This thesis describes the synthesis and biological evaluation of a library of compounds containing the tetramic acid core in search of novel antibacterial drug candidates. Chapter 1 discusses the need for new antibiotics due to the emergence of virulent bacterial strains resistant to clinically available drugs and the hiatus in the discovery of new replacement antibitoics that has become a global threat to human health. Different platforms for antibacterial drug discovery and the re-emergence of natural products-based approach that has gained importance in the quest for novel antibiotics are discussed. In this regard, the intrinsic antibacterial activity of natural products containing a tetramate core structure and the strategies developed to synthesise the core scaffold are described. Chapter 2 discusses the use of ʟ-serine and ʟ-cysteine in tetramic acid synthesis and the application of ʟ-cysteine-derived thiazolidine templates suitable for stereoselective ring closing reactions to obtain the tetramic acid core with scope for further functionalization. Chapters 3 and 4 describe a range of synthetic routes for appropriate substitutions of the tetramate core for compound library generation. Elaboration of the tetramate core via carboxamide tetramate synthesis, Suzuki-Miyaura cross-coupling reactions, glycosylations and their aglycone analogue synthesis, etherification, tetramate-pyroglutamate systems, Buchwald aminations/amidations, cycloadditions and β-lactam hybrids as possible chemical modifications of the tetramate core structure are discussed. Chapter 5 describes the antibacetiral activity and physicochemical properties of the library of compounds synthesised. A preliminary evaluation of their antibiotic activity was conducted against S. aureus and E. coli using the hole-plate method. MICs of the tetramates synthesised were determined against several Gram-negative strains; Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Gram-positive strains; MRSA, Enterococcus faecalis and Streptococcus pneumoniae, in whole-cell bioassays. Physicochemical properties of the compound library were analysed to map the chemical space occupied by tetramates with potent antibacterial activity. Enzyme inhibition studies were conducted to identify possible modes of action that contribute to whole-cell antibiotic activity and in this regard, the inhibition of enzymes S. aureus topoisomerase IV, S. aureus RNA polymerase, E. coli RNA polymerase, E. coli gyrase and M. tuberculosis gyrase are discussed. Since plasma protein binding of compounds is an important factor that determines the bioavailability of antibiotics and their clinical outcome, a study of the binding affinity of these drug candidates to Human Serum Albumin (HSA) by both whole-cell bioassay and NMR spectroscopy-based protein binding experiments are discussed. Finally, a brief note on the potential of tetramic acids to function as proteasome inhibitors in anticancer chemotherapy is included at the end of this chapter.

Resumo: Nas últimas décadas, a capacidade de gerar e coletar dados aumentou rapidamente, gerando a necessidade do desenvolvimento de novas técnicas e ferramentas capazes de processar e analisar esses dados descobrindo informações novas e úteis. Surgindo um proeminente campo de pesquisa para a extração de conhecimento de dados Descoberta de Conhecimento em Banco de Dados. Pela aplicação da metodologia da descoberta de conhecimento indireto aos atributos dos fragmentos cerâmicos coletados ao nível do solo no Sítio Arqueológico Piracanjuba Piraju SP, este trabalho pretende prover aos peritos em arqueologia uma visão sistêmica capaz de auxiliá-los no conhecimento das populações pretéritas que ali habitaram. / Abstract: In the last decades, the capacities to produce and collect data has grown fast and the development of news techniques and tools capable to processes and analyze this datas discovering new and useful information as necessary. Therefore, a huge research area has beginning for the extraction of data understanding Knowledge Discovery in Database. The indirect knowledge discovery applied to ceramic fragment collected at soil level in Piracanjuba's Piraju, SP aims give to archaeology experts a whole vision able to be useful knowledge of the past people living there. / Orientador: Nilton Nobuhiro Imai / Coorientador: Neide Faccio Barrocá / Coorientador: Vilma Tachibana / Banca: Milton Hirokazu Shimabukuro / Banca: Mário Hissamitsu Tarumoto / Banca: José Luiz de Morais / Banca: Emília Mariko Kashimoto / Doutor

Today, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormously increased and stands in need of effective processing, analysis, and visualisation to provide hidden insights owing to the lack of semantic information (particularly in spatiotemporal data), complexity, large volume of trivial records, and absence of effective information visualisation on a large scale. Meanwhile, new technologies such as visual analytics have emerged with great potential in data mining and visualisation to overcome the challenges in handling such data and to support individuals in many aspects of their life. Thus, this thesis contemplates the importance of scalability and conducts a comprehensive investigation into visual analytics and its impact on the process of knowledge discovery from the European Commission project MyHealthAvatar at the Centre for Visualisation and Data Analytics by actively involving individuals in order to establish a credible reasoning and effectual interactive visualisation of such multivariate data with particular focus on lifestyle and personal events. To this end, this work widely reviews the foremost existing work on data mining (with the particular focus on semantic enrichment and ranking), data visualisation (of time-oriented, personal, and spatiotemporal data), and methodical evaluations of such approaches. Subsequently, a novel automated place annotation is introduced with multilevel probabilistic latent semantic analysis to automatically attach relevant information to the collected personal spatiotemporal data with low or no semantic information in order to address the inadequate information, which is essential for the process of knowledge discovery. Correspondingly, a multi-signi ficance event ranking model is introduced by involving a number of factors as well as individuals' preferences, which can influence the result within the process of analysis towards credible and high-quality knowledge discovery. The data mining models are assessed in terms of accurateness and performance. The results showed that both models are highly capable of enriching the raw data and providing significant events based on user preferences. An interactive visualisation is also designed and implemented including a set of novel visual components signifi cantly based upon human perception and attentiveness to visualise the extracted knowledge. Each visual component is evaluated iteratively based on usability and perceptibility in order to enhance the visualisation towards reaching the goal of this thesis. Lastly, three integrated visual analytics tools (platforms) are designed and implemented in order to demonstrate how the data mining models and interactive visualisation can be exploited to support different aspects of personal life, such as lifestyle, life pattern, and memory recall (reminiscence). The result of the evaluation for the three integrated visual analytics tools showed that this visual analytics approach can deliver a remarkable experience in gaining knowledge and supporting the users' life in certain aspects.

Face au danger de la désinformation et de la prolifération de fake news (fausses nouvelles) sur le Web, la notion de véracité des données constitue un enjeu crucial. Dans ce contexte, il devient essentiel de développer des modèles qui évaluent de manière automatique la véracité des informations. De fait, cette évaluation est déjà très difficile pour un humain, en raison notamment du biais de confirmation qui empêche d’évaluer objectivement la fiabilité des informations. De plus, la quantité d'informations disponibles sur le Web rend cette tâche quasiment impossible. Il est donc nécessaire de disposer d'une grande puissance de calcul et de développer des méthodes capables d'automatiser cette tâche.Dans cette thèse, nous nous concentrons sur les modèles de découverte de la vérité. Ces approches analysent les assertions émises par différentes sources afin de déterminer celle qui est la plus fiable et digne de confiance. Cette étape est cruciale dans un processus d'extraction de connaissances, par exemple, pour constituer des bases de qualité, sur lesquelles pourront s'appuyer différents traitements ultérieurs (aide à la décision, recommandation, raisonnement…). Plus précisément, les modèles de la littérature sont des modèles non supervisés qui reposent sur un postulat : les informations exactes sont principalement fournies par des sources fiables et des sources fiables fournissent des informations exactes.Les approches existantes faisaient jusqu'ici abstraction de la connaissance a priori d'un domaine. Dans cette contribution, nous montrons comment les modèles de connaissance (ontologies de domaine) peuvent avantageusement être exploités pour améliorer les processus de recherche de vérité. Nous insistons principalement sur deux approches : la prise en compte de la hiérarchisation des concepts de l'ontologie et l'identification de motifs dans les connaissances qui permet, en exploitant certaines règles d'association, de renforcer la confiance dans certaines assertions. Dans le premier cas, deux valeurs différentes ne seront plus nécessairement considérées comme contradictoires ; elles peuvent, en effet, représenter le même concept mais avec des niveaux de détail différents. Pour intégrer cette composante dans les approches existantes, nous nous basons sur les modèles mathématiques associés aux ordres partiels. Dans le second cas, nous considérons des modèles récurrents (modélisés en utilisant des règles d'association) qui peuvent être dérivés à partir des ontologies et de bases de connaissances existantes. Ces informations supplémentaires peuvent renforcer la confiance dans certaines valeurs lorsque certains schémas récurrents sont observés. Chaque approche est validée sur différents jeux de données qui sont rendus disponibles à la communauté, tout comme le code de calcul correspondant aux deux approches. / The notion of data veracity is increasingly getting attention due to the problem of misinformation and fake news. With more and more published online information it is becoming essential to develop models that automatically evaluate information veracity. Indeed, the task of evaluating data veracity is very difficult for humans. They are affected by confirmation bias that prevents them to objectively evaluate the information reliability. Moreover, the amount of information that is available nowadays makes this task time-consuming. The computational power of computer is required. It is critical to develop methods that are able to automate this task.In this thesis we focus on Truth Discovery models. These approaches address the data veracity problem when conflicting values about the same properties of real-world entities are provided by multiple sources.They aim to identify which are the true claims among the set of conflicting ones. More precisely, they are unsupervised models that are based on the rationale stating that true information is provided by reliable sources and reliable sources provide true information. The main contribution of this thesis consists in improving Truth Discovery models considering a priori knowledge expressed in ontologies. This knowledge may facilitate the identification of true claims. Two particular aspects of ontologies are considered. First of all, we explore the semantic dependencies that may exist among different values, i.e. the ordering of values through certain conceptual relationships. Indeed, two different values are not necessary conflicting. They may represent the same concept, but with different levels of detail. In order to integrate this kind of knowledge into existing approaches, we use the mathematical models of partial order. Then, we consider recurrent patterns that can be derived from ontologies. This additional information indeed reinforces the confidence in certain values when certain recurrent patterns are observed. In this case, we model recurrent patterns using rules. Experiments that were conducted both on synthetic and real-world datasets show that a priori knowledge enhances existing models and paves the way towards a more reliable information world. Source code as well as synthetic and real-world datasets are freely available.

The current work entails the development of a novel high content platform for the measurement of kinetic ligand responses across cell signalling networks at the single-cell level in distinct PBMC subtypes ex vivo. Using automated sample preparation, fluorescent cellular barcoding and flow cytometry the platform is capable of detecting 21, 840 parallel cell signalling responses in each PBMC sample. We apply this platform to characterize the effects of neuropsychiatric treatments and CNS ligands on the T cell signalling repertoire. We apply it to define cell signalling network abnormalities in PBMCs from drug-naïve first-onset schizophrenia patients (n=12) relative to healthy controls (n=12) which are subsequently normalized in PBMCs from the same patients (n=10) after a six week course of clinical treatment with the atypical antipsychotic olanzapine. We then validate the abnormal cell signalling responses in PBMCs from an independent cohort of drug-naïve first-onset schizophrenia patients (n=25) relative to controls (n=25) and investigate the specificity of the abnormal PBMC responses in schizophrenia as compared to major depression (n=25), bipolar disorder (n=25) and autism spectrum disorder (n=25). Subsequently we conduct a phenotypic drug screen using the US Food and Drug Administration (FDA) approved compound library, in addition to experimental neuropsychiatric drug candidates and nutraceuticals, to identify compounds which selectively normalize the schizophrenia-associated cell signalling response. Finally these candidate compounds are characterized using structure-activity relationships to reveal specific chemical moieties implicated in the putative therapeutic effect.

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify new ligands for biological targets. In the technique, library synthesis and hit identification are neatly combined into a single step. A labile functionality between fragments allows the biological target to self-select binders from a dynamic combinatorial library (DCL) of interconverting building blocks. The scope of suitable reversible reactions that proceed under thermodynamic control in physiological conditions has been gradually expanded over the last decades, however DCC has thus far failed to gain traction as a technique appropriate for drug discovery in the pharmaceutical industry. The constraints placed on library size by validated analytical techniques, and the effort-intensive reality of this academically elegant concept have not allowed DCC to develop into a broad-platform technique to compete with the high-throughput screening campaigns favoured by medicinal chemists. This thesis seeks to develop DCL analysis techniques, in an effort to increase the library size and accelerate the analysis of DCC experiments. Using a 19F-labelled core scaffold, we constructed a DCL that could be monitored non-invasively by 19F NMR. Building on NMR techniques developed by fragment screening and non-biological DCC campaigns, the method was developed to circumvent the undesired equilibrium-perturbing side effects arising from sample-consuming analytical methods. The N-acylhydrazone (NAH) DCL equilibrated rapidly at pH 6.2 using 4-amino-L-phenylalanine (4-APA) as a novel, physiologically benign, nucleophilic catalyst. The DCL was designed to target b-ketoacyl-ACP synthase III (FabH), an essential bacterial enzyme and antibiotic target. From the 5-membered DCL, a single combination was identified as a privileged structure by our 19F NMR method. The result correlated well with an in vitro assay, validating 19F NMR as a tool for DCL screening. During the 19F NMR study we identified an established antimicrobial compound, 4,5- dichloro-1,2-dithiole-3-one (HR45), to have potential as a core scaffold from which to develop future DCLs targeting FabH. Despite the potentially tractable chemistry of HR45 for DCC, lack of knowledge around the inhibitory mechanism of the compound prevented us from proceeding. Thus, we used mass spectrometry, NMR and molecular modelling to show that HR45 acts by forming a covalent adduct with S. aureus FabH. The 5-chloro substituent directs attack from the nucleophilic thiol side chain of the essential active site cysteine-112 residue via a Michael-type addition elimination mechanism. Although interesting, this mechanism disfavoured the use of HR45 as a core scaffold for NAH exchange in a DCC campaign. Electrospray ionisation mass spectrometry (ESI-MS) is a powerful technique that allows for larger DCLs by eliminating the size-limitations imposed by the need for spectral or chromatographic resolution of DCL members. We developed a 4-APAcatalysed NAH library targeting the pyridoxal 5’-phosphate (PLP) dependent enzyme 7,8-diaminopelargonic acid synthase (BioA), an essential enzyme in the biotin biosynthesis pathway. We exploited the aldehyde moiety of PLP to form an NAH DCL with a panel of hydrazides, and used the BioA isozymes from M. tuberculosis (Mtb) and E. coli to template the library. A combination of buffer exchange and denaturing ESI-MS allowed us to conduct a DCC experiment with a 29-member DCL. Hits from the DCC experiment correlated well with differential scanning fluorimetry (DSF) results. Of these hits, 5 compounds were selected for further study. In vivo activity was displayed by 2 compounds against E. coli and the ESKAPE pathogen A. baumannii. The identification of compounds with antibacterial activity from a DCL further validates ESI-MS as a platform technology for drug discovery.

Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification. In conventional natural language processing (NLP), this requires experts to annotate scientific text, which is costly and time consuming, resulting in small labelled datasets. This leads to extensive feature engineering and handcrafting in order to fully utilise small labelled datasets, which is again time consuming, and not portable between tasks and domains. In this work, we explore emerging neural network methods to reduce the burden of feature engineering while outperforming the accuracy of conventional pipeline NLP techniques. We focus specifically on the cancer domain in terms of applications, where we introduce two NLP classification tasks and datasets: the first task is that of semantic text classification according to the Hallmarks of Cancer (HoC), which enables text mining of scientific literature assisted by a taxonomy that explains the processes by which cancer starts and spreads in the body. The second task is that of the exposure routes of chemicals into the body that may lead to exposure to carcinogens. We present several novel contributions. We introduce two new semantic classification tasks (the hallmarks, and exposure routes) at both sentence and document levels along with accompanying datasets, and implement and investigate a conventional pipeline NLP classification approach for both tasks, performing both intrinsic and extrinsic evaluation. We propose a new approach to classification using multilevel embeddings and apply this approach to several tasks; we subsequently apply deep learning methods to the task of hallmark classification and evaluate its outcome. Utilising our text classification methods, we develop and two novel text mining tools targeting real-world cancer researchers. The first tool is a cancer hallmark text mining tool that identifies association between a search query and cancer hallmarks; the second tool is a new literature-based discovery (LBD) system designed for the cancer domain. We evaluate both tools with end users (cancer researchers) and find they demonstrate good accuracy and promising potential for cancer research.

This diploma thesis briefly introduces the concept of inquiry based teaching mathematics. In inquiry based teaching, emphasis is placed primarily on the active activity of the pupil, the aim of which is to discover a certain reality. This activity mainly involves solving problems and finding the right paths to achieve the right goal. We can call this path to a goal as a research. The inquiry process involves observing, formulating questions, identifying information, designing possible processes, and verifying them. In this educational method, the role of the teacher is not to pass the facts to the pupils, but to target them and to supervise the correctness of their practices. We can understand the teacher here as a guide, adviser or assistant on the path to the goal (discovery). In the thesis a few examples of mathematics of elementary and secondary schools are given, in which the inquiry based approach is applied. Mathematics in the given examples is not complicated, emphasis is put on the practical use. In the examples interdisciplinary relationships are developed as well, which is an important part of the inquiry based teaching. In each example, additional questions are provided.

O crescimento do uso de sistemas de posicionamento global (GPS) e outros sistemas de localização espacial tornaram possível o rastreamento de objetos móveis, produzindo um grande volume de um novo tipo de dado, chamado trajetórias de objetos móveis. Existe, entretanto, uma forte lacuna entre a quantidade de dados extraídos destes dispositivos, dotados de sistemas GPS, e a descoberta de conhecimento que se pode inferir com estes dados. Um tipo de descoberta de conhecimento em dados de trajetórias de objetos móveis é a classificação. A classificação de trajetórias é um tema de pesquisa relativamente novo, e poucos métodos tem sido propostos até o presente momento. A maioria destes métodos foi desenvolvido para uma aplicação específica. Poucos propuseram um método mais geral, aplicável a vários domínios ou conjuntos de dados. Este trabalho apresenta um novo método de classificação que transforma as trajetórias em séries temporais, de forma a obter características mais discriminativas para a classificação. Experimentos com dados reais mostraram que o método proposto é melhor do que abordagens existentes. / The growing use of global positioning systems (GPS) and other location systems made the tracking of moving objects possible, producing a large volume of a new kind of data, called trajectories of moving objects. However, there is a large gap between the amount of data generated by these devices and the knowledge that can be inferred from these data. One type of knowledge discovery in trajectories of moving objects is classification. Trajectory classification is a relatively new research subject, and a few methods have been proposed so far. Most of these methods were developed for a specific application. Only a few have proposed a general method, applicable to multiple domains or datasets. This work presents a new classification method that transforms the trajectories into time series, in order to obtain more discriminative features for classification. Experiments with real trajectory data revealed that the proposed approach is more effective than existing approaches.

A presente tese analisa a maneira como a probabilidade influencia a formação da decisão sobre os fatos (contexto de descobrimento), bem como a motivação acerca dessas decisões (contexto justificativo). Mediante prévia análise da relevância da verdade no processo judicial, demonstra-se também que no terreno processual somente é possível falar em probabilidade, o que implica a ideia de um modelo probabilístico do juízo de fato, cujo núcleo é o módulo da escolha entre as alternativas possíveis. Portanto, são oferecidos critérios para a escolha das alternativas, os quais são denominados como diretrizes probatórias. De outro lado, a fim de que o convencimento do juiz a respeito dos fatos seja o mais racional possível, também é examinada a obrigatoriedade da motivação das decisões judiciais. Para tanto, é realizada abordagem à luz do Estado Constitucional e, ao final, demonstra-se que a probabilidade igualmente conforma o contexto justificativo, pois faz com que a motivação seja um discurso probatório racional. / This thesis examines how probability influences the fact-finding process (context of discovery) and the motivation about the trial of facts (context of justification). Considering the analysis of the relevance of truth in the judicial process, also in the procedural field only probability is taken into account, which implies the idea of a probabilistic model of factual judgment, whose core is the module of choice among the possible alternatives. Therefore, guidelines are offered for the choice of alternatives, which are called as evidentiary guidelines. On the other hand, in order to achieve the most rational conviction of the trier, mandatory legal motivation is also examined. The approach focuses on the Constitutional State and, in the end, is shown that the probability also conforms the context of justification, in order to make the legal motivation as a rational evidence speech.