Cranfield University at Silsoe (1975-2008)

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Browsing Cranfield University at Silsoe (1975-2008) by Supervisor "Bessant, Conrad"

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    Data analysis tools for safe drinking water production
    (Cranfield University, Cranfield University at Silsoe, 2006-11-08T17:00:01Z) Cauchi, Michael; Setford, S.; Bessant, Conrad
    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.
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    Development of a database with web-based user interface for taqman assay design
    (Cranfield University, 2007-01) Simecek, Nikol; Bessant, Conrad
    TaqMan RT-PCR (reverse transcription-polymerase chain reaction) is a technique used to measure the relative gene expression in a biological sample and is one of the core technologies used by the Molecular Pathology and Toxicology (MPT) Group at GlaxoSmithKline. Conducting TaqMan experiments is a complex process which involves the design of a TaqMan assay specific to a gene of interest. A wealth of data has been generated during assay design, but systems are not currently available to readily share this data within the MPT group. There is a need for a central data storage repository so that data associated with assay design can be organised efficiently and rapidly accessed. Experiments are conducted within limited timeframes and resource is often limited so this would be of great benefit to the MPT group. This thesis describes the development of a database to house data associated with TaqMan assay design, software to populate the database with minimal user interaction and a web based CGI application for members of the MPT group to query and submit data to the database. Finally, the output from testing the software is provided and discussed.
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    Development of medical point-of-care applications for renal medicine and tuberculosis based on electronic nose technology
    (Cranfield University, 2004) Fend, Reinhard; Woodman, Anthony C.; Bessant, Conrad
    INTRODUCTION: Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time consuming, expensive and require special skills, and are therefore not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. METHODS: We applied a gas sensor array based on 14 conducting polymers to monitor haemodialysis in vitro and to detect pulmonary tuberculosis in both culture and sputum. RESULTS and DISCUSSION: The electronic nose is able to distinguish between control blood and “uraemic” blood. Furthermore, the gas sensor array is not only capable of discriminating pre- from post-dialysis blood (97% accuracy) but also can follow the volatile shift occurring during a single haemodialysis session. The electronic nose can be used for both dialysate side and blood-side monitoring of haemodialysis. The pattern observed for post- and pre-dialysis blood might reflect the health status of the patients and can therefore be related to the long-term outcome. Furthermore, the gas sensor array was also able to discriminate between Mycobacterium spp. and other lung pathogens such as Pseudomonas aeruginosa. More importantly the gas sensor array was capable of resolving different Mycobacterium spp. such as Mycobacterium tuberculosis, M. scrofulaceum, and M. avium in both liquid culture and spiked sputum samples. The detection limit for M. tuberculosis in both sputum and liquid culture is 1 x 104 mycobacteria ml-1 and therefore partially fulfils the requirement set by the WHO. The gas sensor array was able to detect culture proven TB with a sensitivity of 89% and a specificity of 91%. CONCLUSIONS: In conclusion, this study has shown the ability of an electronic nose as a point-of-care device in these areas.
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    Machine learning for predicitng the risk of osteoporosis from patient attributes, health and lifestyle history
    (Cranfield University, 2004) Tate, Geoffrey W.; Bessant, Conrad
    The most widely-used method for diagnosis of osteoporosis is to determine bone mineral density (BMD) by bone densitometry. At present mass screening is not, on the basis of resource constraints, considered a option. This project investigates if artificial neural networks (ANN s) or Baysian networks (BNs), using the health and lifestyle history of a patient, (risk factors - used as a generic term for inputs) may be used to develop a preliminary screening system to determine in a patient is at particular risk from osteoporosis and hence in need of a scan. Two databases have been used, one containing 486 records (29 risk factors) of patients examined with a G E Linear Peripheral Densitometer (PIXI) and the other with 4,980 records (33 risk factors) of patients examined with dual energy X ray absorptiometry (DEXA). BNs tend to out-perform AN s particularly where smaller learning sets are involved. The best result was 84% accuracy (sensitivity 0.89 and specificity 0.80) with PIXI and a BN. I general, however, with ANNs the sensitivity achieved with PIXI and DEXA was 0.65 and 0.80 respectively and the corresponding values with BNs were 0.72 and 0.81. The diagnostic performance with ANNs could be achieved with fewer risk factors (PDQ from 29 to 4 and DEXA from 33 to 5) but with BNs a reduction in performance accompanied a reduction in the number of risk factors. l The results also indicate: 0 For Positive patients, the more severely affected by the disease the more accurately they are diagnosed . 0 The lack of continuous values in the DEXA data results in a poor diagnosis of Negative patients. 0 Classifications based on BMD predictions and pattern recognition give similar results. 0 Reasoning with BNs can provide an indication of how a particular risk factor state contributes to a patient`s risk from osteoporosis.