The purpose of this study was to compare physiological characteristics of three different levels of 100 m female sprinters. The 30 subjects in this study (20 female track athletes and 10 recreationally trained females) were assigned, according to their 100 m running time, to one of three different groups: "Fast" (11.8 +/- 0.1 sec), "Average" (12.7 +/- 0.1) and "Slow" (14.2 +/- 0.1 sec). All subjects were tested for performance in the Wingate Anaerobic Test (WAnT), strength (squat exercise), fat % (hydrostatic weighing), reaction time, flexibility (sit-and-reach test), aerobic power (peak VO2) and running skill. The data was analyzed by one-way Analysis of Variance (ANOVA) with post-hoc Tukey test, which was performed on each variable to find differences between the groups. The ANOVA indicated significant differences among all three groups for performance in the Wingate Anaerobic Test and relative strength. Significant differences in fat % and running skill were found between the fast and the slow groups and between the average and the slow groups. However, no significant difference in fat % and running skill existed between the fast and the average groups. The differences in reaction time were significant only between the fast and the average groups. No two groups were significantly different from each other for flexibility and peak VO2. Pearson correlation coefficients (r) were calculated to determine the relationships between the 100m running time and each of the variables tested. Significant and negative correlations were found between the 100m running time and skill, relative strength, and performance in the WAnT. Significant and positive correlations were found between running time and fat %. No significant correlations were found between running time and peak VO2 reaction time and flexibility. Stepwise regression analysis indicated that the combination of performance in the WAnT and strength provided the most efficient (R = 0.92) prediction of 100 m run times. This study demonstrated that the main difference among female sprinters of different performance levels lies in their ability to produce muscular power, strength and running technique. Other physiological components, such as flexibility, peak VO2, and reaction time do not differ among female sprinters of different performance levels as represented in the tested groups.
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Sci Rep
January 2025
Sir Jules Thorn Sleep and Circadian Neuroscience Institute, Kavli Institute for Nanoscience Discovery, Nuffield Department of Clinical Neurosciences, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford, OX1 3QU, UK.
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Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Centre for Analytical Sciences Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands. Electronic address:
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View Article and Find Full Text PDFAnal Chim Acta
March 2025
School of Automation, Central South University, 410083, Changsha, China. Electronic address:
In spectral analysis, selecting the right spectral variables is crucial for effective modeling. It reduces data dimensionality, removes irrelevant wavelength points, and improves both the generalization ability and computational efficiency of the model. However, the number of available samples often falls short of the total possible combinations of wavelengths, making variable selection a non-deterministic polynomial-time (NP) hard optimization problem.
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School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel.
The process of particle picking, a crucial step in cryo-electron microscopy (cryo-EM) image analysis, often encounters challenges due to outliers, leading to inaccuracies in downstream processing. In response to this challenge, this research introduces an additional automated step to reduce the number of outliers identified by the particle picker. The proposed method enhances both the accuracy and efficiency of particle picking, thereby reducing the overall running time and the necessity for expert intervention in the process.
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January 2025
Sports Physical Therapy Laboratory, School of Physical Education and Sports Science, National and Kapodistrian University of Athens, Greece. Electronic address:
Introduction: Breathing and postural control is reported to be both neuromuscularly and mechanically interdependent. To date, the effects of voluntary abdominal and thoracic breathing (VAB and VTB) on the EMG activity of muscles involved in both respiratory and postural functions, as well as gait biomechanics related to these breathing patterns, have not been investigated in young, healthy adults. The aim of the study was to evaluate the EMG responses of neck and trunk muscles, as well as the kinematic, stability, and kinetic parameters of gait induced by VAB and VTB compared to involuntary breathing (INB).
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