We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.

In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in 1H NMR spectroscopic data sets derived from urine. The problems associated with "binned" data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLC-MS), and indeed for other "omics" techniques (i.e., transcriptomics or proteomics) and for integrated studies with "fused" data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent

Administration of high doses of the histamine antagonist methapyrilene to rats causes periportal liver necrosis. The mechanism of toxicity is ill-defined and here we have utilized an integrated systems approach to understanding the toxic mechanisms by combining proteomics, metabonomics by 1 H NMR spectroscopy and genomics by microarray gene expression profiling. Male rats were dosed with methapyrilene for 3 days at 150 mg/kg/day, which was sufficient to induce liver necrosis, or a subtoxic dose of 50 mg/kg/day. Urine was collected over 24 h each day, while blood and liver tissues were obtained at 2 h after the final dose. The resulting data further define the changes that occur in signal transduction and metabolic pathways during methapyrilene hepatotoxicity, revealing modification of expression levels of genes and proteins associated with oxidative stress and a change in energy usage that is reflected in both gene/protein expression patterns and metabolites. The difficulties of combining and interpreting multiomic data are considered.

The capability to detect and quantify sulfur at the part-per-billion (ppb) concentration level in ultrapure hydrochloric acid rinse solutions used in GaAs wafer fabrication is described. Nonvolatile residues formed from the deposition of nanoliter aliquots of solution onto high purity silicon wafers are analyzed using a high performance CAMECA IMS 4f ion microanalyzer. The dynamic SIMS analysis of microdroplet residues is referred to as Microvolume-SIMS (MV-SIMS). The Microvolume-SIMS analyses of two acid solutions are presented. The concentration of total sulfur detected in these solutions was 98 and 650 ppb; both values are below the SEMI specification standard of less than 1300 ppb total sulfur concentration. Despite this, the acid solution containing 650 ppb of total sulfur was responsible for causing an isolation failure halting wafer production. This finding corroborated electrical failure analyses using these same acids.

WSix films are used extensively for contact, interconnect, and, in some cases, diffusion and Schottky barriers in semiconductor devices1. The electrical and barrier properties of these films are affected by a variety of factors, such as film stoichiometry, morphology, impurities, etc. This paper will address the capabilities and limitations of a variety of techniques which are frequently used to characterize WSix films. Techniques which were studied include: Dynamic and Static Secondary Ion Mass Spectrometry (SIMS), Rutherford Backscattering Spectrometry and Elastic Recoil Detection (RBS/ERD), Auger Electron Spectroscopy (AES), Field Emission Scanning Electron Microscopy (FE-SEM), Total Reflection X-ray Fluorescence (TXRF), Atomic Force Microscopy (AFM), and X-Ray Photoelectron Spectroscopy (XPS). Film characteristics which were studied included surface morphology; grain structure; film stoichiometry; surface and interface oxide thickness and composition; and surface, bulk, and interface impurity concentrations including metallic, atmospheric, and dopant impurities. Cross correlation between the techniques was performed whenever possible in order to compare the relative accuracy of the techniques as well.