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Introduction The on-line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on-line near-infrared (NIR) technology. Objective To evaluate the advantages of on-line NIR model development using system optimisation strategy, Glycyrrhiza uralensis Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of Glycyrrhiza uralensis Fisch in three batches. Methods High-performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on-line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality. Results The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models. Conclusion The work demonstrated that system optimisation quantitative model of on-line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during Glycyrrhiza uralensis Fisch extraction process.

The thermosensitive genic male sterile 5 (tms5) mutation causes thermosensitive genic male sterility in rice (Oryza sativa) through loss of RNase ZS1 function, which influences ubiquitin fusion ribosomal protein L40 (UbL40 ) messenger RNA levels during male development. Here, we used ATAC-seq, combined with analysis of H3K9ac and H3K4me2, to identify changes in accessible chromatin during fertility conversion of the two-line hybrid rice Wuxiang S (WXS) derived from a mutant tms5 allele. Furthermore, RNA-seq and bioinformatic analyses identified specific transcription factors (TFs) in differentially accessible chromatin regions. Among these TFs, only GATA10 targeted UbL40 . Osgata10 knockout mutations, which resulted in low expression of UbL40 and a tendency toward male fertility, confirmed that GATA10 regulated fertility conversion via the modulation of UbL40 . Meanwhile, GATA10 acted as a mediator for interactions with ERF65, which revealed that transcriptional regulation is a complex process involving multiple complexes of TFs, namely TF modules. It appears that the ERF141/MADS7/MADS50/MYB modules affect metabolic processes that control anther and pollen development, especially cell wall formation. Our analysis revealed that these modules directly or indirectly affect metabolic pathway-related genes to coordinate plant growth with proper anther development, and furthermore, that GATA10 regulates fertility conversion via the modulation of UbL40 expression.

In recent years, joint inversion has been widely used for integrated geological interpretation. We extended a data-space joint inversion algorithm of magnetotelluric, gravity and magnetic data to include first-arrival seismic travel-time and normalized cross-gradient constraints. We describe the main features of the algorithm and apply it to synthetic data generated for hypothetical models. For the synthetic data, we find that the joint inversion with multiple parameters is superior to the joint inversion with two or even three parameters, which can reduce the multisolution of inversion results more effectively. Furthermore, data-space joint inversion involves fewer memory requirements and better calculation speeds than traditional model-space joint inversion. The normalized cross-gradient constraints can better couple model parameters of different magnitudes compared with traditional unnormalized cross-gradient constraints, resulting in higher levels of structural similarity among resistivity, density, magnetic susceptibility and velocity models.

Almost all living organisms have evolved autoregulatory transcriptional-translational feedback loops that produce oscillations with a period of approximately 24-h. These endogenous time keeping mechanisms are called circadian clocks. The main function of these circadian clocks is to drive overt circadian rhythms in the physiology of the organisms to ensure that main physiological functions are in synchrony with the external environment. Disruption of circadian rhythms caused by genetic or environmental factors has long-term consequences for metabolic health. Of relevance, host circadian rhythmicity and lipid metabolism are increasingly recognized to cross-regulate and the circadian clock-lipid metabolism interplay may involve in the development of obesity. Multiple systemic and molecular mechanisms, such as hormones (ie, melatonin, leptin, and glucocorticoid), the gut microbiome, and energy metabolism, link the circadian clock and lipid metabolism, and predictably, the deregulation of circadian clock-lipid metabolism interplay can increase the risk of obesity, which in turn may exacerbate circadian disorganization. Feeding time and dietary nutrients are two of key environmental Zeitgebers affecting the circadian rhythm-lipid metabolism interplay, and the influencing mechanisms in obesity development are highlighted in this review. Together, the characterization of the clock machinery in lipid metabolism aimed at producing a healthy circadian lifestyle may improve obesity care.

Melatonin has been shown to improve lipid metabolism and gut microbiota communities in animals and humans; however, it remains to know whether melatonin prevents obesity through gut microbiota. Here, we found that high-fat diet promoted the lipid accumulation and intestinal microbiota dysbiosis in mice, while oral melatonin supplementation alleviated the lipid accumulation and reversed gut microbiota dysbiosis, including the diversity of intestinal microbiota, relative abundances of Bacteroides and Alistipes, and functional profiling of microbial communities, such as energy metabolism, lipid metabolism, and carbohydrate metabolism. Interestingly, melatonin failed to alleviate the high-fat-induced lipid accumulation in antibiotic-treated mice; however, microbiota transplantation from melatonin-treated mice alleviated high-fat diet-induced lipid metabolic disorders. Notably, short-chain fatty acids were decreased in high-fat diet-fed mice, while melatonin treatment improved the production of acetic acid. Correlation analysis found a marked correlation between production of acetic acid and relative abundances of Bacteroides and Alistipes. Importantly, sodium acetate treatment also alleviated high-fat diet-induced lipid metabolic disorders. Taken together, our results suggest that melatonin improves lipid metabolism in high-fat diet-fed mice, and the potential mechanisms may be associated with reprogramming gut microbiota, especially, Bacteroides and Alistipes-mediated acetic acid production. Future studies are needed for patients with metabolic syndrome to fully understand melatonin's effects on body weight and lipid profiles and the potential mechanism of gut microbiota.