| 11. |
- Aaltonen, T., et al.
(author)
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Observation of s-Channel Production of Single Top Quarks at the Tevatron
- 2014
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In: Physical Review Letters. - 0031-9007 .- 1079-7114. ; 112:23
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Journal article (peer-reviewed)abstract
- We report the first observation of single-top-quark production in the s channel through the combination of the CDF and D0 measurements of the cross section in proton-antiproton collisions at a center-of-mass energy of 1.96 TeV. The data correspond to total integrated luminosities of up to 9.7 fb(-1) per experiment. The measured cross section is sigma(s) = 1.29(-0.24)(+0.26) pb. The probability of observing a statistical fluctuation of the background to a cross section of the observed size or larger is 1.8 x 10(-10), corresponding to a significance of 6.3 standard deviations for the presence of an s-channel contribution to the production of single-top quarks.
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| 12. |
- Gerkin, RC, et al.
(author)
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The best COVID-19 predictor is recent smell loss: a cross-sectional study
- 2020
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In: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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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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| 13. |
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| 14. |
- Aarestrup, FM, et al.
(author)
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Towards a European health research and innovation cloud (HRIC)
- 2020
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In: Genome medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1, s. 18-
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Journal article (peer-reviewed)abstract
- The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.
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| 15. |
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| 16. |
- Bilski, P., et al.
(author)
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The problems associated with the monitoring of complex workplace radiation fields at European high-energy accelerators and thermonuclear fusion facilities
- 2007
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In: Radiation Protection Dosimetry. - : Oxford University Press (OUP). - 0144-8420 .- 1742-3406. ; 126:1-4, s. 491-496
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Journal article (peer-reviewed)abstract
- The European Commission is funding within its Sixth Framework Programme a three-year project (2005-2007) called CONRAD, COordinated Network for RAdiation Dosimetry. The organisational framework for this project is provided by the European Radiation Dosimetry Group EURADOS. One task within the CONRAD project, Work Package 6 (WP6), was to provide a report outlining research needs and research activities within Europe to develop new and improved methods and techniques for the characterisation of complex radiation fields at workplaces around high-energy accelerators, but also at the next generation of thermonuclear fusion facilities. The paper provides an overview of the report, which will be available as CERN Yellow Report.
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| 17. |
- Burr, H., et al.
(author)
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The demand–control model as a predictor of depressive symptoms—interaction and differential subscale effects : Prospective analyses of 2212 German employees
- 2021
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In: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 18:16
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Journal article (peer-reviewed)abstract
- Testing assumptions of the widely used demand–control (DC) model in occupational psychosocial epidemiology, we investigated (a) interaction, i.e., whether the combined effect of low job control and high psychological demands on depressive symptoms was stronger than the sum of their single effects (i.e., superadditivity) and (b) whether subscales of psychological demands and job control had similar associations with depressive symptoms. Logistic longitudinal regression analyses of the 5-year cohort of the German Study of Mental Health at Work (S-MGA) 2011/12–2017 of 2212 employees were conducted. The observed combined effect of low job control and high psychological demands on depressive symptoms did not indicate interaction (RERI = −0.26, 95% CI = −0.91; 0.40). When dichotomizing subscales at the median, differential effects of subscales were not found. When dividing subscales into categories based on value ranges, differential effects for job control subscales (namely, decision authority and skill discretion) were found (p = 0.04). This study does not support all assumptions of the DC model: (1) it corroborates previous studies not finding an interaction of psychological demands and job control; and (2) signs of differential subscale effects were found regarding job control. Too few prospective studies have been carried out regarding differential subscale effects.
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