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Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex

BACKGROUND: Triatoma dimidiata (Reduviidae: Triatominae) is an important vector of Chagas disease in various countries in the Americas. Phylogenetic studies have defined three lineages in Mexico and part of Central America. While there is a marked genetic differentiation, methods for identifying the...

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Autores principales: Cruz, Daryl D., Arellano, Elizabeth, Denis Ávila, Dennis, Ibarra-Cerdeña, Carlos N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329423/
https://www.ncbi.nlm.nih.gov/pubmed/32611375
http://dx.doi.org/10.1186/s13071-020-04202-2
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author Cruz, Daryl D.
Arellano, Elizabeth
Denis Ávila, Dennis
Ibarra-Cerdeña, Carlos N.
author_facet Cruz, Daryl D.
Arellano, Elizabeth
Denis Ávila, Dennis
Ibarra-Cerdeña, Carlos N.
author_sort Cruz, Daryl D.
collection PubMed
description BACKGROUND: Triatoma dimidiata (Reduviidae: Triatominae) is an important vector of Chagas disease in various countries in the Americas. Phylogenetic studies have defined three lineages in Mexico and part of Central America. While there is a marked genetic differentiation, methods for identifying them using morphometric analyses with landmarks have not yet been fully resolutive. Elliptical Fourier descriptors (EFDs), which mathematically describe the shape of any closed two-dimensional contours, could be a potentially useful alternative method. Our objective was to validate the use of EFDs for the identification of three lineages of this species complex. METHOD: A total of 84 dorsal view images of individuals of the three lineages were used. Body contours were described with EFDs using between 5 and 30 harmonics. The number of obtained coefficients was reduced by a principal components analysis and the first axis scores were used as shape variables. A linear discriminant function analysis and an ordination plot of the discriminant analysis were performed using the shape variables. A confusion matrix of the ordination plot of the discriminant analysis was obtained to estimate the classification errors, the first five PC scores were statistically compared, and a neural network were then performed using the shape variables. RESULTS: The first principal component explained 50% of the variability, regardless the number of harmonics used. The results of discriminant analysis get improved by increasing the number of harmonics and components considered. With 25 harmonics and 30 components, the identification of haplogroups was achieved with an overall efficiency greater than 97%. The ordering diagram showed the correct discrimination of haplogroups, with only one error of discrimination corroborated by the confusion matrix. When comparing the first five PC scores, significant differences were found among at least two haplogroups. The 30 multilayer perceptron neural networks were also efficient in identification, reaching 91% efficiency with the validation data. CONCLUSIONS: The use of EFD is a simple and useful method for the identification of the main lineages of Triatoma dimidiata, with high values of correct identification. [Image: see text]
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spelling pubmed-73294232020-07-02 Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex Cruz, Daryl D. Arellano, Elizabeth Denis Ávila, Dennis Ibarra-Cerdeña, Carlos N. Parasit Vectors Research BACKGROUND: Triatoma dimidiata (Reduviidae: Triatominae) is an important vector of Chagas disease in various countries in the Americas. Phylogenetic studies have defined three lineages in Mexico and part of Central America. While there is a marked genetic differentiation, methods for identifying them using morphometric analyses with landmarks have not yet been fully resolutive. Elliptical Fourier descriptors (EFDs), which mathematically describe the shape of any closed two-dimensional contours, could be a potentially useful alternative method. Our objective was to validate the use of EFDs for the identification of three lineages of this species complex. METHOD: A total of 84 dorsal view images of individuals of the three lineages were used. Body contours were described with EFDs using between 5 and 30 harmonics. The number of obtained coefficients was reduced by a principal components analysis and the first axis scores were used as shape variables. A linear discriminant function analysis and an ordination plot of the discriminant analysis were performed using the shape variables. A confusion matrix of the ordination plot of the discriminant analysis was obtained to estimate the classification errors, the first five PC scores were statistically compared, and a neural network were then performed using the shape variables. RESULTS: The first principal component explained 50% of the variability, regardless the number of harmonics used. The results of discriminant analysis get improved by increasing the number of harmonics and components considered. With 25 harmonics and 30 components, the identification of haplogroups was achieved with an overall efficiency greater than 97%. The ordering diagram showed the correct discrimination of haplogroups, with only one error of discrimination corroborated by the confusion matrix. When comparing the first five PC scores, significant differences were found among at least two haplogroups. The 30 multilayer perceptron neural networks were also efficient in identification, reaching 91% efficiency with the validation data. CONCLUSIONS: The use of EFD is a simple and useful method for the identification of the main lineages of Triatoma dimidiata, with high values of correct identification. [Image: see text] BioMed Central 2020-07-01 /pmc/articles/PMC7329423/ /pubmed/32611375 http://dx.doi.org/10.1186/s13071-020-04202-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Cruz, Daryl D.
Arellano, Elizabeth
Denis Ávila, Dennis
Ibarra-Cerdeña, Carlos N.
Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title_full Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title_fullStr Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title_full_unstemmed Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title_short Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex
title_sort identifying chagas disease vectors using elliptic fourier descriptors of body contour: a case for the cryptic dimidiata complex
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329423/
https://www.ncbi.nlm.nih.gov/pubmed/32611375
http://dx.doi.org/10.1186/s13071-020-04202-2
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