VictorJuiz/thesis huggingface dataset:
Molecular mechanisms in neonatal naive CD4+ T cells contributing to the reduced Th1 immune response in human neonates [electronic resource]

Human neonates are highly susceptible to infection by intracellular pathogens compared to adults, due in large part to a reduction in CD4+ T cell-mediated T helper type 1 (Th1) immunity. We have investigated the molecular mechanisms in the neonatal naive CD4+ T cell response that contribute to this reduction in neonatal Th1 immunity, and have identified differences in gene expression, epigenetic regulation, and T cell receptor signaling between neonatal an adult naive CD4+ T cells. Significantly, neonatal naive CD4+ T cells flux calcium to a higher degree than do adult naive CD4+ T cells in response to CD3 engagement. This enhanced calcium signaling is mediated by higher levels of the microRNA miR-181a, and is not accompanied by an increase in response to CD28-mediated costimulation. In neonatal cells, an increase in calcium signaling relative to a lack of induction of the AP-1 transcription factor indicates that neonatal naive CD4+ T cells may be prone to an anergic response. Neonatal naive CD4+ T cells share an enhanced calcium response with adult CD4+ recent thymic emigrants, a less mature peripheral CD4+ T cell population. We have also confirmed decreased capacity of neonatal naive CD4+ T cells to produce the prototypic Th1 cytokine interferon-gamma (IFN-[gamma]), and have obtained evidence that epigenetic modifications mediated by de novo methyltransferase 3a (Dnmt3a) are responsible for impaired cytokine production. Comprehensive profiling of gene expression and microRNA expression have uncovered further support for our functional observations of increased calcium flux and reduced Th1 function in neonatal naive CD4+ T cells. We have found that neonatal naive CD4+ T cells have reduced Th1 function and have enhanced T cell receptor proximal signaling in the absence of an enhanced response to costimulation, constituting a unique T cell phenotype.

Keywords: Immunology.
Advisor: Lewis David primary advisor. Thesis advisor
Martinez Olivia
Contag Christopher H. advisor. Thesis advisor
Utz Paul advisor. Thesis advisor
Molecular packing in organic solar cells [electronic resource]

Organic solar cells based on blends of a conjugated polymer (donor) with a fullerene derivative (acceptor) offer promise as an inexpensive, printable, and flexible source of renewable energy. It is often assumed that polymer:fullerene blends used in organic solar cells phase separate into pure polymer and pure fullerene phases. However, my research has demonstrated the existence and importance of molecularly mixed polymer:fullerene bimolecular crystals, which form due to fullerene intercalation between the polymer side chains. I will demonstrate methods to control the presence of bimolecular crystals by tuning the fullerene size and the spacing and branching of the polymer side chains. This ability to control fullerene intercalation is used to make solar cells with and without bimolecular crystals so that their properties can be compared. I find that the presence of bimolecular crystals can significantly affect important properties such as absorption, exciton splitting, and charge transport due to the intimate mixing of the donor and acceptor. I will also discuss the determination and significance of the molecular packing in a polymer:fullerene bimolecular crystal using x-ray diffraction techniques, solid-state nuclear magnetic resonance, and molecular mechanics simulations.

Keywords: Materials Science and Engineering
Advisor: McGehee Michael
Salleo Alberto
Toney Michael Folsom advisor. Thesis advisor
Molecular recognition of phosphatases [electronic resource] : insights into specificity and the destabilizing influence of an enzyme active site

Chemical transfer of the phosphoryl group is perhaps the most critical and pervasive reaction in biology, and the enzymes that catalyze these reactions provide some of the largest reaction rate enhancements known. Phosphoryl transfer enzymes have evolved active sites with a remarkable ability to recognize and distinguish similar substrates and transient species along the reaction pathway. A full understanding of the physical features and recognition strategies used by these active sites is lacking and remains as a critical obstacle to the design of novel enzymes for applications in health and industry. All enzymes must provide preferential stabilization to reaction transition states while limiting binding to ground state substrates and products. Alkaline Phosphatase (AP) is one of Nature's most impressive examples, as it prefers stabilizing the phosphoryl group transition state by 10^22-fold compared to its modest binding of the phosphate monoester substrate in the ground state. Binding studies were carried out with a set of highly similar ground state molecules to probe this tremendous molecular discrimination. The results showed that despite the similarities of these molecules, AP has a very strong preference for binding inorganic phosphate (Pi). A vibrational spectroscopy approach was used to dissect the atomic details of Pi binding to AP. These measurements in addition to pH-dependent binding studies suggest that a proton from Pi is transferred to AP upon binding allowing it to bind more strongly than related molecules that lack a transferrable proton. It was proposed that the proton transfer neutralizes electrostatic repulsion between the anionic active site nucleophile of AP and the negatively charged oxygen atoms of the bound ligand. This model implicated a role for anionic active site nucleophiles, when left deprotonated, in destabilizing ground state binding and contributing to the enzyme's enormous preferential stabilization of the transition state. This previously unknown role for anionic nucleophiles was tested further using mutagenesis studies and binding affinity comparisons in AP and another phosphatase. The results provide new insights into how anionic nucleophiles of phosphoryl transfer enzymes contribute to catalysis.

Keywords: Chemical and Systems Biology.
Advisor: Herschlag Daniel primary advisor. Thesis advisor
Chen James
Wandless Thomas advisor. Thesis advisor
Monte Carlo methods for structured data [electronic resource]

Sequential importance sampling is well known to have difficulties in high-dimensional settings. I present a technique called conditional sampling-importance resampling, an extension of sampling importance resampling to conditional distributions that improves performance, particularly when independence structure is present. The primary application is to multi-object tracking for a colony of harvester ants in a laboratory setting. Previous approaches tend to make simplifying parametric assumptions on the model in order to make computations more tractable, while the approach presented finds approximate solutions to more complicated and realistic models. To analyze structural properties of networks, I expand adaptive importance sampling techniques to the analysis of network growth models such as preferential attachment, using the Plackett-Luce family of distributions on permutations, and I present an application of sequential Monte Carlo to a special form of network growth model called vertex censored stochastic Kronecker product graphs.

Advisor: Holmes Susan
Saberi Amin primary advisor. Thesis advisor
Glynn Peter W. advisor. Thesis advisor
Multiclass learning for visual recognition [electronic resource]

Extracting semantic information from raw visual input is one of the long-term goals of computer vision. While humans can almost effortlessly recognize hundreds or thousands of object and scene categories, it poses great computational challenges to design machine learning algorithms that can effectively and efficiently learn and recognize a rich set of concepts. In this work, we consider three challenges when performing multiclass visual perception tasks such as object recognition/detection and scene categorization. First, we address the challenge of scaling up the classifier to a large number of classes in terms of both computational efficiency and statistical accuracy. We propose a learning algorithm that can automatically discover and utilize the hierarchical structure in the semantic label space from data. Empirically, the resulting hierarchical classifier achieves significant improvement over existing methods on both object recognition and scene categorization tasks with hundreds of classes. Second, while image training data is abundant for some object classes, recent studies confirm the heavy-tailed nature of their distribution over categories, fueling the need for learning algorithms that make efficient use of scarce training data. To this end, we study transfer learning applied to multiclass category-level object detection. Built on the HOG (histogram of gradient) feature and template matching detector, we propose a local, spatial and structured prior that can capture the correlation structures on the level of individual features and spatially neighboring pairs of features. We demonstrate improved average performance over all 20 Pascal classes compared to the state-of-the-art deformable part-based model. Last, the broadness of visual classes usually necessitates an ensemble of heterogeneous features to provide good discriminative power but at an expensive computational cost. We propose an active classification scheme to perform efficient inference with an ensemble of features/classifiers, where we view the classification inference task as a sensing problem. Observations, i.e., features/classifiers, are selected dynamically based on previous observations, using a value-theoretic computation that balances an estimate of the expected classification gain from each observation as well as its computational cost. We show that the active scheme can achieve comparable or even higher classification accuracy at a fraction of the computational costs of traditional methods.

Keywords: Electrical Engineering
Advisor: Koller Daphne primary advisor. Thesis advisor
Ng Andrew Hock-soon
Girod Bernd advisor. Thesis advisor
Multidimensional interleavings and applications to topological inference [electronic resource]

This work concerns the theoretical foundations of persistence-based topological data analysis. We develop theory of topological inference in the multidimensional persistence setting, and directly at the (topological) level of filtrations rather than only at the (algebraic) level of persistent homology modules. Our main mathematical objects of study are interleavings. These are tools for quantifying the similarity between two multidimensional filtrations or persistence modules. They were introduced for 1-D filtrations and persistence modules by Chazal, Cohen-Steiner, Glisse, Guibas, and Oudot. We introduce generalizations of the definitions of interleavings given by Chazal et al. and use these to define pseudometrics, called interleaving distances, on multidimensional filtrations and multidimensional persistence modules. We present an in-depth study of interleavings and interleaving distances. We then use them to formulate and prove several multidimensional analogues of a topological inference theorem of Chazal, Guibas, Oudot, and Skraba. These results hold directly at the level of filtrations; they yield as corollaries corresponding results at the module level.

Advisor: Carlsson Gunnar
Guibas Leonidas J. advisor. Thesis advisor
Morozov Dmitriy
Multifidelity optimization for supersonic aircraft design [electronic resource]

Numerical optimization techniques are often used in aircraft design to efficiently search a multidimensional space for improved designs. The optimization process can be excessively time consuming, however, due to the computationally expensive analyses that are typically required to accurately predict many aspects of vehicle performance throughout the design space. Multifidelity optimization techniques incorporate information from less expensive, but possibly less accurate (i.e., low-fidelity), analyses to reduce the time required to find an optimal configuration. The goal of this work was to develop an efficient multifidelity optimization technique and to demonstrate its effectiveness in the conceptual design of a small supersonic aircraft. The novel multifidelity optimization method combines local gradient information and 1-D search, in a manner similar to that used by some of the most efficient single-fidelity optimization strategies, with additional information from low cost, low-fidelity analyses. The 1-D search is performed along a curved path, generated by the optimization of a corrected low-fidelity model, to ensure improvement during each iteration. The effectiveness of this approach was demonstrated on a few simple problems, and then applied to supersonic aircraft design. This involved the integration of analyses of varying fidelity for supersonic inviscid drag predictions into an existing mission analysis routine. The low-fidelity analysis includes a new area rule method capable of providing reliable gradients. The high-fidelity analysis uses an Euler solver, Cart3D, to estimate inviscid drag. The new multifidelity optimization method and a traditional gradient-based optimizer were each used to design a 39 passenger supersonic transport. The multifidelity optimizer required significantly fewer high-fidelity analyses, while achieving similar design objectives, in each of the example problems.

Keywords: Aeronautics and Astronautics
Advisor: Kroo Ilan primary advisor. Thesis advisor
Alonso Juan José
MacCormack R. W. (Robert William)
A multi-regime combustion model for reactive flow in internal combustion engines [electronic resource]

With the ever-rising need for better fuel efficiency and lower emissions, the development of better engine technologies is essential. Developing these technologies requires a good understanding of the interaction between the various coupled multi-physics phenomena present in the engine. Detailed simulations of the engine can potentially provide this. Such simulations are becoming tractable now with the increase in available computational power. Since the combustion process is the primary controlling feature in these engines, an accurate combustion model is essential for enabling these simulations. This model must be efficient and valid across different combustion regimes, since modern engines might operate in hybrid modes. In this work, a framework for studying combustion in engines is developed. In the first part of this work, the focus is on the premixed regime which is dominant in Spark Ignited (SI) engines. The canonical flamelet model used extensively to study constant pressure premixed combustion is extended to variable pressure conditions that are observed in an engine cylinder. An extrapolation procedure is also developed for ensuring a consistent enthalpy and temperature under strong compression. This model is validated against direct numerical simulations with detailed chemistry under different turbulence conditions. The performance of the isobaric model is also evaluated in high turbulence conditions by comparing against detailed experiments of a piloted premixed jet burner with finite rate chemistry and different turbulence levels. In the second part of this work, the autoignition regime is studied, which is the dominant regime in Homogeneous Charge Compression Ignition (HCCI) engines. The Representative Interactive Flamelet (RIF) combustion model has previously been used to describe ignition, combustion, and pollutant formation in direct-injected Diesel engines. It has also been applied successfully for HCCI type combustion. In this work, the RIF model is validated against direct numerical simulation data to evaluate model performance under mixture stratification in Diesel, HCCI, and hybrid regimes. A wide range of thermal stratification and concurrent stratification cases is considered using the model. Since mixed modes are present in a modern engine, a multi-regime combustion model is required. A model is developed in this work by integrating the models for autoignition and premixed regimes. This model is validated against direct numerical simulations with for cases both regimes are important. Finally, simulations of an actual engine are performed. First, the validity of the flow solution is tested by simulating flow bench configurations with no valve motion or reaction. Next, gas exchange simulations of a motored engine are performed. Last, simulation of mixture formation in an engine is performed.

Keywords: Mechanical Engineering
Advisor: Pitsch Heinz
Iaccarino Gianluca advisor. Thesis advisor
Mungal Mark Godfrey advisor. Thesis advisor
Multi-scale data-driven analysis of sex differences in human disease [electronic resource]

There are clear physiological differences between men and women, including dimorphism in disease susceptibility and treatment response. These disparities often stem from biological differences between the sexes. Sex matters at every level of biology, including genes, proteins, pathways and tissues. Sex chromosomes are fundamental determinants of genetic makeup; sex hormones regulate the expression of thousands of genes; immune response pathways differ between the sexes; and clinical variables like heart rate and pain intensity are also divergent. With current technological advances in high-throughput measurement modalities, we can simultaneously probe every gene in the genome or record millions of clinical features in databases for research. However, investigators using large-scale data sources often ignore the question of sex differences. Many experimental studies use only male animals, and clinical trials often exclude one sex in an attempt to reduce variability in their results. This has led to many pharmaceuticals failing to receive approval by the Food and Drug Administration due to toxicity or lack of efficacy in the untested sex. Rather than completely ignoring sex or viewing it only as a variable to control for, we should make sex differences research a priority of every study. In particular, we should incorporate sex analysis into large-scale genomics, proteomics and clinical analyses. In addition, integrating different types of data will enable more powerful mechanistic studies. Investigating sex differences can lead to new insights about how disease operates differently in men and women. These insights will in turn lead us closer to the goal of personalized medicine for both men and women. In this thesis, I describe methods to address informatics challenges in performing sex-differentiated analysis of high-throughput datasets. In particular, I show that I have (1) developed a novel statistical method to systematically analyze genome-wide association study data for sex differences, (2) applied the method to discover and validate a novel sex difference in a top Crohn's disease risk gene, (3) developed methods to mine large electronic medical record databases to discover sex differences in clinical pain measures, and (4) modeled sex-specific gene-gene interactions to discover molecular sex differences in Alzheimer's disease. This dissertation contains a set of methods for (1) genomics and other data-driven researchers to discover sex differences in molecular and clinical measurements and (2) sex differences researchers to integrate large-scale data sources. Many of the methods I have developed are generalizable to any situation in disease or genomics research with a binary variable of interest.

Advisor: Butte Atul J. primary advisor. Thesis advisor
Stefanick Marcia Lynn
Tibshirani Robert advisor. Thesis advisor
Nanostructured materials for alternative energy devices [electronic resource] : supercapacitors, ultracapacitors, and lithium ion batteries

Energy storage and generation are the biggest technological challenges of our generation. Among various devices attracting attention are supercapacitors and ultracapacitors. These devices have higher power densities than rechargeable batteries, meaning that energy can be withdrawn and inserted more quickly, but they currently have insufficiently high energy densities to become widespread within commercial applications. In addition, ultracapacitors tend to use expensive organic electrolytes while supercapacitors tend to use either scarce or expensive metal oxides, and both devices in academia tend to report results based on extremely low mass loadings of active materials (microgram scale). To enhance the commercial viability of supercapacitors and ultracapacitors as feasible complementary energy storage devices to lithium ion batteries such as during the fast time scale regenerative braking process in electric or hybrid cars, these energy density and cost issues must be addressed. In this dissertation, porous 3D metallic nickel foam is used as a high surface area catalyst and substrate for carbon nanofiber growth and ultimately enables mass loadings of carbon nanofibers one or two orders of magnitude higher (tens of milligrams per square centimeter) than in other academically demonstrated devices to enable superior top-down, per-area capacitance, energy density, and power density ultracapacitor results. A different study is also presented that demonstrates that by way of lithium ion battery charging and discharging steps, in which lithium ions are inserted and extracted during each cycle and leave porous voids behind in the host structures, nanostructures such as silicon nanowires, zinc oxide nanorods, and silver nanowires can achieve controllable, tunable porosities. This general method of tuning porosities of nanostructures should provide a novel tool for researchers to control nanostructure morphologies.

Keywords: Materials Science and Engineering
Advisor: Cui Yi
Clemens Bruce A.
Melosh Nicholas A. advisor. Thesis advisor
Nanostructuring molybdenum disulfide to engineer its surface and bulk properties for solar hydrogen production [electronic resource]

The development of readily available and sustainable sources of energy is a key component to ensuring our long-term economic growth and prosperity. Solar technologies, in particular, hold the potential to address a significant portion of our energy demands for decades to come. However, large scale proliferation of solar energy requires further technological advancements to efficiently collect, store, and distribute the energy provided by the sun. Photoelectrochemical (PEC) water splitting is one approach that converts the energy available from solar photons directly into hydrogen (H2) in a manner that is sustainable and carbon-free. In this process, the solar energy absorbed by a PEC device oxidizes water via the oxygen evolution reaction (OER) to produce a sustainable supply of protons (H+) which are concurrently reduced into H2 via the hydrogen evolution reaction (HER). H2 production currently represents a

Keywords: Chemical Engineering
Advisor: Jaramillo Thomas Francisco primary advisor. Thesis advisor
Bent Stacey advisor. Thesis advisor
Prinz F. B. advisor. Thesis advisor