Misclassification rates of dimension reduction classifiers using the trimmed datasets.

<p>Mean misclassification rates for each of the dimension reduction-based methods using the trimmed dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p

Dimension Reduction Classifier Performance Summary.

<p>The performance summary (MCR = Misclassification rate, AUC = Area under the curve, Sens = Sensitivity, Spec = Specificity, No. Components = the number of components used in the model) of each classifier for both the full dataset (“full”) and the trimmed dataset (“trimmed”) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except the OC data which used 200 bootstrap samples.</p

SVM tuning results.

<p>The performance summary (<b>MCR</b> = Misclassification rate) of a SVM-based classifier for both the full dataset (“full”) and the trimmed dataset (“trimmed”) that underwent variable selection using a univariate moderated t-statistic. These are mean values based on 1000 bootstrap samples for each dataset except for the OC data which used 200 bootstrap samples.</p

Misclassification rates of dimension reduction classifiers using the untrimmed datasets.

<p>Mean misclassification rates for each of the dimension reduction-based methods using the full dataset (all variables) in the dataset to build the classification model. <b>A</b>) Is from the OC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Lee1" target="_blank">[16]</a>, <b>B</b>) is from the Gaucher disease dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Hendriks1" target="_blank">[46]</a>, <b>C</b>) is from the LC datasets and <b>D</b>) is from the CRC dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone.0024973-Schleif1" target="_blank">[14]</a>. Blue circles illustrate PLS-LDA classification results, red triangles are from a PLS-RF classifier and purple crosses show results obtained from a PCA-LDA classifier.</p

Comparison of PLS and PCA for dimension reduction.

<p>These plots demonstrate the capacity PLS has to separate classes based on the top 30 variables (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024973#pone-0024973-g004" target="_blank"><i>Figure 4A</i></a>) in the Gaucher dataset when compared to PCA (Note that this class separation is being heavily influenced by the loadings highlighted in Blue. Additionally, the vectors highlighted in red explain the within class variation in the control group. This is a key advantage PLS has over other methods.</p

Urinary biomarker values for mine site employees.^α

α<p>Values are means<u>±</u>SE.</p>*<p>Indicates values are significantly greater than PRE value (P<0.01).</p>#<p>Indicates value significantly greater than PRE value (P<0.05).</p

LC- MS/MS identifies the LG3 peptide of endorepellin, a C-terminal bioactive fragment of Perlecan.

<p>a) Perlecan (<b><u>underlined bold lower case</u></b>), the C terminal of Perlecan containing Endorepellin (lowercase text) and the LG3 Peptide of endorepellin (<b>BOLD CAPITALS</b>). Individual peptides identified by LC-MS/MS of tryptic in-gel digest in </p><p><b>LIGHT GREY</b></p> and <p><b><u>DARK GREY</u></b></p> highlights. Sequence coverage includes the LG3 peptide, however, the first 25 residues of the LG3 peptide were not detected. <b>b</b>) Western blot analysis confirmed that the ∼20 kDa protein observed by SDS-PAGE and the spectral feature at m/z 16881 are derived from endorepellin. Western Blot of worker urine samples using goat anti-human endorepellin polyclonal antibody (1∶10,000).<p></p

The spectral feature at m/z 16881 is a broad tri-phasic peak, visible by SDS-PAGE.

<p><b>a</b>) The hypothesised pattern of intensity of m/z 16881 in stacked replicate spectra, expected to be observed in an SDS-PAGE gel. <b>b</b>) A band which matched the expected pattern of intensity for the feature at m/z 16881 was detected at ∼20 kDa by SDS-PAGE (<b>arrow)</b> suggesting that the bands at ∼20 kDa in the gel were the proteins which constituted m/z 16881 in the spectra. <b>c</b>) The protein at ∼20 kDa was extracted from excised bands from a non-stained replicate SDS-PAGE gel. Examination of the extracted protein by SELDI-TOF MS confirmed that the ∼20 kDa band was the feature originally detected at m/z 16881.</p

Summary of physical exposure relative to different postures and activity.

*<p> <b> = p<0.05.</b></p

Urinary urea and cortisol levels trend toward recovery in operators but not in maintenance crew.

<p><b>a</b>) Urinary, urea levels were determined by an automated kinetic assay (analytic coefficient of variation being <4%). <b>b</b>) Urinary cortisol levels were determined by competitive immunoassay (analytic coefficient of variation being<4%). Both urea and cortisol measurements were standardised for dieresis against urinary creatinine levels which were determined by the Jaffe method (analytic coefficient of variation being<3%).</p