Matching Items (35)
Description
This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods. The goal of this investigation was to 1) identify the genetic variants and SNPs in Type II diabetes to develop a gene regulatory pathway, and 2) utilize this pathway to determine suitable drug therapeutics for prevention and treatment. Using a Gene Set Enrichment Analysis (GSEA), a set of 1000 gene identifiers from a Mayo Clinic database was analyzed to determine the most significant genetic variants related to insulin signaling pathways involved in Type II Diabetes. The following genes were identified: NRAS, KRAS, PIK3CA, PDE3B, TSC1, AKT3, SOS1, NEU1, PRKAA2, AMPK, and ACC. In an extensive literature review and cross-analysis with Kegg and Reactome pathway databases, novel SNPs located on these gene variants were identified and used to determine suitable drug therapeutics for treatment. Overall, understanding how genetic mutations affect target gene function related to Type II Diabetes disease pathology is crucial to the development of effective diagnosis and treatment. This project provides new insight into the molecular basis of the Type II Diabetes, serving to help untangle the regulatory complexity of the disease and aid in the advancement of diagnosis and treatment. Keywords: Type II Diabetes mellitus, Gene Set Enrichment Analysis, genetic variants, KEGG Insulin Pathway, gene-regulatory pathway
ContributorsBucklin, Lindsay (Co-author) / Davis, Vanessa (Co-author) / Holechek, Susan (Thesis director) / Wang, Junwen (Committee member) / Nyarige, Verah (Committee member) / School of Human Evolution & Social Change (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This research project investigated known and novel differential genetic variants and their associated molecular pathways involved in Type II diabetes mellitus for the purpose of improving diagnosis and treatment methods. The goal of this investigation was to 1) identify the genetic variants and SNPs in Type II diabetes to develop a gene regulatory pathway, and 2) utilize this pathway to determine suitable drug therapeutics for prevention and treatment. Using a Gene Set Enrichment Analysis (GSEA), a set of 1000 gene identifiers from a Mayo Clinic database was analyzed to determine the most significant genetic variants related to insulin signaling pathways involved in Type II Diabetes. The following genes were identified: NRAS, KRAS, PIK3CA, PDE3B, TSC1, AKT3, SOS1, NEU1, PRKAA2, AMPK, and ACC. In an extensive literature review and cross-analysis with Kegg and Reactome pathway databases, novel SNPs located on these gene variants were identified and used to determine suitable drug therapeutics for treatment. Overall, understanding how genetic mutations affect target gene function related to Type II Diabetes disease pathology is crucial to the development of effective diagnosis and treatment. This project provides new insight into the molecular basis of the Type II Diabetes, serving to help untangle the regulatory complexity of the disease and aid in the advancement of diagnosis and treatment.
ContributorsDavis, Vanessa Brooke (Co-author) / Bucklin, Lindsay (Co-author) / Holechek, Susan (Thesis director) / Wang, Junwen (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This thesis examines Care Not Cash, a welfare reform measure that replaced traditional cash General Assistance program payments for homeless persons in San Francisco with in-kind social services. Unlike most welfare reform measures, proponents framed Care Not Cash as a progressive policy, aimed at expanding social services and government care for this vulnerable population. Drawing on primary and secondary documents, as well as interviews with homelessness policy experts, this thesis examines the historical and political success of Care Not Cash, and explores the potential need for implementation of a similar program in Phoenix, Arizona.
ContributorsMcCutcheon, Zachary Ryan (Author) / Lucio, Joanna (Thesis director) / Williams, David (Committee member) / Bretts-Jamison, Jake (Committee member) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
High throughput transcriptome data analysis like Single-cell Ribonucleic Acid sequencing (scRNA-seq) and Circular Ribonucleic Acid (circRNA) data have made significant breakthroughs, especially in cancer genomics. Analysis of transcriptome time series data is core in identifying time point(s) where drastic changes in gene transcription are associated with homeostatic to non-homeostatic cellular transition (tipping points). In Chapter 2 of this dissertation, I present a novel cell-type specific and co-expression-based tipping point detection method to identify target gene (TG) versus transcription factor (TF) pairs whose differential co-expression across time points drive biological changes in different cell types and the time point when these changes are observed. This method was applied to scRNA-seq data sets from a SARS-CoV-2 study (18 time points), a human cerebellum development study (9 time points), and a lung injury study (18 time points). Similarly, leveraging transcriptome data across treatment time points, I developed methodologies to identify treatment-induced and cell-type specific differentially co-expressed pairs (DCEPs). In part one of Chapter 3, I presented a pipeline that used a series of statistical tests to detect DCEPs. This method was applied to scRNA-seq data of patients with non-small cell lung cancer (NSCLC) sequenced across cancer treatment times. However, this pipeline does not account for correlations among multiple single cells from the same sample and correlations among multiple samples from the same patient. In Part 2 of Chapter 3, I presented a solution to this problem using a mixed-effect model. In Chapter 4, I present a summary of my work that focused on the cross-species analysis of circRNA transcriptome time series data. I compared circRNA profiles in neonatal pig and mouse hearts, identified orthologous circRNAs, and discussed regulation mechanisms of cardiomyocyte proliferation and myocardial regeneration conserved between mouse and pig at different time points.
ContributorsNyarige, Verah Mocheche (Author) / Liu, Li (Thesis advisor) / Wang, Junwen (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
Description
Information about the elemental composition of a planetary surface can be determined using nuclear instrumentation such as gamma-ray and neutron spectrometers (GRNS). High-energy Galactic Cosmic Rays (GCRs) resulting from cosmic super novae isotropically bombard the surfaces of planetary bodies in space. When GCRs interact with a body’s surface, they can liberate neutrons in a process called spallation, resulting in neutrons and gamma rays being emitted from the planet’s surface; how GCRs and source particles (i.e. active neutron generators) interact with nearby nuclei defines the nuclear environment. In this work I describe the development of nuclear detection systems and techniques for future orbital and landed missions, as well as the implications of nuclear environments on a non-silicate (icy) planetary body. This work aids in the development of future NASA and international missions by presenting many of the capabilities and limitations of nuclear detection systems for a variety of planetary bodies (Earth, the Moon, metallic asteroids, icy moons). From bench top experiments to theoretical simulations, from geochemical hypotheses to instrument calibrations—nuclear planetary science is a challenging and rapidly expanding multidisciplinary field. In this work (1) I describe ground-truth verification of the neutron die-away method using a new type of elpasolite (Cs2YLiCl6:Ce) scintillator, (2) I explore the potential use of temporal neutron measurements on the surface of Titan through Monte-Carlo simulation models, and (3) I report on the experimental spatial efficiency and calibration details of the miniature neutron spectrometer (Mini-NS) on board the NASA LunaH-Map mission. This work presents a subset of planetary nuclear science and its many challenges in humanity's ongoing effort to explore strange new worlds.
ContributorsHeffern, Lena Elizabeth (Author) / Hardgrove, Craig (Thesis advisor) / Elkins-Tanton, Linda (Committee member) / Parsons, Ann (Committee member) / Garvie, Laurence (Committee member) / Holbert, Keith (Committee member) / Lyons, James (Committee member) / Arizona State University (Publisher)
Created2022
Description
Characterizing the surface mineralogy of asteroids is critical to constraining their formation history and provides insight into the processes of planetary formation. One method of determining the surface mineralogy of asteroids is comparison of their visible to near-infrared reflectance (VNIR) spectra with laboratory spectra from meteorites and minerals. Subsequent in-situ investigation of these asteroids by spacecraft can supplement or supersede interpretations derived from Earth-based observations.I investigated a suite of aubrites, sulfide minerals, and metal-rich chondrites in a variety of forms (hand samples, powders, and slabs) to identify similarities with ‘spectrally featureless’ asteroids. I collected VNIR spectra and powder X-ray diffraction patterns of these samples and compared their overall reflectance and spectral slope with X-complex and T-, L-, and D-type asteroid spectra.
The Psyche Mission will orbit asteroid (16) Psyche beginning in 2026. I provide a pre-flight assessment of the surface composition of Psyche by comparing spectra of Psyche to a large spectral library of possible surface analog materials (e.g., iron meteorites, mesosiderites, pallasites, sulfides, enstatite, ordinary, and metal-rich chondrites, endmember silicates, and mixtures of silicates, metal, and sulfides). Spectra of Psyche are generally consistent with iron meteorite powder, mixtures of iron meteorite powder and low-Fe, low-Ca pyroxene, sulfide minerals, and the CH/CBb chondrite Isheyevo. Next, I demonstrate some anticipated capabilities of the Psyche Multispectral Imager by comparing spectral parameters derived from Imager-convolved data to those from high resolution laboratory spectra. I offer preliminary strategies for classifying surface composition based on Imager filter ratios and overall reflectance.
Last, I present an assessment of a benchtop, commercial-off-the-shelf (COTS) version of the Psyche Imager. The COTS Imager uses the same model CCD and a similar f-number commercial camera lens. I measured the gain, full well, linearity, read noise, quantum efficiency, and modulation transfer function to compare with eventual calibration data from the flight Imager. I validate the results of a radiometric model developed for the flight Imager with signal measurements from the COTS Imager. This work demonstrates that the COTS Imager is an effective testbed for validating Imager requirements and developing software and procedures for eventual calibration of the flight instrument.
ContributorsDibb, Steven (Author) / Bell, James (Thesis advisor) / Hardgrove, Craig (Committee member) / Garvie, Laurence (Committee member) / Elkins-Tanton, Linda (Committee member) / Bose, Maitrayee (Committee member) / Arizona State University (Publisher)
Created2021
Description
ABSTRACTWith the National Aeronautics and Space Administration (NASA) Psyche Mission, humans will soon have the first opportunity to explore a new kind of planetary body: one composed mostly of metal as opposed to stony minerals or ices. Identifying the composition of asteroids from Earth-based observations has been an ongoing challenge. Although optical reflectance spectra, radar, and orbital dynamics can constrain an asteroid’s mineralogy and bulk density, in many cases there is not a clear or precise match with analogous materials such as meteorites. Additionally, the surfaces of asteroids and other small, airless planetary bodies can be heavily modified over geologic time by exposure to the space environment. To accurately interpret remote sensing observations of metal-rich asteroids, it is therefore necessary to understand how the processes active on asteroid surfaces affect metallic materials. This dissertation represents a first step toward that understanding. In collaboration with many colleagues, I have performed laboratory experiments on iron meteorites to simulate solar wind ion irradiation, surface heating, micrometeoroid bombardment, and high-velocity impacts. Characterizing the meteorite surface’s physical and chemical properties before and after each experiment can constrain the effects of each process on a metal-rich surface in space. While additional work will be needed for a complete understanding, it is nevertheless possible to make some early predictions of what (16) Psyche’s surface regolith might look like when humans observe it up close. Moreover, the results of these experiments will inform future exploration beyond asteroid Psyche as humans attempt to understand how Earth’s celestial neighborhood came to be.
ContributorsChristoph, John Morgan M. (Author) / Elkins-Tanton, Linda (Thesis advisor) / Williams, David (Committee member) / Dukes, Catherine (Committee member) / Sharp, Thomas (Committee member) / Bell III, James (Committee member) / Arizona State University (Publisher)
Created2023
Description
The central question of my dissertation is "How old are the inner moons of Saturn?" This question is of critical importance for the refinement of how solar systems and giant planet systems form and evolve. One of the most direct ways to test the ages of a planet's surface is through the use of impact craters. Here I utilize images from the Cassini Imaging Science Subsystem (ISS) to count the craters on the mid-sized moons of Saturn, Tethys and Dione. I present a statistical analysis of the craters and the likely impactor sources that crated these craters. On Tethys I find that the impact craters can be explained by a planetocentric source that is local to the Saturnian system and is not found elsewhere in the outer planets. I also find that the majority of mapped regions are likely close in age. On Dione, I have mapped four areas at a regional-scale resolution ( ~ 200 m/ pix) and have found that resurfacing has greatly affected the small crater population and that the overall size-frequency distribution of craters is most representative of a planetocentric source unique to Saturn. Elliptical craters provide another means of assessing the bombardment environment around Saturn, as they record the primary direction of the object that created the crater upon impact on the surface. I have mapped these craters on Tethys and Dione, to analyze the global distributions of these craters and their orientations. Across both satellites, I find that in the equatorial regions between 30° N and 30°S in latitude, the orientations of the elliptical craters are consistent with an East/West orientation for their direction, which also is suggestive of a local planetocentric source. Throughout the main three studies presented in this dissertation I find that the main impactor source is a planetocentric source that is unique to Saturn and is not seen on the moons of the other giant planets.
ContributorsFerguson, Sierra Nichole (Author) / Rhoden, Alyssa R (Thesis advisor) / Desch, Steven J (Thesis advisor) / Robinson, Mark (Committee member) / Williams, David (Committee member) / Bose, Maitrayee (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021