The purpose of this study was to investigate energy system contributions and energy costs in combat situations. The sample consisted of 10 male taekwondo athletes (age: 21 ± 6 years old; height: 176.2 ± 5.3 cm; body mass: 67.2 ± 8.9 kg) who compete at the national or international level. To estimate the energy contributions, and total energy cost of the fights, athletes performed a simulated competition consisting of three 2 min rounds with a 1 min recovery between each round. The combats were filmed to quantify the actual time spent fighting in each round. The contribution of the aerobic (W(AER)), anaerobic alactic (W(PCR)), and anaerobic lactic [Formula: see text] energy systems was estimated through the measurement of oxygen consumption during the activity, the fast component of excess post-exercise oxygen consumption, and the change in blood lactate concentration in each round, respectively. The mean ratio of high intensity actions to moments of low intensity (steps and pauses) was ~1:7. The W(AER), W(PCR) and W([La(-)]) system contributions were estimated as 120 ± 22 kJ (66 ± 6%), 54 ± 21 kJ (30 ± 6%), 8.5 kJ (4 ± 2%), respectively. Thus, training sessions should be directed mainly to the improvement of the anaerobic alactic system (responsible by the high-intensity actions), and of the aerobic system (responsible by the recovery process between high-intensity actions).

The objective of this study was to develop count cut-points for three different accelerometer models: ActiGraph GT3X, RT3 and Actical to accurately classify physical activity intensity levels in adolescents. Seventy-nine adolescents (10-15 years) participated in this study. Accelerometers and oxygen consumption ([Formula: see text]) data were collected at rest and during 11 physical activities of different intensities. Accelerometers were worn on the waist and [Formula: see text] was measured by a portable metabolic system: Cosmed K4b2. Receiver operating characteristic (ROC) curves were used to determine cut-points. Cut-points for sedentary (SED), moderate-to-vigorous (MVPA) and vigorous-intensity physical activity (VPA) were 46, 607 and 818 counts·15s(-1) to the vertical axis of ActiGraph; 180, 757 and 1112 counts·15s(-1) to the vector magnitude of ActiGraph; 17, 441 and 873 counts·15s(-1) for Actical; and 5.6, 20.4 and 32.2 counts·s(-1) for RT3, respectively. For all three accelerometer models, there was an almost perfect discrimination of SED and MVPA (ROC >0.97) and an excellent discrimination of VPA (ROC>0.90) observed. Areas under the ROC curves indicated better discrimination of MVPA by ActiGraph (AUC=0.994) and Actical (AUC=0.993) when compared to RT3 (AUC=0.983). The cut-points developed in this study for the ActiGraph (vector magnitude), RT3 and Actical accelerometer models can be used to monitor physical activity level of adolescents.

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SUMMARYFrom the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long-term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

Recent evidence suggests that some seizures are preceded by preictal changes that start from minutes to hours before an ictal event. Nevertheless an adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence of preictal changes in long-term intracranial recordings from 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe a measure of brain excitability based on the slow modulation of high-frequency gamma activities (40–140 Hz) in ensembles of intracranial contacts. In prospective tests, we found that this index identified preictal changes at levels above chance in 13.2% of the patients (7/53), suggesting that results may be significant for the whole group (p < 0.05). These results provide a demonstration that preictal states can be detected prospectively from EEG data. They advance understanding of the network dynamics leading to seizure and may help develop novel seizure prediction algorithms.