Synchrotron imaging and Markov Chain Monte Carlo reveal tooth mineralization patterns.
Identifieur interne : 000251 ( PubMed/Curation ); précédent : 000250; suivant : 000252Synchrotron imaging and Markov Chain Monte Carlo reveal tooth mineralization patterns.
Auteurs : Daniel R. Green [États-Unis] ; Gregory M. Green [États-Unis] ; Albert S. Colman [États-Unis] ; Felicitas B. Bidlack [États-Unis] ; Paul Tafforeau [France] ; Tanya M. Smith [États-Unis]Source :
- PloS one [ 1932-6203 ] ; 2017.
Abstract
The progressive character of tooth formation records aspects of mammalian life history, diet, seasonal behavior and climate. Tooth mineralization occurs in two stages: secretion and maturation, which overlap to some degree. Despite decades of study, the spatial and temporal pattern of elemental incorporation during enamel mineralization remains poorly characterized. Here we use synchrotron X-ray microtomography and Markov Chain Monte Carlo sampling to estimate mineralization patterns from an ontogenetic series of sheep molars (n = 45 M1s, 18 M2s). We adopt a Bayesian approach that posits a general pattern of maturation estimated from individual- and population-level mineral density variation over time. This approach converts static images of mineral density into a dynamic model of mineralization, and demonstrates that enamel secretion and maturation waves advance at nonlinear rates with distinct geometries. While enamel secretion is ordered, maturation geometry varies within a population and appears to be driven by diffusive processes. Our model yields concrete expectations for the integration of physiological and environmental signals, which is of particular significance for paleoseasonality research. This study also provides an avenue for characterizing mineralization patterns in other taxa. Our synchrotron imaging data and model are available for application to multiple disciplines, including health, material science, and paleontological research.
DOI: 10.1371/journal.pone.0186391
PubMed: 29049333
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Green</name><affiliation wicri:level="1"><nlm:affiliation>Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts</wicri:regionArea></affiliation></author><author><name sortKey="Green, Gregory M" sort="Green, Gregory M" uniqKey="Green G" first="Gregory M" last="Green">Gregory M. Green</name><affiliation wicri:level="1"><nlm:affiliation>Physics Department, Stanford University, Palo Alto, California, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Physics Department, Stanford University, Palo Alto, California</wicri:regionArea></affiliation></author><author><name sortKey="Colman, Albert S" sort="Colman, Albert S" uniqKey="Colman A" first="Albert S" last="Colman">Albert S. 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Bidlack</name><affiliation wicri:level="1"><nlm:affiliation>Forsyth Institute, Cambridge, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Forsyth Institute, Cambridge, Massachusetts</wicri:regionArea></affiliation></author><author><name sortKey="Tafforeau, Paul" sort="Tafforeau, Paul" uniqKey="Tafforeau P" first="Paul" last="Tafforeau">Paul Tafforeau</name><affiliation wicri:level="1"><nlm:affiliation>European Synchrotron Radiation Facility, Grenoble, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>European Synchrotron Radiation Facility, Grenoble</wicri:regionArea></affiliation></author><author><name sortKey="Smith, Tanya M" sort="Smith, Tanya M" uniqKey="Smith T" first="Tanya M" last="Smith">Tanya M. Smith</name><affiliation wicri:level="1"><nlm:affiliation>Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts</wicri:regionArea></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The progressive character of tooth formation records aspects of mammalian life history, diet, seasonal behavior and climate. Tooth mineralization occurs in two stages: secretion and maturation, which overlap to some degree. Despite decades of study, the spatial and temporal pattern of elemental incorporation during enamel mineralization remains poorly characterized. Here we use synchrotron X-ray microtomography and Markov Chain Monte Carlo sampling to estimate mineralization patterns from an ontogenetic series of sheep molars (n = 45 M1s, 18 M2s). We adopt a Bayesian approach that posits a general pattern of maturation estimated from individual- and population-level mineral density variation over time. This approach converts static images of mineral density into a dynamic model of mineralization, and demonstrates that enamel secretion and maturation waves advance at nonlinear rates with distinct geometries. While enamel secretion is ordered, maturation geometry varies within a population and appears to be driven by diffusive processes. Our model yields concrete expectations for the integration of physiological and environmental signals, which is of particular significance for paleoseasonality research. This study also provides an avenue for characterizing mineralization patterns in other taxa. Our synchrotron imaging data and model are available for application to multiple disciplines, including health, material science, and paleontological research.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">29049333</PMID><DateCreated><Year>2017</Year><Month>10</Month><Day>19</Day></DateCreated><DateRevised><Year>2017</Year><Month>11</Month><Day>05</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>10</Issue><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Synchrotron imaging and Markov Chain Monte Carlo reveal tooth mineralization patterns.</ArticleTitle><Pagination><MedlinePgn>e0186391</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0186391</ELocationID><Abstract><AbstractText>The progressive character of tooth formation records aspects of mammalian life history, diet, seasonal behavior and climate. Tooth mineralization occurs in two stages: secretion and maturation, which overlap to some degree. Despite decades of study, the spatial and temporal pattern of elemental incorporation during enamel mineralization remains poorly characterized. Here we use synchrotron X-ray microtomography and Markov Chain Monte Carlo sampling to estimate mineralization patterns from an ontogenetic series of sheep molars (n = 45 M1s, 18 M2s). We adopt a Bayesian approach that posits a general pattern of maturation estimated from individual- and population-level mineral density variation over time. This approach converts static images of mineral density into a dynamic model of mineralization, and demonstrates that enamel secretion and maturation waves advance at nonlinear rates with distinct geometries. While enamel secretion is ordered, maturation geometry varies within a population and appears to be driven by diffusive processes. Our model yields concrete expectations for the integration of physiological and environmental signals, which is of particular significance for paleoseasonality research. This study also provides an avenue for characterizing mineralization patterns in other taxa. 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for Particle Physics and Cosmology, Stanford University, Palo Alto, California, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Colman</LastName><ForeName>Albert S</ForeName><Initials>AS</Initials><AffiliationInfo><Affiliation>Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bidlack</LastName><ForeName>Felicitas B</ForeName><Initials>FB</Initials><AffiliationInfo><Affiliation>Forsyth Institute, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tafforeau</LastName><ForeName>Paul</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>European Synchrotron Radiation Facility, Grenoble, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Smith</LastName><ForeName>Tanya M</ForeName><Initials>TM</Initials><AffiliationInfo><Affiliation>Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Australian Research Center for Human Evolution, Griffith University, Brisbane, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>10</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Science. 2006 Nov 10;314(5801):980-2</RefSource><PMID Version="1">17095699</PMID></CommentsCorrections><CommentsCorrections 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