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When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor’s dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.

Precise dietary assessment is critical for accurate exposure classification in nutritional research, typically aimed at understanding how diet relates to health. Dietary supplement (DS) use is widespread and represents a considerable source of nutrients. However, few studies have compared the best methods to measure DSs. Our literature review on the relative validity and reproducibility of DS instruments in the United States [e.g., product inventories, questionnaires, and 24-h dietary recalls (24HR)] identified five studies that examined validity ( n = 5) and/or reproducibility ( n = 4). No gold standard reference method exists for validating DS use; thus, each study's investigators chose the reference instrument used to measure validity. Self-administered questionnaires agreed well with 24HR and inventory methods when comparing the prevalence of commonly used DSs. The inventory method captured nutrient amounts more accurately than the other methods. Reproducibility (over 3 months to 2.4 years) of prevalence of use estimates on the questionnaires was acceptable for common DSs. Given the limited body of research on measurement error in DS assessment, only tentative conclusions on these DS instruments can be drawn at present. Further research is critical to advancing knowledge in DS assessment for research and monitoring purposes. Expected final online publication date for the Annual Review of Nutrition, Volume 43 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

We develop a generalized partially additive model to build a single semiparametric risk scoring system for physical activity across multiple populations. A score comprised of distinct and objective physical activity measures is a new concept that offers challenges due to the nonlinear relationship between physical behaviors and various health outcomes. We overcome these challenges by modelling each score component as a smooth term, an extension of generalized partially linear single-index models. We use penalized splines and propose two inferential methods, one using profile likelihood and a nonparametric bootstrap, the other using a full Bayesian model, to solve additional computational problems. Both methods exhibit similar and accurate performance in simulations. These models are applied to the National Health and Nutrition Examination Survey (NHANES) and quantify nonlinear and interpretable shapes of score components for all-cause mortality.

Microarray gene expression data contains missing values (MVs). However, some methods for downstream analyses, including some predic- tion tools, require a complete expression data matrix. Current methods for estimating the MVs include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation (imputation) methods depends on the actual gene expression has not been thoroughly investigated. Under this set- ting, we examine how the accuracy depends on the actual expression level and propose new methods that provide improvements in accuracy relative to the current methods in certain ranges of gene expression. In particular, we propose regression methods, namely multiple imputation via ordinary least squares (OLS) and missing value prediction using partial least squares (PLS). Mean estimation of MVs ignores the observed correlation structure of the genes and is highly inaccurate. Estimating MVs using KNN, a method which incorporates pairwise gene expression information, provides substan- tial improvement in accuracy on average. However, the accuracy of KNN across the wide range of observed gene expression is unlikely to be uniform and this is revealed by evaluating accuracy as a function of the expression level.

Ulcerative colitis (UC) patients often avoid foods containing fermentable fibers as some can promote symptoms during active disease. Pectin has been identified as a more protective fermentable fiber, but little has been done to determine the interaction between pectin and bioactive compounds present in foods containing that fiber type. Quercetin and chlorogenic acid, two bioactives in stone fruits, may have anti-cancer, anti-oxidant, and anti-inflammatory properties. We hypothesized that quercetin and chlorogenic acid, in the presence of the fermentable fiber pectin, may suppress the expression of pro-inflammatory molecules, alter the luminal environment, and alter colonocyte proliferation, thereby protecting against recurring bouts of UC. Rats (n = 63) received one of three purified diets (control, 0.45% quercetin, 0.05% chlorogenic acid) containing 6% pectin for 3 weeks before exposure to dextran sodium sulfate (DSS, 3% for 48 h, 3x, 2 wk separation, n = 11/diet) in drinking water to initiate UC, or control (no DSS, n = 10/diet) treatments prior to termination at 9 weeks. DSS increased the fecal moisture content (p < 0.05) and SCFA concentrations (acetate, p < 0.05; butyrate, p < 0.05). Quercetin and chlorogenic acid diets maintained SLC5A8 (SCFA transporter) mRNA levels in DSS-treated rats at levels similar to those not exposed to DSS. DSS increased injury (p < 0.0001) and inflammation (p < 0.01) scores, with no differences noted due to diet. Compared to the control diet, chlorogenic acid decreased NF-κB activity in DSS-treated rats (p < 0.05). Quercetin and chlorogenic acid may contribute to the healthy regulation of NF-κB activation (via mRNA expression of IκΒα, Tollip, and IL-1). Quercetin enhanced injury-repair molecule FGF-2 expression (p < 0.01), but neither diet nor DSS treatment altered proliferation. Although quercetin and chlorogenic acid did not protect against overt indicators of injury and inflammation, or fecal SCFA concentrations, compared to the control diet, their influence on the expression of injury repair molecules, pro-inflammatory cytokines, SCFA transport proteins, and NF-κB inhibitory molecules suggests beneficial influences on major pathways involved in DSS-induced UC. Therefore, in healthy individuals or during periods of remission, quercetin and chlorogenic acid may promote a healthier colon, and may suppress some of the signaling involved in inflammation promotion during active disease.

Dietary supplement (DS) use is widespread in the U.S. and contributes large amounts of micronutrients to users. Most studies have relied on data from one assessment method to characterize the prevalence of DS use. Combining multiple methods enhances the ability to capture nutrient exposures from DS and examine trends over time.The objective of this study was to characterize DS use and examine trends in any DS as well as micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES using a combined approach (i.e., DSMQ and/or ≥1 24HR).NHANES obtains an in-home inventory with a frequency-based DS and Prescription Medicine Questionnaire (DSMQ), and two 24-hr recalls (24HR). The objective of this study was to characterize DS use and examine trends in any and micronutrient-containing (MN) DS use among a nationally representative sample of the U.S. population (≥1y) from the 2007-2018 NHANES, using a combined approach (i.e., DSMQ and/or ≥1 24HR). Trends in the prevalence of use and selected types of products used were estimated for the population and by sex, age, race/Hispanic origin, family income (poverty-to-income ratio (PIR)), and household food security (food-secure vs. food-insecure). Linear trends were tested using orthogonal polynomials (significance set at p0.05).DS use increased from 50% in 2007 to 56% in 2018 (p = 0.001); use of MN products increased from 46% to 49% (p = 0.03), and single-nutrient DS (e.g., magnesium, vitamins B12 and D) use also increased (all p0.001). In contrast, multivitamin-mineral use decreased (70% to 56%; p0.001). Among adults (≥19y), any (54% to 61%) and MN (49% to 54%) DS use increased, especially among men, non-Hispanic Blacks and Hispanics, and low-income adults (PIR≤130%). Among children (1-18y), any DS use remained stable (∼38%), as did MN use, except for food-insecure children, whose use increased from 24% to 31% over the decade (p = 0.03).The prevalence of any and MN DS use increased over time in the U.S. This may be partially attributed to increased use of single-nutrient products. Population subgroups differed in their DS use.

Fatal car crashes are the leading cause of death among teenagers in the USA. The Graduated Driver Licensing (GDL) programme is one effective policy for reducing the number of teen fatal car crashes. Our study focuses on the number of fatal car crashes in Michigan during 1990–2004 excluding 1997, when the GDL started. We use Poisson regression with spatially dependent random effects to model the county level teen car crash counts. We develop a measurement error model to account for the fact that the total teenage population in the county level is used as a proxy for the teenage driver population. To the best of our knowledge, there is no existing literature that considers adjustment for measurement error in an offset variable. Furthermore, limited work has addressed the measurement errors in the context of spatial data. In our modelling, a Berkson measurement error model with spatial random effects is applied to adjust for the error-prone offset variable in a Bayesian paradigm. The Bayesian Markov chain Monte Carlo (MCMC) sampling is implemented in rstan. To assess the consequence of adjusting for measurement error, we compared two models with and without adjustment for measurement error. We found the effect of a time indicator becomes less significant with the measurement-error adjustment. It leads to our conclusion that the reduced number of teen drivers can help explain, to some extent, the effectiveness of GDL.

Viral infections contribute to neurological and immunological dysfunction driven by complex genetic networks. Theiler’s murine encephalomyelitis virus (TMEV) causes neurological dysfunction in mice and can model human outcomes to viral infections. Here, we used genetically distinct mice from five Collaborative Cross mouse strains and C57BL/6J to demonstrate how TMEV-induced immune responses in serum may predict neurological outcomes in acute infection. To test the hypothesis that serum cytokine levels can provide biomarkers for phenotypic outcomes of acute disease, we compared cytokine levels at pre-injection, 4 days post-injection (d.p.i.), and 14 d.p.i. Each strain produced unique baseline cytokine levels and had distinct immune responses to the injection procedure itself. Thus, we eliminated the baseline responses to the injection procedure itself and identified cytokines and chemokines induced specifically by TMEV infection. Then, we identified strain-specific longitudinal cytokine profiles in serum during acute disease. Using stepwise regression analysis, we identified serum immune markers predictive for TMEV-induced neurological phenotypes of the acute phase, e.g., IL-9 for limb paralysis; and TNF-α, IL-1β, and MIP-1β for limb weakness. These findings indicate how temporal differences in immune responses are influenced by host genetic background and demonstrate the potential of serum biomarkers to track the neurological effects of viral infection.

The effective prompt response function full width at half maximum, PRF FWHM of 637 ps (obtained from the prompt gamma pairs of 477 keV and 700 keV associated with the yrast 2+ state in 206Po), and 1007 ps (obtained from the Compton gamma pairs of 189 keV and 237 keV associated with the 192Os(18O,16O)194Os 2 neutron transfer reaction) were used in fitting the time difference spectra obtained from the gamma coincident pairsof 206 keV and 374 keV in a symmetrised LaBr3(Ce) associated with the gamma transitions in 192Os, using the Half-life program. The values of half-life measured by fitting these PRF FWHM of 637 ps and 1007 ps separately show an excellent agreement of 282(16) ps and 272(21) ps, respectively, which correspond to the global half-life value of 282(4) ps for the 192Os. The mean value of 277(12) ps from these two measurements was used in calculating the B(E2; IL ->IL-2) of 4233(114) e2fm4, which is equivalent to be 81(19) W.u.

Dietary data collected from 24-hour dietary recalls are observed with significant measurement errors. In the nonparametric curve estimation literature, much of the effort has been devoted to designing methods that are consistent under contamination by noise, and which have been traditionally applied for analyzing those data. However, some foods such as alcohol or fruits are consumed only episodically, and may not be consumed during the day when the 24-hour recall is administered. These so-called excess zeros make existing nonparametric estimators break down, and new techniques need to be developed for such data. We develop two new consistent semiparametric estimators of the distribution of such episodically consumed food data, making parametric assumptions only on some less important parts of the model. We establish its theoretical properties and illustrate the good performance of our fully data-driven method in simulated and real data. Supplementary materials for this article are available online.

Canonical correlation analysis investigates linear relationships between two sets of variables, but often works poorly on modern data sets due to high-dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach for sparse canonical correlation analysis based on Gaussian copula. Our main contribution is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types without the estimation of marginal transformation functions. The resulting canonical correlation analysis method works well in high-dimensional settings as demonstrated via numerical studies, as well as in application to the analysis of association between gene expression and micro RNA data of breast cancer patients., Accepted to Biometrika. Main text: 19 pages and 3 figures. Supplementary material: 28 pages and 9 figures

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear and Berkson models, and on the concepts of non-differential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary sub-studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the paper deals with more advanced topics.

Neurological dysfunction following viral infection varies among individuals, largely due to differences in their genetic backgrounds. Gait patterns, which can be evaluated using measures of coordination, balance, posture, muscle function, step-to-step variability, and other factors, are also influenced by genetic background. Accordingly, to some extent gait can be characteristic of an individual, even prior to changes in neurological function. Because neuromuscular aspects of gait are under a certain degree of genetic control, the hypothesis tested was that gait parameters could be predictive of neuromuscular dysfunction following viral infection. The Collaborative Cross (CC) mouse resource was utilized to model genetically diverse populations and the DigiGait treadmill system used to provide quantitative and objective measurements of 131 gait parameters in 142 mice from 23 CC and SJL/J strains. DigiGait measurements were taken prior to infection with the neurotropic virus Theiler’s Murine Encephalomyelitis Virus (TMEV). Neurological phenotypes were recorded over 90 days post-infection (d.p.i.), and the cumulative frequency of the observation of these phenotypes was statistically associated with discrete baseline DigiGait measurements. These associations represented spatial and postural aspects of gait influenced by the 90 d.p.i. phenotype score. Furthermore, associations were found between these gait parameters with sex and outcomes considered to show resistance, resilience, or susceptibility to severe neurological symptoms after long-term infection. For example, higher pre-infection measurement values for the Paw Drag parameter corresponded with greater disease severity at 90 d.p.i. Quantitative trait loci significantly associated with these DigiGait parameters revealed potential relationships between 28 differentially expressed genes (DEGs) and different aspects of gait influenced by viral infection. Thus, these potential candidate genes and genetic variations may be predictive of long-term neurological dysfunction. Overall, these findings demonstrate the predictive/prognostic value of quantitative and objective pre-infection DigiGait measurements for viral-induced neuromuscular dysfunction.

BACKGROUND: Most dietary indices reflect foods and beverages and do not include exposures from dietary supplements (DS) that provide substantial amounts of micronutrients. A nutrient-based approach that captures total intake inclusive of DS can strengthen exposure assessment. OBJECTIVE: To examine the construct and criterion validity of the Total Nutrient Index (TNI) among U.S. adults (≥19y; non-pregnant or lactating). METHODS: The TNI includes eight under-consumed micronutrients identified by the Dietary Guidelines for Americans: calcium; magnesium; potassium; choline; and vitamins A, C, D, and E. The TNI is expressed as a percentage of the Recommended Dietary Allowance or Adequate Intake to compute micronutrient component scores; the mean of the component scores yields the TNI score, ranging from 0-100. Data from exemplary menus and the 2003-2006 (≥19y; n = 8,861) and 2011-2014 NHANES (≥19y; n = 9,954) were employed. Exemplary menus were used to determine if the TNI yielded high scores from dietary sources (women 31-50y; men ≥70y). TNI scores were correlated with Healthy Eating Index (HEI)-2015 overall and component scores for dairy, fruits, and vegetables; TNI component scores for vitamins A, C, D, and E were correlated with respective biomarker data. TNI scores were compared between groups with known differences in nutrient intake based on the literature. RESULTS: The TNI yielded high scores on exemplary menus (84.8-93.3/100) and was moderately correlated (r = 0.48) with the HEI-2015. Mean TNI scores were significantly different for DS users (83.5) vs. non-users (67.1), non-smokers (76.8) vs. smokers (70.3), and those living with food security (76.6) vs. food insecurity (69.1). Correlations of TNI vitamin component scores with available biomarkers ranged from r = 0.12 (α-tocopherol) to r = 0.36 (serum 25(OH)D), and were significantly higher than correlations obtained from the diet alone. CONCLUSION: The evaluation of validity supports that the TNI is a useful construct to assess total micronutrient exposures of under-consumed micronutrients among U.S. adults.

We consider analyses of case-control studies assembled from electronic health records (EHRs) where the pool of cases is contaminated by patients who are ineligible for the study. These ineligible patients, referred to as "false cases," should be excluded from the analyses if known. However, the true outcome status of a patient in the case pool is unknown except in a subset whose size may be arbitrarily small compared to the entire pool. To effectively remove the influence of the false cases on estimating odds ratio parameters defined by a working association model of the logistic form, we propose a general strategy to adaptively impute the unknown case status without requiring a correct phenotyping model to help discern the true and false case statuses. Our method estimates the target parameters as the solution to a set of unbiased estimating equations constructed using all available data. It outperforms existing methods by achieving robustness to mismodeling the relationship between the outcome status and covariates of interest, as well as improved estimation efficiency. We further show that our estimator is root-n-consistent and asymptotically normal. Through extensive simulation studies and analysis of real EHR data, we demonstrate that our method has desirable robustness to possible misspecification of both the association and phenotyping models, along with statistical efficiency superior to the competitors.

In nutrition, epidemiology, and other public health fields, composite scores are a common tool used to assess a health behaviour. These composite scores compare an individual's health behaviour to an idealized standard and provide a number, often between 0 and 100, to indicate their compliance to a health behaviour. Crucially, this measure of health behaviour is applied across populations (gender, smoking status, etc.) and health outcomes (colon cancer, breast cancer, etc.) to create a single interpretable score. One such composite score is the 2005 Healthy Eating Index that breaks diet into 12 components and evaluates nutritional intake by adherence to these components. We provide a general method that can be used to reassess the importance of these 12 components using flexible non-linear models, across populations and diseases, based on an asymptotic least squares approximation. We establish oracle properties of this variable selection technique in our context, which is different from the usual one population and one disease context. Although our methods are motivated by the Healthy Eating Index, they are broad enough to be applied to any composite score and a broad range of non-linear models.

Measuring usual dietary intake in freely living humans is difficult to accomplish. As a part of our recent study, a food frequency questionnaire was completed by healthy adult men and women at days 0 and 90 of the study. Data from the food questionnaire were analyzed with a nutrient analysis program ( www.Harvardsffq.date ). Healthy men and women consumed protein as 19-20% and 17-19% of their total energy intakes, respectively, with animal protein representing about 75 and 70% of their total protein intakes, respectively. The intake of each nutritionally essential amino acid (EAA) by the persons exceeded that recommended for healthy adults with a minimal physical activity. In all individuals, the dietary intake of leucine was the highest, followed by lysine, valine, and isoleucine in descending order, and the ingestion of amino acids that are synthesizable de novo in animal cells (AASAs) was about 20% greater than that of total EAAs. The intake of each AASA met those recommended for healthy adults with a minimal physical activity. Intakes of some AASAs (alanine, arginine, aspartate, glutamate, and glycine) from a typical diet providing 90-110 g food protein/day does not meet the requirements of adults with an intensive physical activity. Within the male or female group, there were not significant differences in the dietary intakes of all amino acids between days 0 and 90 of the study, and this was also true for nearly all other essential nutrients. Our findings will help to improve amino acid nutrition and health in both the general population and exercising individuals.

Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.

Numerous statistical methods have been developed for analyzing high-dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high-dimensional data. They are often required to have explicit-likelihood functions. In this article, we propose a "hybrid omnibus test" for high-dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist-Bayesian hybrid score-type test for a generalized partially linear single-index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. We compare our approach with an empirical-likelihood ratio test and Bayesian inference based on Bayes factors, using simulation studies. Our simulation results suggest that our approach outperforms the others, in terms of type I error, power, and computational cost in both the low- and high-dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.

Model averaging generally provides better predictions than model selection, but the existing model averaging methods cannot lead to parsimonious models. Parsimony is an especially important property when the number of parameters is large. To achieve a parsimonious model averaging coefficient estimator, we suggest a novel criterion for choosing weights. Asymptotic properties are derived in two practical scenarios: (i) one or more correct models exist in the candidate model set and (ii) all candidate models are misspecified. Under the former scenario, it is proved that our method can put the weight one to the smallest correct model and the resulting model averaging estimators of coefficients have many zeros and thus lead to a parsimonious model. The asymptotic distribution of the estimators is also provided. Under the latter scenario, prediction is mainly focused on and we prove that the proposed procedure is asymptotically optimal in the sense that its squared prediction loss and risk are asymptotically identical to those of the best—but infeasible—model averaging estimator. Numerical analysis shows the promise of the proposed procedure over existing model averaging and selection methods.

5 pags., 3 figs., 1 tab. -- Open Access funded by Creative Commons Atribution Licence 4.0, Gamow-Teller β decay is forbidden if the number of nodes in the radial wave functions of the initial and final states is different. This Δn=0 requirement plays a major role in the β decay of heavy neutron-rich nuclei, affecting the nucleosynthesis through the increased half-lives of nuclei on the astrophysical r-process pathway below both Z=50 (for N>82) and Z=82 (for N>126). The level of forbiddenness of the Δn=1ν1g →π0g transition has been investigated from the β decay of the ground state of Hg into the single-proton-hole nucleus Tl in an experiment at the ISOLDE Decay Station. From statistical observational limits on possible γ-ray transitions depopulating the π0g state in Tl, an upper limit of 3.9×10 % was obtained for the probability of this decay, corresponding to log⁡ft>8.8 within a 95% confidence limit. This is the most stringent test of the Δn=0 selection rule to date., Support from the European Union seventh framework through ENSAR contract no. 262010, the Science and Technology Facilities Council through grants ST/P005314/1, ST/L005743/1 and ST/J000051/1 (UK), the MINECO projects FPA2015-64969-P, FPA2015-65035-P and FPA2017-87568-P (Spain), FWO-Vlaanderen (Belgium), GOA/2015/010 (BOF KU Leuven), the Excellence of Science programme (EOS-FWO), and the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office (BriX network P7/12) is acknowledged. ZsP acknowledges support by the ExtreMe Matter Institute EMMI at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany. PHR and SMJ ac-knowledge support from the UK Department for Business, Energy and Industrial Strategy via the National Measurement Office.

© 2019 American Society for Nutrition. All rights reserved. The use of dietary supplements (DS) is pervasive and can provide substantial amounts of micronutrients to those who use them. Therefore when characterizing dietary intakes, describing the prevalence of inadequacy or excess, or assessing relations between nutrients and health outcomes, it is critical to incorporate DS intakes to improve exposure estimates. Unfortunately, little is known about the best methods to assess DS, and the structure of measurement error in DS reporting. Several characteristics of nutrients from DS are salient to understand when comparing to those in foods. First, DS can be consumed daily or episodically, in bolus form and can deliver discrete and often very high doses of nutrients that are not limited by energy intakes. These characteristics contribute to bimodal distributions and distributions severely skewed to the right. Labels on DS often provide nutrient forms that differ from those found in conventional foods, and underestimate analytically derived values. Finally, the bioavailability of many nutrient-containing DS is not known and it may not be the same as the nutrients in a food matrix. Current methods to estimate usual intakes are not designed specifically to handle DS. Two temporal procedures are described to refer to the order that nutrient intakes are combined relative to usual intake procedures, referred to as a shrinking the distribution to remove random error. The shrink then add approach is preferable to the add then shrink approach when users and nonusers are combined for most research questions. Stratifying by DS before usual intake methods is another defensible option. This review describes how to incorporate nutrient intakes from DS to usual intakes from foods, and describes the available methods and fit-for-purpose of different analytical strategies to address research questions where total usual intakes are of interest at the group level for use in nutrition research and to inform policy decisions. Clinical Trial Registry: NCT03400436. J Nutr 2019;149:181-197.

© 2019 Frontiers in Bioscience. All rights reserved. Amino acid nutrition studies often involve repeated measures data. An example is that the concentrations of plasma citrulline in steers are repeatedly measured from the same animals. The standard repeated measures ANOVA method does not detect significant time changes in the concentrations of plasma citrulline within 6 hours after steers consumed rumen-protected citrulline, while a graphical analysis indicates that there exists a time effect. Here we describe three mixed model analyses that capture the time effect in a statistically significant way, while accounting for the correlations of measurements over time from the same steers. First, we allow flexible variance-covariance structures on our model. Second, we use baseline measurements as a covariate in our model. Third, we use percent-change from baseline as a data normalization method. In our data analysis, all these three approaches can lead to meaningful statistical results that oral administration of rumen-protected citrulline enhances the concentrations of plasma citrulline over time in ruminants. This supports the notion that rumen-protected citrulline can bypass the rumen to effectively enter the blood circulation.

The structure of Po-208 populated through the EC/beta(+) decay of At-208 is investigated using gamma-ray spectroscopy at the ISOLDE Decay Station. The presented level scheme contains 27 new excited states and 43 new transitions, as well as a further 50 previously observed. rays which have been (re)assigned a position. The level scheme is compared to shell model calculations. Through this analysis approximately half of the beta-decay strength of At-208 is found to proceed via allowed decay and half via first-forbidden decay. The first-forbidden transitions predominantly populate core excited states at high excitation energies, which is qualitatively understood using shell model considerations. This mass region provides an excellent testing ground for the competition between allowed and first-forbidden beta-decay calculations, important for the detailed understanding of the nucleosynthesis of heavy elements.

Summary Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method’s robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with "large p small n", and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before, where p represents the number of variables and n stands for the sample size. The big volume of data does not automatically ensure good quality of inferences because a large number of unimportant variables may be collected in the process of gathering informative variables. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this paper, we develop a screening method for handling large-sized survival data, where the sample size n is large and the dimension p of covariates is of non-polynomial order of the sample size n, or the so-called NP-dimension. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure. Our method can be applied to different situations with large-scale data including genomic data.

Measuring usual dietary intake in freely living humans is difficult to accomplish. As a part of our recent study, a food frequency questionnaire was completed by healthy adult men and women at days 0 and 90 of the study. Data from the food questionnaire were analyzed with a nutrient analysis program ( www.Harvardsffq.date ). Healthy men and women consumed protein as 19-20% and 17-19% of their total energy intakes, respectively, with animal protein representing about 75 and 70% of their total protein intakes, respectively. The intake of each nutritionally essential amino acid (EAA) by the persons exceeded that recommended for healthy adults with a minimal physical activity. In all individuals, the dietary intake of leucine was the highest, followed by lysine, valine, and isoleucine in descending order, and the ingestion of amino acids that are synthesizable de novo in animal cells (AASAs) was about 20% greater than that of total EAAs. The intake of each AASA met those recommended for healthy adults with a minimal physical activity. Intakes of some AASAs (alanine, arginine, aspartate, glutamate, and glycine) from a typical diet providing 90-110 g food protein/day does not meet the requirements of adults with an intensive physical activity. Within the male or female group, there were not significant differences in the dietary intakes of all amino acids between days 0 and 90 of the study, and this was also true for nearly all other essential nutrients. Our findings will help to improve amino acid nutrition and health in both the general population and exercising individuals.