Search Results (82)

Vehicular Ad Hoc Networks (VANETs) enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for enhancing road safety. However, reliable driver stress assessment remains challenging due to noisy sensing, inter-driver variability, and context dynamics. This paper proposes a Fuzzy-based Driver Stress Detection System (FDSDS) that employs an Interval Type-2 Fuzzy Logic System (IT2FLS) to model uncertainty. The FDSDS considers four complementary inputs—Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Steering Angle Variation (SAV), and Traffic Density (TD)—to estimate Driver Stress Level (DSL). Extensive simulations (14,641 test points) show monotonic associations between DSL and the inputs, which reveal that physiological indicators dominate average influence (finite-difference sensitivity: GSR 0.357, SAV 0.239, TD 0.239, HRV 0.235). Under severe physiological conditions (HRV = 0.1, GSR = 0.9), the system consistently outputs high stress (mean DSL = 0.813; range 0.622–0.958), while favorable physiological conditions (HRV = 0.9, GSR = 0.1) yield low stress even in challenging traffic (range 0.044–0.512). The IT2FLS uncertainty bands widen for intermediate conditions, aligning with the inherent ambiguity of moderate stress states. These results indicate that combining physiological, behavioral, and environmental factors with IT2FLS yields interpreted, uncertainty-aware stress estimates suitable for real-time VANET applications. Full article

Purpose: This review consolidates current evidence on how chronic kidney disease (CKD)-especially end-stage kidney disease (ESKD) and its treatments-alters choroidal and retinal vascular permeability, leading to changes in intraocular fluid homeostasis. Methods: A literature search of Medical Literature Analysis and Retrieval System Online (MEDLINE), reference lists, and key ophthalmology-nephrology texts was performed for studies published between 1980 and 2025. One-hundred-forty-four articles (clinical trials, observational cohorts, and case reports) met the inclusion criteria. Data were abstracted on choroidal thickness changes, blood-retinal barrier integrity, incidence of Central Serous Chororioretinopathy (CSCR) and Serous Retinal Detachment (SRD) in dialysis and transplant populations, and systemic variables such as oncotic pressure, hypertension, and corticosteroid exposure, with special attention to retinal pigment epithelium (RPE) pump function. Findings were synthesized qualitatively and tabulated where appropriate. Results: ESKD induces a triad of lowered plasma oncotic pressure, fluctuating hydrostatic forces, and impaired RPE pump function that collectively drive subretinal fluid accumulation. Hemodialysis acutely reduces sub-foveal choroidal thickness by a mean of ≈15–25 µm yet shows inconsistent effects on retinal thickness. Large population data demonstrate a three- to four-fold higher SRD risk and ~1.5-fold higher CSCR risk in dialysis patients versus controls, with peritoneal dialysis conferring the greatest hazard. After kidney transplantation, CSCR prevalence approaches 6%, driven by combined stresses of surgery, hypertension, and long-term corticosteroid or calcineurin-inhibitor therapy. Most reported SRDs resolve as systemic parameters normalize, underscoring the importance of promptly identifying systemic drivers. Conclusions: Systemic fluid-pressure imbalances and treatment-related factors in CKD significantly perturb the outer blood-retinal barrier. Regular ophthalmic surveillance, early visual-symptom screening (e.g., Amsler grid), and close nephrologist-ophthalmologist collaboration are essential for timely detection and management. Future research should quantify the relative contribution of hypoalbuminemia, hypertension, and immunosuppression to ocular permeability changes, and evaluate preventive strategies tailored to high-risk CKD subgroups. Full article

Long-term monitoring of tree crown condition is essential for assessing forest resilience under increasing climatic variability. This study presents a comprehensive evaluation of oak (Quercus spp.) defoliation trends in Poland from 2015 to 2024, based on national forest health monitoring data. Mean defoliation remained relatively stable until 2018, followed by a significant increase in 2019 (+5.1 percentage points; p < 0.001), coinciding with a major drought event across Central Europe. In subsequent years, defoliation gradually decreased and stabilised, indicating partial canopy recovery. Segmented regression and spline models revealed a consistent breakpoint in 2019 across all age classes, with the most severe crown damage recorded in stands older than 100 years. Younger stands showed lower defoliation levels and higher regenerative capacity. A nonlinear relationship between defoliation and growing-season precipitation was also identified, showing that when rainfall fell below 40 mm, canopy loss exceeded 30%. The results confirm that oak defoliation reflects both short-term climatic stress and long-term structural changes. Integrating monitoring data with climatic analyses and statistical modelling improves the detection of stress-related drivers and the assessment of recovery processes. The combined use of these approaches supports adaptive forest management strategies, including the promotion of mixed-species and multi-aged stands, improvement of soil nutrient conditions, and targeted monitoring of drought-sensitive age classes, thereby enhancing the resilience of oak ecosystems to climate change. Full article

Agricultural drought poses a critical constraint to food security and regional sustainable development, particularly in the Huang-Huai-Hai Plain, a major grain-producing region characterized by high spatial heterogeneity in drought risk. Previous studies have demonstrated that the Crop Water Stress Index (CWSI) outperforms traditional meteorological indices in detecting agricultural droughts in various regions. However, there is limited research specifically focusing on its spatiotemporal dynamics and the complex relationships with environmental factors, particularly in the Huang-Huai-Hai Plain. To fill this gap, this study first estimated CWSI using remote sensing evapotranspiration data and systematically assessed the spatiotemporal dynamics of agricultural drought in the Huang-Huai-Hai Plain from 2005 to 2020. Then, an integrated analytical framework that combines Local Indicators of Spatial Association (LISA) with Random Forest (RF) modeling has been proposed to identify primary environmental drivers. Results revealed a general downward trend in CWSI over the study period, with drought hotpots primarily concentrated in the central plains and along the eastern foothills of the Taihang Mountains. LISA identified four distinct spatial cluster types and revealed significant spatial associations between CWSI and six environmental variables. The major driving factors of CWSI included vegetation conditions (NDVI), land surface temperature (LST), rainfall, and temperature-related factors (SAT, DSR), with LST and SAT exhibiting the strongest correlations with CWSI in multiple regions. Among these, LST and SAT exhibited strong positive correlations with CWSI in multiple regions. By integrating spatial clustering and variable importance analysis, we found that agricultural drought patterns are shaped by interacting environmental factors, with region-specific dominant mechanisms. This study provides a novel analytical framework that bridges remote sensing, spatial statistics, and machine learning, offering valuable insights and tools for drought monitoring and attribution at regional scales. Full article

The purpose of the study is to investigate human physiological responses to construction noise exposure using deep learning, applying electroencephalography (EEG) and electro-dermal activity (EDA) sensors. Construction noise is a pervasive occupational stressor that affects physiological states and impairs cognitive performance. EEG sensors capture neural activity related to perception and attention, and EDA reflects autonomic arousal and stress. In this study, twenty-five participants were exposed to impulsive noise from pile drivers and tonal noise from earth augers at three intensity levels (40, 60, and 80 dB), while EEG and EDA signals were recorded simultaneously. Convolutional neural networks (CNN) were utilized for EEG and long short-term memory networks (LSTM) for EDA. The results depict that EEG-based models consistently outperformed EDA-based models, establishing EEG as the dominant modality. In addition, decision-level fusion enhanced robustness across evaluation metrics by employing complementary information from EDA sensors. Ablation analyses presented that model performance was sensitive to design choices, with medium EEG windows (6 s), medium EDA windows (5–10 s), smaller batch sizes, and moderate weight decay yielding the most stable results. Further, retraining with ablation-informed hyperparameters confirmed that this configuration improved overall accuracy and maintained stable generalization across folds. The outcome of this study demonstrates the potential of deep learning to capture multimodal physiological responses when subjected to construction noise and emphasizes the critical role of modality-specific design and systematic hyperparameter optimization in achieving reliable annoyance detection. Full article

Osteoarthritis (OA) lacks disease-modifying therapies, in part because key features of the joint microenvironment remain underappreciated. One such feature is localized acidosis, characterized by sustained reductions in extracellular pH within the cartilage, meniscus, and the osteochondral interface despite near-neutral bulk synovial fluid. We synthesize current evidence on the origins, sensing, and consequences of joint acidosis in OA. Metabolic drivers include hypoxia-biased glycolysis in avascular cartilage, cytokine-driven reprogramming in the synovium, and limits in proton/lactate extrusion (e.g., monocarboxylate transporters (MCTs)), with additional contributions from fixed-charge matrix chemistry and osteoclast-mediated acidification at the osteochondral junction. Acidic niches shift proteolysis toward cathepsins, suppress anabolic control, and trigger chondrocyte stress responses (calcium overload, autophagy, senescence, apoptosis). In the nociceptive axis, protons engage ASIC3 and sensitize TRPV1, linking acidity to pain. Joint cells detect pH through two complementary sensor classes: proton-sensing GPCRs (GPR4, GPR65/TDAG8, GPR68/OGR1, GPR132/G2A), which couple to G_s, G_q/11, and G_12/13 pathways converging on MAPK, NF-κB, CREB, and RhoA/ROCK; and proton-gated ion channels (ASIC1a/3, TRPV1), which convert acidity into electrical and Ca²⁺ signals. Therapeutic implications include inhibition of acid-enabled proteases (e.g., cathepsin K), pharmacologic modulation of pH-sensing receptors (with emerging interest in GPR68 and GPR4), ASIC/TRPV1-targeted analgesia, metabolic control of lactate generation, and pH-responsive intra-articular delivery systems. We outline research priorities for pH-aware clinical phenotyping and imaging, cell-type-resolved signaling maps, and targeted interventions in ‘acidotic OA’ endotypes. Framing acidosis as an actionable component of OA pathogenesis provides a coherent basis for mechanism-anchored, locality-specific disease modification. Full article

Given that urban mobility arises from the interaction between social and environmental conditions, this study constructs a Bayesian network to represent these relationships in David, Panama, using 500 georeferenced household surveys that recorded variables related to demographics, travel behavior, infrastructure, mobility patterns and perceptions of risk, safety, and vulnerability. The Bayesian network was built and validated through a consensus-driven hybrid procedure combining structural learning and expert knowledge, resulting in a directed acyclic graph (DAG) with 127 nodes and 189 arcs; and conditional probability tables (CPTs) were learned from data. The topology of the network was analyzed with Louvain community detection, revealing eleven subsystems that group household economy and mode choice, hydrometeorological mobility barriers, congestion, public-transport quality, and safety in school travel. The inferences show gender-based differences in the risk of harassment on public transport, higher perceived vulnerability on longer trips, and elevated stress among middle-aged drivers. The model highlights potential priority interventions such as reinforcing public-transport safety, promoting self-contained trips, and encouraging short-distance active mobility, based on population perceptions. The resulting DAG functions as both an analytical and communication tool for urban management, is visually understandable to all stakeholders, and provides unprecedented evidence for Panama in a little-studied context. Full article

Medium- and long-term international road safety goals require continued advancement of scientific research, especially with regard to the human component. Recent technological advances in sensor technology offer new opportunities to more accurately characterize driving behavior, helping to reduce the uncertainty associated with driver reactions. This study evaluated the effectiveness of specific physiological variables, detected by low-cost wearable sensors, to obtain reliable indicators of the driver’s workload. Heart rate and skin conductivity were analyzed in a real driving environment, in the absence of evident emotional stresses, to test their sensitivity to an ordinary level of physical and mental engagement. An experiment was conducted on a sample of users (10 drivers) along a rural road in Sicily, Italy. Data analysis, carried out through ANOVA and generalized linear models on three distinct curves, produced preliminary results indicating that subtle road geometry changes can be detected by physiological sensors, validating their potential for integration into driver monitoring systems. Statistically significant mean differences were found for speed (for all curves, p < 0.001), heart rate (R1 vs. R2, p = 0.009), and tonic GSR (R1 vs. R2, p = 0.006; R2 vs. R3, p = 0.013; A vs. B, p = 0.013; A vs. C, p = 0.006) as a function of different radius (R1, R2, R3) and deviation angle values (A, B, C). Future developments will require a significant increase in the sample size and the number of scenarios to achieve results of general utility. Full article

The AP2/ERF transcription factor family plays pivotal roles in plant growth, stress responses, and defense mechanisms, yet its diversity in camphor trees remains underexplored. This study identified 154 AP2/ERF genes in the Camphora officinarum genome, with over 80% belonging to the ERF subfamily, a distribution consistent with other angiosperms. Synteny analysis revealed that tandem and segmental duplications were key drivers of family expansion, suggesting adaptive diversification under ecological pressures. Structural analysis showed that the majority of ERF/RAV subfamily genes possess a single-exon structure, whereas AP2 subfamily genes display muti-exon structures, indicating divergent evolutionary trajectories and potential functional versatility via alternative splicing. Promoter analyses detected numerous hormone- and stress-responsive elements, linking these genes to abscisic acid, auxin, gibberellin signaling, and pathogen defense. Further expression profiling during stem development showed that approximately 60% of CoAP2/ERF genes were constitutively expressed across 17 expression trends, suggesting roles in basal development and stage-specific processes (e.g., lignification). Under Epicoccum poaceicola infection, 23 CoAP2/ERF genes were differentially expressed. Among them, upregulated ERF homologs related to RAP2.2/2.3 suggested roles in hypoxia and antimicrobial responses, while downregulation of ERF5 homologs indicated a growth–defense trade-off, whereby developmental processes are suppressed to prioritize pathogen resistance. Overall, this study deciphers the genomic architecture and structural diversity of CoAP2/ERF genes, along with expression dynamics of these genes in development and biotic stress adaptation of camphor trees. These findings provide critical insights into transcriptional regulation of development and stress responses in camphor trees and establish a theoretical basis for molecular breeding and biotechnological strategies aimed at improving stress resilience in woody plants. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

MYC is a key oncogenic driver frequently overexpressed in non-small cell lung carcinoma (NSCLC) and other cancers, where its protein levels often exceed what would be expected from MYC mRNA levels alone, suggesting post-transcriptional regulation. Strategies to inhibit MYC function by targeting mRNA translation hold potential for therapeutics utility in Myc-dependent cancers. We developed TranslationLight, a high-content imaging platform which detects MYC mRNA translation in human cells. Using this system, we conducted a high-throughput screen of ~100,000 compounds to identify small molecules that selectively modulate MYC translation. Candidate compounds were evaluated by immunofluorescence, ribosome profiling, RNA sequencing, cellular thermal shift assays (CETSA), and subcellular localization studies of mRNA and RNA-binding proteins. We identified a lead compound, CMP76, that potently reduces Myc protein without substantially decreasing its mRNA abundance. Mechanistic investigations showed that the compound induces relocalization of MYC mRNA into stress granules, accompanied by translational silencing. CETSA identified hnRNPK as a primary protein target, and compound treatment triggered its cytoplasmic relocalization together with formation of hnRNPK-containing granules colocalizing with MYC mRNA. Analysis across cancer cell lines revealed that sensitivity to CMP76 was significantly associated with RBM42 dependency. This work establishes a novel therapeutic strategy to inhibit MYC translation mediated by hnRNPK, offering a translationally targeted approach to cancer therapy. Full article

Background. Peripartum cardiomyopathy (PPCM) is a rare but potentially life-threatening condition typically presenting as heart failure with reduced ejection fraction in the last month of pregnancy or in the first five months following delivery in women without other known causes of heart failure. PPCM incidence and prevalence are highly variable in different populations and geographical areas. The etiology of PPCM is likely multifactorial, with genetic predisposition, autoimmune conditions, nutritional deficiencies, hormonal and metabolic changes, myocardial inflammation, enhanced oxidative stress, vascular dysfunction, and angiogenic imbalance all listed as possible contributing factors. Objectives. The complexity and multifactorial nature of PPCM can be explored by large-scale “omics” investigations, and their integration has the potential to identify key drivers and pathways that have the largest contribution to the disease. The scarcity of relevant knowledge and experience with most rare diseases raises the unique need for cooperation and networking. Methods and results. In the context of PPCM, we hypothesize that the creation of prospective patient registries could represent an answer to this criticality. Therefore, we created a multicenter national registry of PPCM in different geographical areas in Italy. Conclusions. We expect that the integration of clinical, imaging and omics-based data might provide novel insights into PPCM pathophysiology and allow in the future early detection, risk assessment, and patient-specific therapeutic interventions, thereby offering new perspectives in precision medicine. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Primary aldosteronism (PA), the most common cause of secondary hypertension, is increasingly recognized as an independent driver of adverse cardiac remodeling, mediated through mechanisms beyond elevated blood pressure alone. Chronic aldosterone excess leads to myocardial fibrosis, left ventricular hypertrophy, and diastolic dysfunction via mineralocorticoid receptor activation, oxidative stress, inflammation, and extracellular matrix dysregulation. These changes culminate in a distinct cardiomyopathy phenotype, often underrecognized in early stages. Multimodality cardiac imaging, led primarily by conventional and speckle-tracking echocardiography, and complemented by exploratory cardiac magnetic resonance (CMR) techniques such as T1 mapping and late gadolinium enhancement, enables non-invasive assessment of structural, functional, and tissue-level changes in aldosterone-mediated myocardial damage. While numerous studies have established the diagnostic and prognostic relevance of imaging in PA, several gaps remain. Specifically, the relative sensitivity of different modalities in detecting subclinical myocardial changes, the long-term prognostic significance of imaging biomarkers, and the differential impact of adrenalectomy versus medical therapy on cardiac reverse remodeling require further clarification. Moreover, the lack of standardized imaging-based criteria for defining and monitoring PA-related cardiomyopathy hinders widespread clinical implementation. This narrative review aims to synthesize current knowledge on the pathophysiological mechanisms of aldosterone-induced cardiac remodeling, delineate the strengths and limitations of existing imaging modalities, and critically evaluate the comparative effects of surgical and pharmacologic interventions. Emphasis is placed on early detection strategies, identification of imaging biomarkers with prognostic utility, and integration of multimodal imaging into clinical decision-making pathways. By outlining current evidence and highlighting key unmet needs, this review provides a framework for future research aimed at advancing personalized care and improving cardiovascular outcomes in patients with PA. Full article

Diabetic retinopathy (DR), a leading cause of blindness in working-age adults, arises from chronic hyperglycemia-induced oxidative stress, inflammation, and vascular dysfunction. Current therapies such as laser photocoagulation, intravitreal anti-vascular endothelial growth factor (VEGF) agents, and steroids target advanced stages but fail to prevent early neuronal and microvascular damage. Emerging evidence highlights oxidative stress as a key driver of DR pathogenesis, disrupting the blood-retinal barrier (BRB), promoting neurodegeneration and angiogenesis. Advances in imaging, particularly optical coherence tomography angiography (OCTA), enable earlier detection of neurodegeneration and microvascular changes, underscoring DR as a neurovascular disorder. Polyphenols, such as resveratrol, curcumin, and pterostilbene, exhibit multitarget antioxidant, anti-inflammatory, and anti-angiogenic effects, showing promise in preclinical and limited clinical studies. However, their low bioavailability limits therapeutic efficacy. Nanotechnology-based delivery systems enhance drug stability, tissue targeting, and sustained release, offering potential for early intervention. Future strategies should integrate antioxidant therapies and precision diagnostics to prevent early irreversible retinal damage in diabetic patients. Full article