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Comparing polysomnography, actigraphy, and sleep diary in the home environment: The Study of Women's Health Across the Nation (SWAN) Sleep Study

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SLEEP Advances 2022年第1期3卷 zpac001页

作者： Lehrer, H. Matthew Yao, Zhigang Krafty, Robert T Evans, Marissa A Buysse, Daniel J Kravitz, Howard M Matthews, Karen A Gold, Ellen B Harlow, Sioban D Samuelsson, Laura B Hall, Martica H Department of Psychiatry University of Pittsburgh Pittsburgh PA United States Department of Statistics and Applied Probability National University of Singapore Singapore Singapore Deparment of Biostatistics and Bioinformatics Emory University Atlanta GA United States Department of Psychology University of Pittsburgh Pittsburgh PA United States Department of Psychiatry and Behavioral Sciences Rush University Medical Center Chicago IL United States Department of Preventive Medicine Rush University Medical Center Chicago IL United States Department of Public Health Sciences University of California Davis School of Medicine Davis CA United States Department of Epidemiology University of Michigan Ann Arbor MI United States

Study Objectives: Polysomnography (PSG) is considered the "gold standard"for assessing sleep, but cost and burden limit its use. Although wrist actigraphy and self-report diaries are feasible alternatives to PSG, few studies have compared all three modalities concurrently across multiple nights in the home to assess their relative validity across multiple sleep outcomes. This study compared sleep duration and continuity measured by PSG, actigraphy, and sleep diaries and examined moderation by race/ethnicity. Methods: Participants from the Study of Women's Health Across the Nation (SWAN) Sleep Study included 323 White (n = 147), African American (n = 120), and Chinese (n = 56) middle-Aged community-dwelling women (mean age: 51 years, range: 48-57). PSG, wrist actigraphy (AW-64;Philips Respironics, McMurray, PA), and sleep diaries were collected concurrently in participants' homes over three consecutive nights. Multivariable repeated-measures linear models compared time in bed (TIB), total sleep time (TST), sleep efficiency (SE), sleep latency (SL), and wake after sleep onset (WASO) across modalities. Results: Actigraphy and PSG produced similar estimates of sleep duration and efficiency. Diaries yielded higher estimates of TIB, TST, and SE versus PSG and actigraphy, and lower estimates of SL and WASO versus PSG. Diary SL was shorter than PSG SL only among White women, and diary WASO was lower than PSG and actigraphy WASO among African American versus White women. Conclusions: Given concordance with PSG, actigraphy may be preferred as an alternative to PSG for measuring sleep in the home. Future research should consider racial/ethnic differences in diary-reported sleep continuity. © 2022 The Author(s). Published by Oxford University Press on behalf of Sleep Research Society.

关键词： actigraphy methods middle-Aged women polysomnography self-report sleep sleep diary

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High-performance nano assemblies for heavy-metal filtration from wastewater

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Nano-Structures and Nano-Objects 2024年 39卷

作者： Dawood, Asadullah Khan, Muhammad Asad Ullah, Sami Ali, Ijaz Saghir, Sana Ullah, Zaka Ayub, Ambreen Jabeen, Zeenat Ahmad, Junaid Khan, Muhammad Saad Department of Physics National Excellence Institute (University) Islamabad 04524 Pakistan Department of Mathematics and Physics University of Campania “Luigi Vanvitelli” Caserta 81100 Italy Department of Physics University of Okara Okara 56300 Pakistan Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science and Technology Hawally 32093 Kuwait Department of Physics University of Wah Wah Cannt 47040 Pakistan Department of Physics Division of Science and Technology University of Education Lahore 54770 Pakistan Department of Physics Women University Mardan Mardan 23200 Pakistan Department of Physics COMSATS University Islamabad Lahore Campus Lahore 54000 Pakistan Department of Statistics Superior University Sargodha Campus 41100 Pakistan

Over the past two decades, concerns regarding global water shortage have escalated owing to population expansion and climate change. Among the numerous issues, the presence of heavy metals in wastewater stands out as a serious health risk that requires immediate action to limit its impact. In response, a slew of innovative technologies have been deployed to address this important issue. Notably, nanotechnology has emerged as a potential area in this field, attracting the interest of academics all around the world. A wide range of nanomaterials, including carbon-based (such as carbon nanotubes, graphene, and carbon nanosheets), metal-based, metal oxide-based, and different composite-based nanomaterials, have been studied for water purification applications. This review summarizes and examines current advances in the use of nanomaterials for wastewater treatment, offering light on their efficacy and potential. Furthermore, it defines the harmful consequences of regularly found heavy metals, emphasising the importance of adequate filtering procedures. The article also provides a complete overview of the most common techniques and methodologies for heavy metal filtering from wastewater. Concluding thoughts and recommendations for future research areas are presented, with the goal of accelerating the evolution of nanomaterial applications in wastewater treatment towards better efficacy and sustainability. © 2024 Elsevier B.V.

关键词： Heavy metals Nanomaterials Wastewater treatment Water purification

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Upregulation versus loss of function of NTRK2 in 44 affected individuals leads to 2 distinct neurodevelopmental disorders

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Genetics in Medicine 2025年 101326页

作者： Berger, Eva Jauss, Robin-Tobias Ranells, Judith D. Zonic, Emir von Wintzingerode, Lydia Wilson, Ashley Wagner, Johannes Tuttle, Annabelle Thomas-Wilson, Amanda Schulte, Björn Rabin, Rachel Pappas, John Odgis, Jacqueline A. Muthaffar, Osama Mendez-Fadol, Alejandra Lynch, Matthew Levy, Jonathan Lehalle, Daphné Lake, Nicole J. Krey, Ilona Kozenko, Mariya Knierim, Ellen Jouret, Guillaume Jobanputra, Vaidehi Isidor, Bertrand Hunt, David Hsieh, Tzung-Chien Holtz, Alexander M. Haack, Tobias B. Gold, Nina B. Dunstheimer, Désirée Donge, Mylène Deb, Wallid De La Rosa Poueriet, Katlin A. Danyel, Magdalena Christodoulou, John Chopra, Saurabh Callewaert, Bert Busche, Andreas Brick, Lauren Bigay, Bary G. Arlt, Marie Anikar, Swathi S. Almohammal, Mohammad N. Almanza, Deanna Alhashem, Amal Bertoli-Avella, Aida Sticht, Heinrich Abou Jamra, Rami Institute of Human Genetics University of Leipzig Medical Center Leipzig Germany Department of Pediatrics Division of Genetics and Metabolism University of South Florida Tampa FL United States CENTOGENE GmbH Rostock Germany Molecular Diagnostics New York Genome Center New York NY United States Institute of Human Genetics University Clinic Carl Gustav Carus Dresden Germany Lead Genetic Counseling Assistant GeneDx University of New England New York NY United States Diagnostics Zentrum für Humangenetik Tübingen Tübingen Germany Department of Pediatrics NYU Grossman School of Medicine New York NY United States Institute for Genomic Health Icahn School of Medicine at Mount Sinai New York NY United States Department of Pediatrics Faculty of Medicine King Abdulaziz University Jeddah Saudi Arabia Departamento de pediatría Universidad de la Frontera Temuco Chile Neurosceinces Unit Queensland Children's Hospital Brisbane Australia Genetics Department Robert Debré Hospital APHP Paris France Department of Genetics Yale School of Medicine New Haven CT United States Genetics and Metabolics Clinic McMaster University Hamilton Canada Department of Pediatric Neurology and Center for Chronically Sick Children Charité-Universitätsmedizin Berlin Berlin Germany National Center of Genetics (NCG) Laboratoire National de Santé (LNS) Dudelange Luxembourg Department of Pathology and Cell Biology Columbia University Irving Medical Center New York NY United States Department of Genetics Centre Hospitalier Universitaire de Nantes Nantes France Wessex Clinical Genetics Service Princess Anne Hospital Southampton United Kingdom Institute for Genome Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Division of Genetics and Genomics Boston Children's Hospital Boston MA United States Institute of Medical Genetics and Applied Genomics Universitätsklinikum Tübingen Tübingen Germany Department of Pediatrics

Purpose: Heterozygous pathogenic variants in NTRK2 (HGNC: 8032) have been associated with global developmental delay. However, only scattered cases have been described in small or general studies. The aim of our work was to consolidate our understanding of NTRK2-related disorders and to delineate the clinical presentation. Methods: We reported an extended cohort of 44 affected individuals, of whom 19 are from the literature and 25 were previously unreported. Results: Our analysis led to splitting the cohort into 2 entities. Conclusion: One group had variants in the cholesterol-binding motif of the transmembrane domain, with most of these being the recurrent variant c.1301A>G p.(Tyr434Cys). These variants probably lead to upregulation of tropomyosin receptor kinase B activity and to a severe phenotype of developmental delay/intellectual disability, muscular hypotonia, therapy-refractory epilepsy, visual impairment and blindness, and feeding difficulties. The second group had truncating variants or variants that presumably disturb the 3D structure of the protein leading to loss of function. These individuals had a remarkably milder phenotype of developmental delay, obesity, and hyperphagia. © 2024 The Authors

关键词： Blindness Obesity Transmembrane domain TRKB Tyrosine kinase domain

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Natural convection heat transport performance of nanofluids under the influence of inclined magnetic field

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Results in Physics 2024年 58卷

作者： Islam, Tarikul Fayz-Al-Asad, Md. Khatun, M.A. Parveen, N. Ahmad, Hijaz Askar, Sameh CMUP University of Porto Porto Portugal Department of Mathematics American International University –Bangladesh Khilkhet Kuratoli Dhaka-1229 Bangladesh Department of Mathematics Jagannath University Dhaka 1000 Bangladesh Department of Mathematics Bangladesh University of Engineering and Technology Dhaka-1000 Bangladesh Near East University Operational Research Center in Healthcare Nicosia PC: 99138 TRNC Mersin 10 Turkey Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Mishref Kuwait Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics and Informatics Azerbaijan University Jeyhun Hajibeyli street 71 AZ1007 Baku Azerbaijan Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia

Two-dimensional free convective temperature transportation and fluid flow within the semi-circular cavity occupied by nanofluid with influence of inclined MHD is studied numerically. While bottom wall is warmed by three distinct thermal conditions (uniform, parabolic, and sinusoidal), the upper circular wall is warmed at lower temperatures. A slopping magnetic field acts upon the enclosure whose strength is unchanging. The governing non-linear equations is resolved with the numerical scheme Galerkin type finite element technique. A strong agreement is found with the earlier work. The outcomes for the model parameters likely Rayleigh number, sloping angle of magnetic field, nanoparticles volume, size, shape, and Brownian effects, and Hartmann number are demonstrated through streamlines, isothermal lines, and mean Nusselt number. In addition, investigation is conducted the thermal features of various nanoparticles and the nanofluid produced from them. The outcomes reveal that thermal transfer rate is significantly enhanced by volume fraction nanoparticles and buoyancy-driven parameter Rayleigh number whilst reduces for higher Hartman number. The inclined angles of magnetic field and different thermal conditions have a positive influence on thermal transport. For Cu-H₂O nanofluids, thermal transport is enhanced by 10.98 % in a uniform thermal system, where it increases by 14.42 % in the parabolic thermal system and by 15.53 % in a sinusoidal thermal system for the only 5 % volume of nanoparticles. Moreover, nano-sized particles’ shape factor has a noteworthy role in thermal transport. It is found that heat transfer rate rises by 6.79 % for blade-shaped nanoparticles. Additionally, as considering Brownian effects of nanoparticles, the temperature transport rate is improved by 21.45 % for small-sized nanoparticles. © 2024 The Authors

关键词： Finite Element Method Inclined magnetic field Nanoparticles Natural convection Semi-circular cavity

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational platforms and environments Databases Infectious diseases

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Transplantation for Congenital Sideroblastic Anaemia Is Feasible and Offers Outcomes Comparable to Other Transfusion Dependent Anaemias. a Joint Retrospective Study of the Paediatric Diseases and Severe Aplastic Anaemia Working Parties (PDWP/SAAWP) of EBMT

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BLOOD 2020年第Sup1期136卷 45-47页

作者： de la Fuente, Josu Eikema, Dirk-Jan Bosman, Paul Wynn, Robert F. Diaz, Miguel Shaw, Peter J. Al-Seraihy, Amal Ar, Muhlis Cem Mohamed, Mohamed Salaheldin Bron, Dominique de Heredia, Cristina Diaz Gedde-Dahl, Tobias Gibson, Brenda Handgretinger, Rupert Gribben, Professor John G. Kerre, Tessa Kitra-Roussou, Vassiliki Lenhoff, Stig Moraleda, Jose Maria Martinez, Antonio Perez Moppett, John Sengeloev, Henrik Tbakhi, Abdelghani Veelken, Hendrik Verburgh, Estelle Visani, Giuseppe Risitano, Antonio Dufour, Carlo Corbacioglu, Selim De latour, Regis Peffault Centre for Haematology Department of Immunology and Inflammation Imperial College London London United Kingdom LUMC Dept. Medical Statistics & Bioinformatics Leiden Netherlands EBMT Data Office Leiden Netherlands Royal Manchester Children's Hospital Manchester United Kingdom HOSPITAL NiÑO JESUS MADRID ESP The Children's Hospital at Westmead Sydney AUS King Faisal Hospital and Research Centre Riyadh Saudi Arabia Department of Internal Medicine Division of Hematology Istanbul University-Cerrahpasa Cerrahpasa Medical Faculty Istanbul Turkey King Faisal Specialist Hospital and Research Center Jeddah SAU Dpt of Hematology Institut Jules Bordet ULB 1 Rue Heger Bordet Belgium Hospital Universitari Vall d'Hebron Vall d'Hebron Barcelona Spain Department of Hematology Oslo University Hospital Rikshospitalet and Institute of Clinical Medicine University of Oslo Oslo Norway Department of Hematology Royal Hospital for Children Glasgow GBR Hematology / Oncology Children's University Hospital Tubingen Germany Barts Cancer Institute Queen Mary University of London London United Kingdom Department of Hematology Ghent University Hospital Ghent Belgium St. Sophia' Children's Hospital Athene Greece Skanes University Hospital Lund Sweden Hematopoietic Transplant and Cellular Therapy Unit Instituto Murciano de Investigación Biosanitaria IMIB-Arrixaca Virgen de la Arrixaca University Hospital University of Murcia Murcia Spain Murcia Spain Hospital Infantil Universitario Niño Jesús Madrid ESP ristol Royal Hospital for Children Bristol United Kingdom Rigshospitalet Copenhagen Denmark Department of Cell Therapy and Applied Genomics King Hussein Cancer Center Amman Jordan Department of Hematology Leiden University Medical Center Leiden Netherlands Groote Schuur Hospital Cape Town South Africa Hematology & Transplant Centre AORMN Hospital Pesaro Italy Department of Clinical Medicine and Surgery University of Naples Naples Italy Hematology Unit IRCCS Istituto Gian

Congenital sideroblastic anaemias (CSA) are a rare group of disorders characterized by the presence of pathologic iron deposits within the mitochondria of erythroid precursors (ring sideroblasts) in the bone marrow due to heterogenous germline mutations leading to defects in mitochondrial heme synthesis, iron-sulfur (Fe-S) cluster biogenesis, or protein synthesis. Patients present with anaemia and relative reticulocytopenia, and systemic iron overload secondary to chronic ineffective erythropoiesis, leading to end-organ damage. The disease is heterogenous underlying the genetic variability and the variable response to treatment. Although a number of CSA patients have received a bone marrow transplant, the outcomes and toxicities are not known. This status makes it very difficult to understand the role of BMT in the management of CSA. A search in the EBMT database identified 28 patients receiving a HSCT for CSA between 1998 to 2018 by 24 participating centres. The median year of transplantation was 2014 (IQR 2004-2016). The distribution was equal between males (n=14) and females (n=14). The median age at transplantation was 7 years of age (3-10 years). Fifteen patients had a sibling HSCT (88%), one a family matched donor HSCT (6%) and one an unrelated matched (6%), the type of transplant being unknown in others (n=11). The source of stem cells was bone marrow in 20 cases (74%), peripheral blood in 4 cases (15%), cord blood in 2 (7%) and combined bone marrow and cord in one (4%). Five cases had a Bu/Cy based conditioning regimen, 4 had Bu/fludarabine based regimen and three fludarabine/treosulfan based conditioning with the rest having a variety of approaches. Eighty-six percent of cases had serotherapy with ATG or alemtuzumab. The median follow-up was 31.6 months (95% CI, 12.2-74.1%). The overall survival at 12 and 24 months was 88% (76-100) and 82% (66-99), respectively (figure 1). The median neutrophil engraftment was 18 (15-21) days and platelet engraftment >20 x

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Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

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Journal of Biomedical Semantics 2011年第5期2卷 S11-S11页

作者： Rebholz-Schuhmann, Dietrich Yepes, Antonio Jimeno Li, Chen Kafkas, Senay Lewin, Ian Kang, Ning Corbett, Peter Milward, David Buyko, Ekaterina Beisswanger, Elena Hornbostel, Kerstin Kouznetsov, Alexandre Witte, René Laurila, Jonas B Baker, Christopher JO Kuo, Cheng-Ju Clematide, Simone Rinaldi, Fabio Farkas, Richárd Móra, György Hara, Kazuo Furlong, Laura I Rautschka, Michael Neves, Mariana Lara Pascual-Montano, Alberto Wei, Qi Collier, Nigel Chowdhury, Md Faisal Mahbub Lavelli, Alberto Berlanga, Rafael Morante, Roser Van Asch, Vincent Daelemans, Walter Marina, José Luís van Mulligen, Erik Kors, Jan Hahn, Udo European Bioinformatics Institute EMBL Outstation Hinxton Cambridge CB10 1SD United Kingdom Erasmus University Medical Center Dept. of Medical Informatics Rotterdam Netherlands St. John's Innovation Centre Linguamatics Ltd Cambridge United Kingdom Friedrich-Schiller-Universität Language and Information Engineering (JULIE) Lab Jena Germany University of New Brunswick Dept. of Computer Science and Applied Statistics Canada Concordia University Dept. of Computer Science and Software Engineering Montreal Canada Academia Sinica Institute of Information Science Taipei 115 Taiwan University of Zürich Switzerland Hungarian Academy of Sciences Research Group on Artificial Intelligence Hungary Nara Institute of Science and Technology Nara Japan IMIM (Hospital del Mar Research Institute) Universitat Pompeu Fabra Research Programme on Biomedical Informatics (GRIB) Barcelona Spain National Center for Biotechnology-CSIC Madrid Spain National Institute of Informatics Tokyo Japan Fondazione Bruno Kessler Trento Italy Universitat Jaume I Spain University of Antwerp CLiPS Belgium Complutense University of Madrid Spain

Background: Competitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions. Results: All four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I. The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants' solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and P

关键词： Chemical Entity Cosine Similarity Semantic Type Semantic Group Annotate Corpus

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