Search Results (2,861)

Quality-of-life (QoL) needs among gynecologic cancer survivors are multifaceted and culturally mediated, yet limited research has examined how survivors in the Middle East prioritize key domains such as sexual function, emotional well-being, and relational quality. This study aimed to identify subgroups of survivors based on the perceived importance of these domains and to explore demographic and clinical predictors of subgroups within the Saudi Arabian context. We conducted a cross-sectional, survey-based study among 129 women with a history of breast or cervical cancer attending a tertiary oncology center in Jeddah, Saudi Arabia. Participants rated the importance of sexual, emotional, and relational QoL domains using a 4-point Likert scale. Latent class analysis (LCA) was used to segment survivors based on their perceived domain importance. Differences in demographic and clinical characteristics across classes were assessed using chi-square tests. A decision tree classifier was employed. Three latent classes emerged: Class 0 (48.8%) prioritized all domains highly; Class 1 (17.8%) reported low importance across domains; and Class 2 (33.3%) emphasized emotional and relational domains while downplaying sexual function. Class group was significantly associated with age (p = 0.001), education (p = 0.04), nationality (p = 0.03), and number of children (p < 0.001). Decision tree analysis identified number of children, age, and marital status as the strongest predictors of high-importance class group. Gynecologic cancer survivors in Saudi Arabia hold diverse priorities regarding QoL domains, primarily shaped by sociocultural context than clinical variables. Tailored survivorship interventions that reflect survivors’ lived values, particularly in relation to age, family structure, and cultural norms, are critical for person-centered oncology care in the region. Full article

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types. Full article

Background: Cervical intraepithelial neoplasia grade 2 (CIN2) represents a controversial lesion in cervical cancer prevention. Traditionally included in the aggregate CIN2+ endpoint for reasons of diagnostic stability and statistical power, isolated CIN2 has unique biological characteristics: greater interobserver variability, a high probability of spontaneous regression and a lower risk of progression compared to CIN3. Objectives: To critically describe the epidemiology, natural history and management strategies of CIN2, integrating data from clinical and population-based studies and comparing the recommendations of the main international guidelines. Methods: A narrative review was conducted using a search of PubMed and Scopus (1990–January 2025). Prospective and retrospective studies on isolated CIN2, screening and vaccination trials with CIN2+ endpoints, biomarker research, and consensus documents (ASCCP, ESGO, GISCi, Ministry of Health, WHO) were included. Results: Clinical studies have shown a high probability of CIN2 regression (50–70% within two years, >70% in those <25 years), compared to a 10–15% risk of progression, especially in the presence of persistent HPV16. Screening trials and vaccine evaluations with CIN2+ endpoints have documented the efficacy of the HPV test and a dramatic reduction in lesions in vaccinated cohorts, which was also confirmed for isolated CIN2. The most recent guidelines have progressively adopted a risk-based approach, which allows for active surveillance in young women or those seeking to conceive, while the WHO maintains a screen-and-treat model in resource-limited countries. Conclusions: CIN2 is not a lesion to be treated automatically, but rather a paradigmatic model for personalized management. Integrating epidemiological and clinical data, supported by biomarkers, allows for reducing overtreatment without compromising oncological safety. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Global food production must meet the dietary requirements of a growing population, which is expected to reach 8–11 billion by 2100, while reducing the environmental impact of agricultural practices. The agricultural sector accounts for 21–37% of global greenhouse gas emissions, 70% of freshwater, and contributes considerably to biodiversity loss and challenges that are further intensified by climate change. Controlled Environment Agriculture (CEA) serves as a sustainable strategy to address global food production and promote consistency and resource-efficient crop production. However, nutrient imbalances remain a key challenge in hydroponic CEA systems. To address these nutrient-related challenges, plant sap analysis is being considered as real-time monitoring tool and precise nutrient management in CEA systems. Compared to traditional nutrient tissue analysis, sap analysis shows stronger correlations with crop performance during active growth. For instance, petiole sap nitrate-nitrogen (NO₃⁻-N) and total nitrogen (N) in tomato leaves show correlation coefficients of r = 0.6–0.8 during their rapid vegetative growth stages. Sap analysis shows potential improvements in nutrient efficiency, crop quality, and sustainability within CEA. This review investigates the principles, methodologies, and advancements in plant sap analysis, contrasting it with traditional nutrient testing methods. It also addresses challenges such as variability in sap composition, the lack of standardized protocols, and economic considerations, while emphasizing real-time nutrient management to achieve and sustainability in CEA. Full article

Background/Objectives: Electrical threshold testing (ETT) offers a promising method for assessing somatosensory function. Despite its growing use, fundamental aspects such as the physiological recovery time required between repeated threshold measurements remain poorly understood. This gap is critical when evaluating sensory, motor, or pain thresholds (EST, EMT, EPT) in pre–post designs or rapid intra-session protocols. The aim is to investigate the short-term recovery dynamics of electrical thresholds following electrical threshold testing, and to determine the minimum interval required for values to return to a stable baseline. Methods: In this pilot, repeated-measures study, 10 healthy adults (20 upper limbs) underwent three progressive stimulation trials (sensory, motor, and pain). Electrical thresholds were assessed at fixed recovery intervals (0–120 s), with duplicate measurements at each time point. Stability was defined as the absence of significant differences between repeated measures. Results: EST stabilized rapidly after sensory or motor stimulation, showing no significant differences beyond 0 and 15 s, respectively. Within pain stimulation, EST recovered at 60 s. EMT showed immediate recovery with motor stimulation and required longer recovery with pain stimulation, with stabilization observed at 90 s. EPT exhibited the highest variability, with the smallest time-dependent differences observed immediately after the first assessment. Conclusion: Recovery time after electrical stimulation varies by threshold type and intensity of the stimuli. EST and EMT can be reliably reassessed immediately after sensory and motor stimulation, respectively. However, when stimulation reaches EPT level, EST requires 60 s to recover and EMT needs 90 s. EPT demonstrates higher variability, indicating the need for further investigation. These findings support the implementation of standardized recovery intervals in ETT and underscore the importance of interpreting EPT results with caution during rapid assessments. Full article

Background/Objectives: This study aimed to investigate the anthropometric characteristics, motor performance, and isokinetic strength profiles of elite Portuguese female handball players, as well as to examine the relationships among these variables. Methods: Sixteen national-team female handball players with an average age of 20.25 ± 0.45 years, height of 171.13 ± 8.13 cm and body mass of 72.24 ± 10.96 kg volunteered. Evaluations were conducted in two sessions within one week (24–48 h apart). The first comprised anthropometric and motor performance tests, while the second focused on isokinetic strength assessments of the upper and lower limbs. Pearson correlations assessed variable associations (p < 0.05). Results: Direct correlations were found between height and arm span (r = 0.910) and between internal rotation total work and internal rotation average power (r = 0.960). The 9 m jump throw was associated with the 7 m standing throw (r = 0.670). External rotation peak torque correlated with squat jump performance (r = 0.540) and the 7 m standing throw (r = 0.760) and 9 m jump throw (r = 0.568). Internal rotation peak torque associated with squat jump performance (r = 0.674) and the 7 m standing throw (r = 0.550). Knee extension peak torque correlated with squat jump performance (r = 0.650), while knee extension total work was strongly associated with external rotation total work (r = 0.870). Knee flexion total work was associated with knee flexion peak torque (r = 0.910). Conclusions: The integrated analysis of anthropometric, motor and isokinetic variables revealed distinct strength–performance associations in female handball players, highlighting the role of upper- and lower-limb muscle function in jumping and throwing. Full article

Since the 1960s, theories on the relationship between people and their environment have explored how elements of the built environment may directly or indirectly influence human behavior. In this context, neuroarchitecture is emerging as an interdisciplinary field that integrates neuroscience, architecture, environmental psychology, and cognitive science, with the aim of providing empirical evidence on how architectural spaces affect the human brain. This study investigates the potential of neuroarchitecture to inform environmental design by clarifying its current conceptual framework, examining its practical applications, and identifying the context in which it is being implemented. Beginning with an in-depth analysis of the definition of neuroarchitecture, its theoretical foundations, and the range of interpretations within the academic community, the study then offers a critical review of its practical applications across various design fields. By presenting a comprehensive overview of this emerging discipline, the study also summarizes the measurement techniques commonly employed in related research and critically evaluates design criteria based on observed human responses. Ultimately, neuroarchitecture represents a promising avenue for creating environments that deliberately enhance psychological and physiological well-being, paving the way toward truly human-centered design. Nevertheless, neuroarchitecture is still an emerging experimental field, which entails significant limitations. The experiments conducted are still limited to virtual reality and controlled experimental contexts. In addition, small and heterogeneous population samples have been tested, without considering human variability. Full article

Canine distemper virus (CDV) is a serious and often fatal disease caused by Morbillivirus canis, which affects domestic dogs and wild carnivores, with case-fatality rates reaching up to 47%. The hemagglutinin (H) protein mediates viral adsorption and shows high genetic variability, making it a valuable molecular marker. This study aimed to detect and characterize the H gene of CDV strains from 14 dogs with fatal neurological disease in the Brazilian states of Mato Grosso and Rondônia. Brain tissue was tested via RT-PCR for the nucleocapsid gene, and positive samples were amplified for the H gene. Ten complete H-gene sequences were obtained. Phylogenetic analysis revealed two distinct clusters within the South America I/Europe lineage: one related to strains from Uruguay and Argentina (with residues 530G/549Y) and another related to Brazilian strains (530S/549Y). One sequence (MT8) showed an intermediate position in the haplotype network but clustered phylogenetically with Uruguay/Argentina-related strains. Most sequences carried 530S/549Y, a pattern linked to altered SLAM receptor usage in wildlife. These findings demonstrate the co-circulation of two CDV clusters in Central–Western Brazil, their regional and international genetic connectivity, and amino acid substitutions potentially influencing host adaptation and antigenicity. Full article

This study investigates the determinants of customers’ perception of business model innovation (BMI) and its impact on customer satisfaction (CS), customer loyalty (CL), and firm sustainability (FS) within Ecuadorian family economic groups (EFEGs). It also examines the moderating role of perceived BMI in the relationships between CS, CL, and FS. Data were collected through an online survey yielding 342 valid responses, using a structured instrument that included socio-demographic variables, perceived EFEG characteristics, and nine validated constructs. Reliability and validity were corroborated through exploratory and confirmatory factor analyses, while structural equation modeling (SEM) and multiple regression analyses were employed to test the proposed relationships. The results reveal that socially responsible consumption (SRC), technological/digital customer skills (TCS), value creation innovativeness (VCrI), value proposition innovativeness (VPI), and value capture innovativeness (VCI) significantly influence customers’ perception of BMI. In turn, BMI positively influences CS, CL, and FS, and moderates the relationships between CS and FS, and CL and FS, though it does not significantly moderate the CS–CL relationship. These findings are consistent with previous research on European family firms, emphasizing the relevance of innovation capabilities, entrepreneurial orientation, and socioemotional wealth in enhancing adaptability and performance in family-owned businesses. This study contributes novel empirical evidence on BMI in the context of an emerging economy dominated by family firms. It underscores BMI as a dynamic capability crucial for fostering customer engagement, improving competitiveness, and ensuring long-term sustainability. Managerial implications suggest that EFEG managers should prioritize digital integration, service innovation, and transparency to strengthen customer trust and loyalty. Future research should broaden the scope to include other Latin American contexts, integrate internal organizational perspectives, and explore intergenerational dynamics and digital transformation processes to deepen understanding of BMI in family business ecosystems. Full article

Lipopolysaccharides (LPS) from Gram-negative bacteria represent a significant challenge across various industries due to their prevalence and pathogenicity and the limitations of existing detection methods. Traditional approaches, such as the rabbit pyrogen test (RPT) and the Limulus Amebocyte Lysate (LAL) assay, have served as gold standards for endotoxin detection. However, these methods are constrained by high costs, lengthy processing times, environmental concerns, and the need for significant reagent volumes, which limit their scalability and application in resource-limited settings. In this study, we introduce an innovative microfluidic platform that integrates the LAL assay within microdroplets, addressing the critical limitations of traditional techniques. By leveraging the precise fluid control and reaction isolation offered by microdroplet technology, the system reduces reagent consumption, enhances sensitivity, and enables high-throughput analysis. Calibration tests were performed to validate the platform’s ability to detect LPS, using colorimetric measurements. Results demonstrated comparable or improved performance relative to traditional systems, achieving lower detection limits and greater accuracy. This work demonstrates a proof-of-concept miniaturisation of the pharmacopoeial LAL assay. The method yielded low intra-assay variability (σ ≈ 0.002 OD; CV ≈ 0.9% over n = 50 droplets per point) and a LOD estimated from calibration statistics after path-length normalisation. Broader adoption will require additional comparative validation and standardisation. This scalable, cost-effective, and environmentally sustainable approach offers a practical solution for endotoxin detection in clinical diagnostics, biopharmaceutical production, and environmental monitoring. The proposed technology paves the way for advanced LPS detection methods that meet stringent safety standards while improving efficiency, affordability, and adaptability for diverse applications. Full article

With worldwide development of wind energy, birds have grown increasingly vulnerable to collisions with wind turbines. For several species of eagles, which in many countries are accorded special protection due to a host of anthropogenic threats, accurate estimates of collision mortality are needed to assess impacts and to formulate appropriate mitigation strategies. Unfortunately, estimates of wind turbine collision mortality are often biased low by failing to account for carcasses that fall beyond the fatality search area boundary, B. In some instances, carcass density is modeled across the fatality search area to adjust for these undetected fatalities. Yet for more accurate fatality estimates, it is important to determine $\widehat{B}$, the search area boundary within which all carcasses could be found. We used eagle carcass data from multi-year fatality studies conducted at the Island of Smøla, Norway, and the Altamont Pass Wind Resource Area, California, USA, to assess carcass density (i) as a contributor to mortality estimation (ii) as a predictor variable of B, and (iii) to test whether the cumulative carcass counts with increasing distance from the wind turbine can predict $\widehat{B}$. We found that carcass counts within 5 m annuli change little with increasing distance from modern wind turbines, and that carcass density is largely a function of the area calculated. Characterization of the spatial distribution of carcasses within the search area varies with the search radius that determines B. However, this may not represent the true spatial distribution of carcasses, including those found beyond B. We assert that the available data are unsuitable for predicting the number of eagle carcasses within and beyond a given search area, nor for determining $\widehat{B},$ but they do indicate that $\widehat{B}$ lies much farther from wind turbines than previously assumed. Ultimately, modeling available carcass distribution data cannot replace the need for searching farther from wind turbines to account for the true number of eagle collision victims at any given wind project. Full article

Urban soundscapes are increasingly recognized as fundamental for both ecological integrity and human well-being, yet the complex interplay between the vegetation structure, seasonal dynamics, and microclimatic factors in shaping these soundscapes remains poorly understood. This study tests the hypothesis that vegetation structure and seasonally driven biological activity mediate the balance and the quality of the urban acoustic environment. We investigated seasonal and spatial variations in five acoustic indices (NDSI, ACI, AEI, ADI, and BI) within a historical urban garden in Castelfranco Veneto, Italy. Using linear mixed-effects models, we analyzed the effects of season, microclimatic variables, and vegetation characteristics on soundscape composition. Non-parametric tests were used to assess spatial differences in vegetation metrics. Results revealed strong seasonal patterns, with spring showing increased NDSI (+0.17), ADI (+0.22), and BI (+1.15) values relative to winter, likely reflecting bird breeding phenology and enhanced biological productivity. Among microclimatic predictors, temperature (p < 0.001), humidity (p = 0.014), and solar radiation (p = 0.002) showed significant relationships with acoustic indices, confirming their influence on both animal behaviour and sound propagation. Spatial analyses showed significant differences in acoustic patterns across points (Kruskal–Wallis p < 0.01), with vegetation metrics such as tree density and evergreen proportion correlating with elevated biophonic activity. Although the canopy height model did not emerge as a significant predictor in the models, the observed spatial heterogeneity supports the role of vegetation in shaping urban sound environments. By integrating ecoacoustic indices, LiDAR-derived vegetation data, and microclimatic parameters, this study offers novel insights into how vegetational components should be considered to manage urban green areas to support biodiversity and foster acoustically restorative environments, advancing the evidence base for sound-informed urban planning. Full article

Partial Differential Equation (PDE)-based hydrologic models demand extensive preprocessing, creating a bottleneck and slowing down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. We present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the Watershed Modeling Framework (WMF), to address this. While primarily designed for the GHOST hydrological model, GMesh’s functionalities can be adapted for other models. GMesh enables rapid mesh generation in Python by incorporating Digital Elevation Models (DEMs), flow direction maps, network topology, and online services. The software creates Voronoi polygons that maintain connectivity between river segments and surrounding hillslopes, ensuring accurate surface–subsurface interaction representation. Key features include customizable mesh generation and variable refinement to target specific watershed areas. We applied GMesh to Iowa’s Bear Creek watershed, generating meshes from 10,000 to 30,000 elements and analyzing their effects on simulated stream flows. Results show that higher mesh resolutions enhance peak flow predictions and reduce response time discrepancies, while local refinements improve model performance with minimal additional computation. GMesh’s open-source nature streamlines mesh generation, offering researchers an efficient solution for hydrological analysis and model configuration testing. Full article

Edible hydrogels are the central material class in 3D food printing because they reconcile two competing needs: (i) low resistance to flow under nozzle shear and (ii) fast recovery of elastic structure after deposition to preserve geometry. This review consolidates the recent years of progress on hydrogel formulations—gelatin, alginate, pectin, carrageenan, agar, starch-based gels, gellan, and cellulose derivatives, xanthan/konjac blends, protein–polysaccharide composites, and emulsion gels alongside a critical analysis of printing technologies relevant to food: extrusion, inkjet, binder jetting, and laser-based approaches. For each material, this review connects gelation triggers and compositional variables to rheology signatures that govern printability and then maps these to process windows and post-processing routes. This review consolidates a decision-oriented workflow for edible-hydrogel printability that links formulation variables, process parameters, and geometric fidelity through standardized test constructs (single line, bridge, thin wall) and rheology-anchored gates (e.g., yield stress and recovery). Building on these elements, a “printability map/window” is formalized to position inks within actionable operating regions, enabling recipe screening and process transfer. Compared with prior reviews, the emphasis is on decisions: what to measure, how to interpret it, and how to adjust inks and post-set enablers to meet target fidelity and texture. Reporting minima and a stability checklist are identified to close the loop from design to shelf. Full article