Search Results (65,524)

A sublingual vaccine comprising the Poly(I:C) adjuvant and influenza HA antigen was evaluated for safety in both mice and macaque monkeys relative to its intranasal counterpart. Safety was assessed in terms of harmful effects corresponding to the upregulation of the inflammation-associated genes Saa3, Tnf, IL6, IL1b, Ccl2, Timp1, C2, Ifi47, Aif1, Omp, Nos2, and/or Gzmb in mice and SAA2, TNF, IL6, IL1B, CCL2, TIMP, C2, AIF1, and GZMB in macaques. Quantitative gene expression analyses were performed using RT-qPCR with RNA samples from four tissue types, the olfactory bulb, pons, lung, tongue, and lymph node, from both mice and macaques. In mice, the intranasally delivered vaccine markedly upregulated the inflammation-related genes in the olfactory bulb 1 day and 7 days after vaccination. The adverse effects of intranasal vaccination were also observed in macaques, albeit to a lesser extent than in mice. The intranasal vaccination also upregulated these genes in the pons of both mice and macaques. In contrast, the sublingual vaccine did not adversely affect the olfactory bulb or pons in either mice or macaques. The intranasally administered vaccine significantly upregulated these genes in the lungs only 1 day after vaccination, but not 7 days later, in both mice and macaques. We conclude that intranasal vaccination results in unfavorable side effects corresponding to upregulated inflammatory genes in the brain (olfactory bulb and pons). Sublingual vaccination, however, did not induce these side effects in either mice or macaques and was hence evaluated as safe. Full article

Stearic acid was used to modify the surface of a mixed flame-retardant powder consisting of aluminum hydroxide and ammonium polyphosphate by an uneven nucleation method, aiming to improve its dispersion in a vinyl resin matrix. This study investigated the effect of stearic acid dosage on the powder’s surface modification, characterized by infrared spectroscopy, activation degree, and laser particle size distribution. The dispersion of the modified powder in the resin matrix was evaluated by measuring the system viscosity, scanning electron microscopy (SEM) images, and bending performance. The results indicated that when the stearic acid content was 1%, the powder exhibited the best overall coating effect, with a uniform particle size distribution and an activation degree of 73.6%. After the composite material was added to the resin, the system viscosity was 923 mPa·s, and SEM images showed good dispersion of the powder in the resin matrix. The cured resin demonstrated a bending strength of 41.86 MPa. However, the flame retardancy slightly decreased, with the limiting oxygen index (LOI) dropping from 24.6% for the unmodified sample to 24.0%. When the stearic acid content exceeded 1%, the powder’s particle size increased dramatically. Although the activation degree also increased, the improvement was not significant. The addition of the powder to the resin resulted in a higher system viscosity, and the flame retardancy deteriorated sharply, with the vertical burning rating dropping from FV-1 to “--”. Considering flame retardancy, mechanical properties, and processing performance, the composite material with 1% stearic acid demonstrated the best overall performance. Full article

Background and Objectives: Oral isotretinoin has revolutionized the treatment of severe acne vulgaris. Isotretinoin is associated with multiple adverse effects, one of which is dyslipidemia (DLP). Materials and Methods: This single-center prospective study recruited 498 patients who were eligible for isotretinoin for severe acne. Risk factors for hyperlipidemia and serum lipids were assessed at baseline. Patients received daily doses ranging from 0.25 to 1 mg/kg of their body weight, and their fasting serum lipids were checked regularly until they reached a cumulative dose of 120–150 mg/kg. Our primary objective is to investigate dyslipidemia incidence and predictors, while the secondary objective is to assess the impact of dose reduction on lipid panels. Results: Our sample was primarily female (n = 380, 76.3%), with a normal Body Mass Index (23.2 ± 4.0) and a mean age of 20.7 (±4.1) years. About 72.5% had a family history of acne, 17.1% a family history of dyslipidemia. Around 17.3% reported tobacco use. A total of 57 (11.4%) patients on isotretinoin developed DLP. Smoking was independently associated with a higher risk of dyslipidemia (OR 1.97, 95% CI [1.01, 3.82], p = 0.046). The mean onset of DLP was at 3.23 (±2.13) months. A total of 52 patients out of the 57 had a dose reduction of 10 mg (n = 5) or 20 mg (n = 47). A dose reduction of 50% was found to significantly improve triglyceride levels. Conclusions: More than 1 out of 10 patients on isotretinoin developed DLP. Tobacco use was significantly associated with developing DLP. Dose reduction significantly impacted a decrease in triglyceride levels. Full article

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry. Full article

Background/Objectives: Intestinal alkaline phosphatase (IAP) is an enzyme expressed in the intestinal brush border, which may exert anti-inflammatory effects by detoxifying lipopolysaccharides (LPSs), thereby preventing metabolic disorders. Various food components have been reported to influence IAP activity. However, few studies have evaluated the effects of fermented milk on IAP activity. In this study, we aimed to investigate fermented milk with high IAP-activating capacity and investigate its effect. Methods: We screened a skim milk culture (SC), a fermented milk model, using differentiated Caco-2 cells. We investigated the effect of SC on IAP activity and gene expression in the Drosophila midgut. Quantitative PCR and immunoblot assays were conducted to examine gene and protein levels. Results: Among the SC samples from different lactic acid bacteria or bifidobacteria, the SC of Lactobacillus johnsonii SBT0309 (LJ0309 SC) demonstrated a particularly strong capacity to activate IAP in Caco-2 cells, demonstrated by significantly increased IAP gene expression and protein levels in Caco-2 cells. Additionally, LJ0309 SC inhibited increased secretion of IL-8 in LPS-stimulated Caco-2 cells. Finally, in Drosophila melanogaster fed LJ0309 SC, we observed an increase in both IAP activity and gene expression in the midgut. Conclusions: LJ0309 SC increased IAP activity and gene expression in both Caco-2 cells and the Drosophila midgut, and inhibited the inflammatory response in LPS-stimulated Caco-2 cells. Although further in vivo studies are required, LJ0309 SC might help to ameliorate LPS-induced inflammation and disease via IAP activation. Full article

Invasive insect pests pose a significant threat to forest ecosystems. Effective pest management relies on detecting these pests, which can be challenging when populations are sparse, newly introduced, or not easily observable. The spotted lanternfly (Lycorma delicatula White), a recent invader to North America, has caused extensive damage across the eastern United States since its introduction in 2014. Conventional monitoring methods, such as traps or visual inspections, are limited in their spatial coverage and may not reliably attract or capture target species. In this study, we explored the potential of bat guano as an additional tool for invasive insect detection. We collected guano samples from five bat species across three forested sites in New Jersey, USA, between 2018 and 2022 and used species-specific quantitative PCR (qPCR) to detect spotted lanternfly DNA. Spotted lanternfly DNA was detected in guano from two bat species: big brown bats (Eptesicus fuscus) and eastern red bats (Lasiurus borealis). Detection probability was strongly influenced by spotted lanternfly phenology, with higher detection rates occurring during the adult life stage. The detection of spotted lanternfly DNA in bat guano demonstrates the feasibility of using guano analysis as a complementary tool for insect pest surveillance. Integrating guano-based monitoring with traditional methods could enhance insect pest detection efforts across diverse forested and agricultural landscapes. Full article

Due to the narrow nulls formed by the Power Inversion (PI) algorithm, it fails to suppress jamming signals in highly dynamic scenarios effectively. This paper proposes a null broadening algorithm based on eigenvalue sorting. Unlike other algorithms, this one does not require prior knowledge of the direction of jamming. It is based on the Covariance Matrix Taper (CMT) algorithm, which orders the eigenvalues of the sampling covariance matrix. The sample covariance matrix’s eigenvalues are sorted to provide new sample data, and the rebuilt covariance matrix is then averaged forward and backward. The experimental results demonstrate that the proposed algorithm can effectively broaden the null. Compared with the CMT algorithm, the null in the jamming direction is, on average, approximately 22 dB deeper under the experimental conditions, and the gain in the direction of the sound signal is increased by around 15 dB. Moreover, the signal can be successfully acquired even when the input jamming-to-signal ratio (ISR) is relatively low. When there is a deviation in the jamming direction, the proposed algorithm demonstrates robust null broadening performance even with a small number of snapshots. The output SINR of the proposed algorithm exhibits a nearly linear relationship with the input SNR. Full article

Many studies have shown that the thermal evolution degree is the main factor affecting the micropore structure of coal reservoirs. However, within the same thick coal seam, the R_o,max of the entire coal seam is not much different, which affects the determination of the main controlling factors of pore structure heterogeneity. Therefore, No. 8 coal collected from Benxi Formation in the eastern margin of Ordos was taken as an example, and 16 samples were selected for low-temperature liquid nitrogen, carbon dioxide adsorption, and industrial component tests. Based on heterogeneity differences of R_o,max, industrial components and pore volume distribution of adsorption pores (pore diameter is less than 100 nm), the main controlling factors affecting the micropore structure of ultra-thick coal seams, were discussed. Then, the surface free energy theory was used to study the influencing factors affecting surface free energy variations during coal adsorption. First of all, R_o,max is not the main controlling factor affecting the micropore-fracture structure, as the effects of industrial components on the micropore structure are obvious, which indicates that industrial components are the main factors affecting vertical differences in the micropore structure within the same thick coal seam. Second of all, R_o,max and industrial components affect the adsorption process. When the adsorption pressure is lower, the adsorption volume and adsorption potential increase rapidly. When the adsorption pressure is higher (pressure is larger than 15 Mpa), the adsorption capacity and potential tend to be stable. Moreover, the maximum surface free energy increases with the increase in coal rank, which indicates that the degree of thermal evolution is the core factor affecting the adsorption free energy, but it is also controlled by the influence of industrial components (ash content). Lastly, micropores affect the adsorption capacity, and mesopores have little effect on the adsorption capacity, since micropores restrict the adsorption capacity and change the adsorption process by affecting surface free energy variations. The refined characterization of pore-fracture structures in deep coal reservoirs plays a crucial role in the occurrence and seepage of coalbed gas. This research can provide a theoretical basis for the efficient development of deep coalbed gas in the target area. This study aims to identify the primary factors controlling micropore structures in No. 8 coal from the Benxi Formation and to analyze the role of industrial components, which has been overlooked in previous research. Full article

Aimed at the limitation that existing hyperspectral classification methods were mainly oriented to small-scale images, this paper proposed a new large-scale hyperspectral remote sensing image classification method, LS3EU-Net++ (Lightweight Encoder and Integrated Spatial Spectral Squeeze and Excitation U-Net++). The method optimized the U-Net++ architecture by introducing a lightweight encoder and combining the Spatial Spectral Squeeze and Excitation (S3E) Attention Module, which maintained the powerful feature extraction capability while significantly reducing the training cost. In addition, the model employed a composite loss function combining focal loss and Jaccard loss, which could focus more on difficult samples, thus improving pixel-level accuracy and classification results. To solve the sample imbalance problem in hyperspectral images, this paper also proposed a data enhancement strategy based on “copy–paste”, which effectively increased the diversity of the training dataset. Experiments on large-scale satellite hyperspectral remote sensing images from the Zhuhai-1 satellite demonstrated that LS3EU-Net++ exhibited superiority over the U-Net++ benchmark. Specifically, the overall accuracy (OA) was improved by 5.35%, and the mean Intersection over Union (mIoU) by 12.4%. These findings suggested that the proposed method provided a robust solution for large-scale hyperspectral image classification, effectively balancing accuracy and computational efficiency. Full article

The analysis of gunshot residues (GSRs) is a critical component of criminal investigations, linking suspects to firearms or shooting incidents. Among the various analytical techniques employed, scanning electron microscopy (SEM) has emerged as a valuable tool due to its ability to provide high-resolution imaging and detailed elemental composition analysis of GSR particles. Recent technological advancements have significantly enhanced the effectiveness of SEM in GSR analysis, incorporating improved detectors and software that facilitate the more accurate detection and characterization of GSR particles. To ensure the reliability of SEM-based GSR analysis, it is essential to adhere to established methodologies for sample collection and preparation, as well as to implement best practices in data interpretation within the forensic context. Through a narrative review, this paper aims to explore the application of SEM techniques for GSR analysis, elucidate the methodological approaches that underpin effective forensic investigations, and highlight the advantages and limitations of SEM, thereby addressing the ongoing challenges and opportunities in the field. Full article

The accurate and efficient simulation of seismic wave energy dissipation and phase dispersion during propagation in subsurface media due to inelastic attenuation is critical for the hydrocarbon-bearing distinction and improving the quality of seismic imaging in strongly attenuating geological media. The fractional viscoelastic equation, which quantifies frequency-independent anelastic effects, has recently become a focal point in seismic exploration. We have developed a novel hybrid staggered-grid Grünwald–Letnikov (HSGGL) finite difference method for solving the fractional viscoelastic equation in the time domain. The proposed method achieves accurate and computationally efficient solutions by using a staggered grid to discretize the first-order partial derivatives of the velocity–stress equations, combined with Grünwald–Letnikov finite difference discretization for the fractional-order terms. To improve the computational efficiency, we employ a preset accuracy to truncate the difference stencil, resulting in a compact fractional-order difference scheme. A stability analysis using the eigenvalue method reveals that the proposed method confers a relaxed stability condition, providing greater flexibility in the selection of sampling intervals. The numerical experiments indicate that the HSGGL method achieves a maximum relative error of no more than 0.17% compared to the reference solution (on a finely meshed domain) while being significantly faster than the conventional global FD method (GFD). In a 500 × 500 computational domain, the computation times for the proposed methods, which meet the specified accuracy levels used, are only approximately 4.67%, 4.47%, 4.44%, and 4.42% of that of the GFD method. This indicates that the novel HSGGL method has the potential as an effective forward modeling tool for understanding complex subsurface structures by employing a fractional viscoelastic equation. Full article

The extensive interconnection and intelligent collaboration of multi-source heterogeneous devices in the industrial Internet environment have significantly improved the efficiency of industrial production and resource utilization. However, at the same time, the deployment characteristics of open-network architecture and the promotion of the concept of deep integration of OT/IT have led to an exponential growth of attacks on the industrial Internet. At present, most of the detection methods for industrial internet attacks use deep learning. However, due to the black-box characteristics caused by the complex structure of deep learning models, the explainability of industrial internet detection results generated based on deep learning is low. Therefore, we proposed an industrial internet intrusion response method xIIRS based on explainable deep learning. Firstly, an explanation method was improved to enhance the explanation by approximating and sampling the historical input and calculating the dynamic weighting for the sparse group lasso based on the evaluation criteria for the importance of features between and within feature groups. Then, we determined the defense rule scope based on the obtained explanation results and generated more fine-grained defense rules to implement intrusion response in combination with security constraints. The proposed method was experimented on two public datasets, TON_IoT and Gas Pipeline. The experimental results show that the explanation effect of xIIRS is better than the baseline method while achieving an average malicious traffic blocking rate of about 95% and an average normal traffic passing rate of about 99%.  Full article

Background/Objectives: Vascular aging is associated with increased arterial stiffness and changes in the wall structure, leading to a loss of elasticity. Silicon is abundant in arteries and plays a key role in the synthesis and stabilization of elastin fibers. In animal models of accelerated cardiovascular aging, a specific nutritional supplement based on silicon-enriched spirulina (SpSi) has been shown to have beneficial effects on vascular function. The present study, designed as a randomized, double-blind, placebo-controlled trial, aimed to evaluate the effectiveness of this SpSi supplement on aging-related changes in vascular function among healthy older adults. Methods: Here, 120 healthy volunteers aged 60–75 years were enrolled and randomly assigned to either the SpSi group (n = 60) or placebo group (n = 60). Over 6 months, the participants received either 3.5 g of specific 1% silicon-enriched spirulina (SpSi group) or placebo tablets daily. The primary outcome was the assessment of arterial wall pressure waveforms, which included blood pressure (BP) readings and the determination of the aortic pulse wave velocity (aPWV). Secondary outcomes included the vasomotor endothelial function through post-ischemic vasorelaxation, measured using the reactive hyperemia index (RHI), and carotid intima–media thickness. Results: When considering the entire sample, none of the studied parameters differed between the placebo and SpSi groups. However, when focusing on individuals with high–normal blood pressure (i.e., systolic BP between 130 and 150 mmHg) and aPWV levels above cutoff values (>10 m/s), the BP decreased by 8% (p < 0.001) and aPWV decreased by 13.5% (p < 0.0001) in subjects receiving SpSi. In individuals with BP and aPWV levels below the cutoff values, no effect was observed. Conclusion: In healthy elderly individuals, SpSi supplementation improved high–normal blood pressure and aortic pulse wave velocity, suggesting an enhanced vascular function. This trial was registered at clinicaltrials.gov as NCT03464760. Full article

Ocean warming poses significant threats to coastal ecosystems. This study investigates the impact of thermal effluents from power plants, as proxies for climate-driven temperatures increase, on Posidonia oceanica meadows and associated fish communities. Using a gradient-based approach, we analyzed environmental variables, seagrass indicators, fish assemblages, and functional group (FG) dynamics across a thermal gradient extending from the effluent outfall itself. Results indicate that temperature is the dominant factor influencing P. oceanica, with reduced leaf length, shoot density, and rhizome weight characterizing samples closest to the effluent. Despite compensatory mechanisms, the overall photosynthetic biomass and resilience declined under thermal stress. Fish assemblages exhibited reduced species richness and biodiversity close to the thermal effluent, with opportunistic and thermophilic species dominating. An FG analysis revealed disrupted seasonal patterns, shifts in trophic dynamics, and functional compensation among species, highlighting potential ecological imbalances. Notably, transient predators thrived near the effluent, while more sedentary and temperate species were displaced. These findings underscore the cascading effects of rising temperatures on coastal habitats such as P. oceanica meadows and their associated communities, emphasizing the urgency for conservation measures. By identifying critical thresholds and adaptive responses, this study contributes valuable insights into the consequences of localized impacts of thermal stress on coastal biodiversity and ecosystem services. Full article

In recent years, the use of street-view images for urban analysis has received much attention. Despite the abundance of raw data, existing supervised learning methods heavily rely on large-scale and high-quality labels. Faced with the challenge of label scarcity in urban scene classification tasks, an innovative self-supervised learning framework, Trilateral Redundancy Reduction (Tri-ReD) is proposed. In this framework, a more restrictive loss, “trilateral loss”, is proposed. By compelling the embedding of positive samples to be highly correlated, it guides the pre-trained model to learn more essential representations without semantic labels. Furthermore, a novel data augmentation strategy, tri-branch mutually exclusive augmentation (Tri-MExA), is proposed. Its aim is to reduce the uncertainties introduced by traditional random augmentation methods. As a model pre-training method, Tri-ReD framework is architecture-agnostic, performing effectively on both CNNs and ViTs, which makes it adaptable for a wide variety of downstream tasks. In this paper, 116,491 unlabeled street-view images were used to pre-train models by Tri-ReD to obtain the general representation of urban scenes at the ground level. These pre-trained models were then fine-tuned using supervised data with semantic labels (17,600 images from BIC_GSV and 12,871 from BEAUTY) for the final classification task. Experimental results demonstrate that the proposed self-supervised pre-training method outperformed the direct supervised learning approaches for urban functional zone identification by 19% on average. It also surpassed the performance of models pre-trained on ImageNet by around 11%, achieving state-of-the-art (SOTA) results in self-supervised pre-training. Full article