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- Genre: Academic theses
- Creators: Yang, Yezhou
Description
Gene manipulation techniques, such as RNA interference (RNAi), offer a powerful method for elucidating gene function and discovery of novel therapeutic targets in a high-throughput fashion. In addition, RNAi is rapidly being adopted for treatment of neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease, etc. However, a major challenge in both of the aforementioned applications is the efficient delivery of siRNA molecules, plasmids or transcription factors to primary cells such as neurons. A majority of the current non-viral techniques, including chemical transfection, bulk electroporation and sonoporation fail to deliver with adequate efficiencies and the required spatial and temporal control. In this study, a novel optically transparent biochip is presented that can (a) transfect populations of primary and secondary cells in 2D culture (b) readily scale to realize high-throughput transfections using microscale electroporation and (c) transfect targeted cells in culture with spatial and temporal control. In this study, delivery of genetic payloads of different sizes and molecular characteristics, such as GFP plasmids and siRNA molecules, to precisely targeted locations in primary hippocampal and HeLa cell cultures is demonstrated. In addition to spatio-temporally controlled transfection, the biochip also allowed simultaneous assessment of a) electrical activity of neurons, b) specific proteins using fluorescent immunohistochemistry, and c) sub-cellular structures. Functional silencing of GAPDH in HeLa cells using siRNA demonstrated a 52% reduction in the GAPDH levels. In situ assessment of actin filaments post electroporation indicated a sustained disruption in actin filaments in electroporated cells for up to two hours. Assessment of neural spike activity pre- and post-electroporation indicated a varying response to electroporation. The microarray based nature of the biochip enables multiple independent experiments on the same culture, thereby decreasing culture-to-culture variability, increasing experimental throughput and allowing cell-cell interaction studies. Further development of this technology will provide a cost-effective platform for performing high-throughput genetic screens.
ContributorsPatel, Chetan (Author) / Muthuswamy, Jitendran (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Jain, Tilak (Committee member) / Caplan, Michael (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2012
Description
A cerebral aneurysm is an abnormal ballooning of the blood vessel wall in the brain that occurs in approximately 6% of the general population. When a cerebral aneurysm ruptures, the subsequent damage is lethal damage in nearly 50% of cases. Over the past decade, endovascular treatment has emerged as an effective treatment option for cerebral aneurysms that is far less invasive than conventional surgical options. Nonetheless, the rate of successful treatment is as low as 50% for certain types of aneurysms. Treatment success has been correlated with favorable post-treatment hemodynamics. However, current understanding of the effects of endovascular treatment parameters on post-treatment hemodynamics is limited. This limitation is due in part to current challenges in in vivo flow measurement techniques. Improved understanding of post-treatment hemodynamics can lead to more effective treatments. However, the effects of treatment on hemodynamics may be patient-specific and thus, accurate tools that can predict hemodynamics on a case by case basis are also required for improving outcomes.Accordingly, the main objectives of this work were 1) to develop computational tools for predicting post-treatment hemodynamics and 2) to build a foundation of understanding on the effects of controllable treatment parameters on cerebral aneurysm hemodynamics. Experimental flow measurement techniques, using particle image velocimetry, were first developed for acquiring flow data in cerebral aneurysm models treated with an endovascular device. The experimental data were then used to guide the development of novel computational tools, which consider the physical properties, design specifications, and deployment mechanics of endovascular devices to simulate post-treatment hemodynamics. The effects of different endovascular treatment parameters on cerebral aneurysm hemodynamics were then characterized under controlled conditions. Lastly, application of the computational tools for interventional planning was demonstrated through the evaluation of two patient cases.
ContributorsBabiker, M. Haithem (Author) / Frakes, David H (Thesis advisor) / Adrian, Ronald (Committee member) / Caplan, Michael (Committee member) / Chong, Brian (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2013
Description
Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the coronary artery wall is the main cause of CHD. Rupture of unstable atherosclerotic coronary plaque is known to be the cause of acute coronary syndrome. The composition of plaque is important for detection of plaque vulnerability. Due to prognostic importance of early stage identification, non-invasive assessment of plaque characterization is necessary. Computed tomography (CT) has emerged as a non-invasive alternative to coronary angiography. Recently, dual energy CT (DECT) coronary angiography has been performed clinically. DECT scanners use two different X-ray energies in order to determine the energy dependency of tissue attenuation values for each voxel. They generate virtual monochromatic energy images, as well as material basis pair images. The characterization of plaque components by DECT is still an active research topic since overlap between the CT attenuations measured in plaque components and contrast material shows that the single mean density might not be an appropriate measure for characterization. This dissertation proposes feature extraction, feature selection and learning strategies for supervised characterization of coronary atherosclerotic plaques. In my first study, I proposed an approach for calcium quantification in contrast-enhanced examinations of the coronary arteries, potentially eliminating the need for an extra non-contrast X-ray acquisition. The ambiguity of separation of calcium from contrast material was solved by using virtual non-contrast images. Additional attenuation data provided by DECT provides valuable information for separation of lipid from fibrous plaque since the change of their attenuation as the energy level changes is different. My second study proposed these as the input to supervised learners for a more precise classification of lipid and fibrous plaques. My last study aimed at automatic segmentation of coronary arteries characterizing plaque components and lumen on contrast enhanced monochromatic X-ray images. This required extraction of features from regions of interests. This study proposed feature extraction strategies and selection of important ones. The results show that supervised learning on the proposed features provides promising results for automatic characterization of coronary atherosclerotic plaques by DECT.
ContributorsYamak, Didem (Author) / Akay, Metin (Thesis advisor) / Muthuswamy, Jit (Committee member) / Akay, Yasemin (Committee member) / Pavlicek, William (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2013
Description
Gold nanoparticles have emerged as promising nanomaterials for biosensing, imaging, photothermal treatment and therapeutic delivery for several diseases, including cancer. We have generated poly(amino ether)-functionalized gold nanorods (PAE-GNRs) using a layer-by-layer deposition approach. Sub-toxic concentrations of PAE-GNRs were employed to deliver plasmid DNA to prostate cancer cells in vitro. PAE-GNRs generated using 1,4C-1,4Bis, a cationic polymer from our laboratory demonstrated significantly higher transgene expression and exhibited lower cytotoxicities when compared to similar assemblies generated using 25 kDa poly(ethylene imine) (PEI25k-GNRs), a current standard for polymer-mediated gene delivery. Additionally, sub-toxic concentrations of 1,4C-1,4Bis-GNR nanoassemblies were employed to deliver expression vectors that express shRNA ('shRNA plasmid') against firefly luciferase gene in order to knock down expression of the protein constitutively expressed in prostate cancer cells. The roles of poly(amino ether) chemistry and zeta-potential in determining transgene expression efficacies of PAE-GNR assemblies were investigated. The theranostic potential of 1,4C-1,4Bis-GNR nanoassemblies was demonstrated using live cell two-photon induced luminescence bioimaging. The PAE class of polymers was also investigated for the one pot synthesis of both gold and silver nanoparticles using a small library poly(amino ethers) derived from linear-like polyamines. Efficient nanoparticle synthesis dependent on concentration of polymers as well as polymer chemical composition is demonstrated. Additionally, the application of poly(amino ether)-gold nanoparticles for transgene delivery is demonstrated in 22Rv1 and MB49 cancer cell lines. Base polymer, 1,4C-1,4Bis and 1,4C-1,4Bis templated and modified gold nanoparticles were compared for transgene delivery efficacies. Differences in morphology and physiochemical properties were investigated as they relate to differences in transgene delivery efficacy. There were found to be minimal differences suggestion that 1,4C-1,4Bis efficacy is not lost following use for nanoparticle modification. These results indicate that poly(amino ether)-gold nanoassemblies are a promising theranostic platform for delivery of therapeutic payloads capable of simultaneous gene silencing and bioimaging.
ContributorsRamos, James (Author) / Rege, Kaushal (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Caplan, Michael (Committee member) / Vernon, Brent (Committee member) / Garcia, Antonio (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this work, a novel method is developed for making nano- and micro- fibrous hydrogels capable of preventing the rejection of implanted materials. This is achieved by either (1) mimicking the native cellular environment, to exert fine control over the cellular response or (2) acting as a protective barrier, to camouflage the foreign nature of a material and evade recognition by the immune system. Comprehensive characterization and in vitro studies described here provide a foundation for developing substrates for use in clinical applications. Hydrogel dextran and poly(acrylic acid) (PAA) fibers are formed via electrospinning, in sizes ranging from nanometers to microns in diameter. While "as-electrospun" fibers are continuous in length, sonication is used to fragment fibers into short fiber "bristles" and generate nano- and micro- fibrous surface coatings over a wide range of topographies. Dex-PAA fibrous surfaces are chemically modified, and then optimized and characterized for non-fouling and ECM-mimetic properties. The non-fouling nature of fibers is verified, and cell culture studies show differential responses dependent upon chemical, topographical and mechanical properties. Dex-PAA fibers are advantageously unique in that (1) a fine degree of control is possible over three significant parameters critical for modifying cellular response: topography, chemistry and mechanical properties, over a range emulating that of native cellular environments, (2) the innate nature of the material is non-fouling, providing an inert background for adding back specific bioactive functionality, and (3) the fibers can be applied as a surface coating or comprise the scaffold itself. This is the first reported work of dex-PAA hydrogel fibers formed via electrospinning and thermal cross-linking, and unique to this method, no toxic solvents or cross-linking agents are needed to create hydrogels or for surface attachment. This is also the first reported work of using sonication to fragment electrospun hydrogel fibers, and in which surface coatings were made via simple electrostatic interaction and dehydration. These versatile features enable fibrous surface coatings to be applied to virtually any material. Results of this research broadly impact the design of biomaterials which contact cells in the body by directing the consequent cell-material interaction.
ContributorsLouie, Katherine BoYook (Author) / Massia, Stephen P (Thesis advisor) / Bennett, Kevin (Committee member) / Garcia, Antonio (Committee member) / Pauken, Christine (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2011
Description
Aqueous solutions of temperature-responsive copolymers based on N-isopropylacrylamide (NIPAAm) hold promise for medical applications because they can be delivered as liquids and quickly form gels in the body without organic solvents or chemical reaction. However, their gelation is often followed by phase-separation and shrinking. Gel shrinking and water loss is a major limitation to using NIPAAm-based gels for nearly any biomedical application. In this work, a graft copolymer design was used to synthesize polymers which combine the convenient injectability of poly(NIPAAm) with gel water content controlled by hydrophilic side-chain grafts based on Jeffamine® M-1000 acrylamide (JAAm). The first segment of this work describes the synthesis and characterization of poly(NIPAAm-co-JAAm) copolymers which demonstrates controlled swelling that is nearly independent of LCST. The graft copolymer design was then used to produce a degradable antimicrobial-eluting gel for prevention of prosthetic joint infection. The resorbable graft copolymer gels were shown to have three unique characteristics which demonstrate their suitability for this application. First, antimicrobial release is sustained and complete within 1 week. Second, the gels behave like viscoelastic fluids, enabling complete surface coverage of an implant without disrupting fixation or movement. Finally, the gels degrade rapidly within 1-6 weeks, which may enable their use in interfaces where bone healing takes place. Graft copolymer hydrogels were also developed which undergo Michael addition in situ with poly(ethylene glycol) diacrylate to form elastic gels for endovascular embolization of saccular aneurysms. Inclusion of JAAm grafts led to weaker physical crosslinking and faster, more complete chemical crosslinking. JAAm grafts prolonged the delivery window of the system from 30 seconds to 220 seconds, provided improved gel swelling, and resulted in stronger, more elastic gels within 30 minutes after delivery.
ContributorsOverstreet, Derek (Author) / Caplan, Michael (Thesis advisor) / Massia, Stephen (Committee member) / Mclaren, Alexander (Committee member) / Vernon, Brent (Committee member) / McLemore, Ryan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Biosensors offer excellent diagnostic methods through precise quantification of bodily fluid biomarkers and could fill an important niche in diagnostic screening. The long term goal of this research is the development of an impedance immunosensor for easy-to-use, rapid, sensitive and selective simultaneously multiplexed quantification of bodily fluid disease biomarkers. To test the hypothesis that various cytokines induce empirically determinable response frequencies when captured by printed circuit board (PCB) impedance immunosensor surface, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) methods were used to test PCB biosensors versus multiple cytokine biomarkers to determine limits of detection, background interaction and response at all sweep frequencies. Results indicated that sensors for cytokine Interleukin-12 (IL-12) detected their target over three decades of concentration and were tolerant to high levels of background protein. Further, the hypothesis that cytokine analytes may be rapidly detected via constant frequency impedance immunosensing without sacrificing undue sensitivity, CV, EIS, impedance-time (Zt) methods and modeling were used to test CHITM gold electrodes versus IL-12 over different lengths of time to determine limits of detection, detection time, frequency of response and consistent cross-platform sensor performance. Modeling and Zt studies indicate interrogation of the electrode with optimum frequency could be used for detection of different target concentrations within 90 seconds of sensor exposure and that interrogating the immunosensor with fixed, optimum frequency could be used for sensing target antigen. This informs usability of fixed-frequency impedance methods for biosensor research and particularly for clinical biosensor use. Finally, a multiplexing impedance immunosensor prototype for quantification of biomarkers in various body fluids was designed for increased automation of sample handling and testing. This enables variability due to exogenous factors and increased rapidity of assay with eased sensor fabrication. Methods were provided for simultaneous multiplexing through multisine perturbation of a sensor, and subsequent data processing. This demonstrated ways to observe multiple types of antibody-antigen affinity binding events in real time, reducing the number of sensors and target sample used in the detection and quantification of multiple biomarkers. These features would also improve the suitability of the sensor for clinical multiplex detection of disease biomarkers.
ContributorsFairchild, Aaron (Author) / La Belle, Jeffrey T (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Nagaraj, Vinay (Committee member) / Pizziconi, Vince (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2012
Description
In a collaborative environment where multiple robots and human beings are expected
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018