Physical education consumption and executive function among older empty nesters in urban community: A double mediation effects.

OBJECTIVE: The main aim of this research was to determine the relationships among executive function, fitness mobile applications (APPs), physical exercise activity and physical education consumption in community-dwelling older empty nesters. METHODS: A cross-sectional design was applied to evaluate the relationships. A sample of 1104 community-dwelling older empty nesters completed the experiments. Physical education consumption scale, fitness APPs by smartphone application scale, physical exercise activity scale, and executive function scale were applied for the evaluation of the elderly alone in urban communities in southeast China. To explore mediating effects, structural equation modeling of AMOS 23.0, SPSS 25.0 and Process V3.5 software packages were applied for statistical processing. RESULT: Physical education consumption positively predicted executive function. Meanwhile, it was also found that physical education consumption and executive function were continuously mediated by fitness APP application and physical exercise activity, with indirect effect value of 0.267, accounting for 76 %. CONCLUSION: This research revealed how physical education consumption affected executive function of older empty nesters. The obtained results had certain implications for older empty nesters to better balance their executive function and life quality. Community managers should provide older empty nesters with favorable physical education environments in terms of positive physical and psychological environments, to improve their use ratio of fitness APPs usage and physical exercise activity, ultimately enhancing their executive function and life satisfaction.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization.

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0.72) and Davies-Bouldin index (1.05), demonstrating higher clustering effectiveness and model stability. Particularly noteworthy is the recall rate (0.82), a key performance indicator, where the ACO model accurately captures different behavioral characteristics of athletes, validating its effectiveness and reliability in athlete behavior analysis. The innovation lies not only in the application of the ACO algorithm to address practical issues in the field of sports but also in showcasing the advantages of the ACO algorithm in handling complex, high-dimensional sports data. However, its generality and efficiency on a larger scale or different types of sports data still need further validation. In conclusion, through the introduction and optimization of the ACO clustering model, this study provides a novel and effective approach for a deeper understanding and analysis of athlete behavior characteristics. This study holds significant importance in advancing sports science research and practical applications.

A Comprehensive Urine Proteome Database Generated From Patients With Various Renal Conditions and Prostate Cancer.

Urine proteins can serve as viable biomarkers for diagnosing and monitoring various diseases. A comprehensive urine proteome database, generated from a variety of urine samples with different disease conditions, can serve as a reference resource for facilitating discovery of potential urine protein biomarkers. Herein, we present a urine proteome database generated from multiple datasets using 2D LC-MS/MS proteome profiling of urine samples from healthy individuals (HI), renal transplant patients with acute rejection (AR) and stable graft (STA), patients with non-specific proteinuria (NS), and patients with prostate cancer (PC). A total of ~28,000 unique peptides spanning ~2,200 unique proteins were identified with a false discovery rate of <0.5% at the protein level. Over one third of the annotated proteins were plasma membrane proteins and another one third were extracellular proteins according to gene ontology analysis. Ingenuity Pathway Analysis of these proteins revealed 349 potential biomarkers in the literature-curated database. Forty-three percentage of all known cluster of differentiation (CD) proteins were identified in the various human urine samples. Interestingly, following comparisons with five recently published urine proteome profiling studies, which applied similar approaches, there are still ~400 proteins which are unique to this current study. These may represent potential disease-associated proteins. Among them, several proteins such as serpin B3, renin receptor, and periostin have been reported as pathological markers for renal failure and prostate cancer, respectively. Taken together, our data should provide valuable information for future discovery and validation studies of urine protein biomarkers for various diseases.