Search Results (3,517)

Traffic-related air pollution (TRAP) is known to contribute to oxidative stress in the central nervous system (CNS) and has been linked to increased risk of Alzheimer’s disease (AD). Alterations in the renin–angiotensin system (RAS), specifically increased angiotensin II (Ang II) signaling via the angiotensin II type 1 (AT₁) receptor, are implicated in increased oxidative stress in the CNS via activation of NADPH oxidase (NOX). As exposure to TRAP may further elevate AD risk, we investigated whether exposure to inhaled mixed gasoline and diesel vehicle emissions (MVE) promotes RAS-dependent expression of factors that contribute to AD pathophysiology in an apolipoprotein E-deficient (ApoE^−/−) mouse model. Male ApoE^−/− mice (6–8 weeks old) on a high-fat diet were treated with either an ACE inhibitor (captopril, 4 mg/kg/day) or water and exposed to filtered air (FA) or MVE (200 µg PM/m³) for 30 days. MVE exposure elevated plasma Ang II, inflammation, and oxidative stress in the hippocampus, associated with increased levels of Aph-1 homolog B (APH1B), a gamma-secretase subunit, and beta-secretase 1 (BACE1), involved in Aβ production. Each of these endpoints was normalized with ACEi treatment. These findings indicate that TRAP exposure in ApoE^−/− mice drives a RAS- and NOX-dependent oxidative and inflammatory response and shifts Aβ processing towards an amyloidogenic profile before overt Aβ deposition, suggesting a potential therapeutic approach for air pollution-induced AD risk. Full article

The escalating incidence of colorectal cancer (CRC), particularly the alarming rise in early-onset cases, necessitates a paradigm shift from a purely genetic perspective to a broader investigation of promising pathways. This review explores the “nutri-epigenetic” interface, positioning liquid biopsy as a critical technology for translating dietary impacts into actionable clinical biomarkers. We contrast the molecular consequences of the Western dietary pattern, characterized by methyl-donor deficiency and pro-inflammatory metabolites, with the protective mechanisms of the Mediterranean diet. Mechanistically, we detail how Western-style diets drive a specific “epigenetic double-hit”: promoting global DNA hypomethylation (destabilizing LINE-1) while paradoxically inducing promoter hypermethylation of critical tumour suppressors (MLH1, APC, MGMT) and silencing tumour-suppressive microRNAs (miR-34b/c, miR-137) via methylation of their encoding genes. Conversely, we highlight the capacity of Mediterranean bioactive compounds (e.g., resveratrol, curcumin, butyrate) to inhibit DNA methyltransferases and restore epigenetic homeostasis. Bridging molecular biology and clinical utility, we demonstrate how these diet-sensitive signatures, specifically circulating methylated DNA and dysregulated microRNAs, can be captured via liquid biopsy. We propose that these circulating analytes serve as dynamic, accessible biomarkers for monitoring the molecular progression toward a carcinogenic state, thereby establishing a novel framework for personalized risk stratification and validating the efficacy of preventive nutritional strategies. Full article

Unstable car-following behavior under truck-induced visual occlusion on mountainous two-lane roads significantly increases rear-end crash risk. However, compared with studies focusing on overtaking or curve risk prediction, the car-following-truck (CFT) risk and its influencing factors have received limited attention. Therefore, this study used unmanned aerial vehicles (UAVs) to collect high-resolution trajectory data of CFT scenarios on both straight and curved segments under truck-induced occlusion. First, the CFT risk was quantified based on an anticipated collision time (ACT) indicator, a two-dimensional surrogate safety measure that accounts for vehicle acceleration variations. Then, extreme value theory (EVT) was applied to calibrate alignment-specific risk thresholds. Finally, an XGBoost-based risk identification model was developed using vehicle dynamics-related features, and feature importance analysis combined with partial dependence interpretability was conducted to obtain key influencing factors. The results show that the calibrated ACT thresholds are approximately 3.838 s for straight segments and 4.385 s for curved segments, providing a reliable basis for risk classification. In addition, the XGBoost-based risk identification achieved accuracies of 90.63% and 95.87% for straight and curved segments, respectively. Further analysis indicates that CFT distance was the contributing factor. Moreover, risk increases markedly within a 10–20 m range on straight segments, while it rises rapidly once spacing falls below about 10 m on curved segments. Speed and acceleration differences exhibited stronger amplifying effects under short-spacing conditions. These findings provide a micro-behavioral basis for safety management and intelligent driving applications on mountainous roads with high truck mixing rates, supporting safer and more sustainable traffic operations. Full article

This study investigated the influence of awareness, knowledge, and risk perceptions on environmental attitudes and behaviours in Türkiye, specifically in the context of climate change, using structural equation modelling (SEM). Data were collected from all 81 provinces covering the seven geographical regions of the country. The results revealed that awareness and risk perception have the strongest direct impact on pro-environmental behaviour. Environmental attitudes also demonstrated a significant positive effect, though the findings suggest that high awareness and risk perception can directly drive action even independently of attitude. Uniquely, this study fills a critical gap in the developing country literature by demonstrating that in Türkiye, perceiving the risk translates directly into action, contrasting with the ‘value-action gap’ often observed in Western contexts. Practically, the findings suggest that policymakers should prioritize risk-communication strategies and disaster-preparedness drills over passive information campaigns to effectively stimulate pro-environmental behaviours. Full article

The rise in antimicrobial resistance (AMR) among the most clinically significant bacteria presents a global threat. The coexistence of resistance mechanisms to both carbapenems and colistin is particularly concerning, as these are last-line treatments, specifically reserved for the most challenging infections caused by clinically multidrug-resistant Enterobacterales. Natural aquatic environments have become environmental reservoirs for the transmission of AMR, particularly concerning mechanisms against these two types of critically important drugs. The crucial role of environmental settings as a driving force for the spread and evolution of AMR associated with these drugs is underestimated, and scientific knowledge on this topic is limited. This review aims to fill an important gap in the scientific literature and comprehensively consolidate the available data on carbapenem- and colistin-associated AMR in the aquatic environment. This study provides a comprehensive synthesis of the current knowledge by integrating bibliographic data with a detailed genomic analysis of 278 bacterial genomes sourced from natural waters. It explores the distribution of carbapenemase and mobile colistin resistance (mcr) genes, identifying their hosts, geographical spread, and complex gene–plasmid–host associations. This review distinguishes two critical host groups for genes that provide resistance to last-resort drugs, Enterobacterales and autochthonous aquatic microbiota, highlighting both confirmed and potential interactions between them. Crucially, genomic analysis highlights the alarming co-occurrence of carbapenem and colistin resistance in single cells and on single plasmids, contributing to the spread of multidrug resistance phenotypes. These findings clearly indicate that aquatic environments are not merely passive recipients but active, evolving hubs for high-risk AMR determinants. Future research should focus on the interplay between allochthonous vectors and autochthonous microbiota to better understand the long-term stabilization of carbapenemase and mcr genes. Such efforts, combined with advanced sequencing technologies, are essential to ensure that carbapenems and colistin remain viable treatment options in clinical settings. Full article

Climate-induced lake expansion on the Tibetan Plateau (TP) has led to two distinct hazard types: outburst floods and passive inundation. However, the divergent driving mechanisms behind these hazards remain insufficiently understood. This study analyzes the spatiotemporal trends of 1352 non-glacial lakes (>1 km²) on the TP from 2014 to 2023 using high-resolution Gaofen-1 (GF-1) and Gaofen-2 (GF-2) imagery. By integrating geomorphic analysis with hazard mechanisms, we screened and categorized lakes prone to outburst floods and inundation using a classification and assessment framework proposed in this study. The results indicate that the net area of these lakes expanded by 2839.53 km² (6.07%), with the Inner TP Basin contributing the largest absolute area gain (1960.60 km²). We identified 21 potentially hazardous lakes (10 outburst-prone and 11 inundation-prone) and systematically categorized them by risk level. Field investigations of high-risk candidates, such as Rulei Co and Xiao Qaidam Lake, validated the accuracy of the hazard classification and risk assessment methodology. Preliminary attribution analysis further suggests that the two hazard types may be associated with distinct climatic factors. Overall, this study provides a scientific basis for disaster mitigation and lake management on the TP. Full article

Investigating the spatio-temporal differentiation patterns and driving factors of ecosystem services (ESs) supply and demand is of great significance for early warning of ecosystem imbalance risks and identifying regional natural resource supply–demand conflicts. This study takes the typical coal-grain overlapping area (CGOA) in Eastern China as the research object, dividing it into mining townships (MT) and non-mining townships (NMT) for comparative analysis. By integrating the InVEST model, ESs supply–demand ratio (ESDR) index, four-quadrant model, and the XGBoost-SHAP algorithm, the study systematically reveals the spatiotemporal differentiation characteristics and driving mechanisms of ESs supply and demand from 2000 to 2020. The results indicated that: (1) grain production (GP) service maintained a continuous supply–demand surplus, with the ESDR of NMT areas surpassing that of MT areas in 2020. The ESDR of water yield (WY) service was significantly influenced by interannual fluctuations in supply, showing deficits in multiple years. The decline in carbon sequestration (CS) service and sharp increase in carbon emissions led to a continuous decrease in the ESDR of CS service, with MT areas facing a higher risk of carbon deficit. (2) The spatial heterogeneity of ESs supply and demand was significant, with GP and CS services exhibiting a typical urban-rural dual spatial structure, and the overall region was dominated by the Type Ⅱ ESs supply–demand matching (ESDM) pattern. The ESDR of WY service generally decreases from Southeast to Northwest across the region. with the Type Ⅳ ESDM pattern dominating in most years. (3) Human activities are the core driving force shaping the supply–demand patterns of ESs. Among these, land use intensity exhibits a nonlinear effect, high population density demonstrates an inhibitory effect, and MT areas are more significantly affected by coal mining subsidence. Natural environmental factors primarily drive WY service. The research findings can provide a scientific reference for the coordinated allocation of regional natural resources and the sustainable development of the human–land system. Full article

Recent advances in Large Vision–Language Models (LVLMs) have demonstrated strong cross-modal reasoning capabilities, offering new opportunities for decision-making in autonomous driving. However, existing end-to-end approaches still suffer from limited semantic consistency, weak task controllability, and insufficient interpretability. To address these challenges, we propose SemAlign-E2E (Semantic-Aligned End-to-End), a semantic-aligned multimodal LVLM framework that unifies visual, LiDAR, and task-oriented textual inputs through cross-modal attention. This design enables end-to-end reasoning from scene understanding to high-level driving command generation. Beyond producing structured control instructions, the framework also provides natural-language explanations to enhance interpretability. We conduct extensive evaluations on the nuScenes dataset and CARLA simulation platform. Experimental results show that SemAlign-E2E achieves substantial improvements in driving stability, safety, multi-task generalization, and semantic comprehension, consistently outperforming state-of-the-art baselines. Notably, the framework exhibits superior behavioral consistency and risk-aware decision-making in complex traffic scenarios. These findings highlight the potential of LVLM-driven semantic reasoning for autonomous driving and provide a scalable pathway toward future semantic-enhanced end-to-end driving systems. Full article

Background: Chemokine CCL5 may drive inflammation and vascular risk in advanced liver disease, but its cardiovascular implications are unclear. Secreted by hepatic, endothelial, macrophage, and lymphocytic cells, CCL5 is involved in cytokine regulation. Its serum levels rise in acute liver injury and hepatocellular carcinoma (HCC), but decline with fibrosis progression in end-stage liver disease (ESLD). CCL5 has also been linked to atherosclerosis. This study aimed to evaluate serum CCL5 levels in ESLD patients listed for liver transplantation (LT) and to assess their potential role as markers of cardiovascular (CV) risk and portal hypertension. Methods: We conducted an observational cohort study. Between 2019 and 2022, patients with ESLD evaluated for LT were enrolled. Data on liver pathology, CV risk, and laboratory parameters were collected. Serum CCL5 concentrations were measured using Sigma Aldrich^® CCL5 ELISA kits (MilliporeSigma, St. Louis, MO, USA). The database was analyzed with IBM^® SPSS^® Statistics version 20 (Chicago, IL, USA). Results: Overall, 46 patients were included, 50% with viral hepatitis and 28.3% with alcohol-related liver disease. HCC was present in 37% of cases. The median CV risk scores (CAD_LT = 7, mCAD_LT = 7, CAR_OLT = 18) placed the population at moderate CV risk. Serum CCL5 levels did not vary significantly between viral vs. non-viral cirrhosis (5511.8 vs. 6272.5 pg/mL, p = 0.15) and were not influenced by the presence of HCC (6098.4 vs. 5771.3 pg/mL, p = 0.55). We did not detect a correlation with MELD score (p = 0.21) or CV risk scores (CAD_LT: p = 0.58; mCAD_LT: p = 0.70; CAR_OLT: p = 0.22). Patients with thrombocytopenia (<100,000/µL, 54.3%) or a history of esophageal variceal ligation had lower CCL5 levels (5170.9 vs. 6750.8 pg/mL, p = 0.002 and 4252.0 vs. 6237.5 pg/mL, p = 0.003, respectively). Similarly, patients with a history of previous variceal bleeding and spontaneous bacterial peritonitis (SBP) had lower levels of CCL5 (4373.8 vs. 6119.9 pg/mL, p = 0.02 and 3404.3 vs. 6606.7 pg/mL, p = 0.01, respectively). We found a negative correlation between CCL5 and QTc interval duration (τ = −0.216, p = 0.037), left ventricle size (LV: τ = −0.235, p = 0.027), and pulmonary artery pressure (RV/RA gradient: τ = −0.225, p = 0.03). CCL5 correlated positively with the inflammatory markers C-reactive protein (CRP) (τ = 0.246, p = 0.018) and fibrinogen (r = 0.216, p = 0.04). Conclusions: In liver transplant candidates, serum CCL5 is not associated with cardiovascular risk scores or coronary atherosclerotic burden, but is inversely associated with clinical markers of portal hypertension severity. These findings suggest that CCL5 may serve as a potential non-invasive surrogate marker of portal hypertension rather than a cardiovascular risk biomarker in ESLD. Full article

The maritime sector is under growing pressure to decarbonize, driving the adoption of alternative fuels such as methane, methanol, ammonia, and hydrogen. This study evaluates their thermal behavior and associated risks using Engineering Equation Solve software for heat transfer modeling and Areal Locations of Hazardous Atmospheres software for dispersion and explosion analysis in pipelines and storage scenarios. Results indicate that methane presents moderate and predictable risks, mainly from thermal effects in fires or Boiling Liquid Expanding Vapor Explosion events, with low toxicity. Methanol offers the safest operational profile, stable at ambient temperature and easily manageable, though it remains slightly flammable even when diluted. Ammonia shows the greatest toxic hazard, with impact distances reaching several kilometers even when emergency shutoff systems are active. Hydrogen, meanwhile, poses the most severe flammability and explosion risks, capable of autoignition and generating destructive overpressures. Thermal analysis highlights that cryogenic fuels require complex insulation systems, increasing storage costs, while methanol and gaseous hydrogen remain thermally stable but have lower energy density. The study concludes that methanol is the most practical transition fuel, when comparing thermal behavior and associated risks, while hydrogen and ammonia demand further technological and regulatory development. Proper insulation, ventilation, and automatic shutoff systems are essential to ensure safe decarbonization in maritime transport. Full article

With rapid socioeconomic development and intensified human activities, nitrogen (N) loads have continued to rise, exerting significant impacts on the environment. Most existing studies focus on single cities or short time periods, which limits their ability to capture nitrogen dynamics under rapid urbanization. Based on statistical data from multiple cities in Zhejiang Province from 2011 to 2021, this study applied nitrogen balance accounting and statistical analysis to systematically evaluate the spatiotemporal variations in nitrogen inputs, outputs, and surpluses, as well as their driving factors. The results indicate that although nitrogen inputs and outputs fluctuated over the past decade, the overall nitrogen surplus showed an increasing trend, with the nitrogen surplus per unit area rising from 49.89 kg/(ha·a) in 2011 to 62.59 kg/(ha·a) in 2021. Zhejiang’s nitrogen load was higher than the national average but remained below the levels of highly urbanized regions such as the Yangtze River Delta and Pearl River Delta. Accelerated urbanization and increasing anthropogenic pressures were identified as major contributors to the rising nitrogen surplus, with significant inter-city disparities. Cities like Hangzhou, Ningbo, Wenzhou, and Jinhua were found to face higher risks of nitrogen pollution. Redundancy analysis and Pearson correlation analysis revealed that nitrogen surplus was positively correlated with cropland area, livestock population, total population, precipitation, GDP, and industrial output, further highlighting the dominant role of human activities in nitrogen cycling. This study provides the long-term quantitative assessment of nitrogen balance under multi-city coupling at the provincial scale and identifies key influencing factors. These findings provide scientific support for integrated nitrogen management across multiple environmental compartments in Zhejiang Province, including surface water, groundwater, agricultural systems, and urban wastewater, under conditions of rapid urbanization. Full article

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

Initially characterized as a component of the extracellular matrix (ECM) in cartilage, cartilage intermediate layer protein 2 (CILP2) is now recognized as a pleiotropic secretory protein with far-reaching roles in physiology and disease. This review synthesizes evidence establishing CILP2 as a key modulator at the nexus of metabolic dysfunction, cancer, and other pathologies. Genomic studies have firmly established the NCAN-CILP2 locus as a hotspot for genetic variants influencing dyslipidemia and cardiovascular risk. Functionally, CILP2 is upregulated by metabolic stress, including high glucose and oxidatively modified LDL (oxLDL), and actively contributes to pathologies such as dyslipidemia, diabetes, and sarcopenia by impairing glucose metabolism and mitochondrial function. Its role extends to fibrosis and neurodevelopment, promoting hypertrophic scar formation and neurogenesis through interactions with ATP citrate lyase (ACLY) and Wnt3a, respectively. More recently, CILP2 has emerged as an oncoprotein, overexpressed in multiple cancers, including pancreatic ductal adenocarcinoma and colorectal cancer. It drives tumor proliferation and metastasis and correlates with tumor microenvironment remodeling through mechanisms involving Akt/EMT signaling and immune infiltration. The dysregulation of CILP2 in patient serum and its correlation with disease severity and poor prognosis highlight it as a promising biomarker and a compelling therapeutic target across a spectrum of human diseases. Full article

As universities increasingly work towards the adoption of their third mission—fostering entrepreneurship and innovation—the concept of the Entrepreneurial University (EntUni) emphasizes the need to cultivate a defined set of entrepreneurial competencies in students, such as opportunity recognition, risk-taking, perseverance, self-efficacy, and adaptability. The purpose of this study is to identify which entrepreneurial competencies are most critical for student readiness within the context of an Entrepreneurial University. However, limited consensus remains on which competencies are most essential. This study identifies the entrepreneurial competencies most critical for students within an Entrepreneurial University context through a mixed-methods approach. A student survey assesses self-perceived competencies; a stakeholder survey captures the perspectives of faculty, industry experts, and entrepreneurs; and qualitative interviews with industry professionals explore best practices for competency development. Findings reveal six core competencies that EntUnis should help students cultivate: proactiveness, perseverance, grit, risk propensity, self-efficacy, and entrepreneurial intention. Industry experts further highlight the importance of teamwork, ethical and sustainable thinking, and ambiguity tolerance—competencies often underdeveloped in academic environments. The study also identifies a disconnect between entrepreneurial education and practical application, with many students demonstrating high entrepreneurial intention but limited participation in start-up activities. These insights offer actionable implications for educators, policymakers, and university administrators. Overall, the study highlights the importance of experiential learning, academia-industry collaboration, and structured competency-building to enhance entrepreneurial readiness. By addressing these gaps, EntUnis can better equip students to drive innovation, economic growth, and societal impact. Full article

In this paper, we propose a generalized odd log-logistic standard Laplace model and study some of its main properties. The novelty of this model is based on classical and risk-based measures to effectively analyze the body mass index (BMI) data. The analysis underscores the importance of a multidisciplinary approach in addressing challenges related to health, performance, and risk management. The proposed methodology not only is helpful to understand the variability of BMI measurements, but also prove how common statistical models considered in financial field can be effectively adapted to other ones, offering insights that drive informed decision-making and strategic planning. Full article