3MNLajS1nPY/WoPwDsLXmAN7Av0NX9psKwIFvJA0/ZIMmm+NnN9Pmno5V9qZcSqQURBWMoOGHCSRM0JGQHU7B56GEAaMfh3kPJGFyD0LnjwMEXgT7ouajwUPvaftI7G8jbOpBK8+AqSdQ9nYboKfiCzOtH+4hteE2zJc9TPqtSpBzpFWPCJNob2vB/bEdOc+CMSIcAF1cHFJK9tdBzKTzifY0Uux1MjUilA0trcRIHV0NBnbj4vvxB/sk7uzRgs4Pfi1MXhtD9+Xx+NI9jHjxYCuQn4OvtVX5mbc5lCAhoEuf/lz24tssu87Hyg/9dDtD0Of8nsxodENpMcboaBpzJUuu9OPQd0UTrcOi64FX1KORVmR0I+4NExEx5ZTF7mB43tj2T5ayhtDgTV6P1XwMLU066trOQyvtSGFE76vGretD8fo24kda0MbGYurWDV1sLJrgrQmkNpSUB275r+5V5Q+jy395/KcQwHtSyruOcM4jZeCDqwiLjt8v8gjj/1veBU6SUu4QQlwETOhwrvWQsT+13k1ANYoVSoPiTvtv0QBNUsoBv+Balb8Iqgj6i/Fl7hp21hQx+8A6PNKHBoG//fdZQrtZIr+0hM9LVwYPV9kbDxNBTa5WbG6lN1aD00akyQrNoNNo8fv9DE/sxvrK/aSFxXHsiEvQCgixxJNHCgV1hZzU9wSuWzKFyrXQmgX/ankeu8dJQcF+ot7ZTXzjE1hivBw3S4fBKhiR2J2ze4wjxRrNeb0mKnvoWcr6ofuYZhmNNSwKK4oVeeJLflqKJDnPmmh1plMw343WbCA0DbQGCE2CMPd0hD8EEIjAjUuNBR9g6DuCsKmKVWrbiz6KF0G3MyH95AdoWzKVmqYJaKTy71rl1hDSA88kIkRPYqSRigYPukLFelafeS9j5lyMISMDIQSZ8WbyntJxfl4kV97sYOaAMDw+SeFbH8BHszBddRUjknoiUf6c9GiV/wCqYl34JCRYDRjN/12tnMizz0YXn0KDsQu1dh9xEQd/PUXAACcEeMor8BWXAFDzzLOYL38cEJjd/RnS9TP6XWzg+ytsaEZ8Scik5fiKejGo+y10S6pg51oXGoyMeVzgDllL7oGVaHedQ9VGgFTawm8HwOLaRELbc+hDdJjrF2P/Rgm4Tp43F6HXE9HVR/2ewOakoK0WwtWWYX9FSlBcYEc6/ktYCswRQjwrpawJuIVCpZTFP3LNGuAs4EMU11k7q4ArhRDvAVHAOOA2FIvSfUKIj9pdTwFrUChQKYTQB+Yp/y/X60g4UCal9AshLkTJNfgxFh9pT0KIQiHE6VLKLwIxTv2klDt+Yi6VvxCqCPoDqXM00+JycKCpgjd2LWRa+hCMWh1PbfkGt8/Lmd3H8vG+FZ2u8Xf8g6bd4tpBDAkgIyyBYYmKNXd7TQFzCzZxfq+J9IpO5dXJVzPnwAbWV+7njSnXUWav6ySW/NKPze3g430rGZnUg/4WmDX1VuqcLSRbo2m5XGKOkqQdK+hWkkxuaTXZX0zFbxuDRppw1krczVBLPSfNeRgpJd+ceE9w/jvyX6W2ZzMxvW1MDiRmeH0uPl71KkvrwxiR2o/MvHQQWrxt4IhaibeoO6vuiEfjVTKMQpLAEge125XaRCPu0xDRNSCKpOTA18pae96G0xb/B//UHTQ7vdgOTKR49hyyr8zu9EyHZYfTkuJl1Yv12LxO7hnaQp/ZkZy0UTLldaUlRW6+D51HkJBnQUwVVOzZRuOKeYTX1sD3C3jy0SncmleG55Cf8f5uNtY8XcYL/Y/snuxITbOLepuHrgkW9DoNQgg2zB5F0x6B5mQbky7REGlV3GljHtHQmAex/aClJJsW43jCXCuwLViAsyCfpMaeVIbfiy3PQHimYPC1VgrqtXiBHhN6kpypx7irC5pAmcPVd0BC7x7ELb2XnOQz0AE+fCAkRtpINz5P1kePUHzGmfh9gf5kQiC9SgxXl8ka8uf4QUC/KyBxuDjCHar8BbibzjFBoATw3v1LJpNS5ggh7gUWCSE0gAe4FvgxEfQv4ONAYPGcDsdnAyOBHSj/qt0upawCvg+4qTYLIdzA/MB+/w8lELs28P+jpSAebb2OvAJ8FWi6+j2HW5IOve+j7elc4NXAM9EDnwbuR+Vvgjhovfz9GDJkiNy8efPvvs5fhT1VeWg1Ghqby5m1fR62wipO29fKMwPCaAg9ust4REJ31lftP+y4QaPD7ffQxR3GKZ5T+Tp8ISXeyuD5awZM44aBSlDvjNkPkddUwZS0Abw46UpWlu3misUvA/DxtFsZFJ8VvK62rZmlJTvJqStE37ASqwvMq20YrAbOufclTObw4NgGp4391ZXU3JSBu1ExS7RYv2boeUPpd2Y6u+qKOf27xwD4dPptRO5IR28VPGB/hZVle3hkzPmcnD0SgJLy5Vz+USH7G+JID23kXz4D1mQf9twotF134vr4Jg4qPsg+DbqepGHxFX60ZjjubQ3G8IPnv7/Ih61YEUvTPvpp64unugbpcZM/bQa4nTx/5sWsGjKd+5/pwaSXNET3FlRs8lGzDXqdpaGh9gAf3X0TAFPiM8i+5FLMAwawtbmVJq+Ppwoqybc7EQiuiI7m5n7JR13b5/Xw3bOP09rUgHXmHfglwerQALNnePHaBZq+LkZebiAsSos1ubPAqNtmo+bsEQg6N8oujXiM0GOmMPI/B7/vvD4XdVsMNOZKup4KB76Gve+68Xn1hOs3427V4zD0B0AKP8d95ScszIAIZKrZli/HvmoVhvQMzL17Yxk0MDh39WaJRg+x/VUB9HshhNgipTxqwO7P4bfODlNR+V9CtQT9CppcrXy2bxVWg4mcgnWsry9HLyRFXuXLSRBwTMdaiKhqYeT+WuYNSTlsnszwBKJNobxyzNWsq9hHhDGEwQkHey9tqT7A/Ws/ZkraQK4aNJzQvW08suFLEkOiGJfSi/N6TgiOndSlH+X2esanKLW+ekd3oVtkMgatjuzIpOA4n9fDNV8/yS5PPb0iYrks3E/jPiNVVa200kp54VayAi6t5kLJKcufQPv/7Z13eFzV0Yff2V31asmybLnJvfeCAWOMIfTeISGUBFIIEBKSEFIgISGQRghJIKGTjwAhYDqmG9MM7h33XmTJ6l27d74/zpW0kiXLHVue93n8aPfc0+6V5P1pZs5MQRJnycV0pScdBsL5911AMNYJomEde/KPE7+DqpK9phcf3eUByj2Pf5vg8bWkxLrSGZU1EZauz2ZMUSUFwTqO6b6WUHoBxQ/cSbBDEeFYV1+LAA21GVb+D2JTPcKVOGtRAcQ16jNOfTyIeooE2v4wrlm5krUXXEh17CDwOhPLBjrX5vLjxA5UTZlGWv+TgRhyxgXJcdU6CBYkI8SDQubVPyVhpHuOo9Pc6dopmakU14VJCQUJyq73ULxtK6tnzwRgaKSMqmAKGcmNwdMJHYSyckipjuPjGyEYF+H0/wSJzxDyZivz/ubRc9j6JgIomJ1NMDWNfmd3peulTY/jSziWj37moWGo2iEEIsVkF9yLUEu8rmJ91uMQho7DoP9FIdI6NN1/yuTJpEye3OK9ZI818XM44AseEz2G0QImgnZBaU0l5XXVzMlbRWpsIsd3Hwp5i2DVG9DtGC6e9SYbygpaHV9vY8usCTOxOsCMiWMZnBTD5YOOp7SmirfWz2V+/lquHnoiF/WfCMCJPUfsNM+Y7L68ct4vG95/ddAJfHXQCS2uefOYc7h5zDkN7zMTUnn53J/v1G/hu28SXr0eeiQzomNfsjt0RWqGUNHhf4RiEuk1oPFE1b2PzSCv5w5u+vB6MsvcyaeSNVBXIQRjIVxbS/6qHaS815k12/7B4px5BOK/RmyikJAeIC62sXZYUXkdFTNzGDWzPyNRlvdextaZU+hQnURkaxJpZ73BwMkB0vvC9nnKPFfmi8p86DYZMgZDep+mH76bP1KKVysdhyrxGUJar9Y/nCOlpWhtLTWBjmzs+AhBr5RhHZIIJFWhJ44iGHK/Eps+UOIzoOMwIUaz6br9cZQwVKS1OG96zM6/SusWzGXaA3+h3/hjmHLVt5CAkNmtBxPOv4TyoiJOGN2VUFw8AV84rZzqoZlhtKKM6tKFwPFEqj2m31TDyY8lsuRJj7L18MX2QUz68Y/xysvIuO46gvHxzPq9x7r/KBtXKpN+79YvWKRU5ntkDHBlSda8ogRrK0iKHUd2+X2UZFzGiQ8nU7hM6T5FGgLcDcMwjhTatQiqjYSpCte0WK6hOXkVxUxdPI3hcSGOSetIcfYIznjpdxRWl6O+nPnHid9hSuE8qCqCLbMRP1q1Png5SSA9IGyOKKmhGMbHx3F8agZnJcUQ//Wr8NYt5K7PnuPPc17ko0vv4fGl7wCwobR1IXWgyOqRy/H/ruCE2jS++7XLCMXEUNVFWfLoMWSNgEBU/acFPeagASU/ezOZZR0BwauFRa/NZchZfXjl3rupmnkaQpCCtNlsWjmb0T8awcSjRxCTIBRv28prf/09GV27c/K3biJ3WCGr3gqxMbaUTwrH89tze7Bx60a8TstJ6V1Jj6Pc2qk9oXxThI0fwNpX3F42TYfCJRGOvsO5veoqlU9vd4VcAQIxyulPBUjIavkDPXHMGLo/8jASE0t2YQLFa+IpqQxTF6qlS6dE6mqqefuny6icPwIJwhlPB8hfAAGckNvxhUfXibsX8Lxq9mdUFBWy8r1VlE31SMyCkTcKR539NUIJTfdXvMZj/l8VCCF0oJpjCUUKiQQzKNsIb1/rEfK1ZDAeMq+5usn4QEzTr1UFyvSbPTQCR98RoNMY5a1vKNUFOeSev526mvsYfcUQknOE1J4mfgzDODJpVyJoY1k+n2z+goLKYmoidbyw6jOKasq557grWbBpPuMrNpCW2YuRY6+kuqaC19//A520jkkpqTyQv4NnyqoIATNyUqmpqaK4pqJBAAEkxcRD75MgGAM5Y3l6VBc+37qC8Z37Ex+KJdEvZFleW0VSTDzSzDWSFHKuioRQHILw6Ck3MSdvFef0OeqgPaN6ug0ayvcefYZgKIQEnOhIyBTG/khYPP0d/vurdxl72hXUrh7Ij48+l6n5H3DmGR0YmiIsfUxY+8W7zHjlPtauGU550Q684CZSqk4lJq4boexUCmMyqRUlBlgz93O2rV7JttUrmfTVqxkwpAsbI4X0qYznkrGT6T4+wKhxyuxHlXU/n8T8cz1GXu/2NPJ7QQqWRKiO0ombPlDKN3skdw0QiofMIVC4HLw6CIRAWtEoVTuUrZ8oXScdQ1yaEMhX5v9didSGOPHcDNJ7BHjjLw+yZv1KshlCTHyIYAIkZTc4Nln5Xxj+zZ3nLt+irHpB6X6CkDnEfd/HnHY+5Vvr6Jx+Khufh5Jy+OBmJXusMukPTTeZkAloxN+8B+LRZcxGCtYkUFuTQOk66HY8pPcVuk/eWbSMukHocaLQwc92EoqHuHSoKYGEjhCbHOCUx5RwBSRkjdm9HxLDMIx2zmEpgiKex6dbv6B7Skc2lOaTm5bN4rxV3PzhEw196sOPg8CLc//H3IoypnRMQvK/4O5P/s01GR24NC6CqiB15UyJC/JCGXQOBUgMhMjo1J8nT7uZTWU7qPPCDMrozpCOfmqNYZcD7kznqb12/kBJjnL/RHNB/2MYlNmdnOQMRIS+6V3om95lPz6ZPSMU23Iem5nPP03J9jwqNr5E8uoBdBjQkzt+fSXrpimzVkHxGiU0eB3kgVeexlk33MC8+6B0cTpdNv+VSLiIupWfkHSce14DJ05m66oVZHbrQVJ6B+pildhO6dQU02CFEBGqlveAMJSubxSehcuV1B4Qnw5bZ9a3CosfU0ZdvobK2bM5/p6zkPh4ytZDKBHiM4Q6z+Urigk0io3Pf+exfQ5s/Vw59s4g1cVQV+6ufXSbMu7HSmaPTiyNeZXijn/lkt//kGAsJHaG7ifCxnchs5WyivP/WMTWeWls/bia0552gcmr/p1F3bvXUzcBhl0n5M1Rts+BcNXO4+PSAozocQeFC4rp+t2zyDj7VJK6jAZg3TSPTR8qg68MtOrqC4SErOGN72OShVOfCBCpcc8DICZRiNm5iL1hGMYRy2F5Ouz2T/5D5caZTK8OU+YpvUMBOoRiWFtTQ6F/OyNjg4SA2bURRsQG6R4M8MeOzi32SDCHa0aeRXjOvyiNeHTo0IMNOceyvqqUo7P7EpvcCQJ7luOlPbHwnWksfv8dAkuuIFjiPvVLE4p5+NS/EBOO4ZvTbiYhnEBszjZqNmfRfXIMHfrBoocaf5bG/3EdPcf0aW0JNKIszt/Ade/9jX4dcnj0lBup2xFg4wdKt+OFRN+d9fZ1EYpXQpejIa5LEetecLXCUnIhvG45XQpup/MF4wic8iMSuyjFKyDuqBIufONuVJXnz/4pnZPcmNl/9Fj7mtLvQmmwNL13fYQdS92eArHOnRSIidDjZFj7cpDELCjbCIO/LvQ9T4hNYycLH8B7Z/+d/LKrCcdN47LXzyUQCLg4nTeUnGPh2N8EidQqebOckIo+3db4TCJEiosJZWbu+TfNaJfsj9NhhmG0zmFnCdpRVUrhxs+4PzOJi7eVskqVS5PjODUhhn+XKYkBYWFthLsyEtka8XiirIZJ8TGckZxMbXwK+XGZfHX4JUhsIjEn/oZMP64n1/9nwPCTTqXP0FN4/fLGE0hbMzZRlOJ8UjtS8ulW2JPazV34bEQRv5ywjRsHdeKso9JZ9BuhYl2A/Pd70XMXXhcJCvML19Bn0QhGrprA+swa+oxPpP+FTcVBztFC2Qaly1FCzsRM1k31QKFsHcAANqXdRcXHs8j7wCMYD5FqCF65gwItBWD1olIKl6fR+yxhxBUFdC59nPQxkwBX32ryfQFWv6gsfFjxasCrBQiy9WM3V5Xvhqsphrh0t7f8+cqGd5X+FwuhRChYBNWdt7Ip4WJQoWjNaWT2TWDMzULPrwgZrrYswVgh59hdPZOgCSCj3SMi5aqavIvrubg6YP/x318FjFXV7+3BGtOBW1R1toi8DlyuqsW76L/OX2OvAzRFZLK/5pl7umcRiQNew9VQ+119IdkjBf/Z1bZVxPZAcEiLoPs/+j9mbpjF4ppa4mMSeO2CO9hcvoMNdWGqVQkDlQrvVdXxteRYJqZncJS4LMGRxGw+L8xjYzBE15EXQM+xxAJNMrhIoIVVDYCkzsIxvwow83dhvKoAyZUpDF8zhrFpAfp0CFHjV/OZN6GQ4tgwz2zeQRdR9Kpa8v5+H5kl3wFatwQBXNDvGEIraggVJrH9daHP+J37DLk6wJCroaZEKd+kBOOcOHEotcFu5HkuVXHAvzaqU2++X/o11r2r5E/txpYypXwLdK99gLL/PkPle6/Q/6MP3ZiQ0O0EWPRIoxUrLgPG/hA2zRByjoHaMuh6bKM4m3OvR9kGqC1Ttm9YTl7+RwwYcy1dIjNI79iHDr1c7FcgxsUSbXxXyT2Vhtw7+8KOpcq8v3p0O14YeJn9/BrtklzgcvbTsX5VPX1/zHOAGQWwP0twiEhIVcP7a75mcwejarXt61whXAmUcmC3RdD+ur9DVgT9/L0HyC1azpwal622pq6K/yz7gBtHn8VXx11G8aZ3+XFamP9W1HJCQggP2Nh1EkclhSC+A8HsoZwCnPKl3sXhTc5E2JK8mc5V3ckp6s7pC88k65t3wZDZ8Oc/EewonNcjkdSwcm5COlSDF6mmNrSaQL8ZtCWC8EJkn11H7SyPfhe2/qOoqrzzbY/Kbc0uBASIbcgnVFcCo78P3SYJw/8wgcxVIH5O2cwhIMUT8GQqlTET8erUFykuoNqL+lWqKYSYxACjb9pZtJRXhQleXk7o/Rhyjk1g7sq/UZ24gY2FBXztkaZJeGuKlRk/8hrinXNP3XcRtPZ1pWi5C8QeeNk+T2ccATx3QmSnZIkXvR/ca4EhIl8DbgRicZmbv+tXby/HVYw/E6gCzlHVPBHphRM0yURlcPbLTPweOA138uA3vgXkbmCQiMwHngCKgBwRmYb7T2Wqqv7Yn+Nk4Fe4MNDVwNWqWt5sv+vwrTwi8iLQHYgH7lPVf7Vxr6finl0QKFDVE0UkCVe9figuS/QdqtpaZurm82UAjwK9cZm7rwO24Up8ZPn3fIGqro4a0xd4EMjC1WS7CFjT0rPzLSp3+s9soIgMBx4AxgJh4Aeq+r5vqTobl0m8+TN9AFfINgH4n6reHvUcn8UVxn1eRC5Q1dH+tX7As/Xvo/Z+rX+PscAq4Aq/7MjjuHpto3DlT44BIv7P1g3AF/4919e4+76qfiwid/j77Q1sEJGuwI2qOt9f7yPg+j0pXXLI/im5sbyQAE0LupzZ22Wvu3jARDoffxsTsnrw56xUTkxO5rPOE7lg0GToeRxktxK9auwRIsL0U59nVZcvEAIklWZR+/HpaJWr57UkZjoxFSV8va4DKfclc0z3DNIrF5Pe5xRiN1zUJMB52f95fH53hLryxrY12yopGVBB9RWFZA5puraqEql1fUvXK3UtJbX3dm6a+xd4+1seQ78hDLxcOP7PAc57XQglQH7tSSztPIc1/IaaYte/ttxjwQMemcMgPgskBD1OgvR+O88NsHBxBeVpNcRcUEH3yUKfvhcRo93pM370Tn1DCZCY7eKMmmd9BqjYqqx/02PlCx6VeS3cTAv0O1/oPB6GX2vH2o228QXQQ7j6YeJ/fchv32NEZBBwCXCsb7WI0FifKwmYqaojgBnAtX77fcADqjoM2Bo13fnASFwR05OAP4hIF+BW4ENVHamq9/p9R/rrDgMuEZHuItIR+Dlwkv/hOxv4QRu3cI2qjsGJghtFpFX/s4hk4Z7dBf49XeRf+hnwnqqOB07w9912HhbHr4B5qjocV3bjSVXdDnwz6p5XNxvzFPB3fw/H4J5ha88OYDRwk6r2x5U0Uf/ZXwY8ISL1GVVH0uyZ1t+fH4c2HDjeF1L17FDV0ar6W6DELyUCcDXwWAv3+4KqjvP3vgz4RtS1bji35/k4wXOvf/8f4n5m7lXVccAFwMNR4wbjvueXAY8AVwGISH8gfk9rtx2ylqAnzv4Zp/3nFs5OUK489psM7D6saYdQDEy4EXBSdhehFsY+8PCl1zBz5GeU3tYHqQkRyBtN5JUJEI6hZ8E4XvroP1w647tIQKmqK+enJR9SmVvLta/n83aHKsZdHMPxkTQW++6muZ9uQ369gG+NOJWumfFsK66hU1rcTsHG79/oUfgFDL5CWPK47pHnsmILzPiRkjlYSOkB66bB3HuVQCwMuARSe0lDHqEPfuiCqaPpPF6QgFBXrtSUwIrnlE0zlG7HweZFcQTPq8NbF8v8TzzKZkyiS2ASmx6BeTsixCQLnScoc+91brSxP4KVT1cy++4gx96dQEp3t27hcuW9652VCGD+/TDqRo++5+36RtN6C8fdc+QG7Rt7zF00rRuG//4u9s7ddCIwBpjl/84mANv9a7XAq/7rOTiLAbj/ni/wX/8buMd/PRF42ner5InIBzgLRGkL676rqiUAIrIUJ+bScR+IH/t7iQU+bWP/N4rIef7r7kA/YEcrfScAM1R1LYBfxBXgZOBsEbnFfx9Po8WiLSbiPwtVfU9EMkUktbXOIpICdFXVqf6Yar99V8/u8/o9++vd74/9QkTWA34ijRaf6UbgYhG5DqcPuuCe8UJ/THSs0sPA1SLyA5yYaiGggaEi8hvc9yoZeDPq2nO7cKmdBAyO+lxIFZH6OLKXVbX+jO1zwC9E5EfANcDjrczXKm2KIP8blNVcnYrIcFVd2Mqw/cIbl//xQE5v7AZrVv2HWcUlDLyjhONDF5M1IosPbvEoALr1Tee2jO8SuBC6HhOgOqOOyroaIgGPL4ZW8OLgEh5YDH+f25+ErDiq8iGztBOrH+vIq1sixCQFOOY3HQjFNv3g9+qcy0fDrmQHSkMyxJD/33m4suX9xqRCXSlUbYdN25X+FwmJnVxsjlcLtRWQe4pbr2yTR3Vh0/EpPWHuX5XK7RHWvAqV28CL8QjUBVj+rkeoIobInzsQAQoH1m/YfVn1MuApy/6vcb6PbvXQiNv0xpe2MPh7Liqtrkxp/utftnG3viWGsSe09uG8ux/azRHgCVX9aQvX6rTxuHGEpp8v+3oMuSbqdf3cArztWwTaxHcVnQQc7btkpuMEzJ4iOOtQk0KPIpK9F3MdCHZZDDaKnZ6p77q8BRinqkW+2yr6GUXP/TxwO/AeMEdVWxKTjwPnquoC3wU3eTf3GQAm1Iu+enxR1DDO/z6+DZwDXIwT6HvELv/sFJGLcb6550VkiYiMi7r8+J4uZhxe1ITreKY4lpe3nc/M506gOjFCIEY47u4Ag6+EHYtgzYuw6n/wyR1Kl2BH/u/0H/LQyTfwgx/3JVEDpJfEUP5OCC8CMZku8GZY3WCq8qF0HUy7Al671GP7fOXzlcW8+sl21i+q49i7hGHXCuN+EmDi3QEm3ytM/F2Ac14Szn01wIn/cG0xUWdMciZCxP/7QIIu7n3bbI9Oo6CT762q9n9Nt89Tpn1dmyRhlJBLMhguh00zoNb/ezQ/x/1fEawUjr6jMStz0RdRD0ug42Aa6776XzUSIKPySQJeGRs/S2Xtax6l6z0+ud19JsSkwOT7YPT3haHfNBeXsd/ZsIftbfEucKGIdAIX4yIiPdsY8zFwqf/6q1HtH+LcMEHf9TQJ+Bwoo/UK8dHMBI71Y2YQkSTfJdIaaUCR/8E5kPojoruef5IvDOrjecBZM27wY5oQkVGtjG+JD/GfgS/KClS1JcsXAKpaBmwSkXP9MXEikkjrz25X6/XHid+dq3Q3kooTGSW+qDttF3urxj2LB2jZFQbu+7hVRGJo+r1vTvPv+Vu42CD8vY/cxdiHgb8Cs1S1aBf9WqQtJ8NtwBjf93s18O8oU6L9j93OeW7Fx7y8ZRvnv9WFo+dmsupB9y0PJQjFo9dzz8U/5d0RzvpdVworX/SoebAnQysGkb09lmdXDeL2+/uTWB2kphDik0MMugKO/3kcner1um/l+fAnHhv/HaTq4VRm/zBI4RIYeHmAULzQ5Sgha0SALhOEQCiAiJAxSEjMhsROkNwNBlwOSZ1dkDO45MvqwdLHlRfP9ojUQt/zYdxP3I/81pm609+mGnYuLIBOI4UR3xG6HA29viIs61NGeVYdEOC0pwJ0HE6zwVCw1AmvfhfDyKiDsRVdLscLpFC6KZHZf1RWvagNlqy6CkjOCdDnnAAxifYrZex3bsMF4EZT6bfvMaq6FBeH85aILATexrlMdsVNwPUisoimB3Sn4twsC3DWhB+r6ja/LSIiC0Tk5l3sJR8XD/K0v5dPgYGt9Qem4awdy3DB1zN30bd+/uuAF0RkAY2uoDtxAdELRWSJ/353uQMY4+/3buDK3RhzBc6NtxB3eqozrT+75vwDCPjP/lngKlWtaaEfAH48zTyc8eM/OAG7K57C2cLfauX6L3DB8x/7c7bGK8B5IjJfRI7DBd6PFZGFvqvu27vY8xycG7A1IbZLdpksUUQW+QFV9e+74Hy+T+Ae5s7RoC2wv5MlGgeHBXnrePMHVXQt6ElCMJahVwfof5ETEQ8ueIO/zH2Zb75xMz3zm54CC8TW59uB9AFQHPV3RzABkrtCySrIOdYV9qzKd9diOyqS6FGzIUj/i4QR321do9eUKG981WsSMB2Mc3W1aktButShJUGoEOr1elIOnP6Ui6f55I4Imz/Yed76vUvQzReuhKSuULHZ32MqnPFMgB1feMyoD8GMqngP0Pc86H5CgPdvjGr09Y0E4bh7hJI1UL5J6T4lQNZwEz9Gy8h+SJa4v0+HGUY9flxUmqr+4kvcQw4wHRioql4b3XeirZigMhHpUx8PpKpbfRPei8CQXYwzDnPUUzqu7EGvLU4kD/xWJf0varRWnhY7iTWhcjqPCbi/r6KoF0AAXY5yhVA3+H8nRKrqEx1CfKY7QRWXCRkDoO+5QdJ6B9mxBHKOdn1qy5RQkpL3mZDSE5JznGAoWNQogAJpdXglMURqIOL/jaNbQmR8tYra+XGUL3HCJ6W7O3VWlQ8ZA2lRBNXvXSNOAEkABlzigrNrCp3AWvOa0ve8AAMv8/jiGcCDUBKoQqQSqoth2VMeiZ2hagfEpcGJfxeCcQBCXJqQvVt/PhjGvuMLHhM9xn5FRKbijqtP+RL38HXgt7ij/3ssgKBtEfQdmrm9VLXMz51w8d4saBweLHlMmfvCDmZMfJW04i7U5ZfCkxcw+OtOUCz8ZTzjCs6n+xTY2MwS0oC4ZIQ1xUrmENixxDUH4+GYnwVY8qRLOgjQ77wA2WPdj1q3Sa5t9Usec/+ipPeF4lVKKBlGfU8IxMDnv20sbeuVxDQuGXJurVCCUPpiIuEKSOvj6nVt+wym3+RRsAgymx02bA1V6HNWgJXPRxoSRC74uztRlr+IBpdauF6QxcH2OY3xRADVBVBdJGQMaGrxCUeUgEAgYJYgwzAOL1T1vLZ7HfA9PAk8uS9z7FIE+RHd5/pBWYtU9U2/vQ7nCzTaKepBaWw1H/V2sXYpsy8gZinEpXn0OSdAeh/YtkMJ9g/TpTaGrR81jKQxKhiWPNrobo1Jce6kUTfA0ic9ile6dgk6y4x6SsVWNzw5R9g0w42tyHP9wuUw6+76+WSnoLT4DDjungDBOIhJgiVPKJtnKMOvC7DqJY+KLY0nsCq27Pr+gwnOapXgZxGZ8Avh87sUVShdCyXrtUHoxGVCjR9w7dVAbY0b3/VYKFrp9pLSven8xRV1zFhSSHxMkCnDMwgFD9mUXYZhGO2WXYogEfkHzu31CXCniIxX1T0JAjMOU4ZeI6R078qW/32N6lA1uetcJHO5n+ps4l0Bpn26g03BCF0zkgl8HsKrrT+12jKRGph8b4D5f/MoXNrYrhF47waP0nVAwNWuPfmRAOW+UEnIhrqylueMz4SaEmf9qS6ErZ8p2z5XwlVw3N0BxtzsxMXKF1z/pM4w9JtC5mBXOX6nLNSAxEBKV+hzjtD1OKF8i8eK55SSNY19ilc4ixY0df8ld4XyzS6p4bBv7ixsts9XCpcpCceHiXhQUROhNqyELPWPYRjGQactd9gkYISfEr3+WJ6JoCMACQq5pwqDYoI8t3QBYy9JYUzFWHqcJNRVKkseUwKxcTCskqzhwsCb3+KDe0fheSFqwx1xvjCauMm8Wpj9B4+8WS4AufvX68hfBJWfxTgBhOvveRCuUgZcLKx+RQm28FPqsjDDUT8LkNYb1r6hFK1Q1ryiVPqWo00fKn3PcaIs3j/cWlMCvU5zyRBH36R89NOmfryMwVBTBMWrYPNHrlBq/nxa1HYjvi0kdhZm/96jDndP438mpOUKoYTGAVtnKvEZkN7H5Q2K1MDgulhGnpJCfGyQxDhTQIZhGF8Gbdnga+szOqpqJXYs/ojjihMn8PINP+DyE8bT+0x3ZH3d0+tY+T+l9D+JHJ3Ska1/j2NbyekMOelTPC/KGtRCnFBFnkt4mHN+hE3dS6gq9wik7twxECOk9RbKNznLSjTxHd1R+NJ1MOeJSlRhw7vK5o9oEEAA8+5Tpp4eoWilR2qu21PFVieEAIpWKtJMYBUudX3AlbwoWOxfUJr8thx7l9DnnAAdhyrZfvYsr9YJnvo9hL0If/zXdD76qcc7345QusEj2XeLrXtDWXQtFPz3gNQ3NAzDMHaDtkTQQP+c/kI/z0D9+0V+zgLjCKRT6R9JiNtEdpfZFHwGmz+EBQ/CnNfPJ0yHXY4t3+BOXfU8TtB1MeiSOLzSgEtmGMAlHRwBv/3rao59ZhrvZa5tcgy+81GQe4pQHxa9dFEBL5wWIX8eDYHLDagLiF43TVnyuOufe5pbf8EDEZY8qtTXIHYnt5pSug56neFOiIUScaIuAP0vgpyj3a/Oezco66MSwW/5CN682mPxox6LC9bzTsFct1cV3rrWpQYAqNwm1JTF8cUz8dSW7msyXcM4eIjIjSKyTESeEpGzReRWv/1xEblwH+a9I6oURXR7rogsbmnMbsy5x/mQROQi//7e35s192Cdcv9rjoj8bz/Md66IDI56/2sROWlf523vtOUOG3RQdmEcVqQdP4EzOQMm3kxJx/Fs+8wjIQu2fOwEQ2wqDQVKgwkw5GooWQPro47SZ+QESJ6VTKk4gbB9LmQOdVmYCxbA7EHbqY3z2DA4n9GX9WLxI+7E1bbPXf2vZX5YvgZA61o3UAbilMQsacgkXZUPM+/0KIrOmRqASAsGmYKF7h80lumISYBh1zX+7RCIiRogNMQNffEfJWNhD07oNKExhLt5lRyNkBY3n5iUcRjGYcR3cQUsN/nvX97dgSISUtWDaf68DZcjaU/4BnCtqn7UZs/dZFf3rapbgL0Wj1Gci8vjt9Sf95f7Yc52zy6TJbY6yBVvu0xVr9+d/pYs8cggf4ESmwoJWU5siEDBEmXefUqnsbAtqrShBBrrgRFwJ6k2f9h4fUtCGUtHb2TMxu502JBCrzNhrV+acco/hLxZyozn8vECHtk12XjROVADkDUa8meDooQ6KcHqQMNprviONCmXEU2n0ZCcA2veoIloCcRAXAeY+FshvW+A4lVK4XKlplhZ/HDLc7kbpdWqSVkDizjunliCqa3WTzSOcPZHssQ/XXLmTskSf/jsq3uVN0hEHsQVqlwOPAoUAWNV9Xt+nalqXIX2VFzullf9mlHn4wpoBoHz/LG9cdmrr1PVhSJyBy7vTF+gI/B7VX1IRHKBV1V1qP/637iK9QDfU9VP/ES+z/rrhnDpXc4AfgQsApaoapOyDSJyGU4kCfCaqv5ERH4J/BjYjCvU+aNmY34CfA1nF35DVW8VkWtxmaVjgVXAFX5pjvrnMQqXMfl+XL6mZOAl4Puqmrw799fa2lH7OgYngEr8fxfgsjW/qqr/E5G7gbOBMPCWqt4iIhfhan9FgBJVneR/r8aq6vf8eV8F/qiq00XkZOBXQBywGrhaVcs5zNntKvJ+fZTLgYuAtcALB2pTxuFJ1ohGi0xssit+uvwZxatrKoAgSgABHfrC+J8GmL690UJz4kUpnJYyhAUfOwWx9rXG/sv+rWSNgMzyLAB6nAF5s6HKj8VJ7wuVqTOpCXUkFO5MZHtyEyNMfWHimGR3Yq2+1EaPk6HbxADp/ZS6amXjO649GO+SPm76AFa/BKN/oEy/2aOuHHp8BQhAfIfGumRNb7Tp2+js030vzyCYakfjjQOHL4AeorGSfE/goT9dciZ7I4RU9dt+nrgTVLXA/9CMJhdXTbwP8H59XS9gNDBcVQtF5H5gnqqeKyJTcHleRvr9huNqeiUB80TkNZqyHfiKqlaLSD/gaZzouhx4U1V/KyJBIFFVPxSR7/lln5rgZxm+B1dwswhXBuRcVf21v6dbVHV2szGn4Qp1HuWLnPpaYi+o6kN+n9/gLEn3+9e6Acf4h4teBh5Q1SdFpDUDQov3t4u1AfCF4Mv4osffS/2+M3HCc6Cqqoik+8N+CZyiqpuj2lpERDriyqWcpKoVviD7AfDrXY07HGjriHx/4DL/XwFOaYuqnnAQ9mYcxnx+d4QtH0NdW38nCIz5Ecy9zyO5Kw0iaMX/IKN/lIKIern1U9j6qTLq+0LOBKFoFXQer8z6ncs5VJFfR8m6AInhvkSkuHGsQFovSOziRFjvM4WtnytFy5wgqi2GT37ZqM6yRkEw1iVZ3ORnl96xTBEJkNTFnSBL7CSc+Sx88Yyy6vlWbjEG1BdaFZsh93ToemyAnGPsnIFxwLmLRgFUT6LffiCySP/Xz9y7UkTW0FjL621VrY/am4izVKCq74lIpojUm0NfUtUqoMqPyRkPzI+aPwb4m19QMwLUF0ydBTzqF+p8UVWjx7TEOGC6Xx8MEXkKdxr6xV2MOQl4zD8kRNT9DPXFTzrOyhMVJchz9YeLgGPr7xtn7bmnhTVau7/W1t4dSnAWqUd8y45vU+dj4HER+S9tGzUmAIOBj31xFYur1XbY05Yl6AvcsfgzVXUVwK4K2hkGQMl6r0mw8K5QVV79bhXxdYlICHqd5dxekUrIGgH5C2l2yqw+GaMw78kI8/4aAE+QIBzzW/j4VqAihkh8PuFAnuurjUNL1jTG7Sx7spbktHwgh7py2LGs6d7y57mSH7GpbprcUyH3lAAlaxX1IDYFvnjKxTTVxxy1eI91EEp2yR7BFWc1AWQcJHrsYfu+0tz5W/++onnHPRxfz81AHjACd5SiGkBVZ4jIJJwL7HER+bOfTfhg8Dhwrp9c+CpgctS15vfdVvxJi/e3L6hqWETGAyfiYo++B0zxrXpH4Z7ZHBEZg3OXRZun/WxoCE7IXrav+znUaMsWfz6wFWfWfEhETsSOyRttkNzZP021GwhCfF0iijuplT8fxt8K/S90r3uc2OzkVsD9HyJ9a6BbHXjux1Ej8MnP/UBlgZTqMwh52cSGOpHSM2o9r4JEbwkBSlGNoaw4p+FaE6uV/1NeugHOfC5A3/Nh5XOw5RNl84dKyerG0hhrXlXCNdD/smaB0j6puXDKY0J6Pxdz1PU4+xUyDhob9rB9X7lIRAIi0gcX87O8hT4fAl8F8GtRFqhqfaGZc0Qk3nfhTMZZeKJJA7b61qYrcDFGiEhPIM93Sz2Mc78B1PnWoeZ8DhwvIh1999llQAvVBJvwNnC1nzOPKJdUCrDVX+errQ3GWV4u9V+31q/F+9vF2tGU+Xtpgogk44qcvo4TWSP89j6q+pkfQJ0PdAfWASP972F3nCUOYCZwbL17U0SSfE/RYU9bZTNeBF4UkSScP/L7QCcReQCYqqpvHfAdGocN5ZuVj3/hkZgNvU6HugpY94YTRP0ugmVPNPYNxEFMoqsGHwmEiU2HSEEM5Rth7n2ANNbjyhgEhcugwwDod0GA6iJhQ24lxf9KJLpMh4bdu87j3SkycLl7ytY3rhsXXkt1KBdPEwkGy4h4jf9nZA6Gim1u/Lo3XFsoEVa9qGyf5VxoSx5Xkrq4k2wZA6GuQlj3hrLxXVejLBBqjDFK6wNH3RYgrbfb31f+tb+fuGG0yW00jQkCF4y8x0fHd5MNOIGRCnzbj21p3ucOnOtqob+XK6OuLQTexwVG36mqW/xg4Xr+ATwvrnDmNBotLZOBH4lIHVAOfN1v/xewUETmRgdG+8XAb/XXqg+MfmlXN6aq03w31WwRqQVexz3HXwCf4YTEZ7QgRHxuAv7jx9O0tlaL97eLtaN5BnhIRG6k6WmzFOAlEYn37/UHfvsf/LgjAd4FFvjta3EnzJYBc/31830r19MiUv9n6c+BFa3cx2HDHp8OE5EOuODoS1T1xN0ZY6fDjgxWvegx776mP08jb4ScCQGSuohfBFXJ+9zF4Hx2p+sbiIHeZ8Hql2FXh2cDMU5gSAh6nq6sfxXUtwSl9IKytU609D7DubbKtzbW9AJAlfjauVTHjgAJEYiB2A4uiLs0qiRGj5Ng4wdhtK7xb4Sux0FVIRQuabqn3NMAFWJTlRX/dW1djnFxSyO+K/S/0AKfjb3nUDsdZhjtjV2KoFZMbg3sbnCWiaAjg7pyZcGDysb3XO2ueoZcIwy+oqkYUE9Z+C9l/VtKTRFNjpIHE11MEEBpfDFruy9nxMqjdl7QL8uROQwm/0moKRVikpWppykoZA6BwuW+sGrlqHrH4dBpFCyNslLV91U8vFAxwXAGI28QJKDMu2/nOeLSG/MixaTAOVMDLsF00Nxexr6xP0SQYRit01ZgdAGwCRcsBU3jgRTn8zUMAGKShbG3CP0v8nj7Wm1wCxUt31l9SEAY8W2hxxRl9aseG96hIbi4XgABLO05n9eO+h9L+8zjsmnfbjqJHzC9YxG8d6Nywn1CMDZASvcIZRugNMoN1lo4YnRCxJ37CsFwBl2Ph4X/1IaCqe5eachkXS+AGkYFd65wbxiGYRx6tGWr/ysuh8I0nN+2t6r28v+ZADJaJLVngHG3CqFEF8/T/xJh/t89dizdWYl06C+M/UGQjoMb28QPBQzEQKeUVIKRIInFabtcs+gLeO0yj3CNx6gbA8RnuEDnFt1ru+OhUm3I9Lz5AxdbVFfqEiZKyA+iVvcvxk9rltIDTvyHyR/DMIzDhbYCo78vLqptMi5S/X4ReQuX8GntQdifcRgTqYXUnsKq55VNH0DebOWUx4J4dcqHt3pUbofj7g6Q3FUYc0uAN77moRGX0fnoX7oCqjPvHE2vp0axO7aVmkJY/owy5MoAQ64W5vwpSnRFV7RvobBrNJmlj7Aj5ZqW1yhyX9P7u8zSXY4Sep4i1JZAXLoJIMMwjMOJNjNGqwsael9E5uGO990JrMSdODCMFsmf56wweXOUwVcKWz5VuhzlRELldtg+1++3UEnuKiR1Fib9Xlj1ojL0m0JqjwBlG5Rtn9EggEKJLsOzRlpb1WWOHnIl9D4zQEKmx0e3+UKoDeHTiNL35qPY8UjT1q6ToOMwoXSD4tUKY24WgnGNoicufXfnNwzDMA4V2soYXX80/hIgC5dVcoyqHqgcE0Y7YcjVQkwK5BwtdBwm9D6j8VpyV2HE9UJlHnQ/oVFIdBrtV5P3qS1rFDx9znGFS2fe6bFtZuvrFi6DqWdESOnh3FQdBjpX2a5I6QllGwCFxM5Ct0uG8dkjvniKhW5Hw4RfBpCAWXoMwzDaE21ZgrbjrD7P+F8VV8dkLICqWv0wo0XiM4Th17UuGnbn6HjmEGHi3QECIcge4+bKGCBsm9lSlLPLF6QRV/G9XvgMuAwyBkDWcKE8T1n2JET8HKwpPSC5Kxz1c6F6h5C/QOlxorDqJX/+AEz+o5A1zI65G8ahjIhMp4V6X4bRFm2JID/zCf1prGFS/8mmWBFVYw8pLllNQdEX9Ox6AsVLElj5gke/CwJNiq9WbFOqdzgRVO9Cq6ff+bDqRagtgYROEMyOUL4oSOMZeCExGyr9YqrLn4bETlCw2GV5bj5X8Rrhgx8ocanK8OuF2vLGQOekztBxqFl/DONgIiLBqHpbhnFAaUsELSY6Ja97nQ98ZIHRxt4we9H91NaWUltXSt6jF1OwCPLmeJz8sEuoWFepvH2tq9De9zwXh9P9hEZLTDBOqCt3lpqq7ZDaqVH8AOQcC73OhI9/2rhm5XacTTMacSe91rzcaFXKu0ZRT5EQ9L9YGPQ1oYVst4ZxWLHp1g93SpbY7e7j9jpZop/BeRqulMIxuNIWjwG/AjrRWBLiPlztqSrgalVd7peouAc4FRep95Cq3i8i63AFur8C/N4/kHMbjdmcf+KvXY6LRz0Z2AZcWl8EFVey4x+4Qqbf8KvI5+KKlfp/2vA9v+J6APgbMAXYCNQBj6rq//waWn/GFUMtAK5S1a17+7yMQ5u27PzJuJTbyVGvxwJviMiluxpoGC2RntobENJSetFtshMY4UrY9GGUi8vXHaumwsxfK0UrGq8F44Sj7xBSc6H32VC+MtAwIK2vEJviBJBEyftAHCR2hh5fgf4X+43qKtVH91M/eFrDUPiFEptiAsg4vPEF0ENAT9wvSk/gIb99X+gL/AlXJX4gcDmuOvwtOPHyBXCcqo4CfokTYQDXAbnASFUdDjwVNecOVR0NzMAJpSnASGCciJzr90kCZqvqEFytr9ujxodUdTyuvFN9+3bgK/68l+DSvoCri5mLq4x+BXA0gF//637gQlUdAzwK/HbPH49xuNDWEflftdTuZ5J+BxcrZBi7zZhhNxDxagkF4+B8qCn2KFnjYnE2feCx8F9KpM4VHa3YBgh8cItHjxOF0Tc5zd51YoCuE2Hj+14TS05NEayb5l4HY6HvJbD6RZfUsHIbbNjmriVkgRd2SRbrCcRB9hhA3UmvvudZHJDRLriLpnXD8N/fBexL6Yy1qroIQESWAO+qqorIIpy4SAOe8GtTKVBfxPQk4EFVl8GrWdWBZ/2v44Dp9RYeEXkKmAS8iLMe1ff7P5qGZNS/nuPvAX/dv/l1tyI0hnVMBJ7zC5VuE5H3/fYBwFDgbd8KHMQVETfaKW0ekW8JVS0U8xMYe4GIOAHkM/SaALV15VQVKJ/e0fh/dem6xjERYM0ryrBvKmWbIKUrbPlUScpRV7A07EpthBIBv1ZYuBKCscLk+5Xp33fv6xMnxnWAMbcIRUth62dK3mzwaiClqzDiuyZ+jHZFjz1s311qol57Ue893OfKncD7qnqe75KavhtzVrTdZSeiT0nU7yFC42fbzUAernJ6AKhuYz4Blqjq0XuxF+MwZK9EkIicgMskbRj7RHVNETM++wWRGiGY8GciVS2LEI3AKxd6RKohGOfyBQVinQACV2qjvNJlm64PqVzymLLkSdx/ifUEoHgFvHudEt8RErMhawzkfe6O0xtGO2MDzgXWUvuBJA3Y7L++Kqr9beBbIvK+qoZFJKOFGpSfA38VkY64z5nLcC4qcELmQpwX4nLgo93YxyZV9UTkSpxlB+Bj4EoReQKX/mUyzjK2HMgSkaNV9VPfPdZfVZe0MLfRDtjln70iskhEFjb7twnnr/3uwdmi0Z6pC1cSDlehwQomPr6KnOMgfSD0OHnnvvVH2yP+33tebdPrEoTsCRCIlvZRAqj/JY0nvwCqC1xV+MyBwvmvB+gxxaxARrvjNqCyWVul334g+T3wOz/JbvRv5MM4AbZQRBbghEwT/CDkW4H3gQXAHFV9yb9cAYwXkcW4mKFft7GPf+DEzgJc7FK9tel5XF3MpTi32lygRFVrcSLrHn/MfFzwt9FOaauKfPO/IBQXvLZHZkurIm/sioLCpUQiNWRnjWpo04iyYymsfMFj0/RWBgZpauVpAQmAKvQ4CY66Lci7341QuAw6HwMF852bbOyPhV6nmQAyDj32RxX5/X067MtERMpVNXk/zZWsquUikomzPh2rqtv2x9zG4cMuRdD+wkSQsbeoKkXLIbUnrH3DY/XLEAhCWh8Y/HWhfKPw2V3uSH09PU+G+CwIxQudxwkZAxrD1+oqlJK1kDnYD5jOg/S+Ft5mHJrsDxHUntjPImg67jh9LPB7VX18f8xrHF6YCDIOe2rLlG2zPBKzID4zQHKOiRqjfWAiyDAOLHsVGG0YhxKxKUKPKcG2OxqGYRhGFBYIYRiGYRjGEYmJIMMwDMMwjkhMBBmGYRiGcURiIsgwDMNoF4jIVSKS82Xvwzh8MBFkGIZhtBeuAkwEGbuNnQ4zDMNox7z+3jU7JUs8fcqje50s0a8FNg2YicumPAt4DPgV0An4KrAKV4G9Ny5D9XWqulBE7vD30dv/+hdV/as/7w+Aa/xlHlbVv/jtX8dVp1dgIa5awUJcOYs6EUnFZZb+MTAWeEpEqnCV4QcDfwaSgQLgKj8jtWEAZgkyDMNot/gC6CFc/TDxvz7kt+8LfYE/4UpRDMSVv5iIEyu34QTRPFUd7r9/MmrsQOAUYDxwu4jEiMgY4GrgKGACcK2IjBKRIcDPgSmqOgK4SVXLcAVZz/DnuxR4QVWfA2YDX1XVkUAYV3PsQlUdgxNlv93H+zbaGWYJMgzDaL/cBSQ2a0v02/eldMZaVV0EICJLgHdVVUVkEZCLE1sXAKjqeyKS6VtsAF5T1RqgRkS2A9k4ATW1viSTiLwAHIez/jynqgX+XPXFVh/GWX5exImna1vY4wBgKPC2iIArtGNWIKMJJoIMwzDaLz32sH13qYl67UW993CfK3W7OTbCXnwOqerHIpIrIpOBoKoubqGbAEtU9eg9nd84cjB3mGEYRvtlwx627y8+xMUG4QuVAlUtbaP/uSKSKCJJwHl+23vARX6RU0QkI2rMkzhr1mNRbWVAiv96OZAlIkf7Y2N895phNGAiyDAMo/1yGy4wOZpKv/1AcgcwRkQWAncDV+6qs6rOBR7HVXP/DBcYPU9Vl+DieD4QkQW4IOd6ngI6AE9HtT0OPCgi83HurwuBe/yx83GB3IbRgBVQNQzDOETZHwVU9/fpsEMFEbkQOEdVr/iy92IcvlhMkGEYRjvGFzyHveiJRkTuB04DTv+y92Ic3pgIMgzDMA4rVPWGL3sPRvvAYoIMwzAMwzgiMRFkGIZhGMYRiYkgwzAMwzCOSEwEGYZhGIZxRGIiyDAMwzjk8DNCXx71/ioR+duXuafmiEj5AZx7soi86r8+5O69vWAiyDAMwzgUycUVZjWMA4aJIMMwjHZM5/fnX975/fnrOr8/3/O/7rWwEJEkEXlNRBaIyGIRucRvXycivxOR+SIyW0RGi8ibIrJaRL7t9xER+YM/blHU2BbbcZmmj/PnvNlvyxGRaSKyUkR+38oed2cvDVYW//3fROQq//XdIrJURBaKyB/9tmwRmerf9wIR2SnztIj8SERm+eN+1creThWRuf4c70Y900dF5HMRmSci5+zp98XYeyxPkGEYRjvFFzwP0VhJvifwUOf357PthJF7k0DxVGCLqp4BICJpUdc2qOpIEbkXV77iWCAeWAw8CJwPjARGAB2BWSIyA1fKoqX2W4FbVPVMf62r/H6jcEVYl4vI/aq6sYV9trWXFvFrlJ0HDFRVFZF0/9JfgQ9U9TwRCQLJzcadDPQDxuMKt74sIpNUdUZUnyzc92KSqq6NqoP2M+A9Vb3GX+9zEXmntT0a+xezBBmGYbRf7qJRANWT6LfvDYuAr4jIPSJynKqWRF17OarPZ6papqr5QI3/4T4ReFpVI6qaB3wAjNtFe0u8q6olqloNLMWJupZoay+tUQJUA4+IyPk01l2bAjwA4O+zpNm4k/1/84C5wECcKIpmAjBDVdf68xRGjb3Vr3c2HSfWeuxij8Z+xCxBhmEY7ZfWPkz36kNWVVeIyGhcuYrfiMi7qvpr/3KN/9WLel3/fn991kTPG9nFvG3tJUxTI0A8gKqGRWQ8cCKu+Or3cAKoLQT4nar+czf6tjT2AlVd3qRRJHsv5jL2ELMEGYZhtF827GH7LhGRHKBSVf8P+AMweg+GfwhcIiJB3zU0CVc1vrX2MiBlb/a5G6wHBotInG8ZOhFARJKBNFV9HbgZ56IDeBf4jt8n2MwNCPAmcI0/HhHpKiKdmvWZCUwSkV5+n4yosTeIiPjto/bfbRptYZYgwzCM9sttNI0JAufiuW0v5xsG/EFEPKAOXxjsJlOBo4EFgAI/VtVtItJa+w4gIiILcHE9RXu5551Q1Y0i8l9cjNBanBsLnOh6SUTicRaaH/jtNwH/EpFv4CxQ3wE+jZrvLREZBHzqa5ly4GvA9qg++SJyHfCCiAT8a18B7gT+Aiz029cCZ+6vezV2jajqAV9k7NixOnv27AO+jmEYRntCROao6th9mcMPjr4L5wLbANy2l0HRhtHuMEuQYRhGO8YXPCZ6DKMFLCbIMAzDMIwjEhNBhmEYhmEckZgIMgzDMAzjiMREkGEYhmEYRyQmggzDMAzDOCIxEWQYhmEcMESk/EtY8yoR+Zv/+g4RueVg78E4PDARZBiGYRjGEYmJIMMwjHbM1Jl5l0+dmbdu6sw8z/96+d7OJSJ3i8j1Ue/vEJFbRCRZRN4VkbkiskhEzmlh7DMickbU+8dF5EK/DMUfRGSWiCwUkW+1svbX/esLROTffluWiDzvj50lIsfu7b0ZRyYmggzDMNopvuB5CFdtXfyvD+2DEHoWuDjq/cV+WzVwnqqOBk4A/lRfC6ulsSISi6vX9RrwDaBEVcfhqsdfW19fqx4RGQL8HJiiqiNwZSwA7gPu9cdeADy8l/dlHKFYxmjDMIz2y100rRuG//4u9iKLtKrOE5FOfiHVLKDIr8MVA9wlIpNwldq7AtnAtqjhbwD3iUgccCowQ1WrRORkYLiIXOj3SwP64Wpo1TMFeE5VC/x9FPrtJ+EKodb3S60vYmoYu4OJIMMwjPZLjz1s3x2eAy4EOuOsOwBfxYmiMapaJyLrgPjoQapaLSLTgVOAS4Bn/EsC3KCqb+7FXgLABFWtjm7c2QhlGC1j7jDDMIz2y4Y9bN8dngUuxQmh5/y2NGC7L4BOwLndWht7NXAcMM1vexP4jm9NQkT6i0hSs3HvAReJSKbfJ8Nvfwu4ob6TiIzch/syjkBMBBmGYbRfbgMqm7VV+u17haouAVKAzaq61W9+ChgrIouArwNftDL8LeB44B1VrfXbHgaWAnNFZDHwT5p5Kfw1fwt8ICILgD/7l270110oIkuBb+/tfRlHJqKqB3yRsWPH6uzZsw/4OoZhGO0JEZmjqmP3ZQ4/CPounAtsA3DbeROyraq8YWAxQYZhGO0aX/CY6DGMFjB3mHFIsqNoGZ/P+xN5+fO+7K0YhmEY7RQTQcaXyrqCCv7w5hes2l7GprICwl4EgNXrX6egaAkr1770Je/QMAzDaK+YO8z4Uvnly4spLlrIh1vKWcEKTssdw70nfJOeXadQW1dObreTvuwtGoZhGO0UE0HGl4JXE6bgkcVc0/EtKnPXUqfCrRtiWF24hqrqQrKzRpGdNerL3qZhGIbRjjF3mPGlEN5eRe2GMryYUgBiAwGu6pHNxSnbWLTssf2yxuxtK/n7/NcoqanYL/MZhmEY7QsTQcZBZ+2Gt5i+9lYKT15JjoynU+ZIxo34AWf3GExGCMort/DuRzezeesn+7TO9e8+yP3zXuWfC6e13dkwjIOKiFzll99o7fqvRWSv/OEisk5EOu797r58ROT7ItK85ImxnzERZBxUSss2sCVvFuFwFZv4iDWpb7Cj+AsyOwyge84kAhKiuqaImtoSFix9mKKCpQ1jC4tXsnz189TUljSZc0veZ6xa9wqeF25srCzkkfwPuKdgJiMz96VCgGEYB4irgBZFkIgEVfWXqvrOwdiIiAQPxjp7yPfZue6bsZ8xEWQcNDYt/y8fzbqDsuIVSNSPXkwoARAikRo89YWMeoDy6cI/snrdawDMX/Igq9e/xoo17sTYwmWP886HNzF/yT9ZsWYqm7c5y5Gqx9o3v0d+/z4kDx3IcTFhDOOI5Z2fXs47P13HOz/1/K97W0EeEblbRK6Pen+HiNziv/6RiMzyszf/ym/LFZFlIvKQiCwRkbdEJMEvljoWeEpE5vtt60TkHhGZiyuR8Xh9UVURGScin4jIAhH5XERSfEvS36L28qqITG5hzy+KyBx//eui2stF5E9+Buqjm42ZLiL3ishsf//jROQFEVkpIr/x+/xaRL4fNea3InKTOP4gIotFZJGIXBLV5yd+2wL/Wfbx77f+ej8RmSsiN+IE4vsi8r5/7WQR+dS//pwVit0/mAgyDhrhuU8C4AWDKB4A2R3HsaLmcobc/iYXPbSWGGIZuHohvTatomveek789BUi7/+G/MJlxMYkA0JGen/qwtVs2jqD2royX1AJaxc9wqaN71FWsYUVSUGq4pOoiUtgc10eqt6Xd+OG8WXhBM9DuFpe4n99aB+E0LPAxVHvLwae9SvB9wPGAyOBMX5Fefz2v6vqEKAYuEBV/wfMBr6qqiNVtcrvu0NVR6tqfXFVRCTWX/cmVR2Bqxxf3393uEZVx+BE14319ceAJOAzVR2hqh+1MK7Wz9b9IPAScD0wFLjKn+NRXIkQRCSAq6f2f8D5/jOo3+sfRKSLiJwGnAMc5d/H71V1NVASVfPsauAxVf0rsAU4QVVP8F17PwdOUtXR/rP7wR48A6MVTAQZB42Oaf3JzttAMFzX0JZXMIt/frCGytoIqwrK6Zi3hl6bVxJQj5rYeIKRCF3yN7FsxVOUlm8ElKrqAmYv+HPDHAp027yS8pggC1f8myXL/0NCamP9xhXrXubj2XcexDs1jEOGu9jZpZLot+8xqjoP6CQiOSIyAihS1Y3Ayf6/ecBcYCBO/ACsVdX5/us5QO4ulni2hbYBwFZVneXvoVRV98S8e6Nv7ZkJdI/aVwR4fhfjXva/LgKWqOpWVa0B1gDdVXUdsENERuHfu6ruACYCT6tqRFXzgA+AcThB9JiqVvr3UejP/zBwte+Su4SWs3tPAAYDH4vIfOBKWi9Sa+wBdkTeOGhUlG9kzPJZFKZmMHPkCaiCCJzVbwnvrBvHpV2LIXso27evY3WPgQAs6zuaivgE0lJzKaks45WV/cnaNI9ju60lPi6DjA6DqK4upDZ/o1tEhKKSnWs3lpZtIByuIhRKOIh3bBhfOq0FxO1LoNxzuArynWkULQL8TlX/Gd1RRHKBmqimCLCrX8I9OcoZpukf8vHNO/jusZOAo1W1UkSmR/WrVtXILuav37dH03vwaPzsfBgX29QZZxnaG54HbgfeA+b4Qqo5Arytqpft5RpGK5glyDjg1NVVMv3Tn1JVvgmAmHAYVPFUyO50FBO6rOet2G9z4/rv0e/zZ5gzxLnng8F4vJGX0XnczXTtfDQzNnTirbW5PLV0NGW1sVTXFLI171Oqa3aQn9W9xbUTEzr5r5SKqu2oemza+jEFhUtb7G8Y7YwNe9i+OzyLc/1ciBNEAG8C19THqYhIVxHp1Mr4espw1ejbYjnQRUTG+XOniEgIWAeMFJGAiHTHueKak4azVlWKyECcRWV/MhU4FWfpedNv+xC4RESCIpIFTAI+B97GWXwS/fvIAFDVan/sA0B0fpDo5zMTOFZE+vpjk0Sk/36+lyMSswQZB5yq6gIqq/JY1mMApR1zkW7jmNDrbGrrSlm68hkSqivIy+jMyu4D6L51DUgAVeiRM5m1G6exJe9TMjsMpm96AamxVXRJLiMuqIALgq6qLmiIMQqF6wiHYhrWrqza3vB65ue/IjmlFyXlaxEJcMIxfyQ+Lv2gPgvDOMjchosJinaJVfrte4WqLhGRFGCzqm71294SkUHApyICUA58DWf5aY3HgQdFpIpmgcnN1qv1g4vvF5EEXDzQScDHwFpgKbAM54ZrzjTg2yKyDCemZu7JvbaFv7f3geIoq9JU3P0swHnrf6yq24BpfuzPbBGpBV6n8fvwFHAe8FbU9P/yx2zx44KuAp4WkTj/+s+BFfvzfo5ERFUP+CJjx47V2bNnH/B1jEOXNeunsXHLDCqqtgGQ0+kotuTNdP6wer8YEKqtpq7E4+/rT+LeU9LZvPk5ahJSUY347q8BZGUMJTYmlVkL/gQIg/pdzqpV/yUSria2porqxGaHJhRnTG5AiI/LYNJRdxIK7WRBN4xDBhGZ4wfn7j0uCPounAtsA3AbJ/3OqsrvB/yA6LnARaq6ch/muQVIU9Vf7LfNGbuFWYKMg0JhyYoGARRA8BY9B9l+WII0KpRwbDzpcTu4ZfDbdJg+ny39RlL/B1aHtL6MHHwtAGW+aw2UFWueJ6J1EAyS23EMK6vXEfGiXfhRKkiVIdnHs7FqHSvWTmVwP3OxG+0cJ3hM9OxnRGQw8CowdR8F0FSgDzBlf+3N2H0sJsg4KCTEZTS89lC2depOaUWIl1cOZtqaAU36plaUUp2QyOxhx5JQXeksRYCnnp8ssRRVj9zuJxMbk0IkUo0QoHPWWHLH/5ChA6+Mmk18kSWgMFa6UeCVUVq2nnUb3yYSqcMwDGNPUdWlqtpbVX+4j/Ocp6rDVbVgf+3N2H3MEmQcFAb3v5zc7l+htGwzi5Y/RjhcwRsbhvDBxr7EBes4tffyhr4bu/RywkeVwtTMBkvR9oK55KnH6vVvAB6hYALDB1/LvMV/RzVCTEwSgUCIisqtBALxxMWmEhNKoLR8PbndTqJf73Mpr9hM3hx3OrhH1ymEI1UEgzEtbdkwDMNo55gIMg4KIgGSErNZseifhMPuFOxx3dcQH6yjS3LZTv1DoUTCkaomrrLGhIee/z5CekpP+uSeSUnJOgCmf3oblb7braq6uiGj2vYdC+nf53yWr5naMF9RyUre/ej79O5xGgP7XrR/b9gwDMM45DERZBxUKks3QBBQj5zkMs7pv/NR9ZFDvuPifKpq0VYOl6Sn9SU2lMSHs26nrq687XWr8ti4eQaFRcsa2ioq8wAoq9jU2jDDMAyjHWMiyDhohMPVlATrrTnSar8Nm6c3OdoOIBKLam3D++qaIopLVrU4Pj4ug+oal4w1JpREXbgKkSBrN7zlr+vHGHm1dM4ay6B+l+71PbWF54WZt/gBqmuKGT3sehLiM9oeZBiGYRwULDDaOGiEQvGEQn66EmlNBAnZWSN2ao0WQACpSd1IjM8iNbknaSm5xMd1bLhWXVNIIBBLZodBTBj9E0BRraO6dsdOR+KLS9cdUGFSWZVPXsE8SsrWsqNoGV5YmXWPx6d3eNRVHvj0FIZhGEbrmCXIOKhMHHc7i754kh1FSxraYmPTUS9MRnp/Il4tnhcmIT6Lqur8VucJBhOZfMw9qCql5RvYUbSclWunEonUH40Xxg6/kWAwjnrLD0A43LTuYiRSvT9vbyeSk7rQN/csqmuK6Zw1mg2fbWPttHSEWHqcJHSdeECXNwzDMHaBiSDjoJKYkEVutym+CAoCEWprixGJpaqqmNKKNRQULm7oHwwkEvGqiBYyAFu3f0re9Nl4XvMj7gHAo0unsYgEqaktJdoF1hxPw3z4+e1U1xQxuN/ldO28b1n1N32xhEXvvsno088hu1cfKN1KclU5weRsPnn1CWY9/SopPfsxpOsf6TRqn5YyDMMw9hETQcZBJztrFBPH/4ot2z5jzYbXAefuKq1Ys1PfiFfZwgxO1DQIoPqs5yLUnxzbvO1jdhQtIyE+C5EgrRWdjkSqKSt3xVfXrH99n0XQ9CceIm/NKipLirng1tspf+JU5g8ZA3lQklflr7mC4AV/Ys22Pgzoc8E+rWcYhmHsPRYTZHwppCZ3Z2DfCxk19DuEgsltD4gis8Mg4uMyiAskklDlnwxrIcaouqaQopLlqIaJCe28RkK8iyMSAgSD8fTrdc4u11268hk+nn0nZRWbm7QXFq+kpraUFatfIGdiLB36dqDDuFJWvftDAlXFJFRV4EXg6ZLjSRmZRq/Tkygs/oLV618jEqltZTXDMAzjQGOWIONLpUuncaQkdefDz3+JaoSBfS5ixdqpLbi5QCSEIHTsMJgdRUsRgvTqdwlrNr3W5jp1YSeWAoEQwwddS2nZOrrnTCIUSiAUjCcYjG11bE1NCes2vce6ja624co1LzO4/2UIwtbts1i68j/ExXagprYIEmBC/7Xkzn6HlT0GUR4XR0xNHd//6FzCGiSn1yi6ZS8lKSGbLtlH7XJdwzAM48BiBVSNQwLPC6OqDdmb5yz6B3n5s4mpraYutumJLnfsvWI3Z3ZxRwCdOo6id/dTCEeqSEnuRkJ8ZpujtxcsZPbCv+zUHhOTTF1dORnpAygsdtmuU8qKKUtO4yufvEJMpI6IBFiRO5i13QewuSwVCcTRt+sQEmQ7wwddvVvrG0c2+6WAqmEYrWLuMOOQIBAINSlf0SPnOOLjOnD8go84eu77dN+8uuFaawKoS6fx9Oz2lYb3wWAiY4ZdTyAQQ1xsGiMGXcPCLx5j9sL7mP7JTyj1Y4F2RWUrJ9Tqs1eXlm8gI30QAGUp6QAUpGcBUJ6Uxoaew+i4YytdU0rJScqnd3YPjhp1iwkgwzCMQwBzhxmHJFmZw5hy7J8orI6HBU+T17FrC70aT33FhlIoq9hMVlwGA3pfQGpKTzLSBxAMxjDl2D8RCMQQCsbh+TE4ikc47I7HR7w6iktWkZ7aZyf3VI+cyazd8CZV1QVN1uzT8wzWb3qP6podlJVviNqSMG/w0awpLyKz/wVEtn5ASXJ61OXg/nlAhmEYxj5j7jDjkKa4dC2fzvldK6e7xHdLNa09NmnCXSQndm5xvuqaIrZs+5zUlO50zBhMSek61myYxtbtn9Ol0zhGDf3OTmPKK/OYMfNn1J88q187IEGSkro0nC5rCVFQtCFwe9jAq+mec1xbt20YgLnDDONAY5Yg45AmPbUXx479BR/Nur1J+4DeF5CW2ptwuIq5i//W0J6U2IWEuNZdTfFxHejd8xQAVq9/g+WrnyMmlASAp16TvmXlmwkGY9i2fTZNBRCA4mmY9JTexMWkUhCV/LFJL4H6EiHJCTkmgAzDMA4hTAQZhzypKd2ZfPTvqakrJb9gAfFxGfToejwAnhdpqBWWmNCJ4yf8drfnjURc3p6YmCRGDvkWHdL7UVtXTnnFNkSET+e0PdfGrR/QIX3Arjup0il9MEOGfGO392YYhmEceEwEGYcFiQkdSUzoSIfU3k3aA4Ego4Z+m5VrXqLbHlpZ+uaeQ1pKL9JSc4mP64Cqxzsf3oTn1ZKVMbzN8SIBVD2KipeTmpJLTW0JkXA14UjT0hwdOw5n7Iib92hvhmEYxoHHRJBx2NMhrS/jR/1wj8cFAkGysxprV6h6DfmJIl4NsTEp1DaLN+qUOYK4uA5kpvcnMaETi5c/SUZ6fwb3v7yhT11dJRu3ziAzfSBJidmEQgl7eWeGYRjGgcQCow0jirz8eeQVzGdQ30uoqytn9qL7qaktIz42jc6dxtI39wxELLOEcXCwwGjDOLCYCDIMwzhEMRFkGAcW+5PWMAzDMIwjEhNBhmEYhmEckZgIMgzDMAzjiMREkGEYhmEYRyQmggzDMAzDOCIxEWQYhmEYxhGJiSDDMAzDMI5IDkqeIBHJB9Yf8IUMwzDaFz1VNevL3oRhtFcOiggyDMMwDMM41DB3mGEYhmEYRyQmggzDMAzDOCIxEWQYhmEYxhGJiSDDMAzDMI5ITAQZhmEYhnFEYiLIMAzDMIwjEhNBxmGLiOSKyOJmbXeIyC0i8riIVIpIStS1v4iIikjHqLZz/baBzeatEpH5IrJURB4UkYB/bZqIFIvIqwfjHg3DMIwDh4kgoz2zCjgHwBcxU4DNzfpcBnzkf41mtaqOBIYDg4Fz/fY/AFccmO0ahmEYBxMTQUZ75hngEv/1ZOBjIFx/UUSSgYnAN4BLW5pAVcPAJ0Bf//27QNkB27FhGIZx0DARZLRnVgBZItIBZ+l5ptn1c4BpqroC2CEiY5pPICKJwInAogO9WcMwDOPgYiLIOJxpreZLdPsLOCvPUcCHzfpFC6NnaOoS6yMi83HWo9dU9Y193q1hGIZxSBH6sjdgGPvADqBDs7YMYG3U+2eBOcATquqJCAAikoGLERomIgoEARWRH/nj6mOCDMMwjHaKWYKMwxZVLQe2isgUaBA2p+ICnev7rAd+Bvyj2fALgX+rak9VzVXV7jjxdNxB2bxhGIbxpWMiyDjc+TrwC9919R7wK1VdHd1BVf/ZvA3n+prarO15dj4l1gQR+RB4DjhRRDaJyCn7snnDMAzjy0NUWwurMAzDMAzDaL+YJcgwDMMwjCMSE0GGYRiGYRyRmAgyDMMwDOOIxESQYRiGYRhHJCaCDMMwDMM4IjERZBiGYRjGEYmJIMMwDMMwjkhMBBmGYRiGcUTy/8/40wCFZiBuAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WjL-a2edSnC1", + "outputId": "f2e30457-9dd0-44ac-f70b-ce5fb22321e0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(164, 159)" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "# Specify the indices of the two groups to be compared\n", + "cell_idx_a = np.where(adata.obs.cell_ontology_class==\"endocardial cell\")[0]\n", + "cell_idx_b = np.where(adata.obs.cell_ontology_class==\"atrial myocyte\")[0]\n", + "len(cell_idx_a), len(cell_idx_b)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Obtaining a low dimensional visualization based on alternative splicing\n", - "adata.obsm[\"X_umap\"] = UMAP(n_components=2).fit_transform(run_pca(adata, 10))\n", - "sc.pl.umap(adata, color='cell_ontology_class')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "WjL-a2edSnC1", - "outputId": "dd0e97b0-e721-40eb-cc19-edd097fb4dcf" - }, - "outputs": [ { - "data": { - "text/plain": [ - "(164, 159)" + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qDmgUEBFSnC2", + "outputId": "94c5cea0-d23c-43f6-c598-fb7ab32d0c6e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "sample sizes: 164 159\n", + "(323, 3044)\n", + "filter_min_cells_per_feature\n", + "filter_singletons\n", + "(323, 265)\n", + "filter_min_global_proportion\n", + "filter_singletons\n", + "(323, 265)\n", + "filter_min_cells_per_intron_group\n", + "filter_singletons\n", + "filter_min_cells_per_intron_group\n", + "filter_singletons\n", + "(323, 165)\n", + "Number of intron groups: 82\n", + "Number of introns: 165\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 82/82 [00:03<00:00, 24.41it/s]\n" + ] + } + ], + "source": [ + "intron_groups, introns = run_differential_splicing(\n", + " adata, cell_idx_a, cell_idx_b, min_cells_per_intron_group=50, min_total_cells_per_intron=50, n_jobs=1,\n", + ")" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Specify the indices of the two groups to be compared\n", - "cell_idx_a = np.where(adata.obs.cell_ontology_class==\"endocardial cell\")[0]\n", - "cell_idx_b = np.where(adata.obs.cell_ontology_class==\"atrial myocyte\")[0]\n", - "len(cell_idx_a), len(cell_idx_b)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "qDmgUEBFSnC2", - "outputId": "2011f44c-2045-428b-ba18-83414b711800" - }, - "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/82 [00:00\n", + "
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p_valuell_nulllln_classesgene_idgene_namemax_abs_delta_psimax_abs_lfc_psirankingp_value_adj
intron_group
Slc25a3_chr10_91119707_-2.783505e-47-358.71158349144565-254.414519572034462ENSMUSG00000061904Slc25a30.80316429.57178102.282474e-45
Tpm1_chr9_67036129_-5.241887e-41-304.4620056272593-214.540019753809932ENSMUSG00000032366Tpm10.8038283.78596712.149174e-39
Atp5c1_chr2_10056162_-4.590732e-39-452.07306917411984-366.598530664809632ENSMUSG00000025781Atp5c10.7231822.77199321.254800e-37
Vdac3_chr8_22583737_-1.121661e-36-250.61923551606782-170.610569857797432ENSMUSG00000008892Vdac30.7980896.17268932.299405e-35
Myl6_chr10_128491034_-1.332322e-29-843.9144475995784-780.08456435469682ENSMUSG00000090841Myl60.5606511.89584742.185008e-28
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Atp5g3_chr2_73911061_-8.684012e-01-285.018287649314-285.00456111069222ENSMUSG00000018770Atp5g30.0104700.036641779.028508e-01
Cd47_chr16_49884164_+8.698196e-01-319.7141268340032-319.700697265386172ENSMUSG00000055447Cd470.0075640.057573789.028508e-01
Sqstm1_chr11_50200948_-9.347043e-01-377.83197645534915-377.828620388962862ENSMUSG00000015837Sqstm10.0035330.025456799.580719e-01
Lsm6_chr8_78813081_-9.549484e-01-159.77279175618725-159.771195976323382ENSMUSG00000031683Lsm60.0047330.018135809.667378e-01
Rpl38_chr11_114668780_+9.739811e-01-464.9025381444468-464.902006255112162ENSMUSG00000057322Rpl380.0015920.006153819.739811e-01
\n", + "

82 rows × 10 columns

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\n", + " \n", + " " + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "# information about all the intron groups that were tested\n", + "intron_groups" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "sample sizes: 164 159\n", - "(323, 3044)\n", - "filter_min_cells_per_feature\n", - "filter_singletons\n", - "(323, 265)\n", - "filter_min_global_proportion\n", - "filter_singletons\n", - "(323, 265)\n", - "filter_min_cells_per_intron_group\n", - "filter_singletons\n", - "filter_min_cells_per_intron_group\n", - "filter_singletons\n", - "(323, 165)\n", - "Number of intron groups: 82\n", - "Number of introns: 165\n" - ] + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 692 + }, + "id": "f7qANOW5SnC3", + "outputId": "3bf38e2c-d4b3-49bc-f4f0-ea91344ca407" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " chromosome start end strand \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 chr10 91119707 91121921 - \n", + "Slc25a3_chr10:91119707-91122206 chr10 91119707 91122206 - \n", + "Myl6_chr10:128491034-128491719 chr10 128491034 128491719 - \n", + "Myl6_chr10:128491034-128492058 chr10 128491034 128492058 - \n", + "Uqcr10_chr11:4702221-4703903 chr11 4702221 4703903 - \n", + "... ... ... ... ... \n", + "Bnip2_chr9:70004342-70007063 chr9 70004342 70007063 + \n", + "Rpl29_chr9:106429577-106429969 chr9 106429577 106429969 + \n", + "Rpl29_chr9:106429640-106429969 chr9 106429640 106429969 + \n", + "Clasp2_chr9:113786635-113813141 chr9 113786635 113813141 + \n", + "Clasp2_chr9:113793467-113813141 chr9 113793467 113813141 + \n", + "\n", + " annotated gene_id_start \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 True ENSMUSG00000061904 \n", + "Slc25a3_chr10:91119707-91122206 True ENSMUSG00000061904 \n", + "Myl6_chr10:128491034-128491719 True ENSMUSG00000090841 \n", + "Myl6_chr10:128491034-128492058 True ENSMUSG00000090841 \n", + "Uqcr10_chr11:4702221-4703903 True ENSMUSG00000059534 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 True ENSMUSG00000011958 \n", + "Rpl29_chr9:106429577-106429969 True ENSMUSG00000048758 \n", + "Rpl29_chr9:106429640-106429969 True ENSMUSG00000048758 \n", + "Clasp2_chr9:113786635-113813141 True ENSMUSG00000033392 \n", + "Clasp2_chr9:113793467-113813141 True ENSMUSG00000033392 \n", + "\n", + " gene_id_end n_genes \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 1 \n", + "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 1 \n", + "Myl6_chr10:128491034-128491719 ENSMUSG00000090841 1 \n", + "Myl6_chr10:128491034-128492058 ENSMUSG00000090841 1 \n", + "Uqcr10_chr11:4702221-4703903 ENSMUSG00000059534 1 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 ENSMUSG00000011958 1 \n", + "Rpl29_chr9:106429577-106429969 ENSMUSG00000048758 1 \n", + "Rpl29_chr9:106429640-106429969 ENSMUSG00000048758 1 \n", + "Clasp2_chr9:113786635-113813141 ENSMUSG00000033392 1 \n", + "Clasp2_chr9:113793467-113813141 ENSMUSG00000033392 1 \n", + "\n", + " gene_id gene_name \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 Slc25a3 \n", + "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 Slc25a3 \n", + "Myl6_chr10:128491034-128491719 ENSMUSG00000090841 Myl6 \n", + "Myl6_chr10:128491034-128492058 ENSMUSG00000090841 Myl6 \n", + "Uqcr10_chr11:4702221-4703903 ENSMUSG00000059534 Uqcr10 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 ENSMUSG00000011958 Bnip2 \n", + "Rpl29_chr9:106429577-106429969 ENSMUSG00000048758 Rpl29 \n", + "Rpl29_chr9:106429640-106429969 ENSMUSG00000048758 Rpl29 \n", + "Clasp2_chr9:113786635-113813141 ENSMUSG00000033392 Clasp2 \n", + "Clasp2_chr9:113793467-113813141 ENSMUSG00000033392 Clasp2 \n", + "\n", + " intron_group intron_group_size \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 Slc25a3_chr10_91119707_- 2 \n", + "Slc25a3_chr10:91119707-91122206 Slc25a3_chr10_91119707_- 2 \n", + "Myl6_chr10:128491034-128491719 Myl6_chr10_128491034_- 3 \n", + "Myl6_chr10:128491034-128492058 Myl6_chr10_128491034_- 3 \n", + "Uqcr10_chr11:4702221-4703903 Uqcr10_chr11_4702221_- 4 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 Bnip2_chr9_70007063_+ 2 \n", + "Rpl29_chr9:106429577-106429969 Rpl29_chr9_106429969_+ 3 \n", + "Rpl29_chr9:106429640-106429969 Rpl29_chr9_106429969_+ 3 \n", + "Clasp2_chr9:113786635-113813141 Clasp2_chr9_113813141_+ 4 \n", + "Clasp2_chr9:113793467-113813141 Clasp2_chr9_113813141_+ 4 \n", + "\n", + " n_genes_per_intron_group psi_a \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 1 1.000000e+00 \n", + "Slc25a3_chr10:91119707-91122206 1 6.494021e-12 \n", + "Myl6_chr10:128491034-128491719 1 7.666660e-01 \n", + "Myl6_chr10:128491034-128492058 1 2.333340e-01 \n", + "Uqcr10_chr11:4702221-4703903 1 3.145444e-02 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 1 3.856290e-01 \n", + "Rpl29_chr9:106429577-106429969 1 8.472850e-01 \n", + "Rpl29_chr9:106429640-106429969 1 1.527150e-01 \n", + "Clasp2_chr9:113786635-113813141 1 4.995530e-01 \n", + "Clasp2_chr9:113793467-113813141 1 5.004470e-01 \n", + "\n", + " psi_b delta_psi lfc_psi \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 0.196836 0.803164 2.344936 \n", + "Slc25a3_chr10:91119707-91122206 0.803164 -0.803164 -29.571781 \n", + "Myl6_chr10:128491034-128491719 0.206015 0.560651 1.895847 \n", + "Myl6_chr10:128491034-128492058 0.793985 -0.560651 -1.766715 \n", + "Uqcr10_chr11:4702221-4703903 0.102419 -0.070964 -1.703142 \n", + "... ... ... ... \n", + "Bnip2_chr9:70004342-70007063 0.866008 -0.480379 -1.167167 \n", + "Rpl29_chr9:106429577-106429969 0.814178 0.033107 0.057502 \n", + "Rpl29_chr9:106429640-106429969 0.185822 -0.033107 -0.283076 \n", + "Clasp2_chr9:113786635-113813141 0.249912 0.249641 0.999217 \n", + "Clasp2_chr9:113793467-113813141 0.750088 -0.249641 -0.583842 \n", + "\n", + " abs_delta_psi abs_lfc_psi \n", + "index \n", + "Slc25a3_chr10:91119707-91121921 0.803164 2.344936 \n", + "Slc25a3_chr10:91119707-91122206 0.803164 29.571781 \n", + "Myl6_chr10:128491034-128491719 0.560651 1.895847 \n", + "Myl6_chr10:128491034-128492058 0.560651 1.766715 \n", + "Uqcr10_chr11:4702221-4703903 0.070964 1.703142 \n", + "... ... ... \n", + "Bnip2_chr9:70004342-70007063 0.480379 1.167167 \n", + "Rpl29_chr9:106429577-106429969 0.033107 0.057502 \n", + "Rpl29_chr9:106429640-106429969 0.033107 0.283076 \n", + "Clasp2_chr9:113786635-113813141 0.249641 0.999217 \n", + "Clasp2_chr9:113793467-113813141 0.249641 0.583842 \n", + "\n", + "[165 rows x 19 columns]" + ], + "text/html": [ + "\n", + "
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_grouppsi_apsi_bdelta_psilfc_psiabs_delta_psiabs_lfc_psi
index
Slc25a3_chr10:91119707-91121921chr109111970791121921-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-211.000000e+000.1968360.8031642.3449360.8031642.344936
Slc25a3_chr10:91119707-91122206chr109111970791122206-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-216.494021e-120.803164-0.803164-29.5717810.80316429.571781
Myl6_chr10:128491034-128491719chr10128491034128491719-TrueENSMUSG00000090841ENSMUSG000000908411ENSMUSG00000090841Myl6Myl6_chr10_128491034_-317.666660e-010.2060150.5606511.8958470.5606511.895847
Myl6_chr10:128491034-128492058chr10128491034128492058-TrueENSMUSG00000090841ENSMUSG000000908411ENSMUSG00000090841Myl6Myl6_chr10_128491034_-312.333340e-010.793985-0.560651-1.7667150.5606511.766715
Uqcr10_chr11:4702221-4703903chr1147022214703903-TrueENSMUSG00000059534ENSMUSG000000595341ENSMUSG00000059534Uqcr10Uqcr10_chr11_4702221_-413.145444e-020.102419-0.070964-1.7031420.0709641.703142
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Bnip2_chr9:70004342-70007063chr97000434270007063+TrueENSMUSG00000011958ENSMUSG000000119581ENSMUSG00000011958Bnip2Bnip2_chr9_70007063_+213.856290e-010.866008-0.480379-1.1671670.4803791.167167
Rpl29_chr9:106429577-106429969chr9106429577106429969+TrueENSMUSG00000048758ENSMUSG000000487581ENSMUSG00000048758Rpl29Rpl29_chr9_106429969_+318.472850e-010.8141780.0331070.0575020.0331070.057502
Rpl29_chr9:106429640-106429969chr9106429640106429969+TrueENSMUSG00000048758ENSMUSG000000487581ENSMUSG00000048758Rpl29Rpl29_chr9_106429969_+311.527150e-010.185822-0.033107-0.2830760.0331070.283076
Clasp2_chr9:113786635-113813141chr9113786635113813141+TrueENSMUSG00000033392ENSMUSG000000333921ENSMUSG00000033392Clasp2Clasp2_chr9_113813141_+414.995530e-010.2499120.2496410.9992170.2496410.999217
Clasp2_chr9:113793467-113813141chr9113793467113813141+TrueENSMUSG00000033392ENSMUSG000000333921ENSMUSG00000033392Clasp2Clasp2_chr9_113813141_+415.004470e-010.750088-0.249641-0.5838420.2496410.583842
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165 rows × 19 columns

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p_valuell_nulllln_classesgene_idgene_namemax_abs_delta_psimax_abs_lfc_psirankingp_value_adj
intron_group
Slc25a3_chr10_91119707_-2.783505e-47-358.71158349144565-254.414519572037252ENSMUSG00000061904Slc25a30.80316429.57178102.282474e-45
Tpm1_chr9_67036129_-5.241887e-41-304.4620056272593-214.540019753810182ENSMUSG00000032366Tpm10.8038283.78596712.149174e-39
Atp5c1_chr2_10056162_-4.590732e-39-452.07306917411984-366.59853066480962ENSMUSG00000025781Atp5c10.7231822.77199321.254800e-37
Vdac3_chr8_22583737_-1.121661e-36-250.61923551606787-170.61056985779632ENSMUSG00000008892Vdac30.7980896.17268932.299405e-35
Myl6_chr10_128491034_-1.332322e-29-843.9144475995784-780.0845643546922ENSMUSG00000090841Myl60.5606511.89584742.185008e-28
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Atp5g3_chr2_73911061_-8.684012e-01-285.018287649314-285.004561110692072ENSMUSG00000018770Atp5g30.0104700.036641779.028508e-01
Cd47_chr16_49884164_+8.698196e-01-319.71412683400314-319.700697265386452ENSMUSG00000055447Cd470.0075640.057573789.028508e-01
Sqstm1_chr11_50200948_-9.347043e-01-377.83197645534915-377.828620388963032ENSMUSG00000015837Sqstm10.0035330.025456799.580719e-01
Lsm6_chr8_78813081_-9.549484e-01-159.77279175618725-159.771195976323532ENSMUSG00000031683Lsm60.0047330.018135809.667378e-01
Rpl38_chr11_114668780_+9.739811e-01-464.9025381444468-464.90200625511212ENSMUSG00000057322Rpl380.0015920.006153819.739811e-01
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82 rows × 10 columns

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" + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 242 + }, + "id": "-gDY_jpbSnC5", + "outputId": "1598316d-bcd3-45b3-b902-161c3975396d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " chromosome start end strand \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 chr10 91119707 91121921 - \n", + "Slc25a3_chr10:91119707-91122206 chr10 91119707 91122206 - \n", + "\n", + " annotated gene_id_start \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 True ENSMUSG00000061904 \n", + "Slc25a3_chr10:91119707-91122206 True ENSMUSG00000061904 \n", + "\n", + " gene_id_end n_genes \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 1 \n", + "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 1 \n", + "\n", + " gene_id gene_name \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 Slc25a3 \n", + "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 Slc25a3 \n", + "\n", + " intron_group intron_group_size \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 Slc25a3_chr10_91119707_- 2 \n", + "Slc25a3_chr10:91119707-91122206 Slc25a3_chr10_91119707_- 2 \n", + "\n", + " n_genes_per_intron_group psi_a \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 1 1.000000e+00 \n", + "Slc25a3_chr10:91119707-91122206 1 6.494021e-12 \n", + "\n", + " psi_b delta_psi lfc_psi \\\n", + "index \n", + "Slc25a3_chr10:91119707-91121921 0.196836 0.803164 2.344936 \n", + "Slc25a3_chr10:91119707-91122206 0.803164 -0.803164 -29.571781 \n", + "\n", + " abs_delta_psi abs_lfc_psi \n", + "index \n", + "Slc25a3_chr10:91119707-91121921 0.803164 2.344936 \n", + "Slc25a3_chr10:91119707-91122206 0.803164 29.571781 " + ], + "text/html": [ + "\n", + "
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_grouppsi_apsi_bdelta_psilfc_psiabs_delta_psiabs_lfc_psi
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Slc25a3_chr10:91119707-91121921chr109111970791121921-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-211.000000e+000.1968360.8031642.3449360.8031642.344936
Slc25a3_chr10:91119707-91122206chr109111970791122206-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-216.494021e-120.803164-0.803164-29.5717810.80316429.571781
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_grouppsi_apsi_bdelta_psilfc_psiabs_delta_psiabs_lfc_psi
index
Slc25a3_chr10:91119707-91121921chr109111970791121921-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-211.000000e+000.1968360.8031642.3449360.8031642.344936
Slc25a3_chr10:91119707-91122206chr109111970791122206-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-216.494020e-120.803164-0.803164-29.5717810.80316429.571781
Myl6_chr10:128491034-128491719chr10128491034128491719-TrueENSMUSG00000090841ENSMUSG000000908411ENSMUSG00000090841Myl6Myl6_chr10_128491034_-317.666660e-010.2060150.5606511.8958470.5606511.895847
Myl6_chr10:128491034-128492058chr10128491034128492058-TrueENSMUSG00000090841ENSMUSG000000908411ENSMUSG00000090841Myl6Myl6_chr10_128491034_-312.333340e-010.793985-0.560651-1.7667150.5606511.766715
Uqcr10_chr11:4702221-4703903chr1147022214703903-TrueENSMUSG00000059534ENSMUSG000000595341ENSMUSG00000059534Uqcr10Uqcr10_chr11_4702221_-413.145444e-020.102419-0.070964-1.7031420.0709641.703142
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Bnip2_chr9:70004342-70007063chr97000434270007063+TrueENSMUSG00000011958ENSMUSG000000119581ENSMUSG00000011958Bnip2Bnip2_chr9_70007063_+213.856290e-010.866008-0.480379-1.1671670.4803791.167167
Rpl29_chr9:106429577-106429969chr9106429577106429969+TrueENSMUSG00000048758ENSMUSG000000487581ENSMUSG00000048758Rpl29Rpl29_chr9_106429969_+318.472850e-010.8141780.0331070.0575020.0331070.057502
Rpl29_chr9:106429640-106429969chr9106429640106429969+TrueENSMUSG00000048758ENSMUSG000000487581ENSMUSG00000048758Rpl29Rpl29_chr9_106429969_+311.527150e-010.185822-0.033107-0.2830760.0331070.283076
Clasp2_chr9:113786635-113813141chr9113786635113813141+TrueENSMUSG00000033392ENSMUSG000000333921ENSMUSG00000033392Clasp2Clasp2_chr9_113813141_+414.995530e-010.2499120.2496410.9992170.2496410.999217
Clasp2_chr9:113793467-113813141chr9113793467113813141+TrueENSMUSG00000033392ENSMUSG000000333921ENSMUSG00000033392Clasp2Clasp2_chr9_113813141_+415.004470e-010.750088-0.249641-0.5838420.2496410.583842
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165 rows × 19 columns

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" + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JhX7KZUYXf-s", + "outputId": "557dd500-fc66-4785-8637-4cef2070a6b2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/scquint/data.py:200: RuntimeWarning: invalid value encountered in true_divide\n", + " return X / intron_group_sums[:,groups]\n" + ] + } ], - "text/plain": [ - " chromosome start end strand \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 chr10 91119707 91121921 - \n", - "Slc25a3_chr10:91119707-91122206 chr10 91119707 91122206 - \n", - "Myl6_chr10:128491034-128491719 chr10 128491034 128491719 - \n", - "Myl6_chr10:128491034-128492058 chr10 128491034 128492058 - \n", - "Uqcr10_chr11:4702221-4703903 chr11 4702221 4703903 - \n", - "... ... ... ... ... \n", - "Bnip2_chr9:70004342-70007063 chr9 70004342 70007063 + \n", - "Rpl29_chr9:106429577-106429969 chr9 106429577 106429969 + \n", - "Rpl29_chr9:106429640-106429969 chr9 106429640 106429969 + \n", - "Clasp2_chr9:113786635-113813141 chr9 113786635 113813141 + \n", - "Clasp2_chr9:113793467-113813141 chr9 113793467 113813141 + \n", - "\n", - " annotated gene_id_start \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 True ENSMUSG00000061904 \n", - "Slc25a3_chr10:91119707-91122206 True ENSMUSG00000061904 \n", - "Myl6_chr10:128491034-128491719 True ENSMUSG00000090841 \n", - "Myl6_chr10:128491034-128492058 True ENSMUSG00000090841 \n", - "Uqcr10_chr11:4702221-4703903 True ENSMUSG00000059534 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 True ENSMUSG00000011958 \n", - "Rpl29_chr9:106429577-106429969 True ENSMUSG00000048758 \n", - "Rpl29_chr9:106429640-106429969 True ENSMUSG00000048758 \n", - "Clasp2_chr9:113786635-113813141 True ENSMUSG00000033392 \n", - "Clasp2_chr9:113793467-113813141 True ENSMUSG00000033392 \n", - "\n", - " gene_id_end n_genes \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 1 \n", - "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 1 \n", - "Myl6_chr10:128491034-128491719 ENSMUSG00000090841 1 \n", - "Myl6_chr10:128491034-128492058 ENSMUSG00000090841 1 \n", - "Uqcr10_chr11:4702221-4703903 ENSMUSG00000059534 1 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 ENSMUSG00000011958 1 \n", - "Rpl29_chr9:106429577-106429969 ENSMUSG00000048758 1 \n", - "Rpl29_chr9:106429640-106429969 ENSMUSG00000048758 1 \n", - "Clasp2_chr9:113786635-113813141 ENSMUSG00000033392 1 \n", - "Clasp2_chr9:113793467-113813141 ENSMUSG00000033392 1 \n", - "\n", - " gene_id gene_name \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 Slc25a3 \n", - "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 Slc25a3 \n", - "Myl6_chr10:128491034-128491719 ENSMUSG00000090841 Myl6 \n", - "Myl6_chr10:128491034-128492058 ENSMUSG00000090841 Myl6 \n", - "Uqcr10_chr11:4702221-4703903 ENSMUSG00000059534 Uqcr10 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 ENSMUSG00000011958 Bnip2 \n", - "Rpl29_chr9:106429577-106429969 ENSMUSG00000048758 Rpl29 \n", - "Rpl29_chr9:106429640-106429969 ENSMUSG00000048758 Rpl29 \n", - "Clasp2_chr9:113786635-113813141 ENSMUSG00000033392 Clasp2 \n", - "Clasp2_chr9:113793467-113813141 ENSMUSG00000033392 Clasp2 \n", - "\n", - " intron_group intron_group_size \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 Slc25a3_chr10_91119707_- 2 \n", - "Slc25a3_chr10:91119707-91122206 Slc25a3_chr10_91119707_- 2 \n", - "Myl6_chr10:128491034-128491719 Myl6_chr10_128491034_- 3 \n", - "Myl6_chr10:128491034-128492058 Myl6_chr10_128491034_- 3 \n", - "Uqcr10_chr11:4702221-4703903 Uqcr10_chr11_4702221_- 4 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 Bnip2_chr9_70007063_+ 2 \n", - "Rpl29_chr9:106429577-106429969 Rpl29_chr9_106429969_+ 3 \n", - "Rpl29_chr9:106429640-106429969 Rpl29_chr9_106429969_+ 3 \n", - "Clasp2_chr9:113786635-113813141 Clasp2_chr9_113813141_+ 4 \n", - "Clasp2_chr9:113793467-113813141 Clasp2_chr9_113813141_+ 4 \n", - "\n", - " n_genes_per_intron_group psi_a \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 1 1.000000e+00 \n", - "Slc25a3_chr10:91119707-91122206 1 6.494020e-12 \n", - "Myl6_chr10:128491034-128491719 1 7.666660e-01 \n", - "Myl6_chr10:128491034-128492058 1 2.333340e-01 \n", - "Uqcr10_chr11:4702221-4703903 1 3.145444e-02 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 1 3.856290e-01 \n", - "Rpl29_chr9:106429577-106429969 1 8.472850e-01 \n", - "Rpl29_chr9:106429640-106429969 1 1.527150e-01 \n", - "Clasp2_chr9:113786635-113813141 1 4.995530e-01 \n", - "Clasp2_chr9:113793467-113813141 1 5.004470e-01 \n", - "\n", - " psi_b delta_psi lfc_psi \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 0.196836 0.803164 2.344936 \n", - "Slc25a3_chr10:91119707-91122206 0.803164 -0.803164 -29.571781 \n", - "Myl6_chr10:128491034-128491719 0.206015 0.560651 1.895847 \n", - "Myl6_chr10:128491034-128492058 0.793985 -0.560651 -1.766715 \n", - "Uqcr10_chr11:4702221-4703903 0.102419 -0.070964 -1.703142 \n", - "... ... ... ... \n", - "Bnip2_chr9:70004342-70007063 0.866008 -0.480379 -1.167167 \n", - "Rpl29_chr9:106429577-106429969 0.814178 0.033107 0.057502 \n", - "Rpl29_chr9:106429640-106429969 0.185822 -0.033107 -0.283076 \n", - "Clasp2_chr9:113786635-113813141 0.249912 0.249641 0.999217 \n", - "Clasp2_chr9:113793467-113813141 0.750088 -0.249641 -0.583842 \n", - "\n", - " abs_delta_psi abs_lfc_psi \n", - "index \n", - "Slc25a3_chr10:91119707-91121921 0.803164 2.344936 \n", - "Slc25a3_chr10:91119707-91122206 0.803164 29.571781 \n", - "Myl6_chr10:128491034-128491719 0.560651 1.895847 \n", - "Myl6_chr10:128491034-128492058 0.560651 1.766715 \n", - "Uqcr10_chr11:4702221-4703903 0.070964 1.703142 \n", - "... ... ... \n", - "Bnip2_chr9:70004342-70007063 0.480379 1.167167 \n", - "Rpl29_chr9:106429577-106429969 0.033107 0.057502 \n", - "Rpl29_chr9:106429640-106429969 0.033107 0.283076 \n", - "Clasp2_chr9:113786635-113813141 0.249641 0.999217 \n", - "Clasp2_chr9:113793467-113813141 0.249641 0.583842 \n", - "\n", - "[165 rows x 19 columns]" + "source": [ + "# Calculate PSI (intron proportions) from intron counts\n", + "# This will contain lots of nan's\n", + "adata.layers[\"PSI_raw\"] = calculate_PSI(adata)" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# information about all the introns corresponding to the tested intron groups\n", - "introns" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "NCfBy1alSnC4", - "outputId": "5c143387-6f07-4c3a-fa31-ac27681e7b7e" - }, - "outputs": [ { - "data": { - "text/plain": [ - "43" + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "8L0qZizyXf-t" + }, + "outputs": [], + "source": [ + "# Select 1 intron from each of the top-10 differentially spliced intron groups\n", + "introns.intron_group = introns.intron_group.astype(str)\n", + "introns = introns[introns.abs_delta_psi > 0.1]\n", + "introns_to_plot = introns[introns.intron_group.isin(intron_groups.head(10).index)].groupby(\"intron_group\").sample(n=1, random_state=42).sort_values(\"delta_psi\").index" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Number of significant differential splicing events\n", - "(intron_groups.p_value_adj < 0.05).sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 205 }, - "id": "-gDY_jpbSnC5", - "outputId": "9cc40ffc-34f7-4ea7-913e-e256ceb2ade8" - }, - "outputs": [ { - "data": { - "text/html": [ - "
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chromosomestartendstrandannotatedgene_id_startgene_id_endn_genesgene_idgene_nameintron_groupintron_group_sizen_genes_per_intron_grouppsi_apsi_bdelta_psilfc_psiabs_delta_psiabs_lfc_psi
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Slc25a3_chr10:91119707-91121921chr109111970791121921-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-211.000000e+000.1968360.8031642.3449360.8031642.344936
Slc25a3_chr10:91119707-91122206chr109111970791122206-TrueENSMUSG00000061904ENSMUSG000000619041ENSMUSG00000061904Slc25a3Slc25a3_chr10_91119707_-216.494020e-120.803164-0.803164-29.5717810.80316429.571781
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\n" + }, + "metadata": { + "needs_background": "light" + } + } ], - "text/plain": [ - " chromosome start end strand \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 chr10 91119707 91121921 - \n", - "Slc25a3_chr10:91119707-91122206 chr10 91119707 91122206 - \n", - "\n", - " annotated gene_id_start \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 True ENSMUSG00000061904 \n", - "Slc25a3_chr10:91119707-91122206 True ENSMUSG00000061904 \n", - "\n", - " gene_id_end n_genes \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 1 \n", - "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 1 \n", - "\n", - " gene_id gene_name \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 ENSMUSG00000061904 Slc25a3 \n", - "Slc25a3_chr10:91119707-91122206 ENSMUSG00000061904 Slc25a3 \n", - "\n", - " intron_group intron_group_size \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 Slc25a3_chr10_91119707_- 2 \n", - "Slc25a3_chr10:91119707-91122206 Slc25a3_chr10_91119707_- 2 \n", - "\n", - " n_genes_per_intron_group psi_a \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 1 1.000000e+00 \n", - "Slc25a3_chr10:91119707-91122206 1 6.494020e-12 \n", - "\n", - " psi_b delta_psi lfc_psi \\\n", - "index \n", - "Slc25a3_chr10:91119707-91121921 0.196836 0.803164 2.344936 \n", - "Slc25a3_chr10:91119707-91122206 0.803164 -0.803164 -29.571781 \n", - "\n", - " abs_delta_psi abs_lfc_psi \n", - "index \n", - "Slc25a3_chr10:91119707-91121921 0.803164 2.344936 \n", - "Slc25a3_chr10:91119707-91122206 0.803164 29.571781 " + "source": [ + "sc.pl.matrixplot(\n", + " adata[adata.obs.cell_ontology_class.isin([\"endocardial cell\", \"atrial myocyte\"])],\n", + " introns_to_plot,\n", + " 'cell_ontology_class',\n", + " vmin=0,\n", + " vmax=1,\n", + " cmap=\"coolwarm\",\n", + " layer=\"PSI_raw\",\n", + " colorbar_title=\"Mean PSI\"\n", + ")" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The introns belonging to the intron group with the lowest p-value\n", - "introns[introns.intron_group==\"Slc25a3_chr10_91119707_-\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/global/scratch/projects/fc_songlab/gbenegas/projects/scquint/scquint/data.py:200: RuntimeWarning: invalid value encountered in true_divide\n", - " return X / intron_group_sums[:,groups]\n" - ] - } - ], - "source": [ - "# Calculate PSI (intron proportions) from intron counts\n", - "# This will contain lots of nan's\n", - "adata.layers[\"PSI_raw\"] = calculate_PSI(adata)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Select 1 intron from each of the top-10 differentially spliced intron groups\n", - "introns.intron_group = introns.intron_group.astype(str)\n", - "introns = introns[introns.abs_delta_psi > 0.1]\n", - "introns_to_plot = introns[introns.intron_group.isin(intron_groups.head(10).index)].groupby(\"intron_group\").sample(n=1, random_state=42).sort_values(\"delta_psi\").index" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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2MFOmmHFD9bSqyD8mGqawbPuWglmN7Hq+UL2KpFtJbeTuG8HLFwPq6p/Xrbbqttettuq21622RmJvWeC/tlep42SS/gJ0VfP0JhZhBIWio8SctleockA4gFmIpJvqSivtVlt12+tWW3Xb61ZbnbDXbeerwlgbW8QAgiAIepRwAEEQBD1KOIBZyxE9YKtue91qq2573WqrE/a67XxVGFNjixhAEARBjxJ3AEEQBD1KOIAgCEYNST/NnfyOkzS+aX+fpN/l3x3cZWNbT9LDuavgxaMwrtdIukHSC5JWaPldpfctHEAQBKNC7tC3hO21SX05mtV7Pwg8mn83j6R3d9HYAP6Ye4hsOCvHlXmJ1K3xlCF+V+l9CwfQATTYJ/kNks7MvY7Pb/XWbZ6jaz47Se+S9Oa8vYmkj0maMNrjGg5Jo9rzIV+lbSnpI809qCV9dDRtNR27iKSFWvYtV2pvBrwHuCBv/xl47wh/NyuY2fm3ylfZ35i1wwLbk2z/d5hfV3rfumYSGWN8If/8OXCg7TWAbwK/LDWYW2Yi6d2SbgCuzI7lAxXtbJh/LiDp5/k29reSligc11HAF4G9JB1PugJ5G/CHEnszOM+BBcd8YIjHZsAnCmydKukzkuareuwQHAusDqwMXCGpIaPwlVG2haSdSc2ZTpZ0hKQ58q860SlvQeC5vP0ssNAIfzcrmNH5bwLeAmwIvF/SarN4bDOi0vsWWkCdoU/S3MCCtq8AsP233ImylHXyzx8DH7L9uKR5gIuAP1Wwsxupg9ovSFcI38m2fweUCBotY3s9AEl/s7183r60wBaSvjrUbmBTYOeK5o4HDmZ6AaP5q4+MZYH/AS6T9AhwMnCm7ek62I2AJWw37hKPAo6VtHuBnbptAWxp+73Z3hbAuZI+3Ya9GfEMg5/Fa4CnRvi7WcGw57c9tSe5pLOBlUidCruBZ6jwvsUdQGcYAM4FJis1qidfOc7dhs0XJC0G/JdBx90HTCq0t4Tt422/bPt8YI6ZHjE0/ZI2kvQxYIKktSUtD5Sq0O1Oah/6YtPjBaBEUe1W4AjbezQ/gMsLbD2Zj18N2JXkDC6VdEaBrQmNK2vbDwIfAr7F0F3tqtr6YBu2IH2nyPZOJznd04GlCu3NiGuAjfL2JsDVI/zdrGDY80tqvoBYi5HpgM0qqr1vtuMxGzyAJUg9l68hBYFuBy4B3l3Rzt+BG4G7SHcoABOAmwrH9RbgEOBLwDLA7/Nj5UJ7xwCvH2L/PqP8/l86zP63FthaA1i0ZV8fqWf2qNnKx34RWHqI795vO/S+7g9cSeo3PgH4dd7fT2oLeyXw81H6zIcb2xeBG/L/4v6jNLY/AY+S+qRvV/q+RSFYB8gB0f8jqQb+CjiIdFu2h+3r27TdT1IkfMZ2LYqJ2eZCtv9Th71uQtJ7gH+S7py2AF6yfU6BnTfa/meN41oMeDdpjfYp4Drbj9Zk+/O2f1eTLTkmiTFLOIAOIOkK4Hukf+5DgK1I/+Qn2n5Poc0+UiraexmcNK4GTrU9uYKdccCWpHX/BYF/k9ayryoc1za2T5C0FCmusBjwNLCz7b8U2KttfJJ+x+D6/0LAw6QA2cK2ty+wt3rLuM5xWnapaudbpADihaRA3WtIt+2X2t6/oq2TWneRviNX2f5Ywdh2tn2gpBVJ0gImLeftbLtk6SzoYsIBdABJV9heJ2/fbfutefsy54Bpgc1jgHtIgdvGpLEJsLztz1Sw89ts5xbSpDM3yZm8anvfgnFdYnuDvBZ+kO0rcgzgMNvrFtj7LSnv+tZ2x9f4HJSi7391U4Da9voVbf0UmKdpXM+TJturbB9TMq6R7p+JrcNJTvcA4EFSXO9o4LOFzqnxeZ4P7Gj7HkmvA053Dg4HY4fIAuoM90s6gTRB3CDpSOBJ0lVjKcvY/mzLvlvy3UZVO1/M2xdLusj2RpIuBCo7AOrPeKpzfOPyeJwn8HZY3YNFP0dKusD2+yRdRIpbVOG/kj5Pytd+jrQ8uBHpO1IJ21+WtDSwC+lqfX/SMlflyT8zT3bg89m+J5/jP5Jq6lMXdBPhADrD50l52Y+Q4gDvIzmDC9uweY2kY5l+0riuop1HJe0B3EZahrg17+8b9ogZM03Gk+2n28x4ah7fBm2Oby9J/bYnN67SlQrUSmoUXpD0OQbft4faGNdnSIHEA0jLSU+TgnlF6ZZ5sv+apGVIS4+lnyXA34BvA3e3fJ6zOg0zmAXEElAHkPQO23d2wO6KpEq/BUn5vtfYvr2ijXGkYOibSMsi5+b9i9p+vN4RV6dlfH9rBGxHe3x5EvxSHtdfgSNtvyJppaqfQZPNhUmxiSdt1zLBRtA2qEI4gA4g6VnSeuy5wEm2b53JISO1uwgw0DxZSFrO9r0V7byeFChcgLQsdZntFwvHNC/wNaYPTv/KBUVSkibYnpi33wm8HXigJAAp6UngDOAk4CLbbXVnz3cP7yA54KdIDvTVAjsbAHuSlnyeJX0OCwK7264kLjZE0HaAlLL4LduXFYxtQ9sXS1ogj3FFUp77j2z/q6q9oLsJB9ABchXsB0iCTR8l5cr/ieQMbiu0+S1gc2AicD+wg+1XG0G7CnZ2AtYD7sw/HyJNGL91KgirOq4zSBNsa3D6k7Y/XGCvEYT8AWnyv4S0nDbO9pcr2roc+CHpM1iftMzScAaV1rSV9J2+RqqheI70d65KCnafUNHWVcCmzQ4y32GcZ3utirZqDdo22Tue9JmeSsp8+j/bJZXiQTczs0KBeBQVaVza8nwuUiro79uweXXT9hYkCYjXA5eUjo20VvxnUrD0osJxXVVl/wjsXTTMe3hpga1LmrZFmsgOBe4osHUlyQk17+sv+TtJlcj/07JvGeCKAlvXA8uTlgOnGW/h+3/JMO//ZSX24tHdjwgCd4Zplitsv0y6kjq1DZvTlOhLeoBUor9IRTuTJa1HqiTeAHjO9oDK1UVPyhk6lzN4Zbw2Q0vVjoQbJO0A3KwkTHYJsAqpkKsqDVEsnGaxK0iCaSUpSi8B6wLNGkdr5/1V+RKwfy4GG0fK3nkUqFybQP1B2yUl3QjM22RvAjBvob2gi4kloNkESV8ELnRTep+SguceHkybHImdJUhaNo1g5r62n5S0iQuWgLLNhUmSBI3g9I22nyixle1tBXwYWDTbu5oUdC2ZbGtB0qIk4bx3kgqjJpHkAPaz3U56b9ej1AxlQY/BSvFeJxxAB5B0Mzn46JxL3a1Ieg0wp9vIsJG0Kik4fVvTvrVtX1nDEGtH0i6292vj+D63GVAexu6Btispnko6lfRdO8NlyqSt9nYgxQ8eaddW0P2EA+gAkq4naQB9lHQVeybJGfy9DZsbAvuQsjwOtX1i3n+u7c0q2PkaaQniJZIE9Lakq9krbO9eMK6fkf7GicBrgW3zHUWl4HSTvS1JuexPAwcCe5GuuH9h+zcVbQ3VK0HAj22vWtHWW4GfkAL6y5LW3u8Evu+KKZwaXvL6a87VyhVs3Q6cRrpjalemGkkPkrJ+5iAtWZ5i++ESW0H3Ew6gAzRPfvkKe3OyM7D9zkKb15AyiyaSHEEf8A3gYleQNZB0LamWYC6SIuiytidJutplWSPNshdrkITvtidN2CUO4DpSxs78JLmKtwIvk4Kt76po6ymG7gfwGdvLTn/EDG1dQlLYfFTS20mSy/sAe7ui5o6k/+TjW8f1LduVZJxbvmtvI33PPgw8YvsjVWxlG5faXj8veW1J0p+aQNKcOriqvaC7iSBwZ5j6j237WVLXpmM1rY54VWz7mby9U65KPYvqwbk+UpXuAqTPfx5JL1D+XZggaZztAds3SvokSQ76jYX2BLySxzlA0gCaLKnkSqXRD2CaNXpJbyiwNReDUh4PkSST783xj6qcB1wwxLjeUmCr+bt2Fyl3f898x1JMXhI8DDgsp5Vu2Y69oDsJB9AZNh1qp+3nhto/Qu5UkySx7aMkPQwcXtHOT0mZLHeTrtSvAl4lXbmXsBewOGn5AduP5KWX0l6px5KWVu7Ptm+Q9BIFGVQepmG37S8MtX8m/By4UdK/gCVJgXSAOwrG1arp1Nj/vYJxfW4YW3cX2IKkXttq6z9U/54FswGxBDSLkHSw7Z1GexydIKcJrkCWSbb91xptz0cKMFeuVJb0IVLmVF19E8aR0m7/6zb+cVS/JHetQfhcKf6S7efyncT8tm8osRV0N+EAOkBe432ItITRuEVfDvh7GzGApUiKj1NIQeD78/69be9Wwc7bbf81T9pfI/UzvQ84pCRwKOmjwFdJdwDvIuWlD5ACrbcU2JuLVOh2Tx7X9qSA9ZG5nqKKrceBx0npricBf3KBdEO2NQ+p0fo0kzZwQlVnoHolr+sOwu8NrEmqT7iGVI39DDDJ9peq2gu6nE5Ul/X6A/gkacni4037zmvT5kXAxiT5hsuArfP+SyvaaVR6/gr4PvA2UlbQ6YXjuoxcIUvSyz+btF5+fqG9M4EfkaSfryUVOX0JOLnA1qX55+qkpa87SG01P1Jg62RSgPVNwJfz+D4LHFhg6+LWzzb/vLDA1hVN22uQKpaXp2KFeJONq/LPfuDeoc4Tj7HziBhAB7D9e0l/BD7ZpJVTLJCf6bd9IYCk9wG/zrfnpbdwy9tupCPeJelThXbGAUsD/yA5E9l+ORcPlTC/U+N2JN3i3CFL0icK7WH7JuAm4DuSViNN5GdUNPM62yfn7fvzFfauuQq6KnVKXtcdhO+XtCxJ2G9OSW8k3QFMKLQXdDHhADqEk9jYCZJ+T9J5v61Nk1Mkvd72v53UMj8n6UckOYIqrJ5L/d/UVOo/DpivcFxfBX6e0wYfBHbI+6s2SWnQPNF8t2m7RKrij607bN8M3Fxg6yalrmy3ke7C/pT3l/wPfZa0zLUc6U6p0aP4kwW2hgrCbwrsVGALYGdS/cVfSXGK40lLej8otBd0MRED6BCquem3kuzyJLesYUta0m1WbSp19FrOhbr2Q9hbyoXFQ0qyxn91U6Vtjle83/ZZhTYXYjBAXSR7ne2sxmCfgsr9jlts1SLtnY+LoG1QRDiADqDhm35f5jYkCIY517a2j614zJq06Pfbvr7w/K3VtgJ2J+nH/2n6I0Zks5FV1Bhfqe7+WsCPSVfpK5BiAI8Buzqn01a0V0u2jWqS9s62ag3aKvoB9BThADqAamz63XTsUBIBIhU6jbiCV9JBpOKxVv3+l12Qppoznm4itaZsxDk+DRxne88Ce3Xq7l9O0t1/KS9RHUrK5Dna9ocq2qot26a56lrSFqS/99PAiQW2rrK9lqR+4C7by+X9Rd81RT+AniJiAJ2htqbfTVxHklhuDSYvXdHOarbXbdl3Wp4sS1gG2JHUKetQ29dKWrNk8s98GVjLTQ1b8uR2GVDJAZA0hPqathfMk/Y8BeNazdNKXpwhqUS+GeqT9obOBW2XsH183j5f0ndn+OpgtiQcQGcYqun3NRQ2/c7cBXzb9jRORNK5Fe3cJ2lPpndOD5QMKq+p/yQXbH1D0v+RZCZKqVN3/3ukyaufJC+xU95fIntdZ7bNbyUt7Sztbft2SVsDexTYqjtoG/0AeohYAppNUNIRetHt97UdB3yQwebyT5Py7c9p13bTON9i+8bC47tSd1/SB4HbmgPuDadne69Cm7UEpzuNoh/AmCUcQAdQzc3IZ3Kulapm72iwuflCpGWpoiBrtvVm0lXof0nFZQeR7ir2tH1dic26aAlo7sFg1XNRQLNbg9PDnKNyb4F8XB8pQD0AnN347kr6aFMdRDBGCAfQAVRjM/IRnOsC2++r8PrhgqyHN635Vjn/FaSlloVIQmJbkSbHE22/p8DeWqRK4DtJufb7Ac8D37N9dUVbtQU0c6Hc1+m+4HRtvQWyvRNI9RwTSZXnn7V9X6m0RNDdRAygM0yxfTlwuSSR1rA/SooJrFhiUNKj5GKf5t2kYqIqzCjIWtkBADiLmEnaz1n/R9LEElvA/sDHSRPs+aSloJdIQdLW4PVIqSOg+RW6Mzi9O0P3Fii961zC9jYAko4iyZjvXmgr6HLCAXSGOpuRN3gKWMctypaqLkVQZ5AVkizCCaQJ6AZJR5KWlUrX61+1/RCApHsba+6SSia0JSXdAMxXQ0CzW4PTdfYWgBTsnsP2q7YfzLGPP5CWDIMxRiwBzSbkpZHb3aLYKemtrqD93hJk7ScFWW+kMMiandrKpLuTJ4D3kZzBhSWxD0mnAJ+wPblpX3+2N+LOZzOw3w8sVDWg2a3B6brJKa4PualHdI4LfLJkiTDobsIBdABJm9s+U6mT0h4kdcYHSIHRf7Rpu+syR1SjrMEMzjFH1YBrrke4XknqYgcGP4dDXbGP7xC2ixvDK8k1/B/wF+BykrLoi6T+wqWNXFrPUblCvOnYjn+eQXdQIrAVzJwd88/DSG0bNyZ1VPpdqUFJa0m6lCSXfBPwJ0l/yIU/bZOXcUqO+xZpff4USUdImiP/6teF9vokfVzSzyUdn39+nLI17Z/kn4eRluX2JtVTVP5blYT3kPSuvKx0uaQbNXTj+ZnxmzyGB0jfjx8AuwG/KBjX8kM83k7qo1AZSTuTPs+T6/g8g+4mYgCdwXmpYR7S0sVk4Pqcg1/K3kyfOfI14GhgxJkjkobSIhKpLqCELVpkDc6V1E7B2+9IzWCOZlqpiqNIBXZVaNzeLm77sLz9d0lfKRhXQ1ZhL+BDth/PQduLGFQGHSmNJAEk7eokV91YTqtKXRXiDbYc5vNsV8486ELCAXSGHwOnkSaw6yRdTdLK/0MbNuvKHNkGGEr7v3R9vU5ZA4BlPH3P3FtyumlVjpB0IvCIpLNIAdyVSVXZVXlBSeH1vwz+3/SRYgFV6ZfUny8MtoKpk3/J/2NdFeIN6v48gy4mYgAdQtKcJDnoRUnaLDe2/pNWtLcesA9pkniFVIF6q6Tv2P5pBTs/AX7WGgSVtKPtnxeM64uku5wHm/YtAexh+4sF9vYl6du3SlX8x/YuBfYWA97P4Odwte07C+wsQUpRfSPJidxLynbazfa1Ve0NYb8fWMwVZbRVU4V4k71aP8+guwkHMAuQtLPtA2u2uXA7DqWbUeoJ0JCqeAa4pmq18zB2t6latDWEjX7S1fAzrSm57SJpE9slqaBD2apcIT6MnQ1tX1zHmILuIxzALEAdqKKsy6akC21vXMeYWuxWqlAegb22J7Q6PwdJx9muGpNoHDv3ULuBs2xv2N7Ipp6jlve/E9/doHuIGMCs4ewO2KwrKNfWd0DSSUPtpv7Cof1JNQbtUCRQNwxLtnHsE0zbP8F5u3KVuOqrEB+Oos5uwexBOIAOodSmr7GM8XdJ89Sct79lTXaG0pKpwhqkxubNGkcCjisx1jShicEsnuIJTdMKuP2xpJ5gGEr7HUDKctp8iKK+kgbzdVWID8kQAflgDBEOoANI+gZpUryD1ED8IWA7Sb8tXeOVdCpJYfQM28/bfrrQznzA/5Im7KNt35X372D70AKTBwHP236i5TyVc9oztU1oGkb4TlJl4TtJc5Eaud9DUhR9Z863P9L2yxWH9jGGzh76aEU7kLSdxpMSA5rZocDWLFWyDUafiAF0AEmXNmQLchn9ucAHSJotGxXavJ2UWvph0hXyycCZrVeRI7BzNqn4aBJp8vimUxevumIKbQWnVZPkRT7mSmDdoQTcbK9V0daZwC3AXCRNoEaa78a2SybuZtttB6ebbLUVtNUsVLINRp+oBO4MkyWtJ2lB4CPAc/mfp533+0nbe9heDdgV+B/gUklnVLQzr+3f2D6alB65m6SiYOYwtKUZb/uq5slf0oZ5f4lEQkPArZlSAbf58/u/KzCH7f1tHwEsXGCrlS/UYKPBbm0eP8X25ba/Tlo6O5ZUaHhbuwMLuo9wAJ1hO1KBzwnAu0hSwgAjztcfgqlBX9t32d7T9uokZ1CFvpw7ju1nSA5qA9Jafh3UXTHazoS2LfAhSVdJul7SVaTJbNsCW809dpvlpOv4H6ozON1u0HYaJVvbV9jegdRMJxhjxBLQbIKkN7qGblFZO+hp28+27F/LWde/TfsLkgqTSvsBtNo7po5ApKTxtkuqdhvHr0jqADalad8E4P22zyqwJ+d/vizt8UrrZ1LRXteJBAbdTziADpCXLfYhBVoPtX1i3n+u7c3asLsm8F4GWxJebfv6ijaWdtJ5F2md9+0kUbITSyZISUN1nToc2L4RYB4tcpB2P9LEOJmU9PAU8N3CauC6WkJ+nXRXeB8pHXRLkhroH2wfXtHW2qSMpFraS2Zn9AVSk/lbSL0LXgT2d5NEdDA2CAfQASRdQwr6TiQ5gj7gG8DFLtS0l3QQqZHJn5lWJO1l2ztVsNNok3gIKXPkYpK0wRolwUxJrwBXkTKdGss/G5MC3p8vsFeb88z6Qds0yytIWork7NauaGu4VpolLSGvJ8mEzEXKKvofUlD+yoLgdG3tJbO9i0gpvPOR/t4fkP7eb7Rz8RJ0J5EG2hmc19cBdpL0OVLmTUknqgar2W4NaJ6WJ4AqNDI5VmiqOr1ASWq6hLcA3ybl7O9v+yFJ55VM/pkfkxzbRGAfSe8mOc+hqmdnRh/JWTbzHGXr9jNqpVk1g+eVbOdFSSc3lssklSxR1dleEqDf9jF5PJ+2fUre/k6hvaCLCQfQGe5sXrO3fZSkh0lLI6XcJ2lPphdJe6CinbMlHQDcI+lQ4BIGxc0q4yQa9vUcW/iuJFM2WTeZrM157gKcJWkyqbH8/KTJsmrgHOptCXmBshqo7W/C1OWlkkynOttLQnIiDZp7CsRcMQaJJaDZBKVeAh+kRSQNOKdqsY6kVYHNaVLIBM6tI89b0v8AK9k+vfD4w4F9m9evJW0EHG572UKbc5Lfs4KirYaNXmkJ+TrgiZY7nfHAyrbrzFYKuoBwALMQSQfa3rnw2Ne0kyXSKYaokN2edFVcUiFb99jmIa2Hr0POkCF1VDvBbXzxa8goqq0lpKTFSYVb7yDd3UwhBYL3sv2vgrG9oyRAHsyehAPoAJKG0tcR8DXbQ2XNjMTmS6Qr/pOBU90ivdAupeqRdVfI5iDtLqSJ7FDb9+f9e9uuVBMg6WRSFestpMD0G0kNVFas6ojrzCjKFcq7kZakfkmqGXke+KUrVopniYwf2b6mad97SP2nK1edS3oWeJBUvX6S7Vur2ghmH2JdrzPsDnyL6Yui2tFVuZ6UnvdR0rr2iww6gxFLL6h+9c75be+Rbd9ie/+8/YlCe0eRCuYmAUdK+kUORJa0rHyd7UZl8v05A2rXEl0hUl/hITOKSLGAKkyxfUW20W5LyLlJ341mbiQ55BJuIWWwbQbsKuktpJaXJ9m+rdBm0KWEA+gM55HSIKdZG87/TKU4B1wPAA6QtDSwNWlJo0rqYK3qndRfIdtv+0IASe8Dfp2XTEpuVW+SdAxJxmA9Bnv3lnzv68woqrMl5M+AKyX9jcH01LcChxTYAiAv3Z0CnJKX+D5Ain98stRm0J3EEtBsgpKC5ZdrsLMD8PvWJSRJH7f9xwJ7dVfIXky60v53074fkVovThj+yGHtrQa8Cfib7b9UPb7JznuBvUnLP42Jtg/4ge0rS+022e8jNa+vLOWQj12OweSAe7NzKRnH7rZ3Lzk2mP0IB9ABJK1p+3qlzk87AMuT0jUPtf3U6I6ufuqqkM225gUmtR4vaUnbrY1PRmJvdVJsYgFSEPgcN/W7LbDXyCh62oUtISXNQQqcP0EqxNueFA84sspy3kzOsa3tY+uwFYxdQgyuM/wk/zyMdLW4Nyn4WCz5K2lRSb+UdIekhyVdKGm3PCFVsbN9/vkWSedKukHSBXmiLBnXNqRiqM+SJtrtgIvz/hI+B7yudWfh5P/TPJ5ngGVJgmbfl1RZW0jS5nlzflLWzfmSjpK0TFVbpMD0ssCmpPduHuBx4PcF41p+iMfbmTaHv4q9tfJ36yBJG0m6RdLl+Q4oGGvYjkfND5LkA8CFLfsvbcPmecDqpGWH95PWftclXTWWjO084O15e0mSrlDJuK4ExrXs6weuKrT3IOmq+Crgm8BS7X4OTc8vyD8vauMzPZU0cU8A1iz5TJuPAe5s5/tBusD4HSl43vx4pPA9uxZ4Aykp4NH83VgIuLz0c4hH9z4iCNwZjpB0IvCIpLNI1aMrk9I4S5nPg9kilwDfsf1NST+saGdSY5mFNNlCEg8rLQKrs0IW4AHbG+bCqy2Bo/MS06m2D65o64VcSXwbKfD9UN7fN+wRw+NcbTsPybFPBq7PBXoltv6XnFIqaVvSclAJdwHfdsvSkaRzC+29avuhbONe5zsvSdEZbAwSDqAD2P6jkhDZ+0nVti8DB7i9ApvTlIS6/kq6Ezgs7/9PRTs7k1IXxwN3S7oZWITUdL2EbYHvSPox6fs0iZSGWKK5PxUn5cnDgMNydWpJD+RPA18itcD8G0ncDAblEqrwYwZrHK6TdDXwNuAPBbY+AWxDKtj6LUlLCcres41JRWTT4HLhtic0KFOxLkzVPKq7z0PQBUQQuENI9eq9N9lZGri/9YqvwNYyDEpB3Osu6f0q6SO2z+iA3cVJwnX/cGFfhRxveTeD79uN7X4OdZK/H28j3UU9NLPXV7Q9hwsD+0H3EkHgDqCk9/4XSWdK+i5wDnCmpLbSOG0/bvuG5kknLx+U2PoH8CqpY9m72xnXUEgqCng3T/6SFpe0vpLQXMkYTs8//5d017Mx8MuCZbMGC5CWbeYhLSOVZgE9KelISZvkFM5iJB2Xf36cJJq3KXCspB0L7b1W0q6Svihpbkn7SvoVsFQ74wy6k7gD6ACqUe+9yeZQEhICjrA94gwNSRfa3jhPEJuQnNNapKBhZclfSfsNM66tbVfOkJF0uu0tJH0J+BQpbrIScL3tPSvausj2RkpS1xs6C5xJutLV+wHsBKxPWrZZjxRPmAD81nYl5U3V2Hhdg/0dLgc2s/1CdipX2a7s2JWqpI8hObrt8zifAvbw9HLkwWxOxAA6Q5167w2uI1Vntq7FLl3RTuOKcyvSpDiZtM5e2g5yG9JE3UpR4xtSIxJIVacbNE/apM5XVbhP0odIMYmPZEewCkl3pyqbOzfzyRPsuaQK2QuoLr08xfblwOWSRAqaf5RU5b1iRVtLKmlPLWL7BQDbU1SkKgHAeNvHQ0oZtn1a3m5bKTboPsIBdIY69d4b1JXtsWy+am+9pR8/1ItHwLHAXbanCUZLKi1CqnPS3pFUiLcK8GHSuv1VpNqAqkyWtB5wOymj6DnbA4VZQNM0XgeuAK5Q2ay9d/65n6T5bT8naT5Smm8Jz0nam7TM9VheLnuCwuWuoLuJJaDZBEnzk5qttxWsldR8G3+jUyvB+YCP2T6yrUHWQHaUO5AyqJZicNLer9XJzOJxLUFqJPMmUibWvk6dtzapugTUzeRA96Ykae97SQV+IrXRfG5GxwazH+EAOoAGNdpXJAXa29Jon8m5VrJ9e8Vj6pRumAf4KklzfwFq0tyvg5wV80NSjGMh0h3YZcCBLpRxqGlcbyal4/4X+BVwEKnCeE/b11W0VVsP5SFs95MKwR6LDKCxSWQBdYZjgGNtv8f2u3KQ9ri8v24q5e+rfumGo4F/kHLrTwDuJ8UZDigxJumtko6QtKOklSSdJ+kUJUXQkrEdTWre/r+kBixXkTT4q45rQ0nXS7pW0qea9pcUXP2W9H24gdSNbT+SEz2owFajh/KGwDslHZqXpYrackpqyHmvT5KG/imp4K1yb4dgNmC0S5HH4oP0T93Xsm88hXIL+fhHSRNG8+NGUpvDKnbqlm64vOX5JfnnhYX2riQ5pg+SKpVXJ+Xvl8g3XNW0PYEstUCLRMQIbV1DusOZGzgYOJR0AXVpga0rmrbvbtq+rOS71vL8c6TMrhsL3//G53cpqZ8C+W++tsRePLr7EUHgznAwQ2u0H9yGzaeAddyydKHqzU3qlm6oU3MfBjNk6miWUlf1NNTbrP7+XCch4AZJRwJPkpbPqnKnpDc6F7fZPkrSw8DhBbYAFpH0AeA1zjEXpzhRZAGNQSIG0CFUo0Z7trcWcLvt51v2v9UV+siqA83NVZPmfrZ1FbCe7cmSFrX9eJ78L3NBHrpqqp5Wjc3q89+zMvAIKcPmfSRncKFHuSJbqfdCg0NsP5OTBPZ3Df0ogu4iHEAHUGq6croLJIxnJWqzufkM7H7X9k9m/soR2+sHFnPFZimSFm5M+pK2IBWU3UfKaBnVK1pJiwADbuoPIWk52/dWtPMe4EekgPL+pLvMeYAf2z67vhEHY5FwAB1A0oOkiWYOknzwKVUnrwrnqtTMXTU2N8/2bmSwXWNjmWY54O+231nVXra5JvBeBrOUrrbd2vd2JHYaVbJ7A68FTictdy1ue7uKtmrL7JL0LWBz0t3XfcAOtl9tjLeirWuAj5Heq/NId3bPke4m3lXFVra3ErAX6e87wPZVef+Rtr9Q1V7Q3UQMoDPUKWkMgOpr5l5nc3NIDm4FUgri9dneebY3LbCFpINI6+p/JhVdvQb4vKRP2t6pxCawVtPy0XlZNqEqxwA/sj1V0jtffR8DbFTR1hbO8h35zuRcSZ8uGBOki7hHJP2bJOX8r2y3dLnxUOCLJOe0v6R32j6IJGcSjDHCAXQQ1yNp3KCuZu51NjfH9r5K/QW+oaSXcyhlDdwbrDbEWv9phZP2CtlxLidpLqdm55CWSKoyN9B6F3IjSe+pKlMF4GyfLukB0t3JIgW2Lsl3AU8Cv5d0ASnmdHOBLUgO5e95e2tJu0v6DTFXjEniQ+0Mh7TuyBkVpZkZkHLEn/f0zdx/UdHOLsBZ+Qqxubn5rqUDc9Kg2TtXK+9EWo8u5T5Je5I0dp4jFUhtROqpXJU1mrYnA2Rn9f0CWwdTX2bXbyUt7dyb2PbtkrYG9qhqyPZukhYkfTcmS3oLaRIvlR15uiWraHdJ29Fmf4egO4kYQIdRDTr0nUCDzc2faboyLrX1euAlJx2at5G6l91QaGscqQbgPQxmUF1DauZeKUNG0gQPCvG9E3g7aXmu5G6iNbPraeC+NjO7Fsq2/m17uqYu7aCaJSokjRvtwHlQP+EAOoBqlDRustmQNVibNGnUKmsgaRfbQ0k7z+y4vUm9cceR/s63kybtSba/VDiWWprpNAWBfwgsD1xCSr8cVzWlUdKatq+XNDdJq2h50l3Joc2ZPCO0tRapgrefFD+5g9SWc9eqFwl5PNPtBs6yvWEVW9nefKSq6QHg6Ebtg6QdbB9a1V7Q3cQSUGeoU9K4wdHAD0gKlxuTyv8bsgYjzs7IRT7T7Sa1KazsAIB1ba+VUzXvsr1cPs8VBbYazXS+Iuk+kgT2liRZ7T/YrrqE1rhiXd9Zyjmf49JhXj8jfkKKwRyWx7U3SWX0BJJ4WhX2BjbNBVaLkuImXyN9xh+qaOuJPJ5GBpbzdlVZ6QYnkgrcJgF/lvRN29cCW+RxBmOIcACdoU5J4wZ1NYU/nrRu3VpZO3/huPolLUtKQ5xTqXvXMyTphRI+Q8psmq6ZDtVjKDfkmoybJe1MugNYhbIYReNWeXHbjYriv6usy9t4BgPB44EFnZRFS4LT95B6FbQWCFatEG8wr+3fZBtnAMdL+mOhraDLCQfQGVp16J8m6QNt14bNumQNbiV1EZum6lfSGwrHtTNwYB7XliQHM0C6WymhtmY6tr8naSvS+7UCKShc2g/gCEknAo9IOoskpbEyadmrKt8Dzs93Ta8w2KS+ZM3+YyQH2UqpeFufcl+BXAX8EeA3TBtQD8YIEQOYjVCNTeG7FUm7AT9tDq7mGopDbH9l9EYGkhYj9SloNIW/uqR4blYg6fUl0h75Du7p1piLpLUaRWHB2CHkoDuIpO1n9Lwqua5AnrYp/JqFY1t6Rs8r2FlK0s8lHSzpTU37957RccNhe+/WzBrbE9uZ/DWtvs10zyuM7THbR9ne1/bhpZO/6pW8Ho6ijmy2/2n72SG+Dx2pZA9Gl3AAnaW1uUelZh/D0DpJlE4an5jJ85FyFEl++AzgyJzPDimNszKSFpd0uKRrJF0n6WpJhyl15CqlNXZQXI8h6bAZPR8hvyEFjx8gBVx/AOwGVK3pQNINQzxuJElCtENd34+gi4kloA7SyTzvbkHSZbbXy9sTgF+TmsJsUFXXJtu4kKElF/a0XVVyYSj7m9kuaeJSGy3v2VW218rbF1dN3ZR0N7BC612TpAttb1zXmIOxSQSBO4CktUnpnlPzvCUV5Xk32WyIkb2DlEFSJEbWgTzvKY315hyw/VxeYinRFYIaJRcktaa1CthS0rq2dymwV4tIHSlzqj9P2ltl26Ls//H7pLTjp1v271VgCw3frnKPwr816GLiDqADKOnWtOZ5f4U04VbN827YrOXKWNLZDOZ5fxn4pu1rVaBEme3NSyr6erVl/5IukMPOS0j/B7RKLhxieyhBvBnZOo+UvvkLoHEHth/wbduV6hQ0rUjds3lcmwAvu1ykrtl+keR13eT6je+RnNwhJAf1FElCu2hZL+he4g6gM9SZ592grivjWvO8sw7QUGxAQSDS9imSTqeGZjq2N81OckdSx7JfAE9WnfwzdYrUTUfW8VmemoKtkk6wXdTn2YMS0PvZviVvT6xjXEF3EQ6gMzTyvPuAV4Fv5P3taLP8jKHFyKYTnpsJteZ550lrut3A9pRnokwhSV00n2db2yUO5RrgmrwsdzSwWMmYqFGkTsPLN+xCxe/IEMtcDVulV+t1tqsMupxYAuoQeU13O9tH1Wiz7TaTTZW6fS3ppEV53pKeA05h+srijW0vWWBvOIdyhLOG/migekXqXmIY+QbbC1e09S+S3lQrB9pevYqtbK/RrvJh0sTfNe0qg/oJB9BBJJ1qe6sOn6PoyljS+bY3qeH81wMfaC1Mk3Su7c0K7NXqUIY5R/HyyBC2KhdcSboVWGco+YaqmTuSfgL8zLmBe9P+HW3/vIqtpmMF/LmO70fQ3YQD6CBKkgHzAzeRhclKsk+yrVqvjJX6CNxMiiM0xva3AjvzAy/WdXVYp0OZwfLI1raXaWOYzeeo1JIzH7Mc8LBbVFwlLdDIyhptJP2UFPBu/n68NKqDCmonHEAHkdQaNMTlWvR1L7W0Lk3Z9udLxpbtzdGcCdT6vIKd2hxKncsjkobqbyBgOdsLFAyvViRtbvvM4Z4X2GtVTHVJlljQ3UQQuIPYvlzSCqQ149aJuyp3kdIXp7syLhzb59ocTyv7At+cwfMRYfs5qM2hHEuSqG5dHikJTs/PMAVXBbYaxx7WLHHR+rwirT0h2uoR4Sb57GDsEncAHUTSaaQsoEY+vNtYAqp7qeWTpOykqXIgttuVD6gNST+z/c3hnlewI2AT239uczxbAxfbfrpl/7qld3XdjKT1gK/TdPESdwBjj3AAHaRkfXgm9moLzkm6hdTMpZ0eBY3MpK1pqZAFTi3J3e8Eks60vXkNdgTsYvunNQyr1fZ3bf+k4LiVSFW/U4ADmnL4j7Q94kZBQ9i9Hfg4TXUJHqNyJr1MLAF1gKaA7f2SPk3S4DeUBVob2Lak2yStT/vBudsZ7JjVDr8jNSU5mmkrZI8iNXephKS5SN2n7gHuI9UTvAQc6fLexS9JOoZp37NfVTWS3/+VJfW1cyemJNbWuPJqLA0uJ2mLgruwQ0kd4SYD+0t6p+2DSI102uEu4B7HFeKYJu4AOsAQAdYGbQVas+22gnNNk8+8JF37RiGTS5aAJF1he52R7h+BvTOBW0gVzusCp5Ecy8a2i5qcSPps6z7bxxTauhxYnORAnUz5YxVt7ErSiDrUWV9H0nm2q7aWRNKVttduer47sATw1ub9FeydTPq7FgcWBv6Sf1X57wy6n3AAHUTSwsBT+cpRwMK2nxjtcdWJpH1Jk0Vrhex/SuIdki5tBCAl3WJ71bxdpFXUZHc+YAEG17MfKrQzXd8E2w8W2JmXFINZgXQV/33bQ/Vrnpmds4Ad3SQyKGk74Ne25yiwN2xfiJK/M+huwgF0ELXI+7Y+L7S5HjUE5yTtb/vbeVvAfo3nBbZWpKVC1vbthbaubtQ1SNrE9vl5e6qEcoHNnwOrAf8ivWfFV7OS5gA2Ztr3v0jyItubn9QSclnb25baGcLuOKfWmqXH72z7wLwt4P8az4OxQ8QAOsv4lueljdKbOYSW4FwhqzU28h3KajN68YywfQdJmnoqklYqdAJfaayxN03+E0iyxKWs5PpkJP5MknGorHTaTJ5Uj7X9GZJ0eN18i6R8WspmpF7Pje/H1OfB2CEcQGe5Q9IhwOWk9ew7ZvL6kVBXcG5A0sq2b5O0Sg3jamV/ko5MJWzfocRxeXLEqc/AWVVtaVB07SZJ7yUpgjaC8aVVrS/Z/m7hsVPJk+qTkpax/Y9SO5KGWjYSqYNXOw5gvKTFbT+q1IuijouXoMuIJaAOI+lDwNtIBUln532qOoHXHZxT6t+7H7Ak6W7iO7bvL7DzKOlqWEyb2dJWhaykg0k9ANqZHC9lUGitmUqB82yrMdF+Avg702Z2/alwfDeTlpKeYjCgXCkQL+kp4GCm/xs/Y3vZknFlu2uQFGjHk2pZdrZ9Y6m9oDsJBzAKlAQ0Z1Vwrmo+uqS/AKt7el2btloS1jE51omGbyRv251YwhkRki4GtnGLIF27dQAzON/nXKPCbTC6hAMYBZozXQqO7WhwrqpzkrQWcLunV7Z8q+27hzlsltLsjPJ7dkGpc5L0duBvTZldy9v+a6Gt5YHPM21AuShNWNLCbpEJ6QTtZmMF3cW4mb8k6ADteN2piph5Gamy5HKdOFWevqDUMrF5f1uTv6TlJR0g6UhJv5P0uzbMTQ3G5/esNThfhUMby3f5Z5HkcuYE4CJSgPtA2guyntjGsVVoV9Mq6CIiCDw6tPNP1OngXOWx5avh1yh3GqtpHCcA36WeFomPSvomg8H4drpbzdnyfKjuXiPlH25To6iJeyV9jjblvUdALBmMIcIBjA7DrSePhP8DTpLUCM5VFkibCdcWHvcu4CFJ91HPmn2dk+Pngf8FvkhqNr9dG7bOUepZfCWwFnBOVQOS9ie9R3MpqYnexmBAuUgsEJgHWCc/yPbaqjofhn92wGYwSkQMYBaimsXhWmy3FZyTtIvtdtIGa6FpcnwH6QLlNtqfHIc71x9tf7zguBVJ/Zjvsn1n3reY7cdGePx0fSIaeJSVRdUZLaagSwkH0AEknTTUbmAt26VNyWd2zhEF52aQN/5jZ9mFwvMvCnyUaQOalbNjZuXk2E4wfghbJZld29g+IW8L+FTjecH5a5H3Vge0mILuJZaAOsMawAZMq7Yp4LgOnnOka/fHM3Te+Pxtnv8skgLoLe0YaUzyQ02ObY5vyNPVaKskrvMFUqyjEUf5fON5Ad+mBnlvYH7be8BULab98/Yn2rQbdCHhADrDQcDzbhF+U+rD2ylGOpndSuoj3Jo3/oY2z/+47cPbtNFMnZPjcNSZ0VLiTOaSNI/tFyXNQ1rHL6Uuee/mpILmiufIGByDhAPoALYPHWb/Hzt42n+O5EUeRoyutGhI0lfz5kRJv2XaCtnKmvtN1Dk5DsdhNdoqcSY/Bq6W9BCpInu3yiedVt77IUltyXvTGS2moEuJGEAHkbQlsAMwB4MqlO8ptFVrcK6ugjINobWfsdtTyfwAsA8wdXK0fV6hrcYkOY4Uo3jaFZvCt9hbGVgOuM/2rXnfm23/vcCWgNcC/23UF0jatPRvrQtJG9q+uGmMGzSeB2OHcAAdRNKtwHq2n63BVq3BudagZbsVnp2YMDoxOealrh1cLn39M1IjnZuA1fPYvlE6nmHOURJQrk3ee6gxqAYp86D7iCWgznIn9b3HdQfn6i4o2w24GKau2X+v8byUPOn/p2X3t4F2ro4fBip3ympiVdtTM5WUOoTVTclyUm3y3pm5GktBkvpJS0zBGCMcQGe5DviHpHvy83aKo+oOzjUKyiYAr9B+QdmsmjAqT45NS0AiLce1I5twu6QNSXdjqwA3KMtOu1xiupWS2/K65b2PAK6UdBOwKlBngD/oEmIJqIMoKVquY/vFGmytCPzVTc3I8+T9ftuVtfLrJssQfBG4mTRh/Nb20R04z6iKkWn6nswNXNe4JP3JFdtDqiZ57xabi5Cay9/vWSA0F8x6wgF0kJz2uY/tR2u2OwfwOqje37axVi9pAWAPYCVSUPlHtv/V5rimmzAkrdIIlNZBlclR0tcY5mq6zQylWsjZTV8CliV9Br+x/ULN56gk79103LykznOL00ZhX9DdRG5vZ3k3+TZa0o2SbmjXoKRdSDo0NwFnUr24rJFq+AuScNimwMlAO2qbANh+wvYNLVeLtUhVS9okn6PKlfGcpCWfF5sec+RH6ThWlHS8pIslXSLpklJbpPf9ReDI/PPkNmwNR2lPhlOB+YCtgReAdutEgi4kYgAdxHa7gbih2ApYE7gUWJ9U2VvCErYbx54vqe02h8NQac1eg20cW23sApxf8dybAls0V8dKmg84ndTtqoQjgC+TZKB3IslflCLbR+TtWyRt1Yatuplg+2BJH7F9kKSzR3tAQf2EA+ggSs1D9gFeDzxGymUvah7SxMs5y2MSsAxJNK0KS+Y7kfkkLWj76RxL6FTQtuoa4xOk4HnDcTSCtysWnLuvVRrB9vOS+gpsNXg5B1pt+xZJB1Q10KTH9LSkvUhxk1WAWpcKG6crPG6ypDmBRyTtCXREwyoYXcIBdJbfAZ+1fbektwDHkq7e22Gv/I+5J0nTp9ISi+03D7F7AKgUdKxA1QnoHmDz1olbSTa5+sml19n+T9Pz15fYaeKW/P5fIulKUsvKqqyRfzayw1YEpgDF/Y9nQKm89/vyhcb2wCZEFtCYJILAHUTSWbY/3PT8bNsfqsl25cbyLccvBuzKYAByv5IgsKQJtifm7XcCbwce8KCoW6UmMZKWAx729D2GF7D9TMWxvQf4FWnJ51FShsyHga/Yvq6KrWHsL0SqKi76HHKQ+phG4FfSlrZPa3dc2VaRvLekbYf7XTuV3UF3EkHgDpAzbADmkHSFpEMaBUOS9pNU8o+5c/65oqTrSBoyN2kG8skz4Q/AucBngD8Bvy+08+c8rh+SagvGAx+XdDhAlck/v/7exuQvaVVJH5Y0joIlKtvXkOIk95MkIO4lVSgXT/7570TSpqQ4TDtVwN8CztCgEN/XC8bzgSEemwGlBYLz5ccWpBjKIsD76dwdYjCKxBJQZziNJAe9T8u+dtiMtNyzP2lZ6R5JryNd3b63wN4Lti/I2+dL2qlwXA0FyvXdpK0/g3z5ESHpQJLsxRq2z1LqCVy5mY7tpykPlA9Fw+F+iiQFcSlpKa6Ef5K6dv1O0u6FNmqV97b9S0iOxfYWjf2S/lQ4vqCLCQfQQVxvA5N5JC0PzGf7nmz/P5JKJYAnSjqewQDkc8rKnhVz5G+QtANwc75LuSTb+2/huBqsYnuDJkfSLd/VuSW9H3jG9iRJU2Z6xAyw/ZCkj5DiRSsUmOiUvPd8TRXPq5LuCoIxRrf8U401Vh8i57+hBloqBfE3kg7O3U3ZO/NRFoQEOKNpu1izx/b3cvri6qQJbA3gatrruwvJQS0DWNJSJLmKUUNJW2cfUhD/f4EDczC4nTu7bQBsvyDp4xTcyQ0n0OZyee/dgJNIdzjfIS1T3UtnGvIEo0wEgTuAamw12Ckk7Wt716bnRRWjnSJP/vuSeu/eDexquxNZMiMdz3XAjqQexesBX28O8BfabKQJL0YKUn/f9l8K7KwKDNi+rWnf2ravLLD1GVLx1+tJXd5OdoHMdTB7EA6gA3TCAeSr4G+T1twPbei8SNrb9ogbiWS5hkVJSw6fy7v7gIOHu5qcib2G0No0u2nvbqfrUM3y2dnG9bSkCduulCasQXnqiSTp7G1tP9nu+PLd5YdIhYdLAGfb3rvUXtCdxBJQZ9i0AzaPIol9TQSOlPQL26cAVRvMvBf4CKm0/1t539LAZSWDsr3GzF81cjR985YXSBlAT7uNJi41sIKkk/K2mp/b/lihzcdt351t3COpVfp6JKxmex0ASWuQsoq2LxzPVHLB3PmkTmyfJWUBhQMYY4QD6ACtOew10d/I2pH0PuDXkt5KxUpb22cCZ+ZjtyZlF91CijEUkwu1TgZOqpqv3zK+NbK9XwM/a7o6rpwiWTO1OrrMHJKuIAVyVyYF4vcDsL3LCG1MkDTO9oDtGyV9kpTS+8aSAeXMsq2ALUkS5KcBn7D9SIm9oLuJJaDZBEkXA9s0Z3tI+hFp3Xh8BTvfJ61h302qBdjNdtt3LHnJYCuSNs5LJM39c2xPKrR3te33Nj2/xoXtNLuVGdVwjDSDTNIHgduaJ2glJc+dbO9VMKYrgFOAU0sKA4PZi3AAswn5n3qS7Vdb9i9Z5epM0tXAI6T88fOBM1xRe34m9lchZY+sQOrmdbrtQwvs7EqSILiDJJVwQTcFqetCSce/WXL5ijZsrUqqdj4HWDyu2oOZEQ5gNkM19H6VtCRp+ed9pIl6J+DSXDRVOq7vkibs20nyBrfk/RfYrlzAlY99HUnw7h/Nej5jBUk/J8ViViFlF6k0s6ilcG6Ndt73oHcIKYjZj2l6vzY/Hym2H7F9cL7yfzewFNPWBYwYSV/NBWR9JA35vwPvahSVUSiXLGn7pkn/xJwnP9ZYzfZHSA5uc1KAv5RVbH+VFDSHiO8FIyAcwOzHgKSVYepyS1vY/pftQ9zU6LwiewHbkiavZ4HnGWy+gu1nC+02Mmu+StIr2rHQTjfTiI+8JGkDUs1DKV1VOBfMHsRVwuzH9sB++Z/8IVJV6miyKLARSTzs3SRtnFPcfhvMeXOm0iu2H1PqfzAmaBRuAV9Xau+5M/BT4Jg2zH6FVDi3MHAA8LV2xxmMfSIGENSGpHVInbautL1ToY3G5PgGUlbRj0n9j39ke5OahjqqSLqI6TuVzQ+cZnujQptvjordoCpxBzCbMEzFLQCjWXGbUxm3JE3Y1wIfa1QpF7IfaXK8jSRFgKTHSTGGscJQncqeU3udyr6fBeD+BPzB9kNtjTDoCcIBzD78ALjW9rOS3gzsQUod3HN0h8WlpAb1D5KKpVZPyUnFFbKdaOPYdajmTmW2t83LSZsCP5G0VKNCOAiGIxzA7MMPmgqjjgJ+SJJc/iWp6closUzdBuueHLuQ3YALJE3XqaxNu28mOeE3kqq7g2CGhAOYfWi0XXwdMLfti/PzUR2U7QdrNtmpybFrsH2NpPVJMhyLM9iprJ06jKtIch5/JF0slPaJCHqICALPJkg6CzgPeBfwd9t7SxoPXGH73aM7unqRtCCDk+OjwLntTI69QASBgxLCAcwmZCmIbYGXgeNsT85Bv3fYPnd0RxeMNkrd3RYltQj9g+3SRkFBDxEOIAjGCJLmIdVjfBKYTKorOCOWg4LhiErgIBg7vI0UBJ4TuJIUDD5nNAcUdDcRBA6CMUAOAt9EkxBf3j/P6I0q6HZiCSgIZmMkHZtrAH5oe7RrQoLZjLgDCILZm2UlfQPYRtITzb+w/atRGlMwmxAOIAhmb75Dato+QFZgzYxugUgwWxBLQEEwGyPpJeBc4Cu2n2jaf4ntDUZvZMHsQGQBBcHszfWkdM8LJDW39ow7gGCmhAMIgtkb2z6H1N7zc5KOyJk/cWsfzJRwAEEwe9NoJv+E7Y+S8v+vBJYe1VEFswXhAIJg9ubE5ie2jwM2B/48OsMJZiciCBwEQdCjxB1AEARBjxIOIAiCoEcJBxAEQdCjhAMIgiDoUcIBBEEQ9Cj/D/oMx2OWjTRFAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 524 + }, + "id": "WkDOTK8GXf-u", + "outputId": "cbdf874c-faa1-4614-a030-3faa7a7debc8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "sc.pl.heatmap(\n", + " adata[adata.obs.cell_ontology_class.isin([\"endocardial cell\", \"atrial myocyte\"])],\n", + " introns_to_plot,\n", + " 'cell_ontology_class',\n", + " layer=\"PSI_raw\",\n", + " vmin=0,\n", + " vmax=1,\n", + " cmap=\"coolwarm\",\n", + ")" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sc.pl.matrixplot(\n", - " adata[adata.obs.cell_ontology_class.isin([\"endocardial cell\", \"atrial myocyte\"])],\n", - " introns_to_plot,\n", - " 'cell_ontology_class',\n", - " vmin=0,\n", - " vmax=1,\n", - " cmap=\"coolwarm\",\n", - " layer=\"PSI_raw\",\n", - " colorbar_title=\"Mean PSI\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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\n", 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" + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "kxQuOpIxSnC6" + }, + "outputs": [], + "source": [ + "# The results can be further visualized with \n", + "# the cellxgene browser https://scquint.ds.czbiohub.org/tabula-muris/\n", + "# and UCSC Genome Browser https://genome.ucsc.edu/s/gbenegas/tabulamuris" ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } - ], - "source": [ - "sc.pl.heatmap(\n", - " adata[adata.obs.cell_ontology_class.isin([\"endocardial cell\", \"atrial myocyte\"])],\n", - " introns_to_plot,\n", - " 'cell_ontology_class',\n", - " layer=\"PSI_raw\",\n", - " vmin=0,\n", - " vmax=1,\n", - " cmap=\"coolwarm\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "kxQuOpIxSnC6" - }, - "outputs": [], - "source": [ - "# The results can be further visualized with \n", - "# the cellxgene browser https://scquint.ds.czbiohub.org/tabula-muris/\n", - "# and UCSC Genome Browser https://genome.ucsc.edu/s/gbenegas/tabulamuris" - ] - } - ], - "metadata": { - "colab": { - "name": "differential_splicing_example.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3.7", - "language": "python", - "name": "python3" + ], + "metadata": { + "colab": { + "name": "differential_splicing_example.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.7", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/setup.py b/setup.py index adbefde..a4adcd5 100644 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ setup( name='scquint', - version='0.3.1', + version='0.3.2', description='scQuint', url='http://github.com/songlab-cal/scquint', author='Gonzalo Benegas',