Performance of islets of Langerhans conformally coated via an emulsion cross-linking method in diabetic rodents and nonhuman primates.

Polyethylene glycol (PEG)-based conformal coating (CC) encapsulation of transplanted islets is a promising ß cell replacement therapy for the treatment of type 1 diabetes without chronic immunosuppression because it minimizes capsule thickness, graft volume, and insulin secretion delay. However, we show here that our original CC method, the direct method, requiring exposure of islets to low pH levels and inclusion of viscosity enhancers during coating, severely affected the viability, scalability, and biocompatibility of CC islets in nonhuman primate preclinical models of type 1 diabetes. We therefore developed and validated in vitro and in vivo, in several small- and large-animal models of type 1 diabetes, an augmented CC method-emulsion method-that achieves hydrogel CCs around islets at physiological pH for improved cytocompatibility, with PEG hydrogels for increased biocompatibility and with fivefold increase in encapsulation throughput for enhanced scalability.

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/.

A mass-differentiated library strategy for identification of sugar nucleotidyltransferase activities from cell lysates.

Sugar nucleotidyltransferases, or nucleotide sugar pyrophosphorylases, are ubiquitous enzymes whose activities have been correlated to disease states and pathogen virulence. Here we report a rapid "one-pot" method to identify a range of sugar nucleotidyltransferase activities of purified proteins or in cell lysates using a mass-differentiated carbohydrate library designed for mass spectrometry-based analysis.