Improving the Taxonomy of Fossil Pollen Using Convolutional Neural Networks and Superresolution Microscopy
Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy Ingrid C. Romeroa,1, Shu Kongb,c, Charless C. Fowlkesc, Carlos Jaramillod,e,f, Michael A. Urbana,g, Francisca Oboh-Ikuenobeh, Carlos D’Apolitoi, and Surangi W. Punyasenaa,1 aDepartment of Plant Biology, University of Illinois at Urbana–Champaign, Urbana, IL 61801; bRobotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213; cDepartment of Computer Science, University of California, Irvine, CA 92697; dCenter for Tropical Paleoecology and Archaeology, Smithsonian Tropical Research Institute, Ancon, 0843-03092, Panama; eInstitut 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; fDepartment of Geology, Faculty of Sciences, University of Salamanca, Salamanca, 37008, Spain; gDepartment of Biology, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada; hDepartment of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, MO 65409; and iFaculdade de Geociencias, Universidade Federal de Mato Grosso, Cuiaba, 78000, Brazil Edited by Peter R. Crane, Yale University, Upperville, VA, and approved September 25, 2020 (received for review April 25, 2020) Taxonomic resolution is a major challenge in palynology, largely material mounted on slides (9). Airyscan captures the three- limiting the ecological and evolutionary interpretations possible dimensional
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