Extraction of environmental information from large aerosol data sets through combined application of cluster and factor analyses

To reveal useful environmental information which is contained in large analytical data sets, an approach, based on the successive application of hierarchical cluster analysis and factor analysis, is proposed. Estimation criteria to determine the most suitable number of clusters and/or factors, are discussed and the interpretation of the cluster and factor analyses results is performed using visual techniques. The data sets were obtained by scanning electron microscope-energy-dispersive X-ray analysis of individual North Sea aerosol particles.     

Identification of noise in linear data sets by factor analysis

With the use of atomic and nuclear methods to analyze samples for a multitude of elements, very large data sets have been generated. Due to the ease of obtaining these results with computerized systems, the elemental data acquired are not always as thoroughly checked as they should be leading to some, if not many, bad data points. It is advantageous to have some feeling for the trouble spots in a data, set before it is used for further studies. A technique which has the ability to identify bad data points, after the data has been generated, is classical factor analysis. The ability of classical factor analysis to identify two different types of data errors make it ideally suited for scanning large data sets. Since the results, yielded by factor analysis indicate correlations between parameters, one must know something about the nature of the data set and the analytical techniques used to obtain it to confidentially isolate errors.     

Extraction of discontinuous structure-activity relationships from compound data sets through particle swarm optimization

The characterization of structure-activity relationship (SAR) features of large compound data sets has been a hot topic in recent years, and different methods for large-scale SAR analysis have been introduced. The exploration of local SAR components and prioritization of compound subsets have thus far mostly relied on graphical analysis methods that capture similarity and potency relationships in a systematic manner. A currently unsolved problem in large-scale SAR analysis is how to automatically select those compound subsets from large data sets that carry most SAR information. For this purpose, we introduce a numerical optimization scheme that is based on particle swarm optimization guided by an SAR scoring function. The methodology is applied to four large compound sets. We demonstrate that compound subsets representing the most discontinuous local SARs are consistently selected through particle swarm optimization.     

HDXFinder: automated analysis and data reporting of deuterium/hydrogen exchange mass spectrometry

Hydrogen/deuterium exchange in combination with mass spectrometry (H/D MS) is a sensitive technique for detection of changes in protein conformation and dynamics. However, wide application of H/D MS has been hindered, in part, by the lack of computational tools necessary for efficient analysis of the large data sets associated with this technique. We report a novel web-based application for automatic analysis of H/D MS experimental data. This application relies on the high resolution of mass spectrometers to extract all isotopic envelopes before correlating these envelopes with individual peptides. Although a fully automatic analysis is possible, a variety of graphical tools are included to aid in the verification of correlations and rankings of the isotopic peptide envelopes. As a demonstration, the rate constants for H/D exchange of peptides from rabbit muscle pyruvate kinase are mapped onto the structure of this protein.     

High-throughput powder X-ray diffraction, IR-spectroscopy and ion chromatography analysis of urinary stones: A comparative study

The instrumental qualitative analysis of urinary stones is a critical step in clinical practice and urological research. A powder X-ray diffraction, IR-spectroscopy and ion chromatography have been applied for the qualitative analysis of 20 urinary stones. Suggestions for a sample preparation and an optimal measurement strategy were formulated. The main difficulties for the powder X-ray diffraction qualitative analysis are a limiting amount of the sample and a preferential orientation of crystals, both issues should be minimized by the special sample preparation. Urinary stones samples have been clustered into four groups using different sets of numerical input data (cation and anion content, phase composition). At the same time a high-throughput multivariate clustering has been applied for powder X-ray diffraction and IR-spectroscopy data. The multivariate whole-profile approach can be used as a tool for a high-throughput time reducing technique for clinical practice, when a quick and stable classification of samples is required. All three sets of the data can be automatically separated into three clusters: oxalate-reach, oxalate-pure and non-oxalate samples. Uricite-pure and uricite-rich samples can be easily clustered.      

Simultaneous quantification and identification using <Superscript>18</Superscript>O labeling with an ion trap mass spectrometer and the analysis software application “ZoomQuant”

Stable isotope labeling with (18)O is a promising technique for obtaining both qualitative and quantitative information from a single differential protein expression experiment. The small 4 Da mass shift produced by incorporation of two molecules of (18)O, and the lack of available methods for automated quantification of large data sets has limited the use of this approach with electrospray ionization-ion trap (ESI-IT) mass spectrometers. In this paper, we describe a method of acquiring ESI-IT mass spectrometric data that provides accurate calculation of relative ratios of peptides that have been differentially labeled using(18)O. The method utilizes zoom scans to provide high resolution data. This allows for accurate calculation of (18)O/(16)O ratios for peptides even when as much as 50% of a (18)O labeled peptide is present as the singly labeled species. The use of zoom scan data also provides sufficient resolution for calculating accurate ratios for peptides of +3 and lower charge states. Sequence coverage is comparable to that obtained with data acquisition modes that use only MS and MS/MS scans. We have employed a newly developed analysis software tool, ZoomQuant, which allows for the automated analysis of large data sets. We show that the combination of zoom scan data acquisition and analysis using ZoomQuant provides calculation of isotopic ratios accurate to approximately 21%. This compares well with data produced from (18)O labeling experiments using time of flight (TOF) and Fourier transform-ion cyclotron resonance (FT-ICR) MS instruments.     

Quantitative microbeam analysis of particles

The ability to characterize individual microscopic particles is important in many areas of science and technology. A variety of microbeam analysis methods are capable of providing information on microscopic particles. With electron probe X-ray microanalysis quantitative analysis is possible under well controlled conditions. Using computer controlled electron microscopes it is feasible to characterize a large number of individual particles. The application of this technique requires the use of multivariate data analysis procedures for the interpretation of the large amount of data obtained.     

Chemometric classification of traditional Chinese medicines by their geographical origins using near-infrared reflectance spectra.

Some raw materials that have different places of production for the plant sources of the drugs Astragalus membranaceus and ginseng have been studied, based on their near-infrared reflectance spectra. The experimentally recorded spectra represent heavily ill-posed and highly correlative data sets. Three related methods, i.e. the Fisher linear discriminant analysis (FLDA), the ridge-type linear discriminant analysis (RLDA) and a newly proposed penalized ridge-type linear discriminant analysis (PRLDA), have been investigated. FLDA over-fits for the training objects of the two data sets to a high extent and is unstable for the predictive objects of the two data sets. RLDA shows obvious improvement in terms of over-fitting and unstability, but the stability for the predictive objects of the two data sets is too sensitive to their ridge-type penalized weights, tending to produce erroneous discrimination results. The proposed PRLDA can circumvent the two aforementioned problems with a large domain of penalized weights for correct discriminant analysis of the two data sets studied. The combination of the PRLDA method and near infrared reflectance spectroscopy can be adapted for the discrimination of the production places of plant sources of these drugs.     

Automated pharmacophore identification for large chemical data sets.

The identification of three-dimensional pharmacophores from large, heterogeneous data sets is still an unsolved problem. We developed a novel program, SCAMPI (statistical classification of activities of molecules for pharmacophore identification), for this purpose by combining a fast conformation search with recursive partitioning, a data-mining technique, which can easily handle large data sets. The pharmacophore identification process is designed to run recursively, and the conformation spaces are resampled under the constraints of the evolving pharmacophore model. This program is capable of deriving pharmacophores from a data set of 1000-2000 compounds, with thousands of conformations generated for each compound and in less than 1 day of computational time. For two test data sets, the identified pharmacophores are consistent with the known results from the literature.     

Experimental and theoretical electron density study of estrone

The electron density and the electrostatic potential (ESP) distributions of estrone have been determined using X-ray diffraction analysis and compared with theoretical calculations in the solid and gas phases. X-ray diffraction measurements are performed with a Rigaku Rapid rotating anode diffractometer at 20 K. The electron density in the estrone crystal has been described with the multipole model, which allowed extensive topological analysis and calculation of the ESP. From DFT calculations in the solid state a theoretical X-ray diffraction data set has been produced and treated in the same way as the experimental data. Two sets of single molecule DFT calculations were performed: (a) An electron density distribution was obtained via a single-point calculation with a large basis set at the experimental geometry and subsequently analyzed according to the quantum theory of atoms in molecules (AIM) to obtain the bond and most atomic properties, and (b) another electron density distribution was obtained with a smaller basis set, but at a geometry optimized using the same basis set for the analysis of atomic energies. An interesting locally stabilizing hydrogen-hydrogen bond path linking H(1) and H(11B) is found which represents the first characterization of such bonding in a steroid molecule. AIM delocalization indices were shown to be well correlated to the experimental electron density at the bond critical points through an exponential relationship. The aromaticity of ring A, chemical bonding, the O(1)...O(2) distance necessary for estrogenic activity, and the electrostatic potential features are also discussed.     

HDX Workbench: Software for the Analysis of H/D Exchange MS Data

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) is an established method for the interrogation of protein conformation and dynamics. While the data analysis challenge of HDX-MS has been addressed by a number of software packages, new computational tools are needed to keep pace with the improved methods and throughput of this technique. To address these needs, we report an integrated desktop program titled HDX Workbench, which facilitates automation, management, visualization, and statistical cross-comparison of large HDX data sets. Using the software, validated data analysis can be achieved at the rate of generation. The application is available at the project home page http://hdx.florida.scripps.edu .     

X射线晶体学结合电子晶体学在复杂无机晶体结构解析中的应用

自然界中的材料,比如无机材料,有机材料,生物材料等等,均有其独特的物理和化学性质。而材料的性能又与材料的结构息息相关,只有充分了解了材料的结构,才能更加深入的研究材料性质。因此,材料结构的确定在化学、物理、生物等学科中的显得尤为重要。X射线晶体学作为传统的结构解析技术仍然是目前最重要的结构解析手段,但是对于复杂结构,X射线衍射晶体学解析结构也存在一些不足,往往需要其他技术手段相补充才能完成复杂结构的结构解析。电子晶体学虽然起步比X射线晶体学晚,但是,经过近几十年的发展,已经是结构解析领域一个非常重要的手段。本文将主要介绍X射线晶体学结合电子晶体学在复杂无机晶体结构解析中的应用。     

Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

In this paper, multivariate calibration of complicated process fluorescence data is presented. Two data sets related to the production of white sugar are investigated. The first data set comprises 106 observations and 571 spectral variables, and the second data set 268 observations and 3997 spectral variables. In both applications, a single response, ash content, is modelled and predicted as a function of the spectral variables. Both data sets contain certain features making multivariate calibration efforts non-trivial. The objective is to show how principal component analysis (PCA) and partial least squares (PLS) regression can be used to overview the data sets and to establish predictively sound regression models. It is shown how a recently developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. In addition, signal compression is tested on the larger data set using wavelet analysis. It is demonstrated that a compression down to 4% of the original matrix size — in the variable direction — is possible without loss of predictive power. It is concluded that the combination of OSC for pre-processing and wavelet analysis for compression of spectral data is promising for future use.     

From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets

The analysis of structure–activity relationships (SARs) becomes rather challenging when large and heterogeneous compound data sets are studied. In such cases, many different compounds and their activities need to be compared, which quickly goes beyond the capacity of subjective assessments. For a comprehensive large-scale exploration of SARs, computational analysis and visualization methods are required. Herein, we introduce a two-layered SAR visualization scheme specifically designed for increasingly large compound data sets. The approach combines a new compound pair-based variant of generative topographic mapping (GTM), a machine learning approach for nonlinear mapping, with chemical space networks (CSNs). The GTM component provides a global view of the activity landscapes of large compound data sets, in which informative local SAR environments are identified, augmented by a numerical SAR scoring scheme. Prioritized local SAR regions are then projected into CSNs that resolve these regions at the level of individual compounds and their relationships. Analysis of CSNs makes it possible to distinguish between regions having different SAR characteristics and select compound subsets that are rich in SAR information.     

On the stability of CoMFA models

Abrupt, smooth, and box methods for the calculation of electrostatic and steric field values in the comparative molecular field analysis (CoMFA) 3D QSAR technique are assessed on three diverse data sets of medicinal chemistry interest. While the standard CoMFA settings are robust to small changes in the position of the lattice, superior results may sometimes be obtained by use of only one field. However, if only the electrostatic field is used, then sometimes large differences between models are apparent. This appears to be due to a lack of column dropping, and these difficulties can be remedied by use of the box method.     

Alignment and clustering strategies for GC×GC–MS features using a cylindrical mapping

Comprehensive two-dimensional gas chromatography coupled to mass spectrometry is a powerful tool to analyze complex samples. For application of the technique in studies like biomarker discovery in which large sets of complex samples have to be analyzed, extensive preprocessing is needed to align the data obtained in several injections (analyses). We developed new alignment and clustering algorithms for this type of data. New in the current procedures is the consistent way in which the phenomenon referred to as wrap-around is treated. The data analysis problems associated with this phenomenon are solved by treating the 2D display as the surface of a three-dimensional cylinder. Based on this transformation we developed a new similarity metric for features as a function of both the cylindrical distance (reflecting similarity in chromatographic behavior) and of the mass spectral correlation (reflecting similarity in chemical structure). The concepts are used in warping and clustering, and include a protection against greedy warping.     

Sub-nm resolution depth profiling of the chemical state and magnetic structure of thin films by a depth-resolved X-ray absorption spectroscopy technique

Development and recent progress of a depth-resolved X-ray absorption spectroscopy (XAS) technique are presented, together with future prospects. The technique has been developed by controlling the probing depth of the electron-yield XAS data, which depends on the electron emission angle. This novel technique enables us to achieve depth profiling of the magnetic structure of thin films with a sub-nm depth resolution by using X-ray magnetic circular dichroism (XMCD) in X-ray absorption, which provides quantitative information on the element-specific spin and orbital magnetic moments. The chemical state and electronic structure at the surface and interface are also investigated by depth-resolved XAS analysis. As for future prospects, a three-dimensional micro XAS technique is being developed by combining an X-ray microbeam with depth-resolved XAS. Moreover, it is expected to manipulate magnetic anisotropy by using element-specific and depth-resolved magnetic anisotropy energies obtained from the depth-resolved XMCD to design thin films and multilayers with proper elements and proper thicknesses. The observation of the spin dynamics at the interface will be also possible in future by adopting the pump-probe method.