Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.

We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition

Toxicological evaluation of complex mixtures by pattern recognition: Correlating chemical fingerprints to mutagenicity

We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition.publishedVersio

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. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 2016;11(6): e0157329.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

Abbreviations used for some variables in the correlation loading plot (Fig 2) and the contribution plots (Figs 4 and 6).

<p>Abbreviations used for some variables in the correlation loading plot (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189443#pone.0189443.g002" target="_blank">Fig 2</a>) and the contribution plots (Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189443#pone.0189443.g004" target="_blank">4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189443#pone.0189443.g006" target="_blank">6</a>).</p

Scatter plot of the current speed north at 18 and 23 m above sea bed for Continental (left) and at 23 m above sea bed for Continental and Aquadopp (right).

<p>A: Aquadopp; C: Continental; N: north.</p

Correlation loading plot after PCA on current speed data 3–23 m above sea bed from both current speed sensors.

<p>A: Aquadopp; C: Continental; E: east; N: north.</p

Score plot with projection of the August data on the calibration model.

<p>Score plot with projection of the August data on the calibration model.</p

Illustration of the multi-block model.

<p>Ind: variable block for individual sensors; A: variable block for Aquadopp profiler; C: variable block for Continental ADCP.</p