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Description
Is it possible to treat the mouth as a natural environment, and determine new methods to keep the microbiome in check? The need for biodiversity in health may suggest that every species carries out a specific function that is required to maintain equilibrium and homeostasis within the oral cavity. Furthermore, the relationship between the microbiome and its host is mutually beneficial because the host is providing microbes with an environment in which they can flourish and, in turn, keep their host healthy. Reviewing examples of larger scale environmental shifts could provide a window by which scientists can make hypotheses. Certain medications and healthcare treatments have been proven to cause xerostomia. This disorder is characterized by a dry mouth, and known to be associated with a change in the composition, and reduction, of saliva. Two case studies performed by Bardow et al, and Leal et al, tested and studied the relationships of certain medications and confirmed their side effects on the salivary glands [2,3]. Their results confirmed a relationship between specific medicines, and the correlating complaints of xerostomia. In addition, Vissink et al conducted case studies that helped to further identify how radiotherapy causes hyposalivation of the salivary glands [4]. Specifically patients that have been diagnosed with oral cancer, and are treated by radiotherapy, have been diagnosed with xerostomia. As stated prior, studies have shown that patients having an ecologically balanced and diverse microbiome tend to have healthier mouths. The oral cavity is like any biome, consisting of commensalism within itself and mutualism with its host. Due to the decreased salivary output, caused by xerostomia, increased parasitic bacteria build up within the oral cavity thus causing dental disease. Every human body contains a personalized microbiome that is essential to maintaining health but capable of eliciting disease. The Human Oral Microbiomics Database (HOMD) is a set of reference 16S rRNA gene sequences. These are then used to define individual human oral taxa. By conducting metagenomic experiments at the molecular and cellular level, scientists can identify and label micro species that inhabit the mouth during parasitic outbreaks or a shifting of the microbiome. Because the HOMD is incomplete, so is our ability to cure, or prevent, oral disease. The purpose of the thesis is to research what is known about xerostomia and its effects on the complex microbiome of the oral cavity. It is important that researchers determine whether this particular perspective is worth considering. In addition, the goal is to create novel experiments for treatment and prevention of dental diseases.
ContributorsHalcomb, Michael Jordan (Author) / Chen, Qiang (Thesis director) / Steele, Kelly (Committee member) / Barrett, The Honors College (Contributor) / College of Letters and Sciences (Contributor)
Created2015-05
Description
I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and python tool designed together. ConstrictPy implements the functions and methods defined in ConstrictR and applies data handling, data parsing, input/output (I/O), and a user interface to increase usability. ConstrictR implements a variety of common data analysis methods used for statistical and subnetwork analysis. The majority of these methods are inspired by Lionel Guidi's 2016 paper, Plankton networks driving carbon export in the oligotrophic ocean. Additional methods were added to expand functionality, usability, and applicability to different areas of data science. Both ConstrictR and ConstrictPy are currently publicly available and usable, however, they are both ongoing projects. ConstrictR is available at github.com/cnegrich and ConstrictPy is available at github.com/ahoetker. Currently, ConstrictR has implemented functions for descriptive statistics, correlation, covariance, rank, sparsity, and weighted correlation network analysis with clustering, centrality, profiling, error handling, and data parsing methods to be released soon. ConstrictPy has fully implemented and integrated the features in ConstrictR as well as created functions for I/O and conversion between pandas and R data frames with a full feature user interface to be released soon. Both ConstrictR and ConstrictPy are designed to work with minimal dependencies and maximum available information on the algorithms implemented. As a result, ConstrictR is only dependent on base R (v3.4.4) functions with no libraries imported. ConstrictPy is dependent upon only pandas, Rpy2, and ConstrictR. This was done to increase longevity and independence of these tools. Additionally, all mathematical information is documented alongside the code, increasing the available information on how these tools function. Although neither tool is in its final version, this paper documents the code, mathematics, and instructions for use, in addition to plans for future work, for of the current versions of ConstrictR (v0.0.1) and ConstrictPy (v0.0.1).
ContributorsNegrich, Christopher Alec (Author) / Can, Huansheng (Thesis director) / Hansford, Dianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Speech nasality disorders are characterized by abnormal resonance in the nasal cavity. Hypernasal speech is of particular interest, characterized by an inability to prevent improper nasalization of vowels, and poor articulation of plosive and fricative consonants, and can lead to negative communicative and social consequences. It can be associated with a range of conditions, including cleft lip or palate, velopharyngeal dysfunction (a physical or neurological defective closure of the soft palate that regulates resonance between the oral and nasal cavity), dysarthria, or hearing impairment, and can also be an early indicator of developing neurological disorders such as ALS. Hypernasality is typically scored perceptually by a Speech Language Pathologist (SLP). Misdiagnosis could lead to inadequate treatment plans and poor treatment outcomes for a patient. Also, for some applications, particularly screening for early neurological disorders, the use of an SLP is not practical. Hence this work demonstrates a data-driven approach to objective assessment of hypernasality, through the use of Goodness of Pronunciation features. These features capture the overall precision of articulation of speaker on a phoneme-by-phoneme basis, allowing demonstrated models to achieve a Pearson correlation coefficient of 0.88 on low-nasality speakers, the population of most interest for this sort of technique. These results are comparable to milestone methods in this domain.
ContributorsSaxon, Michael Stephen (Author) / Berisha, Visar (Thesis director) / McDaniel, Troy (Committee member) / Electrical Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive Bayes classifier, I am able to predict with approximately 85% accuracy a binary change in stock price using term-feature vectors gathered from the news headlines. The accuracy, precision and recall results closely rival the best models in this field of research. In addition to the results, I will also describe the mathematical underpinnings of the two models; preceded by a general investigation of the intersection between the multiple academic disciplines related to this project. These range from social to computer science and from statistics to philosophy. The goal of this additional discussion is to further illustrate the interdisciplinary nature of the research and hopefully inspire a non-monolithic mindset when further investigations are pursued.
ContributorsPriniski, John Hunter (Author) / Haiyan, Wang (Thesis director) / Hazel, Kwon (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
“Social Sports is an application which facilitates the environment fans need to support their teams, in doing so our application aids hospitality businesses market their events and brings business during their downtime. Social Sports allows businesses to market their sports screening events to fans and supporters. Fans and supporters using Social Sports are able to see the percentage of supporters/fans on each side and decide which bar or restaurant to go watch the game. Social Sport’s mission is to connect sports fans with other like minded passionate fans and enable community formation and allow sports fans around the world to socialize with much ease.”
ContributorsWood, Alexander (Author) / Rodin, Dawson (Co-author) / Bhargana, Akshat (Co-author) / Cheshire, Ashley (Co-author) / Fuller, Sarah (Co-author) / Byrne, Jared (Thesis director) / Thomasson, Anna (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05
Description
In the age of information, collecting and processing large amounts of data is an integral part of running a business. From training artificial intelligence to driving decision making, the applications of data are far-reaching. However, it is difficult to process many types of data; namely, unstructured data. Unstructured data is “information that either does not have a predefined data model or is not organized in a pre-defined manner” (Balducci & Marinova 2018). Such data are difficult to put into spreadsheets and relational databases due to their lack of numeric values and often come in the form of text fields written by the consumers (Wolff, R. 2020). The goal of this project is to help in the development of a machine learning model to aid CommonSpirit Health and ServiceNow, hence why this approach using unstructured data was selected. This paper provides a general overview of the process of unstructured data management and explores some existing implementations and their efficacy. It will then discuss our approach to converting unstructured cases into usable data that were used to develop an artificial intelligence model which is estimated to be worth $400,000 and save CommonSpirit Health $1,200,000 in organizational impact.
ContributorsBergsagel, Matteo (Author) / De Waard, Jan (Co-author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Burns, Christopher (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
In baseball, a starting pitcher has historically been a more durable pitcher capable of lasting long into games without tiring. For the entire history of Major League Baseball, these pitchers have been expected to last 6 innings or more into a game before being replaced. However, with the advances in statistics and sabermetrics and their gradual acceptance by professional coaches, the role of the starting pitcher is beginning to change. Teams are experimenting with having starters being replaced quicker, challenging the traditional role of the starting pitcher. The goal of this study is to determine if there is an exact point at which a team would benefit from replacing a starting or relief pitcher with another pitcher using statistical analyses. We will use logistic stepwise regression to predict the likelihood of a team scoring a run if a substitution is made or not made given the current game situation.
ContributorsBuckley, Nicholas J (Author) / Samara, Marko (Thesis director) / Lanchier, Nicolas (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This thesis surveys and analyzes applications of machine learning techniques to the fields of animation and computer graphics. Data-driven techniques utilizing machine learning have in recent years been successfully applied to many subfields of animation and computer graphics. These include, but are not limited to, fluid dynamics, kinematics, and character modeling. I argue that such applications offer significant advantages which will be pivotal in advancing the fields of animation and computer graphics. Further, I argue these advantages are especially relevant in real-time implementations when working with finite computational resources.
ContributorsSaba, Raphael Lucas (Author) / Foy, Joseph (Thesis director) / Olson, Loren (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This project looks at the change in strikeout patterns over the past 19 years of Major League Baseball. New research in 2001 revolutionized the pitching statistics field, and non-coincidentally, the number of strikeouts has ballooned since then. I first detail the statistical nature of the increase, looking at where the additional strikeouts are coming from. Then, a discussion of why this has happened, referencing changes in baseball strategy and talent usage optimization follows. The changes in the ways MLB teams use their pitching staffs are largely the cause of this increase. Similar research is cited to confirm that these strategy changes are valid and are having the effect of increasing strikeouts in the game. Strikeout numbers are then compared to other pitching statistics over the years to determine whether the increase has had any effect on other pitching metrics. Lastly, overall team success is looked at as a verification method as to whether the increased focus on increasing strikeouts has created positive results for major league teams. Teams making the MLB playoffs consistently ranked much higher than non-qualifying teams in terms of strikeout rates. Also included in the project are the details of data acquisition and manipulation, to ensure the figures used are valid. Ideas for future research and further work on the topic are included, as the amount of data available in this field is quite staggering. Further analysis could dive into the ways pitches themselves are changing, rather than looking at pitching outcomes. Overall, the project details and explains a major shift in the way baseball has been played over the last 19 years, complete with both pure data analysis and supplementary commentary and explanation
ContributorsCasalena, Jontito (Author) / Doig, Stephen (Thesis director) / Pomrenke, Jacob (Committee member) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This honors thesis explores and models the flow of air around a cylindrical arrow that is rotating as it moves through the air. This model represents the airflow around an archery arrow after it is released from the bow and rotates while it flies through the air. This situation is important in archery because an understanding of the airflow allows archers to predict the flight of the arrow. As a result, archers can improve their accuracy and ability to hit targets. However, not many computational fluid dynamic simulations modeling the airflow around a rotating archery arrow exist. This thesis attempts to further the understanding of the airflow around a rotating archery arrow by creating a mathematical model to numerically simulate the airflow around the arrow in the presence of this rotation. This thesis uses a linearized approximation of the Navier Stokes equations to model the airflow around the arrow and explains the reasoning for using this simplification of the fully nonlinear Navier Stokes equations. This thesis continues to describe the discretization of these linearized equations using the finite difference method and the boundary conditions used for these equations. A MATLAB code solves the resulting system of equations in order to obtain a numerical simulation of this airflow around the rotating arrow. The results of the simulation for each velocity component and the pressure distribution are displayed. This thesis then discusses the results of the simulation, and the MATLAB code is analyzed to verify the convergence of the solution. Appendix A includes the full MATLAB code used for the flow simulation. Finally, this thesis explains potential future research topics, ideas, and improvements to the code that can help further the understanding and create more realistic simulations of the airflow around a flying archery arrow.
ContributorsCholinski, Christopher John (Author) / Tang, Wenbo (Thesis director) / Herrmann, Marcus (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05