Boone, L. | Search bioRxiv

bioRxiv2022

A NEW VARIANT OF HEPATITIS A VIRUS CAUSING TRANSIENT LIVER ENZYME ELEVATIONS IN MAURITIUS-ORIGIN LABORATORY-HOUSED CYNOMOLGUS MACAQUES

Hepatitis A virus (HAV) infects humans and non-human primates causing an acute self-limited illness. Three HAV genotypes have been described for humans and three genotypes have been described for non-human primates. We observed transiently elevated liver enzymes in Mauritius-origin laboratory-housed macaques in Germany and were not able to demonstrate HAV by serology and PCR. Using deep sequencing, we have identified a new HAV genotype with 86% nucleotide sequence homology to HAV genotype IV capsid proteins and approximately 80% nucleotide homology to other HAV genotypes. In situ hybridization indicates persistence in the biliary epithelium up to 3 months after liver enzymes were elevated. Vaccination using a commercial vaccine against human HAV prevented reoccurrence of liver enzyme elevations. Since available assays for HAV did not detect this new variant, knowledge of its existence may ameliorate potential significant epidemiological and research implications in laboratories globally. Article Summary LineA new genotype of hepatitis A virus, that was not identifiable by available diagnostic assays, caused liver enzyme elevations in laboratory-housed Mauritius-origin Cynomolgus macaques.

microbiology↗

bioRxiv2021

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D Convolutional Neural Network (CNN) with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. 432 subjects were recruited to train the CNNs from four multi-site imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multi-site study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 {+/-} 0.08 and the lowest modified Hausdorff distance of 2.98 {+/-} 4.40 mm. We further validated our models highlighting their robustness on clinical adversarial cases simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io

neuroscience↗