VictorJuiz/thesis huggingface dataset:
In singulo study of the transcription elongation by Mycobacterium tuberculosis RNA polymerase and its inhibition by Aroyl-Aryl-Phenylalaninamide, Streptolydigin, and Pseudouridimycin

Tuberculosis (TB) is an airborne disease that kills 1.5 million people and affects ~ 9 million worldwide. Its etiological agent, Mycobacterium tuberculosis (Mtb), persists in a third of the general population. There is also an emergent problem of drug and multidrug resistance in Mtb that creates the need for novel drugs. In the treatment against TB, one target of antibiotics is the prokaryotic RNA polymerase (RNAP), an essential multitask enzyme that catalyzes RNA synthesis during transcription. Rifampicin, the first-line antibiotic anti-TB, binds and inhibits the MtbRNAP.Consequently, combatting TB requires a better understanding of the primary mechanism of transcription by MtbRNAP. In fact, during Mtb transcription, unique and essential factors might affect the MtbRNAP dynamics and regulate its activity. That regulation plays a significant role in the adaptation and pathogenesis of the parasite. Therefore, understanding how these factors and new antibiotics modulate together the MtbRNAP activity will contribute valid information to address TB. To this end, we use a single-molecule approach based on high-resolution optical tweezers to study transcription elongation by MtbRNAP in the context of its DNA elements, transcriptional factors, and antibiotics.

Advisor: Bustamante, Carlos
Efficient Optimization Algorithms for Machine Learning

Behind all supervised learning problems is an optimization problem. Solving these problems reliably and efficiently is a key step in any machine learning pipeline. This thesis looks at efficient optimization algorithms for a variety of machine learning problems (in particular, sparse learning problems). We first begin by looking at a new class of algorithms for training feedforward neural networks. We then look at an efficient algorithm for constructing knockoff features for statistical inference. Finally, we look at $\ell_0$-penalized and constrained optimization problems and a class of efficient algorithms for training these non-convex problems while providing guarantees on the quality of the solution.

Advisor: El Ghaoui, Laurent
An Edition and Translation of the Scholia to Sophocles Antigone 1-581, with Commentary on the Scholia Vetera

This dissertation puts forward a new way of editing scholiastic texts by providing translation and commentary alongside the scholia. Its theoretical approach is to treat the scholia as a text: it aims to guide the reader through each scholion by elucidating paraliterary terminology and interpretative frameworks when they arise. In providing an edition of the scholia, it draws on a wide range of manuscripts which attest to both the scholia vetera and recentiora, and the variety of forms that any given scholion is found to take are reported in full, either in the apparatus or in a separate entry (the latter in the case of a-group manuscripts AUY, which derive from a systematic attempt to abbreviate the scholia vetera)./nThe scholia to Antigone 1-581 provide important contributions to our understanding of the play and its textual transmission: they demonstrate a sustained engagement with literary devices deployed by Sophocles, his portrayal of characters, and ambiguities that stem from complex phrases or from choices between textual variants.

Advisor: Mastronarde, Donald
Restoring justice: Preservice teachers' moral judgments about school discipline

Research on school discipline and moral reasoning have typically been construed in separate spheres. The former has examined the outcomes of specific practices—from punitive to restorative—whereas the latter has examined reasoning about a variety of issues pertaining to justice, welfare, and rights. Moral reasoning about school discipline has yet to be addressed in the research literature. The goal of the research presented here was to examine moral judgments of preservice teachers about possible school responses to harm and rights’ violations in a hypothetical school setting. Participants were 32 undergraduates, from diverse ethnic backgrounds, drawn from a teacher education program at a large public university. Moral judgments were assessed through interviews around issues of psychological harm (bullying on social media), physical harm (hitting), and theft. Participants were asked to make three different types of evaluations. The first was a general evaluation regarding whether the action entailed a moral transgression. The second involved choosing that which they considered best overall, most fair, least fair, most effective, and least effective among a list of four possible school responses (permissive, retributive/punitive, mixed retributive/restorative, and restorative). The third involved an open-ended response to a repeat-offense. Justifications were elicited for each evaluation. A majority of participants endorsed a restorative school response to all three situations as best overall, most fair, and most effective as well as for the repeat offense. A majority of participants considered the permissive response, as least fair and least effective. Additional findings demonstrate that participants’ approach to school discipline was multifaceted and entailed coordination of different features of the situation alongside myriad goals and considerations, such as the intentions of the actor, the nature of the offense, preventing future offenses, and maintaining school standards. The present research provides evidence of the need for inclusion of restorative practices and training in teacher education and professional development programs.

Advisor: Turiel, Elliot
Embodied Study for Collective Liberation: Everyday Performances of Solidarity

The primary purpose of this dissertation is to talk to and with others like myself with race, class, and/or academic privileges who wish to practice solidarity in the name of collective liberation. I respond to Black feminist thinkers such as Toni Morrison, who called for White people to look at their own complexes in the equation of racism, and Fannie Lou Hamer who proclaimed that “nobody’s free until everybody’s free” by demonstrating the spiritual harms of racial capitalism to people who materially benefit from it, and what can be done to heal from these harms. Treating the Poverty Scholars of POOR Magazine in East Oakland as primary theorists of solidarity to analyze four spaces of learning—across race and/or class difference, within sameness, and in mixed company—I practice Poverty Scholarship’s emphasis to share from self-observation, or “I Journalism,” by including stories and observations from people with race, class, and/or academic privilege practicing solidarity. A major contribution of this dissertation is its use of performance theory to address solidarity as an everyday performance that can be practiced and improved, towards our healing and collective liberation. Ultimately this dissertation offers examples of embodied study for people with race, class, and/or academic privilege to divest from racial capitalism’s extractive and spiritually harmful structures towards evolving and ongoing practices of solidarity, ethical relationality, and collective liberation.

Advisor: Wilkins Catanese, Brandi
SKN-1 is a metabolic surveillance factor that surveils amino acid catabolism pathways to control stress resistance

The deleterious potential to generate oxidative stress and damage is a fundamental challenge to metabolism. The oxidative stress response transcription factor, SKN-1/NRF2, can sense and respond to changes in metabolic state, although the mechanism and physiological consequences of this remain unknown. To explore this connection, we performed a genetic screen in C. elegans targeting amino acid catabolism and identified multiple metabolic pathways as regulators of SKN-1 activity. We found that genetic perturbation of the conserved amidohydrolase T12A2.1/amdh-1 activates a unique subset of SKN-1 regulated detoxification genes. Interestingly, this transcriptional program is independent of canonical P38-MAPK signaling components but requires the GATA transcription factor ELT-3, nuclear hormone receptor NHR-49, and mediator complex subunit MDT-15. This activation of SKN-1 is dependent on upstream histidine catabolism genes HALY-1 and Y51H4A.7/UROC-1 and may occur through accumulation of a catabolite, 4-imidazolone-5-propanoate (IP). Triggering SKN-1 activation results in a physiological trade off of increased oxidative stress resistance but decreased survival to heat stress. Together, our data suggest that SKN-1 is a key surveillance factor which senses and responds to metabolic perturbations to influence physiology and stress resistance.

Advisor: Dillin, Andrew
Statistical Mechanics of Lipid Membranes and Data-Driven Reaction Learning

In this thesis, we apply a common set of tools to two problems that compose two separate parts. Shared between them is the use of importance sampling, in which techniques such as Monte Carlo and molecular dynamics evolve a system in a way that respects its physical conditions under the effect of thermal fluctuations, and the use of techniques from the finite element method, in which a combination of integration by parts and discretization on function spaces allows one to turn strong differential conditions into weaker integral ones./nIn the first part, we consider the modeling of biological membranes, complex materials formed of lipids and proteins that serve as interfacial barriers in cells. At length scales beyond the thickness of a membrane, membrane behavior can be understood using a phenomenological model accounting for fluid in-plane and elastic out-of-plane behavior. A discretization of this model is developed using techniques from the finite element method that is evolved using Metropolis Monte Carlo.Compositional energetics are added to model phase separation into liquid-disordered and liquid-ordered domains that are observed experimentally in multicomponent biological membranes. By including the effect of proteins that can induce domains of the thermodynamically disfavored phase it is found that competition between line tension and curvature prevents macrophase separation and leads to stable microphases, providing a possible explanation for nanoscopic domains hypothesized to exist in cells./nIn the second part, we consider numerical methods to find representations of the committor function in rare event processes. The committor function, the probability a configuration will commit to the product state instead of the reactant state, encodes the complete mechanistic information of a process but is costly to compute. Instead, the transition pathway is homogeneously sampled with importance sampling methods in order to solve a variational form of a partial differential equation the committor function satisfies. A neural network is used as a basis function from which optimization informs the neural network parameters. Coupling this process with fitting to empirical estimates of the committor function, the procedure is found to yield accurate estimates of the committor function and reaction rates.

Advisor: Mandadapu, Kranthi K
ESSAYS ON HOUSE ALLOCATION MECHANISMS

This dissertation studies the problem of allocating heterogeneous indivisible goods to agents without the use of monetary compensation when each agent receives at most one good. The three central concerns in designing allocation mechanisms are incentives of the agents, efficiency, and fairness. There are inherent trade-offs between competing notions of the three desiderata in such assignment mechanisms. This dissertation further explores these tradeoffs and constructs novel mechanisms for such settings./nChapter 2 focuses on random assignment mechanisms for the canonical house allocation problem. It shows that any strategy-proof and envy-free mechanism must sacrifice efficiency in a very weak form captured by the notion of contention-free efficiency. The chapter also characterizes a large family of strategy-proof and envy-free mechanisms called Rank Exchange Mechanisms thereby showing the existence of mechanisms apart from the equal division mechanism that satisfy these two properties./nChapter 3 studies the allocation problem in the presence of arbitrary linear constraints when agents exhibit indifferences in their preferences. It proposes the Constrained Serial Rule, a mechanism that is a generalization of the well-known Probabilistic Serial mechanism, and shows that this mechanism satisfies constrained ordinal efficiency and envy-freeness among agents of the same type./nChapter 4 introduces Generalized Hierarchical Exchange mechanisms and shows that they satisfy strategy-proofness, Pareto efficiency, and admit many bossy mechanisms. Using this generalization, the chapter proposes novel mechanisms called priority trading mechanisms. Finally, it also provides a characterization of an important sub-class of Generalized Hierarchical Exchange mechanisms.

Advisor: Ergin, Haluk
Scalable Robot Learning

For robots to perform tasks in the unstructured environments of the real world, they must be able to be tasked with a desired objective in a general way, and learn to perform the desired task quickly if the robot does not already know how to accomplish it. In this thesis, we explore deep reinforcement learning as a solution to enable this vision for scalable learning-based real-world robotics through two main themes: accelerating reinforcement learning from prior data and self-supervised RL. Accelerating RL from prior data or prior knowledge is important for making reinforcement learning algorithms sufficiently sample-efficient to run directly in the real world. We discuss utilizing human demonstrations to accelerate reinforcement learning, using human-designed residual controllers in combination with reinforcement learning for industrial insertion tasks, and algorithms for offline reinforcement learning that can also benefit from a small amount of online fine-tuning.Concurrently, while sample-efficiency of reinforcement learning algorithms is a well-appreciated problem, additional problems arise around agents that can learn from rich observations such as images: in particular, reward supervision and collecting data autonomously. We discuss self-supervised RL through goal reaching with a generative model, allowing agents to evaluate their own success at reaching goals and autonomously propose and practice skills. In the final section, we consider combining offline policy learning with self-supervised practice, allowing robots to practice and perfect skills in novel environments. These directions enable robots to supervise their own data collection, learning complex and general manipulation skills from interaction.

Advisor: Levine, Sergey
Improving Methods to Validate Forest Growth and Quantify Landscape Tradeoffs in Fire Prone Ecosystems

Land system sciences offer techniques to study tradeoffs and solutions for ecosystems that are vulnerable to–and solutions for–climate change. This dissertation investigates tradeoffs for fire, forest, and carbon management in California. Throughout this work, I focus on methods to improve inference using remote sensing, spatial data analysis, and quasi-experimental methods. In the first chapter, I test assumptions about green landscapes as solutions to threats of fire. I use remote sensing and causal inference techniques to test whether green landscapes can alter fire severity and limit fire spread around communities, using golf courses as an example. I find that golf courses act as buffers, reducing fire severity by 49% relative to similar burned vegetation and that golf courses limit fire spread better than other vegetated landscape like regional parks. In the second chapter, I evaluate models used to make management decisions and discuss how errors from these models have landscape and policy implications. I conduct a model validation study for the widely used Forest Vegetation Simulator and find a significant overestimate in predicted growth rates relative to observed growth. I find that these errors in carbon growth rate are most pronounced on national forest lands, for stands aged 50-100 years old, forests managed as reserves, and for the least productive forest sites. Model assumptions of higher productivity than the observed growth will bias management away from more proactive silviculture techniques and can overestimate their impacts as a natural climate solution. In the third chapter, I document how land use policies for forests create tradeoffs that may undermine policymakers’ objectives. I explore how operationalizing carbon management in forests creates tradeoffs between sustaining high levels of biomass, and carbon, and removing biomass to promote forest structures that are more resistant to disturbance. I find that forests managed for carbon credits are not changing fuels like forests managing for fire risk but are having changes in vegetation greenness as if they were. Throughout this work, I demonstrate how the application of rigorous methods of measurement and estimation can support improved decisions that deliver climate and ecosystem benefits for California.

Advisor: Butsic, Van
Investigating Sperm-Specific Proteins and Physiology to Inform Development of Non-hormonal, Unisex Contraception.

Pharmaceutical contraception is still nonexistent for half the world’s population – people with testes – and nearly half of all pregnancies are still unintended, even in the United States. These unintended pregnancy rates remain stubbornly high despite increasing access to existing contraception, which suggests that new contraceptives are needed to give every person the tools to make independent, voluntary decisions about their own fertility. Spermatozoa are the only human cells that are created in one body and fulfill their function in another, so with proper pharmaceutical properties, the same sperm-impairing drug could be delivered either in bodies that produce or receive sperm, thereby creating the world’s first non-hormonal, unisex contraceptive. To enable this goal, we need a better biological understanding of these unique and understudied cells, to identify which sperm physiological processes and sperm-specific proteins are the most targetable for pharmaceutical inhibition. The studies in this dissertation improve the foundational biological understanding of multiple processes necessary for a spermatozoon to fertilize an egg, from energy metabolism and motility to sperm-egg fusion, using diverse approaches such as in vitro cell physiology, molecular biology, electrophysiology, and structural biology. The first of these studies found that human sperm can maintain their ATP content in the presence of small-molecule uncouplers, even though uncouplers can depolarize the mitochondrial and plasma membranes and impair sperm motility; these results also suggest that the FDA-approved uncoupler niclosamide might be a useful ingredient in spermicide gels. Another study described here revealed that the human sperm-specific protein TMEM95 has a binding partner on the egg membrane and that this interaction is involved in sperm-egg fusion but not binding. The final project produced extremely high-resolution cryo-electron tomography structures of the human and mouse sperm flagellar axonemes, which revealed several species-specific and asymmetrically distributed protein structures connecting various microtubule pairs, with likely significance in the creation of flagellar motility. Together, these findings set the stage for future investigations which may identify new ways to target sperm-specific proteins for the production of novel non-hormonal unisex contraceptives.

Advisor: Lishko, Polina V
The No-Space Between Brazil and Latin America: Utopia, Democracy, and Solidarity in Brazilian Literature and Culture

This dissertation broadly focuses on Inter-American relations and the concept of political utopia. It argues that Brazilian literary and cultural productions that portray progressive revolutionary pursuits ambivalently produce the no-space, or "ou-topos," where Brazil meets Latin America. Brazil's major national essays written by authors such as Eduardo Prado and Sergio Buarque de Holanda developed an argument concerning Brazilian identity and difference by highlighting the positive consequences of its peaceful imperial past compared with Latin America's early tumultuous attempts to implement democracy. Tracing two competing historical narratives, I argue that the Brazilian national essays have overlooked progressive revolutionary events such as the Revolução Praieira (1848), the War of Canudos (1897), and the Intentona Comunista (1935) and made them secondary/regional to Brazilian identity even though they were portrayed in the work of major Brazilian writers and artists Castro Alves, Euclides da Cunha, Jorge Amado, and Glauber Rocha. While highlighting the efforts to abolish slavery and implement a more egalitarian and democratic system, these writers and artists established a network of solidarity with Latin American intellectuals who shared a similar ideology. These connections contradicted the narrative of difference and isolation in the Brazilian national essay. For the republican and abolitionist poet Castro Alves, this ideological connection with Latin American intellectuals is apparent in his references to the Andean region as a space of desired democracy. In the case of Da Cunha, the same link is observed in his unfulfilled desire to write a book on Latin America, and in the letters he exchanged with Latin American intellectuals. Amado's connection with Spanish-speaking countries led him to build friendships with Pablo Neruda and Diego Rivera. For Rocha, this proximity is visible in Terra em Transe (1967). Through the voices of these writers and artists, I follow the thread connecting the abolitionist and republican condoreiro Romantic school (to which Castro Alves belonged) to Latin America across time.

Advisor: Slater, Candace
Tarica, Estelle