Data Science Commons^™

Domain Specific Feature Representation Learning For Diverse Temporal Data, Farhan Asif Chowdhury

Computer Science ETDs

Humans can leverage domain context to recognize novel patterns and categories based on limited known examples. In contrast, computational learning methods are not adept at exploiting context and require sufficient labeled examples to achieve similar accuracy. Many temporal data domain, for example, seismic signals and oil mining sensor data, requires domain expert annotation, which is both costly and time-consuming. The dependency on training data limits the applicability of machine learning algorithms for domains with limited labeled data. This dissertation aims to address this gap by developing temporal mining algorithms that exploit domain context to learn discriminative feature representation from limited …

Usp38 Exacerbates Atrial Inflammation, Fibrosis, And Susceptibility To Atrial Fibrillation After Myocardial Infarction In Mice, Yang Gong, Tingting Yu, Wei Shuai, Tao Chen, Jingjing Zhang, He Huang

Faculty, Staff and Student Publications

BACKGROUND: Inflammation plays an important role in the pathogenesis of atrial fibrillation (AF) after myocardial infarction (MI). The role of USP38, a member of the ubiquitin-specific protease family, on MI-induced atrial inflammation, fibrosis, and associated AF is unclear.

METHODS: In this study, we surgically constructed a mouse MI model using USP38 cardiac conditional knockout (USP38-CKO) and cardiac-specific overexpression (USP38-TG) mice and applied biochemical, histological, electrophysiological characterization and molecular biology to investigate the effects of USP38 on atrial inflammation, fibrosis, and AF and its mechanisms.

RESULTS: Our results revealed that USP38-CKO attenuates atrial inflammation, thereby ameliorating fibrosis, and abnormal electrophysiologic properties, …

Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken

LSU Master's Theses

Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …

Rotational Symmetry Breaking In Superconducting Nickelate Nd08sr02nio2 Films, Haoran Ji, Yi Liu, Yanan Li, Xiang Ding, Zheyuan Xie, Chengcheng Ji, Shichao Qi, Xiaoyue Gao, Minghui Xu, Peng Gao, Liang Qiao, Yi-Feng Yang, Guang-Ming Zhang, Jian Wang

Faculty, Staff and Student Publications

The infinite-layer nickelates, isostructural to the high-Tc cuprate superconductors, have emerged as a promising platform to host unconventional superconductivity and stimulated growing interest in the condensed matter community. Despite considerable attention, the superconducting pairing symmetry of the nickelate superconductors, the fundamental characteristic of a superconducting state, is still under debate. Moreover, the strong electronic correlation in the nickelates may give rise to a rich phase diagram, where the underlying interplay between the superconductivity and other emerging quantum states with broken symmetry is awaiting exploration. Here, we study the angular dependence of the transport properties of the infinite-layer nickelate Nd0.8Sr0.2NiO2 superconducting …

Utilizing Non-Negative Least Squares For Data-Driven Discovery Of Dynamics, Tracey G. Oellerich

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Incorporating Adaptive Human Behavior Into Epidemiological Models Using Equation Learning, Austin Barton, Jordan Klein, Jonathan Greer, Kevin Flores, Patrick Haughey

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Parameter Estimation In Epidemiological And Climate Models Using Ensemble Smoothing With Multiple Data Assimilation, Emmanuel Fleurantin

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Critical Transitions In Mental Health: Van Gogh Case Study, Anna Singley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bayesian Adaptive Smoothing For Activation Detection In Fmri, Juan Florez

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa, Alexander Fulk

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Application Of Physics Informed Neural Networks For Predicting Disease Dynamics, Alonso Gabriel Ogueda, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Bridging Domain Gaps For Cross-Spectrum And Long-Range Face Recognition Using Domain Adaptive Machine Learning, Cedric Armel Nimpa Fondje

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Face recognition technology has witnessed significant advancements in recent decades, enabling its widespread adoption in various applications such as security, surveillance, and biometrics applications. However, one of the primary challenges faced by existing face recognition systems is their limited performance when presented with images from different modalities or domains( such as infrared to visible, long range to close range, nighttime to daytime, profile to f rontal, etc.) Additionally, advancements in camera sensors, analytics beyond the visible spectrum, and the increasing size of cross-modal datasets have led to a particular interest in cross-modal learning for face recognition in the biometrics and …

Mathematical Modeling Of The Impact Of Lobbying On Climate Policy, Andrew Jacoby, Claire Hannah, James Hutchinson, Jasmine Narehood, Aditi Ghosh, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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 …

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 …

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 …

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). …

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