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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Geos-chem adjoint inversion of SO2 and NOx emissions with multi-sensor (OMPS, OMI, and VIIRS) data over China

Wang, Yi 01 August 2019 (has links)
Accurate and timely SO2 and NOx emission inventories are required to simulate and forecast SO2 and NO2 concentrations in the atmosphere. However, bottom-up emission inventories have a time lag of at least one year, as it takes time to collect necessary activity rates and emission factors. This thesis focuses on using satellite data from Ozone Monitoring Instrument (OMI), Ozone Mapper and Profile Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS) to optimize SO2 and NOx emissions through the GEOS-Chem adjoint model. The optimized emission inventories are further applied to improve air quality simulation and forecasts. We firstly integrate OMI SO2 satellite measurements and GEOS-Chem adjoint model simulations to constrain monthly anthropogenic SO2 emissions. The effectiveness of this approach is demonstrated for 14 months over China; resultant posterior emissions not only capture a 20% SO2 emission reduction in Beijing during the 2008 Olympic Games but also improve agreement between modeled and in situ surface measurements. Further analysis reveals that posterior emissions estimates, compared to the prior, lead to significant improvements in forecasting monthly surface and columnar SO2. SO2 and NO2 observations from the newer sensor OMPS are used to optimize SO2 and NOx emissions over China for October 2013 through GEOS-Chem adjoint model. OMPS SO2 and NO2 observations are assimilated separately to optimize corresponding emissions, respectively, and posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, which are validated with both OMI and OMPS observations. The posterior emissions from assimilating OMPS SO2 and NO2 simultaneously are within -3% to 15% of separate assimilations for SO2 emissions and ±1% for NOx, and the joint assimilation saves about 50% computational time. Changes of NH3 emissions modify NO2 lifetime, hence affecting posterior NOx emissions in separate assimilations, and having impacts on both posterior SO2 and NOx emissions in joint assimilation. All these assimilation experiments are conducted at coarse (2°×2.5°) spatial resolution to save computational time, but coarse-resolution simulations underestimate hot spots of surface SO2 and NO2. Thus, the posterior coarse-resolution emissions are further efficiently downscaled to fine resolution (0.25°×0.3125°) according to spatial distributions of prior MIX emissions or VIIRS nighttime lights. Posterior fine-resolution simulation and forecasts, validating with in situ surface SO2 and NO2 measurements, improve on the prior ones.

Discovery and characterization of genetic variants associated with extreme longevity

Gurinovich, Anastasia 01 August 2019 (has links)
Over the last decade, there have been multiple genome-wide association studies (GWASs) of human extreme longevity (EL). However, only a limited number of genetic variants have been identified as significant, and only few of these variants have been replicated in independent studies. There are two possible reasons for this limitation. First, genetic variants might have a varying effect on EL in different populations, and GWAS applied to a dataset as a whole may not pinpoint such differences. Second, EL is a very rare trait in a population, and rare and uncommon variants might be important factors in explaining its heritability but GWASs have focused on the analyses of variants that are relatively common in the population. In this dissertation, I present three projects that address these issues. First, I propose PopCluster: an algorithm that automatically discovers subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects ethnicities. Second, I investigate ethnic-specific effects of APOE alleles on EL in Europeans. APOE is a well-studied gene with multiple effects on aging and longevity. The gene has 3 alleles: e2, e3 and e4, whose frequencies vary by ethnicity. I identify several ethnically different clusters in which the effect of the e2 and e4 alleles on EL changes substantially. Furthermore, I investigate the interaction of APOE alleles with the country of residence. Results of this analysis suggest possible interaction of this gene with dietary habits or other environmental factors. For the third project, I perform a GWAS of rare variants and EL in a case-control dataset with median age of cases 104 years old. I analyze 4.5 million high-imputation quality rare SNPs imputed with HRC panel with minor allele frequency < 0.05. The analysis replicates all previous genome-wide level significant SNPs and identifies a few more potential targets. Additionally, I use serum protein data available for a subset of subjects and find significant pQTLs which have potential functional role. Based on these analyses, both genetic and environmental factors appear to be important factors for EL. / 2020-07-31T00:00:00Z

Approaches for identifying lung cell type responses to perturbation

Corbett, Sean 01 August 2019 (has links)
The use of genomic profiling can provide indications of underlying molecular responses to chemical perturbation, and the characterization of these responses can provide an increased understanding of the greater physiological effects of an exposure and inform clinical decision making. This approach has proven to be effective in understanding the effects of environmental exposures such as cigarette smoke on the airway epithelium, and how they may contribute to associated disease pathogenesis. Because of the existing body of work in genomic profiling towards understanding the effects of environmental exposures, it has relevant applications towards the study of the effects of emerging exposures such as electronic cigarettes, which remain poorly understood. Further, current approaches for genomic profiling could be improved through the development of data resources and computational methods which can identify not only tissue- or sample-level molecular responses to perturbation, but also responses specific to individual cells or cell types. In light of these issues, I investigated the molecular response in airway epithelium to a novel inhaled exposure, and developed methods to support more detailed characterization of such effects. In this dissertation, I describe a clinical observational study in which I examined the gene expression effects of electronic cigarettes on the airway epithelium, and compare these effects to those of conventional cigarettes (Aim 1). Next, I describe CELDA, a novel computational method for identifying cell subpopulations and the co-expressed modules of genes that identify them in single cell RNA-seq (scRNA-seq) data (Aim 2). Finally, I describe the Lung Connectivity Map (Lung CMap), a platform for interrogating lung cell type specific responses to a large set of chemical and molecular perturbations (Aim 3). Collectively, this work encompasses both observational and computational approaches for detailed characterization of the molecular responses to perturbation, and the determination of the relative effects of these novel perturbations versus their more well-described counterparts. / 2021-07-31T00:00:00Z

Genomic analyses of transcription elongation factors and intragenic transcription

Chuang, James 01 August 2019 (has links)
Transcription of protein-coding genes in eukaryotic cells is carried out by the protein complex RNA polymerase II. During the elongation phase of transcription, RNA polymerase II associates with transcription elongation factors which modulate the activity of the transcription complex and are needed to carry out co-transcriptional processes. Chapters 2 and 3 of this dissertation describe studies of Spt6 and Spt5, two conserved transcription elongation factors. Spt6 is a transcription elongation factor thought to replace nucleosomes in the wake of transcription. Saccharomyces cerevisiae spt6 mutants express elevated levels of intragenic transcripts, transcripts appearing to initiate from within gene bodies. We applied high resolution genomic assays of transcription initiation to an spt6-1004 mutant, allowing us to catalog the full extent of intragenic transcription in spt6-1004 and show for the first time on a genome-wide scale that the intragenic transcripts observed in spt6-1004 are largely explained by new transcription initiation. We also assayed chromatin structure genome-wide in spt6-1004, finding a global depletion and disordering of nucleosomes. In addition to increased intragenic transcription in spt6-1004, our results also reveal an unexpected decrease in expression from most canonical genic promoters. Comparing intragenic and genic promoters, we find that intragenic promoters share some features with genic promoters. Altogether, we propose that the transcriptional changes in spt6-1004 are explained by a competition for transcription initiation factors between genic and intragenic promoters, which is made possible by a global decrease in nucleosome protection of the genome. Spt5 is another transcription elongation factor, important for the processivity of the transcription complex and many transcription-related processes. To study the requirement for Spt5 in vivo, we applied multiple genomic assays to Schizosaccharomyces pombe cells depleted of Spt5. Our results reveal an accumulation of RNA polymerase II over the 5 ′ ends of genes upon Spt5 depletion, and a progressive decrease in transcript abundance towards the 3 ′ ends of genes. This is consistent with a model in which Spt5 depletion causes transcription elongation defects and increases early termination. We also unexpectedly discover that Spt5 depletion causes hundreds of antisense transcripts to be expressed across the genome, primarily initiating from within the first 500 base pairs of genes. The expression of intragenic transcripts when transcription elongation factors are disrupted suggests that cells have evolved to prevent spurious intragenic transcription. However, some cases of intragenic transcription are consistently detected in wild-type cells, and some of these cases are known to be important for different biological functions. Chapter 4 of this dissertation describes our efforts to better understand the functions of intragenic transcription in wild-type cells by studying uncharacterized instances of intragenic transcription. To discover uncharacterized instances of intragenic transcription, we applied high resolution genomic assays of transcription initiation to wild-type Saccharomyces cerevisiae under three stress conditions. For the condition of oxidative stress, we show that intragenic transcripts are generally expressed at lower levels than genic transcripts, and that many intragenic transcripts are likely to be translated at some level. By comparing intragenic transcription in three yeast species, we find that most examples of oxidative-stress regulated intragenic transcription identified in S. cerevisiae are not conserved. Finally, we show that the expression of an oxidative-stress-induced intragenic transcript at the gene DSK2 is needed for S. cerevisiae to survive in conditions of oxidative stress.

Optimization and machine learning methods for Computational Protein Docking

Zarbafian, Shahrooz 23 October 2018 (has links)
Computational Protein Docking (CPD) is defined as determining the stable complex of docked proteins given information about two individual partners, called receptor and ligand. The problem is often formulated as an energy/score minimization where the decision variables are the 6 rigid body transformation variables for the ligand in addition to more variables corresponding to flexibilities in the protein structures. The scoring functions used in CPD are highly nonlinear and nonconvex with a very large number of local minima, making the optimization problem particularly challenging. Consequently, most docking procedures employ a multistage strategy of (i) Global Sampling using a coarse scoring function to identify promising areas followed by (ii) a Refinement stage using more accurate scoring functions and possibly allowing more degrees of freedom. In the first part of this work, the problem of local optimization in the refinement stage is addressed. The goal of local optimization is to remove steric clashes between protein partners and obtain more realistic score values. The problem is formulated as optimization on the space of rigid motions of the ligand. Employing a recently introduced representation of the space of rigid motions as a manifold, a new Riemannian metric is introduced that is closely related to the Root Mean Square Deviation (RMSD) distance measure widely used in Protein Docking. It is argued that the new metric puts rotational and translational variables on equal footing as far local changes of RMSD is concerned. The implications and modifications for gradient-based local optimization algorithms are discussed. In the second part, a new methodology for resampling and refinement of ligand conformations is introduced. The algorithm is a refinement method where the inputs to the algorithm are ensembles of ligand conformations and the goal is to generate new ensembles of refined conformations, closer to the native complex. The algorithm builds upon a previous work and introduces multiple new innovations: Clustering the input conformations, performing dimensionality reduction using Principle Component Analysis (PCA), underestimating the scoring function and resampling and refinement of new conformations. The performance of the algorithm on a comprehensive benchmark of protein complexes is reported. The third part of this work focuses on using machine learning framework for addressing two specific problems in Protein Docking: (i) Constructing a machine learning model in order to predict whether a given receptor and ligand pair interact. This is of significant importance for constructing the so-called protein interaction networks, an critical step in the Drug Discovery process. The success of the algorithm is verified on a benchmark for discrimination between Biological and Crystallographic Dimers. (ii) A ranking scheme for output predictions of a protein docking server is devised. The machine learning model employs the features of the docking server predictions to produce a ranked list with the top ranked predictions having higher probability of being close to the native solution. Two state-of-the-art approaches to the ranking problem are presented and compared in detail and the implications of using the superior approach for a structural docking server is discussed.

An Integrative Review of the Literature on Technology Transformation in Healthcare

Phillips, Andrew Bartlett January 2012 (has links)
Healthcare transformation through technology is a core objective of health reform. It is important for decision makers to understand the likelihood that reform policies will in fact transform. This study evaluates evidence of technology transformation in healthcare through an integrative review of the healthcare and business literature, guided by the theory of punctuated equilibrium (TPE). TPE describes the process of transformation within organizations, markets, and groups. The theory explains transformation as a pattern of long periods of incremental change (equilibrium) punctuated by short periods of dramatic change (revolution). An underlying deep structure defines the environment of the organization, market, or group. Radical change in the deep structure of the environment is necessary for transformational change. This integrative review covered the period January 2004 through April 2012. The inclusion criteria required that the article or study address both the implementation of health information technology in the United States and describe one of the three components of TPE. Five hundred twenty articles focusing on transformational change were identified through structured database searches of MedLine/PubMed, Business Source Complete, Social Science Research Network, and others. The articles were reviewed, and coded using the three elements of TPE. A directed content analysis of the coded data produced 10 themes describing the three TPE elements: variations in the environment, market complexity, regulation, flawed risk and reward, theories of technology acceptance, barriers, ethical considerations, competition and sustainability, environmental elements of revolution, and internal elements of revolution. The results describe a healthcare market exhibiting strong equilibrium and substantial resistance to change from HIT. Minimal descriptions of the revolutionary element of TPE were evident. The deep structure of healthcare indicates that the historical provider and hospital-centered market prevails. Conditions that might encourage alteration of this deep structure were: empowering and engaging patients; updating care delivery models; and reducing market uncertainty. The revolutionary changes seen in other complex markets from banking to travel to manufacturing relied heavily on the power of the consumer to alter deep structure. Although the concept of patient centeredness was present in the literature there was little clarity regarding the patient as an agent of structural change. To our knowledge this is the first application of TPE to investigate technology transformation in healthcare. Others have demonstrated TPE as a viable model for explaining transformational change in other markets. The study is limited by the study timeframe and the absence of newer literature reflecting the impact of recent policy changes. Despite this limitation the findings suggest that TPE presents a potentially valuable framework to guide evaluation of the progress of policies that encourage transformation from technology. Some propose that altering the complex deep structure of healthcare may require a complete destruction of existing processes before new processes, innovations, and technologies can emerge. The Affordable Care Act (2010) and the meaningful use provisions of the HITECH Act (2009) are moving healthcare toward new patient centered models of care. Uncertainty around the future of reform policies from possible repeal or amendment likely contributes to resistance to transformational change. This may perpetuate the historical rational and incremental pattern of HIT advancement. Patients as consumers have the potential to influence change given the appropriate tools. The importance of consumers to the transformation process suggests that policies fostering technologies that integrate patients into new care delivery models are likely paramount to realizing technological transformation.

Network based analysis of genetic disease associations

Gilman, Sarah Roche January 2013 (has links)
Despite extensive efforts and many promising early findings, genome-wide association studies have explained only a small fraction of the genetic factors contributing to common human diseases. There are many theories about where this "missing heritability" might lie, but increasingly the prevailing view is that common variants, the target of GWAS, are not solely responsible for susceptibility to common diseases and a substantial portion of human disease risk will be found among rare variants. Relatively new, such variants have not been subject to purifying selection, and therefore may be particularly pertinent for neuropsychiatric disorders and other diseases with greatly reduced fecundity. Recently, several researchers have made great progress towards uncovering the genetics behind autism and schizophrenia. By sequencing families, they have found hundreds of de novo variants occurring only in affected individuals, both large structural copy number variants and single nucleotide variants. Despite studying large cohorts there has been little recurrence among the genes implicated suggesting that many hundreds of genes may underlie these complex phenotypes. The question becomes how to tie these rare mutations together into a cohesive picture of disease risk. Biological networks represent an intuitive answer, as different mutations which converge on the same phenotype must share some underlying biological process. Network-based analysis offers three major advantages: it allows easy integration of both common and rare variants, it allows us to assign significance to collection of genes where individual genes may not be significant due to rarity, and it allows easier identification of the biological processes underlying physical consequences. This work presents the construction of a novel phenotype network and a method for the analysis of disease-associated variants. This method has been applied to de novo mutations and GWAS results associated with both autism and schizophrenia and found clusters of genes strongly connected by shared function for both diseases. The results help elucidate the real physical consequences of putative disease mutations, leading to a better understanding of the pathophysiology of the diseases.

Analysis of trans eSNPs infers regulatory network architecture

Kreimer, Anat January 2014 (has links)
eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. Overall, our work offers insights concerning the topological structure of human regulatory networks and the role genetics plays in shaping them.

Structure-Based Genome Scale Function Prediction and Reconstruction of the Mycobacterium tuberculosis Metabolic Network

Konate, Mariam January 2014 (has links)
Due to vast improvements in sequencing methods over the past few decades, the availability of genomic data is rapidly increasing, thus bringing about the need for functional characterization tools. Considering the breadth of data involved, functional assays would be impractical and only a computational method could afford fast and cost-effective functional annotations. Therefore, homology-based computational methods are routinely used to assign putative molecular functions that can later be confirmed with targeted experiments. These methods are particularly well suited to predict the function of enzymes because most metabolic pathways are conserved across organisms. However, the current methods have limitations, especially when considering enzymes that have very low sequence and structure homology to well-annotated enzymes. We hypothesized that two enzymes with the same molecular function shared significant sequence homology in the region surrounding the active site, even if they appear diverged at the global sequence level. First, we have investigated the limits of sequence and structure conservation for enzymes with the same function during divergent evolution. The goal of this was to determine the sequence identity threshold beyond which functional annotations should not be transferred between two sequences; that is the level of homology beyond which the pair of proteins would not be expected to have the same function. Our analysis, which compares several models of sequence evolution, shows that the sequences of orthologous proteins catalyzing the same reaction rarely diverge beyond 30 % identity, even after approximately 3.5 billion years of evolution. As for structure conservation, enzymes catalyzing the same reactions rarely diverge beyond 3 Ã… root-mean-square distance (RMSD). We have also explored sequence conservation constraints as a function of the distance to the active site. Although residues closer to the protein active site (within a radius of 10 Ã… around the catalytic residues) are mutating significantly slower, the requirement to preserve the molecular function also constrains residues at other parts of the protein. From these results, we have developed a structure-based function prediction method where we employ active site conservation in addition to global sequence homology for functional characterization. We then integrated this method with a probabilistic whole-genome function prediction framework previously developed in the Vitkup group, GLOBUS. The original version of GLOBUS uses sampling of probability space to assign functions to all putative metabolic genes in an input genome by considering sequence homology to known enzymes, gene-gene context and EC co-occurrence. Applying this novel method to the whole-genome metabolic reconstruction of Mycobacterium tuberculosis, we made several novel predictions for genes with apparent links to pathogenesis. Notably, our predictions allowed us to reconstruct the cholesterol degradation pathway in M. tuberculosis, which has been implicated in bacterial persistence in the literature but remains to be fully characterized. This pathway is absent from previously published metabolic models of M. tuberculosis. Our new model can now be used to simulate different environments and conditions in order to gain a better understanding of the metabolic adaptability of M. tuberculosis during pathogenesis.

Understanding and Reducing Clinical Data Biases

Fort, Daniel January 2015 (has links)
The vast amount of clinical data made available by pervasive electronic health records presents a great opportunity for reusing these data to improve the efficiency and lower the costs of clinical and translational research. A risk to reuse is potential hidden biases in clinical data. While specific studies have demonstrated benefits in reusing clinical data for research, there are significant concerns about potential clinical data biases. This dissertation research contributes original understanding of clinical data biases. Using research data carefully collected from a patient community served by our institution as the reference standard, we examined the measurement and sampling biases in the clinical data for selected clinical variables. Our results showed that the clinical data and research data had similar summary statistical profiles, but that there were detectable differences in definitions and measurements for variables such as height, diastolic blood pressure, and diabetes status. One implication of these results is that research data can complement clinical data for clinical phenotyping. We further supported this hypothesis using diabetes as an example clinical phenotype, showing that integrated clinical and research data improved the sensitivity and positive predictive value.

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