Search Results (4,063)

Human-powered electricity generation (HPEG) systems offer promising sustainable energy solutions, yet existing deterministic models fail to capture the inherent variability in human biomechanical performance. This study develops a comprehensive stochastic framework integrating advanced Monte Carlo uncertainty quantification with multi-component fatigue modeling and Pareto optimization. The framework incorporates physiological parameter vectors, kinematic variables, and environmental factors through multivariate distributions, addressing the complex stochastic nature of human power generation. A novel multi-component efficiency function integrates biomechanical, coordination, fatigue, thermal, and adaptation effects, while advanced fatigue dynamics distinguish between peripheral muscular, central neural, and substrate depletion mechanisms. Experimental validation (623 trials, 7 participants) demonstrates RMSE of 3.52 W and CCC of 0.996. Monte Carlo analysis reveals mean power output of 97.6 ± 37.4 W (95% CI: 48.4–174.9 W) with substantial inter-participant variability (CV = 37.6%). Pareto optimization identifies 19 non-dominated solutions across force-cadence space, with maximum power configuration achieving 175.5 W at 332.7 N and 110.4 rpm. This paradigm shift provides essential foundations for next-generation HPEG implementations across emergency response, off-grid communities, and sustainable infrastructure applications. The framework thus delivers dual contributions: advancing stochastic uncertainty quantification methodologies for complex biomechanical systems while enabling resilient decentralized energy solutions critical for sustainable development and climate adaptation strategies. Full article

The endocrine system plays a pivotal role in all stages of cardiac development and in maintaining the structural and functional integrity of the heart. Notably, the heart itself functions as an endocrine organ, producing hormones that regulate blood pressure, fluid balance, and myocardial remodeling. This narrative review explores the endocrine mechanisms underlying cardiac development and function, with a focus on fetal and pediatric life. Special attention is given to the heart’s intrinsic endocrine activity and how hormonal signals interact with the cardiovascular system during early development. Hormonal signaling is essential for maintaining physiological homeostasis and supporting proper heart development during growth. Disruptions in these signals may serve as silent precursors to structural or functional heart disease, potentially manifesting later in life. Understanding these interactions is clinically relevant, as endocrine imbalances can contribute to the onset, progression, and prognosis of pediatric cardiac disorders. Early identification of hormonal dysregulation can help prevent or mitigate adverse cardiovascular outcomes. Furthermore, recognizing age-specific patterns in hormone–heart interactions may enable the development of targeted diagnostic and therapeutic strategies. Full article

The global issues of resource depletion and environmental pollution have led to increased interest in a circular bioeconomy focusing on converting renewable biomass into functional biomaterials. This article explores the transformative potential of hemicellulosic biogels as a sustainable platform to address critical societal challenges, such as water scarcity, food solutions and environmental pollution. Derived from hemicelluloses, an abundant and underutilized polysaccharide in lignocellulose biomass, these biogels offer a fundamentally new approach to developing high-performance, ecofriendly based materials. The review examines their development, characterization, and diverse applications in water treatment, food, agriculture, adhesive and coating systems. In water treatment, these gels exhibit exceptional performance, demonstrating a maximum NaCl uptake of 0.26 g/g and rapid pseudo-second-order adsorption kinetics for desalination. They also show high selectivity for heavy metal removal, with a remarkable binding capacity for lead if 2.9 mg/g at pH 5. For adhesive and coating applications, hemicellulose crosslinked with ammonium zirconium carbonate (AZC) forms water-resistant gels that significantly enhance paper properties, including gloss, smoothness, liquid resistance, and adhesive strength. Furthermore, hemicellulosics exhibit controlled biodegradation in physiological solutions while maintaining their mechanical integrity, underscoring their broad application promise. Overall, this review highlights how hemicellulose-based hydrogels can transform a low-value byproduct from biorefinery into high-performance solutions, contributing significantly to a sustainable economy. Full article

Dementia is a progressive condition that affects cognitive and functional abilities. Psycho-motor agitation represents a frequent and challenging manifestation in People Living with Dementia (PLwD). This behavior contributes to heightened distress and increased risk of harm for patients, while posing a significant burden for caregivers, who must navigate the complexities of managing unpredictable and potentially harmful agitation episodes. Accurately predicting and promptly responding to agitation events is thus critical for enhancing the safety and well-being of PLwD. Leveraging artificial intelligence, tools can be used to monitor behavioral patterns and alert healthcare providers about potential agitation to facilitate timely and effective interventions. Despite the link between poor sleep quality and the likelihood of agitation, there remains a gap in utilizing sleep parameters for predictive analytics in this domain. This study explores the potential of integrating sleep and associated physiological data to predict the risk of agitation in dementia patients the next day, leveraging the Technology Integrated Health Management (TIHM) dataset. Our analysis reveals that the LightGBM model, enhanced with combined feature sets, delivers superior performance, achieving a weighted F1 score of 93.6% compared to standard baseline models. The findings underscore the value of incorporating sleep data into automated models and advocate for continued efforts to develop long-term agitation prediction methods. Full article

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties. Full article

Our previous studies identified the Gcp/TsaD protein as essential for Staphylococcus aureus survival and implicated it in tRNA modification. Here, we demonstrate its broader role in bacterial physiology. Through a morphological analysis, RNA sequencing, network-based bioinformatics, and antibiotic susceptibility testing, we show that Gcp/TsaD influences cell morphology, cell wall integrity, transcriptional regulation, virulence, and antibiotic response. Gcp/TsaD depletion caused reduced cell size and increased cell wall thickness, suggesting its roles in cell division and peptidoglycan biosynthesis. The kinetic transcriptomic analysis revealed widespread changes in gene expression, particularly in the translation and amino acid biosynthesis pathways, supporting its function in maintaining translational fidelity via tRNA modification. Its depletion also upregulated the genes involved in cell envelope biosynthesis, including capsule formation, enhancing resistance to antimicrobial peptides, while downregulating the key virulence genes, indicating a role in pathogenicity. Functionally, the Gcp/TsaD-deficient cells were more susceptible to fosfomycin, reinforcing its importance in cell wall integrity. Together, these findings highlight the multifaceted contribution of Gcp/TsaD to S. aureus physiology and underscore its potential as a therapeutic target, particularly against antibiotic-resistant strains. Full article

This work reviews the complex role of the enzyme triosephosphate isomerase (TIM) (EC 5.3.1.1) within the context of diabetes, a prevalent metabolic disorder. It summarizes the main biochemical pathways, cellular mechanisms, and molecular interactions that highlight both the function of TIM and its implications in diabetes pathophysiology, particularly focusing on its regulatory role in glucose metabolism and insulin secretion. TIM’s involvement is detailed from its enzymatic action in glycolysis, influencing the equilibrium between dihydroxyacetone phosphate and glyceraldehyde-3-phosphate, to its broader implications in cellular metabolic processes. The article highlights how mutations in TIM can lead to metabolic inefficiencies that exacerbate diabetic conditions. It discusses the interaction of TIM with various cellular pathways, including its role in the ATP-sensitive potassium channels in pancreatic beta cells, which are crucial for insulin release. Moreover, we indicate the impact of oxidative stress in diabetes, noting how TIM is affected by reactive oxygen species, which can disrupt normal cellular functions and insulin signaling. The enzyme’s function is also tied to broader cellular and systemic processes, such as membrane fluidity and cellular signaling pathways, including the mammalian target of rapamycin, which are critical in the pathogenesis of diabetes and its complications. This review emphasizes the dual role of TIM in normal physiological and pathological states, suggesting that targeting TIM-related pathways could offer novel therapeutic strategies for managing diabetes. It encourages an integrated approach to understanding and treating diabetes, considering the multifaceted roles of biochemical players such as TIM that bridge metabolic, oxidative, and regulatory functions within the body. Full article

This study proposes a physiologically interpretable framework for stress state classification using respiratory signals. The framework aims to assess whether integrating physiologically meaningful features with an interpretable model can enhance both the accuracy and interpretability of stress state classification. First, a 16-parameter feature set was constructed by extracting rhythm, depth, and nonlinear characteristics of respiratory signals. Subsequently, feature correlations and group differences across stress states were analyzed via heatmaps, multivariate analysis of variance (MANOVA), and box plots. A stacking ensemble model was then designed for three-state classification (normal/stress/meditation). Finally, Shapley additive explanations (SHAP) values were used to quantify feature contributions to classification outcomes. The leave-one-subject-out (LOSO) cross-validation results show that on the wearable stress and affect detection (WESAD) dataset, the model achieves an accuracy of 92.33% and a precision of 93.54%. Furthermore, initial validation shows key respiratory features like breath rate, inspiration time ratio, and expiratory variability coefficient align with autonomic regulation. Key respiratory metrics in other areas like rapid shallow breathing index also play an important role in the stress classification. Notably, increased respiratory depth under a stress state needs further study to clarify its physiological reasons. Overall, this framework enhances physiological interpretability while maintaining competitive performance, offering a promising approach for future applications in multimodal stress monitoring and clinical assessment. Full article

Air pollution is an escalating global challenge with profound implications for agricultural production and food security. This review explores the impacts of deteriorating air quality on global crop yields and food security, emphasizing both direct physiological effects on plants and broader environmental interactions. Key pollutants such as ground-level ozone (O₃), fine particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs) reduce crop yield and quality. They have been shown to inhibit plant growth, potentially by affecting germination, morphology, photosynthesis, and enzyme activity. PAH contamination, for example, can negatively affect soil microbial communities essential for soil health, nutrient cycling and organic matter decomposition. They persist and accumulate in food products through the food chain, raising concerns about food safety. The review synthesizes evidence demonstrating how air pollution undermines the four pillars of food security: availability, access, utilization, and stability by reducing crop yields, elevating food prices, and compromising nutritional quality. The consequences are disproportionately severe in low- and middle-income countries, where regulatory and infrastructural limitations exacerbate vulnerability. This study examines mitigation strategies, including emission control technologies, green infrastructure, and precision agriculture, while stressing the importance of community-level interventions and real-time air quality monitoring through IoT and satellite systems. Integrated policy responses are urgently needed to bridge the gap between environmental regulation and agricultural sustainability. Notably, international cooperation and targeted investments in multidisciplinary research are essential to develop pollution-resilient crop systems and inform adaptive policy frameworks. This review identifies critical knowledge gaps regarding pollutant interactions under field conditions and calls for long-term, region-specific studies to assess cumulative impacts. Ultimately, addressing air pollution is not only vital for ecosystem health, but also for achieving global food security and sustainable development in a rapidly changing environment. Full article

Beta-cypermethrin is widely applied in Korean pine (Pinus koraiensis Siebold & Zucc.) seed orchards to control cone- and seed-infesting moths (e.g., Dioryctria spp.), yet its Wsublethal risks to non-target beneficial arthropods remain insufficiently characterized. Here, we systematically evaluated the ecological and physiological consequences of beta-cypermethrin exposure on the key parasitoid wasp Baryscapus dioryctriae Yang & Song, an important biological control agent in P. koraiensis forests. Adult wasps were exposed to LC₃₀ and LC₅₀ residue concentrations, and sublethal effects were quantified across reproductive, developmental, and biochemical endpoints over two generations. Sublethal exposure resulted in significant reductions in parasitism rates and offspring emergence, as well as altered developmental durations and adult longevity in both F₀ and F₁ generations. Enzymatic assays revealed time-dependent activation of detoxification enzymes (GST, CarE, AChE) alongside suppression of antioxidant defenses (CAT strongly; SOD early with partial recovery; POD biphasic), consistent with a sustained oxidative-stress burden. LC-MS/MS residue analysis further confirmed the accumulation and slow clearance of both beta-cypermethrin and its metabolite 3-phenoxybenzoic acid (PBA) within parasitoid tissues. These findings collectively demonstrate that even non-lethal concentrations of beta-cypermethrin can undermine the ecological fitness and persistence of B. dioryctriae, posing a tangible threat to the sustainability of biological control services. To safeguard beneficial parasitoids, integrated pest management strategies must incorporate selective insecticide use and exposure mitigation, especially in forest habitats where biological control is indispensable. Full article

Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article

Background: Gamification may enhance engagement and higher-order learning in health-care profession education, but evidence from undergraduate nursing programs—particularly in the Middle East—is limited. We evaluated whether integrating structured gamified activities into an anatomy and physiology course improves class engagement and knowledge-based critical thinking. Methods: In this pragmatic, nonrandomized, section-allocated quasi-experimental study at a single Saudi institution, 121 first-year female nursing students were assigned by existing cohorts to traditional instruction (control; n = 61) or instruction enhanced with gamified elements (intervention; n = 60) groups. The intervention (introduced mid-semester) comprised time-limited competitive quizzing with immediate feedback and aligned puzzle tasks. Outcomes were measured at baseline, mid-semester, and end-semester using a four-item Class Engagement Rubric (CER; scale 1–5) and a 40-item high-cognitive multiple-choice (MCQ) assessment mapped to course objectives. Analyses used paired and independent t-tests with effect sizes and 95% confidence intervals. Results: No attrition occurred. From baseline to end-semester, the intervention group had a mean CER increase of 0.59 points (95% CI, 0.42 to 0.76; p < 0.001)—approximately a 15% relative gain—and a mean MCQ increase of 0.30 points (95% CI, 0.18 to 0.42; p < 0.001), an ~8% relative gain. The control group showed no material change over the same interval. Between-group differences in change favored the intervention across CER items and for the MCQ outcome. Semester grade-point average did not differ significantly between groups (p = 0.055). Conclusions: Embedding a brief, structured gamification package within an undergraduate nursing anatomy and physiology course was associated with measurable improvements in classroom engagement and modest gains in knowledge-based critical thinking, with no detectable effect on overall semester GPA. Given the nonrandomized, single-site design, causal inference is limited. Multi-site randomized trials using validated critical-thinking instruments are warranted to confirm effectiveness and define dose, durability, and generalizability. Full article

Drought stress severely impacts the survival of Platycrater arguta, an endangered tertiary relict plant. This study investigated the mechanism by which exogenous melatonin enhances drought tolerance in P. arguta seedlings through integrated physiological, transcriptomic, and metabolomic analyses. Under 30% PEG-6000-induced drought, seedlings exhibited leaf wilting, reduced relative water content (RWC: 78.6% vs. 84.8% in controls), and elevated oxidative damage (malondialdehyde increased by 62.94%, H₂O₂ by 83.78%). Exogenous melatonin application, particularly at 100 μM (T3), significantly alleviated drought symptoms, restoring RWC to 83.7%, reducing membrane permeability (relative electrical conductivity 1.38-fold vs. CK vs. 2.55-fold in PEG), and lowering oxidative markers (MDA and H₂O₂ accumulation by 28.33% and 27.84%, respectively). T3 treatment also enhanced osmolyte synthesis, increasing proline content by 90.14% and soluble sugars by 47.25% compared to the controls. Transcriptome sequencing revealed 31,870 differentially expressed genes in T3, predominantly enriched in carbohydrate metabolism, oxidative phosphorylation, and tryptophan metabolism pathways. Metabolomic profiling demonstrated that 100 μM melatonin elevated endogenous melatonin levels 19.62-fold and modulated tryptophan-derived metabolites, including indole derivatives and benzoic acid compounds. These findings indicate that melatonin mitigates drought stress by enhancing antioxidant capacity, osmotic adjustment, and metabolic reprogramming, with 100 μM as the optimal concentration. This study provides critical insights into melatonin-mediated drought resistance mechanisms, offering a theoretical foundation for conserving and restoring P. arguta populations in arid habitats. Full article

Prolonged sitting and the adoption of unhealthy sitting postures have been a common issue generally seen among many adults and the working population in recent years. This alone has contributed to the alarming rise of various health issues, such as musculoskeletal disorders and a range of long-term health conditions. Hence, this study proposes the development of a novel smart-sensing chair system designed to analyze and provide actionable insights to help encourage better postural habits and promote well-being. The proposed system was equipped with two 32 × 32 pressure sensor mats, which were integrated into an office chair to facilitate the collection of postural data. Unlike traditional approaches that rely on generalized datasets collected from multiple healthy participants to train machine learning models, this study adopts a user-tailored methodology—collecting data from a single individual to account for their unique physiological characteristics and musculoskeletal conditions. The dataset was trained using five different machine learning models—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN)—to classify 19 distinct sitting postures. Overall, CNN achieved the highest accuracy, with 98.29%. To facilitate user engagement and support long-term behavior change, we developed SitWell—an intelligent postural feedback platform comprising both mobile and web applications. The platform’s core features include sitting posture classification, posture duration analytics, and sitting quality assessment. Additionally, the platform integrates OpenAI’s GPT-4o Large Language Model (LLM) to deliver personalized insights and recommendations based on users’ historical posture data. Full article

Background/Objectives: Population aging and its associated chronic conditions have become an unprecedented challenge in the United States and worldwide. Many aged individuals experience certain forms of cognitive decline, which increases their risk of developing a pre-dementia condition called mild cognitive impairment and even dementia. No effective pharmacological treatments are available to treat normal age-associated cognitive decline or mild cognitive impairment. Our previous study has shown the potent anti-inflammatory effects of shiitake mushroom-derived vesicle-like nanoparticles (S-VLNs) in vitro and in an acute inflammatory disease model. In this study, we aimed to investigate the potential benefits of orally administered S-VLNs in aged mice. Methods: S-VLNs were extracted from fresh shiitake mushrooms. S-VLNs in phosphate-buffered saline (PBS) or vehicle only was orally administered to 13-month-old male C57BL/6J mice weekly for 9 months. These mice were subjected to a series of physiological tests, followed by euthanasia at 22 months of age. Their fecal samples were subjected to 16S rRNA and untargeted metabolomics analyses, followed by comprehensive bioinformatics analyses. Results: The long-term oral administration of S-VLNs significantly improved the cognitive function of aged mice. Orally administered S-VLNs did not travel to the brain. Instead, they impacted the composition of the gut microbiota and reshaped the fecal metabolome. Functional predictions of the gut microbiota and fecal metabolome suggested that S-VLNs regulated tryptophan metabolism. Specifically, S-VLNs markedly decreased the tryptophan-related metabolite kynurenic acid (KYNA). The integrative analyses of omics data identified a strong correlation between 18 gut bacterial genera and 66 fecal metabolites. KYNA was found to highly correlate with five genera positively and twelve genera negatively. Conclusions: The oral intake of S-VLNs represents a new and superior dietary approach with the ability to modulate the gut microbiota and fecal metabolome and to improve cognitive function during aging. Full article