The effect of school intervention programs on the body mass index of adolescents: a systematic review with meta-analysis.

Effective obesity interventions in adolescent populations have been identified as an immediate priority action to stem the increasing prevalence of adult obesity. The purpose of this meta-analysis was to make a quantitative analysis of the impact of school-based interventions on body mass index during adolescence. Studies were retrieved from PubMed, Scopus, Science Direct and Web of Science databases. Results were pooled using a random-effects model with 95% confidence interval considered statistically significant. Of the 18 798 possible relevant articles identified, 12 articles were included in this meta-analysis. The global result showed a low magnitude effect, though it was statistically significant (N = 14 428), global e.s. = -0.055, P = 0.004 (95% CI = -0.092, -0.017). Heterogeneity was low among the studies (I2 = 9.017%). The funnel plot showed no evidence of publication bias. The rank-correlation test of Begg (P = 0.45641) and Egger's regression (P = 0.19459) confirmed the absence of bias. This meta-analysis reported a significant effect favoring the interventions; however, future research are needed since the reported the evidence was of low magnitude, with the studies following a substantial range of approaches and mostly had a modest methodological quality.

Searching chaotic saddles in high dimensions.

We propose new methods to numerically approximate non-attracting sets governing transiently chaotic systems. Trajectories starting in a vicinity Ω of these sets escape Ω in a finite time τ and the problem is to find initial conditions x∈Ω with increasingly large τ=τ(x). We search points x' with τ(x')>τ(x) in a search domain in Ω. Our first method considers a search domain with size that decreases exponentially in τ, with an exponent proportional to the largest Lyapunov exponent λ1. Our second method considers anisotropic search domains in the tangent unstable manifold, where each direction scales as the inverse of the corresponding expanding singular value of the Jacobian matrix of the iterated map. We show that both methods outperform the state-of-the-art Stagger-and-Step method [Sweet et al., Phys. Rev. Lett. 86, 2261 (2001)] but that only the anisotropic method achieves an efficiency independent of τ for the case of high-dimensional systems with multiple positive Lyapunov exponents. We perform simulations in a chain of coupled Hénon maps in up to 24 dimensions (12 positive Lyapunov exponents). This suggests the possibility of characterizing also non-attracting sets in spatio-temporal systems.

Interleukin-6 gene -174G>C and -636G>C promoter polymorphisms and prostate cancer risk.

Prostate cancer (PCa) is one of the most commonly diagnosed internal malignancies affecting men. Due to the important roles of IL-6 in different physiological and pathophysiological processes, IL-6 polymorphisms may modulate PCa risk. IL-6 -174 G>C (rs 1800795, also designated -236 G>C) and -636 G>C (rs 1800796, also designated -572 G>C) promoter polymorphisms have been implicated in PCa susceptibility, albeit still controversial. A literature search using PubMed and Highwire databases was conducted, resulting in eight case-control studies concerning the IL-6 -174 G>C polymorphism (11,613 PCa cases and 13,992 controls) and four case-control publications regarding the IL-6 -636 G>C polymorphism (1,941 PCa cases and 3,357 controls). In order to derive a more precise estimation, a meta-analysis based upon these selected case-control studies was performed. There was no significant association between IL-6 -174 G>C polymorphism and PCa increased risk. Nevertheless, the presence of allele C and the CC genotype were statistically significantly associated with decreased PCa risk in the overall analysis for IL-6 -636 G>C polymorphism. Additional studies in larger samples and analyses of functional repercussions of these SNPs in prostate tumor cells are necessary to validate these findings.

Association of the IL-10 polymorphisms and periodontitis: a meta-analysis.

No clear consensus has been reached regarding the association of IL-10 polymorphisms and periodontitis. Therefore, we performed a meta-analysis of case-control studies and a systemic review in an effort to systematically summarize the existing knowledge. Studies were identified by searching PubMed database until December 2011. IL-10 -1082 (-1087) A>G, -819 (-824) C>T and -592 (-597) C>A polymorphisms were included in the present meta-analysis. We calculated the specific odds ratios along with their 95 % confidence intervals to compare the distribution of alleles and genotypes between cases and controls. An additive "per-allele" model (major allele vs. minor allele) was performed, and dominant and recessive models were also considered. The random-effects model was applied for the analysis. Cumulative analysis was also performed. Heterogeneity and publication bias were assessed. Nine case-control studies involving 841 periodontitis cases (644 chronic periodontitis and 197 aggressive periodontitis cases) and 748 controls were included. We found statistically significant association of IL-10 -819 (-824) C>T and IL-10 -592 (-597) C>A polymorphisms in Caucasians. The IL-10 -819 (-824) T and -592 (-597) A alleles may confer a relative increase in the risk for chronic periodontitis in Caucasians. Future studies may be important to reinforce these findings.

Is this scaling nonlinear?

One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g. patents) scale nonlinearly with the population x of the cities in which they appear, i.e. y∼x (ß) ,ß≠1. More recently, the generality of this finding has been questioned in studies that used new databases and different definitions of city boundaries. In this paper, we investigate the existence of nonlinear scaling, using a probabilistic framework in which fluctuations are accounted for explicitly. In particular, we show that this allows not only to (i) estimate ß and confidence intervals, but also to (ii) quantify the evidence in favour of ß≠1 and (iii) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare five different models to 15 different datasets and we find that the answers to points (i)-(iii) crucially depend on the fluctuations contained in the data, on how they are modelled, and on the fact that the city sizes are heavy-tailed distributed.