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Abstract

 

Deep learning is a class of machine learning techniques that uses multi-layered artificial neural networks for automated analysis of signals or data. The name comes from the general structure of deep neural networks, which consist of several layers of artificial neurons, each performing a nonlinear operation, stacked over each other. Beyond its main stream applications such as the recognition and labeling of specific features in images, deep learning holds numerous opportunities for revolutionizing image formation, reconstruction and sensing fields. In fact, deep learning is quite powerful and has been surprising optics researchers in what it can achieve for advancing optical microscopy, and introducing new image reconstruction and transformation methods. From physics-inspired optical designs and devices, we are moving toward data-driven designs that will holistically change both the optical hardware and software of next generation microscopy and sensing systems, blending the two in new ways. Today, we sample an image and then act on it using a computer. Powered by deep learning, next generation optical microscopes and sensors will understand a scene or an object and accordingly decide on how and what to sample based on a given task – this will require a perfect marriage of deep learning with new optical microscopy hardware that is designed based on data. For such a thinking microscope, unsupervised learning would be the key to scale up its impact on various areas of science and engineering, where access to labeled image data might not be immediately available or very costly, difficult to acquire. In this presentation, I will provide an overview of some of our recent work on the use of deep neural networks in advancing computational microscopy and sensing systems, also covering their biomedical applications.

Cellular and tissue engineering promises new therapeutic options for people suffering from a wide range of diseases. Differentiation of stem cells is a powerful renewable source of these functional replacement cells and tissues that can be grown in the laboratory. Diabetes is cause by the death or dysfunction of insulin-secreting islets, which are a tissue type found in the pancreas that contain β cells and other endocrine cell types. We have recently developed approaches combining modulating the actin cytoskeleton and signal transduction pathways during differentiation to produce stem cell-derived islets (SC-islets) capable of undergoing glucose-stimulated insulin secretion, their primary function. We have further expanded this approach to make SC-islets from patients with diabetes and used CRISPR-Cas9 to correct their diabetes-causing mutations. Upon transplantation into mice with severe pre-existing diabetes, these SC-islets rapidly restore normoglycemia and can maintain this functional cure for a year. Our hope is that one day this technology can be used to replace unhealthy islets in patients for therapy and provide a better disease-in-a-dish model to discover new drugs to prevent, stop, or reverse diabetes progression.

Abstract:

Acetalated dextran (Ac-DEX) is an acid-labile polymer originally developed for vaccine formulations, with the idea that vaccine elements could be encapsulated in the biopolymer to be phagocytosed by antigen presenting cells, resulting in pH shift in the phagosome that results in triggered intracellular release. Since its original development, Ac-DEX has also illustrated highly tunable release kinetics based on the cyclic and acyclic acetal coverage of the pendent dextran hydroxyl groups. This finely tunable degradation rate as well as the acid sensitivity has illustrated that microparticulate formulations can be used to optimize universal flu vaccine efficacy. Additionally, these material properties can be used to formulate nanofibrous scaffolds that optimally deliver chemotherapeutics in glioblastoma resection cavities.