A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods

Psychometric properties of the Zarit Caregiver Burden Interview administered to caregivers to patients with Duchenne muscular dystrophy: a Rasch analysis

<p><b>Purpose:</b> To explore the psychometric properties of the full 22-item English (UK and US) version of the Zarit Caregiver Burden Interview administered to caregivers to patients with Duchenne muscular dystrophy.</p> <p><b>Materials and methods:</b> Caregivers to patients with Duchenne muscular dystrophy from the United Kingdom and the United States, recruited through the TREAT-NMD network, completed the Zarit Caregiver Burden Interview online. The psychometric properties of the Zarit Caregiver Burden Interview were examined using Rasch analysis.</p> <p><b>Results:</b> A total of 475 caregivers completed the Zarit Caregiver Burden Interview. Model misfit was identified for 9 of 22 items (mean item fit residual 0.061, SD: 2.736) and 13 of 22 items displayed disordered thresholds. The overall item-trait interaction chi-square value was 499 (198 degrees of freedom, <i>p</i> < 0.001). The mean person fit residual was estimated at −0.213 (SD: 1.235). The Person Separation Index and Cronbach’s α were estimated at 0.902 and 0.914, respectively. Item dependency was low and we found no significant differential item functioning by country or sex.</p> <p><b>Conclusion:</b> Our Rasch analysis shows that the Zarit Caregiver Burden Interview fails to fully operationalize a quantitative conceptualization of caregiver burden among caregivers to patients with Duchenne muscular dystrophy from the United Kingdom and the United States. Further research is needed to understand the psychometric properties of the Zarit Caregiver Burden Interview in other populations and settings.Implications for Rehabilitation</p><p>Duchenne muscular dystrophy is a terminal disease characterized by progressive muscle degeneration resulting in substantial disability and a significant burden on family caregivers.</p><p>The Zarit Caregiver Burden Interview is one of the most widely applied measures of caregiver burden.</p><p>Our Rasch analysis suggests that the Zarit Caregiver Burden Interview is not fit for purpose to measure burden in UK and US caregivers to patients with Duchenne muscular dystrophy.</p><p>Clinicians and decision-makers should interpret Zarit Caregiver Burden Interview data from these populations with caution.</p><p></p> <p>Duchenne muscular dystrophy is a terminal disease characterized by progressive muscle degeneration resulting in substantial disability and a significant burden on family caregivers.</p> <p>The Zarit Caregiver Burden Interview is one of the most widely applied measures of caregiver burden.</p> <p>Our Rasch analysis suggests that the Zarit Caregiver Burden Interview is not fit for purpose to measure burden in UK and US caregivers to patients with Duchenne muscular dystrophy.</p> <p>Clinicians and decision-makers should interpret Zarit Caregiver Burden Interview data from these populations with caution.</p

RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research

<p><strong>Abstract:</strong></p> <p>Despite many examples of excellent practice, rare disease (RD) research is still frequently fragmented by data type and disease. Individual efforts often have little interoperability and almost no systematic connection of detailed clinical information with genetic information, biomaterial availability or research/trial datasets. Linking data at both an individual-patient and whole-cohort level enables researchers to gain a better overview of their disease of interest, while providing access to data from other research groups in a secure fashion allows researchers in multiple institutions to compare results and gain new insights. Funded by the EU Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project which links databases, registries, biobanks and clinical bioinformatics data used in RD research into a central research resource. RD-Connect’s primary objectives are:</p> <p>• Harmonisation and development of common standards for RD patient registries by developing a common registry infrastructure and data elements</p> <p>• Harmonisation and development of common standards and catalogue for RD biobanks that collect and provide standardised, quality-controlled biomaterials for translational research</p> <p>• Development of clinical bioinformatics tools for analysis and integration of molecular and clinical data to discover new disease genes, pathways and therapeutic targets</p> <p>• Development of an integrated platform to host and analyse data from omics research projects</p> <p>• Development of mechanisms for incorporating patient interests and engaging with stakeholders</p> <p>• Development of best ethical practices and a proposal for a regulatory framework for linking medical and personal data related to RD.</p> <p>RD-Connect will accept data generated by IRDiRC projects such as EURenOmics, which focuses on causes, diagnostics, biomarkers and disease models for rare kidney disorders, and Neuromics, which uses next generation whole exome sequencing to increase genetic knowledge of rare neurodegenerative and neuromuscular disorders. The “siloed” nature of individual research efforts is a continued bottleneck for cutting-edge research towards diagnosis and therapy development in RD. RD-Connect aims to unite existing infrastructures and integrate the latest tools in order to create a comprehensive combined omics data, biobanking, data analysis and patient registry platform for RD used by researchers across the world.</p

RD-Connect: first year review

<p>In its first year of operation, RD-Connect has successfully achieved its objectives for the period and has begun to establish its position as an important part of the global rare disease research infrastructure. Owing to the need to integrate with existing initiatives, the primary focus of the year has been on ensuring that RD-Connect activities are fully aligned with the needs of the associated projects that will submit data to the system, and on developing interoperability with related tools and projects operating in the same area.<br>Achievements include:<br>• Established strong collaborations with EURenOmics and Neuromics<br>• Incorporation of new associated partners involved in related work<br>• Representation on IRDiRC Scientific Committees and working groups has helped ensure harmonisation with IRDiRC activities<br>• The foundations for the integrated platform have been put in place, ensuring interoperability with other systems and meeting the requirements of IRDiRC projects generating omics data that will be linked with RD-Connect<br>• The first set of data from NeurOmics and EURenOmics will be uploaded in early 2014 after which time data from other IRDiRC projects may be accepted<br>• Various suites of clinical bioinformatics tools to extract knowledge from high throughput experiments, clinical databases and biobanks are being developed<br>• Extensive engagement with ontology developers and the associated projects has ensured that the submitted omics data will be accompanied by standardised phenotypic descriptions using HPO<br>• An extensive mapping exercise carried out jointly with the registry and biobanking WP has resulted in a list of patient registries and biobanks that will now be surveyed to establish their research focus, utility for research and invited to participate in RD-Connect activities<br>• Progress in evaluating the various options to implement a globally unique identifier<br>• Progress towards the development of the biobank catalogue, database structure and biobanking standards<br>• Proactive engagement with the ethical issues raised by omics experiments and patient data sharing<br>• Draft charter with principles and template for sharing and access to data</p> <p> </p

Fig5_5D

Raw data values and statistical analysis for Fig5D

Fig4_4B STATS

Statistical analysis for Fig4

Fig3_Fig 3 A,B

Raw counts and percentages for lifespan of both control and experimental adult flies. Figure 3

Fig2C Blot2

Original blot to quantify Fig2

Fig2_2A

Raw data values for Fig2A quantificatio

Fig8_8B TTEST

Raw data and statistical analysis for Fig8