Modeling scale morphogenesis in a Devonian chondrichthyan and scale growth patterns in crown gnathostomes

Cui, Xindong; Qu, Qingming; Andreev, Plamen S.; Li, Qiang; Mai, Huijuan; Zhu, Min

doi:10.1080/02724634.2021.1930018

Cited by 5 publications

(11 citation statements)

References 33 publications

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“…The number of superimposed layers of odontodes decreases from the center of the scale to the edge. The younger odontodes cover the older generations of odontodes either completely or incompletely, suggesting a combined areo-superpositional growth (Ørvig, 1968) or a compound growth (Cui et al, 2021). Polyodontode and the compound growth pattern were ever considered to be plesiomorphic in jawed vertebrates (Cui et al, 2021;Qu et al, 2013).…”

Section: Histology Of Yunnanolepidoid Scalesmentioning

confidence: 99%

“…The younger odontodes cover the older generations of odontodes either completely or incompletely, suggesting a combined areo-superpositional growth (Ørvig, 1968) or a compound growth (Cui et al, 2021). Polyodontode and the compound growth pattern were ever considered to be plesiomorphic in jawed vertebrates (Cui et al, 2021;Qu et al, 2013). The conditions in yunnanolepidoid scales offer support for these hypotheses.…”

Section: Histology Of Yunnanolepidoid Scalesmentioning

confidence: 99%

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Squamation and scale morphology at the root of jawed vertebrates

Wang

Zhu

2022

Preprint

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Placoderms, the most basal jawed vertebrates, are crucial to understanding how the characters of crown gnathostomes comprising Chondrichthyes and Osteichthyes evolved from their stem relatives. Despite the growing knowledge on the anatomy and diversity of placoderms over the past decade, the dermal scales of placoderms are predominantly known from isolated material, either morphologically or histologically, resulting in their squamation being poorly understood. Here we provide a comprehensive description of the squamation and scale morphology of a primitive taxon of Antiarcha (a clade at the root of jawed vertebrates), Parayunnanolepis xitunensis, based on the virtual restoration of an articulated specimen by using X-ray computed tomography. Thirteen morphotypes of scales are classified to exhibit how the morphology changes with their position on the body in primitive antiarchs, based on which nine areas of the post-thoracic body are distinguished to show their scale variations in the dorsal, flank, ventral, and caudal lobe regions. In this study, the histological structure of yunnanolepidoid scales was described for the first time based on disarticulated scales from the type locality and horizon of P. xitunensis, demonstrating that yunnanolepidoid scales are remarkably different from the dermal plates of antiarchs including yunnanolepidoids in the absence of developed middle layer. Together, our study reveals that the high regionalization of squamation and the bipartite histological structure of scale might be plesiomorphic for antiarchs, and jawed vertebrates in general.

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Section: Histology Of Yunnanolepidoid Scalesmentioning

confidence: 99%

Section: Histology Of Yunnanolepidoid Scalesmentioning

confidence: 99%

Squamation and scale morphology at the root of jawed vertebrates

Wang

Zhu

2022

Preprint

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“…We dissolved the matrix surrounding the samples from the Xitun Formation (Early Devonian) (Zhao et al, 2021) with a 3 %-7 % acetic acid solution. The fish microfossils were separated under a microscope from the processed samples (Cui et al, 2020;Li et al, 2021;Cui et al, 2021). All fish microfossils were collected and examined at the Institute of Vertebrate Paleontology and Paleoanthropology (IVPP) of the Chinese Academy of Sciences (CAS).…”

Section: Data Preparationmentioning

confidence: 99%

“…Paleozoic vertebrate microfossils provide important evidence for biostratigraphy, paleobiodiversity, and paleogeography (Zhao and Zhu, 2014;Zhao et al, 2018;Ogg et al, 2016;Märss et al, 1995;Žigaitė et al, 2011;Wang, 2006), as well as oil and gas exploration (Hackley et al, 2017;Funkhouser and Evitt, 1959). As one subset of vertebrate microfossils, fish microfossils significantly contribute to the study of early vertebrate evolution (Janvier, 1996;Cui et al, 2020;Wang, 1984;Chen et al, 2016;Botella et al, 2007;Cui et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Paleontologists can nondestructively obtain more compre-hensive three-dimensional (3D) fossil CT data, including 3D microstructures from inside to the surface (Fernandez et al, 2012(Fernandez et al, , 2013(Fernandez et al, , 2015. Digital techniques have also been used to classify fish microfossils in 3D volumes (Cui et al, 2020(Cui et al, , 2021. Semantic image segmentation is a crucial step in the reconstruction of 3D volumes.…”

Section: Introductionmentioning

confidence: 99%

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Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

Hou

Canul-Ku

Cui

et al. 2021

J. Micropalaeontol.

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Abstract. Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.

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Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

et al. 2023

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Micropaleontologists use the fine structures of microfossils to extract evolutionary information. These structures could not be directly observed with the naked eye. Recently, paleontologists resort to computed tomography (CT) images to mine the information, and pursue higher resolution CT images with in‐depth research. Therefore, we propose a new model, weighted super‐resolution generative adversarial network (WSRGAN), for the super‐resolution reconstruction of CT images. The model proposed herein (WSRGAN) obtained higher LPIPS (0.0757) on the experimental dataset, compared with Bilinear (0.4289), Bicubic (0.4166), EDSR (0.2281), WDSR (0.2640), and SRGAN (0.0815). WSRGAN meets the requirements of paleontologists for reconstructing fish microfossils. We hope that more super‐resolution reconstruction methods based on deep learning could be applied to paleontology and achieve better performance.

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Modeling scale morphogenesis in a Devonian chondrichthyan and scale growth patterns in crown gnathostomes

Cited by 5 publications

References 33 publications

Squamation and scale morphology at the root of jawed vertebrates

Squamation and scale morphology at the root of jawed vertebrates

Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

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