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arXiv 2024年

作者： Mendoza, Marie Lisandra Zepeda Agarwal, Sonali Blackshaw, James A. Bol, Vanesa Fazzi, Audrey Fiorini, Filippo Foreman, Amy Louise George, Nancy Johnson, Brett r. Martin, Brian McComb, dave Mutasa-Gottgens, Euphemia Parkinson, Helen romacker, Martin russell, rolf Ségard, Valérien Tan, Shawn Zheng Kai Teh, Wei Kheng Winstanley, F.P. Wong, Benedict Smith, Adrian M. Machine Intelligence AI & Digital Research Novo Nordisk Research Centre Oxford United Kingdom GSK Global Capability Center Bagmane Capital Mahadevapura Karnataka Bengaluru India EMBL-EBI Wellcome Genome Campus Hinxton United Kingdom GSK Vaccines R&D Rue de l'Institut 89 Rixensart Belgium GSK Via Fiorentina Siena53100 Italy Global Genes 28 Argonaut Suite 150 Aliso ViejoCA United States AbbVie 1 N Waukegan Road AP4/B North ChicagoIL United States Semantic Arts 123 N College AvenueSuite 218 Fort CollinsCO United States F.Hoffmann-La Roche Grenzacherstrasse 124 BaselCH-4070 Switzerland TXI 10 S Riverside Plaza Suite 875 ChicagoIL United States Data Representation Novo Nordisk A/S Måløv Denmark Semantic Arts Kilcreggan United Kingdom Unilever R&D Colworth Science Park Sharnbrook Bedford United Kingdom

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become essential for organizations to effectively leverage vast accessible data. This paper is a compilation of concepts that emerged from KM workshops hosted by EMBL-EBI, attended by SMEs and industry. We provide guidance on envisioning, executing, evaluating, and evolving knowledge management platforms. We emphasize essential considerations such as setting knowledge domain boundaries and measuring success, as well as the importance of making knowledge accessible for downstream applications and non-computational users and highlights necessary personal and organizational skills for success. We stress the importance of collaboration and the need for convergence on shared principles and commitment to provide or seek resources to advance KM. The community is invited to join the journey of KM and contribute to the advancement of the field by applying and improving on the guidelines described. © 2024, CC BY.

关键词： Knowledge management

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A COMPUTEr-MOdEL FOr SHIPBOArd ENErGY analysis

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NAVAL ENGINEErS JOUrNAL 1984年第5期96卷 33-45页

作者： dETOLLA, JP FLEMING, Jr Joseph DeTolla:is a ship systems engineer in the Ship Systems Engineering Division SEA 56D5 at the Naval Sea Systems Command. His career with the Navy started in 1965 at the Philadelphia Naval Shipyard Design Division. In 1971 he transferred to the Naval Ship Engineering Center. He has held positions as a fluid systems design engineer and auxiliary systems design integration engineer. Mr. DeTolla has worked extensively in the synthesis and analysis of total energy systems notably the design development of the FFG-7 class waste heat recovery system. He is NA VSEA's machinery group computer supported design project coordinator and is managing the development of a machinery systems data base load forecasting algorithms and design analysis computer programs. Mr. DeTolla has a bachelor of science degree in mechanical engineering from Drexel University and a master of engineering administration degree from George Washington University. He is a registered professional engineer in the District of Columbia and has written several technical papers on waste heat recovery and energy conservation. Jeffrey Fleming:is a senior project engineer in the Energy R&D Office at the David Taylor Naval Ship R&D Center. In his current position as group leader for the future fleet energy conservation portion of the Navy's energy R&D program he is responsible for the identification and development of advanced components and subsystems which will lead to reductions in the fossil fuel consumption of future ships. Over the past several years he has also directed the development and application of total energy computer analysis techniques for the assessment of conventional and advanced shipboard machinery concepts. Mr. Fleming is a 1971 graduate electrical engineer of Virginia Polytechnic Institute and received his MS in electrical engineering from Johns Hopkins University in 1975. Mr. Fleming has authored various technical publications and was the recipient of the Severn Technical Society's “Best Technical Paper of the Year” award in 1

In support of the Navy's efforts to improve the energy usage of future ships and thereby to reduce fleet operating costs, a large scale computer model has been developed by the david Taylor Naval Ship research and development center (dTNSrdC) to analyze the performance of shipboard energy systems for applications other than nuclear or oil-fired steam propulsion plants. This paper discusses the applications and utility of this computer program as a performance analysis tool for design of ship machinery systems. The program is a simulation model that performs a complete thermodynamic analysis of a user-specified energy system. It offers considerable flexibility in analyzing a variety of propulsion, electrical, and auxiliary plant configurations through a component building block structure. Component subroutines that model the performance of shipboard equipment such as engines, boilers, generators, and compressors are available from the program library. Component subroutines are selected and linked in the program to model the desired machinery plant functional configurations. The operation of the defined shipboard energy system may then be simulated over a user-specified scenario of temperature, time, and load profiles. The program output furnishes information on component operating characteristics and fuel demands, which allows evaluation of the total system performance.

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1346P A population-level prognostic score model for early acquired resistance to frontline anti-Pd-(L)1 therapy in metastatic non-small cell lung cancer (NSCLC)

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Annals of Oncology 2024年 35卷 S851-S851页

作者： J.C. Murray Q. Huang C. Hu E. Curran C. Wong L. Glatz T. Wang A. Hasan L. Villaruz A. Schoenfeld Oncology Johns Hopkins Medicine - The Sidney Kimmel Comprehensive Cancer Center Baltimore MD USA Interventional Oncology Johnson & Johnson WHQ New Brunswick NJ USA Biostatistics Sidney Kimmel Comprehensive Cancer Center Baltimore MD USA Global Epidemiology Johnson & Johnson - Janssen R&D Spring House Spring House PA USA Global Epidemiology & Real-World Data Sciences Johnson and Johnson Raritan NJ USA Oncology Johnson & Johnson - Janssen R&D Spring House Spring House PA USA Medicine Hem/Onc Department UPMC Hillman Cancer Center Pittsburgh PA USA Thoracic Oncology MSKCC - Memorial Sloan Kettering Cancer Center New York NY USA

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Benchmarking homogenization algorithms for monthly data

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AIP Conference Proceedings 2013年第1期1552卷 1060-1065页

作者： V. K. C. Venema O. Mestre E. Aguilar I. Auer J. A. Guijarro P. domonkos G. Vertacnik T. Szentimrey P. Stepanek P. Zahradnicek J. Viarre G. Müller-Westermeier M. Lakatos C. N. Williams M. J. Menne r. Lindau d. rasol E. rustemeier K. Kolokythas T. Marinova L. Andresen F. Acquaotta S. Fratiannil S. Cheval M. Klancar M. Brunetti C. Gruber M. Prohom duran T. Likso P. Esteban T. Brandsma K. Willett Meteorological institute of the University of Bonn Germany Meteo France Ecole Nationale de la Meteorologie Toulouse France Center on Climate Change (C3) Universitat Rovira i Virgili Tarragona Spain Zentralanstalt für Meteorologie und Geodynamik Wien Austria Agencia Estatal de Meteorologia Palma de Mallorca Spain Slovenian Environment Agency Ljubljana Slovenia Hungarian Meteorological Service Budapest Hungary Czech Hydrometeorological Institute Brno Czech Republic and Global Change Research Centre AS CR v.v.i. Brno Czech Republic Deutscher Wetterdienst Offenbach Germany NOAA/National Climatic Data Center USA Meteorological and hydrological service Zagreb Croatia Laboratory of Atmospheric Physics University of Patras Greece National Institute of Meteorology and Hydrology -- BAS Sofia Bulgaria Norwegian Meteorological Institute Oslo Norway Department of Earth Science University of Turin Italy National Meteorological Administration Bucharest Romania and National Institute for R&D in Environmental Protection Bucharest Romania Institute of Atmospheric Sciences and Climate (ISAC-CNR) Bologna Italy Grup de Climatologia Universitat de Barcelona Spain and Meteorological Service of Catalonia Area of Climatology Barcelona Catalonia Spain Centre d'Estudis de la Neu i de la Muntanya d'Andorra (CENMA-IEA) Andorra and Grup de Climatologia Universitat de Barcelona Spain Royal Netherlands Meteorological Institute De Bilt Netherlands Met Office Hadley Centre Exeter United Kingdom

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.

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682TiP A phase I, first-in-human study of dS-1471 in patients with advanced/metastatic solid tumors

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Annals of Oncology 2024年 35卷 S531-S531页

作者： S. Koganemaru N. Yoshizuka S. Mizuno S. Hirai N. Yamamoto Department of Experimental Therapeutics National Cancer Center Hospital East Chiba Japan Global Oncology R&D Daiichi Sankyo Inc. Basking Ridge NJ USA Clinical Science Daiichi Sankyo Co. Ltd. Tokyo Japan Data Intelligence Daiichi Sankyo Co. Ltd. Tokyo Japan Department of Experimental Therapeutics National Cancer Center Hospital Tokyo Japan

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1040TiP QuANTUM-First: phase 3, double-blind, placebo-controlled study of quizartinib in combination with induction and consolidation chemotherapy, and as maintenance therapy in patients (pts) with newly diagnosed (Ndx) FLT3-ITd acute myeloid leukemia (AML)

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Annals of Oncology 2017年第SUPPL_5期28卷

作者： r. Schlenk H. dombret S. Amadori P. Montesinos M. Levis M.A. Sekeres J. Cortes A. Perl O. Zernovak d. Mires N. Ge H. Zhang J. Hanyok S. Macintyre S. Gökmen K. Kobayashi H. Erba Trial Center National Center for Tumor Diseases Heidelberg Germany Blood Disease Department University Hospital Saint-Louis Paris France Hematology Tor Vergata University Hospital Rome Italy Hematology Department Hospital Universitario La Fe Valenica Spain Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA Cleveland Clinic Taussig Cancer Institute Cleveland Clinic Cleveland USA Department of Leukemia MD Anderson Cancer Center Houston TX USA Division of Hematology/Oncology University of Pennsylvania Philadelphia PA USA Clinical Development Oncology Daiichi Sankyo Inc. Edison USA Global Oncology R&D Daiichi Sankyo Inc. Edison OH USA Pharmacology Development Daiichi Sankyo Inc. Edison OH USA R&D Biostatistics & Data Management Daiichi Sankyo Inc. Edison OH USA Clinical Development Daiichi Sankyo Inc. Edison OH USA Clinical Development-Oncology R&D Global Oncology R&D Daiichi Sankyo Inc. Edison OH USA Division of Hematology/Oncology University of Alabama at Birmingham Birmingham AL USA

1040TiP QuANTUM-First: phase 3, double-blind, placebo-controlled study of quizartinib in combination with induction and consolidation chemotherapy, and as maintenance therapy in patients (pts) with newly diagnosed (Ndx) FLT3-ITd acute myeloid leukemia (AML) r. Schlenk Trial center, National center for Tumor diseases, Heidelberg, Germany Search for other works by this author on: Oxford Academic PubMed Google Scholar r. Schlenk H. dombret Blood disease department, University Hospital Saint-Louis, Paris, France Search for other works by this author on: Oxford Academic PubMed Google Scholar H. dombret S. Amadori Hematology, Tor Vergata University Hospital, rome, Italy Search for other works by this author on: Oxford Academic PubMed Google Scholar S. Amadori P. Montesinos Hematology department, Hospital Universitario La Fe, Valenica, Spain Search for other works by this author on: Oxford Academic PubMed Google Scholar P. Montesinos M. Levis Sidney Kimmel Comprehensive Cancer center, Johns Hopkins University, Baltimore, Md, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar M. Levis M.A. Sekeres Cleveland Clinic Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar M.A. Sekeres J. Cortes department of Leukemia, Md Anderson Cancer center, Houston, TX, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar J. Cortes A. Perl division of Hematology/Oncology, University of Pennsylvania, Philadelphia, PA, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar A. Perl O. Zernovak Clinical development Oncology, daiichi Sankyo, Inc., Edison, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar O. Zernovak d. Mires global Oncology r&d, daiichi Sankyo, Inc., Edison, OH, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar d. Mires ... Show more N. G

关键词： chemotherapy regimen ms-like tyrosine kinase 3 flt3 gene

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The tracking machine learning challenge: Accuracy phase

arXiv

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arXiv 2019年

作者： Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Estrade, Victor Farrell, Steven Ferreira, diogo r. Finnie, Liam Finnie, Nicole Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Moyse, Edward Puget, Jean-François reina, Yuval rousseau, david Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-roch Wind, Johan Sokrates Xylouris, Trian Yilmaz, Yetkin Département de Physique Nucléaire et Corpusculaire Université de Genève Geneva Switzerland LRI TAU Univ. Paris-Sud INRIA CNRS Université Paris-Saclay Gif-sur-Yvette France Physics Division Lawrence Berkeley National Laboratory University of California BerkeleyCA United States IST University of Lisbon Lisbon Portugal IBM Germany Research and Development Germany Bosch Center for Artificial Intelligence Germany LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France Goethe University Frankfurt am Main Germany UPSud INRIA Université Paris-Saclay Orsay France ChaLearn BerkeleyCA United States National Research University Higher School of Economics Yandex School of Data Analysis Moscow Russia CERN Geneva Switzerland Department of Physics University of Massachusetts AmherstMA United States Data and AI R&D IBM France Lab Biot France Tel-Aviv Israel LAL Univ. Paris-Sud CNRS IN2P3 Université Paris-Saclay Orsay France California Institute of Technology PasadenaCA United States Norwegian University of Science and Technology Oslo Norway Frankfurt Germany

This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document. © 2019, CC BY.

关键词： Machine learning

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Epidemiological and public health requirements for COVId-19 contact tracing apps and their evaluation

arXiv

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arXiv 2021年

作者： Colizza, Vittoria Grill, Eva Mikolajczyk, rafael Cattuto, Ciro Kucharski, Adam riley, Steven Kendall, Michelle Lythgoe, Katrina Abeler-dörner, Lucie Wymant, Chris Bonsall, david Ferretti, Luca Fraser, Christophe INSERM Sorbonne Université Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP Paris France Tokyo Tech World Research Hub Initiative Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan Institute für Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians University Muenchen Muenchen Germany Institute for Medical Epidemiology Biometrics and Informatics Interdisciplinary Center for Health Sciences Martin Luther University Halle-Wittenberg Halle Germany University of Turin Turin Italy ISI Foundation Turin Italy Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford United Kingdom Department of Statistics University of Warwick Warwick United Kingdom

digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust. © 2021, CC BY-NC-SA.

关键词： Public health

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1240MO Quantitative radiomics for the detection of symptomatic pneumonitis following chemoradiotherapy in patients with stage III unresectable NSCLC

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Annals of Oncology 2024年 35卷 S794-S794页

作者： J. Naidoo V.d. Haakensen J. Bar F. Belmans A. Corsi M. Flechet L. Libert C.C. Meca N. Tsoutzidis P. Chander K.A. Patwardhan J. Faria d. de ruysscher Oncology Dept. Beaumont Hospital Dublin Ireland Department of Oncology and Institute for Cancer Research Oslo University Hospital Oslo Norway Institute of Oncology and Faculty of Medicine Sheba Medical Center and Tel Aviv University Ramat Gan/Tel Aviv Israel R&D and Department of Imaging and Pathology Radiomics.bio and KU Leuven Liège/Leuven Belgium Medical Oncology Radiomics.bio Liège Belgium Execution Radiomics.bio Liège Belgium R&D Radiomics.bio Liège Belgium Business Radiomics.bio Liège Belgium Global Medical Affairs AstraZeneca Gaithersburg MD USA Oncology Data Science AstraZeneca Waltham MA USA R&D Oncology AstraZeneca Cambridge UK Radiation Oncology Stichting Maastricht Radiation Oncology (“MAASTRO”) Maastricht Netherlands

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AB1523-HPr USABILITY ANd ACCEPTABILITY OF A NEW AUTOINJECTOr dEVICE ANd ITS ASSOCIATEd APP IN AUSTrALIAN, FrENCH, GErMANY ANd JAPANESE rHEUMATOLOGY PATIENTS

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Annals of the rheumatic diseases 2022年 81卷 1864-1864页

作者： r. Alten M. Latymer d.C. Gruben L. Gunther-Lavergne S. Moss H. Kameda Schlosspark-Klinik University Medicine Internal Medicine Rheumatology Berlin Germany Pfizer Ltd I&I Medical Affairs Sandwich United Kingdom Pfizer Global Biostatistics and Data Management Groton United States of America Farm Design A Flex Company Human Factors Engineering Hollis United States of America Pfizer R&D UK Devices Centre of Excellence Cambridge United Kingdom Toho University (Ohashi Medical Center) Division of Rheumatology Tokyo Japan

Background Smartclic/ClicWise is a new reusable autoinjector with a dose-dispensing cartridge for subcutaneous self-administration of biotherapeutics in development for patients with rheumatoid arthritis (rA) and other diseases. The device can connect to an optional mobile phone app (Smartclic, or ClicNote in Japan) to aid in tracking injections and other treatment or symptom data. Objectives The study objective was to collect ease of use and usability data on the Smartclic injector and companion app from experienced patients. Methods After completing a patient profiling questionnaire, adult patients (≥18 yrs) from Australia, France, Germany, and Japan with rA, psoriatic arthritis, or an original diagnosis of juvenile idiopathic arthritis who were prescribed an injectable biologic were enrolled in this study. For Japan, only patients with rA were enrolled. Each patient received training individually on the use of the Smartclic injector device and insertable cartridge and gained experience by performing simulated injections. Participants completed a questionnaire with evaluations of the device categories (number of questions): ‘ease of use' (14), ‘usability effectiveness' (11), ‘benefit of features' (8), and ‘form factor' (7). Participants also separately received a storyboard presentation summarizing the key features of the app, during which patients could explore the app (on a phone with either an android or iOS operating system), and completed 16 questions on the connectivity, usability, and benefit. responses were recorded as Likert scale ratings from 1 (‘extremely negative') to 7 (‘extremely positive'). respondents also provided an estimate of patient training time for the device. Mean values were reported. The percentage of negative (Likert scale rating 1-2), neutral (3-5), and positive (6-7) responses for each category were determined. results A total of 139 patients (mean age [range], 52 [18-84] yrs; 73% female) participated in the study ( Table 1 ). Mean sco

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