| 1. |
- Björkman, Anne, 1981, et al.
(author)
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Tundra Trait Team: A database of plant traits spanning the tundra biome
- 2018
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In: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 27:12, s. 1402-1411
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Journal article (peer-reviewed)abstract
- © 2018 The Authors Global Ecology and Biogeography Published by John Wiley & Sons Ltd Motivation: The Tundra Trait Team (TTT) database includes field-based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade-offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained: The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (>1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain: Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub-Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain: All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement: Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format: csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.
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| 2. |
- Chambers, Josephine M., et al.
(author)
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Co-productive agility and four collaborative pathways to sustainability transformations
- 2022
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In: Global Environmental Change. - : Elsevier BV. - 0959-3780 .- 1872-9495. ; 72
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Journal article (peer-reviewed)abstract
- Co-production, the collaborative weaving of research and practice by diverse societal actors, is argued to play an important role in sustainability transformations. Yet, there is still poor understanding of how to navigate the tensions that emerge in these processes. Through analyzing 32 initiatives worldwide that co-produced knowledge and action to foster sustainable social-ecological relations, we conceptualize 'co-productive agility' as an emergent feature vital for turning tensions into transformations. Co-productive agility refers to the willingness and ability of diverse actors to iteratively engage in reflexive dialogues to grow shared ideas and actions that would not have been possible from the outset. It relies on embedding knowledge production within processes of change to constantly recognize, reposition, and navigate tensions and opportunities. Co-productive agility opens up multiple pathways to transformation through: (1) elevating marginalized agendas in ways that maintain their integrity and broaden struggles for justice; (2) questioning dominant agendas by engaging with power in ways that challenge assumptions, (3) navigating conflicting agendas to actively transform interlinked paradigms, practices, and structures; (4) exploring diverse agendas to foster learning and mutual respect for a plurality of perspectives. We explore six process considerations that vary by these four pathways and provide a framework to enable agility in sustainability transformations. We argue that research and practice spend too much time closing down debate over different agendas for change - thereby avoiding, suppressing, or polarizing tensions, and call for more efforts to facilitate better interactions among different agendas.
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| 3. |
- Chambers, Josephine M., et al.
(author)
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Six modes of co-production for sustainability
- 2021
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In: Nature Sustainability. - : Springer Science and Business Media LLC. - 2398-9629. ; 4, s. 983-996
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Journal article (peer-reviewed)abstract
- Co-production includes diverse aims, terminologies and practices. This study explores such diversity by mapping differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales. The promise of co-production to address complex sustainability challenges is compelling. Yet, co-production, the collaborative weaving of research and practice, encompasses diverse aims, terminologies and practices, with poor clarity over their implications. To explore this diversity, we systematically mapped differences in how 32 initiatives from 6 continents co-produce diverse outcomes for the sustainable development of ecosystems at local to global scales. We found variation in their purpose for utilizing co-production, understanding of power, approach to politics and pathways to impact. A cluster analysis identified six modes of co-production: (1) researching solutions; (2) empowering voices; (3) brokering power; (4) reframing power; (5) navigating differences and (6) reframing agency. No mode is ideal; each holds unique potential to achieve particular outcomes, but also poses unique challenges and risks. Our analysis provides a heuristic tool for researchers and societal actors to critically explore this diversity and effectively navigate trade-offs when co-producing sustainability.
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| 4. |
- Chiang, Cho-Han, et al.
(author)
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Performance of the European Society of Cardiology 0/1-Hour, 0/2-Hour, and 0/3-Hour Algorithms for Rapid Triage of Acute Myocardial Infarction : An International Collaborative Meta-analysis
- 2022
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In: Annals of Internal Medicine. - 0003-4819. ; 175:1, s. 101-113
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Research review (peer-reviewed)abstract
- BACKGROUND: The 2020 European Society of Cardiology (ESC) guidelines recommend using the 0/1-hour and 0/2-hour algorithms over the 0/3-hour algorithm as the first and second choices of high-sensitivity cardiac troponin (hs-cTn)-based strategies for triage of patients with suspected acute myocardial infarction (AMI).PURPOSE: To evaluate the diagnostic accuracies of the ESC 0/1-hour, 0/2-hour, and 0/3-hour algorithms.DATA SOURCES: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from 1 January 2011 to 31 December 2020. (PROSPERO: CRD42020216479).STUDY SELECTION: Prospective studies that evaluated the ESC 0/1-hour, 0/2-hour, or 0/3-hour algorithms in adult patients presenting with suspected AMI.DATA EXTRACTION: The primary outcome was index AMI. Twenty unique cohorts were identified. Primary data were obtained from investigators of 16 cohorts and aggregate data were extracted from 4 cohorts. Two independent authors assessed each study for methodological quality.DATA SYNTHESIS: A total of 32 studies (20 cohorts) with 30 066 patients were analyzed. The 0/1-hour algorithm had a pooled sensitivity of 99.1% (95% CI, 98.5% to 99.5%) and negative predictive value (NPV) of 99.8% (CI, 99.6% to 99.9%) for ruling out AMI. The 0/2-hour algorithm had a pooled sensitivity of 98.6% (CI, 97.2% to 99.3%) and NPV of 99.6% (CI, 99.4% to 99.8%). The 0/3-hour algorithm had a pooled sensitivity of 93.7% (CI, 87.4% to 97.0%) and NPV of 98.7% (CI, 97.7% to 99.3%). Sensitivity of the 0/3-hour algorithm was attenuated in studies that did not use clinical criteria (GRACE score <140 and pain-free) compared with studies that used clinical criteria (90.2% [CI, 82.9 to 94.6] vs. 98.4% [CI, 88.6 to 99.8]). All 3 algorithms had similar specificities and positive predictive values for ruling in AMI, but heterogeneity across studies was substantial. Diagnostic performance was similar across the hs-cTnT (Elecsys; Roche), hs-cTnI (Architect; Abbott), and hs-cTnI (Centaur/Atellica; Siemens) assays.LIMITATION: Diagnostic accuracy, inclusion and exclusion criteria, and cardiac troponin sampling time varied among studies.CONCLUSION: The ESC 0/1-hour and 0/2-hour algorithms have higher sensitivities and NPVs than the 0/3-hour algorithm for index AMI.PRIMARY FUNDING SOURCE: National Taiwan University Hospital.
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| 5. |
- Neumann, Johannes Tobias, et al.
(author)
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Personalized diagnosis in suspected myocardial infarction
- 2023
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In: Clinical Research in Cardiology. - : Springer. - 1861-0684 .- 1861-0692. ; 112, s. 1288-1301
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Journal article (peer-reviewed)abstract
- Background: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hscTn)- based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.Methods: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability ( ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients.Results: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline- recommended strategy.Conclusion We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care.
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