Securing Cloud-Assisted Connected and Autonomous Vehicles: An In-Depth Threat Analysis and Risk Assessment

As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware-software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware-software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.

Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

A Hybrid Methodology to Assess Cyber Resilience of IoT in Energy Management and Connected Sites

Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors ("hackers"), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today's CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014-present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing "state-of-the-art" mechanisms.

MedSecurance Project: Advanced Security-for-Safety Assurance for Medical Device IoT (IoMT)

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology "accidents".

Unpacking privacy: Valuation of personal data protection

Information about individual behaviour is collected regularly by organisations. This information has value to businesses, the government and third parties. It is not clear what value this personal data has to consumers themselves. Much of the modern economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. One technique to assess how much individuals value their privacy is to ask them whether they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing personal data. Our research extends previous work on factors affecting individuals' decisions about whether to share personal data. We take an experimental approach and focus on whether consumers place a positive value on protecting their data by examining their willingness to share personal data in a variety of data sharing environments. Using five evaluation techniques, we systematically investigate whether members of the public value keeping their personal data private. We show that the extent to which participants value protecting their information differs by data type, suggesting there is no simple function to assign a value for individual privacy. The majority of participants displayed remarkable consistency in their rankings of the importance of different types of data through a variety of elicitation procedures, a finding consistent with the existence of stable individual privacy preferences in protecting personal data. We discuss our findings in the context of research on the value of privacy and privacy preferences.

Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps' privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.

Blockchain and cloud computing-based secure electronic healthcare records storage and sharing

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.

Super-forecasting the 'technological singularity' risks from artificial intelligence

This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12530-022-09431-7.

A Novel Privacy Paradigm for Improving Serial Data Privacy

Protecting the privacy of individuals is of utmost concern in today’s society, as inscribed and governed by the prevailing privacy laws, such as GDPR. In serial data, bits of data are continuously released, but their combined effect may result in a privacy breach in the whole serial publication. Protecting serial data is crucial for preserving them from adversaries. Previous approaches provide privacy for relational data and serial data, but many loopholes exist when dealing with multiple sensitive values. We address these problems by introducing a novel privacy approach that limits the risk of privacy disclosure in republication and gives better privacy with much lower perturbation rates. Existing techniques provide a strong privacy guarantee against attacks on data privacy; however, in serial publication, the chances of attack still exist due to the continuous addition and deletion of data. In serial data, proper countermeasures for tackling attacks such as correlation attacks have not been taken, due to which serial publication is still at risk. Moreover, protecting privacy is a significant task due to the critical absence of sensitive values while dealing with multiple sensitive values. Due to this critical absence, signatures change in every release, which is a reason for attacks. In this paper, we introduce a novel approach in order to counter the composition attack and the transitive composition attack and we prove that the proposed approach is better than the existing state-of-the-art techniques. Our paper establishes the result with a systematic examination of the republication dilemma. Finally, we evaluate our work using benchmark datasets, and the results show the efficacy of the proposed technique.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus

Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). However, a user’s full control over a smart device can allow tampering of the location proof. Witness-oriented location proof systems (LPS) have emerged to resist the generation of false proofs and mitigate collusion attacks. However, witness-oriented LPS are still susceptible to three-way collusion attacks (involving the user, location authority, and the witness). To overcome the threat of three-way collusion in existing schemes, we introduce a decentralized consensus protocol called MobChain in this paper. In this scheme the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network that establishes a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees; as a result, the forging and manipulation of location becomes impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol thereby addressing the three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient and highly available while improving the security of LPS.

Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs

Location privacy is a critical problem in the vehicular communication networks. Vehicles broadcast their road status information to other entities in the network through beacon messages. The beacon message content consists of the vehicle ID, speed, direction, position, and other information. An adversary could use vehicle identity and positioning information to determine vehicle driver behavior and identity at different visited location spots. A pseudonym can be used instead of the vehicle ID to help in the vehicle location privacy. These pseudonyms should be changed in appropriate way to produce uncertainty for any adversary attempting to identify a vehicle at different locations. In the existing research literature, pseudonyms are changed during silent mode between neighbors. However, the use of a short silent period and the visibility of pseudonyms of direct neighbors provides a mechanism for an adversary to determine the identity of a target vehicle at specific locations. Moreover, privacy is provided to the driver, only within the RSU range; outside it, there is no privacy protection. In this research, we address the problem of location privacy in a highway scenario, where vehicles are traveling at high speeds with diverse traffic density. We propose a Dynamic Grouping and Virtual Pseudonym-Changing (DGVP) scheme for vehicle location privacy. Dynamic groups are formed based on similar status vehicles and cooperatively change pseudonyms. In the case of low traffic density, we use a virtual pseudonym update process. We formally present the model and specify the scheme through High-Level Petri Nets (HLPN). The simulation results indicate that the proposed method improves the anonymity set size and entropy, provides lower traceability, reduces impact on vehicular network applications, and has lower computation cost compared to existing research work.

Hybrid Task Coordination Using Multi-Hop Communication in Volunteer Computing-Based VANETs

Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.

Trust in the smart home: Findings from a nationally representative survey in the UK

Businesses in the smart home sector are actively promoting the benefits of smart home technologies for consumers, such as convenience, economy and home security. To better understand meanings of and trust in the smart home, we carried out a nationally representative survey of UK consumers designed to measure adoption and acceptability, focusing on awareness, ownership, experience, trust, satisfaction and intention to use. We analysed the results using theories of meanings and acceptability of technologies including semiotics, social construction of technology (SCOT) and sociotechnical affordance. Our findings suggest that the meaning and value proposition of the smart home have not yet achieved closure for consumers, but is already foregrounding risks to privacy and security amongst the other meaning-making possibilities it could afford. Anxiety about the likelihood of a security incident emerges as a prominent factor influencing adoption of smart home technology. This factor negatively impacts adoption. These findings underline how businesses and policymakers will need to work together to act on the sociotechnical affordances of smart home technology in order to increase consumers’ trust. This intervention is necessary if barriers to adoption and acceptability of the smart home are to be addressed now and in the future.

Future developments in standardisation of cyber risk in the Internet of Things (IoT)

In this research article, we explore the use of a design process for adapting existing cyber risk assessment standards to allow the calculation of economic impact from IoT cyber risk. The paper presents a new model that includes a design process with new risk assessment vectors, specific for IoT cyber risk. To design new risk assessment vectors for IoT, the study applied a range of methodologies, including literature review, empirical study and comparative study, followed by theoretical analysis and grounded theory. An epistemological framework emerges from applying the constructivist grounded theory methodology to draw on knowledge from existing cyber risk frameworks, models and methodologies. This framework presents the current gaps in cyber risk standards and policies, and defines the design principles of future cyber risk impact assessment. The core contribution of the article therefore, being the presentation of a new model for impact assessment of IoT cyber risk.

A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.

A longitudinal analysis of the public perception of the opportunities and challenges of the Internet of Things

The Internet of Things (or IoT), which enables the networked interconnection of everyday objects, is becoming increasingly popular in many aspects of our lives ranging from entertainment to health care. While the IoT brings a set of invaluable advantages and opportunities with it, there is also evidence of numerous challenges that are yet to be resolved. This is certainly the case with regard to ensuring the cyber security of the IoT, and there are various examples of devices being hacked. Despite this evidence, little is known about the public perceptions of the opportunities and challenges presented by the IoT. To advance research in this direction, we mined the social media platform Twitter to learn about public opinion about the IoT. Analysing a longitudinal dataset of more than 6.7 million tweets, we reveal insights into public perceptions of the IoT, identifying big data analytics as the most positive aspect, whereas security issues are the main public concern on the negative side. Our study serves to highlight the importance of keeping IoT devices secure, and remind manufacturers that it is a concern that remains unresolved, at least insofar as the public believes.

The impact of cyberstalking: the lived experience - a thematic analysis

Cyberstalking (CS) can have major psychosocial impacts on individuals. Victims report a number of serious consequences of victimization such as increased suicidal ideation, fear, anger, depression, and post traumatic stress disorder (PTSD) symptomology. Research is largely limited to quantitative outcome research. This study examines the diversity of experiences reported by people who define themselves as having been cyberstalked. Thematic analysis was used to explore 100 CS victim narratives, gathered by means of an online survey questionnaire designed to capture structured text responses. Five emergent themes were evident in the data: control and intimidation; determined offender; development of harassment; negative consequences; and lack of support. Findings identify similarities and differences to traditional stalking, along with the necessity of support for victims and illustration of the negative impacts this form of harassment produces.

Cyberstalking in the UK

The growth in the use of interconnected devices in the UK is well-documented. Society has embraced new technology allowing access to information, systems, and people; children are being described as digital natives and social networking, internet telephony, and accessing digital entertainment are a major part of their lives. However, whilst the ubiquitous nature of modern communication systems has brought many benefits, there exist a minority that uses the technology to harass others. This paper considers the phenomenon of Cyberstalking and presents an analysis of the problem and the responses provided on the first survey that addresses issue specifically. The paper discusses the nature of attacks, the victim-attacker relationship, the impact of the attacks and the actions taken to resolve the issue. The paper also considers both the legal and technological aspects and presents recommendations to help reduce the occurrence of Cyberstalking.

Optimal Placement of the Wind Generators in the Medium Voltage Power Grid

The minimization of power losses in the medium voltage (MV) grid requires adjustment of network of power sources. This problem is particularly important for renewable energy sources, for example for the farms of wind generators. Their placement and nominal power should be selected according to the configuration of the network and the largest loads. The presented problem is solved using a genetic algorithm (GA). The formulation of the GA algorithm and its performance for different numbers of power sources is analyzed. The optimal placements of wind generators were computed for some case problems. The algorithm is validated with a medium sized electrical grid. The formulation of the parallel version of the genetic algorithm is presented. Its properties are verified on the cluster of workstations environment.

Trust Issues on Crowd-Sourcing Methods for Urban Environmental Monitoring

This paper explores trust related issues arising from the use of a crowd-sourcing method. Crowd-sourcing is a relatively new method that utilises modern technologies to gather, analyse and visualise specific “on-the-fly” data. The data is typically acquired through the employment of mobile technology, with each device running a bespoke application that captures the desired information. The resulting “crowd sourced” data is readily available and the corresponding maps produced can in turn, be used to support strategic planning and to facilitate overall, more informed decision-making. The purpose is to provide insights into this novel approach to data collection, aggregation and subsequent visualisation. Specifically, the focus centres upon issues of trust and security that are inherent not merely to the use of crowd sourced data capture itself, but also crucially, to the stewardship and usage of the resultant data sets within e-government settings. A novel community centric usage scenario is presented that seeks to show how issues of trust pervade the technology. Hence, both rewards and risks are revealed and we go on to outline a preliminary approach intended to support a trusted and reputable “crowdy” data architecture.

Defining Minimum Requirements of Inter-Collaborated Nodes by Measuring the Weight of Node Interactions

This paper focuses on defining the minimum requirements to support the inter-cooperation between various scales, dynamically evolved Virtual Organizations (VOs). This proposed method is able to assign a weighted value to each pair-wise path that each member (node) can select in order to locate neighbouring nodes according to their preferences. The method also takes into account the communication overhead between each node interaction. The weight of each path is to be measured by the analysis of prerequisites in order to achieve a mutually agreed interaction between nodes. Requirements are defined as the least parameters or conditions that a node needs to achieve in order to determine its accessibility factor. The motivation behind this work is the vision of the Critical Friends Community model, which is a suitable topology for interoperable grid environments. The topology suggests that capturing inter-cooperated nodes interactions that can be publicly available could lead to knowledge of neighbouring VO members which, in turn, could be used for facilitating a more effective resource discovery and selection decision.

Persistence of varicella-zoster virus viraemia in patients with herpes zoster

BACKGROUND

Herpes zoster is caused by the reactivation of varicella-zoster virus from sensory neurons. The commonest complication following zoster is chronic pain termed post herpetic neuralgia.

OBJECTIVES

To investigate the dynamics of VZV viraemia and viral load following the resolution of zoster and its relationship to PHN development.

STUDY DESIGN

Blood samples were collected at baseline, 1 month, 3 months and 6 month from a prospective study of 63 patients with active zoster. Quantification of VZV DNA in whole blood was performed using a real-time PCR assay.

RESULTS

During acute zoster, all patients had detectable VZV DNA in their blood. VZV DNA remained detectable in the blood of 91% of patients at 6 months although levels declined significantly (p<0.0001). A history of prodromal symptoms (p=0.005) and severity of pain at baseline (p=0.038) as well as taking antivirals (p=0.046) and being immunocompromised (p=0.043) were associated, with longer time to recovery from PHN. Viral DNA loads were consistently higher in patients with risk factors for PHN and higher viral DNA loads over time were associated with longer time to recovery (p=0.058 overall and 0.038 in immunocompetent).

CONCLUSIONS

Based on these observations we hypothesise that VZV replication persists following acute shingles and that higher viral DNA loads contribute to the risk factors for PHN.

The beneficial effects of omega-3 and omega-6 essential fatty acid supplementation on red blood cell rheology

Twenty healthy, non-smoking subjects were enrolled into a study to investigate the effects of dietary supplementation with essential fatty acid (EFAs) on red blood cell rheology. Ten subjects were given 3 months dietary supplementation with long chain polyunsaturated EFAs containing omega-3 and omega-6 EFAs while 10 others were given placebo (sunflower oil). Venous sampling was performed at 0 and 12 weeks and red blood cell (RBC) aggregation and deformability measured by a filtration system. The results showed a reduction in RBC aggregation in the group given omega-3 and omega-6 EFAs but not in the placebo group. This may be related to changes in the RBC membrane and surface receptor characteristics. Such EFAs may be useful in Raynaud's phenomenon.

Soluble cell adhesion molecules--P-selectin and ICAM-1, and disease activity in patients receiving sulphasalazine for active rheumatoid arthritis

The aim of this pilot study was to examine soluble cell adhesion molecules before and after sulphasalazine (SSZ) therapy in active RA. Assessment of RA patients (n = 13) was undertaken before and after 3 months of SSZ. sICAM-1, sVCAM-1, sP- and sE-selectin were measured using an ELISA. The mean (+/-SEM) C-reactive protein (CRP) and sP-selectin levels were significantly reduced from 3.9(0.89) to 2.01(0.53) mg/dl and from 332.8 (48.2) to 116.2 (11.1) respectively, after 3 months of SSZ. The sICAM-1 and sP-selectin levels were significantly higher in RA patients at baseline and a reduction occurred of sICAM-1, sVCAM-1 and sE-selectin levels, however this was not significant. The fall in mean (SEM) sICAM-1, from 345.0 (29.8) to 333.5 (30.2), correlated with the change in CRP (r=0.66; p = 0.018), but the fall in sP-selectin did not. SSZ therapy reduced sP-selectin and sICAM-1 levels in active RA, sICAM-1 correlates with disease activity. SSZ may reduce platelet and/or endothelial activity in RA which may be a useful marker of response, however studies of longer duration and more patients are required.

A circadian variation exists for soluble levels of intercellular adhesion molecule-1 and E-selectin in healthy volunteers

1. We have previously shown a circadian variation in leucocyte activation and endothelial function which may explain why some inflammatory and vascular diseases show a circadian variation in disease activity/ occurrence. 2. We have investigated the circadian variation of two soluble cell adhesion molecules, intercellular adhesion molecule-1 and E-selectin, in 10 healthy volunteers. Soluble intercellular adhesion molecule-1 is released from both activated leucocytes and endothelial cells while soluble E-selectin is released only from activated endothelium. 3. Results show a circadian variation exists for both soluble intercellular adhesion molecule-1 and E-selectin (both P < 0.0001, analysis of variance) with a peak activity at 12:00 h for both measures and a minimum activity at 04:00 h for intercellular adhesion molecule-1 and 00:00 h for E-selectin. 4. These results demonstrate the existence of a diurnal variation in cell adhesion molecules, providing evidence in support of a diurnal pattern in endothelial and leucocyte activation. An alteration in this biological rhythm may help to explain the diurnal variation in disease activity in certain inflammatory and vascular disease states. Furthermore, it stresses the importance of sample time point standardization in clinical studies.

Dietary supplementation with omega 3 and omega 6 fatty acids reduces induced white blood cell aggregation in healthy volunteers

Twenty healthy, non-smoking subjects were enrolled into a study to look at the effects of 3 months' dietary supplementation with long chain polyunsaturated essential fatty acids (EFAs) on white blood cell (WBC) aggregation. Ten subjects received 3 months' supplementation with long chain polyunsaturated omega 3 and omega 6 fatty acids, 10 received 3 months of placebo (sunflower oil). Venous blood was sampled at 0 and 12 weeks; whole blood WBC aggregation in response to formyl-methionyl-leucyl-phenylalanine (FMLP) was measured. The results showed that the 3 months' dietary supplementation with a combination of omega 3 and omega 6 fatty acids significantly reduced WBC aggregation to FMLP in healthy volunteers when compared to placebo. Since WBC aggregation to FMLP is dependent on the activity of WBC surface receptors and independent of eicosanoid production, we suggest EFAs may have other anti-inflammatory actions in addition to their role as modulators of mediator production.

The white blood cell adhesion molecule E-selectin predicts restenosis in patients with intermittent claudication undergoing percutaneous transluminal angioplasty

BACKGROUND

Experimental studies have shown that endothelial dysfunction is an early event preceding restenosis. Monocytes and neutrophils have been shown to bind to damaged endothelium via the cell adhesion molecules (CAMs). The selectins are involved in capturing the leukocytes and tethering them to the endothelium. E-selectin is a CAM that is only expressed on activated endothelial cells. Its ligands are expressed on monocytes and neutrophils and it has been found to exist in a soluble form. This soluble form may represent a marker for endothelial damage and may be a precursor of smooth muscle proliferation.

METHODS AND RESULTS

Fifty-four patients who were undergoing peripheral arterial balloon angioplasty had blood sampled before angioplasty. E-selectin was measured in plasma with the use of an ELISA. At follow-up angiogram, 30% (n=14) of the patients had restenosed at 1 year. There was a significant difference in baseline E-selectin levels in patients who restenosed compared with those who did not (65.3 ng/mL [58.25 to 78.05] versus 52.3 [34.2 to 62.1], Mann-Whitney U, P<.007). Endothelial activation with subsequent adherence of white blood cells is an important step in restenosis.

CONCLUSIONS

We have shown an increased level of shed E-selectin in patients destined for restenosis and suggest that this work further supports a role for white blood cell/endothelial interaction in restenosis after angioplasty.

Cell adhesion molecules in rheumatoid arthritis

Rheumatoid arthritis is a systemic inflammatory disease of the joints and major internal organs that has an unknown aetiology. Cell adhesion molecules (CAMs) are expressed on the surface of cells, enabling homotypic and heterotypic cell-cell interactions that are fundamental in the process of the inflammatory reaction. Three major families of CAMs are now recognised, with numerous subtypes. Many of these molecules play an important role in the mechanism of disease in rheumatoid arthritis. E-Selectin and intercellular adhesion molecule (ICAM)-1 are upregulated on the synovial endothelium, while vascular cell adhesion molecule (VCAM)-1 plays an important role in the synovial lining layer cells and within the synovial stroma. The expression of CAMs may be blocked by monoclonal antibodies and modified by nonsteroidal anti-inflammatory drugs and disease-modifying antirheumatic drugs. This has very important implications in the therapy of rheumatoid arthritis.

Transmission of hepatitis C virus by organ transplantation in the United Kingdom

This study employed a second-generation anti-HCV ELISA, and a second-generation recombinant immunoblot assay and hepatitis C virus RNA detection by polymerase chain reaction to investigate the anti-HCV prevalence in 554 British organ donors and the transmission of hepatitis C virus to heart, liver and kidney recipients between 1984 and 1991. Serum samples from six (1.08%) donors were reactive in the second-generation anti-HCV ELISA and four (67%) of these gave positive or indeterminate results in the recombinant immunoblot assay-2. Of the 15 recipients of these organs from hepatitis C virus-confirmed positive/indeterminate donors, 14 (93%) acquired hepatitis C virus infection and seven (47%) had evidence of hepatitis C virus-related liver disease after transplantation and no evidence of blood transfusion-related transmission. Only six of the 15 (40%) recipients had detectable anti-HCV after transplantation, while 12 of 14 (86%) patients tested had hepatitis C virus RNA in their serum detectable by "nested" polymerase chain reaction. These data indicate a very high rate of transmission with a major risk of the development of liver disease. We believe our study supports the testing of all British organ donors for anti-HCV and that organs from anti-HCV-positive patients should not be transplanted unless the recipient has life-threatening disease and there is a donor shortage, when their use may be justified. Since there are time constraints on organ donor testing, which may frequently be done on call during unsocial hours, we would recommend second-generation ELISA as the current screening test of choice.(ABSTRACT TRUNCATED AT 250 WORDS)

Preliminary study of psychological changes in feeling therapy

10 early, 10 middle, and 10 late participants in an intense, community-oriented psychotherapy (feeling therapy) were measured for differences on two standardized psychological tests (Personal Orientation Inventory and Eysenck Personality Inventory) and on responses to individual and group post-session report questionnaires. Two earlier studies had found measurable physiological changes associated with this form of therapeutic intervention and it was hypothesized that associated psychological changes might be observed. Results partially bore out this prediction; the three groups evidenced some significant differences on the Personal Orientation Inventory and Post-session Report Forms. Eysenck Personality Inventory data did not distinguish between the groups.

Maintenance of psychophysiological changes in feeling therapy

This was the third in a series of studies on the psychophysiological effects of psychotherapy. Two earlier studies have suggested a more relaxed physiological state as measured by body temperature, pulse, and blood pressure taken immediately following therapy sessions. This study investigated the maintenance of these changes over a 10-day period among a group of 5 experienced and 5 inexperienced patients in Feeling Therapy. While neither group was able to sustain the large drops in physiological parameters which followed therapeutic sessions, the experienced patients evidenced a considerably lower level on every parameter throughout the study, suggesting possible long-term effects of this type of therapy.