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- Genre: Academic theses
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
The numerical climate models have provided scientists, policy makers and the general public, crucial information for climate projections since mid-20th century. An international effort to compare and validate the simulations of all major climate models is organized by the Coupled Model Intercomparison Project (CMIP), which has gone through several phases since 1995 with CMIP5 being the state of the art. In parallel, an organized effort to consolidate all observational data in the past century culminates in the creation of several "reanalysis" datasets that are considered the closest representation of the true observation. This study compared the climate variability and trend in the climate model simulations and observations on the timescales ranging from interannual to centennial. The analysis focused on the dynamic climate quantity of zonal-mean zonal wind and global atmospheric angular momentum (AAM), and incorporated multiple datasets from reanalysis and the most recent CMIP3 and CMIP5 archives. For the observation, the validation of AAM by the length-of-day (LOD) and the intercomparison of AAM revealed a good agreement among reanalyses on the interannual and the decadal-to-interdecadal timescales, respectively. But the most significant discrepancies among them are in the long-term mean and long-term trend. For the simulations, the CMIP5 models produced a significantly smaller bias and a narrower ensemble spread of the climatology and trend in the 20th century for AAM compared to CMIP3, while CMIP3 and CMIP5 simulations consistently produced a positive trend for the 20th and 21st century. Both CMIP3 and CMIP5 models produced a wide range of the magnitudes of decadal and interdecadal variability of wind component of AAM (MR) compared to observation. The ensemble means of CMIP3 and CMIP5 are not statistically distinguishable for either the 20th- or 21st-century runs. The in-house atmospheric general circulation model (AGCM) simulations forced by the sea surface temperature (SST) taken from the CMIP5 simulations as lower boundary conditions were carried out. The zonal wind and MR in the CMIP5 simulations are well simulated in the AGCM simulations. This confirmed SST as an important mediator in regulating the global atmospheric changes due to GHG effect.
ContributorsPaek, Houk (Author) / Huang, Huei-Ping (Thesis advisor) / Adrian, Ronald (Committee member) / Wang, Zhihua (Committee member) / Anderson, James (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2013
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
The flow around a golf ball is studied using direct numerical simulation (DNS). An immersed boundary approach is adopted in which the incompressible Navier-Stokes equations are solved using a fractional step method on a structured, staggered grid in cylindrical coordinates. The boundary conditions on the surface are imposed using momentum forcing in the vicinity of the boundary. The flow solver is parallelized using a domain decomposition strategy and message passing interface (MPI), and exhibits linear scaling on as many as 500 processors. A laminar flow case is presented to verify the formal accuracy of the method. The immersed boundary approach is validated by comparison with computations of the flow over a smooth sphere. Simulations are performed at Reynolds numbers of 2.5 × 104 and 1.1 × 105 based on the diameter of the ball and the freestream speed and using grids comprised of more than 1.14 × 109 points. Flow visualizations reveal the location of separation, as well as the delay of complete detachment. Predictions of the aerodynamic forces at both Reynolds numbers are in reasonable agreement with measurements. Energy spectra of the velocity quantify the dominant frequencies of the flow near separation and in the wake. Time-averaged statistics reveal characteristic physical patterns in the flow as well as local trends within dimples. A mechanism of drag reduction due to the dimples is confirmed, and metrics for dimple optimization are proposed.
ContributorsSmith, Clinton E (Author) / Squires, Kyle D (Thesis advisor) / Balaras, Elias (Committee member) / Herrmann, Marcus (Committee member) / Adrian, Ronald (Committee member) / Stanzione, Daniel C (Committee member) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multiphase flows are an important part of many natural and technological phe- nomena such as ocean-air coupling (which is important for climate modeling) and the atomization of liquid fuel jets in combustion engines. The unique challenges of multiphase flow often make analytical solutions to the governing equations impos- sible and experimental investigations very difficult. Thus, high-fidelity numerical simulations can play a pivotal role in understanding these systems. This disserta- tion describes numerical methods developed for complex multiphase flows and the simulations performed using these methods. First, the issue of multiphase code verification is addressed. Code verification answers the question "Is this code solving the equations correctly?" The method of manufactured solutions (MMS) is a procedure for generating exact benchmark solutions which can test the most general capabilities of a code. The chief obstacle to applying MMS to multiphase flow lies in the discontinuous nature of the material properties at the interface. An extension of the MMS procedure to multiphase flow is presented, using an adaptive marching tetrahedron style algorithm to compute the source terms near the interface. Guidelines for the use of the MMS to help locate coding mistakes are also detailed. Three multiphase systems are then investigated: (1) the thermocapillary motion of three-dimensional and axisymmetric drops in a confined apparatus, (2) the flow of two immiscible fluids completely filling an enclosed cylinder and driven by the rotation of the bottom endwall, and (3) the atomization of a single drop subjected to a high shear turbulent flow. The systems are simulated numerically by solving the full multiphase Navier- Stokes equations coupled to the various equations of state and a level set interface tracking scheme based on the refined level set grid method. The codes have been parallelized using MPI in order to take advantage of today's very large parallel computational architectures. In the first system, the code's ability to handle surface tension and large tem- perature gradients is established. In the second system, the code's ability to sim- ulate simple interface geometries with strong shear is demonstrated. In the third system, the ability to handle extremely complex geometries and topology changes with strong shear is shown.
ContributorsBrady, Peter, Ph.D (Author) / Herrmann, Marcus (Thesis advisor) / Lopez, Juan (Thesis advisor) / Adrian, Ronald (Committee member) / Calhoun, Ronald (Committee member) / Chen, Kangping (Committee member) / Arizona State University (Publisher)
Created2011
Description
A method of determining nanoparticle temperature through fluorescence intensity levels is described. Intracellular processes are often tracked through the use of fluorescence tagging, and ideal temperatures for many of these processes are unknown. Through the use of fluorescence-based thermometry, cellular processes such as intracellular enzyme movement can be studied and their respective temperatures established simultaneously. Polystyrene and silica nanoparticles are synthesized with a variety of temperature-sensitive dyes such as BODIPY, rose Bengal, Rhodamine dyes 6G, 700, and 800, and Nile Blue A and Nile Red. Photographs are taken with a QImaging QM1 Questar EXi Retiga camera while particles are heated from 25 to 70 C and excited at 532 nm with a Coherent DPSS-532 laser. Photographs are converted to intensity images in MATLAB and analyzed for fluorescence intensity, and plots are generated in MATLAB to describe each dye's intensity vs temperature. Regression curves are created to describe change in fluorescence intensity over temperature. Dyes are compared as nanoparticle core material is varied. Large particles are also created to match the camera's optical resolution capabilities, and it is established that intensity values increase proportionally with nanoparticle size. Nile Red yielded the closest-fit model, with R2 values greater than 0.99 for a second-order polynomial fit. By contrast, Rhodamine 6G only yielded an R2 value of 0.88 for a third-order polynomial fit, making it the least reliable dye for temperature measurements using the polynomial model. Of particular interest in this work is Nile Blue A, whose fluorescence-temperature curve yielded a much different shape from the other dyes. It is recommended that future work describe a broader range of dyes and nanoparticle sizes, and use multiple excitation wavelengths to better quantify each dye's quantum efficiency. Further research into the effects of nanoparticle size on fluorescence intensity levels should be considered as the particles used here greatly exceed 2 ìm. In addition, Nile Blue A should be further investigated as to why its fluorescence-temperature curve did not take on a characteristic shape for a temperature-sensitive dye in these experiments.
ContributorsTomforde, Christine (Author) / Phelan, Patrick (Thesis advisor) / Dai, Lenore (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2011
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
Next generation gas turbines will be required to produce low concentrations of pollutants such as oxides of nitrogen (NOx), carbon monoxide (CO), and soot. In order to design gas turbines which produce lower emissions it is essential to have computational tools to help designers. Over the past few decades, computational fluid dynamics (CFD) has played a key role in the design of turbomachinary and will be heavily relied upon for the design of future components. In order to design components with the least amount of experimental rig testing, the ensemble of submodels used in simulations must be known to accurately predict the component's performance. The present work aims to validate a CFD model used for a reverse flow, rich-burn, quick quench, lean-burn combustor being developed at Honeywell. Initially, simulations are performed to establish a baseline which will help to assess impact to combustor performance made by changing CFD models. Rig test data from Honeywell is compared to these baseline simulation results. Reynolds averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) turbulence models are both used with the presumption that the LES turbulence model will better predict combustor performance. One specific model, the fuel spray model, is evaluated next. Experimental data of the fuel spray in an isolated environment is used to evaluate models for the fuel spray and a new, simpler approach for inputting the spray boundary conditions (BC) in the combustor is developed. The combustor is simulated once more to evaluate changes from the new fuel spray boundary conditions. This CFD model is then used in a predictive simulation of eight other combustor configurations. All computer simulations in this work were preformed with the commercial CFD software ANSYS FLUENT. NOx pollutant emissions are predicted reasonably well across the range of configurations tested using the RANS turbulence model. However, in LES, significant under predictions are seen. Causes of the under prediction in NOx concentrations are investigated. Temperature metrics at the exit of the combustor, however, are seen to be better predicted with LES.
ContributorsSpencer, A. Jeffrey (Author) / Herrmann, Marcus (Thesis advisor) / Chen, Kangping (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2012