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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.

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.

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.

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.

The identification of valid surrogate markers of disease or disease progression has the potential to decrease the length and costs of future studies. Most available methods that assess the value of a surrogate marker ignore the fact that surrogates are often measured with error. Failing to adjust for measurement error can erroneously identify a useful surrogate marker as not useful or vice versa. We investigate and propose robust methods to correct for the effect of measurement error when evaluating a surrogate marker using multiple estimators developed for parametric and nonparametric estimates of the proportion of treatment effect explained by the surrogate marker. In addition, we quantify the attenuation bias induced by measurement error and develop inference procedures to allow for variance and confidence interval estimation. Through a simulation study, we show that our proposed estimators correct for measurement error in the surrogate marker and that our inference procedures perform well in finite samples. We illustrate these methods by examining a potential surrogate marker which is measured with error, hemoglobin A1c, using data from the Diabetes Prevention Program clinical trial.

Summary Huntington disease is an autosomal dominant, neurodegenerative disease without clearly identified biomarkers for when motor-onset occurs. Current standards to determine motor-onset rely on a clinician’s subjective judgment that a patient’s extrapyramidal signs are unequivocally associated with Huntington disease. This subjectivity can lead to error which could be overcome using an objective, data-driven metric that determines motor-onset. Recent studies of motor-sign decline—the longitudinal degeneration of motor-ability in patients—have revealed that motor-onset is closely related to an inflection point in its longitudinal trajectory. We propose a nonlinear location-shift marker model that captures this motor-sign decline and assesses how its inflection point is linked to other markers of Huntington disease progression. We propose two estimating procedures to estimate this model and its inflection point: one is a parametric method using nonlinear mixed effects model and the other one is a multi-stage nonparametric approach, which we developed. In an empirical study, the parametric approach was sensitive to correct specification of the mean structure of the longitudinal data. In contrast, our multi-stage nonparametric procedure consistently produced unbiased estimates regardless of the true mean structure. Applying our multi-stage nonparametric estimator to Neurobiological Predictors of Huntington Disease, a large observational study of Huntington disease, leads to earlier prediction of motor-onset compared to the clinician’s subjective judgment.

The extremely neutron-deficient isotopes 177,179Au were studied by means of in-beam ?-ray spectroscopy. Specific tagging techniques, ?-decay tagging in 177Au and isomer tagging in 179Au, were used for these studies. Feeding of positive-parity, nearly spherical states, which are associated with 2d3/2 and 3s1/2 proton-hole configurations, from the 1i13/2 proton-intruder configuration was observed in 177Au. Such a decay path has no precedent in odd-Au isotopes and it is explained by the effect of mixing of wave functions of the initial state. © 2020, 262010 2/0129/17 Science and Technology Facilities Council, STFC European Research Council, ERC: 203481 Academy of Finland: 131665 Agentúra na Podporu V?skumu a V?voja, APVV: APVV-15-0225, The authors express their gratitude to the staff of the Accelerator Laboratory at the University of Jyväskylä for their excellent technical support. This work has been supported by the EU-FP7-IA project ENSAR (No. 262010 ), the Academy of Finland (CoE in Nuclear and Accelerator Based Physics, grant to T.G., Contract No. 131665 ), the European Research Council through the project SHESTRUCT (Grant Agreement No. 203481 ), the UK Science and Technology Facilities Council , the Slovak Research and Development Agency under Contract No. APVV-15-0225 , and the Slovak Grant Agency VEGA (Contract No. 2/0129/17 ).

Excited states have been identified in the heaviest known even-Z N = 84 isotone 158W, which lies in a region of one-proton emitters and the two-proton drip line. The observation of γ-ray transitions feeding the ground state establishes the excitation energy of the yrast 6+ state confirming the spin-gap nature of the α-decaying 8+ isomer. The 8+ isomer is also expected to be unbound to two-proton emission but no evidence for this decay mode was observed. An upper limit for the two-proton decay branch has been deduced as b 2 p ≤ 0.17% at the 90% confidence level. The possibility of observing two-proton emission from multiparticle isomers in nearby nuclides is considered.

In biomedical studies, parametric mixture models are widely used for modeling data collected from a non-homogenous population. For instance, in genetic linkage analysis, parametric admixture models have been considered to account for the fact that linkage may exist only in a proportion of families (Liang and Rathouz, 1999; Fu et al., 2006; Di and Liang, 2011). In microarray data analysis, mixture models have been applied to test for partially differentially expressed genes (Ghosh and Chinnaiyan, 2009; Van Wieringen and Van de Viel, 2009). Generally, testing for homogeneity in mixture models is a nonregular problem because the mixture proportion parameter lies on the boundary of its parameter space and some nuisance parameters are absent under the null hypothesis of homogeneity. Therefore, theoretical development for the asymptotic distributions of tests is more difficult than that in regular statistical models. Much work has been done for testing homogeneity in parametric mixture models. For example, a score test is proposed by Liang and Rathouz (1999), and (modified) likelihood ratio tests are derived by Lemdani and Pons (1999), Chen et al. (2001) and Di and Liang (2011), among many others. For more comprehensive overview of parametric mixture models, see McLachlan and Basford (1988) and Lindsay (1995). However, in many modern applications such as the DNA methylation data analysis, the distributions of methylation values are often highly heterogeneous across different CpG sites (Wang, 2011), such that one single parametric mixture model cannot provide sufficient goodness of fit for the methylation data measured at different CpG sites. To relax the parametric assumption, Qin and Liang (2011) proposed a two-sample semiparametric exponential tilt mixture model (ETMM), where the distribution of the reference group is completely unspecified and the distribution of the other group is a mixture of two unknown distributions linked by an exponential tilt with a single parameter. Such an ETMM contains the mixture of natural exponential family (NEF) models (Morris, 2006). However, this ETMM still does not provide sufficient degrees of freedom in modeling, because it only allows one single free parameter which reduces to the non-homogeneity of means in normal models. In applications such as DNA methylation data, the two experimental groups can be different in variations but not in means, or different in both means and variations, and such difference in variation may suggest important associations between the CpG sites and diseases (Teschendorff et al., 2014; Sun et al., 2017). In such circumstances, existing methods are not sufficient to capture these key features of data and can lead to a substantial loss of power. Hong et al. (2017) proposed a pseudolikelihood EM based test (PLEMT) using a multi-parameter generalized exponential tilt mixture model (GETMM). The GETMM generalizes the ETMM by incorporating pre-specified multidimensional basis functions, which offers more flexible characterization of the non-homogeneity between two groups. Unlike the empirical likelihood method in Qin and Liang (2011), a pairwise conditioning procedure was used to eliminate the unspecified distribution function, and a penalized pseudolikelihood was constructed to handle the identifiability problem. Despite the superior power of the PLEMT over existing tests in capturing the signals in mean and variations, there are several limitations. First, it involves choosing multiple tuning parameters including the penalty parameter and number of steps for the EM algorithm. There is no universally accepted values for the tuning parameters. Second, this method is computationally expensive since EM algorithm is used to obtain the test statistic. In this paper, we propose a novel score test for homogeneity based on GETMM. We adopt the same procedure as in Hong et al. (2017) in elimination of the unspecified distribution function of the reference group. Instead of using penalization, we work on the pseudolikelihood directly and show that the parameters of interest are still identifiable. To address the challenge that some nuisance parameters disappear under the null hypothesis, we modify the score function by a novel procedure and derive the asymptotic null distribution of the proposed score test. Unlike the standard asymptotic results in Davies (1977, 1987), we unveil new Wilks phenomenon of the proposed score test, i.e., asymptotic null distributions are independent of nuisance parameters. The simple asymptotic distribution controls the Type I error well, and avoids the need of permutation based procedures. In addition, we show that, with fixed nuisance parameters, the proposed score test is locally most powerful among a class of tests. This justifies the optimality of our method in finite samples. To obtain further insight about the power of the proposed test, we derive the asymptotic power functions under local alternative hypotheses. Two types of local alternatives are studied, with respect to two different geometric paths for approximating the null hypothesis. This result is particularly useful in performing sample size calculation and experimental design. The rest of this article is organized as follows. In Section 2, we introduce the GETMM and construct the pseudolikelihood. In Section 3, we derive the score test based on the constructed pseudolikelihood and derive the asymptotic null distribution. In Section 4, we derive the local asymptotic power of the proposed tests and provide insights on experimental designs. In Section 5, we conduct simulation studies to compare the proposed tests with the existing ones. In Section 6, we apply these tests to the methylation data from an ovarian cancer study. Concluding remarks are given in Section 7.

© Lippincott Williams & Wilkins. Purpose Interest in a variety of physical behaviors (e.g., exercise, sitting time, sleep) in relation to health outcomes creates a need for new statistical approaches to analyze the joint effects of these distinct but inter-related physical behaviors. We developed and tested an integrated physical behavior score (PBS). Methods National Institutes of Health-American Association of Retired Persons Diet and Health Study participants (N = 163,016) completed a questionnaire (2004-2006) asking about time spent in five exercise and nonexercise physical activities, two sedentary behaviors (television and nontelevision), and sleep. In half of the sample, we used shape-constrained additive regression to model the relationship between each behavior and survival. Maximum logit scores from each of the eight behavior-survival functions were summed to produce a PBS that was proportionally rescaled to range from 0 to 100. We examined predictive validity of the PBS in the other half-sample using Cox Proportional Hazards models after adjustment for covariates for all-cause and cause-specific mortality. Results In the testing sample, over an average of 6.6 yr of follow-up, 8732 deaths occurred. We found a strong graded decline in risk of all-cause mortality across quintiles of PBS (Q5 vs Q1 hazard ratio [95% CI] = 0.53 [0.49, 0.57]). Risk estimates for the PBS were higher than any of the components in isolation. Results were similar but stronger for cardiovascular disease (Q5 vs Q1 = 0.42 [0.39, 0.48]) and other mortality (Q5 vs Q1 = 0.42 [0.36, 0.48]). The relationship between PBS and mortality was observed in stratified analyses by median age, sex, body mass index, and health status. Conclusions We developed a novel statistical method generated a composite physical behavior that is predictive of mortality outcomes. Future research is needed to test this approach in an independent sample.

A lifetime measurement of the 19/2 state in Pb has been performed using the Recoil Distance Doppler-Shift (RDDS) method. The nuclei of interest were produced in multi-nucleon transfer reactions induced by a Pb beam impinging on a Mo enriched target. The beam-like nuclei were detected and identified in terms of their atomic mass number in the VAMOS++ spectrometer while the prompt γ rays were detected by the AGATA tracking array. The measured large reduced transition probability B(E3,19/2→13/2)=40(8) W.u. is the first indication of the octupole phonon at high spin in Pb. An analysis in terms of a particle-octupole-vibration coupling model indicates that the measured B(E3) value in Pb is compatible with the contributions from single-phonon and single particle E3 as well as E3 strength arising from the double-octupole-phonon 6 state, all adding coherently. A crucial aspect of the coupling model, namely the strong mixing between single-hole and the phonon-hole states, is confirmed in a realistic shell-model calculation.

The gamma-ray decay of excited states of the one-valence-proton nucleus Sb-133 has been studied using cold-neutron induced fission of U-235 and Pu-241 targets, during the EXILL campaign at the ILL reactor in Grenoble. By using a highly efficient HPGe array, coincidences between gamma-rays prompt with the fission event and those delayed up to several tens of microseconds were investigated, allowing to observe, for the first time, high-spin excited states above the 16.6 mu s isomer. Lifetimes analysis, performed by fast-timing techniques with LaBr3(Ce) scintillators, revealed a difference of almost two orders of magnitude in B(M1) strength for transitions between positive-parity medium-spin yrast states. The data are interpreted by a newly developed microscopic model which takes into account couplings between core excitations (both collective and non-collective) of the doubly magic nucleus Sn-132 and the valence proton, using Skyrme effective interaction in a consistent way. The results point to a fast change in the nature of particle-core excitations with increasing spin. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.

© 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.

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.

© 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.

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 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.

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.

We have recently successfully demonstrated a new technique for production and study of many of the most exotic neutron-rich nuclei at moderate spins. LICORNE, a newly developed directional inverse-kinematic fast neutron source at the IPN Orsay, was coupled to the MINIBALL high resolution γ-ray spectrometer to study nuclei the furthest from stability using the 238U(n,f) reaction. This reaction and 232Th(n,f) are the most neutron-rich fission production mechanisms achievable and can be used to simultaneously populate hundreds of neutron-rich nuclei up to spins of ≈ 16ℏ. High selectivity in the experiment was achieved via triple γ-ray coincidences and the use of a 400 ns period pulsed neutron beam, a technique which is unavailable to other population mechanisms such as 235U(nth,f) and 252Cf(SF). The pulsing allows time correlations to be exploited to separate delayed γ rays from isomeric states in the hundreds of nuclei produced, which are then used to cleanly select a particular nucleus and its exotic binary partners. In the recent experiment, several physics cases are simultaneously addressed such as shape coexistence, the evolution of shell closures far from stability, and the spectroscopy of nuclei in the r-process path near N = 82. Preliminary physics results on anomalies in the 238U(n,f) fission yields and the structure of the 138Te and 100Sr nuclei will soon be published. A future project, ν-ball, to couple LICORNE with a hybrid escape-suppressed spectrometer to refine further the technique and achieve a large increase in the observational limit is discussed. ispartof: pages:395-401 ispartof: Acta Physica Polonica B vol:48 issue:3 pages:395-401 ispartof: location:Zakopane: POLAND status: published

The focus of this paper is dietary intervention trials. We explore the statistical issues involved when the response variable, intake of a food or nutrient, is based on self‐report data that are subject to inherent measurement error. There has been little work on handling error in this context. A particular feature of self‐reported dietary intake data is that the error may be differential by intervention group. Measurement error methods require information on the nature of the errors in the self‐report data. We assume that there is a calibration sub‐study in which unbiased biomarker data are available. We outline methods for handling measurement error in this setting and use theory and simulations to investigate how self‐report and biomarker data may be combined to estimate the intervention effect. Methods are illustrated using data from the Trial of Nonpharmacologic Intervention in the Elderly, in which the intervention was a sodium‐lowering diet and the response was sodium intake. Simulations are used to investigate the methods under differential error, differing reliability of self‐reports relative to biomarkers and different proportions of individuals in the calibration sub‐study. When the reliability of self‐report measurements is comparable with that of the biomarker, it is advantageous to use the self‐report data in addition to the biomarker to estimate the intervention effect. If, however, the reliability of the self‐report data is low compared with that in the biomarker, then, there is little to be gained by using the self‐report data. Our findings have important implications for the design of dietary intervention trials. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Lifetimes of excited states of the 98;100;102Zr nuclei were measured by using the Generalized Centroid Difference Method. The nuclei of interest were populated via neutron-induced fission of 241Pu and 235U during the EXILL-FATIMA campaign. The obtained lifetimes were used to calculate the B(E2) transition strengths and β deformation parameters which were then compared with the recent theoretical predictions obtained with Monte Carlo Shell Model.

The LICORNE neutron source is a new device at the ALTO facility. Its use of inverse kinematics makes the production of naturally focused neutron beams possible with an energy range of 0.5 to 4 MeV. This is perfect for any studies concerning fast neutron induced reaction based on detection setup that require to be placed in a compact geometry - such as Ge based ? detection setup. In this paper, neutron production with LICORNE is described. The development of a gascell target and the extension of the energy range up to 7 MeV with the p(11B,n)11C are presented. An overview of the major research fields studied with LICORNE is given and two types of experiment are presented. The first one dedicated to prompt fission ?-ray emission in fission, as a function of incident neutron energy, is described. Some preliminary results are shown. The second, in the context of the MINORCA campaign, is detailed. The most recent outcomes in the data analysis process are also presented.

In the low-dimensional case, the generalized additive coefficient model (GACM) proposed by Xue and Yang [Statist. Sinica 16 (2006) 1423-1446] has been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables. In this paper, we propose estimation and inference procedures for the GACM when the dimension of the variables is high. Specifically, we propose a groupwise penalization based procedure to distinguish significant covariates for the "large $p$ small $n$" setting. The procedure is shown to be consistent for model structure identification. Further, we construct simultaneous confidence bands for the coefficient functions in the selected model based on a refined two-step spline estimator. We also discuss how to choose the tuning parameters. To estimate the standard deviation of the functional estimator, we adopt the smoothed bootstrap method. We conduct simulation experiments to evaluate the numerical performance of the proposed methods and analyze an obesity data set from a genome-wide association study as an illustration., Published at http://dx.doi.org/10.1214/15-AOS1344 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Motivated by data recording the effects of an exercise intervention on subjects' physical activity over time, we develop a model to assess the effects of a treatment when the data are functional with 3 levels (subjects, weeks and days in our application) and possibly incomplete. We develop a model with 3-level mean structure effects, all stratified by treatment and subject random effects, including a general subject effect and nested effects for the 3 levels. The mean and random structures are specified as smooth curves measured at various time points. The association structure of the 3-level data is induced through the random curves, which are summarized using a few important principal components. 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. We develop an algorithm to fit the model iteratively with the Expectation/Conditional Maximization Either (ECME) version of the EM algorithm and eigenvalue decompositions. Selection of the number of principal components and handling incomplete data issues are incorporated into the algorithm. The performance of the Wald-type hypothesis test is also discussed. The method is applied to the physical activity data and evaluated empirically by a simulation study.

International audience; 206Hg was populated in the fragmentation of an E∕A = 1 GeV 208Pb beam at GSI. It was part of a campaign to study nuclei around 208Pb via relativistic Coulomb excitation. The observation of the known isomeric states confirmed the identification of the fragmentation products. The isomeric decays were also used to prove that the correlations between beam identification detectors and the AGATA γ-ray tracking array worked properly and that the tracking efficiency was independent of the time relative to the prompt flash.

International audience; Preliminary results are presented from a recent campaign of experiments,performed at the PF1B cold-neutron facility at ILL (Grenoble,France), in which $\gamma$-ray spectroscopy of neutron-rich nuclei produced inneutron induced fission on 235U and 241Pu targets was performed. By usingthe EXILL array, consisting of EXOGAM, GASP and ILL-Cloverdetectors, and a digital triggerless data acquisition system, coincidences over several microseconds time windows were studied. This allowed to obtainnew information on nuclei around 132Sn, such as 132Te, 133Sb and130Sn, in which microsecond isomeric states occur. Preliminary results on210Bi, populated by cold-neutron capture, are also shown. The data offerthe possibility to investigate the couplings between core excitations andvalence particles, around the doubly magic nuclei 132Sn and 208Pb.

Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods that accommodate both characteristics simultaneously. Specifically, we consider measurement error and misclassification in generalized linear models under the scenario that an external validation study is available, and systematically develop a number of effective functional and structural methods. Our methods can be applied to different situations to meet various objectives.

The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged

Measurement error causes biases in regression fits. If one could accurately measure exposure to environmental lead media, the line obtained would differ in important ways from the line obtained when one measures exposure with error. The effects of measurement error vary from study to study. It is dangerous to take measurement error corrections derived from one study and apply them to data from entirely different studies or populations. Measurement error can falsely invalidate a correct (complex mechanistic) model. If one builds a model such as the integrated exposure uptake biokinetic model carefully, using essentially error-free lead exposure data, and applies this model in a different data set with error-prone exposures, the complex mechanistic model will almost certainly do a poor job of prediction, especially of extremes. Although mean blood lead levels from such a process may be accurately predicted, in most cases one would expect serious underestimates or overestimates of the proportion of the population whose blood lead level exceeds certain standards.

Primary analysis of case–control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case–control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case–control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case–control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

We have demonstrated that diets containing fish oil and pectin (FO/P) reduce colon tumor incidence relative to control (corn oil and cellulose [CO/C]) in part by inducing apoptosis of DNA-damaged colon cells. Relative to FO/P, CO/C promotes colonocyte expression of the antiapoptotic modulator, Bcl-2, and Bcl-2 promoter methylation is altered in colon cancer. To determine if FO/P, compared with CO/C, limits Bcl-2 expression by enhancing promoter methylation in colon tumors, we examined Bcl-2 promoter methylation, mRNA levels, colonocyte apoptosis and colon tumor incidence in azoxymethane (AOM)-injected rats. Rats were provided diets containing FO/P or CO/C, and were terminated 16 and 34 weeks after AOM injection. DNA isolated from paraformaldehyde-fixed colon tumors and uninvolved tissue was bisulfite modified and amplified by quantitative reverese transcriptase-polymerase chain reaction to assess DNA methylation in Bcl-2 cytosine-guanosine islands. FO/P increased Bcl-2 promoter methylation (P = 0.009) in tumor tissues and colonocyte apoptosis (P = 0.020) relative to CO/C. An inverse correlation between Bcl-2 DNA methylation and Bcl-2 mRNA levels was observed in the tumors. We conclude that dietary FO/P promotes apoptosis in part by enhancing Bcl-2 promoter methylation. These Bcl-2 promoter methylation responses, measured in vivo, contribute to our understanding of the mechanisms involved in chemoprevention of colon cancer by diets containing FO/P. © 2008 Society for Experimental Biology and Medicine.

We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual reported consumption of a particular food item. The second level incorporates the amount of food consumption equalling to zero if not consumed, and the third level incorporates the amount of energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations which can be thought as mean estimates for frequentists. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake.

Functional data analysis has received considerable recent attention and a number of successful applications have been reported. In this paper, asymp- totically simultaneous confidence bands are obtained for the mean function of the functional regression model, using piecewise constant spline estimation. Simulation experiments corroborate the asymptotic theory. The confidence band procedure is illustrated by analyzing CD4 cell counts of HIV infected patients.

We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration., Published in at http://dx.doi.org/10.1214/11-AOS885 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The article addresses the problem of detecting presence and location of a small low emission source inside of an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The goal is to reach the signal-to-noise ratio (SNR) levels on the order of $10^{-3}$. A Bayesian approach to this problem is implemented in 2D. The method allows inference not only about the existence of the source, but also about its location. We derive Bayes factors for model selection and estimation of location based on Markov Chain Monte Carlo (MCMC) simulation. A simulation study shows that with sufficiently high total emission level, our method can effectively locate the source.

To show how the variance of the measurement error (ME) associated with individual ancestry proportion estimates can be estimated, especially when the number of ancestral populations (k) is greater than 2. We extend existing internal consistency measures to esti- mate the ME variance, and we compare these estimates with the ME variance estimated by use of the repeated measure- ment (RM) approach. Both approaches work by dividing the genotyped markers into subsets. We examine the effect of the number of subsets and of the allocation of markers to each subset on the performance of each approach. We used simulated data for all comparisons. Independently of the value of k, the measures of internal reliability provided less biased and more precise estimates of the ME variance than did those obtained with the RM ap- proach. Both methods tend to perform better when a large number of subsets of markers with similar sizes are consid- ered. Our results will facilitate the use of ME correction meth- ods to address the ME problem in individual ancestry pro- portion estimates. Our method will improve the ability to control for type I error inflation and loss of power in asso- ciation tests and other genomic research involving ancestry estimates. If unmodified, the use of this framework implicitly assumes that the predictors are measured without error. We also showed in that study that controlling for admixture alone is not sufficient to control for spurious associations. Investi- gators must also account for the individual's true ancestry proportion as well as the product of the parental ancestries in the association test to completely remove the confound- ing effect of admixture-induced linkage disequilibrium. We define an individual ancestry proportion, with respect to a specific ancestral population, as the proportion of that in- dividual's ancestors who were members of that parental pop- ulation in the generation before the first admixture event. This is in contrast to an individual's admixture, which is the proportion of the individual's genome that is inherited from a specific ancestral population. For example, two full siblings have the same ancestry. However, random variations that happen during meiosis may lead to different admixture proportions both at the global level and at the local level. Therefore, independently of the approach chosen to estimate the IAPEs, the resulting estimate should be seen as an im- perfect or error-contaminated measurement of the true in- dividual ancestry estimate. Existing methods and software for estimating admixture proportions such as STRUCTURE and FRAPPE provide an estimate of the standard error as- sociated with the admixture proportion. Here, the purpose is to go one step further and compute an estimate of the ME associated with the IAPEs. The introduction of error-contaminated covariates in a re- gression model can lead to type I error inflation and loss of power (8). Divers et al. (9) and Padilla et al. (10) discussed how existing measurement correction methods can be tai- lored for application in SATs. These methods assume that an estimate of the ME variance is available. To support such availability, we previously showed how Cronbach's al- pha, a measure of internal reliability, can be used to obtain an estimate of the measurement error variance. The meth- ods that we used in these studies (9, 10) assumed that k, the number of ancestral populations that intermated to cre- ate the admixed population, is equal to 2. In this case, the confounder can be represented by a single predictor in the model. We now extend this approach for estimating the ME variance when k> 2. We focus mainly on the case where k = 3 because it has direct application when con- trolling for admixture-induced confounding in genetic asso

In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication., Published in at http://dx.doi.org/10.1214/10-AOAS446 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box–Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents. Copyright © 2010 John Wiley & Sons, Ltd.

This paper considers identification and estimation of a general nonlinear Errors-in-Variables (EIV) model using two samples. Both samples consist of a dependent variable, some error-free covariates, and an error-prone covariate, for which the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values; and neither sample contains an accurate measurement of the corresponding true variable. We assume that the regression model of interest - the conditional distribution of the dependent variable given the latent true covariate and the error-free covariates - is the same in both samples, but the distributions of the latent true covariates vary with observed error-free discrete covariates. We first show that the general latent nonlinear model is nonparametrically identified using the two samples when both could have nonclassical errors, without either instrumental variables or independence between the two samples. When the two samples are independent and the nonlinear regression model is parameterized, we propose sieve Quasi Maximum Likelihood Estimation (Q-MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspecification, and its semiparametric efficiency under correct specification, with easily estimated standard errors. A Monte Carlo simulation and a data application are presented to show the power of the approach.

We propose a new methodological framework for the analysis of hierarchical functional data when the functions at the lowest level of the hierarchy are correlated. For small data sets, our methodology leads to a computational algorithm that is orders of magnitude more efficient than its closest competitor (seconds versus hours). For large data sets, our algorithm remains fast and has no current competitors. Thus, in contrast to published methods, we can now conduct routine simulations, leave-one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where the object of inference are functions or images that remain dependent even after conditioning on the subject on which they are measured. Supplementary materials are available at Biostatistics online.

We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100$+$ pixels, so that they form 100$+$ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably unbiased manner. Finally, the $\mathrm {Ca}^{2+}$ signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the $\mathrm {Ca}^{2+}$ signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare $\mathrm {Ca}^{2+}$ signals across experimental conditions., Published in at http://dx.doi.org/10.1214/09-AOAS253 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

SIMEX is a general-purpose technique for measurement error correction. There is a substantial literature on the application and theory of SIMEX for purely parametric problems, as well as for purely nonparametric regression problems, but there is neither application nor theory for semiparametric problems. Motivated by an example involving radiation dosimetry, we develop the basic theory for SIMEX in semiparametric problems using kernel-based estimation methods. This includes situations that the mismeasured variable is modeled purely parametrically, purely nonparametrically, or that the mismeasured variable has components that are modeled both parametrically and nonparametrically. Using our asymptotic expansions, easily computed standard error formulae are derived, as are the bias properties of the nonparametric estimator. The standard error method represents a new method for estimating variability of nonparametric estimators in semiparametric problems, and we show in both simulations and in our example that it improves dramatically on first order methods. ¶ We find that for estimating the parametric part of the model, standard bandwidth choices of order O(n−1/5) are sufficient to ensure asymptotic normality, and undersmoothing is not required. SIMEX has the property that it fits misspecified models, namely ones that ignore the measurement error. Our work thus also more generally describes the behavior of kernel-based methods in misspecified semiparametric problems.

We propose a modelling framework to study the relationship between two paired longitudinally observed variables. The data for each variable are viewed as smooth curves measured at discrete time-points plus random errors. While the curves for each variable are summarized using a few important principal components, the association of the two longitudinal variables is modelled 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. The proposed method can be applied in the difficult case in which the measurement times are irregular and sparse and may differ widely across individuals. Use of functional principal components enhances model interpretation and improves statistical and numerical stability of the parameter estimates. Copyright 2008, Oxford University Press.

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics.