Matching Items (27)
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Description
Lonsdaleite, also called hexagonal diamond, has been widely used as a marker of asteroidal impacts. It is thought to play a central role during the graphite-to-diamond transformation, and calculations suggest that it possesses mechanical properties superior to diamond. However, despite extensive efforts, lonsdaleite has never been produced or described as

Lonsdaleite, also called hexagonal diamond, has been widely used as a marker of asteroidal impacts. It is thought to play a central role during the graphite-to-diamond transformation, and calculations suggest that it possesses mechanical properties superior to diamond. However, despite extensive efforts, lonsdaleite has never been produced or described as a separate, pure material. Here we show that defects in cubic diamond provide an explanation for the characteristic d-spacings and reflections reported for lonsdaleite. Ultrahigh-resolution electron microscope images demonstrate that samples displaying features attributed to lonsdaleite consist of cubic diamond dominated by extensive {113} twins and {111} stacking faults. These defects give rise to nanometre-scale structural complexity. Our findings question the existence of lonsdaleite and point to the need for re-evaluating the interpretations of many lonsdaleite-related fundamental and applied studies.
Created2014-11-01
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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 develo

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
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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 develo

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
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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

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
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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

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
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Description
Since the discovery of graphene, two dimensional materials (2D materials) have become a focus of interest for material research due to their many unique physical properties embedded in their 2D structure. While they host many exciting potential applications, some of these 2D materials are subject to environmental instability issues induced

Since the discovery of graphene, two dimensional materials (2D materials) have become a focus of interest for material research due to their many unique physical properties embedded in their 2D structure. While they host many exciting potential applications, some of these 2D materials are subject to environmental instability issues induced by interaction between material and gas molecules in air, which poses a barrier to further application and manufacture. To overcome this, it is necessary to understand the origin of material instability and interaction with molecules commonly found in air, as well as developing a reproducible and manufacturing compatible method to post-process these materials to extend their lifetime. In this work, the very first investigation on environmental stability on Te containing anisotropic 2D materials such as GaTe and ZrTe3 is reported. Experimental results have demonstrated that freshly exfoliated GaTe quickly deteriorate in air, during which the Raman spectrum, surface morphology, and surface chemistry undergo drastic changes. Environmental Raman spectroscopy and XPS measurements demonstrate that H2O molecules in air interact strongly on the surface while O2, N2, and inert gases don't show any detrimental effects on GaTe surface. Moreover, the anisotropic properties of GaTe slowly disappear during the aging process. To prevent this gas/material interaction based surface transformation, diazonium based surface functionalization is adopted on these Te based 2D materials. Environmental Raman spectroscopy results demonstrate that the stability of functionalized Te based 2D materials exhibit much higher stability both in ambient and extreme conditions. Meanwhile, PL spectroscopy, angle resolved Raman spectroscopy, atomic force microscopy measurements confirm that many attractive physical properties of the material are not affected by surface functionalization. Overall, these findings unveil the degradation mechanism of Te based 2D materials as well as provide a way to significantly enhance their environmental stability through an inexpensive and reproducible surface chemical functionalization route.
ContributorsYang, Sijie (Author) / Tongay, Sefaattin (Thesis advisor) / Gould, Ian (Thesis advisor) / Trovitch, Ryan (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2017
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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

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
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Description

Novel hydride chemistries are employed to deposit light-emitting Ge1-y Snyalloys with y ≤ 0.1 by Ultra-High Vacuum Chemical Vapor Deposition (UHV-CVD) on Ge-buffered Si wafers. The properties of the resultant materials are systematically compared with similar alloys grown directly on Si wafers. The fundamental difference between the two systems is a fivefold

Novel hydride chemistries are employed to deposit light-emitting Ge1-y Snyalloys with y ≤ 0.1 by Ultra-High Vacuum Chemical Vapor Deposition (UHV-CVD) on Ge-buffered Si wafers. The properties of the resultant materials are systematically compared with similar alloys grown directly on Si wafers. The fundamental difference between the two systems is a fivefold (and higher) decrease in lattice mismatch between film and virtual substrate, allowing direct integration of bulk-like crystals with planar surfaces and relatively low dislocation densities. For y ≤ 0.06, the CVD precursors used were digermane Ge2H6 and deuterated stannane SnD4. For y ≥ 0.06, the Ge precursor was changed to trigermane Ge3H8, whose higher reactivity enabled the fabrication of supersaturated samples with the target film parameters. In all cases, the Ge wafers were produced using tetragermane Ge4H10 as the Ge source. The photoluminescence intensity from Ge1-y Sny /Ge films is expected to increase relative to Ge1-y Sny /Si due to the less defected interface with the virtual substrate. However, while Ge1-y Sny /Si films are largely relaxed, a significant amount of compressive strain may be present in the Ge1-y Sny /Ge case. This compressive strain can reduce the emission intensity by increasing the separation between the direct and indirect edges. In this context, it is shown here that the proposed CVD approach to Ge1-y Sny /Ge makes it possible to approach film thicknesses of about 1  μm, for which the strain is mostly relaxed and the photoluminescence intensity increases by one order of magnitude relative to Ge1-y Sny /Si films. The observed strain relaxation is shown to be consistent with predictions from strain-relaxation models first developed for the Si1-x Gex /Si system. The defect structure and atomic distributions in the films are studied in detail using advanced electron-microscopy techniques, including aberration corrected STEM imaging and EELS mapping of the average diamond–cubic lattice.

ContributorsSenaratne, Charutha Lasitha (Author) / Gallagher, J. D. (Author) / Jiang, Liying (Author) / Aoki, Toshihiro (Author) / Smith, David (Author) / Menéndez, Jose (Author) / Kouvetakis, John (Author) / Department of Chemistry and Biochemistry (Contributor)
Created2014-10-07
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Description

The emission properties of GeSn heterostructure pin diodes have been investigated. The devices contain thick (400–600 nm) Ge1-y Sny i-layers spanning a broad compositional range below and above the crossover Sn concentration yc where the Ge1-y Sny alloy becomes a direct-gap material. These results are made possible by an optimized device

The emission properties of GeSn heterostructure pin diodes have been investigated. The devices contain thick (400–600 nm) Ge1-y Sny i-layers spanning a broad compositional range below and above the crossover Sn concentration yc where the Ge1-y Sny alloy becomes a direct-gap material. These results are made possible by an optimized device architecture containing a single defected interface thereby mitigating the deleterious effects of mismatch-induced defects. The observed emission intensities as a function of composition show the contributions from two separate trends: an increase in direct gap emission as the Sn concentration is increased, as expected from the reduction and eventual reversal of the separation between the direct and indirect edges, and a parallel increase in non-radiative recombination when the mismatch strains between the structure components is partially relaxed by the generation of misfit dislocations. An estimation of recombination times based on the observed electroluminescence intensities is found to be strongly correlated with the reverse-bias dark current measured in the same devices.

ContributorsGallagher, J. D. (Author) / Senaratne, Charutha Lasitha (Author) / Sims, Patrick (Author) / Aoki, Toshihiro (Author) / Menéndez, Jose (Author) / Kouvetakis, John (Author) / College of Liberal Arts and Sciences (Contributor)
Created2015-03-02
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Description

The development of non-volatile logic through direct coupling of spontaneous ferroelectric polarization with semiconductor charge carriers is nontrivial, with many issues, including epitaxial ferroelectric growth, demonstration of ferroelectric switching and measurable semiconductor modulation. Here we report a true ferroelectric field effect—carrier density modulation in an underlying Ge(001) substrate by switching

The development of non-volatile logic through direct coupling of spontaneous ferroelectric polarization with semiconductor charge carriers is nontrivial, with many issues, including epitaxial ferroelectric growth, demonstration of ferroelectric switching and measurable semiconductor modulation. Here we report a true ferroelectric field effect—carrier density modulation in an underlying Ge(001) substrate by switching of the ferroelectric polarization in epitaxial c-axis-oriented BaTiO3 grown by molecular beam epitaxy. Using the density functional theory, we demonstrate that switching of BaTiO3 polarization results in a large electric potential change in Ge. Aberration-corrected electron microscopy confirms BaTiO3 tetragonality and the absence of any low-permittivity interlayer at the interface with Ge. The non-volatile, switchable nature of the single-domain out-of-plane ferroelectric polarization of BaTiO3 is confirmed using piezoelectric force microscopy. The effect of the polarization switching on the conductivity of the underlying Ge is measured using microwave impedance microscopy, clearly demonstrating a ferroelectric field effect.

ContributorsPonath, Patrick (Author) / Fredrickson, Kurt (Author) / Posadas, Agham B. (Author) / Ren, Yuan (Author) / Wu, Xiaoyu (Author) / Vasudevan, Rama K. (Author) / Okatan, M. Baris (Author) / Jesse, S. (Author) / Aoki, Toshihiro (Author) / McCartney, Martha (Author) / Smith, David (Author) / Kalinin, Sergei V. (Author) / Lai, Keji (Author) / Demkov, Alexander A. (Author) / Ira A. Fulton Schools of Engineering (Contributor)
Created2015-01-01