Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW (2016)
PLOS ONE 11(6): e0157329.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Osterloff, JonasUniBi ; Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim WilhelmUniBi
Abstract / Bemerkung
This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors.
Erscheinungsjahr
2016
Zeitschriftentitel
PLOS ONE
Band
11
Ausgabe
6
Art.-Nr.
e0157329
ISSN
1932-6203
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2903712

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Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 2016;11(6): e0157329.
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M. A., de Souza Tâmega, F. T., & Nattkemper, T. W. (2016). Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE, 11(6), e0157329. doi:10.1371/journal.pone.0157329
Osterloff, Jonas, Nilssen, Ingunn, Eide, Ingvar, de Oliveira Figueiredo, Marcia Abreu, de Souza Tâmega, Frederico Tapajós, and Nattkemper, Tim Wilhelm. 2016. “Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation”. PLOS ONE 11 (6): e0157329.
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M. A., de Souza Tâmega, F. T., and Nattkemper, T. W. (2016). Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE 11:e0157329.
Osterloff, J., et al., 2016. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE, 11(6): e0157329.
J. Osterloff, et al., “Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation”, PLOS ONE, vol. 11, 2016, : e0157329.
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M.A., de Souza Tâmega, F.T., Nattkemper, T.W.: Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 11, : e0157329 (2016).
Osterloff, Jonas, Nilssen, Ingunn, Eide, Ingvar, de Oliveira Figueiredo, Marcia Abreu, de Souza Tâmega, Frederico Tapajós, and Nattkemper, Tim Wilhelm. “Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation”. PLOS ONE 11.6 (2016): e0157329.
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2019-09-06T09:18:38Z
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2 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Underwater hyperspectral classification of deep sea corals exposed to 2-methylnaphthalene.
Letnes PA, Hansen IM, Aas LMS, Eide I, Pettersen R, Tassara L, Receveur J, le Floch S, Guyomarch J, Camus L, Bytingsvik J., PLoS One 14(2), 2019
PMID: 30811426
Computer vision enables short- and long-term analysis of Lophelia pertusa polyp behaviour and colour from an underwater observatory.
Osterloff J, Nilssen I, Järnegren J, Van Engeland T, Buhl-Mortensen P, Nattkemper TW., Sci Rep 9(1), 2019
PMID: 31036904

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