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Applying trained model to new data The work-flow. Step 1: Data source Step 2: Model training Step 3: Predict new data Step 4: Evaluation A: D3.js (3.js) Construction of pluripotent stem cell lines and generation of induced neurons from human embryonic stem cells. Induced pluripotent stem cells are being explored as an in vitro model for neurodevelopmental and neuropsychiatric disease. These lines can be used to investigate the molecular and cellular basis of disease, or as in vitro platforms for drug screening and disease modelling. Human embryonic stem cells (hESCs) are available from both the NIH and the UK Stem Cell Bank. In this protocol we describe a method for the production of induced neural stem cells from hESC lines. The hESC lines are derived from the Swedish H9 cell line. The approach involves the removal of all genetic material from the hESC lines using geneticin and then reintroduction of the genetic material of interest. The generated hESC lines have a unique genetic configuration and therefore lack of chromosomal alterations, which often occur during derivation of induced neural stem cells. The resulting cell lines retain the ability to differentiate into neural stem cells and to subsequently differentiate into neural cells of all major cell lineages in the brain.# -*- coding: utf-8 -*- from tensorflow.keras import Model from keras.layers import * from keras.layers.core import * from keras.layers.utils import to_categorical from keras.optimizers import * from keras.preprocessing.image import ImageDataGenerator from keras.preprocessing.image import img_to_array import os import cv2 import numpy as np import sys # Parameters dataset_path = './datasets/' # Seed seed = 20 # Ignore classes ignore_classes = 0 # Validation split validation_split = 0.2 def get_categories(): # No categories. return np.array(['bg', 'deer', 'people', 'trees']) def get_labels(): # No labels. return np.array(['bird', 'deer', '
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