Absorption of the ω and ϕ Mesons in Nuclei

Because of their long lifetimes, the ω and ϕ mesons are the ideal candidates for the study of possible modifications of the in-medium meson-nucleon interaction through their absorption inside the nucleus. During the E01-112 experiment at the Thomas Jefferson National Accelerator Facility, the mesons were photoproduced from 2H, C, Ti, Fe, and Pb targets. This Letter reports the first measurement of the ratio of nuclear transparencies for the e+e− channel. The ratios indicate larger in-medium widths compared with what have been reported in other reaction channels. The absorption of the ω meson is stronger than that reported by the CBELSA-TAPS experiment and cannot be explained by recent theoretical models

Light Vector Mesons in the Nuclear Medium

The light vector mesons (ρ,ω, and ϕ) were produced in deuterium, carbon, titanium, and iron targets in a search for possible in-medium modifications to the properties of the ρ meson at normal nuclear densities and zero temperature. The vector mesons were detected with the CEBAF Large Acceptance Spectrometer (CLAS) via their decays to e+e−. The rare leptonic decay was chosen to reduce final-state interactions. A combinatorial background was subtracted from the invariant mass spectra using a well-established event-mixing technique. The ρ-meson mass spectrum was extracted after the ω and ϕ signals were removed in a nearly model-independent way. Comparisons were made between the ρ mass spectra from the heavy targets (A\u3e2) with the mass spectrum extracted from the deuterium target. With respect to the ρ-meson mass, we obtain a small shift compatible with zero. Also, we measure widths consistent with standard nuclear many-body effects such as collisional broadening and Fermi motion

The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data.

Multiple imputation can be used as a tool in the process of constructing prediction models in medical and epidemiological studies with missing covariate values. Such models can be used to make predictions for model performance assessment, but the task is made more complicated by the multiple imputation structure. We summarize various predictions constructed from covariates, including multiply imputed covariates, and either the set of imputation-specific prediction model coefficients or the pooled prediction model coefficients. We further describe approaches for using the predictions to assess model performance. We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model's performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model's performance in a real-world clinical setting where some individuals have missing predictors. The approaches are compared through an extensive simulation study based on the UK700 trial. We determine that measures of ideal model performance can be estimated within imputed datasets and subsequently pooled to give an overall measure of model performance. Alternative methods to evaluate pragmatic model performance are required and we propose constructing predictions either from a second set of covariate imputations which make no use of observed outcomes, or from a set of partial prediction models constructed for each potential observed pattern of covariate. Pragmatic model performance is generally lower than ideal model performance. We focus on model performance within the derivation data, but describe how to extend all the methods to a validation dataset.Angela Wood part supported by MRC grant G0701619. Ian White from MRC _Biostatistics Unit with unit programme number U105260558This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/bimj.201400004/abstract;jsessionid=144424FA52D50041821329D8A7741BFD.f02t0

The stability of food intake between adolescence and adulthood: a 21-year follow-up

Studies of the diet of adolescents in the UK demonstrate that dietary habits known to be detrimental to health in adulthood are evident at an early age. For example, Gregory et al (2000) found 4-18 year olds in the UK to have a frequent consumption of fatty and sugary foods and low consumption of fruit and vegetables. Concerns have therefore been expressed regarding the diet of children and adolescents and the continuation of these dietary habits into adulthood (HEA, 1995; Gaziano, 1998). This study aimed to investigate the extent to which these concerns may be justified by determining the stability of food intake of a group of adolescents followed up 21 years later in adulthood. The investigation involved 202 individuals from whom dietary data were collected in 1979-80 (mean age 11.6 years) (Hackett et al. 1984) and again in 2000-1 (mean age 32.5 years). Dietary data were collected at both time-points using two 3 d estimated food diaries followed by an interview to determine portion sizes using the method considered most appropriate at the time, i.e. calibrated food models in 1979-80 and a photographic food atlas (Nelson et al. 1997) in 2000-1. Foods consumed were allocated to one, or a combination of, the five food groups of the ‘Balance of Good Health’ food selection guide (HEA, 1994) according to Gatenby et al. (1995). The weight of food eaten from each of the five food groups was calculated (percentage of total weight of food consumed) and Pearson correlation coefficients generated to provide an estimate of the stability of food intake. The HEA guide advises that a balanced diet should consist of around 33% fruit and vegetables, 33% bread, other cereals and potatoes, 8% foods containing fat and/or sugar, 12% meat, fish and alternatives and 15% milk and dairy products (Gatenby et al. 1995). A shift in the group’s food intake towards the recommendations had occurred with age, most notably with a decrease in foods containing fat and/or sugar and an increase in fruit and vegetables. Nevertheless, at both ages, intakes of foods containing fat and/or sugar, meat, fish and alternatives were higher, and fruit, vegetables, bread, other cereals and potatoes lower than currently recommended. In addition, although there was significant evidence of tracking of relative intake of bread, cereals and potatoes (P<0.01), fruit and vegetables (P<0.01), and meat, fish and alternatives (P=0.02) between 11.6 and 32.5 years, the correlations were not strong. In conclusion, food intake patterns had changed considerably from early adolescence through to adulthood in a direction more in line with the current recommendations. The predictive value of an adolescent’s food intake of their intake in adulthood was found to be significant, but not strong. Further investigations will consider the extent to which this is influenced by factors such as social class, gender and educational level, as well as assessing tracking in terms of relative nutrient intakes

Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates.

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates under weaker assumptions than the standard instrumental variable assumptions. The median-based estimator and MR-Egger are examples of such methods. However, these methods can be sensitive to genetic variants with heterogeneous causal estimates. Such heterogeneity may arise from over-dispersion in the causal estimates, or specific variants with outlying causal estimates. In this paper, we develop three extensions to robust methods for Mendelian randomization with summarized data: 1) robust regression (MM-estimation); 2) penalized weights; and 3) Lasso penalization. Methods using these approaches are considered in two applied examples: one where there is evidence of over-dispersion in the causal estimates (the causal effect of body mass index on schizophrenia risk), and the other containing outliers (the causal effect of low-density lipoprotein cholesterol on Alzheimer's disease risk). Through an extensive simulation study, we demonstrate that robust regression applied to the inverse-variance weighted method with penalized weights is a worthwhile additional sensitivity analysis for Mendelian randomization to provide robustness to variants with outlying causal estimates. The results from the applied examples and simulation study highlight the importance of using methods that make different assumptions to assess the robustness of findings from Mendelian randomization investigations with multiple genetic variants

Birth weight percentile and the risk of term perinatal death.

OBJECTIVE: To estimate the association between birth weight percentile and the risk of perinatal death at term in relation to the cause of death. METHODS: We performed a retrospective cohort study of all term singleton births in delivery units in Scotland between 1992 and 2008 (n=784,576), excluding perinatal deaths ascribed to congenital anomaly. RESULTS: There were 1,700 perinatal deaths in the cohort, which were not the result of congenital anomaly (21.7/10,000 women at term). We observed a reversed J-shaped association between birth weight percentile and the risk of antepartum stillbirth in all women, but the associations significantly differed (P<.001) according to smoking status. The highest risk (adjusted odds ratio referent to 21st-80th percentile, 95% confidence interval) among nonsmokers was for birth weight third or less percentile (10.5, 8.2-13.3), but there were also positive associations for birth weight percentiles 4th-10th (3.8, 3.0-4.8), 11th-20th (1.9, 1.5-2.4), and 98th-100th (1.8, 1.3-2.4). Among smokers, the associations with being small were weaker and the associations with being large were stronger. We also observed a reversed J-shaped association between birth weight percentile and the risk of delivery-related perinatal death (ie, intrapartum stillbirth or neonatal death), but there was no interaction with smoking. The highest risk was for birth weight greater than the 97th percentile (2.3, 1.6-3.3), but there were also associations with third or less percentile (2.1, 1.4-3.1), 4th-10th (1.8, 1.4-2.4), and 11th-20th (1.5, 1.2-2.0). Analysis of the attributable fraction indicated that approximately one in three antepartum stillbirths and one in six delivery-related deaths at term could be related to birth weight percentile outside the range 21st-97th percentile. CONCLUSION: Effective detection of variation in fetal size at term has potential as a screening test for the risk of perinatal death. LEVEL OF EVIDENCE: II.Supported by the NIHR Cambridge Comprehensive Biomedical Research Centre.This version is the author accepted manuscript. This article can also be viewed in advanced access form on the publisher's website at: http://journals.lww.com/greenjournal/pages/articleviewer.aspx?year=9000&issue=00000&article=99411&type=abstract © 2014 by The American College of Obstetricians and Gynecologists. Published by Lippincott Williams & Wilkins

Quantifying anhedonia-like symptoms in marmosets using appetitive Pavlovian conditioning.

Blunted reward responsivity is associated with anhedonia in humans and is a core feature of depression. This protocol describes how to train the common marmoset, Callithrix jacchus, on an appetitive Pavlovian conditioning paradigm to measure behavioral and cardiovascular correlates of anticipatory and consummatory phases of reward processing. We describe how to use intracerebral infusions to manipulate brain regions whose activity is relevant to impaired reward processing in depression and how the paradigm can be used to test antidepressant efficacy. For complete details on the use and execution of this protocol, please refer to Alexander et al. (2019)

Anthocyanins protect the gastrointestinal tract from high fat diet-induced alterations in redox signaling, barrier integrity and dysbiosis.

The gastrointestinal (GI) tract can play a critical role in the development of pathologies associated with overeating, overweight and obesity. We previously observed that supplementation with anthocyanins (AC) (particularly glycosides of cyanidin and delphinidin) mitigated high fat diet (HFD)-induced development of obesity, dyslipidemia, insulin resistance and steatosis in C57BL/6J mice. This paper investigated whether these beneficial effects could be related to AC capacity to sustain intestinal monolayer integrity, prevent endotoxemia, and HFD-associated dysbiosis. The involvement of redox-related mechanisms were further investigated in Caco-2 cell monolayers. Consumption of a HFD for 14 weeks caused intestinal permeabilization and endotoxemia, which were associated with a decreased ileum expression of tight junction (TJ) proteins (occludin, ZO-1 and claudin-1), increased expression of NADPH oxidase (NOX1 and NOX4) and NOS2 and oxidative stress, and activation of redox sensitive signals (NF-κB and ERK1/2) that regulate TJ dynamics. AC supplementation mitigated all these events and increased GLP-2 levels, the intestinal hormone that upregulates TJ protein expression. AC also prevented, in vitro, tumor necrosis factor alpha-induced Caco-2 monolayer permeabilization, NOX1/4 upregulation, oxidative stress, and NF-κB and ERK activation. HFD-induced obesity in mice caused dysbiosis and affected the levels and secretion of MUC2, a mucin that participates in intestinal cell barrier protection and immune response. AC supplementation restored microbiota composition and MUC2 levels and distribution in HFD-fed mice. Thus, AC, particularly delphinidin and cyanidin, can preserve GI physiology in HFD-induced obesity in part through redox-regulated mechanisms. This can in part explain AC capacity to mitigate pathologies, i.e. insulin resistance and steatosis, associated with HFD-associated obesity

The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study.

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.J.K.B. was supported by the Medical Research Council grant numbers G0902100 and MR/K014811/1. This work was funded by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002), UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834) and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The ARIC study is carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C).This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/sim.714

Multiple imputation of missing data in nested case-control and case-cohort studies.

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full-cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI-approx) of White and Royston (2009) and the "substantive model compatible" (MI-SMC) method of Bartlett et al. (2015). We also apply the "MI matched set" approach of Seaman and Keogh (2015) to nested case-control studies, which does not require any full-cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full-cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full-cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code