| 211. |
- Aad, G., et al.
(author)
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- 2016
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In: Physical Review D. Particles and fields. - : American Physical Society. - 0556-2821 .- 1089-4918. ; 93:1
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Journal article (peer-reviewed)
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| 212. |
- Aad, G., et al.
(author)
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- 2015
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In: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 115:3
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Journal article (peer-reviewed)
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| 213. |
- Aad, G., et al.
(author)
-
- 2015
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In: Journal of High Energy Physics. - 1029-8479 .- 1126-6708. ; :3
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Journal article (peer-reviewed)
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| 214. |
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| 215. |
- Ding, Jinzhi, et al.
(author)
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Decadal soil carbon accumulation across Tibetan permafrost regions
- 2017
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In: Nature Geoscience. - 1752-0894 .- 1752-0908. ; 10:6, s. 420-424
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Journal article (peer-reviewed)abstract
- Permafrost soils store large amounts of carbon. Warming can result in carbon release from thawing permafrost, but it can also lead to enhanced primary production, which can increase soil carbon stocks. The balance of these fluxes determines the nature of the permafrost feedback to warming. Here we assessed decadal changes in soil organic carbon stocks in the active layer-the uppermost 30 cm-of permafrost soils across Tibetan alpine regions, based on repeated soil carbon measurements in the early 2000s and 2010s at the same sites. We observed an overall accumulation of soil organic carbon irrespective of vegetation type, with a mean rate of 28.0 g Cm-2 yr(-1) across Tibetan permafrost regions. This soil organic carbon accrual occurred only in the subsurface soil, between depths of 10 and 30 cm, mainly induced by an increase of soil organic carbon concentrations. We conclude that the upper active layer of Tibetan alpine permafrost currently represents a substantial regional soil carbon sink in a warming climate, implying that carbon losses of deeper and older permafrost carbon might be offset by increases in upper-active-layer soil organic carbon stocks, which probably results from enhanced vegetation growth.
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| 216. |
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| 217. |
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| 218. |
- Kaptoge, S., et al.
(author)
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World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
- 2019
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In: Lancet Global Health. - : Elsevier BV. - 2214-109X. ; 7:10
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Journal article (peer-reviewed)abstract
- Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0.685 (95% CI 0 . 629-0 741) to 0.833 (0 . 783-0- 882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
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| 219. |
- Peng, Shushi, et al.
(author)
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Benchmarking the seasonal cycle of CO2 fluxes simulated by terrestrial ecosystem models
- 2015
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In: Global Biogeochemical Cycles. - 0886-6236. ; 29:1, s. 46-64
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Journal article (peer-reviewed)abstract
- We evaluated the seasonality of CO2 fluxes simulated by nine terrestrial ecosystem models of the TRENDY project against (1) the seasonal cycle of gross primary production (GPP) and net ecosystem exchange (NEE) measured at flux tower sites over different biomes, (2) gridded monthly Model Tree Ensembles-estimated GPP (MTE-GPP) and MTE-NEE obtained by interpolating many flux tower measurements with a machine-learning algorithm, (3) atmospheric CO2 mole fraction measurements at surface sites, and (4) CO2 total columns (X-CO2) measurements from the Total Carbon Column Observing Network (TCCON). For comparison with atmospheric CO2 measurements, the LMDZ4 transport model was run with time-varying CO2 fluxes of each model as surface boundary conditions. Seven out of the nine models overestimate the seasonal amplitude of GPP and produce a too early start in spring at most flux sites. Despite their positive bias for GPP, the nine models underestimate NEE at most flux sites and in the Northern Hemisphere compared with MTE-NEE. Comparison with surface atmospheric CO2 measurements confirms that most models underestimate the seasonal amplitude of NEE in the Northern Hemisphere (except CLM4C and SDGVM). Comparison with TCCON data also shows that the seasonal amplitude of X-CO2 is underestimated by more than 10% for seven out of the nine models (except for CLM4C and SDGVM) and that the MTE-NEE product is closer to the TCCON data using LMDZ4. From CO2 columns measured routinely at 10 TCCON sites, the constrained amplitude of NEE over the Northern Hemisphere is of 1.60.4 gC m(-2)d(-1), which translates into a net CO2 uptake during the carbon uptake period in the Northern Hemisphere of 7.92.0 PgC yr(-1).
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