Search Results (401)

Background: Estimating race times for open-water swimming based on pool swimming times could be useful for talent identification and training optimisation. We aimed to compare the swimming speeds of the world’s top and other swimmers in the 2023 Aquatics Championship men’s 10 km OWS race. Methods: Sixty-five swimmers were divided into four groups: G1 (1st–10th positions), G2 (11st–30th positions), G3 (31st–47th positions), and G4 (48th–65th positions). Swimming speed, stroke frequency (SF), and stroke length (SL) for each lap (laps 1–6) were recorded. Critical speed (CS) was calculated from each participant’s personal best times in the 400, 800, and 1500 m freestyle events in the pool. Swimming speed against CS was calculated (%CS). Results: The top performance group (G1) maintained their swimming speed from beginning (lap 1, 1.53 m/s) to end (lap 6, 1.50 m/s), at 92.7 ± 1.9% of CS, characterised by longer SL (1.26 m) and lower SF (72.86 rpm). G3 and G4 were unable to maintain their swimming speed, which decreased from G3: 97.64 ± 1.62% and G4: 96.10 ± 1.96% of CS at lap 1 to G3: 88.39 ± 3.78% and G4: 85.13 ± 5.04% at lap 6. This reduction in swimming speed is consistent with the increased reliance on anaerobic metabolism reported in previous studies under similar conditions. Conclusions: Race pacing for maintaining speeds of 92%CS throughout the race could be an important resilient index in open-water swimming. %CS might be a useful index for estimating the athletic performance level in open-water swimming. Full article

Background: Swimming coaches search for the most efficient training approach and stimuli for swimmers’ improvement. High-intensity interval training (HIIT) is a well-established training approach used by coaches to accelerate swimmers’ improvement. A HIIT variation, which has lately been discussed by many coaches about its possible effectiveness on performance, is Ultra Short Race Pace Training (USRPT). The present case study aimed to examine the effect of a faster-than-race pace test set (FRPtS) on the performance of a middle-distance (MD) swimmer at the freestyle events. Methods: This case study included a 21-year-old national-level MD swimmer with 16 years of swimming experience. The swimmer followed 11 weeks of FRPtS sets in a 17-week training intervention. The FRPtS sets were repeated two to three times per week, the volume ranged from 200 m to 1200 m, and the distances that were used were 25 m, 50 m, and 100 m at a faster pace than the 400 m. Descriptive statistics were implemented, recording the average with standard deviation (number in parentheses), the sum, and the percentages (%). Results: According to the results, the swimmer improved his personal best (PB) and season best (SB) performance in the events of 200 m and 400 m freestyle. Specifically, the improvement from his PB performance was 2.9% (−3.49 s) and 1.0% (−2.55 s), whereas in his SB performance it was 2.9% (−3.53 s) and 4.4% (−11.43 s) for the 200 and 400 m freestyle, respectively. Conclusions: Concluding, FRPtS is assumed to have beneficial effects on the swimming performance of MD events. However, further crossover or parallel studies on different swimming events with more participants and biomarkers must be conducted to clarify the effects of that kind of training on swimming performance. Full article

Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in the literature. These approaches can generally be categorized into three main techniques: pre-processing (modifying data before training), in-processing (incorporating fairness constraints during training), and post-processing (adjusting outputs after model training). Beyond these, an increasingly explored avenue is the direct modification of existing algorithms, aiming to embed fairness constraints into their design while preserving or even enhancing predictive performance. This paper presents a comprehensive survey of classical machine learning models that have been modified or enhanced to improve fairness concerning sensitive attributes (e.g., gender, race). We analyze these adaptations in terms of their methodological adjustments, impact on algorithmic bias and ability to maintain predictive performance comparable to the original models. Full article

Despite advances in cancer care, disparities persist. The collection of the social determinants of health (SDOH) is fundamental to addressing disparities. However, SDOH are inconsistently collected in many regions of the world. This two-phase multiple methods study examined patient and community perspectives regarding SDOH data collection in Canada. In phase 1, a survey was administered to patients at a cancer centre (n = 549) to assess perspectives on an SDOH data collection tool. In phase 2, broader perspectives were sought through a community consultation with patient partners experiencing structural inequality (n = 15). Most participants were comfortable with SDOH data collection. Of survey respondents, 95% were comfortable with the collection of language, birthplace, sex, gender, education, and disability, and 82% to 94% were comfortable with SES, sexual orientation, social support, and race/ethnicity. Discomfort levels did not differ across subgroups, except women were more uncomfortable disclosing SES (OR: 2.00; 95%CI: 1.26, 3.19). Most (71%) preferred face-to-face data collection with a healthcare professional and only half were comfortable with storage of SDOH in electronic health records. Open-ended survey responses (n = 1533) and the community consultation revealed concerns about privacy, discrimination, relevance to care, and data accuracy. SDOH data collection efforts should include a clear rationale for patients, training for providers, strong data privacy and security measures, and actionable strategies to address needs. Full article

Background Objectives: Parental anxiety and depression demonstrate bidirectional connections with child developmental outcomes (e.g., disruptive behavior). Directly targeting child development through behavioral parent training (BPT) has potential for reversing this cycle. Parent–Child Interaction Therapy (PCIT), a BPT with robust research evidence for decreasing child disruptive behaviors, has demonstrated promise in also decreasing caregiver anxiety and depression. However, the mechanisms that explain this relationship are less understood. Methods: The current study examined whether caregivers (N = 840) completing time-limited PCIT experienced significant reductions in depression and anxiety symptoms and improvements in child disruptive behaviors at each time point. Generalized estimate equation analyses assessed whether caregiver anxiety and depression moderated changes in child disruptive behavior. Mediation analyses explored the extent that changes in caregiver–child interactions over time explained changes in family outcomes. Results: Child disruptive behavior and caregiver depression and anxiety symptoms improved significantly at each time point of PCIT. Change in child behavioral outcomes was significantly moderated by caregiver race. Caregivers with higher anxiety reported fewer improvements in child disruptive behavior compared to other caregivers. Changes in caregiver anxiety and depression over the course of treatment were partially mediated by improvement in caregiver–child interaction skills. Changes in child disruptive behavior were not mediated by improvement in caregiver–child interaction skills. Conclusions: Results demonstrate that time-limited PCIT could significantly improve caregiver anxiety and depression, and some PCIT-taught parenting skills are direct drivers of this process. Further research is needed to understand other mechanisms underlying the relationship between PCIT and improved family outcomes. Full article

Background: Optimal nutrition and training regimens are essential for athletes to maximize performance and recovery. Probiotic supplementation, through the modulation of the gut microbiota, and omega-3 fatty acids, known for their anti-inflammatory properties, may enhance physiological adaptations when combined with targeted training. This study evaluated the effects of probiotics and omega-3 supplementation, alongside ultra-short race pace training (USRPT), on performance metrics in competitive sprint swimmers. Methods: In this double-blind, placebo-controlled study, 60 male sprint swimmers (age: 19.2 ± 3.6 years; height: 182.2 ± 5.2 cm; weight: 81.6 ± 4.4 kg) with a minimum of five years of training experience, were randomly assigned to six groups (n = 10 per group): (1) Control (CON), (2) USRPT only, (3) Placebo + USRPT (PLA + USRPT), (4) Probiotics + USRPT (PRO + USRPT), (5) Omega-3 + USRPT (OMEGA + USRPT), and (6) Probiotics + Omega-3 + USRPT (PRO + OMEGA + USRPT). Over the eight-week intervention, the participants in PRO + USRPT consumed one multi-strain probiotic capsule daily (4.5 × 10¹¹ CFU) and a placebo capsule. Those in OMEGA + USRPT ingested 1000 mg of fish oil after lunch (500 mg EPA and 180 mg DHA per capsule) paired with a placebo capsule. The combined supplementation group (PRO + OMEGA + USRPT) received both probiotic and omega-3 capsules. The PLA + USRPT group consumed two starch capsules daily. The USRPT protocol was implemented across all the training groups, where the swimmers performed 17 sets of 25 m and 12.5 m sprints based on weekly recorded race times. Performance assessments included pre- and post-test measurements of sprint times (50 m and 100 m freestyle), vertical jump tests (both in water and on dry land), and other strength and endurance metrics (reaction time, agility T-test, sprint index, fatigue index, and velocity). Results: The combined intervention of probiotics and omega-3 with USRPT produced the greatest improvements in performance. The PRO + OMEGA + USRPT group reduced 50 m freestyle time by 1.92% (p = 0.002, pEta² = 0.286) and 100 m freestyle time by 2.48% (p = 0.041, pEta² = 0.229), demonstrating significant Time × Group interactions consistent with a synergistic effect. Additionally, the sprint index improved (pEta² = 0.139, p = 0.013) and reaction time decreased (pEta² = 0.241, p = 0.009) in the combined group, indicating enhanced anaerobic capacity and neuromuscular responsiveness compared to single interventions. Conclusions: This study suggests that combining probiotics and omega-3 supplementation with USRPT leads to synergistic improvements in sprint swimming performance, enhancing anaerobic power and recovery beyond what is achieved with individual interventions. This integrated approach may provide a practical strategy for competitive swimmers seeking to optimize their performance. Future studies should incorporate mechanistic markers, longer intervention durations, and diverse athlete populations to clarify further and extend these findings. Full article

The off-the-track Thoroughbred’s (OTTTB’s) suitability for equestrian retraining and rehoming should always be subject to behavioral considerations. Certain attributes may be advantageous to a horse’s racing performance but unfavorable to their prospects off the track. It is important to gain a non-biased understanding of how Thoroughbreds (TBs) in equestrian disciplines compare with other horses behaviorally, to minimize risks of poor welfare and safety outcomes. The current study used owner-reported information (n = 1633) from the Equine Behavior Assessment and Research Questionnaire (E-BARQ) global database to compare the behaviors of OTTTBs with those of other ridden horses. Boldness, compliance, rideability, trainability, and responsiveness to acceleration and deceleration signals were evaluated in the context of 27 E-BARQ items, as determined by exploratory factor analysis (EFA). In this study, OTTTBs demonstrated more boldness (t = 3.793; p < 0.001) and lower compliance and responsiveness to deceleration signals (t = 3.448; p < 0.001) than non-OTTTBs. Trainability, rideability, and responsiveness to acceleration signals did not differ significantly between OTTTBs and non-OTTTBs. These findings provide direction for future research into causal factors and improvement opportunities regarding the training and management of Thoroughbreds, on- and off-the-track. Full article

Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. These AI models were analyzed to detect biases they may carry with respect to three variables chosen to represent sensitive populations: gender, race, and first-generation college students. We then describe a method for bias mitigation that uses a combination of machine learning and user interaction. (4) Results and Discussion: We use three scenarios to demonstrate that this interactive bias mitigation approach can successfully decrease the biases towards sensitive populations. (5) Conclusion: Our approach allows the user to examine a model and then iteratively and incrementally adjust bias and fairness metrics to change the training dataset and generate a modified AI model that is more fair, according to the user’s own determination of fairness. Full article

Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article

Biomedical research training initiatives need rigorous evaluation to achieve national goals of supporting a robust workforce in the biomedical sciences. Higher science self-efficacy is associated with the likelihood of pursuing a science-related research career, but we know little about the psychometric properties of this construct. In this study, we report on a comprehensive validation study of the Science Self-Efficacy Scale using a robust sample of 10,029 undergraduates enrolled across 11 higher education institutions that were part of a biomedical training initiative funded by the National Institutes of Health in the United States. We found the scale to be unidimensional with an Omega hierarchical (ω_h) reliability coefficient of 0.86 and a marginal reliability of 0.91. Within the item response theory framework, we did not detect variation in item parameters across undergraduates’ race/ethnicity; however, one item had parameters that varied across gender identity. We determined that the Science Self-Efficacy Scale can be employed across undergraduates enrolled in science, and researchers can use the scale across a diverse group of students. Implications include ensuring that the scale functions consistently across diverse populations, enhancing the validity of conclusions that can be drawn from survey data analysis. Validating this construct with item response theory models strengthens its use for future research. Full article

The commercial sexual exploitation (CSE) of children is a distinct form of sexual trauma, resulting in immediate mental health issues. Few studies explore associations between family-level factors in adolescence and health outcomes in adulthood among this population. Utilizing a nationally representative dataset, we explored differences and associations between mental health outcomes and domains of the Family Health Development framework among respondents who self-reported CSE (N = 502; mean age = 15.03, SD = 1.34; 67% male; 50% white). We conducted ordinary least squares and binary logistic regressions using a hierarchical approach to analyze the CES-D depression scale, anxious personality scale, and self-reported diagnoses of depression and anxiety/panic disorder. At Wave IV, when participants were aged 24–32, 20% of participants reported ever having a diagnosis of depression, and 12% reported ever having an anxiety/panic disorder diagnosis. Family receipt of public assistance during adolescence significantly predicted depression and anxiety symptoms in adulthood, highlighting associations between family structure and mental health. Gender and race significantly predicted anxiety symptoms and having a diagnosis of depression and anxiety/panic disorder. Findings underscore the need for targeted training and comprehensive health screenings for providers to better understand and address the long-term mental health needs of CSE-impacted groups. Full article

Ultramarathon running elicits a profound inflammatory response, characterized by significant increases in interleukin-6 (IL-6) and C-reactive protein (CRP), with comparatively modest changes in tumor necrosis factor-alpha (TNF-α). We reviewed approximately 80 field studies of ultramarathon events (distances >42.2 km) that measured IL-6, CRP, and TNF-α before and after races. IL-6 typically spiked immediately post-race—often rising dozens or even thousands of times above baseline—then rapidly declined, usually returning to near baseline within 24–48 h. CRP, an acute-phase protein, exhibited a slower, sustained elevation, peaking 24–72 h after race completion and remaining above baseline for 2–3 days before gradually returning to normal. TNF-α responses were variable: some studies reported small but significant post-race increases (roughly 1.2–1.7-fold above baseline), while others found no significant change in circulating TNF-α despite the extreme effort. Longer race durations and distances generally correlated with higher peak IL-6 and CRP levels. Experienced ultramarathon runners tended to exhibit attenuated inflammatory responses compared with less-trained individuals, and anti-inflammatory cytokines (e.g., IL-10) increased in tandem with IL-6 in well-trained athletes, helping to mitigate TNF-α elevations. In total, 28 studies were included in the final synthesis, and their quality was assessed using the Newcastle–Ottawa Scale. Visual synthesis tools, including a PRISMA flowchart and time course plots, are provided to enhance the narrative’s interpretability. In summary, ultramarathon running elicits a robust systemic inflammatory response with distinct temporal patterns for IL-6, CRP, and TNF-α. These findings have important implications for athlete recovery, monitoring, and understanding the physiological limits of the inflammatory response to extreme endurance stress. Full article

Background: Gliomas represent the most prevalent and aggressive primary brain tumors, requiring precise classification to guide treatment strategies and improve patient outcomes. Purpose: This study aimed to develop and evaluate a machine learning-driven approach for glioma classification by identifying the most relevant genetic and clinical biomarkers while demonstrating clinical utility. Methods: A dataset from The Cancer Genome Atlas (TCGA) containing 23 features was analyzed using an integrative approach combining Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), and SHapley Additive exPlanations (SHAP) for feature selection. The refined feature set was used to train four machine learning models: Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. Comprehensive evaluation included class distribution analysis, calibration assessment, and decision curve analysis. Results: The feature selection approach identified 13 key predictors, including IDH1, TP53, ATRX, PTEN, NF1, EGFR, NOTCH1, PIK3R1, MUC16, CIC mutations, along with Age at Diagnosis and race. XGBoost achieved the highest AUC (0.93), while Logistic Regression recorded the highest testing accuracy (88.09%). Class distribution analysis revealed excellent GBM detection (Average Precision 0.840–0.880) with minimal false negatives (5–7 cases). Calibration analysis demonstrated reliable probability estimates (Brier scores 0.103–0.124), and decision curve analysis confirmed substantial clinical utility with net benefit values of 0.36–0.39 across clinically relevant thresholds. Conclusions: The integration of feature selection techniques with machine learning models enhances diagnostic precision, interpretability, and clinical utility in glioma classification, providing a clinically ready framework that bridges computational predictions with evidence-based medical decision-making. Full article

Autonomous race driving has emerged as a challenging domain for reinforcement learning (RL) applications, requiring high-speed control while adhering to strict safety constraints. Existing RL-based racing methods often struggle to balance performance and safety, with limited adaptability in dynamic racing scenarios with multiple opponent vehicles. The high-dimensional state space and strict safety constraints pose significant challenges for efficient learning. To address these challenges, this paper proposes an integrated RL framework that combines three novel components: (1) a state mapping mechanism that dynamically transforms raw track observations into a consistent representation space; (2) an action mapping technique that rigorously enforces physical traction constraints; and (3) a safe exploration guidance method that combines conservative controllers with RL policies, significantly reducing off-track incidents during training. Extensive experiments conducted in our simulation environment with four test tracks demonstrate the effectiveness of our approach. In time trial scenarios, our method improves lap times by 12–26% and increases the training completion rate from 33.1% to 78.7%. In competitive racing, it achieves a 46–51% higher average speed compared to baseline methods. These results validate the framework’s ability to achieve both high performance and safety in autonomous racing tasks. Full article

The individual marathon optimal pacing sparring the runner to hit the “wall” after 2 h of running remain unclear. In the current study we examined to what extent Deep neural Network contributes to identify the individual optimal pacing training a Variational Auto Encoder (VAE) with a small dataset of nine runners. This last one has been constructed from an original one that contains the values of multiple physiological variables for 10 different runners during a marathon. We plot the Lyapunov exponent/Time graph on these variables for each runner showing that the marathon wall could be anticipated. The pacing strategy that this innovative technique sheds light on is to predict and delay the moment when the runner empties his reserves and ’hits the wall’ while considering the individual physical capabilities of each athlete. Our data suggest that given that a further increase of marathon runner using a cardio-GPS could benefit of their pacing run for optimizing their performance if AI would be used for learning how to self-pace his marathon race for avoiding hitting the wall. Full article