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Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling. 

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant cost. Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples. AGILE combines a meta-learning algorithm with a novel task augmentation technique which we use to generate an initial adaptive model. It then uses Bayesian dropout uncertainty estimates to actively select the most difficult samples when updating the model to a new task. This allows AGILE to learn with fewer tasks and a few informative samples, achieving high performance with a limited dataset. We perform our experiments using the brain cell classification task and compare the results to a plain meta-learning model trained from scratch. We show that the proposed task-augmented meta-learning framework can learn to classify new cell types after a single gradient step with a limited number of training samples. We show that active learning with Bayesian uncertainty can further improve the performance when the number of training samples is extremely small. Using only 1% of the training data and a single update step, we achieved 90% accuracy on the new cell type classification task, a 50% points improvement over a state-of-the-art meta-learning algorithm. 

Biologically active ligands (e.g., RGDS from fibronectin) play critical roles in the development of chemically defined biomaterials. However, recent decades have shown only limited progress in discovering novel extracellular matrix–protein–derived ligands for translational applications. Through motif analysis of evolutionarily conserved RGD-containing regions in laminin (LM) and peptide-functionalized hydrogel microarray screening, we identified a peptide (a1) that showed superior supports for endothelial cell (EC) functions. Mechanistic studies attributed the results to the capacity of a1 engaging both LM- and Fn-binding integrins. RNA sequencing of ECs in a1-functionalized hydrogels showed ~60% similarities with Matrigel in “vasculature development” gene ontology terms. Vasculogenesis assays revealed the capacity of a1-formulated hydrogels to improve EC network formation. Injectable alginates functionalized with a1 and MMPQK (a vascular endothelial growth factor–mimetic peptide with a matrix metalloproteinase–degradable linker) increased blood perfusion and functional recovery over decellularized extracellular matrix and (RGDS + MMPQK)–functionalized hydrogels in an ischemic hindlimb model, illustrating the power of this approach.