Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Quantifying Morphological Evolution from Low to High Redshifts

Establishing the morphological history of ordinary galaxies was one of the original goals for the Hubble Space Telescope, and remarkable progress toward achieving this this goal has been made. How much of this progress has been at the expense of the Hubble sequence? As we probe further out in redshift space, it seems time to re-examine the underlying significance of Hubble's tuning fork in light of the the spectacular and often bizarre morphological characteristics of high redshift galaxies. The aim of this review is to build a morphological bridge between high-redshift and low-redshift galaxy populations, by using quantitative morphological measures to determine the maximum redshift for which the Hubble sequence provides a meaningful description of the galaxy population. I will outline the various techniques used to quantify high-redshift galaxy morphology, highlight the aspects of the Hubble sequence being probed by these techniques, and indicate what is getting left behind. I will argue that at higher redshifts new techniques (and new ideas) that place less emphasis on classical morphology and more emphasis on the link between morphology and resolved stellar populations are needed in order to probe the evolutionary history of high-redshift galaxies

Collapse of superconductivity in a hybrid tin-graphene Josephson junction array

When a Josephson junction array is built with hybrid superconductor/metal/superconductor junctions, a quantum phase transition from a superconducting to a two-dimensional (2D) metallic ground state is predicted to happen upon increasing the junction normal state resistance. Owing to its surface-exposed 2D electron gas and its gate-tunable charge carrier density, graphene coupled to superconductors is the ideal platform to study the above-mentioned transition between ground states. Here we show that decorating graphene with a sparse and regular array of superconducting nanodisks enables to continuously gate-tune the quantum superconductor-to-metal transition of the Josephson junction array into a zero-temperature metallic state. The suppression of proximity-induced superconductivity is a direct consequence of the emergence of quantum fluctuations of the superconducting phase of the disks. Under perpendicular magnetic field, the competition between quantum fluctuations and disorder is responsible for the resilience at the lowest temperatures of a superconducting glassy state that persists above the upper critical field. Our results provide the entire phase diagram of the disorder and magnetic field-tuned transition and unveil the fundamental impact of quantum phase fluctuations in 2D superconducting systems.Comment: 25 pages, 6 figure

Rehabilitation Enablement in Chronic Heart Failure—a facilitated self-care rehabilitation intervention in patients with heart failure with preserved ejection fraction (REACH-HFpEF) and their caregivers:rationale and protocol for a single-centre pilot randomised controlled trial

This is the final version of the article. Available from the publisher via the DOI in this record.INTRODUCTION: The Rehabilitation EnAblement in CHronic Heart Failure in patients with Heart Failure (HF) with preserved ejection fraction (REACH-HFpEF) pilot trial is part of a research programme designed to develop and evaluate a facilitated, home-based, self-help rehabilitation intervention to improve self-care and quality of life (QoL) in heart failure patients and their caregivers. We will assess the feasibility of a definitive trial of the REACH-HF intervention in patients with HFpEF and their caregivers. The impact of the REACH-HF intervention on echocardiographic outcomes and bloodborne biomarkers will also be assessed. METHODS AND ANALYSIS: A single-centre parallel two-group randomised controlled trial (RCT) with 1:1 individual allocation to the REACH-HF intervention plus usual care (intervention) or usual care alone (control) in 50 HFpEF patients and their caregivers. The REACH-HF intervention comprises a REACH-HF manual with supplementary tools, delivered by trained facilitators over 12 weeks. A mixed methods approach will be used to assess estimation of recruitment and retention rates; fidelity of REACH-HF manual delivery; identification of barriers to participation and adherence to the intervention and study protocol; feasibility of data collection and outcome burden. We will assess the variance in study outcomes to inform a definitive study sample size and assess methods for the collection of resource use and intervention delivery cost data to develop the cost-effectiveness analyses framework for any future trial. Patient outcomes collected at baseline, 4 and 6 months include QoL, psychological well-being, exercise capacity, physical activity and HF-related hospitalisation. Caregiver outcomes will also be assessed, and a substudy will evaluate impact of the REACH-HF manual on resting global cardiovascular function and bloodborne biomarkers in HFpEF patients. ETHICS AND DISSEMINATION: The study is approved by the East of Scotland Research Ethics Service (Ref: 15/ES/0036). Findings will be disseminated via journals and presentations to clinicians, commissioners and service users. TRIAL REGISTRATION NUMBER: ISRCTN78539530; Pre-results .This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-1210-12004). NB, CA, CJG and RST are also supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust; KJ by CLAHRC West Midlands and SS by CLAHRC East-Midlands. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Healt

What do we know about emotional labour in nursing? A narrative review

Nurses have to manage their emotions and the expression of emotion to perform best care, and their behaviours pass through emotional labour (EL). However, EL seems to be an under-appreciated aspect of caring work and there is no synthetic portrait of literature about EL in the nursing profession. This review was conducted to synthesise and to critically analyse the literature in the nursing field related to EL. Twenty-seven papers were included and analysed with a narrative approach, where two main themes were found: EL strategies and EL antecedents and consequences. Hence, EL is a multidimensional, complex concept and it represents a nursing competence to provide the best care. Moreover, nurses have a high awareness of EL as a professional competence, which is a fundamental element to balance engagement with an appropriate degree of detachment to accomplish tasks for best practice, and to provide high-quality patient care

Environmental Factors in the Relapse and Recurrence of Inflammatory Bowel Disease:A Review of the Literature

The causes of relapse in patients with Crohn's disease (CD) and ulcerative colitis (UC) are largely unknown. This paper reviews the epidemiological and clinical data on how medications (non-steroidal anti-inflammatory drugs, estrogens and antibiotics), lifestyle factors (smoking, psychological stress, diet and air pollution) may precipitate clinical relapses and recurrence. Potential biological mechanisms include: increasing thrombotic tendency, imbalances in prostaglandin synthesis, alterations in the composition of gut microbiota, and mucosal damage causing increased permeability

Non-invasive management of peripheral arterial disease.

BACKGROUND: Peripheral arterial disease (PAD) is common and symptoms can be debilitating and lethal. Risk management, exercise, radiological and surgical intervention are all valuable therapies, but morbidity and mortality rates from this disease are increasing. Circulatory enhancement can be achieved using simple medical electronic devices, with claims of minimal adverse side effects. The evidence for these is variable, prompting a review of the available literature. METHODS: Embase and Medline were interrogated for full text articles in humans and written in English. Any external medical devices used in the management of peripheral arterial disease were included if they had objective outcome data. RESULTS: Thirty-one papers met inclusion criteria, but protocols were heterogenous. The medical devices reported were intermittent pneumatic compression (IPC), electronic nerve (NMES) or muscle stimulators (EMS), and galvanic electrical dressings. In patients with intermittent claudication, IPC devices increase popliteal artery velocity (49-70 %) and flow (49-84 %). Gastrocnemius EMS increased superficial femoral artery flow by 140 %. Over 4.5-6 months IPC increased intermittent claudication distance (ICD) (97-150 %) and absolute walking distance (AWD) (84-112 %), with an associated increase in quality of life. NMES of the calf increased ICD and AWD by 82 % and 61-150 % at 4 weeks, and 26 % and 34 % at 8 weeks. In patients with critical limb ischaemia IPC reduced rest pain in 40-100 % and was associated with ulcer healing rates of 26 %. IPC had an early limb salvage rate of 58-83 % at 1-3 months, and 58-94 % at 1.5-3.5 years. No studies have reported the use of EMS or NMES in the management of CLI. CONCLUSION: There is evidence to support the use of IPC in the management of claudication and CLI. There is a building body of literature to support the use of electrical stimulators in PAD, but this is low level to date. Devices may be of special benefit to those with limited exercise capacity, and in non-reconstructable critical limb ischaemia. Galvanic stimulation is not recommended

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente