Shift in social media app usage during covid-19 lockdown and clinical anxiety symptoms: Machine learning-based ecological momentary assessment study

Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.JR was supported by the American Psychiatric Association 2021 Junior Psychiatrist Research Colloquium (NIDA R-13 grant). ES received funding from the European Union Horizon 2020 research and innovation program (Marie Sklodowska-Curie grant 813533). AA is supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018-099655-B-I00), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICO-CM), and the BBVA Foundation (Deep-DARWiN grant)

Ultracool Field Brown Dwarf Candidates Selected at 4.5 microns

We have identified a sample of cool field brown dwarf candidates using IRAC data from the Spitzer Deep, Wide-Field Survey (SDWFS). The candidates were selected from 400,000 SDWFS sources with [4.5] <= 18.5 mag and required to have [3.6]-[4.5] >= 1.5 and [4.5] - [8.0] <= 2.0 on the Vega system. The first color requirement selects objects redder than all but a handful of presently known brown dwarfs with spectral classes later than T7, while the second eliminates 14 probable reddened AGN. Optical detection of 4 of the remaining 18 sources implies they are likely also AGN, leaving 14 brown dwarf candidates. For two of the brightest candidates (SDWFS J143524.44+335334.6 and SDWFS J143222.82+323746.5), the spectral energy distributions including near-infrared detections suggest a spectral class of ~ T8. The proper motion is < 0.25 "/yr, consistent with expectations for a luminosity inferred distance of >70 pc. The reddest brown dwarf candidate (SDWFS J143356.62+351849.2) has [3.6] - [4.5]=2.24 and H - [4.5] > 5.7, redder than any published brown dwarf in these colors, and may be the first example of the elusive Y-dwarf spectral class. Models from Burrows et al. (2003) predict larger numbers of cool brown dwarfs should be found for a Chabrier (2003) mass function. Suppressing the model [4.5] flux by a factor of two, as indicated by previous work, brings the Burrows models and observations into reasonable agreement. The recently launched Wide-field Infrared Survey Explorer (WISE) will probe a volume ~40x larger and should find hundreds of brown dwarfs cooler than T7.Comment: 13 pages, 6 figures, accepted for publication in the June 2010 issue of The Astronomical Journa

Ordinal-Level Phylogenomics of the Arthropod Class Diplopoda (Millipedes) Based on an Analysis of 221 Nuclear Protein-Coding Loci Generated Using Next-Generation Sequence Analyses

Background The ancient and diverse, yet understudied arthropod class Diplopoda, the millipedes, has a muddled taxonomic history. Despite having a cosmopolitan distribution and a number of unique and interesting characteristics, the group has received relatively little attention; interest in millipede systematics is low compared to taxa of comparable diversity. The existing classification of the group comprises 16 orders. Past attempts to reconstruct millipede phylogenies have suffered from a paucity of characters and included too few taxa to confidently resolve relationships and make formal nomenclatural changes. Herein, we reconstruct an ordinal-level phylogeny for the class Diplopoda using the largest character set ever assembled for the group. Methods Transcriptomic sequences were obtained from exemplar taxa representing much of the diversity of millipede orders using second-generation (i.e., next-generation or high-throughput) sequencing. These data were subject to rigorous orthology selection and phylogenetic dataset optimization and then used to reconstruct phylogenies employing Bayesian inference and maximum likelihood optimality criteria. Ancestral reconstructions of sperm transfer appendage development (gonopods), presence of lateral defense secretion pores (ozopores), and presence of spinnerets were considered. The timings of major millipede lineage divergence points were estimated. Results The resulting phylogeny differed from the existing classifications in a number of fundamental ways. Our phylogeny includes a grouping that has never been described (Juliformia+Merocheta+Stemmiulida), and the ancestral reconstructions suggest caution with respect to using spinnerets as a unifying characteristic for the Nematophora. Our results are shown to have significantly stronger support than previous hypotheses given our data. Our efforts represent the first step toward obtaining a well-supported and robust phylogeny of the Diplopoda that can be used to answer many questions concerning the evolution of this ancient and diverse animal group

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Mapping of a blood pressure QTL on chromosome 17 in American Indians of the strong heart family study

Abstract Background Blood pressure (BP) is a complex trait, with a heritability of 30 to 40%. Several genome wide associated BP loci explain only a small fraction of the phenotypic variation. Family studies can provide an important tool for gene discovery by utilizing trait and genetic transmission information among relative-pairs. We have previously described a quantitative trait locus at chromosome 17q25.3 influencing systolic BP in American Indians of the Strong Heart Family Study (SHFS). This locus has been reported to associate with variation in BP traits in family studies of Europeans, African Americans and Hispanics. Methods To follow-up persuasive linkage findings at this locus, we performed comprehensive genotyping in the 1-LOD unit support interval region surrounding this QTL using a multi-step strategy. We first genotyped 1,334 single nucleotide polymorphisms (SNPs) in 928 individuals from families that showed evidence of linkage for BP. We then genotyped a second panel of 306 SNPs in all SHFS participants (N = 3,807) for genes that displayed the strongest evidence of association in the region, and, in a third step, included additional genotyping to better cover the genes of interest and to interrogate plausible candidate genes in the region. Results Three genes had multiple SNPs marginally associated with systolic BP (TBC1D16, HRNBP3 and AZI1). In BQTN analysis, used to estimate the posterior probability that any variant in each gene had an effect on the phenotype, AZI1 showed the most prominent findings (posterior probability of 0.66). Importantly, upon correction for multiple testing, none of our study findings could be distinguished from chance. Conclusion Our findings demonstrate the difficulty of follow-up studies of linkage studies for complex traits, particularly in the context of low powered studies and rare variants underlying linkage peaks

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

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

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

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