Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters.

When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44-70, 32-44, 42-58, and 76-97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.

Liraglutide Improves Nonalcoholic Fatty Liver Disease in Diabetic Mice by Activating Autophagy Through AMPK/mTOR Signaling Pathway.

Background: Type 2 diabetes (T2DM) combined nonalcoholic fatty liver disease (NAFLD) are characterized by metabolic disruptions. Liraglutide has been proved to be effective in T2DM. If LRG could regulate NAFLD combined T2DM has not been reported. Methods: Intraperitoneal injection of 1% streptozotocin (STZ) plus high-sugar and high-fat diet was used to induce NAFLD combined T2DM animal model. Palmitic acid (200 µmol/L) and glucose (25 mmol/L) incubation were used to induce cell model. The cell apoptosis, mRNA and protein expression were measured through flow cytometry, PCR, and Western blotting, respectively. Results: Liraglutide significantly improved the liver injury of NAFLD combined T2DM rats, but Com-C reversed the effect of liraglutide. The decreased AMPK/mTOR signaling pathway in the NAFLD combined T2DM animals was greatly activated by liraglutide. Com-C reversed the protection effects of liraglutide on palmitic acid+glucose induced cell damage. Conclusion: Liraglutide could greatly alleviate the damage caused by NAFLD+T2DM and palmitic acid+glucose. The protection effects of liraglutide were greatly inhibited by suppressing AMPK/mTOR signaling pathway. This research might provide a novel therapeutic strategy for the prevention and treatment of NAFLD combined T2DM disease.