The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulner...Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.展开更多
Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.Howev...Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.展开更多
Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Althoug...Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
BACKGROUND To prevent mother to child transmission(MTCT)of human immunodeficiency virus(HIV),sustained maternal viral load suppression(VLS)and early HIV testing among HIV exposed infants(HEI)is critical.AIM To investi...BACKGROUND To prevent mother to child transmission(MTCT)of human immunodeficiency virus(HIV),sustained maternal viral load suppression(VLS)and early HIV testing among HIV exposed infants(HEI)is critical.AIM To investigate maternal viral load results and infant HIV testing uptake at 6-weeks,and 9-months and 18-months in Rwanda.METHODS Between 2015 and 2022,VLS(<200 copies/mL)was measured among pregnant women living with HIV(WLHIV)from 38-healthcare facilities.Viral loads(VL)were measured at 6-months,12-months and 24-months,respectively.For maternal VL,the unit of analysis was visit-pair,and the pairs were created to define those with VL<200 copies/mL at two consecutive visits as having sustained VLS,persistent viremia(VL≥200 copies/mL at two consecutive visits),viral rebound(VL<200 copies/mL at prior visit only)and newly suppressed(VL<200 copies/mL at subsequent visit only).HEI were considered to have persistent HIV testing if they had all three HIV tests.Poisson regression models with generalized estimating equations were used to estimate the adjusted incidence rate ratio(aIRR)and 95%CI for factors associated with sustained VLS and persistent HIV testing.RESULTS A total of 1145 mother-infant pairs were analyzed.Infant HIV testing uptake at 6-weeks,9-months and 18-months was 1145(100.0%),1089(95.1%),1006(87.9%)respectively.Nine hundred ninety-nine HEI(87.3%)tested for HIV persistently.At 18-months,the incidence of HIV among HEI was 8(0.7%).Of 1145 mothers,1076(94.0%)had≥2 VL results making a total of 2010 visit-pairs(142-single;934-double visit-pairs).The incidence rate of sustained VLS,persistent viremia,viral rebound and new suppression were 91.0%,1.3%,3.6%and 4.0%respectively.Maternal disclosure of HIV status(aIRR=1.08,95%CI:1.02-1.14)was associated with increased likelihood of sustained VLS.Having peer support(aIRR=1.0595%CI:1.01-1.10)was associated with persistent HIV testing among HEI.CONCLUSION Sustained VLS is high among pregnant WLHIV in Rwanda.The low incidence of HIV among HEI may be attributed to high VLS levels.Targeted interventions,including enhanced HIV disclosure and peer support,are crucial for improving sustained VLS and increasing infant HIV testing uptake to reduce MTCT.展开更多
Diel investigations of water environments are one means to holistically understand the dynamics and functional roles of phytoplankton,bacteria and viruses in these ecosystems.They have the potential to substantially i...Diel investigations of water environments are one means to holistically understand the dynamics and functional roles of phytoplankton,bacteria and viruses in these ecosystems.They have the potential to substantially impact carbon(C),nitrogen(N)and phosphorus(P)biogeochemistry through their respective roles.This study characterizes the phytoplankton,bacteria and virus communities and the elemental composition of various C,N and P nutrients flow over three diel cycles in tropical urban lake.Our results show that ratios of C:N:P fluctuated strongly from the lack of dissolved organic phosphorus(DOP)and PO_(4).Specifically,green algae peaked during day time and exudate dissolved organic matter(DOM)that strongly modulate dissolved organic carbon(DOC):DOP ratio to diel DOP limitation.Multiple linear regression and Stella modelling emphasize the roles of viruses together with Synechococcus as important nutrient recyclers of NH_(4)and PO_(4)in nutrients-limited waters.Respective normalised surface PO_(4)and combined surface and bottom NH_(4)concentration selected both viruses and Synechococcus as important drivers.Process model of N and P biogeochemical cycles can achieve 69%and 57%similar to observed concentration of NH_(4)and PO_(4),respectively.A short latent period of 9 hr was calculated,in addition to the calibrated high infectivity of viruses to Synechococcus.Taken together,the rapid turn-over between Synechococcus and viruses has biogeochemical significance,where the rapid recycling of essential nutrients allows for shortcuts in the N and P cycle,supporting a wide range of microbes.展开更多
A complex system is inherently high-dimensional.Recent studies indicate that,even without complete knowledge of its evolutionary dynamics,the future behavior of such a system can be predicted using time-series data(da...A complex system is inherently high-dimensional.Recent studies indicate that,even without complete knowledge of its evolutionary dynamics,the future behavior of such a system can be predicted using time-series data(data-driven prediction).This suggests that the essential dynamics of a complex system can be captured through a low-dimensional representation.Virus evolution and climate change are two examples of complex,time-varying systems.In this article,we show that mutations in the spike protein provide valuable data for predicting SARS-CoV-2 variants,forecasting the possible emergence of the new macro-lineage Q in the near future.Our analysis also demonstrates that carbon dioxide concentration is a reliable indicator for predicting the evolution of the climate system,extending global surface air temperature(GSAT)forecasts through 2500.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resi...Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
Sulfate attack-induced expansion of cement-treated aggregates in seasonally frozen regions is a well-known issue which causes continuous expansion in railway subgrades,and particularly in high-speed railways.According...Sulfate attack-induced expansion of cement-treated aggregates in seasonally frozen regions is a well-known issue which causes continuous expansion in railway subgrades,and particularly in high-speed railways.Accordingly,we investigated the influence of material proportions,the number of freeze-thaw(FT)cycles,and temperature gradients on the expansion mechanism of sulfate attack on cement-treated aggregates subjected to FT cycles.The conditions,laws,and dominant factors causing the expansion of aggregates were analyzed through swelling tests.The results indicate that under FT cycles,3%content cement-treated graded macadam only experiences slight deformation.The maximum strain of graded macadam attacked by 1%sodium sulfate content in each FT cycle is significantly larger than that of 3%content cement-treated graded macadam attacked by 1%sodium sulfate content.Using scanning electron microscopy,needle-like crystals were observed during sulfate attack of cement-treated graded macadam.Through quantitative analysis,we determined the recoverable and unrecoverable deformations of graded macadam under FT cycles.For graded macadam under sulfate attack,the expansion is mainly induced by periodic frost heave and salt expansion,as well as salt migration.For cement-treated graded macadam under sulfate attack,the expansion is mainly induced by chemical attack and salt migration.This study can serve as a reference for future research on the mechanics of sulfate attack on cement-treated aggregates that experience FT cycles,and provide theoretical support for methods that remediate the expansion induced by sulfate attack.展开更多
Unsteady aerodynamic characteristics at high angles of attack are of great importance to the design and development of advanced fighter aircraft, which are characterized by post-stall maneuverability with multiple Deg...Unsteady aerodynamic characteristics at high angles of attack are of great importance to the design and development of advanced fighter aircraft, which are characterized by post-stall maneuverability with multiple Degrees-of-Freedom(multi-DOF) and complex flow field structure.In this paper, a special kind of cable-driven parallel mechanism is firstly utilized as a new suspension method to conduct unsteady dynamic wind tunnel tests at high angles of attack, thereby providing experimental aerodynamic data. These tests include a wide range of multi-DOF coupled oscillatory motions with various amplitudes and frequencies. Then, for aerodynamic modeling and analysis, a novel data-driven Feature-Level Attention Recurrent neural network(FLAR) is proposed. This model incorporates a specially designed feature-level attention module that focuses on the state variables affecting the aerodynamic coefficients, thereby enhancing the physical interpretability of the aerodynamic model. Subsequently, spin maneuver simulations, using a mathematical model as the baseline, are conducted to validate the effectiveness of the FLAR. Finally, the results on wind tunnel data reveal that the FLAR accurately predicts aerodynamic coefficients, and observations through the visualization of attention scores identify the key state variables that affect the aerodynamic coefficients. It is concluded that the proposed FLAR enhances the interpretability of the aerodynamic model while achieving good prediction accuracy and generalization capability for multi-DOF coupling motion at high angles of attack.展开更多
The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an ...The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.展开更多
AIM:To investigate the etiology of ocular pathogens and to establish the various pathogens present in human immunodeficiency virus(HIV)patients with cytomegalovirus retinitis(CMVR).METHODS:A total of 17 HIV-infected p...AIM:To investigate the etiology of ocular pathogens and to establish the various pathogens present in human immunodeficiency virus(HIV)patients with cytomegalovirus retinitis(CMVR).METHODS:A total of 17 HIV-infected patients with concomitant eye disorders were enrolled.Patients were divided into CMVR group(10 patients,18 eyes)and non-CMVR group(7 patients,9 eyes)based on clinical manifestations and the presence of cytomegalovirus(CMV)-DNA in ocular specimens.The viral load of CMV was assessed using polymerase chain reaction in aqueous humor,vitreous fluid,and peripheral blood samples of patients in the CMVR group.Additionally,peripheral blood CD4^(+)T cell counts were measured in both groups.RESULTS:In the CMVR group,the CMV-DNA load in the vitreous and aqueous humor samples was substantially higher than in the peripheral blood samples(P<0.01).CMVDNA load in the aqueous humor and vitreous samples of the two eyes in the CMVR group was determined to be statistically significant(10 patients,16 eyes,P=0.018,0.012).Peripheral blood CD4^(+)T cell counts in the CMVR group were adversely linked with the CMV-DNA load in both the aqueous humor and peripheral blood(P=0.005,0.048).Compared with the non-CMVR group,the peripheral blood CD4^(+)T cell count in the CMVR group decreased significantly(P=0.014).The peripheral blood CD4^(+)T cell count exceeded 300 cells/μL in 85.71%of non-CMVR patients,whereas it was below 100 cells/μL in 90.00%of the CMVR group.The intraocular specimens of the patients who underwent CMVR testing did not include any additional infections.CONCLUSION:In HIV-associated CMVR patients,there may exist alternative,yet unidentified,infection pathways for intraocular CMV in addition to the conventional route.The substantial difference in CMV-DNA load between the eyes of most CMVR patients suggests that CMV may originate from different sources in each eye.The proportion of peripheral blood CD4^(+)T cells in HIV patients is negatively correlated with the quantity of CMV viruses in their eyes.The peripheral blood count of<100 cells/μL indicates a considerable increase in the risk of concurrent CMVR.Multi-ocular pathogen presentations are uncommon in HIV individuals with CMVR.展开更多
To improve the vertical axis wind turbine(VAWT)design,the angle of attack(AOA)and airfoil data must be treated correctly.The present paper develops a method for determining AOA on a VAWT based on computational fluid d...To improve the vertical axis wind turbine(VAWT)design,the angle of attack(AOA)and airfoil data must be treated correctly.The present paper develops a method for determining AOA on a VAWT based on computational fluid dynamics(CFD)analysis.First,a CFD analysis of a two-bladed VAWT equipped with a NACA 0012 airfoil is conducted.The thrust and power coefficients are validated through experiments.Second,the blade force and velocity data at monitoring points are collected.The AOA at different azimuth angles is determined by removing the blade self-induction at the monitoring point.Then,the lift and drag coefficients as a function of AOA are extracted.Results show that this method is independent of the monitoring points selection located at certain distance to the blades and the extracted dynamic stall hysteresis is more precise than the one with the“usual”method without considering the self-induction from bound vortices.展开更多
Several experimental evidence suggests a link between brain Herpes simplex virus type-1 infection and the occurrence of Alzheimer’s disease.However,the molecular mechanisms underlying this association are not complet...Several experimental evidence suggests a link between brain Herpes simplex virus type-1 infection and the occurrence of Alzheimer’s disease.However,the molecular mechanisms underlying this association are not completely understood.Among the molecular mediators of synaptic and cognitive dysfunction occurring after Herpes simplex virus type-1 infection and reactivation in the brain neuroinflammatory cytokines seem to occupy a central role.Here,we specifically reviewed literature reports dealing with the impact of neuroinflammation on synaptic dysfunction observed after recurrent Herpes simplex virus type-1 reactivation in the brain,highlighting the role of interleukins and,in particular,interleukin 1βas a possible target against Herpes simplex virus type-1-induced neuronal dysfunctions.展开更多
Artemisia annua L.is a medicinal herb with multiple therapeutic applications,whereas its antiinfluenza A virus(IAV)efficiency and mechanism of action are still unclear.Here,we investigated the inhibition activity and ...Artemisia annua L.is a medicinal herb with multiple therapeutic applications,whereas its antiinfluenza A virus(IAV)efficiency and mechanism of action are still unclear.Here,we investigated the inhibition activity and mechanism of A.annua leaf methanol extracts(AALME)against IAV in vitro and in vivo.Our results revealed that AALME exhibits potent anti-IAV activity by interacting with IAV particles.Mechanistically,AALME directly targets the IAV nucleoprotein(NP)protein and abolishes the nuclear import of IAV NP.AALME profoundly suppresses IAV-induced mitochondrial apoptosis via suppressing ROS-mediated AIF-dependent pathways.More importantly,we found that AALME plays a crucial role in protecting mice from IAV infection and mitigating IAV pathogenicity.This current work provides mechanistic insight into the mechanism by which AALME controls IAV infection in vitro and in vivo,potentially contributing to the development of antiviral treatments for IAV infection.展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
文摘Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019).
文摘Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.
基金supported by Key Laboratory of Cyberspace Security,Ministry of Education,China。
文摘Transformer-based models have significantly advanced binary code similarity detection(BCSD)by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings.Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code,existing techniques predominantly depend on inserting artificial instructions,which incur high computational costs and offer limited diversity of perturbations.To address these limitations,we propose AIMA,a novel gradient-guided assembly instruction relocation method.Our method decouples the detection model into tokenization,embedding,and encoding layers to enable efficient gradient computation.Since token IDs of instructions are discrete and nondifferentiable,we compute gradients in the continuous embedding space to evaluate the influence of each token.The most critical tokens are identified by calculating the L2 norm of their embedding gradients.We then establish a mapping between instructions and their corresponding tokens to aggregate token-level importance into instructionlevel significance.To maximize adversarial impact,a sliding window algorithm selects the most influential contiguous segments for relocation,ensuring optimal perturbation with minimal length.This approach efficiently locates critical code regions without expensive search operations.The selected segments are relocated outside their original function boundaries via a jump mechanism,which preserves runtime control flow and functionality while introducing“deletion”effects in the static instruction sequence.Extensive experiments show that AIMA reduces similarity scores by up to 35.8%in state-of-the-art BCSD models.When incorporated into training data,it also enhances model robustness,achieving a 5.9%improvement in AUROC.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
文摘BACKGROUND To prevent mother to child transmission(MTCT)of human immunodeficiency virus(HIV),sustained maternal viral load suppression(VLS)and early HIV testing among HIV exposed infants(HEI)is critical.AIM To investigate maternal viral load results and infant HIV testing uptake at 6-weeks,and 9-months and 18-months in Rwanda.METHODS Between 2015 and 2022,VLS(<200 copies/mL)was measured among pregnant women living with HIV(WLHIV)from 38-healthcare facilities.Viral loads(VL)were measured at 6-months,12-months and 24-months,respectively.For maternal VL,the unit of analysis was visit-pair,and the pairs were created to define those with VL<200 copies/mL at two consecutive visits as having sustained VLS,persistent viremia(VL≥200 copies/mL at two consecutive visits),viral rebound(VL<200 copies/mL at prior visit only)and newly suppressed(VL<200 copies/mL at subsequent visit only).HEI were considered to have persistent HIV testing if they had all three HIV tests.Poisson regression models with generalized estimating equations were used to estimate the adjusted incidence rate ratio(aIRR)and 95%CI for factors associated with sustained VLS and persistent HIV testing.RESULTS A total of 1145 mother-infant pairs were analyzed.Infant HIV testing uptake at 6-weeks,9-months and 18-months was 1145(100.0%),1089(95.1%),1006(87.9%)respectively.Nine hundred ninety-nine HEI(87.3%)tested for HIV persistently.At 18-months,the incidence of HIV among HEI was 8(0.7%).Of 1145 mothers,1076(94.0%)had≥2 VL results making a total of 2010 visit-pairs(142-single;934-double visit-pairs).The incidence rate of sustained VLS,persistent viremia,viral rebound and new suppression were 91.0%,1.3%,3.6%and 4.0%respectively.Maternal disclosure of HIV status(aIRR=1.08,95%CI:1.02-1.14)was associated with increased likelihood of sustained VLS.Having peer support(aIRR=1.0595%CI:1.01-1.10)was associated with persistent HIV testing among HEI.CONCLUSION Sustained VLS is high among pregnant WLHIV in Rwanda.The low incidence of HIV among HEI may be attributed to high VLS levels.Targeted interventions,including enhanced HIV disclosure and peer support,are crucial for improving sustained VLS and increasing infant HIV testing uptake to reduce MTCT.
文摘Diel investigations of water environments are one means to holistically understand the dynamics and functional roles of phytoplankton,bacteria and viruses in these ecosystems.They have the potential to substantially impact carbon(C),nitrogen(N)and phosphorus(P)biogeochemistry through their respective roles.This study characterizes the phytoplankton,bacteria and virus communities and the elemental composition of various C,N and P nutrients flow over three diel cycles in tropical urban lake.Our results show that ratios of C:N:P fluctuated strongly from the lack of dissolved organic phosphorus(DOP)and PO_(4).Specifically,green algae peaked during day time and exudate dissolved organic matter(DOM)that strongly modulate dissolved organic carbon(DOC):DOP ratio to diel DOP limitation.Multiple linear regression and Stella modelling emphasize the roles of viruses together with Synechococcus as important nutrient recyclers of NH_(4)and PO_(4)in nutrients-limited waters.Respective normalised surface PO_(4)and combined surface and bottom NH_(4)concentration selected both viruses and Synechococcus as important drivers.Process model of N and P biogeochemical cycles can achieve 69%and 57%similar to observed concentration of NH_(4)and PO_(4),respectively.A short latent period of 9 hr was calculated,in addition to the calibrated high infectivity of viruses to Synechococcus.Taken together,the rapid turn-over between Synechococcus and viruses has biogeochemical significance,where the rapid recycling of essential nutrients allows for shortcuts in the N and P cycle,supporting a wide range of microbes.
基金Natural science foundation of Inner Mongolia(2024LHMS06018)The basic scientific research funding for directly affiliated universities in the Inner Mongolia(JY20250094)。
文摘A complex system is inherently high-dimensional.Recent studies indicate that,even without complete knowledge of its evolutionary dynamics,the future behavior of such a system can be predicted using time-series data(data-driven prediction).This suggests that the essential dynamics of a complex system can be captured through a low-dimensional representation.Virus evolution and climate change are two examples of complex,time-varying systems.In this article,we show that mutations in the spike protein provide valuable data for predicting SARS-CoV-2 variants,forecasting the possible emergence of the new macro-lineage Q in the near future.Our analysis also demonstrates that carbon dioxide concentration is a reliable indicator for predicting the evolution of the climate system,extending global surface air temperature(GSAT)forecasts through 2500.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
文摘Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright.The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification.Some of these features are important perceptual features according to the human visual system(HVS),which means that the embedded watermark should be imperceptible in these features.Therefore,both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions.The two roles will be considered in this paper when designing a robust watermarking algorithm against the most harmful attacks,like volumetric scaling,histogram equalization,and non-conventional watermarking attacks like the Denoising Convolution Neural Network(DnCNN),which must be considered in watermarking algorithm design due to its rising role in the state-of-the-art attacks.The DnCNN is initialized and trained using watermarked image samples created by our proposed Covert and Severe Attacks Resistant Watermarking Algorithm(CSRWA)to prove its robustness.For this algorithm to satisfy the robustness and imperceptibility tradeoff,implementing the Dither Modulation(DM)algorithm is boosted by utilizing the Just Noticeable Distortion(JND)principle to get an improved performance in this sense.Sensitivity,luminance,inter and intra-block contrast are used to adjust the JND values.
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金National Natural Science Foundation of China(Nos.42171130 and 42301158)Pilot Project of China’s Strength in Transportation for the Central Research Institute(No.QG2021-1-4-7)National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2021YFB2601200).
文摘Sulfate attack-induced expansion of cement-treated aggregates in seasonally frozen regions is a well-known issue which causes continuous expansion in railway subgrades,and particularly in high-speed railways.Accordingly,we investigated the influence of material proportions,the number of freeze-thaw(FT)cycles,and temperature gradients on the expansion mechanism of sulfate attack on cement-treated aggregates subjected to FT cycles.The conditions,laws,and dominant factors causing the expansion of aggregates were analyzed through swelling tests.The results indicate that under FT cycles,3%content cement-treated graded macadam only experiences slight deformation.The maximum strain of graded macadam attacked by 1%sodium sulfate content in each FT cycle is significantly larger than that of 3%content cement-treated graded macadam attacked by 1%sodium sulfate content.Using scanning electron microscopy,needle-like crystals were observed during sulfate attack of cement-treated graded macadam.Through quantitative analysis,we determined the recoverable and unrecoverable deformations of graded macadam under FT cycles.For graded macadam under sulfate attack,the expansion is mainly induced by periodic frost heave and salt expansion,as well as salt migration.For cement-treated graded macadam under sulfate attack,the expansion is mainly induced by chemical attack and salt migration.This study can serve as a reference for future research on the mechanics of sulfate attack on cement-treated aggregates that experience FT cycles,and provide theoretical support for methods that remediate the expansion induced by sulfate attack.
基金supported by the National Natural Science Foundation of China(Nos.12172315,12072304,11702232)the Fujian Provincial Natural Science Foundation,China(No.2021J01050)the Aeronautical Science Foundation of China(No.20220013068002).
文摘Unsteady aerodynamic characteristics at high angles of attack are of great importance to the design and development of advanced fighter aircraft, which are characterized by post-stall maneuverability with multiple Degrees-of-Freedom(multi-DOF) and complex flow field structure.In this paper, a special kind of cable-driven parallel mechanism is firstly utilized as a new suspension method to conduct unsteady dynamic wind tunnel tests at high angles of attack, thereby providing experimental aerodynamic data. These tests include a wide range of multi-DOF coupled oscillatory motions with various amplitudes and frequencies. Then, for aerodynamic modeling and analysis, a novel data-driven Feature-Level Attention Recurrent neural network(FLAR) is proposed. This model incorporates a specially designed feature-level attention module that focuses on the state variables affecting the aerodynamic coefficients, thereby enhancing the physical interpretability of the aerodynamic model. Subsequently, spin maneuver simulations, using a mathematical model as the baseline, are conducted to validate the effectiveness of the FLAR. Finally, the results on wind tunnel data reveal that the FLAR accurately predicts aerodynamic coefficients, and observations through the visualization of attention scores identify the key state variables that affect the aerodynamic coefficients. It is concluded that the proposed FLAR enhances the interpretability of the aerodynamic model while achieving good prediction accuracy and generalization capability for multi-DOF coupling motion at high angles of attack.
基金Funded by Chinese National Natural Science Foundation of China(No.U2006224)。
文摘The paper presents experimental investigation results of crack pattern change in cement pastes caused by external sulfate attack(ESA).To visualize the formation and development of cracks in cement pastes under ESA,an X-ray computed tomography(X-ray CT)was used,i e,the tomography system of Zeiss Xradia 510 versa.The results indicate that X-CT can monitor the development process and distribution characteristics of the internal cracks of cement pastes under ESA with attack time.In addition,the C3A content in the cement significantly affects the damage mode of cement paste specimens during sulfate erosion.The damage of ordinary Portland cement(OPC)pastes subjected to sulfate attack with high C3A content are severe,while the damage of sulfate resistant Portland cement(SRPC)pastes is much smaller than that of OPC pastes.Furthermore,a quadratic function describes the correlation between the crack volume fraction and development depth for two cement pastes immermed in sulfate solution.
基金Supported by the Self-funded Research Project of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region(No.Z2016325).
文摘AIM:To investigate the etiology of ocular pathogens and to establish the various pathogens present in human immunodeficiency virus(HIV)patients with cytomegalovirus retinitis(CMVR).METHODS:A total of 17 HIV-infected patients with concomitant eye disorders were enrolled.Patients were divided into CMVR group(10 patients,18 eyes)and non-CMVR group(7 patients,9 eyes)based on clinical manifestations and the presence of cytomegalovirus(CMV)-DNA in ocular specimens.The viral load of CMV was assessed using polymerase chain reaction in aqueous humor,vitreous fluid,and peripheral blood samples of patients in the CMVR group.Additionally,peripheral blood CD4^(+)T cell counts were measured in both groups.RESULTS:In the CMVR group,the CMV-DNA load in the vitreous and aqueous humor samples was substantially higher than in the peripheral blood samples(P<0.01).CMVDNA load in the aqueous humor and vitreous samples of the two eyes in the CMVR group was determined to be statistically significant(10 patients,16 eyes,P=0.018,0.012).Peripheral blood CD4^(+)T cell counts in the CMVR group were adversely linked with the CMV-DNA load in both the aqueous humor and peripheral blood(P=0.005,0.048).Compared with the non-CMVR group,the peripheral blood CD4^(+)T cell count in the CMVR group decreased significantly(P=0.014).The peripheral blood CD4^(+)T cell count exceeded 300 cells/μL in 85.71%of non-CMVR patients,whereas it was below 100 cells/μL in 90.00%of the CMVR group.The intraocular specimens of the patients who underwent CMVR testing did not include any additional infections.CONCLUSION:In HIV-associated CMVR patients,there may exist alternative,yet unidentified,infection pathways for intraocular CMV in addition to the conventional route.The substantial difference in CMV-DNA load between the eyes of most CMVR patients suggests that CMV may originate from different sources in each eye.The proportion of peripheral blood CD4^(+)T cells in HIV patients is negatively correlated with the quantity of CMV viruses in their eyes.The peripheral blood count of<100 cells/μL indicates a considerable increase in the risk of concurrent CMVR.Multi-ocular pathogen presentations are uncommon in HIV individuals with CMVR.
文摘To improve the vertical axis wind turbine(VAWT)design,the angle of attack(AOA)and airfoil data must be treated correctly.The present paper develops a method for determining AOA on a VAWT based on computational fluid dynamics(CFD)analysis.First,a CFD analysis of a two-bladed VAWT equipped with a NACA 0012 airfoil is conducted.The thrust and power coefficients are validated through experiments.Second,the blade force and velocity data at monitoring points are collected.The AOA at different azimuth angles is determined by removing the blade self-induction at the monitoring point.Then,the lift and drag coefficients as a function of AOA are extracted.Results show that this method is independent of the monitoring points selection located at certain distance to the blades and the extracted dynamic stall hysteresis is more precise than the one with the“usual”method without considering the self-induction from bound vortices.
基金supported by UniversitàCattolica(D1 intramural funds to RP)Italian Ministry of University and Research(PRIN 2022ZYLB7B,P2022YW7BP funds to CG).
文摘Several experimental evidence suggests a link between brain Herpes simplex virus type-1 infection and the occurrence of Alzheimer’s disease.However,the molecular mechanisms underlying this association are not completely understood.Among the molecular mediators of synaptic and cognitive dysfunction occurring after Herpes simplex virus type-1 infection and reactivation in the brain neuroinflammatory cytokines seem to occupy a central role.Here,we specifically reviewed literature reports dealing with the impact of neuroinflammation on synaptic dysfunction observed after recurrent Herpes simplex virus type-1 reactivation in the brain,highlighting the role of interleukins and,in particular,interleukin 1βas a possible target against Herpes simplex virus type-1-induced neuronal dysfunctions.
基金supported by grants from the National Natural Science Foundation of China(32170937)Shenzhen Medical Research Fund(SMRF,A2303015)+3 种基金Pearl River Talent Project of Guangdong Province(2021QN02Y426)Shenzhen Peacock Plan Project(827/000655)to Liang Yethe Yunnan Provincial Key Areas Science and Technology Plan Project(202303AC100025)Yunnan Scholar of Yunling(YNWRYLXZ-2019-019)to Rongping Zhang.
文摘Artemisia annua L.is a medicinal herb with multiple therapeutic applications,whereas its antiinfluenza A virus(IAV)efficiency and mechanism of action are still unclear.Here,we investigated the inhibition activity and mechanism of A.annua leaf methanol extracts(AALME)against IAV in vitro and in vivo.Our results revealed that AALME exhibits potent anti-IAV activity by interacting with IAV particles.Mechanistically,AALME directly targets the IAV nucleoprotein(NP)protein and abolishes the nuclear import of IAV NP.AALME profoundly suppresses IAV-induced mitochondrial apoptosis via suppressing ROS-mediated AIF-dependent pathways.More importantly,we found that AALME plays a crucial role in protecting mice from IAV infection and mitigating IAV pathogenicity.This current work provides mechanistic insight into the mechanism by which AALME controls IAV infection in vitro and in vivo,potentially contributing to the development of antiviral treatments for IAV infection.
