The impact of in-season national team soccer play on injury and player availability in a professional club

This study investigated the impact of in-season national team duty on injury rates and player availability in a professional soccer club. Time-loss injuries and exposure time during club and national team duties were recorded prospectively over 5 seasons (2009–2014). A time-loss injury was sustained by 37.7% of squad members participating in national duty, all injuries occurring in match-play. The incidence (per 1000 h exposure) for national team player match-play injuries did not differ (P = 0.608) to that for all players in club competitions: 48.0 (95% CI 20.9–75.5) vs. 41.9 (95% CI 36.5–47.4), incidence rate ratio = 1.2 (CI: 0.8–2.4). The majority (58%) of national team injuries resulted in a layoff ≤1 week. Of all working days lost to injury generally, 5.2% were lost through injury on national duty. Injury incidence in the week following national duty was comparable (P = 0.818) in players participating or not: 7.8 (95% CI 3.6–12.0) vs. 7.1 (95% CI: 4.6–9.6), incidence rate ratio = 1.1 (CI: 0.7–2.7). While approximately 40% of participating players incurred a time-loss injury on national duty, no training injuries were sustained and injuries made up a negligible part of overall club working days lost to injury. Following duty, players had a similar injury risk to peers without national obligations

Ensemble learning based on classifier prediction confidence and comprehensive learning particle swarm optimisation for medical image segmentation.

Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notable successes in medical image analysis, including image segmentation. Due to the fact that medical imaging applications require robust, reliable results, it is necessary to devise effective DNN models for medical applications. One solution is to combine multiple DNN models in an ensemble system to obtain better results than using each single DNN model. Ensemble learning is a popular machine learning technique in which multiple models are combined to improve the final results and has been widely used in medical image analysis. In this paper, we propose to measure the confidence in the prediction of each model in the ensemble system and then use an associate threshold to determine whether the confidence is acceptable or not. A segmentation model is selected based on the comparison between the confidence and its associated threshold. The optimal threshold for each segmentation model is found by using Comprehensive Learning Particle Swarm Optimisation (CLPSO), a swarm intelligence algorithm. The Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. The experimental results on three medical image segmentation datasets confirm that our ensemble achieves better results compared to some well-known segmentation models

Injury prevention strategies at the FIFA 2014 World Cup: perceptions and practices of the physicians from the 32 participating national teams

Purpose The available scientific research regarding injury prevention practices in international football is sparse. The purpose of this study was to quantify current practice with regard to (1) injury prevention of top-level footballers competing in an international tournament, and (2) determine the main challenges and issues faced by practitioners in these national teams. Methods A survey was administered to physicians of the 32 competing national teams at the FIFA 2014 World Cup. The survey included 4 sections regarding perceptions and practices concerning non-contact injuries: (1) risk factors, (2) screening tests and monitoring tools, (3) preventative strategies and (4) reflection on their experience at the World Cup. Results Following responses from all teams (100%), the present study revealed the most important intrinsic (previous injury, accumulated fatigue, agonist:antagonist muscle imbalance) and extrinsic (reduced recovery time, training load prior to and during World Cup, congested fixtures) risk factors during the FIFA 2014 World Cup. The 5 most commonly used tests for risk factors were: flexibility, fitness, joint mobility, balance and strength; monitoring tools commonly used were: medical screen, minutes/matches played, subjective and objective wellness, heart rate and biochemical markers. The 5 most important preventative exercises were: flexibility, core, combined contractions, balance and eccentric. Conclusions The present study showed that many of the National football (soccer) teams’ injury prevention perceptions and practices follow a coherent approach. There remains, however, a lack of consistent research findings to support some of these perceptions and practices

Ensemble of deep learning models with surrogate-based optimization for medical image segmentation.

Deep Neural Networks (DNNs) have created a breakthrough in medical image analysis in recent years. Because clinical applications of automated medical analysis are required to be reliable, robust and accurate, it is necessary to devise effective DNNs based models for medical applications. In this paper, we propose an ensemble framework of DNNs for the problem of medical image segmentation with a note that combining multiple models can obtain better results compared to each constituent one. We introduce an effective combining strategy for individual segmentation models based on swarm intelligence, which is a family of optimization algorithms inspired by biological processes. The problem of expensive computational time of the optimizer during the objective function evaluation is relieved by using a surrogate-based method. We train a surrogate on the objective function information of some populations and then use it to predict the objective values of each candidate in the subsequent populations. Experiments run on a number of public datasets indicate that our framework achieves competitive results within reasonable computation time

Psychological factors and future performance of football players:A systematic review with meta-analysis

Objectives: This systematic review had 3 key objectives: (1) to investigate whether psychological factors were associated with future football performance ( e.g., progression to professional football, better game statistics during the next season); (2) to critically review the methodological approaches used in the included studies and summarize the evidence for the current research question; (3) to provide guidelines for future studies. Design: Systematic ReviewMethods: Electronic databases (SPORTDiscus, PubMed and PsycINFO) and previously published systematic and scoping reviews were searched. Only prospective studies were considered for inclusion. Results: Eleven published studies that reported 39 effect sizes were included. Psychological factors; task orientation, task-oriented coping strategies and perceptual-cognitive functions had small effects on future performance in football (ds = 0.20-0.29). Due to high risk of bias there were low certainty of evidence for psychological factors relationship with future football performance. Conclusions: Psychological factors investigated showed small effects on future football performance, however, there was overall uncertainty in this evidence due to various sources of bias in the included studies. Therefore psychological factors cannot be used as a sole deciding factor in player recruitment, retention, release strategies, however it would appear appropriate to include these in the overall decision-making process. Future, studies with more appropriate and robust research designs are urgently needed to provide more certainty around their actual role

Injury risk factors, screening tests and preventative strategies: A systematic review of the evidence that underpins the perceptions and practices of 44 football (soccer) teams from various premier leagues

Purpose: To systematically review the scientific level of evidence for the ‘Top 3’ risk factors, screening tests and preventative exercises identified by a previously published survey of 44 premier league football (soccer) teams. Also, to provide an overall scientific level of evidence and graded recommendation based on the current research literature. Methods: A systematic literature search (Pubmed [MEDLINE], SportDiscus, PEDRO and Cochrane databases). The quality of the articles was assessed and a level of evidence (1++ to 4) was assigned. Level 1++ corresponded to the highest level of evidence available and 4, the lowest. A graded recommendation (A: strong, B: moderate, C: weak, D: insufficient evidence to assign a specific recommendation) for use in the practical setting was given. Results: Fourteen studies were analysed. The overall level of evidence for the risk factors previous injury, fatigue and muscle imbalance were 2++, 4 and ‘inconclusive’, respectively. The graded recommendation for functional movement screen, psychological questionnaire and isokinetic muscle testing were all ‘D’. Hamstring eccentric had a weak graded ‘C’ recommendation, and eccentric exercise for other body parts was ‘D’. Balance/proprioception exercise to reduce ankle and knee sprain injury was assigned a graded recommendation ‘D’. Conclusions: The majority of perceptions and practices of premier league teams have a low level of evidence and low graded recommendation. This does not imply that these perceptions and practices are not important or not valid, as it may simply be that they are yet to be sufficiently validated or refuted by research

B-type natriuretic peptide predicts deterioration in functional capacity following lung resection

OBJECTIVES Following lung resection, there is a decrease in the functional capacity and quality of life, which is not fully explained by changes in pulmonary function. Previous work demonstrates that B-type natriuretic peptide (BNP) is associated with short- and long-term complications following lung resection, leading to the suggestion that cardiac dysfunction may contribute to functional deterioration. Our aim was to investigate any relationship between BNP and subjective and objective indices of functional deterioration following lung resection surgery. METHODS Twenty-seven patients undergoing lung resection had serum BNP measured preoperatively, on postoperative day (POD)1 and POD2, and at 2 months postoperatively. The functional deterioration was assessed using 6-min walk tests and the Medical Research Council dyspnoea scale. ‘Deterioration in functional capacity’ was defined as either an increase in the Medical Research Council dyspnoea score or a significant decrease in the 6-min walk test distance. RESULTS BNP increased over time (P < 0.01) and was significantly elevated on POD1 and POD2 (P < 0.02 for both). Seventeen patients demonstrated functional deterioration 2 months postoperatively. At all perioperative time points, BNP was significantly higher in patients showing deterioration (P < 0.05 for all). Preoperative BNP was predictive of functional deterioration at 2 months with an area under the receiver-operating characteristic curve of 0.82 (P = 0.01, 95% confidence interval 0.65–0.99). CONCLUSIONS This study has demonstrated, using subjective and objective measures, that preoperative BNP is a predictor of functional deterioration following lung resection. BNP may have a role in preoperative risk stratification in this population, allowing therapy in future to be targeted towards high-risk patients with the aim of preventing postoperative cardiac dysfunction. Clinical trial registration number: NCT01892800

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms

Multi-label classification via incremental clustering on an evolving data stream.

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings