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Development of pH and enzyme dual responsive chitosan/polyaspartic acid nanoparticle-embedded nanofibers for fruit preservation.
Int J Biol Macromol
January 2025
Beijing Key Laboratory for Bioengineering and Sensing Technology, Daxing Research Institute, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing 100083, China. Electronic address:
This study focuses on the development and application of tea polyphenol-loaded chitosan/polyaspartic acid nanoparticles (TP@CS/PASP-Nps) embedded within polyvinyl alcohol (PVA) nanofibers to extend the shelf life of fruit. The nanofibers were fabricated using electrospinning, which enhanced the stability and uniform dispersion of the nanoparticles. Experimental results demonstrated that the TP@CS/PASP nanoparticles exhibit significant pH and protease-responsive release of TP, with a cumulative release of 56.
View Article and Find Full Text PDFExosomal miR-302b rejuvenates aging mice by reversing the proliferative arrest of senescent cells.
Cell Metab
January 2025
Henan Academy of Sciences, Zhengzhou 450000, China; Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. Electronic address:
Cellular senescence, a hallmark of aging, involves a stable exit from the cell cycle. Senescent cells (SnCs) are closely associated with aging and aging-related disorders, making them potential targets for anti-aging interventions. In this study, we demonstrated that human embryonic stem cell-derived exosomes (hESC-Exos) reversed senescence by restoring the proliferative capacity of SnCs in vitro.
View Article and Find Full Text PDFImprovement of the accuracy of breeding value prediction for egg production traits in Muscovy duck using low-coverage whole-genome sequence data.
Poult Sci
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Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China. Electronic address:
Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys.
View Article and Find Full Text PDFA universal strategy for smoothing deceleration in deep graph neural networks.
Neural Netw
January 2025
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China. Electronic address:
Graph neural networks (GNNs) have shown great promise in modeling graph-structured data, but the over-smoothing problem restricts their effectiveness in deep layers. Two key weaknesses of existing research on deep GNN models are: (1) ignoring the beneficial aspects of intra-class smoothing while focusing solely on reducing inter-class smoothing, and (2) inefficient computation of residual weights that neglect the influence of neighboring nodes' distributions. To address these weaknesses, we propose a novel Smoothing Deceleration (SD) strategy to reduce the smoothing speed rate of nodes as information propagates between layers, thereby mitigating over-smoothing.
View Article and Find Full Text PDFImplementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.
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December 2024
Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK; Centre for AI-Physics Modelling, Imperial-X, White City Campus, Imperial College London, W12 7SL, UK.
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.
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