The Detrimental Effects of Mobile Game Addiction on Chinese Primary School Students and Possible Interventions

Smart phones have permeated every part of people’s lives in the mobile internet era and are virtually taken for granted on a daily basis. Chinese primary school students are facing a serious problem with mobile gaming addiction as a result of their easy access to cell phones. The excessive usage of mobile games has negative impacts on children’s academic performance, physical health, and mental health, and may possibly have severe, irreversible implications. It is crucial for educators and the general public to address the issue of mobile game addiction in primary school students. This article examined the current state of mobile gaming among primary school students and the negative effects of mobile game addiction on schoolchildren, looked at the contributing factors from the perspectives of students, parents, schools, and society, and suggested some coping mechanisms

Research on the Post Occupancy Evaluation of Green Public Building Environmental Performance Combined with Carbon Emissions Accounting

AbstractThe development of green building in China has reached a new stage, needs to turn to the total energy consumption control from the technology control[1]. We should avoid packing technologies in green building projects and regard achieving good environmental performance as the fundamental goal. In this paper, we use the method of post-occupancy evaluation and regard the building environmental performance as the core of the evaluation system, in order to reduce the influence on the accuracy of results from the measures evaluation. We establish the evaluation index system of green public building environmental performance in severe cold and cold regions, including the index of building life-cycle carbon emissions accounting. And we set up the application plan of index and the scoring method, then we put forward a kind of evaluation grade based on environmental performance level, finally proposed the POE System of Green Public Building Environmental Performance in Severe Cold and Cold Regions (POE-GPBEPC)

The feasibility and efficiency for constructing arteriovenous fistula with <2 mm vein—a systematic review and meta-analysis

BackgroundAutogenous arteriovenous fistula (AVF) is an efficient hemodialysis access for patients with end-stage kidney disease (ESKD). The specific threshold of vein diameter still not reached a consensus.MethodWe conducted a comprehensive search in PubMed, Embase, and Web of Science databases for articles which comparing the treatment outcomes of AVF with 2 mm as vein diameter threshold. Fixed and random effect model were used for synthesis of results. Subgroup analysis was designed to assess the risk of bias.ResultEight high-quality articles were included finally. Among a total of 1,075 patients (675 males and 400 females), 227 and 809 patients possessed &lt;2 mm and ≥2 mm vein respectively. Apart from gender and coronary artery disease (P &lt; 0.05), there was no significant difference in age, diabetes, hypertension or radial artery between maturation and non-maturation groups. The functional maturation rate was lower in patients with &lt;2 mm vein according to fixed effect model [OR = 0.19, 95% CI (0.12, 0.30), P &lt; 0.01]. There was no significant difference in primary [OR = 0.63, 95% CI (0.12, 3.25), P = 0.58] or cumulative patency rates [OR = 0.40, 95% CI (0.13, 1.19), P = 0.10].ConclusionVein diameter less than 2 mm has a negative impact on the functional maturation rate of AVF, while it does not affect the primary and cumulative patency rates (12 months)

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer. Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis. Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features. Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets

Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data

Purpose: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs. Methods: We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients were divided into three subtypes, namely, estrogen receptor (ER)-positive, ER-negative, and triple-negative groups. The associations between TIL scores and gene expression and somatic mutation were examined separately in three breast cancer subtypes. Both univariate and multivariate survival analyses were performed on 43 TIL image features to examine the prognostic value of TIL spatial patterns in different breast cancer subtypes. Results: The TIL score was in strong association with immune response pathway and genes (eg, programmed death-1 and CLTA4). Different breast cancer subtypes showed TIL score in association with mutations from different genes suggesting that different genetic alterations may lead to similar phenotypes. Spatial TIL features that represent density and distribution of TIL clusters were important indicators of the patient outcomes. Conclusion: Our pipeline can facilitate computational pathology-based discovery in cancer immunology and research on immunotherapy. Our analysis results are available for the research community to generate new hypotheses and insights on breast cancer immunology and development

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets

Microarray-Based Approach Identifies Differentially Expressed MicroRNAs in Porcine Sexually Immature and Mature Testes

MicroRNAs (miRNAs) are short non-coding RNA molecules which are proved to be involved in mammalian spermatogenesis. Their expression and function in the porcine germ cells are not fully understood.We employed a miRNA microarray containing 1260 unique miRNA probes to evaluate the miRNA expression patterns between sexually immature (60-day) and mature (180-day) pig testes. One hundred and twenty nine miRNAs representing 164 reporter miRNAs were expressed differently (p<0.1). Fifty one miRNAs were significantly up-regulated and 78 miRNAs were down-regulated in mature testes. Nine of these differentially expressed miRNAs were validated using quantitative RT-PCR assay. Totally 15,919 putative miRNA-target sites were detected by using RNA22 method to align 445 NCBI pig cDNA sequences with these 129 differentially expressed miRNAs, and seven putative target genes involved in spermatogenesis including DAZL, RNF4 gene were simply confirmed by quantitative RT-PCR.Overall, the results of this study indicated specific miRNAs expression in porcine testes and suggested that miRNAs had a role in regulating spermatogenesis