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| 2. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 3. |
- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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| 4. |
- Aad, G., et al.
(author)
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- 2010
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swepub:Mat__t
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| 5. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 6. |
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| 7. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 8. |
- Aad, G., et al.
(author)
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- 2010
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swepub:Mat__t
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| 9. |
- Aad, G., et al.
(author)
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- 2010
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swepub:Mat__t
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| 10. |
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| 11. |
- Aad, G., et al.
(author)
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- 2010
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swepub:Mat__t
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| 12. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 13. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t (peer-reviewed)
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| 14. |
- Aad, G., et al.
(author)
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- 2011
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swepub:Mat__t
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| 15. |
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| 16. |
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| 17. |
- Aad, G., et al.
(author)
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The ATLAS Simulation Infrastructure
- 2010
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In: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 823-874
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Journal article (peer-reviewed)abstract
- The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.
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| 18. |
- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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| 19. |
- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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| 20. |
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| 21. |
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| 22. |
- Lind, Lars, et al.
(author)
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Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)
- 2021
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In: eLife. - : eLife Sciences Publications Ltd. - 2050-084X. ; 10
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Journal article (peer-reviewed)abstract
- From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions.
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| 23. |
- Aad, G., et al.
(author)
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Commissioning of the ATLAS Muon Spectrometer with cosmic rays
- 2010
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In: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 875-916
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Journal article (peer-reviewed)abstract
- The ATLAS detector at the Large Hadron Collider has collected several hundred million cosmic ray events during 2008 and 2009. These data were used to commission the Muon Spectrometer and to study the performance of the trigger and tracking chambers, their alignment, the detector control system, the data acquisition and the analysis programs. We present the performance in the relevant parameters that determine the quality of the muon measurement. We discuss the single element efficiency, resolution and noise rates, the calibration method of the detector response and of the alignment system, the track reconstruction efficiency and the momentum measurement. The results show that the detector is close to the design performance and that the Muon Spectrometer is ready to detect muons produced in high energy proton-proton collisions.
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| 24. |
- Tran, K. B., et al.
(author)
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The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019
- 2022
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In: Lancet. - 0140-6736. ; 400:10352, s. 563-591
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Journal article (peer-reviewed)abstract
- Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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| 25. |
- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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