Search Results (636)

Parkinson disease (PD) and mood disorders represent two substantial global health burdens that increasingly co-occur as both conditions rise in prevalence worldwide. Diagnosing Parkinson disease in patients with pre-existing mood disorders is clinically challenging due to overlapping symptoms, medication effects, and shared neurobiological mechanisms. Apathy, psychomotor slowing, and fatigue may mimic depressive symptoms, leading to delayed recognition of early parkinsonism. Development of an underlying neurodegenerative disorder could account for some treatment-resistant symptoms or treatment failures if not recognized. Therefore, the identification of PD will change the treatment and management plan significantly. Accurate diagnosis of PD requires a detailed neurologic examination focusing on bradykinesia, rigidity, and resting tremor, supported when appropriate by dopamine transporter imaging (DaT scan) or other emerging biomarkers. Understanding the temporal relationship between psychiatric and motor features helps differentiate prodromal PD from primary mood disorders. Management of patients with both mood disorders and PD integrates dopaminergic replacement therapy for motor symptoms with individualized treatment of psychiatric comorbidities. Levodopa remains the cornerstone for motor control, while dopamine agonists, MAO-B inhibitors, and COMT inhibitors can be added as needed. For depression and anxiety, SSRIs and SNRIs are first-line choices; quetiapine or clozapine are preferred when treatment for psychosis is necessary. Intentional, thoughtful polypharmacy is frequently required. Non-pharmacologic interventions—including cognitive behavioral therapy, structured exercise, and patient–caregiver education—enhance mood, function, and quality of life. Multidisciplinary collaboration between neurology, psychiatry, and allied health professionals is essential for optimal outcomes. This review offers guidance to healthcare providers as well as other interested parties involved in patients with mood disorders who may also be developing or have PD, especially to those who may have limited access to neurologic resources. Full article

This study examined differences in finishing techniques and positional contexts across three temporal windows in the Ultimate Fighting Championship (2003–2004, 2013–2014, and 2023–2024), revealing differences consistent with a shift from greater diversity to a specialized and systematized model. Analysis of 906 finalized bouts demonstrated a marked concentration of submission finishes, with rear naked choke increasing from 15.8% to 46.8% (p ≤ 0.001), while back control was the dominant positional context (45.5%, p ≤ 0.001). In striking-based finishes, punches maintained prevalence, evolving from 77.4% (2003–2004) to 86.1% (2023–2024, p ≤ 0.001), whereas kicks declined from 20.5% to 11.3% (p ≤ 0.001). Submissions increased from 37.0% to 52.0% of all finalized bouts (p ≤ 0.001). These findings indicate a growing emphasis on specific finishing techniques, with modern mixed martial arts demonstrating increased distributional concentration in the methods used to finalize bouts. The increased frequency of certain techniques (e.g., rear naked choke and punches) among finalized fights may reflect strategic preferences, training priorities, or rule-driven changes in technique effectiveness, but cannot be interpreted as evidence of inherent technical superiority without additional data on success rates or efficiency metrics. Our data suggest that contemporary fighters have developed more direct and systematized approaches to finishing fights, reflecting the evolution of training methodologies and competitive strategies. Full article

In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning. Full article

As a significant component of hydraulic cultural heritage within the Grand Canal Cultural Belt, ancient stone bridges serve as vital physical evidence reflecting the evolutionary patterns of water conservancy and settlement spaces in Wuxi. Consequently, understanding their distribution holds critical significance for the holistic protection and revitalized utilization of the heritage. This study investigates 118 ancient stone bridges in Wuxi, China, employing ArcGIS spatial analysis methods, specifically average nearest neighbor, kernel density estimation, and standard deviational ellipse, to examine spatiotemporal characteristics. Additionally, a random forest (RF) model is utilized to quantify the importance of natural environmental factors influencing their distribution. The results reveal the following: (1) Temporally, the distribution transitioned from a random pattern in the Song Dynasty to a highly clustered pattern during the Ming, Qing, and Republic of China periods. (2) Spatially, the distribution centroid exhibited a distinct southwestward trend, evolving from a dispersed structure into a multi nuclei aggregation model centered on Yixing and Wuxi City. (3) Environmentally, bridges are predominantly located in low-elevation plains, gentle slopes (2° to 5°), and stable zones far from geological hazards. They exhibit a preference for northeast and northwest aspects, with the highest concentration within 100 m of rivers and in paddy or yellow–brown soil regions. (4) The RF model identifies rivers as the absolute dominant factor, followed by aspect, geological hazards, slope, and elevation, while soil factors have the lowest importance. These findings enrich the conservation theory for hydraulic cultural heritage and provide a scientific basis for the risk assessment, hierarchical protection, and integrated tourism planning of ancient stone bridges. Full article

The digital transformation of financial services has changed how customers interact with banks through electronic channels, yet the factors influencing channel choice between branch-based and digital banking are not entirely understood, especially in emerging European markets. This study investigates banking channel preferences over three consecutive years (2023–2025) in Constanta County, Romania, quantifying how perceived bank technologization and sociodemographic characteristics are associated with the likelihood of digital banking adoption in the e-commerce context. Using repeated cross-sectional survey data from 785 respondents, we applied pooled and year-specific logistic regression models to evaluate temporal effects and estimate the predictive contribution of a composite perception measure (TechScore). Results show that although digital banking usage increased from 87.7% to 92.4%, time alone did not significantly predict adoption. Technologization perceptions consistently increased the odds of digital banking use, with stronger effects in 2025. Age and living environment were significant determinants, while gender and relationship length were not. As digital financial services mature, perceived bank technologization becomes increasingly influential in channel-use decisions. The study contributes to the electronic commerce and technology acceptance literature by demonstrating the importance of perception-based predictors in digital banking contexts and highlights how perception-based evaluation shapes channel choice in digital service platforms, offering insights applicable to electronic commerce contexts where providers compete across physical and digital channels. Full article

The spatial and temporal conflicts within terminal maneuvering areas, particularly in multi-airport systems, are growing increasingly complex. Traditional independent processing methods face inherent limitations when dealing with multi-source uncertainties, dynamic weather conditions, and high-density operations. This paper proposes MSTAGNN-MARL that systematically integrates the resolution of spatial conflicts and temporal scheduling issues. This framework is based on four crucial innovations: First, a strategic-tactical-execution hierarchical architecture is constructed that integrates multi-criteria decision optimization with graph neural network-based multi-agent reinforcement learning. Second, an uncertainty perception mechanism is designed that explicitly encodes conflict features as dynamic edge attributes in social graphs, incorporating a real-time dynamic weather model and a Gaussian noise-based perception uncertainty model. Third, develop a compliance automated system for behavior cloning that learns the decision preferences of controllers to achieve human–machine collaboration and provide transparent visualization. Fourth, a robustness assurance mechanism for abnormal scenarios is constructed, employing behavior tree-driven emergency strategies to handle unexpected situations. Experiments demonstrate that the proposed method achieves an 89.3% conflict resolution rate, reduces average delays by 6 min compared to existing methods, and exhibits robust performance under varying traffic densities and dynamic weather conditions. Ablation experiments validate the effectiveness of the four innovations. This framework provides a new research paradigm for scheduling and decision-making in Intelligent Transportation Systems (ITS). Full article

This study investigates the multi-locality and multi-temporal characteristics of mobility destinations in Zanjan, Iran, throughout a typical day. Existing approaches often overlook critical geographical concepts, including the influence of multiple motivational factors on destination choice behavior, the clustering of destinations, and the spatiotemporal dynamics of preferred destinations. To address these gaps, Agent-Based Modeling (ABM) was employed to simulate individual daily flows to preferred destinations. An integrated pattern recognition approach combining machine learning clustering (k-means), hotspot analysis, and 3D mapping was utilized to facilitate visual analytics of individual destination choices, with special emphasis on applications for transportation planning. Four optimal destination clusters were identified, with hotspot analysis revealing a concentration of preferred destinations in Cluster 1, located within the Central Business District (CBD), suggesting a monocentric spatial structure. Temporal analysis demonstrated that destination clusters exhibit dynamic spatial and temporal changes over the course of the day. These findings provide new insights into managing travel behavior and offer practical implications for urban planning and transportation policy regarding individuals’ daily movement strategies. Full article

Background: Exposure to environmental pollutants, especially endocrine-disrupting chemicals, disproportionately affects vulnerable populations like pregnant women, lactating mothers, and preterm infants. This study aimed to assess the detection patterns of DiNP-, DEP-, and DEHP-related metabolites in maternal urine and breast milk, examine agreement between matrices, and explore maternal factors associated with phthalate exposure. Methods: Fifty-five mothers who delivered at ≤32 gestational weeks and whose infants were hospitalized in the Neonatal Intensive Care Unit (NICU) were enrolled. Breast milk and urine samples were analyzed using a validated isotope-dilution LC–MS/MS method. Urinary phthalate metabolite concentrations were adjusted for specific gravity. Linear mixed-effects models with a random intercept for mother were used to examine associations between urinary and breast milk phthalate metabolite concentrations, assess temporal changes, and evaluate the influence of breast milk lipid content. Results: DEHP and DiNP metabolites were detected in nearly all maternal urine samples. Breast milk contained predominantly primary metabolites (MEHP and MiNP), while secondary oxidative metabolites were rarely detected. Urine concentrations consistently exceeded breast milk concentrations. Urinary and breast milk phthalate concentrations were not correlated across sampling periods, indicating limited matrix concordance. Conclusions: Mothers of very preterm infants experience sustained phthalate exposure in the postpartum period; however, limited metabolite transfer to breast milk indicates that maternal urine remains the preferred biomonitoring matrix for assessing systemic phthalate exposure. Breast milk phthalate profiles exhibit compound-specific temporal changes and appear largely independent of concurrent urinary exposure biomarkers. Full article

The growth of e-commerce live streaming has expanded sales channel options for fresh agricultural suppliers. This study investigates a two-echelon supply chain consisting of a fresh agricultural supplier and downstream retailers. Using differential game theory, we examine the supplier’s preservation technology level and product greenness, analyzing and comparing equilibrium strategies under three different modes: e-commerce platform sales mode (SP), head streamer sales mode (SH) and ordinary streamer sales mode (SN). The results demonstrate that SP is the dominant strategy when retailers’ marginal profits are low. Conversely, under high marginal profit conditions, the optimal selection depends on streamer cooperation costs: SH is preferred with low head streamer costs; widening cost gaps introduce temporal considerations between SH and SN; further gap expansion makes SN optimal. Furthermore, product greenness is related to supplier’s marginal profit, while the preservation technology level is jointly determined by supplier’s marginal profit and retailers’ inspection costs. Finally, combinations of these modes are also investigated. Full article

Trait-based approaches, particularly those focusing on behavioral traits, have become increasingly important in ecology. However, empirical studies addressing behavioral trait variation in insects remain comparatively scarce. To address this knowledge gap, we investigated the behavior of six wild-living ground beetle species for which no behavioral data have previously been reported. Using standardized behavioral measures, we found that in species occurring in their preferred forest habitats, behavioral traits related to activity, exploration, boldness, and risk-taking showed weak or limited temporal consistency. In contrast, in species inhabiting modified forest habitats, behavioral traits exhibited pronounced and repeatable individual differences, were intercorrelated, and formed behavioral syndromes. Moreover, half of the studied species showed sex-specific differences in personality, reflecting drivers related to reproductive roles and investment. Overall, our findings emphasize that animal personality and behavioral syndromes in ground beetles are not universal species-level properties but emerge from the interaction between intrinsic traits, and sex-specific strategies, underscoring the importance of considering ecological context when interpreting individual-level behavioral variation. Full article

Background: Electronic Health Records (EHRs) are a rich source of clinical information used for patient monitoring, disease progression analysis, and treatment outcome assessment. However, their large-scale, heterogeneity, and temporal characteristics make them difficult to analyze. Topic modeling has emerged as an effective method to extract latent structures, detect disease characteristics, and trace patient trajectories in EHRs. Recent neural and transformer-based approaches such as BERTopic has significantly improved coherence, scalability, and domain adaptability compared to earlier probabilistic models. Methods: This Systematic Literature Review (SLR) examines topic modeling and its variants applied to EHR data over the past decade. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to identify, screen, and select relevant studies. The reviewed techniques span traditional probabilistic models, neural embedding-based methods, and temporal extensions designed for pathway and sequence modeling in clinical data. Results: The synthesis covers trends in publication patterns, dataset usage, application domains, and methodological contributions. The reviewed literature demonstrates strengths across different modeling families, while also highlighting challenges related to scalability, interpretability, temporal complexity, and privacy when analyzing large-scale EHRs. Conclusions: Topic modeling continues to play a central role in understanding temporal patterns and latent structures in EHRs. This review also outlines future possibilities for integrating topic modeling with Agentic AI and large language models to enhance clinical decision-making. Overall, this SLR provides researchers and practitioners with a consolidated foundation on temporal topic modeling in EHRs and its potential to advance data-driven healthcare. Full article

Background/Objectives: Understanding the purchasing behaviour of sweetened beverages is important, as beverages have been highlighted as a key target for reducing sugar intake. This research aimed to provide a comprehensive understanding of trends in per capita volume sales of non-alcoholic water-based beverages (WBB) in Australia and their contribution to dietary sugars between 1997 and 2024. Methods: Volume sales data for the years 2018 to 2024 (Circana Connect) were integrated with three previously published datasets spanning 1997 to 2018, with adjustments to reflect the total market where applicable. Per capita volume sales were determined using national population data (Australian Bureau of Statistics) for each corresponding year. Linear regression analysis was performed to assess trends in per capita volume sales over time. Sugar contributions of each beverage category were modelled based on representative sugar content data. Results: Total WBB sales showed consistent growth over the 28-year period (1.68 L/person/year, 36.2%). Within this, sales of sugar-sweetened beverages (SSB) declined (−1.08 L/person/year), with a concurrent increase in non-sugar-sweetened and unsweetened beverage purchases (2.74 L/person/year). This transition became more pronounced from 2015 and coincided with a decreased contribution of WBB to dietary sugars (−0.13 kg/person/year, p < 0.001). There was variation in sales and sugar contribution trends by beverage category. Functional beverages (e.g., coconut water, protein water) showed increases in sales and sugar contribution. Conclusions: The last 28 years have seen a trend in beverage purchases away from sugar-sweetened to non-sugar-sweetened and unsweetened varieties. This comprehensive analysis of consumer beverage choices makes a valuable contribution to policy and health-focused food industry initiatives. Full article

Background/Objectives: Recent public and scientific discussions have raised concerns about a possible link between prenatal paracetamol exposure and autism spectrum disorder (ASD). However, pharmacovigilance-based evidence remains scarce, and the role of reporting bias has not been systematically assessed. This study aimed to characterize ASD-related adverse event reports involving paracetamol in the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) and to evaluate potential disproportionality signals, considering demographic, temporal, and geographic patterns. Methods: FAERS data from January 2010 to September 2025 were screened for reports listing paracetamol as the suspect drug and ASD-related Preferred Terms. After excluding duplicates and concomitant drugs, 1776 unique cases were analyzed. Patient demographics, reporter type, and country of origin were summarized descriptively. Disproportionality was calculated using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Information Component (IC), and Empirical Bayes Geometric Mean (EBGM). Results: Among 172,129 paracetamol-associated reports, 1776 (1.03%) included ASD-related terms. All were classified as serious and mostly submitted by consumers (98.6%). Gender was available in 47.7% of cases, showing male predominance (68.8%). Most reports referred to fetal exposure during pregnancy. Nearly all originated from the United States (98.4%). A marked rise was observed after 2022, with 562 reports in 2023 and 1051 in 2025. Disproportionality analyses revealed consistently elevated signals (ROR = 69.8, PRR = 69.2, IC025 = 5.60, EB05 = 48.3). Conclusions: The strong disproportionality signal likely reflects increased public attention and reporting dynamics rather than a causal association. Further integration of pharmacovigilance and epidemiologic data is warranted to clarify the clinical significance of these findings. Full article

The persistent expansion in the intensity and scope of human disturbance has become a key driver of global biodiversity loss, affecting wildlife behavior and population stability across multiple dimensions. As a characteristic symbiotic assemblage in the subalpine forest ecosystems of Sichuan, the giant panda (Ailuropoda melanoleuca), red panda (Ailurus fulgens), and forest musk deer (Moschus berezovskii) exhibit significant research value in their responses to human disturbance. However, existing studies lack systematic analysis of multiple disturbances within the same protected area. This study was conducted in the Sichuan Liziping National Nature Reserve, where infrared camera traps were deployed using a kilometer-grid layout. By integrating spatiotemporal pattern analysis and Generalized Additive Models (GAM), we investigated the characteristics of human disturbance and the response strategies of the three species within their habitats. The results show that: (1) A total of seven types of human disturbance were identified in the reserve, with the top three by frequency being cattle disturbance, goat disturbance, and walking disturbance; (2) Temporally, summer and winter were high-occurrence seasons for disturbance, with peaks around 12:00–14:00, while the giant panda exhibited a bimodal diurnal activity pattern (10:00–12:00, 14:00–16:00), the red panda peaked mainly at 8:00–10:00, and the forest musk deer preferred crepuscular and nocturnal activity—all three species displayed activity rhythms that temporally avoided peak disturbance periods; (3) Spatially, giant pandas were sparsely distributed, red pandas showed aggregated distribution, and forest musk deer exhibited a multi-core distribution, with the core distribution areas of each species spatially segregated from high-disturbance zones; (4) GAM analysis revealed that the red panda responded most significantly to disturbance, the giant panda showed marginal significance, and the forest musk deer showed no significant response. This study systematically elucidates the spatiotemporal differences in responses to multiple human disturbances among three sympatric species within the same landscape, providing a scientific basis for the management of human activities, habitat optimization, and synergistic biodiversity conservation in protected areas. It holds practical significance for promoting harmonious coexistence between human and wildlife. Full article

This article presents a systematic review on methods for quantifying three-dimensional, time-resolved (3D+t) deformation and motion of human arteries from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Scopus, Web of Science, IEEE Xplore, Google Scholar, and PubMed on 19 December 2025 for in vivo, patient-specific CT or MRI studies reporting motion or deformation of large human arteries. We included studies that quantified arterial deformation or motion tracking and excluded non-vascular tissues, in vitro or purely computational work. Thirty-five studies were included in the qualitative synthesis; most were small, single-centre observational cohorts. Articles were analysed qualitatively, and results were synthesised narratively. Across the 35 studies, the most common segmentation approaches are active contours and threshold, while temporal motion is tracked using either voxel registration or surface methods. These kinematic data are used to compute metrics such as circumferential and longitudinal strain, distensibility, and curvature. Several studies also employ inverse methods to estimate wall stiffness. The findings consistently show that arterial strain decreases with age (on the order of 20% per decade in some cases) and in the presence of disease, that stiffness correlates with geometric remodelling, and that deformation is spatially heterogeneous. However, insufficient data prevents meaningful comparison across methods. Full article