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Decoding gut microbiota by imaging analysis of fecal samples

The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal sample...

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Detalles Bibliográficos
Autores principales: Furusawa, Chikara, Tanabe, Kumi, Ishii, Chiharu, Kagata, Noriko, Tomita, Masaru, Fukuda, Shinji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652011/
https://www.ncbi.nlm.nih.gov/pubmed/34927025
http://dx.doi.org/10.1016/j.isci.2021.103481
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author Furusawa, Chikara
Tanabe, Kumi
Ishii, Chiharu
Kagata, Noriko
Tomita, Masaru
Fukuda, Shinji
author_facet Furusawa, Chikara
Tanabe, Kumi
Ishii, Chiharu
Kagata, Noriko
Tomita, Masaru
Fukuda, Shinji
author_sort Furusawa, Chikara
collection PubMed
description The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis.
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spelling pubmed-86520112021-12-17 Decoding gut microbiota by imaging analysis of fecal samples Furusawa, Chikara Tanabe, Kumi Ishii, Chiharu Kagata, Noriko Tomita, Masaru Fukuda, Shinji iScience Article The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis. Elsevier 2021-11-22 /pmc/articles/PMC8652011/ /pubmed/34927025 http://dx.doi.org/10.1016/j.isci.2021.103481 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Furusawa, Chikara
Tanabe, Kumi
Ishii, Chiharu
Kagata, Noriko
Tomita, Masaru
Fukuda, Shinji
Decoding gut microbiota by imaging analysis of fecal samples
title Decoding gut microbiota by imaging analysis of fecal samples
title_full Decoding gut microbiota by imaging analysis of fecal samples
title_fullStr Decoding gut microbiota by imaging analysis of fecal samples
title_full_unstemmed Decoding gut microbiota by imaging analysis of fecal samples
title_short Decoding gut microbiota by imaging analysis of fecal samples
title_sort decoding gut microbiota by imaging analysis of fecal samples
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652011/
https://www.ncbi.nlm.nih.gov/pubmed/34927025
http://dx.doi.org/10.1016/j.isci.2021.103481
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