{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "a08cbc06-c9d5-439b-bcd5-dd781f1e4c2a", "metadata": {}, "source": [ "# TCGA database preprocess\n", "\n", "We often download patient survival data from the TCGA database for analysis in order to verify the importance of genes in cancer. However, the pre-processing of the TCGA database is often a headache. Here, we have introduced the TCGA module in ov, a way to quickly process the file formats we download from the TCGA database. We need to prepare 3 files as input:\n", "\n", "- gdc_sample_sheet (`.tsv`): The `Sample Sheet` button of TCGA, and we can get `tsv` file from it\n", "- gdc_download_files (`folder`): The `Download/Cart` button of TCGA, and we get `tar.gz` included all file you selected/\n", "- clinical_cart (`folder`): The `Clinical` button of TCGA, and we can get `tar.gz` included all clinical of your files" ] }, { "cell_type": "code", "execution_count": 1, "id": "b54cdd9a-2f06-4ee7-958f-3166b59b03b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " ____ _ _ __ \n", " / __ \\____ ___ (_)___| | / /__ _____________ \n", " / / / / __ `__ \\/ / ___/ | / / _ \\/ ___/ ___/ _ \\ \n", "/ /_/ / / / / / / / /__ | |/ / __/ / (__ ) __/ \n", "\\____/_/ /_/ /_/_/\\___/ |___/\\___/_/ /____/\\___/ \n", "\n", "Version: 1.6.4, Tutorials: https://omicverse.readthedocs.io/\n", "All dependencies are satisfied.\n" ] } ], "source": [ "import omicverse as ov\n", "import scanpy as sc\n", "ov.plot_set()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "43774ae9-5b14-43c5-8bc4-e3cd630be33b", "metadata": {}, "source": [ "## TCGA counts read\n", "\n", "Here, we use `ov.bulk.TCGA` to perform the `gdc_sample_sheet`, `gdc_download_files` and `clinical_cart` you download before. The raw count, fpkm and tpm matrix will be stored in anndata object" ] }, { "cell_type": "code", "execution_count": 2, "id": "56d85f82-fff8-4db4-b465-920fa90f70fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tcga module init success\n", "...index init\n", "... expression matrix init\n", "...anndata construct\n", "CPU times: user 1min 30s, sys: 4.16 s, total: 1min 34s\n", "Wall time: 1min 34s\n" ] } ], "source": [ "%%time\n", "gdc_sample_sheep='data/TCGA_OV/gdc_sample_sheet.2024-07-05.tsv'\n", "gdc_download_files='data/TCGA_OV/gdc_download_20240705_180129.081531'\n", "clinical_cart='data/TCGA_OV/clinical.cart.2024-07-05'\n", "aml_tcga=ov.bulk.pyTCGA(gdc_sample_sheep,gdc_download_files,clinical_cart)\n", "aml_tcga.adata_init()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "88825e6f-189d-4411-906b-48939b44e205", "metadata": {}, "source": [ "We can save the anndata object for the next use" ] }, { "cell_type": "code", "execution_count": 3, "id": "36dd4b3e-e16e-4acf-a31e-0132c9149eea", "metadata": {}, "outputs": [], "source": [ "aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_raw.h5ad',compression='gzip')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "4bc674b9-5baa-45b2-baa6-d1ccfa1f3ed5", "metadata": {}, "source": [ "Note: Each time we read the anndata file, we need to initialize the TCGA object using three paths so that the subsequent TCGA functions such as survival analysis can be used properly\n", "\n", "If you wish to create your own TCGA data, we have provided [sample data](https://figshare.com/ndownloader/files/47461946) here for download:\n", "\n", "TCGA OV: https://figshare.com/ndownloader/files/47461946" ] }, { "cell_type": "code", "execution_count": 2, "id": "73d6b8cf-80e9-49cd-89e2-13f1ff38f325", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tcga module init success\n", "... anndata reading\n" ] } ], "source": [ "gdc_sample_sheep='data/TCGA_OV/gdc_sample_sheet.2024-07-05.tsv'\n", "gdc_download_files='data/TCGA_OV/gdc_download_20240705_180129.081531'\n", "clinical_cart='data/TCGA_OV/clinical.cart.2024-07-05'\n", "aml_tcga=ov.bulk.pyTCGA(gdc_sample_sheep,gdc_download_files,clinical_cart)\n", "aml_tcga.adata_read('data/TCGA_OV/ov_tcga_raw.h5ad')" ] }, { "attachments": {}, "cell_type": "markdown", "id": "d2627751-eacf-4b18-9176-d832287c164a", "metadata": {}, "source": [ "## Meta init\n", "As the TCGA reads the gene_id, we need to convert it to gene_name as well as adding basic information about the patient. Therefore we need to initialise the patient's meta information." ] }, { "cell_type": "code", "execution_count": 3, "id": "edd96cc3-8a0c-40fc-b541-b6d2724445da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...anndata meta init ['gene_name', 'gene_type'] ['Case ID', 'Sample Type']\n" ] }, { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 429 × 60664\n", " obs: 'Case ID', 'Sample Type'\n", " var: 'gene_name', 'gene_type', 'gene_id'\n", " layers: 'deseq_normalize', 'fpkm', 'tpm'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aml_tcga.adata_meta_init()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "cd40d669-6c51-4adc-b9d3-5173ec5f096b", "metadata": {}, "source": [ "## Survial init\n", "We set up the path for Clinical earlier, but in fact we did not import the patient information in the previous process, we only initially determined the id of the patient's TCGA, so we attracted to initialize the clinical information" ] }, { "cell_type": "code", "execution_count": 4, "id": "fd2c6798-6d6a-4868-bc1d-379c54d96a12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 429 × 60664\n", " obs: 'Case ID', 'Sample Type', 'vital_status', 'days'\n", " var: 'gene_name', 'gene_type', 'gene_id'\n", " layers: 'deseq_normalize', 'fpkm', 'tpm'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aml_tcga.survial_init()\n", "aml_tcga.adata" ] }, { "attachments": {}, "cell_type": "markdown", "id": "2da436e2-cbdf-49f0-a78d-5123a8709172", "metadata": {}, "source": [ "To visualize the gene you interested, we can use `survival_analysis` to finish it. " ] }, { "cell_type": "code", "execution_count": 5, "id": "92350a30-e452-4809-b49b-4c7e9430cd67", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.9528907736380975, 0.328984565709021)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ktGfPHtx9990oLy/H77//jp07d8LBwQHdunVr1j0fBgwYgIiICCQlJYnfRB955JFG9fXoo4/i66+/BgCsXLkSiYmJmDBhAnx8fJCZmYnff/8d+/bts5hSKC4ubtB1Bg8eLP6Zrl69Go8++mij7rc2EydOxPLlyxEfHw8A6N+/P8LCwpr9Oo21du1a5Ofn48CBA0hPT8eYMWPQu3dv3HfffejQoQMYY0hJScHvv/+Ow4cPi+f17dsXGzdurNeySnKDaM06z4QQ69auXcv9/f2t7slQ8xEWFsZ/++03m32VlZXx4cOH2zzfzc2N//LLL/yRRx6pcy8Gd3f3Br2P1157zexaCQkJtbavbS+Gd955p84/i759+/Lvv/9e/HnRokVmfdS1F4Pp9VevXt2g92pkPN+4F0NNx44dM7vnDz/80Gq7huzFYMr0PTTkPFNVVVX8pZde4gqFos4/c0EQ+Lx583hpaWmjrkXaLppiIKQNmjFjBq5du4ZNmzZh1qxZ6NGjBwIDA6FQKODm5oaOHTviwQcfxIYNG3Dp0iWMHTvWZl9OTk7YvXs3vv/+ewwbNgxeXl5wcHBAdHQ0nnnmGZw7dw733nuvXd6H6Q6Pd9xxR5MS8V555RUcOHAADzzwAAICAiCVSuHk5ISIiAiMHz8eP/30E44ePYqHHnpIzOX46aefal290Rr69euH0NBQAIYRnrYyvWBKJpPh3XffRWJiIj744AOMGTMGoaGhcHJyglQqhY+PD+688068+uqruHbtGj7//PMG17UgbR/jvI2l+RJCCCGk1dEIAiGEEEIsUIBACCGEEAsUIBBCCCHEAgUIhBBCCLFAAQIhhBBCLFCAQAghhBALFCAQQgghxAIFCIQQQgixQAECIYQQQixQgEAIIYQQCxQgEELsas2aNWCMgTGGjz76qLVvp8VkZ2fjxRdfRJcuXeDk5AQ3Nzd0794dS5YsQW5ubrNd58iRI5g6dSrCwsKgUCjg7e2N22+/HcuWLUNeXl69+khNTcVLL72Evn37wtPTE3K5HH5+fhg1ahS+/fZbVFVV1dlHeXk5PvzwQwwYMABeXl6Qy+WIiIjArFmzcPLkyaa+TdIaWnu3KELIzW316tXizn8rVqxo7dtpEQcOHOCenp42d0D09fXlhw4datI1tFotnzNnTq07LXp7e/MdO3bU2s+3337LHRwcau2nS5cuPD4+3mYfZ86c4aGhoTbPZ4zx+fPn86qqqia9Z9KypC0ThhBCyK3hypUruPfee6FSqcAYw5QpUzBmzBhoNBps374dO3fuRG5uLsaPH4/jx48jOjq6UddZuHAhvv76awCAs7MzZs+ejV69eqGsrAz79+/Hli1bkJ+fjwcffBB79+7F3XffbdHHxo0b8cQTTwAABEHAxIkTMXLkSLi5uSExMRGrV6/G1atXceHCBQwfPhwnTpyAt7e3WR8JCQkYPHgwlEolACA4OBhPPPEEOnbsiIyMDKxevRrnz5/H559/jvz8fGzcuLFR75e0gtaOUAghN7dbbQRh6NCh4vv9/vvvLY5/9tln4vFx48Y16hrHjx8X+/D39+fJyckWbbZu3coZYxwA79Onj8XxkpIS7uPjwwFwmUzGd+3aZdGmsrKSP/jgg+K15syZY9Hm7rvvFo8PHz6cl5SUmB3XaDR82rRpYpvVq1c36j2TlkcBAiHErm6lAOHEiRPie73//vtttps4caLY7syZMw2+zmOPPSaev3nzZpvt7r33XrHdlStXzI6Z/n955ZVXbPZRVlbGAwICOADu6OjIy8vLxWNHjx4V+wgODuZKpdJqHxUVFbxDhw4cAA8LC+M6na6B75i0BkpSJISQZmI6fD5//nyb7Z555hnx+aZNmxp8HU9PT/To0QPt2rXDuHHjbLbr2rWr+Dw5Odns2O7du8XnM2bMsNmHk5MT7r33XgCAWq3G2bNnxWN//PGH+HzhwoVwd3e32odCocC8efMAACkpKThy5IjN65G2gwIEQlqYMaP/2WefBQD8+eefGDVqFHx9faFQKBAdHY158+YhISGhSdfp3bs3GGOQSqV1Zs1fvnxZvK+pU6daHC8vL8dXX32FCRMmIDw8HK6urlAoFPD398eQIUPw3nvvoaSkpMH3WN8VDkqlUmw3ePBgm+0459i4cSPGjx+P4OBgMat/wIABWLZsGUpLS2u9H+M1GGMWH6j1ceDAAQCGD8S77rrLZrv+/fvDyckJgPmHbH198MEHOHPmDPLz86FQKGy2S0pKEp8HBASYHXNzc0P79u3h7OyM9u3b13o9Ly8v8XlRUZH4/OLFi+Lz4cOH19pH//79xeeHDx+utS1pI1p7CIOQWw2uD8kuXLiQv/POOzYzvx0cHPj27dsbfZ3ly5eLfX3++ee1tn3jjTfEtr/99pvZsf3793NfX99as9wB8ICAAH7u3DmLvmubYqjv9ENRUZHYbtCgQVbbZGZm8ttvv73We6xr9YBp26SkJJvtrNHpdOJqgC5dutTZvkePHhwAl0qlXKvVNuha9XHs2DGuUCjE+9Hr9Y3uy3Sq4vjx4+Lrw4cPF1/Pzc2ttY9r166JbR966KFG3wtpOTSCQEgr2bVrF1599VUAwEMPPYS1a9di1apVuOeeewAAFRUVmDRpEvbs2dOo/qdOnQqJRAKg7mHsDRs2AAB8fX0xcuRI8fXLly9jzJgxyM3NhSAImDx5Mr766its3rwZa9aswdNPPw0PDw8AQFZWVq1D1fZUWFiIAQMG4N9//wVgGD157733sGnTJnz55Ze47777AAC5ubkYMWIEjh071uz3kJ2djYqKCgBAeHh4ne1DQkIAAFqtFpmZmU2+PuccRUVF+PfffzFv3jwMHjwYlZWVcHR0xNdffw3GWKP6TUtLw65duwAYVkt0795dPObi4iI+V6vVtfZjOsKUkZHRqHshLay1IxRCbjUw+ZYqkUj4xo0bLdp88cUXYptOnTpxjUbTqGuNHDmSA+CCIPCMjAyrbU6ePCle6+mnnzY7ZppMt2HDBqvnJycnc3d3d7Hd+fPnzY63xAjClClTxONLliyx+m15586d4jfqiIiIRv+Z2nLmzBnxHmbOnFln+xkzZojtT58+3eTrL1q0yGLEpHfv3vzEiRNN6nfChAlif7NmzTI79vzzz4vHtmzZUms/X3/9tdg2JiamSfdEWgaNIBDSihYvXozJkydbvD537lxMmTIFgOFb/M6dOxvV//Tp0wEAer0eP/30k9U2xtEDAJg2bZr4vLy8HL/99hsA4I477hDvp6awsDCzvIUrV6406l4bKz4+Xnxv999/P5YuXWr12/K4cePwwgsvADDMzTcmObA25eXl4nMHB4c625u2MT23sdLS0ixeS0xMxA8//FBn7oUty5cvx7Zt2wAYkhXfeOMNs+PDhg0Tn3/yySfgnFvtR6/XizUbAKCysrJR90NaFgUIhLQSmUwmfmBZY5rpbvygbqgJEybA2dkZgPVpBs65+HqHDh3Qr18/8ZiTkxOSk5Px999/m/1ytyYiIkJ83hwfdg2xefNm6PV6ABCL/thieryxQZctGo1GfF5b4qC1NlqttsnXHzp0KFavXo0NGzbg5Zdfhre3N5RKJT766CMMGDBALGRUX2vWrMHixYvFnz/77DOLqZPRo0ejQ4cOAIBDhw5h0aJFFkEC5xwvvPACTp06Jb7W2OkO0rKokiIhraRPnz4WVelM3XbbbXBwcEBFRUWj58ydnZ0xYcIErF+/HseOHUNKSgrCwsLE43/99RfS09MBAI888ojF+X5+fvDz87Pad0VFBS5evIijR4+ajU7odLpG3WtjHT16VHyemJiI7du319re0dERarW62fcHcHR0FJ/XZ+8C02/Rcrm8ydd/8sknxedTpkzB888/j9GjRyM2NhZxcXFYvHgxvv3223r19c0332Du3Lnih/3TTz+NWbNmWbQTBAHfffcdBg8eDK1Wiw8//BD79+/H9OnTERISgszMTKxfvx4nT55Ely5doFKpkJKSUq8RFtL6KEAgpJUYv3nZIpVKERgYiMTEROTk5Iiv//XXX8jPz7d5XmhoKHr37i3+PG3aNKxfvx6AYRThxRdfFI/Zml6oKTc3Fzt27MCpU6dw7do1JCQkIDU1VfzmbsrWMLO9GAMcwHzUpS7NuWESYJ6wZ0xWrI1pG9Pgorl4e3tj69at6NChAyorK7Fu3Tq8//77YlKpLf/973/x+uuviz8/9thj+Pjjj222HzBgALZt24aHH34YKpUKZ86cwZkzZ8za9O3bF1u2bBHLPdd1D6RtoACBkFbi5uZWZxvjWnnTDPDXXnsNhw4dsnnOzJkzsWbNGvHn4cOHw9/fH9nZ2WYBglarxc8//wzAkGMQGRlp0ZdOp8Nrr72GDz/80Oq3YplMhjvuuAOOjo74888/63w/9tCY+gsAGj0vb4unp6f4vKCgoM72hYWF4nNfX99mvRej0NBQjBkzBtu3b0dVVRVOnjxps16BVqvFnDlz8N1334mvzZs3D5999lmdUwLjxo3D1atXsXz5cvz6669ISUmBs7MzYmJiMGPGDMycORNSqVQMbGvWZCBtEwUIhLSS+nzLVKlUAFDrVERdJBIJHn74YaxYsQKnTp1CfHw8oqOjsXfvXvEXtq3Rg8ceewzr1q0DYBjRuOuuu9C3b1907doVMTEx6N69OxQKBZYvX27XAKG2PytjEAUYltq11vC1v78/3N3dUVxcjNTU1DrbG9s4ODjYnMZpDp06dRKf2xo1UalUmDhxoll1xddffx1vvfVWva8TEBCADz74AB988IHV4/Hx8WJ+SseOHevdL2k9FCAQ0kqsZZ2bqqqqEteLBwYGiq8fPHiwwdeaPn06VqxYAcCQ1PfSSy+JZYFlMpnVlRT//POPGBxERkZi9+7diIqKstp/QxPgjAShOk+6tkS94uJim8dMP1wzMzOtjoS0lJiYGBw9ehTXrl2DRqOBTCaz2q6qqgrXrl0DAHTp0sXsz6EuBQUFeO2115CQkIBu3bph+fLltbY3Da5cXV0tjpeUlGDUqFFinotEIsGnn36KuXPn1vue6sP0723fvn2btW9iH7SKgZBWcuLECbPM95qOHj0qHh8yZEiTrtWrVy/ExMQAMGTvV1VVYceOHQAMmejt2rWzOOfXX38Vn7/xxhs2gwMAZnPODclBMM3kr22q4Pz58zaP3X777eJzY6ljW3JycjB+/HgsXLjQbCi9uRjLQJeXl9eaWHr06FGxsFBD/9+6uLhg5cqV2LNnD3744Yc6k0JN76Nz585mxyorKzFu3DixjYODAzZv3lzv4OD8+fOYNGkS7rrrLmzZsqXWtsbVMnK5HEOHDq1X/6R1UYBASCspLCzEjz/+aPO46d4EEyZMaPL1jDURjh07hs2bN4vf+m1NL5jOo5sm4NV0/vx5s6Hp2oKemkzn3k+fPm2znTFXwprx48eLz1esWFHrCoLly5djx44d+OSTT/D333/X+z7r68EHHxSff/LJJzbbmSb9WRu9qY1CoRDrD+Tk5NisbwEYvrUbP/y7du2K6Ohos+PPPfecuHGSk5MTfvvtN7M/z7q4u7tjy5Yt+Pvvv/HDDz/YbPf3339j7969AAzv19pIBmmDWrFIEyG3JJhUuvP09OSnTp2yaPPBBx+IbUaMGNEs101NTeWMMXFrXgDczc2Nq9Vqq+3fffdd8R5GjhxptTrh2bNneVRUlNl7+vjjj83a1FYtUalUcqlUKu5J8M8//1hc48svvzTr31olxVGjRonHJ0+ebPU9bdmyhUskErGyZM2Kj81l6NCh4r188cUXFsc//fRT8fiQIUMadY09e/aIffj4+PCLFy9atDl37py4TTMAvm3bNpt9AOBbt25t1L3ceeedHABnjPFdu3ZZHL9w4QIPCgriALhCoeDXrl1r1HVIy6McBEJaCWMMRUVFuOOOOzB79mwMGDAAKpUKP/30k/hty8PDA59//nmzXC8kJASDBg3CwYMHxaWBEydOtJnU9/DDD2Pp0qWorKzE7t270bdvX0ybNg2BgYHIzc3FgQMHsHPnTovcgdryBWpyd3fHxIkTsWnTJmi1WowYMQJz5sxBnz59UFhYiM2bN+PQoUPw9vaGq6ur2e6EplatWoU+ffogJycHmzZtwrFjxzBr1ix06tQJBQUF2LNnj1l9hDfeeANdunSx6Mc0Wz8pKaleeyrU9Omnn6Jv375Qq9WYN28edu/ejfvvvx8AsH37dnFqx9nZGV988YXVPtasWSPWHTD+PzM1fPhwzJ07F19++SXy8vJw22234fHHH0f//v2h0Whw+PBhfP/99+JoytNPP20xMrBkyRLxeb9+/cAYq7OGBGDY5yI0NFT8edmyZRg8eDB0Oh3Gjh2L6dOnY+DAgZBIJDh69Ci+//57MTnxo48+shjFIG1Ya0cohNxqcP0bW/fu3fkTTzxhc+fBsLAwfuHChWa99sqVK82usW/fvlrbr127VvyGb+sRHR3Nd+7cKf48adIksz7q2m8hNzdX3NnQ2sPf358fO3aMDxgwwOYIAuecJyQk8O7du9d6rxKJhL/xxhs2369p24bu5mhqz549ZvtT1Hx4enryAwcO2Dzf9M/M1vvVarV84cKFdb5fa3tTXL58udbzanusXr3a4l7Wrl0r7nNh7eHg4MC//PLLRv95ktZBAQIhLcz4S7NHjx6cc843b97MBw4cyN3c3LijoyPv1asXf/fdd3lJSUmzX1upVIpbEgcHB3OdTlfnOadOneLTpk3joaGhXC6XcwcHBx4cHMzHjBnDV65cycvLyznnnHft2pUD4M7OzrywsFA8vz4bMlVVVfHPPvuM33nnndzd3Z07OTnxmJgY/sorr/C8vDzOOa8zQODc8KG5fv16Pn78eB4cHMwVCgV3cnLinTp14nPnzrW6HbWp5goQOOc8Ozubv/jiizwmJoY7OztzBwcH3rlzZ75o0SKemZlZ67n1CRCMjh8/zh999FEeERHBFQoFd3Fx4TExMfyZZ57hV65csXrOzz//3KwBAueGoOOpp57i0dHRXKFQcEdHR96lSxf+/PPPN/nPkrQOxnkLlz0j5BZnHMbu0aOHRcU5QghpK2gVAyGEEEIsUIBACCGEEAsUIBBCCCHEAgUIhBBCCLFAAQIhhBBCLFChJEJaGC0cIoTcCGgEgRBCCCEWKEAghBBCiAUKEAghhBBigQIEQgghhFigAIEQQgghFihAIOjbty/69u3b2rdBCCGkDaFljgTZ2dmtfQuEEELaGBpBIIQQQogFChAIIYQQYoECBEIIIYRYoACBEEIIIRZu6QDhiy++AGMM48ePb7Y+Dx06hEmTJiEwMBByuRwBAQEYMWIEfvjhh3rX4I+Pj8e8efPQvn17ODg4wMvLC7fddhvef/99lJaWNtu9EkIIIbYwfovuHHPp0iX069cPKpUK999/P7Zv396k/jjnWLRoET788EObbUaMGIEtW7bA1dXVZpuNGzfiscceg1qttno8KioKO3bsQJcuXZp0v6aCg4MBAOnp6c3WJyGEkBvbLTmCkJSUhDFjxkClUjVbn//73//E4MDHxwdvvPEGNmzYgGXLliEqKgoAsGfPHjzyyCM2RxIOHz6MadOmQa1WQyaT4amnnsL69evx5ZdfYuDAgQCAhIQEjBs3DgUFBc1274QQQogFfos5fPgw9/f35wDEx/3339+kPuPj47lMJuMAeFhYGE9PTzc7XlpaygcPHixeb/PmzRZ9aDQa3qFDBw6Ay+Vyvm/fPrPjer2eL168WOxjwYIFTbpnU0FBQTwoKKjZ+iOEEHLju2VGEKqqqvDuu+9i6NChzV4YaMWKFdBoNACAjz76CEFBQWbHXVxcsGnTJjg5OQEA3nnnHYs+tm/fjqtXrwIA5s6di6FDh5odZ4xh2bJlYsXDb775Bnl5ec36PgghhBCjWyJA2Lt3L2JiYvDKK69Aq9VCKpXi1VdfbZa+Oef46aefAABBQUG4//77rbbz9fXF5MmTAQCnT5/GtWvXzI5v3LhRfD5//nyrfTDG8PTTTwMwBDzbtm1r8v0TQggh1twSAcL69euRkJAAAIiJicHff/+N2bNnN0vf58+fF7/JDxs2DIwxm22HDRsmPv/999/Njh08eBAAEBoaivbt29erjz/++KMxt0wIIYTU6ZbZi6Fdu3Z49dVXMX/+fMjlciQnJzdLv3FxceLzbt261drWdOXB2bNnxeeZmZli0mFdfQQFBcHd3R3FxcVmfRBCSGNxzqHX66HX61v7Vm5pjDFIJJJav2i2pFsiQFiwYAG++OILMQegOZkGGuHh4bW2DQkJEZ+npKQ0qg9jP8XFxUhLSwPnvM38ZSKE3FgqKytRVFSE4uJiCg7aEGdnZ7i5ucHV1RUSiaTV7uOWCBDsuZWxaaKgt7d3rW29vLzE56bLFBvSh2k/Wq0WxcXF8PDwqO/t2kWZSoVLcXEoLy+Di8wJEUHh8IgMoMCFkDasvLwcqamp9S7gRlpOWVkZysrKUFhYiNDQUEilrfNRfUsECPZUXl4uPndwcKi1LWMMcrkcVVVVZuc1pI+abcrLy+sVIBiLIVmTlZWFgICAOvuw5VJcHOYffE/8OSCF4dG8nrh/8+sUJBDSBmk0GqSnp4NzDplMBi8vLzg7O0MQbom0tDaLc47KykqUlpaipKQElZWVSE1NRVhYWKuMJFCA0ETG5Y0AoFAo6myvUChQVVUFrVbbpD6MTPtpK7LCOC7vPY8hyTlwj/Bv7dshhNSgVCqh0+kgkUgQFhYGmUzW2rdErpPL5XB1dYWbmxvS09PFgKE1RoopQGgiR0dH8XlVVVWd7SsrKwEY/hI0tY+a/dSmtjLKtY0u1IdgJbLVSTm0lRorrQkhra2srAwA4OHhQcFBG+Xi4gI3NzcUFxejpKSkVQIEGk9qIhcXF/F5RUVFrW0552IAYBoUNKSPmm1M+2ktHWNi8HyvRywPUNITIW2OXq8X93pxdnZu5bshtTHu21NWVtYquSIUIDSRp6en+Lyu/REKCwvF576+vo3qw7QfhUIBd3f3et+rvTi7uKBHz14Wr+eeTQSnIIGQNsV0tQKNHrRtptPJOp2uxa9PAUITdezYUXyemppaa1vT46bLGRvSh2mbsLCw+t6m3TFYJiNyzpH17xXKkiakDTH990hJxG2b6f8fGkG4AZkWP7pw4UKtbU2PmxZE8vT0FFcR1NVHeno6SkpKLPpoaziA85fPQ6/TQUe5CIQQcsOhAKGJ2rdvL27OdODAgVrb7tu3T3w+ZMgQs2ODBw8GAMTHxyMtLa1RfbQleydqkfvlX6jSaZB7OoFGEQgh5AZDAUITMcYwceJEAEBCQgJ+++03q+1ycnKwadMmAEDXrl3NRh4A4MEHHxSff/LJJ1b74Jzj008/BQBIpVLxum2BXGI+l6l2AVTuwJ+zP4ZOq4VGpYauUmP2oKCBEELaLgoQmsHTTz8tJpM8+eST4sZQRiqVCpMnTxYzhxctWmTRx7hx48RchI8++gg7d+40O845x4svvojY2FgAwLRp0+Dv33ZqDAR6+MHNwcXsNZ2UQ69Uozg7H3nnkpB98qrZg0YWCCGk7aIAoR4GDx4MxhgYY1izZo3F8ejoaLz00ksADBsv9enTBy+//DI2btyIDz74AD179sShQ4cAAMOHD8f06dMt+pDJZPj8888hCAK0Wi3Gjx+PRx99FOvXr8c333yDwYMH44MPPgAABAQEYNmyZfZ7w43AGMOCgZbvCwCOvPgdMs5eQ+65JOTFJYsPrboS+qq2V+iJEEIIBQjNZsmSJViwYAEAoLi4GP/3f/+Hhx9+GIsXLxZHFAYNGoTNmzfbLGc6bNgwrF27Fg4ODtDr9Vi7di2mT5+OOXPm4PDhwwAMGzXt3r3bbJlkW1EzH7rUHeDg0KuqkC0rRWEwg2fHIHhEVZd1phEEQkhNpl/K6mvNmjVWv8gtXbpUfD07O7vZ7vHgwYNivxs3bmy2ftsSChCaCWMMn376Kfbv34+HHnoIQUFBkMlk8PDwwODBg/Hdd99h//79ddYtmDZtGuLi4jB//nxER0fD0dERTk5O6NmzJ958803ExcWha9euLfSumubAeC32TtSCgyNDlQetoIdeCgiy6sqLObHXoK2oovwEQghpY27ZUsvh4eH1/hA6ePBgvfsdMmRIk1cXREdH47PPPmtSH61BKrH865QVxqFyB1w/Ow3+cTSulqShvWswtEyP3HNJ8O0egZzYa5Z9OSrg2yuK1mkTQkgruWUDBNL8vJ09EeDYDllq82qQWikHCqqQlJyIyIgoXCtNBw8EVKk50J7TwTg5IYUgBgQ+3cKhr9JCoqBKb4SQxlu6dCmWLl3a2rdxQ6IAgTQbxhhG+d+G/Mwc/Fp5Wnx970QtJn0jA7LKkKitHi0IdvNHnpdeDArkggSRDv4oTjDME+o0WghyKY0iEEJIK6AAgTQrxhi8BPPljsaaCK7fnDN7PRUAOnpBWNATjDFEuASgkmnFqZ+8s4lggoCA2zuC0T71hBDSoui3Lml2al4FZ6Ywe00rtZHvcaUQvioH8cfkilwkCfnIub4kkuv1yDx6iTZ9IoQ0Sn1WMWzduhUjR46El5cXHBwc0KlTJ7zxxhsoKyvDxo0b67WioqioCK+88go6deoER0dH+Pr6Yvjw4diyZYs93laLoACBNAuZRAqpXIZI1yC4BHphvEMfs+PG1QxWz9UxyAQpklRZSC7LglNQO+QEaKHhWmSeS6BNnwghdsE5x5w5czBx4kTs2bMHRUVFqKysxJUrV/D222+jd+/eyMzMrLOfCxcuoEuXLnj33Xdx5coVVFRUIC8vD/v27cOkSZOwcOHCFng3zY8CBNIsGGOICWwPj/aBAAAJJGbH1S6Adkk/BP/3Hvg/M6jm2Qhy9Eawkw8AILksC4JMgoJgAYnaHJw4cwJ6nY6KKhFCmtXrr7+Ob775BgDQvXt3rF+/HseOHcP69evRo0cPXL16FS+//HKd/fz3v/9FVlYWpk6dil9//RUHDx7EO++8A0dHRwCG8vl79+6163uxB8pBIM2GgUGQGZIKwyIj4HzxOMp4pXh8Q+nf8JS7YK7vCLPzMj/Yh/AVD0AmSBHm7I/U8hwkqbIAAHovPdoVAlquoxEEQlqYtqIKxck5Te7HPdwPUgd5o849c+ZMvdqlpqY2qN+kpCSxOu3AgQPx559/wsHBMN15++23Y9KkSRg7diz2799fr/6++uorzJkzR/x50KBB6Nu3L0aNGgUA+OGHHzB8+PAG3WNrowCBNCvGGFyCvMEKGR5y7I/V5YfMjhdVqfBF8W7cCw52fXmjTqlG+Zl0SH1dAQABXAG9lAHejsgsywdgqMiYezoBAf070aoGQlpIcXIONt5tuXdMQ0058gHadQpp1Lm9evVq8vWtWblyJSorKyEIAlavXi0GB0YKhQLr1q1DZGQkqqqqau2rf//+ZsGB0ciRIxEeHo7k5GRcvHixWe+/JVCAQJodk0qghw5hkREIvXQeqTrzughKXRkyuznCLVULl2LDyEPWx4cs+nGI8QOe6gImMJw8E4s7et0OXaWm0d9ECCHE6I8//gAADBgwAJGRkVbbBAUFYdSoURab59U0cuRIm8eio6ORnJyMwsLCxt9sK6EcBNLsGGMo8QIyKwrwgOI2PBM9waLNvhFqbHtcU2vyYsXFHAiFFcj30EIPPSp0Vcg+eVUszUxTDoTc/Djn9XqsXr26QX3GxcUBAHr37l1r29tuu63O/oKDg20eM+70q9XeeDlUNIJAmo1MIoWDTI4OfhHQa7SITc+DnnF4yd2wsOMkfHxls8U5WWEc6ZEcrsUcEi0TRxSMvAokyPdiYAJD7LlY+EWHAicNQQgTBPj2ioJEIaNpB0LswD3cD1OOfNAs/bQlhYWF4gd2u3btam1bn43xXF1d62xzI36hoQCBNBvGGGIC2qO8qgIX065UH+CAl9wVkS4BSLyefGjqwPjqyDpM74WBH5WKQULep0cgdGqH/Lk9oM5TAvGAs7QSMiaBT7dw5MReo30bCLETqYO80bkDbZlpToGeaqzYRFMMpFkxxiCrsWlTaUY+AIaZEaPxbMdJmBpmO5M3RShEVig3m3bQXy6AX7kDnPw84RbqA9doP3Gzp7y4ZGjVldBVauz1lgghNxkvLy/xC0V+fn6tbQsKCmo9fjOjAIHYBZNKwBiDe4QfuJ6D63SGMswKN3R0C0GkS4DNc/dO0mLbYxqzICHv/w4A4BCkEqRU5SI/CCjw5+KwXU7sNaq2SAipF4VCgQ4dOgAAzp49W2vb+i6zvBlRgEDswrjcMUOdbzH3xlj1aMKCDhOsjiioPGAWJOiK1ZAWapCkyhIrLmplgE7gyItLBgCqtkgIqbexY8cCAP755x+bNRSUSqW42uFWRAECaXYyiRSCIKCDXwQ459BBD71WD71WJz64Tg9PuRt8HTxtjiioPID0SI5Sd8OUg27FSQQ7+iDA0ZBUxBjg1cmQPWzct4GqLRJC6mPOnDmQSqXQ6XR44oknoNGYT1Pq9XrMmTMHKpWqle6w9VGAQJqdIVkx2uw1VWYBSlLzzB6q9Hxwbj6iUNOB8VpxOaRWWQ4UqCFl1WWcr6nS4dLeDxqqtEgIaYCOHTviueeeAwDs3r0bd9xxBzZs2IDjx4/j559/xsCBA/HTTz+18l22LlrFQOyCgYFJJRBkUrhHeEImWP5VUyZmG3ITrucreClqXw6pcgf0VTrImQDGGJJUWYhwCcA1VTpKtHkoK1XAT9++xi4QhBBi3bvvvovU1FRs2rQJsbGxmDp1qtnxnj17okOHDvjpp5/Eega3EhpBIHZjzEMo9Rbg2SEYXh1D4NUxBB6RthMUjcshrdFJOTI/2AcACHXyE4OEpLJspGkLUKmvQkYs5SEQQupHIpFg48aN2LRpE4YPH4527dpBoVCgc+fO+O9//4ujR4+KNQ6cnZ1b+W5bHuP02/SWZ6wClp6e3mx9cs5xJv0S9Ho9ruYkIcQrAA5SBRhj0Gu0KLySBmViNtxCfCDIJBbnFlWVIqeiCBtS9omv37tWCs8CAWHv3Q+Zr6shv4HrodXrEZ98DcGCFzwi/TF02EjIHG69aJ+Q+tBoNIiPjwdgKAMsk8la+Y7atgceeADbtm1DTEwMLly40KLXbu3/VzSCQOyiZh5CWmEWUgoyLL7dl2YY8hBqnuulcEM7hZvZ68ayzOn/2w3OORhjkAoSKCRSOPt4IE1rWK98Nv0yjSIQQmr1/vvvY9q0afjwww9ttqmqqsLRo0cBAN26dWupW2szKEAgdiOXyMTSyx38IlCl00Cr117PORDgEekPrufQVWrMVjjotTpwDrNkRABQuwBZoRxaZTnKz6RDk1sqBgohToZyqEmqLBRcSUVZRTmqtLRfAyHEOp1Ohx9++AGLFi3C+fPnrbZZtmwZsrOzAQD33ntvS95em0BTDMQuUwxGnHOUValxJTsRV3OSIDCGKJ8w6DVaFF1NhzIx2+p5EpkUzkHt8MHljVBp1WbHAlIYhm+RgoHBsYs/AhcNA8BQmJyJTE0RunXoAsYEeMWEQiKRoEdwJwiMYmFCgNYftm4rkpOT0blzZ1RUVMDHxwfPPPMMbr/9dri4uCAtLQ2bNm3C1q1bAQB33XUXDh06BEFo2d8jrf3/ilYxELtijMFZ7ijWRbiak4QKbSUkggSCXAaPSH+r5ykTswG9Ho9H3WOxqsG4osG1GFBfyIb6QjYcu/jDMzQAuYkliLt6AS6BXsjL1KBTUDTOpl9Gz+DOtFcDIUQUHh6ONWvWYObMmcjLy8Prr79utd3dd9+Nn3/+ucWDg7bg1nvHpMVZy0dILkhHsRc3W93g1TEEHlGBZufaWtVQ5F1dQCnzg33I/GAfmAB0CItGgMQDwU4+AICrOUnQ6/Wo0tFeDYQQc5MnT8bFixfx3HPPoWvXrnB2doZCoUB4eDjGjh2LDRs2YP/+/fDza1u7UbYUGkEgLcI0H8Hoak4SNEwPiWDINZAKEouI1VhE6UpJGn5M2Su+fvB+Q8VE43SD+kI2tHkqSLycIGUSyAQpItoFIKXYsHvkxax4GkUghFiIjIysNVHxVkYjCKRFGLeC7hbUER39I8XXUwszkZSfhqT8NItVDnqdXqy06OvgYbVf43QDAHF1g5FUkEBgTBxF0OioDDMhhNQXjSCQFsMYg1wqg0witRhNAHA9P6FK/JBXZRRAIpPCJdgbntenGhJVWRb96qQcAINOqYY2r7puOmMMYe2CkJTf/MmXhBBys6MAgbQ442iC8Rt9lU6DK9mJAID0khxUapUIiDBUSlQmZoNrdRBkEsyMGG21gJLpMpzMZfvg8cydpldrgXdECCE3H5piIK3COJogl8rgLHcURxQ6+kdCEeABl4jqpCBjMaW6CigBhm2h9UXmyyIJIYQ0HAUIpNUZRxSMuQmMMaSX5QJgYjElrtWJ7a0VUDLdFppr9Vavw0ElPwghpL4oQCBtQs16CRyAS5TlKAIAeMpd4SJ1NDvfdFvo4vWx4JxbVFG8mBVPlRUJIaSeKEAgbUbNeglcwqDjHO4RfoZRBJ1ObPd41D1W+8gK4yiRVEGbp4LyWiYkTKCVDIQQ0ggUIJA2hZkkFaYVZaHIU4cMdb7FN//atoXeNVljSG7kenCtDmHtgux6z4QQcjOiVQykTam5BFKv0SI2PQ866M2WKxgLKBVVlSK3QmlWREntAhSrK+ADoOhqOtw6VQcIlIdACCH1QyMIpE2xVVAJsNwa2riqoaNbCJyZwqztOscTuHbtKgorS1B4MU0cgTifcRV6bj2JkRBCSDUKEEibIy6BlMiubw3NqvMQTFYzmLZ/tN1Qi9e/V/+Fj65sxvrk3YBOj6s5SQCAs+mXKVmREELqQAECadMYY3AJ8hbzEGqOIhj5+PvBpcR6UaREVSbcHF0BgJIVCSGknihAIG2WTCKFIAjoGGRY2eAU7m3Yn0FnOYogCAIWRk+Ei9J6Xx8fXINwSlYkhJB6owCBtFnGZY/GUYRMdT6y9cXQaXXQa3UWIwlyP1dM2uKKCatkGLLdPP+2pEKFfFVhC949IaSxwsPDwRhDeHh4a9/KLY0CBNKmmW4THeESAC3XoTgjDyWpeVClWyYtBi0eAddihuBEBkeVeV8f7f4Oer0hQZFWMxBCSO0oQCBtmnFVQ+fgDpAo5HAJ9IJHhD88Iv2h02ihq9SYjyQw438YRv0kM+tLpVVDWVwEzjlVVSSEkDpQgEDaPOOqBo/oQLiG+MI5zAdavQ6cc6gyCyxGEoxclbBY/liaV4hTp05Ap9NRoiIhhNSCAgRyw2CMQZBKkKkuQIamEGUBUrhH+EGn0Vpf/giGqW53mb3mlK+H3kZ7Qggh1aiSIrkh1KywyP0icDn1KnTFhpyC0ox8uIX5WZxnWrq5Jo1OC5lECsZstyGEkFsVjSCQG4JphcVO/lGG0QSFDIwJVreEroteo8WlrHicSb9ElRUJuQldvXoVCxYsQKdOneDk5ARXV1d0794d//nPf5CVlWXRvn///mCMoWfPnlb7KywshCAIYIxh4MCBVtskJCSAMQbGGH755ZfmfDutggIEcsMw5iLIJFLxZ/fo6g2bSjPyUdfiBAEMMkGK0+dOIfb4Meh0WqqsSMhN5qOPPkLXrl3x+eef48qVK1Cr1VCpVIiLi8N7772H9u3bY+vWrWbnjBkzBgBw7tw5FBQUWPR56NAh8ffE8ePHUVlZadHmzz//BAAoFAoMGzasud9Wi6MAgdzQBJnEfBRBV2M0oMbnvkQmhXOWFq45enDOEXv8X+h0OlTpNC1304QQu/nmm2/w3HPPQaPRoF27dvi///s//PXXXzhw4ABeeeUVODk5oaysDA8++CD27NkjnmcMEDjnOHjwoEW/pq9VVlbixIkTFm12794NABg4cCCcnZ2b9421AgoQyA0ttTDTbBShptw1x8x+3lL+L9xCfCBlEjFI4FodLXsk5CaQn5+P559/HgAQGhqKU6dO4T//+Q8GDBiAwYMH45133sE///wDd3d36PV6zJo1C1VVVQCAvn37wtvbGwBw4MABi76Nr8lkhuXTR44cMTuu1Wqxf/9+AMDYsWPt8wZbGAUI5IZjLMHcwS8Ces5RpdOIyx6Z1PyvNCsyHwYs1apRzMvhFuIDCQzziVdzk6HX61FWpaYggRATldoqJOalNvlRqa1qkftduXIlysrKAABffPEFQkNDLdr06NED//vf/wAAGRkZ2Lx5MwBDufaRI0cCgPhBb1RQUIDz58+DMYbJkycDsAwQjh49itLSUgDAPffc04zvqvXQKgZywzGWYD6fcRUAkFqUhdLyXFToi+FfCUjcHaArrgAAuBQDjuUMaqfqD34N1wHseuVFR28otTpD0mLaVThIFejsHwWJQkarG8gtL6MoGw+vXNjkfjbM/hiRPpYf1s1t3759AAB/f/9av8XPnDkTzz33HKqqqrB3715MnToVgGGa4ccff8SlS5eQnZ0Nf39/AIbpBc45unXrhpEjR2L9+vX4+++/odfrIQiGLyXG/IPo6Gi0b9/enm+zxdAIArkhmZZg7hgYhUjXIDgEuEPH9Qh8cbjYjoFh+M8Ss3PXJe4SRwqkTILKnGLExh5HbOxxZF6IR/qJS8g9nUCjCYTcYC5dugQA6NOnT60BvrOzM2JiYgAAFy9eFF8fNWqUeJ5pzoHx+aBBg3D77bcDAEpKSnD27FmxjTH/4GaZXgAoQCA3KNNlj50DouHRPhAAkK0vhsTHGRJ3B7GtVGv+i6JUq4ZSVw6JTIripBw4Z2nhlKOFKrMQysRsZJ1PhLqsDBXlalRpNRQoEHKDMK4+8PX1rbOtj48PAKCoqMjstb59+wIwn2YwDRA6dOggjiwYpxkKCgoQGxsL4OYKEGiKgdywTJc9SgQJIlwCEMcLoQNH0CujkPqfHQCuTzOoALVL9bnfJf6GFzpNAa5v3qTV66BSC3BRtENqZj5KSqTIy+IQZIYCTTEB7WnKgdxygjz9sWH2x83ST0toSDCvu75tfM1/16NHj8aJEyfEpMS8vDxcuHABAMT6B4MGDcKmTZtw5MgRPPPMM9i7dy/0ej2cnZ0xePDgZngnbQMFCOSGxxhDZ/8oHLmcAgDIVOcjzMdfzEUwbty0/bHqpYylWjWUmlJ4KdwAADIuQKGVI6UyF+VaJYpUHPm5OghSCTr4RUCj00IulVm9PiE3K4VU3iK5A83Fy8sLWVlZyMvLq7OtsY2Xl5fZ62PGjMHbb7+N+Ph4pKWl4fjx4+Cco1OnTuLIhGmAAFRPLwwdOhQKhfn+LzcymmIgNwW5RAaBCQiRtkNZnhJVeh38XhgiHre2cZOWV1deNCYshjj6IEDigRAnX4R62l4+SQhpe7p06QIAiI2NrXU0obS0VMxXqJlQ2K9fPzFoOHDggBgEDBo0SGxjHCXIycnB5cuXb8r8A6CJIwgPPPBAc92HGcYYtmzZYpe+yc1JopChvVsIrkTqkXa1ABmqXLDKcvE4A8OILTJsf8Cy+pnYhjFIBQmkTAKpIIHAquNnXleJRkJIqxs+fDj27t2LrKws/PHHHzY/sNetWwet1rCb69ChQ82OSSQSjBw5Ehs3bsSBAwcQFxcHwDxA6Ny5M3x9fZGbm4uvvvoK6enpAChAMLN9+3aalyVtAmMMQX06IvlgFkKk7cCUDI5u/kg1aaMvrWhQn8prGeC+hr/fF7Pi0TO4M/19J6QNe+yxx/Dmm29CrVZj/vz5+OuvvxAUFGTW5ty5c3j11VcBAIGBgZg0aZJFP6NHj8bGjRuxa9cucSqi5v4LgwYNws8//4yvvvoKANC1a1erdRduZE3OQbBHhjf9EiaNIXWQo717CMJ7+iPrfCKKvAC4yYES60VarP3NZRIJmMCgTMyGe4QfVCl5uAKGjv6RlIdASAsrKirCokWL6mzn6OiIt99+Gz4+Pvjoo48wZ84cJCcno1evXli0aBEGDBgArVaLPXv24KOPPkJZWRkYY1izZo3VksijR48GYwzZ2dkAgKioKItAwxggGPdkuNlGD4AmBghJSUnNdR+ENBljDH69o5ETew0OkEEi6BH66hhxNUNN6xJ3YVHnKWYBKWOAW6gfipOzUZyUg8BwbxQ3YJdIQkjzKSkpwfLly+ts5+7ujrfffhsA8OSTT6KsrAwvvvgi8vLy8J///MeivYeHB9atW4cRI0ZY7c/Pzw+9evXCqVOnAJhPLxjVXK1AAUINYWFhzXUfhDQL44c9YwxRLoGIFzLFUQSJlXoIRVXVKxnEPgTALcQHJWl5YGBQZeSBB0W32HsghDTNc889h7Fjx+Ljjz/G3r17kZ6eDgcHB0RGRmLChAmYPXs2/Pz8au1jzJgxYoBgbXvnmJgY+Pj4IC8vD+7u7hgwYIBd3ktruqVWMWzfvh333HMPfH19IZfLERwcjPHjx+O3335rdJ9Lly4V9/9uyCM8PNxqf15eXvXu49ixY1b7IAYyZqiPEPqqYZc2Yz0EU6YrGUwx6fWphqRscM6hU1dS0SRCWkhycrJhI7V6PpRKpUUfHTt2xBdffIGrV6+ivLwchYWFOHnyJF599dU6gwMA+O9//yv2P3PmTIvjjDHk5uaK15dKb76qAW0yQDDW024uGo0GU6ZMwYQJE/D7778jLy8PGo0GGRkZ2LFjB8aNG4fp06eLWa0twVi/21RqaqpZVS/SNIwxRLsGQebrAnT0NJRd3mL+j7iwstTqhz5jgGuQt/izMjELx/46gguZ1yhIIITcEuwa8mRnZ2Pbtm24cuUKysvLodPpLH65cs6h0WhQUVGB4uJixMXFIT8/v1k/rOfNm4dNmzYBAMLDw/HUU08hLCwMV65cwVdffYXs7GysX78eHh4e+PTTTxvU95QpU9CzZ8862+l0OsyfPx85OTkAgLfeesuijWld7zfeeAO9evWqtc+OHTs26F5vRQyG0RZhQS94X9VA+eMhs+M/puxFpEsgZkaMMstFAAyjCHKZHBVZBYjjhXAJ9IJbuS/KqtRwljtSMi0h5KZmtwBh06ZNmD17NsrLy+tubIJz3qy/eI8cOYKVK1cCAHr37o0DBw7Aza16znnu3LkYMmQILl68iM8++wyPPvoo+vTpU+/+O3XqhE6dOtXZ7uWXXxaDg2eeeQbTpk2zaGMaIDz66KOIiIio932Q2jHGIPV1tchDAIBEVab1XAQGuIb4ILrCHUWZuSgDoLyWicsyKRzlCiq/TAi5qdlliiE5ORkzZsxAWVlZg+aRAMO34vnz5zfbvSxbtkx8vnLlSrPgADBs6rFx40bxF/0777zTbNc2+vvvv/Hee+8BMKyVNb0nU8YAwc3NzWaOAmk8JhPgUgz4p1h+qK9K+M3mVIPUQQ65IEVFVjESSzNwOfUq1FWV0OhabkqKEEJaml0ChM8//xwajQaMMfTv3x+bNm3C8ePHMWnSJDDGMGPGDJw8eRJ79+7FBx98gOjo6gzx119/HZ988kmz3EdxcTF27doFALjzzjttDtl369ZNXLLy+++/o7S0tFmuDxjyH2bPni3uG/7tt9/CwcHBaltjgNC1a1f6ZmoHUh8XOMUEYMQWKYZvNh88M65osIYxwC3YB/6COwIc20GVVQBlfCblIhBCbmp2CRCM22RGRUXh0KFDePDBB9G3b1+MHz8enHOcPn0avXv3xtChQ/H888/j7NmzGDlyJDjnmD9/fr022qiPw4cPizt2DR8+vNa2w4YNAwBUVlaabfPZVB999BEuX74MAHjiiSfQv39/q+3KysqQkJAAAOjevXuzXZ9UY4whcPEwBC0ajoBUZrGi4aMrm1FYWWJ9JEEqgVQugyo5D6rMQugqq6CvohEEQsjNyy4BQmpqKhhjmDZtGmSy6spzt912GwDgwoULKCkpEV93dHTEhg0b4OHhgZKSEnz77bfNch/GGtqAYZSgNsZNPgDzXICmKCwsFKcs3NzcxEIe1sTFxUF/feth472WlJTgyJEj+OOPP3Dq1KkWXWVxM+Iw5Lc4dvGH1N0Ro36yrIr40ZXNWJv0p0WQwBjgEuwNl4B2LXW7hBDSquwSIBQXFwOARfJeVFQUFAoFOOc4c+aM2TFPT09MmTIFnHP8+eefzXIfycnJ4vO65vRDQkLE5ykpKc1y/ffff1/8s3jhhRfg4+Njs61pUKLX63H//ffDy8sLAwcOxNixY9GnTx/4+PjgP//5j1lwRWpRYyAgQ50vJsEGLh4OVyXgorQ8LVGVidysbKtBgiCt/iej1VBdBELIzcsuAYKLiwsAw65YZhcTBERGRgIALl68aHFe7969AQBXrlxplvswnarw9vaupaX5nuAFBQVNvnZZWZm4iYezszMWLFhQa/tz586Jz59++mn88ssv4vSIkVKpxHvvvYf+/fsjNTW1ZhekhqJrGZBAgMAERLgEGAoeccMoDZhhCeSE72RWg4S05XuQ+cG+WgOAY//8hZxT8RQkEEJuSnYJEIxVqtLS0iyORUVFAQDOnz9vccwYWFiritUYpkssbSUGWjve0KWZ1qxbt058H7NnzzYLQKwxHUFQKBR4+eWXcenSJVRUVCA7Oxvff/+9+Gd36dIl3HvvvVCr1U2+z5uNIJeCCQJ8uoWD6zm4Vo9o1yDLdjJD8GoMEoZsN09aLPEAsrIzkZmZYZaXIJXKIJVKEXf1AgoTMqFSlUJdVk5BAiHkpmOXAOGOO+4A5xzbtm2zONaxY0dwzvH3339bHLt27RoANFvJSo1GIz5XKBS1tjU93hxz/caCS1KpFM8//3yd7Y37iTs7O+Pw4cP43//+h06dOkGhUMDPzw/Tpk3DiRMnxATGc+fO4bPPPqv3/QQHB9t8ZGVlNeIdtk2MMfj2ihJ/LrqWYTbVwK//IPVxgWOMv+EcMLgWm/dz8H4tfpmpxdcFu83yEgSBISosCo4+huWyCaoM/HX4IM6kXYTeODpBCCE3AbsECOPHjwcA/PXXX5g/f77ZnLkxi//cuXPYvXu3+Hp+fj6++uorMMaarUCQo6Oj+LyqyvqWv0bGLTsBQC6XN+m6hw8fxqVLlwAY/izqs0d4cnIylEolLl++jH79+llt4+npie+++0782TiFQcxJFDKzUQS9pnqqxjwPYRjC3rsfIe+MQ/DCobX2aSymBAAyQQKF3AHp+kLEXb2AxNIMaKs0OJt+mUYSSJtnuoSa/r62bcbEdcB6eX57s8sV77vvPvTq1Qucc3z11VcICgrCkSNHAADjxo0Tk/XGjx+Pxx9/HAsXLkSvXr3Eb7JjxoxplvswTlkAQEVFRa1tTY+bBhaNYTpyMnny5Hqf5+7ujuDg4Frb9OnTBzExMQCAxMREZGRk1Kvv9PR0m4+AgIB63+ONoOYoQml8NhiYmIdg3KCJMQaZrysUQR7wCwlCpEvtfw6m5wU7eaNDSDQCJB4AgKu5ydDr9VQ8ibR5EolE/LChacq2raysDIAhOLhpAgQA2LFjB6Kjo8E5R3l5uVjBUKFQYMWKFeCco7KyEmvWrMFnn32GzMxMAICPjw8WLVrULPfg6ekpPq8r8bCwsFB87uvr26Tr/vLLLwAM0wX33HNPk/qypnPnzuJz458bMWc6igAORDr6i8eMowimGGOYGTEa8zxG4r61Uty3VorBO8ynuniN9lKJBFImQbCTD7iVfUYIaYsYY+KXp6KiIrNvqaTt0Gg04ueSu7t7qxTPs9teDMHBwTh//jy++eYbbN68WUywA4CpU6eisrISzz//vLgMEDDsr71hw4Ymf0AbmW5mlJqaKq6SsMZ0VUBTyhyfP38eiYmJAAyjJU0djbDGtLYEsc44ipATa8hrkTGpuJohSZUFHddDyiQW53hKXKAqMMbN5r841yXuwqLOUyw3dQKDKrMAyspMVPlHQSaRUiVM0qa5u7ujpKQEarUaqamp8PT0hKMjbUDW2oybF5aVlUGpVEKn0xl+L5l82W1Jdt3NUS6XY8GCBVaX+M2aNQtTpkzBX3/9hYKCAkRERKBfv37N+hfUtPjRhQsXxNwIay5cuCA+r6uoUm2MpZ0B4IEHHqjXOXFxcdizZw9yc3MxcuRIDB1a+3y4MaERAAIDAxt3o7cA079LDIatn6+WWK6sMWVc3QDAYmMnYzlm46ZOTCKBRCaFMikbKp0SOpcAXEq/CicnJ9rIibRpLi4u8PPzQ05ODtRqNU01tFESiQTBwcF1Jtnbi10DhLo4OjpixIgRduu/f//+UCgUYvnkV1991Wbbffv2ATD8D7n77rsbfU3T1Rm33357vc45f/48XnjhBQBARkZGrQGCUqnEyZMnARiKOwUFWS7hI5aKrmXArVPdwZTUxwUSdwfoiivgUgw4qgB1dSqLmIcAVFdXdKisQl5aKZJUWcjP1aFTUDQ0Oi3kUhrpIW2Xl5cX5HI5iouLoVKpaKqhDXF0dISzszM8PDxadcS4VQMEe3Nzc8OoUaPwyy+/4MCBA4iLi7M6OnD27FkcOnQIADB69Gi4u7s3+prGD+927dohLCysXucMGTIEEokEOp0O27ZtQ3Z2Nvz9/a22fe+998SEypkzZzb6Pm8FpjUR8uKSzVYz2MIYQ9Aro5D6nx1gYBi+RYqdM20nHjIGSKQS+AvucHL0RvNt80WI/bm4uMDFxcVQRIzyaFodY6zVEhKtaVKAYLrr4jPPPGP19cYy7a8pXnjhBfzyyy/gnGPq1KnYv3+/Wcnj3NxcPPzww+I/jMWLFzf6WkqlUhz+79mzZ73P8/f3x+TJk/Hjjz+irKwMU6dOxS+//GK2CgMAVq9eLW4V7evri2effbbR93orqJmHUBSfCX59wIXXrMNsQuZbPYpQ30kCxhgkrPofdW39E9LWMMaarf4MuXkw3oSQURAEcZ7VtCyw6euNVbPMcFM8+uijWLt2LQAgICAAc+fORfv27XHt2jV88cUXyM7OBgA8/vjjWLlypcX54eHh4v4MBw4cELeGrik2NhZ9+/YFADz88MP48ccf632P2dnZ6Nevn1h9MjQ0FE8++SQ6dOiA/Px8bN26FXv37gVgyO349ddfm216xri00jS34WbBOUfWscvgej0yzyUgP1CPlIpcMMYQ5uRn8+9pVU4pUv+zA8p2evxiMoIwNWw4OrqFmJ2n1+pQkpoHl3AfKNsBnYKiIQgCegZ3pjwEQsgNq8kho7HojLXXG6u5f6l+8803KC0txdatW5GVlYU33njDos1DDz2EL774oknXMa1J0NBpCn9/f+zbtw8TJ05EXFwcUlNT8dprr1m08/X1xZo1a+yau3EzMR1FkEIAdHqEOwcgucz6SgYj4ygCYF52+8eUvYh0CcTMiFEWf08lTACDHldzktDBL4LyEAghN7QmBQirV69u0OutRS6XY8uWLdi2bRu+++47nDhxAoWFhXB3d8dtt92GJ554AhMmTGjydUpLq2egPTw8Gnx++/btcfLkSfzwww/YtGkTTp8+jaKiIri7uyMqKgrjx4/HnDlzWm3Jy43K+EHOGIN3DkMKywf3qPucwMXDoXx/p8WxRFUmElSZiHIJNF8pwRjcCxmKfWh6gRBy42vSFAO5OdzMUwyAYTQr93QCtOpKaPRa/HvmOAra6RDs4gu5YLtmgSa3FMkvbseeiVpkh1n+MzGOJAAMJSk54HpuNs3QNagDFNKmle0mhJDW0jZSJQmxI+M0g0938z0+MtX5VqsqGkl9XOAUE4ARW6QYvtlysM24PwNjgGuQ5XbiF7NoK2hCyI3LLgFCWFgYXn75ZatbOhPSGhhjkMhlkDIJ5EyKMIUvwp0DoNFrzWob1DwncPEwBC0ajoBUBv8Uy5EG8dzrhwx5CMDVnCTam4EQckOzS4CQlpaG9957Dz169EDPnj2xfPly2jOAtAmMMYRLfNAul0GVng/Ore/NYNresYs/pO6OGLFFiiHba+zPYGVPB/dCRiMHhJAbnl0ChIAAw655nHPExcXhxRdfRFhYGIYPH461a9dCpVLZ47KE1EqQSyF1VMC3ewSCu7UH1+gR7uBrtsOjNcbiSQwMrsXmx7SFhlUOTCIBExiUidkA5+A6Q1U6qodACLlR2SVASE9Px/79+/HEE0/A09NTrNJ14MABPPbYY/Dz88OUKVPw66+/Nmu9A0JqY5qLwBhDuNQbpRl1jyIAhmWPjjGW1S25Vn+9b8pDIITcXOwSIDDGMHjwYHz99dfIzs7Gzp07MXXqVDg7O4NzDrVajZ9//hn3338/AgIC8PTTT+PYsWP2uBVCzDDGIHNxBBMEBHSLAtMzhDsaRhF03HYtemM+gve028xe1xaWVQcApnkIeo4r2YmUh0AIuWHZfRWDVCrFPffcg/Xr1yM3NxebNm3C+PHjIZfLwTlHfn4+vvjiCwwYMADt27fHm2++ifj4eHvfFrmFGUcSjKMIRlpeey16xhikXs5mr21QH0XGB3vNzmOMwa0IUGXUPipBCCFtWYsuc3RwcMCDDz6IrVu3IicnB9999x3GjBkDmUwGzjkSEhLw1ltvoVOnTi15W+QWVF37gEGv1UOv0yNDlYc0VQ50Gi1sfa7X3FlN7QJcU2chNysbEATD9s+J2ShOyoFeo4Veo4VGp0WVVkPBAiHkhtLqhZLKy8uxZcsWvPrqq8jIyBBLN1NuQsu52QslWaOr1CD75FXknktCsi4PVdwwDZCmLUCAxAMKuQIuwd6oWUNJr9fjvZPrUO5gOR0R6RKIGeGjoNdooUzPRamfgEjXIHjFhIIxBgeZHDEB7Wl/BkLIDaFVtu9SKpXYunUrNm/ejIMHD6KyshJA9ZKxO+64ozVui9xCTFc0+PBwqHWVhsJHKg4PJ1+okvPAdTowqfleDYIgYHb3Cfjk6haLPhNVmVBqSuEpd4NUkKAiS4lEAHmZGghSCe3PQAi5obRYgKBSqbBt2zZs2rQJe/fuhUajAVAdFERHR2PatGmYNm0aIiMjW+q2yC3KmIegrzKMHFRpNVBmSZCXWQWdUg8t10Gr10HQX086NPnW307hhgi5L5Kqci36XZXwGxZ1ngK3YB/wVA5XJ1+4eQYgvdSyLSGEtGV2DRDUajV++eUXbNq0Cbt27bIYKWjXrh0mT56M6dOn4/bbb7fnrRBigTEGicLwbV4iAQSZFEwqQaY6FyqdEsVqQJAIkAlSBDl6m236NLPDWMS9sRmF8gocGF+9SqFUq0ZRVSk8JM6GpEZBAolgfcdIQghpy+wSIBhHCn777TeUlxsKyRiDAgcHB9x7772YNm0axowZA6m0VWY5CDEjk0jhIJOjg284CoskUEoZ3Jx9IEglSFJZbg0tCAI6PTUSqa/thKPKkKxoVFvRJUIIuVHY5dN54sSJYKy63CxjDIMGDcL06dMxadIkuLm52eOyhDQaYwwxAe1RUa5GThZDnpTDzcUfyRU5tZwEMDAM3yLFzpnVowgFlSVoJ3FtgbsmhBD7sdvXd845YmJiMG3aNDzyyCMICQmx16UIaRaMMcilMsgEKWRMAimqRwyslUwWZIbjNdckbEjZh0jnANyPnhbnUOllQsiNwi51ECZMmIB///0X58+fx0svvUTBAbkhFcVnivUQrJVilvoYyi9LtJbLFhPLslDMyy1ep9LLhJAbhV0ChNLSUowdOxZvvPGGPbonxG4EuRRMEODTLRwSPQPTcUS4BFgtxWwsv9z+0UE2toI2tJcwAQJj4hbQVTpNi7wXQghpCrsECGfPnkVhYaG4aoGQG4Vx+aPxeTvD5oy1tpf5ulndCtqoOD4LoV6B4s80ikAIuRHYJUAoKSkBAPTs2dMe3RNiVxKFTBxF4HqA16Oqp7WtoLdUHEdRQhY414NV6cEAGkUghNww7BIgtG/fHgBw8eJFe3RPiF2ZjiIAQH3zCmvmIpTxSjEPoTgxC+5KQRw5oFEEQkhbZ5cAYenSpeCc46OPPsLu3bvtcQlC7Mq0cmJpRj44B3RcX+uHuksx4Kgyf41LGZSJ2VAmZoNV6cD0nEYRCCE3BLsscxw3bhxWrVqF+fPnY8yYMejTpw/uvvtudOrUCR4eHlAoFHX2cd9999nj1gipFzFZsWsYEs8YdmbMKM+HXGJeVREwXe5oWRPB0c8TLoILVBkFYIwhzCsQKcVZAAyjCD2DO9PmTYSQNskuAYKjo6P4nHOO2NhYxMbG1vt8xhi0Wm3dDQmxE+M0Q/bJq5AzKbyVgEQmRZ6HxqKqotTHBRJ3B+iKKyxqInyftAvPt39Q/FnQQ8xF6OAXgbIqNZzljhQkEELaHLtMMXDOxUfNn+v7IKS1SRQyyJwccFvP29Cnex/oNFpwnWEjJ9O/o4bljsMN59TIQyjVqlGkKRV/rpmLcCU7ERezrtHfeUJIm2OXEYQlS5bYo1tCWpRxFEGjUiPzzDUAgDq/GJkSwXKq4fp/jHkIpnsz6BigkEmhTMwGALhH+EHGJLiakwQAtA00IaRNogCBkFowxiBzcYRMIoNfdCgQD7g5+iClItdiqgGwnocABrgEe0NXpRFzEUI9AqAR9EgrzGrhd0QIIfVjlykGQm4mjDH49Y5GtGsQQqXe0Gv1FsWTjImKgOXeDOsSdwHgECTV/9yK47MgYbQNNCGk7aIAgZB6YIyBMQYpJFBlFkCdV2wWJBgTFQEbeQhVpWASCZhgWPbIuR66iirotTrKPyCEtEl2mWI4fPhwk/sYOHBgM9wJIc1DkEshdVTAp2sYCksY0q4WGCosSq4vcbyeqJj22q9W8xC0XAfGANcgb5Sk5QEwJCyWludCWe4IHtSxNd4WIYTYZJcAYfDgwU1atkXLHElbY0xYLCsuwZW/UwFYKbAo5ita5iEUVJZAyiTwkLlCcj1hUct1UOmU0LkEQFtZBYVM3jJvhhBC6sEuAQIAGjYlNx3GGCSy6pUG6WW5iJAGQRAMkUFteQgbUvYBACJdAjEjfBSg16NKowHSlACAo0cOY+io0RAEmvUjhLQNdgkQFi5cWGebiooKFBcX4/z587hw4QIAoF+/fnjrrbfolyRps2QSKQQmIETaDml5BSgqlcItyAdSQYDUxwWOMf5QX8y2yEMwSlRlQqkphZfCDXJBgCAIiLt6Ad06dEF5qQqODo6GKo5UOIkQ0srsEiCsWLGiQe0vXryIadOm4fjx49i8eTO+/vpre9wWIU0mUcjQyScSF7keRSqOzMwCKFU6KGRyBDl6I3DxMJSfSQf/+CD8UxiywyxH0rTcsDukIDBEh0ThaoqhxkL+uSTIBEOug2+vKAoSCCGtqk18VY+JicHvv/8ODw8PrFy5Ert27WrtWyLEKsYYgvp0hF/HcES4BCBE2g4RTv7Q6LXQcT0YY5AHeYCBYcQWKSaskmHwDttxuCCVQJBKoEzMRt75ZOTFJUOrroS+inJwCCGtq00ECADg7++PWbNmgXOOr776qrVvhxCbBEFA1+CO6OgWijCpt7jbI7+etmhc8sjA4FrM4Ka03RdjgKOPO9xCfZDvT2XGCSFtR5sJEACgd+/eAIDjx4+38p0QUjuJQga5VA7/rpHgeg6u1yNDnQ/OudneDHX2wwQIAkNKRS4gAJXQUJBACGkT2lSAUFxcDAAoKipq5TshpHbGZY9SJoEAARHO/uCcQ8f11xvUv58gR2/x5xRtPuJLM6DX6+1w14QQUn9tJkDgnOP7778HAAQEBLTy3RBSN2N1xXCpd92NayFlEsgEKZLKspGmLUClvgoZsVdoJIEQ0qpaPUDQ6/U4ffo0JkyYgGPHjoExhmHDhrX2bRHSAAx6nWF/Bh3X1/nBXvOocRQhyNlXLMV8rSQNukqN/W6ZEELqYJdljl5eXvVqp9VqUV5ebvYLVSKR4Nlnn7XHbRFiN6qMAqhRjAwfQC6Rwofb/qe1LnEXFnWeYraMkTEGhUQKZx8PpOUUwIP7o0qngRRUXZEQ0jrsEiAolUowxho8RCoIAj755BN06dLFHrdFSLMy7s8Q0C0SZaWKGltBV1dVtLV5k5fCzex1xhgCHb1xFYYcnEvZCegd2Y3qIRBCWoVdAoTQ0NB6/VITBAEKhQK+vr7o168fZs2ahc6dO9vjlghpdsZERU1ZBXAGcJCWw1p6rUsx4FjOoHaqDphXJfxmMYoAAFImgDGGJFUWPPXB0Oi0kEtlNbskhBC7s0uAkJycbI9uCWlzDPszGEojSyABcH31gcxk+gAMozZKsf2x6pyC2kYR/AV3lAJQXssED6fRNEJI62j1JEVCbkY5LhWQuDuIP7sqDVtAm1qV8JvFNByTSMAEBlVmITjXQ1dFiYqEkNZBAQIhzUQKAQITEOFiWKbr/1J1sSQGhlE/mU8VGEcRTDEGuAZWL5u8lJ1Ayx0JIa2iVQKE2NhYrFq1CsuWLcPGjRupMBK5KTDGEOUcKP4s9TXs7mhU31EEiVCdh6Dnemh0tC8DIaTlNVsOglKpxHfffYdDhw5hzJgxeOqppyzaXLx4EU8++SSOHj1q9rpCocCzzz6L//73v7TVM7mhKeOzwIMMzw0llw27O2Z9fEgcRaiZi5CgykSUS6CYsGiah0AIIa2lWQKEgwcPYtKkSeJIgL+/v0Wby5cvY+DAgSgqKrL4xlRRUYFly5bh3Llz2Lp1K+RyWvtNbhyCXAomCPDpFo7McwngOkOiIodhXwapr6vY1lUJOHM5yliV+Nq6pD8R6RKImRGjaEkjIaTNaPLX9SNHjmDMmDHiBz/nHAUFBRbtpk+fjsLCQjE4mDx5MtauXYtPP/0UvXr1Auccf/zxB95+++2m3hIhLcq43LEm4+ZNZm3BMGK1ZU5BoirTIh+BEEJaU5MCBJ1Oh3nz5qGyshKcc0ycOBFHjhzBjz/+aNZu+/btiI2NFWvXv/3229iwYQOmT5+O+fPn48SJE5g4cSI453j//feRkpLSpDdFSEszfvM3TVQ0bt4kyCRmbV2VgH+K5UiBluta4lYJIaRemhQg7Ny5ExcuXABjDK+//jp+/vlnDBgwwGKKYM2aNQAMGzJFRUXh5ZdfNr8JQcDXX38NNzc3aDQa/PTTT025LUJaTc1ERQCQ+pgnKzIwjNgixbA9TnX2p9fqoOe0syMhpOU1KUD4/fffAQDt27fH0qVLrbaprKzEnj17xNGDJ554wmoiopeXF+69915wzvHbb7815bYIaVXK+CwYZxaMeQiBi4chcFH1JmQMDM6ZVTZ6MOkrIRP/HD5E2z8TQlpckwIE4+6LEydOtJlc9ffff0OtVotzsffcc4/N/vr37w8ANMVAbjimiYpcz8F1hukCYx4CYwyOXfzNiifZwiQSyGVylGUVIe7qBWgqKlFRrrb3WyCEEDNNChByc3MBoNb9Ew4fPiw+9/PzQ0xMjM22Pj4+AICcnJym3BYhLc40UdFaHoKxTeDi4Tb70F8PohkDXEN8EB0cKR7LO5tIBZMIIS2qSQGCcVmjs7OzzTaHDh0CYPjlOGjQoFr7q6ioAABa5khuSKZ1DKJcAq22EeQSq68DwLqEXWIQwBggkcvABAZlYjY0Wg0qytWo0mooUCCEtIgmBQienp4AAJVKZfV4ZWWlOA0BAIMHD661v4yMDACAt7d3re0Iaeu4Vi/mIei4Hlq9Dpxzs4TFmttAq/QVKKwsEX9mDHD0dgcAJKgycC7tEs4mX8CFzGsUJBBC7K5JhZICAwORl5eHK1euWD2+b98+VFZWAjB8qxoxYkSt/e3ZswcAEBQU1JTbIqTVKROyoWIF4B5ApjofACATpAhy9Ebg4mHQ5qlQmVEER9VeqF2qzyu9lAm3IAFSHxdImACZRII0bQGKVBw4mwUAiG4XhmjfMDjIFK3wzgght4omjSAMHjwYnHNs377d6vEffvgBgCE46Ny5MyIjI622A4ArV67gyJEjYIzVOdJASFskyKWQOirg0y0cAd0iIdEC4Q6+iHAJQIRLADR6LXRcD8YYZL6ucO4ZglG7zafn8tafQMqLO5D5wT4AQLCzH4IdvOGao4dzjg6qzELoKqtwIfUKjSIQQuyqSQHChAkTAACXLl3C//73P7Njx44dw88//yxOL8yYMcNmPxqNBk899RR01zO/x44d25TbIqRVGBMVfXpEgjGGcIkPfHIkiHS2no/AGIPfEwPMXts7UQsODvWFbGjzVBAEBo8QP3iF+cMzyJc2cSKEtJgmBQh33303Ro8eDc45Xn/9dYwaNQrLli3DU089haFDh4of+MHBwZg7d67VPi5duoQxY8bg0KFDYIxh2LBhuP3225tyW4S0GsYYZM4OYIIA3+4RkHIBQi0lDBzauZr9rHYBskI5ODj0Gt31PgFBKoFEKoG/4C625aARBEKI/TR5s6avv/4aQ4cORUJCAvbu3Yu9e/eKxzjnUCgUWLNmDVxdzX8RrlmzBh9//DHi4uLEoVIXFxd8+umnTb0lQlqVcSQhJ/ZanW29FG5wkTpCpa2uc7B3khYBKQyz6phCuJgVj57BnWmDJ0KIXTR5s6aQkBAcPXoUjzzyCCQSibhhE+ccMTEx2Lt3L4YMGWJxXlJSEs6ePSu29fb2xr59+9CxY8em3pJN27dvxz333ANfX1/I5XIEBwdj/PjxzVK58ezZs2K1yLoeDg61F8s5ffo0Zs6cifDwcCgUCvj4+OCuu+7Cl19+iaqquqvvkdZX3w9txhgej7IsHpYVxqHUlVm8LoEAxhiu5iZDr9ejSqexaEMIIc2hyQECYFiW+P333yM9PR27d+/GTz/9hNjYWJw/fx4DBgyweo6fnx8AwMPDA3PnzsXZs2fRt2/f5rgdCxqNBlOmTMGECRPw+++/Iy8vDxqNBhkZGdixYwfGjRuH6dOnQ6tt/Jzu2bNnm+VeP/zwQ9x2221Yt24dUlJSUFVVhfz8fPz999+YN28e7rzzTnE5KLk5eMldEekSYPH66qw9FiWWGWMIcqxeBnwxK56SFQkhdtHkKQZTvr6+GD7cdqU4U4888gjGjRuH0NDQ5rwFq+bNm4dNmzYBAMLDw/HUU08hLCwMV65cwVdffYXs7GysX78eHh4ejZ7iMA0QNm3aVGuxJ4nEerGcH374AS+88AIAQ/GpuXPnok+fPsjNzcV3332Hs2fPIjY2FuPHj8dff/0FhYKWud0MGGOYGTEaV9MT8ENRdeVRtRNHXnYO/ALNgwcpk0BgHFdzktDBLwJVOg0UUiouRghpXozf5F8/jhw5goEDBwIAevfujQMHDsDNzU08npubiyFDhuDixYsAgJMnT6JPnz4Nvs7w4cOxb98+hIaGNmovCaVSiaioKBQWFsLDwwNHjhxB165dxeMajQYzZ87Ehg0bAAAffPCBGEw0VXBwMAAgPT29WfojgK5Sg+yTV5EXlwzXDv6IV2ciSZWFYCcfyATrcbler8ey42uhdqr+J+nCHLC468NgjEGv1aEkNQ8ekf5wDvdFqioHHf0jIQgC5SIQQppds0wxtGXLli0Tn69cudIsOAAMox4bN24Uf7m+8847jbqOcQShe/fujTp/5cqVKCwsBAAsWbLELDgAAJlMhtWrV4tFpJYtW9akKRHSOoybN1kjCAIeDTAvJqbiFSiqKrVoq0rKQXlmIa5kJ0Kv16O8qgJVWg2VYiaENJubOkAoLi7Grl27AAB33nknevXqZbVdt27dxOJMv//+O0pLLX8h1yYzMxP5+YZqeY0NEDZu3AjAsA/FE088YbWNQqHAk08+CQDIy8vD/v37G3Ut0rIMUwLVmzdpuc5mW0+JMxxrVC43tmcSCSQyKZSJ2ShOyoG/1B1cZ8hRuJydgLiMK4jLuIKLWVSKmRDSdDd1gHD48GGxFkNduRHDhg0DYNg/oqEfvKb5B40JEIqLi3H69GkAhi2va9v8ynifAPDHH380+Fqk5TEwRLtWlw+vbRSBMYbhW8ynIEw3cHIJ9oZLUDsAhsBDLpXhak6S2aNCU0VFlAghTXZTBwhxcXHi827dutXatkuXLuLzhq5IMG1vvE5GRgb27duHPXv22NyrwujChQtitro975O0jqJrGZCifqMIgkyCmpkE2sJy8TljgCARrj9nCPMMRIR3CCK8QxDqZb1iIyGENMZNHSAkJyeLz8PDw2ttGxISIj5vaJKh8YNaoVDgwoULuO222xAcHIzhw4dj5MiR6NSpEyIiIvDNN99Y/ebYkPv08PCAi4tLo+6TtBxBLgUTBPh0CwfXc+gqNWZbQNsaRZD6uEBwMV+d8kPh4VpHHGQSKWQSKSSC7a2kCSGkoW7qACEvL098XtcW0l5eXuLzgoKCBl3n3LlzAAzTEw899BBOnjxp0SY5ORlz5szBxIkTxR0uG3Ofpvfa0PskLcdYTdFIGZ+FsvhcMLBaRxEYYwh8zLx2iK1ERUIIsaebOkAoL68emq2reqHpcdPz6lJRUWE2hRAYGIjPP/8caWlpqKysRHx8PN59912x1PS2bduwYMGCRt+naZuG3CdpeRKFTNzd0adbOHRVWkQ6+ovHbY0ieNSSqEgIIS3lpg4QNJrqMrR1FRUyPd6Q5YOJiYniuTExMThz5gzmzZuH4OBgyOVyREVF4aWXXsKRI0fEqYGVK1fixIkTjbpP0zYNuc/g4GCbj6ysrHr3Q+rPdHdHIxmTmuUiVOotlyVaS1S0xdbUg0anFZc90tJHQkhj2CVASE1NRWpqKioqKhp0XnFxMf7880/8+OOPzXIfjo6O4vO69jAwHfavrQpiTTExMSgrK0NmZiaOHDkCHx8fq+169OiBN998U/z5q6++atR9mt5rQ+6TtA7GGCQyqdnPpisastQFVkcS6lvySHkt0+qHv+myR1r6SAhpjGYttWwUHh4OQRCwdetW3HffffU+7/fff8cjjzyC0NBQTJ06tcn3YfzGDqDOYMX0uOkHdn0FBFjW0q9p2rRpYvXDw4erS+o25D5N2zTkPmurkmispEjsT6/VQSqRQSHIEXF9/4UkVRZ0XA8pq1+SIZNIwAQGZWI2PCL9oddoIZHLIBUkkEsMyx5r6uAXAY1OC7lU1qzvhxBy87LbFENjvq0Yh9pzcnKa5R48PT3F53Ul9BmrGAKG6or24Ovri3btDGvYMzMzxdcbcp9A9b3a6z6J/Sjjs1Acn4Uol0BEutR/WaLpvybGANeg6mRW4ygCYwxh7YLEZY+09JEQ0hRNGkE4cOAA0tLSbB7fv38/lEplnf3o9XoUFRVhxYoVAAxL+ZqD6dbRqamp6N27t822qamp4vO6lho2hUxm+Q2u5n3WpqioCCqVIYPNnvdJmo8gl4rJigCQF5cMrtVDJq3/P791ibuwqPMUsSQ4k5qPInCtDkwmFZc9EkJIUzXpN4lWq8Wjjz5qdZMYznmjdkZkjJlVC2wK06JCFy5cwPjx4222vXDhgvi8rmJFpnbt2oWzZ88iNzcXzz77rFk9hZoqKirEEYLAwOpvdp06dYIgCNDr9Wb30Zz3SVqPMVlRU1aBvLOJAAzFk9w62f52L9Ga/5sq1apRWFmCdg7u1/s0jCKUpBmWyOq118sxSyU2N23ioBwEQkj9NWmKYcSIEXjwwQfBOTd7GNV8vT6P9u3b47333mvyGwMMZYuNGf91lU/et28fAMNWzHfffXe9r7Ft2za89NJL+PDDD7F79+5a2x48eFCcRrnzzjvF1x0dHXH77bcDAP75559a8xCM9wkAQ4YMqfd9ktbFGIPM2cGseJLxQ70mQSaBSzEsljpWFNSohWASBygTMlF4JQ3KeOtJiwBwMSueEhUJIfXW5LHIzz77DGPHjjV7bdasWWCMYcGCBbUO6xsJggAnJycEBwejT58+kDZg6LU2bm5uGDVqFH755RccOHAAcXFxVr91nz17FocOHQIAjB49Gu7u7vW+xogRI/DNN98AMKxMeOyxx6x+g9Pr9WY7Rc6YMcPs+IMPPoijR4+ivLwcq1atwvz58y36qKioEK/Vrl27OveXIG2LcSQhJ/YaAMO3fr1WB87Nv91LfVzgFBOA4VsysXNm9VLWa5v+gnTuSMgEKTzlrmabNxmZJi0CgFSQQGAMV3OS0MEvAlU6DRRSWv1CCKkb43b4SiEIAhhj2LZtW4NWMdjD4cOHMWjQIABA165dsX//frOliLm5uRg8eDAuXboEwPAt39i+PiorK9G5c2ckJRkyxxcvXoxly5aZBQlarRbz588XP9yHDRuGvXv3mvVTVFSEDh06ID8/Hy4uLti/fz9uu+028bhGo8GMGTPEXR+XLl2KJUuWNOSPwibjKobaVjqQ5qGr1CD75FXkxSVDw3VI1OYgA0o4+3ogzNlP/HvDOUfq+WtYxf+y2k+kSyBmRowCwMB1Ouh1eqgyCuAR6Q/GBHjFhIp9aXQaJOWno4NfBARBQM/gzjanIQghxMguAcLatWsBGD4I28ISukcffVS8p4CAAMydOxft27fHtWvX8MUXXyA72/AN7PHHH8fKlSstzg8PDxf3PThw4IC4NbTRgQMHMHr0aLGGQf/+/fHII4/Az88PiYmJWLt2rRiAhIaG4q+//rKaq7BmzRrMmjULQPXWzv3790dhYSFWrVqFM2fOADAEOsePH2/UckxrKEBoOZxz5J5OgFZdCc45LpWkoDAhEwVeOoS5+UNqsp9CQUUxPr66xWZfz3acBC+F2/V+gZKUHHA9h0ekP7w6hkC4Xn+Bc46EvBToOUcHvwh0C+pIyx0JIXWyS4DQ1lRVVeHhhx/G1q1bbbZ56KGH8P3331stPlRXgAAYajhMnz7dbLlkTb169cLPP/+MqKgom22WLVuGV199VdymuqauXbti165dCAoKsnq8MShAaFmcc+irtNBptMg4fQXHz5xAur4QHSM6QiaRmLVbfelXJGvzrPbjKnU0W9mg1+hQkpYHj0h/eEQFQpBKxKRF01GErkEdaJqBEFKnm7rUspFcLseWLVuwdetWjBs3Dn5+fpDJZPD29saYMWOwdetWbNq0qUmVCceOHYurV6/i7bffRv/+/eHh4QGZTIbAwECMHj0aq1evxokTJ2oNDgDgP//5D06cOIFHH30UYWFhUCgUcHV1Rf/+/bFixQqcPHmyWYMD0vIYY5AoZJA5O0AQJIZlinoOfY2gkDGGae0GYcIqGe5bK8WQ7ea5OaVatfkmTrUmLVYfpGRFQkh92HUEYdeuXfj222/x77//oqCgABpN/WrCM8YatM8AaRoaQWg95aoyHDm0H3FXL8DJxx3hLv4QpFIYUwQ458h8fx/UF7PBwbH5SQ3U1YU3Mb/DBPg5eF5vC6jS86HTVP/b8Yj0h2eHYAgyqdk0A40iEELqYrcAYdGiRWLhI6BhlRUZYzaH2EnzowCh9WgrqnBg/x4UJmQiTVsAP4k7HGQKuIb4mAUJ2jwVKtOVuPTDAbOVDTWnGTiHzaRFrV5LyYqEkHqzS8m1ffv24cMPPwRjTCwBGxwcDHd392ZbwkjIzUCikKGTTyQucj2KVBxlAAqyCtBB6ylu8sQYg8zXFXqNDlIrBZSKqkrFZEXGrhdLklhWWpRKpWZLHmlvBkJIbezyaW26U+HTTz+NpUuXmu03QAgxYIwhqE9HKFMBj4pAKBMyEccLoeN6WNu6yVhAyXSaYVXCb2ajCIZ+a1Ra1GghAAhx80dSQTrlIBBC6mSXJMV//vkHjDGMGjUKH3/8MQUHhNRCEAR0DeuEzkHtxddsfX4zMIz6yfxbv0WyYnVjkTIxC4VX0lB0NR2labm1VlwkhBDATgGCcb+BSZMm2aN7Qm46jDHIhOoBvfi0BOj11j/AXZWWZZhXJfxm8YFvWmlRfCRlQ5VZCF1lFbSVVc39NgghNxG7BAjGSoXOzs726J6Qm5LC0QESQYJuHbpAr9dDo9VYbVffUQTGAJdgb7iF+ogPl8B24vGjRw5Dr9c3/xshhNwU7BIg9OvXDwAQGxtrj+4JuSkJgoA77h4o/pypzrc5DWBtFCGnogi5FUUorCwRz2MMEKQS8SFXKCAIAuKuXoBOr0OFWm2vt0MIucHZJUB46qmnwDnHypUrxTLGhJC6KaRyCExAiLQd9ODQ8epv+IKsOm2RgWH4FvMc4w0p+/DZ1W346MpmrE3602pwIQgM0SHVxbqoaBIhxBa7BAgjRozAggULoFQqMWzYMBw+fNgelyHkpsMYQ7Tr9UqZNT63pT4ucIzxr/5Za7uGQaIq03riIgDp9c3UklRZKLyajrJKNQUJhBALdlnm+M0336Br167o1KkTLl26hCFDhqBdu3bo3LlzvWohMMawZYvtTWoIuZmx68sP1PnF0Lv6Vb/OGAIXD0P5mXRkfXwILsWAfwpDdpj1D3ctt15sTJBKESD1QGZmIXiHAFxKvwonJyfEBLSnwkmEEJFdAoSnnnpK/EVjLJaUn5+Pv/6yvnUtIcRAkEvBBAEekf5Iu1qADFUuwt0CzP49SX1dDc/BMGKLFK5vDYHg64yCyhJsSNlX5zUYAzyCfJGbUoIkVRbyc7ToFNyeCicRQszYbbMmzrn4sPZabQ9CblWMMQT27mAzD8GiPRg8JS7wdfBEu+vVFOt1HYHBX3BHoKM3VBmGZEiNTosqrQZV2vrtmUIIubnZZQQhKSnJHt0ScksQBAHRrkE4jky7XYNJJBAkAkqTc8H9BHCtDpezE8TjDjI5TTkQcouzS4AQFhZmj24JuWUwNOCD2caX/YLKEvG5lEngKXc1maowlGIuTs2FTJDiam4yBGn1KokOfhEor6qAk9yBggRCblG0cxIhN7jMD/YhfMUDFq/XzEeIdAnEzIhR1R/47PpeEI7ecGsXCEEmhU6vQ2qhYeTicnYCjSQQcgtrkQBBrVbjyJEjOHnyJHJzc1FaWopVq1YBAE6dOgW1Wo0BAwa0xK0QcsMzrYcAADqlGto8FaTu1rZ3qmZc+uhVI1eBMQaJHpAIEkgFCeQSGa7mGKYJO/hFoEqngUIqb943QQhp8+yWpAgAWq0WS5YsQXBwMMaMGYPXX38dn376KdasWSO22bp1KwYOHIgBAwbg8uXL9rwdQm5IvMYcgtTHBRJ3B7PX0v+3Gx4yF0S6BNTal62lj8rELCjjDSMHYe2CEOoVKB6jYkqE3JrsNoJQUlKCkSNH4sSJE5abyJgMVyYnJ4NzjqNHj6Jfv37Yu3evWKqZEAJkqPMR5uRnttQx6JVRSP3PDrGNTqmGLr8MMyNGo6iqVAwEai59NP2XaLqZEwB4RPqDa3UQZNLrFR0ZruYkoYNfBC2BJOQWZLcRhMmTJ+P48ePgnCMiIgJLlizBiy++aNFuzJgxCAkJAQCoVCpMmjQJKpXKoh0htxIpk0CAgDAHX+j1lksdZb6WowiV6Upo81TwlLvC18HT6tLHdYm7zPZpcAn2NtvAqfoYQ1i7IPH1Kh0tfyTkVmOXAOHXX3/Fn3/+CcYYZs2ahUuXLmHJkiW44447LNo+8sgjuHbtGiZOnAgAyMjIwLfffmuP2yLkhsEYQ7jUG6qMAqjzilHzc9lQVXG42WvZnxxCyos7kPnBPvGDXMrM8xJq7vpo2Myp+teA8lqmSRBQPdJ3JTsRcRlXcDHrGgUJhNwi7BIgrFu3DgDQvn17fPPNN5DLa09wksvl2LhxI6KjowEA27dvt8dtEXJDEORSSB0V8OkaDo9If+i1Omg1Gmj1Omj1OvEDWpBbT0pUX8iGNs8wCucpd4WL1NHsuKZGHgKTSMAEBmViNjjXg2sNx00TFo2PCk0VyqsqKEgg5BZglwDh2LFjYIxhxowZkEhqz6w2kkgkmDVrFjjnuHDhgj1ui5AbAmMMvr2i4N09QnwtIT0RqWW5SCvPRcb1baBrbt5kKv1/u8E5B2MMMyNHmx0znWYwXM9QE8FIr9VBr9GCa3UI9QpEhHeIWdLi5ewEGkkg5BZglyTFvLw8AIYRhIaIjIwEAJSWWt+FjpBbBWMMTq4uUEgV6NahC5SJ2XBz9IEglSBJlQUd10MqSBC4eBi0eSpUZSiR9fEh8Xzj0keZrytkNqYZzJY7mpQ5UCZUV3CUKOTwiA60uvyREhcJubnZZQTB2dkZABqcbFhUVAQAcHd3b/Z7IuRGIwgCBgwchI5uoYiU+iHcwQ96rQ6cVy99ZIxB5usKp57BVpMWNbml8JC5WEwzrEr4zXwUwWRFg+lDV1kFrtWJSYumIwlVOkpaJORmZpcAISoqCgBw4MCBBp23Y4dh2ZYxF4GQW50gCJAJUsiYBKrEPJSk5kGdV4yM8vwa0wS2kxazlu/HY5FjzY5ZS1Z0CfaGW6gP3EJ94BJUvbJBr9FCr9ECMBRTMrqSnUhTDYTcxOwSIIwdOxacc2zatAmnTp2q1zk///yzuPJh9OjRdZ9AyC1ATFjsFo6AbpHwigpEEDyg0+ksih7VlrToVszqHkVggCCVGB4Sk5UNiVkovJIGZXwmJEwwS1ys0FShSqdpxndMCGkr7BIgLFiwAO7u7tBqtRg9ejR27txps21ZWRn+7//+D9OnTwdgmJ6YO3euPW6LkBuOMWHRv28H+PaMQrRrdW0CY7KiUW1Jixnv7qlzFMHsulamHHSVVYBOj7B2QQjxqq7YSJUWCbk52SVJsV27dli1ahUeeughFBQUYPz48fDx8THLLZg6dSoyMzNx8uRJqNVqMeP6888/h4+Pjz1ui5AbEmMMEoUhGVDGpBAgIMLZBykVudByHWRMKrarLWnROIqg0qrF122VXjZOOXCdDnqdHqqMAgCGQkoCY3CQKqjSIiE3ObtVUnzggQewadMmuLm5gXOO3NxcxMfHi+ViN23ahCNHjqC8vBycczg4OODbb78VRxIIIZaMBZSMao4i1Ja0WJVRjIed72zAtWA53XC9kFLNSosanRZVWqq2SMjNxK6bNU2cOBFXrlzBa6+9ho4dO4JzbvEIDg7G008/jYsXL+Kxxx6z5+0QclOQQgKBCYhwCQDnlmWYAdtJizmfHDJ7TV+PD3NbhZRM10Zezk5AXMYVqrZIyE3E7ts9+/r64q233sJbb72FoqIiZGRkoKSkBE5OTvDz80NAQO27zxFCzDHGEOUSiAR1FgDL3R6NbCUtmlqXsAuLuzxstoGa5fUMhZRK0vLMXq9ZG8GIphwIuTnYPUAw5enpCU9PT7PXzpw5gx49etT6C4oQYo6ZfHuvudujkTFpUX0xW3xNojVvo9JXWBZNsn5By5euTzNo9YYRBa1ei7RCQ9Ci0WnFdjKJlP59E3IDsusUQ0lJCZYuXYr27dvj0KFDFsdzc3PRu3dvBAQE4M0330RFRYU9b4eQm4aUmU8zVOm1Fns1GJMWw967HyHvjEPIO+MQPXMQHGvUL6u53LEhGGOQSaSQSaSQCtXfN2jKgZAbn90ChMuXL6N79+54++23kZiYiMuXL1u0SUxMBGAozfzWW2+hX79+yM7OtmhHCDHHdXpEuVRXNcxU5yOt3HyvBqA6aVER5AFFkAccgj0x6ifzof/aljtaI+7VUOND39rmTrTBEyE3LrsECOXl5Rg7dixSU1PBOYejo6PVHR39/f2xcOFCBAUFiZs0jR8/Hjqd9aVXhBADZXwWyuJzIRdkiHAJMHto9FqriYuAYdrBgztYjCLkVBTV+wNcmZApFk6quYIirF0QIrxDEOEdYlYrgTZ4IuTGY5cA4auvvkJycjIYY3jooYeQlZWFWbNmWbQLDw/HihUrEB8fjzlz5oBzjhMnTuD777+3x20RckMzraro0y0cuiotIh380dEtFB3dQhHlElRnH4wxBL8yGsO3mKcfbUjZh7VJf9r8ALdVOKl6RUN1/8YpBwepwqLqomluAiGkbbNLgLB161YAQM+ePbFx40a4urrW2l4ul+Ozzz5Dt27dAAA//PCDPW6LkBuasaqiT49Is9dkghQyQQqpUL+t1WW+LpA7Oli8nqjKxJWSNORWFKGwssSiDLNxrwaXwOp9GmobEbC2wZNGZzk1QQhpm+wSIFy8eNGwD/3MmfU+RyKR4JFHHgHnHGfOnLHHbRFyw2OMQSJr2uIjxhg6zhkB/xTLlQU/puzFZ1e34aMrmy1GFKr3arAsnFTr/ZoELjTVQMiNwy4BglptKOXq72+9Lrwt4eHhAIDS0vonTBFCGk6ikGLEFimGb7YdbCSqMpGgsgwAbBdOsq5m8iJNNRByY7BLgBAcHAwAiI+Pb9B5aWlpAAAvL69mvydCbiU6rheXPZoufTSS+rhA6u6IgFRmdSTBaF3Sn1ZHElyDvG2eU5OtqQZjaeaaDxpdIKRtsEuhpK5duyIhIQFr167Fiy++CKm07svo9XqsW7cOjDH07NnTHrdFyM3Hxmdppjrf7GeZIEWQo7dYsMhYijnttV8xYosUKnfA5/lBUHkYphlMJaoyLYspNbDukbWpBlscZHLEBLSn4kqEtDK7BAgzZ87Ejh07EB8fj0cffRTfffed1WWORjqdDvPmzUNcXBwYY5g8ebI9bouQm07RtQy0iwkFYwxSJoFCkCPCxbJ8eZIqCzquh5SZJDIy438YXIsBz0KGwJAgRLoEIFGVZXa+xsaujw1hqzRzTVSqmZC2gXE7jOfpdDrceeedOHHiBBhjiIyMxBNPPIG7774bYWFhcHR0RHl5OVJTU3H06FGsXLkSV65cAWAYfTh9+jQEwa5FHokJ45RQenp6K98JqQ/OObKOXQbX65EXlwyvjsGQyKXiMdMtnLV6HRJUGUhSZSHEyddspUNlhhJpr/5q1rdjF38EvDAUV0vTzUYSXKWOWNR5ivitXq/VoSQ1Dx6R/vCIDIDEUVGvb/ycc7E0c006vQ6phZno4BeBbkEdKUAgpJXZZQRBIpFg69atuP3225GZmYnExES8/PLLtZ5j3Nlx586dFBwQUgvjcsec2GsAzEcRGGOQMev/rGtu6iTILJdFqi9kQ5dfBl93D7PXS7VqXClJg6+DBzzl5suWlYlZkCjk8IgOrDNIMNZJIIS0fXb7JA4KCsKFCxcwe/ZsCIJgdatn40MQBEydOhWnT59GaGiovW6JkJuGRCEDEwT4dAsH13Po61hJAMCsBDNQvZlTTfoqHTzlrnCROpq9/mPKXnH5IwTBrHCStaJJhJAbm12mGGoqKirCH3/8gX///Rc5OTkoLCwUt3vu27cvxowZIw5zk5ZHUww3Jm1FFXJiryEvLhke0QHXaxRIzL7Fc85xqSQFeq5HkioLwU4+kJlsqsQ5R/mZdGR9XL2ZmsTDEeErHkBhVSk+vrLZ6rVnRIxCpHMg9FVaqDIL4BHpD6+OIRCaUKNBo9MiKT8NHfwi0NE/EnKJ5RQD7QxJSMtpkbE+T09PTJ06FVOnTm2JyxFySzD9oFTGG5IKpQoZPNoHmq1WiHYNwtUSwxLimltDM8YgD/Iw61enVEObp4KXj6vVhEXAsPwx0iUQ00OGi68ZRzFYjSClMa5kJ1p9nVY4ENJyaDKQkBuU6d4MRnlxydBrdGLSIgDImFTcGtraagapjwsk7g7QFVdvt16ZroReo8NU5wEoDeTIqyy2vvxRUyr+ElEmZAJAvfMRaqrPKgda4UBIy2mRACE9PR2///47Tp48idzcXKhUKuzda/hlc+DAAWRmZuKhhx6CTEb/6AmpL2Oyor5KC51Gi7yzhm/dpkmLxnamowg1kxVNayIYZX9SPeXg2MUfHV4YanU0IU9TiiCpI5SJ1du0e0T6g2t1YA2cbjAWVLK2ysG4wgGA1SqMNPVASPOza4BQXFyMZ555Bhs2bBC3cOacm/1D/vPPP/H+++/jtddew+rVqzF48GB73hIhNxXGGCQKGQS5VExazItLhraiyiwngZlUNqo5zQAAgtz2Rk/GlQ0zI0bjSkma2UjChpS94lQD13OoMgoANH66oT6rHKwVWaKpB0Kan90ChMzMTNx9991ITk6utXSq8XhKSgpGjhyJrVu3Yty4cfa6LUJuSjWXPtbMSZAyidk0g5brzJZDGlc0qC9mW+2/Ml0JAPBxc7c4lqjKRLG+HB4SZ/G1pk431FTX9ANNPRDS/OyyioFzjttvvx0nT54EAAwYMACzZ89GQUEBFi1aBMaYOKKwd+9evPbaazh+/DgAQ0Lj5cuX4ePj09y3RWygVQw3B845ck8nQKuuFF/Li0uGV6dgSGRSVOk1uFqShiRVlmE4v8YoAucc2jwV9BodNDmlZtMMRg5d/LD3AS0Sy8ynGlyljnih0xSUZRRAp6meAmiO1Q1m91dj+kGr1yKtMIuKKxFiB3apg/Djjz/i5MmTYIzhjTfewJEjRzBz5kxERUVZtB0+fDiOHTuGZ599FgCgVCrx1Vdf2eO2CLmpGUcR/Pt2gE+PyOoD178CmCYrcs6h43qL82W+rlAEeUAR7GH1GhUXcjDVZQBmRIwye71Uq4ZSUwqXYG+4hfrAJbBdc7616vuTSM0eUoHyrAmxF7sECBs3bgQA9O7dG0uXLq3XOR9++CF69+4Nzjl+++03e9wWITc9Y06CxOQbe9G1DDH3J9o1qF792CqiBABco0eUS6BFIaWciiLkVRZBqSsDk1SPTOi1Oug12kY9aGdHQlqPXcLvU6dOgTGGhx9+uEHnTZ06FadOnRL3ZSCENI5F0qK6CoJMAr1eB71WB84tVzOYMqxsGAZtngqV6Uqz6YbMD/YhfMUDmBk5Gp9f3Sa+viFln/g80jkA96MngOp8hMZorhwGQkjD2SVAKCgwZDI3tGyycS68vLy82e+JkFuJRdJigiFnQMN1KNHmQY1iZDCGMGc/mx++ximHmoyFlGTutlc+JJZlodQtBizRetJjfTVkyWSVTmP2My19JKRp7BIguLu7Iz8/H4WFhQ06LyvL8EvM09PTHrdFyC1FopBZFFLinENZIgESgAKdznILaCusFVLSa3TwlLvbrLQIAGWegFwihVSQwFPm2qAPa71Ob7Fk0pS15ZM1qy/S0kdCmsYuAUKnTp3w119/4Y8//sCTTz5Z7/M2bdoExhhiYmLscVuE3FJMCykZ6TRaaE5rcRyZQB3TDKb91CykZHx9ZsRoFFWVQst1KKgsMZtmMJtycAnEzIhRjfqwtjZFYZx6qG35Iy19JKRp7BIg3HfffThy5Ah27tyJP//8E6NGjarznI8//hjHjh0DYwz33HOPPW6LkFuOMWnR7LXrRZPU+cXIkEpqnWYwOclm/14KNwCodSQiUZWJBFUmolzql0/AJBJxt0hrjFMPgkxqUX3RtOpifQIgQoh1dlnFMHfuXPj7+4NzjgceeACffvopSktLrbZNSkrCU089heeffx7s/9u787Aqy/x/4O/nrICAgOy4QOaSa4qmOW1mFo42kpUzGS59TVOrabEm0+ZbTttUU858dcwpS/1lpZVLVmoL4lLZKJVAYrggiiyCIpsInOX5/XE6j2fnrJzD4f26Lq7rgec+z3Pj8XA+574/9+cWBHTr1g0PPPCAL7pF1OnJVAoo5UrE9E5Gd1kMdDodWvWurxbQnG2ApqrB7HHRKsPmTvb8v5NfYt3JL526lyBAWjJp+hWeYr180nL5o1x2OVAprDjOlRBEbvLZds/fffcdxo8fj5YWQ9EWuVyOLl26oK6uDoIgYNSoUSgvL0dp6W/14UURCoUCn3/+OW699VZfdInsYKGkzkXb3IrSg4U4cOggzsfoIFPIoJQpkBIaa/fTfUtZLUqXfG7189CBiUh+Ypz0OFEUpSmHmpYGqw2eAODRfndJow6u0mt1qD9djagrEhHVOxkyhSEYMM1JEEURJ6pPQS+KLKBE5AGfjCAAhuqJ2dnZ6Nmzp6ECmlaL+vp66UX83//+F6WlpRBFEaIoIi4ujsEBUTsQBAFKQQGVoEBsrQKpXZKg0WvRqtdCq9dBq9dZfeqWKW1PH1w6XAltdaPZtWPUkYgPiUa/yB42RxSqmmu98qm+9kQ5aopKUVNUitrj5dI1jZs+EZFnfBYgAMC1116LoqIirF69GrfddhsiIyOlgEAURahUKlx33XV4/fXXcfz4cZ8HB1u3bsXEiRMRHx8PlUqF7t27IzMz02uFmRobG/HPf/4T48aNk+4RExODUaNGYenSpdLyT0diYmIMm+s48fXDDz94pd/UuchUCsjkcoy8eiR6yrpB/G2VQPmlcyhtqkJpUxXKLp0zexN3VDhJ32q9ygC4nMQ4rdctZj//4NQ3Tk81WF3TJDfB9EvX0ir9Hr+1lI40Oi1atRqzL047ELXNZ1MM9jQ2NqK+vh5hYWGIiopql3tqNBpMnz4dGzdutNsmKysLa9asgULhXt7mDz/8gLvvvtvhMH1UVBQ+/PBDZGRk2Dx/+vRp9OrVy+l77t+/H6NHj3a5r5Y4xdD5aJtbcfbHY6jKP4lT+nMI6dUNprMLJxsrkBwaC5VMYTZ0b6twkjwqFKnLptidnqhpqcc/iz6x+rm7Uw2iCIi/7eWi1+rRWH7eas8HjU6Lk+dK0TchzeY1uASSqG3tXsg8PDwc4eHh7XrPBQsWSMFBamoq5s2bh169eqGoqAirVq1CZWUl1q9fj6ioKCxfvtzl6xcVFSEjIwN1dXUADCMnU6dORUpKCqqqqrB582bs2rULtbW1mDx5MnJycjBmzBir6+Tl5UnH//u//4thw4Y5vG+/fv1c7isRYKiRIMhkiB+SBuQDkWFJkCsV0Op1ONFYBsAwomCam9Bm4SQb54DLyYuW9RLONl+AVrQefVAIckSr7NdNEARDzoEl03oJcrmMuz8SecirIwg1NTU4dOgQqqur0bVrV6Snp/t9V8Z9+/bhhhtuAGDYGyInJweRkZc/tVRVVWHs2LEoLCwEAOTm5iI9Pd2le2RkZODLL78EADz77LM2959YtmwZHn/8cQDAgAEDkJ+fD7nc/I/cCy+8gL/+9a8AgOLiYqSl2f70420cQeicjKMIpjs+iqKI4w1laNG3AjCMJHQPi4PSZFMkURRR8ugms8JJPV6cBHVKlN17iaKIovpSm0mLtjhbN8E0adGUXK1C195JVhtSGZdA9k1IQ//E3lDKDb8Xqy4SWfNKDkJJSQnuuusuJCUlYfz48Zg2bRomTpyIxMRE3Hrrrfjpp5+8cRu3vPLKK9Lx6tWrzYIDAIiPj8eGDRukPw4vvviiS9cvKSmRgoPf/e53djeneuyxxzB58mQAQGFhIfbt22fVxjiCEBkZidTUVJf6QeQqW2+Ixg2deodfTvKzzEcwFk5y9V7xIVFOty9uLMeFVttLo82u6yAnATq91e6Ppksgf608gYKyIhSUFaGw4hjzEogseBwg/PDDDxgyZAi2bNkCjUZjloQoiiKys7MxZswYfPDBB97or0vq6uqwc+dOAMCYMWPsDtkPHjwYN910EwBg+/btdms22PLVV19Jx9OnT3fY9o9//KN0bCvB0BggDBo0iJ9mqF3pNTqzVQAhcpXDraHtFU5ypK06CZZsTT9YsqyX0NY206aVF02/mjWt0Oi0Dh9L1Nl4lINw6dIlTJkyBY2NhmVOCoUCo0ePRvfu3VFbW4v9+/ejrq4Ora2tmD17NoYOHYqBAwd6pePO2Lt3L3S/JTPdcovjTzzjxo1DTk4OWlpasGvXLunTvjMGDRqE0tLSNnMCYmJipOMLFy6Ynbt48SJOnDgBABgyZIjT9ybyhtoTFVColYjqkyzlG1wZkYKj9YY6JVpRB+gBuSCzGbxqqxqgSu7qMLC1LM1sybJUs7Ps5STY64Np5UXTqotEZM6jAGHt2rWorKyEIAi45ZZb8O677yIl5fLQ5KVLl7B48WL861//QmtrK9544w288847HnfaWQUFBdLx4MGDHbY1DVzy8vKcDhDmzp3r9H4Thw8flo5jY2PNzhUUFECv15v1tb6+Hnl5eWhsbERCQgKGDBni9ioLIksylcJsM6fqghLotTrIf1sJIJgME1RcMizRNSYtWqr41x6rokm2mJZm9hdj5UVL3hpBYD4DBQuP3m2Mc+9XXHEFvvjiC6s3r9DQUCxbtgzHjh3D9u3bsW3bNk9u57KSkhLpuK05/R49ekjHp06d8npf9Hq9WXB07bXXmp03XcGg1+sxefJkfPHFF9IICGBYJjl37lwsWbLEKpeCyFXGzZw0jZdQnf9btr/JNLxCkEMtUyHNZFrgZGMFWvVaCArr2Ulj0SR7qxlcdb6l3upnba1w8MSvlSe8ch0uoaRg4VGAUFBQAEEQMHv2bIefbBcsWIDt27ejpqYGJSUl7ZaAV11dLR1bfmK3ZDr870xBI1etWLFCWilx5ZVXWgUI+fn50vHDDz9s8xq1tbV49dVX8dlnn2Hnzp3o2bOn1/tJnYsgCJCrLi/1u3CsDN0G9DSbZtCKOqvlj4pwOUIHJOJSoflmSnpN23kDzrI33eDJzpCWHO0G6S4uoaRg4VGAUFVVBQBtFvcZMWKEdFxeXt5uAUJTU5N0HBIS4rCt6XnTx3nD999/jyeffFL6/qWXXrJa4mg6gqBWq/H4449jxowZSEtLQ21tLb7++ms899xzOHHiBI4cOYLbb78dP/zwA0JDQ53qg3Epoy0VFRVISnI+eYyCi0ylgCCTIW5wqvU0w29lmS1HE042ViDx8euhy68yK5qkOdsAmVIORVy4y2/gjnaDNGVc4eCNqQrLnARPMJ+Bgo3HSYoAEBYW5rBdt26XM4tdWSHgKY1GIx2r1WqHbU3Pa7Xey2bOy8vDpEmT0NpqWFc+Y8YM3H333VbtjDUIunTpgl27duGaa66RziUkJCArKwsTJ07ETTfdhPz8fOTn52PFihVmgQeRO4xTDWd/PAbAvOCQ7LdNkIyjCc26VmkkobK5BvJI89eKMVhwJh/Bkr2CSrZYFlkyTj2YMv09TDdzsmQvJ8ETruYzMG+BApFHrwq9Xi/98XDE9NOycXfH9mD66dr4Bm2Pab9UKpVX7n/gwAFkZGRIKxZGjRqFN99802bbkpIS1NXVoaGhwe6n/ejoaLz77rvSiMyqVaucDhAcFUFyNLpAnYPpa7j2+OU3aMuVDSFyldlIQrHc9ry9O/kIjlY5WK5wsDX9cEV4Mqb3uLxaqfbE5U/zcrUKUVcmt9ubsKv5DMxboEAU1CnxpiWdm5ubHbQ0P+/ssL0jO3bswNSpU6UloMOHD8eOHTscjrZ07doVXbt2dXjd9PR0DBgwAIWFhSguLkZZWZnZyhEid1iuaDCqLiiB9lIrZEq5NJpwZUQKLulaUNxYDsSGIuSqBDQfOWt1zZYztQDg0nSDvVUOzkw/FDeWo1bXBNVvhZNMRV2RCFGrg6D03Z88T/IZmLdAgSioA4To6Gjp+Pz58w5zJWpqaqTj+Ph4j+771ltvYcGCBdIKhNGjR2PHjh1e25zqqquukhIey8vLGSCQx4zTDPpWw9C4TqNFdV4xAEONBMB8NMFYelkQBGgeHISeF0ehtazOLB/Bk+kGS85OP+igQ3j32MubOen0aCzzftKxLe7kMzBvgQJZUAcIpoWLTp8+jeHDh9tte/r0aenYkyTKJUuW4KWXXpK+z8jIwCeffIIuXbq4fU1LSiU/ZZD3CYIAudrwf8vWiIJpAqNCkEuVFk82VkCMDYXcstrib7yx/NHe9IOt4kquFE7yNk/yGbxRh4G5DORNQR0gmBY/Onz4MDIzM+22NS1i1FZRJXsefPBBrFy5Uvp+5syZWL16dZvFjQoKCvD111+jqqoKt956K26++WaH7U3zCZKTk93qK5EjpiMKpqMJxjoJlpUWyy+dg9hFBPpFA0UXrK7XcqbWrSWQpisiAqHIki95ow4DcxnIm7wSIBi3MvZm2xkzZnjWKRiG9tVqtVQ+ecmSJXbbZmcbPoXI5XJcf/31Lt/rL3/5i1lwsGjRIrz88stOPfaXX37BwoULAQBlZWUOA4Ta2lrk5uYCMBR34vQC+YrpiIKRaZ0EpaCwKqQkLkrCyZJixNYoULX88oZkplMPrnJlisK0uJJCkKOrzPEKK3/zdh0G5jKQN3klQFi+fHmbbYwvbmfbeiNAiIyMxG233YZt27YhJycHBQUFNkcH8vLysGeP4Q9YRkZGm4mClj766CO89tpr0vd///vf8dRTTzn9+LFjx0Iul0On02HLli2orKxEYmKizbavvvqqlFA5c+ZMl/pJ5A57dRJMCykBgEavRXFjOYS4MMjbqDviClemKCynG67okoTJuNprffE2b9VhYC4D+YLHuzla7t7orS9vMX4yF0UR06ZNM6uuCBiKPd1zzz3SPV2tK1BdXY158+ZJ3z/yyCMuBQcAkJiYKO30ePHiRUybNk1a/WBqzZo10vbV8fHxePTRR126D5E7jNMN9s4pZQrpy+hs+CXDdIOX2JueaGt1Q/HFCtSJ3i185m3GvAVPvky3sSbyFo9GEDrCJ9gbbrgBM2fOxLp16/DLL79g6NChmD9/Pvr06YNjx45h5cqVqKw0LImaPXs2brzxRqtrpKamSvsz5OTkSFtDA8CyZcukOgfR0dEYM2YMtm7d2ma/evbsaZY0+frrr2Pfvn0oLS1FTk4OBg4ciLlz56Jv3744d+4cNm/ejG+++QaAoU7D+vXrzQpQEfmSM8P7ltUWxUVJ0FY3orSuEvHqaChlckMGoRM0ZxusKjSaMuYmOLO6QWsneTJYuZLsyKRGckQQvflxPUC1trbinnvuwebNm+22mTp1Kt577z2bRZIcBQjJycmoqGi78pulmTNnYu3atWY/O3bsGO68806zXSgtxcfHY+3atZgwYYLL97THWCjJUTEl6tx0LRpU5h5FdUEJYvp3l0oxWxJF0WrK4eRvb97GnSCdeUNqKatF6ZLPHbYx5iYAMFvdYLmyYUbo9biyTx9E9U6GPEQVlG+IGp0WJ8+Vom9CmkuPY1IjORLUqxiMVCoVNm3ahC1btuDdd9/FwYMHUVNTg65du2LkyJGYM2cO7rjjDpeve+7cObeCA3v69OmD3NxcvP/++9i4cSN+/vlnXLhwAV27dkXv3r2RmZmJBx54wKy+A1F7M5YwltkoX2xMXgSsRxRONlZAJ+qdKnokU7bdxjQ3wdHqhk3NB/AX8UrUnihv94qK7cXdZEcmNZIjnSJAMLrjjjvcCgRMt402FRsb69V8CcAQzNx333247777vHpdIm8xlmI2LZxki639G3SiHnJR1uYbtCIu3OZukZZs5SZYBiAXxRacOlGMKFmXdqmo6A+uJjsyqZGcEVyvEiLyibYKJ9kjCAIUJgl05ZfOOTXVIAgCkp8cB211o1kQYC83wbReQrQqAuGKUDRqL0ntLkYJ0F5oQGuzGi11aqjUIYgNjw6qkQRfbDpFnRv/NxFRm8wKJ7VqUJ3/21C2EwNotqYaWvVayAXbi6jkgkwqjNTW0kbTYMG0XsLMKzLw76NbpHMby3cbDo7+9gWgf2JvPDx2ZlAFCe5o1WnabhTgmGzpGwwQiMgpbRVOcvQ4y6mG8kvn7LZ3NMLgKDfBNCdB6USew6+VJ3Cu8QLiImLabBvMiiqL/d0FjzHZ0jcYIBCRS+wVTnLE1lbR9jhKZmwrN8E4HeHs5k6aIPj07A5vV3D0NyZb+gYDBCJyiXG64eyPx1x+nGnlRUtavU4aYXB0DdPcBEc5CTNSb0OtphFaUQdRBBrLz+OC7iK2Nf8ota9uNOziqpQrgy4nwRFvVXD0NyZb+hYDBCJymbtvpKbLID25t73cBHs5CQAQ1zMSYon5cPp/9n4gHXe2nAQmNVJb+L+DiAKOVtQBNgogGhMYjZzNSQAACIDCTmIkwJyEjs4b22UHkkBIvGSAQEQe0Wt0NosmeaLi0nmbP7dMYHQ2J8GoqxDmMDehtaUZ+hDfvNEIXv43InPe2C47kARC4iUDBCLySO2JijaLJjnDcjmkLZYJjG3lJFgSBAEz0zKk0syWZZmLi46iTu24OJMzv0e0KsLq3yJYqzj6U7AlW5oKhMRLBghE5DLLwknOrmZwxFESo2kCo07UW00/GIskOby+XA65UoG6k2chA6ACoNS1mrWx3C7aXT3l3XBnyDVmfQrWKo7+FCzJlqYCKfGS/1OJyGXGlQyaxktS0SRHezS4ct22khht1VAwTj04vjYQ3j0Wou7ym4m2VQ0cd6urDp3WnYcuKQQxqkjodXo0ltmeMiHPMdnSd/ivSkRuEQQBctXl4U9n92hwR1vTD8aph7YIgiEXwKibPMqpegnu0MsMwRJRR8UAgYjc5u4eDa6yN/1g3FLak+ua5iR4wjKf4XxLPQDDyEqj7vI+EIKNoKGz1WGgjoEBAhG5zWyPBo0W1XmGOgPemG6wdS9PayjYu66j7aLdZZXPYLIPhC2drQ4DBT4GCETkEVt7NPhyusEVtnZ79BVbpaFdwToMFGgYIBCRV7TXdIMrHFVW9DZn939wpLPuDUGBiQECEXmFvS2hjdMN7nBlikIr6iA6+BBvVVnRyxzlM4giIOp00OtFNJ2tRWSPWHRNTcS55jqzcs/GvSHcxVwG8iYGCETkNY6mG9zhyhRFxaXzELuIQL9ooOiCzTaWlRW9ra18Br1Wh3q5DlEh0YjpGm+VsGgaLLiLuQzkLQwQiMjrbE03uKOtKQpbyx/FRUnQVjeitK4SsfUqVC3f51EffEkp936VPOYykLcwQCAirzOdbnCHrRURpoxTD7aWP2r1OpwQyiCENUIeonLvF2gHeq0OMeoI9ItPQ1GVd0sFM5eBvIEBAhH5hK3pBnfYmqIwnXpwtPyxqtn2VEMgqD1hqN8wLeEmXIhOd6kOg0ylRETPeBgnEaoba7wyPUFkigECEQWctqYoHE09KAQ5ZIIMaeFJKMYxH/fUNcb9IGqLzTeEMu4N4ayoK6IR06UbZHamXpxNdmRSIznCAIGIAo69KQrTqQdHj70yIgVH60utzun0emj1OsgFmV/eFG3tB+EKZ/d1cGU0gUmNZA8DBCIKSG1NUZjmJlguhxRg+82usvQMENUMlVyJlNBYvwUJtsote8KTZEcmNZI9Mn93gIjIHbXHK1Dz6xnU/HoGtcfKIYqidM64uiElLM7sMfq38qFeVYhWncapzZ06itjwaPRLuMLtxzOpkWzhCAIRdRj2chMscxKM0wx10WpYTjRcOlwJ2bk0oEv79Lk9CIKAP988C+caLzj1Zs+kRnIGAwQi6jAscxNMKzbaats1KRZxQ9Ks22j1hhGE3wYR/JWT4E2CILg9TeBpBUd/Y7KlbzBAIKIOxV5ugq2cBEEQMGZpFioPHsV/X9po1r780jnpWClT+C0nIRAEw2gCky29jwECEQUF03oJlnUSuiRGm7VNCY2DOjxK+v5kYwV0ot7jHRk7Cl9UcPQ3Jlt6HwMEIuqwnM1JsFT1+m6MW/0wdKIeJxrLAMBsysETHWG6wpjUWHTW8ZLRjobJlt7FAIGIOixncxIsA4WWC43QVDVAlXB5YyXTKQdPdITpCleTGgMVky19iwECEXVozpR0DkuMhjo6HC0XGqWf6TRam5s9eaqjTFd4ktRInQMDBCIKeoIg4HfPZWHXI6ukn12sMOzTEC8qoRM9fzPXiTqcbqqC2NSI1hA1dDLzMjMypRyKuPCAHlkgMsUAgYg6B4v35f++vNF2Oy8ot/PzkAEJSFg41ipI6Ah5C9T5MEAgok7BXsJie2ouPIvS06cgxIWZ/bwj5C1Q5+P/VwwRkQ9Y1kUIS4y2XTSpnaWoupktsQQ6Tt4CdS4MEIgoKNmqizBmaRaaKi9Ap9E6eKT7RFG02uPhYuUF/Pj3T6Tv07okISLSsEeEVq9zaZmlXq+DVtRBq9dBo9PCIs0BCpmcoxDkNQwQiChoOFMXoUtS+2buK2Ryq++VMus/vc4ss9Tr9GjS1aKhSYba84aREVMquRK9uqUwSCCvYIBAREHDqi6CRovqvMAqBnSx4gLkSgXCEqNdXmap1+pQr5AjKjwRMfE9ILPIqzh69iS0eh2Ucv5pJ8/xfxERBRVn6iL4k3H1RNzQNIx5LgtXRqRAK+raeJSBTqNFjUKGuMieSEjqI/2eGp0Wv1ae8FmfqXNigEBE5EP2Vk9U551EU+UFdEmKgVJw7k+xTAYoBcMUhUqhhFwRuIEQdXwMEIiIfMjR6glfJEvq9M6NRgDBl9TY0betBgyJq+cbL6BcHYZwdRhSY7tDrVD5pS8MEIiIfMi45XRT5QXUn672aYEmADhdY69Mk7VgS2oMxn0ZPrz/X7girqdf7s0AgYg6Bb1WB5nCP5+YBUFAl6QYqxEDY7lnZ+m1OjSdrUV91yqoIsIQ3TcFihAVlHIFQpQq9E1Ic+l6HT2pMRi3rQ4kHfN/BRGRi2qPV0j1EALlE7Onowl/2vcPdOvfA4IgYEBSH2h0zk1ZBEtSY7BuWx0oGCAQUdCyrItgWg/BH3x5X0EQoOpkSYvBsm21Ka1eh7ILlejVLQV9E9KQEp3ot74wQCCioGWsi6C52BwQ9RACpdyzKa3eN1Ul21NseHTAjAp5SqPTolnTguSoBKTF9vBr0McAgYiCmiAI5p/cRf/2xZNyz3qtDnXFZxHdNxmxA1PRNTXB4z6V1lS03SjABVuyZaBggEBEncqFY2XoNqCn395MjAmL7tBptNBcbEFkz3jE9OvudkEod5MaA1VHT7YMVPzXJKKgJ1MpIMhkiBuc6vc8hEDgalJjoAqWZMtA1XlfIUTUaRhzEc7+eMzfXQkYnTGpkVzDAIGIOgXTKQW91vlqg97irxoMRO5igEBEnU7t8fZPzAu0GgxEbWGAQESdgmVNhPbG3AfqaPg/lYg6BWMegr61fRPzdBptQNRgIHIVAwQi6jQEQXB7aaA3GHMfmI9AHQEDBCKidmLMfWA+AnUEDBCIiHzIVu4D8xGoI+D/TiIiHzLNfWA+AnUkDBCIiHzMVu6DO7UY/FG/gTovBghERH7gj1oMRK7oVAHC1q1b8fbbb+PgwYOora1FfHw8RowYgTlz5mDixIkBc4+ff/4Z//znP7Fnzx5UVFQgMjIS/fr1w7333ovZs2dDpVJ5pa9E1L68VYtBEaqGTNWp/ny3SacPjtGVQPo9BFEU/bj5afvQaDSYPn06Nm7caLdNVlYW1qxZA4XCvRedt+7xxhtv4C9/+Qt0Otv/SdLT0/Hpp58iJSXFrX7a0r17dwDAmTNnvHZNIrJNFEWPazHIVAqugADQqtWgoKwIR8+e9HdXvK5vQhoGp/Tz634ZnSJAmDNnDlavXg0ASE1Nxbx589CrVy8UFRVh1apVqKysBAA89NBDWL58ud/u8f777yMrKwsA0KVLF8yfPx/p6emoqqrCu+++i7y8PADAiBEj8O2330KtVrvVV0sMEIioIxJFEYUVx9CsafV3V7wuRKnCgKQ+fg0Egz5A2LdvH2644QYAwPDhw5GTk4PIyEjpfFVVFcaOHYvCwkIAQG5uLtLT09v9HrW1tejduzdqamoQFRWFffv2YdCgQdJ5jUaDmTNn4sMPPwQA/OMf/8DChQtd6qc9DBCIqKMSRbHDb1tti1IeAKNEYpCbOHGiCEAEIP7000822+Tn54uCIIgAxDvuuMMv93jttdekayxbtszmNZqbm8WUlBQRgBgXFydqNBqX+2pLSkqKmJKS4pVrERFRcJD5LTJpB3V1ddi5cycAYMyYMRg2bJjNdoMHD8ZNN90EANi+fTsaGhra/R4bNmwAAKhUKsyZM8fmNdRqNebOnQsAqK6uxq5du5zuJxERkSuCOkDYu3evlOx3yy23OGw7btw4AEBLS4tLb7zeuEddXR1+/vlnAMDo0aPRpUuXNq8BADt27HC6n0RERK4I6gChoKBAOh48eLDDtgMHDpSOjcmA7XWPw4cPQ6/X+7SfRERErgjqAKGkpEQ6Tk1Nddi2R48e0vGpU6fa9R6uXCMqKgrh4eEu95OIiMgVQR0gVFdXS8exsbEO28bExEjH58+fb9d7uHIN0+u40k8iIiJXBHUprqamJuk4JCTEYVvT86aPa497uHIN0zau9NO4lNGWiooKJCUlOX0tIiIKfkE9gqDRaKTjtooKmZ7Xap1fU+uNe7hyDdM2rvSTiIjIFUE9ghAaGiodt7Y6rrTV0tIiHbuy14E37uHKNUyv40o/HRVBcjS6QEREnVNQjyAYk/kAoLm52WFb0/Omb9jtcQ9XrmHaxpV+EhERuSKoA4To6GjpuK2EvpqaGuk4Pj6+Xe/hyjVMr+NKP4mIiFwR1AFCv379pOPTp087bGt6vq2lht6+hyvXuHDhAhobG13uJxERkSuCOkAwLSp0+PBhh21Nz7dVrMjb9+jfvz9kMplP+0lEROSKoA4QRo8eLWX8t1U+OTs7GwAgl8tx/fXXt+s9QkNDMWrUKADA999/7zAPwXgNABg7dqzT/SQiInJF0G/3PHnyZGzbtg2CICAvL8/mp+68vDwMGzYMoihi4sSJ+Pzzz9v9HsuWLcPjjz8OAFixYgUefPBBq2s0Nzejd+/eKC8vR7du3VBWVubUssi2qFQq6HQ61kIgIgoiiYmJyM3NdfvxQT2CAAALFy4EYNgzfNq0aWZVCwGgqqoK99xzD4xx0pNPPumXe8yaNUuqorho0SIcPHjQ7LxGo8F9992H8vJyAMDDDz/sleAAAJRKJeRyuUfXqKioQEVFhVf6Q/7B5zA48Hns+ALlOQz6EQTA8Oa7bt06AEBSUhLmz5+PPn364NixY1i5ciUqKysBALNnz8bq1autHp+amirte5CTkyNt2+zNewDA2rVrcd999wG4vLXz6NGjUVNTg3feeQeHDh0CAAwaNAgHDhwIqGWOxloKjuotUGDjcxgc+Dx2fAHzHIqdQEtLizhlyhQRgN2vqVOnii0tLTYf36tXL6ldTk6OT+5h9Pe//12Uy+V2rzFo0CDxzJkznv6TeF1KSoqYkpLi726QB/gcBgc+jx1foDyHQT/FABjm2Ddt2oTNmzdj0qRJSEhIgFKpRGxsLCZMmIDNmzdj48aNLlUm9NU9nnrqKRw8eBCzZs1Cr169oFarERERgdGjR2PZsmXIzc1FSkqK2/0kIiJyRqeYYiDfC5ghMXIbn8PgwOex4wuU57BTjCAQERGRaxggEBERkRUGCERERGSFOQhERERkhSMIREREZIUBAhEREVlhgEBERERWGCAQERGRFQYIREREZIUBAhEREVlhgEAe2bp1KyZOnIj4+HioVCp0794dmZmZ+OKLL/zdNWpDXl4eBEFw6iskJMTf3SUTK1euhCAIyMzMdKr98ePHsWDBAvTp0wchISGIiYnByJEj8dprr6GhocG3nSWbXHkOhw0b5vRrdcOGDV7ro8JrV6JORaPRYPr06di4caPZz8vKylBWVoZPP/0UWVlZWLNmDRQK/jcLRHl5ef7uArnhyJEjeOqpp5xuv2HDBvzP//wPLl26JP2spaUFubm5yM3NxX/+8x98+umnGDhwoC+6Sza48hxqNBoUFhb6uEe28S83uWXBggVScJCamop58+ahV69eKCoqwqpVq1BZWYn169cjKioKy5cv93NvyRbTAKGtnUblcnl7dInacPLkSUyYMAGNjY1Otd+7dy+ysrKg0+mgVCoxe/ZsXHfddWhoaMCHH36IvXv34sSJE5g0aRJyc3PRrVs3H/8G5Opz+Ouvv6K1tRUAMG/ePNx2220O248cOdLjPkr8u9s0dUR79+4VAYgAxOHDh4t1dXVm58+ePSsOGDBAapObm+unnpIj48aNEwGIPXv29HdXyAl79+4VExMTpdcVAHHy5Ml222s0GrFv374iAFGlUonZ2dlm5/V6vfjkk09K13rooYd8/BuQq8+hKIrie++9J7XdtWtX+3T0N8xBIJe98sor0vHq1asRGRlpdj4+Ph4bNmyAIAgAgBdffLFd+0fOMY4gDBkyxM89IUdaW1vx8ssv4+abb0ZlZaXTj9u6dSuOHj0KAJg/fz5uvvlms/OCIOCVV17BiBEjAABvvfUWqqurvddxkrj7HALmI33t/VplgEAuqaurw86dOwEAY8aMwbBhw2y2Gzx4MG666SYAwPbt25kIFWDKy8tx7tw5AAwQAtk333yDAQMGYPHixdBqtVAoFFiyZIlTjzVNVnvwwQdtthEEAQ8//DAAw5vYli1bPO80mfHkOQQuBwjJycntPgXEAIFcsnfvXuh0OgDALbfc4rDtuHHjABgSonbt2uXzvpHz/PmphJy3fv16nDhxAgAwYMAAfPfdd7j//vudeuzu3bsBAD179kSfPn3stjO+TgFgx44d7neWbPLkOQT8O9LHAIFcUlBQIB0PHjzYYVvTrGhmzAcW0+fD+DyWlZUhOzsbX3/9NYqKivzVNbLQrVs3vPHGG/j5559xzTXXOPWY8vJynD9/HkDbr9OUlBR07doVAF+nvuLOcwgAlZWVqKqqAnD5eWxqasL+/fuxfft2HDhwAM3NzT7pM8BVDOSikpIS6Tg1NdVh2x49ekjHp06d8lGPyB3GNwK1Wo3Dhw9j5syZyM3NNWuTmpqKp59+GnPmzJHySah9PfTQQ1i5ciXCwsJcepwrr1PA8Fqtq6tDaWkpRFHk8+1F7j6HgHnAFh4ejunTp+Pjjz9GS0uL9POwsDDce++9eP7555GQkOCVPhtxBIFcYprEFBsb67BtTEyMdGz8NEOBIT8/H4Bh+mfq1KlWwQFgeJN54IEHcOedd5r9QaL2M2LECLfeWFx5nQKXX6tarRZ1dXUu34/sc/c5BC6/TgHg2Wefxfr1661ei01NTXj77bcxfPhwr48AMUAglzQ1NUnHbVXXMz1v+jjyr+bmZrMphOTkZPz73/9GaWkpWlpacPz4cbz88suIiIgAAGzZsgUPPfSQv7pLbnDldWrZhq/VwGH6hi+TyTB//nz8/PPPaGpqwvnz57FlyxYpUby8vBwTJ06UpiS8gQECuUSj0UjHarXaYVvT81qt1md9ItcUFxdLz82AAQNw6NAhLFiwAN27d4dKpULv3r2xaNEi7Nu3D+Hh4QAMy1kPHjzoz26TC1x5nVq24Ws1cJw5cwaAoVDZli1bsHLlSlx99dUIDQ1FTEwMMjMzsX//fowfPx6AIY9o6dKlXrs/AwRySWhoqHRsrO5lj+lQmKMqfdS+BgwYgIsXL6K8vBz79u1DXFyczXZDhw41+2OzatWq9uoieciV1ynA12qg2r17NxoaGnD8+HH84Q9/sNlGrVZj7dq1UpC3du1ar00JMkAglxg/UQJoM3vW9LzpHywKDElJSWZ5IrZkZWVJx3v37vV1l8hLXHmdWrbhazWwhIeHt5lompycLC1XbWpqsplT5A4GCOSS6Oho6bitxMOamhrpOD4+3md9It+Jj4+XirOUl5f7uTfkLFdep8Dl16parZaWPFLHctVVV0nH3nqtMkAgl/Tr1086Pn36tMO2puedWWpFgUmpVPq7C+QiV16npm169erlsz6Rb/nidco6COQS0+JHhw8fdriX+eHDh6Xjtoq1UPvZuXMn8vLyUFVVhUcffdSsXoWl5uZm6RNocnJye3WRPBQdHY2kpCRUVFSYvQ5tOXPmDOrr6wHwdRpITp48iW3btqGqqgpXX3017r77boftjQmNgPdeqxxBIJeMHj1aSoZpq3xydnY2AEMG7vXXX+/zvpFztmzZgkWLFuGNN97AV1995bDt7t27pYz4MWPGtEf3yEuMe6EcP34cpaWldtsZX6cAMHbsWF93i5xUWVmJRx99FC+99BL+7//+z2FbnU6HnJwcAIZpouHDh3ulDwwQyCWRkZHSfuQ5OTlmpZdN5eXlYc+ePQCAjIwMzmsGEOOSKMCwMkEURZvt9Hq92U6cM2bM8HnfyHtMP3Hae4MRRRHLly8HACgUCtx5553t0jdqW3p6upRL8t1335kVTbL0zjvvoKysDIDhefdaomm7bi5NQWHPnj3S/uSDBg0Sq6qqzM6fPXtWvOqqq6Q2u3fv9lNPyZbm5mYxLS1Nen6efPJJUa/Xm7XRaDTi3LlzpTbjxo3zU2/J0smTJ6XnZfLkyXbbtba2iv369RMBiAqFQty2bZvZeb1eLz7xxBPStWbNmuXjnpORs8/h4sWLpXYDBw4UKysrrdrs2LFDDAsLEwGIoaGh4tGjR73WT0EU7Xx8IHJg1qxZWLduHQDDcrn58+ejT58+OHbsGFauXCnteT579mysXr3an10lG3JycpCRkSGtkR89ejTuvfdeJCQkoLi4GOvWrcORI0cAGHYD/Pbbbx3mKlD7KSkpQVpaGgBg8uTJ2Lp1q9222dnZuPXWW6HX6yGTyTB9+nTccsstaGpqwvvvvy8tXU1KSsKhQ4e42qidOPscXrx4Eb/73e+kioqxsbGYO3cuhg4dioaGBuzYsQObN2+W9s949913MWvWLO911GuhBnUqLS0t4pQpU6To1tbX1KlTxZaWFn93lez44osvxJiYGIfP4bBhw8Tjx4/7u6tkwtlPn0bvvfeeGBISYvc57tGjh1hQUOD7jpPElefw7Nmz4o033ujwdRoRESGuXbvW6/1kDgK5RaVSYdOmTdi8eTMmTZqEhIQEKJVKxMbGYsKECdi8eTM2btzIqmwB7Pe//z2OHj2K559/HqNHj0ZUVBSUSiWSk5ORkZGBNWvW4ODBg+jdu7e/u0oeyMrKQkFBAR588EFceeWVCA0NRVhYGK6++mosXboUBQUFGDRokL+7SXbEx8dj165d+OSTT5CZmYnk5GSoVCpERUXh6quvxjPPPIMjR45g5syZXr83pxiIiIjICkcQiIiIyAoDBCIiIrLCAIGIiIisMEAgIiIiKwwQiIiIyAoDBCIiIrLCAIGIiIisMEAgIiIiKwwQiIiIyAoDBCIiIrLCAIGIiIisMEAgIiIiKwwQiIiIyAoDBCIiIrLCAIGIvG7mzJkQBAGCIKBHjx5oaGhw6fF79uyBTCaDIAhQKpX46aeffNRTIrKHAQIRed2yZcsQHx8PADhz5gwWL17s9GObm5sxd+5ciKIIAHj66acxfPhwn/STiOxjgEBEXhcTE4Ply5dL369cuRI//PCDU4/929/+hqNHjwIAhg4dir/+9a8+6SMROSaIxjCdiMjLMjMz8emnnwIABg8ejB9//BFKpdJu+/z8fKSnp0Or1UKpVOLgwYMYOnRoe3WXiExwBIGIfGblypXo2rUrAKCgoACvvvqq3bZ6vR73338/tFotAOCZZ55hcEDkRxxBICKfevvttzF37lwAQEhICPLz89GnTx+rdsuWLcPjjz8OABg2bBgOHDgAhULRrn0loss4gkBEPjVnzhyMHTsWwOUEREunTp2Scg1UKhXWrVtnFRxoNBq89dZbuO2225CUlAS1Wo2EhASMGzcOK1euRGtra5t9uXDhAl5//XVMmDABPXr0QFhYGEJDQ5GSkoIJEybgzTffREtLi83HPvfcc9LKjObmZmzevBlDhgxBSEgI4uPjMX78eBw/flxqf+LECSxcuBBDhw5FREQEQkJCzO7T3Nzs9L8hkV+IREQ+dvz4cTE0NFQEIAIQ33vvPbPzv//976VzL7zwgtXjjxw5Ivbt21dqY+urd+/e4i+//GK3Dx9++KEYGRnp8BoAxP79+4ulpaVWj3/22WelNu+8847V42JiYsTm5mZRFEVxw4YNolqtdnifnj17ioWFhR7+yxL5DqcYiKhd/OMf/8CTTz4JAEhOTkZRURHCw8OxadMm3HXXXQCAESNGYP/+/WajB8XFxRg5ciRqamoAADfeeCOmTJmCxMREVFZWYsuWLdi9ezcAIDo6Grm5ubjiiivM7p2dnY3x48dDFEWo1WpkZWXh2muvRVRUFOrq6vD999/jgw8+wKVLlwAAkydPxtatW82u8dxzz2Hp0qUADFMlMpkMjzzyCAYOHIhDhw5BpVLhxRdfRGFhIYYNG4bW1lZERETg/vvvx7Bhw6BSqXD69GmsXbsWhYWFAIC+ffvi8OHDnEqhwOTnAIWIOgmtViuOHDlS+gT91FNPiU1NTWKPHj1EAKJarbY5AjB69GgRgCgIgrh69Wqb137rrbek61533XVW59PT00UAolwuF/fu3WvzGgcPHhQVCoUIQFQoFGJtba3ZedMRBADi9u3bbV7nkUcekdp8//33Vuc1Go04btw4qc1nn31m8zpE/sYAgYjaTX5+vqhUKkUAokqlEmfOnCm9Ub788stW7bOzs6XzjzzyiMNrT5s2TWr77bffSj8vLi6Wfn7PPfc4vMaECROktj/++KPZOdMAIT093e41MjIyRABit27d7LbZt2+f2KtXL/Hmm28WN23a5LBPRP7CJEUiajeDBw/GokWLAACtra1Yt24dAGDUqFHS9IOpjz76SDqeM2eOw2ubnv/ss8+k49TUVJw5cwZ79uzB888/7/AaaWlp0nFTU5Pddtdff73dc7GxsQCA8+fP4+2337bZ5rrrrkNJSQmys7MxZcoUh30i8hdOfBFRu1qyZAk++eQTHDlyBIBhPn/t2rWQy+VWbffv3y8d//LLLzh27Jjd65ru95CbmysdC4KAlJQUpKSk2HxcY2MjCgoKsH//fnzzzTfSz3U6nd179e/f3+65P/7xj1i/fj0AYO7cuVixYgX+8Ic/ICMjA6NGjWK+AXUY/J9KRO1KrVbjsccek5Y7Tpkyxe4b7pkzZ6TjP/3pT07fo6qqyubPT506hc8++wx5eXk4duwYTpw4gbKyMmnfB1O2fmYUFRVl99ykSZPw0EMPYcWKFQAM1SHz8/PxwgsvIDIyEuPHj8fkyZORmZmJiIgIp38novbGAIGI2p1puWVHpZfr6+vdur7l7pFNTU3485//jDVr1kCv11u1Dw0NxQ033ID6+nqzUQt71Gq1w/PLly/HrbfeimXLlmHPnj3SPevr67Fp0yZs2rQJERERWLp0KR577DEXfjOi9sMAgYgCVlhYGOrr65GQkIDKykq3rqHX6zFp0iTk5OQAMExp3HTTTRg+fDgGDhwofSkUCjz88MNOBQjOuP3223H77bejuroaO3fuxDfffIPs7GyUlZUBMAQxjz/+OPR6PRYuXOiVexJ5EwMEIgpYCQkJqK+vR01NDVpaWtr85G7Lhg0bpODgmmuuwbZt25CQkGCzbW1trSfdtSkuLg7Tp0/H9OnTAQA//fQTXnvtNWzYsAEA8OKLL+LPf/6zw5EUIn/gKgYiClijRo0CYCiz/O233zpsm5+fjzvvvBMLFy7Epk2bpJ9//vnn0vGrr75qNzgAgEOHDknHjnIQ7GloaMD69evxzDPPSCs0LA0fPhwffPABRowYAcBQ/vn06dMu34vI1ziCQEQB64477pBWBLz66qsYN26c3bYvvPACNm/eDMCQRHjnnXcCMCw3NAoPD7f7+K+++gq//PKL9L1Go3Grz7NmzYJOp0P//v0xY8YMCIJg1UYQBMTExDjVLyJ/4QgCEQWszMxMXHXVVQAMb+DGOXtL//rXv/Dxxx8DMLzZzps3TzpnWttg1apVNu+ze/duZGVlmf3Mnc2UIiIiMGHCBADAr7/+iueee85mu++//14qD92/f3+HoxpE/sIRBCIKWDKZDBs2bMCYMWNw8eJFLFu2DF999RWmT5+OtLQ0VFZWYuvWrVKOAQCsWLECcXFx0vezZs3Cf/7zHwDA6tWrUVxcjDvuuANxcXEoLy/H9u3bkZ2dbTWlUFdX51afX3jhBXz55ZfQaDT429/+hi+//BJTpkxBz549UVtbiwMHDmD9+vXQaDQQBAEvvfSSW/ch8jn/FnIkos5ozZo1UtnimTNnttk+NzdX7NWrl8PdEUNCQsRVq1bZfPyLL77Y5i6OI0aMEN977z3p+yeeeMLsGqallrds2eKwvx9//LHYpUsXh/cLDQ0V33zzTWf/yYjaHacYiCjgpaeno6ioCCtXrsRtt92GxMREKJVKhIeHY+jQoXjiiSdQWFiIBx54wObjFy9ejJycHEyZMgVJSUlQKBQICwtDWloaMjMz8dFHH2H//v2YOnWqlBvw0UcfQavVutXfu+66C0eOHMHixYtxzTXXIDo6GgqFAjExMRg2bBiefvppFBYWmk2FEAUabvdMREREVjiCQERERFYYIBAREZEVBghERERkhQECERERWWGAQERERFYYIBAREZEVBghERERkhQECERERWWGAQERERFYYIBAREZEVBghERERkhQECERERWWGAQERERFYYIBAREZEVBghERERkhQECERERWWGAQERERFYYIBAREZEVBghERERkhQECERERWWGAQERERFb+P9HXknWUDvJ4AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 274, "width": 260 } }, "output_type": "display_data" } ], "source": [ "aml_tcga.survival_analysis('MYC',layer='deseq_normalize',plot=True)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a9970c33-b6e0-4655-9349-8c94950c5575", "metadata": {}, "source": [ "If you want to calculate the survival of all genes, you can also use the `survial_analysis_all` to finish it. It may calculate a lot of times." ] }, { "cell_type": "code", "execution_count": null, "id": "8621986c-649a-4a2d-824b-15afced0fafb", "metadata": {}, "outputs": [], "source": [ "aml_tcga.survial_analysis_all()\n", "aml_tcga.adata" ] }, { "attachments": {}, "cell_type": "markdown", "id": "9564d9eb-a6d4-428a-867c-6325c6dd119a", "metadata": {}, "source": [ "Don't forget to save your result." ] }, { "cell_type": "code", "execution_count": null, "id": "8d56ca64-fd57-4f0e-9def-5eb6885e1b7f", "metadata": {}, "outputs": [], "source": [ "aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_survial_all.h5ad',compression='gzip')" ] } ], "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": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }