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Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification
A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina spe...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684539/ https://www.ncbi.nlm.nih.gov/pubmed/36418429 http://dx.doi.org/10.1038/s41598-022-24522-w |
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author | Cannet, Arnaud Simon-Chane, Camille Akhoundi, Mohammad Histace, Aymeric Romain, Olivier Souchaud, Marc Jacob, Pierre Delaunay, Pascal Sereno, Darian Bousses, Philippe Grebaut, Pascal Geiger, Anne de Beer, Chantel Kaba, Dramane Sereno, Denis |
author_facet | Cannet, Arnaud Simon-Chane, Camille Akhoundi, Mohammad Histace, Aymeric Romain, Olivier Souchaud, Marc Jacob, Pierre Delaunay, Pascal Sereno, Darian Bousses, Philippe Grebaut, Pascal Geiger, Anne de Beer, Chantel Kaba, Dramane Sereno, Denis |
author_sort | Cannet, Arnaud |
collection | PubMed |
description | A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven’t been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy. |
format | Online Article Text |
id | pubmed-9684539 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96845392022-11-25 Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification Cannet, Arnaud Simon-Chane, Camille Akhoundi, Mohammad Histace, Aymeric Romain, Olivier Souchaud, Marc Jacob, Pierre Delaunay, Pascal Sereno, Darian Bousses, Philippe Grebaut, Pascal Geiger, Anne de Beer, Chantel Kaba, Dramane Sereno, Denis Sci Rep Article A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven’t been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy. Nature Publishing Group UK 2022-11-22 /pmc/articles/PMC9684539/ /pubmed/36418429 http://dx.doi.org/10.1038/s41598-022-24522-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cannet, Arnaud Simon-Chane, Camille Akhoundi, Mohammad Histace, Aymeric Romain, Olivier Souchaud, Marc Jacob, Pierre Delaunay, Pascal Sereno, Darian Bousses, Philippe Grebaut, Pascal Geiger, Anne de Beer, Chantel Kaba, Dramane Sereno, Denis Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title | Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title_full | Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title_fullStr | Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title_full_unstemmed | Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title_short | Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification |
title_sort | wing interferential patterns (wips) and machine learning, a step toward automatized tsetse (glossina spp.) identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684539/ https://www.ncbi.nlm.nih.gov/pubmed/36418429 http://dx.doi.org/10.1038/s41598-022-24522-w |
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