Neighborhood environmental factors correlated with walking near home: Using SPACES

Purpose: The physical environment plays an important role in influencing participation in physical activity, although the specific factors that are correlated with different patterns of walking remain to be determined We examined correlations between physical environmental factors and self-reported walking for recreation and transport near home. Methods: The local neighborhood environments (defined as a 400-m radius from the respondent's home) of 1678 adults were assessed for their suitability for walking. The environmental data were collected during 2000 using the Systematic Pedestrian and Cycling Environmental Scan (SPACES) instrument together with information from other sources. We used logistic regression modeling to examine the relationship between the attributes of the physical environment and the self-reported walking behavior undertaken near home. Results: Functional features were correlated with both walking for recreation (odds ratio (OR) 1.62; 95% confidence interval (Cl): 1.20-2.19) and for transport (OR 1.30; 95% Cl: 0.97-1.73). A well-maintained walking surface was the main functional factor associated with walking for recreation (OR 2.04; 95% Cl: 1.43-2.91) and for transport (OR 2.13; 95% Cl: 1.53-2.96). Destination factors, such as shops and public transport, were significantly correlated with walking for transport (OR 1.80; 95% Cl: 1.33-2.44), but not recreation. Conclusion: The findings suggest that neighborhoods with pedestrian facilities that are attractive and comfortable and where there are local destinations (such as shops and public transport) are associated with walking near home.

A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits

Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.

Telomere length attrition, a marker of biological senescence, is inversely correlated with triglycerides and cholesterol in South Asian males with type 2 diabetes mellitus

South Asians have a higher risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) than white Caucasians, for a given BMI. Premature biological ageing, assessed by reduction in telomere length (TL), may be mediated by factors resulting from altered metabolic profiles associated with obesity. We hypothesise that ethnicity and metabolic status represent detrimental factors contributing to premature biological ageing. Therefore we assessed TL in two South Asian, age and BMI-matched cohorts [T2DM (n = 142) versus non-T2DM (n = 76)] to determine the effects of BMI, gender, lipid and CVD profile on biological ageing. Genomic DNA was obtained from the UKADS cohort; biochemical and anthropometric data was collected and TL was measured by quantitative real-time PCR. Our findings indicated a gender-specific effect with reduced TL in T2DM men compared with non-T2DM men (P = 0.006). Additionally, in T2DM men, TL was inversely correlated with triglycerides and total cholesterol (r = -0.419, P <0.01; r = -0.443, P <0.01). In summary, TL was reduced amongst South Asian T2DM men and correlated with triglycerides and total cholesterol. This study highlights enhanced biological ageing among South Asian, T2DM men, which appears to be tracked by changes in lipids and BMI, suggesting that raised lipids and BMI may directly contribute to premature ageing.

Optimal design for correlated processes with input-dependent noise

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical surrogates. In the case of stochastic simulators, which produce a random output for a given set of model inputs, repeated evaluations are useful, supporting the use of replicate observations in the experimental design. The findings are also applicable to the wider context of experimental design for Gaussian process regression and kriging. Designs are proposed with the aim of minimising the variance of the Gaussian process parameter estimates. A heteroscedastic Gaussian process model is presented which allows for an experimental design technique based on an extension of Fisher information to heteroscedastic models. It is empirically shown that the error of the approximation of the parameter variance by the inverse of the Fisher information is reduced as the number of replicated points is increased. Through a series of simulation experiments on both synthetic data and a systems biology stochastic simulator, optimal designs with replicate observations are shown to outperform space-filling designs both with and without replicate observations. Guidance is provided on best practice for optimal experimental design for stochastic response models. © 2013 Elsevier Inc. All rights reserved.

Study on Robustness of Correlated Frailty Model

2000 Mathematics Subject Classifi cation: 62J12.

Short- and long-term changes in corneal aberrations and axial length induced by orthokeratology in children are not correlated

PURPOSE: To assess the correlation between changes in corneal aberrations and the 2-year change in axial length in children fitted with orthokeratology (OK) contact lenses. METHODS: Thirty-one subjects 6 to 12 years of age and with myopia −0.75 to −4.00DS and astigmatism ≤1.00DC were fitted with OK. Measurements of axial length and corneal topography were taken at regular intervals over a 2-year period. Corneal topography at baseline and after 3 and 24 months of OK lens wear was used to derive higher-order corneal aberrations (HOA) that were correlated with OK-induced axial length changes at 2 years. RESULTS: Significant changes in C3, C4, C4, root mean square (RMS) secondary astigmatism and fourth and total HOA were found with both 3 and 24 months of OK lens wear in comparison with baseline (all P0.05). Coma angle of orientation changed significantly pre-OK in comparison with 3 and 24 months post-OK as well as secondary astigmatism angle of orientation pre-OK in comparison with 24 months post-OK (all P0.05). DISCUSSION: Short-term and long-term OK lens wear induces significant changes in corneal aberrations that are not significantly correlated with changes in axial elongation after 2-years.

Weighted Sum of Correlated Lognormals: Convolution Integral Solution

Probability density function (pdf) for sum of n correlated lognormal variables is deducted as a special convolution integral. Pdf for weighted sums (where weights can be any real numbers) is also presented. The result for four dimensions was checked by Monte Carlo simulation.