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The essence of smart pervasive Cyber-Physical Environments (CPEs) is to enhance the dependability, security and efficiency of their encompassing systems and infrastructures and their services. In CPEs, interactive information resources are integrated and coordinated with physical resources to better serve human users. To bridge the interaction gap between users and the physical environment, a CPE is instrumented with a large number of small devices, called sensors, that are capable of sensing, computing and communicating. Sensors with heterogeneous capabilities should autonomously organize on-demand and interact to furnish real-time, high fidelity information serving a wide variety of user applications with dynamic and evolving requirements. CPEs with their associated networked sensors promise aware services for smart systems and infrastructures with the potential to improve the quality of numerous application domains, in particular mission-critical infrastructure domains. Examples include healthcare, environment protection, transportation, energy, homeland security, and national defense. To build smart CPEs, Networked Sensor Environments (NSEs) are needed to manage demand-driven sharing of large-scale federated heterogeneous resources among multiple applications and users. We informally define NSE as a tailorable, application agnostic, distributed platform with the purpose of managing a massive number of federated resources with heterogeneous computing, communication, and monitoring capabilities. We perceive the need to develop scalable, trustworthy, cost-effective NSEs. A NSE should be endowed with dynamic and adaptable computing and communication services capable of efficiently running diverse applications with evolving QoS requirements on top of federated distributed resources. NSEs should also enable the development of applications independent of the underlying system and device concerns. To our knowledge, a NSE with the aforementioned capabilities does not currently exist. The large scale of NSEs, the heterogeneous node capabilities, the highly dynamic topology, and the likelihood of being deployed in inhospitable environments pose formidable challenges for the construction of resilient shared NSE platforms. Additionally, nodes in NSE are often resource challenged and therefore trustworthy node cooperation is required to provide useful services. Furthermore, the failure of NSE nodes due to malicious or non-malicious conditions represents a major threat to the trustworthiness of NSEs. Applications should be able to survive failure of nodes and change their runtime structure while preserving their operational integrity. It is also worth noting that the decoupling of application programming concerns from system and device concerns has not received the appropriate attention in most existing wireless sensor network platforms. In this dissertation, we present a Biologically-inspired Secure Elastic Networked Sensor Environment (BioSENSE) that synergistically integrates: (1) a novel bio-inspired construction of adaptable system building components, (2) associative routing framework with extensible adaptable criteria-based addressing of resources, and (3) management of multi-dimensional software diversity and trust-based variant hot shuffling. The outcome is that an application using BioSENSE is able to allocate, at runtime, a dynamic taskforce, running over a federated resource pool that would satisfy its evolving mission requirements. BioSENSE perceives both applications and the NSE itself to be elastic, and allows them to grow or shrink based upon needs and conditions. BioSENSE adopts Cell-Oriented-Architecture (COA), a novel architecture that supports the development, deployment, execution, maintenance, and evolution of NSE software. COA employs mission-oriented application design and inline code distribution to enable adaptability, dynamic re-tasking, and re-programmability. The cell, the basic building block in COA, is the abstraction of a mission-oriented autonomously active resource. Generic cells are spontaneously created by the middleware, then participate in emerging tasks through a process called specialization. Once specialized, cells exhibit application specific behavior. Specialized cells have mission objectives that are being continuously sought, and sensors that are used to monitor performance parameters, mission objectives, and other phenomena of interest. Due to the inherent anonymous nature of sensor nodes, associative routing enables dynamic semantically-rich descriptive identification of NSE resources. As such, associative routing presents a clear departure from most current network addressing schemes. Associative routing combines resource discovery and path discovery into a single coherent role, leading to significant reduction in traffic load and communication latency without any loss of generality. We also propose Adaptive Multi-Criteria Routing (AMCR) protocol as a realization of associative routing for NSEs. AMCR exploits application-specific message semantics, represented as generic criteria, and adapts its operation according to observed traffic patterns. BioSENSE intrinsically exploits software diversity, runtime implementation shuffling, and fault recovery to achieve security and resilience required for mission-critical NSEs. BioSENSE makes NSE software a resilient moving target that : 1) confuses the attacker by non-determinism through shuffling of software component implementations; 2) improves the availability of NSE by providing means to gracefully recover from implementation flaws at runtime; and 3) enhances the software system by survival of the fittest through trust-based component selection in an online software component marketplace. In summary, BioSENSE touts the following advantages: (1) on-demand, online distribution and adaptive allocation of services and physical resources shared among multiple long-lived applications with dynamic missions and quality of service requirements, (2) structural, functional, and performance adaptation to dynamic network scales, contexts and topologies, (3) moving target defense of system software, and (4) autonomic failure recovery. / Ph. D.

<p dir="ltr">UBE2C is upregulated in castration-resistant prostate cancer and shows strong correlation with high tumor grade. Currently, the scarcity of UBE2C inhibitors is alarming. This study is the first to report UBE2C post-translational modulation mediated by LIMK2 kinase. A proteome-wide screen previously conducted in the Shah lab has identified UBE2C as a direct substrate of LIMK2 using an innovative chemical genetic approach. LIMK2 regulates UBE2C in a variety of ways. First, LIMK2 directly associates with UBE2C in cells. Second, LIMK2 phosphorylates UBE2C at S123 and increases its stability at the protein level. Third, LIMK2 upregulates UBE2C mRNA and protein expression levels in cells. Contrary to its well-established function as an enzyme involved in the ubiquitin-proteosome pathway, UBE2C stabilizes LIMK2 protein expression in a reciprocal loop. This study is the first to show UBE2C stabilizing its substrate. Likewise, UBE2C increases LIMK2 mRNA and protein levels; however, the mechanism is to be elucidated. LIMK2-UBE2C loop is extremely oncogenic creating CRPC pathogenesis <i>in vivo</i>. Targeting LIMK2 is a suitable approach to effectively degrade both UBE2C and LIMK2 which leads to significant inhibition of tumor formation, cancer stem cell phenotype and epithelial to mesenchymal transition <i>in vivo</i>. Additionally, CDK1 for the longest time was thought to be the only protein of the cyclin dependent kinase family which binds to and is activated by cyclin B1 to regulate cell cycle progression. We first showed CDK5 activity in cell division and its importance in maintaining mitotic fidelity. We first established the activation of CDK5 by cyclin B1 <i>in vitro</i>. The phospho-mimetic CDK5 was observed to be less active when bound to cyclin B1 than its wild-type counterpart.</p>

Attached growth biological processes offer advantages over traditional water purification technologies through high biomass retention, easy sludge-water separation, multiple multispecies synergies in proximity, resilience to shock loading, low space requirements, and reactor operational flexibility. Traditionally, attached growth refers to biofilms that require abiotic carrying media for bacteria to attach and grow on. While biofilms have been broadly applied in wastewater treatment, its potential for potable reuse or stormwater treatment has not been well studied. The treatment trains of pre-ozonation followed by biologically active filtration (ozone- BAF) is an advanced biofilm technology for potable reuse that can generate high-quality potable water at reduced energy and chemical demands by removing pollutant through three different pathways: oxidation, adsorption, and biodegradation. However, these pathways can result in both desirable and undesirable effects, and the mechanism behind it is still unclear. To understand the mechanisms of various pollutant removal, parallel performance comparisons of ozone-BAF treatment trains with spent and regenerated granular activated carbon (GAC), along with a range of pre-oxidant ozone doses were performed. Another common issue of BAF is the headloss buildup during its operation, which has become a significant energy and maintenance burden at many utilities. Thus, a mathematical model was developed to predict BAF headloss buildup in response to organic removal and nitrification. For stormwater treatment, the feasibility of using biofilms for stormwater biological nitrogen removal (BNR) is still largely unknown, as very limited research effort has been dedicated to this aspect. Thus, a mathematical model was developed to evaluate the potential of using BNR techniques for stormwater nitrogen removal. Aerobic granules are an even more advanced attached growth process, which eliminates the need for abiotic carrying media. So far, aerobic granular sludge is only formed in sequential batch reactors but not in a continuous flow system. Therefore, continuous flow aerobic granulation from traditional activated sludge was investigated and, for the first time, successfully achieved in continuous flow plug-flow bioreactors fed with real municipal wastewater. Besides, the role and critical value of an essential operational parameter, feast/famine ratio, for continuous flow aerobic granulation were determined. / Doctor of Philosophy / Water scarcity and increasing water demand caused by urban population growth and climate change is a reality throughout the world. Thus, process intensification of the current water and wastewater technologies is gaining increasing attention globally. Comparing to traditional water purification technologies, attached growth biological processes offers advantages such as high biomass retention, easy sludge-water separation, multiple multispecies synergies in proximity, resilience to shock loading, small footprint requirement, and reactor operational flexibility. Traditionally, attached growth refers to biofilms that require abiotic carrying media for bacteria to attach and grow on. While biofilms have been broadly applied in wastewater treatment, its potential for potable reuse or stormwater treatment has not been well studied. For potable reuse, the treatment trains of pre-ozonation followed by biologically active filtration (ozone-BAF) is an advanced biofilm technology that can generate high-quality potable water at reduced energy and chemical demands by removing pollutant through different pathways. However, the mechanism behind it is still unclear. To understand the mechanisms of various pollutant removal, parallel performance comparisons of ozone-BAF treatment trains operated with different operational conditions were performed in this dissertation. Another common issue of BAF is the headloss buildup during its operation, which has become a significant energy and maintenance burden at many utilities. Thus, a mathematical model was developed to predict the headloss buildup during BAF operation. For stormwater treatment, the feasibility of using biofilms for stormwater biological nitrogen removal (BNR) is still largely unknown, as very limited research effort has been dedicated to this aspect. Thus, a mathematical model was developed to evaluate the potential of using BNR technique for stormwater. Aerobic granules are an even more advanced attached growth process. However, aerobic granular sludge is so far only formed in sequential batch reactors which are incompatible with the continuous flow nature of most wastewater treatment plants. Therefore, aerobic granulation from traditional activated sludge was investigated and, for the first time, successfully achieved in continuous flow plug-flow bioreactors fed with real municipal wastewater. Besides, the role of an essential operational parameter, feast/famine ratio, for continuous flow aerobic granulation was determined.

<p dir="ltr">Cancer is the second leading cause of death worldwide, and there is a continued need for effective treatments to combat the disease. A key challenge in cancer therapy persists in the form of therapeutic resistance. While kinase inhibitors (KIs) have shown promise in treating cancer patients with dysregulated protein kinases, treatment failures are common, highlighting the urgent need to address this issue. Despite the approval of 80 protein kinase inhibitors by the United States Food and Drug Administration (FDA), and numerous others in clinical trials, the chemical space explored for protein kinase inhibitors remains limited. Most FDA-approved kinase inhibitors share common core moieties, such as indazole, quinoline, pyrazole, and pyrimidine, indicating a lack of diversification in drug development in this area.</p><p dir="ltr">Efforts to expand the chemical space have led to the identification of a novel 3<i>H</i>-pyrazolo-[4,3-<i>f</i>]quinoline core by the Sintim group. This scaffold can be efficiently synthesized through the Doebner–Povarov multicomponent reaction using readily available ketones, heteroaromatic aldehydes, and 5-aminoindazole. This multicomponent chemistry affords small molecules which inhibit disease-associated protein kinases with sub-nanomolar IC₅₀ values. Additionally, the scaffold presents a unique opportunity to tune for selectivity via judicious substitution patterns, allowing us to target numerous disease-driving kinases, such as FLT3, haspin, and CLK, with the use of simple multi-component chemistry.</p><p dir="ltr">From this work emerged lead amide-containing compound HSK205, which potently inhibits FLT3 and haspin and shows impressive potencies against FLT3-driven acute myeloid leukemia cell lines, with GI₅₀ values between 2 and 20 nM. Western blot analyses indicate that HSK205 inhibits the phosphorylation of FLT3 and histone H3 (substrate of haspin) in Molm-14 AML cells. Further exploration led to the discovery of lead CLK inhibitors, such as HSK1132 and HSK3110, which inhibit the growth of multiple myeloma cell lines <i>in vitro</i> with GI₅₀ values as low as 17 nM. Additionally, these compounds are orally bioavailable and reduce the growth of multiple myeloma RPMI-8226 xenograft model in mice by 69%.</p>

<p dir="ltr">Post-translational modifications (PTMs) play a crucial role in regulating protein function and location. Protein AMPylation, the addition of adenosine monophosphate (AMP), significantly influences protein trafficking, stability, and pathogenic virulence. The Fic Domain family of proteins targets hydroxyl-containing amino acid residues (Ser, Thr, or Tyr), catalyzing the addition of various phosphate-containing moieties such as nucleoside monophosphates (NMPs), phosphocholine, and phosphate. Using gene mining techniques, Dr. Seema Mattoo’s group has identified a clade of Fic domain containing proteins typified by the enzyme originating from <i>Bordetella bronchiseptica</i> (BbFic) which prefers utilizing guanosine triphosphate (GTP) as a substrate over other nucleotides. To understand the physiological role of GMPylation, identifying the proteins modified by BbFic is a first critical step and can be accomplished via mass spectrometry-based proteomics. For a low stoichiometry PTM like GMPylation, however, there is a need to develop chemical tools that enable the targeted enrichment of modified protein. Identifying key interactions between substrate proteins and the BbFic nucleotide binding site will enable development of highly specific molecular tags for Fic substrates.</p><p dir="ltr">The goal of this research project, therefore, is to design chemical probes to tag Fic enzyme substrates, thereby facilitating the identification of GMPylated proteins in chemical proteomics workflows. A set of ATP and GTP analogues carrying either alkyne or azide handles were proposed as possible probes. While 8-azido guanosine showed a high docking score in our in-silico study, literature reports highlight its chemical instability upon exposure to air and light. An alternative probe, the 8-ethynyl guanosine, also showed a high docking score and docks in the same position and orientation as guanosine (the natural ligand) but necessitates synthetically challenging via cross-coupling reactions.</p><p dir="ltr">We considered multiple GMP analogues as potential molecular tags with the assistance of molecular docking with the BbFic enzyme. With predicted binding affinities in hand, we prioritized candidate GTP analogs for synthesis to probe the BbFic-mediated protein GMPylation process. While N6 propargyl guanosine serves as a lead probe for AMPylation, computational analysis reveals challenges with O6 due to its altered hydrogen bond donor/acceptor presentation. The distinctive chemical properties of guanosine, compared to adenosine, require a thorough evaluation of protective group strategies, as not all synthetic methodologies used for ATP analogue synthesis are applicable to GTP analogues. Isolating the triphosphate analogue proved challenging, although purification of the monophosphorylated counterpart is feasible. The Protide analogue benefits from phosphate charge masking, which facilitates purification. While much work remains until the physiological role of GMPylation can be determined, important progress has been made in the design and synthesis of chemical tools for studying this newly discovered PTM.</p>

Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim: The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems. Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques’ prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results: A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents’ reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions: The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for further research, where biologically based ANNs can be employed for general-purpose weather forecasting, as well as for forecasting other severe weather phenomena, such as thunderstorms.

Master of Science / Department of Biochemistry / John M. Tomich / Biologically based adhesives offer many industrial advantages over their chemically synthesized counterparts, not the least of which are reduced environmental impact and limited toxicity. They also represent a renewable resource. In addition, nanoscale biomaterials also show an incredibly large potential for biomedical uses, including possible drug delivery and novel wound bandaging, as well as tissue engineering. Understanding the adhesion mechanisms at work in peptide-based nanomaterials is key for producing viable industrial and clinical biomimetic compounds. Our previous work has shown that small hydrophobic oligopeptide segments flanked by short tri-lysine sequences display adhesion strength that is dependent on the formation of β-structure and large-scale association of monomers. In this study, three oligopeptides were synthesized based on putative amyloid fibril nucleation sites. Two of the sequences originate from the Alzheimer’s beta amyloid peptide Aβ1-40, while the third sequence comes from a nucleation site for islet amyloid polypeptide (IAPP). These peptides show unusual structural properties associated with adhesive ability. Furthermore, they represent a third category of requirements for β-structure formation. In addition, I report the first morphological evidence for the previously predicted structural mechanism underlying our previous peptide based adhesives.

The human vision system is generally recognized as being superior to all known artificial vision systems. Visual attention, among many processes that are related to human vision, is responsible for identifying relevant regions in a scene for further processing. In most cases, analyzing an entire scene is unnecessary and inevitably time consuming. Hence considering visual attention might be advantageous. A subfield of computer vision where this particular functionality is computationally emulated has been shown to retain high potential in solving real world vision problems effectively. In this monograph, elements of visual attention are explored and algorithms are proposed that exploit such elements in order to enhance image understanding capabilities. Satellite images are given special attention due to their practical relevance, inherent complexity in terms of image contents, and their resolution. Processing such large-size images using visual attention can be very helpful since one can first identify relevant regions and deploy further detailed analysis in those regions only. Bottom-up features, which are directly derived from the scene contents, are at the core of visual attention and help identify salient image regions. In the literature, the use of intensity, orientation and color as dominant features to compute bottom-up attention is ubiquitous. The effects of incorporating an entropy feature on top of the above mentioned ones are also studied. This investigation demonstrates that such integration makes visual attention more sensitive to fine details and hence retains the potential to be exploited in a suitable context. One interesting application of bottom-up attention, which is also examined in this work, is that of image segmentation. Since low salient regions generally correspond to homogenously textured regions in the input image; a model can therefore be learned from a homogenous region and used to group similar textures existing in other image regions. Experimentation demonstrates that the proposed method produces realistic segmentation on satellite images. Top-down attention, on the other hand, is influenced by the observer’s current states such as knowledge, goal, and expectation. It can be exploited to locate target objects depending on various features, and increases search or recognition efficiency by concentrating on the relevant image regions only. This technique is very helpful in processing large images such as satellite images. A novel algorithm for computing top-down attention is proposed which is able to learn and quantify important bottom-up features from a set of training images and enhances such features in a test image in order to localize objects having similar features. An object recognition technique is then deployed that extracts potential target objects from the computed top-down attention map and attempts to recognize them. An object descriptor is formed based on physical appearance and uses both texture and shape information. This combination is shown to be especially useful in the object recognition phase. The proposed texture descriptor is based on Legendre moments computed on local binary patterns, while shape is described using Hu moment invariants. Several tools and techniques such as different types of moments of functions, and combinations of different measures have been applied for the purpose of experimentations. The developed algorithms are generalized, efficient and effective, and have the potential to be deployed for real world problems. A dedicated software testing platform has been designed to facilitate the manipulation of satellite images and support a modular and flexible implementation of computational methods, including various components of visual attention models.

O envenenamento ofídico é considerado uma condição tropical negligenciada pela OMS, e no Brasil, o gênero Bothrops está envolvido na maioria dos casos. Primeiramente, estudamos a atividade hidrolítica do veneno de B. jararaca sobre peptídeos biologicamente ativos que podem estar relacionadas com o envenenamento. A hidrólise dos peptídeos que foram substratos para as serinopeptidases não foi eficientemente bloqueada pelo soro antibotrópico produzido pelo Instituto Butantan e, portanto, as causas dessas falhas foram investigadas. Para isso, purificamos quatro serinopeptidases não bloqueadas pelo soro e, por estudos imunoquímicos, observamos que apesar deste não bloquear as atividades destas enzimas, o mesmo é capaz de reconhecê-las. Portanto, decidimos obter soros experimentais contra estas moléculas utilizando camundongos, a fim de compará-los com o soro comercial. Os soros experimentais contra as serinopeptidases mostraram capacidade de reconhecimento e alta afinidade contra elas, e mais importante, capacidade de neutralizar suas atividades in vitro. / Snakebite is considered a neglected tropical condition by WHO, and in Brazil, the Bothrops genus is involved in most of the cases. Initially, we have studied the B. jararaca venom activity over bioactive peptides which could be related with the envenomation. The hydrolysis of the peptides substrate for serinepeptidases were not efficiently blocked by the Butantan Institute bothropic antivenom, therefore, the causes of this flaw were investigated. Thereafter, we purified four serinepeptidases not blocked by the antivenom and, by immunochemistry analysis, we observed that although it could not neutralize the activity, it could well recognize these proteins. Thus, we decided to obtain experimental sera against these serinepeptidases in mice, in order to compare it with the commercial antivenom. The experimental sera against these enzymes demonstrated recognition capability and high affinity, and most important, the ability to neutralize their activity in vitro.