| 1. |
- Jansen, Willemijn J, et al.
(författare)
-
Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum.
- 2022
-
Ingår i: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 79:3, s. 228-243
-
Tidskriftsartikel (refereegranskat)abstract
- One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Alzheimer disease biomarkers detected on PET or in CSF.Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18).This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.
|
|
| 2. |
- Laursen, K. R., et al.
(författare)
-
Airway and systemic biomarkers of health effects after short-term exposure to indoor ultrafine particles from cooking and candles - A randomized controlled double-blind crossover study among mild asthmatic subjects
- 2023
-
Ingår i: Particle and Fibre Toxicology. - 1743-8977. ; 20:1
-
Tidskriftsartikel (refereegranskat)abstract
- BackgroundThere is insufficient knowledge about the systemic health effects of exposure to fine (PM2.5) and ultrafine particles emitted from typical indoor sources, including cooking and candlelight burning. We examined whether short-term exposure to emissions from cooking and burning candles cause inflammatory changes in young individuals with mild asthma. Thirty-six non-smoking asthmatics participated in a randomized controlled double-blind crossover study attending three exposure sessions (mean PM2.5 & mu;g/m(;)(3) polycyclic aromatic hydrocarbons ng/m(3)): (a) air mixed with emissions from cooking (96.1; 1.1), (b) air mixed with emissions from candles (89.8; 10), and (c) clean filtered air (5.8; 1.0). Emissions were generated in an adjacent chamber and let into a full-scale exposure chamber where participants were exposed for five hours. Several biomarkers were assessed in relation to airway and systemic inflammatory changes; the primary outcomes of interest were surfactant Protein-A (SP-A) and albumin in droplets in exhaled air - novel biomarkers for changes in the surfactant composition of small airways. Secondary outcomes included cytokines in nasal lavage, cytokines, C-reactive protein (CRP), epithelial progenitor cells (EPCs), genotoxicity, gene expression related to DNA-repair, oxidative stress, and inflammation, as well as metabolites in blood. Samples were collected before exposure start, right after exposure and the next morning.ResultsSP-A in droplets in exhaled air showed stable concentrations following candle exposure, while concentrations decreased following cooking and clean air exposure. Albumin in droplets in exhaled air increased following exposure to cooking and candles compared to clean air exposure, although not significant. Oxidatively damaged DNA and concentrations of some lipids and lipoproteins in the blood increased significantly following exposure to cooking. We found no or weak associations between cooking and candle exposure and systemic inflammation biomarkers including cytokines, CRP, and EPCs.ConclusionsCooking and candle emissions induced effects on some of the examined health-related biomarkers, while no effect was observed in others; Oxidatively damaged DNA and concentrations of lipids and lipoproteins were increased in blood after exposure to cooking, while both cooking and candle emissions slightly affected the small airways including the primary outcomes SP-A and albumin. We found only weak associations between the exposures and systemic inflammatory biomarkers. Together, the results show the existence of mild inflammation following cooking and candle exposure.
|
|
| 3. |
- Pugh, T. A. M., et al.
(författare)
-
Simulated carbon emissions from land-use change are substantially enhanced by accounting for agricultural management
- 2015
-
Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 10:12
-
Tidskriftsartikel (refereegranskat)abstract
- It is over three decades since a large terrestrial carbon sink (S-T) was first reported. The magnitude of the net sink is now relatively well known, and its importance for dampening atmospheric CO2 accumulation, and hence climate change, widely recognised. But the contributions of underlying processes are not well defined, particularly the role of emissions from land-use change (E-LUC) versus the biospheric carbon uptake (S-L; S-T. = S-L - E-LUC). One key aspect of the interplay of E-LUC and SL is the role of agricultural processes in land-use change emissions, which has not yet been clearly quantified at the global scale. Here we assess the effect of representing agricultural land management in a dynamic global vegetation model. Accounting for harvest, grazing and tillage resulted in cumulative E-LUC since 1850 ca. 70% larger than in simulations ignoring these processes, but also changed the timescale over which these emissions occurred and led to underestimations of the carbon sequestered by possible future reforestation actions. The vast majority of Earth system models in the recent IPCC Fifth Assessment Report omit these processes, suggesting either an overestimation in their present-day ST, or an underestimation of SL, of up to 1.0 Pg Ca-1. Management processes influencing crop productivity per se are important for food supply, but were found to have little influence on E-LUC.
|
|
| 4. |
- Bodin, P., et al.
(författare)
-
Accounting for interannual variability in agricultural intensification : The potential of crop selection in Sub-Saharan Africa
- 2016
-
Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 148, s. 159-168
-
Tidskriftsartikel (refereegranskat)abstract
- Providing sufficient food for a growing global population is one of the fundamental global challenges today. Crop production needs not only to be increased, but also remain stable over the years, in order to limit the vulnerability of producers and consumers to inter-annual weather variability, especially in areas of the world where the food consumed is mainly produced locally (e.g. Sub Saharan Africa (SSA)). For subsistence agriculture, stable yields form a crucial contribution to food security. At a regional to global scale dynamical crop models can be used to study the impact of future changes in climate on food production. However, simulations of future crop production, for instance in response to climate change, often do not take into account either changes in the sown areas of crops or yield interannual variability. Here, we explore the response of simulated crop production to assumptions of crop selection, also taking into account interannual variability in yields and considering the response of agricultural productivity to climate change. We apply the dynamic global vegetation model LPJ-GUESS, which is designed to simulate yield over large regions under a changing environment. Model output provides the basis for selecting the relative fractions of sown areas of a range of crops, either by selecting the highest yielding crop, or by using an optimization approach in which crop production is maximized while the standard deviation in crop production is kept at below current levels. Maximizing simulated crop production for current climate while keeping interannual variability in crop production constant at today's level generates rather similar simulated geographical distributions of crops compared to observations. Even so, the optimization results suggest that it is possible to increase crop production regionally by adjusting crop selection, both for current and future climate, assuming the same cropland cover as today. For future climates modelled production increase is > 25% in more than 15% of the grid cells. For a small number of grid cells it is possible to both increase crop production while at the same time decreasing its interannual variability. Selecting the highest yielding crop for any location will lead to a large potential increase in mean food production, but at the cost of a very large increase in variability.
|
|
| 5. |
- Franke, James A, et al.
(författare)
-
Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change
- 2022
-
Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:1, s. 167-181
-
Tidskriftsartikel (refereegranskat)abstract
- Modern food production is spatially concentrated in global "breadbaskets". A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climaterelated losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end-of-century warming using 7 process-based models simulating 5 major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies.
|
|
| 6. |
- Franke, James A., et al.
(författare)
-
The GGCMI Phase 2 emulators : Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)
- 2020
-
Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 13:9, s. 3995-4018
-
Tidskriftsartikel (refereegranskat)abstract
- Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: Atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: That growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.
|
|
| 7. |
- Franke, James A., et al.
(författare)
-
The GGCMI Phase 2 experiment : Global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)
- 2020
-
Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 13:5, s. 2315-2336
-
Tidskriftsartikel (refereegranskat)abstract
- Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen ("CTWN") for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.
|
|
| 8. |
- Henry, R. C., et al.
(författare)
-
Food supply and bioenergy production within the global cropland planetary boundary
- 2018
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:3
-
Tidskriftsartikel (refereegranskat)abstract
- Supplying food for the anticipated global population of over 9 billion in 2050 under changing climate conditions is one of the major challenges of the 21st century. Agricultural expansion and intensification contributes to global environmental change and risks the long-term sustainability of the planet. It has been proposed that no more than 15% of the global ice-free land surface should be converted to cropland. Bioenergy production for land-based climate mitigation places additional pressure on limited land resources. Here we test normative targets of food supply and bioenergy production within the cropland planetary boundary using a global land-use model. The results suggest supplying the global population with adequate food is possible without cropland expansion exceeding the planetary boundary. Yet this requires an increase in food production, especially in developing countries, as well as a decrease in global crop yield gaps. However, under current assumptions of future food requirements, it was not possible to also produce significant amounts of first generation bioenergy without cropland expansion. These results suggest that meeting food and bioenergy demands within the planetary boundaries would need a shift away from current trends, for example, requiring major change in the demand-side of the food system or advancing biotechnologies.
|
|
| 9. |
- Lindeskog, Mats, et al.
(författare)
-
Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa
- 2013
-
Ingår i: Earth System Dynamics. - : Copernicus GmbH. - 2190-4979 .- 2190-4987. ; 4:2, s. 385-407
-
Tidskriftsartikel (refereegranskat)abstract
- Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr(-1) release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the land-atmosphere carbon flux for the 20th century.
|
|
| 10. |
- Müller, Christoph, et al.
(författare)
-
Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality-Based Model Evaluation
- 2024
-
Ingår i: Earth's Future. - 2328-4277. ; 12:3
-
Tidskriftsartikel (refereegranskat)abstract
- Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.
|
|
