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Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-...

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.

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

// 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.

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.

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.

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.

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

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

High-throughput data have been widely used in biological and medical studies to discover gene and protein functions. Due to the high dimensionality, principal component analysis (PCA) is often involved for data dimension reduction. However, when a few principal components (PCs) are selected for dimension reduction or considered for dimension determination, they are typically ranked by their variances, eigenvalues. However, this approach is not always effective in subsequent multivariate analysis, particularly classification. To maximize information from data with a subset of the components, we apply a different ranking criterion, canonical variate criterion, which considers within- and between-group variance rather than total variance in the classical criterion. Four prevalent classification methods are considered and compared using leave-one-out cross-validation. These methods are illustrated with three real high-throughput data sets, two microarray data sets and a nuclear magnetic resonance spectra data set.

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.

In recent years, there has been a renewed interest in applying statistical ranking criteria to identify sites on a road network, which potentially present high traffic crash risks or are over-represented in certain type of crashes, for further engineering evaluation and safety improvement. This requires that good estimates of ranks of crash risks be obtained at individual intersections or road segments, or some analysis zones. The nature of this site ranking problem in roadway safety is related to two well-established statistical problems known as the small area (or domain) estimation problem and the disease mapping problem. The former arises in the context of providing estimates using sample survey data for a small geographical area or a small socio-demographic group in a large area, while the latter stems from estimating rare disease incidences for typically small geographical areas. The statistical problem is such that direct estimates of certain parameters associated with a site (or a group of sites) with adequate precision cannot be produced, due to a small available sample size, the rareness of the event of interest, and/or a small exposed population or sub-population in question. Model based approaches have offered several advantages to these estimation problems, including increased precision by "borrowing strengths" across the various sites based on available auxiliary variables, including their relative locations in space. Within the model based approach, generalized linear mixed models (GLMM) have played key roles in addressing these problems for many years. The objective of the study, on which this paper is based, was to explore some of the issues raised in recent roadway safety studies regarding ranking methodologies in light of the recent statistical development in space-time GLMM. First, general ranking approaches are reviewed, which include naïve or raw crash-risk ranking, scan based ranking, and model based ranking. Through simulations, the limitation of using the naïve approach in ranking is illustrated. Second, following the model based approach, the choice of decision parameters and consideration of treatability are discussed. Third, several statistical ranking criteria that have been used in biomedical, health, and other scientific studies are presented from a Bayesian perspective. Their applications in roadway safety are then demonstrated using two data sets: one for individual urban intersections and one for rural two-lane roads at the county level. As part of the demonstration, it is shown how multivariate spatial GLMM can be used to model traffic crashes of several injury severity types simultaneously and how the model can be used within a Bayesian framework to rank sites by crash cost per vehicle-mile traveled (instead of by crash frequency rate). Finally, the significant impact of spatial effects on the overall model goodness-of-fit and site ranking performances are discussed for the two data sets examined. The paper is concluded with a discussion on possible directions in which the study can be extended.

In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre-processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre-processing techniques used. One important pre-processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass spectrometry data and show that it improves on performances of peak detection methods.

Estimation of reliability and maintainability parameters is essential in modeling repairable systems and determining maintenance policies. However, because of the aging of repairable systems under imperfect maintenance, failure times are neither identically nor independently distributed, which makes parameter estimation difficult. In this paper, we apply Bayesian methods for estimation of reliability and maintainability parameters based on historical reliability and maintainability (RAM) data. We assume the first failure of the repairable system follows a Weibull probability distribution. The repairable system experiences Kijima Type I imperfect corrective maintenance and Kijima Type I imperfect preventive maintenance. Using a Bayesian perspective, we estimate four parameters for this repairable system: the shape parameter of the Weibull probability distribution (beta), the scale parameter of the Weibull distribution (eta), the imperfect maintenance factor for corrective maintenance (alphar) and the imperfect maintenance factor for preventive maintenance (alphap). The proposed method is illustrated with simulated RAM data.

High-throughput screening technologies recently developed allow scientists to conduct millions of biological and medical tests simultaneously and rapidly. A major bottleneck for the analysis is to reduce the inherent high dimensionality for subsequent analysis. Principal Component Analysis (PCA) is a popular tool for dimensionality reduction by selecting typically a few Principal Components (PCs) ranked by their variances, eigenvalues. Since this selection approach is not always effective in reducing dimensionality, we consider a different ranking criterion, the canonical variate criterion. To further enhance the classification performance, we propose an integrated classification framework to combine the criterion and two hybrid classification methods and compare with several popular classification methods using leave-one-out cross-validation. For illustration, three real high-throughput data sets are considered and analysed to illustrate the methods.

The detection of gene-gene interaction is an important approach to understand the etiology of rheumatoid arthritis (RA). The goal of this study is to identify gene-gene interaction of SNPs at the allelic level contributing to RA using real data sets (Problem 1) of North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Analysis Workshop 16 (GAW16). We applied our novel method that can detect the interaction by a definition of nonrandom association of alleles that occurs when the contribution to RA of a particular allele inherited in one gene depends on a particular allele inherited at other unlinked genes. Starting with 639 single-nucleotide polymorphisms (SNPs) from 26 candidate genes, we identified ten two-way interacting genes and one case of three-way interacting genes. SNP rs2476601 on PTPN22 interacts with rs2306772 on SLC22A4, which interacts with rs881372 on TRAF1 and rs2900180 on C5, respectively. SNP rs2900180 on C5 interacts with rs2242720 on RUNX1, which interacts with rs881375 on TRAF1. Furthermore, rs2476601 on PTPN22 also interacts with three SNPs (rs2905325, rs1476482, and rs2106549) in linkage disequilibrium (LD) on IL6. The other three SNPs (rs2961280, rs2961283, and rs2905308) in LD on IL6 interact with two SNPs (rs477515 and rs2516049) on HLA-DRB1. SNPs rs660895 and rs532098 on HLA-DRB1 interact with rs2834779 and four SNPs in LD on RUNX1. Three-way interacting genes of rs10229203 on IL6, rs4816502 on RUNX1, and rs10818500 on C5 were also detected.