Epidemiology of Injury in English Women's Super league Football: A Cohort Study

INTRODUCTION: The epidemiology of injury in male professional football has been well documented (Ekstrand, Hägglund, & Waldén, 2011) and used as a basis to understand injury trends for a number of years. The prevalence and incidence of injuries occurring in womens super league football is unknown. The aim of this study is to estimate the prevalence and incidence of injury in an English Super League Women’s Football squad. METHODS: Following ethical approval from Leeds Beckett University, players (n = 25) signed to a Women’s Super League Football club provided written informed consent to complete a self-administered injury survey. Measures of exposure, injury and performance over a 12-month period was gathered. Participants were classified as injured if they reported a football injury that required medical attention or withdrawal from participation for one day or more. Injuries were categorised as either traumatic or overuse and whether the injury was a new injury and/or re-injury of the same anatomical site RESULTS: 43 injuries, including re-injury were reported by the 25 participants providing a clinical incidence of 1.72 injuries per player. Total incidence of injury was 10.8/1000 h (95% CI: 7.5 to 14.03). Participants were at higher risk of injury during a match compared with training (32.4 (95% CI: 15.6 to 48.4) vs 8.0 (95% CI: 5.0 to 10.85)/1000 hours, p 28 days) of which there were three non-contact anterior cruciate ligament (ACL) injuries. The epidemiological incidence proportion was 0.80 (95% CI: 0.64 to 0.95) and the average probability that any player on this team will sustain at least one injury was 80.0% (95% CI: 64.3% to 95.6%) CONCLUSION: This is the first report capturing exposure and injury incidence by anatomical site from a cohort of English players and is comparable to that found in Europe (6.3/1000 h (95% CI 5.4 to 7.36) Larruskain et al 2017). The number of ACL injuries highlights a potential injury burden for a squad of this size. Multi-site prospective investigations into the incidence and prevalence of injury in women’s football are require

ANALYSIS OF GENE EXPRESSION DATA USING RPEM ALGORITHM IN NORMAL MIXTURE MODEL WITH DYNAMIC ADJUSTMENT OF LEARNING RATE

Microarray technology is a useful tool for monitoring the expression levels of thousands of genes simultaneously. Recently, mixture modeling has been used to extract expression signatures from gene expression pro¯les. In general, two separate steps are utilized to estimate the number of classes and model parameters, respectively. However, such a method is often time-consuming and leads to suboptimal solutions. In this paper, we therefore apply a one-step approach, namely Rival Penalized Expectation-Maximization (RPEM) algorithm, to analyze the gene expression data. The RPEM algorithm is capable of estimating the parameters of normal mixture model, while determining the number of classes automatically at the same time. Furthermore, we speed up the learning procedure of RPEM by proposing a new mechanism to adjust the learning rate dynamically. The numerical results on real gene expression data demonstrate that our proposed method is indeed effective and efficient