1. |
- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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2. |
- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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3. |
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4. |
- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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5. |
- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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6. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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9. |
- Zuntini, Alexandre R., et al.
(author)
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Phylogenomics and the rise of the angiosperms
- 2024
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In: NATURE. - 0028-0836 .- 1476-4687. ; 629, s. 843-850
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Journal article (peer-reviewed)abstract
- Angiosperms are the cornerstone of most terrestrial ecosystems and human livelihoods(1,2). A robust understanding of angiosperm evolution is required to explain their rise to ecological dominance. So far, the angiosperm tree of life has been determined primarily by means of analyses of the plastid genome(3,4). Many studies have drawn on this foundational work, such as classification and first insights into angiosperm diversification since their Mesozoic origins(5-7). However, the limited and biased sampling of both taxa and genomes undermines confidence in the tree and its implications. Here, we build the tree of life for almost 8,000 (about 60%) angiosperm genera using a standardized set of 353 nuclear genes(8). This 15-fold increase in genus-level sampling relative to comparable nuclear studies(9) provides a critical test of earlier results and brings notable change to key groups, especially in rosids, while substantiating many previously predicted relationships. Scaling this tree to time using 200 fossils, we discovered that early angiosperm evolution was characterized by high gene tree conflict and explosive diversification, giving rise to more than 80% of extant angiosperm orders. Steady diversification ensued through the remaining Mesozoic Era until rates resurged in the Cenozoic Era, concurrent with decreasing global temperatures and tightly linked with gene tree conflict. Taken together, our extensive sampling combined with advanced phylogenomic methods shows the deep history and full complexity in the evolution of a megadiverse clade.
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10. |
- Bivik Stadler, Caroline, 1986-, et al.
(author)
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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
- 2021
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In: Journal of digital imaging. - : Springer-Verlag New York. - 0897-1889 .- 1618-727X. ; 34, s. 105-115
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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