scholarly journals Ontogenetic allometry constrains cranial shape of the head-first burrowing worm lizardCynisca leucura(Squamata: Amphisbaenidae)

2016 ◽  
Vol 277 (9) ◽  
pp. 1159-1167 ◽  
Author(s):  
Christy A. Hipsley ◽  
Marc-Nicolas Rentinck ◽  
Mark-Oliver Rödel ◽  
Johannes Müller
Keyword(s):  
Ontogenetic Allometry ◽  
Cranial Shape

scholarly journals Ontogenetic Allometry and Cranial Shape Diversification Among Human Populations From South America

2011 ◽  
Vol 294 (11) ◽  
pp. 1864-1874 ◽  
Author(s):  
Paula N. Gonzalez ◽  
S. Ivan Perez ◽  
Valeria Bernal
Keyword(s):  
South America ◽  
Human Populations ◽  
Ontogenetic Allometry ◽  
Cranial Shape

scholarly journals The evolution of floral ontogenetic allometry in the Andean genus Caiophora (Loasaceae, subfam. Loasoideae)

2017 ◽  
Vol 20 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Marina Micaela Strelin ◽  
Santiago Benitez‐Vieyra ◽  
Juan Fornoni ◽  
Christian Peter Klingenberg ◽  
Andrea Cocucci
Keyword(s):  
Ontogenetic Allometry

Image processing and machine learning for telehealth craniosynostosis screening in newborns

2021 ◽  
pp. 1-8
Author(s):  
Markus J. Bookland ◽  
Edward S. Ahn ◽  
Petronella Stoltz ◽  
Jonathan E. Martin
Keyword(s):  
Image Processing ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Asymmetry Index ◽  
Optical Measurements ◽  
Shape Classification ◽  
Data Set ◽  
Intraclass Correlation Coefficients ◽  
Optical Scanner ◽  
Cranial Shape

OBJECTIVE The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis within a newborn (< 1 year old) population. Agreement with gold standard craniometric diagnostics was also assessed. METHODS Cranial shape classification software accuracy was compared to that of blinded craniofacial specialists using a data set of open-source (n = 40) and retrospectively collected newborn orthogonal top-down cranial images, with or without additional facial views (n = 339), culled between April 1, 2008, and February 29, 2020. Based on image quality, midface visibility, and visibility of the cranial equator, 351 image sets were deemed acceptable. Accuracy, sensitivity, and specificity were calculated for the software versus specialist classification. Software agreement with optical craniometrics was assessed with intraclass correlation coefficients. RESULTS The cranial shape classification software had an accuracy of 93.3% (95% CI 86.8–98.8; p < 0.001), with a sensitivity of 92.0% and specificity of 94.3%. Intraclass correlation coefficients for measurements of the cephalic index and cranial vault asymmetry index compared to optical measurements were 0.95 (95% CI 0.84–0.98; p < 0.001) and 0.67 (95% CI 0.24–0.88; p = 0.003), respectively. CONCLUSIONS These results support the use of image processing–based neonatal cranial deformity classification software for remote screening of nonsyndromic craniosynostosis in a newborn population and as a substitute for optical scanner– or CT-based craniometrics. This work has implications that suggest the potential for the development of software for a mobile platform that would allow for screening by telemedicine or in a primary care setting.

Consistency of Cranial Shape Measures Obtained From Laser Surface and Computed Tomography Imaging

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Leslie A. Shock ◽  
Sean Greer ◽  
Lucy D. Sheahan ◽  
Arshad R. Muzaffar ◽  
Kristina Aldridge
Keyword(s):  
Computed Tomography ◽  
Laser Surface ◽  
Computed Tomography Imaging ◽  
Tomography Imaging ◽  
Cranial Shape

scholarly journals A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry

PLoS ONE ◽  
2007 ◽  
Vol 2 (11) ◽  
pp. e1245 ◽  
Author(s):  
Hongying Li ◽  
Zhongwen Huang ◽  
Junyi Gai ◽  
Song Wu ◽  
Yanru Zeng ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Conceptual Framework ◽  
Quantitative Trait ◽  
Trait Loci ◽  
Ontogenetic Allometry

scholarly journals The limits of convergence: the roles of phylogeny and dietary ecology in shaping non-avian amniote crania

2021 ◽  
Vol 8 (9) ◽  
pp. 202145
Author(s):  
Keegan M. Melstrom ◽  
Kenneth D. Angielczyk ◽  
Kathleen A. Ritterbush ◽  
Randall B. Irmis
Keyword(s):  
Functional Morphology ◽  
Feeding Ecology ◽  
Cranial Morphology ◽  
Skull Shape ◽  
Morphological Convergence ◽  
Morphometric Methods ◽  
Central Regions ◽  
Dietary Ecology ◽  
Amniote Phylogeny ◽  
Cranial Shape

Cranial morphology is remarkably varied in living amniotes and the diversity of shapes is thought to correspond with feeding ecology, a relationship repeatedly demonstrated at smaller phylogenetic scales, but one that remains untested across amniote phylogeny. Using a combination of morphometric methods, we investigate the links between phylogenetic relationships, diet and skull shape in an expansive dataset of extant toothed amniotes: mammals, lepidosaurs and crocodylians. We find that both phylogeny and dietary ecology have statistically significant effects on cranial shape. The three major clades largely partition morphospace with limited overlap. Dietary generalists often occupy clade-specific central regions of morphospace. Some parallel changes in cranial shape occur in clades with distinct evolutionary histories but similar diets. However, members of a given clade often present distinct cranial shape solutions for a given diet, and the vast majority of species retain the unique aspects of their ancestral skull plan, underscoring the limits of morphological convergence due to ecology in amniotes. These data demonstrate that certain cranial shapes may provide functional advantages suited to particular dietary ecologies, but accounting for both phylogenetic history and ecology can provide a more nuanced approach to inferring the ecology and functional morphology of cryptic or extinct amniotes.

Statistical Techniques for Interpreting Body Size–Related Exercise Performance during Growth

2000 ◽  
Vol 12 (2) ◽  
pp. 112-127 ◽  
Author(s):  
Joanne R. Welsman ◽  
Neil Armstrong
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Body Mass ◽  
Exercise Performance ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
Regression Techniques ◽  
Ontogenetic Allometry ◽  
Log Linear ◽  
Individual Size

This paper reviews some of the statistical methods available for controlling for body size differences in the interpretation of developmental changes in exercise performance. For cross-sectional data analysis simple per body mass ratio scaling continues to be widely used, but is frequently ineffective as the computed ratio remains correlated with body mass. Linear regression techniques may distinguish group differences more appropriately but, as illustrated, only allometric (log-linear regression) scaling appropriately removes body size differences while accommodating the heteroscedasticity common in exercise performance data. The analysis and interpretation of longitudinal data within an allometric framework is complex. More established methods such as ontogenetic allometry allow insights into individual size-function relationships but are unable to describe adequately population effects or changes in the magnitude of the response. The recently developed multilevel regression modeling technique represents a flexible and sensitive solution to such problems allowing both individual and group responses to be modeled concurrently.

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