Does training affect match performance? A study using data mining and tracking devices

FIFA has recently allowed the use of electronic performance and tracking systems (EPTS) in professional football competition, providing teams with novel and more accurate data. Physical performance has not yet taken much attention from the research community, due to the difficulty of accessing this information with the same devices during training and competition. This study provides a methodology based on machine learning and statistical methods to relate the physical performance variation of players during time-framed training sessions, and their performance in the following matches. The analysis is carried out over F.C. Barcelona B, season 2015-2016 data, and makes emphasis on exploiting the design characteristics of the structured training methodology implemented within the club. The use of summarized physical variation data has provided a remarkable relation between higher magnitudes of variation in 3-week time frames during training, and higher physical values in the following matches. With increased data availability this and new approaches could provide a new frontier in physical performance analysis. This is, up to our knowledge, the first study to relate training and matches performance through the same EPTS devices in professional football.Peer ReviewedPostprint (published version

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

A Synergistic Antiobesity Effect by a Combination of Capsinoids and Cold Temperature Through Promoting Beige Adipocyte Biogenesis.

Beige adipocytes emerge postnatally within the white adipose tissue in response to certain environmental cues, such as chronic cold exposure. Because of its highly recruitable nature and relevance to adult humans, beige adipocytes have gained much attention as an attractive cellular target for antiobesity therapy. However, molecular circuits that preferentially promote beige adipocyte biogenesis remain poorly understood. We report that a combination of mild cold exposure at 17°C and capsinoids, a nonpungent analog of capsaicin, synergistically and preferentially promotes beige adipocyte biogenesis and ameliorates diet-induced obesity. Gain- and loss-of-function studies show that the combination of capsinoids and cold exposure synergistically promotes beige adipocyte development through the β2-adrenoceptor signaling pathway. This synergistic effect on beige adipocyte biogenesis occurs through an increased half-life of PRDM16, a dominant transcriptional regulator of brown/beige adipocyte development. We document a previously unappreciated molecular circuit that controls beige adipocyte biogenesis and suggest a plausible approach to increase whole-body energy expenditure by combining dietary components and environmental cues

A kinetic comparison of back-loading and head-loading in Xhosa women

The purpose of this study was to compare the kinetic responses associated with ground reaction force measurements to both head-loading and back-loading in a group of Xhosa women. Altogether, 16 women were divided into two groups based on their experience of head-loading. They walked over a force plate in three conditions: unloaded or carrying 20 kg in either a backpack or on their head. The most striking finding was that there was no difference in kinetic response to head-loading as a consequence of previous experience. Considering the differences between the load carriage methods, most changes were consistent with increasing load. Head-loading was, however, associated with a shorter contact time, smaller thrust maximum and greater vertical force minimum than back-loading. Both loading conditions differed from unloaded walking for a number of temporal variables associated with the ground contact phase, e.g. vertical impact peak was delayed whilst vertical thrust maximum occurred earlier. Statement of Relevance: Consideration of the kinetics of head and back load carriage in African women is important from a health and safety perspective, providing an understanding of the mechanical adaptations associated with both forms of load carriage for a group of people for whom such load carriage is a daily necessity

The influence of push-off timing in a robotic ankle-foot prosthesis on the energetics and mechanics of walking

Background: Robotic ankle-foot prostheses that provide net positive push-off work can reduce the metabolic rate of walking for individuals with amputation, but benefits might be sensitive to push-off timing. Simple walking models suggest that preemptive push-off reduces center-of-mass work, possibly reducing metabolic rate. Studies with bilateral exoskeletons have found that push-off beginning before leading leg contact minimizes metabolic rate, but timing was not varied independently from push-off work, and the effects of push-off timing on biomechanics were not measured. Most lower-limb amputations are unilateral, which could also affect optimal timing. The goal of this study was to vary the timing of positive prosthesis push-off work in isolation and measure the effects on energetics, mechanics and muscle activity. Methods: We tested 10 able-bodied participants walking on a treadmill at 1.25 m.s(-1). Participants wore a tethered ankle-foot prosthesis emulator on one leg using a rigid boot adapter. We programmed the prosthesis to apply torque bursts that began between 46% and 56% of stride in different conditions. We iteratively adjusted torque magnitude to maintain constant net positive push-off work. Results: When push-off began at or after leading leg contact, metabolic rate was about 10% lower than in a condition with Spring-like prosthesis behavior. When push-off began before leading leg contact, metabolic rate was not different from the Spring-like condition. Early push-off led to increased prosthesis-side vastus medialis and biceps femoris activity during push-off and increased variability in step length and prosthesis loading during push-off. Prosthesis push-off timing had no influence on intact-side leg center-of-mass collision work. Conclusions: Prosthesis push-off timing, isolated from push-off work, strongly affected metabolic rate, with optimal timing at or after intact-side heel contact. Increased thigh muscle activation and increased human variability appear to have caused the lack of reduction in metabolic rate when push-off was provided too early. Optimal timing with respect to opposite heel contact was not different from normal walking, but the trends in metabolic rate and center-of-mass mechanics were not consistent with simple model predictions. Optimal push-off timing should also be characterized for individuals with amputation, since meaningful benefits might be realized with improved timing

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences