Fitness Metrics Research Articles

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.

Performance and Sensitivity Analysis of an Automated X-Ray Based Total Knee Replacement Mass-Customization Pipeline

Abstract The objective of this study was to outline a fully automated, X-ray-based, mass-customization pipeline for knee replacement surgery, thoroughly evaluate its robustness across a range of demographics, and quantify necessary input requirements. The pipeline developed uses various machine learning-based methods to enable the automated workflow. Convolutional neural networks initially extract information from inputted bi-planar X-rays, point depth and statistical shape models are used to reconstruct three-dimensional models of the subjects' anatomy, and finally computer-aided design scripts are employed to generate customized implant designs. The pipeline was tested on a range of subjects using three different fit metrics to evaluate performance. A digitally reconstructed radiograph method was adopted to enable a sensitivity analysis of input X-ray alignment and calibration. Subject sex, height, age, and knee side were concluded not to significantly impact performance. The pipeline was found to be sensitive to subject ethnicity, but this was likely due to limited diversity in the training data. Arthritis severity was also found to impact performance, suggesting further work is required to confirm suitability for use with more severe cases. X-ray alignment and dimensional calibration were highlighted as paramount to achieve accurate results. Consequentially, an alignment accuracy of ±5–10 deg and dimensional calibration accuracy of ±2–5%, are stipulated. In summary, the study demonstrated the pipeline's robustness and suitability for a broad range of subjects. The tool could afford substantial advantages over off-the-shelf and other customization solutions, but practical implications such as regulatory requirements need to be further considered.

A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide displacement prediction

Machine learning (ML) has been extensively applied to model geohazards, yielding tremendous success. However, researchers and practitioners still face challenges in enhancing the reliability of ML models. In the present study, a systematic framework combining k-fold cross-validation (CV), metaheuristics (MHs), support vector regression (SVR), and Friedman and Nemenyi tests was proposed to improve the reliability and performance of geohazard modeling. The average normalized mean square error (NMSE) from k-fold CV sets was adopted as the fitness metric. Twenty of the most well-established MHs and the most recent MHs were adopted to tune the hyperparameters of SVR and were evaluated through nonparametric Friedman and post hoc Nemenyi tests to identify significant differences. Observations from a typical reservoir landslide were selected as a benchmark dataset, and the accuracy, robustness, computational time, and convergence speed of the MHs were compared. Significant performance differences among the twenty MHs were identified by Friedman and post hoc Nemenyi tests of the mean absolute error (MAE), root mean squared error (RMSE), Kling–Gupta efficiency (KGE), and computational time, with p values lower than 0.05. The comparison of results demonstrated that the multiverse optimizer (MVO) is among the highest-performing, most stable, and computationally efficient algorithms, providing superior performance to other methods, with nearly optimum values of the correlation coefficient (R), a low MAE (23.5086 versus 23.9360), a low mean RMSE (48.6946 versus 50.1882), and a high mean KGE (0.9803 versus 0.9893) in predicting the displacement of the Shuping landslide. This paper considerably enriches the literature regarding hyperparameter optimization algorithms and the enhancement of their reliability. In addition, Friedman and post hoc Nemenyi tests have the potential for evaluating and comparing various ML-based geohazard models.

Does Minority Case Sampling Improve Performance with Imbalanced Outcomes in Psychological Research?

In psychological research, class imbalance in binary outcome variables is a common occurrence, particularly in clinical variables (e.g., suicide outcomes). Class imbalance can present a number of difficulties for inference and prediction, prompting the development of a number of strategies that perform data augmentation through random sampling from just the positive cases, or from both the positive and negative cases. Through evaluation in benchmark datasets from computer science, these methods have shown marked improvements in predictive performance when the outcome is imbalanced. However, questions remain regarding generalizability to psychological data. To study this, we implemented a simulation study that tests a number of popular sampling strategies implemented in easy-to-use software, as well as in an empirical example focusing on the prediction of suicidal thoughts. In general, we found that while one sampling strategy demonstrated far worse performance even in comparison to no sampling, the other sampling methods performed similarly, evidencing slight improvements over no sampling. Further, we evaluated the sampling strategies across different forms of cross-validation, model fit metrics, and machine learning algorithms.

'Fit for surgery': the relationship between cardiorespiratory fitness and postoperative outcomes.

New Findings What is the topic of this review? The relationships and physiological mechanisms underlying the clinical benefits of cardiorespiratory fitness (CRF) in patients undergoing major intra‐abdominal surgery. What advances does it highlight? Elevated CRF reduces postoperative morbidity/mortality, thus highlighting the importance of CRF as an independent risk factor. The vascular protection afforded by exercise prehabilitation can further improve surgical risk stratification and postoperative outcomes. Surgery accounts for 7.7% of all deaths globally and the number of procedures is increasing annually. A patient's ‘fitness for surgery’ describes the ability to tolerate a physiological insult, fundamental to risk assessment and care planning. We have evolved as obligate aerobes that rely on oxygen (O2). Systemic O2 consumption can be measured via cardiopulmonary exercise testing (CPET) providing objective metrics of cardiorespiratory fitness (CRF). Impaired CRF is an independent risk factor for mortality and morbidity. The perioperative period is associated with increased O2 demand, which if not met leads to O2 deficit, the magnitude and duration of which dictates organ failure and ultimately death. CRF is by far the greatest modifiable risk factor, and optimal exercise interventions are currently under investigation in patient prehabilitation programmes. However, current practice demonstrates potential for up to 60% of patients, who undergo preoperative CPET, to have their fitness incorrectly stratified. To optimise this work we must improve the detection of CRF and reduce potential for interpretive error that may misinform risk classification and subsequent patient care, better quantify risk by expressing the power of CRF to predict mortality and morbidity compared to traditional cardiovascular risk factors, and improve patient interventions with the capacity to further enhance vascular adaptation. Thus, a better understanding of CRF, used to determine fitness for surgery, will enable both clinicians and exercise physiologists to further refine patient care and management to improve survival.

Inclusion of biotic variables improves predictions of environmental niche models

AbstractAimSpecies Distribution Models (SDMs) are correlative models that predict the occurrence or abundance of species in relation to predictor variables. SDMs have become an important part of resource management and conservation biology yet they rarely incorporate species’ biology or demography into their predictions. To explore the possible influence of biotic relationships in explaining patterns of species’ distribution, abundance and explanatory power of SDMs, we chose two intertidal shellfish species with overlapping but different environmental preferences (Austrovenus stutchburyi and Macomona liliana) and modelled their distributions with and without biotic variables.LocationNew Zealand.MethodsThe relationship between environmental and biotic variables on the abundance of our two species was investigated using Boosted Regression Trees (BRTs) with increasing model complexity: (1) BRT models using environmental variables were fitted to each species; (2) BRT models using environmental variables and the co‐occurring abundance of the study taxa not being modelled were fitted; (3) BRT models using environmental variables, the co‐occurring abundance and the estimated abundance of the species’ patch of the study taxa not being modelled were fitted.ResultsA strong, non‐linear effect of the abundance of Austrovenus on Macomona was observed but only a weak effect of Macomona on Austrovenus. The inclusion of biotic variables improved the model fit metrics for both species, as assessed by withheld evaluation data, markedly so for Macomona. The overall deviance explained by the models increased, the correlation of predicted vs observed abundance data increased and the variability in these measures decreased.Main conclusionsThe combination of the improvement in model performance and changes in the influence of variables with the inclusion of biotic variables is of importance when predicting into unsampled space (e.g. when predicting impacts of climate change). Our approach improves classic SDMs by integrating ecological theories of how species interactions can alter species distributions across environmental gradients.

Intra- and Inter-Seasonal Fitness and Training Load Variations of Elite U20 Soccer Players

ABSTRACTInherent physical and anthropometric traits of elite soccer players, influenced by nature and nurture, account for the emergence of performances across time. Purpose: The present study aimed to evaluate inter- and intraseasonal differences and the influence of playing position on training and fitness metrics in talented young soccer players. Methods: A total of 74 male players from U20 teams of a single elite club were tested both at beginning, during, and at the end of three consecutive competitive seasons. Players under went anthropometric measurement and were tested for aerobic, jumping, and sprinting performances; the GPS-derived measures of metabolic power (MP) and equivalent distance index (ED) of every athlete were analyzed. Results: Difference between teams emerged in Mognoni's test, while it did not in countermovement jump and anthropometrics. ED was different across seasons. The model selection criteria revealed that the Bosco–Vittori test achieved the best fit. BMI and countermovement jump (CMJ) increased, and fat mass decreased, during season; different intraseasonal trends for CMJ. MP was slightly greater in midfielder. Conclusion: Network approaches in modeling performance metrics in sports team could unveil original interconnections between performance factors. In addition, the authors support multiparametric longitudinal assessments and a huge database of sports data for facilitating talent identification.

Performance-emission-stability mapping of CI engine in RCCI-PCCI modes under varying ethanol and CNG induced reactivity profiles: A comparative study through experimental and optimization perspectives

The study showed the synergistic benefits of split injection strategies on the emissions, performance and stability of an existing diesel engine fueled with biodiesel-CNG/ethanol. The response parameters of the coefficient of variance of indicated mean effective pressure (COVIMEP), exergy efficiency (XRG), equivalent brake specific energy consumption (EQ_BSEC), nitrogen oxide (NOx), unburned hydrocarbon (HC) and Soot elements were experimented and analyzed. The study performed a comprehensive design-of-experiment (DoE) to determine the operational limits and parametric space for design. It aims to find optimisation by measuring the rate of variability and standard estimation errors through model development on the response surface methodology (RSM) platform. In the DoE-RSM configuration archetype, this study attempts to create a space utilising several robust fitness metrics. The split injection has proven its ability to mitigate lower emissions, and better efficiency requirements from biodiesel fueled experimental studies. The optimum predicted value of highest CNG energy share provides noticeable improvement on XRG, EQ_BSEC, COVIMEP, NOx and HC by 5.51%, 17.58%, 24.73%, 61.49% and 28.20% respectively. However, the optimum predicted value of highest ethanol energy share provides improvement on EQ_BSEC, COVIMEP and HC by 15.83%, 1.07% and 68.05%, respectively.

Novel optimized crow search algorithm for feature selection

Feature selection techniques have been presented to allow us to choose a small subset of the original components’ relevant features by removing irrelevant or redundant features. Feature selection is essential for many reasons such as simplification, performance, computational efficiency, and quality interpretability. Owing to the importance mentioned above, many researchers have proposed and developed many algorithms to solve the feature selection problem. Although these approaches produce useful results, they possess some shortcomings like inadequate feature reduction. In this paper, a novel feature selection algorithm based on the crow search algorithm is presented. The algorithm uses dynamic awareness probability to keep the balance between the local and global search processes. Moreover, a novel neighborhood assigning strategy has been introduced to optimize the local search. Considering the best-selected features in each iteration helps attain more benefits in global search. The main superiority of the proposed algorithm is the significant feature reduction along with retaining the accuracy. Compared to enhanced crow search algorithm, the proposed algorithm has improved the feature reduction metric and fitness metric by 27.12% and 5.16%, respectively, while losing the accuracy metric by only 0.53%. Several popular UCI datasets have been employed to evaluate the proposed feature selection algorithm. The experimental results show that the proposed algorithm outperformed other feature selection algorithms in state-of-the-art related works regarding feature reduction and accuracy.

Physical Frailty is Modifiable in Young Cardiac Rehabilitation Patients.

Frailty is a standardized, quantitative metric used to assess multisystem physiologic reserve and vulnerability to poor health outcomes. Cardiac rehabilitation (CR) positively impacts patient outcomes, including frailty, in adult cardiovascular disease (CVD); however, both the frailty paradigm and CR are understudied in pediatric CVD. This retrospective, single-center cohort study aimed to determine baseline composite frailty for pediatric-onset CVD patients and examine its change throughout CR using a proposed frailty assessment tool. Youth with pediatric-onset CVD participating in CR were stratified into five CVD diagnostic groups: post-heart transplant (HTx) (n = 34), post-ventricular assist device (VAD) (n = 12), single ventricle (n = 20) and biventricular (n = 29) congenital heart disease, and cardiomyopathy (n = 25), and frailty was assessed at baseline and every 30days during CR. Post-HTx and post-VAD groups had significantly higher median frailty scores at baseline (6/10 and 5.75/10, respectively) driven by reduced strength, gait speed, and functional status. All groups except post-VAD displayed a significant absolute reduction in frailty from baseline to 120days (HTx: -3.5; VAD: -3; SV CHD: -1; BV CHD: -1; CM: -1.5), with similar median post-CR scores (1-3/10 in all groups). These improvements did not significantly correlate with number of CR sessions attended. This study established that frailty exhibits discriminatory utility across pediatric-onset CVD groups at baseline and is significantly modifiable over time. Improvements in frailty and other fitness metrics are likely due to a combination of post-operative recovery, post-diagnosis pharmacological and lifestyle changes, and CR. Further study of this frailty tool is needed to explore its prognostic utility.

What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value.

Fitness is at the core of evolutionary theory, but it is difficult to measure accurately. One way to measure long-term fitness is by calculating the individual’s reproductive value, which represents the expected number of allele copies an individual passes on to distant future generations. However, this metric of fitness is scarcely used because the estimation of individual’s reproductive value requires long-term pedigree data, which is rarely available in wild populations where following individuals from birth to death is often impossible. Wild study systems therefore use short-term fitness metrics as proxies, such as the number of offspring produced. This study compared two frequently used short-term metrics for fitness obtained at different offspring life stages (eggs, hatchlings, fledglings and recruits), and compared their ability to predict reproductive values derived from the genetic pedigree of a wild passerine bird population. We used twenty years of precise field observations and a near-complete genetic pedigree to calculate reproductive success, individual growth rate and de-lifed fitness as lifetime fitness measures, and as annual de-lifed fitness. We compared the power of these metrics to predict reproductive values and lineage survival to the end of the study period. The three short-term fitness proxies predict the reproductive values and lineage survival only when measured at the recruit stage. There were no significant differences between the different fitness proxies at the same offspring stages in predicting the reproductive values and lineage survival. Annual fitness at one year old predicted reproductive values equally well as lifetime de-lifed fitness. However, none of the short-term fitness proxies were strongly associated with the reproductive values. The commonly used short-term fitness proxies best predict long-term fitness when measured at recruitment stage. Thus, because lifetime fitness measured at recruit stage and annual fitness in the first year of life were the best proxies of long-term fitness in short-lived birds, we encourage their future use.

Cardiorespiratory Fitness Is Associated With Better White Matter Integrity in Persons Living With HIV.

Despite improved survival rates, neurocognitive impairment persists in persons living with HIV (PLWH). An active lifestyle is linked to improved cognition among PLWH, yet the neural substrates remain unclear. Diffusion tensor imaging and diffusion basis spectrum imaging measure HIV-related changes in brain white matter integrity. We used these measures of structural brain integrity to assess white matter changes, physical fitness, and cognition in a cross-sectional study of PLWH. Forty-four virologically well-controlled PLWH were recruited (average age of 56 years, a median recent CD4+ count of 682 cells/mm3). Diffusion tensor imaging -derived fractional anisotropy (FA) and diffusion basis spectrum imaging-derived axonal density were calculated. Cardiorespiratory fitness [maximal volume of oxygen consumption (VO2 max)] was measured by performing indirect calorimetry during exercise to volitional exhaustion. Cardiovascular risk was assessed by the Framingham risk score. Neuropsychological performance (NP) testing evaluated learning, memory, psychomotor/processing speed, and executive function. Partial correlations assessed the relationships among cardiorespiratory fitness, neuroimaging, NP, and HIV clinical metrics (CD4+ count and time since diagnosis). Higher VO2 max was associated with higher FA and higher axonal density in multiple white matter tracts, including the corticospinal tract and superior longitudinal fasciculus. Better NP in the motor/psychomotor domain was positively associated with FA and axonal density in diverse tracts including those associated with motor and visuospatial processing. However, higher VO2 max was not associated with NP or HIV clinical metrics. An active lifestyle promoting cardiorespiratory fitness may lead to better white matter integrity and decreased susceptibility to cognitive decline in virologically well-controlled PLWH.

Exosymbiotic microbes within fermented pollen provisions are as important for the development of solitary bees as the pollen itself.

Developing bees derive significant benefits from the microbes present within their guts and fermenting pollen provisions. External microbial symbionts (exosymbionts) associated with larval diets may be particularly important for solitary bees that suffer reduced fitness when denied microbe‐colonized pollen.To investigate whether this phenomenon is generalizable across foraging strategy, we examined the effects of exosymbiont presence/absence across two solitary bee species, a pollen specialist and generalist. Larvae from each species were reared on either microbe‐rich natural or microbe‐deficient sterilized pollen provisions allocated by a female forager belonging to their own species (conspecific‐sourced pollen) or that of another species (heterospecific‐sourced pollen). Our results reveal that the presence of pollen‐associated microbes was critical for the survival of both the generalist and specialist larvae, regardless of whether the pollen was sourced from a conspecific or heterospecific forager.Given the positive effects of exosymbiotic microbes for larval fitness, we then examined if the magnitude of this benefit varied based on whether the microbes were provisioned by a conspecific forager (the mother bee) or a heterospecific forager. In this second study, generalist larvae were reared only on microbe‐rich pollen provisions, but importantly, the sources (conspecific versus heterospecific) of the microbes and pollen were experimentally manipulated.Bee fitness metrics indicated that microbial and pollen sourcing both had significant impacts on larval performance, and the effect sizes of each were similar. Moreover, the effects of conspecific‐sourced microbes and conspecific‐sourced pollen were strongly positive, while that of heterospecific‐sourced microbes and heterospecific‐sourced pollen, strongly negative.Our findings imply that not only is the presence of exosymbionts critical for both specialist and generalist solitary bees, but more notably, that the composition of the specific microbial community within larval pollen provisions may be as critical for bee development as the composition of the pollen itself.

The agnostid trilobite assemblage from San José de Gracia, Sonora, México, and the implications of phylogenetics in stratigraphic calibration

ABSTRACT The outcrop of San José de Gracia yields an agnostid assemblage that has been assigned to the middle Cambrian. Although agnostid trilobites are important biostratigraphic markers, the mosaic evolution of this group makes species-level determination difficult, and thus biostratigraphic correlations become cumbersome. The assemblage is described here using morphogroups of the ‘peronopsid’-like agnostids, and specimen-level phylogenetics is used to assess the statistical significance of assigning different ages to the final topology (stratigraphic fit), including the morphogroups as operational taxonomic units. A proper phylogenetic analysis requires considering modularity and ontogenetic development in agnostids, but the overall topology can respond to different stratigraphic calibrations even if the individual relationships are not resolved. Using the distribution of several stratigraphic fit metrics, namely SCI, GER and MSM*, we find that the biozone Plagiura-Poliella (509–598.4 Mya) is the one that offers the best stratigraphic fit for the outcrop of San José de Gracia, and it is consistent with the current biostratigraphic framework that has been built for the middle Cambrian of Sonora.

An adaptive prognostics method based on a new health index via data fusion and diffusion process

Remaining useful life prediction (RUL) is critical in predictive maintenance for components or systems prone to deterioration. However, direct RUL prediction methods have difficulties tracking health trends and realizing online prognostics. To address this issue, this paper proposes a novel health index (HI) based adaptive prognostics method by leveraging the advantages of both data fusion to handle multi-dimensional data and the adaptive extended Kalman filter (AEKF) algorithm for parameter identification in the diffusion process. A fitness metric is proposed for feature selection, and then the composite HI sequence is constructed via data fusion using the genetic algorithm. Furthermore, a diffusion process model is built to characterize HI degradation while considering multi-source uncertainties. Model parameters are then updated using the fitting-based AEKF method. Finally, the proposed method is validated on a real-world dataset of solid-state drives in data centers, and prediction results and comparative studies verify its superiority.

BIMFlexi-VR: A Virtual Reality Framework for Early-Stage Collaboration in Flexible Industrial Building Design

Integrated industrial building design is an interdisciplinary task, in which planning of flexible building structures requires effective communication and collaboration between all stakeholders already in early design stage. This paper presents BIMFlexi-VR, a collaborative framework which implements a real-time bidirectional link between a parametric modelling component created in Grasshopper for Rhinoceros that performs optimized structural calculations of an industrial building, and an immersive Virtual Reality environment in which the automatically calculated building is visualized. Users of BIMFlexi-VR are able to change parameters defining the outcome of the structural calculation directly inside the virtual environment and see the modified building design together with the associated fitness metrics in a matter of seconds. Providing an efficient and intuitive platform for early exploration of industrial building designs, BIMFlexi-VR enables collaborative decision making and facilitates the creation of more efficient and sustainable industrial constructions.

Predictive Fit Metrics for Item Response Models.

The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, "predictive fit," based on the model's ability to predict new data is advocated. The authors define two prediction tasks: "missing responses prediction"-where the goal is to predict an in-sample person's response to an in-sample item-and "missing persons prediction"-where the goal is to predict an out-of-sample person's string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a "model's predictions on average?"). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike's information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.

Health and Fitness of Postmenopausal Women in Urban versus Rural Gurugram: A Cross-Sectional Study.

Throughout that process of menopause, a slew of health and fitness problems arise, all of which have a major effect on a woman's standard of living. Health-related physical fitness has been described as an individual's cardiac fitness (aerobic capacity), musculoskeletal fitness, and body composition. To investigate and compare the health and fitness of postmenopausal women in rural and urban Gurugram. The postmenopausal women of Gurugram, both urban (n = 175) and rural (n = 175), who attended the outpatient department of SGT Hospital in the urban area and a house-to-house survey in the rural area, were the subjects of a cross-sectional survey that was carried out using interviews and a pretested semi-structured questionnaire. Levels of physical activity (PA) were assessed using the International PA Questionnaire (short form). The evaluation of body composition, which was the following step, included determining one's body mass index, waist circumference, and waist-hip ratio. Six-minute Walk Distance Test was used to assess cardiopulmonary fitness. Through chair squat tests, sit-and-reach tests, and grip tests, researchers were able to measure the lower limb strength, flexibility, and upper limb strength of participants. The mean age of subjects was 53.61 ± 5.08 years. Most commonly reported health problems were hypertension (31.3%), hyperlipidemia (21.2%), and diabetes (13.4%). Odds of urban women developing hypertension, hyperlipidemia, and myocardial infarction (MI) were found to be 0.61, 0.42, and 0.96 times higher than rural women. There was a statistically significant difference for the squat test, grip test, body composition parameters, and aerobic capacity; however, no statistically significant difference was seen for sit-and-reach test (P > 0.05). The current research shows that postmenopausal women living in metropolitan areas may face higher health risks since they are more prone to develop hypertension, hyperlipidemia, and MI. Furthermore, all fitness metrics - aside from flexibility - were higher for rural women. The results of the current study highlight the urgent need for health promotion initiatives to enhance the health and fitness of urban postmenopausal women.

Relationship between aerobic fitness and metabolic power metrics in elite male soccer players.

The aim was to assess the relationship between aerobic fitness and metabolic power metrics in elite male soccer players, and the possible differences that playing positions might impose during match play over new metabolic power metrics. Sixty-two elite professional male soccer players (13 central backs, 13 side backs, 22 midfielders, and 14 forwards) took part in the study. Players were monitored during eleven months of full training (including pre-season and in-season) and over all official matches (Serie A matches, Italy Cup matches). Aerobic fitness tests were conducted one week after the start of the preseason, and 8, 24 and 36 weeks after the beginning of the Championship. Players’ aerobic fitness and metabolic power metrics were considered as the mean of all seasonal testing and of pooling data of 38 championship matches and 3 or 6 Italy Cup matches for all the calculations respectively. The velocity at 4 mmol·L-1 (VL4) was significantly related to metabolic power metrics match variables with correlation ranging from trivial to very large (r = 0.32 to r = 0.89). Receiver-operating-characteristic (ROC) analysis showed that speed at VL4 was sensitive in detecting high metabolic power distance (HMPD) changes in all but central back players as revealed by area under the curve (central back .78, 95%CI .47 to .95; full back .93, 95%CI .64 to 0.99; midfielder .88, 95%CI .67 to 0.98; forward .90, 95%CI .62 to 0.99). This study’s findings provide further evidence for the ecological validity of aerobic fitness in elite male soccer players. Players having a HMPD cut-off equal to or higher than > 1450 m for central backs, > 1990 m for full backs, > 2170 m for midfielders and > 1670 m for forwards may be considered as possessing superior aerobic fitness status. In light of this study’s findings, the VL4 test may be considered a valid test to evaluate meaningful information for direct generic aerobic training in soccer players.

Retrospective comparison of motor and somatosensory MEG mapping—Considerations for better clinical applications

While magnetoencephalography (MEG) has proven to be a valuable and reliable tool for presurgical functional mapping of eloquent cortices for at least two decades, widespread use of this technique by clinicians has remained elusive. This modest application may be attributable, at least in part, to misunderstandings regarding the success rate of such mapping procedures, as well as the primary sources contributing to mapping failures. To address this, we conducted a retrospective comparison of sensorimotor functional mapping success rates in 141 patients with epilepsy and 75 tumor patients from the Center for MEG in Omaha, NE. Neurosurgical candidates either completed motor mapping (i.e., finger tapping paradigm), somatosensory mapping (i.e., peripheral stimulation paradigm), or both motor and somatosensory protocols during MEG. All MEG data underwent subsequent time-domain averaging and source localization of left and right primary motor (M1) and somatosensory (S1) cortices was conducted using a single equivalent dipole model. Successful mapping was determined based on dipole goodness of fit metrics ∼ 95%, as well as an accurate and conceivable spatial correspondence to precentral and postcentral gyri for M1 and S1, respectively. Our results suggest that mapping M1 in epilepsy and tumor patients was on average 94.5% successful, when patients only completed motor mapping protocols. In contrast, mapping S1 was successful 45–100% of the time in these patient groups when they only completed somatosensory mapping paradigms. Importantly, Z-tests for independent proportions revealed that the percentage of successful S1 mappings significantly increased to ∼ 94% in epilepsy patients who completed both motor/somatosensory mapping protocols during MEG. Together, these data suggest that ordering more comprehensive mapping procedures (e.g., both motor and somatosensory protocols for a collective sensorimotor network) may substantially increase the accuracy of presurgical functional mapping by providing more extensive data from which to base interpretations. Moreover, clinicians and magnetoencephalographers should be considerate of the major contributors to mapping failures (i.e., low SNR, excessive motion and magnetic artifacts) in order to further increase the percentage of cases achieving successful mapping of eloquent cortices.