b-amyloid is derived from amyloid precursor protein (APP) and tightly associated with the pathogenesis of Alzheimer¡¦s disease (AD). Structurally, APP belongs to type I transmembrane protein family and is composed of a large glycosylated extracellular component, a single membrane-spanning region, and a short cytoplasmic domain. Although physiological function of APP remains unclear, the proteolytic processing of APP by b secretase and g secretase gives rise to the production and secretion of b-amyloid. The C-terminus of APP is believed to participate in the intracellular trafficking of APP and signal transduction via interacting with adaptors and signaling proteins, respectively. Three phosphorylation sites (Thr654, Ser655 and Thr668, numbering for APP695 isoform) and several functional motifs in the cytoplasmic domain of APP have been identified and demonstrated that the phosphorylation can indeed affect APP metabolism including: the rate of secretion, endocytosis and b-amyloid production. In this study, we focused on how APP binding protein1 and the phosphorylation affect on APP metabolism. The reasons are as following: (1) Among many APP associated proteins, APP binding protein 1 (APPBP1) is involved in S-M checkpoint regulation. (2) Recent evidence indicates that aberrantly activation of mitotic events may play an important role in development of AD. Since progression through mitosis is regulated by Cdc2 that has been demonstrated to phosphorylate APP on Thr668-Pro669, the phosphorylation of APP at Thr668 may play the important role in regulating APP metabolism and may also relate to AD development. (3) Moreover, protein phosphorylation induces the conformational change and affects the protein- protein interaction. Phosphorylation of Ser / Thr-Pro motif is a central mechanism controlling progression of the cell cycle, including mitosis. Proline residues provide a potential backbone switch in the polypeptide chain controlled by the cis / trans isomerization. Pin1 is an important mitotic regulator and a highly specific peptidyl-prolyl cis / trans isomerases (PPIase) that catalyzes the isomerization of phosphorylated Ser / Thr-Pro bonds. Our unpublished data have shown that Pin1 can bind to the phosphorylated Thr668-Pro669 APP peptide with high affinity (20 nM) that suggested that Pin1 may interact and regulate mitotic APP. Taken together, these data suggested that the interaction of APP and APPBP1 or Pin1 may affect the APP metabolism and its physiological function. This study investigated the hypothesis above and revealed includes the following results (i) the subcellular localization of the C-terminus of APP and APPBP1; (ii) the interaction between APPBP1 and the C-terminus of APP in vivo and in vitro; (iii) Thr668 of APP is the Cdc2 phosphorylation site; (iv) the binding of APPBP1 to the C-terminus of APP reduces the phosphorylation of APP by Cdc2; (v) the phosphorylation at Thr668 can abolish the interaction between APPBP1 and the C-terminus of APP; (vi) the C-terminus of APP is one of the caspase 3 targets; (vii) the phosphorylation of APP at Thr668 also reduces the caspase 3 activity forward to the C-terminus of APP cleavage; (viii) both APPBP1 and Pin1 can inhibit the C-terminus of APP cleavage by caspase 3 that suggested two novel mechanisms to regulate APP metabolism.

Un intérêt croissant est aujourd'hui porté sur les robots capables de conduire des activités de collaboration d'une manière naturelle et efficace. Nous avons développé une architecture et un système qui traitent des aspects décisionnels de ce problème. Nous avons mis en oeuvre cette architecture pour traiter trois problèmes différents: le robot observateur, le robot équipier et enfin le robot instructeur. Dans cette thèse, nous discutons des défis et problématiques de la coopération homme-robot, puis nous décrivons l'architecture que nous avons développée et enfin détaillons sa mise oeuvre et les algorithmiques spécifiques à chacun des scénarios.Dans le cadre du scénario du robot observateur, le robot maintient un état du monde à jour au moyen d'un raisonnement géométrique effectué sur les données de perception, produisant ainsi une description symbolique de l'état du monde et des agents présents. Nous montrons également, sur la base d'un système de raisonnement intégrant des processus de décision de Markov (MDPs) et des réseaux Bayésiens, comment le robot est capable d'inférer les intentions et les actions futures de ses partenaires humain, à partir d'une observation de leurs mouvements relatifs aux objets de l'environnement. Nous identifions deux types de comportements proactifs : corriger les croyances de l'homme en lui fournissant l'information pertinente qui lui permettra de réaliser son but, aider physiquement la personne dans la réalisation de sa tâche, une fois celle-ci identifiée par le robot.Dans le cas du robot équipier, ce dernier doir réaliser une tâche en coopération avec un partenaire human. Nous introduisons un planificateur nommé Human-Aware Task Planner et détaillons la gestion par notre systeme du plan partagé par un composant appelé Plan Management component. Grâce à se système, le robot peut collaborer avec les hommes selon trois modalités différentes : robot leader, human leader, ou equal partners. Nous discutons des fonctions qui permettent au robot de suivre les actions de son partenaire humain et de vérifier qu'elles sont compatibles ou non avec le plan partagé et nous montrons comment le robot est capable de produire des comportements sûrs qui permettent de réaliser la tâche en prenant en compte de manière explicite la présence et les actions de l'homme ainsi que ses préférences. L'approche est fondée sur des processus décisionnels de Markov hiérarchisés avec observabilité mixte et permet d'estimer l'engagement de l'homme et de réagir en conséquence à différents niveaux d'abstraction. Enfin, nous discutions d'une approche prospective fondée sur un planificateur multi-agent probabiliste mettant en œuvre des MDPs et de sa pertinence quant à l'amélioration du composant de gestion de plan partagé.Dans le scénario du robot instructeur, nous détaillons les processus décisionnels qui permettent au robot d'adapter le plan partagé (shared plan) en fonction de l'état de connaissance et des désirs de son partenaire humain. Selon, le cas, le robot donne plus ou moins de détails sur le plan et adapte son comportement aux connaissances de l'homme ; Une étude utilisateur a également été menée permettant de valider la pertinence de cette approche.Finalement, nous présentons la mise en œuvre d'un robot guide autonome et détaillons les processu décisionnels que nous y avons intégrés pour lui permettre de guider des voyageurs dans un hall d'aéroport en s'adaptant au mieux au contexte et aux désirs des personnes guidées. Nous illustrons dans ce contexte des comportement adaptatifs et pro-actifs. Ce système a été effectivement intégré sur le robot Spencer qui a été déployé dans le terminal principal de l'aéroport d'Amsterdam (Schiphol). Le robot a fonctionné de manière robuste et satisfaisante. Une étude utilisateur a permis, dans ce cas également, de mesurer les performances et de valider le système. / There has been an increasing interest, in the last years, in robots that are able to cooperate with humans not only as simple tools, but as full agents, able to execute collaborative activities in a natural and efficient way. In this work, we have developed an architecture for Human-Robot Interaction able to execute joint activities with humans. We have applied this architecture to three different problems, that we called the robot observer, the robot coworker, and the robot teacher. After quickly giving an overview on the main aspects of human-robot cooperation and on the architecture of our system, we detail these problems.In the observer problem the robot monitors the environment, analyzing perceptual data through geometrical reasoning to produce symbolic information.We show how the system is able to infer humans' actions and intentions by linking physical observations, obtained by reasoning on humans' motions and their relationships with the environment, with planning and humans' mental beliefs, through a framework based on Markov Decision Processes and Bayesian Networks. We show, in a user study, that this model approaches the capacity of humans to infer intentions. We also discuss on the possible reactions that the robot can execute after inferring a human's intention. We identify two possible proactive behaviors: correcting the human's belief, by giving information to help him to correctly accomplish his goal, and physically helping him to accomplish the goal.In the coworker problem the robot has to execute a cooperative task with a human. In this part we introduce the Human-Aware Task Planner, used in different experiments, and detail our plan management component. The robot is able to cooperate with humans in three different modalities: robot leader, human leader, and equal partners. We introduce the problem of task monitoring, where the robot observes human activities to understand if they are still following the shared plan. After that, we describe how our robot is able to execute actions in a safe and robust way, taking humans into account. We present a framework used to achieve joint actions, by continuously estimating the robot's partner activities and reacting accordingly. This framework uses hierarchical Mixed Observability Markov Decision Processes, which allow us to estimate variables, such as the human's commitment to the task, and to react accordingly, splitting the decision process in different levels. We present an example of Collaborative Planner, for the handover problem, and then a set of laboratory experiments for a robot coworker scenario. Additionally, we introduce a novel multi-agent probabilistic planner, based on Markov Decision Processes, and discuss how we could use it to enhance our plan management component.In the robot teacher problem we explain how we can adapt the plan explanation and monitoring of the system to the knowledge of users on the task to perform. Using this idea, the robot will explain in less details tasks that the user has already performed several times, going more in-depth on new tasks. We show, in a user study, that this adaptive behavior is perceived by users better than a system without this capacity.Finally, we present a case study for a human-aware robot guide. This robot is able to guide users with adaptive and proactive behaviors, changing the speed to adapt to their needs, proposing a new pace to better suit the task's objectives, and directly engaging users to propose help. This system was integrated with other components to deploy a robot in the Schiphol Airport of Amsterdam, to guide groups of passengers to their flight gates. We performed user studies both in a laboratory and in the airport, demonstrating the robot's capacities and showing that it is appreciated by users.