Search Results (644)

(1) Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by liver damage similar to alcoholic fatty liver disease, including triglyceride infiltration of hepatocytes, regardless of alcohol consumption. It leads to progressive liver damage, such as loss of liver function, cirrhosis, and liver cancer, and the response rate of drugs under clinical research is less than 50%. There is an urgent need for biomarkers to evaluate the efficacy of these drugs. (2) Methods: MASLD was induced in mice using a High-Fat diet (HF), Western diet (WD), and Methionine/Choline-Deficient diet (MCD) for 20 weeks (4 weeks for MCD). Liver tissue biopsies were performed, and the treatment effects of saponin and non-saponin feeds were evaluated. Fat accumulation and hepatic inflammation were measured, and mRNA sequencing analysis was conducted. The therapeutic effects were validated using patient-derived liver organoids. (3) Results: The NAFLD Activity Score (NAS) significantly increased in all MASLD models compared with controls. Saponin treatment decreased NAS in the HF and WD groups but not in the MCD group. RNA sequencing and PCA analysis showed that the HF saponin response samples were similar to normal controls. DAVID analysis revealed significant changes in lipid, triglyceride, and fatty acid metabolic processes. qRT-PCR confirmed decreased fibrosis markers in the HF saponin response group, and GSEA analysis showed reduced HAMP1 gene expression. (4) Conclusions: Among the diets, red ginseng was most effective in the HF diet, with significant effects in the saponin-treated group. The therapeutic efficacy was better when HAMP1 expression was increased. Therefore, we propose HAMP1 as a potential exploratory biomarker to assess the saponin response in a preclinical setting. In addition, the reduction of inflammation and hepatic iron accumulation suggests that saponins may exert antioxidant effects through modulation of oxidative stress. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

There has been extensive research on the KSHV/HHV8 virus, which has led to a better understanding of viral transmission, pathogenesis, viral-driven lymphoid proliferation, neoplastic transformation, and how we might combat these processes clinically. On an extensive review of the literature, only two true KSHV/HHV8-positive lymphoid neoplasms are described: primary effusion lymphoma (PEL), which can also present as solid or extracavitary primary effusion lymphoma (EC-PEL) and diffuse large B-cell lymphoma (DLBCL). Two lymphoproliferative disorders have also been described, and while they are not true monotypic neoplasms, these lesions can transform into neoplasms: KSHV/HHV8-positive germinotropic lymphoproliferative disorder (GLPD) and multicentric Castleman disease (MCD). This review provides a somewhat concise overview of information related to KSHV/HHV8-positive lymphoid neoplasms and pertinent associated lymphoproliferative lesions. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

In recent years, deep learning-based end-to-end Text-To-Speech (TTS) models have made significant progress in enhancing speech naturalness and fluency. However, existing Variational Inference Text-to-Speech (VITS) models still face challenges such as insufficient pitch modeling, inadequate contextual dependency capture, and low inference efficiency in the decoder. To address these issues, this paper proposes an improved TTS framework named Q-VITS. Q-VITS incorporates Rotary Position Embedding (RoPE) into the text encoder to enhance long-sequence modeling, adopts a frame-level prior modeling strategy to optimize one-to-many mappings, and designs a style extractor based on a diffusion model for controllable style rendering. Additionally, the proposed decoder ConfoGAN integrates explicit F0 modeling, Pseudo-Quadrature Mirror Filter (PQMF) multi-band synthesis and Conformer structure. The experimental results demonstrate that Q-VITS outperforms the VITS in terms of speech quality, pitch accuracy, and inference efficiency in both subjective Mean Opinion Score (MOS) and objective Mel-Cepstral Distortion (MCD) and Root Mean Square Error (RMSE) evaluations on a single-speaker dataset, achieving performance close to ground-truth audio. These improvements provide an effective solution for efficient and controllable speech synthesis. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Background and Aims: An appropriate surveillance system must be established to efficiently identify cases of intraductal papillary mucinous neoplasm (IPMN)-related malignant transformation. We analyzed the initial clinical background that affects long-term prognosis and narrowed the population for whom continued evaluation is inevitable. Methods: We included 1645 patients with IPMN treated at our hospital since 2010. We examined the types and timing of malignant transformation in terms of the worrisome features (WFs). The chi-squared test, log-rank test, and Cox proportional hazards model were used for the analysis (statistical significance at α = 0.05). Results: In total, 123 (7.5%) and 41 patients (2.5%) had IPMN-derived carcinoma (IPMN-DC) and concomitant pancreatic ductal adenocarcinoma (c-PDAC), respectively. Compared with IPMN-DC, a significantly higher proportion of c-PDAC patients were diagnosed with an advanced disease stage that developed earlier. The factors with significantly shorter time for IPMN-DC development were maximum cyst diameter (MCD) ≥ 30 mm, nonbranched type, main pancreatic duct (MPD) diameter ≥ 5 mm, and septal nodal structure (SNS) for IPMN-DC, and MCD ≥ 30 mm, main duct type, MPD ≥ 5 mm, SNS, cyst enlargement (≥2.5 mm/year), and abnormal CA19-9 levels for c-PDAC. Both groups could be significantly stratified by the number of WFs. A relative risk analysis revealed that SNS, MCD ≥ 30 mm, and MPD ≥ 5 mm were significant factors for IPMN-DC, whereas abnormal CA19-9 and SNS were significant for c-PDAC. Conversely, significantly more patients exhibiting these factors initially later developed IPMN-DC or c-PDAC. Conclusions: Ten percent of IPMN cases will develop IPMN-DC or c-PDAC, thereby requiring careful follow-up, especially in cases with SNS, abnormal CA19-9, and MCD ≥ 30 mm. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

Pears are susceptible to internal defects during growth and post-harvest handling, compromising their quality and market value. Traditional detection methods, such as manual inspection and physicochemical analysis, face limitations in efficiency, objectivity, and non-destructiveness. To address these challenges, this study investigates a non-destructive approach integrating X-ray imaging and multi-criteria decision (MCD) theory for non-destructive internal defect detection in pears. Internal defects were identified by analyzing grayscale variations in X-ray images. The proposed method combines manual feature-based classifiers, including Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), with a deep convolutional neural network (DCNN) model within an MCD-based fusion framework. Experimental results demonstrated that the fused model achieved a detection accuracy of 97.1%, significantly outperforming individual classifiers. This approach effectively reduced misclassification caused by structural similarities in X-ray images. The study confirms the efficacy of X-ray imaging coupled with multi-classifier fusion for accurate and non-destructive internal quality evaluation of pears, offering practical value for fruit grading and post-harvest management in the pear industry. Full article

In this study, a novel silicon carbide (SiC) double-trench MOSFET (DT-MOS) combined Schottky barrier diode (SBD) and MOS-channel diode (MCD) is proposed and investigated using TCAD simulations. The integrated MCD helps inactivate the parasitic body diode when the device is utilized as a freewheeling diode, eliminating bipolar degradation. The adjustment of SBD position provides an alternative path for reverse conduction and mitigates the electric field distribution near the bottom source trench region. As a result of the Schottky contact adjustment, the reverse conduction characteristics are less influenced by the source oxide thickness, and the breakdown voltage (BV) is largely improved from 800 V to 1069 V. The gate-to-drain capacitance is much lower due to the removal of the bottom oxide, bringing an improvement to the turn-on switching rise time from 2.58 ns to 0.68 ns. These optimized performances indicate the proposed structure with both SBD and MCD has advantages in switching and breakdown characteristics. Full article

Estimating Remaining Useful Life (RUL) is crucial in modern Prognostic and Health Management (PHM) systems providing valuable information for planning the maintenance strategy of critical components in complex systems such as aircraft engines. Deep Learning (DL) models have shown great performance in the accurate prediction of RUL, building hierarchical representations by the stacking of multiple explicit neural layers. In the current research paper, we follow a different approach presenting a Deep Equilibrium Model (DEM) that effectively captures the spatial and temporal information of the sequential sensor. The DEM, which incorporates convolutional layers and a novel dual-input interconnection mechanism to capture sensor information effectively, estimates the degradation representation implicitly as the equilibrium solution of an equation, rather than explicitly computing it through multiple layer passes. The convergence representation of the DEM is estimated by a fixed-point equation solver while the computation of the gradients in the backward pass is made using the Implicit Function Theorem (IFT). The Monte Carlo Dropout (MCD) technique under calibration is the final key component of the framework that enhances regularization and performance providing a confidence interval for each prediction, contributing to a more robust and reliable outcome. Simulation experiments on the widely used NASA Turbofan Jet Engine Data Set show consistent improvements, with the proposed framework offering a competitive alternative for RUL prediction under diverse conditions. Full article

Silver nanoclusters that are confined inside zeolites can give off intensive tunable emission across the visible region under UV excitation. In this research, a series of silver nanoclusters loaded with R-FAU/Ag (R = Li, Na, K) zeolites were synthesized and then applied as phosphors for LEDs. The XRD and SEM measurements showed the R-FAU/Ag (R = Li, Na, K) zeolites have high crystallinity and a size distribution of 0.7–1.25 μm. Under excitations of 310–330 nm ultraviolet radiation, Li-FAU/Ag, Na-FAU/Ag, and K-FAU/Ag exhibit monotonically declining emission intensities and red-shifted emissions with peak wavelengths of 520, 527, and 535 nm, respectively. By using silicone-based epoxy resin as the packaging material, a series of LEDs were fabricated by mixing R-FAU/Ag (R = Li, Na, K) phosphors. It is indicated that the Li-FAU/Ag-LED shows the strongest intensity of 94.9 mcd, much higher than that of the LEDs made from Na-FAU/Ag (63.7 mcd) and K-FAU/Ag (74.2 mcd) phosphors. Additionally, the chromaticity coordinate of the Li-FAU/Ag-LED is located at (0.2651, 0.4073) and has a high color temperature of 7873 K. Thermal test data showed that upon heating to 440 K, the intensities of R-FAU/Ag (R = Li, Na, K) LEDs decreased to 81%, 79%, and 75% of their initial intensities measured at 280 K, respectively. This research proposes a method for regulating the luminescent properties of silver nanoclusters in FAU zeolite by modifying the extra-framework cations and demonstrates excellent performance in LED products. Full article

Approximately 40% of patients with diffuse large B-cell lymphoma (DLBCL) are not cured with first-line chemoimmunotherapy, resulting in poor prognosis. Schmitz et al. classified DLBCL into four prognostic genetic groups using whole-exome sequencing. We applied a simplified approach using a targeted next-generation sequencing assay (Archer FusionPlex Lymphoma Assay) to analyze samples from 105 patients—53 with a progression-free survival (PFS) < 2 years (the “Relapse group”) and 52 with a PFS > 5 years (the “Remission group”) following first-line systemic treatment. Patients were classified according to Schmitz et al. into the following categories: “MCD” (MYD88^L265P and CD79B alteration), “N1” (NOTCH1 alteration), “BN2” (NOTCH2 alteration and BCL6 translocation), and “EZB” (EZH2 alteration and BCL2 translocation). The predictive value of this simplified genetic classification and of relevant clinical features were evaluated. The “Relapse group” included more patients classified as MCD and N1, while fewer were classified as EZB and BN2. Also, cell-of-origin (COO) characteristics and the size of N1 aligned with the classification of Schmitz et al. However, the limited sample size precludes definitive conclusions about the predictive value of our simplified approach. Additionally, male sex, B symptoms, and bone marrow involvement were associated with relapse. Therefore, these clinical features may be useful in predicting outcomes until an effective molecular classification is widely adopted. Full article

Nervousness is a complex emotional state characterized by high arousal and ambiguous valence, often triggered in high-stress environments. This study presents a mathematical and computational framework for defining and classifying nervousness using facial expression data projected onto a valence–arousal (V–A) space. A statistical approach employing the Minimum Covariance Determinant (MCD) estimator is used to construct 90% and 99% confidence ellipses for nervous and non-nervous states, respectively, using Mahalanobis distance. These ellipses form the basis for binary labeling of the AffectNet dataset. We apply a deep learning model trained via knowledge distillation, with EmoNet as the teacher and MobileNetV2 as the student, to efficiently classify nervousness. The experimental results on the AffectNet dataset show that our proposed method achieves a classification accuracy of 81.08%, improving over the baseline by approximately 6%. These results are obtained by refining the valence–arousal distributions and applying knowledge distillation from EmoNet to MobileNetV2. We use accuracy and F1-score as evaluation metrics to validate the performance. Furthermore, we perform a qualitative analysis using action unit (AU) activation graphs to provide deeper insight into nervous facial expressions. The proposed method demonstrates how mathematical tools and deep learning can be integrated for robust affective state modeling. Full article

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications. Full article