A series of randomly collected human hearts, removed at autopsy from decedents known to be chronic alcoholics, were examined macroscopically and morphometrically using standard histological techniques. An accurate computer assisted semi-automatic method was used for these measurements. Control hearts were collected and examined in a similar manner. It was shown that in chronic alcoholics there are cardiac myocytic changes which eventually result in enlargement of these cells and may lead to cardiac enlargement and dysfunction. A significant degree of interstitial fibrosis was also noted within the myocardium but no vascular abnormalities were noted particularly in the micro circulation. The incidence of chronic alcoholism is on the increase world-wide and sudden death related to cardiac problems in chronic abuse of alcohol is a well known syndrome, whose pathogenesis has been poorly established in clinical and animal studies. Within the limitations of the material available for this study, this work enables a clearer insight into the potential relationships between alcohol and the cardiac myocyte and into pathogenetic mechanisms of alcohol-related cardiac problems.

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This thesis is based on the field of chemoinformatics, in particular Quantitative Structure Activity Relationships (QSAR), data mining and machine learning algorithms. The work has been broken down into seven chapters. In the first chapter an overview of the categories of the substances on the Prohibited List is given, together with a history of doping in sport over the last century and how anti-doping agencies have been set up to combat and punish offenders. Chapter 2 introduces chemoinformatics, the concept of <i>“molecular similarity”, </i>descriptors, chemical space and feature selection and how classification algorithms can be used to partition this chemical space into islands of bioactivity. The basic approach outlined in this chapter is followed throughout much of this thesis. In Chapter 3 the WADA 2005 dataset is presented, with pictorial representations of the most and least likely structures in each of the ten prohibited classes. The objective of this chapter is to use industrial standard two dimensional chemical descriptors and classification algorithms to see whether it is possible to correctly categorise the different classes of substance in the WADA 2005 Prohibited List. Chapter 4 focuses on the development of an ultrafast hybrid chemical descriptor which takes into account both two and three dimensional information and is used in a virtual screening study to rank molecules in a database taken from the National Cancer Institute (NCI) based on their likelihood of being active. Chapter 5 introduces a novel classification algorithm based on inductive logic programming and a Support Vector Machine called SVILP (Support Vector Inductive Logic Programming). SVILP is compared to MOLPRINT 2D on a well known bench mark dataset of around one hundred thousand molecules, with eleven bioactivity classes. In Chapter 6 the concept of rule based learning is applied to the WADA 2005 Prohibited List. In the first half of this chapter the PART rule based learner is used to classify the prohibited substances and generate rules based on two dimensional chemical descriptors to give some insight on why the substances are part of a specific class. These results add more meaning to simple “<i>yes/no”</i> classification responses obtained through work in earlier chapters. In the second half of this chapter rule learning has been extended to identify novel statistically unusual subgroups of WADA substances using the CN2-SD algorithm.

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The analysis of trace amounts of DNA has become one of the most powerful tools in forensic science. DNA profiles can be obtained from as little as 100 pg of purified DNA derived from skin cells. These skin cells may have been transferred to objects by direct contact such as handling or wearing clothing. These are examples of primary transfer but other types of transfer are known to occur. In this work, the primary transfer of skin cells to a variety of substrates is illustrated and a new method of generating a DNA profile proposed. The generation of numerous DNA profiles directly from fabrics and other substrates confirmed the existence of both cellular transfer and the transfer of free DNA. DNA typing methods from various substrates of free DNA, which omits the requirement for prior DNA extraction, were developed. Differences in the resulting DNA profiles from a range of natural and artificial fabrics are illustrated.

It is known that secondary transfer of DNA can occur in different forms: skin to skin contact, skin to object contact and object to skin contact. Until now, the secondary transfer that has been studied concerns the transfer of the DNA from one individual via the skin of another individual. DNA transfer between two surfaces and which does not involve skin contact has not been studied but has been accepted as a possibility. In this project, we investigated such a DNA transfer resulting from the contact between different donor surfaces and recipient fabrics. The aim was to establish experimentally an evidential basis that enables pre assessment in casework by giving ore data about secondary transfer for particular scenarios. For instance, contact between an attacker's gloves bearing the attacker's DNA and the victim's clothes. To answer this question we studied static DNA transfer between two pieces of fabric with an applied pressure similar to that which may be involved in an actual case. The results of these experiments were not conclusive and we extended the nature of the DNA donor surfaces to the non-absorbent surfaces (glass and linoleum) using the same experimental conditions. The significance of friction during contact was also explored and it was found that very little transfer took place in the absence of friction.