Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month.

Higher maternal vitamin D concentrations are associated with longer leukocyte telomeres in newborns.

Gestational vitamin D insufficiency is related with increased risks of various diseases and poor health outcomes later in life. Telomere length at birth or early in life is known to be a predictor of individual health. Both vitamin D and telomere length are related with various health conditions, and vitamin D concentrations are associated with leukocyte telomere lengths in women.