Statistics and Probability Commons^™

Double-Blinded Randomized Control Trial Investigating The Efficacy Of Probiotic Mouth Rinse In Enhancing Oral Health, Sudeep C B Prof. Dr., Sunil P M Prof. Dr.

Annual Research Symposium

The efficacy of probiotic mouth rinse in enhancing oral health was investigated through a double-blinded randomized controlled trial involving 45 children aged 12 to 15 over a two-week period. Comparing probiotic mouth rinse, chlorhexidine mouth rinse, and a control group, plaque and gingival accumulation were assessed using established indices. Results indicated both probiotic and chlorhexidine groups exhibited reduced plaque compared to the control, with the probiotic group showing superior reduction in gingival inflammation. These findings suggest the potential of probiotic mouth rinse in improving oral hygiene and overall oral health.

Comparative Efficacy Of Forsus Frd And Twin Block Appliance In Class Ii Malocclusion Correction, Priyanka Udeshi

Annual Research Symposium

The Forsus Fatigue Resistant Device (FRD) and Twin Block appliances are used to correct Class IImalocclusions. Both appliances work primarily through mandibular repositioning. Despite theirwidespread use, uncertainties remain about their specific mechanisms of correction andcomparative effectiveness. Our study aimed to evaluate and compare the dentoalveolar, skeletal,and soft tissue changes between the Forsus FRD and Twin Block appliance in the treatment of ClassII malocclusions.

Evaluating Pi Angle Efficacy Against Established Cephalometric Angular Parameters, Sumit Bohra

Annual Research Symposium

Cephalometrics is a cornerstone of orthodontic diagnosis. Numerous angularmeasurements are used to assess the anteroposterior jaw relation include the ANB angle, Betaangle, Yen angle, and W angle. However, these have limitations, leading to the development of thePi angle. Our study aimed to evaluate the predictability of the Pi angle in a South Indian populationand compare it with other established antero-posterior dysplasia indicators such as the ANB angle,W angle, Yen angle, and Beta angle. This comparison is crucial for determining the most accuratemethod for assessing sagittal skeletal discrepancies.

Efficacy Of Concentrated Growth Factor Membrane Versus Collagen Membrane In Treating Gingival Recession, Shazneen Kandawalla

Annual Research Symposium

Creating a beautiful smile has always been a cornerstone of dental practice. Gingival recession, characterized by a downward shift of the gum line, causing uneven gum margins and an unattractive appearance.

The Vestibular Incision Subperiosteal Tunnel Access (VISTA) technique, introduced by Zadeh in 2011, offers an innovative solution for root coverage. Concentrated Growth Factor Membrane (CGF) further enhances dental repair.

Spatial Analysis Of Maxillary Central Incisors In Relation To The Nasopalatine Canal And Surrounding Alveolar Bone, Harnoor Dhillon

Annual Research Symposium

The use of mini-implants in orthodontics has enabled greater retraction of incisors than previously achievable. This change underscores the need to understand how incisors relate to the alveolar bone and nasopalatine canal. The varying labiolingual inclination of incisors during orthodontic treatments may influence these anatomical relationships. Our study aimed to assess the spatial relationship between maxillary central incisors and the surrounding alveolar bone and nasopalatine canal.

Implementation Of Best Practice When Collecting Blood Cultures In The Emergency Department (Ed), Parker Grossman

Annual Research Symposium

The purpose of this study is to compare blood culture contamination rates in the ED before and after educational in-services on best practice.

Impact Of Non-Surgical Periodontal Treatment On Salivary Hif-1Α In Gingivitis & Periodontitis Patients, Srijanani S

Annual Research Symposium

To estimate and compare the level of salivary HIF-1α in periodontally & systemically healthy volunteers, generalized chronic gingivitis & periodontitis patients before and after non-surgical periodontal treatment.

Discussion On “Spatial+: A Novel Approach To Spatial Confounding” By Dupont, Wood, And Augustin, Brian J. Reich, Shu Yang, Yawen Guan

Department of Statistics: Faculty Publications

Congratulations to the authors for this thoughtful and timely contribution to the spatial confounding literature. The intuitive nature of the method and simplicity of the estimation procedure will surely make Spatial+ popular with practitioners, and the theoretical developments are a major advance for researchers in this area. There is much to discuss! We have formatted our discussion in two sections: in Section 2 we consider the assumptions and statistical properties of Spatial+, and in Section 3 we examine how Spatial+ fits in the wider literature on spatial causal inference.

Detection And Relative Quantitation Of Changes In Gene Expression Of Hippo Proteins In Doxorubicin-Exposed Human Cells By Rt-Qpcr, Lillian Fleisher, Tricia Domingo, Christopher So, Stephen Lee

Annual Research Symposium

The HIPPO signaling pathway plays a key role in cell proliferation and tumor suppression. It consists of serine/threonine kinase cascades, including MST1/2, LATS1/2, YAP, and TAZ. When the HIPPO pathway is activated, gene expression is altered. Dysfunction of the activity of this cascade may contribute to cancer development and chemoresistance. FAT4 is an intracellular protein that may regulate the HIPPO signaling. The purpose of this study is to determine the relative changes of mRNA of the HIPPO pathway members and FAT4 in doxorubicin-treated and vehicle-treated in vitro cell lines.

Prebiotic Proanthocyanidins Inhibit Bile Reflux–Induced Esophageal Adenocarcinoma Through Reshaping The Gut Microbiome And Esophageal Metabolome, Katherine M. Weh, Connor L. Howard, Yun Zhang, Bridget A. Tripp, Jennifer Clarke, Amy B. Howell, Joel H. Rubenstein, Julian A. Abrams, Maria Westerhoff, Laura A. Kresty

Department of Statistics: Faculty Publications

The gut and local esophageal microbiome progressively shift from healthy commensal bacteria to inflammation-linked pathogenic bacteria in patients with gastroesophageal reflux disease, Barrett’s esophagus, and esophageal adenocarcinoma (EAC). However, mechanisms by which microbial communities and metabolites contribute to reflux-driven EAC remain incompletely understood and challenging to target. Herein, we utilized a rat reflux-induced EAC model to investigate targeting the gut microbiome–esophageal metabolome axis with cranberry proanthocyanidins (C-PAC) to inhibit EAC progression. Sprague-Dawley rats, with or without reflux induction, received water or C-PAC ad libitum (700 μg/rat/day) for 25 or 40 weeks. C-PAC exerted prebiotic activity abrogating reflux-induced dysbiosis and mitigating …

Action Plan: Gym Cleanliness At The Jaeger Center, Blair A. O'Connor

CAFE Symposium 2024

I have created an action plan to assess current patrons' satisfaction with the cleaning materials provided at the Gettysburg College Jaeger Center, and increase the amount or variety if the need is there. Due to a combination of behaviors and bacteria in the Jaeger Center, gym users are at risk of contracting infections. The objective of this plan is for gym users to feel more empowered and safe in their environment. While there may be individuals who feel like increased disinfecting efforts and supplies are not necessary at the Jaeger Center, what may not be a concern for one person …

Principal Component Analysis With Application To Credit Card Data, Eleanor Cain, Semhar Michael, Gary Hatfield

SDSU Data Science Symposium

Principal Component Analysis (PCA) is a type of dimension reduction technique used in data analysis to process the data before making a model. In general, dimension reduction allows analysts to make conclusions about large data sets by reducing the number of variables while retaining as much information as possible. Using the numerical variables from a data set, PCA aims to compute a smaller set of uncorrelated variables, called principal components, that account for a majority of the variability from the data. The purpose of this poster is to understand PCA as well as perform PCA on a large sample credit …

Predicting Crop Yield Using Remote Sensing Data, Mary Row, Jung-Han Kimn, Hossein Moradi

SDSU Data Science Symposium

Accurate crop yield predictions can help farmers make adjustments or changes in their farming practices to optimize their harvest. Remote sensing data is an inexpensive approach to collecting massive amounts of data that could be utilized for predicting crop yield. This study employed linear regression and spatial linear models were used to predict soybean yield with data from Landsat 8 OLI. Each model was built using only spectral bands of the satellite, only vegetation indices, and both spectral bands and vegetation indices. All analysis was based on data collected from two fields in South Dakota from the 2019 and 2021 …

Session 6: The Size-Biased Lognormal Mixture With The Entropy Regularized Algorithm, Tatjana Miljkovic, Taehan Bae

SDSU Data Science Symposium

A size-biased left-truncated Lognormal (SB-ltLN) mixture is proposed as a robust alternative to the Erlang mixture for modeling left-truncated insurance losses with a heavy tail. The weak denseness property of the weighted Lognormal mixture is studied along with the tail behavior. Explicit analytical solutions are derived for moments and Tail Value at Risk based on the proposed model. An extension of the regularized expectation–maximization (REM) algorithm with Shannon's entropy weights (ewREM) is introduced for parameter estimation and variability assessment. The left-truncated internal fraud data set from the Operational Riskdata eXchange is used to illustrate applications of the proposed model. Finally, …

Session 6: Model-Based Clustering Analysis On The Spatial-Temporal And Intensity Patterns Of Tornadoes, Yana Melnykov, Yingying Zhang, Rong Zheng

SDSU Data Science Symposium

Tornadoes are one of the nature’s most violent windstorms that can occur all over the world except Antarctica. Previous scientific efforts were spent on studying this nature hazard from facets such as: genesis, dynamics, detection, forecasting, warning, measuring, and assessing. While we want to model the tornado datasets by using modern sophisticated statistical and computational techniques. The goal of the paper is developing novel finite mixture models and performing clustering analysis on the spatial-temporal and intensity patterns of the tornadoes. To analyze the tornado dataset, we firstly try a Gaussian distribution with the mean vector and variance-covariance matrix represented as …

Mapping Urban Form Into Local Climate Zones For The Continental Us From 1986–2020, Meng Qi, Chunxue Xu, Wenwen Zhang, Matthias Demuzere, Perry Hystad, Tianjun Lu, Peter James, Benjamin Bechtel, Steve Hankey

Earth and Environmental Sciences Faculty Publications

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people’s activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling …

Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete

Dissertations, Theses, and Capstone Projects

This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.

Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing …

A Causal Inference Approach For Spike Train Interactions, Zach Saccomano

Dissertations, Theses, and Capstone Projects

Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …

Making Sense Of Making Parole In New York, Alexandra Mcglinchy

Dissertations, Theses, and Capstone Projects

For many individuals incarcerated in New York, the initial step toward freedom begins with an interview with the Board of Parole. This process, however, is frequently a complex and challenging one, characterized by repeated denials and extended incarcerations. The disparity in outcomes – where one individual may receive over 20 denials and another is granted parole on their first attempt – highlights the ambiguity and inconsistency in the parole decision-making process. This project aims to clarify the factors that influence parole decisions by concentrating on measurable variables. These include age, race, duration of sentence served, proportion of sentence served, type …

Sparse Bayesian Variable Selection In High‐Dimensional Logistic Regression Models With Correlated Priors, Zhuanzhuan Ma, Zifei Han, Souparno Ghosh, Liucang Wu, Min Wang

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we propose a sparse Bayesian procedure with global and local(GL) shrinkage priors for the problems of variable selection and classification in high-dimensional logistic regression models. In particular, we consider two types of GL shrinkage priors for the regression coefficients, the horseshoe (HS)prior and the normal-gamma (NG) prior, and then specify a correlated prior for the binary vector to distinguish models with the same size. The GL priors are then combined with mixture representations of logistic distribution to construct a hierarchical Bayes model that allows efficient implementation of a Markov chain Monte Carlo (MCMC) to generate samples from …