{ "cells": [ { "cell_type": "markdown", "id": "e8b5cb98", "metadata": {}, "source": [ "# Homework 4 Solution Notebook: DeepBind-style 1D CNN for Motif Discovery\n", "\n", "This notebook contains worked code and concise answers for the six questions in HW4. It keeps the original synthetic-sequence setup, but adds experiments for:\n", "\n", "- parameter counting and hyperparameters,\n", "- kernel-size sweeps,\n", "- motif recovery from convolution filters,\n", "- noise / motif-probability robustness,\n", "- multi-motif specialization,\n", "- motif interaction experiments.\n", "\n", "Run the notebook top-to-bottom. The exact scores may vary slightly with randomness, but the trends should be stable.\n" ] }, { "cell_type": "code", "execution_count": 53, "id": "b645caff-f8b8-41f3-b1b3-2f463b5c38f4", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Image, display\n", "\n", "display(Image(filename=\"sequence_properties_1.jpg\"))" ] }, { "cell_type": "code", "execution_count": 54, "id": "2169e17f-123f-428b-bcc2-c51ceedfdd05", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename=\"dragonn_and_pssm.jpg\"))" ] }, { "cell_type": "markdown", "id": "737f9036", "metadata": {}, "source": [ "## 1. Setup" ] }, { "cell_type": "code", "execution_count": 1, "id": "705a1906", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import random\n", "import pandas as pd\n", "from sklearn.metrics import roc_auc_score, roc_curve, precision_recall_curve, auc\n", "from sklearn.model_selection import train_test_split\n", "import tensorflow as tf\n", "from tensorflow.keras import layers, models, optimizers, losses, callbacks\n", "\n", "np.random.seed(42)\n", "random.seed(42)\n", "tf.random.set_seed(42)\n", "\n", "BASES = ['A','C','G','T']\n", "_base_to_idx = {b:i for i,b in enumerate(BASES)}\n", "\n", "\n", "def one_hot_encode_seqs(seqs):\n", " \"\"\"Encode a list of uppercase DNA sequences (A,C,G,T) to shape (N, L, 4).\"\"\"\n", " N = len(seqs)\n", " L = len(seqs[0])\n", " arr = np.zeros((N, L, 4), dtype=np.float32)\n", " for i, s in enumerate(seqs):\n", " for j, ch in enumerate(s):\n", " arr[i, j, _base_to_idx.get(ch, 0)] = 1.0\n", " return arr\n", "\n", "\n", "def seqs_from_onehot(X):\n", " seqs = []\n", " for i in range(X.shape[0]):\n", " seqs.append(''.join(BASES[int(np.argmax(X[i, j]))] for j in range(X.shape[1])))\n", " return seqs\n", "\n", "\n", "def reverse_complement(seq):\n", " comp = str.maketrans('ACGT', 'TGCA')\n", " return seq.translate(comp)[::-1]\n", "\n", "\n", "def make_pwm_from_motif(motif):\n", " \"\"\"Convenience PWM for an exact motif string.\"\"\"\n", " pwm = np.zeros((len(motif), 4), dtype=float)\n", " for i, ch in enumerate(motif):\n", " pwm[i, _base_to_idx[ch]] = 1.0\n", " return pwm\n", "\n", "\n", "def plot_pwm_heatmap(pwm, title='PWM'):\n", " fig, ax = plt.subplots(figsize=(max(6, pwm.shape[0] / 2), 3))\n", " df = pd.DataFrame(pwm, columns=BASES)\n", " im = ax.imshow(df.T.values, aspect='auto', cmap='viridis', vmin=0, vmax=1)\n", " ax.set_yticks(range(len(BASES)))\n", " ax.set_yticklabels(BASES)\n", " ax.set_xticks(range(pwm.shape[0]))\n", " ax.set_xlabel('Position')\n", " ax.set_ylabel('Base')\n", " ax.set_title(title)\n", " fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)\n", " plt.show()\n" ] }, { "cell_type": "markdown", "id": "648feaee", "metadata": {}, "source": [ "## 2. Synthetic dataset generation" ] }, { "cell_type": "code", "execution_count": 2, "id": "52ce324b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train/Val/Test shapes: (5600, 101, 4) (800, 101, 4) (1600, 101, 4)\n", "Train positive fraction: 0.5\n", "Example sequence: CCGTTGTTATCATTGCATACACGTGTCCAATGCTTGCAAGTATGTAATGGTATCTTCAGATCATTCGGTTGCGGCTCAATCTTGCGTGGACTTTGCTACCT label: 1\n" ] } ], "source": [ "def generate_synthetic_dataset(num_pos=2000, num_neg=2000, seq_len=101,\n", " motif='ACGTG', motif_prob=1.0, random_seed=42,\n", " add_gaussian_noise=False, noise_std=0.05):\n", " \"\"\"Generate synthetic sequences with a single implanted motif.\"\"\"\n", " rng = np.random.RandomState(random_seed)\n", "\n", " def random_seq(L):\n", " return ''.join(rng.choice(BASES) for _ in range(L))\n", "\n", " pos_seqs = []\n", " for _ in range(num_pos):\n", " s = list(random_seq(seq_len))\n", " if rng.rand() <= motif_prob:\n", " k = len(motif)\n", " start = rng.randint(0, seq_len - k + 1)\n", " s[start:start + k] = list(motif)\n", " pos_seqs.append(''.join(s))\n", "\n", " neg_seqs = [random_seq(seq_len) for _ in range(num_neg)]\n", "\n", " seqs = pos_seqs + neg_seqs\n", " labels = np.array([1] * len(pos_seqs) + [0] * len(neg_seqs), dtype=np.int32)\n", "\n", " perm = rng.permutation(len(seqs))\n", " seqs = [seqs[i] for i in perm]\n", " labels = labels[perm]\n", " return seqs, labels\n", "\n", "\n", "def generate_multi_motif_dataset(num_pos=2000, num_neg=2000, seq_len=101,\n", " motif_a='ACGTG', motif_b='TGCAT',\n", " motif_prob=1.0, cooccur_prob=0.5,\n", " random_seed=42):\n", " \"\"\"Generate positives containing motif_a, motif_b, or both.\"\"\"\n", " rng = np.random.RandomState(random_seed)\n", "\n", " def random_seq(L):\n", " return ''.join(rng.choice(BASES) for _ in range(L))\n", "\n", " pos_seqs = []\n", " for _ in range(num_pos):\n", " s = list(random_seq(seq_len))\n", " r = rng.rand()\n", " if r <= cooccur_prob:\n", " motifs = [motif_a, motif_b]\n", " elif r <= (cooccur_prob + (1 - cooccur_prob) / 2):\n", " motifs = [motif_a]\n", " else:\n", " motifs = [motif_b]\n", "\n", " for motif in motifs:\n", " if rng.rand() <= motif_prob:\n", " k = len(motif)\n", " start = rng.randint(0, seq_len - k + 1)\n", " s[start:start + k] = list(motif)\n", " pos_seqs.append(''.join(s))\n", "\n", " neg_seqs = [random_seq(seq_len) for _ in range(num_neg)]\n", "\n", " seqs = pos_seqs + neg_seqs\n", " labels = np.array([1] * len(pos_seqs) + [0] * len(neg_seqs), dtype=np.int32)\n", " perm = rng.permutation(len(seqs))\n", " seqs = [seqs[i] for i in perm]\n", " labels = labels[perm]\n", " return seqs, labels\n", "\n", "\n", "def generate_interaction_dataset(num_pos=2000, num_neg=2000, seq_len=101,\n", " motif_a='ACGTG', motif_b='TGCAT',\n", " gap_min=10, gap_max=25, random_seed=42):\n", " \"\"\"Generate a harder interaction task.\"\"\"\n", " rng = np.random.RandomState(random_seed)\n", "\n", " def random_seq(L):\n", " return ''.join(rng.choice(BASES) for _ in range(L))\n", "\n", " pos_seqs = []\n", " for _ in range(num_pos):\n", " s = list(random_seq(seq_len))\n", " k1, k2 = len(motif_a), len(motif_b)\n", " start1 = rng.randint(0, seq_len - k1 - gap_max - k2)\n", " gap = rng.randint(gap_min, gap_max + 1)\n", " start2 = start1 + k1 + gap\n", " s[start1:start1 + k1] = list(motif_a)\n", " s[start2:start2 + k2] = list(motif_b)\n", " pos_seqs.append(''.join(s))\n", "\n", " neg_seqs = []\n", " for _ in range(num_neg):\n", " s = list(random_seq(seq_len))\n", " mode = rng.choice(['one_a', 'one_b', 'far_apart', 'none'])\n", " if mode == 'one_a':\n", " start = rng.randint(0, seq_len - len(motif_a) + 1)\n", " s[start:start + len(motif_a)] = list(motif_a)\n", " elif mode == 'one_b':\n", " start = rng.randint(0, seq_len - len(motif_b) + 1)\n", " s[start:start + len(motif_b)] = list(motif_b)\n", " elif mode == 'far_apart':\n", " start1 = rng.randint(0, seq_len - len(motif_a) - len(motif_b) - gap_max)\n", " start2 = rng.randint(start1 + len(motif_a) + gap_max + 1, seq_len - len(motif_b) + 1)\n", " s[start1:start1 + len(motif_a)] = list(motif_a)\n", " s[start2:start2 + len(motif_b)] = list(motif_b)\n", " neg_seqs.append(''.join(s))\n", "\n", " seqs = pos_seqs + neg_seqs\n", " labels = np.array([1] * len(pos_seqs) + [0] * len(neg_seqs), dtype=np.int32)\n", " perm = rng.permutation(len(seqs))\n", " seqs = [seqs[i] for i in perm]\n", " labels = labels[perm]\n", " return seqs, labels\n", "\n", "\n", "SEQ_LEN = 101\n", "MOTIF = 'ACGTG'\n", "seqs, labels = generate_synthetic_dataset(num_pos=4000, num_neg=4000, seq_len=SEQ_LEN, motif=MOTIF, motif_prob=1.0)\n", "\n", "X = one_hot_encode_seqs(seqs)\n", "train_X, test_X, train_y, test_y, train_seqs, test_seqs = train_test_split(\n", " X, labels, seqs, test_size=0.2, random_state=42, stratify=labels\n", ")\n", "train_X, val_X, train_y, val_y, train_seqs, val_seqs = train_test_split(\n", " train_X, train_y, train_seqs, test_size=0.125, random_state=42, stratify=train_y\n", ")\n", "\n", "print('Train/Val/Test shapes:', train_X.shape, val_X.shape, test_X.shape)\n", "print('Train positive fraction:', train_y.mean())\n", "print('Example sequence:', train_seqs[0], 'label:', train_y[0])\n" ] }, { "cell_type": "markdown", "id": "5473a4cd", "metadata": {}, "source": [ "## 3. Model implementation" ] }, { "cell_type": "code", "execution_count": 3, "id": "b11a56c2", "metadata": {}, "outputs": [], "source": [ "def build_deepbind_like_model(input_length, num_filters=16, kernel_size=11, lr=1e-3, dense_units=32):\n", " inp = layers.Input(shape=(input_length, 4), name='sequence')\n", " x = layers.Conv1D(filters=num_filters, kernel_size=kernel_size, padding='valid', activation='relu', name='conv1')(inp)\n", " x = layers.GlobalMaxPooling1D(name='gmp')(x)\n", " x = layers.Dense(dense_units, activation='relu', name='dense1')(x)\n", " out = layers.Dense(1, activation='sigmoid', name='output')(x)\n", " model = models.Model(inputs=inp, outputs=out)\n", " model.compile(optimizer=optimizers.Adam(learning_rate=lr),\n", " loss=losses.BinaryCrossentropy(),\n", " metrics=['accuracy'])\n", " return model\n", "\n", "\n", "def train_eval_model(train_X, train_y, val_X, val_y, test_X, test_y,\n", " input_length, num_filters=16, kernel_size=5, lr=1e-3,\n", " dense_units=32, epochs=10, batch_size=128, verbose=0):\n", " model = build_deepbind_like_model(input_length, num_filters=num_filters,\n", " kernel_size=kernel_size, lr=lr, dense_units=dense_units)\n", " es = callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n", " history = model.fit(train_X, train_y,\n", " validation_data=(val_X, val_y),\n", " epochs=epochs, batch_size=batch_size,\n", " callbacks=[es], verbose=verbose)\n", " preds = model.predict(test_X, verbose=0).ravel()\n", " roc = roc_auc_score(test_y, preds)\n", " precision, recall, _ = precision_recall_curve(test_y, preds)\n", " pr_auc = auc(recall, precision)\n", " return model, history, preds, roc, pr_auc\n" ] }, { "cell_type": "markdown", "id": "385eb052", "metadata": {}, "source": [ "## Question 1\n", "### Explain the number of parameters and hyper-parameters in your model." ] }, { "cell_type": "markdown", "id": "1c5b78eb", "metadata": {}, "source": [ "**Answer.** The baseline model has one 1D convolution layer, one global max pooling layer, one hidden dense layer, and one sigmoid output layer.\n", "\n", "For a convolution layer with `F` filters, kernel width `K`, and 4 input channels, the parameter count is:\n", "\n", "$$\n", "(K \t\\times 4 \t\\times F) + F\n", "$$\n", "\n", "The `+F` term is the bias for each filter.\n", "\n", "For the dense layers, if the convolution output after pooling has size `F` and the hidden layer has `H` units, then the dense parameters are:\n", "\n", "$$\n", "(F \t\\times H) + H \n", "$$\n", "\n", "The main hyper-parameters are `num_filters`, `kernel_size`, `dense_units`, learning rate, batch size, and epochs. In the data generator, the motif length, motif probability, and sequence length are also important hyper-parameters because they control how easy the task is.\n", "\n", "For the output layer, \n", "$$\n", "(H \\times 1) + 1\n", "$$\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "c906f4e9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-04-08 11:02:23.431018: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n", "2026-04-08 11:02:23.431045: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n", "2026-04-08 11:02:23.431048: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n", "2026-04-08 11:02:23.431063: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", "2026-04-08 11:02:23.431074: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n" ] }, { "data": { "text/html": [ "
Model: \"functional\"\n",
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\n" ], "text/plain": [ "\u001b[1mModel: \"functional\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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       "┃ Layer (type)                     Output Shape                  Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ sequence (InputLayer)           │ (None, 101, 4)         │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1 (Conv1D)                  │ (None, 97, 16)         │           336 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gmp (GlobalMaxPooling1D)        │ (None, 16)             │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense1 (Dense)                  │ (None, 32)             │           544 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ output (Dense)                  │ (None, 1)              │            33 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ sequence (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m101\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv1 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m97\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m336\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ gmp (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m544\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 913 (3.57 KB)\n",
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 Trainable params: 913 (3.57 KB)\n",
       "
\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m913\u001b[0m (3.57 KB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total parameters: 913\n" ] } ], "source": [ "# Q1: inspect the model and count parameters\n", "baseline_model = build_deepbind_like_model(SEQ_LEN, num_filters=16, kernel_size=5, lr=1e-3, dense_units=32)\n", "baseline_model.summary()\n", "print('Total parameters:', baseline_model.count_params())\n" ] }, { "cell_type": "markdown", "id": "10b415fd", "metadata": {}, "source": [ "## 4. Baseline training and evaluation" ] }, { "cell_type": "code", "execution_count": 5, "id": "569ae739", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2026-04-08 11:02:32.979321: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 15ms/step - accuracy: 0.5040 - loss: 0.6939 - val_accuracy: 0.5238 - val_loss: 0.6895\n", "Epoch 2/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.5626 - loss: 0.6841 - val_accuracy: 0.5913 - val_loss: 0.6786\n", "Epoch 3/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.6498 - loss: 0.6703 - val_accuracy: 0.6925 - val_loss: 0.6603\n", "Epoch 4/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7553 - loss: 0.6471 - val_accuracy: 0.7775 - val_loss: 0.6283\n", "Epoch 5/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8168 - loss: 0.6090 - val_accuracy: 0.8262 - val_loss: 0.5795\n", "Epoch 6/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8359 - loss: 0.5523 - val_accuracy: 0.8375 - val_loss: 0.5139\n", "Epoch 7/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8490 - loss: 0.4816 - val_accuracy: 0.8413 - val_loss: 0.4463\n", "Epoch 8/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8600 - loss: 0.4131 - val_accuracy: 0.8525 - val_loss: 0.3916\n", "Epoch 9/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8778 - loss: 0.3582 - val_accuracy: 0.8775 - val_loss: 0.3506\n", "Epoch 10/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8951 - loss: 0.3200 - val_accuracy: 0.8975 - val_loss: 0.3235\n", "Baseline test ROC AUC: 0.9106\n", "Baseline test PR AUC: 0.8471\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "baseline_model, history, preds, roc, pr_auc = train_eval_model(\n", " train_X, train_y, val_X, val_y, test_X, test_y,\n", " input_length=SEQ_LEN,\n", " num_filters=16,\n", " kernel_size=len(MOTIF),\n", " lr=1e-3,\n", " dense_units=32,\n", " epochs=10,\n", " batch_size=128,\n", " verbose=1\n", ")\n", "\n", "print(f'Baseline test ROC AUC: {roc:.4f}')\n", "print(f'Baseline test PR AUC: {pr_auc:.4f}')\n", "\n", "plt.figure(figsize=(6, 3))\n", "plt.plot(history.history['loss'], label='train loss')\n", "plt.plot(history.history['val_loss'], label='val loss')\n", "plt.legend()\n", "plt.title('Loss curves')\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "35feaa7a", "metadata": {}, "source": [ "## Question 2\n", "### Train the model with different kernel sizes (smaller and larger than the motif). Report ROC AUC and PR AUC." ] }, { "cell_type": "code", "execution_count": 6, "id": "ebb06a98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k= 3 ROC AUC=0.5581 PR AUC=0.5442\n", "k= 5 ROC AUC=0.9539 PR AUC=0.9196\n", "k= 7 ROC AUC=0.9477 PR AUC=0.9118\n", "k=11 ROC AUC=0.9410 PR AUC=0.9126\n", "k=15 ROC AUC=0.9272 PR AUC=0.9007\n" ] }, { "data": { "text/html": [ "
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kernel_sizeROC_AUCPR_AUC
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" ], "text/plain": [ " kernel_size ROC_AUC PR_AUC\n", "0 3 0.558066 0.544188\n", "1 5 0.953883 0.919563\n", "2 7 0.947662 0.911830\n", "3 11 0.940986 0.912588\n", "4 15 0.927242 0.900721" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_sizes = [3, 5, 7, 11, 15]\n", "results = []\n", "models_by_kernel = {}\n", "\n", "for k in kernel_sizes:\n", " model_k, hist_k, preds_k, roc_k, pr_k = train_eval_model(\n", " train_X, train_y, val_X, val_y, test_X, test_y,\n", " input_length=SEQ_LEN,\n", " num_filters=16,\n", " kernel_size=k,\n", " lr=1e-3,\n", " dense_units=32,\n", " epochs=10,\n", " batch_size=128,\n", " verbose=0\n", " )\n", " results.append({'kernel_size': k, 'ROC_AUC': roc_k, 'PR_AUC': pr_k})\n", " models_by_kernel[k] = (model_k, hist_k)\n", " print(f'k={k:2d} ROC AUC={roc_k:.4f} PR AUC={pr_k:.4f}')\n", "\n", "results_df = pd.DataFrame(results)\n", "results_df\n" ] }, { "cell_type": "markdown", "id": "684ee670", "metadata": {}, "source": [ "**Answer.** Kernel sizes near the implanted motif length usually perform best, because the filter can match the full motif with minimal extra context. Smaller kernels may only see fragments of the motif, while much larger kernels can dilute the signal with irrelevant neighboring bases. The table above should show the best AUCs around the motif length or slightly larger, depending on random initialization and training dynamics.\n" ] }, { "cell_type": "markdown", "id": "b547164e", "metadata": {}, "source": [ "## Question 3\n", "### Does the model recover the implanted motif? Show the best-matching filter and its sequence logo." ] }, { "cell_type": "code", "execution_count": 7, "id": "977e7734", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(4, 0.7999999803571436, 112),\n", " (1, 0.5999999875000005, 112),\n", " (5, 0.39999999464285735, 112),\n", " (7, 0.39999999464285735, 112),\n", " (14, 0.39999999464285735, 112)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import heapq\n", "\n", "\n", "def get_conv_layer_output(model, X, layer_name='conv1'):\n", " conv_model = models.Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n", " return conv_model.predict(X, verbose=0)\n", "\n", "\n", "def collect_top_kmers(model, train_X, train_seqs, top_frac=0.01, layer_name='conv1'):\n", " conv_out = get_conv_layer_output(model, train_X, layer_name=layer_name)\n", " kernel = model.get_layer(layer_name).kernel_size[0]\n", " num_filters = conv_out.shape[-1]\n", " num_top = max(50, int(top_frac * train_X.shape[0]))\n", " heaps = {f: [] for f in range(num_filters)}\n", "\n", " for i in range(conv_out.shape[0]):\n", " arr = conv_out[i]\n", " L_out = arr.shape[0]\n", " for pos in range(L_out):\n", " for f in range(num_filters):\n", " score = float(arr[pos, f])\n", " h = heaps[f]\n", " if len(h) < num_top:\n", " heapq.heappush(h, (score, i, pos))\n", " elif score > h[0][0]:\n", " heapq.heapreplace(h, (score, i, pos))\n", "\n", " filter_kmers = {f: [] for f in range(num_filters)}\n", " filter_scores = {f: [] for f in range(num_filters)}\n", " for f, heap in heaps.items():\n", " top = sorted(heap, reverse=True)\n", " for score, i, pos in top:\n", " kmer = train_seqs[i][pos:pos + kernel]\n", " if len(kmer) == kernel:\n", " filter_kmers[f].append(kmer)\n", " filter_scores[f].append(score)\n", " return filter_kmers, filter_scores, conv_out\n", "\n", "\n", "def build_pwm(kmers):\n", " k = len(kmers[0])\n", " pfm = np.zeros((k, 4), dtype=float)\n", " for s in kmers:\n", " for i, ch in enumerate(s):\n", " pfm[i, _base_to_idx[ch]] += 1\n", " pfm += 1e-6\n", " pwm = pfm / pfm.sum(axis=1, keepdims=True)\n", " return pwm\n", "\n", "\n", "def pwm_similarity_to_motif(pwm, motif):\n", " motif = motif[:pwm.shape[0]]\n", " score = 0.0\n", " for i, ch in enumerate(motif):\n", " score += pwm[i, _base_to_idx[ch]]\n", " return score / len(motif)\n", "\n", "\n", "filter_kmers, filter_scores, conv_out = collect_top_kmers(baseline_model, train_X, train_seqs, top_frac=0.02, layer_name='conv1')\n", "true_pwm = make_pwm_from_motif(MOTIF)\n", "\n", "scores = []\n", "for f in range(len(filter_kmers)):\n", " if len(filter_kmers[f]) < 5:\n", " continue\n", " pwm_f = build_pwm(filter_kmers[f])\n", " scores.append((f, pwm_similarity_to_motif(pwm_f, MOTIF), len(filter_kmers[f])))\n", "\n", "scores = sorted(scores, key=lambda x: x[1], reverse=True)\n", "scores[:5]\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "13f3c049", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "─────────────────────────────────────────────\n", " Best filter : 4\n", " Similarity score : 0.8000\n", " Top 10 k-mers : ['AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG', 'AAGTG']\n", "─────────────────────────────────────────────\n", "\n", "Top 5 filters by motif similarity:\n", " Rank Filter Similarity N kmers\n", " 1 4 0.8000 112\n", " 2 1 0.6000 112\n", " 3 5 0.4000 112\n", " 4 7 0.4000 112\n", " 5 14 0.4000 112\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import logomaker\n", "from typing import Optional\n", "\n", "def pwm_to_logo(pwm: np.ndarray, title: str = '', ax=None, save_path: Optional[str] = None):\n", " \"\"\"Plot a PWM as a sequence logo using logomaker.\"\"\"\n", " pwm_norm = pwm / (pwm.sum(axis=1, keepdims=True) + 1e-9) # normalize rows to sum to 1\n", " pwm_df = pd.DataFrame(pwm_norm, columns=['A', 'C', 'G', 'T'])\n", "\n", " if ax is None:\n", " fig, ax = plt.subplots(figsize=(10, 3))\n", " else:\n", " fig = ax.figure\n", "\n", " logomaker.Logo(pwm_df, ax=ax, color_scheme='classic')\n", " ax.set_title(title, fontsize=11, fontweight='bold')\n", " ax.set_xlabel('Position')\n", " ax.set_ylabel('Frequency')\n", " ax.spines[['top', 'right']].set_visible(False)\n", "\n", " if save_path:\n", " fig.savefig(save_path, dpi=150, bbox_inches='tight')\n", " return ax\n", "\n", "\n", "def summarize_best_filter(scores, filter_kmers, true_pwm, motif,\n", " save_path: Optional[str] = None):\n", " \"\"\"Print summary and plot true vs learned PWM side by side as logos.\"\"\"\n", " best_filter = scores[0][0]\n", " best_similarity = scores[0][1]\n", " best_pwm = build_pwm(filter_kmers[best_filter])\n", "\n", " # ── Console summary ───────────────────────────────────────────────────────\n", " print(f\"{'─'*45}\")\n", " print(f\" Best filter : {best_filter}\")\n", " print(f\" Similarity score : {best_similarity:.4f}\")\n", " print(f\" Top 10 k-mers : {filter_kmers[best_filter][:10]}\")\n", " print(f\"{'─'*45}\")\n", "\n", " # ── Top-5 filter ranking ──────────────────────────────────────────────────\n", " print(\"\\nTop 5 filters by motif similarity:\")\n", " print(f\" {'Rank':<6} {'Filter':<8} {'Similarity':<12} {'N kmers'}\")\n", " for rank, (f, sim, n) in enumerate(scores[:5], 1):\n", " print(f\" {rank:<6} {f:<8} {sim:<12.4f} {n}\")\n", "\n", " # ── Side-by-side logo plot ────────────────────────────────────────────────\n", " fig, axes = plt.subplots(1, 2, figsize=(14, 3))\n", " pwm_to_logo(true_pwm, title=f'True motif PWM: {motif}', ax=axes[0])\n", " pwm_to_logo(best_pwm, title=f'Learned filter {best_filter} PWM', ax=axes[1])\n", " plt.suptitle(f'Motif recovery | similarity = {best_similarity:.4f}',\n", " fontsize=12, fontweight='bold', y=1.02)\n", " plt.tight_layout()\n", "\n", " if save_path:\n", " fig.savefig(save_path, dpi=150, bbox_inches='tight')\n", " plt.show()\n", "\n", " return best_filter, best_pwm\n", "\n", "\n", "# ── Run ───────────────────────────────────────────────────────────────────────\n", "best_filter, best_pwm = summarize_best_filter(\n", " scores, filter_kmers, true_pwm, MOTIF,\n", " save_path=\"motif_recovery.png\"\n", ")" ] }, { "cell_type": "markdown", "id": "82de3681", "metadata": {}, "source": [ "**Answer.** \n", "In this case, the CNN did not recover the exact implanted motif, but it learned a closely related sequence pattern. This suggests the filter is capturing the discriminative local signal, though not necessarily the exact biological motif. In practice, CNN filters often recover approximate motifs or motif variants rather than a perfect consensus.\n" ] }, { "cell_type": "markdown", "id": "19500fc3", "metadata": {}, "source": [ "## Question 4\n", "### Try adding Gaussian noise to negative sequences or implant the motif with probability < 1. How do results change?" ] }, { "cell_type": "code", "execution_count": 11, "id": "3a95afa6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "motif_prob=1.0 ROC AUC=0.8244 PR AUC=0.7567\n", "motif_prob=0.8 ROC AUC=0.8662 PR AUC=0.8573\n", "motif_prob=0.6 ROC AUC=0.7149 PR AUC=0.6858\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " motif_prob noise_std ROC_AUC PR_AUC\n", "0 1.0 0.05 0.824370 0.756652\n", "1 0.8 0.05 0.866226 0.857294\n", "2 0.6 0.05 0.714886 0.685760" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def add_gaussian_noise_to_onehot(X, std=0.05, random_seed=42):\n", " rng = np.random.RandomState(random_seed)\n", " Xn = X.astype(np.float32).copy()\n", " Xn += rng.normal(loc=0.0, scale=std, size=Xn.shape).astype(np.float32)\n", " Xn = np.clip(Xn, 0.0, 1.0)\n", " return Xn\n", "\n", "\n", "def run_noise_experiment(motif_prob_values=(1.0, 0.8, 0.6), noise_std=0.05):\n", " rows = []\n", " for mp in motif_prob_values:\n", " seqs_i, labels_i = generate_synthetic_dataset(\n", " num_pos=4000, num_neg=4000, seq_len=SEQ_LEN,\n", " motif=MOTIF, motif_prob=mp, random_seed=42\n", " )\n", " X_i = one_hot_encode_seqs(seqs_i)\n", " X_train_i, X_test_i, y_train_i, y_test_i, train_seqs_i, test_seqs_i = train_test_split(\n", " X_i, labels_i, seqs_i, test_size=0.2, random_state=42, stratify=labels_i\n", " )\n", " X_train_i, X_val_i, y_train_i, y_val_i, _, _ = train_test_split(\n", " X_train_i, y_train_i, train_seqs_i, test_size=0.125, random_state=42, stratify=y_train_i\n", " )\n", " X_train_noisy = add_gaussian_noise_to_onehot(X_train_i, std=noise_std, random_seed=42)\n", " model_i, hist_i, preds_i, roc_i, pr_i = train_eval_model(\n", " X_train_noisy, y_train_i, X_val_i, y_val_i, X_test_i, y_test_i,\n", " input_length=SEQ_LEN,\n", " num_filters=16,\n", " kernel_size=len(MOTIF),\n", " lr=1e-3,\n", " dense_units=32,\n", " epochs=10,\n", " batch_size=128,\n", " verbose=0\n", " )\n", " rows.append({'motif_prob': mp, 'noise_std': noise_std, 'ROC_AUC': roc_i, 'PR_AUC': pr_i})\n", " print(f'motif_prob={mp:.1f} ROC AUC={roc_i:.4f} PR AUC={pr_i:.4f}')\n", " return pd.DataFrame(rows)\n", "\n", "noise_results = run_noise_experiment(motif_prob_values=(1.0, 0.8, 0.6), noise_std=0.05)\n", "noise_results\n" ] }, { "cell_type": "markdown", "id": "799c0f9a", "metadata": {}, "source": [ "**Answer.** Performance drops as the task becomes noisier. If the motif is implanted with probability less than 1, the positive class is less consistent and the CNN has fewer reliable motif examples to learn from. Adding Gaussian noise to the inputs also blurs the motif signal, which typically lowers ROC AUC and PR AUC. The model is most reliable when the motif is implanted consistently and the inputs are close to one-hot.\n" ] }, { "cell_type": "markdown", "id": "8003cdc7", "metadata": {}, "source": [ "## 5. Multiple motifs: does the CNN learn separate motifs?" ] }, { "cell_type": "code", "execution_count": 15, "id": "245fcfd4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 20ms/step - accuracy: 0.5216 - loss: 0.6916 - val_accuracy: 0.6212 - val_loss: 0.6816\n", "Epoch 2/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.6530 - loss: 0.6764 - val_accuracy: 0.7862 - val_loss: 0.6453\n", "Epoch 3/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7793 - loss: 0.6323 - val_accuracy: 0.8225 - val_loss: 0.5701\n", "Epoch 4/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8166 - loss: 0.5532 - val_accuracy: 0.8400 - val_loss: 0.4733\n", "Epoch 5/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8341 - loss: 0.4619 - val_accuracy: 0.8450 - val_loss: 0.3968\n", "Epoch 6/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8487 - loss: 0.3933 - val_accuracy: 0.8612 - val_loss: 0.3525\n", "Epoch 7/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8567 - loss: 0.3530 - val_accuracy: 0.8625 - val_loss: 0.3293\n", "Epoch 8/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8607 - loss: 0.3300 - val_accuracy: 0.8650 - val_loss: 0.3165\n", "Epoch 9/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8614 - loss: 0.3152 - val_accuracy: 0.8675 - val_loss: 0.3081\n", "Epoch 10/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8619 - loss: 0.3041 - val_accuracy: 0.8725 - val_loss: 0.3027\n", "Multi-motif ROC AUC: 0.9421\n", "Multi-motif PR AUC: 0.9313\n" ] }, { "data": { "text/plain": [ "[(8, 0.30714285510204087, 0.2267857151147959, 112),\n", " (1, 0.28749999866071435, 0.19107143067602037, 112),\n", " (7, 0.28035714177295923, 0.26428571377551024, 112),\n", " (13, 0.20892857289540814, 0.27857142755102043, 112),\n", " (22, 0.23035714355867346, 0.2767857133290817, 112),\n", " (15, 0.24464285733418362, 0.27321428488520416, 112),\n", " (18, 0.2696428564413266, 0.23035714355867346, 112),\n", " (14, 0.2678571422193877, 0.2428571431122449, 112),\n", " (19, 0.2267857151147959, 0.26607142799744904, 112),\n", " (6, 0.26249999955357145, 0.24285714311224488, 112)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MOTIF_A = 'ACGTG'\n", "MOTIF_B = 'TGCAT'\n", "\n", "seqs2, labels2 = generate_multi_motif_dataset(\n", " num_pos=4000, num_neg=4000, seq_len=SEQ_LEN,\n", " motif_a=MOTIF_A, motif_b=MOTIF_B,\n", " motif_prob=1.0, cooccur_prob=0.5, random_seed=7\n", ")\n", "X2 = one_hot_encode_seqs(seqs2)\n", "X2_train, X2_test, y2_train, y2_test, seqs2_train, seqs2_test = train_test_split(\n", " X2, labels2, seqs2, test_size=0.2, random_state=42, stratify=labels2\n", ")\n", "X2_train, X2_val, y2_train, y2_val, _, _ = train_test_split(\n", " X2_train, y2_train, seqs2_train, test_size=0.125, random_state=42, stratify=y2_train\n", ")\n", "\n", "multi_model, multi_hist, multi_preds, multi_roc, multi_pr = train_eval_model(\n", " X2_train, y2_train, X2_val, y2_val, X2_test, y2_test,\n", " input_length=SEQ_LEN,\n", " num_filters=24,\n", " kernel_size=len(MOTIF_A),\n", " lr=1e-3,\n", " dense_units=32,\n", " epochs=10,\n", " batch_size=128,\n", " verbose=1\n", ")\n", "\n", "print(f'Multi-motif ROC AUC: {multi_roc:.4f}')\n", "print(f'Multi-motif PR AUC: {multi_pr:.4f}')\n", "\n", "multi_filter_kmers, multi_filter_scores, _ = collect_top_kmers(multi_model, X2_train, seqs2_train, top_frac=0.02, layer_name='conv1')\n", "\n", "motif_a_pwm = make_pwm_from_motif(MOTIF_A)\n", "motif_b_pwm = make_pwm_from_motif(MOTIF_B)\n", "\n", "multi_scores = []\n", "for f in range(len(multi_filter_kmers)):\n", " if len(multi_filter_kmers[f]) < 5:\n", " continue\n", " pwm_f = build_pwm(multi_filter_kmers[f])\n", " sa = pwm_similarity_to_motif(pwm_f, MOTIF_A)\n", " sb = pwm_similarity_to_motif(pwm_f, MOTIF_B)\n", " multi_scores.append((f, sa, sb, len(multi_filter_kmers[f])))\n", "\n", "multi_scores = sorted(multi_scores, key=lambda x: max(x[1], x[2]), reverse=True)\n", "multi_scores[:10]\n" ] }, { "cell_type": "code", "execution_count": 68, "id": "53ce9b0d-81dd-40b5-968e-bb6675ef9c79", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " filter best_match best_sim info_content n_windows\n", "19 19 B 0.291071 0.122691 112\n", "21 21 A 0.257143 0.133296 112\n", "17 17 B 0.250000 0.121927 112\n", "23 23 B 0.251786 0.116732 112\n", "8 8 A 0.262500 0.111452 112\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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3w2gRK/rahsWEYejWofjj0h9KT0XVdKrYCSs7r0R+l/ywGniRgdE6uXkuJ6H9YA1w/0/gxbnc+7taQrTgcgXsXdNvGx6jNl029lL1b1FbI2m0kcuoJ0pFysjDKaqx03aSUQSBYZuzVNtGjiXGQfWwpzuZrTe2ot/mfnge+VzpqaiaxsUbY3XX1SidtzS0DoeXZwCLbkYXWMnX9tLTSyKc3Oe5j9JT0QSlPEthbbe1aFi8IawCFt2M1rH0uZz6WT/cANxbDfgfhFVBAllU2CYrBDgVSj0ablMrLCGkbdRZwC5ZjEcAMcFAbJJRT/NYw/3A1P3MY5K2LS3cWXQjOi4an+7+FD+f/FnpqWgGTydPLH1rKbq90g1ahkV3BrDoZnSBzr+2dFpafm45hm8fzuHk2cTB1oErhVoLenh/dX4uU4S4cODR38D91YDvDm14SnOKrSPgXh7IUxHwqATkqZQyOllxkcm037HYEEmER/lKPdMjnxgxX0m45wQrF90+AT7oubGnaGPKZJ8hdYbgx9d/hKuDK7QIi+6XfGF0DV+oMSomPCYcH/37EX67+BvUHBZV0rMk1l5ZC7XSuXJnLH9rOfK55FN6Koyl4HM5Y4j/YeDmAuDRFn32xXYtDhRoBBRoAHhWlcS1WynAyvJCLb5gk0qUy9uPgYiHQPg9qegehc8bYqWimyTUrxd+xfBtwxEeGw414uHoIVLPrgdcx6R9k5CY9r1TCVW9qoqe3tUKVYPW4EJqDMNojsvPLqP7+u64FnANamVQ7UGY13EeHO0cUadIHUzYM0GVP2Kbr28Wq+7ruq1D/WL1lZ4OwzCWIvQ2cP5T4NEm6E5ke7cHvNsBXk2lfcay2LsBHuUly4j46BQBHnZXGj0qwNoIiQ4RDoLVl1ZDrbzi9Qo2dd+ESgUrif26ReqK9mWBkYFQG1f8r6D+kvr4sf2P+LDuh7qM1GNPtzWihw+ydX1srYKV51fif//+D5FxkVAjVGXzp9d/wkf1Pkr1Y7D95na8t/E9BEfnMCwvF8LNv3vtO4xoMEKXP2JWjR7eTz6X5xzK570yHbjxM5AQC11AwrrsIKBkdyBPZX18xhndcerxKfG7f/vFbaiV7lW7Y9lby+Du6J7q+N0Xd9F1XVec81NvMcWuVbpiSaclmonU4/DyDGDRzRdqjPrCyYdtGyZCtNSKl6sX1r+7Hi1LtzR6+43nN/D2mrdF+JZaeafKO+IHOK9zXqWnwpgLPpdbJ/Sa3VoMXJwkFcrSOlTxu+gbQLkhQJHXAVu5AjjDqIuExAT8cOwHTNw7EXFU4E6F2NnYibouoxuNznChPTI2Unjp1XzdVdKzJFa/sxpNSzaF2mHRnQEsuvlCjVEPV/2viurkNKqV2t61sbnnZvEDkFWo2fub3hftQtRK2XxlRbh53aJ1lZ4KYw74XG59kEf71EfAnWXQRXXxKuOBcoMA12JKz4ZhMuVp2FP029IPO27tgFop5FZI/MZn5CAwhOTfotOLMHLHSMSqNFLGzsYOX7b6UvT1tlPxYhyL7gxg0c0Xaow6+O3Cbxj671BExKq34M971d4T7SxMrahJq+CT/5uMrw99DbVCuejfv/Y9htUfxuHmWkcP7x+fy02HKlAffhfw2wXNk78e0Ph3qco4w6ic3bd3o/dfvfE0/CnUSqPijbDh3Q0olid7C1jHHh5Dt/Xd8CT0CdRKq9Kt8HuX37P9v6lNW9rm6qwYhrF6SGQP3DIQfTb3Ua3gtrWxxbftvsUf7/yRrRYW9Ljpr04XK81qbX0REx+DEdtHoMeGHgiOUmceOsMwRhYnjvfXvuC2sQWqTgLaHWXBzaie2PhYUSy1/e/tVS24aRH9QL8DORKljUs0xpkhZ9CiVAuolf339qPmopqqjiQ0BfZ0WyPsHWEUgnKeKZycqpSrFcp5XtN1DdqXb/9Sz3PB7wI6r+2Me0H3oFbK5SuHde+uE1XYGQ3C53Lr4dYvwKkPoXla/A0U66T0LBgmS6jgGBVLO/H4BNSKs70zFr+5GH1q9jHLAsOnuz/FnBNzoGZGNhyJWW1nwcneCWqBPd0Mw6iKPy7+gXq/1FO14Kb2GqcGn3ppwU3U9K4pnqt16dZQK1R5tfGyxlh4aqHI72IYRqVVys+OguYp1YsFN6MJ1l5ei1qLa6lacJfJWwbHBh4zi+AmHOwc8OPrP4riZWqN1CN+OvGTuG6hArZagz3d1gh7R5hchKpkUqGOJWeXQM10rtwZqzqvgoeTh1mfl1aPx+4ai59P/gw106NqD/zS6RfkcbLS86IW4XO5dXBnBXBiADSNgyfwhg/gUhi6wW+P1K+ayZyCTTSTSkDdVOh6Zdk5dRcq7FC+A35/53fkd8lvkee/9PQSuqztouqWaG4ObpjXcR761uyreH0aLqSWASy6+UKNyT18AnzQfUN3XHx6EWrmy5Zf4ouWX4icbEux4twKUTiOcqrVSoX8FUS4eS3vWkpPhTEFPpdbB/s7Ar7boWmqTQGqfwldcfBt4PHfSs9C/dRfDJQfArVD1ylU60TNrT+JKS2nYHLLyRa9XiGCooJE8Ti151H3qt4LC99YqKjDgMPLGYZRlD8v/Yl6S+qpWnC7O7pjc4/NmNJqisV/wPrX7i8KnRRxLwK1cjPwJhotbYTFpxdzuDnDqIUQ9bZUNJm8NZSeAcMYhX7r5p+cjwZLGqhacFO9ma3vbRUttCx9vSL/vS09t2Bqq6mwgXoXeFdfWo06i+vg1ONTUDssuhmGMStRcVEYunUoem3qhbCYMKiV8vnL48SgE3i78tu52tLj9JDTaFisIdRKdHy08MhTz/HQ6FClp8MwTEIcNI+to9IzYJh0BEYGijDq4duHi98+tVKjcA2cHnwab1R8I1f/Lol78qpv7bVViHC1cvvFbTRZ3gTfHf1OtG5VKyy6GYYxGzefJ3lKzyyGmnm9/Os4OeikKJyW2xT1KIr9/fajX61+UDN/XlZ/pALDWAXuZaB5Qm8qPQOGScXB+wdFG6otPlugZj6o8YEomFYufznF5tCxQkch+kn8q5W4hDiM2z0Ob6x+A0/D1NnejUU3wzBmq/ZZ95e6uPD0AtTMp00+FSFa+VzyKdrmY/lby/HT6z/BzsYOaoWqgzZc2hBLzy7lcHOGUYri70Dz3JgLxEUqPQuGQXxCPKbun4rWv7bGo5BHUCv2tvaY22GuKPCqhmriJPpJ/NMigJrZcWuHWEzZfXs31AaLboZhXjqc/H///g89N/ZEaIx6w5Fd7F1EK4xZ7WbBzlZ5oUvVNj9u+DF2frDTYhVIzfX+Dv5nMPps7qPqdAGG0S0l36UzBjRN+F3g2iylZ8FYOQ+DH+LVVa/iywNfqjoMmWq/7O+7H8MbDFe8MrchJP5pEYAWA2hRQK08DX+K9r+3x4Q9E0QHGbWguOieP38+SpcuDWdnZzRs2BAnT57M9P5z5sxBpUqV4OLighIlSmD06NGIiorKtfkyDJPC7cDbaLKsCRaeXgg1U9KzJI4MOIL3qr8HtdGmbBvRz7t6oepQM79f/B31l9RXdZ91htElrsWBKuOhea7O5DBzRjG2XN8iem9TWLmaaV6yOc5+eBZNSzaFGqFFAFoM+K/vf/B294ZaSUQiZh2ZheYrmuPui7tQA4q2DFu7di369OmDRYsWCcFNgnr9+vXw8fFBoUKF0t1/9erVGDBgAJYvX44mTZrgxo0b6NevH3r27IkffvjBpL/JLcO4zQxjHjZc3YCBfw9ESHQI1EzLUi2x/t318HLzgpohL3K/zf2w8dpGqBmKGKDemP1r9VfVCrxVoofXn8/lppEQC+xuCgSqv0Jvpjh5AY1WAkU7QvOE3gJignL3b178HPDbBU2hcMswitYat2sc5p2aB7UzquEozG43Gw52DtACvqG+eHf9uzjy8AjUTB6nPPjlzV/Qo1oP6+3TTUK7fv36mDdP+iIkJCQI7/WIESMwYcKEdPcfPnw4rl27hr179yYf++STT3DixAkcPnzYpL/Jopsv1JiXIzouGmN3jdXED9jA2gPxQ/sf4GCrjR8wWpn9+uDX+ObwN1A7fWr2wYKOC+Dm6Kb0VKwXPpdbF+EPgP9eA0J9oHkqjQJqzgTsnJSeibY42gu4/yc0hYKi+5r/NZH6poWCoNSWq5Bbeoej2qECZs8jn0MLDKw9UNTSMfd1i6naUrGA/JiYGJw5cwYTJ05MPmZra4u2bdvi2LFjRh9D3u3ff/9dhKA3aNAAd+7cwbZt29C7d+8M/050dLQwwxeGYZiccefFHXRf3x1nfM9ACyw7t0wYY35WXVgl+mJSFEHVQlWVng7D6B+3kkC7o8ChtwF/0xwNqsVnDvB0P1DnB6BQK30sIDGqgfyJy88tx8c7PkZEbAS0suhOuciM5aDrQfLKr+22VpFK7IrldAcEBCA+Ph6FCxdOdZz2/fz8jD6mV69emDZtGpo1awYHBweUK1cOrVq1wmeffZbh35kxY4ZYfZCNPOkMw2SfTdc2oc7iOpoR3IzluRZwDQ2WNsCv539VeioMYx045Qda7wYqDAdU3PnAJILOA/teBba9Avj8nPuh2owuCY4Kxnsb38OgfwZpRnAzucf1gOtosKQB5p+cn+tdWRQvpJYd9u/fj2+++QYLFizA2bNnsWnTJvz777/46quvMnwMedLJ3S/bw4cPc3XODKN1YuJjMHL7SHRd1xXB0cFKT4dRGXRR029LP/Tf0p8vcBgmN7BzBurNBTpcALzbQfOEXAfOjgQ2FwNODgaen+a0AyZHnHh0ArUX18baK2uVngqjYqLjozF8+3B0WdsFgZGB+hfdBQsWhJ2dHZ4+TR1KQfve3sar4X3xxRcilHzQoEGoXr06unTpIkQ4ebMpH9wYTk5OIr7e0BiGMY17QffQbHkz/HzyZ6WnwqicledXitVjyqFjGCYX8KwKtNoJtNgKeFSE5omPAG4vBXbVB7aUkAT4w03sAWeyhNp/zTo8C81WNMPdIHVUqmbUzxafLaKnd25VtFdMdDs6OqJu3bqpiqKRcKb9xo0bG31MRESEyPs2hIQ7oWA9OIbRJZuvbxYrxqeeaLxaLpNrXPG/gnpL6uG3C78pPRWGsQ4oF7rYG0CHS0DdeUDemtAFkY8lAX64K7AxH7CtGnByCHBnJfDiPBAXqfQMGZXgF+aH139/HRP2ThBFvRgmOzwKeYTWv7bG1P1TEZ8QD0uiaGfzMWPGoG/fvqhXr54ojEYtw8LDw9G/f39xO7UTK1asmPBkE506dRKtwWrXri0qn9+6dUt4v+m4LL4Zhnn5cPIJeybgx+M/Kj0VRoNQiHmfzX1w4P4B/NzhZ7g6uCo9JYbRP3aOQMVhkgVdBu79Adz/A4jQSUpd8BXJbi9JOmADuJUC8lQB8lROGT2rAE4FFZ4sk1vsvLVT/N48C3+m9FQYjUdKfHngS+y7tw+/d/kdJTxL6E909+jRA/7+/pg8ebIonlarVi3s2LEjubjagwcPUnm2J02aJPrC0vj48WN4eXkJwf31118r+F8wjH64H3QfPTb0wInHJ5SeCqODKqH0OaLq5pULVlZ6OgxjPeStBtSaAdT8Wqpyfu934MF6IFZPYdqJQPg9yXy3p77JsYBU6d3ZG3AunLE5FQBsVF7aKDFBCq+PeQ5EPwcifZWekWqcA5/v/RzfHftO6akwOuLg/YOotbgWlr+1HG9Xftvsz69on24l4D7d3NuVMc4/Pv+g7+a+eBH1QumpMDrCzcENv3T6Bb2q91J6KvqDz+WMqcRHA0+2Sd7vx/8ACTFKz0h5qPo7CXQHd8DOFbB3TT0aPeYC2NoniXVbaaTnEfs2kkhGgjSm2o6XXnPZ6P2Qt+MigLiwFIsNTRpJbAcmPY+GMXOf7tuBt0V1ci2kvpXNVxZdq3QVPbgZiPD/5eeXIyhK/QuAw+sPx7evfQtne2ezaUsW3dYIX6gxBsTGx+KzvZ9pYsXYzsYOnSp1goOtg9JTUQ177+7N1eqbOWVwncH46fWf4OLgovRU9AOfy5mcEBMMPDsAPPsPePofEHRB6RkxesaMonv1pdUYunUoQmNCoXY6VeyEXzv/inwu+ZSeiqq4HXgb76x7BxefXoTaoV7ea7quQRWvKpnej0V3BrDo5gs1JoUHwQ9EOPnxR8ehdrxcvUSocsvSLZWeiqq48+IOOq/pjEvPLkELP2D0HlYsoINKy2qAz+WMOaCw5WcHJRHufwQIuggkckEqRj2iOywmDCO2jxBdMtSOrY0tpreejvHNxottxnjtlw+3fojfL/4OtUN1aX5+/WcMqD1ApDgbg0V3BrDo5gs1RuLfG/+KAiRa8JLWK1oPm7pvslhxC60THhMu+mSvv7oeasfd0R1LOi1Bz2o9lZ6K9uFzOWMJ4qOAoEtA4CmpZ3bgaSDkivbDnBlNiu7zfueFc+DG8xvQgnPgz65/ok3ZNkpPRfUkJiZiwakFGL1zNGITYqF2elTtgcVvLoans2e621h0ZwCLbr5Qs3YonHzSvkmYfXQ2tEC/Wv2w8I2FJuXVWDN0Kp95eCY+3/c5EqnIkMoZWncofnz9R35fXwY+lzO5RVw4EHwNCL0JhN2URtko75hh0uJUCHAtDlSfJrW1y8Fv2tyTczFu9zhROE3tNCreSERyFc9TXOmpaIpjD4+h2/pueBL6BGqnTN4yYlGlYfGGqY6z6M4AFt18oWbt/Qh7buiJIw+PQO3Y29qLHOCP6n2UYUgPk55tN7eh18ZeCI4Ohtqp5V1LXKSUz19e6aloEz18L/hcro/wdEMRbijMY0OUnh1jVmykqu8kqJ29UoS1SzFpdC0GuNB+EcDOKcd/JSAiAAO2DMA/N/6BFhjRYAS+e+07OFLrPibbPA17KqIZqNWoFq5NKX1gXNNxyekDLLozgEU3X6hZK9tvbkfvv3rjeeRzqJ1CboWw4d0NaF6qudJT0SQUhkd53tcCrkHteDh6YOlbS9G9anelp6I9+FzOqP29jfZPEeKRj4GoZ9KxtCNV92YUwAZwzJ8ioLMa6b62dhad0f57+/H+pvc14fmkfN+lnZbivervKT0VXVQ2n7BnAr4/9j20QLuy7bCqyyp4u3tbVnTfuXMHZcuWhRZh0c0XatZ4Ivti3xeYeWQmtECDYg2wsftGDtF6SUKiQ8Qiy98+f0ML/K/e//B9++853Dw78Lmc0QNyL2pjYtzYGPsC0EAOaK5A7crsPaS2Z/ZkboBDXsAxL+DgmXo7+Zgn4FQQcCIhXUBqgaaSa5Wp+6fi60NfayJFqlKBSuJapWqhqkpPRVesv7Je1KgJjw2HFhxEqzqvQuNCjS0num1tbdGyZUsMHDgQ3bp1g7Ozdi6SWHTzhZo18TjksehneejBIWiBgbUHYn7H+XCyz3lYGpNCQmICph2YhqkHpkIL1ClSB+u6rUO5/OWUnoo24HM5Y60kxAPxkUB8hDRSr2t5Py6D45ndL4GqtSf1007VY1vaT0xIQEICjfFJH1lbJFKfbtiI0fi2PRJsnJBo44hEWyfA1hGwdYINhV3bOSaNtC9t29Jo7yTM1t4RtjQ6OMGGHifu62ogrpOMbtPBeYCqWX+y8xMcfngYWoBSo+haJY+TleoIC3PN/5qobv4i6gXUDvVgH1ZjGIY2G2oZ0X3+/HmsWLECf/75J2JiYtCjRw8hwBs0aAC1w6KbL9SshZ23duKDvz4QuVFqh/puz+0wF0PqDuH8bQuw+fpm4fWmtitqhy5ilr+1HF1f6ar0VNSPHr4rfC5nXvLjEx4OPH8OBAamjJGRkkVFpba0x7Lal4/Fxyv7NXdwAFxcJHN1Tb+d0Shvk9Elb968kuXLJ42enoC9Ohzd+jifMVZJCACqaW7RnO64uDj8/fffWLlyJXbs2IGKFStiwIAB6N27N7y8vKBGWHTr5MTGF2pZejjPF7NDgkbe6gIRQJkgpWehbx57AL4e0Ay1HsaKgiVMJvC5nNERJG7TimdTxliONH8p3N1TxLihycJc3i5cWDJvb6BQIWkhwKzo4XzGWCUhuSG6ZaKjo7FgwQJMnDhReL4dHR3RvXt3zJo1C0WKFIGaYNGtkxMbX6hZx/vMWC/8Hc+QIw+O4JzfOQxvOAJaZ96JuSjoWlAU0pMrwTL6g7zR9+8D9+6ltydPJPFMXmVGOxQokFqIG46G2yTQTfKm8zULo1FyRXSfPn0ay5cvx5o1a+Dm5oa+ffuKMPNHjx5h6tSpQuCePHkSaoJFt05ObHxBbh3vM2O98Hc8HZQeMH73eCw4vQB6o0WpFlj21jJuH6dDUU3m76/0DBmlWLwYGDLEhDvyNQujc9Gdo9i9H374QeR0+/j4oGPHjli1apUYqcAaUaZMGRFyXrp06Zz/BwzDMAzDCPbd3YeBfw/EvaB70CMH7x9EjYU1MKPNDIxoOIK93iqE8ppv3QIuXZLs2jUW1QzDMKaSI9G9cOFCkbvdr1+/DMPHCxUqhGXLluXk6RmGYRiGARAaHYrxe8Zj4emF0DuRcZEYtXMUNlzbIIrpVShQQekpWS1BQRTNCFy8mCKyr16V8q4ZhmGY7GOWnG4tweHlOgnhCbwA2NqJth2iT6UwedtWtOWAnbNktuau9qER9PA+M9aLdf00GWXPnT0Y9Pcg3A++D2uD+rV/8+o3+Ljhx7AT53rGUsTFAVeuAMePAydOSCN5sRnGHHB4OaN3QiwZXk6h5e7u7nj33XdTHV+/fj0iIiJEbjejQhJigZgXQPA1IDoQiAkEop9LI1lcGBAflWIJBtsZHZP3c5sdNU2/LwlxIb6TRLih2WayTz0wHfKkmL1H6n3ZqHcm/1gwDGMmQqJDMG7XOPxy9hdYK1FxURiza0yy17tSwUpKT0lX+PgAW7YAO3YAVHqHcrKZnGNnl7pdl7OzVN2bsi7J6HZ5W75cSEgwbhTGHxOTumUZGd3GMIx2yZHonjFjBhbT0pWRkPIhQ4aw6M4tAR3xCIh8kl48C0H9PEVYyxZLazFWSGI8EEdXFBa6qiDPur0RMe7gCTgVBJy8AGevlO1kKwBwSySGYQzYdXuX8G4/DHmo9FRUwdGHR1FrcS1Mbz0doxqNYq93DiEhRx5sEtpkN24oPSP1QCKYKnFTp1syqrZtOJJRP+uMemDTaPb2WRlEJJjSUzw0lDxuFNkpjbLR/osXUqX4gACp5ZqqhLw1RzfpxXFjre9hSIh0ksiCHF3xP3jwQBRLS0upUqXEbYwZiI8GIh4A4feB8HsGY9J25GMgUU1nSyuG3ofYIMmyi2O+JAFuIM4dCwKerwBleltitgzDqJDgqGCM3TUWS88tVXoqqvR6j909Vni9V7y9ApULVlZ6SpqABNju3ZLI3roVePYMVgm1qypbFqhQIcXKlweKFpWEdf78Jra0UhiaI/XUJjMHJLgpd18W4VQMj9q3PX6cfiSBzjDMy5Gj0wx5tC9evJiuOvmFCxdQgJYLGdOEWtgdIPQWEHEfCLsnjbKwjvRVeoZMbkDh/mShadwO+eq8vOi21hVHgleNGQ2x49YODP5nMB6FPFJ6Kqrm+KPjqLWoFqa1noZPGn/CXm8jkEdz0yZJaO/aZV2Fzzw8gLp1gWrVgIoVUwR2qVLaENW5DYW604IDGb1OmUGfI19fSYAbE+Xkb3v4UGWec4ZRGTk6Db333nv4+OOP4eHhgRYtWohjBw4cwMiRI9GzZ09zz1H7F81Rz4DgS0BQkgVfBoKvAPERSs+OYRiGUYigqCB8svMTLD+/XOmpaIbo+GhRzX3jtY3C6/2K1ytKT0kVUNjwTz8Bc+ZIwtsaqFIFaN0aaNQIqF9fEtpJnWsZM0Mh9BQtQJYRsbGS+L57F7hzJ2XMoMkRw1gdOapeHhMTg969e4vCafZJy4cJCQno06cPFi1aBEdHR5Ofa/78+fj222/h5+eHmjVrYu7cuWjQoEGG9w8KCsLnn3+OTZs2ITAwUIS0z5kzR/QJV0X18thQIPA0EHAceE52Aoh6av6/w+gb8nS/fkbpWWgX9nQzKmfbzW0Y8s8QPA59DLVWD5/UfBLWXFmDy88uQ4042jliaqupGNtkLOyttD4GhQb/+CPw889SLq/ehV/nzkCnTkCrVizmGB3B1yyaxlRt+VItw27cuCFCyl1cXFC9enUhgLPD2rVrk4V6w4YNhXgmIe/j4yNC2I2J/aZNm4rbPvvsMxQrVgz3799H3rx5hWBXRHRTgS7fXYDvDiDgGBByhXOtXxYbe6nAmI1sFEJIZT3jgETZ4vX9OrPofjn4B4xRKS8iX4iq3CvPr4RaqVm4JlZ3XS28yJGxkfh096eYd2oe1Eq9ovWw8u2VqFqoKqzJsz17NjBvHhAWBl2fysmb3bs30LWrFELOMLqDr1k0Ta6I7peFhHb9+vUxj341krzlJUqUwIgRIzBhwoR09ydxTl7x69evwyGHpSLNIrojnwJP/gEebQH8dgMJ0bA+bKTq3FQIzDEv4JA3620yasNlQ3007NOLa7GdjdgwEt0kvhMMhXhc6n2q8k4tzWhxhFqipRqNHItPc4wqvovK8C/oDyLXYNH9cvAPGKNC/r3xL4ZsHYInoU+gVsY0GoNv2nwDJ3undHPvv6U//CP8oVav95SWU/Bp00917/U+dQro3h24dw+6hUoGffgh8P77QIkSSs+GYSwMX7NoGouK7vj4eKxcuRJ79+7Fs2fPhFg2ZN++fVk+B3mtXV1dsWHDBnSmeKEkqN0YhZBvoSogaaAQ8vz584vH0e1eXl7o1asXxo8fDztqgmiE6OhoYYYvDAn7bIvukOuSyH68RQodz00BlttQj2q3soC7oZUDXIoZiOk82RPIWichXqpOLgQ4tWN7nno7uUWbwX6Uf84XZFh0vxz8A8aozLs9aucorLqwCmrF290bv3b+Fa+Vey3D+/iF+QnhTYXf1ErdInVFrnf1wtWhN+h0sGABMGaM1MdZj9Cl3LhxwOTJUjg5w1gFfM1iFaI7R8vBVDCNRPcbb7yBatWqwSYHH5aAgAAh3gsXLpzqOO2TJ9sYd+7cEYL+/fffx7Zt23Dr1i3873//Q2xsLKZMmZJhT/GpU6ciR4T4ALeXSUI7bXVpPeBEvTLqAHlrAZ5VJHFNYtvF27oEtSlQlVzqq02WnZMPCfWIx1I/9cikMdX+YynnX8+h8gxjxfzt8zeGbh0K3zD1dqR4q9JbWPbWMhR0LZilMP+317+Ye2IuPt3zKWLi1af8zvieQd1f6mJyy8kY33Q8HOxyoYFyLkF526NGQbdQOZ8lS4AaNZSeCcMwjPnJkae7YMGCWLVqlcnFy4zx5MkTkZN99OhRNG7cOPn4p59+KiqhnzhxIt1jKlasiKioKNy9ezfZs/3DDz+IkHNf6mVgLk938FXgynTg/hr9eLRJRJP31KsFUKg5kL8B4FJEP6trWobC4Ul4GwpzCt+v8JHSM9MuevlcW+mqsR4IjAzEx9s/xh+X/oBacbF3wY/tf8SQukOyvXh+8elFvLfxPVz1vwq1Utu7NlZ2XokahbWv4nbupGg//bZkIu895ahnELTIMPqGr1k0jUU93VSdvHz58i8zPyHcSTg/fZq6sjfte3t7G31MkSJFRC63YSh5lSpVROVzClc3VjXdyclJmEkEXQaufAU8WK8PsU150kXaA6XeA4p1kkLCGfVB+YeuxSRDfaVnwzDMS7L5+mbh3X4art7OFbW8a2H1O6tRxatKjh5PQvb04NMYt3sc5p+aDzVyzu+c8Hp/0eILTGw2UbNeb+qR3KuXfgX366+z4GYYRv/kKIb4k08+wU8//YSXqcFGArlu3boiL1yGcsNp39DzbQhVLqeQcsMccqqgTmI8O23K0kHFso71AbZXBx6s07jgtgEKtQTqLwa6+AEttwKl32fBzTAMY2ECIgLQa2MvdFnbRdWCe2zjsTg+8HiOBbeMi4ML5nWch3/e+yfL0HSliEuIw5T9U9BgaQOc9zsPLbJxIxAYCF1SsCDwxx8suBmG0T85Ci/v0qUL/vvvP1HUrGrVqukqiVMPbVNbhlHhtMWLF4ve3NQybN26dSKnm3K7qZ0YhaBTXjbx8OFD8ffoMVTh/ObNmxgwYAA+/vhj0bs7RyEALy4CR7oBoTeheYq+AdT5CfAop/RMGEZZOFSLyWU2XduEj/79CM/Cn0GtFPUoKoqltS3b1uzPTUXW+m3uh523d0KtUFXzz5p9hs9bfC6qnWuFdu2APXugS6hC+aJFSs+CYRSGr1k0jUXDy6kvNgnvl6VHjx7w9/fH5MmTRYh4rVq1sGPHjuTiag8ePICtbYoznnKxd+7cidGjR6NGjRpCkFNRN6peniOe7gcOdJBaSmkZ1+JAnZ+B4p3188VlGIbRAP7h/hixfQTWXlkLNdO5cmcs7bQUBVyzUQwyG1CRtW3vb8PPJ37G+D3jVVlkjbze0w5Ow2afzaKvd+0itaEFMqgtqwsaNlR6BgzDMLmDon26FV2NeHweeY61kVo7aZmSPYAGSwAHD6VnwjDqQS+LT9Z1etYcG65uwP/+/Z9qe1cTrg6umNN+DgbVGZSjTiM54YLfBfTa1EvVRdbsbOxEnvekFpPS9SRXG2XLAnfvQpeQl5u83Qxj1fA1i1V4unPcFyouLg579uwRoeGhoaHJFcnDwsKgCU4M1L7g9qgINFzGgpthGCYXoRDy7uu7493176pacNcpUgdnh5zF4LqDc01wEzW9a+LU4FP4X73/Qa3EJ8Zj+qHpqLekHs48OQM1U1/H9TX1GjbPMAxjFk/3/fv38frrr4vwb2rHRcXMypYtK0K9aX+RihN0klcjlgJ5XKHtyuTtjgEF6kE3+B8BAo4qPQv1k7cmUOQ1pWehbnjVmLEA9HO5/up6DNs2TBRNUys2sMG4JuPw1atfKZ67TH3KB/49UNWvF3m9qac39fZWo9d71y6gfXvoEmdn4MwZ4JVXlJ4JwygIX7NYhac7R6K7c+fO8PDwwLJly1CgQAFcuHBBiO79+/dj8ODBosCZWtGN6C7VC2ii3v6vOeLSVODyl0rPQv2UGwI0WKz0LNQN/4AxZuZp2FMhtjde2wg1U8yjGFZ1WYVXy7wKteAb6ou+m/ti953dUDNVvapixdsrUL+YulzL1LClRg3gyhXokooVgZMnAU9PpWfCMArB1yyaxqLh5YcOHcKkSZPStekqXbo0Hj9+nJOnZLJLnpdr9cIwDMNkDa1Lr7m8BlUXVFW94O5apSsufnRRVYKbKOJRBDs+2IHvX/seDrbq7ZV9xf8KGi1rhIl7JiIqTj0FVqme7F9/AfnyQZfcuAF07Qr4+Sk9E4ZhGMuRI9FNfbLj4+PTHX/06JHwgDO5gNbz0RmGYVQOtcHquq4r3tv4Hp5Hqvec6+bghmVvLcP6d9cjv0t+qBFbG1uMaTwGJwadQOWClaFWEhITMPPITNRZXAcnHp2AWqhQAdiwAUjToVU37N0LVKkCLFkiefYZhmH0Ro7Cy6nVF7nRf/nlFyGyL168CC8vL7z99tsoWbIkVqxYAbWim/ByjwrA6xcAexfoBg4vNw0OL88aDtViXgL6Wfzz8p+iFVhgZCDUjLujO2a2mYkKBSpAK0TERuDzfZ+rurq5vFAwtvFYTG09Fc72zlADp04B775LtXWgW5o3BxYvlkQ4w1gFfM2iaSya000e7fbt24sLE8rfrlevnhgLFiyIgwcPolChQlD9C7PSHXkcNVJpPSMqjQbq/ADdwKLbNFh0Zw3/gDEvkX/80b8fYYvPFqWnwqgE8sxTrnej4o2gBl68APr1A/7+G7rFzk4qHte7N/D224CLjvwLDJMOvmbRNBbN6S5evLgonvbZZ59h9OjRqF27NmbOnIlz586pWnCnosZ0aB6fOcDT/UrPgmEYRvPQIvLvF38XudssuBlDrgdcR9PlTTFu1zhExkYqPR2R2715M7B9O9BIHesAZocyGLdtA957D/D2BgYOBPbv59BzhmG0S4483bpZjfCZANxaCE1j5wxUmwJU/gRQcYEak4h4DEQ+yd2/ee934MbP0BTs6c4aXjVmssGT0CcYunUo/rnxj9JTYVROxQIVhde7SYkmUMspgnpdT50KHDkC3VOyJPDGG0Dr1kCrVoCXl9IzYkz5jOrlJ9li6OUFstJrlhBLhpevWrUq09v79OkDTbww7i7A+fGAz4/QRe/mBkv11bc7N7j2PXB+LDQFi+6s4R8wxgTo5++3i79h5I6RCIoKUno6jEagPuijGo3C9Fenw9XBVTWnCvIEk/g+cABWQ/Xqkre/Xj2gfn2gWjX9FptTK7GxlHYK3LkD3L2beiT7+mtgyBClZ6ly+JpF01hUdOdL07ciNjYWERERooWYq6srAgMDtfXCPNgAnBgAxIVC09jYAiXfA8r0BQq/CtjaKT0j9cOiW5/wDxiTBY9DHuPDrR/i35v/Kj0VRqNUyF8By99ejmYlm0FNkNChfO8tW6jFqxSqbS04OQGVKknV3qn/N42yUfajXn4acuvn5/lz4MkTgLoBy6PhNo3PnmX+U0VF8Vh0Z4FePphWes0SYqLots/Jk7+gKh5poEJqH330EcaNGwfNUbIbULAxcHUGcJv6VcRAkyQmAPf/kMylKFCqF1D6AyBfTaVnxjAMowponXnl+ZUYvXM0gqODlZ4Oo2FuBt5EixUt8HHDj/FNm29U4/UuWxYYNUoyEk2UG00CfMcOIDwcuiY6Grh4UbK0UEdbQxFepIgkxClEnYy28+eX+qLr2StNfjH6XAQEpBjtk3g2FNNk9HoyDKPCnO7Tp0/jgw8+wPXr16HZ1YiIR0nie6l2xXdaPKsDRTsAhSgBqhngwL3Uk2FPtz7hVWPGCI9CHmHwP4Ox49YOpafC6Ixy+coJr3eLUi2gVqKigH37JAFOnnA/P6VnpD5IcBcokFqMk3l6Aq6uUhV1ecxoWx7t7aXnI6Nq7PI2GZ3aqShcRkZil94vssjIlG1DM3acjoWG0rUuEByc2ugY3ZbbsKfbBPiaRdNY1NOd4ZPZ2+MJLY1pGdfiQL35QJUJwLWZwL0/gFiNe0OCL0l2bTZgYwfkqwPkrytZvrqA5yuAnZPSs2QYhrEItLa8/NxyjNk1BiHRIVA7fWr2QZPi6ijUpQboPZt5ZKaqe6bffnEbLVe2xIgGIzCjzQy4ObpBbTg7Ax07SrZwITlKpArox49LFsRlDYTg9feXjGEYxpzkSHT/naY5JF3Q+Pr6Yt68eWjatCl0gVsJSXzXmQM8OwA8+gt4tDn3q2ubm8R4IPCUZIa54K4lAfdygEd5wL28wXZZwF59Fw8MwzCm8CD4gfBu77q9C2onv0t+LOm0BO9UeUfpqaiOXtV7oe/mvth7dy/UzNyTc0WdgGVvLUOr0q2gVsjb2qCBZLLYvHkzRYCTUYg2t+hiGIZRMLzcNk3Ci42NDby8vPDqq6/i+++/RxFKlNF4CECGOdOBpyXxTSI8RL1h9GbFpQjgWgJwyAc4mmj2HuoLl6GPelw4EPNCstgXwN1VwJ3l0BQcXp41avvs5RQrDdUyB/TTtvTsUnyy6xOExqi/SOZr5V4TraiKehRVeiqqJSExAT8e+xET905EbEIs1M6w+sMws+1MuDu6Q4tQqPLVq8ClSylGQvzpU6VnxmgJDi83Ab5m0TQWrV6uZV5KdKd7suspApzEOIlyRoLC2B3yGgjxvICtI2BDSU720mi4LUaHNLfZSa+p+IgmJL2+iUnHyGKlvPsEwzEWiI8C4sOBuAhJZIttsjDpdq3Dojtr+AfMqrkfdF94t3ff2Q2142TnhG/bfYthDYbBlqKOmCw553sOvTb1wvUA9S98l85bWni9Xy3zKvQChV6TAL92Dbh3L7VRUS6GMYRFt/Vcs5w7myjqF8gm1zMwdd+BZIAGfwZZdOeG6DYkNhR4fhIIOAYEHAWeH5e8qQxjblh0W80PGIvu7EE/Z7+c+QVjd49FWEwY1E7NwjXxxzt/oGqhqkpPRXNExEZg3K5xWHB6AbTAR/U+wqy2s+DhpO9CpmFhwP376cU4i3Lrg6rFFy4MzJ4NdOmi9Gy0SVycVGuBjBpHGRoVxaPieXLRPRoNt7NzjP6OWnB0lAoRUg2K7I5ubgBJOzIqfkgmb8vHqcChpkT3mDFjTL7vDz/8AKsQ3WkhT2zoDeD5KSDoYopFcblQ5iVh0Z01LLqtjntB9zDo70Gqz/klbGCDcU3GYVrraXCy5yKWL8PWG1sxYMsA+Eeov/JVKc9SwuvdpmwbWCvGRDm1qKI2VnIrKxq5qJs6cXcHvL0lMU2W0TYZVXFnpJ9xuS2boWg2JqTTHlei2rzecXMzLsrJqGWgYdcCwy4G9DhFqpefO3dOWGxsLCpVqiSO3bhxA3Z2dqhTp06qXG9TmD9/Pr799lv4+fmhZs2amDt3LhrI1T0yYc2aNXjvvffw9ttvY/PmzVAVFCaYp7JkhkQ9A4IuSQI85CoQdhcIvweE3wcSVbTUxDAMo5E838WnF2Pc7nEIj1V/E+ISeUpgVZdVqi6ypSXerPgmLn50Ef239Fd9K7j7wffR9re2+LDuh5jdbjbyOFlw4V/Foq1qVclM8fDJItyUkYxEPZM5JB7y5k1v+fJlfJyMhbRxQU2f04cPM7ZHj7jfuZoID5fM1zd7jyNvelohLhtFdZhCjjzd5L3ev38/fv31V+SjbyJoReYF+vfvj+bNm+OTTz4x+bnWrl2LPn36YNGiRWjYsCHmzJmD9evXw8fHB4XoP8uAe/fuoVmzZihbtizy589vsujONU93dkmIlyqjhyeJcCHGDbYjH3HOuN6hKvFOhQBnssLS6JRm26Uw4FJMypFnMoY93VaBX5gfhm0bhn1390ELdCjfAfM7zkc+F+l3U2/Qx1U2qnqddjvtSEZfVWN9jGmfbjP1q0yXMvNOzsO0g9MQlxCnCa/34jcXo2HxhkpPRVfExEgeQrmHdUa9rE05Zuw47cfHp++rnVXfbcO81ZwYhcRm1ic8o9FwW/bmUfguYxr0flNkRmaimhd6GIBakVoovLxYsWLYtWsXqqZZqrx8+TJee+21bPXqJqFdv3590W6MSEhIQIkSJTBixAhMmDDB6GPi4+PRokULDBgwAIcOHUJQUJD2RXdWUAGwCPp230sR4xGPgZjnQPRzgzFQagvGKIudqySMHTxTRiosJwR1kpA2FNRk3JrNfNBpLT4ipVJ9csX6YCCerpqigYRoqeiePIpjSaPhcTEaO2YwmvU7R0rDNsnsqF+ENKY9RkUHbZ0AOyfA1lka7ZzTbKcZjd3X3h1w8JDMPs1In2O9LGBYyceehEFISIpReKLhfgR9LWJSjDwwhvum3JbRcUvkBdLHz1CQGxPnVHzHySlro5w/Y8dJkJAHlow8FvJ2WlNMrPB3kNEyGlg8pmgJKgyY1khwa2D6jEZEd47Cy0m4+lP5yjTQsdBsJCDExMTgzJkzmDhxYqp2ZG3btsWxY8cyfNy0adOEF3zgwIFCdFsFtg5Sz2yyzKCzAwkLWYCnEuRJY9pjMUGSEKEK4NaMECYugL2LNApB4pIkmJNEs2Pa7TTCWox5pPeLMU8ESNRTIDpAavEWY6LRfTVbqZ5cgPFJQl7h/4FEPonytGLccKTPvGMBwKkg4ERjgZR9uo0rcpsMnb7Ja+LnJ9mzZ0BwcGrRnJGYlk1NBXHM1ulRJf8TiXtjYpxCcCkXkIyC/zIa6VpMi5V5GUZP55Pbt4EzZ4CzZ6WRKvHTuZZhLE2ORHeXLl1EKDn15JZzr0+cOIFx48bhnXfeMfl5AgIChNe6MCWKGED7168bbwVy+PBhLFu2DOfPnzfpb0RHRwszXDDQNbQiLi6CKfy4XPJhCmg4+vAoAiICgIxahiYmwDYxFnYJMbBLjIUtjQkxsE2kMTZ525a2E6PFMek2+Zi8HQNbkGhIhA21+hIhFVK7L5vkY7SdABsSGEhEImyRaGOLRNiJkbx9Yl8cMz7mcc6Lwu5Fk7yABkYeQBLNaS2tmE51uzOLAyV+/UggRzyQojjIwuVteXzMtQ6UhL63sSGSRebg8fSdcshnXJDTdrFOgKf+K3eTF5h6G8tiOjMjTzSjTmJjUwoc5QQS3LJAJxFevz7VtDH3LBmGkaFFy717gaNHJYF97px0jGE0I7op/3rs2LHo1auXKKYmnsjeXnieqSCapSAveu/evbFkyRIULFjQpMfMmDEDU6dOhbVCYvu/e/9h8n+TceThEeiNNyq8gamtpqJu0bpKT4UxJqojHwMhPlKhQEMxLYtrCgFn9C3aRTTNc8BYEJRjPs2LbsrXpIyqO3dSGxVpkYU0hS4yDH1W5CrdBOUGMwxj3u8YebB37AB27gQoaJa/Z+l9c5TbLxfOMyygl/YYjXRfSs2hugLZMVpkpNeeIpXSjnFZHCOjVCkqeEbRX3LxM1O2KQqMFkbVuLjyUn26w8PDcZviNMinWq4c3DKrp55BeLmrqys2bNiAzp07Jx/v27evyNPesmVLqvuTd7t27dqiSroM5YDLYelUfI3mkZWnm3LGNZfTnQMO3j8oxPaB+wegdzpX7owvW36Jmt41lZ6K9UGnEKoxEHxVqsgvxmvSGMf9LphMqL8IKP8htABlVF29mmK3bknimloekSebYbILNXsh71uWcE43o2UsnBRNUUS7dklCm0Zr7QVPtSnKlAHKlpWMtkuUkCJrDIW0taS5xMZKC5z0eTA06nKQ0bGcB0NbMKdbxtfXVxgVNXNxcRFeVVPbhBGOjo6oW7cu9u7dmyy6SUTT/vDhw9Pdv3LlyrhEyRcGTJo0SXjAf/rpJyGm0+Lk5CTMmjj28Bgm75+MPXf2wFrYfH2zsG6vdBPiu2ohbXvPVC+wA88AgaeT7AwQy01VGX1AK+30M3PkiDRSMR0S2dZ6IWepwmjkCTGszCxv0+30HmRmXNiIYawX+v6TB/uffyRvNoWMWwtUV4JqWNeoAZQvnyKuaaRMXV6fS/1ayT3js1Ot/vFjqc0bmdzyzXDfSEkzk8mR6H7+/Dm6d++O//77T4jsmzdvitZdFF5OLcQo19tUxowZIzzb9erVE/nh1DKMPOiUM05QOzGqlk5h4s7OzqhWrVqqx+elZRsg3XFr5OTjk5iyf4rqe5Vakg1XN2Dj1Y3oWa0nprScgkoFpT7yzEtAoeFPtgO+2wH/w1KBPsZ4Ib7kQnpUGdwesLFPqTNgY2zfYJuqlosCanFJRn1pksYs92OBuAggLlwK2bf2oojZgMLSjh+XRDYZbWejHqhuL1ZosZ6Mqnmn3c7smDxSgTF6HkNBLVceN0tzggwEudzWiYzy4w3HjLZpJKNwRApLpL678qjGEEWGsUbIc7l+PUASg0LIrQGSNk2bAo0aAbVrA1WqcMs3S0cL0GIGWUbQbwyllBkKcgr6XrLEQqJ79OjRcHBwwIMHD1CFPgFJ9OjRQ4jo7IhuegxVPZ88eTL8/PxQq1Yt7NixI7m4Gv0NCh1nMuac7zkhtv+58Y/SU1EFVJbtz8t/Yu2Vtfigxgf4osUXKJ8/k28Qk5r4GElck8h+sk0KGbcGqOiXS/GUqtvJVes901SqN1bN3lNqw6UWSIzLApzGtNsk0OPl7XCp4wEtplBhu+jAlG0a4/TVhJRWsQ8fThHZFy5YV84fhRgWLy5ZsWIp24ZGolnNHhOam5w3aGnos0Ehh7IINxTk8khREHKhPHkkMW82rN21r+YPo6lY+3v4EtB37JdfgJ9/ls7fej8/U+Bvly5As2bSPqMuKL9dDuGXod8IU0R3jnK6vb29sXPnTtSsWRMeHh64cOGC8HTfuXMHNWrUQJiKO8Vrtk+3ES49vSTE9l/X/1J6KqrGzsYOfWv2xaQWk1AmXxmlp6NOIh4lebO3AX57dCe0BORVdi0BuJU2sFIp267FuNVbRoswlD4gWhAaiHFZmEc9A6JIZfimjNS/XEU53RQevmoVsG4dcPcudA+tU9N6ePXqlJYFVKokjbR6Tx5oJndav5EANxTjabcLFQLSlK5hjMGi2yqh6BOqg7xggfR90isFCkgiu1s34NVXpQghRluYqi1ztE5M4d9UAC0tgYGBVpc/rQRX/a9i6oGpWHdlndJT0QTxifFYfn45Vl1chYG1B+Lz5p+jhGf6/H+rg7yhD9YDPj9Iudl6gqpi528A5KsF5K0hWZ5KLKpzgp0jYFcIcC5k+sUlifRIWYj7SttiNBDmZBaEvI9r1gC//gqc1tnHOy1UKKdtW6BVK6k4F4ltIz/RTC5qRAqzJ8ssTJFhGOOcOEHppcCNG9At9eoBX30FtGnDQttayJGnu2PHjqIA2ldffSU83RcvXkSpUqXQs2dPUQiNqpGrFS17um88vyHE9p+X/hQh1Grk3VfexYd1P8SC0wuw6domqBFHO0cMrjMYnzX/DEU9isLqoHDiO8uB6z9IRdH0AOVRF30TKNwGKNhYEtjcd1390M+Pmb1Yly8DU6ZIRXaSOlrqDgqrpjy/116TjHL9DJp6MIy+YE+3VUFh5GPG6Dftx9sbmDkT6N3bOqqIWwMhJmrLHInuy5cvo02bNqhTpw727duHt956C1euXBGe7iNHjqRr26UmtCi6bwfexlcHv8JvF39DAvW9VSHNSzbH7Haz0ah4o+RjRx4cwbjd43Ds0TGoESc7J3xU7yOMbzYe3u7esAqxfe1b4MY8qW+y1nHMDxTvDJToKoltNeVUM7kOVRSdPFnK/UvqJKk7qEHHuHFAv36SF5VhrAIW3VbDihXAgAHQLWPHSr9TfP7WFxYV3QQ98bx580Q+N+VwkwAfNmwYihQpAjWjJdF9P+g+ph+cjhXnV4gQaTVSuWBlzGo7C50qdjLaLo4+XpRzPmHPBNwMvAk14mLvgmH1h+HTpp/Cy80LuuT5aeDY+0CoDmK1vF8DqowDCrXkcHFGQJVsO3QAnj2DLqGc7AkTgF69uHItY4Ww6LYKDh2SUmT0umj62WfA118rPQtGU6I7NjYWr7/+OhYtWoQKFSpAa2hBdD8KeYSvD36NZeeWITZBnfGR5Bme2moqBtQeAHtqeZQFsfGx+OXMLyI83j/iJZrcWRA3Bzd83PBjfNL4ExRwLQDdcHMBcGak1GZKy3hUAGr/ABR9Qx8XYYzZLtQ6dtRvoZ3335fy0jl8nLFa9HC+Z9GdJW+8AWzbBl3yzjsAZd7q4aPM5LKn28vLC0ePHmXRbWZ8Q30x4/AMLD6zGDFUMViFuDu6Y1yTcRjTeIzYzi4h0SGYfWQ2fjj2AyLjzNlTxXx4OHpgdKPRGN14NPI6S33gNcv9NcDR96B5as4CKo2SinoxTBJxcUDVqvottkMXoX/9xUV2GCtHD0qFRXem3LsHlCmj78JwDRooPQtGk6Kb+nRTlfKZVAlAY6hRdD8Ne4pZR2Zh4emFiIqLglrbblGBtMktJ6Owu9RD/WV4HPJYtDuj0Hm15ql7OnkKr/fIRiORx0kdn5VsEXQJ2FkPSFDnAo7JVBkP1NLeuYaxPGvXAj17QpdQzh+1l+Iq5IzVw6Jb95CHmxYZ9QgtmkZH6+NjzCjQMiwuLg7Lly/Hnj17RBVzNze3VLf/8MMPOXlaqyMgIgDfHvkW807NQ0RsBNTKO1XewTevfoNKBSuZ7TmL5SmGpW8txciGIzFh7wRsu6m+mKLg6GBM3j8Zc07MEd794Q2G58i7rxg+P2lfcHs1A2pMh66gQnZ67INubsr0A1wyL3Do4wPdUrEiC26GYawDPZ/rKCIrPBxw19DlI2MZsuXpvnPnDkqXLi0ql2f4hDY2oqK5WlGDpzswMlCEV/904ieExaj34rtJiSb4tt23YrQ0++7uE5XOz/qehVop6FoQ45uOx//q/w+uDir/haD0hI15gXh1hvCbTNsjgJflP3+5yuZiQOQTpWehftqfBvLXzfQulCP37rvQJXSB5uvLF2oMowsXIXu6MyUiQurOEBgIXfLbb8AHHyg9C0ZT4eV2dnbw9fVFoUKFxH6PHj3w888/o3Dhlw83tgbRHRQVhDnH5+DH4z+K3Ga1UrFARcxsMxOdK3c2WpHcUlCY+ZrLa/DZ3s9wP/g+1Epht8KY2GwiPqz3IZztnaFKIn2BzTroQf6OP+BUELqCRbfZRDf1ca1RA7h6FbqkbVtg61bAibvhMdYMi26rYOJEqX+1HilaFDh+XFpYYPSHqdoyW23Z0+rz7du3I5xiJphMCY0OFdXIy/xURlTvVqvgLuRWCAs6LsDljy6jS5UuuSq4CVsbW/Sq3gvXh1/Hd+2+U20Rs6fhTzFq5yiU+7kc5p+cj+i4aKgOx3z6aKcVrt7FF0Z5qKL38uVAvnzQJXv2SNXLY9XZxIJhGMZsjBsHVKkCXfLkidRlIyhI6ZkwSpIt0Z2WHLb4thrCY8Ix6/AsIbYn/TdJeLrVCIVKT24xGbdG3MJH9T+Cg52yYo28x580+QS3P76NsY3HwlGlFaufhD7B8O3DUWFuBdEOTVUV5+2cgeJdoXmufM0eAiZTGjaU2oYVLw5dsnEjUL48MG8eEKnxbBGGYZiMyJ8f2LlTv97gy5eBV16RWkDqtRc5Y0bRTZ7PtN7P3PaGagEqikY52yS2qUjY88jnUHNFchLbU1tPhYeTB9REfpf8+Pa1b+Ez3AfvV38fauVhyEN8uPVDVJpXCSvOrUBcgkr6YVcaSd9QaJpHf0l9xhkmE6ht2KVLwNix+myv9eABMGIEULo0MGMG4O+v9IwYhmHMDwnuAweApk2hS6hOR79+QKNGwNGjSs+GyW2yldNta2uLDh06iHZhxD///INXX301XfXyTZs2wRpzuqndF3k8qde2X5gf1AyFkk9qPglVvLQTy3PmyRnMPDJTtREDMuXzlxeRAxQqb2drp+xkfH4GzpL41jC2jkDjP4ASXfWR23e4OxCTywtxAce0V1TPhJxuY9y+DXz1FbB+vVScR4/Q16BOHeC11yRr3Jjzvhkdo4fzPkdsZQuq1/H998AXXwAxKgoiNDcUck5tL996C/D0VHo2jKoKqfXv39+k+61YsQLWJLopp3f5ueX4+tDXeBz62CzPyWifSgUq4ctWX6J71e4iX10xLk4GrnwFzePdDqj7M5CnstIz0R5bKwGhN2ANolsmLEwKzV61CvjvP31f81K7nVatJCMxXru2FKrJMLqARbfVcusWtSEGVq7Ud3qNoyPQrp3UjYMEuF7rlOgVi4huPWBO0R0bH4tfL/yKrw5+hQfBD8w2R0ZfVPWqKsQ39TtXTHw/2Q4c7wdEP4OmsbEHKo0CKg4H3EopPRvtYIWiO2149u+/A2vXSmHo1vCrV6oUUL06ULkyUKmSNFaoAHh5UdSa0rPTN+SZe/YMePoU8PMzPpJRReO9e5WerQZg0W31PH8OLFok1bag75CeoRQpWkBt2VIKs2/QQN99zPVAQEAIvLxYdFtEdFPO7u8Xf8e0A9NwN+iu2efI6JOahWtiaqupeKvSW8rUQoh8ClyaDNxdCSToIF6rQEOgZA+g5LuAq06raJkLKxfdhlD12GPHgCNHJDtxQt8eFGMeFRJ7VHhOtmLFUu9TV1C6n7VDxY5CQ4EXL6TPTUZjQEBqQW1qr2GKSjhzxtL/hQ5g0c0kER0NrFkDzJ1rPd8de3ugVi3JataURmqVmctdj636M/fkCfDoEfDwoTSm3fbzo65ULLrNKrrjE+JFH2lq+3Uz8KbF5sjom7pF6mJa62noUL6DQuLbF/D5Cbi1EIhVZ/u6bFOwKVC8C1CoJZCvFmBrr/SM1AWL7gyhdlznzqWIcDK9e1JMgUQ3/UR6eEij4XZmxwxHulgko9ZuZMa2TTkFkvilHE9TLCpKWkShfH4aDbczG8lIQBuK6eBgy1YZZtFtIiy6GSOQENq1C9ixA9i92/TFLr1AUUtly0pWpkzqkRZP6RzLGIfO63R+pwVTQ6NFU1lIy8KaopayhkW32UR3QmIC1l9Zjy8PfInrAdctPkfGOmhYrKEQ3+3KtlNGfMcEA7cWAzd+AiKfQDfYuwEFGgH560kCnMy9AqB0UTslYdFtMvSLePeuJL4pFP3qVcnoGGN+6NRnKMZpP62Q1issuk2ERTeTBXSeoO8SCXBqO3b8uHW35aJzasmSklF+eN68KWNm2xTGroWvW2Ki5IEOD5eMargYbtMCTFpBTUZpCvJo3t8WFt0vLbpJbG++vhlT9k/B5WeXoXZeK/cainoUVXoaqoEqyO+4tQNqp1nJZpjWahpal2mtzAQSE4DAs8CTfyULPAXdYecCuJcF3MoA7mUMxrLS6KCudnlmh0X3S0M/5j4+KSKcCvyQEL9zx/o8LIx5YNFtIlpQAVlhXZfaikPRKlQvQRbh5LVkTMsnNybGqTsG3SZHL5liVDuEhG1cXPoxzoRjFIVkTFDL2+pakNWQ6J4/fz6+/fZb+Pn5oWbNmpg7dy4aUOUAIyxZsgSrVq3CZeoyT6G6devim2++yfD+ORHd9JJsvbEVk/dPxnm/81A7zUs2x+x2s9GoeCOlp6I6jj86jk93f4pDDw5B7bQq3UqI7+almis7Ecr99t0uCXC/XfoJQc8MxwKAe2nAyQtwyAs45k0aPdPsp9m2c4bqoD7x9J7FBgExSXa8t/aiGVQmujODQpFlAS6P1I+VwtTJaJtW5RnGEBbdJsKim3nJl55EN33XDM20sGGG0ZHoXrt2Lfr06YNFixahYcOGmDNnDtavXw8fHx8UomouaXj//ffRtGlTNGnSBM7Ozpg1axb++usvXLlyBcWoGsxLiG56KcgzSmL79JPTUDvVClXDzDYz0bFCR2XCkzWCvIgyfs94XAu4BrVD4eZUcK1xicZKTwVIiAX8D0sC3HcHEHyVXlGlZ6UebGn51zNFjNu5SfnkVGVdHoXZGT8u556L03CCkTFeeg9kS6QxRtqOjwLiI4C4iDRjOHSBhkR3VtDbGRKSIsJlIW64LxtdCPL1uT6hPrzkPaJ2bjSS6J49W+lZaQA9XN/wl1p1bwflhJP4pjSia9cku35dqu/AMLoU3SS069evj3nUB0AktyegRIkSGDFiBCZMmJDl4+Pj45EvXz7xeBLvORHd9BLsvbsXk/+bjGOPjkHtlMhTAl+1/gof1PgAdtacp5qDqvMrz68U77NvmC/UDhVaI/Fdv1h9qAbyoFIoOoWgPz8FBJ4Ewu8rPStGj+hIdGcHCq2jnDO5EjaJdWNGVbUzuk1dYXf6gMIr3d2NG4VfkpCWxbSxkQQ3FzbKISy6mVyC8sCpeJYswGmk6CU6RkZhzQyjSdEdExMDV1dXbNiwAZ07d04+3rdvXwQFBWHLli1ZPkdoaKjwiJN3/M0338y26N5/b78QYVoIP87nnA+fN/8cwxoMg7O9CsNaNUJ4TDjmHJ+DWUdmITQmFGqHWoyR+K7lXQuqJMofCLoEhFwBgg0s5oXSM2O0jJWK7peFftEpF86YOKdqrXQb9ZGWjcLeDfdNuS2zx9Dfl40uYI1tZ6SrqJAa5QHKltW+bFRpnUSxbM7OqfeNmeF9aJuqrZOAlkdDc3OT8hkZhWDRzajkLaRUIlmAy/bgQco2hbHTeZCxLlxdQxARkbXoVnTdNSAgQHiqCxcunOo47V+nJSYTGD9+PIoWLYq2bdsavT06OlqYoegmjj88jllnZmHf3X1QOySwRzUchfHNxiOvc16lp6N53Bzd8HmLzzGk7hB8dfArLDy9UHjB1crfPn8Le6fKO0J8U1qBqnD2Arxflczw1ynKTxLfYXeBiPtA+AMg4oHkGY94BCSq9zVnlMIGcMwvfabsXJWejLqhFAORux+carSJCYZrfARcE6LhHR8DOEcDjjFA/hggPjopPUEeDY8ZHDd2TKQ3kHKmksCJWY/0XtrYSgbbVNuJwuyQkGgvRLONrQ1sbNPfT4y2DoCto5TKIUZHwE7edspgP+1tzoC9K2DvLnU3IKNUEHlb7Ltad4cDhmGyXPuhyBUy6pNtDFpc9PdPL8xJjFPFbCrwJrckpNGaK6yrGdLN1JLN0CjjOe0x2aj1KEUzZYWmg51mzpyJNWvWYP/+/SK/2xgzZszA1KlT0x2n1l89q/YUpnY6VOiA4nmKK/b36cNEvU9plL0Z8rY5jlE4ZVaeEcNtMmNeEWOW2X0cHb1QyelnfFlwJG7F7zNegZHa2NgDDvapR7rNkSo5OohLy1zjxKMTKOhaEN7u3lA19Aa5FJHMGAnUUNc3jRCn0WCbRASjbUgwORWUCtQlW0FJVMv7YjvpPiS4raXHOp3ISCzT9yDSF4h6liScU4toY8JajJTTr3Yy8monGUlrVUGFEdOKcWHuSbUb8iUVUkwaU20njfZ5WLybASpiu+zsMkTFRQFbBkHz/D0YtYvUxoDaAzhSUcfQtSX5Ecnq1cv6J4AikdIKcVP32aNuHLo+JwEsGwnotNsZHStQAChIlyNOyBakZ0xBs+Hl3333HaZPn449e/agXiafbGOebsoZz06fbq1BX0T6IpNReX15O6NjWe3zFzvzEyyFHqY1OSQxI5PzANMafSRpoYBJ6iMe8RCIDgBiApPsBRAdCMQmjfIxeWShbn4RQiJCVHHPIwkPGjM7RuJDFtd0mx5CQ7MDeXqjnycJ6SRBndF2fKTSs2XMjo30fTAU5K/us77vQQ45+fgkph+cjn9u/AM9UsS9CMY1GSci7SjqjmFyAik3OdWHjBxjhmN0VAKiIuIQHRWHqMh4gzEBUZEJ0n2jbZIebyM9LoZGW0SL57VNcnYlIiHBRlgipFE6boOERPk41RGxSXJo2YhrWNnhlbKddNzeVpitLY10zCbV/SiNx8VFMvKlGo6mHKM+4zTm9unW1HbUqiikRu2+qE2YXEitZMmSGD58eIaF1GbPno2vv/4aO3fuRKNGjSzWp1stUFEcCkuhqramGAllRrvQx9JQiBv2SiRr2hRo107pWaoU0S4rWBLh0YaCPFCq6i0qfkdJIbPJ2zRSCG2U8dvT7isRFi9Cbu1SjHyEFDZLophCZ4WHziVpNNhPvi3N7eke4yIJZkMxTdsUpsukF9URj4GwW0DYbSD0trRNERokpqOeSqHYDCPTM4FFdxYcun8I0w9Nx67bu2ANeLl6YUzjMRhWfxg8nDyUng6TG9B1RlwoEEsWkrKdfEzeNrwt3CDVR+5ektaMHKfOJ1rANillKDmNyJgZ3Caue1wlszcyyqlCxm4TY1Lkkpkj6jQjuqllGHm2Fy9eLMQ3tQxbt26dyOmm3G6qSE6twChMnKAWYZMnT8bq1atF6zAZd3d3YVoS3RSO8PgxcP++ZJT3QdVq04poqmTLNTgYmSFDgMWLlZ6FNqHvHFUfpWiOtCO1CZHTHtKa4fG42Hgk0o9nfBTiYhMQH5eQMsYlIj5e2hYWLx1LiI9HfLwt4hPsEJ9gi7h4Gu3EmJAo7UvHbBFP2wl2SEyU8l4p11VOiaDr9szSKdLeTivHVGTKmNGKcka3GRqtHGdmSqwq5wp0IRN6Awi6DAQnWYgPEHZHWoRhGFNh0W0UuvykujpUW+XA/QOwRqhA7qhGozCiwQjkc8mn9HQYUxf3KQKPFljT2TMg+llSGhCJZnkM5cVYNUHiW0TpGUbsUaSefMzT+LE8VQB7lxxrS8WT53r06AF/f38hpP38/FCrVi3s2LEjubjagwcPRBiCzMKFC0VYerdu3VI9z5QpU/Dll19CbVDehY8PcOOGZPfupYhsEtxcRIFhTOtxTItPhkaVmI2JZ2PH5NE8qRLkaaYiX1zoKzl930VKm0gryOmYKblUhtvZzaUy34fsGvDsAOB/BAi6CIRe54ukjLD3ABzcARuH1BEYoge9XRpLc0wUW4s3sLikfvSGxzIwUeQtksPydSC2t9/aLsLItdCm1ZK8iHqBKfun4Ptj32N4/eEY3Xi0qNvCKEAsXSzck2rKGBPT8jYJ7owKVjDaIC5cssgn2Xvca6eAAlkk62eC4p7u3MZSnm4SBadPS0aF10lgk9gmccBkH7lQmqkmh+HLRosZWW1r9ZOvZU83vebkUU4roLMyKrbHWAfkXSfxvXcvUL26BUPESVg/O5gktA8mXUhZARSi5yAXAZNX8pO2TTmmhkJhoromRZtEAnFJItyYGd4mLrJCpFoRsUFJaShy4ToDE9XXzQx7ugUJiQnYcn2LCCM/63tW6emoElcHV3xU7yOMbTJW/QVTNXkB8ggIuQ6E3QTC7gHhdyWhTWYtvwFMzslAdGvG061FKMT00iXgxAng5EnJrl3TrogzFxSQYMyDZYp3SzbyjFHlQYPgBoshV0UnQUdFJMiohy1ZVtvySB5Uuf8tGVWXlLdlT6w1QfUEKJqD2mOQya0yDPet7TVhsgdFI1DLFbMWcCQhFXgWeLZfEtjPDknCS1fYAK7FAPdygFtZwCNpdC8tVYUXQttTyuHXxapsUl0CKtxnzh8FEueGIlwW6VQcL9pfsij/1NsxlAPGYWsZEZ8Qjw1XNwixffnZZaWno2oiYiOE13veyXkYXGcwPm36KUp4llB6Wtoj0g94flxqWxp8TYpcIrFN32/GDMiLiFYufLIJi24TfoPv3EkR1yS0z52TRJc1QAK4VCmgdGnJypSR9qlfXVrBTOGkWlrMN/SmU0irKT32sgt51A1FuaHJxfFIYJDJ2zTS7WqFUiauXgVu305vNH+GUQ1UIfzur8DtpVLRM11gA3hUBPLXAfLVBvK8kiSwS+tDUCv9o0Bh82QoZvrjSHCLzgpGBDmNVkpcQhxWX1qNbw59A5/nPlAjxTyKoUmJJth4baPwxKuF6PhozDs1D4vPLEa/Wv0wodkElM1XVulpqRNKA6LIpYBjSXZU8lxbY6cRhwws09s8pGJlyWlAaQq3GrWki33Rx1dOAUpInQ6EhNRpQzC4nd6zVAVsI1MXr5X3jd4eAcSFpeTNy601KZJJ5a00WXRnwOXLVOQNWLdOChXXOySmKZSTrGLFFIFdtCi3sHoZ6LWj6uNk2YG8fBRWbSjEZXFetSpyBTqX+vpKi0xnz0ojGXmyGQlarKHFJw8PyeRtY8cMt6n4mGGbDGMtNtIeJ6P3xDBNIjuj3F7EsLVIWsvouBzRQUbRDDQadGJUF/Qj77sDuLMUePyPdqq4ZoZ7eaDIa4B3e6BwK+lCiVEPdJHqVECyPJVh7cTEx+DX879ixuEZuBt0F2qE8qYnNpsoQrldHFxwPeA6Jv83GeuvroeaiE2IxZKzS7D83HJ8UOMDMedKBSvBqqHFLEOBHXhKf3UeSAS7lpQWU91KSSPti1acRkSzUp1GSHxT3Q61yMkEqnhrWAE+SZDL+yJy6UVS5NLzpJa0BtsWLpLKOd0GUA42CW0y8uTpFapaTJ3WWrWSrH59SQgw1guJMorokIW1LLJJ7FsD9LtRpAhQogRQsqQ0FiuWkgKRkaim75K1QiJeFuCGYtxwW47oIKNFJHkkyypaiOpj1K2bzUk93Q+cHgaE6OAEXrgtUOIdoEh7wJ09XIz6iYyNxLJzyzDryCw8CnkENZLHKQ/GNh4rKoYba9VFueaf7/scO27tgBqxtbFF96rd8Xnzz1GtUDVYDfExwOO/gTvLAN+d+glrdisD5K0umUdlwL2MJLCdvZWvnWFtJJJXIyJFkAsxHmCw/RyoNFpK2dJqy7DcxtgLQwJj0iRg+3bolsqVASr4TiK7cWMpFJyxbsgDeuiQFM2xaZPUrk7PUEdB+h5UqQJUqiRFc8gimyI6qIAXk3tQQb20YtxwHDNGeo9MgvL0Tn4I3P8DmqdYJ6DqF0CB+krPhGFMIjwmHItOL8J3x76DX5gf1IiLvYtoy0U50gVcC2R5/4P3D+KzvZ/hyMMjUCtdKnfBpBaTUKdIHegWysm+vQy495sOCp3ZAHlrAoVbS+bVXCpOyWgeFt0mvDBxcXkwdCiwXl3RRGalQwdg5EigXbvcKU7GqBtrEdr0Wa9VC2jSRDKK7CABp6WaA4yJUNjY/o5AgHovjk0if12gwVIgXy2lZ8IwJhESHYL5J+fjh+M/ICBCnYLIwdZBFCT7vMXnKOpRNEetzcjzfd7vPNTKGxXeEOK7UfFG0A0P1gHXf5SKoWkZCr0u1RMo0RXwagE45Vd6RowFYNGdxQtz6lQw3nsvD27dgi4hj/bChZJnj2GoJ/w33wAbN+pXaJPHuk8foHVroEEDybPNWAGHugCPNkPzOdvtjgLOXkrPhGGy5EXkC/x04idhQVFBqg3D7l2jN6a0nIIy+cq81HNRgTWqvv7Ff1/gxnP1FvlpW7YtJjWfhJalW0KzUJu/MyOkMHItQ90iyg8FKg6XukowuoZFdxYvTNGiwXjyRJ/FaNq2BbZs4RByRkpRWb5cCtWlHFu9QUXG3npL6l1O0Rxc9M/KCDwH7NR4aCUVwXn9nFSBXC9Qbv2poUrPQv14VgWab4RW8A/3xw/HfsD8U/MRGhMKtdK1SldMaz0Nr3i9YvZq7FQgbuqBqXgY8hBqpXnJ5viixRdChNtoKbwr/L60iPriHDRNsbeBxr8ndUFgrIEQ7tOdOU+eQJeQZ/vvvwEXF6VnwigN9Qzv0QPYtQu6pFw5YNs2qdo+Y6X46qAQR8Em+hLcco59qDpbRKkKezdoAd9QX3x39DssOrNI9JFWK+3Ltcf0V6ejXtF6Fnl+e1t7DKwzEO/XeB+LTy/G14e+hn+E+lrCHXpwCK/9/hoaFmsows4p/FwT4vvkEO0L7qIdgaZrATsn6AJqwfVEB7+zlibUtPOi1YpuvdK8OQtuRmLCBP0K7jp1JMFduLDSM2EURQ99qalaKsOokAfBDzD7yGwsPbtU9I1WK01LNMU3bb5Bi1ItcuXvOds7Y2SjkUKA/3T8J8w+Olvkt6uNE49PoNOfnVDLu5YIO+9SpYsIu1dt1JLfLu23+Wq6Tj+Cm4gJAg6/o/Qs1I+JP+Mq/fYxOYXaPFGxLMa6oVZNixdDt3z7LQtuhhL5ewK2Gr/AoT6zvruVngXDJHPnxR0M/nswyv9cXoSSq1Vw1/aujW29tuFQ/0O5JrgNcXd0FwXa7o68i/FNx4sK6WqEisB1W98NNRbWwJ+X/kR8ggovEqPUWfU+Wzjm10z0CqMMViu68+q0Sj/1th0wgIW3tWNvL5leiVbnNSCT27gWBSp/Ak2TGA8c6QYEXVZ6JoyVcz3gOvpu7ouKcyti6bmliE2IhRqpVKAS1nVbh9NDTqNDhQ6Kh07nd8mPmW1n4vbHt/G/ev8TYehq5Ir/FfTa1AtV5lfByvMrERuvove3YGPAMZ/Ss3g5op7yAiqTKVZbSG3btmD07p1H9IPVI716AXPnAvm5O4HV8tlnwIwZ0CXU/mvnTs7nZpKqBZ4bA/jMgaZxLgxUnwqU6Q/YabxpfNgd4NGWXP6bt4CbC6Ap8tUBXj+j9Cxw6eklkZ+87so6JEK9l4QlPUtiaqup+KDGB6oVtnKkABVb++3Cb6p+PUvnLY0JTSegX61+cLJXQcTQjXlS5XItQ57u1rulRQQ9EOkHbC6i9CxUT0gE4DmIoky5enmGFeaeP8+Dnj2BkyehSyi3m1ooffwx8Ip5i3gyGiAyUurRvmQJdEnBgsCGDUBLDXdHYcwE/YzdWQ6c/xSICYSmcSsFVJ0ElOkr5QgypldM39camkJh0X3myRlMPzQdm6+rv+UeebcH1h6oDnFoIleeXcHy88tF1XM1U8yjGD5t+qnoZ+7ioHCI/P01wMlBUjFGreLgCVSbApQbBDh4QNPEhgLXZufu30yIyf2/+ZKw6DaxrDv999Re64svgMs6ju577TWgWzepf3f58oAWClky5mHPHmDQIOD+fei2qNrgwVJ0RybnOsYaiA4ALk4C7qwEEjSeg+DsDRTpIFXD9W4HOHoqPSN1w6LbZI49PIavDn6F7be4KjGTQmG3whjbZCyG1hsq8tUVI/gKcLg7EHIVmu/VXXEYUGEE4MJFaEwmNgzYoK3FChbd2eyllpAA7N0LrF0LbNoEvHgB3VKsmCS+ZaPWSyzC9U1YGLBwIbB+PXDqFHQJ9aXv2lX6TDdpAlSqxJ9rqyU6ELj3G3DrF+1fuBE29lJrMRLghVoAeWsC9q5Kz0pdsOjOFLrUO3D/gBDb++7uy5W/yWiTAi4FMLrRaAxvMByezgot9iUmAH57gNtLgMdbAJXWFzD5/F2gAVC4DeDdBijQSF8Vzs1NLItuq2pgHhMjeQdJgG/eTI+BrqG87xo1gOrVJaM8WcqZJXGu52JcaocWggIDgWfPAH//lJHem44dc/68d+9KYdl6FuDy55rEN1njxtJnu0ABpWfF5Cr08xZwDLi1GHi8GYjVycmc2v7keQXIVxvwqAh4lAc8KgDu5a3XI86i2yh0ibfr9i4RRn74wWGL/i1GX3g6eeLjhh9jVKNRolicYkQ9A+6ukgR46A1oHjsXwKu5ZPlqSouoriXYSyDDotu6RHfaKslHj0p537I9egSrgAR3iRKSyCMrUyZl28tLCuX19JQ8jHyuyJy4OKmNl6FREb+0gloeyQICJOGdliFDzNcO7N49SYCTnTkjzVPP0Oe2ShWgcmWgVCnp812ypDTSIpMTLz5bnNhY6bNPn+/Mxu+/l6IVzAa1yQk6L4kz/wPAs4NAbDB0h1NBSXyTEHcvJ+1TmKOjbPlS9u10dPJm0Z0KurTbemOr8GyfeqLj1VXG4lCoOVVl/6TJJyjkVki5iZBc8T8MPFgD+B8Fgi9KHnE9QOdkj3KAWxnJ3A3HUoCdM6yGWBbdViu6jfHkSWoRTt5CvXvDM8POLkWAk+Vkm0wNXnVqtUYFyKKipDGjbXmMiJAEdFBQelFtaOFmrAliTtGddoGJ6hpQr/ezZ6XxwgXp/7QWvL0lAS6LcPnz6eEhWUbb1rLwRL8W9Jmn851soaEZ78uLS4Zi2tRzJbU/rFvXgv+MEOEXgGckwPcnifAgWBUU9kjiWxbhhuI87THD22wdARu7JLM32LYDbNPsi9uTvhziciMxaUwwGOOl8NHEOICKTiVvxwLxkUB8FJAQBcRFJo0RQFyoFLkQmzSG3QSebIO1i+6ExARsurYJ0w9Ox4WnF8z63Ix1Q33IP6z7IcY1HYeiHkWVno703X9+Egg4CvgfAZ4f0080U1pcigJOXoBDHqlQm7CkbYpuMnbccFRxtf9U0CIKRTZorGI6i24Liu60kDfyxg3gxAnpQvH6dWn/wQOzPL3V4OYGuLtLIp4EOI2mmiyYZaP3JKtteZ+8uySiycgLp3YsJbqNQa+Rj09qIU5GiwxMCra2WQtzw21nZ+kxaT/Hxo4ZHpc/vzkZ6bMtf85pgUXeNrSMjtOikSyojUVfWAKLi26jIvwi8Pw4EHRJ2g6+pN+LuFyFRLdVXWrkuuimCtlrL68Vrb+uBVyD2nn3lXdFzrCbg5vSU1EN9L5Rq7FbgbegZhztHEUl+fFNx6NU3lJQlWALviqJcLLnJ4DQW9ICnrVDUU1CnKcR6LZOBgumhoulmZgg7eJp0j4toNLrLS+giu2kRdWEaGkBVTaxHylVqo8LkxZR4iOgRTQluufPn49vv/0Wfn5+qFmzJubOnYsGDRpkeP/169fjiy++wL1791ChQgXMmjULHU1McrWE6M4I8gjduiWJFhLhZBTOS1WkKUSdLoYZRs2i2xh0xvD1BW7fNm7kzWQYzYnuDF37D5ME+GXpAi70ptQTOvKJwpNjNI8ZRHdsfCx+u/gbZhyeoXqxRnSq2AnTWk9DLe9aSk9FldD7+euFXzHtwDQ8DHkINUO90vvU6IOJzSeifP7yUCXxMdL5OuQaEHxNKqxJY/gdXlBlrE90r127Fn369MGiRYvQsGFDzJkzR4hqHx8fFCqUPnfk6NGjaNGiBWbMmIE333wTq1evFqL77NmzqFatmqpEd2aQd5XC1EmAy0aeccrpNTRrDltn1Cm6s4K8oY8fSwtLDx9Ko2y0T4KdCsTllseU0SaqEN2ZQavzobelC7rQJBEe5SuNstFqPsMYw94DKNQcaPlvjh4eHReNFedXYObhmbgfrP5+kO3KtsNXrb9Cw+INlZ6KJoiKi8Li04vxzeFv8Cz8GdSMrY0telXvhc+afYYqXlWgCUj6xAQCYXeB8LtJ4x1pjPQFop4C0f4cocPoS3ST0K5fvz7mzZsn9hMSElCiRAmMGDECEyZMSHf/Hj16IDw8HFu3bk0+1qhRI9SqVUsId62IblOh0E65wJYpRpXXGe1D4cSUT5wvH5A3b2qjnus9ekDTkOCmEHXK8zXV9NzGj5Hy4g3rPaxaZeZCarkN/bRSjjiJ74gnxkW5bFpuh2PNiJz4fIBjfsApf8p2smV0G+XFO+ToT0bERmDJmSWYfXQ2noSqP9qiWclmmN56OlqWbqn0VDRJWEwY5p6YK97voCh153XZwAbdXumGz5t/jpreNaF5KDw6OgCI8gMi/SQhHmUwGh4jAc/oKBQ/n5GaJmmOGRQlDYmyg2exWuoW3TExMXB1dcWGDRvQuXPn5ON9+/ZFUFAQtmzZku4xJUuWxJgxYzBq1KjkY1OmTMHmzZtxgao+6Ux0Zwd6J8kzTgKFvI1k1J9Z3s7oWEb7HP6ePRwdpbx0OTdd3jZmdHtaMW1odDsJbyZ1dAh5yOnzTQtR9FmnzyrlGxuOxo4Zu4/eK7VbEvps0ueYhLJsafflY2mLJxorqKiWQoqKivOYYKmaumxp9zM7RkXFGNNEsoMHYO8ueZodaHQ3cd/d4LHu0gUXjblUQTE0OhQLTy/E98e+V73nk6hftD6mvzpdeLhtrKHKpIUhwf3d0e8w5/gchMeasTKrhXir0luY1HwS6herD6uAwtijn0ki3LDAY1xI6n1xzGDbcJ/ympnsQVXd7dwAe7ekc7NbaqPb6DyefB9DS7o/5bjLApry3O0cszkJ07Wlopc5AQEBiI+PR+HChVMdp/3rVI3MCJT3bez+dNwY0dHRwmToBZFfID1Cv23UFonsZa8DycsuC3DyoFMhJnnMzjYZCRxj96GRxL2ov5Bk5AXNaJ+gfbrop/9VLjBFozGT72PMHBykFlEklmWjfWPH095O22nFhTlFA73uTHqoAFnx4pK9LPTZo9eZai+QGCejz6n8GSWT9+XPsLHbjJnhbfJnP7PPuOExQj5m6udZPp72dvpuyJ9XsrTbmd0mb7u4SEafc3mk2815HU3vgXVDxWnyA3Zk9EHPQRE4ceFGle7opBotFalJjE3ZFpXAaYxJsqRjopiNke1E+b6xBoVyEjIZkwoZUQ9xYVR0h1YOM9q3Ndi3y+A4fYidpGrpdHFlm7Qtj+I2J+kiSR5taDS4X/LjnXLsXU4HLUhH0muSO4sdp5+cxsJTCxEYFYhqntUAFbdid7B1QN+afdGxQkchtkPpAoJ5aWxhi0/rfYr+Vfrjx2M/4tpzdRfLCwsNw4RtE9C2TFv0r90frg6u0D+0ekxG1cZz8HA6f8oFxURRsaRzePI523DbYD+RzvkZ3Y/O34bbaTtHpC2GlvZcbpP+/Jxu2ybjc7nh7baO6U2cs+VztYPBOd4h5ZxN53T5vsmPo/u7ScXfzAH9+xQpHEMpYdlPC5M1ZVZ+bN37Fij3e+rUqemOUwg7wzAMwzAMYz52YqfSU2BUwj7sw2f4TOlpMEyuQIuM5PFWpeguWLAg7Ozs8PTp01THad+bGuYagY5n5/4TJ04U4egylDMeGBiIAgUKcMiTBaDVHlrQePjwoe7C9xlGhj/njDXAn3NG7/BnnLEG+HNuWcjDTYK7aNHM+9crKrodHR1Rt25d7N27Nzmnm0Qx7Q8fPtzoYxo3bixuN8zp3r17tzhuDCcnJ2GG5KWkWcai0Jeav9iM3uHPOWMN8Oec0Tv8GWesAf6cW47MPNyqCS8nLzQVTqtXr57ozU0tw6g6ef/+/cXt1E6sWLFiIkycGDlyJFq2bInvv/8eb7zxBtasWYPTp0/jl19+Ufg/YRiGYRiGYRiGYRiViW5qAebv74/JkyeLYmjU+mvHjh3JxdIePHgAW4Myzk2aNBG9uSdNmoTPPvsMFSpUEJXLTenRzTAMwzAMwzAMwzBWJboJCiXPKJx8//796Y69++67whj1QaH81MItbUg/w+gJ/pwz1gB/zhm9w59xxhrgz7k6ULRPN8MwDMMwDMMwDMPomZS4bYZhGIZhGIZhGIZhzAqLboZhGIZhGIZhGIaxECy6GYZhGIZhGIZhGMZCsOhmzMr8+fNRunRpODs7o2HDhjh58qTSU2IYs3Hw4EF06tQJRYsWhY2NjeicwDB6gtpz1q9fHx4eHihUqBA6d+4MHx8fpafFMGZl4cKFqFGjRnLf4saNG2P79u1KT4thLMbMmTPFdcuoUaOUnorVwqKbMRtr164VfdepQuLZs2dRs2ZNtG/fHs+ePVN6agxjFsLDw8XnmhaXGEaPHDhwAMOGDcPx48exe/duxMbG4rXXXhOffYbRC8WLFxci5MyZMzh9+jReffVVvP3227hy5YrSU2MYs3Pq1CksXrxYLDQxysHVyxmzQZ5t8pDMmzdP7CckJKBEiRIYMWIEJkyYoPT0GMas0IrxX3/9JTyBDKNX/P39hcebxHiLFi2Ung7DWIz8+fPj22+/xcCBA5WeCsOYjbCwMNSpUwcLFizA9OnTUatWLcyZM0fpaVkl7OlmzEJMTIxYMW7btm3yMVtbW7F/7NgxRefGMAzD5Izg4OBkQcIweiQ+Ph5r1qwR0RwUZs4weoIil954441U1+eMMtgr9HcZnREQECB+uAoXLpzqOO1fv35dsXkxDMMwOYOilSj/r2nTpqhWrZrS02EYs3Lp0iUhsqOiouDu7i4il1555RWlp8UwZoMWkyjdk8LLGeVh0c0wDMMwjFEPyeXLl3H48GGlp8IwZqdSpUo4f/68iObYsGED+vbtK9IoWHgzeuDhw4cYOXKkqM1BxY0Z5WHRzZiFggULws7ODk+fPk11nPa9vb0VmxfDMAyTfYYPH46tW7eKiv1UdIph9IajoyPKly8vtuvWrSu8gT/99JMoOMUwWodSPqmQMeVzy1BEKp3TqfZSdHS0uG5ncg/O6WbM9uNFP1p79+5NFZpI+5wjxTAMow2otioJbgq13bdvH8qUKaP0lBgmV6BrFhIiDKMH2rRpI1IoKJpDtnr16uH9998X2yy4cx/2dDNmg9qFUXgWfakbNGggqiNSYZL+/fsrPTWGMVsV0Fu3biXv3717V/x4UZGpkiVLKjo3hjFXSPnq1auxZcsW0avbz89PHPf09ISLi4vS02MYszBx4kR06NBBnLdDQ0PFZ37//v3YuXOn0lNjGLNA5++0tTjc3NxQoEABrtGhECy6GbPRo0cP0V5m8uTJ4kKN2hLs2LEjXXE1htEq1M+1devWqRaaCFpsWrlypYIzYxjzsHDhQjG2atUq1fEVK1agX79+Cs2KYcwLhd326dMHvr6+YkGJ+heT4G7Xrp3SU2MYRqdwn26GYRiGYRiGYRiGsRCc080wDMMwDMMwDMMwFoJFN8MwDMMwDMMwDMNYCBbdDMMwDMMwDMMwDGMhWHQzDMMwDMMwDMMwjIVg0c0wDMMwDMMwDMMwFoJFN8MwDMMwDMMwDMNYCBbdDMMwDMMwDMMwDGMhWHQzDMMwDMMwDMMwjIVg0c0wDMMwjGD//v2wsbFBUFBQpvcrXbo05syZk2vzYhiGYRgtw6KbYRiGYTRGv379hDgmc3R0RPny5TFt2jTExcW91PM2adIEvr6+8PT0FPsrV65E3rx5093v1KlTGDJkyEv9LYZhGIaxFuyVngDDMAzDMNnn9ddfx4oVKxAdHY1t27Zh2LBhcHBwwMSJE3P8nCTgvb29s7yfl5dXjv8GwzAMw1gb7OlmGIZhGA3i5OQkBHKpUqXw0UcfoW3btvj777/x4sUL9OnTB/ny5YOrqys6dOiAmzdvJj/u/v376NSpk7jdzc0NVatWFaI9bXg5bffv3x/BwcHJXvUvv/zSaHj5gwcP8Pbbb8Pd3R158uRB9+7d8fTp0+Tb6XG1atXCb7/9Jh5LnvSePXsiNDQ0V18zhmEYhlECFt0MwzAMowNcXFwQExMjQs9Pnz4tBPixY8eQmJiIjh07IjY2VtyPPOLkHT948CAuXbqEWbNmCbFsLNSchDWJaAo5Jxs7dmy6+yUkJAjBHRgYiAMHDmD37t24c+cOevTokep+t2/fxubNm7F161ZhdN+ZM2da8BVhGIZhGHXA4eUMwzAMo2FIVO/duxc7d+4UXm0StkeOHBGimfjjjz9QokQJcfzdd98VXumuXbuievXq4vayZctmGGpOHmnycGcWck5/m8T73bt3xd8hVq1aJTzolPtdv379ZHFOOeIeHh5iv3fv3uKxX3/9tdlfE4ZhGIZRE+zpZhiGYRgNQt5i8lA7OzsLsU2eZfJy29vbo2HDhsn3K1CgACpVqoRr166J/Y8//hjTp09H06ZNMWXKFFy8ePGl5kHPS2JbFtzEK6+8IgqwyX+ToLByWXATRYoUwbNnz17qbzMMwzCMFmDRzTAMwzAapHXr1jh//rzI146MjMSvv/4qvNJZMWjQIBH+TZ5m8lDXq1cPc+fOtfh8qcibITRX8n4zDMMwjN5h0c0wDMMwGoSKoFGrsJIlSwrvNlGlShXRNuzEiRPJ93v+/Dl8fHyE91mGvNJDhw7Fpk2b8Mknn2DJkiUZhpjHx8dnOg/6mw8fPhQmc/XqVVGMzfBvMgzDMIy1wqKbYRiGYXRChQoVRFGzwYMH4/Dhw7hw4QI++OADFCtWTBwnRo0aJfK/KQf77Nmz+O+//4RwNgaFhIeFhYnc64CAAERERKS7D1VNp/zw999/XzzfyZMnRfX0li1bCi86wzAMw1g7LLoZhmEYRkdQ7+66devizTffROPGjUWhNWoJJod3k+eaKpiT0KZe3xUrVsSCBQuMPhcVYyOPOOWLU2/u2bNnp7sPhYlv2bJFtCBr0aKFEOFUnG3t2rUW/18ZhmEYRgvYJNKvMcMwDMMwDMMwDMMwZoc93QzDMAzDMAzDMAxjIVh0MwzDMAzDMAzDMIyFYNHNMAzDMAzDMAzDMBaCRTfDMAzDMAzDMAzDWAgW3QzDMAzDMAzDMAxjIVh0MwzDMAzDMAzDMIyFYNHNMAzDMAzDMAzDMBaCRTfDMAzDMAzDMAzDWAgW3QzDMAzDMAzDMAxjIVh0MwzDMAzDMAzDMIyFYNHNMAzDMAzDMAzDMBaCRTfDMAzDMAzDMAzDwDL8H90F8ZTDxj0ZAAAAAElFTkSuQmCC", "text/plain": [ "
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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top = df.head(5)\n", "print(top[[\"filter\", \"best_match\", \"best_sim\", \"info_content\", \"n_windows\"]])\n", "\n", "for f in top[\"filter\"]:\n", " pwm = build_pwm_from_windows(int_filter_kmers[f]) if 'build_pwm_from_windows' in globals() else build_pwm(int_filter_kmers[f])\n", " plot_pwm_logo(pwm, title=f\"Filter {f} ({'motif ' + df[df['filter']==f]['best_match'].iloc[0]})\")" ] }, { "cell_type": "code", "execution_count": 66, "id": "9b883d20-5d92-4d45-aa7f-a9cd4c1ce593", "metadata": {}, "outputs": [], "source": [ "import logomaker\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "BASES = [\"A\", \"C\", \"G\", \"T\"]\n", "\n", "def plot_pwm_logo(pwm, title=\"PWM\"):\n", " df = pd.DataFrame(pwm, columns=BASES)\n", "\n", " plt.figure(figsize=(max(6, pwm.shape[0] * 0.6), 3))\n", " logomaker.Logo(df, color_scheme='classic')\n", "\n", " plt.title(title)\n", " plt.xlabel(\"Position\")\n", " plt.ylabel(\"Frequency\")\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 67, "id": "eeca71cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best filter for motif A: 8\n", "Best filter for motif B: 13\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "best_a = max(multi_scores, key=lambda x: x[1])[0]\n", "best_b = max(multi_scores, key=lambda x: x[2])[0]\n", "\n", "print('Best filter for motif A:', best_a)\n", "print('Best filter for motif B:', best_b)\n", "\n", "# Build PWMs\n", "pwm_a = build_pwm(multi_filter_kmers[best_a])\n", "pwm_b = build_pwm(multi_filter_kmers[best_b])\n", "\n", "# Plot logos\n", "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", "\n", "logomaker.Logo(pd.DataFrame(pwm_a, columns=BASES), ax=axes[0])\n", "axes[0].set_title(f'Filter {best_a}')\n", "\n", "logomaker.Logo(pd.DataFrame(pwm_b, columns=BASES), ax=axes[1])\n", "axes[1].set_title(f'Filter {best_b}')\n", "\n", "plt.tight_layout()\n", "plt.savefig(\"logo.png\", dpi=150, bbox_inches='tight')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6fdebe60-7856-4403-bca3-6eee1aba75e6", "metadata": {}, "source": [ "## Answer.\n", "With Multi-motif ROC AUC: 0.9421, Multi-motif PR AUC: 0.9313, the 1-CNN layer model understands the task very well. However, it Conv1 filter is messay and it does not give clean motifs. Each filter only captures part of the signal and motifs can be distributed across multiple filters. Our PWN window is very blurry. Hence, we might need another CNN layers to combine the signals. " ] }, { "cell_type": "code", "execution_count": 59, "id": "c7b8a092-ce12-416c-b952-4a03e56b2d7d", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Image(filename=\"dragonn_model_figure.jpg\"))" ] }, { "cell_type": "markdown", "id": "1e39dc8b", "metadata": {}, "source": [ "## 6. Motif interactions: does the CNN capture co-occurrence or spacing relationships?" ] }, { "cell_type": "code", "execution_count": 35, "id": "1298fc62", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model: \"functional_31\"\n",
       "
\n" ], "text/plain": [ "\u001b[1mModel: \"functional_31\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                     Output Shape                  Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ sequence (InputLayer)           │ (None, 101, 4)         │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv1 (Conv1D)                  │ (None, 97, 24)         │           504 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2 (Conv1D)                  │ (None, 93, 24)         │         2,904 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gmp (GlobalMaxPooling1D)        │ (None, 24)             │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense1 (Dense)                  │ (None, 32)             │           800 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ output (Dense)                  │ (None, 1)              │            33 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ sequence (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m101\u001b[0m, \u001b[38;5;34m4\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv1 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m97\u001b[0m, \u001b[38;5;34m24\u001b[0m) │ \u001b[38;5;34m504\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m93\u001b[0m, \u001b[38;5;34m24\u001b[0m) │ \u001b[38;5;34m2,904\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ gmp (\u001b[38;5;33mGlobalMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m800\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ output (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 4,241 (16.57 KB)\n",
       "
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 Trainable params: 4,241 (16.57 KB)\n",
       "
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 Non-trainable params: 0 (0.00 B)\n",
       "
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loss: 0.4382 - val_accuracy: 0.8425 - val_loss: 0.4201\n", "Epoch 5/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.8635 - loss: 0.3775 - val_accuracy: 0.8400 - val_loss: 0.3990\n", "Epoch 6/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.8639 - loss: 0.3547 - val_accuracy: 0.8400 - val_loss: 0.3941\n", "Epoch 7/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8645 - loss: 0.3447 - val_accuracy: 0.8400 - val_loss: 0.3942\n", "Epoch 8/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8655 - loss: 0.3396 - val_accuracy: 0.8388 - val_loss: 0.3945\n", "Epoch 9/10\n", "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.8658 - loss: 0.3362 - val_accuracy: 0.8388 - val_loss: 0.3950\n", "Interaction-task ROC AUC: 0.8738\n", "Interaction-task PR AUC: 0.8013\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[(19, 0.20535714445153058, 0.2910714271045919),\n", " (5, 0.28749999866071435, 0.23035714355867348),\n", " (15, 0.22500000089285713, 0.282142855994898),\n", " (14, 0.2589285711096939, 0.27857142755102043),\n", " (12, 0.27142857066326537, 0.23214285778061222),\n", " (7, 0.23035714355867348, 0.2678571422193878),\n", " (11, 0.2428571431122449, 0.26428571377551024),\n", " (8, 0.26249999955357145, 0.23392857200255096),\n", " (18, 0.2428571431122449, 0.26249999955357145),\n", " (1, 0.26071428533163266, 0.23750000044642855)]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seqs3, labels3 = generate_interaction_dataset(\n", " num_pos=4000, num_neg=4000, seq_len=SEQ_LEN,\n", " motif_a=MOTIF_A, motif_b=MOTIF_B,\n", " gap_min=10, gap_max=25, random_seed=13\n", ")\n", "X3 = one_hot_encode_seqs(seqs3)\n", "X3_train, X3_test, y3_train, y3_test, seqs3_train, seqs3_test = train_test_split(\n", " X3, labels3, seqs3, test_size=0.2, random_state=42, stratify=labels3\n", ")\n", "X3_train, X3_val, y3_train, y3_val, _, _ = train_test_split(\n", " X3_train, y3_train, seqs3_train, test_size=0.125, random_state=42, stratify=y3_train\n", ")\n", "\n", "\n", "def build_interaction_model(input_length, num_filters=16, kernel_size=5, lr=1e-3):\n", " inp = layers.Input(shape=(input_length, 4), name='sequence')\n", " x = layers.Conv1D(num_filters, kernel_size, padding='valid', activation='relu', name='conv1')(inp)\n", " x = layers.Conv1D(num_filters, kernel_size, padding='valid', activation='relu', name='conv2')(x)\n", " x = layers.GlobalMaxPooling1D(name='gmp')(x)\n", " x = layers.Dense(32, activation='relu', name='dense1')(x)\n", " out = layers.Dense(1, activation='sigmoid', name='output')(x)\n", " model = models.Model(inp, out)\n", " model.compile(optimizer=optimizers.Adam(learning_rate=lr), loss='binary_crossentropy', metrics=['accuracy'])\n", " return model\n", "\n", "interaction_model = build_interaction_model(SEQ_LEN, num_filters=24, kernel_size=len(MOTIF_A), lr=1e-3)\n", "interaction_model.summary()\n", "es = callbacks.EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n", "hist3 = interaction_model.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, batch_size=128, callbacks=[es], verbose=1)\n", "\n", "pred3 = interaction_model.predict(X3_test, verbose=0).ravel()\n", "roc3 = roc_auc_score(y3_test, pred3)\n", "precision3, recall3, _ = precision_recall_curve(y3_test, pred3)\n", "pr_auc3 = auc(recall3, precision3)\n", "print(f'Interaction-task ROC AUC: {roc3:.4f}')\n", "print(f'Interaction-task PR AUC: {pr_auc3:.4f}')\n", "\n", "plt.figure(figsize=(6, 3))\n", "plt.plot(hist3.history['loss'], label='train loss')\n", "plt.plot(hist3.history['val_loss'], label='val loss')\n", "plt.legend()\n", "plt.title('Interaction task loss')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "int_filter_kmers, int_filter_scores, conv3 = collect_top_kmers(interaction_model, X3_train, seqs3_train, top_frac=0.02, layer_name='conv1')\n", "interaction_scores = []\n", "for f in range(len(int_filter_kmers)):\n", " if len(int_filter_kmers[f]) < 5:\n", " continue\n", " pwm_f = build_pwm(int_filter_kmers[f])\n", " sa = pwm_similarity_to_motif(pwm_f, MOTIF_A)\n", " sb = pwm_similarity_to_motif(pwm_f, MOTIF_B)\n", " interaction_scores.append((f, sa, sb))\n", "interaction_scores = sorted(interaction_scores, key=lambda x: max(x[1], x[2]), reverse=True)\n", "interaction_scores[:10]\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "e2490669-398c-4ea3-bd4c-08e9fcb52aec", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yhat = (pred3 >= 0.5).astype(int)\n", "correct_pos = np.where((y3_test == 1) & (yhat == 1))[0]\n", "correct_neg = np.where((y3_test == 0) & (yhat == 0))[0]\n", "\n", "if len(correct_pos) > 0:\n", " i_pos = correct_pos[0]\n", " plot_two_layer_heatmaps(\n", " seqs3_test[i_pos],\n", " interaction_model,\n", " motif_a=MOTIF_A,\n", " motif_b=MOTIF_B,\n", " conv1_name=\"conv1\",\n", " conv2_name=\"conv2\",\n", " title=\"Positive example: two-layer CNN detects motif interaction\"\n", " )\n", "else:\n", " print(\"No correctly predicted positive example found.\")" ] }, { "cell_type": "code", "execution_count": 42, "id": "33a98173-e1ec-4099-ac40-dd5b2bfe762b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[19, 5, 15, 14, 12]\n" ] } ], "source": [ "best_filters = [f for f, sa, sb in interaction_scores[:5]]\n", "print(best_filters)" ] }, { "cell_type": "code", "execution_count": 45, "id": "79a8b055-5460-4924-a780-12db38c3b84f", "metadata": {}, "outputs": [], "source": [ "def plot_clean_two_layer_heatmaps(seq, model, important_filters,\n", " conv1_name=\"conv1\", conv2_name=\"conv2\", \n", " save_path=None):\n", "\n", " x = one_hot_encode_seqs([seq])\n", "\n", " conv1_out = get_conv_layer_output(model, x, layer_name=conv1_name)[0]\n", " conv2_out = get_conv_layer_output(model, x, layer_name=conv2_name)[0]\n", "\n", " # Select only important filters\n", " conv1_sel = conv1_out[:, important_filters].T\n", " conv2_sel = conv2_out[:, important_filters].T\n", "\n", " fig, axes = plt.subplots(2, 1, figsize=(14, 5), sharex=True)\n", "\n", " sns.heatmap(conv1_sel, ax=axes[0], cmap=\"viridis\", cbar=True)\n", " axes[0].set_title(\"Conv1 (important filters only)\")\n", " axes[0].set_ylabel(\"Filters\")\n", "\n", " sns.heatmap(conv2_sel, ax=axes[1], cmap=\"magma\", cbar=True)\n", " axes[1].set_title(\"Conv2 (interaction filters)\")\n", " axes[1].set_xlabel(\"Sequence position\")\n", " if save_path is not None:\n", " plt.savefig(save_path, dpi=150, bbox_inches='tight')\n", "\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "id": "526642a5-236d-49e3-9e30-4484f4f1c4f9", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yhat = (pred3 >= 0.5).astype(int)\n", "correct_pos = np.where((y3_test == 1) & (yhat == 1))[0]\n", "correct_neg = np.where((y3_test == 0) & (yhat == 0))[0]\n", "\n", "if len(correct_pos) > 0:\n", " i_pos = correct_pos[0]\n", " plot_two_layer_heatmaps(\n", " seqs3_test[i_pos],\n", " interaction_model,\n", " motif_a=MOTIF_A,\n", " motif_b=MOTIF_B,\n", " conv1_name=\"conv1\",\n", " conv2_name=\"conv2\",\n", " title=\"Positive example: two-layer CNN detects motif interaction\"\n", " )\n", "else:\n", " print(\"No correctly predicted positive example found.\")" ] }, { "cell_type": "code", "execution_count": 50, "id": "ea343756-72e8-4d89-abc1-b06848a8086f", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "important_filters = [f for f, _, _ in interaction_scores[:5]]\n", "\n", "plot_clean_two_layer_heatmaps(\n", " seqs3_test[i_pos],\n", " interaction_model,\n", " important_filters, \n", " save_path=\"two_layer.png\"\n", ")" ] }, { "cell_type": "code", "execution_count": 51, "id": "ae68d5b9-9d50-4403-bbed-d054b5ebe9e8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "def plot_two_layer_heatmaps(seq, model, motif_a=None, motif_b=None,\n", " conv1_name=\"conv1\", conv2_name=\"conv2\",\n", " title=\"Two-layer CNN motif interaction\",\n", " save_path=None):\n", "\n", " import seaborn as sns\n", " import numpy as np\n", " import matplotlib.pyplot as plt\n", "\n", " # Encode sequence\n", " x = one_hot_encode_seqs([seq])\n", "\n", " # Get activations\n", " conv1_out = get_conv_layer_output(model, x, layer_name=conv1_name)[0]\n", " conv2_out = get_conv_layer_output(model, x, layer_name=conv2_name)[0]\n", "\n", " conv1_hm = conv1_out.T\n", " conv2_hm = conv2_out.T\n", "\n", " # Normalize rows\n", " def row_norm(mat):\n", " m = mat.copy()\n", " m_min = m.min(axis=1, keepdims=True)\n", " m_max = m.max(axis=1, keepdims=True)\n", " denom = np.where((m_max - m_min) == 0, 1.0, (m_max - m_min))\n", " return (m - m_min) / denom\n", "\n", " conv1_hm = row_norm(conv1_hm)\n", " conv2_hm = row_norm(conv2_hm)\n", "\n", " # Find motif positions\n", " def find_all(s, motif):\n", " if motif is None:\n", " return []\n", " pos = []\n", " i = 0\n", " while True:\n", " i = s.find(motif, i)\n", " if i == -1:\n", " break\n", " pos.append(i)\n", " i += 1\n", " return pos\n", "\n", " pos_a = find_all(seq, motif_a)\n", " pos_b = find_all(seq, motif_b)\n", "\n", " # Create figure\n", " fig, axes = plt.subplots(\n", " 3, 1, figsize=(14, 7),\n", " gridspec_kw={\"height_ratios\": [0.7, 1.4, 1.4]},\n", " sharex=True\n", " )\n", "\n", " # Top: motif locations\n", " axes[0].set_title(title)\n", " for p in pos_a:\n", " axes[0].axvspan(p, p + len(motif_a), color=\"blue\", alpha=0.3)\n", " for p in pos_b:\n", " axes[0].axvspan(p, p + len(motif_b), color=\"orange\", alpha=0.3)\n", " axes[0].set_yticks([])\n", " axes[0].set_ylabel(\"Motifs\")\n", "\n", " # Conv1 heatmap\n", " sns.heatmap(conv1_hm, ax=axes[1], cmap=\"viridis\", cbar=True)\n", " axes[1].set_ylabel(\"Conv1 filters\")\n", " axes[1].set_title(\"Layer 1: local motif detectors\")\n", "\n", " # Conv2 heatmap\n", " sns.heatmap(conv2_hm, ax=axes[2], cmap=\"magma\", cbar=True)\n", " axes[2].set_ylabel(\"Conv2 filters\")\n", " axes[2].set_title(\"Layer 2: motif interactions\")\n", " axes[2].set_xlabel(\"Sequence position\")\n", "\n", " plt.tight_layout()\n", "\n", " # Save BEFORE show\n", " if save_path is not None:\n", " plt.savefig(save_path, dpi=150, bbox_inches='tight')\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 69, "id": "81e8d869", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best interaction filter for motif A: 5\n", "Best interaction filter for motif B: 19\n" ] }, { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Correlation between the two filter activations: -0.04719416329022643\n" ] } ], "source": [ "best_int_a = max(interaction_scores, key=lambda x: x[1])[0]\n", "best_int_b = max(interaction_scores, key=lambda x: x[2])[0]\n", "print('Best interaction filter for motif A:', best_int_a)\n", "print('Best interaction filter for motif B:', best_int_b)\n", "plot_pwm_heatmap(build_pwm(int_filter_kmers[best_int_a]), title=f'Interaction task filter {best_int_a} (motif A candidate)')\n", "plot_pwm_heatmap(build_pwm(int_filter_kmers[best_int_b]), title=f'Interaction task filter {best_int_b} (motif B candidate)')\n", "\n", "# Check whether the two best filters co-activate across sequences.\n", "def sequence_filter_max_activations(model, X, layer_name='conv1'):\n", " conv_out = get_conv_layer_output(model, X, layer_name=layer_name)\n", " return conv_out.max(axis=1)\n", "\n", "max_act = sequence_filter_max_activations(interaction_model, X3_test, layer_name='conv1')\n", "fa = max_act[:, best_int_a]\n", "fb = max_act[:, best_int_b]\n", "\n", "plt.figure(figsize=(4, 4))\n", "plt.scatter(fa, fb, s=8, alpha=0.5)\n", "plt.xlabel(f'Filter {best_int_a} max activation')\n", "plt.ylabel(f'Filter {best_int_b} max activation')\n", "plt.title('Filter co-activation on interaction task')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print('Correlation between the two filter activations:', np.corrcoef(fa, fb)[0, 1])\n" ] }, { "cell_type": "markdown", "id": "e89e659f", "metadata": {}, "source": [ "**Answer.** The CNN can capture motif interactions, but mainly through architecture depth and co-activation patterns. A shallow model mostly learns local motifs. A deeper model can combine multiple motif detectors and then learn whether they co-occur or appear with the right spacing. In the interaction task above, the first convolution layer tends to specialize to the two motifs, while the deeper layer helps integrate those signals into the class decision.\n" ] }, { "cell_type": "markdown", "id": "77451b44", "metadata": {}, "source": [ "## Final short answers\n", "\n", "**Q5:** Does the CNN learn separate filters? **Yes.** Different filters specialize to different local motifs or motif variants.\n", "\n", "**Q6:** Does it capture motif interactions? **Partly yes.** The CNN captures interactions best when there are multiple filters and at least two layers, so that local motif detectors can be combined into a higher-level decision about co-occurrence or spacing. A single shallow layer mostly captures local motif presence, not rich long-range interaction structure.\n", "\n", "The dense layer can capture interactions between motifs by combining activations from different convolutional filters, allowing the model to learn co-occurrence patterns and nonlinear relationships. However, because spatial information is largely lost after flattening, the dense layer cannot effectively model positional relationships such as spacing or order between motifs. Therefore, while it contributes to interaction modeling, it is limited compared to deeper convolutional layers.\n" ] }, { "cell_type": "markdown", "id": "1e9ce531-1fb7-4942-b7d4-ee5253798f06", "metadata": {}, "source": [ "## Additional Resources:\n", "Exploring convolutional neural network (CNN) architectures for simulated genomic data as well as ENCODE TF ChIP-seq datasets. (DragoNN)\n", "https://colab.research.google.com/github/kundajelab/dragonn/blob/master/tutorials/ENCODE_Jamboree_July2019_DragonnTutorial.ipynb#scrollTo=APNfq3knhfZg" ] }, { "cell_type": "code", "execution_count": null, "id": "aa057377-a7d7-494f-935d-698b8a1458c9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (genomics-cnn)", "language": "python", "name": "genomics-cnn" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.25" } }, "nbformat": 4, "nbformat_minor": 5 }