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To explore the influence of pH values on the properties of a compound system containing tea polyphenols (TPs) and low acyl gellan gum (LGG), the color, texture characteristics, rheological properties, water holding capacity (WHC), and microstructure of the compound system were measured. The results showed that the pH value noticeably affects the color and WHC of compound gels. Gels from pH 3 to 5 were yellow, gels from pH 6 to 7 were light brown, and gels from pH 8 to 9 were dark brown. The hardness decreased and the springiness increased with an increase in pH. The steady shear results showed that the viscosity of the compound gel solutions with different pH values decreased with increasing shear rates, indicating that all of the compound gel solutions were pseudoplastic fluids. The dynamic frequency results showed that the G′ and G″ of the compound gel solutions gradually decreased with increasing pH and that G′ was higher than G″. No phase transition occurred in the gel state under heating or cooling conditions at pH 3, indicating that the pH 3 compound gel solution was elastic. The WHC of the pH 3 compound gel was only 79.97% but the WHC of compound gels pH 6 and pH 7 was almost 100%. The network structure of the gels was dense and stable under acidic conditions. The electrostatic repulsion between the carboxyl groups was shielded by H+ with increasing acidity. The three-dimensional network structure was easily formed by an increase in the interactions of the hydrogen bonds. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability. [ABSTRACT FROM AUTHOR]