scholarly journals Convergent Evolution of Cichlid Fish Pharyngeal Jaw Dentitions in Mollusk-Crushing Predators: Comparative X-Ray Computed Tomography of Tooth Sizes, Numbers, and Replacement

2020 ◽  
Vol 60 (3) ◽  
pp. 656-664
Author(s):  
C Darrin Hulsey ◽  
Axel Meyer ◽  
J Todd Streelman

Abstract Dental convergence is a hallmark of cichlid fish adaptive radiations. This type of repeated evolution characterizes both the oral jaws of these fishes as well as their pharyngeal jaws that are modified gill arches used to functionally process prey like hard-shelled mollusks. To test several hypotheses regarding the evolution of cichlid crushing pharyngeal dentitions, we used X-ray computed tomography scans to comparatively examine dental evolution in the pharyngeal jaw of a diversity of New World Heroine cichlid lineages. The substantial variation in erupted tooth sizes and numbers as well as replacement teeth found in these fishes showed several general patterns. Larger toothed species tended to have fewer teeth suggesting a potential role of spatial constraints in cichlid dental divergence. Species with larger numbers of erupted pharyngeal teeth also had larger numbers of replacement teeth. Replacement tooth size is almost exactly predicted (r = 0.99) from the size of erupted teeth across all of the species. Mollusk crushing was, therefore, highly associated with not only larger pharyngeal teeth, but also larger replacement teeth. Whether dental divergence arises as a result of environmental induced plasticity or originates via trophic polymorphism as found in the species Herichthys minckleyi, there appear to be general rules that structure interspecific divergence in cichlid pharyngeal erupted and replacement dentitions.

1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.


2021 ◽  
Author(s):  
Katherine A. Wolcott ◽  
Guillaume Chomicki ◽  
Yannick M. Staedler ◽  
Krystyna Wasylikowa ◽  
Mark Nesbitt ◽  
...  

2021 ◽  
Vol 33 (7) ◽  
pp. 076610
Author(s):  
Chunwei Zhang ◽  
Yun She ◽  
Yingxue Hu ◽  
Zijing Li ◽  
Weicen Wang ◽  
...  

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