Data Science Commons^™

Key Epigenetic And Signaling Factors In The Formation And Maintenance Of The Blood-Brain Barrier, Spiros L Blackburn, Nitin Tandon, Hrishikesh Lokhande, Pierre D Mccrea, Emery H Bresnick, Pramod K Dash, Devin W Mcbride, Arif Harmanci, Lalit K Ahirwar, Dania Jose, Ari C Dienel, Hussein A Zeineddine, Sungha Hong, Peeyush Kumar T

Faculty, Staff and Student Publications

The blood-brain barrier (BBB) controls the movement of molecules into and out of the central nervous system (CNS). Since a functional BBB forms by mouse embryonic day E15.5, we reasoned that gene cohorts expressed in CNS endothelial cells (EC) at E13.5 contribute to BBB formation. In contrast, adult gene signatures reflect BBB maintenance mechanisms. Supporting this hypothesis, transcriptomic analysis revealed distinct cohorts of EC genes involved in BBB formation and maintenance. Here, we demonstrate that epigenetic regulator's histone deacetylase 2 (HDAC2) and polycomb repressive complex 2 (PRC2) control EC gene expression for BBB development and prevent Wnt/β-catenin (Wnt) target genes …

Unlocking The Power Of Data: Enhancing Public Policy Through Advanced Data Infrastructure And Language Model Analysis, Zahid Asghar

CBER Conference

Data is the fundamental building block for advancements in artificial intelligence (AI), general AI (GAI), machine learning (ML), and large language models (LLMs). This study emphasizes the critical need for robust data infrastructure, arguing that without it, countries cannot fully benefit from technological advancements in various economic sectors. Governments possess vast repositories of both structured and unstructured data across multiple domains such as the judiciary, parliaments, and civil bureaucracy. However, these potential goldmines remain untapped due to inadequate data management capabilities and a lack of appreciation for the necessity of high-quality data. The research identifies key issues in public data …

Customer Data And The Digital Age, Mahdi Ansari

CBER Conference

Data is widely regarded as the most valuable resource in today’s economy, yet its value often eludes precise quantification. This paper examines customer data as an intangible capital asset and addresses the challenge of measuring its impact. A novel database was created by merging Compustat with online clickstream data capturing the activity of approximately 200 million users, providing proxies for data inflow based on visit metrics. The analysis documents that the distribution of firms’ customer data stocks follows a rightskewed log-normal pattern with a fat tail. Additionally, a positive relationship emerges between sales and data inflow, data stock, profit, and …

Statistical Analysis For Pre- And Post- Assessments Of Sdq And Idela Scores, Diego Murillo, Franceli L. Cibrian

Student Scholar Symposium Abstracts and Posters

This research aimed to assess the potential of Mazi Umntanakho ("Know Your Child") in tracking developmental milestones in young children. Mazi is a WhatsApp-based conversational agent that assists South African home visitors in evaluating and monitoring children's socio-emotional skills using the Strengths and Difficulties Questionnaire (SDQ) and the International Development and Early Learning Assessment (IDELA). A field study was conducted in low-income South African communities, where 95 home visitors assessed 1,208 children. This detailed analysis of the data was collected during that deployment, focusing on investigating whether assessment scores improved over time and whether the length of time between assessments …

Visualization Of Paleocurrents On A Web Application Using Gplates, Anjan Sapkota

MS in Computer Science Theses

Paleocurrents are flow directions derived from features of sedimentary rocks that reveal the direction of the current of wind or water that deposited the sediment. In 2015, Brand et al. created a global database of paleocurrents, which contains over 1,000,000 measurements worldwide: North America, South America, Australia, Great Britain, parts of Western Europe, China, Africa are fairly well represented; Antarctica, Eastern Europe, and Asia are modestly represented and Russia is poorly represented. The contribution of this thesis is a web application that uses the GPlates’ Application Programming Interface (API) to visualize global paleocurrents through time in an interactive way based …

Graph Neural Networks Powered Scientific Paper Recommendation, Junhao Shen

Computer Science and Engineering Theses and Dissertations

Scientific paper recommendation systems aim to help researchers discover relevant papers amidst the vast and ever-growing body of literature. With the exponential yearly increase in scientific publications, the demand for effective paper recommendation solutions has become both critical and increasingly challenging. In recent years, deep learning techniques have revolutionized recommender systems, and scientific paper recommendations have naturally integrated these advancements. In this dissertation, we address these challenges through three progressive contributions.

First, we enhance traditional content-based methods using Graph Neural Networks (GNNs) by introducing a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) approach. This method improves upon conventional topic modeling techniques …

Applications Of Neural Networks In Parkinson’S Disease Diagnosis, Saladin Minhaaj

Theses

Parkinson's disease (PD) is a complex and debilitating neurodegenerative disorder that affects millions of people worldwide. Early and accurate diagnosis is crucial for effective treatment and management of PD. This thesis explores the application of neural networks in PD diagnosis, leveraging their ability to learn patterns from large datasets and make accurate predictions.

Thesis provides an overview of PD, including its symptoms, diagnosis, and current challenges in diagnosis. We then delve into the fundamentals of neural networks, including supervised learning, mathematical interpretations, and parametric models. This research focuses on the development of neural network models that can accurately diagnose PD …

The Impact Of Student Engagement Activities On Future Climate Change Adaptation: The Case Of Student Simulation Models, Bassel Mostafa Elkalaf

Future Journal of Social Science

This paper explores the critical role of student engagement in addressing the growing challenges of climate change, with a focus on the Model United Nations (MUN) as a case study. As climate-related security threats increase globally, educational platforms that prepare youth for effective leadership in climate politics are more essential than ever. MUN, a widely practiced student activity simulating global policy-making, provides a valuable opportunity for students to deepen their understanding of the interconnectedness between climate change, peace, and security. By participating in MUN simulations, students engage in debates, develop innovative solutions, and practice diplomatic skills, all while exploring the …

A New Reduced Basis Method For Parabolic Equations Based On Single-Eigenvalue Acceleration, Qijia Zhai, Qingguo Hong, Xiaoping Xie

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we develop a new reduced basis (RB) method, named as Single Eigenvalue Acceleration Method (SEAM), for second order parabolic equations with homogeneous Dirichlet boundary conditions. The high-fidelity numerical method adopts the backward Euler scheme and conforming simplicial finite elements for the temporal and spatial discretizations, respectively. Under the assumption that the time step size is sufficiently small, and time steps are not very large, we show that the singular value distribution of the high-fidelity solution matrix U is close to that of a rank one matrix. We select the eigenfunction associated to the principal eigenvalue of the …

Explainable Ai In Medical Imaging: An Interdisciplinary Translational Approach, Caitlyn Chavez

Computational and Data Sciences (PhD) Dissertations

Advances in computer vision and image processing have made a clear impact on many fields, from healthcare diagnostics to autonomous driving. However, as these models become more complex, understanding their decision-making processes has grown increasingly challenging, making explainable AI (XAI) a crucial component of modern AI systems. The focus of this work is to integrate these new technologies alongside foundational methods of image processing to create tools that can be used by domain experts who are not programmers. Prior to delving into the projects which investigate these concepts, the methodologies, background, and the overall frameworks are discussed. In the first …

Optimization Of Markov Chain Modeling In Predicting College Student Retention, Kien Nguyen

Journal of Global Education and Research

College student retention is one of the most important metrics in higher education. With institutions across the US facing decreasing enrollment, developing a reliable retention prediction method is crucial. In recent years, the use of the Markov chain model in forecasting student enrollment and progression has become more common, but there is little work on its application in student retention. One key factor in determining this model's effectiveness is what parameters should be used in the student population’s segmentation or grouping. This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Leveraging High-Frequency Water Quality Data And Machine Learning For Monitoring Harmful Algal Blooms, Ibrahim Busari

All Dissertations

Freshwater management is one of the most critical resources on the earth due to the plethora of water use and its limited availability. Increased algae proliferation is one of the significant problems of freshwater bodies that is triggered by nutrient enrichment and enabling conditions such as light and warm temperatures. This algal proliferation is toxic to the ecosystem through their biomass and potential toxin production that can cause hypoxic conditions and is often referred to as Harmful Algal Blooms (HABs). Current monitoring approaches include laboratory analysis of water samples to observe algal cells, monitoring of water quality parameters using water …

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Basic Safety Message Generation Through A Video-Based Analytics For Potential Safety Application, Abyad Enan

All Theses

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the roadway traffic and predict potential risks of crashes in real-time. If any risky situation or behavior is observed that may lead to a crash, then a safety application can send warnings to the vehicles at risk. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle’s location, speed, acceleration, heading direction, etc. …

Tumor Expression Of Cd83 Reduces Glioma Progression And Is Associated With Reduced Immunosuppression, Alexis Cervantes, Zhung-Fu Lee, Anna Rosenbaum, Peihao He, Carrie Mohila, Arif O Harmanci, Akdes Serin Harmanci, Benjamin Deneen, Ganesh Rao

Faculty, Staff and Student Publications

Immunosuppression in malignant glioma remains a barrier to therapeutic development. CD83 overexpression in human and mouse glioma increases survival. CD83+ tumor cells promote signatures related to cytotoxic T cells, enhanced activation of CD8+ T cells, and increased proinflammatory cytokines. These findings suggest that tumor-expressed CD83 could mediate tumor-immune communications.

Chatgpt Vs Expert-Guided Care Pathways For Postesophagectomy Symptom Management

Faculty, Staff and Student Publications

BACKGROUND: The objective of this study was to compare generative artificial intelligence-initiated care pathways, using ChatGPT, with expert-guided consensus-initiated care pathways from AskMayoExpert (AME) for symptom management of esophageal cancer patients after esophagectomy.

METHODS: A formal protocol for development of 9 AME care pathways was followed for specific patient-identified domains after esophagectomy for esophageal cancer. Domain scores were measured and assessed through the Upper Digestive Disease tool. These care pathways were developed by experts validated by a consensus-driven methodology. ChatGPT was used to answer specific questions similar to the AME care pathway on April 9, 2023, and March 28, 2024. …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

Jürgen Habermas Revisited Via Tim Cook's Wikipedia Biography: A Hermeneutic Approach To Critical Information Systems Research, Reilly Smethurst, Amber G. Young, Arial D. Wigdor

Information Systems Faculty Publications and Presentations

Critical Information Systems (IS) research is sometimes appreciated for the shades of gray it adds to sunny portraits of technology's emancipatory potential. In this article, we revisit a theory about Wikipedia’s putative freedom from the authority of corporate media's editors and authors. We present the curious example of Tim Cook's Wikipedia biography and its history of crowd-sourced editorial decisions, published on Wikipedia's talk pages. We use a hermeneutic method to subject the theory about Wikipedia's “rational discourse” and “emancipatory potential” to a soft, empirical test. When we examined Cook's Wikipedia biography and its editorial decisions, what we found pertained to …