Social physique anxiety across physical activity settings : a meta-analytical review

The purpose of this meta-analytic investigation was to review the empirical evidence specific to the effect of physical activity context on social physique anxiety (SP A). English language studies were located from computer and manual literature searches. A total of 146 initial studies were coded. Studies included in the meta-analysis presented at least one empirical effect for SPA between physical activity participants (i.e., athletes or exercisers) and non-physical activity participants. The final sample included thirteen studies, yielding 14 effect sizes, with a total sample size of 2846. Studies were coded for mean SPA between physical activity participants and non-physical activity participants. Moderator variables related to demographic and study characteristics were also coded. Using Hunter and Schmidt's (2004) protocol, statistical artifacts were corrected. Results indicate that, practically speaking, those who were physically active reported lower levels of SPA than the comparison group (dcorr = -.12; SDeorr.-=-;22). Consideration of the magnitude of the ES, the SDeorr, and confidence interval suggests that this effect is not statistically significant. While most moderator analyses reiterated this trend, some differences were worth noting. Previous research has identified SPA to be especially salient for females compared to males, however, in the current investigation, the magnitude of the ES' s comparing physical activity participants to the comparison group was similar (deorr = -.24 for females and deorr = -.23 for males). Also, the type of physical activity was investigated, and results showed that athletes reported lower levels of SP A than the comparison group (deorr = -.19, SDeorr = .08), whereas exercisers reported higher levels of SPA than the comparison group (deorr = .13, SDeorr = .22). Results demonstrate support for the dispositional nature of SP A. Consideration of practical significance suggests that those who are involved in physical activity may experience slightly lower levels of SPA than those not reporting physical activity participation. Results potentially offer support for the bi-directionality of the relationship between physical activity and SP A; however, a causality may not be inferred. More information about the type of physical activity (i.e., frequency/nature of exercise behaviour, sport classificationllevel of athletes) may help clarify the role of physical activity contexts on SPA.

Conformational analysis of antibody binding site using EDMA methods /

Euclidean distance matrix analysis (EDMA) methods are used to distinguish whether or not significant difference exists between conformational samples of antibody complementarity determining region (CDR) loops, isolated LI loop and LI in three-loop assembly (LI, L3 and H3) obtained from Monte Carlo simulation. After the significant difference is detected, the specific inter-Ca distance which contributes to the difference is identified using EDMA.The estimated and improved mean forms of the conformational samples of isolated LI loop and LI loop in three-loop assembly, CDR loops of antibody binding site, are described using EDMA and distance geometry (DGEOM). To the best of our knowledge, it is the first time the EDMA methods are used to analyze conformational samples of molecules obtained from Monte Carlo simulations. Therefore, validations of the EDMA methods using both positive control and negative control tests for the conformational samples of isolated LI loop and LI in three-loop assembly must be done. The EDMA-I bootstrap null hypothesis tests showed false positive results for the comparison of six samples of the isolated LI loop and true positive results for comparison of conformational samples of isolated LI loop and LI in three-loop assembly. The bootstrap confidence interval tests revealed true negative results for comparisons of six samples of the isolated LI loop, and false negative results for the conformational comparisons between isolated LI loop and LI in three-loop assembly. Different conformational sample sizes are further explored by combining the samples of isolated LI loop to increase the sample size, or by clustering the sample using self-organizing map (SOM) to narrow the conformational distribution of the samples being comparedmolecular conformations. However, there is no improvement made for both bootstrap null hypothesis and confidence interval tests. These results show that more work is required before EDMA methods can be used reliably as a method for comparison of samples obtained by Monte Carlo simulations.

Examining the potential application of childhood stature in assessing adolescent overweight and obesity

Background: Increasing Overweight and Obesity (OwOb) prevalence in pediatric populations is becoming a public health concern in many countries. The purpose of this study was to determine if childhood stature components, particularly the Leg Length Index (LLI = [height - sitting height]! height), were useful in assessing risk of OwOb in adolescence. Methods: Data was from a longitudinal study conducted in south Ontario since 2004. Approximately 2360 students had body composition measurements including sitting height and standing height at baseline. Among them, 1167 children (573 girls, 594 boys) who had weight and height measured at the 5 th year follow-up, were included in this analysis. OwOb was defined using age and sex specific BMI (kg!m 2 ) cut-off points corresponding to adults' BMI ~ 25. Results: Overall, 34% (n=298) of adolescents were considered as OwOb. The results from logistic regression analysis indicated that with 1 unit increase in LLI the odds of OwOb decreased 24% (Odds Ratio, [95% Confidence Interval], 0.76, [0.66-0.87]) after adjusted for age, sex and baseline waist circumference. Further adjusting for birth weight, birth order, breastfeeding, child's physical activity, maternal smoking, education, mother's age at birth and mother's BMI, did not change the relationship. Our results also indicated that mother's smoking status is associated with LLI. Discussion: Although LLI measured at childhood in this study is related to OwOb risk in adolescents, the underlying mechanism is unclear and further study is needed.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.