Les maladies cardiovasculaires (MCV) sont d’une étiologie complexe et elles sont soumises à de nombreux facteurs environnementaux ainsi que génétiques. Malgré les succès obtenus, pendant la dernière décennie, et pour réduire la mortalité CV il est nécessaire l’identification de nouveaux biomarqueurs en utilisant des approches différentes. Cette thèse propose une approche intégrative pour découvrir de nouvelles associations génétiques associés avec les MCV. Nous avons d’abord réuni les résultats existants grâce à des GWAS précédents, puis nous avons recherché la pléiotropie de ces gènes et nous avons dirigé nos efforts vers une possible traduction des résultats obtenus dans l’application clinique. Nous avons détecté les effets pléiotropiques de différent gènes (IL-6R et ABO) avec différents phénotypes lipidiques et inflammatoires. Par ailleurs, nous avons trouvé quelques associations gène-genre intéressantes pour certains gènes étudiés (ABO et GNB3). Concernant l’implémentation clinique des connaissances obtenues par cette thèse, une SNP dans le gène TREM-1, pourrait être utilisé comme un marqueur de risque pour différentes maladies, et nous avons déposé un brevet Européen et nous envisageons de mener des essais cliniques de chez les patients. D’autre part, nous avons détecté une haplotype du gène IL6R qui pourrait être utilisés dans la médecine personnalisée. Nos résultats aident à mieux comprendre comment les gènes étudiés exercent leurs effets au niveau moléculaire, en influant finalement sur l’état des patients souffrant de MCV. Nous espérons que nos résultats vont être pris en compte pour faire progresser la médecine personnalisée / Cardiovascular diseases (CVD) are complex diseases where many environmental and genetic factors are involved. Although the genetic aetiology of the CVD has been extensively investigated the last two decades, alternative approaches are needed in order to keep advancing in the pathophysiology of CVD. In this thesis, we propose an integrative approach to discover new genetic associations potentially involved in CVD. We chose previous GWAS hits and we centred our efforts in studying the pleiotropic and gene-gender interaction effects. Finally, we focused on the implementation of personalized genome-based therapy of the results obtained. New pleiotropic effects were discovered in the IL-6R and ABO genes relating them with different inflammatory and lipid phenotypes. In addition, we studied the gene-gender interaction effects, finding some sex-specific associations in two of the genes studied (ABO and GNB3). Further, we centered our efforts in implementing the results obtained during the thesis at the clinical level. One SNP within the TREM-1 gene was associated with increased levels of its protein and could be used as a predictor or risk biomarker for different diseases. Due to the high potential of this SNP, we applied a European patent and we are planning to start clinical trials in patients. Also, one haplotype in the IL-6R gene could be used in the treatment of personalized medicine. During this thesis, we discovered new gene-phenotype associations involved in CVD and other diseases. Our results help to better understand how the studied genes are exerting their effects at the molecular level. Our results will hopefully be taken into account in future personalized treatments

Face à la surabondance des ressources et de l'information sur le net, l'accès aux ressources pertinentes devient une tâche fastidieuse pour les usagers de la toile. Les systèmes de recommandation personnalisée comptent parmi les principales solutions qui assistent l'utilisateur en filtrant les ressources, pour ne lui proposer que celles susceptibles de l’intéresser. L’approche basée sur l’observation du comportement de l’utilisateur à partir de ses interactions avec le e-services est appelée analyse des usages. Le filtrage collaboratif et le filtrage basé sur le contenu sont les principales techniques de recommandations personnalisées. Le filtrage collaboratif exploite uniquement les données issues de l’analyse des usages alors que le filtrage basé sur le contenu utilise en plus les données décrivant le contenu des ressources. Un système de recommandation hybride combine les deux techniques de recommandation. L'objectif de cette thèse est de proposer une nouvelle technique d'hybridation en étudiant les bénéfices de l'exploitation combinée d'une part, des informations sémantiques des ressources à recommander, avec d'autre part, le filtrage collaboratif. Plusieurs approches ont été proposées pour l'apprentissage d'un nouveau profil utilisateur inférant ses préférences pour l’information sémantique décrivant les ressources. Pour chaque approche proposée, nous traitons le problème du manque de la densité des données et le problème du passage à l’échelle. Nous montrons également, de façon empirique, un gain au niveau de la précision des recommandations par rapport à des approches purement collaboratives ou purement basées sur le contenu / Face to the ongoing rapid expansion of the Internet, user requires help to access to items that may interest her or him. A personalized recommender system filters relevant items from huge catalogue to particular user by observing his or her behavior. The approach based on observing user behavior from his interactions with the website is called usage analysis. Collaborative Filtering and Content-Based filtering are the most widely used techniques in personalized recommender system. Collaborative filtering uses only data from usage analysis to build user profile, while content-based filtering relies in addition on semantic information of items. Hybrid approach is another important technique, which combines collaborative and content-based methods to provide recommendations. The aim of this thesis is to present a new hybridization approach that takes into account the semantic information of items to enhance collaborative recommendations. Several approaches have been proposed for learning a new user profile inferring preferences for semantic information describing items. For each proposed approach, we address the sparsity and the scalability problems. We prove also, empirically, an improvement in recommendations accuracy against collaborative filtering and content-based filtering

With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements. In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2017-03-21T12:43:34Z No. of bitstreams: 1 Felipe Maciel dos Santos Souza.pdf: 1503174 bytes, checksum: d781e0be9bb5887943877b642f573a55 (MD5) / Made available in DSpace on 2017-03-21T12:43:34Z (GMT). No. of bitstreams: 1 Felipe Maciel dos Santos Souza.pdf: 1503174 bytes, checksum: d781e0be9bb5887943877b642f573a55 (MD5) Previous issue date: 2017-03-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Distance education (DE) is a teaching modality that has been standing out in the current scenario, and provides a new platform for the teaching / learning process, which requires consideration of alternative models. This study analyzed the effects of the application of a Personalized System of Instruction (PSI) on student learning in a compulsory subject of the Psychology course of a private university, offered at distance, in relation to: (a) student performance on the initial and final evaluations; assessments of unit, on the bimonthly test, substitute and final exams; (b) interaction with the opportunities offered in the course; (c) possible relationship between student performance and interaction with the opportunities offered in the course. 28 students participated in the study. In all corrections, substantive feedbacks were used. The bimonthly test had a maximum value of 7.5 points, and 15 participants scored at or above 6.0. The others had scores varying from 4.5 to 6.0. It is verified that completing the units is an important factor for a good performance in the bimonthly test, but other factors may also have affected the performance of the participants: the difficulty of the questions presented in the test and the fact that the participants had another evaluation in the same day. The regularity of the work does not seem to be a predictor of the yield in the bimonthly test. In the evaluation of the majority of the participants, the discipline, as taught, was considered better or better than the Institution's traditional distance education method. PSI applied to distance education seems to be a very effective system of teaching, given the high final grades obtained by the students who responded to the initial and final evaluations / A educação a distância (EAD) é uma modalidade de ensino que tem se destacado no cenário atual, e proporciona uma nova plataforma para o processo de ensino/aprendizagem, que requer a consideração de modelos pedagógicos alternativos. Este estudo analisou os efeitos da aplicação do Sistema Personalizado de Ensino (PSI) sobre a aprendizagem dos alunos, em uma disciplina, de caráter obrigatório, do curso de Psicologia de uma universidade privada, oferecida a distância, em relação a: (a) desempenho de participantes nas avaliações inicial e final; de unidade; nas provas bimestral e substitutiva, e no exame final; (b) interação dos participantes com as oportunidades oferecidas no curso e (c) possível relação entre o desempenho dos participantes e suas interações com as oportunidades oferecidas no curso. Foram participantes do estudo 28 estudantes. Em todas as correções realizadas foram utilizados feedbacks substantivos. A prova bimestral teve valor máximo de 7,5 pontos, e 15 participantes tiraram nota igual a ou acima de 6,0. Os demais tiveram notas que variaram de 4,5 a 6,0. Verifica-se que completar as unidades é um fator importante para um bom desempenho na prova bimestral, mas outros fatores também podem ter afetado o desempenho dos participantes: a dificuldade das questões apresentadas na prova e o fato de os participantes já terem realizado outra avaliação no mesmo dia. A regularidade do trabalho não parece ser um preditor do rendimento na prova bimestral. Na avaliação da maioria dos participantes, a disciplina, como ministrada, foi considerada melhor ou muito melhor do que o método tradicional de ensino a distância da Instituição. O PSI aplicado à educação a distância parece ser um sistema muito eficaz de ensino, dadas as altas notas finais obtidas pelos alunos que responderam às avaliações inicial e final

Glycogen is the sole carbohydrate storage molecule found in mammalian cells and plays an important role in cellular metabolism in nearly all tissues, including the brain. Defects in glycogen metabolism underlie the glycogen storage diseases (GSDs), genetic disorders with variable clinical phenotypes depending on the mutation type and affected gene(s). Lafora disease (LD) is a fatal form of progressive myoclonus epilepsy and a non-classical GSD. LD typically manifests in adolescence with tonic-clonic seizures, myoclonus, and a rapid, insidious progression. Patients experience increasingly severe and frequent epileptic episodes, loss of speech and muscular control, disinhibited dementia, and severe cognitive decline; death usually ensues in the second decade of life. LD, like one- third of all epilepsy disorders, is intractable and resistant to antiseizure drugs. A hallmark of LD is the accumulation of intracellular, insoluble carbohydrate aggregates known as Lafora bodies (LBs) in brain, muscle, and other tissues. LBs are a type of polyglucosan body, an insoluble aggregate of aberrant glycogen found in some GSDs and neurodegenerative disorders. Like most GSDs, LD is an autosomal recessive genetic disorder. Approximately 50% of LD patients carry mutations in the epilepsy, progressive myoclonus 2A (EPM2A) gene encoding laforin, a glycogen phosphatase. Remaining patients carry mutations in EPM2B, the gene that encodes malin, an E3 ubiquitin ligase. Laforin and malin play important roles in glycogen metabolism. In the absence of either enzyme, glycogen transforms into an insoluble, hyperphosphorylated and aberrantly branched polysaccharide reminiscent of plant starch. This abnormal polysaccharide precipitates to form LBs and has pathological consequences in the brain. Since a definitive LD diagnosis requires genetic testing, whole exome sequencing has been increasingly used to diagnose LD. As a result, numerous cases of more slowly progressing or late-onset LD have been discovered that are associated with missense mutations in EPM2A or EPM2B. Over 50 EPM2A missense mutations have been described. These mutations map to many regions of the laforin X-ray crystal structure, suggesting they produce a spectrum of effects on laforin function. In the present work, a biochemical pipeline was developed to characterize laforin patient mutations. The mutations fall into distinct classes with mild, moderate or severe effects on laforin function, providing a biochemical explanation for less severe forms of LD. LBs drive LD pathology. As a result, LBs and glycogen metabolism have become therapeutic targets. Since LBs are starch-like, and starch is degraded by amylases, these enzymes are potential therapeutics for reducing LB loads in vivo. However, amylases are normally secreted enzymes. Degradation of intracellular LBs requires a cell-penetrating delivery platform. Herein, an antibody-enzyme fusion (AEF) technology was developed to degrade LBs in vitro, in situ in cell culture, and in vivo in LD mouse models. AEFs are a now putative precision therapy for LD, potentially the first therapeutic to provide a significant clinical benefit. Prior to this work, LD was considered a homogenous disorder and treatments were only palliative. The data herein support a spectrum of clinical progression, a potential therapy for LD, and mechanistic insights into LD pathophysiology. This work illustrates how personalized medicine, both in diagnosis and treatment, can be achieved through basic biochemical approaches to human disease.

Reconceptualization is needed for English language teaching as a response to English-as-an-international language (EIL) contexts. Correspondingly this will lead to what is called “an ecological approach” that invites accommodation of language-learning instruction to the local needs within discourse communities. In this research, the researcher will investigate the English for specific purposes (ESP) needs of business students to explore how best to address their expressed needs through subsequent tutoring. The ecological approach along with vocabulary teaching approach may look deeply into students’ needs. To assess their needs, a questionnaire was developed and administrated to international students in the College of Business and Public Administration (CBPA) at California State University, San Bernardino to investigate what students feel are their strengths as well as needs, in order to succeed in their business studies.

The purpose of implementing educational reform is to improve the academic achievement and social skills of graduating students, but evaluating the benefits of a particular instructional method or curriculum design can be complicated. In an online and problem-based learning environment that allows students to choose content and assessment projects and self-pace, the motivation of students to learn and their engagement in the learning process significantly influences the success of the program. This generic qualitative study focused on the experiences of middle school students participating in an online and problem-based educational setting. The study included interview data and self-evaluation questionnaires about students' levels of motivation and engagement. Vygotsky's zone of proximal development (ZPD), Bandura's theory of self-efficacy, Dewey's experiential learning theory and other motivational theories provided the conceptual framework for this qualitative study of personalizing learning in constructivist environments. The data were analyzed through inductive thematic analysis with constant comparison. The findings highlighted the student perspective and identified factors that influenced students' buy-in to this type of personalized education. The results from this study may be used to help teachers plan and design curriculum and instructional strategies that encourage student motivation to learn and engagement in the learning process. Students who are motivated to learn and engaged in the learning process are more likely to graduate from school with the knowledge and skills required to enter the workforce and become productive knowledge workers in a knowledge economy.

As pessoas estão cada vez mais viajando de avião e, muitas vezes, em viagens longas. A qualidade do ar torna-se uma questão crucial. Uma forma de melhorar a qualidade do ar e as condições de conforto térmico dentro de uma cabine de aeronave está na utilização de novos sistemas de ventilação personalizada. O objetivo do presente trabalho consiste na análise experimental da influência de um sistema de ventilação personalizada (PV) na concentração, dispersão e remoção de partículas expiratórias em cabine de aeronave com sistema por mistura (MV) e por deslocamento (DV). Os ensaios foram realizados em um mock-up de cabine de aeronave comercial de 12 assentos, com 4 assentos por fileira. O ar foi insuflado na cabine a 18°C pelo sistema MV ou DV, correspondendo a uma leve sensação de frio, e a 24°C pelo sistema personalizado (PV), com vazão de 3,0 l/s, operando no assento próximo à fuselagem e ao corredor, alternadamente. As partículas simulando uma pessoa espirrando foram injetadas em dois pontos no fundo da cabine, respectivamente, no assento próximo à fuselagem e naquele junto do corredor, a 1,10m do piso, que corresponde à região de respiração. Foram medidas velocidades e temperaturas do ar e de partículas ao longo de toda a cabine. Os resultados mostraram que no sistema MV o sistema PV somente influenciou o escoamento do ar e a concentração de partículas no assento onde o sistema PV estava operando, com uma eficiência na remoção de partículas de até 30%. No sistema DV, por sua vez, o sistema PV apresentou eficiência de remoção de até 49% nos assentos em que estava operando. Contudo, o sistema PV pode aumentar em até 32% a concentração de partículas no assento próximo da janela quando o sistema PV estava operando no assento próximo do corredor, no sistema DV. Finalmente, os resultados mostraram resultados mais promissores do sistema PV no sistema MV, com melhoria significativa na remoção de partículas nos assentos onde está operando, sem influenciar negativamente no assento ao lado. / People are increasingly traveling by plane and often on long journeys. Air quality becomes a crucial issue. One way to improve air quality and thermal comfort conditions within an aircraft cabin is to use new personalized ventilation systems. The objective of the present work is the experimental analysis of the influence of a personalized ventilation system (PV) on the concentration, dispersion and removal of expiratory particles in aircraft cabin with mixed system (MV) and displacement (DV). The tests were performed in a 12 seat commercial aircraft cabin mock-up, with 4 seats per row. The air was inflated in the cabin at 18 ° C by the MV or DV system, corresponding to a slight cold sensation, and at 24 ° C by the custom system (PV), with a flow rate of 3.0 l / s, operating in the nearby seat the fuselage and the aisle, alternately. Particles simulating a person sneezing were injected at two points in the bottom of the cockpit, respectively, in the seat near the fuselage and next to the corridor, 1.10m from the floor, which corresponds to the breathing region. Air and particle velocities and temperatures were measured throughout the cabin. The results showed that in the MV system the PV system only influenced the air flow and the concentration of particles in the seat where the PV system was operating, with a particle removal efficiency of up to 30%. The DV system together with PV system showed removal efficiency of up to 49% in the seats in which it was operating. However, the PV system can increase particle concentration in the near-window seat by up to 32% when the PV system was operating on the seat next to the aisle in the DV system. Finally, the results showed more promising results of the PV system in the MV system, with significant improvement in particle removal in the seats where it is operating, without negatively influencing the next seat.

Translational Biomedical Informatics (TBMI) is an emerging discipline expanding beyond traditional bioinformatics, with a focus on developing computational technologies for real-world biomedical practice. The goal of my Ph.D. research is to address a few key challenges in TBI, including: (1) the high quality and reproducibility required by medical applications when processing high throughput data, (2) the need for knowledge management solutions that allow molecular data to be handled and evaluated by researchers, regulators, and doctors collectively, (3) the need for near real-time, efficient access to decision-oriented visualizations of integrated data and data processing results, and (4) the need for an integrated solution that can evolve as medical consensus evolves, without requiring retraining, overhaul or replacement. This dissertation resulted in the development and adoption of concrete web-based application deliverables in regular use by bioinformaticians, clinicians, biologists and nanotechnologists. These include: the Chip Artifact Correction (caCORRECT) web site and grid services, the ArrayWiki community microarray repository, and the SimpleVisGrid visualization grid services (including eGOMiner, nanoDRIVE, PathwayVis and SphingoVisGrid).

Title from PDF of title page. / Document formatted into pages; / Thesis (Ph.D.)--University of South Florida, 2001. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Current learner-centered trends, such as supplying students with content on demand (CoD), coupled with research findings that indicate distributed practice is superior to massed practice in terms of increased memory function and that the Personalized System of Instruction (PSI) is superior to traditional instruction in terms of academic achievement, content retention, and student satisfaction, prompted an investigation merging these two lines of research. Although PSI is more feasible today based on advances in technology and students prefer its self-paced component, they often procrastinate. In fact, this problem is resurfacing in distance education courses and is reflected in low completion rates as well as in the number of nonstarters. Numerous researchers have used deadline contingencies to reduce procrastination without adversely affecting student achievement and satisfaction, but few have considered the benefit of enhanced memory. / ABSTRACT: It was hypothesized that, by providing students with CoD, a lesser form of self-pacing, and by using contingencies to regulate the pace of assignment submissions, procrastination would be reduced and content retention subsequently increased without detriment to immediate achievement and student satisfaction. To quantify differences in procrastination level, a comprehensive, sensitive, and reliable measure of procrastination, called the rate of relative digression from a target response (RDTR), was proposed. Undergraduate, preservice teachers in an instructional technology course were randomly assigned to one of three treatments. All groups were given the same deadlines. For one treatment, the deadlines were recommended (R) with one absolute deadline at the end of the treatment interval. For another they were conditional (C) with opportunities to earn bonus and penalty points for early and late work. / ABSTRACT: For a third, they were all absolute (A) with no assignment accepted for credit after its due date. Although many problems experienced by students in A made findings for this group inconclusive, analysis of differences between students in R and C indicated that C was superior in reducing procrastination and enhancing memory function without detriment to immediate achievement, pacing preference, and course satisfaction. Although more research is needed to replicate, extend, and clarify findings, these results support using conditional deadlines for assignments when learners are supplied CoD. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.