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An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track beh...

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Autores principales: Zuazo, Ander, Grinyó, Jordi, López-Vázquez, Vanesa, Rodríguez, Erik, Costa, Corrado, Ortenzi, Luciano, Flögel, Sascha, Valencia, Javier, Marini, Simone, Zhang, Guosong, Wehde, Henning, Aguzzi, Jacopo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662914/
https://www.ncbi.nlm.nih.gov/pubmed/33158174
http://dx.doi.org/10.3390/s20216281
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author Zuazo, Ander
Grinyó, Jordi
López-Vázquez, Vanesa
Rodríguez, Erik
Costa, Corrado
Ortenzi, Luciano
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
author_facet Zuazo, Ander
Grinyó, Jordi
López-Vázquez, Vanesa
Rodríguez, Erik
Costa, Corrado
Ortenzi, Luciano
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
author_sort Zuazo, Ander
collection PubMed
description Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.
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spelling pubmed-76629142020-11-14 An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions Zuazo, Ander Grinyó, Jordi López-Vázquez, Vanesa Rodríguez, Erik Costa, Corrado Ortenzi, Luciano Flögel, Sascha Valencia, Javier Marini, Simone Zhang, Guosong Wehde, Henning Aguzzi, Jacopo Sensors (Basel) Article Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures. MDPI 2020-11-04 /pmc/articles/PMC7662914/ /pubmed/33158174 http://dx.doi.org/10.3390/s20216281 Text en © 2020 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
Zuazo, Ander
Grinyó, Jordi
López-Vázquez, Vanesa
Rodríguez, Erik
Costa, Corrado
Ortenzi, Luciano
Flögel, Sascha
Valencia, Javier
Marini, Simone
Zhang, Guosong
Wehde, Henning
Aguzzi, Jacopo
An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_full An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_fullStr An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_full_unstemmed An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_short An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions
title_sort automated pipeline for image processing and data treatment to track activity rhythms of paragorgia arborea in relation to hydrographic conditions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662914/
https://www.ncbi.nlm.nih.gov/pubmed/33158174
http://dx.doi.org/10.3390/s20216281
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