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Fermented milk is a typically product of co-fermentation by a variety of lactic acid bacteria (LAB). Quorum sensing (QS), as an intercellular communication mechanism of bacteria, may serve as a key factor influencing the quality of fermented milk. In this study, the fermentation performance of Lactobacillus delbrueckiisubsp. bulgaricusDPUL-F36 were evaluated after supplementation with auto-inducer-2 (AI-2). The protein-protein interaction network (PPIN) was used to retrieve the interactions between QS genes and four major metabolic pathway genes. The expression of luxSgene in DPUL-F36 may affect amino acid metabolism, carbohydrate metabolism and cell wall formation. The key differential metabolites were identified by metabolomics analysis. A variety of amino acids including L-Glutamic, L-Tryptophan, L-Threonine, L-Histidine and L-Glutamine were found to be up-regulated in response to the addition of AI-2. Malic acid and succinate gradually accumulate in the tricarboxylic acid cycle. The consumption of citric acid increased with the fermentation process. In terms of rheological and texture characteristics of fermented milk, addition of AI-2 could effectively improve its shear stress resistance, firmness and index of viscosity. Our research provided a theoretical basis for improving the fermentation performance of DPUL-F36 strains, and support for the screening of excellent composite starter culture.

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAFresistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

Preventing screen blockage due to sand poses a significant challenge. The article combines computational fluid dynamics (CFD) and discrete element method (DEM) to establish a fully coupled four-way interaction model. A sophisticated size particle injection model was established, taking into account the particle size distribution (PSD) characteristics of the formation. A layering method was used to study the sand control behavior of a high-performance pre-filled screen. The study also quantitatively investigated the impact of factors such as median sand size (D50), screen structure, and fluid velocity on both wellbore-screen annulus and internal screen plugging patterns. The findings reveal that the sand retention rate within the wellbore-screen annulus exceeds 93.8 %. The risk of pre-filled screen blockage is minimized when the ratio of gravel diameter to sand diameter falls within the range of 3.3 to 5. At a gravel filling rate of 97 %, fine sand predominantly moves radially, resulting in a low risk of blockage at the screen's entrance layer, thereby favoring oil and gas production. In cases with elevated fine sand content, it is advisable to maintain a production flow rate below 3 m/s to mitigate localized erosion damage caused by increased pressure resulting from screen blockage. This model holds significant importance in optimizing completion tools and production parameters.

Inflammation in visceral adipose tissues (VATs) contributes to the pathology of diabetes. This study focused on the inflammatory regulation in VATs by a yeast β-1,3-glucan (BYG) orally administered to ob/ob mice. BYG decreased pro-inflammatory modulators of TNF-α, IL-6, IL-1β, CCL2, and SAA3, and increased anti-inflammatory factors of Azgp1(2.53 ± 0.02-fold change) at protein and/or mRNA levels (p< 0.05). Remarkably, BYG decreased the degree of adipose tissue macrophages (ATMs) infiltration to 82.5 ± 8.3%, especially the newly recruited ATMs. Interestingly, BYG increased the protective Th2 cell regulator GATA3(7.72 ± 0.04-fold change) and decreased immunosuppressors IL-10and IL-1ra, suggesting that BYG elicited inflammation inhibition via stimulating immune responses. Additionally, BYG increased the gut microbiota proportion of Akkermansiafrom 0.07% to 4.85% and improved the microenvironment of VATs through decreasing fibrosis and angiogenesis. These findings suggest that BYG has anti-inflammatory effect in diabetic mice, which can be used as a food component and/or therapeutic agent for diabetes.