Digital Commons Network^™

Dynamic Progression Of Ectopic Lymphoid Structure Formation In Lacrimal Glands Of A Sjögren’S Disease Murine Model, Sara Abdelhamid, Alison V Ramirez, Emre Aksan, Elizaveta A Demianova, Cintia S De Paiva, Maria C Edman, J Andrew Mackay, Sarah F Hamm-Alvarez

Faculty, Staff and Students Publications

Background: Sjögren's Disease (SjD) is a chronic autoimmune condition characterized by lymphocytic infiltration of lacrimal glands (LG) and salivary glands (SG). In SG, these immune structures have properties of ectopic lymphoid structures (ELS) and appear to play a critical role in disease pathology. While the presence of ELS in patients' SG biopsies is linked to disease severity, their presence and composition in LG has not been well characterized.

Methods: The properties and time course of apparent ELS development in LG from the male non-obese diabetes free (NOR) sub-strain of the Non-Obese Diabetic (NOD) mice was investigated at stages encompassing early …

Phase 1 Trial Of Withania Somnifera Leaf Extract (Rh324) In Advanced Non-Small Cell Lung Cancer Including [18f]Fdg Pet/Ct As A Short-Term Metabolic Biomarker To Assess Efficacy: A Novel Model For Assessment Of Complimentary Therapies In Early Phase Human Clinical Trials, Jin Uk Heo, Santosh Rao, Herbert B Newton, Afshin Dowlati, Raymond F Muzic, Arash Kardan

Faculty, Staff and Students Publications

Background: Withania somnifera (WS), commonly known as ashwagandha, has been used in the traditional medical system of India. It has shown significant activity against numerous solid tumor varieties in pre-clinical in vitro and in vivo studies. This study focuses on RH324 (ReHeva Biosciences, Columbus, OH), a pharmaceutical-grade formulation derived from WS, which has received FDA allowance for clinical development as a botanical drug in the treatment of cancer.

Methods: A phase 1 open label dose ranging study of oral RH324 in advanced non-small cell lung cancer (NSCLC) was conducted. The primary endpoint of the study was assessment of short-term safety …

Esr1 Fusions In Breast Cancer: Functions, Mechanisms And Therapeutic Opportunities, Xuxu Gou, Zoya Farooqui, Charles E Foulds

Faculty, Staff and Students Publications

Approximately 70% of all breast cancers are driven by estrogen receptor-alpha (ERα) that binds the predominant circulating female estrogen, 17ꞵ-estradiol (E2). Because of this, drugs collectively termed “endocrine therapy (ET)” that either suppress E2 level or inhibit the activity of ERα have been used as first-line therapy. While initially effective, resistance to ET drugs occurs with subsequent lethal metastasis. The most common genetic alterations are point mutations in the ESR1 gene within the last exon encoding the ligand-binding domain (LBD), resulting in constitutively active ERα proteins. Recently, new oral selective estrogen receptor degraders (SERDs) that effectively target these point mutants …

Evaluating Mesothelin As An Immunotherapeutic Target For Endogenous T Cells, Kiriakos Koukoulias, Ryu Yanagisawa, Alejandro G Torres Chavez, Penelope G Papayanni, Yovana Velazquez, Hyun-Sung Lee, Spyridoula Vasileiou, Ann M Leen

Faculty, Staff and Students Publications

Background: Mesothelin (MSLN) is a GPI-anchored cell surface glycoprotein that is overexpressed in various solid tumors, including mesothelioma, triple-negative breast cancer, colon, ovarian and pancreatic cancer, with restricted normal tissue expression.

Methods: To explore the immunogenicity and immunotherapeutic potential of MSLN to T cells with native receptor specificity, 29 individuals of diverse HLA backgrounds were interrogated for T cell activity against MSLN.

Results: Twenty one (72%) subjects (21/29) mounted a specific T cell response when repetitively challenged with MSLN antigen. Reactive cells were Th1-polarized, polyfunctional, predominantly detected in the CD8+ T cell compartment and cytotoxic toward autologous and MSLN+/HLA-matched tumor …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

A Hybrid Response Surface Methodology And Machine Learning Framework For Quantifying Effects Of Physicochemical Parameters On Pfas Distribution, Harsh V. Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log K_ow, pH_pzc, pK_a, log dose, and log-initial concentration, affect PFAS distribution coefficients (Log K_d). A data set of more than 1000 adsorption observations spanning 15 PFAS compounds, multiple adsorbent types, and a broad operational range was compiled and preprocessed using …

Technological Interventions For Reducing Climate Change Impacts On Health: An Overview Of Future Possibilities, Sujatha Alla, Vijay Kumar Chattu, Bawa Singh

Engineering Management & Systems Engineering Faculty Publications

Globally, over five million deaths annually are attributed to extreme weather events exacerbated by climate change, such as heatwaves, hurricanes, wildfires, droughts, and floods, which create health crises and economic losses. The 2015 Paris Declaration emphasized climate technology mechanisms, including research and development, to enhance resilience and reduce greenhouse gas emissions. Biotechnological advancements have mitigated about 20% of economic losses since the 1960s, highlighting technology’s potential to address climate-induced health risks.

A Three-Stage Causal Root-Cause Diagnostic Protocol For Nonstationary Industrial Time Series Data, Cansu Yalim, Resit Unal, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

Predictive maintenance (PdM) systems effectively forecast failures, but they often fail to find root causes, particularly when system dynamics change over time. This limitation arises from applying static causal models or decoupled segmentation to handle nonstationary industrial time series. For regime-aware causal diagnostics and interventional effect estimation, we introduce a three-stage time-varying dynamic Bayesian network (TV-DBN) protocol. Using a minimum description length (MDL) objective that connects segmentation to mechanism changes, Stage I jointly infers change points and regime-specific graph structure. Stage II produces a completed partially directed acyclic graph (DAG) by orienting edges within each regime using a combination of …

Information-Theoretic Path Planning For Safe And Adaptive Nanobot Drug Delivery, Nagesh Bheesetty, John H. Park, Sujatha Alla, Winifred M. Lo, Aditya Nimmagadda, Mohamadmahdi Samandari, Jae H. Lee

Engineering Management & Systems Engineering Faculty Publications

We present a safe and efficient path planning framework for nanobot navigation in complex vascular networks, enabling precise drug delivery to hard-to-reach tumor regions such as those found in the lungs. The proposed method introduces a safe and efficient path planning strategy that integrates a utility-based function to balance two key goals: exploring uncertain areas to gather valuable information and avoiding hazardous regions to ensure safe and reliable delivery. We evaluate a simplified swarm-based nanobot navigation scenario using synthetic vascular maps that mimic the structural complexity of lung tumor blood vessels. Environmental uncertainty is modeled from vessel topology, blockage density, …

A Sysml V2 Metamodel For -Ilities: Application To A Digital Twin Complex System, Pacifique Munezero, Holly Ann Heine Handley

Engineering Management & Systems Engineering Faculty Publications

Multidisciplinary knowledge and exchange of information are two of the most important aspects of complex system design within digital engineering and model-based systems engineering (MBSE) environments. The next generation of systems modeling language, SysML v2, developed by the Object Management Group, improves the implementation and maturation of model-based approaches. This article focuses on using SysML v2 to model reusable parameterized templates for nonfunctional requirements, referred to as -ilities, to address how they can be treated with the same rigor as functional requirements and mapped directly and explicitly from the requirements engineering phase within digital engineering and MBSE environments. The selected …