scholarly journals A Carboniferous synapsid with caniniform teeth and a reappraisal of mandibular size-shape heterodonty in the origin of mammals

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Adam K. Huttenlocker ◽  
Suresh A. Singh ◽  
Amy C. Henrici ◽  
Stuart S. Sumida

Heterodonty is a hallmark of early mammal evolution that originated among the non-mammalian therapsids by the Middle Permian. Nonetheless, the early evolution of heterodonty in basal synapsids is poorly understood, especially in the mandibular dentition. Here, we describe a new synapsid, Shashajaia bermani gen. et sp. nov., based on a well-preserved dentary and jaw fragments from the Carboniferous–Permian Halgaito Formation of southern Utah. Shashajaia shares with some sphenacodontids enlarged (canine-like) anterior dentary teeth, a dorsoventrally deep symphysis and low-crowned, subthecodont postcanines having festooned plicidentine. A phylogenetic analysis of 20 taxa and 154 characters places Shashajaia near the evolutionary divergence of Sphenacodontidae and Therapsida (Sphenacodontoidea). To investigate the ecomorphological context of Palaeozoic sphenacodontoid dentitions, we performed a principal component analysis based on two-dimensional geometric morphometrics of the mandibular dentition in 65 synapsids. Results emphasize the increasing terrestrialization of predator–prey interactions as a driver of synapsid heterodonty; enhanced raptorial biting (puncture/gripping) aided prey capture, but this behaviour was probably an evolutionary antecedent to more complex processing (shearing/tearing) of larger herbivore prey by the late Early to Middle Permian. The record of Shashajaia supports the notion that the predatory feeding ecology of sphenacodontoids emerged in palaeotropical western Pangea by late Carboniferous times.

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.


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