Search Results (30,775)

Background: Evening meal-timing chronotypes often exhibit lower calcium intake; however, whether this relationship remains significant after accounting for overall diet quality remains unclear. This study examined the association between meal-timing chronotypes and calcium intake and evaluated whether this association is maintained after adjusting for overall diet quality. Methods: This cross-sectional study analyzed 3465 adults aged 30–49 years from the 2016–2018 Korea National Health and Nutrition Examination Survey. Meal-timing chronotypes were identified using dynamic time warping-based K-means clustering of 24-h energy intake distributions. Survey-weighted linear regression assessed the association between meal-timing chronotype and calcium intake and tested their interaction with the Korean Healthy Eating Index (KHEI; excluding dairy) to evaluate the moderating effect of diet quality. Multinomial logistic regression was conducted to estimate odds ratios (ORs) for low calcium intake according to meal-timing chronotypes. Models were adjusted for age, sex, education, occupation, household income, and physical activity. Results: After adjusting for sociodemographic and lifestyle factors, the evening meal-timing chronotype was significantly associated with higher odds of low calcium intake (OR = 2.2, p < 0.001). A significant interaction between chronotype and KHEI tertiles on calcium intake was observed (p < 0.001). Specifically, while calcium intake generally decreased as diet quality declined, individuals with an evening preference consistently showed significantly lower calcium intake across all KHEI tertiles compared to the morning preference group (β = −7.9, p < 0.001). Conclusions: The evening meal-timing chronotype showed a significant association with lower calcium intake, which remained significant even after accounting for overall diet quality. These findings suggest that circadian-related eating patterns, rather than just overall diet quality, play a structural role in determining calcium intake. Full article

Optimizing the trade-off between temperature-tracking precision and energy efficiency remains a significant challenge in industrial refrigeration systems. To address this, this paper presents a novel hierarchical control architecture combining Model Predictive Control (MPC), Fuzzy Logic Controller (FLC), and an Extended State Observer (ESO). Specifically, the MPC manages the system’s physical constraints, while the FLC dynamically tunes the objective function weights online, ensuring an optimal balance between performance and energy savings. Furthermore, the ESO is employed to estimate and actively compensate for exogenous heat load disturbances and model uncertainties. Comparative results confirm that the proposed strategy not only reduces energy consumption by 10.93% but also achieves highly disturbance rejection when compared to conventional PI control. The practical feasibility of the proposed algorithm is rigorously validated via hardware-in-the-loop (HIL) simulations utilizing an STM32F767ZI microcontroller. The successful hardware-in-the-loop validation on an embedded microcontroller demonstrates the industrial viability of the proposed architecture, proving it to be a highly deployable and cost-effective solution for refrigeration system. Full article

Accurate prediction of protein function is fundamental to progress in biotechnology and biomedicine, yet progress remains severely hampered by the widening chasm between exponentially growing genomic data and the limited capacity for functional annotation. High-throughput sequencing and metagenomics have driven an explosion in sequence data that far outstrips experimental characterization. UniProt now contains over 203 million protein entries, of which only ~2% have been experimentally validated. This widening “sequence–function gap” exceeds the reach of traditional homology-based tools such as BLAST (v2.17.0) and HMMER (v3.2), which are inherently constrained by sequence identity thresholds. The emergence of Protein Language Models (PLMs), including ESM and ProtTrans, has introduced a transformative paradigm, thereby shifting functional inference from similarity-based retrieval to geometric reasoning within learned semantic spaces. Nevertheless, current approaches remain largely confined to unimodal or narrowly bimodal frameworks, failing to capture the inherently multidimensional determinants of enzymatic function, including active-site geometry, chemical reaction logic, and literature-embedded semantic context. This review systematically adopts a multimodal global-fusion perspective, elucidating how three-dimensional geometric features, chemical reaction semantics, and textual knowledge graphs are synergistically integrated around PLMs as a core backbone. We delineate complementary mechanisms and integration strategies that together enable fine-grained protein function annotation beyond the performance ceiling of single-sequence methods. Furthermore, we survey the translational potential of such frameworks from computational prediction to real biological applications, and critically examine persistent bottlenecks including activity cliffs, transition-state inference, and conformational dynamics. We identify the integration of physics-informed machine learning with dynamics-aware architectures as a pivotal direction toward a causal, mechanism-level understanding of protein function. Full article

Background: Physical activity (PA), physical fitness (PF), and motor skills (MS) play crucial roles in overall health and well-being, particularly in early childhood, when habits that affect future health are formed. Methods: This study, involving 299 children (150 boys, 149 girls, mean age 6.9 ± 0.96 years), explored the variance explained by external factors such as socioeconomic status (SES), body composition (BC), sex, and geographical location on motor-related physical fitness (MRPF) and health-related physical fitness (HRPF) in children. Using a variety of assessments, including demographics, anthropometric data, BIA, ActiGraphs, the 20 m shuttle run, 10 and 20 m speed tests, and test items from the Körperkoordinations test für Kinder (KTK) and the TGMD-2, a multiple stepwise regression analysis using SPSS (v 28.0) identified the associated factors. Results: The variables tested show modest explained variance for HRPF, MRPF, and MS, with the largest cumulative explained variance of 26.4%. The explained variances for MRPF and MS were lower (medium to small) than the significant, medium-to-large, explained variances for HRPF. Body fat percentage (BF%), moderate-to-vigorous physical activity (MVPA), parental education and income, and BMI emerged as substantial contributors to HRPF, explaining 12.1% to 26.4% of the variance. Sex, BF%, and quintile status were the most influential associated factors for MRPF, and for MS, BMI and sex emerged as the strongest contributors. Conclusions: These findings underscore the importance of holistic approaches that consider individual factors, such as MVPA, body composition (BC), PA levels, sex, and broader social and economic contexts, to promote children’s well-being. The study emphasises the need for comprehensive strategies to address the multifaceted associations with children’s physical development. Full article

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, we propose a resilient cyber–physical framework that jointly optimizes robust dynamic state estimation and collaborative voltage control. At the estimation layer, a novel Persistence-Based Robust Extended Kalman Filter (PB-REKF) is developed, which employs a temporal persistence counter to adaptively switch between Huber M-estimation for sporadic outlier suppression and covariance inflation for rapid tracking of persistent state mutations. At the control layer, a chance-constrained Second-Order Cone Programming (SOCP) strategy directly embeds the real-time posterior covariance from the PB-REKF into the voltage safety constraints, creating a data-quality-adaptive security buffer that provides a $95\%$ probabilistic voltage guarantee. Simulations on 5-bus and IEEE 33-bus systems demonstrate that the proposed framework achieves a $29.5\%$ reduction in global RMSE and a $72.8\%$ reduction in peak outlier-window estimation error relative to the standard EKF, while reducing the voltage violation rate from $8.8\%$ to $3.8\%$. The complete estimation and control pipeline requires $1.341$ ms per update step, confirming real-time feasibility. Full article

Mesenchymal stem/stromal cells (MSCs) are widely recognized as potent modulators of inflammation and immune function in bacterial and viral infections. However, their roles in fungal disease remain comparatively under-defined despite the growing clinical burden of invasive and opportunistic mycoses. This Feature Review synthesizes emerging evidence that MSCs influence antifungal outcomes through two complementary axes: (i) host-directed effects, including modulation of immune responses, particularly macrophage responses, and tissue/barrier conditioning; and (ii) fungus-directed effects (direct antifungal activity mediated by contact-dependent mechanisms and secreted antimicrobial factors). We will summarize how MSCs reshape cytokine and chemokine networks and tune innate immune effector functions, with emphasis on macrophage polarization, pattern-recognition receptor signaling, and downstream phagocytic and fungicidal pathways. In parallel, we will review data suggesting that MSCs can interact more directly with fungal pathogens through sensing, physical engagement, and secretion of antimicrobial mediators while highlighting mechanistic uncertainties and model-dependent limitations. A dedicated section will address MSC-derived secretome products (conditioned media, extracellular vesicles) as a cell-free strategy to enhance antifungal immunity. We will critically evaluate conflicting findings across studies, highlighting that outcomes depend on pathogen and host context. Clarifying these context dependencies is essential to rationally develop MSC or secretome-based interventions that are safe, reproducible, and tailored to specific fungal pathogens and patient populations. Full article

The lower airways are a dynamic environment where physical, microbial, and molecular factors intersect to regulate respiratory health and disease. The muco-microbiotic (MuMi) layer, composed of mucus, resident microbes, and extracellular vesicles (EVs), is not just a passive barrier but also an active site for host–microbe communication. This layer integrates epithelial cell biology, microbial activity, and immune responses within the bronchial environment. New transcriptomic and metatranscriptomic technologies show that it is not only which microbes are present but also their gene activity that closely links to airway inflammation and disease. EV-associated RNAs from both host and microbial cells act as key messengers, influencing epithelial responses, immune activity, mucus properties, and microbial behaviour. This review highlights evidence that positions the MuMi layer as central to understanding lower airway disease, particularly asthma and chronic obstructive pulmonary disease (COPD). Distinct gene expression programs and biomarker profiles, such as exhaled nitric oxide, may reflect different disease mechanisms even in cases with similar clinical features, such as eosinophilia. Multi-omic approaches focused on the MuMi layer enable better disease classification, biomarker discovery, and therapy selection. By putting the MuMi interface at the core of precision pulmonology, we provide a framework for advancing personalised care in chronic respiratory diseases. Full article

Obesity represents a multifactorial health condition influenced by complex interactions among behavioral, environmental, and physiological factors, yet the relative predictive importance of lifestyle behaviors versus medical history indicators remains incompletely characterized. This investigation employed a three-phase machine learning approach to systematically compare the predictive power of behavioral lifestyle factors, medical history variables, and their integration for obesity classification. Phase A utilized a dedicated obesity dataset containing demographic, dietary, and lifestyle predictors to perform seven-category obesity classification, achieving 81.65% test accuracy with an optimized Random Forest ensemble and macro-averaged F1-score of 0.82. Phase B addressed binary obesity classification using health indicators from diabetes screening data, where a Gradient Boosting model with optimized decision threshold achieved 67.84% accuracy and AUC of 0.735, demonstrating substantially lower performance than behavioral predictors. Phase C integrated both feature sets into a unified model, where Gradient Boosting achieved 68.31% accuracy and AUC of 0.747, representing marginal improvement over medical history alone. Cross-validated performance comparisons revealed that behavioral lifestyle factors provided superior discriminative power compared to medical history indicators, with dedicated lifestyle predictors achieving 13.81 percentage points higher accuracy than medical indicators. Feature importance analysis confirmed that transportation mode, physical activity patterns, and dietary behaviors ranked among the most influential predictors in the combined model. These findings demonstrate that behavioral lifestyle factors constitute stronger obesity predictors than medical history variables, with implications for clinical screening strategies and public health intervention targeting that prioritize lifestyle assessment and modification programs. Full article

Background/Objectives: Propolis is well recognized for its antioxidant, anti-inflammatory, and wound-healing properties, supporting its cutaneous application in phytopharmaceuticals for the management of ultraviolet B (UVB)-induced skin damage. However, the application of propolis is limited by its intense coloration, stickiness, and poor user convenience. In situ film-forming systems (FFS) represent a novel dosage form designed to overcome these challenges, although efficacy data for using FFS remains limited. Consequently, this study aimed to develop a propolis-based FFS and evaluate its efficacy in mitigating UVB-irradiated HaCaT keratinocytes. Methods: Apis mellifera propolis was macerated and analyzed for total phenolic content (TPC) and total flavonoid content (TFC), radical scavenging activity (DPPH assay), and nitric oxide scavenging capability. Bioactive compounds were identified using high-performance liquid chromatography analysis (HPLC). The propolis extract was formulated into FFS and investigated on UVB-damaged HaCaT keratinocytes. An MTT viability assay, propidium iodide flow cytometry for cell cycle analysis, and a scratch wound healing assay were used to evaluate the therapeutic effects of the FFS. Results: The 72 h macerated propolis extract contained high levels of TPC, TFC, and targeted phytochemicals. The propolis extract exhibited free radical scavenging and nitric oxide inhibitory activities. Seven formulations exhibited suitable performance, with formulation F7 (FFS-F7) demonstrating superior drying time and dose-dependent free radical scavenging. Notably, FFS-F7 (≥12.5 µg/mL) significantly enhanced HaCaT proliferation, mitigated UVB-induced cell cycle arrest, reduced cellular damage, and accelerated wound closure. Conclusions: This study successfully developed an FFS that not only overcomes these physical drawbacks but also preserves the biological activity of the extract. The significant protective and restorative effects against UVB-induced HaCaT damage demonstrate the therapeutic potential of Thai Apis mellifera propolis and establish the FFS as a versatile base with the potential for delivering other bioactive compounds. Full article

(1) Background: Childhood obesity is one of the greatest challenges facing public health in the 21st century. Currently, there is a great deal of interest in the concept of physical literacy due to its comprehensive nature on the development of physical activity. Physical literacy can be described as the motivation, confidence, physical competence, knowledge and understanding to value and take responsibility for engagement in physical activities for life. The aim of this paper is to provide a systematic review of the literature examining the relationship between physical literacy and risk of obesity in children. This paper also focuses on providing an evaluation of the availability and effectiveness of current physical literacy assessment tools. (2) Methods: This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. (3) Results: A total of 3267 papers were identified from five major databases. Twelve studies met the inclusion criteria, with nine studies included in the final review following quality appraisal. (4) Conclusions: This review identified significant gaps in our understanding of physical literacy emphasising the need for a consistent framework, standardised assessment tools and more experimental research. Full article

The global construction industry urgently requires sustainable alternatives to ordinary Portland cement (OPC) to mitigate its immense carbon footprint. Red mud (RM), a highly alkaline bauxite residue, presents tremendous but challenging potential as a supplementary cementitious material. This review systematically bridges the gap between atomic-level interfacial bonding mechanisms and macroscopic engineering performance, highlighting how these properties are significantly dictated by specific RM sources (e.g., Bayer vs. Sintering processes). We first elucidate advanced pretreatment strategies, notably CO₂ mineralization, which synergistically mitigates extreme alkalinity and sequesters carbon. Crucially, the fundamental bonding mechanisms are decoded: beyond physical filling, RM integration induces significant micro-morphological densification via intense aluminosilicate depolymerization—evidenced by the Al[VI] to Al[IV] coordination shift—and the quantitative integration of approximately 40% reactive iron phases into stable Fe-S-H networks. By clearly distinguishing between traditional hydration and clinker-free alkali-activation pathways, we evaluate holistic structural parameters beyond mere 28-day compressive strength (40–67 MPa), explicitly addressing flexural capacity, modulus of elasticity, and volume stability. Environmental assessments confirm exceptional heavy metal immobilization (>95% efficiency, leaching < 0.010 mg/L) and a substantial 50–80% reduction in Global Warming Potential (GWP), provided the environmental burden of alkaline activators is rigorously accounted for. Furthermore, the long-term risk of Alkali–Silica Reaction (ASR) is evaluated as a primary durability concern. Finally, to overcome persistent rheological bottlenecks, this paper highlights transformative future trajectories, particularly data-driven Machine Learning (ML) for complex mix optimization and 3D concrete printing for advanced infrastructure. Ultimately, this review provides a robust theoretical foundation and a pragmatic roadmap for upcycling RM into safe, high-performance, and ultra-low-carbon building materials. Full article

Polysaccharide-based microparticles have emerged as suitable carriers and stabilizers of active substances, showing potential to stabilize bioactive compounds during storage and gastrointestinal digestion, thereby improving their bioaccessibility and bioavailability. This narrative review provides a comprehensive overview of the main polysaccharides employed as wall materials, including starch, maltodextrin, alginate, pectin, inulin, chitosan, and gum Arabic, and discusses how structural interactions and physicochemical properties can positively influence the microencapsulation of polyphenols and pigments. The principles and main findings of the main microencapsulation techniques, including spray-drying, freeze-drying, extrusion, emulsification, and coacervation, are briefly described. Polysaccharides can entrap both hydrophilic and hydrophobic compounds through physical interactions, forming a barrier around the nucleus or binding to the bioactive compound. Intermolecular binding between polysaccharides in the wall matrix, polyphenols, and pigments in the nucleus can confer up to 90% encapsulation efficiency, primarily governed by hydrogen bonds and electrostatic interactions. The mixture of wall polysaccharides in the microparticles synthesis favors the encapsulation solubility, storage stability, bioaccessibility, and bioactivity of the microencapsulate compounds. Clinical trials regarding the bioefficacy of polyphenols and pigments loaded in polysaccharide microparticles are scarce and require further evidence to reinforce the use of this technology. Full article

The discrete element method (DEM) is widely used to simulate concrete failure, but calibrating its mesoscopic dynamic parameters is computationally expensive due to the high-dimensional parameter space. This study proposes a physics-informed active learning framework to autonomously calibrate these parameters under impact loads. An FDM-DEM coupled split Hopkinson pressure bar model is established to simulate macroscopic dynamic compressive responses. Subsequently, a Plackett–Burman experimental design reduces the parameter optimization space from 16 to 8 core dimensions. A multi-layer perceptron surrogate model is then constructed. By comparing two heuristic active sampling strategies, results indicate that a parameter priority-guided strategy incorporating physical priors significantly outperforms a mid-value exploration strategy. The proposed approach achieves coefficients of determination exceeding 0.9 for predicting multiple macroscopic dynamic indicators on an independent testing set. Building upon this forward mapping, a robust inverse parameter prediction mechanism is established, achieving a closed-loop reconstruction of 0.8662. This framework provides a reliable, data-efficient, and automated pathway for calibrating complex multiphase particulate systems. Full article

Cyclocarya paliurus polysaccharides (CPP) possess various physiological functions such as lipid-lowering and antioxidant activities. However, as a complex plant-based dispersion system, the interfacial characteristics of fermented C. paliurus beverages often restrict the release of bioefficacy of the active ingredients. This study investigated the impact of particle size on the colloidal stability and lipid-lowering activity of C. paliurus beverages fermented by Lactobacillus plantarum and established an empirical correlation between the two. While the 200–300 mesh fraction showed superior physical stability, the 40–60 mesh fraction was identified as the optimal formulation in this study when balancing ROS indicators. In vivo assays using Caenorhabditis elegans demonstrated that the 40–60 mesh formulation significantly reduced MDA levels and inhibited lipid accumulation, decreasing TG content by 19–46%. Notably, the average diameter of lipid droplets decreased by 38.4%, promoting the conversion of large storage-type droplets to small/medium-sized droplets with high metabolic activity. This study reveals the trade-off between physical dispersibility and bioavailability, providing a theoretical basis for optimizing the interfacial structure of functional plant-based beverages. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article