Employing a Toxic Aging Coin approach to assess hexavalent chromium (Cr[VI])-induced neurotoxic effects on behavior: Heads for age differences.

We are facing a rapidly growing geriatric population (65+) that will live for multiple decades and are challenged with environmental pollution far exceeding that of previous generations. Consequently, we currently have a poor understanding of how environmental pollution will impact geriatric health distinctly from younger populations. Few toxicology studies have considered age differences with geriatric individuals. Critically, all top ten most prevalent age-related diseases are linked to metal exposures. Hexavalent chromium [Cr(VI)] is a metal of major environmental health concern that can induce aging phenotypes and neurotoxicity. However, there are many knowledge gaps for Cr(VI) neurotoxicity, including how Cr(VI) impacts behavior. To address this, we exposed male rats across three ages (3-, 7-, and 18-months old) to Cr(VI) in drinking water (0, 0.05, 0.1 mg/L) for 90 days. These levels reflect the maximum contaminant levels determined by the World Health Organization (WHO) and the U.S. Environmental Protection Agency (US EPA). Here, we report how these Cr(VI) drinking water levels impacted rat behaviors using a battery of behavior tests, including grip strength, open field assay, elevated plus maze, Y-maze, and 3-chamber assay. We observed adult rats were the most affected age group and memory assays (spatial and social) exhibited the most significant effects. Critically, the significant effects were surprising as rats should be particularly resistant to these Cr(VI) drinking water levels due to the adjustments applied in risk assessment from rodent studies to human safety, and because rats endogenously synthesize vitamin C in their livers (vitamin C is a primary reducer of Cr[VI] to Cr[III]). Our results emphasize the need to broaden the scope of toxicology research to consider multiple life stages and suggest the current regulations for Cr(VI) in drinking water need to be revisited.

Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection.

For the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner.

Antibiotics-Free Compounds for Chronic Wound Healing.

The rapid rise in the health burden associated with chronic wounds is of great concern to policymakers, academia, and industry. This could be attributed to the devastating implications of this condition, and specifically, chronic wounds which have been linked to invasive microbial infections affecting patients' quality of life. Unfortunately, antibiotics are not always helpful due to their poor penetration of bacterial biofilms and the emergence of antimicrobial resistance. Hence, there is an urgent need to explore antibiotics-free compounds/formulations with proven or potential antimicrobial, anti-inflammatory, antioxidant, and wound healing efficacy. The mechanism of antibiotics-free compounds is thought to include the disruption of the bacteria cell structure, preventing cell division, membrane porins, motility, and the formation of a biofilm. Furthermore, some of these compounds foster tissue regeneration by modulating growth factor expression. In this review article, the focus is placed on a number of non-antibiotic compounds possessing some of the aforementioned pharmacological and physiological activities. Specific interest is given to Aloevera, curcumin, cinnamaldehyde, polyhexanide, retinoids, ascorbate, tocochromanols, and chitosan. These compounds (when alone or in formulation with other biologically active molecules) could be a dependable alternative in the management or prevention of chronic wounds.

GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture.

E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets-a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

Ransomware: Analysing the Impact on Windows Active Directory Domain Services.

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform.

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation's vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage.

Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification.

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file's entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square (χ2), Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. The hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware. During the second phase of testing, 11 candidate entropy calculation techniques were tested against more than 270,000 individual files-resulting in nearly three million separate calculations. The overall accuracy of each of the individual test's ability to differentiate between files encrypted using crypto-ransomware and other file types is then evaluated and each test is compared using this metric in an attempt to identify the entropy method most suited for encrypted file identification. An investigation was also undertaken to determine if a hybrid approach, where the results of multiple tests are combined, to discover if an improvement in accuracy could be achieved.

A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT.

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish-subscribe-based protocol for the communication of sensor or event data. The publish-subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

A Comparative Analysis of Honeypots on Different Cloud Platforms.

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers' activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

BeepTrace: Blockchain-Enabled Privacy-Preserving Contact Tracing for COVID-19 Pandemic and Beyond.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric.

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common security attack vectors. However, healthcare-oriented blockchain solutions can provide a decentralized, anonymous and secure EHR handling approach. This paper presents PREHEALTH, a privacy-preserving EHR management solution that uses distributed ledger technology and an Identity Mixer (Idemix). The paper describes a proof-of-concept implementation that uses the Hyperledger Fabric's permissioned blockchain framework. The proposed solution is able to store patient records effectively whilst providing anonymity and unlinkability. Experimental performance evaluation results demonstrate the scheme's efficiency and feasibility for real-world scale deployment.

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication.

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person's body. The Wi-Fi signals received using non-wearable devices are converted into time-frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.

Trust by Design: Evaluating Issues and Perceptions within Clinical Passporting.

A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening, and appraisals: the bureaucracy associated with each of these processes takes precious time out of a healthcare professional's day. Time that could have been spent focused on patient care. In the midst of the COVID-19 crisis, it is more important than ever to optimize these professionals' time. This article presents the synthesis of a design workshop held at the Royal College of Physicians of Edinburgh (RCPE) and subsequent interviews with healthcare professionals. The main research question posed is whether these processes can be re-imagined using digital technologies, specifically self-sovereign identity? A key contribution in the article is the development of a set of user-led requirements and design principles for identity systems used within healthcare. These are then contrasted with design principles found in the literature. The results of this study confirm the need and potential of professionalizing identity and credential management throughout a healthcare professional's career.

Periodontal-induced chronic inflammation triggers macrophage secretion of Ccl12 to inhibit fibroblast-mediated cardiac wound healing.

Chronic inflammatory diseases, such as periodontal disease, associate with adverse wound healing in response to myocardial infarction (MI). The goal of this study was to elucidate the molecular basis for impaired cardiac wound healing in the setting of periodontal-induced chronic inflammation. Causal network analysis of 168 inflammatory and extracellular matrix genes revealed that chronic inflammation induced by a subseptic dose of Porphyromonas gingivalis lipopolysaccharide (LPS) exacerbated infarct expression of the proinflammatory cytokine Ccl12. Ccl12 prevented initiation of the reparative response by prolonging inflammation and inhibiting fibroblast conversion to myofibroblasts, resulting in diminished scar formation. Macrophage secretion of Ccl12 directly impaired fibronectin and collagen deposition and indirectly stimulated collagen degradation through upregulation of matrix metalloproteinase-2. In post-MI patients, circulating LPS levels strongly associated with the Ccl12 homologue monocyte chemotactic protein 1 (MCP-1). Patients with LPS levels ≥ 1 endotoxin units (EU)/ml (subseptic endotoxemia) at the time of hospitalization had increased end diastolic and systolic dimensions compared with post-MI patients with < 1 EU/ml, indicating that low yet pathological concentrations of circulating LPS adversely impact post-MI left ventricle (LV) remodeling by increasing MCP-1. Our study provides the first evidence to our knowledge that chronic inflammation inhibits reparative fibroblast activation and generates an unfavorable cardiac-healing environment through Ccl12-dependent mechanisms.

Subclinical herpesvirus shedding among HIV-1-infected men on antiretroviral therapy.

OBJECTIVE: We evaluated the subclinical shedding of six different herpesviruses in antiretroviral drug-treated HIV-positive [HIV(+)] MSM, and determined how this is associated with markers of inflammation and immune activation. METHODS: We obtained blood, semen, throat washing, urine, and stool from 15 antiretroviral-treated HIV-1-infected MSM with CD4 T-cell reconstitution, and 12 age-matched HIV-negative [HIV (-)] MSM from the Multicenter AIDS Cohort Study at four timepoints over 24 weeks to measure DNA levels of cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus 1 and 2, human herpesvirus 6 (HHV6), and HHV8. T-cell activation and plasma levels of soluble markers of inflammation and activation were also measured at the corresponding timepoints. RESULTS: HIV(+) participants had a trend for higher total herpesvirus shedding rate. HIV(+) participants also had a significantly higher rate of shedding EBV and CMV compared with the HIV(-) group. Herpesvirus shedding was mostly seen in throat washings. In the HIV(+) group, herpesvirus shedding rate inversely correlated with plasma levels of interferon γ-induced protein 10 and soluble CD163. CMV DNA levels negatively correlated with levels of T-cell activation. There was a trend for a positive correlation between EBV shedding rate and plasma soluble CD14. HHV6 shedding rate negatively correlated with plasma levels of interleukin-6, soluble CD163, and interferon gamma-induced protein 10. Correlations were not observed among HIV(-) individuals. CONCLUSION: Among treated HIV-infected MSM, there are higher subclinical shedding rates of some herpesviruses that occur in different body compartments and negatively correlate with levels of inflammation and immune activation.

Levels of Phonology Related to Reading and Writing in Middle Childhood.

The relationships of different levels of phonological processing (sounds in heard and spoken words for whole words, syllables, phonemes, and rimes) to multi-leveled functional reading or writing systems were studied. Participants in this cross-sectional study were students in fourth- grade (n=119, mean age 116.5 months) and sixth- grade (n=105, mean age 139.7 months). Multiple Indicators and Multiple Causes (MIMIC) modeling was used to analyze whether different levels of sound processing in heard and spoken words were correlated with each other and with multi-leveled reading or writing systems, and if so, which phonological skills explained unique variance in the reading or writing system. The models fit well at both grade levels for both reading and writing. All four phonological skills studied correlated significantly with each other and the latent factor for reading or writing. For reading, phonology for whole words and phonemes explained unique variance in fourth and sixth graders. For writing, at the fourth grade, only phonemes explained unique variance, but at the sixth grade level, syllables, phonemes, and rimes explained unique variance. Thus, the relationships between levels of phonology and reading were stable across grades 4 and 6, but developmental differences were observed in the relationships between levels of phonology and the leveled writing construct.