Digital Commons Network^™

Consumption Pressure In Estuaries Peaks At Intermediate Salinities, Catherine E. De Rivera, Amy A. Larson, Benjamin G. Rubinoff, Luna R. Soto, Seth L. Wright, Edwin D. Grosholz, Gregory M. Ruiz, Andrew L. Chang

Environmental Science and Management Faculty Publications and Presentations

The nature and strength of biotic interactions change along stress gradients, but the importance of these interactions across estuarine gradients is under studied. Here, we examined how consumption varies across estuarine salinity gradients by deploying standardized baits (‘squidpops’) to measure consumption pressure along the gradients of five estuaries in Oregon, USA. The relationship between consumption and stress was nonlinear: consumption pressure peaked slightly at mid salinity and decreased at low salinity, especially as temperature increased, in the five estuaries studied. This finding does not support either of two existing models for consumption across gradients, including the Consumer Stress Model and …

Recovering Population Of The Southern Sea Otter Suppresses A Global Marine Invader, Rikke Jeppesen, Catherine E. De Rivera, Edwin D. Grosholz, M. Tim Tinker, Brent B. Hughes, Ron Eby, Kerstin Wasson

Environmental Science and Management Faculty Publications and Presentations

Understanding the role of apex predators on ecosystems is essential for designing effective conservation strategies. Supporting recovery of apex predators can have many benefits; one that has been rarely examined is control of invasive prey. We investigated whether a recovering apex predator, the southern sea otter (Enhydra lutris nereis), can exert local control over a global marine invader, the green crab (Carcinus maenas). We determined that southern sea otters in Elkhorn Slough estuary in California can consume large numbers of invasive green crabs and found strong negative relationships in space and time between otter and green crab abundance. Green crabs …

Collaborative Wildfire Governance Within The Southwest Idaho Wildfire Crisis Strategy Landscape: Survey Results 2025, Mahmood Muttaqee, Cody R. Evers, Lindsay K. Campbell, Max Nielsen-Pincus, Olivia Awbrey, Kathryn Hixson, Michelle Johnson, Erika Svendsen

Environmental Science and Management Faculty Publications and Presentations

Collaboration plays a critical role in wildfire risk mitigation efforts. This study examines collaborative wildfire governance networks within Southwest Idaho, a priority landscape designated in 2022 under the USDA Forest Service's Wildfire Crisis Strategy (WCS). Through survey data from 64 respondents, the Fueling Adaptation study aimed to identify the structure, capacity, and gaps in governance networks addressing wildfire risk in Southwest Idaho and understand how WCS investments have influenced wildfire management in the area. The findings reveal a growing governance network centered on the USDA Forest Service and the Idaho Department of Lands and supported by numerous non-governmental organizations and …

Nebraska Summary #1293: Fendt 618 Vario, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

Development Of A Phishing Risk Exposure Taxonomy On Mobile Devices In The Healthcare Industry, Christopher P. Collins

CCAC Theses and Dissertations

Phishing emails accessed on mobile devices present substantial risks to healthcare organizations when employees often operate under high cognitive load and with limited cybersecurity training. Despite widespread security awareness initiatives, healthcare workers continue to engage with phishing content on mobile platforms, posing threats to organizational data. Given the high value of healthcare data and the increasing sophistication of phishing schemes targeting healthcare professionals, there is a pressing need to enhance their ability to recognize phishing indicators on mobile devices.

This study developed and validated a Healthcare Workers Phishing Risk Exposure (HWPRE) taxonomy, designed to classify healthcare workers based on their …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Program And Proceedings: Nebraska Academy Of Sciences 1880–2025, 145th Anniversary Year, One Hundred-Thirty-Fifth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program

Aeronautics and Space Science

Biological and Medical Sciences

Biology

Chemistry

Earth Sciences

Science Education

Anthropology

Applied Science and Technology

Physics and Engineering

Forensic Sciences

Ecology, Sustainability, and Environmental Science

Maiben Lecture: Mary Ann Vinton, "State of the Academy"

Friends of Science Awards: David Crouse and Daniel Sitzman

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Quantifying The Vulnerability Of Smooth Dogfish And Winter Skate To Electromagnetic Fields From Offshore Wind Transmission Cables In The Mid-Atlantic Shelf, Rachel A. Sechrist

Theses, Dissertations and Culminating Projects

The development of offshore wind structures in the northeastern U.S. will contribute to renewable energy goals, but will overlap with many marine species as well as economically important fisheries. High voltage transmission cables from offshore wind farms emit electromagnetic fields (EMFs) that elasmobranchs may detect using sensory organs known as ampullae of Lorenzini. However, sensitivity to EMFs varies by species due to differences in the number and arrangement of pores, the length of subdermal canals within the ampullae of Lorenzini sensory network, and the habitats they reside and regions they forage. For species who reside or forage in benthic regions, …

Quantitative Preventive Approaches To Diabetes: Mathematical Modeling And Analysis, Rushi P. Bhatt

Theses, Dissertations and Culminating Projects

The rising prevalence of diabetes presents a pressing global health concern, necessitating effective control strategies. This study aims to construct a mathematical model to analyze the influence of diverse factors on blood sugar levels, with a focus on identifying optimal methods for maintaining healthy glucose levels. Employing ordinary differential equations (ODE), the model investigates variables including leptin resistance, fat mass, glucose, insulin resistance, beta cell mass, daily physical activity, and dietary intake. Utilizing parameter estimates from existing literature, the model’s framework is established, and simulation results elucidate the intricate interplay between lifestyle choices and blood glucose dynamics. Furthermore, the model …

Zinc Or Swim: Investigating Zinc Pollution From Restoration Materials, Megan R. Jensik

Honors Undergraduate Theses

Zinc is an essential nutrient, but can be toxic at high concentrations, negatively affecting various biological functions. Gabions are galvanized metals coated in zinc that are used for many human purposes, including coastal restoration efforts attempting to avoid using plastic materials. However, gabions can degrade in marine environments, potentially leaching metal into the water and soil. There is a knowledge gap regarding the occurrence and impacts of zinc pollution in the areas where these gabions are deployed, including the Indian River Lagoon (IRL). To address this, sediment samples were collected from the IRL and analyzed for their physical and chemical …

The Relevance Of Dnajb1 Protein Dimerization Against The Human Neurodegenerative Diseases, Erina Kotreli, Trang Le, Szymon Ciesielski

Dean's Leadership Council Library Research Prize

All biological functions in living organisms depend on the activity of proteins, long molecules that must twist and fold into specific shapes to become functional. A subset of proteins known as molecular chaperones is essential for maintaining cellular health by assisting other proteins in folding correctly and refolding those damaged by stress. When proteins misfold and clump together, they can form toxic aggregates linked to many neurodegenerative diseases, including Alzheimer’s disease (AD), one of the leading causes of death in the United States. In AD, these protein aggregates form organized, elongated structures referred to as amyloid-like fibrils, which damage neurons …

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu

Computer Science Faculty Publications

Verifying scientific claims is challenging for the general public because most people lack domain knowledge. Manual verification by subject domain experts is accurate, but it is obviously not scalable to meet the rising number of scientific claims on the Web. Whether the emerging large language models and large reasoning models can be used for scientific claim verification, and how their performances compare to humans, are still research questions. To this end, we developed a new benchmark MSVEC2 that consists of 138 claims from credible fact verification websites and science news outlets. Two tasks were given to both human and LLM …

Gramseq-Dta: A Grammar-Based Drug-Target Affinity Prediction Approach Fusing Gene Expression Information, Kasul Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has demonstrated good results in drug–target affinity prediction. However, these approach lacks information on the relative position of the atoms and bonds. To address this limitation, graph-based representations have been used to some extent. However, solely considering the structural aspect of drugs and targets may be insufficient for accurate DTA prediction. Integrating the functional aspect of these drugs at the genetic level can enhance …

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 …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Stephen J. Lippard: Chemistry Meets Medicine, Radwa Elrouby

Natural Sciences Student Research Presentations

Key Study

In "DNA Sequence Context Modulates the Impact of a Cisplatin 1,2-GpG Intrastrand Cross-Link", Pilch, Todd, and Lippard found that the DNA sequence around the binding site influences how tightly cisplatin binds (Pilch, Todd, and Lippard).

Biophysical techniques like melting temperature and circular dichroism showed that some sequences stabilize DNA more strongly after drug binding (Pilch, Todd, and Lippard).

This explains why cisplatin is more effective in some tumor genomes (Todd and Lippard).

Noaa Oceanic And Atmospheric Research Ocean Carbon Observing Science Plan Fy25 To Fy35, United States National Oceanographic And Atmospheric Administration

National Oceanographic and Atmospheric Administration: Technical Reports and Related Materials

United States National Oceanographic and Atmospheric Administration oceanic and atmospheric research ocean carbon observing science plan for fiscal years 2025 through 2035.

Nebraska Water Center Annual Report 2025

Nebraska Water Center: Administrative Materials

Training Nebraska's future water professionals, research publications, water sciences laboratory impact and know your well curriculums and community impact

Impact Of An Herbicide Treatment In An Annual Grass Invaded Sagebrush Steppe, Jordan Dino Santia

Undergraduate Scholarship

Globally, rangelands are being threatened by invasive annual grasses (IAGs) that alter fire regimes, drive vegetation type conversion, and alter ecosystem function. This study evaluates vegetation responses to an Imazapic treatment applied in 2020 to control a population of Taeniatherum caput-medusae, an IAG, that flourished following a severe wildfire in 2013. The site, located on a Bureau of Land Management (BLM) grazing allotment east of Klamath Falls, Oregon, has been monitored annually using the Assessment, Inventory, and Monitoring (AIM) protocol since the Summer of 2022. Over this period, very little T. caput-medusae has been detected while bare ground has …

Igc Oral Presentation Summary: Xxv Igc Congress, S. Ray Smith, Echo Elizabeth Gotsick, J. R. Weinert-Nelson, Alayna Jacobs, Christopher D. Teutsch, Will Fleming, D. R. Woodfield, James L. Klotz, Jeff Lehmkuhler, Greg S. Halich, Brittany Davis, Brittany Hendrix, Krista Lea

IGC Proceedings (1977-2023)

This paper will give an overview of the XXV International Grassland Congress thematic, keynote, and plenary sessions. The theme of the XXV IGC Congress meeting was “Grasslands for Soil, Animal, and Human Health.” We had many people attending, presenting, and participating in all these sessions. Within a month after the conclusion of the Congress, the proceeding’s papers presented at the meeting will be available on the IGC app. By the end of 2023 all papers will be available for download through any search engine at internationalgrasslands.org.

Quantification Of Changes With Combined Shape Mode Analysis And Swimming Simulations, Kelli E. Gutierrez, Becca Thomases, Paulo E. Arratia, Robert D. Guy

Mathematics Sciences: Faculty Publications

Many different microswimmers propel themselves using flagella that beat periodically. The shape of the flagellar beat and swimming speed have been observed to change with fluid rheology. We quantify changes in the flagellar waveforms of Chlamydomonas reinhardtii in response to changes in fluid viscosity using (i) shape mode analysis and (ii) a full swimmer simulation to analyse how shape changes affect the swimming speed and to explore the dimensionality of the shape space. By decomposing the gait into the time‑independent mean shape and the time‑varying stroke, we find that the flagellar mean shape substantially changes in response to viscosity, while …

Reasoning Through Change: Exploring Covariational Thinking In Student Exponential Graph Interpretation, Samantha Lynn Pearson

Honors Theses and Capstones

This study explores how life science students reason about exponential functions in the context of an introductory physics course. While exponential functions are essential in both physics and biology—appearing in scenarios like population growth and temperature cooling—students often encounter challenges in interpreting them. Using a resources framework, we examined the cognitive resources these students bring to exponential reasoning, focusing on what ideas and strategies they already possess. Our methodology involved conducting think-aloud interviews with nine students from PHYS 402. Interviews were coded using a framework informed by prior research on covariational reasoning and exponential understanding. Results show that students generally …

Quantifying Inositol Phosphate Dephosphorylation To Understand The Role Of Recalcitrant Organic Phosphorus Forms On Harmful Algal Blooms In Freshwater Systems, Iffat Tasnim

College of Graduate Studies: Theses & Dissertations

Organophosphorus such as phytic acid, a surrogate species of inositol phosphate (IP), may serve as a source for orthophosphate (OP) in freshwater. In absence of OP, competent aquatic microorganisms upregulate the production of specialized enzymes to obtain growth-sustaining OP from organic phosphorus (org-P) forms. The contribution of recalcitrant org-P to the OP pool has been overlooked due to the lack of capable tools to measure OP production from org-P accurately. The objective of this study was to quantify OP production from phytic acid (a surrogate form of recalcitrant org-P) to assess the contribution of recalcitrant org-P forms to the total …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

Design And Synthesis Of Organocatalyts For Efficient Decontamination Of Organophosphate-Based Nerve Agents And Pesticides, Emmanuel Kingsley Darkwah

Doctoral Dissertations

Exposure to organophosphate-based nerve agents and pesticides poses significant health and security threats to civilians, soldiers, and first responders. Despite extensive efforts to develop chemical detoxification agents for use in topical applications on exposed skin surfaces and for intravenous injections, there remains an unmet need for effective, non-hazardous decontaminating agents. The current state-of-the-art decontaminating agent, Dekon-139 (2,3-butanedione oxime, potassium salt), exhibits adverse effects when applied to the skin.

In this study, we designed and synthesized pharmaceutically relevant aminoguanidine-derived aldimines that are relatively non-toxic and substantially more effective at decontaminating nerve agents and pesticides compared to existing agents, and they act …

Cfd Analysis Of Hydrodynamic Cavitation Through An Orifice: Influence Of Different Inlet Pressures And Number Of Orifice Holes, Lemthong Chanphavong, Vongsavanh Chanthaboune, Keophousone Phonhalath

ASEAN Journal on Science and Technology for Development

Hydrodynamic cavitation (HC) is considered an energy-efficient process with high potential for utilization in many chemical processes. This study presents a computational fluid dynamics (CFD) analysis of cavitating flow through an orifice with a constant flow area. The Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with turbulence and cavitation models, are employed to capture the complex flow behaviors. The effects of inlet pressures and number of orifice-holes on cavitation behavior are investigated. Result of the numerical simulation is validated with the existing experimental data from the literature. The CFD study revealed that cavitation initiates just behind the inlet edge of the orifice …