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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
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Description
Synechocystis sp PCC 6803 is a photosynthetic cyanobacterium that can be easily transformed to produce molecules of interest; this has increased Synechocystis’ popularity as a clean energy platform. Synechocystis has been shown to produce and excrete molecules such as fatty acids, isoprene, etc. after appropriate genetic modification. Challenges faced for large–scale growth of modified Synechocystis include abiotic stress, microbial contamination and high processing costs of product and cell material. Research reported in this dissertation contributes to solutions to these challenges. First, abiotic stress was addressed by overexpression of the heat shock protein ClpB1. In contrast to the wild type, the ClpB1 overexpression mutant (Slr1641+) tolerated rapid temperature changes, but no difference was found between the strains when temperature shifts were slower. Combination of ClpB1 overexpression with DnaK2 overexpression (Slr1641+/Sll0170+) further increased thermotolerance. Next, we used a Synechocystis strain that carries an introduced isoprene synthase gene (IspS+) and that therefore produces isoprene. We attempted to increase isoprene yields by overexpression of key enzymes in the methyl erythritol phosphate (MEP) pathway that leads to synthesis of the isoprene precursor. Isoprene production was not increased greatly by MEP pathway induction, likely because of limitations in the affinity of the isoprene synthase for the substrate. Finally, two extraction principles, two–phase liquid extraction (e.g., with an organic and aqueous phase) and solid–liquid extraction (e.g., with a resin) were tested. Two–phase liquid extraction is suitable for separating isoprene but not fatty acids from the culture medium. Fatty acid removal required acidification or surfactant addition, which affected biocompatibility. Therefore, improvements of both the organism and product–harvesting methods can contribute to enhancing the potential of cyanobacteria as solar–powered biocatalysts for the production of petroleum substitutes.
ContributorsGonzalez Esquer, Cesar Raul (Author) / Vermaas, Willem (Thesis advisor) / Chandler, Douglas (Committee member) / Bingham, Scott (Committee member) / Nielsen, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
The F1Fo ATP synthase is required for energy conversion in almost all living organisms. The F1 complex is a molecular motor that uses ATP hydrolysis to drive rotation of the γ–subunit. It has not been previously possible to resolve the speed and position of the γ–subunit of the F1–ATPase as it rotates during a power stroke. The single molecule experiments presented here measured light scattered from 45X91 nm gold nanorods attached to the γ–subunit that provide an unprecedented 5 μs resolution of rotational position as a function of time. The product of velocity and drag, which were both measured directly, resulted in an average torque of 63±8 pN nm for the Escherichia coli F1-ATPase that was determined to be independent of the load. The rotational velocity had an initial (I) acceleration phase 15° from the end of the catalytic dwell, a slow (S) acceleration phase during ATP binding/ADP release (15°–60°), and a fast (F) acceleration phase (60°–90°) containing an interim deceleration (ID) phase (75°–82°). High ADP concentrations decreased the velocity of the S phase proportional to 'ADP-release' dwells, and the F phase proportional to the free energy derived from the [ADP][Pi]/[ATP] chemical equilibrium. The decreased affinity for ITP increased ITP-binding dwells by 10%, but decreased velocity by 40% during the S phase. This is the first direct evidence that nucleotide binding contributes to F1–ATPase torque. Mutations that affect specific phases of rotation were identified, some in regions of F1 previously considered not to contribute to rotation. Mutations βD372V and γK9I increased the F phase velocity, and γK9I increased the depth of the ID phase. The conversion between S and F phases was specifically affected by γQ269L. While βT273D, βD305E, and αR283Q decreased the velocity of all phases, decreases in velocity due to βD302T, γR268L and γT82A were confined to the I and S phases. The correlations between the structural locations of these mutations and the phases of rotation they affect provide new insight into the molecular basis for F1–ATPase γ-subunit rotation.
ContributorsMartin, James (Author) / Frasch, Wayne D (Thesis advisor) / Chandler, Douglas (Committee member) / Gaxiola, Roberto (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2012
Description
Teleosts have the most primitive adaptive immune system. However, in terms of functionality the teleost immune system is similar to birds and mammals. On the other hand, enteric bacterial pathogens of mammals and birds present conserved regulatory mechanisms that control virulence factors. In this context, deletion of conserved genes that control virulence factors have been successfully used as measure to construct live attenuated bacterial vaccines for mammals and birds. Here, I hypothesize that evolutionary conserved genes, which control virulence factors or are essential for bacterial physiology in Enterobacteriaceae, could be used as universal tools to design live attenuated recombinant bacterial vaccines from fish to mammals. The evolutionary conserved genes that control virulence factors, crp and fur, and the essential gene for the synthesis of the cell wall, asd, were studied in Edwardsiella ictaluri to develop a live recombinant vaccine for fish host. The genus Edwardsiella is one of the most ancient represent of the Enterobacteriaceae family. E. ictaluri, a host restricted pathogen of catfish (Ictalurus punctatus), is the causative agent of the enteric septicemia and one of the most important pathogens of this fish aquaculture. Although, crp and fur control different virulence factors in Edwardsiella, in comparison to other enterics, individual deletion of these genes triggered protective immune response at the systemic and mucosal level of the fish. Deletion of asdA gene allowed the creation of a balanced-lethal system to syntheses heterologous antigens. I concluded that crp, fur and asd could be universally used to develop live attenuate recombinant Enterobacteriaceae base vaccines for different hosts.
ContributorsSantander Morales, Javier Alonso (Author) / Curtiss, Roy Iii (Thesis advisor) / Chandler, Douglas (Committee member) / Chang, Yung (Committee member) / Shi, Yixin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Skeletal muscle (SM) mitochondria generate the majority of adenosine triphosphate (ATP) in SM, and help regulate whole-body energy expenditure. Obesity is associated with alterations in SM mitochondria, which are unique with respect to their arrangement within cells; some mitochondria are located directly beneath the sarcolemma (i.e., subsarcolemmal (SS) mitochondria), while other are nested between the myofibrils (i.e., intermyofibrillar (IMF) mitochondria). Functional and proteome differences specific to SS versus IMF mitochondria in obese individuals may contribute to reduced capacity for muscle ATP production seen in obesity. The overall goals of this work were to (1) isolate functional muscle SS and IMF mitochondria from lean and obese individuals, (2) assess enzyme activities associated with the electron transport chain and ATP production, (3) determine if elevated plasma amino acids enhance SS and IMF mitochondrial respiration and ATP production rates in SM of obese humans, and (4) determine differences in mitochondrial proteome regulating energy metabolism and key biological processes associated with SS and IMF mitochondria between lean and obese humans.
Polarography was used to determine functional differences in isolated SS and IMF mitochondria between lean (37 ± 3 yrs; n = 10) and obese (35 ± 3 yrs; n = 11) subjects during either saline (control) or amino acid (AA) infusions. AA infusion increased ADP-stimulated respiration (i.e., coupled respiration), non-ADP stimulated respiration (i.e., uncoupled respiration), and ATP production rates in SS, but not IMF mitochondria in lean (n = 10; P < 0.05). Neither infusion increased any of the above parameters in muscle SS or IMF mitochondria of the obese subjects.
Using label free quantitative mass spectrometry, we determined differences in proteomes of SM SS and IMF mitochondria between lean (33 ± 3 yrs; n = 16) and obese (32 ± 3 yrs; n = 17) subjects. Differentially-expressed mitochondrial proteins in SS versus IMF mitochondria of obese subjects were associated with biological processes that regulate: electron transport chain (P<0.0001), citric acid cycle (P<0.0001), oxidative phosphorylation (P<0.001), branched-chain amino acid degradation, (P<0.0001), and fatty acid degradation (P<0.001). Overall, these findings show that obesity is associated with redistribution of key biological processes within the mitochondrial reticulum responsible for regulating energy metabolism in human skeletal muscle.
Polarography was used to determine functional differences in isolated SS and IMF mitochondria between lean (37 ± 3 yrs; n = 10) and obese (35 ± 3 yrs; n = 11) subjects during either saline (control) or amino acid (AA) infusions. AA infusion increased ADP-stimulated respiration (i.e., coupled respiration), non-ADP stimulated respiration (i.e., uncoupled respiration), and ATP production rates in SS, but not IMF mitochondria in lean (n = 10; P < 0.05). Neither infusion increased any of the above parameters in muscle SS or IMF mitochondria of the obese subjects.
Using label free quantitative mass spectrometry, we determined differences in proteomes of SM SS and IMF mitochondria between lean (33 ± 3 yrs; n = 16) and obese (32 ± 3 yrs; n = 17) subjects. Differentially-expressed mitochondrial proteins in SS versus IMF mitochondria of obese subjects were associated with biological processes that regulate: electron transport chain (P<0.0001), citric acid cycle (P<0.0001), oxidative phosphorylation (P<0.001), branched-chain amino acid degradation, (P<0.0001), and fatty acid degradation (P<0.001). Overall, these findings show that obesity is associated with redistribution of key biological processes within the mitochondrial reticulum responsible for regulating energy metabolism in human skeletal muscle.
ContributorsKras, Katon Anthony (Author) / Katsanos, Christos (Thesis advisor) / Chandler, Douglas (Committee member) / Dinu, Valentin (Committee member) / Mor, Tsafrir S. (Committee member) / Arizona State University (Publisher)
Created2017
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Many bacteria actively import environmental DNA and incorporate it into their genomes. This behavior, referred to as transformation, has been described in many species from diverse taxonomic backgrounds. Transformation is expected to carry some selective advantages similar to those postulated for meiotic sex in eukaryotes. However, the accumulation of loss-of-function alleles at transformation loci and an increased mutational load from recombining with DNA from dead cells create additional costs to transformation. These costs have been shown to outweigh many of the benefits of recombination under a variety of likely parameters. We investigate an additional proposed benefit of sexual recombination, the Red Queen hypothesis, as it relates to bacterial transformation. Here we describe a computational model showing that host-pathogen coevolution may provide a large selective benefit to transformation and allow transforming cells to invade an environment dominated by otherwise equal non-transformers. Furthermore, we observe that host-pathogen dynamics cause the selection pressure on transformation to vary extensively in time, explaining the tight regulation and wide variety of rates observed in naturally competent bacteria. Host-pathogen dynamics may explain the evolution and maintenance of natural competence despite its associated costs.
ContributorsPalmer, Nathan David (Author) / Cartwright, Reed (Thesis director) / Wang, Xuan (Committee member) / Sievert, Chris (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Alternative polyadenylation (APA) is the biological mechanism in which the same gene can have multiple 3'untranslated region (3'UTR) isoforms due to the presence of multiple polyadenylation signal (PAS) elements within the pre mRNAs. Because APA produces mRNA transcripts that have different 3'UTR isoforms, certain transcripts may be subject to post-transcriptional regulation by regulatory non-coding RNAs, such as microRNAs or RNA binding proteins defects of which have been implicated in diseases such as cancer. Despite the increasing level of information, functional understanding of the molecular mechanisms involved in transcription is still poorly understood, nor is it clear why APA is necessary at a cell or tissue-specific level. To address these questions I wanted to develop a set of sensor strain plasmids capable of detecting cleavage and polyadenylation in vivo, inject the complete sensor strain plasmid into C. elegans and prepare stable transgenic lines, and perform proof-of-principle RNAi feeding experiments targeting genes associated with the cleavage and polyadenylation complex machinery. I demonstrated that it was possible to create a plasmid capable of detecting cleavage and polyadenylation in C. elegans; however, issues arose during the RNAi assays indicating the sensor strain plasmid was not sensitive enough to the RNAi to effectively detect in the worms. Once the problems involved with sensitivity and variability in the RNAi effects are resolved, the plasmid would be able to better address questions regarding the functional understanding of molecular mechanisms involved in transcription termination.
ContributorsWilky, Henry Patrick (Author) / Mangone, Marco (Thesis director) / Newbern, Jason (Committee member) / Blazie, Stephen (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05