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Characterization of a Sporulation Related Two-Component Signaling System in Clostridioides difficile
No full textClostridioides difficile is a spore-forming anaerobic human pathogen, that is commonly associated with antibiotic therapy in adults. The disturbance of the gut microbiota, typically caused by antibiotics, allows for an environment in which this bacterium can grow and cause an infection (CDI). C. difficile produces spores that are dormant, and resistant to antibiotics and common disinfecting methods. These spores are the mode of transmission for this bacteria since they contaminate surfaces and are ingested to infect their hosts. The pathways that regulate the process of sporulation, or spore formation, are still poorly characterized and the initiation of this process still isn’t fully understood.
Two-component systems are present in bacteria, fungi, and plants where they function to sense and adapt to environmental conditions to ensure survival. These systems in bacteria have previously been linked to motility, virulence, cell division, sporulation, and many other essential biological processes. The histidine kinase within the system detects the environmental conditions and signals for the response regulator to provide a specific response such as DNA binding, enzymatic reactions, quorum sensing, etc.. In the C. difficile hypervirulent strain CDR20291, there are 54 response regulators and 57 histidine kinases, but not all of these have been characterized, including response regulator CD1688 and histidine kinase CD1689 which were previously predicted to be involved in sporulation.
This dissertation presents data about a sporulation-related two-component system in C. difficile. Following the introductory chapter, Chapter 2 characterizes the regulatory effect the system composed of RR CD1688 and HK CD1689 has on sporulation. This characterization demonstrated that CD1688 is a negative regulator of sporulation. Chapter 3 further investigates this negative regulator by identifying the specific steps in which activation of CD1688 represses sporulation.
Together the findings from these studies have provided additional insight into the sporulation pathway, and these insights could be exploited for the development of new treatments
An Analysis of the Microphysical and Thermodynamic Properties of Elevated Convective Cells within Snowbands Associated with Winter Storms.
Get PDFIn-situ and remote sensing observations of snowbands were obtained by probes on a cloudpenetrating NASA P-3 and a high-altitude NASA ER-2 aircraft, to identify and characterize elevated convective cells (CCs). Observations were made during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign in 2020. The radar reflectivity and Doppler velocity measured by a Cloud Radar System (CRS) on the ER-2 were used to identify CCs for time periods when the horizontal separation between the ER-2 and the P-3 was less than 1.4 km, with the characteristics of smallscale air motion subsequently determined by the Turbulent Air Motion Measurement System (TAMMS) on the P-3. Through a case study analysis of collocated regions with radar confirmed CCs, an algorithm that considered the statistical significance of the range in small scale vertical velocity from the TAMMS, as well as the magnitude of the largest velocity, was developed to identify CCs using data exclusively recorded by the TAMMS. Using time periods identified as containing elevated CCs from the TAMMS for the entire 2020 IMPACTS campaign, cloud microphysical properties derived from the Rosemount Icing Detector (RICE), Fast Cloud Droplet Probe (CDP), 2D-S Stereo Probe (2DS), and High-Volume Precipitation Spectrometer (HVPS) installed on the P-3 were used to characterize the cloud microphysical properties inside and between CCs. Cloud penetrations were defined as sequences of 20-second time intervals when the P-3 encountered cloud particles at least once every 6-7 seconds, until there was a gap of at least 6-7 seconds between cloud particles. Of the 94 instances of cloud penetration identified from the P-3 analysis, 29 contained at least one CC. Contrary to previous observations of convective cells such as in generating cells in winter storms, distributions of IWC and massweighted mean particle dimension were not statistically different for data collected within and between CCs. However, mean number-weighted particle dimensions were 0.35 mm larger between CCs than within. Total number concentrations and LWC averaged 2.8 times larger and 2.3 times larger, respectively, within CCs than between. Temperatures were on average 2.4 °C greater, and dewpoint depressions 0.77 °C smaller within CCs than between. There was a 9% decrease in supercooled liquid water (SLW) presence between CCs compared to within, and SLW was detected within all TAMMS confirmed CCs. The means for defining CC regions in a substantiated way with in-situ measurements depends on the reliability of contrasting data recorded from the ER-2 and P-3 aircraft during collocated time periods, as well as the number of collocated time periods available. It is because of these limitations, and the unconventional means for detecting CCs using the TAMMS, that differences in the observed characteristics of TAMMS-confirmed CCs may be present when compared to previous studies
Analysis of dynamics of road weather information system data
Get PDFRoad and Weather Information System consists of a network of roadside Environmental Sensor Stations (EES) collecting meteorological data. Equipped with a variety of sensors, these stations gather data including ambient temperature, subsurface temperature, precipitation level and type, brightness, and more. Consequently, RWIS systems have been critical for increasing road safety over the years by providing valuable weather information helpful in anticipating and preparing for adverse weather conditions and reducing traffic collisions. The advancement of Artificial Intelligence (AI) and Machine Learning (ML) methodologies enhances our ability to leverage these systems through the analysis of temporal datasets, unraveling dynamic behavioral patterns over time, and constructing more accurate and dependable predictive models.
The research study reported in this thesis focuses on understanding the dynamics of weather data to enhance road safety and the utilization of RWIS, especially during winter exhibiting hazardous road surface conditions. Utilizing ambient and subsurface temperature data from RWIS stations along Interstate 35, managed by the Oklahoma Department of Transportation (ODOT), the study methodically investigates the climatological patterns across different times of day and seasons. Parametric regression modeling is conducted to characterize the behavioral patterns of weather data for diurnal and nocturnal periods through seasonal progression. A notable aspect of this research is the demonstration of how incorporating data from subsurface temperature probes enhances the performance of machine learning models for the classification of road surface conditions and weather events. The study explored the influence of road clearance time on traffic speed during snowstorms employing statistical data-driven techniques of change in speed pre- and post-clearance. Machine learning classification techniques are employed to automate the detection of hazardous road surface conditions like ice and snow based on weather and speed data
We (Not Them) The People: Populist Rhetoric in the Contemporary U.S. Congress
Get PDFThis dissertation provides evidence that populist rhetoric shapes Congress, parties and individual politicians in rich and contextual ways. I collected a dataset of over 2 million tweets (2012-2020: Chapter 2) and over 150,000 speeches (110th-116th congresses: Chapter 3) from the congressional record, and analyzed these texts using automated dictionary analysis. Populist language on Twitter was associated with greater engagement (favorites, retweets) and with increased candidate fundraising (dollars, number of donors).
Analyzing speeches, members that are ideological extremists and engage in dilatory tactics use populist rhetoric more, while the most productive legislators use it less. My final substantive chapter (4) details survey experiments that gauge the impact of populism on voters' perceptions of Congress as an institution and on candidate perceptions. Candidate characteristics do affect voter perceptions of honesty and authenticity, although overall I caution against the overinterpretation of these results due to their inconsistency and small substantive size
Improving Outcomes in Machine Learning and Data-Driven Learning Systems using Structural Causal Models
Get PDFThe field of causal inference has experienced rapid growth and development in recent years. Its significance in addressing a diverse array of problems and its relevance across various research and application domains are increasingly being acknowledged. However, the current state-of-the-art approaches to causal inference have not yet gained widespread adoption in mainstream data science practices.
This research endeavor begins by seeking to motivate enthusiasm for contemporary approaches to causal investigation utilizing observational data. It explores the existing applications and potential future prospects for employing causal inference methods to enhance desired outcomes in data-driven learning applications across various domains, with a particular focus on their relevance in artificial intelligence (AI). Following this motivation, this dissertation proceeds to offer a broad review of fundamental concepts, theoretical frameworks, methodological advancements, and existing techniques pertaining to causal inference.
The research advances by investigating the problem of data-driven root cause analysis through the lens of causal structure modeling. Data-driven approaches to root cause analysis (RCA) have received attention recently due to their ability to exploit increasing data availability for more effective root cause identification in complex processes. Advancements in the field of causal inference enable unbiased causal investigations using observational data. This study proposes a data-driven RCA method and a time-to-event (TTE) data simulation procedure built on the structural causal model (SCM) framework. A novel causality-based method is introduced for learning a representation of root cause mechanisms, termed in this work as root cause graphs (RCGs), from observational TTE data. Three case scenarios are used to generate TTE datasets for evaluating the proposed method. The utility of the proposed RCG recovery method is demonstrated by using recovered RCGs to guide the estimation of root cause treatment effects. In the presence of mediation, RCG-guided models produce superior estimates of root cause total effects compared to models that adjust for all covariates.
The author delves into the subject of integrating causal inference and machine learning. Incorporating causal inference into machine learning offers many benefits including enhancing model interpretability and robustness to changes in data distributions. This work considers the task of feature selection for prediction model development in the context of potentially changing environments. First, a filter feature selection approach that improves on the select k-best method and prioritizes causal features is introduced and compared to the standard select k-best algorithm. Secondly, a causal feature selection algorithm which adapts to covariate shifts in the target domain is proposed for domain adaptation. Causal approaches to feature selection are demonstrated to be capable of yielding optimal prediction performance when modeling assumptions are met. Additionally, they can mitigate the degrading effects of some forms of dataset shifts on prediction performance
Study On The Effects Of Tubular Restrictions On Liquid Lifting In Natural Gas Wells
Get PDFLiquid loading is a common issue in natural gas wells, resulting in the accumulation of liquids in the well due to the inability of the gas to carry them to the surface. Various methods have been proposed to identify and address liquid loading, with no optimum solution. Several factors must be taken into account including location, costs and fluid properties. The use of partial tubing restrictions, namely inserts, was proposed about 2 decades ago, yet very limited investigation has been done about it. Most of previous studies have been conducted using only air and water as fluids and have identified that inserts best performance is encountered at low vSL. The present study involves the use of air and oil to evaluate the effectiveness of inserts at a range of conditions from annular to churn-slug flow. The measured parameters are pressure drop, liquid holdup, and video recordings of each test. The test matrix involves over 15 superficial gas velocities and 3 superficial liquid velocities. The effect of insert size and insert spacing is experimentally studied and models reported in the literature are benchmarked. The results suggest that inserts are most effective at low vSL, and in churn flow conditions. Their effects are diminished at low vSg ranges of slug flow and high vSg ranges of annular flow. It was found that the insert size is a relevant parameter in tests involving at least two inserts. The 1.5” insert displays a pronounced positive effect within a narrow vSg range, beyond which negative effects are observed. Tests using 1.75” inserts show a favorable performance for a wider range. The spacing setup with the best performance is obtained by using: 2 inserts, followed by the cases with 1 insert. In the 2-insert configuration, significant improvements are achieved regarding liquid holdup, compared to the cases with a single insert. Also, for this configuration the frictional losses are not substantially increased, in contrast with the tests having three inserts. This method could offer an economical, passive, and effective solution for liquid unloading in gas wells
"On Steady Advance..." An Investigation Into The Rhetorical Intent of The Testament of Freedom by Randall Thompson
Get PDFThis document will investigate the events and circumstances that shaped the music and message of Randall Thompson’s The Testament of Freedom. Evidence uncovered in this investigation will provide a more complete historical record regarding the intended message, or rhetorical intent of the work for future conductors, performers, and audiences. The Testament of Freedom was composed in honor of the two-hundredth anniversary of the birth of Thomas Jefferson, the Father of the University of Virginia. It was commissioned by the University of Virginia President’s Bicentennial Celebration Committee in the fall of 1941, just four months before the attack on Pearl Harbor. The work premiered on April 13, 1943, the same day that President Franklin D. Roosevelt dedicated the Jefferson Memorial in Washington D.C. In the intervening time between the initial commission of The Testament of Freedom and its premier, the confluence of world events led Randall Thompson to reshape, not just the original musical parameters of the commission, but the rhetorical intent of the work that ultimately resulted from that commission. This document will prove that knowledge and consideration of Randall Thompson’s rhetorical intent is imperative when assessing its moral value to society and consequently, its performance viability for future generations of conductors, performers, and audiences.
The first chapter will explore the genesis of The Testament of Freedom up to its premier on April 13, 1943, the second chapter will consist of a selective sketch of the life Randall Thompson intended to emphasize the experiences in Thompson’s life that informed the rhetorical intent of The Testament of Freedom. The third chapter will examine the musical rhetoric within The Testament of Freedom to ascertain how Thompson crafted the music of the work to illustrate its rhetorical intent. Evidence will be presented within chapter three to show that Randall Thompson chose rhetorically significant models for both the structure and melodic material within the work. Chapter four’s conclusion will summarize these findings and identify the rhetorical intent of The Testament of Freedom
Optimization of deepwater channel seismic reservoir characterization using seismic attributes and machine learning
Get PDFAccurate subsurface reservoir mapping is essential for resource exploration. In uncalibrated basins, seismic data, often limited by resolution, frequency, quality, etc., algorithms become the primary information source due to the unavailability of well logs and core data.
Seismic attributes, while integral for understanding subsurface structures, visually limit interpreters to working with only three of them at once. Conversely, machine learning, though capable of handling numerous attributes, is often seen as inscrutable "black boxes," complicating the interpretation of their predictions and uncertainties.
To address these challenges, a comprehensive approach was undertaken, involving a detailed 3D model from Chilean Patagonia's Tres Pasos Formation with synthetic seismic data. The synthetic data served as a benchmark for conducting sensitivity analysis on seismic attributes, offering insights for parameter and workflow optimization. The study also evaluated the uncertainty in unsupervised and supervised machine learning for deepwater facies prediction through qualitative and quantitative assessments.
Study key findings include: 1) High-frequency data and smaller analysis windows provide clearer channel images, while low-frequency data and larger windows create composite appearances, particularly in small stratigraphic features. 2) GTM and SOM exhibited similar performance, with error rates around 2% for predominant facies but significantly higher for individual channel-related facies. This suggests that unbalanced data results in higher errors for minor facies and that a reduction in clusters or a simplified model may better represent reservoir versus non-reservoir facies. 3) Resolution and data distribution significantly impact predictability, leading to non-uniqueness in cluster generation, which applies to supervised models as well. Strengthening the argument that understanding the limitations of seismic data is crucial. 4) Uncertainty in seismic facies prediction is influenced by factors such as training attribute selection, original facies proportions (e.g., imbalanced data, variable errors, and data quality). While optimized random forests achieved an 80% accuracy rate, validation accuracy was lower, emphasizing the need to address uncertainties and their role in interpretation.
Overall, the utilization of ground truth seismic data derived from outcrops offers valuable insights into the strengths and challenges of machine learning in subsurface applications, where accurate predictions are critical for decision-making and safety in the energy sector
Willette, Steinlen, and the Interiors and Exteriors of Montmartre
No full textThis dissertation examines artworks by Adolphe Willette and Théophile-Alexandre Steinlen in fin-de-siècle Montmartre, a neighborhood in Paris. Both artists were associated with the Chat Noir cabaret and its eponymous journal. They were hired to decorate the interior space of the cabaret with paintings and, in the case of Willette, stained glass. These were artworks that reflected and helped shape the environments inside spaces that cultivated avant-garde ideas about culture and politics. Following their work with the cabaret, both artists worked in the streets of Montmartre as well. Steinlen became a well-regarded poster artist and Willette created a parade to celebrate and raise money for the artists of Montmartre through moments of media experimentation that expanded their reach to more bigger publics. Through their bold use of media – large-scale paintings, lithographic prints, stained glass, and performance – Willette and Steinlen discovered new ways to reach audiences that eschewed traditional exhibition strategies. Through their works, they participated in dialogues that promoted their shared Bohemian ideologies of anti-capitalism, social justice, and anarchism. Both artists also imbued their work with historical references that aligned with their messaging. Willette turned to Medieval France for his brand of idyllic nationalistic nostalgia, and Steinlen looked to revolutionary history for his. Nostalgia, media, and democratic access will be key themes in a dissertation that illuminates a set of understudied works for and around the cabarets of Montmartre
Combinatorial algorithms in the approximate computing paradigm
Get PDFData-intensive computing has led to the emergence of data centers with massive processor counts and main memory sizes. However, the demand for shared resources has surpassed their capacity, resulting in increased costs and limited access. Commodity hardware, although accessible, has limited computational resources. This poses a challenge when performing computationally intensive tasks with large amounts of data on systems with restricted memory. To address these issues, Approximate Computing offers a solution by allowing selective solution approximation, leading to improved resource efficiency.
This dissertation focuses on the trade-off between output quality and computational resource usage in sorting and searching problems. It introduces the concept of Approximate Sorting, which aims to reduce resource usage while maintaining an accepted level of sorting quality. Quality metrics are defined to assess the ”sortedness” of approximately sorted arrays. The dissertation also proposes a general framework for incorporating approximate computing into sorting algorithms, presenting an algorithm for approximate sorting with guaranteed upper bounds. The algorithms operate under a constraint on the number of comparisons performed.
The dissertation continues to explore searching algorithms, specifically binary search algorithms on approximately sorted arrays. It addresses cases where metrics are given for the input array and cases where metrics are not available. Efficient and optimal algorithms are developed for multidimensional range searches and catalog searches on approximately sorted input. The dissertation further proposes algorithms that analyze patterns in input order to optimize sorting. These algorithms identify underlying patterns and sequences, facilitating faster sorting approaches.
Additionally, the dissertation discusses the growing popularity of approximate computing in the field of High-Performance Computing (HPC). It presents a novel approach to comparison-based sorting by incorporating parallel approximate computing. The dissertation also proposes algorithms for various queries on approximately sorted arrays, such as determining the rank or position of an element. The time complexity of these querying algorithms is proportional to the input metric.
The dissertation concludes by emphasizing the wide range of applications for sorting and searching algorithms. In the context of packet classification in router buffers, approximate sorting offers advantages by reducing the time-consuming sorting step. By capping the number of comparisons, approximate sorting becomes a practical solution for efficiently handling the large volume of incoming packets.
This dissertation contributes to the field of approximate computing by addressing resource limitations and cost issues in data-intensive computing. It provides insights into approximate sorting and searching algorithms, and their application in various domains, offering a valuable contribution to the advancement of efficient, scalable, and accessible data processing