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The alarming rise of antibacterial drug resistance and the dwindling supply of novel antibiotics highlight the need for innovative approaches in combating bacterial infections. Traditionally, antibacterial drug discovery campaigns have largely been conducted in rich media. Such growth conditions are not representative of the host environment and render many metabolic pathways, otherwise needed for survival and infection, dispensable. Such pathways have been overlooked in conventional drug discovery campaigns despite their validity as potential antibacterial targets. The work presented in this thesis focuses on the development and validation of a screening strategy for the identification and mechanism of action determination of novel inhibitors of metabolic pathways in bacteria under nutrient-limited conditions. This screen led to the identification of MAC168425, MAC173979 and MAC13772 as inhibitors that target glycine metabolism, p-aminobenzoic acid biosynthesis and biotin biosynthesis, respectively. Moreover, it established this approach as a general platform that can be applied for different organisms with synthetic or natural product libraries. Additional mechanistic studies of the biotin biosynthesis inhibitor, MAC13772, resulted in solving the crystal structure of BioA in complex with MAC13772. Analysis of the co-structure confirmed our proposed mode of inhibition and provided information for strategies for rational drug design. Investigation of the antibacterial activity of MAC13772 revealed its potency against a number of pathogens. Furthermore, we show how MAC13772 acts synergistically with rifampicin in clearing growing mycobacterial cultures. The potential of this inhibitor as a lead for preclinical pharmacokinetic studies and for antibacterial drug development is discussed. We also discuss our current efforts to develop a metabolomic platform for the characterization of novel antibacterials that can be used in concert with our current approach to chart the metabolic response of bacteria to chemical perturbants and to generate testable hypotheses regarding the mode of action of novel inhibitors of bacterial metabolism. / Thesis / Doctor of Philosophy (PhD)

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<p>    </p> <p>Mutual learning algorithm is a machine learning algorithm where multiple machine learning algorithms learns from different sources and then share their knowledge among themselves so that all the agents can improve their classification and prediction accuracies simultaneously. Mutual learning algorithm can be an efficient mechanism for improving the machine learning and neural network efficiency in a multi-agent system. Usually, in knowledge distillation algorithms, a big network plays the role of a static teacher and passes the data to smaller networks, known as student networks, to improve the efficiency of the latter. In this thesis, it is showed that two small networks can dynamically and interchangeably play the changing roles of teacher and student to share their knowledge and hence, the efficiency of both the networks improve simultaneously. This type of dynamic learning mechanism can be very useful in mobile environment where there is resource constraint for training with big dataset. Data exchange in multi agent, teacher-student network system can lead to efficient learning.  </p>

Background: Destructive leadership seeks to explain how leaders create harmful outcomes in an organizational setting – and why do they choose to do so. However, as with most leadership theories, process models are designed with a traditional office setting in mind which has its own distinct characteristics. Remote working has surged in prevalence in the last two years due to the COVID-19 pandemic and features multiple key differences, including increased social isolation and a decrease in communication quality. The combination of this novel and different context with a high likelihood of employees experiencing destructive leadership during their career, it is of high relevance to critically examine destructive leadership processes in a remote setting. Purpose: The purpose of this study is to adapt the proposed framework of destructive leadership by Krasikova et al. (2013) in a remote working environment, and to provide a greater understanding of destructive leadership processes in a less familiar context. Through exploring a new working context, this research aims to expand the understanding of destructive leadership, its situational factors, processes, and possible destructive outcomes in the ‘modern’ workplace. Method: Our methods were built on the choices of inductive qualitative research. Ten semi-structured interviews with leaders and followers were conducted by utilizing the casemethod, and more precisely, the case-oriented research design. The use of case-oriented research design and thematic analysis allowed us to engage in within- and cross-case comparisons and enabled us to generate new insights and to further develop remote working specific factors in the destructive leadership processes. Conclusion: The results of the study demonstrate that remote working environment influences three main areas of destructive leadership: the organizational context behind the process of choosing to engage in destructive leadership, the process of discovery and organizational response, and by establishing feedback loops from existing destructive leadership that leads to further resource shortages.

Many business intelligence tools aim to digest data into easy, understandable and visualizable information for helping decision-making, while they are still lack of ability to support visualizing the history of selections. This limitation concerns the coming future when everything is about data. Due to it, users are not able to share their thinking paths to the decision. Here a history selection path means a sequence of previous selections. As an approach, it helps users in decision-making and discovery insight. This study investigated an efficient graphical visualization system of history selection paths to support communicating and iterative analysis. We selected tree representation as the main visualization model and also propose features needed for the system. Specifically, we researched the significance of this study, existing solutions and also the proper designs and functions for the idea. It is initiated by user research including targeting users and scenario mapping. Based on the understanding, we applied a parallel design to narroww down the suitable design. As a result, tree representation was selected as the visualization model. To evaluate whether it touched user needs or not, we applied usability test to collect quantitative data and qualitative comments. For making the test environment as real as possible, a webbased interactive prototype supported by D3.js library was implemented for testing. We analyzed the user experience and also consolidated improvements. As a case study, we implemented the solution on Qlik Sense to verify the possibility to place this solution into real data visualization tool. Generally, the result of this study formed a valuable initiative for further development and we saw potentials of this tree model system to be used in other areas when it comes to reviewing history as well. / Många verktyg för affärsintelligens avser till att bryta ner data till enkel, förståelig och visualiserar information för att hjälpa till beslutsantagande, medan det fortfarande saknar förmåga att stödja visualiseringen av urvalens historik. Den här begränsningen berör framtiden när allt är om data. På grund av det, användaren är inte kunniga till att dela deras sökväg till beslutet. Här menas historik urvalsväg en sekvens av tidigare val. Som ett tillvägagångsätt, hjälper det användare att fatta beslut och upptäcka insikt. Denna studie undersökte ett effektivt grafiskt visualiseringssystem av historik urvalsvägar för att stödja kommunikation och iterativ analys. Vi valde trädrepresentation som huvudligavisualisering modell och föreslår också funktioner som behövs för systemet. Specifikt har vi undersökt betydelsen av denna studie, befintliga lösningar och även rätt design och funktioner för denna idé. Det initieras av användare undersökningar inklusive målriktning av användare och scenariokartläggning. Baserat på förståelsen använde vi en parallell design för att begränsa den lämpliga designen. Som ett resultat, valdes trädrepresentation som visualiseringsmodell. För att utvärdera om det rörde användarnas behov eller inte, använde vi användbarhetstest för att samla in kvantitativa data och kvalitativa kommentarer. För att testmiljön ska bli så verklig som möjligt implementerades en webbaserad interaktiv prototyp som stöds av D3.js biblioteket för testning. Vi analyserade användare upplevelsen och konsoliderade förbättringar. Som en fallstudie implementerade vi lösningen på Qlik Sense för att verifiera möjligheten att placera denna lösning i ett verkligt data visualiseringsverktyg. I allmänhet bildade resultatet av den här studien ett värdefullt initiativ för vidare utveckling och vi såg potentialerna i detta trädmodellsystem som kan användas på andra områden när det gäller till att granska historik.

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