Search Results (3,912)

Geographical and botanical origin dominate the chemical composition and biological activity of propolis samples. In this study, thirty Turkish propolis samples collected from different regions within the Black Sea region were comprehensively evaluated to investigate the relationships between phytochemical composition, antioxidant capacity, antibacterial activity, and anticancer potential. The propolis samples were extracted using 70% aqueous ethanol and labeled as K1-K30. Total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and DPPH radical scavenging activity were determined. Antibacterial activity was evaluated against both Gram-positive and Gram-negative bacterial strains using inhibition zone (ZI), minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) tests. Most of the extracts showed measurable antibacterial activity, where Gram-positive bacteria generally showed higher susceptibility compared to the tested Gram-negative bacterial species. The extracts K12, K13, K22, K24, and K28 demonstrated the strongest broad-spectrum antibacterial activity. Pearson correlation analysis revealed that antibacterial efficacy was more strongly associated with antioxidant capacity and flavonoid richness than with total phenolic content alone. FRAP and TFC emerged as the strongest predictors of antibacterial activity, while specific phenolic compounds (particularly quercetin and trans ferulic acid) were associated with enhanced activity against Gram-negative bacteria. Preliminary anticancer activity was assessed using cell viability assays at 24, 48, and 72 h. Cytotoxic responses were highly heterogeneous and strongly time-dependent. K10, among the extracts, exhibited the strongest anticancer activity, showing inhibition rates above 80% at 24 and 48 h, whereas K4 and K23 also demonstrated considerable cytotoxic effects. Notably, antibacterial and anticancer activities were not directly correlated. Extracts with strong antibacterial activity, such as K12 and K22, showed proliferative effects in anticancer assays, whereas K10 displayed weak antibacterial but strong anticancer activity. These findings demonstrate that antibacterial and anticancer properties of propolis are governed by distinct phytochemical compositions. Overall, this study highlights the multidimensional bioactivity of Turkish propolis and emphasizes the importance of comprehensive phytochemical characterization for identifying extracts with application-specific therapeutic potential. Full article

Background/Objectives: The population genetic structure of Albania remains insufficiently characterized at both regional and broader European scales. This study investigated genetic variation within Albania and its relationship with neighboring and European populations using autosomal short tandem repeats (STRs). Methods: Sixteen autosomal STR loci were analyzed in 2000 unrelated individuals from 12 Albanian counties, grouped into Northern, Central, and Southern regions. Population structure was assessed using principal component analysis (PCA), pairwise Weir–Cockerham FST, hierarchical AMOVA, and Bayesian clustering (STRUCTURE Version 2.3.4). Population affinities were further evaluated using Nei’s standard genetic distance, multidimensional scaling, and neighbor-joining analysis of European reference populations. Results: PCA revealed extensive regional overlap, with PC1 and PC2 explaining 2.64% and 2.61% of variation, respectively. Pairwise FST values were close to zero, with confidence intervals overlapping zero, while AMOVA indicated negligible regional differentiation. STRUCTURE identified K = 3 as the strongest relative solution, but ancestry coefficients showed extensive admixture without discrete regional clustering. European comparisons revealed low Nei distances (approximately 0.004–0.014), with Albania showing particularly close affinity to Greece and other Southeastern European populations. Conclusions: Albanian autosomal STR variation demonstrates high regional homogeneity and limited population substructure, while broader affinities are consistent with geographic patterns across Southeastern Europe Full article

Alzheimer’s disease and related dementias are projected to affect more than 150 million people worldwide by 2050. Early staging with validated instruments such as the Clinical Dementia Rating (CDR) scale is essential for timely intervention, yet access to clinician-administered CDR assessment remains constrained by workforce, time, and geographic barriers. This study complements a previously published machine learning pipeline for Alzheimer’s disease prediction by addressing the downstream task of dementia staging. Because the global CDR score is already derived from the six sub-domain ratings through an established rule-based procedure, the contribution reported here lies not in discovering that mapping but in encoding it in a transparent, deployable form: an explainable decision tree classifier embedded in DiAbot, a large-language-model-fronted conversational system that supports self-administered CDR-style assessment. We extracted 13,453 CDR records from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), removed administrative variables, invalid entries, and missing rows (final n = 13,290), and trained decision tree classifiers under two impurity criteria, Information Gain and Gini Index, using a 70/30 stratified record-level hold-out and ten-fold stratified record-level cross-validation. This classifier-level evaluation uses the six domain scores as recorded during ADNI’s clinician-administered assessment, not scores elicited by the DiAbot chatbot; the trained classifier was separately embedded in a web application in which a prompt-engineered large language model conducts a CDR-style interview and normalizes responses to ordinal domain scores, but the end-to-end accuracy of that full conversational pipeline (chatbot elicitation through to final CDGLOBAL) has not yet been measured, and is not what the headline accuracy figures below report. The Information Gain Decision Tree reproduced the established mapping from the six CDR sub-domain scores to the CDGLOBAL with 99.86% accuracy under the record-level hold-out protocol (matching macro-averaged precision, recall, and F1-score), with a ten-fold record-level cross-validated mean of 99.81% (SD 0.07); this result represents fidelity to the established CDR scoring rule rather than independent dementia-diagnosis accuracy. Gini-based trees performed almost identically (99.79% hold-out, 99.74% cross-validated). Memory dominated feature importance, consistent with its role as the primary domain in the official CDR scoring algorithm. Residual misclassifications were confined to adjacent CDR stages. Because the CDGLOBAL is deterministically derived from the six sub-domain scores, these figures should be read throughout as evidence of high-fidelity reconstruction of the established CDR scoring relationship, not as general dementia-diagnosis accuracy comparable to imaging- or biomarker-based classifiers; further, participant-independent generalization remains unverified under the record-level protocol evaluated here. An interpretable classifier embedded in a conversational front-end can nonetheless make standardized CDR-style staging more widely accessible while preserving clinical inspectability; the resulting system is positioned as a screening-stage adjunct to, and not a replacement for, clinician-administered CDR assessment. Full article

Traditional villages preserve regional cultural memory and distinctive rural landscapes while embodying the historical evolution of human–land relationships. This study examines the spatial distribution and influencing factors of 221 nationally listed traditional villages in central Shanxi using GIS-based spatial analysis, standard deviational ellipse analysis, river and road buffer analyses, and the optimal parameters-based geographical detector (OPGD). The villages are significantly clustered in a multi-core, locally contiguous pattern oriented along a northeast–southwest axis. Most are located at elevations of 500–1000 m. Village frequencies vary nonlinearly with distance from rivers and roads, suggesting trade-offs among water access, hazard avoidance, and transport connectivity. Temperature, population density, GDP, and elevation show relatively strong explanatory power, while the interaction between urbanization rate and population density has the highest explanatory power. By linking historical formation conditions with contemporary development pressures, this study proposes a cultural landscape network organized around high-density village clusters, water-system and historical transport corridors, and major mountain–basin landscape units, supported by differentiated conservation zoning and context-specific rural spatial governance. These findings provide region-specific evidence for living heritage conservation and sustainable rural governance. Full article

Urban green space supply (UGSS) is a core component of territorial spatial planning, and the advancement of the park city initiative has been temporally associated with a systematic transformation. Differentiation in UGSS is a comprehensive issue concerning ecological environment, public welfare, and high-quality urban development, and it is closely related to the achievement of United Nations Sustainable Development Goal (SDG) 11.7. This study employs a comprehensive approach combining spatiotemporal dynamic analysis (Mann–Kendall trend test and Theil–Sen slope estimation), differentiation measures (Gini coefficient and Theil index), and the explainable machine learning SHAP model to conduct a large-sample empirical analysis of 1760 towns in China from 2015 to 2024. The findings show the following: First, park city construction corresponds to notable spatiotemporal evolution of UGSS in China’s towns, with approximately 85% of towns showing a significant rise. Second, park city construction coincides with a reduction in differentiation in both the outcomes and processes of UGSS in China’s towns, with both the Gini coefficient and Theil index declining to varying degrees. An analysis of the Theil index further confirms that the observed changes in the Theil index are more pronounced in disadvantaged towns, and the differentiation in UGSS originates more from intra-regional disparities than from inter-regional gaps. Third, the differentiation in UGSS shows deep structural correlates, with the associations of socio-economic and natural ecological factors exhibiting various complex nonlinear associations such as inverted U-shape, arc shape, U-shape, and wave shape. These associations are specifically manifested as mixed directionality of associations, hierarchical intensity of associations, threshold-based evolutionary pathways, geographic spatial heterogeneity, and interactive relationships among factors. This study recommends that the policy design of park city construction and green space system planning should promptly establish a new model combining situational response, threshold management, and collaborative governance. The nonlinear and interpretable analytical paradigm constructed in this study holds significant value for achieving precise supply and equitable sharing of green space resources. Full article

European Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) labels differ in the strength of the relationship that they require between a product and its territory, but comparative evidence on how this distinction is reflected in the protected cheese sector remains limited. This study provides a descriptive and exploratory comparison of the product, territorial and production characteristics associated with PDO rather than PGI status among European protected cheeses already registered under the EU geographical indication framework. A cross-sectional product-level dataset of 253 cheeses was constructed, including 192 PDO and 61 PGI products. Registration status was coded as a binary outcome, where 1 = PDO and 0 = PGI. Explanatory variables described the species, island and mountain location, production requirements, age of registration, estimated retail price, regional GDP per capita and annual production volume. Univariable logistic regression models were first fitted for each explanatory variable, followed by a multivariable logistic regression model obtained through backwards elimination. PDO products represented 75.9% of the dataset. In univariable models, all candidate explanatory variables were significantly associated with the registration status. In the final multivariable model, PDO registration was associated with the species, island location, mountain area and age of registration, while the breed requirement was retained but did not reach conventional statistical significance. Sheep and goat cheeses, island cheeses, mountain cheeses and older registrations showed higher odds of a PDO status. The results are indicative and suggest that PDO and PGI cheeses should not be treated as a homogeneous group in descriptive product-level analyses. Within this dataset, PDO cheeses appeared to be more closely associated with products showing stronger territorial embeddedness, localised production systems and a longer-established geographical indication status. Full article

In the contemporary world, artificial intelligence (AI) is driving profound changes across global institutional, technological, and social processes. However, countries’ readiness for its integration varies substantially by economic development, regional characteristics, and digital maturity. Accordingly, this article aims to assess the alignment between the AI publication footprint, based on Scopus publication data, and national AI readiness, measured by the IMF AI Preparedness Index, across 173 countries, classified by geographic region and income level. The application of analysis of variance (ANOVA), the Kruskal–Wallis test and post hoc comparisons, correlation, regression, and cluster analysis enabled the identification of multidimensional relationships between scientific activity and the four key dimensions of digital maturity: digital infrastructure, human capital/labor market, innovation/economic integration, and regulation/ethics. The results revealed a strong overall global correlation between AI publication footprint and national AI readiness (r = 0.68, R² = 0.46), with the highest level of alignment observed in high-income countries’ readiness (r = 0.67, R² = 0.45), and regions such as the Americas (r = 0.72, R² = 0.51) and Europe (r = 0.57, R² = 0.33). At the same time, lower-income countries demonstrate weak or statistically insignificant relationships, indicating persistent structural barriers. Cluster analysis identified four types of countries, ranging from those with high levels of digital maturity to those with lower levels of national AI readiness. These findings highlight diverse national development trajectories and the need for differentiated policy approaches. However, the AI Publication Footprint is based on absolute cumulative Scopus publication counts and should be interpreted as a proxy for AI knowledge-production capacity rather than a normalized measure of research intensity or actual AI adoption. Given the cross-sectional and primarily bivariate design, the results indicate associations rather than causal effects and do not directly capture educational outcomes. The findings may have implications for education and sustainability by suggesting that disparities in infrastructure, human capital, innovation capacity, and responsible governance may shape the conditions for inclusive and sustainable AI-enabled education; these implications require direct empirical testing. Accordingly, the study emphasizes the need for adaptive policy frameworks that account for regional and economic disparities. Full article

Agriculture is pivotal to China’s dual-carbon goals, yet the nonlinear and spatiotemporally heterogeneous relationships between agricultural carbon emissions (ACE) and their associated socioeconomic, agricultural-production, public-investment, and climatic factors remain poorly understood. Here, we compile a multi-source provincial ACE inventory covering cropland use, rice cultivation, and livestock production for 2000–2023 and combine spatial trend and autocorrelation diagnostics with explainable machine learning using XGBoost–SHAP and geographically and temporally weighted regression (GTWR). We find that national ACE increased from 254.48 to 270.25 Mt, while emission intensity fell by 59.6%, indicating that the carbon efficiency of agricultural production improved substantially, although total emissions did not achieve an absolute decline. ACE exhibited persistent spatial imbalance, significant spatial clustering, and gradual diffusion of high-emission areas. Model benchmarking showed that XGBoost achieved the best overall performance, with a mean cross-validated R² of 0.9131 and an independent temporal-test R² of 0.7056. SHAP importance aggregated across repeated cross-validation identified agricultural public investment (21.3%) and urbanization rate (17.9%) as the two leading factors, followed by precipitation, temperature, agricultural industrial structure, and industrial agglomeration level, with the same six factors consistently identified by the three tree-based models. SHAP dependence plots further revealed nonlinear relationship patterns and approximate transition locations around an agricultural public investment level of CNY 11.84 billion, an urbanization rate of 54.69%, annual precipitation of 698.4 mm, and annual temperature between 13.01 and 22.17 °C. GTWR provided stronger statistical evidence of spatiotemporal nonstationarity for urbanization rate, temperature, and industrial agglomeration level, whereas the coefficient patterns of agricultural public investment, agricultural industrial structure, and, particularly, precipitation received more limited local statistical support. These findings provide support for region-specific agricultural carbon mitigation by improving agricultural input-use efficiency, optimizing the allocation of public investment, facilitating the transition toward low-emission crop and livestock management, and integrating climate adaptation with sustainable agricultural production and food security. Full article

Estuaries support distinct spawning and nursery habitats for migratory fishes, yet the differential environmental response patterns between these two critical early life habitats remain poorly understood from a spatial non-stationarity perspective. Based on six ichthyoplankton surveys conducted during peak and late spawning seasons from 2018 to 2020 in the Yangtze Estuary, this study applied geographically weighted regression (GWR) models to quantify the spatially varying effects of sea surface temperature, sea surface salinity, chlorophyll-a, water depth and distance to coast on the distributions of Coilia mystus eggs and larvae. The results reveal clear divergence in both spatial pattern and environmental drivers between spawning and nursery habitats. Spawning grounds were persistently concentrated in the middle reaches of the South Branch, and shifted approximately 10 km upstream during the spring saltwater intrusion event in 2020. Nursery grounds, by contrast, formed a stable dual-core structure, with the northern core at the North Branch mouth consistently supporting higher larval densities than the southern core in the North and South Passages. Salinity was the primary limiting factor for spawning in spring, while temperature dominated in summer, and chlorophyll-a was never retained in optimal egg models. For larvae, chlorophyll-a emerged as a consistent key driver alongside salinity and temperature, and local regression coefficients spanned a wider range than those for eggs, indicating greater spatial heterogeneity in larval distribution–environment relationships. This study provides the first comparative analysis of spatially non-stationary environmental controls on spawning versus nursery habitats of C. mystus, and offers empirical support for stage-specific habitat conservation and fisheries management in the Yangtze Estuary. Full article

Over 200 million of the world’s 273 million out-of-school children live in Central and Southern Asia or Sub-Saharan Africa, where distance to school is a persistent barrier, and walking remains the dominant travel mode. Tanzania presents a critical case: over 300,000 children dropped out before completing primary school in 2023, and only 8.5% complete upper-secondary education. We estimated walking travel time to the nearest primary and secondary school using a 100-metre resolution cost-surface that incorporated land cover, topography, and road networks. Travel time surfaces were linked to Demographic and Health Survey (DHS) data and analysed using multilevel Poisson regression for primary school-age children (aged 7–13) and single-level models for secondary school-age adolescents (aged 14–19). Among primary school-age children (n = 9213), poor geographic access (walking time > 45 min) was associated with a 27.9% higher prevalence of non-participation after socioeconomic adjustment, consistent across wealth quintiles and maternal education groups. Among secondary school-age adolescents (n = 2585), poor access was not significant. Household wealth and maternal education showed strong protective relationships at both levels. Each additional 10 min of walking travel time was associated with a 2.9% higher prevalence of non-participation, equivalent to approximately 18% per additional hour. Geographic access is an independent barrier to primary school-age participation irrespective of socioeconomic position, as there was approximately a 2.9% higher prevalence of non-participation in school for every additional 10 min of travel time, supporting geographically targeted infrastructure investment at primary level and demand-side interventions at secondary level. Full article

Vernal ponds are temporary, isolated bodies of water that lack vertebrate predators like fish. Palmer Park in Detroit, MI, USA, contains an old-growth forest with multiple vernal ponds that are home to numerous invertebrates, including water mites, which are the main focus of this study. These vernal ponds are unique since they are geographically isolated in the middle of an urban landscape. Previous research discovered new species of non-biting midges in Palmer Park vernal ponds, suggesting the potential for the discovery of previously undocumented organisms and relationships in this ecosystem. Here we document the invertebrate species found in the vernal ponds of Palmer Park, both to illustrate their diversity and to determine their cytochrome oxidase I (COI) barcode sequences. COI barcode sequences are used in this study to verify identification and provide reference sequences for comparison to sequences in the diets of water mites also collected from the ponds. Three taxa of water mites found in Palmer Park Pond A are Hydryphantes waynensis, Parathyas sp., and Hydryphantes sp. (distinct from H. waynensis by having a COI barcode sequence 11.6% different from Hydryphantes sp.). This paper also uses Next Generation Sequencing (NGS) to analyze the complex diets of the water mites at Palmer Park. The diets consisted of a diversity of species of oligochaetes, mosquitoes, non-biting midges, crustaceans, flies, and beetles. Beetles identified as whole organisms in the ponds or from diet-detected bar codes in vernal pond water mites include Copelatus glyphicus, Acilius sp., and Hygrotus sayi. The diets of water mites in these vernal ponds are compared to previous molecular studies in which water mites in a riverine lagoon were identified as opportunistic predators of a diverse invertebrate diet. Beetle DNA was consistently detected in association with water mites, suggesting a potential and previously unreported dietary or ecological interaction; however, alternative explanations such as external contamination by environmental DNA or secondary ingestion cannot be excluded. Beetles have not previously been reported in water mite diets, so this finding represents a new discovery. This molecular analysis revealed taxa and novel COI barcode sequences not observed through traditional sampling, highlighting the value of water mites for community characterization. These results support the hypothesis that water mites are opportunistic predators, uniquely reports beetles as putative components of their diets, and emphasizes their ecological importance and utility in assessing vernal pond biodiversity. Full article

Point-of-interest (POI) recommendation in location-based social networks (LBSNs) predicts a user’s next visit using check-in records. As critical factors influencing user decision making, geographic and social contexts are incorporated to deliver higher-quality recommendation services. However, current methods suffer from two limitations: geographical modeling is confined to distance thresholds, ignoring long-range spatio-temporal transitions, and social graphs remain static, failing to capture dynamic behavioral similarities among unconnected users. To address these gaps, we propose GSTRDFA (Global Spatio-Temporal Relationship- and Dynamic Friendship-Aware), a model comprising three layers. First, we construct spatio-temporal KGs (STKGs) that encode four relationship types: global spatio-temporal and local geospatial POI–POI links, dynamic user–user friendships, and static social ties. Second, four dedicated encoders—STSEncoder (spatio-temporal state embedding), GeoEncoder (geographical convolution), DFEncoder (graph attention network), and SocEncoder (GraphSAGE)—propagate and aggregate user and POI embeddings along these STKG relations. Third, a GRU-based sequence predictor uses the fused embeddings to match candidate POIs to the user. Evaluations on Foursquare datasets (NYC, JK, CA) show that GSTRDFA outperforms existing methods, improving Acc@1/5/10 and MRR by 0.24–3.31%. Key contributions include (1) unifying spatial, temporal, and dynamic social signals via STKGs; (2) jointly modeling global spatio-temporal transitions and dynamic friendships; and (3) enabling balanced short-/long-range and short-/long-term transition prediction. Full article

China’s smart-city and green-transition policies have made digital-green integration an important pathway for improving urban ecological resilience. This study examines whether digital-green integration enhances urban ecological resilience, through which channels this effect occurs, and under what regional conditions it becomes more pronounced. The analysis uses a balanced panel of 510 observations for 30 Chinese provincial-level regions from 2005 to 2021, compiled from national and provincial statistical yearbooks. An indicator system encompassing resistance, recovery, and renewal is used to assess urban ecological resilience, and the composite index is calculated using the entropy-weighting method. Digital–green integration is captured by a coupling coordination index linking digital infrastructure with green finance. Its effect is estimated within a two-way fixed-effects framework, with additional tests used to assess the robustness of the findings and explore the underlying mechanisms, moderating conditions, and regional differences. The results show that: (1) digital–green integration significantly enhances urban ecological resilience, with its effect concentrated primarily in the recovery dimension; (2) industrial-structure upgrading and improvements in energy efficiency serve as important transmission channels, while better traffic infrastructure further strengthens this positive relationship; and (3) the effect is more pronounced in provinces with lower levels of urbanization and stronger environmental regulation. Governments should promote digital-green integration to support industrial-structure upgrading and energy-efficiency improvement, thereby strengthening cities’ capacity to recover from ecological shocks. Greater support should be directed to less urbanized regions, while areas with weaker environmental regulation should improve disclosure, verification, and enforcement. Future research could employ city-level data and spatial econometric models to examine the magnitude, geographic reach, and transmission mechanisms of cross-regional spillovers. Full article

Water erosion is a core issue that threatens the ecological integrity of the highland ecosystems in the Andes Mountains and the agricultural sustainability of the Ramis River basin. This study uses the Revised Universal Soil Loss Equation (RUSLE), which integrates five factors, rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P), to estimate the spatial distribution of potential water erosion rates in this basin. The results show that the very low and low erosion classes together cover 73.21% of the basin, while the high, very high, and extreme erosion classes account for 17.29% of the total area. Among these, the extreme erosion class, with an annual erosion volume exceeding 250 tons per hectare, covers 8.07% of the basin, equivalent to 1190.13 square kilometers. This extreme erosion is concentrated in steep headwater areas and five sub-basins including Cuenca Grande. Comparative model verification shows that the Ordinary Least Squares (OLS) model only identifies a positive correlation between slope gradient and potential soil loss, with an extremely low explanatory power (R² = 0.045). Its residuals exhibit significant spatial autocorrelation (Moran’s I = 0.204, p < 0.001). In contrast, the Geographically Weighted Regression (GWR) model greatly improves the model fit (R² = 0.359, RMSE = 148.288) and eliminates the spatial autocorrelation of residuals, proving that the slope-erosion relationship has spatial non-stationarity. Sensitivity analysis shows that the C factor has the highest sensitivity (0.980), followed by the LS factor (0.626). Based on these findings, this study proposes that cover and management measures such as vegetation restoration should be prioritized in high-risk headwater sub-basins. It should be noted that the values estimated in this study are potential soil loss amounts, rather than actually measured erosion values. Full article

Genetic variation in four wild Prunus taxa (P. fruticosa, P. erythrocarpa, P. verrucosa and P. griffithii var. tianshanica) was investigated for the first time using six chloroplast DNA regions (matK, r rpl16, ycf1_1, ycf1_2, ndhF and trnH–psbA) analysed through CAPS-based SNP detection. The results revealed weak chloroplast differentiation among P. erythrocarpa, P. verrucosa and P. griffithii var. tianshanica. However, chloroplast variation exhibited a strong geographic signal across the studied populations. The observed chloroplast variation primarily reflected geographic structuring rather than clear differentiation among these closely related taxa. In contrast, P. fruticosa showed distinct chloroplast haplotypes not shared with the other taxa. These findings demonstrate that the developed chloroplast CAPS marker system is effective for detecting chloroplast haplotype variation but has limited discriminatory power among closely related wild Prunus taxa. Further studies using nuclear markers and genome-wide approaches will be required to better resolve their genetic relationships and evolutionary history. Full article