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The quantum Hall resistance standard directly traces the resistance unit to fundamental physical constants, greatly enhancing the accuracy and stability of resistance measurement. This paper reviews the current research status of quantum Hall resistance standards based on gallium arsenide, graphene, and the quantum anomalous Hall effect, as well as the decimal quantum Hall resistance standard. It discusses the bottlenecks and challenges faced in the research of quantum Hall resistance standards and anticipates future development directions, aiming to provide valuable insights and references for further research in this field.

Water is a vital resource essential to the survival and development of all creatures. With the rapid growth of industry and agriculture, people face a severe threat of ecological destruction and environmental pollution while living earthly lives. Water pollution, in particular, harms people’s health the most. As a result, water supply security has become a top priority. As a critical point in water supply safety, monitoring water quality effectively and forecasting sudden water contamination on time has become a research hotspot worldwide. With the rapid development and wide applications of artificial intelligence and computer vision technologies, biological activity identification-based intelligent water quality monitoring methods have drawn widespread attention. They were taking fish activities as the water-quality indicator has gained extensive attention by introducing advanced computer vision and artificial intelligence technologies with low cost and ease of carrying. This article comprehensively reviews recent progress in the research and applications of machine vision-based intelligent water quality monitoring and early warning techniques based on fish activity behavior recognition. In detail, it addresses water quality-oriented fish detection and tracking, activity recognition, and abnormal behavior recognition-based intelligent water quality monitoring. It analyzes and compares the performance and their favorite application conditions. Finally, it summarizes and discusses the difficulties and hotspots of water quality monitoring based on the fish’s abnormal behavior recognition and their future development trends.

Background: Depression has become a multifaceted global health issue, with complex connections to obesity. Weight-adjusted-waist index (WWI) can effectively evaluate central obesity, but the relationship between WWI and depression has not been well studied. The study aims to investigate the potential correlation between these two health parameters. Methods: According to the data from National Health and Nutrition Examination Survey, this cross-sectional study used multiple regression analysis, subgroup analysis, and smooth curve fitting to explore the relationship between WWI and depression. The assessment ability of WWI was evaluated and compared to other obesity indicators using the receiver operating characteristic (ROC) curve. Results: This study analyzed 38,154 participants. Higher WWI is associated with higher depression scores (β = 0.41; 95% CI, 0.36–0.47). After adjusting for various confounding factors, the positive correlation between WWI and depression remained significant (P for trend < 0.0001). Nonlinear positive correlation was detected with a breakpoint of 11.14. ROC analysis shows that compared to other obesity indicators (ROCWWI = 0.593; ROCBMI = 0.584; and ROCWC = 0.581), the correlation between WWI and depression has better discrimination and accuracy. DII mediated 4.93%, SII mediated 5.08%, and sedentary mediated 0.35% of the total association between WWI and depression. Conclusion: WWI levels were related to an increased likelihood of depression and showed a stronger relationship than BMI and waist circumference. Our findings indicated that WWI may serve as a simple anthropometric index to evaluate depression.

DNA methylation is known to be associated with cataracts. In this study, we used a mouse model and performed DNA methylation and transcriptome sequencing analyses to find epigenetic indicators for age-related cataracts (ARC). Anterior lens capsule membrane tissues from young and aged mice were analyzed by MethylRAD-seq to detect the genome-wide methylation of extracted DNA. The young and aged mice had 76,524 and 15,608 differentially methylated CCGG and CCWGG sites, respectively. The Pearson correlation analysis detected 109 and 33 differentially expressed genes (DEGs) with negative methylation at CCGG and CCWGG sites, respectively, in their promoter regions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses showed that DEGs with abnormal methylation at CCGG sites were primarily associated with protein kinase C signaling (Akap12, Capzb), protein threonine kinase activity (Dmpk, Mapkapk3), and calcium signaling pathway (Slc25a4, Cacna1f), whereas DEGs with abnormal methylation at CCWGG sites were associated with ribosomal protein S6 kinase activity (Rps6ka3). These genes were validated by pyrosequencing methylation analysis. The results showed that the ARC group (aged mice) had lower Dmpk and Slc25a4 methylation levels and a higher Rps6ka3 methylation than the control group (young mice), which is consistent with the results of the joint analysis of differentially methylated and differentially expressed genes. In conclusion, we confirmed the genome-wide DNA methylation pattern and gene expression profile of ARC based on the mouse cataract model with aged mice. The identified methylation molecular markers have great potential for application in the future diagnosis and treatment of ARC. [ABSTRACT FROM AUTHOR]