Search Results (671)

Wildfires are a major natural hazard causing extensive ecological damage and endangering human survival. Previous studies on wildfires in China have mostly focused on specific regions or individual drivers, with limited systematic assessments at the long-term and national scales. The spatiotemporal patterns of wildfires and their multiple driving mechanisms under China’s diverse climatic regimes remain insufficiently understood. To bridge this gap, we combined MCD64A1 burned area data (2001–2023) with multi-source natural (meteorological, vegetation, and topographic) and anthropogenic factors, using random forest models at both the national and regional scales to examine the spatiotemporal patterns, dominant drivers, and response mechanisms of wildfires in China. The results revealed that: (1) Spatially, wildfires were concentrated in northeastern and southern China, which accounted for 86.20% of the total burned area. Temporally, northern wildfires were primarily a spring-dominated fire regime, with peak activity in March and April, whereas southern wildfires were winter-dominated, peaking in February. (2) At the national scale, elevation was the key topographic factor influencing wildfire occurrence (relative importance = 0.49), with low-elevation and gentle-slope areas being more fire-prone. At the regional scale, the driving factors exhibit spatial differentiation, forming a spatial pattern of topography-dominated and climate-dominated. (3) Partial dependence plot analysis revealed nonlinear and threshold responses. Fire probability increases rapidly when the soil moisture is below 20 mm, while extremely high land surface temperatures in arid regions suppress fire occurrence due to fuel limitations. This study enhances the understanding of spatially heterogeneous wildfire drivers in China and provides a scientific basis for region-specific wildfire prevention and management strategies. Full article

Chronic kidney disease (CKD) is a major global health burden leading to a loss of kidney function via podocyte damage, a non-regenerative renal cell type. Early detection of podocyte injury is crucial but remains limited, highlighting the need for non-invasive biomarkers. Therefore, we analysed urinary exosomal microRNAs (miRNAs) in relation to podocyte morphology in biopsies from 65 CKD patients, including focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD) and healthy controls. Global profiling distinguished CKD patients from controls, with miR-606 consistently upregulated and miR-431 downregulated. In podocytopathies, MCD displayed a predominantly suppressed miRNA profile, with miR-141, miR-429, and miR-660 as key candidates, whereas FSGS exhibited elevated miR-181c, miR-3610, miR-663b, miR-4651, and miR-429. Super-resolution morphometry revealed diffuse foot process effacement in MCD and heterogeneous, focally disrupted architecture in FSGS, providing a structural context for the molecular findings. Regression analyses linked these miRNAs to filtration slit density and length, proteinuria, and 25-Hydroxy-vitamin-D3 levels, integrating molecular, structural, and clinical readouts. These results define a coherent miRNA signature of podocyte injury that distinguishes CKD entities and correlates molecular changes with disease severity. Combining urinary exosomal miRNAs with morphometric analysis facilitates early, non-invasive identification of podocyte damage, enabling earlier therapeutic intervention in podocytopathies. Full article

This paper verifies a method for determining the luminance of a pavement surface made of SMA mixtures at the design stage under laboratory conditions. Tests were conducted on surfaces made of six types of SMA mixtures with varying grain sizes (between 8 and 11 mm) and coarse aggregate types like trachybasalt with a luminance coefficient in diffused light of Q_d—53 mcd/m²/lx, gabbro with Q_d—83 mcd/m²/lx, and granite with Q_d—115 mcd/m²/lx. The effect of the glassblasting process on the changes in the luminance coefficient in diffused light (Q_d) was analyzed while simultaneously monitoring parameters describing skid resistance and macrotexture. Additionally, it was decided that tests would be performed on two sets of specimens differing in their conditioning temperatures. It was found that conditioning at −15 °C significantly improved the binder film removal process from asphalt mixture surfaces compared to those conditioned at 22 °C. Differences were recorded between individual specimens conditioned at −15 °C at the end of the glassblasting. The lowest Q_d values were found for specimens with the darkest trachybasalt aggregate (SMA 8—53.0; SMA 11—51.7 mcd/m²/lx) and the highest for specimens with the lightest granite aggregate (SMA 8—63.9; SMA 11—59.8 mcd/m²/lx). However, considering the differences in Q_d between individual coarse aggregates, the differences between specimens with these aggregates are insignificant. Glassblasting is a cheap and quick procedure for removing a binder from the surface of specimens, preparing them for luminance determination in the laboratory. It should be noted that glassblasted surfaces should not be used to determine the skid resistance and macrotexture changes at the design stage of an asphalt mixture. Full article

Spring vegetation phenology is highly sensitive to climate change; however, climate drivers and their threshold responses at the urban scale remain insufficiently and systematically quantified. Focusing on 31 provincial capitals and municipalities in mainland China, this study integrated MODIS MCD12Q2-derived start-of-season (SOS) for spring green-up and TerraClimate climate data (2001–2023) at a 500 m grid resolution. SOS trends were characterized using the Mann–Kendall test and the Theil–Sen slope estimator. Building on these trend metrics, we developed an XGBoost–SHAP framework using the interannual rate of temperature change (tem_slope) and the interannual rate of precipitation change (pre_slope) as input features, to quantify the nonlinear contributions of climate-change rates to SOS trends and to identify key thresholds. Results indicate that the multi-year mean SOS across China’s provincial capitals and municipalities is primarily distributed between approximately DOY 74 and 138, exhibiting a clear spatial pattern of earlier green-up in the south, later green-up in the north, and delayed green-up on plateaus, with pronounced shifts in distribution centers and dispersion among climatic zones and cities. At the city level, the mean SOS trend shows an overall advancing rate of 0.81 d·year⁻¹ (i.e., the average of city-mean Sen slopes across the 31 cities). Pixel-level trend analyses show that advancing and delaying trends commonly coexist within most cities; among pixels with significant or marginally significant SOS trends identified by the Mann–Kendall test (MK p < 0.10) across all cities, advancing and delaying SOS pixels account for 75.02% and 24.98%, respectively. At the city scale, the proportions of advancing versus delaying pixels vary markedly among cities, forming directional structures characterized by advance-dominant, delay-dominant, or bidirectional coexistence patterns. SHAP dependence relationships further reveal that the effects of tem_slope and pre_slope on SOS trends are generally nonlinear and piecewise, with substantial heterogeneity across climate zones and cities. The identified tipping points and associated sensitive ranges collectively delineate spatially differentiated climate-sensitive intervals, which define the nonlinear response boundaries of spring SOS to sustained warming and precipitation changes. This study provides quantitative evidence for regional differences in urban spring phenological responses to climate change across major Chinese cities and offers a methodological reference for identifying actionable climate thresholds in urban greening design and climate-adaptive management. Full article

Background/Objectives: Differentiating minimal change disease (MCD) from focal segmental glomerulosclerosis (FSGS) remains a diagnostic challenge. We hypothesised that differences in glomerular protein selectivity could translate into distinct serum protein electrophoresis (SPEP) profiles, particularly in severe nephrotic syndrome. Methods: We retrospectively analysed SPEP profiles of adults with biopsy-proven MCD (n = 27), primary FSGS (n = 27), and secondary FSGS (n = 20). Diagnoses were established according to KDIGO guidelines and the Mayo Clinic classification. A severe subgroup was defined by a relative albumin fraction <40% to evaluate patterns in marked hypoalbuminaemia. Results: Secondary FSGS demonstrated significantly higher albumin-to-globulin (A/G) ratios compared with immune-mediated podocytopathies (MCD and primary FSGS), yielding excellent discrimination (AUC > 0.98). In contrast, discriminatory performance between MCD and primary FSGS in the overall cohort was limited (AUC = 0.657). However, within the severe subgroup, the A/G ratio provided clinically meaningful separation (AUC = 0.787). An A/G ratio > 0.49 identified primary FSGS with 86.7% sensitivity and 81.2% specificity. Correlation analysis revealed a strong inverse association between albumin and α2-globulin fractions in immune-mediated podocytopathies (ρ < −0.8), whereas this relationship was attenuated in secondary FSGS (ρ = −0.57). Conclusions: The A/G ratio may represent a practical adjunctive biomarker in the evaluation of podocytopathies. Values > 1.0 strongly favour secondary FSGS, while markedly reduced ratios in severe nephrosis are characteristic of MCD. These findings suggest that differences in glomerular selectivity and the hepatic compensatory response are reflected in routine electrophoretic profiles. Full article

Control charts are widely used in the industrial world to monitor the average and variability of production processes. Max-Half-Mchart is a multivariate control chart that is not particularly effective in handling many outliers. This research aims to develop a control chart that is more resistant to outliers by using Minimum Regularized Covariance Determinant (MRCD). MRCD is a development of the MCD method, which is better at dealing with ‘fat data,” namely, situations in which the number of variables is greater than the number of observations. The performance of a robust Max-Half-Mchart control chart based on MRCD was evaluated using the average run length (ARL) against shifts in the process mean, process variance, and simultaneous shifts. A comparison was also made of the outlier detection accuracy between the robust Max-Half-Mchart based on MRCD and the standard Max-Half-Mchart. Simulation results demonstrated that the MRCD-based robust chart is most sensitive to simultaneous shifts in the mean and variance, significantly outperforming the conventional method in “de-masking” process deviations. The robust framework maintains higher accuracy and AUC levels even at extreme contamination stages of 30% to 40% outliers, where traditional charts typically fail. A practical application to cement quality data further substantiated these findings, as the robust chart successfully identified 14 out-of-control signals (comprising the mean, variability, and simultaneous shifts), whereas the conventional chart detected none. These results indicate that the MRCD-based Max-Half-Mchart offers a more reliable and responsive quality monitoring system for complex industrial datasets. Full article

Understanding land-use and land-cover (LULC) dynamics within protected areas (PAs) is fundamental for assessing conservation effectiveness and ecosystem resilience under increasing anthropogenic and climatic pressures. This study examines the spatio-temporal evolution of LULC across Bavaria’s protected areas between 2000 and 2023 by integrating categorical land-cover data, satellite-derived vegetation indices, and environmental drivers. Annual LULC changes were first quantified using MODIS MCD12Q1 land-cover classifications to evaluate class persistence, transitions, and area trajectories and were subsequently interpreted alongside 16-day MODIS NDVI and SAVI composites to assess associated vegetation greening and browning trends. Ecological stability was characterized by using class-level persistence indicators, coefficients of variation (CVs), and linear trend slopes. The results reveal a marked greening signal after 2010, coinciding with pronounced land-cover transitions, including a decline in evergreen needleleaf forests (−480.6 km²; −32.2%) and substantial expansion of deciduous broadleaf forests (+390.8 km²; +106.1%) and grasslands (+275.8 km²; +28.4%), while wetlands experienced a severe contraction (−203.4 km²; −73.7%), indicating heightened hydrological sensitivity within protected ecosystems. Correlation analysis further indicates that anthropogenic pressure, quantified using the human footprint index, remains a dominant driver of change in croplands and urban areas, even within legally protected boundaries. Overall, this study demonstrates that vegetation trends, land-cover transitions, climatic exposure, and human pressure jointly shape ecological stability in protected areas, highlighting the value of an integrated indicator-based framework. Full article

Kaposi’s Sarcoma-associated herpesvirus (KSHV) has been etiologically linked to several human cancers, including Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD). However, recent studies suggest that KSHV infection may also be associated with the development of other diseases or increased risks, such as KSHV inflammatory cytokine syndrome (KICS), diabetes, malaria, heart disease, and other cancers. In this review, we summarize these findings from clinical observations, epidemiological studies or laboratory research, though more studies are needed in these emerging areas. We believe that this work will enhance our understanding of the molecular mechanisms underlying KSHV pathogenesis and contribute to improving treatments for related human diseases. Full article

Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) has enabled substantial automation, challenges remain in generalizability, particularly for MI detection from non-contrast cine CMR. Objective: This study proposes a comprehensive DL-based framework for automatic segmentation of cardiac structures and myocardial infarction using contrast-free cine CMR. Methods: The framework integrates multiple convolutional neural network (CNN) architectures for cardiac structure segmentation with an attention-based deep learning model for MI localization. Post-processing refinement using stacked autoencoders and active contour modeling is applied to improve anatomical consistency. Segmentation performance is evaluated using overlap-based and boundary-based metrics, including the Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD). Results: The best-performing model achieved Dice scores of 0.93 ± 0.05 for the left ventricular (LV) cavity, 0.89 ± 0.04 for the LV myocardium, and 0.91 ± 0.06 for the right ventricular (RV) cavity, with consistently low boundary errors across all structures. Myocardial infarction segmentation achieved a Dice score of 0.80 ± 0.02 with high recall, demonstrating reliable infarct localization without the use of contrast agents. Conclusions: By enabling accurate cardiac structure and myocardial infarction segmentation from contrast-free cine CMR, the proposed framework supports broader clinical applicability, particularly for patients with contraindications to gadolinium-based contrast agents and in emergency or resource-limited settings. This approach facilitates scalable, contrast-independent cardiac assessment. Full article

Objective: This study examined the pharmacological mechanisms of the therapeutic benefits of SWT to MASLD via regulating the gut–liver axis. Methods: The components of SWT were analyzed by liquid chromatograph mass spectrometer (LC-MS). After establishing an MCD-induced MASLD mice model, we invested the protective mechanism of SWT through 16S rRNA sequencing combined with molecular biological experiments. After eliminating the intestinal microbiota through an antibiotic cocktail experiment, we identified the key microbiota by which SWT improves MASLD. Results: SWT markedly reduced MASLD injury by alleviating intestinal inflammation and restoring the intestinal mucosal barrier, which could be reversed following alcohol exposure. Additionally, SWT altered the intestinal flora of MASLD mice, significantly raising the relative abundance of Parabacteroides goldsteinii-like taxa, while alcohol caused the destruction of P. goldsteinii-like-taxa-centered probiotic habitats and a proliferation of pathogenic bacteria, especially Bacteroides intestinalis-like taxa. After the elimination of intestinal flora, the anti-MASLD effect of SWT was lost. Moreover, the supplement of P. goldsteinii could significantly ameliorate liver damage caused by an MCD diet, functioning similarly to SWT. However, the liver-protective effect of SWT was suppressed following the administration of B. intestinalis. Conclusions: SWT ameliorates MCD diet-induced MASLD via modulating intestinal microbiota homeostasis and restoring intestinal mucosal barriers. Given that P. goldsteinii is effective for treating MASLD, it provides insights into new therapeutic strategies. Full article

This study develops a hybrid deep learning framework for point and interval prediction of Total Nitrogen (TN) concentrations in the Pearl River Delta, China. To address the inherent stochasticity of water quality systems, Bidirectional Long Short-Term Memory (BiLSTM) networks are integrated with residual learning blocks (Res) and Bayesian Optimization (BO). The resulting BiLSTM-Res-BO framework is evaluated within a comparative analysis of eight forecasting models that combine BiLSTM and BiGRU architectures with two uncertainty quantification approaches: Quantile Regression (QR) and Monte Carlo Dropout (MCD). Results from 37 monitoring stations demonstrate that the effectiveness of residual learning is highly context-dependent. For point forecasting, BiLSTM-Res achieves substantial performance gains (12.5–15% RMSE reduction) at complexity-sensitive sites, while providing negligible or slightly degraded performance under hydrologically stable conditions. For interval forecasting, QR-based residual models—particularly Q-BiLSTM-Res—produce notably narrower prediction intervals, with interval width reductions of 16.7–27.3% relative to the baseline BiLSTM model, under comparable levels of empirical coverage. In contrast, MC-dropout-based methods tend to yield wider intervals with different coverage–width trade-offs, reflecting distinct uncertainty propagation behaviors across modeling frameworks. Full article

Deep learning models have become central to automated Printed Circuit Board (PCB) defect detection. However, recent work has raised concerns about how reliably these models express confidence in their predictions, particularly when deployed in safety-critical inspection systems. This study conducts an empirical investigation of epistemic uncertainty across representative architectures used in PCB inspection: the two-stage Faster R-CNN detector, the one-stage YOLOv8 detector, and their corresponding classification counterparts, ResNet-50 and YOLOv8-Cls. Monte Carlo Dropout (MCD) was applied during inference to compute predictive entropy, mutual information, softmax variance, and bounding-box variability across multiple stochastic forward passes on both multiclass and binary inspection datasets. On the multiclass SolDef_AI dataset, Faster R-CNN achieved substantially stronger detection performance (mAP = 0.7607, F1 = 0.9304) and lower predictive entropy, with more stable localisation. In contrast, YOLOv8 produced markedly weaker performance (mAP = 0.2369, F1 = 0.3130) alongside higher entropy and greater bounding-box variability. On the binary Jiafuwen datasets, the YOLOv8-Cls model achieved higher overall performance (F1 = 0.6493) compared with the ResNet-50 classifier (F1 = 0.4904), reflecting its strength in simpler binary inspection tasks. Across uncertainty metrics, predictive entropy and mutual information were more sensitive to dataset size, showing higher and more variable values in the smaller multiclass dataset, whereas softmax variance and bounding-box variability appeared more architecture-dependent. These findings demonstrate that architectural choice, dataset structure, and task formulation jointly influence both performance and uncertainty behaviour. By integrating conventional metrics with uncertainty estimates, this study provides a transparent benchmark for assessing model confidence in automated optical inspection of PCBs. Full article

The Gilé National Park (PNAG for its acronym in Portuguese), located in central Mozambique is one of the most important protected areas in the country. It is one of the last remnants of intact Miombo woodlands, providing critical habitat for endemic biodiversity. Fires are an important ecological factor in Miombo, but changes in fire regimes may compromise the stability of this ecosystem and thus, the conservation value of PNAG. This study assessed fire patterns and mapped fire risk in support of adaptive management in the PNAG. We investigated Miombo fire regime over 23 years (2001 to 2023) in terms of return interval, frequency, temporal distribution, spatial density and intensity, extent, and severity, by using two Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite products (MCD14ML active fire; MCD64A1 burned area). Primary risk drivers were established and spatial fire likelihood mapped, using the Random Forest algorithm. Analysis revealed pronounced late dry season burning (August–October) affecting approximately 60% of the PNAG annually, especially in central-northern and eastern landscapes. Remarkably, 88% of the park maintains a 1-to-2-year fire return interval across the entire fire season (May–October) while only 7% maintains return frequencies of 3-to-4-year cycles. The latter is important for maintaining Miombo ecosystem functionality. Medium to medium–high fire severity covered 98% of the total fire extension. Climate-related drivers and hunting activities were identified as key fire initiators, especially in central areas of the park. The findings demonstrate an urgent need for spatially differentiated fire management action through prescribed burning to maintain PNAG’s ecological resilience and conservation value. Full article

Proton therapy offers superior dose localization, yet the biological effects of low-energy protons relevant to superficial tissues remain underexplored. We report the design and validation of a proton irradiation setup developed at the Tandem Accelerator of NCSR “Demokritos” for controlled radiobiological experiments. Monte Carlo simulations using Geant4 and Monte Carlo Damage Simulation (MCDS—Monte Carlo Damage Simulation) were used to determine proton energy spectra, linear energy transfer (LET), and predicted DNA damage yields. A single layer (15–20 μm in thickness) of human keratinocytes (HaCaT) was irradiated at doses from 0.65 to 3.65 Gy, and γ-H2AX foci were quantified as markers of tracks including one or more DNA double-strand breaks. The system achieved a uniform dose rate of 0.37 Gy/min, as calculated with Geant4, with a mean proton energy of 4.1 MeV (LET ≈ 8 keV/μm). A strong correlation (R² = 0.93) was observed between proton dose and γH2AX foci per nucleus (~10 foci/Gy), reflecting damage-inducing proton tracks rather than individual DNA double-strand breaks. At higher doses, an increased fraction of cells exhibited pan-nuclear γH2AX staining, characterized by a diffuse γH2AX signal throughout the nucleus and commonly associated with extensive or clustered DNA damage and global chromatin phosphorylation. These responses are consistent with the well-established dense ionization patterns produced by low-energy protons, as indicated by the LET spectrum and supported by MCDS-predicted clustered damage yields. While the γH2AX assay does not directly resolve simple versus complex DNA lesions, the agreement between Monte Carlo modeling and the observed cellular stress responses indicates that the irradiation platform reliably reproduces the expected biological signatures of low-energy proton exposure. Consequently, the developed system provides a robust experimental tool for systematic investigations of cellular radiosensitivity and radiotoxicity, with potential applications in skin dosimetry and radioprotection. Full article

Malonyl-CoA decarboxylase (MCD) is an enzyme that controls malonyl-CoA levels and regulates fatty acid synthesis and oxidation. Although its physiological relevance in peripheral tissues is well known, the role of MCD in the central nervous system remains poorly understood. MCD is expressed in mitochondria, cytosol, and peroxisomes and may be regulated by PPAR-α, AMPK, and SIRT4 in tissues such as muscle, liver and kidney. In the brain, MCD expression varies during development and can respond to nutritional states. Inherited MCD deficiency (malonic aciduria) leads to the toxic accumulation of malonic acid and predominantly affects the central nervous system. The underlying mechanisms leading to brain damage in MCD patients remain unclear. Conversely, pharmacological modulation of MCD activity has been studied in obesity, diabetes, and ischemic injury, highlighting its therapeutic potential. There are still major gaps regarding MCD cellular distribution, regulatory pathways, and metabolic interaction with CPT1c (carnitine palmitoyltransferase 1c) in neural metabolism. A deeper understanding of the role of MCD in brain physiology and pathology may indicate novel therapeutic strategies targeting metabolic disorders that involve altered malonyl-CoA dynamics. Here, we discuss the current knowns and unknowns regarding MCD physiology, regulation, and pathophysiology, emphasizing brain aspects. Full article