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Over 60% routinely screened HIV patients for NCDs risk factors Smoking (87.2%), alcohol (90.8%), diet (84.9%) and physical activity (73.5%). Conclusion There were gaps in detailed knowledge on NCDs, but positive attitude towards routine primary care integrated HIV/NCDs management, showing likely support for implementation of such policy. © 2019 Masupe et al.Background HIV and AIDS remains a pandemic that has greatly affected many regions and countries in the world. Africa is the hardest hit region by tthis disease while southern Africa appears to be the melting pot for HIV and AIDS. The HIV and AIDS pandemic remains the greatest sustainable human development and public health challenge for Swaziland. Swaziland is the world's worst affected country with the youth the most vulnerable group to HIV and AIDS due to many factors. Objectives'Methods This study investigated the behavioral factors that contributed to the transmission of HIV and AIDS among female youth of Mbabane in Swaziland and to suggest measures that could encourage positive female youth behavior change in order to mitigate the spread and impacts of the pandemic. The study used a qualitative research approach in order to gain an in-depth experience of female youths in Mabane. Data were collected using a questionnaire, which were distributed to 210 randomly sampled females aged 16 to 24 years in Mbabanthere were still characteristics of resistance to behavior change, because there was evidence of underestimation of HIV risk through engagement in behavior, which contributed to HIV infection and spread. Behavioral change measures using the BCC model is proposed. © 2019 Belle et al.Background Soft tissue sarcomas (STSs) are heterogeneous at the clinical and molecular level and need to be further sub-clustered for treatment and prognosis. Materials And Methods STSs were sub-clustered based on RNAseq and miRNAseq data extracted from The Cancer Genome Atlas (TCGA) through the combined process of similarity network fusion (SNF) and consensus clustering (CC). The expression and clinical characteristics of each sub-cluster were analyzed. The genes differentially expressed (lncRNAs, miRNAs, and mRNAs) between the poor prognosis and good prognosis clusters were used to construct a competing endogenous RNA (ceRNA) network. Functional enrichment analysis was conducted and a hub network was extracted from the constructed ceRNA network. Results A total of 247 STSs were classified into three optimal sub-clusters, and patients in cluster 2 (C2) had a significantly lower rate of survival. A ceRNA network with 91 nodes and 167 edges was constructed according to the hypothesis of ceRNA. Functional enrichment analysis revealed that the network was mainly associated with organism development functions. Moreover, LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1)-competing endogenous gene pairs were identified as hub networks of the ceRNA network, in which each component showed survival significance. Conclusion Integrative clustering analysis revealed that the STSs could be clustered into three sub-clusters. The ceRNA network, especially the subnetwork LncRNA (KCNQ1OT1)-miRNA (has-miR-29c-3p)-mRNA (JARID2, CDK8, DNMT3A, TET1) was a promising therapeutic target for the STS sub-cluster associated with a poor prognosis. Copyright © 2020 Zhu, Jin, Zhang, Zhang and Sun.Giant lampbrush chromosomes (LBCs) typical for growing oocytes of various animal species are characterized by a specific chromomere-loop appearance and massive transcription. Chromomeres represent universal units of chromatin packaging at LBC stage. While quite good progress has been made in investigation of LBCs structure and function, chromomere organization still remains poorly understood. To extend our knowledge on chromomere organization, we applied microdissection to chicken LBCs. In particular, 31 and 5 individual chromomeres were dissected one by one along the macrochromosome 4 and one microchromosome, respectively. read more The data on genomic context of individual chromomeres was obtained by high-throughput sequencing of the corresponding chromomere DNA. Alignment of adjacent chromomeres to chicken genome assembly provided information on chromomeres size and genomic boarders, indicating that prominent marker chromomeres are about 4-5 Mb in size, while common chromomeres of 1.5-3.5 Mb. Analysis of genomic features showed that the majority of chromomere-loop complexes combine gene-dense and gene-poor regions, while massive loopless DAPI-positive chromomeres lack genes and are remarkably enriched with different repetitive elements. Finally, dissected LBC chromomeres were compared with chromatin domains (topologically associated domains [TADs] and A/B-compartments), earlier identified by Hi-C technique in interphase nucleus of chicken embryonic fibroblasts. Generally, the results obtained suggest that chromomeres of LBCs do not correspond unambiguously to any type of well-established spatial domains of interphase nucleus in chicken somatic cells. Copyright © 2020 Zlotina, Maslova, Pavlova, Kosyakova, Al-Rikabi, Liehr and Krasikova.Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an L 1 regularization in mDAG to ensure it can recover a large sparse DAG with limited sample size.