Machine Learning to Quantitate Neutrophil NETosis.

We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in differentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients.

How Müller glial cells in macaque fovea coat and isolate the synaptic terminals of cone photoreceptors.

A cone synaptic terminal in macaque fovea releases quanta of glutamate from approximately 20 active zones at a high rate in the dark. The transmitter reaches approximately 500 receptor clusters on bipolar and horizontal cell processes by diffusion laterally along the terminal's 50 microm(2) secretory face and approximately 2 microm inward. To understand what shapes transmitter flow, we investigated from electron photomicrographs of serial sections the relationship between Müller glial processes and cone terminals.