Genetic Polymorphisms in Replication-related Genes and Risk of Breast Cancer Among European and East Asian Women

Background: The role of common, low to intermediate risk alleles in breast cancer need to be examined due to their relatively high prevalence. Among many cellular pathways, replication has a pivotal role in cell division and frequently targeted during carcinogenesis. Replication is governed by a host of genes involved in a number of different pathways. This study investigates the effects of replication-gene variants in relation to breast cancer and how this relationship is affected by ethnicity, menopausal status and breast tumour subtype. Methods: Data from a case-control study with 997 incident breast cancer cases and 1,050 age frequency matched controls in Vancouver, British Columbia and Kingston, Ontario were used. Unconditional logistic regression was used to calculate odds ratios between 45 replication gene variants and breast cancer risk, assuming an additive genetic model adjusted for age and centre, presented for Europeans and East Asians separately. Polytomous logistic regression was used to assess odds ratios between each SNP and four breast cancer subtypes defined by hormone receptor status among Europeans. All analyses were stratified by menopausal status. The Benjamini–Hochberg false discovery rate (FDR) was used to address multiple comparisons. Results: Among Europeans, the SNPs in FGFR2, TOX3 and 11q13 loci were associated with breast cancer after controlling for multiple comparisons. Test of heterogeneity showed the SNPs rs1045185, rs4973768, rs672888, rs1219648, rs2420946 among Europeans and rs889312 among East Asians conferred differential risk across the tumour subtypes. Conclusions: Specific SNPs in replication genes were associated with breast cancer, and the risk level differed by tumour subtype defined by ER/PR/Her2 status and ethnicity.

Boosting imbalanced data learning with Wiener process oversampling

Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling (WPO) technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperformsmany prevalent imbalance learning solutions.

High body mass index is not a barrier to physical activity: Analysis of international rugby players' anthropometric data

Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

A Petrov-Galerkin method for a singularly perturbed ordinary differential equation with non-smooth data

In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.