Physical performance profile of sub-elite juvenile Gaelic Games players and the prevalence of a Relative Age Effect (RAE)

Gaelic Games are the indigenous sports played in Ireland, the most popular being Gaelic football and hurling. The games are contact sports and the physical demands are thought to be similar to those of Australian Rules football, rugby union, rugby league, field hockey, and lacrosse (Delahunt et al., 2011). The difference in chronological age between children in a single age group is known as relative age and its consequences as the RAE, whereby younger players are disadvantaged (Del Campo et al., 2010). The purpose of this study was to describe the physical and performance profile of sub-elite juvenile Gaelic Games players and to establish if a RAE is present in this cohort and any influence physiological moderator variables may have on this. Following receipt of ethical approval (EHSREC11-45), six sub-elite county development squads (Under-14/15/16 age groups, male, n=115) volunteered to partake in the study. Anthropometric data including skin folds and girths were collected. A number of field tests of physical performance including 5 and 20m speed, vertical and broad jump distance, and an estimate of VO2max were carried out. Descriptive data are presented as Mean SD. Juvenile sub-elite Gaelic Games players aged 14.53 0.82 y were 172.87 7.63 cm tall, had a mass of 64.74 11.06 kg, a BMI of 21.57 2.82 kg.m-2 and 9.22 4.78 % body fat. Flexibility, measured by sit and reach was 33.62 6.86 cm and lower limb power measured by vertical and broad jump were 42.19 5.73 and 191.16 25.26 cm, respectively. Participant time to complete 5m, 20m and an agility test (T-Test) was 1.12 0.07, 3.31 0.30 and 9.31 0.55 s respectively. Participant’s estimated VO2max was 48.23 5.05 ml.kg.min-1. Chi-Square analysis of birth month by quartile (Q1 = January-March) revealed that a RAE was present in this cohort, whereby an over-representation of players born in Q1 compared with Q2, Q3 and Q4 was evident (2 = 14.078, df = 3, p = 0.003). Kruskal-Wallis analysis of the data revealed no significant difference in any of the performance parameters based on quartile of birth (Alpha level = 0.05).This study provides a physical performance profile of juvenile sub-elite Gaelic Games players, comparable with those of other sports such as soccer and rugby. This novel data can inform us of the physical requirements of the sport. The evidence of a RAE is similar to that observed in other contact sports such as soccer and rugby league (Carling et al, 2009; Till et al, 2010). Although a RAE exists in this cohort, this cannot be explained by any physical/physiological moderator variables. Carling C et al. (2009). Scandinavian Journal of Medicine and Science in Sport 19, 3-9. Delahunt E et al. (2011). Journal of Athletic Training 46, 241-5. Del Campo DG et al. (2010). Journal of Sport Science and Medicine 9, 190-198. Delorme N et al. (2010). European Journal of Sport Science 10, 91-96. Till K et al. (2010). Scandinavian Journal of Medicine and Science in Sports 20, 320-329.

Oxygen consumption and muscle activation patterns during constant load exercise

The collective purpose of these two studies was to determine a link between the V02 slow component and the muscle activation patterns that occur during cycling. Six, male subjects performed an incremental cycle ergometer exercise test to determine asub-TvENT (i.e. 80% of TvENT) and supra-TvENT (TvENT + 0.75*(V02 max - TvENT) work load. These two constant work loads were subsequently performed on either three or four occasions for 8 mins each, with V02 captured on a breath-by-breath basis for every test, and EMO of eight major leg muscles collected on one occasion. EMG was collected for the first 10 s of every 30 s period, except for the very first 10 s period. The V02 data was interpolated, time aligned, averaged and smoothed for both intensities. Three models were then fitted to the V02 data to determine the kinetics responses. One of these models was mono-exponential, while the other two were biexponential. A second time delay parameter was the only difference between the two bi-exponential models. An F-test was used to determine significance between the biexponential models using the residual sum of squares term for each model. EMO was integrated to obtain one value for each 10 s period, per muscle. The EMG data was analysed by a two-way repeated measures ANOV A. A correlation was also used to determine significance between V02 and IEMG. The V02 data during the sub-TvENT intensity was best described by a mono-exponential response. In contrast, during supra-TvENT exercise the two bi-exponential models best described the V02 data. The resultant F-test revealed no significant difference between the two models and therefore demonstrated that the slow component was not delayed relative to the onset of the primary component. Furthermore, only two parameters were deemed to be significantly different based upon the two models. This is in contrast to other findings. The EMG data, for most muscles, appeared to follow the same pattern as V02 during both intensities of exercise. On most occasions, the correlation coefficient demonstrated significance. Although some muscles demonstrated the same relative increase in IEMO based upon increases in intensity and duration, it cannot be assumed that these muscles increase their contribution to V02 in a similar fashion. Larger muscles with a higher percentage of type II muscle fibres would have a larger increase in V02 over the same increase in intensity.

Recognising team activities from noisy data

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Representing team behaviours from noisy data using player role

Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

Molecular aspects of tissue engineering in the dental field

Tissue engineering is a multidisciplinary field with the potential to replace tissues lost as a result of trauma, cancer surgery, or organ dysfunction. The successful production, integration, and maintenance of any tissue-engineered product are a result of numerous molecular interactions inside and outside the cell. We consider the essential elements for successful tissue engineering to be a matrix scaffold, space, cells, and vasculature, each of which has a significant and distinct molecular underpinning (Fig. 1). Our approach capitalizes on these elements. Originally developed in the rat, our chamber model (Fig. 2) involves the placement of an arteriovenous loop (the vascular supply) in a polycarbonate chamber (protected space) with the addition of cells and an extracellular matrix such as Matrigel or endogenous fibrin (34, 153, 246, 247). This model has also been extended to the rabbit and pig (J. Dolderer, M. Findlay, W. Morrison, manuscript in preparation), and has been modified for the mouse to grow adipose tissue and islet cells (33, 114, 122) (Fig. 3)...