Overexpression of low-density lipoprotein receptor prevents neurotoxic polarization of astrocytes via inhibiting NLRP3 inflammasome activation in experimental ischemic stroke

Neurotoxic astrocytes are a promising therapeutic target for the attenuation of cerebral ischemia/reperfusion injury.Low-density lipoprotein receptor,a classic cholesterol regulatory receptor,has been found to inhibit NLR family pyrin domain containing protein 3(NLRP3)inflammasome activation in neurons following ischemic stroke and to suppress the activation of microglia and astrocytes in individuals with Alzheimer’s disease.However,little is known about the effects of low-density lipoprotein receptor on astrocytic activation in ischemic stroke.To address this issue in the present study,we examined the mechanisms by which low-density lipoprotein receptor regulates astrocytic polarization in ischemic stroke models.First,we examined low-density lipoprotein receptor expression in astrocytes via immunofluorescence staining and western blotting analysis.We observed significant downregulation of low-density lipoprotein receptor following middle cerebral artery occlusion reperfusion and oxygen-glucose deprivation/reoxygenation.Second,we induced the astrocyte-specific overexpression of low-density lipoprotein receptor using astrocyte-specific adeno-associated virus.Low-density lipoprotein receptor overexpression in astrocytes improved neurological outcomes in middle cerebral artery occlusion mice and reversed neurotoxic astrocytes to create a neuroprotective phenotype.Finally,we found that the overexpression of low-density lipoprotein receptor inhibited NLRP3 inflammasome activation in oxygen-glucose deprivation/reoxygenation injured astrocytes and that the addition of nigericin,an NLRP3 agonist,restored the neurotoxic astrocyte phenotype.These findings suggest that low-density lipoprotein receptor could inhibit the NLRP3-meidiated neurotoxic polarization of astrocytes and that increasing low-density lipoprotein receptor in astrocytes might represent a novel strategy for treating cerebral ischemic stroke.

XMAM:X-raying models with a matrix to reveal backdoor attacks for federated learning

Federated Learning(FL),a burgeoning technology,has received increasing attention due to its privacy protection capability.However,the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks.Former researchers proposed several robust aggregation methods.Unfortunately,due to the hidden characteristic of backdoor attacks,many of these aggregation methods are unable to defend against backdoor attacks.What's more,the attackers recently have proposed some hiding methods that further improve backdoor attacks'stealthiness,making all the existing robust aggregation methods fail.To tackle the threat of backdoor attacks,we propose a new aggregation method,X-raying Models with A Matrix(XMAM),to reveal the malicious local model updates submitted by the backdoor attackers.Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates,unlike the existing aggregation algorithms,we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior.Specifically,like medical X-ray examinations,we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs.Then,we preclude updates whose outputs are abnormal by clustering.Without any training dataset in the server,the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones.For instance,when other methods fail to defend against the backdoor attacks at no more than 20%malicious clients,our method can tolerate 45%malicious clients in the black-box mode and about 30%in Projected Gradient Descent(PGD)mode.Besides,under adaptive attacks,the results demonstrate that XMAM can still complete the global model training task even when there are 40%malicious clients.Finally,we analyze our method's screening complexity and compare the real screening time with other methods.The results show that XMAM is about 10–10000 times faster than the existing methods.

Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers

Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities.This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks.A proportional-integral-observer(PIO)with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles.Then,a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks.In light of such a scheme and the common properties of Laplace matrices,the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one.Furthermore,some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory.The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies.Finally,a simulation example is provided to illustrate the effectiveness of the proposed control strategy.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based institutions.Data indicates a persistent rise in phishing attacks.Moreover,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to identify.Hence,it is imperative to utilize sophisticated algorithms to address this issue.Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors.Machine learning(ML)approaches can identify common characteristics in most phishing assaults.In this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets.After that,we used the normalization technique on the dataset to transform the range of all the features into the same range.The findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best results.While in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction process.Also,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Effect of temperature and time on the precipitation ofκ-carbides in Fe-28Mn-10Al-0.8C low-density steels:Aging mechanism and its impact on material properties

In low-density steel,κ-carbides primarily precipitate in the form of nanoscale particles within austenite grains.However,their precipitation within ferrite matrix grains has not been comprehensively explored,and the second-phase evolution mechanism during aging remains unclear.In this study,the crystallographic characteristics and morphological evolution ofκ-carbides in Fe-28Mn-10Al-0.8C(wt%)low-density steel at different aging temperatures and times and the impacts of these changes on the steels’microhardness and properties were comprehensively analyzed.Under different heat treatment conditions,intragranularκ-carbides exhibited various morpho-logical and crystallographic characteristics,such as acicular,spherical,and short rod-like shapes.At the initial stage of aging,acicularκ-carbides primarily precipitated,accompanied by a few spherical carbides.κ-Carbides grew and coarsened with aging time,the spherical carbides were considerably reduced,and rod-like carbides coarsened.Vickers hardness testing demonstrated that the material’s hardness was affected by the volume fraction,morphology,and size ofκ-carbides.Extended aging at higher temperatures led to an increase in carbide size and volume fraction,resulting in a gradual rise in hardness.During deformation,the primary mechanisms for strengthening were dislocation strengthening and second-phase strengthening.Based on these findings,potential strategies for improving material strength are proposed.

Effects of heating temperature and atmosphere on element distribution and microstructure in high-Mn/Al austenitic low-density steel

The elemental distribution and microstructure near the surface of high-Mn/Al austenitic low-density steel were investigated after isothermal holding at temperatures of 900-1200℃ in different atmospheres,including air,N_(2),and N_(2)+CO_(2).No ferrite was formed near the surface of the experimental steel during isothermal holding at 900 and 1000℃ in air,while ferrite was formed near the steel sur-face at holding temperatures of 1100 and 1200℃.The ferrite fraction was larger at 1200℃ because more C and Mn diffused to the sur-face,exuded from the steel,and then reacted with N and O to form oxidation products.The thickness of the compound scale increased owing to the higher diffusion rate at higher temperatures.In addition,after isothermal holding at 1100℃ in N_(2),the Al content near the surface slightly decreased,while the C and Mn contents did not change.Therefore,no ferrite was formed near the surface.However,the near-surface C and Al contents decreased after holding at 1100℃in the N_(2)+CO_(2)mixed atmosphere,resulting in the formation of a small amount of ferrite.The compound scale was thickest in N_(2),followed by the N_(2)+CO_(2)mixed atmosphere,and thinnest in air.Overall,the element loss and ferrite fraction were largest after holding in air at the same temperature.The differences in element loss and ferrite frac-tion between in N_(2) and N_(2)+CO_(2)atmospheres were small,but the compound scale formed in N_(2) was significantly thicker.According to these results,N_(2)+CO_(2)is the ideal heating atmosphere for the industrial production of high-Mn/Al austenitic low-density steel.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Low-density lipoprotein receptor-related protein 2(LRP2)is required for lipid export in the midgut of the migratory locust,Locusta migratoria

Low-density lipoprotein receptor-related protein 2(LRP2)is a multifunctional endocytic receptor expressed in epithelial cells.In mammals,it acts as an endocytic receptor that mediates the cellular uptake of cholesterol-containing apolipoproteins to maintain lipid homeostasis.However,little is known about the role of LRP2 in lipid homeostasis in insects.In the present study,we investigated the function of LRP2 in the migratory locust Locusta migratoria(LmLRP2).The mRNA of LmLRP2 is widely distributed in various tissues,including integument,wing pads,foregut,midgut,hindgut,Malpighian tubules and fat body,and the amounts of LmLRP2 transcripts decreased gradually in the early stages and then increased in the late stages before ecdysis during the nymphal developmental stage.Fluorescence immunohistochemistry revealed that the LmLRP2 protein is mainly located in cellular membranes of the midgut and hindgut.Using RNAi to silence LmLRP2 caused molting defects in nymphs(more than 60%),and the neutral lipid was found to accumulate in the midgut and surface of the integument,but not in the fat body,of dsLmLRP2-treated nymphs.The results of a lipidomics analysis showed that the main components of lipids(diglyceride and triglyceride)were significantly increased in the midgut,but decreased in the fat body and hemolymph.Furthermore,the content of total triglyceride was significantly increased in the midgut,but markedly decreased in the fat body and hemolymph in dsLmLRP2-injected nymphs.Our results indicate that LmLRP2 is located in the cellular membranes of midgut cells,and is required for lipid export from the midgut to the hemolymphand fat body in locusts.

Mitigating while Accessing:A Lightweight Defense Framework Against Link Flooding Attacks in SDN

Link flooding attack(LFA)is a type of covert distributed denial of service(DDoS)attack.The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet.Recently,the proliferation of Internet of Things(IoT)has increased the quantity of vulnerable devices connected to the network and has intensified the threat of LFAs.In LFAs,attackers typically utilize low-speed flows that do not reach the victims,making the attack difficult to detect.Traditional LFA defense methods mainly reroute the attack traffic around the congested link,which encounters high complexity and high computational overhead due to the aggregation of massive attack traffic.To address these challenges,we present an LFA defense framework which can mitigate the attack flows at the border switches when they are small in scale.This framework is lightweight and can be deployed at border switches of the network in a distributed manner,which ensures the scalability of our defense system.The performance of our framework is assessed in an experimental environment.The simulation results indicate that our method is effective in detecting and mitigating LFAs with low time complexity.

An Improved Framework of Massive Superpoly Recovery in Cube Attacks Against NFSR-Based Stream Ciphers

A critical problem in the cube attack is how to recover superpolies efficiently.As the targeting number of rounds of an iterative stream cipher increases,the scale of its superpolies becomes larger and larger.Recently,to recover massive superpolies,the nested monomial prediction technique,the algorithm based on the divide-and-conquer strategy,and stretching cube attacks were proposed,which have been used to recover a superpoly with over ten million monomials for the NFSR-based stream ciphers such as Trivium and Grain-128AEAD.Nevertheless,when these methods are used to recover superpolies,many invalid calculations are performed,which makes recovering superpolies more difficult.This study finds an interesting observation that can be used to improve the above methods.Based on the observation,a new method is proposed to avoid a part of invalid calculations during the process of recovering superpolies.Then,the new method is applied to the nested monomial prediction technique and an improved superpoly recovery framework is presented.To verify the effectiveness of the proposed scheme,the improved framework is applied to 844-and 846-round Trivium and the exact ANFs of the superpolies is obtained with over one hundred million monomials,showing the improved superpoly recovery technique is powerful.Besides,extensive experiments on other scaled-down variants of NFSR-based stream ciphers show that the proposed scheme indeed could be more efficient on the superpoly recovery against NFSR-based stream ciphers.

Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse systems.Despite the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain threats.This paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT environments.An effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time complexity.By leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world scenarios.Additionally,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack detection.The experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

Rethinking multi-spatial information for transferable adversarial attacks on speaker recognition systems

Adversarial attacks have been posing significant security concerns to intelligent systems,such as speaker recognition systems(SRSs).Most attacks assume the neural networks in the systems are known beforehand,while black-box attacks are proposed without such information to meet practical situations.Existing black-box attacks improve trans-ferability by integrating multiple models or training on multiple datasets,but these methods are costly.Motivated by the optimisation strategy with spatial information on the perturbed paths and samples,we propose a Dual Spatial Momentum Iterative Fast Gradient Sign Method(DS-MI-FGSM)to improve the transferability of black-box at-tacks against SRSs.Specifically,DS-MI-FGSM only needs a single data and one model as the input;by extending to the data and model neighbouring spaces,it generates adver-sarial examples against the integrating models.To reduce the risk of overfitting,DS-MI-FGSM also introduces gradient masking to improve transferability.The authors conduct extensive experiments regarding the speaker recognition task,and the results demonstrate the effectiveness of their method,which can achieve up to 92%attack success rate on the victim model in black-box scenarios with only one known model.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

Recurrent Transient Ischemic Attacks Revealing Cerebral Amyloid Angiopathy: A Comprehensive Case

This case report investigates the manifestation of cerebral amyloid angiopathy (CAA) through recurrent Transient Ischemic Attacks (TIAs) in an 82-year-old patient. Despite initial diagnostic complexities, cerebral angiography-MRI revealed features indicative of CAA. Symptomatic treatment resulted in improvement, but the patient later developed a fatal hematoma. The discussion navigates the intricate therapeutic landscape of repetitive TIAs in the elderly with cardiovascular risk factors, emphasizing the pivotal role of cerebral MRI and meticulous bleeding risk management. The conclusion stresses the importance of incorporating SWI sequences, specifically when suspecting a cardioembolic TIA, as a diagnostic measure to explore and exclude CAA in the differential diagnosis. This case report provides valuable insights into these challenges, highlighting the need to consider CAA in relevant cases.