In this paper, landscape ecological risk in Qinling Mountain was studied. Using the remote sensing images of Landsat TM and DEM data in 1984, 2000, 2005, and 2014, an ecological risk assessment model was constructed, and landscape ecological risk indexes were calculated for four time periods 1984, 2004, 2005 and 2014. The spatial distribution of ecological risk was obtained with ArcGIS and geostatistics, and changes in the landscape patterns and spatiotemporal characteristics of ecological risk were analysed. As shown in the results; (a) from 1984 to 2014, the landscape pattern index of Qinling forest area was relatively stable; fragmentation and segregation decreased, and dominance and area increased. The fragmentation and separation of cultivated land increased over time, and the geographical distribution of cultivated land diversified, while its dominance decreased. (b) The areas of extremely low and extremely high ecological risk level in the study area is gradually reduced. But the area of high ecological risk level increased obviously. The extremely high ecological risk area was mainly distributed in the middle and south‐eastern regions. The extremely low and low risk areas were mainly distributed in the low hilly areas of northern Qinling.

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Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R2 = 0.87, RMSE = 49.22 g/m2, MAE = 42.74 g/m2). The results of multiple stages showed GPR achieved the highest accuracy (R2 = 0.88, RMSE = 49.18 g/m2, MAE = 42.57 g/m2) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.

Thyroid hormone receptor interactor 13 (TRIP13) is a crucial regulator of the spindle apparatus checkpoint and double-stranded break repair. The abnormal expression of TRIP13 was recently found in several human cancers, whereas the role of TRIP13 in the development of bladder cancer (BCa) has not been fully elucidated. Here, we reported that TRIP13 expression was elevated in BCa tissues compared with normal bladder tissues. Notably, the increased expression of TRIP13 was correlated with advanced tumor stage, lymph node metastasis, distant metastasis and reduced survival in BCa patients. Knockdown of TRIP13 in bladder cancer cells suppressed proliferation, induced cell cycle arrest, promoted apoptosis, and impaired cell motility, ultimately inhibiting tumor xenograft growth. Mechanistic investigations revealed that TRIP13 directly bound to epidermal growth factor receptor (EGFR), modulating the EGFR signaling pathway. Furthermore, TRIP13 expression was positively correlated with EGFR expression in BCa specimens, and the high expression of both TRIP13 and EGFR predicted poor survival. Overall, our results underscore the crucial role of TRIP13 in the tumorigenesis of BCa and provide a novel biomarker and therapeutic target for BCa treatment.