Search results

Title: A study of sewage disposal plants in the state of Michigan
Creator: Slaughter, Clare E.
Date: 1924
Collection: Electronic Theses & Dissertations

Title: Lansing engineer's plans for a bridge and grade crossing at Kalamazoo Street, Lansing
Creator: Bauerle, Harold G.
Date: 1924
Collection: Electronic Theses & Dissertations

Title: I. The nature of decay in wood ; II. Development of a method for measuring the rate of decay in wood
Creator: Longyear, B. O. (Burton Orange), 1868-1969
Date: 1925
Collection: Electronic Theses & Dissertations

Title: DATA-DRIVEN COMPUTATIONAL APPROACHES TO UNRAVEL AGE/SEX BIASES & CROSS-SPECIES ANALOGS OF COMPLEX TRAITS AND DISEASES
Creator: Johnson, Kayla A.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Cellular mechanisms and genetic underpinnings of most complex diseases and traits are not well understood. Most diseases also vary in their incidence and presentation in people of different ages and sexes, yet it is still largely unclear how age and sex influence normal tissue physiology and disease at the molecular level. Additionally, while we need research organisms to experimentally study many aspects of human disease etiology, choosing the best genes and conditions in a model organism...
Show moreCellular mechanisms and genetic underpinnings of most complex diseases and traits are not well understood. Most diseases also vary in their incidence and presentation in people of different ages and sexes, yet it is still largely unclear how age and sex influence normal tissue physiology and disease at the molecular level. Additionally, while we need research organisms to experimentally study many aspects of human disease etiology, choosing the best genes and conditions in a model organism for such studies is difficult due to our incomplete knowledge of functional and phenotypic conservation across species. The goal of my research is to address these challenges towards gaining a systematic understanding of the genetic etiology of complex diseases and traits. I have worked towards this goal by developing computational frameworks capable of leveraging massive amounts of publicly-available genomic data with prior knowledge using network analysis and machine learning. These approaches have shed light on the genomic signatures, pathways, and interactions that characterize the age/sex biases and cross-species analogs of complex diseases and traits. I make all the code to reproduce these approaches available by github and have provided tools to make the results searchable by scientists investigating these important biological factors. Collectively, this research will help build infrastructure for advancing biomedical research into the era of precision medicine.
Show less

Title: Unraveling Galaxy Evolution Using Numerical Simulations
Creator: Kopenhafer, Claire
Date: 2022
Collection: Electronic Theses & Dissertations
Description: One of the primary concerns in galaxy evolution is how galaxies form their stars: what keeps thatstar formation going over cosmic time, and what causes it to stop in a processes called “quenching”. Galaxies with mass similar to our own Milky Way occupy a sweet spot between abundance and brightness that makes them easy to find in the sky, and such galaxies also populate a transitionary regime in behavior that make them interesting for studying galaxy evolution. Numerical modeling— from semi...
Show moreOne of the primary concerns in galaxy evolution is how galaxies form their stars: what keeps thatstar formation going over cosmic time, and what causes it to stop in a processes called “quenching”. Galaxies with mass similar to our own Milky Way occupy a sweet spot between abundance and brightness that makes them easy to find in the sky, and such galaxies also populate a transitionary regime in behavior that make them interesting for studying galaxy evolution. Numerical modeling— from semi-analytic models to numerical simulations—are valuable tools for understanding the multiple intersecting physical processes that drive galaxy evolution. These processes act both within and around individual galaxies such that numerical models must necessarily encompass a range of spatial and temporal scales. Multiple approaches are commonly used in order for this modeling to be physically insightful. In this dissertation I will present my efforts to unravel the mechanisms of galaxy evolution affect Milky Way-like galaxies using a variety of numerical models.Addressing the issue of what causes galaxies to stop forming stars, I first investigate an unusualpopulation of galaxies called the “breakBRDs” (Tuttle and Tonnesen 2020). Within the dominant framework for galaxy quenching, galaxies first stop forming stars in their centers and later in their outskirts. This is the “inside-out” quenching paradigm. The breakBRD galaxies possess observa- tional markers that run counter to this narrative. We used the IllustrisTNG cosmological simulation (Pillepich et al. 2018b) to find a set of simulated galaxies that are analogous to the observed breakBRDs in order to better understand their evolution. We found that the breakBRD analogues are galaxies that ultimately become fully quenched, but found no clear cause for the “outside-in” modality. This is not the dominant channel for quenching in the IllustrisTNG simulation, but roughly 10% of quiescent galaxies with 10 < log10 (?∗/M⊙) < 11 had centrally-concentrated star formation similar to the breakBRD analogues.As to what keeps galaxies forming their stars, I used a set of idealized simulations of MilkyWay-like galaxies to study the interactions of the circumgalactic medium (CGM) and its host galaxy. The CGM is an extended volume of gas that accounts for about half of the baryonic matter in a galaxy’s dark matter halo. This gas is also “multiphase,” containing gas at a wide range of densities and temperatures. It may therefore function as a reservoir from which gas may cool, condense, and accrete onto the host galaxy where it can eventually drive star formation and stellar feedback primarily via Type II supernovae. This cycle of condensation and feedback may self-regulate the overall star formation rate of a galaxy. Our idealized simulations include both the CGM and explicit formation of stars but find that stellar feedback can drive outflows that disrupt the CGM with large, hot, low-density cavities. This is true even after we adjust the stellar feedback efficiency to accommodate the “settling” of the initial conditions. We therefore conclude that the picture of star formation self-regulation in Milky Way-like galaxies is missing physical processes at the edge of the galaxy halo that work in tandem with accretion of CGM gas and stellar feedback.The CGM is typically observed via absorption spectra that contain features from numerousmetal ions. In order to better compare the simulated CGM with observations, most simulations need to be post-processed to derive similar information as that extracted from spectra. Therefore, I also present preliminary work quantifying the uncertainties inherent to this post-processing. The results herein focus on the assumption that metals in the CGM follow the abundance pattern of our Sun, which is not physically well-reasoned. We derive plausible alternative abundance patterns using chemical evolution modeling and apply these to a post-processing of the FOGGIE cosmological zoom simulations (Peebles 2020; Simons et al. 2020). We find that adopting a non-Solar abundance affects the column density of CGM absorbers of about ±1 dex.Finally, I present future research directions for all the projects described herein. These includeinvestigating the CGM of the breakBRD analogues from IllustrisTNG, outlining additions to our idealized galaxy simulations that may address the issue of disruptive outflows, and both scaling up our existing uncertainty quantification project as well as including the additional source of uncertainty, ionizing radiation.
Show less

Title: NEAR INFRARED (NIR) SURFACE-ENHANCED RAMAN SPECTROSCOPY AND FLUORESCENCE MICROSCOPY FOR MOLECULAR-GUIDED SURGERY
Creator: Yao, Cheng-You
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Molecular imaging has become an emerging technology to assess tumor margins. As the imaging contrast agents are functionalized with multiple ligands that can bind to different biomarkers – multiplexed molecular imaging, this technique can achieve high sensitivity and specificity for tumor margin detection. Optical-based molecular imaging modalities provide non-hazardous optical radiation, multiplexing wavelengths, and higher spatial resolution than ionizing radiation tomography techniques....
Show moreMolecular imaging has become an emerging technology to assess tumor margins. As the imaging contrast agents are functionalized with multiple ligands that can bind to different biomarkers – multiplexed molecular imaging, this technique can achieve high sensitivity and specificity for tumor margin detection. Optical-based molecular imaging modalities provide non-hazardous optical radiation, multiplexing wavelengths, and higher spatial resolution than ionizing radiation tomography techniques. Two categories of optical contrast agents, fluorescent dyes and Surface-Enhanced Raman Spectroscopy (SERS) nanoparticles are introduced and mainly applied in this dissertation. However, because of the tissue-photon interactions, the imaging contrast and penetration depths are limited by the visible wavelengths. The light in the NIR regime (700~1700 nm) has shown a deeper imaging penetration and better contrast with lower autofluorescence background. Thus, in this dissertation, NIR fluorescent dyes and SERS NPs excited by 785 nm are used for ex vivo and in vivo imaging for biological studies.This work aims to develop a variety of optical instruments for NIR ex vivo and in vivo biomedical imaging applications with deeper penetration, better contrast, and higher sensitivity. The optical instruments include a spectrometric system for SERS Raman detection, a VO2 MEMS scanner for SERS imaging, portable confocal microscopes, and a PZT MEMS scanner-based macroscope for wide-field fluorescence imaging. Chapter 1 briefly introduced the research background, pros and cons of existing techniques, and motivations of this study. In Chapter 2, the spectrometric SERS Raman system and ratiometric analysis have been applied to the detection of Alzheimer's Disease biomarkers and breast cancer image-guided surgery, using different SERS NPs conjugated with ligands. The Raman results were confirmed with histological analysis. In Chapter 3, a VO2 MEMS scanner has been designed, fabricated, and characterized for the Lissajous scanning SERS imaging application. In Chapter 4, two variants of the portable confocal microscopes, the point-scan and line-scan systems were designed with reflective parabolic mirrors for broadband wavelengths from the visible to NIR ranges. Ex vivo and in vivo confocal imaging results have been demonstrated using tumor-bearing mouse tissues. In Chapter 5, a thin-film PZT MEMS scanner has been reported, characterized, and integrated into a wide-field macroscope for fluorescence imaging. In Chapter 6, a novel photodetector - SNSPD has been integrated into the point-scan portable confocal microscope and PZT MEMS scanner-based wide-field macroscope to increase the efficiency and contrast of fluorescent imaging in the NIR range. In the last chapter, the future applications of the advanced VO2 MEMS scanners and fluorescence lifetime imaging microscopy using SNSPD were discussed in detail.
Show less

Title: Understanding Student Experiences in Informal Physics Programs Using the Communities of Practice Framework
Creator: Prefontaine, Brean Elizabeth
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Studies on physics identity have shown that it is one of the main factors that can predict a person’s persistence in the field; therefore, studying physics identity is critical to increase diversity within the field of physics and to understand what changes can allow more women and people of color to identify with the field. Informal physics spaces are not only made up of youth participants, but also facilitators who can be undergraduate or graduate student volunteers. In this work, the...
Show moreStudies on physics identity have shown that it is one of the main factors that can predict a person’s persistence in the field; therefore, studying physics identity is critical to increase diversity within the field of physics and to understand what changes can allow more women and people of color to identify with the field. Informal physics spaces are not only made up of youth participants, but also facilitators who can be undergraduate or graduate student volunteers. In this work, the experiences of facilitators within informal physics programs are investigated as spaces for physics identity development. Thus, the driving question for all of this work is: In what ways can participating as a facilitator within an informal physics program affect identity development? The data for these studies were collected through observations, written artifacts, and semi-structured interviews with those who facilitated the informal physics programs. In order to understand more about the experiences of the facilitators, the informal physics programs were viewed as Communities of Practice (CoP), and the CoP framework was operationalized within the context of these spaces. First, stories from two physics graduate students out of the interview sample are presented to provide a context for testing the feasibility of the extended framework and to identify how experiences within an informal physics program can shape physics identity development. Then, the operationalized CoP framework is used to study three distinct informal physics programs to understand the structures that support physics identity development. Finally, informal programs that combine physics and music/art are examined with the operationalized CoP framework to understand how these blended spaces can form communities of practice and support identity development. Analysis showed that the CoP framework is an effective tool for analyzing informal physics programs and highlights the structures that lead to identity development. These findings indicate that informal physics programs that operate with a CoP structure can provide valuable experiences to undergraduate and graduate facilitators that lead to physics identity growth.
Show less

Title: MACHINE LEARNING APPROACHES FOR PROCESSING AND DECODING ATTENTION MODULATION OF SENSORY REPRESENTATIONS FROM EEG
Creator: saba-sadiya, sari
Date: 2023
Collection: Electronic Theses & Dissertations
Description: This thesis presents novel machine learning algorithms that achieve state-of-the-art performance on a variety of electroencephalography (EEG) tasks, including decoding, classification, and unsupervised / semi-supervised artifact detection and correction. These algorithms are then used within the scope of an EEG experiment that explores how attention to multiple items modulates sensory representations. Using a signal detection paradigm, we demonstrate that attending to multiple items impacts...
Show moreThis thesis presents novel machine learning algorithms that achieve state-of-the-art performance on a variety of electroencephalography (EEG) tasks, including decoding, classification, and unsupervised / semi-supervised artifact detection and correction. These algorithms are then used within the scope of an EEG experiment that explores how attention to multiple items modulates sensory representations. Using a signal detection paradigm, we demonstrate that attending to multiple items impacts the sensitivity of our participants, causing a sharp increase in false-alarm rates and only slightly decreasing hit-rate. We conclude that our behavioral and EEG decoding results contradict simultaneous attention guidance by multiple items (the multiple item template hypothesis).
Show less

Title: Safe Control Design for Uncertain Systems
Creator: Marvi, Zahra
Date: 2021
Collection: Electronic Theses & Dissertations
Description: This dissertation investigates the problem of safe control design for systems under model and environmental uncertainty. Reinforcement learning (RL) provides an interactive learning framework in which the optimal controller is sequentially derived based on instantaneous reward. Although powerful, safety consideration is a barrier to the wide deployment of RL algorithms in practice. To overcome this problem, we proposed an iterative safe off-policy RL algorithm. The cost function that encodes...
Show moreThis dissertation investigates the problem of safe control design for systems under model and environmental uncertainty. Reinforcement learning (RL) provides an interactive learning framework in which the optimal controller is sequentially derived based on instantaneous reward. Although powerful, safety consideration is a barrier to the wide deployment of RL algorithms in practice. To overcome this problem, we proposed an iterative safe off-policy RL algorithm. The cost function that encodes the designer's objectives is augmented with a control barrier function (CBF) to ensure safety and optimality. The proposed formulation provides a look-ahead and proactive safety planning, in which the safety is planned and optimized along with the performance to minimize the intervention with the optimal controller. Extensive safety and stability analysis is provided and the proposed method is implemented using the off-policy algorithm without requiring complete knowledge about the system dynamics. This line of research is then further extended to have a safety and stability guarantee even during the data collection and exploration phases in which random noisy inputs are applied to the system. However, satisfying the safety of actions when little is known about the system dynamics is a daunting challenge. We present a novel RL scheme that ensures the safety and stability of the linear systems during the exploration and exploitation phases. This is obtained by having a concurrent model learning and control, in which an efficient learning scheme is employed to prescribe the learning behavior. This characteristic is then employed to apply only safe and stabilizing controllers to the system. First, the prescribed errors are employed in a novel adaptive robustified control barrier function (AR-CBF) which guarantees that the states of the system remain in the safe set even when the learning is incomplete. Therefore, the noisy input in the exploratory data collection phase and the optimal controller in the exploitation phase are minimally altered such that the AR-CBF criterion is satisfied and, therefore, safety is guaranteed in both phases. It is shown that under the proposed prescribed RL framework, the model learning error is a vanishing perturbation to the original system. Therefore, a stability guarantee is also provided even in the exploration when noisy random inputs are applied to the system. A learning-enabled barrier-certified safe controllers for systems that operate in a shared and uncertain environment is then presented. A safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents that affect the safety of the system. The loss function is defined based on safe set error, instead of the system model error, and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set. The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF (L-ZCBF), which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations. It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents, which is crucial in highly interactive environments. Finally, the cooperative capability of agents in a multi-agent environment is investigated for the sake of safety guarantee. CBFs and information-gap theory are integrated to have robust safe controllers for multi-agent systems with different levels of measurement accuracy. A cooperative framework for the construction of CBFs for every two agents is employed to maximize the horizon of uncertainty under which the safety of the overall system is satisfied. The information-gap theory is leveraged to determine the contribution and share of each agent in the construction of CBFs. This results in the highest possible robustness against measurement uncertainty. By employing the proposed approach in constructing CBF, a higher horizon of uncertainty can be safely tolerated and even the failure of one agent in gathering accurate local data can be compensated by cooperation between agents. The effectiveness of the proposed methods is extensively examined in simulation results.
Show less

Title: CHARACTERIZATION AND APPLICATION OF THE SURFACE CHARGE-INDUCED LONG-RANGE ORGANIZATION IN ROOM TEMPERATURE IONIC LIQUIDS
Creator: Wang, Yufeng
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Room temperature ionic liquids (RTILs) are salts characterized by a melting point below room temperature. RTILs have a wide range of applications, in areas ranging from supercapacitor energy storage to sequestration of toxic gas phase species and use as reusable solvents for selected organic reactions. All these applications stem from their unique physical and chemical properties, which remain understood to a limited extent. Among the issues of greatest importance is the extent to which RTILs...
Show moreRoom temperature ionic liquids (RTILs) are salts characterized by a melting point below room temperature. RTILs have a wide range of applications, in areas ranging from supercapacitor energy storage to sequestration of toxic gas phase species and use as reusable solvents for selected organic reactions. All these applications stem from their unique physical and chemical properties, which remain understood to a limited extent. Among the issues of greatest importance is the extent to which RTILs exist as dissociated ionic species, and the length scales over the organizations are seen to exist in them. Our group have reported previously on the existence of a surface charge-induced free charge density gradient in RTILs with a characteristic persistence length of ca. 50 μm. The existence of such a long-range organization in fluid medium is unusual. The overall goal of this work is to achieve a deeper understanding of this phenomenon, thereby providing an opportunity to better understand the local and long-range organization in RTILs and broad their potential applications which benefit from gaining such knowledge.The induced free charge density gradient (ρf) is probed by measuring the fluorescence anisotropy decay of a trace-level charged chromophore in the RTIL as a function of distance from the indium-doped tin oxide (ITO) support surface. In chapter 2, we characterize the structure-dependence of this charge-induced organization as a function of the RTIL constituent identity, and use these data to evaluate the magnitude of the induced free charge density gradient. The magnitude of this gradient is found to depend on the chemical structures of the cationic and anionic constituents of the RTIL used. In chapter 3, we characterize ρf in three different pyrrolidinium RTILs and two imidazolium RTILs, which aims to expand on prior results (chapter 2) on the chemical structure-dependence of ρf. Our measurements demonstrate that the magnitude of ρf depend on the alkyl chain length of RTIL cation. ρf is larger in the RTIL with longer cation alkyl chain. This dependence has been revealed in both pyrrolidinium and imidazolium ionic liquids. In chapter 4, we report on the existence of a surface charge-induced gradient in the RTIL refractive index (n) and evaluate the relationship between the gradient in n and ρf. Because ρf is uniaxial, the induced change in n is manifested as an induced birefringence. We characterize the ρf -dependent n of the RTIL with an apparatus that uses the RTIL as a lens. ρf is controlled by the surface charge density (σs) of the RTIL support. The far-field image of light passed through the RTIL lens as a function of σs is used to measure charge-induced changes in n of the RTIL. We demonstrate a significant modulation of the n with modest changes in σs of the RTIL support. This report places the relationship between ρf and RTIL dielectric response on a quantitative footing and suggests the utility of RTILs for electro-optic applications. In chapter 5, We report on the dependence of surface charge-induced birefringence in room temperature ionic liquids (RTILs) with different cation constituents. The induced birefringence is related to ρf in the RTIL. We find that in all cases the induced birefringence is proportional to the σs and, that the change in n nearest the ITO surface can be on the order of 30%. Our findings indicate that the induced birefringence depends more sensitively on the cation aliphatic substituent length than on the identity of the charge-carrying headgroup.
Show less

Title: GENOMIC APPLICATIONS TO PLANT BIOLOGY
Creator: Hoopes, Genevieve
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The study of the total nuclear DNA content of an organism, i.e., the genome, is a relatively new field and has evolved as sequencing technology and its output has changed. A shift from model species to ecological and crop species occurred as sequencing costs decreased and the technology became more broadly accessible, enabling new discoveries in genome biology as increasingly diverse species and populations were profiled. Here, a genome assembly and several transcriptional studies in multiple...
Show moreThe study of the total nuclear DNA content of an organism, i.e., the genome, is a relatively new field and has evolved as sequencing technology and its output has changed. A shift from model species to ecological and crop species occurred as sequencing costs decreased and the technology became more broadly accessible, enabling new discoveries in genome biology as increasingly diverse species and populations were profiled. Here, a genome assembly and several transcriptional studies in multiple non-model plant species provided new knowledge of molecular pathways and gene content. Over 157 Mb of the genome of the medicinal plant species Calotropis gigantea (L.) W.T.Aiton was sequenced, de novo assembled and annotated using Next Generation Sequencing technologies. The resulting assembly represents 92% of the genic space and provides a resource for discovery of the enzymes involved in biosynthesis of the anticancer metabolite, cardenolide. An updated gene expression atlas for 79 developmental maize (Zea mays L., 1753) tissues and five abiotic/biotic stress treatments was developed, revealing 4,154 organ-specific and 7,704 stress-induced differentially expressed (DE) genes. Presence-absence variants (PAVs) were enriched for organ-specific and stress-induced DE genes, tended to be lowly expressed, and had few co-expression network connections, suggesting that PAVs function in environmental adaptation and are on an evolutionary path to pseudogenization. The Maize Genomics Resource (http://maize.plantbiology.msu.edu/) was developed to view and data-mine these resources. Through profiling global gene expression over time in potato (Solanum tuberosum L.) leaf and tuber tissue, the first circadian rhythmic gene expression profiles of the below-ground heterotrophic tuber tissue were generated. The tuber displayed a longer circadian period, a delayed phase, and a lower amplitude compared to leaf tissue. Over 500 genes were differentially phased between the leaf and tuber, and many carbohydrate metabolism enzymes are under both diurnal and circadian regulation, reflecting the importance of the circadian clock for tuber bulking. Most core circadian clock genes do not display circadian rhythmic gene expression in the leaf or tuber, yet robust transcriptional and gene expression circadian rhythms are present.
Show less

Title: UNDERSTANDING CAREGIVER PERCEPTIONS ON SCHOOL PARTNERSHIPS
Creator: Rice, Darreth R.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Caregivers play an integral role in a child’s academic development, including their literacy development (Compton-Lilly et al., 2019; Cunningham, 2021; Edwards, 2004; Edwards, 2016; Smith, 2020; Volk, 2021). One way caregivers have supported their children’s literacy development is by assisting them with school activities at home. While some caregivers are willing to assist, schools must recognize that not all caregivers know what specific activities support the development of literacy skills...
Show moreCaregivers play an integral role in a child’s academic development, including their literacy development (Compton-Lilly et al., 2019; Cunningham, 2021; Edwards, 2004; Edwards, 2016; Smith, 2020; Volk, 2021). One way caregivers have supported their children’s literacy development is by assisting them with school activities at home. While some caregivers are willing to assist, schools must recognize that not all caregivers know what specific activities support the development of literacy skills. This partnership between home and school becomes especially important when Michigan schools are in the midst of a state-wide literacy policy aimed at improving students’ achievement levels on the state standardized assessments (Weyer, 2018). This study examined the perspective of the caregiver on this partnership during the implementation of a state-wide literacy policy. Overall, this study sought to understand the degree to which current school outreach to caregivers was aligned to current research on caregiver engagement. The study used the caregivers’ own words (Lumby, 2007), whenever possible. This study further investigated caregivers’ perceptions of the school’s outreach during the 2021-22 school year. Additionally, the study sought to discover caregivers’ desires for future partnerships with schools to continue to support their child in early literacy development. Lastly, this study analyzed the availability of resources, as reported by caregivers and found on school websites, as well as how aligned those resources were to current research on caregiver engagement. To undertake this endeavor, this qualitative study utilized an online survey focused on four distinct areas within a state undergoing a state-wide literacy policy. The four areas were chosen for their diversity in race, ethnicity, location, religions practices, population of immigrants, and population of migrant season farm workers. Following the survey, a subset of the caregivers were interviewed. After the interviews, a review of early literacy materials was conducted using the school’s websites. The review included at least one elementary school from each of the focal areas in the survey and interviews. Additionally, twenty-nine other schools, chosen through random interval sampling, were included in the review. The findings of this study shed light on the partial alignment between the current research and the school outreach to caregivers, specifically in relation to literacy activities focused on student literacy development. Using the frameworks of parental involvement (Epstein et al., 2019), intentionality (Edwards, 2016), and efficacy (Bandura, 1977), this study discovered resources offered to caregivers do not always align with intentionally. Current communication methods do not align with parental involvement framework’s tenet of two-way communication. Lastly, the supports offered to caregivers do not always favor efficacious behavior in caregivers. Implications for this work have wide-reaching opportunities for change in the culture of both policymaking and education. Policymakers can use these findings to understand the importance of including the voice of all policy actors. Teacher educators can view these findings to ensure they teach their teacher candidates how to communicate with caregivers. This includes having difficult conversations. School leadership can support current teachers with stronger engagement with caregivers by using the findings in this study and learning to listen to the caregivers and their concerns. Lastly, caregivers can also learn they are their child’s advocate, and they will have to do their part to work with the child.
Show less

Title: Filling in the Gaps : Modeling the Role of Groundwater in Lake Erie’s Nutrient Budget
Creator: Lanier, Alexis Ann
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Lake Erie is a hotspot for large harmful algal blooms, which damage human health, degrade natural habitats, and impair industries reliant on the lake. The Maumee River watershed, the largest in the Great Lakes, often acts as a major driver for these blooms, as it is the largest contributor of nutrients to the lake, mainly attributed to intense agricultural activity. Consequently, surficial transport of phosphorus and nitrogen within the Maumee River watershed has been extensively studied....
Show moreLake Erie is a hotspot for large harmful algal blooms, which damage human health, degrade natural habitats, and impair industries reliant on the lake. The Maumee River watershed, the largest in the Great Lakes, often acts as a major driver for these blooms, as it is the largest contributor of nutrients to the lake, mainly attributed to intense agricultural activity. Consequently, surficial transport of phosphorus and nitrogen within the Maumee River watershed has been extensively studied. However, there has been very little research into the role of groundwater here, especially groundwater modeling studies. Here, I evaluate the literature that has explored nutrient transport to Lake Erie, with a focus on the Maumee River watershed, and examine groundwater nutrient transport. This knowledge will inform nutrient management decisions, especially those regarding future and legacy nutrient loads. In Chapter 1, I review the current state of literature on hydrologic nutrient modeling in the Lake Erie Basin. I highlight common themes in the literature and detail prominent gaps. Specifically, I focus on the role of groundwater in nutrient modeling studies within the Maumee River watershed and recommend future directions for research. In Chapter 2, I create a spatially explicit, process-based groundwater model of the Maumee River watershed. This model allows me to quantify the contributions of groundwater in the context of total basin loading. I then quantify the role of legacy nutrient accumulation by reducing input loads in a projected future scenario. This research completes the nutrient budget by highlighting ‘hidden’ groundwater nutrient loads and informs the timescale of subsurface nutrient management.
Show less

Title: INVARIANT REPRESENTATION LEARNING VIA FUNCTIONS IN REPRODUCING KERNEL HILBERT SPACES
Creator: Sadeghi, Bashir
Date: 2023
Collection: Electronic Theses & Dissertations
Description: Many applications of representation learning, such as privacy preservation and algorithmic fairness, desire explicit control over some unwanted information being discarded. This goal is formulated as satisfying two objectives: maximizing utility for predicting a target attribute while simultaneously being invariant (independent) to a known sensitive attribute (like gender or race). Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and...
Show moreMany applications of representation learning, such as privacy preservation and algorithmic fairness, desire explicit control over some unwanted information being discarded. This goal is formulated as satisfying two objectives: maximizing utility for predicting a target attribute while simultaneously being invariant (independent) to a known sensitive attribute (like gender or race). Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and invariance when they are competing. Most existing works are empirical and implicitly look for single or multiple points on the utility-invariance trade-off. They do not explicitly seek to characterize the entire trade-off front optimally and do not provide invariance and convergence guarantees. In this thesis, we address the shortcoming mentioned above by considering simple linear modeling and building upon them. As a first step, we derive a closed-form solution for the global optima of the underlying linear IRepL optimization problem. In further development, we consider neural network-based encoders, where we model the utility of the target task and the invariance to the sensitive attribute via kernelized ridge regressors. This setting leads to a stable iterative optimization scheme toward global/local optima(s). However, such a setting cannot guarantee universal invariance.This drawback motivated us to further study the case where the invariance measure is modeled universally via functions in some reproducing kernel Hilbert spaces (RKHS)s. By modeling the encoder and target networks via functions in some RKHS, too, we derive a closed formula for a near-optimal trade-off, corresponding optimal representation dimensionality, and the associated encoder(s). Our findings have an immediate application to fairness in terms of demographic parity.
Show less

Title: Nonlinear Extensions to New Causality and a NARMAX Model Selection Algorithm for Causality Analysis
Creator: da Cunha Nariyoshi, Pedro
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Although the concept of causality is intuitive, an universally accepted objective measure to quantify causal relationships does not exist. In complex systems where the internal mechanism is not well understood, it is helpful to estimate how different parts of the system are related. In the context of time-series data, Granger Causality (GC) has long been used as a way to quantify such relationships, having been successfully been applied in fields as diverse as econometrics and neurology....
Show moreAlthough the concept of causality is intuitive, an universally accepted objective measure to quantify causal relationships does not exist. In complex systems where the internal mechanism is not well understood, it is helpful to estimate how different parts of the system are related. In the context of time-series data, Granger Causality (GC) has long been used as a way to quantify such relationships, having been successfully been applied in fields as diverse as econometrics and neurology. Multiple Granger-like and extensions to GC have also been proposed. A recent measure developed to address limitations of GC, New Causality (NC), offers several advantages over GC, such as normalization and better proportionality with respect to internal mechanisms. However, NC is limited in scope by its seminal definition being based on parametric linear models. In this work, a critical analysis of NC is presented, NC is extended to a wide range of nonlinear models and finally, enhancements to a method of estimating nonlinear models for use with NC are reported.A critical analysis is conducted to study the relationship between NC values and model estimation errors. It is shown that NC is much more sensitive to overfitting in comparison to GC. Although the variance of NC estimates is reduced by applying regularization techniques, NC estimates are also prone to bias. In this work, diverse case-studies are presented showing the behavior of NC estimation in the presence of regularization. A mathematical study of the sources of bias in the estimates is given.For systems that cannot be modeled well by linear models, the seminal definition of NC performs poorly. This works gives examples in which nonlinear observation models cause NC values obtained with the seminal definition to behave contrary to intuitive expectations. A nonlinear extension of NC to all linear-in-parameters models is then developed and shown to address these limitations. The extension reduces to the seminal definition of NC for linear models and offers a flexible weighting mechanism to distribute contributions among nonlinear terms. The nonlinear extension is applied to a range of synthetic data and real EEG data with promising results.The sensitivity of NC to parameter estimation errors demands that special care be taken when using NC with nonlinear models. As a complement to nonlinear NC, enhancements to a algorithm for nonlinear parametric model estimation are presented. The algorithm combines a genetic search element for regressor selection with a set-theoretic optimal bounded ellipsoid algorithm for parameter estimation. The enhancements to the genetic search make use of sparsity and information theoretic measures to reduce the computational cost of the algorithm. Significant reductions are shown and direction for further improvements of the algorithm are given. The main contributions of this work are providing a method for estimating causal relationships between signals using nonlinear estimated models, and a framework for estimating the relationships using an enhanced algorithm for model structure search and parameter estimation.
Show less

Title: Heterogeneous Thalamic Reticular Nucleus Neurons and Their Functional Role in Thalamocortical Processing
Creator: Harding-Jackson, Laura
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The thalamic reticular nucleus (TRN) is an integral regulator of information flow between the thalamus and cortex. The TRN receives synaptic inputs from both cortical and thalamic regions and based upon this information it selectively inhibits thalamic activity. TRN neurons produce action potentials in two distinct modes: a fast, transient burst discharge from a hyperpolarized state, and a prolonged, tonic discharge from a relatively depolarized state. While previous studies have...
Show moreThe thalamic reticular nucleus (TRN) is an integral regulator of information flow between the thalamus and cortex. The TRN receives synaptic inputs from both cortical and thalamic regions and based upon this information it selectively inhibits thalamic activity. TRN neurons produce action potentials in two distinct modes: a fast, transient burst discharge from a hyperpolarized state, and a prolonged, tonic discharge from a relatively depolarized state. While previous studies have characterized burst discharge as a transient high frequency discharge (> 250 Hz), these electrophysiological studies reveal a highly variable range of burst frequencies (4- 342 Hz). In these studies, I aim to discover the mechanisms underlying these highly variable burst frequencies, as well as their functional role in thalamocortical processing.In chapter two, I found that bursts from TRN neurons with relatively higher frequency discharge (>100 Hz) contain more action potentials per burst. These neurons also have higher input resistances, broader action potentials, higher action potential thresholds, and larger somas. The amplitude of the T-type calcium channel-mediated low-threshold spike, which underlies the burst discharge, is positively correlated with both the burst discharge frequency and the number of action potentials per burst. I next investigated whether small conductance calcium-activated potassium channels (SK channels) could mediate the differences in burst firing rate and action potential number. Blocking SK channels increased the frequency and duration of the burst but did not increase the amplitude of the underlying T-type calcium current. Prior studies suggest that T-type calcium channels are distributed along the dendrites in TRN neurons with high frequency burst discharge. In chapter three, I examine the distribution of dendritic calcium activity within the lower frequency bursting neurons. While the calcium signal was lower in these neurons all along the dendrites, the calcium signal was evenly distributed across proximal, intermediate, and distal dendritic regions. Investigation of SK channel activity revealed significant location-specific effects. In lower frequency bursting neurons, SK channels had the greatest influence at proximal and distal locations. In higher frequency bursting neurons, SK channels had the greatest influence at proximal and intermediate dendritic locations. Heterogeneous TRN burst discharge frequencies may represent a diverse cell population with unique dendritic ion channel composition and distribution. These results may improve our understanding of the mechanisms of TRN neuron afferent synaptic integration as well as modulation of thalamocortical inhibition. In chapter four I investigate whether intrinsic properties of TRN neurons are altered in the Fmr1-KO mouse model of Fragile X Syndrome (FXS). Individuals with FXS experience a variety of comorbidities that could involve TRN function, such as altered sensory perceptions, sleep disorders, and epilepsy. Analysis of intrinsic cellular properties revealed no differences in TRN neuron properties. Further investigation of synaptic plasticity, which is an abnormal finding in several other brain regions in FXS, also revealed no pathology. These findings suggest that TRN dysfunction does not contribute to FXS pathology.
Show less

Title: PRECISION DIAGNOSTICS AND INNOVATIONS FOR PLANT BREEDING RESEARCH
Creator: Hugghis, Eli
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Major technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image...
Show moreMajor technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image classification analyses have allowed machine learning approaches to become more accessible to the agricultural field. Here we present the use of gold nanoparticles (AuNPs) for the detection of transgenic gene sequences in maize and the use of machine learning algorithms for the identification and classification of Fusarium spp. infected wheat seed. AuNPs show promise in their ability to diagnose the presence of transgenic insertions in DNA samples within 10 minutes through colorimetric response. Image-based analysis with the utilization of logistic regression, support vector machines, and k-nearest neighbors were able to accurately identify and differentiate healthy and diseased wheat kernels within the testing set at an accuracy of 95-98.8%. These technologies act as rapid tools to be used by plant breeders and pathologists to improve their ability to make selection decisions efficiently and objectively.
Show less

Title: Neuromodulation improves motor and cognitive performance in animal models
Creator: Cywiak, Carolina
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Neurons change the way they respond to a specific stimulus by functional and structural changes, known as neuroplasticity. Neuroplasticity can be modified via different stimuli as electrical, chemical, and mechanical interventions, causing alterations to central and peripheral nervous system functions. Past neuroimaging studies related to chronic pain showed changes associated with altered cortical balance between excitation-inhibition and maladaptive plasticity. However, the mechanisms...
Show moreNeurons change the way they respond to a specific stimulus by functional and structural changes, known as neuroplasticity. Neuroplasticity can be modified via different stimuli as electrical, chemical, and mechanical interventions, causing alterations to central and peripheral nervous system functions. Past neuroimaging studies related to chronic pain showed changes associated with altered cortical balance between excitation-inhibition and maladaptive plasticity. However, the mechanisms behind neuroplasticity and the optimal parameters which induce long-term, and sustainably enhanced performance remain unknown. Previous studies have shown that neuromodulation can induce beneficial changes through neuroplasticity. Therefore, in this study we focused on identifying the best strategies to induce neuroplasticity in the somatosensory cortex (S1). First, we tested if non-invasive repetitive transcranial magnetic stimulation (rTMS) induces neuronal excitability, and cell-specific magnetic activation via the Electromagnetic-Perceptive Gene (EPG). EPG is a novel gene that was identified and cloned from glass catfish (Kryptopterus vitreolus). In response to magnetic stimulation, this gene promotes neural activation, which could potentially restore cortical excitability. The results demonstrated that neuromodulation significantly improved long-term mobility, decreased anxiety, and enhanced neuroplasticity, reinforcing the growing amount of evidence from human and animal studies that are establishing neuromodulation as an effective strategy to promote plasticity and rehabilitation. Second, we identified the best protocol to facilitate the greatest changes in fMRI activation maps in the rat S1. The results showed that a single session of rTMS increased S1 activity, but induced changes that are absent three days after the session. Instead, forepaw stimulation of 10 Hz delivered synchronized with 10 Hz rTMS for five consecutive days demonstrated the greatest increase in the extent of the evoked fMRI responses. These results provide direct indication that pairing peripheral stimulation with rTMS induces long-term plasticity, and this phenomenon appears to follow a time-dependent plasticity mechanism. Given these results, we can conclude that neuromodulation induced by changes on S1 can improve cortical balance, and this therapy could be used in the future to treat different types of disorders.
Show less

Title: Discussion and Democracy : Supporting Students with Disabilities In Historical Thinking and Online Civic Reasoning Through Small Group Discussion
Creator: Daley, Shawn T.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: In the past decade, misinformation has become more challenging to decipher. While separating fact from fiction has always been difficult, it has only grown more difficult with the increasing ubiquity of social media and ongoing political polarization. This is especially the case for students with disabilities, who have traditionally struggled with evaluating written texts. This cross-case analysis, a qualitative review of student experiences, examined how three students responded to an...
Show moreIn the past decade, misinformation has become more challenging to decipher. While separating fact from fiction has always been difficult, it has only grown more difficult with the increasing ubiquity of social media and ongoing political polarization. This is especially the case for students with disabilities, who have traditionally struggled with evaluating written texts. This cross-case analysis, a qualitative review of student experiences, examined how three students responded to an intervention developed to improve their historical thinking skills, including lateral reading, to help them identify misinformation. Grounded in sociocultural theory and shaped by the Universal Design for Learning framework, the intervention consisted of lessons on historical thinking and lateral reading and participation in a structured, peer-mediated small group discussion. Findings showed that while small group discussion may hold promise for supporting students with disabilities to learn lateral reading and historical thinking, it requires further development to help students with the most significant challenges. UDL-informed lessons and associated educational technologies were also evaluated in classroom settings with the case studies students. Results suggest that students with disabilities were aided by the lessons and associated technologies, however, students with more significant disabilities were much less impacted. Finally, this study was conducted during teaching conditions influenced by the COVID-19 pandemic. Findings suggest that certain case study students benefitted from learning in these conditions, but others struggled due to a lack of interpersonal communication with teachers or peers. Overall implications included considerations for how small group discussion is developed and used by teachers in high school social studies and how historical thinking skills and lateral reading are introduced and shared for students with disabilities. Implications for researchers and theorists of sociocultural theory and cultural-historical activity theory are also presented.
Show less

Title: An interactive knowledge-driven multi-objective optimization framework for achieving faster convergence
Creator: Ghosh, Abhiroop
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Users interested in solving real-world optimization problems often have many years of experience. Their intuition or 'knowledge' is often overlooked in academic studies due to concerns regarding loss of generality. Such knowledge can be expressed as inter-variable relationships or functions, which can provide some initial guidance to a suitably-designed optimization algorithm. Alternatively, knowledge about variable interactions can also be extracted algorithmically during the optimization by...
Show moreUsers interested in solving real-world optimization problems often have many years of experience. Their intuition or 'knowledge' is often overlooked in academic studies due to concerns regarding loss of generality. Such knowledge can be expressed as inter-variable relationships or functions, which can provide some initial guidance to a suitably-designed optimization algorithm. Alternatively, knowledge about variable interactions can also be extracted algorithmically during the optimization by analyzing the better solutions progressively found over iterations - a process termed innovization. Any common pattern extracted from good solutions discovered during an optimization run can be used as a repair operator to modify candidate solutions, but the key aspect is to strike a balance between the relevance of the pattern identified and the extent of its use in the repair operator, lest the learned patterns turn out to be properties of unpromising search directions or 'blind alleys'. In this dissertation, we propose a framework combining both user-supplied and algorithmically-extracted knowledge to repair solutions during an optimization run in an online fashion. Such a framework is also interactive, allowing the user to provide inputs at any point during the optimization. We show the step-wise modifications required for an evolutionary multi-objective (EMO) framework to allow for: (a) initial user-provided knowledge, (b) automated knowledge extraction and application using innovization methods, and (c) allowing the user to interact with the framework at any point during the optimization run. The path to creating such a framework is systematically performed one step at a time, starting from creating an efficient method of representing problem knowledge, designing a suitable automated innovization procedure, and finally interleaving human-provided and machine-extracted knowledge. We show that such a framework can achieve faster convergence across a variety of practical optimization problems. Some future research directions are also discussed.
Show less

MSU Libraries

Digital Repository

Enabled Filters

Pages

Pages

MSU Libraries

Digital Repository

You are here

Search results

Pages

Pages