Data Science Commons^™

Modeling Maternal Outcomes By Predicting Geospatial And Social Determinants Of Health, Emily Thompson

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Identification Of Significant Gene Expression Changes Incorporating Heterogeneity In Perturbation Experiments, Katharine Cross

Honors Projects in Biological and Biomedical Sciences

Machine learning methods have been widely applied to the field of genomics and bioinformatics. Specifically utilizing novel machine learning algorithms to study gene-drug interactions has the potential to make a major positive impact on new drug discovery. It is possible that heterogeneity may exist within Vorinostat drug perturbation experiments due to the effects of the perturbations on the gene expressions. Thus, the challenge is to identify the most important genes in a high-dimensional setting while first identifying subpopulations to address population heterogeneity. In this work, clustering techniques are applied to first identify group sub-population structures in the gene expression changes …

Promoting Data Harmonization To Evaluate Vaccine Hesitancy In Lmics: Approach And Applications, Ryan Rego, Yuri Zhukov, Kyrani Reneau, Amy Pienta, Kristina L. Rice, Patrick Brady, Geoffrey Siwo, Peninah Wachira, Amina Abubakar, Ken Kollman

Institute for Human Development, East Africa

Background: Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes.

Methods: We rely on a new software toolkit, Sub-National Geospatial Data Archive …

Effect Of Ultrasound On The Stability Of Partial Nitrification: Under The Interference Of Aeration Rate, Ying Xie, Yichun Zhu, Jieyuan Yang, Guangming Zhang, Shuai Tian, Junfeng Lian, Shanyan Dong

Faculty, Staff and Student Publications

The fluctuation of dissolved oxygen is one of the primary cause of disruptions to the consistent operation of partial nitrification, and the level of dissolved oxygen is mainly controlled by the aeration rate. This study investigated the influence of ultrasonic treatment on the stability of partial nitrification of activated sludge under different aeration conditions. After being treated with ultrasound (energy density = 0.20 W·mL−1, treatment time = 10 min), partial nitrification process operated stably for 67 days, with the nitrite accumulation rate above 83.89 %. The effluent contained 42.50 mg·L−1 of nitrite, much higher than the control reactor (0.30 mg·L−1). …

Ensuring Trust In Genomics Research, Erman Ayday, Jaideep Vaidya, Xiaoqian Jiang, Amalio Telenti

Faculty, Staff and Student Publications

Reproducibility, transparency, representation, and privacy underpin the trust on genomics research in general and genome-wide association studies (GWAS) in particular. Concerns about these issues can be mitigated by technologies that address privacy protection, quality control, and verifiability of GWAS. However, many of the existing technological solutions have been developed in isolation and may address one aspect of reproducibility, transparency, representation, and privacy of GWAS while unknowingly impacting other aspects. As a consequence, the current patchwork of technological tools only partially and in an overlapping manner address issues with GWAS, sometimes even creating more problems. This paper addresses the progress in …

Economic Equity And People With Disabilities: Development And Characterization Of A Novel Index, Bhavneet Walia, Katherine Mcdonald, Joy Hammel, Lex Frieden, Michael Morris, Barry Whaley, Vinh Nguyen

Faculty, Staff and Student Publications

Here we develop two new social indices: The ADA PARC Absolute Economic Opportunity Index and the ADA PARC Relative Economic Opportunity Index. These indices allow us novel examinations of economic equity between people with and without disabilities within a U.S. State and between people with disabilities in different states using aggregations of multiple component economic indicators. These represent the first efforts to offer U.S. indices of this focus, an important development given the distinct economic needs of people with disabilities and the value in accounting for distinct national policies. The indices rely on U.S. Census and other data on economic …

Link Tank

DePaul Magazine

A new JD certificate program in information technology, cybersecurity and data privacy provides DePaul University students with proficiency in both law and tech.

Surface Premelting Of Ice Far Below The Triple Point, Yulin Lin, Tao Zhou, Nathan D Rosenmann, Lei Yu, Thomas E Gage, Suvo Banik, Arnab Neogi, Henry Chan, Aiwen Lei, Xiao-Min Lin, Martin Holt, Ilke Arslan, Jianguo Wen

Faculty, Staff and Student Publications

Premelting of ice, a quasi-liquid layer (QLL) at the surface below the melting temperature, was first postulated by Michael Faraday 160 y ago. Since then, it has been extensively studied theoretically and experimentally through many techniques. Existing work has been performed predominantly on hexagonal ice, at conditions close to the triple point. Whether the same phenomenon can persist at much lower pressure and temperature, where stacking disordered ice sublimates directly into water vapor, remains unclear. Herein, we report direct observations of surface premelting on ice nanocrystals below the sublimation temperature using transmission electron microscopy (TEM). Similar to what has been …

A Dynamic Online Dashboard For Tracking The Performance Of Division 1 Basketball Athletic Performance, Erica Juliano, Chelsea Thakkar, Christopher B. Taber, Mehul S. Raval, Kaya Tolga, Samah Senbel

School of Computer Science & Engineering Undergraduate Publications

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes …

Google Search Trends To Assess Public Interest In And Concern About Vuity For Treating Presbyopia, Taku Wakabayashi, Hana A. Mansour, Robert Abishek, Jayanth Sridhar, Michael N. Cohen, David Xu, Jordan Deaner, Yoshihiro Yonekawa, Jason Hsu, Ajay E. Kuriyan

Wills Eye Hospital Papers

PURPOSE: To assess public awareness, interest, and concerns regarding Vuity (1.25% pilocarpine hydrochloride ophthalmic solution), an eye drop for the treatment of presbyopia, based on Google Trends.

METHODS: We used Google Trends that provides a relative search volume for queried terms, to evaluate searches for Vuity from June 30, 2021, to June 30, 2022, in the United States. The data for this study were downloaded on June 30, 2022. Main outcome measures were changes in relative search volumes for the terms "Vuity," "Eye drops for reading," "Eye drops for near vision," "Presbyopia," "Pilocarpine," and related popular search terms, such as …

Modeling And Estimation Of A Continuous Flexible Structure Using The Theory Of Functional Connections, Riccardo Bevilacqua

Math Department Colloquium Series

This talk presents a novel method for modeling and estimating the dynamics of a continuous structure based on a limited number of noisy measurements. The goal is reached using a Kalman filter in synergy with the recently developed mathematical framework known as the Theory of Functional Connections (TFC). The TFC allows to derive a functional expression capable of representing the entire space of the functions that satisfy a given set of linear and, in some cases, nonlinear constraints. The proposed approach exploits the possibilities offered by the TFC to derive an approximated dynamical model for the flexible system using the …

Evaluating The Impact Of An Mhealth Platform For Managing Acute Postoperative Dental Pain: Randomized Controlled Trial, Bunmi Tokede, Alfa Yansane, Ana Ibarra-Noriega, Joanna Mullins, Kristen Simmons, Nicholas Skourtes, Urvi Mehta, Sayali Tungare, David Holmes, Joel White, Muhammad Walji, Elsbeth Kalenderian

Faculty, Staff and Student Publications

BACKGROUND: Postoperative dental pain is pervasive and can affect a patient's quality of life. Adopting a patient-centric approach to pain management involves having contemporaneous information about the patient's experience of pain and using it to personalize care.

OBJECTIVE: In this study, we evaluated the use of a mobile health (mHealth) platform to collect pain-related patient-reported outcomes over 7 days after the patients underwent pain-inducing dental procedures; we then relayed the information to the dentist and determined its impact on the patient's pain experience.

METHODS: The study used a cluster-randomized experimental study design with an intervention arm where patients were prompted …

Digital Economy Enables Chinese Path To Modernization, Ying Liu, Chaochun Huang, Yongmiao Hong, Shouyang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Firstly, from the perspective of the internal consistency between the characteristics of Chinese path to modernization and the Internet spirit, this study reveals the importance of developing the digital economy for achieving Chinese path to modernization, and expounds the internal logic of digital economy enabling Chinese path to modernization. Then, it analyzes the challenges and problems faced by the construction of Chinese path to modernization from the aspects of the construction of basic scientific and technological capacity of digital economy, the coordination mechanism of data elements, the structural imbalance of digital development, governance system and security system. Finally, based on …

Fall Town Hall On Research Data Management, Megan Hurst, Christine Madsen, Kristi Thompson

Western Libraries Presentations

This Town Hall was held to share recommendations for Research Data Management at Western that result from a gap analysis to understand the RDM needs at Western, conducted by Athenaeum21, a strategy and technology consultancy engaged by Western for this work. The recommendations will inform the implementation of Western’s Research Data Management Strategy, which shapes Research Data Management at Western for all researchers, grant funded or not, student or faculty, regardless of discipline.

Lrtransformer: Learn-Region Transformer For Object-Agnostic Point Cloud Segmentation, Dipesh Gyawali

LSU Master's Theses

3D point cloud segmentation segments the 3D point cloud data into different regions/instances depending on their features that have numerous applications in robotics, autonomous driving, digital twinning, augmented reality, etc. The majority of the existing point cloud segmentation methods depend on class labels to identify 3D objects in the surroundings. Our work focuses on segmenting point clouds into different regions/instances in an object-agnostic manner for any number of objects in the environment. Given the point cloud, our method can segment the entire scene into multiple instances without depending on object shape and size. We leverage the power of the self-attention …

Braf D594a Mutation Defines A Unique Biological And Immuno-Modulatory Subgroup Associated With Functional Cd8+ T Cell Infiltration In Colorectal Cancer, Wenjing Li, Chenyi Zhao, Wenhui Li, Yang Gong, Kaili Ma, Yujie Lu, Xiaowei Liu, Lianjun Zhang, Feng Guo

Faculty, Staff and Student Publications

BACKGROUND: BRAF non-V600 mutation occupies a relatively small but critical subset in colorectal cancer (CRC). However, little is known about the biological functions and impacts of BRAF class III mutation in CRC. Here, we aim to explore how D594A mutation impacts on biological behaviors and immune related signatures in murine CRC cells.

METHODS: BRAF V600E (class I), G469V (class II) and D594A (class III) mutant cell lines were established based on MC38 cells. The biological behaviors of cells were evaluated in respect of cell growth, cell proliferation, cell apoptosis, cell migration and invasion by the methods of colony-forming assay, CCK-8 …

Helping Frontline Workers In Texas-A Framework For Resource Development, Karima Lalani, Meredith O'Neal, Simone Lee Joannou, Bhanumathi Gopal, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

First responders disproportionately experience occupational stress when compared to the general population, and COVID-19 has exacerbated this stress. The nature of their duties as law enforcement officers, firefighters, and medics exposes them to repeated trauma, increasing their risk of developing a broad array of mental health issues, including post-traumatic stress disorder (PTSD), substance use disorder (SUD), and compassion fatigue. This paper describes the need for resources for frontline workers and provides a framework for creating and implementing resources. A team of interdisciplinary subject matter experts developed two major resources. The first resource was a 24/7 helpline to support first responders …

Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based …

Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang

I-GUIDE Forum

Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …

Graph Transformer Network For Flood Forecasting With Heterogeneous Covariates, Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan

I-GUIDE Forum

Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) …

Cross-Scale Urban Land Cover Mapping: Empowering Classification Through Transfer Learning And Deep Learning Integration, Zhe Wang, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma, Xiang Que

I-GUIDE Forum

Urban land cover mapping is essential for effective urban planning and resource management. Thanks to its ability to extract intricate features from urban datasets, deep learning has emerged as a powerful technique for urban classification. The U-net architecture has achieved state-of-the-art land cover classification performance, highlighting its potential for mapping urban trees at different spatial scales. However, deep learning approaches often require large, labeled datasets, which are challenging to acquire for specific urban contexts. Transfer learning addresses this limitation by leveraging pre-trained deep learning models on extensive datasets and adapting them to smaller urban datasets with limited labeled samples. Transfer …

Solving Geospatial Problems Under Extreme Time Constraints: A Call For Inclusive Geocomputational Education, Coline C. Dony

I-GUIDE Forum

To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should …

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

Privacy-Preserving Federated Genome-Wide Association Studies Via Dynamic Sampling, Xinyue Wang, Leonard Dervishi, Wentao Li, Erman Ayday, Xiaoqian Jiang, Jaideep Vaidya

Faculty, Staff and Student Publications

MOTIVATION: Genome-wide association studies (GWAS) benefit from the increasing availability of genomic data and cross-institution collaborations. However, sharing data across institutional boundaries jeopardizes medical data confidentiality and patient privacy. While modern cryptographic techniques provide formal secure guarantees, the substantial communication and computational overheads hinder the practical application of large-scale collaborative GWAS.

RESULTS: This work introduces an efficient framework for conducting collaborative GWAS on distributed datasets, maintaining data privacy without compromising the accuracy of the results. We propose a novel two-step strategy aimed at reducing communication and computational overheads, and we employ iterative and sampling techniques to ensure accurate results. We …

Environmental Loss, Displacement, And Anxiety In Portugal: Analyzing News Articles To Differentiate Manifestations Of Environmental Distress, Robin Greene

Independent Study Project (ISP) Collection

Expressions of environmental distress can take different forms, with different symptoms, causes, and treatments. Existing literature generally identifies three primary categories of environmental distress responses: environmental grief, solastalgia, and eco-anxiety. This paper explores these distinctions and identifies words typically associated with each category in Portuguese news articles using Latent Dirichlet Allocation (LDA). LDA is a Natural Language Processing (NLP) technique that groups words into topics and identifies connections between those words based on how often those words appear together in sequences. This paper uses a modified LDA algorithm called GuidedLDA to identify additional keywords within topics defined by a list …

The Uses And Limitations Of Citizen Science For Monitoring The Australian Grey Nurse Shark (Carcharias Taurus) Population, Leah Corckran

Independent Study Project (ISP) Collection

iNaturalist is a citizen science photographic database, which is an underutilized resource in photographic identification research studies. Grey nurse sharks are critically endangered and there is a lack of knowledge regarding the estimated population size, longevity, and interactions with fisheries of this species off the coasts of Australia. To determine how photos submitted to iNaturalist can be used in Carcharias taurus conservation, the photographs were evaluated on a number of criteria including: location, date, visibility of spot patterns, visible sex characteristics, and visible injuries. In total, 814 photographs of grey nurse sharks were obtained from the iNaturalist database. Only 23.2% …

Surveillance Systems In Western Kenya: Methods, Perceptions, And Effectiveness, Marissa Duffy

Independent Study Project (ISP) Collection

Surveillance is an important tool in monitoring and evaluating infectious disease patterns and trends. Surveillance is vital because it aids public health officials and medical professionals in creating better prevention methods and efficiently managing outbreaks. Kenya is home to many noncommunicable diseases making it an important location to conduct disease surveillance. Within Kenya, each county has its own surveillance unit which tracks and controls outbreaks. In addition, government run surveillance systems were established to determine disease burden, incidence, and patterns in specific at-risk communities around Kenya. One of these major surveillance systems is Population-Based Infectious Disease Surveillance (PBIDS) which has …

A Congressional Twitter Network Dataset Quantifying Pairwise Probability Of Influence, Christian Fink, Nathan Omodt, Sydney Zinnecker, Gina Sprint

Physics Faculty Scholarship

We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. …

Digital Scholarship And Data Science Intersect In Libraries: A Needs Assessment Report, Halie Kerns

Library Created Resources

The following report summarized the results of a needs assessment completed in the fall of 2023 at Binghamton University by the Libraries’ Digital Scholarship team. The aim was to understand how data science-focused programming, as part of the digital scholarship’s offerings, would be utilized on campus. The report evaluates existing literature, summarizes findings from twenty-eight interviews done across campus, and lays out an action plan for the Digital Scholarship team’s future planning.

Improving Semantic Document Classification Accuracy By Integrating Human-Crafted Knowledge, Zachary Weinfeld, Lubomir Stanchev

College of Engineering Summer Undergraduate Research Program

Document classification is a pivotal task in various domains, warranting the development of robust algorithms. Among these, the Bidirectional Encoder Representations from Transformers (BERT) algorithm, introduced by Google, has proven to perform well when fine-tuned for the task at hand. Leveraging transformer architecture, BERT demonstrates stellar language understanding capabilities. However, the integration of BERT with a range of techniques has shown potential for further enhancing classification accuracy. This work investigates several techniques that leverage semantic understanding to improve the performance of document classification models trained with BERT. Specifically, we explore three methods. First, we will balance corpuses afflicted by imbalanced …