Conflict-free access rules for sharing smart patient health records

This research is funded by the EU H2020 project Serums (Securing Medical Data in Smart Patient-Centric Healthcare Systems), grant code 826278.With an increasing trend in personalised healthcare provision across Europe, we need solutions to enable the secure transnational sharing of medical records, establishing granular access rights to personal patient data. Access rules can establish what should be accessible by whom for how long, and comply with collective regulatory frameworks, such as the European General Data Protection Regulation (GDPR). The challenge is to design and implement such systems integrating novel technologies like Blockchain and Data Lake to enhance security and access control. The blockchain module must deal with adequate policies and algorithms to guarantee that no data leaks occur when authorising data retrieval requests. The data lake module tackles the need for an efficient way to retrieve potential granular data from heterogeneous data sources. In this paper, we define a patient-centric authorisation approach, incorporating a structured format for composing access rules that enable secure data retrieval and automatic rules conflict checking.Postprin

A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks

This paper proposes a novel trust-based cooperative system to facilitate efficient Wi-Fi network access trading to solve the network congestion problem in a beneficial manner for both service providers and customers. The proposed system enables service providers to improve their users’ application performance through a novel cooperative Access Point (AP) association solution. The system is based on a Software-Defined Wireless Network (SDWN) controller, which has a global view of users’ devices, requirements, and APs. The SDWN controller is supported by Smart Contracts (SCs) as code of law, to liaise control among service providers according to the terms of their mutual agreements. Evaluation results in dense Wi-Fi network environments show how the system can significantly improve the overall performance for the cooperating network. Specifically, the results have been compared against the standard AP association approach and other centralised algorithms dealing with the same problem, in terms of the data bit rate provided to the users’ stations (STAs), Quality of Experience (QoE), bandwidth and energy consumed by the APs

Meta-Analysis Reveals Transcription Factor Upregulation in Cells of Injured Mouse Sciatic Nerve

Following peripheral nerve injury, transcription factors upregulated in the distal nerve play essential roles in Schwann cell reprogramming, fibroblast activation and immune cell function to create a permissive distal nerve environment for axonal regrowth. In this report, we first analysed four microarray data sets to identify transcription factors that have at least twofold upregulation in the mouse distal nerve stump at day 3 and day 7 post-injury. Next, we compared their relative mRNA levels through the analysis of an available bulk mRNA sequencing data set at day 5 post-injury. We then investigated the expression of identified TFs in analysed single-cell RNA sequencing data sets for the distal nerve at day 3 and day 9 post-injury. These analyses identified 55 transcription factors that have at least twofold upregulation in the distal nerve following mouse sciatic nerve injury. Expression profile for the identified 55 transcription factors in cells of the distal nerve stump was further analysed on the scRNA-seq data. Transcription factor network and functional analysis were performed in Schwann cells. We also validated the expression pattern of Jun, Junb, Runx1, Runx2, and Sox2 in the mouse distal nerve stump by immunostaining. The findings from our study not only could be used to understand the function of key transcription factors in peripheral nerve regeneration but also could be used to facilitate experimental design for future studies to investigate the function of individual TFs in peripheral nerve regeneration.</jats:p

Expression profiling and cross-species RNA interference (RNAi) of desiccation-induced transcripts in the anhydrobiotic nematode Aphelenchus avenae.

BACKGROUND: Some organisms can survive extreme desiccation by entering a state of suspended animation known as anhydrobiosis. The free-living mycophagous nematode Aphelenchus avenae can be induced to enter anhydrobiosis by pre-exposure to moderate reductions in relative humidity (RH) prior to extreme desiccation. This preconditioning phase is thought to allow modification of the transcriptome by activation of genes required for desiccation tolerance. RESULTS: To identify such genes, a panel of expressed sequence tags (ESTs) enriched for sequences upregulated in A. avenae during preconditioning was created. A subset of 30 genes with significant matches in databases, together with a number of apparently novel sequences, were chosen for further study. Several of the recognisable genes are associated with water stress, encoding, for example, two new hydrophilic proteins related to the late embryogenesis abundant (LEA) protein family. Expression studies confirmed EST panel members to be upregulated by evaporative water loss, and the majority of genes was also induced by osmotic stress and cold, but rather fewer by heat. We attempted to use RNA interference (RNAi) to demonstrate the importance of this gene set for anhydrobiosis, but found A. avenae to be recalcitrant with the techniques used. Instead, therefore, we developed a cross-species RNAi procedure using A. avenae sequences in another anhydrobiotic nematode, Panagrolaimus superbus, which is amenable to gene silencing. Of 20 A. avenae ESTs screened, a significant reduction in survival of desiccation in treated P. superbus populations was observed with two sequences, one of which was novel, while the other encoded a glutathione peroxidase. To confirm a role for glutathione peroxidases in anhydrobiosis, RNAi with cognate sequences from P. superbus was performed and was also shown to reduce desiccation tolerance in this species. CONCLUSIONS: This study has identified and characterised the expression profiles of members of the anhydrobiotic gene set in A. avenae. It also demonstrates the potential of RNAi for the analysis of anhydrobiosis and provides the first genetic data to underline the importance of effective antioxidant systems in metazoan desiccation tolerance.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation.

Kinase suppressor of Ras 2 (KSR2) is an intracellular scaffolding protein involved in multiple signaling pathways. Targeted deletion of Ksr2 leads to obesity in mice, suggesting a role in energy homeostasis. We explored the role of KSR2 in humans by sequencing 2,101 individuals with severe early-onset obesity and 1,536 controls. We identified multiple rare variants in KSR2 that disrupt signaling through the Raf-MEKERK pathway and impair cellular fatty acid oxidation and glucose oxidation in transfected cells; effects that can be ameliorated by the commonly prescribed antidiabetic drug, metformin. Mutation carriers exhibit hyperphagia in childhood, low heart rate, reduced basal metabolic rate and severe insulin resistance. These data establish KSR2 as an important regulator of energy intake, energy expenditure, and substrate utilization in humans. Modulation of KSR2-mediated effects may represent a novel therapeutic strategy for obesity and type 2 diabetes.This work was supported by the Wellcome Trust (098497/Z/12/Z; 077016/Z/05/Z; 096106/Z/11/Z) (ISF and LRP), Medical Research Council (MC_U106179471) (NW), NIHR Cambridge Biomedical Research Centre (ISF, IB and SOR), and European Research Council (ISF). This study makes use of data generated by the UK10K Consortium (WT091310). A full list of the investigators who contributed to the generation of the data is available from http://www.UK10K.org.This is the final published version. It first appeared at http://www.cell.com/abstract/S0092-8674%2813%2901276-2

A laboratory-numerical approach for modelling scale effects in dry granular slides

Granular slides are omnipresent in both natural and industrial contexts. Scale effects are changes in physical behaviour of a phenomenon at different geometric scales, such as between a laboratory experiment and a corresponding larger event observed in nature. These scale effects can be significant and can render models of small size inaccurate by underpredicting key characteristics such as ow velocity or runout distance. Although scale effects are highly relevant to granular slides due to the multiplicity of length and time scales in the flow, they are currently not well understood. A laboratory setup under Froude similarity has been developed, allowing dry granular slides to be investigated at a variety of scales, with a channel width configurable between 0.25-1.00 m. Maximum estimated grain Reynolds numbers, which quantify whether the drag force between a particle and the surrounding air act in a turbulent or viscous manner, are found in the range 102-103. A discrete element method (DEM) simulation has also been developed, validated against an axisymmetric column collapse and a granular slide experiment of Hutter and Koch (1995), before being used to model the present laboratory experiments and to examine a granular slide of significantly larger scale. This article discusses the details of this laboratory-numerical approach, with the main aim of examining scale effects related to the grain Reynolds number. Increasing dust formation with increasing scale may also exert influence on laboratory experiments. Overall, significant scale effects have been identified for characteristics such as ow velocity and runout distance in the physical experiments. While the numerical modelling shows good general agreement at the medium scale, it does not capture differences in behaviour seen at the smaller scale, highlighting the importance of physical models in capturing these scale effects

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

A Fresh Look at Combining Logs and Network Data to Detect Anomalous Activity

As data rates have increased, network administrators have increasingly turned to Software Defined Networking (SDN) to increase efficiency, as well as to react quicker to changing network states. However, as SDN flows become the norm to manage network traffic, Network Intrusion Detection Systems (NIDS) still rely on processing packet data directly using techniques such as Deep Packet Inspection (DPI). SDN flows provide only a high level representation of the packets traversing the network, reducing the amount of data available to NIDS. In particular Deep Learning based NIDS may be affected. Deep Learning has been proposed as a solution to 0-day attacks, but these models typically require large volumes of training data with many data points. This paper proposes a solution to this dilemma, by providing more data points for an IDS to monitor through the abstraction of log data generated by the flows. Past papers have shown that the quality of training data can have a marked effect on performance of Deep Learning models. This paper builds on these works by showing that high quality data points can be added in a computationally inexpensive manner, and through adding these data points, accuracy on a real world dataset can be increased by upwards of 1

Intrusion Detection Using Extremely Limited Data Based on SDN

In Western Europe, the number of Internet connected devices is expected to increase from the 2.3 billion devices in 2017, to 4 billion in 2022. Dealing with this growth is an increasing problem for administrators attempting to ensure that Quality of Service levels are maintained. Software Defined Networking (SDN) has been proposed as one of the solutions to some of the problems caused by this increasing volume of data, such as the time it takes to manually reconfigure switches in response to changing network conditions. SDN moves the distributed networking paradigm to a centralised solution, which is easier to manage, but comes with other issues for security focused administrators. SDN can lead to a reduction in the amount of information available for Intrusion Detection Systems (IDSs). This is because IDSs still rely on direct packet sampling techniques, which can provide more information than the aggregated view of networks SDN flow tables provide. As deep learning and other artificial intelligence techniques look likely to become more commonplace in IDSs, this reduction in information becomes an increasing problem. Many of these methods require large training sets with many features. In this paper, we propose a method to correct this imbalance through the creation of a novel framework, which will allow upwards of 90% precision on the state of the art UNSW-NB15 dataset while only using a small fraction of the features available, matching those available within a SDN environment

On the benefits and security risks of a user-centric data sharing platform for healthcare provision

With data breaches on the rise especially after a Covid pandemic, a huge challenge is to design secure platforms for sensitive data sharing and to support vital decisions for both healthcare provision and enhanced personalised patient care. Recently proposed is the design of a patient-centric tool chain to integrate cross-border medical records. The aim is to demonstrate how emerging technologies for authentication, authorisation, and big data storage can converge in a healthcare platform to enable citizens (and researchers) to securely retrieve vital patient health information whilst aligned with data protection regulations and standards. We develop an initial risk model with four common threat scenarios, discussing risk factors such as threat, vulnerability, impact, and likelihood. We detail how the healthcare platform design can mitigate the underlying vulnerabilities with countermeasures that do not compromise the data sharing process transparency and trust for users. </p