Search Results (105)

Background/Objectives: Physical activity (PA) during preschool is vital for supporting physiological development, enhancing cognitive abilities and fostering socio-emotional growth. However, consistent disparities in meeting PA guidelines have been observed. This systematic review aims to identify studies that compared preschoolers’ PA, as measured by technological devices, with recommended PA guidelines. Specifically, it examines (i) factors associated with meeting PA guidelines and (ii) the outcomes observed when children meet these guidelines. Methods: The search strategy was designed based on the PICOS framework. Then, a systematic review was conducted using four databases to identify studies that included children from 0 to 6 years old participating in PA sessions recorded through technological devices. PA is compared with guidelines, and correlations were reported. Results: Of the 52 studies reviewed, most found that meeting PA guidelines in preschool-aged children was linked to favourable outcomes across multiple domains. Children who met the guidelines tended to show better motor competence, emotional regulation and cognitive skills, particularly in areas like working memory and social understanding. However, the relationship with body composition and body mass index was inconsistent, suggesting that the benefits of PA in early childhood extend beyond weight-related measures. Conclusions: Meeting PA guidelines in early childhood is strongly associated with cognitive development, emotional regulation, motor skills and social behaviours. However, adherence varies significantly due to a complex mix of individual, familial, socioeconomic and environmental factors. Full article

Background: The perceived importance of physical practice and its contribution to students’ academic success have evolved considerably throughout the history of the modern educational system. Aim: The purpose of this study was to understand the relationship between physical fitness (measured as VO₂max) and cognitive abilities (attention and concentration) and academic performance in different subjects: sciences, letters, language, arts, and physical education. Method: Fifty Spanish male students who participated in extracurricular sports activities (mean age (SD): 11.59 ± 1.30; range: 9–15 years) were included in the analysis. The 6 min walk test was used to assess physical fitness (6MWT), while for selective attention and concentration, the students completed the D2 test, which is usually considered to analyse the visual ability to select the most relevant stimulus of an exercise and ignore precisely the most irrelevant stimuli. Results: Correlation the individual contribution analyses revealed no significant associations between VO₂max and academic performance in sciences (r = 0.04, p = 0.77), humanities (r = 0.00, p = 0.98), language (r = 0.03, p = 0.83), or arts (r = 0.04, p = 0.76). Similarly, no relationship was found between VO₂max and overall academic performance (r = 0.10, p = 0.46), or cognitive abilities. However, a small positive correlation was observed between VO₂max and physical education scores. Conclusions: Physical fitness showed no significant association with cognitive abilities or academic performance in most subjects, although a small positive correlation with physical education scores was observed. These findings emphasise the importance of promoting physical activity for its health and physical benefits. However, future research should explore broader cognitive outcomes and include more diverse and representative samples. Full article

In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared image quality. Progress in cross-domain pedestrian re-identification is hindered by the lack of labeled infrared image data. To address the degradation of pedestrian recognition in infrared environments, we propose a framework for zero-shot infrared domain adaptation. This integrated approach is designed to mitigate the challenges of pedestrian recognition in infrared domains while enabling zero-shot domain adaptation. Specifically, an advanced reflectance representation learning module and an exchange–re-decomposition–coherence process are employed to learn illumination invariance and to enhance the model’s effectiveness, respectively. Additionally, the CLIP (Contrastive Language–Image Pretraining) image encoder and DINO (Distillation with No Labels) are fused for feature extraction, improving model performance under infrared conditions and enhancing its generalization capability. To further improve model performance, we introduce the Non-Local Attention (NLA) module, the Instance-based Weighted Part Attention (IWPA) module, and the Multi-head Self-Attention module. The NLA module captures global feature dependencies, particularly long-range feature relationships, effectively mitigating issues such as blurred or missing image information in feature degradation scenarios. The IWPA module focuses on localized regions to enhance model accuracy in complex backgrounds and unevenly lit scenes. Meanwhile, the Multi-head Self-Attention module captures long-range dependencies between cross-modal features, further strengthening environmental understanding and scene modeling. The key innovation of this work lies in the skillful combination and application of existing technologies to new domains, overcoming the challenges posed by vision in infrared environments. Experimental results on the SYSU-MM01 dataset show that, under the single-shot setting, Rank-1 Accuracy (Rank-1) andmean Average Precision (mAP) values of 37.97% and 37.25%, respectively, were achieved, while in the multi-shot setting, values of 34.96% and 34.14% were attained. Full article

This study aimed to explore Markov decision methods in order to solve the problem of dairy cow replacement, adding the special characteristics of two types of abortions due to different sanitary reasons that influence the economic, production, and reproduction performance of these animals. The model was successfully validated against other models published in the literature. Python code v.3.13 was used to solve the problem and to ease future extensions with the inclusion of new variables. The results constitute tools that allow the veterinarian to explore more realistic scenarios by running a Markov simulation model that avoids the complexities leading to the problem of dimensionality in dynamic optimization models. In our study, the economic value of the herd considering RA and NLA abortions shows that the maximum net benefit is USD 178.77 per cow, and non-pregnant cows are slaughtered upon reaching six months of lactation, a value that is within the range of values reported by the literature that we have identified. At the optimum, the replacement model extended with abortion generates a difference of USD 0.69 per cow per month compared to the model that does not include the special abortion features. The changes in the net present value of each cow according to the month of culling depend on the variability of milk income and slaughter value and heifers’ replacement values, suggesting that any measure that seeks to improve the economic benefit of dairy cows should take greater account of these variables. Full article

Background: Cardiovascular fitness has been proposed as a key factor influencing executive functioning and academic performance during childhood and adolescence. However, the extent and consistency of this relationship remain unclear across diverse populations and educational contexts. This systematic review aimed to evaluate whether cardiovascular fitness, particularly measured through VO₂max, is consistently associated with improvements in executive function and academic performance among students from early childhood to adolescence. Methods: A systematic search of PubMed, Web of Science, SPORTDiscus, and ProQuest Central was conducted up to 15 November 2022. Studies were included if they examined correlations between VO₂max and cognitive or academic outcomes in students from preschool to high school. Methodological quality was assessed using the MINORS checklist. Results: Out of 271 identified studies, 12 met all inclusion criteria. Evidence suggests that higher VO₂max is generally associated with improved executive function domains such as attention, working memory, and inhibitory control, as well as academic performance indicators including mathematics and reading scores. Neurophysiological studies also indicate links between cardiovascular fitness and brain structure/function. However, the strength and specificity of these associations vary across studies due to methodological differences, limited sample diversity, and inconsistent control for confounders. Conclusions: Cardiovascular fitness appears to have a positive, albeit complex, relationship with cognitive function and academic performance in youth. Future research should adopt longitudinal and experimental designs to clarify causal pathways and consider moderating factors such as sex, age, and psychosocial variables. Full article

Background and Objective: Dysregulated tyrosine kinase signaling is a central driver of tumorigenesis, metastasis, and therapeutic resistance. While tyrosine kinase inhibitors (TKIs) have revolutionized targeted cancer treatment, identifying compounds with optimal bioactivity remains a critical bottleneck. This study presents a robust machine learning framework—leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints—to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. Methods: A curated dataset of 28,314 small molecules from the ChEMBL database targeting 11 tyrosine kinases was analyzed. Using Morgan fingerprints and physicochemical descriptors (e.g., molecular weight, LogP, hydrogen bonding), ten supervised models, including dANN, SVM, CatBoost, and CNN, were trained and optimized through a randomized hyperparameter search. Model performance was evaluated using F1-score, ROC–AUC, precision–recall curves, and log loss. Results: SVM achieved the highest F1-score (87.9%) and accuracy (85.1%), while dANNs yielded the lowest log loss (0.25096), indicating superior probabilistic reliability. CatBoost excelled in ROC–AUC and precision–recall metrics. The integration of Morgan fingerprints significantly improved bioactivity prediction across all models by enhancing structural feature recognition. Conclusions: This work highlights the transformative role of machine learning—particularly dANNs and SVM—in rational drug discovery. By enabling accurate bioactivity prediction, our model pipeline can effectively reduce experimental burden, optimize compound selection, and support personalized cancer treatment design. The proposed framework advances kinase inhibitor screening pipelines and provides a scalable foundation for translational applications in precision oncology. By enabling early identification of bioactive compounds with favorable pharmacological profiles, the results of this study may support more efficient candidate selection for clinical drug development, particularly in regards to cancer therapy and kinase-associated disorders. Full article

The application of superhydrophobic (SH) coatings in road construction has attracted growing attention due to their potential to improve surface durability, reduce cracking, and enhance skid resistance. Among various materials, SiO₂ nanoparticles have emerged as key components in SH coatings by contributing essential surface roughness and hydrophobicity. This review paper analyzes the role of SiO₂ nanoparticles in enhancing the water-repellent properties of coatings applied to road surfaces, particularly concrete and asphalt. Emphasis is placed on their influence on road longevity, reduced maintenance, and overall performance under adverse weather conditions. Furthermore, this review compares functionalization techniques for SiO₂ using different hydrophobic modifiers, evaluating their efficiency, cost effectiveness, and scalability for large-scale infrastructure. In addition to highlighting recent advancements, this study discusses persistent challenges—including environmental compatibility, mechanical wear, and long-term durability—that must be addressed for practical implementation. By offering a critical assessment of current approaches and future prospects, this short review aims to guide the development of robust, high-performance SH coatings for sustainable road construction. Full article

This article explores the impact of internalized oppression on young women of colour in Norway, focusing on how it unfolds across individual life trajectories. Drawing on a qualitative methodology, the study is based on narrative in-depth interviews with thirteen participants aged 18 to 35. The findings reveal that internalized oppression, particularly related to physical appearance, emerges early in life and is often reinforced through social interactions such as bullying, exclusion, and racialized commentary. These experiences frequently convey implicit preferences for whiteness, leading to marginalization and insecurity during adolescence. In response, several participants engaged in practices of assimilation, altering their physical appearance in attempts to embody features aligned with dominant white norms. In adulthood, many of these women have developed a critical awareness of internalized oppression and are engaged in processes of decolonizing their self-perceptions through solidarity with other women of colour. Nevertheless, they continue to grapple with lingering internalized biases. This study highlights the need for further research into the life narratives and everyday experiences of racialized individuals to better understand how they navigate, resist, and unlearn internalized oppression—while also considering the gendered dimension of how such oppression works. Full article

Background and Objectives: Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 lack sensitivity and specificity, particularly for early-stage disease. Advances in metabolomics and machine learning, particularly explainable artificial intelligence (XAI), offer new opportunities for identifying robust biomarkers and improving diagnostic accuracy. This study aimed to identify and validate serum-based metabolic biomarkers for breast cancer using advanced metabolomic profiling techniques and a Light Gradient Boosting Machine (LightGBM) model. Additionally, SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability and biological insight. Materials and Methods: The study included 103 breast cancer patients and 31 healthy controls. Serum samples underwent liquid and gas chromatography–time-of-flight mass spectrometry (LC-TOFMS and GC-TOFMS). Mutual Information (MI), Sparse Partial Least Squares (sPLS), Boruta, and Multi-Objective Feature Selection (MOFS) approaches were applied to the data for biomarker discovery. LightGBM, AdaBoost, and Random Forest were employed for classification and to identify class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). SHAP analysis ranked metabolites based on their contribution to model predictions. Results: Compared to other feature selection approaches, the MOFS approach was more robust in terms of predictive performance, and metabolites identified by this method were used in subsequent analyses for biomarker discovery. LightGBM outperformed the AdaBoost and Random Forest models, achieving 86.6% accuracy, 89.1% sensitivity, 84.2% specificity, and an F1-score of 87.0%. SHAP analysis identified 2-Aminobutyric acid, choline, and coproporphyrin as the most influential metabolites, with dysregulation of these markers associated with breast cancer risk. Conclusions: This study is among the first to integrate SHAP explainability with metabolomic profiling, bridging computational predictions and biological insights for improved clinical adoption. This study demonstrates the effectiveness of combining metabolomics with XAI-driven machine learning for breast cancer diagnostics. The identified biomarkers not only improve diagnostic accuracy but also reveal critical metabolic dysregulations associated with disease progression. Full article

Background/Objectives: Sleep and recovery are critical for optimising exercise performance. However, the efficacy of melatonin supplementation in improving sleep quality and next-day physical performance remains unclear. This study examined the effects of melatonin ingestion on sleep and performance-related outcomes the following day in trained males. Methods: In a randomised, double-blind, placebo-controlled crossover study, 12 trained males (age: 21.92 ± 2.84 years) ingested 6 mg of melatonin (MEL) or a placebo (PLA) the night before performing the 5 m shuttle test (5mSRT). Before and after the 5mSRT, blood samples were collected. Peak heart rate (HR_peak) and rating of perceived exertion (RPE) were recorded throughout the test. Perceived recovery status (PRS) and delayed onset muscle soreness (DOMS) were measured before, 5 min, 24 h, 48 h, and 72 h after the test. The sleep/wake cycle was monitored during the night after ingestion. Results: Data were analysed using paired t-tests, Wilcoxon tests, and two-way ANOVAs, with significance set at p < 0.05. Compared to PLA, MEL did not modify any sleep parameters or blood markers (all p > 0.05). However, MEL improved total distance, fatigue index, the percentage decrement between sprints, and HR_peak (all p < 0.05) in the 5mSRT compared to PLA. MEL also enhanced PRS values up to 72 h post-exercise and reduced DOMS (all p < 0.05). Conclusion: In summary, 6 mg of melatonin taken at night enhanced next-day high-intensity exercise performance and improved perceived recovery up to 72 h post-exercise. Full article

This article examines the interplay between study demands, institutional resources, and individual resources, specifically resilience, with the perceived stress, study engagement, and dropout intentions of student teachers using the Study Demands–Resources model. The aim is to describe the relevance of these variables in relation to student teachers’ intention to drop out of their studies as an indicator of student success. Further, we aim to explore whether the correlation structures can also be confirmed under different conditions, particularly in the context of the COVID-19 pandemic. To answer these questions, data collected under pandemic study conditions (N_LA1 = 510) and post-pandemic study conditions (N_LA2 = 433) are used and analysed by SEM. The results show that the Study Demands–Resources model is applicable in the two different contexts based on its validation in both study contexts. In line with the model, in both contexts, perceived stress and student engagement were significantly related to student teachers’ dropout intentions. Furthermore, study demands and resilience influenced perceived stress, which in turn affected dropout intentions, whereas institutional resources were associated with dropout intentions via student engagement. This article contextualises the findings within the existing research landscape. Based on the results, theoretical implications are discussed and approaches to reduce perceived stress in a sustainable manner to support student teachers and their academic success are described. Full article

This study presents the synthesis, characterization, and evaluation of stearic-acid-coated sand (SACS) as a superhydrophobic material for agricultural water management applications. The fabrication process involves coating silica sand particles with stearic acid in an ethanol-based solution, followed by controlled drying to achieve a stable and uniform hydrophobic layer. Structural, chemical, and physical characterizations confirmed the successful functionalization of the sand surface. The coated sand exhibited a high water contact angle (WCA > 150°), indicating strong water repellency and potential for reducing water loss in soil systems. Experimental results demonstrated enhanced moisture retention in SACS-treated soil, prolonging water availability by up to four additional days compared to untreated samples. Despite its promising performance, potential degradation under acidic or organic solvent exposure remains a concern for long-term application. Overall, this work presents SACS as a low-cost, scalable solution to improve water conservation in dry agricultural areas, supporting sustainable farming practices. Full article

This article methodically reveals how, in woody plants (poplar), the interaction between N and P coordinates root structure and nutrient absorption through a complex hormone signaling network. This study bridges a significant gap in our knowledge of nutrient interaction networks. The results demonstrate that NO₃⁻ significantly enhances the gene expression and enzymatic activity of organic acid synthases (MDH, PEPC) and APs. Furthermore, it synergizes with IAA/ABA signals to refine root structure, enhancing the surface area for P absorption. In low Pi availability environments, NO₃⁻ further promotes P recycling by simultaneously boosting the levels of Pi transport proteins (notably, the PHO family), facilitating myo-inositol phosphate metabolism (via IMP3/ITPK1-mediated PP-InsPs degradation), and augmenting IAA/SA signals. Pi induces the activity of N assimilation enzymes (GS/GOGAT/GDH), facilitating nitrogen metabolism. However, in the absence of N, it leads to a metabolic imbalance characterized by high enzymatic activity but low efficiency. Alternatively, adequate N availability allows Pi to improve root robustness and N assimilation efficiency, mediated by IAA/GA accumulation and ABA signaling (e.g., SNRK2/ABF). We propose the existence of an intricate network in poplar, orchestrated by transcriptional cascades, metabolic regulation, and hormonal synergism. Key modules such as SPX-PHR, NLA, HHO2, and MYB59 are likely central to this network’s function. These findings offer a foundational framework for the development of molecular breeding and precise fertilization strategies, enhancing the efficient use of N and P in forestry. Full article

Background and Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint inflammation and pain. Metabolomics approaches, which are high-throughput profiling of small molecule metabolites in plasma or serum in RA patients, have so far provided biomarker discovery in the literature for clinical subgroups, risk factors, and predictors of treatment response using classical statistical approaches or machine learning models. Despite these recent developments, an explainable artificial intelligence (XAI)-based methodology has not been used to identify RA metabolomic biomarkers and distinguish patients with RA. This study constructed a XAI-based EBM model using global plasma metabolomics profiling to identify metabolites predictive of RA patients and to develop a classification model that can distinguish RA patients from healthy controls. Materials and Methods: Global plasma metabolomics data were analysed from RA patients (49 samples) and healthy individuals (10 samples). SMOTE technique was used for class imbalance in data preprocessing. EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. Comprehensive performance metrics were calculated, and the interpretability of the optimal model was assessed using global and local feature descriptions. Results: A total of 59 samples were analysed, 49 from RA patients, and 10 from healthy subjects. The EBM generated better results than LightGBM and AdaBoost by attaining an AUC of 0.901 (95% CI: 0.847–0.955) with 87.8% sensitivity which helps prevent false negative early RA diagnosis. The primary biomarkers EBM-based XAI identified were N-acetyleucine, pyruvic acid, and glycerol-3-phosphate. EBM global explanation analysis indicated that elevated pyruvic acid levels were significantly correlated with RA, whereas N-acetyleucine exhibited a nonlinear relationship, implying possible protective effects at specific concentrations. Conclusions: This study underscores the promise of XAI and evidence-based medicine methodology in developing biomarkers for RA through metabolomics. The discovered metabolites offer significant insights into RA pathophysiology and may function as diagnostic biomarkers or therapeutic targets. Incorporating EBM methodologies integrated with XAI improves model transparency and increases the therapeutic applicability of predictive models for RA diagnosis/management. Furthermore, the transparent structure of the EBM model empowers clinicians to understand and verify the reasoning behind each prediction, thereby fostering trust in AI-assisted decision-making and facilitating the integration of metabolomic insights into routine clinical practice. Full article

Degenerate third-order nonlinear optical (NLO) responses of organic molecules have a wide range of applications in science and engineering because they relate to the intensity-dependent refractive index (IDRI) and nonlinear absorption (NLA), such as two-photon absorption (TPA). Among the many molecular systems, open-shell molecular species such as intermediate singlet diradicaloids have attracted considerable attention because of their enhanced response, predicted theoretically by Nakano et al. Experimental studies for proofing and evaluating the enhanced nonlinearities play an important role in the development of the field. This tutorial review provides the solid fundamentals of the NLO processes of open-shell molecular species even to those who are not familiar with the experimental works. Its scope ranges from the basics of NLO responses, definitions, and interrelations of the key parameters of the responses, such as hyperpolarizability and TPA cross-section, to the experimental techniques used to evaluate them. Including the recent achievements, the evolution of experimental works on the TPA properties of singlet diradicaloids is also reviewed according to families of molecular structures. Full article