Previously, the CCR5 receptor was found to be a good target for treating prostate cancer (PCa). Dr. Yan Zhang’s laboratory designed several CCR5 antagonists, which were screened for their inhibitory effect on the growth and invasion of the M12, DU145 and PC-3 PCa cell lines. Primary in vitro screening showed one compound (Drug 17) significantly inhibited the proliferation of PCa cells at 1μM concentration, with a half-maximal inhibitory concentration of 237.68 nM. Further in vitro assays including a proliferation, cytotoxicity and invasion assay confirmed the inhibitory effect of drug 17. The physiological effect of drug 17 was tested by the Ware laboratory in vivo by subcutaneous injection of M12 cells into male, athymic nude mice. Tumor growth was slowed in mice receiving injections of drug 17 compared to sham injected controls. Thus, in vitro and in vivo assays suggest drug 17 might be an effective therapy to block PCa progression.

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users' decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain.

The human body is colonized by more than 100 trillion microbes which make up an essential part of the body and plays a significant role in health. We now know the over use and misuse of broad-spectrum antibiotics can disrupt this microbiome contributing to the onset of disease and runs the risk of promoting antibiotic resistance. With antibiotic research still on the decline, new strategies are greatly needed to combat emerging pathogens while maintaining a healthy microbiome. We therefore set out to present a novel species-selective antimicrobial drug discovery strategy. Disruption of the homeostasis within the oral cavity can trigger the onset of one of the most common bacterial infections, periodontal disease. Even though the oral cavity is one of the most diverse sites on the human body, the Gram-negative colonizer, Porphyromonas gingivalis has long been considered a key player in the initiation of periodontitis, suggesting the potential for novel narrow-spectrum therapeutics. By targeting key pathogens, it may be possible to treat periodontitis while allowing for the recolonization of the beneficial, healthy flora. Therefore, we set out to use P. gingivalis and periodontal disease as a model for pathogen-specific antimicrobial drug discovery. In this study we present a unique approach to predict essential gene targets selective for the periodontal pathogen within the oral environment. Using our knowledge of metabolic networks and essential genes we identified a “druggable” essential target, meso-diaminopimelate dehydrogenase, which is found in a limited number of species. This enzyme, meso-diaminopimelate dehydrogenase from P. gingivalis, was first expressed and purified, then characterized for enzymatic inhibitor screening studies. We then applied a computer-based drug discovery method, combining pharmacophore models, high-throughput virtual screening and molecular docking. Utilizing the ZINC database we virtually screened over 9 million small-molecules to identify several potential target-specific inhibitors. Finally, we used target-based and whole-cell based biochemical screening to assess in vitro activity. We conclude that the establishment of this target and screening strategy provides a framework for the future development of new antimicrobials and drug discovery.

Si l’immobilier est de loin la plus importante classe d’actifs de notre économie, elle est également l’une des dernières à ne pas disposer d’un marché de dérivés mature. Des études académiques récentes ont montré que le manque de compréhension de leurs prix en est la principale raison. Ce travail doctoral cherche à y remédier. Par la conduite d’études à la fois théoriques et empiriques, nous sommes parvenus à déterminer leurs caractéristiques statistiques, leurs facteurs de risque mais aussi à appréhender l’intérêt de ces produits en terme de fonction de découverte des prix. Si les dérivés immobiliers constituent un outil de paramétrisation du risque immobilier essentiel, ils offrent également la possibilité aux investisseurs comme aux pouvoirs publics de disposer d’informations qui ne seraient pas disponibles autrement / Despite the fact that real estate is the largest asset class in our economy, it is one of the few that do not have a mature derivatives market. Recent academic studies have shown that the lack of understanding of real estate derivatives’ prices is the main reason for the absence of a market. This dissertation aims to change this. By conducting theoretical and empirical studies we describe their statistical characteristics, their risk factors, and we highlight their importance in terms of price discovery function. Property derivatives are an essential tool for risk management, but they also offer for investors and regulators a source of information that would otherwise not be available

L'agrégation d'activités pour l'identification de catégories de comportements est un enjeu majeur de tous les systèmes socio-techniques complexes actuels. La question clé consiste à réaliser une synthèse de façons de faire (ou praxies) intégrant la variabilité des opérateurs humains impliqués. Dans un cadre aéronautique, l'agrégation d'activités de pilotage vise à accélérer la détermination de procédures améliorant la sécurité des vols et l'efficacité des missions ; elle repose sur les données objectives des paramètres enregistrés des phases de vol significatives et se structure grâce à une interprétation experte. Un modèle d’Agrégation Supervisée : - décomposition, - maïeutique, - reconstruction, est ainsi établi dans cette thèse. Le cœur en est la 2e étape qui généralise et enrichit le concept de « moyenne » classique des approches probabilistes : une base d'apprentissage, constituée d'activités déterminées et caractérisées par l'interprétation experte, est utilisée pour identifier les motifs significatifs de paramètres enregistrés, c'est à dire les praxies qui agrègent donc les éléments essentiels des activités. Ceux-ci sont choisis au sein d'un ensemble de motifs paramétrables génériques, dont les divers seuils sont ajustés de manière incrémentale. Les motifs sont alors évalués selon les deux critères intrinsèques de cohérence et de pertinence de leurs seuils, ainsi que le critère extrinsèque de la conformité des résultats obtenus par leur utilisation aux vols de la base d'apprentissage. Peuvent à ce niveau se faire jour des groupements parmi les éléments de la base d'apprentissage, selon les motifs rendant compte des activités particulières. L'expertise doit également être généralisable pour permettre l'étude de plusieurs points-clé dans cette étape maïeutique.Ce modèle générique définit une activité comme une structure formelle de praxies, et ouvre la voie à un enrichissement de la 3e étape intégrant la multiplicité des rôles des opérateurs. / Aggregating activities in order to identify categories of behaviour is a major topic of actual complex socio-technical systems. The key issue lies in incorporating the variability of implied human operators in the synthesis of ways of doing (or praxis). Aggregation of piloting activities is directed to allow a faster and more secure determination of procedures enhancing flight security and mission efficiency; it is based on the objective data of flight parameters recorded during significant flight phases, and is carried under thorough expert interpretation.A Supervised Aggregation model, consisting in the 3 steps of 1) decomposition, 2) maieutics, and 3) reconstruction, is thus devised in the present PhD. At the heart of this aggregation process, the 2nd maieutic step generalizes and enriches the usual concept of ''mean'', deeply related to probabilistic approaches: a set of activities analyzed and characterized by the expert, the learning basis, is related to significant patterns in the lot of recorded flight parameter values, in other words the praxis resulting of the aggregation of the activities. The patterns are selected from a collection of customizable generic patterns, whose thresholds are incrementally adjusted using the learning basis. The obtained patterns are then assessed according to the three criteria of 1) coherence and 2) likelihood of the thresholds, as well as the 3) conformity of these patterns used on the learning basis. At this stage, groups among the studied behaviours might emerge, gathering those for which an activity would be depicted by similar patterns. Expert-knowledge must be generalized in order to perform the joint analysis of several key points in this maieutic step.This generic model defines an activity as a formal structure of praxis, paving the way towards the further developments of the process, through the enrichment of the 3rd step, incorporating the multiplicity of operating roles.

Le challenge de la consommation d'énergie dans les réseaux de capteurs sans fil constitue un verrou technologique qui reste un problème ouvert encore aujourd'hui. Ces travaux de thèse s'inscrivent dans la problématique de la conservation de l'énergie dans les réseaux de capteurs et s'articulent autour de deux axes. Dans la première partie, nous abordons le dimensionnement des protocoles de découverte de voisinage. Nous proposons deux techniques de dimensionnement de ces protocoles qui visent à optimiser la consommation d'énergie des nœuds capteurs. La première technique, PPM-BM, consiste à dimensionner le protocole de découverte de voisins en fonction du niveau de batterie du nœud. La deuxième approche, ECoND, vise à ajuster la fréquence de découverte de voisins en fonction de la connectivité estimée à chaque instant. Cette technique tire profit des cycles temporels des modèles de mouvement des nœuds. La connectivité est estimée en se basant sur l'historique des rencontres. La découverte de voisins est ajustée en fonction du taux de connectivité estimé. Les résultats enregistrés mettent en évidence l'efficacité de ces deux techniques dans l'optimisation de la consommation d'énergie des nœuds sans affecter les performances de taux de livraison de messages et d'overhead. La deuxième partie de la thèse concerne l'optimisation des performances des réseaux de capteurs en termes de durée de vie. Nous reconsidérons dans cette partie certains protocoles de routage relevant du domaine des réseaux à connectivité intermittente et nous proposons le protocole EXLIOSE qui se base sur la capacité d'énergie résiduelle au niveau des nœuds pour assurer un équilibre énergétique, partager la charge et étendre à la fois la durée de vie des nœuds ainsi que celle du réseau / The challenge of energy consumption in wireless sensor networks is a key issue that remains an open problem. This thesis relates to the problem of energy conservation in sensor networks and is divided into two parts. In the first part, we discuss the design of neighbor discovery protocols. We propose two techniques for modulating these protocols in order to optimize the energy consumption of sensor nodes. The first technique, PPM-BM aims to modulate the neighbor discovery protocol based on the battery level of the node. The second approach ECoND aims to set up the frequency of neighbor discovery based on estimated connectivity. This technique takes advantage of the temporal cycles of nodes' movement patterns. Connectivity is estimated based on encounters' history. A neighbor discovery is set up based on the estimated rate of connectivity. The achieved results demonstrate the effectiveness of these techniques in optimizing the energy consumption of nodes while maintaining acceptable message delivery and overhead rates. In the second part of the thesis, we contribute to the optimization of the performance of sensor networks in terms of network lifetime. We review in this section some routing protocols for networks with intermittent connectivity and we propose EXLIOSE protocol which is based on residual energy to ensure energy-balancing, load sharing and network lifetime extending

D.Phil.

The last decade has demonstrated steady growth and utilization of Web Service technology. While Web Services have become significant in a number of IT domains such as eCommerce, digital libraries, data feeds, and geographical information systems, common portals or registries of Web Services require manual publishing for indexing. Manually compiled registries of Web Services have proven useful but often fail to include a considerable amount of Web Services published and available on the Web. We propose a system capable of finding, binding, and integrating Web Services into an index in an automated manner. By using a combination of guided search and web crawling techniques, the system finds a large number of Web Service providers that are further bound and aggregated into a single portal available for public use. Results show that this approach is successful in discovering a considerable number of Web Services in the GIS(Geographical Information Systems) domain, and demonstrate improvements over existing methods of Web Service Discovery.

The first chapter of this dissertation examines the returns to frequent acquirers from emerging markets and analyzes the cross-country variations in cumulative abnormal returns. The sample consists of 5,147 transactions carried out by firms from 17 common and civil-law countries during the period of January 1985 to June 2008. I find that the cumulative abnormal returns decline over the deal order and it is more pronounced in civil-law countries than in common-law countries. There is also evidence that the premiums paid by acquirers from civillaw countries with a first successful acquisition are higher than those from common-law countries. These findings are consistent with agency problems and the hubris hypothesis, first introduced by Roll (1986). The second chapter examines the information links across futures markets in different nations, using Vector Autoregressive (VAR)-Dynamic Conditional Correlation (DCC) model. The data comprise a large set of commodity and financial futures traded in U.S., U.K., China, Japan, Canada, and Brazil during the period from August 1998 to December 2008. The primary finding is that market interactions are relatively high for commodities for which information production generally is more diverse (metal commodities), while moderate for commodities for which information is more concentrated (agricultural commodities). Furthermore, the strength and persistence of interactions among futures markets decline after excluding the most informative markets. These findings indirectly support the breadth of information being a relevant factor in the extent of information linkage. The results also indicate that the dynamic correlation in futures markets is high in most commodity and financial futures if there is a significant bi-directional return and volatility spillover. Additionally, I estimate a market’s contribution to the price discovery process. In general, the market that has a stronger price impact and a stronger volatility spillover tends to be the market that has greater contribution or leadership in price discovery.

Following changes to the Federal Rules of Civil Procedure in 2006 dealing with the role of Electronically Stored Information, digital forensics is becoming necessary to the discovery process in civil litigation. The development of case law interpreting the rule changes since their enactment defines how digital forensics can be applied to the discovery process, the scope of discovery, and the duties imposed on parties. Herein, pertinent cases are examined to determine what trends exist and how they effect the field. These observations buttress case studies involving discovery failures in large corporate contexts along with insights on the technical reasons those discovery failures occurred and continue to occur. The state of the art in the legal industry for handling Electronically Stored Information is slow, inefficient, and extremely expensive. These failings exacerbate discovery failures by making the discovery process more burdensome than necessary. In addressing this problem, weaknesses of existing approaches are identified, and new tools are presented which cure these defects. By drawing on open source libraries, components, and other support the presented tools exceed the performance of existing solutions by between one and two orders of magnitude. The transparent standards embodied in the open source movement allow for clearer defensibility of discovery practice sufficiency whereas existing approaches entail difficult to verify closed source solutions. Legacy industry practices in numbering documents based on Bates numbers inhibit efficient parallel and distributed processing of electronic data into paginated forms. The failures inherent in legacy numbering systems is identified, and a new system is provided which eliminates these inhibiters while simultaneously better modeling the nature of electronic data which does not lend itself to pagination; such non-paginated data includes databases and other file types which are machine readable, but not human readable in format. In toto, this dissertation provides a broad treatment of digital forensics applied to electronic discovery, an analysis of current failures in the industry, and a suite of tools which address the weaknesses, problems, and failures identified.