VictorJuiz/thesis huggingface dataset:
Mechanistic Analyses of Peroxisome-Related AAA+ Motors

AAA+ ATPases hydrolyze ATP to perform diverse cellular functions. While these complex machines have been the subject of biochemical studies for years, the details on what these motors contribute to cellular processes, how these motors perform work on their substrates, and the identify of their substrates remains unknown for many AAA+ ATPases. Specifically, it is unclear how the activity of two such enzymes, Pex1/Pex6 and Msp1, brings about their cellular functions. It is known that Pex1/Pex6 is essential for peroxisome biogenesis, but the identity of Pex1/Pex6’s substrate is unknown. Furthermore, an understanding of how Pex1/Pex6 processes this substrate, and how this processing contributes to peroxisome biogenesis, is lacking. Msp1 is a poorly studied AAA+ ATPase embedded in the outer-mitochondrial membrane. It is responsible for extracting mislocalized tail-anchored proteins from this surface, but the mechanism of action it uses for extraction and how it recognizes mislocalized proteins remain unclear. Furthermore, although Msp1 has been shown to be present at peroxisomes, details regarding how its function may differ from its mitochondrial duties are unexplored. For my dissertation, I designed and established in vitro assays to study the biochemical mechanisms of both Pex1/Pex6 and Msp1./nTo better understand Pex1/Pex6’s role at the peroxisomal membrane, I focused on characterizing its primarily peroxisomal binding partner, Pex15. I solved the crystal structure of the soluble core domain of Pex15, and exploited its biochemical features to develop an assay that measures its mechanical unfolding. Furthermore, I demonstrated that the Pex1/Pex6 ATPase is capable of unfolding the soluble Pex15 domain, constituting the first direct evidence that Pex1/Pex6 unfolds substrates. I utilized these assays to define both, motor mechanism and substrate requirements to establish that the intrinsically disordered regions of Pex15 play crucial roles in Pex1/Pex6 interaction and engagement./n/nAlthough Pex15 functions as a peroxisomal tether for Pex1/Pex6, it occasionally mislocalizes to the outer-mitochondrial membrane, where it becomes a substrate for Msp1. Msp1 is believed to function as a hexamer; however, it fails to oligomerize when the N-terminal transmembrane domain is removed, presenting a challenge to in vitro characterization of this motor. I developed a system to study Msp1 activity in vitro by fusing it to a hexamerizing scaffold to encourage oligomerization. I used this system to characterize Msp1’s mechanism of action, provided direct evidence that it is capable of unfolding proteins by processive threading, and showed that it is promiscuous in substrate selection. Lastly, I demonstrated that Pex3 directly inhibits Msp1 unfoldase activity. Overall, my work helped elucidate AAA+ motor function and further our understanding of peroxisome biogenesis and proteome quality control at the peroxisome and mitochondria.

Advisor: Martin, Andreas
Investigation and Control of Ultrafast Magnetic Phenomena

Spintronic devices have shown a lot of promise in low power and non-volatile memory applications. However, conventional spintronic devices are limited by the speed of equilibrium magnetization reversal. For more than two decades, the field of ultrafast magnetism, wherein magnetic processes in (sub)picosecond timescales are triggered by the ultrafast non-equilibrium heating of magnetic thin films with femtosecond laser pulses, has provided us with the tantalizing prospect of controlling magnetism in unprecedentedly fast timescales. This dissertation will detail the research conducted over the last 6 years in understanding ultrafast magnetic phenomena, and in controlling and integrating them with conventional spintronic processes to realize fast, non-volatile spintronic devices./nThe first part of the dissertation will focus on work done to understand the fundamental limitations of some spintronic and ultrafast magnetic phenomena. This will include experiments on detecting the current induced spin accumulation due to the spin-orbit effects in heavy metals directly on the heavy metal surface using an optical technique called the magnetization-induced second harmonic generation (MSHG). Insight into the dynamics and timescales of current induced spin accumulation in conventional spin-orbit torque (SOT) devices gained from these experiments will help understand the speed limitations of such devices. The dissertation then focuses on the ultrafast helicity-independent all-optical switching (HI-AOS) in ferrimagnetic GdFeCo and GdTbCo alloys. These experiments shed a light on the underlying mechanism of such a process, and unravel the complex interplay of exchange coupling, elemental damping and other parameters in ultrafast magnetization switching events. The upper limit for the pulse duration of optical excitation that triggers HI-AOS, which has important implications when it comes to integrating these processes on-chip, is also studied./nThe second part of the dissertation will introduce ways to build up on the experimental results of the first part, thereby moving towards the integration of ultrafast magnetic phenomena into conventional spintronic devices. Experiments performed to extend the ultrafast HI-AOS capabilities of GdFeCo to Co/Pt multilayers by controlling the exchange interaction between these two films are presented. This is of technological significance because HI-AOS had thus far only been reported in Gd-based ferrimagnetic films, which are not very attractive for device integration due to their ferrimagnetic nature. Co/Pt multilayers, on the other, are ferromagnetic and are well suited for application in spintronic devices. Then, the ultrafast control of magnetism by picosecond heat current and electrical current pulses will be introduced. Finally, the dissertation will present recent results on demonstrating the deterministic spin-orbit torque switching of a Co/Pt ferromagnet by short, 6 ps electrical pulses.

Advisor: Bokor, Jeffrey
New Algorithms for Three Combinatorial Optimization Problems on Graphs

In this dissertation, we study three NP-hard combinatorial optimization problems phrased on graphs. For each problem, we introduce one or more new algorithms tailored for the problem./nThe first problem is the minimum k-terminal cut problem. Given a graph and a distinguished subset of vertices (``terminals''), we would like to remove the minimum weight set of edges that disconnect the terminals. We present a fixed-parameter-tractable branch-and-bound algorithm for the problem. We also show that (k-1)-stable instances of k-terminal cut can be solved optimally by calculating k-1 minimum isolating cuts: minimum cuts which separate one terminal from the rest. This analysis is tight: there exist (k-1-\epsilon)-stable instances for which the isolating cuts do not return the optimal solution./nThe second problem concerns valid distance drawings of signed graphs. A valid distance drawing of a signed graph in R^k is an embedding of the graph in R^k such that, for every vertex, all its positive neighbors are closer than its negative neighbors. We address the question of finding L(n), the smallest dimension such that every signed graph with n nodes has a valid distance drawing in R^{L(n)}. In general, we show that \floor{\log_5(n-3) + 1} \leq L(n) \leq n-2. We introduce a new algorithm for computing L(n) and then compute L(n) exactly up to n=7. We offer a conjecture for the value of L(n) for all n./nThe third problem is the maximum online perfect bipartite matching problem with i.i.d. arrivals. We introduce seven algorithms in two classes: three ``flow-guided'' algorithms and four ``evaluation-guided'' algorithms. Two of the evaluation-guided algorithms can be interpreted as derandomizations of flow-guided algorithms. We prove that at least three of the algorithms are 0.5-competitive, which is the best possible competitive ratio. The seven algorithms can be partially ordered: for some pairs, one may be expected to perform at least as well as the other on all instances. Through theoretical and empirical results, we determine all but four of the pairwise relations in this partial ordering.

Advisor: Adler, Ilan
Quantum Chemistry Meets X-Ray Absorption Spectroscopy: Theory and Applications to K-Shell Excitations in Molecules

In this thesis, we present our work in pursuit of a novel method for computing core-excited states of molecules: non-orthogonal configuration interaction singles, or NOCIS. NOCIS is a black-box variant of NOCI which, when applied to K-shell excitations, uses A different core-ionized determinants for a molecule with A atoms of a given element to form single substitutions. NOCIS is a variational, spin-pure, size-consistent ab initio method that dramatically improves on standard CIS for core excitations by capturing essential orbital relaxation effects associated with the core hole, in addition to essential configuration interaction. We also describe a more computationally efficient modification of NOCIS called one-center NOCIS (1C-NOCIS) for open-shell molecules which is intermediate between NOCIS and the computationally less demanding static exchange approximation (STEX). These methods are efficiently implemented and applied in the context of various core-excited state types, namely molecules initially in singlet, doublet, and triplet states represented by a single determinant wavefunction. Finally, this thesis also presents an overview of collaborative excited-state work in various settings, including a foray into valence excited states.

Advisor: Head-Gordon, Martin
Accelerating Numerical Simulations with Deep Learning

In many industrial applications, numerical simulations allow us to perform virtual experiments through computers by solving differential equations. However, it often requires us to put large amounts of computational resources, because solving differential equations is computationally expensive and time-consuming in general. This could be more critical when we need to run heavy simulations in real-time applications./nThis work introduces a hybrid approach on how to accelerate numerical simulations by applying the fundamental idea of deep learning to the numerical simulations. Deep learning and numerical simulation have proposed two different ways for engineers and scientists to predict and understand the complex behavior of systems. While numerical simulation is a traditional technology that relies on the fundamental laws of nature, deep learning is an emerging technology that is highly data-driven./nIn the first half of this dissertation, I will review a basic background on deep learning and nonconvex optimization to help readers easily understand the fundamental concepts. In the second half, I will introduce the two engineering problems on accelerating numerical simulations with both reduced simulation costs and desirable accuracy. The first problem is in rapid process control of multiphase flowing foods. The second problem is to optimize the tool path in the Selective Laser Sintering process.

Advisor: Zohdi, Tarek I.
Applications of Near-Term Quantum Computers

Quantum computers exist today that are capable of performing calculations that challenge the largest classical supercomputers. Now the great challenge is to make use of these devices, or their successors, to perform calculations of independent interest. This thesis focuses on a family of algorithms that aim to accomplish this goal, known as variational quantum algorithms, and the challenges that they face. We frame these challenges in terms of two kinds of resources, the number of operations that we can afford to perform in a single quantum circuit, and the overall number of circuit repetitions required. Before turning to our specific contributions, we provide a review of the electronic structure problem, which serves as a central example for our work. We also provide a self-contained introduction to the field of quantum computing and the notion of a variational quantum algorithm./nWe begin the main body of the thesis by studying a generalization of the standard unitary coupled cluster ansatz. We present a sparse version of unitary coupled cluster and show that it can accurately represent the ground and excited states of small molecular systems using fewer quantum gates than the full unitary coupled cluster. We then introduce a strategy for representing molecular ground states as linear combinations of parameterized wavefunctions, allowing for a tradeoff between the number of operations required for each circuit and the number of circuit repetitions. We provide circuit primitives that allow for the efficient measurement of the required matrix elements between these wavefunctions. Subsequently, we show how the cost of estimating the energy of a quantum chemical wavefunction on a near-term quantum computer can be dramatically reduced by using a factorization of the two-electron integral/ntensor. Furthermore, we explain how this measurement strategy helps mitigate against errors during state preparation and measurement. We then present a Monte Carlo version of a classical algorithm for calculating the partition function of two-dimensional lattice models that shows a similar kind of tradeoff between two resources, albeit in a different context. Finally, we show how ideas based on tensor networks can inform the design of quantum circuits for machine learning tasks. We argue that this application is especially tolerant to noise and present numerical data substantiating this argument. We conclude by explaining explicitly how our work addresses the problems of the limited resources available to near-term quantum computers and offering some optimism about the future of the field.

Advisor: Whaley, Katherine B.
Belief Revision in Children and Adults

Learning is a complex process that requires integrating new evidence with existing beliefs. This task is relatively straightforward when the evidence is consistent with and thus supported by existing beliefs. In such instances the evidence serves to confirm and hence strengthen beliefs. It becomes less clear, however, when the evidence is inconsistent—that is, when the evidence directly conflicts with one’s prior knowledge. Will learners revise their beliefs to reflect the new evidence or will they instead maintain their existing beliefs, perhaps ignoring the conflicting data as anomalous? How might their responses differ as a function of development, and what might this reveal about the mechanisms that support learning (and relearning)?/nThe current dissertation addresses these questions by asking how children and adults respond to evidence that violates their beliefs. Chapter 1 begins by describing a surprising developmental pattern: younger children are sometimes better than older children and adults at using statistical evidence to revise their beliefs. I then provide a Bayesian explanation for these findings, noting that younger learners may outperform older ones because they are intrinsically more flexible in their search for alternative hypotheses. Chapter 2 then provides empirical evidence that young children revise their higher-order beliefs by attending to the statistical strength of the data and weighing it against their prior belief. When shown a single event that conflicts with their belief, 4- and 5-year-olds largely maintain their higher-order belief. However, when shown several events that each support the same alternative hypothesis, children reliably update their belief to account for the new data. Chapter 3 then replicates these findings with even younger children, demonstrating that 3-year-olds similarly update their beliefs when the counterevidence is strong enough to outweigh the prior belief. Notably, children’s ability to update their beliefs is independent of their executive ability to deliberately change their behaviors in response to new goals and demands. Chapter 4 then contrasts children’s epistemic flexibility with adults’ inflexibility, exposing a striking developmental decline in adults’ sensitivity to new data. In particular, these findings highlight the robustness of adults’ beliefs by demonstrating that adults continue to endorse a weakly supported belief even after they are primed or incentivized to reason more broadly and creatively. Finally, Chapter 5 discusses the implications of this work and suggests future directions./nTaken together, these experiments contribute to our emerging understanding of belief revision by demonstrating how, as knowledge is accumulated and as beliefs are strengthened, our sensitivity to new evidence decreases and our willingness to revise declines. This pattern, although at times counterproductive, may be adaptive as learners transition from a strategy of exploration to one of exploitation.

Advisor: Gopnik, Alison
Managing disease-related amphibian declines using genomics

Rates of emerging infectious diseases are increasing globally. Impacts of emerging diseases on wildlife populations have been identified as major drivers to species declines and extinctions. Species loss due to disease has necessitated prioritizing disease mitigation in wild populations. In particular, amphibians have been disproportionately affected by the disease, chytridomycosis, caused by a fungal pathogen Batrachochytrium dendrobatidis (Bd). Decades of amphibian species being on the brink of extinction has accelerated the need to interrogate amphibian-Bd interactions. In this dissertation, I focus on an emblematic example of amphibian-Bd dynamics. The mountain yellow-legged frog (Rana muscosa/sierrae), a high alpine species of the Sierra Nevada of California, have declined across more than 90% of their historical range with Bd being a major driver to their decline. While many populations have been lost, there are some remaining frog populations persisting even with Bd present. This devastating loss of a species juxtaposed to potential hope for recovery presents an excellent opportunity to investigate host-pathogen dynamics as well as refining conservation strategies to bolster remaining populations. In this dissertation, I explore this host-pathogen system by integrating a genomic perspective to both species and disease management. For Chapter 2, I focus on a region of the frog species range that is under intensive conservation efforts and used genetic samples from both extant and extirpated populations to inform management actions. In Chapter 3, I take a pathogen perspective and use similar genetic tools in a comparative approach to investigate underlying evolutionary histories of Bd in the Sierra Nevada of California and Central Panama. In Chapter 4, I expand our genomic efforts to the entire mountain yellow-legged frog species range to create an explicit framework for species recovery and management. Together, my work weaves topics of conservation genetics, disease ecology, and evolutionary biology to highlight the use of genomics for applied conservation and builds novel frameworks for addressing species declines in the face of persistent threats.

Advisor: Rosenblum, Erica Bree
Probabilistic models of DNA sequence and complex trait variation under adaptation

Adaptation is a fundamental process in evolution, which leads populations to better survive and reproduce in changing environments. A key insight of population genetics has been that present-day genetic variation is affected by past, and even ongoing, adaptations. Recent expansion of DNA sequencing has afforded us access to genetic variation from now up to nearly millions of individuals. In this dissertation, I develop modeling and inference for DNA sequence variation in order to identify the genetic bases of adaptations, with a focus on evolution of complex traits in humans. First, I review population genetics approaches for detecting natural selection, and argue that these approaches have been hamstrung by the intractability of the so-called ‘full likelihood’ of selection (Chapter 1); I then develop a method to tractably compute this likelihood via importance sampling of the ancestral recombination graph (ARG), enabling us to find targets of selection too subtle to detect with previous methods (Chapter 2); I extend this likelihood method to jointly model DNA sequence variation and complex trait variation (via summary statistics from genome-wide association study, aka GWAS) to quantify the amount of selection acting on a complex trait, and to account for pleiotropy/correlated response in these estimates (Chapter 3); Finally, I present a method to detect polygenic adaptations in the presence of population structure, which explicit accounts for uncorrected stratification and other sources of error in the GWAS via an approach similar to LD score regression (Chapter 4).

Advisor: Nielsen, Rasmus
Liaison of curves and bundles

This thesis is devoted to the study of two central objects in algebraic geometry - algebraic curves and vector bundles - through the unifying lens of liaison theory. The liaison theory of curves originated in the work of M. Noether in the late XIX century, and has since become an instrumental tool in the classification of space curves and the study of Hilbert schemes in general. In Chapter I, we develop a biliaison theory of sheaves and prove several main results in analogy to those from the liaison theory of codimension two subvarieties. In Chapter II, we use the liaison of curves on surfaces with ordinary singularities to prove multiple results about homological invariants of general projections of curves into projective three-space. In Chapter III, we apply biliaison of sheaves to the study of vector bundles. A general program is outlined to describe the moduli of bundles on a projective variety of irregularity zero through a stratification approach. We take the first steps by classifying bundles in the biliaison class of the zero sheaf on projective spaces and describing the moduli. In particular, our results give a description of the moduli of bundles on the projective plane.

Advisor: Eisenbud, David
Literacy in development discourse and practice: Comparative studies in Indonesia

This dissertation study investigates the complexities of the relationship between literacy and international development in Indonesia. I focus on the Literacy Boost program from the international non-governmental organization Save the Children, and I explore how the program was differentially implemented and received in two sites: mega-metropolitan Jakarta, and Belu Regency, a largely rural region in Eastern Indonesia. Drawing on thirteen months of ethnographic research conducted between 2016-2017, I identify both intended and unintended outcomes of the Literacy Boost program.I highlight the tension between the core Literacy Boost program components, standards, and practices, and the program’s heterogenous manifestations in the two sites. I find that in each site, Literacy Boost had to confront systemic challenges at schools before and while implementing literacy programming. Whereas in Jakarta, Literacy Boost focused on issues related to school-based management, in Belu, it focused on banning corporal punishment in classrooms. Thus, Literacy Boost produced different short- and long-term impacts across the two sites, none entirely predictable nor coincidental. My analysis discerns how these distinct trajectories of development unfolded and were experienced by an array of Literacy Boost stakeholders. Furthermore, I explore how Literacy Boost programming was shaped by the exigencies of international development practice, including evaluation requirements and time constraints. These shared constraints notwithstanding, I show how in each site Literacy Boost was modulated by the distinct linguistic context, infrastructure and accessibility, and local notions of progress and development. In demonstrating how a literacy intervention has been taken up in situated contexts of teaching and learning, this dissertation contributes to the understanding of how international development processes succeed and falter across geographic, linguistic, and cultural contexts.

Advisor: Sterponi, Laura