We investigated whether diet-induced changes in the maternal intestinal microbiota were associated with changes in bacterial metabolites and their receptors, intestinal inflammation, and placental inflammation at mid-gestation (E14.5) in female mice fed a control (17% kcal fat, n = 7) or a high-fat diet (HFD 60% kcal fat, n = 9; ad libitum) before and during pregnancy. Maternal diet-induced obesity (mDIO) resulted in a reduction in maternal fecal short-chain fatty acid producing Lachnospiraceae, lower cecal butyrate, intestinal antimicrobial peptide levels, and intestinal SCFA receptor Ffar3, Ffar2 and Hcar2 transcript levels. mDIO increased maternal intestinal pro-inflammatory NFκB activity, colonic CD3+ T cell number, and placental inflammation. Maternal obesity was associated with placental hypoxia, increased angiogenesis, and increased transcript levels of glucose and amino acid transporters. Maternal and fetal markers of gluconeogenic capacity were decreased in pregnancies complicated by obesity. We show that mDIO impairs bacterial metabolite signaling pathways in the mother at mid-gestation, which was associated with significant structural changes in placental blood vessels, likely as a result of placental hypoxia. It is likely that maternal intestinal changes contribute to adverse maternal and placental adaptations that, via alterations in fetal hepatic glucose handling, may impart increased risk of metabolic dysfunction in offspring.

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The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

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