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Vitamin D measurements are influenced by seasonal variation and specific assay used. Motivated by multicenter studies of associations of vitamin D with cancer, we formulated an analytic framework for matched case-control data that accounts for seasonal variation and calibrates to a reference assay. Calibration data were obtained from controls sampled within decile strata of the uncalibrated vitamin D values. Seasonal sine-cosine series were fit to control data. Practical findings included the following: (1) failure to adjust for season and calibrate increased variance, bias, and mean square error and (2) analysis of continuous vitamin D requires a variance adjustment for variation in the calibration estimate. An advantage of the continuous linear risk model is that results are independent of the reference date for seasonal adjustment. (3) For categorical risk models, procedures based on categorizing the seasonally adjusted and calibrated vitamin D have near nominal operating characteristics; estimates of log odds ratios are not robust to choice of seasonal reference date, however. Thus, public health recommendations based on categories of vitamin D should also define the time of year to which they refer. This work supports the use of simple methods for calibration and seasonal adjustment and is informing analytic approaches for the multicenter Vitamin D Pooling Project for Breast and Colorectal Cancer. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study. This approach is applied to the BodyMedia data with 935 males and 84 consecutive days of observation, for a total of 78, 540 observations. We show that sleep efficiency increases with increasing physical activity, while its variance decreases at the same time.

We have shown that dietary fish oil is protective against experimentally induced colon cancer, and the protective effect is enhanced by coadministration of pectin. However, the underlying mechanisms have not been fully elucidated. We hypothesized that fish oil with butyrate, a pectin fermentation product, protects against colon cancer initiation by decreasing cell proliferation and increasing differentiation and apoptosis through a p27Kip1-mediated mechanism. Rats were provided diets of corn or fish oil, with/without butyrate, and terminated 12, 24, or 48 hours after azoxymethane (AOM) injection. Proliferation (Ki-67), differentiation (Dolichos Biflorus Agglutinin), apoptosis (TUNEL), and p27Kip1 (cell-cycle mediator) were measured in the same cell within crypts in order to examine the coordination of cell cycle as a function of diet. DNA damage (N7-methylguanine) was determined by quantitative IHC analysis. Dietary fish oil decreased DNA damage by 19% (P = 0.001) and proliferation by 50% (P = 0.003) and increased differentiation by 56% (P = 0.039) compared with corn oil. When combined with butyrate, fish oil enhanced apoptosis 24 hours after AOM injection compared with a corn oil/butyrate diet (P = 0.039). There was an inverse relationship between crypt height and apoptosis in the fish oil/butyrate group (r = −0.53, P = 0.040). The corn oil/butyrate group showed a positive correlation between p27Kip1 expression and proliferation (r = 0.61, P = 0.035). These results indicate the in vivo effect of butyrate on apoptosis and proliferation is dependent on dietary lipid source. These results demonstrate the presence of an early coordinated colonocyte response by which fish oil and butyrate protects against colon tumorigenesis. Cancer Prev Res; 8(11); 1076–83. ©2015 AACR.

The first fast-timing measurements from nuclides produced via the in-flight fission mechanism are reported. The lifetimes of the first 2+states in 104,106Zr nuclei have been measured via β-delayed γ-ray timing of stopped radioactive isotope beams. An improved precision for the lifetime of the 2+1state in 104Zr was obtained, τ(2+1) =2.90+25−20ns, as well as a first measurement of the 2+1state in 106Zr, τ(2+1) =2.60+20−15ns, with corresponding reduced transition probabilities of B(E2; 2+1→0+g.s.) =0.39(2)e2b2and 0.31(1)e2b2, respectively. Comparisons of the extracted ground-state deformations, β2=0.39(1)(104Zr) and β2=0.36(1)(106Zr) with model calculations indicate a persistence of prolate deformation. The data show that 104Zr is the most deformed of the neutron-rich Zr isotopes measured so far.

In a relative risk analysis of colorectal caner on nutrition intake scores across genders, we show that, surprisingly, when comparing the relative risks for men and women based on the index of a weighted sum of various nutrition scores, the problem reduces to forming a confidence interval for the ratio of two (asymptotically) normal random variables. The latter is an old problem, with a substantial literature. However, our simulation results suggest that existing methods often either give inaccurate coverage probabilities or have a positive probability to produce confidence intervals with infinite length. Motivated by such a problem, we develop a new methodology which we call the Direct Integral Method for Ratios (DIMER), which, unlike the other methods, is based directly on the distribution of the ratio. In simulations, we compare this method to many others. These simulations show that, generally, DIMER more closely achieves the nominal confidence level, and in those cases that the other methods achieve the nominal levels, DIMER has comparable confidence interval lengths. The methodology is then applied to a real data set, and with follow up simulations.

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and non-differentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g. mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and non-identifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, the test with simple asymptotic distribution has computational advantages compared with permutation-based test for high-dimensional genetic or epigenetic data. We propose a pseudolikelihood based expectation-maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. In particular, the proposed test outperforms the commonly used tests under all simulation settings considered, especially when there are variance differences between two groups. The proposed test is applied to a real data set to identify differentially methylated sites between ovarian cancer subjects and normal subjects.

© The Authors, published by EDP Sciences, 2018. Precise spectroscopic information on the fast neutron induced fission of the 238U(n,f) reaction was recently gained using a new technique which involved coupling of the Miniball high resolution y-ray spectrometer and the LICORNE directional neutron source. The experiment allowed measurement of the isotopic fission yields for around 40 even-even nuclei at an incident neutron energy of around 2 MeV where yield data are very sparse. In addition spectroscopic information on very neutron-rich fission products was obtained. Results were compared to models, both the JEFF-3.1.1 data base and the GEF code, and large discrepancies for the S1 fission mode in the Sn/Mo isotope pair were discovered. This suggests that current models are overestimating the role played by spherical shell effects in fast neutron induced fission. In late 2017 and 2018 the nu-ball hybrid spectrometer will be constructed at the IPN Orsay to perform further experimental investigations with directional neutrons coupled to a powerful hybrid Ge/LaBr3 detector array. This will open up new possibilities for measurements of fission yields for fast-neutron-induced fission using the spectroscopic technique and will be complimentary to other methods being developed. ispartof: SCIENTIFIC WORKSHOP ON NUCLEAR FISSION DYNAMICS AND THE EMISSION OF PROMPT NEUTRONS AND GAMMA RAYS (THEORY-4) vol:169 ispartof: 4th Scientific Workshop on Nuclear Fission Dynamics and the Emission of Prompt Neutrons and Gamma Rays (Theory) location:BULGARIA, Varna date:20 Jun - 22 Jun 2017 status: published

© 2018, © 2018 American Statistical Association. We develop a Bayes factor-based testing procedure for comparing two population means in high-dimensional settings. In ‘large-p-small-n” settings, Bayes factors based on proper priors require eliciting a large and complex p × p covariance matrix, whereas Bayes factors based on Jeffrey’s prior suffer the same impediment as the classical Hotelling T2 test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be based on lower dimensional random projections of the high-dimensional data vectors. We choose the prior under the alternative to maximize the power of the test for a fixed threshold level, yielding a restricted most powerful Bayesian test (RMPBT). The final test statistic is based on the ensemble of Bayes factors corresponding to multiple replications of randomly projected data. We show that the test is unbiased and, under mild conditions, is also locally consistent. We demonstrate the efficacy of the approach through simulated and real data examples. Supplementary materials for this article are available online.

© 2018, Institute of Mathematical Statistics. All rights reserved. Studying the relationship between covariates based on retrospective data is the main purpose of secondary analysis, an area of increasing interest. We examine the secondary analysis problem when multiple covariates are available, while only a regression mean model is specified. Despite the completely parametric modeling of the regression mean function, the case-control nature of the data requires special treatment and semiparametric efficient estimation generates various nonparametric estimation problems with multivariate covariates. We devise a dimension reduction approach that fits with the specified primary and secondary models in the original problem setting, and use reweighting to adjust for the case-control nature of the data, even when the disease rate in the source population is unknown. The resulting estimator is both locally efficient and robust against the misspecification of the regression error distribution, which can be heteroscedastic as well as non-Gaussian. We demonstrate the advantage of our method over several existing methods, both analytically and numerically.

This contribution will report on the experimental work on the level structure of 168Dy. The experimental data have been taken as part of the EURICA decay spectroscopy campaign at RIBF, RIKEN in November 2014. In the experiment, a 238U primary beam is accelerated up to 345 MeV/u with an average intensity of 12 pnA. The nuclei of interest are produced by in-flight fission of 238U impinging on Be target with a thickness of 5 mm. The excited states of 168Dy have been populated through the decay from a newly identified isomeric state and via the β decay from 168Tb. In this contribution, scientific motivations, experimental procedure and some preliminary results for this study are presented.

© 2018, Institute of Mathematical Statistics. All rights reserved. Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.

Summary We study the regression relationship between covariates in case–control data: an area known as the secondary analysis of case–control studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical identifiability of such models. Previous work in this context has either specified a fully parametric distribution for the regression errors, specified a homoscedastic distribution for the regression errors, has specified the rate of disease in the population (we refer to this as the true population) or has made a rare disease approximation. We construct a class of semiparametric estimation procedures that rely on none of these. The estimators differ from the usual semiparametric estimators in that they draw conclusions about the true population, while technically operating in a hypothetical superpopulation. We also construct estimators with a unique feature, in that they are robust against the misspecification of the regression error distribution in terms of variance structure, whereas all other non-parametric effects are estimated despite the biased samples. We establish the asymptotic properties of the estimators and illustrate their finite sample performance through simulation studies, as well as through an empirical example on the relationship between red meat consumption and hetero-cyclic amines. Our analysis verified the positive relationship between red meat consumption and two forms of hetro-cyclic amines, indicating that increased red meat consumption leads to increased levels of MeIQx and PhIP, both being risk factors for colorectal cancer. Computer software as well as data to illustrate the methodology are available from http://www.stat.tamu.edu/~carroll/matlab__programs/software.php .

Rapid shape changes are observed for neutron-rich nuclei with A around 100. In particular, a sudden onset of ground-state deformation is observed in the Zr and Sr isotopic chains at N = 60: Low-lying states in $N\ensuremath{\le}58$ nuclei are nearly spherical, while those with $N\ensuremath{\ge}60$ have a rotational character. Nuclear lifetimes as short as a few picoseconds can be measured using fast-timing techniques with ${\mathrm{LaBr}}_{3}$(Ce) scintillators, yielding a key ingredient in the systematic study of the shape evolution in this region. We used neutron-induced fission of $^{241}\mathrm{Pu}$ and $^{235}\mathrm{U}$ to study lifetimes of excited states in fission fragments in the $A\ensuremath{\sim}100$ region with the EXILL-FATIMA array located at the PF1B cold neutron beam line at the Institut Laue-Langevin. In particular, we applied the generalized centroid difference method to deduce lifetimes of low-lying states for the nuclei $^{98}\mathrm{Zr}$ (N = 58), $^{100}\mathrm{Zr}$, and $^{102}\mathrm{Zr}$ ($N\ensuremath{\ge}60$). The results are discussed in the context of the presumed phase transition in the Zr chain by comparing the experimental transition strengths with the theoretical calculations using the interacting boson model and the Monte Carlo shell model.

Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure–outcome relationship is linear on the appropriate scale (e.g. linear, logistic) and the measurement error is classical, that is the result of random noise, the result is attenuation of the effect. When the relationship is non-linear, measurement error distorts the true shape of the association. Regression calibration is a commonly used method for correcting for measurement error, in which each individual’s unknown true exposure in the outcome regression model is replaced by its expectation conditional on the error-prone measure and any fully measured covariates. Regression calibration is simple to execute when the exposure is untransformed in the linear predictor of the outcome regression model, but less straightforward when non-linear transformations of the exposure are used. We describe a method for applying regression calibration in models in which a non-linear association is modelled by transforming the exposure using a fractional polynomial model. It is shown that taking a Bayesian estimation approach is advantageous. By use of Markov chain Monte Carlo algorithms, one can sample from the distribution of the true exposure for each individual. Transformations of the sampled values can then be performed directly and used to find the expectation of the transformed exposure required for regression calibration. A simulation study shows that the proposed approach performs well. We apply the method to investigate the relationship between usual alcohol intake and subsequent all-cause mortality using an error model that adjusts for the episodic nature of alcohol consumption.

© 2018 American Statistical Association. We consider the problem of multivariate density deconvolution when interest lies in estimating the distribution of a vector valued random variable X but precise measurements on X are not available, observations being contaminated by measurement errors U. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density of U is not known but replicated proxies are available for at least some individuals. Additionally, we allow the variability of U to depend on the associated unobserved values of X through unknown relationships, which also automatically includes the case of multivariate multiplicative measurement errors. Basic properties of finite mixture models, multivariate normal kernels, and exchangeable priors are exploited in novel ways to meet modeling and computational challenges. Theoretical results showing the flexibility of the proposed methods in capturing a wide variety of data-generating processes are provided. We illustrate the efficiency of the proposed methods in recovering the density of X through simulation experiments. The methodology is applied to estimate the joint consumption pattern of different dietary components from contaminated 24 h recalls. Supplementary materials for this article are available online.

Copyright © 2017 by the American College of Sports Medicine Introduction/Purpose: To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph). Methods: Participants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL. Results: Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from j4% to j13% lower than DLW, but its accuracy was poorer for physical activity duration (j48%) and sedentary time (j18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (j2% to j7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (j1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (Q TX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (Q TX = 0.48-0.60), as did 4 d of ActiGraph assessment (Q TX = 0.54-0.87). Conclusions: New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.

The (ν 5 2 [532] ⊗ 3 2 [411]) 4− state in 102Zr, populated in the β-decay of 102Y, has been measured to be isomeric with a mean lifetime of 9.5(7) ns. It decays via four transitions, two of which are ∆K = 2 (to the 3+ and 4+ members of the 2+ γ band) and one is ∆K = 4 (to the 4+ member of the ground state 0+ band). The fourth (low-energy) transition is inferred to decay to an as-yet unassigned state. Hindrances of 106 were derived for the ∆K = 2 transitions compared to Weisskopf estimates and the ∆K = 4 transition hindered by a factor of 109 . These values are consistent with the decay pattern of the analogous isomeric state in the neighbouring N = 62 nucleus 100Sr and with the broader systematics of such transitions. A comparison of the hindrances for the ∆K = 4 transitions suggests that 102Zr is hardened against the γ degree of freedom compared to 100Sr.

WOS: 000406755100001, Lifetimes of excited states in the yrast band of the neutron-deficient nuclide W-166 have been measured utilizing the DPUNS plunger device at the target position of the JUROGAM II gamma-ray spectrometer in conjunction with the RITU gas-filled separator and the GREAT focal-plane spectrometer. Excited states in W-166 were populated in the Mo-92(Kr-78, 4p) reaction at a bombarding energy of 380 MeV. The measurements reveal a low value for the ratio of reduced transitions probabilities for the lowest-lying transitions B(E2; 4(+)-> 2(+)) / B(E2; 2(+)-> 0(+)) = 0.33(5), compared with the expected ratio for an axially deformed rotor (B-4/2 = 1.43)., EURONS (European Commission)European Commission Joint Research Centre [RII3-CT-2004-506065]; Academy of Finland under the Finnish Centre of Excellence Programme (Nuclear and Accelerator Based Physics) [213503]; UK Science and Technology Facilities CouncilScience & Technology Facilities Council (STFC); Academy of FinlandAcademy of Finland [131665], This work has been supported through EURONS (European Commission Contract No. RII3-CT-2004-506065), the Academy of Finland under the Finnish Centre of Excellence Programme 2006-2011 (Nuclear and Accelerator Based Physics Contract No. 213503), and the UK Science and Technology Facilities Council. The UK/France (STFC/IN2P3) Loan Pool and GAMMAPOOL network are acknowledged for the EUROGAMdetectors of JUROGAMII. T.G. acknowledge the support of the Academy of Finland (Contract No. 131665).

We report on the first γ-ray spectroscopy of low-lying states in neutron-rich 98,100Kr isotopes obtained from 99,101Rb(p,2p) reactions at ∼ 220 MeV/nucleon. A reduction of the 2+ 1 state energies beyond N = 60 demonstrates a significant increase of deformation, shifted in neutron number compared to the sharper transition observed in strontium and zirconium isotopes. State-of-theart beyond-mean-field calculations using the Gogny D1S interaction predict level energies in good agreement with experimental results. The identification of a low-lying (0+ 2 , 2+ 2 ) state in 98Kr provides the first experimental evidence of a competing configuration at low energy in neutron-rich krypton isotopes consistent with the oblate-prolate shape coexistence picture suggested by theory

Copyright © 2017 John Wiley & Sons, Ltd. A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study.

Excited states in the proton-unbound nuclide $^{177}$Au were populated in the $^92}$Mo($^{88}$Sr, p2n) reaction and identified using the Jurogam-II and GREAT spectrometers in conjunction with the RITU gas-filled separator at the University of Jyvaskyla Accelerator Laboratory. A strongly coupled band and its decay path to the 11/2−α-decaying isomer have been identified using recoil-decay tagging. Comparisons with cranked Hartree-Fock-Bogoliubov (HFB) calculations based on Skyrme energy functionals suggest that the band has a prolate deformation and is based upon coupling the odd 1h11/2 proton hole to the excited 02+ configuration in the $^{178}$Hg core. Although these configurations might be expected to follow the parabolic trend of core Hg(02+) states as a function of neutron number, the electromagnetic decay paths from the strongly coupled band in $^{177}$Au are markedly different from those observed in the heavier isotopes above the midshell. This indicates that a significant change in the structure of the underlying A+1Hg core occurs below the neutron midshell.

Fast-neutron-induced fission of 238U at an energy just above the fission threshold is studied with a novel technique which involves the coupling of a high-efficiency γ-ray spectrometer (MINIBALL) to an inverse-kinematics neutron source (LICORNE) to extract charge yields of fission fragments via γ−γ coincidence spectroscopy. Experimental data and fission models are compared and found to be in reasonable agreement for many nuclei; however, significant discrepancies of up to 600% are observed, particularly for isotopes of Sn and Mo. This indicates that these models significantly overestimate the standard 1 fission mode and suggests that spherical shell effects in the nascent fission fragments are less important for low-energy fast-neutron-induced fission than for thermal neutron-induced fission. This has consequences for understanding and modeling the fission process, for experimental nuclear structure studies of the most neutron-rich nuclei, for future energy applications (e.g., Generation IV reactors which use fast-neutron spectra), and for the reactor antineutrino anomaly.

Using the 168Er(10B,5n) reaction at a beam energy of 68 MeV, new data have been obtained for the population and decay of a T1/2 = 148 ns, Kπ = 21/2− three-quasiparticle isomer at 1717 keV in 173Ta. Revised decay energies and intensities have been determined, together with newly observed members of a rotational band associated with the isomer. By comparison with other isomers in the A ≈ 180 deformed region, the 173Ta isomer properties help to specify the key degrees of freedom that determine K-forbidden transition rates. In particular, when all three quasiparticles are of the same nucleon type, there is a strong dependence of the E2 reduced hindrance factor on the isomer excitation energy.

Summary We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a “full” model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models.

© 2014, © 2014 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, for example, normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails, and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the dataset and R programs implementing our methods are included as part of online supplementary materials.

Summary Motivated by objective measurements of physical activity, we take a functional data approach to longitudinal data with simultaneous measurement of a continuous and a binary outcomes. The regression structures are specified as smooth curves measured at various time-points with random effects that have a hierarchical correlation structure. The random effect curves for each variable are summarized using a few important principal components, and the association of the two longitudinal variables is modeled through the association of the principal component scores. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed effects model framework for model fitting, prediction and inference. Via a quasilikelihood type approximation for the binary component, we develop an algorithm to fit the model. Data-based transformation of the continuous variable and selection of the number of principal components are incorporated into the algorithm. The method is applied to the motivating physical activity data and is evaluated empirically by a simulation study. Extensions for different types of outcomes are also discussed.

Summary. We consider the problem of robust estimation of the regression relationship between a response and a covariate based on sample in which precise measurements on the covariate are not available but error-prone surrogates for the unobserved covariate are available for each sampled unit. Existing methods often make restrictive and unrealistic assumptions about the density of the covariate and the densities of the regression and the measurement errors, for example, normality and, for the latter two, also homoscedasticity and thus independence from the covariate. In this article we describe Bayesian semiparametric methodology based on mixtures of B-splines and mixtures induced by Dirichlet processes that relaxes these restrictive assumptions. In particular, our models for the aforementioned densities adapt to asymmetry, heavy tails and multimodality. The models for the densities of regression and measurement errors also accommodate conditional heteroscedasticity. In simulation experiments, our method vastly outperforms existing methods. We apply our method to data from nutritional epidemiology.

The neutron-rich dysprosium isotopes ¹⁶⁸Dy₁₀₂ and ¹⁶⁹Dy₁₀₃ have been investigated using the EURICA γ-ray spectrometer, following production via in-flight fission of a high-intensity uranium beam in conjunction with isotope separation through the BigRIPS separator at RIBF in RIKEN Nishina Center. For ¹⁶⁸Dy, a previously unreported isomer with a half-life of 0.57(7) μs has been identified at an excitation energy of 1378 keV, and its presence affirmed independently using γ-γ-γ coincidence data taken with Gammasphere via two-proton transfer from an enriched ¹⁷⁰Er target performed at Argonne National Laboratory. This isomer is assigned Jπ = Kπ = (4⁻) based on the measured transition strengths, decay patterns, and the energy systematics for two-quasiparticle states in N = 102 isotones. The underlying mechanism of two-quasiparticle excitations in the doubly midshell region is discussed in comparison with the deformed QRPA and multi-quasiparticle calculations. In ¹⁶⁹Dy, the B(E2) value for the transition de-exciting the previously unreported Kπ = (1/2⁻) isomeric state at 166 keV to the Kπ = (5/2⁻) ground state is approximately two orders of magnitude larger than the E2 strength for the corresponding isomeric-decay transition in the N = 103 isotone ¹⁷³Yb, suggesting the presence of a significant γ-vibrational admixture with a dominant neutron one-quasiparticle component in the isomeric state.

We provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within- and between-variability, and describing the cross-dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed-effects modeling but assuming an approximation to the logistic link function. The primary objectives of this article are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements.

We provide evidence that a root-mean-square test of goodness-of-fit can be significantly more powerful than state-of-the-art exact tests in detecting deviations from Hardy-Weinberg equilibrium. Unlike Pearson's chi-square test, the log--likelihood-ratio test, and Fisher's exact test, which are sensitive to relative discrepancies between genotypic frequencies, the root-mean-square test is sensitive to absolute discrepancies. This can increase statistical power, as we demonstrate using benchmark datasets and through asymptotic analysis. With the aid of computers, exact P-values for the root-mean-square statistic can be calculated eeffortlessly, and can be easily implemented using the author's freely available code., 29 pages, 6 figures

Male Zucker diabetic fatty (ZDF) rats were used to study effects of oral administration of interferon tau (IFNT) in reducing obesity. Eighteen ZDF rats (28 days of age) were assigned randomly to receive 0, 4 or 8 μg IFNT/kg body weight (BW) per day (n=6/group) for 8 weeks. Water consumption was measured every two days. Food intake and BW were recorded weekly. Energy expenditure in 4-, 6-, 8-, and 10-week-old rats was determined using indirect calorimetry. Starting at 7 weeks of age, urinary glucose and ketone bodies were tested daily. Rates of glucose and oleate oxidation in liver, brown adipose tissue, and abdominal adipose tissue, leucine catabolism in skeletal muscle, and lipolysis in white and brown adipose tissues were greater for rats treated with 8 μg IFNT/kg BW/day in comparison with control rats. Treatment with 8 μg IFNT/kg BW/day increased heat production, reduced BW gain and adiposity, ameliorated fatty liver syndrome, delayed the onset of diabetes, and decreased concentrations of glucose, free fatty acids, triacylglycerol, cholesterol, and branched-chain amino acids in plasma, compared to control rats. Oral administration of 8 μg IFNT/kg BW/day ameliorated oxidative stress in skeletal muscle, liver and adipose tissue, as indicated by decreased ratios of oxidized glutathione to reduced glutathione and increased concentrations of the antioxidant tetrahydrobiopterin. These results indicate that IFNT stimulates oxidation of energy substrates and reduces obesity in ZDF rats and may have broad important implications for preventing and treating obesity-related diseases in mammals.

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Systematic investigations into the structure of measurement error of physical activity questionnaires are lacking. We propose a measurement error model for a physical activity questionnaire that uses physical activity level (the ratio of total energy expenditure to basal energy expenditure) to relate questionnaire-based reports of physical activity level to true physical activity levels. The 1999-2006 National Health and Nutrition Examination Survey physical activity questionnaire was administered to 433 participants aged 40-69 years in the Observing Protein and Energy Nutrition (OPEN) Study (Maryland, 1999-2000). Valid estimates of participants' total energy expenditure were also available from doubly labeled water, and basal energy expenditure was estimated from an equation; the ratio of those measures estimated true physical activity level ("truth"). We present a measurement error model that accommodates the mixture of errors that arise from assuming a classical measurement error model for doubly labeled water and a Berkson error model for the equation used to estimate basal energy expenditure. The method was then applied to the OPEN Study. Correlations between the questionnaire-based physical activity level and truth were modest (r = 0.32-0.41); attenuation factors (0.43-0.73) indicate that the use of questionnaire-based physical activity level would lead to attenuated estimates of effect size. Results suggest that sample sizes for estimating relationships between physical activity level and disease should be inflated, and that regression calibration can be used to provide measurement error-adjusted estimates of relationships between physical activity and disease. © 2013 Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2013.

The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso.We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn - δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR.We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen. © The Author 2013. Published by Oxford University Press. All rights reserved.

We have demonstrated that polyphenol-rich sorghum bran diets alter fecal microbiota; however, little is known regarding their effect on colon inflammation. Our aim was to characterize the effect of sorghum bran diets on intestinal homeostasis during dextran sodium sulfate (DSS)-induced colitis. Male Sprague-Dawley rats (N = 20/diet) were provided diets containing 6% fiber from cellulose, or Black (3-deoxyanthocyanins), Sumac (condensed tannins) or Hi Tannin Black (both) sorghum bran. Colitis was induced (N = 10/diet) with three separate 48-h exposures to 3% DSS, and feces were collected. On Day 82, animals were euthanized and the colon resected. Only discrete mucosal lesions, with no diarrhea or bloody stools, were observed in DSS rats. Only bran diets upregulated proliferation and Tff3, Tgfβ and short chain fatty acids (SCFA) transporter expression after a DSS challenge. DSS did not significantly affect fecal SCFA concentrations. Bran diets alone upregulated repair mechanisms and SCFA transporter expression, which suggests these polyphenol-rich sorghum brans may suppress some consequences of colitis.

The properties of excited states in the neutron-rich nucleus 194Os have been investigated using the 192Os(18O,16O)194Os reaction with an 80 MeV beam provided by the IFIN-HH Laboratory, Bucharest. Discrete γ -ray decays from excited states have been measured using the hybrid HPGe-LaBr3(Ce) array RoSPHERE. The current work identifies a number of previously unreported low-lying nonyrast states in 194Os as well as the first measurement of the half-life of the yrast 2+ state of 302(50) ps. This is equivalent to a B(E2 : 2+ → 0+) = 45(16) W.u. and intrinsic quadrupole deformation of βeff = 0.14(1). The experimental results are compared with Hartree-Fock-Bogoliubov–interacting-boson-model calculations and are consistent with a reduction in a quadrupole collectivity in Os isotopes with increasing neutron number.

The first measurement of the low-lying states of the neutron-rich 110 Zr and 112 Mo was performed via in-beam γ -ray spectroscopy after one proton removal on hydrogen at ∼ 200 MeV / nucleon . The 2 + 1 excitation energies were found at 185(11) keV in 110 Zr , and 235(7) keV in 112 Mo , while the R 42 = E ( 4 + 1 ) / E ( 2 + 1 ) ratios are 3.1(2), close to the rigid rotor value, and 2.7(1), respectively. These results are compared to modern energy density functional based configuration mixing models using Gogny and Skyrme effective interactions. We conclude that first levels of 110 Zr exhibit a rotational behavior, in agreement with previous observations of lighter zirconium isotopes as well as with the most advanced Monte Carlo shell model predictions. The data, therefore, do not support a harmonic oscillator shell stabilization scenario at Z = 40 and N = 70 . The present data also invalidate predictions for a tetrahedral ground state symmetry in 110 Zr .

© 2017, Institute of Mathematical Statistics. All rights reserved. While popular, single index models and additive models have potential limitations, a fact that leads us to propose SiAM, a novel hybrid combination of these two models. We first address model identifiability under general assumptions. The result is of independent interest. We then develop an estimation procedure by using splines to approximate unknown functions and establish the asymptotic properties of the resulting estimators. Furthermore, we suggest a two-step procedure for establishing confidence bands for the nonparametric additive functions. This procedure enables us to make global inferences. Numerical experiments indicate that SiAM works well with finite sample sizes, and are especially robust to model structures. That is, when the model reduces to either single-index or additive scenario, the estimation and inference results are comparable to those based on the true model, while when the model is misspecified, the superiority of our method can be very great.

A fast-timing experiment was performed at the Argonne National Laboratory in December 2015 and January 2016, measuring decay radiation of fission products from a Cf-252 fission source. Details of the set-up, integration with Digital Gammasphere, and the data acquisition system are presented. The timing performance of the set-up, capable of measuring lifetimes from the nanosecond region down to tens of picoseconds, is discussed. First preliminary results from the fast-timing analysis of the fission fragment data are presented.

© The Authors, published by EDP Sciences, 2017. This short paper presents the identification of a metastable, isomeric-state decay in the neutron-rich odd-odd, prolate-deformed nucleus 166Tb. The nucleus of interest was formed using the in-flight fission of a 345 MeV per nucleon 238U primary beam at the RIBF facility, RIKEN, Japan. Gamma-ray transitions decaying from the observed isomeric states in 166Tb were identified using the EURICA gamma-ray spectrometer, positioned at the final focus of the BigRIPS fragments separator. The current work identifies a single discrete gamma-ray transition of energy 119 keV which de-excites an isomeric state in 166Tb with a measured half-life of 3.5(4) μs. The multipolarity assignment for this transition is an electric dipole and is made on the basis internal conversion and decay lifetime arguments. Possible two quasi-particle Nilsson configurations for the initial and final states which are linked by this transition in 166Tb are made on the basis of comparison with Blocked BCS Nilsson calculations, with the predicted ground state configuration for this nucleus arising from the coupling of the v(1-/2)[521] and π(3+/2) Nilsson orbitals. ispartof: EPJ Web of Conferences vol:146 ispartof: ND 2016: International Conference on Nuclear Data for Science and Technology location:Bruges, Belgium date:11 Sep - 16 Sep 2016 status: Published online

© 2017, Institute of Mathematical Statistics. All rights reserved. We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root-n consistent, asymptotically normal and locally efficient. Comprehensive simulations and an analysis of an empirical data set are performed to demonstrate the finite sample performance and the bias reduction of the locally efficient estimators.

© 2016 by the American College of Sports Medicine. Introduction The way physical activity (PA) and sedentary behavior (SB) are accumulated throughout the day (i.e., patterns) may be important for health, but identifying measurable and meaningful metrics of behavioral patterns is challenging. This study evaluated accelerometer-derived metrics to determine whether they predicted PA and SB patterns and were reliably measured. Methods We defined and measured 55 metrics that describe daily PA and SB using data collected by using the activPAL monitor in four studies. The first two studies were randomized crossover designs that included recreationally active participants. Study 1 experimentally manipulated time spent in moderate-to-vigorous-intensity PA and sedentary time, and study 2 held time in exercise constant and manipulated SB. Study 3 included inactive participants who increased exercise, decreased sedentary time, or both. The study conditions induced distinct behavioral patterns; thus, we tested whether the new metrics could improve the prediction of an individual's study condition after adjusting for the overall volume of PA or SB using conditional logistic regression. In study 4, we measured the 3-month reliability for the pattern metrics by calculating intraclass correlation coefficients in a community-dwelling sample who wore the activPAL monitor twice for 7 d. Results In each of the experimental studies, we identified new metrics that could improve the accuracy for predicting condition beyond SB and moderate-to-vigorous-intensity PA volume. In study 1, 23 metrics were predictive of a highly active condition, and in study 2, 24 metrics were predictive of a highly sedentary condition. In study 4, the median intraclass correlation coefficients (25-75th percentiles) of the metrics were 0.59 (0.46-0.65). Conclusions Several new metrics were predictive of patterns of SB, exercise, and nonexercise behavior and are moderately reliable for a 3-month period. Applying these metrics to determine whether daily behavioral patterns are associated with health-outcomes is an important area of future research.

Excited states in the neutron-deficient N = 84 nuclide Lu-155 have been populated by using the Pd-102(Ni-58, alpha p) reaction. The Lu-155 nuclei were separated by using the gas-filled recoil ion t ...

Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study. This approach is applied to the BodyMedia data with 935 males and 84 consecutive days of observation, for a total of 78, 540 observations. We show that sleep efficiency increases with increasing physical activity, while its variance decreases at the same time.

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

The 54Fe nucleus was populated from a 56Fe beam impinging on a Be target with an energy of E=A ¼ 500 MeV. The internal decay via γ-ray emission of the 10þ metastable state was observed. As the structure of this isomeric state has to involve at least four unpaired nucleons, it cannot be populated in a simple two-neutron removal reaction from the 56Fe ground state. The isomeric state was produced in the low-momentum (-energy) tail of the parallel momentum (energy) distribution of 54Fe, suggesting that it was populated via the decay of the Δ0 resonance into a proton. This process allows the population of fournucleon states, such as the observed isomer. Therefore, it is concluded that the observation of this 10þ metastable state in 54Fe is a consequence of the quark structure of the nucleons.