Digital Commons Network^™

Natural Language Processing For Disaster Tweets, Akinyemi D. Apampa, Nan Li

Publications and Research

Our goal is to establish an automatic model that identifies which tweets are about natural disasters based on the content of the tweets. Our method is to construct a decision tree based on keyword searching. We will construct the model using 7,645 tweets and test our model on 3,465 tweets as an assessment of the performance.

Learning From Public Spaces In Historic Cities, Cody Josh Kucharski

Symposium of Student Scholars

Successful public spaces in cities are key for enhancing social cohesion and improving health and safety. Learning from historic cities involves the development of representational and analytical tools aimed at capturing their essence as places of human interaction. The research reports findings of the spatial analysis of twenty Adriatic and Ionian coastal cities, which addresses the question of how the network of public spaces calibrates different degrees of spatial enclosure necessary for creating successful social interactions. Cities in the littoral region include well-preserved historic centers that are renowned for the successful integration of urban squares into the urban fabric. For …

The Potential Of Private Health Insurance Ownership Based On The 2018-2020 National Socioeconomic Survey Data, Arief Rosyid Hasan, Adang Bachtiar, Cicilya Candi

Kesmas

In 2014, the Indonesian Government introduced a social security program in the health sector. However, Indonesia’s out-of-pocket expenses remain high due to a lack of public interest in National Health Insurance services. Financing expensive health services with high out-of-pocket expenses has the potential to cause poverty. Private health insurance is considered a solution to this problem. This study aimed to determine the socioeconomic factors of private health insurance ownership and its potential in Indonesia. This study used secondary data from the 2018, 2019, and 2020 National Socioeconomic Surveys. Logistic regression analysis showed that the variables related to private health insurance …

A Bootstrap Method For A Multiple-Imputation Variance Estimator In Survey Sampling, Lili Yu, Yichuan Zhao

Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications

Rubin’s variance estimator of the multiple imputation estimator for a domain mean is not asymptotically unbiased. Kim et al. derived the closed-form bias for Rubin’s variance estimator. In addition, they proposed an asymptotically unbiased variance estimator for the multiple imputation estimator when the imputed values can be written as a linear function of the observed values. However, this needs the assumption that the covariance of the imputed values in the same imputed dataset is twice that in the different imputed datasets. In this study, we proposed a bootstrap variance estimator that does not need this assumption. Both theoretical argument and …

Association Between The Health Belief Model, Exercise, And Nutrition Behaviors During The Covid-19 Pandemic, Keagan Kiely, Bill Mase, Andrew R. Hansen, Jessica S. Schwind

Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications

Introduction: The COVID-19 pandemic has affected our nation’s health further than the infection it causes. Physical activity levels and dietary intake have suffered while individuals grapple with the changes in behavior to reduce viral transmission. With unique nuances regarding the access to physical activity and nutrition during the pandemic, the constructs of Health Belief Model (HBM) may present themselves differently in nutrition and exercise behaviors compared to precautions implemented to reduce viral transmission studied in previous research. The purpose of this study was to investigate the extent of exercise and nutritional behavior change during the COVID-19 pandemic and explain the …

Pandemic Fatigue Impedes Mitigation Of Covid-19 In Hong Kong, Zhanwei Du, Lin Wang, Songwei Shan, Dickson Lam, Tim K. Tsang, Jingyi Xiao, Huizhi Gao, Bingyi Yang, Sheikh Taslim Ali, Sen Pei, Isaac Chun-Hai Fung, Eric H. Y. Lau, Qiuyan Liao, Peng Wu, Lauren Ancel Meyers, Gabriel M. Leung, Benjamin Cowling

Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications

Hong Kong has implemented stringent public health and social measures (PHSMs) to curb each of the four COVID-19 epidemic waves since January 2020. The third wave between July and September 2020 was brought under control within 2 m, while the fourth wave starting from the end of October 2020 has taken longer to bring under control and lasted at least 5 mo. Here, we report the pandemic fatigue as one of the potential reasons for the reduced impact of PHSMs on transmission in the fourth wave. We contacted either 500 or 1,000 local residents through weekly random-digit dialing of landlines …

Laboratory Evaluation And Field Feasibility Of Micro-Encapsulated Insecticide Effect On Rhodnius Prolixus And Triatoma Dimidiata Mortality In Rural Households In Boyaca, Colombia, Lidia Gual-Gonzalez, Manuel Medina, Cesar Valverde-Castro, Virgilio Beltran, Rodrigo Caro, Omar Triana-Chavez, Melissa Nolan Ph.D., Mph, Omar Cantillo-Barraza

Faculty Publications

Chagas disease is a neglected vector-borne zoonosis caused by the parasite Trypanosoma cruzi that is primarily transmitted by insects of the subfamily Triatominae. Although control efforts targeting domestic infestations of Rhodnius prolixus have been largely successful, with several regions in Boyacá department certified free of T. cruzi transmission by intradomicile R. prolixus, novel native species are emerging, increasing the risk of disease. Triatoma dimidiata is the second most important species in Colombia, and conventional control methods seem to be less effective. In this study we evaluated the efficacy and usefulness of micro-encapsulated insecticide paints in laboratory conditions and its …

Convexity Of Regularized Optimal Transport Dissimilarity Measures For Signed Signals, Christian P. Fowler

Mathematics & Statistics ETDs

Debiased Sinkhorn divergence (DS divergence) is a distance function of

regularized optimal transport that measures the dissimilarity between two

probability measures of optimal transport. This thesis analyzes the advantages of

using DS divergence when compared to the more computationally expensive

Wasserstein distance as well as the classical Euclidean norm. Specifically, theory

and numerical experiments are used to show that Debiased Sinkhorn divergence

has geometrically desirable properties such as maintained convexity after data

normalization. Data normalization is often needed to calculate Sinkhorn

divergence as well as Wasserstein distance, as these formulas only accept

probability distributions as inputs and do not directly …

Statistical Methods For Differential Gene Expression Analysis Under The Case-Cohort Design, Lidong Wang

Mathematics & Statistics ETDs

Differential gene expression analysis has the potential to discover candidate biomarkers, therapeutic targets, and gene signatures. How to save money when using an unaffordable sample is a practical question. The case-cohort (CCH) study design can blend the economy of case-control studies with the advantages of cohort studies. But it has not been seen in the medical research literature where high-throughput genomic data were involved.

A score test does not need to fit the Cox PH model iteratively; hence, it can save computing time and avoid potential convergence issues. We developed a score test under the CCH design to identify DEGs …

Evaluation Of Circular Logistic Regression Models With Asymmetrical Link Functions, Feridun Tasdan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Estimating R0 For Dengue Emergence In Central Argentina Using Statistical Models, Sahil Chindal

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Incorporating Interventions To An Extended Seird Model With Vaccination: Application To Covid-19 In Qatar, Elizabeth Amona

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Functional Data Analysis Of Covid-19, Nichole L. Fluke

Mathematics & Statistics ETDs

This thesis deals with Functional Data Analysis (FDA) on COVID data. The Data involves counts for new COVID cases, hospitalized COVID patients, and new COVID deaths. The data used is for all the states and regions in the United States. The data starts in March 1^st, 2020 and goes through March 31^st, 2021. The FDA smooths the data and looks to see if there are similarities or differences between the states and regions in the data. The data also shows which states and regions stand out from the others and which ones are similar. Also shown …

Statistical Methods For Reliability Test Planning And Data Analysis, Oluwaseun Elizabeth Otunuga

USF Tampa Graduate Theses and Dissertations

This dissertation develops several statistical methods to advance the techniques and applications in the fields of reliability test planning and data analysis as well as statistical modeling and analysis in survival analysis.

The first project focuses on developing new demonstration test plans for lifetime data based on considering multiple objectives. Reliability demonstration tests have been broadly used for assuring reliability performance at the desired confidence level. We consider lifetime data that follows a Weibull distribution which has been broadly used for modeling a variety of shapes of lifetime distributions. When planning a demonstration test, there are often multiple aspects to …

The Dietary Inflammatory Index Is Associated With Subclinical Mastitis In Lactating European Women, Myriam C. Afeiche, Alison Iroz, Frank Thielecke, Antoino C. De Castro, Gregory Lefebvre, Colleen F. Draper, Cecilia Martiínez-Costa, Maria Jose Costeira, Mireille Vanpee, Claude Billeaud, Jean-Charles Picaud, Daryl Lim Kah Hian, Guimei Liu, Nitin Shivappa Mbbs, Mph, Ph.D., James Hébert Scd, Tinu M. Samuel

Faculty Publications

Subclinical mastitis (SCM) is an inflammatory state of the lactating mammary gland, which is asymptomatic and may have negative consequences for child growth. The objectives of this study were to: (1) test the association between the dietary inflammatory index (DII®) and SCM and (2) assess the differences in nutrient intakes between women without SCM and those with SCM. One hundred and seventy-seven women with available data on human milk (HM) sodium potassium ratio (Na:K) and dietary intake data were included for analysis. Multivariable logistic regression was used to examine the association between nutrient intake and the DII score in relation …

Music Genre Classification By Convolutional Neural Networks, Usame Suud

Mathematics & Statistics ETDs

In today’s world, deep learning models are widely used in a variety of fields. Audio

applications include speech recognition, audio classification, and music information

retrieval. In this paper, we will focus on the classification of music genres using an

artificial neural network. The development of audio machine learning techniques has

created an independence from traditional, more time-consuming signal processing

techniques. Starting with raw audio data, we will gain an understanding of what

audio is and its digital representation. Then, the focus will be on obtaining frequency

information from audio signals through the use of spectrograms. Transforming the

spectrograms into the …

Assessing Turgor Loss Point In Bottomland Hardwood Trees Using Leaf Spectroscopy, Alexandra M. Eisley

LSU Master's Theses

Climate change is expected to radically alter our planet’s forests, with higher frequencies of drought- and flood-induced mortality events posing a challenge for forest managers and biologists. Research into the factors underlying plant tolerance to environmental stressors is therefore gaining popularity for incorporation into projective and earth system modeling using remote sensing measures. Leaf turgor loss point (TLP) is a key trait associated with drought tolerance among plants and is defined as the water potential at which leaf turgor pressure reaches zero, causing wilting. Here, I investigated patterns of TLP across the landscape and its role as an indicator of …

Improving The Accuracy Of Interactive Voice Response (Ivr) Technology For Pediatric Experience Scores, Elizabeth Spaargaren Ms, Mph, Cpxp, Abigail Kozak Mba, Cpxp, Cara Herbener Cpxp, Barbara Lawlor Burke Ma, Cpxp

Patient Experience Journal

The increased use of interactive voice response (IVR) in assessing patient and family experience should be paired with evidence-based practices on how to obtain the most accurate information via this survey mode. We added a brief clarification sentence of the survey scale at the start of the IVR call to improve our experience data both qualitatively and quantitatively. Our setting was an urban pediatric hospital. We gathered lived experiences from our patients, families, and providers to understand and design a change to the IVR survey mode that would reduce survey inaccuracies. Outcome measures were assessed by baseline measurement and post-intervention …

Conservative Unconditionally Stable Decoupled Numerical Schemes For The Cahn–Hilliard–Navier–Stokes–Darcy–Boussinesq System, Wenbin Chen, Daozhi Han, Xiaoming Wang, Yichao Zhang

Mathematics and Statistics Faculty Research & Creative Works

We propose two mass and heat energy conservative, unconditionally stable, decoupled numerical algorithms for solving the Cahn–Hilliard–Navier–Stokes–Darcy–Boussinesq system that models thermal convection of two-phase flows in superposed free flow and porous media. The schemes totally decouple the computation of the Cahn–Hilliard equation, the Darcy equations, the heat equation, the Navier–Stokes equations at each time step, and thus significantly reducing the computational cost. We rigorously show that the schemes are conservative and energy-law preserving. Numerical results are presented to demonstrate the accuracy and stability of the algorithms.

Marginal Proportional Hazards Models For Clustered Interval-Censored Data With Time-Dependent Covariates, Kaitlyn Cook, Wenbin Lu, Rui Wang

Statistical and Data Sciences: Faculty Publications

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana’s national adoption of a universal test-and-treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy (i) modified the observed preventative effects of the study intervention and (ii) was associated with a reduction in the population-level incidence of HIV in Botswana. To address these questions, we propose a stratified proportional hazards model for clustered intervalcensored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on …

Identification Of Disease Resistance Parents And Genome-Wide Association Mapping Of Resistance In Spring Wheat, Muhammad Iqbal, Kassa Semagn, Diego Jarquin, Harpinder Randhawa, Brent D. Mccallum, Reka Howard, Reem Aboukhaddour, Izabela Ciechanowska, Klaus Strenzke, José Crossa, J. Jesus Céron-Rojas, Amidou N’Diaye, Curtis Pozniak, Dean Spaner

Department of Statistics: Faculty Publications

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) …

Exploring The Vulnerability Of A Neural Tangent Generalization Attack (Ntga) - Generated Unlearnable Cifar-10 Dataset, Gitte Ost

USF Tampa Graduate Theses and Dissertations

Nowadays, a massive amount of data is generated and stored on servers and cloudsfrom various applications daily. Preventing these data from unauthorized use often becomes necessary and critical in various real-world applications. Many researchers have studied this crucial problem and developed different methods for this purpose. Among them, Neural Tangent Generalization Attack (NTGA) is one of the most efficient methods to make a dataset unlearnable, which means that the dataset is not learnable by machine learning/deep learning methods. That is, the NTGA-generated dataset is protected against unauthorized use. In this thesis, we explore the vulnerability of an NTGA-generated unlearnable CIFAR-10 …

Antibody Prevalence And Risk Factors Associated With Rickettsia Spp. In A Pediatric Cohort: Sfgr Remains Underdiagnosed And Underreported In El Salvador, Kyndall C. Dye-Braumuller, Marvin Stanley Rodríguez Aquino, Kia Zellars, Hanna Waltz, Madeleine Meyer, Lídua Gual-Gonzalez, Stella C.W. Self, Mufaro Kanyangarara, Melissa Nolan Ph.D., Mph

Faculty Publications

Spotted fever group rickettsioses (SFGR) are caused by a group of tick-borne pathogens that are increasing in incidence globally. These diseases are typically underreported and undiagnosed in low- and middle-income countries, and thus, have been classified as neglected bacterial pathogens. Countries with high poverty, low human development index score, and limited health infrastructure—like El Salvador in Central America—lack necessary surveillance for SFGR and other tick-borne pathogens. This paucity of baseline SFGR infection prevalence leaves vulnerable populations at risk of misdiagnosis. Further, tick-borne disease burdens in El Salvador are severely limited. To lay the foundation for tick-borne disease epidemiology in El …

Process Evaluation Methods And Results From The Health In Pregnancy And Postpartum (Hipp) Randomized Controlled Trial, Sarah Wilcox Phd, Alicia A. Dahl, Alycia K. Boutté, Jihong Liu Sc.D., Kelsey Day, Gabrielle Turner-Mcgrievy Ph.D., Rd, Ellen Wingard

Faculty Publications

Background

Excessive gestational weight gain has increased over time and is resistant to intervention, especially in women living with overweight or obesity. This study described the process evaluation methods and findings from a behavioral lifestyle intervention for African American and white women living with overweight and obesity that spanned pregnancy (≤ 16 weeks gestation) through 6 months postpartum.

Methods

The Health in Pregnancy and Postpartum (HIPP) study tested a theory-based behavioral intervention (vs. standard care) to help women (N = 219; 44% African American, 29.1 ± 4.8 years) living with overweight or obesity meet weight gain guidelines in pregnancy and …

Association Between Use Of Remdesivir And Bradycardia, Gibret Umeukeje

USF Tampa Graduate Theses and Dissertations

Remdesivir received the first emergency use authorization from the FDA for the treatment of COVID-19. Multiple adverse drug reactions (ADR) have been reported since its approval in October 2020. Bradycardia, defined by a decrease in heart rate has been reported as an adverse event for patients receiving remdesivir for COVID-19 treatment. The purpose of the research is to systematically investigate the frequency of occurrence of bradycardia in adults receiving remdesivir using clinical data derived from the FDA Adverse Event Reporting System (FAERS) database. Patients receiving remdesivir were compared to those receiving Paxlovid, Regen-Cov, and Dexamethasone for COVID-19 treatment to see …

Higher Adherence To A Mediterranean Diet Is Associated With Improved Insulin Sensitivity And Selected Markers Of Inflammation In Individuals Who Are Overweight And Obese Without Diabetes, Surbhi Sood, Jack Feehan, Catherine Itsiopoulos, Kirsty Wilson, Magdalena Plebanski, David Scott, James Hébert Scd, Nitin Shivappa Mbbs, Mph, Ph.D., Aya Mousa, Elena S. George, Barbora De Courten

Faculty Publications

Insulin resistance (IR) and chronic low-grade inflammation are risk factors for chronic diseases including type 2 diabetes (T2D) and cardiovascular disease. This study aimed to investigate two dietary indices: Mediterranean Diet Score (MDS) and Dietary Inflammatory Index (DII®), and their associations with direct measures of glucose metabolism and adiposity, and biochemical measures including lipids, cytokines and adipokines in overweight/obese adults. This cross-sectional study included 65 participants (males = 63%; age 31.3 ± 8.5 years). Dietary intake via 3-day food diaries was used to measure adherence to MDS (0–45 points); higher scores indicating adherence. Energy-adjusted DII (E-DII) scores were calculated with …

The Impact Of Meal Dietary Inflammatory Index On Exercise-Induced Changes In Airway Inflammation In Adults With Asthma, Katrina P. Mcdiarmid, Lisa G. Wood, John W. Upham, Lesley K. Macdonald-Wicks, Nitin Shivappa Mbbs, Mph, Ph.D., James R. Hébert Scd, Hayley A. Scott

Faculty Publications

Research suggests exercise may reduce eosinophilic airway inflammation in adults with asthma. The Dietary Inflammatory Index (DII®) quantifies the inflammatory potential of the diet and has been associated with asthma outcomes. This study aimed to determine whether the DII of a meal consumed either before or after exercise influences exercise-induced changes in airway inflammation. A total of 56 adults with asthma were randomised to (1) 30–45 min moderate–vigorous exercise, or (2) a control group. Participants consumed self-selected meals, two hours pre- and two hours post-intervention. Energy-adjusted DII (E-DIITM) was determined for each meal, with meals then characterised as “anti-inflammatory” or …

Evaluating Dimensionality Reduction For Genomic Prediction, Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard

Department of Statistics: Faculty Publications

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the …

Evaluating Dimensionality Reduction For Genomic Prediction, Vamsi Manthena, Diego Jarquín, Rajeev K. Varshney, Manish Roorkiwal, Girish Prasad Dixit, Chellapilla Bharadwaj, Reka Howard

Department of Statistics: Faculty Publications

The development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the …

Metabolome-Wide Associations Of Gestational Weight Gain In Pregnant Women With Overweight And Obesity, Jin Dai, Nansi S. Boghossian, Mark Sarzynski Ph.D., Faha, Facsm, Feng Luo, Xiaoqian Sun, Jian Li, Oliver Fiehn, Jihong Liu Sc.D., Liwei Chen

Faculty Publications

Excessive gestational weight gain (GWG) is associated with adverse pregnancy outcomes. This metabolome-wide association study aimed to identify metabolomic markers for GWG. This longitudinal study included 39 Black and White pregnant women with a prepregnancy body mass index (BMI) of ≥ 25 kg/m². Untargeted metabolomic profiling was performed using fasting plasma samples collected at baseline (mean: 12.1 weeks) and 32 weeks of gestation. The associations of metabolites at each time point and changes between the two time points with GWG were examined by linear and least absolute shrinkage and selection operator (LASSO) regression analyses. Pearson correlations between the …