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In this work, we suggest new spatial precipitation interpolation schemes using compressed sensing (CS), which is a new framework for signal acquisition and smart sensor design. Using CS, the precipitation maps are recovered in high resolution by obtaining sparse coefficients of radial basis functions(RBFs). Two types of methods are designed according to the construction methods of CS matrix. In the first type, the CS matrix is derived as the product of an m × n (n ≫ m) weights matrix of inverse distance weighting (IDW) and an n × n radial basis function (RBF) matrix. The second type of CS matrix consists of an m × n RBF matrix that depends on a few observation vectors and a number of n unknown vectors. The advantage of the proposed CS methods is that it can be represented at a high resolution because it is interpolated based on a large number of bases (or degrees of freedom). This prevents the variance value from being much smaller than the actual value due to interpolation using a few observation scales. To test our CS interpolation schemes, interpolation results were compared with IDW, Ordinary Kriging (OK) and RBF interpolation methods for analytic test function and some actual rainfall data. In the case of an analytic test function, when the proposed method is compared at high resolution, the error from the true value is the smallest. In real rainfall data, comparison with real values is not possible at high resolutions, but the error with the observed data is the smallest in terms of ‘spatial variogram’. In addition, the proposed CS method generates hight resolution data from rainfall cases, showing promising results when identifying peaks.

The charging of various airborne particles was investigated using single-particle levitation and charge-balance equations. Though radioactive decay and triboelectrification can induce charging, it is typically assumed that the aerosols in a radioactive plume will not carry significant charge at steady state since atmospheric particles can have their charge neutralized through the capture of adjacent counter-ions (i.e., diffusion charging). To assess this assumption, we directly measured the surface charge and charge density of various triboelectrically charged aerosols including radioactive uranium oxide (1 μm), urban dust, Arizona desert dust, hydrophilic and hydrophobic silica nanoparticles, and graphene oxide powders using an electric field-assisted particle levitator in air. Of these particles, uranium oxide aerosols exhibited the highest surface charge density. Charge balance equations were employed to predict the average charge gained from radioactive decay as a function of time and to evaluate the effects of diffusion charging on triboelectrically charged radioactive and non-radioactive particles in the atmosphere. Simulation results show that particles, initially charged through triboelectrification, can be quickly discharged by diffusion charging in the absence of radioactive decay. Nevertheless, simulation results also indicate that particles can be strongly charged when they carry radionuclides. These experimental and simulation results suggest that radioactive decay can induce strong particle charging that may potentially affect atmospheric transport of airborne radionuclides.

Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified outcome. The approach simultaneously corrects bias from error in the outcome and examines possible change in the exposure effect estimation assuming the presence of a binary unmeasured confounder. We assess the influence of unmeasured confounding on the exposure effect estimation through two sensitivity parameters that characterize the associations of the unmeasured confounder with the exposure status and with the outcome variable. The proposed approach is illustrated in the study of the effect of female employment status on the likelihood of domestic violence. An extensive simulation study is conducted to confirm the efficacy of the proposed approach. The simulation results indicate accounting for misclassification in outcome and unmeasured confounding significantly reduce the bias in exposure effect estimation and improve the coverage probability of credible intervals.

Observational data with clustered structure may have confounding at one or more levels which when combined critically undermine result validity. We propose using multilevel models in Bayesian propensity score analysis to account for cluster and individual level confounding in the estimation of both propensity score and in turn treatment effect. In addition, our approach includes confounders in the outcome model for more flexibility to model outcome-covariate surface, minimizing the influence of feedback effect in Bayesian joint modeling of propensity score model and outcome model. In an extensive simulation study, we compare several propensity score analysis approaches with varying complexity of multilevel modeling structures. With each of proposed propensity score model, random intercept outcome model augmented with covariates adjustment well maintains the property of propensity score as balancing score and outperforms single level outcome model. To illustrate the proposed models, a case study is considered, which investigates the impact of lipid screening on lipid management in youth from three different health care systems.

Remote sensing data are often measured or reported over wide spatial footprints with heterogeneous ground cover. Different types of vegetation, however, have unique signatures that evolve throughout the growing season. Without additional information, signals corresponding to individual vegetation types are unidentifiable from satellite measurements. In this paper, we propose a parametric mixture model to describe satellite data monitoring crop development in the US Corn Belt. The ground cover of each satellite footprint is primarily a mixture of corn and soybean. Using auxiliary data from multiple sources, we model the aggregate satellite signal, and identify the signatures of individual crop types, using nonlinear parametric functions. Estimation is performed using a Bayesian approach, and information from auxiliary data is incorporated into the prior distributions to identify distinct crop types. We demonstrate our parametric unmixing approach using data from the European Space Agency’s Soil Moisture and Ocean Salinity satellite. Lastly, we compare our model estimates for the timing of key crop phenological stages to USDA ground-based estimates. Supplementary materials accompanying this paper appear online.

This paper proposes a nonparametric Bayesian approach using a Dirichlet process mixture model with Pareto kernels to estimate the density of observed upper record values and predict future upper record values. A reference distribution, G0, in the nonparametric Bayesian model is provided on the basis of an objective prior for unknown parameters of the Pareto distribution to avoid difficulties caused by finding values of hyperparameters in G0. For the posterior computation, two sampling algorithms are employed and compared. Finally, the approach provides the estimated distribution of observed upper record values and prediction of future upper record values in two real problems, average annual temperatures and carbon dioxide emissions.

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

Recently, the area of sea ice is rapidly decreasing due to global warming, and since the Arctic sea ice has a great impact on climate change, interest in this is increasing very much all over the world. In fact, the area of sea ice reached a record low in September 2012 after satellite observations began in late 1979. In addition, in early 2018, the glacier on the northern coast of Greenland began to collapse. If we are interested in record values of sea ice area, modeling relationships of these values and predicting future record values can be a very important issue because the record values that consist of larger or smaller values than the preceding observations are very closely related to each other. The relationship between the record values can be modeled based on the pivotal quantity and canonical and drawable vine copulas, and the relationship is called a dependence structure. In addition, predictions for future record values can be solved in a very concise way based on the pivotal quantity. To accomplish that, this article proposes an approach to model the dependence structure between record values based on the canonical and drawable vine. To do this, unknown parameters of a probability distribution need to be estimated first, and the pivotal-based method is provided. In the pivotal-based estimation, a new algorithm to deal with a nuisance parameter is proposed. This method allows one to reduce computational complexity when constructing exact confidence intervals of functions with unknown parameters. This method not only reduces computational complexity when constructing exact confidence intervals of functions with unknown parameters, but is also very useful for obtaining the replicated data needed to model the dependence structure based on canonical and drawable vine. In addition, prediction methods for future record values are proposed with the pivotal quantity, and we compared them with a time series forecasting method in real data analysis. The validity of the proposed methods was examined through Monte Carlo simulations and analysis for Arctic sea ice data.

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people’s lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.

Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables produced by a single data-generating process. Of the few studies that have performed QR on count data, none have accounted for excess zeros from a Bayesian perspective, as does the hurdle model that we propose. In this article, we propose a Bayesian two-part QR model for count data with excess zeros. The proposed model is compared to a frequentist approach via simulation, and its usefulness is displayed on two real datasets. In each application, multiple covariates are found to have differing effects across the response distribution, with special attention given to the nature of those effects in the outermost response distribution quantiles.

Jinkyung Park,1 Darryn S Willoughby,2 Joon Jin Song,3 Brian C Leutholtz,2 Yunsuk Koh2 1Department of Kinesiology, George Mason University, Manassas, VA, USA; 2Department of Health, Human Performance, Recreation, Baylor University, Waco, TX, USA; 3Department of Statistical Science, Baylor University, Waco, TX, USA Purpose: The current study examined the relationship between exercise-induced changes in stress hormones (epinephrine, norepinephrine, and cortisol) and vascular inflammatory markers (soluble intracellular adhesion molecule-1 [sICAM-1], soluble endothelial selectin [sE-selectin], and soluble vascular adhesion molecule-1 [sVCAM-1]) in obese men over a 24-hour period following exercise at lower and higher intensity.Patients and methods: Fifteen physically inactive, obese, college-aged men performed a single bout of cycling exercise at lower and higher intensities (lower intensity: 50% of maximal heart rate, and higher intensity: 80% of maximal heart rate) in random order. Overnight fasting blood samples were collected at baseline, immediately postexercise (IPE), 1-hour PE (1-h PE), and 24-hour PE. Changes in stress hormones and inflammatory markers were analyzed with a repeated-measures analysis of variance using Bonferroni multiple comparisons and a linear regression analysis (p

Bayesian propensity score regression analysis with misclassified binary responses is proposed to analyse clustered observational data. This approach utilizes multilevel models and corrects for misc...

// Joseph H. Taube 1, 3, 4, * , Nathalie Sphyris 1, * , Kelsey S. Johnson 3 , Keighley N. Reisenauer 3 , Taylor A. Nesbit 3 , Robiya Joseph 1 , Geraldine V. Vijay 1 , Tapasree R. Sarkar 1, 5 , Neeraja A. Bhangre 1 , Joon Jin Song 6 , Jeffrey T. Chang 7, 8 , Min Gyu Lee 9, 10 , Rama Soundararajan 1 and Sendurai A. Mani 1, 2 1 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 2 Metastasis Research Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 3 Department of Biology, Baylor University, Waco, Texas, USA 4 Institute of Biomedical Sciences, Baylor University, Waco, Texas, USA 5 Department of Integrative Bioscience, Texas A & M University, College Station, Texas, USA 6 Depatment of Statistical Science, Baylor University, Waco, Texas, USA 7 Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Texas, USA 8 Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center at Houston, Texas, USA 9 Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 10 Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA * Co-first authors Correspondence to: Sendurai A. Mani, email: smani@mdanderson.org Joseph H. Taube, email: Joseph_Taube@baylor.edu Keywords: epithelial-mesenchymal transition, mesenchymal-epithelial transition, KDM6A, bivalent genes, GSK-J4 Received: July 20, 2016 Accepted: April 26, 2017 Published: July 10, 2017 ABSTRACT The deposition of the activating H3K4me3 and repressive H3K27me3 histone modifications within the same promoter, forming a so-called bivalent domain, maintains gene expression in a repressed but transcription-ready state. We recently reported a significantly increased incidence of bivalency following an epithelial-mesenchymal transition (EMT), a process associated with the initiation of the metastatic cascade. The reverse process, known as the mesenchymal-epithelial transition (MET), is necessary for efficient colonization. Here, we identify numerous genes associated with differentiation, proliferation and intercellular adhesion that are repressed through the acquisition of bivalency during EMT, and re-expressed following MET. The majority of EMT-associated bivalent domains arise through H3K27me3 deposition at H3K4me3-marked promoters. Accordingly, we show that the expression of the H3K27me3-demethylase KDM6A is reduced in cells that have undergone EMT, stem-like subpopulations of mammary cell lines and stem cell-enriched triple-negative breast cancers. Importantly, KDM6A levels are restored following MET, concomitant with CDH1 /E-cadherin reactivation through H3K27me3 removal. Moreover, inhibition of KDM6A, using the H3K27me3-demethylase inhibitor GSK-J4, prevents the re-expression of bivalent genes during MET. Our findings implicate KDM6A in the resolution of bivalency accompanying MET, and suggest KDM6A inhibition as a viable strategy to suppress metastasis formation in breast cancer.

A key challenge in rainfall estimation is spatio-temporal variablility. Weather radars are used to estimate precipitation with high spatial and temporal resolution. Due to the inherent errors in ra...

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens' personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies., This manuscript has been approved for public access. Please put this version online. Previously, this version was removed by arXiv administrators because the author did not have the right to agree to our license at the time of submission

OBJECTIVE: Troponin values above the threshold established to diagnose acute myocardial infarction (AMI; >99th percentile) are commonly detected in patients with diagnoses other than AMI. The objective of this study was to compare inpatient mortality and 30-day readmission rate in patients with troponin I (TnI) above and below the 99th percentile in those with type 1 AMI and type 2 myocardial injury. METHODS: Between January 1, 2016 and December 31, 2016, there were 56,895 inpatient hospitalizations; of these 14,326 (25.2%) patients received troponin testing. We evaluated mortality and readmissions in the entire cohort based on the primary discharge International Classification of Diseases, Tenth Edition (ICD-10) diagnosis and grouped into type 1 AMI versus other diagnoses comprising the type 2 AMI group (including ICD-10 codes for congestive heart failure, sepsis, and other). Among those with TnI drawn, we evaluated in-hospital mortality and 30-day readmissions based on troponin values > 99th percentile (≥ 0.1 ng/ml). RESULTS: Among the entire cohort, the inpatient mortality rate was significantly higher in those with TnI testing (5.0%, 95% CI 4.6%–5.3%) compared to those without testing (0.7%, 95% CI 0.6%–0.7%, P < 0.01). In the tested cohort 3,743 (26%) patients had troponin levels above the 99th percentile (> 0.1 ng/ml), and 10,583 (74%) had troponin levels below the 99th percentile (≤ 0.1 ng/ml). Comparing type 2 AMI with type 1 AMI and troponin testing, TnI values ≥ 0.1 ng/ml were associated with higher inpatient mortality (11.6% vs. 3.9%) and 30-day readmission rates (16.9% vs. 10.7%). CONCLUSIONS: A higher inpatient mortality and 30-day readmission rates were found in patients with type 2 AMI compared to type 1 AMI group.

We examine the effect of female employment on the odds of physical spousal violence using a Bayesian misclassification model combined with propensity score regression estimation. While a classical propensity score model finds a significant violence-provoking effect of female employment, our model finds no evidence of a significant effect. This suggests that misleading inferences are caused by falsely small standard errors in a model that does not account for uncertainties around propensity scores. Further, we confirm our misclassification model as a preferred specification using Deviance Information Criterion (DIC).

Pediatric lipid management recommendations have evolved from selective screening to universal screening to identify and target therapy for genetic dyslipidemias. Data on the success of the selective screening guidelines for lipid testing, dyslipidemia detection, and lipid management are conflicting.To determine temporal trends in lipid testing, dyslipidemia categories and pharmacotherapy in a cohort of 653,642 individual youth aged 2 to 20 years from 2002 to 2012.Summary data on lipid test results, lipid-lowering medicine (LLM) dispensings, and International Classification of Diseases, Ninth Revision diagnoses were compiled from the virtual data warehouses of 5 sites in the Cardiovascular Research Network. Temporal trends were determined using linear regression.Among the average 255,160 ± 25,506 children enrolled each year, lipid testing declined from 16% in 2002 to 11% in 2012 (P .001 for trend). Among the entire population, the proportion newly detected each year with low-density lipoprotein cholesterol190 mg/dL, a value commonly used to define familial hypercholesterolemia, increased over time from 0.03% to 0.06% (P = .03 for trend). There was no significant change over time in the proportion of the yearly population initiated on LLM or statins specifically (0.045 ± 0.009%, P = .59 [LLM] and 0.028 ± 0.006%, P = .25 [statin]).Although lipid testing declined during 2002 to 2012, the detection of familial hypercholesterolemia-level low-density lipoprotein cholesterol increased. Despite this increased detection, pharmacotherapy did not increase over time. These findings highlight the need to enhance lipid screening and management strategies in high-risk youth.

Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables and without considering spatial effects. Of the few studies that have performed QR on count data, none have included random spatial effects, which is an integral facet of the Bayesian spatial QR model for areal counts that we propose. Additionally, we introduce a simplifying alternative to the response variable transformation currently employed in the QR for counts literature. The efficacy of the proposed model is demonstrated via simulation study and on a real data application from the Texas Department of Family and Protective Services (TDFPS). Our model outperforms a comparable non-spatial model in both instances, as evidenced by the deviance information criterion (DIC) and coverage probabilities. With the TDFPS data, we identify one of four covariates, along with the intercept, as having a nonconstant effect across the response distribution.

Hwang, PS, Andre, TL, McKinley-Barnard, SK, Morales Marroquin, FE, Gann, JJ, Song, JJ, and Willoughby, DS. Resistance training-induced elevations in muscular strength in trained men are maintained after 2 weeks of detraining and not differentially affected by whey protein supplementation. J Strength Cond Res 31(4): 869-881, 2017-Resistance training (RT) with nutritional strategies incorporating whey protein intake postexercise can stimulate muscle protein synthesis and elicit hypertrophy. The early phases of training-induced anabolic responses can be attenuated with longer-term training. It is currently unknown if short-term detraining (DT) can restore these blunted anabolic responses during a subsequent retraining (ReT) period. Twenty resistance-trained men (age 20.95 ± 1.23 years; n = 20) were randomized into one of 2 groups (PRO or CHO; 25 g) in a double-blind manner. Participants followed a 4-day per week RT program (4-week RT; 2-week DT; 4-week ReT) while consuming their respective supplement only on workout days during RT and ReT, but every day during DT. At baseline, 4 weeks after RT (post-RT), 2 weeks after DT (post-2-week DT), and after 4 weeks of ReT after DT (post-ReT), leg press strength (LPS) was assessed and rectus femoris cross-sectional area and lean mass changes were assessed by ultrasonography and dual-energy x-ray absorptiometry, respectively. A factorial 2 × 4 (group by time) analyses of variance with repeated measures were used with a probability level at ≤0.05. LPS was elevated throughout the 10-week training study (p = 0.003) with no decrease in LPS after DT in both groups. Although not statistically significant, both groups retained lean mass after DT. A 2-week period of DT appeared to retain muscular strength in resistance-trained men. Therefore, a short-term period of DT can potentially retain lower-body strength in young resistance-trained men irrespective of supplementing with 25 g of whey protein postexercise.

This study investigated the influences of district-related variables on a district’s academic performance. Arkansas augmented benchmark examination scores were used to measure a district’s scholastic achievement. Spatial analysis fit each district’s performance to its geographical location; spatial autocorrelation measured the amount of influence one district’s scores has on its neighbours. Regression, both ordinary least squares regression (OLS) and spatial auto regression (SAR), quantified how much a district’s academic scores were accounted for by the proportion of white students enrolled, the appraised value of property within a district, and the proportion of students receiving federally assisted lunches. The OLS model was able to account for 30% to 60% of the variation in scores. When ethnicity was predominately white, the district’s scores were higher; the more federally assisted lunches a district’s enrolment received, the lower scores tended to be. Spatial analysis indicated that a district’s performance was highly influenced by the surrounding districts. Major findings showed that, for 2008 data, once fit to an OLS regression model, the spatial dependency completely disappears for mathematics responses, but not literacy. Similar results were seen in 2009 and 2010, though they were not as systematically patterned. Keywords: Arkansas education, regression, spatial analysis and spatial autocorrelation.

The threshold to diagnose acute myocardial infarction is defined as the 99th percentile, 0.09 ng/ml in our healthcare system. However, the optimal threshold appears to be higher in our patient population, (0.13 - 0.23 ng/ml). We hypothesized that patient characteristics might further modify this

Microarray is one of the most powerful detection systems with multiplexing and high throughput capability. It has significant potential as a versatile biosensing platform for environmental monitoring, pathogen detection, medical therapeutics, and drug screening to name a few. To date, however, microarray applications are still limited to preliminary screening of genome-scale transcription profiling or gene ontology analysis. Expanding the utility of microarrays as a detection tool for various biological and biomedical applications requires information about performance such as the limits of detection and quantification, which are considered as an essential information to decide the detection sensitivity of sensing devices. Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system. Two different types of limits of detection and quantification are proposed by the prediction or tolerance interval for two common cyanine fluorescence dyes, Cy3 and Cy5. Besides oligonucleotide, the proposed method can be generalized to other microarray formats with various biomolecules such as complementary DNA, protein, peptide, carbohydrate, tissue, or other small biomolecules. Also, it can be easily applied to other fluorescence dyes for further dye chemistry improvement.

The randomized-response (RR) technique is an effective survey method when collecting sensitive information. In this technique, a probability mechanism using randomization devices is commonly involved in answering to sensitive questions. In order to evaluate the survey at the most accurate extend, self-protection (SP) is introduced to describe the responses by participants who give the evasive answer without taking the result of the randomization device into account. In this study, we propose a Bayesian approach to modeling RR sum score variables under SP assumption. RR data from a Dutch survey on non-compliance with social security regulation in 2004 is used to demonstrate the proposed models.

This work describes a Bayesian approach for model selection in Gaussian conditional autoregressive models and Gaussian simultaneous autoregressive models which are commonly used to describe spatial lattice data. The approach is aimed at situations where all competing models have the same mean structure, but differ on some aspects of their covariance structures. The proposed approach uses as selection criterion the posterior model probabilities computed using some default priors for the model parameters. The proposed methodology is illustrated using two real datasets.

Global RNA expression in breast muscle obtained from a male broiler line phenotyped for high or low feed efficiency (FE) was investigated. Pooled RNA samples (n = 6/phenotype) labeled with cyanine 3 or cyanine 5 fluorescent dyes to generate cRNA probes were hybridized on a 4 × 44K chicken oligo microarray. Local polynomial regression normalization was applied to background-corrected red and green intensities with a moderated t-statistic. Corresponding P-values were computed and adjusted for multiple testing by false discovery rate to identify differentially expressed genes. Microarray validation was carried out by comparing findings with quantitative reverse-transcription PCR. A 1.3-fold difference in gene expression was set as a cutoff value, which encompassed 20% (782 of 4,011) of the total number of genes that were differentially expressed between FE phenotypes. Using an online software program (Ingenuity Pathway Analysis), the top 10 upregulated genes identified by Ingenuity Pathway Analysis in the high-FE group were generally associated with anabolic processes. In contrast, 7 of the top 10 downregulated genes in the high-FE phenotype (upregulated in the low-FE phenotype) were associated with muscle fiber development, muscle function, and cytoskeletal organization, with the remaining 3 genes associated with self-recognition or stress-responding genes. The results from this study focusing on only the top differentially expressed genes suggest that the high-FE broiler phenotype is derived from the upregulation of genes associated with anabolic processes as well as a downregulation of genes associated with muscle fiber development, muscle function, cytoskeletal organization, and stress response.

Accurate weather forecasts have substantial economic value. We examine the provision of accurate forecasts both theoretically and empirically. Theoretically, we use a simple Neo-Hotelling model. In that model, the public forecaster, the National Weather Service (NWS), tries to achieve socially-efficient forecast accuracy operating under a per capita tax constraint; on the other hand, the private providers compete against each other for profits by choosing their optimal level of forecast accuracy in a monopolistically competitive market in which each private provider caters to a market niche while co-existing with the NWS. Empirically, we use a unique data set on daily maximum temperature forecasts for 704 U.S. cities and estimate the nearest neighbor matching and the state fixed effect (FE) models. Our empirical findings are consistent with the predictions of our simple public good model: we find that forecast accuracy is sensitive to economic variables such as population and average household income in that the accuracy increases in these economic variables. Our most surprising theoretical and empirical finding is that population and income sensitivity is found not only for private forecasters but also for the public forecaster, the NWS.

Troponin testing is integral to the diagnosis of acute myocardial infarction (AMI). Positive results are defined as values >99th percentile which is 0.1 ng/ml in our health care system. However, a review of test results indicates poor positive predictive value using this threshold. We hypothesized

Background Prepregnant obesity is a global concern and gestational weight gain has been found to influence the risks of preterm birth. Objective To assess the relationship between gestational weight gain and risk for preterm birth in obese women. Search strategy Four electronic databases were searched from 18 February through to 28 April 2015. Selection criteria Primary research reporting preterm birth as an outcome in obese women and gestational weight gain as a variable that could be compared to the 2009 Institute of Medicine's recommendations. Data collection and analysis Two reviewers independently assessed trials for inclusion. The Newcastle Ottawa Scale was used to assess study bias. Main results Our search identified six studies meeting the inclusion criteria; five were conducted in the USA and one in Peru. Four studies with a total of 10 171 obese women were meta-analysed. Significant heterogeneity was found between studies in the pooled analysis. Results for indicated preterm birth in obese women with gestational weight gain above the Institute of Medicine's recommendations showed increased risk (adjusted odds ratio 1.54; 95% CI 1.09–2.16). Conclusions Available science on this topic is limited to special populations of obese pregnant women. Generalisable research is needed to assess the variation in risk for preterm birth in obese women by differences in gestational weight gain and class of obesity controlling for significant variables in the pathway to preterm birth. This research has the potential to illuminate new science impacting preterm birth and interventions for prevention. Tweetable abstract A meta-analysis of gestational weight gain in obese women and preterm birth.

We consider several Bayesian multivariate spatial models for estimating the crash rates from different kinds of crashes. Multivariate conditional autoregressive (CAR) models are considered to account for the spatial effect. The models considered are fully Bayesian. A general theorem for each case is proved to ensure posterior propriety under noninformative priors. The different models are compared according to some Bayesian criterion. Markov chain Monte Carlo (MCMC) is used for computation. We illustrate these methods with Texas Crash Data.