| 1. |
- Botvinik-Nezer, Rotem, et al.
(författare)
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Variability in the analysis of a single neuroimaging dataset by many teams
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 582, s. 84-88
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Tidskriftsartikel (refereegranskat)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. |
- Alfonso, B., et al.
(författare)
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Tracing genetic variation of Gelidium canariense (Rhodophyta) based on new and historical collections
- 2023
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Ingår i: Phycologia. - 0031-8884. ; 62:4, s. 383-390
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Tidskriftsartikel (refereegranskat)abstract
- Genetic sequencing of herbarium specimens provides invaluable information on species genetic history. However, several factors hinder the extraction of high-quality DNA from long-term preserved specimens. Our goal was to study the genetic variability of the endemic and habitat-forming macroalga Gelidium canariense over the last 40 years using the mitochondrial intergenic marker cox2-3 spacer. We also studied the genetic diversity at the mesoscale (<100 km), i.e. between two localities on the north coast of Tenerife and at the macroscale (>100 km) using specimens collected on the island of La Palma. We found the presence of the same haplotype at the same location for the last 40 years. This haplotype also coincided within populations of Puerto de la Cruz and Garachico (Tenerife Island) and between populations of different islands (Tenerife and La Palma). This study provides a baseline (i.e. extraction method, PCR protocol for cox2-3 spacer molecular marker, level of DNA degradation of herbarium samples) of the genetic characterization of G. canariense that can be used in future molecular studies to better understand the distribution of genetic diversity in this vulnerable species.
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| 3. |
- Statz, Giselle M., et al.
(författare)
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Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement : State-of-the-Art Review
- 2023
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Ingår i: JACC: Advances. - 2772-963X. ; 2:3
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Forskningsöversikt (refereegranskat)abstract
- Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.
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