Digital Commons Network^™

Uv- Altered Macrophages: Impact Of Uv Radiation On Phagocytic Receptor Expression In M2 Macrophages And Implications For Tissue Repair And Immunosuppression, Hala Hafez

Honors Undergraduate Theses

Ultraviolet radiation is an environmental stressor known to suppress immune function in the skin, but its effect on the immunoregulatory M2 phenotype macrophages is poorly studied. This study aims to investigate the impact of UV exposure on key markers of the M2 macrophage, which are critical for tissue repair and immunosuppression. THP-1 monocytes were differentiated and polarized into the M2 phenotype using Phorbol-12 myristate (PMA) and Cytokines IL-4 and IL-10. Once differentiation was confirmed, the macrophages were exposed to varying durations of UV radiation. Real Time Quantitative PCR was used to analyze expression changes in phagocytic receptors (CD206, CD163, Arg-1), …

Calcium: "The Element Of Stability", Paige Doolittle, Dahlia Debarros

CHM 103 Honors, Elements, Fall 2025

Student presentation from the Fall 2025 semester of CHM 103 Honors, Elements, taught by Professor George Dombi.

Good Forestry In The Granite State: Forest Health, Cooperative Extension

UNH Cooperative Extension

No abstract provided.

Good Forestry In The Granite State: Public Review Draft Third Edition - May 2025, Cooperative Extension

UNH Cooperative Extension

No abstract provided.

2025-2026 Teen Leadership And Civic Engagement Guidebook, Kristen Lyons

UNH Cooperative Extension

No abstract provided.

2026 4-H National Conference Application, Kristen Landau, Kristen Lyons

UNH Cooperative Extension

No abstract provided.

Evaluating Nurse Conscientious Objection: Application Of A Novel Framework, Maya Zumstein-Shaha, Lucia D. Wocial, Vicki D. Lachman, Norah Louise Johnson, Cynda Hylton Rushton, Pamela J. Grace

College of Nursing Faculty Research and Publications

Certain moral beliefs and/or values about what is good or harmful can cause nurses and other healthcare professionals to object to participating in some clinical actions. Such objections are also called conscientious objections. Invocation of a conscientious objection (CO) can produce complexities in patient care and health care delivery and must be mindfully evaluated for its soundness. In this manuscript, a recently developed framework, The Ethical Evaluation of a Nurse’s Conscientious Objection (EENCO), is applied to expose hidden elements and nuances in a proposed or actual CO by nurses or other healthcare professionals, thereby illuminating strategies that can lessen associated …

Reducing Opioid Use Following Posterior Spinal Fusion Procedures: A Pilot Project, Kevin Matthew Cornett

Yale School of Nursing Digital Theses

Importance: Opioid misuse and mortality remain a prevalent problem in the United States and there is a high rate of chronic opioid-use after spinal fusions. Current guidelines recommend the optimization of non-opioid analgesics as a primary pain control strategy with opioids acting as a secondary agent. Objective: To pilot an analgesic decision tree (ADT) featuring non-opioid analgesics to reduce postoperative pain, opioid consumption, and the incidence of adverse events during the first 48 hours after patients undergo a posterior spinal fusion. Methods: A literature review of opioid-sparing analgesics was conducted and a protocol developed as an interdisciplinary collaboration. The ADT …

Readmission Reduction Of 1st Year Transplant Recipients, Kristin Freed

Yale School of Nursing Digital Theses

Abstract Readmission Reduction of 1st Year Transplant Recipients Clear communication with patients at their health-literacy level improves patient outcomes, fosters trust, and is associated with readmission reduction. Readmission of transplant recipients are correlated with morbidity, medical errors, higher healthcare economic costs, and indicative of decreased survival post- transplant. Literature suggests that up to 72% of transplant recipients may have low health literacy increasing risk of readmission. The Agency of Healthcare Research and Quality created the REALM-SF assessment as a tool for medical providers to evaluate a patient’s health literacy status in a timely manner. This Doctor of Nursing Practice (DNP) …

New Leader Mentoring: Guiding Nursing Leaders Of The Future, Renee Marie Hammond

Yale School of Nursing Digital Theses

Nursing leaders throughout our health care system are retiring or leaving at an alarming rate – over 60% of the nursing leader workforce is expected to leave in the next five years. Retention of new nurse leaders is crucial to retention of clinical nurses, morale, and patient outcomes. Development of a formal mentorship program between new nurse leaders and experienced nurse leaders is essential to reducing the intent to leave of leaders. The Aims of this project are to develop a mentorship program for new nurse leaders using an adaptation of the American Organization for Nursing Leadership Core Competencies, to …

Improving Equity In Atherosclerotic Cardiovascular Disease Risk, Jordan Levandoski

Yale School of Nursing Digital Theses

The current use of racial categorization in Atherosclerotic Cardiovascular Disease (ASCVD) risk, limits assessment of confounders beyond cholesterol. The Life’s Essential Eight (LE8) categories of healthy diet, sleep, physical activity, smoking, body mass index, hypertension, total cholesterol, and blood glucose could reduce two million ASCVD events per year. The documentation of Z codes 55 – 65 describe social drivers of health (SDH) like housing, economics, and social systems which complicate care. Development of the ASCVD risk assessment and management protocol (ASCVD-RAMP) assessed ASCVD risk without race modification in patients aged 45 – 75 and improved health equity by pairing SDH …

Increasing Professional Interpreter Use To Improve Patient Outcomes On The Inpatient Oncology Unit, Kimberly Medina

Yale School of Nursing Digital Theses

Language barriers in healthcare contribute to significant disparities including delays in diagnosis and treatment, patient confusion, and increased mortality (Kwan et al., 2023). Approximately 21.5% of the people in the United States speak a language other than English at home and 8.2% of that population subset report speaking English less than very well (U.S. Census Bureau, 2021). Furthermore, in 2023 8% of Medicare beneficiaries have limited English proficiency (LEP) speaking and 7% have LEP reading (Centers for Medicare &Medicaid, 2024). Ensuring meaningful language access to LEP patients is essential for improving clinical outcomes and maintaining compliance to federal anti-discrimination policies. …

Developing A Standardized Workplace Violence Prevention Program For Healthcare Leaders To Enhance Safety, Kaleena Soorma

Yale School of Nursing Digital Theses

Abstract

Developing a Standardized Workplace Violence Prevention Program for Healthcare Leaders to Enhance Safety

The increasing threat of Type 2 workplace violence (client on staff) in healthcare disproportionately impacts employees such as Nurses. Desensitization to this growing trend in healthcare has contributed to perceptions that WPV has become an unavoidable risk, especially for those delivering care in high-risk areas such as the emergency department (ED).

The critical need to reevaluate WPV prevention strategies in healthcare, while highlighting systemic failures such as underreporting, is integral to driving a culture of safety. This project aimed to provide a post-incident Type 2 WPV …

Generated Image Quality Assurance And Generative Adversarial Network Augmentation For Machine Learning, Dirk Holscher

School of Engineering, Computing and Mathematics Theses

The advancement of machine learning is heavily dependent on the quality of data used for training models. This thesis explores the enhancement of data quality and augmentation techniques using Generative Adversarial Networks (GANs), pecifically focusing on the Pix2Pix architecture. The research addresses the critical challenges of improving image quality, optimizing hyperparameters, and detecting fake images generated by GANs, aiming to enhance the robustness and eliability of machine learning models. This work presents a collection of metrics for measuring data quality for both structured and unstructured data. Furthermore, it defines data quality for machine learning, divided into three quality levels. Advanced …

Coming Back Differently: An Exploratory Case Study Of Near Death Experiences Of Webpages, Lesley Frew, Michael L. Nelson, Michele Weigle

Computer Science Faculty Publications

In this case study, we use web archives to analyze 8,824 webpages that were taken offline and subsequently put back online, thus experiencing a “near death experience.” We enumerate the stages of a webpage’s near death experience, including the change from a successful HTTP status code to non-successful and back, the intermediate stage with markers such as an under construction banner, and an analysis of how the pages came back differently.

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 …

From Philosophy To Nlu: Evolving Definitions Of Research Hypotheses, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term hypothesis for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as …

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 …

Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang

Computer Science Faculty Publications

Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved …

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 …

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. …

The Invisible Influencer In Information Infrastructure, Herbert Van De Sompel, Michael L. Nelson

Computer Science Faculty Publications

The UPS Prototype was a proof-of-concept web portal built in preparation for the Universal Preprint Service Meeting held in October 1999 in Santa Fe, New Mexico. The portal provided search functionality for a set of metadata records that had been aggregated from a range of repositories that hosted preprints, working papers, and technical reports. Every search result was overlaid with a dynamically generated menu, called an SFX-menu, that provided a selection of value-adding links for the described scholarly work. The meeting eventually led to the Open Archives Initiative and its Protocol for Metadata Harvesting (OAI-PMH), which remains widely used in …

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 …

Securitization Of African Migrants In Europe And North America, Chick Edmond

Political Science & Geography Faculty Publications

The securitization of African migrants in Europe and North America refers to the framing of immigrants as an existential security threat rather than a socioeconomic or humanitarian concerns. This discourse driven by political rhetoric, media narratives, and policy measures, often depict African migrants as risks to national security, cultural identity, and economic stability. Governments in host countries employ stringent border controls, detention and deportation policies often justified by counterterrorism and crime prevention frameworks. However, critics argue that securitization exacerbates xenophobia, violates human rights, and fails to address root causes of migration, such as conflict, poverty, and climate change. This paper …

The Impact Of Professionalism Education And Formative Feedback In Doctor Of Physical Therapy Students Utilizing The Abbreviated Cpi Feedback Form In The On-Campus Clinic Environment, William L. Scott

Dissertations

Problem

The American Physical Therapy Association (APTA) developed the Clinical Performance Instrument (CPI) to assess the professional behaviors required for a Doctor of Physical Therapy (DPT) student to be considered "entry level" in their clinical training. Feedback from our clinical partners has indicated that, historically, students from the Andrews University DPT program have scored lower than students from other programs in several areas of the CPI during their clinical experiences. According to subjective reports from our clinical partners, these areas included safety, professional behavior, accountability, communication, and critical thinking. These five areas have been the focus of our efforts in …

Rethinking Trust In The Refugee Resettlement Process: How Service Providers Can Enable Refugee Agency Through Diasporic Connections, Mahfoudha Sidelemine, Emily D. Campion

Political Science & Geography Faculty Publications

Current guidance for refugee resettlement agency workers encourages the simultaneous pursuit of trust-building and administrative tasks (e.g., housing, transportation and employment). This dual goal is resource-intensive, and focusing on the former may come at a cost to the latter. The purpose of the current research is to challenge the importance of trust-building by resettlement agencies. Drawing from our qualitative data from in-depth interviews with resettled refugees (N = 20) and agency workers (N = 15), we adopt a grounded theory approach and find that the burden of responsibility for both caregiving and administrative responsibilities can overtax agency workers …

Manifesting A Shift In The "Overton Window": The Threat Of Project 2025 On The Lgbtq+ Community In Higher Education, Athena M. King, Sara Sanatkar

Political Science & Geography Faculty Publications

Since the first Trump administration, historically marginalized groups in the United States have been subjected to greater instances of bigotry and discrimination due to conservative influence on sociopolitical institutions. These actions suggest a shift in the "Overton Window," whereby policy preferences previously deemed "unacceptable" are given consideration in the mainstream, especially by conservative policy actors. "Project 2025" is a comprehensive plan to restructure the federal government according to conservative dictates in the second Trump administration. This article is an examination of how higher education may be impacted by this plan, especially as it relates to LGBTQ+ faculty, administration, staff, and …

Enhancing Cybersecurity In Smart Education With Deep Learning And Computer Vision: A Survey, Guma Ali, Aziku Samuel, Maad M. Mijwil, Kholoud Al-Mahzoum, Malik Sallam, Ioannis Adamopoulos, Ayodeji Olalekan Salau, Indu Bala, Klodian Dhoska, Engin Melekoglu

Mesopotamian Journal of Computer Science

The rapid digital transformation of education, driven by the widespread adoption of smart devices and online platforms, has ushered in the era of smart education. While this shift enhances learning experiences, it also introduces significant cybersecurity risks that threaten the confidentiality, integrity, and availability of educational resources, student data, and institutional systems. This survey examines how deep learning (DL) and computer vision (CV) techniques can enhance cybersecurity in smart education environments. By reviewing 202 peer-reviewed research papers published between January 2022 and June 2025 across leading publishers such as ACM Digital Library, Frontiers, Wiley Online Library, IGI Global, Nature, Springer, …