Search

There is ample evidence suggesting that restrained eating behaviors are very common during adolescence. In most research, restrained eating is represented by an overall score, limiting our understanding of the latent individual heterogeneity in restrained eating behaviors. The main purpose of current study was to investigate patterns of restrained eating and explore its association with multiple interpersonal factors (family, peer and school) in a sample of 552 adolescents (46% females, aged 11-16 years) from two middle schools in Changsha, China. Latent profile analysis (LPA) revealed that in the best-fitting model, restrained eating formed three profiles of adolescents: 'low restrained eating group' (57.3%), 'moderate restrained eating group' (31.9%), and 'high restrained eating group' (10.8%). The results of multinomial logistic regression showed that these profiles differed on multiple interpersonal factors including parental monitoring, deviant peer affiliation, and peer relationships. The current study is the first to identify patterns of restrained eating in relation to multiple interpersonal contexts, and the results may inform targeted prevention and intervention strategies., Author(s): Sicheng Xiong [sup.1] , Yi Xu [sup.2] , Bin Zhang [sup.1] , Lihui Zhu [sup.3] , Jianhui Xie [sup.3] Author Affiliations: (1) grid.488482.a, 0000 0004 1765 5169, Department of [...]

Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field. [ABSTRACT FROM AUTHOR]

Simple Summary: After approximately two decades of controlled breeding at the Shanghai Academy of Agricultural Sciences in China, Luchuan (LC) pigs have developed two distinct phenotypes: one with obesity (FLC) and the other with leanness (LLC). Since recent research suggests a link between microorganisms and the development of these different phenotypes, this study investigated the fecal microbiota profiles and serum metabolites of FLC and LLC pigs. We found significant changes in the microbiota and metabolites between the two groups. The correlation studies showed a significant link between the modified microbiota, metabolites, and the observed phenotypic differences in the LC pigs. Notably, Jeotgalicoccus was positively correlated with the body weight and chest circumference, but negatively correlated with certain metabolites like 2-mercaptobenzothiazole and N1-pyrazin-2-yl-4-chlorobenzamide, which were positively associated with Bacteroides. These findings provide strong evidence for a new connection between the gut microbiome, metabolome, and phenotypic variation in LC pigs. Luchuan (LC) pigs are a Chinese breed renowned for their distinctive black and white coloring, superior meat quality and rapid reproduction, but their growth rate is slow. Over the course of approximately two decades of controlled breeding, the LC pigs maintained at the Shanghai Academy of Agricultural Sciences (Shanghai, China) have diverged into two phenotypes: one characterized by obesity (FLC) and the other by leanness (LLC). Recent studies indicate a correlation between microorganisms and the differentiation of host phenotypes. In this study, we examined the fecal microbiota profiles and serum metabolites of FLC and LLC pigs. The body weight, chest circumference, and alanine aminotransferase and aspartate aminotransferase enzyme activities were increased in the FLC pigs compared to the LLC pigs. Conversely, the levels of the Fusobacterium and Streptococcus genera were lower in the FLC pigs, while the number of Firmicutes, Lactobacillus, Phascolartobacterium, and Rikenellaceae_RC9_gut_group members were higher. A total of 52 metabolites were altered between the two groups, with many playing crucial roles in prolactin signaling, oocyte meiosis, and aldosterone-regulated sodium reabsorption pathways. The correlation analyses demonstrated a significant association between the modified microbiota and metabolites and the phenotypic variations observed in the LC pigs. Specifically, Jeotgalicoccus was positively correlated with the body weight and chest circumference, but was negatively correlated with metabolites such as 2-mercaptobenzothiazole and N1-pyrazin-2-yl-4-chlorobenzamide, which were positively associated with Bacteroides. These results provide compelling evidence for a novel relationship between the gut microbiome and metabolome in the phenotypic differentiation of LC pigs. [ABSTRACT FROM AUTHOR]