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The Thompson family of groups F < T < V are well-known as interesting (counter-) examples in group theory. Working algebraically with these objects is difficult, and yet the groups are computationally tractable. For instance, T and V are infinite simple groups; despite this, both are finitely presented. This thesis studies the middle group T, using the piecewise-linear function point of view. We present a solution to the conjugacy problem in this group, adapting the approach of Kassabov and Matucci1 to 1 Kassabov and Matucci 2012. the same problem in F. Conjugacy of elements in T was shown2 to 2 Belk and Matucci 2014. be decidable by Belk and Matucci; however our approach constructs explicit conjugators (when they exist). Later, we refine the description given by Matucci3 of nontorsion elements’ centralisers in T. 3 Matucci 2008, Chapter 7. * * * The first chapter introduces the world of Thompson’s groups. The sections on cyclic order, the generalised groups PLS,G and groupoid PL2, and on the Cantor space are particularly important for readers interested in the rest of the thesis. The second chapter discusses Thompson’s groups from a dynamical point of view. We summarise how F, T and V rerrange the interval, noting the distinction between dyadic and nondyadic points. Focussing on T, we introduce the rotation number and explain what we can learn from it. Amidst all this we present a number of intermediate results, forming a toolkit for use in later chapters. The third chapter studies conjugacy in T. We narrow the search space by finding constraints that a conjugator must satisfy. Next, we break the conjugacy problem into a search for a coarse and fine conjugator, the product of which—if they exist—is a bona fide conjugator. We solve these search problems,4 and thus solve the conjugacy problem in T. 4 Lemma 3.3.2 and Algorithms 3.3.5 and 3.4.8. In the fourth chapter, we study element centralisers in T via a particular group extension. We focus on nontorsion elements, providing small details missing from Matucci’s proof which identifies the extension’s kernel.5 We explain how to find the size of the extension’s quotient, by 5 Theorem 4.2.1 and Remark 4.2.2. reducing the problem to a search for coarse conjugators.6 6 Algorithm 4.2.3. The final chapter describes the extension structure of CT(α) in more detail.7 We do so by classifying α into one of four cases. In all but 7 Section 5.2. one case, this extension splits (as a wreath or direct product); in the remaining case, we identify8 exactly when the extension splits (again as 8 See Proposition 5.2.15 and the summary in in Theorem 5.3.2. a wreath product). In each case, we describe the centraliser’s structure9 9 Proposition 5.2.17. in terms of integer parameters. We then show how to construct10 an 10 Corollaries 5.2.6, 5.2.16, 5.2.18, 5.2.23 element of T whose centraliser has a given list of parameters.

This thesis argues that narratives about Unidentified Flying Objects (UFOs) act as the central point of contact between conspiracist and popular millennial fields. Their confluence has come to form a field here termed ‘metaphysical conspiracism’, combining teleological narratives, the promise of soteriological knowledge and the threat of occluded malevolent agencies. I argue that metaphysical conspiracism offers a unique perspective on the interplay of knowledge, power and the construction of the other in contemporary popular discourse. Narratives about UFOs (and their extra-terrestrial occupants) have their roots in the Cold War period, but from the 1980s were increasingly constructed within a supernatural framework. Discourse analysis of popular literature from this period reveals a process of discursive transfer as the UFO narrative is contested and negotiated between conspiracist discourses concerning powerful, hidden agencies and popular millennial discourses of personal and planetary transformation, including ‘New Age’, 'Ascension' and '2012'. Using historical discourse analysis, supported by small-scale ethnographic sampling, I examine this discursive transfer in the work of three popular writers who together offer a broad overview of the field. Whitley Strieber was a central figure in the 'alien abduction' narrative in the 1980s, but his speculations on its meaning led him increasingly towards millennial and conspiratorial narratives. David Icke's well-known theory that a conspiracy of reptilian extraterrestrials has secretly seized control of the planet is demonstrated to have developed in the 1990s from a post-Theosophical narrative of benevolent UFOs as harbingers of the 'New Age'. Although less well-known, David Wilcock's work demonstrates that UFOs were also instrumental in the incorporation of conspiracist material into the recent '2012' millennial narrative. I seek to answer two questions with this thesis. Firstly, what is the common mechanism which facilitates the hybridisation I uncover between conspiracy narratives and popular millennialism? Secondly, how do the resulting metaphysical conspiracist narratives serve their subscribers? Despite a number of structural similarities, I argue that the common mechanism is the mobilisation of counter-epistemic strategies; that is, those predicated upon access to non-falsifiable sources of knowledge. The UFO narrative is particularly well-suited to suggesting sociological uncertainty about the boundaries between scientific and other strategies for the legitimisation of knowledge, encouraging its adoption by both conspiracist and millennial discourses. Secondly, metaphysical conspiracism reconciles the utopian vision of popular millennial discourse with the apocalyptic critique of modern global society announced by conspiracists. I therefore argue that metaphysical conspiracism supplies an effective popular theodicy with a Gnostic flavour in which these millennial prophecies did not ‘fail’, but were prevented from arriving by hidden malevolent others.

The aim of this study was to examine, understand and conceptualise the critical thinking and clinical reasoning adopted by new graduate occupational therapists as they enter the workforce to become newly autonomous practitioners. The study obtained the perspectives of new graduates, their supervisors and service managers on the means by which critical thinking and clinical reasoning develop to meet the expectations of employers. Factors which impeded the transition between new graduate and autonomous practitioner were identified and explored. Ethical approval was obtained to conduct the study. The study adopted a qualitative phenomenological research approach; Interpretative Phenomenological Analysis (IPA), which informed framing, data gathering and analysis. Semi-structured interviews were conducted with new graduates (n=6), supervisors (n=7) and managers (n=7) from multiple sites within one National Health Service Board. Interviews were transcribed verbatim from audio-recordings. The findings indicate that new graduates are expected to develop critical thinking and clinical reasoning in a manner that might challenge traditional conceptualisations of the transitioning process. A phenomenon, historically named the “shock of practice”, was reflected on by therapists in each phase of the study and adaptive and mal-adaptive responses to this in the thinking and behaviour of new graduates was identified. The clinical supervisor-supervisee relationship appeared to be the key source of support, and the supervisor the most significant knowledge resource, for new graduates. This relationship was supplemented by both peer support and Preceptorship. Discharge planning was a significant source of anxiety and development of an algorithm to support this process is proposed. Recommendations for further research and theoretical implications for practice and undergraduate education are discussed.

Gravitational waves are travelling distortions in the curvature of space-time. They are a prediction of most relativistic theories of gravity, including general relativity. Their coupling to matter is extremely weak and the only sources likely to be detectable are violent astrophysical events where huge amounts of matter are accelerated very rapidly. Gravitational radiation is quadrupole in nature. It causes a tidal strain in space. With the prototype detector at Glasgow we aim to detect this strain by measuring the differential length change induced in two orthogonal reference arms. This is done using optical interferometry. Chapter 1 is a general introduction to gravitational radiation. The first part is a short review of the sources likely to be detectable by terrestrial interferometric detectors and bar detectors. This is followed by a review of the main features of both resonant bar detectors and interferometric detectors, including a discussion of the main noise sources in both types of detector. The frequency of the illuminating laser needs to be stabilised in interferometric gravitational wave detectors and this is dealt with in the next three chapters. Chapter 2 is a review of the methods used to improve the frequency stability of lasers. It concentrates on continuous, single mode gas lasers such as the argon ion laser used to illuminate the detector at Glasgow. In our prototype detector the laser is frequency locked to one of the 10m long Fabry-Perot cavities. To achieve the desired sensitivity the residual frequency noise must be 2 x 10e7 at 1kHz. Chapter 3 describes a new frequency stabilisation system which acheives this performance. The system is narrow band, as would be required for a large scale detector. It employs two nested servo loops to achieve the desired gain. Chapter 4 is an analysis of the gain and stability of the nested servo loop system described in the previous chapter. The next two chapters concern a 100 hour data run with the prototype detector. Chapter 5 describes the preparation for the run. It includes details of the timing system used and a description of each of the signals that were recorded. Chapter 6 describes the preliminary analysis of the data taken during the run. This analysis was carried out to look at the behaviour of the detector rather than to look for signals from astrophysical sources. The variation of the noise level with time, noise statistics of the data and possible contamination of the data by acoustic and seismic noise were investigated. Chapter 7 starts with a discussion of the results of some calculations of the seismic isolation and thermal noise in some single and double pendulum suspension systems. These show that thermal noise in the lowest stage of the suspension system is likely to be dominant. Measurements of the Q of the suspension of the test masses and of the test masses themselves are then described. Appendix A contains diagrams of the circuits used in the frequency stabilisation system described in chapters 3 and 4, some of their special features are commented on. In appendix B the results of intensity noise measurements in three large frame argon ion lasers are given. These show that the level of high frequency intensity noise varies widely depending on the type of plasma tube used. The measurements were taken in the range 0-7MHz. Appendix C contains two of the data analysis programs.

Background: The development of new biomarkers allows for the accurate prediction of breast cancer therapy response in the neoadjuvant setting. The implementation of these tools in routine NHS operations could have the potential for better clinical outcomes and a more cost-effective use of resources. AIMS: A decision-analytic modelling platform was created to evaluate the clinical effectiveness and cost-effectiveness of on-treatment biomarker-based predictive tools in a routine NHS implementation. The biomarker-based tool used in the simulation was EER4, developed at Edinburgh Institute of Genetics and Cancer. Methods: Patient simulation models were constructed by integrating molecular biomarker data with clinical outcomes and healthcare cost data. The simulation benefits from the incorporation of NHS patient data and it features two complementary sub-models: a Discrete Event Simulation for the neoadjuvant setting and a Markov cohort model for the adjuvant setting. The results of the simulation compare the per-patient costs and health outcomes, expressed as Quality Adjusted Life Years (QALY), for each of the evaluated strategies. Moreover, this study includes a decision and budget impact analysis of OncotypeDX after its introduction in Edinburgh's Breast Clinic. Results: Treatment-decision strategies that utilize EER4 are likely to be cost-effective. Specifically, EER4 in conjunction with PREDICT shows lower costs and marginally superior QALYs, compared to PREDICT alone or OncotypeDX with PREDICT. At a threshold of 20,000£/QALY, EER4 with PREDICT has an 86% probability of being a cost-effective alternative to the current standard of care. EER4 in conjunction with neoadjuvant Letrozole increases breast-conserving surgery rates, displacing radical mastectomy by 16%. The Probabilistic One-way Sensitivity Analysis shows that results are robust to the uncertainty of EER4 per-unit costs and clinical performance. The decision and budget impact analysis of OncotypeDX indicates that while this technology might reduce the number of chemotherapies administered, the unit cost is greater than any savings produced by chemotherapy displacement. Conclusion: The early cost-effectiveness analysis shows that these biomarker-based technologies are likely to be cost-effective. However, further research is needed to assess the clinical effectiveness of EER4. The simulation platform developed in this study has the potential for further evaluation of decision-making tools for other subtypes of breast malignancies.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes and mutations implicated in cancer exhibit. Currently, we have no clear explanation for why patterns of replacements of amino acids are frequent in cancer, and what their effects may be on the protein. Such patterns would be expected to provide an understanding of how these amino acid replacements drive cancer progression and reveal the properties that distinguish them from replacements that are non-cancer associated. Moreover, the identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease. These characteristics can be used to recognise and prioritise therapeutic drug targets with an enhanced likelihood of success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. In this work, we identified certain amino acid residues and replacements to be highly enriched in cancer. In particular, we highlight 17 substitutions showing high enrichment rates also we find that very frequently in cancer a residue is replaced with either a Cys or an aromatic residue. We explained the role of Cys in forming disulphide bonds and the aromatic amino acids in forming stacking interactions; both are known to be vital in binding activities highly enriched in cancer-associated gene functions. We also identified properties, such as protein stability and hydrophobicity that have distinguished patterns in these cancer-associated replacements compared to other non-cancer-associated mutations. We used these properties to train a machine learning model predicting cancer-associated replacements related to specific protein using only the amino acids residue position and physico-chemical properties. In terms of cancer-associated genes, we investigated gene essentiality and found that essentiality scores tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in the identification of novel genes and targets for further research.

In 1933 the German geneticist Richard Goldschmidt presented his theory that major evolutionary events have been driven, not by the slow accumulation of small variants, in line with Darwinian thought, but instead large-scale mutations which he called macromutations. His theory was called 'The hopeful monsters hypothesis', as any species arising from such an event would often result in evolutionary dead end. This theory was, however, by and large rejected due to its stark contrast with that of Darwin's theory of evolution. 86 years on, thanks to tremendous advancements in genomic technologies and evolutionary analysis, we are aware of the existence of a myriad of what could be considered macromutations (large-scale duplications) in the human genome, and the significant role they have played in our evolution. Whilst beneficial in the process of shaping who we are as a species, these large-scale mutations are not always benevolent, having been found to have significant links with a plethora of human diseases. Using evolutionary data, alongside important biological features of human genes, we explore the links between large-scale mutations, particularly whole genome duplications, introgressed genes, and heritable, parasitic, and viral disease. We find that there are strong links between ancient fragility, exposed by divergence between duplicated genes, and heritable disease; that co-evolution between humans and the soil borne helminth parasite Trichuris trichura has been facilitated by strong selective pressures exerted on the ancient genes of the TGFb superfamily; and that viruses target ancient and important gene families and functions. It is clear that large-scale mutations, in particular gene and genome duplications, have provided the genetic redundancy that contributed to the evolution of the complex species we are today. However, given the fragility and propensity of genes of this kind to be associated with disease of all varieties, our future evolvability, and ability to adapt is in question. We must, therefore, readdress Goldschmidt's theory as a question - Are we merely the descendants of hopeful monsters?

Phylogenetics, the reconstruction of evolutionary relationships between species, underpins evolutionary biology. These relationships are most often inferred from either or both morphology and molecules, but molecular data are often preferred because of the objective atomization and large number of characters, the relative neutrality of some genomic regions and the sophisticated statistical techniques that these benefits allow. Morphology, by contrast, remains relatively neglected. However, morphological data remain essential, particularly for the phylogenetic placement of fossil species for which no molecular data are available. Morphology nevertheless suffers from issues such convergence and non-independence of traits, the extent and distribution of which remain largely unknown. In this thesis I compare osteological, dental and other partitions of morphological data in tetrapods, using molecular trees as a benchmark. I assess differences in levels of homoplasy, ages of character transition, tree-based correlations and internal consistency. To do this, I compute the retention index of characters and partitions on molecular trees, perform ancestral state reconstructions to estimate character transition ages, apply correlated and uncorrelated models of character pair evolution, perform cluster analyses, and build trees using subsets of data. I find heterogeneity between these partitions, both with and without respect to molecular trees. Specifically, I find that osteological characters are more homoplasious and transition earlier than either dental or soft characters. Further, characters are more correlated within partitions than between partitions. These results highlight the importance of partitions, implying differences in convergence, evolutionary rates and integration between different morphological subsets. As well as shedding light on the nature of morphological evolution in tetrapods, these results have important implications for phylogenetic inference, and suggest the need for careful consideration of the properties of morphological data when reconstructing evolutionary history. Specifically, composite coding and partitioning may be necessary in model-based approaches.

In recent years, topic group mining and massive crowd opinion analysis from on-line social network platforms have become some of the most important tasks not only in research area but also in industry. Systems of this sort can identify similar topics from a very large dataset, group them together based on the topic, and analyse the inclination of the content's owner. To solve this problem, which involves research from a number of different areas, an integrated platform needs to be proposed. Most community mining techniques treat the network as a graph where nodes represent users and edges reflect user relationship between two users. One obvious drawback of these approaches is that it can only utilise the explicit user relationships provided by on-line social network platforms. All other possible relationships will be ignored. Some on-line social network platforms restrict the length of content a user can publish. This causes traditional document clustering methods to perform poorly. Meanwhile, the restriction of content length also affects opinion mining performance since most content lacks contextual features. Hence, other context features that are not immediately or obviously related need to be investigated to improve performance in user inclination classification. This research proposes a novel three layered platform. Two core technologies of the platform are topic group mining and user inclination analysis. The integrated approach was evaluated by a series of experiments to examine each core technology. The results indicate that the proposed integrated platform is able to produce the following results. 1) Scores up to 0.82 by V-measure evaluation function in topic group mining. 2) High accuracy rate in inclination mining. 3) A flexible and adaptable platform design which can accommodate different on-line social networks easily.

G protein-coupled receptors (GPCRs) comprise a superfamily of membrane proteins whose associated signalling pathways control crucial physiological functions. Although this superfamily originated from duplication events, the duplication history of the receptors and their pathway interactors is largely unknown. By using a system-level approach, this work aimed at characterising the duplication patterns among the GPCR signalling system components and establishing their role in disease development and accumulation of genetic variation across the pathway. A comprehensive data set including upstream, downstream and peripheral regulatory and crosstalk components was used to determine the frequency of gene duplication within the GPCR signalling system components. We find that the duplication retention frequency was not constant and that the signalling components tend to differ in their peak frequencies of duplication over evolutionary time, with the exception of whole-genome duplication (WGD). Analysis of GPCR subsets with selective ligand binding and G protein coupling profiles revealed that specific interaction types relate to duplication frequency and tissue specificity. We also find that the evolutionary origin of gene expression in most tissues was influenced by the duplication history of the signalling components, highlighting the importance of duplication and divergence in the evolution of GPCR signalling in specific tissues and cell types. Disease-associated genes within the GPCR signalling system were further analysed with regard to their duplication history. We found that most disease associated genes are WGDs (ohnologues) in all signalling components, although duplicate genes are not overrepresented within ligands. Disease incidence is associated with the most ancient signalling components (G proteins and downstream signalling effectors) and tends to be inherited in an autosomal dominant manner, suggesting that genes within these components are dosage-balanced. We also found that the highest percentages of complex diseases are associated within ligands and GPCRs, indicating that the duplication events that expanded the pathway also created more opportunities for disease to emerge. Heritable genetic variation was also investigated within the GPCR signalling system. We found that the majority of pathway genes tend to accumulate more variation than protein-coding genes, and that for GPCRs this is not due to more transcript isoforms resulting from alternative splicing. A relationship between the accumulation of genetic variation and duplication patterns could not be established, however, differences within variant predicted effects point to distinct selective pressures across the pathway. Highly disruptive mutations indicate that upstream ligands are the least constrained elements, and an excess of intronic variants suggests that downstream signalling effectors are more evolutionarily constrained. The findings in this thesis expand the knowledge of this signalling system and contribute to a better understanding of the evolution of disease and interpretation of genetic variation in a system-wide context.

Viral populations exhibit extensive genomic diversity that may be be profiled with contemporary nucleotide sequencing instruments, offering insight into virus transmission and the evolution of clinically relevant phenotypes within intrapatient infections. Emerging understanding of the role of the microbiome in human health and disease has pushed routine study of viruses beyond that of specific pathogen populations to include prospecting entire viral communities. In the current absence of high fidelity long read sequencing, analyses of such data must begin with reconstructing the short sequence fragments generated by sequencing instruments through either alignment to an appropriate reference sequence or de novo fragment assembly. By consequence of their error prone polymerases, RNA viruses in particular can present intrapopulation diversity to an extent that is problematic for the application of either of these approaches. In this thesis I present novel computational methodologies for robust de novo and reference-guided inference of viral consensus sequences, together with methods for characterising both interspecific and intraspecific genomic variation within viral populations. Through development and application of a wavelet-based sequence comparison approach to simulated viral populations, we demonstrate that seeding alignments using reduced dimensionality sequence representations provides accuracy broadly comparable with—and in some circumstances exceeding—that of the most accurate available aligners. Separately, I present a methodology that identifies sample-specific preprocessing and assembly parameters for optimal reconstruction of specific viruses, together with a companion tool that enables visual exploration of metagenome assemblies with taxonomic annotations retrieved using web services. I also present Kindel, an efficient approach for inferring population consensus sequences from read alignments of arbitrary quality, that unlike existing approaches is able to reconcile both aligned insertions/deletions and leverage unaligned sequence context to locally reassemble discordant alignment regions of up 1.5x read length. I apply Kindel to the characterisation of highly diverse and difficult to assemble intrapatient hepatitis C virus populations, and propose a feature extraction and decomposition methodology for monitoring temporal shifts in the state of specific viral populations. Used in conjunction with the taxonomic annotation and filtering tool Tictax, these approaches represent efficient and composable tools for the reconstruction and exploration of viral genomic and metagenomic communities using a portable computer. In the final chapter I describe the application of these methodologies to profiling the nasopharyngeal viromes of a cohort of asthmatic children and healthy controls, identifying differences in viral composition indicative of microbial dysbiosis within the asthmatic virome.

Molecular networks are commonly used to explore cellular organisation and disease mechanisms. Function is studied using molecular interaction networks, such as protein-protein networks. Although much biological insight has been gained using these models of molecular function, they are hindered by their reliance on available experimental data and an inability to capture the complexity of biological processes. Functional modules can be identified based on molecular network topology, making it essential that the edges accurately depict molecular interactions. However, these networks struggle to depict the temporal nature of interactions, giving the impression that all interactions are constant. This misrepresentation can result in functionally heterogeneous clusters. The notoriously inaccurate nature of experimental protein interaction data, along with variable conformity among network clusters and functional modules further impedes functional module extraction. Representation of genes by single nodes artificially merges the functions of pleiotropic genes, distorting the arrangement of function within molecular networks. This thesis therefore explores a more suitable model for representing function. Pathways are composed of sets of proteins that are known to interact within a particular cellular context, corresponding to a discernible biological function. Their representation of context dependent cellular activity makes them ideal for use as nodes within a new pathway level model. Using combinatorial algorithms a reduced redundancy pathway set was produced to represent global cellular systems. Enrichment analysis provides reliable functional annotations for each pathway node, attributing independent functions to pleiotropic genes. Edges are based on functional semantic similarity, generating a network representation of functional organisation. Both yeast and human biological systems are presented as functionally connected pathway networks. Pathway annotation and experimentation with semantic similarity measures provides insight into the cross-talk between biological processes. Pathway functional modules elucidate the intracellular implementation of processes. Disease modules highlight the effects of functional perturbations and disease mechanisms. The pathway model provides a complementary, high-level functional model that begins to bridge the gap between molecular data and phenotype. The utilisation of pathway data provides a large, well-validated data source, avoiding the inaccuracies inherent with molecular data. Pathway models better represent components of biological complexity such as pleiotropy and linear implementation of functions.

Human immunodeficiency virus (HIV-1) is the pathogenic agent of HIV infection thatprecedes the total breakdown of cellular immunity, a condition known as acquiredimmunodeficiency syndrome (AIDS). The pandemic nature of the disease has promptedintense research into its biology. Already, much is known about HIV-1 infection, lifecycle,and progression to aids. Systems biology enables the combination of complex data fromthese studies into a framework where their effect on the various levels of cellularorganization (i.e. Pathways, cells, tissues, organs and the whole body) could be studied insilico. In this thesis, first, we reviewed our knowledge of the HIV-1 Human InteractionDatabase. We examined its contents and identified processes that HIV-1 was not previouslyknown to interact with. Then, we attempted an in silico dynamic model of HIV-1 interaction. We built a model of HIV-1 interaction with the CD4 T cell activation pathway comprised of137 nodes (16 HIV-1, 121 human) and 336 interactions. The model reproduced expectedpatterns of T cell activation. Using interaction graph properties, we identified 26 host cellfactors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activationor inhibition of viral proteins. By following a logical Boolean formalism, we identified 9 hostcell factors essential to the functions of viral proteins in the activation pathway. This wasthe first attempt to model dynamic viral-host interaction relationships. Then, we organize HIV-1 interacting host genes into modules to represent cellular processesneeded by the virus. We combined HIV-1 interactions with host gene GO annotations toclassify host genes according to these needed cellular processes. We obtained 201 modulesand found the same set of viral proteins do not interact with host genes having similarmodules suggesting intelligence in its co-ordination of host processes. This work is one of agrowing list that explores coordination of HIV-1 interactions. But more importantly, it would bebeneficial to functionally downsize the large dynamic HIV-1 interaction network. Finally, in our discussion, we discuss our results and suggest possible ways in which our workon dynamic models could be improved. This work is opening up a new field of systems virologythat studies the effect of viruses on the host in terms of its temporal and spatial aspects.

Intestinal dwelling parasites live in close association with the complex microbiota that inhabit our intestinal tracts. The intestinal helminth, Trichuris muris, depends on these bacteria for egg hatching and successful establishment of infection within the epithelium of the caecum and colon. Infection causes significant alterations to the host intestinal microbiota, including a decrease in bacterial diversity and shifts in proportions of certain bacterial groups. This is accompanied by a decrease in Foxp3+ regulatory T cells and changes to the metabolic potential of the host microbiota, consequently impacting host health. However, the factor(s) driving these changes and the existence and role of its own intestinal microbiota is unknown. Infection of C57BL/6 and immunodeficient SCID mice with a high dose (~ 200 embryonated eggs) and a low dose (~ 20 embryonated eggs) of T. muris was used to determine the impact of worm burden and the adaptive immune system on the host intestinal microbiota, in comparison to naïve controls. Microbiota analysis was performed by 16S rRNA gene denaturing gradient gel electrophoresis (DGGE) and Illumina sequencing. This revealed that infection-induced microbiota changes were dose dependent and high level infection caused an increase in the Bacteroidaceae and Enterobacteriaceae families, independently of the host adaptive immune system. Development of a surface sterilisation protocol enabled the internal T. muris microbiota to be analysed by 16S rRNA gene DGGE and fluorescence in situ hybridisation (FISH). The resulting data indicated that T. muris requires its own diverse intestinal microbiota that is derived from, but distinct to, that of its host. A core microbiota is selected and maintained by the parasite regardless of the surrounding host microbiota. The parasite microbiota is important for its fitness, shown in vitro using an antibiotic motility assay and in vivo using germ free (GF) mice. Furthermore, infection with T. muris causes a significant reduction in caecal butyrate concentrations and consequently a decrease in the expression of butyrate transporters in caecal tissue. Interestingly, the T. muris microbiota is able to produce the short-chain fatty acid (SCFA) butyrate, which the parasite is unable to make itself yet secretes into its local environment. Together these strategies promote the long term survival of T. muris within the intestinal niche, adding a new level of complexity to the interaction between the pathogen, the host and their respective microbiotas that underpins successful chronic nematode infection.

Method forms the basis of scientific research, enabling criticism, selection and extension of current knowledge. However, methods are usually confined to the literature, where they are often difficult to find, understand, compare, or repeat. Bioinformatics and computational biology provide a rich opportunity for resource creation and discovery, with a rapidly expanding "resourceome". Many of these resources are difficult to find due to the large choice available, and there are only a limited number of sufficiently populated lists that can help inform resource selection. Text mining has enabled large scale data analysis and extraction from within the scientific literature, and as such can provide a way to help explore the vast wealth of resources available, which form the basis of bioinformatics methods. As such, this thesis aims to survey the computational biology literature, using text mining to extract database and software resource name mentions. By evaluating the common pairs and patterns of usage of these resources within such articles, an abstract approximation of the in silico methods employed within the target domain is developed. Specifically, this thesis provides an analysis of the difficulties of resource name extraction from the literature, then using this knowledge to develop bioNerDS - a rule-based system that can detect database and software name mentions within full-text documents (with a final F-score of 67%). bioNerDS is then applied to the full-text document corpus from PubMed Central, the results of which are then explored to identify the differences in resource usage between different domains (bioinformatics, biology and medicine) through time, different journals and different document sections. In particular, the well established resources (e.g., BLAST, GO and GenBank) remain pervasive throughout the domains, although they are seeing a slight decline in usage. Statistical programs see high levels of usage, with R in bioinformatics and SPSS in medicine being frequently mentioned throughout the literature. An overview of the common resource pairs has been generated by pairing database and software names which directly co-occur after one another in text. Combining and aggregating these resource pairs together across the literature enables the generation of a network of common resource patterns within computational biology, which provides an abstract representation of the common in silico methods used. For example, sequence alignment tools remain an important part of several computational biology analysis pipelines, and GO is a strong network sink (primarily used for data annotation). The networks also show the emergence of proteomics and next generation sequencing resources, and provide a specialised overview of a typical phylogenetics method. This work performs an analysis of common resource usage patterns, and thus provides an important first step towards in silico method extraction using text-mining. This should have future implications in community best practice, both for resource and method selection.

Sociality is widespread across the animal kingdom and explanations for its incidence and persistence are numerous. Whilst various drivers of sociality have been identified and tested, controversies remain and we are still far from a complete understanding of the mechanisms underlying social structure. Here I use a combination of field observations on a free-living population of feral horses Equus caballus and laboratory behavioural experiments on the Pacific beetle roach Diploptera punctata to investigate the drivers of sociality in these species. I explore four key aspects of sociality: the influences of kinship on sociality and social development, the strength and persistence of social bonds, the relationship between inbreeding avoidance and dispersal and the potential influence of individuals on social structure. Whilst kinship is a major driver of social structure in most mammalian species, I present evidence in Chapter Three that horse society is not structured by levels of kinship; however, in Chapter Five, I show that kinship levels to potential mates are significant in female dispersal choices in this species. In Chapter Eight, I provide evidence for significant effects of kinship to companions upon social and physical development in D. punctata, indicating a clear potential benefit of kin-based associations. The stability of social bonds can have substantial effects upon social structure. In Chapter Three, I show that the bonds between female horses show significant stability and are formed independently to kinship levels, a rare result in a non-primate species. I also provide evidence consistent with the hypothesis that these bonds are driven by male harassment. Similarly, in D. punctata, I find in Chapter Eight that female clustering occurs within resting aggregations and that the most likely explanation is the avoidance of male harassment. I therefore propose that this driver of female sociality may be a highly prevalent force structuring animal societies. Inbreeding depression has been demonstrated in a variety of species and contexts. Here I show in Chapter Five that in horses, female dispersal is likely to be influenced by kinship levels with potential mates. In Chapter Four, I then show that more heterozygous males have a higher reproductive success, most likely due to their ability to utilise a larger home range. Finally, local population structure can be highly influenced by individual association choices and behaviour. In Chapter Two, I show that in horses, mothers may allow their sons to postpone dispersal by the maintenance of stronger mother-son bonds, permitting an extended period of social learning. In Chapter Seven, I demonstrate that consistent inter-individual variation in personality traits exists in D. punctata which is stable across life stages, despite age effects on the strength of boldness. This is a source of variation which may be extremely important for decision-making social groups. My main conclusion from this work is that male harassment is often a key driver of sociality which may frequently be overlooked. I also demonstrate that the effects of kinship are far-ranging but not omnipresent. This thesis therefore makes a major contribution to our understanding of the mechanisms underlying animal sociality and presents clear potential avenues for future research.

A central concept in evolutionary biology is to understand how new species arise and are maintained. Studying the temporal and spatial distribution of genealogical lineages provides insight into evolutionary processes which govern diversification while the study of disease vectors has additional implications for human health. Within Africa, medically important Aedes mosquitoes provide an interesting case in which to study evolutionary processes because they are behaviourally and morphologically diverse. These mosquitoes are also inherently dependent of forests and so provide an ideal study organism in which to test the refuge hypothesis of historical climate change, which has been suggested as a pivotal force in species evolution. Before their evolutionary history can be determined, reliable identification of target species is required. In Chapter 3, we have developed a PCR mediated method to distinguish between isomorphic species of the Simpsoni Complex and use this method to provide inferences on ecology and species distributions; findings reveal an east-west difference in the distribution of the yellow fever vector Ae. bromeliae and non-vector Ae. lilii across the African continent, while these species meet in Uganda where they use the same larval habitats. In Chapters 4 and 5 we use a standard phylogeographic approach coupled with Approximate Bayesian Computation to uncover the evolutionary history of Aedes mosquitoes. Analysis reveals common phylogeographic signals within Aedes species which show populations have been subject to historical lineage diversification, admixture and recent demographic structure, in accordance with the refuge hypothesis of climate induced vicariance and secondary contact. Findings suggest that recent climate change throughout the late Pleistocene and early Holocene was important in generating African biodiversity. We find additional differences in the population structure of species between East and West Africa which could reflect more general biodiversity patterns within Africa. As the region connecting East and West African populations, Central Africa could be an important area regarding the diversification of species, including diseases and their vectors. We have identified a putative role for ecological speciation; for example in Chapter 4 we have provided additional evidence that worldwide populations of Ae. aegypti stem from a particularly successful source, indicating these mosquitoes possessed a trait integral for range expansion. In Chapters 4 and 5 we find that historical admixture within Africa characterises populations of Aedes mosquitoes and so may have played a key role in their evolutionary success. Since admixture can combine novel combinations of genetic material and raise adaptive potential, admixture may have been selectively advantageous for Aedes mosquitoes. Similarly, climate related secondary contact is likely to have been an important force for the evolution of other forest dependent species within Africa.

HIV evolves very quickly permitting it to escape the immune system of an infected individual, limiting the effectiveness of drug treatment and making vaccine design extremely difficult. For these reasons identifying whether there are constraints on HIV evolution is of primary importance. In this thesis, I examined the relationships between sequence diversity and a number of factors including protein structure, co-evolution and RNA secondary structures, all of which contribute to evolutionary constraints. Firstly, I demonstrate that while there is an increase in sequence diversity over time, this variation has a tendency to be limited to specific structural regions. Relating the sequence variability of individual amino acid residues with three- dimensional protein structure, I find a significant difference between evolutionary rates in regions buried in the core of the protein as compared with those exposed on the surface. This result indicates that missense mutation is affected by structural constraints. Secondly, by relating recombinant breakpoint positions with the potential numbers of losses in amino acid interactions within the structure, I propose that as well as missense mutation, recombination is also affected by structural constraints, due to the need to maintain intra-molecular interactions. Thirdly, I demonstrate a relationship between conservation of RNA secondary structure and limited sequence variation in protein level, indicating RNA secondary structures are additional evolutionary constraint. This link between sequence and protein and RNA structures not only demonstrates the limits of recent HIV-1 evolution but also highlights the origins of evolutionary constraint on viral change. Lastly, I examined whether evolutionary constraints and co- evolution patterns of HIV genomes are applied in novel mosaic vaccine strategy and suggest that polyvalent mosaic vaccine sequence may generate proteins with stable structures and co-evolving units. Detailed understanding of the constraints that restrict HIV’s possible evolution will inform analysis of drug and immune escape.

This thesis was presented by Aaron Webber on the 4th December 2013 for the degree of Doctor of Philosophy from the University of Manchester. The title of this thesis is ‘Transcriptional co-regulation of microRNAs and protein-coding genes’. The thesis relates to gene expression regulation within humans and closely related primate species. We have investigated the binding site distributions from publically available ChIP-seq data of 117 transcription regulatory factors (TRFs) within the human genome. These were mapped to cis-regulatory regions of two major classes of genes,  20,000 genes encoding proteins and  1500 genes encoding microRNAs. MicroRNAs are short 20 - 24 nt noncoding RNAs which bind complementary regions within target mRNAs to repress translation. The complete collection of ChIP-seq binding site data is related to genomic associations between protein-coding and microRNA genes, and to the expression patterns and functions of both gene types across human tissues. We show that microRNA genes are associated with highly regulated protein-coding gene regions, and show rigorously that transcriptional regulation is greater than expected, given properties of these protein-coding genes. We find enrichment in developmental proteins among protein-coding genes hosting microRNA sequences. Novel subclasses of microRNAs are identified that lie outside of protein-coding genes yet may still be expressed from a shared promoter region with their protein-coding neighbours. We show that such microRNAs are more likely to form regulatory feedback loops with the transcriptional regulators lying in the upstream protein-coding promoter region. We show that when a microRNA and a TRF regulate one another, the TRF is more likely to sometimes function as a repressor. As in many studies, the data show that microRNAs lying downstream of particular TRFs target significantly many genes in common with these TRFs. We then demonstrate that the prevalence of such TRF/microRNA regulatory partnerships relates directly to the variation in mRNA expression across human tissues, with the least variable mRNAs having the most significant enrichment in such partnerships. This result is connected to theory describing the buffering of gene expression variation by microRNAs. Taken together, our study has demonstrated significant novel linkages between the transcriptional TRF and post-transcriptional microRNA-mediated regulatory layers. We finally consider transcriptional regulators alone, by mapping these to genes clustered on the basis of their expression patterns through time, within the context of CD4+ T cells from African green monkeys and Rhesus macaques infected with Simian immunodeficiency virus (SIV). African green monkeys maintain a functioning immune system despite never clearing the virus, while in rhesus macaques, the immune system becomes chronically stimulated leading to pathogenesis. Gene expression clusters were identified characterizing the natural and pathogenic host systems. We map transcriptional regulators to these expression clusters and demonstrate significant yet unexpected co-binding by two heterodimers (STAT1:STAT2 and BATF:IRF4) over key viral response genes. From 34 structural families of TRFs, we demonstrate that bZIPs, STATs and IRFs are the most frequently perturbed upon SIV infection. Our work therefore contributes to the characterization of both natural and pathogenic SIV infections, with longer term implications for HIV therapeutics.

Virology, like many other biological research topics, has benefited from the application of large-scale data generation and analysis. Particular effort has been applied to developing a greater understanding of prevalent human-pathogenic viruses including type-1 Human immuno-deficiecy virus (HIV-1) and Hepatitis C virus (HCV). For example, host-virus interaction networks have been researched and important factors required for virus replication or innate cellular defence have been elucidated. Thus, large-scale data sources have provided a wealth of information regarding virus replication and virus-host interaction that may directly influence research and development of new antiviral treatments. In this thesis, we present research into the interaction between pathogenic viruses -- HCV, HIV-1 and SIV (the simian equivalent of HIV) -- and their host cells. Our research is largely integrative, computational research of large-scale data sources. In particular, we employ network models and related modes of analysis, with emphasis on identifying novel drug targets among cellular factors. Initially, we provide relevant background on virology, large-scale data sources and associated computational methods. Following this, we present four research projects that investigate either HIV-1, HCV or SIV interaction with host cells. Finally, we present a detailed analysis of the relationships between large-scale network data and biological function using the Saccharomyces cerevisiae model and demonstrate the importance of composite interaction networks. In our research we show that integration of large-scale data, combined with bespoke computational analyses, can provide a means for investigating specific aspects of viral infection. In particular, using this approach we provide insight into host-virus interactions that influence HIV-1, SIV and HCV infection and we infer cellular functions and specific host factors that may be useful in the search for novel antiviral drug targets. Thus, we recommend that computational methods for analysing large-scale data sources continue to be developed, with particular emphasis on methods that integrate data sources, so that results can be derived from multiple types of information and more complete biological models.

Over the course of the 20th and 21st Centuries the judiciary have increasingly made decisions that have affected the substantive content and the procedural implementation of public policy. The aim of this thesis is to provide an explanation for this political behaviour in judges by introducing the Legislative Politicisation of the Judiciary Theory to the debate. The theory proposes that the key independent causal variable is the language of Parliamentary legislation. The argument is that as legislation has been increasingly used to delegate power from Parliament to its various agents, the language used has become more indeterminate in order to enable discretion. Such indeterminacy creates an institutional problem where the orders of the sovereign Parliament are not clear, and to resolve this uncertainty in the Rule of Law the judges must intervene. The political behaviour of judges is therefore stimulated by a change in the legislative supply-side rather than a change in the behavioural demand-side, and the judges are acting as professional technocrats charged with ensuring the efficacious implementation of Parliamentary legislation. A new discourse analysis methodology has been created for this thesis that provides evidence of change in the language of legislation between 1920 and 2010. A total of 8,328 sections of primary and secondary legislation have been hand-coded, with results showing that 3% of sections in 1920 (21 sections in real terms) were “Henry VIIIth clauses”, where power to make new law was delegated by Parliament; by 2010 this had increased to 16% (400 sections in real terms).

Within a distributed system, autonomous agents may find it necessary to cooperate in order to achieve their objectives. Interaction protocols provide standard frameworks within which to conduct common classes of interaction, but they are only useful when the agents using them have a common interpretation of the constraints imposed by those protocols. In open systems, where there are no system-wide objectives and components are contributed from a variety of sources, this is difficult to ensure. An agent within a sufficiently complex environment will find it necessary to draw inferences from information sources of varying integrity and completeness. Given flawed or incomplete information, it may be necessary for an agent to resort to nonmonotonic reasoning in order to be able to make concrete decisions within limited windows of opportunity. This can be expected to create inconsistencies in the joint beliefs of agents which can only be repaired by dialogue between peers. To verify and repair all possible sources of inconsistency is impractical for any sizable body of inference however—any belief revision must therefore be subject to prioritisation. In this thesis, we introduce a mechanism by which agents can perform opportunistic argumentation during dialogue in order to perform distributed belief revision. An interaction portrayal uses the protocol for a given interaction to identify the logical constraints which must be resolved during the interaction as it unfolds. It then compares and reconciles the expectations of agents prior to the resolution of those constraints by generating and maintaining a system of arguments. The composition and scope of arguments is restricted in order to minimise the information exchange whilst still trying to ensure that all available admissible viewpoints are adequately represented immediately prior to any decision. This serves both to make interaction more robust (by allowing agents to make decisions based on the distributed wisdom of its peer group without being explicitly directed by a protocol) and to reconcile beliefs in a prioritised fashion (by focusing only on those beliefs which directly influence the outcome of an interaction as determined by its protocol).

Service oriented computing is crucial to a large and growing number of computational undertakings. Central to its approach are the open and network-accessible services provided by many different organisations, and which in turn enable the easy creation of composite workflows. This leads to an environment containing many thousands of services, in which a programmer or automated composition system must discover and select services appropriate for the task at hand. This discovery and selection process is known as matchmaking. Prior work in the field has conceived the problem as one of sufficiently describing individual services using formal, symbolic knowledge representation languages. We review the prior work, and present arguments for why it is optimistic to assume that this approach will be adequate by itself. With these issues in mind, we examine how, by reformulating the task and giving the matchmaker a record of prior service performance, we can alleviate some of the problems. Using two formalisms—the incidence calculus and the lightweight coordination calculus—along with algorithms inspired by information retrieval techniques, we evolve a series of simple matchmaking agents that learn from experience how to select those services which performed well in the past, while making minimal demands on the service users. We extend this mechanism to the overlooked case of matchmaking in workflows using multiple services, selecting groups of services known to inter-operate well. We examine the performance of such matchmakers in possible future services environments, and discuss issues in applying such techniques in large-scale deployments.

Research into human disease has classically been 'bottom-up', focussing on individual genes. However, the emergence of Systems Biology has prompted a more holistic 'top-down' approach to decoding life. Less than a decade since the complete draft of the human genome was published, we are increasingly in a position to model the interacting constituents of a cell and thus understand molecular perturbations. Given biological systems are rarely attributable to individual molecules and linear pathways, we must understand the complex dynamic interplay as cellular components interact, combine, overlap and conflict. The integrative approach afforded by Network Biology provides us with a powerful toolset to understand the vast volumes of omics data. In this thesis, I investigate both infectious disease, specifically HIV infection and heritable disease. HIV, the causative agent of AIDS, represents an extensive perturbation of the host system and results in hijacking of cellular proteins to replicate. I first introduce the HIV-interaction data and then characterise HIV's hijack, revealing the ways Network Biology can greatly enhance our understanding of host-pathogen systems and ultimately the systems itself. I find a significantly greater propensity for HIV to interact with ''key'' host proteins that are highly connected and represent critical cellular functions. Unexpectedly, however, I find there are no associations between HIV interaction and inferred essentiality and genetic disease-association. I hypothesise that these observations could be the result of ancestral selection pressure on retroviruses to minimise interactions with phenotypically crucial proteins. Investigating inherited disease, I apply a similar integrative approach to determine the relationships between inherited disease, evolution and function. I find that 'disease' genes are not a homogenous group, and that their emergence has been ongoing throughout the evolution of life; contradicting previous studies. Finally, I consider the consequence of bias in literature-curated interaction datasets. I develop a novel method to identify and correct for ascertainment bias and demonstrate that failure to do this weakens conclusions. correct for ascertainment bias and demonstrate that failure to do this weakens conclusions. The aim of this thesis has been to explore the ways Network Biology can provide an integrative biological approach to studying infectious and inherited disease. Given billions of people around the world are susceptible to disease, it is ultimately hoped that a Systems Biology approach to understanding disease will herald new pharmaceutical interventions.

The task of developing a framework for which agents can communicate reliably and flexibly in open systems is not trivial. This thesis addresses the dichotomy between reliable communication and facilitation of the autonomy of agents to create more flexible and emergent interactions. By the introduction of adaptations to a distributed protocol language, agents benefit from the ability to communicate interaction protocols to elucidate the social norms (thus creating more reliable communication). Yet, this approach also provides the functionality for the agent to unilaterally introduce new paths for the conversation to explore unforeseen opportunities and options (thus restoring more autonomy than possible with static protocols). The foundation of this work is Lightweight Coordination Calculus (LCC). LCC is a distributed protocol language and framework in which agents coordinate their own interactions by their message passing activities. In order to ensure that adaptations to the protocols are done in a reasonable way, we examine the use of two models of communication to guide any transformations to the protocols. We describe the use of FIPA's ACL and ultimately its unsuitability for this approach as well as the more fecund task of implementing dialogue games, an model of argumentation, as dynamic protocols. The existing attempts to develop a model that can encompass the gulf between reliability and autonomy in communication have had varying degrees of success. It is the purpose and the result of the research described in this thesis to develop an alloy of the various models, by the introduction of dynamic and distributed protocols, to develop a framework stronger than its constituents. Though this is successful, the derivations of the protocols can be dificult to reconstruct. To this end, this thesis also describes a method of protocol synthesis inspired by models of human communication that can express the dialogues created by the previous approaches but also have a fully accountable path of construction. Not only does this thesis explore a unique and novel approach to agent communication, it is tested through a practical implementation.

In this thesis we are concerned with the role of formal argumentation in artificial intelligence, in particular in the field of knowledge engineering. The intuition behind argumentation is that one can reason with imperfect information by constructing and weighing up arguements intended to give support in favour or against alternative conclusions. In dynamic argumentation, such arguements may be revised and strengthened in order yo increase to decrease the acceptability of controversial positions. This thesis studies the theory, architecture, development and applications of formal arguementation systems from the procedural perspective of actually generating argumentation processes. First, the types of problems that can be tackled via the argumentation paradigm in knowledge engineering are characterised. Second, an abstract formal framework are built from an underlying set of axioms, represented here as executatble logic programs. Finally an architecture for dynamic arguementation systems is defined, and domain-specific applications are presented within different domaind, thus grounding problems with very distinctive characteristics into a similar source in argumentation. The methods and definitions desribed in this thesis have been assessed on various bases, including the reconstruction of informal arguements and of arguments captured by existing formalisms, the relation between our framework and these formalisms, and examples of dynamic argumentation applications in the safety-engineering and multi-agent domains.

The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are EMYCIN -type expert system shells. The main argument of this thesis is that these shells make knowledge-based modelling unnecessarily difficult and that tools which exploit knowledge acquisition technologies empower learners to build better models. We describe how such tools can be designed. To evaluate their usability a model-building course was conducted in five secondary schools. During the course pupils built hundreds of models in a common range of domains. Some of the models were built with an EMYCIN -type shell whilst others were built with a variety of knowledge acquisition systems. The knowledge acquisition systems emerged as superior in important respects. We offer some explanations for these results and argue that although problems remain, such as in teacher education, design of classroom practice, and assessment of learning outcomes, it is clear that knowledge acquisition systems offer considerable potential to develop improved forms of educational knowledge-based modelling.

Knowledge acquisition from databases is a research frontier for both data base technology and machine learning (ML) techniques,and has seen sustained research over recent years. It also acts as a link between the two fields,thus offering a dual benefit. Firstly, since database technology has already found wide application in many fields ML research obviously stands to gain from this greater exposure and established technological foundation. Secondly, ML techniques can augment the ability of existing database systems to represent acquire,and process a collection of expertise such as those which form part of the semantics of many advanced applications (e.gCAD/CAM).The major contribution of this thesis is the introduction of an effcient induction algorithm to facilitate the acquisition of such knowledge from databases. There are three typical families of inductive algorithms: the generalisation- specialisation based AQ11-like family, the decision tree based ID3-like family,and the extension matrix based family. A heuristic induction algorithm, HCV based on the newly-developed extension matrix approach is described in this thesis. By dividing the positive examples (PE) of a specific class in a given example set into intersect in groups and adopting a set of strategies to find a heuristic conjunctive rule in each group which covers all the group's positiv examples and none of the negativ examples(NE),HCV can find rules in the form of variable-valued logic for PE against NE in low-order polynomial time. The rules generated in HCV are shown empirically to be more compact than the rules produced by AQ1-like algorithms and the decision trees produced by the ID3-like algorithms. KEshell2, an intelligent learning database system, which makes use of the HCV algorithm and couples ML techniques with database and knowledgebase technology, is also described.