Multivariate analysis of a biologically activated carbon (BAC) system and its efficiency for removing PAHs and aliphatic hydrocarbons from wastewater polluted with petroleum products

The efficiency of a biologically activated carbon system for treating wastewater polluted with petroleum products was examined and the effects of process parameters on its efficacy were evaluated. In each experiment 17 alkylated and 19 non-alkylated polycyclic aromatic hydrocarbons (PAHs) and total petroleum hydrocarbons (TPHs, C₁₀–C₄₀) were extracted using semipermeable membrane devices from wastewater before and after treatment. The acquired data during experiments were analyzed using principal component analysis (PCA). The treatment system robustly removed dissolved PAHs across the studied ranges of the process parameters, providing overall removal efficiencies of 96.9–99.7% for the sum of 36 PAHs. However, the major contributor to their removal was sorption rather than biodegradation, and despite the general efficiency of the process there was up to a 9-fold range in the sums of quantified PAHs in the effluents between experiments. Combinations of long process contact time (24 h) with high temperature (24 °C) and moderate oxygen concentration (6–7 mg O₂ L⁻¹) resulted in good removal of bioavailable PAHs. The removal of TPHs was more dependent on biological activities during the wastewater treatment, and consequently more dependent on the process parameters. In addition, small but significant proportions of PAHs were volatilized and released during the wastewater treatment.     

High-throughput global peptide proteomic analysis by combining stable isotope amino acid labeling and data-dependent multiplexed-MS/MS

In this work, we describe the application of a stable isotope amino acid (lysine) labeling in conjunction with data-dependent multiplexed tandem mass spectrometry (MS/MS) to facilitate the characterization and identification of peptides from proteomic (global protein) digests. Lysine auxotrophic yeast was grown in the presence of 13C-labeled or unlabeled lysine and combined after harvesting in equal proportions. Endoproteinase LysC digestion of the cytosolic fraction produced a global proteomic sample, consisting of heavy/light labeled peptide pairs. Then data-dependent multiplexed-MS/MS was applied to simultaneously select and dissociate only labeled peptide ion pairs. The approach allows differentiation between N-terminal (e.g., b-type ions) and C-terminal fragment ions (e.g., y-type ions) in resulting tandem mass spectra, as well as the capability of differentiation between near-isobaric glutamine and lysine residues. We also describe the utility of peptide composition and fragment information to support peptide identifications and examine the potential application of lysine labeling for differential quantitative protein analysis.     

Application of Bayes linear discriminant functions in image classification

In statistical image classification it is usually assumed that feature observations given class labels are independently distributed. Even in the case when training sample is formed by dependent feature observations, the feature observations to be classified are usually assumed to be independent from training sample. In this paper we propose the original method of the incorporation of spatial information into the per-pixel classifiers. Our approach is based on the retraction of the independence assumption by proposing stationary Gaussian random field (GRF) model for features. The conditional distribution of class label of observation to be classified is assumed to be dependent on its spatial adjacency within the spatial framework of the training sample. For a given training sample, plug-in version of the Bayes discriminant function (PBDF) is proposed for classification. Performance of the proposed PBDF is tested and compared with ones ignoring dependence among feature observations to be classified and training sample. For illustration the image of figure corrupted by the additive GRF is analyzed. The advantage of the proposed classifier against the competing one is shown visually and numerically in the first example. In the second example, three spatial sampling designs for training data are compared on the basis of the actual error rate values of the proposed PBDF. For the remotely sensed image, the advantage of the proposed classification method against popular unsupervised classification method is shown in terms of visual evaluation and empirical errors of misclassification.     

Estimation of water sampling rates and concentrations of PAHs in a municipal sewage treatment plant using SPMDs with performance reference compounds

Semipermeable membrane devices (SPMDs) were exposed at ten sampling points, each representing a different stage in the treatment process, in a municipal sewage treatment plant. Differences in SPMD uptake kinetics of polycyclic aromatic hydrocarbons (PAHs) due to variations in conditions at the sampling sites were evaluated by using five performance reference compounds (PRCs) with log K(ow) values of 4.20 to 6.34. PRC release rate constants (k(e,PRC) values) were calculated for PRCs for which 50-98% of the initial amounts were lost during the sampling period. The k(e,PRC) values were high, ranging from 0.08 to 0.11 day(-1) for the studied PRCs, at sampling site W1 (raw sewage), the only sampling site where significant amounts of the PRCs with log K(ow) values > 5 were released from the SPMDs. At the other sampling sites, only PRCs with log K(ow) values between 4.20 and 4.50 were released in significant amounts. The release rates at these sites were lowest (0.04 day(-1)) at sampling site W9 (the secondary clarifier) and highest (0.18 day(-1)) at W8 (the active sludge aeration basin). Differences between sampling rates (R(s)) obtained using published laboratory-calibrated data and PRC-corrected R(s) values were visualized by principal component analysis (PCA). The water concentrations of 24 studied PAHs fell substantially during the course of the sewage treatment process. However, low molecular weight PAHs were more effectively removed than high molecular weight PAHs. Significant deviations between actual and estimated water concentrations may arise unless PRC-corrected R(s) values are applied.