Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.
Ingrid C RomeroShu KongCharless C FowlkesCarlos JaramilloMichael A UrbanFrancisca Oboh-IkuenobeCarlos D'ApolitoSurangi W PunyasenaPublished in: Proceedings of the National Academy of Sciences of the United States of America (2020)
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.
Keyphrases
- convolutional neural network
- deep learning
- machine learning
- high resolution
- single molecule
- artificial intelligence
- high speed
- big data
- high throughput
- climate change
- optical coherence tomography
- gene expression
- men who have sex with men
- magnetic resonance imaging
- dna methylation
- magnetic resonance
- risk assessment
- south africa
- human health
- single cell
- mass spectrometry
- high intensity