Matching Items (130)
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Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of

Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

ContributorsRutter, Erica (Author) / Stepien, Tracy (Author) / Anderies, Barrett (Author) / Plasencia, Jonathan (Author) / Woolf, Eric C. (Author) / Scheck, Adrienne C. (Author) / Turner, Gregory H. (Author) / Liu, Qingwei (Author) / Frakes, David (Author) / Kodibagkar, Vikram (Author) / Kuang, Yang (Author) / Preul, Mark C. (Author) / Kostelich, Eric (Author) / College of Liberal Arts and Sciences (Contributor)
Created2017-05-31
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Description

This paper discusses the theoretical approximation and attempted measurement of the quantum <br/>force produced by material interactions though the use of a tuning fork-based atomic force microscopy <br/>device. This device was built and orientated specifically for the measurement of the Casimir force as a <br/>function of separation distance using a

This paper discusses the theoretical approximation and attempted measurement of the quantum <br/>force produced by material interactions though the use of a tuning fork-based atomic force microscopy <br/>device. This device was built and orientated specifically for the measurement of the Casimir force as a <br/>function of separation distance using a piezo actuator for approaching and a micro tuning fork for the <br/>force measurement. This project proceeds with an experimental measurement of the ambient Casmir force <br/>through the use of a tuning fork-based AFM to determine its viability in measuring the magnitude of the <br/>force interaction between an interface material and the tuning fork probe. The ambient measurements <br/>taken during the device’s development displayed results consistent with theoretical approximations, while<br/>demonstrating the capability to perform high-precision force measurements. The experimental results<br/>concluded in a successful development of a device which has the potential to measure forces of <br/>magnitude 10−6 to 10−9 at nanometric gaps. To conclude, a path to material analysis using an approach <br/>stage, alternative methods of testing, and potential future experiments are speculated upon.

ContributorsMulkern, William Michael (Author) / Wang, Liping (Thesis director) / Kwon, Beomjin (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT,

Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.

ContributorsBennett, Justin Klark (Author) / Kuang, Yang (Thesis director) / Kostelich, Eric (Committee member) / Phan, Tin (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This paper investigates near-field thermal radiation as the primary source of heat transfer between two parallel surfaces. This radiation takes place extremely close to the heated surfaces in study so the experimental set-up to be used will be done at the nanometer scale. The primary theory being investigated is that

This paper investigates near-field thermal radiation as the primary source of heat transfer between two parallel surfaces. This radiation takes place extremely close to the heated surfaces in study so the experimental set-up to be used will be done at the nanometer scale. The primary theory being investigated is that near-field radiation generates greater heat flux that conventional radiation governed by Planck’s law with maximum for blackbodies. Working with a phase shift material such as VO2 enables a switch-like effect to occur where the total amount of heat flux fluctuates as VO2 transitions from a metal to an insulator. In this paper, the theoretical heat flux and near-field radiation effect are modeled for a set-up of VO2 and SiO2 layers separated by different vacuum gaps. In addition, a physical experimental set-up is validated for future near-field radiation experiments.

ContributorsSluder, Nicole (Author) / Wang, Liping (Thesis director) / Wang, Ropert (Committee member) / Mechanical and Aerospace Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic

Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic brain tumors, this thesis analyzes the volume measurements of the tumor sizes from the BNI data and attempts to explain such size distributions through mathematical models. More specifically, a basic stochastic cellular automaton model is used and has three-dimensional results that show similar size distributions of those of the BNI data. Results of the models are investigated using the likelihood ratio test suggesting that, when the tumor volumes are measured based on assuming tumor sphericity, the tumor size distributions significantly mimic the power law over an exponential distribution.
ContributorsFreed, Rebecca (Co-author) / Snopko, Morgan (Co-author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
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Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given

Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and

Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
Solar energy has become one of the most popular renewable energy in human’s life because of its abundance and environment friendliness. To achieve high solar energy conversion efficiency, it usually requires surfaces to absorb selectivity within one spectral range of interest and reflect strongly over the rest of the spectrum.

Solar energy has become one of the most popular renewable energy in human’s life because of its abundance and environment friendliness. To achieve high solar energy conversion efficiency, it usually requires surfaces to absorb selectivity within one spectral range of interest and reflect strongly over the rest of the spectrum. An economic method is always desired to fabricate spectrally selective surfaces with improved energy conversion efficiency. Colloidal lithography is a recently emerged way of nanofabrication, which has advantages of low-cost and easy operation.

In this thesis, aluminum metasurface structures are proposed based on colloidal lithography method. High Frequency Structure Simulator is used to numerically study optical properties and design the aluminum metasurfaces with selective absorption. Simulation results show that proposed aluminum metasurface structure on aluminum oxide thin film and aluminum substrate has a major reflectance dip, whose wavelength is tunable within the near-infrared and visible spectrum with metasurface size. As the metasurface is opaque due to aluminum film, it indicates strong wavelength-selective optical absorption, which is due to the magnetic resonance between the top metasurface and bottom Al film within the aluminum oxide layer.

The proposed sample is fabricated based on colloidal lithography method. Monolayer polystyrene particles of 500 nm are successfully prepared and transferred onto silicon substrate. Scanning electron microscope is used to check the surface topography. Aluminum thin film with 20-nm or 50-nm thickness is then deposited on the sample. After monolayer particles are removed, optical properties of samples are measured by micro-scale optical reflectance and transmittance microscope. Measured and simulated reflectance of these samples do not have frequency selective properties and is not sensitive to defects. The next step is to fabricate the Al metasurface on Al_2 O_3 and Al films to experimentally demonstrate the selective absorption predicted from the numerical simulation.
ContributorsGuan, Chuyun (Author) / Wang, Liping (Thesis advisor) / Azeredo, Bruno (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Biogas’s potential as a renewable fuel source has been an area of increased research in recent years. One issue preventing wide-spread use of biogas as a fuel is the trace amounts of impurities that damage fuel-burning equipment by depositing silicon, sulfur, calcium and other elements on their surface. This study

Biogas’s potential as a renewable fuel source has been an area of increased research in recent years. One issue preventing wide-spread use of biogas as a fuel is the trace amounts of impurities that damage fuel-burning equipment by depositing silicon, sulfur, calcium and other elements on their surface. This study aims to analyze the effects of a high concentration of L4 linear siloxane on solid oxide fuel cell performance until failure occurs. L4 siloxane has not been extensively researched previously, and this investigation aims to provide new data to support similar, though slower, degradation compared to D4, D5 and other siloxanes in solid oxide fuel cells. The experiments were conducted inside a furnace heated to 800℃ with an Ni-YSZ-supported (Nickel-yttria-stabilized zirconia) fuel cell. A fuel source with a flow rate of 20 mL/min of hydrogen gas, 10 mL/min of nitrogen gas and 0.15 mL/min of L4 siloxane was used. Air was supplied to the cathode. The effects of siloxane deposition on cell voltage and power density degradation and resistance increase were studied by using techniques like the current-voltage method, electrochemical impedance spectroscopy, and gas chromatography. The results of the experiment after reduction show roughly constant degradation of 8.35 mV/hr, followed after approximately 8 hours by an increasing degradation until cell failure of 130.45 mV/hr. The initial degradation and stagnation match previous research in siloxane deposition on SOFCs, but the sharp decline to failure does not. A mechanism for solid oxide fuel cell failure is proposed based on the data.
ContributorsRiley, Derall M. (Author) / Milcarek, Ryan J (Thesis advisor) / Wang, Liping (Committee member) / Phelan, Patrick E (Committee member) / Arizona State University (Publisher)
Created2021
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Description
The thermal conductivity of cadmium sulfide (CdS) colloidal nanocrystals (NCs) and magic-sized clusters (MSCs) have been investigated in this work. It is well documented in the literature that the thermal conductivity of colloidal nanocrystal assemblies decreases as diameter decreases. However, the extrapolation of this size dependence does not apply to

The thermal conductivity of cadmium sulfide (CdS) colloidal nanocrystals (NCs) and magic-sized clusters (MSCs) have been investigated in this work. It is well documented in the literature that the thermal conductivity of colloidal nanocrystal assemblies decreases as diameter decreases. However, the extrapolation of this size dependence does not apply to magic-sized clusters. Magic-sized clusters have an anomalously high thermal conductivity relative to the extrapolated size-dependence trend line for the colloidal nanocrystals. This anomalously high thermal conductivity could probably result from the monodispersity of magic-sized clusters. To support this conjecture, a method of deliberately eliminating the monodispersity of MSCs by mixing them with colloidal nanocrystals was performed. Experiment results showed that mixtures of nanocrystals and MSCs have a lower thermal conductivity that falls approximately on the extrapolated trendline for colloidal nanocrystal thermal conductivity as a function of size.
ContributorsSun, Ming-Hsien (Author) / Wang, Robert (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2022