Biology subjects

Vernygora, O.

Publications and source records attributed to Vernygora, O..

3 recordsLinked to original sources

bioRxiv2025

Testing for a wildlife reservoir of divergent SARS-CoV-2 in white-tailed deer

The 2021 discovery of a divergent lineage (B.1.641) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer (Odocoileus virginianus) from Ontario raised concerns that deer were a potential reservoir. To assess whether white-tailed deer continued to be infected with B.1.641 and to test for spillover into other species, we established a surveillance program in Ontario by sampling wildlife via existing monitoring programs and through active surveillance of captive and wild animals. Between 2022 to 2024, we tested 2,839 animals, identifying one active SARS-CoV-2 infection (a likely spillover of a recombinant XBB.2.3.11.3 lineage), but no cases of B.1.641. Overall, 93 animals (6.8%) tested positive for SARS-CoV-2 antibodies, including 89 white-tailed deer, two Virginia opossums (Didelphis virginiana), one American mink (Neogale vison), and one river otter (Lontra canadensis). In Southwestern Ontario, where B.1.641 was originally detected, 15.2% of deer samples were seropositive. Generalized Linear Models demonstrated that seropositive deer were more likely to be found in areas with a higher fall deer harvest and human population density, and closer to previous B.1.641 cases. Our data suggest that deer-associated B.1.641 may have caused a relatively localized epizootic without forming a stable reservoir. This study underscores the importance of One Health-focused surveillance.

pathology↗

bioRxiv2025

WaveSeekerNet: Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning

BackgroundInfluenza A virus (IAV) poses a significant threat to animal health globally, with its ability to overcome species barriers and cause pandemics. Rapid and accurate IAV subtypes and host source prediction is crucial for effective surveillance and pandemic preparedness. Deep learning has emerged as a powerful tool for analyzing viral genomic sequences, offering new ways to uncover hidden patterns associated with viral characteristics and host adaptation. FindingsWe introduce WaveSeekerNet, a novel deep learning model for accurate and rapid prediction of IAV subtypes and host source. The model leverages attention-based mechanisms and efficient token mixing schemes, including the Fourier Transform and the Wavelet Transform, to capture intricate patterns within viral RNA and protein sequences. Extensive experiments on diverse datasets demonstrate WaveSeekerNets superior performance to existing models that use the traditional self-attention mechanism. Notably, WaveSeekerNet rivals VADR (Viral Annotation DefineR) in subtype prediction using the high-quality RNA sequences, achieving the maximum score of 1.0 on metrics including the Balanced Accuracy, F1-score (Macro Average), and Matthews Correlation Coefficient (MCC). Our approach to subtype and host source prediction also exceeds the pre-trained ESM-2 (Evolutionary Scale Modeling) models with respect to generalization performance and computational cost. Furthermore, WaveSeekerNet exhibits remarkable accuracy in distinguishing between human, avian, and other mammalian hosts. The ability of WaveSeekerNet to flag potential cross-species transmission events underscores its significant value for real-time surveillance and proactive pandemic preparedness efforts. ConclusionsWaveSeekerNets superior performance, efficiency, and ability to flag potential cross-species transmission events highlight its potential for real-time surveillance and pandemic preparedness. This model represents a significant advancement in applying deep learning for IAV classification and holds promise for future epidemiological, veterinary studies, and public health interventions.

bioinformatics↗

bioRxiv2020

Megaevolutionary dynamics in reptiles and the role of adaptive radiations in evolutionary innovation

Adaptive radiations are long believed to be responsible for the origin of phenotypic diversity and new body plans among higher clades in the fossil record. However, few studies have assessed rates of phenotypic evolution and disparity across broad scales of time to understand the evolutionary dynamics behind the origin of major clades, or how they relate to rates of molecular evolution. Here, we provide a total evidence approach to this problem using the largest available data set on diapsid reptiles. We find a strong decoupling between phenotypic and molecular rates of evolution, with many periods of accelerated phenotypic evolution or expansion of phenotypic disparity at the origin of major reptile clades and body plans that do not correspond to periods of adaptive radiation. We find heterogeneous rates of evolution during the acquisition of similarly adapted functional types, and that the origin of snakes is marked by exceptionally high evolutionary rates.

evolutionary biology↗