Computer Sciences Commons^™

A Genome-Wide Association Study Of Cocaine Use Disorder Accounting For Phenotypic Heterogeneity And Gene–Environment Interaction, Jiangwen Sun, Henry R. Kranzler, Joel Gelernter, Jinbo Bi

Computer Science Faculty Publications

Background: Phenotypic heterogeneity and complicated gene-environment interplay in etiology are among the primary factors that hinder the identification of genetic variants associated with cocaine use disorder. Methods: To detect novel genetic variants associated with cocaine use disorder, we derived disease traits with reduced phenotypic heterogeneity using cluster analysis of a study sample (n = 9965). We then used these traits in genome-wide association tests, performed separately for 2070 African Americans and 1570 European Americans, using a new mixed model that accounted for the moderating effects of 5 childhood environmental factors. We used an independent sample (918 African Americans, 1382 European …

Outlier Profiles Of Atomic Structures Derived From X-Ray Crystallography And From Cryo-Electron Microscopy, Lin Chen, Jing He, Angelo Facchiano

Computer Science Faculty Publications

Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic structures, including one derived from X-ray data with better than 1.5 Å resolution. Cryo-EM data sets contain structures released by December 2016 and those released between 2017 and 2019, derived from resolution ranges 0–4 Å and 4–6 Å respectively. Results: The distribution of HBOS values in five sets of X-ray structures show that HBOS is sensitive distinguishing …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Mixed Mode Device-To-Device Communication Scheme For Congestion Reduction And Channel Usage Optimization In 5g Cellular Networks, Chiza M. Christophe, Omar F. Hamad, Libe V. Massawe, Abdi T. Abdalla

Tanzania Journal of Science

Device-to-Device (D2D) communication schemes have gained more attention in cellular networks particularly in normalization process of the upcoming 5G networks. They have been investigated in core network offloading, congestion reduction and channel usage optimization.  The two last cases are among the major constraints in current cellular networks and are the main concerns of this paper. The paper presents a mixed mode D2D communication scheme to decentralize data collection between devices and the base station in order to reduce the number of direct connections at the base station of ultra-dense cells characterized by different levels of channel utilizations or target data …

Design Of Reinforced Concrete Deep Beams Using Particle Swarm Optimization Technique, Qais F. Hasan, Dler A. Al-Mamany, Omer K. Fayadh

Karbala International Journal of Modern Science

Researches available in literature interrelating neural networks to civil engineering design problems, especially for beep beams, are very rare. Therefore, an optimization algorithm is developed and verified in this study and coded using MATLAB functions to determine the optimum cost design of reinforced concrete deep beams. ACI 318-14 code method is used benefiting from iterative particle swarm optimization technique due to its efficiency and reliability. Minimizing total cost is used as the objective function in terms of four decision variables. Self-adaptive penalty function technique is used to handle constraints for each of the 300 randomly selected particles, and in each …

Artificial Bee Colony Algorithm For Solving Fuzzy Multi-Objective Bed Allocation Model, Abdulhakeem Luqman Hasan

Karbala International Journal of Modern Science

With the improvement of the medical services frameworks rivalry, hospitals face more and more challenges. In the interim, allotment of resource has a crucial influence on performing competitive benefits in a hospitals. To choose the suitable beds number is one of the most essential tasks in hospital administration. Anyway, in true condition, bed allotment choice is a multiple-side problem with weakness and haphazardness of the information available. It is so sophisticated. Therefore, the research about bed allotment difficulty is comparatively rare under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we …

Evaluation Of Atmospherically Gases Using Models Flaash And Quac To Hyper-Spectral Imagery, Asmaa Maher

Karbala International Journal of Modern Science

The important step is correction of the effect of atmospheric on hyper-spectral imagery of the VIS “visible”, short wave & NIR “near-infrared” spectral range. In general, the cause for limiting the use of hyperspectral images is the atmospheric effects, so, atmospheric correction is necessary for any accurate processing. In this work, two atmospheric correction techniques have been applied on Hyper-spectral image. From the raw original image and also from the FLAASH and QUAC atmospheric corrected images the spectra of vegetation, water and soil were extracted. The acquisition data for study contained Hyperion bands for year “2015” images over each of …

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.

Informing Field Management Decisions To Enhance Alfalfa Seed Production Using Remote Sensing, Thomas V. Van Der Weide

Boise State University Theses and Dissertations

The development rate of alfalfa seed crop depends on both environmental conditions and management decisions. Crop management decisions, such as determining when to release pollinators to optimize pollination, can be informed by the identification of plant development stages from remote sensing data. I first identify what electromagnetic wavelengths are sensitive to alfalfa plant development stages using hyperspectral data. A Random Forest regression is used to determine the best Vegetation Index (VI) to monitor how much of the plant is covered in flower. The results indicate that Blue, Green, and Near-Infrared are the important electromagnetic wavelengths for the VI. Imagery collected …

Overlap Matrix Completion For Predicting Drug-Associated Indications, Menhyun Yang, Huimin Luo, Yaohang Li, Fang-Xiang Wu, Jianxin Wang

Computer Science Faculty Publications

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated …

Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon

Theses

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …

Integrating Mathematics And Biology In The Classroom: A Compendium Of Case Studies And Labs, Becky Sanft, Anne Walter

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Quantifying Pollen Traits To Build A Mathematical Model Of Pollen Competition - A Mathematician's Perspective, Montana Ferita, Julie Fucarino, Alex Capaldi, Charlotte Beckford

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Period Drift In A Neutrally Stable Stochastic Oscillator, Kevin Sanft

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

An Agent-Based Model Of An Endangered Florida Tillansia Utriculata Population, Erin N. Bodine, Alexandra Campbell, Anna C. Kula

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Development Of An Autonomous Aerial Toolset For Agricultural Applications, Terrance Life

Mahurin Honors College Capstone Experience/Thesis Projects

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will …

Properties Of Concrete Modified With Ultra-Fine Slag, Panga Narasimha Reddy, Javed Ahmed Naqash

Karbala International Journal of Modern Science

The supplementary cementitious materials (SCM) can be used as a replacement of cement in the construction industry to minimize the drawbacks of normal concrete such as the emission of carbon dioxide so as to be eco-friendly. This paper presents the effect of ultra-fine slag (i.e. 25% of alccofine) as a replacement of cement for different water to binder ratios (i.e. 0.38, 0.4 and 0.45). The effect of alccofine on the concrete strength properties were studied at 7 and 28 days wherein considerable strength enhancement was observed compared to normal concrete. Thermogravimetric analysis was also carried out in which mass loss …

Analytical Approach To Investigation Of Free Vibration Of Thin Rectangular Plate Immersed In Fluid, Resting On Winkler And Pasternak Foundations, Obanishola Sadiq, Gbeminiyi Sobamowo, Saheed Salawu

Karbala International Journal of Modern Science

Dynamic behaviour of free vibration of rectangular plate is investigated. This study considered an analytical approach to investigation of free vibration of thin rectangular plate immersed in fluid, resting on Winkler and Pasternak foundations.The governing nonlinear partial differential equation is analyzed using two-dimensional differential transform method. The accuracy of the analytical solutions obtained is verified with existing results in literature and confirmed in excellent agreement. Thereafter, the analytical solutions are used for investigation of effect of elastic foundation, fluid and aspect ratio on vibrating plate.From the result, it is observed that, increase elastic foundation parameters increases natural frequency, increase aspect …

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

Deep Machine Learning Techniques For The Detection And Classification Of Sperm Whale Bioacoustics, Peter C. Bermant, Michael M. Bronstein, Robert J. Wood, Shane Gero, David F. Gruber

Publications and Research

We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) …

Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification, Nelson Zange Tsaku

Master of Science in Computer Science Theses

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological …

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

LSU New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

Designing And Sample Size Calculation In Presence Of Heterogeneity In Biological Studies Involving High-Throughput Data., Sudhir Srivastava

Electronic Theses and Dissertations

The designing and determination of sample size are important for conducting high-throughput biological experiments such as proteomics experiments and RNA-Seq expression studies, thus leading to better understanding of complex mechanisms underlying various biological processes. The variations in the biological data or technical approaches to data collection lead to heterogeneity for the samples under study. We critically worked on the issues of technical and biological heterogeneity. The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values (MVs) and data heterogeneity. We considered a proteomics data set generated from human kidney …

High Performance Computing Techniques To Better Understand Protein Conformational Space, Arpita Joshi

Graduate Doctoral Dissertations

This thesis presents an amalgamation of high performance computing techniques to get better insight into protein molecular dynamics. Key aspects of protein function and dynamics can be learned from their conformational space. Datasets that represent the complex nuances of a protein molecule are high dimensional. Efficient dimensionality reduction becomes indispensable for the analysis of such exorbitant datasets. Dimensionality reduction forms a formidable portion of this work and its application has been explored for other datasets as well. It begins with the parallelization of a known non-liner feature reduction algorithm called Isomap. The code for the algorithm was re-written in C …

Rcrab: An R Analytics Tool To Visualize And Analyze The Movement Of Horseshoe Crabs In Long Island Sound, Ismael Youssef, Samah Senbel, Jo-Marie Kasinak, Jennifer Mattei

School of Computer Science & Engineering Faculty Publications

Mark-recapture programs are important for studying the ecology and population dynamics of wildlife. An R shiny analytics tool was developed to track the movement of horseshoe crabs in Long Island Sound based on tag and resight data. The crabs were tagged and recaptured by volunteers of Project Limulus, a community-based research program. The dataset contains tag and recapture location information for 14,065 horseshoe crabs over 18 years. The dataset was initially cleaned by removing records with missing, duplicate or incorrect data. A new data structure was developed to save the data and simplify processing: Three dimensions were used, one for …

Iamhappy: Towards An Iot Knowledge-Based Cross-Domain Well-Being Recommendation System For Everyday Happiness, Amelia Gyrard, Amit Sheth

Kno.e.sis Publications

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people’s feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internetof Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every …

A New Approach To The Potential Energy Of Solids, Radhika Chauhan, Sanjeev K. Verma, Anushri Gupta, Anita Kumari, B. D. Indu

Karbala International Journal of Modern Science

A general theory is developed to investigate the expression for potential energy without using (i) empirical results and (ii) process of parameterizing. The simple approach of thermodynamics is adopted to obtain the expressions for the interaction energy of solids in terms of interatomic separation and crystal volume. The new findings have been applied to obtain the expressions for bulk modulus and pressure. The variation of the potential energy function, which provides a means to understand the stability of a crystal has been found in excellent agreements to the earlier results. The use of obtained harmonic and anharmonic force constants may …

Kinetic Study For The Effect Of New Inhibitors On The Activity Of Purified Gpt From Blood Of Cardiovascular Patients, Firas Maher

Karbala International Journal of Modern Science

In this study several biochemical parameters for cardiovascular patients have been measured CK-MB ,LDH ,GPT and GOT in addition to lipid profile , MDA ,Vitamin C and Vitamin E . New derivatives of ascorbic acid have been synthesized and tested to inhibit the purified GPT from the blood of cardiovascular patients Glutamate pyruvate transaminase (GPT) was purified from cardiovascular patients using ammonium sulphate salt for precipitation then dialysed using 0.1 M Tris-HCL buffer pH7.8 , then filtrated by gel filtration chromatography using Sephadex G-100 , followed by electrophoresis using poly acrylamide – bis acrylamide and sodium dodecyl sulphate (SDS). Derivatives …