Search Results (2,859)

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease characterized by chronic inflammation, extracellular matrix degradation, and smooth muscle cell apoptosis. Porphyromonas gingivalis (P. gingivalis), a key periodontal pathogen, has been implicated in the progression of cardiovascular diseases, including AAA, but the underlying mechanisms remain unclear. In this study, we investigated the role of GroEL, a bacterial heat shock protein 60 homolog derived from P. gingivalis, in AAA development. We employed a CaCl₂-induced AAA mouse model to evaluate the in vivo effects of GroEL. Mice received periaortic CaCl₂ application followed by intravenous injections of recombinant GroEL. Histological analyses were performed to assess aneurysmal dilation, elastin degradation, and inflammatory cell infiltration. Flow cytometry and immunohistochemistry were used to determine macrophage phenotypes, while cytokine profiles were quantified via ELISA. In vitro, THP-1 monocytes were treated with GroEL to evaluate its impact on macrophage polarization and cytokine expression. Our results showed that GroEL administration significantly enhanced aortic diameter expansion and elastin breakdown, accompanied by increased infiltration of M1-like macrophages and elevated levels of pro-inflammatory cytokines such as TNF-α and IL-6. In vitro findings confirmed that GroEL promotes M1 polarization and inhibits M2 marker expression in THP-1-derived macrophages. These findings suggest that P. gingivalis-derived GroEL plays a pathogenic role in AAA by modulating macrophage polarization toward a pro-inflammatory phenotype. Targeting microbial components such as GroEL may offer new therapeutic strategies for AAA management. Full article

Gas hydrate-bearing sediments in marine environments represent both a future energy source and a geohazard risk, prompting increasing international research attention. In the Shenhu area of the South China Sea, a large volume of drilling and laboratory data has been acquired in recent years, yet a comprehensive framework for evaluating the characteristics of key reservoir parameters remains underdeveloped. This study presents a spatially integrated and statistically grounded framework that captures regional-scale heterogeneity using multi-source in situ datasets. It incorporates semi-variogram modeling to assess spatial variability and provides statistical reference values for geological and geotechnical properties across the Shenhu Area. By synthesizing core sampling results, acoustic logging, and triaxial testing data, representative probability distributions and variability scales of hydrate saturation, porosity, permeability, and mechanical strength are derived, which are essential for numerical simulations of gas production and slope stability. Our results support the development of site-specific reservoir models and improve the reliability of early-phase hydrate exploitation assessments. This work facilitates the rapid screening of hydrate reservoirs, contributing to the efficient selection of potential production zones in hydrate-rich continental margins. Full article

The rapid development of Internet technology, while providing convenient services for users, has also aroused deep concern among the public about the issue of privacy leakage during image data transmission. To address this situation, this article proposes a color image encryption algorithm based on RNA extended dynamic coding and quantum chaos (CIEA-RQ). This algorithm significantly improves the ability of the system to withstand cryptographic attacks by introducing RNA extended dynamic encoding with 384 encoding rules. The employed quantum chaotic map improves the randomness of chaotic sequences and increases the key space. First, the algorithm decomposes the plaintext image into bit planes and obtains two parts, high 4-bit and low 4-bit planes, based on different weights of information. Then, the high 4-bit planes are partitioned into blocks and scrambled, and the scrambled planes are confused using RNA extended coding rules. Meanwhile, the low 4-bit planes employ a lightweight XOR operation to improve encryption efficiency. Finally, the algorithm performs cross-iterative diffusion on the processed high 4-bit and low 4-bit planes and then synthesizes a color ciphertext image. Experimental simulations and security assessments demonstrate the superior numerical statistical outcomes of the CIEA-RQ. According to the criteria of cryptanalysis, it can effectively resist known-plaintext attacks and chosen-plaintext attacks. Therefore, the CIEA-RQ presented in this article serves as an efficient digital image privacy safeguard technique, promising extensive applications in image secure transmission for the upcoming generation of networks. Full article

Catalytic combustion gas sensors are critical for safety and environmental monitoring, yet face persistent challenges in sensitivity and detection limits amid expanding market demands. This study innovatively employs attapulgite as a support material functionalized with noble metal catalyst Pd to fabricate a low-cost, high-sensitivity sensor. Characterization (SEM/EDS) confirms a unique Pd/attapulgite core–shell structure with engineered Pd gradient distribution (7.5–75.8 wt% from core to surface). The sensor achieves methane catalytic combustion below 300 °C, delivering 0.7 µV/ppm sensitivity and ~36 ppm detection limit. Reaction kinetics follow the Eley–Rideal mechanism, with voltage difference ($\mathsf{\Delta }\mathrm{U}$) versus methane concentration (C) conforming to the Langmuir equation ($\mathsf{\Delta }\mathrm{U}={\displaystyle \frac{{\mathrm{U}}_{\mathrm{m}\mathrm{a}\mathrm{x}}\cdot \mathrm{K}\cdot \mathrm{C}}{1+\mathrm{K}\cdot \mathrm{C}}}$, R² > 0.99, ${\mathrm{U}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ = 41.80 mV). Cost-effective fabrication and exceptional performance underscore its potential for practical deployment in industrial, residential, and environmental safety monitoring. Full article

Older adults, aged 65 years and older, develop and experience frailty at different rates. Yet, this heterogeneity is not well understood, nor are the factors, such as geographical residence, that influence different frailty trajectories and subsequent healthcare outcomes. We aim to identify factors that impact older adult frailty trajectories, skilled nursing facility (SNF) placement, and death. Medicare beneficiaries ≥ 65 years from the National Health and Aging Trend Study (2011–2021) with complete data using Fried’s frailty phenotype on ≥ 2 occasions (n = 6082) were included in the analysis. Rural/urban residence was defined using Office of Management and Budget criteria. Latent class growth analysis (LCGA) helped identify four frailty trajectories: improving, stable, mildly worsening, and drastically worsening. Cox proportional hazard analysis and logistic regression determined the association of social determinants of health (sex, race/ethnicity, education and income level, healthcare and transportation access, and social support) on death and SNF admission, respectively. The mean age was 75.12 years (SE 0.10); 56.4% female, 18.6% (n = 1133) rural residence. In the overall sample, 1094 (23.0%) older adults were classified as robust, 3242 (53.0%) as pre-frail, and 1746 (24.0%) as frail. Urban residence did not modify the relationship between frailty trajectories and SNF placement, nor did geographic residence on death. Higher income was associated with lower odds of a worse frailty trajectory, SNF admission, and a lower hazard of death, all reaching statistical significance. Future work should examine the factors that influence older adult participation in research and the impact of standardizing the definition of geographic rurality on older adult frailty and health outcomes. Full article

Coccidiosis is a major parasitic disease that suppresses poultry productivity and causes significant global economic losses. Currently, controlling Eimeria parasites relies primarily on the use of anticoccidial drugs or live vaccines. However, these conventional control strategies face the dual constraints of escalating drug resistance and unsustainable economic expenditures. In this study, the efficacy of a chimeric subunit vaccine comprising Eimeria maxima Elongation Factor-1α (EmEF1α) and chicken chemokine Ligand-1 (ChXCL1) was assessed for protection against experimental Eimeria maxima infection. The synthetic gene fragment ChXCL1-EmEF1α was ligated to the pET28a vector and expressed in vitro. Western blot analysis confirmed the successful expression of the recombinant ChXCL1-EmEF1α protein. Chickens immunized with the ChXCL1-EmEF1α exhibited a significantly stronger IgY response and higher secretion of IL-2 and IL-17 compared to those vaccinated with recombinant ChXCL1 alone or challenged solely with E. maxima. Furthermore, the ChXCL1-EmEF1α group demonstrated enhanced anticoccidial effects, including reduced intestinal lesions, higher body weight gain, and lower oocyst shedding compared to control groups. Following E. maxima challenge, the EmEF1α and ChXCL1-EmEF1α groups demonstrated robust protective efficacy, achieving high ACI values of 182 and 178, respectively. In contrast, the ChXCL1 and UC groups exhibited significantly lower ACI values (150 and 149, respectively), indicating minimal protection. This improvement was also reflected in the immune response, with significantly elevated levels of CD4⁺ and CD8⁺ T cells in the ChXCL1-EmEF1α-treated chickens. Moreover, ChXCL1 acts as an effective adjuvant when fused with EmEF1α, enhancing the vaccine’s anticoccidial efficacy. These results suggest that the ChXCL1-EmEF1α chimeric immunogen is a promising candidate for developing subunit vaccines against E. maxima infections. Full article

Mycoplasmal pneumonia of sheep (MPS), caused by Mesomycoplasma (Mycoplasma) ovipneumoniae, profoundly impacts ovine productivity and survival. Although gut–lung microbiota interactions are increasingly recognized in respiratory diseases, whether similar crosstalk occurs between the lung and rumen microbiota in MPS-affected sheep remains unknown. To investigate alterations in the lung and rumen microbiota of sheep with MPS, the crosstalk between these microbial communities, and their impacts on growth phenotypes. From a cohort of 414 naturally infected six-month-old male Hu sheep, we selected 10 individuals with severe pulmonary pathology and 10 healthy controls for detailed phenotypic and microbiome analyses. Assessment of 359 phenotypic traits revealed that MPS significantly impairs feed efficiency and growth rate (p < 0.05). Through 16S rRNA gene sequencing, we found that MPS significantly altered the pulmonary microbiota community structure (p < 0.01), with a noticeable impact on the rumen microbiota composition (p = 0.059). Succinivibrionaceae_UCG-001 was significantly depleted in both the rumen and lungs of diseased sheep (p < 0.05) and strongly associated with reduced average daily feed intake (p < 0.05). In addition, pulmonary Pasteurella and ruminal Succinivibrionaceae_UCG-002 were significantly enriched in MPS-affected sheep, showed a strong positive correlation (p < 0.05), and were both negatively associated with feed efficiency (p < 0.05). Notably, Pasteurella multocida subsp. gallicida may act as a keystone species influencing feed efficiency. These findings point to a previously unrecognized rumen-lung microbial axis that may modulate host productivity in sheep affected by MPS. This work provides new insights into the pathogenesis of MPS and offers potential targets for therapeutic intervention and management. Full article

To study the effect of aggregate type on the adhesion between asphalt and aggregate, limestone, basalt, diabase, and 70# asphalt with SBS asphalt were selected. The mineral phase composition of the aggregates was analyzed by X-ray diffraction. The surface energy theory was used to calculate the adhesion work and the work of flaking. The modified water boiling method combined with image processing technology was used to quantitatively characterize the flaking behavior of the asphalt. The results show that the aggregate type is closely related to the asphalt–aggregate adhesion. The mineral compositions of different types of aggregates vary significantly, with limestone, being a strongly alkaline aggregate predominantly comprising CaCO₃, exhibiting better adhesion with asphalt. The contact angle test and modified boiling method also yielded the same results, and the adhesion relationship with asphalt was limestone > basalt > diabase. Image processing technology effectively characterizes the spalling situation of asphalt and conducts quantitative analysis. Full article

Cutting beech plywood using abrasive water jet (AWJ) technology represents a significant area of research due to increasing demands for precision, quality, and environmental sustainability in manufacturing processes within the woodworking industry. AWJ technology enables non-contact cutting of materials without causing thermal deformation or mechanical damage, which is crucial for preserving the structural integrity and mechanical properties of the plywood. This article investigates cutting beech plywood using technical methods using an abrasive water jet (AWJ) at 400 MPa pressure, with Australian garnet (80 MESH) as the abrasive material. It examines how abrasive mass flow rate, traverse speed, and material thickness affect AWJ lag, which in turn influences both cutting quality and accuracy. Measurements were conducted with power abrasive mass flow rates of 250, 350, and 450 g/min and traverse speeds of 0.2, 0.4, and 0.6 m/min. Results show that increasing the abrasive mass flow rate from 250 g/min to 350 g/min slightly decreased the AWJ cut width by 0.05 mm, while further increasing to 450 g/min caused a slight increase of 0.1 mm. Changes in traverse speed significantly influenced cut width; increasing the traverse speed from 0.2 m/min to 0.4 m/min widened the AWJ by 0.21 mm, while increasing it to 0.6 m/min caused a slight increase of 0.18 mm. For practical applications, it is recommended to use an abrasive mass flow rate of around 350 g/min combined with a traverse speed between 0.2 and 0.4 m/min when cutting beech plywood with AWJ. This balance minimizes jet lag and maintains high surface quality comparable to conventional milling. For thicker plywood, reducing the traverse speed closer to 0.2 m/min and slightly increasing the abrasive flow should ensure clean cuts without compromising surface integrity. Full article

The emergence of SARS-CoV-2 variants highlights the urgent need for novel therapeutic strategies, particularly entry inhibitors that could efficiently prevent viral infection. Medicinal herbs and herbal combination formulas have long been recognized for their effects in treating infectious diseases and their antiviral properties, thus providing abundant resources for the discovery of antiviral candidates. While many candidates have been suggested to have antiviral activity against SARS-CoV-2 infection, few have been validated for their mechanisms, including possible effects on viral entry. This study aimed to identify SARS-CoV-2 entry inhibitors from medicinal herbs and herbal formulas that are known for heat-clearing and detoxifying properties and/or antiviral activities. A SARS-CoV-2 pseudoparticle (SARS-CoV-2pp) system was used to assess mechanism-specific entry inhibition. Our results showed that the methanol extract of Anemarrhena asphodeloides rhizome, as well as the water extracts of Cimicifuga foetida rhizome, Xiao Chai Hu Tang (XCHT), and Sheng Ma Ge Gen Tang (SMGGT), have substantial inhibitory effects on the entry of SARS-CoV-2pps into host cells. Given the observation that Cimicifuga foetida exhibited the most potent inhibition and is a constituent of SMGGT, we further investigated the major compounds of the herb and identified caffeic acid as a bioactive component for blocking SARS-CoV-2pp entry. Entry inhibition of Cimicifuga foetida and caffeic acid was validated on both wild-type and the currently dominant JN.1 strain SARS-CoV-2pp systems. Moreover, caffeic acid was able to both inactivate the pseudoparticles and prevent their entry into pretreated host cells. The results support the traditional use of these herbal medicines and underscore their potential as valuable resources for identifying active compounds and developing therapeutic entry inhibitors for the management of COVID-19. Full article

A total of 32 fish species were detected in Xinglin Bay using a combination of environmental DNA metabarcoding (eDNA) and traditional morphological survey methods (TSM), covering eight orders, fifteen families, and twenty-six genera. The dominant order was Perciformes, accounting for 43.75% of the total species. Among the identified species, there were ten non-native fish species. Compared with the TSM, the eDNA detected 13 additional fish species, including two additional non-native fish species—Gambusia affinis (Baird and Girard, 1853) and Micropterus salmoides (Lacepède, 1802). In addition, the relative abundance of fish from both methods revealed that tilapia was overwhelmingly dominant, accounting for 80.75% and 75.68%, respectively. Furthermore, the AS-ISK assessment revealed that all non-native fish species were classified as medium or high-risk, with five identified as high-risk species, four of which belong to tilapia. These findings demonstrated that tilapia are the dominant and high-risk invasive species in Xinglin Bay and should be prioritized for management. Population reduction through targeted harvesting of tilapia is recommended as the primary control strategy. Additionally, the study highlights the effectiveness of eDNA in monitoring fish community structure in brackish ecosystems. Full article

Chrysanthemum × morifolium (Ramat) Hemsl. (Hangbaiju), which has been widely consumed as a herbal tea for over 3000 years, is renowned for its biosafety and diverse bioactivities. This study investigates the impact of polyphenol-rich Hangbaiju extracts (HE) on high-fat diet-induced obesity in mice. HE contains phenolic acids and flavonoids with anti-obesity properties, such as apigenin, luteolin-7-glucoside, apigenin-7-O-glucoside, kaempferol 3-(6″-acetylglucoside), etc. To establish the obesity model, mice were randomly assigned into four groups (n = 8 per group) and administered with either HE or water for 42 days under high-fat or low-fat dietary conditions. Administration of low (LH) and high (HH) doses of HE both significantly suppressed body weight growth (by 16.28% and 16.24%, respectively) and adipose tissue enlargement in obese mice. HE significantly improved the serum lipid profiles, mainly manifested as decreased levels of triglycerides (28.19% in LH and 19.59% in HH) and increased levels of high-density lipoprotein cholesterol (44.34% in LH and 54.88% in HH), and further attenuated liver lipid deposition. Furthermore, HE significantly decreased the Firmicutes/Bacteroidetes ratio 0.23-fold (LH) and 0.12-fold (HH), indicating an improvement in the microecological balance of the gut. HE administration also elevated the relative abundance of beneficial bacteria (e.g., Allobaculum, norank_f__Muribaculaceae), while suppressing harmful pathogenic proliferation (e.g., Dubosiella, Romboutsia). In conclusion, HE ameliorates obesity and hyperlipidemia through modulating lipid metabolism and restoring the balance of intestinal microecology, thus being promising for obesity therapy. Full article

Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation $SD$ decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article

To develop high-performance electrode materials for supercapacitors, in this paper, a heterostructured composite material of cerium sulfide and zinc sulfide quantum dots (CeS₂/ZnS QD) was successfully prepared by hydrothermal method. Characterization through scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM) showed that ZnS QD nanoparticles were uniformly composited with CeS₂, effectively increasing the active sites surface area and shortening the ion diffusion path. Electrochemical tests show that the specific capacitance of this composite material reaches 2054 F/g at a current density of 1 A/g (specific capacity of about 256 mAh/g), significantly outperforming the specific capacitance of pure CeS₂ 787 F/g at 1 A/g (specific capacity 98 mAh/g). The asymmetric supercapacitor (ASC) assembled with CeS₂/ZnS QD and activated carbon (AC) retained 84% capacitance after 10,000 charge–discharge cycles. Benefited from the synergistic effect between CeS₂ and ZnS QDs, the significantly improved electrochemical performance of the composite material suggests a promising strategy for designing rare-earth and QD-based advanced energy storage materials. Full article

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article