VictorJuiz/thesis huggingface dataset:
Priming by streptococcus pneumoniae causes changes in gene expression in Drosophila melanogaster [electronic resource]
Keywords: Microbiology and Immunology.

Insects are typically described as having no memory yet they can be primed by past exposure to survive a specific pathogenic challenge. To determine how a primed immune response might work in the context of a transcriptional response to a pathogen, we performed a microarray analysis that identified genes modulated by priming during a S. pneumoniae infection in Drosophila melanogaster. We followed 22 transcripts at a high level of resolution using qRT-PCR of tight timelines of infected primed and naïve flies. We knocked down expression of 17 of these transcripts/genes and observed changes in survival and bacterial growth dynamics. We found that priming affected the transcriptional response in three ways. First, the basal levels of some transcripts changed permanently upon priming. Second, the induced maximal expression level of genes was altered between the first and second infection. Third, there were changes in sensitivity where genes were induced at earlier times in the infection in the primed flies. We hypothesized that these changes are evolved to adapt, where the fly's immunity changes in a fashion that allows it to respond more effectively to that infection in subsequent exposures. We highlight 5 genes that follow our gene induction model and show that when knocked down, primed flies exhibit survival and bacterial growth phenotypes that demonstrate their important role in the primed response. These data show how a simple signaling network can possess memory which allows it to respond more effectively to threats based upon past experiences. We also added Serratia marcescens to the repertoire of pathogens that induce the primed immune response. We showed that this memory lasts for the life of the fly and the Toll pathway is necessary for the response. We demonstrated that Serratia is not cleared in primed flies despite longer survival times, indicating a tolerance effect. We found the highest protection to be species specific and nutrient deprivation to negatively impact priming. It is the first time gram-negative bacteria have been characterized in the primed response and the first time tolerance-based priming against any pathogen has been demonstrated in Drosophila.
Prior-knowledge-based optimization approaches for CT metal artifact reduction [electronic resource]
Keywords: Electrical Engineering.

The steak artifacts caused by metal implants have long been recognized as a problem that limits various applications of computed tomography (CT). This type of artifacts typically occurs from metallic implants like dental fillings, hip prostheses, implanted marker bins and branchy-therapy seeds. The artifacts not only blur the CT images and lead to inaccuracies in diagnosis, but also make delineation of anatomical structures intractable, which is important in image-guided intervention procedures. This dissertation focuses on utilizing prior knowledge wisely to reconstruct artifact-reduced high-quality images. Several optimization approaches that integrate prior knowledge in both image and projection spaces are proposed. First, we propose a constrained optimization model that features an anisotropically penalized smoothness objective function, subject to a data tolerance constraint and an image non-negativity constraint. Numerical examples and experimental examples are presented to demonstrate that the algorithm is capable of significantly reducing metal streak artifacts, suppressing noise and preserving edge structures. Second, a sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradient (IG). This approach, by altering a single parameter in the weighting function, flexibly controls the sparsity of IG and reconstructs artifacts-free images in a two-stage process: binary reconstruction and background reconstruction. It is therefore a systematic approach that first identifies metal traces from projection space and then reconstructs metal-free image based on metal-trace-removed projection data. Finally, we propose a projection in-painting method that takes advantage of the piece-wise smoothness of projection data. A penalized-least-squares (PLS) model is used to obtain a smoothed projection image that realistically represents the ideal noise-free projection. Experimental phantom studies and clinical examples are presented to demonstrate the performance of the proposed in-painting algorithm.
Processes of sedimentation, stratigraphic architecture, and provenance of deep-water depositional systems [electronic resource] : the upper Miocene Upper Mount messenger formation, Taranaki Basin, New Zealand and Pliocene Repetto and Pico formations, Ventura Basin, California
Keywords: Geological and Environmental Sciences.

This dissertation characterizes the processes of sedimentation, lithofacies, lithofacies associations, depositional architecture, depositional environments, provenance, and overall evolution of the Upper Miocene Upper Mount Messenger Formation, Taranaki Basin, New Zealand and Pliocene Repetto and Pico formations, Ventura Basin, California. First, the Upper Mount Messenger Formation represents a deep-water slope apron composed of at least five genetically-linked, north-trending, paired channel-levee elements and large-scale overbank avulsion cycles that systematically stack to the southwest. Second, the uniformly fine- to very fine-grained sandstone of the Upper Mount Messenger Formation is largely derived from low-grade metamorphic forearc sandstone and graywacke recycled from the Torlesse and Waipapa terranes. Third, the Repetto and Pico formations represent a type of braided system composed of lobe complexes that lack upslope channel-levee feeders that would necessitate the term 'frontal' lobe. These lobes spilled from canyon mouths lining the Ventura Basin margin and were redirected to flow westward along the basin axis. This type of deep-water system is analogous to how deposits form at the mouths of alluvial fans that feed terrestrial braided river valleys. In addition to the application of this dissertation to understanding the basic science surrounding processes of sedimentation from density flows and specific evolution of flows that deposited the sediment in these formations, models presented here can be used to predict sediment distribution patterns in other deep-water hydrocarbon basins lacking sufficient data.
The promise of a borderless world [electronic resource] : diaspora, nativism, and the Kenyan Somali population (1890-2010)
Keywords: History.

This dissertation examines the intellectual and political strategies of the Kenyan branch of the Somali diaspora (a population comprised of the Isaaq and Harti Somalis, who originate from British Somaliland and Aden, and the nomadic Somali-speaking people, who inhabit the borderlands of Kenya, Ethiopia, and Somalia). This projects analyzes how the political strategies of Kenyan Somali traders, migrants, nomads, and elites evolved and came into crisis during three pivotal moments: the shift towards formal empire in the late nineteenth century; the transition from empire to nationalism in the 1950s and 60s; and the move in the 1990s towards political and economic liberalization, which accompanied the collapse of the Somali state. I argue that historically specific, non-Western traditions of trans-nationalism have challenged and continue to challenge the hegemony of the nation-state.
Quantile function methods for decision analysis [electronic resource]
Keywords: Management Science and Engineering.

Encoding prior probability distributions is a fundamental step in any decision analysis. A decision analyst often elicits an expert's knowledge about a continuous uncertain quantity as a set of quantile-probability pairs (points on a cumulative distribution function) and seeks a probability distribution consistent with them. Quantile-parameterized distributions (QPDs) are continuous probability distributions characterized by quantile-probability data. This dissertation demonstrates the flexibility of QPDs to represent a wide range of distributional shapes, examines a QPD's range of parametric feasibility, and introduces various means of using QPDs when encoding relevance between uncertainties. A decision maker may or may not believe a continuous uncertain quantity has well-defined bounds. For the former case, I offer a toolkit for engineering the support of a QPD. For the latter, I develop a theory for comparing tail heaviness between probability distributions and offer methods for engineering the tail behavior of a QPD. I conclude with an example decision analysis: a pharmaceutical company CEO's decision whether to market or license a drug. This analysis uses QPDs to encode prior probability distributions with bounded and unbounded supports. It introduces three tools that use QPDs: data compression of multivariate probabilistic simulation, sensitivity analysis of the tail heaviness of a prior probability distribution, and valuation of probability assessment.
Quantitative approaches to understanding cellular recognition of the centromere [electronic resource]
Keywords: Biochemistry.

Faithful propagation of the genome during division is a process fundamental to all cells. Eukaryotic cells segregate their chromosomes by attaching them via the kinetochore, a large multi-protein complex, to a microtubule spindle that physically moves the chromosomes to the daughter cells. The kinetochore is assembled at the site of the centromere, a specialized chromatin domain and set of associated proteins. In most organisms, the identity of the centromere is not determined by an underlying DNA sequence but by the incorporation of a centromere-specific histone H3 variant called CENP-A. How the cell recognizes these centromere-specific histones as the site for kinetochore assembly and the site at which to replenish CENP-A during cell division remains a major unanswered question. Many experimental techniques have been used to probe the function of the centromere, but quantitative fluorescence imaging has proved especially useful because of its molecular specificity, high sensitivity, and ability to examine single cells and single centromeres. Accurate quantitative analysis of images is critical to being able to apply fluorescence imaging to centromeres, where often meaningful intensity differences between treatments can be a factor of two or smaller, and meaningful spatial distances are far smaller than the diffraction limit of light. Empirical determination of the accuracy of an image analysis method is necessary to have confidence in these subtle measurements. We first present a series of quantitative image metaanalysis tools whose aim is to provide a quantitative metric to compare different analysis methods, and we develop an accurate and linear automated method for quantifying the intensities of centromeres in fluorescence images. Next, we describe a new superresolution microscopy methodology that can be used to measure and correct the optical aberrations introduced by cells, a critical step for measuring spatial distances at centromeres. Finally, we combine fluorescence imaging with a system for biochemical reconstitution of centromeric chromatin to examine the question of how CENP-A is recognized as the site at which the cell will incorporate additional CENP-A during cell division. We find that the six amino acids at the CENP-A C-terminus are both necessary and sufficient for this recognition.
Quantitative magnetic characterization of single cells [electronic resource]
Keywords: Applied Physics.

Iron oxide based magnetic nanoparticles are used in a variety of biomedical applications, such as targeted drug delivery, cancer therapy and MRI. As many of these applications approach the single cell level, it becomes necessary to develop magnetic characterization techniques with improved sensitivity and spatial resolution. In this thesis, I present magnetic measurements of individual magnetotactic bacteria and iron loaded cancer cells, made with scanning SQUID microscopy. I also describe the modeling technique we use to calculate the dipole moments of these individual cells from their corresponding scanning SQUID magnetometry images. By measuring many individual cells, we can compare our results with bulk magnetometry measurements to gain additional insights. Such single cell magnetic measurements will be helpful for optimizing image contrast in single cell MR tracking and other single cell applications.
Quantitative medical image analysis for decision support [electronic resource]
Keywords: Electrical Engineering.

A crucial challenge for radiology is maintaining high interpretation accuracy in the face of increasing imaging workload and limited time to review and interpret the images for each patient. Variation in interpretation accuracy among radiologists is a recognized challenge. Two techniques that could help radiologists improve their interpretation are Content-based Image Retrieval (CBIR) for diagnostic support, and lesion size tracking for evaluating response to treatment. CBIR provides assists radiologists to find images from a database that are similar in terms of shared imaging features to the images they are interpreting. The performance of CBIR hinges critically on features that characterize lesions. In the first half of the talk, I will focus on the development of a novel quantitative imaging feature that describes the margin characteristics of lesions. This new margin sharpness feature is robust to variation in lesion segmentation, and achieves excellent CBIR performance in clinical datasets. Tracking lesion size in response to treatment is a crucial component for patient management as well as towards finding the best cancer therapy through clinical trials. Traditionally, tracking lesion size using serial Computed Tomography (CT) scans is largely a manual and tedious process. In the second half of the talk, I will present a novel method to automatically track and segment lymph nodes in serial CT scans. My method has achieved excellent overall segmentation performance compared to manual segmentation provided by radiologists. Ultimately, I envision that the translation of both of the above methods to the clinic will improve diagnostic accuracy, precision, and efficiency.
Quantum dot P-I-N structure solar cells by atomic layer deposition [electronic resource]
Keywords: Mechanical Engineering.

With projections suggesting large increases in global energy demand over the coming decades, it is paramount that clean and renewable methods of producing energy are investigated. The sun is an attractive source of renewable energy, offering approximately 5,000 times the world's energy needs. However, current commercial solar technology's low efficiencies and high production costs limit them from being a competitive alternative to traditional power generation. This thesis presents the theoretical background, fabrication, characterization, and measurement of a novel p-i-n structure quantum dot (QD) solar cell fabricated entirely by atomic layer deposition (ALD). As ALD is a low temperature and scalable deposition technique, this type of architecture may be well suited for mass production. The use of different sized QDs may allow for the device to more efficiently absorb the solar spectrum and break through the theoretical efficiency limits of traditional single junction solar cells. Therefore this type of architecture has the potential to open up a new class of high efficiency and low cost solar cells which may help meet the world's future energy demands. Transmission electron microscopy (TEM) and external quantum efficiency (EQE) measurements confirmed the successful fabrication and functionality of the ALD QD solar cell. Furthermore, novel architectures were explored which utilized graded quantum dot layers which showed an increase of charge extraction of over 300%.
Radiative bottomonium spectroscopy at the [upsilon](2,3S) resonances at BaBar [electronic resource]
Keywords: Physics.

The compact bound state consisting of a bottom and anti-bottom quark pair interacting via the strong nuclear force is called "bottomonium." A wealth of long-lived bottomonium states can be both experimentally produced and theoretically described, providing a unique tool to probe calculation techniques with experiment. Bottomonia with total angular momentum J = 1 and orbital angular momentum L = 0 at a variety of radial excitations n -- called [upsilon](nS) -- can be produced at electron-positron colliders. The BABAR experiment, located at the interaction point of such a collider (the PEP-II storage ring), has observed 122 million [upsilon](3S) and 100 million [upsilon](2S) decays. Some of these involve a transition to the bottomonium state [chi]bJ(nP) (L = 1 and J = (0, 1, 2)), emitting a photon, with subsequent transition to a lower [upsilon](nS), also emitting a photon. The ﬁnal [upsilon](nS) can be identiﬁed through a decay to two muons. The dependence of the branching fractions and photon energies in this process on the spin state of the intermediate [chi]bJ(nP) is a key test of phenomenological models. To this end, this dissertation contains a nearly comprehensive study of these transitions with an emphasis on experimentally optimal discrimination between various models. This focus spurs innovative techniques that complement a large array of physics results, both presented in detail herein.
A random walk through combinatorial probability [electronic resource]
Keywords: Mathematics.

This thesis comprises a collection of three stand-alone problems in combinatorial probability. A common aspect to all three problems is the use of perturbation based techniques to show that a quantity we wish to estimate is asymptotically equivalent to another known quantity. The first problem generalises Arratia, Barbour and Tavare's (2000) theory of logarithmic combinatorial structures, showing that conditional independence of component counts is not necessary for results such as the central limit theorem, the Poisson-Dirichlet limit and the Erdos-Turan limit, and unifying these theorems within a single universality result that sheds greater insight into why the same limit theorem should hold for all families of combinatorial structures in this class. The second problem studies the Hopfield model in the limit where the number of patterns is superlinear in the number of sites, computing the asymptotic behaviour of the free energy by applying Guerra and Toninelli's (2002) interpolation technique to the related Sherrington-Kirkpatrick model. The third problem describes a new strategy for sampling combinatorial structures, including many simple demonstrative examples, and with particular application to the case of labelled graphs with given degree sequence, where we describe an algorithm whose domain of applicability and running time compare quite favourably to existing algorithms in the literature.
Rapid and high-specificity assay for micro-RNA detection using combined on-chip isotachophoresis and affinity hydrogel purification [electronic resource]
Keywords: Mechanical Engineering.

microRNAs are short, non-coding RNA molecules that regulate gene expression in animal and plant cells and play important roles in diverse cellular functions. Dysregulation of microRNAs has been linked to diseases such as diabetes, alzheimer's disease, and many forms of cancer. microRNA expression signatures have therefore emerged as important new clinical biomarkers with both diagnostic and prognostic value. Despite growing interest, quantitative microRNAs continues to pose challenges for traditional RNA profiling methods due to their low abundance, high degree of sequence similarity, and complex biogenesis. In this work, we demonstrate the development of a microfluidic microRNA detection assay which addresses these challenges by leveraging two key technologies: on-chip isotachophoresis (ITP) and photopatterned functionalized hydrogels. ITP is a robust electrokinetic technique which uses a heterogeneous buffer system to perform greater than 10,000-fold focusing. In addition to providing signal enhancement, ITP focusing also accelerates reaction kinetics of nucleic acid hybridization. Our microRNA detection assay first uses ITP to enhance hybridization between microRNAs and complementary DNA reporters. Following hybridization, the ITP zone migrates into a purification region which a hydrogel decorated with DNA capture probes. Excess (unhybridized) reporters bind to the capture probes and become immobilized, while reporters hybridized to microRNAs remain focused in ITP and can be detected downstream. In the first part of the dissertation, we present a proof of concept study to assess the feasibility of a microRNA assay and characterize assay sensitivity and dynamic range under low stringency conditions. We demonstrate our technique performs quantitative analysis of synthetic microRNAs with 4 orders of magnitude dynamic range, near 1 pM (30,000 molecules) sensitivity, and assay run time of only 10 min. This constitutes a 100- fold improvement in dynamic range and limit of detection over previous ITP-based approaches. In the second part of the dissertation, we present modifications to our basic assay which allow us to detect microRNAs with single-nucleotide specificity (high stringency) and using total RNA extracted from real tissues. Using this modified assay, we quantify concentration of let-7a microRNA in mouse and human tissues, and validate these measurements, and our method more generally, by comparison with qPCR. We further develop a numerical model which demonstrates that our modified assay leverages both thermodynamics (in the first stage) and off-rate kinetics (in the second stage) to enhance hybridization specificity.
Rate adaptation in MIMO wireless systems [electronic resource]
Keywords: Electrical Engineering.

In Multiple Input Multiple Output (MIMO) systems, predicting performance prior to data transmission is needed to appropriately select transmitter parameters (e.g. QAM modulation, code rate). We want to select transmitter parameters aggressive enough to take advantage of the available channel capacity, but not so aggressive that we do not meet desired performance (error) requirements. The prediction must, therefore, be accurate. But, practical considerations must be accommodated as well. The prediction needs to be easy to compute, given limited resources. An algorithm taking too much time to run or excessive circuitry to implement cannot be deployed. Further, the prediction must be tolerant to imprecise data, as deployed hardware often does not know the environment with complete accuracy. This dissertation presents a family of algorithms to accurately predict the average bit error rate (BER) and packet error rate (PER) of a MIMO system. Further, we address these practical considerations of low computational complexity, and imprecise data. Since the algorithms make no a-priori assumptions about the channel, they are applicable to a wide variety of random channels.