Common variation in the fibroblast growth factor receptor 2 gene is not associated with endometriosis risk

BACKGROUND Endometriosis is a polygenic disease with a complex and multifactorial aetiology that affects 8-10% of women of reproductive age. Epidemiological data support a link between endometriosis and cancers of the reproductive tract. Fibroblast growth factor receptor 2 (FGFR2) has recently been implicated in both endometrial and breast cancer. Our previous studies on endometriosis identified significant linkage to a novel susceptibility locus on chromosome 10q26 and the FGFR2 gene maps within this linkage region. We therefore hypothesized that variation in FGFR2 may contribute to the risk of endometriosis. METHODS We genotyped 13 single nucleotide polymorphisms (SNPs) densely covering a 27 kb region within intron 2 of FGFR2 including two SNPs (rs2981582 and rs1219648) significantly associated with breast cancer and a total 40 tagSNPs across 150 kb of the FGFR2 gene. SNPs were genotyped in 958 endometriosis cases and 959 unrelated controls. RESULTS We found no evidence for association between endometriosis and FGFR2 intron 2 SNPs or SNP haplotypes and no evidence for association between endometriosis and variation across the FGFR2 gene. CONCLUSIONS Common variation in the breast-cancer implicated intron 2 and other highly plausible causative candidate regions of FGFR2 do not appear to be a major contributor to endometriosis susceptibility in our large Australian sample.

Multi-objective optimisation of bijective s-boxes

In this paper we investigate the heuristic construction of bijective s-boxes that satisfy a wide range of cryptographic criteria including algebraic complexity, high nonlinearity, low autocorrelation and have none of the known weaknesses including linear structures, fixed points or linear redundancy. We demonstrate that the power mappings can be evolved (by iterated mutation operators alone) to generate bijective s-boxes with the best known tradeoffs among the considered criteria. The s-boxes found are suitable for use directly in modern encryption algorithms.

Prospective association between physical activity and falls in community-dwelling older women

Abstract Objective: To explore associations between physical activity and risk of falls and broken or fractured bones in community-dwelling older women. Design, setting and participants: This was a prospective observational survey with 3- and 6-year follow-ups. The sample included 8562 healthy, community-dwelling women, aged 70-75 years in 1996, who completed surveys as participants in the Australian Longitudinal Study on Women’s Health. Outcomes were reports of a fall to the ground, injury from a fall, and broken or fractured bones in 1999 and 2002. The main predictor variable was physical activity level in 1996, categorized based on weekly frequency as none/very low, low, moderate, high, and very high. Covariates were demographic and health-related variables. Logistic regression models were computed separately for each outcome in 1999 and 2002. Main results: In multivariable models, very high physical activity was associated with decreased risk of a fall in 1999 (odds ratio 0.67, 95% CI 0.48 to 0.93) and in 2002 (odds ratio 0.62, 95% CI 0.42 to 0.92). High/very high physical activity was associated with decreased risk of broken or fractured bones in 2002 (odds ratio 0.64, 95% CI 0.42 to 0.96). No significant association was found between physical activity and injury from a fall. Conclusions: The results suggest that at least daily moderate to vigorous physical activity is required for the primary prevention of falls to the ground and broken or fractured bones in women aged 70-75 years.

The impact of nonlinear exposure risk relationships on seasonal time series data: modelling danish neonatal birth anthropometric data

Background Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship. Methods Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns. Results The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data. Conclusion In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.