Progress on the Improvement of Quality and Functional Properties of Fermented Milk by Complex Strains of Bacteria

Fermented milk has a long history of being fermented by lactic acid bacteria. Fermented milk contains many elements, including protein, minerals, and vitamins. Fermented milk is gaining more and more attention from customers as people’s desire for a high quality of life improves. Most of the fermented milk on the market today are prepared with single strains or traditional lactic acid bacteria (Streptococcus thermophilus and Lactobacillus bulgaricus) as fermenting agents. However, this production method results in issues like an excessively long fermentation time, a mildly inferior taste, and poor stability. Compound strains have recently gained attention in the field of fermented milk preparation. By utilizing interactions and synergies between various strains, it is possible to increase the quantity and diversity of metabolites, enhancing the quality and functional properties of fermented milk and compensating for some of the shortcomings of conventional fermented milk in terms of product morphology and sensory experience. This study examines how complexing strains have accelerated pH reduction, improved the product’s sensory qualities, rheological characteristics, and water-holding capacity, as well as increased their capacity for lipid-lowering, anti-inflammatory, antioxidant, and bacteriostatic effects. Finally, the future research paths for fermented milk innovation are intended to offer suggestions for the diverse, functionalized, and precise production of fermented milk

Single-cell RNA sequencing reveals cellular dynamics and therapeutic effects of astragaloside IV in slow transit constipation

The cellular characteristics of intestinal cells involved in the therapeutic effects of astragaloside IV (AS-IV) for treating slow transit constipation (STC) remain unclear. This study aimed to determine the dynamics of colon tissue cells in the STC model and investigate the effects of AS-IV treatment by single-cell RNA sequencing (scRNA-seq). STC mouse models were developed using loperamide, with subsequent treatment using AS-IV. Colon tissues and feces were collected for scRNA-seq and targeted short-chain fatty acid quantification. We integrated scRNA-seq data with network pharmacology to analyze the effect of AS-IV on constipation. AS-IV showed improvement in defecation for STC mice induced by loperamide. Notably, in STC mice, epithelial cells, T cells, B cells, and fibroblasts demonstrated alterations in cell proportions and aberrant functions, which AS-IV partially rectified. AS-IV has the potential to modulate the metabolic pathway of epithelial cells through its interaction with peroxisome proliferator-activated receptor gamma (PPARγ). AS-IV reinstated fecal butyrate levels and improved energy metabolism in epithelial cells. The proportion of naïve CD4+T cells is elevated in STC, and the differentiation of these cells into regulatory T cells (Treg) is regulated by B cells and fibroblasts through the interaction of ligand-receptor pairs. AS-IV treatment can partially alleviate this trend. The status of fibroblasts in STC undergoes alterations, and the FB_C4_Adamdec1 subset, associated with angiogenesis and the Wingless-related integration (Wnt) pathway, emerges. Our comprehensive analysis identifies perturbations of epithelial cells and tissue microenvironment cells in STC and elucidates mechanisms underlying the therapeutic efficacy of AS-IV

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced

Study on Short Text Clustering with Unsupervised SimCSE

Traditional shallow text clustering methods face challenges such as limited context information,irregular use of words,and few words with actual meaning when clustering short texts,resulting in sparse embedding representations of the text and difficulty in extracting key features.To address these issues,a deep clustering model SSKU(SBERT SimCSE Kmeans Umap) incorporating simple data augmentation methods is proposed in the paper.The model uses SBERT to embed short texts and fine-tunes the text embedding model using the unsupervised SimCSE method in conjunction with the deep clustering KMeans algorithm to improve the embedding representation of short texts to make them suitable for clustering.To improve the sparse features of short text and optimize the embedding results,Umap manifold dimension reduction method is used to learn the local manifold structure.Using K-Means algorithm to cluster the dimensionality-reduced embeddings,and the clustering results are obtained.Extensive experiments are carried out on four publicly available short text datasets,such as StackOverFlow and Biomedical, and compared with the latest deep clustering algorithms.The results show that the proposed model exhibits good clustering performance in terms of both accuracy and standard mutual information evaluation metrics

Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.</jats:p