Quantum Money with Classical Verification

We propose and construct a quantum money scheme that allows verification through classical communication with a bank. This is the first demonstration that a secure quantum money scheme exists that does not require quantum communication for coin verification. Our scheme is secure against adaptive adversaries - this property is not directly related to the possibility of classical verification, nevertheless none of the earlier quantum money constructions is known to possess it

New Clox Systems for rapid and efficient gene disruption in Candida albicans

Acknowledgements: We are grateful to Janet Quinn, Lila Kastora, Joanna Potrykus, Michelle Leach, and others for sharing their experiences with the Clox cassettes. We thank Julia Kohler for her kind gift of the NAT1-flipper plasmid pJK863, Claudia Jacob for her advice with In-fusion cloning, and our colleagues in the Aberdeen Fungal Group for numerous stimulating discussions. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. The sequences of all Clox cassettes are available in GenBank: URA3-Clox (loxP-URA3-MET3p-cre-loxP): GenBank accession number KC999858. NAT1-Clox (loxP-NAT1-MET3p-cre-loxP): GenBank accession number KC999859. LAL (loxP-ARG4-loxP): GenBank accession number DQ015897. LHL (loxP-HIS1-loxP): GenBank accession number DQ015898. LUL (loxP-URA3-loxP): GenBank accession number DQ015899. Funding: This work was supported by the Wellcome Trust (www.wellcome.ac.uk): S.S., F.C.O., N.A.R.G., A.J.P.B. (080088); N.A.R.G., A.J.P.B. (097377). The authors also received support from the European Research Council [http://erc.europa.eu/]: DSC. ERB, AJPB (STRIFE Advanced Grant; C-2009-AdG-249793). The European Commission also provided funding [http://ec.europa.eu/research/fp7]: I.B., A.J.P.B. (FINSysB MC-ITN; PITN-GA-2008-214004). Also the UK Biotechnology and Biological Research Council provided support [www.bbsrc.ac.uk]: N.A.R.G., A.J.P.B. (Research Grant; BB/F00513X/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Co-Creation of a Multi-Component Health Literacy Intervention Targeting Both Patients with Mild to Severe Chronic Kidney Disease and Health Care Professionals

Limited health literacy (LHL) is common in chronic kidney disease (CKD) patients and frequently associated with worse self-management. Multi-component interventions targeted at patients and healthcare professionals (HCPs) are recommended, but evidence is limited. Therefore, this study aims to determine the objectives and strategies of such an intervention, and to develop, produce and evaluate it. For this purpose, we included CKD patients with LHL (n = 19), HCPs (n = 15), educators (n = 3) and students (n = 4) from general practices, nephrology clinics and universities in an Intervention Mapping (IM) process. The determined intervention objectives especially address the patients’ competences in maintaining self-management in the long term, and communication competences of patients and HCPs. Patients preferred visual strategies and strategies supporting discussion of needs and barriers during consultations to written and digital strategies. Moreover, they preferred an individual approach to group meetings. We produced a four-component intervention, consisting of a visually attractive website and topic-based brochures, consultation cards for patients, and training on LHL for HCPs. Evaluation revealed that the intervention was useful, comprehensible and fitting for patients’ needs. Healthcare organizations need to use visual strategies more in patient education, be careful with digitalization and group meetings, and train HCPs to improve care for patients with LHL. Large-scale research on the effectiveness of similar HL interventions is needed

Performance Analysis of the Decentralized Eigendecomposition and ESPRIT Algorithm

In this paper, we consider performance analysis of the decentralized power method for the eigendecomposition of the sample covariance matrix based on the averaging consensus protocol. An analytical expression of the second order statistics of the eigenvectors obtained from the decentralized power method which is required for computing the mean square error (MSE) of subspace-based estimators is presented. We show that the decentralized power method is not an asymptotically consistent estimator of the eigenvectors of the true measurement covariance matrix unless the averaging consensus protocol is carried out over an infinitely large number of iterations. Moreover, we introduce the decentralized ESPRIT algorithm which yields fully decentralized direction-of-arrival (DOA) estimates. Based on the performance analysis of the decentralized power method, we derive an analytical expression of the MSE of DOA estimators using the decentralized ESPRIT algorithm. The validity of our asymptotic results is demonstrated by simulations.Comment: 18 pages, 5 figures, submitted for publication in IEEE Transactions on Signal Processin

Deep Neural Networks for Anatomical Brain Segmentation

We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture the local spatial context while large, compressed 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks