Digital Commons Network^™

Emotion Detection Using Ensemble Learning, Priya Harika Yerapothu

Master's Projects

Emotion detection is gaining exponential necessity in today’s technological age. This research seeks to delve into ways conversational AI could be enhanced by integrating emotional intelligence using an ensemble learning approach. Traditional machine learning along with advanced neural network architectures are implemented to improve the understanding and intricacies of emotion detection from textual data. The dataset we use is GoEmotions dataset, annotated with 27 emotional labels, to conduct a detailed analysis of emotion recognition. Various machine learning models, such as HistGradientBoosting, LightGBM, CatBoost, and MLP, will be evaluated side by side with advanced models of Bidirectional Long Short-Term Memory (BiLSTM) …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Community Detection Using Deep Learning: Variational Graph Autoencoder Enhanced With Leiden And K-Truss Techniques, Jyotika Hariom Patil

Master's Projects

Community detection in networks is essential for understanding the complex structures of connected systems. Traditional deep learning (DL) methods such as Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) have shown promised results in supervised tasks, like classification, but often fail in unsupervised tasks like community detection because of the lack of labels. Self- supervised approaches where we integrate crucial community information offer a solution. This project seeks to explore DL methods for community detection, focusing specifically on using Graph Variational Autoencoders (VGAEs). While classical approaches can efficiently handle small to medium-sized networks, they typically struggle with larger-sized structures. …

Influence Maximization Using Triadic Closures, Communities, And Quotas, Matthew Fu

Master's Projects

Online social networks have exploded in popularity in the last decade. In addition, traditional advertising methods such as television advertising have greatly decreased. This allows companies to utilize viral marketing more effectively. With viral marketing, companies can spread information on a product to a social network by reaching out to a small group of early adopters, who will go on to inform the people around them of the product. The problem is selecting the early adopters that can maximize the spread of influence. The Influence Maximization (IM) problem is finding a social network’s most influential (early adopters) starting nodes, called …

Characterizing Nanopore Sequencing Artifacts With Deep Learning, David Zhou

Master's Projects

Oxford Nanopore sequencing is a revolutionary new technology for sequencing DNA molecules in long stretches. However, it has a significantly higher error rate than conventional short-read sequencing, resulting in numerous sequencing artifacts. These artifacts can be indistinguishable from low frequency somatic variants, which is a roadblock for cancer diagnosis using liquid biopsies. In this study, benchmarked human genome samples from Genome in a Bottle were used to create a dataset of labeled variants, including artifacts and true variants. Variant features, including sequence context, were used to train various deep learning models. The multi-input neural network combining sequence context features and …

Employing Large Language Models And Retrieval Augmented Generation For Enhanced Predictive Flexibility In Cancer Mortality Prediction, Mridang Kejriwal

Master's Projects

Today, cancer is a major health risk to thousands of people, and there are over a two-hundred different types of cancer. Luckily, over the past several years, the outcomes and survival rates have increased, all thanks to machine learning, specifically Recurrent Neural Networks (RNN) and Long Short-Term memory (LSTM) networks. However, the current prognostic models don’t allow healthcare professionals to adapt the variables to mimic all the different features of every type of cancer, resulting in a model that works but is not as accurate as it could be. This study explores improving the accuracy and adaptability of the current …

Temporal Dynamics In Diabetes Prediction: A Sensor-Driven Time-Series Exploration, Monica Meduri

Master's Projects

Diabetes is a lifelong illness that, if not detected or managed appropriately, turns into serious complications. Correct glucose forecasting is critical to ensuring timely interventions, thereby minimizing risks of hyperglycemia and hypoglycemia, and optimizing the management strategies of the disease. Classical machine learning models have been applied in the blood glucose forecasting problem for a long time, however, usage of transformer-based architectures is still scarce within the literature. Due to the self-attention mechanism, transformers can capture temporal relationships very effectively, which makes them suitable for time-series data. TFT is a novel framework proposed here to utilize time-series data from CGM …

Comparison Of Protein Structures Predicted By Genai Tools In A Zero-Shot Manner, Kruthi Shankar Rao

Master's Projects

Generative AI models have vast applications and one such critical application explored in this study is protein structure prediction. The 3D structures of proteins determine their function. Our study mainly focuses on using generative AI models such as ESMFold and ColabFold to predict and examine naturally occurring and mutated sequences. The workflow begins with collecting antimicrobial resistance (AMR) and toxin-antitoxin (TA) protein data. The sequences are applied over pretrained AI models to predict protein structures. Following this, models are fine-tuned with original and mutated target datasets. A comparison of models’ performances is done using metrics such as root mean square …

Artifacts In Low-Pass Whole Genome Sequencing, Nguyen Mai Anh Do

Master's Projects

Low-pass whole genome sequencing (LP-WGS) provides a cost-effective way to achieve broad genomic coverage, but it comes with the challenge of sequencing artifacts that can complicate accurate variant detection. To address this, we developed a bioinformatics pipeline using Nextflow. Starting with raw sequencing data, the pipeline performed variant calling using VarDict, with Genome in a Bottle (GIAB) high-confidence variants serving as the benchmark for variant validation. We explored machine learning approaches, testing classifiers such as AdaBoost, ExtraTrees, and RandomForest, to evaluate variant classification. Twenty-two features generated by VarDict were fed into Machine Learning pipeline, with AdaBoost standing out for its …

Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande

Master's Projects

Coral reefs, made up of thousands of polyps - tiny sac-like marine invertebrates sea anemones and jellyfish, are important to marine ecosystems and prevent loss of life by acting as a natural barrier against storms, floods, and waves. These reefs support a wide range of species, many of which are underexplored and new species being discovered regularly. Crustose coralline algae (CCA) is one of the vital algal species that provides reef structure. Studying the abundance of CCA is important in helping marine biologists analyze coral reef health while understanding the impact of climate change on the marine lifeforms. This study …

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …

Robust Phylogenetic Regression, Richard Adams, Zoe Cain, Raquel Assis, Michael Degiorgio

Entomology and Plant Pathology Faculty Publications and Presentations

Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has shown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using …

Rcs-Slam: Range Of Communication Swarm Slam, Adam J. Hoburg

Open Access Master's Theses

This work presents Range of Communication Swarm SLAM (RCS-SLAM) as a novel approach to simultaneous localization and mapping (SLAM) that is better suited for swarm robotic systems. RCS-SLAM introduces a novel SLAM front-end that leverages the effective range of an inter-robot communication medium to add inequality constraints between nodes in a pose graph whenever two robots communicate or relay communications. The centralized SLAM back-end then converts the inequality constrained pose graph into an unconstrained optimization problem using the penalty method to enforce the maximum possible range between communicating nodes. Converting to an unconstrained problem allows for the estimate to optimized …

Is Mitigation Translocation An Effective Method For Reducing Laysan Albatross–Military Aircraft Collisions?, Brian E. Washburn, Katherine D. Rubiano, William P. Bukoski

Human–Wildlife Interactions

Wildlife–aircraft collisions (wildlife strikes) pose a serious risk to civil and military aircraft. Each year, Laysan albatrosses (Phoebastria immutabilis) attempt to establish a breeding colony on the airfield at the U.S. Navy’s Pacific Missile Range Facility (PMRF) located on the island of Kaua‘i, Hawai‘i, USA, resulting in a hazard to safe military aircraft operations at this facility. A long-term management program, with an emphasis on mitigation translocation (e.g., live-capture and translocation away from the area) of problematic individuals, has been conducted by the U.S. Department of Agriculture, Wildlife Services. However, the efficacy of mitigation translocation as a nonlethal …

Efficacy Of A Laser As A Starling Deterrent And Its Effect On Lactating Dairy Cow Behavior, Callan A. Lichtenwalter, Marcos Marcondes, Kyle R. Taylor, Craig Mcconnel, Amber Adams Progar

Human–Wildlife Interactions

European starlings (Sturnus vulgaris) cause damage (including structural damage) on dairies, eat cattle feed, and can potentially spread disease through their fecal matter. Deterring starlings from dairies without affecting cow (Bos taurus) welfare is vital, and lasers have been effective starling deterrence tools in urban roosts and in some crops. To evaluate if the use of lasers on dairies would affect starling use of freestall barns as night roosts, or lactating cow behavior, 1 laser was installed in each of the 2 freestall barns at the Knott Dairy Center at Washington State University, Pullman, Washington, USA. …

Snake Translocation Policies And Practices Within The United States, Robin E. Bedard, Megan Rottenborn, Emily Taylor

Human–Wildlife Interactions

Human–wildlife conflict with nuisance snakes (Serpentes) is rapidly increasing with human population growth and development rates. Translocation of nuisance snakes has become a widespread practice, aided by social media pages connecting people to volunteer and for-profit snake translocators. However, translocators often struggle to find information and resources from wildlife agencies about the policies required, and some may unintentionally use translocation procedures that disregard the health and survival of the snakes. The goals of this study were to (1) obtain data on policies, permitting, and training required for translocating nuisance snakes in each U.S. state that has snakes and (2) compare …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Electromagnetic-Circuital-Thermal-Mechanical Multiphysics Numerical Simulation Method For Microwave Circuits, Huan Huan Zhang, Zheng Lang Jia, Peng Fei Zhang, Ying Liu, Li Jun Jiang, Da Zhi Ding

Electrical and Computer Engineering Faculty Research & Creative Works

An electromagnetic-circuital-thermal-mechanical Multiphysics numerical method is proposed for the simulation of microwave circuits. The discontinuous Galerkin time-domain (DGTD) method is adopted for electromagnetic simulation. The time-domain finite element method (FEM) is utilized for thermal simulation. The circuit equation is applied for circuit simulation. The mechanical simulation is also carried out by FEM method. A flexible and unified Multiphysics field coupling mechanism is constructed to cover various electromagnetic, circuital, thermal and mechanical Multiphysics coupling scenarios. Finally, three numerical examples emulating outer space environment, intense electromagnetic pulse (EMP) injection and high-power microwave (HPM) illumination are utilized to demonstrate the accuracy, efficiency, and …

The Meaning And Challenges Of An Interdisciplinary Sentencing Exercise: Reflections From A Death Penalty Mitigation Practice, Anup Surendranath, Maitreyi Misra

Socio-Legal Review

Due to the death penalty sentencing framework’s requirement of taking into account the context of the individual accused who is to be punished, the Supreme Court of India has characterised death penalty sentencing as socio-legal in nature. In this paper, we consider the tensions that have emerged when the legal is forced to interact with the social—when death penalty sentencing law, legal practice, and process has to account for perspectives from the social sciences. One of the mechanisms through which the law conducts this socio-legal examination at the stage of sentencing is mitigation. Mitigation is an exercise by the defence …

Interpretive Divergence In The New York Court Of Appeals, Ethan J. Leib

Faculty Scholarship

This Article focuses attention on the New York Court of Appeals, which is decidedly formalist about contract interpretation but decidedly contextualist about statutory interpretation. It explores some recent exemplary cases to show where the New York Court of Appeals tends to land in what turns out to be, for this court at least, two different battlefields in the law of interpretation. Finding that there is “interpretive divergence” between statutory and contract cases, the Article then reflects on the practice of divergence more generally, revisiting assumptions about why anyone might have thought harmonization was sensible in the first place.

Protein-Centric Omics Analysis Reveals Circulating Complements Linked To Non-Viral Liver Diseases As Potential Therapeutic Targets, Yingzhou Shi, Hang Dong, Shiwei Sun, Xiaoqin Wu, Jiansong Fang, Jianbo Zhao, Junming Han, Zongyue Li, Huixiao Wu, Luna Liu, Wanhong Wu, Yang Tian, Guandou Yuan, Xiude Fan, Chao Xu

Faculty, Staff and Student Publications

BACKGROUND/AIMS: To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets.

METHODS: We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options.

RESULTS: In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk …

Disentangling Accelerated Cognitive Decline From The Normal Aging Process And Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach, Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao, Alzheimer’S Disease Neuroimaging Initiative

Faculty, Staff and Student Publications

BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

OBJECTIVE: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

METHODS: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. …

Descriptor:Benchmarking Secure Neural Network Evaluation Methods For Protein Sequence Classification (Idash24), Arif Harmanci, Luyao Chen, Miran Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

To uniformly test and benchmark the secure evaluation of transformer-based models, we designed the iDASH24 homomorphic encryption track dataset. The dataset comprises a protein family classification model with a transformer architecture and an example dataset that is used to build and test the secure evaluation strategies. This dataset was used in the challenge period of iDASH24 Genomic Privacy Competition, where the teams designed secure evaluation of the classification model using a homomorphic encryption scheme. Combined with the benchmarking results and companion methods, iDASH24 dataset is a unique resource that can be used to benchmark secure evaluation of neural network models.

Transdisciplinary Perspectives For Health Systems Science, Jiang Bian, Nan Liu, Shauna M Overgaard, Rema Padman, Peter A D Steel, Victoria Tiase, Clifford Alan Whitcomb, Jiajie Zhang, Yiye Zhang

Faculty, Staff and Student Publications

Health systems science uses systems thinking as part of a transdisciplinary approach that transcends traditional disciplinary boundaries. It integrates and synthesizes knowledge from multiple disciplines to address real-world problems in healthcare with pragmatic solutions. This editorial defines health systems from the perspectives of systems thinking, science, and engineering, discusses their current challenges and opportunities, and envisions how health systems science as a field can advance the continuous learning and improvement of health systems.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations For Targeted Therapy Through Efficient Ngs Data Filtering System, Jinlian Wang, Hui Li, Hongfang Liu

Faculty, Staff and Student Publications

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a …

Developing Medical Genetics In A Low-Income Country: Unveiling The Journey Of The Pakistani Society Of Medical Genetics And Genomics (Psmg), Aisha Furqan, Syed A Ahmed, Rizwan Naeem, Myla Ashfaq

Faculty, Staff and Student Publications

No abstract provided.

Human Equilibrative Nucleoside Transporter 1: Novel Biomarker And Prognostic Indicator For Patients With Gemcitabine-Treated Pancreatic Cancer, Jianchun Xiao, Fangyu Zhao, Wenhao Luo, Gang Yang, Yicheng Wang, Jiangdong Qiu, Yueze Liu, Lei You, Lianfang Zheng, Taiping Zhang

Faculty, Staff and Student Publications

AIM: This article aimed to find appropriate pancreatic cancer (PC) patients to treat with Gemcitabine with better survival outcomes by detecting hENT1 levels.

METHODS: We collected surgical pathological tissues from PC patients who received radical surgery in our hospital from September 2004 to December 2014. A total of 375 PC tissues and paired adjacent nontumor tissues were employed for the construction of 4 tissue microarrays (TMAs). The quality of the 4 TMAs was examined by HE staining. We performed immunohistochemistry analysis to evaluate hENT1 expression in the TMAs. Moreover, we detected hENT1 expression level and proved the role of hENT1 …

Visualsphere: A Web-Based Interactive Visualization System For Clinical Research Data, Shiwei Lin, Shiqiang Tao, Wei-Chun Chou, Guo-Qiang Zhang, Xiaojin Li

Faculty, Staff and Student Publications

Clinical research data visualization is integral to making sense of biomedical research and healthcare data. The complexity and diversity of data, along with the need for solid programming skills, can hinder advances in clinical research data visualization. To overcome these challenges, we introduce VisualSphere, a web-based interactive visualization system that directly interfaces with clinical research data repositories, streamlining and simplifying the visualization workflow. VisualSphere is founded on three primary component modules: Connection, Configuration, and Visualization. An end-user can set up connections to the data repositories, create charts by selecting the desired tables and variables, and render visualization dashboards generated by …

Mouse Models Of Chronic Lymphocytic Leukemia And Richter Transformation: What We Have Learnt And What We Are Missing, Maria Teresa Sabrina Bertilaccio, Shih-Shih Chen

Faculty, Staff and Student Publications

Although the chronic lymphocytic leukemia (CLL) treatment landscape has changed dramatically, unmet clinical needs are emerging, as CLL in many patients does not respond, becomes resistant to treatment, relapses during treatment, or transforms into Richter. In the majority of cases, transformation evolves the original leukemia clone into a diffuse large B-cell lymphoma (DLBCL). Richter transformation (RT) represents a dreadful clinical challenge with limited therapeutic opportunities and scarce preclinical tools. CLL cells are well known to highly depend on survival signals provided by the tumor microenvironment (TME). These signals enhance the frequency of immunosuppressive cells with protumor function, including regulatory CD4

A Relay Velocity Model Infers Cell-Dependent Rna Velocity, Shengyu Li, Pengzhi Zhang, Weiqing Chen, Lingqun Ye, Kristopher W Brannan, Nhat-Tu Le, Jun-Ichi Abe, John P Cooke, Guangyu Wang

Faculty, Staff and Student Publications

RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust …