Computer Sciences Commons^™

Khealth Digital Personalized Healthcare Technology For Pediatric Asthma, Utkarshani Jaimini, Hong Y. Yip, Revathy Venkataramanan, Dipesh Kadariya, Vaikunth Sridharan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra, Amit Sheth

Kno.e.sis Publications

Episodic:Traditional Clinician Centric Healthcare

Questions to be answered:

1.Can we reduce the number of asthma attacks through continuous monitoring of the patient's health condition?

2.Can we predict the asthma attack based on the data collected from the patient?

3.Can we predict the asthma vulnerability score for a patient?

4.Can we predict the asthma severity level of a patient?

5.Can we understand the casual relationship between the asthma symptom and the possible factors responsible for it?

Personalized Health Knowledge Graph, Amelia Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth

Kno.e.sis Publications

Our current health applications do not adequately take into account contextual and personalized knowledge about patients. In order to design “Personalized Coach for Healthcare” applications to manage chronic diseases, there is a need to create a Personalized Healthcare Knowledge Graph (PHKG) that takes into consideration a patient’s health condition (personalized knowledge) and enriches that with contextualized knowledge from environmental sensors and Web of Data (e.g., symptoms and treatments for diseases). To develop PHKG, aggregating knowledge from various heterogeneous sources such as the Internet of Things (IoT) devices, clinical notes, and Electronic Medical Records (EMRs) is necessary. In this paper, we …

Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz

Theses and Dissertations--Computer Science

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree …

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Ultra-Fast And Memory-Efficient Lookups For Cloud, Networked Systems, And Massive Data Management, Ye Yu

Theses and Dissertations--Computer Science

Systems that process big data (e.g., high-traffic networks and large-scale storage) prefer data structures and algorithms with small memory and fast processing speed. Efficient and fast algorithms play an essential role in system design, despite the improvement of hardware. This dissertation is organized around a novel algorithm called Othello Hashing. Othello Hashing supports ultra-fast and memory-efficient key-value lookup, and it fits the requirements of the core algorithms of many large-scale systems and big data applications. Using Othello hashing, combined with domain expertise in cloud, computer networks, big data, and bioinformatics, I developed the following applications that resolve several major …

Algorithms For Reconstruction Of Gene Regulatory Networks From High -Throughput Gene Expression Data, Wenping Deng

Dissertations, Master's Theses and Master's Reports

Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with increased accuracy from microarray and RNA-Seq high-throughput gene expression data sets.

The first algorithm (Chapter 2) …

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

The Use Of Machine Learning To Detect Suckling In Pre-Weaned Calves, Sukumar Katamreddy

Theses

The weaning of cattle is a process which is known to be labour intensive and to have stressful effects on both cow and calf Common methods used in the weaning process include the temporary removal of a mother from the calf and manual observation and intervention. Early and speedy weaning is known to have a number of benefits, including health benefits for both cow and calf, additional weight gains for the calves as well as reduced labour and feed requirements. The process known as Two-Stage Weaning is recognised to be an effective low-stress approach to weaning in which the calf …

Pollinator Power: Supporting Bees Through Ecoregion Specific Planting Guides, Maya Thomas

Scripps Senior Theses

The pollination of flowering crops by bees is an invaluable ecosystem service that supports biodiversity and much of the global agricultural system. Pollinators move pollen between the male structures of a plant to the female structures of a plant of the same species. This fertilizes the female plant, which then produces the next generation. This process also provides the pollinator with the nectar or pollen it needs to survive. While some plants transfer pollen through different means, the majority of plants need help from pollinators to reproduce. Depending on the means of pollination, pollination can be classified as abiotic or …

Qualitative Change Detection Approach For Preventive Therapies, Cristina Mitrea

Wayne State University Dissertations

Currently, most diseases are diagnosed only after disease-associated changes have occurred. In this PhD dissertation, we propose a paradigm shift from treating the disease to maintaining the healthy state. The proposed approach is able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility of therapeutic interventions before the occurrence of symptoms. The change detection method exploits knowledge from biological networks and longitudinal data using a system impact analysis approach. This approach is validated on eight datasets, for seven different model organisms and eight biological phenomena. On these data, our proposed method performs well, consistently identifying …

A Systematic Approach To Rna-Associated Motif Discovery, Tian Gao, Jiang Shu, Juan Cui

School of Computing: Faculty Publications

Background: Sequencing-based large screening of RNA-protein and RNA-RNA interactions has enabled the mechanistic study of post-transcriptional RNA processing and sorting, including exosome-mediated RNA secretion. The downstream analysis of RNA binding sites has encouraged the investigation of novel sequence motifs, which resulted in exceptional new challenges for identifying motifs from very short sequences (e.g., small non-coding RNAs or truncated messenger RNAs), where conventional methods tend to be ineffective. To address these challenges, we propose a novel motif-finding method and validate it on a wide range of RNA applications.

Results: We first perform motif analysis on microRNAs and longer RNA fragments from …

Crop Height Estimation With Unmanned Aerial Vehicles, Carrick Detweiler, David Anthony, Sebastian Elbaum

School of Computing: Faculty Publications

An unmanned aerial vehicle (UAV) can be configured for crop height estimation. In some examples, the UAV includes an aerial propulsion system, a laser scanner configured to face downwards while the UAV is in flight, and a control system. The laser scanner is configured to scan through a two-dimensional scan angle and is characterized by a maxi mum range. The control system causes the UAV to fly over an agricultural field and maintain, using the aerial propulsion system and the laser scanner, a distance between the UAV and a top of crops in the agricultural field to within a programmed …

Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery

Doctoral Dissertations

"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for …

Quantification Of Twist From The Central Lines Of Β-Strands, Tunazzina Islam, Michael Poteat, Jing He

Computer Science Faculty Publications

Since the discovery of right-handed twist of a β-strand, many studies have been conducted to understand the twist. Given the atomic structure of a protein, twist angles have been defined using atomic positions of the backbone. However, limited study is available to characterize twist when the atomic positions are not available, but the central lines of β-strands are. Recent studies in cryoelectron microscopy show that it is possible to predict the central lines of β-strands from a medium-resolution density map. Accurate measurement of twist angles is important in identification of β-strands from such density maps. We propose an effective method …

Atomistic Simulations And Network-Based Modeling Of The Hsp90-Cdc37 Chaperone Binding With Cdk4 Client Protein: A Mechanism Of Chaperoning Kinase Clients By Exploiting Weak Spots Of Intrinsically Dynamic Kinase Domains, John Czemeres, Kurt Buse, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

A fundamental role of the Hsp90 and Cdc37 chaperones in mediating conformational development and activation of diverse protein kinase clients is essential in signal transduction. There has been increasing evidence that the Hsp90-Cdc37 system executes its chaperoning duties by recognizing conformational instability of kinase clients and modulating their folding landscapes. The recent cryo-electron microscopy structure of the Hsp90-Cdc37- Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have combined atomistic simulations with protein stability and network-based rigidity decomposition analyses to characterize dynamic …

Remote Sensing Of Forests Using Discrete Return Airborne Lidar, Hamid Hamraz, Marco A. Contreras

Forestry and Natural Resources Faculty Publications

Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests, however, remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. …

Virtual Reality As A Training Tool To Treat Physical Inactivity In Children, Adam W. Kiefer, David Pincus, Michael J. Richardson, Gregory D. Myer

Psychology Faculty Articles and Research

Lack of adequate physical activity in children is an epidemic that can result in obesity and other poor health outcomes across the lifespan. Physical activity interventions focused on motor skill competence continue to be developed, but some interventions, such as neuromuscular training (NMT), may be limited in how early they can be implemented due to dependence on the child’s level of cognitive and perceptual-motor development. Early implementation of motor-rich activities that support motor skill development in children is critical for the development of healthy levels of physical activity that carry through into adulthood. Virtual reality (VR) training may be beneficial …

Accurate Determination And Application Of Local Strain For Studying Tissues With Gradients In Mechanical Properties, John Boyle

McKelvey School of Engineering Graduate Student Theses & Dissertations

Determination of the mechanical behavior of materials requires an understanding of deformation during loading. While this is traditionally accomplished in engineering by examining a force displacement curve for a whole sample, these techniques implicitly ignore local geometric complexities and local material inhomogeneities commonly found in biologic tissues. Techniques such as normalized cross correlation have been classically applied to address this issue and resolve deformation at the local level; however, these techniques have proven unreliable when deformations become large, if the sample undergoes a rotation, and/or if strain fields become incompatible (e.g. at or near failure).

Presented here is a toolbox …

The Ability Of Different Imputation Methods To Preserve The Significant Genes And Pathways In Cancer, Rosa Aghdam, Taban Baghfalaki, Pegah Khosravi, Elnaz Saberi Ansari

Publications and Research

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase …

Study Of Self-Similarity In Brain Data, Jennifer Holst

Student Theses

In the area of computer science, past research has found that the concept of self-similarity is present in local and Internet-based network traffic. This study considers the possibility that data traveling through the neuronal network in the human brain is also self-similar. By analyzing publicly available raw EEG data and estimating its Hurst parameter, we find indications that brain data traffic may in fact be self-similar.

A Deep Learníng-Based Data Minimization Algorithm For Big Genomics Data In Support Of Lot And Secure Smart Health Services, Mohammed Aledhari

Dissertations

In the age of Big Genomics Data, institutes such as the National Human Genome Research Institute (NHGRI),1000-Genomes project, and the international cancer sequencing consortium are faced with the challenge of sharing large volumes of data between internationallydispersed sample collectors, data analyzers, and researchers, a process that up until now has been plagued by unreliable transfers and slow connection speeds. These occur due to the inherent throughput bottlenecks of traditional transfer technologies. One suggested solution is using the cloud as an infrastructure to solve the store and analysis challenges. However, the transfer and share of the genomics datasets between biological laboratories …

Using Data To Improve Services For Infants With Hearing Loss: Linking Newborn Hearing Screening Records With Early Intervention Records, Maria Gonzalez, Lori Iarossi, Yan Wu, Ying Huang, Kirsten Siegenthaler

Journal of Early Hearing Detection and Intervention

The purpose of this study was to match records of infants with permanent hearing loss from the New York Early Hearing Detection and Intervention Information System (NYEHDI-IS) to records of infants with permanent hearing loss receiving early intervention services from the New York State Early Intervention Program (NYSEIP) to identify areas in the state where hearing screening, diagnostic evaluations and referrals to the NYSEIP were not being made or documented in a timely manner. Data from 2014-2016 NYEHDI-IS and NYEIS information systems were matched using The Link King. There were 274 infants documented in NYEIS Information System as receiving early …

Examining The Electrical Excitation, Calcium Signaling, And Mechanical Contraction Cycle In A Heart Cell, Kristen Deetz, Nygel Foster, Darius Leftwich, Chad Meyer, Shalin Patel, Carlos Barajas, Matthias K. Gobbert, Zana Coulibaly

Spora: A Journal of Biomathematics

As the leading cause of death in the United States, heart disease has become a principal concern in modern society. Cardiac arrhythmias can be caused by a dysregulation of calcium dynamics in cardiomyocytes. Calcium dysregulation, however, is not yet fully understood and is not easily predicted; this provides motivation for the subsequent research. Excitation-contraction coupling (ECC) is the process through which cardiomyocytes undergo contraction from an action potential. Calcium induced calcium release (CICR) is the mechanism through which electrical excitation is coupled with mechanical contraction through calcium signaling. The study of the interplay between electrical excitation, calcium signaling, and mechanical …

Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …

Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.

Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …

Big Data: Computational Biology Opens A New Window On The World's Challenges For Colby Scientists, Kate Carlisle

Colby Magazine

"What makes us 'us' and not a plant? Not a bacteria, or a virus," asks Andrea Tilden, the J. Warren Merrill Associate Professor of Biology and a genomics expert. "Any one genome has six thousand novels worth of information. Computational biology is the tool we use to read them."

Analysis Of Complex Vertebrate Genomes: Computational Challenges And Solutions, Jeramiah J. Smith

Commonwealth Computational Summit

No abstract provided.

Registration And Grouping Algorithms In Protein Nmr Derived Peak Lists And Their Application In Protein Nmr Reference Correction, Andrey Smelter, Xi Chen, Eric C. Rouchka, Hunter N. B. Moseley

Commonwealth Computational Summit

Nuclear magnetic resonance spectroscopy of proteins (protein NMR) is a powerful analytical technique for studying structure and dynamics of proteins. Almost all aspects of protein NMR have been accelerated by the development of software tools that enable the analysis of NMR spectral data and its utilization in studying protein structure and dynamics. This includes software for raw NMR processing, spectral visualization, protein resonance assignment, and structure determination. However, full automation of protein NMR data analysis is still a work in progress and data analysis still requires an expert NMR spectroscopist utilizing an array of software tools.

While manual resonance assignment …

The Lamprey Genome: Illuminating Genomic Change Across Eons And Embryogenesis, Jeramiah J. Smith, Courtney K. M. Waterbury, Melissa C. Keinath, Cody B. Saraceno, Vladimir A. Timoshevskiy, Nataliya Y. Timoshevskaya

Commonwealth Computational Summit

The lamprey genome provides unique insights into both the deep evolutionary history of vertebrate genomes and the maintenance of genome structure/integrity over development. The lamprey lineage diverged from all other vertebrates approximately 500 million years ago. As such, comparisons between lamprey and other vertebrates permit reconstruction of ancient duplication and rearrangement events that defined the fundamental architecture and gene content of all extant vertebrate genomes. Lamprey also undergoes programmatic changes genome structure that result in the physical elimination of ~20% of its genomic DNA (~0.5Gb from a ~2 Gb genome) from all somatic cell lineages during early embryonic development. Here, …

Use Of Hpc To Analyze Changes In Gene Expression During Fruit Fly Spermiogenesis, Sepideh Dadkhah, Douglas A. Harrison, Jeramiah J. Smith

Commonwealth Computational Summit

In the fruit fly, Drosophila melanogaster, JAK/STAT signaling during spermiogenesis is known to play a crucial role in the maintenance of stem cells of the testis. Recent studies in our lab have shown that activation of the JAK/STAT pathway in somatic cyst cells is also required for the later stages of spermiogenesis like individualization.

The main goal of this project is to characterize the events downstream of JAK/STAT signaling in spermiogenesis and more specifically to determine the mechanism by which JAK/STAT activation regulates individualization, a later stage in spermiogenesis where 64 individual spermatids are formed from a 64-interconnected spermatid …