From 4bbf1bc6d86316e0497157a3ce370504644afae5 Mon Sep 17 00:00:00 2001 From: Jan Lause Date: Wed, 24 Jul 2024 16:37:21 +0200 Subject: [PATCH] paper revision July 2024 --- .gitignore | 1 + 01_prepare_tasic.ipynb | 311 ++++++++---- ...plot_tasic_homogeneous_Fig2_S1_S2_S4.ipynb | 87 ++-- ...nb => 03_prepare_tasic_heterogeneous.ipynb | 400 +++++++-------- ...ot_tasic_heterogeneous_Fig3_S5_S6_S8.ipynb | 24 +- 05_compute_tasic_qumis_census.R | 56 ++ 06_load_tasic_qumi_census.ipynb | 161 ++++++ 07_process_tasic_qumi_census.ipynb | 366 ++++++++++++++ 08_S8.ipynb | 411 --------------- 08_plot_tasic_qumi_census_S7.ipynb | 269 ++++++++++ 09_simulate_tasic_like_data.ipynb | 478 ++++++++++++++++++ 10_process_simulated_tasic.ipynb | 460 +++++++++++++++++ 11_plot_simulated_tasic_S9.ipynb | 249 +++++++++ ...asets.ipynb => 12_prepare_ziegenhain.ipynb | 0 ...pynb => 13_plot_ziegenhain_Fig4_Fig5.ipynb | 18 +- 07_S7.ipynb => 14_plot_ziegenhain_S3.ipynb | 30 +- 15_plot_alpha_per_cell_S10_S11.ipynb | 304 +++++++++++ ....ipynb => 16_prepare_tasic_homo_zinb.ipynb | 72 ++- ...pynb => 17_plot_tasic_homo_zinb_Fig6.ipynb | 16 +- README.md | 38 +- data/tasic/.gitignore | 7 + data/tasic/qumi_census_comparion/.gitignore | 3 + data/tasic/simulations/.gitignore | 4 + figures/S_single_cell_alpha_mean_hist.pdf | Bin 0 -> 34997 bytes figures/S_single_cell_alpha_scatter.pdf | Bin 0 -> 37679 bytes figures/S_single_cell_mean_scatter.pdf | Bin 0 -> 37707 bytes figures/S_tasic_hetero_param_influence.pdf | Bin 610775 -> 610775 bytes figures/S_tasic_hetero_pseudogenes.pdf | Bin 146872 -> 146876 bytes figures/S_tasic_hetero_scanpy.pdf | Bin 125863 -> 125863 bytes figures/S_tasic_homo_genelength.pdf | Bin 249968 -> 249725 bytes figures/S_tasic_homo_pseudogenes.pdf | Bin 492276 -> 492384 bytes figures/S_tasic_homo_theta.pdf | Bin 403683 -> 403683 bytes figures/S_tasic_qumi_census.pdf | Bin 0 -> 229511 bytes figures/S_tasic_simulation.pdf | Bin 0 -> 163217 bytes figures/S_ziegenhain_overlap.pdf | Bin 162052 -> 0 bytes figures/S_ziegenhain_pseudogenes.pdf | Bin 291505 -> 291505 bytes figures/main_dropseq_broken_zeta.pdf | Bin 88613 -> 88613 bytes figures/main_observed_amplification.pdf | Bin 54612 -> 54612 bytes figures/main_tasic_hetero_tsne.pdf | Bin 323238 -> 323238 bytes figures/main_tasic_homo.pdf | Bin 216700 -> 216700 bytes .../main_tasic_vs_simulated_readcounts.pdf | Bin 214710 -> 214710 bytes r41_env.yml | 16 + readcount_tools.py | 78 +++ 43 files changed, 2998 insertions(+), 861 deletions(-) rename 02_Fig2_S1_S2_S3.ipynb => 02_plot_tasic_homogeneous_Fig2_S1_S2_S4.ipynb (50%) rename 03_prepare_Fig3_S4_S5.ipynb => 03_prepare_tasic_heterogeneous.ipynb (70%) rename 04_Fig3_S4_S5_S6.ipynb => 04_plot_tasic_heterogeneous_Fig3_S5_S6_S8.ipynb (98%) create mode 100644 05_compute_tasic_qumis_census.R create mode 100644 06_load_tasic_qumi_census.ipynb create mode 100644 07_process_tasic_qumi_census.ipynb delete mode 100644 08_S8.ipynb create mode 100644 08_plot_tasic_qumi_census_S7.ipynb create mode 100644 09_simulate_tasic_like_data.ipynb create mode 100644 10_process_simulated_tasic.ipynb create mode 100644 11_plot_simulated_tasic_S9.ipynb rename 05_prepare_umi_datasets.ipynb => 12_prepare_ziegenhain.ipynb (100%) rename 06_Fig4_Fig5_.ipynb => 13_plot_ziegenhain_Fig4_Fig5.ipynb (99%) rename 07_S7.ipynb => 14_plot_ziegenhain_S3.ipynb (98%) create mode 100644 15_plot_alpha_per_cell_S10_S11.ipynb rename 09_prepare_Fig6.ipynb => 16_prepare_tasic_homo_zinb.ipynb (90%) rename 10_Fig6.ipynb => 17_plot_tasic_homo_zinb_Fig6.ipynb (99%) create mode 100644 data/tasic/qumi_census_comparion/.gitignore create mode 100644 figures/S_single_cell_alpha_mean_hist.pdf create mode 100644 figures/S_single_cell_alpha_scatter.pdf create mode 100644 figures/S_single_cell_mean_scatter.pdf create mode 100644 figures/S_tasic_qumi_census.pdf create mode 100644 figures/S_tasic_simulation.pdf delete mode 100644 figures/S_ziegenhain_overlap.pdf create mode 100755 r41_env.yml diff --git a/.gitignore b/.gitignore index 3d4df3e..f41b7ac 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ __pycache__/ data/tasic/.ipynb_checkpoints/ figures/.ipynb_checkpoints/ +qumis_census.log diff --git a/01_prepare_tasic.ipynb b/01_prepare_tasic.ipynb index d7237ce..0bbf950 100644 --- a/01_prepare_tasic.ipynb +++ b/01_prepare_tasic.ipynb @@ -20,6 +20,7 @@ "import pandas as pd\n", "\n", "from scipy import sparse\n", + "from scipy.io import mmwrite\n", "import pickle\n", "import anndata\n", "import scanpy as sc\n", @@ -27,7 +28,8 @@ "import mygene\n", "mg = mygene.MyGeneInfo()\n", "\n", - "from readcount_tools import compute_gene_stats" + "from readcount_tools import compute_gene_stats\n", + "\n" ] }, { @@ -62,15 +64,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 1819385856 bytes == 0xdbeb0000 @ \n" + "tcmalloc: large alloc 1819385856 bytes == 0xd8bc4000 @ \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2min 49s, sys: 6.24 s, total: 2min 56s\n", - "Wall time: 2min 56s\n" + "CPU times: user 3min 28s, sys: 7.49 s, total: 3min 35s\n", + "Wall time: 3min 38s\n" ] } ], @@ -176,7 +178,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 1729986560 bytes == 0x17ee3c000 @ \n" + "tcmalloc: large alloc 1729986560 bytes == 0x17b66c000 @ \n" ] } ], @@ -202,7 +204,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_34177/1803155156.py:1: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + "/tmp/ipykernel_25122/1803155156.py:1: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", " adata = anndata.AnnData(X=counts,\n" ] } @@ -235,8 +237,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 1729986560 bytes == 0x10f79c000 @ \n", - "tcmalloc: large alloc 1729986560 bytes == 0x1e68f8000 @ \n" + "tcmalloc: large alloc 1729986560 bytes == 0x1e4e32000 @ \n", + "tcmalloc: large alloc 1729986560 bytes == 0x1e4e32000 @ \n" ] }, { @@ -337,8 +339,8 @@ "querying 41001-42000...done.\n", "querying 42001-42776...done.\n", "Finished.\n", - "538 input query terms found no hit:\n", - "\t['73270', '74282', '619302', '69813', '100042464', '72937', '73001', '73133', '654799', '214604', '7\n", + "555 input query terms found no hit:\n", + "\t['73270', '74282', '76619', '619302', '69813', '100042464', '72937', '73001', '73133', '654799', '21\n", "Pass \"returnall=True\" to return complete lists of duplicate or missing query terms.\n" ] } @@ -453,8 +455,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 4076044288 bytes == 0x24dad0000 @ \n", - "tcmalloc: large alloc 4076044288 bytes == 0x340a08000 @ \n" + "tcmalloc: large alloc 4076044288 bytes == 0x27f8f6000 @ \n", + "tcmalloc: large alloc 4076044288 bytes == 0x37282e000 @ \n" ] } ], @@ -548,16 +550,199 @@ "adata.obs.columns" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add gene length info" + ] + }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], + "source": [ + "def add_gene_length(ad):\n", + " \n", + " #make a working copy so we can re-index without downstream problems\n", + " adata = ad.copy()\n", + " \n", + " adata.var['gene_entrez_id'] = genesDF.loc[adata.var.index,'gene_entrez_id']\n", + "\n", + " adata.var['ensembl_gene'] = gene_annotations.loc[adata.var.gene_entrez_id.astype(str),'ensembl.gene'].values\n", + "\n", + " df_lengths = pd.read_csv(f'{basepath}mart_export_genelength.txt')\n", + " df_lengths.set_index('Gene stable ID',inplace=True)\n", + " ensemble_ids = np.unique(adata.var.ensembl_gene.dropna())\n", + "\n", + " adata.var.set_index('ensembl_gene',inplace=True)\n", + "\n", + " adata.var['transcript_len_max'] = np.nan\n", + " adata.var['n_transcripts'] = np.nan\n", + "\n", + " for e_id in ensemble_ids:\n", + "\n", + " #ignore cases where we dont have a length info\n", + " if e_id in df_lengths.index:\n", + "\n", + " df_gene = df_lengths.loc[e_id,:]\n", + "\n", + " #handle case where we only have one transcript = one length\n", + " if type(df_gene) is pd.core.series.Series:\n", + " lens = [df_gene['Transcript length (including UTRs and CDS)']]\n", + " else:\n", + " lens = df_gene['Transcript length (including UTRs and CDS)'].values\n", + "\n", + " #compute stats\n", + " n_transcripts = len(lens)\n", + " maxlen = np.max(lens)\n", + "\n", + " #assign results only if the ensmbl ID is unique\n", + " if len(adata.var.loc[e_id,:].shape)==1:\n", + " adata.var.loc[e_id,'n_transcripts'] = n_transcripts\n", + " adata.var.loc[e_id,'transcript_len_max'] = maxlen\n", + "\n", + "\n", + " \n", + " #change back index\n", + " adata.var['ensembl_gene'] = adata.var_names\n", + " adata.var.set_index('genes',inplace=True)\n", + " #add length infos back to original adata\n", + " ad.var['transcript_len_max'] = adata.var['transcript_len_max']\n", + " ad.var['n_transcripts'] = adata.var['n_transcripts']\n", + " ad.var['gene_entrez_id'] = adata.var['gene_entrez_id']\n", + " ad.var['ensembl_gene'] = adata.var['ensembl_gene']\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "add_gene_length(adata)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], "source": [ "adata.var_names_make_unique()\n", "adata.write_h5ad(f'{basepath}adata.h5ad')" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 23822 × 42776\n", + " obs: 'clusters', 'class', 'non_neuronal_idx', 'n_genes_by_counts_allGenes', 'total_counts_allGenes', 'clustercolor', 'sample_id', 'subclass', 'clustername'\n", + " var: 'genes', 'gene_id', 'chromosome', 'gene_name', 'n_cells_by_counts_allCells', 'mean_counts_allCells', 'pct_dropout_by_counts_allCells', 'total_counts_allCells', 'n_cells_allCells', 'marker_idx', 'name', 'symbol', 'type_of_gene', 'ensembl.type_of_gene', 'coarse_types', 'gene_var_allCells', 'gene_mean_allCells', 'gene_FF_allCells', 'gene_fraction_zeros_allCells', 'transcript_len_max', 'n_transcripts', 'gene_entrez_id', 'ensembl_gene'\n", + " uns: 'clustercolors', 'clusternames', 'markernames'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute TPM\n", + "(for processing with Monocle and qUMI in R)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(23822, 27841)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#get genes for which we have length info\n", + "adata_tpm = adata.copy()[:,~np.isnan(adata.var['transcript_len_max'])]\n", + "adata_tpm.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 2652913664 bytes == 0x27f8f6000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x31dafa000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x465f66000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x5a2b6e000 @ \n" + ] + } + ], + "source": [ + "#convert counts to TPM\n", + "reads_per_kb = adata_tpm.X / (adata_tpm.var['transcript_len_max'].values/1000)\n", + "adata_tpm.layers['tpm'] = (reads_per_kb / reads_per_kb.sum(axis=1)) * 1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "tpm_df = adata_tpm.to_df(layer='tpm')\n", + "#save transposed sparse version for further processing in R\n", + "mmwrite(basepath + 'tasic_tpm_transposed.mtx',sparse.csr_array(tpm_df.T))\n", + "pd.DataFrame(tpm_df.columns).to_csv(basepath+'tasic_tpm_genes.csv')\n", + "pd.DataFrame(tpm_df.index).to_csv(basepath+'tasic_tpm_cells.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 5305827328 bytes == 0x465f66000 @ \n" + ] + } + ], + "source": [ + "#also write out adata\n", + "adata_tpm.layers['tpm'] = np.array(adata_tpm.layers['tpm'])\n", + "adata_tpm.var_names_make_unique()\n", + "adata_tpm.write_h5ad(f'{basepath}adata_tpm.h5ad')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -567,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -628,8 +813,10 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 33, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -786,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -803,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -815,95 +1002,11 @@ } ], "source": [ + "#save\n", "adata_single_cluster = adatas_neuronal[1]\n", - "print(adata_single_cluster.uns['clustername'],adata_single_cluster.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get gene length data for single cluster data" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "adata_single_cluster.var['gene_entrez_id'] = genesDF.loc[adata_single_cluster.var.index,'gene_entrez_id']" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "adata_single_cluster.var['ensembl_gene'] = gene_annotations.loc[adata_single_cluster.var.gene_entrez_id.astype(str),'ensembl.gene'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "df_lengths = pd.read_csv(f'{basepath}mart_export_genelength.txt')\n", - "df_lengths.set_index('Gene stable ID',inplace=True)\n", - "ensemble_ids = np.unique(adata_single_cluster.var.ensembl_gene.dropna())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "adata_single_cluster.var['ensembl_gene'] = gene_annotations.loc[adata_single_cluster.var.gene_entrez_id.astype(str),'ensembl.gene'].values" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "adata_single_cluster.var.set_index('ensembl_gene',inplace=True)\n", - "\n", - "adata_single_cluster.var['transcript_len_max'] = np.nan\n", - "adata_single_cluster.var['n_transcripts'] = np.nan\n", + "print(adata_single_cluster.uns['clustername'],adata_single_cluster.shape)\n", "\n", - "for e_id in ensemble_ids:\n", - " \n", - " #ignore cases where we dont have a length info\n", - " if e_id in df_lengths.index:\n", - " \n", - " df_gene = df_lengths.loc[e_id,:]\n", - " \n", - " #handle case where we only have one transcript = one length\n", - " if type(df_gene) is pd.core.series.Series:\n", - " lens = [df_gene['Transcript length (including UTRs and CDS)']]\n", - " else:\n", - " lens = df_gene['Transcript length (including UTRs and CDS)'].values\n", - " \n", - " #compute stats\n", - " n_transcripts = len(lens)\n", - " maxlen = np.max(lens)\n", - "\n", - " #assign results only if the ensmbl ID is unique\n", - " if len(adata_single_cluster.var.loc[e_id,:].shape)==1:\n", - " adata_single_cluster.var.loc[e_id,'n_transcripts'] = n_transcripts\n", - " adata_single_cluster.var.loc[e_id,'transcript_len_max'] = maxlen " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ + "adata_single_cluster.var.set_index('ensembl_gene') #for consistency with previous versions\n", "adata_single_cluster.var_names_make_unique()\n", "adata_single_cluster.write_h5ad(f'{basepath}adata_single_cluster.h5ad')" ] diff --git a/02_Fig2_S1_S2_S3.ipynb b/02_plot_tasic_homogeneous_Fig2_S1_S2_S4.ipynb similarity index 50% rename from 02_Fig2_S1_S2_S3.ipynb rename to 02_plot_tasic_homogeneous_Fig2_S1_S2_S4.ipynb index 6e1ba38..0760d5a 100644 --- a/02_Fig2_S1_S2_S3.ipynb +++ b/02_plot_tasic_homogeneous_Fig2_S1_S2_S4.ipynb @@ -43,6 +43,16 @@ "text": [ "L6 IT VISp Penk Col27a1 (1049, 33914)\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] } ], "source": [ @@ -88,7 +98,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "def plot_compound_var(ad,ax,alphas,thetas,colors=None,show_theta=False,xkey='gene_mean_withinCluster',plot_kwargs={}):\n", "\n", " for alpha,theta,color in zip(alphas,thetas,colors):\n", @@ -145,21 +154,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 9201278976 bytes == 0xc1c0000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x231124000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x231124000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xc1c0000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x231124000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xc1c0000 @ \n" + "tcmalloc: large alloc 9201278976 bytes == 0xd410000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x235998000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x235998000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xd410000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x235998000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xd410000 @ \n" ] }, { @@ -319,7 +328,11 @@ "output_type": "stream", "text": [ "/gpfs01/berens/user/jlause/.local/lib/python3.8/site-packages/scanpy/preprocessing/_simple.py:249: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", - " adata.var['n_counts'] = number\n" + " adata.var['n_counts'] = number\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" ] }, { @@ -387,21 +400,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xc030000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xac4d3c000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xac4d3c000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xc030000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x45602a000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x67af30000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0x89fe36000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xac4d3c000 @ \n", - "tcmalloc: large alloc 9201278976 bytes == 0xc030000 @ \n" + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xd2c4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xac95b0000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xd2c4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xac95b0000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xd2c4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x45a89e000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x67f7a4000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0x8a46aa000 @ \n", + "tcmalloc: large alloc 9201278976 bytes == 0xac95b0000 @ \n" ] }, { @@ -486,7 +499,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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BqQsRorzUHvEmV1ze4K8PqqKKv6BFt6Y0rXqW/jY9E26nf/wIFry7l+8f3MDRAx5UBDyqCESgIhLAgKYLBJDx6wKKLqAioSCiIaHqIq3rbyfFV4ebAy/SU1AYl5tHSA2Itblp7D3G8+NWMnPdVABembWEyWu2VWi/y5MqJ1jFKWzdlU7OB08hijqpffszfPj1REeGVLRZVVwEDH15INc/3KeizahUtI7uzkffjEc2Svzw6GayCyR8mpkCRSGgmfDrEk7NhE+V8OpGPLoJnyaiaiI6ApouUCMmkyE9fuKwswYPyo/QyqByc5hKpMGNRfERneem3ZgfeWz5l+w+lsPRgqKK7na5UeUEq2DlvG0c2XcMgLlLU/hk/Dskhq8g4tHOXPPM+Io1roqTmPXRAg5tO1LRZlRRztzY9k7e//YdijNcLBybQqGioSLh0kQCmoxXM+LXZVRdQtcl3KoJt27CqZrw6gacmoHE2HT6dFjI+sxWvFzrJo5Vt+L2+ZlXnMnse8KJz3HQ6NupiI29PHNd94rucrlR5QQvcVRV4+0nJjPts98BMBZkcXXhfLL8ITxbdJoQ7yoqDLfDw8RRk1n07fKKNqWKCmDYNQ/T+Z727JuXxoH1RRT7Tdxc4xlUQvCqMsVKKKWKGYdqxocJVRfx6xIBTQZdxI+BDo030aHRRuKicjHrAdodKSZc1/k1tJT1PWPptjoN666duHx+vl6xiVKPt6K7fd65ZJ1gSkoK119/PQcPHqxoUyoUSRL5aP6TXHVXZ174eDbqmBeJ3w6v5lzFQzf2rGjzKgXl9SxZ7Ra+3jOOYW8MOq/XOd8U/jyV7TcMwldUVUv0n/Lj29Pp/MxlxLaIxaWacasyIxt9TsfoG1GBJuE9UAFFE/FoRvy6Ab8u4ddlFE3AqZm5odMc6sUfRhI0mqQVMSY7F5+iMq+bQkGUma7LtrMhNZ135q9kw6GMiu7yeafSRoeeUG34IwMHDuTBBx/E7XZzzTXXlG0/IXP0R5KTk09Rg7iU0DQNv1fBbDWSVC+Wd8fPpNGvX1DLVcJIl4eZn07EZLo0UiIq07MUUyOo3bt9+W5qN0sitBKu1ZYUrGPl9gxKFu+m680dK9qcSkX10EQ6DryKY74MJAQ+PfQFw+vcw6ysNSSam3Jr0iOkOXvx1ZE38KnFGESFACZAxCzp6IgUqDZCNIVl2zuj1F/Lret30DFOZ72jhI8ebkVBdAS5u6djqG+hXvWoiu7yeeeSHQle6nw+Zi5DrniDgF+hxOVlQFyAbmmH0OJcJD488pJxgJWRopwSnu71Oj+/O+ek7aqi8tVb89m3I71C7PK6fXzz9jxyM88cVOHP2U+tnK/o/2QTOvZvV47WXTy81fI1wrdWY/awXyn2wEcHfiKginSI7sjOkt28tPs9MjwyDtVGcSCEIsVKkWLFGTCCDhoiAU1i/d52TEq9nszqNp7JyAEgJyQfSVJRvB5clFLkvfjzXitsJLhr1y5mz55N69at6dWr1z8+/nRv5CewWq1n3Q9BXcBFixZx8OBBXn75ZSTp4tZa1HUdj9uHKIiYrUbadW1E6rFi7h83jd2H0/hk/bcYbCrPWSKZ8twTFW1uuVLZnqWI2DBenjGSBq3rnLTdWepl9rersIaYaNj878t9/VNeH/4FboeX1396+KTtGam5/PzJEhLrxHD1Tac6OF3XOTTsduJiDGRc3osaxko7EVXhNIpsQFFKDvnLCoi6yoRBMNHQ3oSXUsYhixJ+TaCOtSFpnh3Igo4qqjg0Kz5FxSZ60WWFjk3XsWTLlay8sg63Zu7kRbvKkvgw5IxCRo9fxZoh17EnK4/8HAeFx1wM7N3qoqz6ct6ewj/X73K5XDz44IMYjUa6devG4cOHCQ0NPV+X/0tq1KjBDz/8UGHXL29+HLeAH8f/ijE6nMmrnkcMMbHhQBb5ZiMtY9NJrFvM78ZQWtw2tqJNrXRUxLN02TWtT/p5/pT1pO45xuS1L2INOb+j+AYta+Lz+E/ZXi+5Bt+te4mo2LDTHudatggOOtiqmune479XIrmUeWDAMF6pM5ptk9fQpes9WI02FmbtpJatLmbRQrY3nRyfh1rWRhx1p+JUNAyCil/XsYp+NF2kdcNtLN/elZlZV9OycQZyhJm61gLWRtUiYBKpuWAZL2rhtHDHcnRPHr06NSI81FrRXT/nnLfp0BP1u04wY8YMBgwYwMSJE5k9eza5ubkMGzaMtWvXnvb4ZcuWMXr0aEaPHn1KyZIq/jnNO9Wn4zUtuWZQR0wWA1KEAUuXKAxRHsYIs3E1N5By+xie7fvPR+UVzcaNG3nrrbf45ptvKtqUCiP9UC77dqRjs5vP+9v6zQ/1QBElJo1beMq+MzlAgMw3X0a2qOT3vOOiHFGUJ6IoctmgvjgPZHMkJRO3qvFD2gp+P3aUlJJM3IqZvaXFXB1zI01DeyPoUXg1A27FQK4/hPyADb+skVxnF1sPtmBNcnVy423synGTlbWZdV3iaZKaz+SOl/He/f1o2rQ6u47kVHS3zwvltib4x7pckiRxyy23MG7cOKKiTr/w2q1btzInWKtWrfIy86IluUM9np1wN8Oe6sO3izbz5BfzUHPTGDdtOis3eHjY2Z7EhAtTQDc1NZVhw4aVFUR1uVzceeedDB8+nMmTJ9OuXTsKCwsr2MryIzeriG1rT45Eve/Z6/hwxiPn7Boel5fV87aiaafXkzx2tJDsjOA9//TVX/j2/UVnPV/pxk1oaUWkRzi59t5nzpmdlzKv3vcUglEmd8F2BiV2J1yO5JXk2+gS0YlGtmbcW3sg3xxewNxjq2kfdRVezRBUm9FMgIAHK60bbKFhjf0EAmbC3C4al5bg8PpJuVxCkQTyZv6ALIoczMpnz9GL0wmW26R8YmIiGRkZtGzZEk3T6NSpE506dSqvy1/SFBc6CT8eRegLKMzcmkKuO5u3Nv2G6HLz2HIXbQZ05tbGF6Y+6J+rgp+YVbjuuuu45ZZbGDx4MG+++SYfffTRaY8/WyHUysj3H/zG77O3MnPbqxjO07ra79M28NHTP/Le3Kdo3K7OKfufGX9b2efCPAcBv3LW8x19931KVZWJ4Vdypc12zu29FGleox5dHhrMAZPCxL1bWNjnSTrPe4/igAtJAFFSeKzRVeR6/UxJS0EllBA5QLjJTaHfhNGsEBmVy4DuM5AFnWYbC7n2UDFTaieRoRayNzmSxN9Xs2nIYQo0Hz8s38o9fS+r6G6fc86bE/xz/a5HH32Uhx9+mHnz5nHdddf99wt8fe2p25peD+2Hg98Nk2/+/+13zTul6TfffENqaiotW7YkJSUFgJ49e3LZZRfXL3ndin289MQPvP35XTRvU4ven3/D0UARLx5cRmJRCaMdGZhv68XcoQ8ji5UjWDgjI4NmzZoBwVmF2bNns2XLFuLj40/b/myFUIFK9yzd+URveg5od94cIMBVN19GdHwEjdrW/su2z31w+xn3FZW4ibCbSeqZTEbHznxyxy3n0sxLnscfeZKnNs7iiKOYWam76BhTh7lHd6IiIKsmksNrMidrPVbRRJEqUhwwUc0cQomSh11V0XSQhBJcjlD2JGg03l9ES4POZoeDBb1bIHgkbo8QeGlwD6JDLr71QDiPTvB09bu+/vrr83W5f0xoaCgmkwmfz4fdbueJJ57gxRdfvKic4FcfL+bw/mwGDLkcp1Gj8/sTyNSdDMjZQvv9WYQnl7I/LZS7772n0jhAOHVWoV+/fvTr16/C7CnvZykqNpSo2PMbVGa2GunQs9l/Osf73yxlytzNfHtPBPWOvkH0VZ/y1Y8buH/oFdisVSk454J+NZvw/eolrN68hZG+eaQO+R+iYuSXwzt574r+fL57IzFCHXL8hYTIbnQxwDFvMbpuJU9QiTWrpBXF89PsO7mmw3x62I5wbamD9UY7BYnFeOUazDi8l+YRSdzQJLmiu3teqLwxyqd5Iy/DaD37fuDGG28E4L777qNx48bn0rIKw+v2880bs+hze2dqNkpAliWsIWb63tmR+7+eSYa1GNEeoPfvB7DX8PDy0QKct9/Dw8lXVLTpZ+W8zypcIs/SZ89OoXZyIj1v++fLEF6Pn9T92WzffIRb7+rytwJbnG4fdSlF++IL0qpLrMuJY+5vWxnQr02VEzxHSKJIwfQl5P22hCu/GU+zSe8ztmtv2kQn0juxCT/t34lT83LYUUjn2Ppk+tPxqA5MsoJXN1EU0IkKKSUyrICdacmkJf5K7wOFTGsXQ2Sogni0kNiVP/D7tddzT5OLswJ95XWC/5FFixaxadMmYmJicDgcvPrqqycpg1RGCnNLmPv1cpIaxFOzUQJ33BcUwZ2/fS97vLkQqdFZTqVL60OsttdgkdSHKbc9RTXLha06cqHPKpzvZ6mo0Mm6tYfo1ac5ovjvoyo3/74LRVFP6wRTNh+hRp1qhEWcfr1u6PXvEx5uJSu9iP63dMBqO7MT27TnKEeyChl5z1X89uMY9GKdaUokI8Z3p/91XTCbDP+6D1WcSp8+17J81hwObUghsWVTil1e3l6/mkZhMSw/nM5tDZsz5eo7mLB3Jb/vPkCE1YxbUTELftyCjCFgok7SIbaktOFgsyia7C/gWovELouZ0CMO+i06RGHb/zYrcCFzyTrBnj170rPnxaWNmVCrGq/NGEH9ptUB2H00h/s+nI6tg4mQ2GIen7uG2m3yKJRNvNukMwsvf4H6YadfR6vi73OunyVV1Vi79iDt29fBaJT5bVEKn3/6O02aJlCzVrV/fd7P171y2u2OYjdP3fkF/Qd35P5nTrM+CvQb2IFa9WJo2rzGKQ5w9ZoDTJ68hrFv3oLdbubdH5Zx9FgRHUw+6uXlYmrgIP6GVxEEocoBngeG3zyQUQ88SOGWnejVk/h800ZaxsQybtUaWoXFsyk9i0Gzp7C2OBWrTcITMGEW/BT5rRgNLpyqjWqxGWg727PSn0zpDTIFkpH8Ygc5zcPQBLgy1VHR3TxvVJ6FoCr+kqICJ/976Dt+/HIFWw9mMmfPXnITnRwLpDJ66lJa7c3hvbXQ9ZNCwpWEKgd4AbJwwQ4effhbXnxxOqtW7Qfg+hva8tGEof/JAZ4Ne7iVlz4czE13/X/VEL8vwO8LduA/HvV527CuXH5Fo9OOFDVNIy2zkNEfBKeN2yYn0a55TdLeGgsGjal+PzfdeubgmSr+G5EREVRv0hTv7n00jIzinlZtGdO9FynZORwrcSIhsjY9HUGVcDtldL8Nn9+CXzMSCESh6xBTLQ+T0cv+zIb4zTKaprPmYDYZajFZNe0IGzaypyi3ort6XqhyghcREVEhPDtmAFFNY7n1m5/4YtsmhDgXz85aRb20ImZeG8Uv83ZjuawF31/3fEWbW8VpcDq8KIrKiy/eQEi4hcxjRRhNMo0a//0czlnfrmbD8r1/u/3cGZuZO3cbUTH/H2yzYfUBxj4/g81r/7oyRpfODRl45+XooRIPfTSD23q0oYYjg6j9exiXlU9Rq2GI8iU76VQu3NKvH76cbO6u35Q7WrbCIAi4HAEaWqOZPmAQw1q0oZ4tChCIMYXgC0gEVAFHADQgV7HTvdt8OrVZiS3fy8CVqcSYZNzuEg42CafGkQJumjqB5ZmpFd3Vc84l+2SmpKTw/PPP884775RJXvXs2RNJkpg/fz66rp8+nP4CojDPgSgKhEeFoGk6KTvTkeJt/DBnFYFQBZvNy6jfltF8ey7V2pRQPf0Ypggzy748e2JzFf+Mc/UsZR4ronWHOuzKyKNB4zjuePhrOrevx+in/lnwz7SvVtCsbW3aX9HotPvzjhUTFmnDeHxq8tc5W8k5Voyu60BwzbFj14a89sFgWneo+7eueecNHbjy2Qmoik52iYNNaYeJTqiFfdBd/JoRIG7uWh7tXzH1Kf8s2Th48GAAvvzySzZu3Eh6ejrNmzdnzJgx1K1blx49etC6dWvuvffeCrH33/DsiCdYSAjr0nMZeBlMWLkRfDouj592737KgBbJ3NmwDT8f3MGu4mMYJROqxYxPd1Pkt2CWFEIiSpFMAbyKkRq5DpIjI1nqUkhvbiWwVOLx8BjaxiRWdFfPOZXWCaYNueOUbfY+vYm87TY0j4f0e+8r217zu29PaXui/E1RUdFJYe0Ar7zyCh9++CF5eXlUq3Z+pqDOBc/d+w1Gk8z7Pz3A6lX7GT16BrG9E9lTmk1St2waGHOpN6uQ0OYuHHX8PP5WCd1fuYWIiIiKNv2C4kJ4lnRd566HvyG5SQJb92Zy7VXN+OD1W9my7Qg3D/2UEQ/0YNPmI9w/vDsGw9kFuj+b+wRGo8yCaRsJjwzhsiv/P2LV7fJxd5/3uPaWDmXrf20vq0sgoCJJ/z8xJMkS7S6vf9brADz7xXzCQy00rR+PbDdwX9e2tKqdwL21UqgXtxe5+8s094lc1ijpL8/1T8nJySE2Npbly5eTmJhI3bqnd9inE1cAGDZsGMOGDeOJJ55g6NChAISEhODxeMrUrSoLUVFRfDRkIGv2p1Hk8rA9LQtBh54N65HncnG4sJDJKduxWQyYQkzoKJS4RcyShFswU+JXQIJ1+xpSPTKLm2z76ex08rvBxu7q8NoHnegQGY3NYKzorp5zKq0TPJdURh3DRfO2c8zhoV2jOuzank679nWI6BHHOmMOCZfnkBRSgiCI1Lw6H3vAz22p4UR1DGXGyFO/xKs4d/zbZ6nU4UVBo7DEw4jhV9GmedBp/L58D7l5Dlas3s/SpXsYfOtlRP6hhuCRQ7kUFThp1f7/VV1sIWYApn25guq1q53kBK02E/c81Yfk1jXLtt11PIr43+Dw+Fix7zCLDh1EFXRWpaUiTHybfjEbmZQfxbq9R/j63oH/+vxn47333mP48OFMnTqVkpISvvvuu9O2+7O4wh/xer0cPnyYhg0bArB161Z0Xefaa6+lT58+p5zrQlYfmjNjBl/9NJXezWfg8Pr4X48u3NWpLcM7taPPJ5OoYQ7FbJeJrWZDEXRUUyn7vQeJNKh4VZkIg8auvS0ojosiI3oZV2WX8Fp1Gx5fKbGhkUw+tJ3p+zOY32c4UeaLR/Wn0jrB072Rn0C0WM66H04uf2MwGMrC2kVR5JVXXkHX9XIdBf723XJ2LNvFyC8f/FvtN61Pxe3ysW7dAewRVpxhsDU6G6vFy30rNpF4rITYHsXEeN182zGZ6r2TmNHq+4u+ZNS/4UJ4ltxeP43qxJFf4mRfag7XAT6/QpHLywv/u46ruzbmsQd7YDafHF351Se/s2v7UaYvfhqAbZsOU7NODBGRNt7/+SHk04wa+w8OFrKdO2szbpefgYM74nJ6+Xbicq6/pT3xCWefKVA1DU3XEQWBl+7qyU/rd/DRinX0a9kYffo0mm3ajPtymU+3iCwcd/7SjrKzs/nuu+94/PHH+eyzz87Y7s/iCn9k2rRpZXmeEBSmBjCbzWiaVvbzCf5SfagCiZFUinduoIbNyMLH7z5p37ibrmH5gcN8unEDqaVFeCwBJHMAk9WISfYjiwqCKBBdLYecvHjSW4bSJK2Am5Ii8YWHELoplyE/pPDEHdeQ43ZUOcGLgbOVv2nfvvyTQjP2ZbFr7f7T/uGdjieeuZZ+t7TliQlzmO3PYPySbbSon8btc3bSZF4Rhb0l9rQMY0GeiRXpNh5Kvr/KAZ4nzsWzlLI7k127M5kwbgiNG8RT6vQSCCis25xKcsNgFO+fHSDAI09fg6M0WPjUUerh6Qe/4/pb2vPgyN7Y7OazXnPD2kMUF7kYOLgjmRlFzJq6gcbNEtGBXSkZ9DiDYswTX88hu9iBFCazLeMY17dugj3UyNbU3bydsglLrI8fsx289NJnxIadvxzUu+++m4yMDOrVq3dWdZ4bb7zxJHGFIUOGlI0ap06dyk8//QTAvn37GDs2WEqsW7duf+vv8ELihBbzN1N/4fIrr2baup2YDDKdGtXiujaNyXO60BWdKIOVyTffzINrpnHAmYPHbwYESk1eqkVnk5Femz22OBrHFlDbEGCLbiHc5CQxy8HDokSTyLiK7eg55pJ1ghcad702iLteG3TK9sKcElylHmrUDz54K5ftZf7sLbz0xs0cKiyh0O3FYQ0QZvFwx7SdNFpcRKC7guNBOJxn5fnX9mCu4eTHOzuWd5eq+Af06NaElsk1qBZtx+sLcOODn9Pv6ubM++7h07b/4psVZB4r4qVn+lPtuISaPdTCG+/fRu36sX/rmi+/eTO6HvzcoFE80xY8iT3UwqefLmHG9I2071CXELsZt9fPkdxiFm7ehznEwKajmVgjjBzOLUUywpKsVGzhJvrOXoTB7yOqZQmfzQ9neqfzGwhjNptZuHAhCxcu5MEHzzyDYrPZThJXOLEmCPDLL7+UfW7YsCFfffXV+TG2HGjbti1Go5EPvpvCdp+NfTn5aKqGiECD+GjyCpzUjYpkd3YeCSGhpBc7EJBABFlUUXUJIcwJwE5PfbQrFNx+jQNZx/BXt6MJULh5C57b/Fjki2dtsHK96lyCjH9sEk9e+xbvj57F+Bdn4nJ62ZKWTfdHPmZl1lFq3RagWZsDjN/4C40WF6Fd5adZTC62FJj4ajYBt8Jb739yUT20FyvVou0AmIwy3Ts1ZPHm/Wzac/S0bQVROO36Y9uO9Yg6fp4zUVjkov/NHzB/4Y6TFGjsoRYA7ryzMxM+u5uwMCufTFtF75Gfs2pXKtNW7aBGZBjdm9blqOoAEygmnTzFTeyRQ/Tct5+pNWvRbnIxx+p34Mv1W/7trfhbfPrpp3zxxRdMnDiRiRMnntdrVQbMZjN16jem+EgqjWJj+P6RW6kWGoLPH+DeCdN5efoSnuzSiRibjenbUhjRsgsRJhten4woChT7TRjsboxGLw5XKKouIqiQlldEmi9AXnUrDdIL6LXoA/yaWtHdPWdUOcELnCHP9Gfkx3dhMMrs3J1BgdNDSbSIX9dYULCFavZtXFHtEGIvL9oAH01i8jmWYOWlQ0nsX5hOt3tu4d4rK05cuop/jiAItGiSSHahk1e/WIR6mpp+w+7owouj/t7v1ecLMPXnDZSUuAGwWAy0aVWT6mdY+7NaTdStG8OMVTvJLHXgNKso6Cx98376t2vKjkAeAVHDYBCQ7QK6rLAv0sT6Di34rVN73I0v5+vXXuZ/V3X99zfhb6DrOoIgIIri8fSOKmJqN0aSTcxYuZ2ECDsLXhhGocNNUYkHXdd5fsoiskudbEk/Rs+kBuR7XSiKiDcgoWsCZtnP9ddPpknjbbQ4lMOouVsIMYh4fC6y69modaSYcJOALFw8rqNCp0PHjBlD7dq1ufXWWyvSjArn8N4sDCYDibVPDZ6o3yIYxde+RzNGPjeFSRu3E9UgjIyoI9yRuIraK92k9wkhzuairsHJsSgbs1rWYEP/VViSqvHr+G/KuTdV/BOWbNjPR1NWMvGFW4gO///1s75dmpJ6rIC6iVF0f/hjHrqpM7dc1QqvL8CM33dQWOTk8JF8nrznauJjzlzNHWDfvmwmfLqEsDALPXs1w2I28uKz/QF4eeICasSG065pEs9+PI/n7+5Jh+TgM7f5QAZpecV0aVuXH/bu5NYrWpLlcRATbsHql/BqARRZRdA0okUnA+osYUVIKKb77qJv6xbn76Yd57777uOuu+4C4KGHHjrv16sMLJn5PWt3H2HzvgwCSvDlafyw/uxOz2FfTj7v/rqC+69qR53YSOKtoVS3hJPpKcLrMxNnU/HrBjwYsRCgOMSCpOskSALpHg/pzeMJuCS6R0QhVsKI+jNx3pxgamoqr7/+OiUlJUybNu2UhNXExESaNWuG0+k8XyZUGl55YBKRMaG8O+Xsf8jDH+rOwLe+x+txcVf4BvqMzkJy6XTtmk70aieOKAMLr6jFHlcC0dca+fLO1zAaq6ZBL1RmLNvBZ7PWEGo3M/CdyXxy/w00Sowp2//Tuh14/H461kuiYVJw+5TFW/l4ykpEFWySgWKH5y+dYPPmNfj8i2Eoks7w16fw2v19iI0KxR9QmLt2N6IosCsth5wiB/LxXMFjJQ46tatDRG4In63bBKrGrTN+IiLcwqa8dJA0BLNOs6M5DF2ylYhOTnQ0NhQbMIZ7cPh92I3nt1LEihUr+P7778/rNSobsizx7fxNbNmfwcKN+7DbLXw/ahAdG9UkKtTKwYzG7DqWwxfrNjFm3QqaJFXjmLcYl1+myKdTTRTIzotlXUoz9NaLGMgeGqsK+wMCW1tGorasTr+Iv7fmXFk4b07wr6qBd+jQgeLiYkpLSy/ZkWBxgZP1S/fw9Hu3YbYGnZWu66iKhmyQUBSVnEIHA16YxC3dW9Lv6mSE2m7ejppG/Ve8SBad0HEe3PECB3vaSDWHsbKkLntLY5nz1oc0i7pwtUELCgqIioqqaDMqDI8vwBs/LUFUodDgx2Y2ked0UTsQYNvRbNrUqk7Legms3ptGbHwYLesHRdGb1I4jLiaUJ2/rTtfW9U457+pNh2hcL47I8JND2OvWjeHDqSvYkprJyu2H6XVZQ35Zu4swuwWvonJ33w7cfGULjCHBCNRPlq9j6uYUdAtUC7eQrbo45Csk3mcFo4ogQKjPw9MzVyGKGh2Vw7y0XiWzaAX1H09G4vyPFFatWsWYMWMICwu+BJwtOOZSYtusD8n1yFz1wEgUXS+Lct18KJNftuxBEyEh1k7DhFiKPR7qWKMQTH4EqRi/ZsRq8JOXF09qcRJFNhOtPF5+NdswqKX4BCtGv7eCe3huKbfp0D8nrI4YMYIjR46wbt2607a/kJNSzxVLftnCF2PnM2HuE8TXjMLl8PLN+IWsXbKHrxY9yaBbPiahVSweg8be/Bz2H5nGC8pG6r/hRaytEjPMgexWOaZaKQozMzO7KTMeXEbStdfRrNeF6wAhKFn19NNPV7QZFYbL50eXBVo0SODG7i3QBJ37vptF3WqRpGYXMvaW3nx0/w28P3cVe4/lUuB0ExVipV2TJFo3qsGmfRmnOMGCIhdPvzmTwf3b8eCQYI3IL+dvYMaqncx4+U48uopgFeneth7Ld6by7vQVTHx8AG0bBNVRpm9M4cXvZ9KkTixbC7ORq4k0qRbDNkcmuuRHNEKB5kOUQRZU/vfTKsJdXrhGIU80MG5FCdFPdcJgkDDI5z8d56mnnir7XBkFL84XFpNMceoREiPDeOTmLtw79mc6t6jDTd2as3THQdKKi7k6uQHzMw9wxFGEHqJijXZjUmwk2AsJtxdjMPjILogjJyKE7oUONvZoioqRm1/fTqiWyh2POnmuZU8ahlX+UWG5OcHTJazWqlWLWrVqnbb9hZyUeq647vbLadqmFjXrxzLmiR/Yve0o9z93HbYQMwaDxNU9klngzyDgh5Jmc2lrPkbDyDwMl8nE9i8hdKsPT4LEwbqRLClpyLIJByjZkcFjL3Sr6K79JbNnz+bQoUOEhoYiCAJvvfVWRZtUrrw693cG9GnJ8CvasSXjGFfUqcnl9ZLondyAbVnHaF4zDlEUqJcQxaSVm7nj4ynM+19w/UsShdOuyURF2PjgpYHU+0O1CZNJIrvUwbzNe+jfsSnxUaFE2q30adeI2rGR6Aa465tp2MwmXuvXg7VHjvJb6iE0o45PU9jnzMEaEcCnKoiSiCSohJh8DJqRQquD2czvX4cnLKu4b5UBJS6GuSNeoFlU9XK5hxkZGWzbto23336bjz/+mK5dz28gTmWhaXIzVq1eze/r9vLIzV2wWYyYjTJz1u5mU0o634+6jWk7d9HYEk2mUoJf09AVAU2GIl8INWwlhEUUUlQUSUqrakRE25EFHUUHV4yBGmtySCstoNTvq+iunhPOmxM879XAKxF71h9A13Tmf7UUe6SN+8YGy8oYjTKNWgTlsa66vjXJbWvT6eqmRDesRteRn9C8VXX25ubRouV+7s3eSI06Lmr1MkB4CfJWH0cSQ1ncMYmlRY3Zsg4O/LiNIUOGMGzA+ZGpOpdMnjy57O39Uovsm7Z5Jwv2HgQJVmUeZV9eAUnRYYy9phclLg9Td+xi3p79vNW/NwsOHiQ2yk6uy8WIyXMZe2sfRt1xNdIfErk1TafU48UgS7RpFnye8h0u3p6/Aq/Xj9+s41VUvtu4jfgwO4IgUOz28NnmjZQ4vGxIzUCXYDRX8Uivjog7Yc7h3XgUP0qIArqA0aAiiCoGUSFc99L6UDaLu9VixtXJLN4gs+j32VS7v1+5OUCAtWvXEhMTXCu9WGeL/g2XX9aeCZ98xKFDB9l3JIdnhlyNQZawmAzUiAmnSc1Y5F27cfh9KA6d+KQQjrlVwkK8CAIU+y3Yw4o5mlqfA7FRiPE66dlFpJUESKoVRvMl+Uyu352IiIsjaf68OcELvRp4efLBQ1+iqRr1255e4HfiB78hGyTueuBKANYfTMfp9rMqcy/9am9i6Ec7ER0C1X80IG52Iu7zktfAzPdNktlfHEtqcSip7/9MZEQk48aNK8+u/Wt27NjBd999hyiKDBo0iJo1a/71QRcJz81bjG7Q4XjRhsQIO0eKi7hv9iySQsMw22T6N2pEvtPFwbwC3hl8LbdO+JFle1Px+hUGjPuetnUSef3WXji8Pi5/9VPQoUZkGPOfDI4W9x7LY+62vSDAJ8Oup23d6syatAebMbjm98Kvi1mYehCDUcQQKuIRVVILCnh393LWH8vAbPcTZRARZTdGWUcUFHQd7LIfLwbGPdmBELxIgsLeHC9SdCi3DxpclrZQHsjHyzOVlJSQnZ1dLtesDLRq1QoAX0EmiXER3DF6MlazgXYNk3hwYFDA4NlrulPs8fDwnLkoZpVj3mIUTcQs+wlgJCw6jxiXDY/PQLxWisHnp8Dh5mh8sKTX2F8+ZG2TOqy7dhRyJVPW+TNVijHlwPM/PU76oVwWztrK3U/2P2V/bnYJBoOMqmnc8skPbC/JJeTqQh4uXEXHMTlINoh8y4MWIlIcLVFkCWF1k0T258ewJrMernU78B/JZMLPP1eaYJM5c+bw888/A/DAAw/Qv/+p9+VipMTrRZeD/k8zQ2S0Fb9LRQvoFCle3MUBfJoKJgGXGGDOg3dglCSWPH0PUTYrc3ftRTXoOHQ/h/ILmb5lJ4quowugScER9fiVa6gfHcWNlzXFI6o8t2ox3Y7WZkthNnVrRaJqGi/3vpJVk9Lw6Sr1G4RRort4cud0cj3FyBYJq8mHUfYTbvahoxMue+j+SxoxWR6m31+PKMnNOxt+Y3yDy5nZtzmmLu2Zk7OHER4HCdbQs9+Ec8TQoUN57733uP/++/nf//5XLtesDDRq1Ije115PmsvO8+/P5YlB3VizPZXpS7Zx3RVNWbXzML0ua4QsSZT6fBx2FpBki8SpeQloEp6AQI2ETBrVPoRBVLhjzk6s8VGsF4w4411oAsRnFqE3UQlo6sXtBN9//31SUlKYOHEir776Ki+88EJ52XVRUb1eHFmZxaRsOoyjxI3NbubQ/myaNA8GJNx4dyeMBpl8l4tdublIcX4e2riWTr/k4G8Iif9zY5QU9gbMaA1kDviqsb0knm05iegaRDXtyE/LHqpUayIej4ejR4NqKC6Xq4KtKT/eWr4SVdLRjSBLAisyj4AAggyiQFCJwwSTdm9D1+Dddavp26AhNzRqQlyYne1Z2WT6HRxJK8W6zki76tXRrKCJOhmCk45ffoaj1A8mnZYJ8axJTQcdthVlodpUph7YxdOdu1ItJISkWlb2uTPZFygKVhe3O4k36dhkPwZJxSwpKLpEpMfJtZ+lUn9rEbu6RWISFR7duwG3bGCVbsfjFzEZZb7tNKzcHCDA3Llz+eKLLzAajSxYsKDcrnuhI8syP0/5kf4Pf87h9Hy6tKxDl5Z1eHhgV1bvPMwHU1cyJ2UfXkVhzqiheJUACzL28Oy2aaiYiDR7cGtmbKoXKzp5YVZalrogzEgxCutujmd/TDS9qzdg/KbVDGvelhjr+dOIPd+c1QkeOnSorK6Ww+EoF4MuVtpd0YipG0cD8PO3q/nioyVM/OkBatapxkvj51EUo3I41kFU21w61zhMnfQC9F4BYm7xELrChS7B/muTOBSIZJurJvuyqlHqNiEc8JDyauWrEj969Gg+/PBDgLLaexc7fkXhh9070I06sixyV7tWTNi+AXQdUZSIt9m5tXEz3lm3CjQBSRLxKgpT9+xi7oG9zL/tTkSLSMCuo2gaLZLiuKl5Ml3r1WLS1q18vHUDLpef6hF2MtwOVmUfoW58BH3q1uOjHWvBIlIzysanBxcRIpvIIwujQcEkq4QYvRglDV3XiTR7qGZy4glINFmfQ8fvczA5VNYNjWV3r2j67D1I3ZIi3mvQlnnDZxDXryN17uhH08iEcr2fW7duLRsNrlmzht69e5fr9S9kQqwmhvRJxq0Ev+L3p+Uy7bdtPDb4Cqa8cgfZDicun58ij4d5e/dzXZMGGLbJSHgp8pmJMjlZubwnFslPn6hUWhzKwRAZSYnHz5b+iej+2thEC5/t2EibuOr0rPXXtScvVM7qBAVBwOPxkJKSQlZWVnnZdNHTo29LwiNDSKodDcCQuzvy3Jol1NOP0iewB7vFhTpMIeJoCXGL/WghAoeutrLU3QBVg9RjkThcFhyz1uNasIg9t91O48aN/+KqFxYLFy7k7bffBmD8+PE8/vjjFWtQOfDRlnXoRh0EUCw+Jh5YByYddAFN8pOhFvBeynIMITIx5hCuSWzI5zs3YJRF3H6VG+Z+T58aDVANwejqd7evYn3RUZZlHMYZ8HNf+zZM2LEBl81FSIiKouika1l8dfQY8dXM+CnCIRTxW84BrEaFGLsRW8BAdUs2NoOfaiYXFtGHWfRTqlipKbnoPDkHd6TMqmdjKa5jpUZhMf0P7GNzXDzfb3ChugMMGTCAMX0eKff72aRJE5544gkeffRRSkpKyv36FzIffvghjz32GPn5+QDsP5LLwtV7GNqvA3WrR1OX4HfPxPWbGLt0JXUjI5BFE06fTmyIGx0RyRCgsDCK3DpWjKpGXZuJgGzA7zUQcjiF1THFbLz9BapZK3dZpbM6wZEjR/LJJ5/w3XffMWbMmPKy6aLhz0ECazccYsr0Dbz+4o307NuCJ3+cx9wj+1DjXfRx72bYD9tR4wVSxlm5bHs29p0BXPESq7vEsleIxe2UOFwSTWFGBGp2ESXzF9Djyitp1KhRBfbyn/PUU0+xZs0ajhw5gq7rHDx48D85wXnz5rFp0ybq1q3L7bfffu4MPYcomsb7W9aCqCOYNJBBQ0eQdYySgF/VQRPQRBWLKFKiORmc3IIvD68joKuIJp1iXWVB9i4+6X0dy44eZsahFH5J34ko6oRYLEzJWYctzoNXFVBUGcmgEWLxYTd4sUgqAQRkSSfS4CTU6CNCdmESFYyCSpjuwbJXo8ZmB+F7/OwYE0KIRWXf6xbccTJ2yYNXMZDoKaXYYubbJsnsf3URUc1rUadFkwq5p/379ycyMpJJkybxySefVIgNFyoNGjQAYOfOnXTr1o2+VySzZN1+vv1lPaOG9yxrZzMYEHQYu2EFNpMFt+gloAmUKmZsocVkp9fiqDUcgFssIuvjwglbW8pN7+9j4etNKr0DhL9wgjt27ODNN98E4Ndffy2bGq3ir0k7kscDw7/msRG96dWnOQCHM/LZmJrFT/M28/HuzbhVBbG6m7u2baLfgv246wmkPWuipqEIJaCT39DIsjY1OOKPZHpaC3wBE86UCEJyBTLmTcdmNvP5559XukThhx9+mJYtW9K5c2dEUSQu7uyh1n8lwTd48GC6detWNr16ITLjQAqIOsgaglEHUUOSdQRBB1HBqOsE/CIGGQKSF9C5bskETEbwq1qw5JGqUxzw8tjmKcRbQ5DCnIiahMXiAzwYZQWTAKoqAD7CTB7CzD40JCQCJMilCKWQWFREdnQYBrtK0g4HzaflYUnVkPygy+BtpVPPlYfTbEGsqaFpVgK6hFFQ2F09muVRtdmzNBtXloNJH3zNTfWuqpB72rZtWyAovvHII+U/Er2QOSFM8t3388ryrZPiwokIs57UbuXeI0TLZpKiwtiVfoyGCRF49QC67iM8rBiAVD2BXzqWkhttRdQ1ShKDbqNNcXn15vxyVie4fPly+vbtC8DKlSvp06dPuRh1MRAeYSO5eQ3efHc+tjALmU4HH89cTZNGcRAm4TD4EGJcjF78Oy1ScsntbsQytISGxlIcosDClrXZ74/D6veyorA+LtWC52Ao1myZwm2rKE7dz8SJEyvli0nNmjWZNWtWWVrEJ598clbJq7+S4Bs0aBBvv/32vx5N5ox5E819cnCOuXlzIm6+GYDsV15BDwRO2m9t146wfv3QNY3sl1465Zy2Tp0J7d0LzeMh+/XXyTywg8cBQdAQRJ0NTRLY2Kg6YT4fQ+ZvRxR0RFEPOjBdYE3LGmyrH081j5NBC1OQABEhmFOpafzWri7+ZBvV8x30//kAoq4jaxqSqmHUVNbeWJ3sJjZq7i/h2k8OYfIoGLz/n4955FUDWoJKpMWPRRKgr4o7WcfbHEx2BRsgaAJeZGRdo0lKIflmN4sT6mGUNfbMTiU0PrzConpLSkrK5NKqOJWEhATMZhsrVm4o2/bEnVeyaNUefl2xmz5dg6P3Zgmx7DmWy2/7DhEWaSTNlY/FpBBm1sEWlEc7VhLLvoZ5FLkDLNp1mGbVEwgYBJRDqRXSt3PNWZ1gXl4eS5YsQRAEjh07Vl42VXry8h2MemU6g25oR+2GsUhmiXc+W4rZIPO/e3syJ2831uQi4kMchKz2kfOASFTrQhovduANE1nYK5EUb3VyA2GsTK2DpohoWRasxRJxsXYa1YvCEdqHYcOGVXRX/zUnIkMB0tLS/tGxf5bge+uttygpKWH9+vX06tXrlPZ/JcHnWrcOtajopG3CH8SfnStXoftOVseQwsODH3Qd5/IVp5zTkBAMEtEVhfylS2l7Qm9R0BGAzAQ7O4wKIf4AbQ4G/7Z0QeBE8uChuuFYjZHYdC9NU/OC2wUBgj6SlJYxFBjMmDSN6gWlIIEiiiCBYNCxCx4MVj8R1VwUtTagWo3IdgVbuAdDpEK1Bi4kSUdsq2Npr+JHwAZYdR2DAAEdfIKArIvUPVhMi+0F7Kml44mTCWgy7V/pyUDDNWW5euXNG2+8QWRkZFVqxBkQBIFWrZojihoD7/kMV0Dh+l4t2LI3g4CiljnBxvExJEaEcVfrtogmnbf3zSegqmiajsnqpW693YSGOokucZNU5GKuquFTPBQlmCndtw+f6sckVW6R/rM+wR988AGTJ08GgukSVZwZTdP54otl7M/M53BaPvl5DjbvPMr/Hu2D1x/AnmAjV3Nz/aLJdMzeRrOmAjUiCvG85KXOLgeNl5RSGmlgQ5cYMgJRHHRFk+ez43GZEf0StaQI1G35VI/V+Oqbz1EUpdJNg/4RSZJ44YUXEAShTEbv7/JnCb5Ro0adtf1fSfDV+WXWWY+v99uiM+4TJIn6K5afcb9kt9P/oWuCnkvQCLe70QUBAbBICs4IAyNf6YWADoKGJICiBeXJjGiUJlh56bWuiALIooIsaoCITXITIbjx1dL5ZkwzTJKKiI5NdBNh9KEDZgIYavo5+IidOEMxBkEjQXYioeNGJAQNUdAxCRAtSPjRUHRwH3eEVjSqHXTRYJ2TI/GhLGlbEz0ggQ5eyUrNRk3Pet/OJ4qi8O2337J58+aytfcTeadVBBkxYgSKonDomJ2cIic1q0cy5KYO/FGgqXvDOnRvWAcAVdf4NHUpquTimDuMRuE5NG+9kXCjmyY782h3IJt36tbE4Q1Qkmiixl5PmQNUNe0kFaPKxFmd4NGjRykpKcHn8zF+/PhLJpT9n+Lx+Fn6+25mz9lK4xaJuHwBomqH06FzMGx449EMss0eUDQGLVvLgI0pbO4eg/Soi86rsok/6iGtVghL29VkUWkTrKKHDGc4eUV2JK+IfaeI4HRRO7aIhvWDMlEV9QZ+rnjrrbfYs2cPwF9GtlZmCb59RdnIsoqmiUiSH6NBBQHQBSRRxSj6QRAxSjqaLhBQQRJBlgVE3Y9ZVjBJGioSZtGPiI6Iit3oQ9cFLJIPUdBQNBmb5CNE9mEQVMJEF+EGH+g6BvwkSsWEihoCYBdFqosCuiYjiQKKriEIApIuIAhgRqdUg6gdfmpu8XIs3sqiy2vhwoQ7z8X0+1fQfdRQarevOPHkd999l7vvvpukpKBMnN1urzBbLlQGDBjArl2ZJCfrJCcn/mV7SRD5ttOdPLb1M4r8JYiijlc1UOIIIyc0BEnTiTKIOH1+dvaJRbm2Az9tmYKv2MKqtHTW3HEfJqnyfS+d1eL33nuPESNGYDAYysueSsnS33fz7tvz6dS7CUUoVE+uRmphES/9vAjHjPm0b5WEbHfxzG+LaXk4hwNXh6I+4MUkKkh+nd+bJ7GqfhI5njDcqoEDudEUHI0GD3SNTEJsoOPen85PX7/PtddeC9xd0V3+z8yePZvJkydTWlqKyWRi1qxZZ2xbmSX4blj2KbJBQMRPpN2LgI5RUjGIGqIQdEo6GooGFlnHLGkYRFA1HVnSMYoqAjrhkhtNF1CQsEteRAEkUcUu+9F0gTCjgxDRi1FQsEoKIYIbFQmb4CXO4MYkqISLAgYkEEBExCQF/66Ngo6ia2hoqAQnZF2aAYsWIKOWhcUdauHSjaR7Itg0ZRfFRxyMvfIRYs2nr0xfXsyYMaNsWr1GjRpVL+l/IhAI8OJLEzGZQvlh8oiztv1h63ZqRIYydM0PxIeYsdnzCagyWYcasHdnSxr1CgrcJ6Kx1eunoKmFejVvZvzmn7gitC3X1WuMUTz/lUPOB2d1gsnJySQnJ5eXLZWSPfuPsTc9j5CkMLYeySbX5cYXAqpVQFN0BLvAwfSVfDx1KeFOL0vvTSS+ZSE2DdyCmfndkjjkj8cdMLKzKA6Pz0j+0ShEn4CpSCArv4i5r91Fhw4dCA0NvWhCwX/99VeaNGnCM888w3vvvVfR5pwXXH4voqih6SJmo4ok6IiChixoCAJI6MhS0BH6AVEQkUUVURAINfgwihqgIgrB6UlB0LCIfsxSAAMaGmCT/Mj4CZN8GESVUNGDiI5V9KMjECm4sKBhFkRkgiM9CwYkQUInOC+moaPpKl5NRT6sIEgChQmhZCaH4dNlcn0WMgLRlLhl9sw4SL9+/ahX79RahuVNUVEREydOBE4uq1RFkOzsbGZMf5k33ninbJvXG8BkksuWUoaM+4l9WXmUVFOpGxNJZLiZ/ICLBE0gzxdCiD24Vn5YTwC20UZTKKkWgVn1s3Lmq8TKoVzfphFtIoPPQ3pGIe98sJAnHupBrZrR5d7nf8NZneDSpUtZtmwZJpOpas79DIx8aSpur596dWL43yO9GfP9Yrbm5mAONVAY4kKLVTHafRTEWdhxZzhNA9lcseIoe+pHsahVbVYV1CHbF47XJ+PwmaDYjOQWkR0C9eyRjLy2Ky+++CLbtm1j1qxZxMZW/vpdEBzduVwuNmzYwL59+yranPNCryVjMMoKggA2QwCb7AWE445ORxIBXQ8WqDUGtwGIgoZRVEEHq+hHEwRCRA9mKZhSYRBUBF3HICqYRA0DKrKoI6JjFFRkQcUsqISKPsLEoAMUAFmQMAgiIsG1GwEBFQ1FVfAf9hK5I4AxT6Mg0YAeLyIIAm7NTKkWSkCTSV1wCF+xlyeeeKLC7ukfKS4uZtKkSQiCQEFBwRnbnS6lBoLrw3v27CEiIoIXX3yR+Ph47r//fmRZpk6dOowcObK8unJeqF69OjabjaysNL7+bhUdO9RjxAtTuOm6Ngy7vTPTft9OSamHKLuVzk3jmb1vLwNrN2G5IwWfUkqoQSMktBSALGccpRYjXd0ecpvWxe8XuOK9LTS8qTOtI4KFAXZkZSO7NLJzSnB7/BXZ9X/EWZ3gnDlzztuFK0OC81+RkVWE0+mlZXINBt7VEcUELbvVYumWTGSbgyG7trOrZSRGm86yJ6vz4K4NNMgqYm/NCOYn1+WoO5JcXyiaAsUZoQQEEXueGVOBRmJYKLOfHcrGjRt56623uOeeey4akekjR47w8MMPI0kS3333Hffdd19Fm3Re8OPBZtQwy37Mso50fARoFFRENHRBQBaDwSkaAiYxgK4LWCUfIipWg1I2ehQFHVnQQdcQ0TBJKjIqMn5CJD8WwY9JVJEJYEbBiIIo6AhlQaUCEgKqriEKIqqu4tcVpH0+LBv92Dw6/hCBA51sZNSxkhMIxaFb8GkSRX4rTsXAjsn7CKufeMFo1H722WcsWrQIXddPmS7/I39OqTnhBGVZxmg0YjAYCA8PZ9WqVSQnJ/PII48wZMgQ/H4/RmPljXwURZG6deuxcsUGdh1KxGo1cl3vFrRpUROfX2H2kh1YdIHZbwxjc1Ymsw7s4UBJAfkBD2bNjFHyEGd3IggaRSWRTLuiER6zAVH1ogoGSmJN5O07wOu7f+Tmaj0Z8PWPPHP1FUyZdH9Fd/0fcVYn+OabbzJz5kxsNhv5+fns2LHjb5/4Ykhw/iviYsN46K5utGxdk36TJoMo4AtRiBJLeWHK7zQ8WsispLq4Whp4YvMaIpxeFraow6wajdmTH49fFdE0kdyD0Qh+CaNT55HL2vHj9M3c168dAK1bt+b999/nrrvuqtjOnkPee+89wsLCeOaZZ7j//sr1B/N36bfsRUKMCpKkIQogomI6PgKUBRWE4DhMFlQENCySCohIBLU8BU44Pq3sGIHglKpJVBAAg6BgEYMjTUkIRtjKgo5B1DERnGYVAzpSkYLk0hEcfuQSDbFAxXelBU84mCXwxYjk1TNwrLoJr2DEp8kEkPDrMnn+ULL9YaiKRtjAHnSv3fSCiUo2GAzH18jPzp9Tak7w7LPPIoois2fP5osvvqBatWplebcxMTEUFBQQHx9/0rn+Kt3mQsNmrcbWrZvYMuV2GjaIRxSDv7v8IidHMwt5aFAX1uxLIzLEwrrh97Ewcx/bth9G0UT8ioAo6thDSnGUhlEYakZEY+bmbJKiQumSYKZ6hoPwyPrUi47kwa4duK5pwwru8T/nrE4wOzub3r178/LLL/Puu+/+oxOf7wTnikbTdH5asY0v92/Hv38TAYuOKGq0VNL438+rMPsUptzbGGMPD3F+Nx7ZwM+dm7HZFkNKfg1EUcftM+BwGhFKBCQVZJfI/JV78ZX6Wb3xEJ2bVScqKuqiU8NISkpi8uTJZGZmloW3f/XVVxVn0OxHwFt68raal0OH4yPU6cNB/dP0Tt3u0GYoaBpMO/UF5TK1kCVx9bBofv63dwXS8alOgeD05+aEWLYnJGD3+xm8czvHywsfj/6ELbXi2RcfRbjHzTVbDyLqwfxCUdeR0NjSMJb86hbsxX6uWJuJpGnIqo6sasgBjSNdzbhqySjZGhGL/j/HUbUKKBEC3oCKS5UoqGXEW9NEQJBI80ehI+DXJbL8kRQErCiahD8gkO8Np+GVjXi18x3n6KaXH39OqTmBeDykPyYmhpSUFFq1asX27duBYI706cqS/VW6zYVGj56dWLtuMd/+sJLIqDCeeiwoMh4dEcKiiQ9hNMhc8eIEmtaI45XBPVmXlhEUdBBE7CaVXH8oyc02EW5xEunw0CStgLlGMy6fj6IEC7W3lNIluiXLj6bxwZZ1NI6vRq96lUtM+6xOMDo6Gk3TePXVV//RKPB0nOsE54ok7Vght7zxPQ5ZQQgV8Jo0iPXQ/cghHp26nqJoM1OeTqa74SApajVcJiOjL+tGQDewpzgGTZdwHLPic5sQ3WAplrBZDBgDOs/eeRUWUWbF4pk0btyYdevWUadOnYru8jnlySefZNCgQVSvXn5VyM9K/gFwF568LTzp/z/n7QXl5GR5Yk6kdeiQu+ekXdneAqJiaiGgYxX81HQVB52ccDw7AkgNRCCKOrKuEuN0/WF/MFneFAggCICqY/f4g0nyx9sgCAgaKIioiHgsMpoEyKBLoBt0Sqwykq7jjJJI72lCsAl4QwSMJgGPBg49qBnp1I2omkihZkPRBVREcgJ2PJoBnyaT5owk64Ab18oD9BwylFohF069yuzsbJYvX47vuJDBHXec3kHfeOONJ6XUDBkyhO+++4433niD9PR08vPz+eCDD4iLi+OHH37gscceo0WLFpV6KvQEQ4bcTpcundm5VyE8/ORyR0ZD8Ov/64duJsRsYvnRw8zbfxBCjLzZ+VoWFc2iJOAhPOEYNczF2PP9XLY7i7imtTjkVfDWkBA1ndR9G2lQK5nHO3akY42k05lxQSPo+h9TJ0/P1q1bqVev3r/KxRkwYADTpk3ju+++IyIigr59+3Lrrbfy008//e1zjB49+oJ569pxMJPnP/+VAjxkxfnQZR0hREEODZBUVMSwlVtIu8vCzXv2Ui+3iBkdGrA4ph67ixKC00yqgNNlQskJRSwFQ6FI/cgIsg8UgU+jU+1EOjYPZfCQ67n66quZM2dO2RtrFf+d8niWei17lBNhJ+FGLwYJJEEpW9eTUJFEHYMYLF0kChoSBHVEAQktOGJEAyHoFg2oGCUV9GDahK6LiIKGQQggCRomQcUuujGiYhF9QZk1UUfSgwLZRlELrilqOqW6CRHw6jKFWggu1UgAI6qmUxAIwa2byfWFkOOx41YNbH9zKSUr9pKTmXVBFW0ePHgwgwYNKkvhOt3L9PnkQvpeOhtut4/JP6yl33WtiI09s9ScruvsKchjbuYOvk5dTfWwIsLNHuJkJ67iMJqH7OeJRRv5X+Mk5nvhmpp1cBeE0LBpT2bt8yEg8lv/e8qxZ+eGM44En3766bIR2L9RZKjMCc5n4773Z+D3Biiqr6GbIFYpoteaAyy6oTYJicUcrBPOwxvXEer1MbNlfb6wtsdTaKLQY0XTBJwOM0KhkTjVhuIIoIgaRQeKwadhc2psXprCpAlfEBMTw6RJk6ocYCVjb+Hh44EoweK0sqQTnObUMAgnHJ6OgH787wpAQENH1AV0dLSgSiiCICLowelRVQC/KqAh4FU1rJICgoCqS+hIGFBxahY0BGyYkAWNEN2LhhE0DVlV8QkmDPjRBAOaBh7dgIqEjohfE0n3RZDns+PTDQRUKA5Y8eT7KF62mwG333ZBOUCAdu3alWkbV3Fmvv12ChO/WEPtWtXO6gQFQaBBZDQ/LtuOySggiToexUipK4wVv1+LrXsAv7yZ2n4/YMRj9uNNkskLFJDmczGodtvy69Q55IxOcOzYsWzatIl27dr9qxNX5gTnM/HGvN8piPbjD9dB1mmTeZRRvy5HQCe7r4WWeVkM37oZt9HAJ13as8GcQHZhOKomoGkCbpcJIU/CUCrgLPVgydNomhDNO2/dwLRpG5g2dT2atIaSknzmzFlBdHTlyLP5N3z++ecsXrwYQRDo3r37RRMgMzLlvbL1P4OkHf+sI4s6gqAFkxOOO0kdAV0HXQ+uCIqCjq5rCIJ0XNpKQz7+DqSqOpogYRKCzs+n6hg0BYkABlGkVDdjl7wYRQWPpmMSFEp0E4ouEcCAhIZR1ChVwpBQcWoGPJoVoxCgIGDDqZkpDVhQkAhoAsfcEXgDAukzdqIFFF4d9VwF3M2zM336dH777TesVmtVCtdZePPNF2nTpj2XX16fux79hv59WnB9n1anbZvrcnJNjcZ4dD/tY8OYkvkjtpCg7m1eaTRFdjNt3R46NKyGQRaptzIbp+CkfbuujGpVMdVE/itndIKCILB06dJ/7QQvNn7YsI1vNm9DCQFRUrl930Zu+W0XxxJC+OmhJphr+Am4BA5Vi+DDppeRqYdyOD8avyKj6+A9ZsZYaEQWRK5KrMWKbakk14vHU+RFCagUFLuxR1kwquGMGzeOjh07VnSXzys7d+4s+9KqrMFRp0MDTGIwIlTQNCRJC6Y1HA+oFAXQdFAREPXgFGhQ9VNA1HRUXSSYSyigahJ+Pegwg4qhAoomYhAUdCR8goymmwiV/YBOQLEFr6tr2CQ/CiISKhGyB0UX8WgCPt2IR5UpVc0YRR2HaqQgYAcBAppEgdeEU7Hi8cuUlogUL9zEtX37ltWnu5BYuXIlDocDqJJNOxsNGzYkLS0VHZ3UtHy+nbqeOYtTmPj27WXRogAzd+1m5KJfkUJAD/Ow3lGKSbIRFeLBZPJQWhpOsd1EnWIPjWIsOFWB5OX5BLRiNrf2csiRQ7OIylfV5qyBMbNnz2bJkiWEhoZe8m9aoxcvBVlAMPoZuW453TcdYV3r6qwcXJ0ezoPslGM5FBLN++1iOOa2c6Q0GlUDRRFRSmQknxGzKhKmmni45+Wk78hFLQ1wLLOY7LxSulxen1pJ0dwx6MJIRD7flJSUsHx5UHi6sLDwL1pXDu5e9zIg4td0rFIA4XjFCATQdAEQCOg6CCK6LqAeHwFKQnD9z6fLHC+YhKapKLpIQJWRUFGQUHWx7DwmScWIH1GEfL8RAwEQIMzgRddl3IoJq+BDFkXyAhIO1YyOgKqBTzfg1cx4VYmAJiGLCgUeGyoieR4bIgJOpwVDoULnTp156sknK+yeno3XXnutrAJJlWzamWnYsCFff/01VouRd18ewKSp65BkkROZLgFVZcgHU2jfMJEwsxm36kOU/GgaWEwKAV0mxF5KcWk4S66uiSYLFLtVnIqf0gQjiRtdHHO5mZm+BUPAjMvvp03iBRL09jc4qxNctWpVedlxQfPMooUoJg3sPuRIhU1t4imqZULpGGDcjvlYFIXU6qEUmcwU+q2klUbh9plQXSJakYk2sQlcU7MeH85aTVy8je9nbiAnz0HAGiCpdjRTZi1izcJv+PHHHy+YHKzzzfjx4/nxxx/LPl8MHPMHVUtMQiBY7UEQ0fSgDFpwelNEBdB1NB00ITjXqWk6Ad0QlFMD/JqALsgomowsgEcVg+uDQtBx6ggEAiKqbsAuBxAFFb8gYhB18vwyRsGPJIAHGxI6Hi0Y5SgKGgFNQlU1VEHEr0oouoRLMeIIBFWhdF2k2GEigjDyDH7e++YrWsbEn667FU5hYWGVbNrfoEGDBjidTjIzs6hdM5o9+7IYeuvlZOeVEh8ThigIRNut2KxmXB4/T3TpxC9F68nx5yCaFWRBw24vITc7Ac0gARobjxbjU6FtQgg2R4C2FoWnmvbh1sk/k+twsfahyiOAcVYn+OWXX/Lzzz+jKAqqqpalLFxKFLrd/HhgB10LD5CQVcrvV9akoI2RrtnFDNm0jUKLmbfadWEXsRSXWvApMmpAJ+CS0Xwi1hyRg+k5LIkBoywx4sYrmPHLZsKtZvr3asmEHxcyfclHRIbZLimh8qysLBwOBz6fj48++qjSv8U/tvFtIDjyk6UTWX8Cfl1E1IUyseyAFpz6PBH9qekCmi4BOi7NCCcco6ohSCLFARlVB1WX0PTgcZKoYpY0/JqETzUgCaAfn3KVBRXh+BSXSFBmzSQqiGiUBky4FBM6ApIIXkXCrwTP6/QYUFQDmqaj+Ez0sIXTrlntC9YBwv/LpgFnlU271GnYMJjAfsfgsbz6xsPM+PoBVmw8yIAHJ/Ll2NtpVDeOj+65HoCBHZoxYsV8ukQ0ZVZBFooejFBOrHeAVg23YfQG6LI9gy32MLZ5NVwJQReiHM1ERKAg4CEq3HomUy5IzuoEt23bRseOHRk9ejRvvfVWedl0QdHmm/e5e80Wbt6cwt5aUey8JoqHD62nR1oqKbHV+KJla9K1KESCkXuFRTZ8LhO6ImLMEZECAviherid8ff1o1p4CJO+XkmkyYSMwr6lXyBofubOXXqKOsXFzMVWoWS/5yjG47Jouq4jisF1dV0T0QUdvyqhEUxrEASBgBaMEQ06NghoQjD/T9fRdQGPZiIQEOC47qd+PElQ0QT8moRHEZFFBQmBYIJF8DiDpGKR/GjHEy1MYoAiX3C9zK/IiGLQ+XoVGUUT8PiN6Ij4AgZAIOCUkRBZ9eGX/JCWRv/U1As2QvmEbNqJz1Wcnssvv5x7736HjHSFF16YztdfD6d9i1oMG3g5tRJPjvgNM5uRBBFd1PGrBlwBPx7FQFxEPmEGBzrQ+HAhdRta2KhKHIsO/v1G5LlYnL2Bif2ux1TJyryd0dpRo0ZRVFSE3W7n+++/Z8uWLeVp1wXBQzO/5vVffqNV2jGWdUpi/qD6xIWWkhoTwTxTPeY3bES2x0qR10iJ14rDacbntEBAwFggICoSkg8GdWzBjF+3clXz+mzcmUau5iUu2sK7rz+J35XHvHnzaNGiRUV3t1y5mCqUzExfjq6DJoCoayCCTxUQjuvABB2jcNz5CUiCfnwEKBDQRAJa0GEZUPFpMqoe/LP0q1IwslgX0QFVPb6GKOoYZA2n34wsaBhkFUEHBB2vasSrSMH6gIJGgd+EQdIJqHJZNKrbbwSCI0e/34ASkNB1HV0TICBSvzTAohUreOeddy5YBzh+/HiysrI4kea8fPnyS+5Ffc+ePbz33ns0bdqU7t27n/E7xGaz8dmXI0k9lMvR9AJqHq/uMHRARxat3kPHlrUJs1tYdSiN+37+hZ/uHEizhDg8W/PZ7lhFQJfwKEbyMxqhRmXisuyhrs8HgpW8KI0JX7YgYK7JVXHtMFfCKvNndIK33347X331FXv37qVx48a89NJL5WlXhZNTUMBNYz8hyunmu9uToZHCraXbWB+ZyM74WFZHJZHvsOEKmCj0hBDwi/hcZvCBoUREdomYS3SMgsShvTnYvSK7d2aQVVhCWqkDze+mpLiIr776ip49e1Z0d8udi6lCySeHZiALArouIMsCOhKKqqMLEujaiaxARCHo0DQE5OOFjDRdQCMY8OLWZPyqiCiAqgXX5wJqMIDBFxCDa4sIqLqOoqmIIqi6iMctYZB0fIqIQVJRkTFKGiI6XsWAJxB0noGAHMxCFAXUgEwgYABBA01E9wsIbgPRFgvO3+cQGhrK8OHDK/bGnoVbbrmFwsJCQkKCKihOp7OCLfr3rFu3jk2bNrF7925uuOEGevTo8beO27JlC9WrV0cQBMxmMx9++CGBQIARI06tHThlyhRee+Vnbht8J926BdWOUjPyefmTX7n/ls7c0b89sXYbrZPimbhlE0/bO7O79CjFfgt2QzEqIivWXEWgxVqK7ato6PSC3UqJTyU+UmVYrYEomkZewEE1c+WK1D2jE0xOTua9994jEAgwbtw4rrjiCnJzc8vTtgrlskUf0aNLM1wNRa40pHJXymYywsLYmJRArj+U4oANFYFirxVnkQVNEcEnYsqTkDUBu0dCETUUr0qx10Od2tWYmrIbj6ZgUTRu6NmJn9+9/6KQZvo3nM8KJeWNdnzNj+PRl5oOGjK6fmK6U0AlqPup6kEn51IhoMvBSNDjU6ABTUZRRVQtGD0aUEFALDsPwnF3qsmomgFJCk6TSqKGXwmO8kSM+FUdPyDLSnD0qAsoqoiuSwiaRJwlgjR3KchQXY4ks8AFCPSt34AH6jSk2b2PMHLkSEJDQyv2xp6F+Ph4Jk6cWLaW/Oyzz/LGG29UsFX/jpkzZzJ27FgeeeQRunXrhqqqJwl9n4nU1NQyxZrc3Fw0TTutAwSYNm0aRSWbueHG/y9YUC+pGm+O7M+zn81FNIn8sCcFj0Fle2EOi517UQQfMZHHc1pFsIeUkl8SSUmoiQZHS+jZ2o7VKpO4oogd3zzBOwOv5UiJxIo+F2Y08Zk4oxP0+/1MmzaNmTNnkpCQwMKFC8vTrgpB8/vJeeMN3rOXYKkTSsaVVl46/DsdMjLZmJDA982bU+C34lBM6JpObokNt9+EpgW/aMwFMpIbQgQZ1aeiqxrtGiYy6o6r+WzBOpwbc8hcMpX2dapxW+/HLpr1sH/Df6lQciHR/fcRiIKITwODqKBqEugCIhoBXQhKYh+PDFV0PagdowfXW0RBR9UE/GpwXKhpYnCkqAWl0mRBwhsIpkToejC5PuhNg0KiflXDJMrYJRMFATe6JuD3CMhGFR3wuqzEmq3YjRYOOgvQFZFqViuOUg18MoITjukeBEVECoi4SwLs3LoVm83GY489VpG39S+5+eab2b17NykpKQCVus5mWFgYH3/8MS6XiwkTJtC3b19q1679l8ft3buX8ePHEx8fz8yZM7ntttsoKCg4rbJPgwYNmDlzJuvWH+RoeiE9ewSXIjq1rM3NV7WiTaMavL16NZZQI14xQM+EuuT5isiiFLdixCBqhIUWB3MFq5tJMLmoZzfgknSij3io/3sROcPjGFCj5bm+PeedMzrBQYMGMWDAAL7//ntMJlN52lQhBHJyyHz0MTzbt2Pv2YiIBnX5NGU2ie5SpjZuwqLa9VAQyfGE4VFkcopC0RQJxS0hOEQsRiMNjRGk+grpnlyb3zYeICkmgvceuZ6eL03E4fIi7FhI4d71tB3w/CXtAOG/VSi5kNCCWewACJqEKAI6eLXg71cEtOO5gZquoahCcJSoCSh6sGaEqomoGqiahCCAooCqGgiRDMSbzVQz23G6NfY5c0AH3c/xKFIJDwJupwIYwaAFnaNLpqYtnDStiByHlzzdj0E34keloMAHIhgFiYCqgQqyT+CyWom81PNKkiLC6du3LzabrULu599l6tSp7Nq1i6ZNmwKgqmoFW/TvefbZZ8s+5+bmEhMT87eOmzx5ctnnW2655axtGzRogKqqzJmzglatmtOzRzJ79h3D6fLx+K1XsGDjXupFRZHrdfHNLTcybOV0rqgRT5pXwqUYCDO4CbWXcCC3ETvrR7G7QST5Tj85rgBRCUYMfp1brM1JrFH5YhvOuOo9ffp0Bg0adEk4QNeGDRy+8SZ8Bw7w4dA2/N6/NiGhAdYk1eCdDpfza+0GuHUzbsWAqum4fCY0TUT1S6BImHwyzT3R5BU6aRpbjRVbU2lfPxGjJnDza99RKzqUjCU/smPlb7z44ou88sorFd3lCic6OhpJknjttddYt25dRZvzr7jyt2dQEdB0kI7n8KmagE8VgeCoTtFFFF0KJqkHDCiaAUWV0HSZgCrh9st4fDL+gIymSfgDEoGAAVURKHYHSCvwsDkjjz1FBYh+A5pHAp8MPoHrqyeDXyTJFg6aSDU5BFwSBETSiksxeUzgktBdAopb56kWXREUkUQpjDFdetI9tjaSS0AMCIzs2AmD1wNwwTvAE3z55Zdln5977sKTdfs3/F0H+E85kSahBIp46sk+AHz29XLe+WABpS4vz3+1gAahkbx+Yy+QBWqbolhbeBi3T8Zu9KMD9tBSAooRl9uGKOgcKvSz8ogDV0Jw6nbKyq+YkbH6vNh/PqlcsaznAe++fRy9626MNWrw9eNd6Vy6kmink9TYCJbWqQuCjl8RcCgmij0mMgsj0PwCmltGUCUkB5hyBPbqeaDBntI87riiFfuO5nIgtwDNIrB+xhSK9m7mjTfe4JlnnqnoLlc4s2fP5vnnn2fHjh0oisKjjz5a0Sb9K9yaiiyJqLpGsKZt0CHqCGgax6Mxg0EpfjXoFAUIrt8RHPlpmoiqHp8y1UUSzGGkexygixgECb+qBUsoacH0iCijjeKAF0mV+GX3fm5skMzBwgIGt27FF1s2Ifol0KFJTDVMskxKdg46EGI0clWN2ojtBT5atZ6a9ggah0azznMUQYOZq7by8eN3Mnz4cMaMGVNBd/Sf4Xa7yz5X5sCY8uCEE9yz9yBpaQU0aBDHc09ei8+vEGozM/nZ26geHUaIxcQPu7aTVlKMz6YSFipgl0IIoFC35j4aJu1DMnnosewIJeGhbNYNFFYL1mgUjqbyc/pibkzsVJFd/cdcmPHP5cCJ0GpTgwbEPvsMtSZ9xvVZM7ktfQdti7MwSjqqLpDvsZDtDiPXacPhM6N5RDSvATSRkFKJiAIZSRdAg0jZjNGp89O8zaw7moFqC97ehGad+Pjjj6sc4HEWLFgAwIQJE2jduvUFHYBxJn45sgFVE46LowcjPo/Xcj/u+IL/NC2YAxjQJBQtmCKhI+EPyAQCEoGAhK5LaKqEqkgcLXJCQEYPgOIHQRGCuRcBEPwiRSV+bH4TilfHIsisTUvHG1AocXp55YqrMQsyNtHA3sw8rq5Rl5CAgbe69WTr/Q/x8JS5rNmXxvYRD/HRr2uYu2Evd7Rrwewn7iA0bz8FBQWVqipDp06duPHGG7npppto27ZyVjAoLyIiIjh0KIMGDbpy9GhQWKBatJ3EhAgAGtaIIcQSnPW7vn4T5t0yhMsTkgioAiWKE1kIIBg0QqwuZFHD7gzQ2BGcOcg1q7iiZMI0lXEtH6qYDv4HLsmRoD8tjaxRzxD/6iuY6tUjsntTsr++jOY+D+826MS2urHIuk66OxxNE3AFLDh9Ev6AAV0TEFQBOU+AEh2/CqIGsg+i7RY8ggckkYCzGAoO8eT9j9D38qbERIT8pV2XCmlpacyfP7/sf4Brrrmmgq36Z7y8e9bxtIigCLauiwiCfnwkGIwY1fVgIrt2fJSoasF8PF9ABF1CVY+rwyigaxBpsFHg9wZHhZqIBqAJCCpEG22EGyzklboIBFREn4BgEHAFAnhLFb5I30x1m50r42vx275DWGSZzYczcbkCvDFvGTXsYdzcPJlmNWL5Zf0uqofY2ZaTwdSF27n/qva8P24cnTt3plOnyvMWP2TIEIYMGVLRZlQaYmOjadWqFgcO5dC1a0OMxlO//pcfOsy9U2ah2HX+17UzdouPw54ifKKBADLb9rQg2laMI/Rw0AlGhOIN+JnxST2KlRAaONKJt1Su6jcVNhJcsWIFb775Jt9++225XtexbBmHbx6ILzUVpaAQcnahfdkDUdd5qk0f1iQl4vCbyPGEomoiinZ8PcdrIOAxoPskrEdkTE6JZ2/oRoc6ibRLjEfy6XRolMQNN7SigGwOzfiQtKWz6N2qRpUD/BMDBw4kLy+v7P/8/Pz/dL4VK1Zw6623niPr/h4nkt/RjyfDH9+mHB8d6rqIPyDiV0UCmoCiBINfvD4JXZPQdQFdE7ilVitQJHS/TLI9NliGQgN0aBYeixgQED0iN9ZtQl6xi2m3DqK+NRLJJyD7BUQPvNrnasb170N2qYOV+9IQVIE2CQk827MbnZJq4HT4+Xb1Fj6av4ZRk37l3dkrWbb9EK0bJHBfv47MmjGN9PT0SjdT8eabb9KhQweuvPJKmjdvXtHmXPAsXDiPdeu+5efpG9m779hp25iQ6FynJiaTyIbco+wuLA4Gb+kSZjHAvkNN2HUwmdJQI4mlfiwyFHo0JBEEVF7bM4m1+bvKuWf/jfM2EkxNTeX111+npKSEadOm4XK5ePDBBzEajXTr1o2MjAxGjRrF2LFjz5cJJ6FrGvmffkr+Rx9jatSIxA8/wJiYCLrOdzWbsCChHm6T6biKRzDM3eOXKfWa8flNKCUG0AVC8gxc2ag2AbfKN7M3oTr9OLx+jEaJx2/rRrcHniH1xw+ICgtjwa+LSUhIKJf+VSbuvPPOf9T+r56lwYMHs2bNmvNk7al0WvBaUMRaA6OoExzlBUeDEBTE1nSJgC6ALqKooCrS8Vy9YDqNUZCpZYtkWdpRNFUATWRl1tFgeoUCYbKFW+o1Z73pKAt2HWLmpt10SUqifnQU0TYboaZS7AYjJlGmb5OGLNi5H4smIUnB5HpZE0gItVOQ46R+WCS9GzegW4Pa/LRyOwM7NOOH37by4qAe1IqLpHPnJ2nRogV9+vQpt3t4LriQIoy7det2yraBAwfy4IMP4na7T5rpOJ0Gc0pKCs8//zzvvPMO9erV+9vX/SfHHTp0iMW/zWbDxrE0b3ZqyaNSj5f7Js6gR8sGeHwqy/L3YjAqVJMN2GQvTsVIZFgBefmxOOobkTSd4XUEPKFGjGsdXDUjjQ9HhlLqd/1t+y8EzpsTrFOnDl9++SUDBgwAYMaMGQwYMIDrrruOW265hTZt2pz1+GXLlpU9LCcq3P8Xin76ifwPPyKsfz/iRj6A+NsI6DOWPinvQa1gWK+sB8vXuAMyflXGp8r4vAKaT0TwiRjzRHQ/LN9+mFrWUApKXPxvUHf2Zeax61gO9a+5naNLfqZhw4b8On8+NWvW/M92V/HXz9LgwYPL1R6n4gOC8mXBFAeO5/EF8yMCmhQMetGD64XBBPoThXKDBZY9isZ+VyGoIpIgoQk6si6iBgRkBEyazMu/LkXUg9PtpYqP/FIXBU43Xw6+EQBfQEEHPP4A785bgVmQQQG3O0BBkZuvlmzkcE4RsaEhvDNjOSOv68rXDw6kuNTNgM7/H8q+YMECMjMzK10Fk+joaDRN47XXXmP79u0Vbc5/Ijk5meuvv/68HteoUSMA5i9YxfrNWRw6nMu4N/9/BiXUYmb0TT2Ii7Yzfe5u7FoofiG/TLzdpxmICC3kwJGGFIZayI6zECuL5MoQgpvYVA/VctzsSEilV0L7f9yXiqLc1gQzMjJo1qwZAJIkcdlll/Hmm29Svfrp605169at7O3qhCrCv0FXVQRJInzAAKSwMEKbhiNM6gF+FzsOLCyr8C2g4AoYUZCPT4PKlJSEoDtkRIeI5BaQ/SKCP6jSUeT0EB1i5Y1pSwkYwRQAdIEmrdqxavECwsLC/rXNVZydPz9LO3fuZPXq1SxatOi0EnTn8oXq2sXj0HUdWdIJ1mnQgjUhjq8FqrrEieR2UZPxBXQ0LRhCrqvBQBnU4w5RDQpfa4qOEBBRFEDXSY6LpaTAS7HiQZRAVSFSNpFyKId356/kjYG9ePDzmbStm8iVzeqx4eBRjhU4CLOYKfF4ibCZScsspH73aIQA3NyhGbNX72LP0Rx2bj3KghW7aRNTjR69kunRqxkhISFl0YOVBV3X6d27N23atGHr1q0VHmF8tgo7Vqv1H1fg8Xg8WCyW/2bUnzjhBH+cspB77hlGTPSp8mY3tAvmXc4YOIghi39G1yQCioCsqxglFcnmBQT2SAl4roYcl87Gox7ax5kBaF5kZ0b2Th6qfz1W2XxO7T9flNuaYGJiIhkZGQBomkbXrl0ZNWrUeV3YLpk3j8M33oRaUoJoMBAWlY7wbT8w2WH4Ep727kJERxZUPAETCjKaBk6vgcICK1qJDH4BySvSPLQafZs2RArA2Nv70KVFHfL9bryyF9exA1iNBqZ+9Cbb16+ucoDnmT8/S82aNWPOnDln1GDt1q0bo0ePZvTo0dSqVes/XTvdU0QwweHEPwFFDaZABFQBVRXw+WR8AQMun4iqyOiqhKaIQWm948FVKMGFRItoAF/wsyRApMnCy92vonl8HIIGIZqBBpGRtEpMoEvDWtx3ZQeAMnHuL5ZsYMzMZVxWtwZ9WzXiq/sH8PAVHcCvUzsmgrF3XsMtXVsy97VhPDmwGzf1acWIu6/E71dYtfp3mjRpQmpq6n+6JxWBIAj8/vvvCILwlxHGLpeLO+8Mpn/8McH8zTff5L777uOmm24iIyODI0eO0LJlS+6//36mT59eHt0oIz09nUWLFvHNN9+gqiqvvvrqvzrubNSuXRuj0cgVneJ48rHePPPktWds2yq2OrOvG0KoxYCGiFs34VdEYsNzEAUVj9uOJOiU+nQ2ZfnJj1DQBTCl76V/9fYnCWn7/ApFxRfuFOl5GwkWFBTw3HPPsXXrVsaMGcOjjz7Kww8/zLx587juuuvO12UB0AMBct95l8JJk7C0bo0eCMCmr2DB/6DhNXDDBHqtewlJAEEQ8SmUiRgXu024XVZ0RQhOVbkFDCUC+4oKOHSggJE3dMGp+3n8pq5sPLiLXZ+/j+IsYV3aUeJiq53Xfl2qVOSz9EeGrQwmZ0sS/HHm8ERNQF0Hf0A+LpWmH1//C+YA6qqMrgUTJyRdRFMFPr3yOt5ctpIs1UENg50sp4PVo+5FRGBgi2TW7DlC25qJLN+eSpv28fRq1oBXf/iNccP78fG91wOQlltEvzZNaFOvOtLxig/bUjIQ3ToWo4HebU8e4dWvFUP9WjFcd3Vz2rRpg6IoJCUllcftO+fMnj2bJUuWEBoaelYR9jNNn48aNQoIancuXbqULl26EBISgtvtLvd7UqNGDX744Qcg+GI3cuTIf3zcXyHLMu3bt2fvgWPMWbid63q1QNN0DqTm0LBeHEs27qdO9ShqJ0Txyab1fLx5Hf1btmRZ0Sp0XUAVYqgbvYvn7xhDmMFLm9V51HLrTLZEU6L5ccYYSMj10Lt+v5Om1sd/tpilq/cx/4dHEcULb8r9vDnBqKgoJkyYcNK2r7/++nxdrgwlP5/MESNxb9hAxODBxD79FILJBKG3Ajq0uRvEYMSeJpyoz2bAF5DwBCSUE19WhTKSV6COEIZX8lPs8dGsbhzfbt1GVokD35EJpE37BoMs89uv86sc4Hmkop6lP7OhKA1B0FFVHVk+7vw0AVUNpjNoWrDkUZzJTkapI+j8dILRnsdf0kWXjCgI2EUjo+b9Ru3wcDICpdRPiKJpXCwmSWLmpl28MPU3rmnWAKMkYZNkdh3JIb/AxaYDGbw7fRm/bTnAs7dexS+rUjiQlodJkpj3znAkUWTglS2pXyOauMhQSp1eQkNOnZaaO3cuW7du5auvvkKuZPXfIOgAV61axY4dO/4yMvTP0+d/xOl08vPPP/P5558TEhLCqlWrcLvd3HrrrcyePfuUc53rWIXTIYriafU/zwUrV67knscmkZFZBMCSlXt49d15vPPyAF74dD69OzbmxeG98AYUfFKAKQe3EhEBgh5NQDtMAAmDpCIKOook0LDQg1wDijw6Ba1MOGSJyUcWYZbsDEi6HIA+VyVTr3bMBekA4SLME8x54w08O3aQMPZNwprYYHJ/GDwNTCHQ7h4Aui4egSwKKLqM73hl7SK3BUnQ8blEDOkm8AmYXALZqoPuDetglCWqJ4YzYdVGCjcsJ2/xbKKrJ7Fg3lzaNL846uJV8VcIQNAB6hplw0FVD84iiKpMt5j6HCkqRVO8wUMCIroKaCAcHxVqmo7LEcAsyew7mkd8SAgvX9ODd+YsZ1NqJr2aN+BAVj7fL99Kg7hoLm9ck8MZhXRNrk2vNg14/YclBFQVXde5q3d7NuxOQ1ODpZqWbTxA+2Y1+T/2zjO6iqoLw8/MremdJJCEFDoBQu8KKFXpIiJFUBREpH5gAQWlKYoFEKQKIipNmiCg0rt0Qg8hQBIS0pOb22fm+3EhilQVEgL3WYu1yMyZOftMJrNnztn73U1rlGXg+KUcPHmZZZ+/TEigT8EoFEVhzJgxREZG0qNHj0K/iveDDRs20K5dO77++mtmzJhxx7bXp89jYmKQHUmbAOTm5vLGG28wefJkPDz+XB9zdb19ZfT7FatQlMz98s/o7Ho1Ixn82lNUqxzKog974O/tkMxz12qRzODlpUOyirh7GhEEhSy7C0nxkVxND6RMiW9wOScToFPINMuc7edBps2VPxL2kGqyU9u3HKXd/alaKYSqlUKKarh35ZFxgrLFgqjTEfjOO/i98gr69PWweAIERoM5x+EEgQnHliNfC293yFyJjrwuWcRk0qBO0oBNwFWjQbbaEQX4IzmRwGAv1hw+h6wHmyGbMrXrcei3jTf88Th5dKmyZgwgoRK5VuVBQbaLSLKCze7IC5Ql+D3xPGqbFmTwd3Ejx27DJskIkkCPStVQ20W+O3AUD72WZT270vubFbxYvRqSJLEv7jKCLDCgRV3eat+E80kZRIcF8mLj6vT45Hs+/2k7K9/vzbJRvfBw1eHj7gicqFcxjE6D5pJ11cCmvacpH1GCYb2a4eGqAxnSMgw3OMENGzZw+PBhFi5cWGyF3P+J4EKnTp1umD7v2bMnixYtonfv3thsNiZMmMDzzz+PRqPh22+/xWg08uKLLxbmcAqNffv28VKfvvhEPMOS2SMJK+VL52drABAV4khy//r3vWw+e4F5LToy/eI2jmcnccFgo46LFZ0gcTnXhz/OVcdQ5jsAwlR2EqxaEES0okQNn3z8gpoR6FI8YiOKvROULRZSxo3DlpRE2Jw5qD20qH8bD2fWQ5Xnoe2XoP3zzW510gFEUXXtYeaosm3KV2M16BAAjdGhvfheiye5mJLFvJ2HyJQtpJw9g91uQhdUii8mf0rPBjUe2qrbTh4UjmhQR5qDQwsUcKRGKAKyBIpNQFIkUARcFC1ru7zExzt3oFGrOH0pjbi0DEQJWpWOYt+5RLKyjMz7dT+7TlxgZp8OvPjFD1xOy6ZOVAg+bi4MerYRb81bR2qWgUaVwvF01eOmv7EGpSAINK4ZRZVywZQNL8FXS7bz5sRlrJ32Gi91qEeFyBvLDLVo0YLly5fTvn37wrpw952/Cy7cKb3Dzc3thunz62uCP/30001tGzdu/K/s6Tprz03bnq0aTM/64ZisEr2/2V+wfUm/+je1XbBgAfHx8cTExNCpU6cCkf0WLVpQr1692/a7evVqYmNjsVqtfPDBB3c9zt3dnTOnYqkVXAf7LQJpjl9MYcbPe0AnEJ+eRbbRRm2fSPx9kzHYUwjU5RDgnY4kq0lW+QNpDNEZSKjoQdZFA0++m0fKmxVo1b345JwWaydoS04mcdBgzLGx+PXv55ieWjsEzm2C1pOhzms3RDDUX/8+iNcrdoNdEpDsAlaDi0OeKlNEsYDKCh8u/h0PLx3mEgqm+PNc/f5bVC5uvDNvES81cuoUPk5cuHABSXbkBcqy4Ch3JF0rYitrkOzXvg7tAh6CKwIKeRYriYY8pu3ew89HzxDg6oaHSsur9Wqx5Uw8288kYLFJiLKAJMo0qhBOYkYOrz5dh1bVy/PLgdNoVCKpWXlsOnTWIYodGniTA7zOsN7NANh3PAGAxjXL4O6mp4LHjVN7iqKgUqno3LnzA71mD5p/KrjwsOPp6YlOp8NisZCVlYWHhwdDhw7l/fffv6MTbN++PW3btmXw4MH3dFy5cuVQqzVcSbrA7iMXCA/xv2GtLtTfi+cbVqV305qE+Hnzgq0Kp3NS+N+R6YS6a7EpalSeDs3Q+PxSJJePR/EW0Qt28JfRZcuUulK8CoUXWyeYv3cvSUOHoVithHw1HY+mTUBUQfMPoM6rULrBDe1jMxMxKxKCLKAScJS8sasxGrRgAbVBpITWDRedQLrJiNUT0jCRs3snGT+vQe8fwOdz5tO/bfMiGa+TouOZowsBoeB9ym4XsEsqFMlRABe7iCAJKJJAnmwFCfSKBrtdYX9iMj56FwTA382VKoGBrN13Ci+djqcqRDGiVWM0KjUTV2zm61/2sGvCAD5fs5PL6dnMGuBwVN+N7MbpS1dpHB1JakYef5y8xLONHflcuw/HM37WRr4e05WwYF/qVgnnt9kDcXe9uQSa2Wymfv36jBw5km7duhXS1Xs8uNXX3XVctKo77gfHlC04IlZbtWp1w5etzWZDFMVbVptXFIXx48czcOBAgLsKHmg0Gty9AjHmpPDVou1ElytJTIU/1+u83VwY/dxTALz7+6/8cv4sZv8sRNGVAH0+sgIlvVMRBJnErJJcaqDHaofvz6go62uiYqCajBOb8c4/T6hb1B1teVgolvN5itXKlffeR+XrS/iSH/CQtsCSno75Te+wmxwgwIu7Zjo0HhUBuyxiMqsxZuuRTVqCde4EyW645al4unpZujSrgtpVJnndD2SsWYVnhUqknD1D/3bFS+TZyf2lvk9Z7JJDT1aWBCRZRJZBsgvINgEkAbVNRCOpsBolVGaBpNQcPm7VgvZlK/BM+fL0+2YV2UYTl9Ozeeu7X/hh11H8PFw5fikFWYFeXyxh95kE8s3Wgn4rlw6ic+OqCMCqrccYN2cjqRl5AOw5egG7XUKrUZOanguAq17L0iX7SLiQdoP9U6dO5ciRI8W6CvujyqZNm5g4cSIajQYfHx/y8vIYN24cbdq0YcGCBVy6dOmWx3344YckJyezc+fOm467HXXr1MRqvEpUmQAmzNlEbr75lu3OpWdgtUu8WrY+3noBWZG5avXERW0hPOgiKlFCo0h4m+0cS1dzKVfCXFrA85KVOMNxNiadYkHcw18rtFh+CQpaLaGzvkbtrka1fgAk7HBEfioSt/LrLX/+iOvLd3ZJBARMeS4odhWCWSAj14xbrkiuTeH7XUcxByhIegXJlE/g063ZMG+uMwH+MaXKivFU8w7haF4i267GoygqFBxFdGVJcNxydsc9pUbAU6Wnqn8gTcMjmb55L3kmC2/8uAZBgpolS+Ku0vDL8N40Hz8Ps2wn0MsRsNWtcQyfr97B+ZQMRFEkM8PI0fhkdscm8M2GP3DTaMgzmJn7dleerFmGQD9HQNbRk0n4ebnxw8r9rNp4lBWz+yEiMHfuVvKNFvpEOFJ30tLSmDBhAs888wzNmjUrmovp5La0aNHiBrGH9957r+D/kZGRty22O2bMmBt+/utxt6Nzx2fwdNeSmZ2PXVaQJEfE7FtfrqGEjzvDezVj/4VEmgaGE1XSm68PHmHMk0+w/spS1KJMqtWTfm0WEK5JI+C8nbAdZkIre3E1X8AUqaLUH2YEq8zKpFjO5F6ld5kbp2UXfreLzMx8hg66tbhFYVMsvwQBdLosVN+1hsQ/oMNMeGYKqG4d6XZFyUeRBWRZwS6JWK0iig0wC2jyRVQmaBAdji1YITP7LFZjDoIo8va0aVxYv5qYMKcI9uOKBYWD2Vfw17oVFMhVZAG75HCCKCJqlQCSgmgVeTI0HJtJ5lxSOu0rVECwQ4i3J9++8jyCAkaLje93HaVz3SoIgI+rI8KzT9Na1I4MARk+eakNwb4eHIlLQqtRIUkyuQYzMWVLcTkxk2C/P9VRAnzcqRpVkhXrDtOkfjl8vd24kJBGj14NiDuVzIX4qwCMHj0ao9HIJ598UujX0Ml/435Xm3/11VdZunQp62cOYNOsN/DxdKwbe3u44HUt4vjXk+f4ZtdBuparwgf1nuZARiJxud6IgFHRY1UEsmUXjN6Ov4ko0UZivogxRiHtWTU2s4FPandgVbPXbuo/KyufjIyHpwhysfwSxG6Fpb1BEOHljVAy5rZNq6wZ46j3BtjsGqxWFZJZg2BRIZpENDkCkh5+TThPxoEtZP28HveatfjfxEm83/ypwhqRk4ecUdFtGLhvhSPPTwLJpkaFgGQRKKH1JMViQJYUgjTubEg4ywEpCV+1jmolg/j25S58snobLzaIIdTXm6qhQVzJckxn7jyVQOsaDk3HIe0b89uRs5y/nEGNiFJ8+dNO3DRaBrZviM0u8VRMGXr+71tye5jp3q42AFPe6YTJbCU00Jtnnq6CKAosW76f83GpSCYbmZn5CGICc+fOZdCgQVSsWLHIrqGThwur1YpWq8VktvHF91vp2KwqXy7fTuL8DQx9sQk/HDnGiGUb+aHP83y+ZTV6rQ6bLOCpspCf7cOY3wcwuPECygtHqGy3sNHuQlZZO1K0ip15a4lNyOb1qJs1XYe8+XB8AV6neDpBtRa6LnKs/7n63rZZzTUfALJDzsruUIKRjRqETC2iJKC2CVi9FCS7hbTlS8g/dhSPmBh+/eEH6pYpHou6Th4sZzu/R7kV4xi4/ye4nhIhCSCDpDja5JjNuIhqFAmu5OShVqnw17qQmmOga8sq5JksrNhznN2nL9KkciQDZq/i+6Hd8Nbr+HnfKZ6KLkOIrydVSgcRUcKHFiNm06FRNBNebsXmg3E0iA6nfJjja+DDQW34fvk+qpYtSZWKpZAkGRe9lh6d6xbYPHpUO6xWCV8ft4Jtv//+O9WrVy+8C+fkH/FvUyQOHjzIli1bSE9P56OPPrrn4+rWrYvO3Z+R731MeKgfq7cdx8/bjQqlA/HzdEWWFUQEEnKyqbNgFq7+KlCbyTS7UlKfg6STSM0pQVxmOBbvo1TPsxDqbUclSejtKsoZDKRq4gC4bMjhUFoS7SMq3ccrdv8ottOhlIy5owMEsCKjKOK1it5gM2jAoAWVI6pK56XCYsgg6asvyT9+jPBOncg+eNDpAJ0Q9eNEon6cWPDzdQk0wapBsYuIikAl7xJU9QzGYpFwRYPdItMgqjSxbw+ilLsHgg1iQksiyQrP1qxIk8qRlPTxBAXW7T9FCXd3ArzcGPPNRrpP/AGj2YpGpWLamx0Y1uVJWtepyCevty1wgACVygQjywqKomCzSXTqM5NZ326/wfb0jHwUR90msrIc8lhNmjRxrms/SL555uZ/++c49lmNN26/BbdKkXj//fcLhABuR82aNVGpVOTn5/+j4wIDAzl29Aibdp/Cbpdwd9MTU74ULzavwW9H4riQlEHP+tURFQG1LGCRZaySQIbFhXxJh0GlxtstmwtpYdj9RKpkWPiyTha1fQyU+ECg1keX8FA5CvcuPH2QITvWkmU2/fPrWggUXyd4FyqtGoskg80uIkkqJJuIbL62ZiiBi5eaXJUVwUOPqNfTdcIE4pcvdybAO7mB8j9O5Gzn9xyVIGQRq+RIlu9RtgbVvUtxIuUqSJBlMlPaw4sNJ84C8MkLbRj5zBOE+XmzaMtB1uw9SbC3J/lGK2HenqRm5uHr4cqGsX15uWVtWtUqx1NvzOC9r9fz+qRlnIxP4djZ5JvsKRnkzYKpfahaKQSL1UazxhWoUvHGcmRvjljMp9M2Eh8fT0REBAsWLCiEK+Xkv9CpUydGjRpVUBfx7ykSd6oQMXToUMqWLXvTcXeiZs2a5GReIe5CMq99vJT3Xm1JncqlMVlsXMnIJd9kpU+dGoQFeoNdoLyPPza74/lpkdWAQEm/K5xNCye3gprselo0CAgKSGXtuF6QwWxCViQGV23I2mdewkd/f0tD3S+K53ToPWCXQBQcyfAIYDeqUIsiOkGFwc3MlR278KhZG5XWlfcXLODdW1SGdvL44omaXOx8ULM5b+/7mcreJYhNTwPFIbG37ORxLBYFtSiiQkCFSLCrO/FXMjh66QrVwoKJDg7k7YXrGd7hSbbFxnMkPomth8+j16rZm3WRzZ+8jqIoZKXl06F+NB4aLVXLlCTHYOL7Xw6w/VA82+cNQq26+cXMYrXTYdAc2japQoPaUazfcYKt+88x/KVmjB7xLN5eLvTp8wKKojijQQuDPutuv0/reuf9OFIkDhw4cNsUiaeffpqIiIibjlu7di2nTp0iLS3tnlMkAOrXrw+Kgl7JpGbZaCqUdsw2JKRmkZ6dj8Vmx9/djUmtm9N1xRJCNP4gShjJJSHfjyi/q4QEJHPyUkVyvD0JDDbz22Ud3yfo+KyiCUFWEZNoRKyuwkOrItov6O7XsIgoUie4bt06Dhw4QFRU1H0V8i23Yhygwi6BZHeUrREkNZJNIs9uIH32EszHTyMKapaP+4gmEZH3rW8njwaHXxgJQLnl41AUkGwqqvqUIj4rkxyTDZvNMd1ot8kIdpEXqlbm5z9OYTTaGP3jRt5p35S9py6y5/RFtGqRNaN7oyjwTak/qFcxjGAfT/RaNRk5+azZdpwgf09G9nqKn7Yc48jZJMqU8sddp2HUF2uYMLgtdklGr/sz+lmrUdGmSWXcvR0VIs5eTGP7wfN46HW890ZrJk6cyPbt2/nmm2+Kbamkx4l/myLRtm3bG8qJ3UuKBEC9evUQRZFL8SeIqBCDj4cLyzYfoXmdckwf3InaFUI5l5LO5uPn8VbpMdgk4jONBAcqaEQ7AgqhJS/TyLCXPJuWEvl5eBlUpFlcySxnJxgoGe/QIpUUmUUXtvBEicpEuj98zvC+OcH4+HgmTJhATk4Oy5cvJz8/nwEDBqDVamnSpEmBXt9feeaZZ2jSpAnTpk27X2YUoCggWUUUSQU2R/lTa3oK6V8twp6ZjV/Xjlye/x0u2uIl8eOk8LHbQbELHMm4wvYO/VhyJpaL6dnsvZxIltWEpCgcupSMi15DCTc3R8mj84n8tCcWg9nKz/tO8UKTGABeaVXnhnP7ebmxYebr6LUa0nPy+eT7zVSIDKR9oyrsOHCOAF8PRk/7mTMXUlk59c9w840Hz3LVaOLHncfo3rIWL7WtQ7mQAGpGh7J161bee+89unbt+sjJiz2O3O8UCXCsQX744YeUr1iFWZsu8/HC31m95wR6rYa2jRxqRL+djOO7PYeJKhuAyqKiZ4XqbEhPwSKpuWp1JdA3jR6NjxKgz8NtnZV6dgncPTmDmpLBEpbDp1l/eTg6TSPmx+9HQHgoneB9WwCLjIxk3rx5BT9fL2Q5Z84c1qxZw6pVq+jduze9e/dmyZIlgKN45CeffEL//v3viw19f1tM1I8TkWxgt4gokggoCBo7phMnSJ34FYrVSskR/Un/8SenA3RyE+WWj6Pc8j+rem9pPQika++KCrRevZARtRvzcrWa5BnMuKjUeGt1THy2OWW9/fikaxva1qjAhSuZjOvegicqRvLFqh18umwrTYfNJDPXeFOfeq3jC89mk/B2d6F1/Yp0alaVZvXKczUvnwyDkdR8I9uOnWfzsTimrNzG2wvWcyEjk4XvdkOvVfPZvN/5bO7vBPp7Eh8fT8WKFZk9e/Y9rxE5efwYNWoU1es0IN9kxVWnoWXdCkz/aQfStXJTXetWJdjDkyCtG0eTrlLay5Mcqysp+R4IghoTOqxouZQTgClIRUiajUCdzIlsLR4DLOQ/byLJdIAjmTNYWK8fPSKaFOl4b8cDmw79eyHLDh060KFDhxvaTJ48mZycHPbt20fLli1v2PdvilduSb9Y8H9RbUOlERBUChazGk1Jf3QVImk4pB+/9Rn6r8fl5PGizfq5BLq4kmIwoigCUV6OiOS9iZexoWC32h0L/gpcTM8i12RGJYhsOXGeMiX92HXmAt2b1aB5TFkOxSUxc91uRnV7+oY+Ri/cgM1uJyaqFL983q9ge1aeicSr2TzbtDLZio0PvvuVLIsJjUqFKAqUDy1BeKAvH83ZxJN1y9KisSMH8OWXX6Z79+7odDfrhzpxch1FUTBkJLF4bAciIiLYcugcvp6uqESRjQfPMnPDbgx2C0npOdT0CyI2OQs/rTsGOYc0iz96MYnfTzRh7cEWbG43EB9yqKO1siVTh/bJfMIUI2ZbKselELZd/QFPbUU6hXQo6mHfxANzgrcrZPlX3n777dse/2+KVzb1L82W9Iu46C2o1WDNyid1wwk8nmmMJtiP/b9voYp/8D8dipPHmA9rt+Ljw5sRVQqyDY6mp9D8h2+QJIU3a9dl3r6DTO38DJtOxjHu+RZcSM9iUNuGCKJA44oR2GwSKo2ISiNSKSKQc2kZZBiMZOebMNvsVA4JRFEU4pIz+ONsIt2a/JnL1+uZ2mw6GcfHq7czvMMT1K9Qmq837CHY14Mu9atx5WoOL767kIw0A6El3Jg/9X2GDx9OmzZtnA6wmPFv8wQBVqxYwdatW5k2bdo/Oi4/P586depQo25rxowdT6tm0TSt4Ygynb9xPxeTs/DxdyU+K5tz2VmYs2y4ByjotRqyJRVBsoqAoCRkRcWW/Gq8pN1OG7MRv1ABiwz2wyp8JDO+1cqSaD5Lgkl6tJ1gRkYGo0aN4vDhw0yaNIlBgwbdUMiyMNiZex6NFjQaMJxOImHSCqQ8E641K3B52NRCscFJ8ebsc47Aggo/OR4mTfwjUYkiKpWAm1pNrkEizpCOYhY46pFC7P8GcSUnj/m7DvLz8VMkZxnwdXelfuVwlh2O5d02TWg0diYnk1M5ePkKBpOVTUfPsv7oGa7mGFj0Rlcm9G5NlsHIzwdPY7HZ0Wn+/LN8vU09hsxfy/7zl/DxdOFieja/HY3jSGwSBqMFXy9X3unZkDde68WRI0d48803i+S6Pe702dDnpm0tw1vyQoUXMNlNDPhtQMH2b1p9c1Pbf1tK6fjx47i6uuLl5fWPjgNHbcFGjRpx8MgfzF+7j592HOezEZ1wd9Ux/Y0ObDkah8Fqw8vHhc937yIp347ircGuMRKf40ewPptK/sm46w38nlCD7qV20+BKPnWb6JHQcHmhBhczVJ59jJ5Ru/7B1Sxc7psT9PPz4+uvv75h218LWT5I/rqGIwgK6esPkTR7E2o/DyIm9uG80wE6+ZfszT2NqIEhVVtz4OplWsVUYtTOX5EEBeu13K1gLw/qlwtja3wCOq3I+pNnqBIYyOZT5+nZoDr5Ooktly7SNDKcC9nZrDtzljGdnuL171bRZPIcFvV9nvScfCav2YbZbqd6RElqRYaQkp3H6sOnMCOx40wC22MTaFolkpjwYBqXi8AuyfirjXTo0IaUlBRWrlzJs88+W8RXzMm/4d+WUtq8eTOKorB//37MZvM/XgPu3Lkzmze/QZCnlX3nslm55Rg9n6lNgJc7pQN9ee2rFbzZviGpefmoBBGD2Ya7TkQlqbBKKvIUHZVCT3PwYhVOP+dBiLsNV0Vkb4aask3s5M7QIl9I4ntLayp696C6380BkkXNI5YnmIll6XYSlx7Gs2YEFzYdwNf3zqoyTpzcitOd3gegxvrRKArMvrCBo8+OY3PieVR6cNVq+F/DRhxJvUJ5P38616zM5ssXiAkL5qX61bmab6R70+pE+PtQNzyEVGM+v11JoLyPH22rVsDH3YUUgwF/DzciS/gSExbMoDYN+XLDLkp4ubN59Kus+uMEG46coX650rhoVGw9Ek9WjpEv+zoqwp85c4Zq1eri5+fHtm3bqFOnzp2G5OQBcquvu+u4qF3uuB/+fZ7g4MGDAUhPTyc4OPie8wSv88ILLzB06FDEnDNMGT6IymWC+OzHrWQaTMSUKUnrGuVJvpqDn1qPQW1Dwo5NUiGbRRLNvvhrDdQsd4T952qxPbUm3fz282sqvHtcy6xqMj6CgstmEc8eiUjyzSWbjmReJsTVB3+9+z3Z+yB4JJzg9SmsltuGkFYzDFGr5tyCPbd8c3Li5J/godKRbbOgKA4VojbBMehUKkK9vHDXaGm+ZD4l9O5k5JsY27IZE7Zso//atSBBptFErs3C09XKMPa3LQgixOdl4efpygebNqNyUdGoUjjuOi1pefmsOBRLkJ8Hn734DDZJYsbGPagQ2HfqIkOaN2CH6TwlXFw5ffo0FSpUoFy5crz//vv07dv3gYTROyk8/m2e4HXGjx9/03H3gq+vLx06dGDZsp9Qu9Unrn443/96CH9vd/RaNe56LVa7hDHPhiiAu4eOvFwZvaeZfIuaOEMJavgl0Kf5tzSOOAipVprtNODhW4ofjWqGNLBj+VlDxIvepFj2cS43imybmspe1VEJWnrvmk+H0BjGxrT/R3bfTx4JJ9hmZkuyE3IIbVMB6oZzaOSqojbJySPCtpbvEZuZSPed81AUhTWJx7AjYldcuWjIAi3kSmZklcK6C2foFlOFb48dBRnQwdxDBynn5+fQ3lbAokgMWLkWRQGVDCuOnKBiQAA/7D7KpYwcBOBY/BXemr+eno2qY5ckWsaUx5Kdg2tqLOumz+Or4fGcP3+ekiVL8u677xbxFXLyoHnQLzhTpkxh++4LbN4WR5CXB40qhfP809X5bNk23Fy0TB/aiaonSnL4UjLfXzwOXgKyrOKqyQtZUJHnqsO/1FVO2P3x0yfjmyXT3S+fr6+60/NZCx4nRMSUC+SVcmNd0njSrB4YpX48WaIV0+q8SKSH/wMd390o1kKZiqLw6aefsmHgr5z+6SyuWomNT35R1GY5KWS2b9/ORx99xLfffvtAzh/tG8LRdmP4oXFf9IIWRYYzORn03bwCBAWz3Q6CwoErSaw5d8pxkKPOLjIKp9PTQQCNACoBZEWBayWZsMOktVuxWGxULOlPlJcP01ZsIzE1k2phwTQt5cbrL3amYe0q7Fr7DXqdlhkzZuDvX7QPDiePDiEhIbz4fGPata3Ge2MXEhd/ldHT1wJw5mIq6dn5rNt9itW7ThKodsPb6oarqEUl2LHYROyCGqui5nh8ZV5cN5b0Mq70jsvCTyUzTtLjNi8fl3CJ0uJRPDRB+GqtnMo9xY+XvqdBiSi+T9jBvvRzRTb+YvslaDAYePnll1m2bBmdO3fG+01v5tafW9RmOXkA3E2NKDExkbfffpuPP/74H5/7w9h2pJ7NpW/E5wB8cMKxFpitDkAX7EmWyZXM0ykodhWugjuiLFJB70ucZMDm5YksKVgvXkawg4/ahQyzFavNhtrLB42vH4pkx3T2HJ4qNTl5RhTJjmKxowsMwaNkCNZcA4bdv5GclUOkt56rKVe4kpzE51On0aJGeU6dOoVarWbMmDF07NiRqlWr3tdr+zhwO/Wq2NhYJk2aBMA777xDdHQ0b7/9NkajEVdXVz766KOiNLvQmff1eE7u+w2T3AW3oIq4uVlBAklWOHkxFU+1lpYhZVl09hiyBLqSWjSuEvEGf8Ld0ynpnUJqjh8v2N9nrfsYPrqSxtiS/thcBRRAu0BPWLk/sMaU4LxhDzk2F0rqo9iQfJRkYzblPEvio3W7q533m2LpBN8//CyzX9jB1bhcPv74Y0aMGOFUxniEua5G9NxzzwF/qhG1bduWrl27UrNmzf90/oWv7mFGZo0btgU3r0CVUS0Bhfi3FqFY7QX7jgHuTevi+0InsEPqR18BkPKX472aNMG3dVtkq43UeXNJ/Vuf/k+2pHn9BjRpFMCw76bi6e6Ju96T8g0bEB4eTv3atQCoWLEiu3Y9vOHlxYG/3y/XneCXX37JV199hSAIjBw5klGjRmGz2Zg6dSojRozg8uXLhIaG3lMfF3v2ummbR+tW+L74IrLJxOXX/hRBKL3o5hmL2NhYRo8ezaeffkqZMmVuyPdTqVSsX78eRVFuypmOi4vjhx9+IDAwkNdee+0f5Qn+nS8+n0K7du2I3TofF3dfKkZXIVtVkhdHK9jUMklHfuHcVhDcVchqBZvWhqFqKEGt3cg3qvh+bgJlXT7lrHUgT4hfMF8zkVFIeGhl4o7rsWzUE/IjuHtkEOyXRb6LwLpyb6FqXY24nWf5afIUlL8apMAu7yj+8GhM+YzjtDYeRERAFAVk2VHbzBDZGK1Um74fdUet+XfurFg6QbVGpFaX0viHuzGy78iiNsdJIfN3NaJ69erx0UcfUapUqVu2v5v6UMcJ1Xku+B0Apsd9CYDB87rGoUDptzphtwmOqDhJBFlA5esLKA49xEGvYzHbARFRFPHQ6ujdrAm5ejfOXE2j59wFfHfgGCAy5fl2WIF6FcoRERwIQM+0tPtzYZzckr/fL9fJycnB29sbgLy8PJKSkgqcXlhYGImJiTc5wX+jZHUvREdHFyhq/T3fD+DDDz9k2rRppKWlERAQUHDct99+S4kSJRBF8R/nCf6diIgIDh06xA8//MCaNWs4djwWwdONYD8P0gx5ZBzZjigICNem9CVFQaVtTH7zquRaRfYvPIcogKA7jlvlp2lVujOtI/fyEce5bNbwS3eF0HMC5eMVgjIkSqZCqk8eogD+GTnUPmFFUG60KaVWCn94QZB8lbonrDfZvE4by69rsun2Tke8/D3/2UW/RrF0gu9Hr+HDF9rxfvSaojbFSRHwdzWiJ554gieeeOK27e+kPvR+9BqI/vPn9twiSq3Vf7d5bB8Zi82Om86pV1vY3E69ysvLi5ycHARBwMPDg1KlSpGYmAjA5cuXb5J5hNvfS7f6uruO6OJyx/234k4zWyaTCRcXR22+nJwcXn/9db755hvsdvt/nhHTaDT06tWLXr1u/rJl1rCC/yqKYy3cjkSmJReL3UCPCy+iVs7jIV1FLRzFU7UUN1GNTYEqNfMIioEkm44kmzeHrb6kWTzJsXiiWG0k1K1M/xoxqK1uBGlKUM0nlGoBwdRTNDyBiIeuKxqVSEiQN+5uegy5JiS7nbd83DEbLbi46f/1mIulEwScDvAx4mFQI/qvqEURtdMBFgmdOnW64X7p2bMnixYtYvDgwQUKOyNHjiQsLAyNRsOwYcPQ6XT3PBV6P7h8+TKbNm0iLi6ODz744IZ8P1EU+fDDD1EUhYCAAN59910mTpwIQI8ePZgxYwZGoxE/P79/nCf4bxEEAReNBtDgodEDJSjnffuSdEFAxfvYv7vnnwV6/4sDBBAURVHu3qxoGTt27D3rhzpxciec95KT+0VR3EuyLJOVlYWfn1+h9vsoU6xTJJw4ceLkcUIURacDvM84naATJ06cOHlsKRbToX379iUkJOS+nCshIYHw8PD7cq6i7ONh7ic8PJzevXs/MHv+C/frXnpYr/2j1M/DfB+B814qTv3c8V5SHjPGjBnzSPTxKPZTnHjUrv2j1k9x4lG79sWtH+d0qBMnTpw4eWx57Jzg9Ryf4t7Ho9hPceJRu/aPWj/FiUft2he3forFmqATJ06cOHHyIHjsvgSdOHHixImT6zidoBMnTpw4eWxxOkEnTpw4cfLY4nSCTpw4ceLkscXpBJ04ceLEyWOL0wk6ceLEiZPHFqcTdPLYYrVaefPNNxkyZAi9e/dm//79t2w3duxYYmNjC82u5557rtD6+iuvvfYa/yRjSpZlRo0axZtvvsnChQsB6NOnD/369aNPnz5IksSOHTt4/fXXadeuHatWrQJgypQpDBw4kH79+qEoCitWrGDDhg0PYkhOnNyVYltP0ImT/8rcuXNp06YNrVu3xm6307lzZ1avXk2VKlXo3bs3Bw8eZN68eQXte/fuzcyZM8nJyWHChAlMmzYNcGgYdu/eneeff54zZ84wffp0+vfvj6enJ5UrV6Znz56MGjUKi8WCJEl88cUXdO3aleXLl7NhwwZSUlKoV68eY8aMoVy5chgMBgAGDRqEWq3GbrczdepUPv/8cy5dukROTg41atSgbdu2TJkyBUVRiIqKokOHDvTq1Yt27dpx4sQJ5s+fz2+//ca6deswmUx07tyZ9PR0tm7dioeHBxMnTkSn0wFw7NgxypQpgyAIPPvsszRp0oTY2FiGDRtG1apVb3n9Vq9eTWJiIn5+fgUamt988w0AgwcPJjk5mcaNG9O4cWOysrIYPXo0bdq04dChQyxevJjp06ezc+dO2rdvT9++fWnV6j5UL3bi5B/i/BJ08tgSGxtL7dq1AVCr1bi6uiJJEiEhIQwfPpz69etz5MiRgva9evVi8eLFzJ49m379+t1wrooVKzJ48GDKli3Lvn37SElJoWnTpnTr1o1ff/2VhIQEvL29MRgMJCUl3WTLnDlz+OijjxgzZgxWq5XY2Fh8fHz47LPP8PPzIzY2lp07d/L555/TunVrAGbMmIGLiwt+fn4cP34cgMqVK/O///0PX19fUlJSmDp1Kt7e3gQHB7N//37Onz9P1apVGTJkSIEDBNi+fTsxMTEFPw8bNozhw4ezbt06MjMzGTJkyA3/4uLiOHPmDA0aNOCzzz5j5syZBceePn0ai8VSUJR2wYIFdO7cmeeee46MjAwCAgIAKF26NImJiajVajIzM//Db9KJk3+P80vQyWNLpUqVOHjwIC1btsRut2M0GlGpVLi5uQGg0WiwWCwF7Zs1a8Y333yDoihER0ffcC673Q6AzWYDYMmSJWzZsoXevXvTs2dPGjZsyKBBgwrai6Lj/TM/P79gm1arRaVSoVarURQFQRAARxXvv05TXt8uyzI9e/Ys+FJLSEi4yXZZlhk9ejRq9Z9/6kePHmXEiBGMGzeOsmXLApCbm4u3tzcAer0eURRvGP/18V1HURRCQkLQarUAqFQqwPFi8cUXXzBjxoyCtr1796Z79+506dKFpUuXkp6eDsClS5cKbNfpdNhsNjQaza1+VU6cPDCcTtDJY8urr77K8OHDWb9+PTk5Obzzzjt3PaZy5cqUL1/+pu3nzp3j3Xff5cqVKwwePJihQ4fi4uJCuXLlaNmyJf3792fEiBFkZ2czbdo0qlatysSJEzl//jyNGzemb9++TJgwgcjISACqVKnCrFmzGDFiBCaTiSpVqtC4cWOGDh1KRkYGzZo1Y+DAgbz77rsEBwfj4eHBSy+9dJNdgwYNom/fvvj6+lKrVi0MBgPnzp27qThr2bJlSUhIoE6dOjedw9fXl+nTp9+0vVSpUrz55pvs2LGDJ554AlmWad68Oa1bt2bQoEGMHj2a/fv3s2XLFoxGIz169ECr1VKjRg0GDx6MxWJhwIABgMOhOx2gk6LAqR3qxMk9snbtWpYuXcrChQsLvuTA8QU2ffp0Pv300wfa/+bNm/ntt99ITk5mypQp97XCuNFoZNSoUXz++ef37Zz3SkJCAnPnzmX8+PGF3rcTJ04n6MSJEwA2bdpE8+bNC6ZbC4v9+/cTFRV1X526Eyf3itMJOnHixImTxxZndKgTJ06cOHlscTpBJ06cOHHy2FIsnOCCBQuK2gQnjwjOe8nJ/cJ5Lz0aFAsnmJCQUNQmOLmPLFiwgKCgoCLp23kvOblfOO+lR4M7OsFVq1ZRqVIlQkJCblLIcOLkv2AymWjZsiXBwcHs27evqM1x8ghw8uRJatasSVBQEG+99VZRm+OkmHBHJygIApIkoSgKs2fPLlB6cOLkv2K1Wlm+fDlPPfUUn3zySVGb4+QR4MMPPyQsLIyUlBRGjx59w774+HheeeWVm8TJY2Nj6d69O927dy8QSX/77bcZNGgQb7/9dqHZ7qTouKNizOzZs3nyySfp1q0bzZo1w2Aw4O/vX1i2OSkidq/+g8oNy+Pl7/lA+xEEodBz0pwUHhaThb0/H6JRxzqo1KpC6fO6iMFfxQwAIiMjmTdv3k1O8Msvv+Srr75CEARGjhzJqFGjsNlsTJ06lREjRnD58uUCDdS7sWh3DNm/qCjhVgtREEk1H0JQgz4a3CvL2FPtmHaqEAUQBQlRUFAh41/dSEi5fIwpGlL3u6MWQaNV0KgVVGo7/hXNqAJ05OWFkJ/lj5/302SIqUQFdEJ20RHkEkWePQtvbcD9uYiPGXd0gp07d2bYsGGkpaUVlj1Oipgr8amM6TiZXmOep+eYLg+sH61WS8eOHTl+/DgrV658YP04KTq2L9vL5N7T+fjX96nxVJUH3t/o0aPp2bMnQUFB9OrVi8mTJ9/1mJycnALN1Ly8PJKSkgqcXlhYGImJiTc4wa1bt7J161bg5jXBjMMqWi4zAtsBuK4u+3tDHe6V1WTsEmn+TT5/Z29bdyLKGbm03ZtKc2x/2SMAGo50c6Nhn6vs+ymbGt9dBU7iB2SzBBlY1t+Pq80C8NmXRYV1WeQHelGvWkf8KlRDX7486pIlSTHl4qdzR6sqnJeR4sQdneDLL7/Myy+/XFi2OHkICI4M5KONoylfu8wD66N379707t37gZ3fycPBk10b4OnnTkzTyoXSX3R0NIcPH/5Hx3h5eZGTk4MgCHh4eFCqVCkSExMBuHz5Mh06dLihfZMmTWjSpAngqDN5w7mibaxqq8NbW5VU41kUyYIsKbhVFQhARo6wMreGCptVwmYBuw0EAcoE5CArCkJYLpue8cLXS4UKAVkSkCWIrJuPSgX6ciLbnnYDSUa0KYh2CbVdwuTjgotgQ3UpH1WWlYiLaZi2zSbxml3npo3mLUMc7pl2JjzZhRZlbl0a63HlsRXQ3rt3LwcOHODkyZN07NiR5s2bF7VJDw01m1crahOKFc576dZodRrqPlOzqM0AICMjg1GjRnH48GEmTZrEyZMnWbRoEYMHD+bNN98EYOTIkYSFhaHRaBg2bBg6ne6ep0IB+jx5DJ68Q4MywLXJlQMX3iYt/0eSzpsoF6lCBg4fz2T8Zxfx8BDo9YIbgwd4EhqoRVEUjIpIvYYp2OvDJbsrubKeq3Yv0kweSCoVoghxFWuTqFIooY3DTbxIhlHBlqUh3rYbvdqXvrsOE/r1Bq527Ypvnz6ofH2dyxE8xk5w5cqVfPzxx7z55pvOh5aT/4TzXnr48fPz4+uvv75pe3R0NN9+++0N2yZNmvTA7akV8RHw0Z9zpkCfvhWpWuUqP35v4uv5BhYszue9//kwpJ8nboIWHTJZgg0XwQ6imVzRBV+XPCyKljxJi290EvYKGg4lVuP8ua6E2QxUL3GcXDdws+SzMyoJdZZE/fnzyfj+BxZUrkGTkcNoXaXiAx/vw8xj6wS9vLz46quvyM/Px2Qy4eLiUtQmOSmmOO8lJ/eDyLBTRIZBly5weFcwoz/I4chxKypBhYyCGvAQVVhVdtSyjIdowiQ7HuFawY5WlNForESExRNYKomUq4HsMQfjpk4jwJBPH3Uy454NYGljkYHb3Oj9x05YGwlOJ/h48u677xa1CQ8nNhO288dQl6mJoH5sb49/hPNecnK/qd7wCiu+W4vNOBhBEEhKtIMAAcEC3qKASVZwVdnxVkxkSGpsoh5RlpBQoxZltIoNL/9MdHIeNknFqfRolku+LEn+lol+vrzbMZMhqTVp0ul5Xp29glEdmxEe4FPUwy4SioVijJNCZNtkNMvbQU5SUVvixMljjb5EWzzC45EVmS6vpNDmhWRyshQEBFwEGREZGRGtYEelSMiATRFAEUEQ0IoSKuyoRRm91sra3Ga8rBnGuxm59Mo1s76ciUyNSELiFfI//wwpL6+oh1wkOJ2gkwKyVizHtGMVVO6E4Fe6qM25Z/744w8mT57s1HJ08kiiCT7PJ2P9uHDZTq/XM9BIKnSigicmVIKMRlTQCDYURQUIICgosoKsCGhEAVFQ8A25jH/F0xzNrkYfl9d5PTuHmBQVyxPO8FWzaMQ1q0geMRJFlot6uIWO0wk6AcASF0fSe2NYu07E3PSDojbnBv6u9pGfn89LL73Eq6++yuLFi6lduzaZmZlFbOWDIfl8CsY8U1Gb4aSIadoxiakT/di8w8SU6Tm4CSq0KoVQdRYqbFgVTxRAQcAqi0iCCkEAQVBQYUdRFPzDL+EVeolj6bV4uuRgFvtUY+mpP2gfN4mEXh0wbN1K5t+ChB4HiswJnjhxgkmTJrFx48aiMsHJNRSbjYsD+5Frs/FzqRD03iWK2qQbuK72cZ2ffvqJ5557jjlz5rBmzRoAPvroIwwGQ1GZ+ECw2+z0jR7GzCHfFLUpTh4CXnrtd55r58qEz7M5d0bCAzDKatwFCwIm9KLN4QgVEZuiQVIEFMAuu6ISBQQBfMvFoXI3kCV7gcZOc7ccvLQ5zCobi1vTJqR99jnWixeLdqCFzANzgnd7e1+5ciVubm4Pqvu7EhsbS4cOHYiLiysyGx4W0qdMREpIJiUsiwnTpxe1OXflryoeKpWKNWvWMHbsWDQazS3bb926lbFjxzJ27NgHovz/oO4ltUbN0Nn96DCozX09r5Piic69KlMmNOLFLm54eAnoBfBVWdEINiQc+X5WWYVB1qIoYJdFTDY1FkVGEUAj2JEECKxzEJeSVxAVmfcuLWVAeg7nc+PY3b0qgkbD1SmfFfFIC5cHFv73d62+62/vbdu2pWvXrgQGBjJ06FA++eQTWrZs+Y/Pf1214a88//zzDBgwAKPRSJs2fz44rssc/ZXo6Oib1CAeR0zHj5O+8EdcQo2kPt3/HyUHFxUhISEkJiYSExODLMu0a9eOdu3a3bb9nVQ+ru//Ow/LvdS8552yr508boRFb2Tqp6EICOTLdlwEmQBVPsmiEQUP7LIaSVEhywomSYtNdqwTioBVViMKoFMJlHYpzR8pZr7wb8anKSuZ51GJzxK+pU/H53mxxwtFPcxCpdCmQ//+9t61a1c+//xz/Pz8btn+Qb+9O3Ggy9tFQHQuC9UCfYe9X9Tm3JKMjAz69+9foPbRqVMnVqxYweuvv07btm2L2jwnTgoVWRzI4VgzY8bmAAoqBERBxI4ZGVAQMUtarLIKCRWgIkAXzHMhLRDQkm8TSTVnYz8fySrDk1zxDGd8dgYmycDXulPkuD9Y4fyHjUJLBPv723vDhg1p2LDhbdvf7e39Vm/k13F1db3jfnDoAm7atIm4uDg++OADVI+ZsKyiKCjJpxG3fIhX65p0rT/5ttOJRc2t1D6++eb+rZM57yUnxQmfoLc4+N0k5swz0uxpHdUbCPioDVyw+OGuspJn15EvaQEV5T2iuGo0kWDIIzZ7K7IiACpyhTz8SxlJT/JnZ72uPH/4Y9qpOjF0wGd4Zedz6ZX/4T/wDVyrVy/q4T5wHtiX4MP+9h4aGsr333/P+PHjH8uHVs5PP3Gua18s7jFoOs2gQsXHWzXiv1Cc7qU9aw+Qn2ssajOc/Ec6dXwFf3+ROXOMqASQFYEI3VUEQXa84KLCJgscyrrEJVMGJsmKcG3dUJKhhN6HHJ94UATeuxDMSZcIqgv++Lv5oPL2xhQbS+Y3C4p2kIXEA/sSfNBv707+PeazZ0kZN57TisJry0W2DgrBKaP76BN/7CLvt/+Yfp/24rlhD/5F1J6WhqDXo/LweOB9PW6ER02hW/dZTJ+aT/wlOy4lreRKOoySFpUoICtgtYu0LtmIdqUaMPHkEo5lpKIgEKDzwU/rSbzLFRQvA5Z0H3pXeJWrWSaEs79w+Oou3uzcicxvF2FPS0Md8GjXKXTmCT5mSIZ8kgYOQBGsvHX+BF27dXMqyT8mRFYtzYer36J136cKpb/UjyYR/+yzKFZrofT3uNGxaykAVi0z4yfmIioybqIFHVZcVWokxYVNV47zv4NLSM2XcFW7ohNcSDdZ+aRGV3Y/M5zSoQoICukGOz5qLbO3L2X1+dVkPl0D7HayV64q2kEWAk4n+BihKApX3nkb6+UkfGpcJSImmn79+hW1WU4Kkfpta+Hm6frA+7FeukTuLxvwDMpAcD5lHgj1aq6i3XMu6N1EBAX8NQbUKhlfnQmzpKKydzBjo7tzMS+bFKOBCm6RZJklDFYbicYsPDR63H2saMrHoWisDM9axZwrG1CLalbbDkJ0VXJWr0ZRlKIe6gPlsVVIXrBgAfHx8cTExBAbGwtAixYtqFevXhFb9uBQTCbky7GUqJbDa3tyGP/tLw/9GlZx4HG8l+5G+tezUASFY+5mGttl9I/tk+bB4aKtyOhPfBEFEbMsAgpuooV8RUcNnxI8E9yCzcnxmGxQ0sWDwxmJlHYJ4FxOFrtTEoh0D+BcbjrV/UqyLzGJ3a6V6JF1kLruZVh1di0J9moMfqIuSBI8wmL6xXdk3zxz87bKHaDOq2A1wuIuf27vs+6mpp6enuh0OiwWCx4eHgwdOpT333//kX5wiakHCKl6mNWX3Als0pdatWoVtUkPB8576b6i2O1YExJYY5X4Ja4k2/T6ojbpkSXY42MuZr1DQiL4h4q4ilbMsj+xuefJsq7lUKqAKEDbsBher9CYwTtXcS4zh0vZebReO5/vnupGxxVLES5XYLOPJwbRlXqZuexS56DrVBWXFl0e+Woyj+1ERadOnRg1ahRbt2595NfEzKdOcanPy9hXvosYEMWzX8XyySefFLVZjwyP0710LwiCQspzjRhzPo5+/foXtTmPNKV8uzNqSAbDeyQhKzIuoh2rbMRDawPFgyyrgbahVVDsKhINOdQLDEdAJNTDmydLRlLJtwTvNmiAXqPCWwghsWRDWicdw5MwdsbG893qHRi2bSvqYT5Qiq+Lv8UbeQFa1zvvBzZt2sSBAwcoUaIEeXl5jBs37gZlkEcFW0oKl/v1B1HkcLvXKV+tHJ5u3jycGYFFxEN6Lx3bcYqwCqXwDig+ycu25GSEuF+od+xtmkf70Llz56I26ZGnan0Ptv6cyvmzNgLLGJAJRY2V88YTNAquiqfKk69O7uK3S/HUDgihf7mG9KlYm+ZL5zPY8DOzWnbgYvxxlh64xFdCANNkI1sb/o/TjSvg9es6Lvd7m6iNG9CWLj6VZf4JxdcJ/kdatGhBixYtitqMB4qUm8vlV19DNuTiOX06dZ9tS7NmzVi+fHlRm/ZI8SDupbysfEa0mEiHAS14fUrP+3ruB0nKhAmY9m7BVkemcquX0el0RW3SI0+jp2oyddR69v2eT/uyHqgFK54aLblWF/akJBOslXHX6HBVa0g1Glhw/jBmqx0vjZ6N5+MYvfk3wkoEY5egaeOB4N8VTVgDImQrtuaNyP98CoatW/F96aWiHuoD4bGdDn3UkU0mLr8+AMuFeELqJrNzyQjy8vL48MMPi9o0J/eAh48b41f9j+eHP3vD9ivxqYzpNIUr8alFZNntMR44gOH3zfhG5pAd3ZP+rw8oapMeCzrXXkNEJT0Hd+RjVdSU0vtisIu4acx4uyioNXa81W48USqC0bWaIcoiq86dIiknl3ZRFVh8/CgXDEmAzM6LZohsQoYtj8Y/NmataT/aiAgMu3YV9TAfGI/tl+CjjpSbi5SWQqlGJogsQZ9Rexg+/C0qVapU1KY5uUdqt6x207asqzkc23GKrKs5BEcGFoFVt0aRZVI/nozaXYVvbU8a9vsC1NqiNuuxQBRVVG7oxS8LrmLKl5F1Z/BW+5NiUhOgc8NsVlO9RBDZJjN5Ngs1AkryevW6fHP4IHsvX2Z7776EeHriIR+jlJeGzfu34HbsO0J9SrI7eTctGjYke/lyZKsVUfvo/U6dTvARQ7HZQBTReLsR2c4EeVae/UmDX3AY7733XlGb5+Q/UqleOVamzS1qM24iZ9VqzMePU6KBkW1SHerZJFwekqdLfn4+AwYMQKvV0qRJE7p37w7AvHnz+OOPP7h8+TJVq1Zl0qRJREVF0bx5c2rUqMFrr71WxJbfO08/F0PFhpexq7UY5WtpT2ImNrk0F81XOJ2ZjdUokJZn5HR6OvVKhVLZvwR5VguhXl5km8x8e3QzHjod1bnK3JzlxPi3Y8PVQ+gbPIfy3XeYDh/BrW6doh3oA+AhuU3/ORd79rppm0frVvi++KJjKvC1P5PASy+6uVpybGwso0eP5tNPP+X7778HHGs7KpWK9evXoyjKLYW7H2YUm42kESMRdTqCa11FyDhJbtt5sHMe097pV6T1Gx9mnPfSf8dy5gwu1arR/2omB3/dyZn/PfhHS2pqKoGBgWzbto2QkBCioqJu2e7vZdyuO8FXXnmFV155haFDh9K7d28A3N3dMZlMxaKk2F/p+cRsll3sgQLoZBmNoGCV1WTZE3HTumHIAXetljOp6eRbbRxJvUKj0NIE45C083bR83m7NiRl2cky5JC1ZxYuZ+IwBZlIKONBxXU/o42MLNpBPiAe2zXB6zXgsrKy8PDw4P3332f9+vWsXbuWMWPG4OfnR1paWlGbCcDmH3Zyat+5O7ZRrFaShv+PvA0b0JUvj1D/DXj2CzxrdOLnn3++Y709J/+Nh/FekuwS+bmmQusvcEg/lA/GsGL9b3Tv3bdQKpJ89tlnxMXFsWzZsju+ZPy9jNtfMZvNXLhwgfLlywNw+PBh5s+fz5dffnnLcz2sJd589WHEHc1ly+IrIAioBQuuajVuWiuSYuHJkAgMJitx+RkIMqiuqQWfS0un67c/su7EGZqViWTyugQ2nchmP+F0ldIQENiXdQRdVNQjm/5TbL8Eb/VGfh3RxeWO+//Ow/7LnT5wLjFPVeH9pcNvuV+2WEgaMhTDli0EDnwZ35f7APDRd7/yvE88kY/oG9z94lG8l2aOWsq2VQf48cRkVGoVyRfT0bto8S1xf9MtTMeOIWi16A+NhdNHEQSBV1999b72cTtSUlJYtGgRQ4YMYdasWbdt9/cybn9l+fLldOrUqeBnUXR8F+j1emRZLvj5Oncr8VaUHP49my3zLlG9XTD5Gh16MReTLRC1SsDfRYebTkuv8jXYdyGJr/bvp0ZwSRJzcjmWlMJH2dtpWjYSjcZCVr4LqV6VaZmzjLExX1I9/ElMx4+TvXQpgaNHIz5iEb/F1gn+V/5aA06j0RTkdomiyIcffoiiKAQ8JOrpMw99gpvX7fUek4YOw7BlC0GvtscnfQIcK83SkyLvvPMOgiDw1ltvFaK1jx8P473UqG0NAkr5oFI7vnze6jGL0KgSTFxw/xyUZMgnadhwBFEmst4BfvhDTZs2bQgLC7tvfdyJl19+mcTERMqUKXNHdZ5OnToxcOBA1q1bR9u2benZsyeLFi0CYNmyZfz4448AnDlzho8//hhwOLu/O8CHner1qvH77IucP2LAv5YPVkVFpIcJkyWKEu5ulPcqwQ+njjGkWkNkReG1Zas5n5HJK3VqsmDvIYxWKwObVOWTjWdYY/Glg0qPcsZMRLUI8lJ/I3vZcrw6dMC1Zs2iHup9pUid4KRJk4iIiOCFF14o9L6v14C7FXXqPFyLv4Glb/8AtdslLlZoiF6vwsswB0LrsumIF717d6ZMZFmGDh1aiJY+njyM91JMo/LENCpf8PPgCc/h4X1/hbNTx32ILTmZ0j1CkQVv9ipRvP766/e1jzuh1+vZuHEjGzduZMCA26djuLm53VDG7fqaIMDq1asL/l++fHnmz5//YIwtBP7XdiZThDXEH80jtJ4dQVKTL8k0CAhDkgSOpCcT5u5NjaBgdGo1W07Fk2bIx1WjwUOn44+ERDacPA64UKlmezqeiiLtcA5CtTXUK1cGANORo04neK/Ex8czYcIEcnJyWL58+U0RWiEhIVSpUgWDwfCgTHikSTt2Bk38Kc4HV2Hlz1uYWPcnrtqDCHpxCWuHv49JyWfa1GloH8GQ5kcJo8nKmyO+p3O7mrRpUeWB9VPrifJ3b/QPyFq6lJzVa/Dv0RZX6yxoMZ4177x5X/u4GzNnzmTu3LkoisKAAQMeW63W6/j7BRIQ5c7lozkoQEmdSLZN4PfkBBIM2dQsEcnFzDz6rVlDSTcPvEUdbzd7gq2n47FJEiabjaz8LEDPqRQj60a8wtErZ+m7pSvv1XuPGiEhmI4eLeph3nce2Pd+ZGQk8+bNK/j5eoTWnDlzWLNmDQcPHmT//v3s3bv3QZnwyLJ56grOd+nKlQmTqBoiMPmJ9aj9SuM9eBMn4pP4es5MevfuTatnWha1qU7ugkoU8PJ0Qa8vPkJ25jNnSB03HrdGjfAvl4bkFkRSqcKXHFQUBUEQEEXxkS/3cy+oBBXBVXxJPW/AKosYpByqe1Wjln8AJitcseWiV6lBpTD2qWYkZGXjptHQtFwkXWtWpVP1aH4e2IMnysi83aYy0vGV1F7fDx+dN8fTj+NSrdoj6QQLbTo0MTGRKlUcb7oqlYphw4aRkJBwWye4detWtm7dCvBQRWEVJkkJaVxNyqZ6w7IF2wy7dhG8YCIWdxdKzZ+LplQkdJgKYQ1QeQZTSu3GoEGDeOedd4rQcif3ik6n4bOJXR94P1aLjS3rj/FkqyroXf7b7ICuTBn833gDnxe6Ini48enbrzO2QjSpqal4ehaezmm/fv3o08cRBPbGG28UWr8PM08Pr4HWVUFBwChpuJh1Bp3ogyJrSLPn0SigHB/Ue4pgDw9Wv9IDnVpNVr6R7nOWoFYEhjZvxLd922GzS4yeu5mPVceoHPIssemxuMR0xnT8OFJuLqpC/D0/aArNCd4qQis8PJzw8PBbtn+Yo7AKi2+/2MSeX2P5Yd8Y3Nz1ZK/4iStjxkCpMFz7dsJFk8zJQ25UqvGnSLG3tzdTpkwpQqud3A2z1Y6CQq7RQqC3e6H0eXhfPJ+PWYmrm47GLaL/1Tks8fEIWh3akFL4v/ISSFZMVjuT56+kbdu2heoAAbZv3853331XqH0+7DQIacMf2b+AAqKgUMFTw//Kv8WW4ATe3L6a9uUqEOrlxdj1v7PuxBkahZVmT9xFck0WrmYbWLb/OHWiIlm05yJRNZ+BI98TjY7dObHoXuhImZ49inqI950HNh2akZFB//79OXz4MJMmTaJTp06sWLGC119/nbZt2z6obh8pXhz4NDZR5LsZmwGQ8w241anNzuhKhB8fiWHlO/yv61fEnUgkLS2NZs2aceTIkaI1+h7JyMgoahOKhK0nzlPv3em8tegX2n70DXZJvvtB94Hajcoyef4rNHjq38nmGQ8d5uKL3UkePtwx9bjrS5hei7VLFpCZmUm/fv3ufpL7zM6dO5k0aRIzZsxgxowZhd7/w0hN/yZs+uQ4+36IR0Ah1XKV/Zl7eTasIq9Vqod4LT+wZcWy9K1fi/YxFelSM5r5vTujV6uZsGYzA+avZe7OC5QIiwH3QKoYDciKzOms00U7uAfEA/sS9PPz4+uvv75h218jtJzcTMrFdNbO38aL/2uDm4cLpcsEMnJ8B8p42ADw7dULn6p6+q55HZtPBdSdfmBwmVQiKpTklVdeZseOHYWSpHw/mDdvHiNHjixqMwodX09XnqlRgTY1KtKoYjhqVeGE4YuiSNVaEf/4OEVRyFr8PVc//hh1cDAlJ3+MkHUBdn4G5Vrx5ZffUa5cOZo1a/YArL4zI0aMKPj/w5KfWdSUditL0pFMrp7NpfLzFbBIIj8lbSDT4sK2pHh2JCbw7urfaFOmLB8/24pjF6/wzeaDxCVn8Gn3Z8jJM7H9zEXAjQ2xlwk1+lEtNYEhlb6gpLosKRMnIucZKDlpYlEP9b5RvBJhHnFOH7rAqjmbuZKQDoA9PZ2IHz7DOHIw1sxM2DcbYdVrCKF10b66Ho9SYbTsUpsdO7azcOFCRowYQeXKlYt4FPfGmjVr6NevHyNGjHisnOGwn9aTpphoWKE0zzeo+o+PX7zxIPtOXHwAlt2MlJ3NpZdfJnX8eFwb1Cdi6RK0YWHw8zAQNSRVGci+ffvo169fkTihxMREfv75Z5588kmOHz9e6P0/rEREl+bqqSwUWUEl6DHZBT49tYb/1axHSoYBWVL45eQ5zDY7kYG+VC9dkufqVEGjEjl4PonuDapRuaQ3l7OsHNdWwe5VnhlLjrJ+7ynk3DwM27c/UoFIj22y/MNIk461qfFkRTx93TGfPUti/9exZ2bi9cE4mr+/mHmV91CmfBt4bh5oXACwWCz069ePiIgIRo8eXcQjuHcWL15c8OB8lP6g7sbgZg3xdXUp+FlRFH4/F0+90qG46+4esPLNun00rhZF3cr3p8CpZJewmG24uusLttkzMlD7+SF6eIBdImjsGLy7dnX8vg4uhPgt0OZTSlWsTUJCAh4eHvfFln/Knj17KFGiBPD4Bs/diirVqrN3yWEyEs20rPIE61J2MzmmPzOPHCVHbeLnnr0JcvdAr1GDRs2iN/4MzPr9/ddYuvMo3to8jqeYeWnMLARBYGj0Kq7Ke9BXqkjOqlXY09LQXLv2xR2nE3zI8PR1x7B9OxcHD0Hj7o73V9PZn5tH5ciS5DWdCGVDQOX4tZ05fJFRI8dy9uxZNmzYgKvr/U2GfpAcO3aMRYsWIYoi3bp1o/R/qFq9bt06Dhw4QFRUFD16PJwL97lmM7VnfE2otyeLujzHV3/sZdWpU0T7B/LzsTOMfroJL9WqftfzrPzoZfTa+zflPX3MSvZuOs68+c9h3LWb3E0bsV1OpMyWLajc3Qj7duGNX3kXtkN4Y5RaLyPgCHgrKtRqx99BTk4OKSkpRWbHw8YTtZowh/mkn8tmZ8gmxkW/y+L4E+zPiMdN40qlOzgvtUpk9b6T5AseaFQuZOZb8XPTkiHHs/DEt5gPdKUjDsF0pxN08kBYvOswwhdf46VxZdszz9Fp/RvUELPZ5DmO6hXCb2h77tglrBe8WbTge1q2LF45gWvXrmXp0qUAvP7667Rv3/62be8mvNC9e3eaNGnCtGnTCsv8f8y0fXuwyhLn87MY8MtaUOBCdhbnM7MY07wpz1WpzPnMDHxcXPB1cWXPxcuk5+fTtlKFG84jqAS2xV3gqfL3LmgsW63IeXlIOTlImZnYr17FtV491L6+POmdyVPmVVzs4vhd6KtWJeDNN7l+6pv66DwXLHnMnTef5cuXs2zZskKPCr1O7969+eyzz+jfv79TGvAvtKzTCo8QD2xWGQWBRRcX81uqnQBPN0ZUvPva7aKhXZEBnVqFIAikjSuP5B6G7Ctx0kOiI2A+fRr3xo0f+FgKgzs6wS+//JLY2FjmzJnDuHHjnPXo7hFTvhlZknHzvPcvM8VqxZ6Rwfd7jpJYtgalIiOYK80iQJ3MpIy2ePl739BelmUad6hKqxcboNaobn3ShxiTycSlS5cAR723O3FdeOG5554Dbi6N061bNz755BOGDBnyr2xJGjYcKS/vhm1udevg17cvAJffGIhitd6w3/3JJ/Ht0R1Fkrjc/2apMM+WLfB+7jnk/HwSBw+hdkYapfPz8dW7UNLDk/zmTVhZrTxyRg7RUyZzWaPl8JVkfF1cqejnz/bSEfwcEITh8iVcpkyhWlAgIgIpOXlkZWVzashgKnVqj/nsWZLeeRe7zYZGUVDsdhSrlcD3RnMqNAqv2GPIb/3vJvtCZ8/C/YknKN+6ITmWNFxiYnCrWwf17TROd34B0Z3AOwxF58FXX30FUGRToQA///wzc+fORavVsmHDhiKz42HD39OfTis6IQggKQrn8lMo5Vqa2DQ7ow7+Qt3ACELcvW57vFZzo1vIE9yJMTmiubu8XAWPvOao/R8OXeX7wR2d4Pnz5wtKkOT97SHh5PaM7TiZrNQcZh/9M18vPSkT7xKeqDU3X3J7VhZJgwaTn5yMrV0XelfS0efiItyROdboK6qpo6kQfmMV8blz5zJmzBj27Nlz21zLh5mxY8cWfLm9//77/+jYvwsvTJ48mZycHPbt23fLL+K7CS9I176S/ops/LMMkZSTjWK13bjfZPzL/huPdew3A6AoIOXmIuQb8LTbqOjiC3YbcZkZ/KrOR5eTR+MrSahEES9BRZhag5yfT+9qVXj56ac5cvgQoiiAXUIRBQLcXdFr1IT6Oh5igkZDgqSQZLIQ7OONVq8jLCAAtbc3AxasopxgZ+qQwYjuHqi8vFD5+qAOCHAEuAAu0ZVxif4zmMpstt2sXrNvFvw2BuxmaPI2+/bt4+jRo8ycObNIozIPHz5c8DW4e/duWrVqVWS2PEwIgoCg6JCxgiKgFmQC3STqeTVixrH9LI87zpCYRnc9z4R1J1EUaOUSSe28HYhKMIeSjtN+2tRCGEXhcUcnKAgCJpOJ2NhYkpOTC8umYs+z/VtizP3zIZmbkUevcoPpMuxZ+oy7UR3Ecu4cR3v3wSUnh4t9XyMh30iD3B8w2AW+i/iQgc1fpMbfzp+cnMzIkSOpUaPGf1pLK0o2btzIJ598AsAXX3zxj77i/i688Pbbb9+x/d2EF8LmzL7j8eF3SMgWVCoili657X6VuxtfDerJ8rhYfPQuHH5hMF+f3MPBtMuYr8TjXsqLt15rg01SmFK/HRWjbozubdygPoNGvMYz4RVoH+XI8dt0/hzD9+zkh/zaBEREEDxrJsfPxDFu7z4UBYY2rs8bMTFE7z8HCowSVHzepS2uujuvJf70/R4WzNjMglWD8fW/lsR/bBn88haUfwaecKQkzJw5E3d39xuEqIuCSpUqMXToUAYNGkTOLV5EHmeubk5l27TfabOoEyo3FRbJjKQGUSvzXNl706i9mGHkfJqB0Y1bwPpN6IxeZJqyAVDsdhBFhGJWaeNW3HEEw68lxi5atIhJkyYVlk3Fnsad6tKyd9OCnz183en94fM82aX+De1yt27l1HNdsBuNjGzbmuQ6VVj9SncqDVjFSzl9yVLdWJLmTFwKk778hQED3sBisTBr1qximR81YsQIFi9ezMiRIxkxYgTbtm27Y/viLLxgtFtZceE4olqmQoAPaWYDZ3JSSTbl8kblhqRbDNgUiVZh5YjxD+Rg+uWCY3++dJJ6K6ay6dI5vj9zhKtGh9j8xwe2k24z8OXBPXy4azO+7i7UiQzhtca1cHFXM/foQQAWvNaFCoH+7D5zkU1Hz97V1srVwmjVoQbuntciRY/8ACv7QXgjR0SyqCIjI4MlS5bQo0ePIp0KBWjfvj2+vr4sXLiQFi1aFKktDxulvMLIv2Ig72I2IlDRqxxPBIcyrX5HSrne2xpu+SAPEtKNrDvjeMZ86vscQZdbkLd1K2eq18ASF/cAR1B43PFL8NixY3z00UcA/PLLLwVTo07+GYIg8PzwGx/WBy4lkjxuPCoPDz5q2ZRBqq2E/b6BcWffIjXeQIenm9K2zo3qHvEX0/nxx6Uc27uKSZMmUbZsWYojAwcOJCYmhkaNGiGKIkFBQXdsX5yFF3alXkCtBlmQOZB5kV475yPJCipRjateIMhLgxYtKdIV+uxaRKo5j00t3sBDq2fu2Z3ky2ZUWpGLlnTqrZ7K9IYdqRMcwvdnj/DrpXNoBBU/nj1Gx4jK/HTmBGa7hLdOj8Fi4WJWNm+2akiwjydPVy1zV1vLVy5F+cqlSIhLpVSoN5oD8xwO8IXvC1JyrtdLfOaZZx70pbsrtWrVAhxT4m++WbgVLB52ujXowhymkXE+G48KgfyScoSlF06SkePDFWMer1Soy564iyzccYihLRpSvtTNkZ7lAj2QFIVPf82iduverNifxb7U47xZvxWKzYbl3Dn05coVwejuL3f8EvzrG/qOHTseuDGPA7LVipSbiyiKTOjwFOO6PcFsl3V0sR5lH+GcSsggKsiPNrUrEh7oW3DctB+3c/ZqBjUrCsTExDB8+K2rzBcHSpcuTXp6OqVLlyY0NJQ5c+YUtUkPjLjcqwR5uKLXW4nydifVnEWWLZtAN5HfUw5Tys2Nku46LpmS8XER6Bpeg1f2zqHbjq+4bL6Kl7vM8a5DifYJRK9SU9G7BKPrN0XUKqhdFLpGR+PppuGSJQs/b1c6V6zEr71689baTXSc/z3n0zPIMBq5VSqmoih8MGk16zceK9iWciqWt1+czMrF+6DbEui+DHR/6pt6enoyYsQIKlX6d/Jr9wvn9OedqVe5NqJWRU58NgDNSlSlZ8RTdCtTnbKeAdhlmW+2H2TniQRenPojq7cf56VxizH/Ze27XKDjS79773aU6PYlLw3pSGSXY1z0sYBK9ch8Cd7RCaalpfH777+zefNmrly5Ulg2FWve6zSFD1/48pb77OnpXOjZk119etPpt4VE+0kszfyOCEMCQ1w7E1/hNZqUj2T4k/UZ+tzXHNoXj9Fs5UJyBpk5RjKy85k3bx6bN28uNvJot+N6ZCjAxYuFo4BS2JzMSWbW+Y2gzUGnVvDUyUR5uVLCRc0Zw3kS8pOIN15Eo7ajUcmkWtN4v3objJKJHHs+TweXxYaFTcmxzG3SlVE1mzJg749cys9kUPX6zG7SmWOZyaSYc9l95SIICsMbNOLL/XuI9PfBz9WF86mZzNt6gLjUm7VaFQUuJ2aRnmkAaz7smELgypZM6XqOJq2rgpsfqHUF7ffv38+iRYuw/i1StiiYOHFiQRV4JzfjotHjGe5DTnwmMiKx2cfoW+YpBlRsRJ/1PzHjyF4+faENbnot9cuHIQgCrnotgiBw7GQiLw9egF6yUTXEi0A/D7CZCBZyOZ5ziFOGOLTh4VjOnivqYd4X7jgdOnXqVBYvXgw40iWc3J3ytSJvSln4dPFmUg8d5qX9q1FyspndqgGiSmFEwgL0ip2uvj3RBdfj+85tUIsi6VdzqdO4HCWCvJjx0y4W7ziChy2dPLOK/l0aUtLfp4hGd/9QqVS89957CIJQUFXkUePb+C2IIpgkM08GR6JSQWz2BSyY0KoFBGx4aFwZWakjr+6fgkZloduuMeg1eYCW47kn0WuszI3/hdnnN5JpsRKkDuTDI+s5mpFEtE8QvyWfQRAFhldvSOeoqpy8epXFx4/yQ6euDG/iiABsVC6cAA83AE5fTOXDbzYx4bU2RJT0Y+6HjeHwIvi8O5gyEcq3oVTLieDrfdN4PvroI3bs2MHzzz9fiFfx1tjtdr799lsOHjxYUFfwet6pEwehTcthMVuQZYF8LDTf8g51fWMYXqsxzUtH4emqZ9cH/Wn09gyMBivzRzh+rwKOJRytWsWagdeiSH95i6BD3+IaHsb57PM0iIrCcvbu68zFgTs6wUuXLpGTk4PFYuGLL774x6HsjyM93u1407bAM4d4asNCMl20fPBCCxLLeIAsMyiwLXkWN1BCWNXiWdTXIq38S3gyelIXANRH1VgwcWLJVwQEBOHtXnyk0e7E5MmTOXXqFAAVK1YsYmvuP+mWHHZmHEGvFqnnF0qn0FrMiv8JjcqIIihoFBVGWUZSshl+dBJ+OhX5MuRIOWjVArJsJ1/KR6MSyLLlosgabLLCJWsSslFHo+AI3jq4Eo1WQZYV/FxdeGrFXLqUrcL+l/vj7+ZweksPHKduRChxV9IZPGslr9YKpC6HEGxNAD+HA9z1JZRvAw0HQ2idW44nKSmJNWvWMHz4cHQ63S3bFCZTpkzh5ZdfJuxaqkdRB+k8jDR4uQVXbakASLKCRRLYeOU0ixs2oay3PwB2SUFWIC3bQEpmHkG+HlSpFMK8L1668WQBFRBsRqLcQziffR7PNl2wRv+7klwPG3d0gp999hnDhg0r9lNvRYlsNlNvx3riSpZgTJsGjFA2Y01R8X6JFpzTlUAwalDlmpiw8nem9m5Pdp6J7DwT4SUd64FB/h4k71iFZDKweul3uOr/W0HUh4U1a9awePFicnNz0el0rFq1quiMyUsB5W9fo2o9uF5bk829AvxtUU3jCi7ejjnFvJuXCr4+9xOioKBTWcjNOsCinD/wEgRcZBlR0JErQrBbAOmWLHzMJtSCQoCiYJdBRqRSiVpszj2LVlLhZTLgpfZAq1JxKS+XMq4B1PIOYkdqHF6KTHnDFS4dXsb7Hm6UyzvK9BnziKjVmbZ1WrNizbfU9j1ASTmbn5SL6A5IDgOl9kAo1OkHdfuDx52Dk+bMmYMsy0VSMul2/PTTTwXT6qGhoc6X9L8xsdpgeu1+GxQFUaNCEEArQPsNC5nV6HmeCimDXqvmjVb1+XzlDs4mpRHk60F2rpFXh3xLg9pRBNYoz5RNZ9jTvRxaIErjxY7sODy7Fi+FqjtxRycYHR1N9APy9sVB7/G/IBuNCFotu7KuIH7yAStSdjPz5AJqGpOZGVSHKFd/MGi5lJqNWhAY0LweAB8v/J3dxy6wbbYj2m329z+Qc+IPho0YWRAN9yjwyy+/UKlSJd555x0+++yzojXm60aQn3bjtirPQ+drATtTq4PddOP+Wi/Ds587nOdnN3/JVgyrxJ4yNfG2G5m7Y+VN+3+MqsLP5URCZQNTd/180/7FFZLxCquErzGNGX+svWn/xLSziN4VqWhP59vLS+HPzApigFkXQlHVf4YpXVpTcvd+DNooNhsjOJDuRsXazega1sDR2DP4TlcGAJvNxuzZs2nVqhWRkZF3bV9YZGVlFQRV/bWskhMHVxNT+aXlHKqOaEqpluXx0IpkWdWoRZh7cj9PhTgihmeu30OTalE8UcXxu/362+2kZhsoVyYQu05FRr6Vi2J5ygJVBT3J3lFY7VbErBxQqVD7+t7BioefOzrBLVu2sHXrVnQ63T+ec38U9B7/LedPniRhwAAulg1lfLOKVLYmMytpFb42I/+LaMXPrpUZVqYarQIr8kbGGkY/05TUqwb+N30ttauG0blDdYasXEe9ID+O/DifyPIVmDR+XFEP677i5+dHfn4++/fv58yZM0VrTIsJNzs537887Nt8Aop0437/8tf+I0DbG9fLN6fuYT+ZaEUJD1cLC6tUQ75WzlSSRURR4ZKnD1W9KhBnP87c6FqoRRGbLDne1kUtmpAn0cmJ5Lpo+bRSQ+yI2GQNTwTG8GvqWY7pSuChVXPC5s3kGkPpVb4xEw7vo0ulOqSZBL7cfAjbwSO0r1CDPvIbPFuhAnMT9vNq59o0q1YW1DfOKBzefY6EMyl07HOjHqTdZudU7Bn8/f0ZMGDAf7jI95/s7GwWLnQIfN+pSPOtnj3gEE44deoUPj4+vP/++wQHB9O/f3/UajWRkZHFOgIboEzpKFAE8hKysMsCdsWCh9aDkq4+TKj5p7rO/CHP4+fpVvBzj851aVi7DI3rluFEsiMK92yuhrKu/nSxqejSbh6y2cyZJ57E/403CBj4RqGP7X5yRye4du3Nb6D3yoPWe3wYkWSZ5F27MQwfhpfJzMYnKxPkms2C88uxCyIvV+zMMU0QljwdgqIizM+bRX26MPn7zYSV8OGyMY+Lf5ygbFQAx3OusvaUheef78Ibr7+OVvtoTIOCQ7ps4MCBqFQqFi1aVPRTbNW63nl/jZ633yeKULP3DZs2H08hPk/AXTQjagX2h4cgCgpWWY2AgoyIjIDKcJinQ5qx3WUjMgJ2yTFlpQCKcgpBUCHrBHaWDEdBwGxXsV80s6jjLPZkxDPp2Hp0ri7MyrOwNS6e2qVrsyNPzQuVqjLTP5KYUsGkZRtIzckjKtiPjR/0ve0wNq86xL4tp25ygjP/9x2bf9jFwcSDt5T8K0pmzZrFpk2bUBTlpjzSv/L3Z891J6hWq9FqtWg0Gry9vdm5cyfR0dG8+eab9OzZE6vVWqz/7tRqNe6hPhguZqEWBSRJAbWVfmUb0+GnxbxcpSZDajWgUtiNkowhwT6EBDuC76IC3BEEiLtqcLwMejvWYEW9Hk2pUljjzxf6uO43d7yrP/roI1auXImbmxvp6ekcO3bsTs3vyP3We3wY+XnKZ0R88w0ZPm580rcJhkgdamTGV2jKGY0fiSofzEYtVfwCeLWyIwAhO8/EtiPnGfdKaz59+Vk+3PQ7h+wpKHqFyoEl+frdd4p4VPefzz77DC8vL9555x369+9f1ObcVy7kXSDFfBJfrYxalNEINgQEZEFEJ1oBEQUZCQEvlY7dmRsQAY0go9NISIqIVVKhCICioBbBBRtGSYufzp00s5X2O8fTwKcGWVYjWkGDu0bHuYxsTiYfQTAJ7LpwiaYlI4gpGUyZIH82vP0KACazjW9W7qFds6qEBHrfYPeb4zrzmvnm1IeqT5fHK9ANzX0s33S/0Gg095S0//dnz3XeffddRFFkzZo1zJ07l4CAgAJBkBIlSpCRkUFw8I3TxcXtueQfHkjq+SQAnixRhQ2JKYw/sYpO5RpQO6jUXY/Xa1SU9HIhPt0AT3dCURS6rn2eJ0KeoF1kBJYLCQ94BA+eOzrBlJQUWrVqxQcffMCUKVPu1PSu3G+9x6Jm/Cc/4+6uZ8jrTwNgS02l3HffERtVgrk9oxl5dTuxV4NYE1SBIz6B5FpcEWSo6h1E+fxS9Fu8ik87tSbQz4OdX73JOxs3kSdbuOyTgy0xnfRvfmTA518U7SAfEGFhYSxevJikpKSC8Pb58+cXtVn3hdnnv0Qr2kEAN5UFjSChEhQkRUCSBSRBhaKAKIBNMaMTBRRFQiUI2BQVoKATZBRBi6QoSApIAuhFGyV0AaSYLuOm0vDLlaOAiFrQ8Fr5xmyMP4+3xgWTQUK2K8zZfYAmZSKoHRbC+SsZRAX7kZyWw+K1BwgN8rnJCWp1arS6mx8Hyzd9zw8//ECXoc/i4uJy0/7iwN+fPdcRr0VjlyhRgtjYWKpXr87Ro0cBR460n5/fTed62J9Lf8ct1Afj9lPYbBJxhkSeDalOntXK2Op3L6l0nS61QvB20WBMu0L6H79itZs5m3UWXUQExj8OoMhysdYQvaMT9Pf3R5Zlxo0b94+/AjMyMhg1alSB3uOgQYMYOHAg69ate+j1Hu8FvV6DXqfmZOJlshQZtd7GrjF92Ks+zbSz6yhjyCDB2wcXtR2brMJFbSPtgh9Hc3M5YTlJkM6NHjN+JMjLg1l9O3EhK4s0bQ6KaCH/26Xo8/LpWa/+3Q0phvzvf/+jW7dulCp19zfR4kaOlIYgCLiLJtTIiCgIgCgoIIKA7EjEwqHuLyM4/ggFBa3g+BKUBAGbbEUrapAFEWQNdlkiy5qCAuTZzIiCFlElYrHJyLLA5bxcKocHMa6V46Xsg9W/szX2PEdPJ/HV6t1MfuUZmtcox9qZ/fH1urcSXwaDgYULF9KuXbuH0gGmpKSwbds2LBYLAL169bplu06dOt3w7OnZsyeLFi1i4sSJXL58mfT0dKZOnUpQUBDff/89gwcPplq1asV6KvQ6tZrWx6yxI9kUUsyZ2O1nsdhdMNjMuGv093SOIU87pNF+//RjnjJMpFS1Z7iQcwFtREMUkwl7SgqakiUf5DAeKHd0gqNHO3LSDh8+/I/X7oqz3uO90P6FmtgvJ3C16wusrBvNrqd9qWRPZdbpdeglOx/HNGaffyiCAiGa0lT2qsGX5/5AhQgKGCxWhrRsiLteyzv7fuaE9gJGm52sH9aTd+ESa9eupWQxvrHuxqPoAD87PRqVIKER7LipbaAoaEQJSRFQFBFBBLsiXAuSUaMgo0IGFLj2JagIICBgF0QEFAQk7LIZSdGQazejKCoUBDw0evqXeZoupevSY+MSonx8cJd1ZJpM+Lq4kJVv5GhyFucTM3ilRW3qlHes5dyrAwT47rvvyMvL4403Hs7Ah+HDh9OtWze8vb3v2M7Nze2GZ8/1NcF33333prYzZ868rzYWNTN7jqdDhKOepKzAhbxc8my5vL1/NTkZIh888RSR3neP7jRa7VR7thX8OJFyOjd2p59C90RtgidNQnR3v+vxDzO3dYIjR44smPN2KjI4SMnOY8Xe4/RpWpsPJnzMkM2b0KkELpbSECpnMfv0KnJ1Oj6u14gkDy/sJhEQOJF3hd/O7Kesvx+Jl/NY93pPdGoVfX9dxXljChpPE4ooYv/jBHm/7aP/oIE8++yzRT1cJ/+QRNNZNKKAq2hDQEYtKIiCgoiMXVBQFBGNqKAoMiBdyzxUEAQFx1qhHQE1MgJ6UYsi27DIarQqCaOkQyNqABlJhlyrlUnHfsVFdOP7Vi+wPSGBV5avxGi08kGLp/jixbZkGUycvJxKw4rh/3gsiqIwY8YMqlevTr169e7nZbpv1K5d2/l3chd0Ki2WjHyQFdT+7vi5yqgN3vySEIc635Uskwm873yOPxIy6fL1Hr7rU4tGKi3hNit2xU6qj0BExw6FMYwHym2d4Mcff8yBAweoXbt2Ydrz0GK02Nh/7jLTdu4hfvkc3t61j4v+3szsXwNDoJZSbkYWV63KiRIB5Gt1oIBVVmOyaZDsHjwVFElGlpmG4b4Ee3oy4+heztsT0XjZ0WusSIpI4pZD1G9Qn6mfFnHeXCEwe/ZsfvvtNwRBoGnTpsU+QOZk1gFEQcFNZUYtyOgEKypBcDg6AUQkZBQkhGvToeK1r0BHNKisyKgFAUGRsChqZMWMCgHh2syBWrBjlc2Udy/PvvRUBAGsVpGph/ZS2askTSIiiCkRRL7lz+AWH3eXf+UAwTH7c/z4cebOnfvQlutasWIFv/76K66urs6X9Duw+9UfCagXTvSIp7BLKgxKFjKuKFqZKG8fPlm7jVYx5akSGlTwwfNXwnwdswcXMk008o2kkiGPdlHtEAUR8+nTKDY7LlWKr3rMbZ2gIAhs2bLF6QSB1GwDbSbOo3bt0kQoqfTduZddFUNZ1b0M76Zs5RfPcmT56DgQWhKtIKFGwmDTkWfSkGVyp35ABKfPZpBmMCLpU6mx5AvsahvubhZkFDx0Fow2LZ2mjGBKlc6PhULP8ePHCx5aj0KazLeXPkIUQI2EWpDRiMq1dAhQEFAUAUGAP2MT5Wv7FRwToA7fKCkyouBYOxQUNTKKI4hG1qAgEpt7GY3ggtGuIMkiZsmOdK1ExNwuHa+d6b9To0YNjhw58lCX69qxYwd5eXmAUzbtTriGepN/KQtBALOci1bwQa1V+L55F+x2hSV7j+Hn4cbe7XGs+/04y2a9huYv+sclPHS4aVWcTckDvzKUTTvDhO4/AXDhledReXgQNn9eUQ3vP3PHNcE1a9bw+++/4+np+Vi/ac3avR+VJ6j0AmdC/BnVqyk+VfOZeWotwcY8EsM8OKQuiYyARVKTaXElzeRGttkdWRKJ8Qxnq2UfuCsokoJZsuPuZqGUVxYWu0j8t/t5rk9fPn7ylYf2rft+k5OTU1CqKzMzs4it+W/IsoyCgggIgoyL6ChHI+Ao06KgoAgCiiI7vuwKHJWCWnCs1Th+4tpZHE7QjoiIjFnWYFMcZ/q8+iv46byZd2YfP184S/fyMVTwCUBRFJ6f9QNGi5Ulr3UjxNfrP4+rWrVq//kcD5Lx48cXVCBxyqbdHs8wf5J3nHO8bgkKTwSF0Dq4EaGuvsw5dIDvBnZl/6nLhEcE0LhuGdTqPyM9FUXBYrGjs1hZv+0s494dDYKIoiiY7Ca04eEYDx4ousHdB+7oBHfu3FlYdjy0rDl9mv37t/DRhk3MdWtCQEUID8jkwyO/owgwr0EMF0r4ohZkJAVSLN5IigqzpEWRFUK1Jfj88C5wlfHyELFhxsPFSpRPGiZJzcmZu0lYcpjwJ/ogNHk8HCDAF198wQ8//FDw/+LM+BN90YoSInbUooTMtRplAjdIjjrU+a87PccOWfmziSQLSIrjASQjYJVFbJLKkTqhQJR7GHWuKdVMrNWOiX9R0Zv0yzYSM3JAhn1xlwmp8++d4IQJEzh79izz58+/Ia/uYSMzM9Mpm3YPlCtbnotrj2HJs6B20/Nrynm0Kk9sRpE5Bw/ialIzf/1+vhnUheENmt9w7Pw521iz+iBRzaoTn2uFEg6JwH6bXsMiWfg0oi65a9cim0yID2EE8b1wRyc4b948li5dit1uR5KkgiTRx4WkvFyWT53OuO1bMbjoyRIt1LWk8smJDVxxc2dFwwpke+hRy3ZARZbNFbuiJceiIcfogjlfx3klm+tzXVbRjJ+rgXDPdNQqgTOrzpCw5DAd+nRjwCsPjzBxYZCcnExeXh4Wi4Xp06cX67d4o5KDClALCtprKRHXnZ0ogFTgCAVkxREEcx0FBRTHdKmMiCgo2GSQFBWyomBUdEiKgFVWIcuu2GUJtXizYyrl5YmHi46xzzSjTUyFfz0Wq9XKtGnTqFWr1kPtAOFP2TTgjrJpjztPVm3AryzDeDkbn0rBeGs1dA6tRYxvOPv79cdX70Kr6HKULxVw07E1a4UjKwqRT1QiMcsE5lw4vowglZ7tWWfRhTvUlqyXLqEvX/6m44sDd3SCR44coX79+owdO5bJkycXlk0PBYoss/n9Ubyz+XfOhPjzaZ/6+IUauKp2Z1m5Spwt643KRURUQCNAqtmFqxYPMkwuZJtdyDfrEDUygiIjSQp6NzvB7tmU80lHK0rE70zh8JSdlG1YhWWzvy3q4RY6j0qFkiumBGRFQEHER20rmOlUlOsBLxTsd2wRHY4PAOFa+gRIqJEUsCMiKSKKImKWtICCClckReRI9gV67/6K2XX7878dv9C9QgwNS5YG4KWGNXipYY1b2ihJMirVvSUzL1u2jNTUVN58883/clkKheuyadf/7+TWNKzXgEpvNUcX5InJBlbZxHlDCjG+4fi7OoJebuUAAWJqhBNTI/zPDcZMWDeM8NpdWGnOwFbRIblmvXDh0XOCb7/9NllZWXh4ePDdd99x6NChwrSryNm3aD61fvmNX6tF8kuXSCanrmdVSAXy1Brior3RiAp2RUFBIMumJz7fD7Okw2DTIisCKlFBUCuIooJWbaGi3xWi3LIcQsiSij1TjxBRMZKDv+xCrX64NBkLgwdZoaQw+eLsWwBocSRsO8JcQAZkRXQ4QoRr633Cn9+A1xtyPXBGBsUdm2wjX9GjwRs7VnSinjybGqtdxippOJSezu6r8exPTaRxqXAaUvqO9q1eso9vZmxmwcpBePu63bEtwLRp0yhXrhzNmze/a9ui5IsvviA5ORnlWlDQtm3bHrsX9VOnTvHZZ59RuXJlmjZtets13MZla1KyZSWEazmoNqvCz4mHyDIK9ClXG9U9qL3IskJCRj7ueldKuPhQ2uIQnL/iJxCx4Bv0xbgm6G2fvj169GD+/PmcPn2aihUrMmbMmMK0q8i4LgHUQ0yjznNPoKtuZmHccnwsZg7nB3PR2/NayDuoUEizupNlc8Vo06IAVruIza5CEGVEQcFDZ8bb1Yiv1oSn2ohV0WJU1HSY0ZUJVT94bKPa/kuFkocFu92OhIwIaAX52vedgKPehHjtZ/GaQwRRELBfe2griuiIGFXUmBQcmqGyjEXRYZVU5MsmJEmFpNKSbbZjklyRFYEyHiVoHBTFiKo2bFbpVmbdQJkKwTzZvDKubncuhPvtJ+vYs3sP+/btY9q0aQWSYg8rXbt2JTMzE/dridoGg6GILfr37N27lwMHDnDy5Ek6dux4zy8ghw4dolSpUgiCgF6vZ9q0adhsNoYNG3ZDO5WoQr5kICcnB9+qpYhyK83JnFR2Jm2iRUg5wtwdYtlnL1zlvSlr+GDos1SIurG+ZL7VTrMp2xjZqjwD/MoQnpsGAiRYkomuV7xzNW97p0dHR/PZZ5+xZMkSrly5wpNPPlmYdhUJxgMHuNC+A0v2/IKslilVLpuZJ39Gq0jMaFybK6VccROtiIqEWVaRZXMh0+5OUr4XdkWN0aYFBFQqGbVKwtPFSIx/Io394qjknoopMYcNn57ELgcxp/lMgoLuXMj0UWbt2rWsWbOGZcuWFUsHCDDp9FAkRURWFETRMe0JwjUHBzZFwC471vtAQJK5tv6nwrE+KGJDdijFXJsatcsqJEXEImuxKhrSjAJGSYsAuAp6fmo6gLisDCbv2cGUPbvuamPlamEMHd3ulrqgf8fTzZuRI0fy0ksv3bVtURMcHMyKFSsoXbo0pUuXZvHixUVt0r9m5cqVBVVVmjRpgiTd/eUGHOXqxo4dy+DBg/Hx8UGW5Zsc4HViv97KmS+3ogBPBEbir/XhmbAKBQ4QQKtR4e3pilZ7873ioddQwkPHhbR88CtLaMYlXq3yKmW8y2A8dIjsoiyK/R+57V+G1Wpl+fLlrFy5kpIlS7Jx48bCtKvQyVq2jJQPx6EpVYoFZzbQXkjlkwsbOO/lw6qG5cl31eKKIxHZImvJk1xRFIWUfDfyr02B2iXBUU1cLeGjN+KnyyPKLR030UR6ooUvex/FYoF57w1BFB7uN+0Hzf2sUFJUZNjSEAC1YEVBhR3HQqCk4HB0Ati59tWHzPX5TxHHOqBVEbEqIgIiKsGFXLtjr6SI2GTHfKlJVhAFAatdIMcK7/7xM0tOnAarQFlXX974YTVPlovk+ZpV/tNYeo14Brh7RYaHhS5dunDy5EliY2MBCAwMvMsRDy9eXl589dVX5Ofn8/XXX/Pss88SERFx1+NOnz7NF198QXBwMCtXruTFF18kIyPjlsLfgZElOXH4IsgK/jofUszZvFOlzQ1twkP8mDXxxRu2KYrCksV7qFUnkgh/N+LT85ErRqLN+55B0a+A1o0rU8eSu/4XvNq3L5YpXrd1gt26deO5557ju+++Q6e781RKcUax20n9eDJZixbh1rAh/Tt4YXO1I7vKbNSU4Y+Kgai0yrUkaLDKIoIgYJD0mOyOt3RRULBKIhrVtSlQvYlAl3xCdRlE6VI4l6Biaq8TWIwyu7btpULUv4/ee1S4nxVKioJdaVuRFYeohFZ0THFKivpa9KcjulORHWvGjhqB6msrg2BXQEaDpMjIihoBsOGJjMkhiWbT4a1xZ2j5V5l8ciUX87LwVfmRqOTTJLgMboobdoPCkj+Ok6exUj30v2vMLl68mMDAQJ5++un/fK7CYNmyZZw4cYLKlSsD3PPX08PIXzVMr169SokSJe7puL9+/XbteueamCGR4Ry37MCclsdvnnuYXucF6geUu2sfeXlmFs7ZyoE/4gmoXo7dl3P48WwlVu4ayvjLaah8r+AbEYGcm4uUnY3ax+eu53zYuO3nyIoVK+jWrdsj7QAB0mfPJmvRInxf6sVv/aow+PJWot1TQS+yrXIoGq2MSnCof5gkNWZZIM3iQpZFT5LRG7ssYpMcoe2CAK4aK7W8L1Hf6xx1POJJSzDxac/jWIwSv/+6leoxt47ge9zw9/dHpVIxfvx49u7dW9Tm/GMWXpyPpAioBQmLosUsq5AUFVZZ40hnUEBChYQKAT2yImJXHFOeFkWFHQFJVmGVBGyyG9lWEyBgkjRIigpPjS/70+NJyM9EhSdXzAZq+oTRMqQCY+o9RY9q1QhwdcWuyPRt9N9UnUwmE0OGDGH69On35+IUEvPm/alSMmrUqCK05P5xrw7wn/JEVYf+qykxixO5lxgbu4jVl44iKfIdj/P0dGHGN305fuwylrRsMvOtVKhfjVqNYnjpq2G88HM3NKUdwVnWCxceiO0Pmsd2Tu56VJlvr5co9cXn+LzYmI6/TaRxRgLlTamU0ObiqTaBICMpCrIiYJR1mCQXLpv8ybXpMdkdLwhqlYK71oK/az5V/s/emcfpVP0P/H3vffbZ98UMY+y77FsMylIpSUkRJSVJRZJQVFIp7VIR8tMihL7WKFuLfZusYzDGLGZ9ZubZ7/L748mUrMUYw/N+vbyYe88953OuM/dzls8SfJKG/iepbMzDICoU5ngQRIENP/1K6xZtyrPL1wxLly5l3Lhx9OzZk9tuu+2Mj9l/YcOGDdx///1XSLpLQ9VAJ3pXeqf/KHgtPVUkXJrO6/4AOFUFtyYia3rcmh5Vk/AoAk5NwqUZsSsqiiZQ5PEmX2oS1AijEMwdMc1QZQNO1U2nqLocKszHpcoA1IqIYEznDlQLCmFnWgYATpeHbQdOXJL8q37+g09meyP2zJs3j9zc3AoXvs5ut5f+uyIbxlwN+re5CwBbWgEAInpe2L6YvQUZF322amIkX34zlOf7t2J6vybUqRnN8GY76ZYYgEt1UhTtNe7zKcEKRMn69aQNfBjV4UDy9yMwugBxTg9cko432txMQYQJg6AgALKmx6UasSpmHIqOYtnrDi2hIiKjF1QCdE7CjMVEGa3UNGcSItkoznEioBLdJJJDB47StIkvButpVq5cCcD06dNp0qQJgYGBFyyfmprKoEGD6N27NwA2m40BAwYwePBg5s2bR/v27WncuHFZi13Kw1sfRkGkWDagxw9VFbErOmyyEaemw63pkDUJh2rApRoQMaOoIh4VVESv87umR9VEFNW7MnSqXkOZegG12Jh7gGTrcZ7b+TWqIGBzKRwpLGBmh/ux6Lw57jKsRUQF+rP/ZA5f/up1X1qwbjdD3v6OH7ceZMu+NDJyrBQWO87Zhz370vllcwqapjF16lRuuummCmf81rZtW3r16sU999xDs2bNLv7ADUxsbCzNpt5NTOeaGEWV+sFRzG73EI1D4y7p+aioIGpEB9Ktfgxmow7zoUV0j/BOQk74u0Cvr7BKsNwc1DZs2MCvv/5KbGzseZNhXmk0TSP/iy849fY7GGvXRikuRtw9B1aO5kh4GN+1qINkVvCTPLhV0Ru1g78CIOe5/JE18Cg6HIqETtLw07uxSB6qmvNpHZhClK6Y39fZGDc0h8cnxfPM4LmE+l+/eQH/C8ePH2f58uWlfwPcdttt5y2fmJjIzJkzS5XgokWL6N27Nz169KBPnz6l+eGuFrLqXQUaULEpCqKg47Q9gKaqfzrKe7fIVU2iWJUBnTcQtqKhqXoUQcOjing0CRUJneDHe41HEGmMpFv2ePwNARywZmMQ9SgqyKp3UubweHh5/VqOZxeSmlNAoNlIiJ83XNUdbesRHuzHkvV7OXIyD1GF6vERvP9cL6/csvfc7Nc9x+jXpzXPDQ1g5cqV7N+/ny+//LLCGTX079+f/v37l7cYFQJBEAhvGg94ff72FqUyOcybY3L4/5YRHxTIqJtvvmg9m1PzMOhEbgqvTpXCLDDBsZI0eq1YgT6qbLZyy5oyU4KpqalMmjQJq9XKggULsNlsDB06FIPBQFJSEunp6bzwwgu8+eabZSXCGaguF1kvvYx1yRICunYldvLriBYL6LuzKuVTfqhdG4vOQbho90bu0EQ0QaDQbUbWRDKcIWiagEPWYZN1iIJGkNGFhEa0oZC2gYcJ19lYvbCISaPzSKhl5Nn7fqSK/8UPn2807rvvPnJyckr//rcf3/T0dBo08FpDSpLE3r17+eWXX1i9ejVdunQ5q/y6detKQ/6dzpH5X/kp61ecigGL5kLVeSdJsioDeiRcuNAjCQISGqqqcVr5iX9mjFCQSi1GVQQ8qoiiStQKrMGYPTMJkiKQNAPHiosRBSPRxlDySgqJMPnTKCyWrJJilh8+xOCbmjGiXVtuqhSD/s/wZsH+Zrq1rENibDgLNuzhpsQYKkUEl8o+bOpCPB6FfYeyuLtTQ54f0JmioiKaN29+UcOKa5HrwcL4aiIednB0z0HiezVG0TxsPLWXLelW1uen0lNfl2EzFjPqrg5UiTi/ccuL3++leqQ/n4bXJGrnFsxVKnGs6BiG2vfz08ItBIX607Rj3avYq8unzJTgxWbvTZs2veDzV/LDBZA1YSLWJUsIHzaM8D5dEDZOgltfZXzaRJSGVQjESYSuBBkBjyJR6DHhxmukUOIxIKsSsgYlshFJkDFKKhbJg0Vy0zlkH3FSPnOmFfPRlCKatDWxcNFvVIn0KcBzcbl+aHFxcaSnp9O4cWNUVaVBgwb88MMP5y2flJREUlISABMmTListj8/+hUg4tZ0GJEIkPzJduciCRoezQACKICKWpoFHk2HgIJBFJE1FY8qof5pHKMhIWs6fss9jKxKnFCdpWHWJAFKPN7V2468k6iqSrR/AJsGDmbmjh1UCw/FoNPh8shMXLSGnk3r0aJ6PJkFRSzYtIfOTarTsPpfuxBdWtRG0zSe6dOB+Cjvh65Pnz7cd999FW4VCBXfwvjvJCcnM27cON5++22qV69eJs/Zd5xi/7QNxHavi2owM27P19zs355WleK4t0Y9Ht+wiLxi+wWVYGKEP6k5NqhdA8Fdwis3PUtC9E3Yd+4kY+KrrKvXxacEz8c/Z++tWrXijTfeoFKlSucsfyU/XADhQ5/Av1NHAhMlmNkZBImiRr1xaEVISARKdhRNoFg1o2gSRYoZAQGrW0++HAiahk02oBcVgg0uqvtlU92YRUNzBmgetm+X+WhKEbfc5cfSb7Ixmy4eosrHpZGXl8fYsWPZuXMnkydPZvjw4QwbNoxly5bRo0ePqyZHpj0Hu6JgEBUQBOyygkOxgiah4A2ALaCgIAEa4XozJUqxV8FoBt5p9BHjkl8mx5WPTtDjkb0Wxy71LwUUbQxAJ/hzyJqDRbKQVuKtX5UFZuzfymP1WrL1ZDrTNm+mfmQkXWvUwOH2sG5fKvXiomhRPZ4ODasx/N6bcf/D8q9XUsMzft60aROtWrWqsGH7wsPDUVWV1157jd27d5erLKe/VX/nvvvuY+jQodjt9jO2+8+ViKB+/fr07NnzX7f7b567uWFr1rEEW7oVU6KZIIOR79J20TysMj+ePMLG155AvMhkqFqEP+sP5qCEVEcy+NMtsAaE1qZw/WKaulPoMe7tf92H8uaqjf5/zt7bt29P+/bty7TNouXLKdm4iZjXJ2GIi8Nw8n/wfy9CRG3o+zWvp43ELMroBA9G0fNn1BcdqqBDRo9NNngNYVTwaCJm0YNeUjCIMrXNmcToCwkUXagING5u5t05UTzdP7NCzqqvZcLCwpg+ffoZ12bNmnXV5XAoTk7bknUIa8GRkuOcdKZTM6A2B4rSkDUZf70fimrDIBgJ1MeS4z6KXhRRNYVPjszGKAXgVKyogKLovXFF/0yf5FF1ZDud6FBRNIlC2Ub7yJpszDpGk4hKHLcWct8PX7PlRDqv3XoLXf9MeBvsZ+bXiUPPkHXexp0gwHcj+xH855lhWk4hK3ce5OHOzUhPSyMpKYnx48dXyJCImqbRrVs3mjZtys6dOxk+fHh5i3RFcTgcmK9waqIG9byLEFtaAQHVI9EJGvXDwqkiRTBjx3YGNWlKoNEEwP7DmWSdKqJj2zODYleL8MOtqKQFNaXqmHRynXnsOr6GZlW8ixkx+yTUq1iBtMtMCZbn7F1TVXLe/4C8Tz/F3LQpqs2O9Otk+PVDqH0H3D2dz45ORSco+OtciKgUy2ZKFCM2zYSs6nArYJN12GU9blWHUZKp5GdFL8pUNebQxJSONcvFA0MLGf1yAFUaCjzdP8OnAK9jntz5NiAhChp2VeOYIx9Z04Nmxql6E+YWu2WMUhBV/aqS484GwK14U+6mFp8g11WEjIQASII3TJqAiFm0eLdRBR0tQquxJuMIHlUm2GikVmAUv2en8XvGSURV5PbqteiUUO2Csg67vS0vfbeabGtJqRJ88auV7D6WybKt+4nL2Iooijz66KNl+9LKCEEQ+Omnn2jWrBlNmlzY9/af9ginDaneeOMNjh49Sm5uLu+//z6yLNOzZ09atWrFrbfeyj333HPJ8lwozZzFYrloGroTJ06wevVqUlJSmDhxIq+++iqvv/76Rdv953MXSn/Vo1knEAVKjnuTWBfLNgZUq8lDVZMY2zapVAECzPhqEzv3nqBNs0SMxr8yvVSP9MZqTcmxUzUigO3Z23lu/XPM7zgbANeRIwR06nRRua8lykwJltfsXSmxkTF6NCVr1xJ8b2+ix49HMBigZnfQmSFpDIgiKa49CEg4VRFVM3h9ADUjdo+EU9FT6DHjlkVkvAPAX3JgltxYJBf3Bv3BgWQnjzxcgN2h4SipReuEX8u8bz6uETSRDTnbiTCEYZR0OBQRoxhEiVyCQdBT5HGyo3A/ggB6wYyIioxChquwNKi2pqk4FBWPqkNRRYoUNxoiHSJq0S+hAytOHkLRBPLcNtJKCqkSFEyuzUmbqAQ6Vk5EFAS+2reb++s0POcW1l3N6nLbTbVKjWYAXrn/Vt7/3y8Eah6mvPYFDz300HmPIyoCS5cuZe3atQQGBl4wCPv5rIlfeOEFwBu78+eff+bmm2/G398fu91O5cqVr1o/AOLj4/nqq68AUFWVkSNH/uvnLobJZMIcHURJWiGKBpIo4lI0FE05QwHaHW627UtDkVVW/vQHd3VvXHqvTkwg73WpxufD5xDRNZXCwhSoCUflbGpERuI+knrpnb5GqJiHAedB0zTSn3gC+/btRL34IiG3NETYOQtaPg4Jbb1/gFf3Pg4IeOfnIg5VR4lswKboKfBYcMg6vNkAdKBp1AvO5KaAE8ToC2ljzGPpCjvDhhcSFi7y+dw47u/iU4DXO51/HgGIWHQysiaAppHuKCTMGEqYwUiYIYxijxOnJiMKAkkRLch3F3DMfpJiTwnSnwG2zaI/NtmGjESwPogAKZRMexF5iotgnR8/Ze0nxhSFgEaT4MpYJANLuj7CocJc5uzfzo8nDvHj8UPcEluLNUeP0Co2nsTg0DNkXZtyhBbxcQQYjfzftl04ZZlHWzUjMSqMdwbeQatu9+ByuRg9enT5vMwrwNKlS9m0aRN79uyhYcOGFyz7T3uEv1NSUsL8+fP57LPP8Pf3Z9OmTdjtdu6//36WLl1aZvJfCFEUzxn/80rQ5oOByAEqmiZgEQL45NDPNAtNpGlYQmkZo0HHHZ0aoqnqGQoQwKSXaFo1jC3VowjUfqeTeJDXhVBSranUr14NpbCwTOQuS64rJSgIAuFPDkVTFPxDcmFWNzCHQKO+YPI6ZKcXHyVfyUcCLIIbtypidRvJk/0ochlwKkYU1Zv+JtpkINx4lFC9g8q6fFqaitj4i5tHHyukSRM9n8/qR5OGX5Rrn31cTQTMooVixUGcKRqPptEoqBZLT271TqgEERAQBViWuROT6DWTAR2CphIgGrHKTiKN4eS6bNwS1Zq12XsoUZwE6/3IddqoGxTPF0c2giBw2HaKonyZDHsxd1ZuSI2QMH7PTkOnE1lz8hCCQUD/Z7Jcm8eNn97A4dw8Hv9+CaPat+Pxli3YkHqMEpebR1s1Y8a6rXz+8xbcuWl07no7Nf48U6yIrFy5kjvvvJPp06czbdq0C5b9pz3CaYqKinjyySd56623zkhpZvkz0ey5uNJW61ebavHVOFTsXa1FWfxpElaFUEMAmqaVHuVIksiox8+fzum4LFB7QAcS1UL4cS2V/RtwpPAI8Z99hlABjayui4gxBd98S+7nnwPg16IF/p6NMP8hiKoHg38uVYAAUw6PASRUBNKdoRwojiHNEYrVZcaumFA1ARUNg6QQbzlMp+BD3Buykw6WEvQCtGttZML4ABYuftmnAG8Qntn2Cf5iEMEGEw5FRkRC1gwMSLgbj6rj7krtSPCL9lqJqgKKKqCqUMO/Cjq8cURVVaRY8aAhYJb8sHlUtuYdZnbLUXzSfBgFbhuSCFmOfJqFVuGuuEbMazeIqv6h7Ldmc6wkj/nHdhIZYGZA7ab4m4xoosaLv63iqTU/UH/WB3yxZzs1wsP4vFdP7m1Qn/25p3isXXOe6egN12cy6Eiqk8jWLb+z8Jv/K+e3enn8M+DC6aAL56JXr14sXLiQJ554gh49epQ62A8cOJDCwkImTZrETz/9xMaNGxk8eDAPPvggDzzwwDnrSkpKYsKECUyYMIGEhISy6FqZ0kd3E4c++BlbZjH5ziJ6VmpFl5WfsCRt7yXXsWZ/Nm+uOIAW7jWAqWYMJ6UwpUIqQKjgK0HN4yF78mQKvvoa/w4d0B55BOH7xyB5gXf1d8d7oP9rr/vV5PHeDN8aCAIUK0Y0RFyyHpeiQ1YFb4JKyU3D4EwaWU7Q2FSIodDDQ2MLeWtiCCGRGk+P/D+Cg+4st377uLrssHpnzreG10XTNHbmp3G4OI+X9s5DUTUETQDRu8IQEUAQEVDZVZiGTvwzxyCgE0BC4nBxJgoiB4tPcs/GSXSOusmb9VsTKXDbKXAfpVi2E2S0kOUsQhJVPKqbYLNEl5jqfHN8K63jEkDW8WNaCr0S6lEjOIxjJfnYPG46Vktk7Pof+XrfHgxIuJ0q49t3YOqSNSTVTsSo12MMCiq393kl+DcBF/z8/M6wRzh9Jrho0aKzyt58CVFTzkWfT38769odDWPo3zoBh1th4Kwtpde/fbz1WWVnocM/SAAArD9JREFUz55NamoqjRs3plevXrzyyisAdOnShVatWp233SVLlpCcnIzb7WbixIkXfa6kxEbG0l0E3VSZvOhAZqWuZGT9jjQMrsSoFSt4rEULalxkK7ZGlD82t0KWsRoxwPCQxujq98aTkUHW668TNnAglgoUxq7CrgTlggLSBj9GwVdfE/rII8RN+xhBkiChHdz6KvT85AwFCHDMkYZH0+FUdGTagzhl9yfL7odNNnoNFDQdkqASYy6ipjmHLn7pWA/ZSbo9m+VrHGzf5yAgeodPAd5gWERvoPTaAdWINceS67YBGrKqgibiVsEiWNA0kDWJNmH1/wy3B7KqoceAooq4ZBFBNYHmPXNWgWCDH7UCYqliDiPOEoQgaKiaRmpJFsO3ziXcrCfGEsjiE7sp8JSQ5y5BReaoPZsPbu5BRKCRNTkHGNa8JV8e3sGmzGPYPG7urFEbgyQR7mehamAwm46l0aTwOPOeG4LVai2/l3mFGDBgwBl/rlboxbIiMDAQo9GIy+WioKCAgIAAXnrppQuucAHuuusuxowZQ35+/iU917lpWxCg5GgusiqS67bSJ7Exf2TlsHD/Pr7effGoO7WivFvH+x1BEN2ARHMklQMrI1oslKxZi+MS6riWqJArQdXt5njfB/CcPEnM5MkEN4mCw6ug9m3Q7OFzPjN8ywgUQFYl3KpItjMQVRO9f1TQCR4EQUeb4MP0CP+DanqZzWsU+j2ZRUCAyPKF4bS5dTt6XcVN3unj39Nn/VS6RbRk+alf+OjwUqKMoWhAgBBMrlLyp7ODQKHHTbwlAlVQCTMGIKKnfURd1mbvQVY1JFFAQKBxSDV0op6fT+0hQDKR6cxjyqEFyKqKBhgkHf4GPwpcNg4UnUQnSIiI1A4Ow+6R2VGYgiJ4OOko4O0/fsSqFFMzIIYTzlwmtu7I81t+INzkx3e3PERokBFR1uicWI3elSrTqN8D9O7dm6AKvgq8FjnX6u40ZoN0wfvg3bIFr8Vqt27dzljZejweRFE8p/uDpmm89tprDBs2DOCiLlp+fn6YYoKxH8sDILU4j06r38bPHk1UpB8PNLqwkRFAjT+V4MFsO52GbMIhO1i0fx4NwhtgjozEdfjwReu4lqiQK0HRYCBs8GAqfzmH4CrFMKcHrHsdVPWc5Tfn7CRTtuNWdBS5BXIc/jhlHW5Zwinr8agS1YLy6Rh9mDvC/iBe52DDche9Hsmkdg0965dF0/G2Uxj18Ve5pz7Km6OOXL5K/43B1W9D1USynVaCpBBy3Tb0go53mwygXmClPzOOaCT6xeCUVewejSPFOYCITtBhEIwImp5CjxOj6E2kW6LYQdPQNBlJ8OatjDL54afTo5ME9IKChoy/QccxezanPPk4VBttI+OIMEvMT9tMg/AIHq3bhNkpv/HO/lWEGA0UyTYe37CADHchAQYzn+/cxvPjxiHLMhMnTiznN+rjXKxevZrXX38dvV5PSEgIxcXFvPrqq9x2223Mnj2btLS0cz73yiuvkJGRwaZNm8567nzUqlMH+7FcQMCjaMiaSp+adWkbU4Ugs+m8z50myKwnJsjEsVwbADpBx9vb3mZt2lqM1atXOCVYIVeCAME974Qfx8Pv0yAxCe6dDeK5dfrbh2eiaeBSDeQ4LXgUHbIqes8G0bBIbmpasjBK0MIMEmZu7aDy7JAgXnzuDsISK7YRgY/L597K7Zl55CdssptilwtFgydqdOaTlPXoRCM6wYDN4+GkPZ9NOQcwigaOlJzCT2ck0T+SNPspPDjZW3iMZOsRBARMkh6X6kZA8FrnIXDKVcBp9xxJB4GimWLZhkUnoWoeEvyjyJFzkcUimkUkMKBqe0bsmEezsJpsyT1GjpqLqonstypUsgSSXmTFnZ3FD99+y7AnnyQxMbG8X6WPc9ClS5czgr+PHz++9N+JiYnnTbb7z2g/f3/ufPRo3Zkp+/ahKSpmvQ6zaCTEbGHq/i20io2nd536F61jxdM3E2TWw/7/oV82kmrVa3Ow4CDGmjUp+OorNFmuMIYyFXIliOyCr+71KsCWQ+DBhV5XiHPw5JY3KZGhRJbIdZpxKzpUTcAlgySoxAVY6Rm3i84Bx7nbtZenXjhFiUPGLwBeGT/IpwBvcLZ0e51GIbF0XjuODmENcHg0bKqHeyu3wiyZybQXkRRZm7YRNXEoMoeLcwjWWzBLelqG1qRfQkcmN36IEtlFiN4fnQCi4E29K2tuwg1+GEQFP52ITlSwSHpEAQyijFEUaRoai5/BRZRFwt/kRBMLeLx6BwIMek65M5ieupBws4eD9mQi/EESFOoGRRBkNFIjNIR64VEk2TwE+Pszbty48n6dPv4DVzrb/MSJE2n15RBEnYAqeChwufg0dT0/PjCQXrXrXVIdwRaDd+vVFAQlWdQ0hHI4/zDmxo0x1a2LUlBwRWUuSyqmEtQZvfE/7/wQur8J0vlnHKnODNBEQIcoei1DnbIOnSSh06n4S3Y6mE+gHbSSdEcW874rYe9+N1LkFvxiKnZkeh//nW8Ob+KWH18DIMwYAAisz91HmCEQRYOFx3ey8Pg2JEHki5SNWN0uTH+OwyhTMC5FZmt+Kl1jGqFqKjGmEPpUvplAgwFRAJ2kEGk0E2Y0ohNBwYNZFNEEBxZJR93gGPz1KjuKkpFEjWKlEFGAIrmA6alzGVC1LVX9QnAoNhRkdJJC9YBgqgSEcNSRQZatmF+z0pjRrSfz3n2PP/74g4iIiHJ8oz6uFURRRNaUP0+zoX5wFJ1ialE9NOyiAbRPczTXxrPf7uKwmABALUXglOMUclJzEr75Gl0FGmsVUwkCdJsMTS5sEdb55xG4FZAVAYdHR7FTj9NjAA0CdXaGxq/nvcrrOPRjAbfcnYkgwE9LomnT7QQ6XfhV6oiPa5EpKSsokB088dsM3mg8AIvkT77bQYDBQmVTJC5FodDl4KS9EKvbwYGiDAo9DkRB4GBRBg7Vg4rM6F1z6P/bFHrENWa39QBO2YpecGOWNEqUQrJcGRglFybJg17nRhI1zDqFfHc2kUYTkiATonPhr3MTbHASYrATZHCwveBHusXWYXqzEYyvez9BehmPZqNYyUMTNOICLLicLtbv2g5QocOj+bjypLyzkuNzNiEJGnZymXjT7f/qeVGA73eeZFu2BiEJ1CkpQEAgtdDrTqRpWlmIXSZUjE3b/8Cq9K14VBDRYZMl3B4Rj6pHEKBRaAbtww5zR8BJZs9x8czYPFo3N/LNjCgqNzxe3qL7uAbQISKj8knrRzlWkk278FosTt9OnquI3vFt+PTwevSSiCCoiKKIR3UgohBu8CfC5M/h4gwMOpUMZwYqKinFJylw52LUqehEBQQRCQFFA9C8q0NBwF/nxiDokBG5LbYlSzJWgODGjIJZ0v8ZqFvDKufzv8yvWXzyW9yqDgE9Jxyn0AQJARMWk0S35AJ6Dkni4MGDFdKxu0Ix6xxKpF5PaDEY3HaYd+9f1x9edlbR/+onuH37dn7++Wdyc3N54403Lvk5V3YR9mM5iAPbUNkSetZ9m9ON2aBHFM+9MqwcaiHAqCP5pBVib+Kmk9v5/cnNWPQWMsaORT6VQ+XPPztv+9cSFXcleBEmHZwPSLgVEU0TEUUBk85DFb8C7ojYS++AoxhFA53bmxn6SCAr51fzKUAfpWzu/irbu0+i/dpRPPjr2yzO+B1RkLApbhqFxtEz/iYsOh1hRj80QUVBQS95ty3TnOkY9CoCEKCz0CasLqJYglXOIFCvRxJARMUo2qkTEESgwUmQ3kmoXvX6CQrFNA+JZpd1OcH6U9QPiCJEbydMf4pYYyHxJpVgnQO94LXMQwCD5CHGEIjNLaGpAkmOML744GP69u3rU4AVgP/qJ9i0aVMkScJms/2r5xrc1IiSIzloHjcaChtP7Su9Z3O6uWX8Z0xbfv6YyIIg0DA+iF0nCqH2HYg1urNq1q8U5hYjmszYt29Hk+V/9Q7Ki+tyJfj0llmoHglNUvAoEioiAZJAl5ht3Bm0DzVLZsqndsaNCCaxmsTbr/XBHFMxZi0+rj6iCFXMkbQIr8ai47v5w5pOk/BYNubuompgJOk2Ny7NRfeYVvjpRFZlryVAb6BZSE3y3TmonMAs6Qk2uPEXZWyqC6Po8SZvlo8TrPOnWHEBVgIk0DByyrUbRZPRixpF8j4CdCp6UUNEQdPykQQjZsmBqimYRRG3bOKkq4hAo0h2vsS0cROJiIjg3XffLe/Xd2NwjtVdKQbLhe/z3/0EAZ599lk++OAD4OJ+gqd56vaH2DjrB2wpp9hfR8d7B5dyc6Q3I7zZoOfBDjfRtm7CBetoUjmEaeuOYK/VkzRPM9774AUWmufxUeNeaPPm4Tp8GFOdOpckT3lSrkpw2bJlbNu2jWrVqtGvX78rVu9v+YcBAUkTEQSBKHMRSRGHqarP4+h2B488VoDHo/HAvf5Uq7MPs8952Md5+KLJM4zY8wE5nhOsP1XM6zfdzfb8w8w5uokQg4U0xxE0QSRIb2SndR1NQmpjksCpOtlasAud6CFED3luBT/JDYJKoOBBFSQCBRVEI4qaj14AjyYQYPCjd1w/fsj8BLviJlxX7A25hoJT1SEKIIgaqgCKKuJAh6rpkDU9elFG9piotuw4PxxMYcWKFYSEnNtq2se1xerVq9m2bdt5/QRvueUWqlatetZzP/zwA/v37ycnJ+eS/QQBbm7bDoDifRmENYhiYNV2pfdEUWDYHW3Pesbl8nAyo5DEqhFs//0Iv3/zGwl1q5BldVK7YSx9Rjbhk8y5FNXwJnh27Np1YynB1NRUJk2ahNVqZcGCBedNZPl3br/9dpKSkvjwww+vlBh/oiKgYtbJRBis/Blonz++P8Hr4/KoEq/jm1nh1G918gq366Oic8+v3l/ghW28mQmOuA4jigKCIhBg0Pg6bTV6wUCJUsB98e3RtLokRTbGrhTzxoEP2F+0i1hzCd75uECc2Z8i+RQuVSLWGIZVzsJfdOHWRPSiiqqAS9QhoKITwKzLZ0POexhFB9WN0ZSoObhUHUbBQ4DkwqYaUBHRoyAKYNZ5cHiMOP90/VEUMw1iq1Jz5Ei6detWbu/Rx7/jv/oJ9ujR44wk5ZfiJwgQHR1NZJtqGAKMaJrAzKNL6RHX7oLPzPv2d+Z+/SsP3N2C9auTiTDomD6gGWER/vBFd27BzScS7JDSqRsejn3nTkL69r0kecqTK6YEExMTmTlzJr179wbOTmTp5+fH4sWLAejevTt9+vRBVVWmTJnCM888c0VkeHDDhxywZWGSFIw6GUmEW4P30TTgBHOmWnn/gxI6djDy+bRQqtXNuCJt+ri++e7E/+gUXQdVg10Fu+gW3YSUkoMIRNM5qgVV/SqRbk9hScbHxJgLqGSqSr7bjSRYsYgORO0UesFErCEfi1RAtL4m+Z6jBAoleDQTBsmGKOlR8UY7kjCjqacIkmQUNR8/AQIkG4o3NDcSMsWaBYvoIlcORP0zTROaSoHdn75VW/PMpNsqlHWejwtzpf0ET9PxrX7keawIKMiqg1E7P+f1Rg+jF8+tFrp3aUhkRCAGUaBWgzjGju+JKAqoqoZYqQnVt3xGUPVabMveTrv+/ZFCK8YuRJlth/4zkWXPnj3p2bPnGWXeeustrFYrmzdvpmvXrmfc+y95uw7YsgBQNAGz6KRz6EFaBKShCQLNW+h5bLAfE8bVJabylovU5ONGpYohBrNkBuBYYQrvNp6AUTSgAW/uT+H3vHm4FAOj675FSvEvLEibg0UKJ99dRNPAm9CU34ky63Cq+ZgEJxIaKlbcmh4dToxsJlan4NJMgJ1gXRx25ThuVSTYEIQHUBTvSlLRBARBQ4eCJgi4NR0aIKgaMhKKJmCT9Xg0kfxjNlJfXUKT//MGd7/UsyEfNy6doprz7dGVqLIHnZ+OHGcBh4sySS2y0i2uLrp/ROCKiQ7iju6NAOjS1Rtj9NutaUxZdZBfe7XF8NtHNA2owrbsbYQ99mqFGYNlpgTPl8jy77zwwgvnfT4pKYmkpCQAJkyYcEltbu8+iVt/nETv+NWE64pQj59i1eoS7utjoX2Skc5JU4muNPBf9sTHjcTUZt7tpMn7vGcqjQJv47a4YazKnEaglEah5iDEVMRPGS+iokcU3ChaHjUNuQQLNgoowCwaCBYLkQAPAhbRTaCkp0h24Sd6AA2EYu/2pWYFSY+fBJrmwE8IxS140NAwCAqKIKIg4tF06AUVGUATUDXQCSpuVUJO15P8/EKMmo6oEJ9/a0Xkv7pIACxcuJB169bx4Ycf/qvn7o+8maEN76Z6/6bU7NcUp1LII5unUWQ3EmYaQOvIs88g/0lkgIncEjfbhAa0kYwkeQTsAfHYPDZMNg9qURGGKlUu8S2UD1dMCebl5TF27Fh27tzJ5MmTGT58OMOGDWPZsmVn7FmXJW/uu5NOlWScqsCBjfm8+UwmJpPAHT1M1KmVfVVk8HF9kV00l/lHFlE3bBzpxfOxiHpcmgZKMrF+3eka8xTrMwYRJJYgkoufDtAkzKITSdCw4EZGQC+AWaciIeDRRFREQEMRRQI1J4LgdUB2qMWY/9yOUhGQNQEPOjyaiIIOq+qPrIk4VQPFion0Hbn8MmYDfnoLa9auoVatWuX6vm5UHl55dvaargldub/2/ThkB0PXDC29PqvbrLPKnstF4tlnn+Wll166oDLbu3cvFouFoKCgf/Xc6TbDq0SRvekoNfs1xSBp3Fe5HfNT95LjKgIgq6SYaP+A89bRMjEUk15k5aEi2iS04+70/dz9lDdAw5G770AXEUGV2Wf391riiinBsLAwpk+ffsa1vyeyLEtOGzM0MqvoDBoHv9rHp2/kUKu2js9mhvgUoI9/zZi6Xj+rr1JaomlWThYMo2HI0xQ4NyN7NiMTiuT6iuy8ZXSvtJS92V0RNDcmQcEkKqCBJICsgUFQERCwiCDidXNQUfBo3rBVrj+VHYBBEEBQUDTBqygFAZeioSGiISDiVY52zcThLQ7WDltFtcRqLFu2jOrVq5ffC/NxWfxXF4mffvoJTdPYsmULTqfzX29BPtTnIaZMfB17VhFaFGiUUDsoirFblvNLWjrfbTnIwzfdxPikjrw6fw3peVY+feKe0uctBh2dakeyfG8WLz84AklvAk0j15lHYNcu5E7/FDk3F134tbtDcV35Cd4qbOf9SVa+/dpBt9uMLJifh5+fX3mL5aMCUyfwEbKK3wc0BPtrRAl+FIpGAsTjGPCgU21k57QgXlJRENABJsFrxCKjYfgzea4K6AVvTkG7JmNARBM0XJqGXtNwCxoyIArelaJOAIcK3mgyAgIqTsWISxXIzNdhDAym361daPx8A5577jmfK0Q5c67V3WnMOvMF78N/d5F4+umnAcjNzSUmJuaSXSRO81DfB5ky8XXSfkyh+oNNWZn9C0FiVSxmD21jq7BYPMycHTsZ3qo18eHBGHRnK+LbGsSwfG8Wv8qNuLlqBGuOr2Hk+pF81+YdmKZStHwFoQ/1vyR5ygNBqwBmZBMmTLikc8Fj6THM/NxGcbHK1LeLEM+TWsnHjculjqW/k170Eyl5gwkWXQgCmFEQAZ3An1FbvCs0oyD9mVHemxbJhA5JEFHxrgQ9mgKAV6VpeFBwayqqpmEHVA1kTcCJhKaBXdVj04yowCm3H9t+9bDx//LI+MPF4ZTDBAUEX9F34+Pf8V/G0n/h1KlTZWYhChDbtAb5J7Lo+M0ABEkk1lCDbXmnmNduCKJbz+6sLO5veP5ku06PwntrDtOvVWXiPGnk/j6NWws30rtmbx58dy9yYQHVli9HOI+zf3lzXWiJVSsj+ObrMAAGDfbjvaklPgV4A7FhwwbeeOMNvvzyyzKpPy6wE0lVj6ATKiGhohcgUBLwE0VMgoReEDALOgyCDrOgxw8DfoIBnSAhIKAX9EhIGAU9kiD+6ezgzWgiIuAB+HMqKmsCbk3ApUm4NIm0VA/vv5TD00nJfDRoDyf3unhq2HB0or5M+urj2qMsFSDAyFfGU/+VHmiSiAbI2HiuXkeySuz0+fn/OGGzUuuj95i/Z+85nzfpJV7oXpu4EAu4ignfMYem1mAWHl4I99+JJ+0Ezn37zvnstUCF3w6dP38+AwbkklBFx48/hhMXn1neIvm4wlwsEEN6ejovvPACb775ZpnKUb/yZgBOnOyAxiFEBPR/KjUd3r9FvFGKNLTSv1VNRdM0XLKKw6XicGrojBp6Pw2rXWbjL25y8lQyslXSTsLJEzK9Hg6mQXsz6Tka6xYVUqtNNE+9PZZ+vR/GZLp49m8fPi6VYd36kr7TyW5rMordTaoll89Sl/N+42doE10Ft6Jg0umIDw68YD3JJ618udnA0/4tmViUzJ3B0Uzx28iUhd9hrntpeQrLgwqrBFVVZcKECbz66qs0a6ZnxuchPgV4nXKxQAxNmzb9z3VnnYyl2+25WK3eVEOKcgKALl2NjHvZ+0vfOSkHl9O7VDt9eHDfPRbGvRCER1ap3yITNAFNA1XVUFUY+kgQ40eGUFioUqnRMTyeM9t98flAnhnuT26exiMPF5ZeDwwWiY7XE6ofyM3xQ4kN3MhzuV0JsIT95z7e6JwvelVycjKTJ08GYMyYMdSvX58XXngBu92OxWLhjTfeKE+xrxpGSc+7zR6j2v1tyN5ymLoTe6KPDOax3z/l5Qb3UCOgEiNbtcWku/DuQ3qBnfnb0lFrPMtr6YMZesrOp8JWCtoEYwGK163DVKsW+piYq9OxS6RCKsG0ozEMGVrAipUuHn74YT755BOMRmN5i+XjKvHPQAytWrXijTfeOG/OvIsFXmh6kx5ZbQ+A3f4dAInV/vrVaNHSgMetlaaVEQWo8ed9SYSunc1e4xUBJFFAFKBBXe99nUll+GOB6PUCkk7DaBTQG6FpUx0lqkpApMg3SypDiJE6VfoSGnITEeZ2GP/MZ9k44oHLfFs+/jlpOq0E33//fT7++GMEQeD5559n7NixeDwePvjgA0aNGsWJEyeIj4+/KjImJyczbtw43n77bapXr36Gv58kSSxfvhxN0846g0xJSeHrr78mKiqKxx577F/5Cf6TqQNf5J4f7mHboFmEtKyOoVosw3afRIivjuoSELelYhH1tImtzO8H0xBVgVb1G9KpWXPqJ0TiTNnGPQkiCw9rpDKCmcoUKssRRJtiyNi7m1PDhyKqkJEYQkGkPy6Lgdi23ejS50kO7t3MxjlnTzqqd+hJUo8BrFuyiFNpudz31GP/7QVfgAqpBPV6CAkWmTghkPEvzawwkQl8XBn+GYihffv2tG/f/rzlLxR4IbpSBjPPMNybc9bzX8+7sDyz54EjczqwEJFTQDEqGjImTH61eXnsUSTLAIx+/XC6t5OVNxC9VB0/v7uJ8+tDvcRYNE1BEK5Nw4GKzj8nTaexWq0EBwcDUFxczMmTJ0uVXuXKlUlPTz9LCZ5vQnW8/9kJvgO6dyP0gQdQHQ5OPPZ46fUqc88+u65fv35pRK1/+vsBvPLKK3z44Yfk5OQQ8bes7V9++SWRkZGIoviv/QT/yV3d7mD1lg08NOZJcn47hHvTQUJub0nQ/dVRnSppn84A4O+ne1lNb2HvfhuNEkOZPtHrK2mIqkZJy3voVHUifjt3EdU1naP5u/n8YeiyBZoezqfKoXwANtm/gT5PkrxpGW3/d+AsmTY5/o+kHgP4ZeFsTp0o5M5BAzBZruyCp0IqQUEQeHtKEDFxvu3PG4FrIRDDxTDHDAGGXLScn64LMdL3mPQNEEVL6XWfAiw7zhe9KigoCKvViiAIBAQEUKlSJdLT0wE4ceLEWWEe4b9FsvovXGhi73A4MJu9of2sVitPPPEEs2bNQpbly14QdKrXkhNLtrK74DhF1hLynDZcoolDuadImz4Nj1uldaV41iQfJkBvoGmNmjSvVYvK0cE83rMxAOlpeQQEmrH4GSgouImGjStTtagn9hMCnu7FHO+jcdxRgmRz0qx+GwDa3DGATapyljytW9wKwF1PvUhBdtEVV4BwnblI+PBxMXxj6cbDZrMxbNgwTCYT7dq1Y+XKlcydO5fk5GTeeustAJ5//nnq16/PmDFjcLlcGI3G0vPC83Elx9KJEycYPXo0iYmJTJw4kddffx2AW2+9FVEUWblyJZqm8fLLL/Piiy+W3t+6dStLly7FbrczZcoUJk2aVPrcv10J3qj4lKCPGwrfWPJxpSiPsaSqKgUFBYSF+QylrhQ+ZzofPnz4qCCIouhTgFcYnxL04cOHDx83LBViO/TRRx8lLi7uitR17NgxEhISrkhd5dnGtdxOQkICAwcOLDN5LocrNZau1Xd/PbVzLY8j8I2litTOBceSdoPx8ssvXxdtXI/tVCSut3d/vbVTkbje3n1Fa8e3HerDhw8fPm5YbjgleNrHp6K3cT22U5G43t799dZOReJ6e/cVrZ0KcSbow4cPHz58lAU33ErQhw8fPnz4OI1PCfrw4cOHjxsWnxL04cOHDx83LD4l6MOHDx8+blh8StCHDx8+fNyw+JSgDx8+fPi4YfEpQR/XBG63m6eeeopnnnmGgQMHsmXLlnOWmzBhAsnJyeet59ixY0yYMOG8IZJ69+59zn9fCE3TmD17NrNnzy7NGrBhwwbmz59/Sc9fCU73+6WXXsLhcPzrZx944AH69u3LnDnepMGdO3fm3XffBeCFF14oTRC7aNEiWrRocd72rzSzZ8/ml19+ueTymqYxZMgQhgwZwogRIzjt4ZWZmUliYmKpjC+88ALDhw/nhRdeAGDx4sUMGTKE22+/nd9//x2Xy8WwYcOueH98VDwqZFJdH9cfM2bM4LbbbqN79+7Issw999zDkiVLaNCgAQMHDmT79u3MnDmztPzAgQP55JNPsFqtTJo0iQ8//BDwRtk3mUxYLN6EtcOHD0en0yHLMs8//zz79u1jwoQJPProo6SkpDBmzBj27dvHN998w4EDB5g9ezayLNO6dWvi4uKYMmUKbdu2pUqVKgiCgMlkAmD16tWMGjWKdu3a0atXL7Zt28bbb7+N3W5nwoQJmEwmevToQaNGjXjooYe48847+eOPP/jiiy9KE5/KsszQoUMJCAigQYMG9OjRg2eeeYbAwEAaNmzI448/zuDBg4mIiOC3336jd+/epKWloSgKAwcOpEaNGpw8eZKePXvSvn17Bg0aRNWqVVmxYgXr16/H39+/9H29+OKL1K5dm4EDBzJgwABCQkLYtWsXhYWFZ/w/LFmyhAcffJANGzbQvn370uubNm3i1KlTPPDAA3z++efMmTOHRYsW4XK5WLVqFTVq1CA/P5/u3bvTvHlzXn75ZTRNIyAggKeffponn3yShIQE7rjjjjPqXbNmDQMHDmT27NmsX7+exMRERFFk7Nix5xwn+fn5eDweZs6cyVtvvcUvv/xCu3bteOutt7j33nsBSEtLw+Px8MEHHzBq1KjSBLk9e/Zk586drFmzhlatWmE2m8nMzCQmJua/Dlsf1wE+JejjmiA5OZn77rsPAJ1Oh8ViQVEU4uLiGDlyJB9++CG7du0qLf/QQw8xb948MjIyePzxx0uvV65cuXT2n5ycTEhICBMnTmTixIkUFhZSt27d0tVcREQEkydP5r333mPXrl1MmzaNatWqAbBz507i4uJo06YNL7744lnyFhYWEhQUREBAACNGjOCHH35g/fr1bN++nVdffZWqVaty77330qhRI+rVq8dzzz3HyJEjycrKKv3orl69mubNmzN48GAAPvroIx555BE6duzIvffeS5s2bYiNjWXixIk88cQTZ8nw6KOPYrFYeO6553C5XHTp0oUBAwawc+fOs8q++eab7Nmzh/fff7/02qhRo3jzzTdLfz558iTBwcH069eP0aNHn6Gs2rVrR+/evalfvz5r1qzh8OHDLFy4kNmzZ7Nq1SoGDBhAbGwsDzzwAIcPH8bhcBATE8OhQ4coKChAkiR69uxJ69atS+ssLi4+Q8Zu3brRp08f+vbtW/o+UlJSSu+3aNGCBx54gHr16vHMM89QUFBA5cqVmTVrFr179+bHH38s7Ud8fHzpeEhPTyc+Pp533nmHH374oXTC1KhRIzZs2ECfPn3Oel8+bhx8StDHNUHdunXZvn07Xbt2RZZl7HY7kiTh5+cHgF6vx+VylZbv1KkTs2bNQtM06tevf846NU0rXXUJgnDGzwBBQUEAmEwmXC4Xbrebp59+mpCQEADWrVtXWubvpKamlirLf8r3zzbPVeall17C4/HQrl07RPGvE4l/ygdgMBgAMBqNZ8nh5+eHTqc74738vd2/M3r0aMLCwnjttddKw03Vr1+foqIiCgoKAO/WZFpaGmPHjuW3337DarWW9v/vcg4ZMoTHHnuM5s2bo9frAe+qVlVVFEVBVVVuv/127rzzztJnPvjgAxYtWsSPP/7Iyy+/DHiVYGBg4Bn9Of0eABRFQZbl0vuqqgIwYsQIAMaPH0/t2rX59NNP2b17N7///jt5eXmMGjWK9PR0gNJVIMDIkSPp378/o0ePZtasWYSEhJCRkXHWu/JxY+FTgj6uCQYPHszIkSNZvnw5VquVMWPGXPSZevXqUatWrfPeb9CgAZ9++imjRo3C4XDQoEGD0lXZ008/fVb50aNH89RTTxEVFUVCQgINGjQ4Z73/+9//uP3228957/HHH2f8+PFYLJbSFc0/eeWVVwDweDw8+eSTHDp0iIYNG9K3b1+effZZFi1aRMeOHWnQoAEzZswoXaleiFtvvbV0izcjI6N02/bvxMTEYLFY2L17d+m109ukkyZN4pdffmH58uUALF26lHnz5jF06FAAmjdvzpQpU3jkkUfo0KEDAI899lhpPZ999hnp6ek89thjtGjRgmHDhrFx40bcbjePPvooM2fOxOl0csstt5Q+ExUVRV5e3nn7dK7/I/Aqv9zcXCIjI2ncuDGffPIJ4D237N27N5UrV0av1zNixAiMRiPx8fHMmDGD3bt3Y7VaS1feR44coVGjRhd8rz6uf3yxQ31USH744Qfmz5/PnDlzzlilXA2GDRvGRx99dFXbvBTeeecdsrKyCAwMZPz48WXWztSpU7FarUycOBHwns9+9NFHZ5xBXiqjR4/mxRdfPOeKu6wZOHAgM2fORJKkq962j2sHnxL04cNHuXHixAmKi4upW7fuVW3X5XLxyy+/0KlTp6varo9rD58S9OHDhw8fNyw+P0EfPnz48HHD4lOCPnz48OHjhqVCKMHZs2eXtwg+rhN8Y8nHlcI3lq4PKoQSPB3SyYePy8U3lnxcKXxj6fqgQihBH9cXs2fPJjo6urzF8OHDh48LK8HFixdTt25d4uLizghN5cPH5eJwOOjatSsxMTFs3ry5vMXxcR2wb98+mjZtSnR0NKNHjz7jXmpqKoMGDToraHpycjIPPvggDz744HmDb/u4vrlgxBhBEFAUBU3T+Oyzz5g0aRLh4eFXSzYf1zg56XmExYb8J2d1t9vNggULeOKJJ5gyZQoLFiwoAwl9VBROpeUQWTnisup45ZVXqFy5Mtu3bz8rLmliYiIzZ848Swm+//77fPzxxwiCwPPPP8/YsWPPCr59Og7pxVh9sBauAh3NYxcCAr9l3EPr2EVsKxyIMUBE586GkjqIQiEGIQeTUIRRF4LJrxCLIQZJdSPICjrRhE5KRBQtCIKASCySEASYwVQVza8JohiMKIpomgpIgBNRvPoBB64HLqgEP/vsMzp06EDfvn3p1KkTJSUlV0wJfv/99xw/fpyioiJeeumlK1Knj6tH+qEMBtV9hiFTB3L38Nv+Ux2CIJwzzqWPG4vVc9Yx5eGPmbbtTWo0Sbysuk5PyC51Yma1WgkODga8sUzPF3z7Ujj6s5H273ko4G4AagMF3EV6eyPVxjhJWe5Hu08P/+2JQEDht57h3PLUSX75LpxanymADdgLgAqk9Ddy+yMlrP3GQuiXHhQJ7x89KEYIGOyi8s1OTu0XKNpgxBSrIFYKJ77xZAJj26MTDRwoPgxo1AmsfUl9uZG4oBK85557GDFiBDk5Of+64tTUVCZNmoTVamXBggXYbDaGDh2KwWAgKSmJgIAAMjMziYuL+8/C+yg/IioF02/0HbS8vcl/et5gMHD33Xezd+9evv/++yssnY+KxE2dG9BvfG/iasVeVj3jxo2jf//+REdH89BDD/HWW29d9JmgoCCsViuCIBAQEEClSpXOGXz7NOvWrWPdunXA2YYxljgP65rrkTAha07QQNPAHqcBGi5/Jz821KOpIKABAgKgGZwIgCnWw9bWBkQB74MqoEGlKgIaGrKfiZTqCoKiISoaogI6DziKg6lNLod+9qPOwtPS2DnF06QFwrFJevbFtMOmRPB6o4n/9fVet5R5xJjevXuzYMEC5s6dS3BwMD169KBPnz507NiRIUOG8NprrzFu3LgL1jFhwoTS9Dc+rg22PdQNZedxEpcsIyLx8mbvVxPfWLoxycvLY+zYsfz44488+uij7Nu3j7lz55KcnFyqLJ9//nnq16/PmDFjcLlcGI1GJk+efN46/8tYSjk+iSz3L+Rnbyc7zcOJFJnu3UxUjpX4v7l2XnixiJbNDTz6sB933W5CpxMxoENFRUBAh4ggCLg8RjKLQzieH0qTSofxM7uZ/WNjctKKCZQzcDvc2Ir1RJ8SqDRaRg01Ypptwpisw//B+2jW62mEqxxz91rlqmWRSE9PL43KL0kSZrOZKVOmlKZP+ScXmnFdCX7//Xe2bdvGvn37uPvuu7n11luveBvXK45fVuC39RjuSIWwKlXKW5xyxzeWrn3CwsKYPn36Wdfr16/Pl19+eca1Cym+y6V6lbFUB6hx5nVZLuSWW+5iXMlOvvrazuChBUytreONSUG0bmFEFAQUVAQN9IKEIrmIDS6hckgm3iWnQP/OO/gjN5YVB2/h1321aaP7g7DErYSFZpMvGNnhsXFztoD/uM84NHctxx9/hHeUPL7t0o9go7nM+nytc9WmAnFxcaXbDKqqMmDAAEaNGsWzzz57zvJJSUmlM62EhIQrLs/333/PsGHDkCTJ99H6F6h2G5nPP4/OBLEfzkX0ReD3jaXLRFOU8hah3NHpgmnafD2vTipi/8FcPvw4hOISjbGvFZMpQ76qomgaLjTcKHjQcKLg1Dy4kPGgIAsqNcLTGN52EbMffovAVsWk1KhHmN5NvCMf/9gSnhkMn9wlUXjqGJVGjuXWzTuRBK8a2HYknRO5heX7IsqBMlOCeXl5DBkyhJ07dzJ58mR69erFwoULeeKJJ+jRo0dZNXvJBAUF8fHHH2Oz2XA4HOUtToUhdd6z+EcVEDNiIBENW5W3ONcEvrF0eZyaOpW0wY/5lOGfGPTBDBuaT/Ifx3jrszgKNCPHbCZSSnTs8wST7PbjiMdIngJ2TaVIUclUFIoVBQWQ0dBJbh5osob7m6xEL8DuvNrUoxKLTmaRXktm2MOwtY0f2U08FHqKAHjys8VM/WFj+Xa+HCiz7dBzbT/MmjWrrJr717z44ovlLUKF4+i2NVRJ+4btifG06u/zoTqNbyz9dzTZzamjvxMWWw/Bt6twBkGBcXRvdoSfj97F84N/QtQJvDLdD1Wvp0Q1cdwThlnwYBJlYnVW3GiYVa81jiII6DQNQQBZg+0nG/Byxq1MSPyEuembeCY2mmmdZSpFCgzZ8hK37wnlw0cfJS4srLy7fdXxnYz6uCTcBQUkD3yWyStMVH5ifnmL4+M6Yd93k2gQuYb9Me7yFuWapWPVJTTvWp8d64qZ/KqdDDmEbDkAt6bHjY4S1UiyK5Y0TyApnmBS5SAKFD0lmkiRImHXdDzSZgGd62xkQuoTrEi8iWnpmUzQOYk122h+PIOOnyzC/M0sYkMDy7u7V52rZhjjo2Kzqd8DJHg8ePqNJLZavfIWx8d1gOp0Ylg2i/0hAm1H+3yFL8Rn437jQEpLNs7ZQlTjKKp1roRZUhEFjXB9MSKQ5gon1pBPgOTAqvhhUZ2EijY0TcClivS/eREZRVGM2f80nrbvUFhFwOKxsyvaSnS7WDrM+47idh0J6NixvLt7VfGtBH1clJQpY4g5cozkSgHcPfr58hbHx3XC0SljkLf5ka60xmS2lLc41zw/fPIj4XVi+OHVvexNDybFEcUhWyS/FtRmU2E1HKqOY64IdpXE41BEctx+HPWEUqD6UYwFJyIDO36NUe9iecHNGCUZs62Qo6fy+bKTTE7lcDLGjUcpKirvrl5VblglmJycTM+ePUlJSSlvUa5pPEcPosxdjBQq02n2N74IL+fAN5b+PZqi4Fq8AinITaPxn5W3OBWCIHMgS79egiEskMx0lRNFgaQWhlHo0pHtCGRzflWO26uwrySOLUXVOeCM47AzlnzZQoliIEsORm9281yvD7il+c8IaIz8NZm+bjfFnnym9YpAzs/jj8mvlHdXryoVdjs0KSnprGv33XcfQ4cOxW63c9ttf4XyOu1v+Hfq169/VjQIH2dT8OZwUFUS3p2KoXK18hanTPCNpauPddlSBJvA8ZvCqBlzaWHJfEDrRs0ZseBzvkrZi+ByIIoap0oisZhKsKsCWYIdgySR6QokTLNjUIw4VAPBkg2zJFOkWDAYPJjQOFUSwpYqlRi57yjLa1Rlv/9Jvr2jLuYqfjQs745eRW7YlaCPi3N0+QdERP1OxNO3YGhZ/m4tPq4PNE2jYPb/oU9IoOXHy8tbnArHy417EK2ZSF+RgrXYTIlH5VSxGZvLQJ7DTLHbQLHHSI47gGxXAEWKhQxPCC5Vj4KEUzOSao3is0WP8YX7NuwmA0OsxaiKnfUdjHS/b1B5d/GqUmFXgueakZ/GYrFc8D544wKuXr2alJQUJk6ciOQzzz6Dor172TDjbazRIpWHXd9GC76xdHWxH0lGLiok/PEhBIb6stL8WyRRJHHrSX6ZNh9LfCWEypGIOhWHG26Krcq2/GNEW/SUaLnoBAm7x4hBcuOSJfx0LsIMNkwWF5ViT7DjUFN+ariGvskH+bZOQ4oMJtYeWkPs5mKC774bQ+XK5d3dMueGXQnGx8fz1Vdf8dprr/k+Wv9AKSnhwMCHCdwhkdF1BqERUeUt0jWNbyz9O44uHkNE020U1q9Z3qJUWN4e/SJ6o5G85ZtQXCJul0AlSyg7MwsJESK5KbABRiWBXKcfDlmPrOrJ8wSQ5wkk3RFMocdMtVr7cLlNfK10pMRg4FGPE8RAfsnYTd6MGeTPnlPe3bwq3LBK0MfZ7Fy/n6c6TeLAwHvwKylhR7Om3HbXPeUtlo/rCPfRAyTkbmJjjj8JtW+kk6crS2REBD3vuw/77zvxZDuJEAI5mleI1eXmpLWEHLuTI0VFKKpEiUdPsdtAkUvnzWqhmrArRgLD8gkOyWXfkYa83LEji2rWQ5YdnBCz0G7pgHXJElSbrby7Wub4lKCPUjRNo5lnN2JyGiWVbTw3c2Z5i+TjOiN16COc+jkcrfUwn6XxZTJs6FA0j4x9+y5O2RxosoDmgShTADk2Gy63SFGJEVkRcat6SjwWMmz+FLpMlMgG7IqeSokplBQFkumIwKNJ4EjDad3HvIYWVJuNopUry7ubZY5PCfoAwFbsZN/yjbQs2Is5WsH/lVkEBfkyVfu4cjh2bUc7WsCpQCfdBowsb3EqPO1btSIiMRFPVjaCB6paQhEQySiy8UdOLqpHjyJLeDx6ihxGFE0DQcKuGCn0GClRTATGZNGm+zIM/g7uStnP1CP7APglOBtP5TjyFy68iBQVnwprGHO5zJ49m9TUVBo3bkxycjIAXbp0oVWrGzModOaJPIwpMwlOsBH24lvoW99S3iJdMlu3buXnn38mMjKSgQMHXvX2fWPp0kifPAFRr5La5jZaGQzlLc51wfHkZJYcOsSLP60hK8+GYBbQVA1NABTQVBELQRSrJciygWJNRC/KqKoZneBAQcRi9CCrAtkGP+4tKSQoOoa8kkPMqZFIpwIXVWUZQXf9qopy69kff/zB0qVLadKkCV27dv33Fcy6/exr9XpCi8HgtsO8e/+6/vCys4oGBgZiNBpxuVwEBATw7LPP8tJLL91wH66iQjs5WYXEqH9QveFBVuZWolub+8tbrDNITU1l0qRJWK1WFixYgM1mY+jQoRgMBpKSknjwwQdZuHAhkZGR/60B31gqc5wHDyLvTkG+rTPdhr9a3uJcN5jNZmL8AtEXQ+0q4ZgCdPyedQKdKOFRFJA0rC6ZuiGV2WtNI8hPQ0NAUMCt6hAEKCz2J213Ywor7+dxYRttnDIr9C5WJ1ViQMdnrmsFCGW4HZqamsqgQYPo3bs3ADabjQEDBjB48GDmzZvH999/f96EuleDXr16MXbsWNatW3dDn018/vYK5t37PDsfGsPoZR4Cer9f3iKdRWJiIjP/dj65aNEievfuzeeff87SpUsBeOONNygpKSkX+Xxj6eIUr1iO6O9P3ZcnE3oDZiooS8Y/P4a0zz6gf6PG7DqaTZwhGEXWEBWRxiGV2HTnkwgCqKqEzaVHVUHW9JR4dLhkAfQytsJg0k9VZWtoJQbkZoGgQxILaBgei5yXV95dLFPKTMWf/nCdVoKnP1w9evSgT58+REVF8eyzzzJlypT/thI8x4y8FIPlwveB1atXs23bNiIjIykuLubVV189IzLIjcI97cKxLdrFr4VWlHuH0Dbp2k8Km56eToMGDQCQJImlS5eyY8cOYmJizll+3bp1pb5+x44dO7uAbyxdcea8voQd6/bx/uoxoKrIBR9zvEUYNQICylu0645W9Wuz8ftvcecXYBZ15BfaCQ+xEOpvYXd2FnaXm/uqNmZXXjoet4RHJ6JqIGDEI6joRBVLeC4lpyL5pVZlRuWdoI65K1UTbiV/3jyyJ71OjU0b0YWGlndXy4Srts7954erT58+vPvuu4SdZ1Z40Q/XZdKlSxe6dOlyxeutCGiahiAIyHl5uCeORGd0sdRk5rvXXy9v0S6JuLg40tPTady4Maqqcuedd3LnnXeet3xSUlJpaLQJEyZccXlu5LF0PsJjQ4irHg2AffO3xAg5fOuIoIvos8W70jzctw9TXp3IzK++RY6rQVKDBN66oxuNP/sIBGg/7wsiAiwkVarDzzn78cg6FElDEjVUUUAQNMzheZRkxfCTrh6Bld0UGfxYdXIfUYYYuqsqJRs2EHydhga8aiPy9IcLQFVV2rZty7hx43jqqafOWT4pKYkJEyYwYcIEEhISrpaY1z3/m72ee6qPIP9kHieHDEItKiG7joOXpv8fJpOpvMU7J3l5eQwZMoSdO3cyefJkevXqxcKFC3niiSfo0cMXzu1a5PaB7Rk17WGcf/zB8UdfIeWQnqYDJ5e3WNcltWvXJi6+Mlt+Xs9dtWrzTLu2FDvdPNGsJV4LGcix21mbmorm1qMoBtwePR5ZRFFFPIqEIbQQ0DhZWIXZcU3JlXTI1jVs8U+D8DBK1q0v1z6WJWW2EszLy2Ps2LGlH67hw4czbNgwli1b5vtwlSMJdSrR7o6bMDlOYc08SEySRK239yKYQ8pbtPMSFhbG9OnTz7g2a9ascpLGx7/h1BsTECWVxY5QRrbvUN7iXJcIgsAdt9/GF7PnsPi3PaxJPUL9SlHMGHgPJS43eQ4ba9NTeahRIz7dtwW3U8RoElA0EUEFRRMwSjJ+lTKQzE4siosGBVms1ZzsydnFmogQOv76K5VUFeE6XMmXmRL0fbiuTeq3rE69ZlXxfHkPcR2zcPZbck0rwBudY4ez+XzKCp6e0JPI2ODyFudfYd+xE9vWZIy1iql875vlZjR0LmtigJkzZ7J161ZOnDhBw4YNmTx5MtWqVePWW2+lSZMmPPbYY+Ui73+hX79+hEREEd+yBTuyczmQnkN+iY1gzUimqwS9rKNRaCwhgh9O0Ynd6UZTTbglN2ajG1UUCax5BASN6gW5vHdkBbdUbUg22dhu6oK0/3tcBw5gqlu3vLt6xbm+bV8vQHJyMuPGjePtt9/mq6++ArxnO5IksXz5cjRNK5Pzo/Km6McfsS5aSJYhmS83Oxl8b/ANlTalLCjLsWQtsJF6IBNbifMKSly2bFy5l1lvr+DF0B0QHMTamk14+L6+V7yd7OxsoqKiWL9+PXFxcVSrdu5UX/80yjutBAcNGsSgQYN49tlnS/1L/f39cTgcxMdXrPRObdu2pV6jJhxIO0XNhEqMnr+Szu99gVORubtJPYqsLp783/9QdCoYFDCBqgogSLg8BhRFxc+g4JFFdujicYg6mrjcrDC6WFFJYcikSehjY8u7m2VChVWCx/s/dNa1gO7dCH3gAVSHgxOPPV56vcrcL88qezoHXEFBwRm+XQCvvPIKH374ITk5OURERJRdJ64y9h07yXhuFCXh4bT/OZXxr75Kw4Y+FXitjqWfVu5h7fI9fLl2FHr9uX9V53+0mvQj2Yx4t/+/qrssyDtVhKST8A8yUytEwZO8l6hxYxn8wANl0t7UqVMZPHgw3333HVarlblz556z3D+N8v6O0+nk6NGj1KpVC4CdO3eiaRq333473bt3P6uusjbYuxw++34dM79bwcY5r/NoUnO+/H0HDWKiCDQZkWTQizoi/C3IRg/ZrhJkjwdBk9A0AVVSEICizc0xxmay2z+GHkV5rIgwk2XIIbDX3YjXqfvP9bfB+x+4EXy7XIcPc+Lxx9BZFIan7KRRixY8P3p0eYt13XElx5KtxEVBbgmqop23TFF+CflZ1ivW5uUwetBMXnv2K25qXZ3R73XB7zaRY+FlN8/Oyspi7ty5PPPMM0RHR5+33D+N8v7OggUL6NWrV+nPoigiSRImk+mssnBtG+wd+fV/pK78lJPZeSiKQmGJk62pJ/k9JY0okx+KXeXV9rfwUvNb0DwCkWIwBs2Ad8iKaIKA5G/DUxjMdr9Y2pbkIEoJyGoA8zetoXDBArRzvJOKToVdCZ5rRn4a0Wy+4H04MwecXq8v9e0SRZFXXnkFTdOum1WgOy2NtEceQVRthLXOpfBrB9/PmYPuOo8Ecalcq2OpR+/m9Ojd/IJlHn2p1wXvlyVvD5vNicNZvL/qBQAGjeiG2WJAKSzEvXICfuJxpq/aSJMu95VJ+4888gjp6elUr179gtF5evXqdYZRXv/+/UtXjd999x3ffPMNAAcPHuTNN98EvMpOrGBGIG3bteOz6dMY8cYMln/2EtHBATg8MuGBfpwqLuGeJvUZs3w1608eQxBFspzFIGoIqojJIKCICoJ/CUpmNNsrxyGymVZKFJG1bqdJajaZr0zC1LAhpprXVwqsG/YreDoH3Llo0aLFVZambFFLihHVYuI65GDtM43JHQyl2z8+Lp8baSz9nbotqhFR6S+jqtYd66AUFXGky62EVk7nvWwPj3/5XJm1bzKZWLVqFatWrWLo0KHnLefn53eGUd7pM0GAJUuWlP67Vq1afPHFF2Uj7FXglo5e69sYo43O4z8jqV4i9apF82PyYVbsPUSg0UiPurVpFBtDp5qJPPrjQlKKc0ERcbk1prXqwxTXFlIzRLYoNehe4yEOi2GIhzdRLEXwOODYsfO6U4IVa6rj45KZPmkp6+b/AoApbR6JnY9iuP91Ilvey1133VXO0vn4r+RlWym22stbDABue+hmBow5cyzlfPgRirUIOcRJeqXbqVKlSpm1/8knnzBjxgw+//xzPv/88zJrp6IQGxtLfHxldmzZguyU+SM9m9eXrmP7sZMIAry4aDUOu5tBLZpSNTiEZXcPoEuVGqCJaIrIG3vWclLIAMBpCyHVFIEgFGFUv+cn5SBSeDiOnTvKuZdXHp8SvE45sHoLQW+OInfaR2hH1vJ9ZgT3vrWqvMXycQG2bD9K6tGcC5Z55u73mfLsvNKfl331GzPf/F9Zi3ZJOA8coGDePIKr2ZiaXMLQZ54v0/ZORz4SRRFNO/+56Y1Es+YtOJ6yj0FJzflocE9evedWHm7VhJqR4YT6W5iyeqM3sDZg0ulpEBSDv86b0eN4QRE6vYaUcAwhxEo7Zy4vZW1F03S45Cw8tWvi2LO3PLtXJviU4HWI68gRntQ2YhYVttpCme7pxb0zj9CtW7fyFs3HeVizbh+jxn/H2FcWXbDc4BfvpPdjHUt/TklOZ8/m1LIW76JoikLmyy8jBQdxpFVrTkR3K/Ot4Mcff5yHH36Yhx9+mMGDB5dpWxWFVya+zNbfNvBYz9bERwSzNeUEn67ewlv3dafQZie32M6YRd7J8MgVK3h/y+9UNod4I8toEqqqIYXnIZhchNmz6V+4BwMB6MQ8/Bs2xH3sGEo5BaovK27YM8HrFcfu3V6TflHD0CmQd2at55ejX9Kt+20MGjSovMXzcR5ualiZu25rzK2dLuyM3P6Oxmf8/PTr95674FXGdeQI7pQjRL/8EjXvvJN2V6HNDRs28H//939XoaWKQ/369c/4+aXet2C7w43N7aFWRAQxoYEk1U4kw1rEsn0HqRIUxOjm7Rn40wI0wCVrCKoOigPYbawEQKxH4pg+n3GBJiatWonk718OPSs7fErwOsHlkflo9nK6fDwBc4g/lVseQRdXCY//FsxmMzNmzLimXEHy8vLOGzz9RuO9GWvZvieNuR88XN6i/GdM1auTOLQ6f9iTaaDcfpY/XlmwadMmJk+eTFBQEMAFjWNuJL788kvCw8O57bbbMBv0mA16tu89xIGMHAItJtYdSGXk4hU0jovhjbu68uWBHaiyRu3QcHI1KwUFIurRRI4kKpSIBhq6nBw3qBwhH0/A9aUAwbcdet2QX2Tnm62pLEmoQ3TLw+hjIkjr8AGpmblMmzbtvGmGyou/5we80alRNZKmDStfcvldO46Rl1tchhJdOposU7RyJVryQvQnVvDO5AnMmTPnqrQ9atQo2rRpQ7169c5aAd3IvPnmm3zyyScApGblMWHealolxrPwqQdJtxbx+5ETVAoK5K76dfg9/QRbjp3EKBs4WJhDvtOJZnICGordn2RjFF1sBUj6jqzoM5R5419h0diXyreDVxjfSrCCY7M7KH7vPfxbtuDHkc0xffsWVrcRsc9CEqKrceDAgXJNXnw+li5dypEjRwgMDEQQBN56663yFqncuL1zgwvedzjc5OeVUCkuFIfdzfMjvuK2OxrzzHPln7Mw56OPyJv+KZXvEMmW/Fl+QmR6nz5Xpe309HR27drFlClT+Pjjj2nfvv1Vafdap2nTpqxZswaAlMw8ftiyn4gQP6LCAzmZa0WQYNTN7XmgeSPe3LCBk8VFuBUVzSOiiSDoVDA5Eex+JFuiaGc/RqE7msPWYpqfyiZ27+5y7uGVpVxXgpMnTy51VPVxaWxY8weH9nnNmB35Bfzvjp4UfPkli2d/x66jGTh0Udh7fsunXy/F7XZfkwoQYN68eYwdO5annnqKJ598srzFuersP5LFrQM/ZO1vBy9adsZnP/PIwM9w2N2YLQbeeKcv/QbefFntn0zLw+2WL6uO4nXryPv0M4La1cLPP53HFpzi8SFPXLUx99tvvxHwZ5Leay2EWXnSqHFjMjMzeXn6QkL9LVSJDmHW2m28vXg9VcKDkQSBhnHRnCiwEmowU1zsRpJBEAVQRDS3hGByoTrMvB7Rie5VBiEKVr45uJjmnTpgyM4qNY45eSKfiaO+JTuzsHw7fRmU2UowNTWVSZMmYbVaWbBgwVmR3OPi4mjQoAEl15mlUVky+vHZ7N2ZRptOdXj6kUbse3QwdXNy+K5VUxYG1aLmLif2BT0Q17/H/1Z/Q6NGjejUqVN5i31O9uzZw9y5cxFFkb59+5apP9m1yIGj2ZTY3RTZnKz+/QC1EqKoEn3ubB497mxCtepRmC1eU/YmTateVtsFeSU8eveHVK4eyahX7qZ6rQtvlaf8cZKEmtHo9H+d87lSUsgY9TzGmjWIrryNPc44Npw4zJfDhl2WbP+G0xGPrFYrWVlZV63da52gSG/w7+Tkvaw9kkWxx03t+EhqxUewePd+bq1fgzB/P26d9gWyqIEONBmCBCOFqhMEAc1gh8JgVJcBdDIG9QgrU1dzd/hoQgHXgQNYmjUjP7eYXVtTKcgrISomuFz7/V8ps5VgYmLiGec+pyO5f/755yxdupTt27ezZcsWfv/997IS4bqjTsN47nuoLZuDjnHont6oRUUs71SXF6osY1T1k3z57P08/lJrlv34LYMHD75mFSDADz/8wPz58/nmm29YuXJleYtz1el2c10Wffwo3W6uy8ufruDrldvPWzahagS33d4YgKxTVkaM/ZYjR0/957aDQ/14YHAHTp7I5/iRC/slHtmXwVN3f8DybzaXXtM8Hk48+SSCyUj8xx/D3e8zdp3GAw88QOxVzDQwcOBAUlJSGDJkCCNHjrxq7V7r3NXN60JTLUQiLiwIDQj1N/PKfV0Y0LYJ3RvU4vZpc1AV6FqzBuFmCzGBAdQPieHOanVoG1sZLbAQqh0AQWb6qSV0chYiCDJvZB0CwLn/AAANbqrC9+vGULt+XDn19vK5ameC/4zkPmLECI4dO3ZeJXgtR2svL7o+1IInflhKsgLfNG9ITGwxo3U/sl2twr0Dx+B2O3lmxJMkJCTwzjvvlLe4F8ThcJCWlgZ4873dSMz/eTfvzl/PotcGYjbqmftKP6JCA85bfvuRdCqFBhEdEoDd7ub4iTxKbK7/3L4gCPQf0pEHH0tCFC9sMZxQM4onX+5Ju25/nVsKej3RL76IFBqKPi4O4uJYvLEnxcVX11jnf//7HzNmzMBgMNyQE6nzEREezoGUYzz83lI6VAnljlZ12XcimxKHkzkbt9OqWmWebN+Sgzm5vHr7rRQ4HKxPO0aAwUDPOnV5aMV3mHQi/iYDNrdKolzE/YKBTcB+IZuS0FDCrddG0PYrwQVXgu+//36pE+qrr756WQ2dK5J7QkIC999//znLX8vR2suD6evWsGXQQOzHDyCYFOrXzmSEbh0nIjowZf9DnMjy8Pzzz3Ps2DHmzJlTelZyrTJhwgQ+/PBDPvzww9K0QzcCqqpR6HFye+s6hARYAKgeH0FacRHp5/iwyIrKYx8v5KPl3hB4iQkRLPxyKI3qX36+u4spQABJJ3HHg60JDvPHfewYRStWAODfthXm355C3jobh8OBJEkEBwdftkz/hp07dzJw4ECysrL49ddfr2rb1zp+AUGoisrq7QfZtPcovxw8zm2TZqNDpEW1eJ7o0Ir3et/BBxt+o9P0L5i1eQd+egMOj4cNx49jUvXkZhhx5gaxRwwl8dQRNE3AYiom9IelRAy7fs7xL7gSPHLkSGlyyX87y8vLy2Ps2LHs3LmTyZMnM3z48DMiufu4dGZ8N5vaUz4i1OYgOiGCIKOV/vZtzHa0oVrzKVQ+tA2LxciQIUOoV68eN998eUYTV4NVq1YxZcoUAN577z2eeeaZ8hXoKrFq9yE+XPUrtzSqgcng/fX7LS2NfvMXYFBFkp8bjvS37AU6SeSzJ++hUmjQeev8csMOUrJyeeW+LmUmd8nGjWQ8NwrBYMC/QwfE36dCxk7WKS154PYqbN68mapVL++s8t9St25dnn32WYYPH471OlqZXAlS9u0ie8PXNOoxiDH3deJ4bgFfb9qNoigMaN8EgH2Zp/hy804EUSCtoJCX1qylGDd6VcKueKA4Ek3RcTAokt7qXgQ1gfZVAql2nfn3XlAJCoKAw+EgOTmZjIyMf1VxWFgY06dPP+Pa3yO5+7g4e3My8SxcQIsPppMfYGbcw53YEx2LdFLP3bohZFij2dg4kZaNq2IwGIitFEKjRo3KW+yLMmrUKH799VeOHTuGpmmkpKTcMErQqrloV68qj3ZsVnqtxO3GoJO4r179UgW4PfMkSw8d4OX2nWha7cLnLTnWEtLzykYJKCUl5Lz7HgXz5mGsWZO4jz5ELDgAm95Fa3Q/I15eQXR0dLns1tx1112EhoYyZ84cpk2bdtXbv5bJyMjg2B+bGfLUs5j1OppUrURWQTFLt+6DP8OsfvTTr1h0emyyh/qRkXSuU513tvwCAmioCHo3OP04qI8EoFpxbWoE9Ma+fTun3n6H2ClTMMRVKsdeXhkuqARHjhzJtGnTmDt3LpMnT75aMl337Pn1EIs/X8dzHz6Exd90zjI/njjEN1On8NyKTWyrFcOvXSrxRd4CnsrrRXJ2Ik5DNb6b0BujXkevXr2Iioo6a9JxrTJs2DAaN25Mu3btEEXxgglRrydybTZeWrWWZnVimbB3LfWzI1h27CBNw+Po06YuTzbw5sRTVJXnf1rFkcICGkdHc3etegAM+X4JBknigzvvOKPekT2urH/cb38cZ9yMFXz29J2oD/dDzsoipF8/IkeOQBQV+KwX+Efxs7Ebe/d+xqxZs8olGlGzZt6JhCRJPPXUU1e9/WuZevW8Y2blmk18/2sGql7j9SF38H/P9i0t0yqxMr8ePwECJOecYk/eKUICTBS6nJhFA7JJxl0kckCtxBZdPAGaHxuPZPJgDX8cO3fiOnjg+leCe/bs4Y033gBgxYoVpVujPi6PnJMF7N+eitPuOksJZtiK+GjPGvY50klrHclnATdRNS6XjzOWctAQwSl9GCN6dCDGz5+E6FC++OILFi9ezNtvv11Ovfn3VKlShcWLF5e6RUybNu2GCHkV7ufHD4P6s/BYMp/v/53t1uOY0PNLdipFLg+/nzpOo4ho6gRFkerMRzMp/FGUzd14P2i1IsLRixJHi/OxSHqiLBc+98112nDIHiLN/qw6dphuCTUxSBLpRVZCTWYO5uXilhUax8RQ5HDx6g9rGBYbQsjOPdSvGkdgcCDSoEGYGzbA3LCht9J9SyA/FR5azJtDJhEdHU3fvn0vKEdZYLVaS8Ol+TibWrVqIUkSgZKd0MggmtaPp06VqDPKPNS6CaEBFt5cs548p4PxXTpRMzKMexd8g9stE+EvkXkKPLp4HgobhJ/Og2JbTUZ4HfSA6/BhAjp3Lp8OXkEuqATXr1/PHXd4Z50bN26ke/fuV0Wo6x2bQ8auCCjKmelfFFXhp2lTuPWHFewf3YLYYLgz/A86ZhzjB0tt3rTdS6ISz8N/bqUdOXKEp59+mo4dO/Lss8+WQ0/+O6ctQwGOHz9+WXUtW7aMbdu2Ua1aNfr163e5opUJBwtP8ci6b6gbHkh8UABdakaS7y5hX0E+bkXAqBM5peSx+Hguq/T7WHpXf/qv/ZYD1mwOW08RZQ7k0RZN6b7qcz7/cR2RpkCWdBmEQdTx4YE1dI6pS8OQMyepI379gRRrLr0TGvH+jt+YccvdJASE0OXr2STFJbL+yDFiC6x0zsygjyTxwOYtKE4H6PW0GzuF9+dv4LXHHjyzI3Xvgqe2cyjXw+rVq5k0aRJGo/Eqvkkvr7/+OqGhoYwePfqqt10RMJlMVK9eHdGVz8Ipj5x1f/ZP2zickUu+7CTC4Ef7qlUJNBjo/+0CBECVIMedD4RRUKLQvk48GQXHyRa2cVQ5Tr1KlXAdOnzV+1UWXFAJ5uTksHbtWgRBIDMz82rJdN1Tp0kVut7bgqDQvyJr2IryWfFUX5puTuNQvRAETaNrTio35x/ntdBOzJBaE1So44F2jQGQZZl+/fohSRJz5sxBFCtWGFhJkhg/fjyCIJRaC5+PiwVeePDBB0lKSuLDDz/8T7IU//wzmttzxjV9THTp6qd4zRo05UwZDfFxmOrWRdM0ilf/eFadhoQETLVqorndFP+8jo+2f081p4Kq95CpVzgVE0hmdAD+qkaLQ7l4NJkAnQmr2wGCyA+GxYxv1ZY3f1vEm++upWlYJZqFx9HtxGFyXXkkV4rggwPLiHC6yFy6jLTwWrj1JtweJ1G6QApaNqR9XDRK2j4C35vKRJuHmr8e5I+jh5lTZGPVow8yPimJ5P+bx/2bNuEKDye/fn1q33sPAR068PNPf5Br/ZsdQOo6QIDEDhBalRohGhs2bCjddrvayLLMl19+yfbt20vzCs6fP79cZLlWad68OXa7HZvdxVffb6Fn98ZE/OmKk5KZy9Yj6WQ6igkIMFE9OoxnFi8nKMCERdJjMRoY3LQJ4zeuQ0Cj8YGlvO7cTvcq4RzIP8hN1RJxpZZ/Cq8rwQWV4AcffMC8ed4Enu+///5VEeh6xO3y4HK4CQj2Kr0a9eNIqBPDiTwriZGhHNq2luwRo6iT42DTvZXY3CGKYlHPd4Z6bNBXZZunEjWOBdKhWlW63VQLgH379rF//34+/fTTCrlN/dZbb7F//34A6tSpc8GypwMv9O7dG/gr8EKPHj3o06cPffv2ZcqUKec1rrmYz2nmuPEoeXlnXAvs0YNKU7zxTE8+NwrN6TzjfvD9fYiZMAFUlZNPP31WnaGDHsE0ahSq08nJp5/mn5u9/7ujOktiaxGm2hg655eznv/WVsTcgGyqexyM/Oav+/ecvt+/Ln+UbMe4/yQj528HvM72foAbWPjoTWy7qQqJTjcN96cRHBLOEdtx8iwGjkYEsb04D0dJJulN6/NAZBhBEVGcKrSxuVs3JKORob3a/iXMyR2oXz+Ix78Kxqc2gSgiCEK5WiG/8847PPLII1Su7A08fq27BJUHc+fOBWD7nuN8+d3v7PrjBB+//gAArz3YjWXb9vF/m3ezOyOL5vGVyHKUcOhUHq91v5XHlyxhU3oagqYhCFAjKoG4Y+sQ5QS+2rWe/k16IB68eMi/isAFlWBaWhpWqxWXy8V77713Q/lzXQ4Om4spLy7gjvtb0qR1dT4Y8x1bft7HjPVjCQw0AzBv407e+d9G2tzloesHS4lzyywZU422chp9N+9iUJO72O+KIScnnKgMP5zJxaRW+Ws13rBhQ1JSUggPDy+vbl4WS5cuZd68eRQVFWE0Glm8ePElP/vPwAtvvfUWVquVzZs307Vr17PKJyUlkZSUBHj9E/9JlS/noMnKGdekwL8+qgnzvy21qCu9H/zneZQoUnXJkrPqlEKCvbf9/Nj43qP8nrcXnaCiACZRJc/fSIjJgVOQeOfllgiIuBXwIGEQFayBRmTy8EsM4OPXmqNoGjZFh0nSCDQ4cYQImKU0nNUk3p3aAlFSsGlG3Ojw6ESckg5RdHC4psBLb97BzJbD+WzL9+zJyUHRQPOIPF2lOtNy8kmsVYW0zCJcssyejCzaVv1bCLuTO2Du3RQ6DEz4rTMfPC3y6quvkpOTw/vvv1+u6bkWLVpUuq0eHx/v+z6dh6YNq9C/d0sa1jnTynjljkNkZFjR60Q++XkzVSqF4nB7mLrhF+qGRfJwo6asTs5GdQSzVsugBxDgNuLwKyai32COnMyjyOYk0O/cxn0VhQsqwalTpzJixAj0ev3Vkue6IOVABof2ppObVBuAW3o3Z/eBDEaP+IpPZngT2+rNBbRpmUEmdlYOSqC+kM2AfbuoZC3mm0r12VMQj1JownTIiKJAreqRTH3p3tItwUceeaTCKkDwGlrVrVuXMWPGMHXq1H/17OnAC40bN0ZVVV544YXLksVYrdoF75tq1jzvPUEQMNU6//0cTwHrgvYjhQgYJA2TYMKuudHJGhZNwV8nU+xnwKnoCTPY0DQNSVQJV0twqAbcYhGWah7cso4oXTGCqKGiw6BqmHARHiTjCJAoUUz4Y8epGJAVEBQTLk2HJImgOXjgl3cp9kCVkFAO59nRJIUf0w6xryCbVjHxOGQPekVk/s7kUiW4Zs5HtD/2GoagCGxdvuDxeyIpKSlh6tSpdO7cudzzUxYUFPD5558DXrcbH2eSnp7O/fffz+jRo3ms39m+2VMG3k7nCZ9TPzKKdnUS+G5vMvUiI3mkXTP0ksjXu/aAogOXP1vt3ri2d8ZU4nvHYQpKSuj/xlf0alufMQ/ecrW7dkW5oBKsX7++L0/Xv2Tj2n28Nno+kz7sR7PW1QGo3yIRY5VQatfxmhOv+3keCePfJiJax68vx1CvJIse21JQBJGX6nfiK3dTZI+eSoGBvPRoZxrGRhEW6IdeJ/HAA4P47rvvaNWqVbmdx1wJwsLCsNlsbNmyhYMX2VapyIEXxuwejVGngqahExVktQQRHUZRQQRUDYyCQojegYqApIFZ8iDpFKLFQlyaDlXQYzG6CZZs6AQVu6pDEAXcmoSCSLDkwl/nAQ1cmocS2YhZs2P36HGpeoplAUnyoFeNpNsK0eslFFXk18zjyKJCnstOmNmCzihQ3T+YjmOn81q/bvhnbCRHi6DSI6uID/TGBP3ggw8oLCzkueeeK98XCxQWFjJnzhwEQSDvH9vZf+dcZ8jg3RXYv38/ISEhvPTSS8TExDBkyBB0Oh2JiYkVPh5pWFgYv/76Kzt27KBHjx4s+3EvK9bs5Z1X78No0GEy6Fkx/hHMBj1707N4f/1v2B0eluzZj79Rz7IDBxEFEwowoF0vXD9/RM8SHfUbzeL212YwfccigmvpgOtYCf7888+sW7cOo9HoO3i+RBo3r8qjw2+lXqN40tPziYsLRdPAYDEQGu/PpvfGE/75QkqC9RzpG4BRkKmfmctJv0A+aNyGTQUJqLKA4IQxHTrRtfZfq4xZs2bxzTff8Nprr1VoBXjs2DGGDRuGJEnMnTuXxx9//ILlK2rgBU3TMOqcoGkEGZwYRBmHokfRZDyiDg0HekFBL3q3YkVNQ0XDIMgYRIUAnQtB0DAgo2gqBgEcmpEwyYlR9KAioSHgUCXsigk3egRUkDSsshk/vQePx7uLIwoa4p9O0KrmzQaR47aBqHE8swjVo1JVCGb9xsXEO2SOZDXnoVFzQfWAwXuWLcsy7777Lm3btqVVq1bl81L/xqeffsrq1avRNO2CPrL/PEM+rQR1Oh0GgwG9Xk9wcDCbNm2ifv36PPXUU/Tv3x+3243BYLha3bnimM1mEhISOHDgwHnLBJq9W5lOt4xZp8Mpy2w+doIClxODTsIlugGYt/0gHppz4oTAsrRV3NOyIZZfFmHO+ndBVK5FLqgEf/jhh6slx3VDQKCZex9qy/p1+3llwvdMmfoAterHot1SgmXmaMKS88hsYSb/UYFQczEOUWR+4zpkuEI45gglPzcQVInWB8MJaPyXxeeBAwcYNmwYSUlJl739V95MnTqVoKAgxowZw5AhQ8pbnDLjpT334y/JGCU3FlFG0wAJPIqEn86NpgnoBTcGUcOtiugElUCdA5MgYxBcgIiGhr/kRtO8CtBfcwEqChIeBBRNRC8o+OncGFWZIsWMTtDQCQqyJqGpHsCIJKgoioqGAAI0C4tD57FwrNDKnTXrkHPwF54XVhBWsAp7zWZYOjb9sxd/KYFFixZx7Nixf719XVbo9Xpuv/32i5b75xnyaV588UVEUWTp0qXMmDGDiIiIUiOzyMhI8vLyiIk5M81URQvsX7t27VIDtNtvbUC3TvWY/7/ttGmWyIvTlnHHzfXp260Jx3ML0ZwqHw28i8HfLsZi1GGXZZpXjmVLkUJanoN3/TrRKCKSMGkVNepEY6leHffRo+Xcw8vngkrwjTfe4Pvvv8fPz4/c3Fz27NlzteSq8OQKbprfUpMVOak88n//R9PKB4jNL2bXI2FENi7kzg0nyQ7xY26HMLYVVSGlIAK1wIAx00iPxrWwZRSxY8NBwiQddZtUoW/fvlgsFubNm3fGL3JFpHLlysybN4+TJ0+Wmrd/8cUX5S3WFccglWARVARNwU/yoGkCRs2DLEp4NAlB0PCT3KgaRBkcGEQZWdVAkDAKCibRiVFQEAQVTYMg7AgI2DUDqibg1HQgCDg1I3bVW59ZcuNS9ZglD3luPSYdWGUBFQGdJGAQJRQFjhQUkWPPokvOYR7L/JzA/EPYBTOrAu+k68CPztmfxo0bM3r0aO68886r/CYvj3+eIZ/mtFtRZGQkycnJ3HTTTeze7c2anpOTQ9g5YmRezMjqWqNOnTr8/PPPqKqKKIpk5RQx7cv1eDwKMeFBhPxpqNe3dSPualqXEqeL0be0J8hsYPLajezLOoXBGINep2PhwAcZ8PnnFEcf5o11X7G4Uj3kn34q/R2uqFxQCWZlZdGtWzcmTpx4zafmKS+Ki52czCygds0zZ4w/HUglV2fn5u0/0rFjLkYLbHgrmj4H95Lwq42sMAuLmtYi3RHCsaJwBMDtlIgzmnmpa2d0t8L9LV9BKHBQv1lVpkyZgqZpVzVfW1nx3HPP0bdvXypVqvghl87Hx/v7YRY9SIKCn+jyrsoEAYMgIwgaICAhYxRVJEEGRIy4MejcBIhuXKoOUVQR0TAJCqoAGiJooOHGIwiIGnhUEScqOmRcGDk9PdIQMYgqbpdC5aICQpxOQu12YkuKqOYoYHxkV+rGtiQu/xCSZITuU/iuoAY6SzCYglBkheQdx2nQLKFUWdSsWbM0gtS1QFZWFuvXr8fl8qaVeuihh85ZrlevXmecIffv35+5c+fy+uuvc+LECXJzc/nggw+Ijo7mq6++4umnn6ZRo0YVeiv0NK1bt+bw4cMUFxcTFBREpehgvv5oENGRQTwkiWzdn0ZmbhGrdh9i1k9bUYxQLTqMU247JS43sqoyd2BLGleKYd68SfwmT6WlpwmKxYalWjUKv/8epaAAXWhoeXf1P3NBJRgeHo6qqrz66qtXfBVYEaJ8XIziEicPD/2C3LwSfpg/nIC/hUDr2MQPw2tTqXEwl4iQEJSWMnf8coTAApk/6oSwoX4V9tpjKZbNKE4Bd4GJuBR//ESBAJORH7/fjtutUK+ld3vmllsq9uHzP7mmFOCK0eD6R5aUuObQ7GHvv394GpQznelJuBka9wVVhaVnZ1OvbN7Hwfhw/GUXHXacAPBuRQICGsfjAsmL16N3Q/PtmYheFYeIigDkJ+pxxoPRDlFbnAgaaJrgrUMVOFbLj+xYEaMV2m7ORlQ1BBlERUMna8xvXI9t4THUyi3khZ0bS+VyCDqOGEMJdDpwOGW2+9/C/WonfmjZnwF/k3/jj3/wxgvf8dq0h2jWtgZvvfUWnTp1Ko3XeS0wcuRI+vbte9EUTn5+fmecIZ8+E3zxxRfPKvvJJ59cURnLm969e9O7d29Wr96LyaRna3Iat93SgLiYEGRZ4al3FtKhaXUO5eVTZHfxeMdWTN+4hSCLiXrhEVQJD6ZllXiyiov5/oSDR4Bwj0iWfyGBTZpC3/vRZLm8u3lZXFAJjhs3DvDm7fq3Uf7LOsrHtUB+gQ2DUUfvns0I8Dex5eQJHlg2n/a5mTzx/WqMLoXDT1oo7ihQRSrCZZL4rVMo28MjWZzVGLtspNBmwnnKj0piCOZ8J++/3BOA9t0bkpV7gnv6d2HatGkVdqJQIUj7Dez5Z14z/s35+tgmkP+RxDbgdNBvDY5uOOOWS7ETXtVAoVSARVOolF0CgreoIGhogCdMw6MPxM/jIDLbifeo7k9nRAE8sRqioEOvavjnKWiCV4lqgoAqgCRrSIAqSCBouPQSTqMOt6ijSDRRbDBS5DHzh5+FCbU6kiaGcFIfSGhwHbZmFoDDgHziJANr3ERuZgk935jD4hf+UoOtOtRm5Ku9aNi8Kvv27WP06NFMmDDhmlKCzZs3Lw3r6OPCzPu/XwgLCyA57RSJVSKoUzOGjTtTUd0aVWPDWPvHEWYM7029qtF8sWk7YQEW9qZnI0kiySetPDZ3C4VyLAoCiYpGmuqiqFoUMS+/XN5du2zOqwRPJ2gF/lNYosuN8nGts3jlLt79fC1zP3iYypW8WwEj1i+nx76dPLZyOwXxRtYPi+Ee9x5OiQZsooHtt4Sxzx1DuiMUFQk0AXumP13FaqTuy0YuclNS5I1M4nTZee3t0VgsFjpfB0Fq/8lnn33GmjVrEASBjh07lq+BzOMbLnz/qe3nvydK/9/eeYdHUXV//DMzW9MbCUkoCaFLKIIUQUBUUIoiKtgQUFRUxK4o+rMjYNdX1BexIKIviAUbYkEFkQ5C6CWBhBDSN9k+5f7+2BBBioChBObzPDzJ7s7MvXNz2TP33HO+B+7J2u+t9zaehyQJIuQgul3i+0Gp2GUDQ+gY2LBIKhYEkZIPS7jOmsHRgMAm6SgI7JJRXe1ajZLIuSoMXYBXKASFhYBQ8AsrqpDxRDn45oLGuHULlUYEuiFRpEVgCBkjKKi0h/N9XBO8qg0hZK5MyaRdrMp3m7YSFrAyc9FaRrY9m2hpf/1PR5iNiy5tB4TUWZxOJ3fccWoVUp09ezY//PADYWFhZvT6YWjdujVdunTl2fGvUlLuwecPeTVaN0lh+ICOXHfh2XRtk86Nr8zixZH9WfjoKFRdJ2vXHu799BsemP01+eUx1EtIYpc7hnMklZyoNFxBF3W1OhhuN16LYz9PWG3ikEZw4sSJLF++nHPOOadGGjpalY9TPQorLiGCXue3YJunDFEp8cDy/2GNLcTdWWGtGk/D6/K4Y8kOHC4DtY7MxpRk3IaDja5ktlYmUO5xUlYcwQWxjVj2y07aNEpmyitXYbEoGIbB0KFD2bZtGz///PMBEWqnA2vXrq3+0qqtD0IHY2fFH9hkA5ukEiaFUhlkwJBk7JJAwk+kFMAqaUgSWBHogIRBOIBEyACGgjhD6i4INELBLVLoIzQUDBQ8ho2AsKBKdkCnXIvCp9lQDZmg7sCthePXZFTNghq0MH7lrySJGGZdeh1qQOeHDVuZ880aUuOjWbc2j2VLtnHDjd2rq87v3r2b6dOnM3LkyFNOnGHBggXVxb5N2bRD43A42L59K06njdde/pqsjfl8O2008THh3H5VNwBSjWgubNuE6HAHj378Pf06NOeOj79Ek0A3wKbE0bFBBjnrE+hYXsTq5LE0j2vO9kGDKFPCuD/YlqmvDaNRwzon+W6PnkMaQUmSmD9/fo0ZwaNV+ThVo7B25JcSE+nk7Tl/8KcoYtoXG+kRXMt5O/fw/ZA0zj87izYxe2jyUyW6TWJ17xi21YllVyCO0oCTta56aLpMSXE0FyZnsOqrnZzTvD4v3NofiyUU1vDkk0/y1Vdf8dprr9G9e83WijtVcLlc/PrrrwCUlpb+w9G1h58L7kZCQUZgl1SskhGK8CQUlRgmqVgwsEghJTZJEtiFwCrBwQLsNEATEpqQ0IWE35Bw48RAxmcoaFU5f4YBmqGgI+E3rOhCpjzgwB00CGoWdENGCBAehQLNixCChnExLFy4la6t0hnUPZPfv1vHF58u46prOhMeHloZvvbaa2iaxr333nuCRvDIeeaZZ6orkJiyaYemefPm1QuK24Z2p6zcy4dfLuXLn9fw1hNXkxAbQXG5h7TYaBw2C/PXbaPE46Vdagp39+lGXLiDIf9dzpw/t+NTOhBBgO5N0wGw1atP+PqNXHH12dStUztLWx229MCcOXPo06cPV111FYMHDz6qC5eUlDBq1KhqlY9BgwYxe/ZsbrvttlNe5eNQFJe5uebu97h3/GwuvuwstEiV2//8hQemLyYzdw+ZYQV027CLZosr8dRV+POyaPbUdVKp2VnrTmWNqx7egIWK7dGE7bFy91ld6FenPk0Ddm695m38VW6K+Ph4Ro4cyejRBwZcnC688sorZGVlkZWVxSuvvHKyu1MjBAIVhEkaMbKHJIsLhxzEKQVxSBpOSSO8yihapNDKT5YM7IQMoFK1ArQACiEDqQGGkPAIC35hxSds+HCgC5mAYcGlO9GETKkWhtewU6RG4FLDcKs2KoJ2DCEhYUFTraArGJoCusztZ3ciJ6+MIpeHOtERtExLonmDREbc3JNPPh9TbQABYmJiuOmmm8j4B2m5k0FpaSlTpkxhypQp1StCkwNp1qwZubm5eDwe0usncHZmAyq9fnYVuvhlWagc0m9/buP975bhsFj48oEbWLojlw35RbRPS+W1+Ysp9pRhCCsrHa1JPu8m1vo/o/f7N5AfHo0oyOeOYecRFlY7o2kPGxizcOHCY75wbVX5OBS6YbBkYy4tm9dlZ8tKpi7+hJfn/kZavouVFyci3eSmX/ga1FjBHquVnS2cBCWFUr+Tle6G5Luj2J0Xg+pxInsl7G74acUWlszfSPNWqbRtn1YdGDFmzJhan3vzT+Tn51NZWUkgEOA///nPafEUPyevJxbZwCbpyAickoYshVLeLZKOhMBKSLll7388S5XbUyK0EhQCdEJG0DBCFSEMZGQkBDKSEGhCwWfY8Bs2gljxalY8hoOgYUUAFaodIWQaOzNZ6tmBrslcXK85X2dlk+iM4Iq0Fgyc+CHXd2/HS7f/lfMnyxLhf9vXOZXr9e2VTQMOK5t2ptO0Svt2y5Yt6HIsBUUV3H5Ndy46tzmx0eEYhuDGvp2IDHMw+MkPaZaWyFf3Die7uJT+r36Aokhc1b45c/7MxuvzElm2kV3sYY+2lR32VrTSNIJ5edjT00/ynR4bhzWCU6dOZebMmWiahq7r1UvqM5EZv6zi5Y9/o3HXOPZY8nhz+jzsQZ0fHqhHRnQJnZfkUniRBc0ps7p5NB4jjA3eZLI89Sn32xGBCKTycBS/hBw06NAglZv6dib/9zzObp9Ol26pZLZuxeTJk7nwwgtPawMIp6c4uw0Vu6RhFUEiZBVFEiBJ2NCQJQllbxpE1Z/WAlVpEVVGT4R+6gJUAQFkgsKC25DQDQtBbJRqDjwiHAPwCgcB3UKpFomOhRK/g6CwYhgO7LKddcXFeDyhFWDP5KY813EANkXhyokfklm/Lrdc2OmQ9xIMBvnxxx+5+OKLT9lalXtl0/b+bnJw2rVrx2233cYH7y0i1yWhSYJ+F2QSGx3OZfe/w8jLOjNiQCca1o0lIyWewT3akFYnlk9XrGV7YSlChi9Hd6RBnM6KNXu4ef19vNz2cgxLJQtcAVoBweyc09MIrl69mi5duvDEE08wadKkE9WnUw6/qjHpm99wtisjJ7GA9AgX39+aTrMG+Vy97U8SswJUNlCQNAuVipUyPYpSI5JN7kTyXZHscUURUVYH3RfAabHwaN8eDG6fiSRJTHrpOkpLS+natStFRUWnRTL8kXC6ibN/n90Zp6TilDWs6FiqEt1hr5uzKkuwauUnh36FfVZ/hgi5QFUhoaIQFDJe3YKGHRWJoLDgMRwYkkTAUAgYVip0Oz7NgkZIVFtTJSoCElZJpsKvITQrkoAeKY2ItIXcnCMv7EhMuJPYCOch72fGjBmMGDGCn376iV69eh3PoTsmXnnlFfLz8xEiNMa//vrrGfcdtWHDBl566SXOOusszj//fNq0aXPQ4xo3bszkyZN5Z8ovNNV0rr4mpPsaE+nkhr4d6NqmEQCdWzakc8u/ymiNubArKTFRuPx+hr0/i9SYKBaVagQVBwmVlSBBluxHGXkLtvS0436/x4tDGsGxY8dSVlZGZGQk06dPZ+XKlSeyX6cUI2Z+Skb4bu78ZD5reiWx5pI4mjfI5/JlW7AFdEq7WHG3sBBAwa9bqdBtbHLXYXNFEgUFcaDKBMq8RO6RaRgVxpAOrSkprCA2IQKfz0f//v3Zvn078+bNo2XLlif7dk8Ip5s4u0xZlUETWGStStq6yvhJfxlAhSrjx1+rP52QIaxKJQxFhRoGPmHFgwMQaELCrVtxGeEEDCtuw4FHtRE0FLyGk6Au4VOtFHki8Kk20sJjibOFs6msBKHBQ7O/J7+4grl3j+DSc1oe1tNgGAbPP/88rVu35vzzzz+ew3bMDBkyhNLSUiIiIgBwu90nuUfHzuLFi1m+fDnr16/n8ssv56KLLjqi81auXElqamqonJfDweuvv46qqgcNYtI0jcsGnkWdOn9Fb1oUmVsHdWXDjj1s3FlI8waJ1Z+9PXcxAsGoi7vw8OdzWbw9n6Bqp2395mwriEHakQ314eExPWna8Mj6e6pySCN4/fXX8+6777Jx40ZatGjB46dBUuSx8OX21TTc8DnXfbGWQLiCK81KpnMXly/YDFbY0duBUkcmIGSWexpQKZysctWjwBsFyNhsKoYnDMmQcPgkGjSIoWBXGTcOeIUb7jif92dNYMmSJcyaNYsePXqc7Ns9YZxO4uwLcrqhEEqCd0gqtqpIz70BLnudifs6FQ32MYYCNCSEAK8IrRHLhQ2tSrzarVnw46DMiCBoWBDIBI3QvqBXVfBqVgJqBGUB0IUFCShw+Smv8GGRZERQomt6Q2Zv/ZPrn/kIw68z45mDS4wBfP3116xfv57p06efsm755ORkpkyZUr2X/MgjjzB+/PiT3Ktj4/PPP2fixInceeed9OzZE13Xj0gfePv27dWR84WFhRiGccgo3n79+lFeXs6SJUuq33tvzhK+/WM92UVlJMRFMnfSzdWfvfvDMnyaxuWdM5mXtQUwsCoKhRUaO+QEukmFTLO2QVMVtJIStMJCHC1a/KtxOFkc0gi2atWKl156CVVVefnll+nRoweFhYUnsm8nnecXfUr0C1MYtn4n2ZnRrLslhqi6lSQ7K9h+YRj2SAPFDl4DXLqFIj0Kv2EluyKBPSWR6JqVq2I7MGfteoZ1OxspSeXqfu2Z9eYvdOrWhM49mzPv9xT++9//MmjQoJN9uyeU00mc3aAAqySwoGOX9wo0S4gqCbTQSwldiKpAmdC+397P1KrffYaEHxtBoeDR7RhIFOnRBIQVr2FHCCgJhuPSwzAMCV0oFPvDkWQZt2oQ1GxgWEmyRdGvfhveWbKSuzp2pkNyPTrVr8+q1blYBPh2eVm7Po/MlvX+fisIIZg4cSJpaWkMGTLkuI/dsXLVVVexfv16srJCQgVJSUknuUfHTnR0NG+88QYej4e33nqL/v37k34E+2sbN27klVdeITk5mc8//5xrr72WkpKSgwp/Z2RkMGPGjP0C7hRZQjUMBNAjM52XZv1K11bpdGrRgNkP38CO4jKSYiK4qGVjvli9kTCboG5UHGe37EHCyjcpXtGJRbJKmz9fxLNwIU0W/IPoxCnKIY1gMBjk008/5fPPPyclJYXvv//+RPbrpKMZOpv//Ioxm3KZf2U9xLkGdyxfRkkTCxUdLVjiBSpQqVuoNGws8Tbil8LGFHqjkRAE3Q7wK3xfupXr27cm3RHFaz/Np22LeqxZtpUmbZNIy6jL1KlTT9mn7ePJ6SLOvr3kreqoXhtatUsTIRBV+38GElKVIpoh9hq90N9cB/SqmoB+7ICg0rDhxQFCQkcOXUMIKnUnHt1O0LBiw0aBT0YXCoGgBU0oGIaCHHAwPL0b7/+5ElmTqOOMoFNVeaBJN/ZjwMjJ4NVZsiL7oEawsLCQvLw8HnjgASyWw4YMnFRmzZrFunXrqutq6rp+knt07OyrYVpYWEhiYuJhjv6Ljz76qPr3f3pgadasGS6Xi5ce+ZC7nroOi1Xhhv4duaF/Ryo8fi55cAo6ApvVQodm9ViycQf9O7VkV5mLOSs3Isvg8leQU2pjefNz+UEJYCDx5dJ13JyeilZUhO52o1S5p2sTh5zl11xzDVdeeSXTp0/Hbrcf6rDTDmEY+FatYmTwDWI6BPnm9fr0K9xC5tIS/LESNLMQIRsEq77I/MJCXjCaRWWNKQ1E4PbZQZOQKq1Y/DKf3Xottz35P34Ianz131GE2S286fyDn2f+ye3jBlXvaZxp7BVnf+aZZ6rL19RGdlW+gAQo6Cgye9U/QzqfYm++n8AAZCQCoQxB9KoEeA0FWRj4CMma+QwLHpyh+oGGhWI1goCw4go6cGlOgoYVvy5T4A9VjZcw8AWtqJpCu5iGdIlrglOx4AxaSBLhDNzHReV0WLl+YCcym6ZwdmaDg95PUlISW7durQ44OZWZOnVqdW3DcePGnVIVLo6VIzWAR0uzZs0A+GLG99x03+XEJPylsBMZZmdIz7ak142lf7ez+OTX1Uya9QtZOwr4v2svYkjHTAoq3czfXEmJW+Xen7cSYU2jUbvtbA0s4ufcIXQCgjk7cLaqfcW+D2kEZ8+efSL7cUqglZSQ//DDuBcsoN6TmSTVK+e6dVnULfGws2k4gS4G1iqpjxw1lh3BGLyGnT9d9dheGovfbyfothLmd2B1SfRqkcF3C9bjVlWSoyIxdJWBA6/ku+++49VXXz1jDeCcOXN49NFHWbNmDZqmMWbMmJPdpWMitD0Q2t2zEVLS37seUQit8HRCEmehwBeBhdDKUCCjI2EIGY+woWNBR8ZlOPAJK2V6BEJIlKnhBIWFCs1OUNjwaUpIFk2E4k3jLEk80bI/S4tyuTixJSM+/5xwmx1LuaB1g2Qs++wtSZLEiKu6HPZ+oqKicDhqhwak1+ut/r02B8acCPbmCl770Hn7GUAIzYst2wuZt2A9A85rRffMdF7+YgF1YyORJInHL7uQRdt2oIkNpEanMmvFVvqF5ZCYEsWWXC+xZ4UiSoPZ2aeXETzT8CxZSv7996OWl7F0RCJJLYto7SsgxuNjQ48IrI0NLEgEBLiEA5fhYLcWh0sPY21xMhUV4RiqAqqMHJBoGh/Pti1FnNMpmf7dWjLy4nZc2r8fixYtYsqUKYwcOfJk3/JJY+7cuVx66aW89dZbTJ48+WR355jZ5D0Xa5XYtSLtdYSGqsFr+0R7GlW6n0JAhbBWH+UTNgxkvIYdTUgEDBm/cKAZCkHDGsoFNOxohkSl6kCWJNyqDb9uI6jJ6LrEZpeXJ5f8QnmZyofqeoJBgwvSMyiggvsu6IahG6zZvJu2B3F9/p0777yTNWvWsG7dulM2N3BfunbtyqBBg5AkqdaqUJ0oGjZsyPjx4+l2XteDfn7n1d2p8PgAiI+M4IK2TWiXESp3tnpnPqkx0TwzsBtRdhtRznJuWDiR33M7AiA3jgRJqrVV5k0jCBS/9RZFr76GLS2NBfdLNLMXEWG3oYYr/H5FIkn2SmwCgpJEjh4JQuZPdyo5/gR2VURR7IrCMBTQZNLkWIr2uGl0Vhw/bdzM7vIKHrruAoYPH86yZcuYOXNmdWWNM5UdO3bw7bffVv8E6Nu370nu1bGgV0V8hiJDhQgphBpVLk8wMISMT1hDr0WoarxfhNLmZYyQBJqwoBoKHmHDb8iUqRGohg2X5kA1LLhUB5qw4FGtqLqNgCrjCdrQVQtWNYx8v4c4xUm7lPpc16ItPdPT8fiDWBWZ735dz4S35vHGU0No2+LQhnDTpk3MmjWLsWPH1goDCDB06FCGDh16srtRK1AUhYcffni/93K27uGh26fx8Pgradvhr0Ach83Cc8MvqX49atoXtKqXxMK8XHQDJF2mGwk0qiyCSHj0y8+ZN3Ei0a0zT9j91CSmEQRkp5PoSy/lh0t+5fJVOSS6vMyPTyasjg+HVcMiQbFuBWS2BJLRDZmdgQR8up2i8lh0f0j1RPLLuEp8tEyuQ0ZMLP1vHUCXlmkAvPDCC9x0002cd955J+9GTxEGDx5MUVFR9c/aGBi0IKcxStUOoCQZGFUGUKtKcVCFTAA7hhHa99NR0BEIHYLYsUo6bt1JpXCiGgqyJOE3LLg0B+VaOJoh4dMdaEImzpLGZncRAokwxUqGM4P15YUUVujE28Jwa24iHHam9L0cgJ9XbWX8Bz/QoXl9Hh16EbIs0arJ4SuRPPfcczgcDu65557jPHI1x+kUYXwiKC4uZuvWrXTuHEqWD4tw0Lh5MtExYQc9/o63PycpJpK3h13Ohj2FrC8rJr8wCVnxsdNWh95lmyA8no5totmc3IKXJ8xj8tNXU7dO1Im8rX/NGWsEK3/5BXSdyAsuIPaGG9g6/1auXbABzSKzqlcMzkQfkZKKjEBCYkcwAU1IlGnh5HjjWFOYTGWFAy3gCAXCBCRkXSIQ1Nmwo4jtaws5L17jlU2LmDlzJgkJCaYBrGLYsGH/fFAtICSCFiqEJKRQsItRJQHjxY4QEh7sKEIHSUY3wCvsSAgq9dDenyYsuDQnPmFHxsCnWXBrDoK6lYAhoekSOwOlgAVNF4xq1oc6lrrcsfVLDFWiwgiw/PrbqRMWXt2vj35aidWm0LdzCyLC7fQ7//DKPNnZ2UyfPp3Ro0fvl0x9qnO6RBgDZGVl8eijj/LCCy/QuHHj43LeSy+9xPPPP4/X68VqtZJYN5pnXzt4se4Vm3NZvTWf1o2SaVM/mdtmzaHE40WSgwjDxmbiuVKv5IqGN9E1oxvROyq4ILgTu7V2eBH25aQZwd9++41FixaRkpLCDTccOnG3phHBIIUvvUzp++8Tds45RPTqhfTtAzRZ9j/2JDrZ0d1OWJSGs0roWBMSq3yp7NFi0YXE70WNqQzYKCuPBE1BVKkdy0jIQYl4ix2X34d76S+8lvUN7du3p6Ki4pSrxXY68dtvvzF58mQ++eSTE9LegpzG7HWBygh0QwJJoBngE3aCKCElGCFjIBEwrAgUvIYVVVhQhQUQ+LGj6jY8uhVJkvBWuUA1IePRZCpVB5ohY4hQGSQ9kEC5T6JJggNDlbAYMkPSWhNh3V+9/7XRAzEMg8iwIwtw+fjjj1EUhQceeKDGx+p4cipFGO8t+7YvgwcP5vbbb8fr9e7n7j+YBnOrVq0YOHDgUbd7NOc1b94cTdPYtm0bzZs3P+yxM35che7RiQp38N785Uy4tA9FbjdfrCzlj22VtM48D/78nm0/VdKlTkPq56+i16LZRHhug5jaFfB33Izg9u3befbZZ3G5XHz66ad4PB5uv/12bDYbPXv2JC8vj7FjxzJx4sTj1YUDCObmsuve+/CvXUvstdeS+NCDSJLEAvscojIT2NgqlpbO3UgI3IZCsRFBcTCcAiMWgURRMJx8dxRCgKIYaMGQ60vyS7RPSqWdPZHuzerzzMMP8v3arxk8eDDvvfceYWEHdzeYHBn/NJeuu+46Fi1adML7pSCqYkJlNCHhJZTgHjQsGEiU62GhtAdsCMNAGAJVsiFL4NMV/IaMS3eEjKIwUEUo/cGvWykPhqMbEoahEC1Fkl2poWsqb2T9wTcXn8W5SQ1YmL2DD5b+ye5SD5MHhQJDlm/KxW61kNnoyAsxP/zww1x++eWkpqYel3E6HgghuPjii2nfvj2rVq2qtRHGh8Ln8+F0Hlrb9VhoUZUus3HjRnKyg9SvH0fTZslYrQeq0zwxvA++gMoTs3/k9405jDi/A7srKnngi6UYehLPbTPQu71P43IHc1Yuo35CPBIQyM7BWovmERxHI9ioUSOmTp1aHQTy2WefceWVVzJgwACGDBlC+/btj1fTB0XNzyf78kEgy6S++ipRsTtgzXQWJeZRmu6gQjJoqJRUlbSR0FHwCgduwigJhrPTG0uBOwKfz4rqsyKEvDcTmnOcdXm598XUS4jhiiuuYN43X/L0008zbty4WrnfdarxT3PpuuuuO+z5v/zyS/XTd05Ozr/qy+aczYi9uqCSgU9YECgYQqAaEgFs+IWNgAEBEVqhaULGb9hRhQVJGAhkyjUHQcNCQFeo1MPQhYyqK0jCRrHXibpPnt5OrwpCIsJwUuFRmbVxLcPOOptkWyQbC4rp0zRU629zbhH3Tp5DckQ4A9q14NpBHasrxB+KvV+2LWqZ5JUkSfz888906NCBs88++2R357AVdsLCwv6xAk9ubi7z5s1j69atPPnkkzz99NNHJAP39/MOJ7e2N1dw9eq1/L5gI0KWGHh5e0bffiEAn3y8GJfLy8ArO/DQxC8YM/x8Jo8ciD+oMn/9NvIqKshMjubPbCjwOrh/SQ53XhDPp5uep7P8JB2A4Pbt0O3gEainKifMHZqXl0dmZih6SFEUOnfuzIQJEw759FlTX1x7ZYKsKSnE33oL0b26Yl32LPzxNTTvT1ncSmJlFV2Eqn77dIlcPZ4yI4J8fzQ5vni2e+oQ0CwUlUaiqvsMmYC4oJMtW4vo93/vMvXuq3jssccYMWIE/fv3P+Y+mxyev8+ltWvX8vvvvzNv3jx69+59wPE9e/asdlft1Vo8VnbTn71PP6Fq7yFFF1XIlOkRGMioWPEbMpWGI1QayQgFxhgCgrodl+HAEDIVaqj0kW5IGEKi3B+JjB1VaAgBQVXCH7QBMo+268PSHQWohs6Hy/5ktrQBBFya3pTX5/5Bm8RkJv5vPn5VIzMliXdnLKTvBa2Iiw3HXeHjvRe+Y9CN55Ga9tee386dO2nbti1Tp07l8ssv/1fjcjKYM2cOP/30E1FRUYcVYT+Y5wBCgTXZ2dkUFxfz6quvomkaAwcOpHPnzlx00UVcccUVJ+xe6tevz4wZM4CQgPl999131Of9E1FRUaSmppKdvZX7HrqfSS99R8O0v7ZpduwopqTkwOLE363ZzLhP56GHgdNi55zGGazZo9LbWIdzUygoMKVFFHJUFIHs7UfUl1OJE2YE69WrR15eHm3btsUwDLp370737t0PeXxNfHEFtm4l/5FxJD/zNI6mTUm4uA18OgQqd0PvZ5lX92UsWAkIhWjZQ7HuJCBsqCi4NRubvMns8UYQ0GSKysLRtKrhkkFyySRYwrm5UVuenDEe3VNBxsTbiI2sT9u2bY+pvyZHxt/nUmZm5gkR5F6T8+w+yfAGASETxArCoFRzogkFSYJyzRGKBhUyQWElaIBGSPuzTA1DR0bVQBVWDAEVQQc2KRKfHkQyFGItUez2+AjoIlReSZVItcfSPt7Ca0v+YFyPHrz3x0ockoUPl6ymfUpdSis8rN2+m+dv6U/XzHRGXX0ecbGhYJmi/HLmzlxK606N9jOC48ePx+12n3CvTE0wZ84cFi5cyJo1a2jduvVhjz2U52Ds2LFASMB6/vz5nHfeeUREROD1emnQ4OCKOicCWZYPqv9ZE7z77rukpqaSUKc+F11wFp3PaVT92UNj/3pwf//5v+I0UqMiub93N6YsWU6F349qFKFIKhcb62mcvYvX0qNYV7yZjIwMglu2Hpd+H0+OmxEsKSlh3LhxrFq1iueee44xY8YwevRovvnmm+Oe2CqEwPXZZxQ8/QxyWBh6eTmU58J7fSEqBW78ngXaaGxIGELHjoohyUgipPJRpoWz0VOXioCdQk8Umg66HgpmkCRAlYm22+kf1YBn7ruT3ZtW0nfAQCKdp0+B2FOJkzmX9mWPmAbsTYwPrfhAwiPseA0nfuz4dBu6UNCQKFHDkZFw6zaCuoJepRiqI+HRbbhVJ6puJSOiHnGWeL5zbUIYgnLDjWHIWGWZhs44tpa7+W7bFq5s3IpzU+szbfEqtpeUIemgGLB2SwENro3l99dGY7WE3GEJ8X8FJ6Q3T2b2yqdwhP0VQJOdnc3UqVO55ZZbTuoX/rFyNIILf/cc7Ivb7WbmzJn897//JSIigoULF+L1ern66quZM2fOAdeqSdf6yWBfT8nDD/Q7onMmfvoL5R4f5fgREmzYVUZMeBxuRyOaVKwnxprGbl8uyc8+g1wLVbCOmxGMj4/nrbfe2u+9995773g1V43udlPwxJNUfP01YZ07kzL+KawpIQFhLn8LmlwEjmhETiEKEhbUUFSeHk6hGsU2fx22uRMIGDZ2V0ai6TL+gLWqDo5ALrcTITvwz8/mublPoXvKue2eR3jjxWfM/b/jxMmaSwcSqgHvkIJoRuiVR1co1SNQsVRJmYWE1ErUcDRhRRcSAd2CXiWdFjQslPpDye+6IRHQIKtsD1ekNqFLnQx+27UDXZOxSDLPtO/Plj2lbNm1lDh7GF3qN+C1XxaxpbiUDvVSuLxlSzbmFtKkTjwJUeGH7PXcWUtZMHctT709HKXKSD711FNYLBbGjRt3IgauxjkawYW/ew72UlFRwR133MGkSZOIjPxLSuxwgWw16Vo/GRQWFjJ37lz69u27X8T6Hyu2s2RVNmNu7HXAPvJLIwfw5rzFbC0uYV1REQqJeDzR9Bt4GfKXX5HhrMuaPRuwn9fo783VCk67PMGy6dOp+PZb6tw1hvgLmyF93BuumAJp3SAzFFixICcNCQUZDUkKbbL7dBtu4WCHLwGv5qAiaMPrVzCEEhITFhIYYOiCyrJKcue8jcVq4/uf5tOzh5n/d7qzIvtZdAwUQNOhTIShYcGlOxBIaEJGNcBvyHh1B17NFqr9J0K6MqouU6HZCWh2ArpEUJMJ6DYMQ8LQ7Hy5Yx0L+99Dv++msrPChV9VeXLJT9zYuCPoEp+v2UDv+o0pdvuQg5AWGcPg9plwBJ7MgF/F5wmwN9YmPz+f6dOnM2bMGFJSUo7ruB0vjkZwYdCgQft5DoYOHcqHH37I8OHDUVWVZ599lsGDB2O1Wpk2bRper5drr732BN4NvP/++2zfvp22bdsyaNAgnnrqKSC0ctub3H4wvvzyS7KysggGgzz55JP/eN6mTZsYNmwY33zzTfVDg64bvPfx7+zILyV3Vyk3X3cedeIjiI8NreoaJsYy4fqQgszy7DwWZ3t5Yd4Wthp1aQuEbREUGI3QSkspn/UpkRf0wn4UuY4nm9PCCAoh0AqLsCYlEn/jjYR37oyz5Cv46GFIaArOuOpjF+QMIOTMMrDKENBlcoIJlOhRbPPE4QrYqQg4UXUZCO3xSIBhyBhlGkol9K7fnMHfzKF169Zm/t8Zwh4+Biwo6LgIB0nB0CVCirJQroe+MIqDTrzCCUIQNCzohowuLNikeIJ6ZeihCqr2omVUXUIxZMo8Og/+8Q2J9ig2qaXEK5GkOKP5NCuLvmlNWZKdx8sL/mDmsKv5cs0GWtRJQNX0avfnlk27+fDdBdwzth+xsfuvCi8b2pXLhv4VsZeSksLy5ctrVUrE3zkawYXw8PD9PAd79wQ/++yzA449VkGLIW//ccB7/VsnM7RLGr6gzvD3lla//79bDxQxj4qKwm63EwgEKCsrIzIyknvuuYf/+7//O6wRvOyyyxgwYAB33XXXEZ23t/TUunXr6Nu3L8GghqvcR15+GX26t+DXJVv5Yu5q5s5fx8y3byExIRLDEIx59TN6tM2gXfN6tEoJbfu8t8HLC0Lm8qh4+ne6mcLiUlwvv4wcGWEawROJVlbG7nGP4t+4gUZz5qDo5ThXjoWdf0C76+GSSWD760tBIgubBH6hoOsGq/xpqEJmTzCSHE8CHtWOL2jBEHLV8WAYoG4qoHDyx9TrfiFX9bucXm1qzx/ZpKYI1ZBUhVyl+VmVH6hbUQ2ZgCHhEw4MIaEZMj7dhmbIaEJhd9AP2DAMmTq2BHZ6VPyGiqRZ8aogDJ1lBbvY4XIRZ3VS5PZR7PZh9VtY4s+jZ/10LkxvxFWvf8Rzl/fmjgmz6XtuS54Y0QeAkmI369bm4qn0H2AE9yUYDGKz2WjTps2JGTKTI2JvUe2xY8dy8cUX77eyVVUVWZYPmv4ghOCZZ55h9OjRAP+4JRMXF0dycnJ1MeIXnpnD2lU7mTHtZqKiw7h3VG+ydxaTlBBZHVj155ZdrNu4m7S6sTzz5XxGXBgyrj9t2kNf+xNMuHgQw6e8iUV18nFEBIHNm//9gJxAarUR9K5Ywa777kcrKSHp/vuQw8Nh8QdQsBYGvQOtr9rv+AU56UhIqCKU6VWmR5DrjyU/EEOBNxKPagdC4eq6AbIMmgbuL1dT8tkcLM5wXrtlOBeYBvCM4rNt7QArYVIAkAkYof2/Ei2cgLASFHYCuoVSPYKgbkETEn5DCanF6DK6IaFpFvy6ggUbEfZYyv27ABmh65yb2IgIi4Ohzc7m+u9mUurzY1MUhCoxol07Zixew/yt2xl2djsaxMegaaF9rcapf3khOndtwuxv76Os1E1ZqZvYuAMDFIQQ9OrVi06dOtV6mbFTjYOt7vbitCmH/Rxg3rx5LF++HKvVSmxsLJWVlTz99NP07duX999/nwsvvPCg1eafeuopdu/ezcKFC7npppv2O+9QtGrVqtoIXnhJa9IbJ3H9kDe46upODLuxB+kNEkhvkIBhCF546wfiEyKoExPO1b3OJjYhgvNapjP5j6X4VYVuZ59FpFOBpl+h551NYVwSjo2bjnDUTg1qpREUhkHJf6dQ9PrrWFNTSZv+Ac4kC0gSdLoNWlwKMfX3OyckdSVjiJB+o0+zsj6YQoXmpDJoRzOqEp51C4YAw5AIugIUTf4Wz4o/yTi7PYu++/a4Fb00OfWxS40p1vLwGza8wgGShaBhpVKz4dFt6EZIQEEYMoah4A5a8Rk2ZGS8Vek1AaGjSDI9khuzYNdO6oZFEW+L5NvsTbSMTiLcYufixk14oXtoD0aSJEa0bY9NUYgPD+PdkaF97T/evgtln2oPxcWVPPLAJwQ8AcLD7Ex+/+YD+j9r1ix+//13RowYcQJGy+Ro6N27936Rm4899lj1740aNTrk987jjz++3+t9zzsUmZmZTJ48GU3T6NilMe07NsIXCHJOp/0f7jVdZ+HSrfS/MJOZE0Nz5suFWSzfmMtrV3fmgY8+Z+uSZZSse5L6LVIpi6tkd0wiSZv/RBgGUi2pRlI7evl3hMCzeDFRffqQ/s4LOBffBR8MAL8rtHz7mwEMqXxQ9U+QryWwQ0tkVUVDigIRFPuc+DQbhgjt4chSqCyOb0MBnlVr6X/LLWxettQ0gGcogzJWAVCg7qJCOAkYFgKGgkeXcet2NKHgFzaChkSlaqdStVERDKVLGIZEor0OMkrowUqV2VBaQl1bLNGWMFRNcG/brqi6wZtrluIOBqljCWNtYSGSJBFQNdbl7iH6b4Vulb99wUiA1apw8YB2jLj1/APuwev18sADD9C6dWuGDx9+vIbK5DhQ0987999/P9nZ2VgsoQczRZG58ebzaXnW/nvEmmbQskEibZv/9f4dl3blhgs70L1pHaIcCrFWgy7aGhrKsbjlAnz1GiBUFa2wsEb7fDyplStBSVGo/+ZkpC1fI02/BGQFBk4GR/RBj8/jEiQRquK9KZBMqRZBuRqGalhC0XkiFL6OEPj8Mv5tBSiJaUQ3yGTmypV0N/dPzlj+t/UcAKwSBLAhkHCJMFRhwa3b0YWFkoATjdA+sl9TUA2FcCUShIFHM8iucJEWnsSGshL6p7Ymq7iIT7atJiMygWxXGeMWz0OSwKdqXNMsk7dXruDdVavoHF2fYEBjzfbdTBkxiK5NGh6yn/EJkbzx3xsP+fmkSZPYuXMn06ZNO6y0lsnpT3LykenKBoMa27YXUlzirn6vT/uQ9FpRZYBm9ZqxPdsDCiR7DHBUYunbi2YTxyFZa0/OdK00guga8rwHYNWHUL8zXPHOAau/vfyQ3QJQUHWF3VosARwYkoUSLQLDEHg0K0gSwpAIVATIf2kO3hUbyXxiLB+OHE6bI5wwJqc3gzJW8ebG7qgi9OAUFAqaUCjxOwkKK349VDE+oIcK6BYFghiGRLQlkj3BIBvLS0i0R3NRagtmb9lAcngUpT4fuhD8vnsHmQl12bCnmE83rqdXw3RSIqOIw0m4zcbN555D54yDz+8jwev1MnnyZIYMGUKPHj1qblBMai2vvvoqcXFxhy1KHBMdxv+m3XbQz9blFvPzVh8OLZoSJZyuis4sHZq3CqtVBhBqqxGUFdCDcN590PMRUA59GzogCZk9WiQe4UDVJbI9MZQEw/BoNoK6BV9QojyrkPyXZ6OVVtBpxPV8f/+jRNewirtJ7WNI42V8sOVc3t7YHY9hp1ILQ0fCpTnQhUIQa3VVeV1IgGW/hOzSgIZAoOmw21vJq2sXgASugA9ZWEASPH9uX85PbcSzS34jt9xFpNXOQ127U+zyMPXXZQxu2wpFlgmqGk9PnUdGchxLVubw3H2XEhP1zxVKwsLCWLVqVbX7y+Q48N5B1FfOGggdb4agFz7aJ0hvxDcHHHqseYIrVqxg/vz5FBcXM2HChCM+b8aMGYSFhR3WCB6KgKrxxLSvQKRSPzEZPbItnUp38u4V79IyviUl776HtmcPSQ+PPeprnwxq556gJMHlb8MF/3dYA/htdiuMqnD2Qj0Wt+FgrTuFokAEft2KIhMqkfTlCnY+9h7CgKdnvM/idz4wDeAZzgdbzuWDLedWvw4IC6qhEMBKwAipvTjlGDLCG6IaFiySFaschWYYaIaCpktkRqUT0DWEkKjriEZCZpOrEEkSxDvCqdT8WCSJJ5b+SPtP3mBhfg4rd+YzZ9MmBk//hLlZm5mzcgMFrpA7KqjqrNiQy66iClyVPnRDHKr71WzduhUhBKmpqSQlJR238TL5dxwsT/D//u//qtVwDkX79u1RFAWPx3PU561YsQJd1w97nM8X5IVn5rBx3a7q9xauzcZV7iNM1sh3abjqtCfCGcc5ddoRZg0jmL2d8i++CImM1AJqpxGEkCE8DJ9ta4ffUAgKmZXeNIrVcDZUJuLXrQQNCyCh6jLCgKBXJ+LsluzasJlHBx/9k5HJ6csHW85lWJNFlOqR+IUdVZOoUB2owka4pQENnE1wSNFEWurgCvoRQsYQEiCxqiwHISQMAXZZDq0QJbiyUWssioxDsaAJgUdTAXDKFs6KSyQMC1tzi3nv9+UsfGwUzVNCote+oMqEOwdwfd8OzHhpBPEx4ZQUVTL9rZ/xeQMH9D0vL48OHTrw4IMPnsghOzMZ8c2B/zpWRejawvZ//yAMGjSIcePGVRcH/nue4OGM1T333EOTJk0OOO9wnHvuuVRWVrJu3brDHueu9PPbz+vJ3vZXoEuPNhlMHjOIgec0wqdbyG9xI9w4l/Xlm3no24l4G2ZguFyoO3ceUV9ONqe1f0QImT1qBOV6OIX+CDQsWBTQAwrFq3cR8AuMxmeRdNlFLB54D5H2I6vEbXLm0DTqXp7NGoFmWPDqMl7DhpDAo1rZULmdP8t3oAoFzQiGhBWECIVqIhAo2CULXl2nKOBBksEuKXydu45edZuQ7SojNSyaXZ5K0KGg0s2uCjft45PJKizAK6kUVrhZmJXNC7N/JdZqZ09xBQ2io/loQkgxZc2y7Ux/cz5nd2lMyzZ/CWEbhlEtCzZq1KiTMnYmR86x5gl+9dVXbNiwgaKiogPOOxznnhvycvzxxx+HrcJRJzGKL358aD890fJKL29+tpDk9IYIJBJj43ntq995d8276PWWkuy6jT6Ab80abA0PHcx1qnBSjeA333zD8uXLycjI4Prrr6+x6/5v6zkIIVGuhpGvx1OuOZEkCUUYBFSDbR8sYcf0xYQ3T2X8J6MZk2kGC5jsz7AmoUr1z63vS0BY0Q0Fn27FQCGgK1VC2TIGcpWstgFSSGghJLYQ+qkKnfTweAq8PiQJ/LoGAr7N3YgsS5T6fBAEyZCQkPjPZf1IdUbylrKETTuKWJOzm+ISNz5/kNH9zkUP6jRv+Jdbs3vvVrRo3YC69WL36/+ECRP46aefeOedd8jIyDiBI2dyLBxrnuCAAQP2q6RyJHmCAOnp6dSvX5/du3cf9PNgQOOVBz+m1+Ud6NBz/4LL+cUV5Be5KPJuokOUnd/XbSR90VjudPp4BWjcryHyjHC8K1YQfQKrvBwrNeYO3b59OzfddFN19W+Px8OwYcO4+eab+eijjw56Tr9+/bj//vvJy8urqW5UU6hGsy1QlzI1DEkYyJKBu9DLvDt/Yse0P4jt3pKvvv7ONIAm/4giVNyGHaXKyOlCBklCEgYSRpW4gkA1QvvPAglZlqgfFg9IbHeXYbcoKJKEJIXKcQ1Ma0Wv1CZ4VRXZItM3rTnzr72JS5o0pUFsDK3r1qW83MtHv6yidaMUWjdK4YL2Tbjygra0ahyKWM7fWcKgdv/H+hXZ+/X3xx9/5LHHHuPqq6/mxhsPnTZhUjs4HvnJkiSRnZ19yEoYakBl9cIt5G07MN+vsMxNZWWATk3rM3H4xXRqWp8GqSlcp+zGIlnJcmUReeGFyLXEs1ZjK8FGjRoxderUaiP490KW4eHhfPHFFwBccsklDBkyBMMweP7557n77rtrpA8fbLkOyCZfdRLQwpElgSRAkcGdX8m3N3yH7tdocHc/Voz/mARn5D9e0+TMYvKmUKL57c3mA1DH1om1gfWhlV/1Pr+OEDISAgmBgYwkyTixE5QMhBAYhgJSSHdWliSEMEh0RrLLXYEh4Odd23modS/mbc4miSjOik0iJTIKgKVbc5k89w8UJArL3XRq3oBOzQ+s+RcdF06Pfm1o2KTufu+HhYXRo0cPpkyZYpb3Mjkkh8sXDY9yMmPFUwf97MJzmhLQNB57/3vqpqbw5m9buDOuEW3UuThcTfhm04+kXTSJwb3aHqee1yzHzR3690KWAwcOZODAgfsdM2nSJFwuF0uWLKFPnz77fXZsxStDT8SJssYWYUVIYJU0ArpMvrMBCX1a02NQPz4ecmQuAxOTrd61OGSo1C2oVY4TuwK6Hnq4MgwJIUI7gKrQcco2KjUNXRjscJcCEgIoV31IagBJCrk9K9UA9SKjmH7JYGasWkNieDjrcgvwqxq92zTly7EJJMdEkrW9gP9++Qc3X9r5AIMWHuHg3gmDq197vV7CwsI499xz+emnn0wDaHJYKisr6devH9dee+1R7xv369KS+kkx3P/RQlxqOFm20Hf9BbZIvlTzmTh7Lt1ap5MSaUc+xSPtj5sRPFQhy30ZO/bQeST/pnjlbjUMp6xStCvIV0+uofGdF6Anwh2P3sW4zGuO6lomZzYRchTluhunrKEbVnRJoOoKCgZBIYdqBgIWYcOtGwT0UP34UJCMhCxBengC58Sn81XOesq0UBK9osssz8vngoYZvDngUgCGvv4JpW4f3zw8gvTEOH5euYX3vllKzu5Sru/TnjCH7ZD9zM/Pp1evXgwfPpyxY8eaBvAEM2LugXqsfdL6cHXzq/FpPm7/8fbq99+7+MCC0MeaJwgwe/ZsfvnlF15//fWjOi8yMpL8/Hy+/vprRo0axc7cEoJBnbS0BDZvL6B+chyREYd2aTZKjifBYbDZIyggkbLoljyMi8LKh2hxYRL6Q/eQHxFJvddfO2w/TjY1ZgRLSkoYN24cq1at4rnnnmPMmDH7FbI8EVTqoT+YRVFY9V0hXz+xGgEk7a5k6bUH35c0MdmXvW7Q/2zsBUCSJY4K3YaOjl3W8OkKYEGWqCrMDH49VJnEIisEdQFISCgYRigPtcBTyayKNfg0HUPIKCgk2iN5a/kycstdvNQ7FMk34bpLCGp/hcKv3ppPUYWHryeNPKwB3LBhA5dccgklJSV069bteA2NyXHkWOsJrl27lrCwMKKjo4/qvL0MGDCAyZMnU1ZWxqSXvqOszMPAKzvw6tT5RIbb+W76nYc8989t+WzN2UNMfBgbi4LovUcRLrn5b+er2LK7GNu6Rrhmz8bweEIVfk5RaswIxsfH89Zbb+333r6FLI8ne/dxANSAwcxnc1g1K5s6ZyXQ/anufDpo9gnph8npR7FRSaxVoTgYAxjYZNCEjkezYwgHBiIUKCPAkEIGUEHBbnEQ0FSEgAAGPk0gDAkMaB2VzOrsIlCh0h1gZ0k59eOiSY3bX/v23sE9GHFhB2a8u5Crru1MQsKBe9izZ89mxIgROJ1O5s+fT4cOHU7IuJjsz8FWd3txWpyH/RyOvZ7gzz//jBCCpUuX4vf7j9oDMHToUF555RVmzpzJfXcNIhDQmPX9Kqw2hcsvaUMgqGK3HVwGrX5iLH06NCO8TjJvLcyjx2ceVjx3KyOmvEiW+JzHkoaTGQhQOf8XovsfRFHnFKH2JssfBL+Aue/sYtWsbDrc0Iy8lbtMA2hyTIxu/jOjm/8MKKiGTLjsIUqJRBN7q4zIaAJkYUOuivrUDQMhwKtBtDWMBGsUChYkw0paWDx22UqKNZZtxRVIAhyKhfZ1k7lk0nt8tiyUtDxx1nweeOfr6n7k7izhs5lL2ba54IA+5uTkMHjwYFq0aMHy5ctNA1iLmTdvHuPHjz9knuDOQySe33XXXdx999107NiR5OTkI84T3Eu7du1o1aoVU6ZMIa1hAs2bJXNJz7MYekUnpn+zgjdnLDzkuQ0SY3h6xMUM69YERfiQkHCIIBl7VoGjgrw2XqwNGlD24YfHNCYnCknUAm2bJ5544rD7gl9tH4XbtYBdjnoEfTo7Vpfz9o3LT1wHTWoN/zSX/k5W4a98WvgiAd2CJhQqNTtezUGlbsMw7FQEZQxA1SUMYcGvhiTVkhzR2EUkee5yIi1hhFnslLt0RrbswNdbNpG1Yw9On0L3jHTa1U3msvYt+W3NNt74ehEVngBLXr0Ta9WTf4XLS1R0SCO0sLCQuXPncsMNNwDw/fff06tXL6y1TLT4dOBo59KxUlhYeFzLuM2cOZNduQWkxLWhw7nNyGgWijae/f1qduSXoigyd93Qk4LiCnYVlNO+1V+Ryr6AypeLsnCGWdGFYFBMLnxyDSNb9mCDXsKn6k2Uj3+etFkzcVYFSp5q1PqVoNvt5uX7/sf9g7bSQMvjkbN/NA3gGcZvv/3GhAkTmDZtWo1fu1ViD55oNQedUMV4SQKXakfVFaxEoRPKGwyV45IIagq6oVDph43lxbiDGv3qtyRNqoMRFKRFxrApp5hEazjDO53NdR3bUsfuZH12AUs25VLpCdAxPbXaAAIYIsjMmTO56qqrqF+/PsOHDyc7OxQJ3adPH9MAnuYc7zqmgwcPpkn9zrz7+s/cOWwKwaAGwBV92rKroJzflm5B1w3e+d8i7n52Nms37SKvoByAj39exaSZvzD5qz/474/L+U9eGptEMrdmZ1ERrOABsZyNV9yAr0Ejysu9fP3dn+j6wQMlTxa1WjZt9erVDBkyhK1byxlyZyI2R9eT3SWT48D27dt59tlncblcfPrpp3g8Hm6//XZsNhs9e/YkLy+PsWPHMnHixOPWh0ltZgLwzvaP2OX9AwOJErUSVbMgkAnqFrz+kCatYUBRIBDKIjRgdtYmiiu9yD6Ju7/4lhjDhrciCCoEPSpPffQjANee05wudcPJiPCyY8cOGjZsyPfff0/fvn0xDIM6depw2223MWrUqINKaJkcnL/Pl+uuuw6ArKwsnnvuOQAefvhhWrVqxdixY6tTTSZMmHAyu31CuWRQexYunceKlYvRdZW9pqFVRl3Wrd+FP6By6zVd6d+rFWNfnEPjBnVonFGHD+euIL1BHHdc0ZWVBQFe+DGbxbYRfOB9jj4VUXwfuYAs75X0mPYDFzkUXp6dRbOmdWmSceqIuddKI6jvbsKb77u4/8kK4uPj+emn+dXpFCanH/8kxNC+fftjvrZR0JQXJpfhle8AYFPZmyDAmpFM695JFATD+O2tbQSDEl7NhmZIBA2FiCb1CO/YBAyd/Gm/oRugqlUBMkEZe/N0wto1x/CobPlkOnpAQ9YMDE3DEjSIOqsdHxbp1LtEsP1/4/G5K3nwzb9EsC2vvcadd95JmzZtePTRR7nooovo0qWLWRD3GPj7fNlrBF999VXeeOMNJEniwQcfZNy4caiqymuvvcYDDzxAbm4u9esfex3HoyErK4tHH32UF154gcaNG++X6qAoCt9++y1CiAPcr1u3buXjjz8mKSmJW2655ahSJPZFsSgk14/kp5e+JTMzk1tvvZVzzjmH3l2bcN3lHbFaFFzlJbhdpfQ6O4X4+Ehem/EzjdKSefv+qzCEwdTPP0bx2lhIKx7kRp4pfpdUdxoxF/clsrKcBs/ez6SIcPLfKWB300ZY4+JIadGOhk1bsW3dZvJyVmGzh7wa5SUVRESHUbd+C5DicDiD5G1bdUC/05q1Ibl+BsUFeWzJWooaCJLSoC2NM5sf8b3XTiOow8efV3J+V4Vpn6ymTp06J7tLJieQvwsxdO7cmQkTJpCamnrQ4/9JeOHFN8soLH5yv/fOHqDQuncSEhZWvbceLbiPir8kkdg3SETHJgQ1hYLPlwBSSEoNQJLBYqXvBQP4Zc8mPKvWYrPaqBMdhc1mw+l0cssFXeh0UX/S48IZdGl/4uPjiYtLoF69VNLT0zjrrLMAqFu3Lk8+uX/fTI6Ov8+XvbhcLmJiYoBQ4viuXbuqjV6DBg3Iy8s7YiO4Y+gNB7wXecnFxF17LYbPR+4tt1a/3/DDA932rVq1qhYT+XuqA8BTTz3F66+/TlFR0X7fd9OmTSMxMRFZlo8pRWJf7rrrLlq0aMG4ceOqK4+MHj2a119/Hb/ff9D/X0kDhxIfPYrfV65jxlOhe4xo04dPOl9FduzjaDnraZNYzMKlX1C/v8bVv5WR/vm86vN/6JrAyKkLePmeu7lj51/yfzFVP7/qEM8PWb3o2HotVy/deED7X/dtys0vfcmc1x+ky+yQkXwxIYX//DrviB8Ya6URtFgkvvowhahIGYtpAM84/i7E0L17d7p3737I4/9JeGH32kbIdTcD8NX2kKJ+IDCIHP4gkgRGLbwenwYOSyOCAYU9qkGrqMb8UbiN5MgErlgyh2RHHKpuUOj1MrxFe+o4win2elnTpBWdbruLcJuNCPvBc/1OVCrRmcqhhDuio6NxuVxIkkRkZCSpqanVOsa5ubkHKFzBsSpZHT2HS3Xw+Xw4q1RYXC4Xt912G++99x6apv1rkYS9Qt75+fmsX7++ej/SYrHw1ltvsX1HEXuKK+l8djqSJNGuXTsAzm7RiFvufZTUhGiS4iLxB30U+e1ENu/LoAvOIeAqItwXYPFlfhaUlBPnUYlQDeq2vwiAThdfyq9ZH6FYQmEqQb+GxaoQ16InA87qSGJiAxY6Sg/ob9q5odSLRl0uYWHRLnTNoFOTS4/KY3JaRIeanN7sFWL44YcfGDlyZLUQg8PhoFu3btXurSPBnEtnHh6PZ7/5MnfuXD788EOysrKYNGkSAA8++CCtWrXi4YcfJhAIYLfbq/cLD0VNzqXc3FweeughGjVqxJNPPsn48eMBuOiii5Blmblz5yKE4PHHH+eRRx6p/nzZsmXMmTMHr9fL888/z7PPPlt93tGuBM9UTCNockZhziWTmuJkzCXDMCgrKyM+Pv6Etns6U+tTJExMTEzOFGRZNg1gDWMaQRMTExOTM5Za4Q4dOXIk9erVq5Fr5eTkkJaWViPXOpltnMrtpKWlMXz48OPWn39DTc2lU3XsT6d2TuV5BOZcqk3tHHYuiTOMxx9//LRo43RspzZxuo396dZObeJ0G/va1o7pDjUxMTExOWM544zgiVCWOVHqNadbO7WJ023sT7d2ahOn29jXtnZqxZ6giYmJiYnJ8eCMWwmamJiYmJjsxTSCJiYmJiZnLKYRNDExMTE5Y6mVAto1zTfffMPy5cvJyMjg+uuvr7Hr/vbbbyxatIiUlJTqSuDHg+PV/4Px3HPPkZ6eztVXX31c26mNHM+/gzmXzizMuXR0/Ju5dNoZwX8qwHowseV+/frRs2dPXn/99Rptq6aKvR7JPR1L/4+2nXr16pGZmYnb7f5X7dQGTuQ8OpL2zLlUezHn0ik+l2ok2/AU5IorrhBCCDFt2jQxZ84cIYQQgwcPFp9//rkYNmyYGDZsmPjkk0+EEELoui4ef/xxUVZWVqNtTZw4UQghxIQJE/7NrfxjO/+2/0fazosvvigee+wxcdddd9VIO7WBEzmPDteeOZdqP+ZcOj7t/Nu5dNqtBP/O3wtqDhw48IA6YZMmTcLlcrFkyRL69OlTY239U7HXmmqnpvr/T+3ce++95OTksHjx4hpro7ZwIufRwdoz59LpgzmXaradfzuXTnsjeKiCmvsyduzY49LWPxV7ral2aqr//9QOhDT4ToQu4KnGiZxHB2vPnEunD+Zcqtl24F/OpRpZp55CFBcXi1tvvVU0atRIjB8/XrjdbjF8+HAxatQoMX369FrZ1unWTm3gRI/F6fY3NufSX5hz6dRux1SMMTExMTE5YzHzBE1MTExMzlhMI2hiYmJicsZiGkETExMTkzMW0wiamJiYmJyxmEbQxMTExOSMxTSCJiYmJiZnLKYRNDExMTE5YzGNYBXvv/8+bdu2BUBVVRo2bMjXX3990vpzKCWJQ7F8+XL+97//HafemBwp5jwyqSnMuXRiOO1l046GZs2asWjRIvbs2UOXLl0A2LFjBy+++CJCCDIyMrjlllsYP3485eXltGnThptvvplu3boxaNAgli9fzgsvvEBKSgoAmqYxbtw4AoEAuq7zzDPPMHLkSN555x1uuukmpk+fzgUXXMBVV13FunXreOKJJ5gyZQqlpaW0a9eOsLAwFi9eTEVFBXfeeSc//fQTO3bsIDo6mscee4xhw4ZRr149unbtSkxMDEVFRZSUlHD33XcTFRVF69atufXWW8nMzGT48OGsWLGCqVOn4nQ6T+Ywn/aY88ikpjDn0vHHXAnuw5VXXsns2bOZN28evXv3BmDy5Mk4nU7i4+NZu3YtkiShaRpxcXHMnDkTgMjISO69916uueYafv311+rr/fDDD+Tk5BATE4Pb7cblcjFmzBg6d+7M448/jsPhwOl0cvfdd3P77bczbdo0AIYMGcKIESP4z3/+Q3R0NImJiSxdupScnBw6dOjAmDFjCAQCeDweLrnkEvr371/d5scff8yNN97IG2+8wY8//giEtPbuu+8+unTpwurVq0/QaJ65mPPIpKYw59Lxx1wJ7sPep5GkpCRkOfR8YBgGQ4cOpXXr1gDMnj2bli1bcsMNN3D++ecDEB4eDoDVaiUQCFRfzzAMunbtypgxY6rf++2336hbty4VFRVA6MkMQu6OvURHR1f354knntjvesuWLWPEiBHMmDGDDz/8kHnz5jF69OjqYpJCCCRJ2u++DtU/k+ODOY9MagpzLh1/TCP4NyZNmoQkSdVPQKNHj+aRRx4hOTmZyMhIhg4dytixY9m9eze6rh/2Wn369GHUqFE88MADlJeXc9999/HLL7/www8/cN1119GqVSsMw2DcuHFs2bKFl19+mSlTplSff/3113PLLbfgdDrp168fK1eupLi4mLi4OFwuF5MmTUJRFM4666zqc6655hruuecePvvss+r/ECYnHnMemdQU5lw6vpgC2ieZK6+8kk8//fRkd8OklmPOI5Oa4kybS6YRNDExMTE5YzEDY0xMTExMzlhMI2hiYmJicsZiGkETExMTkzMW0wiamJiYmJyxmEbQxMTExOSMxTSCJiYmJiZnLP8P9nujJFIomiMAAAAASUVORK5CYII=\n", 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" ] @@ -527,7 +540,7 @@ "tags": [] }, "source": [ - "### Suppl. Figure 3 - transcript length" + "### Suppl. Figure 4 - transcript length" ] }, { @@ -537,7 +550,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -563,7 +576,7 @@ { "data": { "text/plain": [ - "11097" + "11069" ] }, "execution_count": 18, @@ -584,7 +597,7 @@ { "data": { "text/plain": [ - "9849.400000000001" + "9851.599999999995" ] }, "execution_count": 19, diff --git a/03_prepare_Fig3_S4_S5.ipynb b/03_prepare_tasic_heterogeneous.ipynb similarity index 70% rename from 03_prepare_Fig3_S4_S5.ipynb rename to 03_prepare_tasic_heterogeneous.ipynb index 144c9ea..f2ac3be 100644 --- a/03_prepare_Fig3_S4_S5.ipynb +++ b/03_prepare_tasic_heterogeneous.ipynb @@ -18,11 +18,11 @@ "import scanpy as sc\n", "import anndata\n", "import openTSNE\n", - "from openTSNE.initialization import rescale as rescale_pca\n", "from sklearn.decomposition import PCA\n", + "from openTSNE.initialization import rescale as rescale_pca\n", "sc.settings.verbosity = 2\n", "from tqdm import tqdm\n", - "from readcount_tools import pearson_residuals_compound, get_tag" + "from readcount_tools import get_tag, compute_residuals, select_hvgs, scanpy_preproc_baseline" ] }, { @@ -71,7 +71,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 1729986560 bytes == 0xb081e000 @ \n" + "tcmalloc: large alloc 1729986560 bytes == 0xb0518000 @ \n" ] }, { @@ -108,25 +108,12 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [ - "def compute_residuals(adata,alpha,theta,clipping=True,tag_suffix=''):\n", - " infostr = get_tag(alpha,theta,clipping) + tag_suffix\n", - " print(infostr)\n", - " adata.layers[infostr] = pearson_residuals_compound(counts=adata.X.toarray(),theta=theta,alpha=alpha,clipping=clipping)\n", - " adata.var[infostr+'_var'] = np.var(adata.layers[infostr],axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/5 [00:00= np.sort(resvar)[-n_hvgs]\n", - " adata.var['top%u_%s'%(n_hvgs,get_tag(alpha=alpha,theta=theta,clipping=clipping))] = hvg_idx" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "tags": [] }, @@ -257,7 +231,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:00<00:00, 308.73it/s]\n" + "100%|██████████| 5/5 [00:00<00:00, 135.12it/s]\n" ] }, { @@ -266,7 +240,7 @@ "[None, None, None, None, None]" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -293,7 +267,7 @@ "Layers with keys: pr_theta100_alpha1.0, pr_theta100_alpha10.0, pr_theta100_alpha100.0, pr_theta100_alpha1000.0, pr_theta100_alpha50.0" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -304,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "tags": [] }, @@ -329,7 +303,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 20%|██ | 1/5 [00:20<01:23, 20.90s/it]" + " 20%|██ | 1/5 [00:16<01:07, 16.98s/it]" ] }, { @@ -344,7 +318,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 40%|████ | 2/5 [00:41<01:02, 20.71s/it]" + " 40%|████ | 2/5 [00:33<00:50, 16.88s/it]" ] }, { @@ -359,7 +333,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 60%|██████ | 3/5 [01:03<00:42, 21.08s/it]/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " 60%|██████ | 3/5 [00:49<00:32, 16.25s/it]/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", " utils.warn_names_duplicates(\"var\")\n" ] }, @@ -375,7 +349,7 @@ "name": "stderr", "output_type": "stream", "text": [ - " 80%|████████ | 4/5 [01:24<00:21, 21.26s/it]" + " 80%|████████ | 4/5 [01:05<00:16, 16.10s/it]" ] }, { @@ -391,7 +365,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [01:57<00:00, 23.51s/it]\n" + "100%|██████████| 5/5 [01:32<00:00, 18.59s/it]\n" ] } ], @@ -438,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": { "tags": [] }, @@ -463,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "tags": [] }, @@ -483,74 +457,74 @@ "TSNE(n_jobs=38, random_state=42, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", - " --> Time elapsed: 7.34 seconds\n", + " --> Time elapsed: 7.27 seconds\n", "===> Calculating affinity matrix...\n", - " --> Time elapsed: 0.28 seconds\n", + " --> Time elapsed: 0.33 seconds\n", "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", - "Iteration 50, KL divergence 5.9971, 50 iterations in 1.6028 sec\n", - "Iteration 100, KL divergence 4.6969, 50 iterations in 1.6963 sec\n", - "Iteration 150, KL divergence 4.4263, 50 iterations in 2.3782 sec\n", - "Iteration 200, KL divergence 4.2650, 50 iterations in 2.4978 sec\n", - "Iteration 250, KL divergence 4.1859, 50 iterations in 2.4451 sec\n", - " --> Time elapsed: 10.62 seconds\n", + "Iteration 50, KL divergence 5.9971, 50 iterations in 1.7796 sec\n", + "Iteration 100, KL divergence 4.6969, 50 iterations in 1.6073 sec\n", + "Iteration 150, KL divergence 4.4263, 50 iterations in 1.5620 sec\n", + "Iteration 200, KL divergence 4.2650, 50 iterations in 1.6657 sec\n", + "Iteration 250, KL divergence 4.1859, 50 iterations in 1.6947 sec\n", + " --> Time elapsed: 8.31 seconds\n", "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", - "Iteration 50, KL divergence 3.0453, 50 iterations in 2.1287 sec\n", - "Iteration 100, KL divergence 2.6213, 50 iterations in 2.1608 sec\n", - "Iteration 150, KL divergence 2.4315, 50 iterations in 2.6608 sec\n", - "Iteration 200, KL divergence 2.3261, 50 iterations in 3.5881 sec\n", - "Iteration 250, KL divergence 2.2603, 50 iterations in 4.0187 sec\n", - "Iteration 300, KL divergence 2.2159, 50 iterations in 4.9849 sec\n", - "Iteration 350, KL divergence 2.1853, 50 iterations in 6.5094 sec\n", - "Iteration 400, KL divergence 2.1642, 50 iterations in 6.9770 sec\n", - "Iteration 450, KL divergence 2.1485, 50 iterations in 8.0114 sec\n" + "Iteration 50, KL divergence 3.0453, 50 iterations in 1.6123 sec\n", + "Iteration 100, KL divergence 2.6213, 50 iterations in 1.7060 sec\n", + "Iteration 150, KL divergence 2.4315, 50 iterations in 1.6303 sec\n", + "Iteration 200, KL divergence 2.3261, 50 iterations in 2.6629 sec\n", + "Iteration 250, KL divergence 2.2603, 50 iterations in 2.8507 sec\n", + "Iteration 300, KL divergence 2.2159, 50 iterations in 3.7701 sec\n", + "Iteration 350, KL divergence 2.1853, 50 iterations in 4.4227 sec\n", + "Iteration 400, KL divergence 2.1642, 50 iterations in 6.1565 sec\n", + "Iteration 450, KL divergence 2.1485, 50 iterations in 6.1260 sec\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 20%|██ | 1/5 [01:19<05:19, 79.94s/it]" + " 20%|██ | 1/5 [01:04<04:18, 64.63s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 500, KL divergence 2.1353, 50 iterations in 8.3937 sec\n", - " --> Time elapsed: 49.44 seconds\n", + "Iteration 500, KL divergence 2.1353, 50 iterations in 6.4367 sec\n", + " --> Time elapsed: 37.38 seconds\n", "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=38, random_state=42, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", - " --> Time elapsed: 7.53 seconds\n", + " --> Time elapsed: 8.09 seconds\n", "===> Calculating affinity matrix...\n", - " --> Time elapsed: 0.34 seconds\n", + " --> Time elapsed: 0.25 seconds\n", "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", - "Iteration 50, KL divergence 5.5512, 50 iterations in 2.1618 sec\n", - "Iteration 100, KL divergence 4.4510, 50 iterations in 2.0197 sec\n", - "Iteration 150, KL divergence 4.1906, 50 iterations in 1.7325 sec\n", - "Iteration 200, KL divergence 4.0650, 50 iterations in 1.7479 sec\n", - "Iteration 250, KL divergence 3.9927, 50 iterations in 1.7246 sec\n", - " --> Time elapsed: 9.39 seconds\n", + "Iteration 50, KL divergence 5.5512, 50 iterations in 1.5163 sec\n", + "Iteration 100, KL divergence 4.4510, 50 iterations in 1.3975 sec\n", + "Iteration 150, KL divergence 4.1906, 50 iterations in 1.5069 sec\n", + "Iteration 200, KL divergence 4.0650, 50 iterations in 1.3944 sec\n", + "Iteration 250, KL divergence 3.9927, 50 iterations in 1.7286 sec\n", + " --> Time elapsed: 7.54 seconds\n", "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", - "Iteration 50, KL divergence 2.9647, 50 iterations in 1.8333 sec\n", - "Iteration 100, KL divergence 2.5013, 50 iterations in 1.6393 sec\n", - "Iteration 150, KL divergence 2.2674, 50 iterations in 2.2911 sec\n", - 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"100%|██████████| 5/5 [07:02<00:00, 84.55s/it]" + "100%|██████████| 5/5 [06:29<00:00, 77.83s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 500, KL divergence 1.8087, 50 iterations in 9.7372 sec\n", - " --> Time elapsed: 47.63 seconds\n" + "Iteration 500, KL divergence 1.8087, 50 iterations in 10.5529 sec\n", + " --> Time elapsed: 55.20 seconds\n" ] }, { @@ -741,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -760,65 +734,40 @@ " finished (0:00:01)\n", "If you pass `n_top_genes`, all cutoffs are ignored.\n", "extracting highly variable genes\n", - " finished (0:00:04)\n", + " finished (0:00:05)\n", "normalizing counts per cell\n", " finished (0:00:00)\n", "--------------------------------------------------------------------------------\n", "TSNE(n_jobs=38, random_state=42, verbose=True)\n", "--------------------------------------------------------------------------------\n", "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", - " --> Time elapsed: 7.03 seconds\n", + " --> Time elapsed: 7.04 seconds\n", "===> Calculating affinity matrix...\n", - " --> Time elapsed: 0.26 seconds\n", + " --> Time elapsed: 0.28 seconds\n", "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", - "Iteration 50, KL divergence 5.4656, 50 iterations in 1.4089 sec\n", - "Iteration 100, KL divergence 4.4254, 50 iterations in 1.6354 sec\n", - "Iteration 150, KL divergence 4.1669, 50 iterations in 1.5159 sec\n", - "Iteration 200, KL divergence 4.0382, 50 iterations in 1.7217 sec\n", - "Iteration 250, KL divergence 3.9628, 50 iterations in 1.5821 sec\n", + "Iteration 50, KL divergence 5.4656, 50 iterations in 1.4626 sec\n", + "Iteration 100, KL divergence 4.4254, 50 iterations in 1.5978 sec\n", + "Iteration 150, KL divergence 4.1669, 50 iterations in 1.5271 sec\n", + "Iteration 200, KL divergence 4.0382, 50 iterations in 1.6023 sec\n", + "Iteration 250, KL divergence 3.9628, 50 iterations in 1.6826 sec\n", " --> Time elapsed: 7.87 seconds\n", "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", - "Iteration 50, KL divergence 3.0145, 50 iterations in 1.5583 sec\n", - "Iteration 100, KL divergence 2.6037, 50 iterations in 1.6180 sec\n", - "Iteration 150, KL divergence 2.4109, 50 iterations in 2.0063 sec\n", - "Iteration 200, KL divergence 2.3022, 50 iterations in 2.5981 sec\n", - "Iteration 250, KL divergence 2.2343, 50 iterations in 3.4081 sec\n", - "Iteration 300, KL divergence 2.1874, 50 iterations in 3.8527 sec\n", - "Iteration 350, KL divergence 2.1560, 50 iterations in 5.0336 sec\n", - "Iteration 400, KL divergence 2.1335, 50 iterations in 5.7583 sec\n", - "Iteration 450, KL divergence 2.1152, 50 iterations in 6.4413 sec\n", - "Iteration 500, KL divergence 2.1000, 50 iterations in 6.7470 sec\n", - " --> Time elapsed: 39.03 seconds\n" + "Iteration 50, KL divergence 3.0145, 50 iterations in 1.6652 sec\n", + "Iteration 100, KL divergence 2.6037, 50 iterations in 1.5923 sec\n", + "Iteration 150, KL divergence 2.4109, 50 iterations in 1.6699 sec\n", + "Iteration 200, KL divergence 2.3022, 50 iterations in 2.5603 sec\n", + "Iteration 250, KL divergence 2.2343, 50 iterations in 3.6713 sec\n", + "Iteration 300, KL divergence 2.1874, 50 iterations in 3.7426 sec\n", + "Iteration 350, KL divergence 2.1560, 50 iterations in 4.7974 sec\n", + "Iteration 400, KL divergence 2.1335, 50 iterations in 5.8488 sec\n", + "Iteration 450, KL divergence 2.1152, 50 iterations in 6.7618 sec\n", + "Iteration 500, KL divergence 2.1000, 50 iterations in 6.8048 sec\n", + " --> Time elapsed: 39.12 seconds\n" ] } ], "source": [ - "n_hvgs=3000\n", - "adata_seurat = adata.copy()\n", - "sc.pp.normalize_total(adata_seurat)\n", - "sc.pp.log1p(adata_seurat)\n", - "hvg_seurat = sc.pp.highly_variable_genes(adata_seurat,flavor='seurat',n_top_genes=n_hvgs,inplace=False)\n", - "\n", - "adata.var[f'top{n_hvgs}_seurat'] = np.array(hvg_seurat['highly_variable'])\n", - "ad_hvg_seurat = adata[:,adata.var[f'top{n_hvgs}_seurat']].copy()\n", - "ad_hvg_seurat.uns['hvg'] = 'Seurat'\n", - "ad_hvg_seurat.uns['hvg_plotlabel'] = 'Seurat'\n", - "ad_hvg_seurat.uns['hvg_criterion'] = hvg_seurat['dispersions_norm']\n", - "\n", - "def logmedian_PCA(ad,ncomp):\n", - " ad.layers['logmedian'] = sc.pp.normalize_total(ad,inplace=False)['X']\n", - " sc.pp.log1p(ad,layer='logmedian')\n", - " \n", - " pca = PCA(random_state=42)\n", - " ad.obsm['pca'] = rescale_pca(pca.fit_transform(ad.layers['logmedian'].A))\n", - " ad.obsm[f'pca{ncomp}'] = ad.obsm['pca'][:,:ncomp]\n", - "\n", - "logmedian_PCA(ad_hvg_seurat,ncomp=ncomp)\n", - "\n", - "pca_data_after_HVG = ad_hvg_seurat.obsm[f'pca{ncomp}']\n", - "tsne = openTSNE.TSNE(random_state=42,verbose=True,n_jobs=38)\n", - "pca_init = ad_hvg_seurat.obsm['pca'][:,:2]\n", - "ad_hvg_seurat.obsm['tsne'] = np.array(tsne.fit(X=pca_data_after_HVG,initialization=pca_init))" + "ad_hvg_seurat = scanpy_preproc_baseline(adata,n_hvgs=3000,n_comps=1000)" ] }, { @@ -830,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "tags": [] }, @@ -843,13 +792,20 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "ad_hvg_seurat.uns['hvg_criterion'] =ad_hvg_seurat.uns['hvg_criterion'].values\n", "ad_hvg_seurat.write(f'{basepath}ad_hvg_seurat.h5ad')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/04_Fig3_S4_S5_S6.ipynb b/04_plot_tasic_heterogeneous_Fig3_S5_S6_S8.ipynb similarity index 98% rename from 04_Fig3_S4_S5_S6.ipynb rename to 04_plot_tasic_heterogeneous_Fig3_S5_S6_S8.ipynb index 93b8d88..bd8477e 100644 --- a/04_Fig3_S4_S5_S6.ipynb +++ b/04_plot_tasic_heterogeneous_Fig3_S5_S6_S8.ipynb @@ -45,11 +45,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 3669549056 bytes == 0x6e568000 @ \n", - "tcmalloc: large alloc 3669549056 bytes == 0x1490f6000 @ \n", - "tcmalloc: large alloc 3669549056 bytes == 0x2244ba000 @ \n", - "tcmalloc: large alloc 3669549056 bytes == 0x2ff048000 @ \n", - "tcmalloc: large alloc 3669549056 bytes == 0x3da3d6000 @ \n", + "tcmalloc: large alloc 3669549056 bytes == 0x70034000 @ \n", + "tcmalloc: large alloc 3669549056 bytes == 0x14abc2000 @ \n", + "tcmalloc: large alloc 3669549056 bytes == 0x226020000 @ \n", + "tcmalloc: large alloc 3669549056 bytes == 0x300bae000 @ \n", + "tcmalloc: large alloc 3669549056 bytes == 0x3dbf3c000 @ \n", "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", @@ -390,7 +390,7 @@ "tags": [] }, "source": [ - "### Suppl. Figure 4" + "### Suppl. Figure 5" ] }, { @@ -518,7 +518,7 @@ "tags": [] }, "source": [ - "### Suppl. Figure 5" + "### Suppl. Figure 8" ] }, { @@ -629,15 +629,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 2976530432 bytes == 0x8b7024000 @ \n", - "tcmalloc: large alloc 2976530432 bytes == 0x988ee0000 @ \n", - "tcmalloc: large alloc 2976530432 bytes == 0xa3a584000 @ \n", - "tcmalloc: large alloc 2976530432 bytes == 0xaebc28000 @ \n" + "tcmalloc: large alloc 2979618816 bytes == 0x8d8ffe000 @ \n", + "tcmalloc: large alloc 2979618816 bytes == 0x98b194000 @ \n", + "tcmalloc: large alloc 2979618816 bytes == 0xa3cb2a000 @ \n", + "tcmalloc: large alloc 2979618816 bytes == 0xaee4c0000 @ \n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/05_compute_tasic_qumis_census.R b/05_compute_tasic_qumis_census.R new file mode 100644 index 0000000..90968f4 --- /dev/null +++ b/05_compute_tasic_qumis_census.R @@ -0,0 +1,56 @@ +suppressPackageStartupMessages(library(SingleCellExperiment)) +suppressPackageStartupMessages(library(EDASeq)) +suppressPackageStartupMessages(library(Matrix)) +library(scRNAseq) +library(quminorm) +library(monocle) +library(scran) + +print('reading data') +mtx_tpm = readMM('data//tasic//tasic_tpm_transposed.mtx') + +print('make sce object') +sce = SingleCellExperiment(list(tpm=mtx_tpm)) +cellnames = read.csv('data//tasic//tasic_tpm_cells.csv')$cells +genenames = read.csv('data//tasic//tasic_tpm_genes.csv')$gene_name +rownames(sce) = genenames +colnames(sce) = cellnames + +print('compute qUMIs') +system.time(sce<-quminorm(sce,assayName="tpm",shape=2,mc.cores=16)) +qumi=assay(sce,"qumi_poilog_2") + +print('write out qUMIs') +writeMM(qumi,'data//tasic//qumi_transposed.mtx') + + +#monocle/census in batches +print('preparing batches') +batchsize_max = 1000 +batch_id <- ceiling(seq(nrow(colData(sce)))/batchsize_max) +batches <- lapply(unique(batch_id), function(x) sce[, batch_id == x]) + +print('compute census counts') +print('number of batches:') +print(length(unique(batch_id))) + +system.time( +for (i in 1:length(batches)) { + print('working on batch') + print(i) + sce_batch = batches[[i]] + cds_batch = convertTo(sce_batch,"monocle",assay.type='tpm') + census_batch = relative2abs(cds_batch,cores=16,verbose=TRUE,return_all=TRUE,method="num_genes") + census_counts_batch = census_batch$norm_cds + if (i==1){ + results = census_counts_batch + } + else { + results = cbind(results,census_counts_batch) + } +} +) + +print('write out census counts') +writeMM(results,'data//tasic//census_transposed.mtx') + diff --git a/06_load_tasic_qumi_census.ipynb b/06_load_tasic_qumi_census.ipynb new file mode 100644 index 0000000..6606cf2 --- /dev/null +++ b/06_load_tasic_qumi_census.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93a983c4-19a3-42ed-92f2-8c7e7dedaa20", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata\n", + "from scipy.io import mmread" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "796f7be1-ae2f-412c-abc3-52df176da0a6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1545371648 bytes == 0x5f7f6000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5min 14s, sys: 1.93 s, total: 5min 16s\n", + "Wall time: 5min 16s\n" + ] + } + ], + "source": [ + "%%time\n", + "#5min\n", + "qumis_transposed = mmread('data/tasic/qumi_transposed.mtx')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dec8b688-1daf-43ae-95c4-bcbc71427acb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1545371648 bytes == 0x117b86000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6min 21s, sys: 3.33 s, total: 6min 24s\n", + "Wall time: 6min 24s\n" + ] + } + ], + "source": [ + "%%time\n", + "#6min\n", + "census_transposed = mmread('data/tasic/census_transposed.mtx')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9d8da830-13c9-4afc-833a-3e0b975337a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 5305827328 bytes == 0x1d2e32000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 692 ms, sys: 4.41 s, total: 5.1 s\n", + "Wall time: 49.1 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "%%time\n", + "#30sec\n", + "adata_qumi_census = anndata.read_h5ad('data/tasic/adata_tpm.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ac923473-613f-40a3-aa8c-648c22f53cce", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1545371648 bytes == 0x33de5a000 @ \n" + ] + } + ], + "source": [ + "adata_qumi_census.layers['qumis'] = qumis_transposed.T.tocsc()\n", + "adata_qumi_census.layers['census'] = census_transposed.T.tocsc()\n", + "adata_qumi_census.layers['readcounts'] = adata_qumi_census.X.astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3be38def-65af-4360-9f9e-249d65c5471c", + "metadata": {}, + "outputs": [], + "source": [ + "adata_qumi_census.var_names_make_unique()\n", + "adata_qumi_census.write_h5ad('data/tasic/adata_qumi_census.h5ad')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/07_process_tasic_qumi_census.ipynb b/07_process_tasic_qumi_census.ipynb new file mode 100644 index 0000000..4cded18 --- /dev/null +++ b/07_process_tasic_qumi_census.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93a983c4-19a3-42ed-92f2-8c7e7dedaa20", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata\n", + "import numpy as np\n", + "import scanpy as sc\n", + "sc.settings.verbosity = 2\n", + "import openTSNE\n", + "from readcount_tools import readcount_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ee8cb54b-fde9-41ab-ad7f-c9af264a057f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1545371648 bytes == 0x63ed6000 @ \n", + "tcmalloc: large alloc 1545371648 bytes == 0xee9f8000 @ \n", + "tcmalloc: large alloc 1545371648 bytes == 0x17937a000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x20400a000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.73 s, sys: 7.2 s, total: 8.93 s\n", + "Wall time: 19.9 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "%%time\n", + "#30sec\n", + "adata_qumi_census_readcounts = anndata.read_h5ad('data/tasic/adata_qumi_census.h5ad')\n", + "adata_qumi_census_readcounts.var_names_make_unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2e55438f-c764-45d5-ab94-980fdd898526", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 5305827328 bytes == 0x4e223c000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x4b4158000 @ \n", + "tcmalloc: large alloc 5305827328 bytes == 0x4b4158000 @ \n" + ] + } + ], + "source": [ + "adata_qumis = adata_qumi_census_readcounts.copy()\n", + "adata_qumis.X = adata_qumi_census_readcounts.layers['qumis'].copy()\n", + "del adata_qumis.layers\n", + "\n", + "adata_census = adata_qumi_census_readcounts.copy()\n", + "adata_census.X = adata_qumi_census_readcounts.layers['census'].copy()\n", + "del adata_census.layers\n", + "\n", + "adata_readcounts = adata_qumi_census_readcounts.copy()\n", + "adata_readcounts.X = adata_qumi_census_readcounts.layers['readcounts'].copy()\n", + "del adata_readcounts.layers\n", + "adata_readcount_umi_processing = adata_readcounts.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc495871-5819-4c34-b6a4-94abad22013c", + "metadata": {}, + "outputs": [], + "source": [ + "n_hvgs=3000\n", + "n_comps=1000" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "87b9ab49-ec3d-4d5d-b291-f7980e534f62", + "metadata": {}, + "outputs": [], + "source": [ + "def umi_pipeline(adata,n_comps,n_hvgs):\n", + " sc.pp.filter_genes(adata,min_cells=5) \n", + " sc.experimental.pp.recipe_pearson_residuals(adata,n_comps=n_comps,n_top_genes=n_hvgs)\n", + " tsne = openTSNE.TSNE(random_state=42,verbose=True,n_jobs=38)\n", + " adata.obsm['tsne'] = np.array(tsne.fit(X=adata.obsm['X_pca']))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7ef9d9bd-de5d-4d01-8a5d-01cc9f1d58e6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "filtered out 2593 genes that are detected in less than 5 cells\n", + "extracting highly variable genes\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/gpfs01/berens/user/jlause/.local/lib/python3.8/site-packages/scanpy/experimental/pp/_highly_variable_genes.py:73: UserWarning: `flavor='pearson_residuals'` expects raw count data, but non-integers were found.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "computing analytic Pearson residuals on adata.X\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/gpfs01/berens/user/jlause/.local/lib/python3.8/site-packages/scanpy/experimental/pp/_normalization.py:43: UserWarning: `normalize_pearson_residuals()` expects raw count data, but non-integers were found.\n", + " warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " finished (0:00:02)\n", + "computing PCA\n", + " on highly variable genes\n", + " with n_comps=1000\n", + " finished (0:01:56)\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 19.63 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.37 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.68 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.4157, 50 iterations in 3.3952 sec\n", + "Iteration 100, KL divergence 4.4559, 50 iterations in 3.3226 sec\n", + "Iteration 150, KL divergence 4.2254, 50 iterations in 3.1868 sec\n", + "Iteration 200, KL divergence 4.1156, 50 iterations in 1.2663 sec\n", + "Iteration 250, KL divergence 4.0524, 50 iterations in 1.2570 sec\n", + " --> Time elapsed: 12.43 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 3.0076, 50 iterations in 1.2805 sec\n", + "Iteration 100, KL divergence 2.5424, 50 iterations in 1.5271 sec\n", + "Iteration 150, KL divergence 2.3078, 50 iterations in 1.6307 sec\n", + "Iteration 200, KL divergence 2.1692, 50 iterations in 2.5215 sec\n", + "Iteration 250, KL divergence 2.0796, 50 iterations in 3.3487 sec\n", + "Iteration 300, KL divergence 2.0178, 50 iterations in 4.4553 sec\n", + "Iteration 350, KL divergence 1.9743, 50 iterations in 5.2469 sec\n", + "Iteration 400, KL divergence 1.9413, 50 iterations in 6.0174 sec\n", + "Iteration 450, KL divergence 1.9173, 50 iterations in 7.2087 sec\n", + "Iteration 500, KL divergence 1.8970, 50 iterations in 8.4333 sec\n", + " --> Time elapsed: 41.68 seconds\n", + "filtered out 2593 genes that are detected in less than 5 cells\n", + "extracting highly variable genes\n", + "computing analytic Pearson residuals on adata.X\n", + " finished (0:00:02)\n", + "computing PCA\n", + " on highly variable genes\n", + " with n_comps=1000\n", + " finished (0:02:13)\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 7.11 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.22 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.48 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.8391, 50 iterations in 1.4792 sec\n", + "Iteration 100, KL divergence 4.5435, 50 iterations in 1.4337 sec\n", + "Iteration 150, KL divergence 4.2464, 50 iterations in 1.4476 sec\n", + "Iteration 200, KL divergence 4.1110, 50 iterations in 1.4827 sec\n", + "Iteration 250, KL divergence 4.0321, 50 iterations in 1.5239 sec\n", + " --> Time elapsed: 7.37 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 2.9904, 50 iterations in 1.4994 sec\n", + "Iteration 100, KL divergence 2.5367, 50 iterations in 1.5127 sec\n", + "Iteration 150, KL divergence 2.3076, 50 iterations in 1.7027 sec\n", + "Iteration 200, KL divergence 2.1723, 50 iterations in 3.2029 sec\n", + "Iteration 250, KL divergence 2.0847, 50 iterations in 3.9929 sec\n", + "Iteration 300, KL divergence 2.0242, 50 iterations in 5.0589 sec\n", + "Iteration 350, KL divergence 1.9823, 50 iterations in 6.0494 sec\n", + "Iteration 400, KL divergence 1.9511, 50 iterations in 7.2497 sec\n", + "Iteration 450, KL divergence 1.9275, 50 iterations in 8.2088 sec\n", + "Iteration 500, KL divergence 1.9095, 50 iterations in 8.8633 sec\n", + " --> Time elapsed: 47.34 seconds\n", + "CPU times: user 1h 16min 14s, sys: 1h 16min 10s, total: 2h 32min 25s\n", + "Wall time: 7min 43s\n" + ] + } + ], + "source": [ + "%%time\n", + "for adata in [adata_census,adata_qumis]:\n", + " umi_pipeline(adata,n_comps=n_comps,n_hvgs=n_hvgs)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5b73bbaf-cf03-40ad-b753-4621160c2063", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "filtered out 2593 genes that are detected in less than 5 cells\n", + "pr_theta100_alpha50.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 4811669504 bytes == 0x7ebf28000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0x99569c000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0xab4360000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0x99569c000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0xbd3824000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0xcf24e8000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha50.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 6.65 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.22 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.93 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.8027, 50 iterations in 1.7785 sec\n", + "Iteration 100, KL divergence 4.5160, 50 iterations in 1.6048 sec\n", + "Iteration 150, KL divergence 4.2299, 50 iterations in 1.4758 sec\n", + "Iteration 200, KL divergence 4.0997, 50 iterations in 1.4825 sec\n", + "Iteration 250, KL divergence 4.0235, 50 iterations in 1.5101 sec\n", + " --> Time elapsed: 7.85 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 2.9855, 50 iterations in 1.4687 sec\n", + "Iteration 100, KL divergence 2.5024, 50 iterations in 1.4485 sec\n", + "Iteration 150, KL divergence 2.2559, 50 iterations in 2.1952 sec\n", + "Iteration 200, KL divergence 2.1088, 50 iterations in 3.1664 sec\n", + "Iteration 250, KL divergence 2.0120, 50 iterations in 4.7697 sec\n", + "Iteration 300, KL divergence 1.9467, 50 iterations in 6.0298 sec\n", + "Iteration 350, KL divergence 1.8993, 50 iterations in 6.9023 sec\n", + "Iteration 400, KL divergence 1.8654, 50 iterations in 8.1080 sec\n", + "Iteration 450, KL divergence 1.8394, 50 iterations in 9.5167 sec\n", + "Iteration 500, KL divergence 1.8183, 50 iterations in 10.3671 sec\n", + " --> Time elapsed: 53.98 seconds\n", + "CPU times: user 23min 28s, sys: 6min 16s, total: 29min 44s\n", + "Wall time: 2min 15s\n" + ] + }, + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 23822 × 3000\n", + " obs: 'clusters', 'class', 'non_neuronal_idx', 'n_genes_by_counts_allGenes', 'total_counts_allGenes', 'clustercolor', 'sample_id', 'subclass', 'clustername'\n", + " var: 'genes', 'gene_id', 'chromosome', 'gene_name', 'n_cells_by_counts_allCells', 'mean_counts_allCells', 'pct_dropout_by_counts_allCells', 'total_counts_allCells', 'n_cells_allCells', 'marker_idx', 'name', 'symbol', 'type_of_gene', 'ensembl.type_of_gene', 'coarse_types', 'gene_var_allCells', 'gene_mean_allCells', 'gene_FF_allCells', 'gene_fraction_zeros_allCells', 'transcript_len_max', 'n_transcripts', 'gene_entrez_id', 'ensembl_gene', 'n_cells', 'pr_theta100_alpha50.0_var', 'top3000_pr_theta100_alpha50.0', 'means', 'pr_theta100_alpha50.0_afterHVG_var'\n", + " uns: 'clustercolors', 'clusternames', 'markernames', 'cpr_alpha', 'cpr_theta'\n", + " obsm: 'pca1000', 'tsne'\n", + " layers: 'pr_theta100_alpha50.0', 'pr_theta100_alpha50.0_afterHVG'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "readcount_pipeline(adata_readcounts,alpha=50,theta=100, n_hvgs=n_hvgs,n_comps=n_comps)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e5c4d1f0-503d-4161-a262-124addd471d1", + "metadata": {}, + "outputs": [], + "source": [ + "#save\n", + "adata_qumis.write_h5ad('data/tasic/qumi_census_comparion/adata_qumis_processed.h5ad')\n", + "adata_census.write_h5ad('data/tasic/qumi_census_comparion/adata_census_processed.h5ad')\n", + "adata_readcounts.write_h5ad('data/tasic/qumi_census_comparion/adata_readcounts_processed.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0dd87195-7d43-4df0-87b9-587951d43ba8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/08_S8.ipynb b/08_S8.ipynb deleted file mode 100644 index ed447b9..0000000 --- a/08_S8.ipynb +++ /dev/null @@ -1,411 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import seaborn as sns \n", - "import scanpy as sc\n", - "sc.settings.verbosity = 2\n", - "import anndata\n", - "from readcount_tools import plot_mean_vs\n", - "\n", - "#import plotting constants\n", - "from readcount_tools import PAPER_CONTEXT, PAGEWIDTH_IN, SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE,LINEWIDTH,POINTSIZE,TICKLENGTH,LEGEND_FONTSIZE,SPINEWIDTH,LETTER_LOC_X,LETTER_LOC_Y" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "exp2run = {'CELseq2A':'A',\n", - " 'CELseq2B':'B',\n", - " 'DropSeqA':'A',\n", - " 'DropSeqB':'B',\n", - " 'MARSseqA':'A',\n", - " 'MARSseqB':'B',\n", - " 'SCRBseqA':'A',\n", - " 'SCRBseqB':'B',\n", - " 'Smartseq3_Fibroblast':'A',\n", - " 'Smartseq3_Fibroblast_PE':'A'}\n", - "exp2prot = {'CELseq2A':'CEL-seq2',\n", - " 'CELseq2B':'CEL-seq2',\n", - " 'DropSeqA':'Drop-seq',\n", - " 'DropSeqB':'Drop-seq',\n", - " 'MARSseqA':'MARS-seq',\n", - " 'MARSseqB':'MARS-seq',\n", - " 'SCRBseqA':'SCRB-seq',\n", - " 'SCRBseqB':'SCRB-seq',\n", - " 'Smartseq3_Fibroblast':'Smart-seq3 SE',\n", - " 'Smartseq3_Fibroblast_PE':'Smart-seq3 PE'}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CEL-seq2\tA\t(34, 23555)\n", - "CEL-seq2\tB\t(37, 25479)\n", - "Drop-seq\tA\t(42, 23530)\n", - "Drop-seq\tB\t(34, 22104)\n", - "MARS-seq\tA\t(29, 20307)\n", - "MARS-seq\tB\t(36, 21150)\n", - "SCRB-seq\tA\t(39, 24054)\n", - "SCRB-seq\tB\t(45, 24281)\n", - "Smart-seq3 SE\tA\t(145, 23161)\n", - "Smart-seq3 PE\tA\t(319, 25427)\n" - ] - } - ], - "source": [ - "### Use only mouse datasets\n", - "experiments = ['CELseq2A', 'CELseq2B',\n", - " 'DropSeqA', 'DropSeqB', \n", - " 'MARSseqA', 'MARSseqB', \n", - " 'SCRBseqA', 'SCRBseqB', \n", - " 'Smartseq3_Fibroblast',\n", - " 'Smartseq3_Fibroblast_PE']\n", - "\n", - "basepath = 'data/reads_per_umi_tables/'\n", - "\n", - "\n", - "bottom_criterion = 'ff_reads'\n", - "n_bottom = 7000\n", - "\n", - "adatas_by_exp = []\n", - "bottom_genes = []\n", - "genes_by_exp = []\n", - "n_genes = []\n", - "\n", - "for exp in experiments:\n", - " fullpath = (f'{basepath}ad_readcounts_umicounts_hd1_{exp}')\n", - " #fullpath = (f'{path}00_preload_ziegenhain_ad_readcounts_umicounts_{hd}_{exp}')\n", - " ad = anndata.read_h5ad(fullpath)\n", - " \n", - " #ad.var.set_index('genes',inplace=True)\n", - " ad.obs['experiment'] = ad.uns['experiment']\n", - " ad.obs['index'] = ad.obs['experiment'] + '_' + ad.obs['cells'] \n", - " ad.obs.set_index('index',inplace=True)\n", - " \n", - " ad.obs['protocol']=[exp2prot[exp] for exp in ad.obs.experiment]\n", - " ad.obs['run']=[exp2run[exp] for exp in ad.obs.experiment]\n", - " ad.obs['plotlabel'] = [p+' '+r for p,r in zip(ad.obs['protocol'],ad.obs['run'])]\n", - " \n", - " ad.uns['experiment']=exp\n", - " ad.uns['protocol']=exp2prot[exp]\n", - " ad.uns['run']=exp2run[exp]\n", - " ad.uns['plotlabel']=ad.uns['protocol']+' '+ad.uns['run']\n", - " #remove .. A from SE and PE runs because we only have one run there\n", - " ad.uns['plotlabel'] = ad.uns['plotlabel'].replace('E A','E') \n", - " \n", - " sc.pp.filter_genes(ad,min_counts=1)\n", - " sc.pp.filter_cells(ad,min_counts=1)\n", - " \n", - " #compute readcount statistics within each exp\n", - " variances_reads = np.var(ad.layers['reads'].A,axis=0)\n", - " means_reads = np.squeeze(np.mean(ad.layers['reads'].A,axis=0))\n", - " ffs_reads = variances_reads/means_reads \n", - " ad.var['var_reads'] = variances_reads\n", - " ad.var['ff_reads'] = ffs_reads\n", - " ad.var['mean_reads'] = means_reads\n", - " \n", - " bottom_criterion_cutoff = np.sort(ad.var[bottom_criterion])[n_bottom]\n", - " ad.uns['bottom_criterion_cutoff']=bottom_criterion_cutoff\n", - " ad.var['bottom_cloud_idx'] = ad.var[bottom_criterion] <= bottom_criterion_cutoff\n", - " bottom_genes.append(ad.var.index[ad.var['bottom_cloud_idx']])\n", - "\n", - " genes_by_exp.append(ad.var.index) \n", - " adatas_by_exp.append(ad)\n", - " \n", - " print(ad.uns['protocol'],ad.uns['run'], ad.shape,sep='\\t')\n", - " n_genes.append(ad.shape[1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "adatas_by_exp_A=[ad for ad in adatas_by_exp if ad.uns['run']=='A']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Pairwise intersection fraction matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_intersections(bottom_genes,return_raw=False):\n", - " intersection_fraction = np.zeros((len(bottom_genes),len(bottom_genes)))\n", - " for i_a,bottom_a in enumerate(bottom_genes):\n", - " for i_b,bottom_b in enumerate(bottom_genes):\n", - "\n", - " if return_raw:\n", - " intersection_fraction[i_a,i_b]=len(np.intersect1d(bottom_a,bottom_b))\n", - " else:\n", - " intersection_fraction[i_a,i_b]=len(np.intersect1d(bottom_a,bottom_b))/len(bottom_a)\n", - "\n", - " np.fill_diagonal(intersection_fraction,val=np.nan)\n", - " \n", - " return intersection_fraction" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "n_shared_genes = compute_intersections(genes_by_exp,return_raw=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "intersection_matrix_raw = compute_intersections(bottom_genes,return_raw=True)\n", - "intersection_matrix = compute_intersections(bottom_genes)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_chance_intersections(n_genes_shared_between_runs,n_genes_per_run,n_subset=3000):\n", - " n_genes_chance_intersection = np.zeros((len(n_genes_per_run),len(n_genes_per_run)))\n", - " for i_a,n_a in enumerate(n_genes):\n", - " for i_b,n_b in enumerate(n_genes):\n", - " \n", - " n_shared = n_genes_shared_between_runs[i_a,i_b]\n", - " n_genes_chance_intersection[i_a,i_b] = (n_subset**2 * n_shared) / (n_a * n_b)\n", - "\n", - " np.fill_diagonal(n_genes_chance_intersection,val=np.nan)\n", - " \n", - " return n_genes_chance_intersection" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "chance_intersections = compute_chance_intersections(n_genes_shared_between_runs=n_shared_genes,n_genes_per_run=n_genes,n_subset=n_bottom)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Paper version: Fig S3B" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_intersection_matrix(intersection_fraction,runs,\n", - " ax=None,vmin=None,vmax=None,\n", - " print_floats=True,print_thousands=False,print_pct=False,\n", - " fontsize=12):\n", - "\n", - " if ax:\n", - " plt.sca(ax)\n", - " \n", - " plt.imshow(intersection_fraction,vmin=vmin,vmax=vmax) \n", - " plt.xticks(ticks=np.arange(len(runs)),labels=runs,rotation=90)\n", - " plt.yticks(ticks=np.arange(len(runs)),labels=runs)\n", - "\n", - " for row in range(len(runs)):\n", - " for clm in range(len(runs)):\n", - " if row<=clm:\n", - " continue\n", - " if print_floats:\n", - " plt.text(clm,row,f'{intersection_fraction[row,clm]:.1f}',ha='center',va='center',fontsize=fontsize,fontweight='bold')\n", - " elif print_thousands:\n", - " number = np.round(intersection_fraction[row,clm]/1000,decimals=1)\n", - " plt.text(clm,row,f'{number}k',ha='center',va='center',fontsize=fontsize,fontweight='bold')\n", - " elif print_pct:\n", - " pct = int(np.round(intersection_fraction[row,clm]*100))\n", - " plt.text(clm,row,f'{pct}%',ha='center',va='center',fontsize=fontsize,fontweight='bold')\n", - " else:\n", - " plt.text(clm,row,'%u'%int(intersection_fraction[row,clm]),ha='center',va='center',fontsize=fontsize,fontweight='bold')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tcmalloc: large alloc 4438712320 bytes == 0x18120000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x120a36000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x2378c2000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x3401d8000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x4492ee000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x18120000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x4492ee000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x12013a000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x2378c2000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x33f8dc000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x2378c2000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x18120000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x4492ee000 @ \n", - "tcmalloc: large alloc 5172264960 bytes == 0x18120000 @ \n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ncols = 6\n", - "figheight_in = PAGEWIDTH_IN/ncols * 5.4\n", - "\n", - "path='figures/S_ziegenhain_overlap.pdf'\n", - "\n", - "with mpl.rc_context(PAPER_CONTEXT):\n", - " \n", - " gridrows = 24\n", - " \n", - " fig = plt.figure(figsize=(PAGEWIDTH_IN,figheight_in))\n", - " axes_toprow = np.array([plt.subplot2grid(shape=(gridrows,ncols), loc=(0, col), colspan=1,rowspan=5) for col in range(6)])\n", - " ax_left = plt.subplot2grid( shape=(gridrows,ncols), loc=(5, 0), colspan=3,rowspan=19)\n", - " ax_right = plt.subplot2grid( shape=(gridrows,ncols), loc=(5, 3), colspan=3,rowspan=19)\n", - " axes_all = list(axes_toprow) + [ax_left] + [ax_right]\n", - "\n", - "\n", - " \n", - " plot_mean_vs(adatas_by_exp_A,axes=axes_toprow,\n", - " ykey='ff_reads',xkey='mean_reads',s=POINTSIZE,)\n", - " \n", - " \n", - " min_y = min([min(ad.var['ff_reads']) for ad in adatas_by_exp_A])\n", - " max_y = max([max(ad.var['ff_reads']) for ad in adatas_by_exp_A])\n", - " min_x = min([min(ad.var['mean_reads']) for ad in adatas_by_exp_A])\n", - " max_x = max([max(ad.var['mean_reads']) for ad in adatas_by_exp_A])\n", - " \n", - " for ad,ax in zip(adatas_by_exp_A,axes_toprow):\n", - " ax.hlines(ad.uns['bottom_criterion_cutoff'],\n", - " xmin=min_x,\n", - " xmax=max_x,\n", - " lw=LINEWIDTH,\n", - " color='tab:red',\n", - " linestyles='--',\n", - " label=f'Lowest\\n{n_bottom}')\n", - " axes_toprow[0].legend(loc=(0,0.75),frameon=False)\n", - " axes_toprow[0].set_ylabel('Fano factor')\n", - " \n", - " [ax.set_xlim(min_x,max_x) for ax in axes_toprow]\n", - " [ax.set_ylim(min_y,max_y) for ax in axes_toprow]\n", - " [ax.set_yticks([10**0,10**2,10**4]) for ax in axes_toprow]\n", - " [ax.set_yticklabels([]) for ax in axes_toprow[1:]]\n", - " [ax.set_xticks([10**-2,10**1,10**4]) for ax in axes_toprow]\n", - " [ax.set_xlabel('Mean expr.',labelpad=-2) for ax in axes_toprow]\n", - " \n", - " plt.minorticks_off()\n", - " run_labels = [ad.uns['plotlabel'] for ad in adatas_by_exp]\n", - " \n", - " ax_left.set_title('Number of shared genes with low FF')\n", - " plot_intersection_matrix(intersection_matrix_raw,run_labels,print_floats=False,print_thousands=True,ax=ax_left,fontsize=LEGEND_FONTSIZE)\n", - " \n", - " plot_intersection_matrix(intersection_matrix_raw/chance_intersections,run_labels,ax=ax_right,fontsize=LEGEND_FONTSIZE)\n", - " ax_right.set_yticklabels([]) \n", - " ax_right.set_title('[Observed / Expected] shared low FF genes')\n", - "\n", - " letters=['a','b','c','d','e','f',\n", - " 'g','h']\n", - " x_corrections=[0.12]*5 + [0.15] + [0.2, 0.29]\n", - " y_corrections=[0.15]*6 + [0.09]*2\n", - " for i,(ax,letter,letter_x_correction,letter_y_correction) in enumerate(zip(axes_all,letters,x_corrections,y_corrections)): \n", - " if i<6:\n", - " sns.despine(ax=ax)\n", - " axes=['bottom','left']\n", - " else:\n", - " axes=['bottom','left','top','right'] \n", - " for axis in axes:\n", - " ax.spines[axis].set_linewidth(SPINEWIDTH)\n", - " ax.text(LETTER_LOC_X+letter_x_correction,LETTER_LOC_Y+letter_y_correction,letter,transform=ax.transAxes,fontweight='bold')\n", - " \n", - " fig.savefig(path, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/08_plot_tasic_qumi_census_S7.ipynb b/08_plot_tasic_qumi_census_S7.ipynb new file mode 100644 index 0000000..1d7d4da --- /dev/null +++ b/08_plot_tasic_qumi_census_S7.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93a983c4-19a3-42ed-92f2-8c7e7dedaa20", + "metadata": {}, + "outputs": [], + "source": [ + "import anndata\n", + "import pandas as pd\n", + "import numpy as np\n", + "import scanpy as sc\n", + "sc.settings.verbosity = 2\n", + "from matplotlib import pyplot as plt\n", + "import matplotlib as mpl\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "295229fb-01ec-48a0-9803-940204e3aea7", + "metadata": {}, + "outputs": [], + "source": [ + "from readcount_tools import PAPER_CONTEXT, PAGEWIDTH_IN, POINTSIZE_HIGHLIGHT,POINTSIZE_SMALL,SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE,LINEWIDTH,POINTSIZE,TICKLENGTH,LEGEND_FONTSIZE,SPINEWIDTH,LETTER_LOC_X,LETTER_LOC_Y\n", + "from readcount_tools import get_tag" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4cd51812-00f9-41fc-bd78-bd4e9d3d198f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1545330688 bytes == 0x5990000 @ \n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "tcmalloc: large alloc 1545330688 bytes == 0xe94fc000 @ \n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "tcmalloc: large alloc 1545330688 bytes == 0x1a9c10000 @ \n", + "tcmalloc: large alloc 4811669504 bytes == 0x233eae000 @ \n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "#load\n", + "adata_qumis = anndata.read_h5ad('data/tasic/qumi_census_comparion/adata_qumis_processed.h5ad')\n", + "adata_census = anndata.read_h5ad('data/tasic/qumi_census_comparion/adata_census_processed.h5ad')\n", + "adata_readcounts = anndata.read_h5ad('data/tasic/qumi_census_comparion/adata_readcounts_processed.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f3354502-3ed0-4d92-a20e-63f87f2e9878", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(23822, 25248)\n", + "(23822, 25248)\n", + "(23822, 25248)\n" + ] + } + ], + "source": [ + "print(adata_qumis.shape)\n", + "print(adata_census.shape)\n", + "print(adata_readcounts.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3a409032-eb79-41d1-b0e2-5cdfe602bd52", + "metadata": {}, + "outputs": [], + "source": [ + "#adjust varnames/obsnames\n", + "adata_readcounts.var['residual_variances'] = adata_readcounts.var['pr_theta100_alpha50.0_var']\n", + "adata_readcounts.obsm['tsne'] = adata_readcounts.obsm['tsne_pr_theta100_alpha50.0']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "385ba1c2-dd34-425d-baf1-023fdfbb9920", + "metadata": {}, + "outputs": [], + "source": [ + "n_hvgs=3000" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3568df8f-7549-4c0a-993b-252314efc8a5", + "metadata": {}, + "outputs": [], + "source": [ + "def mean_resvar_plot(ax,adata,label,ylabel,xlims,ylims,hlinetext=False):\n", + " \n", + " resvar_cutoff = np.sort(adata.var['residual_variances'])[-n_hvgs]\n", + "\n", + " ax.scatter(x=adata.var['means'],y=adata.var['residual_variances'],lw=0,s=POINTSIZE_SMALL,rasterized=True)\n", + " ax.scatter(x=adata.var['means'][adata.var['marker_idx']],\n", + " y=adata.var['residual_variances'][adata.var['marker_idx']],\n", + " lw=1,c='tab:red',s=5,label='Marker genes',rasterized=True)\n", + " ax.set_ylim(ylims)\n", + " ax.set_xlim(xlims)\n", + " ax.set_yticks([1,100])\n", + " \n", + " ax.hlines(resvar_cutoff,xlims[0],xlims[1],color='tab:red',linestyle=':',lw=LINEWIDTH)\n", + " if hlinetext:\n", + " ax.text(xlims[0]*1.3,resvar_cutoff*1.4,f' {n_hvgs}\\nHVG-cutoff',fontsize=LEGEND_FONTSIZE,c='tab:red')\n", + " ax.set_ylabel('Residual variance')\n", + " ax.set_xscale('log')\n", + " ax.set_yscale('log')\n", + " sns.despine()\n", + " ax.set_title(label)\n", + " ax.set_xlabel(ylabel)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b83841fa-584a-48bb-996c-fe257fe11459", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_tsne(ax,adata,label):\n", + " ax.scatter(*adata.obsm['tsne'].T,linewidth=0,s=POINTSIZE,c=adata.obs['clustercolor'],rasterized=True)\n", + " ax.axis('off')\n", + " ax.set_aspect('equal')" + ] + }, + { + "cell_type": "markdown", + "id": "c94fafa8-be85-4bbc-a334-989ca7379189", + "metadata": {}, + "source": [ + "### Figure S7" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e41c3d16-97bc-4648-a0d1-abe8f099dfa3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_28765/1248326064.py:46: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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2ZLebF979mfV5TqbnH0UAFOBARFIF66H3jBWsfqSff/Hwim9bseT2pyFg3ZevLb0ysAkHXzi5PuJELdlaLZCkpCQ2btzIn3/+yapVq/61MJWq+ISwZsoA2k9dxpo8D3ujmiGLIjlNWqOJjKww/zF5/jY2pVb1dQf6OQP/D0xj4VtVqo5Kzyx9pv/OE3NWkTxlCZPnb2NwhzgkUeCZfvfw6GivNcHxjsAn60CZBa71CBxVvjKwSZXBQk2yVa3OhofsdnOw18WMencKM76bioiCAhyJTubJ/vfx4Lfb/ZPna6YMqLB4uHH6QV4Z2MTfXgLl3//j7Zi6dUUWRTITW52QK/RE+4xaLZDnn3+eoKAgbrjhBn744YcTKlClnN4zVpBeaCHMYcEZEsbgjvFsSi3kuj9/YshzP9GmbVPWjOlaJRor0A/uGwlUHqEGdiyvH19V2v/j7awRBFVhnCEUWeaaZxdx728f077oKF0ik5nCRIx6LR4FLC6FNh2T+WlbBj9ty+Dwy0P81/pcXRcmR1awRH2ynzx/G5tSCvjy+IrizMRW/jmw6lDbQMPDeeQInrIyBEBEQQDcCLx7+QRQxAqDjU2phax+tD/7o5vRKu8IB6ObMeHj7ax57HJmje5SIRz8wmZRNHn0s9M/BxIXF4fBYODSSy9VU5mcID6/9+T52xAUmel/vc+Xy5/nuVXv8OuuTABaPvkLZmMom44W+RvDhcmR1YbgLd+dXevoFMoXCa157PIqForK6aHD0z+z4qrrmPnd03QsTEGjyLQrTOXG1iFYnR6SIoyM6JrAptRChnVJYFiXhArX+xSHz90Z6ELwfxYEbu0wllZ/rmLJ/6aqbuUGTmDy0/ZTl9H24/1YNAYUwIOAG9gT1Yz9Di1Wp4c1Uwb4Ay8A+sxcyetX3c+8h97htaseYM1jl/t/2/PclRX6kwe/28FLd/ar8zZTqwUydOhQdDodY8aMoV+/fnV647rCnZdH6c8/4zx6lEYPPID59z/wlJQghYYQMmgQ+e+/DwpET7yrwm/h111X53Xxvejtpy5j+e5smglWOhSkIKHQNj8FQ1kpBBsA70TY4A5xfuUB1Y8afeGePtZMGVBlXkVVFmcOnyyM5hJiju73ux08COyNTOarA2VIokCBxclP2zKqKA7Ab3n4yqvOAgWv7HvPWMEjf6T7XWCVz6ntmMrZgyLLrLjqOmKOHWBPZDK2yyaCKHLTkGk0Ls3jWHAjwlw2nCFhNA7WVxhgbkot9LcFRRBZdNROVJCuwmB0+e5sBneI8/cR9bVWpEYLJCUlhfDwcEwmE9OmTaN///7/+Wb1gaZRI7RNm+LOy0eQJJxpx4j63+0409OxrFtH2JAhhF17LZa1ayv8Vtf0nrGCH7Zm0H7qMhwuD9EmDWN/n+PvUHKSWmELDqPA4sSj8K/Ko7If03cs8HNgowpsFGrncXpoP3UZP2zNoNmUxTyy8UskFGTAIulRUNBJAgLeaKnAgYDPFeFzW/nwdQCB7knfZ59V4nNv1RRx40OV/9lL7xkruOSxBcQcO4BGkWlfmEqYwwKAS9BwNCweRdJQbAjB6pJJK7KxKbWQH7ZmsGbKAAoszgptYXCHOP+gY82UASDLDG9q4PUbOlfoI3z3hort479sA1GjBfLrr7+Sk5PjL1QQBKZOnXrSNzgdhPTrhyBJuPPy/CZadaZabb+dCr74fYfLg0cBScD/uTQ7n3YFKf4oiuJSGwIKgzrE+62UqCCdf6Tg81kO7hDnP+bDd67P2vCNQCq7OXxUOCbLFdacqPw3FFnmkscWkCUY/VZAmMNC+8JUBEAWRAweBxqgRV4K7YweUmUtAI3DjX7l4ZNh8mNLSYowVvjuo/eMFVzYJPy4/DpXkV+gEvG1hcr/q5w9+PoLq9MDgpE9kcm0L0xlT2QyxfrgKudLx8XtmytLfmwpyY8txaSTKmSuCHR7bk7J576lb9A2P4UV37bm9V8W8OB3OwAqeCx8nwO3gfBF9p1MBu8az5wwYQLh4eE888wzPPPMM3WuPFavXs2YMWP+czn2/fsp+PhjzCtWIIaFoU1qQsHcuWgTEgjq1YuSpUsp+elHgi65pMJvdUFgtFRShBG9VuLwy0OQBCjRmXBodCiAALQtPkaow8IPWzP8owDf6uNNqYUM7hDn/xwVpGNTaiGbUgvpPWMFUUE6v6WyfHc2qx/phzs/n9dv6Oy///Ld2RXqNnn+Nl6//gJv2Gfffqy4cuQZ2+K0rmR9pukz/XcW9B3Gh4unMeOv9xAUbzx9sT6YPZHJuAWR3ZHJ7IlshlsQ2ROZzB6bBofLQ/upy0grspFZbKPA4vSXmTp9SIVjJp3kbx9ZRRaumfs8+/v05cd+w/zyC2x3/gCL46PI12/oXKNL63Rwrsi6rpk8fxs/bM3wKg8AQeCxyyZy6+CnmXLZ3RVckj48ivefr8MXFJn2BhcOpxuAH7ZmAOWKYVNqIcH2MtoVpCApMrHHDnDJYwsquLvB22Z8lklgJFfZ5i0nnd2iRgtEEAQ2btzI119/7d+V8Oqrrz7hgo8cOcKLL75ISUkJ33//PRaLhXvuuQedTke/fv24+eabWbt27UlVtjoMbdpgaNPG/z18xPAKv8c+8kiNv50slX2MDpe3MXgUSCuyAfhHkOFOK3qPy2+B7ItogiYykiRBIK3IRup076jC5//+aZu3MfiE7Etx4TvH12kMbhfDj/2v9UZeNGrOnxOfZdaN3SrU0dd5BDaOuLQD9bbF6emS9Zmi94wVFFic6EqLaZOfUsHtUGwI8XcGYQ4LxfpgBBT/Z0kU0GslHC7vRHqBxYnV6cHhsvmtTo/iTU3hGywA/LQtg9GtQoj58QCiItMq7wgzth6m97FioJJCkGWvonn+AMHdu/F6NetD6soaOddlXVcEysfX0VdGEURv+6mBpAgjmcXefiWryMKsdR/SIvcIeyOTeXbAvX4LJbPYRovHlzKsSwI/bbWSndSamGMHyG3SmhJ9cJUszoEZDyZ/s5UHjkf2nWi2g0BqnUS/4oorcDqd5AW4hk6U5s2bM2fOHEaNGgXAwoULGTVqFEOHDmX06NHcfPPNtV6/atUq/9qT1NTUk7p3fTFrdBdaPL6Un7Zl4KnBXSgJ3g6hWB/M3shk2hWmciA8iYd63wvFdv95lfd18CjeTsOnoDxK+TkFFid7nruS9lOXkSzaGZd3BFH2dirXDGzC5Pnb/L/vee5Kes9YQWaxjddv6IypW1fMm7aQk9Saj39PY9aYul9keC7K2kegS8l63NKozu0Q2BkoCP7PPuVg0kn+QYbvxfcpC6vTe7zA4sTh8vgn2m2KQm4Tb2dwuFFz1s4YxYPfbge8FmdUkI4Ci5PhTQ00Tj8IsuwfRdbXYtL/Kms4u+VdF/gGmj55nyq+JQA/bEkn3GGhRe4Rf3Sf1lyKEhoOeF2jviANDwLP9r+XYHsZZkMIjhI7m1IL/X0DeNuZf0ARsJvlqbi5a1Ugw4YN448//sBisZz0H1+Z9PR0OnXqBIAkSezcuZO///6bX3/9lUGDBlU5v1+/fv7Ir2nTpgGQ99bbRN89EUGjIefl6cQ88jAIArmvvkZwn97Ytu9A0GrQNW1KyBVX/Gudct94g5gHHqhwzJGSQvE33xA6ZAilS5fyRswl2IPD+GFrBiO6el9sj+J1NfjN0QD8ikUQmBIwKq1soqYV2fwjjB+2ZmDSSQzuEMdP2zL87ihfA0yKMPon55e+dC0/rpxDi9wjHG7UnCGvrMOk1zB5/jYcLo/fcmk/dRkPfrudzR3Gclm/O3nxjr5cfprmQOpC1mcan8uhApUsjercDjXhs1Z9BFqtvrYUFaQjs9jmVw6ZxTY2978XV2ERvbq15KEnfqZxuJELkyP9bs1NqYXYgsIwdetK2eYtBHfvdlr3Qj9ZWcPZKe+6InDA8V/wLQHwDVYeu/SuqoMXp8c/+AjkWLGD1OnD/XMumcU29Frv9ta+ha11FXTzr6lM3G43AwcO5Pfff+e22247pZsAJCYmkp6eTpcuXZBlmU6dOrF48eKTLqfwiy8RRAHbjh2UrVmDIIoEXXYplnXriZn8QJXzLevWY9u5A11yMrK5DFP3blg3b0HfsgWO/QewrN+AdeNGpNAQxNBQXv9hC+01NgZt3wEaDQv2FaEx2pCEiqZodcqjMpVNVEkAvdbbWUiCtwMJVEQ/bM3wuznAqzjAO2LwjVabPf4zTa6639+pmPbk+GO+fYrDZ7lsSi1kzWPlivR0TazWlazPBP82evw3t0NNBFqsHgVGdE3gh60ZSELFtjSsSwLLd2eTVmTzrxvJlUws35ODR8HvPvVe47VWf9iaweKEGwiLHkKfnm2YdTxFiirr00e1A47/gC8ww+8udVpPavDiU2QmnYRH8Vq7LR5f6h+ABEb3/ZdNymqdbo+JiSE+Pp7bbruNtm3bnlTBBQUFTJw4ka1bt/Lyyy8zcuRIFixYwN13383QoUNPucKRt95C5NixmC66CNvmzVjWrSfo4ov9v5csXkzhvHlY1q6leNEiLOvXET1+PKEDB3ofuqKA7EEKC0PfpjVBPS9CLisj8rbbcOzbz5R7riG9xQWMXVXA/Yd0eDTeRTw1uaxOBo/iHYn6FAngX2TmUyo+Hzl4FUxgR5ZZ7O1UFEGkV/dWLN+Tg8Pl8SuM5MeW+t0aPoUTmJSxuljwuqC+ZH06mTx/Gy0eX8oPWzP+s+vhRPDNefnwDSgCJ1p9dfENOgKvM+m87cdnrcZHBGGKi/Hn16ov5VHXsras30DeW28DcHjwlThSUrDt2k3KSO86rZzpMyj45FMADvbugysnF8v6DRy91TuYzXp6KkXzvwVgf7fueMosmFesJG3i3QBkPPQwJYuXALC3bTsAShYvIeOhhwFIm3g35hUr8ZRZ2N+tOwBF878l62lv0NDRW2/Dsn4DrpxcDvbu430Gn3xKzvQZTJ6/jWWXDmLQxA/YsGYbH/02HYCb9y7n5r3LAfjot+kklOXRsjid2StnATBu50+MPLgKgC+XPUekrYROeYeYscabsPa+rd9xcdYu9kQm++dP2xQd45l1n1JsCOHRzV/RL80bUPHLIu/f0S9tC49umgfAtH/m0DNrNy2DBb5Y+BgAnbau5N4t33FhciTXfPoclvUbmNmvMZ8smVbhbwL8z/5EEJRagn9//vlnJEnivffeo0OHDrz44osnXHBdMm3aNKZNm1bBhZX7xhtIwcEobg/RE+/C8s8/2HbuQgwOQtBoiLjhBgDv8V270DdvjjYhAfOvv+LKyCT6nrspmv8tQZdcgm37NqQQrwUyflk6LqcLjyAiKTLbYlr/5/r75kWqOx7YOVT+DlVdZdWV5Ts2omuC3/3hc5FVDvc92/HJ+nTSfuqyE7Io/wuCIp+S66smV6nvuG8+Ra+Vqiw6bQicCXnXFb1nrKjXwcaptpnqkAQqWB//1fLwUasFkpuby6WXXsqiRYvOmPIIpNH/TULQeL1uMQ88QNS4cURPvAuAoIsvJnrCeCJvusmvPPzHx48n5PLLMbRtS6P77qPxjOnomjYl9tFHCL7sUl6KvpTI226j+3otu2Nasi2mNTsbtawT5QHVKw/f8cDOofJ3qN1VJgnlJip4R6c+X7tJJ9W6wv1s5XSNSCfP38betu1IfmwpFx3eWGX0ZnTZWbDkSQCuSl3HfVu/A2DGmnfplHeISFsJXy57DoCRB1cxbudPAMxeOYuWxekklOXx8W/TCbebuWXPMj799SW+WP48ixY/QaI554RHpFanh/u2fsdVqesAWLDkSYwuO52O7mDaP3PQayUe3jiPezyHgbofZYN3RGrbtfu/C/ccofeMFSQ/ttQfIVVf+N2ldTB3GTjnVtsGdidLrRbIsmXLWLhwIYqiMHz4cIYMGVLTqfXKI1ddzaNXX02j/5vE4cFXkvj+e8gWK9lTp9Js4QJyps9AExND1B3/42DvPiR//z3O1FTy336bpl98TtbTUzF07EjE6BvY3607LVevxrphA8XffkvS++/x3hU3sTWhA7/Gd+GXRQ9z1fBX6Ze2hYty9jLzwpuZ9s8cfknuxY7oFny5/Hmuu+ZFrkpdR6uiNGZ3vZ4Za97ly7aDyAhuxOw/3+SWK6cy8uAqIu2lfNxpGLNXzuKtLtchyTIPbfmG8QMf45a9y9C7XczpeA0f/z6DV7uNwS1K3Lfte+7rP5lxO3+i0BDKj6378dkvz3Ff3/tJKMvjln2/MqX3Pdy39TsORiTxS3IvFix5klsGP02XgsMMTlnHzL4TmLH3O4627sakF+9lb9t2tNu3l5LFSyhbtYqE114lbeLdhN9wA6aLLuJQnz602bKZovnfYt+1i/jnn+PorbcRPWkSuuRkUkeNotWa1RR88inu3FxiH5tCysjriHvuOYwdO9SprE/XiNQXqVYXrsnqCJwEPRCeSOvidDSKjFsQuXXw06c0j1IdvsAO30LEhkZDskB8bt+6nOs43aROH1KnHolaFQhASUkJn332Ge+88w779++vk5ueLPXdyOoqcqImKkdUPH7pBF7++0Pv94imCAK0Kzzqjba4bCKKcOIrQStj0jVMV4aP09GhnA6XVbjdzBfLn/cqDQQORDShdXEaeyKTa1w4diqM6JrQoCzMyjQEBVJhBXkDxRecU9d9Q61RWLfffjsGg4FRo0axd+/eOr3x+UTliIokc16F7whC1cVp/4G68m+ea9S3zzqQ4kprRh679C7CnNY68Wf78I0mG9IcV0PCF1lV0xxmQyHQnV3X1KpA3nvvPYxGY73c+HyicmdyNCS2/HtEUxCEWnPinCg+d4bamVTE18GeLuUBVLtmpK7cVr7w7rrMqqpSkUCvRENWHr6MF/U1yKhVgajKo46opjOpKfXFfxmdVk7CeL7jC2H2LdY83ZzqmpHaUAcJ9ce5ti2Cr61A/bWXWhWISt1RuTOpKfXFqZAUYaywh8T5jE9pnFZr4zQgCd5FhqqM65bAdEENeY4jEF/4/umgRgVy/fXXIwhChXTu33777WmplMrJoSqPcnx7z9e0fqIh0tADI85WzsXBhs/qOF3zYjUqkO+++67eb94QCFyk53ODBHZOgRNsvnNMOsmfywjqx4dq0nkzvAbuq63ifXHOFcUB3valuiXrnsnzt1VIq3828F8m630LBeH0ujdrdWH99NNPzJs3D7PZjE6nY9GiRaepWqcXn3vAlwnXt1KzupH9vwnnRIXX4vGl/0mxDO4Qp1odVBxp+aJmApV9Q6UhZhFoKPie6X99B+sK32DQl4UiKcJYJU+eL3Ci8qDUF557pvqDWhXIL7/8Qvv27Xn88cd5/fXXT1edTjvVjeKr8yHWpYAC73my61Aaeux/XRKoPDalFiIJVfNMNTRU5VH/+PZiqU98isHX2fusBJ/lU10STSjP1B2YONOXedl3rq+Nn2n3da0r1qKiorBYLGzYsOGMLSI8H/CF2v0bJp1UIbJCpSJpRTb/Lm4NAV8eK0koT0vj6zhU5VG/1FWC1JO5R2A6kcEd4jDpJL+S8cl6RNcETDqJNVMG+KMqfVmZ10wZ4Fc0w7oknBVBFbVaIJMmTUKSJL788ksmTpx4uup0XpI6fUitlohqddSMb8fA/4LPbQAn74v+LxP2lVNL9J6xQpVzPeJbVV7fBCZG9Q0KfPOjvrbiy5h9YXJkhS0YfPX0bdMQWPfAdnI2LBau1QL54IMPeO+99ygtLWX58uWnq04qAaROH1Jl4ZhKRdZMGXBSHbgvFXoggZE4gS4HqZplOYGWA5zY3jC++0qCdzCQFGH07zzo25sBqnedqtQdm1IL6yXIwtcWkiKM/gGIL/ghs9jmd68O7hBHUoTRP9+6ZsoAZo3uwpopA/yBN3ueu5Llu7P9bcJ3rk+5nC3KA/7FAhkzZgwAFouF+fPnn5YKnc8EWiG+SC5f5kx1VFo9pzKi9KVBr21nycD9yytPyvv2OA9MvV+dBeMr26ew9jx3JS0e98o3cFtR1V11+qivkF3foCOwDfjSoPjmLBqHG6ss9vXJvnKGXN/W1NUpirNFecC/KJA2bdr4Py9YsKDeK6PiRY37PzlqGlH6RoXVuaN8nX9NyiOtyLsLZVKEsUpEl29i1Hetb79zn6Xo60QCI2qSIoz0nrHCb3X4OhFVedQ/vmfscxPVB4FuT9/21D9szUCvlfhpm/d/nxURuJ+9b5AI5RmVfS7Zyn3A2dhWanVhXX/99dxwww2MGTOGli1b1vnNV69e7bdyVLykTh9yTiqPupS1L4Fg+6nL/jVc12cJVHZF1ebG8CmAwEnPymVW59pKK7JRYHH67ycJXv+2L/ChwOL0W0u+juBs6xDqgrPtvfaN8E/W+qgc5FDdbz587Qy8bStwPdiwLgn++Q7A/34P7hDnVyo+5eGzRHzzH4Fu67OxrdRogVitVubOnev/LvxLjqYjR47w4osvUlJSwvfff4/FYuGee+5Bp9PRr18/br755irX9OnTh7Vr15567VXOCGda1rNGd/GHYdY2ge1bAArePGG+zbZ8LiiPUtVtGOhy8u3sWFPZ4O1IfOUG7m8euLYIqBCWW58j4brmTMu6LjjVuUOfjD0KFSzJmoItvErC6bc8oXzQEOiSCpR/4PxG5TVNZ6PCqEyNCuTee+9l165dxMTEoNPpOHr0KFu2bKmxoObNmzNnzhxGjRoFwMKFCxk1ahRDhw5l9OjRBAUF+RciXnXVVYwePbrWiq1atYpVq1YBkJqaenJ/lUq9cjbI2qcAqrMEAvlpW0aFlzwwWwBUXIPj6/zL/69eeQRuJxuYpcBXH9/9NqUWVnFFTJ6/rUGt8ahrWcPpf7dnje7yr2utKiuDwMGE1enxK41A6zRw4jytyOb/LTBIwzdXFqg8KkcM+iwknxLx0RDaSI0K5NNPP+Wpp57ihRdeAODNN988qYLT09Pp1KkTAJIkMXz4cIYPH17hnJ07d/L333/z66+/MmjQoAq/9evXj379+gGc9RvOnO+cTln7XqphXco77UB8HYHvpQ5csZtWZOOnbRkVFAD8+77zlX8P7FhMOokftmZUmP9IijCSWWyrMrIM7AzO9o6hJv6rrOH0v9uVFw1WZ7VWbkeBg4maMhv45tEyi20V2ozV6SEqqOKCQKBCdgufwrgwOZL2U5f5XVyB2TAaArXOgaSkpPDZZ5/x9ddfs2/fvpMqODExkfT0dABkWa72nE6dOrF48eJqG5lKw+F0ytqXgqKmyCvfyNCXCsL3L3DkGKg8Al1QgWX4GNE1wf+7T0mA95rAjihwBBro9vKFaZ4rNMT3unIuscD5CChfxBn4PfBzZeXhawc+2fsWr/rK8LlNfe3OJ//MYpu/3foUhc+9dWFyZL1t+lSf1KpAPvnkE6KioggNDf1XC6SgoICJEyeydetWXn75ZUaOHMmCBQu4++67GTp0aJ1WWuXMcqZkHWjeV+70fVS2KgZ3iKsQauubEA1csR748geWAVSwciqvFfEpoqQIo/9632pyX7RVQ1+709Df68nzt1VJbVN5Xiswoq4y1UXwBV7vk3vg/Nem1ELSimxEBelIijDS4vGlbEot9Cc7TH5sKWlFNn9Ib/upy/yKpSFZH1DLnuhvvPEGmZmZFdK5z5w587RWzkdD2DdZpW6oTdaB4Y6VO4HKbim9Vqow0vSt6fC5l3zuhxFdE/xhlZV92IM7xPnnMXydQ+CGTpUjZAInyc+mWP2zmfp+t/8tYeKIrgn+ebLKrsvqMhL42k9tZQZmxg2c7wgMsPC1T9/vvkWEDS27do1zIKNHj8bp9P5xgfuCqKicSf5NeTQO9851+Dp/n1tg+e5sJMH7Quu1kj8qZlNqIVFBOi5MjqTAUu4WG9whzq9YfJlOA7eQ9SmJ6pSGqjzODibP31btXFZgJFXl9CK+zr0mC7emtheIR6FKwsRAq9Y3eHG4bP622FCp0YUVHx/PqlWriIuLY8qUKXz66aens14qKlVYvju7SgRLUoSRqCCdX3n41l344urB2+H74vErv6wXJkf6U00M7hDHnueuZM9zV/rdCIHKwDcRXp3loSqNsw9fCLWPmtxUgXMZvu8+F6ev468p4WltZQb+FqjIfMEXDV15wL/MgezatYsVK1Zw2223YbFYTledVFSqJSpIV8GVFBjhMqxLAmlFNv9K38oTkoG+5cAkdrNGd6FxuDd9ui9iyhen73u5fUrCd9yXu8hHQ/JZn0/49s6oTG0LCiu7pnzfT3bLhX/DN1Bp6NSayqS0tJQPPviAb775hiVLlpyuOqmoVMA34vdGqnhHlr6No4AKlgbgd03VFEdfYHFWsCJ8rqjAlcCV5zFUJdHwOFNp/X3zJL6FpOeydVqrAnnttdfYu3cvBoOBxx9//HTVSUWlAr7Q3cCJ8cA06IFzE1CuUHzHKy/Oqm7kdy6nFjmf8WUE8BG48rtyO2loq8DPBmpVIJMnT0an09GjRw9efvll3nnnndNVLxUVoPxl9s1vVD4ONXf61S3cCzymdhTnNie6h87JblutUk6tCiQkJITw8HAADAbD6aiPikoFfPsiVOcGCLRAanvpq/tNVR7nPqp8659aJ9Gjo6NZu3YtDz/8MJJUdRMeFZX6pLK7qaZFeZWtihNB7VxUVP47tVogTzzxBCNHjkSWZVJSUk5XnVRUqrUuqvteGVUxqKicPmq0QGbOnMlNN93Eli1bmD59upoRV+W0sGxX9ftlVJ7orImGnjpERaUhUWMqk//97398+umnXHbZZfz+++9ndA5k3LhxJCYmnvb7pqamkpycfNrveyKcLXVLTk7m9ttvr7PyfLJetiubKzvG+f+vb86W51kdZ0vd6lrWcGbe7bPleVbH2VK3E5a1UgPXXHONsnTpUuXaa69Vli5dqixdurSmU89ZnnnmmTNdhRo5m+vWEDmbn+fZXLeGyNn8PM/mulVHjXMgo0aNIi8vjxEjRpCXl1eHuk1FRUVF5VygRgUyduzY01mPsxLfpjdnI2dz3RoiZ/PzPJvr1hA5m5/n2Vy36qhxDkRFRUVFRaU2al0HoqKioqKiUhOqAlFRUVFROSVUBaKioqKickqoCkRFRUVF5ZRQFchJsnHjRmbOnMncuXPPdFUAWL16NWPGjDnT1TgnOdtkDaq86wtV1qfGea1Ajhw5wp133smoUaMAsFgsjB07lvHjxzNv3rxqr+nRoweFhYXV/nYm6tinTx+6dOly2urTUGkIsgZV3nWBKuvTyJleyXg2cN111ymKoiiff/658tNPPymKoig33HCD8sMPPyhjx45Vxo4dq3zzzTcVrnnrrbfOijoqiqK8/PLLp7UuDZmGIOva6qkoqrxPFFXW9U+t2XjPN9LT0+nUqRMAkiQxfPhwhg8fXuGcn376iS1bthAfH38Gali1jjt37uTvv//m119/ZdCgQWekTg2RhiBrUOVdF6iyrj9UBRJAYmIi6enpdOnSBVmWqz1n2LBhDBs27DTXrJzKdezUqROLFy8+Y/VpqDQEWYMq77pAlXX9cV7PgRQUFDBx4kS2bt3Kyy+/zMiRI1mwYAF33303Q4cOPdPVAxpGHRsCDeU5NpR6ns00lGfYUOpZG2oqExUVFRWVU+K8tkBUVFRUVE4dVYGoqKioqJwSqgJRUVFRUTklVAWioqKionJKqApERUVFReWUUBWIioqKisopoSoQFRUVFZVT4rxVIHPnzvUnKnO5XDRt2pQlS5bU+X18idIAHnvsMVJTU7n99tu5//77Adi5cycGg4GysjJuv/12ysrK6vT+q1at4u23367TMs926ku2s2fPZsKECdx999188MEH/7m8+mbatGns2rXrTFfjjFAfbSA1NZWuXbsyYcIEbrvtNk52CV1gX/Bfqcuy/gvndSqTNm3asHbtWnJycrj44osBOHr0KK+99hqKotCiRQsmTJjASy+9RHFxMZ07d2b8+PFcdtlljBw5kk2bNvHqq6/SuHFjAOx2O3feeSfJycn88ssvrF69usZ72+12bDYb8+bN48orr6z2nKNHj/LUU08RExPDiBEj0Gg0vPvuuwDcc8892O12du3axaRJkxgzZgzffPMN11xzDf369WPXrl08+OCD/PXXX6xdu5bw8HBuueWWOn6CZy91Ldtdu3aRmZnJhx9+CIDT6axS3vDhw7ntttsYNmwYu3fv5pNPPuGJJ57AbreTlJTEgw8+yKhRo/j+++95//33adu2LZs3b+bo0aOEhYXx/PPP++u/fPlyvvnmGyIiIpgyZQqLFi1ix44dlJaW8sYbb/DWW28xatQoOnbs6Jd9p06duP3229m8eTNz5szhr7/+Ijc3l5tuuonLLrvs9AvhDFPXbQDg8ssv59VXX2XixImUlJSwfPly1q1bR2lpKf/3f/+HxWJh8eLFZGdn89RTTxEaGsr//d//0bJlS9LT0wF45ZVXOHbsGOHh4Tz//PPcd999aDQa3G43s2fP9reRffv28c0339CvXz9ef/11LrvsMrKysnjooYfYs2cP06ZNY9y4cSQmJp6R5wvnuQIZNWoUCxYswGq1+pOVvfvuuxiNRoxGIzt37kQQBNxuN5GRkXz77beMHz+ekJAQHnzwQRYvXsyff/7JjTfeCMBvv/3GoEGDGDt2LNu2bav13iNHjuSrr77CYrEQHh5e7TnvvPMOU6dOpVWrVgDcfPPNzJkzB4Dx48dz5513Vnvdgw8+yO7du1myZAmXXXbZeac8oO5lu2fPHi688EJ/+Tqdrkp5w4cPp0OHDjz88MM89NBDZGdnk56ezrBhwxg8eHC19UxNTaVHjx5cddVVFY5/8MEHLFiwAEEQAK9CWbhwIX/++Sdff/11tWUlJiby0EMP8dZbb7Ft2zYuu+wyv5I5H6nrNgCwcuVKRo0aRWRkJOHh4bz99ttcfvnlGAwGNmzYQNeuXXE6nZhMJhYuXIjRaGTixIn079+f1atXU1ZWxs6dO/n8888B78AkIiKCZ599lmeffbZGi/HSSy/l0Ucf5cYbbyQxMZH27dszbdq0en+G/8Z5rUCMRiMAsbGxiKLXmyfLMrfeeisXXHABAAsWLKB9+/bcdttt9O/fH4CgoCAAtFotDoej2rJ9L74kSTgcDvR6Pbm5uURERADeBnH55Zfz4osv8uWXX1ZbhqIo/nr5vvvKVRQFvV6P2+0GvHsJABgMBkRR9Nct8PrzibqWbfv27fnqq6/8rgOn01mlvNTU1CrXf/LJJ/z999+MGTOGn3/+2V8Xn7zefPNNNm7cyP/+9z+++uorQkNDgfL2UxlBECrIXlEUbDZbtXU/X2Xvoz7e7/79+/Pqq68yYcIE8vPzMRqNFTryUaNG8fXXX7N27VpWrlyJ0WhEp9MBoNfrK7zDUPGd9sm2chsJrJPPbVZT+zjdnNcKBGDmzJkIguAfEUyaNIknnniC+Ph4QkJCuPXWW3nsscfIysrC4/HUWtbAgQMZN24c+/fvJzMz01/euHHjCA8Pp2nTpoSFhfnP/+uvv9BqtTUqkHvuuYdp06YRHx/PsGHDmDRpEhMnTvT/1rlzZ9566y1mzZrF0aNHqy2jdevWvP766wQFBfG///3vpJ9PQ6YuZduxY0diYmK466670Gg0dO7cuUp5Y8eOrXLdk08+iSzLNG/eHICEhAReffVV/vrrL7p3787MmTPJz88nMjISk8nkv27cuHFMmDCBqKgoHnzwQa644gruu+8+ioqKmDVrFunp6cyePZsOHTrU6Ivv0aMHr7zyCnfccQd9+/Y91cfYoKnLNhDIpEmTmD59OrfccgsTJkzAaDQyZMgQ+vbtyzPPPIPFYiEiIoLRo0fz1FNPsWHDBoqLiwkJCaF9+/ZMnjyZyMhInn76aT744AMeeeQRbDYbnTp1YvDgwTzxxBO4XC6/4qiMz9K9//77SUpKqpNndSqoyRTridtvv523336b4ODgM10VFRUVlXpBVSAqKioqKqfE+e0kVVFRUVE5ZVQFoqKioqJySqgKREVFRUXllFAViIqKiorKKXHOKJBevXqdFQtrVFRU/jspKSk0btyYxo0bs3///jNdHZUaaNAK5PDhw7Rq1YqePXuSnZ19pqujUs/s3r2bHj160LRpU6655hqsVuuZrpJKPTF69GiysrIQRZGYmJgzXR2VGmjQCuS9994jNjaWv//++0xXpQoeRT7TVTjneP755zl48CCCIPD333/z22+/nekq/Ssu15muQcNk5syZABw6dMifveFMo5zEQsPzhQa/El0QhLNmWT94Uw1cv/1lkAAB5rV/BL2oPakyijxlREjqAsTKKIrCVVddxddff83OnTtp0qTJma5SjZQWwO3RCiAw3wFa3Zmukcp/oXDoCAAUGaSLehL+zGNnuEZnBw3aApk4cSJZWVn06NEDWT47Rvzj985GkRQUvOszb97zyglf+2rWAj7M+ZnPi9YwI+8nns76nkJ33aZ3b8g8+eST7Nmzh9jYWEaPHo3T6TzTVaqRF0bu834QVeVxLmCzWUFREET+XXkkTIPG02DrsdNRtTOKuhK9jhm14SUUo4KiKFgL00mKbsNLLW6nkbF2M/xA6XJWWYrIRcSGz6JS6KbJYHDY1QTr2tR/5VXqFEWBs8g4VvkPLGzTmn7N2qC3xhO0+sPaT248rfxz5rSazjonaNAWyNlI7xwD1s0HsP+5hWJnNo7MUvqmXU+hvajW6zZbCghGIB4Io1ynhwkWUso+Z3/xY5TYttdz7VXqElV5nDuM3H+A8O++Qulz0b+f3OP4/iHvjqjfSp0FqAqkjvnxUBbK0Uzs+aVI6zwcCz/ML8mfE2mIwC17aPHw3aw7uNd//t8529mQvx0ZDRqcGLBTPmMikOaORCt6J++yHF+TUvwrijpBf9Zhk89ed5pK3SCGhBD8wjg8DheKoqDIMkdnvYwsy+R8+gl7WzTh6MOT4ZObYeNkGN75TFe53mnwk+hnG26PSC6xNBFzSBM9jHFG8coPN5Ke1YYdWQcRhCZ45PKhqVaQCNWYEF0gI5KkycTubkrB8VmUQ0oUWbYgOmo1rHdpCc98Fq3jFR645OyPQDqfmH3kH2asPkaU3oDZ5UISBDLG3vjvF6o0GCxHD5Nx5d0cimxETFk6GjmTUncRpR98gMblHdRZf1iAfeI9GFq2qqaEQ4DvuBtvpE3DRrVA6pg/H5qOHit2IK9JDDFJnfk5KJTt2QcxR4fiko4RpLeyNXUzNo+N/dlb+G7FXCwWG25FJNsdTgqgIAICIGIhiD02JzHOdRjDS4kNjj2zf6RKBS78bhGP/X4Yj6xwxGym1GEn02Yj5tN5Z7pqKnVE0acLSb16LJLbRuucVEKcDvRWD1FOo195+NA1aVpDKYFKZVi91fV0ok6i1wOlNitbrbt51PwyKVkexGIX2oMe8vQRhBWYyX58CQB/ZvzBpv2bsZVY8DSKIKRlAja8Wt2CgIA3dFUri4iePKJc27m35ftn8k9TCaDH9z+SWWYh025HAqpbJRBvMvBZ/z4MTDpz+1ar/Hcs3SbjcbjIdu7FpSmhRE4n2hOHRylBwICCBZBoNPZJDJ1aETyi6gZelrRgTAkWBBFAB1S/m2lDQrVA6oFQo4k9ZQdIPaLBXBiMIUnBY5LQyBJFyTH+tStFUj7JbRNp2r4pccEhmF0ykXiVB+CdCxEE9AJcH3mlqjzOEjyyzNy9B2gZGor9+OKympaYZXncrEzPqngwJxtW/wIeD3jcsHcTnkJz/VZa5T9hNEQRopcoC84gX+9CRwRep3M0vm7UqGmOa/5KzE/NIavXhCplSLpfcJoNx79ZqvzeEFEVyKlSaoO5G2v8+Za44eijzTRpl4sx2EXYACcxFxXSuFX5ug67PQJFEQkKM6EL1hCRU0yxP4RXwIlABHo0ooY9zsx6/oNUTpTxK1fzvz//YvHRYxT+21LzaCMfBuZykmVY/hns2AWfvQrfv4Wy6Q88C96t30qrnDSK24kt7xAAnk9vRpGDaBEUTygxmNx23EoxbnJA8M5liKITSWPD7DxAcd4aCuZ/VaE8Q2xv9GE2QOFcmX5WFcgpULJ9I0rbGfDEUthbfQ6uEH0Q7yU/gr4kHptZiyDBgLhOpPb4zn/O9U0HYZYEchE4HBxEfJOk49aHAMdDeR0eG0bMROoiq9zj5odGM/SuK+vjT1SpgR+PpDD/8FEGNW6MrbbUFpFG7/8ZZoY3DfCJf318DYFvnOByIiigVcCzUJ0zqU8Coxf3/fE9B7b9WuWc2VeFMe9q77uW98NwFk3ry+pZt+MO0bHS/T7/2MooMiahSGG4jw/y3LHJ6KSWFAm55Fi3YpPT8GAm94lzf7X6uaEGTzPSphy8HbwAv+yDdnEAvH4bJHeGkQ95zxsSOoBLOl6EW/Zg1GoIFoMqlKMRBQaGX8pfZQcwSFqiTAYudnvYU5aLpMg0FUxogiLpa2pHhN6b2uT2ZyfhROaeEbcz77X5fPzNvyxqUjkpXshawMTwHqws3kCPsItI1Ddmi3U/F4V0AOBgiRmb7CHVUkatwdQ2b1ivAMw7eIiP+/f2Hh95Gyx4C8ICrlYAEURLen38SSrH+fWjIETBjk4bxpFUmdwSHZ5PNXSLvxJNzmFkt53WOiMel4Mfb2lHcNfeFBRt4sCvPxHR7hrE0DAExU2ZCEpIFGZDRxC1BHlEmhvCkWxHcEU1QuPxIJUWo72o55n+k+sddRL9VLC7yB4zkbCiBIx/Puc/PFIAULjkxiIe/so7inE4ICICvvkGhtUQeJHpLMKNTBNdFJnObPZZ9zEgvB/zV0yiZdvRdG/c23/uTc/e4//81TOq26MuKHZbsLocrDJvxKMUoZG9PboVgTAhGJ1k5IqoS9lhS6NXUAt+TTnGRXGNiPjs638tO8agp9DuwDXxjvKDZcVQmAerF5Yf8+mTNv2g57nf8ZwJ5jzbiOZN8hEEDR6PG5cbbGYdpbta0So4BNHlQdLqUBQFc8puMhSZFFkDAhgwEO/sTogc6w1v8TjB4wKtCQQBo+ggOkwh12ogTshFKczHZXfTc8f6auuy49Ae2jdrjUbSMPLeobjcLhZ/sOw0P5H/jmqBnAC2XXtQXG709z3FgcxtBA8fQeIPH7Pr8n50CjgvLA5KshW2/ur2HwtvcgS7vQnXXquhJlXdWBcR8DmOxjqvRTN6wNtVzp058Wle+HwW7ZP/PbVJnmM/oigQpW19Yn/oecovpTvIdBZiVSz0EAoxE4ILHXqgVC4jTXEQYjlME10UsqIwePnvNNJWSpBp0IDTTWWzxO7x8PlVAyoeDA7HvWgeogyiz4ns81oeWQutm0FEDGUlJXw1y5uVdsRdk2gUH18Pf/35wy3dHGSXQYpDQZJAkuBYpgFLRCk6JRYFB4rHg6J4SBUkchTQKOBRQGeyE4QbNOlQlgSCCGL5ex6mtRJsNZNpjSHHKeIwtKJFn6Rq61F5EDhl/BNM//Clev/76wPVAjkBsh54EvfRTGy7VuP2uFAE7+SRU6ul877DtV7rS2fxf/8Hs2fXf13/3DaTv7d8woQxv/FdwdUgwJhGi4nQJ9f/zRsYiiKzoegl8pTObHSaSUSgu/4ABxwtkNHh8cgUp+fgttgZd+k9hBhCAPji4H6sDpk3Dx9kb1Z+zTeINBFq99AuKY51/S8vPz7vVVA85bmyrIATkAW45TaI9A4gPp/5EnarBUVRKC0o4J6XXyUoJKTense5zoFVN6Mv+IOssjzsiozW0JwE/ViC23Un5e2XEVCw56ZTZDBzqEjB4QbZKRDbvAlKYRZKWRKNjHrMZj2SHIFJCcc3jdwnwYai0bDvWB4lShRO9Axf9VG19TiXvAiqBVILssNB5oC+HCg6QLwjBpFIPEIRAm5AJkq8mKwOtxH6yl3IGjMhg6pOaBcdT4EVHn7i93W73RwuyMMg6YkIMqI1imzek8KLn37LzLtuo2OLplVS2Muym5c/bsOFF/bn2qse4LuCqxHwhgJ/kzeUODGeEY2rThqer9hcx0izvotBhjSXBTdajiHjdrRDxDupbXO5KMouom33FvxWtISR8d6V5dc3a0HC599wYVQkeysXHBMMcSFgcUDzRuARWNf6kornKN7Jd8FndciAA5RsHRS5EY7HS/QafBWrfvgeQRDQm0yq8viPmNOOIYhhXBRiwe7WkBYVS3hkGDpxBx6rGVdpAdqIOIwh0cToM4hvbERZUkr/d9Yj6vTsnv8eWT98gfbwLtyJl6HY3ICOkOJUNln0uCPiCRUl2l2UQLOXXwYgLyeTLz55m4SkJoy+ZSJQs9K4KQzspfDEErhwyOl6Kv8N1QKpAY/Txdoe/YkoO4YDNyYMgIyACOgQMRGka4MoShAEjpYukr+aX2uZJTYHty/5g6HNY/h72xckhEayuUTHJcltefIKb+K1ojIr173+FBopmkvad6VtfAx/bFvP8hVbcTq9HY9OEjm24GMcuLDjJLdwOXvTvmbf+i1EhxmxKnbErjr0JolQAdyEokdhZOLaen5qDYcDJY+hKN6MufPtXXAiAQoSEAm4FCjOL4WoMDoLLkoFkXHxY/zXy7KM9OHcKuVqGgXj7hANkgiSN4/7O3FtuMcUVX7SpzPAI4BB8SoQD2ARUI5qEW69HDpd4D+1KC+XksICktu08x/7eskc0jKP8Mj4F86qvXDOdoqWtcfgKkSLBSexaBOKEYJcCIKE3GgtlsxiCrPnkz3/T8IaGwhrJGOO6UK769/HkZONxgarrmmPpI/g4qDWiMiUhGrJKLVTZnMjtexAwq3DSbp6IACHDuzmlecf8t9fq9Px9pyfAHC6Heg0+gr1Gyl4ABF0AgsbyBpDVYHUwIE2F5NnDCPMfIAIfS+yNdswS7E0Kk1BQEYIiYWyMkJ1nQmLc5AfF4fYszcJE0ch6StuACFO/4RJXVoyd9t2zBgBhVuidiErCk5Fi2xLx+Kw8fvuP0gM7UqLyIGEGxMQRBEaQWFBIfkZ+RSkF+Bxe2gSE8V1/S6hT6c0Dv7zFiDjAhwtJcRDHnyBxfoEkZ4d9WhEcNohbX8zruw1g/jo9qf5aZ5dyLKHj3I+wCTYMAgetsnNjv9SnkY/CAXRoyDKMmgFShSJ68MvooupKWaniz8yMukUEcHqrCzu+NO7I2ZyaBBFHeMo0dlBthAU2QqD3sRArZ6vgwNWor/+GtgVaGOEPAvICuRpvP9Pe6jWNL7vvfAC2/76i7hr22GVXMwYPxtRUKPxT4jvG4Ngw+GJpbT1//DkvkxIkgeHx0Nosz/ZMOEGCh2RhEbpsRcZ0EVG0++9ryncvwfXHS8hlyk4H78C2w/LaJFhQyNpcAc5ET74gN/ve432k26iyRXlARD3T7gOu63igsFpMz7k9SXeea2+LfszpP9I/yDAWibzzJV5vPJXw0lVpLqwqsFtMaMVI4kuO4aCgiIcoX1QMhbRQIktDbl1O8wlDrArOOSjFJeGoRSXIuz7hZR/VtLym4q+TwV4a9tBwAjH13ksLGjDzY12Y3FqCNIZ2JOxD4/iweoqRRA0IAgoigIyRMRFENYojJL8EjxlHu66djDDLr2I0vw/OCjKCBrQOEE85PGnQpGBYJeC7JbJypPxFHsQpP3MXzyKB/6353Q/0rOKN3f0xWq8ACm0J2YlCKg8hhKwA4IkIEsSIhAraOli8q7n8CgyZqcL2Wjl0XWbeOSCjvySls5hl4PmBW5cbRoTHhaBx1FMUEgE3x9YwpHcX1jf73jU1oMPIXtkxB8/AEEBSUCxQJFZQ9Deo+jbJ9dY95KCAtAJmM2lSOFGVXmcDIZCcINeyCAquhme9oc4tq0LYfp80vY8hTuyEdGrPMSMi+TwJgUUAQSJQ19+RHshGkXjIuj625ETL0ZjCoLuyf7M2VcuetObWeA4LpezivIA+PnHbwAwyhK7D21Gr9UxsPdQAEzBYoNSHqAqkGrJfuFlNApYTTG4ZTeSJ5ij+ZloglwUeZyUFmswi9EQm4ROLxIXp8FoziM0KIgiV8XO6NcU35oR8I1wJdzoRBlFgWCNE5MYjNVlBcBkbIRVW4xWa8TkCuOQfQNNtV1Q3NCmRxuamayUFP5Bo/Ar2LZhOeklRpo1teEuBcENQXiFmgPYixWOpThp0kyHtrGEtVCmXaObTtdjPIvJJ8J1BKfnCGZpHOWWRzme48eaB0Vwe0h/pOMd9QsH13Isv5SFe0sYWhLJG5dcyvObt6LXCNw/IJpvsBCV/S02ayK27N+gyQhiwoPZsPNH5h76nNtb3gZA2XdfEuIqRXAK4BTAI6ARQbNuJ1SjQD6c/Qo9L76Ux2bNIicvk5joeNV9daJ80xoUG25PAoJgRxIF8jfch2STcMcVkpvhJPvFvRgv0OHu2IWMNTKmpHBkxYPLauWiF2bjLC1BFxqG6+sf0N5YzT4fa9fCokVwfC93h8NebVUOHdiNLcyN+YiZsNAIBk7wKg9W29i7bR3t7utfTw+hfjjvXVj5g0cQ9NFsjE3KQ+5ypszE89MWosK05OrTsBWISC4TOWxC0rQkK6K5178tKEiSSAdjEZrsI7jH/x8R/QcQ0bp85XHr9xdysLgYr6M7cLSo0ERbQr+oAmLD41iy+yf2pm9CSApBMUj0No3FWByCW+MkODIKrU5Ls7Bw7uvZg+LSTDq17MnnH7bCrZHRHI8mtIoSBtlDAdAoWULnUFDKFOJ66RElgcJ0MAR34KoLap+rOV9we5zsceZg9TjI0GWzrSDbH4UrAPFCCD2Ckvm9dBtPJIwGYMjSLRwpSGdfaYg3hYVQBkIBoqDl2jZR9OrTjqnrnsaZswzFVVLhfnr02Ecfj6r44i0osEKhCBoBSjQ43QJiqBHN8948Sl+8/wqH920hPDqBvTt2AfDBt961ArIsc+3dVzfItQOnnS8TwWPCo+gRcAESggIyHuyeUjKb5nD4/+Iw5sUghOqROsRiTRTQGbXYHMUMvuVVhIRY8FjxdOqKZCn511sC3HP7NXg87grHel4ygPU/rqBRQgJjH3yQVh28C1R39XVR2FFivflP8iK9lsvU5wcSHKKvUu7ZhGr/Ata778N66T0oJd6EdlK2BYM2CLstgjyHndyQUPbGRuCSwsiKauWfHMVlI8pZjJQn4wmLJmfRjwTFR/vLPVpcxhFrPpK+FC0VRyQiCsdcEfyUF0+3Vr0Yc+0rFMzKQgnrBlI4ablbKHPkEOppxBWNm/P9LXfwytCRxEU1plPLnny8/WuathpGsK7c5DXKHqwIRAFJLbQUSwr2GBFJI2CxeNBb47ms9Zv1/jzPZjxK+cZPGklHR0MiPYKaM1J3Kd2Dm9JWH48eAZPsprMhlEy5DFtuKIVOGwA/5y2kEAWE4+UoISA3RfbEsmSfwKNSIpfH56O4ivFansdnySUFR2D21R0e0AnehSAlGhRFQSMqWJLLBx9H9m8FoCgvnffn/+JXHgAOp7c93f3UuPp4TOcU9tCeyFqLd+5SkfDgAQXcxaWYcl0cfiKewlxvp20y2QkR3bgjokgv+oNjx37n25evxPPzBpzL9iDddEOV8mW5emtj2owPvPOYxwkKCuGqYTfywbJlvDBnjl958Bs4o7Rc8qFA8rEIf7PZsvnsz0xwXruwCr9bgCi6QZCwh+ZiPD75Hf7RE+zvfSWK1UpxWSr6UC1uMZKimN4gW0EwAQpoTJhtJewWFaIikynMsyA7XBAQbRlvNOG07KCUprg4fh0CMiDiob0pj78sX3F7i/uJNBgRwy5DDg3j2viOzOzyICuyDxEc4q3Xpux/2FY2j3Et32ZbwR6aixJ2W3lyRgEIOu4uO/KHHUMTAVOEhBswGDRkZaaQm7WKkGbl0UTnCw/tHYJOKqaJqZTro74n2uhdiCkKAlZ7Pt+vvYOhfb5ho5JCN2MyTfXhSEgYRQMH9BsRENicW8xzHW9lYe4a3O54SmxKQBZeAZemmLZfzKVP8w5oIlfhLvRAuBskGaI94AkYr0WFIR8sBVFGULzzYoKgIG/bR8HKeKL6eyOxBEFg7KSnKrir7n7lUSKDI1Tr4wSx5+uwZ0QSlqQgCzIyTkpCnJQ2KiGu9zrMr1+BiECTGUlYj1rxRMSgFP+N02ahNE8iqzibT/dMICG6NVetXw3A5x+2BFEhsb8erV7iksYbkERjhfvGxCbw/mc/s23zP3To1B2tTlelbr8/eph2c2PokheMAjg7JaA3lRDbpC1NmkdUOf9s47xWIIIi4xFFNIpM6PdfIBi85uLim7pTEh5CeFBz9MVmSoVo3MhoFBEEI2g04HSBKBNXtJXC6BbYDx5ENsViiA73l58YasLu0lLkaUuQIY/+usPsKYM0+QJkJGL1O9kf0pzUlHhe7ux1oXlueYRWa69hr/sgOq2Gz7Xvs7nkb/aHb+TCuIu5kIsBeHvA87zw2dfEShvRustTMHJ8cZoAeI4phMR4KDsmU3rAzZARvxARfn6uSo83ZiEJMhLwY8GN3Jm4xf+bThtC+4QBRGiCGBTaESduDpFBKxIAeLR9DwBWZR/ix8xt2EO34pF0aAxFeDwm70S4aAEliGxzMVJpCD3Ei9nXOJwSjR25uBQObaN0RFp5hVo2pSxtJ26PSLDOa4jYXCAKYNd4X8tnZlXM5gpwIO0wJdYySqxl3PPq47z78Mv199DOEcLv+BpkD3zWGanUgiAJSO5gFI+Mfd2DXJ+fg8tqRWsysfe3O7BlpmGylBLT1IRWI3N4UykZUg6Jo8YC8O3nPQEFUQvOYhk5GN757ALaxHbBsnc2TpvCmKe8+6LbrBZcxWXVzLJ5aTevMY3zvCneBaDPqgyMY7sSud9D0oRyhTP3KxdoNNx+w9k173Veu7BCLx/Ab6l7CV28CDGofPQQ3rovruA0coI2URrcGafWhEMwgyAg6LR0n3I7fT98ksRWEYTceTfN7puEsU9/hq79pkL5kigSphNIZh1GuZTfgzpg1sdj1BeBToPLFESBGEKmJZqL531O8Mc/kbTgGy4yt+LK8AHctvkV1hw7zLGQY9XW/44hgzH2GItF0JQngT/+QQFyTVrcioghzIAuyXDeKo8ffroF1lkJFj00AgZHexdyHS19D5s7DY1k4MI2k/3nO1wuNIiIAa/HmoJdfJ0zA3Pon8iKE1NEGm5FB/oyJGMOGp2ZeMsumthW8M+e9bzT/VUSdJDQ9ALaJjZlyah5hEgBpmmSG6NBQaeBLDOkl0CxXaDUChH5Na9ub5XY3P9ZVR4ngShB9OVQbEYsMiNqRIxiMCHZWbitVn7r1w+ApK6PEd95AqDDYNJxeFOp93oBrnxkBkeKX8Pu9B7zOCB3m4v8HU6MokyOcxulBQ5K872uyhXff8vnM15k858rmP9u9a7j+MzyfieFMjbviuWy/FIS3/4HybgEtpegeBTISke7fzv7f9leb4/oVDivJ9HnRUaiiYrghgOHKFy1nKj+3pXk2eu3sO7tq9hT4KaptQ+KYiHY0JQ2N16NJEDrsdVEYdTAXb/8xZG8vfxjzcLoVCjTx+LS2vHogmla+BfpkT3wCEbEokTCwrNw6K381f96pqd+zLayw94wYmRW9pzJnMMfMKzx1RDUiq7Hs/MuXDoWc8ZfCHDcLebFBeyPj6B3pA13TgxplmPcOGQ5jSJb1ukzbAi89mELnEA0YOibjGt9JhGmlgwe9g5GbWNv2PRx0nLy6HHXFABeuPNG7hhyOTbZSZf9N3D57hJWxLREQGTNxTOJ/WI66BPRKAqSbKNF0UbcsoZ2Le7FLoZxY+tYxraNxYkTDRpSF8/HWpBL4w6DiHQdIX1xDkqOGY8gIMsACh6DHkNkEJGjLkXboQm//Z3Gxh15WDLNiBLMeOny6v5ElRNhzhBI/8f72RABCcUgNIKb95H52280HjjQf6qiyGz+7CbQNef312cwftFhohKTyS77gdSdGzmwszwQJaSZBtGUTMnBQ+CC28Z70xt9OO1J/zlanYH/PfE0AJs+ykdapaGoj4F+Ew2IKMdnymQcIS4E8+84BTfhioZDTULZHhmB3HUXktPC4DfHEBQVXN9P6oQ5rxXI15GRiMA1ybdS7E4n/IeXCGrRht3v9yDr7cPkhkBqiBGTHMX4BasICqu6J8eJYHc5aPrt7+ic4aS7CxCCcgmzplGij0NR9IAI5uYYjHnc2jyRz7O3cnlcCIdYhYyHzDZ/EKEJw1lWwMCSgRjjHuaj2A5+v7jH42TpDyNxyQrW0qO4ZRuS3og+0ok+WCCE7lzev6o75Fzn/h/7086pYHaVYC8rJTYBxCzQHQ+1siEQrAslqumltGw1ipaJfel42/3kl5bvDnj/qGv4dMPvZDyyDE+wBVlnJyarHTeX3M3qo1lszTyKZDHjaNwLj9MAehHKAE9TLk8S+H1YeSLFdU94J7wdG6/lkoVDsXnsWJ/4EElWMDsE0IjYDHoSHhnBoTwXi9ekIWq8QwK3zUXujnQEEd76+DqyM0tIanr2+8jPOn57FsIToEcNwQd5efDqq/DCC1A5YSZwYO+3rFvzeIVjigSyR0BC4cM+7Rj61z6mjDvE9r9Ws/735QDc8vBjmIK9FuiXb5ei0+rQafS0XCrQ6mc3OoeEm6PsDrLS2XLk+GBQIT3ZzsE2NvKD7MguM32GNKHxXdfX6SP5L5zXLqwbCwsZXViINcqMISySoOZeF8/enUVsj4fIMvBgwy6Volgt8Ndfp3Qfg1ZPv0ZGPIDeFUKSbhvxnn2YPCWIkgucIYCA3RbDwv0OWkZl8GbnYfQP7YERE01SriRDn0+eLYevjn7DnF1P02rjFB776Vq2Hl6CJOm4evh3WIr3geQmNK45WbKN1h2e5dp++85L5QEQkZ9GdlEahrJSogA5G3QG/Ms+FI+C3VlCTuoq1q2dyitfL6JxI2+nHBYThincxEZPIW07X8DFC/+HaV5TXKINXV4CK82ruSA8BoepB9aEK/AUmAh1l4LVA24Zo+KhbdyP5Dl3V6lX0oNutCEQGm5A07IJ+u7tELQSulATrnwDHw35DbPZG9mjuL3aTtRKRLWKw21xsHTeZt6Zs5VfLlnEgg9cOIw5OP44HU/07ObPtFxilv/FCzsP1nzSwGdqVh47d0JMjHctx/rq07CHhVe04D0ylFk0SCgYogTuL03hogF69pfMpfNlfZgw7UUmTHvRrzwASouteNwePBYz6Ul7ebTzfDaGH6CYfSzvuIm/GuWSL1qwCE5K7okhXbGhVwS0phDW/ZHPj5POno3HzttJdEWW+WzypYx95gN2XhvDgEnT/cfjYzzk5wis18iAgCZYIDgsCqzlqfO+/s6JQ9bQNPNP+k/+98U/8wcN4HCJjTu/GEfRUTPW6JuwCyXITh0IvpGOk9YtstiT24wL/phM7vC3ecD6I19umQ9/l4FW9PqmCpZzuGA5B8L7ExPlDQXUaIzcNqH2zMDnE18vHefNww0UAd176pD0AuY0F7YUb6dskI4nwfUI3DBoLsEhTbE4HKTnppDUMRlTmAlBENBatITa4hHDgU3xFB2Q0bc20zcsnI+PZYPbQGNhCznFHRHDnYTo8pHsZXy1sSPhBhsvHE9t1eulj6vUM7pNApacEmJ7t6foaB5ydi5NrmqLyyXjMtsJLy1DMmjIE3RoLTJN3SYOfJ/PtY8N5Jd9S2hhhnWDIrHtUwj9OA2DDN3mNzktz/hsIt1s5vqP3ifv4v48fSyTrVYbTSxt0Jg0vNJLqnL+ovVHWbErgxaJegZ1SKZdYhR06gS33QavvAIxMazbvZ9bX3iTSaN6odPnMOGq+1i2eAwy4CtRFCDE4EYMh0ad9SiKgrNU4VDpXJI7XIteW9FKnPnIP2jCOlFm1yKUllL85woUOZQ/2mbQbnMQzTf3otGMEjY/v4rEqDiCUmUS06Gog4Lb7UEURbJ3ZtT34zxhzksLZP+SLvw1oSnRuR42znwJxWLz/yak9yKxexGK23Vcu8oMGnIbh1c8x2HbJv95GhRwOcnfmsYvD37Np1e+yTfXvUvmzmOU5BYzr9unVPYOtggz8u7IaRglLSXOMIIczUA0gj0MX/D3VlsjwnSxtPFcwW+pO1hfcAhBcYFZgQJNhVDQbc4jNA5Lrscn1XDp0Opa9IABuKC7DlOYiMEI7kLF//aLx3/Xyxb+WHYPWTlbSLBNZe5Lz6IP1iNJEoIsIDgEBAR6Ft2CaZuBYmkFVxpupWfLzvRLyEa2unB49LiCwCgUElSWR5JrNZexhnc2LOehlUs4RA5XL72So+4sUtbu5sh36zC/vRT3nnRMRj36ECOHkprQ8Z7LGT3zCgZe0Zz+A5pTKuoxRYdw+00dGTiyJce0NvqXdmT97lSiL7wWWavhYD8NRYU29vaIJ21wEhtPzVBu0DRbu538Xn280VYeD38cKUbK1qEIIo9vcrE0xcPD3x6j0Ozigfe28/2qPLJy4Y2f93P5y4vLC/rsM5RGjXj1y48Y/uTLmG12Xp//B4v+3I/VYafg0ilkGstd2YIAbo0es8uIoAVRJyAZBIIi8vhh5cUV6qgooJQFExZswObRUeS0sqtrF4qNNq6O6YjBFYfWHcz+KSIeAbZb03B/mopLELDanLgcbgrySs+qOZDzUoGU/qzBuTsJgxiJtUgmuKCYtV/qyTn4GfkHcolupGf8CxpGX+GmvSSQvvQjzHu+J3vvZ/4yrr9eT0frIQSbDWuGmeBQPRqNxOLJ3/PJFW+Tac9FdpfvLrR9yz+s/fM3whUd6x5YwNMXteX1S1uDJQQoBWSaRpYwuUkSM7t1YHeRlbd27iUkM5vLwnsCWkKb/w9jnM+nrpDiScGj1LIvN7Ao6whWV62br56TXNB6KO3ba2nfVUNWhhu7Taa42EORW0FqaayS/arUfJiFW2dQMPApZIMFnS9mXwBFUpDxoFdMhBxrBEVBzNkznkhdOJmZEVCqocDZChwaNFYPiluDU2jCDrsJOwYWp2cRTxipTYKRJC1KlgUEAdEt43IpkFeCdX8ufcf2QnepN+uuIAgMvDSRp57ty933X0zr1lFk782ja7GdA8Ns9NppxVG0jxK7jNOi0HqZB71kRwbi7z7eJiYCG0/XEz+zRJfJhLo0UGKDMhedHBIWJQNHXi5lRWWszBPxJDTmuc1uZIPOmyJf0qERRebcW+5BkGWZ6+d9yjqPm9CYcAZ0KKJZXBmtYo7y3peT6LEuC53H+z55OD7sEzVoBT2Fh5yIooAuSEQUBUTZQ6k1y1/2zr+LuXNlB6wO0EsKxaUuulit9O/SixRhI3bRA9pSlLh1KIKMJAlkPByDa0AERWYXpSU2zEV2cvfXsgfNaea8dGHZFglonHZcSWGIOh0laZl4DupJK92I/S8N+siWhLQ9SuouF1HxYbS/4x7y0tdw4TVzK5STuXw9Qpkdd4gOQQBziY3i/DIElwGXLZhlo9cyZGFvJt9zA1ZzaYVr3/vsZ0RR5EBhGlkOhfU5Zvo3TiI3P5cx/xxBBDYWi1gNPYkWwwnvtYCQEBMZGx9HNCYi2zLBKaMRqxfh11tLSXMJ/Gqcz6pDD/JG77M7JUJdk1m4DqegoVEjhdBGAvt2OwgRwa0TsO63UflpiIqHWy5+iTTtdoLFLL6/5Q5u+3IOFiuUOPPYpv2ZpLxW5GXvgAI3w3vdSOd3PiPDGQqAjBZcAiWuBGxSGDlOAybycBcIfP+/6wjCwJ5O33vPHR7J9rl7MYcGs3+ZFWVHNkKQnhumgLMUMn6Cpjd514YYDF75Hpx8mIsP5LFNhrxfN/FX214kpx4lrZGGsmATfw1tRJxRiyDA4mEpXH9JHNEpEiXNBcriHSQkhp3Ox3/aSRAlEsua8XPkUXQWK7EGPXkhG9DtaUxwbDySRo9k1OMstqNr0wolbzsCMLZnB65qX776/4Yv5notVElP255tKdiWhlaQ6dM+iU7tx5Oy8RcGthzHtk2vUgyEAx6jFU2YSFqRN9JPUUDxyITHaymzFRBq8u4kmbZ8Bx/qdlG4CHRhUXTtfz2y3ujdGqC0AGOvTGS3B8dBEz0vaIuss7E5tZC0IiseUyi2jAxEBwjVvPIH047x5hdzCTEaeHnyo/X8tMs5Ly2QuLvG4GmsRfo7B2dRITaxjD63tkGx7MFdWoiiQOneplCmpyA9j/SvFtKp2avod5Vr/u+fLaCsUzfMEmQeK6KkyEZethkUCBeMlGQ1J2+1d+FYZeUB3jw5AItSjtAk0kVyuMLn+3Yz5+ARwBuSW+Zwoc2SccUlUuLWY/ZEEN7tTWI7Pkloq3GEJVRNqwCw8aiVvw5YSE0ro33GvQgeNxtTiur4KZ7dGA2NCA5ri9Ms4HHKtLpAT2hTDZ16GjAbvFNJPhQF3DJs/PtZjv32KVardz3PJ2NuQRFcOJVSgi3BZAYfJMgQwkXJF5FqKWJA8GFGReyknSE3oDQRpycUGR1lJGLSRNE8rOJGUD89bWXtl004uiuMG5b1QN8snLJcmX2vwddhDlI/l9l8d8W/R0nKJ9uWh9HpwWizorMUoXU4yc0tYPfKZZjKNJQVKLRYpHDB/kYE7Q4iPcrAUo3I+tfOfQt004hLeW6QjhH2XMzXDeC7ERdi2tccQQTBI+DMLcFtdaANC0IRBRyhMlFxDv5vaNuKBRXhffk8oBW06DtfTq4jjlZt7kCnD2KZsoo9Wg0yYAKygcwShcxjHgzBYMObxDcrR6Jd9Ns0jupIUamNB+77mWW7MzA1jiCsRRRauYRW3XNpFunCfOhvDG47ogQavUT3bpHYS8KJ1oQysDCRxGIFbUYOIWiIaRHFoGlVd5t664u5KLJMkbmMn1f/We/P28d5ZYEorizsmS/R+pm30OhNpL/xEdI+G0qPaDYsLiG+S1uEZuVdS1hIJHqng25vrIQnFnjt1R7evSMiVlgpxYTLFIPHcQhbugV0Xp+oonGjc4poW9U8mako3pd6VOsOvLh+R5XfE4MzSbc3xtEtAYe7FESJ4tJ8wkLDMSZdw+Hmt2KspP+P5NrQCjIf/1UAgKQzoA0zoSgwL12iU5KMQXN+jBkiTC24quP37M6awtaDi4htKqJ1CaTYuyAHZSLYj/rPVRTYl59In37Dadl2lP+4RqNn4cSJ/u8L1i/j/d8/I996hE6NYwAIFdx0M2Wy194I/PkAvOlqmkWEs/fOa9GXOaDZixCkgYNPkbHOW17aViNr7tpBaPMg0teWsCt9H24SseVqCOnkPceaVsLeeVvI/Gw3hpBQFIeTkKwWtJX2stMeQpjRSWRuOEFfLqNPwUUklEVhjg7FGqaQsFvguie1/PHIuW19+Ghk0NMrJoa3ljpYe3A9iiITpItHlt2IaHHllSBHBOGx2OnTqin/d3lMhetHTH8bESNCjgASKIKMJkpHoxZNuaD5pUiiwOzR3lQmC/cvI6twmz+7mQDo2ukpRMDukrFECWiivRPoP/9xBBQwto5BV+LClVvAS/NuJqZRECsc+3H+WoYHCa0CEZKRSH0wjlAXggB2CaISk0g/UIzF7USbXkan67pV+dvTivJxltmICgvn6j596/MxV+D86E2Oc+SPsexa9D7K4qFYfnsbxQ20CQKtgmyKJLdUIMdjJlUpRkYhavBY+n+fgfuNb1hZ/DHWZrmk3TMJgFbDrChmG00GtKdDbhA9s6MJtYmECFoiInW8oAzi5n8uIzur5oRosuyhd1wsySZTld/SyxqDG8goBrfinRwESkqL8RxZUEV5ALzy01GmfncUWZa9OyU6vftuuwrNOHOKWbCtmB2FdfEkGw4d4mfQMvZWZJeM3elC2LSLWENmBQvEJULzmPQKyqM6+rS9jPF9H2Xri+uRRAmLrMPi0bDXGkkQDrwCU9DjJM6kZel1/dFrJLjuUwAUiwvztq3cuyKY7rdL3PS+HkSQ9CIYYP0nZrYHbSK3XSpKGaTceBTD3VvRLs0iISQCUe9AMGhA3EXGzizizRJ3/O3ikSNabjxqoXGZN0tscD7IxfnkhBXw5qhV7NNtqfmPauBc8finXHjPu+xPySI+JISStKPM/vlx8kuO4XI7MBni0OiMoCgoioLH4sDQKJzW1eSZOpy/Frfs8K78dgIOgZLCTC67vhHzcpZxhCPMPPgawnwTJX2uxUUcAsdjMqIlrCgUC2DXiWiNOsIkb3qi+LhQQptFYYgOwhGi4c4EN0dTiiktdVDiErj56ctpEp9M10bdaBMUj1aQCA11eNPbSAI73EVkH2yNJjuREIxsn3moSt07d7mAqwdezufTX6nX512Z88oCabxjA85ebpwlO2gywEjRTgi9zEDRkRL6v7KE2fc2Qw4qJSLMgTOjHfHd+7N2xjh2/fklVruHrbv/AFngzmMP0+ShNjS620Xh6hRWux24JDdBej2mi+IJW1XuLoqLT6R3v6tZs+rnCnXRanWIosT9f68j1WqtudJZpSBJoJUQE8IQBSjYFQltKp521+5DtOkQzq69hV7lIQig1yB73EhBBjThwfyTUco+ET7f70JxSSQHu/m/blUTvJ1r9GrzFPM++5SQZA1JPRzsWevi3Wu6M3XVZuxWAVNoBAZT+S6Nt/z+LKLsIVIfRIwpiieOrxtoFBbMDZd1BWDG6BeZMv9JPDIkGcrYYm8Cx5PBB0keDt41imD98TQVC+/A3u4xBLSERCV76zTWO2jo/d4FfN9jFWFJBqLzCsgqlXCZsxAimpJgSUMQRdqHNiYrV08TfSGbCo7gkCDOYqDxoUKiXGGAjAUZHUa0eLADkWYjB5OKyY+0EJ2i4NzkRnfhufe6WywOZKuVO2Z+w9/vTWbp0l24jVYOZf1F46j2OD124k2dMTaKRhAEbBl5PDYwgghj1dDeYJMFp6sMSasFUcQs2Mloncuzyc8guQyE/R5PYYF3v887Vk7EdWcp84+OIMSdQOHhEtL/2E5kfwVBK3Bn47VoRO9MW3S4HkknoXhkjPHBLClsyosXefOsjRjuXXuWcEVLXIVupB8OY19cQrE+AtFeRNz7F6C90UWpBQyKgoAFh9VZpe7Tbj4zWZnPvRZVC9rgxrTeEkJJtJugGOjyUyYW51VkbN6P7HLSa2BTstPWIooysUYH+ugIjqYfQR+pReMykLbbQqNmOj6/6xL+75dMjCYtW2/+kahQE5gk4kMNJG9rhJxsIu36pSR95/VV3vS/e/nnr99wu8vHvU+96M3HdCRg1XO1uBVwexAzShgjhfL1gSP8fG25D1SWZSZ+8AFv3XUXn+xVkDSFWMsKMJoiCY2PxyU78FhtOBxmFJuH7kEmfs10YHSHkJqmwdwWQqoaQOccN489zPoDt+BWCmkcV8TUnbuIuyAac67A4EuXYNB70/CvOrIGm6OU+IgkRElDgeKgz7f3IIsyz7cYSYugRjRp3ZXwoHCu6zGClXv+5rMUn/tK4u7ObXj3qksr3FsxaZEX30LZn6sxPzAW5/btBBuiifluHvp2bRm1sR+LBv5Fs6CtZOzowBEK6OW0Yhf1aD02BEQktFhFF/a9VyNp08h3JaKP+5tGuW52Ggu50NIObcsoOCSgRUDBxA/X7QIZ8oOtbD+SSY8Lz731IX+/cRfT5y7miTuGsSsllTydd54yMiiBJo06A2AvzgfZu2HXdb1iiAmu2u1dNPVSjhakU2z5h+aJA0iJ2IFDZ0VqVsYl7t5sFjZQWJpJoNNG+30oOrQ4RpdAa1AUBYsnB0nQ+5UHgNOjIAgCiiDQctch9sQmVrj3X5N24jK76f9ZVxjXloI4yF4m47ErrP3qINtWaEEAucxAUHw3irZU3enwTHF+KJDpySjGSBwRdvTFIo6gMsqiShGFizAmdCd5yEIOL55MuMGCNcSFR4acpr0QN31BSFQoh7YqoHeBAMZgDUNe+sFftGCMQPaEoQkvRjzWlibtFbQ6PR5beaCoKEq89t53ZKSlsmfnJnr06kNcvLcR9YyK5O+Cf/ErpZcgA/OLc1Bkhd6x5b5bURR54aab+OiPXaxPceGyO8DjBDeEFiRhk3IpOngQSZIwaBtR7CzgZ3cWg+WWhLpD0VfN1nDO0rP1lwD8kX4jFsNWQl3XMmDAExXO+Wz/UvSyjF7jfTAOl5NNuXtxu508YjEzPXoATVp7rZBBna5gUKcrqC2lYdb/PQYrMpFlDx7RQqGcSpCiYNJpKZv5OvpPPwRg+G+X4bb3JHLKWoJDdCTfYCH4SSsgIIqg07sIlyMRBAOKqyXxV9sQQsLYcOUA2tyxBgk7LNdDBNgWu/Cg0EhOwrnFjkES6HHDuac8AERR4Ik7hnHdy9PZnHoUY0QUNnM+W16fCoDL7eHLtZmE6RWu7paAQVvR8piz8lM2pGxCFDWU2kpwalzsi/oHEJBEmWQas45/kAvdoNdVjL4QJAhrh4KCgMBXwgZKNDbuoV+Fe7ROjuDnP9MRRIHDnVpDjncLBneRGfeGQ7TsJrP66SzA266SroHMRSJFOzzs3+JAZzQg6bXklcqEJgUfz5wFTqeDj157ilvufoKw8DOT1ub8yIU1uycUHUSJjULQ63EKThw6BzqXQFFUGE7TAcIEOJhmp8y71wy7D4aQFD6Sax+dy8exwVgviKdJ22KCYoIZ+HQK4B1xLIv4A5cngfCua6EojnYRIUSHeR9pyaPNCOoej9sDxpDqe+ru3y1ka0FxlXUJ1dEtPJzNY0ZW+9v4d74nx2qk1JxNTHgz0oqKaRnS9Ph8iEholINfg0UOhbl4rnkiHAlhs34Zi/rfcipP9Jxl7LIpuN0u4mJaAFBsK+WXg2vJKvMGJkTpw9h606ckhcXVWs6+e+7H+esSonS9EBDQ6oqwWvSkm3IIL0vzLmK8eggJzzyP+ccfiLpzfIXrLeP+Iv1wJr8UHmBMbFfyNA4Ob2yKsXUihztYiVsRhOe+MK6/7/wKz65MicXGoHtnY3EUUBZmIyy6MTZLMQdmzfjXa1ft+ZOv130LgEeWOVaYwZoDfxPcoz8F9n38NvoT7tn3EQAF1pUU2NLBHQQ4aNRrDnnr7yX2olkcSb4ZEyJL2IEZOzdyUbX3++qnfRzYW0CHY0fp/eTVhG0/jEFRUI7kkr7FQUG75nR9pDMeB6y+Fkr3yuQeM3PlNonNX8HQ54zkbS3BFKsjtFkwz04u3566utT/p4PzwwIpOp4bp7AEJa4ROkWL1qkDXSmx9jTMgoLOpWDUCJTJCqJopE0zM95APRiXU8axrR9wZMmzBCeXN46fG2cjl3RAxkO4EoYpxka40QSCRJmgY8NLkfyy/B+0IUbCExT6jwii1/Ptefdyrwl678pgHDJo8c7Z/RtPX9S12uOjZ77N+oM7QYSOSRei6AWCjEHIeAABt+zhXaMZgo2INhuv/PY7S665kf1bO536Mz3LObB3J+/Pfg5LmRmtTs/w62/niiv/PYvy0fRdICvExbTA7LDwydbyVcqSR6SRM5q44Cj/sYgIKC6G9HRI8Lq1Se15K1aPg2DjRZQ5jlEUE4TR4cCjOUasJRw3ehQcFP68iLxffiBI0RF+y1ik4xua2XLNfHcoF0/RMZwmA4tibUi7j2BvfIThh5uQ0tEEcRKWktZAi7p8bA2ORz/4GbskYXC40EUmozEa0IhaLrn/Wda8/hSSVHWuA+CdzXvYs/tnXLKArCi4PC7CQ6Mp+zCPnVn7aRPTjPePlc9bvtn2I9Zad/LekbcxRnbHI5iI6vQIV8X0x3TcrRWWGcPSQ4e5sU/1db1pWFuUqz3sG7+Zsic/J2pET5BERI9M4zYKbmMBRxZsIPna7vReJFG4WcQQJ/HFOO922O9fWcK9KysmdFUUhZBTTPJaF5wfUVidbvb+L0koNhsIgjdJutMAtnAMpcmIYhiNjUbiwppiCGqKVivhdLzvT0cS3tSbRrssuzw5XsINIh3xZtMtzehMvCYSq9uB7PFwIKozQREGLhobg63UgrKvEOthOw6zk/KtTmHX6JG1Ko/nunn9uFqNyPDmzfzHuz7zKt2enUXnp6ez/vBOf4LAArOTCxMvIC6+BbIMO3KPsdR5mKDwUHSChjghGm1wFF/vOsSXwzvXxdM969iy8W9ee+kRLGXe+SWX08F38z5g9Yqf/+VKePmK5zCZwrDZLaw8VJ66RpIhWonCaDcx8LOH/Mdtht0gOnn11fIyzMkCujgDHkHLwagEXCVlCM4i9JIbp3KMfH0IRbpwXKJAiKLDqQli8TV3+K//qN97WIsOIsgaFKeTkl37kIudDEyL4efWRwnLOgpJmSSZU8Alg3zuOxFqomDnFmxpB7HqdCg2FzZLEQ6XhSLZwYYDqTVeN3XNFraXhrCtLAYQ0Ut6wrUGdh1LZ8d+M0atgR+yyhMqmkUr73S8j+U93ieq+xsExVyEu9EIPs/NYLu5mNhPvmTR4RS6RHpdSTvyCrh66TJ2FRSSZwtIlaSRvDsZSwLFu4/hMluhe3NKIkwQF3T8JNAYIOZSCG0RzLilRkDAFFMe8FJaXExuvo2L+4/koWffqctHelKcHxbIiNmQ8iuCZEcw+jZwURCMeShuPYo7HKHDEwQ1vhLjptkkl37AAYuRbIuE21mKVh9GaGRL+j2d6S9SkRVEKQNr2wzi3Ho2XQO/2e3cmupBiwZNqIBOKxJrbsqU7Qks7vEn2lANGx/dT/tLw7hkSmN/Wb6VA9XxwlbvGhEpYC3YU98twiO7kACjKZwyrR6Hy0HvywfxULfOXNgyimJLMI+vCAdrLo9e1pXVipbteYdp6jBwMCSScLH6kdm5wAezn6/2+Pwv3qPPgKtrvfbi5O70yxmEuSSfoyXeiJsoQYvWHU6cHItDdFKUV54+xpGXDLKGGwLWdIYbTcghCtutMrKsp0DfkhjNMQRLKWUuCSUoDpdsI7y4AA2NcWh1GEvKJ0bjk5MpTDuGwQCC1Y0iQliXRuwQrYTLQWh0GmSbi9K0DL6dnsHV2/IIXlB1bcD5QILbzmHwBpoYNWh1OpwOMxe1b8nF7aq3zr7fm4LL5eJvewyRog2d6F38d8AWzlffrWD3ndfQ483RFLe4HNHgYXGvB2gd7J2zHBg/gMJj67GYbWD0duhdflkGTiev796LDrh/7Xocx5X6L2mLEAHPxPIBgjvIgLbMjvlgFrZCBX1sM2InXEmY3QUiiJWsJr1Jx70rK0ZLPnn/nbgdNgZfe2ZTu58fCkQQ4P6dcGgZytonUQQPIm4USUQRwdJiENYt4zm6xskl1x1AkO6lyYqLEBt3RauvfhHW4b6rEClDuj0a/V8WMhU7okZP6S1x2EP0tOttYOW1Ltz5Bo6+loPFJbFvThq35F3OV7faaXfUTlxH77yIPPEOmn/xDSmW8nDeC8LDsMkyB0vN7Bg1nE7R5WZqkE6P7HYg4wJBRPQEgU7LlAH96BLtda+EB+l562otXH01giDSeOt+jm3eiTknn6/uuJOBHc7NSdXacLtdlJlLCQ4JrfW8J9d9wgXRyVzXrj+7Dx0kBCPB1lgEBPBAtCnIf67iDoLdu6FjR/jgAzxjx5KTZyHOlk+YKRZFYyA6Nwc5Mw88JbSMbkOI3kiWJY9MMRyP4iHOaSLBUz6EuP5nrzbKO5jDl8M+AA9kphUSlxiBTiche2SMjU3YLGBtncBPLWK4qcpfcX7w5hev+T/3njqDN2+7jm4Blnp1fLXnMFpRAo9CoRzMemsIviFcuFTGjT/OJyx2MJ2DRD5JD2ZDQR4jP7gfEHjz9lnogiKwuD3eFahWJ1gc/rJ1QFkli1AG3t6+g0mdvWmZgxsF4bJ685a5C3Io+2ARsf3vo+zPNdgLbcTdWB5lOeuz91i+diXvT3uD5PjyQedbny1AlmvPg3c6OD8m0QPImXE1JjENQ5yEJa4d4fpSlN6LKfwhmHSbm85jzCDVMDFZ5qSw+18EyQrSISsZBhfab9qjv6sVIQcVMj7fjHWLFk/XKDrc1ZTf+zkpWisR299NRNBSgkKgSX+B7KBGeHRaDIkRNO5Vvs3sh3v28euxdNpGRPBCz+41/g2XPjMDO1pseYW4D+ehj2/KoDaxvPb4nXX9uBoky5d8z8L5VVOnX9JnEGPHP3hSZZU5LIiILNqwls//WgHAuAGDGdUzwNGt0YDHA82bY1m5ms0TpqCXS8mzaAh3mCkISkJRPMTkbMQp5dFUboTbHYRWG4KFUsJpgc19hKZ/L0EbE1vh/jn7sjm6KYXuoy/ih5s+xqN46DaxL62u6HDyD0bFT+wbX5Jr97mTwesH8M41iIlOZEDvKsapDeGC/LWUEIxBMvDjbc/T4dAG3GYnRBvB7IAdWeXFtGnk3Z/YUtEx3S+2EStHDAUgZ9LbaBSFEquCKCgYJJm4adeibPwHpSAdMdgI108CUeTKu7yDCUNwCG9MeYHkmPjT8HROnPNOgciyzNFNL9K022MIuBE0xn+/6Dhuwy8IDhd2wY1R0eGgOUdvKaT56O7YWprZMWkpzTUxmOOiaTu3Z/mFJVbkL/5E0ErkayScEjhCgonu1RJTQjg9ttzAO52e4xLt8U6hfXvYuxfatIF9+6rU47qX3mFfZjbOQjNCqZOQ5AtY88odmIzn/qLAEyUz4xgvPHUvnuNrb9p26Mbkx146oWs9ioLk21y+Bi5eOZXro3oi5BUzqe9otBqvMW/JyGb9+KewWlzIbjdBcilmfSzeQH4PptJt6IRo4iNl7J4IPLZ9hJYlEK5EE/rCLehGXum/xz9b1nEw5RC3XadGytU1wvRPjn8qVyAabLTQ/E26rjeWCD1ILgyOEhJKd+IwBjHr6nFsSJN5Zfuu8oL0IjhqzzUm4PUy+MiZ8CYOD8QHg0bCu211exeSR4GggO44rBFrGrfjxdkv8d4Ls2mWVLtldSY4PybRAxBFkWYXPY2o0Z6U8gDwbL8ShSR0ioQbHRLBfF6aSWGejeUP/IHdrXDAmsXi+HJ/tutABmzaj5AQjq20jGhJIXpYT5KHXUhoQhQSIrHZVj4r9Y5ucTi8ygOw//03Q++oGjn01rgbSRZCubJpIjg9LHhkqKo8KtE4oQnvfrqYD75YxgdfLDsh5WH3wOTVdh7+y8WmbBeF9urHVguKf+No8HYe2foNL277lq7PXOP/TacrxH7B7whCGYga9IkJx2MmZBAlrOHdKApNYqcnmGNyMAWWDKKkJKJ0zdAN6uPd4u7FF6GoiM8XfcNXSxfw9ldVrSmVE8Pt8VTZlwfgp+uuCPgmIOAiUdpKvmEAFsV4PK2ZFrsugoygtuSEdWT8in28sn0XiYbjHgqN8K/KA+D1i8sjN/O//QtRgAgDONzg8cgIBg/iIRFHmohsC7iwOI/e8XE8MGQEzRrFVi34LOD8mAOpA8wpZZRevYdGBvDYG6ERSnit027CpQQWF2ax3emkTffLcedtY8JjlwDexuHZdBin240jtxSn2YaxXRP0keUbwggILLv6l/Ib6fVe3yow/Lj5mpaTQ1JseQMaMvUrhAIbS7+bStnfawlu6o0fzUk7xo9zPgBA1OgY99Qz9fpMzgXuen83STFBlDROQhFFHGU2dHo9847ocKRn8+4N5S6DEpuVHrv+hzu4CEOIDMHhWFxB2PJKafHwrUAoh16aRbNmobSbvgDZ7WHpFWOAsOP5FRU0tiLsxmBcGNBE56DRNMYz/FK4rA9s34p1yhycQTbCp4TQu+dlpORksmLbRibd5E1VYXc40Ou8+1mo1E6JzcLoN7wDh5k33ckFTZv7fxvqT3Tq3V9Q0UGukIxRe5j2jv0cUvoiuwRCQ80EBZlIKwzDbI9lRFMjmY4isu15uN3/7ry5vXVzHujcEYDLfx/KbMd4kkQwaUEQFURBAFlBiVWQj2mR0xVoISMIIDiApd9ypRH4/i1vgQOuh8bNa7zf6ea8s0BOldLNpWREWPDYSzEHt+Fo/MWYgz3E9O0B5gT6JLVGp9FzcbdEQkL07NqVyxNdPiZ3azbmvWkUHMrCmlZAxs/r8TicFC3fguNYnrfwA1urvectQ0aB3sBbS7/F5SmfMJN0OoKTvAol+NJL/Me//+Bt/2hLdjs5uKNqll+Vcix2F1aHhwPZFjyygi09H8x2nAUleEqtRDSLYWt+eSehl7SIJhs6gwwKhEgWQsvyjr9EWrSSFkGno91d3ucuaiT6fT4bTUwkTqEMi+cICGVojGUoWjOuUi37TW7C77oBoVtzdi1dji2sAFmygEbDTUNGsfjteVw1wOvW+nvzJh5+dTr3vjDttD+rhsgfOzf7Pz/61RyGvPx0NWcdj3hyg9UZR4HYnP0xl6O3leEua4OntCWJIY1B0OAx5LO7NI+P+17KiSTIF4FPB/Q7XryHgb+l4e4ZR7HDu9eLKACiDB4JQZExNnKhiQCnVY/bjXfVuwgooDi9Riy/f/cfnkjd8//t3Xl8FOX9wPHPHHsfuS8SQoBwGCIQTgG5FGypWhUVD1qslaKtWrWvqqDWiopab6otrf5EtFW8r2qLRUBQBEW55D7CGXKSa7Pn7Mz8/hhIiATRlBDA5/16wSvDzvPsM8tmvzvP9f3BjYF8XysW7+TNNzZyttqVIc/mQngjm0Zm8krCWjy5eSiqSkJ6Fzz1VWg2O7+6zUV5TZQNG6soLEilYerzKArW7kQ2cKhQE7a+kOb6QVXj0DtuvY+zu0H3PpDdfPrhT393PXYUrrv0csYMGdpiOwEevuFa/MnJyLL1kTb57vsafxYOF4pp/PKxlSR2ycGe5MeIacTrQ8h2Fdlhx5GeSM2XG+mc6WZTv3f5U/ZEHquexbzIEuJhk8gmG+Y6jTMGn8kb50xvrHfP6mVkPPQk9rokgo/+Dk+v7pixOhYO7UE8GmdNjoKua+T4/ThMNxPe3ABA+X8WIj31BDYTDFkl4fWXUA/s1Dz9ledYs+wrEn1+XE4n544cxU+Gj2qPl+2kcdEjfyQcjwOwTykjwfDyydSZjY/XhCMkz3wJgK7KYortZ2CaTlypKlpIJq6lgns/hA6sEJXqGJqewuLxQ7E9M+eoz393vz5MH3T4ZBjtppnYUnWolyBbs1JV52vWamL5wB8bjcMz5sG/TJB04Je3t+4FaQOiC+soXn99HaiwILaF3KxUum3rS3V+kMEDziIUUyitrcTEIJSQylXXGqhuhWy3nexsK4nQ+vQEXPvrMAG3HRSHjQRDQ5ZAcRqQo0OVDBkGlGyFij1w2U3N2hALRsjIzOSdRR+xa+9mrrn06hbbetvTVvfV7i2byO3es8VzhCbLq2yM+EkBK9fWENi+Cac3CUW2IesKOBQkSUILR9iyPcJD511Njj2Bh9Km8vzCr0nfZeDZE+fB66ZyUdaoxjpXzJ3Fv+/5DemaBHkmqdet5IL3/4tN38EX6Rp61EQ3DUaUOmhIMxjzVFMC89SiXtQlJmFmZOB74F5UV9MY3TVnn8fkjz8jFKlkzv0PkHmC9omfSPIzsllbshMJiWpqKPOXUhKoINtn7SWX5HLy7sUj+WBrKS9+HSTdUcOFPS9kT32Mz8IhajEg1DRlGzOJfJ8fVZY5LyeH9/fupdDvY119gCy3i3SbnTV1dY2ntxQ8AGwqVkBIMaFKhVRr/QdOICJBxARrk2WQQIpK4DIhIRXOP7FmWoo7kKP4svde5g3dSqKSwG9mFRHxwY5RsOV82FcNMS1EoktHczdQUARnDu7QrPz8379Ml/2VqBKkuCCmg9cBNsVEyohDSLLePF3jTYV+1vwbxiv/eZ+Pln2Gw25nxm9vItGfxMaVK3nhiSeI6BFG//ISLhhzWdu/GKeIR1bEKItbv8UT82Ue/ufnQASAhLQuGIaGTfbicqVRsX0VhmEwaUwuPxqQQVALMeSL8YzzX82H+xYSWiwxLC+F56fMAGB6DwlJhl4jkihdEuVXvxiH4/Y30NY8whNTZxABetTINPjSuGb+ZgDi9QG2z3qIHrfPaJ8X5BTWEA3y6qqPmby86bU1b/7ssPN+MX8jOT4ndw/sRMwwGfvOSr6ujhA85NfygUE5TBt4+Eyo0mAIv82Gx25DNwz2hULkeDzfPk5VVQepB9aYxaJQvAa+XAyDxsLWtVBzIJe6BlSqkBmHS34Dbt8Rq2wPon/jKLbeUsNFXwzgsn/1Ja6CEjEpGWwiKZCbquOev4y9W4vpXRDmzMEdWLUCdN3ENKFmP9Sf0ZcGh5N0L7hs4HeC7UC6DuIGpBnQIW7lIjroycfh3aYdfy8fdx6P3DqNu669nkS/tVXCW3NmU1NfheJV2Vwsxjq+q6gGZcUqZqVBsHQ/Ez9bTrXXjiQnILkTiGgNBGvLiWi1lO9Zgy7FeLf4K8b2s761emxuzkwYyFdVHxDeuoMU204qSr8grlv/gb3PTqJDn46s/7SGkDdEyX+dAEQyL0dCQgG2JIAWq4S4NVuvfOLVdHlZJ/yvpd9o7XfZIU34NmE9xsJdXx71vDljT+P+MzpjV2S8NgUDk5BWd2BCi0miYrQYPACyPG48dmtRsCLLdPR6kSSJuXOtsY6WVNTu48s//d46sDug5yD42a3QvS+cOwkuvtF6TAEy4iA7TrjgASKAHFUsJrG/yIm/UsKugSMGZ92jk+mpJPDeYpRghLEjfTxx54fs3FnDGwuW8sDfVvL4rK08/eJ6CvpkknX/z5HUgwNnJkgmhmkQjErgMKxb10N3LwiZUL2lWTuSfD6y0tIajy+56dc4B6Qh908h7hAzcr4rRQI5IhGNBHnTu4dN9jjzcxpYGq+gXjeRJDuGEaemcjs18TqKtSocNmvb8INKg1Uk1LvxVtjwytZwqqpYvcGSTeW00R3wp8okZsLy9EUAfH5NEX50OkoSqmwQ8JuYi6zUo8kzxhNKr4PTsg9p6ULAQVOa3FM/r3lbyHtuPC+Xfth43AkYOHcyK8o2sLl61xHLzT2ngGR1N6oUJdWps2XSwO/93JMnW/Fn9Ojm/24YBsWzHyNeV3vkwi631RMx6Xa46nb42c3f+/mPBxFAjuL8kb2oz1WZPy3E3t4lhL0Su86GotFpZA3rQ4+He/Leup10Hl7AX2etRLYpGKZJg1FHXAriKikmPdWNfP4IdAlAsmZT2A3cIVvTncehMSA3DklZ1pqQA4o3r2P6LVeydNEHAPTo2otH73gOJAhET5wEMyc6VYVHrpL4y4QEKs/pT/rSj8A02dPLzX86BgiFDAw9zua9S0hNSOLuc09ny2MTm9XRJ68Qs5OHlG5pqJikDWn6cEnukIBWsZuu/f24ZC/JiYWsuu8qYrpGF1lh2Ll+bpwuM3ZCEnS3cpG4el+J/8O/4MrPO+RZnv1Gy8VdZmssGd80aD7QnUiu28eI/H7M3b2Ie1e9wL1fvtBiuS4JXoqy+nN9P6i8ZjRpbjef7qujOmrdFT5Y+h4vVTd1ha2vfJentzbf3fqeewBJY+na7c0rN06dLwNiDOQ7qJ4P83dHOH2pRs4CH66NYIRNlr5cxVk3pmGaJtPuXIxpmuwvLiN7WFdkWcaI6oztEOPMK4ZbFb06n9jyDagSSJKJlBIF3QadteZP6PZDOAxjLocMa0zl4N7/hmEyfebc43n5p7ROC5cRCjkorE+gwldPL3s6xUv+QVHPCcTNCDMndKHf4zex7Y6/N5a5t3wWi5bPJym7N5LDhtkQZmBKL6Z1v4J4tJaN826k66j7cCfksfbeC/H2HEbV4ndA1yi8dhvla03INgmsyCFUXcSgB2cjqy3lizn0W0WQg+kFhO/HMA1Gzb6AfgVnIyuqtacZENQivL1uIeXXfvCt5U3Tyig4Z+8mvMn1bKsrJ6BHQQEHEbpKKvvi63EE3+OK7JmkeUc1lk1N0+ncZw/Vff9MQYd0/vW7qW15qcedCCDHQNWiEFVZAea8uJFwfRBvThKSLNG1eBe/+OsEpEM7Qp9/FWP7frRwDIeqQ+c4eI9c98EB9Xlv/5Pli603ut2dTjgU5L4nxQrl/1VlTCNjwXJ+ulUnK6E/LmSWlD6Low5qG6pYNP1W0pObb6gZ1MP8fMNjSEiNA6U5JPFk4a8B6wNHi4WZf89ppHh8yCEPcsCOEY9SG9iNbIKtQKd7Tgc2f9IRe2IWQx/95h2HcCyNeOZH9CkYg93ubgwIAFd3HUdhypEX5q2oXkZ5rIx+GcNZZ+zi66oy6owQmKBLB/fRNrCZOn69jp+nDSDN2by7a+Ksu1i+fT9xezGr73yNJE/LG7SejEQAOQZ2TKglfaabhetLWLZiL5KikJHqZnhhCkWDOjY7d9f8d0hQTBL1COzZDTYrnYOpyyi2A7e2u1RrYF3G6v/8hmsnWAvL/v7avLa+tB8M0zQZufp2xu+7k7eXvcWu+tUQsWZmFf/9b9aK4UNcsvYBkEyQJG7KPJ/hqU3dF3uKV7Drg2eJ2edQtakTFVGNznISGXiIB+spj+t4jAy0oDXtW3XIpA07j8LJzadvC8fOqGd+jIGJLCt0yxuEJCv079Cba3tecMQyC0o/pA4r3fRG2YkkyQRiGjmyQViCmmYjAHEudazFJpn0Smqe5Ljr1NFIQGmndMJOO08NvZrre5zVBld5/IkAcowZhkkwHMfnObxLwjRNPr/TSl3a/47HsdXth0/nY+oB6jLOJ7F+ISSnw+piSNStHoxJJ86ioR+KyU/PZV2iQtnHC6zJDabEoxMnccnI5os4G/QwXuXw/dT27VzD2zPHkdO5lNIqH4GAk5RoIr3ifrRAHarXx7aK8WRkr8TjUcntvI/yLRH63TgFqeuU43SVPyyhWIhxcy5qlndnyZQPj3g+wFulVldx6Z5yJNlDSNcId0wlYtJsepWfGENtO0lR6tGB05MealbPJS9PY8metdSm+HBpcaI5TurOnY1DPvn3rxMLCY8xWZZaDB5As3nhisMF2Z3hsilIQCIAB7Z2H9nGjRS+1f/dcAWaptPtkwXYXX7iYYPHnniNtZ9u4N47Jzee983goUWD1O74mI2fXkSPQmuxqMfdgKOhgD6X38/6GX9ANXz484soHHw6gdc/J7q+mO3dMzBCYTYNmc1pIoC0CbfdzeIDASOkhXHbjr6RqoJC3IxTvbcaqMZI9pFqJFGvKDQcEop8KCgohA0XftXZrI5AOMLqdSvxTzAxgmH8ySFMAkSNqAggwtGEsfpImwY/ffmFxCMNyLaWg4xwYgjGYmTkFSFJECqvotal8fbmSq6v2E9GekqLZT6bm4pCDEU1kGQ3phFClk0Scveyf+UzdJp4Hkq4G/bkDMLb6qn7qgQFGbO+HLVII7nw6Dnbhf/ddwkeAD/JuIg3172C6rCjOhx43W4SZAUXYMPEhrVSpwQJR9xLf3uAfP9dzeqQJQmpyIOaWE+qPwgSVJe68Ksn3pqO1hBdWG3mEeC2Q47Fy3wyufKvc9heFyYYqAJNJ7qpElmVMIMaW+bNYswNj1Edklg55/eNZVa8M5xtWyo5LWszASUZI1aNBOR40jBiPgzXWbjcg0gKvk7J0u5UvbcMz/g9BBck4+0+lj5zn2q/CxaOaIm5hrAUI1xaSogIEhAmTDkuwoCODBjcnvZj/Orh2S4Ldo3BtIbLsGtOCvT+vNxj+mHnnYzEOpA2M63VJWvDDcxY/I9j2Bbh+/rDhT8mHqoDUwdZwpGVDDaZ8QO7YwCllbVIbjehSNMU7IEXfsI5595G99RsOibmkWaXOcPnI9ldhx07htmAumI2S17YTPj0Z8h+Yg22H1VSsq+CmrojL2oT2tcwqZAR9MbGwTsXEwcqOZiNE609OFsMHgBvZz3DEFsRt/qmsDr//VMmeIC4A2lD84BxB37uAJS0eNbnpeuZvfF9/rH234TRuCB+Dk6HjRWRddzRdxLdsrIZUdj7eDVaOMS2snIunPk3/E47BUmZ/Pm6ibjtTf3Weypr6ZiWeFi5r19JISkhj1R9FzEHGK4GXCVZqI5y9m/pREOXvdgdCs4yL4vvCRILgzspnQu2bz6OVyd8X4Zp8GrZG0CUNGJoSi6JjlxSHH66O5vyxnxdspsPN6/lllHjUGTlyBWeAkQAaVM1QFKLj8zfvoR/V6xCUhTWl23jk+JVhNEYHjkDh2lnm7qDHvF8AOZNE5vsnUyWvyjTw6Pg9OQQkFXSKnVC9gBuU4GIHQw/RqgBs7aOHe5L8fW5nMzRp8a0TgG6TZ+MgxA5zgr8wzry2pnPt3eT2owYRG9TLQcPwzR4e+9SXC7rlrdjQhZhvgDgS9sq/IafvnEri5mK1GIdwolr8EQN7fOLsVV8hDPusPJKyF70qANZA8nQkJ0ekMJ0u/mZ9m6ucAzphkFXdhNHRorCaMeQ9m5SmxJjIO3gjx/NoDZQ1XjstNlJcyYCYFM0/KYd80AGmTsuvrJ9Gim0mqQo2Ia8xf7KXhBKQsWGpMlEzRiVn4YJaRBUs5Bu3tDeTRWOoekfvcaGshLAxI5Opj+LXw88tadlizuQdrBy92piikFGah6yLFMTrqcyUgtAv7y+dFTT6OrpxLThE7G3uEeScKIz9Aj1uxVSTgsgRR24ozZMm4pjSAyzy1BsPSeBq+XpwMLJ5a71z7GhfgemXWNN1Taeu+F5MhIzG3doPpWJO5B2kJtbiM+bQmnFNkzTJNWT2PjYBakDKC7fy8TeY0TwOIkpqpsut7yIFEuHuA7BEFIkhhoIYOs1BXJHH70S4aQQlXRMWUL2eZAlE8lp/0EEDxABpF08PnIqofoq9u3bRE1tGbFouPGxT8p3k0V/8lNy2rGFwjGRnA9n/AEaDAgEobwKwlF4aVJ7t0w4hh4pmILicSHZVEzD4Ic0LUnMwmpHF836LXldskhwuTgvbywDcnu1d5OEthCuhofzm44HXQfjHmi/9gjHnGGaTFhvbaL4RuEd7dya40cEEEEQBKFVRBeWIAiC0CoigAiCIAitIgKIIAiC0CoigAiCIAitIgKIIAiC0CoigAiCIAitIgKIIAiC0CoigAiCIAitIgKIIAiC0Cr/Dw+TN6xIWljuAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncols = 3\n", + "nrows = 2\n", + "panelsize_tsne_in = PAGEWIDTH_IN/ncols\n", + "panelheight_resvar_in = panelsize_tsne_in/2\n", + "figheight_in = panelsize_tsne_in + panelheight_resvar_in\n", + "\n", + "path='figures/S_tasic_qumi_census.pdf'\n", + "\n", + "with mpl.rc_context(PAPER_CONTEXT):\n", + "\n", + " fig = plt.figure(figsize=(PAGEWIDTH_IN,figheight_in))\n", + "\n", + " nrows_grid = 11\n", + " ncols_grid = 3\n", + " axes_resvar = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(0, col), colspan=1,rowspan=4) for col in range(ncols_grid)]\n", + " axes_tsne = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(6, col), colspan=1,rowspan=5) for col in range(ncols_grid)]\n", + "\n", + " adatas=[adata_qumis,adata_census, adata_readcounts,]\n", + "\n", + " xlims = [np.min([np.min(ad.var['means']) for ad in adatas]),\n", + " np.max([np.max(ad.var['means']) for ad in adatas])]\n", + " ylims = [np.min([np.min(ad.var['residual_variances']) for ad in adatas]),\n", + " 1.25*np.max([np.max(ad.var['residual_variances']) for ad in adatas])]\n", + "\n", + " labels=[r'$R_{UMI}$(qUMIs, $\\theta=100$)',r'$R_{UMI}$(Census, $\\theta=100$)',r'$R$(Readcounts, $\\alpha=50, \\theta=100$)']\n", + " ylabels=['Mean qUMI count', 'Mean Census count', 'Mean Readcount']\n", + " for i,(ax,adata,label,ylabel) in enumerate(zip(axes_resvar, adatas, labels, ylabels)):\n", + " if i==0:\n", + " hlinetext=True\n", + " else:\n", + " hlinetext=False\n", + " mean_resvar_plot(ax,adata,label,ylabel,xlims=xlims,ylims=ylims,hlinetext=hlinetext) \n", + " for i,(ax,adata,label) in enumerate(zip(axes_tsne, adatas, labels)):\n", + " plot_tsne(ax,adata,label)\n", + " \n", + " \n", + " for ax in axes_resvar:\n", + " for axis in ['bottom','left']:\n", + " ax.spines[axis].set_linewidth(SPINEWIDTH)\n", + " \n", + " letters=['a','b','c',\n", + " 'd','e','f']\n", + " for ax,letter, in zip(np.concatenate((axes_resvar, axes_tsne)),letters):\n", + " ax.text(LETTER_LOC_X+0.2,LETTER_LOC_Y+0,letter,transform=ax.transAxes,fontweight='bold')\n", + "\n", + " plt.tight_layout()\n", + " fig.savefig(path, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bd08252-7e87-4606-964a-53a6b4ff22e1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/09_simulate_tasic_like_data.ipynb b/09_simulate_tasic_like_data.ipynb new file mode 100644 index 0000000..d5d7920 --- /dev/null +++ b/09_simulate_tasic_like_data.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "69589703-921d-45f7-87af-cceaaf41ad23", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import anndata\n", + "from scipy import sparse" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3a1fac73-e7e5-4623-a8ec-bd64af4af845", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "basepath = 'data/tasic/'\n", + "adata_tasic = anndata.read_h5ad(f'{basepath}adata.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3aa328a0-754d-4a5a-850b-c22f58ed6be4", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate(adata,only_marker_genes_DE,read_umi_factor = 100, theta_umi = 100, theta_z = 1, seed = 42):\n", + "\n", + " cluster_ids = np.unique(adata.obs['clusters'])\n", + "\n", + " readcounts_sim = []\n", + " umi_counts_sim = []\n", + " clusterlabels_sim = []\n", + " clustercolors_sim = []\n", + "\n", + " housekeeping_idx = ~adata.var['marker_idx']\n", + " across_cluster_ps = np.array(adata.X.sum(axis=0) / adata.X.sum()).flatten()\n", + "\n", + " for cluster_i in cluster_ids:\n", + "\n", + " print(f'simulating cluster {cluster_i}..')\n", + "\n", + " cluster_idx = adata.obs['clusters'] == cluster_i\n", + " ad_cl = adata[cluster_idx]\n", + "\n", + " ###get UMIs\n", + " #get seqdepths per cell\n", + " readcount_ns = np.array(ad_cl.X.sum(axis=1)).flatten()\n", + " umi_ns = readcount_ns / read_umi_factor\n", + " #get proportions per gene\n", + " ps = np.array(ad_cl.X.sum(axis=0) / np.sum(readcount_ns)).flatten()\n", + " \n", + " if only_marker_genes_DE:\n", + " #set ps to across-cluster ps for all non-marker genes\n", + " ps[housekeeping_idx] = across_cluster_ps[housekeeping_idx]\n", + "\n", + " #sample UMIs from NB(n*p)\n", + " mus = np.outer(umi_ns,ps)\n", + " nb_p = theta_umi / (theta_umi + mus)\n", + " np.random.seed(seed)\n", + " umi_counts = np.random.negative_binomial(np.ones_like(nb_p)*theta_umi, nb_p)\n", + "\n", + " ###amplify UMIs to get readcounts\n", + " #intialize readcounts\n", + " readcounts=np.zeros(umi_counts.shape)\n", + " #only nonzero counts need amplification observations \n", + " umi_nonzero_idx = umi_counts>0\n", + " umi_nonzero_counts = umi_counts[umi_nonzero_idx]\n", + " #index for which amplification sample(s) get to amplify which UMIs\n", + " split_idx = np.cumsum(umi_nonzero_counts).astype(int)\n", + " #one large sample instead of separate ones is more efficient (one sample per molecule observed)\n", + " \n", + " mean_z = read_umi_factor\n", + " np.random.seed(seed) \n", + " zs = np.random.geometric(1/mean_z,size=sum(umi_nonzero_counts))\n", + "\n", + " #splitting up into separate groups of samples for each (gene-x-cell)-observation\n", + " zs_per_cell_x_gene=np.split(zs,split_idx[:-1])\n", + " #summing reads for each (gene-x-cell)-observation\n", + " zs_sums =[sum(z) for z in zs_per_cell_x_gene]\n", + " #mapping back to count matrix\n", + " readcounts[umi_nonzero_idx]=zs_sums\n", + "\n", + " n_cells = sum(cluster_idx)\n", + " clusterlabels_sim += [cluster_i]*n_cells\n", + " clustercolors_sim += [adata.uns['clustercolors'][cluster_i]]*n_cells\n", + " readcounts_sim += [readcounts]\n", + " umi_counts_sim += [umi_counts]\n", + " \n", + " adata_sim = anndata.AnnData(X=sparse.csc_matrix(np.concatenate(readcounts_sim)))\n", + " adata_sim.obs['clustercolor'] = clustercolors_sim\n", + " adata_sim.obs['clusters'] = clusterlabels_sim\n", + " adata_sim.layers['umis_sim'] = sparse.csc_matrix(np.concatenate(umi_counts_sim))\n", + " adata_sim.uns['theta_umi']=theta_umi\n", + " adata_sim.uns['theta_z']=theta_z\n", + " adata_sim.uns['seed']=seed\n", + " adata_sim.uns['read_umi_factor']=read_umi_factor\n", + " adata_sim.var_names = adata.var_names.copy()\n", + " adata_sim.var['marker_idx'] = adata.var['marker_idx'].copy()\n", + " \n", + " return adata_sim\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "905c29d2-8d85-4634-9909-5fa8556dc675", + "metadata": {}, + "outputs": [], + "source": [ + "theta_umi = 100\n", + "theta_z = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "27026c62-3f79-4499-9ce9-4777bbf8c6ad", + "metadata": {}, + "outputs": [ + { + "name": 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In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + " adata_sim = anndata.AnnData(X=sparse.csc_matrix(np.concatenate(readcounts_sim)))\n", + "tcmalloc: large alloc 8152080384 bytes == 0x4505e4000 @ \n", + "tcmalloc: large alloc 1985470464 bytes == 0x72832c000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulating cluster 0..\n", + "simulating cluster 1..\n", + "simulating cluster 2..\n", + "simulating cluster 3..\n", + "simulating cluster 4..\n", + "simulating cluster 5..\n", + "simulating cluster 6..\n", + "simulating cluster 7..\n", + "simulating cluster 8..\n", + "simulating cluster 9..\n", + "simulating cluster 10..\n", + "simulating cluster 11..\n", + "simulating cluster 12..\n", + "simulating cluster 13..\n", + "simulating cluster 14..\n", + "simulating cluster 15..\n", + "simulating cluster 16..\n", + "simulating cluster 17..\n", + "simulating cluster 18..\n", + "simulating cluster 19..\n", + "simulating cluster 20..\n", + "simulating cluster 21..\n", + "simulating cluster 22..\n", + "simulating cluster 23..\n", + "simulating cluster 24..\n", + "simulating cluster 25..\n", + "simulating cluster 26..\n", + "simulating cluster 27..\n", + "simulating cluster 28..\n", + "simulating cluster 29..\n", + "simulating cluster 30..\n", + "simulating cluster 31..\n", + "simulating cluster 32..\n", + "simulating cluster 33..\n", + "simulating cluster 34..\n", + "simulating cluster 35..\n", + "simulating cluster 36..\n", + "simulating cluster 37..\n", + "simulating cluster 38..\n", + "simulating cluster 39..\n", + "simulating cluster 40..\n", + "simulating cluster 41..\n", + "simulating cluster 42..\n", + "simulating cluster 43..\n", + "simulating cluster 44..\n", + "simulating cluster 45..\n", + "simulating cluster 46..\n", + "simulating cluster 47..\n", + "simulating cluster 48..\n", + "simulating cluster 49..\n", + "simulating cluster 50..\n", + "simulating cluster 51..\n", + "simulating cluster 52..\n", + "simulating cluster 53..\n", + "simulating cluster 54..\n", + "simulating cluster 55..\n", + "simulating cluster 56..\n", + "simulating cluster 57..\n", + "simulating cluster 58..\n", + "simulating cluster 59..\n", + "simulating cluster 60..\n", + "simulating cluster 61..\n", + "simulating cluster 62..\n", + "simulating cluster 63..\n", + "simulating cluster 64..\n", + "simulating cluster 65..\n", + "simulating cluster 66..\n", + "simulating cluster 67..\n", + "simulating cluster 68..\n", + "simulating cluster 69..\n", + "simulating cluster 70..\n", + "simulating cluster 71..\n", + "simulating cluster 72..\n", + "simulating cluster 73..\n", + "simulating cluster 74..\n", + "simulating cluster 75..\n", + "simulating cluster 76..\n", + "simulating cluster 77..\n", + "simulating cluster 78..\n", + "simulating cluster 79..\n", + "simulating cluster 80..\n", + "simulating cluster 81..\n", + "simulating cluster 82..\n", + "simulating cluster 83..\n", + "simulating cluster 84..\n", + "simulating cluster 85..\n", + "simulating cluster 86..\n", + "simulating cluster 87..\n", + "simulating cluster 88..\n", + "simulating cluster 89..\n", + "simulating cluster 90..\n", + "simulating cluster 91..\n", + "simulating cluster 92..\n", + "simulating cluster 93..\n", + "simulating cluster 94..\n", + "simulating cluster 95..\n", + "simulating cluster 96..\n", + "simulating cluster 97..\n", + "simulating cluster 98..\n", + "simulating cluster 99..\n", + "simulating cluster 100..\n", + "simulating cluster 101..\n", + "simulating cluster 102..\n", + "simulating cluster 103..\n", + "simulating cluster 104..\n", + "simulating cluster 105..\n", + "simulating cluster 106..\n", + "simulating cluster 107..\n", + "simulating cluster 108..\n", + "simulating cluster 109..\n", + "simulating cluster 110..\n", + "simulating cluster 111..\n", + "simulating cluster 112..\n", + "simulating cluster 113..\n", + "simulating cluster 114..\n", + "simulating cluster 115..\n", + "simulating cluster 116..\n", + "simulating cluster 117..\n", + "simulating cluster 118..\n", + "simulating cluster 119..\n", + "simulating cluster 120..\n", + "simulating cluster 121..\n", + "simulating cluster 122..\n", + "simulating cluster 123..\n", + "simulating cluster 124..\n", + "simulating cluster 125..\n", + "simulating cluster 126..\n", + "simulating cluster 127..\n", + "simulating cluster 128..\n", + "simulating cluster 129..\n", + "simulating cluster 130..\n", + "simulating cluster 131..\n", + "simulating cluster 132..\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 8152080384 bytes == 0x3e4c00000 @ \n", + "tcmalloc: large alloc 2127740928 bytes == 0x79ddc8000 @ \n", + "/tmp/ipykernel_28877/474217884.py:64: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + " adata_sim = anndata.AnnData(X=sparse.csc_matrix(np.concatenate(readcounts_sim)))\n", + "tcmalloc: large alloc 8152080384 bytes == 0x3e4c00000 @ \n" + ] + } + ], + "source": [ + "adata_sim = simulate(adata_tasic,theta_umi=theta_umi,theta_z=theta_z,only_marker_genes_DE=False)\n", + "adata_sim_only_marker_genes_DE = simulate(adata_tasic,theta_umi=theta_umi,theta_z=theta_z,only_marker_genes_DE=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4071c2d7-f4af-4d76-aaa3-14a00718e0d2", + "metadata": {}, + "outputs": [], + "source": [ + "adata_sim.write_h5ad(f'data/tasic/simulations/adata_sim_thetaUMI_{theta_umi}_GeomZ.h5ad')\n", + "adata_sim_only_marker_genes_DE.write_h5ad(f'data/tasic/simulations/adata_sim_only_marker_genes_DE_thetaUMI_{theta_umi}_GeomZ.h5ad')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/10_process_simulated_tasic.ipynb b/10_process_simulated_tasic.ipynb new file mode 100644 index 0000000..2c60fa3 --- /dev/null +++ b/10_process_simulated_tasic.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ddbdebb6-7315-41f9-a67c-6fd7844940cd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scanpy as sc\n", + "import anndata\n", + "import openTSNE\n", + "from openTSNE.initialization import rescale as rescale_pca\n", + "from sklearn.decomposition import PCA\n", + "sc.settings.verbosity = 2\n", + "from readcount_tools import get_tag, readcount_pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4071c2d7-f4af-4d76-aaa3-14a00718e0d2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "path = 'data/tasic/simulations/adata_sim_thetaUMI_100_GeomZ'\n", + "adata_sim = anndata.read_h5ad(path+'.h5ad')\n", + "path_only_marker_genes_DE = 'data/tasic/simulations/adata_sim_only_marker_genes_DE_thetaUMI_100_GeomZ'\n", + "adata_sim_only_marker_genes_DE = anndata.read_h5ad(path_only_marker_genes_DE+'.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "038d0893-ba02-4b0c-b24c-68def3f087fc", + "metadata": {}, + "outputs": [], + "source": [ + "theta_umi = 100\n", + "n_hvgs = 3000\n", + "n_comps = 1000\n", + "alphas = [1,50,199]\n", + "adatas = [adata_sim, adata_sim_only_marker_genes_DE]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a9bcdad6-2198-41de-a302-64eb51b7e135", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "filtered out 12124 genes that are detected in less than 5 cells\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "tcmalloc: large alloc 2920775680 bytes == 0x22eef4000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 2920775680 bytes == 0x2dd06c000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x38b9e4000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x439b5c000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x4e7cd4000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x4e7cd4000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x439b5c000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x22eef4000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha1.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 6.69 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.19 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.48 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.7719, 50 iterations in 1.6141 sec\n", + "Iteration 100, KL divergence 4.8098, 50 iterations in 1.7537 sec\n", + "Iteration 150, KL divergence 4.5192, 50 iterations in 1.7109 sec\n", + "Iteration 200, KL divergence 4.3580, 50 iterations in 1.7292 sec\n", + "Iteration 250, KL divergence 4.2699, 50 iterations in 1.6265 sec\n", + " --> Time elapsed: 8.44 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 3.1270, 50 iterations in 1.4984 sec\n", + "Iteration 100, KL divergence 2.7664, 50 iterations in 1.6778 sec\n", + "Iteration 150, KL divergence 2.6197, 50 iterations in 1.6208 sec\n", + "Iteration 200, KL divergence 2.5438, 50 iterations in 2.0810 sec\n", + "Iteration 250, KL divergence 2.5037, 50 iterations in 3.0172 sec\n", + "Iteration 300, KL divergence 2.4771, 50 iterations in 3.0070 sec\n", + "Iteration 350, KL divergence 2.4608, 50 iterations in 3.7391 sec\n", + "Iteration 400, KL divergence 2.4470, 50 iterations in 4.3596 sec\n", + "Iteration 450, KL divergence 2.4362, 50 iterations in 4.5543 sec\n", + "Iteration 500, KL divergence 2.4285, 50 iterations in 4.4424 sec\n", + " --> Time elapsed: 30.00 seconds\n", + "filtered out 12200 genes that are detected in less than 5 cells\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "tcmalloc: large alloc 2913533952 bytes == 0x439b5c000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha1.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha1.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 8.48 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.26 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.56 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 6.7454, 50 iterations in 1.5842 sec\n", + "Iteration 100, KL divergence 6.6367, 50 iterations in 1.8444 sec\n", + "Iteration 150, KL divergence 6.6601, 50 iterations in 1.5287 sec\n", + "Iteration 200, KL divergence 6.7405, 50 iterations in 1.5460 sec\n", + "Iteration 250, KL divergence 6.6861, 50 iterations in 1.6035 sec\n", + " --> Time elapsed: 8.11 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 5.3491, 50 iterations in 1.5099 sec\n", + "Iteration 100, KL divergence 5.2262, 50 iterations in 1.5089 sec\n", + "Iteration 150, KL divergence 5.2099, 50 iterations in 1.5328 sec\n", + "Iteration 200, KL divergence 5.1632, 50 iterations in 1.5627 sec\n", + "Iteration 250, KL divergence 5.1539, 50 iterations in 1.5571 sec\n", + "Iteration 300, KL divergence 5.1826, 50 iterations in 1.5338 sec\n", + "Iteration 350, KL divergence 5.1508, 50 iterations in 1.5250 sec\n", + "Iteration 400, KL divergence 5.1488, 50 iterations in 1.7321 sec\n", + "Iteration 450, KL divergence 5.1740, 50 iterations in 1.6481 sec\n", + "Iteration 500, KL divergence 5.1835, 50 iterations in 1.7786 sec\n", + " --> Time elapsed: 15.89 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha50.0\n", + "pr_theta100_alpha50.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 7.05 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.18 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.53 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.5140, 50 iterations in 1.4108 sec\n", + "Iteration 100, KL divergence 4.2558, 50 iterations in 1.6000 sec\n", + "Iteration 150, KL divergence 3.8645, 50 iterations in 1.7418 sec\n", + "Iteration 200, KL divergence 3.6439, 50 iterations in 1.4766 sec\n", + "Iteration 250, KL divergence 3.5034, 50 iterations in 1.2979 sec\n", + " --> Time elapsed: 7.53 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 2.7388, 50 iterations in 1.3047 sec\n", + "Iteration 100, KL divergence 2.3147, 50 iterations in 1.2554 sec\n", + "Iteration 150, KL divergence 2.1128, 50 iterations in 1.8623 sec\n", + "Iteration 200, KL divergence 2.0034, 50 iterations in 3.1324 sec\n", + "Iteration 250, KL divergence 1.9400, 50 iterations in 3.4603 sec\n", + "Iteration 300, KL divergence 1.8983, 50 iterations in 4.1856 sec\n", + "Iteration 350, KL divergence 1.8700, 50 iterations in 4.6611 sec\n", + "Iteration 400, KL divergence 1.8474, 50 iterations in 5.8678 sec\n", + "Iteration 450, KL divergence 1.8275, 50 iterations in 6.8222 sec\n", + "Iteration 500, KL divergence 1.8108, 50 iterations in 7.7638 sec\n", + " --> Time elapsed: 40.32 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha50.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha50.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 7.42 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.20 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.65 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 6.3205, 50 iterations in 1.6200 sec\n", + "Iteration 100, KL divergence 6.1152, 50 iterations in 1.7312 sec\n", + "Iteration 150, KL divergence 6.0870, 50 iterations in 1.4613 sec\n", + "Iteration 200, KL divergence 6.0233, 50 iterations in 1.6383 sec\n", + "Iteration 250, KL divergence 6.0007, 50 iterations in 1.6976 sec\n", + " --> Time elapsed: 8.15 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 4.4068, 50 iterations in 1.6780 sec\n", + "Iteration 100, KL divergence 4.1653, 50 iterations in 1.5881 sec\n", + "Iteration 150, KL divergence 4.0690, 50 iterations in 1.6254 sec\n", + "Iteration 200, KL divergence 4.0138, 50 iterations in 1.7634 sec\n", + "Iteration 250, KL divergence 3.9815, 50 iterations in 2.2699 sec\n", + "Iteration 300, KL divergence 3.9592, 50 iterations in 2.6683 sec\n", + "Iteration 350, KL divergence 3.9448, 50 iterations in 2.7302 sec\n", + "Iteration 400, KL divergence 3.9325, 50 iterations in 3.0386 sec\n", + "Iteration 450, KL divergence 3.9235, 50 iterations in 3.1905 sec\n", + "Iteration 500, KL divergence 3.9153, 50 iterations in 3.4846 sec\n", + " --> Time elapsed: 24.04 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha199.0\n", + "pr_theta100_alpha199.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 6.48 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.17 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.53 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 5.6379, 50 iterations in 1.3402 sec\n", + "Iteration 100, KL divergence 4.2452, 50 iterations in 1.4593 sec\n", + "Iteration 150, KL divergence 3.8193, 50 iterations in 1.7282 sec\n", + "Iteration 200, KL divergence 3.6030, 50 iterations in 1.5404 sec\n", + "Iteration 250, KL divergence 3.4588, 50 iterations in 1.6651 sec\n", + " --> Time elapsed: 7.73 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 2.6926, 50 iterations in 1.3907 sec\n", + "Iteration 100, KL divergence 2.2558, 50 iterations in 1.6048 sec\n", + "Iteration 150, KL divergence 2.0367, 50 iterations in 1.9848 sec\n", + "Iteration 200, KL divergence 1.9183, 50 iterations in 2.9041 sec\n", + "Iteration 250, KL divergence 1.8484, 50 iterations in 3.4553 sec\n", + "Iteration 300, KL divergence 1.8029, 50 iterations in 4.8830 sec\n", + "Iteration 350, KL divergence 1.7713, 50 iterations in 8.8511 sec\n", + "Iteration 400, KL divergence 1.7473, 50 iterations in 10.8025 sec\n", + "Iteration 450, KL divergence 1.7275, 50 iterations in 11.0360 sec\n", + "Iteration 500, KL divergence 1.7112, 50 iterations in 12.8367 sec\n", + " --> Time elapsed: 59.76 seconds\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha199.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pr_theta100_alpha199.0_afterHVG\n", + "--------------------------------------------------------------------------------\n", + "TSNE(n_jobs=38, random_state=42, verbose=True)\n", + "--------------------------------------------------------------------------------\n", + "===> Finding 90 nearest neighbors using Annoy approximate search using euclidean distance...\n", + " --> Time elapsed: 7.57 seconds\n", + "===> Calculating affinity matrix...\n", + " --> Time elapsed: 0.23 seconds\n", + "===> Calculating PCA-based initialization...\n", + " --> Time elapsed: 0.49 seconds\n", + "===> Running optimization with exaggeration=12.00, lr=1985.17 for 250 iterations...\n", + "Iteration 50, KL divergence 6.2091, 50 iterations in 1.6845 sec\n", + "Iteration 100, KL divergence 5.9686, 50 iterations in 1.5772 sec\n", + "Iteration 150, KL divergence 5.8932, 50 iterations in 1.5383 sec\n", + "Iteration 200, KL divergence 5.8192, 50 iterations in 1.5880 sec\n", + "Iteration 250, KL divergence 5.8379, 50 iterations in 1.6605 sec\n", + " --> Time elapsed: 8.05 seconds\n", + "===> Running optimization with exaggeration=1.00, lr=1985.17 for 500 iterations...\n", + "Iteration 50, KL divergence 4.1810, 50 iterations in 1.7171 sec\n", + "Iteration 100, KL divergence 3.8760, 50 iterations in 1.5234 sec\n", + "Iteration 150, KL divergence 3.7329, 50 iterations in 1.4186 sec\n", + "Iteration 200, KL divergence 3.6467, 50 iterations in 1.6202 sec\n", + "Iteration 250, KL divergence 3.5889, 50 iterations in 2.5226 sec\n", + "Iteration 300, KL divergence 3.5466, 50 iterations in 2.9095 sec\n", + "Iteration 350, KL divergence 3.5140, 50 iterations in 3.4653 sec\n", + "Iteration 400, KL divergence 3.4880, 50 iterations in 4.4669 sec\n", + "Iteration 450, KL divergence 3.4681, 50 iterations in 4.3761 sec\n", + "Iteration 500, KL divergence 3.4519, 50 iterations in 5.5018 sec\n", + " --> Time elapsed: 29.53 seconds\n" + ] + } + ], + "source": [ + "for alpha in alphas:\n", + " [readcount_pipeline(ad,alpha=alpha,theta=theta_umi,n_hvgs=n_hvgs,n_comps=n_comps) for ad in adatas]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3ca881a1-9e2a-4684-b4e0-5517083314fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(23822, 30652)\n", + "(23822, 30576)\n" + ] + } + ], + "source": [ + "print(adata_sim.shape)\n", + "print(adata_sim_only_marker_genes_DE.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b44cf65a-8778-4785-b4c2-f777229d23ad", + "metadata": {}, + "outputs": [], + "source": [ + "adata_sim.write_h5ad(path+'_processed.h5ad')\n", + "adata_sim_only_marker_genes_DE.write_h5ad(path_only_marker_genes_DE+'_processed.h5ad')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/11_plot_simulated_tasic_S9.ipynb b/11_plot_simulated_tasic_S9.ipynb new file mode 100644 index 0000000..889877f --- /dev/null +++ b/11_plot_simulated_tasic_S9.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "18a40d9a-1d1e-4a6c-814d-12ec158cbeda", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scanpy as sc\n", + "import anndata\n", + "sc.settings.verbosity = 2\n", + "from readcount_tools import get_tag\n", + "from matplotlib import pyplot as plt\n", + "import matplotlib as mpl\n", + "import seaborn as sns\n", + "from readcount_tools import PAPER_CONTEXT, PAGEWIDTH_IN, POINTSIZE_HIGHLIGHT,POINTSIZE_SMALL,SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE,LINEWIDTH,POINTSIZE,TICKLENGTH,LEGEND_FONTSIZE,SPINEWIDTH,LETTER_LOC_X,LETTER_LOC_Y" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "038d0893-ba02-4b0c-b24c-68def3f087fc", + "metadata": {}, + "outputs": [], + "source": [ + "theta_umi = 100\n", + "n_hvgs = 3000\n", + "n_comps = 1000\n", + "alphas = [50,199,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "28ecca64-aaba-41e5-a6b3-55931c7e46fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 2920775680 bytes == 0x41938000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0xefab0000 @ \n", + "tcmalloc: large alloc 2920775680 bytes == 0x19e50e000 @ \n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "tcmalloc: large alloc 2913533952 bytes == 0x2e5d06000 @ \n", + "tcmalloc: large alloc 2913533952 bytes == 0x393f96000 @ \n", + "tcmalloc: large alloc 2913533952 bytes == 0x441a26000 @ \n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] + } + ], + "source": [ + "#load\n", + "path = 'data/tasic/simulations/adata_sim_thetaUMI_100_GeomZ'\n", + "adata_sim = anndata.read_h5ad(path+'_processed.h5ad')\n", + "path_only_marker_genes_DE = 'data/tasic/simulations/adata_sim_only_marker_genes_DE_thetaUMI_100_GeomZ'\n", + "adata_sim_only_marker_genes_DE = anndata.read_h5ad(path_only_marker_genes_DE+'_processed.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8f682e39-018a-41cc-879c-d0354dfcd4d1", + "metadata": {}, + "outputs": [], + "source": [ + "def mean_resvar_plot(adata,ax,alpha,xlims,ylims,set_title=False,set_xlabel=False,set_ylabel=False):\n", + " \n", + " marker_idx = adata.var['marker_idx'].values\n", + " \n", + "\n", + " tag = get_tag(alpha,theta_umi)\n", + " ax.scatter(adata.var[\"means\"],adata.var[tag+\"_var\"],s=POINTSIZE_SMALL,lw=0,rasterized=True)\n", + " ax.scatter(adata.var[\"means\"][marker_idx],adata.var[tag+\"_var\"][marker_idx],s=POINTSIZE_HIGHLIGHT,lw=1,c='tab:red',rasterized=True)\n", + " ax.hlines(1,xlims[0],xlims[1],label='Residual variance = 1',colors='k',lw=LINEWIDTH/2)\n", + " ax.set_ylim(ylims)\n", + " ax.set_xlim(xlims)\n", + "\n", + " \n", + " ax.set_xscale(\"log\")\n", + " ax.set_yscale(\"log\")\n", + " ax.set_yticks([1,100])\n", + " ax.minorticks_off()\n", + " if set_title:\n", + " ax.set_title(fr\"$\\alpha=${alpha} $\\theta=${theta_umi}\")\n", + " if set_xlabel:\n", + " ax.set_xlabel('Mean expression (read counts)')\n", + " if set_ylabel:\n", + " ax.set_ylabel('Residual variance')\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9c81438e-1e70-4143-b6b9-478b9d979db4", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_tsne(adata,ax,alpha):\n", + "\n", + " tsne = adata.obsm[f'tsne_{get_tag(alpha=alpha,theta=100)}']\n", + " label = fr\"$\\alpha={alpha}$, $\\theta=100$\"\n", + " ax.scatter(*tsne.T,linewidth=0,s=POINTSIZE,c=adata.obs['clustercolor'],rasterized=True)\n", + " ax.axis('off')\n", + " ax.set_aspect('equal')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "51ef2c1d-1f15-44ae-85e1-24ff951382ac", + "metadata": {}, + "outputs": [], + "source": [ + "adatas = [adata_sim, adata_sim_only_marker_genes_DE]\n", + "xlims = [np.min([np.min(ad.var['means']) for ad in adatas]),\n", + " np.max([np.max(ad.var['means']) for ad in adatas])]\n", + "\n", + "all_resvars = np.concatenate([np.concatenate([adata_sim.var[f'{get_tag(alpha,theta_umi)}_var'].values for alpha in alphas]) for ad in adatas])\n", + "ylims = [np.min(all_resvars),np.max(all_resvars)*1.25]" + ] + }, + { + "cell_type": "markdown", + "id": "806188a9-8b3c-4edf-aacb-aaed38228361", + "metadata": {}, + "source": [ + "### Figure S9" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "21944916-4fa2-4876-9bf2-095019ecf1eb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncols = 3\n", + "nrows = 3\n", + "panelsize_tsne_in = PAGEWIDTH_IN/ncols\n", + "panelheight_resvar_in = panelsize_tsne_in/2\n", + "figheight_in = panelsize_tsne_in + panelheight_resvar_in*3\n", + "\n", + "path='figures/S_tasic_simulation.pdf'\n", + "\n", + "with mpl.rc_context(PAPER_CONTEXT):\n", + "\n", + " fig = plt.figure(figsize=(PAGEWIDTH_IN,figheight_in))\n", + "\n", + " nrows_grid = 16\n", + " ncols_grid = 3\n", + " axes_resvar1 = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(0, col), colspan=1,rowspan=4) for col in range(ncols_grid)]\n", + " axes_resvar2 = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(5, col), colspan=1,rowspan=4) for col in range(ncols_grid)]\n", + " axes_tsne = [plt.subplot2grid(shape=(nrows_grid,ncols_grid), loc=(11, col), colspan=1,rowspan=5) for col in range(ncols_grid)]\n", + "\n", + " \n", + " ##top row\n", + " for icol,(ax,alpha) in enumerate(zip(axes_resvar1,alphas)):\n", + " if icol==0:\n", + " set_ylabel=True\n", + " ax.text(-0.4,-0.1,'Only marker genes DE',rotation=90,fontweight='bold',fontsize=MEDIUM_SIZE,transform=ax.transAxes)\n", + " else:\n", + " set_ylabel=False\n", + " \n", + " mean_resvar_plot(adata_sim_only_marker_genes_DE,ax,alpha,xlims=xlims,ylims=ylims,\n", + " set_ylabel=set_ylabel,set_title=True)\n", + " \n", + " \n", + " #middle row\n", + " for icol,(ax,alpha) in enumerate(zip(axes_resvar2,alphas)):\n", + " if icol==0:\n", + " set_ylabel=True\n", + " ax.text(-0.4,-1.5,'All genes can be differentially expressed',rotation=90,fontweight='bold',fontsize=MEDIUM_SIZE,transform=ax.transAxes)\n", + " else:\n", + " set_ylabel=False\n", + " \n", + " mean_resvar_plot(adata_sim,ax,alpha,xlims=xlims,ylims=ylims,\n", + " set_ylabel=set_ylabel,set_xlabel=True)\n", + " \n", + " for icol,(ax,alpha) in enumerate(zip(axes_tsne,alphas)):\n", + " plot_tsne(adata_sim,ax,alpha)\n", + " \n", + " sns.despine()\n", + " \n", + " letters=['a','b','c',\n", + " 'd','e','f',\n", + " 'g','h','i']\n", + " for ax,letter, in zip(np.concatenate((axes_resvar1, axes_resvar2, axes_tsne)),letters):\n", + " ax.text(LETTER_LOC_X+0.2,LETTER_LOC_Y+0,letter,transform=ax.transAxes,fontweight='bold')\n", + " \n", + " fig.savefig(path, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e0d6e5-86d4-4e9a-a5d0-4e4fdcaee41d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d776648-e7f4-4dcf-a025-8dd57b0f1989", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/05_prepare_umi_datasets.ipynb b/12_prepare_ziegenhain.ipynb similarity index 100% rename from 05_prepare_umi_datasets.ipynb rename to 12_prepare_ziegenhain.ipynb diff --git a/06_Fig4_Fig5_.ipynb b/13_plot_ziegenhain_Fig4_Fig5.ipynb similarity index 99% rename from 06_Fig4_Fig5_.ipynb rename to 13_plot_ziegenhain_Fig4_Fig5.ipynb index 1ac6047..a2005fb 100644 --- a/06_Fig4_Fig5_.ipynb +++ b/13_plot_ziegenhain_Fig4_Fig5.ipynb @@ -46,17 +46,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 1073741824 bytes == 0x76e60000 @ \n", - "tcmalloc: large alloc 2147483648 bytes == 0xf1712000 @ \n", - "tcmalloc: large alloc 4294967296 bytes == 0x1f79b0000 @ \n" + "tcmalloc: large alloc 1073741824 bytes == 0x7a8d6000 @ \n", + "tcmalloc: large alloc 2147483648 bytes == 0xe08de000 @ \n", + "tcmalloc: large alloc 4294967296 bytes == 0x1b86a2000 @ \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 46.3 s, sys: 29.2 s, total: 1min 15s\n", - "Wall time: 1min 15s\n" + "CPU times: user 59.4 s, sys: 30.9 s, total: 1min 30s\n", + "Wall time: 1min 35s\n" ] } ], @@ -295,8 +295,8 @@ "Smrtsq3:\t145\t1216\t5.4\t8.0\t21549849\t13.4\t\n", "Smrtsq3_PE:\t319\t989\t3.8\t4.5\t48073893\t8.3\t\n", "Smrtsq3xprA:\t96\t173\t1.3\t0.3\t6940889\t1.6\t\n", - "CPU times: user 2min 53s, sys: 8.29 s, total: 3min 1s\n", - "Wall time: 2min 58s\n" + "CPU times: user 3min 48s, sys: 21.6 s, total: 4min 9s\n", + "Wall time: 4min 9s\n" ] } ], @@ -412,8 +412,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 56.4 s, sys: 668 ms, total: 57.1 s\n", - "Wall time: 57.1 s\n" + "CPU times: user 1min 4s, sys: 1.15 s, total: 1min 5s\n", + "Wall time: 1min 5s\n" ] } ], diff --git a/07_S7.ipynb b/14_plot_ziegenhain_S3.ipynb similarity index 98% rename from 07_S7.ipynb rename to 14_plot_ziegenhain_S3.ipynb index 49c3d5c..de968bd 100644 --- a/07_S7.ipynb +++ b/14_plot_ziegenhain_S3.ipynb @@ -146,7 +146,7 @@ "tags": [] }, "source": [ - "### Fig S7" + "### Fig S3" ] }, { @@ -185,20 +185,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 4438712320 bytes == 0x23fbc000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x12c8d2000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x23dcee000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x346604000 @ \n", - "tcmalloc: large alloc 4438712320 bytes == 0x44f71a000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x44f71a000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x23dcee000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x345d08000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x23fbc000 @ \n", - "tcmalloc: large alloc 4429291520 bytes == 0x12bfd6000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x23dcee000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x23fbc000 @ \n", - "tcmalloc: large alloc 4628766720 bytes == 0x44f71a000 @ \n", - "tcmalloc: large alloc 5172264960 bytes == 0x23dcee000 @ \n" + "tcmalloc: large alloc 4438712320 bytes == 0x2533a000 @ \n", + "tcmalloc: large alloc 4438712320 bytes == 0x12dc50000 @ \n", + "tcmalloc: large alloc 4438712320 bytes == 0x23ee06000 @ \n", + "tcmalloc: large alloc 4438712320 bytes == 0x34771c000 @ \n", + "tcmalloc: large alloc 4438712320 bytes == 0x450832000 @ \n", + "tcmalloc: large alloc 4429291520 bytes == 0x450832000 @ \n", + "tcmalloc: large alloc 4429291520 bytes == 0x23ee06000 @ \n", + "tcmalloc: large alloc 4429291520 bytes == 0x346e20000 @ \n", + "tcmalloc: large alloc 4429291520 bytes == 0x2533a000 @ \n", + "tcmalloc: large alloc 4429291520 bytes == 0x12d354000 @ \n", + "tcmalloc: large alloc 4628766720 bytes == 0x23ee06000 @ \n", + "tcmalloc: large alloc 4628766720 bytes == 0x2533a000 @ \n", + "tcmalloc: large alloc 4628766720 bytes == 0x559148000 @ \n", + "tcmalloc: large alloc 5172264960 bytes == 0x23ee06000 @ \n" ] }, { diff --git a/15_plot_alpha_per_cell_S10_S11.ipynb b/15_plot_alpha_per_cell_S10_S11.ipynb new file mode 100644 index 0000000..2611834 --- /dev/null +++ b/15_plot_alpha_per_cell_S10_S11.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import pickle\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "\n", + "from readcount_tools import broken_zeta, add_largedot_legend\n", + "#import plotting constants\n", + "from readcount_tools import PAPER_CONTEXT, PAGEWIDTH_IN, POINTSIZE_HIGHLIGHT,POINTSIZE_SMALL,SMALL_SIZE,MEDIUM_SIZE,BIGGER_SIZE,LINEWIDTH,POINTSIZE,TICKLENGTH,LEGEND_FONTSIZE,SPINEWIDTH,LETTER_LOC_X,LETTER_LOC_Y" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Load preproc results" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "basepath = 'data/reads_per_umi_tables/'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tcmalloc: large alloc 1073741824 bytes == 0x769ac000 @ \n", + "tcmalloc: large alloc 2147483648 bytes == 0xf125e000 @ \n", + "tcmalloc: large alloc 4294967296 bytes == 0x1f74fc000 @ \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 45.7 s, sys: 27.2 s, total: 1min 12s\n", + "Wall time: 1min 12s\n" + ] + } + ], + "source": [ + "%%time\n", + "#1min30sec\n", + "with open(f'{basepath}reads_per_umi_dfs_hd1_preproc.pickle','rb') as f:\n", + " dfs = pickle.load(f) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dfs_A = [df for df in dfs if not 'qB' in df.index[0]]\n", + "labels = [df.index[0] for df in dfs_A]\n", + "\n", + "labels2pretty = dict(CELseq2A='CEL-seq2 (A)',\n", + " CELseq2B='CEL-seq2 (B)',\n", + " DropSeqA='Drop-seq',\n", + " MARSseqA='MARS-seq',\n", + " SCRBseqA='SCRB-seq',\n", + " Smartseq3_Fibroblast='Smart-seq3 (SE)',\n", + " Smartseq3_Fibroblast_PE='Smart-seq3 (PE)',\n", + " Smartseq3xpressA='Smart-seq3 (Xpress)')\n", + "pretty_labels = [labels2pretty[l] for l in labels]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### $\\alpha_Z$ and E[Z] per cell (Figures S10/S11)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncols = 4\n", + "nrows = 2\n", + "panelsize_in = PAGEWIDTH_IN/ncols\n", + "figheight_in = panelsize_in*nrows\n", + "\n", + "path_hist='figures/S_single_cell_alpha_mean_hist.pdf'\n", + "path_alpha='figures/S_single_cell_alpha_scatter.pdf'\n", + "path_ez='figures/S_single_cell_mean_scatter.pdf'\n", + "with mpl.rc_context(PAPER_CONTEXT):\n", + "\n", + "\n", + " fig_hist, axes = plt.subplots(2,4,figsize=(PAGEWIDTH_IN,figheight_in))\n", + " fig_alpha,bxes = plt.subplots(2,4,figsize=(PAGEWIDTH_IN,figheight_in))\n", + " fig_ez, cxes = plt.subplots(2,4,figsize=(PAGEWIDTH_IN,figheight_in))\n", + "\n", + " for i,(df,ax,bx,cx,label) in enumerate(zip(dfs_A,axes.flatten(),bxes.flatten(),cxes.flatten(),pretty_labels)):\n", + "\n", + "\n", + " cellstats = df.groupby('RG').agg({'N': [np.mean, np.var, np.sum]})\n", + " cellstats[('N','alpha')] = cellstats[('N','mean')] + cellstats[('N','var')] / cellstats[('N','mean')]\n", + "\n", + " n_cells=cellstats.shape[0]\n", + "\n", + " alphas = cellstats[('N','alpha')]\n", + " means = cellstats[('N','mean')]\n", + "\n", + " minval = np.min(np.concatenate([alphas,means]))\n", + " maxval = np.max(np.concatenate([alphas,means]))\n", + " nbins = min([30*2,int(n_cells)*2])\n", + " bins = np.linspace(0,maxval,num=nbins)\n", + "\n", + " ax.hist(alphas,bins=bins,label=r'$\\alpha_Z=E_Z + FF_Z$') \n", + " ax.set_title(f'{label}')\n", + " n_per_bin,_,_ = ax.hist(means,bins=bins,label='$E_Z$',alpha=0.6)\n", + " maxtick = np.round(np.max(n_per_bin)/10)*10\n", + " ax.set_yticks(np.array([maxtick/2,maxtick]).astype(int))\n", + " ax.set_xlabel(r'$\\alpha_Z$ and $E_Z$ per cell')\n", + " ax.set_ylabel('Frequency') \n", + " \n", + " if i==0:\n", + " ax.legend(frameon=False,fontsize=LEGEND_FONTSIZE)\n", + "\n", + " bx.set_title(f' {label}')\n", + " \n", + " total_counts = cellstats[('N','sum')]\n", + " bx.scatter(cellstats[('N','alpha')],total_counts,s=POINTSIZE_HIGHLIGHT) \n", + " \n", + " max_total_counts = np.max(total_counts)\n", + " scale_total_counts = 10**np.floor(np.log10(max_total_counts))\n", + " maxtick = np.round(max_total_counts/scale_total_counts)*scale_total_counts\n", + " bx.set_yticks(np.array([maxtick/2,maxtick]).astype(int))\n", + " \n", + " bx.set_xlabel(r'$\\alpha_Z$ per cell')\n", + " bx.set_ylabel('Total counts per cell')\n", + " bx.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n", + "\n", + " cx.set_title(f' {label}')\n", + " cx.scatter(cellstats[('N','mean')],total_counts,s=POINTSIZE_HIGHLIGHT) \n", + " cx.set_xlabel(r'$E_Z$ per cell')\n", + " cx.set_ylabel('Total counts per cell')\n", + " cx.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n", + "\n", + " for ax in np.concatenate([axes.flatten(),bxes.flatten(),cxes.flatten()]):\n", + " for axis in ['bottom','left']:\n", + " ax.spines[axis].set_linewidth(SPINEWIDTH)\n", + " \n", + " letters=['a','b','c','d',\n", + " 'e','f','g','h']\n", + " for xes in [axes, bxes, cxes]:\n", + " for ax,letter, in zip(xes.flatten(),letters):\n", + " ax.text(LETTER_LOC_X+0.2,LETTER_LOC_Y+0.13,letter,transform=ax.transAxes,fontweight='bold')\n", + " \n", + " for xes in [axes, bxes, cxes]:\n", + " for irow,row in enumerate(xes):\n", + " for icol,ax in enumerate(row):\n", + " if irow==0:\n", + " ax.set_xlabel('')\n", + " if icol>0:\n", + " ax.set_ylabel('')\n", + " \n", + " \n", + " sns.despine(fig_hist)\n", + " sns.despine(fig_alpha)\n", + " sns.despine(fig_ez)\n", + " fig_hist.tight_layout()\n", + " fig_alpha.tight_layout()\n", + " fig_ez.tight_layout()\n", + " \n", + " fig_hist.savefig(path_hist, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)\n", + " fig_alpha.savefig(path_alpha, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)\n", + " fig_ez.savefig(path_ez, dpi=300, format=None, bbox_inches = 'tight', pad_inches = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Look at Z distribution shape per cell" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,axes = plt.subplots(2,4,figsize=(16,6))\n", + "\n", + "for df,ax,label in zip(dfs_A,axes.flatten(),pretty_labels):\n", + "\n", + " cells = df.groupby('RG')\n", + "\n", + " for cell_i,(cellcode,cell) in enumerate(cells):\n", + " Zs = cell['N']\n", + " eZ = np.mean(Zs)\n", + " ffZ = np.var(Zs)/eZ\n", + " alpha = eZ + ffZ\n", + " bins,counts = np.unique(Zs,return_counts=True)\n", + " ax.scatter(bins,counts,s=2.5,lw=1,label=fr'$E_Z={eZ:.0f}$, $\\alpha={alpha:.0f}$')\n", + " ax.set_xscale('log')\n", + " ax.set_yscale('log')\n", + " add_largedot_legend(ax,loc='best',kwargs=dict(frameon=False,fontsize=8))\n", + " if cell_i>=3:\n", + " break\n", + " \n", + " ax.set_title(f'{label}, 4 cells')\n", + " ax.set_xlabel('Reads per UMI')\n", + " ax.set_ylabel('Frequency')\n", + "\n", + "sns.despine()\n", + "plt.tight_layout() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/09_prepare_Fig6.ipynb b/16_prepare_tasic_homo_zinb.ipynb similarity index 90% rename from 09_prepare_Fig6.ipynb rename to 16_prepare_tasic_homo_zinb.ipynb index ecc4f42..bc20a9c 100644 --- a/09_prepare_Fig6.ipynb +++ b/16_prepare_tasic_homo_zinb.ipynb @@ -157,20 +157,15 @@ "tags": [] }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "L6 IT VISp Penk Col27a1 (1049, 33914)\n", - "11549 genes > 5\n", - "." - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_37176/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n", + "/tmp/ipykernel_28976/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['mus_fit']=np.nan * np.ones(ad.shape[1]).astype(bool)\n" ] }, @@ -178,7 +173,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "...................................................................................................................saved to data/tasic/simulations/ZINBfit_single_cluster_meanCutoff_5.h5ad\n" + "L6 IT VISp Penk Col27a1 (1049, 33914)\n", + "11549 genes > 5\n", + "....................................................................................................................saved to data/tasic/simulations/ZINBfit_single_cluster_meanCutoff_5.h5ad\n" ] } ], @@ -186,6 +183,7 @@ "mean_cutoff=5\n", "adata_single_cluster = anndata.read_h5ad(f'{basepath}adata_single_cluster.h5ad')\n", "print(adata_single_cluster.uns['clustername'],adata_single_cluster.shape)\n", + "adata_single_cluster.var_names_make_unique()\n", "\n", "clustername=adata_single_cluster.uns['clustername']\n", "\n", @@ -407,9 +405,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:218: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:225: FutureWarning: X.dtype being converted to np.float32 from int64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", " ad = anndata.AnnData(X=sparse.csc_matrix(readcounts_sim),layers=dict(molecules=molecules_sim))\n", - "/tmp/ipykernel_37176/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_28976/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['mus_fit']=np.nan * np.ones(ad.shape[1]).astype(bool)\n" ] }, @@ -425,12 +423,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 16377569280 bytes == 0x1d98c000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x7c04a0000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x7c04a0000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x7c04a0000 @ \n" + "tcmalloc: large alloc 16377569280 bytes == 0x1d59c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x7c02fa000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x7c02fa000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x7c02fa000 @ \n" ] }, { @@ -445,9 +443,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:218: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:225: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", " ad = anndata.AnnData(X=sparse.csc_matrix(readcounts_sim),layers=dict(molecules=molecules_sim))\n", - "/tmp/ipykernel_37176/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_28976/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['mus_fit']=np.nan * np.ones(ad.shape[1]).astype(bool)\n" ] }, @@ -464,12 +462,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x1a422000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0xb9177e000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x1a422000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n" + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x1b716000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0xb915d8000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x1b716000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n" ] }, { @@ -484,9 +482,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:218: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:225: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", " ad = anndata.AnnData(X=sparse.csc_matrix(readcounts_sim),layers=dict(molecules=molecules_sim))\n", - "/tmp/ipykernel_37176/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_28976/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['mus_fit']=np.nan * np.ones(ad.shape[1]).astype(bool)\n" ] }, @@ -503,12 +501,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0xb9177e000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x1a422000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0xb9177e000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n", - "tcmalloc: large alloc 16377569280 bytes == 0x3ef1c2000 @ \n" + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0xb915d8000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x1b716000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0xb915d8000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n", + "tcmalloc: large alloc 16377569280 bytes == 0x3ef01c000 @ \n" ] }, { @@ -523,9 +521,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:218: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + "/gpfs01/berens/user/jlause/countstats/read-normalization/readcount_tools.py:225: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", " ad = anndata.AnnData(X=sparse.csc_matrix(readcounts_sim),layers=dict(molecules=molecules_sim))\n", - "/tmp/ipykernel_37176/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_28976/940645334.py:17: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['mus_fit']=np.nan * np.ones(ad.shape[1]).astype(bool)\n" ] }, diff --git a/10_Fig6.ipynb b/17_plot_tasic_homo_zinb_Fig6.ipynb similarity index 99% rename from 10_Fig6.ipynb rename to 17_plot_tasic_homo_zinb_Fig6.ipynb index b912fcd..5b978fc 100644 --- a/10_Fig6.ipynb +++ b/17_plot_tasic_homo_zinb_Fig6.ipynb @@ -48,6 +48,16 @@ "text": [ "L6 IT VISp Penk Col27a1 (1049, 11549)\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n", + "/usr/local/lib/python3.8/dist-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n", + " utils.warn_names_duplicates(\"var\")\n" + ] } ], "source": [ @@ -318,7 +328,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_37383/1405998313.py:35: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_29711/1405998313.py:35: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var['overflow_only_idx'] = overflow_only\n" ] } @@ -600,9 +610,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_37383/4067560376.py:5: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + "/tmp/ipykernel_29711/4067560376.py:5: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", " ad.var[f'{key}_clipped'] = ad.var[key]\n", - "/tmp/ipykernel_37383/553178152.py:173: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n", + "/tmp/ipykernel_29711/553178152.py:173: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n", " plt.tight_layout()\n" ] }, diff --git a/README.md b/README.md index 6f6708b..b07aa65 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,25 @@ -# Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs +# Compound models and Pearson residuals for single-cell RNA-seq data without UMIs -This repository holds the code needed to reproduce the analyses and figures presented in [Lause et al. (2023).](https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1) +This repository holds the code needed to reproduce the analyses and figures presented in [Lause et al. (2023).](https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1) (release v1) and the updated code after the first round of revisions (release v2). # Code Some of the notebooks depend on each other. -All plots based on the Tasic datasets require `01_prepare_tasic` to run first. Then, +All plots based on the Tasic 2018 dataset require `01_prepare_tasic` to run first. Then, -- to reproduce Figure 2, S1, S2, S3, run `02` -- to reproduce Figure 3, S4, S5, S6, run `03` to compute t-SNEs and `04` to make the figures -- to reproduce Figure 6, run `09` to prepare simulated data and `10` to make the figure +- to reproduce Figures for the homogeneous within-cluster data (Figure 2, S1, S2, S4), run notebook `02` +- to reproduce Figures for the full Tasic data (Figure 3, S5, S6, S8), run notebook `03` to compute t-SNEs etc. and notebook `04` to make the figures +- to reproduce Figure S7 for the Census/qUMI comparison, run Census and qUMI with `05_compute_tasic_qumis_census.R` (using our separate R environment, see below for setup instructions), and then run notebooks `06`-`08` to load the data, process and plot +- to reproduce Figure S9 for the Tasic-like simulated data, use notebooks `09`-`11` to simulate, process and plot +- to reproduce Figure 6, run notebook `16` to prepare simulated data and notebook `17` to plot -All plots based on the reads-per-UMI tables requires `05_prepare_umi_datasets` to run first. Then, +All plots based on the reads-per-UMI tables from the Ziegenhain/Hagemann-Jensen datasets requires `12_prepare_ziegenhain` to run first. Then, + +- to reproduce the main Figures 4 and 5, run notebook `13` +- to reproduce Figure S3 on Pseudogenes, run notebook `14` +- to reproduce Figure S10 and S11 on per-cell amplification parameter estimates, run notebook `15` -- to reproduce Figures 4 and 5, run `06` -- to reproduce Figure S7, run `07` -- to reproduce Figure S8, run `08` # Datasets @@ -26,7 +29,7 @@ All plots based on the reads-per-UMI tables requires `05_prepare_umi_datasets` t # Compute environment -We ran the code in Python 3.8.10 on an Ubuntu machine with 40 CPUs and 440 GB RAM. The following package versions were used: +We ran all notebooks in Python 3.8.10 on an Ubuntu machine with 40 CPUs and 440 GB RAM. The following package versions were used: - `scanpy 1.9.0` - `anndata 0.8.0` @@ -39,4 +42,17 @@ We ran the code in Python 3.8.10 on an Ubuntu machine with 40 CPUs and 440 GB RA - `mygene 3.2.2.` - `scipy 1.8.0` +Census and qUMI where run in a separate R conda environment specified in `r41_env.yml`. To install it, create the environment from that file with + +``` +conda env create -f r41_env.yml +``` + +Then, to install qUMI, activate the environment with `conda activate r41_env_full`, start R and run + +``` +remotes::install_github("willtownes/quminorm") +``` + + diff --git a/data/tasic/.gitignore b/data/tasic/.gitignore index c4c83cd..a707335 100644 --- a/data/tasic/.gitignore +++ b/data/tasic/.gitignore @@ -7,4 +7,11 @@ adata_single_cluster.h5ad ad_hvg_seurat.h5ad ads_hvg.pickle cluster_df.pickle +adata_qumi_census.h5ad +adata_tpm.h5ad +census_transposed.mtx +qumi_transposed.mtx +tasic_tpm_cells.csv +tasic_tpm_genes.csv +tasic_tpm_transposed.mtx diff --git a/data/tasic/qumi_census_comparion/.gitignore b/data/tasic/qumi_census_comparion/.gitignore new file mode 100644 index 0000000..ed7c1c6 --- /dev/null +++ b/data/tasic/qumi_census_comparion/.gitignore @@ -0,0 +1,3 @@ +adata_census_processed.h5ad +adata_qumis_processed.h5ad +adata_readcounts_processed.h5ad diff --git a/data/tasic/simulations/.gitignore b/data/tasic/simulations/.gitignore index c165a51..719de5f 100644 --- a/data/tasic/simulations/.gitignore +++ b/data/tasic/simulations/.gitignore @@ -6,3 +6,7 @@ ZINBfit_single_cluster_meanCutoff_5.h5ad zeta_params_list.pickle zeta_params_info_list.pickle gridsearch_broken_zeta +adata_sim_only_marker_genes_DE_thetaUMI_100_GeomZ.h5ad +adata_sim_only_marker_genes_DE_thetaUMI_100_GeomZ_processed.h5ad +adata_sim_thetaUMI_100_GeomZ.h5ad +adata_sim_thetaUMI_100_GeomZ_processed.h5ad diff --git a/figures/S_single_cell_alpha_mean_hist.pdf b/figures/S_single_cell_alpha_mean_hist.pdf new file mode 100644 index 0000000000000000000000000000000000000000..74ff8f39f39f49d9a8725981b7a8328404b20dcb GIT binary patch literal 34997 zcmd43cT^Qi(=TiS5fKy+B*{jC1PPn4H=uxkM9Gq)DySqIG`pbvy6?U?jwCWm>L-8fiU2&?_>vK7BdH9 z3jD4Gf7fC#4H)wyBW*on3qu&eh0b^z1Wa8jt;pAXtMY1BH;bpXKK-Uvv z$m!c!+S}*?>p1cM)& z7${+DXQQuej_DM&9Q`nusxCBodxybffJNnQ)!52q#$O1MV6z7ZGb$3<%U8{KAJby< 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# to install qUMI package from github source + - bioconductor-monocle #compute census counts + - bioconductor-singlecellexperiment + - bioconductor-edaseq + - bioconductor-scrnaseq + - bioconductor-scran # convert sce data to monocle cds data diff --git a/readcount_tools.py b/readcount_tools.py index c81d939..615305c 100644 --- a/readcount_tools.py +++ b/readcount_tools.py @@ -5,6 +5,13 @@ import pandas as pd import anndata from scipy import sparse +import scanpy as sc +from sklearn.decomposition import PCA +from openTSNE.initialization import rescale as rescale_pca +import openTSNE + + + SMALL_SIZE = 7 MEDIUM_SIZE = 7 @@ -315,4 +322,75 @@ def simulate_amplification(molecules, return readcounts, dict(mean=mean,median=median,var=var,ff=ff,alpha=empirical_alpha,max=maxx) +def scanpy_preproc_baseline(adata,n_hvgs,n_comps): + + adata_seurat = adata.copy() + sc.pp.normalize_total(adata_seurat) + sc.pp.log1p(adata_seurat) + hvg_seurat = sc.pp.highly_variable_genes(adata_seurat,flavor='seurat',n_top_genes=n_hvgs,inplace=False) + + adata.var[f'top{n_hvgs}_seurat'] = np.array(hvg_seurat['highly_variable']) + ad_hvg_seurat = adata[:,adata.var[f'top{n_hvgs}_seurat']].copy() + ad_hvg_seurat.uns['hvg'] = 'Seurat' + ad_hvg_seurat.uns['hvg_plotlabel'] = 'Seurat' + ad_hvg_seurat.uns['hvg_criterion'] = hvg_seurat['dispersions_norm'] + + def logmedian_PCA(ad,n_comps): + ad.layers['logmedian'] = sc.pp.normalize_total(ad,inplace=False)['X'] + sc.pp.log1p(ad,layer='logmedian') + + pca = PCA(random_state=42) + ad.obsm['pca'] = rescale_pca(pca.fit_transform(ad.layers['logmedian'].A)) + ad.obsm[f'pca{n_comps}'] = ad.obsm['pca'][:,:n_comps] + + logmedian_PCA(ad_hvg_seurat,n_comps=n_comps) + + pca_data_after_HVG = ad_hvg_seurat.obsm[f'pca{n_comps}'] + tsne = openTSNE.TSNE(random_state=42,verbose=True,n_jobs=38) + pca_init = ad_hvg_seurat.obsm['pca'][:,:2] + ad_hvg_seurat.obsm['tsne'] = np.array(tsne.fit(X=pca_data_after_HVG,initialization=pca_init)) + + return ad_hvg_seurat + +def compute_residuals(adata,alpha,theta,clipping=True,tag_suffix=''): + infostr = get_tag(alpha,theta,clipping) + tag_suffix + print(infostr) + adata.layers[infostr] = pearson_residuals_compound(counts=adata.X.toarray(),theta=theta,alpha=alpha,clipping=clipping) + adata.var[infostr+'_var'] = np.var(adata.layers[infostr],axis=0) + +def select_hvgs(adata,alpha,theta,n_hvgs=3000,clipping=True): + resvar = adata.var[get_tag(alpha=alpha,theta=theta,clipping=clipping)+'_var'] + hvg_idx = resvar >= np.sort(resvar)[-n_hvgs] + adata.var[f'top{n_hvgs}_{get_tag(alpha=alpha,theta=theta,clipping=clipping)}'] = hvg_idx + +def compute_pca_on_hvgs(adata, alpha, theta, n_hvgs,n_comps,clipping=True): + tag = get_tag(alpha=alpha, theta=theta,clipping=clipping) + #subset to HVGs + ad = adata[:,adata.var[f'top{n_hvgs}_{tag}']].copy() + #recompute residuals + compute_residuals(ad,theta=theta,alpha=alpha,tag_suffix='_afterHVG',clipping=clipping) + #compute PCA + pca = PCA(random_state=42,n_components=n_comps) + ad.obsm[f'pca{n_comps}'] = pca.fit_transform(ad.layers[tag+'_afterHVG']) + + return ad + + +def readcount_pipeline(adata,alpha, theta, n_hvgs,n_comps,clipping=True): + sc.pp.filter_genes(adata,min_cells=5) + + compute_residuals(adata,alpha=alpha,theta=theta,clipping=clipping) + select_hvgs(adata,alpha=alpha,theta=theta,n_hvgs=n_hvgs,clipping=clipping) + + adata.uns['cpr_alpha']=alpha + adata.uns['cpr_theta']=theta + adata.var['means'] = np.mean(adata.X.A,axis=0) + adata_hvg=compute_pca_on_hvgs(adata,alpha=alpha,theta=theta,n_hvgs=n_hvgs,n_comps=n_comps,clipping=clipping) + + tsne = openTSNE.TSNE(random_state=42,verbose=True,n_jobs=38) + tsne_output = np.array(tsne.fit(X=adata_hvg.obsm[f'pca{n_comps}'])) + adata_hvg.obsm['tsne'] = tsne_output + adata.obsm[f'tsne_{get_tag(alpha=alpha,theta=theta)}'] = tsne_output + + return adata_hvg

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