Cetin, E, Hindistan, IE, Ozkaya, YG. Effect of different training methods on stride parameters in speed maintenance phase of 100-m sprint running. J Strength Cond Res 32(5): 1263-1272, 2018-This study examined the effects of 2 different training methods relevant to sloping surface on stride parameters in speed maintenance phase of 100-m sprint running. Twenty recreationally active students were assigned into one of 3 groups: combined training (Com), horizontal training (H), and control (C) group. Com group performed uphill and downhill training on a sloping surface with an angle of 4°, whereas H group trained on a horizontal surface, 3 days a week for 8 weeks. Speed maintenance and deceleration phases were divided into distances with 10-m intervals, and running time (t), running velocity (RV), step frequency (SF), and step length (SL) were measured at preexercise, and postexercise period. After 8 weeks of training program, t was shortened by 3.97% in Com group, and 2.37% in H group. Running velocity also increased for totally 100 m of running distance by 4.13 and 2.35% in Com, and H groups, respectively. At the speed maintenance phase, although t and maximal RV (RVmax) found to be statistically unaltered during overall phase, t was found to be decreased, and RVmax was preceded by 10 m in distance in both training groups. Step length was increased at 60-70 m, and SF was decreased at 70-80 m in H group. Step length was increased with concomitant decrease in SF at 80-90 m in Com group. Both training groups maintained the RVmax with a great percentage at the speed maintenance phase. In conclusion, although both training methods resulted in an increase in running time and RV, Com training method was more prominently effective method in improving RV, and this improvement was originated from the positive changes in SL during the speed maintaining phase.
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Front Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
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Northwest Sichuan Gas District of Southwest Oil and Gasfield Company, Jiangyou, 621700, China.
With the wide application of hydrogen-doped natural gas (HBNG) in end users, laying pipelines in urban, comprehensive pipe corridors has become increasingly common. However, the leakage and diffusion of hydrogen-doped natural gas in confined or semi-confined spaces (e.g.
View Article and Find Full Text PDFTrials
January 2025
School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
Background: Alzheimer's disease is caused by modifiable and non-modifiable risk factors. Randomised controlled trials have investigated whether the strongest genetic risk factor for Alzheimer's disease, APOE4, impacts the effectiveness of exercise on health. Systematic reviews are yet to evaluate the effect of exercise on physical and cognitive outcomes in APOE genotyped participants.
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January 2025
Division of Pediatric Cardiology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA.
The integration of genomic medicine into pediatric clinical practice is a critical need that remains largely unmet, especially in socioeconomically challenged and rural areas where healthcare disparities are most pronounced. This review seeks to summarize the barriers responsible for delayed diagnosis and treatment, and examines diverse care models, technological innovations, and strategies for dissemination and implementation aimed at addressing the evolving genomic needs of pediatric populations. Through a comprehensive review of the literature, we explore proposed methodologies to bridge this gap in pediatric healthcare, with a specific emphasis on understanding and speeding implementation approaches and technologies to mitigate disparities in underserved populations, including rural and marginalized communities.
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Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037.
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e.
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