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Abstract ObjectivesRespiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. Materials and MethodsThis study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. ResultsWe identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. ConclusionOur proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions. 

Abstract Chromosome number change is a driver of speciation in eukaryotic organisms. Carnivorous sundews in the plant genus Drosera L. exhibit single chromosome number variation both among and within species, especially in the Australian Drosera subg. Ergaleium D.C., potentially linked to atypical centromeres that span much of the length of the chromosomes. We critically reviewed the literature on chromosome counts in Drosera, verified the taxonomy and quality of the original counts, and reconstructed dated phylogenies. We used the BiChrom model to test whether rates of single chromosome number increase and decrease, and chromosome number doubling differed between D. subg. Ergaleium and the other subgenera and between self-compatible and self-incompatible lineages. The best model for chromosome evolution among subgenera had equal rates of chromosome number doubling but higher rates of single chromosome number change in D. subg. Ergaleium than in the other subgenera. Contrary to expectation, self-incompatible lineages had a significantly higher rate of single chromosome loss than self-compatible lineages. We found no evidence for an association between differences in single chromosome number changes and diploidization after polyploidy or centromere type. This study presents an exemplar for critically examining published cytological data and rigorously testing factors that may impact the rates of chromosome number evolution. 

Abstract In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19 hospitalization prediction. By drawing the analogy between locations and lattice sites in statistical mechanics, we use the Ising dynamics to guide the model to extract and utilize spatial relationships across locations and model the complex influence of granular information from real-world clinical evidence. By leveraging rich linked databases, including insurance claims, census information, and hospital resource usage data across the U.S., we evaluate the HOIST model on the large-scale spatiotemporal COVID-19 hospitalization prediction task for 2299 counties in the U.S. In the 4-week hospitalization prediction task, HOIST achieves 368.7 mean absolute error, 0.6$${R}^{2}$$ $R^2$and 0.89 concordance correlation coefficient score on average. Our detailed number needed to treat (NNT) and cost analysis suggest that future COVID-19 vaccination efforts may be most impactful in rural areas. This model may serve as a resource for future county and state-level vaccination efforts.