Immunological and biochemical techniques in the analysis of tear proteins

This study is concerned with the analysis of tear proteins, paying particular attention to the state of the tears (e.g. non-stimulated, reflex, closed), created during sampling, and to assess their interactions with hydrogel contact lenses. The work has involved the use of a variety of biochemical and immunological analytical techniques for the measurement of proteins, (a), in tears, (b), on the contact lens, and (c), in the eluate of extracted lenses. Although a diverse range of tear components may contribute to contact lens spoilation, proteins were of particular interest in this study because of their theoretical potential for producing immunological reactions. Although normal host proteins in their natural state are generally not treated as dangerous or non-self, those which undergo denaturation or suffer a conformational change may provoke an excessive and unnecessary immune response. A novel on-lens cell based assay has been developed and exploited in order to study the role of the ubiquitous cell adhesion glycoprotein, vitronectin, in tears and contact lens wear under various parameters. Vitronectin, whose levels are known to increase in the closed eye environment and shown here to increase during contact lens wear, is an important immunoregulatory protein and may be a prominent marker of inflammatory activity. Immunodiffusion assays were developed and optimised for use in tear analysis, and in a series of subsequent studies used for example in the measurement of albumin, lactoferrin, IgA and IgG. The immunodiffusion assays were then applied in the estimation of the closed eye environment; an environment which has been described as sustaining a state of sub-clinical inflammation. The role and presence of a lesser understood and investigated protein, kininogen, was also estimated, in particular, in relation to contact lens wear. Difficulties arise when attempting to extract proteins from the contact lens in order to examine the individual nature of the proteins involved. These problems were partly alleviated with the use of the on-lens cell assay and a UV spectrophotometry assay, which can analyse the lens surface and bulk respectively, the latter yielding only total protein values. Various lens extraction methods were investigated to remove protein from the lens and the most efficient was employed in the analysis of lens extracts. Counter immunoelectrophoresis, an immunodiffusion assay, was then applied to the analysis of albumin, lactoferrin, IgA and IgG in the resultant eluates.

The application of expert systems techniques in the design of durable concrete

This thesis describes work done exploring the application of expert system techniques to the domain of designing durable concrete. The nature of concrete durability design is described and some problems from the domain are discussed. Some related work on expert systems in concrete durability are described. Various implementation languages are considered - PROLOG and OPS5, and rejected in favour of a shell - CRYSTAL3 (later CRYSTAL4). Criteria for useful expert system shells in the domain are discussed. CRYSTAL4 is evaluated in the light of these criteria. Modules in various sub-domains (mix-design, sulphate attack, steel-corrosion and alkali aggregate reaction) are developed and organised under a BLACKBOARD system (called DEX). Extensions to the CRYSTAL4 modules are considered for different knowledge representations. These include LOTUS123 spreadsheets implementing models incorporating some of the mathematical knowledge in the domain. Design databases are used to represent tabular design knowledge. Hypertext representations of the original building standards texts are proposed as a tool for providing a well structured and extensive justification/help facility. A standardised approach to module development is proposed using hypertext development as a structured basis for expert systems development. Some areas of deficient domain knowledge are highlighted particularly in the use of data from mathematical models and in gaps and inconsistencies in the original knowledge source Digests.

The use of spot simulation imagery for forestry mapping and management

SPOT simulation imagery was acquired for a test site in the Forest of Dean in Gloucestershire, U.K. This data was qualitatively and quantitatively evaluated for its potential application in forest resource mapping and management. A variety of techniques are described for enhancing the image with the aim of providing species level discrimination within the forest. Visual interpretation of the imagery was more successful than automated classification. The heterogeneity within the forest classes, and in particular between the forest and urban class, resulted in poor discrimination using traditional `per-pixel' automated methods of classification. Different means of assessing classification accuracy are proposed. Two techniques for measuring textural variation were investigated in an attempt to improve classification accuracy. The first of these, a sequential segmentation method, was found to be beneficial. The second, a parallel segmentation method, resulted in little improvement though this may be related to a combination of resolution in size of the texture extraction area. The effect on classification accuracy of combining the SPOT simulation imagery with other data types is investigated. A grid cell encoding technique was selected as most appropriate for storing digitised topographic (elevation, slope) and ground truth data. Topographic data were shown to improve species-level classification, though with sixteen classes overall accuracies were consistently below 50%. Neither sub-division into age groups or the incorporation of principal components and a band ratio significantly improved classification accuracy. It is concluded that SPOT imagery will not permit species level classification within forested areas as diverse as the Forest of Dean. The imagery will be most useful as part of a multi-stage sampling scheme. The use of texture analysis is highly recommended for extracting maximum information content from the data. Incorporation of the imagery into a GIS will both aid discrimination and provide a useful management tool.