Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS478 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Validity of oral fluid test for Delta-9-tetrahydrocannabinol in drivers using the 2013 National Roadside Survey Data

Background Driving under the influence of marijuana is a serious traffic safety concern in the United States. Delta 9-tetrahydrocannabinol (THC) is the main active compound in marijuana. Although blood THC testing is a more accurate measure of THC-induced impairment, measuring THC in oral fluid is a less intrusive and less costly method of testing. Methods We examined whether the oral fluid THC test can be used as a valid alternative to the blood THC test using a sensitivity and specificity analysis and a logistic regression, and estimate the quantitative relationship between oral fluid THC concentration and blood THC concentration using a correlation analysis and a linear regression on the log-transformed THC concentrations. We used data from 4596 drivers who participated in the 2013 National Roadside Survey of Alcohol and Drug Use by Drivers and for whom THC testing results from both oral fluid and whole blood samples were available. Results Overall, 8.9% and 9.4% of the participants tested positive for THC in oral fluid and whole blood samples, respectively. Using blood test as the reference criterion, oral fluid test for THC positivity showed a sensitivity of 79.4% (95% CI: 75.2%, 83.1%) and a specificity of 98.3% (95% CI: 97.9%, 98.7%). The log-transformed oral fluid THC concentration accounted for about 29% of the variation in the log-transformed blood THC concentration. That is, there is still 71% of the variation in the log-transformed blood THC concentration unexplained by the log-transformed oral fluid THC concentration. Back-transforming to the original scale, we estimated that each 10% increase in the oral fluid THC concentration was associated with a 2.4% (95% CI: 2.1%, 2.8%) increase in the blood THC concentration. Conclusions The oral fluid test is a highly valid method for detecting the presence of THC in the blood but cannot be used to accurately measure the blood THC concentration

The roles of herbal remedies in survival and quality of life among long-term breast cancer survivors - results of a prospective study

<p>Abstract</p> <p>Background</p> <p>Few data exist on survival or health-related quality of life (QOL) related to herbal remedy use among long-term breast cancer survivors. The objective of this report is to examine whether herbal remedy use is associated with survival or the health-related QOL of these long-term breast cancer survivors.</p> <p>Methods</p> <p>In 1999-2000, we collected the information of herbal remedy use and QOL during a telephone interview with 371 Los Angeles Non-Hispanic/Hispanic white women who had survived more than 10 years after breast cancer diagnosis. QOL was measured using the Medical Outcomes Study Short Form-36 (SF-36) questionnaire. Patients were followed for mortality from the baseline interview through 2007. 299 surviving patients completed a second telephone interview on QOL in 2002-2004. We used multivariable Cox proportional hazards methods to estimate relative risks (RR) and 95% confidence intervals (CI) for mortality and applied multivariable linear regression models to compare average SF-36 change scores (follow-up - baseline) between herbal remedy users and non-users.</p> <p>Results</p> <p>Fifty-nine percent of participants were herbal remedy users at baseline. The most commonly used herbal remedies were echinacea, herbal teas, and ginko biloba. Herbal remedy use was associated with non-statistically significant increases in the risks for all-cause (44 deaths, RR = 1.28, 95% CI = 0.62-2.64) and breast cancer (33 deaths, RR = 1.78, 95% CI = 0.72-4.40) mortality. Both herbal remedy users' and non-users' mental component summary scores on the SF-36 increased similarly from the first survey to the second survey (<it>P </it>= 0.16), but herbal remedy users' physical component summary scores decreased more than those of non-users (-5.7 vs. -3.2, <it>P </it>= 0.02).</p> <p>Conclusions</p> <p>Our data provide some evidence that herbal remedy use is associated with poorer survival and a poorer physical component score for health-related QOL among women who have survived breast cancer for at least 10 years. These conclusions are based on exploratory analyses of data from a prospective study using two-sided statistical tests with no correction for multiple testing and are limited by few deaths for mortality analysis and lack of information on when herbal remedy use was initiated or duration of or reasons for use.</p

Mutations in Polymerase Genes Enhanced the Virulence of 2009 Pandemic H1N1 Influenza Virus in Mice

Influenza A virus can infect a wide variety of animal species with illness ranging from mild to severe, and is a continual cause for concern. Genetic mutations that occur either naturally or during viral adaptation in a poorly susceptible host are key mechanisms underlying the evolution and virulence of influenza A virus. Here, the variants containing PA-A36T or PB2-H357N observed in the mouse-adapted descendants of 2009 pandemic H1N1 virus (pH1N1), A/Sichuan/1/2009 (SC), were characterized. Both mutations enhanced polymerase activity in mammalian cells. These effects were confirmed using recombinant SC virus containing polymerase genes with wild type (WT) or mutant PA or PB2. The PA-A36T mutant showed enhanced growth property compared to the WT in both human A549 cells and porcine PK15 cells in vitro, without significant effect on viral propagation in murine LA-4 cells and pathogenicity in mice; however, it did enhance the lung virus titer. PB2-H357N variant demonstrated growth ability comparable to the WT in A549 cells, but replicated well in PK15, LA-4 cells and in mice with an enhanced pathogenic phenotype. Despite such mutations are rare in nature, they could be observed in avian H5 and H7 subtype viruses which were currently recognized to pose potential threat to human. Our findings indicated that pH1N1 may adapt well in mammals when acquiring these mutations. Therefore, future molecular epidemiological surveillance should include scrutiny of both markers because of their potential impact on pathogenesis

A Phosphatidic Acid Binding/Nuclear Localization Motif Determines Lipin1 Function in Lipid Metabolism and Adipogenesis

A polybasic motif in the metabolic regulator lipin1 is both a membrane anchor and a nuclear localization sequence required for lipin1 function in phospholipid metabolism and adipogenesis

Estrogenic botanical supplements, health-related quality of life, fatigue, and hormone-related symptoms in breast cancer survivors: a HEAL study report

<p>Abstract</p> <p>Background</p> <p>It remains unclear whether estrogenic botanical supplement (EBS) use influences breast cancer survivors' health-related outcomes.</p> <p>Methods</p> <p>We examined the associations of EBS use with health-related quality of life (HRQOL), with fatigue, and with 15 hormone-related symptoms such as hot flashes and night sweats among 767 breast cancer survivors participating in the Health, Eating, Activity, and Lifestyle (HEAL) Study. HRQOL was measured by the Medical Outcomes Study short form-36 physical and mental component scale summary score. Fatigue was measured by the Revised-Piper Fatigue Scale score.</p> <p>Results</p> <p>Neither overall EBS use nor the number of EBS types used was associated with HRQOL, fatigue, or hormone-related symptoms. However, comparisons of those using each specific type of EBS with non-EBS users revealed the following associations. Soy supplements users were more likely to have a better physical health summary score (odds ratio [OR] = 1.66, 95% confidence interval [CI] = 1.02-2.70). Flaxseed oil users were more likely to have a better mental health summary score (OR = 1.76, 95% CI = 1.05-2.94). Ginseng users were more likely to report severe fatigue and several hormone-related symptoms (all ORs ≥ 1.7 and all 95% CIs exclude 1). Red clover users were less likely to report weight gain, night sweats, and difficulty concentrating (all OR approximately 0.4 and all 95% CIs exclude 1). Alfalfa users were less likely to experience sleep interruption (OR = 0.28, 95% CI = 0.12-0.68). Dehydroepiandrosterone users were less likely to have hot flashes (OR = 0.33, 95% CI = 0.14-0.82).</p> <p>Conclusions</p> <p>Our findings indicate that several specific types of EBS might have important influences on a woman's various aspects of quality of life, but further verification is necessary.</p

Fine-Scale Mapping of the 4q24 Locus Identifies Two Independent Loci Associated with Breast Cancer Risk

Background: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored. Methods: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium. Results: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10−4; OR, 1.04; 95% confidence interval (CI), 1.02–1.07] and rs77928427 (P = 1.86 × 10−4; OR, 1.04; 95% CI, 1.02–1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r2 ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor–binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue. Conclusion: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2. Impact: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk

Full-scale approximations of spatio-temporal covariance models for large datasets

Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset