Assessing the contribution of natural sources to the global mercury cycle: the importance of intercomparing dynamic flux measurements

In order to constrain the contribution of natural sources of mercury to the global atmospheric cycle we need to: 1. assess the methods used to measure mercury flux, 2. characterize those factors most important in controlling emissions, 3. develop a database of emissions from representative locations, and 4. develop a means of scaling up measured emissions to estimate fluxes on a regional basis. This paper describes how an international multi-collaborator project, the Nevada SToRMS Project, held September 1997 in Reno, Nevada, USA, contributed to our ability to constrain natural source mercury emissions. This study entailed a field intercomparison of those methods typically applied to measure mercury flux from substrate combined with evening workshops and round-table discussions. The project was unique in that it focused on assessing our ability to measure the flux of an environmental contaminant. This is more difficult than measurement of the concentration of a contaminant because of the number and nature of the variables which influence the field flux measurements, including experimental design, spatial heterogeneity, and temporally changing environmental conditions. As a result of the Nevada SToRMS Project, rapid and significant advances in our understanding of how to constrain emission fluxes from large areas of mercury enrichment were realized. Because this intercomparison was a multi-investigator project, the results and implications of the project have been broadly circulated. The sincere scientific collaboration that evolved amongst those working on the study has led to significant advancements in our understanding of the fate and transport of mercury in the environment.     

Automated annotation and quantification of metabolites in <Superscript>1</Superscript>H NMR data of biological origin

In ¹H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of ¹H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several ¹H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.     

A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D ¹H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic ¹H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples. Figure From chaos to order: heatmaps of a H-NMR spectral segment prior and post sorting on one peak position. Post sorting sample order reveals that peak positions exhibits distinctive patterns which are modeled by the GFHT to establish correspondence. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.