Computer Sciences Commons^™

A Theoretical Model Of Underground Dipole Antennas For Communications In Internet Of Underground Things, Abdul Salam, Mehmet C. Vuran, Xin Dong, Christos Argyropoulos, Suat Irmak

Faculty Publications

The realization of Internet of Underground Things (IOUT) relies on the establishment of reliable communication links, where the antenna becomes a major design component due to the significant impacts of soil. In this paper, a theoretical model is developed to capture the impacts of change of soil moisture on the return loss, resonant frequency, and bandwidth of a buried dipole antenna. Experiments are conducted in silty clay loam, sandy, and silt loam soil, to characterize the effects of soil, in an indoor testbed and field testbeds. It is shown that at subsurface burial depths (0.1-0.4m), change in soil moisture impacts …

Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an …

Question Answering For Suicide Risk Assessment Using Reddit, Amanuel Alambo, Usha Lokala, Ugur Kursuncu, Krishnaprasad Thirunarayan, Amelia Gyrard, Randon S. Welton, Jyotishman Pathak, Amit P. Sheth

Kno.e.sis Publications

Mental Health America designed ten questionnaires that are used to determine the risk of mental disorders. They are also commonly used by Mental Health Professionals (MHPs) to assess suicidality. Specifically, the Columbia Suicide Severity Rating Scale (C-SSRS), a widely used suicide assessment questionnaire, helps MHPs determine the severity of suicide risk and offer an appropriate treatment. A major challenge in suicide treatment is the social stigma wherein the patient feels reluctance in discussing his/her conditions with an MHP, which leads to inaccurate assessment and treatment of patients. On the other hand, the same patient is comfortable freely discussing his/her mental …

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim …

Automatic Identification Of Individual Drugs In Death Certificates, Soon Jye Kho, Amit Sheth, Olivier Bodenreider

Kno.e.sis Publications

Background:

Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging due to the variability of drug names.

Objectives:

To automatically identify individual drugs in death certificates.

Methods:

We use RxNorm to collect variants for drug names (generic names, synonyms, brand names) and we algorithmically generate common misspellings. We use this automatically compiled list to identify drug mentions from 703,106 death certificates and compare the performance of our automated approach to that of …

Empathi: An Ontology For Emergency Managing And Planning About Hazard Crisis, Manas Gaur, Kaeedeh Shekarpour, Amelia Gyrard, Amit P. Sheth

Kno.e.sis Publications

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people.
A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts describing the domain of emergency managing and planning of hazard crises.
Although empathi has a coarse-grained view, it considers the necessary …

Exploring Strategies To Integrate Disparate Bioinformatics Datasets, Charbel Bader Fakhry

Walden Dissertations and Doctoral Studies

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating …

Gene Ontology-Guided Force-Directed Visualization Of Protein Interaction Networks, James Lowell King

CCAC Theses and Dissertations

Protein interaction data is being generated at unprecedented rates thanks to advancements made in high throughput techniques such as mass spectrometry and DNA microarrays. Biomedical researchers, operating under budgetary constraints, have found it difficult to scale their efforts to keep up with the ever-increasing amount of available data. They often lack the resources and manpower required to analyze the data using existing methodologies. These research deficiencies impede our ability to understand diseases, delay the advancement of clinical therapeutics, and ultimately costs lives.

One of the most commonly used techniques to analyze protein interaction data is the construction and visualization of …

Citationally Enhanced Semantic Literature Based Discovery, John David Fleig

CCAC Theses and Dissertations

We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if …

Adaptive Knowledge Networks: A Time Capsule, Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L. Shalin

Kno.e.sis Publications

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal

Theses and Dissertations--Computer Science

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …

Walking With A Robotic Exoskeleton Does Not Mimic Natural Gait: A Within-Subjects Study, Chad Swank, Sharon Wang-Price, Fan Gao, Sattam Almutairi

Kinesiology and Health Promotion Faculty Publications

Background: Robotic exoskeleton devices enable individuals with lower extremity weakness to stand up and walk over ground with full weight-bearing and reciprocal gait. Limited information is available on how a robotic exoskeleton affects gait characteristics.

Objective: The purpose of this study was to examine whether wearing a robotic exoskeleton affects temporospatial parameters, kinematics, and muscle activity during gait.

Methods: The study was completed by 15 healthy adults (mean age 26.2 [SD 8.3] years; 6 males, 9 females). Each participant performed walking under 2 conditions: with and without wearing a robotic exoskeleton (EKSO). A 10-camera motion analysis system synchronized with 6 …

A Hybrid Cognitive Architecture With Primal Affect And Physiology, Christopher L. Dancy

Faculty Journal Articles

Though computational cognitive architectures have been used to study several processes associated with human behavior, the study of integration of affect and emotion in these processes has been relatively sparse. Theory from affective science and affective neuroscience can be used to systematically integrate affect into cognitive architectures, particularly in areas where cognitive system behavior is known to be associated with physiological structure and behavior. I introduce a unified theory and model of human behavior that integrates physiology and primal affect with cognitive processes in a cognitive architecture. This new architecture gives a more tractable, mechanistic way to simulate affect-cognition interactions …

Microarray Data Analysis And Classification Of Cancers, Grant Gates

Williams Honors College, Honors Research Projects

When it comes to cancer, there is no standardized approach for identifying new cancer classes nor is there a standardized approach for assigning cancer tumors to existing classes. These two ideas are known as class discovery and class prediction. For a cancer patient to receive proper treatment, it is important that the type of cancer be accurately identified. For my Senior Honors Project, I would like to use this opportunity to research a topic in bioinformatics. Bioinformatics incorporates a few different subjects into one including biology, computer science and statistics. An intricate method for class discovery and class prediction is …

Phenogeneranker: A Tool For Gene Prioritization Using Complete Multiplex Heterogeneous Networks, Cagatay Dursun, Naoki Shimoyama, Mary Shimoyama, Michael Schläppi, Serdar Bozdag

Computer Science Faculty Research and Publications

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally-related genes tend to be close to each other in the biological networks.

In this study, we present PhenoGeneRanker, an improved version of a recently developed network propagation method called Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH). PhenoGeneRanker allows multi-layer gene and disease networks. …

Aligning Best Practices In Student Success And Career Preparedness: An Exploratory Study To Establish Pathways To Stem Careers For Undergraduate Minority Students, Kimberly D. Kendricks, Anthony A. Arment, K. V. Nedunuri, Cadance A. Lowell

Journal of Research in Technical Careers

Undergraduate minority retention and graduation rates in STEM disciplines is a nationally recognized challenge for workforce growth and diversification. The Benjamin Banneker Scholars Program (BBSP) was a five-year undergraduate study developed to increase minority student retention and graduation rates at an HBCU. The program structure utilized a family model as a vehicle to orient students to the demands of college. Program activities integrated best K-12 practices and workforce skillsets to increase academic preparedness and career readiness. Findings revealed that a familial atmosphere improved academic performance, increased undergraduate research, and generated positive perceptions of faculty mentoring. Retention rates among BBSP participants …

Volume 11, Jacob Carney, Ryan White, Joseph Hyman, Jenny Raven, Megan Garrett, Ibrahim Kante, Summer Meinhard, Lauren Johnson, William "Editha" Dean Howells, Laura Gottschalk, Christopher Siefke, Pink Powell, Natasha Woodmancy, Katharine Colley, Abbey Mays, Charlotte Potts

Incite: The Journal of Undergraduate Scholarship

Table of Contents:

Introduction, Dr. Roger A. Byrne, Dean

From the Editor, Dr. Larissa "Kat" Tracy

From the Designers, Rachel English, Rachel Hanson

Synthesis of 3,5-substituted Parabens and their Antimicrobial Properties, Jacob Coarney, Ryan White

Chernobyl: Putting "Perestroika" and "Glasnot" to the Test, Joseph Hyman

Art by Jenny Raven

Watering Down Accessibility: The Issue with Public Access to Alaska's Federal Waterways, Meagan Garrett

Why Has the Democratic Republic of the Congo outsourced its Responsibility to Educate its Citizens? Ibrahim Kante

Art by Summer Meinhard

A Computational Study of Single Molecule Diodes, Lauren Johnson

Satire of …

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

Utilization Of Various Methods And A Landsat Ndvi/Google Earth Engine Product For Classifying Irrigated Land Cover, Andrew Nemecek

Graduate Student Theses, Dissertations, & Professional Papers

Methods for classifying irrigated land cover are often complex and not quickly reproducible. Further, moderate resolution time-series datasets have been consistently utilized to produce irrigated land cover products over the past decade, and the body of irrigation classification literature contains no examples of subclassification of irrigated land cover by irrigation method. Creation of geospatial irrigated land cover products with higher resolution datasets could improve reliability, and subclassification of irrigation by method could provide better information for hydrologists and climatologists attempting to model the role of irrigation in the surface-ground water cycle and the water-energy balance. This study summarizes a simple, …

Neuroprotective Effects Of Melatonin And Celecoxib Against Ethanol-Induced Neurodegeneration: A Computational And Pharmacological Approach, Lina T. Al Kury, Alam Zeb, Zain Ul Abidin, Nadeem Irshad, Imran Malik, Arooj Mohsin Alvi, Atif Ali Khan Khalil, Sareer Ahmad, Muhammad Faheem, Arif Ullah Khan, Fawad Ali Shah, Shupeng Li

All Works

© 2019 Al Kury et al. This work is published and licensed by Dove Medical Press Limited. Purpose: Melatonin and celecoxib are antioxidants and anti-inflammatory agents that exert protective effects in different experimental models. In this study, the neuroprotective effects of melatonin and celecoxib were demonstrated against ethanol-induced neuronal injury by in silico, morphological, and biochemical approaches. Methods: For the in silico study, 3-D structures were constructed and docking analysis performed. For in vivo studies, rats were treated with ethanol, melatonin, and celecoxib. Brain samples were collected for biochemical and morphological analysis. Results: Homology modeling was performed to build 3-D …

Estimating Waterbird Abundance On Catfish Aquaculture Ponds Using An Unmanned Aerial System, Paul C. Burr, Sathishkumar Samiappan, Lee A. Hathcock, Robert J. Moorhead, Brian S. Dorr

Human–Wildlife Interactions

In this study, we examined the use of an unmanned aerial system (UAS) to monitor fish-eating birds on catfish (Ictalurus spp.) aquaculture facilities in Mississippi, USA. We tested 2 automated computer algorithms to identify bird species using mosaicked imagery taken from a UAS platform. One algorithm identified birds based on color alone (color segmentation), and the other algorithm used shape recognition (template matching), and the results of each algorithm were compared directly to manual counts of the same imagery. We captured digital imagery of great egrets (Ardea alba), great blue herons (A. herodias), …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

Polya Db3: A Database Cataloging Polyadenation Sites(Pas) Across Different Species And Their Conservation, Ram Mohan Nambiar

Theses

Polyadenation is an important process occurring in the messenger RNA that involves cleavage of 3 end nascent mRNAs and addition of poly(A) tails. For this thesis,I present PolyA DB3 ,a database cataloging cleavage and polyadenylation sites (PASs) in several genomes specifically for human,mouse,rat and chicken. This database is based on deep sequencing data. PASs are mapped by the 3’ region extraction and deep sequencing (3’READS) method, ensuring unequivocal PAS identification. Large volume of data based on diverse biological samples is used to increase PAS coverage and provide PAS usage information. Strand-specific RNA-seq data were used to extend annotated 3’ ends …

Application Of Graphical Models In Protein-Protein Interactions And Dynamics, Amir Vajdi Hoojghan

Graduate Doctoral Dissertations

Every organism contains a few hundred to thousands of proteins. A protein is made of a sequence of molecular building blocks named amino acids. Amino acids will be referred to as residues. Every protein performs one or more functions in the cell. In order for a protein to do its job, it requires to bind properly to other partner proteins. Many genetic diseases such as cancer are caused by mutations (changes) of specific residues which cause disturbances in the functions of those proteins.

The problem of prediction of protein binding site is a crucial topic in computational biology. A protein …

The International Conference On Intelligent Biology And Medicine (Icibm) 2018: Bioinformatics Towards Translational Applications, Xiaoming Liu, Lei Xie, Zhijin Wu, Kai Wang, Zhongming Zhao, Jianhuan Ruan, Degui Zhi

Publications and Research

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10–12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

Discerning Novel Splice Junctions Derived From Rna-Seq Alignment: A Deep Learning Approach, Yi Zhang, Xinan Liu, James N. Macleod, Jinze Liu

Computer Science Faculty Publications

Background: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results: …

Pasnet: Pathway-Associated Sparse Deepneural Network For Prognosis Prediction From High-Throughput Data, Jie Hao, Youngsoon Kim, Tae-Kyung Kim, Mingon Kang

Faculty Articles

Background: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis maybe caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. Results: In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes …

Phr: Patient Health Record, Quinn Nelson

Theses/Capstones/Creative Projects

The rapid development of information technology systems has expanded into multiple disciplines and results in systems that are limited by initial design and implementation: the Healthcare Information Technology (HIT) space is no different. The introduction of the Electronic Health Record (EHR) system has changed the way healthcare operates. Initial designs of these systems were focused on serving the needs of insurance companies and healthcare billing departments. Research shows that the design of EHR systems negatively impact provider-patient interactions and the care they receive. This capstone project capitalizes on the collaboration efforts between UNO and UNMC – by joining a research …

Integrating Node Embeddings And Biological Annotations For Genes To Predict Disease-Gene Associations, Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li

Research Collection School Of Computing and Information Systems

Background: Predicting disease causative genes (or simply, disease genes) has played critical roles in understandingthe genetic basis of human diseases and further providing disease treatment guidelines. While various computationalmethods have been proposed for disease gene prediction, with the recent increasing availability of biologicalinformation for genes, it is highly motivated to leverage these valuable data sources and extract useful information foraccurately predicting disease genes. Results: We present an integrative framework called N2VKO to predict disease genes. Firstly, we learn the nodeembeddings from protein-protein interaction (PPI) network for genes by adapting the well-known representationlearning method node2vec. Secondly, we combine the learned node …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …