Dataset Information

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

ABSTRACT: 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, [Formula: see text]) 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.

SUBMITTER: Osterloff J

PROVIDER: S-EPMC4902238 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

Osterloff Jonas J Nilssen Ingunn I Eide Ingvar I de Oliveira Figueiredo Marcia Abreu MA de Souza Tâmega Frederico Tapajós FT Nattkemper Tim W TW

PloS one 20160610 6

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 ...[more]

PMID: 27285611

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Project description:Marine biofouling is a natural process by which many organisms colonize and grow in submerged structures, causing serious economic consequences for the maritime industry. Geniculate calcareous algae (GCA; Corallinales, Rhodophyta) produce bioactive secondary metabolites and are a promise for new antifouling compounds. Here, we investigated the antifouling activity of four GCA species-Amphiroa beauvoisii, Jania sagittata (formerly Cheilosporum sagittatum), Jania crassa, and Jania prolifera (formerly Amphiroa flabellata)-from the Brazilian coast against macro- and microorganisms. Simultaneously, metabolomic tools were applied to assess the chemical profiles of these seaweeds using gas chromatography coupled to mass spectrometry (GC-MS). Data analysis by principal component and molecular networking analyses used the global natural products social molecular networking platform (GNPS). Our results showed that all extracts were active against different strains of marine bacteria and that the J. sagittata (JsSI) extract showed the highest percentage of bacterial inhibition. The J. sagittata (JsSI) extract was the most active against the mussel Perna perna, showing 100% byssus inhibition. Regarding toxicity, only the J. crassa (JcP) extract showed a 20% mortality rate. The chemical profiles of the evaluated GCA extracts differed qualitatively and quantitatively. Yet, the steroid (3β)-cholest-5-en-3-ol was the major compound commonly identified in all extracts, with the exception of J. sagittata (JsSI). Moreover, we observed intra- and interspecific chemical variabilities among GCA extracts for the different populations, which could explain their antifouling activity variability. This study contributed new information about the chemical compounds produced by this group of seaweeds and showed its antifouling potential. These GCA species may be the subject of future studies to obtain new bioactive compounds with biotechnological potential in maritime areas.

Dataset Information

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

Publications

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation.

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