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The COVID-19 pandemic has underscored the global need for widespread testing. While current gold standard diagnostics return accurate results, they require expensive lab equipment making them difficult to use in resource-limited settings where the burden of disease is greatest. The lateral-flow immunoassay (LFA) is suitable for point-of-care diagnosis in resource-limited settings, but it suffers from limited sensitivity and lack of quantitative results.In Chapter 2, we increase the sensitivity of the LFA by developing a 3D printed casing that incorporates the LFA, dehydrated signal enhancements reagents, and a buffer reservoir to detect SARS-CoV-2 N-protein in serum at concentrations as low as 0.1 ng/mL. In Chapter 3, we address the lack of quantitative results by integrating the conventional LFA with the anisotropic etching of gold nanorods (GNRs) to achieve a multicolor, semi-quantitative assay that can be interpreted by the naked eye to determine digoxin concentrations in a serum sample.

This document contains a series of studies that were inspired by the international activity of firms in the rubber and tire industries. Several aspects of the production and commercialization of tires make these firms face challenges that are essential problems studied in the field of international trade. This industry has been at the core of my family's business practice for decades, allowing for a frequent interaction with many stakeholders in the production, wholesale and retail stages. Such interaction proved this industry to be a rich source of debate that can inform research on international trade.Some of the challenges facing these firms include the imposition of stringent and non-conventional trade barriers, the constant need for new export destinations and, from the production perspective, the volatility of rubber prices. These challenges are addressed in this dissertation. The first chapter uses tire industry data only, while chapters two and three analyze a wider range of industries. Overall, these studies draw insights and policy recommendations for a broad scope of industries. The welfare effects of trade policy crucially depend on import allocations and price effects. In chapter one, I show that these outcomes depend on whether importing firms are matched with multinational suppliers or with single-country producers. I study an antidumping duty imposed by Colombia on the imports of Chinese truck tires. In the data I observe the full network of Colombian importers and global exporters, some of which are multi-origin and some of which are single-origin. Due to the policy, imports of Chinese tires were almost fully reallocated into other origins. Moreover, the bulk of this reallocation was undertaken by importers matched with multi-origin exporters. Estimating a quantitative trade framework to match the reallocations and price changes that I see in the data, I find that under a counterfactual network without multinational suppliers, the increase in prices

Intermediate filament (IF) proteins self-assemble into coiled-coil structures via a central rod domain. Disordered head or tail domains of low complexity flank this region and are required for assembly into unit length filament bundles of eight or more coiled-coil tetramers that anneal end to end to make fully formed filaments. These disordered regions can be responsible for many cellular regulation processes and are implicated in numerous diseases that are associated with mutations in these regions. However, it is difficult to characterize these disordered regions in the context of whole protein and assemblies using high resolution techniques such as crystallography and solution state nuclear magnetic resonance (NMR). To pursue a high-resolution structure of an IF protein, solid state NMR is used to address the limitations of many low-resolution techniques and other high-resolution techniques that are incompatible with large or disordered proteins. The head and tail domains in full-length vimentin and the tail domain fibrils of atypical isoform of tropomyosin Tm1 I/C are investigated using CC DARR, NCACX and NCOCX NMR experiments with isotopic labeling techniques such as paramagnetic relaxation enhancement, segmental labeling, 50:50 mixed labeling and glycerol labeling. The distance restraints from these experiments will contribute to a calculation of a high-resolution structure. Understanding details of the structure of the disordered domains in the tail domain polymers and fully assembled filaments can give insight to the assembly mechanism and function of other intermediate filament proteins.

Pathogenic multidrug resistant bacteria cause a range of serious infections in humans. These bacteria have developed mechanisms to counteract the lethal effects of currently used antibiotics, creating a need for novel therapeutics. Gram-positive bacteria display a wide assortment of cell surface proteins that are important for bacterial survival and host-pathogen interactions. These key surface structures included pili, proteinaceous fibers that assist in microbial survival by mediating adhesion to host tissues and aid in the formation of biofilm. A large number of gram-positive bacterial species assemble pili and append surface proteins to the cell wall using sortase cysteine transpeptidase enzymes. These enzymes link the components of the pilus together via covalent lysine isopeptide bonds which confer enormous tensile strength. This dissertation describes my investigation of the assembly mechanism of the archetypal SpaA-pilus from Corynebacterium diphtheriae. It also describes my contributions to develop small molecule sortase inhibitors that could function as anti-infective agents and my work towards exploiting the activity of sortase enzymes as a protein engineering tool. This thesis focuses on sortase enzymes and can be divided into two major sections: studies to determine how sortases construct pili (Chapters 2-4) and work designed to discover a small molecule inhibitor for the Staphylococcus aureus Sortase A enzyme (Chapter 5). All of the studies have been published in peer reviewed papers, with the exception of work detailed in Chapter 4. Chapter 2 describes the NMR solution structure of the lysine isopeptide bond interface that connects the pilin components of the pilus. This structural information combined with biophysical and cellular analyses led to the formulation of the “latch” mechanism of pilus assembly. Chapter 3 describes an enzyme kinetic study of the sortase enzyme from C. diphtheriae (CdSrtA) which catalyzes the formation of lysine isopeptide b

Seamounts are isolated elevations in the seafloor with circular or elliptical plans, comparatively steep slopes, and relatively small summit areas (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim and Wessel, 2011). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 10,796 new seamounts, expanding the catalog by 1/3. 739 well-surveyed seamounts, having heights ranging from 421 m to 2500 m, were then used to estimate the typical radially-symmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to their height – their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median-absolute deviation. These data were fitted by a 3-parameter Gaussian model that explained 99 percent of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape, we show that VGG can be used to estimate the height of small seamounts to an accuracy of about 270 m.

This dissertation takes up questions incited by encounters with novels by Tim Z. Hernandez, Emma Pérez, Edward P. Jones, Colson Whitehead, Carmen Boullosa, Luis Alberto Urrea, Toni Morrison, and other contemporary authors of historical fiction. While contemporary historical novels frequently receive critical recognition as well as literary prizes, few studies examine the formal aspects of historical fiction as a genre, and those genre-oriented studies that exist tend to ignore works by writers of color. I posit that the absence of an analysis of how Latina/o studies, Black studies, biopolitical theory and its historiography, and historical fiction as a genre intersect makes it difficult for us to perceive how these historical fictions operate. Invested in both the study of contemporary historical fiction as a form and in Latina/o studies, Black studies, and biopolitical theory, my dissertation produces such a synthesis. My project examines how these authors play with genre conventions, particularly through the rejection of realism and the frequent use of experimental literary forms to critique dominant modes of writing history. These authors also emphasize a face-to-face encounter with lost individuals and events of the past. I argue that their works are not antiquarian depictions of how things really were; rather, their speculative and often deliberately anachronistic historical fictions attempt to reframe the past from the perspective of the present. They do so in order to challenge nationalist accounts of U.S. history, read archival documents against the grain, and give voice to individuals and groups who have been erased, forgotten, or otherwise elided by dominant historical narratives. I term these novels and works like them “presentist historical fictions” because they situate analyses or critiques of the past in terms of the present. For these works, anachronism and a “presentist” orientation is not an error due to a faulty understanding of history but a stra

The giant kelp, Macrocystis pyrifera, exists as one species with distinct morphological variants—or “ecomorphs”—in different populations, yet the mechanism for this variation is unclear. One ecomorph (“pyrifera”) has a conical, mound-shaped base while the other (“integrifolia”) has a flattened, elongated base that forms denser kelp beds. My research on the drivers of these distinct phenotypes can inform decisions about management and restoration of kelp populations, as well as selective breeding for certain traits in aquaculture. First, I assessed the patterns of giant kelp ecomorph spatial distribution and genetic differentiation across its Western Hemisphere range. I collected samples from 18 kelp populations in Chile and California and used whole-genome sequencing to evaluate the degree of genetic divergence among populations. My results demonstrate that “pyrifera” and “integrifolia” are genetically distinguishable, yet divergence patterns differ between Northern and Southern Hemispheres. Next, I empirically tested the effect of local environment on giant kelp morphology. I collected reproductive tissue from adult individuals of both ecomorphs naturally occurring at the same location, cultured the spores in the laboratory, and outplanted the resulting baby kelp to the seafloor in a common ocean environment. Weekly monitoring revealed distinguishing morphological features at multiple developmental stages, ultimately culminating in morphologically distinct adults which also differed in reproductive timing. By including a mixed treatment with equal numbers of spores from each ecomorph, I observed the morphologies that emerge in a competitive context. This study also revealed for the first time how the characteristic “integrifolia” morphology forms in nature. Finally, I investigated ecomorph variation in production of alginate, one of the most commercially important chemicals from kelp. I compared alginate yield and composition of “pyrifera” and “integrifolia” popula

Stroke is a devastating neurological condition and the leading cause of lasting motor, sensory and cognitive deficits. These disabilities occur due to the central nervous system’s limited ability to repair itself after injury. Following cortical ischemic stroke, adjacent surviving tissue initiates a limited process of repair characterized in part by axonal sprouting and cortical remodeling. Neurons that undergo axonal sprouting have a unique transcriptional profile, differentially regulating genes in a time and age-specific manner. Among these genes are a number of differentially regulated extracellular matrix proteins, including those that are important in peripheral nervous system regeneration. The extracellular matrix is comprised of a network of proteins that provide support to cells throughout the body. In the central nervous system, the extracellular matrix is implicated in development, ensuring developing axons are guided to the correct destination, modulating signaling pathways that coordinate proliferating cells, and providing a general environment of support. In the mature brain, the extracellular matrix plays key roles in plasticity and support, and undergoes dramatic changes in composition and function following injury. This thesis investigates two differentially regulated extracellular matrix proteins for their ability to induce axonal sprouting and functional recovery following stroke. Matrilin-2 (Matn2) is an adapter protein important for peripheral nerve regeneration but paradoxically is one of the most down-regulated genes in sprouting neurons in young animals. Unique cartilage matrix associated protein (Ucma) is a novel extracellular matrix protein that is one of the most upregulated genes in young animals, but whose expression is almost totally abolished in aged animals following stroke, potentially contributing to the lack of recovery seen as a result of age. Both proteins increased neurite outgrowth in primary neurons, and rescued neurite outgro

We aimed to elucidate the characteristics of HIV molecular epidemiology and identify transmission hubs in eastern China using genetic transmission network and lineage analyses. HIV-TRACE was used to infer putative relationships. Across the range of epidemiologically-plausible genetic distance (GD) thresholds (0.1-2.0%), a sensitivity analysis was performed to determine the optimal threshold, generating the maximum number of transmission clusters and providing reliable resolution without merging different small clusters into a single large cluster. Characteristics of genetically linked individuals were analyzed using logistic regression. Assortativity (shared characteristics) analysis was performed to infer shared attributes between putative partners. 1,993 persons living with HIV-1 were enrolled. The determined GD thresholds within subtypes CRF07_BC, CRF01_AE, and B were 0.5%, 1.2%, and 1.7%, respectively, and 826 of 1,993 (41.4%) sequences were linked with at least one other sequence, forming 188 transmission clusters of 2-80 sequences. Clustering rates for the main subtypes CRF01_AE, CRF07_BC, and B were 50.9% (523/1027), 34.2% (256/749), and 32.1% (25/78), respectively. Median cluster sizes of these subtypes were 2 (2-52, n = 523), 2 (2-80, n = 256), and 3 (2-6, n = 25), respectively. Subtypes in individuals diagnosed and residing in Hangzhou city (OR = 1.423, 95% CI: 1.168-1.734) and men who have sex with men (MSM) were more likely to cluster. Assortativity analysis revealed individuals were more likely to be genetically linked to individuals from the same age group (AIage = 0.090, P<0.001) and the same area of residency in Zhejiang (AIcity = 0.078, P<0.001). Additionally, students living with HIV were more likely to be linked with students than show a random distribution (AI student = 0.740, P<0.01). These results highlight the importance of Hangzhou City in the regional epidemic and show that MSM comprise the population rapidly transmitting HIV in Zhe

In resourced-constrained countries like India, ambitions for environmental protection are often limited by the paucity of funds, and limited capacity within governments to implement transformational programs. But an excessive focus on funding and government action fails to account for several ways in which motivated individuals and communities voluntarily come together to solve environmental problems. Such initiatives might be small and dispersed but can be scaled-up by understanding the underlying structure of incentives and motivations that drive people towards contributing labour and finding collective solutions. My dissertation is motivated by the desire to develop practical insights for fostering greater citizen participation and collective action. In particular, my research is focused on understanding how low-cost and non-monetary incentives motivate individuals towards pro-social and pro-environmental behaviours.The first project examines the factors that contribute to better performance on tasks that managers cannot easily observe. Here, I study frontline employees of an NGO in India who are responsible for improving social development outcomes for rural women. These employees perform two broad categories of tasks – some are observable by their managers and others are more difficult to observe or unobservable. Unobservable tasks like cajoling, convincing, dispute resolution, and emotional labour are critical for frontline work and meeting the NGO’s organizational mission. However they remain unrewarded because they are not observed. Why do frontline employees perform these tasks then? Through a survey of 15,000 rural women and 150 frontline employees, I find that pro-social motivation is associated with improved performance on all types of tasks. On the other hand, I find that monitoring is not associated with improved performance. I also find that understanding the broad principles for action are more important than knowing which specific tasks the manageme

The rapid invasion of Drosophila suzukii (Matsumura) throughout Europe and the Americas has led to an increased reliance on calendar-based broad-spectrum insecticide programs among berry and cherry growers. Relatively few active ingredients (AIs) are currently available for effective D. suzukii management, and studies from multiple growing regions indicate that susceptibility to at least some of these materials is declining. Greater effort is needed to understand the status of susceptibility across field populations and the potential for increased resistance to develop, as well as the possible fitness costs incurred by resistant individuals. However, current bioassay protocols used for resistance monitoring and selection studies (i.e. resistance risk assessments) are labor-intensive and costly, making large-scale studies difficult to conduct. Here, we first present a novel bioassay protocol using larvae that requires little effort or cost to implement beyond what is needed for basic D. suzukii laboratory colony maintenance. We then perform dose-response bioassays using this protocol to identify larval lethal concentrations for three commonly used insecticides (malathion, spinosad and zeta-cypermethrin) in a susceptible population. Finally, resistance risk assessments were conducted using a population of D. suzukii from commercial caneberry fields near Watsonville, CA. We find that five generations of larval selection with a discriminating dose is sufficient to significantly increase both larval (malathion and spinosad) and adult (spinosad) resistance to the target AIs. This approach provides a simple, cost-effective tool for assaying susceptibility of D. suzukii populations to insecticides and for selecting resistant insect lines for resistance management research.

We undertook a prospective temporal study collecting blood samples from consenting pregnant women, to test the hypothesis that circulating extracellular vesicles (EVs) carrying specific non-coding microRNA signatures can underlie gestational diabetes mellitus (GDM). To test this hypothesis, miRNA cargo of isolated and characterized EVs revealed contributions from the placenta and differential expression at all three trimesters and at delivery between pregnant and non-pregnant states. Many miRNAs originate from the placental-specific chromosome 19 microRNA cluster (19MC) and chromosome 14 microRNA cluster (14MC). Further a positive correlation emerged between third trimester and at delivery EVs containing miRNAs and those expressed by the corresponding post-parturient placentas (R value = 0.63 to 0.69, p value = 2.2X10-16), in normal and GDM. In addition, distinct differences at all trimesters emerged between women who subsequently developed GDM. Analysis by logistic regression with leave-one-out-cross validation revealed the optimal combination of miRNAs using all the circulating miRNAs (miR-92a-3p, miR-192-5p, miR-451a, miR-122-5p), or using only the differentially expressed miRNAs (has-miR-92a-3p, hsa-miR-92b-3p, hsa-miR-100-5p and hsa-miR-125a-3p) in GDM during the first trimester. As an initial step, both sets of miRNAs demonstrated a predictive probability with an area under the curve of 0.95 to 0.96. These miRNAs targeted genes involved in cell metabolism, proliferation and immune tolerance. In particular genes of the P-I-3-Kinase, FOXO, insulin signaling and glucogenic pathways were targeted, suggestive of placental connectivity with various maternal organs/cells, altering physiology along with pathogenic mechanisms underlying the subsequent development of GDM. We conclude that circulating EVs originating from the placenta with their miRNA cargo communicate and regulate signaling pathways in maternal organs, thereby predetermining development of GDM.

Compounding, the creation of words by combining two or more words, has long been a topic of interest in various fields of linguistics. This is because compounds seem to straddle the boundary between “words” and “phrases” and have some amount of internal structure (Scalise and Vogel 2010). This raises a question for research on the syntax-prosody interface about how syntactic structure is mapped onto prosodic structure when compound words are concerned. Whereas the fundamental difference between syntactic structure and phonological structure has traditionally been held to be that syntactic structure allows recursion and phonological structure disallows recursion, phenomena like compounding call to question whether disallowing recursion in phonological structure is tenable. If recursion is allowed in phonological structure, however, the issue becomes just how much recursion is allowable. Compound words provide a crucial case for investigation because while they seem to act like words on the one hand, a well-known property of compound words in many languages is that they are infinitely recursive, such that novel compounds can be created productively. Furthermore, compounds have been noted to be able to include phrasal structure such as sentence fragments. It is plausible, then, to expect that recursion may occur in phonological structure when considering such recursive cases and cases in which phrasal structure is involved.Compounds in Japanese have long been observed to exhibit a large variety of compound prosodies. Ito and Mester (2003, 2007, 2018a, 2021) have developed a theory that accounts for the variety of compound prosodies in Japanese by crucially proposing that recursive structure is involved. The theory they develop predicts a set of structures of which a subset is observed in Tokyo Japanese. In this dissertation, I demonstrate that Kansai Japanese exhibits a compound type, which I refer to as the word-phrase compound, which was predicted by Ito and Mester’s

Infertility is a broad disorder with numerous causes including physical, genetic and environmental. While techniques are currently in use to address certain causes of infertility, such as in vitro fertilization and hormonal therapies, there is currently no treatment option for those who are either unable to make or no longer possess viable gametes. Recently, advances have been made in the development of in vitro gametogenesis which, if perfected, promises an option for gametes to be derived from a patient’s own tissue. In order to bring this technique to fruition, further research is needed into the mechanics directing the specification and epigenetic reprogramming of the earliest stage of the germline, the primordial germ cells (PGCs).Mammalian PGCs are specified early in embryonic development and give rise to the entire adult germline. Following specification, PGCs undergo epigenetic reprogramming in order to establish a permissive epigenetic landscape for proper gametogenesis prior to differentiation into either oocyte or spermatogonial progenitors. Any errors in either of these processes can result in the complete loss of the germline and infertility. In order to better understand the mechanisms underlying PGC development, we utilized the in vitro PGC-like cell (PGCLC) differentiation to study human PGC specification and PGC-specific conditional knockout mice to assess epigenetic remodeling. In our studies into specification, we further characterized the differences between mouse and human PGC specification mechanisms. Using CRISPR/Cas9 gene editing we identified that EOMES directs human PGCLC specification, whereas in the mouse this role is accomplished by T. Our exploration into epigenetic reprogramming utilized a Cre/lox driven PGC-specific conditional knockout mouse to assess the role of epigenetic regulatory proteins during PGC differentiation. We used two knockouts, the first being UHRF1 which interacts with DNMT1 to promote DNA methylation maintenance and

Bilingualism continues to increase with more than 350 different languages spoken in the United States, and more than 21% of people over the age of five (approximately 66 million people) speaking a language other than English at home (U.S. Census Bureau, 2020). Many bilingual children in the US speak a minority first language (L1) and English as their second language (L2). For a bilingual child, development of both languages are important for different purposes and contexts. Although the L1 is oftentimes not the language of school instruction in the US, L1 maintenance can lead to closer family cohesion (Park et al., 2012), a stronger sense of identity (Phinney et al., 2001) and overall better health outcomes (Mulvaney-Day et al., 2007). However, bilingual children are at risk for L1 attrition (Pearson, 2007) and this risk is even greater for bilingual children with a language disorder (Ebert et al., 2014).Speech-language pathologists (SLPs) lack resources to assess and treat bilingual children, especially in the L1. Despite the evidence for best practice, SLPs have resorted to an English-only approach to assessment and intervention (e.g., Caesar & Kohler, 2007, Williams & Mcleod, 2012, Arias & Friberg, 2017). Clearly, more work is needed to improve the quality of care for bilingual children. There are limited resources on how to include L1 in assessment and intervention as compared to the L2 (English). This dissertation contributes to evidence-based resources for SLPs to better incorporate the L1 and, in turn, improve service delivery for bilingual children and families.

Rationale: Prevalence of mental health challenges (MHC) in graduate students has increased, which may impair academic performance (AP). Graduate students from racially minoritized groups are a growing demographic who may be faced with greater MHC than those from majority groups. This study examined (1) differences in MHC, discrimination, and AP by minoritized status and sex; (2) interplay of MHC, discrimination, and AP, and (3) roles of resilience, advisor contact, social support in buffering relationships. Methods: Archival data from the Healthy Minds Study 2018-2019 annual survey included self-reported depression severity (PHQ-9), anxiety severity (GAD-7), suicidal ideation (yes/no in past year, SI), mental health burden (diagnoses of depression, anxiety, both), frequency of discrimination (past year), and AP (GPA of A or below A). Analyses included 5,820 graduate students (mean(SE) age = 28(8.2) years, 72% female, and 61% in master’s program), categorized into non-Latino White (n=4032) and minoritized (n=1,788) groups, and used generalized linear mixed models. Results: Minoritized graduate students experienced higher levels of discrimination (F(1574)=13.9, p<.001) and were more often in the lower AP category compared to majority groups (F(5320)=33.3, p<.001); no significant racial/ethnic differences for any MHC were observed (p’s>.05). Women reported higher anxiety (F(3909)=41.9, p=<.001), more anxiety diagnoses (t(1634)=2.84, p=.004), were more likely to experience discrimination once in a while (F(1639)=11.8, p<.001), and had better AP compared to males (F(5320)=15.9, p<.001). Non-Latino White men were most likely to report SI (F(5159)=12.04, p<.001). Moderate levels of discrimination were associated with higher MHCs (p’s <.05). Moderate levels of any MHC were associated with lower AP (all p’s <.005). Race, sex, advisor contact, or social support (all p’s >.05) did not significantly moderate relationships of MHC with AP or discrimi

Entropy is a central concept in physics and has deep connections with Information theory, which is one of the foundations of modern machine learning. Specifically, energy-based models are unsupervised machine learning models that adopt a simple yet general formulation based on the principle of maximum entropy. Three Chapters in my thesis are related to energy-based models, and one Chapter uses a Gaussian Coding rate function, which is also related to entropy.The Boltzmann machine is an energy-based model with strong connections to spin systems in Physics. Boltzmann machines were conceived with bipolar real-valued spin states (up and down) and later generalized to complex valued spin states with unit length. Building on the previous work on complex Boltzmann machines, here we study a generalization where the complex spin states can vary in both, phase and amplitude. Complex Boltzmann machines are closely related to networks of coupled stochastic oscillators and thus can be efficiently implemented in coupled oscillator and also neuromorphic hardwares. Neural Network Energy-based model (EBM) provide a unified framework for a diverse set of functions, such as sample synthesis, denoising, outlier detection, and Bayesian reasoning. However, the downside of EBMs is that their standard training method based on maximum-likelihood requires expensive sampling and is therefore extremely slow. Denoising score matching is an attractive alternative. Inspired by [143], we study a new method of training EBM in high-dimensional space using multiple scales with denoising score matching. The resulting model exhibits strong performance on data generation and inpainting. Another approach that could make training of EBMs efficient is developing an efficient MCMC (Markov Chain Monte Carlo) sampler. The entropy of the proposa distribution of a sampler is an effective measure of the efficiency of the sampler with-out reference to any detail of the target distributions, thus has the potential

Zeolites are crystalline microporous solids composed of corner-sharing, tetrahedrally- coordinated silicate (SiO4) units. The isomorphic substitution of a framework Si atom by an Al atom is charge-compensated by a proton, introducing Brønsted-acidic bridging-hydroxy groups. These proton-exchanged zeolites are used in a large number of processes, including hydrocarbon cracking, isomerization, and alkylation, and conversion of petroleum to trans- portation fuel. In addition to catalytic processes, zeolites can be used as adsorbents for carbon capture, molecular sieving, and pollution control technologies. It is therefore of great interest to predict the impact of zeolite structure and composition on its functional proper- ties to screen new catalysts and improve existing catalysts. Quantum chemical calculations can provide molecular-scale information on zeolite-adsorbate interactions, as well as model the energetic changes and dynamics of important reactions that occur within the channels and pores of zeolite catalysts. However, the application of quantum chemical calculations for the study of chemical reactions occurring in zeolites is made difficult by the lack of reliable methods to generalize the theory beyond zero Kelvin. Modeling adsorption and desorption free energies is particularly troublesome, relying on a subtle balance between enthalpic and entropic terms. While the enthalpic term is becoming ever more accurate through density functional development, the much more temperature-sensitive entropic term remains gener- ally underquantified by frequently-assumed harmonic approximations. The consequence is an inability to computationally replicate experimental observations, such as rate coefficients or equilibrium constants, with chemical accuracy. This work is concerned with a re-examination of harmonic approximations, including quantifying its failures in reaction kinetics of aldol condensation on isolated metal sites, exploring alternative approximation method

Ecosystems in the Western US face a combination of climate-driven threats including warming temperature, drought, and wildfire. How ecosystems respond to these threats is directly relevant for human health, water availability, and carbon storage toward climate mitigation, among other services. However, there currently exist large uncertainties regarding the impact of a changing climate on ecosystems and the reliability of these ecosystems to serve as carbon sinks going forward. In my dissertation I used statistical and machine learning techniques along with large-scale geospatial datasets to explore several questions at the intersection of climate change and ecosystems. In my first study, I developed a new framework for wildfire prediction. I found that vapor pressure deficit at the time of ignition and the density of black spruce trees surrounding ignition sites could be used to predict in Alaskan ecosystems whether a fire would become large. These two pieces of information could help managers triage wildfires to protect vulnerable ecosystems given limited resources. In my second chapter, I quantified the future impacts of climate change on carbon storage in California ecosystems, finding that warming temperatures are likely to drive a net loss of carbon and increase the challenges associated with meeting the State’s climate mitigation goals. Projected losses were greatest for the mid-elevation mountains, northern coast region, and locations of current forest carbon offset projects. This study also revealed the largest sources of uncertainty to future ecosystem projections, most notably the uncertainty in future precipitation for California and uncertainty in tree species migration rates relative to the climate velocity. In my third chapter, I focused on California’s forest carbon offset projects and used remote sensing datasets to assess whether these projects have led to additional carbon sequestration in the 5-10 years since their initiation. Five lines of evide

Holographic dualities like AdS/CFT provide a non-perturbative definition of a bulk theory of quantum gravity in terms of a lower-dimensional boundary quantum field theory. Elucidating quantum gravity thus becomes the challenge of deciphering the dictionary between bulk and boundary physics, and using it to translate basic properties of the latter into lessons about the former. A crucial intermediary between these two realms is the gravitational path integral, which defines the bulk theory in terms of an integral over spacetimes subject to conditions from the boundary theory. Though shallowly understood and rather formal, this piece of technology has thus far been able to provide deep insights into quantum gravity. This thesis is organized in three parts, each focusing on a different basic property in quantum theory and its consequences for quantum gravity through the gravitational path integral: entanglement, causality, and factorization. Part I addresses the emergence of spacetime from entanglement, with a focus on understanding constraints on the entanglement structure of quantum states to posses classical geometries as holographic bulk duals. These constraints can be expressed as linear inequalities and used to define the holographic entropy cone (HEC). A systematic study of the HEC is accomplished by reformulating the holographic computation of von Neumann entropies as a graph-theoretic one, thereby recasting a complicated problem in differential geometry as a purely combinatorial one. This allows to prove important properties of the HEC, devise proof methods and algorithms for constructing it, derive precise relations to other polytope structures, and ultimately pursue a top-down understanding of the HEC from the universal quantum inequality of subadditivity. This part concludes with an exploration of how the machinery involved in the study of the HEC may also be generalized to settings where the von Neumann entropy receives contributions from bulk quantum fiel

Soft robotics represents a shift in engineering design methodology: one that draws inspiration from biology and strives to move away from complex, rigid assemblies of numerous discrete parts and towards soft assemblies comprised of relatively few parts. Proponents of this shift advocate that soft robots will enable safe operation around humans, increased resistance to mechanical damage, operation in hazardous environments, and other functions that are presently inaccessible via traditional (i.e., rigid) robotics. However, bringing these visions to life requires efficient energy systems, soft controllers, and new material systems. This dissertation is an exploration of the myriad ways that light-material interactions can be leveraged to offer innovations in each of these areas. Light serves as the common thread among each of these investigations as it is an accessible form of energy that can be modulated (e.g., by wavelength or intensity) and transmitted (e.g., lasers) or harvested (e.g., sunlight) over long distances with highly tunable spatial and temporal precision. First, we examine the capabilities of soft heat engines, identify their major sources of losses and offer three key insights that soft roboticists can leverage to minimize them (Chapter II). Second, we investigate the photokinetics of a novel class of photoswitches, which constitute the basis for a thermofluidic switch (Chapter III). Finally, we investigate the mechanical properties of bio-inspired stiff-soft multi-material composites that exhibit impressive toughness (Chapter IV). These endeavors at the intersection of materials, mechanics, chemistry, and thermodynamics put forth foundational knowledge that advances the field of soft robotics and also inspires new avenues for meeting the growing demand for adaptive and autonomous materials and devices.

The electrochemical conversion of carbon dioxide is of increasing interest because it offers a means of achieving the generation of value-added products, mitigation of greenhouse gas (GHG)-emissions, and storage of intermittent renewable energy. Aqueous gas-diffusion electrode (GDE) systems for electrochemical CO2 reduction allow for an order-of-magnitude increase in obtainable limiting current densities compared to planar systems due to their inherent increase in CO2 flux. Despite this improvement, aqueous GDEs exhibit significant ohmic resistances that limit the current densities that can be achieved at applied overpotentials, making them impractical for industrial implementation. Membrane-electrode assemblies (MEAs), consisting of humidified gaseous feeds at one or both porous electrodes and no aqueous electrolyte (only a solid ion-conducting polymer or ionomer), can overcome the limitations of aqueous GDEs. Commercial-scale generation of carbon-containing products by means of electrochemical CO2 reduction (CO2R) requires electrolyzers operating at high current densities and product selectivities. MEAs have been shown to be suitable for this purpose. In this dissertation, various MEA designs are considered: the Full-MEA, the H2O-MEA, and the Exchange-MEA. Based on recent modeling and experimental findings presented and cited within this dissertation, the Exchange-MEA emerges as the cell design of choice for ultimately developing an efficient and scalable CO2 reduction device because it enables good ionomer and membrane hydration, as well as high catalyst activity and selectivity. However, despite its outwardly simple design, the MEA is a complicated system that warrants further investigation into its structure and function. There is significant complexity in the CO2R MEA, specifically in its ionomer-based catalyst layers. In particular, the cathode catalyst layer and the multiple phenomena occurring in this region contribute extensively to the rate of CO2R, overa

Soil Taxonomy has proven to be a valuable tool for communicating soils information. Since its inception in 1975, soils have been classified by manually working through criteria established in the Keys to Soil Taxonomy. Navigating this system is often time consuming, prone to error, and requires significant pedologic knowledge and training. Despite recent advancements in soil data storage and analysis, no method currently exists to automatically calculate soil taxa. Here, a new suite of computer algorithms is presented to aid in the determination of soil classification. This tool is designed to take in soil lab and field observations, calculate diagnostic features, and output a final classification to the suborder level for all orders and great group level for Gelisols, Histosols, and Alfisols. Demonstrations prove that this tool can reach classifications that match human-verified results. However, the tool’s accuracy is highly dependent on the quality and completeness of input data. The automated process requires a massive collection of data and observations for every possible classification outcome. Thus, the tool inherently lacks the human/pedologist expertise that would allow a user to simplify the classification process and reduce data demand by only considering regionally relevant taxa. However, the tool can be constrained by users to better mimic expert behavior. This tool shows incredible potential for reducing the burden of updating outdated pedon taxonomy in NRCS databases by processing large amounts of data quickly and accurately. This tool also provides a computer resource that will help make Soil Taxonomy more accessible.

Unlocking one third of plant biomass as a renewable feedstock for fuels and materials depends on the effective deconstruction of the secondary plant cell wall. Over 90% of the native secondary plant cell wall is composed of cellulose and hemicellulose polysaccharides and the lignin heteroaromatic polymers. Deconstruction refers to the processes which digest polymers into desired subunits. Present deconstruction processes are centered on lignin-first extraction to overcome the recalcitrance of biomass: the accumulation of indigestible plant polymers during deconstruction. Recent availability of 13C-enriched plant biomass has enabled the use of solid-state Nuclear Magnetic Resonance (NMR) experiments to refine the model of native cell wall structure in plant tissue in biofuel relevant crops. Solid-state NMR has the advantageous ability to non-invasively probe the structure of the secondary plant cell wall throughout deconstruction pathways and potentially refine methods development for the conversion of plant biomass to sustainable products. The mechanical preprocessing of plant material to solubilize the plant cell wall could introduce recalcitrance at the beginning of the deconstruction pathway. In this research solid-state NMR of Sorghum bicolor stems mechanically preprocessed on the lab scale (by vibratory ballmilling at 30 Hz for 2 and 15 minutes) supported recalcitrant reorganization in lignin and hemicellulose. Recalcitrance related to lignin becoming more rigid, potentially trapping other polymers was supported by a reduction in highly dynamic signals of arabinosyl hemicellulose (correlated in lignin-hemicellulose cross linkages), lignin linkages, and lignin probed in the refocused Incredible Nuclear Enhancement by Polarization Transfer (rINEPT) experiment. Cross polarized experiments targeting rigid polymers supported potential recalcitrance from structural hemicellulose as signals: which persisted after milling at a greater signal intensity than cellulose. R

Search is a fundamental process that illustrates adaptive, goal-directed behavior across multiple scales and contexts: from organisms foraging for food independently or collectively, to networked teams searching for solutions to a problem. Efficient search in an environment or a problem-space requires managing a primary trade-off between exploiting available options, resources, or information and exploring for more. In collaboration with other co-authors, I present experimental and modeling work on how individuals and groups can balance this and other related trade-offs to search efficiently under different constraints, and the interplay between individual and group search trade-offs and behavior. Using a virtual game environment with ecologically-valid constraints, I show that individual humans can efficiently search a previously unknown environment by balancing explorative and exploitative search modes and flexibly adapting this balance based on their task and cognitive constraints (Chapter 2). However, as part of a group, individuals are faced with another essential trade-off: whether to use social information to find resources or search independently. Using an agent-based model of collective foraging, I show that selective use of social information and high levels of independent, explorative search can allow groups to simultaneously take advantage of collective information while maintaining exploration for new resources (Chapter 3). Further, I show that groups may not always evolve to be composed of explorers that maximize a group's efficiency and that competition for resources can result in less efficient groups composed of both explorers and exploiters. I further demonstrate how simple heuristics like area-restricted search can benefit explorers and maximize both individual and group search efficiencies (Chapter 4). Finally, a group's ability to search for resources or solutions over a problem-space can also be affected by patterns of social interactions betwe

In this study, I examine how low-income Hmong, Chinese, and Mexican American students decide to pursue graduate school or not as well as the resources and networks they have for doing so at two public research universities. I ask the research questions: 1) What factors influence low-income and racially marginalized students’ aspirations to pursue graduate school?, 2) How does the formal and informal infrastructure of their educational institution influence their aspirations?, and 3) How are students racialized within this process? Through in-depth interviews with 48 students, I find that these students are independent in their decision-making of graduate school and other post-baccalaureate pathways. Because they are first-generation students and professionals, many of them lack mentors or family members whom they could ask about these pathways. Instead, they decide on what would be best for them and their future. Some do have university mentors who help them and steer them in new directions but very few have that support. Family members, especially parents, do aspire students to do better but do not pressure them into graduate school or certain career paths. I also find that students who are embedded in the research and academic networks are more likely to learn about graduate school than those not embedded in these networks. Lastly, I find that students use a transformational capital process to help them gain access to information, opportunities, and resources for graduate school or other post-baccalaureate goals. They transform the capital through seeking out mentorship, modeling what others are doing, and taking self-initiative to do things themselves. These findings show how underrepresented students create agency and capital in ways that benefit them. They also indicate how universities may fall short in helping these students prepare for graduate school or other post-baccalaureate pathways.

As mobile robots become increasingly prevalent in society, there is a need to design robots to be robust to deployment in natural terrain environments. Natural environments feature diverse sets of challenges that are terrain specific, for example the strategies and robot morphologies needed for movement over sand dunes differs greatly from those needed for movement through dense forest underbrush, or through caves systems. As we seek to extend the range of robots from the human engineered spaces that they mostly inhabit today, new robot designs and movement strategies are needed in order to ensure robust and efficient locomotion. To date, much work has been done on complex sensing and control strategies to compensate for irregular environmental features, but these control strategies often involve expensive components and are computationally expensive. Alternatively, we can use morphology solutions to offload computation to the body of the robot, reducing sensor needs and freeing some computational power to be used for other tasks. To achieve this, we can harness new actuation strategies, unconventional materials, and novel robot appendage designs to modulate robot-environment interactions using only basic control loops. This dissertation describes the design of robot-environment interfaces that to improve physical interactions between a robot and it’s environment in order to enable locomotion through that environment. First, I use granular jamming, a mechanism to achieve variable stiffness, and create shear-strengthened jamming robot appendages that can conform over irregular terrain. I demonstrate that, when internally reinforced with abrasive fibers, these granular jamming feet allow a hexapod robot to walk faster and with more traction over rough terrain. Next, I present a robot featuring directionally compliant telescoping appendages that allow it to passively compress to enter both vertically and horizontally confined spaces without sacrificing performance over

During infection with an acute microbial infection, responding CD8+ T cells give rise to effector cells that provide acute host defense and memory cells that provide sustained protection. An alternative outcome is exhaustion, a state of T cell dysfunction that occurs in the context of chronic infections and cancer. Although it is evident that exhausted CD8+ T cells (TEX) are phenotypically and molecularly distinct from effector and memory CD8+ T cells, the factors regulating the earliest events in the differentiation process of TEX cells remain incompletely understood. Here we performed single-cell RNA-sequencing and single-cell ATAC-sequencing of CD8+ T cells responding to LCMV-Armstrong (LCMV-Arm) or LCMV-Clone 13 (LCMV-Cl13). Compared to CD8+ T cells that had undergone their first division in response to LCMV-Arm (Div1ARM) cells, CD8+ T cells that had undergone their first division in response to LCMV-Cl13 (Div1CL13) expressed higher levels of genes encoding transcription factors that have been previously reported to promote exhaustion, along with Ezh2, the catalytic component of the polycomb repressive complex 2 (PRC2) complex which mediates epigenetic silencing. Modulation of Ezh2 by genetic deletion or retroviral overexpression approaches resulted in decreased or increased expression, respectively, of exhaustion-associated molecules by CD8+ T cells responding to LCMV-Cl13. Taken together, these findings indicate that acquisition of phenotypic, transcriptional, and epigenetic features associated with T cell exhaustion can occur earlier than previously appreciated and raise the possibility that TEX cells may not need to transit through an effector intermediate state.

Mobile robots are commonly used to perform tasks in underwater environments that are difficult for humans to endure, such as exploration, long-duration measurements, or maintenance and repair of underwater structures. Traditional underwater robots are often bulky and disruptive to the environment around it and are often adapted from engineered systems that were designed for operation in air. However, the underwater fluid environment is significantly different from the in-air environment and motivates the development of new robot paradigms specifically to address the challenges that arise from the surrounding water. I have taken inspiration from nature, which has evolved fast and efficient mechanisms for underwater locomotion, to design soft, bioinspired walking and swimming robots. In this work, I have explored several ways to design mobile robots specifically for the underwater fluid environment. To mitigate the negative effects of flow on an underwater walking robot, I created a soft inflatable structure that can be attached to the robot to alter the lift and drag forces on the robot and increase traction in flow. To create locomotion independent of the flow on the robot, I designed soft suction discs and soft linear actuators that enable adhesive-based locomotion. By leveraging interactions with fluid, I created a steerable, shape-changing robot that uses vectored jet propulsion to swim through open water. This work has the potential to enable more efficient locomotion in underwater environments more closely resembling the capabilities of biological systems.

Aluminum Nitride (Al-N) is a white, hydrophilic, thermally-conducting, high-band-gap ceramic with low visible-light-spectrum emissivity. Aluminum Nitride being hydrophilic makes it capable of utilizing capillary action, which means it can be used as a material for wicking. Here we report on the light-induced evaporation of saltwater through a capillary wick composed of drop-cast, loosely bonded Al-N microparticles. With 405-nm LED light illuminating the wick, water evaporation rates are increased to levels above equivalent-energy thermal evaporation rates. We checked for the effect of humidity and crystallization on the trials, and showed how both act as inevitable inhibitors to the evaporation process. Oscillations in the evaporation rate occur as well, caused by a dominance in either crystallization or dissolution in the pores. Our observations point to the potential of a novel solar non-thermal desalination approach.

As an RNA-binding protein, G3BP1 forms ribonuclear protein complexes with free mRNAs and proteins that leads to their separation from solution into biomolecular condensates. As a condensate, these species are removed from the processes of the cell thereby regulating transcription and metabolism which makes G3BP1 a compelling protein to study. In this paper, the foundation for producing isolated, in vitro G3BP1 is investigated for future spectroscopic and biochemical studies. Specifically, the human protein’s sequence was inserted into an E. coli plasmid designed for heterologous expression and immobilized metal affinity purification. Afterward, the affinity tag was cleaved, and the protein was isolated from other contaminants as verified by SDS-PAGE. Since the solution pH and concentrations of G3BP1 and sodium chloride conflicted with those of spectroscopic studies, additional tests were conducted to uncover the favorable conditions. Optimization of the expression, purification, and cleavage of the affinity tag of G3BP1 was successful in isolating and producing large enough quantities of the full-length and intact protein for further experiments but maintaining the viable protein in buffers amenable to solution state NMR proved challenging.

The Standard Penetration Test (SPT) N60 value is one of the most common measurements used to estimate in-situ soil strength and density of sands. During SPT testing, a sampler is attached to a series of rods and driven through the bottom of a borehole. A hammer strike at the surface generates energy that propagates through the rods and into the soil at a sampler head. The amount of energy driving the sampler through the soil is dependent on the hammer type, drill rig, and wave propagation along the rod. However, the magnitude of energy loss as a function of the rod length has not been well characterized at depths greater than 15 meters. This study presents the results of four field studies that attempt to quantify the amount of energy loss that can be attributed to increasing rod length. Two SPT rods were instrumented with a series of strain gauges and accelerometers which yielded force and velocity measurements. Measurements acquired from these sensors were integrated to determine the energy loss along the sampler rod. Drilling was performed to depths of up to 85 meters across various sites and soil types to develop the relationship between rod length and energy loss. The study revealed that approximately 1.4% of the theoretical hammer energy is lost for each 3 m of rod added to the sampler rod.

Researchers have recently explored the effects of gradation on earthquake-induced liquefaction behavior, showing that broadly graded sands have a distinct response compared to poorly graded sands. However, current state of practice applies liquefaction procedures developed from poorly graded sands to liquefiable natural alluvial deposits that are much more broadly graded. The goal of this research is to better understand and quantify the systematic effects of sand gradation on embankment performance and the consequences of liquefaction. The soil’s coefficient of uniformity (Cu) is used as a proxy for gradation as it is a single parameter that affects multiple index and density metrics, which in turn generate distinct responses.The testing series evaluated herein incorporates results from two centrifuge tests, including four different soil types with Cu values ranging from 2 to 12. The centrifuge testing was performed on the 9 m-radius centrifuge at the Center for Geotechnical Modeling (CGM) at UC Davis. The model container was designed with two identical parallel sloping embankments of different gradations, prepared to the same relative density (Dr) of 40%, and configured with arrays of accelerometers and pore pressure transducers. High speed cameras mounted outside the model container recorded embankment plane-strain deformations of dyed soil columns through the clear polycarbonate sidewalls. The models were subjected to four strong motion events of increasing intensity. The results reveal that at the same relative density, horizontal displacements and, strains decreased by over 50% with increasing Cu, whereas settlements reduced to zero and even resulted in net upward displacement due to the strong dilatancy of the broadly graded sands. Despite similar numbers of cycles to liquefaction triggering, the rate of dissipation of excess pore pressures occurred 3-4 times faster for the more broadly graded sands, reducing the duration of soil matrix instability. The accel

E-bike incentive programs are being utilized across the United States to encourage the adoption of active transportation. This study assesses the impacts of three e-bike rebate programs in Northern California using survey results by the three agencies that administered the programs. Through this research, I answer three research questions: “How has e-bike ownership impacted the mode choices, trip purpose, and travel frequency of new adopters”, “How much do e-bike rebate recipients reduce their transportation-related GHG emission?”, and “How did the design of each program impact who was able to participate and the program outcomes?”. To answer these questions, we explored survey responses through descriptive statistics and undertook an estimation of GHG emissions reductions. I decided against more complex data analysis given data quality issues that arose during the cleaning process. Despite that, our analysis revealed changes in travel behavior, car travel replacement, the impact of program designs, and various equity impacts. E-bike recipients reported more regular bike use after getting their e-bike, although their frequency of bicycle use began to decline in the long-term while remaining above previous rates. Respondents also reported high rates of occasional car trip replacement (1-3 times per week and 1-3 times per month), indicating that e-bikes substituted occasional car trips. While there was evidence of regular car trip replacement, the vast majority of e-bike use in our sample was for recreational travel. Given that this data was collected during the COVID-19 pandemic when many restrictions were still in place, these high rates of reported recreational travel were unsurprising. Our GHG reduction analysis estimated a monthly diversion of 12-44 kilograms of CO2 per rebate participant, similar to the GHG emissions reductions observed in other research. We conclude with an equity analysis that explores how program design influenced who participated in these re

Gene regulatory systems contribute to an organism’s fitness by providing a way to dynamically tune cellular responses in response to fluctuating environmental conditions. In this work, we compare the regulatory tuning of genes involved in the catabolism of glucose, maltose, and sucrose in the metabolically versatile halophilic archaeon Halogeometricum borinquense through functional transcriptomic profiling, targeted qRT-PCR, and sugar utilization assays. This work provides the first transcriptomic study for this organism, preliminarily characterizes the unique and shared responses of H. borinquense to single- and multiple-sugar growth conditions, identifies key metabolic regulatory points in the organism, and advances the discussion of archaeal carbon catabolite repression (CCR) by identifying putative targets of CCR that can be probed in future work to further explore underlying mechanisms.Halophilic organisms are also suitable for use in educational settings. Here, I present the development and study examining the impact of a quantitative biology-focused Course Based Undergraduate Research Experience (CURE) on the development of student interest in and self-efficacy for quantitative biology. Students in the course isolate halophilic organisms from environmental samples, quantitatively characterize their growth, and thereafter sequence the genomes of the isolates. We found that although students initially reported high math-biology values, we saw improvements with regard to students seeing higher value in using quantitative calculations in biology and increased interest in pursuing future quantitative experiences (e.g., coursework, occupation).Lastly, I present collaborative research that explores how students express affect when they engage with one another in the context of an online course textbook-based annotation and discussion forum. This work includes efforts to use machine learning to predict student affect and better understand student confusion, to increa

The RNA binding protein, DDX5, is a polyfunctional regulator of gene expression, yet its role in CD8+ T cell biology has not been extensively investigated. In this study, we demonstrate that deletion of Ddx5 reduced differentiation of terminal effector (TE), effector memory (TEM), and terminal effector memory (t-TEM) cells while increasing the generation of central memory (TCM) cells. In addition, DDX5 deficiency also increases the proportion of CD69+ CD103+ tissue-resident memory (TRM) cells and promotes Eomes and CD122 expression in established TRM cells from the small intestine intraepithelial layer (IEL). Single-cell CITE-sequencing analyses revealed increased expression of genes that promote TCM cell differentiation, including Tcf7 and Eomes, in response to deletion of Ddx5. Moreover, we have shown a putative association between DDX5 and components of the polycomb repressive complex 2 (PRC2) in in vitro activated CD8+ T cells indicating that DDX5 may mediate histone methylation at memory gene loci. Together, these findings uncover a role for DDX5 in regulating the differentiation of CD8+ T cell subsets in response to microbial infection.

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

The tripartite motif (TRIM) family of E3 ubiquitin ligases is well known for its roles in antiviral restriction and innate immunity regulation, in addition to many other cellular pathways. In particular, TRIM25-mediated ubiquitination affects both carcinogenesis and antiviral response. While individual substrates have been identified for TRIM25, it remains unclear how it regulates diverse processes. Here we characterized a mutation, R54P, critical for TRIM25 catalytic activity, which we successfully utilized to "trap" substrates. We demonstrated that TRIM25 targets proteins implicated in stress granule formation (G3BP1/2), nonsense-mediated mRNA decay (UPF1), nucleoside synthesis (NME1), and mRNA translation and stability (PABPC4). The R54P mutation abolishes TRIM25 inhibition of alphaviruses independently of the host interferon response, suggesting that this antiviral effect is a direct consequence of ubiquitination. Consistent with that, we observed diminished antiviral activity upon knockdown of several TRIM25-R54P specific interactors including NME1 and PABPC4. Our findings highlight that multiple substrates mediate the cellular and antiviral activities of TRIM25, illustrating the multi-faceted role of this ubiquitination network in modulating diverse biological processes.

Capsular polysaccharides are common virulence factors of extracellular, but not intracellular bacterial pathogens, due to the antiphagocytic properties of these surface structures. It is therefore paradoxical that Salmonella enterica subspecies enterica serovar Typhi, an intracellular pathogen, synthesizes a virulence-associated (Vi) capsule, which exhibits antiphagocytic properties. Here, we show that the Vi capsular polysaccharide has different functions when S. Typhi interacts with distinct subsets of host phagocytes. The Vi capsular polysaccharide allowed S. Typhi to selectively evade phagocytosis by human neutrophils while promoting human macrophage phagocytosis. A screen of C-type lectin receptors identified human DC-SIGN as the receptor involved in macrophage binding and phagocytosis of capsulated S. Typhi. Consistent with the anti-inflammatory activity of DC-SIGN, purified Vi capsular polysaccharide reduced inflammatory responses in macrophages. These data suggest that binding of the human C-type lectin receptor DC-SIGN by the Vi capsular polysaccharide contributes to the pathogenesis of typhoid fever. IMPORTANCE Salmonella enterica subspecies enterica serovar Typhi is the causative agent of typhoid fever. The recent emergence of S. Typhi strains which are resistant to antibiotic therapy highlights the importance of vaccination in managing typhoid fever. The virulence-associated (Vi) capsular polysaccharide is an effective vaccine against typhoid fever, but the role the capsule plays during pathogenesis remains incompletely understood. Here, we identify the human C-type lectin receptor DC-SIGN as the receptor for the Vi capsular polysaccharide. Binding of capsulated S. Typhi to DC-SIGN resulted in phagocytosis of the pathogen by macrophages and induction of an anti-inflammatory cytokine response. Thus, the interaction of the Vi capsular polysaccharide with human DC-SIGN contributes to the pathogenesis of typhoid fever and should be further investigated in th

The advent of high-throughput sequencing has radically altered the theory and practice of biology. Massive volumes of sequence data have necessitated commensurate advances in computational approaches to biological analysis; methods for the assembly of shotgun sequencing data into complete genomes, transcriptomes, and metagenomes have been particularly foundational in enabling downstream study. More recently, sketching methods have allowed classification and comparison to scale to hundreds of thousands of samples. This dissertation extends that work by exploring streaming implementations of several core approaches. First, we introduce a method for single-pass construction of the compact de Bruijn graph, with support for novel methods of dynamic assembly graph analysis. Next, we explore the saturation behavior of streaming sequence sketches, and introduce a novel sketch we call draff which uses Universal k-mer Hitting Sets to represent the shape of assembly graphs. Finally, we introduce several pieces of research software for sequence comparison, transcript annotation, and phylogenetic tree building, which showcase accessible, open access design philosophies. We also introduce the goetia library and toolset for efficient de Bruijn graph algorithms in C++ and Python.

The creation of new carbon–carbon bonds with a high degree of stereocontrol is a powerful strategy employed in targeted synthesis of complex organic molecules. Chiral benzene-fused 5-membered rings represent a significant portion of medicinally relevant molecules. This dissertation describes the construction of benzodihydrofurans, indanes and indolines by C–H insertion reactions of donor/donor carbenes. Chapter one discusses the development of a stereochemcially predicative model for insertion reactions to form benzodihydrofurans. From this study, a predictive model for diastereoselecitivty was established. Chapter two discusses methodology development for insertion reactions to make indolines and indanes. These cores were produced in high yields, with a high degree of stereocontrol and functional group tolerance.

Signaling reactions often originate at the membrane, where competing membrane reactions decide if and how the signal will be passed on to downstream signaling cascades. In some cases, membrane reactions can even lead to polarization of signaling molecules that convey spatial information to downstream pathways. Due to the microscopic size of cellular structures, reactions at the membrane are confined and limited to low molecular copy numbers. Signaling decisions are therefore subject to strong stochastic effects. Understanding how membrane reactions can be influenced by stochastic effects is an imperative step toward deciphering the molecular logic of cellular signaling. However, the space of possibilities remains sparsely mapped. In this dissertation, I aim to expand our understanding of this topic by studying two-dimensional confinement effects on phosphatidylinositol signaling and small GTPase signaling reactions, both of which are ubiquitous membrane signaling mediators. Reducing the size of the membrane can lead to two distinct types of stochastic effects: size-dependent reaction speed and stochastic bistability. The underlying physical mechanism is intrinsically stochastic and cannot be predicted by classical deterministic kinetics. The biochemical requirements and potential cellular regulation of these stochastic effects are further discussed. In membranes with asymmetric shapes, the difference in local stochastic effects that the reaction experiences could emerge as stochastic polarization. This stochastic polarization has no analog in continuum reaction-diffusion systems and exhibits a striking ability to polarize in a consistent direction when constrained to cellular shapes. The requirements of size-dependent reaction speed, stochastic bistability, and stochastic polarization are very basic, and largely met in cellular environments, such that many membrane signaling reactions may be subject to these effects. Overall, the discussions here provide a basis to

Light alkanes, namely ethane and propane, derived from shale gas are an important chemical feedstock for the production of a variety of commodity chemicals. One commercial use of light alkanes is their conversion to aromatics, in particular benzene, toluene, and xylene. This reaction, termed dehydroaromatization, is catalyzed using heterogeneous, Lewis acidic, gallium-exchanged H-MFI zeolites (Ga/H-MFI); however, zinc-exchanged H-MFI (Zn/H-MFI) has also attracted attention for this purpose due to its reported high activity and selectivity, and the lower cost of Zn relative to Ga. Additionally, dehydrogenation of light alkanes is used to manufacture alkenes, namely ethene and propene, which also serve as chemical intermediates. Hydroformylation of light alkenes, that is, their reaction with CO and H2, is an important commercial process for the synthesis of aldehydes, and is carried out using homogeneous Rh catalysts. However, heterogeneous single-atom catalysts also show promise as hydroformylation catalysts. Moreover, dealuminated BEA zeolites (DeAlBEA) containing nests of ≡Si-OM-OH groups (M-DeAlBEA), where M denotes a nonprecious metal atom, are effective supports for atomically dispersed noble metal atoms. This dissertation is centered on developing an understanding of the reaction pathway, or of several individual steps, involved in the dehydroaromatization or hydroformylation of alkane or alkene reactants over zeolite catalysts, and elucidating how the identity and structure of the catalytically active metal center, or of extraframework metal sites in the zeolite support, influence the catalyst reactivity. Understanding these relationships will aid in the development of active and selective catalysts for these reactions and for related alkane and alkene transformations. The first three chapters of this dissertation explore individual steps of the process of alkane dehydroaromatization over H-MFI zeolite catalysts. The first step of alkane dehydroaromatization i

The brain, while being small, low-power, and robust, performs complex computations that we cannot yet replicate or fully understand. Oscillatory signals are ubiquitously observed in the brain across multiple scales, e.g., from individual neural membranes to large-scale averages measured in electroencephalograms. Explaining the computational function and generation of brain oscillations is an active area in neuroscience. Computation with oscillatory signals is also interesting from an engineering standpoint in the field of analog computing. Digital computing represents objects with discrete, Boolean variables. In contrast, analog computing investigates how to use the continuous dynamics of physical systems to perform fast, energy-efficient computing. The potential advantage of analog computing has been hard to realize due to the challenges of working with analog systems and competing with rapid advances in digital computing. Recently, neuromorphic computers and coupled oscillator networks have shown potential as efficient analog computers for certain applications. Thus, motivated by neuroscience as well as engineering, here we explore computations in oscillatory systems that efficiently perform specific functions. Our results demonstrate that models of computation using oscillator neural networks can be used as tools for neuroscience and as the basis of efficient analog computers. Specifically, we investigate inference in feedforward deep neural networks, the analog implementation of associative memories, and optimization performed through the dynamics of coupled oscillator networks. Presumably, the function of a brain critically relies on a combination of continuous and discrete signals, e.g., membrane voltages in neurons and their averages, local field potentials, and action potentials or spikes. Neuromorphic computers that use this combination of signaling are emerging as alternatives to traditional computers for certain tasks. How information is encoded by spikin