The use of smart phones and their mobile applications among older adults in Hong Kong: An exploratory study

The purpose of this study was to explore social participation using smart phones by the older population in Hong Kong. The present study was conducted from 10-June-2013 to 16-August-2013. It was a cross-sectional survey study, and data were collected from street interviews. Potential participants were approached and invited to respond to a questionnaire. The locations for collecting data were evenly distributed on Hong Kong Island, Kowloon, and the New Territories. The size of the samples for Hong Kong Island, Kowloon, and the New Territories were calculated based on their respective proportion of the Hong Kong population in 2011. The estimated time to complete the questionnaire was approximately 10 minutes. The questionnaire included questions on demographic data and the use of smart phones and their related features. A total of 982 participants were interviewed, 46% of whom were male and 54% female. The participants were divided into the following two groups: the young-old (age 50-69) and the old-old (age 70 or above). The mean age was 67.93±10.386. The findings showed that, in comparison with the young-old group (age 50 to 69), a smaller percentage of the old-old group (70 and over) used smart phones and mobile messaging applications to communicate with others. There were no differences in patterns with regard to the type and frequency of the mobile applications being used. However, a smaller percentage of the old-old group had installed the mobile app by themselves and introduced the mobile app to others. This study reveals the behavioral patterns of the young-old and the old-old groups in the use of mobile devices to communicate. The young-old and old-old groups exhibited the same patterns in terms of the types and frequency of the mobile apps used; however, a smaller percentage of the old-old group used mobile apps to communicate. Different educational programs on the importance of social support should be established, and the promotional strategies for these programs need to be tailored to older adults

Synaptopathies: Dysfunction of Synaptic Function Confirmed rare copy number variants implicate novel genes in schizophrenia

Abstract Understanding how cognitive processes including learning, memory, decision making and ideation are encoded by the genome is a key question in biology. Identification of sets of genes underlying human mental disorders is a path towards this objective. Schizophrenia is a common disease with cognitive symptoms, high heritability and complex genetics. We have identified genes involved with schizophrenia by measuring differences in DNA copy number across the entire genome in 91 schizophrenia cases and 92 controls in the Scottish population. Our data reproduce rare and common variants observed in public domain data from >3000 schizophrenia cases, confirming known disease loci as well as identifying novel loci. We found copy number variants in PDE10A (phosphodiesterase 10A), CYFIP1 [cytoplasmic FMR1 (Fragile X mental retardation 1)-interacting protein 1], K + channel genes KCNE1 and KCNE2, the Down's syndrome critical region 1 gene RCAN1 (regulator of calcineurin 1), cell-recognition protein CHL1 (cell adhesion molecule with homology with L1CAM), the transcription factor SP4 (specificity protein 4) and histone deacetylase HDAC9, among others (see http://www.genes2cognition.org/SCZ-CNV). Integrating the function of these many genes into a coherent model of schizophrenia and cognition is a major unanswered challenge

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine