Alpha7 nicotinic acetylcholine receptors (α7nAChRs) are interesting not only because of their physiological effects, but because this receptor requires chaperones to traffic to cell surfaces (measured by alpha-bungarotoxin [αBGT] binding). While knockout (KO) animals and antibodies that react across species exist for tmem35a encoding the protein chaperone NACHO, commercially available antibodies against the chaperone RIC3 that allow Western blots across species have not been generally available. Further, no effects of deleting RIC3 function (ric3 KO) on α7nAChR expression are reported. Finally, antibodies against α7nAChRs have shown various deficiencies. We find mouse macrophages bind αBGT but lack NACHO. We also report on a new α7nAChR antibody and testing commercially available anti-RIC3 antibodies that react across species allowing Western blot analysis of in vitro cultures. These antibodies also react to specific RIC3 splice variants and single-nucleotide polymorphisms. Preliminary autoradiographic analysis reveals that ric3 KOs show subtle αBGT binding changes across different mouse brain regions, while tmem35a KOs show a complete loss of αBGT binding. These findings are inconsistent with effects observed in vitro, as RIC3 promotes αBGT binding to α7nAChRs expressed in HEK cells, even in the absence of NACHO. Collectively, additional regulatory factors are likely involved in the in vivo expression of α7nAChRs.

We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey (DES) and images from the Vera C. Rubin Observatory.

No abstract