An invariant of smooth 4-manifolds

We define a diffeomorphism invariant of smooth 4-manifolds which we can estimate for many smoothings of R^4 and other smooth 4-manifolds. Using this invariant we can show that uncountably many smoothings of R^4 support no Stein structure. (Gompf has constructed uncountably many smoothings of R^4 which do support Stein structures.) Other applications of this invariant are given.Comment: 19 pages. Published copy, also available at http://www.maths.warwick.ac.uk/gt/GTVol1/paper6.abs.htm

Correction: A simple method for analyzing data from a randomized trial with a missing binary outcome

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Extent, duration and predictors of exclusive breastfeeding in a longitudinal study: adjusting for missing data using an accelerated failure time model and multiple imputation

Background: The World Health Organization recommends at least 6 months of exclusive breastfeeding (EBF). Longitudinal studies facilitate estimation of EBF duration, but often suffer from loss to follow-up and missing information. The study estimates the prevalence of EBF, duration and predictors of EBF duration while adjusting for missing data using multiple imputation (MI). Methods: A longitudinal study was conducted on all women giving birth between September 2009-February 2010 in selected hospitals (N=2119). Data on EBF and socio-demographic and other characteristics were collected at birth, and at 2, 6, 12 and 24 months. Information on EBF status and duration was missing for 29%. To deal with missing data, we generated multiple datasets using logistic regression-based MI to impute missing EBF practice, and an accelerated failure time (AFT) model to impute missing duration of EBF. The latter model also identified factors associated with EBF duration. Results: The observed 64% of women practicing EBF (95%CI; 62%-66%) was adjusted, after imputation, to 62% (95%CI; 60%-65%). After imputation, the estimated median time of EBF among women practicing EBF was 4.9 months. Predictors of EBF duration were stated intention to breastfeed, religious observance, and giving formula milk while in hospital. Conclusion: Adjusting estimates of EBF practice and duration using MI is feasible and potentially important. Using an AFT model for EBF duration enables the execution of MI in such studies and allows direct interpretation of the impact of various factors on EBF duration.&nbsp

Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies?

Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes

Intake_epis_food(): An R Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

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

Statistical issues related to dietary intake as the response variable in intervention trials.

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

A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment

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.Comment: 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

Modulation of Kv Channel Expression and Function by TCR and Costimulatory Signals during Peripheral CD4+ Lymphocyte Differentiation

Ionic signaling pathways, including voltage-dependent potassium (Kv) channels, are instrumental in antigen-mediated responses of peripheral T cells. However, how Kv channels cooperate with other signaling pathways involved in T cell activation and differentiation is unknown. We report that multiple Kv channels are expressed by naive CD4+ lymphocytes, and that the current amplitude and kinetics are modulated by antigen receptor–mediated stimulation and costimulatory signals. Currents expressed in naive CD4+ lymphocytes are consistent with Kv1.1, Kv1.2, Kv1.3, and Kv1.6. Effector CD4+ cells generated by optimal TCR and costimulation exhibit only Kv1.3 current, but at approximately sixfold higher levels than naive cells. CD4+ lymphocytes anergized through partial stimulation exhibit similar Kv1.1, Kv1.2, and/or Kv1.6 currents, but approximately threefold more Kv1.3 current than naive cells. To determine if Kv channels contribute to the distinct functions of naive, effector, and anergized T cells, we tested their role in immunoregulatory cytokine production. Each Kv channel is required for maximal IL-2 production by naive CD4+ lymphocytes, whereas none appears to play a role in IL-2, IL-4, or IFN-γ production by effector cells. Interestingly, Kv channels in anergized lymphocytes actively suppress IL-4 production, and these functions are consistent with a role in regulating the membrane potential and calcium signaling

DNA Repair Biomarker for Lung Cancer Risk and its Correlation With Airway Cells Gene Expression.

Background: Improving lung cancer risk assessment is required because current early-detection screening criteria miss most cases. We therefore examined the utility for lung cancer risk assessment of a DNA Repair score obtained from OGG1, MPG, and APE1 blood tests. In addition, we examined the relationship between the level of DNA repair and global gene expression. Methods: We conducted a blinded case-control study with 150 non-small cell lung cancer case patients and 143 control individuals. DNA Repair activity was measured in peripheral blood mononuclear cells, and the transcriptome of nasal and bronchial cells was determined by RNA sequencing. A combined DNA Repair score was formed using logistic regression, and its correlation with disease was assessed using cross-validation; correlation of expression to DNA Repair was analyzed using Gene Ontology enrichment. Results: DNA Repair score was lower in case patients than in control individuals, regardless of the case's disease stage. Individuals at the lowest tertile of DNA Repair score had an increased risk of lung cancer compared to individuals at the highest tertile, with an odds ratio (OR) of 7.2 (95% confidence interval [CI] = 3.0 to 17.5; P < .001), and independent of smoking. Receiver operating characteristic analysis yielded an area under the curve  of 0.89 (95% CI = 0.82 to 0.93). Remarkably, low DNA Repair score correlated with a broad upregulation of gene expression of immune pathways in patients but not in control individuals. Conclusions: The DNA Repair score, previously shown to be a lung cancer risk factor in the Israeli population, was validated in this independent study as a mechanism-based cancer risk biomarker and can substantially improve current lung cancer risk prediction, assisting prevention and early detection by computed tomography scanning.This work was funded by grants from NIH/NCI/EDRN (#1 U01 CA111219), the Flight Attendant Medical Research Institute, Florida, the Mike Rosenbloom Foundation and Weizmann Institute of Science to ZL and TPE; and by grants from Cancer Research UK to BP and to the Cancer Research UK Cambridge Centre; and by a UK National Institute for Health Research Senior Fellowship to BP; and by the Cambridge Biomedical Research Centre and the Cancer Research UK Cambridge Centre to RCR. Volunteer participant recruitment through the Cambridge Bioresource was funded by the Cambridge Biomedical Research Centre