Search Results (134,035)

Understanding how individuals balance health and pleasure in food choices is important for promoting healthier diets. This study examined 6300 adults across ten countries (630 per country) using the General Health Interest and Pleasure subscales of the Health and Taste Attitude Scales. Participants were grouped into four categories—HH-HP (High Health, High Pleasure), HH-LP (High Health, Low Pleasure), LH-HP (Low Health, High Pleasure), and LH-LP (Low Health, Low Pleasure)—based on their scores. Clear cross-national differences were observed. Respondents in Peru and China prioritized both health and pleasure, while those in Mexico and Russia scored higher on pleasure but lower on health. A polarized pattern was found in Japan, and a more balanced distribution appeared in Thailand and Spain. Australia, the United Kingdom, and the United States showed generally lower scores for both dimensions. Females tended to report higher health interest and greater pleasure in eating than males. Older age and higher education were also associated with stronger interest in health and food enjoyment. These results emphasize the importance of considering cultural and demographic variations when designing strategies to encourage healthy eating, and they support the cross-cultural validity of the Health and Taste Attitude Scales. Full article

High-renewables grids are planned in min but judged in milliseconds; credible studies must therefore resolve both horizons within a single model. Current adequacy tools bypass fast frequency dynamics, while detailed simulators lack multi-hour optimization, leaving investors without a unified basis for sizing storage, shifting demand, or upgrading transfers. We present a two-layer Hierarchical Model Predictive Control framework that links 15-min scheduling with 1-s corrective action and apply it to Germany’s four TSO zones under a stringent zero-exchange stress test derived from the NEP 2045 baseline. Batteries, vehicle-to-grid, pumped hydro and power-to-gas technologies are captured through aggregators; a decentralized optimizer pre-positions them, while a fast layer refines setpoints as forecasts drift; all are subject to inter-zonal transfer limits. Year-long simulations hold frequency within ±2 mHz for 99.9% of hours and below ±10 mHz during the worst multi-day renewable lull. Batteries absorb sub-second transients, electrolyzers smooth surpluses, and hydrogen turbines bridge week-long deficits—none of which violate transfer constraints. Because the algebraic core is modular, analysts can insert new asset classes or policy rules with minimal code change, enabling policy-relevant scenario studies from storage mandates to capacity-upgrade plans. The work elevates predictive control from plant-scale demonstrations to system-level planning practice. It unifies adequacy sizing and dynamic-performance evaluation in a single optimization loop, delivering an open, scalable blueprint for high-renewables assessments. The framework is readily portable to other interconnected grids, supporting analyses of storage obligations, hydrogen roll-outs and islanding strategies. Full article

Alzheimer’s disease (AD), the most prevalent form of dementia, is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, ultimately leading to loss of independence and reduced quality of life. Since current treatments are most effective in early stages, the development of reliable and noninvasive biomarkers for early diagnosis and monitoring is crucial. Abnormal tau protein aggregation is a key pathological hallmark of AD, disrupting neuronal integrity, accelerating progression, and associating closely with cognitive decline and the transition to mild cognitive impairment, a prodromal stage of AD. Currently, tau pathology is evaluated mainly by cerebrospinal fluid analysis and tau positron emission tomography (tau PET), which are invasive or costly, limiting their clinical applicability. This systematic review and meta-analysis synthesized evidence on tau as a blood-based biomarker for dementia, with emphasis on its relationship to tau PET, the gold standard for in vivo tau assessment. Findings indicate that elevated plasma tau levels such as p-tau181, p-tau217 and p-tau231 consistently reflect brain tau pathology, supporting their role as surrogate markers. Large-scale longitudinal validation is warranted to establish blood-based tau as a practical, accessible tool for early detection and disease monitoring, thereby improving therapeutic outcomes in AD. Full article

This cross-sectional study examined whether physical activity (PA) in nature and overall PA mediate the relationship between nature exposure and quality of life (QoL) across four domains: physical, psychological, social, and environmental, while controlling for perceived financial security. A cross-sectional online survey was conducted, involving 924 adults aged 18 to 79 years (m = 40.0, SD = 12.4); 73.6% were women. Nature exposure, PA in nature, overall PA, and financial security were assessed using nationally language-validated self-report scales and questionnaires. QoL was measured using the WHOQOL-BREF, covering four domains. Mediation models were tested using the regression-based PROCESS macro with 5000 bootstrapped samples. Nature exposure was positively associated with both types of PA and all QoL domains, while financial security was positively linked to PA in nature. PA in nature significantly mediated the relationship between nature exposure and psychological QoL, but not the other domains. In contrast, overall PA was a significant mediator across all QoL domains. In all models, nature exposure and financial security remained significant direct predictors of QoL. Bootstrapped confidence intervals confirmed the significance of indirect effects through overall PA for physical, psychological, social, and environmental QoL. While nature exposure was independently associated with better QoL, this relationship was partly explained by PA. These findings highlight the broader role of PA in linking nature exposure to QoL and underscore the importance of supporting active lifestyles in nature to enhance QoL. To achieve a higher QoL, policies that increase access to and opportunities for nature-based physical activity should be implemented. Full article

Olive cultivation is one of the most widespread agro-industrial activities in the Mediterranean area. However, required pretreatments often affect the anaerobic digestion process, promoting or inhibiting the overall yield. Therefore, the efficiency of Anaerobic Digestion (AD) processes cannot be established in advance but needs to be experimentally validated for each biomass-pretreatment combination. Following the present purpose, these biomasses were firstly treated: the olive pomace (OP) with a procedure based on the use of an ionic liquid (IL) composed of triethylamine and sulfuric acid [Et₃N][HSO₄] to remove hemicellulose and lignin and recover the insolubilized OP, while olive mill wastewater (OW) was processed via freeze-drying. The resulting materials, the pulp from olive pomace (POP) and freeze-dried OW (FDOW), were then digested using lab-scale anaerobic reactors. The biogas production was then compared with the quantity obtained by digesting the same untreated biomasses (OW and OP). The FDOW showed the highest biogas production due to the freeze-drying treatment that led to some morphological and structural surface modifications of OW (respectively, 658 mL vs. 79 mL/g for the two matrices), prompting microorganism activity. Conversely, the method based on the use of IL significantly reduced the nitrogen content of POP, thus resulting in the lowest biogas production, which ceased by the second day. To address this issue, we co-digested POP with the brewery’s spent grain, a biomass rich in nitrogen. This step enhanced the biogas yield of POP, resulting in an extended anaerobic digestion period and the production of 466 mL/g. Additionally, we tested FDOW in co-digestion with BSG to evaluate improvements in production. The codigestion of the two matrices increased the biogas yield of FDOW from 944 to 1131 mL/g. Full article

Cathepsin C (CTSC) mediates neutrophil serine protease (NSP) maturation, contributing to inflammatory cascades, making it a key therapeutic target. In this study, through large-scale screening of a natural product library, marrubiin, a diterpenoid lactone compound, was identified as a potent CTSC inhibitor, which holds potential value in the treatment of inflammatory diseases. It inhibited human recombinant CTSC (IC₅₀ = 57.5 nM) and intracellular CTSC (IC₅₀ = 51.6 nM) with acceptable cytotoxicity, and reduced the activity and protein levels of downstream NSPs in vitro. Functionally, marrubiin inhibited lipopolysaccharide-induced nitric oxide release and regulated the levels of cytokines and chemokines. Docking result predicted marrubiin may achieve CTSC activity inhibition by using lactone structure as a covalent unit to target Cys234. In vivo study indicated that high-dose marrubiin (IC₅₀ = 30 mg/kg) reduced CTSC and NSPs activities in blood and bone marrow in mice without toxicity, and its efficacy was comparable to that of positive compound AZD7986. In the adjuvant-induced arthritis model, high-dose marrubiin (IC₅₀ = 60 mg/kg) exerted a therapeutic effect by reducing the activities of CTSC and NSPs. These findings indicated marrubiin is a promising natural CTSC inhibitor, which can be used for the treatment of neutrophil-related inflammatory diseases. Full article

Obesity is a highly complex, multifactorial disease influenced by dynamic interactions among genetic, epigenetic, environmental, and behavioral determinants that explicitly position genetics as the core. While advances in multi-omic integration have revolutionized our understanding of adiposity pathways, translation into personalized clinical nutrition remains a critical challenge. This review systematically consolidates emerging insights into the molecular and nutrigenomic architecture of obesity by integrating data from large-scale GWAS, functional epigenomics, nutrigenetic interactions, and microbiome-mediated metabolic programming. The primary aim is to systematically organize and synthesize recent genetic and genomic findings in obesity, while also highlighting how these discoveries can be contextualized within precision nutrition frameworks. A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science up to July 2024 using MeSH terms, nutrigenomic-specific queries, and multi-omics filters. Eligible studies were classified into five domains: monogenic obesity, polygenic GWAS findings, epigenomic regulation, nutrigenomic signatures, and gut microbiome contributions. Over 127 candidate genes and 253 QTLs have been implicated in obesity susceptibility. Monogenic variants (e.g., LEP, LEPR, MC4R, POMC, PCSK1) explain rare, early-onset phenotypes, while FTO (polygenic) and MC4R (monogenic mutations as well as common polygenic variants) represent major loci across populations. Epigenetic mechanisms, dietary composition, physical activity, and microbial diversity significantly recalibrate obesity trajectories. Integration of genomics, functional epigenomics, precision nutrigenomics, and microbiome science presents transformative opportunities for personalized obesity interventions. However, translation into evidence-based clinical nutrition remains limited, emphasizing the need for functional validation, cross-ancestry mapping, and AI-driven precision frameworks. Specifically, this review systematically identifies and integrates evidence from genomics, epigenomics, nutrigenomics, and microbiome studies published between 2000 and 2024, applying structured inclusion/exclusion criteria and narrative synthesis to highlight translational pathways for precision nutrition. Full article

Background and Objectives: Spinal stenosis, low back pain, and radiating pain to the lower extremities are caused by inflammation of the spinal canal and impaired blood flow around the nerves. Because JOINS tablets are known to have anti-inflammatory, pain-relieving, and blood circulation-enhancing properties, this research was conducted based on the assumption that they could improve spinal stenosis. Materials and Methods: This was a prospective, randomized, single-center, open-label clinical trial with a 6-month follow-up period. A total of 100 patients with lumbar spinal stenosis were randomized into two groups: 50 patients in the test group and 50 patients in the control group. The control group was prescribed the usual spinal stenosis medications (Naproxen, Limaprost, and Pregabalin), while the test group was prescribed JOINS tablets in addition to the usual medications. Results: The severity of low back pain and radiating leg pain was assessed using a Visual Analog Scale. Spinal functional outcomes were assessed using the Oswestry Disability Index (ODI) and Roland-Morris Disability Questionnaire (RMDQ), and quality of life was assessed using the Short Form 36 (SF-36), with division into Physical Component Score (PCS) and Mental Component Score (MCS). At 6 months, the JOINS group showed a greater reduction in low back pain compared with controls (p < 0.001). At all follow-up periods, the functional outcomes did not show statistically significant differences between the test and control groups. Conclusions: The significant reduction in pain suggests that JOINS tablets may be an effective adjunct for pain relief, particularly in patients at high risk of adverse effects from long-term NSAID use. Full article

Object detection in degraded environments such as low-light and nighttime conditions remains a challenging task, as conventional computer vision techniques often fail to achieve high precision and robust performance. With the increasing adoption of deep learning, this paper aims to enhance object detection under such adverse conditions by proposing an improved version of YOLOv11, named DRF-YOLO (Degradation-Robust and Feature-enhanced YOLO). The proposed framework incorporates three innovative components: (1) a lightweight Cross Stage Partial Multi-Scale Edge Enhancement (CSP-MSEE) module that combines multi-scale feature extraction with edge enhancement to strengthen feature representation; (2) a Focal Modulation attention mechanism that improves the network’s responsiveness to target regions and contextual information; and (3) a self-developed Dynamic Interaction Head (DIH) that enhances detection accuracy and spatial adaptability for small objects. In addition, a lightweight unsupervised image enhancement algorithm, Zero-DCE (Zero-Reference Deep Curve Estimation), is introduced prior to training to improve image contrast and detail, and Generalized Intersection over Union (GIoU) is employed as the bounding box regression loss. To evaluate the effectiveness of DRF-YOLO, experiments are conducted on two representative low-light datasets: ExDark and the nighttime subset of BDD100K, which include images of vehicles, pedestrians, and other road objects. Results show that DRF-YOLO achieves improvements of 3.4% and 2.3% in mAP@0.5 compared with the original YOLOv11, demonstrating enhanced robustness and accuracy in degraded environments while maintaining lightweight efficiency. Full article

Federated learning (FL) enables privacy-preserving adaptation of large language models (LLMs) across distributed clients. However, deploying FL in edge environments remains challenging because of the high communication overhead of full-model updates. Recent advances in parameter-efficient fine-tuning (PEFT), particularly low-rank adaptation (LoRA), have substantially reduced update sizes by injecting lightweight trainable matrices into pretrained transformers, thereby making FL with LLMs more feasible. In this paper, we propose LoRaC-GA, a communication-aware optimization framework that dynamically determines the optimal number of clients to participate in each round under a fixed bandwidth constraint. We formulated a max-min objective to jointly maximize the model accuracy and communication efficiency and solved the resulting non-convex problem using a genetic algorithm (GA). To further reduce the overhead, we integrated a structured peer-to-peer collaboration protocol with ${log}_{2}K$ complexity, enabling scalable communication without full connectivity. The simulation results demonstrate that LoRaC-GA adaptively selects the optimal client count, achieving competitive accuracy while significantly reducing the communication cost. The proposed framework is well-suited for bandwidth-constrained edge deployments involving large-scale LLMs. Full article

Background: Iron oxide nanoparticles (IONPs) have broad-spectrum antimicrobial activity, with negligible potential for resistance development, excellent biocompatibility, and therefore, could be promising alternatives to conventional antimicrobials. However, their industrial-scale production relies on chemical synthesis that involves toxic reagents, imposing potential environmental hazards. In contrast, green synthesis offers an eco-friendly alternative, but our previous study found that green-synthesized IONPs (IONPs-G) exhibited a lower antibacterial activity and a higher cytotoxicity compared to chemically synthesized counterparts, likely due to nanoparticle aggregation. Objectives: To address this challenge, the current study presents a simple, effective, economic, scalable, and eco-friendly strategy to optimize the physicochemical properties of IONPs-G post-production without requiring extensive modifications to synthesis parameters. Methods: IONPs-G were dispersed in a solvent mixture containing Tween 80 (Tw80). Subsequently, in vitro antimicrobial and in vivo cytotoxicity studies on rabbits’ skin and eye were conducted. Results: The formed nanoparticles’ dispersion (IONPs-GTw80) had a particle size of 9.7 ± 2.1 nm, a polydispersity index of 0.111 ± 0.02, and a zeta potential of −11.4 ± 2.4 mV. MIC of IONPs-GTw80 values against S. aureus and E. coli were reduced by more than ten-fold compared to IONPs-G. MBC was twice MIC, confirming the bactericidal activity of IONPs-GTw80. In vivo studies of IONPs-GTw80 confirmed their biocompatibility with intact/abraded skin and eyes; this was further confirmed by histopathological and biochemical analyses. Conclusions: IONPs-GTw80 might be recommended as a disinfectant in healthcare settings or a topical antimicrobial agent for treatment of infected wounds. Nevertheless, further studies are required for their clinical translation. Full article

Introduction: The efficacy of percutaneous vertebroplasty (PV) versus placebo and conservative treatment (CT) in patients with osteoporotic vertebral fractures (OVFs) has been debated in recent years. The aim of this study was to conduct an updated systematic review with a meta-analysis on the efficacy of randomized controlled trials (RCTs) comparing PV versus placebo and CT in pain relief, functionality and quality of life in patients with OVFs. Methods: A systematic search was conducted in PubMed, Web of Science, EMBASE, and CENTRAL, resulting in a total of 15 RCTs. The risk of bias was assessed using the Risk of Bias v.2 tool. A meta-analysis was performed using the weighted inverse variance method to analyze the standardized mean difference (SMD) in pain (VAS/NRS scales), functionality (RMDQ/ODI scales) and quality of life (QUALEFFO scale) in the short (<1 month), medium (1–6 months) and long terms (≥6 months). Heterogeneity was assessed using I² and τ². Subgroup analyses were performed according to the type of control, geographic region, number of institutions, fracture chronicity, and risk of bias. In addition, sensitivity (leave-one-out) and publication bias (funnel plots and Egger’s tests) analyses were performed. Results: Overall, PV showed benefits over the combined control groups in pain relief in the short (SMD: −0.68; 95%CI: −1.28–−0.07), medium (SMD: −0.63; 95%CI: −1.18–−0.07), and long terms (SMD: −0.59; 95%CI: −1.02–−0.15). No statistically significant differences were found in functionality and quality of life, although several trends toward significance were observed favoring PV. Subgroup analyses showed greater advantages of PV at several time intervals in acute (<8 weeks) OVFs, multicentric trials and studies with a low risk of bias. There were cues suggestive of potential publication bias in functionality, but not in pain or quality of life. Conclusions: PV shows significant benefits in pain relief, particularly in acute OVFs, but its efficacy in terms of functionality and quality of life remains unclear. These results support the use of PV in appropriately selected patients. However, given the high heterogeneity found, more controlled, multicenter trials are still required. Full article

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance. Full article

With the rapid development of 3D sensor technology, point cloud semantic segmentation has found widespread applications in autonomous driving, remote sensing, mapping, and industrial manufacturing. However, outdoor traffic scenes present significant challenges: point clouds are inherently disordered, unevenly distributed, and unstructured. As a result, traditional point cloud semantic segmentation methods often suffer from low accuracy and unstable performance in complex tasks such as semantic segmentation and object detection. To address these limitations, this paper proposes an improved point cloud semantic segmentation method based on Cylinder3D. The proposed approach integrates the PointMamba and MS3DAM modules, which enhance the model’s ability to capture global features while preserving local details, thereby improving adaptability and recognition across multiple feature scales. Furthermore, leveraging the linear computational complexity of Mamba enables the method to maintain high efficiency when processing large-scale point cloud data. In addition, incorporating the KAT module into the encoder improves the model’s perceptual capacity and robustness in handling point clouds. Experimental results on the SemanticKITTI dataset demonstrate that the proposed method achieves a mean Intersection over Union (mIoU) of 64.98%, representing a 2.81% improvement over Cylinder3D, thereby confirming its superior segmentation accuracy compared with existing models. Full article

The increasing demand for monoclonal antibody (mAb) therapeutics has intensified the need for more efficient and consistent biomanufacturing processes. We present a data-driven, machine-learning (ML) approach to exploring and predicting upstream yield behavior. Drawing on industrial-scale batch records for a single mAb product from a contract development and manufacturing organization, we applied regression models to identify key process parameters and estimate production outcomes. Random forest regression, gradient boosting machine, and support vector regression (SVR) were evaluated to predict three yield indicators: bioreactor final weight (BFW), harvest titer (HT), and packed cell volume (PCV). SVR outperformed other models for BFW prediction (R² = 0.978), while HT and PCV were difficult to model accurately with the available data. Exploratory analysis using sequential least-squares programming suggested parameter combinations associated with improved yield estimates relative to historical data. Sensitivity analysis highlighted the most influential process parameters. While the findings demonstrate the potential of ML for predictive, data-driven yield improvement, the results should be interpreted as an exploratory proof of concept rather than a fully validated optimization framework. This study highlights the need to incorporate process constraints and control logic, along with interpretable or hybrid modeling frameworks, to enable practical deployment in regulated biomanufacturing environments. Full article