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Neuroendocrine regulation of social dominance and reproductive success in a highly social cichlid fish
No full textSexual reproduction requires animals to be in the right state at the right time to successfully reproduce. How does an animal regulate its readiness to mate and how does it respond to the social interaction that is mating before ultimately producing viable offspring. Using the highly social African cichlid fish, Astatotilapia burtoni, my dissertation research employed neurobiological and transcriptomic approaches to investigate the function and evolution of neuroendocrine gene networks in the regulation of reproduction. A. burtoni males exhibit remarkable social status-dependent plasticity whose neuromolecular underpinnings have been well characterized. Specifically, my research asked how animals get ready to reproduce, reproductive state, investigated the act of reproduction, reproduction synchronization, and analyzed the consequences of reproduction, reproductive success. Taken together, my dissertation research traces how an individual prepares, participates, and succeeds at reproduction, thereby providing a powerful framework for the study of social behavior and its neuromolecular underpinnings in non-traditional model systems.Ecology, Evolution and Behavio
Application of mathematical optimization to the resilience and reliability of the electric grid
No full textAs the electric grid is fundamental to modern society, it is imperative to maintain the functioning of the electric grid in the face of both high-impact, low-probability events and low-impact, high-probability events. Namely, the electric grid must receive the support needed for it to exhibit resilience as well as reliability. It is then considered that mathematical optimization is the branch of applied mathematics concerned with finding the best solution to a problem. Consequently, while mathematical optimization is not the focus of this dissertation, it does contain decidedly novel research contributions to the application of mathematical optimization for the resilience and reliability of the electric grid. The contributions include not only optimization models developed but also other concepts devised in the process of developing the models. One electric-grid resilience issue that this dissertation tackles with optimization is the application of mobile energy storage (MES) to assist with black-start (BS) restoration of a transmission system. MES-assisted BS restoration is assessed from the perspectives of pre-placement before a blackout and multi-period operations over a time horizon following a blackout. In the former case, the concept of a discretized expected value realization is devised to address the inadequacy of the expected value problem when presented with an integer-valued random parameter vector. Another electric-grid resilience issue this dissertation optimally approaches is the enhancement of the flood resilience of a transmission system. Resilience of the transmission system to a particular, imminent flood event is enhanced with mobile-substation (MS) resources, and resilience to multiple potential flood events over a multi-year horizon is enhanced with substation hardening. In the former case, an intricate optimization model considering MS resources arises, so a parallel heuristic is devised to efficiently solve instances of the model. Lastly, an electric-grid reliability issue that this dissertation confronts with optimization is the installation of fault indicators (FIs) to facilitate impedance-based fault location within a radial distribution system. The optimization model for placing FIs leverages the concept of an expanse, which is devised to account for continuous ranges of fault locations.Electrical and Computer Engineerin
An investigation of inorganic nanomaterial synthesis via extracellular electron transfer by Shewanella oneidensis
No full textInorganic materials play a role in an ever-growing part of our everyday lives. These materials have multiple applications in several industries but find expanded use as nanomaterials. Changes in surface area, porosity, and structural organization convey distinct properties in particles that present advantages to their macro-sized counterparts. These materials are typically synthesized using physical and chemical methods, but recent advances in in genomic tools, synthetic regulations, and non-natural biomolecules as well as mild reaction conditions and enhanced stability are advantages of biological synthesis. Despite these advances, there are limitations to tunability and our understanding of these systems. To address this, we utilize the extracellular electron transfer (EET) mechanism in Shewanella oneidensis as a platform for biosynthesis of inorganic nanomaterials. This work seeks to explore the diversity of inorganic nanomaterials that can be synthesized, modulated, or controlled using EET. The first part of this work explores the functionality of linking EET with metal reduction to evaluate the breadth and properties of materials that can be formed via biological redox reactions. This work specifically looks to expand knowledge of the types of nanomaterial formation achievable with S. oneidensis as the source of synthesis. It also aims to understand the functional properties associated with those materials. In chapter 2, we show the application of this system toward the formation of palladium nanoparticles. In chapter 3, we demonstrate ability of this system for the reduction of ruthenium. The second part of this work investigates EET mechanics and evaluates for key components and system changes associated with different electron accepters. This objective expands on the first, specifically probing the relationship between the abiotic/biotic interface to elucidate specific metabolic impacts when utilizing EET pathways with varying electron acceptors. In this case, we seek to explore how the phenotypic factors associated with material formation are impacted by changes in culture workflow and genotypic changes enabled by microbial engineering. To achieve this, we used a variety of knockouts, gene expression systems using genetic logic, and modified proteins with binding peptides. This work provides foundational knowledge for the advancement of biological synthesis of inorganic materials using biological electron transfer.Chemical Engineerin
Computational modeling of protein fluorosequencing
Full text linkSingle molecule protein sequencing is a field of new technologies for proteomics with great potential. I developed a machine learning-based interpretive framework called whatprot to analyze data produced by the single molecule protein sequencing technique we call fluorosequencing. whatprot accurately fits and classifies fluorosequencing data using specially customized implementations of k-Nearest Neighbors (kNN) and hidden Markov models (HMM). In particular, the transition matrices of the hidden Markov models are factored to dramatically improve runtime and enable direct parameter estimation with a modified Baum-Welch implementation that we have been unable to find in existing literature. I also compared this parameter estimation method with a method developed by Kent VanderVelden using DIRECT followed by Powell’s method as a sanity check on our implementation. Lastly, I began to explore peptide and protein inference from fluorosequencing data, guiding a master’s degree student, Sophia Zhou, in using expectation maximization (EM) for this purpose.Computational Science, Engineering, and Mathematic
Studies on the cellular localization and spore morphology of Hip1r phosphomutants in Dictyostelium discoideum
Full text linkClathrin mediated endocytosis is a multistep process that facilitates the transport of material across membranes in eukaryotic cells. Cargo molecules internalize in clathrin coated vesicles on the plasma membrane and are transported into the intracellular space. Clathrin and several accessory and adaptor proteins participate in this process. Epsin and Hip1r are two adaptor proteins that provide a structural link between the plasma membrane and the clathrin pits. Using Dictyostelium discoideum as our model organism, previous studies in our lab showed that epsin is required for the phosphorylation and proper localization of Hip1r. Furthermore, cells lacking epsin or Hip1r share defects in spore morphology and the dynamic assembly of clathrin and actin on the plasma membrane. Because cells lacking epsin contain only non-phosphorylated Hip1r, we hypothesized that Hip1r phosphorylation could be related to the phenotypic defects. Having identified three sites of phosphorylation in the Hip1r sequence, we tested both the cellular localization and function of Hip1r phosphomutants expressed in cells lacking Hip1r. A special focus of this project was on residue S417. We found that Hip1r null cells expressing a phosphosilent version of Hip1r (Hip1r [superscript S417A]) were indistinguishable from non-transformed wild type cells in the cellular localization of the protein and spore morphology. Hip1r null cells expressing a phosphomimetic version of Hip1r (Hip1r [superscript S417D]) showed wild type spore morphology, but had a different distribution on the plasma membrane than wild type. A helix breaking mutant (Hip1r [superscript S417G]) rendered the protein non-functional, with round spore morphology and cellular distribution closer to the Hip1rS417D. Expression of the same mutants in epsin null cells had similar effects in localization but failed to rescue the defective spore morphology of epsin null cells. These results show that phosphorylation is not important for spore morphology but it may act as a spatiotemporal switch; non-phosphorylated Hip1r may become phosphorylated to promote dissociation from the clathrin pits. The function of Hip1r is dependent on the integrity of the helix. Finally, epsin has an additional role in the process than mediating Hip1r phosphorylation.Microbiolog
Data-driven modeling and control of high-dimensional and nonlinear systems with application to turbulent flows
Full text linkControl of high-dimensional and nonlinear dynamical systems, such as turbulent flows, which are expensive to model due to their large state spaces, has led to the need for approximate models that are computationally tractable and amenable to classical control algorithms. Such complex and uncertain dynamical systems are often easier to observe and collect data from through simulations or experiments than to approximate and control from first principles. The first part of this work focuses on developing methods for reduced-order and control-oriented modeling of such high-dimensional systems from simulation data. In the second part, data-driven modeling methods are used along with optimal control methods to design a novel flow control scheme that targets large-scale motions in turbulent boundary layers for separation delay. Both parametric and non-parametric methods for dimensionality reduction and modeling are explored. Parametric methods focus on dynamic mode decomposition (DMD) - a data-driven, projection-based model reduction method that approximates the evolution of time-resolved data as discrete-time linear dynamics. The widely-used sparsity-promoting DMD is extended to systems with control inputs and non-sequential data, and its amenability to linear optimal control and estimation methods is demonstrated in flow control applications. Furthermore, for systems with unknown parameters where a single linear system fails to sufficiently capture the dynamics, a flowfield and parameter estimation framework, referred to as multiple-model DMD, is proposed. The second class of reduced-order modeling methods is based on Gaussian Process Regression (GPR). These non-parametric probabilistic models offer a number of benefits, including flexibility, uncertainty estimates, smooth performance degradation in unexplored areas of the state space, and the ability to incorporate prior knowledge through the selection of suitable kernel and mean functions. A method that merges the model-reduction capabilities of DMD with the strengths of GPR in handling nonlinearities and uncertainties in the low-dimensional DMD subspace is introduced for high-dimensional systems. The proposed methods are demonstrated in the optimal control of nonlinear partial differential equations, showcasing the ability to control such systems while accounting for model uncertainties. The data-driven modeling methods developed in this work are demonstrated in a novel and challenging turbulent flow control application. Turbulent boundary layers are dominated by large-scale motions (LSMs) of streamwise momentum surplus and deficit that contribute significantly to the statistics of the flow. This work explores the effect of manipulating LSMs in a moderate Reynolds number turbulent boundary layer for separation delay via well-resolved large-eddy simulations. In particular, a model predictive control scheme based on a reduced-order model of the flow that moves LSMs of interest closer to the wall in an optimal way via a body force-induced downwash is developed. The performance gain of targeting LSMs for separation delay versus a naive actuation scheme that does not account for LSMs is demonstrated.Aerospace Engineerin
Modulation of superconductivity in two-dimensional materials
Full text linkSuperconductivity is a phenomenon where under certain conditions the resistance of a material drops to zero and all magnetic fields inside the material are expelled. This state is incredibly useful since zero resistance means that electricity can flow through the superconducting material without losses. However, most materials only become superconductors at very low temperatures, limiting practical applications. Thus, a considerable amount of research has been conducted to try and discover new superconducting materials and find ways to enhance the superconducting critical temperature in existing superconducting materials; this work focuses on the latter subject. The first chapter focuses on the theory and basic physics of superconductivity. Chapter 2 then provides a brief literature review of superconductivity in two-dimensional materials, focusing on the different methods researchers have used to enhance superconductivity in two-dimensional superconductors such as proximity effects and doping/intercalation. Chapter 3 covers our attempt to enhance the critical temperature of NbS₂ using two-dimensional antiferromagnets; chapter 4 discusses our attempts to modulate the critical temperature of FeSeTe using two-dimensional ferroelectrics. While neither experiment yielded the desired critical temperature enhancement, several interesting phenomena were observed, namely a reduction in the critical temperature of NbS₂ when placed in contact with the antiferromagnet MnPSe₃ and the appearance of a two-step superconducting transition in FeSeTe when placed in contact with the ferromagnets CuInP₂S₆ and CuInP₂Se₆₋. In chapter 5 we report on our attempt to intercalate lithium ions into FeSeTe, with the main phenomena observed being a reduction in the critical temperature and the appearance of a hysteresis loop in the resistance of sample when rotated in a magnetic field. Next, chapter 6 reviews the growth and superconducting properties of Mo-doped NbSe₂, discussing the non-monotonic relationship the doping level has with the critical temperature, critical current, and critical field with a focus on the samples in which these parameters are enhanced. Finally, in chapter 7 we conclude by discussing potential avenues for future research.Electrical and Computer Engineerin
Undergraduate engineering students' moral sensitivity and effects of ethics education
Full text linkEngineering ethics refers to the ethical requirements of engineering as a profession to ensure the fulfillment of public commitments. Due to the crucial role that engineers play in our society, it is important that they have the appropriate level of awareness of social responsibilities. This research seeks to investigate instructional methods for improving engineering students' moral sensitivity. Moral sensitivity is the first component of James Rest's Four-Component Model of Moral Behavior that places an emphasis on social justice. This study's primary goals are to examine (1) the moral sensitivity of undergraduate engineering students and (2) university-level engineering ethics education in this regard. To accomplish this, we used combined qualitative and quantitative analysis methods applied to 52 semi-structured interviews with undergraduate engineering students at two large public universities. The interviews leveraged a story modified from a New York Times article about Hurricane Ida in Southern Louisiana in 2021 to evaluate students’ moral sensitivity based on the disaster’s complex effects on engineered, natural, and social systems. The findings demonstrate that during the interview, undergraduate engineering students were aware of and discussed socioeconomic inequity issues the most amongst issues present in the case study. Students, while having little access to learning about socioeconomic disparities in their curricula, indicated that they relied heavily on student organizations as a source of ethics education for this topic. Institutional contexts of students’ universities and the type of student organizations had significant relationships with moral sensitivity. This study highlights the lack of university curricula in fostering moral growth in engineering students and the potential for extracurricular activities to fill these gaps. Universities and educators are encouraged to expand their ethics education programs by encouraging extracurricular activities for students. Using a contemporary disaster event to measure moral sensitivity was effective in this exercise as students indicated they were aware of and in many instances experienced similar challenges. This approach, using a contemporary disaster case study, identifies opportunities to develop useful techniques for assessing moral sensitivity as well as other components of moral behavior.Civil, Architectural, and Environmental Engineerin
Antecedents and consequences of changes in self-perceptions in middle childhood and adolescence
Full text linkAdolescence is a time of great social, cognitive, and behavioral change, and work to understand how contextual factors influence beliefs about the self, and how those beliefs are manifested into maladaptive behaviors across this period, is important for understanding the important role self-perceptions play in shaping positive health. This dissertation included two studies that examined antecedents and consequences of changes in self-perceptions in middle childhood and adolescence. In a sample of 8,830 children from the Early Childhood Longitudinal Study Kindergarten Class of 1998-1999 (ECLS-K), the first study explored the links between school context (i.e., school climate, school safety, academic press) and changes in self-perceptions (i.e., reading and mathematics competence, peer relations) in middle childhood and early adolescence. Using data from a longitudinal study of 859 adolescents residing in the Northeast, the second study examined how changes in adolescents’ self-perceptions (i.e., scholastic competence, social acceptance, behavioral conduct, global self-worth) were linked with initiation and changes in risky behaviors (i.e., positive and negative alcohol expectancies, smoking behaviors, delinquency, physical and non-physical aggression) across middle and high school. Findings from the first study revealed school safety and academic press were linked with children’s academic and social self-perceptions. More specifically, children that attended schools with greater safety problems in 3rd grade experienced steeper declines in peer relations self-perceptions over the later elementary school years, and children in schools with less academic press reported lower reading competence self-perceptions at that time point. The second study found that steeper declines in youth’s self-perceptions in early adolescence were related to steeper increases in substance use cognitions and antisocial behaviors over the middle and high school years. Youth with increasing middle school behavioral conduct self-perceptions reported increases in negative alcohol expectancies across the high school transition. Students with less steep declines in global self-worth and social acceptance self-perceptions were more likely to report steeper declines in non-physical aggression across adolescence. Similarly, adolescents with more attenuated declines in middle school scholastic competence reported decreasing delinquent behaviors over the later middle school years. Taken together, the results from these two studies suggest that school processes do play a role in youth’s declining self-perceptions, and these declining self-perceptions put students at risk for engagement in unhealthy behaviors, which can carry serious mental and psychical health risks across the life course (Kann et al., 2016). Findings from the current study can inform public health and school policy efforts to prepare youth for healthy, successful futures.Human Development and Family Science
