Bristol Laboratories (United Kingdom)

We report the case of a 13-year-old female patient presenting with presyncope and palpitations. Her electrocardiogram revealed an abbreviation of the rate-corrected QT interval with imaging showing significant left ventricular dysfunction. Carnitine levels were measured as part of her diagnostic workup, discovering a rare, reversible cause of short QT syndrome (SQTS) and associated cardiomyopathy-primary carnitine deficiency (PCD) caused by a homozygous mutation in the SLC22A5 gene, leading to an in-frame deletion mutation (NP_003051.1:p.Phe23del) affecting the organic cation transporter 2 (OCTN2) protein. Following the treatment with oral carnitine supplementation, her QT interval returned to within the normal range with significant improvement in left ventricular function.

CEMIP (KIAA1199) was identified as migratory indicator protein which had been crudely studied in the last decade. Firstly its mutation site was reported to cause hearing loss due to the folding change of protein structure, meanwhile the over-expression of CEMIP referred to dreadful invasion and uncontrolled proliferation of tumor with distant metastasis, dedifferentiation, and limited survival opportunity of patients. Especially, over-expressed CEMIP also protected malignant tumor from strict microenvironment in hypoxia, low glucose and cracked barrier, leading to enhanced adaptability of tumor by stimulating the Wnt, EGFR, FGFR pathway. Here, we intend to elaborate the clinical function and dysregulation of CEMIP under the tumorous circumstance since CEMIP plays an important role in cytokine pathway and its over-expression in tumors provide a novel target for individual therapy. Targeting CEMIP would thereby dysregulate the cytokine pathway which would in turn, decide the growth and death of the vicious tumour cells.

Keyword and feature extraction is a fundamental problem in data mining and document processing. A majority of applications directly depend on the quality and speed of keyword and feature extraction pre-processing results. In the current paper we present novel algorithms for feature extraction and change detection in unstructured data, primarily in textual and sequential data. Our approach is based on ideas from image processing and especially on the Helmholtz Principle from the Gestalt Theory of human perception. The improvements due to the novel feature extraction technique are demonstrated on several key applications: classification for strengthening document security and storage optimization, automatic summarization and segmentation for problems of information overload. The developed algorithms and applications are the result of research collaboration between Cardiff University School of Mathematics and HP Laboratories.

Accountability is a critical prerequisite for effective governance and control of corporate and private data processed by cloud-based information technology services. This chapter clarifies how accountability tools and practices can enhance cloud assurance and transparency in a variety of ways. Relevant techniques and terminologies are presented, and a scenario is considered to illustrate the related issues. In addition, some related examples are provided involving cutting-edge research and development in fields like risk management, security and Privacy Level Agreements and continuous security monitoring. The provided arguments seek to justify the use of accountability-based approaches for providing an improved basis for consumers’ trust in cloud computing and thereby can benefit from the uptake of this technology.

Background. Common childhood respiratory virus infection may cause increased rates or density of nasopharyngeal (NP) carriage of bacteria such as S.pneumoniae (Sp), M.catarrhalis (Mc), H.influenzae (Hi) or S.aureus (Sa), possibly leading to changes in colonization dynamics and transmission rates to new hosts. We used LAIV, an effective vaccine against influenza which causes mild symptoms of URTI to test this hypothesis.

In recent years we have seen how Cloud Computing is changing the way of doing businesses and how services are delivered over the Internet. This disruption is a major challenge for Service Providers and Independent Software Vendors when creating new services and software applications for the Cloud. BonFIRE offers a federated, multi-site cloud testbed to support large-scale testing of applications, services and systems. This is achieved by federating geographically distributed, heterogeneous clouds testbeds where each exposes unique configuration and/or features while giving to the experimenters (users) an homogeneous way to interact with the facility. All those testbeds are controlled by a central set of services commonly denominated “Broker”. Additionally, BonFIRE is federated with different network facilities like the Virtual Wall, FEDERICA and AutoBAHN to provide high-level interfaces to network control functionality, in order to simulate diverse network QoS scenarios, enabling vertical federation.

Cloud governance, and in particular data governance in the cloud, relies on different technical and organizational practices and procedures, such as policy enforcement, risk management, incident management and remediation. The concept of accountability encompasses such practices, and is essential for enhancing security and trustworthiness in the cloud. Besides this, proper measurement of cloud services, both at a technical and governance level, is a distinctive aspect of the cloud computing model. Hence, a natural problem that arises is how to measure the impact on accountability of the procedures held in practice by organizations that participate in the cloud ecosystem. In this paper, we describe a metamodel for addressing the problem of measuring accountability properties for cloud computing, as discussed and defined by the Cloud Accountability Project (A4Cloud). The goal of this metamodel is to act as a language for describing: (i) accountability properties in terms of actions between entities, and (ii) metrics for measuring the fulfillment of such properties. It also allows the recursive decomposition of properties and metrics, from a high-level and abstract world to a tangible and measurable one. Finally, we illustrate our proposal of the metamodel by modelling the transparency property, and define some metrics for it.

A private set intersection (PSI) protocol allows two parties to compute the intersection of their input sets privately. Most of the previous PSI protocols only output the result to one party and the other party gets nothing from running the protocols. However, a mutual PSI protocol in which both parties can get the output is highly desirable in many applications. A major obstacle in designing a mutual PSI protocol is how to ensure fairness. In this paper we present the first fair mutual PSI protocol which is efficient and secure. Fairness of the protocol is obtained in an optimistic fashion, i.e. by using an offline third party arbiter. In contrast to many optimistic protocols which require a fully trusted arbiter, in our protocol the arbiter is only required to be semi-trusted, in the sense that we consider it to be a potential threat to both parties’ privacy but believe it will follow the protocol. The arbiter can resolve disputes without knowing any private information belongs to the two parties. This feature is appealing for a PSI protocol in which privacy may be of ultimate importance.