UC Irvine

As new materials and manufacturing techniques are developed to suit the needs of several key industries, creative methods of designing microstructures to tailor mechanical behavior must be explored to control deformation and prevent failure. Additionally, the underlying mechanisms that dictate such control must be well understood. To this end, this dissertation includes three distinct investigations: a study on the underling mechanisms that control the microstructure of samples fabricated with ultrasonic vibration-assisted directed energy deposition; an exploration of the role of phase state and composition on the room-temperature mechanical behavior of entropy stabilized oxides; and an analysis of the role of microstructure and phase state on the high-temperature deformation of entropy stabilized oxides. In the first study, ultrasonic vibration (UV) was applied in situ to directed energy deposition (DED) of 316L stainless steel single tracks and bulk parts. For the first time, high-speed video imaging and thermal imaging were implemented in situ to quantitatively correlate the application of UV to melt pool evolution in DED. Findings show that UV increases the melt pool peak temperature and dimensions, while improving the wettability of injected powder particles with the melt pool surface and reducing powder particle residence time. Through in situ imaging we demonstrate quantitatively that these phenomena, acting simultaneously, effectively diminish with increasing build height and size, consequently decreasing the positive effect of implementing UV assisted (UV-A) DED. Thus, this research provides valuable insight into the effects of UV on DED melt pool dynamics, the stochastic interactions between the melt pool and incoming powder particles, and the limitations of build geometry on the UV-A DED technique. In the second study, we investigate the influence of these secondary phases on the mechanical behavior of the (CoCuMgNiZn)O transition metal ESO (TM ESO). TM-ESOs of equimolar, Co deficient, and Cu deficient compositions were fabricated, heat treated to form secondary phases, and characterized. Room-temperature indentation was used to measure the hardness and elastic modulus of as-sintered single-phase and as-heat-treated multiphase bulk samples. As the atomic fraction of secondary phase increases, equimolar and Co-deficient TM ESO harden then soften, and Cu-deficient TM ESO continuously hardens. Hardness trends were analyzed by evaluating strengthening mechanisms, indicating that hardness is significantly influenced by the interactions between dislocations and secondary phases. The elastic modulus varies as a function of composition and quantity of secondary phases but falls within a range of values predicted by a composite model. Changing composition influences the hardness and elastic modulus of as sintered single-phase TM-ESOs due to changes in cation-dislocation and cation-cation interaction energies. Overall, our findings indicate that the entropic phase transformation can be manipulated to tailor the room-temperature mechanical properties of TM-ESOs. In the third study, we begin to address the high-temperature deformation behavior of TM ESOs. The microstructure and phase state of TM-ESOs were varied. Fine-grained and coarse-grained TM-ESOs were deformed at increasing loads over a range of elevated temperatures in both their single-phase and multiphase states. Stress exponent values were determined for all conditions, indicating that fine-grained and coarse-grained TM-ESO deformed superplastically. In fine grained TM-ESO samples, the secondary phases did not have a significant effect on the stress exponent values. At low deformation temperatures, coarse-grained TM-ESO samples had higher stress exponent values than fine-grained samples, and the stress exponent increased with the presence of secondary phases. At high deformation temperatures, the stress exponents for single phase and multiphase coarse-grained samples decreased. The drop in stress exponent for the coarse grained samples at higher deformation temperatures indicates a temperature-induced switch in the deformation mechanism from grain boundary sliding to solute-drag creep. Overall, this work demonstrates that microstructure and phase composition of TM-ESO can be used to tailor the high temperature deformation of TM-ESO. This dissertation highlights that unconventional methods can be used to tailor the microstructure of polycrystalline metal alloys and oxide ceramics to control their mechanical behavior. Future studies examining the mechanical properties of individual secondary phases in TM-ESOs, the kinetics of the reversible phase transformation of TM-ESOs, and the reversible phase transformation and room-temperature mechanical behavior of nanocrystalline TM-ESOs would be meaningful additions to the studies included in this dissertation.

We study the capacity of the K-star-graph private information retrieval (PIR) problem introduced by Sadeh et al. The problem is so labeled because the storage graph corresponds to a star-graph with K edges (corresponding to the edges) and K + 1 vertices (corresponding to the servers): K messages are separately (one each) stored in K dedicated servers and meanwhile a universal server stores all K messages. While it is one of the simplest PIR settings to describe, the capacity CK of K-star-graph PIR is open for K ≥ 4. We study the critical K = 4 setting, for which prior work establishes the bounds 2/5 ≤ C4 ≤ 3/7. As our main contribution, we characterize the exact capacity of 4-star-graph PIR as C4 = 5/12, thus improving upon both the prior lower-bound as well as the prior upper-bound. The main technical challenge resides in the new converse bound, whose non-trivial structure is deduced indirectly from the achievable schemes that emerge from the study of a finer tradeoff between the download costs from the dedicated servers versus the universal server. A sharp characterization of this tradeoff is also obtained for K = 4. The connection of the PIR problem to caching and interference alignment indicates that our result may provide insight for these problems as well.

Background: Deficits in cerebrovascular reactivity (CVR) to experimentally-induced hypercapnia are observed in older adults with mild cognitive impairment (MCI), suggesting a role for CVR as a biomarker of vascular contributions to MCI. Spontaneous CVR at rest has also been studied without experimental induction of hypercapnia. The present study sought to determine whether spontaneous CVR in whole brain and medial temporal regions is associated with MCI and memory impairment.

Methods: Independently living older adults without history of clinical stroke, dementia or other major neurological or psychiatric disorder were recruited from the community. Participants underwent clinical interview, comprehensive cognitive testing, venipuncture for plasma Alzheimer’s disease (AD) biomarkers, and brain MRI. Pseudo-continuous arterial spin labeling MRI quantified whole brain perfusion during 5min of rest with simultaneous monitoring of end-tidal CO2 (ETCO2) levels. Spontaneous CVR maps were quantified as %∆CBF/∆ETCO2 at rest.

Results: A total of 161 older participants (mean age = 69.5 years; SD = 7.6; age range 55–89 years; 34.8% male) were studied. Spontaneous whole brain CVR was negatively associated with associated with age, [B = -0.41, 95% CI (-0.65, -0.17), p = 0.00096, but not MCI. Analysis of medial temporal regions revealed that spontaneous CVR in the parahippocampal gyrus (PHG) was significantly lower in participants with MCI compared to those who were cognitively unimpaired, t (148) = 2.90 95% CI (1.93, 10.46), p = 0.005. Participants with amnestic MCI also showed significantly lower spontaneous PHG CVR relative to cognitively unimpaired, t (134) = 3.055 95% CI (2.59, 13.29), p = 0.005. Decreased spontaneous PHG CVR was associated with worse memory composite z-scores, B = 0.26, 95% CI (0.56, 3.12), p =0.005. Findings remained consistent after controlling for AD biomarkers and vascular risk factor burden.

Conclusion: Deficits in spontaneous CVR at rest are observed in older adults with MCI, specifically in the PHG, and particularly in those with memory impairment. These findings further implicate medial temporal microvascular dysfunction in cognitive decline and memory impairment among older adults at risk for dementia, independent of AD pathophysiologic change. Spontaneous PHG CVR may be a useful marker for vascular contributions to memory decline.

Marine bacterial metabolism of oxygen (O), carbon (C), nitrogen (N), phosphorus (P), iron (Fe), and silica (Si) are essential for elemental cycling on our planet. Because of this, understanding the spatial distribution of marine bacteria, how these bacteria are functioning and interacting with physical processes, and what environmental conditions impact them is essential for understanding current biogeochemical dynamics and for predicting how these dynamics might change in the future. My dissertation research is divided into three projects that aimed to connect large microbial genomic datasets with geochemical and physical processes in open ocean and coastal ecosystems. These studies consisted of a biogeographic survey of the Indian Ocean, an in-depth analysis of mesoscale eddies, and a coastal study at Newport Beach, California.In the first chapter, I linked broad-scale spatial patterns of marine bacteria with geochemical and physical dynamics across the eastern and western Indian Ocean. To achieve this, I incorporated 16S rRNA gene sequences from 465 samples with in-situ CTD data, nutrient concentrations, particulate organic matter (POM) concentrations, stoichiometric ratios, and remote sensing data of sea surface height and geostrophic currents. This extensive analysis revealed 23 unique community structures within the Indian Ocean. It highlighted the southeastern gyre as the area with the largest gradient in bacterial alpha-diversity, identified that the Indian Ocean microbiome was dominated by a core set of taxa, and linked changes in community structure with transitions in physical and geochemical conditions. Importantly, the study identified distinct community structures within mesoscale studies, which served as the foundation for my next chapter. In the second chapter, I investigated the role of mesoscale eddies in shaping bacterial community composition and function across various Indian Ocean regions. This project integrated genomics (16S rRNA and short-read metagenomes), nutrient concentrations, and remote sensing data of 26 eddies of varying age, intensity, and size. Within this study, mesoscale eddies were viewed as physical disturbances that altered communities through dispersal and/or environmental selection. It was found that the origin of the eddy (i.e., coastal waters versus open ocean waters) played a pivotal role in determining how dispersal and environmental selection affected microbial community outcomes. In the third chapter, I examined the interactions between pollutants, marine bacterial diversity and function, and ecosystem recovery within the coastal waters of Newport Beach, California, following the Orange County oil spill. This investigation incorporated short-read metagenomic data, flow cytometry data, polycyclic aromatic hydrocarbon (PAH) concentrations, nutrient concentrations, and POM concentrations. The acute bacterial response to the oil spill was assessed by comparing metagenomically derived taxonomic and functional trends to a 10-year time-series. Notably, there was a rapid and anomalous decline in the abundance of the dominant picophytoplankton, Synechococcus. This decline was coupled with an increase in sulfur-oxidizing and potential hydrocarbon-degrading heterotrophic lineages. There was a lagged response in taxonomy and function to peaks in total PAHs. One week after peaks in total PAH concentrations, the largest shifts in taxonomy were observed, and one week after the taxonomy shifts were observed, unique functional changes were seen. This pattern of response was observed two times during our sampling period and corresponded with the potential resuspension of PAHs. Thus, the impact of the oil spill was temporally extended and demonstrates the need for continued monitoring long after initial exposure.

Sociologists of education have a longstanding interest in studying the relationship between schooling and inequality in society. While we know education matters for who gets ahead, we still know relatively less about the processes and mechanisms behind this relationship. In my dissertation, I focus on higher education as a key site where stratification plays out and examine various ways in which individuals are ranked and sorted relative to one another. Conceptually, I extend insight into the positional (i.e., hierarchical) and relational (i.e., network) dynamics of postsecondary schooling. I draw upon sociological theories relevant to the social stratification, organizations, and social networks literatures. Through both regression-based and social network analyses, my dissertation adds to our understanding of differential sorting into and through the first year of college. In so doing, I shed additional light on how and why students end up at distinct institutions, as well as the causes and consequences of the social networks that emerge once they arrive on campus.

This dissertation outlines three studies investigating linguistically diverse students’ (including bilingual and monolingual students) language use and self-efficacy in writing. Bilingual students are those who speak a language other than English, including students with various levels of English proficiency and language experience. The first two studies (Study 1 and Study 2) examine the relations of language use features to writing performance, while the third study examines which dimensions of writing self-efficacy are more crucial for educators to target. The Direct and Indirect Effects Model of Writing (DIEW; Kim & Graham, 2022) and the Writer(s)-Within-Community serve as the guiding theoretical frameworks for this dissertation. According to DIEW and WWC, many linguistic, socioemotional, and cognitive skills and knowledge are needed to write proficiently. The present dissertation focuses on language skills and self-efficacy in writing. Study 1 is a meta-analysis that aims to investigate the relations between syntactic features and writing performance systematically. Previous studies have inconsistent results regarding the relation between syntactic complexity and writing quality and the moderating roles of measurement (e.g., genre, syntactic complexity measures) and writers’ (i.e., language proficiency, age) in the relation. The results show a weak relation between syntactic features and writing performance, and the relation can be moderated by measurement and writers’ characteristics. Study 2 examines the predictive accuracy of machine learning in predicting multiple dimensions of writing quality (structure, evidence use, historical thinking, and language use) in secondary history source-based argument writing. In addition, based on linguistic features that contribute to the prediction of writing quality, the study further examines language use patterns in bilingual and monolingual writers with different EL designations. Results indicated that machine learning models can explain a higher variance in writing quality scores. English Learners (EL) performed lower on lexical sophistication and global cohesion, but bilingual students designated as proficient in English performed equivalently with their monolingual peers. Using the same data as in Study 2, Study 3 examines secondary students’ writing self-efficacy. The study examines (1) in which dimension(s) students feel less efficacious in writing, (2) which dimension(s) is more predictive of students’ writing quality, and (3) to what extent students of different EL designations perform differently. Results showed that students had the lowest writing self-efficacy in self-regulation, which was the dimension that was significantly associated with writing quality. In addition, EL students and students designated as Reclassified Fluent English Proficient (RFEP) had lower writing self-efficacy in ideation and revision, whereas their writing self-efficacy in self-regulation was equivalent to students who were initially fluent in English since they entered school. These findings suggest that the text-based linguistic features can provide information about students’ language skills and areas of improvement in an authentic writing context. Students, in general, had lower writing self-efficacy in self-regulation, which is a critical skill to navigate through the writing process. Students who had the experience of being an EL may have lower writing self-efficacy in certain aspects but had equivalent writing self-efficacy in self-regulation.

The lower urinary tract, consisting of the bladder and urethra, functions to facilitate storage and voiding of urine while maintaining low intravesical pressures to prevent renal damage. A variety of congenital and acquired pathologies including spinal cord injury and urethral stricture disease can lead to anatomical or functional obstruction of the lower urinary tract which can elevate urinary storage pressures and ultimately cause renal deterioration. In addition, malignant conditions such as bladder cancer and developmental abnormalities including bladder exstrophy can also result in tissue loss or malformed urogenital tissues which can compromise organ continuity, disrupt normal micturition, and in some cases negatively impact patient fertility. Surgical correction of urogenital defects is conventionally accomplished with autologous tissue grafts derived from extragenital sources, however this strategy is encumbered by donor site morbidity, limited tissue availability, and failure to restore native tissue functionality. Tissue engineering approaches utilizing acellular biomaterials composed of decellularized tissue grafts or synthetic polymers either alone or seeded with ex vivo expanded primary or progenitor cell sources have been previously explored as alternatives to autologous tissue grafts for urogenital reconstruction in both preclinical studies and clinical trials. Despite the successful performance of these constructs in non diseased animal models, none of these technologies have been adopted into widespread clinical practice due to poor functional outcomes, abnormal tissue formation, and serious adverse events encountered in human studies. Clinically viable biomaterial configurations must possess optimal structural, mechanical and degradative characteristics sufficient to provide for initial defect reinforcement, but allow for gradual scaffold dissipation and subsequent formation of site-appropriate functional tissue (constructive remodeling). Moreover, validation of prospective tissue engineered implant designs in diseased animal models which mimic underlying patient pathology is necessary to accurately evaluate graft potential prior to clinical translation. Alterations in the regenerative capacity of host tissues can occur as a function of disease or past injury and can ultimately influence implant functional performance. Therefore, advancements in urinary tract reconstruction are dependent on new scaffold designs which can promote host regenerative responses in diseased settings and overcome deficiencies related to autologous tissue deployment.The focus of my thesis is centered on 2 main areas of investigation: (1) creation of novel preclinical models of urinary tract disease for medical device testing and (2) evaluation of bi-layer silk fibroin (BLSF) grafts for urogenital tissue reconstruction. My first goal will be accomplished by developing and characterizing new rabbit and swine models which respectively recapitulate clinical phenotypes of Peyronie’s disease and urethral stricture disease. My second goal will involve testing the efficacy of protein-based, BLSF grafts to serve as urinary conduits for management of bladder cancer following radical cystectomy as well as biological substitutes for repair of focal vaginal defects and long urethra stricture defects in male and female porcine models.

Taking a relational and systemic approach, this dissertation offers theoretical, methodological, and empirical advancements in understanding the social forces that drive or inhibit human migration. We consider migration flows among geographical areas as a network system, analyzing its dynamics using the exponential-family random graph models (ERGMs) and simulation methods. Chapter 2 grapples with the computational hurdle for modeling valued/weighted networks using ERGMs. We propose and implement an efficient parallelizable subsampled Maximum Pseudo-Likelihood Estimation (MPLE) scheme, which enables fast and accurate computation of ERGMs for big valued networks with high edge variance. The comparative simulation experiments further show whether and how the performance of existing computational approaches vary by the network size and the variance of edge values, providing guidelines for choosing and tuning those methods for different use cases. Chapter 3 applies the implemented method to study intercounty migration in the United States (U.S.), whose migration rates have declined for decades and reached a historical low. We found a pattern of "segmented immobility," where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. We also propose a "knockout experiment" framework to quantify the impact of segmentation on population immobility. The chapter reveals the social and political cleavages that underlie population immobility in the U.S., bridging the scholarly domains of (im)mobility, segregation, and polarization. Motivated by debates about California’s net migration loss ("California Exodus"), Chapter 4 examines the scale of and the mechanisms behind the migration-induced population redistribution among U.S. states. We combine ERGMs, knockout experiments, and a protocol for functional form visualization to understand the complex effects of political climates, housing costs, racial dynamics, and urbanization. The chapter offers an analytical framework for migration-induced population redistribution and demonstrates how generative statistical models can provide mechanistic insights beyond hypothesis-testing.

My dissertation, “An Asset-Based Approach to the Mental Health of Undocumented Students” is a three article-based dissertation that examines how undocumented students experience their mental health and promote their psychological wellbeing in an exclusionary and unpredictable socio-legal context. I address the following questions: 1) How do undocumented students experience emotional distress and psychological wellbeing? 2) How do undocumented students practice agency to promote psychological wellbeing? and 3) To what extent is engaging in acts of resistance associated with emotional distress, measured as anxiety and depression? To answer these questions, I conducted 66 in-depth interviews and analyzed a unique survey data set composed of 1,277 Californian undocumented college students. I argue that, despite a multitude of legal barriers and vulnerabilities, undocumented students practice agency to protect their wellbeing. While previous work has highlighted undocumented students’ poor mental health outcomes, I take an asset-based approach to illustrate how undocumented college students take action to manage their mental health and promote their psychological wellbeing. Throughout, I advance the concept of acts of resistance to capture undocumented students’ agency in navigating and contesting their structural marginalization.My first chapter establishes the process through which undocumented students experience coexisting feelings of emotional distress and psychological wellbeing. I build on my interviewees’ descriptions of their mental health as a rollercoaster to illustrate how this process is shaped by both the socio-legal context and students’ agency. I find that as students confront their legal realities, they experience emotional distress which leads them into downward motions (i.e., spirals and plunges). These downward motions are comprised of students’ feelings of legal insecurity and uncertainty. However, students promote their psychological wellbeing by employing their agency which in turn allows for them to experience upward motions. Student agency encompasses students drawing strength from their undocumented immigration status, and envisioning opportunities for themselves to experience upward motions or psychological wellbeing. Yet, these emotions collide because of structural constraints of the socio-legal context, leaving students feeling like they are in a perpetual rollercoaster ride. Contrary to prior research which has focused on adverse mental health outcomes, this manuscript sheds light on the multidimensional nature of mental health and shows how it is shaped by socio-legal barriers. My second chapter examines the agentic actions employed by students to resist structural constraints and foster their psychological wellbeing. I find that students utilize undocumented student programs on their campus, engage in political actions, expand their critical consciousness, and practice self-care to mitigate the negative consequences associated with their legal status. These actions promote wellbeing by demystifying the constraints imposed by exclusionary immigration policy and helping students reframe their negative thoughts. I conceptualize these efforts as acts of resistance, or actions taken to resist structural inequality and socio-legal barriers and theorize these as a unique dimension of the stress process. Whereas stress process theory focuses on coping actions as a critical pathway to protect mental health by buffering against the effect of stressors, acts of resistance are unique actions taken to mitigate legal vulnerabilities. My third chapter is a quantitative analysis that examines undocumented students’ political engagement, critical consciousness raising, and undocumented student programming usage to see if these acts of resistance are associated with mental health outcomes. I use 2020 survey data collected by the UC Collaborative to Promote Immigrant and Student Equity (UC PromISE), for which I served as a graduate student researcher allowing me to add measures of interest. Findings suggest that students who report higher political engagement and critical consciousness raising report higher depression and anxiety symptomatology. This work highlights that undocumented college students’ efforts to navigate and contest their marginalization harms their mental health outcomes. The complete manuscript is published in Society and Mental Health; I am first author with co-authors Dr. Flores Morales who aided with data analysis at the revision stage and Dr. Enriquez who is PI on the UC PromISE study.

Perceptual decisions are accompanied by a level of confidence that tends to track our decisional accuracy. However, this correspondence can be dissociated in noisy and atypical environments or in some clinical populations. This raises an important question: what are the neural computations of perceptual metacognition if their output can diverge from perceptual decisions themselves? In a recent paper, it was argued that tuned inhibition — i.e., the degree to which a neuron is inhibited by neighboring neurons with opposing tuning preferences, which varies from neuron to neuron — is a crucial part of the underlying mechanism. In this dissertation, we aimed to investigate the neural mechanisms underlying perceptual metacognition by seeking evidence of tuned inhibition via functional magnetic resonance imaging (fMRI). We first explored how we might validate the tuned inhibition model using fMRI data, by simulating the activity of ‘voxels’ of different compositions in the presence of evidence for and against a perceptual decision in a decision and confidence task. We showed that it is possible to quantify how a voxel’s level of tuned inhibition dictates its predictive power for confidence judgments, thus providing support for use of these stimuli and analyses in fMRI data to validate the model. The observed relationships from our model simulations were then applied to human fMRI data. We identified evidence of the model within decision-making and density-sensitive regions of the brain using fMRI. Finally, we provided further evidence supporting tuned inhibition as a model of confidence by decoding high- versus low-confidence responses on a trial-by-trial basis from voxels within higher order regions, such as the dorsal prefrontal cortex.