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Cancer represents one of the major public health issues in sub-Saharan Africa (SSA), with increasing incidence and mortality rates as a result of late diagnosis, limited healthcare infrastructure, and financial difficulties. Traditional medicine plays an important role in healthcare across different populations in SSA, as more than 80% of the population depend on indigenous plant-based remedies for treating or managing different ailments, including cancer. This study aimed to document medicinal plants traditionally used to treat cancer in SSA. A systematic search of all documents available in the last two decades (2006–2026) was conducted using PubMed, Web of Science, and Google Scholar databases. After screening studies using the predefined inclusion and exclusion criteria, 55 studies met the eligibility requirements and were selected for analysis based on their relevance to the topic, geographic scope, and reported applications in cancer management. The scientific names of the identified plant species and their taxonomic authorities were verified using the Plants of the World Online database. A total of 556 species, belonging to 110 families, were recorded as medicinal plants used to treat various forms of cancer in SSA. The top five families with the most frequently used plants were Fabaceae (51 species), Asteraceae (34 species), Euphorbiaceae (25 species), Apocynaceae (22 species) and Lamiaceae (22 species). Frequently cited plants include Kigelia africana, Annona muricata, Adansonia digitata, Carica papaya, and Tamarindus indica. A total of 11 plant parts were documented, with leaves (41.20%), roots (18.75%), and bark (17.25%) being the dominant plant parts utilised. The primary methods of preparation were decoction (38.23%), powdering and grinding (14.51%), and infusion and tea preparation (49.73%), while the main modes of administration were oral (66.88%) and topical (26.46%). The results show that traditional medicinal plants hold significant potential as sources of novel anticancer drugs in SSA. However, a significant gap exists between ethnobotanical knowledge, laboratory research, and clinical application. Rigorous pharmacological and toxicity evaluations and well-designed clinical trials on the identified medicinal plants are needed to integrate effective and safe plant-based therapies into evidence-based oncology. Full article

Understanding trade-offs and synergies among ecosystem services (ESs) along environmental gradients is crucial for sustainable oasis management. This study investigated four key ESs—carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC)—in three typical oases along water–heat gradients in arid northwestern China. The InVEST model was used to quantify ESs in 1990, 2005, and 2022, and Pearson correlation, geographically weighted regression, K-means clustering, and random forest models were applied to analyze service relationships, ecosystem service bundles (ESBs), and driving factors. The results showed that CS and HQ maintained strong synergies, while the WY–SC relationship shifted from weak trade-offs under drier conditions to stronger synergies under more favorable water–heat conditions. Geographically weighted regression revealed spatial heterogeneity and directional asymmetry in ES relationships. Four ESB types were identified: ecologically fragile zones, ecological transition or buffer zones, agricultural production zones, and core ecological source zones. Driving-factor analysis indicated that vegetation-related services were mainly associated with land-cover structure and vegetation growth, whereas hydrological and erosion-related services were more closely linked to precipitation, potential evapotranspiration, temperature, and topography. These findings support differentiated oasis management through ecological restoration, development regulation, water-saving agriculture, and strict ecological protection. Full article

Brucellosis is a globally prevalent zoonotic disease affecting both humans and animals. Its nonspecific clinical manifestations often complicate diagnosis, underscoring the need for reliable laboratory confirmation. Traditional serological assays, though widely used, suffer from limitations such as inconsistent sensitivity and false-positive results. To address these challenges, this study mapped IgM and IgG epitopes of the Brucella Omp-2a protein using sera from infected patients. Epitope identification was performed through SPOT synthesis on cellulose membranes, followed by assessment of potential cross-reactivity using peptide database analysis and ELISA validation. Three major IgM and seven IgG linear B-cell epitopes were identified, six of which demonstrated strong reactivity in peptide-ELISA. Importantly, no significant cross-reactivity with proteins from other human pathogens was detected. Two chimeric multi-epitope peptides, composed of 50 and 60 amino acids and integrating Brucella-specific IgM and IgG epitopes, exhibited excellent diagnostic performance in ELISA, achieving near 100% sensitivity and specificity. These findings support the potential of synthetic peptides as reliable and cost-effective alternatives to native antigens in serological assays. Further validation in larger, geographically diverse cohorts will be essential to confirm their diagnostic robustness and facilitate their integration into routine brucellosis diagnostics. Full article

Rapid urbanization reshapes urban land systems and intensifies surface thermal heterogeneity, yet nonlinear day–night land surface temperature (LST) responses to grey–green spatial organization and building morphology remain insufficiently understood, particularly in thermally stressed areas across the urban–rural gradient. Using Xi’an, China, as a case study, this study develops a priority-area-based land–climate interaction framework. Priority areas were defined as grid cells where elevated LST coincided with relatively strong local explanatory relationships between LST and land-cover or morphological variables. Multiscale geographically weighted regression (MGWR), gradient boosting decision trees (GBDTs), SHAP-based interpretation, and threshold sensitivity analysis were combined to identify dominant drivers, nonlinear response patterns, and interaction structures of daytime and nighttime LST. The results show pronounced day–night differentiation: daytime hotspots were concentrated in the built-up core, whereas nighttime hotspots extended toward the urban–rural fringe. Daytime LST was mainly associated with building coverage and grey-space organization, while nighttime LST was more strongly related to mean building height and the cooling contribution of green-space coverage. The analysis further identified localized empirical response ranges for built-up intensity, grey-space connectivity, building height, and green-space coverage within the priority areas. These findings clarify how land-cover configuration and building morphology jointly shape day–night surface thermal responses and provide context-specific evidence for land-use planning and targeted urban heat mitigation. Full article

Background: Increasing evidence suggests that breast milk and its bioactive components, including short-chain fatty acids and the milk microbiome, are influenced by maternal nutrition and body mass index (BMI). Bioactive components transferred to the infant through breast milk play a pivotal role in infant growth and development and have indications in the child’s future short- and long-term health outcomes. This study aimed to assess the impact of maternal pre-pregnancy BMI (PP-BMI) on human breast milk macronutrient composition, short- and long-chain fatty acid profiles, and breast milk microbiome profiles. Approach: This was an exploratory cohort study of forty-four lactating Caucasian women, two to fourteen weeks postpartum, divided into groups based on pre-pregnancy body mass index (BMI). Study participants signed informed consent, completed health and nutritional surveys, and provided a breast milk sample. Breast milk samples were subjected to proximate analysis, microbiome identification and short- and long-chain fatty acid extraction and analysis. Results: Maternal age, maternal physical activity, infant birth weight, and time of lactation at sample collection were not significantly different between the maternal PP-BMI groups. PP-BMI was significantly different between the two maternal groups. No significant differences were found between the maternal BMI groups concerning nutritional intake. No differences in breast milk microbiomes were observed in alpha diversity and beta diversity between the maternal PP-BMI groups. For long-chain fatty analysis in breast milk samples, myristic acid was significantly higher in the PP-BMI overweight/obese group while stearic acid was significantly higher in the PP-BMI normal-weight group. Butyric, valeric, and isocaproic acid concentrations in HBM were significantly higher in the PP-BMI normal-weight group and lower or undetectable in the PP-BMI overweight/obese group. Conclusions: Data from this exploratory cohort study indicate that maternal diet and pre-pregnancy BMI may be associated with differences in selected HBM fatty acids. There were no significant differences in microbiomes for alpha and beta diversity in breast milk between maternal PP-BMI groups; however, lower relative abundance was observed in the breast milk of the PP-BMI overweight/obese group. These findings should be interpreted in the context of the study’s limitations, including convenience recruitment from a Facebook group, the modest sample size, and restriction to Caucasian women from a single geographic region. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

The development of sustainable tourism requires the use of planning methods that combine environmental protection with inclusive access to nature-based destinations. This article presents a macro-level spatial decision-support framework for planning service infrastructure for specialized off-road electric wheelchairs in mountain destinations. The proposed framework combines predefined static vehicle-related constraints, Geographic Information System (GIS) analysis using QGIS and OpenStreetMap data, and Multi-Criteria Decision Analysis (MCDA). The spatial filtering stage evaluates terrain feasibility using an adopted maximum longitudinal slope threshold and minimum path-width requirement. The location–allocation stage combines Simple Additive Weighting (SAW) with a spatial-dispersion procedure to identify service hubs that are both suitable and regionally distributed. The method is not a dynamic engineering model of vehicle performance, but a GIS-MCDA planning tool for preliminary regional infrastructure siting under predefined operational constraints. Full article

The genus Tulipa L. is a common group of ornamental plants, characterized by high morphological variability and a complex taxonomy. Despite considerable interest in this group, assessments of its species composition remain inconclusive, as evidenced by discrepancies between contemporary taxonomic sources. The number of recognized taxa varies across major taxonomic databases, including Plants of the World Online, World Flora Online, and Euro+Med PlantBase, reflecting ongoing taxonomic revisions and differences in species concepts. In terms of distribution patterns, 7.6% are widely distributed taxa across transcontinental regions, 28.0% occur across multiple countries within a continent, and 66.9% are range-restricted taxa. The latter group includes 4.2% transnational endemics, 44.1% single-country endemics, 8.5% single-region endemics, and 10.2% single-site endemics. Recent taxonomic and evolutionary studies of Tulipa increasingly rely on molecular approaches, particularly DNA barcoding and chloroplast genome analyses, which have improved phylogenetic resolution and species delimitation in several cases. However, truly comprehensive studies combining morphological, cytogenetic, and molecular datasets remain limited and are typically restricted to individual taxa or species complexes rather than the genus as a whole. Modern molecular genetic studies demonstrate the high informativeness of both nuclear and plastid markers for studying the phylogeny, systematics, and genetic diversity of Tulipa species. Natural populations of Tulipa are under pressure from anthropogenic factors and climate change, resulting in reduced range and habitat degradation. According to the International Union for Conservation of Nature Red List of Threatened Species, among 118 taxa of the genus Tulipa, T. sprengeri Baker is classified as Extinct in the Wild, 5.9% as Critically Endangered, 5.9% as Endangered, 8.5% as Vulnerable, 11.9% as Near Threatened, and 11.0% as Least Concern. The use of exclusively national assessments to determine species extinction risk may be insufficiently objective, whereas global assessments provide a more informative and reliable approach for evaluating conservation status. In this review, we combine investigations of the morphology, taxonomy, and geographic diversity; population genetic structure and molecular diversity; and molecular phylogenetics and plastome-based genomics of the genus Tulipa. Furthermore, the review examines current challenges and future research prospects, emphasizing that studies of the genus Tulipa should integrate morphological, genomic, and ecological approaches to refine taxonomy and conserve genetic resources. Full article

Connected vehicle platforms enable large-scale collection of vehicle dynamics data from production fleets, creating opportunities for passive roadway monitoring using onboard sensing systems. While existing vibration-based approaches primarily focus on pavement roughness estimation, the ability of fused onboard signals to capture defect-level characteristics remains insufficiently explored. This study investigates whether Road Surface Monitoring (RSM) signals, developed by Honda as an integrated OEM sensing approach, contain distinguishable patterns associated with specific road surface defects. A framework is developed to analyze, detect, and classify defect-related vibration signatures using these fused signals. The approach introduces the Defect Consistency Index (DCI), which measured a 29% average difference between pothole and patching signal signatures within the dataset. A threshold-based Defect Identification Algorithm (DIA) was then applied to detect defective segments, achieving 89% detection accuracy. A machine learning pipeline using shape-based features was subsequently used to classify potholes and patching, achieving up to 90% classification accuracy on the evaluated dataset. The framework was evaluated using real-world RSM data collected from a single instrumented vehicle within a limited geographic region. The results indicate that fused vibration signals contain recurring defect-related patterns that may support defect-level analysis using compact, non-visual measurements. These findings indicate the potential of connected vehicle vibration sensing for scalable roadway monitoring while highlighting the need for broader validation across vehicles, environments, and defect conditions. Full article

Potato sorting in post-harvest processing relies heavily on manual visual inspection, which is physically demanding, subjective, and insufficiently scalable for modern packing lines. This study investigates the feasibility of a low-cost RGB-based optical inspection system for automated potato quality detection using deep learning-based object detection. A controlled imaging platform was constructed using commodity hardware, and a dataset of 19,805 manually annotated instances across 1361 images was collected from two geographically distinct farm locations in Slovenia. A systematic benchmark of 25 model configurations spanning five YOLO architecture families—YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLO26—was conducted across three practical quality classes (Edible, Feed, Rotten) using a strict cross-location evaluation protocol in which models were trained on one location and tested on a completely unseen second location. All models achieved strong in-distribution performance (F1 ≥ 0.906), but showed considerable variation under cross-location conditions, with external F1 ranging from 0.792 to 0.918. The yolo26_l configuration achieved the best cross-location performance (F1 = 0.918, mAP@0.5:0.95 = 0.816, ΔF1 = 0.029), demonstrating that transferable representations are achievable under a standard supervised training protocol. Per-class analysis identified feed detection as the primary generalization bottleneck. The results confirm that affordable RGB-based sorting systems are technically feasible and highlight cross-location evaluation as an essential protocol for assessing real-world deployment readiness. Full article

This study evaluated the microbiological quality, phenolic compound profile, antimicrobial activity against foodborne pathogens, and the presence of potential chemical markers associated with microplastic polymers in 35 commercial bee pollen samples obtained from the seven geographical regions of Türkiye. Microbiological analyses included the enumeration of total mesophilic aerobic bacteria, coliforms, yeasts and molds, lactobacilli, lactococci, and psychrophilic bacteria. Antimicrobial activity was determined against Escherichia coli O157:H7, Staphylococcus aureus, and Salmonella Enteritidis using the disk diffusion method. Phenolic compounds were analyzed by HPLC-DAD, while characteristic pyrolysis products associated with microplastics were analyzed by PY-GC/MS. The results indicated that the pollen samples generally exhibited low microbial contamination levels and variable antimicrobial activity, depending on their geographical origin. Quercetin was identified as the predominant phenolic compound, and samples with higher phenolic content tended to show stronger antimicrobial effects, particularly against S. aureus. PY-GC/MS analyses revealed the presence of several chemical markers potentially associated with plastic polymers in a considerable proportion of the samples. Spearman correlation analysis showed a positive correlation between total phenolic content and particularly S. aureus inhibition. These findings highlight the nutritional and functional value of bee pollen while also drawing attention to emerging food safety concerns related to possible exposure to plastic-associated environmental contaminants. Regular monitoring of bee pollen is therefore recommended to ensure product quality and consumer safety. Full article

Background/Objectives: No study has investigated the effect of the catechol-O-methyltransferase (COMT) Val158Met rs4680 polymorphism in cognitive and executive performance in migraine. The current study investigated the potential influence of the Val158Met rs4680 polymorphism in cognitive performance/executive function in women with migraine. Methods: One hundred and forty women with migraine (70 chronic and 70 episodic) and 70 healthy controls completed the following neurocognitive tests (D2 Attention test and Rey–Osterrieth Complex Figure) and executive functions (subtest “Digits D/R/I” of the Wechsler Adult Intelligence Scale WAIS-IV battery for, the 5-Digit test, the Symbol Search for and the Zoo Test) for evaluating selective attention, visual perception, working memory, mental inhibition, processing speed and planning/decision making, respectively. Thus, three genotypes (Val/Val, Val/Met, and Met/Met) of the Val158Met polymorphism were identified by polymerase chain reaction. The effect of group and Val158Met genotype in neurocognitive tests and executive functions was evaluated with multivariate analysis of covariance (MANCOVA). Results: The MANCOVA revealed a significant Val158Met polymorphism* group interaction on neurocognitive performance (Wilk’s λ = 0.393, F [76,688] = 2.425, p < 0.001, n2p = 0.208, 1 − β = 0.999), not influenced by age (Wilk’s λ = 0.920, F [19,174] = 0.743, p = 0.734, n2p = 0.035, 1 − β = 0.120), educational level (Wilk’s λ = 0.875, F [19,174] = 1.024, p = 0.440, n2p = 0.047, 1 − β = 0.190) and prophylactic medication (Wilk’s λ = 0.855, F [19,174]= 1.000, p = 0.467, n2p= 0.145, 1 − β = 0.686). Post hoc analyses revealed that women with chronic migraine with the Met/Met genotype exhibited domain-specific better performance (i.e., higher selective attention, visuospatial memory) and executive functioning (i.e., working memory, planning/decision making) than those women with chronic migraine carrying Val/Val or Val/Met genotypes. Conclusions: We found an association of the Met/Met genotype with neurocognitive performance/executive functioning, particularly in women with chronic migraine since women with chronic migraine carrying the Met/Met genotype showed domain-specific better cognitive performance/executive functioning than those with the Val allele. Future studies including large sample sizes from different geographic locations are needed to better generalizability and validity of the current results. Full article

In the context of stock-based development, assessing the development potential of idle rural resources and formulating differentiated utilization pathways are important for improving resource-use efficiency and stimulating endogenous rural development in mountainous counties. However, existing studies mainly focus on single resource types and lack both an integrated framework for multiple idle rural resources and effective links between potential assessment and classified utilization. Taking Suichang County, Zhejiang Province, as a case study, this study constructs an evaluation index system for idle rural resource development potential. GIS-based spatial analysis and geographically weighted regression (GWR) reveal the spatial differentiation of development potential and its driving factors. On this basis, a three-dimensional framework of “potential–driving force–resistance” is used to classify resource utilization types and formulate differentiated pathways. The results show significant spatial heterogeneity in the development potential of idle rural resources in Suichang County, characterized by “central agglomeration, two-wing diffusion, and peripheral weakening” and a “three cores and two zones” pattern. The driving factors display significant spatial non-stationarity. Idle rural resources are classified into six utilization types with corresponding utilization strategies. This study provides a scientific basis and practical reference for classified revitalization, zoned policy implementation, and sustainable rural transformation in similar mountainous counties. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article