Search Results (19,383)

Objectives: Periapical periodontitis, which is a periodontal dysfunction, is a current clinical problem. Due to the frequency of occurrence and the adverse effects they cause, they are considered a social disease. They require detailed diagnostics to implement appropriate treatment. Mathematical calculations based on data obtained from radiological images used in routine clinical practice may help differentiate the forms of periodontitis. This study aimed to evaluate the areas affected by periodontitis in comparison to the healthy tissues of the periapical area. Methods: The study analyzed texture components using the gray-level co-occurrence matrix (GLCM) and the gray-level run-length matrix (GRLM) on an orthopantomogram (OPG) from 50 patients with clinically confirmed periodontitis treated at the Department of Maxillofacial and Plastic Surgery, University of Bialystok. Texture analysis was performed on defined regions of interest (ROIs) to distinguish diseased from healthy tissues. We employed four classification algorithms to assess model performance. Results: The data set included 50 patients, with 76 cases of periodontitis and 50 healthy ROIs. The reference standard was clinical diagnosis confirmed by two specialist doctors. The best-performing algorithm achieved an AUC of 98%. Conclusions: The obtained results showed significant statistical differences in the inflamed regions compared to the control, which may aid in diagnosing and selecting the treatment method for periodontitis. Full article

This paper addresses the issue of rotor swings in a high-power synchronous generator during stable operation with a stiff power grid. The analysis of electromechanical swings was conducted using a circuit model incorporating fractional-order derivatives. Assuming that variations in the load angle under small disturbances from a stable equilibrium are minor, a linearized differential equation describing the electrodynamic state of the synchronous machine was derived. Based on this linearized equation of motion and the identified parameters of the equivalent circuit, calculations were performed for a 200 MW turbogenerator. The results indicate that the electromechanical swings are characterized by a constant pulsation and a low damping factor. Calculations were also carried out using a lumped-parameter equivalent circuit model. Based on the obtained results, it can be stated that the fractional-order model provides a more accurate fit of the frequency characteristics compared with the classical model with the same number of rotor equivalent circuits. The relative approximation errors for the fractional-order model are, for the d-axis (one rotor equivalent circuit), relative magnitude error δ_m = 1.53% and relative phase error δ_φ = 6.32%, and for the q-axis (two rotor equivalent circuits), δ_m = 3.2% and δ_φ = 8.3%. To achieve comparable approximation accuracy for the classical model, the rotor electrical circuit must be replaced with two equivalent circuits in the d-axis and four equivalent circuits in the q-axis, yielding relative errors of δ_m = 2.85% and δ_φ = 6.51% for the d-axis, and δ_m = 1.86% and δ_φ = 5.49% for the q-axis. Full article

Listeria monocytogenes (L. monocytogenes) is a Gram-positive intracellular pathogen capable of causing severe infections. The Listeria pathogenicity island 4 (LIPI-4) encodes a phosphotransferase system (PTS) with its EIIB component playing a critical role in carbohydrate phosphorylation and virulence. However, the precise function of EIIB in virulence regulation across diverse pathogenic strains remains unclear. Here, we generated an EIIB deletion mutant (LM873ΔEIIB) and its complemented strain (CLM873ΔEIIB) from the low-virulence strain LM873, and performed comparative analyses with the high-virulence strain LM928 and its corresponding mutants. Deletion of EIIB differentially modulated biofilm formation: suppressing it in LM928 while enhancing it in LM873, accompanied by corresponding transcriptional changes in biofilm-associated and virulence genes. Both mutants exhibited impaired hemolytic activity, whereas motility attenuation was specific to LM928ΔEIIB. At the cellular level, LM873ΔEIIB enhanced adhesion to and invasion of Caco-2 but impaired intracellular proliferation in JEG-3; In contrast, LM928ΔEIIB promoted Caco-2 invasion while attenuating JEG-3 adhesion, invasion, and intracellular replication, as well as reducing invasion and proliferation in RAW264.7 macrophage. Animal experiments demonstrated that EIIB deletion attenuated LM928 colonization in the liver and spleen, but had no significant impact on LM873. Collectively, our findings establish EIIB as a strain-dependent virulence regulator in L. monocytogenes, particularly modulating biofilm formation and host–pathogen interactions. Full article

Background/Objectives: Human ovarian granulosa cells (hGCs) are crucial to ovarian follicle development and function, exhibiting multipotency and the ability to differentiate into neuronal cells, chondrocytes, and osteoblasts in vitro. 3,4,5,4′-tetramethoxystilbene (DMU-212) is a methylated derivative of resveratrol, a natural polyphenol found in grapes and berries, with a wide spectrum of biological activities, including notable anticancer properties. Interestingly, DMU-212 exhibits cytotoxic effects predominantly on cancer cells while sparing non-cancerous ones, and evidence suggests that similar to resveratrol, it may also promote hGC differentiation. This study aimed to investigate the effects of the liposomal formulation of this methylated resveratrol analog—lipDMU-212—on the osteogenic differentiation ability of hGCs in a primary three-dimensional cell culture model. Methods: lipDMU-212 was formulated using the thin-film hydration method. GC spheroids’ viability was evaluated after exposure to lipDMU-212, an osteoinductive medium, or both. Osteogenic differentiation was confirmed using Alizarin Red staining and quantified by measuring Alkaline Phosphatase (ALP) activity on days 1, 7, and 15. RNA sequencing (RNA-seq) was performed to explore molecular mechanisms underlying lipDMU-212-induced differentiation. Results: lipDMU-212 promoted osteogenic differentiation of hGCs in the 3D cell culture model, as evidenced by increased mineralization and a ~4-fold increase in ALP activity compared with the control. RNA-seq revealed up-regulation of genes related to cell differentiation and cellular identity. Furthermore, JUN (+2.82, p = 0.003), LRP1 (+2.06, p = 0.05), AXIN1 (+3.02, p = 0.03), and FYN (+3.30, p = 0.01) were up-regulated, indicating modulation of the Wnt/β-catenin signaling pathway, a key regulator of osteoblast differentiation. Conclusions: The ability of GCs to differentiate into diverse tissue-specific cell types underscores their potential in regenerative medicine. This study contributes to the understanding of lipDMU-212’s role in osteogenic differentiation and highlights its potential in developing future therapies for degenerative bone diseases. Full article

Egg weight monitoring provides critical data for calculating the feed-to-egg ratio, and improving poultry farming efficiency. Installing a computer vision monitoring system in egg collection systems enables efficient and low-cost automated egg weight measurement. However, its accuracy is compromised by egg clustering during transportation and low-contrast edges, which limits the widespread adoption of such methods. To address this, we propose an egg measurement method based on a computer vision and multi-feature extraction and regression approach. The proposed pipeline integrates two artificial neural networks: Central differential-EfficientViT YOLO (CEV-YOLO) and Egg Weight Measurement Network (EWM-Net). CEV-YOLO is an enhanced version of YOLOv11, incorporating central differential convolution (CDC) and efficient Vision Transformer (EfficientViT), enabling accurate pixel-level egg segmentation in the presence of occlusions and low-contrast edges. EWM-Net is a custom-designed neural network that utilizes the segmented egg masks to perform advanced feature extraction and precise weight estimation. Experimental results show that CEV-YOLO outperforms other YOLO-based models in egg segmentation, with a precision of 98.9%, a recall of 97.5%, and an Average Precision (AP) at an Intersection over Union (IoU) threshold of 0.9 (AP90) of 89.8%. EWM-Net achieves a mean absolute error (MAE) of 0.88 g and an R² of 0.926 in egg weight measurement, outperforming six mainstream regression models. This study provides a practical and automated solution for precise egg weight measurement in practical production scenarios, which is expected to improve the accuracy and efficiency of feed-to-egg ratio measurement in laying hen farms. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Against the backdrop of global population growth and intensified resource competition, the sustainable development of the water-energy-food system (WEF) is facing challenges. Wetlands, as key ecological hubs, play a crucial role in regulating water cycles, energy metabolism, and food production, thus serving as a breakthrough point for resolving the bottleneck of resource synergy. Incorporating wetlands into the WEF framework helps us comprehensively understand and optimize the interrelationships among water, energy, and food. This paper proposes an indicator system based on WEFW to study the coupling of water-energy-food-wetland systems and analyzes the evolution of the comprehensive development index of WEFW and its coupling relationship in Jiangxi Province from 2001 to 2022. It uses the grey correlation model to explore the sustainable development capacity of wetland resources, water resources, energy resources, and food resources in Jiangxi Province, and employs a geographical detector model to quantify the contribution of wetlands to WEFW. The research results show that (1) the comprehensive evaluation of WEFW systems in various cities in Jiangxi Province has generally improved, but there is imbalance in regional development. Cities such as Nanchang and Jiujiang have performed well, while cities like Jingdezhen and Xinyu need to enhance resource integration and sustainable development. (2) The coupling coordination degree (CCD) has experienced a process of “stability-fluctuation-recovery”, with a significant increase after 2014, and the spatial differentiation characteristics are obvious. (3) Wetlands play a dominant role in the spatial differentiation of CCD, and their interaction with water, energy, and food resources significantly enhance the explanatory power of their impact on CCD. (4) The grey model indicates that the CCDs of WEFW systems in most cities of Jiangxi Province have a projected annual growth rate of 1.8% (2022–2032), reaching 0.71–0.73 in leading cities. These results emphasize the importance of wetland protection and sustainable resource management in promoting regional coordinated development. The research and prediction of the coupling coordination relationship of water-energy-food-wetland systems can provide a scientific basis for the sustainable development of Jiangxi Province and also offer important scientific references for other regions to achieve a balance between ecological protection and resource utilization. Full article

Effective station–city integration is crucial for sustainable development around high-speed rail stations. However, research assessing public preferences regarding the aspects of this integration remains limited. We constructed a performance evaluation model for station–city integration in high-speed rail station areas. By considering the high-speed rail station area in the Yangtze River Delta region as a research object, which is located in the metropolitan cities centered on Shanghai, China, we dissected the five dimensions of population, industry, land use, function, and environment into 15 indicators that flow into the three value objectives of attraction–retention–integration (NPI). Subsequently, we systematically analyzed the performance differentiation characteristics of station–city integration in the Yangtze River Delta region’s high-speed rail station areas by employing a multiple regression model to delve into the influence mechanisms affecting the performance differentiation patterns of station–city integration. Our findings indicate the following. (1) Regarding station–city integration performance grade differentiation, a few high-speed rail station areas in the Yangtze River Delta region exhibit a high-efficiency integration level, whereas more areas fall within the higher and general integration levels. (2) Spatially, the station–city integration performance in high-speed rail station areas within the Yangtze River Delta region exhibits a distinct distribution characterized by “high-grade point-block dependence and low-grade concentrated contiguous patches.” (3) The spatial distribution of the five dimensions of station–city integration performance exhibits significant disparities. (4) Regarding the development types of station–city integration performance advantages, efficient integration of stations and cities represents a multidimensional advantageous development type and higher integration falls into the same category. (5) Station–city integration performance results from the comprehensive effects of four factors: government policy inducement, station energy level attraction, station–city relationship adhesion, and urban energy level promotion. This study advances a systematic framework—encompassing performance measurement, mechanistic inquiry, and strategy formulation—for examining station–city integration in HSR station areas. By integrating the perspective of cyclical cumulative development into the node–place model from urban planning and geographical viewpoints, we articulate a new performance model that clarifies critical influencing factors and mechanisms, thus broadening the theoretical scope of HSR station area research. We believe that the NPI evaluation model can provide valuable insights for guiding the integrated development of high-speed rail station areas and enhancing the quality of urban development. Full article

Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear relationships, temporal dynamics, and firm heterogeneity. This study proposes a hybrid machine learning (ML) framework to explain and predict corporate rating transitions and defaults, addressing key limitations in existing literature. We benchmark four classification algorithms—LR, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM)—on a structured corporate credit dataset. Our approach integrates segment-specific modeling across rating bands, out-of-time validation to simulate real-world applicability, and SHapley Additive exPlanations (SHAP) values to ensure interpretability. The results demonstrate that ensemble methods, particularly XGBoost and RF, significantly outperform LR and SVM in predictive accuracy and early warning capability. Moreover, SHAP analysis reveals differentiated drivers of rating transitions across credit quality segments, highlighting the importance of tailored monitoring strategies. This research contributes to the literature by bridging predictive performance with interpretability in credit risk modeling and offers practical implications for regulators, rating agencies, and financial institutions seeking robust, transparent, and forward-looking credit assessment tools. Full article

Drought-induced forest mortality threatens biodiversity globally, particularly in arid, and semi-arid woodlands. The continual development of remote sensing approaches enables enhanced monitoring of forest health. Herein, we investigate the ability of a limited ground-truthed canopy dieback dataset and satellite image derived Normalised Difference Vegetation Index (NDVI) to make inferences about forest health as temporal and spatial extent from its collection increases. We used ground-truthed observations of relative canopy mortality from the Pinus edulis-Juniperus osteosperma woodlands of southeastern Utah, United States of America, collected after the 2017–2018 drought, and PlanetScope satellite imagery. Through assessing different modelling approaches, we found that NDVI is significantly associated with sitewide mean canopy dieback, with beta regression being the most optimal modelling framework due to the bounded nature of the variable relative canopy dieback. Model performance was further improved by incorporating the proportion of J. osteosperma as an interaction term, matching the reports of species-specific differential dieback. A time-series analysis revealed that NDVI retained its predictive power for our whole testing period; four years after the initial ground-truthing, thus enabling retrospective inference of defoliation and regreening. A spatial random forest model trained on our ground-truthed observations accurately predicted dieback across the broader landscape. These findings demonstrate that modest field campaigns combined with high-resolution satellite data can generate reliable, scalable insights into forest health, offering a cost-effective method for monitoring drought-impacted ecosystems under climate change. Full article

As one of the primary contributors to carbon emissions in China, the transportation sector plays a pivotal role in achieving green and low-carbon development. Considering the spatio-temporal dependency characteristics of transportation carbon emissions driven by economic interactions and population mobility among provinces, this study proposes a predictive framework for transportation carbon emissions based on a spatio-temporal attention mechanism from the perspective of multi-province spatio-temporal synergy. First, the study conducts transportation carbon emission accounting by considering both transportation fuel consumption and electricity usage, followed by feature selection using an enhanced STIRPAT model. Second, it integrates the spatio-temporal attention mechanism with graph convolutional neural networks to construct a multi-province transportation carbon emission collaborative prediction model. Comparative experiments highlight the superior performance of deep learning methods and spatio-temporal correlation modeling in multi-province transportation carbon emission collaborative prediction. Finally, three future development scenarios are designed to analyze the evolution paths of transportation carbon emissions. The results indicate that technological innovation can significantly improve the efficiency of transportation emission reduction. Moreover, given that the eastern region and the central and western regions are at distinct stages of development, it is essential to develop differentiated emission reduction strategies tailored to local conditions to facilitate a green and low-carbon transformation in the transportation sector. Full article

Background: Perianal Crohn’s disease (pCD) is one of the most disabling complications of inflammatory bowel disease, characterized by fistulas and abscesses that demand precise imaging for diagnosis, treatment planning, and follow-up. Magnetic resonance imaging (MRI) is considered the reference standard, but endoanal ultrasound (EAUS) with high-frequency 360° probes provide a readily available, cost-effective, and repeatable alternative. Methods: We performed a narrative review of the literature, evaluating studies on the EAUS technique, diagnostic applications, distinguishing features of Crohn’s-related fistulas, and comparative analyses with MRI. Consensus documents and structured reporting initiatives were also included. Results: EAUS provides high-resolution visualization of the anal sphincter complex and intersphincteric space, enabling the reliable detection of fistulas and abscesses. Characteristic features such as tract width > 4 mm, bifurcation, hyperechoic debris, the Crohn’s Ultrasound Fistula Sign (CUFS), and the rosary sign assist in differentiating Crohn’s from cryptoglandular fistulas. EAUS is well-suited for serial monitoring, perioperative seton guidance, and therapeutic decision-making. Emerging tools such as Doppler and shear wave elastography provide additional information on activity and fibrosis. MRI remains indispensable for supralevator disease, deep pelvic sepsis, and standardized activity indices. Comparative studies indicate similar sensitivity for simple fistulas, with MRI superior in complex cases; combining both modalities maximizes accuracy. Conclusions: EAUS is a practical and repeatable imaging tool that complements MRI in the multidisciplinary management of perianal Crohn’s disease. Advances such as 3D imaging, contrast enhancement, and elastography may enable validated activity scoring—for example, a future PEACE (Perianal Endosonographic Activity in Chron’s Evaluation) Index—further strengthening its role in longitudinal care. Full article

Plantsterol esters (PSEs) exert beneficial effects on animal product quality, indicating their potential as a nutritional intervention strategy. This study investigated the effects of dietary PSE supplementation on lactation performance and lipid metabolism in Tarim Bactrian camels (Camelus bactrianus) to establish a scientific basis for its application in their husbandry. Thirty-two mid-lactation female camels were randomly allocated to four groups (n = 8): CON (basal diet), L (200 mg/kg PSE), M (400 mg/kg PSE), and H (800 mg/kg PSE). Since lactation performance is closely linked to metabolic status, biochemical and lipidomic analyses were conducted on serum and milk samples. Analysis revealed that the H group showed significantly increased milk yield, lactose yield, and milk fat yield compared to other groups. Serum cholesterol levels decreased progressively with higher PSE supplementation, while serum urea levels rose dose-dependently. Blood Glu was lower in the L group but higher in the H group relative to CON. Lipidomic profiling identified 644 and 257 differential metabolites in milk and serum, respectively. Milk metabolites were enriched in the EGFR inhibitor resistance, MAPK, and ErbB signaling pathways; serum metabolites were linked to glycerophospholipid, arachidonic acid, and linoleic acid metabolism. These findings indicate that PSE-modulated metabolites in serum and milk significantly influence lactation performance and glucolipid metabolism in Tarim Bactrian camels, supporting further investigation into precision nutrition strategies. Full article

During the suspension flight of high-altitude scientific balloons in near-space, they are highly vulnerable to time-varying wind field disturbances, which tend to excite multiple distinctive torsional modes of the balloons themselves, thereby interfering with the observations of balloon-borne equipment. Focusing on the azimuth control of the balloon-borne gondola, this paper designs a simplified decoupling mechanism and a reaction wheel as actuators. Specifically, the reaction wheel achieves azimuth tracking through angular momentum exchange, while the simplified decoupling mechanism performs the functions of decoupling and unloading. To fully utilize the control performance of the actuating structure, this paper further proposes a control algorithm based on a nonlinear differential tracker and neural network PID. Simulation results demonstrate that under typical wind disturbances and sensor noise conditions, the proposed system exhibits excellent smoothness and high-precision and stable control performance. This research provides a significant basis for stable observation platforms in precise near-space observation missions. Full article

Pine wilt disease (PWD), transmitted by Monochamus alternatus (JPS), poses a severe threat to global pine forests. Although the entomopathogenic fungi Beauveria bassiana (Bb) and Metarhizium anisopliae (Ma) represent environmentally friendly biocontrol alternatives, their practical application is limited by inconsistent field performance and an incomplete understanding of host–pathogen interactions. We employed dual RNA-seq at the critical 48 h infection time point to systematically compare the transcriptional responses between JPS and Bb/Ma during infection. Key findings revealed distinct infection strategies: Bb preferentially induced autophagy pathways and modulated host carbohydrate metabolism to facilitate nutrient acquisition, triggering corresponding tissue degradation responses in JPS. In contrast, Ma primarily co-opted host amino acid and sugar metabolic pathways for biosynthetic processes, eliciting a stronger immune defense activation in JPS. Notably, the mTOR signaling pathway was identified as a key regulator of the differential host responses to various entomopathogenic fungi. Further functional validation-specifically, the application of a chemical inhibitor and RNAi targeting mTOR in JPS-confirmed that mTOR inhibition selectively enhanced Bb-induced mortality in JPS without affecting Ma virulence. Our findings reveal the molecular determinants of host–pathogen specificity in PWD biological control and indicate that mTOR regulation could serve as an effective strategy to improve fungal pesticide performance. Full article