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The Effects Of Abused Drugs On Ferroptosis Pathways: Potential Therapeutic Targets For Substance Use Disorders, Tamara Bouchard, Brooke Russell, Liam Liyang Guo, Tina Nguyen, Yan Cheng, Yan Y. Sanders, Ming-Lei Guo

Department of Biomedical and Translational Sciences Faculty Publications

Substance use disorders (SUDs) remain major public health concerns worldwide, particularly in developed countries. SUDs are characterized by persistent neuroinflammation and synaptic dysfunction in the brain. Despite decades of extensive investigation, the detailed mechanisms underlying SUDs remain elusive. Ferroptosis is a highly regulated cell death process deeply affected by iron metabolism, lipid peroxidation, reactive oxygen species (ROS) production, and antioxidant systems. It has been implicated in multiple neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and ischemic stroke. Recently, emerging evidence has highlighted the role of ferroptosis in drug-induced pathological changes. Various types of abused substances including alcohol, cocaine, …

Senescent Fibroblasts In Aging And Pulmonary Fibrosis, Sara B. Palega, Aiwei Y. Borengasser, Yan Y. Sanders

Microbiology & Molecular Cell Biology Faculty Publications

Aging is a major risk factor for many chronic lung diseases, including Idiopathic Pulmonary Fibrosis (IPF), a fatal and incurable disease characterized by progressive fibrotic remodeling. Age-associated structural alterations, impaired regenerative capacity, and dysregulated cellular signaling collectively create a pro-fibrotic microenvironment. A central driver of this pathological shift is the accumulation of senescent cells, which undergo irreversible growth arrest and develop a robust pro-inflammatory senescence-associated secretory phenotype (SASP). Emerging evidence identifies senescent lung fibroblasts as critical mediators of IPF pathogenesis. These cells promote excessive extracellular matrix deposition, myofibroblast differentiation, and tissue stiffening, while simultaneously impairing epithelial regeneration. Together, these effects …

Single-Cell Transciptomic Analysis Reveals Age-Related Remodeling Of Brain Endothelial Cells, Hai Duc Nguyen, Summer Siddiqui, Diana G. Bohannon, Robert V. Blair, Hong-Wen Deng, Alexandre Prat, Woong-Ki Kim

Microbiology & Molecular Cell Biology Faculty Publications

Blood–brain barrier (BBB) integrity naturally declines with age. Brain endothelial cells (ECs) and pericytes (PCs) form the BBB, and aging impairs tight junctions, likely via altered PC-to-EC signaling. However, the molecular mechanisms underlying this impairment remain unclear. Using single-cell RNA sequencing, we profiled 68,316 brain ECs expressing 15,564 genes from young and old mice. Unsupervised clustering and annotation revealed five distinct EC subtypes—Capillary EC1, Capillary EC2, Arterial EC, Venous EC1, and Venous EC2—defined by marker genes Mfsd2a, Plvap, Bmx, Nr2f2, and Vcam1, respectively. Aging shifted EC subtype distribution, with reduced Capillary EC1 (45% vs. 57%) and increased Arterial …

Mepolizumab Improves Outcomes In Patients With Crswnp With One Or More Prior Nasal Polyp Surgeries, Joseph K. Han, Joaquim Mullol, Martin Wagenmann, Laura Walrave, Lingjiao Zhang, Ana R. Sousa, Peter Howarth, Claire Hopkins

Department of Otolaryngology (ENT) Faculty Publications

BACKGROUND: Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) often undergo multiple nasal surgeries. The Phase III SYNAPSE study (GSK ID:205687; NCT0308579) demonstrated that mepolizumab reduces the need for repeat surgery. We explored whether outcomes differed based on the number of prior surgeries. METHODOLOGY: This post hoc analysis of SYNAPSE stratified patients by history of 1 or more than 1 prior surgery. Patients were randomised to receive mepolizumab or placebo every 4 weeks, alongside standard of care, for 52 weeks. Outcomes included change from baseline in endoscopic nasal polyp (NP) score, visual analogue scale (VAS) scores for nasal obstruction, loss …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

An Investigation Of Federated Gnns Under Aggregation, Data Poisoning, And Differential Privacy For Icu Length-Of-Stay Prediction, Shakib Mahmud Dipto, Soumya Banerjee, Sandip Roy, Ahmad F. Al Musawi, Preetam Ghosh, Sachin Shetty, Pratip Rana

Computer Science Faculty Publications

Accurate prediction of ICU Length of Stay (LoS) is essential for clinical decision-making and healthcare resource management. Graph Neural Networks (GNNs), such as GraphSAGE, offer a natural fit by capturing patient data from Electronic Health Records (EHRs) through graph structures. However, the distributed and sensitive nature of this data raises both privacy and legal concerns regarding the aggregation and training of GNN models. This additionally leads to issues with data imbalance and model robustness. In this study, we perform an analysis of the Federated Graph Neural Network (GNN-FL) framework to enable decentralized learning on EHRs derived from the MIMIC-III dataset. …

Larval Crowding In Culex Pipiens Generates Cumulative Cascading Effects Under Changing Environmental Conditions, Sengul Talay, Zafer Sakaci, Michele C. Weigle, Bugrahan Regaip Kilinc, Jenah Parman, Holly Gaff, Deniz Sirin, Bulent Alten, Dennis Bente, Sirri Kar

Computer Science Faculty Publications

Density-dependent population regulation is a fundamental driver in the population dynamics of living systems, ensuring a balanced and sustainable maintenance in nature. It has been suggested that the adverse developmental and morphological effect of high larval density in container mosquitoes represents a significant limiting factor for population density, and the need to investigate this possible importance within the framework of field-based principles has been emphasized. This semi-field study, conducted under the natural thermal regime and using a natural population in Turkish Thrace, aimed to examine the effects of non-food-limiting larval density (crowding effect) in the mosquito species, Culex pipiens ( …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

Chromnet: A Multi-Task Learning Framework For Cross-Cell Type Prediction Of 3d Chromatin Interactions Using Epigenetic Signals, Bin Wang, Shaokai Wang, Liqing Ding, Hongdong Li, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

The 3D organization of chromatin plays a fundamental role in gene regulation, cellular function, and disease mechanisms. However, current experimental techniques, such as Hi-C, remain costly and labor-intensive, limiting their application in large-scale and disease-related studies. To address this challenge, ChromNet is presented, a multi-task learning framework that integrates epigenetic signals across diverse cell types to enable high-precision prediction of chromatin architecture. By incorporating noise perturbation and auxiliary classification tasks, ChromNet improves the identification of topologically associating domains (TADs) and cell-type-specific chromatin structures, demonstrating superior generalization performance. Notably, ChromNet accurately predicts chromatin interactions in acute myeloid leukemia (AML) samples by …

A Comprehensive Overview Of Neurophysiological Correlates Of Cognitive Impairment In Amyotrophic Lateral Sclerosis, Seyyed Bahram Borgheai, Brie E. Achorn, Alyssa H. Zisk, Sarah M. Hosni, Karl E. G. Richter, Frank S. Menniti, Yalda Shahriari

Computer Science Faculty Publications

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to the gradual loss of motor control, typically resulting in paralysis and death within 3 to 5 years of diagnosis. ALS shares neuropathological and genetic associations with fronto-temporal dementia (FTD), a neurodegenerative condition primarily impacting cognitive functions. These two conditions are increasingly viewed as manifestations of a single molecular disease process that affects distinct brain systems, impacting motor neuronal pathways in ALS and fronto-cortical functions in FTD. However, this simple dichotomy belies the complexity of these conditions. In particular, patients with primary motor ALS can also experience significant cognitive …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

A Phenomenological Analysis Of Sense Of Belonging Among Community College Educators, Muhammad Owais Aziz

EVMS School of Health Professions Theses & Dissertations

This phenomenological study explored the complex phenomenon of sense of belonging (SB) among diverse health professions educators (HPEs) and non-health professions educators (non-HPEs) at a Canadian community college. SB, as a dynamic construct shaped by an individual's perception of being valued and respected, is influenced by personal preferences and by complex interactions with the individual's surroundings. To better understand SB and foster a more inclusive environment for diverse educators, it is necessary to investigate SB among these educators. This qualitative study used eleven semi-structured interviews and seven steps of data analysis. The results of this study can inform strategies, policies, …

Twim #261: Overwhelming Microbial Greatness, Jenny Stoval, Briana Lanzarotta, Luke Boseman, Nicole L. Podnecky, Angela Wilson

School of Medical Diagnostics & Translational Sciences Publications

Podcast annotation TWiM #261: Overwhelming Microbial Greatness from the weekly podcast series "This Week in Microbiology" (TWiM), a podcast where experts in microbiology discuss academic papers in their field in an informal way.

Sciteuq: Toward Uncertainty-Aware Complex Scientific Table Data Extraction And Understanding, Kehinde Ajayi

Computer Science Theses & Dissertations

Scientific tables report critical research insights, data, and findings for scientific progress. Because Portable Document Format (PDF) is the de facto standard format for scientific paper publishing, there has been an emerging need for an automatic method to extract data from PDF files. A significant fraction of scientific tables exhibit complex structure and content, making it challenging for machine learning tools to accurately extract the content directly from PDF files. Despite the advancements in Table Structure Recognition (TSR), automated extraction of data from complex scientific tables remains a challenge due to variations in table structures and contents. In this dissertation, …

Aluminum As A Tracer Of Dust Deposition To The Ocean: A Case Study From The Bermuda Region, Tara Elizabeth Williams

OES Theses and Dissertations

Aluminum (Al), a major component of mineral aerosol (dust), partially dissolves in seawater and is widely used as a tracer for estimating time‐averaged dust fluxes to the ocean. Such estimates rely on dissolved Al (DAl) inventories in the surface mixed layer (SML), an assumed SML residence time of DAl (_TDAl), the fractional solubility of Al in dust (Al_S), and the mass fraction of Al in dust. In this study, dust flux estimated from seasonal, water-column DAl data from the Bermuda Atlantic Time-series Study (BATS) region are compared with direct dust flux estimated from contemporaneous measurements of …

Evaluation Of Ocean Lidar Enhancement Through Miniaturization And Gated Pmt-Based Range Extension, Chandler Austin Slater

OES Theses and Dissertations

Oceanographic lidar systems remotely characterize the vertical structure of the upper ocean by recording the light backscattered from a laser pulse as it propagates through the water column. Historically these systems have been constrained by limited detection ranges, often capturing only a portion of the illuminated water column, typically fading to noise around the 10% isolume. A primary constraint has been the limited dynamic range of digitizers, which inhibits the ability to resolve both intense near-field and faint far-field backscatter signals within a single acquisition. To address this, we developed a novel optical lidar system incorporating gated photomultiplier tubes (PMTs) …

Observed Parenting, Parent-Child Aggression, And Intimate Partner Violence As Predictive Of Family Dysfunction, Paige Munshell

Psychology Theses & Dissertations

Family systems theories contextualize violence as occurring across multiple levels of the family, yet much of the existing literature focuses on a single family member’s outcome (i.e., the child) and neglects to include data collected from fathers. This study examined the associations between indicators of functioning at the dyadic-level in the family system—observed parenting, Intimate Partner Violence (IPV) perpetration, and Parent-Child Aggression (PCA) risk—and later overall family dysfunction. Participants were 180 mothers and 144 of their male partners evaluated when their child was 18 months old and re-evaluated at 4 years old. Measures of observed parenting, IPV perpetration, and PCA …

Schema, Stigma, And The “We-Them” Divide: Perspectives On Ageism From Everyday Life To Healthcare, Temple D. West

English Theses & Dissertations

Ageism remains a pervasive and often underexamined form of discrimination, affecting older adults across both everyday and clinical contexts. This paper explores how cognitive schemas—mental frameworks that shape perception and experience—and stigma interact to reinforce a “we–them” divide between and older patients and healthcare providers, as well as between older and younger individuals in broader society. Through focus groups and listening to the voices of older patients, this study examines how assumptions of age-based decline are embedded in society and inform schemas of ageism. It considers how those schemas shape the spoiled identity of older people as a stigmatized group, …

Tracing The Journey Of Dissolved Organic Matter: From Leaf Litter Photodegradation To Microbial Processing In Tropical Streams, Samantha Nicole Sullivan

Chemistry & Biochemistry Theses & Dissertations

Dissolved organic matter (DOM) is a major reservoir of organic carbon in aquatic ecosystems and plays a central role in global carbon cycling through its transformation and remineralization to carbon dioxide (CO₂). As DOM moves from terrestrial environments into streams and rivers, its chemical composition is altered by a combination of microbial and photochemical oxidation processes that regulate both its reactivity and its persistence during transport. However, the molecular mechanisms governing these oxidative transitions, particularly in tropical ecosystems characterized by strong hydrologic seasonality and substantial inputs of plant-derived material, remain insufficiently resolved. This dissertation integrates ultrahigh-resolution mass spectrometry, optical characterization, …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Margalefidinium Polykrikoides Group Iii Va, Usa Strain Growth And Sac-Like Pellicle Cyst Dynamics, Eduardo Pérez Vega

OES Theses and Dissertations

Margalefidinium polykrikoides is a harmful cosmopolitan dinoflagellate that blooms in coastal waters. The effect of temperature and salinity on the growth of M. polykrikoides VA strain was examined using microscopy and growth models. M. polykrikoides Group III VA strain grew better at warmer temperatures and lower salinities than M. polykrikoides Group III NY strain, Group I Korea strain, and Japan strain (unknown group). Modelers need to use the temperature and salinity growth responses from M. polykrikoides Group III VA strain to better simulate and predict M. polykrikoides blooms in the Chesapeake Bay.

Dinoflagellates produce cysts as a strategy to withstand …

Twim #222: Biosensors In Bacteria, Madison Hayes, Alexis Austin, Dillon Nguyen, Angela Wilson, Rebecca Seipelt-Thiemann

School of Medical Diagnostics & Translational Sciences Publications

Podcast annotation TWiM #222: Biosensors in Bacteria from the weekly podcast series "This Week in Microbiology" (TWiM), a podcast where experts in microbiology discuss academic papers in their field in an informal way.

Twim #243: Beef And Bacillus, Sakiem Winston, Madison Wolford, Rebecca Seipelt-Thiemann, Angela Wilson

School of Medical Diagnostics & Translational Sciences Publications

Podcast annotation TWiM #243: Beef and Bacillus from the weekly podcast series "This Week in Microbiology" (TWiM), a podcast where experts in microbiology discuss academic papers in their field in an informal way.

Twim #286: Integrons And Invasion, Andrea Ayala-Lopez, Brittney Heist,, Thomas Salazar, Rebecca Seipelt-Thiemann, Mel Melendrez-Vallard, Angela Wilson

School of Medical Diagnostics & Translational Sciences Publications

Podcast annotation TWiM #286: Integrons and Invasion from the weekly podcast series "This Week in Microbiology" (TWiM), a podcast where experts in microbiology discuss academic papers in their field in an informal way.