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Maternal exercise (ME) has been established as a useful non‐pharmacological intervention to improve infant metabolic health; however, mechanistic insight behind these adaptations remains mostly confined to animal models. Infant mesenchymal stem cells (MSCs) give rise to infant tissues (e.g., skeletal muscle), and remain involved in mature tissue maintenance. Importantly, these cells maintain metabolic characteristics of an offspring donor and provide a model for the investigation of mechanisms behind infant metabolic health improvements. We used undifferentiated MSC to investigate if ME affects infant MSC mitochondrial function and insulin action, and if these adaptations are associated with lower infant adiposity. We found that infants from exercising mothers have improvements in MSC insulin signaling related to higher MSC respiration and fat oxidation, and expression and activation of energy‐sensing and redox‐sensitive proteins. Further, we found that infants exposed to exercise in utero were leaner at 1 month of age, with a significant inverse correlation between infant MSC respiration and infant adiposity at 6 months of age. These data suggest that infants from exercising mothers are relatively leaner, and this is associated with higher infant MSC mitochondrial respiration, fat use, and insulin action. [ABSTRACT FROM AUTHOR]

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination ( R 2 ) values of 0.72 and 0.67 and root mean squared error ( R M S E ) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics. [ABSTRACT FROM AUTHOR]

In this study, WC–AlxFeCoCrNi (x = 0 to 0.5) cemented carbides were prepared using spark plasma sintering, and the phase composition, microstructure, and mechanical properties of the WC–AlxFeCoCrNi composites were studied. The FeCoCrNi high-entropy alloy powder showed a single FCC crystal structure, and the Al additive created a BCC phase. The hardness of the WC–AlxFeCoCrNi (x = 0.1 to 0.5) composites at room temperature and 600 °C/800 °C was found to be higher than that of the WC–FeCoCrNi composite, mainly due to the suppression of WC grain growth by the Al additive and the formation of BCC phase. [ABSTRACT FROM AUTHOR]