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Erratum to: Methods for evaluating medical tests and biomarkers

Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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ESICM LIVES 2016: part one Milan, Italy. 1-5 October 2016 Abstracts

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INTENSIVE CARE MEDICINE EXPERIMENTAL 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Academic Medical Center University of Amsterdam Amsterdam Netherlands UMCU Utrecht Netherlands Autonomous University of Barcelona Barcelona Spain Hospital São Francisco Xavier Lisbon Portugal CHU Henri Mondor Medical ICU Créteil France Université de Paris Est-Créteil Groupe de Recherche Clinique CARMAS Créteil France Hôpital Tenon APHP Medical and Surgical ICU Paris France Université Paris Diderot Sorbonne Paris Cité UMR 1153 Paris France Interdepartmental Division of Critical Care University of Toronto Toronto Canada University Health Network University of Toronto Critical Care Medicine Toronto Canada University of Milan - Bicocca Health Sciences School of Medicine and Surgery Monza Italy St Michael’s Hospital University of Toronto Anesthesia Toronto Canada CHU d’Angers Medical ICU Angers France Interdepartmental Division of Critical Care University of Toronto Saint Michael’s Hospital and Keenan Research Centre Toronto Canada University of Birmingham Institute for Inflammation and Ageing Birmingham UK University of Sheffield Mathematics and Statistics Help Centre Sheffield UK Heart of England NHS Foundation Trust Intensive Care Medicine Birmingham UK Department of Surgery University Hospitals Birmingham NHS Trust Birmingham UK University Hospitals Birmingham NHS Trust Anaesthesia and Critical Care Medicine Birmingham UK Queen’s University of Belfast Wellcome-Wolfson Institute for Experimental Medicine Belfast UK Royal Victoria Hospital Intensive Care Medicine Belfast UK University of Warwick Warwick Clinical Trials Unit Coventry UK University of Bari ‘Aldo Moro’ Bari Italy Hospital Israelita Albert Einstein São Paulo Brazil Mayo Clinic Jacksonville USA Mahidol University Faculty of Tropical Medicine Bangkok Thailand University Medical Center Utrecht Intensive Care Utrecht Netherlands University Medical Center Utrecht Julius Center for Health Sciences and Primary Care Utrecht Netherlands Academic Medical Center University of Amste

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Noise characteristics of 340 nm and 280 nm GaN-based light emitting diodes

Noise characteristics of 340 nm and 280 nm GaN-based light e...

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作者： Sawyer, Shayla Rumyantsev, Sergey L. Pala, Nezih Shur, Michael S. Bilenko, Yuriy Gaska, Remis Kosterin, Pavel V. Salzberg, Brian M. Department of Electrical Computer and Systems Engineering Center for Broadband Data Transport Science and Technology CII 9017 Rensselaer Polytechnic Institute Troy NY 12180-3590 United States Sensor Electronic Technology Inc. 1195 Atlas Road Columbia SC 29209 United States Departments of Neuroscience and Physiology University of Pennsylvania School of Medicine Philadelphia PA 19104 United States

Low frequency fluctuations in light intensity of 340 nm and 280 nm GaN-based light emitting diodes (LEDs) are compared with noise properties of other commercially available UV and visible wavelength LEDs and halogen lamps. At low frequencies, LEDs can exhibit lower levels of noise than halogen lamps. An LED noise quality factor β is estimated for the UV LEDs. © World Scientific Publishing Company.

关键词： Light emitting diodes

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NOISE CHARACTERISTICS OF 340 nm AND 280 nmGaN-BASED LIGHT EMITTING DIODES

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International Journal of High Speed Electronics and Systems 2004年第3期14卷 702-707页

作者： SHAYLA SAWYER SERGEY L. RUMYANTSEV NEZIH PALA MICHAEL S. SHUR YURIY BILENKO REMIS GASKA PAVEL V. KOSTERIN BRIAN M. SALZBERG Department of Electrical Computer and Systems Engineering Center for Broadband Data Transport Science and Technology CII 9017 Rensselaer Polytechnic Institute Troy NY 12180-3590 USA Sensor Electronic Technology Inc. 1195 Atlas Road Columbia SC 29209 USA Departments of Neuroscience and Physiology University of Pennsylvania School of Medicine Philadelphia PA 19104 USA

Low frequency fluctuations in light intensity of 340 nm and 280 nm GaN -based light emitting diodes (LEDs) are compared with noise properties of other commercially available UV and visible wavelength LEDs and halogen lamps. At low frequencies, LEDs can exhibit lower levels of noise than halogen lamps. An LED noise quality factor β is estimated for the UV LEDs.

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Noise characteristics of 340 nm and 280 in GaN-based light emitting diodes

Noise characteristics of 340 nm and 280 in GaN-based light e...

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IEEE Conference on High Performance Devices

作者： S. Sawyer S.L. Rumyantsev N. Pala M.S. Shur Y. Bilenko R. Gaska P.V. Kosterin B.M. Salzberg Department of Electrical Computer and Systems Engineering Center for Broadband Data Transport Science and Technology Cll 9017 Rensselaer Polytechnic Institute Troy NY USA Sensor Electronic Technology Inc.orporated Columbia SC USA Departments of Neuroscience and Physiology University of Pennsylvania School of Medicine Philadelphia PA USA

关键词： Light emitting diodes Optical noise Fluctuations Low-frequency noise Light sources Frequency Photodiodes Resistors Proteins Batteries

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Equivalence of quantitative models for tumour response to ionizing radiation in treatment Field optimisation procedures

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Acta Oncologica 1979年第1期18卷 1-10页

作者： Graffman, S. Groth, T. Jung, B. Sköllermo, G. Departments of Oncology and Radiation Physics Akademiska Sjukhuset Uppsala University Data Center S-75010 Uppsala Sweden Department of Computer Science University of Uppsala S-75010 Uppsala Sweden

Three cell kinetic models of tumour response to ionising radiation were compared with regard to their prediction of variation in curability when the dose to the tumour varied between 54 and 66 Gy given in 30 fractions. It was found that a simple model emulated the results of the more complex ones when the parameters of the simple model were properly adjusted. A comparison of the simple model and the CRE formula gave a similar result.

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Decentralized control of an iner-connected electric energy system subject to information flow constraints

Decentralized control of an iner-connected electric energy s...

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IEEE Conference on Decision and Control

作者： T. D. Linton D. R. Fischer E. C. Tacker C. W. Sanders Data Acquisition and Simulation Laboratory University of Wisconsin Madison USA Department of Electrical Engineering Louisiana state University USA Departments of Engineering Science Electrical Engineering and Chemical Engineering Louisiana state University USA Department of Electrical and Computer Engineering University of Wisconsin Madison USA

This paper considers the problem of designing a decentralized feedback controller for a class of interconnected linear dynamical systems. The structure of each local controller is constrained to be such that it can be defined via a constant output feedback matrix, and necessary conditions for the resulting parameter optimization problem are given in the form of simultaneous nonlinear algebraic equations. Two methods for solving these equations are discussed and compared relative to computational efficiency. The design methodology is then illustrated via an application to an interconnected electric energy system.

关键词： Distributed control Output feedback Constraint optimization Equations Computational efficiency Design methodology

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Pangenome Graphs

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Genomics and Human Genetics 1000年第1期21卷 139-162页

作者： Jordan M. Eizenga Adam M. Novak Jonas A. Sibbesen Simon Heumos Ali Ghaffaari Glenn Hickey Xian Chang Josiah D. Seaman Robin Rounthwaite Jana Ebler Mikko Rautiainen Shilpa Garg Benedict Paten Tobias Marschall Jouni Sirén Erik Garrison 1Genomics Institute University of California Santa Cruz California 95064 USA email: erik.garrison@ucsc.edu 2Quantitative Biology Center University of Tübingen 72076 Tübingen Germany 5Saarbrücken Graduate School for Computer Science Saarland University 66123 Saarbrücken Germany 4Max Planck Institute for Informatics 66123 Saarbrücken Germany 3Center for Bioinformatics Saarland University 66123 Saarbrücken Germany 7School of Biological and Chemical Sciences Queen Mary University of London London E1 4NS United Kingdom 6Royal Botanic Gardens Kew Richmond TW9 3AB United Kingdom 9Department of Data Sciences Dana-Farber Cancer Institute Boston Massachusetts 02215 USA 8Departments of Genetics and Biomedical Informatics Harvard Medical School Boston Massachusetts 02215 USA

Low-cost whole-genome assembly has enabled the collection of haplotype-resolved pangenomes for numerous organisms. In turn, this technological change is encouraging the development of methods that can precisely address the sequence and variation described in large collections of related genomes. These approaches often use graphical models of the pangenome to support algorithms for sequence alignment, visualization, functional genomics, and association studies. The additional information provided to these methods by the pangenome allows them to achieve superior performance on a variety of bioinformatic tasks, including read alignment, variant calling, and genotyping. Pangenome graphs stand to become a ubiquitous tool in genomics. Although it is unclear whether they will replace linearreference genomes, their ability to harmoniously relate multiple sequence and coordinate systems will make them useful irrespective of which pangenomic models become most common in the future.

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A Review of and Roadmap for data science and Machine Learning for the Neuropsychiatric Phenotype of Autism

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Biomedical data science 1000年第1期6卷 211-228页

作者： Peter Washington Dennis P. Wall 1Department of Information and Computer Sciences University of Hawaiʻi at Mānoa Honolulu Hawaiʻi USA 2Departments of Pediatrics (Systems Medicine) Biomedical Data Science and Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford California USA email: dpwall@stanford.edu

Autism spectrum disorder (autism) is a neurodevelopmental delay that affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case–control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights that are relevant to neurological behavior analysis and digital psychiatry more broadly.

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