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Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility

(1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but...

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Autores principales: Riekeles, Max, Schirmack, Janosch, Schulze-Makuch, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828299/
https://www.ncbi.nlm.nih.gov/pubmed/33445805
http://dx.doi.org/10.3390/life11010044
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author Riekeles, Max
Schirmack, Janosch
Schulze-Makuch, Dirk
author_facet Riekeles, Max
Schirmack, Janosch
Schulze-Makuch, Dirk
author_sort Riekeles, Max
collection PubMed
description (1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but recent increases in computational power make the use of automated in-situ analyses feasible. (2) Methods: Here, we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. (3) Results: While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates for the selected species does not exceed 82%. (4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming life-detection missions. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.
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spelling pubmed-78282992021-01-25 Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility Riekeles, Max Schirmack, Janosch Schulze-Makuch, Dirk Life (Basel) Article (1) Background: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potential alien life forms. The technology was not advanced enough for onboard machine analysis of microscopic observations to be performed in past missions, but recent increases in computational power make the use of automated in-situ analyses feasible. (2) Methods: Here, we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. (3) Results: While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates for the selected species does not exceed 82%. (4) Conclusions: Motility is an excellent biosignature, which can be used as a tool for upcoming life-detection missions. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System. MDPI 2021-01-12 /pmc/articles/PMC7828299/ /pubmed/33445805 http://dx.doi.org/10.3390/life11010044 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Riekeles, Max
Schirmack, Janosch
Schulze-Makuch, Dirk
Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title_full Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title_fullStr Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title_full_unstemmed Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title_short Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
title_sort machine learning algorithms applied to identify microbial species by their motility
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828299/
https://www.ncbi.nlm.nih.gov/pubmed/33445805
http://dx.doi.org/10.3390/life11010044
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