Search Results (757)

Wild and synanthropic birds harbour a diverse range of yeasts, including species of recognised clinical relevance. Given their close interaction with human activities, these birds represent a valuable source for investigating environmental yeasts and assessing the performance of identification methods. We investigated yeasts recovered from cloacal and crop samples of birds from Gran Canaria and compared routine identification methods with molecular sequencing. Twenty-four isolates were examined by biochemical profiling (API ID32C) and MALDI-TOF MS. Molecular identification based on ITS sequencing was carried out only for the sixteen isolates for which the two routine methods yielded discordant results, allowing resolution of their taxonomic identification. Phenotypic and proteomic methods showed limited agreement at the species level (8/24; 33.3%), with 16 discordant identifications resolved by sequencing. Pigeon isolates were dominated by members of the Kazachstania telluris complex, chiefly K. bovina (11/24; 45.8%), while partridges yielded taxa of clinical importance, including Candida parapsilosis and Pichia kudriavzevii (formerly Candida krusei). Rhodotorula mucilaginosa, Debaryomyces spp., and Saccharomyces cerevisiae were also detected. Comparative tests confirmed significant host-associated differences in species distribution (p < 0.05), and Cohen’s kappa indicated substantial agreement between API and MALDI-TOF at the genus level when benchmarked against ITS (κ = 0.71), although concordance was lower at the species level. In conclusion, these findings strengthen the case for integrating sequencing into diagnostic workflows, highlight the potential One Health relevance of yeast carriage by wild birds, and underscore the need for targeted surveillance at urban and game-handling interfaces where human exposure is likely. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Pegmatite-related rare-metal exploration in high-altitude mountainous regions is commonly limited by rugged terrain, complex structural frameworks, and uneven bedrock exposure. This study presents a multi-source remote-sensing workflow for regional-scale rare-metal prospectivity assessment in the Pusharong area of western Sichuan, China, by integrating GF-5A Advanced Hyperspectral Imager (AHSI) data, ASTER thermal infrared (TIR) data, and structural interpretation. GF-5A hyperspectral data were used as the primary source for extracting mineral-related anomaly responses associated with muscovite, tourmaline, cookeite, and spodumene. Mixture Tuned Matched Filtering (MTMF) was applied to enhance weak target-related spectral responses, whereas Spectral Angle Mapper (SAM) provided an independent spectral-consistency constraint to reduce potential over-identification. ASTER TIR-derived Quartz Index (QI) and Feldspar Ratio Index (FRI) responses were used as supplementary lithological and differentiation-related background constraints rather than as continuous quartz–feldspar mineral-distribution maps. Structural interpretation was further integrated to evaluate the spatial relationship between mineral-related anomalies and favourable fault settings. Preliminary point-based validation shows a high degree of consistency between the mapped anomaly zones and available field or geochemical observations, with an overall consistency of 92.86% and a Kappa coefficient of 0.91. The integrated workflow delineated four prospective target zones for follow-up verification, with T1 showing the strongest multi-source support, followed by T2 and T3, whereas T4 is regarded as a lower-priority verification target. These results demonstrate the usefulness of the workflow for first-pass regional target prioritisation in complex mountainous terrain, but the delineated targets require further field, mineralogical, geochemical, and drilling verification before any deposit-scale interpretation can be made. Full article

Background/Objectives: This prospective observational cohort study aimed to evaluate the influence of heated tobacco (HT) and electronic cigarettes (ECs) on bone remodeling markers such as receptor activator of nuclear factor kappa-B ligand (RANKL) and osteoprotegerin (OPG), and periodontal status, at baseline and at 3 months after initial periodontal therapy. Methods: The sample comprised 236 participants (130 women, 106 men; mean age 38.96 ± 7.69 years), distributed across non-smokers (n = 72), heated tobacco/HT product users (n = 83), and electronic cigarette/EC users (n = 81). For each patient, the periodontal charting included periodontal probing depth (PPD), bleeding on probing (BOP), and clinical attachment loss (CAL). Unstimulated saliva samples were analyzed for RANKL and OPG levels. All patients underwent nonsurgical periodontal therapy (scaling and root planing). Between-group comparisons were performed using the Kruskal–Wallis test followed by Bonferroni-adjusted pairwise comparisons, while within-group changes over time were assessed using the Wilcoxon signed-rank test. To complement the primary nonparametric analyses, two-way mixed-design ANOVA and ANCOVA models adjusted for baseline values and periodontitis stage were performed as sensitivity analyses. Statistical significance was set at p < 0.05. Results: At baseline, both product user groups exhibited significantly higher PPD (p = 0.005) and CAL (p = 0.001) compared with non-smokers, with no differences between HT and EC users. Salivary RANKL levels were significantly higher in HT and EC users than in non-smokers, and OPG levels did not differ significantly. Following non-surgical periodontal therapy, all parameters improved significantly across groups (p < 0.001). At the 3-month follow-up, both product user groups maintained higher PPD (p = 0.008), CAL (p = 0.001), and salivary RANKL levels, compared with non-smoking individuals (p < 0.001). The RANKL/OPG ratio remained significantly different only for EC users compared with non-smokers (p < 0.001). Conclusions: HT and EC use were associated with differences in periodontal parameters and higher RANKL levels, while differences in the RANKL/OPG ratio were observed in EC users compared with non-smokers. Non-surgical periodontal therapy improved clinical parameters and reduced the RANKL/OPG ratio, highlighting the importance of biofilm control. Full article

Intracranial hemorrhage (ICH) is a life-threatening neurological condition that requires rapid and accurate diagnosis to reduce mortality and improve patient health. Computed tomography (CT) imaging is widely used for ICH detection. However, manual interpretation can be time-consuming and prone to errors, particularly in high-volume clinical settings. Recent studies have demonstrated the effectiveness of deep learning techniques in automating medical image analysis and improving diagnostic accuracy. In this study, we propose a novel deep learning model, MGiT-X, for the automated detection of intracranial hemorrhage using head CT images. The MGiT-X model is a hybrid deep learning architecture that uses dual scale Swin Transformer modules to extract features at multiple scales, capturing local and global contextual information on CT images. It has a Gradient Fusion mechanism to enhance feature representation by combining complementary features to distinguish between hemorrhagic and healthy tissue. In addition, to further improve feature representation, the use of Jensen–Shannon divergence is used to provide better mutual alignment and consistency between the distribution of features. An adaptive weight strategy is also employed to provide refinement to the importance of features for classification. MGiT-X is evaluated on two publicly available datasets including the Head CT Hemorrhage dataset and the Brain CT Hemorrhage dataset. The proposed approach leverages advanced feature extraction and classification capabilities to distinguish between hemorrhage and healthy cases effectively. Experimental results demonstrate that the proposed MGiT-X achieves high performance across both datasets. On Dataset 1, the model attains an overall accuracy of 95.87% and a Kappa score of 91.80%, while on Dataset 2, it achieves an improved accuracy of 99.12% with a Kappa score of 98.20%. Class-wise evaluation further shows strong performance, with F1-scores exceeding 95% for both hemorrhage and healthy classes across datasets. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

Protecting native coastal wetland vegetation and controlling the invasion of Spartina alterniflora (SA) have long been key ecological and management priorities in China. The accurate and rapid mapping of vegetation distribution is critical for effective invasion control and wetland restoration. While phenological information improves remote sensing classification, most studies rely on the Normalized Difference Vegetation Index (NDVI), which has limited capability to distinguish morphologically similar species in coastal wetlands. To better exploit the unique reddening phenology of one such species, Suaeda salsa (SS), this study builds on our previously developed Red Suaeda salsa Index (RSSI) and introduces two novel phenological indicators: the Redness Contribution Coefficient (RCC) and Reddening Rate Index (RCI). Using the coastal wetlands of Jiangsu Province as the study area, we employed multi-temporal Sentinel-2 image composites (spring, summer, autumn) from 2019, 2022, 2024, and 2025 to construct a multi-dimensional feature set and implemented classification using a random forest algorithm. Results showed that the feature scheme integrating SS reddening phenological parameters achieved the highest accuracy, with an overall accuracy of 97.32% and a Kappa coefficient of 0.9625 in 2019, confirming the method’s reliability at the provincial scale. Between 2019 and 2025, SA coverage in Jiangsu decreased by 90.8%, with most cleared areas converting to non-vegetated land, indicating the remarkable effectiveness of recent control projects. This study scales up a locally validated high-precision classification approach to the provincial scale, supporting sustainable coastal wetland management in line with United Nations (UN) SDG 14 (Life Below Water) and SDG 15 (Life on Land). Full article

Contrary to the common assumption of Maxwell–Boltzmann (MB) distributions, space plasmas are characterized by kappa distributions and reside in thermodynamic stationary states out of classical thermal equilibrium, owing to the correlations between the charged plasma particles. In this study, we extend prior work to include realistic 3D collisions and Debye-like shielding of the correlations to show how these two processes compete in the development of realistic plasma particle velocity distributions. We modify our prior numerical experiment to incorporate both 3D collisions and correlations that include realistic Debye-like shielding of plasma particles and run it over many collisions until it becomes stationary. While 3D collisions alone produce Maxwell–Boltzmann (MB) distributions of the particles (κ → ∞), introducing correlations drives the distributions to stationary states with finite thermodynamic kappa (κ), where stronger correlations produce lower values of κ, as observed in space plasmas. Further, development of correlation clusters around each collision rapidly produces thermodynamic systems where the Debye length is proportional to $\sqrt{1+1/{{\kappa }_0}_{\mathrm{th}}}$, for invariant thermal kappa ${{\kappa }_0}_{\mathrm{th}}$, just as predicted by theory. This simple numerical experiment explores much more realistic particle interactions to show how 3D collisions and properly shielded correlations compete to produce stationary states of plasma particle kappa distributions and illuminates how long-range interactions correlate particles over the scale of the Debye lengths. Full article

Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often contain implicit multi-label semantics due to spectral mixing and spatial overlap. This mismatch distorts prediction structure, exacerbates generalization errors, and limits the effectiveness of standard domain generalization (DG) techniques focused solely on feature or prediction invariance. We propose Class-Aware Semantic Calibration (CASC), a systematic semantic structure calibration framework that addresses three complementary distortions induced by mismatched patch supervision: (i) Balance corrects class frequency bias via reweighted supervision; (ii) Separability enhances boundary decision stability through margin-based logit calibration; and (iii) Independence reduces domain-specific spurious co-occurrence via prediction covariance decorrelation. To preserve calibrated semantics under pseudo-source shift, we further introduce a complementary DualAlign (DA) module, which jointly aligns feature statistics and prediction distributions, enforcing consistency at both representation and semantic levels. Extensive experiments on three cross-scene benchmarks (Houston, Pavia, and WHU-Hi) demonstrate that CASC-DA consistently improves performance over strong baselines, achieving an average gain of 3.0% in overall accuracy and 4.9% in Kappa coefficient compared with the best-performing baseline on each dataset. These results underscore the importance of semantic structure calibration for domain-generalized HSI classification. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Oligodendrocytes (OLs) are specialized glial cells essential for the formation and maintenance of the myelin sheath within the central nervous system (CNS). Historically, OLs were considered a functionally homogeneous population. However, the advent and widespread application of single-cell and single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) technologies since 2015 have revealed substantial transcriptional heterogeneity, varying according to developmental stage, anatomical region, and disease state. In this review, we synthesized current advances in the understanding of OL heterogeneity. Nine OL cell classes have been identified in the mouse somatosensory cortex and hippocampal CA1 region, later expanding to 13 distinct subpopulations across ten CNS regions. Furthermore, we characterized disease-associated oligodendrocytes (DAOs)/disease-associated oligodendrocyte lineages (DOLs), identified in various neurological diseases, including multiple sclerosis (MS), Alzheimer’s disease (AD), and spinal cord injury, focusing on their molecular markers, spatial distribution, and pathophysiological roles. We summarized key transcriptional regulatory networks underlying DAO induction, including the signal transducer and activator of transcription (STAT)/interferon regulatory factor (IRF) family, the Yin Yang 1 (YY1)/nuclear factor kappa B (NF-κB) axis, and the SOX9/SOX10 regulatory system. The utility of region-specific brain analyses using spatial transcriptomics (ST) in conjunction with these approaches was also discussed. Finally, we compiled the implications of patient stratification according to white matter glial response patterns derived from large-scale snRNA-seq analyses of patients with progressive MS. Our synthesis shows that oligodendrocytes consist of multiple distinct subtypes that vary across development, brain regions, and disease conditions. In pathological states, they adopt specific disease-associated programs that reflect context-dependent responses and may influence disease progression and repair. This work provides a framework for understanding how oligodendrocyte diversity contributes to neurological disease and may support the development of targeted remyelination therapies. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Aquaculture ponds and salt pans represent the dominant forms of coastal production spaces along the Jiangsu coast, China; however, their long-term co-evolution and mutual transitions remain poorly understood. To bridge this gap, this study developed a 40-year (1985–2025) spatiotemporal dataset of these land covers leveraging Landsat imagery via the Google Earth Engine (GEE) platform. We established an integrated classification workflow encompassing single-scene water mask extraction, annual Modified Normalized Difference Water Index (MNDWI)-based water frequency statistics, Otsu automatic thresholding, connected-component labeling, and the masking of natural water bodies. The resulting dataset demonstrated high reliability, achieving overall accuracies (OA) ranging from 92.32% to 94.15% and an average Kappa coefficient of 0.89. Based on multi-metric analyses of area dynamics, annual change rates, and transition patterns, we identified three distinct co-evolutionary stages: simultaneous expansion (1985–1995), internal reorganization (1995–2015), and overall contraction (2015–2025). Notably, transitions between the two production spaces were highly asymmetric over the 40-year period; the area converted from salt pans to aquaculture ponds was approximately 15.23 times greater than the reverse conversion. Furthermore, their distribution exhibited strong spatial heterogeneity at the county level, underscoring the critical role of localized coastal planning in balancing economic production and wetland conservation. Ultimately, this work provides foundational data and methodological insights for long-term coastal ecological monitoring and sustainable production space management. Full article

AI methods (machine learning and deep learning methods) presented promising results concerning the accuracy of dental age estimation and sex determination. Therefore, this pilot study aims to evaluate the efficacy of an artificial intelligence system to estimate age and determine sex in a Greek population sample. Panoramic radiographs from 110 adult subjects comprised this study’s sample. Males and females were equally distributed (1/1) in the sample. The dental status of each patient was different. The sample’s age ranged from 9 to 84 years of age, with a mean age of 48.87 years (±16.14 yrs). The methodology employed beta versions of convolutional neural networks (CNNs) developed by the University of Zagreb. Separate CNNs were trained on 4000 panoramic radiographs: one for sex estimation and another for age estimation. The AI program overestimated the subjects’ age on average by 4.16 years. A statistically significant correlation was found between true and estimated sex (p-value < 0.001). In males, the rate of agreement was 56.36%, while for females it was 89.47% (z-test for two proportions; p-value < 0.001). For the overall sample, Kappa = 73.21%, indicating a very good agreement. The results concerning age estimation are not quite satisfactory and further research is needed. Full article

Tool wear state monitoring under varying operating conditions is important for machining quality and production reliability. However, changes in cutting parameters can shift monitoring-signal distributions and reduce the generalization ability of data-driven models. This paper proposes a cross-condition tool wear state monitoring method based on multi-source sensor signal fusion and supervised transfer learning. X-axis vibration, Z-axis vibration, and spindle current signals are organized as multi-channel time-series inputs. A deep model integrating a multi-scale convolutional neural network, bidirectional long short-term memory, and an attention mechanism is developed to extract discriminative wear-related features. Source-domain pretraining, target-domain warm-up fine-tuning, and source-target joint fine-tuning are organized as a progressive supervised transfer procedure to improve target-condition adaptation. Experiments are conducted on a custom multi-condition dataset using an hp0 + hp1 → hp2 transfer task. Under the unified XZI input configuration, the proposed method outperforms CNN-LSTM, DANN, and CORAL. Input ablation results show that X, XZ, and XZI achieve accuracies of 0.6000, 0.7647, and 0.8588, respectively. In repeated random-seed experiments, the method obtains an Accuracy of 0.7929 ± 0.0499, a Macro-F1 of 0.7292 ± 0.0706, and a Cohen’s Kappa of 0.6542 ± 0.0840. The results demonstrate the effectiveness of multi-source sensor fusion and supervised target-condition adaptation for cross-condition tool wear monitoring. Full article