Data Science Commons^™

Evaluation Of Variable Selection Techniques On The Genetic Architecture Of Flowering Time In Maize, Felix Yeboah

Data Science and Data Mining

In this project, we investigate several variable selection procedures to give an overview of how well they perform on a genomic dataset using three different penalized regression approaches. Comparisons between different methods were performed. These methods include Ridge, lasso, and Elastic Net. We utilized 4494 observations with 7389 SNPs gene scores to predict time to male flowering (dtoa). We assessed the performance of these three models in terms of mean square error. Not surprisingly, Lasso and Elastic Net perform better than Ridge Regression. Overall, Elastic Net performed better in predicting the time of male flowering (dtoa).

Pull Or Play? The Interpretation Of A Novel, Ai-Powered On-Field Decision Support Tool., Lynne Becker, Dafne Badilla, Osho Yonzon, Xiaoyu Wu, Roland Rocafort, Erik Viogan Phd, Devansh Manocha

Journal for Sports Neuroscience

The document titled "Pull or Play? The interpretation of a novel, AI-powered on-field decision support tool" explores the development and application of the Injury Impact Severity Score (IISS)™ for assessing traumatic brain injuries (TBIs), particularly in sports settings. It addresses the limitations of current assessment tools like the Glasgow Coma Scale (GCS) and proposes a more objective approach using patient-reported data and machine learning algorithms.

Key points include:

• Background: TBIs are a significant health concern with under-reported cases and a lack of effective research. Current assessment tools like the GCS have limitations in accuracy and speed, especially in dynamic …

Scproatlas: An Atlas Of Multiplexed Single-Cell Spatial Proteomics Imaging In Human Tissues, Tiangang Wang, Xuanmin Chen, Yujuan Han, Jiahao Yi, Xi Liu, Pora Kim, Liyu Huang, Kexin Huang, Xiaobo Zhou

Faculty, Staff and Student Publications

Spatial proteomics can visualize and quantify protein expression profiles within tissues at single-cell resolution. Although spatial proteomics can only detect a limited number of proteins compared to spatial transcriptomics, it provides comprehensive spatial information with single-cell resolution. By studying the spatial distribution of cells, we can clearly obtain the spatial context within tissues at multiple scales. Spatial context includes the spatial composition of cell types, the distribution of functional structures, and the spatial communication between functional regions, all of which are crucial for the patterns of cellular distribution. Here, we constructed a comprehensive spatial proteomics functional annotation knowledgebase, scProAtlas (https://relab.xidian.edu.cn/scProAtlas/#/), …

Aspdb: An Integrative Knowledgebase Of Human Protein Isoforms From Experimental And Ai-Predicted Structures, Yuntao Yang, Himansu Kumar, Yuhan Xie, Zhao Li, Rongbin Li, Wenbo Chen, Chiamaka S Diala, Meer A Ali, Yi Xu, Albon Wu, Sayed-Rzgar Hosseini, Erfei Bi, Hongyu Zhao, Pora Kim, W Jim Zheng

Faculty, Staff and Student Publications

Alternative splicing is a crucial cellular process in eukaryotes, enabling the generation of multiple protein isoforms with diverse functions from a single gene. To better understand the impact of alternative splicing on protein structures, protein-protein interaction and human diseases, we developed ASpdb (https://biodataai.uth.edu/ASpdb/), a comprehensive database integrating experimentally determined structures and AlphaFold 2-predicted models for human protein isoforms. ASpdb includes over 3400 canonical isoforms, each represented by both experimentally resolved and predicted structures, and >7200 alternative isoforms with AlphaFold 2 predictions. In addition to detailed splicing events, 3D structures, sequence variations and functional annotations, ASpdb uniquely offers comparative analyses and …

Metsdb: A Knowledgebase Of Cancer Metastasis At Bulk, Single-Cell And Spatial Levels, Sijia Wu, Jiajin Zhang, Yanfei Wang, Xinyu Qin, Zhaocan Zhang, Zhennan Lu, Pora Kim, Xiaobo Zhou, Liyu Huang

Faculty, Staff and Student Publications

Cancer metastasis, the process by which tumour cells migrate and colonize distant organs from a primary site, is responsible for the majority of cancer-related deaths. Understanding the cellular and molecular mechanisms underlying this complex process is essential for developing effective metastasis prevention and therapy strategies. To this end, we systematically analysed 1786 bulk tissue samples from 13 cancer types, 988 463 single cells from 17 cancer types, and 40 252 spots from 45 spatial slides across 10 cancer types. The results of these analyses are compiled in the metsDB database, accessible at https://relab.xidian.edu.cn/metsDB/. This database provides insights into alterations in …

Crisprofft: Comprehensive Database Of Crispr/Cas Off-Targets, Grant Wang, Xiaona Liu, Aoqi Wang, Jianguo Wen, Pora Kim, Qianqian Song, Xiaona Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) programmable nuclease system continues to evolve, with in vivo therapeutic gene editing increasingly applied in clinical settings. However, off-target effects remain a significant challenge, hindering its broader clinical application. To enhance the development of gene-editing therapies and the accuracy of prediction algorithms, we developed CRISPRoffT (https://ccsm.uth.edu/CRISPRoffT/). Users can access a comprehensive repository of off-target regions predicted and validated by a diverse range of technologies across various cell lines, Cas enzyme variants, engineered sgRNAs (single guide RNAs) and CRISPR editing systems. CRISPRoffT integrates results of off-target analysis from 74 studies, encompassing …

On Solving Differential Equations Describing The Ecology Of Shallow Lakes: A Comparison Of Classical And Modern Computational Methods Through Python, Vruddhi Kapre, Jeffrey R. Anderson, Alessandro Maria Selvitella

Spora: A Journal of Biomathematics

In this exposition paper, we describe some computational methodologies to solve numerically differential equations modeling the shallow lake ecosystem, with particular focus on the dynamics of the phosphorus. We describe in detail Python implementations of classical algorithms, like Runge-Kutta, and more modern approaches, including physics-informed neural networks.

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Biotime 2.0: Expanding And Improving A Database Of Biodiversity Time Series, Maria Dornelas, Laura H. Antão, Amanda E. Bates, Viviana Brambilla, Jonathan M. Chase, Cher F. Y. Chow, Ada Fontrodona-Eslava, Anne E. Magurran, Inês S. Martins, Faye Moyes, Alban Sagouis, Samuel Adu-Acheampong, Daniel Acquah-Lamptey, Dušan Adam, Penelope A. Ajani, Aitor Albaina, Pablo Almaraz, Jeongseop An, Roger Sigismund Anderson, Madelaine Jean Robertson Anderson, Alexsander Z. Antunes, Ivan Arismendi, Linda Armbrecht, Pedro Aros-Mardones, Sreejith Kalpuzha Ashtamoorthy, Narayanan Ayyappan, Gal Badihi, Joseph J. Bailey, Andrew H. Baird, Mark Edward Baird, Sreekumar Vadakkethil Balakrishnan, José António L. Barão-Nóbrega, Adi Barash, Miguel Barbosa, Jos Barlow, Claus Bässler, Matthieu Beaumont, Natalie Beenaerts, Tiago Octavio Begot, Wallace Beiroz, Ricardo Beldade, David M. Bell, Alecia Bellgrove, Jonathan Belmaker, Lisandro Benedetti-Cecchi, Cassandra E. Benkwitt, Pamela Medina-Van Berkum, Brandon T. Bestelmeyer, Matthew C. Betts, Maxwell Kelvin Billah, Anne D. Bjorkman, Magdalena Błażewicz, Christopher P. Bloch, Shane A. Blowes, Antonio Bode, Juliano A. Bogoni, Thomas Bolger, Timothy C. Bonebrake, Erik Bonsdorff, Roberta Bottarin, Luke N. Brokensha, Rob W. Brooker, Andrew J. Brooks, Helge Bruelheide, Thiago Almeida Bueno, Claire Laguionie, Mariana Lopes Campagnoli, James Cant, Erica Pellegrini Caramaschi, Alexandre Caron, Tadhg Carroll, Tancredi Caruso, Juan Carvajal-Quintero, Giuseppe Castaldelli, Edward Castañeda-Moya, Pedro V. Castilho, Sonia Zanini Cechin, Shahar Chaikin, Uchangi Manjunatha Chandrashekara, Tory J. Chase, Chaolun Allen Chen, Jorge José Cherem, Sei-Woong Choi, Erica M. Christensen, Alexander V. Christianini, Jackson Wing Four Chu, Peter Coad, Carl Van Colen, Lise Comte, Elisabeth J. Cooper, J. Hans C. Cornelissen, Eddy Cosson, Unai Cotano, Luc Crevecoeur, Shannan Kyle Crow, Graeme S. Cumming, Vanessa S. Daga, Gabriella Damasceno, Gergana N. Daskalova, Claire H. Davies, Robert A. Davis, Frank P. Day, Sussy De-La-Zerda, Amy Elizabeth Deacon, Indradatta De Castro-Arrazola, Steven Degraer, Kharran Deonarinesingh, Juan C. Diaz-Ricaurte, Christopher R. Dickman, Tara Dirilgen, Ciaran John Dolan, J. Emmett Duffy, Timothy E. Dunn, Giselda Durigan, Ciara Dwyer, Steven Earl, Dor Edelist, Graham John Edgar, Sally Edmonson, Ashley K. Elgin, Kari Elsa Ellingsen, Sarah C. Elmendorf, Ruth S. Eriksen, S. K. Morgan Ernest, Ruben Escribano, Paula Cabral Eterovick, Brian S. Evans, Jason D. Everett, Vesela Evtimova, Dan A. Exton, Andrew J. Fairbairn, Filipe Moreli Fantacini, Fabiano Turini Farah, Fábio Zanella Farneda, Mario E. Favila, Philippe Fernandez-Fournier, Braulio Fernández-Zapata, Diogo F. Ferreira, Carola Ferronato, Christopher R. Du Feu, Alessandra Fidelis, David A. Fifield, Vilmar Picinatto Filho, Walter Mesquita Filho, Robert N. L. Fitt, Carlos A. H. Flechtmann, William R. Fraser, Donna L. Fraser, Lídia Freixas, John Fryxell, Garrett J. Fundakowski, Scott Stanley Gabara, Elise Gallois, Mariana Garcia Criado, Emili García-Berthou, Joaquim Garrabou, Andrew R. Gates, Roberto Cazzola Gatti, Anna Gavioli, Tal Gavriel, Benoit Gendreau-Berthiaume, Xingli Giam, Carina Gjerdrum, Michael Glemnitz, Jasmin Annica Godbold, Daniel Gómez-Gras, Rodrigo Barbosa Gonçalves, Andy Goold, Richard R. Gordon, Menachem Goren, Fernando Vilas Boas Goulart, William G. Gould, Meagan M. Graboski, Nicholas A. J. Graham, Maurício Eduardo Graipel, Laura J. Grange, Aaron C. Greenville, Gary D. Grossman, Valeria A. Guinder, Peter Haase, Gary N. Haskins, Kris Havstad, Luise Hermanutz, Michael Julian Hames Hickford, Pamela Hidalgo, Pedro Higuchi, Andrew S. Hoey, Gert Van Hoey, Annika Hofgaard, Kristen T. Holeck, Robert D. Hollister, Richard T. Holmes, Mia Odell Hoogenboom, Joaquín Hortal, Tammy Horton, Chih-Hao Hsieh, Christine L. Huffard, Ida-Maria Huikkonen, Allen H. Hurlbert, Julian Hynes, Pascal Irz, Natalia Macedo Ivanauskas, Akemi Iwayama, Darren K. James, Ute Jandt, Anna M. Jażdżewska, Merlijn Jocque, Sophie T. Johnston, Samuel E. I. Jones, Faith A. M. Jones, Julia A. Jones, Edite Jucevica, Ugis Kagainis, Maiko Kagami, Jungwon Kang, Xuejia Ke, Erin Colleen Keeley, Rebecca Kinnear, Kari Klanderud, Uwe Klinck, Roel Van Klink, Stefan Klotz, Carolien Kockaert, Halvor Knutsen, Matti Koivula, Alessandra Kortz, Peter Kriegel, Chao-Yang Kuo, David J. Kushner, Rosina Kyerematen, Raphaël Lagarde, Lesley T. Lancaster, Ori Frid Landau, Wouter Van Landuyt, Eric R. Larson, Mai Lazarus, Cheol Min Lee, Jonathan S. Lefcheck, Jonas J. Lembrechts, Renato A. Ferreira De Lima, Romullo Guimarães Lima, Nathália G. S. Lima, Cristina Linares, Sandra C. Lindstrom, Francisco Lloret, John David Lloyd, Cleonice Maria Cardoso Lobato, David M. Lodge, Peter Richard Long, Celeste López-Abbate, Adrià López-Baucells, Julio Louzada, Maite Louzao, Antonella Lugliè, Micheli Ribeiro Luiz, S. Ellen Macdonald, Joshua S. Madin, André Lincoln Barroso Magalhães, Rajindra Mahabir, David Maphisa, Thomas Edward Martin, Marcio Martins, Patrick T. Martone, Silvia Matesanz, Shin-Ichiro S. Matsuzaki, Thomas J. Matthews, Iain Mccombe Matthews, Connie J. Maxwell, Kent P. Mcfarland, Brian J. Mcgill, Diane Marie Mcknight, Michael J. Mcwilliam, Jason Meador, Henning Meesenburg, Kristin Meier, Viesturs Melecis, Peter L. Meserve, Christoph F. J. Meyer, Anders Michelsen, Natali Olivia Roman Miiller, Marco Milardi, Nataliya Milchakova, Robert J. Miller, Jonathan Millett, Tom Moens, Luciano F. A. Montag, Jon Moore, Jörg Müller, Akhil Murali, Shauna Ann Murray, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nakamura, Sasi Nayar, Francis Neat, James A. Nelson, Michael Paul Nelson, Boris P. Nikolov, Rym Nouioua, Collins Ayine Nsor, Michael O' Connor, Edward Adzesiwor Obodai, Amy Marie Offland, Romà Ogaya, Hisako Ogura, Thomas A. Okey, Julian D. Olden, Luiz Gustavo Rodrigues Oliveira-Santos, Jeffrey C. Oliver, Esben Moland Olsen, Vladimir G. Onipchenko, Daniel Oro, Dais Ozolins, Krzysztof Pabis, Bachisio Mario Padedda, Facundo X. Palacio, Alain Paquette, Sinta Trilestari Pardede, David M. Patersib, Sarah Pausina, Raphaël Pélissier, Steven C. Pennings, Josep Penuelas, Felipe Walter Pereira, Nivaldo Peroni, Sergio Picó, Francesca Pilotto, Hudson Tercio Pinheiro, Oscar Pizarro, Roberto Pizzolotto, Francesco Pomati, Paulo Santos Pompeu, Dominique Ponton, Eric Post, Nicolas Poulet, Juha Pöyry, Steven J. Presley, Herbert H. T. Prins, Pieter Provoost, Kathleen L. Prudic, Vignesh Punjayil, Petr Pyšek, Pascal Querner, Juan Pablo Quimbayo, Indar W. Ramnarine, Daniel C. Reed, Peter Bernard Reich, Suzanne M. Remillard, Cerren Richards, Anthony James Richardson, Itai Van Rijin, Victor H. Rivera-Monroy, Christian Rixen, Kevin Peter Robinson, Ricardo Rocha, Ricardo R. Rodrigues, Cassy Rodrigues, Bjørn De Roos, Denise De C. De Rossa-Feres, Loreta Rosselli, Peter Charles Rothlisberg, Ana Rubio, Lars G. Rudstam, Catalina S. Ruz, Nancy B. Rybicki, Gunther Van Ryckegem, Andrew L. Rypel, Jon P. Sadler, Victor Satoru Saito, Sofia Sal, Renato Portela Salomão, Nathan J. Sanders, Flavio A. M. Santos, Tiago Gomes Dos Santos, Swapan Kumar Sarker, Sara E. Scanga, Marcus Schaub, Jochen Schmidt, Inger Kappel Schmidt, Robert L. Schooley, Alfred Schultz, Alberto Scotti, Amanda Serpell-Stevens, Filipe C. Serrano, Elizabeth H. Shadwick, Matthew Shaft, Thomas W. Sherry, Erika Mayumi Shimabukuro, Jacek Siciński, Caya Sievers, Fernando Rodrigues Da Silva, Ana Carolina Da Silva, Juliana M. Silveira, Tadeu Siqueira, Arunkumar Kavidapadinjattathil Sivadasan, Prasad Theruvil Parambil Sivan, Agnija Skuja, Amalia L. Slaughter, Jasper A. Slingsby, Joseph R. Smith, Bruno Eleres Soares, Martin Solan, Flaviana Maluf Souza, Gabriel B. G. Souza, Joshua L. Sprague, Ulrich Stachow, J. John Stadt, Christopher D. Stallings, Radoslav Hristov Stanchev, Emily H. Stanley, Brian M. Starzomski, Jose Mauro Sterza, Maarten Stevens, F. Gary Stiles, Stefan Stoll, Rick D. Stuart-Smith, Yzel Rondon Súarez, Laura Super, Sarah R. Supp, Tapio Sutela, Iain M. Suthers, Anna Suuronen, Kerrie M. Swadling, Daniel K. Szydlowski, Hisatomo Taki, Sara Jeanne Snell Taylor, Pablo A. Tedesco, Nils Teichert, Akira Terui, Gary P. Thiede, Anne Thimonier, Oliver Thomas, Peter Allan Thompson, Simon Thorn, Jeremy S. Tiemann, Luís Felipe Toledo, Anne Tolvanen, Maria Teresa Zugliani Toniato, Ignasi Torre, Marcos Adriano Tortato, Kumiko Totsu, Andrew Trant, Robert R. Twilley, Hirokazu Urabe, Pierre Valade, Nelson Valdivia, Martha Isabel Vallejo, Thomas J. Valone, Jan Vanaverbeke, Tiago Silveira Vasconcelos, Teppo Vehanen, Fábio Venturoli, Hans M. Verheye, Hendrik Jannes Wietse Vermeulen, Arne Verstraeten, Marcelo Vianna, Rui Vieira, João Paulo Santos Vieira-Alencar, Marc Vilella, Jean Ricardo Simões Vitule, Lien Van Vu, Robert B. Waide, Paige S. Warren, Joseph Paul Wayman, Sara L. Webb, Benjamin Weigel, Ellen A. R. Welti, Fritha West, Fulgor Westermann, Matthew A. Whalen, Ethan P. White, Claire E. Widdicombe, Richard Williams, Mark Williamson, Michael R. Willig, Sonja Wipf, Eric J. Woehler, Alje Woldering, Kerry D. Woods, Wu-Bing Xu, Ruthy Yahel, Zeren Yang, Kyle J. A. Zawada, Camila Zornosa-Torres, Assaf Zvuloni

Biological Sciences Faculty Publications

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised …

Leveraging Gpt-4o For Automated Extraction Of Neural Projections From Scientific Literature, Rashmie Abeysinghe, Gorbachev Jowah, Licong Cui, Samden D Lhatoo, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Sudden Unexpected Death in Epilepsy (SUDEP) is a major cause of death for epilepsy patients having uncontrolled seizures. Understanding the complex neural circuits within the central nervous system is crucial for understanding the mechanisms underlying cardiorespiratory regulation, particularly in the context of SUDEP. This study explores the potential of GPT-4o, a cutting-edge language model, to automate the extraction of neural projections from scientific literature. We developed prompts to extract neuroscientific structures, extract projections, and perform synonym harmonization. Applying the approach to four neuroscientific articles, the method extracted 205 projections. A random sample of 100 projections identified was handed over to …

Gene Expression Changes In Human Cerebral Arteries Following Hemoglobin Exposure: Implications For Vascular Responses In Sah, Chathathayil M Shafeeque, Arif O Harmanci, Sithara Thomas, Ari C Dienel, Devin W Mcbride, Kumar T Peeyush, Spiros L Blackburn

Faculty, Staff and Student Publications

Subarachnoid hemorrhage (SAH), characterized by the presence of hemoglobin (Hb) in the subarachnoid space, significantly impacts cerebral vessels, leading to various pathological outcomes. The toxicity of cell-free Hb released from erythrocytes and its metabolites after SAH causes vasoconstriction and neuronal damage, and correlates with delayed ischemic neurological deficits (DIND). While animal models have provided substantial and invaluable data in the research of aneurysmal SAH, the specific effects of subarachnoid blood on cerebral arteries remain greatly understudied. Here, we describe the changes in the genetic profile of human cerebral arteries exposed to free Hb for 48 h. We performed an ex …

Depletion Of Adipose Stroma-Like Cancer-Associated Fibroblasts Potentiates Pancreatic Cancer Immunotherapy, Joseph Rupert, Alexes Daquinag, Yongmei Yu, Yulin Dai, Zhongming Zhao, Mikhail G Kolonin

Faculty, Staff and Student Publications

This study shows that populations of CAFs have distinct effects on pancreatic cancer progression and shows that depletion of CAFs expressing adipose markers potentiates tumor/metastasis suppression effects of immune checkpoint blockade.

A Bayesian Deep Segmentation Framework For Glioblastoma Tumor Segmentation Using Follow-Up Mris, Tanjida Kabir, Kang-Lin Hsieh, Luis Nunez, Yu-Chun Hsu, Juan C Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jay-Jiguang Zhu, Roy F Riascos, Mahboubeh Madadi, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this …

Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem

Dissertations, Master's Theses and Master's Reports

Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …

Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small

Honors Undergraduate Theses

Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a ’black box’. This thesis confronts the interpretability knowledge …

A Method For Empirically Assessing Small Area Estimators Via Bootstrap-Weighted K-Nearest-Neighbor Artificial Populations, With Applications To Forest Inventory, Grayson W. White, Jerzy Wieczorek, Zachariah W. Cody, Emily X. Tan, Jacqueline O. Chistolini, Kelly S. Mcconville, Tracey S. Frescino, Gretchen G. Moisen

Faculty Journal Articles

National Forest Inventories monitor forest attributes across a variety of spatial and temporal scales in a given country. Increased interest in reporting and management at smaller scales has driven National Forest Inventories to investigate and adopt small area estimation (SAE) due to the promise of increased precision at these scales. However, comparing and evaluating SAE models for a given application is inherently difficult. Typically, many areas lack enough data to check unit-level modeling assumptions or to assess unit-level predictions empirically; and no ground truth is available for checking area-level estimates. Design-based simulation from artificial populations can help with each of …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Cropsync: Ai-Powered Sustainable Crop Management, Ziad Doughan, Ibrahim Mneimneh, Zouheir Nakouzi, Noor Al Khaib, Samer Damaj, Jamal Chaaban, Hamza Mrad, Sari Itani

BAU Journal - Science and Technology

CropSync is a smart agriculture system that uses AI and IoT technologies to enable sustain- able crop management and precision farming. The system aims to address the challenges faced by the agriculture sector, such as increasing food production to meet global population demands while minimizing environmental impact. CropSync integrates sensors, cameras, and cloud-based analytics to provide farmers with real-time insights and recommendations for optimizing crop cul- tivation. The system upholds engineering professional and ethical standards, considering broader social, environmental, and economic implications. From a social perspective, CropSync improves food security and enhances farmers’ livelihoods through increased productivity and efficient re- …

An Ontology-Based Approach For Understanding Appendicectomy Processes And Associated Resources, Nadeesha Pathiraja Rathnayaka Hitige, Ting Song, Steven J Craig, Kimberley J Davis, Xubing Hao, Licong Cui, Ping Yu

Faculty, Staff and Student Publications

Background: Traditional methods for analysing surgical processes often fall short in capturing the intricate interconnectedness between clinical procedures, their execution sequences, and associated resources such as hospital infrastructure, staff, and protocols.

Aim: This study addresses this gap by developing an ontology for appendicectomy, a computational model that comprehensively represents appendicectomy processes and their resource dependencies to support informed decision making and optimise appendicectomy healthcare delivery.

Methods: The ontology was developed using the NeON methodology, drawing knowledge from existing ontologies, scholarly literature, and de-identified patient data from local hospitals.

Results: The resulting ontology comprises 108 classes, including 11 top-level classes and …

Advancing Continuous Manufacturing: The Role Of Process Analytical Technology In Process Development, Samuel R. Henson

Electronic Theses and Dissertations

The pharmaceutical industry is actively pursuing technologies which improve manufacturing processes with the goal of producing high-quality pharmaceutical products for patients, manifesting in an industry-wide investment in continuous manufacturing (CM). Process analytical technology (PAT) has been recognized for its successful monitoring of critical quality attributes during routine production and is often cited alongside CM due to its ability to make timely, in-line measurements of intermediate materials. Various PAT tools are valuable in process development, particularly as continuous wet granulation processes are developed for use within pharmaceutical manufacturing. This work applied PAT and chemometric modeling during CM process development to enhance …

Key Epigenetic And Signaling Factors In The Formation And Maintenance Of The Blood-Brain Barrier, Jayanarayanan Sadanandan, Sithara Thomas, Iny Elizabeth Mathew, Zhen Huang, 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 …

Tumor Expression Of Cd83 Reduces Glioma Progression And Is Associated With Reduced Immunosuppression, Malcolm F Mcdonald, Rachel Naomi Curry, Isabella O'Reilly, Brittney Lozzi, 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, Mohamad K Abou Chaar, Giovanna Grigsby-Rocca, Ming Huang, Shanda H Blackmon

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, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, 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 …

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 …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …