Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images.

Objective: We developed an optimized decision support system for retinal fundus image-based glaucoma screening.

Methods: We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. Optic boundary detection, optic cup, and optic disc segmentations were conducted using TernausNet.

Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma.

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response.

New York State Tick Blitz: harnessing community-based science to understand range expansion of ticks.

Ticks and tick-borne diseases are an immense public health burden in New York State (NYS), United States. Tick species and associated pathogens are expanding into new areas, shifting threats to human, and animal health across the state. For example, the invasive tick, Haemaphysalis longicornis Neumann (Acari: Ixodidae), was first detected in the United States in 2017 and has since been identified in 17 states, including NYS.