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Title: THE ORIGINATION AND IMPLEMENTATION OF THE NATIONAL WETLANDS POLICY OF UGANDA : ENVIRONMENT, KNOWLEDGE, AND POWER FROM THE LATE NINETEENTH CENTURY TO PRESENT
Creator: Doyle-Raso, John
Date: 2022
Collection: Electronic Theses & Dissertations
Description: In the 1980s, following widespread environmental and intellectual changes associated with “swamp reclamation” that in Uganda had started in the early twentieth century, proponents of the emerging science of “wetland conservation” sought to influence the practices and thinking of people across the country. To do so, they created a national wetlands policy based on decentralized “community-based” projects. Yet, farmers’ and investors’ engagements with reclamation have continued. Meanwhile, the...
Show moreIn the 1980s, following widespread environmental and intellectual changes associated with “swamp reclamation” that in Uganda had started in the early twentieth century, proponents of the emerging science of “wetland conservation” sought to influence the practices and thinking of people across the country. To do so, they created a national wetlands policy based on decentralized “community-based” projects. Yet, farmers’ and investors’ engagements with reclamation have continued. Meanwhile, the Ugandan wetlands policy became internationally influential for its groundbreaking approach to interdisciplinary questions about governance, emphasizing economic analyses based on concepts such as “ecosystem services” and “Environmental Economic Valuation.” Ugandan wetland conservationists have had more influence abroad than domestically, as in Uganda neoliberalization and recentralization have limited the power of the community-based groups who have worked through the national policy. Using a range of sources including but not limited to archives and interviews with conservationists, this dissertation historicizes the Ugandan wetlands conservation policy. It comprises two parts addressing overlapping time periods. The first three chapters consider the origination of this policy by analyzing environmental and intellectual changes in southeastern and southwestern Uganda, leading to the creation in the late-twentieth century of environmental regulations. The latter three chapters examine how conservationists have tried implementing the policy in rural and urban places, and in relation to the national emblem of Uganda – the Grey Crowned Crane. They have focused their efforts on community-based projects outside Protected Areas promoting indigenous knowledges and practices to obtain economic benefits from wetlands that conservationists. This approach was an early manifestation of connected trends in international developmentalist networks. Furthermore, the limitations on its implementation have become pivotal in the global histories of neoliberalization, decentralization, and recentralization. Historicizing Ugandan wetland conservationism contributes to four scholarly literatures. 1) Analyzing community-based projects outside “Protected Areas” advances the historiographies of conservation and watershed management in Africa by considering the significances of neoliberalization, decentralization, and recentralization beyond extraordinary legal cases. 2) Examining intellectual changes in this history – including an emphasis on community-based projects, use of the concept of ecosystem services, and the promotion of indigenous knowledges and sciences – reveals connections between changes in environmental science and global trends in developmentalism. 3) Focusing on these changes in Uganda builds on analyses of environmental management in political power there by identifying the importance of an underexamined resource in entrenched land conflicts, and by uncovering early institutional bases of recentralization. 4) Because Ugandan wetland conservationists were global leaders in policy creation, citizen science, and more changes in scientific thinking, researching their work reveals how African scientists have navigated tensions between their local, national, and international interlocutors to become internationally influential. Studying the history of Ugandan wetland conservationism reveals how different people’s engagements with changes in environmental thinking have reshaped environments and livelihoods, as well as influenced international scientific networks.
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Title: PROBE EFFECTS DURING CONCENTRATION DETERMINATION IN SCANNING ELECTROCHEMICAL MICROSCOPY
Creator: Mirabal, Alex
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Efficient, sustainable chemical reactions will play a large role in addressing many growing issues, including alternative energy production, greenhouse gas conversion, and pharmaceuticals. Electrochemical reactions are attractive due to their relatively mild reaction conditions and direct use of electricity. The understanding and design of the local liquid-solid interface will guide future progress in electrocatalytic reactions.Over time, nature has evolved many highly efficient reactions...
Show moreEfficient, sustainable chemical reactions will play a large role in addressing many growing issues, including alternative energy production, greenhouse gas conversion, and pharmaceuticals. Electrochemical reactions are attractive due to their relatively mild reaction conditions and direct use of electricity. The understanding and design of the local liquid-solid interface will guide future progress in electrocatalytic reactions.Over time, nature has evolved many highly efficient reactions through enzymatic reactions. These long-studied catalysts provide complex reaction environments that: 1) enhance interaction with reactants, 2) protect intermediates from side reactions, 3) increase the rates of reactions, and 4) selectively react to a specific product. The overarching lesson to be learned is that the local reaction environment plays a large role in the catalyst’s reactivity, selectivity, and efficiency. One way to characterize the local environment is through scanning electrochemical microscopy (SECM), in which a small electrochemical probe is rastered over an interface. A quantitative correlation of the probe response to concentration provides a direct measurement of the local environment. The presence of the SECM probe itself can induce changes in the local environment. Comparing the changed local environment (in situ) to what it would be without the probe present (operando), shows large differences of up to 120% under specific operating conditions. A few physical parameters such as the surface site geometry are shown to have an impact on how significant the probe effects are. Additional parameters such as the tip geometry and tip-surface separation are also to have an impact. A finite element method (FEM) simulation informed by experiments is used to examine the above-mentioned tip effects. It is found that fitting responses to other frequently used electrochemical measurements, such as approach curves and CVs, to parameterize the model appropriately describes experimental SECM results. We first apply this method to study platinum nanoparticles, where a ~50 nm resolution is the highest resolution to our knowledge for AFM-SECM. Through statistical analysis of the surface, an isolated nanoparticle SECM response is correlated with a concentration profile. It is found that the concentration profile has minimal probe effects due to the use of a conical electrode. Applying a similar approach, we also study the probe effects in pH detection during hydrogen evolution and CO2 reduction. Where we match experimental results to parameterize the system. It shown that there is a pH difference of up to ~7 pH units underneath the probe due to hindered diffusion. However, even with these large differences, the probes are still able to reflect the trends seen without the probe present. Moreover, it is shown that the physical parameters have correlated responses, indicating that hindered diffusion is controlled by the insulation radius and tip-surface separation. Finally, the importance of the analyte is discussed with regard to its interaction with the tip. In addition to the concentration impact on the response signal, the compatibility with the tip need be considered. Degradation of the tip and/or the redox couple of choice will detrimentally affect the ability to examine the local interface. We show that, of the redox couples examined, ferrocene-based compounds appear to best satisfy the most crucial factors of stability and mild redox potentials. Overall, this work studies and removes the impact of the probe for local concentration detection using SECM. This work acts as a guide to quantitatively study the local environment of electrocatalyzed reactions. This is realized through a combined experimental-FEM approach in which the simulation is informed by experiments such that it’s representative of the experimental environment.
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Title: Three Essays on Panel Data Models with Interactive and Unobserved Effects
Creator: Brown, Nicholas Lynn
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Chapter 1: More Efficient Estimation of Multiplicative Panel Data Models in the Presence of Serial Correlation (with Jeffrey Wooldridge)We provide a systematic approach in obtaining an estimator asymptotically more efficient than the popular fixed effects Poisson (FEP) estimator for panel data models with multiplicative heterogeneity in the conditional mean. In particular, we derive the optimal instrumental variables under appealing `working' second moment assumptions that allow...
Show moreChapter 1: More Efficient Estimation of Multiplicative Panel Data Models in the Presence of Serial Correlation (with Jeffrey Wooldridge)We provide a systematic approach in obtaining an estimator asymptotically more efficient than the popular fixed effects Poisson (FEP) estimator for panel data models with multiplicative heterogeneity in the conditional mean. In particular, we derive the optimal instrumental variables under appealing `working' second moment assumptions that allow underdispersion, overdispersion, and general patterns of serial correlation. Because parameters in the optimal instruments must be estimated, we argue for combining our new moment conditions with those that define the FEP estimator to obtain a generalized method of moments (GMM) estimator no less efficient than the FEP estimator and the estimator using the new instruments. A simulation study shows that the GMM estimator behaves well in terms of bias, and it often delivers nontrivial efficiency gains -- even when the working second-moment assumptions fail.Chapter 2: Information equivalence among transformations of semiparametric nonlinear panel data modelsI consider transformations of nonlinear semiparametric mean functions which yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I first derive a unified theory of algebraic equivalence for moment conditions created by a given linear transformation. The main equivalence result states that under standard regularity conditions, transformations which create conditional moment restrictions in a given empirical setting need only to have an equal rank to reach the same efficiency bound. Example applications are considered, including nonlinear models with multiplicative heterogeneity and linear models with arbitrary unobserved factor structures.Chapter 3: Moment-based Estimation of Linear Panel Data Models with Factor-augmented ErrorsI consider linear panel data models with unobserved factor structures when the number of time periods is small relative to the number of cross-sectional units. I examine two popular methods of estimation: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses the cross-sectional averages of the independent and response variables to project out the space spanned by the factors. I show that the classical CCE assumptions imply unused moment conditions which can be exploited by the QLD transformation to derive new linear estimators which weaken identifying assumptions and have desirable theoretical properties. I prove asymptotic normality of the linear QLD estimators under a heterogeneous slope model which allows for a tradeoff between identifying conditions. These estimators do not require the number of cross-sectional variables to be less than T-1, a strong restriction in fixed-$T$ CCE analysis. Finally, I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.
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Title: HELLGATE TO HIGHWAY : ISLAND MAKING, DREDGING, AND INFRASTRUCTURE IN THE DETROIT RIVER, 1874-1938
Creator: Swayamprakash, Ramya
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This dissertation exposes the tensions, trials, and tribulations along the U.S.-Canada border in the Detroit River between 1874 and 1938. I study how dredging—a seemingly inert process of removing river bottom sediment and depositing it elsewhere—helped create landforms across and along the political border, in turn, revealing the myriad social and political tensions that undergird it. By exposing how infrastructure revealed border tensions, especially those related to resource extraction,...
Show moreThis dissertation exposes the tensions, trials, and tribulations along the U.S.-Canada border in the Detroit River between 1874 and 1938. I study how dredging—a seemingly inert process of removing river bottom sediment and depositing it elsewhere—helped create landforms across and along the political border, in turn, revealing the myriad social and political tensions that undergird it. By exposing how infrastructure revealed border tensions, especially those related to resource extraction, scarcity, and national security—on both sides of the Canada-U.S. border—this dissertation offers a new way to link environmental and border history as well as environmental diplomacy.The lower Detroit River forms the ideal study site for two interrelated reasons. One, its narrow and rocky riverbed along the shipping channel was a dangerous bottleneck, slowing traffic on one of the busiest waterways in the world. Two, dredging the lower part of the river kept this busy waterway running efficiently. The Livingstone Channel fundamentally reordered the Detroit River when it was carved out of the riverbed where hitherto there existed fish spawning grounds and shallow water. Concentrating on the lower Detroit River in general and the Livingstone Channel in particular, this dissertation will show how conflict and cooperation overlapped when it came to international diplomacy in the Great Lakes. Cultural and social historians have analyzed Great Lakes borderlands. Environmental historians though have yet to fully analyze these lakes. The political border between the United States and Canada has often been portrayed as being benign and uncontested. Yet, as this dissertation shows, border infrastructure, such as shipping channels, was seldom uncontested. By focusing on the political border, this dissertation aims to bring attention to the border as a site rather than a liminal space or an end zone of state sovereignty. The border in this reading is the origin of state sovereignty. Studies of the Canada-U.S. borderlands have often explored the role of international environmental diplomacy, especially in the joint management and conservation of binational water bodies like the Great Lakes through policy mechanisms such as the Boundary Waters Treaty (BWT) of 1908 and the International Joint Commission. As my dissertation shows, however, the BWT was an important staging point on which the different intercultural and international misunderstandings were exposed. The Great Lakes have often been cast as being abundant, yet there is little or no work on how that plentitude was not just manufactured in thought, but also embodied in infrastructures. As a transformative process, dredging does not seem monumental. Yet, dredging in the Detroit River has permanently lowered the levels of Lakes Huron and Michigan by at least 25 cm. Dredging thus reveals how environmental transformation lies at the heart of Great Lakes geography as we know it. By exposing dredging in a connecting channel, this dissertation shows that infrastructural creation and imagination undergirds the Great Lakes environment. Infrastructure, as I show, is an important and unseen filter to understand intercultural and international relationships. This is especially true of countries such as the U.S. and Canada which pride themselves in intercultural similarities more than differences. Studying conflict and contestation offers a novel way to understand the cooperative mechanism that drives current borderlands diplomacy. Studying dredging along the lower Detroit River in the nineteenth and twentieth centuries reveals ideas about nature as well as historical challenges and contestations to them.
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Title: EXAMINING THE RELATIONSHIP AMONG PHYSICAL ACTIVITY, STRESS, DEPRESSION, AND ANXIETY IN COLLEGE STUDENTS
Creator: Hayden, Dorian James
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Mental health challenges have been steadily increasing around college campuses, with consistent findings pointing to ethnic minorities and low socioeconomic groups adversely affected and needing more support than their counterparts. On the other end, physical activity (PA) has consistently been associated with positive mental health outcomes. Therefore, further understanding the relationship among race, socioeconomic status, and PA, as well as college students personal experiences on campuses...
Show moreMental health challenges have been steadily increasing around college campuses, with consistent findings pointing to ethnic minorities and low socioeconomic groups adversely affected and needing more support than their counterparts. On the other end, physical activity (PA) has consistently been associated with positive mental health outcomes. Therefore, further understanding the relationship among race, socioeconomic status, and PA, as well as college students personal experiences on campuses, is of great importance to improve college student well-being. This two-study dissertation sought to address these relationships. Study 1 evaluated differences in mental health across race, parental education (proxy for SES) and PA. Student obstacles to using on-campus mental health and PA resources were examined in a mixed methods design. Most of the data was collected prior to the establishment of COVID-19 restrictions at a large Midwest university. Mental health levels were anticipated to vary between the variables of race, parental education, and PA, which was partially supported. Results showed that low SES participants had significantly higher levels of depression, while PA was associated with lower levels of anxiety and stress. Study 2 sought to replicate findings of study 1 and test the relationships within the context of a diathesis-stress model that includes PA using an expanded sample of students drawn from a large Midwest university and a smaller East Coast university. In addition to replicating study 1 findings, study 2 yielded several themes that revealed common obstacles of college students and how they overcome those obstacles. The hypothesized relationship among race, SES, PA and mental health was partially supported. Specifically, participants whose parents or guardians had lower levels of education reported higher levels of anxiety, while there were significant differences in mental health across different levels of PA. The other goal of this study was to map the above relationship onto a PA moderating model, including variables of race, parental education, depression, stress, and anxiety, based on an adapted diathesis-stress model. This relationship was not supported by the data. Free response answers revealed interesting themes related to the college student experience and campus resources. Focus groups added to this through discussions on topics like the COVID-19 pandemic, mental health, and advice for future students. Data from study 2 was collected while COVID-19 pandemic restrictions were in place. Overall results expanded knowledge on the experience of COVID-19 on college campus and the interconnection between race, parental education, PA, and mental health. Further social relations were important for student wellbeing. Students’ also shared obstacles they faced with the use of on campus mental health and other resources.
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Title: Profiles of Student Engagement in Synchronous and Asynchronous Science Instruction
Creator: Schell, Matthew J.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Virtual instruction at the K-12 level is on the rise, yet we know very little about the ways students engage in different types of virtual instruction. The goals of this study were to: 1) describe high school students’ engagement in virtual science courses in terms of behavioral, affective, cognitive-value, and cognitive-self regulatory dimensions; 2) explore whether students’ engagement patterns across these dimensions differed depending on whether science activities were synchronous or...
Show moreVirtual instruction at the K-12 level is on the rise, yet we know very little about the ways students engage in different types of virtual instruction. The goals of this study were to: 1) describe high school students’ engagement in virtual science courses in terms of behavioral, affective, cognitive-value, and cognitive-self regulatory dimensions; 2) explore whether students’ engagement patterns across these dimensions differed depending on whether science activities were synchronous or asynchronous; and 3) examine whether these engagement patterns were associated with students’ final course grades or over-summer retention in a virtual high school. Students enrolled in a range of science courses at virtual high school (n=124) provided multiple reports (n=493) of their engagement during both synchronous and asynchronous learning activities. Latent Profile Analysis (LPA) conducted with these data suggested five distinct situational engagement profiles representing different constellations of the affective, behavioral, cognitive-value, and cognitive-self-regulatory dimensions of engagement. During synchronous instruction, students tended to engage in ways characterized by higher engagement in all dimensions compared with asynchronous instruction. These high engagement profiles were also associated with higher final course grades. There were few differences in the extent to which profiles predicted retention; however, lower self-regulation was associated with higher rates of retention.
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Title: Examining an Important Assumption in the Faultline Literature
Creator: Guo, Zhiya
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Group faultlines are defined as hypothetical dividing lines that split a team into subgroups based on the alignment of team members’ attributes. Prior faultline research has almost exclusively focused on the implications of between-subgroup relationships assuming that “team members form homophilous ties on either side of a faultline by associating with others in the team who have similar demographic attributes” (Ren et al., 2015, p. 390). However, this important assumption has not been tested...
Show moreGroup faultlines are defined as hypothetical dividing lines that split a team into subgroups based on the alignment of team members’ attributes. Prior faultline research has almost exclusively focused on the implications of between-subgroup relationships assuming that “team members form homophilous ties on either side of a faultline by associating with others in the team who have similar demographic attributes” (Ren et al., 2015, p. 390). However, this important assumption has not been tested. Drawing from social comparison theory and its “similarity hypothesis,” I argue that homogeneous, faultline-based subgroups may serve as a hotbed for social comparisons, and comparisons on social power can engender conflict under certain circumstances, triggering within-subgroup conflict. More specifically, consistent with the emerging research that recognizes different types of group faultlines, I outlined a) different dimensions that different faultline-based subgroups are more likely to compare and b) the downstream effects of these comparisons. Hypotheses were tested using multi-wave, round-robin data from multiple intact work teams of full-time employees. Results largely supported my predictions regarding knowledge-based subgroups but not so much for identity-based subgroups or resource-based subgroups. Implications and future directions are discussed.
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Title: Leveraging Angiosperm Pangenomics to Understand Genome Evolution
Creator: Yocca, Alan E.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: My dissertation work focused on species-level comparative genomics and pangenomics to describe patterns of genetic variation. I studied multiple systems and unsurprisingly discovered different patterns of variation. Within a species, individuals are genetically diverse. There are some DNA regions present in every individual (core), while others may be specific to a single individual or lineage (variable). The sum of the genetic sequences found across an entire taxonomic group is called the...
Show moreMy dissertation work focused on species-level comparative genomics and pangenomics to describe patterns of genetic variation. I studied multiple systems and unsurprisingly discovered different patterns of variation. Within a species, individuals are genetically diverse. There are some DNA regions present in every individual (core), while others may be specific to a single individual or lineage (variable). The sum of the genetic sequences found across an entire taxonomic group is called the pangenome. This DNA variation greatly contributes to observed phenotypic differences between individuals. Therefore, to understand genome evolution and the link between genotype and phenotype, we must understand the pangenome. In this work, I compare the core and variable genetic regions both coding and noncoding across different flowering plant lineages. I note many consistent features across lineages as well as ways in which each pangenomic pattern is unique. These consistencies and differences can be leveraged in the future to better understand genome evolution as well as how genotype relates to phenotype. Specifically, my dissertation includes four chapters; (1) Evolution of Conserved Noncoding Sequences in Arabidopsis thaliana, (2) Machine learning identifies differences between core and variable genes in Brachypodium distachyon and Oryza sativa, (3) Current status and future perspectives on the evolution of cis-regulatory elements in plants, and (4) A pangenome for Vaccinium.
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Title: Patient-specific prediction of abdominal aortic aneurysm expansion using efficient physics-based machine learning approaches
Creator: Jiang, Zhenxiang
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Computational vascular Growth and Remodeling (G&R) models have been developed to capture key physiological and morphological features during the arterial disease progression and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, the translation of computational G&R models into their applications has yet to wait for clinical practice. Partly, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R...
Show moreComputational vascular Growth and Remodeling (G&R) models have been developed to capture key physiological and morphological features during the arterial disease progression and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, the translation of computational G&R models into their applications has yet to wait for clinical practice. Partly, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R simulations typically require hours or even days, which hinders its time-consuming applications such as patient-specific parameter estimation, disease prediction, verification, validation, and sensitivity analysis. Furthermore, the typical Finite Element Method (FEM) based computational G&R model should be extended to provide the uncertainty quantification associated with simulation and prediction results. Therefore, to enhance practicality of the G&R modeling, we develop a novel and computationally efficient simulation framework that comprehensively combines physics-based G&R simulations and data-driven machine learning methods using a Multi-Fidelity Surrogate (MFS) approach. This greatly enhances the computational efficiency of arterial G&R simulations, enabling more time-consuming applications such as personalized parameter estimation. The proposed framework is then tested for a specific disease, Abdominal Aortic Aneurysms (AAAs), by estimating G&R model parameters from follow-up CT images in 21 patients.
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Title: Characterization of the human gut resistome, microbiome, and metabolome during enteric infection
Creator: Hansen, Zoe A.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The human gut environment is replete with host-microbe and microbe-microbe interactions that shape human health. This system is also a known reservoir for antimicrobial resistance (AMR). The ubiquity of AMR is alarming, as greater than 2.8 million antibiotic-resistant infections and 35,000 deaths occur annually in the United States. Multiple human pathogens have demonstrated reduced susceptibility to various antibiotics, including enteric pathogens such as Campylobacter, Salmonella, Shigella,...
Show moreThe human gut environment is replete with host-microbe and microbe-microbe interactions that shape human health. This system is also a known reservoir for antimicrobial resistance (AMR). The ubiquity of AMR is alarming, as greater than 2.8 million antibiotic-resistant infections and 35,000 deaths occur annually in the United States. Multiple human pathogens have demonstrated reduced susceptibility to various antibiotics, including enteric pathogens such as Campylobacter, Salmonella, Shigella, and STEC, which cause millions of foodborne infections each year. The increasing incidence of antibiotic resistant enteric infections substantiates a need to further characterize these pathogens’ role in the curation and dissemination of AMR across environments. In this dissertation, a total of 223 human stools were assessed using shotgun metagenomics sequencing to investigate gut microbiome changes associated with enteric infection. Sixty-three stools were collected from patients suffering from enteric infection between 2011-2015 by the Michigan Department of Health and Human Services (MDHHS). Sixty-one of these patients submitted a follow-up sample between 1- and 29-weeks post-infection, and 99 healthy household members also submitted stools to serve as controls. In Chapter 2, a subset of patients infected with Campylobacter spp. and their related controls were investigated to assess the gut resistome, or collection of all antimicrobial resistance genes (ARGs) and their genetic precursors, related to infection. This examination revealed significantly higher ARG diversity in infected patients compared to healthy controls. Specifically, levels of multi-drug resistance (MDR) were greatly increased during infection. Three case clusters with distinct resistomes were identified; two of these clusters had unique ARG profiles that differed from those of healthy family members. In Chapter 3, a larger subset of 120 paired samples (60 infected vs. 60 recovered) were investigated to further characterize resistome and microbiome fluctuations related to infection and recovery. Again, infected patients harbored greater resistome diversity; however, recovered individuals displayed higher diversity in their microbiota composition. Despite their lower overall microbial diversity, patients with acute infections showed an increase in the abundance of members of Enterobacteriaceae, with specific expansion of the genus Escherichia. Host-tracking analysis revealed that many Enterobacteriaceae carried ARGs related to MDR and biocide resistance, a finding with broad implications for the ecology of resistance during infection. The fourth chapter explored metabolic capacity of gut microbial communities. In addition to metabolic pathway prediction, untargeted metabolomics was performed via LC/MS for 122 paired samples. Pathway annotation suggested that infected individuals contain greater microbial functional capacity, but metabolomics indicated greater overall metabolite diversity among recovered patients. Infection was associated with enhanced nitrogen and amino acid metabolism pathways. Although many metabolites remain uncharacterized, their presence or absence among individuals suggest their importance during and after infection. Altogether, the findings of this dissertation further characterize ecological consequences related to enteric infection in the human gut. Specifically, this research illustrates the importance of enteric infection in the dissemination and persistence of resistance determinants. Moreover, the expansion of Enterobacteriaceae and the evident increase in nitrogen- and amino acid-related metabolism during infection represent potential targets for future intervention practices.
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Title: Interpreting Gravitational Waves and Developing Relativistic Multiphysics Solvers for Core-collapse Supernova Simulations
Creator: Pajkos, Michael Anton
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Core-collapse supernovae (CCSNe) mark the endpoint for millions of years of massive stellar evolution. After a successful explosion, supernovae increase the metallicity of the interstellar medium, generate intense electromagnetic radiation ionizing their surroundings, generate compact objects such as black holes or neutron stars, and create ripples in spacetime---gravitational waves (GWs). Advances in supernova theory over the past few decades have furthered our understanding of CCSNe....
Show moreCore-collapse supernovae (CCSNe) mark the endpoint for millions of years of massive stellar evolution. After a successful explosion, supernovae increase the metallicity of the interstellar medium, generate intense electromagnetic radiation ionizing their surroundings, generate compact objects such as black holes or neutron stars, and create ripples in spacetime---gravitational waves (GWs). Advances in supernova theory over the past few decades have furthered our understanding of CCSNe. However, constraints on the physics enshrouded in the supernova center would further illuminate their explosion mechanisms. Advances in high performance computing (HPC) resources and the ever-increasing sensitivities of GW observatories have positioned the field of astrophysics between two recent technological advances. The work presented here leverages HPC to perform CCSN simulations, allowing astronomers to translate between GW signals and internal physics. Using this insight, astronomers are better positioned to constrain the physics driving these explosive events that have such a widespread influence throughout astronomy.Investigating the evolution of 12-, 20-, 40-, and 60 solar mass progenitors, I perform axisymmetric neutrino radiation-hydrodynamic CCSN simulations, to relate the convective activity behind the supernova shock to the expected GW strength. I quantify how the rotational content of the supernova lowers GW frequencies. I present a novel method that combines two features of a single GW event to constrain the mass distribution within the stellar progenitor. By only requiring the two most detectable parts of the GW signal, astronomers can also potentially predict the explosion properties ~days before shock breakout. I present work with my undergraduate research assistant, that considers the impact of viewing angle on detecting GWs from CCSNe. Presented is a novel analysis method to identify the distribution of GW emission over all angles, accompanied with results showing that the preferred direction of GW emission for CCSNe migrates over time. Lastly, I present new numerical solvers targeted at exascale computing platforms that account for magnetized fluid evolution with velocities near the speed of light and in extreme spacetimes. These solvers are accompanied with stringent baseline tests, paired with 1D and 2D supernova simulations making use of these features.
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Title: GOOD AT THIS BUT NOT AT THAT : MULTIDIMENSIONAL SELF-EVALUATIONS AND DIMENSIONAL COMPARISONS AT WORK
Creator: Mitchell, Rebecca
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Social comparison theory (Festinger, 1954) underlies findings and theory in many organizational behavior literatures, such as identity, justice, and compensation. Yet, the field has neglected to incorporate comparison theories introduced in other psychology literatures. Dimensional comparison theory (DCT; (Möller & Marsh, 2013) argues that, in addition to external comparisons to referent other, individuals also make internal comparisons across different dimensions of the self, defined within...
Show moreSocial comparison theory (Festinger, 1954) underlies findings and theory in many organizational behavior literatures, such as identity, justice, and compensation. Yet, the field has neglected to incorporate comparison theories introduced in other psychology literatures. Dimensional comparison theory (DCT; (Möller & Marsh, 2013) argues that, in addition to external comparisons to referent other, individuals also make internal comparisons across different dimensions of the self, defined within a multidimensional self-evaluation. This dissertation argues that DCT is related to, but distinct from, existing concepts within organizational behavior and is thus critical to integrate into our understanding of work. In three studies, a vignette study, one experiment, and one field study, I propose examining the effect that dimensional comparisons along these abilities have on individuals’ psychological investment as well as the resulting achievement and satisfaction in these dimensions. Further, I build upon existing DCT research in educational psychology through explicitly hypothesizing the interactive effect of dimensional and social comparisons, considering the role that the importance of the dimension to the group and the individual plays in these relationships, and examining dimensional comparisons using polynomial regression techniques.
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Title: IMPROVED DETECTION AND MANAGEMENT OF PHYTOPHTHORA SOJAE
Creator: McCoy, Austin Glenn
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Phytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of...
Show morePhytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of soybean (Glycine max L.) worldwide, causing Phytophthora stem and root rot (PRR). PRR disease management during the growing season relies on an integrated pest management approach using a combination of host resistance, chemical compounds (fungicides; oomicides) and cultural practices for successful management. Therefore, this dissertation research focuses on improving the detection and management recommendations for Phytophthora sojae. In Chapter 1 I provide background and a review of the current literature on Phytophthora sojae management, including genetic resistance, chemical control compounds (fungicides; oomicides) and cultural practices used to mitigate losses to PRR. In my second chapter I validate the sensitivity and specificity of a preformulated Recombinase Polymerase Amplification assay for Phytophthora spp. This assay needs no refrigeration, does not require extensive DNA isolation, can be used in the field, and different qPCR platforms could reliably detect down to 3.3-330.0 pg of Phytophthora spp. DNA within plant tissue in under 30 minutes. Based on the limited reagents needed, ease of use, and reliability, this assay would be of benefit to diagnostic labs and inspectors monitoring regulated and non-regulated Phytophthora spp. Next, I transitioned the Habgood-Gilmour Spreadsheet (‘HaGiS’) from Microsoft Excel format to the subsequent R package ‘hagis’ and improved upon the analyses readily available to compare pathotypes from different populations of P. sojae (Chapter 3; ‘hagis’ beta-diversity). I then implemented the R package ‘hagis’ in my own P. sojae pathotype and fungicide sensitivity survey in the state of Michigan, identifying effective resistance genes and seed treatment compounds for the management of PRR. This study identified a loss of Rps1c and Rps1k, the two most widely plant Phytophthora sojae resistance genes, as viable management tools in Michigan and an increase in pathotype complexity, as compared to a survey conducted twenty years ago in Michigan (Chapter 4). In Chapter 5 I led a multi-state integrated pest management field trial that was performed in Michigan, Indiana, and Minnesota to study the effects of partial resistance and seed treatments with or without ethaboxam and metalaxyl on soybean stand, plant dry weights, and final yields under P. sojae pressure. This study found that oomicide treated seed protects stand across three locations in the Midwest, but the response of soybean varieties based on seed treatment, was variety and year specific. Significant yield benefits from using oomicide treated seed were only observed in one location and year. The effects of partial resistance were inconclusive and highlighted the need for a more informative and reliable rating system for soybean varieties partial resistance to P. sojae. Finally, in Chapter 6 I present conclusions and impacts on the studies presented in this dissertation. Overall, the studies presented provide an improvement to the detection, virulence data analysis, and integrated pest management recommendations for Phytophthora sojae.
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Title: CONSTRAIN NEUTRON STAR PROPERTIES WITH SpiRIT EXPERIMENT
Creator: Tsang, Chun Yuen
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The study of nuclear matter is an interdisciplinary endeavor that is relevant to both astrophysics and nuclear physics. Astrophysicists need to understand the properties of nuclear matter as some astrophysical objects are made of nuclear material. Nuclear physicists also need to understand the properties of nuclear matter as they are fundamental to the understanding of the existence of nuclei, their composition and the dynamics of nuclear collisions.Recent measurements of gravitational waves...
Show moreThe study of nuclear matter is an interdisciplinary endeavor that is relevant to both astrophysics and nuclear physics. Astrophysicists need to understand the properties of nuclear matter as some astrophysical objects are made of nuclear material. Nuclear physicists also need to understand the properties of nuclear matter as they are fundamental to the understanding of the existence of nuclei, their composition and the dynamics of nuclear collisions.Recent measurements of gravitational waves from binary neutron star mergers and precise neutron star radii from X-ray data of pulsars open a new channel for physicists to study nuclear matter. Such astronomical observations of neutron stars are sensitive to nuclear matter at high density that is usually inaccessible on earth. One of the ways physicists are able to reach such high density in laboratory is through heavy-ion collision. Transport model calculations that simulate nuclear collisions show that head-on collisions of heavy nuclei at high beam energy compress the overlapping region momentarily to densities comparable to that of the interior of neutron stars. To study neutron star where number of neutrons far exceeds that of protons, the dependence of nuclear properties on neutron-to-proton ratio (N/Z) needs to be understood. This dependence is quantified by the symmetry energy, which describes the difference in binding energy between pure neutron matter and matter with equal amount of protons and neutrons. The latter is also known as symmetric nuclear matter (SNM) which has been fairly well constrained. The amount of internal neutron star pressure that supports itself from gravitational collapse depends on the value of symmetry energy. Most of the existing heavy-ion collision data comes from collisions of stable isotopes. This limits the range of available N/Z in nuclear experiments. Extending results to a wider range of N/Z is one of the goals of SpiRIT experiment using projectiles provided by the cutting-edge Radioactive Isotope Beam Factory in RIKEN, Japan. SpiRIT time projection chamber (TPC) is constructed to measure charged pions spectra from the collision of neutron-rich system (132Sn + 124Sn), neutron-poor system (108Sn + 112Sn) and intermediate system (112Sn + 124Sn) at 270 MeV/u. By comparing fragmentation patterns for reactions with different number of neutrons, symmetry energy effects can be isolated. Some results from the analysis of pion spectra have been published and will be briefly reviewed in this work before we focus on light fragment observables that are also available from the TPC data. The data analysis software, with highlights on correction of some major detector aberrations, is discussed in details. Monte Carlo simulation of the SpiRIT TPC is then performed to understand the behavior of SpiRIT data and validate our data analysis procedure. Finally, Bayesian analysis is performed to compare transport model simulations with selected light fragment measurements using Markov-Chain Monte Carlo and Gaussian emulators. The observables are chosen to minimize systematic uncertainties from both the experiment and model. The posterior provides a comprehensive constraint on the symmetry energy parameters. Although previous analyses of pion spectra have already constrained the slope of symmetry energy at saturation density (L), its uncertainty can be reduced by 39% if pion results are combined with our new Bayesian posterior. The implications of symmetry energy constraint for neutron star will be discussed to demonstrate the importance of data from rare isotope heavy-ion collisions.
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Title: CLICKABLE POLY(PROPARGYL METHACRYLATE) PREPARED BY ATOM TRANSFER RADICAL POLYMERIZATION AND ITS DERIVATIVES AS ENZYME STABILIZERS
Creator: Hsiao, Po-Jen
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Enzymes are nearly perfect catalysts with excellent selectivity and high turnover frequencies. A long-standing goal has been enabling enzymes to operate ex vivo in non-aqueous solvents, but the structures of native enzymes are typically compromised under these conditions. Polymer-enzyme bioconjugates have shown some promise—albeit limited—in this regard. The clickable poly(propargyl methacrylate) (PPMA) was proposed as a platform to enhance different polymer structures versus the residual...
Show moreEnzymes are nearly perfect catalysts with excellent selectivity and high turnover frequencies. A long-standing goal has been enabling enzymes to operate ex vivo in non-aqueous solvents, but the structures of native enzymes are typically compromised under these conditions. Polymer-enzyme bioconjugates have shown some promise—albeit limited—in this regard. The clickable poly(propargyl methacrylate) (PPMA) was proposed as a platform to enhance different polymer structures versus the residual enzymatic activities. The degree of polymerization and polydispersity are two factors that affect the polymer properties and can affect the enzymatic activities of the polymer-enzyme bioconjugates. The literature examples of PPMA with degree of polymerization greater than 200 are limited. In the atom transfer radical polymerization (ATRP) conditions we discovered, the degree of polymerization and the polydispersity of poly(trimethylsilylpropargyl methacrylate) (PTMSPMA) can be precisely adjusted by the initiator and monomer ratio, the copper catalyst loading, and the reducing agent loading (copper wire). After deprotection, PPMA is further reacted with different mole fraction compositions of hydrophilic triethylene glycol monomethyl ether (mDEG) azide and hydrophobic dodecyl azide to prepare amphiphilic polymers as enzyme stabilizers. The activities of the model enzyme, Subtilisin Carlsberg (SC), and polymer-SC bioconjugates were determined by 4-nitrophenolate and 4-thiopyridone assays, and the polymer-enzyme bioconjugate SC@82%mDEG-PPMA was found to be more active than SC alone in toluene. The SC@82%mDEG-PPMA is also more active than SC@100%mDEG-PPMA in 4-nitrophenolate assay, proving that the side chain structure of the polymer micelles can affect the polymer-enzyme bioconjugates. The micelle 80%mDEG-PPMA may isolate the enzyme from the bulk toluene better than 100%mDEG-PPMA. Deprotonated amino acid salts are great alternatives to the synthesized alkylamines as post-combustion CO2 absorbents due to their non-toxic and low volatile nature. For CO2 capture, gas uptake was measured when solutions of monodeprotonated amino acids were sparged with CO2. The speciation between dissolved CO32–, HCO3– , and CO2(aq), and CO2 captured as carbamates of the deprotonated amino acids, was quantified by 13C{1H} and 1H NMR spectroscopy. Less hindered amino acids like glycine tend to have faster CO2 absorption kinetic and higher carbamate concentrations due to the formation of relatively stable carbamates. One equivalent of carbamate forms requires one equivalent of amino acid as sacrificial base. Therefore, the formation of carbamate decreases the total CO2 absorption capacity and is an unfavorable pathway for CO2 capture. While the amino acids containing substituents at the α carbon atom adjacent to the amino group, like alanine and proline, destabilize their carbamates by unfavorable steric interaction and lead to carbamate hydrolysis to CO32–/HCO3– and enhance the CO2 capture capacity. Therefore, mixing different amino acids can have the fast absorption kinetics and higher absorption capacity. Based on the results, the mixture amino acid solutions were observed to have higher CO2 absorption capacity than the single amino acid counterparts.
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Title: ASSESSING DISASTER MANAGEMENT EFFECTS ON RECOVERY OUTCOMES IN RURAL POST-DISASTER JAPAN
Creator: Ward, Kayleigh
Date: 2022
Collection: Electronic Theses & Dissertations
Description: As a country frequented by natural disasters, Japan has robust disaster management systems that can be employed quickly to mitigate human, environmental, and economic harm and losses. However, these systems tend to be most effective when handling small-scale localized disasters. In the face of the 2011 Great East Japan Earthquake which decimated the northeastern communities of the Tohoku region, Japan’s disaster management system collapsed, unable to handle such large scale and widespread...
Show moreAs a country frequented by natural disasters, Japan has robust disaster management systems that can be employed quickly to mitigate human, environmental, and economic harm and losses. However, these systems tend to be most effective when handling small-scale localized disasters. In the face of the 2011 Great East Japan Earthquake which decimated the northeastern communities of the Tohoku region, Japan’s disaster management system collapsed, unable to handle such large scale and widespread damage. In the ten years since the disaster many rural communities have contended with a variety of social and economic problems, often left unremedied despite on-going government intervention. In this context, this dissertation will explore the complex problems in Minamisanriku, Miyagi—a rural coastal community decimated by the 2011 Great East Japan Earthquake. By engaging and collaborating with organizations in this community, I assess the connections between disaster management and post-disaster recovery outcomes through various applications of social capital and power. I first investigate how historical legacies of national government policies influenced recovery outcomes in the Tohoku region and how have these processes influenced economic restructuring and social development in Minamisanriku during reconstruction. Next, I consider how governance structures within Miyagi prefecture influenced the social and economic development of Minamisanriku during reconstruction. Lastly, I look to how disaster management affects the ability of residents to handle locally-identified and in turn, how residents utilize their social capital to driver social and economic recovery. I assess several key ideas on the connections between forms and theories of social capital and how they affect long-term disaster recovery outcomes through the disaster management process. The dissertation is situated to improve our understanding of how social capital affects rural communities’ ability to respond to these troubles and to craft context specific solutions to them. It also offers a variety of policy recommendations about how to improve community-centered recovery within disaster management frameworks.
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Title: NEUTRON SCATTERING AND TRANSPORT STUDIES OF QUANTUM MATERIALS
Creator: Zhang, Heda
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Quantum material is an multi-disciplinary research topic that continues to thrive in recent years. The term \textit{Quantum material} covers all systems which demonstrate physical phenomena beyond the scope of single-particle, semi-classical/quantum theory. Among many sub-fields of quantum materials, topological systems and strongly correlated systems are two topics which have receive growing attention from the scientific community. We begin with a discussion on a van der Waals magnet VI$_3$...
Show moreQuantum material is an multi-disciplinary research topic that continues to thrive in recent years. The term \textit{Quantum material} covers all systems which demonstrate physical phenomena beyond the scope of single-particle, semi-classical/quantum theory. Among many sub-fields of quantum materials, topological systems and strongly correlated systems are two topics which have receive growing attention from the scientific community. We begin with a discussion on a van der Waals magnet VI$_3$ in chapter three. VI$_3$ hosts ferromagnetism on a honeycomb lattice, which was one of the proposed models for topological magnon bands. There have been ample theoretical studies on ferromagnetic honeycomb lattice. However, there has not been any physical realization of such model. In our study, we show that the is a strong anomalous thermal Hall effect in VI$_3$, the underlying mechanism of which is the non-trivial topological nature of the magnon bands.In chapter four, we discuss our transport studies on some magnetic topological metals. The non-zero Berry curvature in the reciprocal space of topological metals can lead to anomalous transverse conductivities ($\kappa^A, \sigma^A, \alpha^A$) in the system. We found large anomalous transverse conductivities in TbMn$_6$Sn$_6$ and verified its intrinsic nature through first-principle calculations. Furthermore, we have found large exchange-bias behavior in TbMn$_6$Sn$_6$, which renders it as a promising system for anomalous Nernst effect based thermoelectric device. We will also discuss the topological Nernst effect observed in Fe$_3$Sn$_2$, which is potentially due to the Skyrmion bubble phase revealed by the Lorentz transmission electron microscopic studies.In chapter five, we discuss our inelastic neutron scattering study on a unique quantum spin chain system in Cu$_2$(OH)$_3$Br. The system hosts alternating ferromagnetic and anti-ferromagnetic spin chains with finite inter-chain couplings. This allows for the coexistence and interactions between magnons and spinons.
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Title: MUTANT ANALYSIS OF A POLYOL MONOSACCHARIDE TRANSPORTER IN ARABIDOPSIS INVOLVED IN LIGNIFICATION
Creator: Tran, John Dang Khoa
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Monolignols have important roles in plant development and primarily serve as monomers for lignin polymerization in secondary cell walls. Monolignols are synthesized in the plant cytoplasm prior to entering the apoplast where oxidation occurs. Upon oxidation, monolignols are incorporated into the cell wall. Several mechanisms have been suggested to explain how monolignols cross the plasma membrane, including endocytosis, diffusion, and active transport. However, evidence for those models...
Show moreMonolignols have important roles in plant development and primarily serve as monomers for lignin polymerization in secondary cell walls. Monolignols are synthesized in the plant cytoplasm prior to entering the apoplast where oxidation occurs. Upon oxidation, monolignols are incorporated into the cell wall. Several mechanisms have been suggested to explain how monolignols cross the plasma membrane, including endocytosis, diffusion, and active transport. However, evidence for those models relied on theoretical calculations or produced results using in vitro approaches. Further, only one active transporter protein has been characterized to date. Yet, of the three monolignols tested, the transporter was only demonstrably shown to transport p-coumaryl alcohol, the least abundant monolignol present in Arabidopsis.Here we show that AtPMT4 is likely a monolignol transporter, particularly for the more abundant monolignols: coniferyl alcohol and sinapyl alcohol. Gene expression analysis performed on AtPMT4 in dicots and monocots shows coexpression with lignin biosynthetic genes. Cell-specific expression analysis of the inflorescence stem, a tissue that undergoes intense lignification to provide plant structural support, shows that AtPMT4 is expressed higher in cell types that lignify. We demonstrate that Arabidopsis Col-0 plants transformed with a CRISPR-Cas9 construct targeted near the TSS of AtPMT4, a member of the POLYOL/ MONOSACCHARIDE TRANSPORTER family, which is a subfamily of the MONOSACCHARIDE TRANSPORTER-LIKE family, displayed altered lignin phenotypes. We quantified the total lignin, free monomer subunits, and digestibility of the inflorescence stem in pmt4. Our studies show lower amounts of lignin and increased digestibility when AtPMT4 is mutated. Further, we show that pmt4 is sensitive to monolignols when grown in the presence of coniferyl alcohol. pmt4 displayed shorter root length compared to Col-0 at low concentrations of coniferyl alcohol. In conclusion, we provide evidence for an understanding of monolignol translocation and lignification by which transporters are likely involved in a proton-coupled manner.
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Title: ELUCIDATING THE POTENTIAL ROLE OF ARYL HYDROCARBON RECEPTOR IN THE PATHOGENESIS OF CAMPYLOBACTER JEJUNI.
Creator: Ahmed, Husnain
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Campylobacter jejuni is a leading cause of human foodborne gastroenteritis in the US, with an incidence rate of 13.6 diagnosed cases per 100,000 individuals. The most frequent cause of C. jejuni infection in the US is the consumption of chicken contaminated during processing. Macrolide antibiotics such as azithromycin and ciprofloxacin are the drug of choice to treat C. jejuni infection in human populations. However, the over-use of antibiotics has led to the emergence of antimicrobial...
Show moreCampylobacter jejuni is a leading cause of human foodborne gastroenteritis in the US, with an incidence rate of 13.6 diagnosed cases per 100,000 individuals. The most frequent cause of C. jejuni infection in the US is the consumption of chicken contaminated during processing. Macrolide antibiotics such as azithromycin and ciprofloxacin are the drug of choice to treat C. jejuni infection in human populations. However, the over-use of antibiotics has led to the emergence of antimicrobial-resistant C. jejuni strains and reduced treatment efficacy. The development of antimicrobial resistance traits in C. jejuni isolates has augmented the need to develop innovative strategies to treat drug-resistant C. jejuni infections in human and animal populations.Members of the genus Lactobacillus are commonly used as probiotics, however the mechanisms by which they provide protective health effects remain elusive. In the first study, we described a novel mechanism by which L. murinus attenuates pro-inflammatory responses in the human intestinal epithelial cells. The results showed that L. murinus activates aryl hydrocarbon receptor (AHR) to decrease the secretion of IL-8 in response to exogenous stimulation by TNF-alpha in the human intestinal epithelial cells. Furthermore, activating the AHR with its defined ligand also reduced the secretion of IL-8 upon TNF-alpha stimulation. These results suggest that AHR can a novel target for inflammatory bowel disease (IBD) treatment. Furthermore, these results suggest that L. murinus can be a novel probiotic for treating IBD. In the 2nd study, we determined the effect of prophylactic inoculation of L. muirnus on the pathogenesis of C. jejuni in the BALB/c IL-10-/- mice. A total of 41 BALB/c IL-10-/- mice were used in this study. 11 mice were sham inoculated, 10 mice received only L. murinus, 10 mice received only C. jejuni, and 10 mice in the test group received both L. murinus and C. jejuni such that L. murinus was inoculated 32 days before C. jejuni infection. In addition, 30 days post-C. jejuni challenge mice were sacrificed and assessed for gut pathology. Fecal samples were also collected to access bacterial colonization levels in the gut through routine culture techniques and 16S sequence analysis. Both positive control group for C. jejuni and test groups mice developed severe colitis. 16S analysis of fecal DNA revealed that bacterial diversity in the test and positive control group for C. jejuni was significantly less (P<0.001) than in the Lactobacillus only and negative control group. These results suggest that prophylactic administration of L. murinus does not protect BALB/c IL-10-/- mice from developing disease following C. jejuni infection. Overall, this dissertation contains identification of a novel mechanism of action of L. murinus. The results provide insights for the identification of novel targets to treat C. jejuni disease without using antibiotics. This dissertation provides a basis for the future studies to further dissect the role of the AHR in the pathogenesis of C. jejuni.
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Title: TENSOR LEARNING WITH STRUCTURE, GEOMETRY AND MULTI-MODALITY
Creator: Sofuoglu, Seyyid Emre
Date: 2022
Collection: Electronic Theses & Dissertations
Description: With the advances in sensing and data acquisition technology, it is now possible to collect datafrom different modalities and sources simultaneously. Most of these data are multi-dimensional in nature and can be represented by multiway arrays known as tensors. For instance, a color image is a third-order tensor defined by two indices for spatial variables and one index for color mode. Some other examples include color video, medical imaging such as EEG and fMRI, spatiotemporal data...
Show moreWith the advances in sensing and data acquisition technology, it is now possible to collect datafrom different modalities and sources simultaneously. Most of these data are multi-dimensional in nature and can be represented by multiway arrays known as tensors. For instance, a color image is a third-order tensor defined by two indices for spatial variables and one index for color mode. Some other examples include color video, medical imaging such as EEG and fMRI, spatiotemporal data encountered in urban traffic monitoring, etc.In the past two decades, tensors have become ubiquitous in signal processing, statistics andcomputer science. Traditional unsupervised and supervised learning methods developed for one- dimensional signals do not translate well to higher order data structures as they get computationally prohibitive with increasing dimensionalities. Vectorizing high dimensional inputs creates problems in nearly all machine learning tasks due to exponentially increasing dimensionality, distortion of data structure and the difficulty of obtaining sufficiently large training sample size.In this thesis, we develop tensor-based approaches to various machine learning tasks. Existingtensor based unsupervised and supervised learning algorithms extend many well-known algorithms, e.g. 2-D component analysis, support vector machines and linear discriminant analysis, with better performance and lower computational and memory costs. Most of these methods rely on Tucker decomposition which has exponential storage complexity requirements; CANDECOMP-PARAFAC (CP) based methods which might not have a solution; or Tensor Train (TT) based solutions which suffer from exponentially increasing ranks. Many tensor based methods have quadratic (w.r.t the size of data), or higher computational complexity, and similarly, high memory complexity. Moreover, existing tensor based methods are not always designed with the particular structure of the data in mind. Many of the existing methods use purely algebraic measures as their objective which might not capture the local relations within data. Thus, there is a necessity to develop new models with better computational and memory efficiency, with the particular structure of the data and problem in mind. Finally, as tensors represent the data with more faithfulness to the original structure compared to the vectorization, they also allow coupling of heterogeneous data sources where the underlying physical relationship is known. Still, most of the current work on coupled tensor decompositions does not explore supervised problems.In order to address the issues around computational and storage complexity of tensor basedmachine learning, in Chapter 2, we propose a new tensor train decomposition structure, which is a hybrid between Tucker and Tensor Train decompositions. The proposed structure is used to imple- ment Tensor Train based supervised and unsupervised learning frameworks: linear discriminant analysis (LDA) and graph regularized subspace learning. The algorithm is designed to solve ex- tremal eigenvalue-eigenvector pair computation problems, which can be generalized to many other methods. The supervised framework, Tensor Train Discriminant Analysis (TTDA), is evaluated in a classification task with varying storage complexities with respect to classification accuracy and training time on four different datasets. The unsupervised approach, Graph Regularized TT, is evaluated on a clustering task with respect to clustering quality and training time on various storage complexities. Both frameworks are compared to discriminant analysis algorithms with similar objectives based on Tucker and TT decompositions.In Chapter 3, we present an unsupervised anomaly detection algorithm for spatiotemporaltensor data. The algorithm models the anomaly detection problem as a low-rank plus sparse tensor decomposition problem, where the normal activity is assumed to be low-rank and the anomalies are assumed to be sparse and temporally continuous. We present an extension of this algorithm, where we utilize a graph regularization term in our objective function to preserve the underlying geometry of the original data. Finally, we propose a computationally efficient implementation of this framework by approximating the nuclear norm using graph total variation minimization. The proposed approach is evaluated for both simulated data with varying levels of anomaly strength, length and number of missing entries in the observed tensor as well as urban traffic data. In Chapter 4, we propose a geometric tensor learning framework using product graph structures for tensor completion problem. Instead of purely algebraic measures such as rank, we use graph smoothness constraints that utilize geometric or topological relations within data. We prove the equivalence of a Cartesian graph structure to TT-based graph structure under some conditions. We show empirically, that introducing such relaxations due to the conditions do not deteriorate the recovery performance. We also outline a fully geometric learning method on product graphs for data completion.In Chapter 5, we introduce a supervised learning method for heterogeneous data sources suchas simultaneous EEG and fMRI. The proposed two-stage method first extracts features taking the coupling across modalities into account and then introduces kernelized support tensor machines for classification. We illustrate the advantages of the proposed method on simulated and real classification tasks with small number of training data with high dimensionality.
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