Metabolite profiling, antioxidant and anti-glycemic activities of Tartary buckwheat processed by solid-state fermentation(SSF)with Ganoderma lucidum.

The aim of this study was to investigate the effect of Ganoderma lucidum fermentation on antioxidant and anti-glycemic activities of Tartary buckwheat. Xylanase, total cellulase (CMCase and FPase) and ß-glucosidase in fermented Tartary buckwheat (FB) increased significantly to 242.06 U/g, 17.99 U/g and 8.67 U/g, respectively. And the polysaccharides, total phenols, flavonoids and triterpenoids, which is increased by 122.19%, 113.70%, 203.74%, and 123.27%, respectively. Metabolite differences between non-fermented Tartary buckwheat (NFB) and FB pointed out that 445 metabolites were substantially different, and were involved in related biological metabolic pathways. There was a considerable rise in the concentrations of hesperidin, xanthotoxol and quercetin 3-O-malonylglucoside by 240.21, 136.94 and 100.77 times (in Fold Change), respectively. The results showed that fermentation significantly increased the antioxidant and anti-glycemic activities of buckwheat. This study demonstrates that the fermentation of Ganoderma lucidum provides a new idea to enhance the health-promoting components and bioactivities of Tartary buckwheat.

Effect of L. Plantarum Y279 and W. Cibaria Y113 on microorganism, lipid oxidation and fatty acid metabolites in Yu jiaosuan, A Chinese tradition fermented snack.

Fatty acids are one of the main sources of flavour in fermented Yu jiaosuan (YJS) in southwest China. Bacilli (50.18 %) and Oxyphotobacteria (32.70 %) were the dominant class. Lactiplantibacillus (40.51 %) and Weissella (20.43 %) were the dominant species in the inoculated fermented group (HY). The peroxide value (ZY: 0.025 g/100 g, HY: 0.016 g/100 g) and lipoxygenase (LOX) (ZY: 5.7654 U/min·g, HY: 3.3856 U/min·g) in the HY group were significantly lower compared with the natural fermentation group (ZY), while acid lipase activity (ZY: 0.3184 U/h·g, HY: 0.7075 U/h·g) and neutral lipase activity (ZY: 12.65443 U/h·g, HY: 20.25142 U/h·g) were significantly higher than the control sample. Totally 40 differential fatty acid metabolites were screened. Arachidonic acid metabolism, unsaturated fatty acid biosynthesis and linoleic acid metabolism were potential metabolic pathways. Seven major bacterial species were closely associated with 15 differential fatty acid. This study contributes to the targeted production of fatty acid functional active substances of YJS.

Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier.

Escherichia coli (E. coli) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making.

Elucidation of the effects of autochthonous starter on nitrogen-containing compounds during fermentation of Yujiangsuan by metabolomics.

To understand the role of microorganisms in nitrogen (N)-containing compound changes during the processing of Yujiangsuan by autochthonous starter cultures, the GC-TOF-MS-based metabolomics method was adopted to investigate the effects of Weissella cibaria and Lactobacillus plantarum. The results demonstrated that inoculation of autochthonous strains led to differential metabolites, such as fatty acids, organic oxygen compounds, and carboxylic acids on day 4 to day 12 of fermentation. The N-containing compounds under the inoculated fermentation group showed a faster relative concentration change. Nucleotide metabolism and arginine and proline metabolism exerted an influence on the formation of N-containing compounds. Apart from that, the effect of W. cibaria and L. plantarum on the hydrolysis of macromolecules was the main factor causing differences in major N-containing compounds.

Copper-Catalyzed Reaction Cascade of Thiophenol Hydroxylation and S-Arylation through Disulfide-Directed C-H Activation.

Copper-catalyzed thiophenol C-H activation is described. Through an initial attempt to conduct C-arylation with arylboronic acid, a rather surprising sequential C-H activation and S-arylation was discovered. Mechanistic investigation revealed the disulfide intermediate as the key component in directing C-H oxidation. The overall reaction proceeded under mild conditions with molecular oxygen as the oxidant. Discovery of disulfide as the directing group provides a potential new direction for catalytic C-H functionalization under mild conditions.