The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020.

In recent decades, extreme floods and droughts have occurred frequently around the world, which seriously threatens the social and economic development and the safety of people's lives and properties. Therefore, it is of great scientific significance to discuss the causes and characteristic quantization of extreme floods and droughts. Here, the terrestrial water storage change (TWSC) derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to characterize the floods and droughts in the Yangtze River basin (YRB) during 2003 and 2020. To reduce the uncertainty of TWSC results, the generalized three-cornered hat and least square methods were used to fuse TWSC results from six GRACE solutions. Then combining precipitation (PPT), evapotranspiration, soil moisture (SM), runoff, and extreme climate index data, the influence of climate change on floods and droughts in the YRB was discussed and analyzed. The results show that the fused method can effectively improve the uncertainty of TWSC results. And seven droughts and seven floods occurred in the upper of YRB (UY) and nine droughts and six floods appeared in the middle and lower of YRB (MLY) during the study period. The correlation between TWSC and PPT (0.33) is the strongest in the UY, and the response time between the two is 1 month, while TWSC and SM (0.67) are strongly correlated with no delay in the MLY. The reason for this difference is mainly due to the large-scale hydropower development in the UY. Floods and droughts in the UY and MLY are more influenced by the El Niño-Southern Oscillation (ENSO) (correlation coefficients are 0.39 and 0.50, respectively) than the Indian Ocean Dipole (IOD) (correlation coefficients are 0.19 and 0.09, respectively). The IOD event is usually accompanied by the ENSO event (the probability is 80%), and the hydrological hazards caused by independent ENSO events are less severe than those caused by these two extreme climate events in the YRB. Our results provide a reference for the study on the formation, development, and recovery mechanism of regional floods and droughts on a global scale.

Effects of norepinephrine­induced activation of rat vascular adventitial fibroblasts on proliferation and migration of BMSCs involved in vascular remodeling.

Vascular remodeling caused by vascular injury such as hypertension and atherosclerosis is a complex process involving a variety of cells and factors, and the mechanism is unclear. A vascular injury model was simulated by adding norepinephrine (NE) to culture medium of vascular adventitial fibroblasts (AFs). NE induced activation and proliferation of AFs. To investigate the association between the AFs activation and bone marrow mesenchymal stem cells (BMSCs) differentiation in vascular remodeling. BMSCs were cultured with supernatant of the AFs culture medium. BMSC differentiation and migration were observed by immunostaining and Transwell assay, respectively, while cell proliferation was measured using the Cell Counting Kit-8. Expression levels of smooth muscle actin (α-SMA), TGF-ß1 and SMAD3 were measured using western blot assay. The results indicated that compared with those in the control group, in which BMSCs were cultured in normal medium, expression levels of α-SMA, TGF-ß1 and SMAD3 in BMSCs cultured in medium supplemented with supernatant of AFs, increased significantly (all P<0.05). Activated AFs induced the differentiation of BMSCs into vascular smooth muscle-like cells and promoted proliferation and migration. AFs activated by NE may induce BMSCs to participate in vascular remodeling. These findings may help design and develop new approaches and therapeutic strategies for vascular injury to prevent pathological remodeling.

Spatial interpolation of regional PM2.5 concentrations in China during COVID-19 incorporating multivariate data.

During specific periods when the PM2.5 variation pattern is unusual, such as during the coronavirus disease 2019 (COVID-19) outbreak, epidemic PM2.5 regional interpolation models have been relatively little investigated, and little consideration has been given to the residuals of optimized models and changes in model interpolation accuracy for the PM2.5 concentration under the influence of epidemic phenomena. Therefore, this paper mainly introduces four interpolation methods (kriging, empirical Bayesian kriging, tensor spline function and complete regular spline function), constructs geographically weighted regression (GWR) models of the PM2.5 concentration in Chinese regions for the periods from January-June 2019 and January-June 2020 by considering multiple factors, and optimizes the GWR regression residuals using these four interpolation methods, thus achieving the purpose of enhancing the model accuracy. The PM2.5 concentrations in many regions of China showed a downward trend during the same period before and after the COVID-19 outbreak. Atmospheric pollutants, meteorological factors, elevation, zenith wet delay (ZWD), normalized difference vegetation index (NDVI) and population maintained a certain relationship with the PM2.5 concentration in terms of linear spatial relationships, which could explain why the PM2.5 concentration changed to a certain extent. By evaluating the model accuracy from two perspectives, i.e., the overall interpolation effect and the validation set interpolation effect, the results showed that all four interpolation methods could improve the numerical accuracy of GWR to different degrees, among which the tensor spline function and the fully regular spline function achieved the most stable effect on the correction of GWR residuals, followed by kriging and empirical Bayesian kriging.