Search Results (77,312)

Visual orthopedic gait assessment in dogs is recognized as subjective and is limited by interobserver variability. Objective detection of lameness is offered by biomechanical analysis, where asymmetry between limbs is quantified through kinematic parameters and symmetry indices. However, the minimum number of trials (full stride cycles) required to reliably discriminate lameness has remained a challenge. In this study, six healthy adult dogs were used. Mild, reversible lameness was induced in one forelimb using a cotton pad. Dogs were walked along a straight runway, and kinematic data were captured with a high-speed video camera. Stride length (SLE), support time (ST), and elbow range of motion (ROM) were measured. Symmetry indices (for linear and temporal parameters) and the symmetry angle (for angular parameters) were computed. The asymptotic distribution of these indices was derived using the delta method, which allowed for the construction of confidence intervals (CIs) and hypothesis tests for an asymmetry threshold of 3%. The number of trials required to achieve reliable detection was estimated through statistical simulations. Results indicated that the required number of trials was highly dependent on both the kinematic parameter and the magnitude of asymmetry. While detecting subtle asymmetries (≈4%) required a high number of trials (up to 347 for stride length), the requirements decreased substantially for more pronounced lameness. For a true asymmetry of 6%, 11–39 trials per limb were sufficient to achieve 80–90% power. It is concluded that the collection of only five trials is insufficient for detecting mild asymmetries. A statistical framework and practical recommendations for kinematic gait studies in dogs are provided. Full article

This study aims to establish an origin identification method for Scutellariae radix that integrates multidimensional quality indicators and machine learning algorithms, enabling accurate and rapid traceability of Scutellariae radix medicinal materials from four production areas: Hebei (HB), Shanxi (SX), Shaanxi (SAX), and Chengde (CD). The study collected a total of 43 batches of Scutellariae radix samples from the aforementioned origins. It systematically measured 12 key quality indicators covering flavonoids, physicochemical parameters, chromaticity values, and biological activity. These specifically include four flavonoid components: baicalin, wogonoside, baicalein, and wogonin; three physicochemical parameters: moisture content, ash content, and alcohol-soluble extract; four chromaticity values: L*, a*, b*, and ΔE; and in vitro anti-inflammatory activity (IC₅₀ value for NO clearance). On the basis of these parameters, in this study there were five machine learning models constructed based on the following algorithms and methods: Random Forest (RF), Extreme Learning Machine (ELM), Backpropagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). A comparative analysis was conducted to evaluate the origin identification performance of each model. The results indicate significant differences (p < 0.05) in the contents of baicalin, wogonoside, L*, a*, b*, ΔE, and alcohol-soluble extract among Scutellariae radix from different origins. The comparative analysis of four machine learning models reveals that RF outperforms ELM, BP, and RBF in multiclass classification, achieving a test accuracy of 75% and consistent precision, recall, and F1-score of 79.17%. In contrast, the three neural networks attain only 66.67% test accuracy, with RBF showing high precision but low recall, ELM delivering moderate performance, and BP performing poorly. These results underscore the strength of ensemble methods like RF in small-sample settings, where they mitigate overfitting and enhance generalization, whereas neural networks struggle with limited data. We therefore recommend RF for deployment under current data constraints and suggest future work should focus on data expansion, especially for under-performing classes, along with hyperparameter tuning to further improve classification. Full article

Chromatin dynamics are usually modulated by histone epigenetic post-translational modifications, which rapidly and reversibly govern accessibility and transcriptional responsiveness. During microbial infection, this regulatory layer becomes a highly contested interface where host defense mechanisms and pathogen-driven subversion strategies converge and compete. Many infectious agents exploit chromatin to reprogram gene expression, creating cellular environments that are conducive to infection, proliferation, and persistence. Diverse strategies have been described for viruses, bacteria, fungi, protozoa and nematodes, including the direct secretion of acetyltransferases and methyltransferases, interference with host chromatin-binding proteins, subcellular localization of transcriptional factors or epigenetic regulators, and metabolic availability manipulation. Concurrently, host cells activate immune and stress-response genes to mount rapid, adaptable antimicrobial responses. Recent advances in genome-wide, single-cell, and spatial omics profiling have begun to reveal the temporal and cell-type-specific dynamics of the host genome at the core of infection. This review synthesizes current insights into how chromatin is rewired by the major categories of pathogens during infection, highlighting representative case studies across infective agents and the functional consequences for immunity and cell fate. In addition, we discuss emerging techniques for epigenomic and transcriptomic data collection, and the potential of targeted host-directed therapeutic strategies. Chromatin regulation is thus a promising field of study and a possible target for next-generation interventions. Full article

The aim of this article is to present the current state of knowledge regarding the use of GLP-1 agonists in the treatment of type 2 diabetes, obesity, and other potential clinical indications, including neurodegenerative conditions. The article describes the characteristics of the diseases discussed, with particular emphasis on the pathophysiological mechanisms and the impact of metabolic disorders on the course of the diseases. In addition, the specific role of GLP-1 receptor agonists and their mechanisms of action leading to improved clinical outcomes were discussed, including their impact on molecular pathways involved in glucose metabolism regulation, inflammatory processes, carcinogenesis, and neuroprotection. Based on meta-analyses of available clinical trials, the evidence supporting the effectiveness of GLP-1 agonist therapy in glycemic control, weight loss, and improvement of metabolic parameters was synthesized. Additionally, potential benefits beyond the metabolic system are discussed, including neuroprotective effects and impact on patients’ cardiovascular profiles, as well as risks and adverse effects associated with the use of GLP-1 agonists. The collected data indicate the growing role of GLP-1 agonists as an innovative and effective therapeutic strategy, while emphasizing the need for further research in the context of new clinical indications. Full article

The application of vibration monitoring integrated with sparse Auto-Encoder (SAE) networks is investigated in this paper with the objective of detecting and localizing structural anomalies or damages. Unlike previous studies on SAE networks, the methodology proposed is based on the definition of a single SAE model, trained with the signals simultaneously collected from several sensors. Once the SAE has been trained using measurements that represent the baseline (undamaged) condition of the structure, the network is likely to reconstruct well newly collected data if the structure maintains its intact condition. When damage or structural degradation processes start developing, an increase in the reconstruction error—defined as the residual between the original input and the reconstructed output—has to be expected, so that a deviation from the normal state is highlighted. Moreover, this rise in reconstruction errors is typically more significant near the damaged areas, allowing for precise localization of the affected zones. The performance and robustness of the proposed approach are illustrated and validated using experimental data from two real-world bridge structures. Full article

Against the backdrop of global digitalization and green development, digital innovation ecosystems have emerged as key drivers for advancing regional green innovation cooperation and achieving sustainable development goals. This study constructs a theoretical analytical framework encompassing “Actor-Resource-Environment.” Utilizing panel data from 30 Chinese provinces spanning 2012–2022, it employs machine learning and dynamic QCA methods to dissect the dynamic causal relationship between digital innovation ecosystems and regional green innovation cooperation. Key findings include: (1) Green innovation cooperation networks are evolving from a “core-periphery structure” toward new characteristics of multi-centered mutual coupling and coordination. (2) Different machine learning models yield varying effects on how digital innovation ecosystems influence regional green innovation cooperation, with the XGBoost model demonstrating the strongest performance. (3) No single element within the digital innovation ecosystem can serve as a necessary condition for driving regional green innovation cooperation. (4) Three configuration patterns emerge for achieving high-level regional green innovation cooperation, with digital innovation funding, digital talent resources, and digitally inclusive financial environments consistently serving as core prerequisites. These findings deepen our understanding of the complex causal mechanisms involving multi-factor matching and linkage that influence regional green innovation cooperation, offering valuable insights for advancing high-quality regional green innovation development. The research findings reveal the complex configuration pathways through which multidimensional elements of the digital innovation ecosystem collectively drive regional green innovation cooperation. This provides practical governance pathways for breaking down regional barriers and building highly resilient green innovation cooperation networks. Full article

Driving rain or ‘wind-driven rain’ (WDR) arrives at the ground on an oblique trajectory, and drops may strike at a speed greater than their still-air terminal velocity. Oblique rain can affect a range of geomorphic processes including the splash dislodgment and transport of soil particles, and hydrological processes including overland flow, canopy interception, and the generation of stemflow. The mean rain inclination angle at which WDR strikes the ground has been estimated from the catch of paired gauges, one with a conventional horizontal orifice, and one with a vertical orifice, or by related forms of vectopluviometers. Such data allow the resolution of rain vectors to find the rain inclination. However, rain-collecting devices of this kind do not permit the real-time recording of the rain inclination from moment to moment. Here, a new acoustic method for measuring the rain inclination is introduced that provides an inexpensive tool for the continuous, real-time monitoring of WDR. Furthermore, the method also permits the simultaneous recording of rainfall duration and intermittency at a high temporal resolution, with no additional apparatus. Data on rain inclinations collected during showers on a tropical coast exposed to strong trade-winds are presented to illustrate the operation of the acoustic measurement system. However, the focus of this paper is the presentation of the new method itself, and not on the climatology of WDR. Full article

Collaborative projects under the Horizon Europe Framework Program of the European Union typically involve a large number of partners from multiple countries. Data-centric projects, among them, often require integration of disparate data source formats and collection methods, leading to complex data management architectures and policies. This article is an extended version of an article presented at the 1st International Conference on Big Data Analytics and Applications (BDAA’2025). It explores design decisions, organisational principles, and technological solutions to address these challenges by focusing on data integration of data sources and the hybridisation of data services. This experience was gathered while working on DiverSea, a project dedicated to the analysis of biodiversity dynamics along European coastlines—ranging from the Black Sea to the Mediterranean and the North Sea. While grounded in established technologies, the project’s takeaways offer valuable insights for environmental data projects across aquatic, terrestrial, and atmospheric domains. Full article

Priority habitat 91E0* (alluvial forests with Alnus glutinosa and Fraxinus excelsior) constitutes a key riparian biodiversity hotspot, yet it is increasingly threatened by woody invasions that alter the community composition and reduce the habitat’s heterogeneity. Ten permanent plots (15 m radius) were surveyed in the Nestos River delta (NE Greece) in 2019 and 2023, following a manual control campaign conducted in 2021, targeting Amorpha fruticosa and Acer negundo. Because systematic plot-level vegetation data were collected only in 2019 and 2023, the study evaluates before–after changes rather than continuous annual dynamics. Woody species composition and diversity, community turnover (Bray–Curtis dissimilarites/PCoA; PERMANOVA), invasive dynamics (negative binomial GLMs), and community-weighted Ellenberg-type indicator values and their relationships with the soil properties (0–30 cm) were assessed. Across the surveys, 18 woody taxa were recorded, dominated by native riparian trees and shrubs, together with four established alien species. The total alien abundance declined from 943 to 385 individuals between 2019 and 2023, driven by A. negundo (−68%) and A. fruticosa (−39%). The woody community composition differed significantly between years (R² = 0.12; p = 0.013) and river banks, whereas plot-scale diversity indices changed modestly and evenness increased. The mean community-weighted moisture affinity increased (CWM_F: 6.28 → 7.07), nutrient affinity remained high, and reaction values declined slightly. The soil’s properties did not differ between the treated and control plots; nevertheless, Shannon diversity was positively correlated with organic C, total N, exchangeable Ca and K, and clay content. Permanent plot resurveys thatintegrate soil properties and indicator-based community metrics provide robust baselines to support Article 17 reporting under the EU Habitats Directive and to guide spatially targeted invasive-species management in Mediterranean alluvial forests (habitat 91E0) undergoing restoration actions. Full article

Background: Missed nursing care, defined as essential patient care that is omitted or delayed, is a growing source of concern due to its effects on healthcare quality and patient safety. Our aims in this study were twofold: first, we examined the extent and types of missed nursing care, and second, we analyzed the relationship between the care missed by hospital nurses and the staffing and resource adequacy in an academic medical center. Methods: A descriptive cross-sectional study was conducted during the period between November 2022 and July 2023. Data were collected using a self-administered questionnaire that comprised items on socio-demographic and work-related characteristics, items on staffing and resource availability, and items from the ‘MISSCARE’ Survey. Results: The most frequently missed nursing care involved pressure-relieving interventions (Mean = 2.39) and ambulation/mobilization (Mean = 2.27), while medication administration (Mean = 1.60) and glucose monitoring (Mean = 1.56) were missed the least. Labor resource inadequacy (β = 0.315, p < 0.001) and communication and teamwork deficits (β = 0.285, p < 0.001) were positively associated with missed nursing care, whereas staffing and resource adequacy showed an inverse association (β = −0.164, p = 0.006). The model explained 49.8% of the variance in missed nursing care (R² = 0.498). Conclusions: These findings highlight that missed nursing care is a system-level issue primarily associated with staffing and resource constraints rather than individual characteristics. Improving staffing adequacy, resource availability, and interprofessional collaboration may reduce care omissions and enhance patient safety in Saudi Arabian academic medical centers. Full article

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country. Full article

Fibromyalgia is a complex chronic pain condition characterized by pervasive pain, persistent fatigue, and cognitive disturbances. Despite advances in understanding its neurobiological mechanisms, diagnosis largely relies on subjective symptom assessment and exclusion criteria, contributing to underdiagnosis and treatment delays. Recent research has increasingly focused on identifying objective biomarkers and leveraging digital phenotyping to improve diagnostic precision. Promising biomarkers include neuroimaging indicators of altered pain processing, neuroinflammatory signatures in cerebrospinal fluid and blood, and dysregulated neuroendocrine and autonomic patterns. In addition, metabolomics and transcriptomics have revealed molecular profiles associated with fibromyalgia pathophysiology. Concurrently, digital health tools (e.g., wearable sensors, ecological momentary assessment, and machine learning-based symptom clustering) offer opportunities for continuous, real-world data collection and individualized disease characterization. This body of work suggests that integrating biological and digital metrics could enable a transition from subjective to objective data-driven fibromyalgia classification, facilitating earlier diagnosis and improved therapeutic outcomes. Full article

Cohesion among individuals reflects the quality of relationships and interpersonal interaction within a community. Elements such as social connections, trust, and a sense of belonging serve as key indicators of societal cohesion and are often rooted in acts of good neighborliness. Despite this, limited knowledge exists regarding perceptions and behaviors related to good neighborliness within South African society. The present study examines how perceptions and practices of good neighborliness contribute to the development of cohesive communities. Research was conducted in four South African communities: Philippolis, Lambert’s Bay, Caledon, and Grabouw. Utilizing an interpretivist approach, the study adopted a qualitative methodology involving interviews with 25 participants, including family members and community stakeholders. Data were collected through in-depth interviews, and thematic analysis facilitated the identification of recurring patterns and key themes. The principal themes identified were everyday mutual support and practical assistance, moral norms and values of care, social familiarity and community connectedness, trust and good neighborliness, and intergroup relations and cohesion across diversity. The findings demonstrate the crucial role of good neighborliness in advancing social cohesion. For communities and families to thrive, it is vital that members experience safety and cultivate trusting relationships, which often requires openness about their vulnerabilities and needs. Full article

Background/Objectives: This study examined the prevalence of adverse childhood experiences (ACEs) among patients with myasthenia gravis (MG) and explored associations between ACE exposure and psychological outcomes. In addition, this study conducted a preliminary evaluation of an online “Positive Mental Health BMI Learning Program” and its association with changes in psychological well-being. Methods: A total of 77 patients with MG were included, with data collected between January 2024 and January 2025. Sociodemographic characteristics, ACE exposure, and psychological and disease-related indicators were assessed, including the Myasthenia Gravis Activities of Daily Living Scale (MG-ADL), the Myasthenia Gravis Quality of Life 15-item scale (MG-QOL15), the indicator of mental health BMI on well-being (mBMI), and the Patient Health Questionnaire-9 (PHQ-9). Using a single-group pre–post design, this exploratory pilot study examined associations between ACEs and psychological outcomes, along with pre–post changes among participants who completed the online program. Results: Among the 32 participants who completed the online program, mBMI scores showed an increase, primarily reflecting improvements in emotional stability (21.41 ± 4.70 to 23.03 ± 4.49, p < 0.01); however, in the absence of a control group, these changes cannot be attributed solely to the intervention. In contrast, no significant pre–post changes were observed in PHQ-9, MG-ADL, and MG-QOL15. Across the full sample, higher ACE exposure was associated with greater depressive symptom severity, as measured by the PHQ-9 (p < 0.05). Overall, 42.9% of participants reported at least one ACE, with emotional abuse being the most frequently endorsed, followed by parental separation or divorce and emotional neglect. Conclusions: ACE exposure was common among patients with MG and was associated with greater depressive symptoms. Participation in the online positive mental health BMI learning program was associated with improvements in positive psychological well-being. Full article

Timely monitoring of cotton defoliation progress is crucial for optimizing the quality of mechanical harvesting. To accurately assess the defoliation status prior to mechanical picking, a field experiment was conducted in Hejian, Hebei Province, China, in 2022. Using a DJI P4M multispectral drone, canopy images of cotton were collected before and after defoliation at three flight altitudes: 25 m, 50 m, and 100 m. The study employed machine learning algorithms including linear regression, Support Vector Machine (SVM), Generalized Additive Model (GAM), and Random Forest (RF) to invert the Leaf Area Index (LAI). Additionally, SVM-based supervised classification was introduced to eliminate background interference from soil and open cotton bolls, while the XGBoost model and SHAP method were used to analyze the main factors influencing LAI inversion. Key findings include the following: The univariate linear relationship between EVI and LAI proved to be the most robust, with the model constructed from 100 m flight altitude data performing best (validation set: R² = 0.921, RMSE = 0.284). The rate of LAI change showed a strong positive correlation with field-measured defoliation rate (r = 0.83–0.88), confirming its reliability as a proxy indicator for defoliation progress. Soil and open cotton bolls were identified as major negative factors affecting LAI inversion accuracy. The optimal machine learning prediction model varied with days after spraying, demonstrating significant temporal variability. This study demonstrates that high-throughput LAI inversion based on drone-derived multispectral EVI enables precise and dynamic monitoring of cotton defoliation. The approach provides farmers and field managers with an efficient, non-destructive monitoring tool. By delivering real-time insight into defoliation progress, it plays a pivotal role in enabling precision defoliation management, reducing excessive chemical use, optimizing the scheduling of mechanical operations, and ultimately enhancing both the sustainability and profitability of cotton production. Full article