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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]

BACKGROUND: Protein–polysaccharide gels have significant and unique properties in food formulations. However, they are susceptible to environmental influences like heat and pH. The present work investigated the effects of acid and alkali treatments on the gel properties and microstructural changes of whey protein isolate (WPI) high acyl gellan gum (HG). RESULTS: The results showed that the pH had a strong effect on the gel hardness, water‐holding capacity (WHC), free sulfhydryl groups (‐SH), and other properties of the composite gel. The hardness reached a maximum level of 282.50 g and the best WHC was 98.33% at pH 7, indicating that a suitable pH could promote this cross‐linking between the WPI and HG molecules. The rheological analysis demonstrated that the pH affected the gel formation time. Meanwhile, the gel formation time reached a maximum at pH 7, and the gel's storage modulus G′ value was the largest in the final state. Fourier transform infrared spectroscopy (FTIR) results showed that pH affected the interaction between WPI and HG. Scanning electron microscopy (SEM) analysis also indicated that the composite gel formed a three‐dimensional network structure at pH 7–9. CONCLUSION: These results could broaden the application of protein‐polysaccharide gels in food and delivery systems. © 2023 Society of Chemical Industry. [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]

A refractive index (RI) sensor has been realized to measure RI, hardly affected by temperature. A fused biconical taper machine is used to receive two same diameter tapers, which are cascaded by a fusion splicer. This paper studies the spectral characteristics of the RI sensor with a different bending angle in tapers, and finds that with the increase of bending angle (from 0° to 100°), the extinction ratio will be improved while the spectrum moved down. When two tapers are bent to 90°, the RI sensor can serve as a sensing probe (the RI sensitivity was $\sim 126.15$ nm/RIU). The RI sensor has significant applications, and the measurement error is <1%. [ABSTRACT FROM PUBLISHER]

Liver cancer is one of the most common malignant diseases worldwide. Segmentation and reconstruction of liver tumors and vessels in CT images can provide convenience for physicians in preoperative planning and surgical intervention. In this paper, we introduced a TransFusionNet framework, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels. In addition, we applied the transfer learning approach to pre-train using public datasets and then fine-tune the model to further improve the fitting effect. Furthermore, we proposed an intelligent quantization scheme to compress the model weights and achieved high performance inference on JetsonTX2. The TransFusionNet framework achieved mean IoU of 0.854 in vessel segmentation task, and achieved mean IoU of 0.927 in liver tumor segmentation task. When profiling the Computational Performance of the quantized inference, our quantized model achieved 4TFLOPs on Node with NVIDIA RTX3090 and 132GFLOPs on JetsonTX2. This unprecedented segmentation effect solves the accuracy and performance bottleneck of automated segmentation to a certain extent.

Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Song, Dai, Wang, Wang, Zhang, Zhang and Jiao.)