Search Results (563)

Fat embolism is a critical medical emergency often resulting from long bone fractures or amputations, leading to acute respiratory distress syndrome (ARDS). The NOD-like receptor pyrin domain-containing 3 (NLRP3) inflammasome, a key regulator of innate immunity, is activated by reactive oxygen species and tissue damage, contributing to inflammatory responses. This study examines the role of NLRP3 in fat embolism-induced ARDS and evaluates the therapeutic potential of MCC950, a selective NLRP3 antagonist. Fat embolism was induced by fatty micelle injection into the tail vein of Sprague Dawley rats. Pulmonary injury was assessed through lung weight gain as an edema indicator, NLRP3 expression via Western blot, and IL-1β levels using ELISA. Histological damage and macrophage infiltration were evaluated with hematoxylin and eosin staining. Fat embolism significantly increased pulmonary NLRP3 expression, lipid peroxidation, IL-1β release, and macrophage infiltration within four hours, accompanied by severe pulmonary edema. NLRP3 was localized in type I alveolar cells, co-localizing with aquaporin 5. Administration of MCC950 significantly reduced inflammatory responses, lipid peroxidation, pulmonary edema, and histological damage, while attenuating MAPK cascade phosphorylation of ERK and Raf. These findings suggest that NLRP3 plays a critical role in fat embolism-induced acute respiratory distress syndrome, and its inhibition by MCC950 may offer a promising therapeutic approach. Full article

The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. Full article

65 Mn is a high-quality carbon structural steel that exhibits excellent mechanical properties and machinability. It finds broad applications in machinery manufacturing, agricultural tools, and mining equipment, and is commonly used for producing mechanical parts, springs, and cutting tools. Fe901 is an iron-based alloy that exhibits excellent hardness, structural stability, and wear resistance. It is widely used in surface engineering applications, especially laser cladding, due to its ability to form dense and crack-free metallurgical coatings. To enhance the surface hardness and wear resistance of 65 Mn steel, this study employs a laser melting process to deposit a multi-layer Fe901 alloy coating. The phase composition, microstructure, microhardness, and wear resistance of the coatings are investigated using X-ray diffraction (XRD), optical microscopy, scanning electron microscopy (SEM), Vickers hardness testing, and friction-wear testing. The results show that the coatings are dense and uniform, without visible defects. The main phases in the coating include solid solution, carbides, and α-phase. The microstructure comprises dendritic, columnar, and equiaxed crystals. The microhardness of the cladding layer increases significantly, with the multilayer coating reaching 3.59 times the hardness of the 65 Mn substrate. The coatings exhibit stable and relatively low friction coefficients ranging from 0.38 to 0.58. Under identical testing conditions, the wear resistance of the coating surpasses that of the substrate, and the multilayer coating shows better wear performance than the single-layer one. Full article

Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. Determining the stages of strawberry growth has important guiding significance for irrigation, fertilization, and picking. Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. The original YOLOv9 showed limitations in detecting strawberries at different growth stages, particularly lower precision in identifying occluded fruits and immature stages. We enhanced the YOLOv9 model by introducing the Alterable Kernel Convolution (AKConv) to improve the recognition efficiency while ensuring precision. The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. Finally, the CIoU function was replaced by the Minimum Point Distance based IoU (MPDIoU) loss function to effectively solve the problem of low precision in identifying bounding boxes. The experimental results demonstrate that, under identical conditions, the improved model achieves a precision of 97.7%, a recall of 97.2%, mAP50 of 99.1%, and mAP50-95 of 90.7%, which are 0.6%, 3.0%, 0.7%, and 7.4% greater than those of the original model, respectively. The parameters, model size, and floating-point calculations were reduced by 3.7%, 5.6% and 3.8%, respectively, which significantly boosted the performance of the original model and outperformed that of the other models. Experiments revealed that the model could provide technical support for the multistage identification of strawberry planting. Full article

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R² lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques. Full article

This paper explores the strategic implications of technological adoption within shipping logistics service supply chains, with a particular focus on blockchain technology (BCT). When integrating new technologies, supply chain stakeholders evaluate associated risks alongside complexity, profitability, and operational challenges, which influence their strategic behaviors. Anchored in the concept of technology trust, this study examines how different risk preferences affect BCT adoption decisions and freight rate strategies. A game-theoretic model is constructed using a mean-variance utility framework to analyze interactions between shipping companies and freight forwarders under three adoption scenarios: no adoption (NN), partial adoption (BN), and full adoption (BB). The results indicate that risk-seeking agents are more likely to adopt BCT early but face greater freight rate volatility in the initial stages. As the technology matures, strategic variability declines and the influence of adaptability on pricing becomes less pronounced. In contrast, risk-neutral and risk-averse participants tend to adopt more conservatively, resulting in slower but more stable pricing dynamics. These findings offer new insights into how technology trust and risk attitudes shape strategic decisions in digitally transforming supply chains. The study also provides practical implications for differentiated pricing strategies, BCT adoption incentives, and collaborative policy design among logistics stakeholders. Full article

Background/Objectives: Oxidative stress constitutes a principal pathophysiological mechanism driving neurodegeneration and brain aging. α-Ketoglutarate (AKG), a key intermediate of the tricarboxylic acid (TCA) cycle, has shown potential in longevity and oxidative stress resistance. However, the role of AKG in oxidative stress-induced neuronal senescence and its interaction with the mTOR signaling pathway during neuronal aging remain poorly understood, posing a key challenge for developing senescence-targeted therapies. Methods: We investigated the neuroprotective effects of AKG using H₂O₂-induced senescence in HT22 cells and a D-galactose-induced brain aging mouse model. Assessments encompassed SA-β-gal staining, EdU incorporation, mitochondrial membrane potential (JC-1), and ROS measurement. Antioxidant markers, ATP levels, and the NAD⁺/NADH ratio were also analyzed. Proteomic profiling (DIA-MS) and KEGG/GSEA enrichment analyses were employed to identify AKG-responsive signaling pathways, and Western blotting validated changes in mTOR signaling and downstream effectors. Results: AKG significantly alleviated H₂O₂-induced senescence in HT22 cells, evidenced by enhanced cell viability, reduced ROS level, restored mitochondrial function, and downregulated p53/p21 expression. In vivo, AKG administration improved cognitive deficits and vestibulomotor dysfunction while ameliorating brain oxidative damage in aging mice. Proteomics revealed mTOR signaling pathways as key targets for AKG’s anti-aging activity. Mechanistically, AKG suppressed mTOR phosphorylation and activated ULK1, suggesting modulation of autophagy and metabolic homeostasis. These effects were accompanied by enhanced antioxidant enzyme activities and improved redox homeostasis. Conclusions: Our study demonstrates that AKG mitigates oxidative stress-induced neuronal senescence through suppression of the mTOR pathway and enhancement of mitochondrial and antioxidant function. These findings highlight AKG as a metabolic intervention candidate for age-related neurodegenerative diseases. Full article

Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenario fire detection. Firstly, to resolve missed detection caused by deformation of smoke and flames, a Scale Adaptive Perception Module (SAPM) is proposed. Secondly, aiming at the suppression of significant fire features by complex backgrounds, a Feature Adaptive Weighting Module (FAWM) is introduced to enhance the feature representation of fire. Finally, considering the difficulty in detecting small flames, a fine-grained Small Object Feature Extraction Module (SOFEM) is developed. Additionally, given the scarcity of multi-scenario fire datasets, this paper constructs a Multi-scenario Fire Dataset (MFDB). Experimental results on MFDB demonstrate that the proposed YOLO-MFD achieves a good balance between effectiveness and efficiency, achieving good effective fire detection performance across various scenarios. Full article

Cold storage is widely used for the postharvest preservation of fruits and vegetables; however, okra, as a tropical vegetable, is susceptible to chilling injury under low-temperature storage conditions, leading to quality deterioration, reduced nutritional value, and significant economic losses. Nitric oxide (NO), as an important signaling molecule, plays a crucial role in the postharvest preservation of fruits and vegetables. To investigate the effects of different concentrations of nitric oxide on the postharvest quality of okra under cold storage, fresh okra pods were treated with sodium nitroprusside (SNP), a commonly used NO donor, at concentrations of 0 (control), 0.5 (T1), 1.0 (T2), 1.5 (T3), and 2.0 mmol·L⁻¹ (T4). The results showed that low-concentration NO treatment (T1) significantly reduced weight loss, improved texture attributes including hardness, springiness, chewiness, resilience, and cohesiveness, and suppressed the increase in adhesiveness. T1 treatment also effectively inhibited excessive accumulation of cellulose and lignin, thereby maintaining tissue palatability and structural integrity. Additionally, T1 significantly delayed chlorophyll degradation, preserved higher levels of soluble sugars and proteins, and enhanced the activities of key antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), contributing to improved oxidative stress resistance and membrane stability. In contrast, high-concentration NO treatments (T3 and T4) led to pronounced quality deterioration, characterized by accelerated membrane lipid peroxidation as evidenced by increased malondialdehyde (MDA) content and relative conductivity, and impaired antioxidant defense, resulting in rapid texture degradation, chlorophyll loss, nutrient depletion, and oxidative damage. These findings provide theoretical insights and practical guidance for the precise application of NO in extending shelf life and maintaining the postharvest quality of okra fruits. Full article

This study was performed to investigate the effects of dietary yeast culture (YC) supplementation on growth performance, digestive function, intestinal inflammatory response, and microbiota composition of largemouth bass Micropterus salmoides (LMB). Six diets were formulated with graded levels of YC (0, 5, 10, 15, 20, and 30 g/kg), referred to as CON, YC5, YC10, YC15, YC20, and YC30, respectively. Each diet was assigned to four replicate tanks of LMB juveniles (initial body weight 8.11 ± 0.05 g) with twenty fish per tank. After an 8-week feeding trial, final body weight and specific growth rate showed an increasing trend with 5~20 g/kg YC and reached a maximum at 15 g/kg YC. Feeding ratio decreased, but feed efficiency ratio (FER) improved in response to dietary YC inclusion, and FER was higher in the YC10 fish than in the YC5, YC20, and YC30 fish. Proximate composition (moisture, protein, and lipid) of the whole fish was not affected by dietary YC levels. The activities of intestinal lipase and trypsin were higher in the YC10 fish, while the relative expression of interleukin-8 (il-8) and il-1β was downregulated in the hindgut of the YC15 fish compared with the CON fish. Histological examination showed that the villus height of the midgut, together with goblet cell density of the foregut and midgut, was higher in the YC10 and YC30 fish than in the CON fish. 16S rRNA sequencing showed that Proteobacteria, Fusobacteria, and Firmicutes dominated the intestinal microbiota in LMB. The decrease in harmful Mycoplasma accounted for the dramatic change in Firmicutes abundance, while the increase in Cetobacterium (specifically C. somerae) accounted for the change in Fusobacteria abundance in the gut of the YC10 and YC30 fish compared with the CON fish. The increase in the beneficial Endozoicomonas was the main reason for the change in Proteobacteria abundance in the intestine of the YC30 fish as compared with the CON fish. Taken together, the alteration of intestinal microbiota composition contributed to the improved digestive function and feed utilization in LMB fed YC-supplemented diets. Based on growth performance, the optimal YC level in the diet for LMB was 15 g/kg. Full article

Helianthus annuus L. (H. annuus) receptacles, a major agricultural by-product generated during seed processing, are currently underutilized. This study aimed to explore the valorization potential of this by-product by extracting H. annuus receptacles total polysaccharides (HRTP) and characterizing their potential as natural ingredients in ultraviolet (UV)-protective cosmetics. A new purified polysaccharide named H. annuus receptacles polysaccharide-1 (HRP-1) was isolated, likely exhibiting a backbone of alternating →4)-α-D-GalA-(1→ and →4)-α-D-GalA(6-OCH₃)-(1→ units, with a weight-average molecular weight (Mw) of 163 kDa. HRTP demonstrated significant protective effects against UV-induced damage in human immortalized keratinocyte (HaCaT) cells by suppressing intracellular reactive oxygen species (ROS) levels and downregulating MAPK-p38/ERK/JNK pathways, thereby inhibiting inflammatory cytokines (IL-1β, IL-6, IL-8, and TNF-α) and matrix metalloproteinases (MMP-1, MMP-3, and MMP-9). Additionally, HRTP exhibited moisturizing properties. These findings highlight H. annuus receptacle polysaccharides as sustainable, bioactive ingredients for eco-friendly sunscreen formulations, providing a practical approach to converting agricultural by-products into high-value industrial biomaterials. Full article

Obesity is a chronic metabolic disorder and a growing global public health challenge, affecting hundreds of millions of individuals worldwide. While diet and physical activity are well-established contributors, increasing evidence underscores the critical role of epigenetic mechanisms in mediating obesity-related processes. Epigenetic modifications—such as DNA methylation, RNA methylation (particularly N6-methyladenosine), histone modifications, non-coding RNAs, and chromatin remodeling—modulate gene expression without altering the DNA sequence. This review aims to provide an overview of the epigenetic mechanisms involved in obesity, with an emphasis on their molecular functions and regulatory networks. Integrating findings from relevant studies, we discuss how these modifications influence obesity-related outcomes through regulating key processes such as adipocyte differentiation and energy metabolism. Advancing our understanding of epigenetic regulation may pave the way for novel, targeted strategies in the prevention and treatment of obesity. Full article

Like other mega cities in China, Beijing is undergoing a large-scale urban renewal process. However, in the context of global warming and the goal of promoting human health and well-being, urban renewal should follow the principle of minimal intervention, draw inspiration from the condition of the climate and environment itself, and pursue the goal of common health and development between humans and non-human beings. This study takes the Panjiayuan Antique Market as the research object. Unlike previous studies that focused on the behavior patterns of vendors and buyers, this study focuses on the increase in users’ expectation on environmental thermal comfort when the Panjiayuan Antique Market transforms from a conventional commercial market into an urban public space. This study aimed to find a minimal intervention strategy suitable for urban public space renewal from the perspective of the microclimate, encouraging people to use outdoor public spaces more, thereby promoting physical and mental health, as well as social well-being. We used a mixed-methods approach comprising microclimate measurements, questionnaires (n = 254), and field measurements. Our results show that the mean radiant temperature (T_mrt) is the key factor that affects thermal comfort, and it is a comprehensive concept that is associated with other microclimate factors. Linking the quantitative sun-related factors, such as the solar position angle (SAA), the shadow area ratio (SAR), and direct sun hours (DSHs), we also found that the correlation between the T_mrt and physical spatial characteristics, such as the ratio of the visible sky (SVF), the aspect ratio (H/W), and orientation of the building layout, helped us to generate design strategies oriented by regulating microclimate, such as controlling thermal mass/radiant heating, solar radiation, and air convection. One of the significances of this study is its development of a design method that minimizes intervention in urban public spaces from the perspective of regulating the microclimate. In addition, this study proposes a new perspective of promoting people’s health and well-being by improving outdoor thermal comfort. Full article

This study investigates how climate-resilient city construction (CRCC) influences the Environmental, Social, and Governance (ESG) performance of Chinese listed firms, employing a difference-in-differences (DID) model with firm-year data from 2012 to 2023. The empirical results demonstrate that CRCC exerts a significant positive effect on firms’ ESG performance, with particularly pronounced improvements in the environmental and social dimensions. The mechanism analysis reveals that strengthening government environmental guidance and stimulating firms’ environmental response strategies are the key channels via which CRCC improves firms’ ESG performance. The heterogeneity tests show more pronounced effects for the central–eastern regions, state-owned firms, non-regulated industries, and non-heavily polluting sectors. A further analysis indicates that better ESG performance drives firms to increase their environmental investment, upgrade their value chains, and enhance new quality productive forces. This study extends the framework of ESG determinants by integrating climate adaptation policies, offering insights for urban climate governance and firms’ low-carbon transitions. Full article

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R² = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems. Full article