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Yue, N.

Publications and source records attributed to Yue, N..

2 recordsLinked to original sources

Spatiotemporal heterogeneity and the long-term impact of meteorological , environmental and socio-economic factors of scrub typhus in China from 2012 to 2018

Large-scale outbreaks of scrub typhus combined with the emergence of this vector-borne rickettsiosis in new areas indicate that this disease remains seriously neglected. This study aimed to explore the long-term changes and regional leading factors of scrub typhus in China, so as to provide fresh insights for the prevention and control of this disease. In this study, a Bayesian space-time hierarchical model (BSTHM) was used to identify the long-term spatiotemporal heterogeneity of scrub typhus and quantify the association between meteorological factors and scrub typhus in southern and northern China from 2012 to 2018. GeoDetector model was used to quantify the dominant forces of environmental and socioeconomic factors in the Northern and the Southern China. Scrub typhus often appeared in summer and autumn (June to November), and epidemically peaked in October, with obvious temporal seasonality. Spatially, the hot spots (high-risk regions) were concentrated in the south, on the contrary the cold spots (low-risk regions) in the north. In addition, the main meteorological factor, average temperature, gave a significant impact in both areas. The average temperature increased by 1 {degrees}C, resulting in a decrease of 1.10% in southern China and an increase of 0.96% in northern China in the risk of scrub typhus. The determinant environmental and socio-economic factors of scrub typhus in the two areas were altitude and per capita GDP, with q-values of 0.91 and 0.87, respectively. Meteorological, environmental and socio-economic factors had a significant impact on the distribution of scrub typhus, with obvious seasonality and spatial heterogeneity. This study provides helpful suggestions and basis for reasonably allocating resources and controlling the occurrence of scrub typhus. Author summaryScrub typhus is a natural-focus disease caused by the bite of chigger mite larval. In this study, we use BSTHM to capture the overall temporal trend and spatial hot spots of scrub typhus, and quantify the relationship between the disease and major meteorological factors. Meanwhile, Geodetector model was used to quantify the influence of other potential risk factors and estimate the spatio-temporal heterogeneity of scrub typhus. The results showed that scrub typhus had significant seasonality, with a q value of 0.52, and spatial heterogeneity, with a q-value of 0.64. Scrub typhus mainly occurred in summer and autumn, and high-risk areas were mainly distributed in southern China (Yunnan, Hainan and Guangdong). These heterogeneity were closely related to the vector and host. Whether in the South or the north, scrub typhus was closely related to risk factors such as temperature, per capita GDP, NDVI, altitude and the percentage of children aged 0-14. These results suggest that the relevant departments should strengthen the monitoring of the ecological environment, the host and vector of Orientia tsutsugamushi, and strengthen the risk awareness, so as to prevent and control the possible increased risk of scrub typhus under these meteorological, environmental and socio-economic conditions. Considering the differences in different regions, resources should be allocated reasonably.

ecology↗

GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNets output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.

bioinformatics↗