Field-based team sports, such as soccer, rugby and hockey are popular worldwide. There have been many studies that have investigated the physiology of these sports, especially soccer. However, some fitness components of these field-based team sports are poorly understood. In particular, repeated-sprint ability (RSA) is one area that has received relatively little research attention until recent times. Historically, it has been difficult to investigate the nature of RSA, because of the unpredictability of player movements performed during field-based team sports. However, with improvements in technology, time-motion analysis has allowed researchers to document the detailed movement patterns of team-sport athletes. Studies that have published time-motion analysis during competition, in general, have reported the mean distance and duration of sprints during field-based team sports to be between 10-20 m and 2-3 seconds, respectively. Unfortunately, the vast majority of these studies have not reported the specific movement patterns of RSA, which is proposed as an important fitness component of team sports. Furthermore, there have been few studies that have investigated the physiological requirements of one-off, short-duration sprinting and repeated sprints (<10 seconds duration) that is specific to field-based team sports. This review examines the limited data concerning the metabolic changes occurring during this type of exercise, such as energy system contribution, adenosine triphosphate depletion and resynthesis, phosphocreatine degradation and resynthesis, glycolysis and glycogenolysis, and purine nucleotide loss. Assessment of RSA, as a training and research tool, is also discussed.

These results suggest that the relative contribution of the aerobic energy system during track running events is considerable and greater than traditionally thought.

Limited information exists about the movement patterns of field-hockey players, especially during elite competition. Time-motion analysis was used to document the movement patterns during an international field-hockey game. In addition, the movement patterns of repeated-sprint activity were investigated, as repeated-sprint ability is considered to be an important fitness component of team-sport performance. Fourteen members of the Australian men's field-hockey team (age 26+/-3 years, body mass 76.7+/-5.6 kg, VO2max 57.9+/-3.6 ml.kg(-1).min(-1); mean+/-s) were filmed during an international game and their movement patterns were analysed. The majority of the total player game time was spent in the low-intensity motions of walking, jogging and standing (46.5+/-8.1, 40.5+/-7.0 and 7.4+/-0.9%, respectively). In comparison, the proportions of time spent in striding and sprinting were 4.1+/-1.1 and 1.5+/-0.6%, respectively. Our criteria for 'repeated-sprint' activity (defined as a minimum of three sprints, with mean recovery duration between sprints of less than 21 s) was met on 17 occasions during the game (total for all players), with a mean 4+/-1 sprints per bout. On average, 95% of the recovery during the repeated-sprint bouts was of an active nature. In summary, the results suggest that the motion activities of an elite field-hockey competition are similar to those of elite soccer, rugby and Australian Rules football. In addition, the investigation of repeated-sprint activity during competition has provided additional information about the unique physiological demands of elite field-hockey performance.

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%.

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Long-term effects of training are important information for athletes, coaches, and scientists when associating changes in physiological indices with changes in performance. Therefore, this study monitored changes in aerobic and anaerobic capacities and performance in a group of elite cross-country skiers during a full sport season. Thirteen men (age, 23 ± 2 years; height, 182 ± 6 cm; body mass, 76 ± 8 kg; V2 roller ski skating VO2max, 79.3 ± 4.4 ml·kg·min or 6.0 ± 0.5 L·min) were tested during the early, middle, and late preparation phase: June (T1), August (T2), and October (T3); during the competition phase: January/February (T4); and after early precompetition phase: June (T5). O2-cost during submaximal efforts, V[Combining Dot Above]O2peak, accumulated oxygen deficit (ΣO2-deficit), and performance during a 1,000-m test were determined in the V2 ski skating technique on a roller ski treadmill. Subjects performed their training on an individual basis, and detailed training logs were categorized into different intensity zones and exercise modes. Total training volume was highest during the summer months (early preseason) and decreased toward and through the winter season, whereas the volume of high-intensity training increased (all p < 0.05). There was a significant main effect among testing sessions for 1,000 m time, O2-cost, and ΣO2-deficit (Cohen's d effect size; ES = 0.63-1.37, moderate to large, all p < 0.05). In general, the changes occurred between T1 and T3 with minor changes in the competitive season (T3 to T4). No significant changes were found in V[Combining Dot Above]O2peak across the year (ES = 0.17, trivial). In conclusion, the training performed by elite cross-country skiers induced no significant changes in V[Combining Dot Above]O2peak but improved performance, O2-cost, and ΣO2-deficit.

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In this study, we investigated the age-related differences in repeated-sprint ability and blood lactate responses in 134 youth football players. Players from the development programme of a professional club were grouped according to their respective under-age team (U-11 to U-18). Following familiarization, the participants performed a repeated-sprint ability test [6 x 30-m sprints 30 s apart, with active recovery (2.0-2.2 m . s(-1)) between sprints]. The test variables were total time, percent sprint decrement, and post-test peak lactate concentration. Total time improved from the U-11 to U-15 age groups (range 33.15 +/- 1.84 vs. 27.25 +/- 0.82 s), whereas no further significant improvements were evident from U-15 to U-18. No significant differences in percent sprint decrement were reported among groups (range 4.0 +/- 1.0% to 5.5 +/- 2.1%). Post-test peak lactate increased from one age group to the next (range 7.3 +/- 1.8 to 12.6 +/- 1.6 mmol . l(-1)), but remained constant when adjusted for age-related difference in body mass. Peak lactate concentration was moderately correlated with sprint time (r = 0.70, P > 0.001). Our results suggest that performance in repeated-sprint ability improves during maturation of highly trained youth football players, although a plateau occurs from 15 years of age. In contrast to expectations based on previous suggestions, percent sprint decrement during repeated sprints did not deteriorate with age.