Lipids are an essential component of living beings and an important group of nutrients. As the gut microbiota plays important roles in the intestinal absorption and extraintestinal metabolism of dietary lipids, the current review addresses the recent progress regarding the interactions between the gut microbiota and lipid metabolism in aquatic animals, with a focus on fish. We discuss in detail how dietary lipid sources and content affect the composition of the gut microbiome and the mechanism by which the gut microbiota affects the lipid metabolism of the host. This interaction is largely mediated via microbial lipases, short‐chain fatty acids and the gut‐liver axis. The latter refers to the metabolism of biliary salts and acids, the regulation of their synthesis by gut microbes and their impact on the lipid metabolism. Finally, we briefly discuss how probiotic supplementation modulates the host's microbiome, and how probiotics have a beneficial effect on health and welfare of farmed aquatic animals. Although the influence of intestinal microbiota on lipid metabolism has been explored before, further research is needed to profoundly investigate the molecular mechanisms by which microbial metabolites (SCFAs and bile acids) induce lipid metabolism.

Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson’s correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses

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