{ "cells": [ { "cell_type": "markdown", "id": "acfe011d", "metadata": {}, "source": [ "## Exploring Protein Relationships Through ESM-2 Embeddings\n", "\n", "Proteins are molecular machines with unique structures that determine their functions. ESM-2 treats protein sequences as a language, learning representations that capture evolutionary and functional relationships without relying on traditional sequence alignment.\n", "\n", "In this notebook, we'll explore how ESM-2 embeddings reveal relationships between six human proteins:\n", "\n", "**Oxygen Transport & Storage:**\n", "- **Hemoglobin Beta**: The oxygen-carrying protein in red blood cells, part of the tetrameric hemoglobin complex\n", "- **Myoglobin**: The oxygen storage protein in muscle tissue, structurally similar to individual hemoglobin subunits\n", "\n", "**Defense & Immunity:**\n", "- **Lysozyme C**: An antimicrobial enzyme that breaks down bacterial cell walls, found in tears, saliva, and mucus\n", "- **Defensin Beta 4A**: A small antimicrobial peptide that directly kills bacteria and other pathogens\n", "\n", "**Structural Support:**\n", "- **Alpha-1 Type I Collagen**: The most abundant protein in the human body, providing strength to bones, skin, and connective tissues\n", "- **Elastin**: The protein that gives tissues their elasticity, crucial for arteries, lungs, and skin" ] }, { "cell_type": "markdown", "id": "20f98f7f", "metadata": {}, "source": [ "### Setup\n", "\n", "Here we import all neccessary libraries." ] }, { "cell_type": "code", "execution_count": 1, "id": "2bacd1ff", "metadata": {}, "outputs": [], "source": [ "import mlx.core as mx\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.decomposition import PCA\n", "from sklearn.manifold import TSNE\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "5563c495", "metadata": {}, "source": [ "These are our protein sequences, obtained from [UniProt](https://www.uniprot.org/)." ] }, { "cell_type": "code", "execution_count": 2, "id": "b8e9d6d2", "metadata": {}, "outputs": [], "source": [ "proteins = [\n", " # Oxygen Transport\n", " (\"Hemoglobin Beta\", \"MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH\"),\n", " (\"Myoglobin\", \"MGLSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASEDLKKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKHKIPVKYLEFISECIIQVLQSKHPGDFGADAQGAMNKALELFRKDMASNYKELGFQG\"),\n", "\n", " # Antimicrobial Defense\n", " (\"Lysozyme C\", \"MKALIVLGLVLLSVTVQGKVFERCELARTLKRLGMDGYRGISLANWMCLAKWESGYNTRATNYNAGDRSTDYGIFQINSRYWCNDGKTPGAVNACHLSCSALLQDNIADAVACAKRVVRDPQGIRAWVAWRNRCQNRDVRQYVQGCGV\"),\n", " (\"Defensin Beta 4A\", \"MRVLYLLFSFLFIFLMPLPGVFGGIGDPVTCLKSGAICHPVFCPRRYKQIGTCGLPGTKCCKKP\"),\n", "\n", " # Structural Proteins\n", " (\"Alpha-1 Type I Collagen\", \"MFSFVDLRLLLLLAATALLTHGQEEGQVEGQDEDIPPITCVQNGLRYHDRDVWKPEPCRICVCDNGKVLCDDVICDETKNCPGAEVPEGECCPVCPDGSESPTDQETTGVEGPKGDTGPRGPRGPAGPPGRDGIPGQPGLPGPPGPPGPPGPPGLGGNFAPQLSYGYDEKSTGGISVPGPMGPSGPRGLPGPPGAPGPQGFQGPPGEPGEPGASGPMGPRGPPGPPGKNGDDGEAGKPGRPGERGPPGPQGARGLPGTAGLPGMKGHRGFSGLDGAKGDAGPAGPKGEPGSPGENGAPGQMGPRGLPGERGRPGAPGPAGARGNDGATGAAGPPGPTGPAGPPGFPGAVGAKGEAGPQGPRGSEGPQGVRGEPGPPGPAGAAGPAGNPGADGQPGAKGANGAPGIAGAPGFPGARGPSGPQGPGGPPGPKGNSGEPGAPGSKGDTGAKGEPGPVGVQGPPGPAGEEGKRGARGEPGPTGLPGPPGERGGPGSRGFPGADGVAGPKGPAGERGSPGPAGPKGSPGEAGRPGEAGLPGAKGLTGSPGSPGPDGKTGPPGPAGQDGRPGPPGPPGARGQAGVMGFPGPKGAAGEPGKAGERGVPGPPGAVGPAGKDGEAGAQGPPGPAGPAGERGEQGPAGSPGFQGLPGPAGPPGEAGKPGEQGVPGDLGAPGPSGARGERGFPGERGVQGPPGPAGPRGANGAPGNDGAKGDAGAPGAPGSQGAPGLQGMPGERGAAGLPGPKGDRGDAGPKGADGSPGKDGVRGLTGPIGPPGPAGAPGDKGESGPSGPAGPTGARGAPGDRGEPGPPGPAGFAGPPGADGQPGAKGEPGDAGAKGDAGPPGPAGPAGPPGPIGNVGAPGAKGARGSAGPPGATGFPGAAGRVGPPGPSGNAGPPGPPGPAGKEGGKGPRGETGPAGRPGEVGPPGPPGPAGEKGSPGADGPAGAPGTPGPQGIAGQRGVVGLPGQRGERGFPGLPGPSGEPGKQGPSGASGERGPPGPMGPPGLAGPPGESGREGAPGAEGSPGRDGSPGAKGDRGETGPAGPPGAPGAPGAPGPVGPAGKSGDRGETGPAGPAGPVGPVGARGPAGPQGPRGDKGETGEQGDRGIKGHRGFSGLQGPPGPPGSPGEQGPSGASGPAGPRGPPGSAGAPGKDGLNGLPGPIGPPGPRGRTGDAGPVGPPGPPGPPGPPGPPSAGFDFSFLPQPPQEKAHDGGRYYRADDANVVRDRDLEVDTTLKSLSQQIENIRSPEGSRKNPARTCRDLKMCHSDWKSGEYWIDPNQGCNLDAIKVFCNMETGETCVYPTQPSVAQKNWYISKNPKDKRHVWFGESMTDGFQFEYGGQGSDPADVAIQLTFLRLMSTEASQNITYHCKNSVAYMDQQTGNLKKALLLQGSNEIEIRAEGNSRFTYSVTVDGCTSHTGAWGKTVIEYKTTKTSRLPIIDVAPLDVGAPDQEFGFDVGPVCFL\"),\n", " (\"Elastin\", \"MAGLTAAAPRPGVLLLLLSILHPSRPGGVPGAIPGGVPGGVFYPGAGLGALGGGALGPGGKPLKPVPGGLAGAGLGAGLGAFPAVTFPGALVPGGVADAAAAYKAAKAGAGLGGVPGVGGLGVSAGAVVPQPGAGVKPGKVPGVGLPGVYPGGVLPGARFPGVGVLPGVPTGAGVKPKAPGVGGAFAGIPGVGPFGGPQPGVPLGYPIKAPKLPGGYGLPYTTGKLPYGYGPGGVAGAAGKAGYPTGTGVGPQAAAAAAAKAAAKFGAGAAGVLPGVGGAGVPGVPGAIPGIGGIAGVGTPAAAAAAAAAAKAAKYGAAAGLVPGGPGFGPGVVGVPGAGVPGVGVPGAGIPVVPGAGIPGAAVPGVVSPEAAAKAAAKAAKYGARPGVGVGGIPTYGVGAGGFPGFGVGVGGIPGVAGVPGVGGVPGVGGVPGVGISPEAQAAAAAKAAKYGAAGAGVLGGLVPGAPGAVPGVPGTGGVPGVGTPAAAAAKAAAKAAQFGLVPGVGVAPGVGVAPGVGVAPGVGLAPGVGVAPGVGVAPGVGVAPGIGPGGVAAAAKSAAKVAAKAQLRAAAGLGAGIPGLGVGVGVPGLGVGAGVPGLGVGAGVPGFGAGADEGVRRSLSPELREGDPSSSQHLPSTPSSPRVPGALAAAKAAKYGAAVPGVLGGLGALGGVGIPGGVVGAGPAAAAAAAKAAAKAAQFGLVGAAGLGGLGVGGLGVPGVGGLGGIPPAAAAKAAKYGAAGLGGVLGGAGQFPLGGVAARPGFGLSPIFPGGACLGKACGRKRK\"),\n", "]" ] }, { "cell_type": "markdown", "id": "c9621578", "metadata": {}, "source": [ "### Loading the model and tokenizing a sequence\n", "\n", "First, load the ESM-2 model. Change the path below to point to your converted checkpoint.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "05696400", "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"..\")\n", "\n", "from esm import ESM2\n", "\n", "esm_checkpoint = \"../checkpoints/mlx-esm2_t33_650M_UR50D\"\n", "tokenizer, model = ESM2.from_pretrained(esm_checkpoint)" ] }, { "cell_type": "markdown", "id": "2916adbb", "metadata": {}, "source": [ "Here, we tokenize and decode the protein sequence for human Insulin." ] }, { "cell_type": "code", "execution_count": 4, "id": "47178dcd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequence: MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\n", "Tokens: array([0, 20, 5, ..., 23, 17, 2], dtype=int32)\n", "Decoded: MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\n" ] } ], "source": [ "human_insulin_sequence = \"MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN\"\n", "tokens = tokenizer.encode(human_insulin_sequence)\n", "print(f\"Sequence: {human_insulin_sequence}\")\n", "print(f\"Tokens: {tokens}\")\n", "print(f\"Decoded: {tokenizer.decode(tokens)}\")" ] }, { "cell_type": "markdown", "id": "c1b73ded", "metadata": {}, "source": [ "### Embedding sequences\n", "\n", "To compute the embeddings of our proteins, we pass each protein sequence through ESM-2's tokenizer to convert amino acids into token IDs, then extract the final layer representations using `get_sequence_representations()`. This process gives us a vector for each protein that captures its learned functional and evolutionary features." ] }, { "cell_type": "code", "execution_count": 5, "id": "cb470957", "metadata": {}, "outputs": [], "source": [ "def extract_embeddings(model, protein_list):\n", " embeddings = []\n", " names = []\n", " for name, sequence in protein_list:\n", " tokens = model.tokenizer.encode(sequence, return_batch_dim=True)\n", " embedding = model.get_sequence_representations(tokens, layer=-1)\n", " embeddings.append(embedding[0])\n", " names.append(name)\n", " return mx.stack(embeddings), names" ] }, { "cell_type": "code", "execution_count": 6, "id": "38e83142", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Embedding shape: (6, 1280)\n", "Each protein represented by 1280 features\n" ] } ], "source": [ "embeddings, protein_names = extract_embeddings(model, proteins)\n", "print(f\"\\nEmbedding shape: {embeddings.shape}\")\n", "print(f\"Each protein represented by {embeddings.shape[1]} features\")" ] }, { "cell_type": "markdown", "id": "fccd2a99", "metadata": {}, "source": [ "### Protein embedding similarity matrix\n", "\n", "We can measure how similar the protein embeddings are by calculating a similarity matrix. We normalize each embedding to unit length and compute cosine similarities between all pairs, producing a matrix where values close to 1 indicate highly similar proteins and values close to 0 indicate dissimilar ones." ] }, { "cell_type": "code", "execution_count": 7, "id": "93d14fff", "metadata": {}, "outputs": [], "source": [ "def compute_similarity_matrix(embeddings):\n", " normalized = embeddings / mx.linalg.norm(embeddings, axis=1, keepdims=True)\n", " similarity_matrix = normalized @ normalized.T\n", " return similarity_matrix" ] }, { "cell_type": "code", "execution_count": 8, "id": "3485f854", "metadata": {}, "outputs": [ { "data": { "image/png": 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GVNXdyuoeaaDMl6B6O7ooBl4UJNCAFQruZXXa2X1Nmb3WjAYGfQ7VQ1Ng8fDhQxG8UI0hBUY0X1ktOdWMp/W52u4vRV1DqGadgnYK9Og10GAeMmzYsGzPl2pnKfCkekaqIZUNksoIBX6yAXJU+0ufJ/3Y0zpKr41+1FUtj8/JzvKi56TaXqqdVhxsm1WyoIHqVXNSdtdz+v7T8qfPgWrwaeeXxhtkhNa/tDvFFLCpCpjpPVIdMQVUVNeathb+W8nsO5nRfRm1MlS1jlDXEfou0GDftNtA2inICnosbX9VrfeqnlP2XLSTntHr/JKAOaOxDbQsFLexst+AzJ43LVrXaOeddr4WLFggLjSokerAaeyFqvdMO1ZUY840B3fJYN+djLoy0AaTRjPLsr+KaAAIHbnLaKOqal6yQVSfSptUXmgDqm6UEZEFBBcvXkReRAPWKFimgZP0Pw0moh8d6gJAA9Qy8i3O5EcdICgYlfVxpn7fFJhlJ1Mo06dPH9FBgEbY0w+ybMBpRigIoddAATZlU+lHl4I2Wh5ZycLm5PKigZw0uI8yYvR4ann2JTtxsh1UWYCVF9ZzWv70OdDnQYENfT4Zoc897fdaVaBGARBl3qlPNPXapZOZaCLqJJEWfd60A6k4oFe2DaTlmNk2UIYeS9tfVVlVVc8pe660HU0ye0xOkL2vL3leCrqpLSIlT2iw365du0RmnLYhqtYtWg60Q6AqecPyLg6Y2X+GrPWTqjY/stsUD1/KermqCg7Kli0rNqw0cjq7WdXclNEJIfIKKsEglJ1L69q1azn2PJl9pjI0Kp5+yCizvH//fvHDRkHi15B1YKAfU+rQQkdMPrc86DE0Kl8RBfC0Q5Gd95UdlIWkH3wql6GOAhMnThTZb1VlSBmRlUxRJjevrOe0/OlzoM+DPpcv7bigKlimdZeCZQqcstpuLy9S9X2jEhFaFxTb51E5i2JpxufIukuomr+q2zKbnlqyZdRa7mvJnldV5w76/n2u1SeV6VG7SDoiSm3zaKeX1g9FVE5EQXPackKWt3HAzP4zqJUSoR97xdILCohkAYBsGiKrgVS1YaaMAtVy0oabfiBVBc10aDijLEVOo9IPykqqyt7QDx2dlpikDcDyCjrETaheVxHVolJNak7J7DOVofpiyjxS/1w6pE61mGl7VWcH9TOmlmxUUvG59mq0PKhGXrHch4JhWtdUHSHJyvvKDvpeULBELdioLzhlTannOP2f1R0ZCgro9WVUM6uO9Zx2MOhzoM+D+gB/DVkZBgXLtEMhew2aisoL3r17p1QuJTsjneL3YPTo0WI7SCU6Pj4+6eZDdcGKNc9UZkWop7NiqRrttKgqn6HPkMocqEe24naBstb0vczpnUPF7x7VL1OJD+0kKqLPNu2REjoSpCq4pvdIJ+uh7Ufa77vsu0DlX0xzcA0z+8+gnqO0cafDtjQ4Q1ZOQT1K6QeCakwV+5JSPStlC6jWkQ6n0yA8OiRN86DDhRRMULaPThBAvZfpsVT3Rj8A1L+YMhEUbGRUC5eTKGCnkwXQIXsabEJZEsqA0+FDGpRD749+fKifal5ENaXUF5YGudGOBn0+lJGkH0saQJW2/2t2yQbiUN9aCkhlh0Rp2aXtyUwDEGmgIa0nOfEZ0vtTPMVzZmgdozPWUdBAdZBUNkBHQWjdohKBtEdJsvq+vgT1YJYFyLI6VdqZoMPNdBIbqhemdfxzg/noO0MBJQ2co/Uwo1M+0/LOKENMZTk0CC4n13M6KQ5dcqKEh4JlKpWh/rtpz0gqCzSz+tkTOrqR0ZgNWg6ZlSl9LZo/LVfqe0yD3KgPM30X6cgLfRdk6DtKA90ocUAnRaF+4dSHmI5GUM9w2tml971p0yYxPQ2Eo373shOs0Peagk0KSuk56buuiIJMOmkRzZd21mR9mKnchgaI0tnyqDf0t0C/EbQ9p6Mr9PtAmWLa1lM2nW6n74YsCKbsMQXYNKiQvhc0IJoCZXo/NFiXdnLp90MRrS+0s0G9sZkGUXebDsay24dZFZqW2lRlZsuWLaI3qrGxsbjQ33SbKtR6iuZnZmamsv8m9f+ltln16tWTmJubi5ZU1K7JxcVFsnHjRqV+o5m1lUvb2u1L3o+sjdPJkycl48ePF62c7OzsRLskek3UE5raTin2ns7suVW1Y5OhdlsZtdxS9Vq/tA9zly5dRDsq2eeyZ8+eDKf/3LLJ6H1QX2Cat5GRkcperml7sZ4+fVrypRTbyn1ORm3lqOcttbuiZUF9art37y567craf6WdPrP3ldlnqmrdpN7kTk5OohXXq1ev0k3/559/ium7du36RT2JqV/3l7aVU2zB9zXreVa3IV/aVi4rrz+rreWy0laO3ntW2gFmtJ4ovmZV09M6Rq32SpQoIdr10bKYN29ehr2u7969K+nZs6foQ0xtJ2ldpfV22rRpEjc3N6VpaXtJPZqpnRrNm/5fuHCh5PXr1xluB6ltHfUrpvWa2rdRD3hqa/klbSMz21ZktE2jNoO0zlDfc9r+t27dWvLs2TPRWk+xvR+1d6T1umXLlpKCBQuK90XrJb3mXbt2pWs7Sr2aaZ4dO3ZU+XpY3qVF/6g7aGeMsbyEDrNTJpQynpQt+9oz1DFpezAqJ6FSF16eTBNRGQhl0SmrnN1Bh3T0gDruUAZe1ZkWWd7FWy3GGEuDDhtTVwAqzeDgLmdQ/Scd2qcSJ8byMqqHV1UnT+MQaNxKdk+hTvOlMifqpsLBsubhDDNjjCn8IFIW9I8//hD1m+7u7kqttNjXoZpUqrWWDQBjLC+iAYvUb7lFixaiNplq52mg3r1798RJX+hkRl960iBCR6uo1Ryt/5SpZpqFA2bGGFMYoEaj2mnQEw38+ZKzrTHGvg/UGYROTEMDDGngL5VoUYDcunVrzJ49W7QkZP89fKyRMcY+ofwB/VhSJomDZcb+m6gbDHUAoVN7UwtS2iZQKQZ1/OBgOT3qGkKdjqiLiexspJ9DnX6oAw91EKEuJGlPP0/Wr18vustQlyDq+3337l2oEwfMjDHGGGMsW6Kjo8VROQpws3r20B9++EG0GqR+15TNp5NDUWtIGdmJkqhFJLX0o/nTCYdy69wGqnBJBmOMMcYY+2paWlrihECZDYycOnWqOHcB9dyX6dmzp6gdl/Ufp4xyjRo1sG7dOnGdzozo5OQketRPmzYN6sAZZsYYY4wxJkcnlaFyFMVLfHx8jsybTuhFJ6NRRNljup1QCQwNrFSchroV0XXZNOrAZ/pj362UgFLqfgl5zoygSup+CXnKlUV11P0S8pyAuup+BXlL/nta6n4JeU6iES+TtB7+8dN39Vu3aFNvcTZbRXPnzlV5JssvRWdApC4kiug6BeXU45rOVko9r1VNQ3Xl6sIBM2OMMcYYk5s+fbqoIVZkkOYU3/81HDAzxhhjjGmoFKTk+DwpOP5WAbK9vX26MyXSdXNzc9GnXUdHR1xUTUOPVReuYWaMMcYYY7miTp06uHDhgtJt586dE7fL2vpVq1ZNaRoa9EfXZdOoA2eYGWOMMcY0VLIkRa3BYVRUFF6/fq3UNo7axVlbW6NQoUKivOP9+/fiLIdk5MiRovvFlClTMHjwYHGCmH379onOGTJUDjJgwABUr14dNWvWxKpVq0T7ukGDBkFdOGBmjDHGGNNQKVBvd+D79++LnsoystpnCnjphCT+/v7w8fGR31+0aFERHP/0009YvXo1ChYsiL/++kt0ypDp0aMHgoODMWfOHDFIsHLlyqLlXNqBgLmJ+zCz7xZ3yUiPu2Qo4y4Z6XGXDGXcJSM97pKRt7pkxPoXzfF5Gjm8yfF5ajrOMDPGGGOMaahvMeiPpccBM2OMMcaYhkrmQoFcwV0yGGOMMcYYywRnmBljjDHGNJS6B/39V3CGmTHGGGOMsUxwhpkxxhhjTEMlc4Y5V3DAzBhjjDGmobgkI3dwSQZjjDHGGGOZ4AwzY4wxxpiG4rZyuYMDZsYYY4wxDcWnLckdXJLBGGOMMcZYJjjDzBhjjDGmobhLRu7gDDNjjDHGGGOZ4AwzY4wxxpiGSuYEc67ggJkxxhhjTEPxoL/cwSUZjDHGGGOMZYIzzIwxxhhjGioZWup+Cf8JHDAzxhhjjGmoFK5hzhVcksEYY4wxxlgmOMPMGGOMMaahuCQjd3CGmTHGGGOMsUxwwJzHaWlp4ciRI1mefuDAgejYsWOm0zRu3BgTJkzIgVfHGGOMMXVnmHP6wv5DJRkUOEZERKQLNi9fvowmTZogPDwclpaW+C86dOgQ9PT0vnr5bt++XX7d2toaNWrUwO+//45KlSp99eeUF9x7AmzZDbi6A8GhWli7QILmDTJ/zN1HwOL1wGtvwCE/MLIf0Km18jT/HAa27AFCwoAyxYGZ44FKZVPvj48HlmwATl4EEhOBejWAOT8BttbIE7xOh8LjWDDiIpJgUdgQlQY7wrqkscppU5IkcD8cBJ8rEYgNS4SpowEq9LGHXRUz+TRu+wLxcn+Q0uNouharS8mvJyek4NkOf7y78QEpiRLYVTaF81BHGFp+3XqcU7q0qIw+7arD2sIEr32CsWLbRbzwDMhw+h6tq6JTc2fY25ohIjIOl+64Y+Oea0hITBb39+9QE41qlERhR2vEJyThmbsfNuy+Ch//cPk81s/ujqrlnJTme/j8E/z+93moW7+KlTG8anXkMzaBW0gw5l29iCeBGS+PQc5V0beiMxzNzBAWG4dTr93x+61rSEiWLg9tLS1MqFkHHUuXQz4TYwRGR+OgmyvW3rst7tfV1sbPteuhceGiKGRhicj4eNx49xZLbl5DUHQ08oKuzZzRp0112FiYwMM3GMv/dwkvvDJeJj1bVUHnps6wszHHh8hYXLznjg37r8vXkc5NK4n7HfOZi+te70Px95HbuPXUWz4PfT0djO/VCC1ql4aerg7uPHuL37dfQNjHGKhb98bO6N+imlge7u+C8fueS3D1Dsxw+t7NqqBrw0qwtzZHRFQsLjz0wNrD15GQJF0edF+3RpXgYPNpefiHYvPxO7jpmro8bMyNMaFLQ9QqWwgmhvrwDgzD3yfv4uKj19BkKRIOcHPDdxsws4xRcJsTXFxcsHXrVvF3QEAAZs2ahbZt28LHxwffg9hYoHQJoHMbYNzsz0//zh8YOQ3o0R5YOgu4/RCYvRTIZwPUrymdhoLgJeuBeROBSuWAHfuBYZOAkzsBGyvpNIvWAVdvA6vmA2YmwK+rpM+/az3U7t2NCDzb7o/Kwx1hVcIYnidCcPO3N2ixujQMLNJvTl7sCYDv1QhUGVkAZgUMEfg4EreXvkWj34rDsqiRfDozJwPUn11Ufl1LR/kH4Nk2fwQ8jEStiYWga6yDJ3+/x51lPmi0oDjUrVnt0hjXr5EIVF1f+6NH62pYOa0Lev68BeEfY9NN37JuGYzq2QAL/ziDp+5+KORghVmjXCCRSLBm5xUxTZWyBXHw7GO4eQVAR1sbI3vWx6rpXdF78lbExSfJ53XkwlP8uf+G/HpcQup96vJDydKY2aARZl06j8cB/hhcuRq2t++CZju3IJS+VGm0L1UGU+s2wJQLZ/DA3w/FLK2wtLkLJJDgt+vS5TGyWg30qVgZk86fgntoKCrlt8PvzV1EYLzt6SMY6eqiQj47rLt3WwTo5gaGmNuwCf78oSM67PsH6ta8VimM790IS7ZdgKunP3q2qorVkzuj+5StCI9UsY7UKYPR3Rpgwd9n8czDD4XsrTB7WCtQM4TVu6TLJCgsChv2XYdvYDgoIfhD/fJYOqED+s3eiTfvQ8U0E3o3Rr3KRTF97XFEx8ZjUv+mWDyuHYYv2Jvry0Dp/VUvhYldG2Lhrgt49iYAfZpVxfpxndFp7jaVy8OlRmn82Kk+5m8/iyde/iic3xLzB9LykGDF/qtimqCIKKw5fB0+QREiP9quTjmsHN0evRb8I4Jn8ssgF5gZGeCnDUcRERUHl5qlsWT4D+i7cBde+Qbn+nJgmoVLMgBcv34dDRo0gJGREZycnDBu3DhEK2QlihQpggULFqB///4wNTVF4cKFcezYMQQHB6NDhw7iNsqq3r9/X2m+Bw8eRPny5WFgYCDmsXz5cqX7/f398cMPP4jnLVq0KHbt2iWmW7VqVYav9dmzZ2jatKl4jI2NDYYPH46oqKh0082fPx/58uWDubk5Ro4ciYSEhAxLMug5Fy5ciMGDB8PMzAyFChXC5s2bP7vc6H3Z29uLS+XKlTFt2jT4+vqK5SJD17t37y6y+RSo0/Ly9pbu8c+bN09kqY8ePSpKT+hCRwDI1KlTUapUKRgbG6NYsWKYPXs2Eindmosa1gYmDAVaNMza9HuOAgUcgKljgOJFgD6dgZaNgO37U6fZvg/o1lYahJcoAsz7GTA0BA6dlN4fGSX9m+ZRuypQvjSwcBrw6LkWHrtC7V4fD0GRZlYo3MQa5k6GqDy8AHT0teF9MUzl9BQsl+6cH/ZVzWFip49irWxgX9UMr/8NUZpOW1sLhlZ68ouBeWrwnRidDO+L4ag4wAH5KprCqrgRqo0piLBXMQhzV3+mrNcP1XDs4jOcuOIK7/dh+P3vc4hPSETbxhVVTl+xlCOeub/H2ZsvERDyEXefvcW5my9RrriDfJqfFh/CyauuePMuVGSsF2w8DYd85ihT1E5pXvQ8YR9i5JeY2NTvuboMrVwNe12f4YCbK16Hh2HmpXOITUpEt3Kql0c1B0fc93+PY+4v8T7yI675vsW/Hi/hbJe6PKraO+Kc12tc8n4jpjnl6YFrPt5wtrMX90cmJKDf0QM48dodXhHheBzoj7lXLqCSnT0cTVOPZqhLL5dqOHr5OY5fc8UbvzAs3nZe7Pi0a1RB5fSVSjjiqYcfzt56Cf+Qj7jz/C3O3n6J8sWk75dcf+yFm0/fwDcwAr4BEdh04AZi4hJR4dN6ZGKkj/aNKogA+4GbL156B+HXP8/AuVQB+TTq0qd5VRy+/hzHbr7AG/8w/PbPebGz16Gu6uXhXNwRTzz9cPreK/iHfsRtNx/xd/kiqcvj6lMv3HjuDd+gCBE0rz96EzHxiaiosMycizlg76XHIpP9PuSDyC5HxsSjbCHl75Wm4ZKM3PGfD5g9PT1FprRLly54+vQp9u7dKwLosWPHKk23cuVK1KtXD48ePRJBbr9+/UQA3bdvXzx8+BDFixcX1ylLRB48eCACxZ49e4ogl4JDCvq2bdsmnydN7+fnJ4JECq4pSA0KUj40rYiC+FatWsHKygr37t3D/v37cf78+XSv9cKFC3BzcxPz3b17tyjBoAA6MxTMV69eXby/0aNHY9SoUXj16lWWlyMF7Tt37kSJEiVEIE8owKXXS0H4tWvXcOPGDbFzQcubAvhJkyaJZUTXaeeBLnXr1hWPpcfQsnrx4gVWr16NP//8U3wGeRkFtHWqKd9Wv4b0dpKQKC3vUJxGW1t6XTYN3Z+YpKU0TbHCgIOdRO0Bc0piCiK8YpGvkqn8Ni1tLXE9o8A1OVECbT3ljS8F2KEvlQ+TRwXE49RwN5wZ8xL3VvsgJjg18KPnlCRLlJ6XstVGtnpqD5h1dbRRuqgd7j1PPapCmwC6XqGk6qCEyivoMeWKS3/IHfNboG7lorj12CvD5zE1NhD/f4yKU7q9Zb2yOLV5NHb+PgCjetaHgb56DxrqaWujQn47XPdVWB4Abvj6oKq96uVBWeWK+e3kwa+TuYUorbj8NnV5PAzwQz2nQihqKT0MU9Y2H2o4FsDlt28yfC1mBgZIkUjwkWqc1LyOlClih7uub5XXkRdvUbGE6mXy9LUfyhTJj3Kfgj3HfBao61wUN56ofr9UstKiVmkYGeji+Ws/cRs9J5Vh3HVN/Sze+oeLALxCBs+bW8uDAtQ7bsrfmTsvfVCpmOrXRcFy2UL5Ub6INLAtYGuB+hWK4MbzjJcHZbGN9HXx1Ms/dT5e/uJ2c2MDaGlJM90Gerp44O4LTZYM7Ry/sP9YScbx48dFgKYo+VNNnMyiRYvQp08feca1ZMmSWLNmDRo1aoSNGzfCkNJ/ANq0aYMRI0aIv+fMmSPuo5rdbt26yTOiderUQWBgoMi4rlixAs2aNRNBMqFsKQV/S5cuFXW7L1++FMEuBb4UqJK//vpLPH9GKAMdFxeHHTt2wMTERNy2bt06tGvXDkuWLIGdnXRjoq+vjy1btojsLGW4f/nlF0yePBm//vortClCU4HeHwXKsvdCwemlS5dQunTpLC1fCuYdHBzEbbLnoJ2PlJQU8b4oe0yohIOyzRTMt2zZUmTK4+PjxTJTROUdihlwCq737NmDKVOmIK+immTbT2UVMjbWQFS0FuLiJfgYSeufFmyslLvMUynGm0+/HSGhgJ6eBOZpkmI0X5q/OsVHJkOSgnSlF4YWuoh6rzoosXM2FVlp23ImIsMc/CwKfnc+iPnIWJU0RrUxTjB11EdceJKoZ746xwvNVpSEnpGOqJXW1tWCvolOuueNi8jdow5pWZobiQAg7IPyDgBle6n+WBXKLFuYGWHTvJ4ij6Orq4ND5x5j+9G7Kqenr86E/o3x5OV7eL0LTZ3PDTeRoQ4Jj0bxQrYY06shCjlYY/rKY1AXKyMjUU8cEqO8PEJiYlDcSvXyoMyytaER9nWRLg89HR3sfPYYG+6nLo+N9+/CVN8A5/sOQnJKiihTWXbrOo66v1Q5T30dHUyt21DMOypRvVl3S7NP60iaumGxjjhksI7ceglLUyNsntVDvo4cvPAE2/9VXkeKF7TFX3N6Ql9PF7FxCZi6+l+RwSY2liZISExCVEx8uuelumF1ofcllkdkmuXxMQZF7NNsQD+hbDI9bsvkHqL8hNaR/VeeYMupe0rTlXC0wbapn5ZHfAJ+3vSvyGDLTN18AkuGtcHllaORmJwssto/bzwG3+AP3+jdsu/Jdx0w0+A+CmwV3blzR2SFZZ48eSIyy//8k1rnRlliCvTevHmDsmWlo7EUB7LJAtOKFSumu40yxBT8UYaXyg8UUYaayi0oaKfsra6uLqpWrSq/n7KzlD3OCM3T2dlZHizL5kmvleYnew00DQXLMhTIUwaYyiOonEQVxfdHwS29h8yy3WmXLw2i3LBhA1q3bo27d++K56Fl+/r1a5EtVkRBP2X2M0PBNu240HT02pOSkkR5SUYo6KaLIr34FBgY8J6yOlUa5IhHf7zHufHuIvCjoLlQEyu8vZg6eM1eYQCgRWFpAH1m1Eu8v/kBRZrlkZGOOYjqkwd0rIWlWy7gxWt/FLSzxIQBTTCoUzS2HpYOYlM0aVAzFHOyxYh5e5RuP3rxmfxvT98QhEZEY92s7iiQ3wLvgzQnAKhVoCBGV6+FOZcviFKKwhaWmNOwCX6sES0f1Ed10R1KlcX4MyfgERaKcrb5MLtBEwRGR+HQyxdK86OAfb1LOxFozr6k/gGQ2VG1TEEMbFdTDNBz9QwQ68jEvo0xOKIWthy9I5/urX8Y+s3aCVNjfTStUQpzhrfCqIX75EHz96JaqYIY3LomFu26iOdv/OGU3xKTejTG0Da18NfJ1OXhHRiOXgt2wtTIAM2qlsQvA1th6PL98qB5dIc64mjNyJUHEB4ViyaVS4ga5iFL9+G1X+rOqKbhQX+547sOmCmwpCBU0bt375SuUzBGmWOqW06LanllFLtKyLKlqm6j4FUTpe2aQe/nc+8l7fKlTLKFhYUon6Cab1q21apVU9oZkaH66ozcunVLZP2pjIRKOmielF1OWwOe9khB2rKTOT9bY+4kaXlIbqAuFiGpcaAQGgaYmkhgaCAtv9DRkSA07TThqR0wbG2olEULHyOVs8w0X3V3yTAw04GWNhD/QXlgWdyHJBhYqt6UUDa69pTCostFQmQyDK114fpPgAicM0KZZOqSER0gzQwaWuqKbhsJ0clKWWZ6XnV3yYj4GIuk5BTRHUORtYWxCGBVGd69Hk5fe4F/Lz2TB7uGhnqYNrQFth25LQ5Py/w8sCnqVS2OUfP3IDgs/VgFRTTgkBS0t1RbwBweG4uklBTYGisvD1tjYwSnyTrLUHeLw69eYO8L6fJ4FRoCYz09LGzSQgzio8UxvV4jbHpwF8c9XsmnKWBmLgJtxYCZguV1Lm1RwNwMvQ/vV3t2mUREflpHzI3TrSNpj0zIjOhSF6duuuHYlefiuue7EBgZ6GH6oObYeuyOfB2h+b4LihB/U41y2WJ26NGyqqiRpvWPMq0UICpmmcW6mcHz5gbqcCGWh1ma5WFOr0t1idXo9nVx8o4bjtyQLg8Kbml5zOzbHH+fUl4esmyxm0+QqHHu3bQKfvvnAgraWqBnkyroOm+HfBCgx7sQVClRAN0bVxYDEBnLzH8+/UYZXiqVoMAv7YVKG7KLMtNUs6uIrlNpho6Ojih1oKwp1QzLUDaWMrWZzZOytooDEmmeVAKhWDpB08QqjEa/ffu2KJ2gAY3fEgXZ9Fpkz03L1sPDA/nz50+3bCkIJrSM05bJ3Lx5U2SoZ86cKcpVqEzl7dvU+j9Vpk+fjg8fPihdpv2Ycbb+W6hcHrj9QPm2m/eltxN9PaB8KeVpaJ+EumnIpqH79XQl4jYZKtfwD9SST6Mu2nrasCxmhOBnqeufJEUiyiysS6luK6dYt2xkowdJMuB3+yMcamR8tCApNlkEy4ZW0iCcnpO6ZtDzyES+j0dsSOJnn/dbox/oV28CUb1C6s417TtXL18Izz1SaycVGerridpaRSkp0utaCoNtKFhuVKMExi7YB//gj599LaUK5xf/h2QQqOeGxJQUPA8KRL2CCssDQF2nQngYkMHy0E2/PJJly+NTIoK6YKSbRiJR+gGTBctFLK3Q9/ABRMQp13urcx156R2IGuWV15Ea5Qrh2aedHJXryKdlICNLYCiuI6pqd/X0pDuV9JyJScnieWSo24aDrTmeZ/C8ubU83HwCUbOsk9LyqFnGSaneWJGhvorPX8V3Ji1tKt/Q1ZHPg8jGGSkuVxp0rMl40F/u+M8HzFSvSwEaDZx7/PixCPCoa0PagXRf6ueffxaD76hu2N3dXXSDoHpjqsUlZcqUQfPmzUWXCyphoMCZ/qaaXtmPRFqUdaWa6gEDBuD58+eixvjHH38UAxBl5RiEBtQNGTJE7AicPHkSc+fOFe8no/rl7KISCGonRxcqF6HXQlllqqmWvV5bW1tRmkKD/qjEhWqXKZsvy/RTfTKVxFBJSUhIiBgoSAEytaajrDKVZFBpxuHDhz/bsYNKNhQvX1uOER0DuHlIL7K2cfS336dWoSs2A1N/S52+ZwfpNEs3Al5vgV2HgdOXgQHSMndhQHdg/wngyGnA0xuYv0Lavk7Wq9nMVNpBg3o533kIuL4CZiymgFqi9oCZlGhrC+8LYXh7ORwf38Xh8Z9+SI5PQeEm0p2T+2t9RQZZJswjBu/vfEB0YAJC3KJFCzr6wSrZIfUIA/VXDnGNQnRQAkJfReP2Uh+RyS5YT9onXc9EB0WaWol2dsHPoxDuGYuHG96JYFndATPZfeIB2jepiDYNy4m65SmDm8PQQA/HP2UH54xyEQPyZK4/9ETn5s5oXqe06HxRo2JhDO9WF9cfesmDgkmDm6FV/bKYu+6k6HxBWUG60AAlQmUXgzrVRumi+WFva4761Ypj9ujWeOTmC08f5Q4kue2vxw/Qs3xFdC5TTtQtL2jSHMa6ejjwQro8lrdwweQ6qcvjwhtP9KnojLYlS6OguTnqOxXGxNp1ccE7dXlc8PbEmBq10KRIUZFZblmsBIZUqYYzXq/lwfKG1u1QMb89fjp7UgRAlNWmCw1EVLfdpx+gQ6OKaFO/HIo4WmPqgE/ryFXpSN65w10wulvqMrn22AtdmlUSA/kowK1ZvhCGd6knbpctE5q+cukC4n6qZabrVcs44cxNN3F/dGyCyFBTO7tqZZ3EIEJqTUfdN557qi9gJv+cf4hO9Suibe1yKGpvjRm9m8FIXw/HbkqXB5VSjO1YT6kDBvVZpkF6jjbmoo8yZZ2vPU1dHjR91ZIFRB9mqmWm69VKOeHUXWmdu3dAOHwCwzGzbzMxeJAyzn2bV0WtsoVx6bFm92FOlmjn+IX9x0oysoJqd69cuSKymdRajn7MqeNFjx49vmq+lF3dt2+fGCBIQTMNiKPBdzTgT4YG71Fg27BhQ1EzTGUFrq6u8oGGaVFd8pkzZzB+/Hgx4JCuU3cPGmCoiAYbUtBJ86WgtlevXqJLR047ffq0eF+E6pRpJ4A6d1DbOtnrvXr1qtgp6dy5MyIjI1GgQAHx+mT1yMOGDRNBNGWSKdimnYD27dvjp59+EkE+vX7qSkKDJ7/Fe8gMBasDJqTuvCxZL/27o4sEi6bTyUwAf4Uy74IOwKbFwOJ1wP8OAvb5gF8np/ZgJm2aAuERwJot0kF8ZUsAm5cql1tMHyst3xg/R9pZQ3bikryAgtj4j0lw2xuIeDpxSRFD1J1ZVF4aQVlfxf29lIQUuO0OFMGwrqG2OGFJtR+dlEorYkMTcW+1ryjZ0DfXgU0ZEzRaWFxpcGHFgQ5i9556L6ckpSC/sxkqD3VEXnDh9itYmRthaNd6sLE0hsfbYPy0+CDCPx1etrM1V8qObTssLTMY0b0e8lmbil7NNx56YdPe60onQiEb5ihvh37deFq0m0tMSkGNioXECVAo8AoKjcTlux4qa6Bz2wmPV7AxMsLEWvVga2IMt+BgDDx2ECGx0uXhaKq8PGRlF1SaYW9qKno1X3zjhaW3UpfHvCsXMbF2PfzaqDlsjI3EiUt2P3+KNXdvifvtTEzRopi0POxkr/5Kr6fnob248165FC+3nb/jDkszYwzvXBc2FsZw9wnGhKWH5AMB7WzMlJbJ1qNUmiPBiK71kM/KFBGRMbj+yAsbD6QetbQyNxaBtq2lCaJiE/DaNxjjlx5U6oqxatdlMZ9FP7YTJzG5/cxb1EWr29n77rAyNcKo9nXEyURevQvG2DWH5QMB7a2VlwfVKdO1MR3qIZ+lKcKjYkSwvO7ITfk0VOJBgbathXR5eLwPwZg1h+TdOKhU6Md1RzCuU32sGtMBxgb6ogXd3G1nRDs6xj5HS5L2+ARTG8q6UtkEdc+goJJ9nZSA1DPFMakZQVk/C+N/wZVFddT9EvKcAGlnR/ZJ/nt8eDqtRCNeJmk9/EN9WY0r3jn/W9eoiHuOz1PT/eczzOp08eJFkVWlbhvUg5haplGJAmWGGWOMMcZY3sABsxpRve6MGTPg5eUlShropB3UUSJtxwrGGGOMMVV4kF7u4IBZjahlGl0YY4wxxrKDB+nlDl7KjDHGGGOMZYIzzIwxxhhjGiqFSzJyBQfMjDHGGGMaKpmLBXIFL2XGGGOMMcYywRlmxhhjjDENxYP+cgcvZcYYY4wxxjLBGWbGGGOMMQ2VwrnPXMEBM2OMMcaYhkqWcJeM3MC7JYwxxhhjjGWCM8yMMcYYYxqK28rlDg6YGWOMMcY0VAp3ycgVvJQZY4wxxhjLBGeYGWOMMcY0FJdk5A5eyowxxhhjjGWCM8yMMcYYYxqK28rlDg6YGWOMMcY0FJ+4JHfwUmaMMcYYYywTnGFmjDHGGNNQydxWLldwwMwYY4wxpqFSwDXMuYF3SxhjjDHGGMsEZ5gZY4wxxjQUl2TkDl7KjDHGGGOMZYIzzIwxxhhjGorP9Jc7OGBmjDHGGNNQKXziklzBuyWMMcYYY4xlgjPMjDHGGGMaiksycgcHzOy7NSOokrpfQp6zMP9Tdb+EPKWVbxV1v4Q8Rz/CWN0vIU+RaEvU/RLyHFP/JHW/BKYghbtk5ApeyowxxhhjjGWCM8yMMcYYYxoqmc/0lys4w8wYY4wxxlgmOMPMGGOMMaahuIY5d3DAzBhjjDGmobgkI3fwbgljjDHGGGOZ4AwzY4wxxpiG4pKM3MFLmTHGGGOMsUxwhpkxxhhjTEMlc4Y5V3DAzBhjjDGmoVJ40F+u4N0SxhhjjDHGMsEBM2OMMcaYBpdk5PTlS61fvx5FihSBoaEhatWqhbt372Y4bWJiIn755RcUL15cTO/s7IzTp08rTTNv3jxoaWkpXcqUKQN14oCZMcYYY0xDpUi0cvzyJfbu3YuJEydi7ty5ePjwoQiAW7VqhaCgIJXTz5o1C3/88QfWrl2LFy9eYOTIkejUqRMePXqkNF358uXh7+8vv1y/fh3qxAEzY4wxxhjLlhUrVmDYsGEYNGgQypUrh02bNsHY2BhbtmxROf3//vc/zJgxA23atEGxYsUwatQo8ffy5cuVptPV1YW9vb38YmtrC3XigJkxxhhjTEMlQzvHL1mVkJCABw8eoHnz5vLbtLW1xfVbt26pfEx8fLwoxVBkZGSULoPs4eEBR0dHEVT36dMHPj4+UCcOmBljjDHGmFJQ+/HjR6VLfHx8uulCQkKQnJwMOzs7pdvpekBAgMp5U7kGZaUpIE5JScG5c+dw6NAhUXYhQ3XQ27ZtE7XNGzduxJs3b9CgQQNERkZCXThgZowxxhjTUN+ihnnRokWwsLBQuixatChHXu/q1atRsmRJMYhPX18fY8eOFeUclJmWad26Nbp164ZKlSqJAPvkyZOIiIjAvn37oC7ch5kxxhhjTEOlfIPc5/Tp08VAPkUGBgbppqO6Yh0dHQQGBirdTtep7liVfPny4ciRI4iLi0NoaKgou5g2bZoovciIpaUlSpUqhdevX0NdOMPMGGOMMcaUgmNzc3Oli4GKgJkyxNWqVcOFCxfkt1GZBV2vU6dOps9BdcwFChRAUlISDh48iA4dOmQ4bVRUFDw9PeHg4AB14QwzY4wxxpiGSv7CNnA5beLEiRgwYACqV6+OmjVrYtWqVYiOjhZlFqR///4iMJaVdNy5cwfv379H5cqVxf/Uc5mC7ClTpsjnOWnSJLRr1w6FCxeGn5+faFlHmexevXqp7X1ywMwYY4wxpqG+tG9yTuvRoweCg4MxZ84cMdCPAmEarCcbCEjdLRTrk6kUg3oxe3l5wdTUVLSUo1ZzVHYh8+7dOxEcU8kGlXDUr18ft2/fFn+rCwfMjDHGGGMs28aOHSsuqly+fFnpeqNGjcQJSzKzZ88e5DUcMDPGGGOMaaiUbJzKmn05XsqMMcYYY4xlgjPMjDHGGGMaKhnqrWH+r+CAmTHGGGNMQ6l70N9/BZdksCzz9vaGlpYWHj9+nOXHNG7cGBMmTMh0miJFiog2NIwxxhhjeRFnmPOwgQMHYvv27RgxYgQ2bdqkdN+YMWOwYcMG0fuQzreuye7duwcTExPkRV6nQ+FxLBhxEUmwKGyISoMdYV3SWOW0KUkSuB8Ogs+VCMSGJcLU0QAV+tjDroqZfBq3fYF4uT9I6XE0XYvVpeTXkxNS8GyHP97d+ICURAnsKpvCeagjDC31oG73ngBbdgOu7kBwqBbWLpCgeYPMH3P3EbB4PfDaG3DID4zsB3RqrTzNP4eBLXuAkDCgTHFg5nigUtnU++PjgSUbgJMXgcREoF4NYM5PgK018oT2nauhW6/asLY2hadnINavPItXbn4ZTt+pWw2061QN+e3M8SEiFtcuu+HvPy4hMSE5S/M0MzNE/yENUa1msU/ziMGNq+7Y9tcVxETHQ916V3fGkDrVkM/UBC8Dg/Hr6Ut45qd8JjBFA2pWQa/qleBgbo7wmFiccfPA8ovXkZCcujzym5lgcrMGaFC8CIz09PA2PAIzjp3Fc3/pfBe1b4nOzuWV5nvttTeG7j6MvKBrM2f0bV0dNhYm8PAJxrKdl/DiTUCG0/dsWQVdmjjDzsYcHyJjcfG+O9YfuI6EROky6dKkEjo3dYaDrbm4/uZ9KP46ehu3nnmL6+YmhhjeqQ5qlS8s5hERGYMrDz2x6dANRMcmQN06tamCnp1qwNrKBJ5vgrB68wW4eWS8PLq1r4YOLpVhl88MHz7G4vJNd2zecVW+PJzLFxTzK13cHrY2ppjx22Fcv5P+rHCFC1pj5IBGcK7gBB0dLXj7hmL2oqMIComEpuJBf7mDA+Y8zsnJSbRXWblyJYyMjOQ9DHft2oVChQrhe6DOvoqZeXcjAs+2+6PycEdYlTCG54kQ3PztDVqsLg0Di/RfnRd7AuB7NQJVRhaAWQFDBD6OxO2lb9Hot+KwLCr97IiZkwHqzy4qv66lo3w47dk2fwQ8jEStiYWga6yDJ3+/x51lPmi0oDjULTYWKF0C6NwGGDf789O/8wdGTgN6tAeWzgJuPwRmLwXy2QD1a0qnoSB4yXpg3kSgUjlgx35g2CTg5E7Axko6zaJ1wNXbwKr5gJkJ8Osq6fPvWg+1a9S0LEaMbY41y07B7YUfOneviUUremJwr02IiIhJN32TFuUxdGRTLFt8HC+evUNBJ2tMntkOEgnwx7rzWZqnja2ZuGxefwFv3wTDzt4C4ye3ho2tKX6dfQjq1LpcKUxv0RBzT17Ak/cBGFCrKv7u3RkuG7YhLCY23fRtK5TGz83qY8a/Z/HI1x9FbCyxuH0rSCDB4nNXxTTmhgbYPbAH7ni/w7Ddh0VQXdjaEh/i4pTmdfX1G0w/dlZ+XTHgVqfmNUthQs9GWLz9Aly9/NGzZVWsmdQZ3aZtRXhk+mXSqnYZjOnWAAv+Pounr/1QyM4Kc4a2EuvIqj1XxDSB4VFYv/86fAPDRQXrD/XLY9n4Dug3Zye8/EJha2kCW0tTrN57VQTTFFhPG9Bc3D59/XGoU9P6pTFmSGMs33AOL9z9RTC8bH439Bn1NyI+pP/ONG9YFsP7N8SSNafx/OV7ODlaY/r41mJ5rN9ySUxjaKAHzzfBOHn+OX6b0VHl8zraW2Ld4t44cf4Ztuy+geiYBBQtZCMPujVVCtcw5wreLcnjqlatKoLmQ4dSfwTpbwqWq1SpIq7v2LEDNjY2iKc0nIKOHTuiX79+8usbN25E8eLFxaksS5cuLRqFK3r58qVoDk6nqyxXrhzOnz8vSjDonO8ZuXLlijizD50yk05ZSeeDp9NcKqLr1J/RwsJCnHd+9uzZkNCWLoOSDHrOv/76C506dYKxsTFKliyJY8eOIbe9Ph6CIs2sULiJNcydDFF5eAHo6GvD+2KYyukpWC7dOT/sq5rDxE4fxVrZwL6qGV7/G6I0nba2Fgyt9OQXA/PU4DsxOhneF8NRcYAD8lU0hVVxI1QbUxBhr2IQ5p7+hyS3NawNTBgKtGiYten3HAUKOABTxwDFiwB9OgMtGwHb96dOs30f0K2tNAgvUQSY9zOdMhU4dFJ6f2SU9G+aR+2qQPnSwMJpwKPnWnjsCrXr0rMWTv37GGdOPoWPdwhWLz2J+LgktGrrrHL68hUKwvWZLy6dc0VgwAc8uPcGl867okw5xyzP0/tNMH6ZdRC3b3jA3y8Cjx++xdbNl1G7Xklop9kBy22DalfFvkfPcejJC3iGhGHuifOIS0xCl8oVVE5fpaAjHvr64fjzV3j/4SNuePmIvys52sunGVa3BgI+RomgmjLV7yKk0/mGf1CaFwXIIdEx8svHOPVn20nvVtVw5MpzHL/uijd+YVi8/TziEpLQrqHqZVKxhCOeevjhzO2X8A/5iDuub3H2zkuUK5a6TK4/9sLNp2/gGxgBn8AIbDx4AzFxiahQQnrqYK/3oZi27l8x3fvgD7jv5ouNB6+jQeVi0NFW7zrSvUN1HD/7FKcuPMdb31As33AWcfGJ+KG56uVRoawjnru9x/mrbggI+oh7j71x4ZobypZKXR53Hr7BX/9cx7XbHhk+77C+9XH7gRc2bbsCD68g+AVE4MZdT5VBOmNpccCsAQYPHoytW7fKr2/ZskV+yknSrVs3JCcnKwWVQUFBOHHihHgsOXz4MMaPH4+ff/4Zz58/F2UeNI9Ll6R75/R4CrApQKXTVm7evBkzZ87M9HXRKS3pDD01atTAkydPRED+999/Y8GCBUrTUVmJrq4u7t69i9WrV2PFihUiIM7M/Pnz0b17dzx9+lQ8R58+fRAWpjpQ/RZSElMQ4RWLfJVM5bdpaWuJ6xkFrsmJEmjrKf8QUYAd+jJa6baogHicGu6GM2Ne4t5qH8QEpx4epeeUJEuUnpey1Ua2enkiYP5SFNDWqaZ8W/0a0ttJQqK0vENxGjohFF2XTUP3JyZpKU1TrDDgYCdRe8Csq6uNUqUc8PD+G/lttC9I18uVL6jyMa7P36FkaQeULisNkO0dLVGzdgncvfU62/MkJiaGohwjJTl1ZzS36Wlro7yDHW6+8ZHfRq+GrlcpKA3k0nr0zg/lHfKjoqP0rGAFLS3QqGQRXHmd+v6bliqG536BWN3lB9ycOAKHh/VBtyrpg6uahQuK+0+PHoB5rZvC0sgQ6qaro40yRexw78Vbpc/znutbVCyuepk8e+2HMkXyo1xRaUDomM8CdSsVFQGyKtpaWmhRqzSMDHTFYzNiamQgyjGSU9S3joj1u4Q97j9WXh4PnrxF+TKpO42Knrv5oVRxO5QtKV0eDnYWqF2tmAh+s0pLC6hTvTh8/cKxbF5XHN0xGpuW9kH9WiXwPZwaO6cvLD0uydAAffv2xfTp0/H2rXQDc+PGDVGmITt7DpVq9O7dWwTVFDyTnTt3iiw0Dbojy5YtEzXRo0ePlp/7nU4zSbc3adIE586dg6enp5invb10o/Tbb7+hRYsWGb4uqqGm7Pe6detEVrhMmTLinO9Tp04Vp8iUnQqTpqGSEpqGMtvPnj0T14cNG5bhvOm1ys4Zv3DhQqxZs0YE3C4uLsgN8ZHJkKQgXemFoYUuot6rzlrZOZuKrLRtORORYQ5+FgW/Ox/EfGSsShqj2hgnmDrqIy48SdQzX53jhWYrSkLPSEfUSmvrakHfRCfd88ZFJELTUE2y7aeyChkbayAqWgtx8RJ8jKSdNS3YWCn/gFMphizmCgkF9PQkME8tBRdovjR/dbKwMIaOrjbCw5R3iui6U2EblY+hzLKFhRFWbugvfsR1dXXw7+EH2P2/m9mep7mFEfoMrI+T/2Z9QO63YGVsBF1tbYRGKe/chUbHoFjaFeETyiZbGRlh18Ae4sCyno4Odt9/gj9u3JNP42RlIWqct95+iE037qKigz1mtWqCxOQUHHkqPWPYNU9vnHv5Gu8iPsDJyhITm9TDn706ocfWPUhROKKV2yzNjETQHJYmixn2MQaFHVQX4VNm2cLUCH/OlC4TWkcOXnyCbcfvKk1XvKAt/p7VE/p6uoiNT8CUtf+KDLYqFqaGGNy+No5ceQZ1sjCXLo/wNOVKYRExKFRA9fKgzDI9jsopZN+ZI6ceY+f+O1l+XisLExgb66NPl5r4a+d1bNp+FbWqFsGC6R0xfuYePHF999XvjX3fOGDWkBrfH374QQzuo1IG+ptKGxRR8EmZXsr6FihQQExLQScFqcTNzQ3Dhw9Xeky9evVExpe8evVKBLayYJlQqUVmaJ516tSRP4dsnlFRUeI88LIa69q1aytNQ49Zvny5yGrr6CgHhjKVKlWS/00DAs3NzUXWPCNUjpK2JCUpIRm6+qrn/y1UGuSIR3+8x7nx7mKjTkFzoSZWeHsxXD6NvcIAQIvC0gD6zKiXeH/zA4o0yyMj2Ng3ValKIfTqVw9rl5+G24v3KFDQGqPHt0CfkPr4Z/v1L54fBQELlvbAW+8Q7PhbWvOrSSgrPKJ+Tcw/eRFP/fxRyMoSM1s1xuioWthwTRoQ0faDMswrL90Q190CglEyvw16VqsoD5hP0qGIT9yDQvEqMAQXfhws5n/b2xeapGqZghjUriZ+33EBz70C4JTfEhP7NMbg9rWw5VhqkPjWPwx95+yEqZE+mtYohblDW2Hk4n3pgmYTQ32s/KkT3viFYvORW9A0lSs4oW+32lix6Rzc3P1RwMEK44Y1Rf8edbBjb9bej9an4+k0EHD/sQfi79dvglChTAF0aF1ZowNmHvSXOzhg1hBUWiE7T/v69elHOlE9s7Ozs6hnbtmyJVxdXUVJhqbS01PuCEE/mCkpCqnaNBYtWiTKOBTVG1kW9UeVy9bzG5jpiA1s/Afleuy4D0kwsFT9taFsdO0phUWXi4TIZBha68L1nwAROGeEMsnUJSM6QFqWYWipK7ptJEQnK2WZ6XnzQpeML0VdLEJS9xeE0DDA1EQCQwNp+YWOjgShaacJT+2AYWtDnTG08DFSOctM81V3l4wPH2KQnJQCK2vlLi90PTxUOUMsM3BoI5w/8wynjkuzwd5ewTA01MOEKW2wa8f1L5qnkZE+Fi7vhdiYBMybsR/JyRl/R3IDDcZLSkmBjalyJxkbE2OEpMk6y4xvXBfHnrrhwOPn8mDXWF8Pv/zQHBuv3RElHcGR0fCkQw0KvELC0KpMyQxfC2Waw6JjxOBAdQbMEZGxSEpOgbWF8jKxNjdG6AfV68jITnVx8qYbjl6VLhPPdyFiUNuMgc2x9d87ooSB0HzfBUWIv1++DUK5onbo0aKqqJGWMTbUw+qfOyMmjjLQx9S+jlCHC3rdVpZploelMcIiVC+PIX3q4+wlV5w4J82Oe70NEd+ZyWNa4n/7bsmXx2efNylZ1EwrevsuFBXLZVzqpAm4D3Pu4N0SDUGlCAkJCUhMTESrVq1UTjN06FCRWabSjObNm4uMsUzZsmVFKYciuk6D+wiVSvj6+iIwMFCp3VtmaJ63btHGSqI0TzMzMxQsmLoBoppoRVQKQgP5MsouZweVrHz48EHpUntI6WzPT1tPG5bFjBD8LHUDLkmRiDIL61Kq28op1i0b2ehBkgz43f4IhxrStk+qJMUmi2DZ0EoahNNzUtcMeh6ZyPfxiA1J/Ozz5kWVywO3pckcuZv3pbcTfT2gfCnlaWi/iLppyKah+/V0JeI2GSrX8A/Ukk+jLklJKXB390eVakXkt9HRBbr+IoOMlYGhntJ3hsh2BmnHMKvzpMzy4pW9RBAwZ+o+pZZ06pKYkgJX/0DUKZK67aGf8jpFnfCIWqaoYKinixQRFqeS1djKjkw9fOeHolTLo6CItZUYJJgROzNTWBobIThKdRCWWyg4fOkdiBrlUrsa0duqXq4QnnmqXiYGBnpie6NyHcmkIwLVMuvr6ShlltdO6oLE5GT8vPponugGIdbv1wGo5lxYaXlUrVQYri9V118bGuhm+p3J6vO+9AiAU5qyj4KO1ggIUh48ypgqnGHWEBRcUgmE7G9VqI550qRJ+PPPP0WmWdHkyZPFIDrKRFMw/e+//4puG9QJg1CtMnXQoL7Ov//+OyIjIzFr1qxMN0hUD03dLX788UeR/aayjrlz54r6aFn9MvHx8RG30UDDhw8fYu3ataIkIydRlw66KPracowSbW3xYP07WBY3glUJI3ieCEVyfAoKN5HWYt5f6wsjaz2U7yMtYwnziBH9ly2LGIn/X+4LFBv5kh1S2+ZRf2WHamYwykc1zIlw2xskMtkF61mK+/VMdFCkqZVoZ6dvqgNdIx083eInguW8EDBHxwA+71OvUwzk5kF1iQCN2VqxGQgMBpZ8Gi/aswOw6zCwdCPQpY00ED59Gdi0OHUeA7oD0xcBFcoAFcsAOw5I29fJejWbmUo7aFAvZwszyk4DC1ZTQC1Re8BMDu65gykz28P9pb/ok9ype00YGunhzImn4v4ps9ohJDgSW/6QjjmgzhZdetTCa/dAvHzxHo4FrDFgaCNxe8qnIOlz85QGy71hYKCLxb8chbGJgbgQ6sksm486UJ3xkg6t8Nw/CE/9AkSPZeqbfOiJdIQm3RcYGYUVF6U78JfcvURnjRcBQXj6PkCUZFDWmW6X1R5vv/0Quwf1wIh6NXDqhTsqFbBH96oVMeeEdPtlrKeHsQ1r48xLD5HJpprnyc0b4G1YBK55pg4uU5ddZx5g7jAXuL0JhKtXgGgrZ2Sgh+PXpMtk3jAXBIVHYcMBaUkOdbbo1aoqXvkEwdXTHwXtLDGicz1ce5y6TEZ3rY9bT98gICwSxob6ohVd1TJOGLf8oDxYXjO5Cwz1dTHnj1OibIMuJPxjrFrruvcdvY/pE9rg1esAUWLRrX11GBnq4eQFaUZ9xoQ2CAmLxOYd18T1m/c8RWcNd6+gTyUZliLrfPOup3xdp8dTqYYMDQwsUTQ/PkbGynss7z58D/MmtxPlF4+e+aBW1aKoW7M4xs/YA03GbeVyBwfMGoTqeDNDbdu6dOkiSjGo44Uiuk71yjTIj7plFC1aVGSiZYMCKQin9nGUpaZa6GLFimHp0qVo166daDOnCtVKnzx5UgTjVA5ibW2NIUOGyANtmf79+yM2NlbURNPz0POnrafOiyiIjf+YBLe9gYinE5cUMUTdmUXlpRGU9VXcl0hJSIHb7kBEByVA11BbnLCk2o9OSqUVsaGJuLfaV5Rs6JvrwKaMCRotLK40uLDiQAdx7Id6L6ckpSC/sxkqD1U9ejy3ub4CBkxIfdNL1kv/7ugiwaLpdDITwF+h1JwaI1BwvHgd8L+DgH0+4NfJqT2YSZumQHgEsGaLdBBf2RLA5qXK5RbTx0rLN8bPkXbWkJ24JC+4ctENlpYmIuilsgnP14GY8fMeRIRLM5v57SyUsoVUp0yxysBhjWBLJ2GIiBHB8pbNl7M8zxKl7VG2fAHx9459Y5ReT9+u60S7OnWhgNba2AjjGtVBPlNjuAUGY+iuw2LgH3EwN1MK1mRlFxMa1xNZ4bCYGBEsr7wkHQRJnvkHYuz+fzGxaX2MaVhblFssPHsZ/z5/Ke5PlqSglJ0tOjqXg5mhAYIio0TbudWXb4rsqrqdv+sOKzNjDO9UFzYWxnD3Ccb45YfEwD9iZ6O8TLYcuy12tkd2rod8VqbipCMULFPrOMWSjrnDXWBrYYKo2AS89g0WwfJdV+lo2dJF8su7cBxeOkTp9XSY9JdoV6cuF6+/gqWFMQb3ridOXPLaKwiT5h2QDwSkk5MoZpSpTpmuDu1bH/msTRHxMVYEy3/ulAbUpHQJe6xZ2FN+/cehTcX/1Lpu0epT4m9qObd841n07Vob44c1hc/7cMxZfBTP3BSyABqISzJyh5Yk7XEOptGaNWuG8uXLi64SX4vKK6gv8+vXr0X2WdNMe9pV3S8hz1mYX5qhZFKtug5Q90vIc3xaqf9IRl5i4ck/kWkZhql/JySvuXpsstqeu9ftnE9A7a69Ocfnqek4w/ydCA8PFy3h6ELt3rKDejWbmpqK+mIKkikTTF0vNDFYZowxxv4LuEtG7uCA+TtBtckUNC9ZskQM4MsOqlumHspUc0xt66jWOadrjRljjDHGNA0HzN8Jb2/vr54H1RrThTHGGGOagWuYcwcHzIwxxhhjGoq7ZOQOLnxhjDHGGGMsE5xhZowxxhjTUFySkTs4YGaMMcYY01AcMOcOLslgjDHGGGMsE5xhZowxxhjTUJxhzh2cYWaMMcYYYywTnGFmjDHGGNNQnGHOHRwwM8YYY4xpKO7DnDu4JIMxxhhjjLFMcIaZMcYYY0xDcUlG7uAMM2OMMcYYY5ngDDNjjDHGmIbiDHPu4ICZMcYYY0xDccCcO7gkgzHGGGOMsUxwhpkxxhhjTENxhjl3cMDMGGOMMaahJBww5wouyWCMMcYYYywTnGFmjDHGGNNQfKa/3MEZZsYYY4wxxjLBGWbGGGOMMQ3Fg/5yBwfMjDHGGGMaigf95Q4uyWCMMcYYYywTnGFmjDHGGNNQXJKROzhgZowxxhjTUFySkTu4JIMxxhhjjLFMcIaZMcYYY0xDcUlG7uCAmX23riyqo+6XkOe08q2i7peQp5w5sF3dLyHPcWnfV90vIU/Rjo5X90vIc5Ld3NX9EhjLdRwwM8YYY4xpKIlE3a/gv4EDZsYYY4wxDcWnxs4dPOiPMcYYY4yxTHCGmTHGGGNMQ3FbudzBATNjjDHGmIbiLhm5g0syGGOMMcYYywRnmBljjDHGNBR3ycgdnGFmjDHGGGMsE5xhZowxxhjTUDzoL3dwwMwYY4wxpqE4YM4dXJLBGGOMMca+G15eXjk+Tw6YGWOMMcY0uK1cTl80XYkSJdCkSRPs3LkTcXFxOTJPDpgZY4wxxjS4S0ZOXzTdw4cPUalSJUycOBH29vYYMWIE7t69+1Xz5ICZMcYYY4x9NypXrozVq1fDz88PW7Zsgb+/P+rXr48KFSpgxYoVCA4O/uJ5csDMGGOMMabBg/5y+vK90NXVRefOnbF//34sWbIEr1+/xqRJk+Dk5IT+/fuLQDqrOGBmjDHGGGPfnfv372P06NFwcHAQmWUKlj09PXHu3DmRfe7QoUOW58Vt5RhjjDHGNNT3lBHOKRQcb926Fa9evUKbNm2wY8cO8b+2tjRPXLRoUWzbtg1FihTJ8jw5YGaMMcYY01DfwRi9HLdx40YMHjwYAwcOFNllVfLnz4+///47y/PkgJkxxhhjjH03zp07h0KFCskzyjISiQS+vr7iPn19fQwYMCDL8+QaZsYYY4wxDcWD/tIrXrw4QkJC0t0eFhYmyjGygwNmxhhjjDH23ZBk0Ew6KioKhoaG2Zonl2QwxhhjjGkqLmKWoxOVEC0tLcyZMwfGxsby+5KTk3Hnzh3Rozk7OGBmjDHGGNNQ30MJRU559OiRPMP87NkzUacsQ387OzuL1nLZwQEzY4wxxhjTeJcuXRL/Dxo0SJzpz9zcPMfmzQEzY4wxxpiGyqBc9z9t69atOT5PDpgZY4wxxjQUl2RI0Smw6WQklFWmvzNz6NAhfCkOmBljjDHGmEazsLAQg/1kf+c0DphzGZ11JiIiAkeOHFH3S8mzqFj/zz//FGfgcXV1ha6uLkqUKIG+ffti+PDhSqNev7UuLSqjT7vqsLYwwWufYKzYdhEvPAMynL5H66ro1NwZ9rZmiIiMw6U77ti45xoSEpPF/f071ESjGiVR2NEa8QlJeObuhw27r8LHP1w+j/Wzu6NqOSel+R4+/wS//30eeUH7ztXQrVdtWFubwtMzEOtXnsUrN78Mp+/UrQbadaqG/Hbm+BARi2uX3fD3H5eQmJCcpXmamRmi/5CGqFaz2Kd5xODGVXds++sKYqLjoU73ngBbdgOu7kBwqBbWLpCgeYPMH3P3EbB4PfDaG3DID4zsB3RqrTzNP4eBLXuAkDCgTHFg5nigUtnU++PjgSUbgJMXgcREoF4NYM5PgK018oR2XaujW9+6sLYxhZdHINYvO4VXL1SvI0s39odztfSnp71z3QOzJ+4Wf1tam2Do2GaoVqs4TMwM8ezRW6xfdhp+vmFKjylbsSAGjWqCMuULIDlZAi+PAEwf9w8S4pOgbu161ULXQQ1gZWsKr1cB2LDwONyfvVM57e9bh6BSzWLpbr975RXmjN4h/jY01sfgn1qhTtOyMLc0RsD7cBzdeQsn992VT0/PNfRnF1SpWwLGxgZ45x2C3Zsv48Y5V6hb+9Gt0G1Se1jbW8LzyVusH7cFr+69Vjntsovz4Ny4fLrb75x4iFntFsmvFypTAEMX90WlRuWgrasNnxfvML/rcgT7Svvx6hnoYeTy/mjco574+/6Zx1gz5i9EBH2ARuMMs1IZBsUQ8+fPR758+WBkZIScwn2YWZ7Tr18/TJgwAR06dBAF/I8fP8bs2bNx9OhRnD17NtdeR7PapTGuXyP8ffAWBs74HzzeBmPltC6wMlf9BWxZtwxG9WyALQdvoefP27DwjzNoVqc0RvaoL5+mStmCOHj2MYbN2YXxCw9AV1cbq6Z3haGB8r7rkQtP8cPIjfLLul1XkRc0aloWI8Y2x86t1zBqyN/weh2ERSt6wtJS9U5MkxblMXRkU/xv6zUM6fMHViw+jsbNymHw8CZZnqeNrZm4bF5/AcP6bcbS3/5FjdrF8PO0H6BusbFA6RLA7AlZm/6dPzByGlCrCnD4L6B/V2D2UuB6aowjguAl64ExA4CDfwKliwPDJgGhqftUWLQOuHwTWDUf2LEaCAoBxs1GntCoeTmMmNASO/+6gtH9N4ugdeGaPrC0Ur2O/DJ1H3q0Xi6/DOu5EclJKbh64YV8mnlLe8ChgBXmTtqL0X03I8j/A5as6wtDQz2lYHnh6t54cNsLPw76Gz8O/AtH99+DJEX9BZ4NXSpi2JQ22LnhIsZ2Wy8C5t/+GAgLaxOV0/8yYRd6NVokv4xovxrJScm4dvaZfJrhU9qgev2SWDptP4a3W4Uj/7uJMTPbonaTMvJpJi3sioJF82He2J0Y2WkNbpx3xYzlPVG8jOpTBeeWRt3rYsTyAdj5y36MqjYVXk/fYtHpmbDMp3qA1vwuy9DdYZj8MrTCT2J5XD1wSz6NQzE7rLz2K3xevcfPTeZihPMk/LPgIBLjEuTTjFo5ELXbVsev3Vfg58ZzYeNojXkHs9cxgSlbv349ihQpInoc16pVC3fvKmzU0khMTMQvv/wiTi5C01PnitOnT3/VPBVRwExJtnfvVO+QZhcHzHmA7MNdtmyZ0u0UKNLhhdevX4tp5s2bJ07naGBgAEdHR4wbN04+bXh4OPr37w8rKyuRgW3dujU8PDzk9zdu3FjMK+3F29tbnG+9bdu26VZoxfOs0+N//PFHEcjSc9jZ2YkscHR0tBiNamZmJt7DqVOnlObz/Plz8VpMTU3FYygYVnX2HZl9+/bhn3/+we7duzFjxgzUqFFDfGEoeL548SKaNEkNtL61Xj9Uw7GLz3Diiiu834fh97/PIT4hEW0bV1Q5fcVSjnjm/h5nb75EQMhH3H32FuduvkS54qk/Tj8tPoSTV13x5l2oyFgv2HgaDvnMUaaondK86HnCPsTILzGxqRt9derSsxZO/fsYZ04+hY93CFYvPYn4uCS0auuscvryFQrC9ZkvLp1zRWDABzy49waXzruiTDnHLM/T+00wfpl1ELdveMDfLwKPH77F1s2XUbteSWjrqDez0rA2MGEo0KJh1qbfcxQo4ABMHQMULwL06Qy0bARs3586zfZ9QLe2QOc2QIkiwLyfAeqzf+ik9P7IKOnfNI/aVYHypYGF04BHz7XwWP2JQ3TpXQenjjzE2eNP4PMmBKsXn0B8XCJatauicvrIj3EID42WX6rWLIa4uERc+xQwFyhkjXIVC2LNkpNwd/PDO59QrFlyAgYGemjcqoJ8PiMntMSRvXexd8cNvPUKFtNdPf8CiZ+O7qhT5wH1cPrAfZw78hA+nsFYO/+odJl0rqZy+qgPsQgPiZJfKENMy+TqmefyacpVLoTzRx/h6b03CPSLwKn990QgXrpiwdRpqhTCsX9uiUx2wLtw7P7jMqIj41CyfAGoU5ef2uLUXxdwZttl+Li9w+qRmxEfk4BWg5uqnD4yPArhgRHyS9UWlRAXE4+r+1MD5kELeuHuyUf4a+pOeD72hr9XIG79ex8RwR/F/cbmxnAZ3BSbft6Ox5eew+OhF5YNXo/y9cqgbK2S0PRBfzl9+RJ79+4V/Y/nzp2Lhw8figC4VatWCAoKUjn9rFmz8Mcff2Dt2rV48eIFRo4ciU6dOslbwmVnnorodNglS5ZEaGjol72Rz803R+fGsoUCVwpa047qpOsNGzYUgejBgwexcuVKsZJRIEwlHRUrVlQq9bh//z6OHTuGW7duiQC7TZs2IvCVFbj7+/vLL1QQX7p0aRHEDh06VOzd0e0yx48fR0xMDHr06CG/bfv27bC1tRV7eRQ8jxo1Ct26dUPdunXFCt2yZUsRENPjCJWeNG3aFFWqVBGvjZ4jMDAQ3bt3z3BZULBMr4sCZFXL6VvUJamiq6ON0kXtcO+5j/w22ojQ9QolVWdnqLyCHlOuuL247pjfAnUrF8Wtx14ZPo+psYH4/2NUnNLtLeuVxanNo7Hz9wEY1bM+DPTVXz1F2fBSpRzw8P4bpWVC18uVT/2RVuT6/B1KlnZA6bLSANne0RI1a5fA3Vuvsz1PYmJiKMoxUpLVnz38EhTQ1kkTI9WvIb2dJCRKyzsUp9HWll6XTUP3JyZpKU1TrDDgYCdRe8BMn2fJMg54dE/586TrlAHOCpf2lXHl3HMRIBI9Pem6r1hWQfNMTExCBWdp6RJlr2n+EeHRWPnXIOw9NRHLNg1A+U/3q5Oung5KlnPEo0/rPKHt86Pbr1HWuVCW5kGB9ZVTzxAfK10m5MVjH5FNtskvzcpWqlkUBYrY4sGN1Od58chHZLdNLYzE9rNR64rQ19fFk3sZb5O+NV09XZSqVgwPzz9VWh50vVztUlmaR+vBzXB5700RNBN6b7V+qIp3Hn5YdGom9gX8hTW3FqJuhxryx9Bz6unrKj2v7ys/BL4NRtk6WXvePEvyDS5fYMWKFRg2bJhInpUrVw6bNm0SibstW7aonP5///ufSIhRjFKsWDERS9Dfy5cvz/Y801q8eDEmT54sknY5Rf2/wkwe8NJZaSgYrVmzpgh0d+3aJc86+/j4wN7eHs2bN4eenp7INNN0hAJoCpRv3LghgldZ4Onk5CQCawpqra1Tixsp8KZsLZ3xhup76DEUpNJKPGXKFHmwTo+jzLAM7eHRniGZPn26WCEpgKaVmtDr37hxI54+fYratWtj3bp1IlheuHChfB60stPrcnd3R6lS6TdS9F7otaibpbmRCJrDPkQr3U7ZXqo/VoUyyxZmRtg0ryco76mrq4ND5x5j+1HVh5FobMKE/o3x5OV7eL1L3RM+e8NNZKhDwqNRvJAtxvRqiEIO1pi+8hjUycLCGDq62ggPU14mdN2psI3Kx1Bm2cLCCCs39Bfvl5bJv4cfYPf/bmZ7nuYWRugzsD5O/vsYmoZqkm2tlG+zsQaiorUQFy/Bx0g6G5UWbKyUf7FsrIA3n/bdQkIpiJTA3Ex5PjRfmr86US1txp+n7WcfX7qcI4qWsMOKBf/Kb/P1DkGgfwQGj2mK1YtOIC42AZ1710Y+OwtY20oXgn0B6ULtN6wRNq8+B0/3QLT4oRKWrO+H4b02pat1zv1looOI0Cil2+m6U9F8n318qYoFUbSUPVbOOax0+8bf/sW4+R3xz6WpSEpMRopEgtVzD+P5A2/5NAt/3iNKMA7cnCWmoaz2L+P/gb+P+paHha2ZWB7hgcp1w+FBH+BU5vOZ79I1SqBoxUJYPnSj/DbL/BYwNjNCj6kdsW32Hvw17R9Ud6mMuQcnYXLT+Xh69QWs7C2REJ+I6A8xys8b+EHUUbPsSUhIwIMHD0RMoJjhpViFkneqxMfHpzs9NcUi169fz/Y806Ij7pS8o7iFTliStpY5LOzLvwMcMOcRVGLxww8/iICSAuF///1XrFQUtBL6f9WqVWJvzMXFReyNtWvXTgyIc3NzE/9TjY+MjY2NCDzpPkVUMjFt2jQxf8WAlbLMmzdvFgEzZYFpOgqqFVWqVEn+t46OjngOxSw3ZauJ7JDJkydPRA2yYtAt4+npqTJgzuj8759Dy4ouilKSk6Ctk3urONUnD+hYC0u3XMCL1/4oaGeJCQOaYFCnaGw9fDvd9JMGNUMxJ1uMmLdH6fajF1PrFD19QxAaEY11s7qjQH4LvNewwSmVqhRCr371sHb5abi9eI8CBa0xenwL9Ampj3+2SzeOX8LYWB8LlvbAW+8Q7Pg7b9R1s5zj0r6KGCSoOEAwOTkFv0zdj4mz2uHQhSmivvnhPS/cveEhHxGv/en/E4ekpSDE0z0AlasXhUu7ytiyQXlbpklcOlfDm1cB6QYItu9TB2UrOWHumP8hyC8cFaoXxZhZ7REWFIlHtz3FNP1/bC4GSU4b/LcYLFu3aTkRQE/q/ye8PQKhiVyGNBU1z4oDBLW1pZ//raP3cWjVCfG35xNvlK9TGm1HtBAB8/fsW7SVU/WbamBgIC6KqMSSTjkt+/2XoesvX75UOW8qraAMMh1BpzrmCxcuiKPgNJ/szjMtipdyGgfMeQgFrVTSQBlgyvBSOYSsIwRlZV+9eoXz58/j3LlzGD16NJYuXYorV65kef5UK9SzZ0+RGabyibR7YxRI097bzZs3UbRoUTRooDzcnzLbiujHSvE22Y9XSkqK+D8qKkoE9UuWLEn3WhwcVJc1UBCd1S+EokWLFolRsYoKlG8Bp4qtkB0RH2ORlJwiumMosrYwFgGsKsO718Ppay/w76Vn8mCXBiVNG9oC247cVqoL+3lgU9SrWhyj5u9BcJhy5ikt19fSUpmC9pZqDZg/fIgRwYpVmoFKdJ1qT1UZOLQRzp95hlPHpdlgb69gsUwmTGmDXTuuf9E8jYz0sXB5L8TGJGDejP0ikNI01MUiRGHwHgkNA0xNJDA0kJZf6OhIlAb4iWnCUztg2NpQOYIWPkYqZ5lpvurukvExIuPPMyxNhjUtWi8atyyP7X9cTnefx0t/jOq7GcYmBtDT0xHB35otQ0RNM5HN2+dNsNLjqCY+v33ulHFlvkySYWmjnDig61SfnBkDIz00al0JO9Ypd8jRN9DFwAkt8Ou4Xbh79ZW47Y17IIqXdkCXQfVFwOzgZI0OfeqIAYNvPaVJDAq8K1QrjHa9amPtL0ehDh9CIsXysLJT/lys8lsgPCAi08caGhugSY962D53b7p5JiUm4a2br9LtPi/foUI96SBImre+gR5MLIyVssz0OsI+87x53jeoTFP1mzp37lwxlupr0Rn46Mh0mTJlRNxAQTOVXmS13CIrBgwYgJzGNcx5CGWNTUxMRFkD1ftSXbMiOqRAAeiaNWtw+fJlEdzSudLLli2LpKQkUWIhQ8XuFGBT7Y9sj40e26VLF/z000/pnpuyxR07dhSBOjX+ppX3a1WtWlW0haNBe1SHrXih96lK7969RbkGdcRQlX3+8EF1wEiHbug+xUuBcs2y/dopWH71JhDVK6TWGNL+QPXyhfDcI7XWW5Ghvp44LKoo5dMIfS1RpJEaLDeqUQJjF+yD/6cBKZkpVTi/+D8kg0A9tyQlpcDd3R9VFFqA0TKh6y9cVY9GNjDUS3fUQLZDRRvKrM6TMsuLV/ZCUlIy5kzdp9SSTpNULg/cfqB828370tuJvh5QvpTyNLS4bj9MnYbu19OViNtkqFzDP1BLPo260OdJwW3lGkWVPk/K9Lpl0EJNpkGzcqJe+cLp1CMsaVHdOgXLjk7WKFnWAbc+BYsBfhEICfqIgmnKeAoWskagv3qPylAphMcLP1SuXVx+G637lWsVh9uT1DESqjRsVQF6+jq4mKb8iEqbaFnJti+K3y1Z4oK+e+I2FdskrU8ZWXWgwNb9gReqNEs9Okmvma6/uO2e6WMbdqsDPQNdnN95Nd08X93zhFMp5ZKOAiUdEfhWOsicnjMxIUnpeQuWcoRd4Xxwu5X58/4XqfpNna5QIiFDZZl0xJmOTCui61RGqgq1e6NyUWoa8PbtW5EkoyPRdAQ9u/PMTFxcHD5+/Kh0yQ4OmNWAVjzqgKF48fX1FSsI1TLTSkkjPOvUqSN/DAWx1LGCCti9vLywc+dOEUAXLlxYTEuD5GiPjWqAqBSCehYXKFBAPniOAmXKVtPeYUBAgPwiOwQiy3DTwD4q48iJvbMxY8aIOqFevXrh3r17ogzjzJkzIhhXfF5FNCCQMuv0GKp9psGC9IWiQYhUvyQ7T3xadJiIzu6jePnacozdJx6gfZOKaNOwnKhbnjK4OQwN9HD8inQQwZxRLmJAnsz1h57o3NwZzeuUFp0valQsjOHd6uL6Qy/5j9akwc3Qqn5ZzF13UnS+oIw1XQw+DWyisotBnWqjdNH8sLc1R/1qxTF7dGs8cvOFp0/G3UVyy8E9d9CmXRW0cKmIQoVtMG5Saxga6eHMCelAmimz2mHwiMby6amzRduO1UQrOXsHC1StXhQDhjYSt8t+7D83T2mw3FtkIJcvOi6yjJSxpIvsUKy6RMcAbh7Si6xtHP3t92k7v2IzMPW31Ol7dpBOs3Qj4PUW2HUYOH0ZGCCtvBIGdAf2nwCOnAY8vYH5K6Tt62S9ms1MpR00qJfznYeA6ytgxmIKqCVqD5jJwV230KZDVVFD7FTEFuOm/iD9PD8dZZg8rwMGj07fDcGlQxXcvPISkR9i093XoFlZVKpaWAwardOwFBav7YubV17hwZ3UwWv7d95Cxx410aBpWTgWtMKAEY1F3fTpY6kj79Xl0PYbaN21Opp3qAKnYvnw45z2MDTSx9nDD+Tt3wZNUD7qR1p1ro6bF9zSLRPacXh61wtDJ7mgUo2isCtghRYdq6BZ+yq4+am7iO+bYLx/G4JxczuIOmjKOFO3jip1iuOWQss+dTi48jjaDG2GFv0bid7J4zYOg6GJAc5slW7fp2wbi8ELe6d7HHW5uHHkHiJVHJXbv+wYGvWoi9ZDm8GxuD06jHFBnXbVcGzjGXF/zMcYnN5yESOXDxA9nUtWLYZJW0bD9eYruN1J7SilqSUZOX1R9ZtqkKYcg1B9cLVq1URZheKOG11XjGFUoTpmilMo4UeNDWTxytfMU4aC8bFjx4puX5Sgo+5eipfs4JIMNaDsMA2GUzRkyBD89ddf4n8KFNNmeC0tLUUpBbVZoWCTaoepDpkyw4Qyw+PHjxft4ahgnmqDTp48KS+ZuHpVukdOAbaiN2/eiAwwoYCUSiXKly8vaqq/Fs2DBiJOnTpVlIBQPRQ9P9VgUwG/KpRpoMGOVE9Nh2d+++03UZ9NOwVUNkK1T7nlwu1Xoufy0K71YGNpLPow/7T4IMI/Hc6zszVXyt5sO3xbHBkb0b0e8lmbIvxjLG489MKmvdeVToRCNsxJ7T5Cft14WrSbS0xKQY2KhcQJUCg4DwqNxOW7HiproNXhykU3WFqaiKCXAlbP14GY8fMe0Z2A5LezUOp7S3XKtIgGDmsE23xmIjtIwfKWzZezPM8Spe1R9lMbrB37xii9nr5d14l2depCweqACalB+5L10r87ukiwaDqdzATwV+iCVNAB2LQYWLwO+N9BwD4f8OtkoL50/K7QpikQHgGs2SIdxFe2BLB5qXK5xfSx0vKN8XOknTVkJy7JC66cfwELKxP0H94YVnTiEvdAzBy/CxFhqtcRUrCQDSpWLoRpY3eqnKeNjZloG2dpbYqwkEicP/kU/6SpYT+8547oADHyp5YwMzeCp0cgpv24E/7v09S3qMHV089Ez+V+Y5vBytYMXi/9MWvENkR8KjvK72CR7khMwSK2qFCtCKYPVX2YetHkvSLInrKkO8wsjBDkF4Hta87hxF7pIGMqjZk9cgcGT2yJ+ev6wchYH36+oVg+4yDuXVNvRvXKvpui5/KA+T3EYDxqAzej9W/yE4jkL2Sbfh0p5YiKDcpiastfVc7zxpG7WD1qM3pN64Qxqwfj3Ss/zO+6DK43Ukv8Nv60DZKUFMw5MElkqh+ceSJOXMK+zsSJE0WSrXr16mIMFtUPy1rOEvrtpsCYyjwIHQ1///49KleuLP6nRB4FxLKmA1mZ5+fQvCjBRkfsqdSVejrTc1GnMYqlskNLkt1RVuybuHbtGpo1ayYyzmkL3r81qjmmlZqC78+dh10T1OmV2qKGSZn6Krev+687c2C7ul9CnuPSvq+6X0Keoq3ms0nmRcluXMKQ1rkUhWbquazIjuwFgJnx7j/ti6Zft26dGFdFR64pEKbSUVkjAjqPAyXm6Eg5obFX1EqOjpZTKQaVo1IQmzZRl9k8P4c6ie3YsUM8N2XHqfUtlYNSNzA6zwMlFL8UZ5jzCMq+BgcHiz0t6oiRm8Ey7dlRjTP1QKRMdvv27XPtuRljjDH2NdR/auyxY8eKS0ZH1RU1atRINCH4mnl+DpWDymqiKWCWtZGrX7++CNazg2uY8wja46FyBTrZx++//56rz009nilAp1IIKoOgEgjGGGOMMU1UrFgxUXJKqBsHnUWYUCkrJQazgyOjPIIG+9FFHehQCVfmMMYYYxqIf77ToVpnaoBA2WxqmUtdwqjEg04KRz2gs4MDZsYYY4wxTcUBczqK7XOpoQG1rqOzB1Ids+JJ2L4EB8yMMcYYY+y7Vbhw4XRdwr4UB8yMMcYYY5rqG5waWxOtWbMmy9OOGzfui+fPATNjjDHGGNNoK1euzNJ0dL4HDpgZY4wxxv5DeMy+lKwrxrfCATNjjDHGmKbigDlXcMDMGGOMMcY02sSJE/Hrr7/CxMRE/J2Z7LSW44CZMcYYY0xT8aA/4dGjR6LPsuzvzGqYs4MDZsYYY4wxDaXFJRnCpUuXVP6dU/jU2IwxxhhjjGWCM8yMMcYYY5qKM8zpxMXFYe3atSLTHBQUhJSUFKX7Hz58iC/FATNjjDHGGPtuDBkyBGfPnkXXrl1Rs2bNbNctK+KAmTHGGGNMU/Ggv3SOHz+OkydPol69esgpHDAzxhhjjGkqLslIp0CBAjAzM0NO4kF/jDHGGGPsu7F8+XJMnToVb9++zbF5coaZMcYYY0xTcYY5nerVq4uBf8WKFYOxsTH09PSU7g8LC8OX4oCZMcYYY0xTccCcTq9evfD+/XssXLgQdnZ2POiPMcYYY4wxRTdv3sStW7fg7OyMnMIBM2OMMcaYpuIuGemUKVMGsbGxyEk86I8xxhhjjH03Fi9ejJ9//hmXL19GaGgoPn78qHTJDs4wM8YYY4xpKC2uYU7HxcVF/N+sWTOl2yUSiahnTk5OxpfigJkxxhhjTFNxwJwOnRI7p3HAzBhjjDHGvhuNGjXK8XlywMwYY4wxxjTa06dPUaFCBWhra4u/M1OpUqUvnj8HzIwxxhhjTKNVrlwZAQEByJ8/v/ibapWpZjktrmFmjDHGGPuP4UF/Um/evEG+fPnkf+c0DpjZdyugrrpfQd6jH2Gs7peQp7i076vul5DnnD62U90vIU+pM2Wkul9CnhPVjjeueQr3YRYKFy6s8u+cwn2YGWOMMcaYxnN3d8fdu3eVbrtw4QKaNGmCmjVrilNlZxcHzIwxxhhjmkryDS4aaurUqTh+/Lj8OpVmtGvXDvr6+qhTpw4WLVqEVatWZWveXJLBGGOMMaapNDjAzWn379/HlClT5Nf/+ecflCpVCmfOnJF3x1i7di0mTJjwxfPmDDNjjDHGGNN4ISEhKFiwoNIJTCjDLNO4cWN4e3tna94cMDPGGGOMaXCXjJy+aCpra2v4+/uLv1NSUkTGuXbt2vL7ExISVLaaywoOmBljjDHGmMZr3Lgxfv31V/j6+opaZQqa6TaZFy9eoEiRItmaN9cwM8YYY4xpKg3OCOe03377DS1atBBt5XR0dLBmzRqYmJjI7//f//6Hpk2bZmveHDAzxhhjjGkqDpjlKHvs5uYGV1dXcRITR0fH1DsBzJ8/X6nG+UtwwMwYY4wxxr4Lurq6cHZ2VnlfRrdnab5f8ZoYY4wxxpgaafIgPU3CATNjjDHGmKbiU2PnCu6SwRhjjDHGWCY4w8wYY4wxpqm4JCNXcIaZMcYYY4x9V65du4a+ffuiTp06eP/+vbyt3PXr17M1Pw6YGWOMMcY0FJ/pL72DBw+iVatWMDIywqNHjxAfHy9u//DhAxYuXIjs4ICZMcYYY0xTSb7BRcMtWLAAmzZtwp9//gk9PT357fXq1cPDhw+zNU8OmBljjDHG2Hfj1atXaNiwYbrbLSwsEBERka15csDMGGOMMaahuCQjPXt7e7x+/Trd7VS/XKxYMWQHB8yMMcYYY5qKSzLSGTZsGMaPH487d+5AS0sLfn5++OeffzBp0iSMGjUK2cFt5RhjjDHG2Hdj2rRpSElJQbNmzRATEyPKMwwMDETA/OOPP2ZrnhwwM8YYY4xpqu8gI5zTKKs8c+ZMTJ48WZRmREVFoVy5cjA1Nc32PDlgZowxxhhj3x19fX0RKOcEDpgZY4wxxjTU9zBIL6dFR0dj8eLFuHDhAoKCgkR5hiIvL68vnicHzIwxxhhj7LsxdOhQXLlyBf369YODg4Mo0fhaHDAzxhhjjLHvxqlTp3DixAlxopKcwgEzY4wxxpim4pKMdKysrGBtbY2cxH2Ys2jz5s1wcnKCtrY2Vq1a9c2fz9vbWxxCePz48Td/LsYYY4xpJj5xSXq//vor5syZI1rK5ZTvNsM8cOBAbN++Xfytq6sr9jQqVaqEXr16ifso8M2qjx8/YuzYsVixYgW6dOkiTq34rVFw7u/vD1tb26+aj2Ldjo6ODhwdHdG1a1csWrRI9CT8kvkcPnwYHTt2RE65ceMGGjVqhAoVKqjcMbh16xbq168PFxcXcWhFHfpVrIzhVasjn7EJ3EKCMe/qRTwJDMhw+kHOVdG3ojMczcwQFhuHU6/d8futa0hIThb3a2tpYULNOuhYuhzymRgjMDoaB91csfbebXG/rrY2fq5dD40LF0UhC0tExsfjxru3WHLzGoKio5EX9K7ujCF1qiGfqQleBgbj19OX8MwvMMPpB9Ssgl7VK8HB3BzhMbE44+aB5Revy5cJyW9mgsnNGqBB8SIw0tPD2/AIzDh2Fs/9pfNd1L4lOjuXV5rvtdfeGLr7MPKCdl2ro1vfurC2MYWXRyDWLzuFVy/8VE67dGN/OFcrku72O9c9MHvibvG3pbUJho5thmq1isPEzBDPHr3F+mWn4ecbpvSYshULYtCoJihTvgCSkyXw8gjA9HH/ICE+Cepy7wmwZTfg6g4Eh2ph7QIJmjfI/DF3HwGL1wOvvQGH/MDIfkCn1srT/HMY2LIHCAkDyhQHZo4HKpVNvT8+HliyATh5EUhMBOrVAOb8BNjmbJIp27o2c0afNtVhY2ECD99gLP/fJbzwynhb0rNVFXRu6gw7G3N8iIzFxXvu2LD/OhISpd+bAW1roHH1kijsYI34xCQ88/DDur3X4BMQLp9HgfwWGNezEZxLOUJfTwe3nnqL5w37mHNBRHb1qu2MQY2qwdbUBK/8g7Hw2CU8e5fxdqRfvSroUbsSHCzNER4di3PPPbDy9HUkJEmXx+jmtTGmeR2lx3gFhaHdCmkcQLYO74qaxZyUptl7+yl+OXIhx98fU6/ly5fD09MTdnZ2KFKkCPT09JTuf/jw4RfP87sNmAkFWlu3bkVycjICAwNx+vRpceaXAwcO4NixYyKQzgofHx8kJibihx9+EMXjuYGCWzq1Y06gZUDLgt7DkydPMGjQIJiYmIg9MHWhc7n3799fNBWnz0aVv//+WzQYp//pLD0U7OemH0qWxswGjTDr0nk8DvDH4MrVsL19FzTbuQWhsbHppm9fqgym1m2AKRfO4IG/H4pZWmFpcxdIIMFv16+IaUZWq4E+FStj0vlTcA8NRaX8dvi9uYsIjLc9fQQjXV1UyGeHdfduiwDd3MAQcxs2wZ8/dESHff9A3VqXK4XpLRpi7skLePI+AANqVcXfvTvDZcM2hMWkXyZtK5TGz83qY8a/Z/HI1x9FbCyxuH0rsUwWn7sqpjE3NMDugT1wx/sdhu0+LILqwtaW+BAXpzSvq6/fYPqxs/LrigG3OjVqXg4jJrTEmsUn8NL1PTr3rIWFa/pgSLf1iAhPH5j8MnUfdPV05NfNLYyxaecIXL3wQn7bvKU9kJyUjLmT9iImOh5detfGknV9MazHRsTFJcqD5YWre2PPthsimE5OSkGxUnaQpKg3PURfjdIlgM5tgHGzPz/9O39g5DSgR3tg6Szg9kNg9lIgnw1Qv6Z0GgqCl6wH5k0EKpUDduwHhk0CTu4EbKyk0yxaB1y9DayaD5iZAL+ukj7/rvVQu+a1SmF870ZYsu0CXD390bNVVaye3Bndp2xFeGT6703LOmUwulsDLPj7rAiEC9lbYfYw+t4Aq3dJtyVVyjjhwPnHePEmELraWhjVrT7WTOmCntO2IS4hCYb6ulgzuYsIzscsPiAeM6JLXSz7qSOG/LILEjWuJi6VSmFK24aYf/gCnvkGoF+9qvhjSGe0XbYNYdHpl8cPzqXxk0t9zD5wFo98/FHE1hK/dWsFiUSC309ItyPEIyAEQ/86KL+elKYzAtl/5xnWnbspvx6bqL6dyxzzHWSEc1pOJvf+EyUZlEGloLNAgQKoWrUqZsyYgaNHj4pi8G3btikFbzSiMl++fDA3N0fTpk1FYElouooVK4q/6fzjlGmlcglC86L5Ghoaivvmz5+PpKTULx9N+9dff6FTp04wNjZGyZIlRaAuEx4ejj59+ojnNTIyEvdTcKuqJOPy5cviOrVIqV69uphf3bp18erVq88uB0tLS7EcKGvdtm1bdOjQId3eVWbvhfbOCL0Peg2y67T3RvOiPThqBl6jRg2cP38+S5/NyJEj0bt3b9Spo5wRkKEm43v37hWnsKQdFcXPK7cMrVwNe12f4YCbK16Hh2HmpXOITUpEt3LS9SGtag6OuO//HsfcX+J95Edc832Lfz1ewtkudSerqr0jznm9xiXvN2KaU54euObjDWc76c5RZEIC+h09gBOv3eEVEY7Hgf6Ye+UCKtnZw9HUDOo2qHZV7Hv0HIeevIBnSBjmnjiPuMQkdKlcQeX0VQo64qGvH44/f4X3Hz7ihpeP+LuSY+rO4LC6NRDwMUoE1ZSpfhchnc43/IPSvChADomOkV8+xsUjL+jSuw5OHXmIs8efwOdNCFYvPoH4uES0aldF5fSRH+MQHhotv1StWUwEwdc+BcwFClmjXMWCWLPkJNzd/PDOJxRrlpyAgYEeGrdKXc4jJ7TEkb13sXfHDbz1ChbTXT3/AomfMpDq0rA2MGEo0KJh1qbfcxQo4ABMHQMULwL06Qy0bARs3586zfZ9QLe20iC8RBFg3s+AoSFw6KT0/sgo6d80j9pVgfKlgYXTgEfPtfDYFWrXy6Uajl5+juPXXPHGLwyLt51HXHwS2jVS/b2pVMIRTz38cPbWS/iHfMSd529x9vZLlC+W+r2ZsOwQTlx/gTfvQ+HhG4Jf/jwDB1tzlClqJ+53LlUADvnM8evmM/B8FyIu8zefRtmidqherhDUaUD9qjhw9zmOPHgBz6AwzD9yXgT5naurXh6VCzvi0Vs/nHjyCn7hH3HTwwcnn7xCRSflpFJySgpComLkl4gY5Z1uEpeYqDRNdHzCN3ufTH3mzp2b6SU7vuuAWRUKhp2dnXHo0CH5bd26dRN9+iiQfvDggQgcKfMZFhaGHj16yIPAu3fvijIJCjyvXbsmMqSUsX7x4gX++OMPEdT99ttvSs9HgWf37t3x9OlTtGnTRgTINF8ye/Zs8Vh6Xjc3N2zcuPGzJRh05ho61HD//n2RIR88ePAXvX93d3dcvHgRtWrVkt/2ufdy79498T8F8/T+ZdcpqKX3REH8o0ePRBa7Xbt2IiOfGZoP9UDMbKXdt28fypQpg9KlS6Nv377YsmWLyCbkFj1tbVTIb4frvqnvhZ79hq8PqtqrPspAWeWK+e3kwa+TuYUorbj8NrXf48MAP9RzKoSiltK0WFnbfKjhWACX377J8LWYGRggRSLBRzrmrEa0TMo72OHmG+VlQterFFS9TB6980N5h/yo6Cj9ES9oaYFGJYvgyuvU99u0VDE89wvE6i4/4ObEETg8rA+6VUn/w1mzcEFx/+nRAzCvdVNYGhlC3XR1tVGyjAMe3Ut9P7Sa0nXKAGeFS/vKuHLuuTxzrKcnPfKlWFZB80xMTEIFZ+nhZEsrYzH/iPBorPxrEPaemohlmwag/Kf7NQkFtHWqKd9Wv4b0dpKQKC3vUJyGKuroumwauj8xSUtpmmKFAQc7idoDZl0dbZQpYoe7rm+VPs97L96iYgnV35unr/1Qpkh+lPsUIDvms0Bd56K48STj7YSpkbTE7mOUNEjU09URzyMrWSBUzkHbEgqm1UVPRxvlCtjh1muF7YgEuP3aB86FVS+Px2/9UK5AflQs+Gk7Ym2BBqWL4OpL5eVRyNYKl2YMw+nJg7GkhwscLNInGX6oXAbXZ4/EkQn9MKFVPRh++r5pNMk3uLB0voM15ctRIEYBLLl+/boIhClgltX0Llu2DEeOHBGlG8OHD4eNjY24nTLBsjIJCoTpXOUDBgwQ1ykrSyUOU6ZMUQoEqV6a6qbJwoULsWbNGvF8FFxSYFmlShWRMSayzG1mKIilul9Cz0/Z17i4OJEZzgg9P5V4UMY4Pj5eZJmnT58uv/9z74Xet2KmWoZ2POgiQ4+hOmfKolPNtyoeHh7iuShIz6wkhsowKFAmtKw+fPggeio2btwYucHKyEjUE4fEKNcNh8TEoLiV6qJIyixbGxphX5eeoMpxPR0d7Hz2GBvu35VPs/H+XZjqG+B830EiG6KjrY1lt67jqPtLlfPU19HB1LoNxbyjEtWbCbEyli6T0CjlMoPQ6BgUs/10XDwNyibTstw1sId8mey+/wR/3JDudBEnKwtR47z19kNsunEXFR3sMatVEyQmp+DIU2nW9ZqnN869fI13ER/gZGWJiU3q4c9endBj6x4RAKiLuaUxdHS1ER6mvJ7QdafCnx9/ULqcI4qWsMOKBf/Kb/P1DkGgfwQGj2mK1YtOIC42AZ1710Y+OwtY20oDAPsC0uXdb1gjbF59Dp7ugWjxQyUsWd8Pw3ttSlfrnJdRTXLa1cfGGoiK1kJcvAQfI4HkZC3YWCl/zlSKIdt3CwmlHQ0JzNPERzRfmr86WZoZiaA5bd1w2IcYUX+sCmWWLU2NsHmW9Hujq6uDgxeeYPu/qdsSRTRU5ae+jfHE/T283oeK2557+iMuPhFjezQQtc80nzE9GojXYmthAnWxpO2IjortSFQMiuZTvR2hzLKliRH+N7IH6I3QdmTP7Sf483LqduSpTwBm7j8D7+Bw5DMzwajmtbFjZHd0WLkDMbTXRaU9j6UZ6qCPUSjlkA8TW9dHkXxWmLDzODTZ9zBILyfQWDVKClLikbpkZNZ7WZa4/BL/yYCZMpWyBUmlF5QplQXFMrGxsaLkICP0OBq0pphRplppCl5pVCaVTBAaaChDdcNU8kHBOaFyAxpESOURLVu2FDU3VGaRGcX5yeqpaX6FCmV8iG3lypVo3ry5eH10TvWJEyeKZt579uz5oveSFi23efPmiQF5lHmmgJyWW0YZZponlWFQgF6qVKkMXy+VmdBOBQXfhAJryvRTEJ1RwEw7AnRRJElMglYuZg9qFSiI0dVrYc7lC6KUorCFJeY0bIIfa0TLB/VRXXSHUmUx/swJeISFopxtPsxu0ASB0VE49DK1hpVQcLrepZ34oZt9KWulLnkNZYVH1K+J+Scv4qmfPwpZWWJmq8YYHVULG67dEdPQd5EyzCsv3RDX3QKCUTK/DXpWqygPmE9SCvET96BQvAoMwYUfB4v53/b2haZyaV9FDBJUHCCYnJyCX6bux8RZ7XDowhRRm/zwnhfu3vCQb7do8Cg5cUhaCkI83QNQuXpRuLSrjC0bLqrpHbGcULVMQQxsVxO/b6ea5wAUtLPExL6NMTiiFrYclX5vFE3u3wzFCthgxIK98tsiImMxY91xTBnQDN1bVBE7luduv8TLN4Fq3cnMjhrFCmJ4k5r49ehFPPXxRyFbS0xv1xjBTWth00Xp8rjuLi2VJO4BIXjqG4Bz04aIeulD96WHGfbffSafxiMwFCGR0dgyrCucrC3gG6ZcAsY0z8qVK2FmZib/OydOVoL/esBM5Q9FixaVB30UeFKNcFqUUc0IPY4Cv86dO6e7TzHbm3ZkJn2AslM0tm7dGm/fvsXJkydx7tw5UQYyZswYkeHOiOL8ZCtD2lM+pkVZ4RIlSoi/qcQhMjJSZJ0XLFggbs/qe0lr0qRJ4nXT66X5UB02deBISFCdCaXnpVISKt+QZaDptdMODAXFZ8+eFSUzFBhT8K04yI+moSMA69atU9mlhLp+0HtQZOHSAlZtWiE7wmNjxYARW2PlTIytsTGC02SdZai7xeFXL7D3hXSj/Co0BMZ6eljYpIUYxEc/UdPrNcKmB3dx3OOVfJoCZuYi0FYMmClYXufSFgXMzdD78H61Z5cJDcajZWJjqrwDZWNiLGoBVRnfuC6OPXXDgcfP5cGusb4efvmhOTZeuyOWSXBkNDwpRajAKyQMrcqUzPC1UKY5LDpGDA5UZ8D8MSJGBLRW1srrCV0PC43K9LGGhnpo3LI8tv+Rftvj8dIfo/puhrGJAfT0dPAhIgZrtgwRNc1ENm+fN8FKj/PxDkF++2/fxScnUReLkNTGDkJoGGBqIoGhgbT8QkdHgtC004SndsCwtaGSFS18jFTOMtN81d0lgwLXpOQUWJsrf2+sLYwR9kH1toQG55266YZjV6TfG6o/NjLQw/RBzbH12B2lAXuT+jVF/crFMOK3vQgKV17nqPa5y+QtsDA1RHKKBFEx8Ti5ZgT8gtUXHEbQdiRZxXbENOPtyI8t6uLYQzccvPdcHuxSN515nZvjj0vKy0MmMi4eb4PDUcgm499xCr4JTaPRAbNm7f98M7Kj5LKj+zntP1fDTPW7z549E5ldQvXKAQEBImCjoE/xklk9MT2OMqFpH0OXL2lZR+UO9CHv3LlT9Hemfs/fGpVnEMoGZ/W9UKBOGWJFlJWmlZIGA9LASArMZQMiVaHsOi17Gsgou9DgPwri6W+qq6ZAeceOHaJOW3E6yoJTAL17t7TtVlpUYkJlG4oXyxbNsr2MElNS8DwoEPUKpmbuafekrlMhPAyQbmTTMtTVS5e5oR8pxZ0b6oKRbhqJROmLKAuWi1haoe/hA4hI0y1CXWiZuPoHok6R1DpZeld1ijrhEbU6UIHqA1OQ+TJ5+M4PRekYvIIi1lZikGBG7MxMxaHd4Cj1ttpLSkoRwW3lGtIdcEJvizK9bs/eZfrYBs3KiXrlC6dTs15pUYcMCpYdnaxRsqwDbl2V7mgF+EUgJOgjChZWPjJWsJA1Av0164e/cnng9gPl227el95O9PWA8qWUp6EcAXXTkE1D9+vpSsRtMlSu4R+oJZ9GXSg4fOkdiBrlFbYlWkCNcoXw7HUG3xt9PaSk6XYiS4xoiW9darDcqFoJjFm8XwwOzMiHqDgRLFcr6wQrc2NcfZjx0dNvjUqtXrwPRO0SCtsRLaBWCSc8eZvxdiTtGBbZdlRxeSiiHXMnG0uxQ56RMo75xf+ZTcM008OHD0W8odjYgI7iU/OHjJJ6/+kMMx2ip2BYsa0cZSKphpcGuREqVaBODbQgf//9d1EqQC3MqMyAAkFZfXFa1BCb5kOlEJRVpcCSgrrnz5+LzG1W0DyqVauG8uXLi9d6/PhxlC2r0Fg0h1AXEFoOtMGlGuJffvlFvE/Zc2XlvVB9NQ3uo9NMUqaX6oOoqwcNnqSBfhT80CDGzLLdNF/quawof/78Iostu51qx6l7yJAhQ9Jlkmknh7LPFGSnRa8pbV/pry3H+OvxAyxv7oKnQQGi9/LgylVhrKuHAy+kWY7lLVwQEBWFpbeui+sX3nhiSJVqcA0OEiUZRSysMLF2XVzw9pJv3C94e2JMjVrwi/oo2sqVz5dfPGb/p3lSsLyhdTuUz2eHoccPQ1tbS2S1CbVZo6BVnajOeEmHVnjuH4SnfgGixzJleg49kR7ypPsCI6Ow4qK0vOKSu5forPEiIAhP3weIkgzKOtPtsmWy/fZD7B7UAyPq1cCpF+6oVMAe3atWxJwT0jIUytKPbVgbZ156iAwU1TxPbt4Ab8MicM0zdSCVuhzcdQuT53aEh5sfXrr6ibZyhkZ6OHNc2uFm8rwOCA2KTFcm4dKhCm5eeYnID+nbaDVoVhYfwmMQFPABRUvkx6iJLrh55RUe3EkdQLp/5y30H95IlHRQOUaLH5xF3fSv06QtxNQlOgbweZ96nfal3DwAC3OAxn6u2AwEBgNLZkrv79kB2HWY+lMDXdpIA+HTl4FNi1PnMaA7MH0RUKEMULEMsOOAtH2drFezmam0gwb1cqZxXqYmwILVFFBL1B4wk92nH2DOMBe4vQkUvZd7tqwKQwM9HL8q/d7MHe6C4PAoUWtMrj32Qm+XqnB/GyRqkZ3sLDG8Sz1xu+x7M3lAU7SqXQaTVx1DdFyCyFiT6JgE0ZeZtG1QHt5+YQiPjEHFEo6irGP3mQdKvZrVYfv1h1jYrRVc3wVJ28rVrwIjfT0cfiBdHgu7t0LQhyisOiPdjlx+6SU6a7j5BYlSC8oIU9b5slvq8pjUpoG47hcRKfq6j2lRR4wToW4ahMouaMDf1VdvRPeM0va2mNK2Ee55vRMlHJqMa5jTGzFihBgvRck8ajJAZZ10FH3//v2i1DQ7J6D7rgNmCpCp3IKyxxTg0QA1GnRHGV1Z5pQCPSqJoO4T1J84ODhYZEobNmwo2qVlpFWrViLApeBzyZIlIgNLgwmpPV1W6evri8woZWWpnKFBgwbyuuKcRO9L9l5l740GIMoG3WXlvVDGl2qf//zzT9Gmj14znciFunRQ3TVl46dOnSpO8vI1KCCmnRhVZRcUMNNODQ3YVKzl/lZOeLyCjZERJtaqB1sTY7gFB2PgsYMIiZUeNnQ0NVfKFsvKLqg0w97UVPRqvvjGSx5Qk3lXLmJi7Xr4tVFz2BgbiROX7H7+FGvu3hL325mYokUxafnMyV7SnTqZnof24s77zLOW3xoFtNbGRhjXqA7ymRrDLTAYQ3cdFgP/iIO5mdIykZVdTGhcT2SFw2JiRLC88lJqH9Rn/oEYu/9fTGxaH2Ma1hblFgvPXsa/z6UDIZMlKShlZ4uOzuVgZmiAoMgo0XZu9eWbSMwDvZivnH8BCysT9B/eGFZ04hL3QMwcvwsRnwYC5rezSNcbuWAhG1SsXAjTxu5UOU8bGzPRNs7S2hRhIZE4f/Ip/vk7td8sObznDvT1dTHyp5YwMzeCp0cgpv24E/7v1RsMub4CBkxIzfotWS/9u6OLBIum08lMAH/pMA6BGqxQcLx4HfC/g4B9PuDXyak9mEmbpkB4BLBmi3QQX9kSwOalyuUW08dKyzfGz5F21pCduCQvOH/HHZZmxhjeuS5sLIzh7hOMCUsPyQcC2tkof2+2Hr0tMqojutZDPitTRETG4PojL2w8IA0gSddmlcX/m2Z2V3quXzafFu3mSCEHK4zuVh/mpoYiA03lHLtPf/kJG3La6afusDYxwtgWdWBrZoyXfsEYseWwfCCgg6WZUkb5j4vSsotxLeshv4UpwqNjRHC8+kzqdsTOwgxLe7WBpbGh6OX80NsPvTfsESc5IbStqF2ikDgBCgXnAR8icf75a3kNtEbjgDkdGvxXubL0O0JBMjVL2LVrlzgy3rNnz2wFzFqS3OzVxVguKrp2ubpfQp6jH/Gfq8LKVOF/I9X9EvKc08dUB/H/VXWmpD+i9V8X5Zizg6m+B66L1bd3Vm7myhyf54vf8sjeZjZRCSi1CaYj4S1atBBH0al1LjUloDJQWUnql/iuM8yMMcYYY981TnumQ+W0VFJKR6ypJS2d54K8efMm0+qBzHDAzBhjjDGmobiGOT0quaATxdG4KCq5lXUKo/NrfK59b0Y4YGaMMcYYY9+NSpUqKXXJkFm6dKm8U9iX4oCZMcYYY0xTcYY5Q1THTOfeIOXKlRNtdLOLA2bGGGOMMfbdCAoKEq3kqH5ZdhI6arHbpEkT0Y2MzoHxpXjIPGOMMcaYJmeYc/qi4X788UdxFmNXV1eEhYWJC51bglrfjhs3Llvz5AwzY4wxxpiG4kF/qs/Dcf78eaWTwVFJxvr169GyZUtkB2eYGWOMMcbYdyMlJUWchC0tui2zMxJnhgNmxhhjjDFNlQdKMtavX48iRYrA0NAQtWrVwt27dz/b9o1OIEJnOXZycsJPP/2EuLg4+f3z5s0TZydWvNAZiLOqadOm4kQlfn5+8tvev38vnqdZs2Zf/ga5JIMxxhhjTHOpuyRj7969mDhxIjZt2iSCZQqGW7VqhVevXiF//vzppqdTVE+bNg1btmwRPZHpNNYDBw4UQfGKFSvk05UvX16UVcjo6mY9ZF23bh3at28vgngKyImvry8qVKiAnTuzdzZTDpgZY4wxxli2rFixAsOGDcOgQYPEdQqcT5w4IQJiCozTunnzJurVq4fevXuL6xTU9urVC3fu3FGajgJke3v7bL0mCpIfPnwoAu6XL1+K26iemc78l10cMDPGGGOMaapvkGGOj48XF0UGBgbioighIUH0Op4+fbr8Nm1tbRGY3rp1S+W8KatMWV4q26hZsya8vLxw8uRJ9OvXT2k6Dw8PODo6ijKPOnXqYNGiRShUqFCW3wNlrFu0aCEuOYFrmBljjDHGmBwFpxYWFkqXRYsWpZsuJCQEycnJsLOzU7qdrgcEBKicN2WWf/nlF9SvX18MwitevDgaN26MGTNmyKeh0o5t27aJbhcbN27Emzdv0KBBA0RGRmb6ui9evCi6YVD7uLQ+fPggyjyuXbuG7OCAmTHGGGNMU32DQX+UMaYAU/EyXSGL/DUuX76MhQsXYsOGDaJs4tChQ6KE49dff5VP07p1a3Tr1k2c4prqoSkDTSce2bdvX6bzpvppKg8xNzdPdx8F/SNGjFCqk/4SXJLBGGOMMaahtL7BPFWVX6hia2sLHR0dBAYGKt1O1zOqP549e7Yovxg6dKi4XrFiRURHR2P48OGYOXOmKOlIi87WV6pUKbx+/RqZefLkCZYsWZLh/dSDedmyZcgOzjAzxhhjjLEvpq+vj2rVquHChQvy26jPMV2numNVYmJi0gXFFHQTiUR1QTadtc/T0xMODg6Zvh4K1FX1X1YcSBgcHIzs4ICZMcYYY0xTqbkP88SJE/Hnn39i+/btcHNzw6hRo0TGWNY1o3///krlHO3atRN1yXv27BG1yefOnRNZZ7pdFjhPmjQJV65cgbe3t+iq0alTJ3EfddPITIECBcQpsDPy9OnTzwbdGeGSDMYYY4wxDaXuPsw9evQQWds5c+aIgX6VK1cWg/VkAwF9fHyUMsqzZs0SHSzofzqZSL58+USw/Ntvv8mneffunQiOQ0NDxf00QPD27dvi78y0adNGBN8uLi6iu4ai2NhYzJ07F23bts3W+9SSZJT/ZkzDFV27XN0vIc/Rj+CDSooK/5v5iOv/otPHstfU/3tVZ8pIdb+EPCfK8VtUzWo218U/qe25nSeszPF5PlmlvvfzNagko2rVqiIbPXbsWHE2QUK9mOlshNTRgwYapu3qkRWcYWaMMcYY01Sc9pSjQJhKOKgshMpAZDlhymhTtw0KmrMTLBMOmBljjDHG2HehcOHCog1deHi46KpBQXPJkiVhZWX1VfPlgJkxxhhjTFNxhlklCpBr1KiBnMIBM2OMMcaYhlL3oL//Ch4BxBhjjDHGWCY4w8wYY4wxpqk4w5wrOGBmjDHGGNNQXJKRO7gkgzHGGGOMsUxwhpkxxhhjTFNxhjlXcIaZMcYYY4yxTHCGmTHGGGNMQ3ENc+7ggJl9t/Lf01L3S8hzJNq8ZVWkHR2v7peQ59SZMlLdLyFPufX7JnW/hDzHeekodb8Epog367mCSzIYY4wxxhjLBGeYGWOMMcY0FWeYcwUHzIwxxhhjGoprmHMHl2QwxhhjjDGWCc4wM8YYY4xpKs4w5wrOMDPGGGOMMZYJzjAzxhhjjGkoLQmnmHMDB8yMMcYYY5qK4+VcwSUZjDHGGGOMZYIzzIwxxhhjGorbyuUOzjAzxhhjjDGWCc4wM8YYY4xpKs4w5woOmBljjDHGNBSXZOQOLslgjDHGGGMsE5xhZowxxhjTVJxhzhUcMDPGGGOMaSguycgdXJLBGGOMMcZYJjjDzBhjjDGmqTjDnCs4w8wYY4wxxlgmOMPMGGOMMaahuIY5d3DAzBhjjDGmqSQcMecGLslgjDHGGGMsE5xhZowxxhjTUFySkTs4YGaMMcYY01QcMOcKLslgjDHGGGMsE5xhZowxxhjTUFop6n4F/w05nmG+fPkytLS0EBERkeXHzJs3D5UrV87pl8KygT67I0eOiL+9vb3F9cePH6v7ZTHGGGOMaVaG+datW6hfvz5cXFxw4sQJ5GXjxo3DjRs38Pz5c5QtWzbT4I+C/SZNmmQ6v0uXLqFx48bITbRDQUHs5wLXgwcPYu3atXj06BGSk5NRrFgxdO3aFWPHjoW1tXWuvd7vSddmzujTpjpsLEzg4RuM5f+7hBdeARlO37NVFXRu6gw7G3N8iIzFxXvu2LD/OhISk8X9nZtWEvc75jMX173eh+LvI7dx66m3fB76ejoY36sRWtQuDT1dHdx59ha/b7+AsI8xyCvLpG/rT8vEJxjLdl7CizeZLJOWVdClicIyue+O9QdSl0mXJtJl4mArXSZv3ofir6O3ceuZdJmYmxhieKc6qFW+sJhHRGQMrjz0xKZDNxAdm4C8oF2vWug6qAGsbE3h9SoAGxYeh/uzdyqn/X3rEFSqWSzd7XevvMKc0TvE34bG+hj8UyvUaVoW5pbGCHgfjqM7b+Hkvrvy6em5hv7sgip1S8DY2ADvvEOwe/Nl3Djniu/tezOgbQ00rl4ShR2sEZ+YhGcefli39xp8AsLl8yiQ3wLjejaCcylH8R2i7xQ9r7q/N/eeAFt2A67uQHCoFtYukKB5g8wfc/cRsHg98NobcMgPjOwHdGqtPM0/h4Ete4CQMKBMcWDmeKBS2dT74+OBJRuAkxeBxESgXg1gzk+AbR75KehZzxkDG1eDrZkJXvkFY9HhS3juG5jh9H0bVEH3upXgYGWOiOhYnHvigVUnryMhSbqOjGpZG6Nb1VF6zJugMLRfsl1+XV9XB5PbN4RL5dLi7xuv3uK3gxcRGpU3tq3ZxjXMeTdg/vvvv/Hjjz+K//38/ODo6Ii8bPDgwbhz5w6ePn2a6XR169aFv7+//Pr48ePx8eNHbN26VX5bXg08Z86ciSVLluCnn37CwoULxWfi4eGBTZs24X//+594L+zLNK9VCuN7N8KSbRfg6umPnq2qYvXkzug+ZSvCI2PTTd+yThmM7tYAC/4+K37QC9lbYfawVmJbtnrXFTFNUFgUNuy7Dt/AcEAL+KF+eSyd0AH9Zu8UgSKZ0Lsx6lUuiulrjyM6Nh6T+jfF4nHtMHzBXqhb85qlMKFnIyzefgGuXv7o2bIq1kzqjG7TVC+TVrXLYMynZfL0tR8K2VlhztBWom3oqj3SZRIYHoX1+6XLRAvSZbJsfAf0m7MTXn6hsLU0ga2lKVbvvSqWEQXW0wY0F7dPX38c6tbQpSKGTWmDtfOP4tUzX3TsVw+//TEQQ9uuxIew6HTT/zJhF/T0dOTXzS2MseHQWFw7+0x+2/ApbVC5VjEsnbYfge/DUbVeSYyd1Q5hwR9x+9JLMc2khV1ham6EeWN34mN4NJr84IwZy3tiXPcN8HyZuh37Hr43Vco44cD5x3jxJhC62loY1a0+1kzpgp7TtiEuIQmG+rpYM7mLCM7HLD4gHjOiS10s+6kjhvyyS61tamNjgdIlgM5tgHGzPz/9O39g5DSgR3tg6Szg9kNg9lIgnw1Qv6Z0GgqCl6wH5k0EKpUDduwHhk0CTu4EbKyk0yxaB1y9DayaD5iZAL+ukj7/rvVQu1aVS4nA9dcDF/DUJwD9GlTFH8M7o92SbQiLSr+OtKlSGhN+qI85e8/isbc/CuezxIKetI5IsPTYVfl0Hv4hGPbHQfn15BTlWoUpHRqhYdmi+HnHCUTFxWNGpyZYObAd+q9T/7b1a3CXjDxakhEVFYW9e/di1KhR+OGHH7Bt27ZMp6f7LS0tRYa0ZMmSMDQ0RKtWreDr65tuWgrsihQpAgsLC/Ts2RORkZHy+06fPi2y2jQvGxsbtG3bFp6enp99vWvWrMGYMWNEtvVz9PX1YW9vL78YGRnBwMBA/O3u7g4nJyeEhYUpPWbChAlo0KDBF73Xo0ePomrVquJ+el3z589HUlISsuvu3bsiSF6+fDmWLl0qAn9aji1atBBZ5wEDBsin3bhxI4oXLy7ea+nSpcUyzyrKWg8ZMgRFixYVy4Yev3r1aqVp6H1QVl/2OU2dOlU8f8eOHeXTpKSkYNGiRfL5ODs748AB6Y+cYlnPhQsXUL16dRgbG4v39OrVK+SmXi7VcPTycxy/5oo3fmFYvO084uKT0K5RBZXTVyrhiKcefjh76yX8Qz7izvO3OHv7JcoXs5dPc/2xF24+fQPfwAj4BkRg04EbiIlLRIXiDv9v7yygo7q6KHyQEDy4u7u7trhTpLgXKIW2UNy1QCnOj5VSrKVAKVqKFSju7i5BEyRocOZf+4Q3eZOElFLIZN7sb61AZuaN3Tw599x99tHHY0SLIjVK59BAYd+Jy3Lyoq8M+XGN5M6U3L6NM2lUMb8s3XRUVmx9PSaz12nAUr1UyGOS8/WYrNn5ekyOXZK1u05KtjeMibfPXZmy6PWYZEhqz8L3nPiHbnf15j3Ze+KyTFm0VUrmSSeRIiLEdi61mxeX1b/vlb+W7hfvczc1cH765LlUrJ0/xO0f3nssfrce2n+QIX7y5LlsXnPUvk22PKlk3bIDcnjPBfG5dldWLdyjmevMOVMEbpM3lSyfu0Mz2Teu+Mm8HzbKowdPJGP25OJMPsRx02nUYvlz63GdMJ25fEsG/7hGJ05Z0ibWx3F8JE0YW4ZMWyPnrtzSn0HTVkvWtImlQLZU4kxKFRHp1FqkfKm3237+MpHkSUV6dBBJn0akcW2RCqVFZi8M3Gb2byKfVgsIwjOkERnYRSRqVJHFKwMef/Aw4He8RpF8ItkziwzrKXLgaAQ56PwFCGlWKp8s2nlUlu45Lud97sjgRevk8fMXUqtQyPtInjTJ5MDFa7LywCm55ndfdpz2llUHTkmOVIH7iBEg337gb/+5++iJ/bGYUaNI7UI5NMDeffayHL/iK/0WrJW8aZNJriCvQ8h7CZh/++03yZIliwZLTZo0kRkzZojtH6bv/v7+MnToUJkzZ47KI6BvRkBsBsEvAs0VK1boz6ZNm+S7776zP/7o0SPp3Lmz7N27VwOpiBEjSq1atTT4CgtKlSqlwa05wHz+/LnMnTtXM9hv+123bNkizZo104zv8ePH5YcfftBAG895V/AZYsaMKe3btw/xcQSvYMmSJfq+Xbp0UYnK559/Li1btlSZyduAsU6RIoUsXLhQP3v//v2ld+/euk8YIMuNz4OsPL4/MvSGJtoAwTLGB9nvY8eOaVYc+xL+5kGz5pgE4G8eOXJkh3H+0ESOFFGypEksu49dst+H3XzP8UuS83UgFxRkULOkSWQPBpMl9JJiudPKtkMXQtw+YoQIUr5wZonmGVmOnr2m9+E9IcPYfczbvt2l634aSBgBpLMwxgRj4DAmxy5JzjcE80eMMUlrGpNcaTVA/qcxwXPfRMxonirHePnKuamVyB6RJGO2ZHJgx1n7fTgfHth5VrLmfrtADYH1plVH5Onj5/b7jh/0liIfZ5H4iQJkKrkKpZXkaRLIvm2B73P8gLdmt2N6RdMJZunKOSVKlMhyaM95cRZhcdwYf39w/2FAQIRjBu9jLM8DyDle2WwaTLsSCGiLBplrlSgYcD949jxA3mHeJmLEgNvGNnj8+YsIDtukSy2SNLHN6QEz9pFsKRLLzjOB5zj87Xae9pbcqUPeRw5evCbZUiSSHCkDJkgp4nlJyaxpZMsJx30kVYK4sr5/G1nVu5V817iSJIkTy/4Y3hP7Cd7H4IKvn1y7c19yp3F+MuI/gQF83z/kv0syIMNAcAOgYb53754GOqHpehFYTpw4UQoXLqy3Z8+erXpiZEYLFSpkD8YQOMaKFbCDN23aVANjI5CsU6eOw2siUE+YMKEGbjlyhDwrfd8gu4pAsFu3bnr7jz/+kCdPnki9evXe+rsim9yzZ0971hdB+JAhQ6R79+4yYMCAd/pckF7gdTw8PELdbtSoUdKiRQt7YI0JyM6dO/X+f9JuA7w+Pr8BMsTQsyNgNsYAGupevXrpZAZgLFauXGnS1T3VbPi6deukaNEAvRk++9atW3XyULp0afu2+NsbtzFmWNHAeCMz/6GJEyuantiD6h/v3PNXHWVIIEMWJ2Y0mda3vkoLIkeOJIvWH5LZfwTqTkH6FAlkev8GEsUjsjx+8kx6jP9DM3EgfpwY8uz5C3no/zTY+0IP6kzsY3IvyJjcf/OYILPsFTOa/NjHNCYbDsmsFcHH5Ke+r8fk6TPp/r/AMQmKV8yo0qpGEVm6KVDC4CygL44UOZLcvf3Q4X7cTpk24T8+P1POFJI2UxIZ23+Jw/1Thv4hXw/6ROb+3UNevA78xg9YIkf3BWrdh3WZrxKM37f31W2Q1R7cca5c9w553MKCD3ncGESIIPJNk4/k0OmruvoAjp67Lk+ePpcv65dU7TNep0P9kvpZEjj5uPm3QJOc4LWswiB+PJGHjyLIk6c2uf8Aq30RJH5cx6AGUowLr2PBW7dxvrZJ7MB4UcHr4vWdSdwYAfsIMsBmoCNOmyjIF38NMstxYkSTOV/WVymbR6RIsmD7IZm+fo99myPeN6Tf/DVy8aafJIgdQzXNszvUk1qj5oj/0+eSIFZ0efbihTx48jTY+0JH7cpQkhEOM8xYEkfg17BhQ72NrF/9+vU1iA4NbFewYEH7bWSokfU8ceKE/T5ICIxgGSRNmlR8fX0dgkK8L4Kr2LFj6/bA2zvgDFG5cmXNsuIne/bs8iFAsHn27FkNMgECfASKMWLEeOvveujQIRk8eLD9s+KnTZs2qp1Gdvpd+KcMvwE+Q/HixR3uw23z3+GfmDRpkuTPn18nK/js06ZNs/8NMHny8fGxT4JApEiRdHsDjB++J+Qi5jFAxjmoxCZXrlwO+wMw7xNmEIgjm23+efXy3WUu70K+LCmkRfVCWqDXrP9c6T5+uWqRW9UMmDwZXLp+R5r2/UU+G/SrLN5wWPq3rShpk4VPbfz7GJOWGJM566XpwLnSfcJyKZ47rbSqEXxMmvT/RVoN/lUWbTgsA1qHPCYxokaRsd/UkgvXbsu0pTvE1alUO79cOHUjWIFgjcZFJWuulDKgw8/yVb1J8uPIVdKhbw3JWyS9fZtmX5WTGLGiSs9WP8lX9SfL4tnbNIBOkzEgC+cqvO1xY9CtWVlJlzy+9J0UWHB+98Fj6T1xhZTIk042TvtK1v/wpcSK7iknL/joZIO4NgXSp5A2ZQvJt4s3SP0xc6XjzOWqRf68XOA+svXkRVl7+Iycvn5Ltp+6JO1/XCqxonlKxdyZnPrZiZtmmBEYQ6NqLvJDsAadLzKJ0B6/K0Gzo1hiNMstqlevLqlTp5Yff/xR3x+PIbP87FlAlfz06dPlMaorQnit90WiRIn0cyDLjOzqqlWrVG/7bzXgyNLWrl072GPvmjnNlCmTZmiR3f5Q3x3Mnz9funbtqjIJZIcxwYFmGgWV/+b7A7irJE/uuFSK/ciM+btgfwBvkuBA5mHOfoNkuSpIitwV5V3ABfjFy1cSL3Z0h/vjeUWXO/eCF3IZRUartp+Q5ZsCtKjQUUbz9JBeLcvJzOW77KtceN0rvgG2i9AoZ02XWOpXyKdaz9t3H2mWNWZ0T4csM9739hveN6ywj4lXkDGJ/ebP1q5WMVm5/YQs2xw4JlE9PaR3i3Iy8483jMklX8mWNrHUL59PNdIG0aN6yPgutcX/CTLQy+XlS+ebj96/6y8vX7yUOPFjOtyP29Anh4ZnNA8pXTmXzJkY+B1BFM/I0qJTeRny9a+ye3OAbv/CaR9Jnzmp1GlZQg7sPCdJU8aTmo2Lyuc1xsulcwGTSATeOfKnluoNi8j/Bi8TZ/AhjxvQtWkZDYo/H7pAfP0cxxfa5zrdZugKBKQ6OH5WTvhcrt28J64EXCxuBZp/KLfviMSMYZOongHyi0iRbHI76DZ+gQ4YCeJjtTOC3H/gmGXG6zrbJcPvUcA+Ej+W4z4SP2b0YFlngy8rFZM/9p2QxbsC9pEzN25L9Cge0v/TcjJtveM+YoBM8qWbfpIqQYAk8dYDf4kSObLEiurpkGXG+9564Nxz63+Gc8LwlWFGoIwsIIIl2JsZP8iYIoCdN29eqM+FDtWcqYa2F1KFt+H27dv6nL59+0rZsmX1eX5+jmcLBF8ZMmTQHwTWH4rWrVtr0SMyqyieC5qx/afvimI/3Gd8VvMPdNnvQqNGjTQQnTx5coiPG57Y+AzQFZvB7WzZsr3V+2BbFN9B0pE3b179zOasMCZMiRMnlj179jgUCu7fv99+G++FwBhZ6aDfH0WV7wpkIMhwm3+S5Sj7zq+HE/rJiz5SMHugDhUxe8FsqeTI2ZAdCKJG8ZBXQTS1RoAfQReJ5Y26XcM1Ae/5/MVLfR8DuAagwOnoG943rLCPSTbHMUFR1ZFzIX82T08Psb3jmMAazJxZ/l/XOvL85UvpMn6Z3W7M2UAKceb4NcljyvxicpencHo5cShQKxkSpSrmEI8okWTDH452kZAkeHhEDnFfMiaOnlEDJpNBs6d4TgQnFkJ+yOMGwXLp/Bmkw3cLVdP/Ju49fKLBcv6sKSVu7Oiyef8/F4eHJ/JkF9m5z/G+7XsD7gdRPESyZ3LcBsMFNw1jGzzuEdmm9xlArnHdJ4J9G2fuI8ev+EjhjCkd9pEiGVPKoUsh7yPRPCIHW0l9+fr2m84j0aJ4SMoEceTm/YBgGO+Jc6v5fdMkjCvJ4sWWQxede24lFsswoxAPQSp0vEEzydAXI/vcrl27EJ+LTCFs6OBYAckCfIGLFCnisHQfGnHjxlXHBQSpWJpHsAVN69sACQCCyRs3bmgG2vAyRuAGp4h/C1wvIAn59ttvVVrxb78rCuXg8JEqVSr1SEaQjEkHivDwmu8C9NLQQKOY7+rVq6ofxiQG3x2FdXAXQbEftNeQkCDYLVeunGqwFy9erHritwHOH5g0rVmzRjPsKIBEcIzfDfDdke1FAAw5CjTN2G+MCz2y0shSo9APF0V8NgS3CMYxrmZHj38DgvCgGeqIkf5bI8t5q/dJ/zaV5MQFH/WQhYUasqMrNgdUzQxoW0lu+j1UzSTYcvC8NKqUT05f8lVNZcrEcaRtneJ6vxHYtP+0hBa8+dx+INGjRpGKRbNIviwppePIACskFLIh0wZbrvuPnqitXJemZdRFAK/pbH5ds08GvB6TY6/HBNlAOCKAgW0qaeZv8u8BYwJni4YV88kpb1+1GEuROI58XjvImNQtITsOX5Abd16PSZGAMfl69CJ7sAzLMFiH9f9hlcSMFkV/gN/9x05fcocUouuwOnLm2FU5deSK1GpaTKJGiyJrl+yz27/d9r0vM8etdXhexdoFZPv6E/LgnqONlv+jp3J493lp3bWSPHv6XF0ychVMI2Vr5JVp3wfUA1y+cFOuXrolXw+oKT+OWi0P7vqrZ3PeoullQPu3d775EHyI46Zb8zK6X3Qbt1wePXlmX+V45P9MfZlBtZLZ5eK1O+L3wF/dWTo3+Ujmrdnn4NXsDB75i3hfdbSNO3FGxCu2SLLEImOmifjcFBnRJ+DxBjVFfl0iMnKKSJ0qAYHw6o0iUwNr4KV5PZFew0VyZBHJmUVkzu8B9nWGV3OsmAEOGvBy9oqF7LTIt+MRUNucHjCDOZv3y9AGFeXYZV/VHjctlVcD3KW7A/aRoQ0riu+9hzJ+ZUCCZ+Px89KsdD45cTVge2SNkXXedDxwH+lSvaRsOnZervk9kIReMaRDxaLqmgE3DfDwyTNZvPuodKtRWu75P5FHT59Jr1ofa0EhrO1cGWqYw4a3jigQECPICkl2gYD5+++/f6PPMWzBYC+GTCgCOtiw/ZPu2QyCSsgBYFcGGQYcOhCQvk0DEWSEze4LCBbBhQsX7DrofwM+C7TMKFyD28W//a4IuDH5QLANRwkE2Ags8Tn/C3gtaIWhMUaQjGAUGXAE5UYQCms32MChyA8BNAJdyEvethELXDXQFAW6dQTA0JQj2wxpigG+OyYnGBvol9u2bavfGb8boMgRGmgE1ufPn1eNNzLvcNwIT6zbdVrixIoubWsXk/he0eW0903pNHKxvaApcfxYDsHazGU7NQvyed3ikjBuTG2wsfXAeZnye2BWHxkvBAzwEH74+JmcvXxTg2WzK8a4Xzfq6wz/qrpmWXceuaj6zvDAut2nJS7GpFbgmHQc/eYxmbE8YEza1Q4cEwRCsI4ziGeMiVfgmCBYNsYkc5pEdheOJSM/c/g8NbtODzXbGBZsXn1EvOLFkKZflpW4CWLJ+ZPXpe/ns+Tu7YDMVqKkXsGyYynSJJAc+dNIr9YzQnzN4d0WSMtOFaT7iHoSyyua+F67K7Mn/CV/LggohHv54pX0azdHWnWuIIMmNpVo0aPItcu3ZXTvRbJny2mx2nFTt2xAJ9ipfQILrMHgaavVbg6kShpXJ6SxY0bVfQJyjnmrTSlWJ3HslEjzToFZ0BGTAn7/pJJNhvdCMxOR66bSjBRJA4Lj7yaK/LxIJElCkSHdAj2YQZUyIn53RSbMCCjiy5pBZNpIR7lFry8D5Bsd+wc4axiNS8IDaw6elngxomlQmyB2dDl59aa0+3GJvYFI0jixHI6Zaet2qergq8rFJZFXTPF76K/B8oSV2+3bJPaKJSOaVJE4MaKK38PHsv/CNWk8Yb5KQAy+X7ZJXxfeyygc3H7qouqiXR7q9MOECLa3rRh7R1AYB6/if9MqO7yDLPvNmzdl+fLllv+u/xUE7pCCILONQDksKdxsTJi+nytgezfVj2WJtyfAZYEE4pc/vrM/Qrhix/dTnf0Rwh25R37h7I8Q7jgy2nmzkZKfjHzvr7llaYAbGAnkv61ZuxmQDhw5ckR+/fXXYMEyCeDSpUuydu1atYODcwWKQZHNR8adEEIIIe8XSjLCBgbM/4KaNWuqrR602rBFIyFLVpBph04ZixeQ0EAj/bYFnoQQQgj5FzBgtkbADL0vfqzAP1nIWem7vitwugjqxEEIIYQQ4soww0wIIYQQ4qJQkhE2sASIEEIIIYSQUGCGmRBCCCHEVQnS+Id8GBgwE0IIIYS4KoyXwwRKMgghhBBCCAkFZpgJIYQQQlwUFv2FDcwwE0IIIYQQEgrMMBNCCCGEuCo2ppjDAgbMhBBCCCEuCiUZYQMlGYQQQgghhIQCM8yEEEIIIa4KM8xhAgNmQgghhBAXJQI1zGECJRmEEEIIIYSEAjPMhBBCCCGuyitnfwD3gBlmQgghhBBCQoEZZkIIIYQQF4Ua5rCBATMhhBBCiKvCeDlMoCSDEEIIIYSQUGCGmRBCCCHEVaEkI0xgwEwIIYQQ4qKwNXbYQEkGIYQQQgghocCAmRBCCCHElSUZ7/vnXzJp0iRJkyaNRI0aVQoXLiy7d+8Odftx48ZJ5syZJVq0aJIyZUr55ptv5MmTJ//pNT80DJgJIYQQQsg7sWDBAuncubMMGDBA9u/fL7lz55aKFSuKr69viNv/+uuv0rNnT93+xIkT8tNPP+lr9O7d+51fMyxgwEwIIYQQ4qJEePX+f/4NY8aMkTZt2kjLli0lW7ZsMnXqVIkePbrMmDEjxO23b98uxYsXl0aNGmkGuUKFCtKwYUOHDPK/fc2wgAEzIYQQQoir4kRJxrNnz2Tfvn1Srlw5+30RI0bU2zt27AjxOcWKFdPnGAHy+fPnZeXKlVKlSpV3fs2wgC4ZhBBCCCHEztOnT/XHjKenp/6YuXXrlrx8+VISJ07scD9unzx5MsTXRmYZzytRooTYbDZ58eKFtGvXzi7JeJfXDAsYMBPL8jxaBGd/hHBHzOsvnP0RwhUvT5x29kcIdzysXszZHyFckXvkF87+COGOQ92mOPsjhEO+cd5bfwBbueHDh8ugQYMc7hswYIAMHDjwP7/2xo0bZdiwYTJ58mQt5jt79qx07NhRhgwZIv369ZPwCgNmQgghhBAXJcIHaFzSq1cvLboz4xkkuwwSJEggkSJFEh8fH4f7cTtJkiQhvjaC4qZNm0rr1q31ds6cOeXRo0fStm1b6dOnzzu9ZlhADTMhhBBCCHEIjmPHju3w4xlCwBwlShTJnz+/rF+/3n7fq1ev9HbRokVDfG1/f3/VJJtBgAwg0XiX1wwLmGEmhBBCCHFVnNwau3PnztK8eXMpUKCAFCpUSD2WkTGGwwVo1qyZJE+eXGUeoHr16uqCkTdvXrskA1ln3G8Ezv/0ms6AATMhhBBCCHkn6tevLzdv3pT+/fvLjRs3JE+ePLJ69Wp70Z63t7dDRrlv374SIUIE/f/q1auSMGFCDZaHDh361q/pDCLYkP8mxILk+3yssz9CuINFf454rnBu56jwyLXuLPozE/G5sz9B+INFf8GJmMR5BcQVCg1+76+5dnf/9/6arg4zzIQQQgghLsqHKPojwWHRHyGEEEIIIaHADDMhhBBCiKvCDHOYwICZEEIIIcRVYcAcJlCSQQghhBBCSCgww0wIIYQQ4qq8cvYHcA+YYSaEEEIIISQUmGEmhBBCCHFRaCsXNjBgJoQQQghxVRgwhwmUZBBCCCGEEBIKzDATQgghhLgqzDCHCQyYCSGEEEJcFQbMYQIlGYQQQgghhIQCM8yEEEIIIa4KfZjDBGaYCSGEEEIICQVmmAkhhBBCXBT6MIcNDJgJIYQQQlwVBsxhAiUZhBBCCCGEhAIzzIQQQgghrsorZpjDAmaYCSGEEEIICQVmmAkhhBBCXBVqmMMEBsyEEEIIIa4KA+YwgZIMEioRIkSQpUuXftD3SJMmjYwbN+6DvgchhBBCyLvCDLOb06JFC5k9e3aw+ytWrCirV69+r+81a9Ys6dSpk9y9e9fh/j179kiMGDEkPFLvo9zSrHx+ie8VQ05fuSnfz/9bjl30eeP2jcrmlbqlckmSeLHl7sPHsn7/Gfnfkq3y7MVLfRyPfVo6lySNH1tvn79+W6at2CXbj120v0b82NGlU51SUjhrKokRNYpc9LkjP63cLRsOnJXwQK0qeaVBrYISL24MOXfBV8ZPWy8nztx44/af1sgvNSvlkcQJY8m9+49l4/bTMm3OZnn2PGBMcmdPoa+XOX0SSRA/pvQeukS27gr+XVOniCftmpeW3DlSSqRIEeTi5dvSb/gy8b31QJxNjfYV5dOuNSRekjhy7tAlmfT1DDm1J+S/16gNAyX3R9mD3b/rz/3St/pw++1UWZJL6++aSK7S2SRi5IjiffyKDKo7Wm5evqWPe3h6SLvRzeSj+sX1971rDsqEDtPlru89cTYNi+SWlqXzS4KYMeTU9ZsybPnfcuTKm4+bpsXzSv0iuSRpnNji9+ix/HX0jIxdHXjctC9XRDqUK+rwnPO+d6T6mMBz18y2daVQupQO2yzYeVgGL10v4YEGxXNLi4/yS4JYMeTUtZsyfMnfcvTym8ekScm8Uq9YLkkaN7bcxZgcOiPjVgaOyRcVikj7io5jcsH3jtQYETgmUSJHkm41SkmlPJn1922nLsnQRRvk9kN/cSZ7DonMmCdy7LTIzdsR5H/f2qRcydCfs/uAyHeTRM5eFEmaSKRdU5FalR23mbtEZMZ8kVt3RLKkF+nTUSRX1sDHnz4VGTFZZOUGkefPRYoXFOn/jUiCeOLaMMMcJjBgJlKpUiWZOXOmw32enp5h9v4JEyaU8EiFApmkc91SMuzX9XLkwg1pXDafTPq6ttQaMEv8HjwOtn2lgpnlq1olZNDstXLo/HVJnSiODGpRUWxikzELN+s2vncfyoQlW8Xb965EEJHqRbPJ2PY1pOG3czV4BoNbVpJY0Tzlm8nL5O7DJ1KpUGYZ0baqNBn2q5y6fFOcSZkSmaXDZx/J6Ml/yfHT1zUYHjXoU2n8xU9y917wi3C5UlmlbbNSMmLCajl68qqkTBZPenWsrOf3STP+1m2ienrIuQs3ZeW6ozK09ychvm+yJHFk4neN5M91R2TGvG3yyP+ZpE0V3x50O5PS9YrJ56Oby4QvpsmJXWeldqeqMnx1H2mVpaPcvXk/2PaD6oySyFECT72x48eUHw6Oks2/77DflzRdYhm7ZYismrFBZg9cIP73H0ua7Cnl+ZNn9m2+GNtCClfJJ0PqjZFH9/zly/99JgMXdZVOJfuJM6mUK5N0r1ZKBi1ZL0cu35CmxfPJD5/VlmqjZsmdR8GPm6q5M8s3lUpIv9/XygHv65ImQRwZ+mlFsdls8v2fAccNOHPjlrSevsh++8Wr4P2AF+46IhP/2m6//fj5CwkPVMyTSQPXIb+vl8PeN6RpyXzyQ9vaUn3ELLnzMPiYVMmbWTpVLSH9F6yVgxevS+qEceTbBgHnkpHLTWNy/Za0+SFwTF4GGZPuNUtLqaxppcucP+Xhk6fSu9bHMrZFdWk2cYE4k8ePRTJnEKldReTrt9hdr1wXaddTpH4NkZF9RXbuF+k3UiRhfJEShQK2QRA8YpLIwM4iubKJzFko0qaryMpfROLHDdhm+ESRzTtFxg0SiRVDZMi4gPf/dZK4NnTJCBMoySAaHCdJksThJ27c12eYIPTo0UMyZcok0aNHl3Tp0km/fv3kOabqrzl06JB8/PHHEitWLIkdO7bkz59f9u7dKxs3bpSWLVvKvXv3VOaBn4EDB4YoycBj06dPl1q1aun7ZMyYUZYvXy5hTeNy+WTJ1qOyfPtxuXD9jgydu06ePHshNYvlCHH73OmTyaFz12T1nlNy/fZ92XnCW3/PniaJfZvNh8/LtqMX5bLvXQ2aJy3bLv5Pn0vOdIHb5E6XVBb8fVAz2Vdv3dPs8gP/p5I1VWJxNvVqFpAVaw/LqvVH5dLl2zJ68lp58vS5VC0X8pjkyJpMjp64Kus2n5Abvvdlz8GLsn7LCcmaKfD77tp/QabP3Spbdp554/u2aVJCdu47L1NnbZIz533l2o27sm33uRCD9LCmzjfVZNX09bJm1kbxPnFFxrebJk/9n0nFVmVC3P6B30Px87lr/8lXPpc88X8qmxcGBswtv20ou1cekOk9fpFzBy/K9fM+suOPvfYAPHrs6FKpVRmZ2mW2HPz7qJzZf15GtZok2YtnkayFM4ozaV4in/y++6gs3XdczvnekUFLA46b2gVC3kfypE4mBy5dkz8PnZJrfvdl+xlvWXnolORMGbiPGMHgrYf+9p+7/k+CvdaT588dtnn0NHCC4Uyalconi3YelaV7jst5nzsyeNE6DeZrFXrDmKRJJgcuXpOVBwLGZMdpb1l14JTkSBV8TG4/8Lf/3H0UOCYxo0aR2oVyaIC9++xlOX7FV/otWCt50yaTXEFeJ6wpVUSkU2uR8qXebvv5y0SSJxXp0UEkfRqRxrVFKpQWmb0wcJvZv4l8Wi0gCM+QRmRgF5GoUUUWrwx4/MHDgN/xGkXyiWTPLDKsp8iBoxHk4LEP8z2JtWDATP4VCIQhrTh+/LiMHz9efvzxRxk7dqz98caNG0uKFClUZrFv3z7p2bOneHh4SLFixTQoRhB9/fp1/enatesb32fQoEFSr149OXz4sFSpUkVf986dO2H0LUUiR4qoAequE972+5AV3XXSW3KlSxricxAsZ02VSLKnCQhskyfwkhI50si2oxdC3D5ihAiaxY4WJbIcPn898HXOX9f7Y0f3lAgRAjLdnh6RZd/py+JMIkeOKJkyJJG9By85jMm+Q5cke5ZkIT7n6Ilrkil9YsmaMeACnTSxlxTJn06D37cFY1C0QHq5fM1PRg2sK8vmtJepIxtLicIZxNlE9ogsmfKnk/3rDtvvQ2YUt7MVyfRWr1G5VVnZuGC7Bs3GhLFw1Xxy5cw1Gb6qj/x2Y7pM2DFMitUsaH8O3tMjSmSH97186pr4XLopWYu+3ft+CDwiRZRsyRPLjrOOx83Os96SO3XIx83BS9ckW/JEkjNFwHGTIp6XlMycRjafdDxuUiWIK3/3biOru7WSEfUrSVKvWMFeq2qeLLK1XztZ2qmpdKpYXKJ6OH8RFeeSbCkSy84zQcbkdChjcvGaZEuRSHKkNI1J1jSy5UTwMVnfv42s6t1KvmtcSZLECRwTvKdH5Ej6PgYXfP3k2p37kjtNyO8bXkFAWzS/430lCgbcD549D5B3mLeJGDHgtrENHn/+IoLDNulS45xkc/2A2fbq/f+QYDj/bEKczooVKyRmzJgO9/Xu3Vt/gtK3b1/778gMI+idP3++dO/eXe/z9vaWbt26SZYsWfQ2ssMGXl5eGgwgg/022uqGDRvq78OGDZMJEybI7t27VT4SFsSJGU0vdHceOGYw79z3lzRJQs6+I5uM583oVl+gt/CIFEkWbjokM1btcdguQ7L4MqtHA4niEVkeP30mXab+oRlsgx7T/pQRbarIxrHt5fnLl5qd6zJluVy+6VxtqlfsgDHxuxtkTO76S6rkIYsAkVnG8yCnQOAbOXIkWbrqoPyycNdbv29crxgSPXoUaVynkEz/ZatMnb1ZCudLI9/2+kQ69pkvh45dEWfhlSCWRIocSfx8HP82fr73JGWW5P/4/MwFM0janKlkdOsp9vviJPKS6LGiSf0en8isfvNles+5UqBSHhmwqKt0KzNIDm8+LnGTxJFnT5+rFMPhfX3uqY7aWcSJHrCPBNXI4nbahCEfN8gsx4kRTX5uF3jczN95SH7cGHjcQMbQZ+EauXjTTxLGiiFflCsic9rVk5pj54g/oiUsyR8MyMb63n8omZImlM6VS0iahHGl0y8rxJnEjfF6TB6EMCaJQh4TZJYxJnO+DByTBdsPyfT1gWNyxPuG9JsfMCYJYsdQTfPsDvWk1qg5umqVIFZ0efbihTx48jTY+0JH7UpAk5wgyFDFjyfy8FEEefLUJvcfiLx8GUHix3WUJkCKceH1fOHWbREPD5vEDjLPwuvi9Qn5JxgwE5VQTJkSeMEG8eKFHAAtWLBAg9dz587Jw4cP5cWLF5o1NujcubO0bt1afv75ZylXrpx8+umnkj59+n/9mXLlymX/HQWBeA9fX983bv/06VP9MfPq5QuJGCnsdvH8mVJIq8qFZPivG+ToheuSMlEc6Vr/I2ldpbBMXxkYIF708ZOG3/4iMaN5Stl8GWVwi4rSevRCe9DcvmZRiRndU9qN/V38Hj6Wj/NkUA3zZyN/k7PXAnTOrkKeHCmlyadFZMzUv+TE6euSPGlc+bpNGWlWv6jMWRAoQQiNCK/XwVAIuHD5Pv397AVfyZEludSsnMepAfN/pdJnZeT84UsOBYIRI0LdLrJj2V5ZPO5P/f3coYuSvWhmqfZ5eQ2YrUTBdCmk7ceFZMiyDXLY+7qkShBHelX/SG6WKSxTNwQcN1tPBxbFnr5xSw5fviF/9fxM9dKL9wakBxfuPmLf5ozPbbn14JHMaFNXUsbzkst3nF8I+W8okD6FtClbSL5dvEGOXLouKRPEkZ6ffCSflyssP6x7PSYnTWNy/ZYcuXRD1vT9TCrmziRLdrt6ypT8K1j0FyZQkkE0IM2QIYPDT0gB844dO1QaAYkEstIHDhyQPn36yLNngTpB6JKPHTsmVatWlQ0bNki2bNlkyZIl//ozQcZhBpnpVyEU+RgMHz5cM9jmH58D6+RdgcPFi5evJF6s6A73x4sdXW6/QTfbvkYxWbnrhCzddlQD278PnpNJS7dJy8oFNbtqgNdFtviEt69MXLpNTl+5JY3K5NXHUiTwkgYf55VBs/+S3Scvy5krt2Taip1y/JKv1PsojzgTOFzgs8eNE2RM4kSXO3cfhficzxqXkLV/H5M//zoi5y/dUp3ytJ+3SJO6hR3G5B/f98VL1UybuXTltiROGDhZcwb3bj2Qly9eStzEXg73x03kJX43HN1gghI1uqd8XL+4rJ6xIdhrvnj+Qi6dcJTgeJ+8IolSJdDf8dpRPD0khpfj3wKf484/vO+H5K5/wD4SP6bj58JtaIpD4qvyxWT5/hOyaM9RDXTXHzsn41Zvk9YfOR43ZpA1vXTTT1LFf3M2HcE3CG2bsACuHzomsYKPSdCss8GXlYrJH/tOyOJdR+XMjduy4eg5mbBym3xW9i3GJEHA9731wF+iRI4ssaJ6Bv9bPAj5eA2vwMXilp/jfbfviMSMYRN8vTheIpEi2eR20G38Ah0wEsSHM0YEzUabweu6vEsGiv7e9w8JBgNm8tZs375dUqdOrUFygQIFVG5x6VKgntUARYHffPONrF27VmrXrm134IgSJYq8fPlhXA169eqlBYXmn8R5y73z6+ECd8LbRwplDbSpwoWqUJaUDnpjM1GjRJZXQWb6L1+feCKoJ0bIIKEIraHxGoYO1gwmC0bm0Vm8ePFKTp+9Iflzp3YYk3y5Usuxk9dCfE5Uz8ghfpeA50Z46/c9eeaGpAwi+0iRLJ7ccLKFGgLb0/vOS96yOe334Xvh9vGdp0N9bqlPi4qHZ2RZ98vmYK95as85SZnJUdKRPGMy8bkUYCmH93z+7IXD+6bIlEwSp04oJ3aE/r4fkucvX8nxqz5SJIPjcVM4Q0o5dOkNx41HCPuILfTjJnoUD0kZP47cDCXwy5Iskf4f2jZhAc4lx6/4SOGMjmNSJOObxyRaCGPy8h/GJBrGJEEcuXk/4PviPZ+/eOnwvpCoJIsXWw5dDPl9wyt5sovsDFhcsrN9b8D9IIqHSPZMjtvgNAM3DWMbPO4R2ab3GUCucd0ngn0bQkKDkgyiUoYbNxx9dCNHjiwJEgRkswwQIEOjDM1ywYIF5c8//3TIHj9+/Fj1y3Xr1pW0adPKlStXtPivTp06ds0zZBzr16+X3LlzqwMGft6X00dQK7z/KseYu26/2sIdv+grxy7eUI9lXJSWbw9Y7oSUAjZxyBIbDhhw1jjp7StHL9xQSQayzlsOn7cHAF9+Ulw9l6/feSAxPD2kUqEskj9TSukwYbE+fvGGn3j7+EmfJmVl7O+b5d7DJ/JRnvRSOGtq6TjpwzaQeRt+W7ZXenWqIqfO3lCJxac1Cki0qB6ycv1Rfbx3pypy684DmTZni97evuecOmucPu/7WpIRR7PO23efk1evJxN4PqQaBigMzJA2kdx/8NjusTxvyR4Z2K26yi8OHPGWwvnSSrFC6aVj7/nibBaNXSHdZ3WQ03vPyandZ6VWp6oSNYanrJkZYJvXfdaXcuvaHZnR+1eH58HlYtvSPfLgzsNgr7lw1HLpM/8bObzluBz6+5gUrJRHilbPL10+DnCW8b/vr5npdqOb6/NhO9dhQis5tv2UnNj1ZreRsGD21v0y7NOKcuyKb4CtXImA42bJvoDjZli9iuJ776GMWxNw3Gw8eV6dNU5c81WpBTLCyDpvPBF43HStUlJvX7v7QBLFiiEdyhdVhwi4aQDILlDwt/nUBXXPyJwkgXSvVlr2nL+iEg5nM2fzfhnaoKIcu+yr2uOmpQLGZOlr6cTQhgFjMn7l6zE5fl6alc4nJ64GbI+sMbLOm44HjkmX6iVl07Hzcs3vgST0iiEdKgaMCdw0wMMnz2Tx7qPSrUZpuef/RB1DetX6WAsKoQl3Jo/8RbyvOtrGnTiDOgmRZIlFxkwT8bkpMqJPwOMNaor8ukRk5BSROlUCAuHVG0Wmfhf4Gs3rifQaLpIji0jOLCJzfg+wrzO8mmPFDHDQgJcz6kVjxhD5djwCapvrB8yUZIQJDJiJNihJmtSxajpz5sxy8uRJh/tq1KihmeMvv/xSg2zILmArZ9jDRYoUSW7fvi3NmjUTHx8fDbiRYYbjBYBTRrt27aR+/fq63YABA+zPDY+s3Xta4saMJl/UKKrNRE5duSlfTlhiLwRMEi+WQ0YZOmXc6lCzuCSME1P8HvprsDxxaaAvLCQeCLQTeMWQh4+fyZmrtzRYNtw44C371cSl8nWtEjKuQ02J7hlFLegGzFqjdnTOZsPWUxLHK7q0alRcG5ecPe8rXQf+bi8ERHMSc2YMOmXcbN2khCSMF1Pu3n+swfKPvwQE1CBzhiQyYVgD++2vWgfYscG6bvj4Vfo7pByjp6yVJnWLSMc2ZcT7qp/0/26ZHDlhuuo6iU2/bZc4CWNL80H1tRgPNnC9Kw+1NxCBjMIWZIkT2eCcJbNKjwpDQnzNbUt3y/gvpknDnrWkw/hWcuXUNRlUd5Qc2xZ4TE75ZpbYXr2S/r931Uz1vjWHtHGJs1l9+LTEixFNvixfVAvPTl67KZ/PWGIvBEwax3Ef+WHDLt1Hvq5QXBJ5xRS/R/4aHI9fE3jcJPaKJSMbVpE40aOql/P+i9ek0eT5KncAKI4tkiGVNkBBIHrj3gNZd/SsXQPtbNYcDBgTBLUJYkeXk1dvSrsf3zwm09YFnEu+qvx6TB76a7A8YaXjmIxoUkXixIiqtQ77L1yTxhMCxwR8v2yTvi68l1E4uP3URdVFO5tjp0SadwrMlI+YFPD7J5VsMrwXmpmIXDeVrKRIGhAcfzdR5OdFIkkSigzpFujBDKqUEfG7KzJhRkARX9YMItNGOsoten0Z4J7RsX+As4bRuMTlYcAcJkSwBV33IcQi5Ps80O6OBBDzevho5BBe8Fyx29kfIdxxrXsxZ3+EcEXEQJt58ppD3RyLxIlIxCTOk0JVTvH1e3/NVVcmvPfXdHWYYSaEEEIIcVWY9wwTWPRHCCGEEEJIKDDDTAghhBDiqoRiuUreHwyYCSGEEEJcFUoywgRKMgghhBBCCAkFZpgJIYQQQlwVZpjDBAbMhBBCCCGuCltZhwmUZBBCCCGEEBIKzDATQgghhLgoNhtdMsICZpgJIYQQQggJBWaYCSGEEEJcFWqYwwQGzIQQQgghrgpdMsIESjIIIYQQQggJBWaYCSGEEEJcFbbGDhMYMBNCCCGEuCqUZIQJlGQQQgghhBASCswwE0IIIYS4KDZKMsIEZpgJIYQQQggJBWaYCSGEEEJcFWqYwwQGzIQQQgghrgobl4QJlGQQQgghhBASCswwE0IIIYS4KjYW/YUFzDATQgghhBASCswwE0IIIYS4KDZqmMMEBsyEEEIIIa4KJRlhAiUZhBBCCCGEhAIzzIQQQgghLgolGWEDA2ZCCCGEEFeFkowwgZIMQgghhBBCQiGCzcaeioR8KJ4+fSrDhw+XXr16iaenp7M/TriAY+IIxyM4HJPgcEwc4XiQsIYBMyEfkPv374uXl5fcu3dPYseO7eyPEy7gmDjC8QgOxyQ4HBNHOB4krKEkgxBCCCGEkFBgwEwIIYQQQkgoMGAmhBBCCCEkFBgwE/IBQTHKgAEDWJRigmPiCMcjOByT4HBMHOF4kLCGRX+EEEIIIYSEAjPMhBBCCCGEhAIDZkIIIYQQQkKBATMhhBBCCCGhwICZEEIIIYSQUGDATAghhBBCSChEDu1BQggh/57t27fL/v37JXr06FKzZk2JHz++uCswYooQIYL+/urVK4kYkXmaN42FeawIIeELnrkI+ZcXOfLPuLNb5axZs6RZs2aye/duefr0qcSLF0/c+XhBAHjnzh3x9/d3CBDdeR8xB8u+vr5y69Yt/R1j5c7jQkh4hj7MhLzDRW7btm3y4MEDiRkzppQoUULcGfO4vHz5UiJFiiTuyrx586R169Yye/Zs+eSTTyRyZC7inTt3TgoXLiwpU6aUESNGSIoUKSRbtmz2x90569y/f39ZuHChfv/ixYvLtGnTxJ0xMuw4t+JcEidOHGd/JELs8GxOyFueyI2Leu/evWXx4sXy8OFDSZUqlaRNm1bmzp0r7j4u48ePl2PHjmkA1KdPHx0bdwqer1y5IpMmTdIgqG7duvb73X2Z/ebNm5I9e3ZJnTq1TJ48Wa5evSrVq1eXxo0bS/r06d0qWDZPDmbOnKkB8uDBgzUDP3bsWLl8+bIsWrRIpTzuCI6TpUuXypgxY+T27ds66fziiy90kkWIs3GfMxUh/wEj4Pnuu+9kxowZ+nPhwgX5+OOPNatYtWpVcdfldvDtt99Kv3799L41a9ZI+fLlZeXKlfLs2TNxF7CsfubMGc2mmjHrdwGkCe4Egh1MnD799FM9Vnr27ClLliyRLl26SKtWrcTb21vu3bsn7oARLOMYweoDguS2bdvqmOB4OXr0qNSuXdut9hHzIveOHTt0nyhUqJBOOjEJ/+abb3RcCHE6kGQQQkLm5cuX9t/Pnj1rK1++vO3PP//U26tWrbLFjBnT1r59e1uqVKlsNWrUsLkjly5dsjVp0sS2fft2+32VK1e2ZcyY0bZs2TLb06dPbe7AokWLbHHjxrXduHFDbz9//jzYNnjsxx9/tD1+/NjmTkyZMkX3BxxDBjly5LBFiBDBlj17dluDBg1s8+bNs7169cpmdc6cOaPfGz/YF8zs27fPljJlSluVKlVsDx8+tFmZFy9eONw+d+6cbdq0abbhw4fb7ztw4IAtadKktjp16tiOHDnihE9JSCDMMBPyFhkhZMCwfNy0aVPJly+fuiBAqzp69Ghdhq9WrZr88ccfwbKLVuOnn37SIiWDH374QfLkySPHjx+XuHHj2u9HtixDhgzStWtXzaah+M3q5MiRQ7/njz/+qLeRQQxaJLpgwQLZvHmzJaUqQb8rNKgGWFrPnDmzSnYAsog4pk6dOiXdu3fXLHz79u11Gd5qBC0TgjRl+fLlmnlfvXq1w2M4tyxbtkzWrl2rKzZWZeTIkfo9jf0EqzM4X0B+cf/+fft2OLesWLFCa0awinXo0CEnfmri9piCZ0LIa/766y/b//73P/0dGeQ2bdo4PN63b19bs2bN7JnCUaNG2WrVqmVr3bp1sMyJVZg7d65m2M1Zd2RRixQpotkyZJPNj4Fq1arZYsWKZduyZYvN6ty+fdtWs2ZNW4YMGWy//fZbsMefPHmimbI+ffrYrIaRGT5x4oRtwoQJ9vvNx0KHDh1spUuXtjVv3tyWLFky265duxy2w/hZDfPxgHOFv7+//TaOF6xQtWjRItjzTp06ZdnzCPaVunXr2k6ePOkwRjjnRo8e3VahQgWbt7e3fVsj0+zh4aHnXHdZsSLhDwbMhATh/v37emIuVKiQrWLFihrwHT161GGbhg0b2goUKKC/P3v2zFa7dm3b2LFj7Y9b9WJnfK9Nmzbp0rJxX548eWxZsmTRICjosnqXLl0sOx5B2bx5sy1dunQqM4AMAdy7d8926NAhDQTy589vl2pYTX4ACUGiRIlskSJF0gmlgRHg+Pn5qXQJS+wIgKyOOVj+7rvvdLKUKVMmnTBt27ZN71++fLktduzYtpYtW4b4GlY7boLu85hI//rrr7ZHjx7p7bVr1+r+07ZtW9u1a9ccnoNjCBMJQpwFA2ZCQsDHx8eWO3duzZyaM4JGsPPHH3+oJhPbIHDOli2bZQMh83fC/zt27LBFjRpVg6ILFy7YL+w5c+bUcQgpaDa2cQc2bNhgK1q0qM3T01N1uunTp7cVLlzYVqpUKZ1cWXUssP8XLFhQV1ny5s1r69Wrl/0xfF98dzyGVQcDKx4rQcE4JEiQwDZnzhydROXLl0/H59atWzouOJfEiRNHVyesCv7Oxt/aOAYA6j4wwVywYIE9aF69erU9aL5+/br9+YQ4G2qYCQlBiwktKjSp0F5u3LhRdcrA8NUtXbq0Wh/h/7Jly6q2Do9Bj2c1CzGzLRr+L1KkiAwcOFB+/vlntcaCWwg0uQcOHBAPDw/VdkNzGFS7aTXd7pss7OGc8uuvv6peGfsGdO+9evWSDRs26Pi8ePHCcmMBsP9DpwwLOThi/P777zJgwAB9DJp/fHfolKHP/e233/R+qx0rQYG7w59//qmuINgPMmbMKCdOnJCvvvpKuz9iP0D9A+zl4IxhxcZIxvnD0KdjP1i/fr3uE7DnxHl2+PDhWgOCMahYsaKO2Zw5c6Rz587i4+Nj+f2EuAjOjtgJCQ8E1d4aXLlyxdaqVSvNEE6cONHhsZs3bzrcDskVwdUxZ4OCZkVRzZ48eXJb//79befPn7dvkyRJElvjxo1tVt9H3rTPhIYVM8vm7wWt//jx41Wvjf3DyLBDinD37l3dpmrVqiphwjZWB9ITaNoxPnBRgWbZkOogozp//nzNNP/X/Sq8c+fOHc0k41yxcuVKXbmDHMU4b2KfgKwL42FkmvF4/Pjx7dIMQpwNA2bi9pgvULA1+uabb2yffPKJLg3iQoeg+bPPPrOVKFFCdcrYvmzZsqrNtSp79+51uI0gqF69ehoQjRkzJljQPGDAAHvQjPGxWmBo3kemTp1q+/zzz3U5edasWW8M/MzL0FZfUja+HwIeFPUBjAuCIMh3jPsANKtmezmrENLfGMcR5BfYZ7y8vGyTJk1y0O/CTg/aXKuDgBnnVui1sT/8/vvver9RNG0OmiHPMCz1rG6tR1wLSjKI22NYx8HeClIDNNtIliyZVK5cWa2MkidPrkvquXLlUiN92B9hmRDLiFYEy+hYOseyKBg2bJh27osXL552Ihs3bpxUqFBBH0PDha+//lqlGRMmTJDr16/reGKp2WwrZpV9pEePHjJkyBCJGjWqSlNatmypt0NqNIFlZLOUxcoY3w/jYlh/ffnll7o/YIwg18F+Aho2bKgWjVaVLaE5C+wWQf78+fX8Abs0dAjFcQUeP36s5w/sN5AkWB1YTuJ7ouU1gA2lsb88efJE5Tzo8IfuoN26dVMrSuCuHQ9J+IStsQl53XkLmlN4fubNm1f2798vU6ZMUU0mwAUeLY8bNGigml209UVQCD2qoWu2ClWqVJGtW7eqbtvPz08OHjyoFzPocTGZgI8wfHRr1qypXqqYaODCj+2SJElifx2r6XQ3bdqk2lu0LobfNjSYIFOmTLywv6ZYsWLqMwxdLoJk6P8TJUoko0aNUg03PLxx26rtrvfs2SPTp0/XduAIEuvVq6ddQevUqSMTJ07UYwLHEDS8N27c0DHCc82vYcWJBDy34TuN8wp03Zh4ok4ECQkjaMb/0DS3aNFC/ZfdYaJJXAxnp7gJCQ/ANxdLgsaSMbSGkydPttuCwV82KFaTHZiXlffv32/7+OOPbZUqVVKLNHThMuuaFy9erLrUdevWBXuuVeUH+M7lypXT3xcuXOigR4Vl2sGDB23uDpbWM2fOrK4Q2IcMfH19g2n+reiGAS/24sWL674B15hffvnFblUJv+VixYqpnAue1EbNgxVrH8znAbiAQM5lWOnBb3vcuHHqDNKvXz/79jNmzHDoFkpIeIMBM3E7oEc1vGGNkzpa1EJraPiiGsGy0bADvsxWbKwQEsaYQH+JoBkFOkZgaG6HnTBhQntAEPS5VmTFihWqsZw+fXqwfWTJkiXaztidC5QMnTeOk9OnT7vFPmFg7BMI+FDcuHv3blv16tW1qQ8m4AZ4LGjjHyuDSSZ87FHsZ94noGlGPQjGrEmTJrbOnTurlRx9lkl4xnprQISEAiQEkBOUK1dObYuMJT/olWPEiKEyA+iYoTk0tIbz58/X7cytn62G2c7KGBPoLw3rPOgysVxqgLFIkCCBSlLMWGEJ9U3WXtBgJkyYUHWoaPlt3keg4YbG2yxJcRcMez1ICvA7xgH2aVbaJ/6Jw4cPqxylaNGi4uXlJQULFtRaAHx3aNwh9wJ4zJBeYKysJucyc/r0aenYsaPKcQYNGqT7BL4z2qFjDDp16iSTJ0+WkydPqpxr7969Km8iJLxi3aOVkCDA6xTaOXjEpkyZUrVyuLhDb5k4cWJp1KiRFqXAQxhBIgrcpk6dKlevXtVgERc/c3GPVTDrJ6HlRkFjtGjRpEyZMqolHD16tHTp0kU13Dt37lQ996pVq/R5TZo0ESuBv68xFijcunjxosSMGVMLkaDNxfdFIRsK2+AbC+3lTz/9JNeuXVPdO/YNK+pRjf3+yJEjelzgO5YoUULixInjcFxY7dj4J4y/NSZS+/btU80/JpMYD0w4UeiICTrqAbAdzj0GVh+rR48e6QQStQ93795V33acRxEg58uXT0aOHKm1ICgCxbaxYsVy9kcmJHScneImJCyApZGHh4cuERr6Y+hzYQtmSC1w38yZM9U+Llq0aNq1DJpEK3dnMwObvBQpUqhvLLxzoUM1dIf79u2zlSlTRpdNocEcOnSofTnZiuMyePBg1aHCXjB69OiqSz158qRda4n7YY+FfaVu3bqW3kcMSQV8hNH6Gi3jscz+6aef2v78889g21mZN3kkYxzQ2RG2cWaZBWQ8sB9EW2x4T0PLbGXM+8CRI0d0TBo1amRLnTq1djKE/SQkKji/mKUqhLgCDJiJ5fnrr79Uh2v2DwYoZkML43jx4mnB36pVq+yPHT9+XPWGxgXA6lrDn3/+Wcdhz549OoGAT27Dhg3VO9bwicX/KGRCMGmMi1UCxKCBEAq0Nm3apL+jsQQu8NC4m4s/4Tvt7+/vFvvI+vXrdQL1ww8/2I+pKFGiaBEk9NtWbroR0ndDQ5LNmzer5tb4+w8ZMkQnlN9//71qmKFnx3kFXuXwdMc5CPdbEWMMDE9y41jAOfWLL77Qsbl48aJ9e0xAkZwgxJVgwEwsD4r2ChQooAGg0VwD2R50nsJJG1nmZMmSaYHO5cuXg10crRYEwOEh6HcaNGiQZtPNoKkACpcQKBoNBBAwGkGyVTKKQQOhrVu3amMSc3MNOKUgaM6fP7+6YQT97lYZi5AaryAIQlFW165d9X4cQxgLZNlxXOEHTghWxvz3hRtGtmzZ1OUBRbEdO3a070MjR47URj6JEyfWrCom5SgwvnDhgrqHHDt2zGbVscEkCsXRyCT36NFDnVGA0bnPoHfv3jpGGBNCXAkGzMRtgmZc3OrXr6/Sgnz58jmcsI12rUZW0arABg7fExkfc6DYvXt3W5o0aey3jaAYdnuYWHh7ezu8jlUyy+ZACJIUtPVGO16MEaQX5sexnA4rPQRCZ86csVkNY38w/22NCSQcU7DqgokDAmS0iwfIssaIEUMnm0uXLrVZHUiREAxv2LBB94fWrVurdAedDI3xw0oMuvghq2zchwlH1qxZ7UGkFTAfG1hlwDigSyqOo5IlS6qFnmEliHGAkwjOvzjGzJaDhLgKDJiJpTEHhZAdQIIBK6O1a9fqfYb2FBc4ZIOg1bU6P/30ky1y5MgaNBvBEbKqkFt8++23DhkhTCCyZMli+VbGmEhgEgXNKcYCmWTsK1hSNm8HmQ48Za0yYQgKdNrYL4zJUty4cTVoNpbakUnGOBnL68gqYpywYhN0UuXqmD3GwdGjR3VigEAY4ByCyQJWrjJmzKiTiKArNwgMGzdurHIWrF5YgaDtqjFBwGTAkOvAcjJp0qS67+CcagTNGzdu1AmGUQtAiKvBgJlYHvNFbN68ebbSpUtroRYugMbj1apVUz2m1eQXbwK+0xEjRlQ9siG/wNIyitiw9A79JfSZlStXtvy4oBC0ZcuWulRsgEwgAkGMR9Cg2cCKQTMKsZBdx/EBPS7kSsD4/gsWLNDgEBNMgMYTWH63WjHb/PnzdRwwuTSD2z4+PppZR6YUxxGAnAmTUEiYzJl6BJMIpFEAZwWGDRumE4Dr16876Ns/++wze7AMuQ5uY2KBMcJxdOPGDX3c8L8nxBVhwEzcMtP80UcfaZU/NIXIjkFfaGSbrRgcmr+TuVkLgmZUrhuZIwSNuXPn1vtz5MihjghWGxfz90DRHlw/4HgBNwMzCJqxrIwJlrmwzeqgCx3+/gj+gk4KEPhhJQINXCDNwGqNVTscDhw4UJ11ICUIOmH66quvbO3atbMHgH369FGpF+4PepwYx48rY3wnuIFgIoFCPnPQbBTDYvUFrhiG/r1UqVK6PRyHsC9ZUetP3AcGzMRtMF/I0KEOgRIq/aFLNS5qVnQ6MF+ksGwKyYERCAUNmvH9Hzx4oFlVVPQb21lxXJYtW6aZdWTTGzRooPuBkTE0B82w2UOAYHWM4A8BMzLuCHQwgYJLiHk/2rlzpzo/oPgtpJbxrgwy5ocPH7bfxnGBTDuCZjOYXFWoUMF+XkFG3pAkGPdZBeO7QMMOaRay6zhnYMJw9epV+3bYTzDZNrp/QsaD/QirFleuXHHa5yfkfcGAmVgC8wUqaEbHHDCat8PyKpYOjWDQikGh+fuiHS08hZH12bVrl/0xI2iGU0ZIF3qrSQ/wHc+dO6cBIbyFAS78WFbH2AS1u/Lz87PcGIR0fAQ9buBdjjFCYIx9x8BsD2YlYKeI74tMsXkiEFLQDL9lZNmx+oBVCLhmWM09BhjnA+ivce6YMGGC3saEGucMuMkYmWbsP7CLw0QCk45u3bpp/QODZWIVGDATl8d8gZo4caJqBlGtDY2yccJ/U9BsYMVg2QwuXhgXLI3iwoeLPTLI5qAZ2XZsZ6XsWGggQwbnFENfaQTNCIIM7a4ZKwbNxnGBAjeMB5bUcewYmmQ4hRiZZqMgEC4RmERYMTDEPgCLSUi2kFENGjQbKxDIpk6ePFn9upGRt2ITH2NMILnBOaNnz57B9hkjaMYqDcAEFFlmaJfhruMORdTEfWDATFwa80UbDg+oWkfWGFXpKDZBkV9IQbOVLvb/BLJh8IxFUxIsqUK3jcwPtKjmoHncuHFa5Ga1sQk6ATCCGxT7QadttrhCwISubKj6N3exszIYBxw3sD7r37+/LVeuXLa0adOqNAfMmTNHLcMwycJxBYs5K2LsF8iIIuB7U9CMzHtoz7cCZos8BMvmgljDhhOSCziFIGhu27atTqKMyQTOK2aNMyFWgAEzcVnMgR0ubCg2gT4XwBoNDg9YIoSGLqSg2V34+uuvNQg0Az9duB2gqA+a1KDjY8VxWrNmjb1xjQGW0+GQYgY2apAhWClb+Cbg+ACbuPHjx9tdDpBhhf2XGUh4sAxvRTlGSKtPCJqRSQ8aNKMQECsxGC+rT8BhE4gJElYdzGCVAY1HDJchuGEgaIbO38g0E2JFGDATlwNLoYa3J8AyKbJi8M41NyNBpqNKlSoaNCPT7A4BkBnjIt6kSRMNDA1Q6GZkDrHcjqwyLOSMgMEqF3/z33vHjh2aUYerw6hRo+weu7jYYyVi+/btIWYJrbjPmP++WHFANhkSDGTXU6RIodlCA7iDWMHl4W1WHzDJxt/f+L4IGBMlShQsaEYmHo05rHKcvAmcSyHhQoGjkYhAsSeCaEyezMeH0fob1pRWPGYIARGFEBdi5syZsnHjRokbN679vo8++kiiRIkiJ06ckF27dtnvjx8/vvzyyy8SL148GTBggGzYsEGszKtXrxxuR4gQQf9v166dHDlyREaMGKG3o0aNqv/HihVLOnToID4+PtKpUye9L2LEiPbnufpYRIoUSX/HPpAkSRLZsmWLDB48WJYtWyZt27aV1q1by927d+XGjRuye/du3TZy5MgOr2O8hhX2i6dPn+r/z549sz+G4yhdunTyxx9/SNGiRaVq1aoyadIkfez8+fPy+++/y7Zt28SKYFywv4Pvv/9eGjVqJAULFpR+/frJ9u3bJWXKlLJ37145fvy4HicnT57UbUePHi2bNm3S4wRJJ6uSJk0amTt3ru4vGB8cM2PGjNH7KlWqpNsY41eyZEk5ePCgfP7555Y4ZggJEc4biKthZDBgmG9klJENwtIyskG43wwyzWjGYeXMhzlT9vfff6u1E5pLGOODpWS0voYTBvyWMV7Ivo8cOVK3ixYtmj3LaqWx6Nu3r2a+ILswMoKQXCBjVrhwYV1uxuPIJELbbTWMsYDrA6zz0IQGxX2nT5/W+7EvQLqEMUDHOjMoAIXXstW1qJDfoCvd6NGjtYANLg9ovoEuhgDHCmQq0LubJSlWzzAbYPWpfPnyeo7A6gwwfJYND2p09gvaAZAQq8GAmbgM5gsUvEBTp06tgbDRkhf6VBQmwV85aNBsYOWg2QhycHHHBT9VqlS2vHnzaktajB2CY0gSoM3E0jtkLBgPFOhgWf7MmTM2KwXL0G7Hjx9fi0Br1qwZbFsswUOqgYApXrx49q5uVtlHzC4HXl5etjZt2qg2GS4gcHYwpAcIArGvwFJv6tSp6k/dvn17fQ6KvqwKjglMHBAIo4jNAEWgcL/A/YZOF9rukBq5uAuQ7mAigckVzr1m32o0/UFBMSFWhwEzcQlCyubgZI0MGFrzmoNmBIk4uZsvgu4ANMnQFyJjjA52mzZtUis5BMdGK2NkC3/77TctgDMu/t27d9dxRJMOq9CpUydbrFixdH/A5AlNSVDVbwSRQZ0zYKWGbo9WCYiM7wc/XDhgINNugL93/fr1dTyMIi34UletWlXdU+ApXLFiRUsGy5gk4ccYH3QuhAuEoWk32LZtm9qjLV26NNhrWGUf+bdgclGpUiXdNzCpGDFihAbLVnVNISQoDJhJuCe0oiPYYCGrbA6aIUNAFTcKUKxMUEcLZNvRccwMipUgO8D9hk2YAbJnWJ5HJtFK7Y0hSUHwawR8sIeDrZ65UDTovoUsOyZayCRaBRTxQWoBKUbQyQRWZ2Cdh8kUvMsBAkk074CECb9bjT/++EMLGtF9DgExgAc3pDmQYxjFsAbZs2dXuQFxDJohb4KECW3DGSwTd4JFfyTccuDAAf3fw8ND///f//4nDRs2lC+++EImT56s9w0aNEhq1aola9eu1WKly5cva7HKnj17tDjHqmCyaxTnGUVcKOa7cOGC3L9/375d1qxZtSgSRUqPHz92KHg6d+6cvs7WrVsld+7c4qoELbwyvm+uXLn0dsaMGSVOnDj2ojeAIkfsK8a+9dtvv2mRW4wYMcQqJEuWTPLnz6/HEQoewciRI2XatGnSt29f/alZs6Z07NhRj5/o0aNrgSyKZfG7lZgxY4a0atVKCxxr1KghxYoV0/sTJ04s+fLlk4kTJ+oYGMfSgwcPdF9A4R8JBMfSqFGjpEiRIrpfYf8ixG1wdsROSEjAvgg2YIZ9EYrWsMTevHlz1aNiKdBcpISmApAVIGMKb1l3WT5FG2ejqcD8+fO1sA8Ff+ZsMqQZyMIHzZ5iWRrSDVfGLK3Adzb/7Q3Q1jlhwoT2bBikB9Asmy3kUMyELLNVMO/3sFWEJAUFbZDsmOUHWJXBagys0qzK8uXLdRUFx8ebxqh27dqq44cFI84l6AAJDbOVmpG8T6xsNUjIm2CGmYRLkLlInz692hnBFgzZv+XLl8usWbNk0aJFsnLlSlmzZo1mjcDAgQM1s/jkyRNJmDCh/XWsbHGEzOrff/8t69ev19v169fXMejRo4fMnj1bDh8+LFevXpWhQ4dKggQJgmXLYAkVLVo0ceXvb9ha4TtWrlxZbcHq1Kmj2cLnz5/rY/gfmWRYyGGMzp49q1ZysJB7+fKlbtOlSxd9rlXAfv/ixQv9HSsI+Nsjs9yrVy8pW7asfTsvLy9Jnjy5Zl6tBvYPZIyXLFkizZo105Uo82oExsj4++Oc0r59e12FwOoEzj379+932EdIIMbKDCFuxRtDaUKcDKqxa9WqpdkeZMhQqW3W7KKaH9pUtGc1sHKnOnM2NWhHMrS/NoADAgqWPD097Q1djIxQ0GI3K4CMINww0H0NGXd0L0QzkhkzZti/NzLs0POioM24zx2yh+bvCMvFdOnSqWuKkV1F4Sz0zEE7IFoFrDrAAWTw4MEhPm4cD3fv3rXfh2JIA3fYRwghbwczzCTcYWSBYIb/9ddfq5bwzJkz9qYkhnY3Z86cEjNmTLl37579uUYzASs03wiKkU01/47GE9BkYmyMjCr0mAsWLJAVK1bI+PHj9TFkhJBxNL+Gq4O/85UrV2Tx4sUyZcoU3VdatGihGfdUqVLpfdBpA2QMM2TIIIcOHbKPRdAmJVYE39HINGM1ArpmjBGaTKBBB1ZwMH5p06YVKwItMjLJ0LAHbdoCcDxA89+7d285evSo3ufp6Wnfv9xhHyGEvB3WuXoSy3QkMwe7kBj07NlTKlSoIGPHjtUubQaQGaDQ7dGjRw6vY7VgGfKC+fPn6+8obETXsVOnTulSMYqzULg1b9482blzp/05mTNnlnLlyun4GUvPVrj4m5fU8XfGd3r48KFdeoOACJMoSFKuXbsmP//8s96PjnXo1GYEkFYYi3cJmlH8lzp1apWfYDKFjnYoerMqSZMm1eJPdKjz8/PTjqBBJRboEHrp0qVghY5WO48QQv4bDJhJuGtTC20yXAugXUbWtHjx4tK/f3+9+H3zzTfa3hguGY0bN9ZAqUmTJmJV0JYYbWhRmY7vjOAH49O8eXPVZOJiD00qgmhoVBE8hoQVtNzYR4wgBplDgNUHZIwNHTcCIuwzyBIWKlTIYTywf+E1rBYsG5MIBILm9ujmyYU5aEZreWi5N2/ebOlg2aBevXpa29CyZUu5deuWw7EA5xhk2aHltmqWnRDyfrDWlYO4LEaw3LVrV82mohgNFzMU8yFwhg0UMs0IlnGBw4UexV1YTjYyqFYICoOCC7wRGCLQwSQB8hRk2qdOnWovdLtz544GiggQkWE1T0CsgPn7YKUBGXbsD7AQHDFihDRo0EDlBn369NEAGtt7e3sHs8uz0pgAQ360atUqtU7DMVC1alVp2rRpsAypObuOFQkrEtJ5APsGpDhz5syxTz5jxYqltoITJkyQ27dvy759+3S8rHbcEELeI2+pdSbkgzN79mwt3jpw4IA2FMAPOkslTZpUG3CArVu32kqWLGnr2bOnvbDP6tZxTZs21bbFsL4qUaKEbeHChfbH0LVv0KBBWvyIoja0NLYyaM6CIkfsK+hOZxR2jR071hY5cmTtQgbrQbR/RmMOqxZtmYtaUfQaLVo0W+PGjdVyEfuBubOfO2DuSmg+H5g7O44ZM0YLYKNEiaJNNwoWLKgNfYwiUKufRwgh/40I+Od9BuCEvA1Lly5Vfa1RjGM0IYEZPh4zZ3pQ/IfbkCcAbIPMIR63aoEfgMUVpAVz585Vq6vPPvtMs+s3b97U3yHLMIDtHor9UMyFgj+ztZ5VwPfq3LmzZtcLFCig9yGrjiw85DrQcI8bN04zzGi+gUyiYQtmxdUHcP36ddUlwyYPRY/YZxYuXKj7B6zyhg0bJlYH2nRk1atXr65/f2D+m5vPJah3gF0c9htIMLBCgfOHu+naCSHvwH8MuAl5p0YCyIJ9//33tnv37tnv/+KLLzQrGNTeCS1t0VTAsJUzsKJF2oYNG2zTp093uO/atWvaXAI2adevX9dMMyzCfv75Z4ftkHGNESOGbcGCBTYrEPTvO3ToUFvZsmXtbb3Rzhj7S9y4cW3Dhg0L8TlWyjAji459wfz3xnGUJEkS27Rp0xy2xb6BLKo7ZJp9fX21sREsFLt162a/35wxDs1m0ornEULI+4diLRLmIBOEttVoogAdLhpKABTlQLcMnbLZ3gmFXPgJapZvNa0hbL9QwNemTRvVWmJsYHWF7CmypWjAAH3ykCFDNIOKJi4//vijPYuG5hNoWevj4yNWwPj7GgV9sIVDMxZoUqFf37t3r+4zWJmAdhm65qD7hFWyhnB4gPOHUewIsF8MHz5c77t48aLeZywYohAW+wcaumB/sTJYTUHRK/aLP//8U7p37x6sMUloq1BWO48QQj4M1riaEJfBWCqF2wWCvG7duun9uOBlzZpVGjVqpBc9FK8hCELwh8IcWGGlSJFCrAy6sUF+giAPS+vHjx+Xvn37ql8uxgrLyZBclChRQicVGDMEkMZF/9dff5UNGzaom4ZVwBiUL19enUGqVaumkytMHFAciskFltUxqcBEwZhgWRH4be/evVsnjTt27FCfaXTo+/LLL/VxTD5xfHzxxRf25+BYwvY5cuQQqwOLSUw0ERjDShATh5EjR1q6IJgQErZQw0zCjJAq0JE5RUYIWku4HkCfi6wpLNKgTUUQAMsntPc13A+snBE6ffq0Bj/QWEKTios9xgKZ99WrV6vnMjyFEQAgq4jAyRgPBJMYv4wZM4pVwMQBbY2xH2BVAhh6U+wLePzTTz9V2zD4VVt538CpGt83e/bs6hmMSQScQfDd4RyCCSZ8us1Bs7uBc8b06dM1aK5SpYoGzYBBMyHkP/MBZB6EBMOsIZw/f77qkg1GjhypWszhw4ernhDaw4cPH+o2O3bssGsRraRHDY2TJ0+qO0iFChVsJ06c0O9/7Ngx22effWY7ePBgsPE0xszVeZOWdNy4cbYECRLYbt68ab/v0aNHuh9By503b15Lt/42/tZPnz7V/y9cuKAtvosUKWK7evWq3vf48WPVccMBAtpudwb7Cc4l2bNnt3Xv3t3ZH4cQYhEYMJMPjjmIQaESrOOqVKliW79+fbCgecSIETY/P79gr2GFgPDfcPr0aQ2Y8bN582aHx6wYFJrBpODKlSsO9xUuXNj21Vdf2b87AsUJEyaozZwxkbLihMoIlv/++2+1RTMKXy9dumTLlClTsKAZRX4oggzpGHJ13lS4F9L9CJq/++47nWhNnDgxDD4dIcTqUJJBwgxILlC8BHnFhQsXJE+ePFqQBC2qIc+AHAE/PXr00GYd7gwalECWAbDcDu2y1YHMAE03oOeGtKBixYr6u9EWffny5RI7dmzd1t/f397O2IpL7oZl4qJFi6RFixYqXapdu7ZKMgDaOaP9OfS72AbyDEg2oP9HUaiVMEuxYKEIeRbGB5IkEJK9pK+vr+5P0HJbbd8ghIQ9DJhJmIBCNBSwQWeKizku6p988okGQ2h7XaZMGd0Onf3WrVun3rJW9Vf+t0EzCiRR/PjTTz9Jrly5xIqYA6K//vpLvXKhP82bN6+2uEbQg/8xqXInjS4K/OAqg7GAI4gBJp4oBETQDEcVgIJPOGdYDXMwPGDAANXyIxhOlCiRdr40JpVveo5VJ1SEkDDG2Slu4h60a9fOVqtWLf3dWFaHPCNZsmS24sWL2/766y/7tsbjoXmnuhPocti5c2fLSTHWrVtnGzJkiP22odE1f+8ff/xRpQfYR2LHjm0rVqyYg5bZ6owfP1512sDf3191/fDhRrfLSZMm6f3nz5+3FShQQLXNVgYdLePFi6f7DbT9jRo1UhnXqVOnnP3RCCFugHVLykm4yRwCVPIjqwyQ+cHSMXyDR4wYoR3a0KVu165d9udZuYPfvwV2e3CIQAbWGE9XB3//3377TX8MJwN4bRu+ufj743u3bt1a/ZVbtWqlbhjIuBpWeu6wOBYtWjR1fsBxAjnGDz/8oA4qhQsXVtnSoUOH1FoP44KudVbl3r17sn37dpk5c6ZKuM6ePSsrV66UKVOmSKZMmXRMCCHkQ8KAmbxXggZ0xjI7mgpAaoELHgJhwzMXy6R169bVIAg6VeM5DJZDxiq2afj7Y3kdUhzobxEQGvsD9iHj72/sT5AjwC4MLZ/RrAM2e1bbR4wJwLNnz/QHwIMaUpT58+erfAlBMnTcaEyCCQUa2VipQcubwATrwIEDai8IXXLDhg11P/j888/1sTFjxqhHOSGEfCiscfUl4U6HunHjRr3IQ1d5/fp1Ld5CMR8ucPCKvXHjhupy0WwDGSME0sg2QrtK3AMUqaEQtGDBgtqMxAiazZl0+EqjCBD7CUDhI1YrrDJxCLqismLFCv2+OF4WLFigWWP4kuM4gh+3UfiJSQYCxVixYonVMK8cGL/Dix0FjqiFqFevnq64tGvXTh+7evWqbNu2TYsBCSHkQ2HttAQJU4wgBtX8uNiDqFGjavCzdOlSbdOLjBi6tGEZHsvvceLEkebNm2uGGUvLVqvuJ6GTJEkSdQDBvoGgGQESgmjsS5hoQYaBAq8ZM2bo9ufOndPleDhkWKmzH4JltEbHSkz9+vVVYoBCR0gu0M0PkwuAVZo//vhD5syZo9uj8M2qk24UNmJccI7A37pAgQLSpUsXza6jmY3RrOerr77SFQc09SGEkA8FA2byXpk1a5YGN7ioZ86cWTvXwdmgePHiqrNElhkXtpMnT+oyctWqVXUZHtllXBjd3UrO3YNmTKywP0CzDAcEBE3Hjh1TGzGsSqA9OFqnwyHCSmC1Zc+ePdrx0nB9gCtK586d9XcEzbDTW7VqlWZS4SJjxZbXRrAMtxzsC1hNQOdKjAvcYm7fvq1ZZui5ca7APvHgwQMdO0POY7XVB0JI+IC2cuS9Ao0llkh/+eUX+31XrlxRKzBkzRYuXOiwjIyCLgTUWGJGxix37txO+uTE2SD4QWC0e/dunVAhq4oMK4JlwxYM2l4UB1oFnH4RACMohCVcv3797FIDI2hGoIgsKjLvxhhgcmklzIEuCvl69+4tgwcP1qJH3L5//75MmDBBKleurOeWo0ePajEkPKkxNph8Gy3TCSHkQ8CzC3mv4KKF4hyz7ykKdapUqaK6Q7PuEr8jWEDQsGnTJsmZM6eTPz1xdqYZgRImXfHixdNGJQiWzYGQ1YJlSA7Sp08v3377rfqUI5uO4NBozoIiRwSS+B/fHQG10azFShjBMnza8feeOnWqSlMAnFKwEtWxY0fJnz+/SjKCgvMNg2VCyIeEZxjyTrxp6bNkyZKqs4QsA5XsRhU/5BnIFmFJHZ3JAHSJKOT5+OOPVetMCILmcePGaZEX9i8rZg1DskzERAFBH5xDkG2GJMU4duAQgjFAcaTV5Abm8wgm2tWqVdO/OTLrAJIMnBsgw0EBJNwwvvvuu2Cvw6YkhJAPjbWuRCTMLvjGRW7x4sVagJUwYUKt7EdXMsgrUNmPghxUtCM4+P777yV58uT2VrYGyCDihxADQ5+MYMqqwTJWVKDzhzc5jgtkjvGDxyHBAOagGc4ZVsQ4j1y4cEG7OkJ2ASkGJt2YKCBYhpQL5wjItXCuIYQQZ2CtdAUJE4zsGAq1UK0ODTK0hYbGEoV/uPjBVg7NSRBEQ2+IAAHPtUrzDfJhsVo2FWD/xyQTmVQEy1h1QTa1WLFiGiyjTfygQYPUaQb2i1iRsTo4L9SqVUvu3LmjLiHIsqPgr0OHDg7ZY29vb0tJcgghroW10jckTLJj+B/+uOjMB79lNFTYvHmzagtRsY4LPTSI165dk3379mmBElwycOGz4hI7IW8CBWuQWEB6ZBTAIpOMphtwvrh48aLMmzfPwfECWmYcR1iVadu2reWcY0KSpMBC8NKlSzrRxqoUQKYddpNYlcJ5A5OHkOQYhBASFtAlg/xrrSHcDDUa25kAAA/fSURBVBAwIzOGYiVj2RhFWrjYoTkJtIZBA2NzISAhVgeZYljlwTfakCIdOXJEvaVPnDihbjJFixbVgjZMMAFs47BaA7AqY+j9rQImB/CORt0CMM4psImDheDq1au1tgGFj9gWEwsUOaJxTZ48eXRbTroJIc7Aemue5INgXNjgo4x2vbioI0A2d9eCvzL8lBFIt2nTRrWHZhgsE3cBwd/OnTvVGQbBstG2Ge4fCBjR3hqrLgiWJ06caLdYRGAIv3JgtSY+P/zwg3prYxKADLNZcgNpF5qPwCUEwCUELhkohoT38uzZsy0t1SGEhH945iGhYtYbI+MDXTJ8YmHxhGVU6JeRKTMHzXDIQBEPA2TirsDlAwVq8B2fO3eu5MuXT+VJKGLDol7dunWlVKlSmlk2sqXTp0/XCShs5kBQ2YIrg7oG+CUjg45mRgia//e//9knEhUqVFBHDGPyACDlQtCMIkCcd2AvBxgwE0KcASUZ5K1AUxFYO2XNmlU9YQH0y7jQIWsEaQaq/YPCzlvE3TD2eWRM4UGO/zGxbN++vT6+f/9+KVOmjP4gIEycOLEWAqLdNWoBcuXKJVYCjUaQQYYeGxME6Lihzz5+/LgGzDifoA04iv66du2quu9ChQrZnw95xsyZMzXA3rZtm44XIYSENQyYyT+CwiRcxFHVj4IkZHwMcIGHRAMFf6huD2obR4i7gi6FKGJDthla5QULFtib9kB2AVcZrMTgPmRcYalmtU6XkGGgy2fhwoXVEQTnCdjlgcuXL6tsBb7baGKEQkcU9qEtNrLJ5poHPIbbVutwSAhxHRgwk1DdMIxl4S1btqgvaurUqWXEiBFSoEAB+/Z4rHTp0jJkyBC1miOEBGiSoWWGR/lHH32kqzPIJBtFsvfu3dMAEccY7jO3jLcCyAhDuvXXX39JlixZtP7h3Llz0rx5c802G/j4+KhDxtixY3VbFPlt375ds/OEEBJeYMBM3iihgBMG9JXwPoW11YYNG3T5tESJEtK5c2fNnpmzadmzZ2f1OnFbjAkm7BRxHEGvbLhcoItdjRo1ggXNVgUZYdjmoajRsImDMwhcLxA04zxiZJrNrF+/Xlew4MGM54VkQUcIIc6AATOxY744we8UmmVkwNBlC0ursHWClhkXOgTNXbp0sVs9GdDyibjzsYOGG8YqCzpdIpsKjT8mk9DrImiG5zLcZKweNKPoEdliTB4wNvh5U9D87Nkze1MSBNmQscAxhBBCwgusxiJ2jGAZjRVgh4WqdgTKCJo/+eQTdcWAfypcMKDBRGBw5swZh9dgsEzc9dhBdhQtrOFDDkcMaHdR3IYgEWByia52aIuNIjir5yoQLAOsWBnnFmTYYRWXIUMGPY+gmA8gWDYcedKkSaOOGQiiCSEkvMCA2c0JetFGUxJc+FGxjyVR3IZtXI8ePSRp0qRaeIOgefz48VrEY1hgEeLuxxCkFihq+/rrr7XpCLyDUbwGCzmAIBBFfSh0Qx2Au0oNDD0zOiAi27xixQp7YA3pCpqXoEU422ATQsITlGS4OSi2QSGfAbJhkFt4e3vL1q1b9WI/cuRI9V5Gpnny5MlasIMlUwNaxxF3wtjfjf8NOQYmmPAhR9c6ZFCrVaumNmp4DBIMFP8ZHe6sxLvqjNH1EM2PEDybPdvRqMRqTVsIIa4Poxw3Bl210qZNq0ujBsj6FCtWTLp3767BMirXESwD+KeuW7dOdu3apbeNuRaDZeIOGJIBIzhEYZv5dpIkSdSPPHPmzFKnTh1twoHH0PFyyZIlqv+Hxt9qwO0D/NvcS86cOdWmEsEyVq6M8WWwTAgJjzDScWOQWe7WrZtqLSHBALhwwc4J2kIUKxndtVDAA2cMBADlypXT+9x1SZm4H0Y2GZ7kQ4cOlZIlS6q8AscIOvkBFMHGjRtXA0cUzaJYFscTfIXRcAP6Zqtp/FGYB49pyEwMK8p3AUEzJ96EkPAMJRluDrJDaJiApiO4+DVo0ECr+7G8DB1mpkyZNAuNCz7uRzETAgHKMIi7YOzrkBAgcwwPcngmo0nPTz/9pE034PgAO7RFixZpgIwmPwULFtSJ5u7du2XNmjUONoxWAFpjTBhgnQeJFs4jRYoUoRUcIcSSMGB2U7A0jCDACHpRmQ7dMuQZLVq00Da106ZN00AZxX3p0qXT5WZkyGgdR9wtWIbPOLT9aG8Nza3Rce706dN6XCB4hGsMGnXgPhxH0OJisonJJzTNVgLnB0i1IEMpW7asrlChayFqHBg0E0KsCANmNwLuF7CDg27QzKeffqoXeSwz44KHrBm6+oV00TO3qyXEHTh79qzqbbt27ardLI1jwJg4wlMYTTrQ6hlaZdQBuAPLly/X5iwVKlSQzZs3a4trSFaMoJkQQqwE04RuApaNUamPgBmSCtjEASwxI1hGkxLYxkGD2aZNGw0EoLkMCoNl4m4ZZmSLIcGAywUwitRwjGBSCWtFeAuj/fXRo0cdAmYrZlqN74QmLAalSpXS+yHLQE3ElClTNGhG22t0PrSaHIUQ4n4wYHYTPD09VaeMRgroRobMEOQWyJ7hNvSYoGfPnroEjQ5lCBAqVark7I9OiNPAsYDsMbTI0PjjfxwjCJqNDnYgf/786u6A5j5mrBYsh/SdDNlK6dKl9TaCZrS2RjZ+0KBBOlbbt2930qclhJD3AyUZbgYu6MOGDdOMMgr+Dh8+LMmTJ3fQJaNgaf78+aplplaZkICGPnDH2LNnj9SqVcu+QmPIMzD5RJCIbHS+fPnE3YB1HlauwJYtW9S7HQ1J0N0QNpTGY4QQ4qrQ5sDNgOwCGubq1atrQdK8efP0fgTGuPiDmDFjqp2cUeBHiLuD4jYU9cH5AjpldOozS5TgjpE4cWItnnUHjDwLiiEBAmLjPui9T506JYUKFVKHEDzG8wghxNVhwOyG4MKOSn/4p/7+++8OF3+jeYABM8yEhB40wyVj1qxZMnr0aIkXL55YHUPDjDEoXry4ZtcB7kP776+++krPJcg001WHEGIVKMlw82VmyDPgrYyWvbjwE0LeTp6B7CqKaSFrQtBoNSlGSF7rRrAMh4xGjRrJmDFjpG3btg7boBsoCiAZLBNCrAQDZjcHF3+0wUYR4A8//GDJIiVCPsRxA2cMZFEXLlyoWl2rBsso2Hv27JlKtdC0BY916tRJOx2iYcubHEFoQUkIsRIMmIk2IUAjBlwgrWiDRciH4ObNmxo8QuJkJcznAEhQfv75Zw2Wz5w5o1IuFDxGjx6d5wlCiFtBDTNR3SWCZbNNFiEkdGC7aLVgGRjnAMi1Zs6cqXZ6x48f10AZsi3Y6iHDbsCcCyHEHaC4jNgJqlckhLgn6NiH2oaJEydqS3AU+KGD3+eff65NSRBUQ8qVIkUKTrIJIW4BA2ZCCCEOQKJVt25dqVixouzcuVM6duyoTUjggAF5Btpg379/X51B0LCFEEKsDlOKhBDipkBOEdRK0giYq1WrJjFixNBOoGhzbRT4oU14+fLl5fz58xI3blwnfGpCCAl7mGEmhBA3xHCxMCQV6O7p4+OjuuwaNWpoYAzbPDQhgVwL9nAIriHV6Nq1q5QpU+aN9nOEEGI16JJBCCFuRrdu3eTIkSPy559/atDcuXNn+eWXX+wFwOnTp9fbXl5e+n+zZs3UW9nX11eff/DgQQ2g6apDCHEXmBYghBA34vnz55I8eXK5ffu2NG/eXM6dO6dFfuvXr5c9e/aoVvnWrVtSvXp1uXv3rjRp0kTmzZsnWbNm1cyzESwjQ81gmRDiLjDDTAghbgZaWCNzPH36dG1ahEK+BQsWqGYZgfDKlSvVVi5KlCiqYYZWGYG2h4eHPp8d/Agh7gYDZkIIcSMMGcXjx49lzpw5Mm3aNM0oX7p0yb6NETSPGDFCGxsh84xgmhBC3BVKMgghxA0w3DAMGUW0aNFUbtG+fXu9Xa9ePc0iA+iaq1SponZyJUuW1Cw0IYS4M8wwE0KIxTE7WezevVuDZmSMs2XLpkEy2l9PmjRJMmXKpFlnQ3phfp7hqkEIIe4IM8yEEGJxjKAX7hhVq1aVWrVqSYECBfQ25Bgo/kOm+cyZM9KyZUt59uyZw/MAg2VCiDvDqg1CCLEoZts3ZJbhtbx48WK1j4OtHIJkBMzjx4+Xxo0b6/ZDhgzRgr+BAwc6++MTQki4gQEzIYRYFCNYnjBhgly9elUaNWqkmmSQPXt2SZAggdrH5ciRQ7p06SINGzbUxiXQLxNCCAmEkgxCCLEQDRo00EYjBujet27dOhk5cqRcvnzZbguHn3Llykn37t3VXg6ey9A1I4CG/AKaZUIIIQEwYCaEEIuAIr1KlSrJokWL1OECIGOMZiRwxIAcY8eOHeqhbGiS4bGMjn7Ro0d3eC1qlgkhJBBKMgghxCKgSK9p06Ya/LZo0UKzxBMnTpS8efNKjx495OHDh1KhQgVZvny55MqVS90w0B47YcKEdmcMQgghwWHATAghFirwQ2a4Tp06ehuOFwBBMzTLgwcP1tvly5eXFClSSOXKlVWKsW3bNn2uuUiQEEJIIAyYCSHExTH8ko3/ETTXrVtXHzMHzSjugzwjTpw4snDhQpVvTJkyRR83t74mhBDiCANmQghxYWAVt3btWunZs6ckT57c3sIaQXPt2rWDBc05c+aUr7/+Wvz9/aVt27aSKlUqlWxQs0wIIW+GATMhhLgo9+/fl759++r/e/fulUKFCqltHBqRAGSMP/nkE3vQjOwzLOby5Mkj/fv319sFCxaUPXv2aNBMCCEkZBgwE0KIi4Jscr169SR16tQa+G7YsEE6deqkGWdoltHJz9PTU+rXr6/6ZPgwY1t4LqMtNrLSUaNGtWelCSGEhEwEG86ihBBCXJJVq1ZpQLx161Z1vnjy5Il26vv22281kwxfZjQigX4ZAXWpUqXUVs4AbbCjRIni1O9ACCHhHQbMhBDi4nTo0EH/nzRpkv6P7HKmTJkkQ4YMcujQIW1cMnPmTLtUA01LzEEzIYSQ0OEZkxBCXJx8+fJpQOzn5ydly5bVZiSzZ8+W2LFja0vsLVu22F0zAINlQgj5dzDDTAghFgAFfyj8g+QCHf3ixYsXbBtmlgkh5N1ga2xCCHFhjJwHrOIgxRg9erQGyyHlQhgsE0LIu8GAmRBCXBijM9/HH38st2/flr/++svhfkIIIf8dBsyEEGIB0LSkV69eMmrUKDl+/LizPw4hhFgKrs8RQohFgH0cdMxZsmRx9kchhBBLwaI/QgixEDilQ47x8uVLtrsmhJD3BANmQgghhBBCQoEaZkIIIYQQQkKBATMhhBBCCCGhwICZEEIIIYSQUGDATAghhBBCSCgwYCaEEEIIISQUGDATQgghhBASCgyYCSGEEEIICQUGzIQQQgghhIQCA2ZCCCGEEEJCgQEzIYQQQggh8mb+DywYPKZu86R1AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "similarity_matrix = compute_similarity_matrix(embeddings)\n", "\n", "plt.figure(figsize=(8, 6))\n", "similarity_np = np.array(similarity_matrix)\n", "\n", "sim_df = pd.DataFrame(similarity_np, \n", " index=protein_names, \n", " columns=protein_names)\n", "\n", "sns.heatmap(sim_df, annot=True, cmap='viridis', \n", " fmt='.3f', square=True, cbar_kws={'label': 'Cosine Similarity'})\n", "plt.title('Protein Similarity Matrix (ESM-2 Embeddings)', fontsize=14)\n", "plt.xticks(rotation=45, ha='right')\n", "plt.yticks(rotation=0)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1532dfc5", "metadata": {}, "source": [ "The similarity matrix shows both expected and unexpected relationships in ESM-2’s learned representations. We see clear functional clustering for oxygen-binding proteins such as Hemoglobin Beta and Myoglobin, which have a similarity score of 0.986, and for structural proteins such as Collagen and Elastin, which have a similarity score of 0.852. Interestingly, Lysozyme C shows high similarity to both Myoglobin, with a score of 0.906, and Hemoglobin Beta, with a score of 0.921, even though it is an antimicrobial enzyme. This is because ESM-2 has likely learned that these three proteins share a fundamental blueprint of compact globular proteins, including similar size, folding patterns, and structural stability that go beyond their specific biological functions. We will use PCA and t-SNE to better visualize how these proteins cluster in the high dimensional embedding space." ] }, { "cell_type": "markdown", "id": "66794597", "metadata": {}, "source": [ "### PCA visualization\n", "\n", "PCA (Principal Component Analysis) reduces high-dimensional data to a lower-dimensional representation by finding the directions of maximum variance in the data. This allows us to visualize our high-dimensional protein embeddings in 2D space while preserving the most important patterns of similarity and difference between proteins." ] }, { "cell_type": "code", "execution_count": 9, "id": "67afd74e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA(n_components=2)\n", "pca_result = pca.fit_transform(np.array(embeddings))\n", "\n", "plt.figure(figsize=(12, 5))\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.scatter(pca_result[:, 0], pca_result[:, 1], s=100, alpha=0.7)\n", "for i, name in enumerate(protein_names):\n", " plt.annotate(name, (pca_result[i, 0], pca_result[i, 1]), \n", " xytext=(5, 5), textcoords='offset points', fontsize=9)\n", "plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.1%} variance)')\n", "plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.1%} variance)')\n", "plt.title('PCA of Protein Embeddings')\n", "plt.grid(True, alpha=0.3)" ] }, { "cell_type": "markdown", "id": "4dd24749", "metadata": {}, "source": [ "The PCA analysis reveals clear groupings among the proteins. Hemoglobin Beta and Myoglobin, both oxygen-binding proteins, cluster tightly in the bottom right, while Lysozyme C and Defensin Beta 4A, both antimicrobial proteins, group together in the middle. In contrast, Collagen and Elastin appear isolated and far apart. However, this apparent separation is misleading. Because PCA is a linear dimensionality reduction method that captures only the directions of maximum variance, the high similarity between Collagen and Elastin (0.852 in our matrix) may lie in dimensions that contribute little to the variance represented by the first two principal components." ] }, { "cell_type": "markdown", "id": "9099082f", "metadata": {}, "source": [ "### t-SNE visualization\n", "\n", "t-SNE (t-distributed Stochastic Neighbor Embedding) is a non-linear dimensionality reduction method designed to preserve local structure, ensuring that points close in the original high-dimensional space remain close in the low-dimensional representation. Unlike PCA, which captures global variance through a linear transformation, t-SNE can uncover intricate clustering patterns and subtle local relationships that linear methods may obscure." ] }, { "cell_type": "code", "execution_count": 10, "id": "4f24b218", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tsne = TSNE(n_components=2, random_state=42, perplexity=3)\n", "tsne_result = tsne.fit_transform(np.array(embeddings))\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(1, 2, 2)\n", "plt.scatter(tsne_result[:, 0], tsne_result[:, 1], s=100, alpha=0.7)\n", "for i, name in enumerate(protein_names):\n", " plt.annotate(name, (tsne_result[i, 0], tsne_result[i, 1]), \n", " xytext=(5, 5), textcoords='offset points', fontsize=9)\n", "plt.xlabel('t-SNE 1')\n", "plt.ylabel('t-SNE 2')\n", "plt.title('t-SNE of Protein Embeddings')\n", "plt.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f312fb11", "metadata": {}, "source": [ "The t-SNE visualization uncovers more nuanced clustering than PCA, revealing relationships that were previously obscured. Notably, the structural proteins Collagen and Elastin now cluster together in the upper right, reflecting their functional similarity that PCA failed to capture. The oxygen-binding proteins Hemoglobin Beta and Myoglobin remain grouped in the bottom left, consistent with both analyses. In contrast, the antimicrobial proteins separate, with Lysozyme C positioned centrally and Defensin Beta 4A isolated in the bottom right. This separation indicates that ESM-2 has likely learned to distinguish between fundamentally different antimicrobial strategies: Lysozyme, a large enzyme (148 amino acids) that enzymatically cleaves bacterial cell walls, and Defensin, a small peptide (64 amino acids) that disrupts bacterial membranes via direct interaction. Despite their shared antimicrobial role, ESM-2 appears to encode them as distinct in both architecture and mechanism." ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }