Computer Sciences Commons^™

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

Utilizing Natural Language Processing For Automated Clinical Text Review: Identification Of Care Preference Documentation In Patients’ Discharge Summaries, Saksham Arora

Computer Science Senior Theses

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than more appropriately broad population of `seriously ill' patients. This paper addresses this gap by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods in identifying care preference documentation.

In order to capture a broad spectrum of seriously ill patients, we matched decedent patients to non-decedent counterparts by utilizing a propensity score matching, accounting for important variables like age, gender, primary diagnoses and commodities. We trained and fine-tuned Bio_ClinicalBERT …

Creating Project Contrast: A Video Game Exploring Consciousness And Qualia, Pierce Papke

Honors Projects

Project Contrast is a video game that explores how the unique traits inherent to video games might engage reflective player responses to qualitative experience. Project Contrast does this through suspension of disbelief, avatar projection, presence, player agency in storytelling, visual perception, functional gameplay, and art. Considering the difficulty in researching qualitative experience due to its subjectivity and circular explanations, I created Project Contrast not to analyze qualia, though that was my original hope. I instead created Project Contrast as an avenue for player self-reflection and learning about qualitative experience. While video games might be just code and art on a …

Vitreoscilla Globin Promoter Cloning And Testing In Escherichia Coli, Lauren J. Coffey

Rose-Hulman Undergraduate Research Publications

No abstract provided.

Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer

MODVIS Workshop

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …

Development And Evaluation Of An Automated Tactical Tillage Tool To Control Weeds In Row-Crop Production Systems, Grace Mccormick Friday

Theses and Dissertations

Weed control is an integral part of a successful overall production strategy in row- cropping systems and has the potential to reduce or eliminate yield losses that negatively affect profitability. Timely and correctly selected herbicide applications are the major keys for effective weed control in a majority of instances. However, there are negative factors that contribute to ineffectiveness and weed escape issues that currently lack viable options for management. Sparsely populated late-season weeds that emerge after lay-by herbicide applications and weeds that have become tolerant and resistant to traditional herbicide chemistries are of greatest concern. Historically, these weeds would have …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works, Caitlyn Aldersea, Justin Bravo, Sam Allen, Anna Block, Connor Block, Emma Buechler, Maria De Los Angeles Bustillos, Arianna Carlson, William Christensen, Olivia Kachulis, Noah Craver, Kate Dillon, Muskan Fatima, Angel Fernandes, Emma Finch, Colleen Cassidy, Amy Fishman, Andrea Francis, Stacia Fritz, Simran Gill, Emma Gries, Rylie Hansen, Shannon Powers, Jacqueline Martinez, Zachary Harker, Ashley Hasty, Mykaela Tanino-Springsteen, Kathleen Hopps, Adelaide Kerenick, Colin Kleckner, Ci Koehring, Elijah Kruger, Braden Krumholz, Maddie Leake, Lyneé Alves, Seraphina Loukas, Yatzari Lozano Vazquez, Haley Maki, Emily Martinez, Sierra Mckinney, Audrey Mitchell, Kipling Newman, Audrey Ng, Megan Lucyshyn, Andrew Nguyen, Stevie Ostman, Casandra Pearson, Alexandra Penney, Julia Gielczynski, Tyler Ball, Anna Rini, Christina Rorres, Simon Ruland, Helayna Schafer, Emma Sellers, Sarah Schuller, Claire Shaver, Kevin Summers, Isabella Shaw, Madison Sinar, Claudia Pena, Apshara Siwakoti, Carter Sorensen, Madi Sousa, Anna Sparling, Alexandra Revier, Brandon Thierry, Dylan Tyree, Maggie Williams, Lauren Wols

DU Undergraduate Research Journal Archive

DU Undergraduate Showcase: Research, Scholarship, and Creative Works

Do Plants Have The Cognitive Complexity For Sentience?, Ricard V. Solé

Animal Sentience

Are plants sentient? Like other aspects of the cognitive potential of plants, this is a controversial issue, often driven by analogies and seldom supported on solid theoretical grounds. Sentience is understood in cognitive sciences as the capacity to feel. I suggest that because of plants’ evolved adaptations to morphological plasticity, sessile nature and ecological constraints, they are unlikely to have the requisite cognitive complexity for sentience.

Trace Dna Detection Using Diamond Dye: A Recovery Technique To Yield More Dna, Leah Davis

Master's Theses

This study aspires to find a new screening approach to trace DNA recovery techniques to yield a higher quantity of trace DNA from larger items of evidence. It takes the path of visualizing trace DNA on items of evidence with potential DNA so analysts can swab a more localized area rather than attempting to recover trace DNA through the general swabbing technique currently used for trace DNA recovery. The first and second parts consisted of observing trace DNA interaction with Diamond Dye on porous and non-porous surfaces.

The third part involved applying the Diamond Dye solution by spraying it onto …

A Programmatic Geographic Information Systems Analysis Of Plant Hardiness Zones, Andrew Bowen

Electronic Theses and Dissertations

The Plant Hardiness Zone Map consists of thirteen geographical zones that describe whether a plant can survive based on average annual minimal temperatures. As climate change progresses, minimum temperatures in all regions are expected to change. This work programmatically evaluates predicted future climate projection data and converts it to United States Department of Agriculture-defined hardiness zones. Through the next 80 years, hardiness zones are projected to move poleward; in effect, colder zones will lose area and warmer zones will gain area globally. Some implications include changes in crop growing degree days, which could alter crop productivity, migration and settlement of …

Rattus Norvegicus As A Biological Detector Of Clandestine Remains And The Use Of Ultrasonic Vocalizations As A Locating Mechanism, Gabrielle M. Johnston

Master's Theses

In investigations, locating missing persons and clandestine remains are imperative. One way that first responder and police agencies can search for the remains is by using cadaver dogs as biological detectors. Cadaver dogs are typically used due to their olfactory sensitivity and ability to detect low concentrations of volatile organic compounds produced by biological remains. Cadaver dogs are typically chosen for their stamina, agility, and olfactory sensitivity. However, what is not taken into account often is the size of the animal and the expense of maintaining and training the animal. Cadaver dogs are typically large breeds that cannot fit in …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Volume 14, Ireland Seagle, Dalton C. Whitby, Cassandra Poole, Rachel Cannon, Heidi Parker-Combes, Devon G. Shifflett, Antonio Harvey

Incite: The Journal of Undergraduate Scholarship

Table of Contents:

• Introduction: Dr. Amorette Barber
• From the Editor: Dr. Larissa "Kat" Tracy
• From the Designers: Rachel English, Rachel Hanson
• Hungry Like the Wolf: The Wolf as Metaphor in Paramount Network’s Yellowstone: Ireland Seagle
• “Floating Cities”: Illustrating the Commercial and Conservation Conflict of Alaskan Cruise Ship Tourism: Dalton C. Whitby
• What Can You Do When Your Genes are the Enemy? Current Applications of Gene Manipulation and the Associated Ethical Considerations: Cassandra Poole
• La doble cara: un tema romántico en las obras de Larra y Hawthorne: Rachel Cannon
• Resolving a Conflict: How to …

In Situ Microwave Fixation Provides An Instantaneous Snapshot Of The Brain Metabolome, Jelena A. Juras, Madison B. Webb, Lyndsay E. A. Young, Kia H. Markussen, Tara R. Hawkinson, Michael D. Buoncristiani, Kayli E. Bolton, Peyton T. Coburn, Meredith I. Williams, Lisa P. Y. Sun, William C. Sanders, Ronald C. Bruntz, Lindsey R. Conroy, Chi Wang, Matthew S. Gentry, Bret N. Smith, Ramon C. Sun

Markey Cancer Center Faculty Publications

Brain glucose metabolism is highly heterogeneous among brain regions and continues postmortem. In particular, we demonstrate exhaustion of glycogen and glucose and an increase in lactate production during conventional rapid brain resection and preservation by liquid nitrogen. In contrast, we show that these post- mortem changes are not observed with simultaneous animal sacrifice and in situ fixation with focused, high- power microwave. We further employ microwave fixation to define brain glucose metabolism in the mouse model of streptozotocin-induced type 1 diabetes. Using both total pool and isotope tracing analyses, we identified global glucose hypometabolism in multiple brain regions, evidenced by …

Attenuated Skeletal Muscle Metabolism Explains Blunted Reactive Hyperemia After Prolonged Sitting, Cody Anderson, Elizabeth Pekas, Michael Allen, Song-Young Park

UNO Student Research and Creative Activity Fair

Introduction: Although reduced post-occlusive reactive hyperemia (PORH) after prolonged sitting (PS) has been reported as impaired microvascular function, no specific mechanism(s) have been elucidated. One potential mechanism, independent of microvascular function, is that an altered muscle metabolic rate (MMR) may change the magnitude of PORH by modifying the oxygen deficit achieved during cuff-induced arterial occlusions. We speculated that if MMR changes during PS, this may invalidate current inferences about microvascular function during PS. Objective: Therefore, the objective of this study was to examine if peripheral leg MMR changes during PS and to ascertain whether the change in the oxygen deficit …

The Effects Of Demographics And Risk Factors On The Morphological Characteristics Of Human Femoropopliteal Arteries, Sayed Ahmadreza Razian, Majid Jadidi, Alexey Kamenskiy

UNO Student Research and Creative Activity Fair

Background: Disease of the lower extremity arteries (Peripheral Arterial Disease, PAD) is associated with high morbidity and mortality. During disease development, the arteries adapt by changing their diameter, wall thickness, and residual deformations, but the effects of demographics and risk factors on this process are not clear.

Methods: Superficial femoral arteries from 736 subjects (505 male, 231 female, 12 to 99 years old, average age 51±17.8 years) and the associated demographic and risk factor variables were used to construct machine learning (ML) regression models that predicted morphological characteristics (diameter, wall thickness, and longitudinal opening angle resulting from the …

An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali

All Works

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …

A Fog Computing Framework For Intrusion Detection Of Energy-Based Attacks On Uav-Assisted Smart Farming, Junaid Sajid, Kadhim Hayawi, Asad Waqar Malik, Zahid Anwar, Zouheir Trabelsi

All Works

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. …

Kegg_Pull: A Software Package For The Restful Access And Pulling From The Kyoto Encyclopedia Of Gene And Genomes, Erik D. Huckvale, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background: The Kyoto Encyclopedia of Genes and Genomes (KEGG) provides organized genomic, biomolecular, and metabolic information and knowledge that is reasonably current and highly useful for a wide range of analyses and modeling. KEGG follows the principles of data stewardship to be findable, accessible, interoperable, and reusable (FAIR) by providing RESTful access to their database entries via their web-accessible KEGG API. However, the overall FAIRness of KEGG is often limited by the library and software package support available in a given programming language. While R library support for KEGG is fairly strong, Python library support has been lacking. Moreover, there …

Psf Sampling In Fluorescence Image Deconvolution, Eric A. Inman

Master's Theses

All microscope imaging is largely affected by inherent resolution limitations because of out-of-focus light and diffraction effects. The traditional approach to restoring the image resolution is to use a deconvolution algorithm to “invert” the effect of convolving the volume with the point spread function. However, these algorithms fall short in several areas such as noise amplification and stopping criterion. In this paper, we try to reconstruct an explicit volumetric representation of the fluorescence density in the sample and fit a neural network to the target z-stack to properly minimize a reconstruction cost function for an optimal result. Additionally, we do …

Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …

Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco

SDSU Data Science Symposium

Self-propelled sprayers are commonly used in agriculture to disperse agrichemicals. These sprayers commonly have two boom wings with dozens of nozzles that disperse the chemicals. Automatic boom height systems reduce the variability of agricultural sprayer boom height, which is important to reduce uneven spray dispersion if the boom is not at the target height.

A computational model was created to simulate the spray dispersion under the following conditions: a) one stationary nozzle based on the measured spray pattern from one nozzle, b) one stationary model due to an angled boom, c) superposition of multiple stationary nozzles due an angled boom, …

E Pluribus Unum: Prospective Acceptability Benchmarking From The Contouring Collaborative For Consensus In Radiation Oncology Crowdsourced Initiative For Multiobserver Segmentation, Diana Lin, Kareem A Wahid, Benjamin E Nelms, Renjie He, Mohammed A Naser, Simon Duke, Michael V Sherer, John P Christodouleas, Abdallah S R Mohamed, Michael Cislo, James D Murphy, Clifton D Fuller, Erin F Gillespie

Faculty, Staff and Student Publications

Purpose: Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement.

Approach: Participants who contoured ≥1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. …

Synthesize Heterogeneous Biological Knowledge Via Representation Learning For Alzheimer’S Disease Drug Repurposing, Kang-Lin Hsieh, German Plascencia-Villa, Ko-Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in …

Wrapper-Based Deep Feature Optimization For Activity Recognition In The Wearable Sensor Networks Of Healthcare Systems, Karam Kumar Sahoo, Raghunath Ghosh, Saurav Mallik, Arup Roy, Pawan Kumar Singh, Zhongming Zhao

Faculty, Staff and Student Publications

The Human Activity Recognition (HAR) problem leverages pattern recognition to classify physical human activities as they are captured by several sensor modalities. Remote monitoring of an individual's activities has gained importance due to the reduction in travel and physical activities during the pandemic. Research on HAR enables one person to either remotely monitor or recognize another person's activity via the ubiquitous mobile device or by using sensor-based Internet of Things (IoT). Our proposed work focuses on the accurate classification of daily human activities from both accelerometer and gyroscope sensor data after converting into spectrogram images. The feature extraction process follows …

Analysis Of Ab Initio Protein Structure Prediction Methods, Maytha Alshammari, Jing He

College of Sciences Posters

Protein structure prediction produces atomic models of three-dimensional structure of a protein from its amino acid sequence. Understanding the function mechanism of proteins requires knowledge of three-dimensional structures. When developing new enzymes and drugs, it's essential to understand the structure of the target protein. In this study, we analyze models predicted using two ab initio protein structure prediction methods, trRosetta and Quark. A set of thirty protein chains was used to evaluate the effectiveness of the methods. The thirty chains were collected from Protein Data Bank (June – November, 2020). The length and the relative position of the predicted secondary …

A Computational Analysis Of Hybrid Genome Assembly Strategies, Joseph Walewski

Computer Science Honors Papers

The central dogma of molecular biology states that DNA is transcribed to RNA and then translated into proteins. Since DNA is the starting material for many of biology’s macromolecules, it has been referred to as “nature’s instruction book.” The sum of all DNA in a cell is referred to as the genome, and genome sequencing is how we interpret the DNA.

Due to limitations on currently available technology, it is not possible to retrieve the entire genome in one contiguous set of data. Therefore, genome sequencing is a computer science problem as sequencing “reads” must be stitched together to obtain …

Teloportwrapper: A New Tool For Understanding The Dynamic World Of Fungal Telomere Ends, Trey Stansfield

Mahurin Honors College Capstone Experience/Thesis Projects

Telomeres are repetitive DNA sequence motifs found at eukaryote chromosome ends. Telomeres help protect chromosome ends from DNA damage and promote chromosome stability. Chromosomes play important roles in aging, mutation, and cancer. Eukaryotic pathogens also use telomeres to mutate and manage virulence genes. In response to chromosome end breakage newly formed telomeres, called de novo telomeres, are formed to recreate the lost telomere and sub-telomeric regions.

Magnaporthe oryzae is a fungal pathogen which causes wheat blast, a deadly plant disease in wheat. Magnaporthe oryzae is also known for its highly variable sub-regions which show high amounts of induced variability due …