Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets

Recently, Bayesian solutions to the quantile regression problem, via the likelihood of a Skewed-Laplace distribution, have been proposed. These approaches are extended and applied to a family of dynamic conditional autoregressive quantile models. Popular Value at Risk models, used for risk management in finance, are extended to this fully nonlinear family. An adaptive Markov chain Monte Carlo sampling scheme is adapted for estimation and inference. Simulation studies illustrate favourable performance, compared to the standard numerical optimization of the usual nonparametric quantile criterion function, in finite samples. An empirical study generating Value at Risk forecasts for ten major financial stock indices finds significant nonlinearity in dynamic quantiles and evidence favoring the proposed model family, for lower level quantiles, compared to a range of standard parametric volatility models, a semi-parametric smoothly mixing regression and some nonparametric risk measures, in the literature

The TIGR Maize Database

Maize is a staple crop of the grass family and also an excellent model for plant genetics. Owing to the large size and repetitiveness of its genome, we previously investigated two approaches to accelerate gene discovery and genome analysis in maize: methylation filtration and high C(0)t selection. These techniques allow the construction of gene-enriched genomic libraries by minimizing repeat sequences due to either their methylation status or their copy number, yielding a 7-fold enrichment in genic sequences relative to a random genomic library. Approximately 900 000 gene-enriched reads from maize were generated and clustered into Assembled Zea mays (AZM) sequences. Here we report the current AZM release, which consists of ∼298 Mb representing 243 807 sequence assemblies and singletons. In order to provide a repository of publicly available maize genomic sequences, we have created the TIGR Maize Database (). In this resource, we have assembled and annotated the AZMs and used available sequenced markers to anchor AZMs to maize chromosomes. We have constructed a maize repeat database and generated draft sequence assemblies of 287 maize bacterial artificial chromosome (BAC) clone sequences, which we annotated along with 172 additional publicly available BAC clones. All sequences, assemblies and annotations are available at the project website via web interfaces and FTP downloads

Associations with photoreceptor thickness measures in the UK Biobank.

Spectral-domain OCT (SD-OCT) provides high resolution images enabling identification of individual retinal layers. We included 32,923 participants aged 40-69 years old from UK Biobank. Questionnaires, physical examination, and eye examination including SD-OCT imaging were performed. SD OCT measured photoreceptor layer thickness includes photoreceptor layer thickness: inner nuclear layer-retinal pigment epithelium (INL-RPE) and the specific sublayers of the photoreceptor: inner nuclear layer-external limiting membrane (INL-ELM); external limiting membrane-inner segment outer segment (ELM-ISOS); and inner segment outer segment-retinal pigment epithelium (ISOS-RPE). In multivariate regression models, the total average INL-RPE was observed to be thinner in older aged, females, Black ethnicity, smokers, participants with higher systolic blood pressure, more negative refractive error, lower IOPcc and lower corneal hysteresis. The overall INL-ELM, ELM-ISOS and ISOS-RPE thickness was significantly associated with sex and race. Total average of INL-ELM thickness was additionally associated with age and refractive error, while ELM-ISOS was additionally associated with age, smoking status, SBP and refractive error; and ISOS-RPE was additionally associated with smoking status, IOPcc and corneal hysteresis. Hence, we found novel associations of ethnicity, smoking, systolic blood pressure, refraction, IOPcc and corneal hysteresis with photoreceptor thickness

Closing the gap on causal processes of infection risk from cross-sectional data: structural equation models to understand infection and co-infection

Background: Epidemiological studies of disease exposure risk are frequently based on observational, cross-sectional data, and use statistical approaches as crucial tools for formalising causal processes and making predictions of exposure risks. However, an acknowledged limitation of traditional models is that the inferred relationships are correlational, cannot easily distinguish direct from indirect determinants of disease risk, and are often considerable simplifications of complex interrelationships. This may be particularly important when attempting to infer causality in patterns of co-infection through pathogen-facilitation. Methods: We describe analyses of cross-sectional data using structural equation models (SEMs), a contemporary advancement on traditional regression approaches, based on our study system of feline gammaherpesvirus (FcaGHV1) in domestic cats. Results: SEMs strongly supported a latent (host phenotype) variable associated with FcaGHV1 exposure and co-infection risk, suggesting these individuals are simply more likely to become infected with multiple pathogens. However, indications of pathogen-covariance (potential facilitation) were also variably detected: potentially among FcaGHV1, Bartonella spp and Mycoplasma spp. Conclusions: Our models suggest multiple exposures are primarily driven by host phenotypic traits, such as aggressive male phenotypes, and secondarily by pathogen-pathogen interactions. The results of this study demonstrate the application of SEMs to understanding epidemiological processes using observational data, and could be used more widely as a complementary tool to understand complex cross-sectional information in a wide variety of disciplines.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by BioMed Central. The published article can be found at: http://www.parasitesandvectors.com

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

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy