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- Genre: Masters Thesis
- Status: Published
Description
It has been well established that mitochondria play a critical role in the pathology of Friedreich's Ataxia. This disease is believed to be caused by a deficiency of frataxin, which research suggests is responsible for iron sulfur cluster assembly. This incomplete assembly of iron sulfur clusters is believed to be linked with dysfunctional complexes in the mitochondrial respiratory chain, increased oxidative stress, and potential cell death. Increased understanding of the pathophysiology of this disease has enabled the development of various therapeutic strategies aimed at restoring mitochondrial respiration. This thesis contains an analysis of the biological activity of several classes of antioxidants against oxidative stress induced by diethyl maleate in Friedreich's Ataxia lymphocytes and CEM leukemia cells. Analogues of vitamin E α-tocopherol have been shown to protect cells under oxidative stress. However, these same analogues show various levels of inhibition towards the electron transport chain complex I. Bicyclic pyridinols containing a ten carbon substituent provided favorable cytoprotection. N-hydroxy-4-pyridone compounds were observed to provide little protection. Similarly, analogues of CoQ10 in the form of pyridinol and pyrimidinol compounds also preserved cell viability at low concentrations.
ContributorsJaruvangsanti, Jennifer (Author) / Hecht, Sidney (Thesis advisor) / Woodbury, Neal (Committee member) / Skibo, Edward (Committee member) / Arizona State University (Publisher)
Created2012
Description
The spread of invasive species may be greatly affected by human responses to prior species spread, but models and estimation methods seldom explicitly consider human responses. I investigate the effects of management responses on estimates of invasive species spread rates. To do this, I create an agent-based simulation model of an insect invasion across a county-level citrus landscape. My model provides an approximation of a complex spatial environment while allowing the "truth" to be known. The modeled environment consists of citrus orchards with insect pests dispersing among them. Insects move across the simulation environment infesting orchards, while orchard managers respond by administering insecticide according to analyst-selected behavior profiles and management responses may depend on prior invasion states. Dispersal data is generated in each simulation and used to calculate spread rate via a set of estimators selected for their predominance in the empirical literature. Spread rate is a mechanistic, emergent phenomenon measured at the population level caused by a suite of latent biological, environmental, and anthropogenic. I test the effectiveness of orchard behavior profiles on invasion suppression and evaluate the robustness of the estimators given orchard responses. I find that allowing growers to use future expectations of spread in management decisions leads to reduced spread rates. Acting in a preventative manner by applying insecticide before insects are actually present, orchards are able to lower spread rates more than by reactive behavior alone. Spread rates are highly sensitive to spatial configuration. Spatial configuration is hardly a random process, consisting of many latent factors often not accounted for in spread rate estimation. Not considering these factors may lead to an omitted variables bias and skew estimation results. The ability of spread rate estimators to predict future spread varies considerably between estimators, and with spatial configuration, invader biological parameters, and orchard behavior profile. The model suggests that understanding the latent factors inherent to dispersal is important for selecting phenomenological models of spread and interpreting estimation results. This indicates a need for caution when evaluating spread. Although standard practice, current empirical estimators may both over- and underestimate spread rate in the simulation.
ContributorsShanafelt, David William (Author) / Fenichel, Eli P (Thesis advisor) / Richards, Timothy (Committee member) / Janssen, Marco (Committee member) / Arizona State University (Publisher)
Created2012
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
This study examined perception of K12 schooling systems as experienced by a randomsample of adults in Phoenix, AZ. It explored whether the values purported as key factors
in the American K12 schooling system - as presented in academic literature - were compatible with the lives, interests and goals of ‘users’, student-participants. In addition, it offered opportunity for post-K12 student-participants to share their views on the purposes, goals, and outcomes they held to be important. The sample consisted of 139 post-K12 stu-
dents/individuals residing in Phoenix, AZ. Mean age of student-participants was 29. Results indicated a mismatch between purported K12 schooling goals and important outcomes embedded in the system and values held by the K12 student-participants. The participants in this research generally perceived K12 schooling as valuable, both to themselves and to society at large, but stressed that the deficiencies they perceived in the system were particular to delivery platforms as they relate to the learning styles of students and belonging. Future life skills and success - in and after K12 schooling - whether related to college or not were also of importance. Results revealed that the initial hypothesis of income, age, and ethnicity as
key factors in satisfaction with K12 schooling was not borne-out. Rather it revealed that a sense of belonging and the suitability of learning platforms to the individual learning styles of students were of greatest significance.
ContributorsParker-Anderies, Margaret (Author) / Janssen, Marco (Thesis advisor) / Garcia, David (Committee member) / Mishra, Punya (Committee member) / Arizona State University (Publisher)
Created2022
Description
Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.
ContributorsYeh, Huai-Ming (Author) / Yan, Hao (Thesis advisor) / Pan, Rong (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Modern, advanced statistical tools from data mining and machine learning have become commonplace in molecular biology in large part because of the “big data” demands of various kinds of “-omics” (e.g., genomics, transcriptomics, metabolomics, etc.). However, in other fields of biology where empirical data sets are conventionally smaller, more traditional statistical methods of inference are still very effective and widely used. Nevertheless, with the decrease in cost of high-performance computing, these fields are starting to employ simulation models to generate insights into questions that have been elusive in the laboratory and field. Although these computational models allow for exquisite control over large numbers of parameters, they also generate data at a qualitatively different scale than most experts in these fields are accustomed to. Thus, more sophisticated methods from big-data statistics have an opportunity to better facilitate the often-forgotten area of bioinformatics that might be called “in-silicomics”.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
ContributorsSeto, Christian (Author) / Pavlic, Theodore (Thesis advisor) / Li, Jing (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2018
Description
Acoustic emission (AE) signals have been widely employed for tracking material properties and structural characteristics. In this study, the aim is to analyze the AE signals gathered during a scanning probe lithography process to classify the known microstructure types and discover unknown surface microstructures/anomalies. To achieve this, a Hidden Markov Model is developed to consider the temporal dependency of the high-resolution AE data. Furthermore, the posterior classification probability and the negative likelihood score for microstructure classification and discovery are computed. Subsequently, a diagnostic procedure to identify the dominant AE frequencies that were used to track the microstructural characteristics is presented. In addition, machine learning methods such as KNN, Naive Bayes, and Logistic Regression classifiers are applied. Finally, the proposed approach applied to identify the surface microstructures of additively manufactured Ti-6Al-4V and show that it not only achieved a high classification accuracy (e.g., more than 90\%) but also correctly identified the microstructural anomalies that may be subjected to further investigation to discover new material phases/properties.
ContributorsSun, Huifeng (Author) / Yan, Hao (Thesis advisor) / Fricks, John (Thesis advisor) / Cheng, Dan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the
absence of severe alcohol consumption. It is widely prevalent in the United States
and in many other developed countries, affecting up to 25 percent of the population.
Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if
not treated at the right time.
Currently, liver biopsy is the gold standard to diagnose NASH, but being an
invasive procedure, it comes with it's own complications along with the inconvenience
of sampling repeated measurements over a period of time. Hence, noninvasive
procedures to assess NASH are urgently required. Magnetic Resonance Elastography
(MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance
Imaging based proton density fat fraction have been successfully combined to predict
NASH stages However, their role in the prediction of disease progression still remains
to be investigated.
This thesis thus looks into combining features from serial MRE observations to
develop statistical models to predict NASH progression. It utilizes data from an experiment
conducted on male mice to develop progressive and regressive NASH and
trains ordinal models, ordered probit regression and ordinal forest on labels generated
from a logistic regression model. The models are assessed on histological data collected
at the end point of the experiment. The models developed provide a framework
to utilize a non-invasive tool to predict NASH disease progression.
ContributorsDeshpande, Eeshan (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2021
Description
Student retention is a critical metric for many universities whose intention is to support student success. The goal of this thesis is to create retention models utilizing machine learning (ML) techniques. The factors explored in this research include only those known during the admissions process. These models have two goals: first, to correctly predict as many non-returning students as possible, while minimizing the number of students who are falsely predicted as non-returning. Next, to identify important features in student retention and provide a practical explanation for a student's decision to no longer persist. The models are then used to provide outreach to students that need more support. The findings of this research indicate that the current top performing model is Adaboost which is able to successfully predict non-returning students with an accuracy of 54 percent.
ContributorsWade, Alexis N (Author) / Gel, Esma (Thesis advisor) / Yan, Hao (Thesis advisor) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021