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  • Acharya, B. S., et al. (author)
  • Introducing the CTA concept
  • 2013
  • In: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 43, s. 3-18
  • Journal article (other academic/artistic)abstract
    • The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. (C) 2013 Elsevier B.V. All rights reserved.
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  • Gerkin, RC, et al. (author)
  • The best COVID-19 predictor is recent smell loss: a cross-sectional study
  • 2020
  • In: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
  • Journal article (other academic/artistic)abstract
    • BackgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.MethodsThis preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.ResultsBoth C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.ConclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
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  • Result 1-10 of 321
Type of publication
conference paper (155)
journal article (128)
other publication (14)
book chapter (10)
reports (9)
research review (3)
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book (2)
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Type of content
other academic/artistic (321)
Author/Editor
Tu, C. W. (48)
Buyanova, Irina, 196 ... (33)
Chen, Weimin, 1959- (32)
Chen, Weimin (25)
Chen, C. (24)
Buyanova, Irina (21)
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Metra, M (17)
Hill, JA (17)
Borer, JS (17)
Agewall, S (17)
Luscher, TF (17)
Guzik, TJ (17)
Dominiczak, AF (17)
Timmis, A (17)
Nallamothu, BK (17)
Vrints, C. (17)
Erol, C (17)
Saksena, S. (17)
Camici, PG (17)
Natale, A (17)
Remme, WJ (17)
Baranchuk, A (17)
Booz, GW (17)
Chen, PS (17)
Grines, CL (17)
Gropler, R (17)
Heinemann, MK (17)
Iskandrian, AE (17)
Knight, BP (17)
London, B (17)
Musunuru, K (17)
Puttisong, Yuttapoom (11)
Pearton, S. J. (11)
Chen, Deliang, 1961 (10)
Abernathy, C. R. (10)
Hong, Y. G. (10)
Chen, Y. (9)
Li, L. (9)
Zheng, W. (9)
Chen, Q. (9)
Zhang, L. (8)
Kim, J. (8)
Carrero, JJ (8)
Chen, Z. (8)
Halldin, C (8)
Ikram, MA (8)
Chang-Claude, J (8)
Lichtenstein, P. (8)
Hayward, C. (8)
Xin, H.P. (8)
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University
Karolinska Institutet (193)
Linköping University (65)
Uppsala University (16)
University of Gothenburg (15)
Örebro University (11)
Chalmers University of Technology (9)
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Umeå University (6)
Royal Institute of Technology (6)
Stockholm University (6)
Lund University (5)
Linnaeus University (3)
Jönköping University (1)
Malmö University (1)
Mid Sweden University (1)
University of Borås (1)
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Language
English (321)
Research subject (UKÄ/SCB)
Natural sciences (62)
Medical and Health Sciences (26)
Engineering and Technology (7)
Social Sciences (4)
Agricultural Sciences (1)

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