The Associations Between Social Support and Problematic Mobile Phone Use Among Children and Adolescents: A Three-level Meta-analysis.

Numerous studies have investigated the relationship between social support and problematic mobile phone use among adolescents, yet a definitive consensus remains elusive. The high prevalence of problematic mobile phone use among children and adolescents requires urgent clarity on this issue. However, previous meta-analyses on this topic have primarily focused on college students, overlooking this association in younger age groups. The present study thus concentrated on children and adolescents, conducting a three-level meta-analysis to combine existing research findings and analyze various moderators to identify sources of research heterogeneity. A systematic literature search retrieved a total of 33 studies with 135 effect sizes for this meta-analysis, and 25,537 students (53.83% female, age range 7-19, grades range 3rd-12th) were included. The results showed a negative correlation (r = -0.139) between social support and problematic mobile phone use in children and adolescents. Age, social support measurement, sources of social support, and symptoms of problematic mobile phone use were found to have a significant moderating influence. Specifically, social support showed a stronger negative correlation with problematic mobile phone use in older adolescents compared to their younger counterparts. The correlation was more pronounced when using the Multidimensional Scale of Perceived Social Support than other scales. Family support exhibited a stronger negative correlation with problematic mobile phone use compared to other sources of support. Among the symptoms of problematic mobile phone use, the inability to control craving has the strongest negative correlation with social support. This meta-analysis suggested that providing more social support, particularly in the form of family support, during the development of children and adolescents may help alleviate problematic mobile phone use.

Imputation method for single-cell RNA-seq data using neural topic model.

Single-cell RNA sequencing (scRNA-seq) technology studies transcriptome and cell-to-cell differences from higher single-cell resolution and different perspectives. Despite the advantage of high capture efficiency, downstream functional analysis of scRNA-seq data is made difficult by the excess of zero values (i.e., the dropout phenomenon). To effectively address this problem, we introduced scNTImpute, an imputation framework based on a neural topic model. A neural network encoder is used to extract underlying topic features of single-cell transcriptome data to infer high-quality cell similarity. At the same time, we determine which transcriptome data are affected by the dropout phenomenon according to the learning of the mixture model by the neural network. On the basis of stable cell similarity, the same gene information in other similar cells is borrowed to impute only the missing expression values. By evaluating the performance of real data, scNTImpute can accurately and efficiently identify the dropout values and imputes them accurately. In the meantime, the clustering of cell subsets is improved and the original biological information in cell clustering is solved, which is covered by technical noise. The source code for the scNTImpute module is available as open source at https://github.com/qiyueyang-7/scNTImpute.git.