Search Results (818)

Bone radiodensity is often reduced to global Hounsfield Unit (HU) summaries, although its spatial distribution may contain biologically relevant information. This study introduced a template-based framework for spatial radiodensity mapping of the adult human hyoid and tested whether spatial descriptors capture age- and sex-related variation better than whole-bone HU measures. Clinical head-and-neck CT scans from 300 adults, including 180 females and 120 males, were analyzed. Hyoids were segmented, normalized into a common anatomical template, and divided into 1500 supervoxels for spatial analysis. Scalar HU summaries were compared with PCA-based spatial descriptors, and regional effects were assessed using supervoxel-wise models with false-discovery-rate correction. The registered radiodensity maps showed spatial coherence, with neighboring supervoxels displaying more similar profiles than distant ones. In this cohort, spatial descriptors outperformed mean HU for age prediction, although performance remained limited (R² = 0.255). Sex classification was moderate (ROC AUC = 0.810; balanced accuracy = 0.725). After the spatial features were adjusted for native volume, sex-classification AUC fell to 0.671, and none of the 51 unadjusted regional sex associations survived volume adjustment. Age-related associations involved the hyoid body, the body–greater horn transition, and the proximal-to-mid greater horns. These findings demonstrate spatial coherence in the registered maps but show that the sex-related pattern is strongly size-dependent. Full article

Remote electroencephalography (EEG) systems require compact representations that remain useful when communication noise corrupts the transmitted message. We evaluated whether task-oriented residual refinement of filter bank common spatial pattern (FBCSP) features compressed by principal component analysis (PCA) improves four-class motor imagery (MI) decoding without increasing the transmitted dimension. The BNCI2014-001 dataset was assessed in nine subjects using bidirectional subject-specific cross-session evaluation. All methods transmitted $K\in \{16,32,64\}$ power-normalized real values through additive white Gaussian noise (AWGN) at seven signal-to-noise ratios (SNRs) and a noise-free reference. Balanced accuracy was averaged over 20 paired noise realizations per noisy condition, and paired subject-level differences were evaluated with exact joint sign-flip max-$\left|t\right|$ inference. At $K=32$, the proposed method achieved 41.10%, 49.46%, and 56.15% balanced accuracy at $-10$, $-5$, and 0 dB, compared with 38.63%, 46.28%, and 53.26% for conventional FBCSP–PCA transmission. Ten of the 24 semantic-versus-conventional comparisons were significant after family-wise max-$\left|t\right|$ correction, including all nine comparisons at $-10$, $-5$, and 0 dB. Receiver-only controls closely reproduced conventional performance at all three message dimensions, whereas alternative loss weights, uniform-SNR training and selection, and removal of the 0 dB/noise-free reference-condition guard retained positive low-SNR gains. Overall, baseline-preserving task-oriented refinement improved MI decision robustness under severe AWGN without increasing the number of transmitted values. Full article

Fungi play essential roles in ecosystems as pathogens, mutualists, and ubiquitous decomposers. However, like many important microbes, the spatial distribution of species and natural populations remains poorly understood compared to plants and animals. Many fungi are described as global generalists because they occur across wide geographic areas, but it remains unclear how and if these species are constrained by climate or geographic barriers. In this study, we used Species Distribution Models to infer the global climatic suitability of three common and globally distributed fungi: Aspergillus flavus, Penicillium chrysogenum and Aspergillus fumigatus. Models were constructed using global occurrence data from the Global Biodiversity Information Facility and were trained with Bioclimatic variables from the WorldClim dataset. All species’ models showed high prediction fit, with predicted occurrence concentrated in the temperate and subtropical regions and broadly structured patterns. Each species showed distinct predicted distributions, but they displayed considerable spatial overlap on a global scale. Together, these results demonstrate that even apparently globally occurring and generalist fungal species occupy climatically structured niches. This study highlights the utility of SDMs and it provides a framework for future studies integrating ecological, genomics and evolutionary perspectives among the difficult to assess geographically widespread and common fungi. Full article

Rapid urbanization across global South megacities has accelerated the overexploitation of urban aquifers, creating complex socio-hydrological crises that threaten long-term water resilience for a vast population. Conventional urban-water management frequently relies on uniform, city-wide regulatory mandates that fail to account for localized hydrogeological heterogeneities and the socio-economic drivers of private extraction. This study performed a seasonal assessment (post- and pre-monsoon) of groundwater levels (GWLs), across the National Capital Territory of Delhi, India, using a 13-year archival dataset (2010–2022) of 77 ‘common’ wells, with a sequential spatial–statistical framework. No statistically significant ‘seasonality’ was found in GWLs, except for isolated years. About 13% of the observations appeared as ‘deep outliers’, the call for more process-level hydrogeologic investigations. Spatial interpolation via the Inverse Distance Weighting (IDW) interpolation technique, alongside Global Moran’s I, Local Indicators of Spatial Association (LISA), and spatially Constrained Hierarchical Cluster Analysis (sHCA), revealed a recurrent spatial pattern: persistent, deep GWLs, within the fracture-dominated, low-yielding Alwar Quartzite (Delhi Ridge) of South and Southeast Delhi. The spatial clustering demonstrates the migration of the deep-GWL hotspots toward the unconfined alluvial aquifers of the Yamuna River floodplains to the east, threatening future baseflow stability. These spatial drawdown patterns represent a structural response to municipal Drinking Water Services Framework (DWSF) deficits, where intermittent supply and informal water markets incentivize the growth of more unregulated and unrestricted private pumping of groundwater. Achieving sustainable urban groundwater governance requires replacing blanket administrative mandates with a more data-driven, micro-zoned socio-hydrological framework across the city—combining area-specific extraction caps, economic instruments for geologically targeted aquifer storage and recovery, informal market regulation, and facilitating more participatory, community-based (Water users Associations, WUA) initiatives in the future to protect groundwater resources in Delhi. However, it requires specialized monitoring data, which is still largely lacking, and detailed investigations involving the aquifer hydrogeology and groundwater pumping patterns. 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

Normalized Difference Vegetation Index (NDVI) is an important remote sensing indicator for characterizing vegetation growth status and ecosystem changes. Improving NDVI prediction accuracy is of great significance for regional ecological monitoring and conservation. However, existing prediction methods often rely on single meteorological drivers or numerous explanatory variables that are difficult to obtain for future periods, limiting their applicability to pixel-level NDVI forecasting over spatially distributed areas. In this study, a Dual-Factor-Driven Temporal Network (DfT-Net) was proposed for pixel-level NDVI prediction in the Hulunbuir grassland based on MODIS NDVI data and ERA5-Land temperature and precipitation data from 2013 to 2024. The model was independently applied to valid 1000 m grassland pixels, with the same model parameters shared across pixels. This pixel-wise prediction strategy enables the model to learn common meteorological–vegetation response patterns across different pixels while generating spatially distributed NDVI predictions. For temporal feature modeling, Time Series Decomposition (TSD) was first applied to the temperature and precipitation sequences to decompose them into trend, seasonal, and residual components, thereby characterizing their multi-scale temporal variations and recurrent seasonal patterns. Subsequently, one-dimensional convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) were employed to extract local temporal patterns and long-term temporal dependencies, respectively. Experimental results showed that DfT-Net achieved an RMSE of 0.100, an MAE of 0.074, and an R² of 0.828 on the independent temporal test set (2022–2024). Under the same experimental setting, DfT-Net achieved the lowest RMSE and MAE and the highest R² among the evaluated models, including LSTM, CNN, TSD-CNN, TSD-LSTM, and CNN-LSTM. These results indicate that DfT-Net effectively integrates dual-factor meteorological driving, multi-scale temporal feature representation, and pixel-level prediction, providing a useful framework for spatially distributed grassland NDVI forecasting and ecological monitoring. Full article

Ground deformations induced by underground mining significantly affect the geometric condition and serviceability of buildings located in mining areas. Conventional assessments are commonly based on ground deformation indicators, which do not necessarily reflect the actual structural response. This study presents a terrestrial laser scanning (TLS)-based methodology for assessing the three-dimensional deformation of an eleven-storey residential building located in the Legnica–Głogów Copper District (LGCD), Poland. The analysis was performed using a high-density point cloud acquired from ten scanning positions. Following registration and filtering, building geometry was reconstructed and corner positions were determined from 123 horizontal cross-sections. Horizontal displacements, tilt profiles, and rotation about the vertical axis were subsequently analysed within a local coordinate system. The results revealed pronounced spatial variability in both displacement magnitude and direction. The maximum horizontal displacement reached approximately 0.18 m, corresponding to a local tilt of 5.9 mm/m. Corner displacements at the highest common observation level ranged from 8.6 mm to 178.3 mm, indicating that the observed geometry is inconsistent with a simple rigid-body model subjected to uniform tilting. Analysis of geometric changes with height further identified an overall increase in torsional rotation with height, accompanied by local variations. Comparison of TLS-derived geometry with a theoretical mining-induced ground deformation model showed that the measured structural response does not directly reproduce the underlying ground deformation pattern. The largest discrepancies occurred along the building longitudinal axis, indicating that structural stiffness and soil–foundation–structure interaction significantly modify the transfer of ground movements to the superstructure. These results demonstrate the capability of TLS for detailed assessment of mining-affected buildings and provide quantitative insight into the relationship between ground deformation and actual structural response. Full article

Since NADC30-like PRRSV-2 was first detected in Henan Province in 2012, it has continued to spread and has become one of the predominant PRRSV-2 groups in mainland China. However, the long-term spatiotemporal dynamics, interprovincial transmission patterns, transmission drivers and recombination associated evolutionary features of sublineage L1C (L1C; NADC30-like) remain incompletely resolved. Here, we analysed 9541 quality-controlled lineage 1 ORF5 sequences from 12 countries, including ORF5 sequences from 62 laboratory-derived complete genomes assigned to L1C. Globally, 3787 sequences were classified as L1C. Among the 1648 lineage 1 sequences from China, 1362 were assigned to L1C, accounting for 82.65% of Chinese lineage 1 sequences. Phylodynamic analysis dated the global common ancestor of L1C to around 2002 and suggested that strains circulating in mainland China were likely introduced from US-related strains around 2008. Before the African swine fever (ASF) outbreak, inferred interprovincial transmission links were concentrated in a limited number of key provinces. During the early ASF period, observable links decreased, but they subsequently recovered and expanded across more provinces. Transmission-driver analysis suggested that pig inventory and pig output were associated with stronger inferred interprovincial L1C transmission links, whereas geographic distance was associated with a spatial-decay effect. Whole-genome recombination analysis revealed extensive recombination signals in L1C genomes involving other PRRSV-2 lineages. Among inter-lineage associations, L8E (HP-PRRSV/JXA1-like) was the most frequently implicated background, followed by L5 and L3. These findings provide systematic evidence for the persistent prevalence, regional transmission and recombination-driven evolution of L1C in mainland China, and support molecular surveillance, regional risk warning and optimization of PRRSV-2 control strategies. Full article

As a common benign bone tumor, the pathological diagnosis of chondrogenic tumors needs to accurately analyze the calcification pattern of the cartilage matrix, key spatial topology information, and other important indicators. However, chondrogenic tumors are a heterogeneous group of tumors and a rare disease: doctors lack sufficient reference data and experience in diagnosis. At the same time, complex spatial structural information also increases the difficulty of diagnosis, which leads to inconsistencies in the diagnosis’s results of different doctors. In contrast, with the development of technology, artificial intelligence (AI), with its fast, accurate, and robust characteristics, can effectively improve the efficiency and accuracy of diagnosis. However, the application of AI in this field is still unrecognized, so it is urgent to develop a model that can help doctors in diagnosis to improve accuracy and efficiency. In this study, we propose XChondNet, an explainable spatial-context-aware synergistic deep feature fusion model for WSI-based chondrogenic tumor classification. The model proposes a fusion mechanism of pathological and positional features so that the model can effectively perceive spatial structural information and a parallel classifier mechanism based on potential coding, which can effectively solve the problem of class imbalance in chondrogenic tumor data. We evaluated the XChondNet model on our chondrogenic tumor dataset and the experimental results verified its effectiveness in the classification of the chondrogenic tumor subtype. In the test phase, XChondNet consistently achieved superior performance across different feature extractors. Compared with two strong MIL baselines, DTFD-MIL and RRT-MIL, XChondNet improved the average ACC from 85.16% to 86.58% (1.42 percentage points), with statistically significant differences confirmed by a paired t-test ($p=2.61\times {10}^{-5}$) and Wilcoxon signed-rank test ($p=0.0078$). Moreover, the attention maps of XChondNet explicitly reflect regions consistent with pathologists’ diagnostic concerns, enhancing the interpretability of AI-assisted diagnosis. Full article

Ground deformation associated with rapid urbanization has emerged as a common global challenge facing cities. However, identifying its contributing mechanisms remains difficult because multiple factors, including groundwater management, engineering loads, and extreme weather events, are often superimposed in space and time. This study proposes a scale-aware analytical framework that integrates Small Baseline Subset InSAR (SBAS-InSAR), Stationary Wavelet Transform (SWT), and Independent Component Analysis (ICA) to separate deformation signals by spatial scale and statistical structure, and then support the interpretation of their potential drivers. The original deformation field is first decomposed into a large-scale background component and a small-scale local component using SWT. ICA is then applied to the large-scale background component to extract two statistically separated deformation modes with distinct spatiotemporal characteristics. These modes are further examined using Least-Squares Cross-Wavelet Analysis (LSCWA) and ancillary hydrological information to evaluate their associations with groundwater-related processes. We applied and validated this framework in Zhengzhou, China, as a representative case. Results show that Zhengzhou exhibits a clear “central uplift and peripheral subsidence” pattern. The large-scale component contains two statistically separated modes. The first mode shows a long-term uplift pattern that is spatially consistent with the groundwater no-take zone and temporally associated with confined-aquifer water-level recovery. The second mode shows an annual oscillation that is coherent with groundwater storage anomaly, with an estimated deformation lag of approximately 1–2 months. The small-scale local component highlights subsidence bowls that are spatially and temporally associated with engineering and land-use features, including newly constructed transportation corridors, high-density building areas, landfills, and waterfront park projects. The proposed framework provides a scale-aware pathway for interpreting complex urban deformation fields and may support more targeted geohazard monitoring and urban planning in areas affected by coupled hydrological and anthropogenic pressures. Full article

Accurate daily gridded precipitation estimates are needed to characterize spatial rainfall variability and support retrospective hydrological analysis in Southeast Asia, where complex rainfall regimes and sparse rain gauge coverage remain major challenges. This study develops an Extended Triple Collocation Analysis (ETCA)-based merged daily precipitation dataset for Southeast Asia using 15 multi-source gridded precipitation products for 2000–2014. The products include gauge-based, satellite-based, reanalysis-based, and merged datasets, all regridded to a common 10 km × 10 km grid. ETCA was applied to all 455 possible three-product combinations to estimate product-level reliability, expressed as the squared correlation coefficient and error variance at each grid cell. The results reveal substantial spatial variability in product reliability. The final ETCA-merged product was generated through pixel-wise reliability-based product selection, quantile-based distributional adjustment using the locally highest-ranked product as an internal reference, and equal-weight averaging of the selected adjusted products. Validation against GSOD daily observations shows that the ETCA-merged product achieves lower RMSD of 2.95 mm day⁻¹, compared with 2.96 mm day⁻¹ for the simple all-product ensemble and 3.01 mm day⁻¹ for the ensemble of the five most regionally reliable products. The corresponding temporal correlations are 0.79, 0.81, and 0.78, respectively. The merged product also improves the representation of high-percentile rainfall, although very intense rainfall remains underestimated. Spatial climatology and annual cycle analyses indicate that the ETCA-merged product preserves the main rainfall patterns and seasonal evolution of Southeast Asia while introducing local adjustments based on product reliability. These findings demonstrate that ETCA provides a useful framework for developing uncertainty-informed precipitation datasets in regions with sparse gauge observations and spatially heterogeneous product performance. Full article

Endometrial cancer (EC) is a common gynecologic malignancy arising from the epithelial cells of the endometrium. The marked cellular heterogeneity of EC and features of its tumor immune microenvironment (TIME) contribute to disease complexity and have been associated with poor prognosis. This study integrates single-cell RNA sequencing, spatial transcriptomics, and Mendelian randomization (MR) to identify candidate genes associated with EC. Single-cell analysis identified MM0 as a putative stemness-associated transcriptional subpopulation with the highest CytoTRACE-inferred score; irGSEA indicated enrichment of proliferation- and stress-response pathways. Spatial data were analyzed with RCTD, MISTy, and stLearn to estimate spatial associations and pathway activities. MR and colocalization analyses integrating eQTL data and EC GWAS summary statistics prioritized DNAJA4, HSPA6, and LMNA as candidate genes with potential causal associations with EC risk. The expression patterns of these candidate genes were further examined in patient-derived samples. In Ishikawa cells cultured under high-estrogen conditions, siRNA-mediated knockdown of DNAJA4 and HSPA6 significantly suppressed proliferation and migration, accompanied by reduced CDK1 and Cyclin B expression. Collectively, these findings provide insight into EC heterogeneity and support further mechanistic investigation of candidate genes associated with malignant epithelial proliferation. Full article

Extreme precipitation events have grown more common as a result of global climate change, and conventional static hazard assessments find it difficult to account for the dynamic progression of flood disasters. This study considers extreme precipitation factors for different return times and creates different extreme precipitation scenarios based on multiyear historical precipitation data and actual storm events. The study proposes a method for the dynamic assessment of regional flood hazard that takes extreme rainfall scenarios into account by simulating the dynamic flood inundation processes under each scenario using the Accumulated Runoff and Flood Estimation Model (AccRo v.1.0), iterative flow accumulation, and hydrological calculations. A dynamic assessment and zoning of flood hazards was carried out in Laiyuan County, Hebei Province. The results reveal that high-hazard zones coincide with the distribution of historically badly damaged townships, concentrated in the river valley plains along the Juma River. The results show that spatial patterns are simultaneously influenced by precipitation, terrain, and the river network. In the temporal dimension, under Scenario 3, the superimposition of the 50-year return period daily maximum rainfall at the 12th hour increased the high-hazard area by approximately 110% compared with that at the 11th hour. In addition, the non-uniform multi-peak rainfall pattern in Scenario 4 represented the rise, peak, and recession stages of the flood process. A combined assessment of water depth and flow velocity can effectively distinguish between two disaster-causing modes—deep water with low flow velocity and shallow water with high flow velocity—thereby addressing the underestimation of hazard in transition zones associated with the use of water depth as a single indicator. Full article

Background: Major depressive disorder (MDD) is a common psychiatric disorder. Electroencephalography (EEG) provides physiological information for depression detection, but many existing methods rely on dense multichannel recordings, limiting their use in lightweight EEG screening. Frontal sparse-channel EEG reduces acquisition burden but provides limited spatial information, requiring effective within-window representation learning and cross-window temporal modelling. Methods: We propose FSC-BiMamba, a dual-branch bidirectional Mamba network for subject-independent window-level MDD classification from frontal sparse-channel EEG. For each window, a Time–Frequency Map Encoder (TFME) learns local time–frequency patterns, while a Frequency-Domain Statistical Descriptor Encoder (FSDE) encodes complementary frequency-domain statistical descriptors. An Adaptive Dual-Token Fusion (ADTF) module integrates the resulting tokens through feature-wise gating to form a unified window representation. Representations from eight consecutive windows are then processed by a bidirectional Mamba (BiMamba) module to capture cross-window context. Finally, a softmax classifier converts each contextualized representation into a window-level class prediction. Results: Performance was evaluated using stratified subject-independent five-fold cross-validation. FSC-BiMamba achieved window-level accuracies of 82.70 ± 2.37% and 84.55 ± 8.01% on MODMA and Mumtaz2016, respectively. Together with its compact architecture, these results indicate a favourable balance between classification performance and model size. Conclusions: FSC-BiMamba effectively integrates complementary window-level representations with cross-window temporal context. It achieved the highest accuracy among the compared models on both datasets, demonstrating a favourable performance–size trade-off for lightweight EEG-based MDD screening. Full article

Coupling coordination degree (CCD) between persistent late-night radiance and population density may vary nonlinearly with urban functional density, yet linear and single-city analyses cannot distinguish shared from city-specific patterns. A method-controlled dual-city XGBoost-SHAP framework was applied to 14,215 H3 cells in Shanghai and 31,067 in Beijing using six point-of-interest density factors. XGBoost outperformed OLS, with random hold-out R² values of 0.899 and 0.918 vs. 0.617 and 0.626. Public service (X2) ranked first in both cities, accounting for 39.77% and 58.73% of total mean absolute SHAP magnitude. The secondary hierarchy diverged as follows: commercial finance (X3) ranked second in Shanghai at 27.91% and formed the strongest interaction with X2, whereas transport infrastructure (X6) ranked second in Beijing at 18.63% and formed the strongest interaction with X2. Nonlinear analysis identified reproducible negative-to-positive crossings for X2, X3, and X6, peak-type responses for X4 and X5, and no stable second saturation threshold. Spatial OOF, grid, rank, LOWESS, and residual checks supported the leading-factor contrast while showing scale sensitivity and residual spatial dependence. The results identify a common leading attribution alongside city-specific secondary, nonlinear, and spatial patterns within the two observed megacities. Full article