Exploring two-dimensional chromatography and chemiluminescence selectivity for complex sample analysis

 Human, animal and plant samples contain a considerable number of chemical substances critical for supporting their existence. This thesis presents improved techniques to find and analyse these components to help us understand more about their roles in the complex jigsaw of life.

Structural equation modelling of complex sample survey : an application to brand signalling data

The purpose of this paper is to present an empirical analysis of complex sample data with regard to the biasing effect of nonindependence of observations on standard error parameter estimates. In a two-factor confirmatory factor analysis model, using real data, we show how the bias in standard errors can be derived when the nonindependence is ignored. We demonstrate that the standard error bias produced by the nonindependence of observations can be considerable and we briefly discuss solutions to overcome the problem.

Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC-HRMS)

This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds.

Multiplexed detection: fast comprehensive sample analysis of tobacco leaf extracts using HPLC with AFT columns

Detector-based comprehensive screening analysis of complex samples of natural origin using High Performance Liquid Chromatography (HPLC) can be a complicated and time-consuming task. There are a number of ways multidetection characterization can be achieved; however, there are limitations associated with each technique. Active Flow Technology (AFT) in Parallel Segmented Flow (PSF) mode allows for multiplexed detection HPLC analysis within a single injection, whereas maintaining chromatographic performance and allowing the use of multiple destructive detectors to achieve a comprehensive yet efficient screening of a complex sample. In this study, a comprehensive characterization analysis of tobacco leaf extract was carried out through multiplexed detection using a PSF column for the detection of biomolecules by UV-Vis detection, DPPH• for reactive-oxygen species (ROS) detection, and mass spectrometry, the latter two detection methods being sample destructive.

Using genome-Wwide complex trait analysis to quantify 'missing heritability' in Parkinson's disease

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered. © Published by Oxford University Press 2012.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.

A hybrid approach to selecting susceptible single nucleotide polymorphisms for complex disease analysis

An increasingly popular and promising way for complex disease diagnosis is to employ artificial neural networks (ANN). Single nucleotide polymorphisms (SNP) data from individuals is used as the inputs of ANN to find out specific SNP patterns related to certain disease. Due to the large number of SNPs, it is crucial to select optimal SNP subset and their combinations so that the inputs of ANN can be reduced. With this observation in mind, a hybrid approach - a combination of genetic algorithms (GA) and ANN (called GANN) is used to automatically determine optimal SNP set and optimize the structure of ANN. The proposed GANN algorithm is evaluated by using both a synthetic dataset and a real SNP dataset of a complex disease.

Advanced pattern mining for complex sata analysis

The thesis has researched a set of critical problems in data mining and has proposed four advanced pattern mining algorithm to discover the most interesting and useful data patterns highly relevant to the user’s application targets from the data is represented in complex structures.

Influence of a co-substituted A-site on structural characteristics and ferroelectricity of (Pb, Ba, Ca)TiO3 complex perovskites: analysis of local-, medium- and long-range order

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)