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Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters

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Nature chemical biology 2018年第9期14卷 901-901页

作者： Kiryl D Piatkevich Erica E Jung Christoph Straub Changyang Linghu Demian Park HoJun Suk Daniel R Hochbaum Daniel Goodwin Eftychios Pnevmatikakis Nikita Pak Takashi Kawashima ChaoTsung Yang Jeffrey L Rhoades Or Shemesh Shoh Asano YoungGyu Yoon Limor Freifeld Jessica L Saulnier Clemens Riegler Florian Engert Thom Hughes Mikhail Drobizhev Balint Szabo Misha B Ahrens Steven W Flavell Bernardo L Sabatini Edward S Boyden Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA. Simons Center Data Analysis Simons Foundation New York NY USA. Department of Mechanical Engineering MIT Cambridge MA USA. Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA. Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA. Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA. Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria. Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA. Department of Biological Physics Eotvos University Budapest Hungary. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. esb@media.mit.edu. Department of Biological Engineering MIT Cambridge MA USA. esb@media.mit.edu. MIT Center for Neurobiological Engineering MIT Cambridge MA USA. esb@media.mit.edu. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. esb@media.mit.edu. MIT McGovern Institute for Brain Research MIT Cambridge MA USA. esb@media.mit.edu.

In the version of this article originally published, the bottom of Figure 4f,g was partially truncated in the PDF. The error has been corrected in the PDF version of this article.

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Monitoring quality indicators for screening colonoscopies

Monitoring quality indicators for screening colonoscopies

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2015 systems and Information engineering Design Symposium, SIEDS 2015

作者： Charles, Malcolm Miano, Thomas N. Zhang, Xinhe Barnes, Laura E. Lobo, Jennifer M. Data Science Institute University of Virginia United States Department of Systems and Information Engineering University of Virginia United States Department of Public Health Sciences University of Virginia United States

ISBN: (纸本)9781479918324

The detection rate of adenomas in screening colonoscopies is an important quality indicator for endoscopists. Successful detection of adenomas is linked to reduced cancer incidence and mortality. This study focuses on evaluating the performance of endoscopists on adenoma detection rate (ADR), polyp detection rate (PDR), and scope withdrawal time. The substitution of PDR for ADR has been suggested due the reliance of ADR calculation on pathology reports. We compare these metrics to established clinical guidelines and to the performance of other individual endoscopists. Our analysis (n = 2730 screening colonoscopies) found variation in ADR for 14 endoscopists, ranging from 0.20 to 0.41. PDR ranged from 0.38 to 0.62. Controlling for age, sex, race, withdrawal time, and the presence of a trainee endoscopist accounted for 34% of variation in PDR but failed to account for any variation in ADR. The Pearson correlation between PDR and ADR is 0.82. These results suggest that PDR has significant value as a quality indicator. The reported variation in detection rates after controlling for case mix signals the need for greater scrutiny of individual endoscopist skill. Understanding the root cause of this variation could potentially lead to better patient outcomes. © 2015 IEEE.

关键词： Correlation methods

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Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2018年第12期50卷 1754页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Igor Cima Min-Han Tan Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Institute of Bioengineering and Nanotechnology Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. science Transl. Med. 8, 345ra89, 2016).

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Multivariate Spearman's p for Aggregating Ranks Using Copulas

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Journal of Machine Learning Research 2016年 17卷 1-30页

作者： Bedo, Justin Ong, Cheng Soon Walter and Eliza Hall Institute 1G Royal Parade ParkvilleVIC3052 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Data61 CSIRO 7 London Circuit CanberraACT2601 Australia Research School of Computer Science Australian National University Department of Electrical and Electronic Engineering VIC3010 Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's p, which measures correlation between a set of ranked lists. Multivariate Spearman's p is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's κ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008. © 2016 Justin Bed}o and Cheng Soon Ong.

关键词： Benchmarking

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Excitations in the field-induced quantum spin liquid state of α-RuCl3

arXiv

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arXiv 2017年

作者： Banerjee, A. Lampen-Kelley, P. Knolle, J. Balz, C. Aczel, A.A. Winn, B. Liu, Y. Pajerowski, D. Yan, J.-Q. Bridges, C.A. Savici, A.T. Chakoumakos, B.C. Lumsden, M.D. Tennant, D.A. Moessner, R. Mandrus, D.G. Nagler, S.E. Quantum Condensed Matter Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Department of Physics Cavendish Laboratory JJ Thomson Avenue CambridgeCB3 0HE United Kingdom Chemical Sciences Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Neutron Data Analysis and Visualization Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Neutron Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN37831 United States Max Planck Institute for the Physics of Complex Systems DresdenD-01187 Germany

The Kitaev model on a honeycomb lattice predicts a paradigmatic quantum spin liquid (QSL) exhibiting Majorana Fermion excitations. The insight that Kitaev physics might be realized in practice has stimulated investigations of candidate materials, recently including α-RuCl3. In all the systems studied to date, significant non-Kitaev interactions induce magnetic order at low temperature. However, in-plane magnetic fields of roughly 8 Tesla suppress the long-range magnetic order in α-RuCl3 raising the intriguing possibility of a field-induced QSL exhibiting non-Abelian quasiparticle excitations. Here we present inelastic neutron scattering in αRuCl3 in an applied magnetic field. At a field of 8 Tesla the spin waves characteristic of the ordered state vanish throughout the Brillouin zone. The remaining single dominant feature of the response is a broad continuum centered at the Γ point, previously identified as a signature of fractionalized excitations. This provides compelling evidence that a field-induced QSL state has been achieved. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum theory

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Efficient mining of class association rules with the itemset constraint

Efficient mining of class association rules with the itemset...

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作者： Nguyen, Dang Nguyen, Loan T.T. Vo, Bay Pedrycz, Witold Division of Data Science Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City University of Technology Viet Nam College of Electronics and Information Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

Mining class association rules (CARs) with the itemset constraint is concerned with the discovery of rules, which contain a set of specific items in the rule antecedent and a class label in the rule consequent. This task is commonly encountered in mining medical data. For example, when classifying which section of the population is at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as gender, age, and marital status in the rule antecedent, and HIV-Positive in the rule consequent. There are two naive strategies to solve this problem, namely pre-processing and post-processing. The post-processing methods have to generate and consider a huge number of candidate CARs while the performance of the pre-processing methods depend on the number of records filtered out. Therefore, such approaches are time consuming. This study proposes an efficient method for mining CARs with the itemset constraint based on a lattice structure and the difference between two sets of object identifiers (diffset). Firstly, a lattice structure is built to store all frequent itemsets in the dataset. To reduce memory usage, instead of the entire set of object identifiers, the diffset is used. Secondly, the lattice is traversed to generate only rules which satisfy the itemset constraint. The experimental results show that the proposed algorithm outperforms existing methods in terms of both the mining time and memory usage. © 2016 Elsevier B.V. All rights reserved.

关键词： data mining

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Abstracts from Hydrocephalus 2016 Abstracts

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FLUIDS AND BARRIERS OF THE CNS 2017年第SUPPL 1期14卷 1-21页

作者： [Anonymous] Johns Hopkins University Baltimore MD USA. Department of Neurosurgery Johns Hopkins University Baltimore MD USA. Johns Hopkins Biostatistics Center Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. Department of Neurosurgery Johns Hopkins University School of Medicine Baltimore MD USA. Johns Hopkins Aramco Healthcare Dhahran Saudi Arabia. Johns Hopkins Hopkins Aramco Healthcare Dhahran Saudi Arabia. Johns Hopkins Aramco Healthcare Ras Tanura Saudi Arabia. Primary Care Johns Hopkins Aramco Healthcare Ras Tanura Saudi Arabia. Bloomberg School of Public Health Johns Hopkins University Baltimore MD USA. Department of Neurosurgery Rhode Island Hospital Providence RI USA. Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA. The Interface Group Institute of PhysiologyUniversity of Zurich Zurich Switzerland. Institute of Physiology University of Zurich Zurich Switzerland. Neuroscience Center Zurich and the Zurich Center for Integrative Human Physiology University of Zurich Zurich Switzerland. BioFlowImage Laboratory University of Picardie Jules Verne Amiens France. Reims Mathematics Laboratory University of Reims Champagne-Ardenne Reims France. Image Processing Laboratory University Hospital of Amiens-Picardie Amiens France. BioFlowImage Laboratory Department of Medical Image Processing University Hospital of Picardie Jules Verne Amiens France. Department of Geriatric University Hospital of Toulouse Toulouse France. Departments of Geriatric University Hospital of Toulouse Toulouse France. Department of Geriatry University Hospital Toulouse Toulouse France. Neurosciences department University of Cambridge Cambridge UK. Brain Physics Lab Academic Neurosurgery University of Cambridge Cambridge UK. Department of Nuclear Medicine University Hospital of Toulouse Toulouse France. Department of Nuclear Medicine University Hospit

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Energy Spectrum of Cosmic-Ray Electron and Positron from 10 GeV to 3 TeV Observed with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2017年第18期119卷 181101-181101页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka M. G. Bagliesi G. Bigongiari W. R. Binns S. Bonechi M. Bongi P. Brogi J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol V. Di Felice K. Ebisawa H. Fuke T. G. Guzik T. Hams M. Hareyama N. Hasebe K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel A. Javaid K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo H. S. Krawczynski J. F. Krizmanic S. Kuramata T. Lomtadze P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake K. Mizutani A. A. Moiseev K. Mori M. Mori N. Mori H. M. Motz K. Munakata H. Murakami S. Nakahira J. Nishimura G. A. de Nolfo S. Okuno J. F. Ormes S. Ozawa L. Pacini F. Palma P. Papini A. V. Penacchioni B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi R. Sparvoli P. Spillantini F. Stolzi I. Takahashi M. Takayanagi M. Takita T. Tamura N. Tateyama T. Terasawa H. Tomida S. Torii Y. Tsunesada Y. Uchihori S. Ueno E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida T. Yuda Department of Physics University of Florence Via Sansone 1–50019 Sesto Fiorentino Italy INFN Sezione di Florence Via Sansone 1–50019 Sesto Fiorentino Italy of Physics University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Research Institute for Science and Engineering Waseda University 3-4-1 Okubo Shinjuku Tokyo 169-8555 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3–56127 Pisa Italy Department of Physics Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy University of Rome “Tor Vergata ” Via della Ricerca Scientifica 1 00133 Rome Italy INFN Sezione di Rome “Tor Vergata ” Via della Ricerca Scientifica 1 00133 Rome Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan CRESST and Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA St. Marianna University School of Medicine 2-16-1 Sugao Miyamae-ku Kawasaki Kanagawa 216-8511 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Gradua

First results of a cosmic-ray electron and positron spectrum from 10 GeV to 3 TeV is presented based upon observations with the CALET instrument on the International Space Station starting in October, 2015. Nearly a half million electron and positron events are included in the analysis. CALET is an all-calorimetric instrument with total vertical thickness of 30 X0 and a fine imaging capability designed to achieve a large proton rejection and excellent energy resolution well into the TeV energy region. The observed energy spectrum over 30 GeV can be fit with a single power law with a spectral index of −3.152±0.016 (stat+syst). Possible structure observed above 100 GeV requires further investigation with increased statistics and refined data analysis.

关键词： Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles Particle dark matter

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MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORING

MODULAR SENSOR SYSTEM (MSS) FOR URBAN AIR POLLUTION MONITORI...

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IEEE Sensors

作者： Wei-Ying Yi Kwong-Sak Leung Yee Leung Mei-Ling Meng Terrence Mak Institute of Future Cities Department of Computer Science and Engineering Stanley Ho Big Data Decision Analytics Research Center The Chinese University of Hong Kong China Department of Systems Engineering and Engineering Management Department of Electronics and Computer Science University of Southampton UK

Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation.

关键词： plug-and-play air pollution monitoring wireless sensor network modular sensor node

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159 Dermoscopy versus videocapillaroscopy in the assessment of nailfold capillaroscopy images in patients with systemic sclerosis and healthy controls

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Rheumatology 2018年第SUPPL_3期57卷

作者： Dinsdale, Graham Peytrignet, Sebastien Moore, Tonia Berks, Michael Roberts, Christopher Manning, Joanne Allen, John Anderson, Marina Cutolo, Maurizio Hesselstrand, Roger Howell, Kevin Pizzorni, Carmen Smith, Vanessa Sulli, Alberto Wildt, Marie Taylor, Christopher Murray, Andrea Herrick, Ariane Centre for Musculoskeletal Research University of Manchester Salford Royal Hospital NHS Foundation Trust MAHSC Manchester UNITED KINGDOM Centre for Imaging Sciences Division of Informatics Imaging & Data Sciences University of Manchester Manchester UNITED KINGDOM Centre for Biostatistics Division of Population Health Health Services Research & Primary Care University of Manchester Manchester UNITED KINGDOM Microvascular Diagnostics Northern Medical Physics and Clinical Engineering Freeman Hospital Newcastle-upon-Tyne UNITED KINGDOM Institute of Ageing and Chronic Disease University of Liverpool Liverpool UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Department of Internal Medicine University of Genova Genova ITALY Department of Clinical Sciences Section of Rheumatology Lund University Lund SWEDEN Institute of Immunity and Transplantation University College London Royal Free Campus London UNITED KINGDOM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Genova ITALY Department of Rheumatology Ghent University Hospital Faculty of Internal Medicine Ghent University Ghent BELGIUM Research Laboratory and Academic Division of Clinical Rheumatology Dept. Internal Medicine University of Genova Manchester ITALY NIHR Manchester Musculoskeletal Biomedical Research Centre Central Manchester NHS Foundation Trust Manchester Academic Health Science Centre Manchester UNITED KINGDOM

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