VictorJuiz/thesis huggingface dataset:
Neurons alter endoplasmic reticulum exit sites to accommodate dendritic arbor size

Nervous systems consist of neurons with a wide range of form and function. This neural diversity is critical for nervous system function. How neurons achieve such diverse morphologies has been a central question in neuroscience since its inception. Neurite outgrowth is regulated on several levels. First, a neurite's particular shape is determined by guidance receptors on the neurite surface interacting with environmental ligands to inform neurite pathfinding. Second, intracellular cytoskeleton is coordinated by these guidance receptors to generate the force required for neurite progress through the sub-strate. Third, a neuron must coordinately scale its biological processes, such as biosyn-thesis and secretion, to supply sufficient building blocks, including lipids and surface proteins, for its expanding surface area. Considerable work has been done to elucidate mechanisms of neurite pathfinding and force generation. However, relatively little is known about how neurons scale biosynthetic and secretory machinery to accommodate cell membranes of vastly different size. Using the nematode Caenorhabditis elegans (worm), we observed that neurons with larg-er plasma membranes displayed higher numbers of the early secretory organelles, Endo-plasmic Reticulum Exit Sites (ERESs). ERES function is required for dendrite growth and ERES size and number are intimately linked with the biosynthetic and secretory flux of non-neuronal cells. However, how neurons of different size scale secretory machinery, such as ERESs, to meet vastly differing growth requirements is not known. Using timelapse microscopy, we show that the worm's largest neuron, PVD, has espe-cially high ERES numbers, which are initially established during neuron birth via asym-metric cell division. We show that maintenance of PVD's high ERES number during neu-ron development does not require normal dendrite outgrowth, but depends on cell fate transcription factors, nutrient availability, and master growth regulator ceTOR/LET-363. Together these factors coordinately maintain ERES number and somato-dendritic growth throughout PVD development. Our data suggest a model in which cell fate transcription factors drive both asymmetric cell division and subsequent nutrient sensor levels to set the biosynthetic output that specifies ERES number and cell size. This work takes a first step toward identifying the mechanisms regulating neuronal bio-synthetic and secretory output, and highlights several advantages and challenges of pur-suing novel mechanisms for cell-specific size and organelle scaling in multicellular or-ganisms

Keywords: Medicine.
Advisor: Shen Kang
Clandinin Thomas R. (Thomas Robert)
Luo Liqun
Skotheim Jan
A new approach to the diffusive limit of the random schrodinger equation

In this thesis we introduce a new approach to the diffusive limit of the weakly random Schrodinger equation, first studied by L. Erdos, M. Salmhofer, and H.T. Yau. Our approach is based on a wavepacket decomposition of the evolution operator, which allows us to interpret the Duhamel series as an integral over piecewise linear paths. We relate the geometry of these paths to combinatorial features of a diagrammatic expansion which allows us to express the error terms in the expansion as an integral over paths that are exceptional in some way. These error terms are bounded using geometric arguments. The main term is then shown to have a semigroup property, which allows us to iteratively increase the timescale of validity of an effective diffusion. This is the first derivation of an effective diffusion equation from the random Schrodinger equation that is valid in two and more dimensions.

Keywords: Humanities and Sciences.
Mathematics.
Advisor: Ryzhik Leonid
Chatterjee Sourav
Papanicolaou George
A new model : the mammalian brain is derived from two separate regional progenitors

The mammalian brain is a truly remarkable organ, and its mature form is the culmination of a complex sequence of developmental biology. A key component of this complexity lies in the fact that the brain is not a homogeneous tissue, but rather a modular system with distinct sub-regions that work in close concert with one another. In the developing organism, the first identifiable brain sub-regions are the forebrain, midbrain, and hindbrain. All sub-regions of the brain are derived from the ectoderm germ layer of the embryo, yet the precise embryonic cues for inducing these regions, and the developmental timing of those cues, remains incompletely understood. In this thesis, I have employed two complementary approaches to elucidate the first steps of brain development and sub-regional patterning. First, I used mouse embryonic lineage tracing to label and follow populations of cells in the neural ectoderm in vivo. I found that cells labeled in the posterior domains of mouse gastrula-stage embryos exclusively gave rise to the hindbrain, but not forebrain or midbrain. These data revealed that regional identity may be established concomitantly with neural identity. Second, I used human pluripotent stem cells (hPSCs, which have the developmental potential to become any cell in the body) to precisely determine the sequence of signals required to generate embryonic forebrain, midbrain, and hindbrain. Surprisingly, hPSC-derived anterior neural ectoderm (aNE) was restricted to generate forebrain and midbrain, and hPSC-derived posterior neural ectoderm (pNE) was restricted to hindbrain fates, consistent with my in vivo data. I thus propose a revised model of brain development in which the neural ectoderm is already restricted to specific sub-regions of the brain, which has substantial implications for our ability to model human brain development using hPSCs.

Keywords: Medicine.
Advisor: Loh Kyle
Beachy Philip
Monje-Deisseroth Michelle
New perspectives on online prediction and decision making

The problem of predicting the future has a long history, and it has become ever more important in recent years, as uncertainties arise with evolving geopolitical crises and quickly unfolding pandemics around the globe. Can we accurately predict the future and make near-optimal decisions, even if the world is complex, behaves arbitrarily, and may not satisfy any of the strong traditional assumptions in the literature? This thesis studies several different settings of sequential prediction and decision-making, especially when distributional assumptions are absent: 1. Sequential calibration: Facing a sequence of intrinsically noisy outcomes, to what extent can probabilistic forecasts be well-calibrated? We develop a novel "sidestepping" technique to prove the first non-trivial lower bound on the calibration error in sequential binary prediction. 2. Selective prediction of arbitrary data: Can we accurately predict the unseen data in the future, even when distributional structure of data and advice from domain experts are absent? We formulate models of selective prediction and learning and study the optimal accuracy of a forecaster that may choose to predict over a carefully selected interval. 3. Optimal stopping with costly probing: Can we make high-stake decisions optimally even when obtaining information is difficult and associated with high opportunity costs? We define and study the problem of online pen testing, which abstracts the challenge in this decision-making problem

Keywords: Engineering.
Advisor: Valiant Gregory
Charikar Moses
Tan Li-Yang
Nonlinear mechanisms in encapsulated micromechanical resonators

Resonant Micro-electromechanical systems (MEMS) are an important technology that underlies many of the resonant sensors and oscillators used in scientific research and commercial products. Studying core properties of the resonators that make up these systems allows for future improvements in the performance of existing systems and enables new applications of resonators. Capacitively coupled readout is a common method to measure the motion of a MEMS resonator; in this work, I consider the system of an encapsulated mechanical resonator capacitively coupled to a lossy readout. I demonstrate that losses induced due to coupling are highly dependent on the parasitic capacitance in the readout and the properties of the readout amplifier, and can reduce the quality factor of the mechanical system by a factor of 15. Nonlinear properties of resonant systems affect their applications as resonant sensors and oscillators in the linear regime, and in novel nonlinear modes. I explore a case of measurements of a doubly clamped beam resonator that features negative nonlinear dissipation. By performing both directly-driven and parametrically-pumped characterizations of the system, I measure nonlinear dissipation that makes the effective linewidth of the device decrease with amplitude. The dissipation appears to be drive-induced, as it does not appear in the free response of system. I then study the impacts of nonlinear phenomena on the phase noise of resonant systems, which is relevant for improving the performance of MEMS clocks. Using parametric pumping tuned to achieve maximum parametric suppression, which reduces the phase to frequency noise conversion in our amplifier-noise dominated measurement setup, I decrease the measured resonator phase noise by almost threefold. Nonlinearity also can be used as a means to readout a MEMS resonator. I demonstrate an implementation of dispersive readout in our epitaxially encapsulated resonators and characterize its performance by measuring a Lamé mode resonator at room temperature. This novel readout achieves a displacement resolution of 522 fm/rtHz, representing a state-of-the-art resolution for measurements of encapsulated bulk-mode resonators. I further demonstrate that the dispersive readout technique can be used to measure mechanical system properties from 12 K to below 1 K. At the lowest temperature of 500 mK, I measure the resonator quality factor as 7.8 million, and an f x Q product of 7.9 x 10^13 Hz, which is the highest known measured value for an encapsulated bulk-mode resonator.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Kenny Thomas
Howe Roger
Safavi-Naeini Amir
“Not Just Pawns On A Chessboard”: Exploring Challenges and Nuances To Traditional Explanations of Southeast Asian Countries Hedging Between the US And China

Is hedging between the US and the People’s Republic of China (PRC) the primary driver of Southeast Asian countries’ behavior in relation to the two? At the center of US and PRC strategic interests in the Indo-Pacific, from the South China Sea to the Strait of Malacca, Southeast Asia is a dynamic region becoming increasingly vital to both great powers. Washington currently buys into the narrative that Southeast Asian countries, caught in the middle of outsized US-PRC competition and the risks of aligning too closely with one side, are constantly hedging between both to maximize benefits and avoid hostilities. Current literature that has informed US policymakers’ perspectives have applied realist assumptions to traditional hedging theory to explain that Southeast Asian countries are behaving as small states reacting to threatening imbalances of power presented by larger rivals and primarily guided by a need to ensure security against both fronts. This thesis seeks to paint a more complete and nuanced picture of the forces at work driving Southeast Asian countries’ policymaking, moving away from viewing the region through hegemonic lenses. Through two case studies—the negative reaction to the AUKUS pact from Indonesia and Malaysia and defense cooperation between the US and the Philippines and Singapore—this thesis uses historical, discourse, and interview analysis to demonstrate that significant confounding factors exist that drive Southeast Asian countries to act in ways that may seem to “hedge” towards the US or the PRC, but are actually independent from security considerations vis a vis great power competition. This thesis finds that Southeast Asian countries, exemplified by the four examined in these case studies, exercise agency and operate on their own agendas with respect to their specific contexts—including colonial histories, the Non-Aligned Movement, and fundamental principles such as pragmatism and Association of Southeast Asian Nations (ASEAN) centrality—both in cases when they act in alignment and misalignment with hedging theory. These findings have serious implications for how the US approaches relations with Southeast Asian countries and crafting policies that will resonate with the region during the “Asian century”.

Keywords: Center for International Security and Cooperation
Southeast Asia
ASEAN
Indonesia
Malaysia
Philippines
Singapore
Hedging
United States
China
Great powers
International relations
Text
Thesis
Advisor: Cohen David
Vardi Gil-li
Novel inflation and black hole dynamics with applications

Black holes are interesting astrophysical objects that have been studied as systems sensitive to quantum gravitational data. The accelerated geometry in the exterior of extremal black holes can induce large center-of-mass energies between particles with particular momenta at the horizon. This is known as the Bañados-Silk-West (BSW) effect. For point particles, the BSW effect requires tuning to have the collision coincide with the horizon. However, this tuning is relaxed for string-theoretic objects. String scattering amplitudes are large in the Regge limit which occurs at large center-of-mass energies and shallow scattering angles, parametrically surpassing quantum field theoretic amplitudes. In this limit, longitudinal string spreading is induced between strings with a large difference in light-cone momenta, and this spread can be used to 'detune' the BSW effect. With this in mind, quantum gravitational data, as described by string theory, may play an important role in near horizon dynamics of extremal Kerr black holes. Further, though it may be hard to realize astrophysically, this system acts as a natural particle accelerator for probing the nature of small-scale physics at Planckian energies. The inflationary epoch of the early universe described by the rapid acceleration of space, was introduced to explain the distribution of matter in our universe. In particular, models using 'slow-roll inflation' have been successful in predicting this distribution, as have more general models with stronger interactions, such as DBI inflation obtained from a gauge theory with many 'color' degrees of freedom. We find a novel analogous mechanism for inflation by coupling the scalar field governing the expansion, the inflaton, to a family of flavor fields. Its effective action leads to a Coleman-Wienberg potential term that generates viable inflation, via a speed limit deriving from a logarithmic-- rather than a square root-- branch cut. Interestingly, a version of the model can be cast as an energy-conserving-descent optimization algorithm. A machine learning algorithm utilizing this logarithmic Hamiltonian has interesting properties for finding minimal loss solutions while effectively spanning large regions of phase space.

Keywords: Humanities and Sciences.
Physics.
Advisor: Silverstein Eva
Graham Peter
Kachru Shamit
Nucleic acids as direct effectors in gene regulation : from argonaute proteins to scalp epigenomics

Much of the complexity of biology lies in the problem of how cells sharing the same, relatively static genetic code produce the vast diversity of cellular states and functions necessary for multicellular life. DNA and RNA are the storage and message molecules, respectively, of genetic information transfer, but these macromolecules have also been co-opted as direct functional players in the control of specific, context-dependent gene regulation. The vastness of sequence space and the challenge of generating quantitative, genome-scale datasets make understanding, predicting, and intervening in these modes of gene regulation daunting. The first part of this work addresses Argonaute family proteins, which load short nucleic acid guides to program specific binding to nucleic acid targets to regulate gene expression, host defense, and other biological functions. We deploy multiple high-throughput sequencing-based assays to measure the association rates, binding affinities, and single turnover cleavage rates for mouse Ago2 loaded with specific RNA guides against > 40,000 unique RNA targets. We map sequence to structure to function relationships for Ago2 binding and cleavage, and show that our in vitro measurements can be used to predict gene repression in an engineered cellular system. We next use similar methodologic approaches to study an Argonaute protein derived from the bacterium Thermus thermophilus, TtAgo, that uses DNA guides to bind and cleave DNA targets at extreme temperatures. By measuring the binding of multiple DNA guides against thousands of targets each, we were able to construct general, quantitative models of association kinetics and binding affinity. We also show that guide sequence composition has dramatic effects on cleavage activity, suggesting that only a subset of guides are capable of cleaving targets at physiologically relevant temperatures. In the second part of this work, we examine a different form of gene regulation-- the use of enhancers and other cis-regulatory elements to control gene expression in the many distinct cell types comprising human scalp. We generated paired single cell RNA- and ATAC-sequencing datasets of primary human scalp. We use these integrated datasets to identify 'highly-regulated genes' linked to a disproportionately large number of enhancers and show that for a given highly-regulated gene expressed in multiple cell types, a greater number of linked enhancers is associated with higher levels of transcription. We demonstrate that genetic variation associated with skin and hair disease is specifically enriched in open chromatin regions of implicated cell types, including a strong association between dermal papilla cells and androgenetic alopecia. Using machine learning approaches, we further prioritize specific genetic variants that putatively disrupt transcription factor binding sites, leading to altered expression at disease-relevant genes

Keywords: Medicine.
Genetics.
Advisor: Greenleaf William James
Fire Andrew Zachary
Kay Mark Allan
Oro Anthony
Numerical methods for the simulation of quantum ultra light dark matter

Ultra light dark matter is an interesting dark matter candidate. In this model we work with a particle so light that its wave nature manifests on astrophysical scales. Here we operate on the interface between cosmology and quantum mechanics. Simulations have emerged as one of the most powerful methods for studying this model. Typically, the classical field theory is used to simplify the numerics of the problem on a wide variety of time and length scales. However, the applicability of this approximation on some of these scales has become a subject of some debate in the literature. The numerical complexity of quantum mechanical simulations has resulted in much of the previous work on this topic relying on analytic approximations or small toy systems. In this thesis we discuss the development and application of numerical methods to study the classical limit of quantum mechanics by simulating quantum corrections to test systems many orders of magnitude larger than had been previously studied in this context. Much of the thesis draws on four publications, one conference proceedings, and one paper in preparation, that I have worked on over the last six years. Each of the chapters 2-6 presents a different method for simulating ultra light dark matter, which are, in order: the classical field theory, full quantum simulations, the field moment expansion, and the truncated Wigner approximation. In each chapter we study both the numerical implementation of each method as well as what they can tell us about the nature of quantum corrections to the classical theory. We use these simulations to estimate both the effect of the quantum corrections on the predictions of classical field theory as well as approximate the time scales on which they become large

Keywords: Humanities and Sciences.
Physics.
Advisor: Abel Tom G.
Graham Peter
Wechsler Risa H. (Risa Heyrman)
Numerical studies in rarefied gas dynamics, cosmological summary statistics, and scalar field dark matter

In this thesis, we study and develop several numerical techniques used in astrophysics and cosmology. On the astrophysical front, we examine the ability of unified gas kinetic schemes to simulate rarefied gas dynamics beyond the domain of the Navier-Stokes approximation and discuss potential applications. We showcase our massively parallel open-source implementation of the Coupled Discrete Unified Gas Kinetic Scheme (CDUGKS) on common test problems and discuss performance benchmarks. On the cosmological front, we further develop the theoretical and numerical techniques of k-Nearest Neighbor (kNN) statistics, a novel cosmological summary statistic which shows promise as a more informative alternative to traditional N-point statistics. We describe the theory of kNN statistics in a linear theory of cosmology, and discuss several measurement techniques including our open source high performance implementation of the kD-Tree algorithm specialized for kNNs in cosmology. Finally, we evaluate the validity of current techniques used for the simulation of ultralight scalar field dark matter at astrophysical and cosmological scales, and present new codes and techniques for going beyond the regime of validity of the current techniques

Keywords: Humanities and Sciences.
Physics.
Advisor: Abel Tom G.
Blandford Roger D.
Madejski Grzegorz
Numerically anchored theory for quantum materials

Quantum materials retain manifestly quantum behavior at macroscopic scales, where regular materials can be approximated classically. Developing theories that describe these macroscopic quantum states is challenging due to Avogardo's number of quantum particles involved, which often experience strong correlations and symmetry confinements. Such theories must be tested by quantitatively comparing their results to experiments to determine if they can capture the correct physics. Studying these theories is the cornerstone of this thesis. The first section presents an investigation of the role of the rare-earth element $R$ in the newly-discovered superconducting infinite-layer nickelates RSr_(d)Ni_(1-d)O2. The electronic structure calculations of the parent compound RNiO2 with lanthanide substitution show a strong interplay between bandwidth renormalization, charge transfer, compensation, number of electron pockets, and magnetic exchange when traversing from La to Lu. These calculations gave rise to a microscopic model Hamiltonian built from Wannier downfolding, which then in turn was used to directly calculate the doping dependence of the superconducting nickelates. The second section of this thesis explores the rich landscape of surprising transport signatures in the EuX2Y2 compounds, where $X$ is a transition metal, and $Y$ is a pnictide. There are unexpectedly large contributions of the europium orbitals near the Fermi level which seem to play a significant role in transport, even though nominally, they should be far away in energy. The third section of this thesis examines spectroscopic signatures in high-disorder environments. Simulating x-ray spectroscopy and using statistical methods, a coordination number mechanism was developed for superionic conductivity in solid-state electrolytes. The study of theories that are quantitatively tied to experiments elucidates the complex behavior in multiple classes of quantum materials, and paves a path for future many-body studies.

Keywords: Humanities and Sciences.
Physics.
Advisor: Devereaux Thomas
Cui Yi
Shen Zhi-Xun