Computing explanations for interactive constraint-based systems

Constraint programming has emerged as a successful paradigm for modelling combinatorial problems arising from practical situations. In many of those situations, we are not provided with an immutable set of constraints. Instead, a user will modify his requirements, in an interactive fashion, until he is satisfied with a solution. Examples of such applications include, amongst others, model-based diagnosis, expert systems, product configurators. The system he interacts with must be able to assist him by showing the consequences of his requirements. Explanations are the ideal tool for providing this assistance. However, existing notions of explanations fail to provide sufficient information. We define new forms of explanations that aim to be more informative. Even if explanation generation is a very hard task, in the applications we consider, we must manage to provide a satisfactory level of interactivity and, therefore, we cannot afford long computational times. We introduce the concept of representative sets of relaxations, a compact set of relaxations that shows the user at least one way to satisfy each of his requirements and at least one way to relax them, and present an algorithm that efficiently computes such sets. We introduce the concept of most soluble relaxations, maximising the number of products they allow. We present algorithms to compute such relaxations in times compatible with interactivity, achieving this by indifferently making use of different types of compiled representations. We propose to generalise the concept of prime implicates to constraint problems with the concept of domain consequences, and suggest to generate them as a compilation strategy. This sets a new approach in compilation, and allows to address explanation-related queries in an efficient way. We define ordered automata to compactly represent large sets of domain consequences, in an orthogonal way from existing compilation techniques that represent large sets of solutions.

Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems.

Fractionation and compositional studies of rabbit skeletal muscle membranes

The observations of Hooke (1665), Schleiden & Schwann (1839) and Virchow (1855) led to the identification of the cell as the basic structural unit of living material. In the intervening years, it has been firmly established that the chemical processes which underlie the proper functioning, development and reproduction of the organism are cellular activities. The development of the electron microscope has enabled cell structure to be studied in detail. A picture of the cell as an entity with a complex and highly organised internal structure has emerged from the work of Palade, Porter, Fernandez-Moran and many others. Although cells from different tissues and organisms differ in aspects of their structure and consequently in function, they have several features in common. A retentive membrane encloses a number of cell constituents, which include membrane-enclosed subcellular structures known as organelles. The cells of most tissues also contain a reticulum or system of branching tubules. The interplay of the biochemical activities of these structures enables the cell to function. Almost thirty years ago, Claude, Palade, Schneider, Hogeboom, de Duve and others set out to analytically fractionate the subcellular components obtained after the fragmentation of liver cells. This approach has become known as subcellular fractionation, and signalled a major conceptual breakthrough in biochemistry (reviewed by de Duve, 1964, 1967, 1971). The significance of this breakthrough has been underlined by the award of the 1974 Nobel Prize in Medicine to de Duve, Palade and Claude. This thesis is concerned with the application of subcellular fractionation techniques to the separation and characterisation of the membrane systems of the rabbit skeletal muscle cell.

Immune and stress factors in the pathophysiology of the mdx mouse model of Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is a fatal multi-system neuromuscular disease caused by loss of dystrophin. The loss of dystrophin from membranes of contractile muscle cells and the dysregulation of the DAPC, induces chronic inflammation due to tissue necrosis and eventual replacement with collagen which weakens muscular force and strength. Dystrophin deficiency may cause under-diagnosed features of DMD include mood disorders such as depression and anxiety and dysfunction of the gastrointestinal tract. The first study in the thesis examined mood in the dystrophin-deficient mdx mouse model of DMD and examined the effects of the tri-cyclic antidepressant, amitriptyline on behaviours. Amitriptyline had anti-depressant and anxiolytic effects in the mdx mice possibly through effects on stress factors such as corticotrophin-releasing factor (CRF). This antidepressant also reduced skeletal muscle inflammation and caused a reduction in circulating interleukin (IL)-6 levels. In the second and third studies, we specifically blocked IL-6 signalling and used Urocortin 2, CRFR2 agonist to investigate their potential as therapeutic targets in mdx mice pathophysiology. Isometric and isotonic contractile properties of the diaphragm, were compared in mdx mice treated with anti IL-6 receptor antibodies (anti IL-6R) and/or Urocortin 2. Deficits in force production, work and power detected in mdx mice were improved with treatment. In study three I investigated contractile properties in gastrointestinal smooth muscle. As compared to wild type mice, mdx mice had slower faecal transit times, shorter colons with thickened muscle layers and increased contractile activity in response to recombinant IL-6. Blocking IL-6 signalling resulted in an increase in colon length, normalised faecal output times and a reduction in IL-6-evoked contractile activity. The findings from these studies indicate that for both diaphragm and gastrointestinal function in a dystrophin-deficient model, targeting of IL-6 and CRFR2 signalling has beneficial therapeutic effects.