Influence V0.2
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The purpose of this study was to investigate the influence of exercise modality on the 'overshoot' in V(O2) that has been reported following the onset of moderate-intensity (below the gas exchange threshold, GET) exercise in endurance athletes. Seven trained endurance cyclists and seven trained endurance runners completed six square-wave transitions to a work-rate or running speed requiring 80% of mode-specific GET during both cycle and treadmill running exercise. The kinetics of V(O2) was assessed using non-linear regression and any overshoot in V(O2) was quantified as the integrated volume (IV) of O(2) consumed above the steady-state requirement. During cycling, an overshoot in V(O2) was evident in all seven cyclists (IV = 136 +/- 41 ml) and in four runners (IV = 81 +/- 94 ml). During running, an overshoot in V(O2) was evident in four runners (IV = 72 +/- 61 ml) but no cyclists. These data challenge the notion that V(O2) always rises towards a steady-state with near-exponential kinetics in this exercise intensity domain. The greater incidence of the V(O2) overshoot during cycling (11/14 subjects) compared to running (4/14 subjects) indicates that the overshoot phenomenon is related to an interaction between high levels of aerobic fitness and exercise modality. We speculate that a transient loss in muscle efficiency as a consequence of a non-constant ATP requirement following the onset of constant-work-rate exercise or an initially excessive recruitment of motor units (relative to the work-rate) might contribute to the overshoot phenomenon.
Aerobic exercise performance is indicated by maximal oxygen uptake per minute (VO2max) and primarily determined by the efficiency of mechanisms supplying active muscles with oxygen from the air [1]. Other factors affecting aerobic performance include body mass (BM) and body composition [2]. Obese and overweight persons, whose high BM is caused by high body adiposity, display a considerably lower VO2max relative to their body mass [3], [4]. However, a high body mass, as well as a high body mass index (BMI), can also be caused by a high amount of lean body mass (LBM) in persons with normal (or even low) body fat (BF). Publications to date have presented results of research on the influence of obesity and overweight on physical fitness and have established correlations between body composition and performance on fitness tests for athletes engaged in different disciplines [5], [6]. However, no attempts have thus far been made to conduct a comprehensive analysis of the influence of body composition on aerobic performance. The influence of body composition may be particularly important for sports disciplines in which athletes are required to have an appropriately high aerobic performance together with high muscle mass (e.g., boxing, basketball, or handball).
The aim of the study was to determine the influence of body composition and increased BM on aerobic performance. High body mass can be caused by an increased amount of BF or increased muscle mass (i.e., LBM), or both. The study sought to isolate the influence of both factors. The study also sought to assess, on the one hand, the influence of high body adiposity and, on the other hand, the influence of high LBM in men with similar body mass and normal values of other parameters for body composition (with the exception of body height). Results show that most of the analyzed physiological and biochemical parameters were similar between the HBF and HLBM groups.
A person's maximum rate of oxygen consumption during aerobic exercise, their VO 2 max , is the gold standard of measurement in aerobic fitness scoring. It is often normalized to a person's body weight to allow for comparison between different people. However, other factors aside from body weight, such as age, can also influence an individual's VO 2 max score. This calculator converts a VO2 max score to a percentile rank, so people of different ages can be compared.
The differences in exercise modalities, alcohol doses, types of beverages and participant characteristics, including their habitual alcohol intake, may explain the contradictory and inconclusive results found among studies [28]. However, there are no studies that investigate the influence of a moderate consumption of beer or alcohol on the physical fitness response to a highly demanding training program in conditions reflecting the real-life situation of healthy adults. Thus, the purpose of this study was to evaluate the effects of a HIIT program on physical fitness parameters in healthy young adults, and to analyze the possible influence of daily but moderate beer consumption or its alcohol equivalent on it. The primary hypothesis was that HIIT would improve cardiorespiratory fitness, muscular strength and power outcomes, but the intake of beer, even in moderate amounts, may mitigate these positive effects due to its alcohol content.
This study shows that a 10-week structured and highly demanding exercise intervention improves cardiorespiratory fitness and hand grip strength in healthy adults. This was not influenced by the concurrent daily intake of beer or ethanol in moderate amounts. No significant improvements were found in muscular power variables. Therefore, moderate and daily beer consumption accompanying meals, like in the present study, appears not to be an issue of concern affecting physical fitness parameters such as cardiorespiratory fitness and muscular strength after a HIIT program in healthy young adults.
This study shows that a 10-week structured and highly demanding exercise intervention improves cardiorespiratory fitness and hand grip strength in healthy adults. This was not influenced by the concurrent intake of beer in moderate amounts. Although we did not find deleterious effects of post-exercise alcohol consumption neither on cardiorespiratory fitness nor on muscular strength, the use of alcohol after strenuous exercise should be managed carefully. Therefore, more information is required if recommendations on appropriate alcohol use during the post-event period are to be made.
The rest period between RT sets is another important variable to consider because shorter rest periods may result in a greater stimulation of the cardiovascular system, which could potentially influence the change in VO2max. To illustrate, a study by McCarthy et al. [35] reported an increase in VO2max following whole body RT with short rest periods (about 75 s). In contrast, another study using shorter rest periods (60 s) [11] observed no significant changes in VO2max. Therefore, with respect to what is currently known in the literature, the rest period between sets does not appear to be a significant modulating factor for VO2max. It is conceivable, however, that shorter rest periods (30 s) may induce a greater cardiovascular demand and thus improve VO2max. Nevertheless, that is speculative and currently unknown.
Cardiorespiratory endurance is a key element of health and fitness. In sports, it is reflected in the ability to sustain exercise over an extended period of time. How well the body can take up oxygen, clear lactate, and move efficiently, determines your level of cardiorespiratory endurance, and these factors are largely influenced by age, physical training and genetics.
VO2max is a frequently used measure in sports and science to determine the effect of training, to study the influence of genetic factors on endurance capacity. And, when combined with heart rate and performance tests it can be extremely useful in customizing training plans.
A large proportion of your ability to improve VO2max in response to training is influenced by your DNA. In fact, genetics can account for as much as 47% of the inter-individual variance in training responses10. FitnessGenes test for several genes that influence the trainability of your VO2max - the ACE, PGC1A, CKM, AMPD1, AKT1, HIF1A, VEGF and ADRB2 genes.
This packages offers two modes of computation to calculate the influencefunctions. The first mode is called calc_img_wise, during which the twovalues s_test and grad_z for each training image are computed on the flywhen calculating the influence of that single image. The algorithm moves thenon to the next image. The second mode is called calc_all_grad_then_test andcalculates the grad_z values for all images first and saves them to disk.Then, it'll calculate all s_test values and save those to disk. Subsequently,the algorithm will then calculate the influence functions for all images byreading both values from disk and calculating the influence base on them. Thiscan take significant amounts of disk space (100s of GBs) but with a fast SSDcan speed up the calculation significantly as no duplicate calculations takeplace. This is the case because grad_z has to be calculated twice, once forthe first approximation in s_test and once to combine with the s_testvector to calculate the influence. Most importantnly however, s_test is onlydependent on the test sample(s). While one grad_z is used to estimate theinitial value of the Hessian during the s_test calculation, this isinsignificant. grad_z on the other hand is only dependent on the trainingsample. Thus, in the calc_img_wise mode, we throw away all grad_zcalculations even if we could reuse them for all subsequent s_testcalculations, which could potentially be 10s of thousands. However, as statedabove, keeping the grad_zs only makes sense if they can be loaded faster/kept in RAM than calculating them on-the-fly.
TL;DR: The recommended way is using calc_img_wise unless you have a crazyfast SSD, lots of free storage space, and want to calculate the influences onthe prediction outcomes of an entire dataset or even >1000 test samples.
The test image on the top left is test image for which the influences werecalculated. To get the correct test outcome of ship, the Helpful images fromthe training dataset were the most helpful, whereas the Harmful images were themost harmful. Here, we used CIFAR-10 as dataset. The model was ResNet-110. Thenumbers above the images show the actual influence value which was calculated. 59ce067264
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