Search Results (61,479)

Camellia oleifera Abel, recognized as a woody oil-producing tree species, possesses considerable economic significance. To improve the breeding efficiency of C. oleifera, it is crucial to elucidate the genetic foundation underlying the mechanisms regulating fruit traits. In this study, a total of 6,252,197 high-quality single-nucleotide polymorphisms (SNPs) were identified from 109 germplasm accessions. Through genetic structure analysis, these accessions were categorized into two distinct populations. The average fixation index (Fst) was found to be 0.0153, indicating weak population differentiation. The genome-wide association analysis (GWAS) identified 157 significant loci. From these loci, 110 candidate genes were selected, which were associated with disease resistance, reproduction, development, and RNA biosynthesis. Twenty-three genes were involved in metabolic pathways, including genetic information-processing protein families, metabolic protein families, terpenoids, and polyketides. The identification of gene loci closely related to fruit traits not only provides genetic data for studying the molecular mechanisms of fruit traits but also offers new research avenues for molecular breeding of C. oleifera. Full article

Global forced displacement has reached unprecedented levels, with more than 123 million people uprooted by the end of 2024. Although older adults represent a growing proportion of refugee populations, their mental health needs remain overlooked. This scoping review synthesized current evidence on depression among older adult refugees aged 50 years and older. Guided by the Joanna Briggs Institute methodology and reported using PRISMA-ScR standards, searches were conducted in CINAHL, PsycINFO, AgeLine, and Medline for English-language publications from 2015 to 2025. A total of 1971 records were identified, with nine studies (N = 1370 participants) meeting eligibility criteria. Most studies employed cross-sectional designs and were conducted in high-income countries. Depression prevalence was consistently elevated, with rates ranging from 22% to over 70%, depending on population and measurement tools. Risk factors included female sex, widowhood, low socioeconomic status, chronic illness, functional impairment, trauma exposure, language barriers, social isolation, and limited access to care. Protective influences such as family support, higher socioeconomic status, and improved living conditions were identified but inconsistently reported. Findings indicate that older refugees are at high risk of depression, often shaped by intersecting aging- and displacement-related vulnerabilities. Findings highlight the need for culturally specific tools and longitudinal research to inform culturally safe care for older refugees. Full article

This article evaluates how artificial intelligence (AI) and educational technology (EdTech) support inclusive learning in Russia and Kazakhstan, two Eurasian countries that share post-Soviet educational legacies but differ in their levels of digital infrastructure and teacher preparedness. Using an asymmetric mixed-methods design, the study draws on a primary survey of N = 2570 educators and staff in four Russian cities (Moscow, Saint Petersburg, Yekaterinburg, Novosibirsk; October–December 2024; response rate 59.8%) and secondary policy/indicator analysis for Kazakhstan. Russia exhibits higher broadband access, AI/EdTech platform adoption, and teacher digital skill levels compared with Kazakhstan. Structural equation modeling (SEM; SmartPLS 4.1) tested four latent constructs—learning environment (LE), general digital competencies (HCg), specialized AI skills (HCs), and inclusion (I)—with satisfactory validity (AVE > 0.5; HTMT ≤ 0.85). A three-stage Measurement Invariance of Composite Models (MICOM) procedure confirmed configural, compositional, and full mean/variance invariance across Russian city subgroups, enabling pooled path analysis. Kazakhstan indicators from secondary sources are discussed as a descriptive benchmark. Semi-structured interviews with 24 stakeholders (12 Russia, 12 Kazakhstan; March 2025; analyzed with NVivo 14) revealed four themes: policy coherence, teacher readiness, infrastructure access, and ethical AI governance. Key SEM paths were LE → HCg (β = 0.278), HCg → HCs (β = 0.652), and HCs → I (β = 0.188), all p < 0.001. A formal mediation analysis confirmed a significant indirect effect across the full LE → HCg → HCs → I chain. The findings indicate that infrastructure is necessary but insufficient: the key to inclusion lies in sustained development of both basic and specialized digital skills, supported by coherent policies and continuous professional development. China and India are discussed as secondary international benchmarks drawn from published reports, not as sites of primary data collection. Full article

Winter survival in barley depends on freezing tolerance and de-acclimation tolerance, yet their genetic determinants under increasingly unstable winters remain poorly understood. Here, 188 Polish barley accessions were evaluated over two consecutive growing seasons (2021–2022) using genome-wide association studies (GWAS) with a mixed-linear model (MLM) and high-density single nucleotide polymorphism (SNP) and diversity arrays technology sequencing (DArTseq) markers. Freezing and de-acclimation tolerance were quantified by 16 chlorophyll fluorescence parameters and post-freezing survival rates in plants subjected to 21 days of cold acclimation (4 °C/2 °C, day/night) and 7 days of de-acclimation (12 °C/5 °C, day/night). The results showed that freezing and de-acclimation tolerance are related but genetically distinct. The cold-acclimated (CA) state exhibited significant marker–trait associations on chromosomes 2H and 6H, whereas the de-acclimated (DA) state displayed a broader, more complex genetic architecture, particularly on chromosomes 2H and 7H. F_v/F_m showed the strongest associations for both SNP and DArTseq markers in both states. PI_(csm), followed by PI_(cs0) and PI_(total), showed high SNP associations in the DA state, indicating a strong relationship between photosynthetic performance and freezing tolerance after de-acclimation. Notably, the DArTseq marker 11400277 on chromosome 7H showed multiple marker–trait associations across both states. These findings provide a genomic basis for marker-assisted selection of climate-resilient winter barley cultivars. Full article

Background: Tourists often travel within their own country or abroad for business, leisure or to receive planned healthcare. However, they are often not prepared for unexpected medical emergencies that occur far from home. Seeking emergency healthcare during travel may pose various barriers and challenges to tourists. Aims: This systematic review aimed to identify the challenges and barriers tourists face while seeking emergency healthcare during travel. Methods: A comprehensive search was performed in PubMed, Scopus, Web of Science and ScienceDirect from 1st January 1995 to 31 October 2025. The review included studies focusing on tourists who sought emergency healthcare abroad. Due to the methodological heterogeneity of the studies making meta-analysis impossible, a narrative synthesis of the results was conducted. The review protocol was registered with PROSPERO (ID CRD420251156975). Results: From 608 initial titles (603 from database searches and 5 additional from similar articles), 10 studies were selected—5 cross-sectional and 5 retrospective. Most (7/10) were conducted in Asian countries, while others were conducted in Europe (1), the U.S.A. (1) and multiple countries (1). The participant number ranged from 37 to 2333. All studies included both genders, apart from one that focused exclusively on pregnant women. The most common challenges identified were language and cultural barriers, limited access to healthcare services in terms of appropriateness and timeliness of care and financial and insurance coverage issues. Conclusions: The findings underscore that tourists face multiple barriers when seeking emergency healthcare abroad, resulting in negative tourist travel experiences. Once identified, specific strategies should be adopted to improve accessibility and the overall quality of care for tourists. Full article

Hearing loss affects approximately 1.5 billion people globally, yet access to comprehensive audiometric datasets for research remains limited due to privacy constraints. Synthetic data generation offers a promising solution, enabling broader data sharing while preserving privacy. This study developed and validated two complementary approaches for synthesising audiometric data: Kernel Density Estimation (KDE) and Variational Autoencoders (VAE). Using the National Health and Nutrition Examination Survey (NHANES) dataset comprising 36,676 participants with comprehensive hearing assessments, we trained both generative models and evaluated synthetic data quality through a rigorous Train-on-Synthetic-Test-on-Real (TSTR) machine learning validation framework and blinded expert clinical assessment by two independent audiologists. The VAE approach achieved 86.3% utility for hearing loss prediction, as compared to the benchmark real data (Train-on-Real-Test-on-Real). Both methods demonstrated strong privacy preservation, with zero exact record matches and robust membership inference attack resistance. Statistical validation confirmed equivalence within clinically negligible margins (<1 dB HL) across all audiometric frequencies. Blinded assessment of 85 patient profiles by two independent expert audiologists revealed that VAE synthetic data achieved high clinical plausibility ratings, with 96.7% of VAE profiles rated as plausible, compared to 13.3% for KDE. Inter-rater reliability was moderate (Cohen’s weighted $\kappa =0.553$, ICC $=0.556$), with 84.7% of ratings within one point, and both raters independently ranking VAE above real data above KDE. These findings establish validated methodologies for generating privacy-preserving synthetic audiometric data suitable for machine learning applications and clinical education, addressing a critical gap in hearing health research infrastructure. Full article

Background: Given global population growth and aging, it is imperative to prioritize early eye disease detection and treatment. However, as patient volume increases, providers are facing a shortage of workforce capacity, particularly in areas where eye doctors are already scarce, making it important to consider alternative innovative solutions that could help increase eye screening capabilities. This study compared virtual reality (VR) platform of vision screening exams that are used to evaluate ocular health, such as 24-2 perimetry, Ishihara tiles, and the Amsler grid, against their in-clinic counterparts. Methods: A total of 86 subjects were recruited from Mount Sinai’s ophthalmology clinic (New York, USA) for a comparison trial that was internally controlled across healthy eyes and those with glaucoma and retinal diseases. VR and in-office tests were administered to the patients during their clinical visit, including 24-2 perimetry, Ishihara tiles, and the Amsler grid in a randomized order, and the results were compared for each test. Results: Perimetry results from Humphrey Visual Field Analyzer (HVFA) and VR suprathreshold testing demonstrated a good sensitivity both overall (80% OD, 84% OS) and across control (86% OD, 89% OS), glaucoma (69% OD, 78% OS), and retinal disease (76% OD, 80% OS) groups. A Garway-Heath anatomical map showed an overall 70–80% agreement. Ishihara plate tests did not show a significant difference between the two testing modalities (p = 0.12; Mann–Whitney U test), which remained true across all groups. Amsler grid testing differences were also non-significant within each subgroup (p = 0.81; Mann–Whitney U test). Patient time required to complete VR exams was significantly improved (p < 0.0001; Welch’s t-test) compared to the clinical standard tests. Conclusions: All VR-based exams tested in this study showed high sensitivity and percent agreement when compared to their in-office standards. Given the results of this study, VR has a promising potential in visual function screening, which, in addition to its portable design and easy use, could assist eye doctors in screening for prevalent diseases such as glaucoma and retinal conditions. Translational Relevance: VR-based vision exams that test vision fields, color vision and visual distortions provide comparable results in healthy patients, as well as those with glaucoma and retinal diseases, indicating its potential as a screening technology for different ocular pathologies. Given VR’s portable and low-profile features, it is important to consider leveraging VR to augment delivery of vision care. Full article

Background/Objectives: Despite a wide range of international studies examining antibiotic prescribing practices among physicians, research from Central Asia remains scarce. To address this gap, the present study aimed to investigate antibiotic resistance awareness and prescribing practices among general practitioners (GPs) in Kazakhstan, Kyrgyzstan, Uzbekistan, and Tajikistan. Methods: The online questionnaire was completed by 1231 GPs, including 469 from Kazakhstan, 274 from Kyrgyzstan, 369 from Uzbekistan, and 119 from Tajikistan. Results: Most physicians (71.1%) acknowledged that their antibiotic prescribing behavior influences the development of antibiotic resistance in their regions. More than half reported discussing antibiotic resistance with their patients often or very often. However, the strategy of delayed antibiotic prescribing was unknown to 27.1% of GPs. Factors associated with good knowledge of indications for antibiotic prescribing included female sex, older age, working in Uzbekistan, practicing in urban areas, seeing 20 or more patients per day, and use of practice guidelines. Clinical practice guidelines were the most frequently reported source of current information on antibiotic therapy and resistance (20.4%), followed by continuing professional education (15.9%) and textbooks (14.1%). The vast majority of GPs (94.4%) indicated a need for additional information resources to support more rational antibiotic prescribing. The most commonly cited needs were higher-quality clinical practice guidelines (22.5%) and better access to existing guidelines (17.7%). Conclusions: These findings suggest that, despite generally high awareness of antibiotic resistance, important knowledge gaps remain among GPs in Central Asia. Strengthening access to clinical guidelines and continuing professional education may support more rational antibiotic prescribing. Full article

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

A high prevalence of psychological distress and unmet mental health needs among youth, combined with a preference for self-reliance and informal support (e.g., peers), poses a major public health challenge. Growing reliance on digital platforms highlights the potential of anonymized online peer support forums as accessible first-line avenues of support. In India, research on peer support interventions remains scarce. This study aimed to identify the concerns for which Indian youth engage in an anonymized, moderated online peer support forum, as well as the purposes for posting. A retrospective qualitative design was employed, analyzing all 137 posts of 124 unique users received between February 2024 and October 2025 on the forum using hybrid thematic analysis. Findings revealed that user posts encompassed diverse concerns across personal, relational, social, and achievement domains. Feeling states emerged as the most prominent theme, frequently co-occurring with other themes, followed by self-related concerns. Several posts explicitly mentioned mental health concerns such as depression and social anxiety, often without mention of professional help-seeking suggestions. Family-related issues, romantic relationship concerns, academic pressures, social comparisons, and unmet needs for approval were some of the other themes that emerged. Shifting from content to motivations for posting, the analysis identified purposes such as venting distress, seeking suggestions, sharing reflections, engaging in meaning-making, and seeking reassurance or validation. Future work needs to examine whether such forums can function not only as spaces for strengthening self-help and peer support processes but also as avenues for improving professional help-seeking through normalization and encouragement of the same when appropriate. Full article

Background: Depression remains a major global health burden, yet fragmented care often leads to waiting times and unmet needs. Therefore, the Belgian collaborative Integrated Depression Care (IDECA) project strengthened primary care depression management by introducing a Reference Person Mental Wellbeing (RPMW) who functions as a case manager, supported by shared-care tools, structured psychoeducation modules, and targeted training for general practitioners (GPs). This study examines normalization in primary care practice. Methods: A single-arm, mixed-method study was implemented over 18 months in two Flemish Primary Care Zones (PCZ). Implementation outcomes were assessed every four months using the NoMAD questionnaire and analyzed using Wilcoxon signed-rank tests. Peer review sessions with professionals and interviews with patients were analyzed thematically. Caseload and service delivery were assessed using process evaluation logs. Results: Twenty-two professionals (17 GPs, two RPMWs, and three PCZ staff members) completed the NoMAD questionnaire. Intervention familiarity increased during the first eight months (T0–T1: p < 0.001; T1–T2: p = 0.022) and continued to rise thereafter (T3–T4: p = 0.008). Integration into daily practice and perceived impact on professional work improved progressively, reaching near-ceiling scores. Peer review sessions highlighted the RPMW’s central role in trust-building and care coordination. Over 12 months, one full-time equivalent RPMW supported 175 patients (mean age 40.7 years; 75% female), with an average of five consultations per patient. Patients reported high satisfaction, emphasizing accessibility, empathy, and practical support. Conclusions: Sustained results suggest successful normalization and support the potential of collaborative, low-threshold depression care. Future work will assess clinical and economic outcomes. Full article

Organophosphate pesticides (OPs) are widespread contaminants in agricultural and aquatic environments. Growing evidence indicates that even low-level, chronic exposure to OPs is associated with neurotoxic effects and long-term neurological risks. Over the past decade, substantial progress has been made in developing porous carbon materials capable of efficiently removing OPs from water, food systems, and other environmental matrices. However, adsorption studies have largely focused on equilibrium performance metrics rather than on conditions relevant to real exposure scenarios. This review introduces an exposure-oriented perspective for evaluating porous carbon materials for OP mitigation by linking adsorption science with exposure-driven neurotoxicity considerations. By analysing recent studies on OP adsorption, we demonstrate that equilibrium adsorption capacity alone is often a poor predictor of real-world exposure mitigation. Instead, adsorption kinetics at low concentrations, pore accessibility, and surface chemical heterogeneity emerge as key factors governing sustained OP sequestration. The review further highlights how hierarchical pore architectures and balanced surface functionalization can enhance adsorption efficiency under environmentally realistic conditions. By integrating environmental carbon research with exposure-relevant considerations, this work outlines design principles for carbon adsorbents to reduce long-term OP exposure and associated neurological risks. Full article

Background: Qualitative motion analysis revealed that the cervical spine moves according to a consistent pattern. Current data analysis methods are limited by the extensive time required to process the retrieved data. A previous study demonstrated the feasibility of using a deep-learning model to automate analysis methods. However, segmentation accuracy needed to be improved. This study aims to improve segmentation model performance to enable reliable motion analysis. Methods: Four nnU-Net configurations were tested: baseline (A), pre-trained (B), with histogram equalization (C), and pre-trained with histogram equalization (D). Segmentation performance was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU) and 95th percentile Hausdorff Distance (HD95). Vertebral rotation was estimated using mean shapes. Reliability was assessed using the Intraclass Correlation Coefficient (ICC). Sensitivity analyses were conducted. Results: Across all models, mean DSC ranged from 0.67 to 0.92, mean IoU from 0.55 to 0.85, and mean HD95 from 2.35 to 19.67 mm. After sensitivity analysis for low segmental range of motion (sROM) and low-quality recordings, the mean ICC ranged from 0.617 to 0.837 for model A, from 0.609 to 0.780 for model B, from 0.409 to 0.689 for model C, and from 0.480 to 0.835 for model D. Conclusions: This study shows that Models A and B can accurately analyze cervical motion patterns. High image contrast and an adequate sROM are essential for robust model performance. It also marks an important step toward automated qualitative motion analysis, increasing the accessibility of motion pattern evaluation. Full article

Urban agriculture (UA) provides numerous social, environmental, economic, and spatial benefits for urban residents. The development of UA is supported, among other factors, by the participatory budget, an inclusive mechanism of democratic governance that facilitates investment decisions tailored to local communities’ needs. The aim of the study presented in this article was to identify and assess projects on vegetable cultivation as part of UA submitted in nine completed editions of the Warsaw Participatory Budget (WPB) conducted between 2015 and 2023. The research developed a typology and carried out a comparative analysis of vegetable-related projects, focusing on those selected through public voting. The projects, which varied in scale and scope, were classified into six types and subsequently evaluated across four dimensions: social, functional, ecological, and economic. The assessment also incorporated nine thematic categories and six social categories to highlight the primary beneficiaries of the projects. The results showed that vegetable-related projects in the WPB were decidedly niche, as only 101 such initiatives were submitted during the analyzed period, of which only 31 were implemented. The social dimension proved dominant, with children as the main target group. Most projects focused primarily on education in vegetable cultivation and the development of healthy eating habits and, to a lesser extent, on ensuring access to healthy food. The ecological dimension is fulfilled primarily through sustainable vegetable cultivation combined with educational activities; however, education concerning other pro-environmental solutions and their implementation is very limited. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article