Search Results (652)

Background/Objectives: The duodenum is commonly involved in systemic amyloidosis. This retrospective observational study describes histoanatomical distributions of different types of duodenal amyloidosis to improve the diagnostic value of duodenal biopsies. Methods: We examined 21 biopsy and 16 autopsy specimens from duodenal amyloidosis patients. Immunohistochemical typing was performed using a broad panel of antibodies against different amyloid types. Results: AL lambda amyloidosis was determined in 5 (13%) biopsies and 7 (18%) autopsies, exhibiting interstitial and intravascular amyloid deposition patterns in 11 (92%) cases; AL kappa amyloidosis—in 7 (18%) biopsies and 1 (3%) autopsy, presenting with a combined interstitial and intravascular deposition pattern in 6 (75%) cases; transthyretin amyloidosis—in 2 (5%) biopsies and 2 (5%) autopsies, showing focal interstitial and intravascular deposits; and AA amyloidosis—in 7 (19%) biopsies and 6 (16%) autopsies, demonstrating a combined pattern of amyloid deposition. Regardless of the specific amyloid type, in 33 (89%) of 37 cases, amyloid deposits were determined in the muscularis mucosae and submucosa of the small intestine, while in the lamina propria, amyloid depositions were found only in 29 (78%) cases. Conclusions: When diagnosing duodenal amyloidosis, superficial biopsies can lead to false negative results. This is particularly true for ATTR amyloidosis, where mucosal involvement is rare. The most extensive amyloid deposits were observed in AL kappa amyloidosis. Gastrointestinal bleeding was a more frequent complication of AA amyloidosis stemming from the extensive amyloid deposits within the lamina propria which cause vascular fragility and friability. Full article

Background/Objectives: Dermatophytosis is a widespread superficial fungal infection affecting skin, hair, and nails. Its diagnosis is often based on conventional methods such as microscopy and fungal culture. Laboratory confirmation is essential for guiding appropriate treatment and preventing the misuse of antifungal agents, which can contribute to the emergence of antifungal resistance. We retrospectively assessed the burden and species distribution of dermatophytosis in individuals attending a public dermatology institute in Italy over a 3-year period (2019–2021). Methods: We analyzed 3208 samples from 3037 individuals with clinical suspicion of superficial mycosis. All samples underwent direct microscopic examination and fungal culture. Data were stratified by demographics, body site, and fungal species. Agreement between diagnostic methods was assessed using raw concordance and Cohen’s Kappa statistic. Results: Dermatophytes were confirmed in 667 samples (20.8%). Buttocks and genitals showed the highest positivity rates (37.5% and 36.4%, respectively). T. rubrum (56.8%) and T. mentagrophytes (30.7%) were the predominant species among the dermatophyte-positive specimens. Agreement between microscopy and culture was good (raw concordance: 91.6%, Cohen’s Kappa: 0.77, 95% CI: 0.74–0.79). Younger age and male gender were significantly associated with dermatophyte positivity. Conclusions: Our data provide updated epidemiological insights into dermatophytosis in Italy and support appropriate antifungal stewardship. Laboratory confirmation remains essential for an accurate diagnosis and species identification, thus avoiding other non-dermatophytic or non-infectious conditions being treated as dermatophytosis. Full article

To accurately grasp the land and ecological dynamics in the Henan section of the Yellow River Basin (YRB) and provide detailed local data for the ecological protection of the YRB, this article takes the Henan segment within the YRB as the research area, explores the spatio-temporal evolution of land use (LU) and landscape ecological risks (LERS), and predicts LU and LERS under various scenarios in the future based on the PLUS model. We found that: (1) From 2000 to 2020, object types in research area were given priority with cultivated land, forest land, and construction land, with construction land and cultivated land experiencing the largest changes of 5.71% and −6.34%, respectively. Changes in other land types varied within a ±3% range. The expansion of construction land principally encroached upon cultivated land, indicating significant urban sprawl. (2) The high-ecological-risk areas were clustered in the area centered in Zhengzhou, and the low-ecological-risk areas were distributed in the edge of the study area. As risk levels increased, the risk center gradually shifted towards the central regions, particularly around Luoyang and at the junction of Luoyang, Zhengzhou, and Jiaozuo. (3) The LU status in 2030 was projected using the PLUS model under three varied scenarios. The Kappa coefficient of the model was 0.81, and the overall accuracy was about 88.13%. Cultivated land, forest land, and construction land still accounted for the main part, and the area of cultivated land and construction land changed significantly. Based on this analysis of LERS prediction, the distribution of risk levels in different scenarios was different, but in general, high-ecological-risk areas and higher-ecological-risk areas accounted for the main part, while the study area’s edges were where low-ecological-risk zones were situated. Research can offer scientific and technological support for the sensible utilization and administration of resources, along with the protection of the ecological environment and regional sustainable development. Full article

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

This study proposes a novel, scalable framework for the automated classification and synthesis of survey literature by integrating state-of-the-art Large Language Models (LLMs) with robust ensemble voting techniques. The framework consolidates predictions from three independent models—GPT-4, LLaMA 3.3, and Claude 3—to generate consensus-based classifications, thereby enhancing reliability and mitigating individual model biases. We demonstrate the generalizability of our approach through comprehensive evaluation on two distinct domains: Question Answering (QA) systems and Computer Vision (CV) survey literature, using a dataset of 1154 real papers extracted from arXiv. Comprehensive visual evaluation tools, including distribution charts, heatmaps, confusion matrices, and statistical validation metrics, are employed to rigorously assess model performance and inter-model agreement. The framework incorporates advanced statistical measures, including k-fold cross-validation, Fleiss’ kappa for inter-rater reliability, and chi-square tests for independence to validate classification robustness. Extensive experimental evaluations demonstrate that this ensemble approach achieves superior performance compared to individual models, with accuracy improvements of 10.0% over the best single model on QA literature and 10.9% on CV literature. Furthermore, comprehensive cost–benefit analysis reveals that our automated approach reduces manual literature synthesis time by 95% while maintaining high classification accuracy (F1-score: 0.89 for QA, 0.87 for CV), making it a practical solution for large-scale literature analysis. The methodology effectively uncovers emerging research trends and persistent challenges across domains, providing researchers with powerful tools for continuous literature monitoring and informed decision-making in rapidly evolving scientific fields. Full article

A multicenter molecular biomarker survey was conducted in Multiple Sclerosis (MS) centers across Central-Eastern European countries, encompassing a population of 107 million. Our aim was to provide a “snapshot” for future studies investigating the use of molecular biomarkers in MS. A self-report questionnaire was distributed via email to MS centers in seven Central-Eastern European countries (Croatia, Czech Republic, Poland, Romania, Serbia, Slovakia, and Slovenia) and to four reference centers (two in Austria, one in Germany, and one in Denmark), focusing on cerebrospinal fluid (CSF) analysis and molecular biomarkers in MS. Responding centers routinely request CSF oligoclonal band (OCB) testing in suspected MS cases, although no consensus exists on the number of CSF-restricted bands required to define OCB positivity, either within or between countries. More than half of the surveyed centers in the Czech Republic, Slovakia, Slovenia, and the reference centers request kappa free light chain (κFLC) testing in patients with suspected MS. Neurofilament light chain (NfL) is frequently used as a molecular biomarker for MS in Romania, Slovakia, and the reference centers. In summary, besides the use of CSF-specific OCB there is no consensus among the surveyed countries regarding the use of molecular biomarkers in MS. Full article

Context: It has been claimed that recently, in England, the places with the greatest amount of housing built were the places that least needed them. This is an accusation that has echoes in a number of countries around the globe. The lack of construction leads to greater unaffordability and a lower level of economic activity than could have been achieved if labour, particularly those with high human capital, was not so constrained as to where they could afford to live. The recent National Planning Policy Framework for England imposes mandatory targets on housing planning authorities. As such, the following question is raised: will the targets result in additional residential homes being located in places of greater need than the prevailing pattern? Research Questions: The paper sets out to consider the spatial mismatch between housing additions and national benefit in terms of unaffordability and productivity. Specifically, do the concentrations of high and/or low rates of the prevailing rates of additional dwellings and the target rates of adding dwellings correspond with the clusters of high and/or low unaffordability and productivity? A further question considered is: does the spatial distribution of additional dwellings match the clusters of population growth? Method: The values of the variables are transformed at the first stage into Anselin’s LISA categories. LISA maps can reveal unusually high spatial concentrations of values, or clusters. The second stage entails comparing sets of the transformed data for agreement of the classifications. An agreement coefficient is provided by Fleiss’s kappa. Data: The data used is of additional dwellings, the total number of dwellings, population estimates, gross value added per hour worked (productivity data), and house price–earnings ratios. The period of study covers the eight years prior to 2020 and the two years after, omitting 2020 itself due to the unusual impact on economic activity. All the data is at local authority district level. Findings: The hot and cold spots of additional dwellings do not correspond those of house price–earnings ratios or productivity. However, population growth hot spots show moderate agreement with those of where additional dwellings are concentrated. This is in line with findings from elsewhere, suggesting that population follows housing supply. Concentrations of districts with relatively high targets per unit of existing stocks are found correspond (agree strongly) with clusters of house price–earnings ratios. Links between productivity and housing are much weaker. Conclusions: The strong link between targets and affordability suggests that if the targets are met, the claim that the places that build the most housing are the places that least need them can be challenged. That said, house-price–earnings ratios present a view of unaffordability that will favour greater building in the countryside rather than cities outside of London, which runs against concentrating new housing in urban areas consistent with fostering clusters/agglomerations implicit in the new modern industrial strategy. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article

Monitoring salt marsh vegetation in the Yellow River Delta (YRD) wetland is the basis of wetland research, which is of great significance for the further protection and restoration of wetland ecological functions. In the existing remote sensing technologies for wetland salt marsh vegetation classification, the object-oriented classification method effectively produces landscape patches similar to wetland vegetation and improves the spatial consistency and accuracy of the classification. However, the vegetation classes of the YRD are mixed with uneven distribution, irregular texture, and significant color variation. In order to solve the problem, this study proposes a fine-scale classification of dominant vegetation communities using color-enhanced aerial images. The color information is used to extract the color features of the image. Various features including spectral features, texture features and vegetation features are extracted from the image objects and used as inputs for four machine learning classifiers: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) and maximum likelihood (MLC). The results showed that the accuracy of the four classifiers in classifying vegetation communities was significantly improved by adding color features. RF had the highest OA and Kappa coefficients of 96.69% and 0.9603. This shows that the classification method based on color enhancement can effectively distinguish between vegetation and non-vegetation and extract each vegetation type, which provides an effective technical route for wetland vegetation classification in aerial imagery. Full article

In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations. Full article

Atherosclerosis is a multifactorial, chronic inflammatory disorder characterized by the progressive accumulation of plaque within the arterial wall. Recent research has highlighted the pivotal role of bioactive peptides in modulating vascular homeostasis and inflammation. Among these, salusin-α and salusin-β have emerged as critical regulators of atherogenesis. These peptides are generated via differential proteolytic processing of preprosalusin: an amino acid precursor encoded by the torsin family 2 member A gene. Despite their common origin, salusin-α and salusin-β exhibit divergent biological activities. Salusin-β promotes vascular inflammation by enhancing oxidative stress, activating the nuclear factor kappa B signaling pathway, and upregulating proinflammatory cytokines as well as adhesion molecules, and it also facilitates foam cell formation by increasing the expression of acyl-CoA/cholesterol acyltransferase 1 and scavenger receptors, thereby contributing to plaque progression. In contrast, salusin-α appears to exert protective, anti-inflammatory, and anti-atherogenic effects by increasing the expression of the interleukin-1 receptor antagonist and inhibiting key proinflammatory mediators. Additionally, these peptides modulate the proliferation of vascular smooth muscle cells and fibroblasts, with salusin-β promoting cellular proliferation and fibrosis via calcium and 3′,5′-cyclic adenosine monophosphate-mediated pathways, while the role of salusin-α in these processes is less well defined. Altered plasma levels of salusins have been correlated with the presence and severity of atherosclerotic lesions, suggesting their potential as diagnostic biomarkers and therapeutic targets. This review provides a comprehensive overview of biosynthesis, tissue distribution, and dual roles of salusins in vascular inflammation and remodeling, emphasizing their significance in the pathogenesis and early detection of atherosclerotic cardiovascular disease. Full article

This study examines the variation and trends in wave parameters across the Arctic, including the marginal ice zone (MIZ), by comparing historical data (1980–2009) with projections for a future climate scenario (2070–2099) as outlined by the IPCC. Utilizing the WAVEWATCH III (WW3) numerical wave prediction model, we simulate the wave climate for these periods, incorporating advanced parameterizations to account for wave-ice interactions within the MIZ. Our analysis focuses on the extreme values of significant wave heights (Hs), mean wave periods (T0), and dominant mean wave direction (MWD), calculated for both winter and summer seasons. To assess changes in wave climate under future climate scenarios, we first use a similarity matrix, applying the kappa variable and cell-by-cell numerical comparison methods to assess model congruence across different conditions. We also follow a standard approach, by assessing the extreme wave conditions for 20 and 100-year return periods using standard stochastic models, including Gumbel, exponential, and Weibull distributions. Full article

Accurate urban land use classification is vital for urban planning, resource allocation, and sustainable management. Traditional remote sensing methods struggle with fine-grained classification and spatial structure identification, while socio-economic data, like points of interest and road networks, face issues of uneven distribution and outdated updates. To explore the role of building morphology characteristics in enhancing urban land use classification and their potential as a substitute for socio-economic information, this study proposes a method integrating architectural features with multi-source remote sensing data, evaluated through an empirical analysis using a random forest model in Shenzhen. Three models were developed as follows: Model 1, utilizing only remote sensing data; Model 2, combining remote sensing with socio-economic data; and Model 3, integrating building morphology with remote sensing data to evaluate its potential for enhancing classification accuracy and substituting socio-economic data. Experimental results demonstrate that Model 3 achieves an overall accuracy of 80.09% and a Kappa coefficient of 0.77. Compared to this, Model 1 achieves an accuracy of 74.56% and a Kappa coefficient of 0.70, while Model 2 reaches 79.56% accuracy and a Kappa coefficient of 0.76. Model 3 also shows greater stability in complex, smaller parcels. This method offers superior generalization and substitution potential in data-scarce, heterogeneous contexts, providing a scalable approach for fine-grained urban monitoring and dynamic management. Full article