Medicine and Health Sciences Commons^™

Key Brain Region Identification In Obesity Prediction With Structural Mri And Probabilistic Uncertainty Aware Model, Walia Farzana, Megan A. Witherow, Ahmed Temtam, Liangsuo Ma, Melanie Bean, F. Gerry Moeller, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

An Overview Of Video Game Biometrics Collection And Considerations For Cyberbiosecurity, Lucas Potter, Christen Westberry, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Over the past fifty years, the global cost of consumer electronics has significantly decreased, leading to greater accessibility to both biosensing systems and interactive entertainment platforms. This increased access has naturally resulted in higher usage of medical and entertainment electronics. However, the intersection of these technologies, combined with invasive data harvesting practices, has raised concerns about the potential misuse of biological signals to manipulate individuals' behavior both within and beyond the video game environment. Currently, biometric data in video games are employed in various ways, such as using Heart Rate Variability (HRV) as a performance metric and integrating eye tracking …

Evaluating The Emotional Accuracy Of Ai-Generated Facial Expressions In Neurotypical Individuals, Antonio Pagán, Katherine A Loveland, Ronald Acierno

Faculty, Staff and Student Publications

This study examines the ability of generative artificial intelligence to produce facial expressions representing basic emotions in a neutral context using black-and-white cartoon imagery. Mentalization, the capacity to recognize and interpret one’s own and others’ mental states, is critical for social interaction and emotional regulation. We explored the emotional validation of artificial intelligence (AI)-generated images by assessing the agreement between human interpretations of emotions and those generated by an AI model. Thirty-four participants evaluated images depicting six basic emotions: sadness, anger, happiness, surprise, fear, and disgust. Our findings revealed significant variability in human agreement, with higher concordance for sadness, anger, …

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Spatial Analytics To Elucidate The Incubation Period And Drivers Of Visceral Leishmaniasis: Case Of Turkana County In Kenya, Kennedy Senagi, Maureen Nzilani, Evans Omondi, David P. Tchouassi, Tobias Landmann, Damaris Matoke-Muhia, Emmanuel Okunga, Barrington Gesimba, Elfatih M. Abdel-Rahman, Dawn Maranga, Joseph M. Ndungu, Daniel Masiga

All Peer-Reviewed Publications

Introduction: Visceral leishmaniasis (VL) is a severe and neglected tropical disease of public health concern. VL is fatal if not treated. Kenya has experienced multiple outbreaks of the disease since 2017. The underlying drivers of the disease risk dynamics, as well as the incubation period, are not well understood. Methods: We implemented statistical (spatial logistic regression and Bayesian spatial) and machine learning (random forest, support vector machine, AdaBoost, logistic regression, and extra trees) models to estimate the incubation period and predict areas of low/high risk in Turkana County, an endemic VL foci in Kenya. Two-year (2019–2020) patient data were sourced …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Food Rescue, Food Waste, And Policy In New York: A Mixed-Methods Study Of Food System Impacts, Mariana Torres Arroyo

Electronic Theses & Dissertations (2024 - present)

This dissertation investigates how food donation policies in New York State influence the recovery, redistribution, and waste of surplus fresh produce, with a focus on fruits and vegetables. Using an interdisciplinary, systems-based approach that integrates system dynamics modeling with qualitative, community-engaged research, the study analyzes the effectiveness and unintended consequences of three major policies: Nourish New York, the Farm to Food Bank tax credit, and the Food Donation and Food Scraps Recycling Law.

Chapter 2 uses simulation modeling to examine how the tax credit and waste ban affect produce recovery and redistribution. Results show that while both policies can boost …

Precision Haptics For Rehabilitation: Quantifying Directional Bias And Force Threshold Effects On Motor Learning, Conor J. Nolan

UNF Graduate Theses and Dissertations

Hand rehabilitation represents a critical challenge in modern physical therapy, with significant implications for patients' quality of life and functional independence. Despite technological advancements across healthcare, traditional hand assessment methods often rely on subjective measures or lack task-specific biomechanical assessment capabilities. This thesis investigates the effects of haptic force feedback, handedness, and rotation direction on circle-tracing task completion using a within-subjects factorial design with 20 university participants examining varying resistance levels (0.0N, 0.5N, 1.2N) across different movement configurations using a 3D Systems Touch X haptic device with 0.023mm precision. Results demonstrate that moderate haptic force (0.5N) significantly enhanced spatial accuracy …

Environment Scan Of Generative Ai Infrastructure For Clinical And Translational Science, Hua Xu, Jiang Bian, Chunhua Weng, Yifan Peng, Betina Idnay, Zihan Xu, William G. Adams, Mohammad Adibuzzaman, Nicholas R. Anderson, Neil Bahroos, Douglas S. Bell, Cody Bumgardner, Thomas Campion, Mario Castro, James J. Cimino, I. Glenn Cohen, David Dorr, Peter L. Elkin, Jungwei W. Fan, Todd Ferris, David J. Foran, David Hanauer, Mike Hogarth, Kun Huang, Jayashree Kalpathy-Cramer, Manoj Kandpal, Niranjan S. Karnik, Avnish Katoch, Albert M. Lai, Christophe G. Lambert, Lang Li, Christopher Lindsell, Jinze Liu, Zhiyong Lu, Yuan Luo, Peter Mcgarvey, Eneida A. Mendonca, Parsa Mirhaji, Shawn Murphy, John D. Osborne, Ioannis C. Paschalidis, Paul A. Harris, Fred Prior, Nicholas J. Shaheen, Nawar Shara, Ida Sim, Umberto Tachinardi, Lemuel R. Waitman, Rosalind J. Wright, Adrian H. Zai, Kai Zheng, Sandra Soo-Jin Lee, Bradley A. Malin, Karthik Natarajan, Nicholson Price, Rui Zhang, Yiye Zhang

Articles

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of …

Synthetic Data Generation Of Health And Demographic Surveillance Systems Data: A Case Study In A Low- And Middle-Income Country, Dorcas G. Mwigereri, Nigel T. Kamotho, Akbar K. Waljee, Ryan T. Rego, Eileen M. Weinheimer-Haus, Farhana Alarakhiya, Anthony K. Ngugi, W. Nicholson Price, Ji Zhu, Stephen Peter Wong, Geoffrey H. Siwo

Articles

Objective: To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and Methods: Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/ Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results: CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving …

Clear Speech Intervention In Parkinson’S Disease: A Single Case Design Examining Acoustic And Aerodynamic Outcomes Following A Structured Protocol, Zachariah Jordan Backus

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

Clear Speech Intervention in Parkinson’s Disease: A Single Case Design Examining Acoustic and Aerodynamic Outcomes Following a Structured Protocol

Zachariah Backus

Purpose:

Parkinson Disease (PD) is a neuro-progressive movement disorder that impacts all movement of the body, including speech production. Ninety percent of individuals living with PD will experience hypokinetic dysarthria, a motor speech disorder, during the progression of their disease. Clear speech instruction is a treatment strategy used to increase motor speech outcomes in those with PD. Studies have shown positive changes in articulation, prosody, voice, and listener perception measures in those with PD after clear speech instruction …

Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms, Kevin Reisch

Graduate Theses, Dissertations, and Problem Reports (ETD)

Left ventricular ejection fraction (LVEF) is a critical biomarker for heart failure, but manual estimation from echocardiograms is time-consuming. Artificial intelligence can be used to accelerate this process, allowing clinicians to focus on other critical tasks. Current methods typically train models from scratch on echocardiogram datasets; however, this approach is limited by the scarcity of large medical imaging datasets, which are expensive and difficult to acquire. We present a transfer learning approach that leverages pretrained models from massive datasets, enabling continuous improvement as foundation models advance. Our method employs visual prompting to generate trainable masks for echocardiogram videos, transforming the …

Computational Approaches For Diagnosing Neurological Impairments And Restoring Functional Movement, Serhii Bahdasariants

Graduate Theses, Dissertations, and Problem Reports (ETD)

Movement is the primary means by which the nervous system converts intention into interaction with the environment. Voluntary limb motion enables standing, walking, reaching, and grasping—actions that underpin self‑care, communication, and economic participation. When diseases or injuries disrupt the pathways that couple neural commands to muscle forces—whether through cortical stroke, spinal cord injury, or peripheral nerve damage—mechanical outcomes such as weakness, loss of coordination, and ultimately reduced mobility emerge. These impairments cascade into secondary complications, increased dependence on caregivers, and diminished quality of life. Quantifying how the lesion alters the mechanics of movement is therefore essential for designing targeted interventions …

Rational Design Of Small Molecules Targeting Mitochondrial Redox Proteins, Matheus Barbosa Belchior

Graduate Theses, Dissertations, and Problem Reports (ETD)

Reactive oxygen species play a crucial role in many cellular processes. Despite being a natural byproduct of cellular metabolism, overexpression of these species causes damage to the cell, leading to cell dysfunction. Central to the regulation of these highly reactive molecule is the mitochondria. Impairment in its function has been associated with oxidative stress within the cell. Herein, a ligand-based and structure-based approach to design new small molecules to inhibit mitochondrial redox proteins associated with oxidative stress and reactive oxygen species. In the first project two molecules were synthesized and characterized as potential monoamine oxidase B inhibitors. Results showed two …