Search Results (2,548)

Skin aging is closely related to mitochondrial dysfunction and cell cycle abnormalities, and developing intervention strategies targeting mitochondrial quality control is an important direction for anti-aging research. In this study, we investigated the anti-aging mechanism of Camellia japonica flower (CJF) extract and its active ingredient hyperoside based on a doxorubicin (DOX)-induced endogenous senescence model in human skin fibroblasts (HSFs). LC-MS proteomics analysis revealed that CJF extract and hyperoside specifically activated the FUNDC1-mediated mitochondrial autophagy pathway, significantly ameliorated the DOX-induced decrease in mitochondrial membrane potential and the accumulation of reactive oxygen species (ROS), and alleviated the cellular S-phase blockade and reversed the high expression of senescence-associated β-galactosidase (SA-β-gal). Further studies showed that the two cleared damaged mitochondria by enhancing mitochondrial autophagy and restoring cellular energy metabolism homeostasis while promoting type III collagen and elastin synthesis and repairing the expression of Claudin 1 related to skin barrier function. For the first time, the present study reveals the molecular mechanism of CJF extract in delaying skin aging by regulating the FUNDC1-dependent mitochondrial autophagy pathway, which provides a theoretical basis and a candidate strategy for developing novel anti-aging agents targeting mitochondrial quality control. Full article

Multi-vessel formation shipping demonstrates significant potential for enhancing maritime transportation efficiency and economy. However, existing route planning systems inadequately address the unique challenges of formations, where traditional methods fail to integrate global optimality, local dynamic obstacle avoidance, and formation coordination into a cohesive system. Global planning often neglects multi-ship collaborative constraints, while local methods disregard vessel maneuvering characteristics and formation stability. This paper proposes GLFM, a three-layer hierarchical framework (global optimization–local adjustment-formation collaboration module) for intelligent route planning of transport ship formations. GLFM integrates an improved multi-objective A* algorithm for global path optimization under dynamic meteorological and oceanographic (METOC) conditions and International Maritime Organization (IMO) safety regulations, with an enhanced Artificial Potential Field (APF) method incorporating ship safety domains for dynamic local obstacle avoidance. Formation, structural stability, and coordination are achieved through an improved leader–follower approach. Simulation results demonstrate that GLFM-generated trajectories significantly outperform conventional routes, reducing average risk level by 38.46% and voyage duration by 12.15%, while maintaining zero speed and period violation rates. Effective obstacle avoidance is achieved, with the leader vessel navigating optimized global waypoints and followers maintaining formation structure. The GLFM framework successfully balances global optimality with local responsiveness, enhances formation transportation efficiency and safety, and provides a comprehensive solution for intelligent route optimization in multi-constrained marine convoy operations. Full article

It has long been accepted that breathing gases that are physiologically inert include helium (He), neon (Ne), nitrogen (N₂), argon (Ar), krypton (Kr), xenon (Xe), and hydrogen (H₂). The term “inert gas” has been used to describe them due to their unusually high chemical stability. However, as investigations have advanced, many have shown that inert gas can have specific biological impacts when exposed to high pressure or atmospheric pressure. Additionally, different inert gases have different effects on intracellular signal transduction, ion channels, and cell membrane receptors, which are linked to their anesthetic and cell protection effects in normal or pathological processes. Through a selective analysis of the representative literature, this study offers a concise overview of the state of research on the biological impacts of inert gas and their molecular mechanisms. Full article

Faba bean (Vicia faba L.), a nutrient-rich legume beneficial to human health, is valued for its high L-3,4-dihydroxyphenylalanine (L-DOPA) and phenolic content. This study investigated phytochemical diversity and bioactivity across 29 Chinese faba bean varieties. Phenolics were profiled using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and quantified via high-performance liquid chromatography (HPLC). Antioxidant capacity was evaluated, including DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid)) radical scavenging activity, and ferric reducing antioxidant power (FRAP), along with α-amylase/α-glucosidase inhibitory effects. Twenty-five phenolics were identified, including L-DOPA (11.96–17.93 mg/g, >70% of total content), seven phenolic acids, and seventeen flavonoids. L-DOPA showed potent enzyme inhibition (IC₅₀ values of 22.45 μM for α-amylase and 16.66 μM for α-glucosidase) but demonstrated limited antioxidant effects. Lincan 13 (Gansu) exhibited the strongest antioxidant activity (DPPH, 16.32 μmol trolox/g; ABTS, 5.85 μmol trolox/g; FRAP, 21.38 mmol Fe²⁺/g), which correlated with it having the highest flavonoid content (40.51 mg rutin/g), while Yican 4 (Yunnan) showed the strongest α-amylase inhibition (43.33%). Correlation analysis confirmed flavonoids as the primary antioxidants, and principal component analysis (PCA) revealed geographical trends (e.g., Jiangsu varieties were particularly phenolic-rich). These findings highlight faba beans’ potential as functional foods and guide genotype selection in targeted breeding programs aimed at enhancing health benefits. Full article

As an efficient clean energy technology, water electrolysis for hydrogen production has its efficiency limited by the sluggish oxygen evolution reaction (OER) kinetics, which drives the demand for the development of high-performance anode OER catalysts. This work constructs bimetallic (Al, Mn) co-doped nanoporous spinel CoFe₂O₄ (np-CFO) with a tunable structure and composition as an OER catalyst through a simple two-step dealloying strategy. The as-formed np-CFO (Al and Mn) features a hierarchical flaky configuration; that is, there are a large number of fine nanosheets attached to the surface of a regular micron-sized flake, which not only increases the number of active sites but also enhances mass transport efficiency. Consequently, the optimized catalyst exhibits a low OER overpotential of only 320 mV at a current density of 10 mA cm⁻², a minimal Tafel slope of 45.09 mV dec⁻¹, and exceptional durability. Even under industrial conditions (6 M KOH, 60 °C), it only needs 1.83 V to achieve a current density of 500 mA cm⁻² and can maintain good stability for approximately 100 h at this high current density. Theoretical simulations indicate that Al and Mn co-doping could indeed optimize the electronic structure of CFO and thus decrease the energy barrier of OER to 1.35 eV. This work offers a practical approach towards synthesizing efficient and stable OER catalysts. Full article

Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection. Full article

Spin transport behaviors in heavy metal/ferromagnetic insulator (HM/FI) bilayers have attracted considerable attention due to various novel phenomena and applications in spintronic devices. Herein, we investigate the planar Hall effect (PHE) in Pt/Tm₃Fe₅O₁₂ (Pt/TmIG) heterostructures at low temperatures; moment switching in the ferrimagnetic insulator TmIG is detected by using electrical measurements. Double switching hysteresis PHE curves are found in Pt/TmIG bilayers, closely related to the magnetic moment of Tm³⁺ ions, which makes a key contribution to the total magnetic moment of TmIG film at low temperature. More importantly, a magnetoresistance (MR) curve with double switching is found, which has not been reported in this simple HM/FI bilayer, and the sign of this MR effect is sensitive to the angle between the magnetic field and current directions. Our findings of these effects in this HM/rare earth iron garnet (HM/REIG) bilayer provide insights into tuning the spin transport properties of HM/REIG by changing the rare earth. Full article

Background: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. Methods: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. Results: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. Conclusions: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery. Full article

Background/Objectives: Elevated serum uric acid levels are associated with the occurrence, development, and adverse events of coronary heart disease (CHD) and CHD risk factors. However, the extent of any pathogenic effect of the serum uric acid on CHD and whether CHD risk factors play a confounding or mediating role are still unclear. Methods: The potential causal associations of serum uric acid with CHD were evaluated via cross-trait linkage disequilibrium score regression analysis and Mendelian randomization. The pleiotropy of genetic tools was analyzed via a Bayesian colocalization approach. Moreover, we utilized two-step MR to identify risk factors mediating the relationship between uric acid and CHD. Results: Mendelian randomization results derived from two genetic instrument selection strategies support that serum uric acid levels have a significant causal relationship with coronary artery disease, stable angina pectoris, and myocardial infarction. This causal relationship was partially mediated by diastolic blood pressure, mean arterial pressure, and serum triglycerides. Transcriptomic analysis revealed that serum uric acid may directly contribute to the development of atherosclerosis by inducing transcriptomic changes in macrophages. Conclusions: Our findings highlight that the control of serum urate concentration in the long-term management of CHD patients may be necessary. Well-designed clinical trials and foundational research are presently required to furnish conclusive proof regarding the specific clinical scenarios in which adequate reduction in urate concentrations can confer cardiovascular advantages. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

The value realization of ecological products is an important part of rural and agricultural development. As a significant force for protecting traditional agricultural systems and promoting rural revitalization, agricultural heritage systems (AHSs) have formed diverse value realization paths of ecological products in the process of dynamic protection and adaptive management. Through theoretical research, this article analyzed the characteristics of ecological products in AHS sites (EPAHSSs) and summarized the framework of value realization paths of EPAHSSs. Then, the Wannian Rice Culture System in China was selected as a case for conducting empirical research. The results showed that EPAHSSs exhibit obvious uniqueness in terms of climate environment, germplasm resources, farming and breeding models, and cultural heritage. The value realization paths of EPAHSSs mainly include industrial development support, such as the extension of agricultural industrial chains and the development of tourism, as well as fiscal transfer payments. The case analysis results indicated that Wannian County contains a rich variety of ecological products and developed a value realization pathway mainly based on the integration of industries and supplemented by fiscal transfer payments during the process of protection and development. However, further optimization is needed to promote the development of tourism and other paths. This study not only contributes to the sustainable development of the Wannian Rice Culture System, but the proposed framework is also applicable to other heritage systems and similar regions. Full article

Accurate classification of chest X-ray (CXR) images is crucial for diagnosing lung diseases in medical imaging. Existing deep learning models for CXR image classification face challenges in distinguishing non-lung features. In this work, we propose a new segmentation-assisted fusion-based classification method. The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. Then, a fusion of the masked CXR image with the original image enables classification using the improved lightweight ShuffleNetV2 model. The proposed method is trained and evaluated on segmentation datasets including the Montgomery County Dataset (MC) and Shenzhen Hospital Dataset (SH), and classification datasets such as Chest X-Ray Images for Pneumonia (CXIP) and COVIDx. Compared with seven segmentation models (U-Net, Attention-Net, SegNet, FPNNet, DANet, DMNet, and SETR), five classification models (ResNet34, ResNet50, DenseNet121, Swin-Transforms, and ShuffleNetV2), and state-of-the-art methods, our PCSNet model achieved high segmentation performance on CXR images. Compared to the state-of-the-art Attention-Net model, the accuracy of PCSNet increased by 0.19% (98.94% vs. 98.75%), and the boundary accuracy improved by 0.3% (97.86% vs. 97.56%), while requiring 62% fewer parameters. For pneumonia classification using the CXIP dataset, the proposed strategy outperforms the current best model by 0.14% in accuracy (98.55% vs. 98.41%). For COVID-19 classification with the COVIDx dataset, the model reached an accuracy of 97.50%, the absolute improvement in accuracy compared to CovXNet was 0.1%, and clinical metrics demonstrate more significant gains: specificity increased from 94.7% to 99.5%. These results highlight the model’s effectiveness in medical image analysis, demonstrating clinically meaningful improvements over state-of-the-art approaches. Full article

Reductive soil disinfestation (RSD) significantly alters soil characteristics, yet its combined effects with bacterial inoculation on subsequent rhizospheric microbial community composition remains poorly understood. To address this knowledge gap, we investigated the effects of RSD and endophytic Brevibacillus laterosporus inoculation on the composition, network, and predicted function of peanut rhizospheric bacteria and fungi. Our results demonstrated that RSD and B. laterosporus inoculation substantially increased rhizospheric bacterial diversity while reducing fungal diversity. Specifically, B. laterosporus-enhanced RSD significantly reshaped the bacterial community, resulting in increased relative abundances of Chloroflexi, Desulfobacterota, and Myxococcota while decreasing those of Firmicutes, Gemmatimonadota, and Acidobacteriota. The fungal community exhibited a more consistent response to RSD and B. laterosporus amendment, with reduced proportions of Ascomycota and Gemmatimonadota but an increase in Chytridiomycota. Network analysis revealed that B. laterosporus inoculation and RSD enhanced the bacterial species complexity and keystone taxa. Furthermore, canonical correspondence analysis indicated strong associations between the soil bacterial community and soil properties, including Eh, EC, NO₃⁻-N, and SOC. Our findings highlight that the shifts in bacterial taxa induced by B. laterosporus inoculation and RSD, particularly the keystone taxa identified in the network, may contribute to the suppression of soil-borne pathogens. Overall, this study provides a novel insight into the shifts in rhizospheric bacterial and fungal communities and their ecological functions after bacteria inoculation and RSD treatment. Full article

Retired chemically polluted sites in southern Jiangsu Province, China, are characterized by dense nonaqueous phase liquids (DNAPLs) and extremely thick aquifers (>30 m), which pose substantial challenges for determining investigation and remediation depths during redevelopment and exploitation. This study constructed a 2D groundwater transport model using TMVOC to systematically investigate the migration, diffusion, and natural attenuation processes of two typical DNAPLs—1,2-dichloroethane (DCE) and carbon tetrachloride (CTC)—under three scenarios: individual transport, mixed transport, and heterogeneous aquifer conditions, with a simulation period of 35 years. In individual transport scenarios, DCE and CTC showed distinct migration behaviors. DCE achieved a maximum vertical transport distance of 14.01 m and a downstream migration distance of 459.58 m, while CTC reached 13.57 m vertically and 453.51 m downstream. When transported as a mixture, their migration was inhibited: DCE’s vertical and downstream distances decreased to 13.76 m and 440.46 m, respectively; and CTC’s to 13.23 m and 420.32 m, likely due to mutual solvent effects that altered their physicochemical properties such as viscosity and solubility. Under natural attenuation conditions, both DNAPLs ceased downstream transport by the end of the 6th year. DCE concentrations dropped below its risk control value (0.81 mg/L) by the 14th year, and CTC (with a risk control value of 0.23 mg/L) by the 11th year. By the 10th year, DCE’s downstream plume had retreated to 48.65 m, and CTC’s to 0.95 m. In heterogeneous aquifers, vertical upward transport of DCE and CTC increased to 14.82 m and 14.22 m, respectively, due to the partial absence of low-conductivity silt layers, while their downstream distances decreased to 397.99 m and 354.11 m, constrained by low-permeability lenses in the migration path. These quantitative results clarify the dynamic differences in DNAPL transport under varying conditions, highlighting the impacts of multicomponent interactions, natural attenuation, and aquifer heterogeneity. They provide critical references for risk management, scientific determination of remediation depths, and safe exploitation of retired chemically polluted sites with similar hydrogeological characteristics. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article