Experiences in Pattern Recognition for Machine Olfaction

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings

Linking Bistable Dynamics to Metabolic P Systems

Bistability, or more generally multistability, is an important recurring theme in biological systems. In particular, the discovery of bistability in signal pathways of genetic networks, prompts strong interest in understanding both the design and function of these networks. Therefore, modelling these systems is crucial to understand their behaviors, and also to analyze and identify characteristics that would otherwise be di cult to realize. Although di erent classes of models have been used to study bistable dynamics, there is a lag in the development of models for bistable systems starting from experimental data. This is due to the lack of detailed knowledge of biochemical reactions and kinetic rates. In this work, we propose a procedure to develop, starting from observed dynamics, Metabolic P models for multistable processes. As a case study, a mathematical model of the Schl ogel's dynamics, which represents an example of a chemical reaction system that exhibits bistability, is inferred starting from observed stochastic bistable dynamics. Since, recent experiments indicate that noise plays an important role in the switching of bistable systems, the success of this work suggests that this approach is a very promising one for studying dynamics and role of noise in biological systems, such as, for example, genetic regulatory networks

Depth or breadth : towards a contingency model of innovation strategy in the automotive sector

The thesis explores the strategic choices made by automotive manufacturers in developing and deploying technology that is discontinuous and potentially disruptive. It studies the deployment of seat belts, airbags, hybrid vehicles and fuel cell electric vehicles, drawing on product deployment histories, patents and the opinions of industry experts. The thesis identifies two fundamental strategies called depth and breadth and shows how the different manufacturers’ approach to these four technologies is arrayed along a continuum between these two choices. The thesis contributes to the theory of the technology-based firm which focuses on the management of scale, scope, time and space by making operational the idea of scope with depth and breadth. It also explicitly links the theory to the literature on coevolution and dynamic capabilities and adds to the understanding of the co-evolutionary dynamics at play in the automotive industry by applying the idea of technological pathways to the technologies under study. This discussion yields some potentially interesting insight for practitioners. The thesis also reviews the literature concerning the potential changes to automotive power train technology and adds to it by using the theory of the technology-based firm as well as environmental literature and the non market strategy lens in order to develop a nonbiased view of the state of development of fuel cell and hybrid technology. Finally, the thesis provides a rigorous review of the use of patents in management science over the last 50 years and makes one of the first attempts in the academic literature to study patents using a patent mapping tool to help make sense of the large amounts of data available in line with the new ideas concerning the importance of developing visualisation techniques in data intensive scientific enquiry.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multivariate analysis methods for veterinary diagnostics using SIFT-MS

Selected ion flow tube mass spectroscopy (SIFT-MS) is an analytical method for the investigation of volatile organic compounds (VOCs). It produces mass to charge (m/z) ratio ion counts with a range of 10-200 m/z. Current data analysis involves sifting through the spectra files one at a time looking for peaks of interest. This is time consuming and requires expert knowledge. This thesis proposes, implements and demonstrates a novel approach to the analysis of SIFT-MS data using multivariate techniques similar to those employed to analyse electronic nose and gas chromatography mass spectroscopy (GCMS) data. The methodology was developed using a set of samples created in the laboratory that belonged to two groups which contained different VOCs found in biological samples. The methodology requires the removal of the m/z peaks associated with the precursors, then principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) methods were evaluated for biomarker discovery and sample classification. Both methods produced excellent results, identifying the volatiles in the mixtures and being able to classify samples with 100% accuracy. This methodology was then tested using a variety of samples. Ammonia was found as a possible marker for bovine TB (Mycobacterium bovis) infection using serum samples taken from wild badgers. Discrimination results of an accuracy of 67%±6% were acquired. The number of sample needed to build the best performing model from this dataset was empirically shown to be 120. It was shown to be effective for the discrimination of serum samples from cattle taken before and after introduction of bovine TB (Mycobacterium bovis) bacteria in a clinical trial (accuracy of 85% achieved). A similar dataset pertaining to infection by Mannheimia haemolytica failed to produce models that performed as well as the others - this is suspect to be due to a poor experimental design. Finally, discrimination accuracies of 88% for urine samples collected from cattle from herds infected with Mycobacterium paratuberculosis and 90% for urine samples collected in the same bovine TB trial as above were achieved. The novel multivariate approach to SIFT-MS data analysis has been shown to be effective with a number of datasets but it is sensitive to the experimental design. Recommendation for the consideration required for analysis using this method have been made.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evaluation of a combination of SIFT-MS and multivariate data analysis for the diagnosis of Mycobacterium bovis in wild badgers

The currently accepted gold standard tuberculosis (TB) detection method for veterinary applications is that of culturing from a tissue sample post mortem. The test is accurate, but growing Mycobacterium bovis is difficult and the process can take up to 12 weeks to return a diagnosis. In this paper we evaluate a much faster screening approach based on serum headspace analysis using selected ion flow tube mass spectrometry (SIFT-MS). SIFT-MS is a rapid, quantitative gas analysis technique, with sample analysis times of as little as a few seconds. Headspace from above serum samples from wild badgers, captured as part of a randomised trial, was analysed. Multivariate classification algorithms were then employed to extract a simple TB diagnosis from the complex multivariate response provided by the SIFT-MS instrument. This is the first time that such multivariate analysis has been applied to SIFT-MS data. An accuracy of TB discrimination of approximately 88% true positive was achieved which shows promise, but the corresponding false positive rate of 38% indicates that there is more work to do before this approach could replace the culture test. Recommendations for future work that could increase the performance are therefore proposed

Optimisation of machine learning methods for cancer detection using vibrational spectroscopy

Early cancer detection drastically improves the chances of cure and therefore methods are required, which allow early detection and screening in a fast, reliable and inexpensive manner. A prospective method, featuring all these characteristics, is vibrational spectroscopy. In order to take the next step towards the development of this technology into a clinical diagnostic tool, classification and imaging methods for an automated diagnosis based on spectral data are required. For this study, Raman spectra, derived from axillary lymph node tissue from breast cancer patients, were used to develop a diagnostic model. For this purpose different classification methods were investigated. A support vector machine (SVM) proved to be the best choice of classification method since it classified 100% of the unseen test set correctly. The resulting diagnostic models were thoroughly tested for their robustness to the spectral corruptions that would be expected to occur during routine clinical analysis. It showed that sufficient robustness is provided for a future diagnostic routine application. SVMs demonstrated to be a powerful classifier for Raman data and due to that they were also investigated for infrared spectroscopic data. Since it was found that a single SVM was not capable of reliably predicting breast cancer pathology based on tissue calcifications measured by infrared micro-spectroscopy a SVM ensemble system was implemented. The resulting multi-class SVM ensemble predicted the pathology of the unseen test set with an accuracy of 88.9%, in comparison a single SVM assessed with the same unseen test set achieved 66.7% accuracy. In addition, the ensemble system was extended for analysing complete infrared maps obtained from breast tissue specimens. The resulting imaging method successfully detected and staged calcification in infrared maps. Furthermore, this imaging approach revealed new insights into the calcification process in malignant development, which was not previously well understood.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Data analysis tools for safe drinking water production

Providing safe and high quality drinking water is essential for a high quality of life. However, the water resources in Europe are threatened by various sources of contamination. This has led to the development of concepts and technologies to create a basis for provision of safe and high quality drinking water, which had thus resulted in the formation of the Artificial Recharge Demonstration project (ARTDEMO). The overall aim of this thesis in relation to the ARTDEMO project was to develop a realtime automated water monitoring system, capable of using data from various complementary sources to determine the amounts of inorganic and organic pollutants. The application of multivariate calibration to differential pulse anodic stripping voltammograms and fluorescence spectra (emission and excitation-emission matrix) is presented. The quantitative determination of cadmium, lead and copper acquired on carbon-ink screen-printed electrodes, arsenic and mercury acquired on gold-ink screen-printed electrodes, in addition to the quantitative determination of anthracene, phenanthrene and naphthalene have been realised. The statistically inspired modification of partial least squares (SIMPLS) algorithm has been shown to be the better modelling tool, in terms of the root mean square error of prediction (RMSEP), in conjunction with application of data pre-treatment techniques involving rangescaling, filtering and weighting of variables. The % recoveries of cadmium, lead and copper in a certified reference material by graphite furnace atomic absorption spectrometry (GF-AAS) and multivariate calibration are in good agreement. The development of a prototype application on a personal digital assistant (PDA) device is described. At-line analysis at potential contamination sites in which an instant response is required is thus possible. This provides quantitative screening of target metal ions. The application imports the acquired voltammograms, standardises them against the laboratory-acquired voltammograms (using piecewise direct standardisation), and predicts the concentrations of the target metal ions using previously trained SIMPLS models. This work represents significant progress in the development of analytical techniques for water quality determination, in line with the ARTDEMO project's aim of maintaining a high quality of drinking water.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A study of FT-IR spectroscopy for the identification and classifcation of haematological malignancies

The aim of the work presented in this thesis was to explore the use of FT-IR spectroscopy, as a complementary clinical tool for haematological laboratory analysis. FT-IR spectra were measured from air-dried and frozen cell lines derived from lymphoma, lymphoid, myeloid leukaemia and normal and chronic lymphocytic leukaemia blood samples. Multivariate statistical analysis was used to extract important spectral information with the greatest discriminative power. Principal component fed linear discriminant spectral models have been tested with leave one out cross validation procedures. A preliminary unfiltered classification model using 50 frozen and air-dried samples correctly classified 54% of 18556 spectra. The performance improved with the three cell line group datasets, with 71% of 19903 spectra correctly classified. Furthermore, the use of the frozen spectra improved the performance of the three cell line group classification model considerably. Findings showed that 73.3% of 9920 spectra were correctly classified in the frozen datasets, whereas in the air-dried only 41.5% of 9983 spectra are correctly classified. Optimisation of the spectral models by selection of principal components, application of Savitsky-Golay filters and selecting spectra using standard deviation and absorption filter tool was investigated. Using the first 25 significant PCs, a 0 th derivative Savitsky-Golay filter and the absorbance filter tool on the frozen five cell line spectral dataset were shown to be the optimal parameters for constructing a classification model. When tested with leave one batch out cross validation 90% of the spectra were correctly classified for the five cell line model. Blood component classification models tested with leave one batch out cross validation performed well. The whole blood model correctly classified 70% of 1736 spectra, measured on 22 samples. The plasma model correctly classified 80.6% of 331 spectra and the buffy coat model correctly classified 99.5% of 1438 spectra. This demonstrated that the buffy coat (containing white blood cells) holds the key biochemical information for discrimination between the pathology of the blood samples. Partial least squares analysis has been demonstrated as a method to support whole blood count tests for real time prediction of cellular constituents. These findings demonstrate the potential of FT- IR spectroscopy as a clinical tool although more work is needed if it is to be applied in clinical practice.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bioinformatics solutions for confident identification and targeted quantification of proteins using tandem mass spectrometry

Proteins are the structural supports, signal messengers and molecular workhorses that underpin living processes in every cell. Understanding when and where proteins are expressed, and their structure and functions, is the realm of proteomics. Mass spectrometry (MS) is a powerful method for identifying and quantifying proteins, however, very large datasets are produced, so researchers rely on computational approaches to transform raw data into protein information. This project develops new bioinformatics solutions to support the next generation of proteomic MS research. Part I introduces the state of the art in proteomic bioinformatics in industry and academia. The business history and funding mechanisms are examined to fill a notable gap in management research literature, and to explain events at the sponsor, GlaxoSmithKline. It reveals that public funding of proteomic science has yet to come to fruition and exclusively high-tech niche bioinformatics businesses can succeed in the current climate. Next, a comprehensive review of repositories for proteomic MS is performed, to locate and compile a summary of sources of datasets for research activities in this project, and as a novel summary for the community. Part II addresses the issue of false positive protein identifications produced by automated analysis with a proteomics pipeline. The work shows that by selecting a suitable decoy database design, a statistically significant improvement in identification accuracy can be made. Part III describes development of computational resources for selecting multiple reaction monitoring (MRM) assays for quantifying proteins using MS. A tool for transition design, MRMaid (pronounced „mermaid‟), and database of pre-published transitions, MRMaid-DB, are developed, saving practitioners time and leveraging existing resources for superior transition selection. By improving the quality of identifications, and providing support for quantitative approaches, this project brings the field a small step closer to achieving the goal of systems biology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo