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David Bancroft Johnson Personal Papers - Accession 2, David Bancroft Johnson

Manuscript Collection

This collection consists of the personal correspondence and papers of Dr. David Bancroft Johnson, founder and first president of Winthrop.  The letters address various subjects and people, both personal and professional, concerning issues from the original funding of Winthrop in 1886 to the minutiae of ordering flour and cotton seed, as well as personal letters to family, friends, and acquaintances. This collection also includes news clippings, brochures, programs, and various documents belonging to Dr. Johnson.  The letters and documents are by organized by each item chronologically.

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 …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Design And Analysis Of Modern Quantum Neural Network Architectures For Intelligent Systems, Lakshmi Chandrakanth Kasireddy, Prabhakara Rao Kapula, Dineshkumar Rajendran, Neha Bharani, Srikanth Pulipeti, Islombek Khushvaktov

Computer Science Faculty Publications

Quantum neural networks (QNNs) offer a principled pathway for integrating quantum computation with machine learning through superposition- and entanglement-based representations. This chapter proposes an architecture-aware design and evaluation framework for modern QNNs, emphasizing robustness and system feasibility alongside predictive performance. Multiple architectures variational QNNs, quantum convolutional neural networks, tensor-network hybrids, and fully quantum models—are assessed under a unified protocol. Experimental analysis shows that the proposed architecture-search–guided QNN achieves 91.8% classification accuracy and an F1-score of 0.914, outperforming fixed-template variational QNNs by approximately 5.6 percentage points. Under depolarizing noise with probability p = 0.10, the proposed model retains 85.3% accuracy, whereas …

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 …

Voxvista: Enhancing Screen Reading Experience For Online User Comments, Yash Prakash, Akshay Kolgar Nayak, Mohammed Shoaib Alyaan, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to …

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 …

Exploring Marshall–Olkin Models Through Bibliometric And Topic Modeling Approaches Uses Latent Dirichlet Allocation (1981-2025): A Study Based On Scopus Data, Humberto Llinás, Brian Llinás, Carlos López, Daniela Nuñez

Computer Science Faculty Publications

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

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 …

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 …

The Deregulation Of Cancer, Daniel G. Aaron

Utah Law Faculty Scholarship

Fifty years ago, the U.S. embarked on a radical new path. Under pressure from the environmental movement, Congress passed an array of statutes aimed at regulating carcinogens out of our world. This regulatory experiment sprawled from air and water to food and consumer products.

Legal scholars have paid scant attention to this wave of cancer regulation despite its size and importance and the public’s apparent interest in avoiding carcinogens. But there is a growing literature finding flaws with individual regimes within the framework, such as the Clean Air Act, the Toxic Substances Control Act, and the Tobacco Control Act. In …

The Crisis In U.S. Cancer Care: Law, Markets, And Privatization, Daniel G. Aaron

Utah Law Faculty Scholarship

Cancer is surging among youth and young adults in the U.S., yet instead of public regulation addressing its root causes, we have outsourced the management of cancer to the private sector.  A suite of laws, embodying faith that corporations will cure cancer, has subsidized the cancer biomedical enterprise and transformed quasi-public institutions into marketized, profit-seeking entities.  These changes, across patent law, healthcare law, innovation law, and Food and Drug Administration (FDA) law, have privatized cancer. 

The market failures I describe in U.S. cancer care raise larger questions about who we entrust with managing critical social problems.  This Article provides theoretical …

Environmental Pesticide Exposure In The Etiology Of Pediatric Brain Tumors And Leukemia: A Scoping Review Of Epidemiological Studies, G. N. Vandesteeg, A. R. Russman, M. R. Sandbulte, E. G. Rogan, M. G. Rhodes

School of Natural Resources: Faculty Publications

No abstract provided.

Dynamic Social Impact Theory And The Consolidation And Dispersal Of Clusters In Squid Game: The Challenge, Adrienne Darrah, Robert J. Darrah

Iowa Journal of Communication

This study utilizes a dynamic social impact theory (DSIT) framework to analyze human influence on the popular Netflix television series Squid Game: The Challenge. A British reality competition, Squid Game: The Challenge is comprised of ten-episodes and features 456 contestants vying for $4.56 million. As contestants self-organize into social alliances, their shared attitudes, beliefs, and values transform the physical space into a dynamic social space. The creation of clusters through these alliances, the correlation between contestant attitudes and behaviors, and the consolidation of contestant attitudes and behaviors reflective of alliance needs is reinforced by the physical proximity of contestants, the …

Richard Victor Alvarus Mattessich [Inducted 2026], American Accounting Association

Accounting Hall of Fame Brochures

No abstract provided.

Donald James Kirk [Inducted 1996], American Accounting Association

Accounting Hall of Fame Brochures

No abstract provided.

Research News, Publications, 2026, Volume 1, Soumyajit Majumdar

Publications (2015-)

Publications that appeared during the period of January 1 - March 31, 2026.

Research News, Publications, 2026, Volume 2, Soumyajit Majumdar

Publications (2015-)

Publications that appeared during the period of April 1 - June 30, 2026.

Research News, Publications, 2026, Volume 3, Soumyajit Majumdar

Publications (2015-)

Publications that appeared during the period of July 1 - September 30, 2026

Metastasis-Directed Radiotherapy For Oligometastatic Urothelial Carcinoma Of The Bladder And Upper Tract, Patrick Carriere, Omar Alhalabi, Jianjun Gao, Amishi Shah, Sangeeta Goswami, Matthew Campbell, Ruitao Lin, Lauren Mayo, Osama Mohamad, Karen Hoffman, Henry Mok, Charmaigne Lozano, Ashish Kamat, Neema Navai, Kelly Bree, Byron Lee, Charles Guo, Michael Rooney, Chad Tang, Seungtaek Choi, Comron Hassanzadeh

Faculty, Staff and Student Publications

Background: Metastasis-directed therapy (MDT) for oligometastatic cancer is utilized for genitourinary malignancies including prostate and kidney cancers. Clinical research on MDT for urothelial carcinoma (UC) remains sparse, especially as systemic therapy advances have improved outcomes.

Objective: We investigated the role of MDT, specifically radiotherapy, for patients with oligometastatic bladder or upper-tract UC.

Methods: Data were collated on patients with metastatic UC with 5 or fewer metastatic sites undergoing MDT with ablative radiotherapy with or without preceding systemic therapy during January 2016 to July 2024. Endpoints were progression-free survival (PFS), and overall survival (OS). Cox proportional hazards analysis was conducted to …

Contralateral Breast Cancer After Radiotherapy And Hormone Therapy In Two Cohorts Of Us Breast Cancer Survivors, Lene H S Veiga, Gretchen L Gierach, Susan A Smith, Rebecca M Howell, Matthew M Mille, Monjoy Saha, Rochelle E Curtis, Cody Ramin, Clara Bodelon, Heather Spencer Feigelson, Erin J Aiello Bowles, Diana S M Buist, Sheila Weinmann, Jacqueline B Vo, Choonsik Lee, Amy Berrington De Gonzalez

Faculty, Staff and Student Publications

Background: Radiotherapy increases contralateral breast cancer risk, while hormone therapy reduces it; their combined effects are unclear.

Methods: Data from two US retrospective cohort studies of 5-year breast cancer survivors (stage I-III, ages 20-84), Kaiser Permanente (KP, 1990-2012) and SEER (1990-2013), were analysed. Contralateral breast radiation doses were estimated for the KP cohort. Multivariable Poisson regression estimated relative risks (RRs) and excess relative risks per Gray (ERR/Gy), stratified by hormone therapy use.

Results: KP cohort (n = 9053) included 353 contralateral breast cancer cases (73% ER+); SEER cohort (n = 244,834) included 10,470 cases (72% ER+). Among women with ER+ …

Trial Interviews To Explore Glycogen Storage Disease Type Ia Patient Experiences Following Gene Therapy, Diane M Turner-Bowker, Jessica Butler, Shayna Egan, David A Weinstein, David F Rodriguez-Buritica, Ayesha Ahmad, María-Luz Couce, Rebecca Riba-Wolman, John J Mitchell, Christina Theodore-Oklota

Faculty, Staff and Student Publications

Background: Glycogen storage disease type Ia (GSDIa) is a rare, inherited, autosomal recessive deficiency of glucose-6-phosphatase (G6Pase), an enzyme necessary in glycogenolysis and gluconeogenesis. To maintain normal blood glucose levels and ensure survival, individuals living with GSDIa must frequently consume complex carbohydrates (eg, uncooked cornstarch). Dietary management can result in chronic complications and significant patient burden. DTX401 (pariglasgene brecaparvovec) is an investigational adeno-associated virus serotype 8 vector (AAV8)-based gene therapy designed to restore endogenous glucose production.

Objectives: Patient experience interviews were conducted as part of an open-label, phase 1/2 dose-escalation trial (NCT03517085) evaluating the safety and efficacy of …

Reconsidering The Definition Of Triple-Negative Breast Cancer In The Immune Checkpoint Inhibitor Era: An Optimal Cut-Off Value For Hormone Receptor Percentage Of Her2-Negative Invasive Breast Cancer, Takeo Fujii, Toshiaki Iwase, Yu Shen, Jami Fukui, Naoto T Ueno

Faculty, Staff and Student Publications

The optimal cut-off values of estrogen receptor (ER) and progesterone receptor (PgR) expression to define the positivity of ER and PgR have been under discussion for over a decade but remain controversial. The American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) and the St. Gallen International Expert Consensus recommended that breast cancers with ≥1% of ER or PgR expression should be considered hormone receptor (HR)-positive tumors but ER/PR expression of 1% to 10% should be reported as HR-low positive; however, among HER2-negative disease, data on the overall benefit of adjuvant endocrine therapies for patients with HR-low positive disease is …

T Cells Modulate The Development And Maintenance Of Painful Paclitaxel-Induced Peripheral Neuropathy In Rnu Rats, Ahmed Olalekan Bakare, Gerard Limerick, Vasudha Goel, Ratan K Banik, Lei Zheng, Andrew J Shepherd, Kristine Glunde, Qin Zheng, Eellan Sivanesan

Faculty, Staff and Student Publications

The role of T cells in chemotherapy-induced peripheral neuropathy (CIPN) is complex and shaped by biological and experimental factors, including sex, hormonal status, genetic background, and cancer model. This complexity has contributed to inconsistent findings among studies, limiting therapeutic progress. In this study, we investigate how T cells contribute to painful paclitaxel (PTX)-induced peripheral neuropathy (PIPN). Adult male T cell-competent (RNU+/−) and T cell-deficient (RNU−/−) rats were subcutaneously inoculated with tumor cells and subsequently treated with intraperitoneal PTX (8 mg/kg total dose). Reflexive (mechanical, heat, cold) and non-reflexive (burrowing, gait) pain behaviors were assessed from baseline through week 6. Immunohistochemistry …

Digital Communication Strategies For Culinary Medicine In Cancer Prevention And Survivorship: Information-Seeking Habits And Content Preferences Of Online Cooking Media Among Adults Participating In Four Cancer Prevention And Control Cohort Studies, Margaret Raber, Sara Fares, Maria Vazquez, Larkin Strong, Carrie Daniel-Macdougall, Lorna Mcneill, Karen Basen-Engquist

Faculty, Staff and Student Publications

Introduction

Poor diet and excess weight have been linked to increased risk for at least 13 types of cancer. Culinary medicine utilizes experiential cooking skill development to improve individuals’ capacity for healthy eating. Digital communication strategies offer pathways for scalable culinary medicine interventions, but little research has explored how online cooking tools could be leveraged for cancer prevention messaging. We conducted a cross-sectional survey study exploring online cooking information-seeking habits and content preferences among participants in four cancer prevention and control cohorts to inform future digital culinary medicine interventions.

Methods

A cross-sectional survey study was conducted with a convenience sample …

Interventional Oncology: A Primer For Clinicians On The Role Of Ablation And Embolization For Solid Tumors, Mohamed E Abdelsalam, Kamran Ahrar, Rahul A Sheth, Ketan Y Shah, Steven Yevich, Varshana Gurusamy, Bruno C Odisio, Alda L Tam, Armeen Mahvash, Peiman Habibollahi

Faculty, Staff and Student Publications

Interventional radiology (IR) is a rapidly evolving medical field that combines advanced imaging with minimally invasive techniques for both diagnosis and treatment. Interventional oncology (IO), a subspecialty of IR, focuses on the minimally invasive, image-guided intervention for cancer and cancer-related conditions. IR plays an important and increasingly recognized role within the multidisciplinary care of patients with cancer, contributing meaningfully to diagnosis, therapy, and palliation. IO therapies, particularly tumor ablation and transarterial embolization, aim to target tumors directly while preserving surrounding healthy tissue. These therapies are increasingly supported by clinical guidelines and have shown favorable outcomes in cancers such as hepatocellular …

Nampt Inhibition Uncovers Therapeutic Vulnerabilities To Venetoclax And Chemotherapy In Acute Myelogenous Leukemia, John R Sanchez, Chaomei Liu, Vishakha Pawar, Yanqing Huang, Daisy Diaz-Rohena, Jyotsana Singh, Priya Koppikar, Tzung-Huei Lai, Lisa St John, Vinay Puduvalli, Jeffrey Molldrem, Palaniraja Thandapani, Nitin Jain, Rosa Lapalombella, Deepa Sampath

Faculty, Staff and Student Publications

Acute myeloid leukemia (AML) cells depend on nicotinamide adenine dinucleotide (NAD