Search Results (341)

This paper propose a practical field-based screening protocol for evaluating the risk of hamstring injury. This is done by discerning the most important factors that better explain the underlying structure among various measurements. Following a cross-sectional study design, ninety-nine professional and semi-professional football players were assessed at the team’s facilities during the preseason period. The collected data included aspects of demographic characteristics; previous injuries; athlete sense of burnout (Athlete Burnout Questionnaire (ABQ)); hamstring (HS) flexibility (passive single leg raise test); isometric hamstring strength (make and brake test); isometric quadriceps strength; single-leg triple hop for distance; endurance of the core muscles (prone bridge, side bridge and Biering–Sørensen tests); and hamstring strength endurance (single leg hamstring bridge test). Subsequently, Exploratory Factor Analysis was performed. Following a summarized dimension reduction process, the twenty-three assessment variables were grouped into a parsimonious model of six main risk factors. Specifically, the resulting model explains 55.7% of the total variance, comprising HS and core endurance (20.2% of the variance), HS strength (12.8%), previous injuries (8.9%), ABQ (5.8%), lower limb strength (4.1%), and strength limb symmetry (3.8%). The proposed model provides a practical protocol, facilitating sports scientists in evaluating the risk for HI in the highly complex reality of field-based situations. Full article

The allostatic load index (AL_index) measures the cumulative physiological burden on the body due to stress. This prospective cohort study examined the relationships between certain molecular biomarkers, physical variables, and psychometric variables during deload and overload microcycles to contribute to developing an AL_index in professional team-sport athletes. Twelve elite male basketball players (18.3 [0.9] years; 77.2 [5.7] kg; 185 [9.0] cm) were monitored during two microcycles (deload and overload). Blood creatine kinase (CK) and urea levels, countermovement jump (CMJ), session-RPE (RPE × session duration [min], its exponentially weighted moving average [EWMA]), and a cumulative wellness score (sleep, stress, fatigue, muscle soreness, and mood) were assessed at different time points. Bayesian and robust statistics (Cohen’s ξ) were employed. CK rose from 222 U/L (deload) to 439 U/L (overload; +98%, large effect ξ = 0.65), while session-RPE load more than doubled (270 [269] AU to 733 [406] AU, ξ > 0.8). No difference was found in urea and wellness scores (cumulative or other components). CK levels showed moderate positive correlations with both EWMA of session-RPE (ρ = 0.346, p = 0.002) and reduced sleep quality (ρ = 0.25, p = 0.018). Bayesian modeling identified the EWMA of session-RPE as the strongest predictor of jump-defined fatigue (β = 0.012, 95% HDI [0.004, 0.021]), while CK demonstrated a small negative association (β = −0.009, HDI [−0.016, −0.001]). Finally, a principal component analysis (PCA) revealed that CK and the EWMA of session-RPE were robust indicators of physiological stress. A parsimonious index based on PCA loadings ([0.823 × CK] + [0.652 × EWMA of session-RPE]) demonstrated strong discriminative validity between microcycle phases (overload: 515, 95% HDI [442, 587] versus deload: 250, 95% HDI [218, 283], BF₁₀ > 100,000). CK and session-RPE may serve as sensitive biomarkers for inclusion in the AL_index for team sport athletes. Full article

In recent years, the passenger flow volume of conventional transit in major cities has declined steadily. Ground public transit often suffers from congestion during rush hours caused by frequent stops (e.g., conventional fixed-route buses) or excessively high operating costs (e.g., demand-responsive transit). While rail transit offers reliable service with dedicated right-of-way, its high capital and operational costs pose challenges for integrated planning with other transit modes. The joint design of rail, conventional buses, and DRT remains underexplored. To bridge this gap, this paper proposes and analyses a new hybrid transit system that integrates conventional transit service with demand-adaptive transit (DAT) to feed urban rail transit (the system hence called hybrid microtransit system). The main task is to optimally design the hybrid microtransit system to allocate resources efficiently across different modes. Both the conventional transit and DAT connect passengers from their origin/destination to the rail transit stations. Travelers can choose one of the services to access urban rail transit, or directly walk. Accordingly, we divide the service area into three parts and compute the user costs to access rail transit by conventional transit and DAT. The optimal design problem is hence formulated as a mixed integer program by minimizing the total system cost, which includes both the user and agency (operating) costs. Numerical experiment results demonstrate that the hybrid microtransit system performs better than the system that only has conventional transit to feed under all demand levels, achieving up to a 7% reduction in total system cost. These may provide some evidence to resolve the “first-mile” challenges of rail transit in megacities by designing better conventional transit and DAT. Full article

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R² of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Full article

Introduction: Tunnelled central venous catheters are increasingly used for vascular access in patients undergoing haemodialysis for chronic kidney disease. However, catheter dysfunction is a frequent and clinically relevant complication, impairing treatment efficacy and increasing morbidity. This study aimed to develop and internally validate predictive models for catheter dysfunction using routinely collected haemodialysis session data, with the goal of facilitating early detection and proactive clinical decision-making. Methods: We conducted a diagnostic, retrospective, cross-sectional, and analytical study based on 60,230 HD sessions recorded in 2021 across dialysis centres in Spain. A total of 743 patients with functioning catheter were included. Clinical, technical, and haemodynamic variables were analysed to identify those associated with catheter dysfunction in the subsequent session. Five logistic regression models were built; the dataset was split into training (two-thirds) and internal validation (one-third) cohorts. Model performance was evaluated using the area under the ROC curve (AUC) and the Hosmer–Lemeshow test. Results: Significant predictors included venous pressure, effective blood flow, catheter location, convective techniques, and line reversal. The bootstrapping model, selected for internal validation due to its parsimony and performance, achieved an AUC of 0.844 (95% CI: 0.824–0.863), with a sensitivity of 81.6% and a specificity of 70.9% at a 0.019 threshold. Conclusions: The bootstrapping-based predictive model is a valuable clinical tool for anticipating catheter dysfunction using routine haemodialysis data. Its implementation may enable earlier intervention, reduce reliance on reactive treatments, and enhance vascular access management in haemodialysis patients. Full article

Determining the transmission routes of pathogens in indoor environments is challenging, with most studies limited to specific case analyses and pilot experiments. When pathogens are instantaneously released by a patient in an indoor environment, the peak infection risk may not occur immediately but may instead appear at a specific moment during the pathogen’s spread. We developed a concise model to describe the temporal crest of infection risk. The model incorporates the transmission and degradation characteristics of aerosols and surface particles to predict infection risks via air and surface routes. Only four real-world outbreaks met the criteria for validating this phenomenon. Based on the available data, norovirus is likely to transmit primarily via surface touch (i.e., the fomite route). In contrast, crests of infection risk were not observed in outbreaks of respiratory diseases (e.g., SARS-CoV-2), suggesting a minimal probability of surface transmission in such cases. The new model can serve as a preliminary indicator for identifying different indoor pathogen transmission routes (e.g., food, air, or fomite). Further analyses of pathogens’ transmission routes require additional evidence. Full article

The average body size (fork length) of juvenile salmonids in small streams varies across landscapes and can be influenced by stream temperature, density dependence, catchment size, and physical habitat. In this study, we compared sets of 16 mixed-effects linear models representing these four potentially influencing indicators for three species/age classes to assess the relative importance of their influences on body size. The global model containing all indicators was the most parsimonious model for juvenile coho salmon (Oncorhynchus kisutch; R²m = 0.4581, R²c = 0.5859), age-0 trout (R²m = 0.4117, R²c = 0.5968), and age-1 or older coastal cutthroat trout (O. clarkii; R²m = 0.2407, R²c = 0.5188). Contrary to expectations, salmonid density, catchment size, and physical habitat metrics contributed more to the top models for both coho salmon and age-1 or older cutthroat trout than stream temperature metrics. However, a stream temperature metric, accumulated degree days, had the only significant relationship (positive) of the indicators with body size in age-0 trout (95% CI 1.58 to 23.04). Our analysis identifies complex relationships between salmonid body size and environmental influences, such as the importance of physical habitat such as pool size and boulders. However, management or restoration actions aimed at improving or preventing anticipated declines in physical habitat such as adding instream wood or actions that may lead to increasing pool area have potential to ensure a natural range of salmonid body sizes across watersheds. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Expansive clays damage the foundations, slabs, and utilities of low- and mid-rise buildings, threatening daily operations and incurring billions of dollars in costs globally. This study pioneers a domain-informed machine learning framework, coupled with a collinearity-aware feature selection strategy, to predict soil swell potential solely from routine index properties. Following hard-limit filtering and Unified Soil Classification System (USCS) screening, 291 valid samples were extracted from a public dataset of 395 cases. A random forest benchmark model was developed using five correlated features, and a multicollinearity analysis, as indicated by the variance inflation factor, revealed exact linear dependence among the Atterberg limits. A parsimonious two-variable model, based solely on plasticity index (PI) and clay fraction (C), was retained. On an 80:20 stratified hold-out set, this simplified model reduced root mean square error (RMSE) from 9.0% to 6.8% and maximum residuals from 42% to 16%. Bootstrap analysis confirmed a median RMSE of 7.5% with stable 95% prediction intervals. Shapley Additive Explanations (SHAP) analysis revealed that PI accounted for approximately 75% of the model’s influence, highlighting the critical swell surge beyond PI ≈ 55%. This work introduces a rule-based cleaning pipeline and collinearity-aware feature selection to derive a robust, two-variable model balancing accuracy and interpretability, a lightweight, interpretable tool for foundation design, GIS zoning, and BIM workflows. Full article

Background: Clostridioides difficile infection (CDI) is a major cause of healthcare-associated morbidity and mortality. Understanding the predictors of in-hospital mortality and recurrence of CDI is key for improving outcomes. This study combined traditional statistical methods and machine learning approaches to identify risk factors for these outcomes. Methods: We conducted a nationwide, retrospective study using the Spanish Minimum Basic Data Set at Hospitalization, analyzing 34,557 admissions with CDI from 2020 to 2022. Logistic regression combined with the least absolute shrinkage and selection operator (LASSO) was used to identify the most relevant predictors. Survival analyses using Cox regression and LASSO were also performed to assess time-to-mortality predictors. Results: Mortality and recurrence rates increased over the study period, reflecting the increasing burden of CDI. LASSO identified a parsimonious subset of predictors while maintaining predictive accuracy (area under the curve: 0.71). Older age (OR = 2.10, 95%CI: 1.98–2.22), Charlson Comorbidity Index ≥ 2 (OR = 1.42, 95%CI: 1.33–1.52), admission to the intensive care unit (OR = 3.09, 95%CI: 2.88–3.32), congestive heart failure (OR = 1.71, 95%CI: 1.61–1.82), malignancies (OR = 1.76, 95%CI: 1.66–1.87), and dementia (OR = 1.36, 95%CI: 1.25–1.48) were strongly associated with all-cause hospital mortality. For recurrence, age ≥ 75 years (OR = 1.19, 95%CI: 1.12–1.27), chronic kidney disease (OR = 1.15, 95%CI: 1.08–1.23), and chronic liver disease (OR = 1.43, 95%CI: 1.16–1.74) were the strongest predictors, while malignancy appeared protective, likely due to survivor bias. Conclusions: Our study provides a robust framework for predicting CDI outcomes. The integration of traditional statistical methods and machine learning applied to a large dataset may improve the reliability of the results. Our findings highlight the need for targeted interventions in high-risk populations and emphasize the potential utility of machine learning in risk stratification. Future studies should validate these models in external cohorts and explore survivor bias in malignancy-associated outcomes. Full article

The Values in Action Inventory (VIA) is one of the most widely used measures for assessing character strengths. While the original version includes 240 items, shorter versions such as the VIA-72 have been developed to enhance its applicability. Psychometric studies of the VIA-72 in Spanish are still limited. This study examined the factorial structure, reliability, and convergent validity of the Spanish VIA-72 in a sample of 470 adults. Three alternative models—comprising three, five, and six factors—were tested using confirmatory factor analysis. All models showed acceptable fit, but the three-factor solution—Caring, Self-Control, and Inquisitiveness—showed the best performance in terms of parsimony, fit indices, and conceptual clarity. Internal consistency for the three-factor model was high across dimensions and comparable to previous studies. Convergent validity was supported through meaningful correlations with personality traits, particularly with conscientiousness. The factorial structure largely replicated findings obtained with both VIA-72 and VIA-240. These results support the Spanish VIA-72 as a reliable and valid instrument for assessing character strengths, offering a concise, theory-based alternative for Spanish-speaking populations. Full article

The challenge of detecting nuanced, context-dependent offensive language highlights the need for Large Language Model (LLM) adaptation strategies that can effectively address inherent data and task asymmetries. Standard Parameter-Efficient Finetuning (PEFT) methods like Low-Rank Adaptation (LoRA), while efficient, often employ a more uniform, or symmetric, update mechanism that can be suboptimal for capturing such linguistic subtleties. In this paper, we propose Dynamic Sparse LoRA (DS-LoRA), a novel technique that leverages asymmetric adaptation to enhance LLM finetuning for nuanced offensive language detection. DS-LoRA achieves this by (1) incorporating input-dependent gating mechanisms, enabling the asymmetric modulation of LoRA module contributions based on instance-specific characteristics, and (2) promoting asymmetric sparsity within LoRA update matrices via L1 regularization. This dual asymmetric strategy empowers the model to selectively engage and refine only the most pertinent parameters for a given input, fostering a more parsimonious and contextually aware adaptation. Extensive experiments on benchmark datasets demonstrate DS-LoRA’s significant overperformance over standard LoRA and other strong baselines, particularly in identifying subtle and contextually ambiguous offensive content, underscoring the benefits of its asymmetric adaptive capabilities. Full article

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article

Accurately modeling the degradation of food antioxidants in oils is essential for understanding oxidative stability and improving food shelf life. This study presents an innovative machine learning approach integrating neural differential equations and sparse symbolic regression to derive a parsimonious differential equation for myricetin degradation in stripped soybean oil. Despite being trained on a small experimental dataset, the model successfully predicts degradation trends across a wide range of initial concentrations and extrapolates beyond the learning data. This capability demonstrates the robustness of machine learning for uncovering governing equations in complex food systems, particularly when experimental data is scarce. Our findings provide a framework for improving antioxidant efficiency in food formulations. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article