Search Results (103,752)

Osteoarthritis (OA) represents a degenerative joint disease which advances through cartilage breakdown, synovial inflammation, and subchondral bone transformation until it causes persistent pain and mobility loss. The scientific community lacks complete knowledge about OA disease mechanisms and post-operative healing processes despite arthroplasty surgery providing effective symptom relief. This study investigated plasma proteomic changes in OA patients before and after arthroplasty. The cohort included eight OA patients undergoing knee or hip arthroplasty and ten age-, sex-, and body mass index-matched healthy controls. Plasma proteins were analyzed using liquid chromatography–tandem mass spectrometry following enzymatic digestion and depletion of high-abundance components. The bioinformatic analysis together with quantitative methods showed that OA patients experienced changes in inflammatory pathways, extracellular matrix remodeling, immune system regulation and coagulation processes. A total of 93 proteins were differentially abundant in the pre-operative comparison. Among these, 63 proteins were consistently up-regulated and 23 were consistently down-regulated across both pre- and post-operative time points. In addition, 20 proteins exhibited post-operative-specific changes. These findings highlight both persistent disease-associated alterations and transient proteomic shifts linked to post-operative recovery. Overall, this study identifies candidate plasma proteomic signatures associated with OA and surgical intervention, providing exploratory insights into disease monitoring and potential personalized therapeutic strategies. Full article

The mountainous regions of southwest China represent one of the world’s most distinctive and sensitive areas. Against the backdrop of rapid urbanization and water conservancy construction, rural landscapes in these regions face challenges such as fragmentation, homogenization, and loss of local distinctiveness. Responding to the initiative of the European Landscape Convention (ELC), this study takes the Longchuan River Basin in Southwest China as a case study, and constructs a rural Landscape Character Assessment (LCA) framework adapted to the multi-level governance system. We established a multi-scale evaluation system covering large scale (county-level), medium scale (township-level), and detailed scale (reservoir area-level). The large scale integrated 6 categories of natural variables, while the medium scale involved 4 categories of natural variables and 4 categories of cultural variables. Using a Principal Component Analysis–Two-Step Clustering coupled method and eCognition software, landscape character types and areas were identified respectively. The results show that 11 landscape character types and 41 landscape character areas were identified at the large scale, and 6 landscape character types and 73 landscape character areas at the medium scale. At the detailed scale, 4 typical reservoir areas were selected for field surveys, which verified the in-depth impact of hydropower construction on landscape characteristics. The study provides a transferable technical pathway and policy recommendations for monitoring and managing rural landscapes in mountainous regions. Supports the long-term sustainability and resilience of rural landscapes in China. Full article

Understanding spatiotemporal variation in soil CO₂ flux (FCO₂) along elevational gradients is essential for predicting carbon–climate feedback in alpine ecosystems. However, how temperature- and moisture-related factors jointly regulate daily-scale FCO₂ and how their contributions vary with elevation remain unclear, particularly in the Sejila Mountains (Southeastern Tibetan Plateau). We conducted continuous in situ measurements of daily-scale FCO₂, air temperature (Ta), relative humidity (RH), soil temperature (ST, 0–10 cm), and volumetric soil water content (SW) across five elevational bands (3000–4200 m) in 2024–2025. Across both years, FCO₂ showed a unimodal seasonal cycle and a robust nonmonotonic spatial pattern, with the highest efflux at 3000 and 4200 m and peak rates exceeding 5.0 µmol CO₂ m⁻² s⁻¹. Cumulative carbon loss at 4200 m (909.90 g C m⁻²) exceeded that at mid-elevation sites. Linear mixed-effects models identified Ta as the most consistent positive predictor; the ST × SW interaction was not significant, indicating that temperature and moisture effects are largely additive at the daily scale. Piecewise regression revealed nonlinear SW thresholds (θ) in the FCO₂ response, with θ varying nonmonotonically with elevation. Multiple linear regression further showed that thermal predictors (Ta, ST) explained substantially more variance than moisture predictors (RH, SW), and the relative importance of thermal drivers increased with elevation. These results challenge the common expectation of a monotonic decline in soil respiration with elevation and suggest that, when SW remains above critical thresholds, warming may amplify soil carbon losses at high elevations on the Tibetan Plateau. Full article

Background/Objectives: Paediatric cataract is among the most common treatable causes of childhood blindness, caused by a genetically diverse disorder with variable clinical features. Although genetic factors significantly contribute to the development of paediatric cataracts, recent data on their genetic makeup and genotype–phenotype relationships in Saudi Arabia is limited. This study aims to investigate the genetic spectrum, inheritance patterns, and genotype–phenotype correlations of paediatric cataract in a Saudi population over twenty years. Methods: We conducted a retrospective cohort study of children diagnosed with congenital or juvenile cataracts between 2000 and 2019 at two major referral centres in Riyadh. Clinical, ocular, and systemic data were collected through multidisciplinary evaluations. Genetic analysis involved whole-exome and whole-genome sequencing performed at College of American Pathologists (CAP)-accredited laboratories. Variant interpretation was supported by bioinformatic and Artificial Intelligence (AI) prediction tools. Genotype–phenotype relationships were systematically analysed. Results: The study included 28 cases of genetically confirmed paediatric cataracts. Variants classified as pathogenic or likely pathogenic were identified in 13 genes. Autosomal recessive inheritance was predominant, with many patients exhibiting homozygous variants, often due to consanguinity. Two novel variants were identified in the Collagen Type XVIII Alpha 1 Chain (COL18A1) and the RAB3 GTPase-activating protein catalytic subunit 2 (RAB3GAP2) genes. Considerable phenotypic variability was observed, even among patients with the same mutation, particularly those with the recurrent CRYBB1 c.171del (p.Asn58fs) mutation. Syndromic cataracts were more frequently associated with loss-of-function variants and multisystem features. Conclusions: This study offers updated insights into the genetics and clinical presentation of paediatric cataract in Saudi Arabia. It highlights high genetic diversity, unique inheritance patterns, and notable genotype–phenotype variability, emphasising the importance of early genetic testing and multidisciplinary assessment for improved diagnosis, management, and counselling. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

The Mediterranean mussel, Mytilus galloprovincialis, is currently facing unprecedented mass mortality events (MMEs) that threaten the economic and ecological stability of Mediterranean aquaculture. The present review gathered and analyzed current knowledge on climate change and environmental disorders that may cause MMEs in Mediterranean mussels, compromising mussel physiology and immune competence. Biological agents, which proliferate under stress conditions, can either trigger direct disease or act as co-factors in mortality. The impact of the economic loss following MMEs in mussel production in the Mediterranean Sea is also described. The main key drivers used in the analysis of the literature were “M. galloprovincialis”, “MMEs”, “environmental stressors”, “climate change”, “pathogens”, “pollutants”, “economical losses”. The One Health–One Welfare framework recognizes the inextricable interconnection between the health of human, mussel, and marine ecosystems. This approach is essential for developing holistic monitoring programs, robust risk assessment strategies, and adaptive management policies capable of ensuring the long-term sustainability of Mediterranean mussel production and the ecological stability of coastal systems. In the future, the development of integrated water monitoring systems where mussels are both farmed species and active biological sentinels is possible. The implementation of a digital monitoring system will offer a transformative strategy for mitigating MMEs in Mediterranean mussel populations. Full article

The assessment of landscape ecological risk and the exploration of its driving factors is a critical approach to alleviating the conflict between the growing demand of human activities and ecological environment conservation, and the Five Lakes Area in Central Yunnan serves as a typical representative of landscape ecological risk issues in plateau lake regions. Therefore, this study, based on the land use transfer change characteristics of the Five Lakes Area in Central Yunnan across four periods (1995–2024), employed the landscape pattern index method to calculate the spatiotemporal variation characteristics of the landscape ecological risk index; additionally, 10 driving factors (including natural and socio-economic factors) were selected, and the XGBoost-SHAP model and Random Forest model were applied to explore the driving factors, with the results showing that: (1) In terms of land use transfer, farmland, forest, and Grass land were transferred among each other, the inflow of Construction land increased, and Grass land had the largest outflow area; (2) regarding landscape ecological risk, the landscape pattern was unstable, the loss degree increased, and the moderate and moderately high-risk areas expanded; and (3) for driving factors, the dominance shifted from natural factors to socio-economic factors; among these, Precipitation, NDVI (Normalized Difference Vegetation Index), Land use intensity, and Night-time light index were significant influencing factors. Based on the above results, a zoning management and control strategy for landscape ecological risk was proposed, aiming to provide a scientific reference for policy formulation to reduce risks and alleviate human–land conflicts. Full article

In this study, a three-dimensional simulation of a walking-beam reheating furnace was developed to improve the steel slab reheating process and reduce surface oxidation kinetics using computational fluid dynamics (CFD). Combustion, heat transfer, fluid dynamics, and chemical reaction models were integrated into the numerical framework of this study. In addition, dynamic mesh remeshing was coupled through user-defined functions (UDFs), enabling the simultaneous simulation of slab movement and evolution of the involved transport phenomena. Turbulence was modeled with the realizable k-ε formulation, combustion with the Eddy Dissipation model, and radiation with the P-1 model coupled with WSGGM to include CO₂ and H₂O gas radiation. Scale formation was modeled using customized functions based on Arrhenius-type kinetics and Wagner’s oxidation model, evaluating its growth as a function of time, temperature, and furnace atmosphere. The predicted thermal evolution inside the furnace was validated using industrial data, yielding an average deviation of 5%. Furthermore, the proposed operating conditions led to an average slab temperature of 1289.77 °C at the exit of the homogenization zone, which was 16 °C higher than that under the current operation but still within the target range (1250 ± 50 °C). The reduction in combustion air decreased energy losses and improved product quality, lowering the molar oxygen content in the furnace atmosphere from 4.9 × 10² mol to 6.7 × 10¹ mol. Additionally, annual savings of 4,793,472 kg of natural gas and 13,884 tons of steel were estimated owing to reduced oxidation losses. The proposed air–fuel adjustment led to estimated annual energy savings (equivalent to 4,793,472 kg of natural gas) and a reduction in material loss due to oxidation from 4.5% to 3.75% (an absolute reduction of 0.75 percentage points; relative reduction ≈ 16.7%), which has a significant industrial impact on metal conservation and descaling cost reduction. Although there are CFD studies on plate overheating and scale growth separately, this work presents three main contributions: (1) the integration, within a single numerical framework, of combustion, radiation, species transport, oxidation kinetics, and actual plate movement using a dynamic mesh; (2) validation against continuous industrial records (16 thermocouples) and quantification of operational benefits such as fuel savings and reduced material loss; and (3) a comparative analysis between actual and optimized conditions, which standardize the air–methane ratio. Full article

Chronic hypoxia is a hallmark of aging and retinal diseases such as age-related macular degeneration (AMD), yet the molecular mechanisms that enable retinal pigment epithelium (RPE) cells to survive under sustained low-oxygen conditions remain poorly understood. To address this, we conducted transcriptomic profiling and a genome-wide CRISPR-Cas9 loss-of-function screen in ARPE-19 cells exposed to chronic hypoxia (1% and 5% O₂), mimicking the retinal disease environment. The CRISPR screen identified genes whose loss compromises RPE viability or fitness under hypoxia, while transcriptomic profiling revealed oxygen-dependent shifts in key functional modules. These findings converged on pathways related to mitochondrial function, extracellular matrix remodeling, vascular signaling, and cell cycle regulation, identifying unique functional nodes specific to RPE cells. These core processes are also implicated in retinal diseases, such as AMD. Together, these complementary approaches provide an integrated view of the molecular networks driving RPE adaptation to hypoxic stress and highlight novel gene candidates that may serve as therapeutic targets in retinal disease. Full article

Ornithine decarboxylase (ODC) functions as the rate-limiting enzyme in the polyamine (PA) biosynthetic pathway. It catalyzes the decarboxylation of L-ornithine to produce putrescine, thereby initiating the biosynthesis of polyamines. Polyamines are a class of widely distributed polycationic aliphatic compounds in living organisms, including putrescine, spermidine, and spermine. They serve not only as critical regulators of cell growth, proliferation, and differentiation, but also as important signaling molecules involved in plant responses to environmental stress and key precursors in the biosynthesis of diverse secondary metabolites. Focusing on recent advances in plant ODC research, this review summarizes the characteristics and evolutionary relationships of the ODC gene family, the biochemical properties and catalytic mechanism of the enzyme, and its multiple physiological roles in growth, development, secondary metabolism, and stress adaptation. Furthermore, we discuss the complex regulatory mechanisms governing ODC activity at both transcriptional and post-translational levels, with a critical gap in understanding the post-translational regulation of ODC in plants, particularly the mechanisms governing its degradation. Unlike in animals, where antizymes mediate ODC degradation, functional analogs of antizymes have not yet been identified in plants, leaving the degradation pathway largely unexplored. Finally, we review the applications of plant genetic modification targeting ODC in enhancing the production of valuable secondary metabolites in medicinal plants and improving stress tolerance in crops, along with perspectives on future research directions. This review illustrates the diversity of ODC functions and the complexity of its regulatory mechanisms in plant growth, development, stress responses, and secondary metabolism. It also provides a theoretical foundation and insights for exploring ODC as a target for plant genetic modification, which is promising for improving the economic traits and stress resistance of horticultural plants. Full article

To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control. Full article

Natural grasslands are among the most endangered habitats in Northern, Central and Eastern Europe due to the agricultural intensification, land abandonment and afforestation, urban expansion, and the loss of traditional low-intensity management, on which their biodiversity depends. One way to increase the number of natural grasslands is by integrating them into urban green infrastructure as a nature-based solution to enhance ecological resilience and urban livability: diverse grassland systems support pollinators, improve soil structure and stormwater infiltration, mitigate urban heat and provide restorative, experience-rich public spaces. The aim of the study is to explore opportunities and barriers to integrating different types of grasslands into the green infrastructure of Latvian cities, with a primary focus on public perceptions and institutional aspects of urban grassland implementation and management. A mixed-methods approach was applied, combining resident surveys, interviews with municipal experts—territorial development specialists, planners and maintenance managers—and comparative policy analysis. Results show that although residents acknowledge the ecological benefits of urban grasslands, they prefer them in peripheral or underused areas rather than in city centres and residential zones, as these areas are often aesthetically perceived as “untidy” or neglected, conflicting with cultural norms that favour short, intensively mown lawns and raising concerns about insects. Acceptance increases through communication and participatory practices. Municipal approaches range from structured maintenance guidelines, including delayed mowing, biomass removal, and invasive species control, to flexible experimentation. The study contributes scientifically grounded insights into governance, perception, and management interfaces critical for mainstreaming socially accepted urban grasslands. Full article

Positional consumption is spending valued mainly for relative standing rather than intrinsic usefulness. A progressive consumption tax can, in principle, reduce the social costs of status-driven spending by taxing consumption rather than saving, but it may face resistance. We examine a behavioral evaluation channel in which status quo bias and loss aversion can sustain positional consumption and reduce support for this reform. We combine a fully specified, reproducible in silico simulation of tax acceptance with a real-participant gain–loss questionnaire that benchmarks positional-choice patterns under matched items. In grouped fractional-response estimates from the simulated data, the post-condition increases predicted acceptance from about 0.11 to about 0.22 and is statistically significant (p < 0.001), while higher status quo and loss-aversion proxy intensity predicts lower acceptance and is statistically significant (p < 0.001). Policy framing increases predicted acceptance relative to the Neutral frame. In the questionnaire, loss framing shifts choices toward absolute outcomes relative to gain framing, consistent with attenuated positional motives. The framework provides a transparent way to stress test how framing and bundled communication and comprehension supports can shift acceptance of progressive consumption taxation under stated assumptions. Full article

Prolonged water injection in unconsolidated sandstone reservoirs can induce pore rearrangement and modify flow pathways, thereby affecting reservoir performance. However, quantitative characterization of pore evolution in both temporal and spatial dimensions remains limited. This study investigates the mechanisms of pore-structure evolution during extended injection through a series of multi-scale experiments. Scanning electron microscopy and X-ray diffraction analyses were employed to compare mineral composition and microstructural characteristics before and after injection, while in situ nuclear magnetic resonance (NMR) monitoring captured the dynamic evolution process, enabling pore-size classification from T₂ spectra and fractal assessment of structural complexity. Segmented NMR measurements at different distances further resolved spatial heterogeneity. The results show that prolonged water injection reduced permeability by 10.4–32.1%, whereas porosity exhibited only minor variation, indicating that the decline in flow capacity is primarily controlled by pore–throat structural adjustment rather than pore volume loss. Mineralogical redistribution and fine-particle migration decreased the median pore radius by 21.5–51.8% and the micropore fractal dimension by 23.8–76.5%, with stronger responses observed at higher permeabilities, while meso- and macropore fractal dimensions remained nearly unchanged, indicating preferential modification of micropores with preservation of the main connected flow framework. Consistently, NMR responses reveal pronounced spatial heterogeneity along the flow direction. The NMR signal changes at the injection end were 11.2–18.4% and 7.7–21.7% during the early and intermediate stages, respectively, both exceeding those at the distal end (2.9–12.4% and 1.9–17.1%). These results indicate a downstream-attenuating structural modification gradient. The findings provide new insights into pore-structure evolution during prolonged water injection and offer a scientific basis for optimizing water-injection strategies in unconsolidated sandstone reservoirs. Full article

Mobile Laser Scanning (MLS) integrated with Simultaneous Localization and Mapping (SLAM) has emerged as a key technology for digitizing GNSS-denied environments, such as underground mines. However, the automated interpretation of unstructured, high-density point clouds into semantic engineering models remains challenging due to extreme geometric anisotropy in point distributions and severe class imbalance inherent to narrow tunnel environments. To address these issues, this study proposes a highly automated scan-to-model framework for precise semantic segmentation and vectorized two-dimensional (2D) profile reconstruction. First, an enhanced hierarchical deep learning network tailored for point clouds is introduced. The architecture incorporates a context-aware sampling strategy with an expanded receptive field of up to 10 m to preserve axial continuity, coupled with a spatial–geometric dual-attention mechanism to refine boundary delineation. In addition, a composite Focal–Dice loss function is employed to alleviate the dominance of wall points during network training. Experimental validation on a field-collected dataset comprising 16 mine tunnels demonstrates that the proposed model achieves a mean Intersection over Union (mIoU) of 85.15% (±0.29%) and an Overall Accuracy (OA) of 95.13% (±0.13%). Building on this semantic foundation, a robust geometric modeling pipeline is established using curvature-guided filtering and density-adaptive B-spline fitting. The reconstructed profiles accurately recover the geometric mean surface of the tunnel wall, yielding an overall filtered Root Mean Square Error (RMSE) of 4.96 ± 0.48 cm. The proposed framework provides an efficient end-to-end solution for deformation analysis and digital twinning of underground mining infrastructure. Full article