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Essential hypertension is a common cardiovascular disease with complex etiology, closely related to genetic and environmental factors. The pathogenesis of hypertension involves alteration in vascular resistance caused by sympathetic nervous system (SNS) and renin angiotensin system (RAS). Susceptibility factors of hypertension vary with regions and ethnicities. In this study, we conducted target capture sequencing on 54 genes related to SNS and RAS derived from a collection of Han nationality, consisting of 151 hypertension patients and 65 normal subjects in Xinjiang, China. Six non-synonymous mutations related to hypertension were identified, including GRK4 rs1644731 and RDH8 rs1801058, Mutations are predicted to affect 3D conformation, force field, transmembrane domain and RNA secondary structure of corresponding genes. Based on protein interaction network and pathway enrichment, GRK4 is predicted to participate in hypertension by acting on dopaminergic synapse, together with interacting components. RDH8 is involved in vitamin A (retinol) metabolism and consequent biological processes related to hypertension. Thus, GRK4 and RDH8 may serve as susceptibility genes for hypertension. This finding provides new genetic evidence for elucidating risk factors of hypertension in Han nationality in Xinjiang, which in turn, enriches genetic resource bank of hypertension susceptibility genes. [ABSTRACT FROM AUTHOR]

The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application. [ABSTRACT FROM AUTHOR]