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The change of wetland ecosystem in the region can reflect the quality of regional ecological environment. Accurate and objective assessment of wetland degradation risk is the key to wetland management and sustainable development. In this study, four counties in Zhangjiakou, Hebei Province are selected as the research area, and the characteristics of wetland degradation are analysed by using the multi-period land use and landscape index data from 1990 to 2020. Eight indicators are selected to construct a wetland degradation risk evaluation index system. Based on two machine learning algorithms, random forest (RF) and support vector machine (SVM), the risk of wetland degradation in the Bashang Region is evaluated and partitioned. The results show that the wetland area in the Bashang Region is obviously degraded from 1990 to 2020, and the total area is reduced by 290.79 km2, and the salinization of the wetland is more serious. The results of model evaluation accuracy show that the AUC values of RF and SVM algorithms are 0.549 and 0.613, respectively, indicating that the SVM model is more suitable for wetland degradation risk assessment in this area. The analysis of wetland degradation risk assessment results based on SVM model shows that the proportion of severe degradation risk area is 23.94%, which is mainly concentrated in the western edge of the study area and the central Angulinao watershed. The proportion of mild degradation risk area is 22.54%, which is scattered, mostly seasonal lakes with small area and high fragmentation. The degradation risk of areas with good connectivity such as rivers and ditches between lakes is relatively low. The weight analysis of the influencing factors of wetland degradation shows that the weight of nighttime light accounted for the largest proportion, reaching 17.74%, indicating that human activities are the main influencing factors. The results of the study are helpful for local relevant departments to identify the risk area of wetland degradation in Zhangjiakou Bashang, and provide scientific decision-making guidance for maintaining the sustainable development of regional wetlands. [ABSTRACT FROM AUTHOR]

] AIM: To evaluate the therapeutic effect of Shen-Xiankang formula on renal interstitial fibrosis induced by unilateral ureteral obstruction (UUO) in mice and to elucidate its underlying mechanisms. METHODS: Thirty-two C57BL/6 mice were randomly divided into sham group, UUO model group, and Shen-Xiankang formula intervention groups receiving either a low dose (150 mg'kg-l-d-1) or a high dose (450 mg'kg-l-d-1), with 8 mice in each group. All mice except those in sham group underwent UUO to establish chronic kidney disease (CKD) model. After modeling, corresponding doses of Shen-Xiankang or an equivalent volume of saline were administered daily for 7 d. Upon completion of treatment, renal tissues were collected for hematoxylin-eosin (HE) and Masson staining to assess tissue damage and fibrosis. Immunohistochemistry and Western blot analyses were used to detect the markers of fibrosis, oxidative stress, and ferroptosis. The effect of Shen-Xiankang formula on the interaction between activating transcription factor 3 (ATF3) and Smad3 was assessed using co-immunoprecipitation (Co-IP). RESULTS: The untreated UUO model group exhibited notable pathological changes such as expanded renal tubules and collagen deposition. Shen-Xiankang treatment significantly alleviated these changes (P<0. 05). It markedly reduced Smad3 phosphorylation, ATF3, 4-hydroxynonenal (4-HNE), and NADPH oxidase 4 (NOX4) aberrant expression, while increasing glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11) expression. Co-IP results indicated a significant modulation of the ATF3-Smad3 interaction by Shen-Xiankang. CONCLUSION: Shen-Xiankang formula effectively mitigates UUO-induced renal interstitial fibrosis in mice. The mechanism may involve modulating the ATF3/Smad3 interaction, which in turn attenuates oxidative stress and ferroptosis, consequently leading to the amelioration of renal fibrosis. These findings provide important insights for further research and clinical application of Shen-Xiankang formula. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short-term wind speed and power forecasts as well as reduce the impact of wind power uncertainty on the grid system, this study attempted to use wind speed observations to select the optimal numerical forecasting ensemble members that closely matched the actual wind speed within the forecast window. Those selected ensemble members then formed an optimized ensemble for training and testing machine learning models. Unlike the conventional method that relied solely on ensemble averaging, this method considered the forecast discrepancies among different ensemble members, avoided the introduction of members with large errors, and thus helped to improve wind speed prediction. The optimal number of ensemble members for wind farms with different altitudes and terrains in Henan and Gansu was determined based on the results of ensemble performance and sensitivity experiments. Comparative analyses demonstrated that the ensemble selection forecasts outperformed ensemble averaging in predicting wind speed fluctuations during different weather processes, closely aligning with actual wind speed observations. The sea-level pressure field estimates generated by the ensemble selection method exhibited a higher level of agreement with ERA5 data. Evaluation of wind speed and power prediction over eleven consecutive months in different wind farms showed that the ensemble selection method improved the accuracy of diurnal variation and monthly average wind speed compared to the original ensemble averaging method. Analysis of the observed data of upslope and downslope winds with different durations and speed changes in two wind farms showed that there were the most wind speed changes of 2 -4 m s-1 within 0-2 h and 2-4 h. Compared to ensemble averaging, the ensemble selection method significantly improved the prediction accuracy for these upslope and downslope winds. Furthermore, using the machine learning algorithm to train the optimal selection ensemble can further reduce the absolute deviation and root mean square error of wind speed, thereby effectively improving the accuracy of power prediction. [ABSTRACT FROM AUTHOR]