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Description
The purpose of information source detection problem (or called rumor source detection) is to identify the source of information diffusion in networks based on available observations like the states of the nodes and the timestamps at which nodes adopted the information (or called infected). The solution of the problem can be used to answer a wide range of important questions in epidemiology, computer network security, etc. This dissertation studies the fundamental theory and the design of efficient and robust algorithms for the information source detection problem.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
ContributorsZhu, Kai (Author) / Ying, Lei (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Liu, Huan (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2015
Description
The manipulation of biological targets using synthetic compounds has been the focal point of medicinal chemistry. The work described herein centers on the synthesis of organic small molecules that act either as probes for studying protein conformational changes or DNA–protein interaction, or as multifunctional radical quenchers.
Fluorescent labeling is of paramount importance to biological studies of proteins. For the development of new extrinsic small fluorophores, a series of tryptophan analogues has been designed and synthesized. Their pdCpA derivatives have been synthesized for tRNA activation and in vitro protein synthesis. The photophysical properties of the tryptophan (Trp) analogues have been examined, some of which can be selectively monitored even in the presence of multiple native tryptophan residues. Further, some of the Trp analogues form efficient FRET pairs with acceptors such as acridon-2-ylalanine (Acd) or L-(7-hydroxycoumarin-4-yl)ethylglycine (HCO) for the selective study of conformational changes in proteins.
Molecules which can bind with high sequence selectivity to a chosen target in a gene sequence are of interest for the development of gene therapy, diagnostic devices for genetic analysis, and as molecular tools for nucleic acid manipulations. Stereoselective synthesis of different alanyl nucleobase amino acids is described. Their pdCpA derivatives have been synthesized for tRNA activation and site-specific incorporation into the DNA-binding protein RRM1 of hnRNP LL. It is proposed that the nucleobase moieties in the protein may specifically recognize base sequence in the i-motif DNA through H-bonding and base-stacking interactions.
The mitochondrial respiratory chain accumulates more oxidative damage than any other organelle within the cell. Dysfunction of this organelle is believed to drive the progression of many diseases, thus mitochondria are an important potential drug target. Reactive oxygen species (ROS) are generated when electrons from the respiratory chain escape and interact with oxygen. ROS can react with proteins, lipids or DNA causing cell death. For the development of effective neuroprotective drugs, a series of N-hydroxy-4-pyridones have been designed and synthesized as CoQ10 analogues. All the analogues synthesized were evaluated for their ability to quench lipid peroxidation and reactive oxygen species (ROS).
Fluorescent labeling is of paramount importance to biological studies of proteins. For the development of new extrinsic small fluorophores, a series of tryptophan analogues has been designed and synthesized. Their pdCpA derivatives have been synthesized for tRNA activation and in vitro protein synthesis. The photophysical properties of the tryptophan (Trp) analogues have been examined, some of which can be selectively monitored even in the presence of multiple native tryptophan residues. Further, some of the Trp analogues form efficient FRET pairs with acceptors such as acridon-2-ylalanine (Acd) or L-(7-hydroxycoumarin-4-yl)ethylglycine (HCO) for the selective study of conformational changes in proteins.
Molecules which can bind with high sequence selectivity to a chosen target in a gene sequence are of interest for the development of gene therapy, diagnostic devices for genetic analysis, and as molecular tools for nucleic acid manipulations. Stereoselective synthesis of different alanyl nucleobase amino acids is described. Their pdCpA derivatives have been synthesized for tRNA activation and site-specific incorporation into the DNA-binding protein RRM1 of hnRNP LL. It is proposed that the nucleobase moieties in the protein may specifically recognize base sequence in the i-motif DNA through H-bonding and base-stacking interactions.
The mitochondrial respiratory chain accumulates more oxidative damage than any other organelle within the cell. Dysfunction of this organelle is believed to drive the progression of many diseases, thus mitochondria are an important potential drug target. Reactive oxygen species (ROS) are generated when electrons from the respiratory chain escape and interact with oxygen. ROS can react with proteins, lipids or DNA causing cell death. For the development of effective neuroprotective drugs, a series of N-hydroxy-4-pyridones have been designed and synthesized as CoQ10 analogues. All the analogues synthesized were evaluated for their ability to quench lipid peroxidation and reactive oxygen species (ROS).
ContributorsTalukder, Poulami (Author) / Hecht, Sidney M. (Thesis advisor) / Woodbury, Neal (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2016
Description
Sunlight, the most abundant source of energy available, is diffuse and intermittent; therefore it needs to be stored in chemicals bonds in order to be used any time. Photosynthesis converts sunlight into useful chemical energy that organisms can use for their functions. Artificial photosynthesis aims to use the essential chemistry of natural photosynthesis to harvest solar energy and convert it into fuels such as hydrogen gas. By splitting water, tandem photoelectrochemical solar cells (PESC) can produce hydrogen gas, which can be stored and used as fuel. Understanding the mechanisms of photosynthesis, such as photoinduced electron transfer, proton-coupled electron transfer (PCET) and energy transfer (singlet-singlet and triplet-triplet) can provide a detailed knowledge of those processes which can later be applied to the design of artificial photosynthetic systems. This dissertation has three main research projects. The first part focuses on design, synthesis and characterization of suitable photosensitizers for tandem cells. Different factors that can influence the performance of the photosensitizers in PESC and the attachment and use of a biomimetic electron relay to a water oxidation catalyst are explored. The second part studies PCET, using Nuclear Magnetic Resonance and computational chemistry to elucidate the structure and stability of tautomers that comprise biomimetic electron relays, focusing on the formation of intramolecular hydrogen bonds. The third part of this dissertation uses computational calculations to understand triplet-triplet energy transfer and the mechanism of quenching of the excited singlet state of phthalocyanines in antenna models by covalently attached carotenoids.
ContributorsTejeda Ferrari, Marely (Author) / Moore, Ana (Thesis advisor) / Mujica, Vladimiro (Thesis advisor) / Gust, John (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2016
Description
Conductance fluctuations associated with quantum transport through quantumdot systems are currently understood to depend on the nature of the corresponding classical dynamics, i.e., integrable or chaotic. There are a couple of interesting phenomena about conductance fluctuation and quantum tunneling related to geometrical shapes of graphene systems. Firstly, in graphene quantum-dot systems, when a magnetic field is present, as the Fermi energy or the magnetic flux is varied, both regular oscillations and random fluctuations in the conductance can occur, with alternating transitions between the two. Secondly, a scheme based on geometrical rotation of rectangular devices to effectively modulate the conductance fluctuations is presented. Thirdly, when graphene is placed on a substrate of heavy metal, Rashba spin-orbit interaction of substantial strength can occur. In an open system such as a quantum dot, the interaction can induce spin polarization. Finally, a problem using graphene systems with electron-electron interactions described by the Hubbard Hamiltonian in the setting of resonant tunneling is investigated.
Another interesting problem in quantum transport is the effect of disorder or random impurities since it is inevitable in real experiments. At first, for a twodimensional Dirac ring, as the disorder density is systematically increased, the persistent current decreases slowly initially and then plateaus at a finite nonzero value, indicating remarkable robustness of the persistent currents, which cannot be discovered in normal metal and semiconductor rings. In addition, in a Floquet system with a ribbon structure, the conductance can be remarkably enhanced by onsite disorder.
Recent years have witnessed significant interest in nanoscale physical systems, such as semiconductor supperlattices and optomechanical systems, which can exhibit distinct collective dynamical behaviors. Firstly, a system of two optically coupled optomechanical cavities is considered and the phenomenon of synchronization transition associated with quantum entanglement transition is discovered. Another useful issue is nonlinear dynamics in semiconductor superlattices caused by its key potential application lies in generating radiation sources, amplifiers and detectors in the spectral range of terahertz. In such a system, transition to multistability, i.e., the emergence of multistability with chaos as a system parameter passes through a critical point, is found and argued to be abrupt.
Another interesting problem in quantum transport is the effect of disorder or random impurities since it is inevitable in real experiments. At first, for a twodimensional Dirac ring, as the disorder density is systematically increased, the persistent current decreases slowly initially and then plateaus at a finite nonzero value, indicating remarkable robustness of the persistent currents, which cannot be discovered in normal metal and semiconductor rings. In addition, in a Floquet system with a ribbon structure, the conductance can be remarkably enhanced by onsite disorder.
Recent years have witnessed significant interest in nanoscale physical systems, such as semiconductor supperlattices and optomechanical systems, which can exhibit distinct collective dynamical behaviors. Firstly, a system of two optically coupled optomechanical cavities is considered and the phenomenon of synchronization transition associated with quantum entanglement transition is discovered. Another useful issue is nonlinear dynamics in semiconductor superlattices caused by its key potential application lies in generating radiation sources, amplifiers and detectors in the spectral range of terahertz. In such a system, transition to multistability, i.e., the emergence of multistability with chaos as a system parameter passes through a critical point, is found and argued to be abrupt.
ContributorsYing, Lei (Author) / Lai, Ying-Cheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Chen, Tingyong (Committee member) / Yao, Yu (Committee member) / Arizona State University (Publisher)
Created2016
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Glycans are monosaccharide-based heteropolymers that are found covalently attached to many different proteins and lipids and are ubiquitously displayed on the exterior surfaces of cells. Serum glycan composition and structure are well known to be altered in many different types of cancer. In fact, glycans represent a promising but only marginally accessed source of cancer markers. The approach used in this dissertation, which is referred to as “glycan node analysis”, is a molecularly bottom-up approach to plasma/serum (P/S) glycomics based on glycan linkage analysis that captures features such as α2-6 sialylation, β1-6 branching, and core fucosylation as single analytical signals.
The diagnostic utility of this approach as applied to lung cancer patients across all stages as well as prostate, serous ovarian, and pancreatic cancer patients compared to certifiably healthy individuals, nominally healthy individuals and/or risk-matched controls is reported. Markers for terminal fucosylation, α2-6 sialylation, β1-4 branching, β1-6 branching and outer-arm fucosylation were most able to differentiate cases from controls. These markers behaved in a stage-dependent manner in lung cancer as well as other types of cancer. Using a Cox proportional hazards regression model, the ability of these markers to predict progression and survival in lung cancer patients was assessed. In addition, the potential mechanistic role of aberrant P/S glycans in cancer progression is discussed.
Plasma samples from former bladder cancer patients with currently no evidence of disease (NED), non-muscle invasive bladder cancer (NMIBC), and muscle invasive bladder cancer (MIBC) along with certifiably healthy controls were analyzed. Markers for α2-6 sialylation, β1-4 branching, β1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from controls; but NED, NMIBC, and MIBC were not distinguished from one another. Markers for α2-6 sialylation and β1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. These two glycan features were found to be correlated to the concentration of C-reactive protein, a known prognostic marker for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation.
The diagnostic utility of this approach as applied to lung cancer patients across all stages as well as prostate, serous ovarian, and pancreatic cancer patients compared to certifiably healthy individuals, nominally healthy individuals and/or risk-matched controls is reported. Markers for terminal fucosylation, α2-6 sialylation, β1-4 branching, β1-6 branching and outer-arm fucosylation were most able to differentiate cases from controls. These markers behaved in a stage-dependent manner in lung cancer as well as other types of cancer. Using a Cox proportional hazards regression model, the ability of these markers to predict progression and survival in lung cancer patients was assessed. In addition, the potential mechanistic role of aberrant P/S glycans in cancer progression is discussed.
Plasma samples from former bladder cancer patients with currently no evidence of disease (NED), non-muscle invasive bladder cancer (NMIBC), and muscle invasive bladder cancer (MIBC) along with certifiably healthy controls were analyzed. Markers for α2-6 sialylation, β1-4 branching, β1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from controls; but NED, NMIBC, and MIBC were not distinguished from one another. Markers for α2-6 sialylation and β1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. These two glycan features were found to be correlated to the concentration of C-reactive protein, a known prognostic marker for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation.
ContributorsRoshdiferdosi, Shadi (Author) / Borges, Chad R (Thesis advisor) / Woodbury, Neal (Committee member) / Hayes, Mark (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation treats a number of related problems in control and data analysis of complex networks.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
ContributorsWang, Lezhi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Papandreoou-Suppappola, Antonia (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
Online social media is popular due to its real-time nature, extensive connectivity and a large user base. This motivates users to employ social media for seeking information by reaching out to their large number of social connections. Information seeking can manifest in the form of requests for personal and time-critical information or gathering perspectives on important issues. Social media platforms are not designed for resource seeking and experience large volumes of messages, leading to requests not being fulfilled satisfactorily. Designing frameworks to facilitate efficient information seeking in social media will help users to obtain appropriate assistance for their needs
and help platforms to increase user satisfaction.
Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the
syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on
issues of public importance.
Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them.
and help platforms to increase user satisfaction.
Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the
syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on
issues of public importance.
Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them.
ContributorsRanganath, Suhas (Author) / Liu, Huan (Thesis advisor) / Lai, Ying-Cheng (Thesis advisor) / Tong, Hanghang (Committee member) / Vaculin, Roman (Committee member) / Arizona State University (Publisher)
Created2017
Description
Though DNA nanostructures (DNs) have become interesting subjects of drug delivery, in vivo imaging and biosensor research, however, for real biological applications, they should be ‘long circulating’ in blood. One of the crucial requirements for DN stability is high salt concentration (like ~5–20 mM Mg2+) that is unavailable in a cell culture medium or in blood. Hence DNs denature promptly when injected into living systems. Another important factor is the presence of nucleases that cause fast degradation of unprotected DNs. The third factor is ‘opsonization’ which is the immune process by which phagocytes target foreign particles introduced into the bloodstream. The primary aim of this thesis is to design strategies that can improve the in vivo stability of DNs, thus improving their pharmacodynamics and biodistribution.
Several strategies were investigated to address the three previously mentioned limitations. The first attempt was to study the effect length and conformation of polyethylene glycol (PEG) on DN stability. DNs were also coated with PEG-lipid and human serum albumin (HSA) and their stealth efficiencies were compared. The findings reveal that both PEGylation and albumin coating enhance low salt stability, increase resistance towards nuclease action and reduce uptake of DNs by macrophages. Any protective coating around a DN increases its hydrodynamic radius, which is a crucial parameter influencing their clearance. Keeping this in mind, intrinsically stable DNs that can survive low salt concentration without any polymer coating were built. Several DNA compaction agents and DNA binders were screened to stabilize DNs in low magnesium conditions. Among them arginine, lysine, bis-lysine and hexamine cobalt showed the potential to enhance DN stability.
This thesis also presents a sensitive assay, the Proximity Ligation Assay (PLA), for the estimation of DN stability with time. It requires very simple modifications on the DNs and it can yield precise results from a very small amount of sample. The applicability of PLA was successfully tested on several DNs ranging from a simple wireframe tetrahedron to a 3D origami and the protocol to collect in vivo samples, isolate the DNs and measure their stability was developed.
Several strategies were investigated to address the three previously mentioned limitations. The first attempt was to study the effect length and conformation of polyethylene glycol (PEG) on DN stability. DNs were also coated with PEG-lipid and human serum albumin (HSA) and their stealth efficiencies were compared. The findings reveal that both PEGylation and albumin coating enhance low salt stability, increase resistance towards nuclease action and reduce uptake of DNs by macrophages. Any protective coating around a DN increases its hydrodynamic radius, which is a crucial parameter influencing their clearance. Keeping this in mind, intrinsically stable DNs that can survive low salt concentration without any polymer coating were built. Several DNA compaction agents and DNA binders were screened to stabilize DNs in low magnesium conditions. Among them arginine, lysine, bis-lysine and hexamine cobalt showed the potential to enhance DN stability.
This thesis also presents a sensitive assay, the Proximity Ligation Assay (PLA), for the estimation of DN stability with time. It requires very simple modifications on the DNs and it can yield precise results from a very small amount of sample. The applicability of PLA was successfully tested on several DNs ranging from a simple wireframe tetrahedron to a 3D origami and the protocol to collect in vivo samples, isolate the DNs and measure their stability was developed.
ContributorsBanerjee, Saswata (Author) / Yan, Hao (Thesis advisor) / Angell, Austen (Committee member) / Woodbury, Neal (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Resilience is emerging as the preferred way to improve the protection of infrastructure systems beyond established risk management practices. Massive damages experienced during tragedies like Hurricane Katrina showed that risk analysis is incapable to prevent unforeseen infrastructure failures and shifted expert focus towards resilience to absorb and recover from adverse events. Recent, exponential growth in research is now producing consensus on how to think about infrastructure resilience centered on definitions and models from influential organizations like the US National Academy of Sciences. Despite widespread efforts, massive infrastructure failures in 2017 demonstrate that resilience is still not working, raising the question: Are the ways people think about resilience producing resilient infrastructure systems?
This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure.
Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.
This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure.
Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.
ContributorsEisenberg, Daniel Alexander (Author) / Seager, Thomas P. (Thesis advisor) / Park, Jeryang (Thesis advisor) / Alderson, David L. (Committee member) / Lai, Ying-Cheng (Committee member) / Arizona State University (Publisher)
Created2018