Ingeniero Civil Industrial / En la siguiente memoria se busca corroborar, por medio de una experiencia práctica y aplicada, si a caso el uso de las técnicas de Web Opinion Mining (WOM) y de herramientas informáticas, permiten determinar las tendencias generales que pueden poseer un conjunto de opiniones presentes en la Web. Particularmente, los reclamos publicados en el sitio web Reclamos.cl, y que apuntan contra instituciones pertenecientes a las industrias nacionales de Educación y de Gobierno. En ese sentido, los consumidores cada vez están utilizando más la Web para publicar en ella las apreciaciones positivas y negativas que poseen sobre lo que adquieren en el mercado, situación que hace de esta una mina de oro para diversas instituciones, especialmente para lo que es el identificar las fortalezas y las debilidades de los productos y los servicios que ofrecen, su imagen pública, entre varios otros aspectos. Concretamente, el experimento se realiza a través de la confección y la ejecución de una aplicación informática que integra e implementa conceptos de WOM, tales como Knowledge Discovery from Data (KDD), a modo de marco metodológico para alcanzar el objetivo planteado, y Latent Dirichlet Allocation (LDA), para lo que es la detección de tópicos dentro de los contenidos de los reclamos abordados. También se hace uso de programación orientada a objetos, basada en el lenguaje Python, almacenamiento de datos en bases de datos relacionales, y se incorporan herramientas pre fabricadas con tal de simplificar la realización de ciertas tareas requeridas. La ejecución de la aplicación permitió descargar las páginas web en cuyo interior se encontraban los reclamos de interés para la realización experimento, detectando en ellas 6.460 de estos reclamos; los cueles estaban dirigidos hacia 245 instituciones, y cuya fecha de publicación fue entre el 13 de Julio de 2006 y el 5 de Diciembre de 2011. Así también, la aplicación, mediante el uso de listas de palabras a descartar y de herramientas de lematización, procesó los contenidos de los reclamos, dejando en ellos sólo las versiones canónicas de las palabras que los constituían y que aportasen significado a estos. Con ello, la aplicación llevó a cabo varios análisis LDA sobre estos contenidos, los que arbitrariamente se definieron para ser ejecutados por cada institución detectada, tanto sobre el conjunto total de sus reclamos, como en segmentos de estos agrupados por año de publicación, con tal de generar, por cada uno de estos análisis, resultados compuestos por 20 tópicos de 30 palabras cada uno. Con los resultados de los análisis LDA, y mediante una metodología de lectura e interpretación manual de las palabras que constituían cada uno de los conjuntos de tópicos obtenidos, se procedió a generar frases y oraciones que apuntasen a hilarlas, con tal de obtener una interpretación que reflejase la tendencia a la cual los reclamos, representados en estos resultados, apuntaban. De esto se pudo concluir que es posible detectar las tendencias generales de los reclamos mediante el uso de las técnicas de WOM, pero con observaciones al respecto, pues al surgir la determinación de las tendencias desde un proceso de interpretación manual, se pueden generar subjetividades en torno al objeto al que apuntan dichas tendencias, ya sea por los intereses, las experiencias, entre otros, que posea la persona que realice el ejercicio de interpretación de los resultados.

AMANDA-II est un télescope à neutrinos composé d'un réseau tri-dimensionnel de senseurs optiques déployé dans la glace du Pôle Sud.<p>Son principe de détection repose sur la mise en évidence de particules secondaires chargées émises lors de l'interaction d'un neutrino de haute énergie (> 100 GeV) avec la matière environnant le détecteur, sur base de la détection de rayonnement Cerenkov.<p><p>Ce travail est basé sur les données enregistrées par AMANDA-II entre 2000 et 2006, afin de rechercher des sources cosmiques de neutrinos.<p>Le signal recherché est affecté d'un bruit de fond important de muons et de neutrinos issus de l'interaction du rayonnement cosmique primaire dans l'atmosphère. En se limitant à l'observation de l'hémisphère nord, le bruit de fond des muons atmosphériques, absorbés par la Terre, est éliminé.<p>Par contre, les neutrinos atmosphériques forment un bruit de fond irréductible constituant la majorité des 6100 événements sélectionnés pour cette analyse.<p>Il est cependant possible d'identifier une source ponctuelle de neutrinos cosmiques en recherchant un excès local se détachant du bruit de fond isotrope de neutrinos atmosphériques, couplé à une sélection basée sur l'énergie, dont le spectre est différent pour les deux catégories de neutrinos.<p><p>Une approche statistique originale est développée dans le but d'optimiser le pouvoir de détection de sources ponctuelles, tout en contrôlant le taux de fausses découvertes, donc le niveau de confiance d'une observation.<p>Cette méthode repose uniquement sur la connaissance de l'hypothèse de bruit de fond, sans aucune hypothèse sur le modèle de production de neutrinos par les sources recherchées. De plus, elle intègre naturellement la notion de facteur d'essai rencontrée dans le cadre de test d'hypothèses multiples.La procédure a été appliquée sur l'échantillon final d'évènements récoltés par AMANDA-II.<p><p>---------<p><p>MANDA-II is a neutrino telescope which comprises a three dimensional array of optical sensors deployed in the South Pole glacier. <p>Its principle rests on the detection of the Cherenkov radiation emitted by charged secondary particles produced by the interaction of a high energy neutrino (> 100 GeV) with the matter surrounding the detector.<p><p>This work is based on data recorded by the AMANDA-II detector between 2000 and 2006 in order to search for cosmic sources of neutrinos. A potential signal must be extracted from the overwhelming background of muons and neutrinos originating from the interaction of primary cosmic rays within the atmosphere.<p>The observation is limited to the northern hemisphere in order to be free of the atmospheric muon background, which is stopped by the Earth. However, atmospheric neutrinos constitute an irreducible background composing the main part of the 6100 events selected for this analysis.<p>It is nevertheless possible to identify a point source of cosmic neutrinos by looking for a local excess breaking away from the isotropic background of atmospheric neutrinos;<p>This search is coupled with a selection based on the energy, whose spectrum is different from that of the atmospheric neutrino background.<p><p>An original statistical approach has been developed in order to optimize the detection of point sources, whilst controlling the false discovery rate -- hence the confidence level -- of an observation. This method is based solely on the knowledge of the background hypothesis, without any assumption on the production model of neutrinos in sought sources. Moreover, the method naturally accounts for the trial factor inherent in multiple testing.The procedure was applied on the final sample of events collected by AMANDA-II. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Antibiotic resistance has been a developing problem for mankind in recent decades and multi-drug resistant bacteria are now encountered that are resistant to all treatment options available. In 2014, the World Health Organization announced that this problem is driving us towards a “post-antibiotic era” that will change the face of modern medicine as we know it. If lack of novel antibiotic development and FDA approval continues, by the year 2050, 10 million people will die each year to an antimicrobial resistant bacterial infection. With lack of pharmaceutical industry involvement in developing novel antibiotics, the responsibility now lies within the academic institutions to identify potential novel therapeutics to fuel the antibiotic drug discovery pipeline. Combinatorial chemistry is one technique used to expedite the discovery process by assessing a large chemical space in a relatively short time when compared to traditional screening approaches. Combinatorial libraries can be screened using multiple approaches and has shown successful application towards many disease states. We initially discovered broad spectrum antibacterial bis-cyclic guanidines using combinatorial libraries and expanded on the knowledge of the physiochemical attributes necessary to inhibit Gram negative bacterial pathogens. Following this success, we continued to assess the combinatorial libraries for adjunctive therapeutics that potentiate the activity of obsolete clinical antibiotics. The polyamine efflux pump inhibitors discovered in this subsequent study prove the benefits of using the large chemical space provided in the combinatorial libraries to identify a variety of therapeutics. Our studies always begin with identifying an active compound and active compounds undergo hit-to-lead optimization. This optimization studies are of utmost importance in developing a novel antibacterial agent for therapeutic applications. Our medicinal chemistry work described here is proof of the success of careful structure activity analyses to optimize a hit scaffold to create a more effective antibacterial agent. Overall, our work described here reveals the potential role of academic institutions in fending off the impending “post-antibiotic era”.

Abstract INVESTEGATION OF ANTICOAGULATION PROPERTIES OF SULFATED GLYCOSAMINOGLYCAN MIMETICS By Elsamani Ismail Abdelfadiel, MS A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University, 2017. Supervisor: Umesh R Desai Professor, Department of Medicinal Chemistry The existence of thrombosis in numerous pathophysiological situations formed a vast necessity for anticoagulation therapy. Thrombin and factor Xa are the only two factors of the entire coagulation cascade that have been major targets for regulation of clotting via the direct and indirect mechanism of inhibition. Our recent discovery of sulfated non-saccharide glycosaminoglycan mimetics, especially G2.2, that demonstrates highly selective cancer stem-like cells (CSCs) inhibition activity. G2.2 inhibited the growth of CSCs from multiple cancer cell lines. To evaluate its in vivo anticoagulation effect, we asked a contract research organization (CRO) to produce 20 g of material, labelled as G2.2Y. Evaluation of G2.2C in HT-29 xenograft mouse model showed a significant reduction in tumor volume and CSC markers, but unexpected bleeding consequences in some animals were observed. Also in a tail bleeding experiment, G2.2Y showed a significant enhancement in bleeding volume. Comparable studies with G2.2 synthesized in our laboratory had shown no bleeding effects. To investigate the difference between the two G2.2 samples (G2.2W (white) and G2.2Y (Yellow) that were performed using UPLC-MS characterization, we were able to determine that the G2.2Y sample was an 85:15 blend of two compounds. Elemental, NMR and MS data revealed that G2.2W was fully sulfated flavonoid derivative, as expected, but G2.2Y contained one less sulfate group. We tested both agents for their inhibition of various coagulation factors and revealed that G2.2Y inhibited fXIa nearly 2-fold better in comparison to G2.2W. Furthermore, activated partial thromboplastin time assay (APTT) indicated that G2.2W exhibited almost 3-4-fold less anticoagulant activity compared to G2.2Y. This indicates that the loss of just one sulfate group could induce substantial side effects and lead to a discovery of new anticoagulant agent. Such structure–activity relationship is important to understand if the in vivo metabolism of the agents leads to accumulation of de-sulfated products.

Structure-based drug design and virtual screening are areas of computational medicinal chemistry that use 3D models of target proteins. It is important to develop better methods in this field with the aim of increasing the speed and quality of early stage drug discovery. The first part of this thesis focuses on the application of structure-based drug design in the search for inhibitors for the protein 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), one of the enzymes in the DOXP/MEP synthetic pathway. This pathway is found in many bacteria (such as Mycobacterium tuberculosis) and in the parasite Plasmodium falciparum. In order to evaluate and improve current virtual screening methods, a benchmarking data set was constructed using publically available high-throughput screening data. The exercise highlighted a number of problems with current data sets as well as with the use of publically available high-throughput screening data. We hope this work will help guide further development of well designed benchmarking data sets for virtual screening methods. Conformal prediction is a new method in the computer-aided drug design toolbox that gives the prediction range at a specified level of confidence for each compound. To demonstrate the versatility and applicability of this method we derived models of skin permeability using two different machine learning methods; random forest and support vector machines.

Abstract In today's world, enormous amounts of data are being collected everyday. Thus, the problems of storing, handling, and utilizing the data are faced constantly. As the human mind itself can no longer interpret the vast datasets, methods for extracting useful and novel information from the data are needed and developed. These methods are collectively called knowledge discovery methods. In this thesis, a novel combination of feature selection and data modeling methods is presented in order to help with this task. This combination includes the methods of basic statistical analysis, linear correlation, self-organizing map, parallel coordinates, and k-means clustering. The presented method can be used, first, to select the most relevant features from even hundreds of them and, then, to model the complex inter-correlations within the selected ones. The capability to handle hundreds of features opens up the possibility to study more extensive processes instead of just looking at smaller parts of them. The results of k-nearest-neighbors study show that the presented feature selection procedure is valid and appropriate. A second advantage of the presented method is the possibility to use thousands of samples. Whereas the current rules of selecting appropriate limits for utilizing the methods are theoretically proved only for small sample sizes, especially in the case of linear correlation, this thesis gives the guidelines for feature selection with thousands of samples. A third positive aspect is the nature of the results: given that the outcome of the method is a set of conditional probabilities, the derived model is highly unrestrictive and rather easy to interpret. In order to test the presented method in practice, it was applied to study two different cases of steel manufacturing with hot strip rolling. In the first case, the conditional probabilities for different types of retentions were derived and, in the second case, the rolling conditions for the occurrence of wedge were revealed. The results of both of these studies show that steel manufacturing processes are indeed very complex and highly dependent on the various stages of the manufacturing. This was further confirmed by the fact that with studies of k-nearest-neighbors and C4.5, it was impossible to derive useful models concerning the datasets as a whole. It is believed that the reason for this lies in the nature of these two methods, meaning that they are unable to grasp such manifold inter-correlations in the data. On the contrary, the presented method of conditional probabilities allowed new knowledge to be gained of the studied processes, which will help to better understand these processes and to enhance them.

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application is market basket analysis. If we want to mine temporal association rules on time series data, changes need to be made. During temporal association rule mining, the temporal ordering nature of data and the temporal interval between the left and right patterns of a rule need to be considered. This thesis reviews some mining methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series. Both algorithms are scalable and efficient. In addition, temporal association rules are generated from the patterns found. Finally, the usability and efficiency of the algorithms are demonstrated by evaluating the results.

The infrequency of known T cell targets in high grade serous ovarian carcinoma (HSGC) is a substantial barrier to the development of targeted immunotherapies. Due to their infrequency, antigen discovery is a crucial component of immunotherapeutic design. In our cohort of HGSC cases, the cancer testis (CT) antigen lactate dehydrogenase C (LDHC) is expressed in 76% of tumours (22/29). As LDHC presents with tumour specificity in women, I hypothesize that LDHC is an immunogenic target in HGSC patients, and that LDHC-specific T cells can be activated and expanded for therapeutic purposes. As such, I sought to examine whether endogenous LDHC-specific T cells were present in the ascites of HGSC patients. A standard Rapid Expansion Protocol was used to expand CD8 T cell cultures from patient ascites. These cultures were screened for reactivity to a peptide library encompassing all possible epitopes of the LDHC protein by interferon-γ ELISpot. With this approach, T cell clones from one of five patients were identified that were reactive to minimal peptides contained within LDHC. In this patient, the antigenic LDHC peptide differentiated from LDHA by a single amino acid at its C-terminus (YTSWAIGLSVM versus YTSWAIGLSVA). In recognition assays, tumour cell lines expressing endogenous LDHC, autologous ascites, or autologous B cells transfected with LDHC were unable to elicit T cell responses. Although this study suggests that LDHC is not immunogenic, continued screening of LDHC and other CT proteins will likely provide additional immunotherapeutic targets. / Graduate

The aim of this study is to explore the perceptions of Mathematics teachers using Information and Communications Technology (ICT) as educational tool in their classrooms. This study focuses on the Mathematics teachers’ 21st century-oriented pedagogical practices that propagate learning outcomes that are considered essential for all learners to prosper in this ever-changing and demanding information society. The learning competencies considered are termed lifelong competencies as they transcend the classroom and school environment and can thus be used to solve authentic problems in day-to-day life. The development of these learning competencies, especially by using ICT, has become vital in equipping learners with the necessary skills to become confident citizens in this globalised world. The role the teacher plays is increasingly acknowledged as having a major impact on this process. An essential assumption of this study is that learning activities facilitated by teachers utilising ICT efficiently and effectively as an educational tool have the potential of enhancing the quality of learning competencies. Moreover, as the role of the teacher in these activities is highly important, the teacher’s characteristics and background have the potential to determine the overall success of the learners. Using the underlying principles of Activity Theory and the conceptual framework of SITES 2006 this study investigates the relationship between these three components, i.e. ICT integration, learning competencies, and teacher background and characteristics. The intricate relationships that exist among these three components are investigated in this study in the context of Mathematics education. This is a secondary data analysis study that utilises data from the SITES 2006 South African Mathematics teachers’ questionnaire. Only Mathematics teachers who indicated using ICT as an educational tool in the discovery of Mathematics principles and concepts were considered. Using Spearman’s correlation coefficient, the data was analysed to determine the strength of the relationships among the variables. Findings of the study suggest that certain teacher characteristics do indeed influence the probability of teachers developing certain learning competencies in learners. Moreover, the findings indicate that a number of the learning competencies investigated in this study are not as readily attained as others. / Dissertation (MEd)--University of Pretoria, 2013. / gm2013 / Science, Mathematics and Technology Education / unrestricted

Les systèmes autonomes sont des objets mobiles communicants capables de réaliser un certain nombre de tâches sans intervention humaine. Le coût (e.g. argent, poids, énergie) de la charge utile requise pour effectuer certaines missions est parfois trop important pour permettre aux engins d’embarquer la totalité des capacités nécessaires (i.e. capteurs et actionneurs). Répartir ces capacités sur plusieurs entités est une solution naturelle à ce problème. Un tel groupe d’entités constitue une flotte à laquelle il devient nécessaire de fournir un mécanisme de découverte permettant aux différents engins de partager leurs capacités respectives afin de résoudre une mission globale de façon collaborative. Ce mécanisme, outre l’affectation des tâches, doit gérer les conflits et les pannes potentielles qui peuvent survenir à tout moment sur tout engin de la flotte. Fort de ces constations, nous proposons un nouveau mécanisme collaboratif nommé AMiRALE qui apporte une solution aux problèmes ci-dessus pour les flottes hétérogènes d’engins mobiles autonomes. Notre système est entièrement distribué et repose uniquement sur des communications asynchrones. Nous proposonségalement un nouvel outil nommé NEmu permettant de créer des réseaux virtuels mobiles avec un contrôle important sur les propriétés de la topologie du réseau ainsi que sur la configuration des noeuds et des inter-connexions. Cet outil permet la réalisation d’expérimentations réalistes sur des prototypes d’applications réseaux. Enfin, nous proposons une évaluation de notre système collaboratif AMiRALE au travers d’un scénario de nettoyage de parc utilisant une flotte autonome de drones et de robots terrestres spécialisés. / We call autonomous systems, mobile and communicating objects which are able to perform several tasks without any human intervention. The overall cost (including price, weight and energy) of the payload required by some missions is sometimes too important to enable the entities to embed all the required capabilities (i.e. sensors and actuators). This is the reason why it is more suitable to spread all the capabilities among several entities. The team formed by those entities is called a swarm. It then becomes necessary to provide a discovery mechanism built into the swarm in order to enable its members to share their capabilities and to collaborate for achieving a global mission.This mechanism should perform task allocation as well as management of conflicts and failures which can occur at any moment on any entity of the swarm. In this thesis, we present a novel collaborative system which is called AMiRALE for heterogeneous swarms of autonomous mobile robots. Our system is fully distributed and relies only on asynchronous communications. We also present a novel tool called NEmu which enables to create virtual mobile networks with a complete control over the network topology, links and nodes properties. This tool is designed for performingrealistic experimentation on prototypes of network applications. Finally, we present experimental results on our collaborative system AMiRALE obtained through a park cleaning scenario which relies on an autonomous swarm of drones and specialized ground robots.