气候变化领域集成服务门户(CCPortal): Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

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DOI	10.1073/pnas.2007324117
	Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy
	Romero I.C.; Kong S.; Fowlkes C.C.; Jaramillo C.; Urban M.A.; Oboh-Ikuenobe F.; D’Apolito C.; Punyasena S.W.
发表日期	2020
ISSN	0027-8424
起始页码	28496
结束页码	28505
卷号	117 期号:45
英文摘要	Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution microscopy and machine learning to create a quantitative and higher throughput workflow for producing palynological identifications and hypotheses of biological affinity. We developed three convolutional neural network (CNN) classification models: maximum projection (MPM), multislice (MSM), and fused (FM). We trained the models on the pollen of 16 genera of the legume tribe Amherstieae, and then used these models to constrain the biological classifications of 48 fossil Striatopollis specimens from the Paleocene, Eocene, and Miocene of western Africa and northern South America. All models achieved average accuracies of 83 to 90% in the classification of the extant genera, and the majority of fossil identifications (86%) showed consensus among at least two of the three models. Our fossil identifications support the paleobiogeographic hypothesis that Amherstieae originated in Paleocene Africa and dispersed to South America during the Paleocene-Eocene Thermal Maximum (56 Ma). They also raise the possibility that at least three Amherstieae genera (Crudia, Berlinia, and Anthonotha) may have diverged earlier in the Cenozoic than predicted by molecular phylogenies. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Airyscan microscopy \| automated classification \| Detarioideae \| machine learning \| palynology
语种	英语
scopus关键词	Africa; article; consensus; convolutional neural network; Eocene; fossil pollen; legume; microscopy; Miocene; molecular phylogeny; nonhuman; Paleocene; palynology; quantitative analysis; South America; taxonomy; workflow
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160739
作者单位	Romero, I.C., Department of Plant Biology, University of Illinois at Urbana–Champaign, Urbana, IL 61801, United States; Kong, S., Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, United States, Department of Computer Science, University of California, Irvine, CA 92697, United States; Fowlkes, C.C., Department of Computer Science, University of California, Irvine, CA 92697, United States; Jaramillo, C., Center for Tropical Paleoecology and Archaeology, Smithsonian Tropical Research Institute, Ancon, 0843-03092, Panama, Institut des Sciences de l’Évolution de Montpellier, Université de Montpellier, CNRS, Ecole Pratique des Hautes Études, Institut de Recherche pour le Développement, Montpellier, 34095, France, Department of Geology, Faculty of Sciences, University of Salamanca, Salamanca, 37008, Spain; Urban, M.A., Department of Plant Biology, University of Illinois at Urbana–Champaign, Urbana, IL 61801, United States, Department of Biology, University of New Brunswick, Fredericton,...
推荐引用方式 GB/T 7714	Romero I.C.,Kong S.,Fowlkes C.C.,et al. Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy[J],2020,117(45).
APA	Romero I.C..,Kong S..,Fowlkes C.C..,Jaramillo C..,Urban M.A..,...&Punyasena S.W..(2020).Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.Proceedings of the National Academy of Sciences of the United States of America,117(45).
MLA	Romero I.C.,et al."Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy".Proceedings of the National Academy of Sciences of the United States of America 117.45(2020).