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This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modularization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to explore the organization and functioning of the brain to discover new structural and learning inspirations that can be subsequently utilized to design artificial neural network. The artificial neural networks are touted to be a neurobiologicaly inspired paradigm that emulate the functioning of the vertebrate brain. The brain is a highly structured entity with localized regions of neurons specialized in performing specific tasks. On the other hand, the mainstream monolithic feed-forward neural networks are generally unstructured black boxes which is their major performance limiting characteristic. The non explicit structure and monolithic nature of the current mainstream artificial neural networks results in lack of the capability of systematic incorporation of functional or task-specific a priori knowledge in the artificial neural network design process. The problem caused by these limitations are discussed in detail in this dissertation and remedial solutions are presented that are driven by the functioning of the brain and its structural organization. Also, this dissertation presents an in depth study of the currently available modular neural network architectures along with highlighting their shortcomings and investigates new modular artificial neural network models in order to overcome pointed out shortcomings. The resulting proposed modular neural network models have greater accuracy, generalization, comprehensible simplified neural structure, ease of training and more user confidence. These benefits are readily obvious for certain problems, depending upon availability and usage of available a priori knowledge about the problems. The modular neural network models presented in this dissertation exploit the capabilities of the principle of divide and conquer in the design and learning of the modular artificial neural networks. The strategy of divide and conquer solves a complex computational problem by dividing it into simpler sub-problems and then combining the individual solutions to the sub-problems into a solution to the original problem. The divisions of a task considered in this dissertation are the automatic decomposition of the mappings to be learned, decompositions of the artificial neural networks to minimize harmful interaction during the learning process, and explicit decomposition of the application task into sub-tasks that are learned separately. The versatility and capabilities of the new proposed modular neural networks are demonstrated by the experimental results. A comparison of the current modular neural network design techniques with the ones introduced in this dissertation, is also presented for reference. The results presented in this dissertation lay a solid foundation for design and learning of the artificial neural networks that have sound neurobiological basis that leads to superior design techniques. Areas of the future research are also presented. / Ph. D.

Indirect potable reuse has been practiced with the potential to enhance sustainability of water resources if planned accordingly. Depending on the pretreatment implemented for potable reuse, emerging contaminants; such as pharmaceuticals, personal care products, industrial solvents, bacterial/viral pathogens, and disinfection byproducts, might be present in source water and difficult to remove via various water treatment technologies. Low molecular weight organic compounds are especially challenging to remove and may require treatment optimization. The overarching purpose of this study was to demonstrate the feasibility of a carbon-based advanced treatment train; including ozonation, biological activated carbon (BAC) filtration and granular activated carbon (GAC) adsorption to achieve water quality suitable for potable reuse and assess the impact of a range of operating conditions for emerging contaminant removal at pilot-scale. The results from this study showed that carbon-based treatment train is equally effective as more commonly used, and more costly, membrane-based treatment trains in terms of pathogen and disinfection byproduct removal. A multiple-barrier approach was implemented, with each treatment stage capable of removing total organic carbon (TOC). GAC was responsible for removal of most of the TOC and emerging contaminants and this removal depended on the number of bed volumes of water processed by GAC. Empty bed contact time was another factor that dictated the extent of TOC removal in the BAC and GAC units as the carbon media was exhausted. Among the emerging contaminants detected, sucralose, iohexol and acesulfame-k were present in the highest concentrations in the influent and were detected consistently in the GAC effluent, thus making them good indicators of treatment performance. Apart from organics removal, BAC played an important role in removal of nutrients, such as ammonia via nitrification. N-Nitrosodimethlyamine (NDMA) was formed in the treatment process by ozone, but was shown to be effectively removed by BAC. EBCT, temperature, ozone dose and presence of pre-oxidants, such as monochloramine, played an important role in determining the amount of NDMA removed. These factors can be further optimized to improve NDMA removal. Sodium bisulfite was used for dechlorinating monochloramine residual post ozone. Nitrification in the BAC was shown to be inhibited by excess of sodium bisulfite dose. Thus monochloramine residual needs to be dechlorinated with sodium bisulfite to help with NDMA degradation but at the same time the sodium bisulfite dose needs to be monitored to allow complete nitrification in the BAC. 1,4-dioxane, another contaminant of emerging concern, was monitored in the treatment process. Biodegradation of 1,4-dioxane was enhanced via addition of tetrahydrofuran as a growth substrate. Biodegradation of 1,4-dioxane can help reduce energy and capital costs associated with advanced oxidation processes that are currently used for 1,4-dioxane removal. Further, relying on biodegradation for the removal of 1,4-dioxane can help avoid the formation of disinfection byproducts associated with advanced oxidation processes such as ozone with peroxide or ultraviolet disinfection with peroxide. The results from this project can be useful for designing potable reuse treatment trains and provide a baseline for removal of organic carbon and emerging contaminants. The conventionally used reverse osmosis and ultrafiltration approach is useful for organics removal in areas where the rationale behind potable reuse is water scarcity. Operational difficulties encountered during this study can prove to be important as this treatment process is scaled up to treat a total of 120 MGD of water for managed aquifer recharge. Overall the lessons learnt from this study can give a better understanding of a carbon-based treatment and further the advancement of reuse projects that have drivers other than water scarcity. / Doctor of Philosophy / The increased growth in urban areas has been accompanied by an increase in potable water demand, leading to depletion of surface and groundwater. Further, the discharge of nutrients such as nitrogen and phosphorus into some of these water bodies can lead to algal blooms. Water reuse involves treating used water and discharging into either a surface or groundwater body. Water reuse has been sought after as a solution to prevent these nutrients being discharged into surface water and to provide a sustainable solution for depletion of water sources. Direct or indirect potable reuse can include a combination of advanced treatment methods such as membrane filtration using ultrafiltration and reverse osmosis, biological filtration, granular or powdered activated carbon adsorption and disinfection methods such as ozonation and ultraviolet disinfection. This study focused on Hampton Roads Sanitation District's managed aquifer recharge project 'sustainable water initiative for tomorrow' (SWIFT), two different advanced water treatment strategies, namely carbon-based and membrane-based were implemented on a pilot-scale (20,000 L/day). The driver for indirect potable reuse in this study was not related to water shortage but aimed at reducing the nutrients discharged into the Chesapeake Bay. Other reasons for implementing reuse included recharging the depleting groundwater levels, land subsidence, and preventing flooding and seawater intrusion near the coastal areas. Membrane-based treatments, such as reverse osmosis, have been well established and studied for potable reuse. The less frequently used carbon-based treatment, that includes used of activated carbon for adsorption and biodegradation of organics (not involving any membrane barrier), was shown to be cost-effective and provided equal protection as that of the membrane-based system in terms of removal of pathogens. Further, since there is no membrane involved in the carbon-based treatment the energy requirements are less than that of the membrane-based treatment and concentrated brine solution is not produced, which makes it favorable for potable reuse where water scarcity is not an issue. This carbon-based treatment which included ozonation and activated carbon filtration and adsorption was further monitored and optimized for removal of organic contaminants and disinfection byproducts. Emerging contaminants such as pharmaceuticals, industrial solvents and personal care products can be harmful to human health and water ecology even at low concentrations. These contaminants are often present in wastewater effluent and can enter drinking water sources if untreated. These emerging contaminants were shown to be effectively removed by ozonation and granular activated carbon adsorption. The formation of disinfection byproducts such as N-nitrosodimethylamine in the treatment process and its removal in the biological activated carbon filtration was also monitored. The impact of temperature, presence of pre-oxidants and design factors such as ozone dose and empty bed contact time affected the removal of all these contaminants. This study provided an understanding of implementing carbon-based treatment for managed aquifer recharge for optimizing removal of bulk organic carbon and emerging contaminants. The results from this study can be utilized for designing advanced water treatment systems and can prove to be a guideline for monitoring and removing emerging contaminants.

<p dir="ltr">Humans encode approximately 100 deubiquitinating enzymes (DUBs) which are categorized into seven distinct subfamilies. Each family and representative has a unique expression, function and binding topology to ubiquitin. In addition to human DUBs, parasites, bacteria, and viruses contain DUBs with unique structures and functions. One subfamily of DUBs, the ubiquitin C-terminal hydrolases (UCH), has four structurally similar human members and two known members within the <i>Plasmodium falciparum</i> genome. Human UCHL1 and UCHL3 are genetically validated targets in oncology and <i>Plasmodium falciparum</i><i> </i>UCHL3 (PfUCHL3) is a prospective target for antimalarial drug development. Though these three UCH enzymes have potential as therapeutic targets, there is a significant lack of quality small molecule chemical probes to understand the underlying biology and function of the enzymes, pharmacologically validate the targets, and serve as leads for drug development in oncology and malaria.</p><p dir="ltr">The UCH enzymes are cysteine proteases, which our lab has leveraged to identify novel covalent small molecule inhibitors of each enzyme. The workflow for each hit identification and optimization campaign is similar. Covalent fragment screening of electrophilic small molecule libraries against the respective recombinant enzyme was performed to identify chemical space around each enzyme. Subsequent medicinal chemistry hit-to-lead optimization was undertaken to improve upon the moderately potent hit molecules to provide improved small molecule inhibitors for each enzyme. Inhibitor identification and optimization for UCHL1 is described in Chapter 2, revealing a novel scaffold and a cocrystal structure reveals a unique binding pose for UCHL1 inhibitors. These molecules were also characterized in breast cancer cells to validate UCHL1 as a therapeutic target in breast cancer. First-in-class covalent inhibitors of UCHL3 are described in Chapter 3. Medicinal chemistry optimization along with a cocrystal structure of the initial hit has revealed the molecular interactions of this novel inhibitory scaffold. PfUCHL3 inhibitor identification is described in Chapter 4. Characterization of these molecules against Plasmodium falciparum is described along with a comparison to a recently identified reversible PfUCHL3 inhibitor. Finally, conclusions and future directions toward the development of potent, drug-like inhibitors of each UCH enzyme is presented in Chapter 5.</p>

<p dir="ltr">Immunotherapy has revolutionized the field of oncology. While a lot of antibodies and small molecule inhibitors have been developed for this, a lot of targets remain undruggable in humans.</p><p dir="ltr">Targeted protein degradation has opened a new horizon in drug discovery where we can target these undruggable proteins. Proteolysis targeting chimeras using the ubiquitin-proteasomal system is one of the most popular TPD strategies that complement lysosomal degradation strategies to degrade intracellular proteins, typically using bifunctional small molecule degraders. Recently, large biomolecular and antibody conjugates have been developed for degrading membrane and extracellular proteins in cells, such as lysosomal targeting chimeras (LYTACs) and genetically encoded LYTACS, among several others. However, larger molecules have limitations in penetrating solid tumors. This dissertation work focused on the development of bifunctional small molecule degraders for programmed death-ligand 1 (PD-L1), a transmembrane protein ligand for the immune checkpoint programmed cell death 1 (PD-1). PD-L1 is highly expressed on several tumors, such as triple-negative breast cancer (TNBC), non-small cell lung carcinoma, and renal cancer, and is known to suppress cancer-killing immune cells via interaction with PD-1 on T-cells. In addition, PD-L1 is also present on macrophages in the tumor microenvironments leading to further immune suppression and acquired resistance to anti-PD-1 therapy is associated with the upregulation of alternative immune checkpoints, thereby reducing anti-tumor efficacy. We have designed and synthesized bifunctional small molecules as PD-L1 degraders with different recruiters and linkers guided by computational studies with known PD-1/PD-L1 structures to show both cell surface and total protein degradation in human TNBC cells. In a separate project, we also developed small molecule conjugates to degrade an intracellular integral membrane protein of the endoplasmic reticulum with an unknown 3D structure, namely Diglyceride acyltransferase 2 (DGAT2). Recently, our lab identified DGAT2 as a new target for combating Alzheimer’s disease. Specifically, DGAT2 catalyzes triacylglycerol (TAG) synthesis using diacylglycerol and fatty acyl CoA as substrates. The accumulation of TAGs, mechanistically linked to DGAT2, results in “fat” or lipid droplets (LDs) inside the cells. Our lab showed that microglial cells (resident immune cells in the brain) accumulate LDs in the postmortem brains of human patients and mouse models (5xFAD) of Alzheimer’s disease and that the LD accumulation is driven by amyloid-beta (Ab) – a hallmark of Alzheimer’s disease – via DGAT2 pathway. Further, these LD-laden microglia have phagocytic defects and are spared Aβ thereby affecting plaque accumulation and clearance. Inhibiting DGAT2 reduces the amount of TAG in the brain, which in turn reduces LDs and restores microglial ability to phagocytose Ab. However, commercially available DGAT2 inhibitors were unable to reduce LD load in older 5xFAD mice. Using AlphaFold’s models of DGAT2, we designed and identified sites to synthesize bifunctional DGAT2 degraders that resulted in reduced LDs in mouse primary microglial cells and enhanced phagocytosis of Aβ plaques in vivo in aged 5xFAD mice. Our approach shows a framework to develop bifunctional small molecule degraders for membrane proteins to potentially combat immune dysregulation in chronic diseases.</p>

Biological organisms, phenomena and strategies, herein referred to as biological systems, provide a rich set of analogies that can be used to inspire engineering innovation. Biologically-inspired, or biomimetic, designs are publicly viewed as creative and novel solutions to human problems. Moreover, some biomimetic designs have become so commonplace that it is hard to image life without them (e.g. velcro, airplanes). Although the biologically- inspired solutions are innovative and useful, the majority of inspiration taken from nature has happened by chance observation, dedicated study of a specific biological entity (e.g., gecko), or asking a biologist to explain the biology in simple terms. This reveals a fundamental problem of working across the engineering and biological domains. The effort and time required to become a competent engineering designer creates significant obstacles to becoming sufficiently knowledgeable about biological systems (the converse can also be said). This research aims to remove the element of chance, reduce the amount of time and effort required to developing biologically-inspired solutions, and bridge the seemingly immense disconnect between the engineering and biological domains. To facilitate systematic biologically-inspired design, a design methodology that relies on a framework of tools and techniques that bridge the two domains is established. The design tools and techniques that comprise the framework achieve: Identification of relevant biological solutions based on function; translation of identified biological systems of interest; functional representation of biological information such that it can be used for engineering design activities; and conceptualization of biomimetic engineering designs. Using functional representation and abstraction to describe biological systems presents the natural designs in an engineering context and allows designers to make connections between biological and engineered systems. Thus, the biological information is accessible to engineering designers with varying biological knowledge, but a common understanding of engineering design methodologies. This work has demonstrated the feasibility of using systematic design for the discovery of innovative engineering designs without requiring expert-level knowledge, but rather broad knowledge of many fields. / Graduation date: 2011

Capillary zone electrophoresis is an analytical method frequently used in many laboratories for solving various analytical problems. This diploma thesis describes one of many applications of capillary zone electrophoresis using a unique laboratory apparatus composed of a short capillary and dual conductivity/UV detector placed in one detection point of the separation capillary. In the first part of this thesis, the laboratory apparatus was tested by the separation of small inorganic and organic ions. Sodium, potassium, tyramine and histidine ions were used to test the two parts of the dual detector. Experimentally obtained mobilities of these ions were compared with those calculated from the tabulated values. In the second part, the apparatus was used for determination of analytes in samples with more complex matrixces, pharmaceuticals Acylcoffin and B-komplex produced by Zentiva, a.s. One analyte was chosen from each pharmaceutical preparation for determination of its concentration in the preparation, caffeine from Acylcoffine and thiamine from B-komplex. The concentrations were calulated using three different calibration methods and the experimentally obtained values were compared with those specified by the pharmaceuticals producer. Key words: capillary electrophoresis; short capillary;...

Visão é um processo que envolve uma grande quantidade de informações, as quais precisam ser otimizadas de alguma forma para propiciar um processamento eficiente. Grande parte das informações visuais estão contidas nos contornos de uma imagem e uma grande redução no volume dos dados pode ser conseguida com a análise dos contornos. Além dos contornos, a detecção de segmentos de reta é o próximo passo na compressão das informações visuais. A detecção de retas ocorre no sistema visual humano, e também no de outros seres vivos. Entre os invertebrados terrestres, o melhor sistema de visão é o das aranhas da família Salticidae e este apresenta características que facilitam a detecção de retas. Este trabalho propõe um novo método de detecção de retas, baseado no sistema visual das aranhas saltadoras, que aborda este problema através de um enfoque inédito, por otimização. O método realiza a busca por retas através de janelas lineares. Para isso, a detecção de retas será feita em um espaço de parâmetros, com a utilização do algoritmo de maximização de funções \"Downhill Simplex\". O método desenvolvido leva em consideração a natureza discreta da imagem e do espaço de parâmetros utilizado, e este trabalho inclui um estudo detalhado destes espaços discretos. O método incorpora, para lidar adequadamente com as peculiaridades do problema, características como \"Simulated Annealing\" e largura adaptativa. O desempenho do método depende de um conjunto de parâmetros cujo comportamento é de difícil previsão, e a escolha de um conjunto foi realizada através de um algoritmo genético. O trabalho envolve também a construção de um protótipo para a realização de testes utilizando o método desenvolvido. Os resultados foram analisados quanto a precisão na detecção de retas, ao tempo de processamento e a movimentação das janelas lineares, relacionada aos esforços na busca por retas. / Vision is a process that involves a large amount of information that need to be somehow optimized to allow efficient processing. Most of the visual information is contained in the contours of an image and a considerable reduction in the amount of data can be achieved by fmding and processing these contours. The next step to further compress the visual data is to fmd straight segments, and represent the contours in terms of these entities. Straight-line segment detection is performed by the human visual system, as well as by other creatures. Among terrestrial invertebrates, the best visual system is that of the Salticidae family of spiders, also known as jumping spiders. This visual system presents some characteristics that facilitate the detection of straight-lines. The present work proposes a new method for straight-line detection, based on the visual system of the jumping spiders, using linear windows. This method approach the straight-line detection problem through an optimization point of view yet unexplored in literature. The detection will be accomplished in a parameter space, using the \"Downhill Simplex\" maximization algorithm. The method considers the discrete nature of both the image and the parameter spaces, and this work includes a detailed analysis of these discrete spaces. The method also incorporates, to adequately deal with the specific characteristics of the problem, resources such as \"Simulated Annealing\" and adaptive width of the linear windows. The performance of the method depends on a set of parameters, which behavior is hard to predict, and the choice of an adequate set was made using a genetic algorithm. The work also involves the project and construction of a prototype, to evaluate the proposed method. Results were analyzed regarding their precision, processing time and the movements of the linear windows, related to the effort made to detect the straight lines.

Sistemas de vigilância são geralmente empregados no monitoramento de áreas de grandes dimensões nas quais seus usuários visam detectar ou observar fenômenos de seu interesse. O uso de redes de sensores sem fio nesses sistemas apresenta especial interesse, uma vez que essas redes podem apresentar soluções de baixo custo e robustas para cobrir áreas extensas. Neste contexto, novas aplicações têm surgido propondo o uso de redes de sensores sem fio compostas por nós sensores estáticos e móveis. Uma das motivações para esta tendência é a redução do custo de implantação e operação do sistema, além da possibilidade de proporcionar incremento em suas funcionalidades. O foco desta tese se concentra na proposta de soluções para redes de sensores sem fio com uso cooperativo de sensores estáticos e móveis, com particular atenção a sensibilidade ao contexto na configuração e execução de missões de sensoriamento. O objetivo é manter um baixo custo de comunicação associado às soluções propostas. Esta preocupação se dá pelo fato da comunicação aumentar o consumo de energia em redes de sensores, o que é um problema importante para nós sensores com limitada fonte de energia, i.e. baterias. No caso de nós sensores móveis, esta limitação pode não ser relevante, uma vez que seu movimento deve consumir uma quantidade muito mais expressiva de energia do que a comunicação. Neste caso, o problema se relaciona à estabilidade dos enlaces, bem como ao curto intervalo de tempo disponível para transmitir e receber dados. Logo, o melhor é comunicar o menos possível. Com relação à interação entre nós sensores estáticos, os problemas de disseminação e alocação de missões de sensoriamento são estudados e uma solução que explora o uso de informações locais é proposta e avaliada. Esta solução emprega agentes de software móveis que têm a capacidade de tomar decisões autônomas através do uso de informações de contexto local. Para redes de sensores móveis, o problema estudado se refere a como transferir missões entre os nós sensores de acordo com seu movimento e localização em relação aos locais onde as missões devem ser executadas. Para tratar este problema, uma abordagem baseada em agentes móveis é proposta, na qual os agentes implementam a migração das missões de sensoriamento usando informações de contexto geográfico para decidir a respeito de suas migrações. Para redes de sensores com sensores estáticos e móveis, a cooperação entre eles é abordada através de um mecanismo com inspiração biológica para realizar a realizar a entrega de dados emitidos pelos sensores estáticos aos sensores móveis. Para isto, explora-se uma analogia baseada no comportamento de formigas na construção e seguimento de trilhas. As soluções propostas são flexíveis, sendo aplicáveis a diferentes domínios de aplicação. Resultados experimentais evidenciam sua escalabilidade, avaliando, por exemplo, seu custo em termos de comunicação, além de outras métricas de interesse para cada uma das soluções. Estes resultados são comparados aos atingidos por soluções de referência (solução ótima teórica e baseada em inundação), indicando sua eficiência. Estes resultados são próximos do ótimo teórico e significativamente melhores que aqueles atingidos por soluções baseadas em técnicas de inundação. / Surveillance systems are usually employed to monitor wide areas in which their users are interested in detecting and/or observing events or phenomena of their interest. The use of wireless sensor networks in such systems is of particular interest as these networks can provide a relative low cost and robust solution to cover large areas. Emerging applications in this context are proposing the use of wireless sensor networks composed of both static and mobile sensor nodes. Motivation for this trend is to reduce deployment and operating costs, besides providing enhanced functionalities. This work focuses on the proposal of solutions for wireless sensor networks including static and mobile sensor nodes specifically regarding cooperative and context aware mission setup and performance. The goal is to keep the communication costs as low as possible in the execution of the proposed solutions. This concern comes from the fact that communication increases energy consumption, which is a particular issue for energy constrained sensor nodes often used in wireless sensor networks, especially if battery supplied. In the case of the mobile nodes, this energy constraint may not be valid, since their motion might need much more energy, but links instabilities and short time windows available to receive and transmit data. Therefore, it is better to communicate as little as possible. For the interaction among static sensor nodes, the problems of dissemination and allocation of sensing missions are studied and a solution that explores local information is proposed and evaluated. This solution uses mobile software agents that have capabilities to take autonomous decisions about the mission dissemination and allocation using local context information. For mobile wireless sensor networks, the problem studied is how to perform handover of missions among the nodes according to their movements and locations in relation to the place where the missions have to be performed. To handle this problem, a mobile agent approach is proposed in which the agents implement the sensing missions’ migration from node to node using geographical context information to decide about their migrations. For the networks combining static and mobile sensor nodes, the cooperation among them is approached by a biologically-inspired mechanism to deliver data from the static to the mobile nodes. The data delivery mechanism explores an analogy based on the behaviour of ants building and following trails, inspired by the ant colony algorithm. The proposed solutions are flexible, being able to be applied to different application domains. Obtained experimental results provide evidence of the scalability of these proposed solutions, for example by evaluating their cost in terms of communication, among other metrics of interest for each solution. These results are compared to those achieved by reference solutions (theoretical optimum and floodingbased), providing indications of the proposed solutions’ efficiency. These results are considered close to the theoretical optimum one and significantly better than the ones achieved by flooding-based solutions.

Pronking is a legged locomotory gait in which all legs are used in synchrony, usually resulting in slow speeds but long flight phases and large jumping heights that may potentially be useful for mobile robots locomoting in cluttered natural environments. Instantiations of this gait for robotic systems suffer from severe pitch instability either due to underactuated leg designs, or the open-loop nature of proposed controllers. Nevertheless, both the kinematic simplicity of this gait and its dynamic nature suggest that the Spring-Loaded Inverted Pendulum Model (SLIP), a very successful predictive model for both natural and robotic runners, would be a good basis for more robust and maneuverable robotic pronking. In the scope of thesis, we describe a novel controller to achieve stable and controllable pronking for a planar, underactuated hexapod model, based on the idea of &ldquo / template-based control&rdquo / , a controller structure based on the embedding of a simple dynamical template within a more complex anchor system. In this context, high-level control of the gait is regulated through speed and height commands to the SLIP template, while the embedding controller based on approximate inverse-dynamics and carefully designed passive robot morphology ensures the stability of the remaining degrees of freedom. We show through extensive simulation experiments that unlike existing open-loop alternatives, the resulting control structure provides stability, explicit maneuverability and significant robustness against sensor noise.