This dissertation contributes to the state of the art in protein interface prediction and detection of native-like docked poses by re-ranking them using protein interface knowledge. We started by investigating binding site patterns among homologues of a target protein in order to create a 3D motif. This structural binding site descriptor enables the re-ranking of docked complexes of the target protein. Although 3D motifs provide biological insight of protein interactions and have usage in real applications, they are not suitable for high through-put analysis. Therefore, we introduced a novel protein interface prediction framework which uses a weighted scoring schema to detect interface residues of the target protein using its homologues. The weights quantify both homology closeness between the target protein and its homologues and the diversity between the interacting partners of these homologues. The main novelty of this predictor is that it takes into account the nature of homologues interacting partners. It was further exploited for the development of a method for re-ranking docked conformations using predicted interface residues. We have evaluated both our interface predictor and re-ranking of docked poses using standard benchmarks. Comparisons to current state-of-the-art methods reveal that the proposed approaches outperform all their competitors. However, similarly to current interface predictors, our framework does not explicitly refer to pairwise residue interactions which leaves ambiguity when assessing quality of complex conformations. In addition, the performance of our interface predictor generally does not outperform the best available homologue interfaces if it was used as prediction. Therefore, we investigated the detection of the best homologue using the 'binding site transitivity' concept: given two query protein chains, interfaces of the first query protein are structurally compared against binding sites of the homologues' partners of the second query chain. This method not only allows detection of the best homologue for a reasonable number of proteins but also produces a docked structure of the two query chains. Finally, experiment suggests a meta interface predictor combining the prediction of our former interface predictor with the latter predictor based on binding site transitivity could further improve interface prediction.

In western countries at least one third of the population develops cancer. The main cause of death in cancer patients is metastasis and there is no effective treatment for this complication. The situation can be improved by a better understanding of the cancer invasion process. In order to re­ veal new aspects of this dynamic process, a novel image-processing-based direct viewing cancer-cell invasion assay was developed and used with inverted wide-field microscopy. The combination of high-resolution 3D image-processing approaches with a custom-made flow chamber system enabled the quantification of the sarcoma-cell invasion process through a monolayer of endothelial cells in vitro. The image processing entailed the separation of positive cell signal from background noise and blur, which are inherent in 3D wide-field microscopy. The preparation and cell signal segmentation of wide-field images prior to quantification featured stochastic as well as deterministic techniques. The stochastic approach was based on a Gaussian Mixture Model to separate noise and background signal characteristics from positive cell signal which performed well in conditions with high signal-to-noise ratios. The. deterministic segmentation approach was based- on linear diffusion and performed well despite low signal-to-noise ratios as it assessed the diffusion rates of cell signal over multiple convolutions. The image-processing-based assay included the definition of two new parameters to quantify the invasion: Relative Invasion (RI) and Opening Rate of the Endothelial Monolayer (O REM). The first parameter RI measured the invasion as the percentage of sarcoma cell signal below the reconstructed monolayer surface. The second parameter O RE M evaluated the speed at which the sarcoma cells disassemble the monolayer in their strive to exit the flow channel. This assay was applied to metastatic rat sarcoma cells where the cells invaded monolayers of rat endothelial cells. After adhesion, the sarcoma cells initially invaded significantly faster under flow conditions compared to situations without shear stress. Later, however, the rate of invasion underflow decreased and the sarcoma cells without shear stress achieved significantly higher levels of invasion. These observations thus revealed the non-linear modulation of a tumour-cell invasion process by shear flow, demonstrating that tumour cells can respond to flow by enhancement of invasiveness in a similar way to white blood cells. In summary, the newly developed direct viewing assay provides a quantitative image-processing-based approach to assessing cancer invasion dynamics, which should lead to a better understanding of the mechanisms involved in cancer invasion and metastasis.

The scoped of this dissertation is to procure and propose a novel fully automated computer aided detection and measurement (CAD/CAM) system of thoracic aortic aneurysms. More explicitly, the objective of the algorithm is to facilitate the segmentation of the thoracic aorta, as accurately as possible and detection of possible existing aneurysms in the Computer Tomography Angiography (CT) images. In biomedical imaging, the manual examination and analysis of aortic aneurysms is a particularly laborious and time-consuming undertaking. Humans are susceptible to committing errors and their analysis is usually subjective and qualitative due to the inter- and intra-observer variability issue. Objective and quantitative analysis facilitated by the application developed in this project leads to a more accurate diagnostic decision by the physician. In this context, the project is concerned with the automatic analysis of thoracic aneurysms from CTA images. The project initially examines the theoretical background of the anatomy of the aorta and aneurysms. The concepts of image segmentation and, in particular, segmentation of vessels methods are reviewed. An algorithm is then developed and implemented, such that it will conform to the requirements put forth in the stated objectives. For purposes of testing the proposed approach, a significant amount of 3D, clinical CTA datasets of thoracic aorta form the framework of the CAD/CAM system. It is followed by presentation and discussion of the results. The system has been validated on a clinical dataset of30 eTA scans of which 28 eTA scans contained aneurysms. There were 30 eTA scans used as training dataset for parameter selection and another 30 eTA scans uses as a test dataset, in total 60 for clinical evaluation. The radiologist visually inspected the CAD and CAM component results and confirmed it correctly detected and segmented the T AA on all datasets, proving to have 100% sensitivity. We were able to conclude that there is distinct potential for.use of our fully automated CAD/CAM system in a real clinical setting. Although other CAD/CAM systems have been developed for other organ detection and even small sections of the thoracic aorta, to this date no fully automated CAD/CAM of the entire thoracic aorta has been developed hence its novelty. To facilitate the proposed CAD/CAM system is integrated in a Medical Images Processing, Seamless and Secure Sharing Platform (MIPS3) which is a friendly user interface that has been developed alongside with this project.

The development of mathematics is foundational. For the most part in early childhood it is seldom insurmountable. Various constructions exhibit conceptual change in the child, which is evidence of overcoming the learning paradox. If one tries to account for learning by means of mental actions carried out by the learner, then it is necessary to attribute to the learner a prior structure , one that is as advanced or as complex as the one to be acquired, unless there is emergence. This thesis reinterprets Piaget's theory using research from neurophysiology, biology, machine learning and demonstrates a novel approach to partially resolve the learning paradox for a simulation that experiences a number line world, exhibiting emergence of structure using a model of Drosophila. In doing so, the research evaluates other models of cognitive development against a real-world, worked example of number-sense from childhood mathematics. The purpose is to determine if they assume a prior capacity to solve problems or provide parallel assumptions within the learning process as additional capabilities not seen in children. Technically, the research uses an artificial neural network with reinforcement learning to confirm the emergence of permanent object invariants. It then evaluates an evolved dialectic system with hierarchical finite state automata within a reactive Argos framework to confirm the reevaluated Piagetian developmental model against the worked example. This research thesis establishes that the emergence of new concepts is a critical need in the development of autonomous evolvable systems that can act, learn and plan in novel ways, in noisy situations.

The research presented in this thesis focuses on applications of Contrast Enhanced Ultrasound (CEUS) imaging and is coordinated to address current clinical requirements in the assessment, quantification and evaluation of liver cancer and in particular focal liver lesions (FLLs). The main outcomes of this research are methods to assist radiologists with automating these routinely performed manual image interpretation tasks, with the intention of supporting them to make their diagnostic decisions faster, more easily and with greater confidence. Such automatic analysis is challenging mainly because of the relative motion between the ultrasound transducer and the liver, the physiological motion of the patient and the dramatic intensity changes over time caused by the contrast-enhancing agents intravenously injected during a CEUS examination. The work described in this thesis can be divided into three principal themes. These are addressed in turn below. Firstly, a set of methods are proposed to assist in automating initialisation tasks required for the offline assessment of data acquired during CEUS liver scans. These tasks relate to the delineation of the area comprising the ultrasonographic image, the identification of the optimal reference frame for initialising an FLL, as well as the segmentation of the FLL boundaries on this frame. The potential clinical value of the proposed methods is that they can lead to easier and faster assessment of FLLs, whilst producing results less dependent on the human initialisation and hence improving the repeatability and reproducibility of the assessment of the examination and increasing the confidence of radiologists when making a diagnosis. Secondly, a variety of methods are investigated to estimate the motion observed within the ultrasonographic image of CEUS screening recordings and then compensate for this, allowing for an accurate quantification of the perfusion of tissue regions. Obtaining a perfusion curve for an image region, without compensating for the observed motion, may lead to erroneous diagnostic results as the specified image region may correspond to different tissue along the video sequence. Quantitative evaluation of the presented methods demonstrates their potential as reliable real-time motion compensation methods for such recordings. Finally, an alternative fully automatic method for the identification and localisation of potential malignancies is proposed. For such identification, and hence distinction between cases that include potentially malignant and benign lesions, an innovative assessment of the global spatial configuration of local variations of perfusion curves is presented. For the localisation of tissue regions of potential malignancy, a novel feature is proposed that encompasses spatio-temporal information (Le. the combination of both the variation in these local perfusion curves and the location they relate to) to cluster together neighbouring regions with similar dynamic behaviour. The clinical value of the identification part is the early diagnosis of an FLL’s type and the possibility for the discharge of patients with benign FLLs, leading to less distress to the patients and their families, as well as reduced healthcare costs. Additionally, the localisation part assists in enhancing the radiologist’s awareness of tissue regions with potentially malignant behaviour, as well as providing effortless localisation of such regions allowing for an objective initialisation of computer-aided segmentation methods improving the repeatability and reproducibility of the assessment of CEUS data. The key findings of this research indicate that: i) the optimal reference frame can be reliably identified in a fully automatic and deterministic manner, ii) the segmentation of an F LL can be performed in a rapid semi-automatic manner, which produces results that are, at worst, of comparable consistency as different manual annotations, iii) the apparent observed motion can be compensated in real-time, either locally or globally, and a simple translation is sufficient to achieve this, iv) the distinction between benign and malignant lesions can be performed in a fully automatic and deterministic manner, without missing a single malignancy, and v) potential malignancies can be localised reliably in a fully automatic manner. Quantitative analysis of all results on real clinical data, from a multi-centre study, is used to evaluate the level of confidence of the decision of the proposed methods and demonstrates the value of these methods in a diverse dataset acquired using the protocol of current standard care. A system incorporating the proposed methods could improve the current clinical practice for assessing, quantifying and evaluating FLLs in CEUS recordings. Specifically, it would be beneficial to radiologists, for cancer research, providing easier and faster assessment of FLLs whilst producing results less dependent on the human initialisation and therefore increasing the confidence of radiologists in their diagnostic decisions.

Smoking has become one of the major global health concerns. Though there are various awareness activities being undertaken, the prevalence of smoking across the world is increasing at alarming levels. However, the extent of this increase varies among different countries. Even in culturally rich countries where smoking is considered as antagonistic behaviour both religiously and culturally, like Saudi Arabia, the prevalence of smoking is increasing at alarming levels. As smoking is mostly a behavioural aspect bundled with other factors, CBT (Cognitive Behavioural Therapy) integrated with m-health technologies represents a good strategy towards smoking cessation. This study focuses on developing a mobile smoking cessation management system - SMOKE MIND - using CBT intervention, and assessing its impact on achieving smoking cessation. This study uses mixed methods approach, where different methods are used at different stages of the research. Based on the systematic reviews and other literature reviewed, a questionnaire-based survey is conducted to assess the requirements of smokers in Saudi Arabia regarding the system for smoking cessation. The system developed uses CO readings of smokers, entered daily by participants through the mobile application, and assesses their improvement. Additionally, smokers enter CBT data if their CO readings are high, also through the mobile application. Based on these readings and CBT data, physicians recommend various activities and send motivational messages. The system is trailed for four weeks with an intervention group, who had access to the system, and a control group who did not. At the end of the study, another survey is conducted for evaluating the usability aspects of the SMOKE MIND system. The results achieved at the end of the study in evaluating the SMOKE MIND System reflect significant improvements in the participants in quitting smoking, and high satisfaction levels of the participants using the system. The values of p in both one-talied (0.0061) and two tailed (0.01) t-test are < 0.05, indicating that results are significant. 81.8% of the participants in intervention group and 40% participants in the control group quit smoking at the end of the study. A majority of the participants were highly satisfied with the various features used in the SMOKE MIND System.

Peripheral Arterial Disease (pAD) afflicts more than 2.7 million people in the U.K. per year, and it is projected to increase rapidly within the current decade. PAD is a product of obstruction (stenosis or occlusion) of vessels feeding the body's extremities, and it is most often encountered in the lower extremities. Treatment of the disease is dependent on the specific anatomic segments afflicted, the degree of stenosis and its length. A common technique for imaging PAD is Computed Tomography-Angiography (CTA). The acquired CTA images are then investigated by a radiologist for disease assessment. However due to the large size of the PAD CTA datasets (1000-2000 slices) the radiologist's examination is time consuming and laborious. This project brings a contribution to the investigation of PAD in CTA datasets by the development of a tool for the radiologist, a fully automatic system for the detection and measurement of PAD, as currently there are no such systems efficacious for the disease. The proposed system is comprised of two components: a Computer Aided Detection (CAD) component and a Computer Aided Measurement (CAM) component. The CAD component is designed for artery segmentation and stenosis detection. The stage of artery segmentation is accomplished by using a 3D region growing method and an innovative 3D fast morphology operation. CAD methodologies commonly employ morphological operations as a tool in the segmentation process, along with extended series of CTA images. This large dataset requires careful attention to be paid towards optimizing the computational process in terms of time efficiency. In order to meet this goal, an optimized morphology algorithm is presented, which reduces the computation time by a factor of 10. A skeletonization based centreline technique is applied on the detected artery, and it then provides the basis for the measurement stage. Orthogonal planes to the centreline are used in order to obtain cross sectional images. The artery profile is then built based on vessel areas measured in the cross sectional images and an automated process of stenosis detection is performed. The CAM component of the system accurately measures and quantifies the stenosis and overcomes the challenge brought by the partial volume effect. In this respect, a hybrid method for partial volume correction is employed locally, on the candidate areas of stenosis detected by the CAD component, based on Maximum a Posterior (MAP) and Markov Random Field (MRF) expectation maximization method. The CAD-CAM system has been successfully implemented and applied on phantom and patient data (twenty data sets from The University Hospital of Lausanne (CHUV)) and the evaluation was carried out through the visual judgment of two experienced radiologists. Within the CAD component, the artery segmentation was evaluated and a total of 15 peripheral arterial trees were correctly extracted. The proposed stenosis detection method was evaluated on 525 arterial segments (each dataset was partitioned into 35 segments) from which 132 exhibited stenosis caused by soft plaque. The system achieved a sensitivity of 88% and a specificity of 96%. The CAM component has been evaluated using phantom data, and the average error of the diameter measurement was 8%.

This thesis describes research into communication protocols required by a wireless mesh network (WMN) that would be deployed to support emergency rescue teams in a disaster recovery scenario. WMN applications in emergency and disaster recovery require the network to facilitate multimedia group communications to enable rescue team members to share information with each other and provide access to broadband services via www and email. The work presented in the thesis proposes a scheme to improve the performance of WMN to satisfy such application requirements. Several protocols have been designed and implemented to support the exchange of information between these protocols in order to meet the QoS requirements of real-time multimedia traffic and to avoid congestion whilst routing Internet traffic in a multiple gateway environment. A novel implementation of the MAODV routing protocol is developed and modifications are proposed to enhance the protocol's performance and reliability to support the multimedia multicast operation ofWMNs. A novel Load-Balanced Gateway Discovery routing protocol called LBGD-AODV is designed and implemented which provides a multiple gateway environment and balanced Internet traffic loading to more effectively utilize the available gateway resources and minimise network congestion. The proposed QoS scheme enables both protocols to exchange information on network congestion in order to calculate the network bandwidth consumption using a novel scheme at the network layer. This information is used to provide rate-adaptive admission control for multimedia traffic at the application layer. Furthermore, the scheme also provides strategies to support efficient priorities for multimedia traffic to ensure that all the critical priority flows are facilitated even when the network is congested. LBGD-AODV uses the bandwidth information to avoid congested routes to gateway nodes for Internet traffic. A WMN testbed is designed and developed using cross-platform hardware to evaluate the performance of the proposed protocols. The test results show that the objectives set in this study have been successfully achieved by improving the WMN performance for both UDP real-time multimedia traffic and TCP internet traffic.

Understanding knowledge management is critical for organisations because the persons concerned are not trained adequately to access, use and benefit from this knowledge effectively. The main problem is that the workers themselves do not understand how to organize and manage a huge amount of knowledge capital has, and the importance of this to improve organizational performance. This research is based on understanding the importance of knowledge management practices and policies within an organisation. This is based on research to understand the importance of knowledge management practices and policies within the organization. This research focuses on building a framework based on the knowledge of social issues within the organization and its impact on business. This research will explore strategies that can be used for organizations in order to integrate this knowledge into their business social benefit from social ontology. To enable this framework, a business organization knowledge management integrated social ontology framework (BOKMISOf) was built with a focus on telecommunications industry in Saudi Arabia and the Gulf states. In order to understand the social aspects of an organisation, a social ontology was developed which was incorporated into the knowledge management framework. The BOKMISO framework was evaluated with case studies, within telecommunication companies, in Saudi Arabia and the Gulf states. The employees of these companies contributed in data collection activity via questionnaire, whereas the managers of these companies contributed via semi-structured interview. Results that were gathered from data collection showed that BOKMISO framework was valid, appropriate, useful and added value to an organisation. Further work can be done to apply the BOKMISOf within other telecommunications companies in Saudi Arabia.

This thesis is concerned with the design and implementation of algorithms in Computer Algebra - a discipline which pursues a symbolic approach to solving mathematical equations and problems in contrast to computing solutions numerically. More precisely, we study sys¬tems of pseudo-linear equations, which unify the classes of linear differential, difference and q-difference systems. Whilst the classical mathematical theory of asymptotic expansions and the notion of formal solutions of this type of solutions are well established for all these indi-vidual cases, no unifying theoretical framework for pseudo-linear systems was known prior to our work. From an algorithmic point of view, the computation of a complete fundamental system of formal solutions is implemented by the formal reduction process. The formal reduction of linear differential systems had been treated in the past, and linear difference systems were also investigated and partly solved. In the case of linear q-difference systems, the structure of the formal solution is much easier which results in an alleviated formal reduction. However, no satisfying algorithm had been published that would be suitable to compute the formal solutions. We place ourselves in the generic setting and show that various algorithms that are known to be building blocks for the formal reduction in the differential case can be extended to the general pseudo-linear setting. In particular, the family of Moser- and super-reduction algorithms as well as the Classical Splitting Lemma and the Generalised Splitting Lemma are amongst the fundamental ingredients that we consider and which are essential for an effective formal reduction procedure. Whereas some of these techniques had been considered and adapted for systems of difference or q-difference equations, our novel contribution is to show that they can be extended and formulated in such a way that they are valid generically. Based on these results, we then design our generic formal reduction method, again in-spired by the differential case. Apart from the resulting unified approach, this also yields a novel approach to the formal reduction of difference and q-difference systems. Together with a generalisation of an efficient algorithm for computing regular formal solutions that was devised for linear differential systems, we finally obtain a complete and generic algorithm for computing formal solutions of systems of pseudo-linear equations. We show that we are able to compute a complete basis of formal solutions of large classes of linear functional systems, using our formal reduction method. The algorithms presented in this thesis have been implemented in the Computer Algebra System Maple as part of the Open Source project ISOLDE.