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Given the complex geometry of white matter streamlines, Autoencoders have been proposed as a dimension-reduction tool to simplify the analysis streamlines in a low-dimensional latent spaces. However, despite these recent successes, the majority of encoder architectures only perform dimension reduction on single streamlines as opposed to a full bundle of streamlines. This is a severe limitation of the encoder architecture that completely disregards the global geometric structure of streamlines at the expense of individual fibers. Moreover, the latent space may not be well structured which leads to doubt into their interpretability. In this paper we propose a novel Differentiable Vector Quantized Variational Autoencoder, which are engineered to ingest entire bundles of streamlines as single data-point and provides reliable trustworthy encodings that can then be later used to analyze streamlines in the latent space. Comparisons with several state of the art Autoencoders demonstrate superior performance in both encoding and synthesis. 

The pathogen Ophidiomyces ophidiicola (O.o.), widely known as the primary cause of snake fungal disease (SFD) has been detected in Texas’s naïve snakes. Our team set out to begin to characterize O. ophidiicola’s spread in east Texas. From July 2019 until October 2021, we sampled 176 snakes across east Texas and detected 27 positives cases (qPCR confirmed 27/176). From a ribbon snake with clear clinical display, we isolated and cultured what we believe to be the Texas isolate of O. ophidiicola. With over 1/10 snakes that may be infected in East Texas, gives credence to the onset of SFD in Texas.