Digital Commons Network^™

Hemolysis Detection Using The Gem 7000 At The Point Of Care In A Pediatric Hospital Setting: Does It Affect Outcomes?, Ridwan B Ibrahim, Nazmin Bithi, Charlene Hernandez, Hanna Uhrova, Brandy D Recio, Sridevi Devaraj

Faculty, Staff and Students Publications

No abstract provided.

Nivolumab Plus Ipilimumab Induce Hyper-Progression In Renal Medullary Carcinoma: Results Of A Phase Ii Trial And Preclinical Evidence, Melinda Soeung, Xinmiao Yan, Ciro Zanca, Jing Qian, Menuka Karki, Fei Duan, Hania Khan, Li Zhang, David H Peng, Mariah Williams, Rong He, Ziheng Chen, Luigi Perelli, Jianfeng Chen, Rebecca S Tidwell, Pankaj K Chauhan, Courtney N Le, Truong N A Lam, Nirjar Bhattacharya, Rutvi Shah, I-Lin Ho, Jason P Gay, Caroline C Carrillo, Ningping Feng, Kang Le, Guang Gao, Teresa L Perry, Faika Mseeh, Yongying Jiang, Quanyun A Xu, Niki Marie Zacharias, Rahul A Sheth, Tharakeswara K Bathala, Priya Rao, Najat C Daw, Durga N Tripathi, Cheryl L Walker, Mohammad M Mohammad, Jianhua Zhang, Guangchun Han, Yanshuo Chu, Ruiping Wang, Minghao Dang, Enyu Dai, Fuduan Peng, Yunhe Liu, Akshaya Jadhav, Wenhua Lang, Claudio A Arrechedera, Leticia Campos Clemente, Edwin R Parra, Hsinyi Lu, Cara L Haymaker, Ignacio I Wistuba, Andrew Futreal, Andrea Viale, Michael J Soth, Philip Jones, Joseph R Marszalek, Timothy Heffernan, Giulio F Draetta, Nizar M Tannir, Jianjun Gao, Linghua Wang, Giannicola Genovese, Pavlos Msaouel

Faculty, Staff and Students Publications

Therapeutic options for patients with renal medullary carcinoma (RMC) are limited. Here we report the results of a phase II clinical trial (NCT03274258) of anti-PD1 nivolumab plus anti-CTLA4 ipilimumab in patients with RMC, with objective response rate as primary outcome. Enrollment was halted for futility at a prespecified interim analysis as all 10 treated patients experienced rapid disease progression. 5/10 met radiological criteria for hyperprogression and median progression-free survival (secondary outcome) was 1.38 months (95% confidence interval: 1.28, 1.60). In a post-hoc single-cell RNA sequencing analysis, data from patients with RMC before and after nivolumab plus ipilimumab treatment indicated that …

Access To Healthy And Nutritious Food In Utah: Results From The 2024 Utah Wellbeing Survey, Heather H. Kelley, Palak Gupta, Courtney G. Flint

All Current Publications

Every Utahn, regardless of income, ZIP code, race, age, or health status, deserves reliable access to affordable, nutritious, and culturally meaningful food. Yet, too many communities across the state face persistent barriers that limit their ability to eat well and live healthy lives. This fact sheet reviews results of a 2024 Utah State University (USU) Extension needs assessment that identified food and nutrition security as a high-priority issue in Utah. It explores the current landscape, identifies key challenges, and outlines strategic opportunities for USU Extension and its partners to enhance food security statewide.

Leaping Between Branches: Hybridisation And The Tangled Evolutionary History Of True Lemurs, Giacomo Mercuri, Giovanni Merici, Kyle Kai-How Farh, Lukas F K Kuderna, Jeffrey Rogers, Tomàs Marques-Bonet, Giuseppe Donati, Riccardo Percudani, Cristian Capelli

Faculty, Staff and Students Publications

The true lemurs (genus Eulemur) are a genetically diverse and spatially widespread group of species inhabiting most of Madagascar's forests. Including 12 recognized species, the genus can be divided into four major evolutionary groups: E. rubriventer, E. mongoz, the Brown Lemur Species Complex (BLSC), and the coronatus-macaco-flavifrons complex (CMFC), although monophyly for the CMFC is not always supported. Recent genome-based studies highlighted topological and chronological differences between nuclear and mitochondrial phylogenies of true lemurs, which could be explained by events of hybridisation. In order to reconstruct the evolutionary history of the genus, we test for gene-flow between Eulemur clades using …

Deep Learning Reveals How Cells Pull, Buckle, And Navigate Fibrous Environments, Abinash Padhi, Arka Daw, Atharva Agashe, Medha Sawhney, Maahi M Talukder, Mehran M H Pour, Mohammad Jafari, Guy M Genin, Farid Alisafaei, Sohan Kale, Anuj Karpatne, Amrinder S Nain

2020-Current year OA Pubs

Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the extracellular matrix (ECM) are known to drive behaviors, including cell motility on flat nonfibrous substrates, the interplay between fiber architecture and stiffness in fibrous ECM is not known. Here, we find that in fibrous environments, the directionality of mechanical forces overrides ECM stiffness as the primary regulator of contractility in migrating cells. Using an approach combining phase microscopy with deep learning to map forces in …

Cpx-351 Vs. Conventional Chemotherapy Cardiotoxicity In High-Risk Aml: A Post Hoc Phase Iii Trial Analysis, Joshua D Mitchell, Michael Pfeiffer, John Boehmer, John Gorcsan, Shunsuke Eguchi, Yoshiyuki Orihara, Nalina Dronamraju, Sonja Dhani, Stefan Faderl, Tara L Lin, Geoffrey L Uy, Jeffrey E Lancet, Jorge E Cortes

2020-Current year OA Pubs

BACKGROUND: CPX-351, a dual-drug liposomal encapsulation of daunorubicin and cytarabine in a synergistic 1:5 molar ratio, has demonstrated significantly improved overall survival in acute myeloid leukemia (AML) compared with 7 + 3, but its impact on cardiac function remains unclear. In a post hoc analysis of the pivotal clinical trial, we sought to determine the relative cardiotoxicity of CPX-351 vs. 7 + 3 in high-risk AML.

METHODS: We evaluated cardiotoxicity in 102 patients with AML (CPX-351, n = 57; 7 + 3, n = 45) who had normal baseline left ventricular ejection fraction (LVEF) ≥ 53% and at least one …

Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

“It Haunts Me Still”: Exposure To Potentially Morally Injurious Events In First Responders, Miranda Worthen, Shira Maguen, Soma De Bourbon

Faculty Research, Scholarly, and Creative Activity

Moral injury is a biopsychosocial, behavioral, and spiritual consequence that may occur for individuals in high-risk occupations after acting, failing to prevent, or witnessing events that go against their deeply held morals or values. While there has been extensive research on the types of events that may lead to moral injury for veterans, and a growing literature on healthcare workers, limited research has explored the experiences of first responders. This study builds on a multi-year participatory action research study with a large fire department in northern California aimed at understanding and mitigating moral injury among first responders. The present article …

Single-Cell Transcriptomic Analysis Of Peripheral Blood Mononuclear Cells Reveals Key Immune Responses In St-Segment Elevation Myocardial Infarction, Zheng Zhang, Shengfang Wang, Yahui Liu, Gaohan Li, Qianqian Cheng, Wei Yang, Gan-Xin Yan, Chuanyu Gao

Department of Medicine Faculty Papers

BACKGROUND: Inflammation plays a crucial role in the pathogenesis of ST-segment elevation myocardial infarction (STEMI). However, the precise immunological mechanisms remain incompletely understood. Single-cell RNA sequencing (scRNA-seq) provides a powerful approach to dissect immune cell heterogeneity and dynamic changes at single-cell resolution.

METHODS: Peripheral blood mononuclear cells (PBMCs) were collected from 7 STEMI patients (within 6h after primary percutaneous coronary intervention) and 3 healthy controls. Single-cell suspensions were prepared and subjected to scRNA-seq using the 10x Genomics Chromium platform and Illumina NovaSeq 6000. Data were processed using Cell Ranger and analyzed with Seurat for quality control, clustering, and annotation. Differentially …

Changing Perspectives In California: A Transformative Culture Exchange For Stockholm University And Sonoma State University Criminology Students, Bryan Burton, Sofie Hellmer, Vida Wåhlmark, Hope Ortiz, Diana Grant

csuglobalaction

From June 13 to 20, 2024, Sonoma State University (SSU) hosted 16 criminology students from Stockholm University (SU) for an educational and cultural exchange. Five SSU undergraduate “student ambassadors” participated, representing California and SSU while assisting the Swedish students. Surveys indicated the exchange was a meaningful academic and cultural experience for all participants. The Swedish students deepened their understanding of the U.S. criminal justice system through presentations by criminologists and discussions with practitioners on issues such as policing and prison reform. They also gained broader perspectives on California and the United States through interactions with SSU ambassadors and visits to …

Gc-0258 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur- Rahman, Soarov Borty, Shakib Quddus

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) is one of the leading causes of global health concern, but current risk assessments are limited to episodic clinical visits. Most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of …

Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics​, Syeda Umme Salma

C-Day Computing Showcase

Caregivers face distinctive emotional and logistical burdens, yet many mental-health apps overlook their needs and show usability issues. We introduce an automated pipeline that analyzes 317K app-store reviews from 9 apps, mapping them to Nielsen’s usability components and heuristics, together with sentiment. To assess reliability, we run a human–AI agreement study (N=50) where a domain expert (A2) and a non- expert (A1) label reviews. For heuristics, the pipeline achieves 66% exact agreement and moderate κ=0.579 with the expert, outperforming human–human agreement; components remain harder, revealing a need to refine the codebook (e.g., learnability vs satisfaction). Complementary clustering and sentiment analyses …

Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan

C-Day Computing Showcase

Edge Artificial Intelligence (AI) refers to running AI inference directly on local devices such as wearables, sensors, and mobile systems rather than relying on cloud computing. The growth of Edge AI has created strong demand for efficient inference on resource-limited devices. Edge AI devices must perform real-time inference while operating under strict battery constraints. Although significant model optimizations exist for managing power-intensive inference models, operating system (OS) level support is limited. Existing OS schedulers often neglect energy limits in edge devices as they prioritize fairness or throughput. In this research we proposed an OS level framework to bridge this gap …

Grp-1230 Environmental Protection: Development Of A Real-Time Multi-Stream Water Quality Monitoring System, Faruk Muritala

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Grp-1184 Edge-Llm Anomaly Detection On Raspberry Pi: Syscall Dataset Collection And Prototype Llm Explanation Layer, Shiva Shrestha, Shiva Shrestha

C-Day Computing Showcase

This research work offers a light-weight, end-to-end, syscall-level anomaly detection approach for the Raspberry Pi platform. The proposal involves the collection of around 2000 NORMAL and 200 ANOMALY syscall observation groups using the Linux Auditd safe synthetic generators. The work also utilizes a prototype LLM Explanation Layer, allowing the provision of human-friendly explanations pertaining to identified anomalies leveraging small LLM models like the Gemma-3 1B, Phi-3 Mini, or other sub 1B LLMs employing the Ollama platform. The LLM inference layer in this research work has partial implementations, as the fine-tuning of the model remains to be done.

Uc-1276 Ci-Cd Pipeline Team 2, Cameron Arnold

C-Day Computing Showcase

Our project is about creating a basic cloud-native pipeline that can build and deploy an application in a more automated way. We will also try to add some security checks and monitoring tools so that we can see how everything is working. The goal is to get hands-on experience with the process and show a working demo at the end of the semester.

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

Grm-0204 Unpacking Early Burnout Through Predictive Risk Boundaries, Soarov Chakra Borty

C-Day Computing Showcase

Caregiver burnout is a significant issue in healthcare delivery and management, as it directly impacts caregivers' health and compromises the standard of care, often leading to negligence, health deterioration, or withdrawal from caregiving duties. Caregivers play a crucial role in supporting the health, well-being, and quality of life of care recipients by providing both personal and professional services. However, the continuous needs and stress associated with caregiving duties can affect their health and everyday life, leading to caregiver burnout. This study applied data analytics and machine learning by merging several feature selection methods on the NHATS dataset, including LightGBM, XGBoost, …

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

Uc-0223 Predicting Nba Player Re-Injury Using Net Rating, Anaya Tention

C-Day Computing Showcase

This project examines whether player performance data can signal injury risk before an absence occurs. Using game-by-game net rating trends, I applied an exponentially weighted control-chart approach to detect early shifts in performance that might indicate a rising risk of re-injury. The method successfully identified 71% of re-injury cases with an average 20-game lead, suggesting that performance declines can serve as an early warning signal. While the false-alarm rate was high, the results show that performance-based monitoring has potential value for teams seeking proactive player-health insights.

Uc-1232 Stronghold, Caitlin Tigani, Adam Tucker, Camden Lloyd, Dale Balsor

C-Day Computing Showcase

Our goal for this game is to create a game using both a single agent and multi agent AIs. In our game you can either play against the current AI or train the AI up as it fights against another AI. The training will use genetic AI. So whichever AI wins that row will be the one that move on. The loser will have their weights adjusted. This means the more that you train the Stronghold AI the harder the AI will be to fight against. The final game mode available is being able to play against another.

Uc-1240 Project Ibis, Isaac Alderman, Braden Mizell, Collin Sutton

C-Day Computing Showcase

Project Ibis (working title) is a first person, narrative heavy, puzzle-lite RPG that follows the story of a renaissance era plague doctor and their attempt to alter the minds of three subjects; a gardener, a street urchin, and a priest. The game’s narrative is set in historically accurate 1637 Florence, Italy, in the wake of the Great Plague of Milan, and draws heavily from renaissance culture. Each subject being treated is a complex person with personal conflicts and issues; our focus is on tackling mental and emotional health through empathy and nuance rather than diagnosis. Different aspects of each subject’s …

Uc-1274 Cloud-Native Ci/Cd Pipeline, Enitan Meduteni, Rami Elmostafa, Matt Crowley, Kade Fleming, Cecily Graffree

C-Day Computing Showcase

This project documents a 12-week capstone implementing a cloud-native CI/CD pipeline using industry-standard DevOps tools. The system integrates Jenkins for continuous integration, Kubernetes for container orchestration, GitOps (ArgoCD) for automated deployments, DevSecOps practices including RBAC and vulnerability scanning, and comprehensive monitoring using Prometheus and Grafana. Mentored by Sudheer Amgothu, Principal Cloud Operations Engineer.

Grm-1245 A Synthetic Data Engine For Explainable Injection-Area Perception, Yukang Shen

C-Day Computing Showcase

Vision-Language-Action (VLA) systems are beginning to support everyday clinical workflows. Deltoid intramuscular injection is a representative task, but progress is limited by data scarcity, privacy constraints, and the cost of expert annotation. Recent text-to-image (T2I) models make large-scale data synthesis possible, yet ensuring anatomical correctness, diversity, and label quality remains difficult. To address this gap, we propose a Synthetic Data Engine tailored for medical perception, integrating cold-start filtering, controlled T2I generation, CLIP-based quality checks, and iterative segmentation training. We further introduce an anthropometry-grounded formulation of injection safety that produces interpretable safe-zone guidance. Experiments show that synthetic data can effectively bootstrap …

Mobile Computer Vision Application For Agricultural Disease Detection Of Pepper Diseases Using Two-Stage Deep Learning System, Carlos Jose Estevez, Mai Dang, Ryan Bass

SMU Data Science Review

Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves …

Predictive Analytics In Public Health: Developing Ai Strategies To Combat High-Temperature Impacts On Violence And Overdoses In Las Vegas, David Camacho, Stephanie Duarte, Kosi Okeke, Chris Papesh, Jacquelyn Cheun-Jensen

SMU Data Science Review

Public violence and overdoses are key issues that Nevada agencies aim to address in the Las Vegas Metropolitan Area. A proven and effective strategy is via the implementation of a Cardiff Violence Prevention Model. This model provides a way for communities to gain a clearer picture about where violence is occurring by combining and mapping both hospital and police data on violence. In an effort to support this model, the exploration and implementation of AI models to predict the impacts of high temperatures on key public data and outcomes based upon historical data will be applied.

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Analyzing The Global Happiness Index, Victoria Hernandez, Christy W. Wachira

SMU Data Science Review

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and …