Biostatistics Commons^™

Tropical Fish Study In Tahiti, French Polynesia, Miranda Brainard, Caitlyn Swango, Paityn Houglan, Richard Londraville

Williams Honors College, Honors Research Projects

In May of 2023, I embarked on an exciting research journey to Moorea, French Polynesia, alongside fellow students and faculty members from the University of Akron and Syracuse University. This expedition was part of the university-sponsored Tropical Vertebrate Biology course, where we delved into the exploration of various tropical species inhabiting the island, including sea urchins, geckos, and my primary focus, the blackspotted rockskipper.

My research team, composed of my co-authors and me, was particularly intrigued by the unique refuge-seeking behavior displayed by blackspotted rockskippers. These amphibious fish are renowned for their remarkable ability to inhabit tide pools and rocky …

Multiscale Modelling Of Brain Networks And The Analysis Of Dynamic Processes In Neurodegenerative Disorders, Hina Shaheen

Theses and Dissertations (Comprehensive)

The complex nature of the human brain, with its intricate organic structure and multiscale spatio-temporal characteristics ranging from synapses to the entire brain, presents a major obstacle in brain modelling. Capturing this complexity poses a significant challenge for researchers. The complex interplay of coupled multiphysics and biochemical activities within this intricate system shapes the brain's capacity, functioning within a structure-function relationship that necessitates a specific mathematical framework. Advanced mathematical modelling approaches that incorporate the coupling of brain networks and the analysis of dynamic processes are essential for advancing therapeutic strategies aimed at treating neurodegenerative diseases (NDDs), which afflict millions of …

Interactions Between Sediment Mechanical Structure And Infaunal Community Structure Following Physical Disturbance, William Cyrus Roger Clemo

Theses and Dissertations

Shallow, river-influenced coastal sediments are important for global carbon storage and nutrient cycling and provide a habitat for diverse communities of invertebrates (infauna). Elevated bed shear stress from extreme storms can resuspend, transport, and deposit sediments, disrupting the cohesive structure of muds, and sorting and depositing sand eroded from beaches. These physical disruptions can also resuspend or smother infauna, decreasing abundances and changing community structure. Infaunal activities such as burrowing, tube construction, and feeding can impact sediment structure and stability. However, little is known about how physical disturbance impacts short and long-term sediment habitat suitability and whether disturbance-tolerant infauna influence …

From Formulas To Futures: Mathematical Insights Into Endosomal Escape, Fnu Nisha

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bayesian Adaptive Smoothing For Activation Detection In Fmri, Juan Florez

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Estimating & Modeling The Growth Of Bromeliads, Erin N. Bodine, Helen O. Udeochu, Alana G. Wells

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

All-Cause And Opioid-Related Mortality Compared Between Traumatic Spinal Cord Injury And The Us General Population, Jaden Whitehead, Beatrice Ugiliweneza

The Cardinal Edge

Individuals with spinal cord injury (SCI) are susceptible to the misuse of opioids due to the introduction of these substances for pain management. There are very few studies examining the relationship between unintentional deaths caused by opioid usage following spinal cord injury. The objective of this study was to evaluate the trend of opioid-related mortality of individuals with spinal cord injury (SCI) over the years and compare these findings to the mortality rates due to opioid misuse in the general population. In this study, we used data provided by the National Spinal Cord Injury Model Systems (NSCIMS) for SCI 1999-2016 …

Nonparametric Methods For Analysis And Sizing Of Cluster Randomization Trials With Baseline Measurements, Chengchun Yu

Electronic Thesis and Dissertation Repository

Cluster randomization trials are popular in situations where the intervention needs to be implemented at the cluster level, or logistical, financial and/or ethical reason dictates the choice for randomization at the cluster level, or minimization of contamination is needed. It is very common for cluster trials to take measurements before randomization and again at follow-up, resulting in a clustered pretest-posttest design. For continuous outcomes, the cluster-adjusted analysis of covariance approach can be used to adjust for accidental bias and improve efficiency. However, a direct application of this method is nonsensical if the measures are incompatible with an interval scale, yet …

History And Current Status Of Mediterranean Spotted Fever (Msf) In The Crimean Peninsula And Neighboring Regions Along The Black Sea Coast, Muniver T. Gafarova, Marina E. Eremeeva

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Mediterranean spotted fever (MSF) is a tick-borne rickettsiosis caused by Rickettsia conorii subspecies conorii and transmitted to humans by Rhipicephalus sanguineus ticks. The disease was first discovered in Tunisia in 1910 and was subsequently reported from other Mediterranean countries. The first cases of MSF in the former Soviet Union were detected in 1936 on the Crimean Peninsula. This review summarizes the historic information and main features of MSF in that region and contemporary surveillance and control efforts for this rickettsiosis. Current data pertinent to the epidemiology of the disease, circulation of the ticks and distribution of animal hosts are discussed …

Produção De Artigos Científicos No Estudo Longitudinal De Saúde Do Adulto (Elsa-Brasil), 2011-2023, Arthur Sandi Bauermann, Maria Antônia Mylius De Oliveira, Clara Akemi Basso Aseka, Luiza Dalmolin Beneduzi

AMNET XX Conferencia Internacional

No abstract provided.

Construction And Performance Optimization Of Bioconjugated Nanosensors For Early Detection Of Breast Cancer And Pro-Inflammatory Diseases, Pooja Gaikwad

Dissertations, Theses, and Capstone Projects

In recent years, nanosensors have emerged as a tool with strong potential in medical diagnostics. Single-walled carbon nanotube (SWCNT) based optical nanosensors have notably garnered interest due to the unique characteristics of their near-infrared fluorescence emission, including tissue transparency, photostability, and various chiralities with discrete absorption and fluorescence emission bands. Additionally, the optoelectronic properties of SWCNT are sensitive to the surrounding environment, which makes them suitable for in vitro and in vivo biosensing. Single-stranded (ss) DNA-wrapped SWCNTs have been reported as optical nanosensors for cancers and metabolic diseases. Breast cancer and cardiovascular diseases are the most common causes of death …

Prediction Of Factors For Patients With Hypertension And Dyslipidemia Using Multilayer Feedforward Neural Networks And Ordered Logistic Regression Analysis: A Robust Hybrid Methodology, Wan Muhamad Amir W Ahmad, Mohamad Nasarudin Bin Adnan, Norhayati Yusop, Hazik Bin Shahzad, Farah Muna Mohamad Ghazali, Nor Azlida Aleng, Nor Farid Mohd Noor

Makara Journal of Health Research

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia.

Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping.

Results: Variable …

A Review Of Recent Gene Expression-Based And Dna Methylation-Based Mathematical Cell Type Deconvolution Methods, Chenxiao Tian

Arts & Sciences Electronic Theses and Dissertations

In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn …

Genetic Associations Of Alzheimer’S Disease And Mild Cognitive Impairment, Scott Hebert

Masters Theses

Over 6 million people are estimated to have been living with Alzheimer’s Disease (AD) in 2020, with another 12 million living with Mild Cognitive Impairment (MCI). Research has been conducted to evaluate genetic links to AD, but more research is needed on the subject. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been conducting a longitudinal study of AD and MCI since 2004 and offering their data to research teams around the world. Diagnostic and demographic data was collected from participants, as well as data regarding single nucleotide polymorphisms (SNPs). SNP data was transformed to a binary format regarding whether the …

Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross

Masters Theses

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.

In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …

Statistical Inference On Lung Cancer Screening Using The National Lung Screening Trial Data., Farhin Rahman

Electronic Theses and Dissertations

This dissertation consists of three research projects on cancer screening probability modeling. In these projects, the three key modeling parameters (sensitivity, sojourn time, transition density) for cancer screening were estimated, along with the long-term outcomes (including overdiagnosis as one outcome), the optimal screening time/age, the lead time distribution, and the probability of overdiagnosis at the future screening time were simulated to provide a statistical perspective on the effectiveness of cancer screening programs. In the first part of this dissertation, a statistical inference was conducted for male and female smokers using the National Lung Screening Trial (NLST) chest X-ray data. A …

Modeling Biphasic, Non-Sigmoidal Dose-Response Relationships: Comparison Of Brain- Cousens And Cedergreen Models For A Biochemical Dataset, Venkat D. Abbaraju, Tamaraty L. Robinson, Brian P. Weiser

Rowan-Virtua School of Osteopathic Medicine Faculty Scholarship

Biphasic, non-sigmoidal dose-response relationships are frequently observed in biochemistry and pharmacology, but they are not always analyzed with appropriate statistical methods. Here, we examine curve fitting methods for “hormetic” dose-response relationships where low and high doses of an effector produce opposite responses. We provide the full dataset used for modeling, and we provide the code for analyzing the dataset in SAS using two established mathematical models of hormesis, the Brain-Cousens model and the Cedergreen model. We show how to obtain and interpret curve parameters such as the ED50 that arise from modeling, and we discuss how curve parameters might change …

Statistical Methods For Single Cell Sequencing Data Analysis, Fei Qin

Theses and Dissertations

The recent emergence of single cell sequencing (SCS) technology has provided us with single-cell DNA or RNA sequencing (scDNA/RNA-seq) information to investigate cellular evolutionary relationships. Despite many analysis methods have been developed to infer intra-tumor genetic heterogeneity, cluster cellular subclones, detect genetic mutations, and investigate spatially variable (SV) genes, exploring SCS data remains statistically challenging due to its noisy nature.

To identify subclones with scDNA-seq data, many existing studies use an independent statistical model to detect copy number profile in the first step, followed by classical clustering methods for subclone identification in downstream analyses. However, spurious results might be generated …

Approaches To Detecting And Modeling Over-And Underdispersion In Alternative Count Data Distributions And An Application Of Logistic Regression And Random Forest Modeling To Improve Screening Tools For Tic Disorders In Children, Rebecca C. Wardrop

Theses and Dissertations

This dissertation focuses on theory and application of discrete data methods, particularly approaches to over- and underdispersion relative to the Poisson distribution and an application of random forest and logistic regression modeling. The first chapter derives a score test for over- and underdispersion in the heaped generalized Poisson distribution. Equi-, over-, and underdispersed heaped generalized Poisson and heaped negative binomial data are simulated to evaluate the performance of the score test by comparing the power it achieves to that of Wald and likelihood ratio tests. We find that the score test we derive performs comparably to both the Wald and …

A Bayesian Spatial Scan Statistic For Normal Data, Laasya Velamakanni

Theses and Dissertations

Scan statistics are useful methods for detecting spatial clustering. While they were initially developed to detect regions with an excess of binomial or Poisson events, spatial scan statistics have been extended to detect hotspots in other types of data including continuous data. They have many applications in different fields such as epidemiology (e.g. detecting disease outbreaks), sociology (e.g. detecting crime hotspots), and environmental health (e.g. detecting high-pollution areas). Spatial scan statistics identify a ‘most likely cluster’ and then use a likelihood ratio test to determine if this cluster is statistically significant. Spatial scan statistics have been extended to the Bayesian …