Neurotransmitter landscape and neurodegeneration patterns in Alzheimer's Disease.

Future landscape pattern optimization based on runoff response across different temporal scales: A SWAT-PLUS coupled model simulation in the Three Gorges Reservoir Area

Habitat fragmentation may amplify antibiotic resistance genes (ARGs), yet the ecological pathways linking landscape patterns to host resistomes in intertidal systems remain unclear. Macrobenthic organisms as potential reservoirs and dispersal nodes are ideal models. Focusing on the horseshoe crab (Tachypleus tridentatus), a food web hub and habitat indicator, we integrated landscape metrics, metagenomics, and path modeling (PLS-PM) to examine, across fragmented habitats, links among sediment physicochemistry, larval diet, gut microbiota, mobile genetic elements (MGEs), and ARGs. Results revealed that more fragmented habitats promoted individuals with higher ARG abundance and diversity, alongside stronger MGE enrichment and increased ARG-MGE co-occurrence, indicating enhanced mobility potential. Fragmentation also coincided with greater dietary diversity but higher among-individual convergence, selective assembly of gut microbiota with higher diversity, and tight ARG-MGE association. PLS-PM supported a diet-gut microbiota-MGE-ARG cascade, while the direct effects of sediment chemistry were not significant. Attributing ARG hosts at the MAG level, Enterobacteriaceae and Vibrionaceae dominated ARG abundance and enrichment, indicating lineage selectivity. Multidrug and polymyxin resistance was most prominent. These findings identify key AMR risk pathways and inform priority interventions for T. tridentatus and habitat conservation. The developed assessment framework is scalable and offers a paradigm for One Health management in mudflat systems.

Landscape pattern is closely associated with pollution in rapidly urbanizing watersheds, but most studies still focus on single pollutants or single environmental media. This study developed a watershed-based framework to compare coupled water and air pollution in the Liaohe River Basin, China. A total of 156 hydrologically connected sub-basins were used as common spatial units. Landscape metrics were calculated for 2000, 2010, and 2020. Total nitrogen and total phosphorus loads were simulated using the Soil and Water Assessment Tool, while annual mean PM2.5 and O3 concentrations were aggregated from gridded products to the same sub-basin scale. Coupling coordination degree was used to identify relative co-pollution patterns within the aquatic and atmospheric systems. GeoXGBoost with spatial block cross-validation was used to evaluate predictive performance, and GeoSHAP was used to interpret model-based predictor contributions. The aquatic coupled pollution index was predicted more accurately than the atmospheric index, indicating a stronger landscape association with nutrient coupling. Cropland proportion was the most stable predictor of aquatic coupling, whereas forest proportion was the most stable predictor of atmospheric coupling. These results suggest that water-oriented management should focus on cropland structure and ecological buffering, while air-oriented management should emphasize forest continuity and fragmentation control. The framework provides a spatially explicit basis for differentiated watershed management and territorial spatial planning.

<bold></bold> We investigated how landscape patterns influence water quality across the Lower Niger River Basin, Nigeria, using multi-scale spatial analysis, contributing to Sustainable Development Goal 6 (Clean Water and Sanitation). Land use composition and configuration metrics were quantified within 5 km and 10 km buffer zones, and their effects on water quality were examined using stepwise multiple linear regression. Results showed that cropland fragmentation was the most significant driver of water quality degradation, strongly associated with elevated biochemical oxygen demand (BOD) and nitrite concentrations, particularly at the 10 km scale, indicating cumulative watershed effects. Urban-related metrics exhibited localized impacts, with built-up patch density influencing total dissolved solids (TDS) and BOD at the 5 km scale. Forest cover was generally linked to improved water quality, although fragmentation reduced its buffering capacity. Overall, configuration metrics outperformed composition metrics in explaining water quality variation, highlighting the importance of spatial arrangement in addition to land cover extent. These findings demonstrate that landscape fragmentation from agriculture and urban expansion exerts strong, scale-dependent effects on water quality. Integrating landscape configuration into watershed management can support evidence-based strategies for improving water quality and achieving SDG 6 in rapidly changing Afro-tropical river systems.

Activity cliffs (AC) correspond to large potency differences between highly similar compounds and pose a persistent challenge for both predictive modeling and de novo molecular design, particularly in small and underexplored areas of the chemical space. In this study, we introduce an AC-aware generative framework for the de novo design of anticancer thiazolidinone derivatives relevant to non-small cell lung cancer. We identify difficult regions of structure-activity landscape and extract chemical pattern signals hidden in its discontinuities directly from AC pairs using a dedicated algorithm. These signals are incorporated into a fragment-based generative pipeline to guide molecular construction and candidate selection. Compared with a standard QSAR-guided approach, the proposed framework yields a higher fraction of candidates with favorable docking profiles across multiple cancer-relevant targets and produces ligands with more stable binding modes in molecular dynamics simulations. Among the top five candidates selected by a molecular dynamics, multi-metric consensus combining docking affinity, binding stability (ligand and pocket RMSD/RMSF), and energetic criteria, four were generated using the AC-aware CAFE LATE strategy. The two highest-ranked hits exhibited binding energies of - 7.78 and - 7.70kcal/mol, markedly more favorable than the native reference (- 4.09kcal/mol), and consistently outperformed native ligands across all evaluated stability metrics. Those results suggest that explicit use of AC information can improve the quality of de novo generated small-molecule candidates in data-limited settings.

Land-use simulation is essential for understanding future spatiotemporal changes on the Earth’s surface. Most existing methods simulate land use change by modelling dynamic processes among cell labels, often overlooking the landscape-scale patterns that emerge from these cells. Moreover, reliably constructing evolutionary rules and key parameters to simulate future land use change remains a fundamental challenge. We proposed MapsGT, a novel generative deep learning approach for the simulation of land use change over multiple classes. The framework consists of two core components: MapsVAE, which learns compact landscape encodings to capture spatial information from historical maps of land-use and geographic environment; and EMTrans, which models spatiotemporal dependencies within these encodings to generate future maps of land use. Experiments in the Pearl River Delta and the Changsha-Zhuzhou-Xiangtan urban agglomerations showed that MapsGT excels in short-term simulation, achieving a high Figure of Merit and User’s Accuracy. Additionally, a newly developed regional consistency index (RCI) reveals the model’s ability to capture and reproduce complex landscape patterns with high spatial authenticity. MapsGT provides a valuable framework for geospatial artificial intelligence that complements existing simulation methods of land use change.

Development intensity and landscape pattern are two fundamental components of village development systems. Understanding their coupling coordination and underlying driving mechanisms is important for guiding territorial spatial development and conservation in rural areas. This study integrates a development intensity model, landscape pattern indices, bivariate spatial autocorrelation, a coupling coordination degree model, and an optimal parameter-based geographical detector to construct an analytical framework at the micro-scale village level. Using data from 563 villages in Mengcheng, Lai'an, and Xiuning counties in Anhui Province, China, we find marked variability and spatial heterogeneity in both development intensity and landscape pattern, with High-High and Low-Low clusters dominating. The overall coupling coordination degree is at a medium-to-low level, with pronounced regional differences. Mengcheng is characterized by high levels of mild maladjustment and transitional states, Lai'an by low maladjustment and a high proportion of transitional states, and Xiuning by high maladjustment and limited transitional states, indicating relatively more pronounced development-landscape conflicts. Vegetation coverage and the proportion of construction land emerge as the primary shared drivers, while the effects of other factors vary across regions. Overall, coupling coordination is jointly shaped by socio-economic and environmental factors. Based on these findings, region-specific development strategies are proposed. This study contributes to the understanding of village development and landscape patterns and provides a scientific basis for rural planning and territorial spatial management.

ABSTRACT This article examines urban regeneration in Istanbul’s Bomonti district, focusing on the social and spatial transformations shaped by gentrification from the 1940s to the present. Using a mixed-methods approach, including on-site observations, resident surveys, and interviews with local representatives, this longitudinal study spans 13 years, with fieldwork conducted in 2012 and again in 2025, tracing changes in the urban landscape, economic structure, and redevelopment patterns over time. The study introduces an analytical framework to understand how globalization has reshaped urban space through economic shifts that stimulated housing investments and new residential typologies, including informal settlements, residences, and gated communities. Land regulation policies and earthquake-resistant construction requirements further influenced regeneration processes. Findings indicate that as upper-income groups initiated profit-driven projects in this former industrial zone, class distinctions evolved into spatial segregation, reshaping the district’s social fabric. By critically analyzing these dynamics, the investigation contributes to urban planning, urban sociology, and geography by highlighting the socio-spatial consequences of urban regeneration in rapidly transforming cities.

Under global climate change and intensified human activities, habitats in northwestern China’s Altay region—comprising forests, grasslands, deserts, and alpine areas—are increasingly fragmented. This study assessed the suitable habitat distribution and fragmentation characteristics of three ungulate species: red deer, goitered gazelle, and Siberian ibex. An ensemble species distribution model based on BIOMOD2 was used, and fragmentation metrics were calculated with the landscapemetrics R package. Red deer had the largest suitable area (20,688.54 km²), followed by goitered gazelle (16,342.25 km²) and Siberian ibex (11,477.62 km²). Fragmentation analysis revealed that red deer habitat patches in forest areas had the highest patch density (PD = 0.042); grasslands showed the largest dominant patches (LPI = 63.31) and highest aggregation (AI = 74.45). Gazelle habitats in deserts were the least fragmented (PD = 0.017, AI = 92.06), while ibex habitats in grasslands had higher fragmentation (PD = 0.046) but moderate aggregation (AI = 75.31). The results reveal clear interspecific differences in habitat suitability and landscape patterns, highlighting species-specific responses to environmental gradients. This study provides a scientific basis for habitat management, conservation planning, and connectivity enhancement for large herbivores in cold-arid ecotones.

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City.

Oral and maxillofacial space infections (OMSIs) are potentially life-threatening polymicrobial conditions requiring timely and appropriate antimicrobial therapy. We conducted a systematic review and meta-analysis of 53 studies (12,408 cases) to evaluate microbial epidemiology and resistance patterns. Streptococcus was the predominant pathogen (45.51%), followed by Prevotella (15.89%) and Porphyromonas (15.61%). Methicillin-resistant Staphylococcus aureus (MRSA) accounted for 19.18% of S. aureus isolates. Diabetic patients exhibited a higher prevalence of Klebsiella (27.09% vs. 8.13% in nondiabetics). Antimicrobial resistance was lowest for levofloxacin (11.03%) and cephalosporins (16.83%), and highest for gentamicin (39.66%) and erythromycin (39.35%). The microbiological landscape and antimicrobial resistance patterns were influenced by multiple factors, including infection source, diabetic status, diagnostic methods, national income level, and temporal trends. Incorporating these variables with local antimicrobial surveillance data can support more targeted and effective empirical therapy. Well-designed, representative studies across diverse settings are urgently needed to optimize antimicrobial use amid rising resistance.

The heterogeneous role of spatial configuration in shaping aboveground carbon storage of terrestrial ecosystems: Evidence from China.

Urban lake scenic areas serve as crucial ecological barriers but face acute conflicts between expansion and conservation. Existing research has often overlooked microscale landscape fragmentation and its associated ecological effects. Focusing on the Wuhan East Lake ecotourism scenic area (Wuhan East Lake), this study investigated the spatiotemporal impacts of micro-scale land-use transitions on ecosystem service value (ESV). To evaluate the historical evolution of ESV from 2010 to 2024, an improved equivalent factor method was coupled with a patch-generating land-use simulation (PLUS) model. Spatial autocorrelation and landscape pattern metrics were then incorporated to diagnose structural degradation and establish a foundation for simulating the four development scenarios for 2035. Results demonstrate that sporadic construction expansion led to a decline in total ESV from 2.445 to 2.216 billion CNY, driving a pronounced “core-hot vs. edge-cold” spatial disparity. Among future projections, the Sustainable Development pathway emerges as optimal, effectively balancing economic demands with the need to minimize ecological fragmentation. Ultimately, this study contributes to the literature by integrating microscale landscape fragmentation analysis with a PLUS-based multi-scenario simulation to provide a refined understanding of ecosystem service dynamics in urban lake systems, thereby offering a scientific reference for resilient spatial planning and policymaking.

ABSTRACT Understanding the complex linkages between landscape patterns and ecosystem service multifunctionality (ESMF) and ecosystem service (ES) trade‐offs is crucial for sustainable land management. However, few studies have examined how these relationships vary non‐linearly and spatially under alternative future development pathways. This study developed an integrated framework that combined land‐use simulation, ES assessment, machine learning, and spatial analysis. It was employed to examine how landscape patterns influence ESMF and ES trade‐offs in 2040 under the business‐as‐usual (BAU), cropland protection (CP), and ecological priority (EP) scenarios in the Xiamen–Zhangzhou–Quanzhou metropolitan area. The results show that the EP scenario generally provided the favorable basis for maintaining multifunctionality and avoiding severe trade‐offs. Landscape composition dominated variation in ESMF and ES trade‐off intensity, whereas landscape configuration moderated ES trade‐offs across scenarios and sub‐basins. The combined partial dependence plot (PDP) and geographically weighted regression (GWR) analysis further show that landscape‐ES relationships were governed by scenario‐dependent non‐linear response ranges together with strong local spatial heterogeneity. Forest cover generally promoted ESMF, with the most effective response range occurring at approximately 40%–50%. By contrast, built‐up land consistently suppressed ESMF and shifted several trade‐off relationships toward low‐level equilibrium state. Cropland became more influential under the CP and EP scenario, and the ecological meaning of landscape diversity varied across local landscape contexts. These findings show that similar landscape changes may generate contrasting ES outcomes across scenarios and sub‐basins, and they provide a spatially explicit, scenario‐dependent basis for differentiated land management.

The comparison of semiotic landscapes of warning safety in Chinese and Russian languages can help native speakers of Russian in China and native speakers of Chinese in Russia to understand the danger of space, and prepare to avoid potential dangers. This paper presents photographs of semiotic landscapes of warning safety in three large and medium-sized cities in China and Russia, clarifies the linguistic and symbolic patterns of these landscapes in China and Russia, and compares the similarities and differences between them. The comparison shows that both Chinese and Russian landscapes use three patterns, but there are differences in the internal expression of each pattern.

As the main distribution area of cities, the rapid expansion of urbanization has profoundly altered landscape patterns in large and medium-sized river basins, triggering potential ecological risks. However, the dynamic relationship between urbanization and landscape ecological risks (LER) in agricultural-dominated river basins remains poorly understood, limiting effective environmental management. Focusing on the Mainstream of the Liaohe River Basin in northern China-a region characterized by intensive cropland and accelerating urbanization. Based on the landscape pattern indices and urbanization progress, this study employed a coupling coordination degree model to evaluate the spatiotemporal patterns and coupling relationship between urbanization and LER from 1990 to 2019. The results indicate that urbanization increased notably by 86.36%, primarily driven by infrastructure expansion. Meanwhile, the overall LER persisted at a moderate level, yet with substantial spatial heterogeneity. The dynamic coupling coordination degree (CCD) between urbanization and LER transitioned through three distinct phases: strengthening, weakening, and then strengthening again, yet it generally remained at a low level. Due to the wide distribution of cropland, the CCD is shown as urbanization lagging. These results not only revealed the spatiotemporal heterogeneity of LER under rapid urbanization but also underscored the implementing gradient management policies based on basin-specific urbanization-LER synchronization types. This study provides actionable insights for balancing socioeconomic growth and ecological security in agricultural basins undergoing rapid urbanization.

Research on the spatio-temporal patterns of landscapes is crucial for understanding the complexity of human-land relationships. With the in-depth application of Geographic Information Systems (GIS) technology, the research paradigm in this field has undergone significant evolution, progressing from static pattern identification to dynamic process simulation and expanding towards multidimensional frontiers. This paper systematically reviews the recent applications of GIS in landscape studies in China. At the level of pattern identification, methods of spatial visualization and quantification have revealed the spatial differentiation rules of cultural landscapes, ecological elements, and urban-rural settlements. At the level of process simulation, dynamic models and multi-source historical data have enabled the mechanistic analysis and process reconstruction of urban expansion, landscape evolution, and historical spatial reconstruction. At the level of frontier expansion, the integration of 3D GIS and interdisciplinary methods has deepened research from two-dimensional planes to three-dimensional spaces and from single analyses to integrated simulations. Despite significant progress, current research still faces challenges in integrating historical data, improving the accuracy of 3D models, and intelligently fusing multi-source data. In the future, deepening the application of 3D technology, promoting AI-driven intelligent analysis, developing interactive simulation platforms, and strengthening interdisciplinary methodological innovation will be key directions for advancing research on landscape spatio-temporal patterns to a deeper level.

Geographical-XGBoost and SHAP reveal ecosystem service supply-demand responses to landscape patterns in karst regions.

Revealing the evolution and driving factors of the landscape pattern-ecosystem service-human well-being cascade: Social network and geodetector based analysis.