| 1. |
- Botvinik-Nezer, Rotem, et al.
(author)
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Variability in the analysis of a single neuroimaging dataset by many teams
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 582, s. 84-88
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Journal article (peer-reviewed)abstract
- Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
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| 2. |
- Downey, Harriet, et al.
(author)
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Training future generations to deliver evidence-based conservation and ecosystem management
- 2021
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In: Ecological Solutions and Evidence. - : Wiley. - 2688-8319. ; 2:1
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Research review (peer-reviewed)abstract
- 1. To be effective, the next generation of conservation practitioners and managers need to be critical thinkers with a deep understanding of how to make evidence-based decisions and of the value of evidence synthesis.2. If, as educators, we do not make these priorities a core part of what we teach, we are failing to prepare our students to make an effective contribution to conservation practice.3. To help overcome this problem we have created open access online teaching materials in multiple languages that are stored in Applied Ecology Resources. So far, 117 educators from 23 countries have acknowledged the importance of this and are already teaching or about to teach skills in appraising or using evidence in conservation decision-making. This includes 145 undergraduate, postgraduate or professional development courses.4. We call for wider teaching of the tools and skills that facilitate evidence-based conservation and also suggest that providing online teaching materials in multiple languages could be beneficial for improving global understanding of other subject areas.
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| 3. |
- Sridhar, Arun R., et al.
(author)
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Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram.
- 2022
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In: Cardiovascular digital health journal. - : Elsevier. - 2666-6936. ; 3:2, s. 62-74
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Journal article (peer-reviewed)abstract
- Background: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications.Objective: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE).Methods: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data.Results: 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance.Conclusion: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.
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| 4. |
- Widemann, Thomas, et al.
(author)
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Venus Evolution Through Time : Key Science Questions, Selected Mission Concepts and Future Investigations
- 2023
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In: Space Science Reviews. - : SPRINGER. - 0038-6308 .- 1572-9672. ; 219:7
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Research review (peer-reviewed)abstract
- In this work we discuss various selected mission concepts addressing Venus evolution through time. More specifically, we address investigations and payload instrument concepts supporting scientific goals and open questions presented in the companion articles of this volume. Also included are their related investigations (observations & modeling) and discussion of which measurements and future data products are needed to better constrain Venus' atmosphere, climate, surface, interior and habitability evolution through time. A new fleet of Venus missions has been selected, and new mission concepts will continue to be considered for future selections. Missions under development include radar-equipped ESA-led EnVision M5 orbiter mission (European Space Agency 2021), NASA-JPL's VERITAS orbiter mission (Smrekar et al. 2022a), NASA-GSFC's DAVINCI entry probe/flyby mission (Garvin et al. 2022a). The data acquired with the VERITAS, DAVINCI, and EnVision from the end of this decade will fundamentally improve our understanding of the planet's long term history, current activity and evolutionary path. We further describe future mission concepts and measurements beyond the current framework of selected missions, as well as the synergies between these mission concepts, ground-based and space-based observatories and facilities, laboratory measurements, and future algorithmic or modeling activities that pave the way for the development of a Venus program that extends into the 2040s (Wilson et al. 2022).
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