Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users¡¯ exploration in such environments is usually impeded due to several problems: 1) Valuable information is hard to discover, when too much data is visualized on the screen. 2) They have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists. 3) Their discoveries based on visual exploration alone may lack accuracy. 4) They have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users¡¯ visual exploration process. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, data mining techniques are applied to refine the nuggets to best represent the patterns existing in datasets. Lastly, the resulting well-organized nugget pool is used to guide users¡¯ exploration. To evaluate the effectiveness of NMS, we integrated NMS into XmdvTool, a freeware multivariate visualization system. User studies were performed to compare the users¡¯ efficiency and accuracy of finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS. Keywords: Visual Analytics, Visual Knowledge

Staphyloccocus aureus is an opportunisitic pathogen that causes soft skin and tissue infections (SSTI) such as endocarditis, osteomyelitis and meningitis. In recent years, the re-emergence of antibiotic-resistant S. aureus such as MRSA presents a formidable challenge for infection management worldwide. Amidst this global epidemic of antimicrobial resistance, several research efforts have turned their focus towards exploiting the cell-wall biosynthesis pathway for novel anti-bacterial targets. Recently, the lipoteichoic acid (LTA) biosynthesis pathway has emerged as a potential anti-bacterial target. LTA is an anionic polymer found on the cell envelope of Gram-positive bacteria. It comprises of repeating units of glycerol-phosphate (GroP) and is important for bacterial cell physiology and virulence. For example, it is critically involved in regulating ion homeostasis, cell division, host colonization and immune system invasion. Several reports showed that bacteria lacking LTA are unable to grow. At the same time, they suffer from severe cell division defects and also exhibit aberrant cell morphologies. The key protein involved in the LTA biosynthesis pathway is the Lipoteichoic acid synthase (LtaS). LtaS is located on the cell membrane of Gram-positive bacteria and can be divided into two parts: a transmembrane domain and an extra-cellular domain responsible for its enzymatic activity (annotated eLtaS). Given that LtaS is important for bacterial survival and there are no known eLtaS homologues in eukaryotic cells, this protein is an attractive antibacterial target. In 2013, a small molecule eLtaS inhibitor (termed 1771) was discovered. Although 1771 was able to deplete LTA production, the binding mechanism of 1771 to eLtaS remains unknown. Additionally, 1771 could only prolong the survival of infected mice temporarily because of its in vivo instability. Therefore, the need for finding more potent and metabolically stable inhibitors of eLtaS still remains. Computational-aided drug design (CADD) is a cost-effective and useful approach that has been widely integrated into the drug discovery process. The protein eLtaS lends itself to be a good target for CADD since its crystal structure and a known inhibitor (with limited structure-activity data) is available. In this work, I have targeted eLtaS using CADD methodology followed by prospective validation using various biophysical, biochemical and microbiological assays. My project can broadly be sub-divided into three phases: (a) identification of small molecule binding “hot spots”, (b) optimization of existing inhibitor and (c) discovery of new hits. Through a systematic use of different computational approaches, I modelled a plausible 1771-bound eLtaS complex and used the structural insights to generate new inhibitors against eLtaS. To this end, I discovered EN-19, which is a more potent inhibitor of eLtaS. Additionally, by targeting transient cryptic pockets predicted by Molecular Dynamic simulations, I have discovered a new inhibitor chemotype that seems to exhibit a different mode of action against eLtaS. Taken together, my work presents a computational platform for future drug design against eLtaS and reinforces the notion that targeting eLtaS is a viable strategy to combat Gram-positive infections.

Zero-resource speech processing is a growing research area which aims to develop methods that can discover linguistic structure and representations directly from unlabelled speech audio. Such unsupervised methods would allow speech technology to be developed in settings where transcriptions, pronunciation dictionaries, and text for language modelling are not available. Similar methods are required for cognitive models of language acquisition in human infants, and for developing robotic applications that are able to automatically learn language in a novel linguistic environment. There are two central problems in zero-resource speech processing: (i) finding frame-level feature representations which make it easier to discriminate between linguistic units (phones or words), and (ii) segmenting and clustering unlabelled speech into meaningful units. The claim of this thesis is that both top-down modelling (using knowledge of higher-level units to to learn, discover and gain insight into their lower-level constituents) as well as bottom-up modelling (piecing together lower-level features to give rise to more complex higher-level structures) are advantageous in tackling these two problems. The thesis is divided into three parts. The first part introduces a new autoencoder-like deep neural network for unsupervised frame-level representation learning. This correspondence autoencoder (cAE) uses weak top-down supervision from an unsupervised term discovery system that identifies noisy word-like terms in unlabelled speech data. In an intrinsic evaluation of frame-level representations, the cAE outperforms several state-of-the-art bottom-up and top-down approaches, achieving a relative improvement of more than 60% over the previous best system. This shows that the cAE is particularly effective in using top-down knowledge of longer-spanning patterns in the data; at the same time, we find that the cAE is only able to learn useful representations when it is initialized using bottom-up pretraining on a large set of unlabelled speech. The second part of the thesis presents a novel unsupervised segmental Bayesian model that segments unlabelled speech data and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types|the system essentially performs unsupervised speech recognition. In this approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this embedding space while jointly performing segmentation. We first evaluate the approach in a small-vocabulary multi-speaker connected digit recognition task, where we report unsupervised word error rates (WER) by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% WER, outperforming a previous HMM-based system by about 10% absolute. To achieve this performance, the acoustic word embedding function (which maps variable-duration segments to single vectors) is refined in a top-down manner by using terms discovered by the model in an outer loop of segmentation. The third and final part of the study extends the small-vocabulary system in order to handle larger vocabularies in conversational speech data. To our knowledge, this is the first full-coverage segmentation and clustering system that is applied to large-vocabulary multi-speaker data. To improve efficiency, the system incorporates a bottom-up syllable boundary detection method to eliminate unlikely word boundaries. We compare the system on English and Xitsonga datasets to several state-of-the-art baselines. We show that by imposing a consistent top-down segmentation while also using bottom-up knowledge from detected syllable boundaries, both single-speaker and multi-speaker versions of our system outperform a purely bottom-up single-speaker syllable-based approach. We also show that the discovered clusters can be made less speaker- and gender-specific by using features from the cAE (which incorporates both top-down and bottom-up learning). The system's discovered clusters are still less pure than those of two multi-speaker unsupervised term discovery systems, but provide far greater coverage. In summary, the different models and systems presented in this thesis show that both top-down and bottom-up modelling can improve representation learning, segmentation and clustering of unlabelled speech data.

The purpose of this bachelor’s thesis is to examine how students at Högskolan i Borås experience the web-scale discovery service Summon, based on the theoretical model of “The Five Dimensions of Usability”, defined by Whitney Quesenbery. The study aims to answer the following research questions: How do the students execute search assignments in the Summon interface? How intuitive and easy to navigate is the system from a user perspective? What are the students’ opinions about Summon, regarding usability?Five students with varying information seeking skills are asked to perform search assignments during controlled observation, followed by semi-structured interviews. Our findings are presented in the context of previous research and show correspondence with the results of related studies. Our results show that users are comfortable in navigating the Summon interface, but that more user education is desirable for them to be able to fully take advantage of the service provided. / Program: Bibliotekarie

Tuberculosis is a disease that kills more people every year than any other infectious disease and is caused by the human pathogen, Mycobacterium tuberculosis (Mtb). This disease can be treated by a standard six month course of four antimicrobial drugs that have been in use since the 1960s. However, the rise of multi-drug resistant and extensively drug-resistant strains of TB has complicated the efforts to eradicate the disease. Therefore, there is a critical need for the development of new anti-TB drugs with a novel mechanism of action that can speed up treatment duration and help avoid resistance. The discovery of twenty genes encoding cytochrome P450 enzymes in the Mtb H37Rv genome sequence has pointed to the significance of these enzymes in the physiology and pathogenicity of this bacterium. Consequently, the characterisation of these Mtb P450 enzymes may define their physiological roles of which can be a novel anti-tubercular drug target. To date, the characterisations of selected Mtb P450 enzymes have highlighted their diverse and unexpected roles in the metabolism of cholesterol and lipids and the production of secondary metabolites. Biochemical and biophysical studies of these enzymes provided knowledge of their active site properties that may be exploited for drug discovery. Therefore, with the prospect of defining novel functions and identifying novel drug targets, characterisations of the remaining orphan Mtb P450s is of interest. M. tuberculosis CYP141A1 and CYP143A1 are orphan enzymes with unknown physiological function in Mtb which is characterised in this study through use of various spectroscopic and biophysical techniques. Interestingly, CYP141A1 can be expressed in form of which 54 amino acids (Del54CYP141A1) are deleted from the N-terminus. Although Del54CYP141A1 still retain spectroscopic characteristics, this form of P450 cannot be crystallized. Optimisation of full-length CYP141A1 buffer composition resulted to the formation of reproducible crystals and determination of CYP141A1 structure. Spectroscopic and structural characterisations presented in this thesis revealed many characteristics of CYP141A1 and CYP143A1 are comparable to previous Mtb P450s reported to date. CYP141A1 and CYP143A1 active site consist of b-type heme iron ligated by cysteine residue and a water molecule at its proximal and distal face, respectively. Both enzymes bind tightly to azole antifungal drugs highlighting their potential as a drug target. In addition, fragment-based screening applied to CYP141A1 and CYP143A1 provided the starting point for the development of potent, isoform-specific inhibitors for both orphan Mtb P450 enzymes. The first crystal structure of CYP141A1 and identification of new fragment binders of CYP141A1 and CYP143A1 are presented in this thesis. Overall, this research remains significant in providing new knowledge on the spectroscopic and structural properties of the M. tuberculosis P450s CYP141A1 and CYP143A1.

To complete an online world Flora by 2020 rapid progress is required towards understanding the taxonomy and distributions of the world's plants. This ambitious target set by the Global Strategy for Plant Conservation is hampered by two facts; first, many species of seed plant remain poorly known and second, the process of improving taxonomy and discovering species is not well understood. Here I investigate in detail the taxonomy and process of species discovery in a genus of tropical plants, Aframomum by examining specimens, taxonomic literature and authors of specimen determinations. I demonstrate that >50% of Aframomum specimens did not have the correct name prior to a recent comprehensive revision, that the number of specimens in herbaria doubled between 1970 and 2000, and that these results are also found in other taxa. I deconstruct the process of ‘species discovery' by identifying four key events: Initial collection, publication, conservation assessment, and distribution mapping. The time lags between the initial collection and completion of a) an accurate conservation assessment (101 years) and b) a comprehensive distribution map (115 years) demonstrate that many seed plant species published in the last 100 years are not fully understood. This is partly due to the fact that most species protologues (>90%) cite too few specimens at publication to produce an accurate conservation assessment. Furthermore, I explore variation in species' distribution patterns over time, taking account of specimen misidentification. Taken together the thesis identifies the lack of taxonomic capacity to efficiently deal with the tremendous influx of specimens since 1970, the poor current state of taxonomic knowledge of many taxa, and three significant time lags in the process of species discovery. Focused taxonomic effort is required for the successful completion of a world online Flora with conservation assessments to meet the 2020 GSPC target.

Kynurenine 3-monooxygenase (KMO) is an enzyme central to the kynurenine pathway of tryptophan degradation. KMO is emerging as an increasingly important target for drug development. The enzyme is implicated in the development and progression of several neurodegenerative disorders, in the regulation of the immune response and in sterile systemic inflammation. Production of recombinant human enzyme is challenging due to the presence of transmembrane domains, which localise KMO to the outer mitochondrial membrane and render KMO insoluble in many in vitro expression systems. Although several in vitro KMO assay techniques have been reported in the literature these methods are typically insensitive or require purified protein for use in high-throughput screening assays of human KMO enzyme. The first report of bacterial expression of soluble active human KMO enzyme is described here. Fusion protein tags were used to optimise soluble expression and enable characterisation and partial purification of the active protein constructs. Functional enzyme was used to develop several novel high-throughput drug screening techniques for the discovery of inhibitors specifically targeting human KMO. These screening techniques were fully characterised and validated using known KMO inhibitors from the patent literature. One of the novel KMO assay techniques was implemented for compound screening and several hit compounds were identified, validated and their in vitro DMPK characteristics determined. In addition to assay development, KMO was characterised at the cellular level when overexpressed in HEK293 cells. These experiments indicated that KMO overexpressing cells undergo bidirectional adaptation via alteration of kynurenine pathway homeostasis. As a result, these cells are protected from cytotoxicity mediated by 3-hydroxykynurenine (3-HK), the toxic product of KMO catalysis. The development of novel high throughput screening techniques targeting KMO has enabled screening of potential new inhibitors specifically targeting the human enzyme. Implementation of these screening assays will allow accelerated and improved discovery and development of novel KMO inhibitors for the potential treatment of numerous disease states.

Le Web est devenu une plateforme universelle d’hébergement d'applications hétérogènes. Dans ce contexte, les services Web se sont imposés comme une technologie clé pour permettre l’interaction entre diverses applications. Les technologies standards proposées autour des services Web permettent la programmation, plutôt manuelle, de ces applications. Pour favoriser une programmation automatique à base de services web, un problème majeur se pose : celui de leur découverte. Plusieurs approches adressant ce problème ont été proposées dans la littérature. L’objectif de cette thèse est d’améliorer le processus de découverte de services en exploitant trois pistes de recherche. La première consiste à proposer une approche de découverte qui combine plusieurs techniques de matching. La deuxième se base sur une validation des services retournés par un processus de découverte automatique en se basant sur les compétences utilisateurs. Ces approches ne prennent pas en considération l’évolution de services dans le temps et les préférences des utilisateurs. Pour remédier à ces lacunes plusieurs approches incorporent des techniques de recommandation. La majorité d'entre eux sont basées sur les évaluations des propriétés de QdS. Pratiquement, ces évaluations sont rarement disponibles. D’autres systèmes exploitent les relations de confiance. Ces relations sont établies en se basant sur les évaluations de services. Or, invoquant le même service ne signifie pas obligatoirement avoir les mêmes préférences. D’où, nous proposons, l’exploitation des relations d’intérêts entre les utilisateurs pour recommander des services. L’approche s’appuie sur une modélisation orientée base de données graphes. / The Web has become an universal platform for content hosting and distributed heterogeneous applications that can be accessed manually or automatically. In this context, Web services have established themselves as a key technology for deploying interactions across applications. The standard Web services technologies allow and facilitate the manual programming of these applications. To promote automatic programming based on Web services, a major problem arises : that of their discovery. Several approaches addressing this problem have been proposed in the literature. The aim of this thesis is to improve the Web services discovery process. We proposed three approaches. We proposed a Web services discovery approach that combines several matching techniques. The second consists on the validation of the services returned by an automatic process of discovery using users’ competencies. These approaches do not take into account the evolution of services over time and user preferences. To address these shortcomings, several approaches incorporate referral techniques to assist the discovery process. A large majority of these approaches are based on assessments of QoS properties. In practice, these assessments are rarely available. In other systems, trust relationships between users and services are used. These relationships are established based on invocations evaluations of similar services. However, invoking the same service do not necessarily mean having the same preferences. Hence, we propose, in our third approach, the use of the relations of interest between users to recommend services. The approach relies on modeling services’ ecosystem by database graphs.

Lung cancer is the leading cause of death from cancer in the world. First, we hypothesized that microRNA expression is altered in the bronchial epithelium of patients with lung cancer and that incorporating microRNA expression into an existing mRNA biomarker may improve its performance. Using bronchial brushings collected from current and former smokers, we profiled microRNA expression via small RNA sequencing for 347 patients with available mRNA data. We found that four microRNAs were under-expressed in cancer patients compared to controls (p<0.002, FDR<0.2). We explored the role of these microRNAs and their gene targets in cancer. In addition, we found that adding a microRNA feature to an existing 23-gene biomarker significantly improves its performance (AUC) in a test set (p<0.05). Next, we generalized the biomarker discovery process, and developed a visualization tool for biomarker selection. We built upon an existing biomarker discovery pipeline and created a web-based interface to visualize the performance of multiple predictors. The “visualization” component is the key to sorting through a thousand potential biomarkers, and developing clinically useful molecular predictors. Finally, we explored the molecular events leading to the development of COPD and ILD, two heterogeneous diseases with high mortality. We hypothesized that integrative genetic and expression networks can help identify drivers and elucidate mechanisms of genetic susceptibility. We utilized 262 lung tissue specimens profiled with microRNA sequencing, microarray gene expression and SNP chip genotyping. Next, we built condition specific integrative networks using a causality inference test for predicting SNP-microRNA-mRNA associations, where the microRNA is a predicted mediator of the SNP’s effect on gene expression. We identified the microRNAs predicted to affect the most genes within each network. Members of miR-34/449 family, known to promote airway differentiation by repressing the Notch pathway, were among the top ranked microRNAs in COPD and ILD networks, but not in the non-disease network. In addition, the miR-34/449 gene module was enriched among genes that increase in expression over time when airway basal cells are differentiated at an air-liquid interface and among genes that increase in expression with the airway wall thickening in patients with emphysema. / 2019-07-31T00:00:00Z

Research has revealed that current antidepressant treatment is less than adequate at alleviating behavioral endophenotypes associated with major depressive disorder (MDD) and there is a need for appropriate animal models to validate novel antidepressant pharmacological targets. In the present study, we wished to establish an ethologically relevant social defeat stress model in combination with a chronic unpredictable stress model, to more accurately mimic severe stress that is common in MDD. Before each day of the introduction of the stressor, animals were given saline or a 40 mg/kg dose of 3-aminobenzamide (3-AB), a poly ADP-ribose (PARP) inhibitor. PARP is a DNA repair enzyme that is increased in activity in response to DNA oxidation, which is elevated in the prefrontal cortical white matter in MDD post-mortem donors. One stressed group was given the common antidepressant fluoxetine (10mg/kg) to serve as a positive control. Results of this study demonstrated that 3-AB alleviated decreases in sucrose preference, a natural reward, along with avoidance on a social interaction test given at the end of social defeat. Preliminary telemetry readings indicated 3-AB was able to significantly decrease heart rate and blood pressure in response to SDS as compared to saline treated rats. Therefore, it appears that PARP inhibition alleviated behavioral endophenotypes associated with stress and represents a new pharmacological treatment for MDD in humans.