Diarrhoea is a common clinical condition; its pathogenesis is strongly associated with gut microbiota dysbiosis. Limonitum is a well-known traditional Chinese medicine that exerts appreciable benefits regarding the amelioration of diarrhoea. However, the mechanism through which Limonitum ameliorates diarrhoea remains unclear. Here, the efficacy and underlying mechanism of Limonitum decoction (LD) regarding diarrhoea were explored from the aspect of gut microbiota. Castor oil (CO) was used to induce diarrhoea in mice, which were then used to evaluate the effects of LD regarding the timing of the first defecation, diarrhoea stool rate, degree of diarrhoea, diarrhoea score, intestinal propulsive rate, and weight of intestinal contents. The concentrations of short-chain fatty acids (SCFAs), including acetic, propionic, isobutyric, butyric and valeric acids, were analysed by gas chromatography-mass spectrometry (GC-MS). The 16S rRNA high-throughput sequencing technology was applied to evaluate changes in the gut microbiota under exposure to LD. LD was found to effectively ameliorate the symptoms of diarrhoea, and the diversity and relative abundance of gut microbiota were restored to normal levels following LD treatment. Additionally, LD significantly restored the observed reductions in SCFAs. These results provide strong evidence that LD can sufficiently ameliorate diarrhoea in mice by regulating their gut microbiota. The findings presented here highlight that Limonitum may constitute a prospective remedy for diarrhoea.
When we apply ecological models in environmental management, we must assess the accuracy of parameter estimation and its impact on model predictions. Parameters estimated by conventional techniques tend to be nonrobust and require excessive computational resources. However, optimization algorithms are highly robust and generally exhibit convergence of parameter estimation by inversion with nonlinear models. They can simultaneously generate a large number of parameter estimates using an entire data set. In this study, we tested four inversion algorithms (simulated annealing, shuffled complex evolution, particle swarm optimization, and the genetic algorithm) to optimize parameters in photosynthetic models depending on different temperatures. We investigated if parameter boundary values and control variables influenced the accuracy and efficiency of the various algorithms and models. We obtained optimal solutions with all of the inversion algorithms tested if the parameter bounds and control variables were constrained properly. However, the efficiency of processing time use varied with the control variables obtained. In addition, we investigated if temperature dependence formalization impacted optimally the parameter estimation process. We found that the model with a peaked temperature response provided the best fit to the data., H. B. Wang, M. G. Ma, Y. M. Xie, X. F. Wang, J. Wang., and Obsahuje bibliografii