Organizational Unit: Computer Science
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Investigating the Role of pH-sensing G Protein-Coupled Receptors GPR4 and GPR132 in Colorectal Cancer: Multi-Statistical, Survival, and Structural Analysis Approach
(East Carolina University, May 2025) Ochoa, Carlos Andres
Colorectal adenocarcinoma (COAD) is one of the leading causes of cancer-related mortality worldwide. Understanding the molecular mechanisms that contribute to cancer progression is a critical step for identifying therapeutic targets to combat COAD. GPR4 and GPR132, which are pH sensing G protein-coupled receptors (GPCRs), have recently emerged as a point of interest and linked to tumor progression, tumor microenvironment, and molecular signaling pathways. However, despite this, the roles these GPCRs play are still not fully understood. This study investigates the clinical and structural relevance of GPR4 and GPR132 in COAD through gene expression, survival, and structural analyses.
Gene expression and clinical data were collected from The Cancer Genome Atlas (TCGA) and analyzed using various statistical and survival methods. Statistical tests and survival models revealed an increase in GPR4 expression and that higher stages were significantly associated with worsening patient survival outcomes, which suggests GPR4 to be a potential biomarker and therapeutic target. In contrast GPR132 showed a limited amount of clinical significance and was hindered by a lack of comprehensive clinical data. Additionally, AlphaFold and APBS were used to model wildtype and mutation GPR4 structures and electrostatic potential (ESP) maps across different pH levels. While electrostatic differences were inconclusive and need further in-depth investigation, structural comparisons discovered notable spatial changes between the transmembrane domain that contains position 115 and two other transmembrane domains. Overall, this study highlights prognostic potential in COAD and provides preliminary insights into how mutations may influence its structure and function.
ACTIVITY SIMULATION IN UNITY FOR OLDER ADULTS IN SMART HOMES
(East Carolina University, May 2025) Montes, Kenly
The increased desire to age in place among older adults has led to a growing interest in smart home technologies. Within these smart homes, independent living is supported while maintaining the safety of older adults with timely interventions. This thesis presents the design and implementation of a 3D simulation created in Unity to visualize daily activities within a smart home environment. The simulation models different sensors to simulate a virtual resident interacting within a scanned apartment layout. The data to simulate these activities is obtained from actual sensors previously set up in the living space of an older adult. Along with taking in data, the simulation allows for the creation of scenarios to generate potential behavioral patterns that can be represented in sensor data. This work demonstrates how 3D simulations can close the gap between raw sensor data to an intuitive visualization to further enhance eldercare.
Sampling and Selection Methods for Applying 2D Neural Networks to 3D Gaussian Splats
(East Carolina University, May 2025) Dusablon, Raphael
We propose a novel approach for applying interpolation methods to unstructured volumetric data that allows for the operation of 2D neural networks directly on 3D Gaussian splats. Gaussian splatting is at the cutting edge of volume rendering methods, 2D neural networks have achieved a dominant and lasting degree of success and real-life application. We propose leveraging the advantages of both, an approach which is the first of its kind. We extend the method for interpolated convolution on 3D surface meshes with 2D CNNs by Hart et al to the unstructured 3D volumetric data of Gaussian splats and present an end-to-end pipeline for our work. We showcase our results with style transfers on 3D Gaussian splats performed by a 2D convolution model with no retraining. Our results compare favorably with those of current approaches to performing style transfers on 3D Gaussians using purpose-built and purpose-trained 3D models.
A FRAMEWORK FOR TEMPORAL-BASED PREDICTION OF EYE DISEASES
(East Carolina University, May 2025) Jaiswal, Saumya Singh
Diabetic retinopathy (DR) is a leading cause of preventable blindness, and deep learning has shown promise in automating its diagnosis. However, most models treat retinal images as static inputs, overlooking the temporal nature of disease progression. In this work, we propose a Temporal Vision Recurrent Transformer (TVRT): a hybrid architecture combining a fine-tuned ViT-Tiny backbone with a bidirectional LSTM, to capture both spatial features and temporal evolution from fundus image sequences.
To address the lack of temporal data in the APTOS 2019 dataset, we introduce two synthetic sequence generation methods: (1) stage-based augmentation using contrast and geometric transformations to mimic progressive DR stages, and (2) neural style transfer to simulate intra-stage variability using higher-stage fundus images as style references.
Experimental results show that while ViT and ResNet perform well on static classification, TVRT significantly outperforms them on progression modeling, achieving an F1-score of 0.86 on synthetic sequences with 5+ timesteps. Furthermore, soft attention maps derived from the ViT encoder provide interpretable visualizations that highlight clinically relevant features like hemorrhages and exudates. Our findings suggest that temporal modeling not
only enhances predictive accuracy but also improves interpretability, offering a promising direction for intelligent, progression-aware eye care systems.
METAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS
(East Carolina University, May 2025) Anthamola, Harishwar Reddy
Large Language Models (LLMs) have made significant progress in Natural Language Processing, yet they remain susceptible to fairness-related issues, often reflecting biases from their training data. These biases present risks, mainly when LLMs are used in sensitive domains such as healthcare, finance, and law. This research proposes a metamorphic testing approach to uncover fairness bugs in LLMs systematically. We define and apply fairness-oriented metamorphic relations (MRs) to evaluate state-of-the-art models like LLaMA and GPT across diverse demographic inputs. By generating and analyzing source and follow-up test cases, we identify patterns of bias, particularly in tone and sentiment. Results show that tone-based MRs detected up to 2,200 fairness violations, while sentiment-based MRs detected fewer than 500, highlighting the strength of this method. This study presents a structured strategy for enhancing fairness in LLMs and improving their robustness in critical applications.
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