VictorJuiz/thesis huggingface dataset:
Stimulated emission depletion super-resolution fluorescence microscopy : addressing biophysical questions from bacteria to eukaryotic cells

Fluorescence microscopy is a widely used tool in biological studies, allowing specific labeling and imaging of proteins and structures within living cells. As reviewed in Chapter 1, conventional fluorescence microscopy is unable to distinguish structures below ~250 nm in size due to the diffraction limit of light. Over the last two decades, the field of super-resolution microscopy has overcome this diffraction limit, allowing fluorescence imaging of structures just a few tens of nanometers in size. This thesis discusses one super-resolution technique, stimulated emission depletion (STED) microscopy, and its application to various cellular and molecular biophysics questions. In Chapter 2, the bespoke STED microscope used for much of this thesis is described in detail. Some guiding principles behind STED microscopes and their design are discussed. We next discuss the major improvements to the STED microscope: fast scanning and two-color. Fast scanning protects fluorophores from photobleaching during imaging, improving image quality and expanding the range of available fluorophores for STED imaging. Two-color imaging allows the examination of two different protein species, a universal requirement in many biological studies. In Chapter 3, we apply STED microscopy to study how the bacteria Caulobacter crescentus assembles a 2D paracrystalline surface layer (S-layer) composed of the protein RsaA. We show that S-layer assembly is localized to highly curved regions of the cell, and that this localized assembly depends upon the presence of a pre-existing crystalline S-layer. Together, our results suggest the topology of the cell surface combined with continuous protein crystallization localizes S-layer assembly. In Chapter 5, 2-color STED microscopy is used to examine protein interactions important for the 3D organization of the genome in eukaryotic cells. We quantify clustering of the DNA binding protein CCCTC-binding factor (CTCF), as well as the molecular coupling between CTCF and cohesin, two proteins hypothesized to play a major role in organizing topologically associating domains (TADs). We show that cohesin plays a regulatory role in CTCF clustering by perturbing its activity and monitoring cluster size. Finally, we discuss several applications of STED imaging to support larger studies. Chapter 4 discusses applying a genetically encoded fluoromodule for STED imaging in live bacteria cells. In the first half of Chapter 6, STED reveals the localization of the centriole protein Cby1 to the transition zone, supporting a role for Cby1 in recruiting Ahi1, another transition zone protein. Finally, the second half of Chapter 6 uses two-color STED microscopy to demonstrate that deletion of the intracellular cargo transport protein IFT25 causes simultaneous defects in the cilium cytoskeleton as well as ciliary membrane morphology

Advisor: Moerner W. E. (William Esco)
Huang Kerwyn Casey
Shapiro Lucy
Stochastic modeling and control of autonomous mobility-on-demand systems

The last decade saw the rapid development of two major mobility paradigms: Mobility-on-Demand (MoD) systems (e.g. ridesharing, carsharing) and self-driving vehicles. While individually impactful, together they present a major paradigm shift in modern mobility. Autonomous Mobility-on-Demand (AMoD) systems, wherein a fleet of self-driving vehicles serve on-demand travel requests, present a unique opportunity to alleviate many of our transportation woes. Specifically, by combining fully-compliant vehicles with central coordination, AMoD systems can achieve system-level optimal strategies via, e.g., coordinated routing and preemptive dispatch. This thesis presents methods to model, analyze and control AMoD systems. In particular, special emphasis is given to develop stochastic algorithms that can cope with the uncertainty inherent to travel demand. In the first part, we present a steady-state modeling framework built on queueing networks and network flow theory. By casting the system as a multi-class BCMP network, the framework provides analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput. Moreover, we present a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. The framework provides a large set of modeling options, and specifically address cases where the operational concerns of congestion and battery charge level are considered. We validate our theoretical results on a case study of New York City. In the second part, we leverage the insights provided by the steady-state models to present real-time control algorithms. Specifically, we cast the real-time control problem within a stochastic model predictive control framework. The control loop consists of a forecasting generative model and a stochastic optimization subproblem. At each time step, the generative model first forecasts a finite number of travel demand for a finite horizon and then we solve the stochastic subproblem via Sample Average Approximation. We show via simulation that this approach is more robust to uncertain demand and vastly outperforms state-of-the-art fleet-level control algorithms. Finally, we validate the presented frameworks by deploying a fleet control application in a carsharing system in Japan. The application uses the aforementioned algorithms to provide, in real-time, tasks to the carsharing employees regarding actions to be taken to better meet customer demand. Results show significant improvement over human based decision making.

Advisor: Pavone Marco
Rajagopal Ram 
Jain Rishee
Strategies of repression in Pinochet's Chile

Authoritarian repression is understood as a means to eliminate dissent. Yet most dictators also require some popular support to survive. How does the need to maintain supporters influence repression? This dissertation argues that repression can serve to appeal to supporters, by signaling to a regime's backing coalition that authoritarian control is uniquely capable of managing political and social threats to the state. The three chapters bring to bear new findings on a variety of repressive methods used during Augusto Pinochet's dictatorship in Chile (1973-1989). Chapter 2 demonstrates how the need for support shapes patterns of political killings. Chile's military justified a coup on the basis of a communist threat that was, in fact, exaggerated. Original data on 3,000 victims, along with qualitative evidence, show that killings targeted suspicious individuals in otherwise high-support areas to demonstrate that a communist threat existed and the military was uniquely capable of managing it. Chapter 3 shows how supporters can constrain violence: the more prominent the opposition leader, the more likely their death or detention will mobilize opposition and alienate supporters. New data on the repression of candidates for national office shows that election -- by increasing visibility -- constrained violent repression. The regime instead substituted it with exile. Chapter 4 demonstrates that dictators can use even seemingly punitive policies to reward supporters. Text analysis of all 8,000 movies reviewed for distribution during the dictatorship shows bans largely targeted immoral content, rather than political themes. Qualitative and quantitative evidence link this to the regime's need to appeal to conservative groups. Findings provide new insight into how dictators balance security and popular support.

Keywords: Political Science.
Advisor: Laitin David D. 
Magaloni Beatriz 
Weinstein Jeremy M.
STRETCHING HUMAN LAWS TO APPLY TO MACHINES: THE DANGERS OF A “COLORBLIND” COMPUTER

Automated decision making has become widespread in recent years, largely due to
advances in machine learning methods. As a result of this trend, automated systems are increasingly used in high-impact applications, such as university admissions decisions. The weightiness of these decisions has prompted the realization that, like humans, machines must also comply with the law. But the human decision-making process is quite different from the computational decision-making process, which creates a mismatch between the laws and the decision makers they were designed to apply to. This mismatch can lead to counterproductive outcomes. I take antidiscrimination laws in university admissions as a case example, with a
particular focus on Title VI of the Civil Rights Act of 1964.

Keywords: Machine learning
artificial intelligence
discrimination
employment
Title VII
Thesis
Strong field ionization and probing of nonadiabatic dynamics in water

Intense, ultrafast laser fields can be useful tools for studying the electronic structure of molecules. Electronic structure can be studied via ionization, where the strong laser field can preferentially interact with electrons based on their binding energies and spatial orbital geometries. Strong fields can also induce nuclear motion during the ionization process. The effect that nuclear geometry has on the electronic structure will affect how strong field ionization occurs, enhancing the ionization rates at special geometries. In this thesis, I study the strong field ionization of molecular water. The goal of this study is to place the strong field multiple ionization of water among existing theories and experiments on enhanced ionization. The strong field ionization experiments in this work use intense, 800nm laser pulses with 40 fs pulse duration. Dissociation channels from the dication, trication, and tetracation are studied using the ion coincidence technique. This allows for detailed analysis of the enhanced ionization geometries. By analyzing all two and three body fragmentation channels from each isotope of water, I am able to differentiate different ionization pathways leading to the observed channels. It is found that enhanced ionization proceeds primarily from a nearly linear, symmetrically elongated molecule. Furthermore, alignment of the laser polarization parallel to the H-H bond seems to be the preferred configuration for enhanced ionization. It is shown that much of this alignment dependence is due to dynamic alignment effects that are much stronger when the molecule is linear. The electronic structure of water is also studied in the context of nonadiabatic coupling. Nonadiabatic coupling in light molecules such as water is commonplace, where two electronic states can be coupled by vibrational motion near points of degeneracy in the potential energy landscape. In this work, strong fields are used to probe an ultrafast dissociation process in neutral water where the dissociation only occurs due to nonadiabatic coupling between an excited electronic state and the ground state. The strong field probe adds an additional coupling between the two states, and as a result affects the dynamics of the dissociation channel. The effect of changing the strong field probe wavelength is investigated, as this changes the points of effective strong field coupling between the two electronic states. It is found that the dissociation is most affected when shorter strong field wavelengths are applied. This is shown to be mostly due to the effects of coupling to an intermediate state that is not involved in the field free dissociation dynamics.

Keywords: Applied Physics.
Advisor: Bucksbaum Philip H. 
Martinez Todd J. (Todd Joseph)
Schleier-Smith Monika
Structural and functional insights into membrane transport mechanisms

All cells are surrounded by a lipid bilayer that prevents many chemical species from crossing including key biomolecules. Transport proteins facilitate the passage of a wide range of molecules across the cell membrane. Molecular details of transport protein function, such as mechanisms of substrate specificity, gating, and conformational change can be gleaned from high-resolution structures. To this end, I utilized structural approaches to develop models for different transport mechanisms. In the first section, I focus on a family of sugar transporters called SWEETs. SWEETs play central roles in plant biology and are found across all lifeforms including humans. Their prokaryotic homologs, SemiSWEETs, are amongst the smallest natural transporters. I focused on SemiSWEET as a representative system for SWEET function and as a model for other more complex transporters. High resolution structures of SemiSWEET in all major conformational states revealed the movement of hydrophobic gate residues controls the accessibility of the substrate-binding binding pocket. A glucose-bound structure revealed the substrate recognition residues. Structural observations were corroborated by mutagenesis, sugar uptake experiments, and unguided molecular dynamic simulations. The simulations faithfully recapitulate the experimental structures while demonstrating that the substrate doesn't induce conformational changes, but instead takes a "free ride" through a stochastically fluctuating transporter. In addition to the characterization of SemiSWEET, I developed more general protein engineering strategies to facilitate structural studies. The final section details the molecular structure of the mitochondrial calcium uniporter, which required a transmembrane-targeting nanobody in order to achieve the highest resolution crystal structure.

Advisor: Feng Liang
Kobilka Brian K. 
Maduke Merritt C.
Structural insights into G protein-coupled receptor activation and signaling

G protein coupled receptors (GPCRs) are a class of cell-surface receptors that mediate complex cellular responses to a myriad of physiological stimuli, from photons and neurotransmitters, to hormones, peptides, and even full proteins. GPCR signaling underlies almost all of physiology; this central importance to human health and disease is reflected in the fact that approximately 30% of all FDA approved drugs target GPCRs. Ligand action at GPCRs mediates intracellular signaling through heterotrimeric G proteins, as well as G protein-independent pathways. Over the past decade, a structural framework has allowed for a more precise understanding of ligand-mediated activation in rhodopsin-like GPCRs, as well as the molecular mechanism for GPCR signaling through the stimulatory G protein, Gs. My work expands this structural understanding of GPCR activation and signaling by exploring two novel signaling systems in the GPCR family for which structural information is lacking. First, I describe work on the structure of a peptide-activated mu-opioid receptor (muOR) in complex with the inhibitory G protein, Gi. As signaling through Gi is responsible for the beneficial aspects of opioids, the structure of this complex can help guide the design and discovery of novel therapeutics with reduced liabilities. I then show the structural mechanism of activation in a family C GPCR, the metabotropic glutamate receptor 5 (mGlu5). Family C receptors are unusual in that, as well as the GPCR-defining 7-transmembrane (7TM) domain, they possess relatively large amino-terminal extracellular domains (ECDs) that form obligate dimers and contain the orthosteric ligand-binding sites. Taken together, this work presents novel insights into GPCR activation and signaling.

Keywords: Structural Biology.
Advisor: Kobilka Brian K. 
Feng Liang
Skiniotis Georgios
The structure and function of neural circuits supporting incentivized inhibition

What humans choose and how their brains make choices are central questions in psychology and neuroscience. Neuroimaging research is advancing models of functional brain activity that can not only correlate with, but can also predict, value-based choices in humans. Physiological research in animals suggests that monosynaptic axons structurally connect the brain circuits predictive of choice. However, similar structural connections have only recently been characterized in humans, and researchers are now starting to explore links from structural brain connections to functional brain activity to value-based choices. This dissertation first reviews recent attempts to link structural white-matter tracts to individual differences in value-based choices. Next, two peer-reviewed articles present studies that characterize novel white-matter tracts and link the structural coherence of the tracts to functional activity at tract endpoints to value-based behaviors. A third study presents applications of the neuroimaging tools in a clinical context to characterize brain structural deficits in patients who used stimulant drugs, and further to predict patient relapse after intervention. The dissertation concludes with guidelines for researchers interested in linking multimodal neuroimaging measurements to individual differences in value-based choice. Together this work demonstrates the promise of combining measures of brain structure, brain function, and behavior to better explain how the brain chooses.

Keywords: Psychology.
Advisor: Knutson Brian 
Gotlib Ian H.
Poldrack Russell A.
Studies in stochastic optimization and applications

All machine learning problems reduce to some kind of stochastic optimization problem, which can be solved with variants of algorithms in stochastic approximation literature. In this thesis, we study the classical stochastic optimization algorithms and their extensions to two financial applications. In the first part of this thesis, we revisit the classical stochastic approximation algorithm introduced by Robbins and Monro in 1951, now referred to as the Robbins-Monro procedure. We establish its consistency and utilize the Martingale Central Limit Theorem to prove comprehensive asymptotic normality results for the algorithm. In the second part of this thesis, we introduce a trading algorithm to solve the optimal execution problem in the context of trading in dark pools. The stochastic optimization problem to minimize cost is solved together with an estimation problem to learn the underlying unknown distribution of trading volume limits. Our algorithm solves the two problems which are related, and updates the allocation strategy and the estimations of volume limits alternatively. In the third part of this thesis, we estimate a general non-linear asset pricing model with deep neural network applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We include the no-arbitrage condition in the objective and consider a GMM type problem with infinite moment conditions. We combine different neural network structures in a novel way and modify the stochastic optimization algorithms to solve a minimax optimization problem. Our model allows us to understand the key factors that drive asset prices, identify mis-pricing of stocks and generate the mean-variance efficient portfolio.

Advisor: Papanicolaou George 
Pelger Markus 
Giesecke Kay
Studying therapeutic potentials of human mesenchymal stromal cells with Cas9/AAV-based genome editing

Human mesenchymal stromal cells (hMSCs) are a promising source for cell-based therapies that require short-term interventions in injured tissues without replacement of resident cells. Genetic engineering of hMSCs could elucidate their mechanisms of function, enhance their therapeutic efficacy, and install novel functions, yet existing methods for MSCs are limited in their site specificity and ease of design, which ultimately constraint their applicability to research and clinical uses. Here, we describe an optimized Cas9/AAV6-based genome editing tool platform for hMSCs. Our results show that the system is applicable to cells derived from the bone marrow, adipose tissue, and umbilical cord blood. We successfully integrated more than 3 kilobases of exogenous DNA sequence into the hMSC genome without altering ex vivo characteristics of the cells. As a proof of concept, we applied the technique to generate in vivo bioluminescent hMSCs for biodistribution studies following infusion and local injection, and produced therapeutic factor-secreting MSCs that can exert enhanced therapeutic efficacy for wound healing in diabetic (db/db) mice over non-edited hMSCs. The genome editing platform is versatile in its application and is compatible with existing tissue engineering approaches for wound treatment and beyond. This study demonstrates the potential for precise genetic engineering of hMSCs and establishes a platform for future development of mechanism of action studies and engineered MSC-based therapies.

Advisor: Porteus Matthew H.
Gurtner Geoffrey
Longaker Michael T.
Sub-Nyquist receiver for digital predistortion of RF power amplifiers

Digital predistortion (DPD) is an essential building block of modern wireless base stations: it linearizes the radio frequency (RF) power amplifier (PA) such that the transmitter can be operated efficiently. A crucial ingredient to a DPD system is the feedback receiver which includes an analog-to-digital converter (ADC) which digitizes the PA output signal. Ever increasing signal bandwidths push conventional DPD receivers sampling at the Nyquist rate to their practical and economical limits. This issue is exacerbated in emerging technologies like 5G and beyond where the trend towards a denser grid of pico- and femto cells demands efficient and inexpensive components. Furthermore, DPD systems employing conventional Nyquist-rate feedback receivers do not allow efficient implementation of transmitter arrays (massive MIMO) due to the enormous bit rates. This thesis presents a framework for nonlinear system identification which allows to obtain the PA model with fewer samples than conventional methods. Furthermore, it is shown that the sampling rate as well as the ADC acquisition bandwidth is irrelevant for obtaining the PA model. To my knowledge, this is the first method which decouples the requirements of the feedback ADC from the input signal bandwidth. The presented framework is then used to implement a feedback receiver prototype for DPD of RF PAs. In contrast to conventional architectures, the receiver samples in the frequency domain. By randomly selecting a frequency bin in each sampling interval, the PA model is identified with substantially lower acquisition bandwidth and sampling rate. The receiver prototype is implemented in 28nm FD-SOI technology and samples at 4MS/s instead of the full Nyquist rate of 168MS/s for a 20MHz Long Term Evolution (LTE) signal with spectral regrowth. The prototype is shown to linearize a 4W small base station power amplifier from an adjacent channel leakage ratio (ACLR) of −35dBc to −47.7dBc after processing only about 250 samples. It is to my knowledge the first sub-Nyquist DPD feedback receiver implemented on an integrated circuit (IC).

Keywords: Electrical Engineering.
Advisor: Murmann Boris 
Arbabian Amin 
Narasimha Madhally