Computer Sciences Commons^™

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.

Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan

Mathematics Faculty Publications

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as …

Methods For Extending Biomedical Reference Ontologies And Interface Terminologies For Ehrr Text Annotation, Vipina Kuttichi Keloth

Dissertations

Biomedical ontologies and terminologies are a cornerstone in various electronic health record systems (EHRs) for encoding information related to diseases, diagnoses, treatments, etc. Ontologies in general represent entities (concepts) and events along with all interdependent properties and relationships in an efficient way to facilitate easy access, retrieval and sharing. With the landscape of medicine rapidly changing, biomedical ontologies and terminologies need to rapidly evolve to support interoperability, medical coding, record keeping, and healthcare activities in general, and to facilitate interdisciplinary research. Extending ontologies by identifying new and missing concepts plays a vital role in the maintenance of ontologies to keep …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Knowing What We Know: Leveraging Community Knowledge Through Automated Text-Mining, Justin Gardner, Jonathan Tory Toole, Hemant Kalia, Garry Spink Jr., Gordon Broderick

Advances in Clinical Medical Research and Healthcare Delivery

No abstract provided.

Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano

Computer Science and Computer Engineering Undergraduate Honors Theses

Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …

Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke

Computer Science and Computer Engineering Undergraduate Honors Theses

Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after …

Corn Co-Product Logistics: An Application Of Linear Programming, Dmitry Kalashnikov Adams

Department of Agricultural Economics: Dissertations, Theses, and Student Research

The purpose of this thesis is not to explore new ways to apply or to study the general field of linear programming. Rather the emphasis is on applying a particular type of linear programming to a specific problem. In this thesis the classic case of linear programing - the transportation problem – is used to optimize corn co-product logistics between six ethanol producing facilities. At the core, the problem of corn germ logistics lies in transporting products from areas of excess supply to areas with excess demand. The challenge of optimizing corn germ logistics lies in managing transportation between producing …

Addressing Challenges In Aggregating And Analyzing Agroecological Data, Sarah E. Mccord

Open Access Theses & Dissertations

Agroecosystems face multiple threats including land degradation and climate change, changing and competing land uses, invasive species and disease spread, and biodiversity loss. While scientists seek to understand rapidly changing ecosystems, land managers are struggling to maintain ecosystem services amid transitions to novel ecosystem states. Understanding agroecosystem drivers and ensuing responses requires quality information about ecosystems that span biomes, trophic scales, ecological processes, spatiotemporal scales, land use, and land ownership. Yet, using multi-scale agroecosystem information can be frustrating for both scientific researchers and land managers as it is difficult to locate data that are trustworthy, easily accessible, standardized, and connected …

Incorporating Demographic Structure And Variable Interaction Types Into Community Assembly Models, Akhil Reddy Alasandagutti, Nayan Chawla

Honors Theses

Theoretical studies of ecological food webs have allowed ecologists to remove the constraints of specific location and timescales from their study of ecological communities; food webs are generally complex and thus empirical study is difficult. Further, this theoretical approach allows ecologists to compare ecological processes and outcomes across any possible food web structures. However, these simulated communities are only as useful as the model from which they were constructed. Modifying existing considerations in these models, and generating new ones, are the jobs of theoretical ecologists that seek to achieve the shared goal of a majority of simulations: representation of real …

Machine Learning Models For Deciphering Regulatory Mechanisms And Morphological Variations In Cancer, Saman Farahmand

Graduate Doctoral Dissertations

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances …

Using Information Theory To Extract Patterns From Categorical Raster Data, David Percy

Complex Systems Faculty Publications and Presentations

Information theory -- Reconstructability Analysis (RA) implemented in the Occam software -- was used to extract patterns from National Land Cover Data. The aim was to predict temporal change in evergreen forests from time-lagged and spatially adjacent states. The NLCD satellite data were preprocessed with Python and submitted to Occam for analysis, and Occam output was also explored with R-studio. The effectiveness of RA methodology for the analysis of this type of categorical space-time grid data was demonstrated.

The Whole Is Greater Than Its Parts: Ensembling Improves Protein Contact Prediction, Wendy M. Billings, Connor J. Morris, Dennis Della Corte

Faculty Publications

The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks—AlphaFold, trRosetta, and ProSPr—can be substantially …

Iot Based Agriculture 4.0: Challenges And Opportunities, Halimjon Khujamatov, Temur Toshtemirov Mr., Doston Turayevich Khasanov Mr., Nasiba Saburova Ms., Ilhom Ikromovich Xamroyev Mr.

Bulletin of TUIT: Management and Communication Technologies

In recent years, the world's population growth has been intensifying, resulting in specific problems related to the depletion of natural resources, food shortages, declining fertile lands, and changing weather conditions. This paper has been discussed the use of IoT technology as a solution to such problems.

At the same time, the emergence of IoT technology has given rise to a new research direction in agriculture. Soil analysis and monitoring using Zigbee wireless sensor network technology, which is part of the IoT, will enable the creation of an IoT ecosystem as well as the development of smart agriculture. In addition, entrepreneurship, …

Nanopore Guided Regional Assembly, Eleni Adam, Desh Ranjan, Harold Riethman

College of Sciences Posters

The telomeres are the “caps” of the chromosomes and their vital role is to protect them. Possible telomere dysfunction caused by telomere rearrangements can be fatal for the cell and result in age-related diseases, including cancer. The telomeres and subtelomeres are regions that are hard to investigate. The current technology cannot provide their complete sequence, instead the DNA is given in multiple pieces. Current methods of assembling the pieces of these regions are not accurate enough due to the region’s high variability and complex repeated patterns. We propose a hybrid assembly method, the NPGREAT, which utilizes two of the latest …

Sound In Color, Amber Rhodes

Honors Scholars Collaborative Projects

“Sound in Color” is an interactive audio-visual experience designed to explore the relationship between sound, color, and emotions. Taking place on the Massey Concert Hall stage, the project is inspired by synesthesia and incorporates research on color psychology. Participants are invited to select an emotion and color. As the user hums into a microphone, they hear their emotions expressed through sound in their headphones and watch as the lights on stage respond to their vocal cues.

Selfish Gene Theory Explains Oedipus Complex, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Sigmund Freud famously placed what he called Oedipus complex at the center of his explanation of psychological and psychiatric problems. Freund's analysis was based on anecdotal evidence and intuition, not on solid experiments -- as a result, for a long time, many psychologists dismissed the universality of Freud's findings. However, lately, experiments seem to confirm that indeed men, on average, select wives who resemble their mothers, and women select husbands who resemble their mothers. In this paper, we provide a possible biological explanation for this observational phenomenon.

Predictions Of Knee Joint Contact Forces Using Only Kinematic Inputs With A Recurrent Neural Network, Kaileigh Elisabeth Estler

Human Movement Studies & Special Education Theses & Dissertations

BACKGROUND: Knee joint contact (bone on bone) forces are commonly estimated using surrogate measures such as external knee adduction moments (with limited success) or musculoskeletal modeling (more successful). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience and knowledge. Therefore, the purpose of this study was to design a novel prediction method for knee joint contact forces that is equal or more accurate than modeling, yet simplistic in terms of required inputs. METHODS: This study included all six subjects’ (71.3±6.5kg, 1.7±0.1m) data from the opensource “Grand Challenge” datasets (simtk.org) and two subjects from the "CAMS" …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Mixed Dish Recognition With Contextual Relation And Domain Alignment, Lixi Deng, Jingjing Chen, Chong-Wah Ngo, Qianru Sun, Sheng Tang, Yongdong Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Mixed dish is a food category that contains different dishes mixed in one plate, and is popular in Eastern and Southeast Asia. Recognizing the individual dishes in a mixed dish image is important for health related applications, e.g. to calculate the nutrition values of the dish. However, most existing methods that focus on single dish classification are not applicable to the recognition of mixed dish images. The main challenge of mixed dish recognition comes from three aspects: a wide range of dish types, the complex dish combination with severe overlap between different dishes and the large visual variances of same …

Mach-Zehnder Quantum Interference Rules In Hydrocarbons With Substituents, Alaa Al-Jobory, Zainelabideen Y. Mijbil

Karbala International Journal of Modern Science

We have investigated the conditions of quantum interferences in hydrocarbons with substituents using density functional theory and tight binding approximation combined with non-equilibrium Green’s function technique. The chosen model systems, namely benzene and tripyridyl–triazine molecules, have elucidated three prominent rules. The ‘first rule’ is the occurrence of inevitable, destructive quantum interference when 1,3-benzene ring incorporates single substituent at the fifth site. The ‘second rule’ is the chaotic occurrence of quantum interferences due the position and/or type of the substituents. The ‘third rule’: the substituents decrease (increase) the probability of destructive (constructive) interferences

Physical-Chemical Characterization And Heavy Metals Assessment Of Waters And Sediments Of Sebou Watershed (Top Sebou, Morocco), Mohamed Kabriti, Eda Mahougnon Léonce, Chaimaa Merbouh, Bensaber Abdelfattah, Abdelmajid Achkir, Abdlhakim Aouragh, Iounes Nadia

Karbala International Journal of Modern Science

The main purpose of this study was to investigate the impact of human activities, geochemical background and seasons on pollutant pathway. Surface water, groundwater and sediments were assessed to highlight and confirm those impacts. Sixteen physico-chemical parameters were measured (T°, pH, O2, salinity, conductivity, BOD5, COD, SM, Cl-, NO2-, NO3-, NH4+, TAC, TH, SO₄²- and PO43-) and twelve metallic trace elements were analyzed (Ar, Cr, Zn, Mn, Ni, Fe, Al, Cd, Cu, Pb, K and Na). Five sampling campaigns were carried out in 18 sampling points for over one year, between July 2018 and July 2019 in the upstream part …

Molecular Structure Vibrational & Electronic Properties Of Some Isatin Derivatives, Fatma K. Fkandermili Prof, Fatma M. Aldibashi S, Hakan S. Sayiner Prof

Karbala International Journal of Modern Science

Abstract

In this study, Natural Bonding Orbitals (NBOs) were applied to isatin, 5‑fluoroisatin, 5‑chloroisatin, 5‑methylisatin and 5‑methoxyisatin using the Lee-Yang-Parr correlation functional B3LYP with 6‑311++G(2d,2p) basis set. Natural bonding analysis was performed to consider the transfer interactions of intra-molecular charge, pre-hybridization and electron density within the isatin, 5‑fluoroisatin, 5‑chloroisatin, 5‑methylisatin and 5‑methoxyisatin. In natural bonding orbital analysis, the wave functions of the electrons were explicated in sets of occupied Lewis type terms, (bonds or lone pairs) and sets of unoccupied non‑Lewis (anti‑bond and Rydberg) localized natural bonding orbitals. The electron density between these orbitals was correlated to stabilize the interaction …

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.

The Dna Cloud: Is It Alive?, Theodoros Bargiotas

LSU Doctoral Dissertations

In this analysis, I will firstly be presenting the current knowledge concerning the materiality of the internet based Cloud, which I will henceforth be referring to as simply the Cloud. For organisation purposes I have created two umbrella categories under which I place the ongoing research in the field. Scholars have been addressing the issue of Cloud materiality through broadly two prisms: sociological materiality and geopolitical materiality. The literature of course deals with the intricacies of the Cloud based on its present ferromagnetic storage functionality. However, developments in synthetic biology have caused private tech companies and University spin-offs to flirt …

3d Printing Of Human Microbiome Constituents To Understand Spatial Relationships And Shape Parameters In Bacteriology, Jacques Izard, Teklu Kuru Gerbaba, Shara R. P. Yumul

Department of Food Science and Technology: Faculty Publications

Effective laboratory and classroom demonstration of microbiome size and shape, diversity, and ecological relationships is hampered by a lack of high-resolution, easy-to-use, readily accessible physical or digital models for use in teaching. Three-dimensional (3D) representations are, overall, more effective in communicating visuospatial information, allowing for a better understanding of concepts not directly observable with the unaided eye. Published morphology descriptions and microscopy images were used as the basis for designing 3D digital models, scaled at 20,000×, using computer-aided design software (CAD) and generating printed models of bacteria on mass-market 3D printers. Sixteen models are presented, including rod-shaped, spiral, flask-like, vibroid, …

Machine Learning Approaches For The Prediction Of Bone Mineral Density By Using Genomic And Phenotypic Data Of 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han, Robert A. Greenes, Kenneth G. Saag

School of Medicine Faculty Research

The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold …

Non-Transgenic Crispr-Mediated Knockout Of Entire Ergot Alkaloid Gene Clusters In Slow-Growing Asexual Polyploid Fungi, Simona Florea, Jolanta Jaromczyk, Christopher L. Schardl

Computer Science Faculty Publications

The Epichloë species of fungi include seed-borne symbionts (endophytes) of cool-season grasses that enhance plant fitness, although some also produce alkaloids that are toxic to livestock. Selected or mutated toxin-free endophytes can be introduced into forage cultivars for improved livestock performance. Long-read genome sequencing revealed clusters of ergot alkaloid biosynthesis (EAS) genes in Epichloë coenophiala strain e19 from tall fescue (Lolium arundinaceum) and Epichloë hybrida Lp1 from perennial ryegrass (Lolium perenne). The two homeologous clusters in E. coenophiala—a triploid hybrid species—were 196 kb (EAS1) and 75 kb (EAS2), and …

Deep Learning For Multi-Tissue Cancer Classification Of Gene Expressions, Tarek Khorshed

Theses and Dissertations

We contribute in saving the lives of cancer patients through early detection and diagnosis, since one of the major challenges in cancer treatment is that patients are diagnosed at very late stages when appropriate medical interventions become less effective and full curative treatment is no longer achievable. Cancer classification using gene expressions is extremely challenging given the complexity and high dimensionality of the data. Current classification methods typically rely on samples collected from a single tissue type and perform a prerequisite of gene feature selection to avoid processing the full set of genes. These methods fall short in taking advantage …

Mapping Transcription Factor Networks And Elucidating Their Biological Determinants, Yiming Kang

McKelvey School of Engineering Graduate Student Theses & Dissertations

A central goal in systems biology is to accurately map the transcription factor (TF) network of a cell. Such a network map is a key component for many downstream applications, from developmental biology to transcriptome engineering, and from disease modeling to drug discovery. Building a reliable network map requires a wide range of data sources including TF binding locations and gene expression data after direct TF perturbations. However, we are facing two roadblocks. First, rich resources are available only for a few well-studied systems and cannot be easily replicated for new organisms or cell types. Second, when TF binding and …