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Ehrlichia and Anaplasma are important tick-borne obligate intracellular bacteria that cause clinically and economically significant infections in humans and animals. Despite their importance, vaccine development for these pathogens remains limited, and the overall progression of vaccine-related research in this field has not been quantitatively characterized. In this study, we conducted a comprehensive bibliometric analysis to characterize the trajectory and the research landscape of vaccine-related research for these pathogens. The literature was retrieved from the Web of Science Core Collection using the search terms (Ehrlichia OR ehrlichiosis OR Anaplasma OR anaplasmosis) AND (vaccine OR vaccines). A total of 483 publications published between 1965 and 2025 were included in the analysis. Bibliometric analyses were conducted using Biblioshiny (Version 4.3.3) and VOSviewer (Version 1.6.20) to assess publication trends, citation patterns, leading countries, institutions, and major research themes. The literature showed gradual growth over time, with more consistent publication output from the late 1990s onward. Scientific output was concentrated in a relatively small number of countries and institutions, led by the United States, South Africa, Brazil, Spain, and France. Keyword frequency and co-occurrence analyses showed that the field has been shaped mainly by bovine anaplasmosis, heartwater, and tick-associated vaccine research, with Anaplasma marginale and Ehrlichia ruminantium emerging as dominant pathogen-focused themes. Major surface proteins, msp1a, subolesin, and Bm86, appeared frequently, indicating continued interest in both pathogen-derived and tick-associated vaccine targets. In contrast, human and canine pathogens such as Ehrlichia chaffeensis, Anaplasma phagocytophilum, and Ehrlichia canis were less represented. Our analysis maps the research landscape of Ehrlichia and Anaplasma vaccine development, quantifying how research activity has been distributed across pathogens, geographic regions, institutions, collaborations, and vaccine-related themes. It also identifies areas that remain comparatively less represented in the published literature and provides a quantitative baseline for future comparisons with independent measures of disease burden and research funding data. Full article

Background: Myocardin-related transcription factor B (MRTFB) acts as a transcriptional coactivator for serum response factor (SRF) and regulates actin cytoskeletal dynamics. Despite its biological significance, the phosphoregulatory network, upstream kinases, and interactors remain poorly understood. Methods: To explore this, we analyzed global phosphoproteomic datasets to identify predominant phosphosites. We categorized phosphosites in other proteins as positively or negatively co-regulated with the predominant phosphosites of MRTFB and also integrated predicted upstream kinases and interactors of MRTFB. Further, phosphosite conservation, co-occurrence patterns, and functional and pathway enrichment analyses of co-regulated proteins were also performed. Results: We identified S66 and S921 as predominant phosphosites in MRTFB, with the highest detection frequency across multiple experimental conditions. These phosphosites were found to be conserved within the MRTF family and across multiple species. From the predicted upstream kinases, MAPK1, MAPK3, MAST3, and CSNK1A1 were identified as predicted upstream kinases. Co-occurrence analysis revealed high positive co-occurrence between the predominant phosphosites, S66 and S921, suggesting similar functional roles. Functional enrichment analysis highlighted the involvement of co-regulated proteins in the actin cytoskeleton pathway. Conclusions: This study provides the detailed phosphoproteomic landscape of MRTFB, mapping its upstream kinases and co-regulated proteins involved in actin cytoskeleton regulation. Full article

To address the limitation of traditional cellular automata models in effectively integrating temporal and spatial information, this study extends the previously developed Land use Simulation and Decision-Support system (LandSDS). By incorporating a graph attention network (GAT), a transformer, and an agent-based model (ABM) into a cellular automata framework informed by remote sensing time series, GT-LandSDS is constructed. Specifically, GAT dynamically captures higher-order spatial dependencies among land parcels; the self-attention mechanism of the transformer extracts land use change characteristics from multi-period observations; and ABM captures human behavioral decisions of three types, namely traffic, resident, and government. Based on this framework, GT-LandSDS derives CA transition rules from multiple dimensions and enhances the dynamic exploration of land use change across space and time. Using Guangxi Zhuang Autonomous Region as a case study, the model was validated with remote sensing land use data from six periods (2000, 2005, 2010, 2015, 2020, and 2023), and scenario-based future predictions were generated. The results show that: (1) The overall accuracy reaches 0.926, while the Kappa coefficient is 0.820, and the figure of merit (FoM) for change simulation is 0.034, indicating a relative advantage over ANN-CA, LSTM-CA, and UESP in overall pattern simulation, although fine-scale change reproduction remains limited; (2) Three development scenarios were then assessed: continuing historical trends, theoretical high-intensity urban expansion, and karst landform conservation under a green transformation development policy. The land use pattern of the area from 2023 to 2035 was predicted. The findings reveal that accelerating urbanization leads to rapid expansion of construction land, increasing by more than 88% compared with 2023, and causes substantial cropland loss. In contrast, intervention through the green transformation development policy limits construction land growth to 26.5%, effectively curbing urban sprawl while protecting forest, grassland, and cropland resources in the karst landscape. This study offers new insights into land use change simulation in ecologically fragile regions subject to strong policy interventions. It provides a scientific basis for coordinating ecological conservation and high-quality development in karst areas. Full article

Wildfire impact assessment requires information on both burned areas and the biomass exposed within burned landscapes. We developed a field-calibrated, multi-source remote-sensing framework to estimate above-ground biomass (AGB) and quantify annual wildfire-related potential AGB exposure across the boreal forests of Quebec and Ontario, Canada, during 2018–2024. The dataset comprised 3725 plot-year AGB observations linked to optical, Sentinel-1 C-band, ALOS L-band synthetic aperture radar, environmental, and geographic predictors. Product-wise screening reduced the 91 candidate predictors to 28. An optimized extreme gradient boosting (XGBoost) model was evaluated using five-fold grouped cross-validation, with repeated observations from each plot assigned to a single fold. The model achieved an RMSE of 25.08 ± 0.36 t ha⁻¹, an MAE of 20.89 ± 0.39 t ha⁻¹, and an R² of 0.53 ± 0.02. The full multi-source configuration outperformed all reduced-source and source-only configurations, while removing ALOS L-band SAR or environmental/geographic predictors produced among the largest performance declines. The model was applied to 9937 land-cover-stratified points within wildfire polygons using predictors from the year preceding each fire. Under the complete-loss assumption, cumulative potential AGB exposure was 269.20 Mt across 6.66 Mha of effective burned area, with a 95% bootstrap interval of 254.19–284.06 Mt reflecting finite-point sampling uncertainty and an area-weighted mean exposure intensity of 40.43 t ha⁻¹. The 2023 fire season accounted for 206.44 Mt, representing 76.7% of cumulative exposure and 73.2% of effective burned area. Effective burned area and total potential exposure were strongly correlated (r = 0.99), whereas exposure intensity followed a distinct pattern and peaked in 2022 at 46.86 t ha⁻¹. Thus, burned area was the primary correlate of regional potential biomass exposure, whereas exposure intensity reflected variation in pre-fire biomass among burned landscapes. These estimates represent potential exposure rather than measured combustion, mortality, or carbon emissions and demonstrate the value of integrating spatially explicit pre-fire AGB with wildfire perimeters. Full article

Near-infrared imagery is essential for vegetation monitoring, precision agriculture, and environmental remote sensing, but multispectral UAV systems remain significantly more expensive and less accessible than conventional RGB imaging platforms. This study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV. The proposed methodology formulates the reconstruction task as a pixel-wise nonlinear regression problem and employs a compact multilayer perceptron (MLP) containing only 609 trainable parameters, without exploiting spatial neighborhood information. The framework was developed and evaluated using 280 synchronized multispectral UAV image sets acquired with a DJI Phantom 4 Multispectral platform over a heterogeneous agricultural landscape in the Republic of Moldova. Of these, 252 image sets were used for model development, and 28 were reserved as a held-out within-mission test subset. Quantitative evaluation on a held-out test dataset from the same acquisition mission yielded a mean squared error of 0.010329, a root mean squared error of 0.101632, a mean absolute error of 0.079883, a coefficient of determination of 0.253383, and a Pearson correlation coefficient of 0.683637 between measured and reconstructed normalized NIR digital intensities. The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations. An illustrative NDVI-based assessment showed that broad spatial vegetation patterns remained identifiable. Rather than introducing a new neural network architecture, this work establishes a compact empirical baseline to investigate the practical performance and limitations of pixel-wise NIR reconstruction from a single red-band value with minimal model complexity. Full article

Under climate change, the demand for high-quality urban ecological security is rising. This study focuses on how rapid urbanization and climate change affect ecological risk, resilience, and land use functions (LUFs) spatial co-variation in the Pearl River Delta. Land use was simulated under Shared Socioeconomic Pathways (SSPs) using system dynamics (SD) and the interaction network–Patch-generating Land Use Simulation (intPLUS) model. Ecological risk was quantified via the landscape ecological risk index (LERI) and habitat degradation index (HDI), while ecological resilience was obtained using an adaptability–resistance–recovery framework, producing a comprehensive ecological risk–resilience index (CERRI). Ecosystem services were assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, geographically weighted regression (GWR), constraint lines, and extreme gradient boosting with SHapley Additive exPlanations (XGBoost-SHAP) revealed LUFs patterns, nonlinear relationships and threshold effects, and driving factors. The results indicate that construction land expands mainly at the expense of cropland (~4049–4157 km²) from 2023 to 2035, while woodland and water remain largely stable. CERRI shows a concentric pattern (2023 domain mean = 0.638), with safer peripheral belts and a more risk-dominated central–southern core; under coupling-weight uncertainty with 2023-fixed common-reference normalization, SSP245 was preferred in all Monte Carlo iterations (best-scenario probability = 1.000). EF–LF, EF–PF and LF–PF retain stable nonlinear forms. Elevation, economic vitality and transport accessibility are the leading drivers, with model-derived breakpoints near low-elevation, high-vitality and moderately accessible transport nodes. This study provides the CERRI framework to support ecological security monitoring and adaptive land-use management, contributing to more sustainable regional development under climate change. Full article

Background: Research on women’s health is essential for addressing sex-specific healthcare needs and informing evidence-based policy. However, women-only health research in Taiwan has not been systematically examined. Methods: We conducted a Scopus-based bibliometric analysis of health research exclusively involving female populations in Taiwan from 2015 to 2025. We included English-language, peer-reviewed journal articles involving only female participants and conducted in Taiwan or using Taiwanese datasets. Mixed-sex studies, including those reporting female-specific or sex-stratified findings, were excluded. Publication trends, citations, international collaboration, secondary data sources, and thematic patterns were analyzed using Biblioshiny and VOSviewer. Results: A total of 1345 publications across 446 journals were included. Publication output increased from 126 articles in 2015 to a peak of 153 in 2021, followed by a decline, with an annual growth rate of −0.4%. The publications involved 3412 authors and received an average of 14.52 citations per document. International co-authorship accounted for 19.48% of publications, with the United States as the leading collaborator. The National Health Insurance Research Database was the most frequently reported secondary data source (n = 371, 27.6%). Research was concentrated in cancer, reproductive health, and mental health, whereas cardiovascular health accounted for a comparatively smaller share of the literature. Conclusions: Women-only health research in Taiwan remained active throughout the study period, despite a decline in publication output after 2021. The extensive use of national health databases and international collaboration further characterized the research landscape. These findings provide an evidence-informed basis for future research priority-setting, including efforts to broaden research across women’s health conditions and life-course stages while considering research activity alongside population health needs. Integrating bibliometric evidence with population health priorities may support a more balanced and comprehensive women’s health research agenda in Taiwan. Full article

The increase in the number and complexity of interconnected systems requires new methods to identify potential threats in today’s hyperconnected world. This trend affects systems ranging from smart homes and Internet of Things (IoT) devices to critical infrastructure which must be equipped with the corresponding cyber defense methods. Given the new landscape, it is more difficult for classical cybersecurity systems to stay up to date with novel threats, as well as to keep track of all interconnected devices defined by various protocols and behaviors. Artificial intelligence (AI) represents a strong candidate to complement traditional cyber defense methods due to its adaptability to variation and capability to identify complex data patterns, which has led researchers to develop state-of-the-art anomaly detection systems. The current critical review aims to analyze the scientific literature on three dimensions including used algorithms and datasets, domain challenges hindering AI deployment in productive environments, and the capability of explainable artificial intelligence (XAI) to support cyber security experts with insights into the model’s inner workings and decision rationale. Compared to existing scientific reviews, this paper moves beyond algorithmic comparison by providing a methodological interpretation of AI anomaly detection landscape, demonstrating how data availability, learning paradigms, and explainability collectively influence the evolution of cyber defense research towards operational deployment. This approach revealed that AI development for cyber defense is highly heterogenous, and that the available datasets strongly influence the algorithm of choice, rather than the models being chosen methodologically based on proven performance. The analysis further indicates that operational deployment remains challenging, as the literature continues to report substantial limitations related to data quality, computational requirements, and model interpretability. Full article

Conducting landscape ecological risk (LER) assessments and identifying their driving mechanisms is a key approach to balancing regional development with ecological conservation and enhancing ecological security. Taking the Beipanjiang River Basin (Guizhou section)—a typical karst mountainous watershed—as the study subject, this research analyzed the spatiotemporal evolution of LER in the basin using landscape pattern indices and spatial autocorrelation analysis based on five sets of land-use data from 1985 to 2024. The optimal geographical detector (OPGD) was employed to identify the driving factors. The results are as follows: (1) From 1985 to 2024, the average LER index in the study area decreased by 37.07%. The proportion of the area classified as the lowest-risk zone increased from 18.76% to 38.83%, while the proportions of the medium-risk and higher-risk zones decreased to 14.97% and 3.55%, respectively, indicating an optimization of the ecological security pattern. (2) The center of gravity of the lowest-risk zones shifted northwestward, while the medium-risk and higher-risk zones contracted northeastward. The global Moran’s I ranged from 0.477 to 0.574. High–high clustering zones contracted in the north, and regional stress capacity decreased. Low–low clusters expanded in the south, forming a continuously consolidated ecological barrier zone. (3) The interactions between landscape fragmentation and factors such as elevation, precipitation, and NDVI all exhibited a two-factor enhancement effect, with the risk pattern being primarily driven by the synergy of multiple factors. This study provides a scientific basis for adjusting the land-use structure of the watershed, mitigating ecological risks, and promoting high-quality sustainable development. Full article

Conflicts between development and conservation on tourism-driven islands are concentrated in bay-adjacent terrestrial areas, yet land-use evolution and scenario responses are rarely compared among functionally distinct bays on the same island. This study examined Haitan Bay and Tannan Bay on Pingtan Island using 2 m remote-sensing images from 2008, 2016, and 2024, landscape metrics, and a coupled System Dynamics–Patch-generating Land Use Simulation (SD–PLUS) model. Natural Development (ND), Ecological Restoration (ER), and Economic Development (ED) scenarios were simulated for 2035; ER and ED outputs were further compared pixel by pixel with Minimum Mapping Unit (MMU) sensitivity testing. From 2008 to 2024, Construction Land expanded and became more aggregated in both bays, mainly at the expense of Cropland and Grassland. Relative to 2024, Construction Land increased by 1.74% and 3.97% under ER in Haitan Bay and Tannan Bay, versus 12.27% and 10.89% under ED. New Construction Land mainly extended around existing built-up areas in Haitan Bay but clustered along transportation corridors in Tannan Bay. Scenario-consistent Areas accounted for 87.56% and 89.29%, while some patch metrics were MMU-sensitive. These results reveal distinct land-use responses between Haitan Bay and Tannan Bay and support differentiated spatial planning for the two bay-adjacent terrestrial areas. Full article

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R²Y > 0.9, Q² > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production. Full article

Impervious surface expansion is a defining manifestation of urbanization, but changes in total area alone cannot reveal how transitions in development density reshape landscape structure. Adopting an integrated area–density perspective, this study examined impervious surface expansion and landscape reconfiguration in Zhengzhou, China, using Landsat observations from 1990, 2000, 2010, and 2019. Impervious surface fractions were estimated using dynamic endmember linear spectral mixture analysis. Impervious surface area (ISA) was assessed using expansion statistics, spatial autocorrelation, and standard deviational ellipses, while transitions among six impervious surface percentage (ISP) classes were analyzed using transition matrices and landscape metrics. Total ISA increased from 71.12 to 523.34 km². Absolute expansion peaked during 2000–2010, whereas annual expansion intensity declined from 14.70% in 1990–2000 to 4.07% in 2010–2019. ISA expansion exhibited significant positive spatial autocorrelation (Moran’s I = 0.404–0.663), and the footprint retained a southeast–northwest orientation. Natural surfaces declined from 92.16% to 12.75%, accompanied by growth in high-density and fully impervious classes. Decreasing CONTAG and increasing SHDI indicated weakened landscape continuity and greater configurational diversity. Urban development shifted from outward land conversion toward heterogeneous density reconfiguration. Sustainable planning should prioritize reconnecting residual natural surfaces, managing growth in fragmented transition zones, and promoting redevelopment in high-density areas. Full article

Land use and land cover change represent major anthropogenic carbon emission sources, yet most existing studies on landscape patterns and carbon emissions predominantly focus on plain urban agglomerations, with limited empirical evidence from terrain-restricted hilly regions. Taking Xuancheng, a typical hilly city in the Yangtze River Delta, as the study area, this paper utilized seven time-series of Landsat remote-sensing datasets spanning 1990–2020. Integrating the land use dynamic degree, transfer matrix, landscape pattern indexes, calibrated carbon coefficients, Spearman correlation analysis and grey relational analysis, this study explored the associations between land use patterns and carbon emissions. Results show that built-up areas expanded 4.08-fold in the past three decades and emerged as the dominant carbon source, while forests maintained persistent carbon sequestration. The largest patch index of built-up area exhibited the strongest correlation with carbon emissions; cultivated land and forest displayed temporally synchronous fluctuations with emissions rather than exerting independent causal effects. Terrain constraints drive axial urban sprawl along transport corridors, elevating correlations of edge-related indexes and generating carbon response patterns distinct from those observed in plain cities. The two-step correlation analysis framework proved suitable for small-sample long-term datasets. These findings suggest that curbing contiguous built-up area expansion, optimizing urban morphology, and strengthening ecological connectivity should be prioritized in low-carbon spatial planning for hilly cities. Full article

This study reconstructs the agricultural landscape of Cyprus in the early 1830s, a time of extraordinary socio-economic pressures on the island and immediately prior to the economic and administrative transformations of the later nineteenth century, drawing on the Ottoman property survey of 1832/33, an island-wide register documenting over 20,000 households and their assets across 622 principal settlements. The records were converted into a geospatial database and analysed through GIS, spatial statistics, and a transparent, reproducible landscape classification framework, complemented by historical cartographic evidence. The reconstruction reveals distinct agricultural regions shaped by environmental conditions, settlement patterns, and the interplay between subsistence and market-oriented production, with marked concentrations of viticulture, olive cultivation, cotton, grain, sericulture, and pastoralism. A hierarchical classification assigns 471 settlements (77%) to a single-dominant landscape type, 92 (15%) to co-dominant zones, and 50 (8%) to diversified categories, collectively synthesised into a broader interpretation of the landscape as an integrated socioecological system. The findings demonstrate that Ottoman surveys offer considerable, albeit bounded, potential for historical landscape reconstruction, providing a dated baseline for the study of Mediterranean agricultural landscapes and a transferable methodological framework applicable to any region with comparable historical records. Full article

Extreme hydrological events induced by climate change pose significant challenges to the ecological stability of river-connected lake-wetland systems. Poyang Lake, China’s largest freshwater lake, provides an ideal case for investigating landscape responses to compound drought and flood disturbances. This study quantifies the propagation process from meteorological drought to hydrological drought and identifies critical water level thresholds governing abrupt landscape transitions. By integrating remote sensing data, drought indices, Morphological Spatial Pattern Analysis (MSPA), and piecewise regression, we evaluated nonlinear landscape responses to extreme water level fluctuations. The results showed that the optimal lag time for lake water area response to meteorological drought was 21 days. Landscape connectivity exhibited nonlinear responses to water level variations, with 12.50 m and 12.73 m identified as critical thresholds for maintaining lake-wide connectivity and core deep-water habitats, respectively. The 9.75–14.44 m water level range was identified as the key sensitive interval associated with rapid landscape reorganization. Moreover, landscape responses showed significant spatial heterogeneity, with the Nanji Wetland National Nature Reserve being more sensitive to drought-induced fragmentation than the Poyang Lake National Nature Reserve. These findings provide quantitative thresholds and adaptive management implications for improving the resilience of floodplain lake-wetland systems under increasing hydrological extremes. Full article