Biology subjects

Taylor, G. W.

Publications and source records attributed to Taylor, G. W..

3 recordsLinked to original sources

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

C. elegans provide milk for their young

Adult C. elegans hermaphrodites exhibit severe senescent pathology that begins to develop within days of reaching sexual maturity (Ezcurra et al., 2018; Garigan et al., 2002; Herndon et al., 2002; Wang et al., 2018). For example, after depletion of self-sperm, intestinal biomass is converted into yolk leading to intestinal atrophy and yolk steatosis (pseudocoelomic lipoprotein pools, PLPs) (Ezcurra et al., 2018; Garigan et al., 2002; Herndon et al., 2002; Sornda et al., 2019). These senescent pathologies are promoted by insulin/IGF-1 signalling (IIS), which also shortens lifespan (Ezcurra et al., 2018; Kenyon, 2010). This pattern of rapid and severe pathology in organs linked to reproduction is reminiscent of semelparous organisms where massive reproductive effort leads to rapid death (reproductive death) as in Pacific salmon (Finch, 1990; Gems et al., 2020). Moreover, destructive conversion of somatic biomass to support reproduction is a hallmark of reproductive death (Gems et al., 2020). Yet arguing against the occurrence of reproductive death in C. elegans is the apparent futility of post-reproductive yolk production. Here we show that this effort is not futile, since post-reproductive mothers vent yolk through their vulva, which is consumed by progeny and supports their growth; thus vented yolk functions as a milk, and C. elegans mothers exhibit a form of lactation. Moreover, IIS promotes lactation, thereby effecting a costly process of resource transfer from postreproductive mothers to offspring. These results support the view that C. elegans hermaphrodites exhibit reproductive death involving a self-destructive process of lactation that is promoted by IIS. They also provide new insight into how the strongly life-shortening effects of IIS in C. elegans evolved.

genetics↗

bioRxiv2019

Amyloid damage to islet β-cells in type 2 diabetes: hypoxia or pseudo-hypoxia?

Aggregation of islet amyloid polypeptide (IAPP) and amyloid deposition in the islets of Langerhans may significantly contribute to the multifactorial pathogenic mechanisms leading to type 2 diabetes. A direct toxic effect on {beta}-cells of oligomeric IAAP has been demonstrated in in vitro models, but the mechanism operating in vivo is still unclear. Mice models presenting amyloid deposition and glucose intolerance represent a good tool for exploring in vivo a putative mechanism of toxicity directly related to the physical expansion of the extracellular matrix by the amyloid fibrillar aggregates. Based on our hypothesis that deposition of amyloid may influence the oxygen perfusion, we have calculated that the mean distribution of oxygen partial pressure would drop by more than 50 % in the presence of amyloid deposits in the islet. This condition of hypoxia caused by the remodelling of the extracellular space may explain the metabolic abnormalities in the Langerhans islets, otherwise interpreted as pseudo-hypoxic response to IAPP oligomers.

biochemistry↗