VictorJuiz/thesis huggingface dataset:
Solid-state NMR studies of peptidoglycan and teichoic acids in the bacterial cell wall and bacterial biofilms
Keywords: Chemistry.

Gram-positive bacteria surround themselves with a cell wall that is essential to cell survival and serves as a major target for antibiotics. The cell wall is composed primarily of two polymeric macromolecules: peptidoglycan (PG) and teichoic acid (TA). During infection, bacteria can further elaborate a multi-component extracellular matrix beyond the cell wall to form a biofilm community, enmeshing many cells, that improves adhesion, increases virulence, and helps avoid host immune responses. Cell walls and biofilm matrices are insoluble and heterogeneous materials that pose a challenge to analysis with biophysical techniques like X-ray crystallography, solution NMR, and mass spectrometry. We are implementing solid-state NMR methods to access atomic-level compositional parameters of cell walls and the extracellular matrix, particularly doing so in the context of intact biofilms to address questions that cannot be answered by attempted digestions and solution-based analysis. I will report on our new approach to quantify the relative proportions of PG and TA in isolated cell walls through 13C analysis, and how we have extended this approach to intact whole cells and protoplasts to identify several characteristic spectral features of cell walls. These allow us to gauge cell wall levels quantitatively. Further 15N analysis revealed a uniquely shift-resolved peak in whole cells entirely attributed to TA D-alanylation. We use these markers alongside isotopic-labeling experiments to characterize the distinct mode of actions of a panel of cell wall synthesis inhibitors. Through our analysis, we also found that cells in stationary phase exhibit reduced levels of TA with increased PG. We have now discovered that TA D-alanylation decreases significantly in cells that make a robust extracellular matrix, as visualized by EM. Similarly, cells treated with a small molecule inhibitor of teichoic acid synthesis appear to make more developed biofilms. Our solid-state NMR measurements are integrated with proteomic and microscopic analysis of biofilm cells and reveal direct composition-function relationships. Together, our results demonstrate that S. aureus PG and TA production are tunable, and that decreased TA production or modification occurs as cells form biofilms.
Some hierarchical and group regularization methods and applications
Keywords: Statistics.

We analyze methods to model data with group effects that may be hierarchical. We begin by introducing a problem inferring human results from experiments done on mice in Chapter 2. Since we have paired experimental results across many genes and diseases, this suggests a hierarchical random effects model. Alternatively, we can also simplify the generative assumptions and consider a fixed effect model with regularization. While these basic models can outperform the baseline estimate of using the results on mice directly, they do not take full advantage of the two factors in the data. We impose either a hierarchical model or an additive model to account for the two effects. In Chapter 3, we propose a two factor random effects model with a more general covariance structure, which captures correlation across related genes and diseases and thus, allows interactions between the factors. The benefit of this model extends to general linear regression problems with networked data. Finally, in Chapter 4, we analyze pliable lasso, which extends the lasso, for regularizing two factor fixed effects with potential interactions between them. While the lasso can be used for all terms, including interactions, the large amount of coefficients introduces variance into the estimator. Pliable lasso imposes a hierarchical structure, that allows variance reduction. Since pliable lasso is a more general method for estimating interaction effects and not just fixed effects, our analysis is done with general main and interaction effects.
A source rock first approach to petroleum system analysis in the middle Magdalena Valley, Colombia
Keywords: Geological and Environmental Sciences.

The Middle Magdalena Valley (MMV) is an intermontane basin and petroleum province in northern Colombia. A succession of organic-rich source rocks deposited in the Cretaceous show excellent petroleum potential and have generated economic oil and gas accumulations within the valley. However, understanding the chronology of source rock maturation and generation with respect to the other petroleum system elements is complicated by the impact of the Andean orogeny on northern South America. To help resolve the MMV petroleum system, biomarker chemometrics of oil samples from discoveries in the basin are used to identify distinct petroleum tribes with characteristics indicating the presence of multiple source rocks. An examination of pyrolysis data from MMV wells finds six Cretaceous intervals with favorable organic matter quality and quantity within the Tablazo, La Luna, and Umir formations. Finally, a 3-D model of source rock maturation and generation from the Cretaceous to the present-day enables the incorporation of all petroleum system elements within the context of the basin evolution. The timing of maturation-generation for the Cretaceous interval and the petroleum tribes identified both support that the upper members of the La Luna Formation and the Umir Formation generated the majority of hydrocarbons discovered in the MMV
Spatial multiplexing in optical communications
Keywords: Electrical Engineering.

The information capacity of optical communication systems can be increased by multiplexing signals in co-propagating spatial and polarization modes of multimode or multicore fibers. In this dissertation, we discuss some fundamental aspects of spatial multiplexing in long-haul and short-reach transmission systems. We describe methods for modeling the temporal dynamics of modes in the presence of random environmental vibrations and show, both theoretically and experimentally, that the rate of channel dynamics increases with the number of coupled modes. We study the implications of fast channel dynamics on the adaptive multiple-input multiple-output digital signal processing at the receiver. Furthermore, we discuss the combined impact of mode-dependent gain and modal dispersion on the capacity of spatially multiplexed links, and derive the requirements for optical amplifiers in ultra-long-haul systems. We propose techniques for optical equalization using meshes of Mach-Zehnder interferometers. In particular, we evaluate the performance of the self-configuration method to configure mesh phase shifters and propose variants of this method that can track microsecond timescale channel dynamics. Finally, we propose novel design algorithms to optimize fiber refractive index profiles for various applications. Our algorithms enable effective control of fiber properties such as mode coupling, chromatic and modal dispersions.
Stability of oil/water interfaces
Keywords: Chemical Engineering.

The stability of oil/water interfaces affects the manufacture of many different products in industries ranging from pharmaceuticals to oil industry. In the first part of the talk, the stability of asphaltene-laden interfaces will be discussed. Asphaltene molecules are complex molecules from crude oil that are defined by their solubility in hydrocarbons. These molecules have the propensity to adsorb onto oil/water interfaces where they create a viscoelastic network and stabilize oil/water emulsions. This causes difficulties, for instance, in the desalting process, one of the first processes in an oil refinery. During the desalting process, high concentrations of salt in crude oil are reduced by a liquid-liquid extraction where crude oil is mixed with water. Then, this mixture is brought to a settling tank called desalter where the two phases are expected to separate. However, the presence of asphaltenes may negatively impact this separation process as they stabilize oil/water emulsions. We built an experimental setup in the Fuller laboratory to reproduce in a smaller scale and in a controlled way the separation process that occurs in a desalter. Our goal was to study the impact of asphaltenes on the coalescence of both water and oil drops against a flat oil/water interface. We found that water drops are more stable than oil drops upon coalescence. In addition, the time it takes for water drops to coalesce as a function of the aging time of the interface presents a maximum due to a spontaneous appearance of micron-sized droplets at the oil/water interface. This spontaneous appearance of droplets is a phenomenon known as spontaneous emulsification and, in our system, the majority of the droplets formed consist of asphaltene-laden water droplets. In the second part of the talk, the spontaneous emulsification phenomenon in the presence of asphaltenes will be discussed. We used a co-flow microfluidic device to study the shrinkage due to spontaneous emulsification of suspended water drops in asphaltene/heptane-toluene solutions. The initial size of water drops and the concentration of solvents used in the oil phase are varied in our study. We found that drops will only decrease in size in the presence of asphaltenes. In addition, we found that drops shrink faster as their initial drop size decreases. In fact, the drop's radius decreases linearly with time and the angular coefficient of this relationship is a strong function of the drop's initial size. Finally, we found that the internal pressure of asphaltene-coated water drops does not follow a simple Young-Laplace equation and its internal pressure is inversely proportional to the drop's initial surface area. The internal drop pressure was found to be a potential force for spontaneous emulsification. An understanding of the spontaneous emulsification phenomenon in the presence of asphaltenes has strong implications in the oil industry, as it may affect problems ranging from oil refining to oil spill cleanups.
Statistical methods for adaptive data analysis
Keywords: Statistics.

We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection procedure. After defining the selected parameters, the conditional correction for selection requires knowledge of how the selection is affected by changes in the underlying data. We address two important issues arising in selective inference methodology: statistical power of selective inference methods and generality of the selection procedures addressed by the methods. We provide two methods that improve on the power of the original selective inference methods. The first way to improve statistical power after data exploration is to do selection on a noisy version of the data, thus using less information in selection and leaving more for inference. We also introduce the bootstrap version of this method and prove asymptotic guarantees. By redefining the selected parameters to require as little as possible information from selection, the second method we introduce here improves greatly on the power of the original selective inference methods. We apply the method to conduct powerful inference after Lasso in high-dimensional settings. The third method enables inference after black box model selection algorithms, without having explicit selection. In this work, we assume we have in silico access to the selection algorithm. We recast the inference problem into a statistical learning problem which can be fit with off-the-shelf models for binary regression. We apply this method to stability selection, which was previously out of reach of this conditional approach.
Stereospecific functionalization of vicinal dihalides and efforts toward the total synthesis of archaeal lipids
Keywords: Chemistry.

Chapter 1 shows that while nucleophilic substitution reactions are universally taught, they are often much more complicated than we think. A huge amount of work has been done to understand neighboring-group participation in SN1-type reactions both in a qualitative and quantitative sense. Studying the reactivity of dibromides and their surrogates led to important insights in physical organic chem-istry. However, the enantiospecificity and potential synthetic use of such processes are underexplored. Chapter 2 is a reprint of a review I coauthored entitled "Chiral Alkyl Halides: Un-derexplored Motifs in Medicine". While alkyl halides are valuable intermediates in synthetic organic chemistry, their use as bioactive motifs in drug discovery and me-dicinal chemistry is rare in comparison. This is likely attributable to the common misconception that these compounds are merely non-specific alkylators in biologi-cal systems. A number of chlorinated compounds in the pharmaceutical and food industries, as well as a growing number of halogenated marine natural products showing unique bioactivity, illustrate the role that chiral alkyl halides can play in drug discovery. Through a series of case studies, we demonstrate in this chapter that these motifs can indeed be stable under physiological conditions, and that halogenation can enhance bioactivity through both steric and electronic effects. Chapter 3 demonstrates that dihalides could be used to generate a configurational-ly stable bromonium ion either through Lewis acid activation or by using a strongly ionizing solvent. This bromonium ion can then be trapped by a host of nucleophiles inter- and intramolecularly. Using this strategy, a variety of natural products and natural product relevant motifs were synthesized in our laboratory. Chapter 4 describes the importance of studying archaeal lipids and the ongoing efforts toward their total syntheses. Archaeal lipids are structurally unique, their biosynthesis has several unanswered questions, and their biophysical properties have not been studied on pure compounds. Our work describes a highly convergent strat-egy toward GDGT-0, which exploits the molecule's C2 symmetry. We have shown the feasibility of a key enantioselective hydrogenation step, and alternative strategies have been described that may circumvent certain challenges.
Strategies of repression in Pinochet's Chile
Keywords: Political Science.

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.
STRETCHING HUMAN LAWS TO APPLY TO MACHINES: THE DANGERS OF A “COLORBLIND” COMPUTER
Keywords: Machine learning
artificial intelligence
discrimination
employment
Title VII
Thesis

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.
Strong field ionization and probing of nonadiabatic dynamics in water
Keywords: Applied Physics.

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.
Structural insights into G protein-coupled receptor activation and signaling
Keywords: Structural Biology.

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.
The structure and function of neural circuits supporting incentivized inhibition
Keywords: Psychology.

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.