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Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018–2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest R2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.90, 0.10, and 0.08, respectively (grain starch content (GSC)); 0.87, 0.10, and 0.08, respectively (grain protein content (GPC)); and 0.74, 0.13, and 0.10, respectively (grain oil content (GOC)). In addition, the combination of the tasseling, grain-filling, and maturity stages further improved the predictive power for GSC (R2 = 0.96). The combination of the grain-filling and maturity stages further improved the predictive power for GPC (R2 = 0.90). The prediction accuracy developed in the combination of the jointing and tasseling stages for GOC (R2 = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.

Lactic acid, a metabolic by-product of host and intestinal microbiota, has been recovered as an active signal molecule in the immune system. In this study, a lactic acid biosynthesis pathway that directly produces lactic acid from glucose rather than ethanol with high production was reconstructed in Saccharomyces cerevisiae. The engineered S. cerevisiae showed anti-inflammatory activity in dextran sulfate sodium (DSS)-induced mice with improved histological damage, increased mucosal barrier, and decreased intestinal immune response. Lactic acid regulated the macrophage polarization state and inhibited the expression of pro-inflammatory cytokines in vivo and in vitro. Increasing the macrophage monocarboxylic acid transporter-mediated active lactic acid uptake suppressed the excessive activation of the NLRP3 inflammasome and the downstream caspase-1 pathway in macrophages. Moreover, lactic acid promoted histone H3K9 acetylation and histone H3K18 lactylation. Meanwhile, the engineered S. cerevisiae altered the diversity and composition of the intestinal microbiota and changed the abundance of metabolic products in mice with colitis. In conclusion, this study shows that the application of engineered S. cerevisiae attenuated DSS-induced colitis in mice via suppressing macrophage pyroptosis and modulating the intestinal microbiota, which is an effective and safe treatment strategy for ulcerative colitis.

Scope: Gut microbiome-derived metabolites are the major mediators of diet-induced host-microbial interactions. Aryl hydrocarbon receptor (AHR) plays a crucial role in glucose, lipid, and cholesterol metabolism in the liver. In this study, we aimed to investigate the role of indole-3-acetic acid (IAA) and AHR in sulforaphane (SFN) alleviates hepatic steatosis in mice fed on a high-fat diet (HFD).Methods and Results: The HFD-fed male C57BL/6 mice were intervened with SFN for 6 weeks. HFD-mice showed classical pathophysiological characteristics of hepatic steatosis. The results showed that SFN significantly reduced body weight, liver inflammation and hepatic steatosis in HFD-fed mice. SFN reduced hepatic lipogenesis by activating AHR/SREBP-1C pathway, which was confirmed in HepG2 cell experiments. Moreover, SFN increased hepatic antioxidant activity by modulating Nrf-2/NQO1 expression. SFN increased serum and liver IAA level in HFD mice. Notably, SFN manipulated the gut microbiota, resulting in reducing Deferribacteres and proportions of the phylum Firmicutes/Bacteroidetes and increasing the abundance of specific bacteria that produce IAA. Furthermore, SFN upregulated Ahr expression and decreased the expression of inflammatory cytokines in Raw264.7 cells.Conclusions: SFN ameliorated hepatic steatosis not only by modulating lipid metabolism via AHR/SREBP-1C pathway but regulating IAA and gut microbiota in HFD-induced NAFLD mice.

The sequence of changes in crop responding to soil water deficit and related critical thresholds are essential for better drought damage classification and drought monitoring indicators. This study was aimed to investigate the critical thresholds of maize growth and physiological characteristics responding to changing soil water and to reveal the sequence of changes in maize responding to soil water deficit both in seedling and jointing stages based on 2-year’s maize field experiment responding to six initial soil water statuses conducted in 2013 and 2014. Normal distribution tolerance limits were newly adopted to identify critical thresholds of maize growth and physiological characteristics to a wide range of soil water status. The results showed that in both stages maize growth characteristics related to plant water status [stem moisture content (SMC) and leaf moisture content (LMC)], leaf gas exchange [net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs)], and leaf area were sensitive to soil water deficit, while biomass-related characteristics were less sensitive. Under the concurrent weather conditions and agronomic managements, the critical soil water thresholds in terms of relative soil moisture of 0–30 cm depth (RSM) of maize SMC, LMC, net Pn, Tr, Gs, and leaf area were 72, 65, 62, 60, 58, and 46%, respectively, in seedling stage, and 64, 64, 51, 53, 48, and 46%, respectively, in jointing stage. It indicated that there is a sequence of changes in maize responding to soil water deficit, i.e., their response sequences as soil water deficit intensified: SMC ≥ LMC > leaf gas exchange > leaf area in both stages. This sequence of changes in maize responding to soil water deficit and related critical thresholds may be better indicators of damage classification and drought monitoring.