University Students’ Sport Participation: The Significance of Sport and Leisure Careers

There is now national and international evidence which indicates that those who have higher educational qualifications are more likely to be present-day and future sport participants than those who leave education once they reach the minimum school-leaving age. In Britain, despite significant government policy and financial investment in interventions designed to boost youth sport participation alongside other favourable trends, including a doubling in the proportion of students entering higher education (HE) since the 1980s, the rates of sport participation among the general population, including young people, have remained relatively static. This is particularly significant for, if attending HE does indeed help explain why university students are more likely to become present-day sport participants and remain sports-active into later life, then one might have expected to observe increases in participation by young people and adults over the last three decades or so. Since this has not happened, definitive conclusions about whether there is a HE effect on sport participation and, if so, what this effect/these effects are, cannot yet be drawn. The central objective of this study, therefore, was to explore this apparent paradox by analysing the development of 124 20-25-year-old undergraduate students’ present-day sport and leisure participation via a retrospective analysis of their sport and leisure careers. The study employed a cross-sectional, mixed methods, research design incorporating structured and semi-structured interviews held at two universities in England between March and July 2011. The findings indicated that the two clearest predictors of differences in the present-day sport participation and sport careers of university students were subject of study and sex, with sport students and males being the most likely participants over the life course and whilst at university. These differences first emerged during childhood, widened from age 12-13-years-old, and remained relatively set from age 16 onwards. The differences in the present-day sport participation of university students, and the richness of their overall sport careers, could thus not be attributed to a ‘HE effect’ as previous research has suggested. It was during childhood, rather than youth, when the preconditions required for constructing short- or longer-term sport (and leisure) careers were formed. The differential childhood socialization practices students’ experienced played a crucial role in the development of sporting habituses and dispositions within their unfolding networks (or figurations) which provided the foundations upon which present-day inequalities in participation were based. In this regard, the assumed contribution attending HE has previously been expected to make to students’ current and future sport participation appears to have been over-stated, and in so doing diverted attention from other processes associated with the inequalities that underlie students’ differential engagement in sport. It seemed that the context of university did little to promote overall levels of student participation, the numbers of sports they played, and the facilities they used. At best, attending HE may have simply delayed the drop-out from sport among those with already established and longer-running sport careers prior to attending university. In this regard, the present focus on raising sport participation among 14-25-year-olds by various sports organizations and facilitators would appear misguided and perhaps doomed to failure, for the evidence of this study suggests that a more appropriate focal point for policy interventions concerned with boosting longer-term participation is not with youth, but with children.Green, KenRoberts, KenThomas, NigelCarnegie, Evely

Extending reservoir computing with random static projections : a hybrid between extreme learning and RC

Reservoir Computing is a relatively new paradigm in the field of neural networks that has shown promise in applications where traditional recurrent neural networks have performed poorly. The main advantage of using reservoirs is that only the output weights are trained, reducing computational requirements significantly. There is a trade-off, however, between the amount of memory a reservoir can possess and its capability of mapping data into a highly non-linear transformation space. A new, hybrid architecture, combining a reservoir with an extreme learning machine, is presented which overcomes this trade-off, whose performance is demonstrated on a 4th order polynomial modelling task and an isolated spoken digit recognition task

Quantum And Classical Dynamics Of Atoms In A Magneto-optical Lattice

The transport of ultra-cold atoms in magneto-optical potentials provides a clean setting in which to investigate the distinct predictions of classical versus quantum dynamics for a system with coupled degrees of freedom. In this system, entanglement at the quantum level and chaos at the classical level arise from the coupling between the atomic spin and its center-of- mass motion. Experiments, performed deep in the quantum regime, correspond to dynamic quantum tunneling. This nonclassical behavior is contrasted with the predictions for an initial phase space distribution produced in the experiment, but undergoing classical Hamiltonian flow. We study conditions under which the trapped atoms can be made to exhibit classical dynamics through the process of continuous measurement, which localizes the probability distribution to phase space trajectories, consistent with the uncertainty principle and quantum back-action noise. This method allows us to analytically and numerically identify the quantum-classical boundary.Comment: Contribution to the Proceedings of the 7th Experimental Chaos Conference describing recent experimental and theoretical result

Circulating Selenium and Prostate Cancer Risk: A Mendelian Randomization Analysis.

In the Selenium and Vitamin E Cancer Prevention Trial (SELECT), selenium supplementation (causing a median 114 μg/L increase in circulating selenium) did not lower overall prostate cancer risk, but increased risk of high-grade prostate cancer and type 2 diabetes. Mendelian randomization analysis uses genetic variants to proxy modifiable risk factors and can strengthen causal inference in observational studies. We constructed a genetic instrument comprising 11 single nucleotide polymorphisms robustly (P < 5 × 10-8) associated with circulating selenium in genome-wide association studies. In a Mendelian randomization analysis of 72 729 men in the PRACTICAL Consortium (44 825 case subjects, 27 904 control subjects), 114 μg/L higher genetically elevated circulating selenium was not associated with prostate cancer (odds ratio [OR] = 1.01, 95% confidence interval [CI] = 0.89 to 1.13). In concordance with findings from SELECT, selenium was weakly associated with advanced (including high-grade) prostate cancer (OR = 1.21, 95% CI = 0.98 to 1.49) and type 2 diabetes (OR = 1.18, 95% CI = 0.97 to 1.43; in a type 2 diabetes genome-wide association study meta-analysis with up to 49 266 case subjects and 249 906 control subjects). Our Mendelian randomization analyses do not support a role for selenium supplementation in prostate cancer prevention and suggest that supplementation could have adverse effects on risks of advanced prostate cancer and type 2 diabetes

Recent developments in mendelian randomization studies

Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel's First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions.In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR.In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future