Aliza's research aimed to retrieve the PETase gene sequence from the NCBI database and obtain the upstream region as a source of potential promoter elements using UNIPORT. Afterward, conserved motifs were identified within the PETase gene sequence using the MEME Suite that may serve as binding sites for transcription factors involved in PETase regulation.

Anaiya's project involved a rigorous investigation into the influence of social determinants of health (SDOH) on a range of cardiovascular diseases (CVD) and related conditions, encompassing heart disease, stroke, and diabetes. Data collection and analysis revealed compelling evidence that non-medical factors do impact the rate of CVD mortality.

Charitie's project included designing and conducting a pilot study to validate if we can control cognitive engagement by regulating cognitive load induced by visual complexity.In a subsequent study to this one, cognitive load and visual complexity will be assessed with TBI population, a group known to suffer from various cognitive impairments including difficulties with attention, memory, processing speed, executive functions, and problem-solving.

Chelsea's project involved multiple regression models in machine learning that are used to assess how various social vulnerability factors, race, and eviction rates affect the infant mortality rate for sudden infant death syndrome (SIDS). The dataset extracted from Indiana Network for Patient Care (INPC) focused on the Marion County population from March 2020 to April 2023. Consideration of these factors were critical in determining how the community's demographics and socioeconomic status relate to the rising infant mortality rate and the population's overall health in the Covid-19 pandemic and post-pandemic period.

Cheyenne's project involved RNA modification databases and how they have been identified and categorized. Using 15 databases that have already been established, an overview of the species and biotypes within them was given, along with who can access the databases and how current each database is as of 2023.

Chitra's project focused on drug repurposing for Alzheimer's Disease. As part of this process,she studied the spatio-topological signature of trichostatin-a in mouse and human brains to identify which regions would be most affected by treatment. These results are beneficial as we now know that trichostatin-a is a potential drug that can be used to treat Alzheimer's. This method is very time and cost-efficient and helps make significant advancements in finding a treatment method for Alzheimer's.

David's project under Dr. Janga and other mentors involved investigating programmable RNA editing using RNA-targeting CRISPR Cas13 knockdown, a potent tool for precise RNA information manipulation in research and disease treatment. These findings pave the way for future research utilizing RNA-targeting CRISPR Cas13 knockdown in diverse scientific and therapeutic application.

Kalynn's project was about delirium. Delirium is a serious cognitive disorder that, although common in ICU patients, is inadequately understood. Further comprehension can be obtained via analyzing biomarkers and their values relevant to delirium patients in comparison to healthy subjects. By researching delirium-specific biomarkers in reference to the muscle and brain biomarkers, it was aimed to unveil a connection that makes it easier to diagnose and treat patients with delirium in an ICU setting.

Kelly's project involved the use of machine learning approaches and MRI scans to estimate brain ages by calculating the difference between predicted brain age and chronological brain age also known as Delta to determine if there is association between accelerated BrainAGE and cognitive decline.

Melina's project involved the use of ad-hoc low-order enumerations and clustering-based enclosed graph strategies from past cancer studies to investigate the CNs in kidney spatial proteomics associated with chronic kidney disease (CKD) and acute kidney infection (AKI). These investigations will then allowed to analyze the differences in spatial organizations between CKD and AKI. In the time duration of this program, Melina along with her mentors was able to develop a methodology to produce a binary mask of the segmented cells within the tissue image, as well as the necessary coordinates that will be used to analyze the spatial organization of the tissue sample through the use of an image processing software.

Nhan's project involved Identification of patients with PCC and evaluation by applying machine learning tools to Electronic Health Record (EHR) data. A rules-based phenotype that is based on symptoms or conditions after the acute infection phase of COVID will be used as a reference for identification of PCC-positive patients. Then, XGBoost was used with all features present within the EHR data that are not already used by the phenotype. Results were examined, and feature selection was applied. Features will be studied to evaluate the potential impact.

Rachel's project was about comparing the EK228 knockout strain to the N2 wildtype strain of C.elegans because the EK228 strain of C.elegans may be vital to studying the early embryonic effects of DYRK1A in mammalian development, and characterization of this strain may provide insight into the roles of DYRK1A in individuals with Down syndrome.

Simranjit's project involved an extensive study examining the roles of Matrix Metalloproteinases (MMPs) in various health conditions. By leveraging innovative methodologies including Genome-Wide Association Studies (GWAS), eQTL analysis, and Mendelian Randomization (MR), they discovered novel associations between MMPs and these diseases. The findings were robustly validated using the independent "All of Us" Research Workbench data. These results provide new insights into disease development and progression, potentially guiding personalized treatment strategies. Despite certain limitations, the work represents a significant step forward in understanding MMPs' implications in human health.

Zaidon's project involved RNA modification datatbase and how they have been identified and categorized. Using 15 databases that have already been established, an overview of the species and biotypes within them was given, along with who can access the databases and how current each database is as of 2023.