Matching Items (36)
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Moore's law has been the most important driving force for the tremendous progress of semiconductor industry. With time the transistors which form the fundamental building block of any integrated circuit have been shrinking in size leading to smaller and faster electronic devices.As the devices scale down thermal effects and the short channel effects become the important deciding factors in determining transistor architecture.SOI (Silicon on Insulator) devices have been excellent alternative to planar MOSFET for ultimate CMOS scaling since they mitigate short channel effects. Hence as a part of thesis we tried to study the benefits of the SOI technology especially for lower technology nodes when the channel thickness reduces down to sub 10nm regime. This work tries to explore the effects of structural confinement due to reduced channel thickness on the electrostatic behavior of DG SOI MOSFET. DG SOI MOSFET form the Qfinfet which is an alternative to existing Finfet structure. Qfinfet was proposed and patented by the Finscale Inc for sub 10nm technology nodes.
As part of MS Thesis we developed electrostatic simulator for DG SOI devices by implementing the self consistent full band Schrodinger Poisson solver. We used the Empirical Pseudopotential method in conjunction with supercell approach to solve the Schrodinger Equation. EPM was chosen because it has few empirical parameters which give us good accuracy for experimental results. Also EPM is computationally less expensive as compared to the atomistic methods like DFT(Density functional theory) and NEGF (Non-equilibrium Green's function). In our workwe considered two crystallographic orientations of Si,namely [100] and [110].
As part of MS Thesis we developed electrostatic simulator for DG SOI devices by implementing the self consistent full band Schrodinger Poisson solver. We used the Empirical Pseudopotential method in conjunction with supercell approach to solve the Schrodinger Equation. EPM was chosen because it has few empirical parameters which give us good accuracy for experimental results. Also EPM is computationally less expensive as compared to the atomistic methods like DFT(Density functional theory) and NEGF (Non-equilibrium Green's function). In our workwe considered two crystallographic orientations of Si,namely [100] and [110].
ContributorsLaturia, Akash (Author) / Vasileska, Dragica (Thesis advisor) / Ferry, David (Committee member) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2016
Description
Thermal effects in nano-scaled devices were reviewed and modeling methodologies to deal with this issue were discussed. The phonon energy balance equations model, being one of the important previous works regarding the modeling of heating effects in nano-scale devices, was derived. Then, detailed description was given on the Monte Carlo (MC) solution of the phonon Boltzmann Transport Equation. The phonon MC solver was developed next as part of this thesis. Simulation results of the thermal conductivity in bulk Si show good agreement with theoretical/experimental values from literature.
ContributorsYoo, Seung Kyung (Author) / Vasileska, Dragica (Thesis advisor) / Ferry, David (Committee member) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
Description
The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive immune system is further split into two main categories: humoral and cellular immunity. The humoral immune response produces antibodies against specific targets, and these antibodies can be used to learn about disease and normal states. In this document, I use antibodies to characterize the immune system in two ways: 1. I determine the Antibody Status (AbStat) from the data collected from applying sera to an array of non-natural sequence peptides, and demonstrate that this AbStat measure can distinguish between disease, normal, and aged samples as well as produce a single AbStat number for each sample; 2. I search for antigens for use in a cancer vaccine, and this search results in several candidates as well as a new hypothesis. Antibodies provide us with a powerful tool for characterizing the immune system, and this natural tool combined with emerging technologies allows us to learn more about healthy and disease states.
ContributorsWhittemore, Kurt (Author) / Sykes, Kathryn (Thesis advisor) / Johnston, Stephen A. (Committee member) / Jacobs, Bertram (Committee member) / Stafford, Phillip (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2014
Description
From 2D planar MOSFET to 3D FinFET, the geometry of semiconductor devices is getting more and more complex. Correspondingly, the number of mesh grid points increases largely to maintain the accuracy of carrier transport and heat transfer simulations. By substituting the conventional uniform mesh with non-uniform mesh, one can reduce the number of grid points. However, the problem of how to solve governing equations on non-uniform mesh is then imposed to the numerical solver. Moreover, if a device simulator is integrated into a multi-scale simulator, the problem size will be further increased. Consequently, there exist two challenges for the current numerical solver. One is to increase the functionality to accommodate non-uniform mesh. The other is to solve governing physical equations fast and accurately on a large number of mesh grid points.
This research rst discusses a 2D planar MOSFET simulator and its numerical solver, pointing out its performance limit. By analyzing the algorithm complexity, Multigrid method is proposed to replace conventional Successive-Over-Relaxation method in a numerical solver. A variety of Multigrid methods (standard Multigrid, Algebraic Multigrid, Full Approximation Scheme, and Full Multigrid) are discussed and implemented. Their properties are examined through a set of numerical experiments. Finally, Algebraic Multigrid, Full Approximation Scheme and Full Multigrid are integrated into one advanced numerical solver based on the exact requirements of a semiconductor device simulator. A 2D MOSFET device is used to benchmark the performance, showing that the advanced Multigrid method has higher speed, accuracy and robustness.
This research rst discusses a 2D planar MOSFET simulator and its numerical solver, pointing out its performance limit. By analyzing the algorithm complexity, Multigrid method is proposed to replace conventional Successive-Over-Relaxation method in a numerical solver. A variety of Multigrid methods (standard Multigrid, Algebraic Multigrid, Full Approximation Scheme, and Full Multigrid) are discussed and implemented. Their properties are examined through a set of numerical experiments. Finally, Algebraic Multigrid, Full Approximation Scheme and Full Multigrid are integrated into one advanced numerical solver based on the exact requirements of a semiconductor device simulator. A 2D MOSFET device is used to benchmark the performance, showing that the advanced Multigrid method has higher speed, accuracy and robustness.
ContributorsGuo, Xinchen (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen (Committee member) / Ferry, David (Committee member) / Arizona State University (Publisher)
Created2015
Description
Studies of ancient pathogens are moving beyond simple confirmatory analysis of diseased bone; bioarchaeologists and ancient geneticists are posing nuanced questions and utilizing novel methods capable of confronting the debates surrounding pathogen origins and evolution, and the relationships between humans and disease in the past. This dissertation examines two ancient human diseases through molecular and bioarchaeological lines of evidence, relying on techniques in paleogenetics and phylogenetics to detect, isolate, sequence and analyze ancient and modern pathogen DNA within an evolutionary framework. Specifically this research addresses outstanding issues regarding a) the evolution, origin and phylogenetic placement of the pathogen causing skeletal tuberculosis in New World prior to European contact, and b) the phylogeny and origins of the parasite causing the human leishmaniasis disease complex. An additional chapter presents a review of the major technological and theoretical advances in ancient pathogen genomics to frame the contributions of this work within a rapidly developing field. This overview emphasizes that understanding the evolution of human disease is critical to contextualizing relationships between humans and pathogens, and the epidemiological shifts observed both in the past and in the present era of (re)emerging infectious diseases. These questions continue to be at the forefront of not only pathogen research, but also
bioarchaeological and paleopathological scholarship.
bioarchaeological and paleopathological scholarship.
ContributorsHarkins, Kelly M (Author) / Buikstra, Jane E. (Thesis advisor) / Stone, Anne C (Thesis advisor) / Knudson, Kelly (Committee member) / Kumar, Sudhir (Committee member) / Krause, Johannes (Committee member) / Arizona State University (Publisher)
Created2014
Description
Peptide microarrays are to proteomics as sequencing is to genomics. As microarrays become more content-rich, higher resolution proteomic studies will parallel deep sequencing of nucleic acids. Antigen-antibody interactions can be studied at a much higher resolution using microarrays than was possible only a decade ago. My dissertation focuses on testing the feasibility of using either the Immunosignature platform, based on non-natural peptide sequences, or a pathogen peptide microarray, which uses bioinformatically-selected peptides from pathogens for creating sensitive diagnostics. Both diagnostic applications use relatively little serum from infected individuals, but each approaches diagnosis of disease differently. The first project compares pathogen epitope peptide (life-space) and non-natural (random-space) peptide microarrays while using them for the early detection of Coccidioidomycosis (Valley Fever). The second project uses NIAID category A, B and C priority pathogen epitope peptides in a multiplexed microarray platform to assess the feasibility of using epitope peptides to simultaneously diagnose multiple exposures using a single assay. Cross-reactivity is a consistent feature of several antigen-antibody based immunodiagnostics. This work utilizes microarray optimization and bioinformatic approaches to distill the underlying disease specific antibody signature pattern. Circumventing inherent cross-reactivity observed in antibody binding to peptides was crucial to achieve the goal of this work to accurately distinguishing multiple exposures simultaneously.
ContributorsNavalkar, Krupa Arun (Author) / Johnston, Stephen A. (Thesis advisor) / Stafford, Phillip (Thesis advisor) / Sykes, Kathryn (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2014
Description
Understanding the interplay between the electrical and mechanical properties of single molecules is of fundamental importance for molecular electronics. The sensitivity of charge transport to mechanical fluctuations is a key problem in developing long lasting molecular devices. Furthermore, harnessing this response to mechanical perturbation, molecular devices which can be mechanically gated can be developed. This thesis demonstrates three examples of the unique electromechanical properties of single molecules.
First, the electromechanical properties of 1,4-benzenedithiol molecular junctions are investigate. Counterintuitively, the conductance of this molecule is found to increase by more than an order of magnitude when stretched. This conductance increase is found to be reversible when the molecular junction is compressed. The current-voltage, conductance-voltage and inelastic electron tunneling spectroscopy characteristics are used to attribute the conductance increase to a strain-induced shift in the frontier molecular orbital relative to the electrode Fermi level, leading to resonant enhancement in the conductance.
Next, the effect of stretching-induced structural changes on charge transport in DNA molecules is studied. The conductance of single DNA molecules with lengths varying from 6 to 26 base pairs is measured and found to follow a hopping transport mechanism. The conductance of DNA molecules is highly sensitive to mechanical stretching, showing an abrupt decrease in conductance at surprisingly short stretching distances, with weak dependence on DNA length. This abrupt conductance decrease is attributed to force-induced breaking of hydrogen bonds in the base pairs at the end of the DNA sequence.
Finally, the effect of small mechanical modulation of the base separation on DNA conductance is investigated. The sensitivity of conductance to mechanical modulation is studied for molecules of different sequence and length. Sequences with purine-purine stacking are found to be more responsive to modulation than purine-pyrimidine sequences. This sensitivity is attributed to the perturbation of &pi-&pi stacking interactions and resulting effects on the activation energy and electronic coupling for the end base pairs.
First, the electromechanical properties of 1,4-benzenedithiol molecular junctions are investigate. Counterintuitively, the conductance of this molecule is found to increase by more than an order of magnitude when stretched. This conductance increase is found to be reversible when the molecular junction is compressed. The current-voltage, conductance-voltage and inelastic electron tunneling spectroscopy characteristics are used to attribute the conductance increase to a strain-induced shift in the frontier molecular orbital relative to the electrode Fermi level, leading to resonant enhancement in the conductance.
Next, the effect of stretching-induced structural changes on charge transport in DNA molecules is studied. The conductance of single DNA molecules with lengths varying from 6 to 26 base pairs is measured and found to follow a hopping transport mechanism. The conductance of DNA molecules is highly sensitive to mechanical stretching, showing an abrupt decrease in conductance at surprisingly short stretching distances, with weak dependence on DNA length. This abrupt conductance decrease is attributed to force-induced breaking of hydrogen bonds in the base pairs at the end of the DNA sequence.
Finally, the effect of small mechanical modulation of the base separation on DNA conductance is investigated. The sensitivity of conductance to mechanical modulation is studied for molecules of different sequence and length. Sequences with purine-purine stacking are found to be more responsive to modulation than purine-pyrimidine sequences. This sensitivity is attributed to the perturbation of &pi-&pi stacking interactions and resulting effects on the activation energy and electronic coupling for the end base pairs.
ContributorsBruot, Christopher, 1986- (Author) / Tao, Nongjian (Thesis advisor) / Lindsay, Stuart (Committee member) / Mujica, Vladimiro (Committee member) / Ferry, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate epitope antigen subsequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide microarrays. The method first maps peptide sequences to linear expansions of highly-localized one-dimensional (1-D) time-varying signals and uses a time-frequency processing technique to detect recurring patterns in subsequences. This technique is matched to the aforementioned mapping scheme, and it allows for an inherent analysis on how substitutions in the subsequences can affect antibody binding strength. The performance of the proposed method is demonstrated by estimating epitopes and identifying potential mimotopes for eight monoclonal antibody samples.
The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.
In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.
The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.
In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.
ContributorsO'Donnell, Brian (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Johnston, Stephen A. (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014