La plupart des travaux réalisés dans le domaine de l'assistance à l'autonomie à domicile (AAL) reposent sur l'utilisation de capteurs visuels et audio tels que des caméras. Or, ces capteurs sont souvent rejetés par le patient à cause de leur caractère invasif. Des approches alternatives requièrent l'utilisation de capteurs embarqués sur la personne (GPS, bracelets électroniques ou puces RFID dans les vêtements...), et leur pertinence est donc ramenée à l'hypothèse que les personnes les portent effectivement, sans jamais les rejeter ni les oublier. Pour ces raisons, dans cette thèse, nous trouvons plus pertinentes les approches uniquement basées sur l'utilisation de capteurs binaires intégrés dans l'habitat, tels que les détecteurs de mouvements, les tapis sensitifs ou les barrières optiques. Dans un tel contexte technologique, il devient intéressant d'utiliser les paradigmes, les modèles et les outils des systèmes à événement Discrets (SED), initialement plutôt développés pour la modélisation, l'analyse et la commande des systèmes industriels complexes. Dans ces travaux de thèse, l'objectif est de construire une approche pour la modélisation et le suivi des habitudes de vie, basée sur les modèles et les paradigmes des SED et répondant à une problématique qui s'énonce de la manière suivante : L'objectif est de développer un cadre global pour découvrir et reconnaître les activités de la vie quotidienne d'un habitant vivant seul dans une maison intelligente. Cette maison intelligente doit être équipée uniquement de capteurs binaires, l'étiquetage par des experts des activités observées ne doit pas être nécessaire et les activités peuvent être représentées par des modèles probabilistes. La première méthode présentée dans cette thèse permet, à partir d'une base de données d'apprentissage et d'une description experte des activités à modéliser listées par des médecins, de construire pour chaque activité un modèle sous la forme d'un automate à état-fini probabiliste (PFA). La seconde méthode développée lors de cette thèse permet d'estimer en temps réel, à partir des seules données observées par les capteurs ambiants, quelle activité la personne observée réalise effectivement. Les méthodes décrites dans cette thèse sont illustrées en utilisant les données générées localement via l'utilisation d'un appartement prêté par ENS Paris-Saclay équipé pour répondre aux besoins expérimentaux de cette thèse. / Most of the work done in the field of ambient assisted living (AAL) is based on the use of visual and audio sensors such as cameras. However, these sensors are often rejected by the patient because of their invasiveness. Alternative approaches require the use of sensors embedded on the person (GPS, electronic wristbands or RFID chips in clothing ...), and their relevance is therefore reduced to the assumption that people actually wear them, without rejecting nor forgetting them. For these reasons, in this thesis, we find more relevant the approaches based on the use of binary sensors integrated into the habitat only, such as motion detectors, sensory mats or optical barriers. In such a technological context, it becomes interesting to use paradigms, models and tools of Discrete Event Systems (DES), initially developed for modeling, analysis and control of complex industrial systems. In this thesis work, the goal is to build an activity of daily living modeling and monitoring approach, based on the models and the paradigm of the DES and answering a problem that is expressed as follows:The objective is to develop a global framework to discover and recognise activities of daily living of an inhabitant living alone in a smart home. This smart home have to be equipped with binary sensors only, expert labeling of activities should not be needed and activities can be represented by probabilistic models. The first method presented in this thesis allows to build a probabilistic finite-state automata (PFA) from a learning database and an expert description of the activities to be modeled given by the medical staff. The second method developed during this thesis estimates, according to the observations, the activity performed by the monitored inhabitant. The methods described in this thesis are applied on data generated using an apartment lent by ENS Paris-Saclay and equipped according the experimental needs of this thesis. / La maggior parte dei lavori nel settore dell’Ambient Assisted Living (AAL) si basasull’uso di sensori visivi e audio come le telecamere. Tuttavia, questi sensori sonospesso rifiutati dal paziente a causa della loro natura invasiva. Gli approcci alternativi richiedono l’uso di sensori integrati nella persona (GPS, bracciali elettronici o chipRFID...), e la loro rilevanza è quindi ridotta all’ipotesi che le persone li indossino effettivamente, senza mai rifiutarli o dimenticarli.Per questi motivi, in questa tesi, troviamo approcci più rilevanti basati esclusivamente sull’uso di sensori binari integrati nell’habitat, come rilevatori di movimento,tappeti sensoriali o barriere fotoelettriche.In tale contesto tecnologico, diventa interessante utilizzare i paradigmi, i modelli egli strumenti dei sistemi ad eventi discreti (SED), inizialmente sviluppati per la modellazione, l’analisi e il controllo di sistemi industriali complessi.In questo lavoro di tesi, l’obiettivo è quello di presentare un metodo per la modellazione e il monitoraggio delle abitudini di vita, basato sui modelli e paradigmi di SEDe rispondendo ad un problema che si esprime come segue : L’obiettivo è quello di sviluppare un quadro globale per rivelare e riconoscere le attività della vita quotidiana di una persona che abita da sola in una smart home chedovrebbe essere dotata solo di sensori binari. Inoltre si suppone che non sia necessarial’etichettatura delle attività osservate da parte di un esperto e tali attività sono rappresentate da modelli probabilistici.Il primo metodo presentato in questa tesi permette di costruire un modello probabilistico di automa a stati finiti (PFA) ottenuto da un database di apprendimento e unadescrizione delle attività da parte di medici. Il secondo metodo sviluppato in questa tesi stima, alla luce delle osservazioni, qualeattività svolge la persona osservata. I metodi descritti sono illustrati utilizzando dati generati localmente attraverso l’usodi un appartamento messo a disposizione da ENS Paris-Saclay e attrezzato per soddisfarele esigenze sperimentali di questa tesi.

Indiana University-Purdue University Indianapolis (IUPUI) / Precision medicine aims to employ information from all modalities to develop a comprehensive view of disease progression and administer therapies tailored to the individual patient. A set of genomic features (gene CNVs, mutations, mRNA expressions, and protein abundances) is associated with each patient and it is hard to explain the phenotypic similarities such as gene essentiality or variability in drug response in a single genomic level. Thus, to extract biological principles it is critical to seek mutual information from multi-dimensional datasets. To address these concerns, we first conduct an integrated mRNA/protein analysis in both breast cancer cell lines and tumors, and most interestingly in the breast cancer subtypes. We identified cell lines that provide optimum heterogeneity models for studying the underlying biological processes of tumors. Our systematic observation across multi-omics data identifies distinct subgroups of cancer cells and patients. Based on this identified signal transduction between mRNA and RPPA, we developed a biclustering model to characterize key genetic alterations that are shared in both cancer cell lines and patients. We integrated two types of omics data including copy number variations, transcriptome, and proteome. Bi-EB adopts a data-driven statistics strategy by using Expected-Maximum (EM) algorithm to extract the foreground bicluster pattern from its background noise data in an iterative search. Using Bi-EB algorithm we selected translational gene sets that are characterized by highly correlated molecular profiles among RNA and proteins. To further investigate cell line and tissue in breast cancer we explore the relationship vii between genomic features and the phenotypic factors. Using in vitro/in vivo drug screening data, we adopt partial least square regression method and develop a multi-modular approach to predict anticancer therapy benefits for ER-negative breast cancer patients. The identified joint multi-dimensional modules here provide us new insights into the molecular mechanisms of drugs and cancer treatment. / 2021-12-28

The congenital myasthenic syndromes (CMS) are a group of rare genetic diseases affecting the neuromuscular junction (NMJ). These syndromes affect signal transmission and result in fatigable muscle weakness. In this study we performed exome analysis of 2 CMS patient cohorts and identified SYT11, a synaptotagmin inhibitor of clathrin mediated endocytosis (CME), and MGAT5B, a glycosylation protein, as potential novel CMS genes using bioinformatic analysis on the RD-Connect Genome Phenome Analysis Platform (GPAP). To validate them, we utilized morpholino knockdown models of zebrafish orthologues syt11a, syt11b, and mgat5b and conducted functional assays measuring chorion activity and escape response. Our results show that co-knockdown of syt11a/b or syt11b alone (and not mgat5b) results in a substantial neuromuscular deficit, with ablation of chorion activity and severely reduced escape response. Immunofluorescent studies showed both motor neuron growth and NMJ formation was inhibited by syt11a/b knockdown. In conclusion, syt11b causes a severe neuromuscular phenotype in zebrafish which supports SYT11 as a novel CMS-causing gene.

During the last three decades, research and activity within international entrepreneurship have accelerated. The international entrepreneurial process consists of, for instance, international opportunities that are recognized as a critical function. Over time, the research view of the entrepreneur within the entrepreneurial process has shifted from being an individual to being more frequently discovered as plural, and more recent studies emphasize that the entrepreneurial team is the primary catalyst for new venture creation. However, the complexity differs for the entrepreneurial teams that internationalize since uncertainty and increased risk, which may require appropriate levels of collective cognition affecting the teams and international opportunities. This thesis aims to combine aspects of international entrepreneurship and international business with exploring to what extent the collective cognition of entrepreneurial teams determines the discovery and exploitation of international opportunities within Swedish SMEs. By investigating from the entrepreneurial team perspective, this thesis strives to present a more accurate picture of the impact of collective cognition on organizations' international opportunities. An abductive research approach combined with a qualitative method has been applied to ensure a more extensive understanding of the subject. The literature review provides definitions as well as an in-depth understanding of the thesis’ four core concepts; collective cognition, internationalization, international opportunities, and entrepreneurial teams. Finally, the theoretical framework concludes with a conceptual framework model explaining how the concepts are related. The empirical findings tie together with the theoretical framework, and the concepts are further analyzed in relation and contrast to each other. Lastly, the findings are gathered in the final chapter, concluding that entrepreneurial teams’ international opportunities are influenced by collective cognition in Swedish SMEs, which can play an essential role in the internationalization and discovery and exploitation of further international opportunities. / Nej

The data mining is still little investigated area. This project is aimed firstly generally to the knowledge discovery from the structured data, especially from the datas in XML format. Furthermore the tree algorithm HybridTreeMiner is presented here with aim of its application for the knowledge discovery from XML documents. The practical part of this project is dedicated to the design of the conception for the algorithm integration to the mining system developed in FIT. This system is implemented in the programming language Java, it has modular   structure and its parts communicate each other by means of the language DMSL. Reached results are presented and discussed in the end.

京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第18221号 / 薬科博第25号 / 新制||薬科||4(附属図書館) / 31079 / 京都大学大学院薬学研究科医薬創成情報科学専攻 / (主査)教授 掛谷 秀昭, 教授 高須 清誠, 准教授 大野 浩章 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19132号 / 情博第578号 / 新制||情||101(附属図書館) / 32083 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 高橋 達郎, 教授 原田 博司, 教授 梅野 健 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20737号 / 情博第651号 / 新制||情||112(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 石田 亨, 教授 美濃 導彦, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21425号 / 工博第4535号 / 京都大学大学院工学研究科合成・生物化学専攻 / (主査)教授 浜地 格, 教授 杉野目 道紀, 教授 秋吉 一成 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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