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Uncovering the chromatin-mediated transcriptional regulatory network governing cold stress responses in fish immune cells.

J Genet Genomics

January 2025

Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China; International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China; Marine Biomedical Science and Technology Innovation Platform of Lin-gang Special Area, Shanghai 201306, China. Electronic address:

Temperature fluctuations challenge ectothermic species, particularly tropical fish dependent on external temperatures for physiological regulation. However, the molecular mechanisms through which low-temperature stress impacts immune responses in these species, especially in relation to chromatin accessibility and epigenetic regulation, remain poorly understood. In this study, we investigate chromatin and transcriptional changes in the head kidney and thymus tissues of Nile tilapia (Oreochromis niloticus), a tropical fish of significant economic importance, under cold stress.

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Objectives: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.

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The distribution of substitutional aluminum (Al) atoms in zeolites affects molecular adsorbate geometry, catalytic activity, and shape and size selectivity. Accurately determining Al positions has been challenging. We used synchrotron resonant soft x-ray diffraction (RSXRD) at multiple energies near the Al K-edge combined with molecular adsorption techniques to precisely locate "single Al" and "Al pairs" in a commercial H-ZSM-5 zeolite.

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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.

PLoS One

January 2025

Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

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Mosquitoes are important vectors for the transmission of some major infectious diseases of humans, i.e., malaria, dengue, West Nile Virus and Zika virus.

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