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The Ili River Valley and adjacent Xinjiang regions contain introduced cattle, local cattle, and yak and therefore provide a useful regional system for examining cattle–yak genomic differentiation. Using whole-genome resequencing data from 79 individuals, we analyzed Angus (ANG), Simmental (SIM), Holstein (HOL), Xinjiang Brown Cattle (XH), Kazakh Cattle (KAZ), Altay White-headed Cattle (AWH), and yak (WY). The six domestic cattle groups were merged into a composite domestic cattle group (PTN, n = 69) and compared with WY (n = 10). Sequencing generated 2996.28 Gb of raw data and 2939.56 Gb of clean data. Alignment to the Bos taurus ARS-UCD1.2 reference genome yielded mapping rates of 96.71–99.78% and depths of 5.98×–17.26×. Genome-wide PTN-WY comparisons showed extremely high differentiation: the median weighted F_ST was 0.846 and the 95th percentile was 0.943. The joint F_ST–π scan identified 832 candidate highly differentiated windows and 533 unique ENSBTAG gene IDs, whereas the low-differentiation set contained only five windows and three genes. The longest contiguous highly differentiated segments were located on chromosomes 26, 29, 8, 21, and 7. WY had the highest median Tajima’s D (1.173) and the slowest LD decay, while KAZ had the lowest median Tajima’s D (0.345) and the fastest LD decay. Treemix supported non-tree-like covariance components, and PSMC indicated broadly similar deep-time demographic profiles across individuals. Overall, the dominant genomic signal between PTN and WY is deep phylogenetic divergence, with locally enhanced highly differentiated segments superimposed on this background. These segments were enriched for functions and pathways related to reproductive behavior, neuroendocrine regulation, circadian rhythm, and membrane transport, but they are not interpreted here as recent within-species selective sweeps. The results provide a cautious regional framework for conservation and breeding of bovine genetic resources in Xinjiang. Full article

Evapotranspiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BHFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (R_n) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that R_n has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season. Full article

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into “time” and “event” components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset ($n=663$ individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models’ predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy $0.811$), support vector machines (of accuracy $0.845$), random forests (of accuracy $0.836$), artificial neural networks (of accuracy $0.806$) outperform the Cox proportional hazard model (of accuracy $0.796$), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is $0.627$ for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies. Full article