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Spatial and temporal changes in daily temperature and rainfall indices are analyzed for the source region of Yellow River. Three periods are examined: 1960-1990, 1960-2000 and 1960-2006. Significant warming trends have been observed for the whole study region over all the three periods, particularly over the period 1960-2006. This warming is mainly attributed to a significant increase in the minimum temperature, and characterized by pronounced changes in the low temperature events composing a significant increase in the magnitude and a significant decrease in the frequency. In contrast to the temperature indices, no significant changes have been observed in the rainfall indices at the majority of stations. However, the rainfall shows noticeable increasing trends during winter and spring from a basin-wide point of view. Conversely, the frequency and contribution of moderately heavy rainfall events to total rainfall show a significant decreasing trend in summer. To conclude, this study shows that over the past 40-45 years the source region of the Yellow River has become warmer and experienced some seasonally varying changes in rainfall, which also supports an emerging global picture of warming and the prevailing positive trends in winter rainfall extremes over the mid-latitudinal land areas of the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

Much of the discussion on hydrological trends and variability in the source region of the Yellow River centres on the mean values of the mainstream flows. Changes in hydrological extremes in the mainstream as well as in the tributary flows are largely unexplored. Although decreasing water availability has been noted, the nature of those changes is less explored. This article investigates trends and variability in the hydrological regimes (both mean values and extreme events) and their links with the local climate in the source region of the Yellow River over the last 50 years (1959-2008). This large catchment is relatively undisturbed by anthropogenic influences such as abstraction and impoundments, enabling the characterization of widely natural, climate-driven trends. A total of 27 hydrological variables were used as indicators for the analysis. Streamflow records from six major headwater catchments and climatic data from seven stations were studied. The trend results vary considerably from one river basin to another, and become more accentuated with longer time period. Overall, the source region of the Yellow River is characterized by an overall tendency towards decreasing water availability. Noteworthy are strong decreasing trends in the winter (dry season) monthly flows of January to March and September as well as in annual mean flow, annual 1-, 3-, 7-, 30- and 90-day maxima and minima flows for Maqu and Tangnag catchments over the period 1959-2008. The hydrological variables studied are closely related to precipitation in the wet season (June, July, August and September), indicating that the widespread decrease in wet season precipitation is expected to be associated with significant decrease in streamflow. To conclude, decreasing precipitation, particularly in the wet season, along with increasing temperature can be associated with pronounced decrease in water resources, posing a significant challenge to downstream water uses. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China., Methods: We collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model., Results: The average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening., Conclusion: This study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD., (© 2024. The Author(s).)