Skip to main content

D4 Research Symposium


 

Join us for the first Dependable Data-Driven Discovery D4 Annual Research Symposium at Iowa State University on August 14, 2024, at 1 pm at Hach Hall. This exciting event showcases the interdisciplinary research of the D4 National Research Trainees of 2024.

Our talented trainees from diverse disciplines—including Mathematics, Statistics, Neurobiology, Bioinformatics and Computational Biology (BBC), Biochemistry, Chemical and Biological Engineering (CBE), and Computer Science—will deliver an engaging five-minute lightning talk and present insightful posters detailing their work in Dependable Data-Driven Discovery (D4).

You will have the opportunity to speak with the trainees about their posters and learn more about their innovative projects. This event highlights the collective strength of interdisciplinary collaboration. All are welcome to attend and celebrate our trainees' achievements and learn more about D4.


D4 Presenters

Detection of Brain Midline Shift using Convolutional Neural Networks

Laura Zinnel (MATH) 

Traumatic brain injury (TBI) is a prevalent neurological disorder that can have life-long impacts, and a quick diagnosis of TBI can play an essential role in the effectiveness of treatment. Typically, computerized Tomography (CT) scans are used by radiologists to check for signs of TBI in a patient's brain, but this process is time-consuming. My work focuses on developing a method to automatically detect signs of TBI from CT scans using deep learning models. This would speed up the diagnosis of patients with TBI and lead to faster treatment.


Nonlinear Dynamic Bayesian Modeling for Disease Outbreak Forecasting

Spencer Wadsworth (STAT)

To better infom public decision-makers and the healthcare system, the US Centers for Disease Control (CDC) hosts an annual collaborative flu forecasting initiative involving dozens of research teams who build weekly forecasts of flu hospitalizations. To maintain uniformity, all forecasts are submitted as a series of predictive quantiles. I explore a new statistical model for recovering the continuous probability distributions from which the predictive quantiles are estimated thus allowing the various forecasts to be compared and aggregated into an ensemble under well-established scoring and aggregation methods.


Understanding Associations Between Pathogenicity and Transmission Dynamics in Avian Influenza

Sigournie Brock (BCB)

The transmission of avian influenza virus (AIV), otherwise known as “bird flu,” poses a significant threat to both avian populations and the larger public due to its potential to reassort within avian hosts and has majorly impacted domestic poultry and wild bird populations with recent global outbreaks. The goal of my work is to elucidate the evolution of the transition from low pathogenicity AIV (LPAIV) - the ability of the virus to cause disease in poultry - to high pathogenicity AIV (HPAIV), which is associated with high morbidity and mortality rates. This presentation will outline a comprehensive Bayesian phylogenetic framework that implements state-dependent speciation and extinction (SSE) models to analyze how pathogenicity influences speciation (i.e., transmission), extinction (i.e., becoming non-infectious), and the rate of evolution between high and low pathogenicity.


Geng Ding (ComS)

The swine industry serves as a vital economic cornerstone for Iowa. Iowa is consistently the top producer of hogs and pigs in the country, and in 2023 hog sales accounted for a total of $10.9 billion. However, Porcine Reproductive and Respiratory Syndrome (PRRS) is the most economically impactful virus disease in US swine production. The multi-institutional Swine Disease Reporting System (SDRS) at Iowa State University is constantly collecting large numbers of genetic sequencing data of PRRS. This project aims to provide risk measures and threat predictions based on the data submitted to SDRS using computational biology approaches, focusing on understanding the phylogenetic diversity of PRRS over the years. Our goal is to develop cutting edge AI/ML online tools that could inform stakeholders about the early health threats with the information provided by the ever-updating genome-scale database.


Austin Sympson (BCE)

Vision loss and blindness affect an estimated 7 million people in America, equating to roughly twice the population of Los Angeles. Retinal degenerative disease is the leading contributor to vision loss and blindness worldwide. Age-related macular degeneration, diabetic retinopathy, and genetic and neural-based diseases all result in apoptosis of varying cell layers, causing vision loss or blindness. The unique irreversibility of Retinal Degeneration has made it a critical target for stem cell-based therapies. However, the unique interdependence of the retinal neural layers and biologically complex in vivo growth conditions have halted reliable in vitro cell production and successful clinical application. There is a need to investigate robust methods of directing the differentiation of cost-effective eternal progenitor cell lines to address the morphology, functionality, and cell specificity required for transplantation treatment. In this work, we propose using an interdigitated capacitor to stimulate retinal progenitor cells electrically, induce differentiation to vary retinal phenotypes, evaluate the morphological and chemical outcome through immunocytochemistry, and model the conditions resulting in the differentiated state. We aim to uncover the factors and conditions necessary to direct differentiation of retinal progenitor cells by a cost-effective and robust electrical stimulation protocol.


Allison Triebe (IGG)

Cell wall dynamics are regulated during root development through the activity of cell wall-modifying enzymes. My lab has found that the pectin-modifying enzyme GALACTURONOSYLTRANSFERASE 10 (GAUT10) is involved in both primary root elongation and cell division. Through data mining and BiFC assays, we have found that GAUT10 interacts with three other GAUTs: GAUT 3, 8, and 11. To test the overlapping function of these GAUT proteins during root development, mutant combinations of these four GAUT genes have been made. Preliminary phenotyping has shown that these genes have non-redundant and epistatic interactions.


Xiang Ma (ComS)

In our study, we introduce Seq-Dock, a computational protocol designed to utilize natural language encodings from protein pairs jointly trained on normalized binding strength to identify essential amino acids driving binding. By iteratively substituting amino acids with alanines along polypeptide backbones, we identify critical residues for binding. Validation on 15 known protein-protein complexes confirms Seq-Dock's accuracy. Our analysis highlights the challenge of predicting interacting residues in mutated protein sequences, demonstrating the importance of assessing predictive model robustness.


Cyna Nguyen (MATH)

In the last few years, machine learning with neural networks have been explored to directly solve partial differential equations (PDEs). We consider combining the learning mechanisms of neural networks and PDE theories to develop fast neural network solvers for time dependent PDEs. In particular, we propose assigning different feed forward networks to approximate different physical terms of the PDEs. The new method shows a better order of convergence when comparing to the original cell-average neural network (CANN) network. The linear convection-diffusion equation is used as the model equation