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Instead of relying on nanoparticles to directly destroy every tumor cell, immunotherapeutic approaches target immune cells to train them to recognize and destroy tumor cells, which, due to the amplification and specificity of an adaptive immune response, may be a more effective approach to treating metastatic cancer.
Moreover, specialists labeled a set of cells belonging to images with fluorescent cells since our recognition approach requires the labels of individual cells to train the corresponding classifier.
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Datasets matching this criteria show a distribution of genes that appears visually similar to that in the human lymphoblastoid cells used to train the classifier.
Typically, scientists use a set of manually validated cell images to "train" the software's learning algorithms on the desired morphological traits.
The brightfield and darkfield features of the training set as well as the ground truth of these cells are used to train the ensemble.
The engineered APCs can then be given back to the patient, where they can instruct the patient's T cells as they naturally would, offering a relatively simple way to train T cells to attack cancer cells.
For this purpose we need an annotated set of cells where the actual cell state is known to train the classifier and to test its predictive power.
This target was chosen because the patients had a type of leukemia that affects B-cells, so the goal was to train the patients' T-cells to destroy B-cells.
Spain alleges Banna and Deghayes belonged to the cell which provided members to train in terror camps in Afghanistan and Indonesia.
Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways.
After that, the selected spoken-style sentences and the existing spoken text, CELL-TRN, are used to train the spoken LM.
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