Relationship of physical activity with motor skills, aerobic fitness and body fat in preschool children: a cross-sectional and longitudinal study (Ballabeina).

Adiposity, low aerobic fitness and low levels of activity are all associated with clustered cardiovascular disease risk in children and their high prevalence represents a major public health concern. The aim of this study is to investigate the relationship of objectively measured physical activity (PA) with motor skills (agility and balance), aerobic fitness and %body fat in young children. This study is a cross-sectional and longitudinal analyses using mixed linear models. Longitudinal data were adjusted for baseline outcome parameters. In all, 217 healthy preschool children (age 4-6 years, 48% boys) participated in this study. PA (accelerometers), agility (obstacle course), dynamic balance (balance beam), aerobic fitness (20-m shuttle run) and %body fat (bioelectric impedance) at baseline and 9 months later. PA was positively associated with both motor skills and aerobic fitness at baseline as well as with their longitudinal changes. Specifically, only vigorous, but not total or moderate PA, was related to changes in aerobic fitness. Higher PA was associated with less %body fat at baseline, but not with its change. Conversely, baseline motor skills, aerobic fitness or %body fat were not related to changes in PA. In young children, baseline PA was associated with improvements in motor skills and in aerobic fitness, an important determinant of cardiovascular risk.

Physical activity, bodyweight, health and fear of negative evaluation in primary school children.

Fear of negative evaluation (FNE) is regarded as being the core feature of social anxiety. The present study examined how FNE is associated with physical activity (PA), body mass index (BMI) and perceived physical health (PPH) in children. Data were collected in a sample of 502 primary school children in first and fifth grades taking part in a randomized-controlled trial ("Kinder-Sportstudie KISS") aimed at increasing PA and health. PA was assessed by accelerometry over 7 days, PPH by the Child Health Questionnaire and FNE by the Social Anxiety Scale for Children--Revised. BMI z-scores were calculated based on Swiss norms. Cross-sectional analyses indicated that children high in FNE exercised less, reported lower levels of PPH and had higher BMI z-scores (P<0.01). Using mixed linear models, the school-based PA intervention did not manage to reduce FNE scores. Overweight children demonstrated a greater increase in FNE (P<0.05) indicating that enhanced weight may be a risk factor for FNE. In conclusion, the associations among high FNE, low PA and increased BMI should be considered when promoting an active lifestyle in children.

Change in physical activity after smoking cessation: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

AIMS: To estimate physical activity trajectories for people who quit smoking, and compare them to what would have been expected had smoking continued. DESIGN, SETTING AND PARTICIPANTS: A total of 5115 participants in the Coronary Artery Risk Development in Young Adults Study (CARDIA) study, a population-based study of African American and European American people recruited at age 18-30 years in 1985/6 and followed over 25 years. MEASUREMENTS: Physical activity was self-reported during clinical examinations at baseline (1985/6) and at years 2, 5, 7, 10, 15, 20 and 25 (2010/11); smoking status was reported each year (at examinations or by telephone, and imputed where missing). We used mixed linear models to estimate trajectories of physical activity under varying smoking conditions, with adjustment for participant characteristics and secular trends. FINDINGS: We found significant interactions by race/sex (P = 0.02 for the interaction with cumulative years of smoking), hence we investigated the subgroups separately. Increasing years of smoking were associated with a decline in physical activity in black and white women and black men [e.g. coefficient for 10 years of smoking: -0.14; 95% confidence interval (CI) = -0.20 to -0.07, P < 0.001 for white women]. An increase in physical activity was associated with years since smoking cessation in white men (coefficient 0.06; 95% CI = 0 to 0.13, P = 0.05). The physical activity trajectory for people who quit diverged progressively towards higher physical activity from the expected trajectory had smoking continued. For example, physical activity was 34% higher (95% CI = 18 to 52%; P < 0.001) for white women 10 years after stopping compared with continuing smoking for those 10 years (P = 0.21 for race/sex differences). CONCLUSIONS: Smokers who quit have progressively higher levels of physical activity in the years after quitting compared with continuing smokers.

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

The ghost of past species occurrence: improving species distribution models for presence-only data

1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.

Evaluating the ability of habitat suitability models to predict species presences.

Models predicting species spatial distribution are increasingly applied to wildlife management issues, emphasising the need for reliable methods to evaluate the accuracy of their predictions. As many available datasets (e.g. museums, herbariums, atlas) do not provide reliable information about species absences, several presence-only based analyses have been developed. However, methods to evaluate the accuracy of their predictions are few and have never been validated. The aim of this paper is to compare existing and new presenceonly evaluators to usual presence/absence measures. We use a reliable, diverse, presence/absence dataset of 114 plant species to test how common presence/absence indices (Kappa, MaxKappa, AUC, adjusted D-2) compare to presenceonly measures (AVI, CVI, Boyce index) for evaluating generalised linear models (GLM). Moreover we propose a new, threshold-independent evaluator, which we call "continuous Boyce index". All indices were implemented in the B10MAPPER software. We show that the presence-only evaluators are fairly correlated (p > 0.7) to the presence/absence ones. The Boyce indices are closer to AUC than to MaxKappa and are fairly insensitive to species prevalence. In addition, the Boyce indices provide predicted-toexpected ratio curves that offer further insights into the model quality: robustness, habitat suitability resolution and deviation from randomness. This information helps reclassifying predicted maps into meaningful habitat suitability classes. The continuous Boyce index is thus both a complement to usual evaluation of presence/absence models and a reliable measure of presence-only based predictions.

Clinical performance of cervical restorations: a meta-analysis.

OBJECTIVES: To carry out a meta-analysis in order to assess the influencing factors on retention loss and marginal discoloration of cervical restorations made of composites and glass ionomer (derivates). METHODS: The literature was searched for prospective clinical studies on cervical restorations with an observation period of at least 18 months. RESULTS: Fifty clinical studies involving 40 adhesive systems matched the inclusion criteria. On average, 10% of the cervical fillings were lost and 24% exhibited marginal discoloration after 3 years. The variability ranged from 0% to 50% for retention loss and from 0% to 74% for marginal discoloration. Hardly any secondary caries was detected. When linear mixed models with a study and experiment effect were used, the analysis revealed that the adhesive/restorative class had the most significant influence, with 2-step self-etching adhesive systems performing best and 1-step self-etching adhesive systems performing worst; 3-step etch-and-rinse systems, glass ionomers/resin-modified glass ionomers, 2-step etch-and-rinse systems and polyacid-modified resin composites were ranked in between. Restorations placed in teeth whose dentin/enamel had been prepared/roughened showed a statistically significant higher retention rate than those placed in teeth with unprepared dentin (p<0.05). Beveling of the enamel and the type of isolation used (rubberdam/cotton rolls) had no significant influence. SIGNIFICANCE: The clinical performance of cervical restorations is significantly influenced by the type of adhesive system used and/or the adhesive class to which the system belonged and whether the dentin/enamel is prepared or not. 2-Step self-etching- and 3-step etch&rinse systems shall be chosen over 1-step self-etching systems and glass ionomer derivates. The dentin (and enamel) surface shall be roughened before placement of the restoration.

Pooling of dentin microtensile bond strength data improves clinical correlation.

Purpose: To evaluate whether the correlation between in vitro bond strength data and estimated clinical retention rates of cervical restorations after two years depends on pooled data obtained from multicenter studies or single-test data. Materials and Methods: Pooled mean data for six dentin adhesive systems (Adper Prompt L-Pop, Clearfil SE, OptiBond FL, Prime & Bond NT, Single Bond, and Scotchbond Multipurpose) and four laboratory methods (macroshear, microshear, macrotensile and microtensile bond strength test) (Scherrer et al, 2010) were correlated to estimated pooled two-year retention rates of Class V restorations using the same adhesive systems. For bond strength data from a single test institute, the literature search in SCOPUS revealed one study that tested all six adhesive systems (microtensile) and two that tested five of the six systems (microtensile, macroshear). The correlation was determined with a database designed to perform a meta-analysis on the clinical performance of cervical restorations (Heintze et al, 2010). The clinical data were pooled and adjusted in a linear mixed model, taking the study effect, dentin preparation, type of isolation and bevelling of enamel into account. A regression analysis was carried out to evaluate the correlation between clinical and laboratory findings. Results: The results of the regression analysis for the pooled data revealed that only the macrotensile (adjusted R2 = 0.86) and microtensile tests (adjusted R2 = 0.64), but not the shear and the microshear tests, correlated well with the clinical findings. As regards the data from a single-test institute, the correlation was not statistically significant. Conclusion: Macrotensile and microtensile bond strength tests showed an adequate correlation with the retention rate of cervical restorations after two years. Bond strength tests should be carried out by different operators and/or research institutes to determine the reliability and technique sensitivity of the material under investigation.

Wear of two artificial tooth materials in vivo: a 12-month pilot study.

STATEMENT OF PROBLEM: Wear of methacrylate artificial teeth resulting in vertical loss is a problem for both dentists and patients. PURPOSE: The purpose of this study was to quantify wear of artificial teeth in vivo and to relate it to subject and tooth variables. MATERIAL AND METHODS: Twenty-eight subjects treated with complete dentures received 2 artificial tooth materials (polymethyl methacrylate (PMMA)/double-cross linked PMMA fillers; 35%/59% (SR Antaris DCL, SR Postaris DCL); experimental 48%/46%). At baseline and after 12 months, impressions of the dentures were poured with improved stone. After laser scanning, the casts were superimposed and matched. Maximal vertical loss (mm) and volumetric loss (mm(3)) were calculated for each tooth and log-transformed to reduce variability. Volumetric loss was related to the occlusally active surface area. Linear mixed models were used to study the influence of the factors jaw, tooth, and material on adjusted (residual) wear values (alpha=.05). RESULTS: Due to drop outs (n=5) and unmatchable casts (n=3), 69% of all teeth were analyzed. Volumetric loss had a strong linear relationship to surface area (P<.001); this was less pronounced for vertical loss (P=.004). The factor showing the highest influence was the subject. Wear was tooth dependent (increasing from incisors to molars). However, these differences diminished once the wear rates were adjusted for occlusal area, and only a few remained significant (anterior versus posterior maxillary teeth). Another influencing factor was the age of the subject. CONCLUSIONS: Clinical wear of artificial teeth is higher than previously measured or expected. The presented method of analyzing wear of artificial teeth using a laser-scanning device seemed suitable.

Neocortical maturation during adolescence: change in neuronal soma dimension.

During adolescence, cognitive abilities increase robustly. To search for possible related structural alterations of the cerebral cortex, we measured neuronal soma dimension (NSD = width times height), cortical thickness and neuronal densities in different types of neocortex in post-mortem brains of five 12-16 and five 17-24 year-olds (each 2F, 3M). Using a generalized mixed model analysis, mean normalized NSD comparing the age groups shows layer-specific change for layer 2 (p < .0001) and age-related differences between categorized type of cortex: primary/primary association cortex (BA 1, 3, 4, and 44) shows a generalized increase; higher-order regions (BA 9, 21, 39, and 45) also show increase in layers 2 and 5 but decrease in layers 3, 4, and 6 while limbic/orbital cortex (BA 23, 24, and 47) undergoes minor decrease (BA 1, 3, 4, and 44 vs. BA 9, 21, 39, and 45: p = .036 and BA 1, 3, 4, and 44 vs. BA 23, 24, and 47: p = .004). These data imply the operation of cortical layer- and type-specific processes of growth and regression adding new evidence that the human brain matures during adolescence not only functionally but also structurally.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.