Matching Items (29)
Description
Semiconductor nanowires are featured by their unique one-dimensional structure which makes them promising for small scale electronic and photonic device applications. Among them, III-V material nanowires are particularly outstanding due to their good electronic properties. In bulk, these materials reveal electron mobility much higher than conventional silicon based devices, for example at room temperature, InAs field effect transistor (FET) has electron mobility of 40,000 cm2/Vs more than 10 times of Si FET. This makes such materials promising for high speed nanowire FETs. With small bandgap, such as 0.354 eV for InAs and 1.52 eV for GaAs, it does not need high voltage to turn on such devices which leads to low power consumption devices. Another feature of direct bandgap allows their applications of optoelectronic devices such as avalanche photodiodes. However, there are challenges to face up. Due to their large surface to volume ratio, nanowire devices typically are strongly affected by the surface states. Although nanowires can be grown into single crystal structure, people observe crystal defects along the wires which can significantly affect the performance of devices. In this work, FETs made of two types of III-V nanowire, GaAs and InAs, are demonstrated. These nanowires are grown by catalyst-free MOCVD growth method. Vertically nanowires are transferred onto patterned substrates for coordinate calibration. Then electrodes are defined by e-beam lithography followed by deposition of contact metals. Prior to metal deposition, however, the substrates are dipped in ammonium hydroxide solution to remove native oxide layer formed on nanowire surface. Current vs. source-drain voltage with different gate bias are measured at room temperature. GaAs nanowire FETs show photo response while InAs nanowire FETs do not show that. Surface passivation is performed on GaAs FETs by using ammonium surfide solution. The best results on current increase is observed with around 20-30 minutes chemical treatment time. Gate response measurements are performed at room temperature, from which field effect mobility as high as 1490 cm2/Vs is extracted for InAs FETs. One major contributor for this is stacking faults defect existing along nanowires. For InAs FETs, thermal excitations observed from temperature dependent results which leads us to investigate potential barriers.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Ferry, David (Committee member) / Tracy, Clarence (Committee member) / Arizona State University (Publisher)
Created2011
Description
Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.
ContributorsGlembo, Tyler J (Author) / Ozkan, Sefika B (Thesis advisor) / Thorpe, Michael F (Committee member) / Ros, Robert (Committee member) / Kumar, Sudhir (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
The entire history of HIV-1 is hidden in its ten thousand bases, where information regarding its evolutionary traversal through the human population can only be unlocked with fine-scale sequence analysis. Measurable footprints of mutation and recombination have imparted upon us a wealth of knowledge, from multiple chimpanzee-to-human transmissions to patterns of neutralizing antibody and drug resistance. Extracting maximum understanding from such diverse data can only be accomplished by analyzing the viral population from many angles. This body of work explores two primary aspects of HIV sequence evolution, point mutation and recombination, through cross-sectional (inter-individual) and longitudinal (intra-individual) investigations, respectively. Cross-sectional Analysis: The role of Haiti in the subtype B pandemic has been hotly debated for years; while there have been many studies, up to this point, no one has incorporated the well-known mechanism of retroviral recombination into their biological model. Prior to the use of recombination detection, multiple analyses produced trees where subtype B appears to have first entered Haiti, followed by a jump into the rest of the world. The results presented here contest the Haiti-first theory of the pandemic and instead suggest simultaneous entries of subtype B into Haiti and the rest of the world. Longitudinal Analysis: Potential N-linked glycosylation sites (PNGS) are the most evolutionarily dynamic component of one of the most evolutionarily dynamic proteins known to date. While the number of mutations associated with the increase or decrease of PNGS frequency over time is high, there are a set of relatively stable sites that persist within and between longitudinally sampled individuals. Here, I identify the most conserved stable PNGSs and suggest their potential roles in host-virus interplay. In addition, I have identified, for the first time, what may be a gp-120-based environmental preference for N-linked glycosylation sites.
ContributorsHepp, Crystal Marie, 1981- (Author) / Rosenberg, Michael S. (Thesis advisor) / Hedrick, Philip (Committee member) / Escalante, Ananias (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT An Ensemble Monte Carlo (EMC) computer code has been developed to simulate, semi-classically, spin-dependent electron transport in quasi two-dimensional (2D) III-V semiconductors. The code accounts for both three-dimensional (3D) and quasi-2D transport, utilizing either 3D or 2D scattering mechanisms, as appropriate. Phonon, alloy, interface roughness, and impurity scattering mechanisms are included, accounting for the Pauli Exclusion Principle via a rejection algorithm. The 2D carrier states are calculated via a self-consistent 1D Schrödinger-3D-Poisson solution in which the charge distribution of the 2D carriers in the quantization direction is taken as the spatial distribution of the squared envelope functions within the Hartree approximation. The wavefunctions, subband energies, and 2D scattering rates are updated periodically by solving a series of 1D Schrödinger wave equations (SWE) over the real-space domain of the device at fixed time intervals. The electrostatic potential is updated by periodically solving the 3D Poisson equation. Spin-polarized transport is modeled via a spin density-matrix formalism that accounts for D'yakanov-Perel (DP) scattering. Also, the code allows for the easy inclusion of additional scattering mechanisms and structural modifications to devices. As an application of the simulator, the current voltage characteristics of an InGaAs/InAlAs HEMT are simulated, corresponding to nanoscale III-V HEMTs currently being fabricated by Intel Corporation. The comparative effects of various scattering parameters, material properties and structural attributes are investigated and compared with experiments where reasonable agreement is obtained. The spatial evolution of spin-polarized carriers in prototypical Spin Field Effect Transistor (SpinFET) devices is then simulated. Studies of the spin coherence times in quasi-2D structures is first investigated and compared to experimental results. It is found that the simulated spin coherence times for GaAs structures are in reasonable agreement with experiment. The SpinFET structure studied is a scaled-down version of the InGaAs/InAlAs HEMT discussed in this work, in which spin-polarized carriers are injected at the source, and the coherence length is studied as a function of gate voltage via the Rashba effect.
ContributorsTierney, Brian David (Author) / Goodnick, Stephen (Thesis advisor) / Ferry, David (Committee member) / Akis, Richard (Committee member) / Saraniti, Marco (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2011
Description
We propose a novel solution to prevent cancer by developing a prophylactic cancer. Several sources of antigens for cancer vaccines have been published. Among these, antigens that contain a frame-shift (FS) peptide or viral peptide are quite attractive for a variety of reasons. FS sequences, from either mistake in RNA processing or in genomic DNA, may lead to generation of neo-peptides that are foreign to the immune system. Viral peptides presumably would originate from exogenous but integrated viral nucleic acid sequences. Both are non-self, therefore lessen concerns about development of autoimmunity. I have developed a bioinformatical approach to identify these aberrant transcripts in the cancer transcriptome. Their suitability for use in a vaccine is evaluated by establishing their frequencies and predicting possible epitopes along with their population coverage according to the prevalence of major histocompatibility complex (MHC) types. Viral transcripts and transcripts with FS mutations from gene fusion, insertion/deletion at coding microsatellite DNA, and alternative splicing were identified in NCBI Expressed Sequence Tag (EST) database. 48 FS chimeric transcripts were validated in 50 breast cell lines and 68 primary breast tumor samples with their frequencies from 4% to 98% by RT-PCR and sequencing confirmation. These 48 FS peptides, if translated and presented, could be used to protect more than 90% of the population in Northern America based on the prediction of epitopes derived from them. Furthermore, we synthesized 150 peptides that correspond to FS and viral peptides that we predicted would exist in tumor patients and we tested over 200 different cancer patient sera. We found a number of serological reactive peptide sequences in cancer patients that had little to no reactivity in healthy controls; strong support for the strength of our bioinformatic approach. This study describes a process used to identify aberrant transcripts that lead to a new source of antigens that can be tested and used in a prophylactic cancer vaccine. The vast amount of transcriptome data of various cancers from the Cancer Genome Atlas (TCGA) project will enhance our ability to further select better cancer antigen candidates.
ContributorsLee, HoJoon (Author) / Johnston, Stephen A. (Thesis advisor) / Kumar, Sudhir (Committee member) / Miller, Laurence (Committee member) / Stafford, Phillip (Committee member) / Sykes, Kathryn (Committee member) / Arizona State University (Publisher)
Created2012
Description
Graphene, a one atomic thick planar sheet of carbon atoms, has a zero gap band structure with a linear dispersion relation. This unique property makes graphene a favorite for physicists and engineers, who are trying to understand the mechanism of charge transport in graphene and using it as channel material for field effect transistor (FET) beyond silicon. Therefore, an in-depth exploring of these electrical properties of graphene is urgent, which is the purpose of this dissertation. In this dissertation, the charge transport and quantum capacitance of graphene were studied. Firstly, the transport properties of back-gated graphene transistor covering by high dielectric medium were systematically studied. The gate efficiency increased by up to two orders of magnitude in the presence of a high top dielectric medium, but the mobility did not change significantly. The results strongly suggested that the previously reported top dielectric medium-induced charge transport properties of graphene FETs were possibly due to the increase of gate capacitance, rather than enhancement of carrier mobility. Secondly, a direct measurement of quantum capacitance of graphene was performed. The quantum capacitance displayed a non-zero minimum at the Dirac point and a linear increase on both sides of the minimum with relatively small slopes. The findings - which were not predicted by theory for ideal graphene - suggested that scattering from charged impurities also influences the quantum capacitance. The capacitances in aqueous solutions at different ionic concentrations were also measured, which strongly suggested that the longstanding puzzle about the interfacial capacitance in carbon-based electrodes had a quantum origin. Finally, the transport and quantum capacitance of epitaxial graphene were studied simultaneously, the quantum capacitance of epitaxial graphene was extracted, which was similar to that of exfoliated graphene near the Dirac Point, but exhibited a large sub-linear behavior at high carrier density. The self-consistent theory was found to provide a reasonable description of the transport data of the epitaxial graphene device, but a more complete theory was needed to explain both the transport and quantum capacitance data.
ContributorsXia, Jilin (Author) / Tao, N.J. (Thesis advisor) / Ferry, David (Committee member) / Thornton, Trevor (Committee member) / Tsui, Raymond (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2010
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Description
Background
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
“Stoichioproteomics” relates the elemental composition of proteins and proteomes to variation in the physiological and ecological environment. To help harness and explore the wealth of hypotheses made possible under this framework, we introduce GRASP (http://www.graspdb.net), a public bioinformatic knowledgebase containing information on the frequencies of 20 amino acids and atomic composition of their side chains. GRASP integrates comparative protein composition data with annotation data from multiple public databases. Currently, GRASP includes information on proteins of 12 sequenced Drosophila (fruit fly) proteomes, which will be expanded to include increasingly diverse organisms over time. In this paper we illustrate the potential of GRASP for testing stoichioproteomic hypotheses by conducting an exploratory investigation into the composition of 12 Drosophila proteomes, testing the prediction that protein atomic content is associated with species ecology and with protein expression levels.
Results
Elements varied predictably along multivariate axes. Species were broadly similar, with the D. willistoni proteome a clear outlier. As expected, individual protein atomic content within proteomes was influenced by protein function and amino acid biochemistry. Evolution in elemental composition across the phylogeny followed less predictable patterns, but was associated with broad ecological variation in diet. Using expression data available for D. melanogaster, we found evidence consistent with selection for efficient usage of elements within the proteome: as expected, nitrogen content was reduced in highly expressed proteins in most tissues, most strongly in the gut, where nutrients are assimilated, and least strongly in the germline.
Conclusions
The patterns identified here using GRASP provide a foundation on which to base future research into the evolution of atomic composition in Drosophila and other taxa.
ContributorsGilbert, James D. J. (Author) / Acquisti, Claudia (Author) / Martinson, Holly M. (Author) / Elser, James (Author) / Kumar, Sudhir (Author) / Fagan, William F. (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-09-04